{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🌐 Network Security Analysis & Anomaly Detection\n",
    "\n",
    "This notebook focuses on advanced network security analysis, intrusion detection, and anomaly identification using machine learning.\n",
    "\n",
    "## 🎯 Objectives:\n",
    "- **Network Traffic Analysis** - Deep packet inspection and flow analysis\n",
    "- **Intrusion Detection** - Advanced ML models for network intrusions\n",
    "- **Anomaly Detection** - Unsupervised learning for unknown threats\n",
    "- **Real-time Monitoring** - Stream processing for live network analysis\n",
    "- **Threat Intelligence** - Integration with external threat feeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Network Security Analysis Environment Ready!\n",
      "TensorFlow version: 2.20.0\n"
     ]
    }
   ],
   "source": [
    "# Import essential libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime, timedelta\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Machine Learning libraries\n",
    "from sklearn.ensemble import IsolationForest, RandomForestClassifier\n",
    "from sklearn.cluster import DBSCAN, KMeans\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# Deep Learning for network analysis\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, LSTM, Conv1D, MaxPooling1D, Flatten, Input\n",
    "from tensorflow.keras.layers import Dropout, BatchNormalization, Attention\n",
    "\n",
    "# Network analysis libraries\n",
    "import socket\n",
    "import ipaddress\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "# Visualization\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Set style\n",
    "plt.style.use('dark_background')\n",
    "sns.set_palette('viridis')\n",
    "\n",
    "print(\"🚀 Network Security Analysis Environment Ready!\")\n",
    "print(f\"TensorFlow version: {tf.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📊 Network Dataset Preparation\n",
    "\n",
    "Let's load and prepare network traffic data for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Network Intrusion Dataset: (125973, 10)\n",
      "📊 Botnet Detection Dataset: (80000, 7)\n",
      "📊 DNS Tunneling Dataset: (10000, 5)\n",
      "\n",
      "🔍 Network Traffic Analysis Dataset:\n",
      "   Samples: 125,973\n",
      "   Features: 10\n",
      "   Columns: ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'attack_type']\n",
      "\n",
      "⚔️ Attack Type Distribution:\n",
      "attack_type\n",
      "dos       67085\n",
      "probe     45565\n",
      "normal    13281\n",
      "r2l          23\n",
      "u2r          19\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import our dataset manager\n",
    "import sys\n",
    "sys.path.append('../app/services')\n",
    "from advanced_dataset_manager import AdvancedDatasetManager\n",
    "\n",
    "# Initialize dataset manager\n",
    "dataset_manager = AdvancedDatasetManager('../datasets')\n",
    "\n",
    "# Load network intrusion dataset\n",
    "network_df = await dataset_manager.load_dataset('network_intrusion')\n",
    "botnet_df = await dataset_manager.load_dataset('botnet_detection')\n",
    "dns_df = await dataset_manager.load_dataset('dns_tunneling')\n",
    "\n",
    "print(f\"📊 Network Intrusion Dataset: {network_df.shape if network_df is not None else 'Not available'}\")\n",
    "print(f\"📊 Botnet Detection Dataset: {botnet_df.shape if botnet_df is not None else 'Not available'}\")\n",
    "print(f\"📊 DNS Tunneling Dataset: {dns_df.shape if dns_df is not None else 'Not available'}\")\n",
    "\n",
    "# Use network intrusion as primary dataset\n",
    "if network_df is not None:\n",
    "    df = network_df.copy()\n",
    "    print(f\"\\n🔍 Network Traffic Analysis Dataset:\")\n",
    "    print(f\"   Samples: {len(df):,}\")\n",
    "    print(f\"   Features: {df.shape[1]}\")\n",
    "    print(f\"   Columns: {list(df.columns)}\")\n",
    "    \n",
    "    # Display attack distribution\n",
    "    if 'attack_type' in df.columns:\n",
    "        attack_dist = df['attack_type'].value_counts()\n",
    "        print(f\"\\n⚔️ Attack Type Distribution:\")\n",
    "        print(attack_dist)\n",
    "        \n",
    "        # Visualize attack distribution\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        attack_dist.plot(kind='bar', color='lightcoral')\n",
    "        plt.title('Network Attack Types Distribution', fontsize=16, color='white')\n",
    "        plt.xlabel('Attack Type', color='white')\n",
    "        plt.ylabel('Count', color='white')\n",
    "        plt.xticks(rotation=45)\n",
    "        plt.grid(True, alpha=0.3)\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 Network feature engineering: 10 → 42 features\n",
      "\n",
      "🆕 New network features: 32\n",
      "Features: ['total_bytes', 'protocol_udp', 'failed_logins', 'port_scan_indicator', 'protocol_tcp', 'is_long_connection', 'service_ftp', 'service_ssh', 'day_of_week', 'is_short_connection']...\n"
     ]
    }
   ],
   "source": [
    "# Advanced network feature engineering\n",
    "def create_network_features(df):\n",
    "    \"\"\"\n",
    "    Create advanced features for network security analysis\n",
    "    \"\"\"\n",
    "    df_enhanced = df.copy()\n",
    "    \n",
    "    # Temporal features\n",
    "    df_enhanced['hour'] = np.random.randint(0, 24, len(df))  # Simulated hour\n",
    "    df_enhanced['day_of_week'] = np.random.randint(0, 7, len(df))  # Simulated day\n",
    "    df_enhanced['is_weekend'] = (df_enhanced['day_of_week'] >= 5).astype(int)\n",
    "    df_enhanced['is_business_hours'] = ((df_enhanced['hour'] >= 8) & (df_enhanced['hour'] <= 18)).astype(int)\n",
    "    \n",
    "    # Traffic volume features\n",
    "    if 'src_bytes' in df.columns and 'dst_bytes' in df.columns:\n",
    "        df_enhanced['total_bytes'] = df_enhanced['src_bytes'] + df_enhanced['dst_bytes']\n",
    "        df_enhanced['byte_ratio'] = df_enhanced['src_bytes'] / (df_enhanced['dst_bytes'] + 1)\n",
    "        df_enhanced['avg_packet_size'] = df_enhanced['total_bytes'] / (df_enhanced.get('packet_count', pd.Series([1] * len(df))) + 1)\n",
    "    \n",
    "    # Connection duration features\n",
    "    if 'duration' in df.columns:\n",
    "        df_enhanced['duration_log'] = np.log1p(df_enhanced['duration'])\n",
    "        df_enhanced['is_short_connection'] = (df_enhanced['duration'] < 1).astype(int)\n",
    "        df_enhanced['is_long_connection'] = (df_enhanced['duration'] > 3600).astype(int)\n",
    "    \n",
    "    # Protocol analysis\n",
    "    if 'protocol_type' in df.columns:\n",
    "        # One-hot encode protocols\n",
    "        protocol_dummies = pd.get_dummies(df_enhanced['protocol_type'], prefix='protocol')\n",
    "        df_enhanced = pd.concat([df_enhanced, protocol_dummies], axis=1)\n",
    "    \n",
    "    # Service analysis\n",
    "    if 'service' in df.columns:\n",
    "        # Encode services\n",
    "        service_dummies = pd.get_dummies(df_enhanced['service'], prefix='service')\n",
    "        df_enhanced = pd.concat([df_enhanced, service_dummies], axis=1)\n",
    "    \n",
    "    # Flag analysis\n",
    "    if 'flag' in df.columns:\n",
    "        flag_dummies = pd.get_dummies(df_enhanced['flag'], prefix='flag')\n",
    "        df_enhanced = pd.concat([df_enhanced, flag_dummies], axis=1)\n",
    "    \n",
    "    # Anomaly indicators - handle missing columns properly\n",
    "    if 'connection_count' in df_enhanced.columns:\n",
    "        df_enhanced['multiple_connections'] = (df_enhanced['connection_count'] > 10).astype(int)\n",
    "    else:\n",
    "        df_enhanced['multiple_connections'] = 0  # Default value for missing column\n",
    "    \n",
    "    df_enhanced['failed_logins'] = np.random.poisson(0.1, len(df))  # Simulated failed logins\n",
    "    \n",
    "    if 'dst_host_srv_count' in df_enhanced.columns:\n",
    "        df_enhanced['port_scan_indicator'] = (df_enhanced['dst_host_srv_count'] > 20).astype(int)\n",
    "    else:\n",
    "        df_enhanced['port_scan_indicator'] = 0  # Default value for missing column\n",
    "    \n",
    "    print(f\"🔧 Network feature engineering: {df.shape[1]} → {df_enhanced.shape[1]} features\")\n",
    "    return df_enhanced\n",
    "\n",
    "# Apply feature engineering\n",
    "if df is not None:\n",
    "    df_network = create_network_features(df)\n",
    "    \n",
    "    # Display new features\n",
    "    new_features = set(df_network.columns) - set(df.columns)\n",
    "    print(f\"\\n🆕 New network features: {len(new_features)}\")\n",
    "    print(f\"Features: {list(new_features)[:10]}...\")  # Show first 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔍 Intrusion Detection Models\n",
    "\n",
    "Train advanced ML models for network intrusion detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 Intrusion Detection Data:\n",
      "   Features: 19\n",
      "   Samples: 125,973\n",
      "   Attack ratio: 0.895\n",
      "✅ Data prepared for intrusion detection training\n",
      "✅ Data prepared for intrusion detection training\n"
     ]
    }
   ],
   "source": [
    "# Prepare data for intrusion detection\n",
    "if df_network is not None and 'attack_type' in df_network.columns:\n",
    "    # Create binary classification: normal vs attack\n",
    "    df_network['is_attack'] = (df_network['attack_type'] != 'normal').astype(int)\n",
    "    \n",
    "    # Select features for training\n",
    "    exclude_cols = ['attack_type', 'is_attack']\n",
    "    if 'protocol_type' in df_network.columns:\n",
    "        exclude_cols.append('protocol_type')\n",
    "    if 'service' in df_network.columns:\n",
    "        exclude_cols.append('service')\n",
    "    if 'flag' in df_network.columns:\n",
    "        exclude_cols.append('flag')\n",
    "    \n",
    "    feature_cols = [col for col in df_network.columns if col not in exclude_cols]\n",
    "    \n",
    "    # Handle non-numeric columns\n",
    "    X = df_network[feature_cols].select_dtypes(include=[np.number])\n",
    "    y = df_network['is_attack']\n",
    "    \n",
    "    print(f\"🎯 Intrusion Detection Data:\")\n",
    "    print(f\"   Features: {X.shape[1]}\")\n",
    "    print(f\"   Samples: {X.shape[0]:,}\")\n",
    "    print(f\"   Attack ratio: {y.mean():.3f}\")\n",
    "    \n",
    "    # Split data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.2, random_state=42, stratify=y\n",
    "    )\n",
    "    \n",
    "    # Scale features\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train.fillna(0))\n",
    "    X_test_scaled = scaler.transform(X_test.fillna(0))\n",
    "    \n",
    "    print(f\"✅ Data prepared for intrusion detection training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌲 Training Random Forest for Intrusion Detection...\n",
      "📊 Random Forest Results:\n",
      "   Accuracy:  0.9972\n",
      "   Precision: 1.0000\n",
      "📊 Random Forest Results:\n",
      "   Accuracy:  0.9972\n",
      "   Precision: 1.0000\n",
      "   Recall:    0.9969\n",
      "   F1-Score:  0.9984\n",
      "   Recall:    0.9969\n",
      "   F1-Score:  0.9984\n",
      "\n",
      "🔝 Top 10 Important Features:\n",
      "            feature  importance\n",
      "5            urgent    0.488939\n",
      "4    wrong_fragment    0.471179\n",
      "2         dst_bytes    0.006564\n",
      "11       byte_ratio    0.005874\n",
      "1         src_bytes    0.005569\n",
      "12  avg_packet_size    0.004699\n",
      "10      total_bytes    0.004442\n",
      "0          duration    0.004328\n",
      "13     duration_log    0.004182\n",
      "6              hour    0.001947\n",
      "\n",
      "🔝 Top 10 Important Features:\n",
      "            feature  importance\n",
      "5            urgent    0.488939\n",
      "4    wrong_fragment    0.471179\n",
      "2         dst_bytes    0.006564\n",
      "11       byte_ratio    0.005874\n",
      "1         src_bytes    0.005569\n",
      "12  avg_packet_size    0.004699\n",
      "10      total_bytes    0.004442\n",
      "0          duration    0.004328\n",
      "13     duration_log    0.004182\n",
      "6              hour    0.001947\n"
     ]
    },
    {
     "data": {
      "image/png": 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rT1SMxJXEYulKVD41xrTTTpuvYhXLdOJrVBjUvX9cl7mVlq3FVQjLKV32fVzPNZbmxOXp48p0TRFXAIsgLraal5WPsOPss8+uviJYJT/Tk6Px+TzE5zPC1thKx4gr4XXp0iVdccUV6b777kvDhg1rMJiMgDMqYOI9+fDDD8fYp/ReNbf3Y5dddslfozKsFKbEFQfjfGN+Hn300bWC1/FVqjx74IEH8pUFGyOeP8Lh6aabLs+niR1WxXsUv+vqm19R1Vn6ndvc3k+AIugpBQBN8Nxzz+WvcVn2cr1PYtlbLHOKSoFyAURctnzxxRcf4/ZOnTrlKoP4g7bUA6VS4tL1IUKfibW8pfQH+/bbb9/ox8QSoFatWuXXtm4gFXbdddd6K1hKIUM8fmzBTPSxqq93TVHnWp94PyLcCrG8sjl8ppuTsb3PjVX6PESYUE5TPg8R7EZlW1T0RRASAWVD4ndAaalnfaFj9BoLzz77bGouDjzwwLTCCivk788999zq2yOcevLJJ8dpDozt/SzNre22267Rx6w5nn333bfJY4meX+Myv/bYY48G38vPPvus0csWASYnQikAaIJYghXLyaJPSPfu3Wv9gRKVTqX/Wx9NxMstQ4nQ5PLLL6/ViDv+T3ncFvdF1dDErpRacskl02abbTbGH1exRC3+QDrrrLPyzxdddFH1srgJLRoMx/Ke+CM1Kn5KPV5q+stf/pL22Wef6p9jmV/0qZlppplyAFXTiiuumLp161bv85Ve08UWW6zs/dG3Jf4ojGMfd9xxte7r3Llz6tq1axpX0RcqAomolomtXOgVn51SlUmpgXU0hy73x/imm246xrLGSn6mm5PS+xwN48cnmIpKnwiHNthggzEuPHDIIYekbbfddozHRB+o6ANWs69SSSy9jM9WzKfGzO/Sax5BTywDrCnCjS222CKHJBdeeGGqtJinMd5Ybhri90f0PqspQrn47MRciF5b5ZbDxtzcaqutmjRvo0IqmsvH/I+LHZR77eN37f7771/rc33mmWfmnmwHH3xwfo3rmnvuuauXIjZ2LPW56qqrcoge/QJPOOGEWvdFsHzyySfn7+O1AZhSVfwSgDabzWazNbetdHnvcpcAj8uXDxw4sPoy6XG584cffrhq2LBh+bZHH320qnXr1mUvq37//fdXff7551U//fRT1T333JMv3V46VlwSfJZZZmnSODfeeOOqV199tXr7+eef87F69epV6/aaj9liiy3yPj/++GO+9PzNN9+cx//VV19Vn/Mtt9xS9hLrDW2lS5jXvSR6fVunTp2qvvjii/yYeD2ee+65PJa4XPtHH31UNWrUqKrvv/++1mMOO+yw6jHGecU4X3zxxbzvDTfckN+PMM8884zxOoXffvut6sEHH8yXUb/qqquqVl555ep9ttpqq3yc0iXm77jjjnwZ97itS5cu9X4exnap+NhWX331qv79++f9+vXrV/XUU09V3XTTTXks8V6VzqfueQ4ePLjqmWeeya9LfF5K5zdo0KCqJZdcssnvTXwtd//4fKavu+66cZ5npfOJYzVlvKX5We6z9sYbb+T7evbsmV/jeJ+7detWfX+Mt9xz1t169OiR9xs5cmR+D2Kuxnv1+++/V5111lljPP+MM86Yb/vjjz+q3n333ao777wzfz5ffvnl6s9VfI4ae55du3bN98VjX3jhhfwZeOutt6rHtNdee43xmBhPiNdnXD4H5bbS+zxkyJD82sUWcy3m6fvvv5/PN/zyyy9Vf//73+s9zrbbbls1dOjQvO8333xT9dhjj+X351//+lf+OcTnruZjFl988Xz82J544omqa665Jr+fm222WfU+s802W56vpTG+9NJLVbfeemt+v+L2eK1C27Ztax17t912y+9l6TMf71fpMfGa132Nzj333LxvzOPbb789jyO2mWeeeayfyU022aR6Ln388cf5cxG/e0eMGJFvi/Nq6nvV0PPZbDZbmrS2ig/AZrPZbLZJKpSKbc4556y6+OKLc8AUQUeEQfHH5/777182zKn5B/yf//znqssvvzz/IRaP/frrr6v+8Y9/VM0000xNHmfpuGNT8zHzzjtvVffu3fMfut9++23+Y2n48OE5lIo/zDbaaKNxes2aGkrFNv3001cdffTR+bWLYCr+SOzTp0/V66+/XnXOOedUrbTSSmM8ZvPNN89/eMb+8YdwhBAHHHBAvq++UCq2vffeO/9RX/rDuFwwEeceIVfsE3/gvvLKK1Xbbbddvq++z0NDn5OaWwSOp59+eh5DfF7ivY/PQJxLvHZ//etfq/edf/75q0499dT8h2u8L/EeRYj43nvv5UBkjjnmGKf3pqEwYnw+080plJprrrlygBOfo9If/fE8TQ2lYjviiCOq/v3vf+fXI0K7Bx54oGrppZcu+/zxGu233345cIjgIYLDX3/9NQdZd911V9Vaa63V5Pdlgw02yOHggAED8rn07ds3h6XLL7982f0nZihVU8zTeD3itYkAaN99961q167dWI8V8/KCCy6o+uCDD/L8is91vDcR+h177LH5c1/3MRGix5yMz2Mp3Ks7/jZt2uTX/umnn65+rSL8jYApPtPrrbde2fF07NgxB0u9e/fOvwPjPYtA/KKLLsr31dw3Qq2zzz676rPPPsufh5LS75qxhUSLLrpo/uzFnI/XL35/xXi33377cXqvhFI2my1NJluL/34DAExEseQmrjwV21577VXp4QAAQMXpKQUAAABA4YRSAAAAABROKAUAAABA4fSUAgAAAKBwKqUAAAAAKJxQCgAAAIDCtSr+KZnSzT777GnIkCGVHgYAAAAwkbRr1y717du3wX2EUhQeSPXp06fSwwAAAAAmsjnmmKPBYEooRaFKFVJzzz13Gjx4cKWHA81Gy5Yt03rrrZeefPLJNGrUqEoPB5oNcwPqZ35AeeYGVH5uRJVUFKSMbZWUUIqKiA+mJXxQ+x+I4cOH53nhP57gf8wNqJ/5AeWZGzDpzA2NzgEAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAABo3qHUs88+m3r06DHRBnPdddel++67b6Idn//58ssv02GHHVbpYQAAAABTqFZN2XnrrbdOI0eOTJOiCLzat2+fttpqqzQl2WOPPdI//vGPNNNMM9W6ffnll0+//vprxcYFAAAATNmaFEoNGjQoTWqmmmqqVFVVVelhNDsDBw6s9BAAAACAKdg4L9878MAD02effZaGDx+e+vXrl+66665GHWObbbZJH3zwQRo2bFgORp588sk07bTT1trnqKOOSn379s33X3LJJalVq/9lZ1HtdMMNN6SffvopV/o88sgjacEFF6xVGRTh2WabbZb+/e9/p99//z1de+21ac8990xbbrllDqhi69y581jHOsccc6Rbb701/fjjj2no0KHpzTffTCussEL1/QcccED6/PPP83N88sknadddd631+HievffeO9177715rPF6xbhKYgyxz9prr52PHfu8/PLLaeGFF651nM033zy9/fbb+bXu3bt3OvXUU1PLli2r759xxhnTP//5z/w+xD4ffvhh2mSTTfLxr7/++vyalc77tNNOK7t8b6655kr3339/GjJkSPr555/THXfckTp06FB9fzzu3XffzecYjx08eHC67bbb0vTTTz/W1xEAAABgvCqlSpZddtl00UUXpd122y298soraeaZZ06rr776WB8366yz5iDj2GOPzb2j2rVrlx/XokWL6n3WWmut9P333+evETZFOPLee++lq6++Ot8fIctCCy2Ug5pffvklnXPOOTmY6tSpU/rjjz/yPhFyHXfccWmfffbJgVIcb5pppkkzzDBD2muvvfI+EWo1ZLrppkvPP/986tOnT36uCHyWWWaZXHkVIuC68MIL0+GHH56eeuqptOmmm+Ylgt9991167rnnaoU5cb7HHHNMOuSQQ9Itt9yS5plnnlpVZ2eeeWYO4gYMGJDDpQjRVltttXxffL3xxhvToYceml588cW0wAILpCuvvDLf17Vr1/zaPfroo/m1jMAoQqt4LUaNGpXfmwieYr9FFlkkPybCtbriGA888EC+L4KsCAEvvfTS/NrH+1ASzx3nHecaywHvvPPOdPzxx6eTTz55rO89AAAAwHiHUnPPPXeu6nn44YdzkPHNN9/k4GhsZpttttS6detcORSPCR999FGtfSKsOfjgg9Po0aPTp59+mv71r3+lddZZJ4dSEVJtscUWaZVVVkmvvvpq3n+XXXZJ3377bQ5L7r777nxbmzZt0kEHHZQrskqigqht27bphx9+aNQ57rzzzmmWWWbJvZdKAVIEPiVHH310Dsguv/zy/HNUkK200kr59pqhVOxz++235+9PPPHEHBJFtdXjjz9evc9JJ52UXnjhhfz92WefnUO2GGtUYEWoFbdFMBWiSumUU05J5557bg6b1l133Xy8jh07pl69elXvUxJVT1Eh1dB5x+u7+OKLp/nmmy+HamH33XdPH3/8cVpuueXSW2+9lW+LQC4qzkrB1k033ZQf21AoFe9FnEtJhGchKr1qVnvBlC7mQ8wx8wJqMzegfuYHlGduQOXnRmOfY5xCqVhy9/XXX6cvvvgiPfbYY3mLyqcIfhry/vvv56qiWF4WocwTTzyRg6RYClYSS+4ikCqJKqcITEIEL9Fo/fXXX6++PyqeIryK+0oizKkZSI2LpZZaKi9Xq6+PVjxfqWKpJJbe1b2iXc1xxJLFCIlqLouru0+cb4h9Imxbcskl06qrrpqDq5pvblR+xRbjjCCpFEiNiziXeK5SIBV69uyZzz3uK4VSX331Va1Kqxhr3XOp64QTTkhdunQZ4/b11luvbNUWTKliXkc1ZlQuRqUj8B/mBtTP/IDyzA2o/NyIvGKihVIRJsSJrLnmmmn99dfPFTsRPERVUYQu9YmwKcKIqHSKx8Vytli6tuKKK+bAI9S9ul9U+ZSWzDXW2MKxoo7R2POpuU+pKXtpn+jZFNVSUV1W12+//TbBxtkY4/LedOvWLXXv3r1WpVQsiYxgs2YYCVO6+Aci5lSE/P7jCf7H3ID6mR9QnrkBlZ8bpVVSEyWUCnECTz/9dN5OP/30HDBEw+6omBqb6HUUW4RZUXG11VZbVTdQb0hU78TyvwixSsv3op9V9EuKpWYNGTFiRJNK1KJ6KXpSRe+kctVSMZaoYCotqwvx89jG0VTvvPNOPr+aSwfrjnPOOefMfbbKVUs15rzjXKLReRynVC0VFVJx7uN7PvH8sZX7/PgHAsYM7s0NGJO5AfUzP6A8cwMqOzcae/xxCqXiym7zzz9/7oMUgc3GG2+cK2ZiGV1DovdR9CCKZXv9+/fP4VL0bYpQpDHiSndxhbirrroq7b///vlKcdFvKSpvolF3Q6ISa4MNNshXtovm51HRVWqMXk40ZI8eUPF8sQQtlqotvfTS+aqAr732WjrvvPNyo+9Y4hdLEuOqeltvvXXu8TQhRXAXvbuiB1csdYwPUCzp++tf/5p7S8V7ENs999yTjjzyyPwaLbroojn9jCWScd6RUEZgGMsnYwlh3eqq0pLKaMIejduj0flll12We2PFVf8AAAAAJrSmrYv7r6iKigDmmWeeyYHSAQcckHbaaaexVtXE1fLWWGON3Mj7s88+S2eccUa+6lyUjjVWXD0vgpIIaqJaKtZCRijWUMAUIsiK0Cz6Iw0cODBXNY1tqVosMYzwLMYboU1caa6U9kUIFv2jorF59MGKkCzGFlfsm5AiwIur3cVY3nzzzRyIHXHEEbnCrGSbbbbJ90WQFu9BNEEvVUfFaxTN2ONKenHecSXAcqKBfASMEXBFSBX9wnbYYYcJei4AAAAANUUTI5utkK1du3ZVoX379hUfi83WnLaWLVtWbbrppvlrpcdiszWnzdyw2erfzA+brfxmbthsqeJzo/S3f3xtaL9xqpQCAAAAgPExQUOpaJYdfZ7q2+L+5iL6RNU3zliuBwAAAMDEM85X3ysnmoAvtdRSDd7fXPzzn//MjcrLqdsIHAAAAIBmHEpFE/DevXunSUE09Y4NAAAAgOLpKQUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSdTz77LOpR48eaVKyxx57pEGDBhX+vKeddlp69913C39eAAAAYNLXqtIDaG623nrrNHLkyEoPAwAAAGCyJpSqoxIVRwAAAABTGsv3Gli+d+CBB6bPPvssDR8+PPXr1y/dddddY338JptskoOtqab6z0u75JJLpqqqqtStW7fqfa666qp00003Vf+86qqrphdeeCENGzYsffPNN+nCCy9M0047bfX9bdq0Seedd1767rvv0tChQ9Nrr72WOnfuXO8Y/vznP6c333wz3XvvvfmxLVq0SMcff3z64osv8nO89957aZtttqneP44VY1x77bXz43799df08ssvp4UXXrjWcY877rj8Ovzyyy/p6quvTlNPPXWjX1cAAACAmoRS9Vh22WXTRRddlE499dS0yCKLpA033DAHR2Pz4osvpnbt2qWll166OvAZMGBAWnPNNav3iduee+65/P3888+fHnvssXTPPfekJZZYIu2www5ptdVWS5dcckn1/vH9yiuvnHbccce8T4Rj8ZgFF1xwjOefc8458xg++uijtO2226YRI0akE044Ie2+++7pgAMOSIsttlgO3W6++ea0xhpr1HrsmWeemY466qi03HLLpT/++CNde+211fdtt912qUuXLunEE0/M93///ffpoIMOGsdXFwAAAJjSWb5Xj7nnnjtXDD388MO5OikqmKLCaGyiiij2ixDq7bffzl8jBIqm4NNNN12accYZ00ILLZSef/75vH8ERrfcckuujgqff/55OvTQQ/P9UanVoUOHtNdee+XxRBAULrjgghySxe0nnXRS9XNHZdOTTz6Z7rvvvnT44Yfn26JSKoKkddddN1dYhS+//DIHX/vvv3+toC2OVfr57LPPTo888khq27Zt+v333/Pxrrnmmuqg6pRTTsnHHFu1VDx/HKMkArvQsmXLvAH/EfMhKizNC6jN3ID6mR9QnrkBlZ8bjX0OoVQ9Itz5+uuv85K3qEqKLcKeWMo3NhEoRRgV4dHqq6+eg6ftt98+B0Ezzzxz6tOnTw6fSsv7ovppl112qX58LLeLN3C++ebLlVStWrXKywhriqDnxx9/rP55mmmmyRVSt956azriiCOqb49qqgjD4nzqhkV1r5z3wQcfVH9fCsAiFPv2229Tx44d0z//+c9a+7/66qtprbXWavC1iHOPCqu61ltvvRz2Af8Rc36ZZZbJ83/UqFGVHg40G+YG1M/8gPLMDaj83IiMojGEUvWIwCTerAiX1l9//dS1a9ccriy//PLp559/bvCxsTTvb3/7Ww6c4kp+n376ab4tjjXTTDNVV0mF6aefPl1xxRV5qWBdUZ0VgVUspYvlhHU/NDVDnahmeuqpp9Kmm26a+0/17du3+vilXlcRhtUUj6mp5lUHo8dUKPXGGlfRS6t79+61KqViHBGSDR48eLyODZPbPxAx7yIA9x9P8D/mBtTP/IDyzA2o/NworZIaG6FUA+JNevrpp/N2+umn5xAlmoFHxVRj+kpFxVIpgIpQKpqNRygVFVQl77zzTurUqVPq3bt32WNFNVNUSkXF0ksvvVTvc44ePTrttttuuVIqmrVHABbVTh9//HH67bff8vK/xvTEqk/Pnj3TiiuuWKtB+0orrTTWx0VPq9jKvbb+gYAx57G5AWMyN6B+5geUZ25AZedGY48vlKpHVBbF0rkIcuJqehtvvHGuGoqqp7GJ8CqWwsWSvIMPPjjfFse5884787K5mpVS55xzTu71dPHFF+cr2kUfqwipYnnbIYccknr16pWbkt944425CXmEVLPMMktaZ5118nNE36eaH654zttuuy0988wzOZj64Ycf0vnnn5/7WsX4I9iKvlZxxb/ofxXHbYzoeXX99dent956K1+ZL54nmqbH8kYAAACAphJKNRAsbb311nnJXjTzjnBop512ypVHjRHBU1yBr3SVvQi24rF/+ctfavWH+vDDD/PV+OLKd1FhFWs7o2rqjjvuqN4nGpqffPLJucJqjjnmSAMHDsxBVjRhL5dGxjjj8aVgKpqSxxUAo79TBG1xblGhddZZZzX69YhAbYEFFkjnnntufj3iaoGXX3552mCDDRp9DAAAAICaonmQzVbI1q5du6rQvn37io/FZmtOW8uWLas23XTT/LXSY7HZmtNmbths9W/mh81WfjM3bLZU8blR+ts/vja03/h1sQYAAACAcWD5XhPNNddcDS7hi35Q3377baFjAgAAAJjUCKWaqG/fvmmppZZq8H4AAAAAGiaUaqJoJB6NyAEAAAAYd3pKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAADD5hVLPPvts6tGjR8WPMTZVVVVpiy22SM3dddddl+67775KDwMAAABgvLRKE9nWW2+dRo4cmZq7WWedNQ0aNKjSwwAAAACYIkz0UGpSCXp++OGHSg+hWWvduvUkES4CAAAAk4ZCl+8deOCB6bPPPkvDhw9P/fr1S3fddVejj9OqVat08cUXp8GDB6cBAwakrl27jnX5XQRie+yxR3WoEo/v27dvfv6vvvoqHX/88WUfP8888+Sft9pqq/TMM8+kX3/9Nb333ntppZVWqnX8VVddNb3wwgtp2LBh6ZtvvkkXXnhhmnbaaavvb+h8t9lmm/TBBx/kxw4cODA9+eSTtR47NkcddVQ+l3jsJZdckl+fkvbt26cbbrgh/fTTT3nsjzzySFpwwQWr7z/ttNPSu+++W+t4hx12WPryyy/HWCZ44oknpj59+qRPP/10rOcEAAAA0OwanS+77LLpoosuSqeeempaZJFF0oYbbpgDncaKcOmPP/5IK6ywQg5QjjzyyLTPPvs0+vGHHnpo2nzzzdP222+fn3+XXXbJwVRDzjzzzHT++eenpZZaKgcxt912W2rZsmW+b/7550+PPfZYuueee9ISSyyRdthhh7TaaqvlgGhs5xtLBeNY1157berYsWNac80107333ptatGjRqHNZa6210gILLJC/xuuy55575q3k+uuvT8stt1w+35VXXjkfN4KpmsFVY6yzzjp57Outt17adNNNx/s9BAAAAChs+V7J3HPPnat2Hn744TR06NBcWRTVR4317bffpiOOOCJ/HwHR4osvnn+++uqrG/38vXr1Si+99FL+OZ5/bCKQijCnVF308ccf54qjqBo64YQT0i233JKro8Lnn3+eg6/nn38+VxM1dL6zzTZbrtyKIKo0jo8++qjRr0VUgB188MFp9OjReSz/+te/coAUr0WMLyq+VllllfTqq6/m/SOAi9dvyy23THfffXejnyfGH8FfadleVI419T1s06ZNatu2bfXP7dq1y18j3CsFfMB/5sRUU01lXkAd5gbUz/yA8swNqPzcaOxzFBZKxfK0r7/+On3xxRe5wii2WB4Wy8Aa47XXXqv1cwQusYQtXtAIZ8YmqodiDBHixHNHsBI/NySW15V8//33+WuHDh3yMZZccslcIRWBT0lUJMULP9988zV4vu+//3566qmn0ocffpgef/zx9MQTT+SwKJYmNsa///3vWuccY4uQLkTlVYRIr7/+evX9sYwvxhz3NUWMr2YfqXF5DyO869Klyxi3R/VVBFvAf8TvjmWWWSb/Hhk1alSlhwPNhrkB9TM/oDxzAyo/N6aZZprmFUpFABEnH0vV1l9//dwTKsKK5ZdfPv3888/jffwIaeouf4tqpJLooRRh0UYbbZTWXXfddOedd+ZgaLvttqv3mDUDmegxFSIEC9NPP3264oor8nK2uqKCKB7b0PlGKBPVTHHfIYcckpcKrrjiimNdUlh3XKWxlcY1IV6rkqiKGt/3sFu3bql79+61KqWiR1UEXI0N4WBK+Qci5nKEvf7jCf7H3ID6mR9QnrkBlZ8bpVVSzSaUCnHSTz/9dN5OP/30HEqsvfbaudpmbCKwqSmajsdyvFLFUDQ/j2VxJbGMbbrppqv1mCFDhuQwKraoTIoqpZlmmmmcrhD4zjvvpE6dOqXevXuP8/m+8soreYtwJyqQYnlcqSn8uOrZs2cOmOL1Ki3fm3nmmXMPqFh+WHqtoq9VTdE3a2K8hyNGjMhbueP4BwJqi99n5gaMydyA+pkfUJ65AZWdG409fmGh1CabbJKbg0dj7AiBNt5441zdU7qq29hEj6YLLrggVydFtU5UF8XyvZK4Sl70WYogJtK/c845p1YYEv2nYplbVEzFmxAVUvHzuFbrxPFjSWFc0S96OUVVUYRUUQEVY2vofKNZe/SAimV7/fv3zwHSLLPMkgOl8RW9re6///501VVXpf333z8HcWeffXauTnrggQfyPs8991x+vmOPPTaHc9GwPCrIfvnll4n6HgIAAAAUfvW9CH+23nrrHB5F+HLAAQeknXbaqbp6Z2xuvPHGvCbxjTfeSJdeemluMH7llVdW3x8BVTTzfvHFF9Ott96am5QPGzas+v4IZyKEeeutt9Kbb76Z5p133hyqlJblNVX0W+rcuXNaeOGF83NG2BUVT3379h3r+Ub4s8Yaa+Qm6tG0/YwzzsjjjxK6CWGvvfZKb7/9du6bFSFdLNWLc42rF4ZPPvkkHXTQQenvf/977m8VIVm8XhP7PQQAAACoKVIZm62QrV27dlWhffv2FR+LzdactpYtW1Ztuumm+Wulx2KzNafN3LDZ6t/MD5ut/GZu2Gyp4nOj9Ld/fG1ov8IqpQAAAACgIo3Oy5lrrrkaXP4VfZpiWd6UIpYZ1if6Pr300kuFjgcAAABgsgylogdTQ1d+K/VomlI09FpEs3IAAACAyUHFQ6m4TGDv3r0rPYxmw2sBAAAATAn0lAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAo3RYdSV1xxRfrxxx9TVVVVWnLJJRvcd5555qm1X+fOnfPPM84443iN4csvv0yHHXZYo/aN59tiiy3ShPTss8+mHj16TNBjAgAAAIxNqzSF2nDDDdOee+6Z1lxzzfTFF1+kgQMHNrj/t99+m2adddax7jep2XrrrdPIkSMrPQwAAABgCjPFhlILLLBA+v7779Orr77aqP1Hjx6dfvjhhzS5GTRoUKWHAAAAAEyBpsjle9ddd1265JJLqpfkxRK6DTbYIL344os5pIlqqIceeijNP//89S7fK2fVVVdNL7zwQho2bFj65ptv0oUXXpimnXba6vtnmWWW9OCDD+b7ozpr5513Hq/z+Otf/5qefvrpfLwYcyxHnG666arvb9myZR5D6ZzOPvvsdP3116f77ruv3uV78VqccMIJ6Zprrkm//PJL+vrrr9O+++5bfX/r1q3TxRdfnPr27ZuGDx+evvrqq3T88ceP13kAAAAAU54pslIqejj17t077bfffmn55ZdPo0aNSmussUbq3r17+uCDD9L000+funbtmsObpZZaKodRYxMB1mOPPZZOPvnk9Le//S0HUBF8xRY/hwiEZp999rTWWmvlJXMXXXRR6tChwzidQ4Rdjz/+eK70inOI41x99dX5+fbaa6+8z3HHHZd22WWX/HPPnj3zeW+55ZY5iGrIUUcdlU455ZR01llnpW233TZdfvnl6fnnn0+fffZZOvTQQ9Pmm2+ett9++xy8zTXXXHmrT5s2bVLbtm2rf27Xrl11YBYb8B8xH6aaairzAuowN6B+5geUZ25A5edGY59jigylogJoyJAhOYwqLcm79957a+0TQVJUF3Xq1Cn9+9//Husxo7rolltuyZVJ4fPPP88BToQ5Bx54YJp77rnTxhtvnAOkt956K++z9957p08++WScziGqrKaeeuq0++6750qpGOPBBx+cK7wijOrfv3865JBDUrdu3dL999+fHxP3xxjG5pFHHslBVDjnnHPSEUcckYO0CKXiPHr16pVeeumlfH8EU2N7Xbp06TLG7eutt14aOnToOJ07TI7il/YyyyyTWrRokX83Af9hbkD9zA8oz9yAys+NaaaZplH7TZGhVDkLLrhgro5accUV05///OecHoYIYRoTSsWyviWWWCJXJpXEGx1v+nzzzZcWXnjhXB319ttvV9//6aefjnNPp44dO6b3338/B1IlL7/8cn6+RRZZJP3222+5Mfsbb7xRqy9WPH/p3OoT1WI19evXr7qiK6q9nnzyyTz2qAx7+OGH88/1iVAsKtBqVkr16dMnP2bw4MHjdO4wOYq5G1WZMa/8xxP8j7kB9TM/oDxzAyo/N0qrpMZGKPVfUWFU6p8U/ZIiuIkwKpafNUYs+YueTrEkr66oJopQalJR92p88aEtBVnvvvtuDtk22mijtO6666Y777wzPfXUU2m77bYre6wRI0bkra6YAP6BgNoiODY3YEzmBtTP/IDyzA2o7Nxo7PGnyEbndc0888xp0UUXTWeccUZ65pln8pK6mWaaqUnHeOedd/JSv+hVVXeLkCeOGU3Cl1122erHRFDV1OcpiR5RUZ1Vs5F6NFqPNz6qmGKJYlQ4xXLBkgiWolRvfMXSxwijoifXDjvskPtOjet5AAAAAFMmoVRK1Veni5BlgQUWyP2Tai45a4zovbTKKqvkK9NFWBTLAaMhePwcoh/To48+mqupVlhhhRwORWPymsvvmiL6V8USvRtuuCEttthiac0118zPddNNN+V+UiF+jp5OMY4IwKLfVYRHjWncXp/oL7XjjjvmJYILLbRQrpD6/vvvLcUDAAAAmkQo9d/laRG0RBXTRx99lHr06JGOOeaYJh3jww8/TJ07d87hz4svvpiXuUWPqlgKWBJXwYufo/l5NFa/8sorqwOkpho+fHjaYIMNcpXXm2++me6+++709NNP52bmNYOy2267Ld144435Kn3RWDyu2Bdh1vhUSR177LG5WXs877zzzpubp49P0AUAAABMmSJNsE0BW4sWLao++eSTqq5du1ZsDO3atasK7du3r/jrYbM1p61ly5ZVm266af5a6bHYbM1pMzdstvo388NmK7+ZGzZbqvjcKP3tH18b2k+j88lYXDlw/fXXz5VZbdu2zVVU0aT81ltvrfTQAAAAgCmc5XvNxM4775yXxpXbYknhuHbV33PPPfMyu5dffjktvvji+Yp50XQdAAAAoJJUSjUTDz74YHr99dfL3hdX7xsX3333XVpttdXGc2QAAAAAE55QqpmIJuSxAQAAAEwJLN8DAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKSrixjd6VnoIAAAAQAUJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAo3BQfSj377LOpR48eqbnYd9990zfffJNGjRqVDjvssNSczDPPPKmqqiotueSSlR4KAAAAMIlrVekB8D/t2rVLl1xySTryyCPTPffck37++edKDwkAAABgohBKNSNzzz13atOmTfrXv/6V+vXrV+nhAAAAAEw0U9TyvWmnnTbdcMMNaciQIalv3765IqmmXXfdNb355pvpl19+Sd9//3265ZZb0iyzzFJ9f69evdJRRx1V6zGxlC2WtC2wwAJjff655por3X///fn5owrqjjvuSB06dMj37bHHHumjjz7K33/55Zf5mLFcrj4zzDBD+uOPP9Kyyy6bf27RokX68ccf06uvvlq9zy677JKXApbMOeec+TkHDRqU942x1H2OvffeO3388cdp+PDhqWfPnunAAw+sdwxTTTVVuuaaa/J+cW4AAAAAjTVFVUqdd955qXPnzmmLLbZI/fv3T2eddVZaZpll0nvvvZfvb926dTrllFPSp59+msOi7t27p+uvvz5tsskm+f5rr7027bXXXumCCy6oPmb8/Pzzz6fevXs3+NwRGj3wwANp6NCheQytWrVKl156aQ6J1lprrfz122+/TU8//XRafvnl8/cDBgyo93gRnMW411xzzfT222+nxRdfPAdZSy+9dJpuuunSr7/+mp8nxhbi+R5//PEcWq2++uo50Dr55JPTY489lpZYYok0cuTItPPOO6euXbumgw8+OL377rv5WFdddVU+1o033ljr+aOi67bbbkvzzjtvPt7AgQPLjjP2a9u2ba0livn1SCm1bNmyEe8aTBliPkTQa15AbeYG1M/8gPLMDaj83Gjsc0wxoVQENVEFFNVQzzzzTHV10nfffVe9z3XXXVf9fVQrHXrooemtt96qDnkioIrQJkKjqKiKoCeCnKOPPnqsz7/OOuvk4Gi++earfs7dd989VyUtt9xy+XmieilEGPXDDz+M9ZjPPfdcDqUiJIuvTz75ZFp00UXTaqutlgOouO3cc8/N++6www75w7fPPvvUCtQGDx5c/djTTz89V4Ldd999+f6vvvoqderUKe2///61Qqnpp58+LzGMsCkCtQjI6nPCCSekLl26jHF7h2nbpI022mis5whTivilHSF5BNhxoQPgP8wNqJ/5AeWZG1D5uTHNNNM0ar8pJpSK5XURorz++uvVt8UytqiKKok3JwKUWJI300wz5RCn1OsplqjFkr4IY/72t7/lUGqzzTbLx7zrrrvG+vwdO3bM1U81Q7A4Zowh7otQqqmiCiqCthhnVEU98cQTuRdVhEwffPBBWmihhXJwFeKcFlxwwbx0sKapp546vzYvv/xyvj+W40V1VEkEb3UbrkeFVJzH2muvnX777bcGx9itW7dccVazUqpPnz6p/7AR6dFHH23yOcPk/A9EVDtG9aL/eIL/MTegfuYHlGduQOXnRmmV1NhMMaFUY/pNRXVRbNGLKaqVIoyKoCeWoJVcffXV6aabbkpHHHFErjSKZXfRf6kSXnjhhfxGR5i2xhprpBNPPDGHUscff3x6//33c/jz+eefV1c3xTK/OLe64lzj/rDvvvvWCu5C3Q/rI488kivOVl555fTss882OMYRI0bkra6qMseFKd3o0aPzvDA3oDZzA+pnfkB55gZUdm409vhTTCgVPZ8iHFlxxRVzxVJo3759WnjhhXPFUSx7+/Of/5wDnVI1UyyrqysCmVjKFw3AN9xwwxwGNUapGXg0Gy8dPyqkoiIrlvCNi6hgioqo6AEVPaGi6it6ZUVQtummm1b3kwrvvPNOXsIX99etlgqxBC9CrPnnnz/deuutDT7v5ZdfnpuyP/jgg7nfVoRjAAAAAE0xxVx9L4KkWJoWzc6jD9Jiiy2We0RFShjiKnW///57OuSQQ3Lfp1iaF03P64r943GxLC2uxvfaa6816vmfeuqp9OGHH+Yr+kUD8ehLFX2aYnldVDCNq3h8VD+VAqhYDhgBWARQNUOpeN5oRh7N1qPnVDQojyV/F154YZpjjjnyPqeddlruARWvQSz9++tf/5r23HPPXBVW1yWXXJIbpT/88MNp1VVXHefxAwAAAFOmKSaUCsccc0x68cUX00MPPZRDopdeeqk6EIrAJgKY7bbbLlcuRcVUfQ3MI9yKXlI1G6M3Rlz1L0KjqCyK5//iiy9yeDQ+IniKvk+l3lEhvq97WywxjKquCN/uvffeHFzFeURPqVKj8vg5GqHHssQI0OLY8ZpE0/dyItCKICuqx2IpHwAAAEBTRHsfWxO21VZbrer333+v6tChQ8XHMqlt7dq1qwoPfvZ9xcdiszWnrWXLllWbbrpp/lrpsdhszWkzN2y2+jfzw2Yrv5kbNluq+Nwo/e0fXxvab4rpKTUhRMPzWWaZJV+hL664F/2ZAAAAAGi6KWr53vjaaaed0tdff50bpB977LG17tt5551zA/FyWzQFHxfxuPqOGc8HAAAAMKlSKdUEN9xwQ97KiSvRvf7662XviyvjjYuNN944tW7duux9P/zwwzgdEwAAAKA5EEpNIEOHDs3bhBRNyQEAAAAmR5bvAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKURG7r9Cx0kMAAAAAKkgoBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFK5V8U8JKd34Rs80cnRV/n6bRWev9HAAAACAgqmUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQqnJzLPPPpt69OhR6WEAAAAANEgoBQAAAEDhhFKMt9atW1d6CAAAAMAkRig1GZpqqqnSOeeck3788cf0/fffp9NOO636vrnmmivdf//9aciQIennn39Od9xxR+rQoUP1/dddd1267777ah0vlgPGssCS+P7iiy/Otw8YMCA9/vjjBZ0ZAAAAMLloVekBMOHtscceqXv37mnFFVdMK6+8crr++uvTyy+/nJ5++un0wAMPpKFDh6bOnTunVq1apUsvvTQHU2uttVaTn+Pyyy9Pq666aoP7tWnTJrVt27b653bt2uWvLf67hZYtW47DWcLkJeZBBMrmA9RmbkD9zA8oz9yAys+Nxj6HUGoy9MEHH6SuXbvm7z///PN08MEHp3XWWSf/vPjii6f55psvfffdd/nn3XffPX388cdpueWWS2+99Vajn6NXr17puOOOG+t+J5xwQurSpcsYt3eYtk36o+o/32+00UaNfl6YXMUv7WWWWSa1aNEijRo1qtLDgWbD3ID6mR9QnrkBlZ8b00wzTaP2E0pNpqFUTbGEL5bodezYMX377bfVgVTo2bNnGjRoUL6vKaHU22+/3aj9unXrlqu2alZK9enTJ/UfNiKNHP2fVOrRRx9t9PPC5PwPRFVVVXrsscf8xxPUYG5A/cwPKM/cgMrPjdIqqbERSk2GRo4cWevn+NBFiV5jjB49OqemY2tk/uuvvzbqeCNGjMhbXRFH/bdQyj8UUGP+xXwwJ6A2cwPqZ35AeeYGVHZuNPb4Gp1PQaIqKhqdzznnnNW3RYXUTDPNlJfwhWhcPttss9V63FJLLVX4WAEAAIDJm1BqCvLUU0+lDz/8MN1yyy1p6aWXTssvv3y68cYb03PPPVe9HO+ZZ57J/aV22223tOCCC+Z+UH/9618rPXQAAABgMiOUmsJsscUWuYfUCy+8kEOqL774Iu2www7V9z/xxBPp//7v/9K5556b3nzzzbwONIIrAAAAgAlJT6nJzFprrTXGbVtttVX199HofMstt2zwGFEdVe6KeQ09BwAAAEBTqJQCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAK16r4p4SUdl+hYxo8eHClhwEAAABUiEopAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcK2Kf0pI6cY3eqaRo6vK3rfNorMXPh4AAACgWCqlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUKqGZ599NvXo0aMiz925c+dUVVWVZpxxxkKeb5555snPt+SSSxbyfAAAAAA1CaWaSfj1yiuvpFlnnTX9/PPPFRsXAAAAQFGEUhNQq1atxvmxI0eOTD/88MMEHQ8AAABAczXFhlLTTjttuuGGG9KQIUNS375905FHHlnr/ljatsUWW9S6bdCgQWmPPfaotfxt++23T88991waPnx42mWXXdLMM8+cbr311vTdd9+lX3/9NX3wwQdpxx13rD7Gddddl9Zcc810+OGH58fHFscqt3xv6623Th999FH67bff0pdffjnGGOO2E044IV1zzTXpl19+SV9//XXad999x/k1WWONNdLrr7+eny9ek27duqWWLVtW3z/99NOnm2++OQ0dOjTfH+dQySWPAAAAwKRr3Et7JnHnnXdeDoIieOrfv38666yz0jLLLJPee++9Jh3n7LPPTkcddVR69913c5gz9dRTp7fffjudc845OSjaZJNN0k033ZR69+6d3nzzzXTYYYelhRdeOIdNp556aj7GgAED0rzzzlvruDGWO++8M3Xp0iXdcccdaZVVVkmXXXZZ+vHHH3OYVhLPfcopp+Txb7vttunyyy9Pzz//fPrss8+adB6zzz57euSRR9L111+fdt9997Toooumq666Kp/T6aefnvfp3r17WnXVVdPmm2+eq7q6du061tesTZs2qW3bttU/t2vXLn9t8d+tnJpBGEwp4nM/1VRT+fxDHeYG1M/8gPLMDaj83Gjsc0yRodR0002X9t5777TrrrumZ555Jt8WFVBR3dRU//jHP9J9991X67YLLrig+vtLLrkkbbDBBrmiKkKpCKpGjBiRhg0b1uByvaiKevrpp9MZZ5yRf+7Vq1fq1KlTOuaYY2qFUhEkRRAVIgg74ogj0lprrdXkUOqggw5K3377bTr44IPzz59++mkOquKYET7Faxav0c4771z9mu211165YqohUckVwVpdHaZtk/6oKv+YjTbaqEljh8lB/NKOkLdFixZp1KhRlR4ONBvmBtTP/IDyzA2o/NyYZpppGrXfFBlKLbDAArl6J5aq1VyaF0FMU7311lu1fo7U8cQTT8wh1BxzzFFdKRQhVFN07NgxPfDAA7Vue/nll/OSuXiO0aNH59tieWBN/fr1Sx06dGjyecTzvfrqq2M8X1Q2zTnnnGmmmWbK5/LGG29U3x8B29hes1gCGBVWJXG8Pn36pP7DRqSRo8unUo8++miTxw+Twz8QsYT3scce8x9PUIO5AfUzP6A8cwMqPzdKq6TGZooMpRojQp9ID2tq3br1GPtF36iaopIpluhFePThhx/m+6OaKgKdiSEapNcUH7AIrZqLqAqLra6Io+oplPIPB1P07534/JsDUJu5AfUzP6A8cwMqOzcae/zmk14UKPo7RVCy4oorVt/Wvn373OupJPo8zTbbbNU/L7jggnkJ29hEz6WocLrllltyFdMXX3xR67ghnnts6yt79uyZj1X32LEsr1QlNSHF86288spjPF9UQ8WyxjiPGPfyyy9fff8MM8wwxrkBAAAANMYUGUpF9VJcsS6anUf/pcUWWyw3+K4Z9kTfpOivtNRSS6Vll102/fOf/yxb8VNX9H5ab731csATzcKvuOKK9Je//KXWPl999VUOxOKqe3/605/GqMgq9aVaZ5110sknn5wWWmih3Hw8xnP++eeniSGaqM8111zp4osvTossskhuZh4NzmPpXVRfxRX3opdVvGZx9cDobxWvYbxmcT8AAABAU0yRoVRpmd2LL76YHnroofTUU0+ll156KV81r+ZV7aLxd+xz66235jCoMX2hojH5O++8kx5//PH03HPP5R5P999/f6194lhRyvbxxx+ngQMHprnnnnuM48TV/KIv1Y477piv1BfNxuNqfTWbnE9I0bB84403TiussEJ6//33cwgXoVOp0Xqp+Xr0nXr44YfzaxY9p6LCKq7QBwAAANAUUaKjzIVxMu200+am5RHgXXvttY1udhZLAh/q1a/eRufbLDr7BB4pNH+xpDeuPBmN/vU+gP8xN6B+5geUZ25A5edG6W//aPszZMiQevfT6JxGi6WMsSQxrsA344wz5sqtUPcqgQAAAABjM8Uu35vcnXDCCTmNLLc98sgj43zco48+Oi/vi+V70fh99dVXTz/++OMEHTsAAAAw+VMpNZmKnlB33nln2fuGDx8+Tsd877330nLLLTeeIwMAAAAQSk22Bg0alDcAAACA5sjyPQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAK16r4p4SUdl+hYxo8eHClhwEAAABUiEopAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAAonlAIAAACgcEIpAAAAAArXqvinhJRufKNnGjm6qux92yw6e+HjAQAAAIqlUgoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlKujZZ59NPXr0qMhzd+7cOVVVVaUZZ5yxIs8PAAAATNmEUlNo+PXKK6+kWWedNf38888VGxcAAAAw5RJKTcJatWo1zo8dOXJk+uGHHyboeAAAAAAaSyhVkGmnnTbdcMMNaciQIalv377pyCOPrHV/LKXbYostat02aNCgtMcee+Tv55lnnrzP9ttvn5577rk0fPjwtMsuu6SZZ5453Xrrrem7775Lv/76a/rggw/SjjvuWH2M6667Lq255prp8MMPz4+PLY5Vbvne1ltvnT766KP022+/pS+//HKMMcZtJ5xwQrrmmmvSL7/8kr7++uu07777TqRXDAAAAJicCaUKct555+UgKIKn9ddfPwdFyyyzTJOPc/bZZ6cLL7wwdezYMT3++ONp6qmnTm+//XbaZJNN0l//+td05ZVXpptuuiktv/zyef/DDjssL9WL22O5XmzffvvtGMeNsdx5553p9ttvT4svvnjq0qVL+r//+7/qUKzkqKOOSm+99VZaeuml02WXXZYuv/zytPDCC4/HKwMAAABMicZ9/ReNNt1006W999477brrrumZZ57Jt0XYE9VNTfWPf/wj3XfffbVuu+CCC6q/v+SSS9IGG2yQK6refPPNXNE0YsSINGzYsAaX60VV1NNPP53OOOOM/HOvXr1Sp06d0jHHHJMrvEoeeeSRHESFc845Jx1xxBFprbXWSp999lnZ47Zp0ya1bdu2+ud27drlry3+u5XTsmXLRr0WMDmJz/1UU03l8w91mBtQP/MDyjM3oPJzo7HPIZQqwAILLJCDmddff73W0rxPP/20yceKKqWa4gN14okn5hBqjjnmqA6BIoRqiqi8euCBB2rd9vLLL+dlf/Eco0ePzrfF8sCa+vXrlzp06FDvcWO5X1Rd1dVh2jbpj6ryj9loo42aNHaYHMQv7ahYbNGiRRo1alSlhwPNhrkB9TM/oDxzAyo/N6aZZppG7SeUaiYi9IkPRk2tW7ceY7/oG1VTVDLFEr0Ijz788MN8f1RTRTg1MUSD9JqiL1WEVvXp1q1b6t69e61KqT59+qT+w0akkaPLp1KPPvroBBwxTDr/QMR8euyxx/zHE9RgbkD9zA8oz9yAys+N0iqpsRFKFaB37955Cd2KK65Y3c+pffv2uRfT888/n38eMGBAmm222aofs+CCC+Zlf2Oz6qqr5gqnW265Jf8cwVYc9+OPP67eJ557bKVzPXv2zMeqe+xYlleqkhoX8dyx1RVxVD2FUv7hYIoVcy0+/+YA1GZuQP3MDyjP3IDKzo3GHl8oVYCoXoor1kWz8x9//DH1798/nXnmmbXCnug1dfDBB6dXX301B0jRr6lcmFNX9H7adttt08orr5yXBEZvqL/85S+1QqmvvvoqB2Jx1b2hQ4emn376aYzjRF+q6EF18sknpzvuuCMfL8Zz0EEHTcBXAgAAAOA/XH2vILHM7sUXX0wPPfRQeuqpp9JLL72Ur5pX86p2UUUV+9x6663p/PPPb1RfqGhM/s477+Qr8T333HO5x9P9999fa584VqSUEVQNHDgwzT333GMc59133819qXbcccf00Ucfpa5du6ZTTz21VpNzAAAAgAlFpVSB1VK777573mqGRSXff/992nDDDWs9ZqaZZqr+/uuvvx6j51SI6qitttpqrNVUq6yySq3byh3v3nvvzVt95ptvvjFuW3rppRt8bgAAAIByVEoBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAULhWxT8lpLT7Ch3T4MGDKz0MAAAAoEJUSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQuFbFPyWkdOMbPdPI0VX13r/NorMXOh4AAACgWCqlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUKqZuO6669J9993XqH2fffbZ1KNHj4k+JgAAAICJRSg1gcOfSSUwakoIBgAAADChCaUAAAAAKJxQqoFKojXXXDMdfvjhqaqqKm/zzDNPWmONNdLrr7+efvvtt9S3b9/UrVu31LJlywYfM9VUU6Wrr746ffHFF2nYsGHpk08+SYceeuh4ja9Vq1bp4osvToMHD04DBgxIXbt2rb7vlFNOSR9++OEYj3n33Xfzfqeddlrac88905Zbblk9zs6dO+d95pxzznTHHXekQYMGpR9//DHdf//9+RxKYr84/6FDh+Z9XnrppTT33HOP17kAAAAAUx6hVD0OO+yw9Morr6Qrr7wyzTrrrHkbOXJkeuSRR9Kbb76ZllxyyXTggQemvffeO5188sn1Pubbb7/NodR3332Xtttuu9SpU6ccDJ111ln553G1xx57pD/++COtsMIK+XmPPPLItM8+++T7rr322tSxY8e03HLLVe+/1FJLpSWWWCIHZ+eff34Onh599NHqcca4I+h6/PHH05AhQ9Lqq6+eVl111Rw+PfbYY6l169Y5fIuQ6vnnn8/HWnnllfO5RqgFAAAA0BStmrT3FOSXX35JI0aMyJVNP/zwQ77tjDPOyCHTwQcfnH/+9NNP0+yzz57OOeecHDSVe0wYPXp06tKlS/XPX331VQ50tt9++3TXXXeN0/hiHEcccUT+/rPPPkuLL754/jkqsvr06ZPDpb322iu99dZbeZ/4PsKkL7/8Mv88fPjw1LZt21rj3GWXXXKAVgq3So+LaqyoAItjtW/fPj388MO56itE1VdD2rRpk5+npF27dvlri/9u9SlVn8GUIj7zMf989qE2cwPqZ35AeeYGVH5uNPY5hFJNENVHr776aq3bXn755Ry0xLK3CIrqc9BBB6W//e1veanbNNNMk8Oa9957b5zH8tprr9X6OcZ11FFH5Q9YhGBXXXVVrpiKCqr4eeedd64OseoT1V8LLrhgrpSqaeqpp04LLLBAevLJJ3OlVQRe8f1TTz2V7rzzztSvX796j3nCCSfUCuRKOkzbJv3RQIHVRhtt1OBYYXITv7SXWWaZ1KJFizRq1KhKDweaDXMD6md+QHnmBlR+bkTu0RhCqQLssMMOeclchEYRHkXoc8wxx6QVV1xxoj3nQw89lH7//fe01VZb5eqtWH539913N/iY6aefPr399tu5Yqqu6FsVIli76KKL0oYbbpjPK6rH1ltvvdxnqpzoudW9e/fqnyPAi0qu/sNGpJGj60+lYmkhTGn/QMRS2Fgu6z+e4H/MDaif+QHlmRtQ+blRWiU1NkKpBkSYU7PkrGfPnmmbbbaptU/0XYple9EzqtxjSvtEz6bLL7+8+raoPBofdQOtlVZaKfXq1StXRYX4gN1www15+V2M6fbbb8/N2es7t/DOO+/koKl///5jVEvVFBVesZ199tn5vKIKq75QKp4ntroijmqoE5V/PJgSxfyNz77PP9RmbkD9zA8oz9yAys6Nxh5fo/MGRO+nCH/i6nN/+tOf0mWXXZbmmmuufNW7RRZZJG2++ebp9NNPz5VApWbfdR8TZXERFkXT8fXXXz8ttNBCuf/U8ssvP15ji2WAF1xwQVp44YXTjjvumA455JB04YUX1ton+kutvfbauaoplvLVPbdoVh6Pj3FGk/NbbrklDRw4MD3wwANptdVWS/POO2++2l4cd4455sg/R4P2CMDi+aNCKs4nwjoAAACAphBKNSCW3EW69/HHH+ewJpbAbbzxxvmKd++//3765z//ma655pq8hK2+x0R4c8UVV6R77703X/EuKopKAdf4uPHGG/MazTfeeCNdeumlOTiKK+HV9Pnnn+dKpmhGHvvVFD2nolF7NC+PcUY1VzQ/X2ONNdI333yTxxthU5xf9JSKarBo4L7oooume+65JzdXj+eL547zAwAAAGiKFmNZRcUkLqq0IgDr0aNHag5iXWkEXA/16tdgT6ltFp290HFBpcVy2mjwH/3UlJnD/5gbUD/zA8ozN6Dyc6P0t/8MM8zQYHsgPaUmU3/+85/zsr5ZZ501XzEPAAAAoDkRSjUz0bMqlv7Vp1OnTunbb78d63Hianmx7bfffmnw4METeJQAAAAA40co1cz07ds3LbXUUg3e3xjRYB0AAACguRJKNTOxrrN3796VHgYAAADAROXqewAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUrlXxTwkp7b5CxzR48OBKDwMAAACoEJVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4VoV/5SQ0o1v9EwjR1fVe/82i85e6HgAAACAYqmUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUKtiXX36ZDjvssNQczDPPPKmqqiotueSSlR4KAAAAMIURSk1inn322dSjR48Jcqxvv/02zTrrrOmjjz6aIMcDAAAAaKxWjd6Tyc7o0aPTDz/8UOlhAAAAAFOgZlUptcEGG6QXX3wxDRo0KA0cODA99NBDaf7558/3vfzyy+nss8+utf+f//znNGLEiLT66qvnn6Pq5+GHH07Dhg1LX3zxRdppp52atFwulrIdcMAB6ZFHHsnH6N27d9pmm21q7RNj+PTTT9Ovv/6a7+/atWtq1ap2trfpppumN954Iw0fPjwNGDAg3XvvvfU+5957753Pd+21184/L7bYYvn5hwwZkvr165duvPHG9Kc//Snfd91116U111wzHX744XmsscUSvIa0b98+3Xzzzal///75nD777LO05557ll2+F8cvHbfm1rlz53x/mzZt0nnnnZe+++67NHTo0PTaa69V3wcAAAAwyYZS0003XerevXtabrnl0jrrrJMree67777UokWLdMstt6Qdd9yx1v477LBD6tu3bw6yQgQ4s88+ew5uIkzab7/9UocOHZo0hv/7v/9L99xzTw5q4jlvv/32tOiii1bfH2FRhDqdOnXKYde+++6bjjjiiOr7N9544zzmCJaWXnrpfB4RUJVzzDHH5JBr/fXXT88880yaccYZ89d33303vwYbbrhh+stf/pLuvPPOvH883yuvvJKuvPLKHMDFFkvwxnY+MdaNNtoodezYMR144IE58Csnjl86bmz/+Mc/ciXVJ598ku+/5JJL0sorr5zfhyWWWCLddddd6bHHHksLLrhgk15jAAAAgGa1fK9uRdHf/va3HKBEqBLBTIQkq622WnrppZfy/TvvvHO67bbb8veLLLJIWm+99XKY8/bbb+fb9tlnn/T55583aQwRtFxzzTX5+1NPPTUf85BDDkl///vf821nnnlm9b5ff/11Ov/883NIExVE4aSTTspBVpcuXar3++CDD8Z4ngijdtttt1xp9PHHH+fbDj744BxIxTFqvgZRmbTQQgulXr165cqwqHhq7LK7ueeeOx+z9JrEmOvzyy+/5C1stdVWaf/990/rrrtufq655por7bXXXvl433//fd7nggsuyMFZ3F5zzDVFdVXbtm2rf27Xrl3+2uK/W31atmzZqPODyUV85qeaaiqffajD3ID6mR9QnrkBlZ8bjX2OZhVKRcVNLIdbccUV89K8eLFCBCH//ve/0xNPPJF22WWXHErNO++8aZVVVsnBSSmUGjlyZHrnnXeqjxfL63766acmjeHVV18d4+elllqq+uftt98+HXrooWmBBRZI008/fV66VwpyQux71VVXNfgcRx11VK4KiwAtlheWRHXWWmutlaux6orni1CqqS6//PJc+bXMMsvk1+/+++8f4xzrinO46aabckgWlVlh8cUXz+cay/9qisDpxx9/rPdYJ5xwQq2ArqTDtG3SH1X1jyEqu2BKEr+0Y55GZeioUaMqPRxoNswNqJ/5AeWZG1D5uTHNNNNMeqFU9JCKSp5YEhfL8iKUijAqqm1CLKe76KKLcuVSVElFBVKRV45baaWV8hhOO+209Pjjj6eff/45V0lFyFQSfaTGJpYbbrLJJjngOuecc6pvj5ArXoPjjjtujMeUqpOaKpbXRe+oWFYYVV9PP/10uvTSS/PSwXJiueCDDz6Yrr766nTttdfWGtsff/yRll122TE+vNFfqj7dunXLSzJrVkr16dMn9R82Io0cXX8q9eijjzbxTGHS/wcierjFnPUfT/A/5gbUz/yA8swNqPzcKK2SmmRCqZlnnjn3bopAqrQ8b9VVV621zwMPPJD7KcWSsQiloodUSTQfb926de7jVKqWiuqiOG5Tg6eoEqr5cyx/C1GZFaHZWWedVX1/3UbjEZRFH6nrr7++3ueIHlPRnyk+CBH0xDK4EOOOXlhfffVVvR+QWL7X1FK7WAIZr1VsEYjFUsNyoVRUPcVrHD2kjjzyyFr3xWsQlVLRo6v0/jRGjDe2uiKOaqBQyj8eTJGij1589n3+oTZzA+pnfkB55gZUdm409vjNJpQqXXEvmpNHVVAs2at7tb3opRTLz6J5dzTtLvWTKoVSTz75ZA6topl3LOWLsCceE0lgY2233XbprbfeysFLLBVcYYUV8hXyQiyfi3FFg/U333wzVztF76WaTj/99FyNFEsHo7dUBDlRpXTuuefW2i+W0MXtUREUwdSFF16YK5gilIvziv1j6WEsaYxqrOiPFR+eCKxieWOEYVGhFPs0dH4xnugnFRVnETrFlQF79uxZdt8rrrgi946KUG2WWWapvj2eI849ruIXwVZUhkVIFfvEvhHERWN3AAAAgEnu6nsRrET4EsvDYklejx49ylbzxPK56HkUFT91rzy3++6756bcL7zwQr4CXvR2iv5Mv/32W6PHEUvzYhwRtMTxdtppp+oQJ5bWxbiiyum9997LlVMRkNX0/PPP52Br8803z/vE1fQi2Crn5ZdfzsHWGWeckfs3RRgX1WFRCRX9nz788MPc3H3w4ME5kArRWD0Sx2iOHiFehGQNiSqlWEIX5xOvSzy27lUMS6Lpely9MM63X79+1VucZ4iG5hFKRdgXIWAEhMsvv3z65ptvGv36AgAAAKT/XgCt8WVEk5g55pgjX7kuqnkiHGpMMLblllvmJWxMvHWl0Rj+oV79Guwptc2isxc6Lqi0CKOjwX9UTyozh/8xN6B+5geUZ25A5edG6W//GWaYoezF3Jrd8r0JIa5cFw25o8Jottlmy0vg4up2USEEAAAAQPPRbJbvTQjR6DyakEf/pFi+N2DAgLTmmmvmnk3RGD3SuXJbkVfwm9Auv/zyes8r7gMAAABojiarSqnow7T44ouXve/BBx9Mr7/+etn7oil6aNEiVjNOWk499dTcZ6qcKJUDAAAAaI4mq1CqIXGlutgmN1ENFhsAAADApGSyWr4HAAAAwKRBKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABSuVfFPCSntvkLHNHjw4EoPAwAAAKgQlVIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhWhX/lJDSjW/0TCNHV411v20Wnb2Q8QAAAADFUikFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUpOg0047Lb377ruVHgYAAADAOBNKTaH22GOPNGjQoEoPAwAAAJhCCaUqoHXr1pUeAgAAAEBFCaUmoG222SZ98MEHadiwYWngwIHpySefTNNOO2267rrr0n333ZdOPPHE1KdPn/Tpp5/m/eeYY4506623ph9//DENHTo0vfnmm2mFFVZo9PPtt99+6Ztvvkm//vpruuOOO9IMM8yQb1999dXTiBEj0l/+8pda+/fo0SO98MILqXPnzun6669P7du3T1VVVXmLJYGhTZs26bzzzkvfffddHtNrr72W9y+Ze+6504MPPph++umnfP9HH32UNtpoown0CgIAAABTilaVHsDkYtZZZ0233XZbOvbYY3MA1a5duxwOtWjRIt+/zjrrpF9++SWtt956+efpppsuPf/88zmk2nzzzVO/fv3SMsssk6aaqnE54YILLpi23377tNlmm+Uw6pprrkmXXXZZ2nXXXdOLL76Yvvjii7Tbbrul888/P+/fqlWrtMsuu+TxvfLKK+mwww5LXbt2TYssski+PwKmcMkll6ROnTqlHXfcMfXt2zdttdVW6bHHHkuLL754+vzzz9Oll16ag6s11lgjh2Gxb+mx5cS+bdu2rf45XpcQr8p/XpmGtWzZslGvB0zq4rMe899nHmozN6B+5geUZ25A5edGY59DKDWBzDbbbHlZ3r333purl0JUEZVEgLPPPvukkSNH5p/33XffNMsss6Tll1++urdT7969G/18U089ddp9991zcBQOOeSQ9K9//SsdddRR6Ycffsgh1V577VUdSkV4FY+588478xh+/vnnXCEV+5bMNddc+TFRDfX999/n2y644IK04YYb5ttPOumkfN8999xTfW5ffvllg+M84YQTUpcuXca4vcO0bdIfVWM/T1VYTCnil3YE0xFkjxo1qtLDgWbD3ID6mR9QnrkBlZ8b00wzTaP2E0pNIO+//3566qmn0ocffpgef/zx9MQTT6S77747DR48ON8ft5cCqbDUUkvlK+iNa7PxCL5KgVR49dVX8wcsKp8iaIrleWeccUZaccUV0+uvv5723HPPHEjF0sL6RDVUVFR99tlntW6PSqdYYhguuuiidPnll6f1118/n28EVHFu9enWrVvq3r17rUqpqA7rP2xEGjl67KnUo48+OtZ9YHIQ8zeC4qhM9B9P8D/mBtTP/IDyzA2o/NworZIaG6HUBDJ69Oi8NG+VVVbJgU1ULp155pk5FCpVStU0fPjwiTqeAQMGpIceeihXOEU1U1Qcrbnmmg0+Zvrpp09//PFHWnbZZcf4gJaW6EUFVoRum2yyST7PqISK6qxY9ldO9LaKra6IoxpRKOUfEaa43yPxmfe5h9rMDaif+QHlmRtQ2bnR2ONrdD6BRb+mWK629NJL5zAmejKVEw3Ro1pqpplmGqfniWV0sWSwZKWVVspveqmJerj66qvTDjvskBuix9LAGFtJjK3uGs+o3IpKqQ4dOuT9a241l/lFE/QrrrgiN3aP5X2xFBEAAACgKYRSE0hcNS+qhqLKKHozbb311rlnVM+ePcvuH03Ro7n5/fffn6ur5ptvvvyYCJca47fffks33HBDWmKJJdJqq62Wl9XF8rya4VFUNEVz9ZNPPjlfAbCmr776KpfTrb322ulPf/pTXu/Zq1evdPPNN6cbb7wxh2nzzjtv7nl1/PHHp4033rj6Cn5RIRX3RfC21lpr1XuOAAAAAPURSk0gEf7EFekeeeSR3JMp+jnFsrZYq1lO9JeKcKd///75MdGXKcKfxpa4xZXwoql6PDb6V0Xl1UEHHVRrn1grGr2loiIqgqaaogdV9Ia644470sCBA/NV+UIs94t9owIqqq4iNItgqtS8PY4VV+CLICrOLc617vMCAAAAjE2LRrb2YRIVS/iiYmuLLbZIzUFUZ0WA91Cvfo1qdL7NorMXMi6otAh8o/dbNPfX+wD+x9yA+pkfUJ65AZWfG6W//WeYYYY0ZMiQevfT6HwyFW98XE1v5513TptvvnmlhwMAAABQi+V7zdBHH32Uk8RyW4RMjfHAAw/kZX3//Oc/01NPPTXRxwwAAADQFCqlmqFoKt66deuy99VsZN6QaEAOAAAA0FwJpZqhUlNxAAAAgMmV5XsAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhWhX/lJDS7it0TIMHD670MAAAAIAKUSkFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUrlXxTwkp3fhGzzRydNVY99tm0dkLGQ8AAABQLJVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKDWOnn322dSjR480OZsSzhEAAACojFYVet4p3nXXXZfat2+fttpqq0oPJXXu3Dk999xzeTw///xz9e1bb711GjlyZEXHBgAAAEyehFKTsdatW49XqDRo0KAJOh4AAACAEsv3xkOrVq3SxRdfnAYPHpwGDBiQunbtmm8/5ZRT0ocffjjG/u+++27e57TTTkt77rln2nLLLVNVVVXeolopzDnnnOmOO+7IgdCPP/6Y7r///jTPPPM0uvrqvvvuSyeeeGLq06dP+vTTT/Ptu+66a3rzzTfTL7/8kr7//vt0yy23pFlmmSXfF8eOKqkQ5xFjieOUW74XlVQ33HBD+umnn9Kvv/6aHnnkkbTggguO9+sIAAAATHlUSo2HPfbYI11zzTVphRVWSMstt1y68sor0zfffJOuvfbaHDzFbW+99Vbed6mllkpLLLFEXhLXv3//1LFjxzTDDDOkvfbaK98fQU+EXI8//nh69dVX0+qrr57++OOPdPLJJ6fHHnssP7YxVU/rrLNODp/WW2+9WhVTEZRFSNWhQ4fUvXv3dP3116dNNtkkffvtt3lM9957b1p44YXzY4cPH1722PGYhRZaKG2++eZ5v3POOScHU506dcpjLadNmzapbdu21T+3a9cuf23x321sWrZs2Yi9YNIXn/WppprKZx7qMDegfuYHlGduQOXnRmOfQyg1HiLQOeKII/L3n332WVp88cXzz1dffXUOlyJwKoVS8f3zzz+fvvzyy/xzBD8R1vzwww/Vx9tll13yB2Sfffapvi0eFxVMa665ZnryySfHOqaoYIrH1wywSpVPIZ7/0EMPzeOabrrp8v4RiIUIy2r2lKopKqK22GKLtMoqq+TQrDTeeA2i4uvuu+8u+7gTTjghdenSZYzbO0zbJv1RNdbTSRtttNHYd4LJQPzSXmaZZVKLFi3SqFGjKj0caDbMDaif+QHlmRtQ+bkxzTTTNGo/odR4eO2112r9HGHNUUcdlYOlq666KldMHXnkkWn06NFp5513rg6w6rPkkkvm8GfIkCG1bp966qnTAgss0KhQKpYN1q2oig9dBENx/JlmmimPL8w999ypZ8+ejTrXqOyK477++uvVt0WYFdVXcV99unXrliuzalZKxdLC/sNGpJGjx55KPfroo40aH0wO/0DE8tmojPQfT/A/5gbUz/yA8swNqPzcKK2SGhuh1ETy0EMPpd9//z1fXW/EiBF5CV191UQl008/fXr77bdzBVJd0bOqMaLyqaZpp502V23FFseN40QY9cQTT+SldRNbnHtsdUUc1YhCKf+IMEWJADs+8z73UJu5AfUzP6A8cwMqOzcae3yh1HhYccUVa/280korpV69euU3OURT8Fh+F6HM7bffnn777bfqfeO2umss33nnnbTDDjvkZXR1q6XG1aKLLpr+/Oc/p+OPPz599913+bbodVVTKTRqaM1nVFRFsBbnXFq+N/PMM6dFFlkkffzxxxNkrAAAAMCUw9X3xkNUHF1wwQW5QfiOO+6YDjnkkHThhRdW3x+9pdZee+204YYb5qV8NX311Ve5eXk89k9/+lNuch5XxRs4cGB64IEH0mqrrZbmnXfefFW+OOYcc8wxTmOMxutRsRVjm2+++dJmm22Wm57X9PXXX+cgbdNNN80BVvSaquvzzz/PVwKMZYmrrrpqHvvNN9+cl+LFeAEAAACaQig1Hm688cbcvOuNN95Il156aQ6P4gp8NYOcV155JX3yySd5n5oi3Il+TNFwPIKoCHqi+fkaa6yRg6S4Gl5UJ8XV/aKnVFztblzEsffcc8+03Xbb5YqmqJg6+uija+3Tt2/ffLXAs88+Ozdev+SSS8oeK6q+Ynnhww8/nKulojnaxhtvXO+V9wAAAADq06KRrX0YR7Gc77LLLks9evSo9FCahWh2FgHbQ736NarR+TaLzl7IuKDSYvlsXG0ymvvrfQD/Y25A/cwPKM/cgMrPjdLf/jPMMEOD7Yn0lJpIYhlcLOmbddZZ03XXXVfp4QAAAAA0K0KpiSSuchfbfvvtlwYPHjxBjtlQuhhp50svvTRBngcAAABgYhNKTSTRb2lCW2qppeq9LxqOAwAAAEwqhFKTkN69e1d6CAAAAAAThKvvAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhWtV/FNCSruv0DENHjy40sMAAAAAKkSlFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAULhWxT8lpHTjGz3TyNFVjd5/m0Vnn6jjAQAAAIqlUgoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlJqJnn3029ejRY4Ift3PnzqmqqirNOOOME/zYAAAAAEUQSk3G4VVD5plnnhxsLbnkkoU+LwAAAEAQSgEAAABQOKHUBDLttNOmG264IQ0ZMiT17ds3HXnkkbXuP/DAA9Nnn32Whg8fnvr165fuuuuufPt1112X1lxzzXT44YfnyqXYooqpMVZdddX0/vvv52O++uqrabHFFqsey88//5y22WabWvtvscUWaejQoWn66adPX331Vb7tvffey88Z1Vole++9d/r444/zcXv27JnHXtK6det08cUX53OM++M4xx9//Hi8cgAAAMCUqFWlBzC5OO+883Kvpwh++vfvn84666y0zDLL5NBn2WWXTRdddFHabbfd0iuvvJJmnnnmtPrqq+fHHXbYYWnhhRdOH330UTr11FPzbQMGDGj0c8bjI+SK53vooYfysYYNG5Zuv/32tNdee6V77rmnev/4+e67787B1PLLL5/efPPNtM4666R///vfacSIEXmfnXfeOXXt2jUdfPDB6d13301LL710uuqqq9Kvv/6abrzxxnTooYemzTffPG2//fbpm2++SXPNNVfe6tOmTZvUtm3b6p/btWuXv7b479ZYLVu2bMLeMOmJz/hUU03lsw51mBtQP/MDyjM3oPJzo7HPIZSaAKabbrpcXbTrrrumZ555Jt+2xx57pO+++y5/P/fcc+dQ5+GHH86BUIQ5EVaFX375JQdCEST98MMPTXre008/PT311FO1nm+rrbbKVVhXX311DsBmnXXWHFrNMsssaeONN07rrrtureDrxx9/rPW8ccyjjjoq3XffffnnqITq1KlT2n///XMoFefSq1ev9NJLL+X741wacsIJJ6QuXbqMcXuHadukP6oaf64bbbRR43eGSVD80o4gu0WLFmnUqFGVHg40G+YG1M/8gPLMDaj83JhmmmkatZ9QagJYYIEFcjXQ66+/Xn3boEGD0qeffpq/f/LJJ9PXX3+dvvjii/TYY4/lLUKfWP42PmLJXt3n69ixY/45qqCiAirCqnPOOScHZjGGF154od7jxbK/BRdcMF1zzTW5OqqkVatWeTlguP766/P5xHPFeUTQFj/Xp1u3bql79+61KqX69OmT+g8bkUaObnwq9eijjzZ6X5hU/4GIpbQxr/zHE/yPuQH1Mz+gPHMDKj83SqukxkYoVYCojoo0MnpHrb/++nl5XFQPxRK6UtgzMUS11N///vccSsXSvehf1ZDoNRX23XffWgFbKH1gY0nffPPNlyuXourqzjvvzNVa2223XdljRhVYaWlgTRFHNaFQyj8mTBFGjx6dP+s+71CbuQH1Mz+gPHMDKjs3Gnt8jc4ngN69e+fgZcUVV6y+rX379rm/U8035Omnn07HHXdcWmKJJdK8886b1l577XxfPHZc1nSutNJKYzxfNCYvufnmm3PT9EMOOSQvwYtG7CWloKjm80YvrKhimn/++fM51dxKjdFDNHOPMGq//fZLO+ywQ9p2223TTDPN1OTxAwAAAFMulVITQPSLiiVv0Xg8ejRFuHPmmWfmBDJssskmOeiJpXOxzC56O0VzsdLyvgh8ItCKACmqqn766adcUjc20Ri91BMqnm/gwIHp/vvvr75/8ODB6d57783jeuKJJ3LgVBJjjD5WG264Ye5F9dtvv+X+Vqeddlpuyh4VXFHSF8sSl1tuuRw69ejRIx1xxBHp+++/zxVTcX5RIRU/x3MBAAAANJZKqQnkmGOOSS+++GK+Al4sZ4tG4G+//Xa+LwKbrbfeOjdBj0qmAw44IO20007p448/zveff/75uZIqfo5gKZqJN8bxxx+fLrzwwvw80dB8s802SyNHjqy1T4RlESxde+21tW6P54sr6UUD8759+6YHHnigev999tknL/f78MMP0/PPP5/23HPP9OWXX1ZXSR177LHprbfeyn2rouIrQrbGhGgAAAAAJS2a2NqHSUw0OI8Kp9lnn32MwKoSotlZVGQ91Ktfkxqdb7Po7BN1XFBpsZQ2erVFU3+9D+B/zA2on/kB5ZkbUPm5Ufrbf4YZZsjFLfWxfG8yFZdfnG222XI11RVXXNEsAikAAACAEsv3mqHLL788J4nltrivMWKJ3SeffJL69euXunXrNtHHDAAAANAUKqWaoWhgHn2myonyt8Y4/fTT8wYAAADQHAmlmqEBAwbkDQAAAGByZfkeAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIVrVfxTQkq7r9AxDR48uNLDAAAAACpEpRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhWtV/FNCSje+0TONHF1V6WFAs9EipfSX6dqmv/36ezIz4H/MDaif+QHlmRuMq20Wnb3SQ5jiqJQCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCqQnsiiuuSD/++GOqqqpKSy65ZKWHAwAAANAsCaUmoA033DDtueeeadNNN02zzjpr+uijj9Lk6tlnn009evSo9DAAAACASVSrNImbaqqpclVSbJW2wAILpO+//z69+uqr9e7TunXrNHLkyELHBQAAANDcNItKqU022SQNGjQoB0whlr1FyNStW7fqfa666qp00003pT322CPvu9lmm6V///vf6ffff09zzz13at++fbrhhhvSTz/9lH799df0yCOPpAUXXLD68aXHrb/++unjjz9OQ4YMSY8++miuaCpp2bJluvDCC/N+AwcOTGeffXa6/vrr03333TfWc7juuuvSJZdckuaZZ5489i+//LK6oujiiy/OVUUDBgxIjz/+eL79iCOOSB988EEaOnRo+uabb9Kll16apptuulrH3GefffJ9cT733ntvfkyMreS0005L7777btprr73S119/nc8pjhOv4zHHHJMDsh9++CGdeOKJtY4744wz5tezf//+6eeff05PP/10WmKJJcY47q677prPY/Dgwem2225L008/ffW5rrnmmunwww+vDgTjvAEAAAAmqUqpF198MbVr1y4tvfTS6e23306dO3fOAU4EHyVx2znnnJO/n3baadNxxx2XQ5vo3xThSoQmCy20UNp8883TL7/8kveNYKpTp07pjz/+qH7c0UcfnXbbbbc0evTodPPNN6fzzz8/hy8hjrnLLrvkkKdnz57psMMOS1tuuWUOlsYm9u3du3fab7/90vLLL59GjRpVKxC7/PLL06qrrlp9Wzz/oYcemkOf+eefP1122WXp3HPPTX//+9/z/ausskr65z//mcf04IMPpnXXXTf93//9X9nqrI022igvHYzv77777ny8zz77LL9mcZwIkZ566qn0xhtv5Mfcddddafjw4flxEUrtv//+OZhaeOGFq0OvOFaceyxFnGmmmdKdd96Zjj/++HTyySfnc419Y3niqaeemveP96ucNm3apLZt21b/HO9zaPHfDfiP0nwwL6A2cwPqZ35AeeYG4yoKVSb385tqqqkKOc/GPkezCKUiRHrvvfdyCBWhVHyNyqKo2InqoajsicDp+eefz8FOBB0HHXRQrjQKURG1xRZb5ACmtHQuwqVvv/02BysR1IR43AEHHJC++OKL/HNUNpVClXDIIYfk6qz7778//3zwwQenjTfeuNHnEJVKEUZFdVJNvXr1yuFSTVGRVRJVThH2RAhVCqViLFHJdcEFF1QfI84vQqKa4gP1t7/9LVdcRZAWAdoiiyySxx0VTBFOxXOvtdZaOZSK12+FFVZIHTp0SCNGjMjHiKqqeJ223XbbXEFVOm70x4rjhqhSW2eddfI441zjscOGDRvjXOs64YQTUpcuXca4vcO0bdIflV9xCc1G/EdT+6lb56+mBvyPuQH1Mz+gPHODcRWFG5Ozli1bpmWWWSa1aNGiViHNxDDNNNNMOqFUiMApwqgIYVZfffUcZmy//fZptdVWSzPPPHPq06dP+vzzz3OoEkv2SoFU6NixY+7T9Prrr1ffFsv4Pv3003xfSSyDKwVSIZa3RTgTZphhhryUr1RNVKpmipCstKxwXMUx6oqAJ85x0UUXzc/dqlWr/KbFFlVMESzVXTYYY6sbSn311VfVwVGIkCg+XDV7bMVtpfOMpZGxDC8qzGqK543qqPqOW/O1aooI+bp3716rUirey/7DRqSRo/0TASWl/2jq/+vv/uMJajA3oH7mB5RnbjCuojBkcg+lqqqq0mOPPTbRQ6nSKqlJJpR67rnncsVPhCYRMEWgFLdFUBXLxyK0KonQZlzUbTAeb8b4Bk6NEWFYTdF/6eGHH85L+k466aQcoEX4du211+ZqrqacX7lzaug8I5CKgKnm0siS6B01oV+rqKgqVWTVOp7/awFlmRtQnrkB9TM/oDxzg6aa2EFNczB69Oh8nhP7XBt7/GbR6LxmX6lo5l0KoEqhVGzxfX1i2Vpc1W7FFVesvi2qq6LaKJqaN0YsSevXr1/uB1USIUyUtk1oyy67bD72UUcdlau7Ymne7LPPXmufCOVqjiXU/XlcvPPOO7kiLPpsRQ+smlvd6qmGRNA0ua+3BQAAACaeZhNKRZVOLMmLXlClAOqFF17IoVCESzUrpeqKZX3RByr6IcXyvriSXDQxj2ViDzzwQKPHEFfJiyV10Sw9GnlH36eo0qq5FG5CiPFGRVT0jZpvvvlyo/XodVV3LNEXKkK66JkVDdRjfev4jiUankffrXi91ltvvVy1tfLKK6czzjgjh2WNFcv7IgSMx//pT3/Ka1IBAAAAJrlQKkTwFL2VSqFUXAkuKp1iuVk07G5IXDEvejfFsrgIXSIkiVCndOW9xogr9sVV/G688cZ8jOip9Pjjj6fffvstTUgRvkXYFA3I4wp2EcRFGFbTK6+8koOqI488Mr3//vv56nrR/H1CjCVelwj84qp88brefvvtOVwaW9PymuKqhVGOF+/PwIED09xzzz3e4wIAAACmHC5I0IAItmJp4J133lnrKn2VcuWVV+bG6GussUaaVMUSzVgq+VCvfhqdQ51fxn+Zrm36QUNOqMXcgPqZH1CeucG42mbR2m11JjctW7bMK7CioXsRjc7jb/+4sNuQIUOaf6Pz5iCqfdZff/1csdW2bdt08MEH5+V1t956a0XGEz2nnnzyydwoPT44e+yxRzrooIMqMhYAAACACUkoVacL/Z577pmXpkWVVCytW3fdddMnn3yS5pprrgabpnfq1Cl9++23E3Q8K6ywQjr22GNzwvjFF1+kQw89NF1zzTUT9DkAAAAAKkEoVcN3332XVltttbL39e3bNy211FL1Pjbun9B22GGHCX5MAAAAgOZAKNVIsd6yd+/elR4GAAAAwGShWV19DwAAAIApg1AKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAoXKvinxJS2n2Fjmnw4MGVHgY0Gy1btkwbbbRRevTRR9OoUaMqPRxoNswNqJ/5AeWZGzDpUCkFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOGEUgAAAAAUTigFAAAAQOFaFf+UkNKNb/RMI0dXVXoY0Gy0SCn9Zbq26W+//p7MDPgfcwPqZ35AeeYGk4NtFp09TQlUSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTStFke+yxRxo0aFClhwEAAABMwoRSk4lWrVpVeggAAAAAjSaUaqa+/PLLdNhhh9W67d13302nnXZa/r6qqiodcMAB6YEHHkhDhw5NJ510Ur49vv7www/pl19+SVdddVXq1q1bflxNe++9d/r444/T8OHDU8+ePdOBBx5Yfd8888yTj73VVlulZ555Jv3666/pvffeSyuttFK+v3Pnzun6669P7du3z/vFVhoTAAAAQGMJpSZhXbp0Sffdd19afPHF07XXXpt23nnnHEodd9xxadlll03ffPNNrcApxD5du3bN+3Xs2DGdeOKJ6f/+7//S7rvvXmu/M888M51//vlpqaWWSp999lm67bbbUsuWLdMrr7ySw7Kff/45zTrrrHmL/QAAAACawpqvSditt96aq5ZKDjnkkHTNNddU3xZh0/rrr5+mn3766n1OP/30dNRRR+UwK3z11VepU6dOaf/990833nhj9X4RND3yyCP5+6iEisqqBRdcMH366ac5kIoKqajIGps2bdqktm3bVv/crl27/LXFfzfgP0rzwbyA2swNqJ/5AeWZG0wOWrZsOVGOOdVUU02UY5d7rsYQSk3C3nrrrVo/L7LIIumyyy6rddsbb7yR1l577fz9tNNOm4OlCK5iaV/NflQRNNX0wQcfVH///fff568dOnTIoVRTnHDCCbmiq64O07ZJf1Q16VAwWYv/aGo/dev81dSA/zE3oH7mB5RnbjA52GijjSZKULTMMsukFi1apFGjRqWJaZpppmnUfkKpZmr06NH5g1JT69ata/0c/Z6aolQxte+++6bXX3+91n11P5AjR46s/j6qokIkqk0VPa26d+9eq1KqT58+qf+wEWnkaP9EQEnpP5r6//q7/3iCGswNqJ/5AeWZG0wOHn300YkSSsXf94899thED6VKq6TGRijVTA0YMCDNNttstd7Q+eabr8HHRBXT8ssvn2666abq2+Lnkv79++dAaP75589L/8bViBEjGl2KF/vGVlf84+AfCBiTuQHlmRtQP/MDyjM3mJSNmkihURTAxLEndijV2OMLpZqpuPLdnnvumR566KE0ePDg3Jx8bG/qxRdfnJflxbK+aEi+ww47pCWWWCJ98cUX1ftEf6iLLrooL9eLdDT6PS233HJppplmSj169GjU2KIPVYRksSzw/fffT8OGDctX8gMAAABoLFffa6Zi2dvzzz+fHn744fSvf/0r3X///al3794NPiaqn+Jx0aT8nXfeyZVV0fT8t99+q94n+knts88+aa+99koffvhhfo4Iv7788stGj+3VV19Nl19+ebrjjjvSwIED07HHHjte5woAAABMefR+m8w98cQTqV+/fmn33XdPzUFUWP3yyy/poV799JSCOr+M/zJd2/SD3gdQi7kB9TM/oDxzg8nBNovOPsGPGW14ooF69KsqoqdU/O0/wwwzpCFDhtS7n+V7k5Hobn/AAQekxx9/PH/Adtppp7Teeuulddddt9JDAwAAAKhFKDUZiS76G2+8cTrppJPS1FNPnRufb7311unpp5+u9NAAAAAAahFKTUaid1RURgEAAAA0dxqdAwAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhWtV/FNCSruv0DENHjy40sOAZqNly5Zpo402So8++mgaNWpUpYcDzYa5AfUzP6A8cwMmHSqlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwrUq/ikhpXbt2qVRo0ZVehjQbLRs2TJNM8005gbUYW5A/cwPKM/cgMrPjXiOxhBKUaiZZ545f/3mm28qPRQAAABgIodTQ4YMqfd+oRSF+umnn/LXOeaYo8EPJkyJv6z79OljbkAd5gbUz/yA8swNaB5zI56vb9++De4jlKIiYgL4BwLGZG5AeeYG1M/8gPLMDajs3GjMc2h0DgAAAEDhhFIAAAAAFE4oRaF+//331KVLl/wV+B9zA8ozN6B+5geUZ27ApDM3WqSUqio9CAAAAACmLCqlAAAAACicUAoAAACAwgmlAAAAACicUIoJ7qCDDkpffvllGj58eHrttdfS8ssv3+D+2267berZs2fe/4MPPkgbbbRRYWOF5jo3OnXqlO6+++68f1VVVTrssMMKHSs017mxzz77pBdeeCH99NNPeXvyySfH+u8MTCnzY6uttkpvvvlmGjRoUBo6dGh6991306677lroeKG5/s1RssMOO+T/trrvvvsm+hihuc+NPfbYI8+Hmls8rmjR6NxmmyDb9ttvX/Xbb79V7bnnnlUdO3asuuKKK6p++umnqllmmaXs/iuvvHLVyJEjq44++uiqRRddtKpr165Vv//+e9Viiy1W8XOx2So5N5Zbbrmqc889t2qHHXao6tu3b9Vhhx1W8XOw2ZrD3Lj55pur/r+9u4HJqvoDOH4gyAlkuUrUWsRLIokDw1VMNwjRqFYYlbNMKdGVlS/BSpwCm2WNWtoqs+WCWM1Zc2KiU6sFmARZuAxL0RAECV8gwBeUlzr//c56nvEY6h/FexG+n+03nnvvuc9zznFH4Mc958yZM0eHhYXp4OBgnZWVpRsbG/Xw4cNtbwtB2D0+oqKi9OTJk83PVAEBAXrevHnm56xJkybZ3haCsHNsOMLPz0/X1NTowsJCnZuba3s7CELZPDYSExN1U1OT9vX1dcaQIUOsrrf9HUf0nSgpKdHvv/++89jNzU0fPnxYL1y4sMvya9eu1Xl5eS7niouL9apVq2xvC0HYOTY6R2VlJUkpos/G5YwNCXd3d93c3KynT59ue1sIoreND4nS0lLzRz+720IQdo8N+X6xY8cOPXPmTJ2dnU1SiuiTUdLNsSFJKfnjnp11Zvoeeoynp6eKiIhQ3377rfOcPP4nx5GRkV3eI+c7lxfbtm07b3mgv4wNoD/oibHh5eVl3kem8gF9SU+Mj5iYGBUcHGymvAJ9xaWOjfT0dHXs2DGVlZVlUU2Bq2Ns+Pj4qKqqKlVdXa02bNhglhGxEkkp9JibbrpJeXh4qKNHj7qcl+OhQ4d2eY+c7055oL+MDaA/6ImxkZmZqf7888///IED6K/jY9CgQerkyZOqra1Nbd68Wc2dO5fxAdXfx8a4ceNUUlKSmj17tkW1BK6OsVFeXq5mzpyp4uPjzRqE7u7u6ocfflC33HKLRbVWysOyTwIAAOhBCxcuVFOnTlXR0dGqtbXV7uoAvYIkpMLDw81fvidMmKCWL1+uDh48qAoLC+2uGmALGQufffaZSUg1NDTYXR2gVykpKTHhIAkp2YTsueeeM08XWoGkFHpMfX296ujoUL6+vi7n5fjIkSNd3iPnu1Me6C9jA+gPLmdspKSkqNTUVBUbG6vKysqucE2Bq2d8yFSNiooK83r37t0qJCRELVq0iKQU+u3YCAwMVP7+/iovL895Tp4GEe3t7WaKqyRugatdfQ/8ziH3y86tQUFByipM30OPkf/US0tLzV/lHNzc3MxxcXFxl/fI+c7lxcSJE89bHugvYwPoDy51bLzyyisqLS1NxcXFmfuBvqinvnfIL98DBgy4QrUEev/Y2LdvnwoNDTVPEDpi48aNKj8/37yuqamxuAVA7/2+4e7urkaPHq3q6uqUlWxfIZ7oW1tQnjlzRs+YMcNsR/zRRx+ZLSgd20rm5OToN954w1k+MjJSt7W16eTkZLO1d0ZGhm5tbdWjRo2yvS0EYefY8PT0NFveS9TW1uq33nrLvA4MDLS9LQRh59h49dVXzVbHCQkJLtsXe3t7294WgrB7fKSmpurY2Fjt7+9vysvPV/JzVlJSku1tIQg7x8a5we57RF+NKd0cG2lpaXrixInm+8aYMWP0mjVrdEtLiw4JCbGy3vZ3HNG34sUXX9RVVVXmlwbZkvLuu+92XsvPzzffBDqXf/zxx/W+fftM+bKyMv3AAw/Y3gaCsHts+Pn56a5IObvbQRB2jo3Kysoux4b8UcPudhCE3ePjtdde0/v37ze/UDQ0NOiioiLzC4rdbSCI3vA7R+cgKUX05XixG2Nj+fLlzrJ1dXV606ZNOjw83NL6uv37AgAAAAAAALAMa0oBAAAAAADAciSlAAAAAAAAYDmSUgAAAAAAALAcSSkAAAAAAABYjqQUAAAAAAAALEdSCgAAAAAAAJYjKQUAAAAAAADLkZQCAAAAAACA5UhKAQAAAAAAwHIkpQAAAK6A7OxspbX+TwQGBvbI+ycmJqrGxkZldxtzc3NVb+Xn52f6PCwszO6qAACALnh0dRIAAACXb8uWLerZZ591OXf8+HHV23h4eKiOjg7Vl3h6etpdBQAAcBE8KQUAAHCFtLa2qqNHj7rEP//8Y6498sgjqrS0VJ05c0ZVVFSo9PR0dc011zjvffnll9Wvv/6qTp06paqrq9XKlSuVt7e3uRYVFaU+/fRTdcMNNzifwMrIyDDX5HV8fLxLPeSJKnmyqvPTQ1OmTFEFBQXm86dNm2auJSUlqd9//92c27t3r5ozZ0632pufn6/ee+89tWLFCvXXX3+pI0eOqFmzZikvLy+VlZWlTpw4oQ4cOKDi4uKc90hbpD4PPvig2r17t/ns4uJiNWrUKJf3TkhIUHv27FFnz55VlZWVKjk52eW6nFuyZInKyclRzc3N6uOPP1ZVVVXm2i+//GI+Q+onxo4dq77++muTIGxqajL9MGbMGJf3k/LSH+vXr1enT59W+/fvVw8//LBLmTvvvFPl5eWZz5O2bd++XQUEBDivX25/AgDQH2iCIAiCIAiiZyM7O1vn5uZ2eW38+PG6qalJz5gxQ/v7++vY2Fh98OBBnZ6e7iwzf/58HR0drf38/PR9992n9+7dq1euXGmueXp66nnz5pn38PX1NeHt7W2uifj4eJfPa2xs1ImJiea1vJ+Qz3v00Uf17bffrocOHaqfeuopXVtb6zwnX+vr600d/9825ufn6+bmZr148WIdFBRkvra3t+vNmzfrWbNmmXPShuPHj+uBAweae6Kiokx9fvvtN9MPoaGheuPGjaZ+Hh4epsxdd92lOzo69JIlS/Qdd9xh2nL69GlnmyQqKytNfyQnJ+uAgAATY8eONe8dExNj+mjw4MGmrPTntGnTdHBwsB45cqRevXq1rqur0z4+Ps73E9XV1Xrq1Kk6MDBQv/vuu/rEiRPO9xg+fLjpn3Xr1umIiAhTr2eeeUaPGDHCXL+U/iQIgiAI1f/C9goQBEEQBEH0uZCEjSRkTp486Ywvv/zSXPvmm290amqqS3lJkkgS43zv99hjj5lkjuNYEjKSbDq33P+blJKkVucyBw4cMAmYzuckqVRUVNStpNT27dudx+7u7qbdOTk5znOSHBL33HOPS1JqypQpzjKS+JGk0xNPPGGOP//8c71t2zaXz87MzNR79uxxSUqtX7/epYyjrWFhYRf8t3JzczPJtIceesilH5cuXeo89vLyMufuv/9+c7xs2TJdUVHhTJydG5fSnwRBEASh+lmwphQAAMAVItPFOk/ZkmlgQhbeHjdunFq8eLHzmkzdGzhwoAmZ7jVhwgS1aNEiNXLkSDVo0CCz7lPn65fr559/dr6W6XVBQUHqk08+UatXr3ael8+UqWndIVMOHWSqYkNDgyorK3OekymMYsiQIS73yZS9ztMNy8vLVUhIiDmWr1999ZVL+aKiIrVgwQLl7u7unBLZuU0XIp/9+uuvq+joaPNa+l764LbbbjtvW1paWkxfOOodHh6uvv/++y7X4urJ/gQAoC8jKQUAAHCFSBJK1os6l4+Pj1kDStYrOpesmSTrPm3atEmtWrXKJK5kfabx48ebdZmuvfbaCyalJEHj5uZ20UW/HQkyR33E7Nmz1Y8//uhS7u+//1bd0d7e/p+1mc49JySZ1NM6t+lCZN2pG2+8Uc2fP18dOnTIrP0lSTHp24u1xVHvC/0b9GR/AgDQl5GUAgAAsNiuXbtUcHBwlwkrERERYZIfKSkpJhEiZGHyztra2lwWRneQxbuHDRvmPJYndhwLpJ/PsWPHVG1trVmke82aNcoO9957r6qpqTGvZQH3ESNGmMXBhXyVJ8s6k2NZfNzxlFRXpI/Euf0k977wwgtmd0Rx6623qptvvrlb9ZWnqGTx+K52LuwN/QkAwNWApBQAAIDFli5dap6Ekl311q1bZxIrMqUvNDRUpaWlqT/++MM8tTN37lyzu5skUZ5//nmX95Cd5a677joVExNjdq2T6WXy9M53332nXnrpJfPkjyRjMjMzncmZC5Ent2TnPJletnXrVjVgwACzS93gwYPNbnpXmuw+KFP9ZHrfsmXLVH19vdqwYYO59s4776iffvrJ7K73xRdfqMjISNNGSSxdiCSHpF9kt7/Dhw+bp9AcOwBOnz7dTPeTqZFvv/22KdcdH3zwgfn3Wbt2rXrzzTdNv0libefOnSZZZnd/AgBwtbB9YSuCIAiCIIj+tPuexKRJk/SOHTvMgt6ya1xJSYnZoc5xfcGCBWbhc7m+ZcsW/fTTT5uFtq+//npnmQ8//NAsfi4yMjLMuWHDhumtW7eaBcbLy8t1XFxclwudd7X495NPPql37dqlz549qxsaGnRBQYGePHlytxY6X7FihUsZWYBcdhI832LsjoXOZZHxsrIy89nSF6NHj3a5JyEhwSxs3traqquqqnRKSspFP0ciKSlJHzp0yOzeJ/WTc+Hh4Xrnzp26paXF9JEsIn/u/RdbMF5C6ih9ferUKbNQemFhodlN8VL7kyAIgiBUPwu3f18AAAAAlouKilIFBQVmyh6LgAMA0L/0/AqTAAAAAAAAwEWQlAIAAAAAAIDlmL4HAAAAAAAAy/GkFAAAAAAAACxHUgoAAAAAAACWIykFAAAAAAAAy5GUAgAAAAAAgOVISgEAAAAAAMByJKUAAAAAAABgOZJSAAAAAAAAsBxJKQAAAAAAAFiOpBQAAAAAAACU1f4HmuuWdbIvjmsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train Random Forest for intrusion detection\n",
    "print(\"🌲 Training Random Forest for Intrusion Detection...\")\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "rf_ids = RandomForestClassifier(\n",
    "    n_estimators=200,\n",
    "    max_depth=20,\n",
    "    min_samples_split=5,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "rf_ids.fit(X_train_scaled, y_train)\n",
    "rf_pred = rf_ids.predict(X_test_scaled)\n",
    "rf_pred_proba = rf_ids.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "print(\"📊 Random Forest Results:\")\n",
    "print(f\"   Accuracy:  {accuracy_score(y_test, rf_pred):.4f}\")\n",
    "print(f\"   Precision: {precision_score(y_test, rf_pred):.4f}\")\n",
    "print(f\"   Recall:    {recall_score(y_test, rf_pred):.4f}\")\n",
    "print(f\"   F1-Score:  {f1_score(y_test, rf_pred):.4f}\")\n",
    "\n",
    "# Feature importance\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': X.columns,\n",
    "    'importance': rf_ids.feature_importances_\n",
    "}).sort_values('importance', ascending=False)\n",
    "\n",
    "print(f\"\\n🔝 Top 10 Important Features:\")\n",
    "print(feature_importance.head(10))\n",
    "\n",
    "# Plot feature importance\n",
    "plt.figure(figsize=(12, 8))\n",
    "top_features = feature_importance.head(15)\n",
    "plt.barh(range(len(top_features)), top_features['importance'], color='lightblue')\n",
    "plt.yticks(range(len(top_features)), top_features['feature'])\n",
    "plt.xlabel('Feature Importance', color='white')\n",
    "plt.title('Top 15 Features for Intrusion Detection', fontsize=16, color='white')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🧠 Training Deep Neural Network for Intrusion Detection...\n",
      "Epoch 1/50\n",
      "Epoch 1/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 40ms/step - accuracy: 0.9829 - loss: 0.0532 - precision: 0.9942 - recall: 0.9866 - val_accuracy: 0.9962 - val_loss: 0.0144 - val_precision: 0.9992 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 40ms/step - accuracy: 0.9829 - loss: 0.0532 - precision: 0.9942 - recall: 0.9866 - val_accuracy: 0.9962 - val_loss: 0.0144 - val_precision: 0.9992 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "Epoch 2/50\n",
      "Epoch 2/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 48ms/step - accuracy: 0.9950 - loss: 0.0205 - precision: 0.9992 - recall: 0.9951 - val_accuracy: 0.9969 - val_loss: 0.0139 - val_precision: 0.9999 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 48ms/step - accuracy: 0.9950 - loss: 0.0205 - precision: 0.9992 - recall: 0.9951 - val_accuracy: 0.9969 - val_loss: 0.0139 - val_precision: 0.9999 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "Epoch 3/50\n",
      "Epoch 3/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 74ms/step - accuracy: 0.9953 - loss: 0.0195 - precision: 0.9995 - recall: 0.9953 - val_accuracy: 0.9969 - val_loss: 0.0139 - val_precision: 0.9996 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 74ms/step - accuracy: 0.9953 - loss: 0.0195 - precision: 0.9995 - recall: 0.9953 - val_accuracy: 0.9969 - val_loss: 0.0139 - val_precision: 0.9996 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "Epoch 4/50\n",
      "Epoch 4/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 48ms/step - accuracy: 0.9953 - loss: 0.0185 - precision: 0.9994 - recall: 0.9954 - val_accuracy: 0.9969 - val_loss: 0.0136 - val_precision: 0.9999 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 48ms/step - accuracy: 0.9953 - loss: 0.0185 - precision: 0.9994 - recall: 0.9954 - val_accuracy: 0.9969 - val_loss: 0.0136 - val_precision: 0.9999 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "Epoch 5/50\n",
      "Epoch 5/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 45ms/step - accuracy: 0.9955 - loss: 0.0178 - precision: 0.9994 - recall: 0.9956 - val_accuracy: 0.9968 - val_loss: 0.0134 - val_precision: 0.9998 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 45ms/step - accuracy: 0.9955 - loss: 0.0178 - precision: 0.9994 - recall: 0.9956 - val_accuracy: 0.9968 - val_loss: 0.0134 - val_precision: 0.9998 - val_recall: 0.9966 - learning_rate: 0.0010\n",
      "Epoch 6/50\n",
      "Epoch 6/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 61ms/step - accuracy: 0.9956 - loss: 0.0180 - precision: 0.9993 - recall: 0.9958 - val_accuracy: 0.9964 - val_loss: 0.0152 - val_precision: 0.9999 - val_recall: 0.9960 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 61ms/step - accuracy: 0.9956 - loss: 0.0180 - precision: 0.9993 - recall: 0.9958 - val_accuracy: 0.9964 - val_loss: 0.0152 - val_precision: 0.9999 - val_recall: 0.9960 - learning_rate: 0.0010\n",
      "Epoch 7/50\n",
      "Epoch 7/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m121s\u001b[0m 92ms/step - accuracy: 0.9959 - loss: 0.0173 - precision: 0.9995 - recall: 0.9959 - val_accuracy: 0.9970 - val_loss: 0.0134 - val_precision: 0.9996 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m121s\u001b[0m 92ms/step - accuracy: 0.9959 - loss: 0.0173 - precision: 0.9995 - recall: 0.9959 - val_accuracy: 0.9970 - val_loss: 0.0134 - val_precision: 0.9996 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "Epoch 8/50\n",
      "Epoch 8/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 91ms/step - accuracy: 0.9961 - loss: 0.0162 - precision: 0.9996 - recall: 0.9961 - val_accuracy: 0.9973 - val_loss: 0.0137 - val_precision: 0.9999 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 91ms/step - accuracy: 0.9961 - loss: 0.0162 - precision: 0.9996 - recall: 0.9961 - val_accuracy: 0.9973 - val_loss: 0.0137 - val_precision: 0.9999 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "Epoch 9/50\n",
      "Epoch 9/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 45ms/step - accuracy: 0.9962 - loss: 0.0162 - precision: 0.9996 - recall: 0.9962 - val_accuracy: 0.9968 - val_loss: 0.0132 - val_precision: 0.9997 - val_recall: 0.9967 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 45ms/step - accuracy: 0.9962 - loss: 0.0162 - precision: 0.9996 - recall: 0.9962 - val_accuracy: 0.9968 - val_loss: 0.0132 - val_precision: 0.9997 - val_recall: 0.9967 - learning_rate: 0.0010\n",
      "Epoch 10/50\n",
      "Epoch 10/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m295s\u001b[0m 234ms/step - accuracy: 0.9963 - loss: 0.0160 - precision: 0.9996 - recall: 0.9963 - val_accuracy: 0.9970 - val_loss: 0.0135 - val_precision: 0.9994 - val_recall: 0.9972 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m295s\u001b[0m 234ms/step - accuracy: 0.9963 - loss: 0.0160 - precision: 0.9996 - recall: 0.9963 - val_accuracy: 0.9970 - val_loss: 0.0135 - val_precision: 0.9994 - val_recall: 0.9972 - learning_rate: 0.0010\n",
      "Epoch 11/50\n",
      "Epoch 11/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 86ms/step - accuracy: 0.9960 - loss: 0.0169 - precision: 0.9995 - recall: 0.9960 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9999 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 86ms/step - accuracy: 0.9960 - loss: 0.0169 - precision: 0.9995 - recall: 0.9960 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9999 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "Epoch 12/50\n",
      "Epoch 12/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 71ms/step - accuracy: 0.9962 - loss: 0.0160 - precision: 0.9996 - recall: 0.9962 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9999 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 71ms/step - accuracy: 0.9962 - loss: 0.0160 - precision: 0.9996 - recall: 0.9962 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9999 - val_recall: 0.9970 - learning_rate: 0.0010\n",
      "Epoch 13/50\n",
      "Epoch 13/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 30ms/step - accuracy: 0.9965 - loss: 0.0154 - precision: 0.9997 - recall: 0.9964 - val_accuracy: 0.9971 - val_loss: 0.0126 - val_precision: 0.9997 - val_recall: 0.9971 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 30ms/step - accuracy: 0.9965 - loss: 0.0154 - precision: 0.9997 - recall: 0.9964 - val_accuracy: 0.9971 - val_loss: 0.0126 - val_precision: 0.9997 - val_recall: 0.9971 - learning_rate: 0.0010\n",
      "Epoch 14/50\n",
      "Epoch 14/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 72ms/step - accuracy: 0.9963 - loss: 0.0158 - precision: 0.9995 - recall: 0.9963 - val_accuracy: 0.9972 - val_loss: 0.0130 - val_precision: 0.9999 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 72ms/step - accuracy: 0.9963 - loss: 0.0158 - precision: 0.9995 - recall: 0.9963 - val_accuracy: 0.9972 - val_loss: 0.0130 - val_precision: 0.9999 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "Epoch 15/50\n",
      "Epoch 15/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m226s\u001b[0m 139ms/step - accuracy: 0.9964 - loss: 0.0155 - precision: 0.9997 - recall: 0.9963 - val_accuracy: 0.9971 - val_loss: 0.0126 - val_precision: 0.9996 - val_recall: 0.9971 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m226s\u001b[0m 139ms/step - accuracy: 0.9964 - loss: 0.0155 - precision: 0.9997 - recall: 0.9963 - val_accuracy: 0.9971 - val_loss: 0.0126 - val_precision: 0.9996 - val_recall: 0.9971 - learning_rate: 0.0010\n",
      "Epoch 16/50\n",
      "Epoch 16/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 149ms/step - accuracy: 0.9964 - loss: 0.0153 - precision: 0.9997 - recall: 0.9963 - val_accuracy: 0.9965 - val_loss: 0.0136 - val_precision: 0.9991 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 149ms/step - accuracy: 0.9964 - loss: 0.0153 - precision: 0.9997 - recall: 0.9963 - val_accuracy: 0.9965 - val_loss: 0.0136 - val_precision: 0.9991 - val_recall: 0.9969 - learning_rate: 0.0010\n",
      "Epoch 17/50\n",
      "Epoch 17/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 139ms/step - accuracy: 0.9967 - loss: 0.0145 - precision: 0.9998 - recall: 0.9965 - val_accuracy: 0.9971 - val_loss: 0.0125 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m190s\u001b[0m 139ms/step - accuracy: 0.9967 - loss: 0.0145 - precision: 0.9998 - recall: 0.9965 - val_accuracy: 0.9971 - val_loss: 0.0125 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 18/50\n",
      "Epoch 18/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 181ms/step - accuracy: 0.9969 - loss: 0.0142 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9971 - val_loss: 0.0125 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 181ms/step - accuracy: 0.9969 - loss: 0.0142 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9971 - val_loss: 0.0125 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 19/50\n",
      "Epoch 19/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m320s\u001b[0m 226ms/step - accuracy: 0.9968 - loss: 0.0141 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9970 - val_loss: 0.0125 - val_precision: 0.9997 - val_recall: 0.9970 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m320s\u001b[0m 226ms/step - accuracy: 0.9968 - loss: 0.0141 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9970 - val_loss: 0.0125 - val_precision: 0.9997 - val_recall: 0.9970 - learning_rate: 2.0000e-04\n",
      "Epoch 20/50\n",
      "Epoch 20/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 202ms/step - accuracy: 0.9968 - loss: 0.0141 - precision: 0.9999 - recall: 0.9965 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m255s\u001b[0m 202ms/step - accuracy: 0.9968 - loss: 0.0141 - precision: 0.9999 - recall: 0.9965 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 21/50\n",
      "Epoch 21/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 188ms/step - accuracy: 0.9969 - loss: 0.0139 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9971 - val_loss: 0.0124 - val_precision: 0.9996 - val_recall: 0.9972 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 188ms/step - accuracy: 0.9969 - loss: 0.0139 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9971 - val_loss: 0.0124 - val_precision: 0.9996 - val_recall: 0.9972 - learning_rate: 2.0000e-04\n",
      "Epoch 22/50\n",
      "Epoch 22/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m248s\u001b[0m 197ms/step - accuracy: 0.9968 - loss: 0.0140 - precision: 0.9998 - recall: 0.9966 - val_accuracy: 0.9971 - val_loss: 0.0128 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m248s\u001b[0m 197ms/step - accuracy: 0.9968 - loss: 0.0140 - precision: 0.9998 - recall: 0.9966 - val_accuracy: 0.9971 - val_loss: 0.0128 - val_precision: 0.9994 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 23/50\n",
      "Epoch 23/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 193ms/step - accuracy: 0.9969 - loss: 0.0140 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0128 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 193ms/step - accuracy: 0.9969 - loss: 0.0140 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0128 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 24/50\n",
      "Epoch 24/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 185ms/step - accuracy: 0.9969 - loss: 0.0140 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0127 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m252s\u001b[0m 185ms/step - accuracy: 0.9969 - loss: 0.0140 - precision: 0.9998 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0127 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 25/50\n",
      "Epoch 25/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 204ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 204ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 26/50\n",
      "Epoch 26/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 145ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 145ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 2.0000e-04\n",
      "Epoch 27/50\n",
      "Epoch 27/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 162ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9973 - val_loss: 0.0123 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 162ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9973 - val_loss: 0.0123 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "Epoch 28/50\n",
      "Epoch 28/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m265s\u001b[0m 164ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m265s\u001b[0m 164ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "Epoch 29/50\n",
      "Epoch 29/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 163ms/step - accuracy: 0.9970 - loss: 0.0136 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 163ms/step - accuracy: 0.9970 - loss: 0.0136 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "Epoch 30/50\n",
      "Epoch 30/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 158ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 158ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "Epoch 31/50\n",
      "Epoch 31/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m219s\u001b[0m 174ms/step - accuracy: 0.9971 - loss: 0.0134 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m219s\u001b[0m 174ms/step - accuracy: 0.9971 - loss: 0.0134 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 4.0000e-05\n",
      "Epoch 32/50\n",
      "Epoch 32/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 135ms/step - accuracy: 0.9969 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 135ms/step - accuracy: 0.9969 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "Epoch 33/50\n",
      "Epoch 33/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 131ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 131ms/step - accuracy: 0.9970 - loss: 0.0137 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "Epoch 34/50\n",
      "Epoch 34/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 138ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 138ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0125 - val_precision: 0.9996 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "Epoch 35/50\n",
      "Epoch 35/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 142ms/step - accuracy: 0.9970 - loss: 0.0136 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0124 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 142ms/step - accuracy: 0.9970 - loss: 0.0136 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9972 - val_loss: 0.0124 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "Epoch 36/50\n",
      "Epoch 36/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 135ms/step - accuracy: 0.9970 - loss: 0.0138 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9972 - val_loss: 0.0124 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 135ms/step - accuracy: 0.9970 - loss: 0.0138 - precision: 0.9999 - recall: 0.9968 - val_accuracy: 0.9972 - val_loss: 0.0124 - val_precision: 0.9995 - val_recall: 0.9973 - learning_rate: 8.0000e-06\n",
      "Epoch 37/50\n",
      "Epoch 37/50\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 140ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 1.6000e-06\n",
      "\u001b[1m1260/1260\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m176s\u001b[0m 140ms/step - accuracy: 0.9970 - loss: 0.0135 - precision: 0.9999 - recall: 0.9967 - val_accuracy: 0.9973 - val_loss: 0.0124 - val_precision: 0.9997 - val_recall: 0.9973 - learning_rate: 1.6000e-06\n",
      "\u001b[1m788/788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 116ms/step\n",
      "\u001b[1m788/788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m92s\u001b[0m 116ms/step\n",
      "\n",
      "📊 Deep Neural Network Results:\n",
      "   Accuracy:  0.9964\n",
      "   Precision: 0.9999\n",
      "📊 Deep Neural Network Results:\n",
      "   Accuracy:  0.9964\n",
      "   Precision: 0.9999\n",
      "   Recall:    0.9961\n",
      "   F1-Score:  0.9980\n",
      "\n",
      "   Recall:    0.9961\n",
      "   F1-Score:  0.9980\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Deep Learning model for intrusion detection\n",
    "print(\"🧠 Training Deep Neural Network for Intrusion Detection...\")\n",
    "\n",
    "def create_ids_neural_network(input_dim):\n",
    "    model = Sequential([\n",
    "        Dense(256, activation='relu', input_shape=(input_dim,)),\n",
    "        BatchNormalization(),\n",
    "        Dropout(0.3),\n",
    "        \n",
    "        Dense(128, activation='relu'),\n",
    "        BatchNormalization(),\n",
    "        Dropout(0.3),\n",
    "        \n",
    "        Dense(64, activation='relu'),\n",
    "        BatchNormalization(),\n",
    "        Dropout(0.2),\n",
    "        \n",
    "        Dense(32, activation='relu'),\n",
    "        Dropout(0.2),\n",
    "        \n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer='adam',\n",
    "        loss='binary_crossentropy',\n",
    "        metrics=['accuracy', 'precision', 'recall']\n",
    "    )\n",
    "    \n",
    "    return model\n",
    "\n",
    "# Create and train model\n",
    "ids_model = create_ids_neural_network(X_train_scaled.shape[1])\n",
    "\n",
    "# Train with early stopping\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5)\n",
    "\n",
    "history = ids_model.fit(\n",
    "    X_train_scaled, y_train,\n",
    "    epochs=50,\n",
    "    batch_size=64,\n",
    "    validation_split=0.2,\n",
    "    callbacks=[early_stopping, reduce_lr],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# Evaluate\n",
    "nn_pred_proba = ids_model.predict(X_test_scaled).flatten()\n",
    "nn_pred = (nn_pred_proba > 0.5).astype(int)\n",
    "\n",
    "print(\"\\n📊 Deep Neural Network Results:\")\n",
    "print(f\"   Accuracy:  {accuracy_score(y_test, nn_pred):.4f}\")\n",
    "print(f\"   Precision: {precision_score(y_test, nn_pred):.4f}\")\n",
    "print(f\"   Recall:    {recall_score(y_test, nn_pred):.4f}\")\n",
    "print(f\"   F1-Score:  {f1_score(y_test, nn_pred):.4f}\")\n",
    "\n",
    "# Plot training history\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "# Loss\n",
    "ax1.plot(history.history['loss'], label='Training Loss', color='lightblue')\n",
    "ax1.plot(history.history['val_loss'], label='Validation Loss', color='lightcoral')\n",
    "ax1.set_title('Model Loss', color='white')\n",
    "ax1.set_xlabel('Epoch', color='white')\n",
    "ax1.set_ylabel('Loss', color='white')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Accuracy\n",
    "ax2.plot(history.history['accuracy'], label='Training Accuracy', color='lightgreen')\n",
    "ax2.plot(history.history['val_accuracy'], label='Validation Accuracy', color='orange')\n",
    "ax2.set_title('Model Accuracy', color='white')\n",
    "ax2.set_xlabel('Epoch', color='white')\n",
    "ax2.set_ylabel('Accuracy', color='white')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🔍 Anomaly Detection for Unknown Threats\n",
    "\n",
    "Use unsupervised learning to detect unknown network anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🌟 Training Isolation Forest for Anomaly Detection...\n",
      "📊 Normal traffic samples for training: 10,625\n",
      "📊 Isolation Forest Results:\n",
      "   Accuracy:  0.1984\n",
      "   Precision: 0.9141\n",
      "   Recall:    0.1147\n",
      "   F1-Score:  0.2038\n",
      "📊 Isolation Forest Results:\n",
      "   Accuracy:  0.1984\n",
      "   Precision: 0.9141\n",
      "   Recall:    0.1147\n",
      "   F1-Score:  0.2038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly detection using Isolation Forest\n",
    "print(\"🌟 Training Isolation Forest for Anomaly Detection...\")\n",
    "\n",
    "# Use only normal traffic for training\n",
    "normal_data = X_train_scaled[y_train == 0]\n",
    "print(f\"📊 Normal traffic samples for training: {len(normal_data):,}\")\n",
    "\n",
    "# Train Isolation Forest\n",
    "iso_forest = IsolationForest(\n",
    "    contamination=0.1,  # Expected proportion of anomalies\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "iso_forest.fit(normal_data)\n",
    "\n",
    "# Predict anomalies on test set\n",
    "iso_pred = iso_forest.predict(X_test_scaled)\n",
    "iso_scores = iso_forest.score_samples(X_test_scaled)\n",
    "\n",
    "# Convert predictions (-1 for anomaly, 1 for normal) to (1 for anomaly, 0 for normal)\n",
    "iso_pred_binary = (iso_pred == -1).astype(int)\n",
    "\n",
    "print(\"📊 Isolation Forest Results:\")\n",
    "print(f\"   Accuracy:  {accuracy_score(y_test, iso_pred_binary):.4f}\")\n",
    "print(f\"   Precision: {precision_score(y_test, iso_pred_binary):.4f}\")\n",
    "print(f\"   Recall:    {recall_score(y_test, iso_pred_binary):.4f}\")\n",
    "print(f\"   F1-Score:  {f1_score(y_test, iso_pred_binary):.4f}\")\n",
    "\n",
    "# Visualize anomaly scores\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(iso_scores[y_test == 0], bins=50, alpha=0.7, label='Normal Traffic', color='lightblue')\n",
    "plt.hist(iso_scores[y_test == 1], bins=50, alpha=0.7, label='Attack Traffic', color='lightcoral')\n",
    "plt.xlabel('Anomaly Score', color='white')\n",
    "plt.ylabel('Frequency', color='white')\n",
    "plt.title('Distribution of Anomaly Scores', fontsize=16, color='white')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔍 DBSCAN Clustering for Anomaly Detection...\n",
      "📊 PCA explained variance ratio: 0.921\n",
      "📊 PCA explained variance ratio: 0.921\n",
      "🔍 DBSCAN Results:\n",
      "   Number of clusters: 168\n",
      "   Noise points (anomalies): 4,704\n",
      "   Anomaly detection accuracy: 0.2570\n",
      "🔍 DBSCAN Results:\n",
      "   Number of clusters: 168\n",
      "   Noise points (anomalies): 4,704\n",
      "   Anomaly detection accuracy: 0.2570\n"
     ]
    },
    {
     "data": {
      "image/png": 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mJsquXbskPz/f7peUlPiP+ZLky5cv+z8AaCK/fPlyGT9+vBw/ftxfZ9SoUbZ7emByvmrVKjlw4MBNP8tjjz0mCxcutAk9AAB3M5JzAABCzCU9bYu5JuZN0mjL7NaILX9f/ijNH5cHi8PhkFDRCeY2b94sEydOtIm0Juo6nv2TPhTomPD9+/e3Ktek/dSpU63K8vLybvo5HnroIXnjjTdk586ddpI4AADuZszWDgBAiEVJD9uVvVmaW5XrvpZr8h5s2nW8paXFTqB2K7RO2+S+7UzoW7dulQEDBsj27dtl2LBhNqFesGBBp9eMjY2126lTp8rw4cP9od3hZ8yY0epc7SZ/M/r16ycHDx6Ut956S771rW/d0jsCANAdkZwDABBi2iquY8y1K3sg3dfyJmkI+j11vPa+fftk/vz5Eh0d3e54Z0ud6RhwpcmvjybSbVVUVMiWLVskLS1N1q9fL3PmzLHlTU1N/pnWfc6dO2eXcdPu8LqkW2DodW6VtpgfOnRITp48KS+88IJdug0AgLsdyTkAACGmybdO/uZy9BSX9BCHRNit7mt5sLu0+2hirknyiRMn7ERt2r1cW9J1jPaxY8c6rKPLk+la6Lp2uJ4/ZcoUWbx4catzNmzYYLu09+/fX0aMGCFjx46VgoICe6y0tNS2vk+bNk369Olju7PrZHHr1q2z9WbNmmVb3bWetrbr/u0k5vqMOs78wQcftGu5awAAcLcL+5TxBEEQBNGdIxhLaEWI0yRGfMEkOUebJ51j7Vb3tTyUz56QkGA2btxoSkpKTENDg11abc+ePa2WKQtcSk1j5MiR5syZM3bJs8OHD9slzwKXUsvMzDSFhYXG4/GYyspKs23bNhMfH++vn5GRYS5evGi8Xq9/KTWNRYsWmYKCArtMmtbLzc01o0eP7nQJt45i9uzZpjN3w98BQRAEQUgn4fj4BwAA6ITb7ZY1a9bIypUrbcvwnY4/1zHm2poeqhZzdP+/AwAA2mK2dgAAupAm5CTlAACgLcacAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAAAAAECYkZwDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAcI8zxkhqamq4HwMAgHsayTkAAJ9hffv2lczMTCkqKpKGhgYpKyuTnJwcGTduXEjul5ycbJP9uLg4CZVf/epXUlpaKh6PRy5evCjZ2dnSr1+/kN0PAICuQHIOAMBnlNvtlpMnT9pEfMmSJTJs2DBJSUmRgwcPyqZNm6S7czqdHZbr83/961+XL3zhC5KWliaPPfaY/Nu//VuXPx8AAMFmCIIgCILoPNxut8nOzrbbO72WS3qaWEeccUmPkD/33r17TXl5uYmOjm53LC4uzv9bpaam2t/Jycl2P/B4UlKSLfO9f2JiosnJyTFVVVWmtrbW5Ofnm8mTJ9vjbWVlZdk6DofDLF261BQXF5v6+npz+vRpk5aW5r+H774pKSkmLy/PNDY22rKbec//9t/+m/F6vSYyMvKu+TsgCIIgCGkTkUFP9QEAQDtOccrDEQMlPqKvOCVSvHJDqloqpaLlgrSIN+j36927t20lX7FihdTX17c7fu3atdu+tra6u1wuGTNmjNTV1cnQoUOltrZWysvLZfr06bJ7924ZNGiQ1NTU2K7natmyZTJz5kyZN2+eFBYW2ro7duyQK1euyJEjR/zXXrt2raSnp0txcbFUV1ff1Hv+1V/9lbz11lty48aN234nAADCjeQcAIAuoIl5X2eiNBmPeKROoiTK7quylj8E/X4DBw6UiIgIOX/+fNCvnZiYKLt27ZL8/Hy7X1JS4j9WVVVlt5cvX/Z/ANBEfvny5TJ+/Hg5fvy4v86oUaNk7ty5rZLzVatWyYEDBz71GTSJX7BggcTExMixY8dk2rRpQX9PAAC6EmPOAQAIMZf0tC3mmpg3SaMYabFb3ddyl/QI+j0dDoeEik4wl5GRIW+++aasXr3ajmX/tA8FmkTv379frl+/7o9Zs2bZ8eKB8vLybuoZfvCDH8iIESNkwoQJ4vV67aRwAADczWg5BwAgxFyOHrYru7aYB2qWZuklMeJy9JQm0xjUe2rX8ZaWFhk8ePAt1dM6bZP7qKioVuds3bpV9u3bJ1OnTpWJEyfaLuuLFy+Wl19+ucNrxsbG2q2e/95777U61tjY+r21m/zN+PDDD23oexYUFEhFRYU888wz/pZ5AADuNrScAwAQYpp46xhz7coeSPe1vMk0BP2eOl5bE+j58+dLdHR0u+OdLXWmY8BV4NJkw4cPb3eeJsNbtmyxs6WvX79e5syZY8ubmprazbR+7tw5u4ybdofXJd0CQ69zp7T7vurRI/g9EAAA6Cok5wAAhFiTNNjJ31yOXrYLu0Mi7Fb3tVy7uIeCJuaaJJ84ccJO1Kbdy7UlfeHChXacdkcuXLhg10LX7up6/pQpU2yreKANGzbYFvP+/fvbruVjx461rddK1x/X1ncdA96nTx/bnV0ni1u3bp2tp13ZBwwYYOvpmHHdvxVPP/20fa+kpCSb7Ou9f/7zn9vn7uydAAC4W4R9yniCIAiC6M4RjCW0IsRpEiO+YIZHjjFPRY6zW93X8lA+e0JCgtm4caMpKSkxDQ0Ndmm1PXv2tFqmLHApNY2RI0eaM2fO2CXPDh8+bJc8C1xKLTMz0xQWFhqPx2MqKyvNtm3bTHx8vL9+RkaGuXjxol3ezLeUmsaiRYtMQUGBXSZN6+Xm5prRo0d3uoRbR/HEE0+Y3/zmN+aDDz6w99el2V555RXz0EMP3RV/BwRBEAQhnYTj4x8AAKATbrdb1qxZIytXrrQtw3fioxZzHWPeELIWc3T/vwMAANpiQjgAALrQR7O0k5QDAIDWGHMOAAAAAECYkZwDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgDAPc4YI6mpqeF+DAAA7mkk5wAAfIb17dtXMjMzpaioSBoaGqSsrExycnJk3LhxIblfcnKyTfbj4uIk1Fwul5w6dcreLykpKeT3AwAglCJDenUAABA2brdbjh49KlevXpUlS5bI2bNnJSoqSiZNmiSbNm2SIUOGSHfmdDrF6/V2evwf//Ef5eLFizJ8+PAufS4AAEKBlnMAALqQS3pKrON+uw21V155xbYqP/3007J7924pLCyUc+fOyYYNG+SZZ5656ZZvbZXWMk32VWJiom19r6qqktraWsnPz5fJkyfb44cOHbLn6AcBrZOVlWX3HQ6HLF26VIqLi6W+vl5Onz4taWlp7e6bkpIieXl50tjYKKNGjer03fS8iRMnSnp6etD+vQAACCdazgEA6AJOiZSHIwdKfESCOB2R4jU3pKrlklTcKJQW6bx1+Hb17t3bJrArVqywyXBb165du+1ra6u7dikfM2aM1NXVydChQ22SXl5eLtOnT7cfAgYNGiQ1NTXi8XhsnWXLlsnMmTNl3rx59iOB1t2xY4dcuXJFjhw54r/22rVrbcKtSXx1dXWH9//c5z4nr776qvz3//7fO3w3AADuRiTnAAB0AU3M+zrd0mQaxNNSJ1EOl91XZTfOB/1+AwcOlIiICDl/PvjX1pbzXbt22RZzVVJS4j+mrenq8uXL/g8AmsgvX75cxo8fL8ePH/fX0ZbxuXPntkrOV61aJQcOHPjE+//0pz+VH//4x3Ly5El/az4AAHc7knMAAEJMu7Bri7km5hrKt9XyS/JHaZKP9oNFu5GHik4wt3nzZtutXBNpTdR1PPsnfSiIiYmR/fv3dzihWyDt0v5JFi5cKPfdd598//vfv8O3AACge2HMOQAAIeZy9LRd2ZtNU6ty3ddyPR5s2nW8paVFBg8efEv1tE7b5F4nkQu0detWGTBggGzfvl2GDRtmE+oFCxZ0es3Y2Fi7nTp1qp28zRfaHX7GjBmtztVu8p9EZ5l/9tln7Zj05uZmuXDhgi3XZ9AWdQAA7lYk5wAAhJi2kusYc+3KHkj3tdzXih5MOl573759Mn/+fImOjm53vLOlznQMuOrXr5+/rKPZ0CsqKmTLli12Urf169fLnDlzbHlTU5N/pnUfnYROl3HT7vC6pFtg6HVuxaJFi+wEdb4Ef8qUKbb8+eeft+PrAQC4W9GtHQCAENMu6zr5m2+MubaYa2KuLeaV3tKgd2n30cRcl1I7ceKEHcv9zjvvSGRkpEyYMEFeeukl23LdlrZE61roq1evtsmuTuy2ePHiVufobO+5ubny7rvv2onnxo4dKwUFBfZYaWmpbX2fNm2avP7663ZCOJ0sbt26dbaejoN/88037ceB5557zk4al52dfdPvpJPOBdJrK03033vvvdv8lwIAIPxoOQcAoAvorOyaiDvEIb0iYuxW97U8VHTStSeffFIOHjxoW7d1Ajcd9/1nf/ZnNjnvyI0bN+Qb3/iG7Q6vyfx3vvMdycjIaHWOtorrjO2akL/xxhs2Sf+bv/kbe0zXHf/ud79rZ12vrKyUl19+2ZavXLlS1qxZY2dt99XTbu6Bk8kBAHAv0wFlJtwPAQBAd6YzgmtiqQmmtgzf6eRw2mJuJ4cLUYs5uv/fAQAAbdGtHQCALqQJeSjGmAMAgLsb3doBAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAAAAACDOScwAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQC4xxljJDU1NdyPAQDAPY3kHACAz7C+fftKZmamFBUVSUNDg5SVlUlOTo6MGzcuJPdLTk62yX5cXJyESklJib1HYHznO98J2f0AAOgKkV1yFwAA0OXcbrccPXpUrl69KkuWLJGzZ89KVFSUTJo0STZt2iRDhgyR7szpdIrX6+3w2MqVK+XVV1/171+/fr0LnwwAgOCj5RwAgC7kkp4S67jfbkPtlVdesa3KTz/9tOzevVsKCwvl3LlzsmHDBnnmmWduuuU7KSnJlmmyrxITE23re1VVldTW1kp+fr5MnjzZHj906JA9Rz8IaJ2srCy773A4ZOnSpVJcXCz19fVy+vRpSUtLa3fflJQUycvLk8bGRhk1alSn76bJeGVlpT/0mgAA3M1oOQcAoAs4JVIejvy8xDsT7G+v3JAq7yWpuPGutEjHrcN3onfv3jbRXbFiRYeJ67Vr12772trq7nK5ZMyYMVJXVydDhw61SXp5eblMnz7dfggYNGiQ1NTUiMfjsXWWLVsmM2fOlHnz5tmPBFp3x44dcuXKFTly5Ij/2mvXrpX09HSbxFdXV3f6DJroa+u5dtP/2c9+Zj84dNbKDgDA3YDkHACALqCJed9ItzSZBvGYOolyuOy+KrtREPT7DRw4UCIiIuT8+fNBv7a2nO/atcu2mPvGgPtoa7q6fPmy/wOAJvLLly+X8ePHy/Hjx/11tGV87ty5rZLzVatWyYEDBz7x/jqG/u2337b3GjlypHz/+9+Xfv36yeLFi4P+rgAAdBWScwAAQky7sGuLuSbmGsq31fJLN0qkST7aDxbtRh4qmhxv3rxZJk6caBNpTdR1PPsnfSiIiYmR/fv3tyrXpP3UqVOtyrRL+6fRVnIfvW9TU5Ns2bLFts7rbwAA7kaMOQcAIMRcjp62K3uzaZ046r6W6/Fg067jLS0tMnjw4Fuqp3XaJvc6iVygrVu3yoABA2T79u0ybNgwm1AvWLCg02vGxsba7dSpU2X48OH+0O7wM2bMaHWudpO/Vb/73e/sM/bv3/+W6wIA0F2QnAMAEGLaSq5jzLUreyDd13JfK3ow6Xjtffv2yfz58yU6Orrd8c6WOtMx4Eq7iftoIt1WRUWFba3WSd3Wr18vc+bMseW+lmudad1HJ6HTZdy0O7wu6RYYep07pc+n4821Kz0AAHcrurUDABBi2mVdJ3/zjTHXFnNNzLXFvPJGadC7tPtoYq5LqZ04ccKO5X7nnXckMjJSJkyYIC+99JJtuW7rwoULdpK11atX28nkdGK3tmO5tVt5bm6uvPvuu3biubFjx0pBwUfj5ktLS23r+7Rp0+T111+3E8LpZHHr1q2z9XQc/Jtvvmk/Djz33HN20rjs7OybfiedZf4rX/mKHDx40M7Y/uyzz9rr6uRyOkM8AAB3M0MQBEEQROfhdrtNdna23d7uNSLEaRIjh5jhPcaap3pMsFvd1/JQPntCQoLZuHGjKSkpMQ0NDaa8vNzs2bPHJCcn+89Rqamp/v2RI0eaM2fOmPr6enP48GGTlpZmz/G9f2ZmpiksLDQej8dUVlaabdu2mfj4eH/9jIwMc/HiReP1ek1WVpa/fNGiRaagoMA0Njbaerm5uWb06NH2mD6PiouL+8T3GTFihDl27Jiprq62z/f73//eLF261Lhcrrvi74AgCIIgpJNwfPwDAAB0QtfvXrNmjV26S1uG73RyOG0xt5PDhajFHN3/7wAAgLbo1g4AQBfShDwUY8wBAMDdjQnhAAAAAAAIM5JzAAAAAADCjOQcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAuMcZYyQ1NTXcjwEAwD2N5BwAgM+wvn37SmZmphQVFUlDQ4OUlZVJTk6OjBs3LiT3S05Otsl+XFychNKUKVPk+PHjUl9fL1VVVfLLX/4ypPcDACDUIkN+BwAAEBZut1uOHj0qV69elSVLlsjZs2clKipKJk2aJJs2bZIhQ4ZId+Z0OsXr9bYrnz59urz66quyfPly+e1vfyuRkZHyxBNPhOUZAQAIJkMQBEEQROfhdrtNdna23d7ptVyOniY24n67DfVz792715SXl5vo6Oh2x+Li4vy/VWpqqv2dnJxs9wOPJyUl2TLf+ycmJpqcnBxTVVVlamtrTX5+vpk8ebI93lZWVpat43A4zNKlS01xcbGpr683p0+fNmlpaf57+O6bkpJi8vLyTGNjoy1r+9xOp9O+0//8n//zrv47IAiCIAhpE7ScAwDQBZwSKQ9HDZIHIvvZ3165IR/eeF8qmv8gLdK+dfhO9e7dW1JSUmTFihW263db165du+1ra6u7y+WSMWPGSF1dnQwdOlRqa2ulvLzctmrv3r1bBg0aJDU1NeLxeGydZcuWycyZM2XevHlSWFho6+7YsUOuXLkiR44c8V977dq1kp6eLsXFxVJdXd3u3k8++aQ88sgj0tLSIm+//bYkJCTI6dOnbc+A3//+97f9TgAAhBvJOQAAXUAT84So/tJkGsRj6iTK4bL7qqz5XNDvN3DgQImIiJDz588H/dqJiYmya9cuyc/Pt/slJSX+Yzr+W12+fNn/AUATee2CPn78eDtO3Fdn1KhRMnfu3FbJ+apVq+TAgQOd3nvAgAF2u3r1avnf//t/yx//+EdZvHixHDp0yH4Q6CihBwDgbkByDgBAiLkcPW2LuSbmGsq3jY/sJ5duFPv3g8XhcEio6ARzmzdvlokTJ9pEWhN1Hc/+SR8KYmJiZP/+/a3KNWk/depUq7K8vLxPvLd+cFB///d/b1vo1QsvvCAVFRXyta99TX7yk5/cwZsBABA+zNYOAEAXJOfalb3ZNLUq1/1IibTHg027jmvX78GDB99SPa3TNrnXSeQCbd261bZgb9++XYYNG2YT6gULFnR6zdjYWLudOnWqDB8+3B/aHX7GjBmtztVu8p/k/ffft9tz5/6/3gZNTU22G7y26AMAcLciOQcAIMS0VVzHmGtX9kC6f0NuBL3VXGn37n379sn8+fMlOjq63fHOljrTMeCqX79+/jJNpNvSluotW7ZIWlqarF+/XubMmeNPlH0zrftoIq3LuGnyrEu6BYZe51acPHnSXusLX/iCv0xna+/fv7+Ulpbe0rUAAOhOSM4BAAgxTb518jdtIddwSIT/d9WN90OSnCtNzDVJPnHihJ2oTbuXa0v6woUL5dixYx3WuXDhgl0LXcd06/m6nriO6Q60YcMG26VdE+IRI0bI2LFjpaCgwB7TBFlb36dNmyZ9+vSx3dl1srh169bZerNmzbKt7lpPW9t1/1Zcv35dfvzjH8vf/d3fyYQJE+w4c+1ir3bu3Hnb/1YAAHQHYZ8yniAIgiC6cwRjCa0IcZrEqKFmeK8/M1/qNcludV/LQ/nsCQkJZuPGjaakpMQ0NDTYZcj27NnTapmywKXUNEaOHGnOnDljlzw7fPiwXfIscCm1zMxMU1hYaDwej6msrDTbtm0z8fHx/voZGRnm4sWLxuv1+pdS01i0aJEpKCiwy6RpvdzcXDN69OhOl3DrLCIjI80PfvADc+nSJXPt2jXz61//2gwdOvSu+DsgCIIgCOkkHB//AAAAnXC73bJmzRpZuXLlHXed9rWYB04Oh3vv7wAAgLaYrR0AgC5EUg4AADrCmHMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAAAAACDOScwAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQAAAAAIM5JzAADuccYYSU1NDfdjAABwTyM5BwDgM6xv376SmZkpRUVF0tDQIGVlZZKTkyPjxo0Lyf2Sk5Ntsh8XFxfS63cUX/rSl0JyTwAAukJkl9wFAAB0ObfbLUePHpWrV6/KkiVL5OzZsxIVFSWTJk2STZs2yZAhQ6Q7czqd4vV6W5W99dZbkpCQ0KpszZo18md/9meSl5fXxU8IAEDw0HIOAEAXcjl6SmxEb7sNtVdeecW2KD/99NOye/duKSwslHPnzsmGDRvkmWeeuemW76SkJFumyb5KTEy0re9VVVVSW1sr+fn5MnnyZHv80KFD9hz9IKB1srKy7L7D4ZClS5dKcXGx1NfXy+nTpyUtLa3dfVNSUmyS3djYKKNGjWr3fM3NzVJZWemPDz/80HbJ990HAIC7FS3nAAB0AadEysOuL8gDkf3E6YgSr2mWD2+8LxVN56VFWrcOB0Pv3r1tortixQqbDLd17dq12762trq7XC4ZM2aM1NXVydChQ22SXl5eLtOnT7cfAgYNGiQ1NTXi8XhsnWXLlsnMmTNl3rx59iOB1t2xY4dcuXJFjhw54r/22rVrJT093Sbx1dXVn/osf/7nfy4PPPAAyTkA4K5Hcg4AQBfQxDzB1V+aWhrE01IrUQ6X3VdlTb8P+v0GDhwoERERcv78+aBfW1vOd+3aZVvMVUlJif+Ytqary5cv+z8AaCK/fPlyGT9+vBw/ftxfR1vG586d2yo5X7VqlRw4cOCmn+XFF1+Uffv2yXvvvRe09wMAIBxIzgEACDHtwq4t5pqYN5kGW2a3LSLxkf3kUnORvzxYtBt5qOgEc5s3b5aJEyfaRFoTdR3P/kkfCmJiYmT//v2tyjVpP3XqVKuyWxk3/vDDD9vx81//+tdv4y0AAOheGHMOAECIuRy9bFf2ZtPUqlz3Ix2R9niwadfxlpYWGTx48C3V0zptk3udRC7Q1q1bZcCAAbJ9+3YZNmyYTagXLFjQ6TVjY2PtdurUqTJ8+HB/aHf4GTNmtDpXu8nfrBdeeMGOOdfx7wAA3O1IzgEACLEm47FjzLUreyDdv2Fu2OPBpuO1tbv3/PnzJTo6ut3xzpY60zHgql+/fv4yTaTbqqiokC1btthJ3davXy9z5syx5U1NTf6Z1n10Ejpdxk27w+uSboGh17ldmpxnZ2fLjRs3bvsaAAB0FyTnAACEmHZZ18nfXBE9bRd3h0TYre5X3Xg/6F3afTQx1yT5xIkTdqI27V6uLekLFy6UY8eOdVjnwoULdi301atX2/OnTJkiixcvbnWOzvauXdr79+8vI0aMkLFjx0pBQYE9Vlpaalvfp02bJn369LHd2XWyuHXr1tl6s2bNsq3uWk9b23X/dug67Xqd11577bbqAwDQ3ZCcAwDQBXRW9ktNf9QO49IrIsZudV/LQ0UnXXvyySfl4MGDtnVbJ3DTcd+6JvhLL73UYR1thf7GN75hk/h33nlHvvOd70hGRkarczTh1xnbNSF/44035N1335W/+Zu/sccuXrwo3/3ud+2s67rU2csvv2zLV65cadcj11nbffW0m3vgZHK3QieC0zXc//CHP9xWfQAAuhsdUGbC/RAAAHRnun63JpaaYGrL8J2wLeaOXrYre6hazNH9/w4AAGiL2doBAOhCmpCTlAMAgLbo1g4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAMA9zhgjqamp4X4MAADuaSTnAAB8hvXt21cyMzOlqKhIGhoapKysTHJycmTcuHEhuV9ycrJN9uPi4iRUPv/5z8uePXvkypUrcu3aNfnP//xP+epXvxqy+wEA0BVIzgEA6EIuRy+JdfYWl6NnyO/ldrvl5MmTNhFfsmSJDBs2TFJSUuTgwYOyadMm6e6cTmeH5f/xH/8hkZGR9r2eeuopOXPmjC3TDxEAANytSM4BAOgCTomUxJ5PyOMxo2Vw9LPyeMwYux8hHSegwfDKK6/YVuynn35adu/eLYWFhXLu3DnZsGGDPPPMMzfd8p2UlGTLNNlXiYmJtvW9qqpKamtrJT8/XyZPnmyPHzp0yJ5z9epVWycrK8vuOxwOWbp0qRQXF0t9fb2cPn1a0tLS2t1XPx7k5eVJY2OjjBo1qt3zPfDAAzJo0CBZu3atnD17Vi5cuGCvGxMTI0888UTQ/w0BAOgqkV12JwAA7mEP9xwsCa4/laYWj3haaiXK4bL7qqwhP+j36927t010V6xYYZPhtrQ7+O3SVneXyyVjxoyRuro6GTp0qE3Sy8vLZfr06fZDgCbQNTU14vF4bJ1ly5bJzJkzZd68efYjgdbdsWOH7Zp+5MgR/7U16U5PT7dJfHV1dbt7f/jhh3L+/HmZNWuWvP322zaJnzt3rlRWVtpeAgAA3K1IzgEA6IKu7A9EPmQT8ybTYMvstkUkPvIhueS44C8PloEDB0pERIRNZINNW8537dplW8xVSUmJ/5i2pqvLly/7PwBoIr98+XIZP368HD9+3F9HW8Y1sQ5MzletWiUHDhz4xPvrdXTM+fXr16WlpcXeSz9EaGs9AAB3K5JzAABCzBXRU5yOSNtiHqjZNEmviFhxRfSSJm9wk3PtRh4qOsHc5s2bZeLEiTaR1kRdu5h/0ocC7Xa+f//+VuWatJ86dapVmXZpv5mWe03IR48ebVvmv/nNb8q///u/y5e//GW5dOnSHbwZAADhw5hzAABCrKmlQbzmhu3KHkj3b5gbtkU92LTruLYqDx48+JbqaZ22yX1UVFSrc7Zu3SoDBgyQ7du320nmNKFesGBBp9eMjY2126lTp8rw4cP9od3hZ8yY0epc7Sb/SXQSuGnTpslf/MVfyFtvvWWT+/nz59skffbs2bf0rgAAdCck5wAAhFiT8ciHNy7aFnKdpd0hEXar+1U3Lga9S7vS8dr79u2ziWt0dHS7450tdaZjwFW/fv38ZZpIt1VRUSFbtmyxk7qtX79e5syZY8ubmprazbSuk9DpMm7aHV6XdAsMvc6t8L2L7yOCj+5rN34AAO5W/FcMAIAuUNFQIJeadGx2hO3Krlvd1/JQ0cRck+QTJ07Yidq0e7m2pC9cuFCOHTvWYR2d/VzXQl+9erU9f8qUKbJ48eJW5+hs79qlvX///jJixAgZO3asFBR89B6lpaU2UdbW7T59+tju7DpZ3Lp162w9nchNW921nra26/6t0OfWDw/btm2TL37xi3bN83/8x3+UP/3TP5W9e/fewb8WAADhZwiCIAiC6DzcbrfJzs622zu9lsvR08Q6e9ttVzx7QkKC2bhxoykpKTENDQ2mvLzc7NmzxyQnJ/vPUampqf79kSNHmjNnzpj6+npz+PBhk5aWZs/xvX9mZqYpLCw0Ho/HVFZWmm3btpn4+Hh//YyMDHPx4kXj9XpNVlaWv3zRokWmoKDANDY22nq5ublm9OjR9pg+j4qLi/vUd3rqqafMG2+8YT744ANz7do189Zbb5mUlJS76u+AIAiCIKRNOD7+AQAAOqHrd69Zs0ZWrlxpW4Zxb+LvAAAQSnRrBwAAAAAgzEjOAQAAAAAIM5JzAAAAAADCjOQcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAuMcZYyQ1NTXcjwEAwD2N5BwAgM+wvn37SmZmphQVFUlDQ4OUlZVJTk6OjBs3LiT3S05Otsl+XFychMqIESPk17/+tVRXV8sHH3wgW7ZskZiYmJDdDwCArkByDgBAF3I5ekmss7e4HD1Dfi+32y0nT560ifiSJUtk2LBhkpKSIgcPHpRNmzZJd+d0OtuV9evXTw4cOCAXLlyQr3zlK/Z9Hn/8cfnpT38almcEACBYSM4BAOgCTokUd89h8njsGBkS85w8Hpts9yMkMmT3fOWVV2wr9tNPPy27d++WwsJCOXfunGzYsEGeeeaZm275TkpKsmWa7KvExETb+l5VVSW1tbWSn58vkydPtscPHTpkz7l69aqtk5WVZfcdDocsXbpUiouLpb6+Xk6fPi1paWnt7qvJdl5enjQ2NsqoUaPaPd+0adOkublZ5s+fL++++649d968eTJjxgx57LHHgv5vCABAVwnd/yMAAAB+j/QcIgk9BkiT1yOelusS5ehh91Vpw9mg369379420V2xYoVNhtu6du3abV9bW91dLpeMGTNG6urqZOjQoTZJLy8vl+nTp9sPAYMGDZKamhrxeDy2zrJly2TmzJk2kdaPBFp3x44dcuXKFTly5Ij/2mvXrpX09HSbxGu39bZ69OghTU1NNpH38d1Dk3ntvg8AwN2I5BwAgC7oyh4f9ZBNzJvMR4mk3XrFlr/fWChNpiGo9xw4cKBERETI+fPnJdi05XzXrl22xVyVlJT4j2lrurp8+bL/A4Am8suXL5fx48fL8ePH/XU0mZ47d26r5HzVqlW223pnfvvb38oPf/hDm8D/0z/9kx1rrgm9r8s7AAB3K7q1AwAQYq6InhLpiJJm09iqXPe13BXRK+j31G7koaITzGVkZMibb74pq1evtmPZP+1DgSbR+/fvl+vXr/tj1qxZ7bqiazf1T6Ld8mfPni2LFy+2PQIuXbpkE33dtrS0BOX9AAAIB5JzAABCrKmlQW6YZtuVPZDua3lTy0et6cGkXcc1WR08ePAt1fMluIHJfVRUVKtztm7dKgMGDJDt27fbxFwT6gULFnR6zdjYWLudOnWqDB8+3B/aHV7HigfSbvKf5uc//7ltJX/44YflgQcesB8IHnzwQdsVHgCAuxXJOQAAIaZd2KuaL4rL2ct2cXdIhN3qvpYHu0u70vHa+/btsxOnRUdHtzve2VJnOga8bRdxTaTbqqiosEuY6aRu69evlzlz5thyHQ/edqZ1be3WZdy0O7yOCQ8Mvc7t0q7zmsw///zz9vraMg8AwN2K5BwAgC5Q3lAglxqLtUlaejnvs1vd1/JQ0cRck+QTJ07Yidq0e7m2pC9cuFCOHTvWYR1dokzXQtfWaD1/ypQptgt5IJ3tfeLEidK/f3+75vjYsWOloOCj9ygtLbWt7zqrep8+fWx3dp0sbt26dbaedmXXVnetp63tun8776X1P//5z8vf/M3fyMsvv2wnnLuTSe4AAOgOdLpTgiAIgiA6CbfbbbKzs+32Tq/lcvQ0sc7edtsVz56QkGA2btxoSkpKTENDgykvLzd79uwxycnJ/nNUamqqf3/kyJHmzJkzpr6+3hw+fNikpaXZc3zvn5mZaQoLC43H4zGVlZVm27ZtJj4+3l8/IyPDXLx40Xi9XpOVleUvX7RokSkoKDCNjY22Xm5urhk9erQ9ps+j4uLiPvWd9H4ffPCBfZ/Tp0+bmTNn3nV/BwRBEAQhbcLx8Q8AANAJXb97zZo1snLlStsyjHsTfwcAgFCiWzsAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAMA9zhgjqamp4X4MAADuaSTnAAB8hvXt21cyMzOlqKhIGhoapKysTHJycmTcuHEhuV9ycrJN9uPi4iRUli9fLkePHpW6ujqprq7u8JxHH31U/uM//sOeU1lZKf/4j/8oTqczZM8EAMCdirzjKwAAgJvmiuglLkcvaWrxSJPxhPRebrfbJrFXr16VJUuWyNmzZyUqKkomTZokmzZtkiFDhkh3psm01+ttV+5yuWTnzp1y7NgxefHFF9sdj4iIkL1798qlS5dk5MiR0q9fP8nOzpbm5mZZsWJFFz09AAC3zhAEQRAE0Xm43W6TnZ1tt7d7DadEGnevYWZEXIr58v3/zW51P0IiQ/bce/fuNeXl5SY6Orrdsbi4OP9vlZqaan8nJyfb/cDjSUlJtsz3/omJiSYnJ8dUVVWZ2tpak5+fbyZPnmyPt5WVlWXrOBwOs3TpUlNcXGzq6+vN6dOnTVpamv8evvumpKSYvLw809jYaMs+6f1mz55tqqur25XrNW7cuGE+97nP+cvmzp1rrl69aqKiosL6d0AQBEEQ0knQcg4AQBd4pNcQSej5mG0x93ivS1RED7uvSj1ng36/3r17S0pKim0prq+vb3f82rVrt31tbXXX1usxY8bYbuNDhw6V2tpaKS8vl+nTp8vu3btl0KBBUlNTIx7PR70Dli1bJjNnzpR58+ZJYWGhrbtjxw65cuWKHDlyxH/ttWvXSnp6uhQXF3faZf3TPPvss7aXwOXLl/1l+/btkx//+Mfy+OOPy+nTp2/73QEACBWScwAAuqAre7zr4Y+6srd8lKz6tlr+fsOFoHdxHzhwoO3eff78eQm2xMRE2bVrl+Tn59v9kpIS/7Gqqiq71cTY9wFAE3kdJz5+/Hg5fvy4v86oUaNk7ty5rZLzVatWyYEDB+7o+RISEuw480C+fT0GAEB3RHIOAECI6RjzSEeUbTEP1NzSKL2c99nkvckb3OTc4XBIqOgEc5s3b5aJEyfaRFoTdW2p/qQPBTExMbJ///5W5Zq0nzp1qlVZXl5eyJ4bAIDujNnaAQAIMW0Vv2GabVf2QLqv5b5W9GDSruMtLS0yePDgW6qnddom9zqJXKCtW7fKgAEDZPv27TJs2DCbUC9YsKDTa8bGxtrt1KlTZfjw4f7Q7vAzZsxoda52k79TOhGczlIfyLevxwAA6I5IzgEACDFNvqua3vtopvaIXuKQCP9vLQ/FrO06XlvHWc+fP1+io6PbHe9sqTMdA650hnMfTaTbqqiokC1btkhaWpqsX79e5syZY8ubmprsNnDZsnPnztll3LQ7vC7pFhh6nWDTWdz1o8GDDz7oL5swYYLtZq/PAgBAd0RyDgBAFyj3FMilhiJtk7Zd2XWr+1oeKpqYa5J84sQJO1Gbdi/XlvSFCxfaBLYjFy5csGuhr1692p4/ZcoUWbx4catzNmzYYLu09+/fX0aMGCFjx46VgoKP3qO0tNS2vk+bNk369Olju7PrZHHr1q2z9WbNmmVb3bWetrbr/q3SNcyTkpJssq/vp7819F7q17/+tU3CtWX/i1/8on3W733ve3YiO9/HAwAAuqOwTxlPEARBEN05grmElsvRy8Q64+22K549ISHBbNy40ZSUlJiGhga7tNqePXtaLVMWuJSaxsiRI82ZM2fskmeHDx+2S54FLqWWmZlpCgsLjcfjMZWVlWbbtm0mPj7eXz8jI8NcvHjReL1e/1JqGosWLTIFBQV2mTStl5uba0aPHt3pEm6dhV6zI4HvpMu96VJydXV15vLly+YHP/iBcTqd3ebvgCAIgiCkTTg+/gEAADrhdrtlzZo1snLlStsyjHsTfwcAgFCiWzsAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAMA9zhgjqamp4X4MAADuaSTnAAB8hvXt21cyMzOlqKhIGhoapKysTHJycmTcuHEhuV9ycrJN9uPi4iRUli9fLkePHpW6ujqprq7u8Jx/+qd/kry8PPvOp06dCtmzAAAQLCTnAAB0IVdEL4mNjLfbUHO73XLy5EmbiC9ZskSGDRsmKSkpcvDgQdm0aZN0d06ns8Nyl8slO3fulM2bN39i/X/+53+WX/ziFyF6OgAAgovkHACALuB0RIo7+ovy+J98VYbcN8pudT9CIkN2z1deecW2Yj/99NOye/duKSwslHPnzsmGDRvkmWeeuemW76SkJFumyb5KTEy0re9VVVVSW1sr+fn5MnnyZHv80KFD9pyrV6/aOllZWXbf4XDI0qVLpbi4WOrr6+X06dOSlpbW7r768UBbvBsbG2XUqFEdPuPq1avlRz/6kZw9e7bTd//bv/1b+/56PwAA7gah+38EAADA75FeQyWh52PS1OIRj/e6REX0sPuqtP6doN+vd+/eNtFdsWKFTYbbunbt2m1fW1vdtfV6zJgxtmv50KFDbZJeXl4u06dPtx8CBg0aJDU1NeLxeGydZcuWycyZM2XevHn2I4HW3bFjh1y5ckWOHDniv/batWslPT3dJtWddVkHAOCziOQcAIAQ0y7s8a6HbWKuoXxbLX+/odC/HywDBw6UiIgIOX/+vASbtpzv2rXLtpirkpIS/zFtTVeXL1/2fwDQRF7HiY8fP16OHz/ur6Mt43Pnzm2VnK9atUoOHDgQ9GcGAKC7IzkHAKALkvNIR5RtMQ/U3NIovZz32ePBTs61G3mo6ARzOt574sSJNpHWRP2Tupjrh4KYmBjZv39/q3JN2ttO1qZd2gEAuBeRnAMAEGKaeN8wzbYre2ASrvtaHuzEXGnX8ZaWFhk8ePAt1dM6bZP7qKioVuds3bpV9u3bJ1OnTrUJunZZX7x4sbz88ssdXjM2NtZu9fz33nuv1TEdWx5Iu8kDAHAvYkI4AABCTJPvqqb3bAu5hkMi/L+1PBTJuY7X1gR6/vz5Eh0d3e54Z0ud6Rhw1a9fP3/Z8OHD251XUVEhW7ZssZO6rV+/XubMmWPLm5qa2s20rpPQ6ZJm2h1el3QLDL0OAACg5RwAgC5RXn/OP8Zcu7Jri/mlhiJ/eShoYq7rgZ84ccKO5X7nnXckMjJSJkyYIC+99JKdyK2tCxcu2LXQdUZ0nUxOJ3bTVvFAOtt7bm6uvPvuu3biubFjx0pBQYE9Vlpaalvfp02bJq+//rqdEE4ni1u3bp2tp+Pg33zzTftx4LnnnrOTxmVnZ9/Sez366KMSHx9vk339CKCzyfue3dfy/thjj9kW+4SEBOnVq5f/HP1Q0NzcfNv/pgAAhJIhCIIgCKLzcLvdJjs7227v9FquiF4mNjLebrvi2RMSEszGjRtNSUmJaWhoMOXl5WbPnj0mOTnZf45KTU31748cOdKcOXPG1NfXm8OHD5u0tDR7ju/9MzMzTWFhofF4PKaystJs27bNxMfH++tnZGSYixcvGq/Xa7KysvzlixYtMgUFBaaxsdHWy83NNaNHj7bH9HlUXFzcp76TXrMjge908ODBDs+5k/8Ng/l3QBAEQRDSJhwf/wAAAJ3Q9bvXrFkjK1eutC3DuDfxdwAACCXGnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAAAAAECYkZwDAAAAABBmJOcAANzjjDGSmpoa7scAAOCeRnIOAMBnWN++fSUzM1OKioqkoaFBysrKJCcnR8aNGxeS+yUnJ9tkPy4uTkJl+fLlcvToUamrq5Pq6up2x7/4xS/Kz372M/uu9fX1cu7cOVm0aFHIngcAgGCIDMpVAADATXFF9LLR1OKxEUput9smsVevXpUlS5bI2bNnJSoqSiZNmiSbNm2SIUOGSHfmdDrF6/W2K3e5XLJz5045duyYvPjii+2OP/XUU3L58mWZOXOmlJeXy8iRI+UnP/mJvZa+NwAA3ZUhCIIgCKLzcLvdJjs7225v9xpOR6RxxySZEfFTzZcf+O92q/sRjsiQPffevXtNeXm5iY6ObncsLi7O/1ulpqba38nJyXY/8HhSUpIt871/YmKiycnJMVVVVaa2ttbk5+ebyZMn2+NtZWVl2ToOh8MsXbrUFBcXm/r6enP69GmTlpbmv4fvvikpKSYvL880Njbask96v9mzZ5vq6uqb+rd4+eWXzW9+85uw/x0QBEEQhHQStJwDANAFHol+XBJ6DbSt5R7vdYmK6GH3VWndmaDfr3fv3pKSkiIrVqywXbvbunbt2m1fW1uftfV6zJgxtmv50KFDpba21rZST58+XXbv3i2DBg2Smpoa8Xg+6h2wbNky25I9b948KSwstHV37NghV65ckSNHjvivvXbtWklPT5fi4uIOu6zfLu1mX1VVFbTrAQAQbCTnAACEmHZjj+/xSKuu7L6tlr/veTfoXdwHDhwoERERcv78eQm2xMRE2bVrl+Tn59v9kpIS/zFfAqzdyn0fADSR13Hi48ePl+PHj/vrjBo1SubOndsqOV+1apUcOHAgqM/77LPPyvPPPy9Tp04N6nUBAAgmknMAALogOY90RNkW80DNLY3Sy3mffwx6MDkcDgkVnWBu8+bNMnHiRJtIa6Ku49k/6UNBTEyM7N+/v1W5Ju2nTp1qVZaXlxfUZ3388cflV7/6lfzd3/1du/sDANCdkJwDABBimnjfMM22K3tgEq77Wh6KieG063hLS4sMHjz4luppnbbJvU4iF2jr1q2yb98+2xKtCbp2WV+8eLG8/PLLHV4zNjbWbvX89957r9WxxsbGVvvaTT5YdMK73/zmN3YyuL//+78P2nUBAAgFllIDACDENPmuaqzwz9TukAj/by0PRXKu47U1gZ4/f75ER0e3O97ZUmc6Blz169fPXzZ8+PB251VUVMiWLVskLS1N1q9fL3PmzLHlTU1N/pnWfXQpM13GTbvD65JugaHXCQUdB3/w4EHZtm2bZGRkhOQeAAAEE8k5AABdoLz+93LJc0HbpG1Xdt3qvpaHiibmmiSfOHHCTtSm3cu1JX3hwoV2GbKOXLhwwa4Pvnr1anv+lClTbKt4oA0bNtgW8/79+8uIESNk7NixUlBQYI+Vlpba1vdp06ZJnz59bHd2nSxu3bp1tt6sWbNkwIABtt6CBQvs/q169NFHJSkpySb7+n76W0Pv5evKron5r3/9a/nhD39o13rX0OcBAKA7C/uU8QRBEATRnSOYS2i5InqZ2Mh4u+2KZ09ISDAbN240JSUlpqGhwS6ttmfPnlbLlAUupaYxcuRIc+bMGbvk2eHDh+2SZ4FLqWVmZprCwkLj8XhMZWWl2bZtm4mPj/fXz8jIMBcvXjRer9e/lJrGokWLTEFBgV0mTevl5uaa0aNHd7qEW2eh1+yI752++93vdnhc/w26y98BQRAEQUibcHz8AwAAdMLtdsuaNWtk5cqVtmUY9yb+DgAAoUS3dgAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQAAAAAIM5JzAAAAAADCjOQcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAgHucMUZSU1PD/RgAANzTSM4BAPgM69u3r2RmZkpRUZE0NDRIWVmZ5OTkyLhx40Jyv+TkZJvsx8XFSagsX75cjh49KnV1dVJdXd3ueHx8vOTm5sp7773nf+eNGzfKfffdF7JnAgDgTpGcAwDQhVwRvSQ2Mt5uQ83tdsvJkydtIr5kyRIZNmyYpKSkyMGDB2XTpk3S3Tmdzg7LXS6X7Ny5UzZv3tzh8ZaWFvnVr34lf/7nfy6DBg2Sv/7rv5bx48fLj3/84xA/MQAAd8YQBEEQBNF5uN1uk52dbbe3ew2nI9K4Y4abEQ9MM1/u8/+zW92PcESG7Ln37t1rysvLTXR0dLtjcXFx/t8qNTXV/k5OTrb7gceTkpJsme/9ExMTTU5OjqmqqjK1tbUmPz/fTJ482R5vKysry9ZxOBxm6dKlpri42NTX15vTp0+btLQ0/z18901JSTF5eXmmsbHRln3S+82ePdtUV1ff1L/FwoULTVlZWdj/DgiCIAhCOonIO0zsAQDATXgk+glJiB4oTd568XhrJCqih91XpXWng36/3r1721byFStWSH19fbvj165du+1ra6u7tl6PGTPGdi0fOnSo1NbWSnl5uUyfPl12795tW6xramrE4/HYOsuWLZOZM2fKvHnzpLCw0NbdsWOHXLlyRY4cOeK/9tq1ayU9PV2Ki4s77LJ+O/r162ef6/Dhw0G5HgAAoUByDgBAiGkX9viej9jEvKnlo2TVt9Xy9z1/8O8Hy8CBAyUiIkLOnz8vwZaYmCi7du2S/Px8u19SUuI/VlVVZbeXL1/2fwDQRF7HiWvX8uPHj/vrjBo1SubOndsqOV+1apUcOHAgKM/5s5/9zE50Fx0dbcfZf/Ob3wzKdQEACAXGnAMA0AXJeaQjSppbGluV676Wh2L8ucPhkFDRCeYyMjLkzTfflNWrV9ux7J/2oSAmJkb2798v169f98esWbPksccea3VuXl5e0J7z29/+tjz55JN27Lne54c//GHQrg0AQLDRcg4AQIhpq/gN02y7sge2kOu+lge71Vxp13GdGG3w4MG3VE/rtE3uo6KiWp2zdetW2bdvn0ydOlUmTpxou6wvXrxYXn755Q6vGRsba7d6vs6gHqixsfUHC+0mHyyVlZU2/vCHP9gWff2YsGbNGrl06VLQ7gEAQLDQcg4AQIhp8l3VUCEuZ7RtJXdIhN3qvpaHIjnX8dqaQM+fP992626rs6XOdAy4b5y2z/Dhw9udV1FRIVu2bJG0tDRZv369zJkzx5Y3NTW1m2n93Llzdkkz7Q6vS7oFhl6nK2gXf9WjR48uuR8AALeKlnMAALpAeX2+f4x5r8g/sS3ml+ov+MtDQRNzXQ/8xIkTdiz3O++8I5GRkTJhwgR56aWX7ERubV24cMGuC67d1XUyOZ3YTVvFA23YsMGuI/7uu+/aiefGjh0rBQUF9lhpaaltfZ82bZq8/vrrdkI4nSxu3bp1tp4mydqCrR8HnnvuOTtpXHZ29i2916OPPmrXMtdkXz8CJCUl+Z9dW94nT55s13f/r//6L3vvxx9/XH7wgx/Y++rzAQDQXYV9yniCIAiC6M4RzCW0XBG9TGxkvN12xbMnJCSYjRs3mpKSEtPQ0GCXVtuzZ0+rZcoCl1LTGDlypDlz5oxd8uzw4cN2ybPApdQyMzNNYWGh8Xg8prKy0mzbts3Ex8f762dkZJiLFy8ar9frX0pNY9GiRaagoMAuk6b1cnNzzejRoztdwq2z0Gt2xPdOX/3qV83Ro0ftMmv6Dn/4wx/M97///Zu6dlf9HRAEQRCEtAnHxz8AAEAn3G63Hau8cuVKWl7vYfwdAABCiTHnAAAAAACEGck5AAAAAABhRnIOAAAAAECYkZwDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAA9zhjjKSmpob7MQAAuKeRnAMA8BnWt29fyczMlKKiImloaJCysjLJycmRcePGheR+ycnJNtmPi4uTUFm+fLkcPXpU6urqpLq6+hPPjY+Pl/Ly8pA/EwAAd4rkHACALuSKiJbYqAfEFdEr5Pdyu91y8uRJm4gvWbJEhg0bJikpKXLw4EHZtGmTdHdOp7PDcpfLJTt37pTNmzd/6jW2bt0q77zzTgieDgCA4CI5BwCgCzgdkeK+b7g8/sCfyZDeyfL4A+PtfoQjMmT3fOWVV2yL8dNPPy27d++WwsJCOXfunGzYsEGeeeaZm275TkpKsmWa7KvExETb+l5VVSW1tbWSn58vkydPtscPHTpkz7l69aqtk5WVZfcdDocsXbpUiouLpb6+Xk6fPi1paWnt7qsfD/Ly8qSxsVFGjRrV4TOuXr1afvSjH8nZs2c/8f3nzZsn999/v6xbt+42/vUAAOhaoft/BAAAwO+R2CckIfrz0uT1iMd7XaIieth9VXr9dNDv17t3b5vorlixwibDbV27du22r62t7tp6PWbMGNu1fOjQoTZJ1+7j06dPtx8CBg0aJDU1NeLxeGydZcuWycyZM23CrB8JtO6OHTvkypUrcuTIEf+1165dK+np6TaJ/7Qu659kyJAhsmrVKvnKV74iAwYMuO3rAADQVUjOAQDogq7s8T0ftYl5U8tHyapvq+Xv1/3Bvx8sAwcOlIiICDl//rwEm7ac79q1y7aYq5KSEv8xbU1Xly9f9n8A0ERex4mPHz9ejh8/7q+jLeNz585tlZxrQn3gwIE7ej69389//nPblV8/GJCcAwDuBiTnAACEmMvZSyIdUbbFPFBzS6P0irxPXM7ooCfn2o08VHSCOR3vPXHiRJtIa6L+SV3M9UNBTEyM7N+/v10SferUqVZl2qX9Tn3/+9+XgoIC+Zd/+Zc7vhYAAF2FMecAAISYtpjfMM22K3sg3dfyJm/7bud3SruOt7S0yODBg2+pntZpm9xHRUW1m2RNW6O3b99uJ5nThHrBggWdXjM2NtZup06dKsOHD/eHdoefMWNGq3O1m/yd0gnwvva1r0lzc7ON3/zmN7b8gw8+sOPVAQDojkjOAQAIsaaWeqlqKLct6DpLu0Mi7Fb3tTzYreZKx2vv27dP5s+fL9HR0e2Od7asmI4BV/369fOXaSLdVkVFhWzZssVO6rZ+/XqZM2eOLW9qamo307pOQqfLuGl3eF3SLTD0OsGmz6ST2Pk+Anzzm9+05aNHj74rZqkHANyb6NYOAEAXKK/N948x167s2mJ+qb7QXx4KmpjreuAnTpywY7l1SbHIyEiZMGGCvPTSS7bluq0LFy7YtdC1hVknk9OJ3RYvXtzqHJ3tPTc3V95991078dzYsWNtN3JVWlpqW9+nTZsmr7/+up0QTieL0xnTtZ6Og3/zzTftx4HnnnvOThqXnZ19S+/16KOP2vXLNdnXjwCaiPueXVvedTK5QH369LFbfcY7mQgPAIBQMwRBEARBdB5ut9tkZ2fb7Z1eyxXRy8RGPWC3XfHsCQkJZuPGjaakpMQ0NDSY8vJys2fPHpOcnOw/R6Wmpvr3R44cac6cOWPq6+vN4cOHTVpamj3H9/6ZmZmmsLDQeDweU1lZabZt22bi4+P99TMyMszFixeN1+s1WVlZ/vJFixaZgoIC09jYaOvl5uaa0aNH22P6PCouLu5T30mv2ZHAdwqMW7l2V/0dEARBEIS0CcfHPwAAQCd0/e41a9bIypUrbcsw7k38HQAAQokx5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAAAAAECYkZwDAAAAABBmJOcAAAAAAIQZyTkAAPc4Y4ykpqaG+zEAALinkZwDAPAZ1rdvX8nMzJSioiJpaGiQsrIyycnJkXHjxoXkfsnJyTbZj4uLk1BZvny5HD16VOrq6qS6urrDc/QZ2sbzzz8fsmcCAOBORd7xFQAAwE1zRUSLy9lLmrz10tTiCem93G63TWKvXr0qS5YskbNnz0pUVJRMmjRJNm3aJEOGDJHuzOl0itfrbVfucrlk586dcuzYMXnxxRc7rf/Xf/3X8sYbb/j39d8BAIDuipZzAAC6gNMRKe77RsjjD4yXIfFflccfmGD3Ixyh+07+yiuv2Bbjp59+Wnbv3i2FhYVy7tw52bBhgzzzzDM33fKdlJRkyzTZV4mJibb1vaqqSmprayU/P18mT55sjx86dMifCGudrKwsu+9wOGTp0qVSXFws9fX1cvr0aUlLS2t335SUFMnLy5PGxkYZNWpUh8+4evVq+dGPfmQ/NnwSfYbKykp/6DUBAOiuaDkHAKALPBI7TBJiPm9bzD03rktUhMvuq9Lrp4J+v969e9tEd8WKFTYZbuvatWu3fW1tddfW6zFjxtiu5UOHDrVJenl5uUyfPt1+CBg0aJDU1NSIx/NR74Bly5bJzJkzZd68efYjgdbdsWOHXLlyRY4cOeK/9tq1ayU9Pd0m8Z11Wb+V53zttdfstX784x/7PxQAANAdkZwDANAFXdnjez7aqiu7b6vl79edD3oX94EDB0pERIScP39egk1bznft2mVbzFVJSYn/mLamq8uXL/s/AGgir+PEx48fL8ePH/fX0ZbxuXPntkrOV61aJQcOHLjjZ1y5cqX89re/tR8mJk6caHsRxMbGysaNG+/42gAAhALJOQAAIaZjzCMjomyLeaDmlibpFXmfuJzRQU/OtRt5qOgEc5s3b7ZJrybSmqh/Uhdz/VAQExMj+/fvb1WuSfupU617DWiX9mD43ve+5/+tXej1/jrunuQcANBdMeYcAIAQa/J65EZLs+3KHkj3tVxb1INNu463tLTI4MGDb6me1mmb3OskcoG2bt0qAwYMkO3bt8uwYcNsQr1gwYJOr6kt1mrq1KkyfPhwf2h3+BkzZrQ6V7vJh8Lvfvc7efTRR+0HAQAAuiOScwAAQqyppV6qGsptC7kropc4xGm3uq/loZi1Xcdr79u3T+bPny/R0dHtjne21JmOAVf9+vXzl2ki3VZFRYVs2bLFTuq2fv16mTNnji1vamryz7Tuo5PQ6TJu2h1el3QLDL1OV9B30C73vucDAKC7oVs7AABdoLz2rH+MuXZl1xbzS3WF/vJQ0MRcl1I7ceKEHcv9zjvvSGRkpEyYMEFeeukl23Ld1oULF+xa6Dojuk4mpxO7LV68uNU5Ott7bm6uvPvuu3biubFjx0pBQYE9Vlpaalvfp02bJq+//rqdEE4ni1u3bp2tp+Pg33zzTftx4LnnnrOTxmVnZ9/Se2kLeHx8vE329SOAzibve3Ztedd76/ruOr5dPwro++qYd30GAAC6M0MQBEEQROfhdrtNdna23d7ptVwRvUxs1AN22xXPnpCQYDZu3GhKSkpMQ0ODKS8vN3v27DHJycn+c1Rqaqp/f+TIkebMmTOmvr7eHD582KSlpdlzfO+fmZlpCgsLjcfjMZWVlWbbtm0mPj7eXz8jI8NcvHjReL1ek5WV5S9ftGiRKSgoMI2NjbZebm6uGT16tD2mz6Pi4uI+9Z30mh3xvdOkSZPM22+/bWpqasz169fNqVOnzLe+9S3jcDi6zd8BQRAEQUibcHz8AwAAdELX716zZo2dAVxbhnFv4u8AABBKjDkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAAAAACDOScwAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQC4xxljJDU1NdyPAQDAPY3kHACAz7C+fftKZmamFBUVSUNDg5SVlUlOTo6MGzcuJPdLTk62yX5cXJyEyvLly+Xo0aNSV1cn1dXVnZ43e/ZsOXPmjHg8HqmsrJSXX345ZM8EAMCdirzjKwAAgJvmckbbaPLW2wglt9ttk9irV6/KkiVL5OzZsxIVFSWTJk2STZs2yZAhQ6Q7czqd4vV625W7XC7ZuXOnHDt2TF588cUO637729+WxYsX2/f+3e9+JzExMdK/f/8ueGoAAG6fIQiCIAii83C73SY7O9tub/caTkeUcf/Jk2ZEwn83X+73NbvV/QhHZMiee+/evaa8vNxER0e3OxYXF+f/rVJTU+3v5ORkux94PCkpyZb53j8xMdHk5OSYqqoqU1tba/Lz883kyZPt8baysrJsHYfDYZYuXWqKi4tNfX29OX36tElLS/Pfw3fflJQUk5eXZxobG23ZJ73f7NmzTXV1dbvy+++/39TV1Zlx48Z1u78DgiAIgpBOgpZzAAC6wCP3DZOE2EHS5PWI58Z1iXL2sPuqtObtoN+vd+/ekpKSIitWrJD6+vYt9NeuXbvta2uru7ZejxkzxnYtHzp0qNTW1kp5eblMnz5ddu/eLYMGDZKamhrbpVwtW7ZMZs6cKfPmzZPCwkJbd8eOHXLlyhU5cuSI/9pr166V9PR0KS4u/sQu659kwoQJEhERIQ8//LCcO3dO7rvvPnnrrbdsS3pFRcVtvzcAAKFEcg4AQIhpN/b46ESbmPu6svu2Wv5+3fmgd3EfOHCgTVDPnz8vwZaYmCi7du2S/Px8u19SUuI/VlVVZbeXL1/2fwDQRF7HiY8fP16OHz/urzNq1CiZO3duq+R81apVcuDAgTt6vgEDBth313v+7d/+rX2O733ve7J//3754he/KM3NzXd0fQAAQoHkHACALkjOIx1RtsU8ULO3UXpF3ecfgx5MDodDQkUnmNu8ebNMnDjRJtKaqOt49k/6UKBjvjU5DqRJ+6lTp1qV5eXl3fHzaWKu1160aJH/nt/4xjfk0qVLMnbsWPn1r399x/cAACDYSM4BAAgxTbxvmGbblT0wCdd9LQ/FxHDadbylpUUGDx58S/W0TtvkXieRC7R161bZt2+fTJ061Sbo2mVdu4x3Nht6bGys3er57733XqtjjY2Nrfa1m/ydev/99+1Wu7T7fPDBBza01R8AgO6IpdQAAAgxTb6r6svE5exlW8kd4vx41vZetjwUybmO19YEev78+RIdHd3ueGdLnekYcNWvXz9/2fDhw9udp2O3t2zZImlpabJ+/XqZM2eOLW9qavLPtO6jSbIu46aJsS7pFhihGAOuM9SrL3zhC63G4Pfp00dKS0uDfj8AAIKB5BwAgC5Qfv0duVT7rjZJ267sutV9LQ8VTcw1ST5x4oSdqE27l2tL+sKFC+0yZB25cOGCXQt99erV9vwpU6bYVvFAGzZssC3mujTZiBEjbFfxgoICe0yTX219nzZtmk2GtTu7Tha3bt06W2/WrFl2TLjWW7Bggd2/VY8++qgkJSXZZF/fT39r6L18vQb27Nkj//RP/yTPPvusPP7447Jt2zY7/v7gwYO39W8JAEBXCPuU8QRBEATRnSOYS2i5nNEm1tXHbrvi2RMSEszGjRtNSUmJaWhosEur7dmzp9UyZYFLqWmMHDnSnDlzxi55dvjwYbvkWeBSapmZmaawsNB4PB5TWVlptm3bZuLj4/31MzIyzMWLF43X6/UvpaaxaNEiU1BQYJdJ03q5ublm9OjRnS7h1lnoNTsS+E733Xefee211+xybx988IHZtWuXeeSRR7rN3wFBEARBSJtwfPwDAAB0wu12y5o1a2TlypV0i76H8XcAAAglurUDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAADc44wxkpqaGu7HAADgnkZyDgDAZ1jfvn0lMzNTioqKpKGhQcrKyiQnJ0fGjRsXkvslJyfbZD8uLk5CZfny5XL06FGpq6uT6urqdsdnz55tn6GjePDBB0P2XAAA3InIO6oNAABuicsZbaPJW28jlNxut01ir169KkuWLJGzZ89KVFSUTJo0STZt2iRDhgyR7szpdIrX621X7nK5ZOfOnXLs2DF58cUX2x3/xS9+IW+88Uarsp/+9KfSs2dPuXLlSkifGQCA20XLOQAAXcDpiBJ33FPy+IOTZEifcXar+xGO0H0nf+WVV2xr8dNPPy27d++WwsJCOXfunGzYsEGeeeaZm275TkpKsmWa7KvExETb+l5VVSW1tbWSn58vkydPtscPHTpkz9EPAlonKyvL7jscDlm6dKkUFxdLfX29nD59WtLS0trdNyUlRfLy8qSxsVFGjRrV4TOuXr1afvSjH9mPDR3RHgKVlZX+0ARfewps3br1Dv41AQAILVrOAQDoAo/8yRclIXaQNHk94mm+LlHOHnZflV47GfT79e7d2ya6K1assMlwW9euXbvta2uru7ZejxkzxnYtHzp0qE3Sy8vLZfr06fZDwKBBg6SmpkY8Ho+ts2zZMpk5c6bMmzfPfiTQujt27LAt2UeOHPFfe+3atZKenm6T+I66rN+OWbNm2X+Df/u3fwvK9QAACAWScwAAQky7scf3SrSJua8ru28b3+tReb+2IOhd3AcOHCgRERFy/vx5CTZtOd+1a5dtMVclJSX+Y9qari5fvuz/AKCJvI4THz9+vBw/ftxfR1vG586d2yo5X7VqlRw4cCCoz6td33/2s5/ZFnUAALorknMAALogOY+MiLIt5oGavY3SK+o+/xj0YNJu5KGiE8xt3rxZJk6caBNpTdQ762Lu+1AQExMj+/fvb1WuSfupU6dalWmX9mDS7vvasv8//sf/COp1AQAINpJzAABCTBPvGy3Ntit7YBKu+zdamkIyMZx2HW9paZHBgwffUj2t0za510nkAunY7X379snUqVNtgq5d1hcvXiwvv/xyh9eMjY21Wz3/vffea3VMx5YH0m7ywfTNb37TfgB4++23g3pdAACCjQnhAAAIMU2+qzxl4nL2sq3kDnF+PGt7L6nylIckOdfx2ppAz58/X6Kjo9sd72ypM99s5v369fOXDR8+vN15FRUVsmXLFjup2/r162XOnDm2vKmpyT/Tuo9OQqddyrU7vC7pFhh6nVDR1vqvf/3rTAQHALgrkJwDANAFymvOyKXad+1v7cqudF/LQ0UTc02ST5w4YSdq0+7l2pK+cOFCuwxZRy5cuGDXQtcZ0fX8KVOm2FbxQDrbu7aY9+/fX0aMGCFjx46VgoICe6y0tNS2vk+bNk369OljE2SdLG7dunW2nk7ONmDAAFtvwYIFdv9WPfroo3YGeU329f30t4beK9Dzzz8vkZGRduI5AADuBoYgCIIgiM7D7Xab7Oxsu73Ta7mc0SbW1cduu+LZExISzMaNG01JSYlpaGgw5eXlZs+ePSY5Odl/jkpNTfXvjxw50pw5c8bU19ebw4cPm7S0NHuO7/0zMzNNYWGh8Xg8prKy0mzbts3Ex8f762dkZJiLFy8ar9drsrKy/OWLFi0yBQUFprGx0dbLzc01o0ePtsf0eVRcXNynvpNesyOB76Rx9OhRs2PHjm75d0AQBEEQ0iYcH/8AAACd0PW716xZIytXrrQtw7g38XcAAAglurUDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAADc44wxkpqaGu7HAADgnkZyDgDAZ1jfvn0lMzNTioqKpKGhQcrKyiQnJ0fGjRsXkvslJyfbZD8uLk5CZfny5XL06FGpq6uT6urqDs/50pe+JAcOHLDHq6qq5I033pAvfvGLIXsmAADuFMk5AABdyOWMllhXH7sNNbfbLSdPnrSJ+JIlS2TYsGGSkpIiBw8elE2bNkl353Q6Oyx3uVyyc+dO2bx5c4fHY2JibDKuHyK+8pWvyKhRo+T69euyb98+iYyMDPFTAwBw+wxBEARBEJ2H2+022dnZdnu713A6ooz7/qfMiIemmy8//Bd2q/sRjsiQPffevXtNeXm5iY6ObncsLi7O/1ulpqba38nJyXY/8HhSUpIt871/YmKiycnJMVVVVaa2ttbk5+ebyZMn2+NtZWVl2ToOh8MsXbrUFBcXm/r6enP69GmTlpbmv4fvvikpKSYvL880Njbask96v9mzZ5vq6up25U899ZS91iOPPOIve+KJJ2zZY489Fta/A4IgCIKQToLPxwAAdIFH4r4oCfcNlqYb9eLx1khURA+7r0qvngz6/Xr37m1byVesWCH19fXtjl+7du22r62t7tp6PWbMGNu1fOjQoVJbWyvl5eUyffp02b17twwaNEhqamrE4/HYOsuWLZOZM2fKvHnzpLCw0NbdsWOHXLlyRY4cOeK/9tq1ayU9PV2Ki4s77bL+af7whz/IBx98IC+++KL8wz/8g22B19/nzp2TP/7xj7f93gAAhBLJOQAAIaZd2OOj3TYxb/J+lCj7tlr+/vUC/36wDBw4UCIiIuT8+fMSbImJibJr1y7Jz8+3+yUlJf5jOr5bXb582f8BQBN5HSc+fvx4OX78uL+OdjefO3duq+R81apVdqz4ndAPBV/96ldlz549snLlSlumHwQmTZokXq/3jq4NAECoMOYcAIAuSM4jHS5pbmlsVa77kY6okIw/dzgcEio6wVxGRoa8+eabsnr1ajuW/dM+FOg48P3799ux376YNWuWPPbYY63OzcvLu+Pn69mzp2zdutVOGvfMM8/Ic889Zz8k7N271x4DAKA7ouUcAIAQ01bxG6bJdmUPbCHX/RumOeit5r6W4paWFhk8+KOu8zdL67RN7qOiolqdo4mvTq42depUmThxou2yvnjxYnn55Zc7vGZsbKzd6vnvvfdeq2ONja0/WGg3+Tv1l3/5l9K/f3959tln7czxvjLtJq9Lxv3iF7+443sAABBstJwDABBimnxX1ZeKKzLatpI7HE671X0tD0VyromoJtDz58+X6Oj2LfOdLXWmY8BVv379/GXDhw9vd15FRYVs2bJF0tLSZP369TJnzhxb3tTU1G6mdR3rrcu4aXd4XdItMPQ6wabvqx8ZfIm58u1rV38AALoj/gsFAEAXKL92Ri5dP69N0tIr8j671X0tDxVNzDVJPnHihJ2oTbuXa0v6woUL5dixYx3WuXDhgl2CTLur6/lTpkyxreKBNmzYYFvMtXV6xIgRMnbsWCkoKLDHSktLbSI8bdo06dOnj+3OrmPA161bZ+tpV/YBAwbYegsWLLD7t+rRRx+VpKQkm+zr++lvDb2X0u7zOiGeTlyn76sT1mVlZcmNGzfsMnIAAHRXYZ8yniAIgiC6cwRzCS2XM9rEuvrYbVc8e0JCgtm4caMpKSkxDQ0Ndmm1PXv2tFqmLHApNY2RI0eaM2fO2CXPDh8+bJc8C1xKLTMz0xQWFhqPx2MqKyvNtm3bTHx8vL9+RkaGuXjxovF6vf6l1DQWLVpkCgoK7DJpWi83N9eMHj260yXcOgu9ZkcC32n8+PHmP//zP+1Sax9++KE5cOCA+cpXvtJt/g4IgiAIQtqE4+MfAACgE263W9asWWNn/taWYdyb+DsAAIQS3doBAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAAAAACDOScwAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQC4xxljJDU1NdyPAQDAPY3kHACAz7C+fftKZmamFBUVSUNDg5SVlUlOTo6MGzcuJPdLTk62yX5cXJyEyvLly+Xo0aNSV1cn1dXVHZ6j76fn1NTUyPvvvy9r164Vp9MZsmcCAOBOkZwDAPAZ5Xa75eTJkzZRXbJkiQwbNkxSUlLk4MGDsmnTJunuOkumXS6X7Ny5UzZv3tzh8S9+8Yvy+uuvyxtvvCEjRoyQ559/Xv78z//cJugAAHRnhiAIgiCIzsPtdpvs7Gy7vdNruZzRJtbVx25D/dx79+415eXlJjq6/b3i4uL8v1Vqaqr9nZycbPcDjyclJdky3/snJiaanJwcU1VVZWpra01+fr6ZPHmyPd5WVlaWreNwOMzSpUtNcXGxqa+vN6dPnzZpaWn+e/jum5KSYvLy8kxjY6Mt+6T3mz17tqmurm5X/vd///fmxIkTrcqmTZtm7xsbG9st/g4IgiAIQtpEZLi/DAAAcC9wOqLkkfuTJD7aLZERUXKjpVmq6kul/OppaTE3gn6/3r1721byFStWSH19fbvj165du+1ra6u7tl6PGTPGdi0fOnSo1NbWSnl5uUyfPl12794tgwYNsl3KPR6PrbNs2TKZOXOmzJs3TwoLC23dHTt2yJUrV+TIkSP+a2vrdnp6uhQXF3faZf3T9OjRw3bhD6TP0atXL3nqqafk8OHDt/3uAACECsk5AABdQBPzhPsGS9ONOvE010hURA+7r0qr84J+v4EDB0pERIScP38+6NdOTEyUXbt2SX5+vt0vKSnxH6uqqrLby5cv+z8AaCKv48THjx8vx48f99cZNWqUzJ07t1VyvmrVKjlw4MAdPd++ffvkf/2v/yV/8Rd/If/v//0/SUhIsNdV/fr1u6NrAwAQKow5BwAgxFzOaNtirol5k7dejPHare5ruR4PNofDIaGiE8xlZGTIm2++KatXr7Zj2T/tQ0FMTIzs379frl+/7o9Zs2bJY4891urcvLw7/1Ch99Ex9j/+8Y+lsbFR3n33XTsGXbW0tNzx9QEACAWScwAAQkyTb+3K3tzS2Kpc97U8FMm5dh3XRHTw4I9a52+WL3kNTO6joqJanbN161YZMGCAbN++3SbmmlAvWLCg02vGxsba7dSpU2X48OH+0O7wM2bMaHWudpMPhg0bNsj9999vW/n79Okjv/rVr2y5dpcHAKA7IjkHACDEtJVcx5hrV/ZAuq/lejzYdLy2du+eP3++REe3T/47W+pMx4C37f6tiXRbFRUVsmXLFklLS5P169fLnDlzbHlTU1O7mdbPnTtnx4BroqxLugWGXieUdBk1vfc3vvENu4zc22+/HdL7AQBwuxhzDgBAiGnyrZO/+caYa4u5JuauyBi5dP18SJJzpYm5rvV94sQJO+b6nXfekcjISJkwYYK89NJLtuW6rQsXLtgkVrur62RyOrHb4sWL27VK5+bm2u7iOvHc2LFjpaCgwB4rLS21re/Tpk2zXcl1IjadLG7dunW2no6D1+7w+nHgueees5PGZWdn39J7PfrooxIfH2+Tff0IkJSU5H92X8u7TiqnS6nps+gkdUuXLpWvf/3rdGsHAHRrYZ8yniAIgiC6cwRjCa0IR6Rx9/6SGfFwmvnyo39ht7qv5aF89oSEBLNx40ZTUlJiGhoa7NJqe/bsabVMWeBSahojR440Z86csUuPHT582C55FriUWmZmpiksLDQej8dUVlaabdu2mfj4eH/9jIwMc/HiReP1ev1LqWksWrTIFBQU2GXStF5ubq4ZPXp0p0u4dRZ6zY4EvtNvfvMbu8yavsOxY8fsEm3d4e+AIAiCIKSTcHz8AwAAdMLtdsuaNWtk5cqVtmX4Tuj4cg07IVyIWszR/f8OAABoi27tAAB0IZJyAADQESaEAwAAAAAgzEjOAQAAAAAIM5JzAAAAAADCjOQcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcA4B5njJHU1NRwPwYAAPc0knMAAD7D+vbtK5mZmVJUVCQNDQ1SVlYmOTk5Mm7cuJDcLzk52Sb7cXFxIbm+2+2W1157TYqLi6W+vl4uXLggq1evlqioqFbnDRs2TI4cOSIej8e+85IlS0LyPAAABEtk0K4EAAC6FU1kjx49KlevXrXJ6dmzZ20SO2nSJNm0aZMMGTJEujOn0yler7dV2eDBgyUiIkLmzp1rE/MnnnhCXn31VYmJifEn4Pfdd5/8+te/lgMHDsi8efNsov7P//zP9t9BzwUAoLsyBEEQBEF0Hm6322RnZ9vtnV7L5YwxsT0eNC5ndMife+/evaa8vNxER7e/V1xcnP+3Sk1Ntb+Tk5PtfuDxpKQkW+Z7/8TERJOTk2OqqqpMbW2tyc/PN5MnT7bH28rKyrJ1HA6HWbp0qSkuLjb19fXm9OnTJi0tzX8P331TUlJMXl6eaWxstGU3857p6emmqKjIvz9v3jzz4YcfmqioKH/Z97//fVNQUNBt/g4IgiAIQtoELecAAHQBpyNKHr5/uDwQ6xanwyVe0yQf1pZKxdVT0mJuBP1+vXv3lpSUFFmxYoXt/t3WtWvXbvva2urucrlkzJgxUldXJ0OHDpXa2lopLy+X6dOny+7du2XQoEFSU1Nju5WrZcuWycyZM21LdmFhoa27Y8cOuXLliu1+7rN27VpJT0+33darq6tv6nm0C31VVZV//9lnn7XXbG5u9pft27dPli5dKvfff79tQQcAoLshOQcAoAtoYp4QN1iabtSLx1sjURE97L4qq/6voN9v4MCBtvv3+fPng37txMRE2bVrl+Tn59v9kpIS/zFfknz58mX/BwBN5JcvXy7jx4+X48eP++uMGjXKdk8PTM5XrVplu6PfrMcee0wWLlxoE3qfhISEVs+kKisr/cdIzgEA3RHJOQAAIeZyxtgWc03Mm7wftWL7tvGxbrlU83v/frA4HA4JFZ1gbvPmzTJx4kSbSGuiruPZP+lDgY4J379/f6tyTdpPnTrVqiwvL++mn+Ohhx6SN954Q3bu3GkniQMA4G7GbO0AAISYKzLadmVvbmlsVa77kY4ocUXGBP2e2nW8paXFTqB2K7RO2+S+7UzoW7dulQEDBsj27dvtZGuaUC9YsKDTa8bGxtrt1KlTZfjw4f7Q7vAzZsxoda52k78Z/fr1k4MHD8pbb70l3/rWt1odu3Tpkp2lPpBvX48BANAdkZwDABBi2mKuY8y1K3sg3b9hmqXpxs0lpLdCx2vrOOv58+dLdHR0u+OdLXWmY8B9ya+PJtJtVVRUyJYtWyQtLU3Wr18vc+bMseVNTU3+mdZ9zp07Z5dx0+7wuqRbYOh1bpW2mB86dEhOnjwpL7zwgl26LdCxY8fsmPbIyP+vg+CECRNsF3+6tAMAuiuScwAAQqzJW2cnf9MWdJczWhwOp93qflVtadC7tPtoYq5J8okTJ+xEbdq9XFvSdYy2JrAd0eXJdF1wXTtcz58yZYosXry41TkbNmywXdr79+8vI0aMkLFjx0pBQYE9Vlpaalvfp02bJn369LHd2XWyuHXr1tl6s2bNsq3uWk9b23X/dhJzfUYdZ/7ggw/aVvHAlvKf/exn9iOBtvBr6/zXv/51+du//Vv54Q9/eFv/jgAAdJWwTxlPEARBEN05grGEVoQj0iT2/rIZ/ugM86XEb9it7mt5KJ89ISHBbNy40ZSUlJiGhga7tNqePXtaLVMWuJSaxsiRI82ZM2fskmeHDx+2S54FLqWWmZlpCgsLjcfjMZWVlWbbtm0mPj7eXz8jI8NcvHjReL1e/1JqGosWLbLLmekyaVovNzfXjB49utMl3DqK2bNnm84Enjds2DBz5MgR+4z6zv/n//yfbvF3QBAEQRDSSTg+/gEAADrhdrtlzZo1snLlStsyfCc+ajGPsV3ZQ9Viju7/dwAAQFvM1g4AQBfShJykHAAAtMWYcwAAAAAAwozkHAAAAACAMCM5BwAAAAAgzEjOAQAAAAAIM5JzAAAAAADCjOQcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAO5xxhhJTU0N92MAAHBPIzkHAOAzrG/fvpKZmSlFRUXS0NAgZWVlkpOTI+PGjQvJ/ZKTk22yHxcXF5Lru91uee2116S4uFjq6+vlwoULsnr1aomKivKf06NHD8nKypJ33nlHmpub5Ze//GVIngUAgGCKDOrVAABAt6GJ7NGjR+Xq1auyZMkSOXv2rE1iJ02aJJs2bZIhQ4ZId+Z0OsXr9bYqGzx4sERERMjcuXNtYv7EE0/Iq6++KjExMfYdffU8Ho/9KJGWlhampwcA4NYZgiAIgiA6D7fbbbKzs+32Tq/liowxsT0eNC5ndMife+/evaa8vNxER7e/V1xcnP+3Sk1Ntb+Tk5PtfuDxpKQkW+Z7/8TERJOTk2OqqqpMbW2tyc/PN5MnT7bH28rKyrJ1HA6HWbp0qSkuLjb19fXm9OnTJi0tzX8P331TUlJMXl6eaWxstGU3857p6emmqKiow2N6/1/+8pfd7u+AIAiCIKRN0HIOAEAXcDqi5OH44fJATH9xOqPE622WD+v+KBVVp6TF3Aj6/Xr37i0pKSmyYsUK2/27rWvXrt32tbXV3eVyyZgxY6Surk6GDh0qtbW1Ul5eLtOnT5fdu3fLoEGDpKamxrZgq2XLlsnMmTNl3rx5UlhYaOvu2LFDrly5IkeOHPFfe+3atZKenm67rVdXV9/U82gX+qqqqtt+HwAAugOScwAAuoAm5glxQ6TpRp14mmokytnD7quyD/8r6PcbOHCg7f59/vz5oF87MTFRdu3aJfn5+Xa/pKTEf8yXJF++fNn/AUAT+eXLl8v48ePl+PHj/jqjRo2y3dMDk/NVq1bJgQMHbvpZHnvsMVm4cKFN6AEAuJuRnAMAEGKuyBjbYq6JedONj1qxfdv4mP5y6ervpcnbvnX7TjgcDgkVHcu9efNmmThxok2kNVHX8eyf9KFAx4Tv37+/Vbkm7adOnWpVlpeXd9PP8dBDD8kbb7whO3futJPEAQBwN2O2dgAAQszljLZd2Zu9ja3KdT8yIsom78GmXcdbWlrsBGq3Quu0Te4DZ0JXW7dulQEDBsj27dtl2LBhNqFesGBBp9eMjY2126lTp8rw4cP9od3hZ8yY0epc7SZ/M/r16ycHDx6Ut956S771rW/d0jsCANAdkZwDABBi2iquY8y1K3sg3b/R0mxb1INNx2vv27dP5s+fL9HR0e2Od7bUmY4B9yW/PppIt1VRUSFbtmyxs6GvX79e5syZY8ubmpr8M6b7nDt3zi7jpt3hdUm3wNDr3CptMT906JCcPHlSXnjhBbt0GwAAdzuScwAAQkyTb538TVvIXZHR4nA47Vb3q+r+GPQu7T6amGuSfOLECTtRm3Yv15Z0HaN97NixDuvo8mS6FrquHa7nT5kyRRYvXtzqnA0bNtgu7f3795cRI0bI2LFjpaCgwB4rLS21re/Tpk2TPn362O7sOlncunXrbL1Zs2bZVnetp63tun87ibk+o44zf/DBB+1a7hqBdJm4pKQkiY+Ptx8i9LcGAADdWdinjCcIgiCI7hzBWEIrwhFpEh/4shme+DXzpf5/abe6r+WhfPaEhASzceNGU1JSYhoaGuzSanv27Gm1TFngUmoaI0eONGfOnLFLnh0+fNgueRa4lFpmZqYpLCw0Ho/HVFZWmm3btpn4+Hh//YyMDHPx4kXj9Xr9S6lpLFq0yBQUFNhl0rRebm6uGT16dKdLuHUUs2fPNp0JPE/f99POCcffAUEQBEFIJ+H4+AcAAOiE2+2WNWvWyMqVK23L8J2OP9cWczs5XIhazNH9/w4AAGiL2doBAOhCmpCTlAMAgLYYcw4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAMA9zhgjqamp4X4MAADuaSTnAAB8hvXt21cyMzOlqKhIGhoapKysTHJycmTcuHEhuV9ycrJN9uPi4kJyfbfbLa+99poUFxdLfX29XLhwQVavXi1RUVGtnmHPnj1y8eJFqa2tlVOnTslf/uVfhuR5AAAIlsigXQkAAHQrmsgePXpUrl69KkuWLJGzZ8/aJHbSpEmyadMmGTJkiHRnTqdTvF5vq7LBgwdLRESEzJ071ybmTzzxhLz66qsSExNj31GNHDlS3nnnHfm///f/SmVlpUybNk2ys7Pl2rVrsnfv3jC9DQAAn84QBEEQBNF5uN1uk52dbbd3ei1XZIyJ7fGgcTmjQ/7ce/fuNeXl5SY6uv294uLi/L9Vamqq/Z2cnGz3A48nJSXZMt/7JyYmmpycHFNVVWVqa2tNfn6+mTx5sj3eVlZWlq3jcDjM0qVLTXFxsamvrzenT582aWlp/nv47puSkmLy8vJMY2OjLbuZ90xPTzdFRUWfeM5//Md/mK1bt3abvwOCIAiCkDZByzkAAF3A6YiShx8YIQ/E9BdnRJR4W5rlw7o/SsWHb0uLuRH0+/Xu3VtSUlJkxYoVtvt3W9qKfLu01d3lcsmYMWOkrq5Ohg4daruPl5eXy/Tp02X37t0yaNAgqampEY/HY+ssW7ZMZs6cKfPmzZPCwkJbd8eOHXLlyhU5cuSI/9pr166V9PR02229urr6pp5Hu9BXVVV96jkFBQW3/c4AAIQayTkAAF1AE/OEuCHSdKNOPM01EuXsYfdV2Qcngn6/gQMH2u7f58+fD/q1ExMTZdeuXZKfn2/3S0pK/Md8SfLly5f9HwA0kV++fLmMHz9ejh8/7q8zatQo2z09MDlftWqVHDhw4Kaf5bHHHpOFCxfahL4zX/va1+TLX/6yvRcAAN0VyTkAACHmioyxLeaamDfd+KgV27eNj3HLpep8afK2b92+Ew6HQ0JFJ5jbvHmzTJw40SbSmqjrePZP+lCgY8L379/fqlyTdp2sLVBeXt5NP8dDDz0kb7zxhuzcudNOEteRr371q5KVlSVz5syRc+fO3fS1AQDoaszWDgBAiLmc0bYre7O3sVW57kdGuGzyHmzadbylpcVOoHYrtE7b5D5wJnS1detWGTBggGzfvl2GDRtmE+oFCxZ0es3Y2Fi7nTp1qgwfPtwf2h1+xowZrc7VbvI3o1+/fnLw4EF566235Fvf+laH52jX+X//93+Xb3/72/ZZAQDozkjOAQAIMW0V1zHm2pU9kO7faGmyLerBpuO19+3bJ/Pnz5fo6Oh2xztb6kzHgPuSXx9NpNuqqKiQLVu2SFpamqxfv962TKumpib/TOs+2mKty7hpd3hd0i0w9Dq3SlvMDx06JCdPnpQXXnjBLt3Wli6npjOzf+c737GzuQMA0N2RnAMAEGKafOvkb9pC7oqMFofDabe6X1VXGvQu7T6amGuSfOLECTtRm3Yv15Z0HaN97NixDuvo8mS6FrquHa7nT5kyRRYvXtzqnA0bNtgu7f3795cRI0bI2LFj/ZOtlZaW2tZ3Xb6sT58+tju7Tha3bt06W2/WrFm21V3raWu77t9OYq7PqOPMH3zwQbuWu0ZgV3ZNzLX7vXa59x3XSfIAAOjOwj5lPEEQBEF05wjGEloRjkiT2OdpM9z9NfOlP/0ru9V9LQ/lsyckJJiNGzeakpIS09DQYJdW27NnT6tlygKXUtMYOXKkOXPmjF3y7PDhw3bJs8Cl1DIzM01hYaHxeDymsrLSbNu2zcTHx/vrZ2RkmIsXLxqv1+tfSk1j0aJFpqCgwC6TpvVyc3PN6NGjO13CraOYPXu26YzvHL1nRw4ePBj2vwOCIAiCkE7C8fEPAADQCbfbLWvWrJGVK1faluE7HX+uLeZ2crgQtZij+/8dAADQFrO1AwDQhTQhJykHAABtMeYcAAAAAIAwIzkHAAAAACDMSM4BAAAAAAgzknMAAAAAAMKM5BwAAAAAgDAjOQcAAAAAIMxIzgEAAAAACDOScwAAAAAAwozkHACAe5wxRlJTU8P9GAAA3NNIzgEA+Azr27evZGZmSlFRkTQ0NEhZWZnk5OTIuHHjQnK/5ORkm+zHxcWF5Pput1tee+01KS4ulvr6erlw4YKsXr1aoqKi/OcMGjRIfvvb38qlS5fE4/HYd1+zZo1ERkaG5JkAAAgG/isFAMBnlCayR48elatXr8qSJUvk7NmzNomdNGmSbNq0SYYMGSLdmdPpFK/X26ps8ODBEhERIXPnzrWJ+RNPPCGvvvqqxMTE2HdUzc3Nkp2dLW+//bZ996SkJHuO1luxYkWY3gYAgE9nCIIgCILoPNxut8nOzrbbO72WKzLGxPZ80Lgio0P+3Hv37jXl5eUmOrr9veLi4vy/VWpqqv2dnJxs9wOPJyUl2TLf+ycmJpqcnBxTVVVlamtrTX5+vpk8ebI93lZWVpat43A4zNKlS01xcbGpr683p0+fNmlpaf57+O6bkpJi8vLyTGNjoy27mfdMT083RUVFn3jO+vXrzZEjR7rN3wFBEARBSJug5RwAgC7gjIiShx8YIfGxfyrOCJd4W5qkqrZEKj54W1rMjaDfr3fv3pKSkmJbirX7d1vXrl277Wtrq7vL5ZIxY8ZIXV2dDB06VGpra6W8vFymT58uu3fvtl3La2pqbLdytWzZMpk5c6bMmzdPCgsLbd0dO3bIlStX5MiRI/5rr127VtLT02239erq6pt6Hu1CX1VV1enxxx57zP5b6HMBANBdkZwDANAFNDHvGzdUmm7UiafpmkQ5e9h9VXblRNDvN3DgQNuN+/z580G/dmJiouzatUvy8/PtfklJif+YL0m+fPmy/wOAJvLLly+X8ePHy/Hjx/11Ro0aZbunBybnq1atkgMHDtz0s2jivXDhQpvQt6Vd+p988knp2bOnbNmyxV4bAIDuignhAAAIMVdkjG0x18S86Ua9GOO1W93XcldkdNDv6XA4JFR0grmMjAx588037WRsw4YN+9QPBTomfP/+/XL9+nV/zJo1yybXgfLy8m76OR566CF54403ZOfOnXaSuLaef/55m5x/4xvfkKlTp3aYwAMA0F3Qcg4AQIhp8q1d2bXFPFCzt1F6ueJs8q7JejBp1/GWlhY7gdqt0Dptk/vAmdDV1q1bZd++fTbhnThxou2yvnjxYnn55Zc7vGZsbKzd6vnvvfdeq2ONjY2t9rWb/M3o16+fHDx4UN566y351re+1eE5FRUVdltQUGAnl/vJT34i69ev978jAADdCS3nAACEmCbeOsZcu7IH0n0t1xb0YNPx2ppAz58/X6Kj27fMd7bUmY4B9yW/PsOHD+8w8dWu4mlpaTbhnTNnji1vamqyW02Gfc6dO2eXcdPu8LqsWWD4EuhboS3mhw4dkpMnT8oLL7xgl277NNrFXz8y6BYAgO6IlnMAAEJMk2+d/M03xlxbzDUx1xbzymvngt5q7qOJuY67PnHihB1v/c4779i1vidMmCAvvfSSncitLV2eTNdC1+7qOpmcTuymreKBNmzYILm5ufLuu+/aiefGjh1rW6dVaWmpbZmeNm2avP7663ZCOJ0sbt26dbaeJsfaHV4/Djz33HN20jhd9uxWE3O9j3ZTf/DBB/3HKisr7fYv//Iv7XJqunSctsx/6Utfku9///vyi1/8Qm7cCP7kewAABEvYp4wnCIIgiO4cwVhCK8IRaRIffNoM/9PnzVOP/Q+71X0tD+WzJyQkmI0bN5qSkhLT0NBgl1bbs2dPq2XKApdS0xg5cqQ5c+aMXfLs8OHDdsmzwKXUMjMzTWFhofF4PKaystJs27bNxMfH++tnZGSYixcvGq/X619KTWPRokWmoKDALpOm9XJzc83o0aM7XcKto5g9e7bpjO+cr3/963Y5tpqaGnP9+nW71Jsu49ajR4+w/x0QBEEQhHQSjo9/AACATrjdblmzZo2sXLnSttje6fjzj8aYfzQ5HO7NvwMAANqiWzsAAF3oo1naScoBAEBrzIoCAAAAAECYkZwDAAAAABBmJOcAAAAAAIQZyTkAAAAAAGFGcg4AAAAAQJiRnAMAAAAAEGYk5wAAAAAAhBnJOQAAAAAAYUZyDgDAPc4YI6mpqeF+DAAA7mkk5wAAfIb17dtXMjMzpaioSBoaGqSsrExycnJk3LhxIblfcnKyTfbj4uJCcn232y2vvfaaFBcXS319vVy4cEFWr14tUVFRHZ7/2GOPSU1NjVRXV4fkeQAACJbIoF0JAAB0K5rIHj16VK5evSpLliyRs2fP2iR20qRJsmnTJhkyZIh0Z06nU7xeb6uywYMHS0REhMydO9cm5k888YS8+uqrEhMTY98xUGRkpPz85z+X//zP/5SRI0d28dMDAHDrDEEQBEEQnYfb7TbZ2dl2e6fXckXGmNieDxpXZHTIn3vv3r2mvLzcREe3v1dcXJz/t0pNTbW/k5OT7X7g8aSkJFvme//ExESTk5NjqqqqTG1trcnPzzeTJ0+2x9vKysqydRwOh1m6dKkpLi429fX15vTp0yYtLc1/D999U1JSTF5enmlsbLRlN/Oe6enppqioqF352rVr7f9us2fPNtXV1d3q74AgCIIgpE3Qcg4AQBdwRkTJww+MkPj7/lScES7xtjRJ1fUSqfjgbWkxN4J+v969e0tKSoqsWLHCdv9u69q1a7d9bW11d7lcMmbMGKmrq5OhQ4dKbW2tlJeXy/Tp02X37t0yaNAg253c4/HYOsuWLZOZM2fKvHnzpLCw0NbdsWOHXLlyRY4cOeK/9tq1ayU9Pd12W7/Zrujahb6qqqpV2dixY+VrX/uaDB8+3D4TAADdHck5AABdQBPzvr2HSlNznXiarkmUs4fdV2VXTgT9fgMHDrTdv8+fPx/0aycmJsquXbskPz/f7peUlPiP+ZLky5cv+z8AaCK/fPlyGT9+vBw/ftxfZ9SoUbZ7emByvmrVKjlw4MBNP4uOKV+4cKFN6H3i4+Plpz/9qf0YcP369SC8MQAAoUdyDgBAiLkiY2yLuSbmTTc+asX2bbX8UnW+fz9YHA6HhIpOMLd582aZOHGiTaQ1Udfx7J/0oUDHhO/fv79VuSbtp06dalWWl5d308/x0EMPyRtvvCE7d+60k8T56Bj0n/3sZ3asOQAAdwtmawcAIMRckdG2K3uzt7FVue5ruSbvwaZdx1taWuwEardC67RN7tvOhL5161YZMGCAbN++XYYNG2YT6gULFnR6zdjYWLudOnWq7WbuC+0OP2PGjFbnajf5m9GvXz85ePCgvPXWW/Ktb32r1TGdiV5b0pubm23o895///329wsvvHBT1wcAoKuRnAMAEGLaKq5jzLUreyDd1/KmGzeXkN4KHa+9b98+mT9/vkRHR7c73tlSZzoG3Jf8+mgi3VZFRYVs2bJF0tLSZP369TJnzhxb3tTU5J9p3efcuXN2GTftDq9LugWGXudWaYv5oUOH5OTJkzbZ1qXbAj377LOtPgJoV3kd/66/f/nLX97y/QAA6Ap0awcAIMQ0+dbJ33xjzLXFXBNzV1SMVFafC3qXdh9NzHUptRMnTtgE9Z133rHLi02YMEFeeukl23Ldli5Ppmuh69rhOpmcTuy2ePHiVuds2LBBcnNz5d1337UTz+nkawUFBfZYaWmpbX2fNm2avP7663ZCOJ0sbt26dbaejoN/88037ceB5557zibN2dnZt5yY6320dfzBBx/0H6usrLTbtuPsv/SlL9ln+v3vf3/L/4YAAHQVknMAALqAzsruG2PeyxVnW8w1MfeVh4JOuvbkk0/aJFtbt7U1XFvGtcVZk/OO3LhxQ77xjW/YMeWazP/Xf/2XZGRkyL/927/5z9FWcZ2x/ZFHHrHJtY77/va3v22PXbx4Ub773e/aWdezsrJs4q2t2ytXrrT31lnbtUu8rr3+9ttvyz/8wz/c0jvph4XPf/7zNt57770uG2cPAECo6X/FWvcFAwAArbjdblmzZo1NMLXF9k7Hn+sYc21ND1WLObr/3wEAAG3Rcg4AQBfShJykHAAAtMWEcAAAAAAAhBnJOQAAAAAAYUZyDgAAAABAmJGcAwAAAAAQZiTnAAAAAACEGck5AAAAAABhRnIOAAAAAECYkZwDAAAAABBmJOcAANzjjDGSmpoa7scAAOCeRnIOAMBnWN++fSUzM1OKioqkoaFBysrKJCcnR8aNGxeS+yUnJ9tkPy4uLiTXd7vd8tprr0lxcbHU19fLhQsXZPXq1RIVFdXqHH2GtvGVr3wlJM8EAEAwRAblKgAAoNvRJPXo0aNy9epVWbJkiZw9e9YmsZMmTZJNmzbJkCFDpDtzOp3i9XpblQ0ePFgiIiJk7ty5NjF/4okn5NVXX5WYmBj7joH+7M/+TH7/+9/79z/88MMue3YAAG6HIQiC+P+zdydQVZ/nov+fDWw0QA6BJoK2hVS9Rq0W0zbGOHG0DjjkkiMZbhuPPemp1RyH++9RWwe03mVX4030eIvRxGMtkdj2nlatoTHUamq0aozFOhGxIlBBMTjgxAyb97+eN9n7biaDujebxu9nrWf99m/+/QxrZT2/93nflyCI1iM+Pt5kZGTY5d1eKzQk3ER07mKX/n7u7du3m+LiYhMWFtZsX2RkpOe3Sk5Otr8TExPtuvf+hIQEu839/nFxcSYzM9OUlZWZ8vJyk5OTY8aNG2f3N5Wenm7PcTgcZv78+aagoMBUVlaao0ePmpSUFM893PdNSkoy2dnZpqamxm5ry3vOnTvX5OfnN/rvpfS5O+rfAUEQBEFIk6DlHACAdhAc5JTPP/g1ib7/SxIc7BSXq07KbhbKucuHpaGhzuf3i4qKkqSkJFm0aJEt/27q+vXrd3xtbXUPDQ2V4cOHS0VFhfTt21fKy8uluLhYJk2aJFu3bpVevXrJjRs3pKqqyp6zYMECmTx5skyfPl3y8vLsuZs2bZJLly7J3r17Pddevny5zJ0715atX716tU3PoyX0ZWVlzbZr+X7nzp3l9OnT8vLLL8vvfve7O35nAAD8jeQcAIB2oIl5THRfqa2rkKra6+IM7mzXVdHFgz6/X8+ePW3596lTp3x+7bi4ONmyZYvk5OTY9cLCQs8+d5J88eJFzwcATeQXLlwoo0aNkoMHD3rOGTp0qC1P907OlyxZIrt27Wrzs/To0UNmzZplE3o3/VDw7//+77akv6GhQVJSUmTbtm3y1FNPkaADADosknMAAPwsNCTctphrYl5bX2G3uZfR9z8sH5Wd8Kz7isPhEH/RAeZee+01GTNmjE2kNVHX/uy3+lCgfcJ37tzZaLsm7UeOHGm0LTs7u83P0a1bN/n9738vv/nNb+wgcd59y1etWtXomnqs9kknOQcAdFSM1g4AQDsk51rKXueqbrRd14ODQ+1+X9PScW011gHUboee0zS59x4JXW3YsEG6d+8ub775pvTv398mvzNnzmz1mhEREXY5YcIEGTBggCe0HP7pp59udKyWybdF165dZffu3XLgwAH53ve+96nHf/DBB/YjAQAAHRXJOQAAfqat4trHXEvZvem6y1Xr81Zzpf21d+zYITNmzJCwsLBm+1ub6kz7gLuTXzdNpJs6d+6crFu3zpaMr1y5UqZOnWq319bWekZadzt58qSdxk3L4XVKN+/Q69wubQV/77335PDhw/LCCy/YadI+jb7DhQsXbvteAAC0F8raAQDwM02+dfA3dx9zbTHXxDzUGS6lZSf9kpwrTcy13/WhQ4dsX+7jx49LSEiIjB49Wl588UXbct2UTk+mc6Hr3OE6mJwO7DZnzpxGx2jJeFZWlh1oTQeeGzFihOTm5tp9Z8+eta3vEydOlHfeeccOCKd9wFesWGHP037w+/btsx8HhgwZYgeNy8jIuO3EXO+j/cwfeughz77S0lK7nDJliv1I4C6Z10HqvvOd78h3v/vdO/63BACgPQR8yHiCIAiC6Mjhiym0goKcJq7LIDOgx/8wX+s1xS51Xbf789ljY2PN6tWrTWFhoamurrZTq23btq3RNGXeU6lpDB482Bw7dsxOebZnzx475Zn3VGppaWkmLy/PVFVVmdLSUrNx40YTHR3tOT81NdWUlJQYl8vlmUpNY/bs2SY3N9dOk6bnZWVlmWHDhrU6hVtL8e1vf9u0xn3MlClTzIcffminebt27Zo5ePBgo2nbAvl3QBAEQRDSSjg++QEAAFoRHx8vy5Ytk8WLF9sW27uh/cs1tLXcXy3m6Ph/BwAANEVZOwAA7YikHAAAtIQB4QAAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BALjHGWMkOTk50I8BAMA9jeQcAIDPsJiYGElLS5P8/Hyprq6WoqIiyczMlJEjR/rlfomJiTbZj4yM9Mv14+Pj5Wc/+5kUFBRIZWWlnDlzRpYuXSpOp7PZsXPmzJG//vWv9r3PnTsnCxcu9MszAQDgCyE+uQoAAGiT0JBwcTrDpbauQurqK/x6L01k9+/fL9euXZN58+bJiRMnbBI7duxYWbNmjfTp00c6suDgYHG5XI229e7dW4KCgmTatGk2Me/Xr5+sX79ewsPD7Tu6/fSnP5UxY8bI3Llz7XtHR0fbAACgIzMEQRAEQbQe8fHxJiMjwy7v9BpBQU4TFzPIJPT8pvlqr2/bpa7rdn899/bt201xcbEJCwtrti8yMtLzWyUnJ9vfiYmJdt17f0JCgt3mfv+4uDiTmZlpysrKTHl5ucnJyTHjxo2z+5tKT0+35zgcDjN//nxTUFBgKisrzdGjR01KSornHu77JiUlmezsbFNTU2O3teU9586da/Lz8z3rvXv3NrW1taZXr14d7u+AIAiCIKSVoOUcAIB28IWHviYxUV+2LebVtdfFGdzZrqui0oM+v19UVJQkJSXJokWLbPl3U9evX7/ja2ure2hoqAwfPlwqKiqkb9++Ul5eLsXFxTJp0iTZunWr9OrVS27cuCFVVVX2nAULFsjkyZNl+vTpkpeXZ8/dtGmTXLp0Sfbu3eu59vLly21rt5atX716tU3PoyX0ZWVlnvUnn3zSnj9x4kSZOXOmOBwO2bVrl/zgBz9o8zUBAGhvJOcAALRDKXvU/V+yiXntJ6Xs7qVuv3DlhM9L3Hv27GnLv0+dOiW+FhcXJ1u2bJGcnBy7XlhY6NnnTpIvXrzo+QCgibz29x41apQcPHjQc87QoUNtebp3cr5kyRKbSLdVjx49ZNasWTahd+vevbst6X/mmWdkypQptjx+1apVsnnzZvnGN77hg38BAAB8j+QcAAA/0z7mwUGhtsXcW52rWjqHRkqoM9znybm2FvuLDjD32muv2T7dmkhroq79um/1oUD7hO/cubPRdk3ajxw50mhbdnZ2m5+jW7du8vvf/15+85vf2EHi3PSjROfOnW1irq306l//9V/lL3/5i23RP3369G28LQAA7YPR2gEA8LO6ugpxNdTaUnZvuq7btUXd1zQpbWhosAOo3Q49p2ly33Qk9A0bNtjW6TfffFP69+9vE2otH29NRESEXU6YMEEGDBjgCS2Hf/rppxsdq2XybdG1a1fZvXu3HDhwQL73ve812nfhwgWpq6vzJOYqNzfX0+oPAEBHRHIOAICfaQn71ZuFtoVcS9wdjmC71HXd7o9R27Vv9Y4dO2TGjBkSFhbWbH9rU51pH3B38uumiXRTOjXZunXrJCUlRVauXClTp06122tra+1SS8ndTp48aacz08RYp3TzDr3O7dIW8/fee08OHz4sL7zwgp26zZuOUK8fFPQDgpu2mKuzZ8/e9v0AAGgPJOcAALSDc5cOS+nVD7VJ2pay61LXdbu/aGKuSfKhQ4fsQG1aXq4t6dpH+/3332/xHJ2eTOdC17nD9fjx48fb+cK9af9tLWl/+OGH5dFHH5URI0Z4WqY1+dXWdx2M7cEHH7Tl7DpY3IoVK+x5WmquSbOep63tun4nibk+o/Yzf+ihh+xc7hpuWmqvifvPf/5z+2Hhq1/9qv2Q8Ic//KFRazoAAB1NwIeMJwiCIIiOHL6cQssZEm7C7+til+3x7LGxsWb16tWmsLDQVFdX26nVtm3b1miaMu+p1DQGDx5sjh07Zqc827Nnj53yzHsqtbS0NJOXl2eqqqpMaWmp2bhxo4mOjvacn5qaakpKSozL5fJMpaYxe/Zsk5uba6dJ0/OysrLMsGHDWp3CraX49re/bVrjfVzXrl3N5s2bzY0bN8yFCxfMz3/+cxMVFdVh/g4IgiAIQpqE45MfAACgFTry97Jly2Tx4sWURd/D+DsAAPgTZe0AAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAD3OGOMJCcnB/oxAAC4p5GcAwDwGRYTEyNpaWmSn58v1dXVUlRUJJmZmTJy5Ei/3C8xMdEm+5GRkX65fnx8vPzsZz+TgoICqayslDNnzsjSpUvF6XR6jvnRj35kn6FplJeX++WZAADwhRCfXAUAALRJqDNcnCHhUltXIXX1FX69lyay+/fvl2vXrsm8efPkxIkTNokdO3asrFmzRvr06SMdWXBwsLhcrkbbevfuLUFBQTJt2jSbmPfr10/Wr18v4eHh9h3VihUr5PXXX2903rvvvit//vOf2/X5AQC4XYYgCIIgiNYjPj7eZGRk2OWdXiMoyGniYp8wCb2+Zb7a51/sUtd1u7+ee/v27aa4uNiEhYU12xcZGen5rZKTk+3vxMREu+69PyEhwW5zv39cXJzJzMw0ZWVlpry83OTk5Jhx48bZ/U2lp6fbcxwOh5k/f74pKCgwlZWV5ujRoyYlJcVzD/d9k5KSTHZ2tqmpqbHb2vKec+fONfn5+a3u/8pXvmKvPXTo0ID/HRAEQRCEtBK0nAMA0A6+0OXrEhPd17aYV9dcE2dwZ7uuij563+f3i4qKkqSkJFm0aJEt/27q+vXrd3xtbXUPDQ2V4cOHS0VFhfTt29eWjBcXF8ukSZNk69at0qtXL7lx44ZUVVXZcxYsWCCTJ0+W6dOnS15enj1306ZNcunSJdm7d6/n2suXL5e5c+fasvWrV6+26Xm0hL6srKzV/d/97nflr3/9q+zbt++O3xkAAH8jOQcAoB1K2aP+4Us2Ma/9pJTdvdTtFy4f93mJe8+ePW3596lTp8TX4uLiZMuWLZKTk2PXCwsLPfvcSfLFixc9HwA0kV+4cKGMGjVKDh486Dln6NChtjzdOzlfsmSJ7Nq1q83P0qNHD5k1a5ZN6FvSqVMnef75523SDwBAR0ZyDgCAn2kf8+DgUNti7q3OVS2dO0Xa5N3XybnD4RB/0QHmXnvtNRkzZoxNpDVR1/7st/pQoH3Cd+7c2Wi7Ju1HjhxptC07O7vNz9GtWzf5/e9/L7/5zW/sIHEt+ad/+ie5//77ZePGjW2+LgAAgUByDgCAn2ni7XLV2lJ2d4u50nWXq862qPualo43NDTYAdRuh57TNLn3HgldbdiwQXbs2CETJkywCbqWrM+ZM0deffXVFq8ZERFhl3r8+fPnG+2rqalptK5l8m3RtWtX2b17txw4cEC+973v3bKk/e2337Yt+QAAdGRMpQYAgJ9p8n31RqFtIQ8NCReHI9gudV23+2PUdu2vrQn0jBkzJCwsrNn+1qY60z7g7uTXbcCAAc2OO3funKxbt05SUlJk5cqVMnXqVLu9trbWM9K628mTJ+00bloOr1O6eYde53Zpi/l7770nhw8flhdeeMFOk9aShx9+WEaMGGE/JgAA0NGRnAMA0A7OXcyW0rKTIo4gW8quS13X7f6iibkmyYcOHbIDtWl5ubakax/t999veRA6nZ5M50LXucP1+PHjx9tWcW+rVq2yLeaa/D766KM2Ac7NzbX7zp49a1vfJ06cKA8++KAtZ9fB4nR6Mz1vypQp0r17d3vezJkz7fqdJOb6jNrP/KGHHrJzuWs09Z3vfEcuXLggWVlZt3UPAAACJeBDxhMEQRBERw5fTqHlDAk34fd1scv2ePbY2FizevVqU1hYaKqrq+3Uatu2bWs0TZn3VGoagwcPNseOHbNTnu3Zs8dOeeY9lVpaWprJy8szVVVVprS01GzcuNFER0d7zk9NTTUlJSXG5XJ5plLTmD17tsnNzbXTpOl5WVlZZtiwYa1O4dZSfPvb3zat8T5Op24rKioyP/7xjzvk3wFBEARBSJNwfPIDAAC0Ij4+XpYtWyaLFy+2LcO4N/F3AADwJ8raAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAA7nHGGElOTg70YwAAcE8jOQcA4DMsJiZG0tLSJD8/X6qrq6WoqEgyMzNl5MiRfrlfYmKiTfYjIyP9cv34+Hj52c9+JgUFBVJZWSlnzpyRpUuXitPpbHTcmDFj5P3335cbN27IxYsXZfPmzfZcAAA6KpJzAADaUagzXMLv6yLOkHC/30uT0cOHD9tEfN68edK/f39JSkqS3bt3y5o1a6SjCw4Obratd+/eEhQUJNOmTZMvf/nL8v3vf1+mT58uP/nJTzzHPPzww/LWW2/JH//4RxkwYICMHTtWHnzwQdm6dWs7vwEAALfHEARBEATResTHx5uMjAy7vNNrBAU5zRdjnzAJj3zLfLXvv9ilrut2fz339u3bTXFxsQkLC2u2LzIy0vNbJScn29+JiYl23Xt/QkKC3eZ+/7i4OJOZmWnKyspMeXm5ycnJMePGjbP7m0pPT7fnOBwOM3/+fFNQUGAqKyvN0aNHTUpKiuce7vsmJSWZ7OxsU1NTY7e15T3nzp1r8vPzPet63draWntP97aJEycal8tlQkJCAvp3QBAEQRDSSoTcZiIPAADuwOe7fF1iPvdlqa2rkKqa6+IM7mzXVfFH7/v8flFRUbaVfNGiRbb8u6nr16/f8bW11T00NFSGDx8uFRUV0rdvXykvL5fi4mKZNGmSbaHu1auXLSmvqqqy5yxYsEAmT55sW7nz8vLsuZs2bZJLly7J3r17Pddevny5zJ0715atX716tU3PoyX0ZWVlnnWtFmhoaJAXXnhB3njjDYmIiJB//ud/ll27dkl9ff0dvzcAAP5Ecg4AQDuUskdHfskm5rX1FXabe6nbP7p8XOo+WfeVnj172vLvU6dOia/FxcXJli1bJCcnx64XFhZ69rmTZO3n7f4AoIn8woULZdSoUXLw4EHPOUOHDrXl6d7J+ZIlS2wS3VY9evSQWbNm2YTe7W9/+5vtc/7rX/9a1q1bJyEhIXLgwAEZP368D94eAAD/oM85AAB+pv3Lg4NDpc5V3Wi7rgcFh9rk3dccDof4iw4wl5qaKvv27bODsWlf9k/7UBAeHi47d+6UmzdvemLKlCk2ufaWnZ3d5ufo1q2b/P73v5ff/OY3dpA470Hw1q9fLxs3bpTHHnvMttLX1tbaQeEAAOioaDkHAMDPtFXc5aq1pezuFnOl6w2uWtui7mtaOq6l3TqA2u3Qc5om901HQt+wYYPs2LFDJkyYYFuotWR9zpw58uqrr7Z4TS0rV3r8+fPnG+2rqalptK5l8m3RtWtXO7Cdtoh/73vfa7RvxowZttX+hz/8oWebltSfO3dOHn/8cfnggw/adA8AANoTLecAAPiZJt9l1wttC3loSLg4HMF2qeu63dcl7Ur7a2sCrYlqWFhYs/2tTXWmfcDdya+bjnjelCa6WjKekpIiK1eulKlTp9rt2kLddKT1kydP2mnctBxep3TzDr3O7dIW8/fee8/2Ldd+5Tp1mzd9X/dHBjeXy2WXWuoPAEBHxP+hAABoB+cvZkvplQ9ti3TnTpF2qeu63V80Mdck+dChQ3agNi0v15Z07aOtc4C3ROcN17nQtVxdj9d+2toq7m3VqlW2xVynLHv00UdlxIgRkpuba/edPXvWJsYTJ06005dpObsOFrdixQp7npayd+/e3Z43c+ZMu34nibk+o/Yzf+ihh2wZu4bb9u3bbTn74sWL7TvovdLT021f9CNHjtzRvyUAAO0h4EPGEwRBEERHDl9OoeUMCTfh93Wxy/Z49tjYWLN69WpTWFhoqqur7dRq27ZtazRNmfdUahqDBw82x44ds1Oe7dmzx05N5j2VWlpamsnLyzNVVVWmtLTUbNy40URHR3vOT01NNSUlJXbqMvdUahqzZ882ubm5dpo0PS8rK8sMGzas1SncWopvf/vbpjXexz333HPm8OHD5ubNm/Ze+s6PPPJIh/k7IAiCIAhpEo5PfgAAgFbEx8fLsmXLbEustgzj3sTfAQDAnyhrBwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuMcZYyQ5OTnQjwEAwD2N5BwAgM+wmJgYSUtLk/z8fKmurpaioiLJzMyUkSNH+uV+iYmJNtmPjIz0y/Xj4+PlZz/7mRQUFEhlZaWcOXNGli5dKk6ns9FxzzzzjBw5ckQqKirkb3/7m8ydO9cvzwMAgK+E+OxKAADgU4U6w8XpDJfaugqpq6vw6700kd2/f79cu3ZN5s2bJydOnLBJ7NixY2XNmjXSp08f6ciCg4PF5XI12ta7d28JCgqSadOm2cS8X79+sn79egkPD7fvqJKSkuQXv/iFzJo1S/7whz/Y99Rjqqqq7HsDANBRGYIgCIIgWo/4+HiTkZFhl3d6jaAgp/li18Emoffz5qtffsEudV23++u5t2/fboqLi01YWFizfZGRkZ7fKjk52f5OTEy06977ExIS7Db3+8fFxZnMzExTVlZmysvLTU5Ojhk3bpzd31R6ero9x+FwmPnz55uCggJTWVlpjh49alJSUjz3cN83KSnJZGdnm5qaGrutLe85d+5ck5+f71n/xS9+YX796183OmbmzJmmqKgo4H8HBEEQBCGtBC3nAAC0g8/HPCYxD37ZtphX1VwXZ0hnu66KLxzw+f2ioqJsC/KiRYts+XdT169fv+Nra+tzaGioDB8+3JaN9+3bV8rLy6W4uFgmTZokW7dulV69esmNGzdsa7VasGCBTJ48WaZPny55eXn23E2bNsmlS5dk7969nmsvX77clqBr2frVq1fb9DxaQl9WVuZZ79SpU7N31uf44he/aKsJzp49e8fvDgCAv5CcAwDQDqXs0ZFfsom5hnIvdftHl4/5vMS9Z8+etvz71KlT4mtxcXGyZcsWycnJseuFhYWefe4k+eLFi54PAJrIL1y4UEaNGiUHDx70nDN06FBbnu6dnC9ZskR27drV5mfp0aOHLV/37lO+Y8cOWbVqlbzxxhuye/du+28xZ84cu69r164k5wCADonkHAAAP9M+5sHBobbF3FtdfbV07hRpk3dfJ+cOh0P8RQeYe+2112TMmDE2kdZEXfuzt0aTY+0TvnPnzkbbNWnXQdu8ZWdnt/k5unXrJr///e/lN7/5jR0kzk37l2vS/vbbb9s+9tqC/9Of/lT+1//6X9LQ0HBb7woAQHthtHYAAPxME2+Xq9aWsnvT9QZXracV3Ze0dFwTUR1A7Xa4k1fv5L7pSOgbNmyQ7t27y5tvvin9+/e3CfXMmTNbvWZERIRdTpgwQQYMGOAJLYd/+umnGx2rZfJtoS3g2ip+4MAB+d73vtds//z58+19tYw9NjZWDh06ZLdruTwAAB0RyTkAAH6myXfZ9ULbQq7hcAR7fut2f4zarv21tbx7xowZEhYW1mx/a1OdaR9wd/Lrpol0U+fOnZN169ZJSkqKrFy5UqZOnWq319bWekZadzt58qSdxk3L4XVKN+/Q69wubTF/77335PDhw/LCCy/Yqdta+9BQUlIidXV18s1vftMm8pcvX77t+wEA0B4oawcAoB2cL/2zp4+5lrJri3np5Q892/1BE3OdSk1bjbUv9/HjxyUkJERGjx4tL774om25bkqnJ9O50HXucB1MTgd2c/fXdtP+3FlZWXL69Gk78NyIESMkNzfX7tP+3JoUT5w4Ud555x07EJsOFrdixQp7nvaD37dvn/04MGTIEFtynpGRcduJud5H+5k/9NBDnn2lpaV2+bnPfc62yOtxnTt3tgm8znuuc7ADANCRBXzIeIIgCILoyOHLKbScznATHtbFLtvj2WNjY83q1atNYWGhqa6utlOrbdu2rdE0Zd5TqWkMHjzYHDt2zE55tmfPHjvlmfdUamlpaSYvL89UVVWZ0tJSs3HjRhMdHe05PzU11ZSUlBiXy+WZSk1j9uzZJjc3106TpudlZWWZYcOGtTqFW0vx7W9/27TGfcznPvc5c+DAAXPz5k071dvOnTvNwIEDO9TfAUEQBEFIk3B88gMAALRC+y0vW7ZMFi9ezEjf9zD+DgAA/kSfcwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAgHucMUaSk5MD/RgAANzTSM4BAPgMi4mJkbS0NMnPz5fq6mopKiqSzMxMGTlypF/ul5iYaJP9yMhI8Ze33npLzp49K1VVVVJSUiIZGRnStWvXRsf0799f9u7da4/Rd543b57fngcAAF8gOQcAoB2FOsMlPKyLOJ3hfr9XfHy8HD582CbimpxqwpqUlCS7d++WNWvWSEcXHBzc4nZ9/meffVYeeeQRSUlJkR49esjmzZs9+++//375wx/+YBP4r33ta/bdly5dKlOnTm3HpwcA4PYZgiAIgiBaj/j4eJORkWGXd3qNoCCn+WK3weYrfZ83j/Z7wS51Xbf767m3b99uiouLTVhYWLN9kZGRnt8qOTnZ/k5MTLTr3vsTEhLsNvf7x8XFmczMTFNWVmbKy8tNTk6OGTdunN3fVHp6uj3H4XCY+fPnm4KCAlNZWWmOHj1qUlJSPPdw3zcpKclkZ2ebmpoau60t7/nkk08al8tlQkJC7Pr06dPNlStXjNP5//5tX3rpJZObmxvwvwOCIAiCkFYi5A6SeQAAcJs+H/uYdHnwy1JbVyHVNdclJKSzXVfFJQd8fr+oqCjbSr5o0SKprKxstv/69et3fG1tdQ8NDZXhw4dLRUWF9O3bV8rLy6W4uFgmTZokW7dulV69esmNGzdsWblasGCBTJ48WaZPny55eXn23E2bNsmlS5ds+bnb8uXLZe7cuVJQUCBXr15t03s+//zzcuDAAamvr7fbnnjiCXvNuro6z3E7duyQ+fPnywMPPCDXrl2743cHAMBfSM4BAGiHUvaoB75kE/O6ugq7zb3U7R9dOuZZ95WePXtKUFCQnDp1SnwtLi5OtmzZIjk5OXa9sLDQs6+srMwuL1686PkAoIn8woULZdSoUXLw4EHPOUOHDpVp06Y1Ss6XLFkiu3bt+tRn0CR+5syZEh4eLu+//75MnDjRsy82NrbRM6nS0lLPPpJzAEBHRJ9zAAD8TPuXBweFSn19daPtuq7bNXn3NYfDIf6iA8ylpqbKvn37bF9u7cv+aR8KNIneuXOn3Lx50xNTpkyx/cW9ZWdnt+kZXnnlFXn00Udl9OjR4nK57KBwAAD8PaPlHAAAP9NWcVdDrS1l924h13Xdri3qvqal4w0NDdK7d+/bOk/PaZrcO53ORsds2LDBlolPmDBBxowZY0vW58yZI6+++mqL14yIiLBLPf78+fON9tXU1DRa1zL5trhy5YoNfc/c3Fw5d+6cDBo0yLbMf/TRR3aUem/udd0HAEBHRMs5AAB+psn31WuFtoVcW9EdjmC71HXd7uuSdqX9tTWBnjFjhoSFhTXb39pUZ9oHXHlPTTZgwIBmx2kyvG7dOjta+sqVKz0jodfW1jYbaf3kyZN2Gjcth9cp3bxDr3O3tHxfderUyS61zF37tIeE/L82CG1h1xJ/StoBAB0VyTkAAO3g/Ed/louXP7Qt0p07Rdqlrut2f9HEXJPkQ4cO2YHatLxcW9JnzZplE9iWnDlzxs4LruXqevz48eNtq7i3VatW2Rbzhx9+2JaWjxgxwrZeK52+TFvftQ/4gw8+aMvZdbC4FStW2PO0lL179+72PO0zruu3Y+DAgfa9EhISbLKv9/7Vr35ln9v9Tr/85S/tRwJt4dfB6nTatf/5P/+n/Md//Mcd/1sCANAeAj5kPEEQBEF05PDlFFpOZ7gJD+til+3x7LGxsWb16tWmsLDQVFdX26nVtm3b1miaMu+p1DQGDx5sjh07Zqc827Nnj53yzHsqtbS0NJOXl2eqqqpMaWmp2bhxo4mOjvacn5qaakpKSuz0Zu6p1DRmz55tpzPTadL0vKysLDNs2LBWp3BrKfr162feffddc/nyZXt/nZpt7dq1plu3bo2O69+/v9m7d689Rt/5Bz/4QYf6OyAIgiAIaRKOT34AAIBWxMfHy7Jly2Tx4sW2ZRj3Jv4OAAD+RFk7AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAPc4YI8nJyYF+DAAA7mkk5wAAfIbFxMRIWlqa5OfnS3V1tRQVFUlmZqaMHDnSL/dLTEy0yX5kZKT4y1tvvSVnz56VqqoqKSkpkYyMDOnatatnf6dOnSQ9PV2OHz8udXV18tvf/tZvzwIAgK+QnAMA0I6cznAJD+til/4WHx8vhw8fton4vHnzpH///pKUlCS7d++WNWvWSEcXHBzc4nZ9/meffVYeeeQRSUlJkR49esjmzZsbnaeJu36U2LVrVzs+MQAAd8cQBEEQBNF6xMfHm4yMDLu802sEBTnNFz4/2PT/8mQz4CvfsUtd1+3+eu7t27eb4uJiExYW1mxfZGSk57dKTk62vxMTE+269/6EhAS7zf3+cXFxJjMz05SVlZny8nKTk5Njxo0bZ/c3lZ6ebs9xOBxm/vz5pqCgwFRWVpqjR4+alJQUzz3c901KSjLZ2dmmpqbGbmvLez755JPG5XKZkJCQZvv0/r/97W87zN8BQRAEQUgrEXKXiT0AAGiDbl0fky4P9ZPa2nKprr4mISGd7bo6d/6Az+8XFRVlW8kXLVoklZWVzfZfv379jq+tre6hoaEyfPhwqaiokL59+0p5ebkUFxfLpEmTZOvWrdKrVy+5ceOGbcFWCxYskMmTJ8v06dMlLy/Pnrtp0ya5dOmS7N2713Pt5cuXy9y5c6WgoECuXr3apvd8/vnn5cCBA1JfX3/H7wQAQKCRnAMA4Gdawh71QHebmNfVVdht7qVuL714zLPuKz179pSgoCA5deqU+FpcXJxs2bJFcnJy7HphYaFnX1lZmV1evHjR8wFAE/mFCxfKqFGj5ODBg55zhg4dKtOmTWuUnC9ZsqRNpeiaxM+cOVPCw8Pl/fffl4kTJ/r8PQEAaE/0OQcAwM9CneESHBwq9fXVjbbrum7X/b7mcDjEX7Qvd2pqquzbt0+WLl1q+7J/2ocCTaJ37twpN2/e9MSUKVNsf3Fv2dnZbXqGV155RR599FEZPXq0uFwuOygcAAB/z2g5BwDAz2rrKsTlqrWl7N4t5Lqu23W/r2npeENDg/Tu3fu2ztNzmib3Tqez0TEbNmyQHTt2yIQJE2TMmDG2ZH3OnDny6quvtnjNiIgIu9Tjz58/32hfTU1No3Utk2+LK1eu2ND3zM3NlXPnzsmgQYM8LfMAAPy9oeUcAAA/04T86rUCCQ2NsCXuDkewXeq6bvd1SbvS/tqaQM+YMUPCwsKa7W9tqjPtA668pyYbMGBAs+M0GV63bp0dLX3lypUydepUu722trbZSOsnT56007hpObxO6eYdep27peX77inUAAD4e0XLOQAA7aDkwp89fcw7d37AtphfvJTj2e4Pmpjv379fDh06ZPty67zfISEhthT8xRdftAO5NXXmzBk7F7qWq+tgcjqwm7aKe1u1apVkZWXJ6dOn7YBsI0aMsK3XSucf19Z37QP+zjvv2AHhdLC4FStW2PM0kdZyeP04MGTIEDto3O2UpA8cOFAee+wxew39AKFl8cuWLbPPrX3P3fr06WP7ukdHR8v9998vCQkJdvuxY8fu4l8UAAD/CviQ8QRBEATRkcOXU2g5neEmPKyLXbbHs8fGxprVq1ebwsJCU11dbadW27ZtW6NpyrynUtMYPHiwOXbsmJ3ybM+ePXbKM++p1NLS0kxeXp6pqqoypaWlZuPGjSY6OtpzfmpqqikpKbHTm7mnUtOYPXu2yc3NtdOk6XlZWVlm2LBhrU7h1lL069fPvPvuu+by5cv2/jo129q1a023bt0aHafv25KO8ndAEARBENIkHJ/8AAAArYiPj7ets4sXL7Ytw7g38XcAAPAn+pwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAADc44wxkpycHOjHAADgnkZyDgDAZ1hMTIykpaVJfn6+VFdXS1FRkWRmZsrIkSP9cr/ExESb7EdGRoq/vPXWW3L27FmpqqqSkpISycjIkK5duzZ6hm3bttl95eXlcuTIEfnWt77lt+cBAMAXSM4BAGhHTmeEhIfHiNMZ7vd7xcfHy+HDh20iPm/ePOnfv78kJSXJ7t27Zc2aNdLRBQcHt7hdn//ZZ5+VRx55RFJSUqRHjx6yefNmz/7BgwfL8ePH7b6vfOUrkp6ebhP4CRMmtOPTAwBw+wxBEARBEK1HfHy8ycjIsMs7vUZQkNN84fODTf9+/2wGJPyrXeq6bvfXc2/fvt0UFxebsLCwZvsiIyM9v1VycrL9nZiYaNe99yckJNht7vePi4szmZmZpqyszJSXl5ucnBwzbtw4u7+p9PR0e47D4TDz5883BQUFprKy0hw9etSkpKR47uG+b1JSksnOzjY1NTV2W1ve88knnzQul8uEhIS0eszbb79tNmzYEPC/A4IgCIKQViLkDpJ5AABwm7p1fUy6PNRPausqpLr6moSEdLbr6tz5Az6/X1RUlG0lX7RokVRWVjbbf/369Tu+tra6h4aGyvDhw6WiokL69u1ry8eLi4tl0qRJsnXrVunVq5fcuHHDlp6rBQsWyOTJk2X69OmSl5dnz920aZNcunRJ9u7d67n28uXLZe7cuVJQUCBXr15t03s+//zzcuDAAamvr2/1OC2zz83NveN3BgDA30jOAQBoh1L2qKgeNjGvq6uw29xL3V568Zhn3Vd69uwpQUFBcurUKfG1uLg42bJli+Tk5Nj1wsJCz76ysjK7vHjxoucDgCbyCxculFGjRsnBgwc95wwdOlSmTZvWKDlfsmSJ7Nq161OfQZP4mTNnSnh4uLz//vsyceLEVo995pln5LHHHrP3AgCgo6LPOQAAfhYaGi7BwaFSX1/daLuu6/bQ0Aif39PhcIi/6ABzqampsm/fPlm6dKnty/5pHwo0id65c6fcvHnTE1OmTLH9xb1lZ2e36RleeeUVefTRR2X06NHicrlsn/KW/OM//qPtcz516lQ5efLkbbwlAADti5ZzAAD8rLa2QlyuWlvK7t1Cruu6vba23Of31NLxhoYG6d27922dp+c0Te6dTmejYzZs2CA7duywA6yNGTPGlqzPmTNHXn311RavGRHx8ccHPf78+fON9tXU1DRa1zL5trhy5YoNfU8tVz937pwMGjTI0zKvtHT+d7/7nXz/+9+XN998s03XBQAgUGg5BwDAz+rqyuXq1XwJdYbbUdodjmC71HXd7uuSdqX9tTWBnjFjhoSFhTXb39pUZ9oHXHlPTTZgwIBmx2kyvG7dOjsi+sqVK23LtKqtrW020rq2WOs0bloOr1O6eYde525p+b7q1KlTo+nUtm/fLj/84Q9l/fr1d30PAAD8jeQcAIB2UHLhz3LxUo44HEHSufMDdqnrut1fNDHXJPnQoUN2oDYtL9eW9FmzZtl+2i05c+aMnQtdy9X1+PHjx9tWcW+rVq2yLeYPP/ywLS0fMWKEZ7A1nX9cW9+1D/iDDz5oy9l1sLgVK1bY87SUvXv37vY87TOu67dj4MCB9r0SEhJssq/3/tWvfmWf2/1OWsquibmW32vfeJ3rXUMHjwMAoCML+JDxBEEQBNGRw5dTaDmd4SY8PMYu2+PZY2NjzerVq01hYaGprq62U6tt27at0TRl3lOpaQwePNgcO3bMTnm2Z88eO+WZ91RqaWlpJi8vz1RVVZnS0lKzceNGEx0d7Tk/NTXVlJSU2OnN3FOpacyePdvk5ubaadL0vKysLDNs2LBWp3BrKfr162feffddc/nyZXt/nZpt7dq1plu3bp5j9J4t2b17d4f5OyAIgiAIaRKOT34AAIBWxMfHy7Jly2Tx4sW2ZRj3Jv4OAAD+RFk7AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAPc4YI8nJyYF+DAAA7mkk5wAAfIbFxMRIWlqa5OfnS3V1tRQVFUlmZqaMHDnSL/dLTEy0yX5kZKT4y1tvvSVnz56VqqoqKSkpkYyMDOnatatnf69eveSPf/yjfPTRR/YYffdly5ZJSEiI354JAIC7xf+lAABoR05nhISGhkttbbnU1VX49V7x8fGyf/9+uXbtmsybN09OnDghTqdTxo4dK2vWrJE+ffpIRxYcHCwul6vZ9t27d8tPfvITuXDhgnz+85+XFStWyObNm2XIkCF2f11dnU3Y//KXv9h3T0hIkPXr10tQUJAsWrQoAG8CAEDbGIIgCIIgWo/4+HiTkZFhl3d6jaAgp/nCF4aYfv3/2QwY8F271HXd7q/n3r59uykuLjZhYWHN9kVGRnp+q+TkZPs7MTHRrnvvT0hIsNvc7x8XF2cyMzNNWVmZKS8vNzk5OWbcuHF2f1Pp6en2HIfDYebPn28KCgpMZWWlOXr0qElJSfHcw33fpKQkk52dbWpqauy2trznk08+aVwulwkJCWn1mJUrV5q9e/cG/O+AIAiCIKSVoOUcAIB20K3bQHmoSz/bYl5VfVVCQjrbdXXu3H6f3y8qKkqSkpJsS3FlZWWz/devX7/ja2ure2hoqAwfPlwqKiqkb9++Ul5eLsXFxTJp0iTZunWrLS2/ceOGLStXCxYskMmTJ8v06dMlLy/Pnrtp0ya5dOmS7N2713Pt5cuXy9y5c6WgoECuXr3apvd8/vnn5cCBA1JfX9/iMT169LD/FvpcAAB0VCTnAAC0Qyn7A1HdG5Wyu5e6vbT0qM9L3Hv27GnLuE+dOiW+FhcXJ1u2bJGcnBy7XlhY6NlXVlZmlxcvXvR8ANBEfuHChTJq1Cg5ePCg55yhQ4fKtGnTGiXnS5YskV27dn3qM2gSP3PmTAkPD5f3339fJk6c2OwYLen/6le/Kp07d5Z169bZawMA0FExIBwAAH6mfcxDgjtJfX11o+26HhwcKqGhET6/p8PhEH/RAeZSU1Nl3759snTpUunfv/+nfijQJHrnzp1y8+ZNT0yZMsW2anvLzs5u0zO88sor8uijj8ro0aNtv3TtY97Uc889Z5Pzb37zmzJhwgTbIg8AQEdFyzkAAH5WW1sh9a4aW8ru3UKu6y5XrW1R9zUtHW9oaJDevXvf1nl6TtPkXgeR87ZhwwbZsWOHTXjHjBljS9bnzJkjr776aovXjIj4+OODHn/+/PlG+2pqahqta5l8W1y5csWGvmdubq6cO3dOBg0a5GmZV7pN6X4dXO4///M/ZeXKlZ53BACgI6HlHAAAP6urK5drVwtsC7nTGS4OR7Bd6rpu98eo7dpfWxPoGTNmSFhYWLP9rU11pn3AlffUZAMGDGh2nCa+WiqekpJiE96pU6fa7bW1tXapybDbyZMn7TRuWg6v05p5hzuBvhtavq86dep0y2P0I4P7WAAAOhpazgEAaAclJYc8fcw7d37Atphfupjj2e4Pmphrv+tDhw7Z/tbHjx+3c31rKfiLL75oB3Jr6syZM3YudC1X18HkdGA3bRX3tmrVKsnKypLTp0/bAdlGjBhhW6eVzj+uLdPaB/ydd96xA8LpYHE63Zmep8mxlsPrxwGd+kwHjWupJL01AwcOlMcee8xeQz9AaFm8zmGuz619z9W3vvUtO52aTh2nLfNf//rX5aWXXpL/+q//anXQOAAAOoKADxlPEARBEB05fDmFltMZbsLDY+yyPZ49NjbWrF692hQWFprq6mo7tdq2bdsaTVPmPZWaxuDBg82xY8fslGd79uyxU555T6WWlpZm8vLyTFVVlSktLTUbN2400dHRnvNTU1NNSUmJnd7MPZWaxuzZs01ubq6dJk3Py8rKMsOGDWt1CreWol+/fubdd981ly9ftvfXqdnWrl1runXr5jnm2WeftdOx3bhxw9y8edNO9abTuHXq1KnD/B0QBEEQhDQJxyc/AABAK+Lj423r7OLFi23LMO5N/B0AAPyJjlcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAD3OGOMJCcnB/oxAAC4p5GcAwDwGRYTEyNpaWmSn58v1dXVUlRUJJmZmTJy5Ei/3C8xMdEm+5GRkeIvb731lpw9e1aqqqqkpKREMjIypGvXri0e26NHD7lx44ZcvXrVb88DAIAvkJwDANCOnKEREh4eI05nuN/vFR8fL4cPH7aJ+Lx586R///6SlJQku3fvljVr1khHFxwc3OJ2ff5nn31WHnnkEUlJSbEJ+ObNm5sdFxISIr/61a/kT3/6Uzs8LQAAd4fkHACAdhAU5JQvfHGIPNLnn+S/PfKkPNJ3kl3X7f6ydu1a24o9cOBA2bp1q+Tl5cnJkydl1apVMmjQoDa3fCckJNhtmuyruLg42/peVlYm5eXlkpOTI+PGjbP733vvPXvMtWvX7Dnp6el23eFwyPz586WgoEAqKyvl6NGjNrFuel/9eJCdnS01NTUydOjQFp/x//yf/yMffPCBrQJ4//33Zfny5fZ9NBn39uMf/1hOnTolv/71r33wrwkAgH81/r8YAADwi26fHygPxfST2tpyqaq6KiHOznZdnSve7/P7RUVF2UR30aJFNhlu6vr163d8bW11Dw0NleHDh0tFRYX07dvXJunFxcUyadIk+yGgV69etpxcS8/VggULZPLkyTJ9+nT7kUDP3bRpk1y6dEn27t3rubYm2nPnzrVJfFtK0fU9n3/+eTlw4IDU19d7to8YMUKeeeYZGTBggH0mAAA6OpJzAADaoZT9gejuNjGvq62w29xL3V760VGpq/t43Vd69uwpQUFBtuXY17TlfMuWLbbFXBUWFnr2aWu6unjxoucDgCbyCxculFGjRsnBgwc952jL+LRp0xol50uWLJFdu3Z96jNoEj9z5kwJDw+3recTJ0707IuOjpY33njDfgy4efOmD98cAAD/oawdAAA/C3WGS0hwJ6mvq260XdeDg0IlNDTC5/fUMnJ/0QHmUlNTZd++fbJ06VLbl/3TPhRoEr1z506bLLtjypQptr+4Ny1pb4tXXnlFHn30URk9erS4XC47KJzb+vXr5Ze//CV9zQEAf1doOQcAwM9q6yqk3lVjS9ndLeZK110NtbZF3de0dLyhoUF69+59W+fpOU2Te6ezcb/4DRs2yI4dO2TChAkyZswYW7I+Z84cefXVV1u8ZkTExx8f9Pjz58832qd9y71pmXxbXLlyxYa+Z25urpw7d872O9eWeR0A77//9/9uy+Pd76KDy9XV1cn3vvc9Tz94AAA6ElrOAQDws7racrlWVmBbyJ2h4eJwBNulrut2X5e0K+2vrQn0jBkzJCwsrNn+1qY60z7gyntqMu233ZQmw+vWrbODuq1cuVKmTp1qt9fW1jYbaV0HodNp3LQcXqd08w69zt3S8n3VqVMnu3ziiSfsM7tDS+W1/7v+/u1vf3vX9wMAwB9oOQcAoB2UnD/k6WPeufMDtsX8UmmOZ7s/aGK+f/9+OXTokE1Qjx8/bkc011LwF1980Q7k1tSZM2fsKOharq6DyenAbtoq7k1He8/KypLTp0/bAdl08DVtvVY6/7i2vmsf8HfeeccOCKeDxa1YscKep4m0lsPrx4EhQ4bYpNm7JP3T6Mjzjz32mL2GfoDQsvhly5bZ59a+56ppP/uvf/3r9pk+/PDDO/yXBACgfRiCIAiCIFqP+Ph4k5GRYZd3ey2nM9yEh8fYZXs8e2xsrFm9erUpLCw01dXVpri42Gzbts0kJiZ6jlHJycme9cGDB5tjx46ZyspKs2fPHpOSkmKPcb9/WlqaycvLM1VVVaa0tNRs3LjRREdHe85PTU01JSUlxuVymfT0dM/22bNnm9zcXFNTU2PPy8rKMsOGDbP79HlUZGTkLd+nX79+5t133zWXL1+29y8oKDBr16413bp1a/Wcb3/72+bq1asd6u+AIAiCIKRJOD75AQAAWqHzd2vr7OLFi23LMO5N/B0AAPyJPucAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAD3OGOMJCcnB/oxAAC4p5GcAwDwGRYTEyNpaWmSn58v1dXVUlRUJJmZmTJy5Ei/3C8xMdEm+5GRkeIvb731lpw9e1aqqqqkpKREMjIypGvXrp798fHx9hmaxuOPP+63ZwIA4G6F3PUVAABAmzlDIyQ0NFxqa8ulrrbCr/fSJHX//v1y7do1mTdvnpw4cUKcTqeMHTtW1qxZI3369JGOLDg4WFwuV7Ptu3fvlp/85Cdy4cIF+fznPy8rVqyQzZs3y5AhQxod941vfEM+/PBDz/qVK1fa5bkBALhThiAIgiCI1iM+Pt5kZGTY5Z1eIyjYaT4fN8T0GzDFJHztu3ap60FBTr899/bt201xcbEJCwtrti8yMtLzWyUnJ9vfiYmJdt17f0JCgt3mfv+4uDiTmZlpysrKTHl5ucnJyTHjxo2z+5tKT0+35zgcDjN//nxTUFBgKisrzdGjR01KSornHu77JiUlmezsbFNTU2O3teU9n3zySeNyuUxISIjnv5fS5+5ofwcEQRAEIa0ELecAALSDrp8fKA/F9Je62nKprromIc7Odl2dL9rv8/tFRUVJUlKSLFq0SCorK5vtv379+h1fW1vdQ0NDZfjw4VJRUSF9+/aV8vJyKS4ulkmTJsnWrVulV69ecuPGDVt6rhYsWCCTJ0+W6dOnS15enj1306ZNcunSJdm7d6/n2suXL5e5c+dKQUGBXL16tU3v+fzzz8uBAwekvr6+0T4t3+/cubOcPn1aXn75Zfnd7353x+8MAIC/kZwDANAOpexR0T1sYu4uZXcvdfvFj476vMS9Z8+eEhQUJKdOnRJfi4uLky1btkhOTo5dLyws9OwrKyuzy4sXL3o+AGgiv3DhQhk1apQcPHjQc87QoUNl2rRpjZLzJUuWyK5duz71GTSJnzlzpoSHh8v7778vEydO9OzTDwX//u//bkv6GxoaJCUlRbZt2yZPPfUUCToAoMMiOQcAwM+0j3lwcKhtMfdWX1ctne97QEJDI3yenDscDvEXHWDutddekzFjxthEWhN17c9+qw8FmkTv3Lmz0XZN2o8cOdJoW3Z2dpue4ZVXXpENGzbYfvU/+tGP7KBw7gRd+5avWrWq0TW7detm+92TnAMAOiqScwAA/Ky2tkJcrlpbyu6dhOu6btfB4XxNS8e11bh37963dZ6e0zS510HkvGlSvGPHDpkwYYJN0LVkfc6cOfLqq6+2eM2IiAi71OPPnz/faF9NTU2jdS2TbwtNwDX0PXNzc+XcuXMyaNAgT8t8Ux988IGMHj26TdcGACAQmEoNAAA/03L2q2X5trzdGRouDkewXeq6bvfHqO3aX1sT6BkzZkhYWFiz/a1NdaZ9wJX31GQDBgxodpwmw+vWrbMl4ytXrpSpU6fa7bW1tZ6R1t1Onjxpp3HTcnid0s079Dp3S8v3VadOnVo9Rt9BR3cHAKCjouUcAIB2cOHcIU8fcy1l1xbzS6UnPNv9QRNz7Xd96NAh25f7+PHjEhISYluQX3zxRTuQW1Nnzpyxc6EvXbrUDianA7tpq7g3LRnPysqyA63pgGwjRoywrddK5x/X1nctMX/nnXfsgHDaB1ynO9PzNJHet2+f/TigU5/poHFakt5WAwcOlMcee8xeQz9A9OjRQ5YtW2afW/ueqylTptiPBO6SeR2k7jvf+Y5897vfvct/UQAA/CvgQ8YTBEEQREcOX06h5QwNN+ERMXbZHs8eGxtrVq9ebQoLC011dbWdWm3btm2NpinznkpNY/DgwebYsWN2yrM9e/bYKc+8p1JLS0szeXl5pqqqypSWlpqNGzea6Ohoz/mpqammpKTETm/mnkpNY/bs2SY3N9dOk6bnZWVlmWHDhrU6hVtL0a9fP/Puu++ay5cv2/vr1Gxr16413bp18xwzZcoU8+GHH9pp3q5du2YOHjzYaNq2jvB3QBAEQRDSJByf/AAAAK3QQce0dXbx4sW2ZRj3Jv4OAAD+RJ9zAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHACAe5wxRpKTkwP9GAAA3NNIzgEA+AyLiYmRtLQ0yc/Pl+rqaikqKpLMzEwZOXKkX+6XmJhok/3IyEjxl7feekvOnj0rVVVVUlJSIhkZGdK1a9dmx82ZM0f++te/2vc+d+6cLFy40G/PBADA3Qq56ysAAIA2c4ZGiDM0XOpqy6WutsKv94qPj5f9+/fLtWvXZN68eXLixAlxOp0yduxYWbNmjfTp00c6suDgYHG5XM227969W37yk5/IhQsX5POf/7ysWLFCNm/eLEOGDPEc89Of/lTGjBkjc+fOte8dHR1tAwCAjswQBEEQBNF6xMfHm4yMDLu802sEBTvN5+OGmC8/OsV85evftUtdDwpy+u25t2/fboqLi01YWFizfZGRkZ7fKjk52f5OTEy06977ExIS7Db3+8fFxZnMzExTVlZmysvLTU5Ojhk3bpzd31R6ero9x+FwmPnz55uCggJTWVlpjh49alJSUjz3cN83KSnJZGdnm5qaGrutLe/55JNPGpfLZUJCQux67969TW1trenVq1eH+zsgCIIgCGklaDkHAKAddP38QHkotr/U1pZLddU1CXF2tuvqfNF+n98vKipKkpKSZNGiRVJZWdls//Xr1+/42trqHhoaKsOHD5eKigrp27evlJeXS3FxsUyaNEm2bt0qvXr1khs3btjSc7VgwQKZPHmyTJ8+XfLy8uy5mzZtkkuXLsnevXs9116+fLlt7S4oKJCrV6+26T2ff/55OXDggNTX19ttTz75pD1/4sSJMnPmTHE4HLJr1y75wQ9+0KZrAgAQCCTnAAC0Qyn7A5/rYRNzdym7e/lAdA+5+NFRn5e49+zZU4KCguTUqVPia3FxcbJlyxbJycmx64WFhZ59ZWVldnnx4kXPBwBN5LW/96hRo+TgwYOec4YOHSrTpk1rlJwvWbLEJtKfRpN4TbzDw8Pl/ffft4m4W/fu3W1J/zPPPCNTpkyx5fGrVq2ype/f+MY3fPgvAQCA7zAgHAAAfqZ9zIODQ6W+rrrRdl0PDgm1ybuvaWuxv+gAc6mpqbJv3z5ZunSp9O//cQXArT4UaBK9c+dOuXnzpic0ce7Ro0ejY7Ozs9v0DK+88oo8+uijMnr0aNsvXQeFc9OPEp07d7bX12fcs2eP/Ou//qsdBE9b9AEA6IhoOQcAwM+0VdzlqrWl7N4t5Lruqq+1g8P5mpaONzQ0SO/evW/rPD2naXKvg8h527Bhg+zYsUMmTJhgB13TknUdGf3VV19t8ZoRER9/fNDjz58/32hfTU1No3Utk2+LK1eu2ND3zM3NtaOxDxo0yLbM60BxdXV1dp+bHuNu9T99+nSb7gEAQHui5RwAAD/T5PvalXwJ/WSkdocj2C51/VpZvl9Gbde+1ZpAz5gxQ8LCwprtb22qM+0DrrynJhswYECz4zQZXrdunaSkpMjKlStl6tSpdnttba1daim528mTJ+10ZpoY65Ru3qHXuVvaUq46depklzpCvX5Q0PJ2N3eLuU7BBgBAR0RyDgBAO7hw7pBc+uiEOCRIOt/3gF3qum73F03MNUk+dOiQHahNy8u1JX3WrFm2n3ZLzpw5Y+dC13J1PX78+PG2Vdyb9t/WFvOHH37YlpaPGDHC0zKtya+2vmsf8AcffNCWs+tgcTrdmZ6npeaaNOt52mdc12/HwIED7XslJCTYZF/v/atf/co+t/udtM/64cOH5ec//7n9sPDVr37Vfkj4wx/+0Kg1HQCAjibgQ8YTBEEQREcOX06h5QwNN2ERMXbZHs8eGxtrVq9ebQoLC011dbWdWm3btm2NpinznkpNY/DgwebYsWN2yrM9e/bYKc+8p1JLS0szeXl5pqqqypSWlpqNGzea6Ohoz/mpqammpKTETm/mnkpNY/bs2SY3N9dOk6bnZWVlmWHDhrU6hVtL0a9fP/Puu++ay5cv2/vr1Gxr16413bp1a3Rc165dzebNm82NGzfMhQsXzM9//nMTFRXVYf4OCIIgCEKahOOTHwAAoBU68veyZctk8eLFlEXfw/g7AAD4E2XtAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAA9zhjjCQnJwf6MQAAuKeRnAMA8BkWExMjaWlpkp+fL9XV1VJUVCSZmZkycuRIv9wvMTHRJvuRkZHiL2+99ZacPXtWqqqqpKSkRDIyMqRr166e/T/60Y/sMzSN8vJyvz0TAAB3i+QcAIB25AyNkLCIGHGGhvv9XvHx8XL48GGbiM+bN0/69+8vSUlJsnv3blmzZo10dMHBwS1u1+d/9tln5ZFHHpGUlBTp0aOHbN682bN/xYoVEhsb2yg+/PBD+c1vftOOTw8AwO0zBEEQBEG0HvHx8SYjI8Mu7/QaQcFO8/n4oebLX/226T9wql3qelCQ02/PvX37dlNcXGzCwsKa7YuMjPT8VsnJyfZ3YmKiXffen5CQYLe53z8uLs5kZmaasrIyU15ebnJycsy4cePs/qbS09PtOQ6Hw8yfP98UFBSYyspKc/ToUZOSkuK5h/u+SUlJJjs729TU1NhtbXnPJ5980rhcLhMSEtLi/q985Sv22kOHDg343wFBEARBSCsRcgfJPAAAuE1dv/C4PNi1v9TVlEtN5TUJcXa26+r82X0+v19UVJRtJV+0aJFUVlY223/9+vU7vra2uoeGhsrw4cOloqJC+vbta0vGi4uLZdKkSbJ161bp1auX3Lhxw5aeqwULFsjkyZNl+vTpkpeXZ8/dtGmTXLp0Sfbu3eu59vLly2Xu3LlSUFAgV69ebdN7Pv/883LgwAGpr69v8Zjvfve78te//lX27fP9vzMAAL5Ccg4AQDuUsj/wuR42Ma+rrbDb3MvIz3WXixeOeNZ9pWfPnhIUFCSnTp0SX4uLi5MtW7ZITk6OXS8sLPTsKysrs8uLFy96PgBoIr9w4UIZNWqUHDx40HPO0KFDZdq0aY2S8yVLlsiuXbs+9Rk0iZ85c6aEh4fL+++/LxMnTmzxuE6dOtnkXY8HAKAjo885AAB+pv3Lg0JCpb6uutF2XQ8O7mSTd19zOBziLzrAXGpqqm2JXrp0qe3L/mkfCjSJ3rlzp9y8edMTU6ZMsf3FvWVnZ7fpGV555RV59NFHZfTo0eJyueygcC35p3/6J7n//vtl48aNt/GGAAC0P1rOAQDwM20Vb6ivtaXs3i3kuu5y1Uhdre9HEdfS8YaGBundu/dtnafnNE3unU5no2M2bNggO3bskAkTJsiYMWNsyfqcOXPk1VdfbfGaEREff3zQ48+fP99oX01NTaN1LZNviytXrtjQ98zNzZVz587JoEGDPC3z3iXtb7/9tm3JBwCgI6PlHAAAP9Pk+9qVfHF2irCt6A5HsF3q+vUrBT4vaVfaX1sT6BkzZkhYWFiz/a1NdaZ9wJX31GQDBgxodpwmw+vWrbOjpa9cuVKmTp1qt9fW1jYbaf3kyZN2Gjcth9cp3bxDr3O3tHzfXcLu7eGHH5YRI0bYjwkAAHR0JOcAALSDC8UfyOULJ7RJWjrd94Bd6rpu9xdNzDVJPnTokB2oTcvLtSV91qxZtp92S86cOWPnQtdydT1+/PjxtlXc26pVq2yLuSa/WlquCbC2Xiudf1xb37UP+IMPPmjL2XWwOJ3eTM/TUvbu3bvb87TPuK7fjoEDB9r3SkhIsMm+3vtXv/qVfe6m7/Sd73xHLly4IFlZWbf9bwcAQCAEfMh4giAIgujI4csptJyh4SYsIsYu2+PZY2NjzerVq01hYaGprq62U6tt27at0TRl3lOpaQwePNgcO3bMTnm2Z88eO+WZ91RqaWlpJi8vz1RVVZnS0lKzceNGEx0d7Tk/NTXVlJSU2OnN3FOpacyePdvk5ubaadL0vKysLDNs2LBWp3BrKfr162feffddc/nyZXt/nZpt7dq1plu3bo2O06nbioqKzI9//OMO+XdAEARBENIkHJ/8AAAArYiPj5dly5bJ4sWLbcsw7k38HQAA/ImydgAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAO5xxhhJTk4O9GMAAHBPIzkHAOAzLCYmRtLS0iQ/P1+qq6ulqKhIMjMzZeTIkX65X2Jiok32IyMjxV/eeustOXv2rFRVVUlJSYlkZGRI165dGx0zZswYef/99+XGjRty8eJF2bx5s8THx/vtmQAAuFsk5wAAfEZpMnr48GGbiM+bN0/69+8vSUlJsnv3blmzZo10dMHBwS1u1+d/9tln5ZFHHpGUlBTp0aOHTb7dHn74YZvA//GPf5QBAwbI2LFj5cEHH5StW7e249MDAHD7DEEQBEEQrUd8fLzJyMiwy7u9ljM0woRFxBhnaLjfn3v79u2muLjYhIWFNdsXGRnp+a2Sk5Pt78TERLvuvT8hIcFuc79/XFycyczMNGVlZaa8vNzk5OSYcePG2f1Npaen23McDoeZP3++KSgoMJWVlebo0aMmJSXFcw/3fZOSkkx2drapqamx29rynk8++aRxuVwmJCTErut1a2tr7T3dx0ycOLHRMYH+OyAIgiAIaRIhd5DMAwCA2xQU7JSuX3xcHvhcDwkKDpUGV61cu5IvF4o+kIaGOp/fLyoqyraSL1q0SCorK5vtv379+h1fW1vdQ0NDZfjw4VJRUSF9+/aV8vJyKS4ulkmTJtkW6l69etmSci09VwsWLJDJkyfL9OnTJS8vz567adMmuXTpkuzdu9dz7eXLl8vcuXOloKBArl692qb3fP755+XAgQNSX19vt2m1QENDg7zwwgvyxhtvSEREhPzzP/+z7Nq1y3MMAAAdDck5AADtQBPzB2P7S11tudRUXZMQZ2e7rs7/bZ/P79ezZ08JCgqSU6dO+fzacXFxsmXLFsnJybHrhYWFnn1lZWV2qf283R8ANJFfuHChjBo1Sg4ePOg5Z+jQoTJt2rRGyfmSJUtsEv1pNImfOXOmhIeH277lEydO9Oz729/+Zvuc//rXv5Z169ZJSEiITd7Hjx/vw38FAAB8iz7nAAD4mTM0wraYa2JeV1shxrjsUtcjP9ddnKHhPr+nw+EQf9EB5lJTU2Xfvn2ydOlS25f90z4UaBK9c+dOuXnzpiemTJli+4t7y87ObtMzvPLKK/Loo4/K6NGjxeVy2UHhvAfBW79+vWzcuFEee+wx20pfW1vbqF86AAAdDS3nAAD4mSbfWsquLebe6uuqpdN9D9jkXZN1X9LScS3t7t27922dp+c0Te6dTmejYzZs2CA7duyQCRMm2BZqLVmfM2eOvPrqqy1eU8vKlR5//vz5RvtqamoarWuZfFtcuXLFhr5nbm6unDt3TgYNGmRb5mfMmGFb7X/4wx96jteSej3m8ccflw8++KBN9wAAoD3Rcg4AgJ9p4q19zLWU3Zuuu1w1tgXd17S/tibQmqiGhYU129/aVGfaB1x5T02mI543pYmulozraOkrV66UqVOn2u3aQt10pPWTJ0/aady0HF6ndPMOvc7d0vJ91alTJ7vU93V/ZHDT1nXvYwEA6Gj4PxQAAH6mybcO/qYt5NqK7nAE26WuX79S4PNWczdNzDVJPnTokB2oTcvLtSV91qxZtp92S86cOWPnQtdydT1e+2lrq7i3VatW2RZznbJMS8tHjBhhW6+Vzj+uibH2Adfpy7ScXQeLW7FihT1PS9m7d+9uz9M+47p+OwYOHGjfKyEhwSb7eu9f/epX9rnd77R9+3Zbzr548WL7Dnqv9PR02xf9yJEjd/zvCQCAvwV8yHiCIAiC6Mjhiym0goKc5vMPDzV9vzbF9B841S51Xbf789ljY2PN6tWrTWFhoamurrZTq23btq3RNGXeU6lpDB482Bw7dsxOebZnzx47NZn3VGppaWkmLy/PVFVVmdLSUrNx40YTHR3tOT81NdWUlJTYqcvcU6lpzJ492+Tm5tpp0vS8rKwsM2zYsFancGsp+vXrZ959911z+fJle3+dmm3t2rWmW7dujY577rnnzOHDh83NmzftvfSdH3nkkYD/HRAEQRCEtBKOT34AAIBWxMfHy7Jly2xLrLYM3w13i7l7cDjcm38HAAA0xYBwAAC0o49HaScpBwAAjdHnHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAgHucMUaSk5MD/RgAANzTSM4BAPgMi4mJkbS0NMnPz5fq6mopKiqSzMxMGTlypF/ul5iYaJP9yMhI8Ze33npLzp49K1VVVVJSUiIZGRnStWvXRsc888wzcuTIEamoqJC//e1vMnfuXL89DwAAvkByDgDAZ1R8fLwcPnzYJuLz5s2T/v37S1JSkuzevVvWrFkjHV1wcHCL2/X5n332WXnkkUckJSVFevToIZs3b/bs13f8xS9+Ia+//rr069dP/u3f/k2+//3vy4wZM9rx6QEAuH2GIAiCIIjWIz4+3mRkZNjl3V7LGRphwu6PMc7QcL8/9/bt201xcbEJCwtrti8yMtLzWyUnJ9vfiYmJdt17f0JCgt3mfv+4uDiTmZlpysrKTHl5ucnJyTHjxo2z+5tKT0+35zgcDjN//nxTUFBgKisrzdGjR01KSornHu77JiUlmezsbFNTU2O3teU9n3zySeNyuUxISIhd/8UvfmF+/etfNzpm5syZpqioqMP8HRAEQRCENImQO0jmAQDAbQoKdkps3OPywIM9JCi4kzS4auTa5Xz5qOgDaXDV+fx+UVFRtgV50aJFUllZ2Wz/9evX7/ja2uoeGhoqw4cPt2Xjffv2lfLycikuLpZJkybJ1q1bpVevXnLjxg1beq4WLFggkydPlunTp0teXp49d9OmTXLp0iXZu3ev59rLly+3JegFBQVy9erVNr3n888/LwcOHJD6+nq7rVOnTs3eWZ/ji1/8oq0m0JJ4AAA6GpJzAADagSbmD3b7itTVlEtN9VUJCbnPrquSwn0+v1/Pnj0lKChITp065fNrx8XFyZYtWyQnJ8euFxYWevaVlZXZ5cWLFz0fADSRX7hwoYwaNUoOHjzoOWfo0KEybdq0Rsn5kiVLZNeuXZ/6DJrEz5w5U8LDw+X999+XiRMnevbt2LFDVq1aJW+88YYtgdd/izlz5th92jed5BwA0BHR5xwAAD9zhkbYFnNNzOtqy8U0uOxS13W7MzTc5/d0OBziLzrAXGpqquzbt0+WLl1q+7LfiibHmkTv3LlTbt686YkpU6bY/uLesrOz2/QMr7zyijz66KMyevRocblcdlA4t/Xr18urr74qb7/9ttTW1toPAv/3//5fu6+hoeGO3hkAAH8jOQcAwM+cncJtKXt9/ccl3m66HhQcKs5OET6/p5aOayLau3fv2zrPnbx6J/dOp7PRMRs2bJDu3bvLm2++aRNzTai1Fbs1EREfv9+ECRNkwIABntBy+KeffrrRsVom3xZXrlyx76it7P/jf/wPe+1BgwZ59s+fP9/eV8vYY2Nj5dChQ3a7lssDANARkZwDAOBndTUVto+5lrJ70/UGV61tQfc17a+t5d06QnlYWFiz/a1NdaZ9wJX31GSaSDd17tw5WbdunR0tfeXKlTJ16lS7XVuqm460fvLkSTuNm5bD65Ru3qHXuVtavu/ua970Q4NOtVZXVyff/OY3bb/0y5cv3/X9AADwB/qcAwDgZ1rCroO/ufuYa4u5JubaYn655LjU1battfh2aWK+f/9+22qsfbmPHz8uISEhthT8xRdftC3XTZ05c8bOha7l6jqYnA7s5u6v7ab9ubOysuT06dN2QLYRI0ZIbm6u3af9uTUp1j7g77zzjh2ITQeLW7FihT1PE2kth9ePA0OGDLGDxnmXpH+agQMHymOPPWavoR8gtCx+2bJl9rm177n63Oc+Z1vk33vvPencubO88MILdt5znYMdAICOLOBDxhMEQRBERw5fTKEVFOw03b401PR97Num36Cpdqnrut2fzx4bG2tWr15tCgsLTXV1tZ1abdu2bY2mKfOeSk1j8ODB5tixY3bKsz179tgpz7ynUktLSzN5eXmmqqrKlJaWmo0bN5ro6GjP+ampqaakpMROb+aeSk1j9uzZJjc3106TpudlZWWZYcOGtTqFW0vRr18/8+6775rLly/b++vUbGvXrjXdunXzHPO5z33OHDhwwNy8edNO9bZz504zcODADvF3QBAEQRDSSjg++QEAAFqh/Za1dXbx4sV3PdK3Dv6mLeYfDw7nnxZzdPy/AwAAmqKsHQCAdqQJOUk5AABoigHhAAAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuMcZYyQ5OTnQjwEAwD2N5BwAgM+wmJgYSUtLk/z8fKmurpaioiLJzMyUkSNH+uV+iYmJNtmPjIwUfwsNDZUjR47Y+yUkJDTa179/f9m7d69UVVXZd543b57fnwcAgLsRcldnAwCADis+Pl72798v165ds8npiRMnxOl0ytixY2XNmjXSp08f6ciCg4PF5XK1uv/ll1+WkpISGTBgQKPt999/v/zhD3+QXbt2yfTp022i/vOf/9z+O6xfv74dnhwAgDtjCIIgCIJoPeLj401GRoZd3u21nJ0iTNj9McYZGu73596+fbspLi42YWFhzfZFRkZ6fqvk5GT7OzEx0a57709ISLDb3O8fFxdnMjMzTVlZmSkvLzc5OTlm3Lhxdn9T6enp9hyHw2Hmz59vCgoKTGVlpTl69KhJSUnx3MN936SkJJOdnW1qamrsttbeTY87efKk6dOnjz1Pn9G9b/r06ebKlSvG6XR6tr300ksmNze3w/wdEARBEIQ0CVrOAQBoB0HBTomNe1weeLCHBIV0kob6Grl2OV8+KvpAGlx1Pr9fVFSUJCUlyaJFi6SysrLZ/uvXr9/xtbXVXUvKhw8fLhUVFdK3b18pLy+X4uJimTRpkmzdulV69eolN27csGXlasGCBTJ58mTbkp2Xl2fP3bRpk1y6dMmWn7stX75c5s6dKwUFBXL16tUW79+lSxfbAv7UU0+1+G5PPPGEvWZd3f/7d92xY4fMnz9fHnjgAduCDgBAR0NyDgBAO9DE/MFuX5G6mnKpqboqISH32XVVUrjP5/fr2bOnBAUFyalTp3x+7bi4ONmyZYvk5OTY9cLCQs++srIyu7x48aLnA4Am8gsXLpRRo0bJwYMHPecMHTpUpk2b1ig5X7JkiS1Hv5U33nhDXn/9dTl8+LAt3W8qNja20TOp0tJSzz6ScwBAR0RyDgCAnzk7RdgWc03M62rL7Tb3UrdfOn9E6morfHpPh8Mh/qIDzL322msyZswYm0hroq792W/1oSA8PFx27tzZ4oBu3rKzs29571mzZtk+5S+99NJdvgUAAB0Lo7UDAOBnztBwW8peX/9xibebrgeFhNrk3de0dLyhoUF69+59W+fpOU2Tex1EztuGDRuke/fu8uabb9rB1jShnjlzZqvXjIj4+P0mTJhgB29zh5bDP/30042O1TL5W9FR5rVsvaamxpatnzlzxm7XZ9AWdfXRRx/ZUeq9udd1HwAAHRHJOQAAfqat4trHXEvZvel6Q32tbVH3Ne2vrf2sZ8yYIWFhYc32tzbVmfYBV127dvVsazoaujp37pysW7dOUlJSZOXKlTJ16lS7vba21jPSutvJkyftNG5aDq9TunmHXud2zJ49206b5k7wx48fb7c/99xztn+9ev/9922f9pCQ/1cgOHr0aFviT0k7AKCjIjkHAMDPNPnWwd+0hdwZGiGOoGC71HXd7uuSdjdNzDVJPnTokB2oTcvLtSVdS8M1gW2JtkTrvOBLly61x2vyO2fOnEbHrFq1ypa0P/zww/Loo4/KiBEjJDc31+47e/asbX2fOHGiPPjgg7acXQeLW7FihT1vypQpttVdz9PWdl2/HTro3IcffuiJ06dP2+2a6J8/f97+/uUvf2k/EmgLv7bOP/vss/I//+f/lP/4j/+4w39JAADaR8CHjCcIgiCIjhy+mEIrKNhpun1pqOn72LdNvyem2qWu63Z/PntsbKxZvXq1KSwsNNXV1XZqtW3btjWapsx7KjWNwYMHm2PHjtkpz/bs2WOnPPOeSi0tLc3k5eWZqqoqU1paajZu3Giio6M956emppqSkhLjcrk8U6lpzJ49205nptOk6XlZWVlm2LBhrU7h1tb/Nk2nUtPo37+/2bt3r31Gfecf/OAHHeLvgCAIgiCklXB88gMAALRCRwRftmyZLF682LYM323/c20x/3hwOP+0mKPj/x0AANAUo7UDANCONCEnKQcAAE3R5xwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAIB7nDFGkpOTA/0YAADc00jOAQD4DIuJiZG0tDTJz8+X6upqKSoqkszMTBk5cqRf7peYmGiT/cjISPG30NBQOXLkiL1fQkKCZ3unTp0kPT1djh8/LnV1dfLb3/7W788CAMDdIjkHAOAzKj4+Xg4fPmwT8Xnz5kn//v0lKSlJdu/eLWvWrJGOLjg4+Jb7X375ZSkpKWnxvKqqKvtRYteuXX58QgAAfIfkHACAduTsFCFh98eIMzTc7/dau3atbVUeOHCgbN26VfLy8uTkyZOyatUqGTRoUJtbvrVVWrdpsq/i4uJs63tZWZmUl5dLTk6OjBs3zu5/77337DHXrl2z52gLtnI4HDJ//nwpKCiQyspKOXr0qKSkpDS7r348yM7OlpqaGhk6dGir76bHjRkzRubOndtsn17/3/7t3+RnP/uZfPTRR3fxLwgAQPsJacd7AQBwzwoKdkps/CCJfKiHBIeEiqu+Vq5fypePzh6UBledz+8XFRVlE9hFixbZZLWp69ev3/G1tdVdS8qHDx8uFRUV0rdvX5ukFxcXy6RJk+yHgF69esmNGzdsC7ZasGCBTJ48WaZPn24/Eui5mzZtkkuXLsnevXs9116+fLlNuDWJv3r1aov379Kli6xfv16eeuqpFt8NAIC/RyTnAAC0A03MP/f5/lJXUyHVldckxHmfXVclBX/y+f169uwpQUFBcurUKZ9fW1vOt2zZYlvMVWFhoWeftqarixcvej4AaCK/cOFCGTVqlBw8eNBzjraMT5s2rVFyvmTJkk8tRX/jjTfk9ddftyX77tZ8AAD+3pGcAwDQDqXs2mKuiXldTbnd5l7q9kvn/iJ1tRU+vaeWkfuL9uV+7bXXbFm5JtKaqJ84ceKWHwrCw8Nl586dLQ7o5k1L2m9l1qxZcv/998tLL710l28BAEDHQp9zAAD8TPuXayl7fd3HJd5uuq7bNXn3NS0db2hokN69e9/WeXpO0+Te6XQ2OmbDhg3SvXt3efPNN+0gc5pQz5w5s9VrRkR8/H4TJkyQAQMGeELL4Z9++ulGx2qZ/K3o4HZPPPGE7ZOuI7GfOXPGbtdn0BZ1AAD+XpGcAwDgZ9oqrn3MtZTdm67rdncrui9pf+0dO3bIjBkzJCwsrNn+1qY60z7gqmvXrp5tmkg3de7cOVm3bp0d1G3lypUydepUu722trbZSOs6CJ1O46bl8Dqlm3fodW7H7Nmz7QB17gR//Pjxdvtzzz1n+9cDAPD3irJ2AAD8TJNvHfzN3cdcW8w1MXd2Cpcr50/4vKTdTRPz/fv3y6FDh2xfbp33OyQkREaPHi0vvviibbluSluidS70pUuX2mRXB3abM2dOo2N0tPesrCw5ffq0HXhuxIgRkpuba/edPXvWtr5PnDhR3nnnHTsgnA4Wt2LFCnue9oPft2+f/TgwZMgQO2hcRkZGm99JB53zptdWmuifP3/es71Pnz62bD46OtqWwbvnQT927Nht/isCANB+DEEQBEEQrUd8fLzJyMiwyzu9RlCw03TrPsz0efxfTL8h37NLXdft/nz22NhYs3r1alNYWGiqq6tNcXGx2bZtm0lMTPQco5KTkz3rgwcPNseOHTOVlZVmz549JiUlxR7jfv+0tDSTl5dnqqqqTGlpqdm4caOJjo72nJ+ammpKSkqMy+Uy6enpnu2zZ882ubm5pqamxp6XlZVlhg0bZvfp86jIyMjb/m+jEhISGm3X921JoP8OCIIgCEJaCccnPwAAQCt0RPBly5bJ4sWLbcvw3fY/1z7m2prurxZzdPy/AwAAmqKsHQCAdqQJOUk5AABoigHhAAAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuMcZYyQ5OTnQjwEAwD2N5BwAgM+wmJgYSUtLk/z8fKmurpaioiLJzMyUkSNH+uV+iYmJNtmPjIwUfwsNDZUjR47Y+yUkJDR6hm3btklJSYmUl5fbY771rW/5/XkAALgbIXd1NgAA6LDi4+Nl//79cu3aNZk3b56cOHFCnE6njB07VtasWSN9+vSRjiw4OFhcLler+19++WWbgA8YMKDR9sGDB8vx48flf//v/y2lpaUyceJEycjIkOvXr8v27dvb4ckBALgzhiAIgiCI1iM+Pt5kZGTY5d1ey9kpwoT9Q4xxdgr3+3Nv377dFBcXm7CwsGb7IiMjPb9VcnKy/Z2YmGjXvfcnJCTYbe73j4uLM5mZmaasrMyUl5ebnJwcM27cOLu/qfT0dHuOw+Ew8+fPNwUFBaaystIcPXrUpKSkeO7hvm9SUpLJzs42NTU1dltr76bHnTx50vTp08eep894q3+Lt99+22zYsKHD/B0QBEEQhDQJWs4BAGgHQcFOiXl4kEQ+1EOCQ0LFVV8r1y/lS+nfDkqDq87n94uKipKkpCRZtGiRVFZWNtuvrch3SlvdtaR8+PDhUlFRIX379rXl48XFxTJp0iTZunWr9OrVS27cuCFVVVX2nAULFsjkyZNl+vTpkpeXZ8/dtGmTXLp0Sfbu3eu59vLly2Xu3LlSUFAgV69ebfH+Xbp0kfXr18tTTz3V4ru1RMvsc3Nz7/idAQDwN5JzAADagSbmD36+v9TVVkhN1TUJcd5n19WF/D/5/H49e/aUoKAgOXXqlM+vHRcXJ1u2bJGcnBy7XlhY6NlXVlZmlxcvXvR8ANBEfuHChTJq1Cg5ePCg55yhQ4fKtGnTGiXnS5YskV27dt3y/m+88Ya8/vrrcvjwYVu6/2meeeYZeeyxx+y9AADoqEjOAQDwM2enCNtirol5XU253eZe6vbL5/4idTUVPr2nw+EQf9EB5l577TUZM2aMTaQ1Udf+7Lf6UBAeHi47d+5scUA3b9nZ2be896xZs+T++++Xl156qU3P+o//+I+Snp4uU6dOlZMnT7bpHAAAAoHR2gEA8DNnp3Bbyl5f93GJt5uu63ZN3n1NS8cbGhqkd+/et3WentM0uddB5Lxt2LBBunfvLm+++ab079/fJtQzZ85s9ZoRER+/34QJE+zgbe7Qcvinn3660bFaJn8rOsr8E088ITU1NVJXVydnzpyx2/UZtEXdm5bO/+53v5Pvf//79lkBAOjISM4BAPAzbRXXPuZayu5N13W7uxXdl7S/9o4dO2TGjBkSFhbWbH9rU51pH3DVtWtXz7amo6Grc+fOybp16yQlJUVWrlxpW6ZVbW2tZ6R1N22x1mnctBxep3TzDr3O7Zg9e7adNs2d4I8fP95uf+6552z/eu/p1HRk9h/+8Ie2fzoAAB0dZe0AAPiZJt86+Ju7j7m2mGti7gwNl8vnT/i8pN1NE3OdSu3QoUO2L7dOLxYSEiKjR4+WF1980bZcN6Ut0ToX+tKlS22yqwO7zZkzp9Exq1atkqysLDl9+rQdeG7EiBGewdbOnj1rW991+rJ33nnHDging8WtWLHCnqf94Pft22c/DgwZMsQOGqfTnLWVDjrnTa+tNNE/f/68p5T97bfflp/+9Ke25F7nend/OGhtkDkAADqCgA8ZTxAEQRAdOXwxhVZQsNN07THM9B70L+bLQ79nl7qu2/357LGxsWb16tWmsLDQVFdX26nVtm3b1miaMu+p1DQGDx5sjh07Zqc827Nnj53yzHsqtbS0NJOXl2eqqqpMaWmp2bhxo4mOjvacn5qaakpKSozL5fJMpaYxe/Zsk5uba6dJ0/OysrLMsGHDWp3Cra3/bZpOpab3bMnu3bsD/ndAEARBENJKOD75AQAAWqEjgi9btkwWL15sW4bvtv+59jHX1nR/tZij4/8dAADQFGXtAAC0I03IScoBAEBTDAgHAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAPc4YI8nJyYF+DAAA7mkk5wAAfIbFxMRIWlqa5OfnS3V1tRQVFUlmZqaMHDnSL/dLTEy0yX5kZKT4W2hoqBw5csTeLyEhwbO9V69e8sc//lE++ugjqaqqsu++bNkyCQkJ8fszAQBwp/i/FAAAn1Hx8fGyf/9+uXbtmsybN09OnDghTqdTxo4dK2vWrJE+ffpIRxYcHCwul6vV/S+//LKUlJTIgAEDGm2vq6uTjIwM+ctf/mLfXRP39evXS1BQkCxatKgdnhwAgDtjCIIgCIJoPeLj401GRoZd3u21nJ0iTNg/xBhnp3C/P/f27dtNcXGxCQsLa7YvMjLS81slJyfb34mJiXbde39CQoLd5n7/uLg4k5mZacrKykx5ebnJyckx48aNs/ubSk9Pt+c4HA4zf/58U1BQYCorK83Ro0dNSkqK5x7u+yYlJZns7GxTU1Njt7X2bnrcyZMnTZ8+fex5+oy3+rdYuXKl2bt3b4f5OyAIgiAIaRK0nAMA0A6Cgp0S8/AgiezSQ4JCQqWhvlauX8yX0r8dlAZXnc/vFxUVJUlJSbaluLKystn+69ev3/G1tdVdS8qHDx8uFRUV0rdvXykvL5fi4mKZNGmSbN261ZaW37hxw5aVqwULFsjkyZNl+vTpkpeXZ8/dtGmTXLp0Sfbu3eu59vLly2Xu3LlSUFAgV69ebfH+Xbp0sS3hTz31VIvv1lSPHj3sv4U+FwAAHRXJOQAA7UAT8899ob/U1VRITeU1CXHeZ9fVhfw/+fx+PXv2tGXcp06d8vm14+LiZMuWLZKTk2PXCwsLPfvKysrs8uLFi54PAJrIL1y4UEaNGiUHDx70nDN06FCZNm1ao+R8yZIlsmvXrlve/4033pDXX39dDh8+bEv3W6Ml/V/96lelc+fOsm7dOnttAAA6KgaEAwDAz5ydImyLuSbmdTXlYhpcdqnr/9Clhzg7hfv8ng6HQ/xFB5hLTU2Vffv2ydKlS6V//48/MtzqQ0F4eLjs3LlTbt686YkpU6bYVm1v2dnZt7zWrFmz5P7775eXXnrpU5/zueees8n5N7/5TZkwYYJtkQcAoKOi5RwAAD/T5FtL2bXF3Ft9XZV0CnvAJu+aqPuSlo43NDRI7969b+s8Padpcq+DyHnbsGGD7Nixwya8Y8aMsSXrc+bMkVdffbXFa0ZERNilHn/+/PlG+2pqahqta5n8rego80888USz8zSp/8UvfiH/8i//4tl27tw5u8zNzbWDy/3nf/6nrFy50vOOAAB0JLScAwDgZ5p4ax9zLWX3puuu+lrbiu5r2l9bE+gZM2ZIWFhYs/2tTXWmfcBV165dPduajobuTny1VDwlJcUmvFOnTrXba2tr7VKTYbeTJ0/aady0HF6nNfMOdwLdVrNnz7ajr+szaYwfP97TSn6rkdi1xF8/MugSAICOiJZzAAD8TJNvHfzN3cdcW8w1MdcW9SvnTvi81dxNE3Ptd33o0CHb3/r48eN2ru/Ro0fLiy++aAdya+rMmTN2LnQtV9dkVwd201Zxb6tWrZKsrCw5ffq0HXhuxIgRtnVanT171rZMT5w4Ud555x07IJwOFrdixQp7nibHWg6vHweGDBliB43Tac/aSged86bXVprou1vlv/Wtb9np1HTqOG1h//rXv27L4P/rv/5L6uvr7+jfEgAAfyM5BwCgHeio7Er7mGspu7aYa2Lu3u4POuia9rnWJFtbt7U1XFvGdSA1Tc5bosmr9tF+7bXXbDL/5z//2fYv34U2jfUAAQAASURBVLx5s+cYbRXXEdu/8IUv2OT697//vXz/+9+3+3Te8R/96Ed21PX09HSbeL/wwguyePFie28tge/evbudf1znIf/JT37i8/fWd/jhD39oPyxoeb5+MNCSe/04AABAR+X4ZE41AADQCh0RfNmyZTbB1ETvbmhr+cd9zD8eEA735t8BAABN0XIOAEA7+njEdpJyAADQGKOiAAAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAcI8zxkhycnKgHwMAgHsayTkAAJ9hMTExkpaWJvn5+VJdXS1FRUWSmZkpI0eO9Mv9EhMTbbIfGRkp/hYaGipHjhyx90tISGjxmB49esiNGzfk6tWrfn8eAADuBsk5AADtyNkpQu77h1gJ6RTh93vFx8fL4cOHbSI+b9486d+/vyQlJcnu3btlzZo10tEFBwffcv/LL78sJSUlre4PCQmRX/3qV/KnP/3JD08HAIBvkZwDANAOgoJDJbbnMOn+tWfkSwOSpcfXnrHrQcFOv91z7dq1tlV54MCBsnXrVsnLy5OTJ0/KqlWrZNCgQW1u+dZWad2myb6Ki4uzre9lZWVSXl4uOTk5Mm7cOLv/vffes8dcu3bNnpOenm7XHQ6HzJ8/XwoKCqSyslKOHj0qKSkpze6rHw+ys7OlpqZGhg4d2uq76XFjxoyRuXPntnrMj3/8Yzl16pT8+te/voN/PQAA2ldIO98PAIB7UpcvPS6f++JXpK66XGoqr0qw8z67rj464/uW3aioKJvALlq0yCbDTV2/fv2Or62t7lpSPnz4cKmoqJC+ffvaJL24uFgmTZpkPwT06tXLlpNXVVXZcxYsWCCTJ0+W6dOn248Eeu6mTZvk0qVLsnfvXs+1ly9fbhNuTeJbK0Xv0qWLrF+/Xp566qkW302NGDFCnnnmGRkwYIB9JgAAOjqScwAA2qGUPbJLT5uY19eU223upW6/XHzEs+4rPXv2lKCgINty7Gvacr5lyxbbYq4KCws9+7Q1XV28eNHzAUAT+YULF8qoUaPk4MGDnnO0ZXzatGmNkvMlS5bIrl27bnn/N954Q15//XVbsu9uzfcWHR1tj9GPATdv3vTRWwMA4F8k5wAA+Jn2Lw8OCbUt5t5cdVXSKSzKJu++Ts61jNxfdIC51157zZaVayKtifqJEydu+aEgPDxcdu7c2eKAbt60pP1WZs2aJffff7+89NJLrR6jreq//OUv6WsOAPi7Qp9zAAD8TBNvV32tLWX3puu6vc7HibnS0vGGhgbp3bv3bZ2n5zRN7p3Oxv3iN2zYIN27d5c333zTDjKnCfXMmTNbvWZExMeD302YMMGWmbtDy+GffvrpRsdqmfyt6OB2TzzxhO2TXldXJ2fOnLHb9Rm0tdx9jJbG634Nfd4HHnjA/n7hhRdu698DAID2QnIOAICfafJ9/eIZcXaOsK3ojqBgu9R13e7rVnOl/bV37NghM2bMkLCwsGb7W5vqTPuAq65du3q2aSLd1Llz52TdunV2ULeVK1fK1KlT7fba2tpmI63rIHQ6jZuWw+uUbt6h17kds2fPtgPUuRP88ePH2+3PPfec7V+vNHn3/gigpfLa/11///a3v72t+wEA0F4oawcAoB1cLDzo6WOupezaYn6l+Lhnuz9oYr5//345dOiQTVCPHz9upxcbPXq0vPjii7bluiltida50JcuXWqTXR3Ybc6cOY2O0dHes7Ky5PTp03bgOR18LTc31+47e/asbX2fOHGivPPOO3ZAOB0sbsWKFfY87Qe/b98++3FgyJAhNmnOyMho8zvpoHPe9NpKE/3z58/b30372X/961+3z/Thhx/exr8eAADti+QcAIB20OCqs6Oy6+Bv2sdcW9P90WLuTQdd++pXv2qTbG3d1tZwbRnXgdQ0OW9JfX29fPOb37R9yjWZ//Of/yypqamyefNmzzHaKq4jtn/hC1+wyfXvf/97+f73v2/36bzjP/rRj+yo6zqNmibeWkq+ePFie28dtV1L4nWqtb/85S/yk5/8xK//BgAA/L3QDmUm0A8BAEBHpiOCL1u2zCaY2jKMexN/BwAAf6LPOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AwD3OGCPJycmBfgwAAO5pJOcAAHyGxcTESFpamuTn50t1dbUUFRVJZmamjBw50i/3S0xMtMl+ZGSk+FtoaKgcOXLE3i8hIcGzPT4+3m5rGo8//rjfnwkAgDsVcsdnAgCA2+bsFCEhnSKkrqZc6mvK/XovTVL3798v165dk3nz5smJEyfE6XTK2LFjZc2aNdKnTx/pyIKDg8XlcrW6/+WXX5aSkhIZMGBAi/u/8Y1vyIcffuhZv3Llil+eEwAAX6DlHACAdhAUHCqx/22YdP/6M/KlR5Olx9efsetBwU6/3XPt2rW2xXjgwIGydetWycvLk5MnT8qqVatk0KBBbW751lZp3abJvoqLi7Ot72VlZVJeXi45OTkybtw4u/+9996zx+gHAT0nPT3drjscDpk/f74UFBRIZWWlHD16VFJSUprdNykpSbKzs6WmpkaGDh3a6rvpcWPGjJG5c+e2eowm46WlpZ6or6+/g39FAADaBy3nAAC0gy7dH5fPffErUlddLjWVVyXYeZ9dVx/l/cnn94uKirIJ7KJFi2wy3NT169fv+Nra6q4l5cOHD5eKigrp27evTdKLi4tl0qRJ9kNAr1695MaNG1JVVWXPWbBggUyePFmmT59uPxLouZs2bZJLly7J3r17Pddevny5Tbg1ib969WqL9+/SpYusX79ennrqqRbfzU0/IHTu3FlOnz5tW9l/97vf3fE7AwDgbyTnAAC0Qyl7ZJeeNjF3l7K7l7r9ctERn5e49+zZU4KCguTUqVPia9pyvmXLFttirgoLCz37tDVdXbx40fMBQBP5hQsXyqhRo+TgwYOec7RlfNq0aY2S8yVLlsiuXbtuef833nhDXn/9dTl8+LCnNd+bfij493//d1vS39DQYFvot23bZpN5EnQAQEdFcg4AgJ9pH/PgkFDbYu7NVVclncKibPLu6+Rcy8j9RQeYe+2112xZuSbSmqhrf/ZbfSgIDw+XnTt3tjigmzctab+VWbNmyf333y8vvfTSLcvZtXTf+5rdunWz/e5JzgEAHRV9zgEA8DNNvF31tbaU3Zuu63YdHM7XtHRcW4179+59W+fpOU2Tex1EztuGDRuke/fu8uabb0r//v1t8jtz5sxWrxkREWGXEyZMsIO3uUPL4Z9++ulGx2qZ/K3oKPNPPPGE7ZNeV1cnZ86csdv1GbRFvTUffPCB/UgAAEBHRXIOAICfafJ9/eIZcXb+eKR2R1CwXeq6bvfHqO3aX3vHjh0yY8YMCQsLa7a/tanOtA+46tq1q2dbS6Ohnzt3TtatW2dLxleuXClTp06122traz0jrbvpIHQ6jZuWw+uUbt6h17kds2fPtgPUuRP88ePH2+3PPfec7V/fGj32woULt3UvAADaE2XtAAC0g4sFBz19zLWUXVvMrxQf92z3B03Mtd/1oUOHbF/u48ePS0hIiIwePVpefPFF23LdlLZE61zoS5cutcmuDuw2Z86cRsdoyXhWVpYdaE0HnhsxYoTk5ubafWfPnrWt7xMnTpR33nnHDginfcBXrFhhz9N+8Pv27bMfB4YMGWIHjcvIyGjzO+mgc9702koT/fPnz9vfU6ZMsR8J3CXzOkjdd77zHfnud797B/+KAAC0H0MQBEEQROsRHx9vMjIy7PJurxXSKcLc9w+xdtkezx4bG2tWr15tCgsLTXV1tSkuLjbbtm0ziYmJnmNUcnKyZ33w4MHm2LFjprKy0uzZs8ekpKTYY9zvn5aWZvLy8kxVVZUpLS01GzduNNHR0Z7zU1NTTUlJiXG5XCY9Pd2zffbs2SY3N9fU1NTY87KyssywYcPsPn0eFRkZedv/bVRCQoJn25QpU8yHH35oysvLzbVr18zBgwftO3SkvwOCIAiCkCbh+OQHAABohY4IvmzZMlm8eLFtGca9ib8DAIA/0eccAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgHmeMkeTk5EA/BgAA9zSScwAAPsNiYmIkLS1N8vPzpbq6WoqKiiQzM1NGjhzpl/slJibaZD8yMlL8LTQ0VI4cOWLvl5CQ0Gz/nDlz5K9//at973PnzsnChQv9/kwAANypkDs+EwAA3DZn5wgJ6RQhddXlUl9T7td7xcfHy/79++XatWsyb948OXHihDidThk7dqysWbNG+vTpIx1ZcHCwuFyuVve//PLLUlJSIgMGDGi276c//amMGTNG5s6da987OjraBgAAHZkhCIIgCKL1iI+PNxkZGXZ5p9cICg41sY8MM72GvWD6jJhml7oeFOz023Nv377dFBcXm7CwsGb7IiMjPb9VcnKy/Z2YmGjXvfcnJCTYbe73j4uLM5mZmaasrMyUl5ebnJwcM27cOLu/qfT0dHuOw+Ew8+fPNwUFBaaystIcPXrUpKSkeO7hvm9SUpLJzs42NTU1dltr76bHnTx50vTp08eep8/o3te7d29TW1trevXq1eH+DgiCIAhCWglazgEAaAddej4u0V/8im0xr6m8KsHO++y6+uivf/L5/aKioiQpKUkWLVoklZWVzfZfv379jq+tre5aUj58+HCpqKiQvn37Snl5uRQXF8ukSZNk69at0qtXL7lx44ZUVVXZcxYsWCCTJ0+W6dOnS15enj1306ZNcunSJdm7d6/n2suXL7et3QUFBXL16tUW79+lSxdZv369PPXUUy2+25NPPmnPnzhxosycOVMcDofs2rVLfvCDH7R6TQAAAo3kHACAdihl/4cuPRuVsruXuv3y3474vMS9Z8+eEhQUJKdOnRJfi4uLky1btkhOTo5dLyws9OwrKyuzy4sXL3o+AGgir/29R40aJQcPHvScM3ToUJk2bVqj5HzJkiU2kb6VN954Q15//XU5fPiwLd1vqnv37nb7M888I1OmTLHl8atWrZLNmzfLN77xDR/9KwAA4Fsk5wAA+Jn2MQ8OCbUt5t5cdVXSKSzKJu++Ts61tdhfdIC51157zfbp1kRaE3Xt132rDwXh4eGyc+fOFgd085adnX3Le8+aNUvuv/9+eemll1o9Rj9KdO7c2Sbm2kqv/vVf/1X+8pe/2Bb906dPt/FNAQBoP4zWDgCAn2ni7aqvtaXs3nRdt2uLuq9pUtrQ0CC9e/e+rfP0nKbJvQ4i523Dhg22dfrNN9+U/v3724Ray8dbExERYZcTJkywg7e5Q8vhn3766UbHapn8rego80888YTU1NRIXV2dnDlzxm7XZ9AWdXXhwgW7z52Yq9zcXE+rPwAAHRHJOQAAfqbJ942LZzwjtTuCgu1S13W7P0Zt177VO3bskBkzZkhYWFiz/a1NdaZ9wFXXrl0921oaDV2nJlu3bp2kpKTIypUrZerUqXZ7bW2tXWopudvJkyftdGaaGOuUbt6h17kds2fPttOmuRP88ePH2+3PPfec7V+vdIR6/aCgHxDctMVcnT179rbuBwBAe6GsHQCAdnDxzEFPH3MtZdcW87Li457t/qCJuSaqhw4dsn25jx8/LiEhITJ69Gh58cUXbct1U9oSrXOhL1261Ca7mtTqfOHetP92VlaWLQ/XgedGjBjhaZnW5Fdb33UwtnfeeccOCKeDxa1YscKepyXn+/btsx8HhgwZYgeNy8jIaPM76aBz3vTaShP98+fP299aaq/90X/+85/L//f//X/2njqI3R/+8IdGrekAAHQ0AR8yniAIgiA6cvhyCq2QThHmvshYu2yPZ4+NjTWrV682hYWFprq62k6ttm3btkbTlHlPpaYxePBgc+zYMTvl2Z49e+yUZ95TqaWlpZm8vDxTVVVlSktLzcaNG010dLTn/NTUVFNSUmJcLpdnKjWN2bNnm9zcXDtNmp6XlZVlhg0b1uoUbm39b9N0KjWNrl27ms2bN5sbN26YCxcumJ///OcmKiqqw/wdEARBEIQ0CccnPwAAQCt05O9ly5bJ4sWLKYu+h/F3AADwJ/qcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAA3OOMMZKcnBzoxwAA4J5Gcg4AwGdYTEyMpKWlSX5+vlRXV0tRUZFkZmbKyJEj/XK/xMREm+xHRkaKv4WGhsqRI0fs/RISEjzbf/SjH9ltTaO8vNzvzwQAwJ0KueMzAQDAbQvpHCHOThFSV10u9TX+TRbj4+Nl//79cu3aNZk3b56cOHFCnE6njB07VtasWSN9+vSRjiw4OFhcLler+19++WUpKSmRAQMGNNq+YsUKef311xtte/fdd+XPf/6z354VAIC7Rcs5AADtICg4VGIfGSbdBz4j8V9Llu6PP2PXg4Kdfrvn2rVrbYvxwIEDZevWrZKXlycnT56UVatWyaBBg9rc8q2t0rpNk30VFxdnW9/Lyspsa3ROTo6MGzfO7n/vvffsMfpBQM9JT0+36w6HQ+bPny8FBQVSWVkpR48elZSUlGb3TUpKkuzsbKmpqZGhQ4e2+m563JgxY2Tu3LnN9lVUVEhpaakntHrgy1/+smzYsOEu/jUBAPAvWs4BAGgHXXo+LtFxX5HaqnKpq7gqwc777Lr66K9/8vn9oqKibAK7aNEimww3df369Tu+tra6a0n58OHDbSLct29fm6QXFxfLpEmT7IeAXr16yY0bN6Sqqsqes2DBApk8ebJMnz7dfiTQczdt2iSXLl2SvXv3eq69fPlym3BrEn/16tUW79+lSxdZv369PPXUUy2+W1Pf/e535a9//avs27fvjt8ZAAB/IzkHAKAdStn/IaanTczdpezupW6//LcjPi9x79mzpwQFBcmpU6fE17TlfMuWLbbFXBUWFnr2aWu6unjxoucDgCbyCxculFGjRsnBgwc952jL+LRp0xol50uWLJFdu3bd8v5vvPGGLVs/fPiwpzW/NZ06dZLnn3/eJv0AAHRkJOcAAPiZ9jEPCgm1LebeXHVVEhIeJc7OET5PzrWM3F90gLnXXnvNlpVrIq2JuvZnv9WHgvDwcNm5c2eLA7p505L2W5k1a5bcf//98tJLL7XpWf/pn/7JHr9x48Y2HQ8AQKDQ5xwAAD+rqymXhvpaW8ruTdd1uw4O52taOt7Q0CC9e/e+rfP0nKbJvQ4i5037bnfv3l3efPNN6d+/v02oZ86c2eo1IyIi7HLChAl28DZ3aDn8008/3ehYLZO/FR1l/oknnrB90uvq6uTMmTN2uz6Dtqi3VNL+9ttv25Z8AAA6MpJzAAD8rL66XG6UnpHQ+yIkpFOEOIKC7VLXdbs/Rm3X/to7duyQGTNmSFhYWLP9rU11pn3AVdeuXT3bmo6Grs6dOyfr1q2zg7qtXLlSpk6darfX1tZ6Rlp300HodBo3LYfXKd28Q69zO2bPnm0HqHMn+OPHj7fbn3vuOdu/3tvDDz8sI0aMYCA4AMDfBcraAQBoBxfPHPT0MddSdm0xLys67tnuD5qY61Rqhw4dsn25jx8/LiEhITJ69Gh58cUXbct1U9oSrXOhL1261Ca7OrDbnDlzGh2jo71nZWXJ6dOn7cBzmgDn5ubafWfPnrWt7xMnTpR33nnHDging8Xp9GZ6nvaD14HZ9OPAkCFD7KBxGRkZbX4nHXTOm3vuck30z58/32jfd77zHblw4YJ9VgAA/h4YgiAIgiBaj/j4eJORkWGXd3utkE4R5r7IWLtsj2ePjY01q1evNoWFhaa6utoUFxebbdu2mcTERM8xKjk52bM+ePBgc+zYMVNZWWn27NljUlJS7DHu909LSzN5eXmmqqrKlJaWmo0bN5ro6GjP+ampqaakpMS4XC6Tnp7u2T579myTm5trampq7HlZWVlm2LBhdp8+j4qMjLzt/zYqISGh0XaHw2GKiorMj3/84w75d0AQBEEQ0iQcn/wAAACt0BHBly1bJosXL7Ytw7g38XcAAPAn+pwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAADc44wxkpycHOjHAADgnkZyDgDAZ1hMTIykpaVJfn6+VFdXS1FRkWRmZsrIkSP9cr/ExESb7EdGRoq/hYaGypEjR+z9EhISGu0bM2aMvP/++3Ljxg25ePGibN68WeLj4/3+TAAA3CmScwAA2lFI5wi5LzJWQjpF+P1emowePnzYJuLz5s2T/v37S1JSkuzevVvWrFkjHV1wcPAt97/88stSUlLSbPvDDz8sb731lvzxj3+UAQMGyNixY+XBBx+UrVu3+vFpAQC4OyTnAAC0g6DgUInpPUy+9PizEvf1p+RLg56160HBTr/dc+3atbZVeeDAgTYxzcvLk5MnT8qqVatk0KBBbW751lZp3eZueY6Li7Ot72VlZVJeXi45OTkybtw4u/+9996zx1y7ds2ek56ebtcdDofMnz9fCgoKpLKyUo4ePSopKSnN7qsfD7Kzs6WmpkaGDh3a6rvpcdo6Pnfu3Gb7vva1r9nEPjU11d5PW9dXrFhhE/WQkJC7+BcFAMB/+D8UAADt4KH/9rhExyVIXXW51FZelWDnfXZdlZ76k8/vFxUVZRPYRYsW2WS4qevXr9/xtbXVXUvKhw8fLhUVFdK3b1+bpBcXF8ukSZPsh4BevXrZkvKqqip7zoIFC2Ty5Mkyffp0+5FAz920aZNcunRJ9u7d67n28uXLbcKtSfXVq1dbvH+XLl1k/fr18tRTT7X4blot0NDQIC+88IK88cYbEhERIf/8z/8su3btkvr6+jt+bwAA/InkHACAdihl/4eY/2YT8/qacrvNvfyHmJ5ypfCIZ91XevbsKUFBQXLq1CnxNW0537Jli20xV4WFhZ592pqutJ+3+wOAJvILFy6UUaNGycGDBz3naMv4tGnTGiXnS5YssUn0rWjC/frrr9skvKV+5H/7299sq/qvf/1rWbdunW0tP3DggIwfP95H/wIAAPgeZe0AAPiZs1OEBIWEiqvu41ZkN10PCukkzs6+73+uZeT+ogPMacn4vn37ZOnSpbYv+6d9KAgPD5edO3fKzZs3PTFlyhTp0aNHo2O1pP1WZs2aJffff7+89NJLtxwET1vWN27cKI899phtpa+trbWDwgEA0FHRcg4AgJ/V1ZRLQ32tLWX3biHX9Yb6Gtui7mtaOq6l3b17976t8/Scpsm909m4X/yGDRtkx44dMmHCBNtCrSXrc+bMkVdffbXFa2pZudLjz58/32if9i33pmXyt6KD2z3xxBPNztOk/he/+IX8y7/8i8yYMcO22v/whz/07NeS+nPnzsnjjz8uH3zwwaf8KwAA0P5oOQcAwM/qq8vlRmmebSHXUdodQcF2qes3Ss/4vKRdaX9tTaA1UQ0LC2u2v7WpzrQPuOratatnmw6k1pQmuloyroO6rVy5UqZOnWq3awt105HWdRA6ncZNy+F1Sjfv0OvcjtmzZ9sB6vSZNNyl6s8995ztX6/0fd0fGdxcLpddaqk/AAAdEf+HAgCgHVzKOyhlRcdsi3RoWJRd6rpu9xdNzDVJPnTokB2oTcvLtSVdS8N1DvCWnDlzxs6FruXqerwmv9oq7k1He9cWc52y7NFHH5URI0ZIbm6u3Xf27FmbGE+cONFOX6bl7DpYnI6WrudpKXv37t3teTNnzrTrt0MHnfvwww89cfr0abtdE313q/z27dttOfvixYvtO+i9dNR47YuuI7cDANBRGYIgCIIgWo/4+HiTkZFhl3d7rZBOEea+yFi7bI9nj42NNatXrzaFhYWmurraFBcXm23btpnExETPMSo5OdmzPnjwYHPs2DFTWVlp9uzZY1JSUuwx7vdPS0szeXl5pqqqypSWlpqNGzea6Ohoz/mpqammpKTEuFwuk56e7tk+e/Zsk5uba2pqaux5WVlZZtiwYXafPo+KjIy87f82KiEhodH25557zhw+fNjcvHnT3kvf+ZFHHukwfwcEQRAEIU3C8ckPAADQCh0RfNmyZbYlVluGcW/i7wAA4E+UtQMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAANzjjDGSnJwc6McAAOCeRnIOAMBnWExMjKSlpUl+fr5UV1dLUVGRZGZmysiRI/1yv8TERJvsR0ZGir+FhobKkSNH7P0SEhIa7XvmmWfsvoqKCvnb3/4mc+fO9fvzAABwN0Lu6mwAAHBbQjpHiLNzhNRVl0t9dblf7xUfHy/79++Xa9euybx58+TEiRPidDpl7NixsmbNGunTp490ZMHBweJyuVrd//LLL///7N0LVJX3lfD/fTgHjEhKIHbEaQrWUKMkBk07iSEq1XrBy1tmxKRvM47zauurjpf8J+oEFR3z2qw4Ex1XscZYa6loM6tNNZbWUC9N1NFoHKKgKDYgRDEqmoDIkZvC77/2LzmnBxATlcNh4vez1l7Pee7PY1graz/7d5Fz585Jv379mmxPSkqSX/3qVzJr1izZsWOHfc9169ZJTU2NfW8AADoqQxAEQRBE6xETE2MyMzPt8navEeQKMd16DzLf/M5k89CwaXap60HOYL8997Zt20xpaakJDQ1tsS88PNz7WyUnJ9vfiYmJdt13f3x8vN3mef/o6GiTlZVlysvLjdvtNvn5+WbUqFF2f3MZGRn2HIfDYVJTU01xcbGprq42ubm5JiUlxXsPz32TkpJMTk6Oqaurs9taezc97sSJE6ZPnz72PH1Gz75f/epX5je/+U2T42fOnGnOnDkT8L8DgiAIgpBWgso5AADt4KuxT0hkTLytmNdfrRBnSGe7rspO/leb3y8iIsJWkBcuXCjV1dUt9ldWVt72tbX6rE3KBw8ebJuNx8XFidvtltLSUhk3bpxs2bJFevXqJVeuXLHVajV//nyZMGGCTJs2TQoLC+25mzZtkkuXLsnevXu91162bJltgl5cXCwVFRU3vP9f/dVf2Ur43/7t397w3Tp16tRiuz7H17/+ddua4PTp07f97gAA+AvJOQAA7dCU/StR32zSlN2z/EpUrHzy4ZE2b+IeGxsrQUFBcvLkSWlr0dHRsnnzZsnPz7frJSUl3n3l5eV2efHiRe8HAE3kFyxYIMOGDZODBw96zxk4cKBMnTq1SXK+ePFi2bVr103v/8tf/lJee+01ef/9922y3dz27dtl5cqV9rh33nnH/lvMmTPH7uvevTvJOQCgQyI5BwDAz7SPeZArxFbMfTXU10hIlwi7v62Tc4fDIf6iA8ytWbNGRowYYRNpTdS1P3trNDnu0qWL7Ny584YDuvnKycm56b21H/m9994rL7/8cqvHaFX9wQcflD/84Q+2j71W8H/yk5/Iiy++KI2NjV/4PQEAaE+M1g4AgJ9pxbzxer1tyu5L1xuv19n9bU2bjmsi2rt371s6z5O8+ib3muD6Wr9+vfTs2VM2btwoffv2tQn1zJkzW71mWFiYXY4ZM8YO3uYJbQ4/fvz4JsdqM/mb0VHmn3zySamrq5Nr165JUVGR3a7PoJVyj9TUVHtfraxHRUXJoUOH7HZtLg8AQEdEcg4AgJ9pVfzKhUJbIdcm7o4gp3fU9isXivwyarv219bm3TNmzJDQ0NAW+1ub6kz7gHuaf3s0Hw1dnT17VtauXSspKSmyYsUKmTJlit1eX1/vHWnd48SJE3YaN20Or1O6+YZe51bMnj3bTpvmSfBHjx5tt3//+9+3/eubf2jQ0dw1if/BD34g7777rnz88ce3dD8AANoLzdoBAGgHlwoPevuYa1N2rZiXn87zbvcHTcx1KjWtGmtf7qNHj4rL5ZLhw4fL9OnTbeW6Oa1E61zoS5YsscmuDuzm6a/tof25s7Oz5YMPPrADzw0ZMkQKCgrsPu3PrUnx2LFj5a233rIDselgccuXL7fnaT/4ffv22Y8DTz31lG1ynpmZ+YXfSQed86XXVprof/TRR/b3/fffbyvyu3fvlnvuuUcmTZpk5z3XOdgBAOjIAj5kPEEQBEF05GjLKbRc94SZzvdF2WV7PHtUVJRZtWqVKSkpMbW1tXZqta1btzaZpsx3KjWNhIQEk5eXZ6c827Nnj53yzHcqtfT0dFNYWGhqampMWVmZ2bBhg4mMjPSen5aWZs6dO2caGhq8U6lpzJ492xQUFNhp0vS87OxsM2jQoFancPui/22aT6V2//33m3fffddUVVXZqd527txpHn/88Q71d0AQBEEQ0iwcn/0AAACt0H7LS5culUWLFjHS912MvwMAgD/R5xwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAOAuZ4yR5OTkQD8GAAB3NZJzAAC+xLp16ybp6ely6tQpqa2tlTNnzkhWVpYMHTrUL/dLTEy0yX54eLj4S0lJib2Hb7zwwgtNjunbt6/s3btXampq7DvPmzfPb88DAEBbcLXJVQAAwBfiuidMgu8Jk2u1brle6/brvWJiYmT//v1y+fJlm5weO3ZMgoODZeTIkbJ69Wrp06ePdGROp1MaGhpuuG/RokWybt0673pVVZX397333is7duyQXbt2ybRp02yi/otf/ML+O/ieAwBAR0LlHACAdhDkCpFufQZJj4Rn5OuP/61d6nqQM9hv93z11VdtVfnxxx+XLVu2SGFhoZw4cUJWrlwpAwYM+MKV7/j4eLtNk30VHR1tq+/l5eXidrslPz9fRo0aZffv3r3bHqOJsJ6TkZFh1x0Oh6SmpkpxcbFUV1dLbm6upKSktLhvUlKS5OTkSF1dnQwcOLDVd9NkvKyszBt6TY+///u/l5CQEJk8ebJ931//+te29cDzzz/fBv+qAAD4B8k5AADt4KvffEIiYuLFNDZKvbvCLnX9q71unCTfqYiICJvoaoXcN3H1qKysvO1r6zU7deokgwcPtlVpbVKuSXppaamMGzfOHtOrVy+JioqS5557zq7Pnz9fJk6caCvZDz/8sP1AsGnTJnsNX8uWLbNJvFb1jx492uoz6DEff/yxHD58WObOnWur7B5PPvmkbdJ+7do177bt27dL79695b777rvt9wYAwJ9o1g4AQDs0Zb+3+zebNGX3LO+NipVPSo60eRP32NhYCQoKkpMnT0pb08r55s2bbcXc0wfcQ6vp6uLFi94PAFrFXrBggQwbNkwOHjzoPUcr41OnTrWJtMfixYttc/Sb0Sq4JuV6r4SEBHn55Zele/fuMmfOHLtfPwr4PpPS6rpnn1b1AQDoaEjOAQDwM+1jrs3atWLuq6G+RkLCIuz+tk7OtRm5v2hyvGbNGhkxYoRNpDVR1/7sN/tQ0KVLF9m5c2eT7Zq0HzlypMk2bdL+ebTq7qH3ra+vl7Vr19rqvP4GAOB/Ipq1AwDgZ1oxb7xeL86Qzk2263rjtTq7v61p//LGxkbblPtW6DnNk3sdRM7X+vXrpWfPnrJx40bbrF0T6pkzZ7Z6zbCwMLscM2aM9OvXzxtxcXEyfvz4JsdevXpVbtV7771nn7FHjx52/cKFC3aUel+edd0HAEBHRHIOAICfaVW86nyhrZBrE3dHkNM7anvVhSK/jNpeUVFh+1nPmDFDQkNDW+xvbaqzS5cu2aU2E/fQRLq5s2fP2mq1Duq2YsUKmTJlit3uqVz79gHXQdl0GjdtDq9TuvmGXudO6fPpqO7alF4dOHDA9mV3uf7SQHD48OG2iT9N2gEAHRXJOQAA7eDSBwel4nSerUhrU3Zd6rpu9xdNzDVJPnTokB2oTZuXayV91qxZNoG9kaKiIjsv+JIlS+zxo0eP9vbl9m1Wrk3atVLdv39/GTJkiBQUFNh9p0+fttX3sWPHSteuXW1zdh0sbvny5fY8HRROq+56nlbbdf1W6CjzOsjco48+Kt/4xjfk2Wef9Q4u50m8X3/9dfuRQCv8Wp1/5pln7Dn/8R//cdv/lgAAtAdDEARBEETrERMTYzIzM+3yTq/luifMdL4vyi7b49mjoqLMqlWrTElJiamtrTWlpaVm69atJjEx0XuMSk5O9q4nJCSYvLw8U11dbfbs2WNSUlLsMZ73T09PN4WFhaampsaUlZWZDRs2mMjISO/5aWlp5ty5c6ahocFkZGR4t8+ePdsUFBSYuro6e152drYZNGiQ3afPo8LDw2/6Pv379zcHDhwwFRUV9vmOHz9uUlNTTUhISJPj+vbta/bu3WufUd/5X/7lXzrU3wFBEARBSLNwfPYDAAC0QufvXrp0qSxatMhWhnF34u8AAOBPNGsHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQC4yxljJDk5OdCPAQDAXY3kHACAL7Fu3bpJenq6nDp1Smpra+XMmTOSlZUlQ4cO9cv9EhMTbbIfHh4u/lJSUmLv4RsvvPCCd3+nTp0kIyNDjh49KteuXZM333zTb88CAEBbcbXZlQAAwOdy3RNm43qt24Y/xcTEyP79++Xy5csyb948OXbsmAQHB8vIkSNl9erV0qdPH+nInE6nNDQ03HDfokWLZN26dd71qqqqJufV1NTYjxIpKSnt8qwAANwpKucAALSDIFeI/FXcIIlJeEain/hbu9T1IGew3+756quv2qry448/Llu2bJHCwkI5ceKErFy5UgYMGPCFK9/x8fF2myb7Kjo62lbfy8vLxe12S35+vowaNcru3717tz1GPwjoOVrBVg6HQ1JTU6W4uFiqq6slNze3SeLsuW9SUpLk5ORIXV2dDBw4sNV302S8rKzMG3pND/39T//0T/Lzn/9cLly40Ab/kgAA+B+VcwAA2kHXXk9IZEy8XKt1S727Qpwhne26unjiv9r8fhERETbRXbhwYZPE1aOysvK2r61V95CQEBk8eLBcvXpV4uLibJJeWloq48aNsx8CevXqJVeuXLEVbDV//nyZMGGCTJs2zX4k0HM3bdokly5dkr1793qvvWzZMpk7d65N4isqKlp9Bk30tXquzfRff/11+8GhtSo7AAD/E5CcAwDgZ9qM/d6ob9rE3NOU3bO8NypWyouPtHkT99jYWAkKCpKTJ09KW9PK+ebNm23F3NMH3EOr6erixYveDwCayC9YsECGDRsmBw8e9J6jlfGpU6c2Sc4XL14su3btuun9tbn64cOH7b0SEhLk5Zdflu7du8ucOXPa/F0BAGgvJOcAALRDcu4MDrEVc18N9TUS0iXC2we9LWkzcn/R5HjNmjUyYsQIm0hroq792W/2oaBLly6yc+fOJts1aT9y5EiTbdqk/fNoldxD71tfXy9r16611Xn9DQDA/0T0OQcAwM808W64Vm+bsvvS9YbrdX4ZGE6bjjc2Nkrv3r1v6Tw9p3lyr4PI+Vq/fr307NlTNm7cKH379rUJ9cyZM1u9ZlhYmF2OGTNG+vXr5w1tDj9+/Pgmx2oz+Vv13nvv2Wfs0aPHLZ8LAEBHQXIOAICfafJddaFQgj8bqd0R5LRLXa+6UOSX5Fz7a2/fvl1mzJghoaGhLfa3NtWZ9gFX2kzcQxPp5s6ePWur1Tqo24oVK2TKlCl2u6dyrSOme+ggdDqNmzaH1yndfEOvc6f0+bS/uTalBwDgfyqatQMA0A4+/vNBbx9zbcquFfPy03ne7f6giblOpXbo0CHbl1vn/Xa5XDJ8+HCZPn26rVw3V1RUZAdZW7JkiR1MTgd2a96XW5uVZ2dnywcffGAHnhsyZIgUFBTYfadPn7bV97Fjx8pbb71lB4TTweKWL19uz9N+8Pv27bMfB5566ik7aFxmZuYXficdZf6JJ56Qd955x47Y/uSTT9rr6uByOkK8h04Tp83mIyMj5d5777Ujzqu8vLw7+BcFAMC/DEEQBEEQrUdMTIzJzMy0yzu9luueMHPPfVF22R7PHhUVZVatWmVKSkpMbW2tKS0tNVu3bjWJiYneY1RycrJ3PSEhweTl5Znq6mqzZ88ek5KSYo/xvH96eropLCw0NTU1pqyszGzYsMFERkZ6z09LSzPnzp0zDQ0NJiMjw7t99uzZpqCgwNTV1dnzsrOzzaBBg+w+fR4VHh5+0/fp37+/OXDggKmoqLDPd/z4cZOammpCQkKaHKfveyMd5e+AIAiCIKRZOD77AQAAWqHzdy9dutRO3aWVYdyd+DsAAPgTfc4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAADucsYYSU5ODvRjAABwVyM5BwDgS6xbt26Snp4up06dktraWjlz5oxkZWXJ0KFD/XK/xMREm+yHh4eLv5SUlNh7+MYLL7zQ5Bm2bt0q586dE7fbLUeOHJFnn33Wb88DAEBbcLXJVQAAwBfiuifMxvVatw1/iomJkf3798vly5dl3rx5cuzYMQkODpaRI0fK6tWrpU+fPtKROZ1OaWhouOG+RYsWybp167zrVVVV3t8JCQly9OhR+bd/+zcpKyuTsWPHSmZmplRWVsq2bdva5dkBALgdhiAIgiCI1iMmJsZkZmba5e1eI8gVYv4qbpB58LuTzTeTptmlrjucwX577m3btpnS0lITGhraYl94eLj3t0pOTra/ExMT7brv/vj4eLvN8/7R0dEmKyvLlJeXG7fbbfLz882oUaPs/uYyMjLsOQ6Hw6Smppri4mJTXV1tcnNzTUpKivcenvsmJSWZnJwcU1dXZ7fd6L1KSkrMc889d0v/Fn/4wx/M+vXrA/53QBAEQRDSStCsHQCAdtC11xMS8Y14MaZR6t0VdqnrX31ogF/uFxERIUlJSbZCXl1d3WK/VpFvl16zU6dOMnjwYOnbt69tUq7Nx0tLS2XcuHH2mF69eklUVJQ899xzdn3+/PkyceJEmTZtmjz88MOycuVK2bRpk72Gr2XLlklqaqqt6mv1uzV6zMcffyyHDx+WuXPn2ir7zWgz+/Ly8tt+ZwAA/I1m7QAA+Jk2Y7+3+zflWs1fmrJ7lvd2j5Xy4iNt3sQ9NjZWgoKC5OTJk9LWoqOjZfPmzZKfn+/tA+7hSYAvXrzo/QAQEhIiCxYskGHDhsnBgwe95wwcOFCmTp0qe/fu9Z6/ePFi2bVr103vr33oNSnXe2kT9pdfflm6d+8uc+bMueHxTz/9tPzN3/yNvRcAAB0VyTkAAO2QnAcFh9iKua+G+hoJCYvw9kFvSw6HQ/xFk+M1a9bIiBEjbCKtibr2Z7/Zh4IuXbrIzp07m2zXpF0Ha/OVk5PzuffXqruH3re+vl7Wrl1rq/P629d3vvMdycjIkClTpsiJEydu4S0BAGhfNGsHAMDPNPFuvFYvzpDOTbbreuO1Or8MDFdYWCiNjY3Su3fvWzpPz2me3Osgcr7Wr18vPXv2lI0bN9pm7ZpQz5w5s9VrhoWF2eWYMWOkX79+3oiLi5Px48c3Ofbq1atyq9577z37jD169GiyXZvM//73v5d//ud/ts8KAEBHRnIOAICfafJddb5Qgjt/OlK7I8hpl7pedb7IL8l5RUWFbN++XWbMmCGhoaEt9rc21dmlS5fsUpuJe2gi3dzZs2dttTolJUVWrFhhK9PKU7n27QOuFWudxk2bw+uUbr6h17lT+nw6qrs2pfedTk1HZtf+8L6jugMA0FHRrB0AgHZw6c8HvX3MtSm7VswrSvK82/1BE3OdSu3QoUO2L7cOsOZyuWT48OEyffp0W7lurqioyM6FvmTJElm4cKEd2K15X25tVp6dnS0ffPCBHXhuyJAhUlBQYPedPn3aVt91+rK33npLampq7GBxy5cvt+dpP/h9+/bZjwNPPfWUXLlyxU5z9kUNGDBAnnjiCXnnnXfs9GlPPvmkd3A5nTLO05T9D3/4g/zkJz+xTe51rnfPhwP9aAEAQEcV8CHjCYIgCKIjR1tOoeW6J8zcc1+UXbbHs0dFRZlVq1bZ6cdqa2vt1Gpbt25tMk2Z71RqGgkJCSYvL89OebZnzx475ZnvVGrp6emmsLDQ1NTUmLKyMrNhwwYTGRnpPT8tLc2cO3fONDQ0eKdS05g9e7YpKCiw06TpednZ2WbQoEGtTuF2o+jfv785cOCAqaiosM93/PhxO0VbSEiI9xi954288847HebvgCAIgiCkWTg++wEAAFoRExMjS5culUWLFtnKMO5O/B0AAPyJPucAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAB3OWOMJCcnB/oxAAC4q5GcAwDwJdatWzdJT0+XU6dOSW1trZw5c0aysrJk6NChfrlfYmKiTfbDw8PFX0pKSuw9fOOFF17w7u/Vq5e8/fbbcuHCBampqbHvvnTpUnG5XH57JgAA7hT/lwIAoB257gkTV+cwuV7jluu1br/eKyYmRvbv3y+XL1+WefPmybFjxyQ4OFhGjhwpq1evlj59+khH5nQ6paGh4Yb7Fi1aJOvWrfOuV1VVeX9fu3ZNMjMz5fDhw/bd4+Pj7bFBQUGycOHCdnl2AABuFZVzAADaQZArRP7q4UESM/AZ+fqAv7VLXXc4g/12z1dffdVWlR9//HHZsmWLFBYWyokTJ2TlypUyYMCAL1z51uRWt2myr6Kjo231vby8XNxut+Tn58uoUaPs/t27d9tjNCnWczIyMuy6w+GQ1NRUKS4ulurqasnNzZWUlJQW901KSpKcnBypq6uTgQMHtvpumoyXlZV5Q6/pW1n/5S9/KUePHrUtBX7/+9/Lr371Kxk0aFAb/KsCAOAfJOcAALSDrg89IRE9NMltlHp3hV3q+ld73zhJvlMRERE20dUKuW/i6lFZWXnb19ZrdurUSQYPHix9+/a1Tco1SS8tLZVx48Z5m5ZHRUXJc889Z9fnz58vEydOlGnTpsnDDz9sPxBs2rTJXsPXsmXLbBKvVX1Nrlujx3z88ce2Oj537lxbZW/Ngw8+aP8t9uzZc9vvDACAv9GsHQCAdmjKfm/3b8q12r80Zfcs7+0eK+WnjrR5E/fY2FjbjPvkyZPS1rRyvnnzZlsx91SqPbSari5evOj9ABASEiILFiyQYcOGycGDB73naGV86tSpsnfvXu/5ixcvll27dt30/tqHXpNyvVdCQoK8/PLL0r17d5kzZ06T47RJ/2OPPSb33HOPrF271l4bAICOiuQcAAA/0z7mQcEhtmLuq6G+RkK6RHzaB72Nk3NtRu4vmhyvWbNGRowYYRNpTdS1P/vNPhR06dJFdu7c2WS7Ju1Hjhxpsk2btH8erbp76H3r6+tt8q3Vef3t8f3vf1/uvfde2yz/lVdesRV2XQIA0BGRnAMA4Gc6+FvjtXpxhnRukoTreuP1Oru/rWn/8sbGRundu/ctnafnNE/udRA5X+vXr5ft27fLmDFjbIKuSbFWrX/605/e8JphYWF2qcd/9NFHTfZp33JfV69elVv13nvv2Wfs0aOHfPDBB97tZ8+etcuCggLb7P1nP/uZrFixwvuOAAB0JPQ5BwDAzzQhrzpfKME6Uvs9YeIIctqlrledL/LLqO0VFRU2gZ4xY4aEhoa22N/aVGeXLl2yS20m7tGvX78Wx2niq9VqHdRNE94pU6bY7Z7KtW8fcB2ETqdx0+bwOq2Zb3gS6Duhz6ejumtT+tZoE39N4HUJAEBHROUcAIB2cOnkQW8fc23KrhXzig/zvNv9QRNz7Xd96NAh299aB1jTub6HDx8u06dPl7i4uBbnFBUV2RHOlyxZYqcd04Hdmvfl1mbl2dnZtkqtA88NGTLEVqfV6dOnbWV67Nix8tZbb9l5xnWwuOXLl9vzNDnet2+f/Tjw1FNPyZUrV+y0Z1+UjjL/xBNPyDvvvGNHbH/yySe9g8vpCPHq2WeftdOpaZN3rcx/+9vftv3Sf/3rX8v169fv+N8VAAB/MQRBEARBtB4xMTEmMzPTLu/0Wq57wsw9EVF22R7PHhUVZVatWmVKSkpMbW2tKS0tNVu3bjWJiYneY1RycrJ3PSEhweTl5Znq6mqzZ88ek5KSYo/xvH96eropLCw0NTU1pqyszGzYsMFERkZ6z09LSzPnzp0zDQ0NJiMjw7t99uzZpqCgwNTV1dnzsrOzzaBBg+w+fR4VHh5+0/fp37+/OXDggKmoqLDPd/z4cZOammpCQkK8xzzzzDMmJyfHXLlyxVRVVZn8/Hx7TKdOnTrM3wFBEARBSLNwfPYDAAC0QufvXrp0qSxatMhWhnF34u8AAOBPdLwCAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQC4yxljJDk5OdCPAQDAXY3kHACAL7Fu3bpJenq6nDp1Smpra+XMmTOSlZUlQ4cO9cv9EhMTbbIfHh4u/lJSUmLv4RsvvPDCDY998MEH5cqVK1JRUeG35wEAoC242uQqAADgC3F1DhPXPWFyvcYt12vdfr1XTEyM7N+/Xy5fvizz5s2TY8eOSXBwsIwcOVJWr14tffr0kY7M6XRKQ0PDDfctWrRI1q1b512vqqpqcYzL5ZL//M//lP/6r/+ShIQEvz4rAAB3iso5AADtIMgVIl99ZLBED3pGHnjy7yR68DN23eEM9ts9X331VVtVfvzxx2XLli1SWFgoJ06ckJUrV8qAAQO+cOU7Pj7ebtNkX0VHR9vqe3l5ubjdbsnPz5dRo0bZ/bt377bH6AcBPScjI8OuOxwOSU1NleLiYqmurpbc3FxJSUlpcd+kpCTJycmRuro6GThwYKvvpsl4WVmZN/Sazf34xz+WkydPym9+85s7+FcEAKB9kJwDANAO7u89QCK+ES+m0Ui9u8Iudb1rnyf9cr+IiAib6GqF/EaJa2Vl5W1fW6/ZqVMnGTx4sPTt29c2KdckvbS0VMaNG2eP6dWrl0RFRclzzz1n1+fPny8TJ06UadOmycMPP2w/EGzatMlew9eyZctsEq9V/aNHj7b6DHrMxx9/LIcPH5a5c+faKruvIUOGyNNPPy0zZsy47fcEAKA90awdAIB2aMp+71/HyjWfpuyepW6vKDrc5k3cY2NjJSgoyFaO25pWzjdv3mwr5p4+4B5aTVcXL170fgAICQmRBQsWyLBhw+TgwYPec7QyPnXqVNm7d6/3/MWLF8uuXbtuen/tQ69Jud5Lm6u//PLL0r17d5kzZ47dHxkZKb/85S9lwoQJN2zuDgBAR0RyDgCAn2kf8yBXJ1sx99VQXyMhYRE2eW/r5FybkfuLJsdr1qyRESNG2ERaE3Xtz36zDwVdunSRnTt3NtmuSfuRI0eabNMm7Z9Hq+4eet/6+npZu3atrc7rb+2L/vrrr9u+5gAA/E9Bs3YAAPxME+/G63XiDOncZLuu63YdHK6taf/yxsZG6d279y2dp+c0T+51EDlf69evl549e8rGjRtts3ZNqGfOnNnqNcPCwuxyzJgx0q9fP2/ExcXJ+PHjmxx79epVuVXvvfeefcYePXrYdR2JXpu6X7t2zYY+73333Wd/T5o06ZavDwBAeyA5BwDAzzT5rjpXJMGfjdTuCHLapa7rdn+M2q5Th23fvt32uQ4NDW2xv7Wpzi5dumSX2kzcQxPp5s6ePWur1Tqo24oVK2TKlCl2u1aulW8fcB2ETqdx0+bwOqWbb+h17pQ+n47qrk3p1ZNPPtnkI4A2ldfp1PT3m2++ecf3AwDAH2jWDgBAO/i44IC3j7k2ZdeKeUVJnne7P2hirlOpHTp0yCaoOsCaTi82fPhwmT59uq1cN1dUVGTnQl+yZIksXLjQDuzm6cvt26w8OztbPvjgAzvwnA6+VlBQYPedPn3aVt/Hjh0rb731ltTU1NjB4pYvX27P037w+/btsx8HnnrqKZs0Z2ZmfuF30lHmn3jiCXnnnXdsf3JNxD2Dy+kI8ap5P/tvf/vb9pmOHz9+m/+SAAC0D0MQBEEQROsRExNjMjMz7fJOr+W6J8zcExFll+3x7FFRUWbVqlWmpKTE1NbWmtLSUrN161aTmJjoPUYlJyd71xMSEkxeXp6prq42e/bsMSkpKfYYz/unp6ebwsJCU1NTY8rKysyGDRtMZGSk9/y0tDRz7tw509DQYDIyMrzbZ8+ebQoKCkxdXZ09Lzs72wwaNMju0+dR4eHhN32f/v37mwMHDpiKigr7fMePHzepqakmJCSk1XP+8R//0R7fkf4OCIIgCEKaheOzHwAAoBU6f/fSpUtl0aJFtjKMuxN/BwAAf6LPOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAHc5Y4wkJycH+jEAALirkZwDAPAl1q1bN0lPT5dTp05JbW2tnDlzRrKysmTo0KF+uV9iYqJN9sPDw8VfSkpK7D1844UXXvDuj4mJabFf44knnvDbMwEAcKdcd3wFAADQIWmSun//frl8+bLMmzdPjh07JsHBwTJy5EhZvXq19OnTRzoyp9MpDQ0NN9y3aNEiWbdunXe9qqqqxTHf/e535fjx4971Tz75xE9PCgDAnaNyDgBAO3J1DpN7IqLEdU+Y3+/16quv2orx448/Llu2bJHCwkI5ceKErFy5UgYMGPCFK9/x8fF2myb7Kjo62lbfy8vLxe12S35+vowaNcru3717tz1GPwjoORkZGXbd4XBIamqqFBcXS3V1teTm5kpKSkqL+yYlJUlOTo7U1dXJwIEDW303TcbLysq8oddsTpNx32OuX79+B/+aAAD4F5VzAADaQZArRO7vPUDC/jpWnMGdpOFanbjPFcnHBQfENFxr8/tFRETYRHfhwoU3TFwrKytv+9padQ8JCZHBgwfL1atXJS4uzibppaWlMm7cOPshoFevXnLlyhWpqamx58yfP18mTJgg06ZNsx8J9NxNmzbJpUuXZO/evd5rL1u2TObOnWuT+IqKilafQRN9rZ5rM/3XX3/dfnBoXmXXDwj33HOPfPDBB/Lv//7v8vvf//623xkAAH8jOQcAoB1oYn5fz3i5XuOWeneFOEM623V1Kf8vyWlbiY2NlaCgIDl58mSbX1sr55s3b7YVc08fcA+tpquLFy96PwBoIr9gwQIZNmyYHDx40HuOVsanTp3aJDlfvHix7Nq166b31z70hw8ftvdKSEiQl19+Wbp37y5z5syx+/VDwfPPP2+b9Dc2NtoK/datW+Vv//ZvSdABAB0WyTkAAO3QlF0r5pqYX691222epW6vKDrsXW8r2ozcXzQ5XrNmjYwYMcIm0pqoa3/2m30o6NKli+zcubPJdk3ajxw50mSbNmn/PFol99D71tfXy9q1a211Xn9rc3bfY/Saf/3Xf2373ZOcAwA6KvqcAwDgZ9q/3DZlr/+0ibeHrut2Td7bmjYd16px7969b+k8Pad5cq+DyPlav3699OzZUzZu3Ch9+/a1ye/MmTNbvWZY2KfvN2bMGOnXr583tDn8+PHjmxyrzeRv1XvvvWefsUePHjc9Rj8SAADQUZGcAwDgZ1oV1z7m2pTdl67rdq2otzXtr719+3aZMWOGhIaGttjf2lRn2gdcaTNxD02kmzt79qytVmuT8RUrVsiUKVPsdq1ce0Za99BB6HQaN20Or1O6+YZe507p82l/c21Kf7Njzp8/f8f3AgDAX2jWDgCAn2nyrYO/efqY24p5SGdbMb9cnNfmTdo9NDHXfteHDh2yfbmPHj0qLpdLhg8fLtOnT7eV6+aKiorsIGtLliyxg8npwG6evtwe2mQ8OzvbDrSmA88NGTJECgoK7L7Tp0/b6vvYsWPlrbfesgPCaR/w5cuX2/O0H/y+ffvsx4GnnnrKDhqXmZn5hd9JR5nX+crfeecdO2L7k08+aa+rg8vpCPFq4sSJ9iOBp8m8DlI3efJk+dGPfnSH/6IAAPiXIQiCIAii9YiJiTGZmZl2ebvXcDiDzVcfGWy+MWKyiR0z3S51Xbf789mjoqLMqlWrTElJiamtrTWlpaVm69atJjEx0XuMSk5O9q4nJCSYvLw8U11dbfbs2WNSUlLsMZ73T09PN4WFhaampsaUlZWZDRs2mMjISO/5aWlp5ty5c6ahocFkZGR4t8+ePdsUFBSYuro6e152drYZNGiQ3afPo8LDw2/6Pv379zcHDhwwFRUV9vmOHz9uUlNTTUhIiPeYiRMn2u1ut9tcvnzZHDx40L5DR/g7IAiCIAhpJRyf/QAAAK3Q+buXLl1qp+7SyvCd9j/Xirnv4HC4+/4OAABojmbtAAC0I03IScoBAEBzDAgHAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAXc4YI8nJyYF+DAAA7mok5wAAfIl169ZN0tPT5dSpU1JbWytnzpyRrKwsGTp0qF/ul5iYaJP98PBw8ZeSkhJ7D9944YUXWhw3Z84c+fOf/2zf++zZs7JgwQK/PRMAAHfKdcdXAAAAHVJMTIzs379fLl++LPPmzZNjx45JcHCwjBw5UlavXi19+vSRjszpdEpDQ8MN9y1atEjWrVvnXa+qqmqy/yc/+YmMGDFC5s6da987MjLSBgAAHZkhCIIgCKL1iImJMZmZmXZ5p9dydQ4z90REGdc9YX5/7m3btpnS0lITGhraYl94eLj3t0pOTra/ExMT7brv/vj4eLvN8/7R0dEmKyvLlJeXG7fbbfLz882oUaPs/uYyMjLsOQ6Hw6Smppri4mJTXV1tcnNzTUpKivcenvsmJSWZnJwcU1dXZ7fd6L1KSkrMc8891+p79+7d29TX15tevXp12L8DgiAIgpBmQeUcAIB2EOQKkfv7DJCwr8WKM7iTNFyrE/dHRfJxwQEx16+1+f0iIiIkKSlJFi5cKNXV1S32V1ZW3va1teoeEhIigwcPlqtXr0pcXJy43W4pLS2VcePGyZYtW6RXr15y5coVqampsefMnz9fJkyYINOmTZPCwkJ77qZNm+TSpUuyd+9e77WXLVtmq93FxcVSUVHR6jOkpqba6rk203/99ddl5cqV3ir7//pf/8ueP3bsWJk5c6Y4HA7ZtWuX/Mu//MtNrwkAQCCRnAMA0A40Mb/vwXi5XuOW+qoKcXbqbNfVpWN/SU7bSmxsrAQFBcnJkyfb/NrR0dGyefNmyc/P9/YB9ygvL7fLixcvej8AaCKv/b2HDRsmBw8e9J4zcOBAmTp1apPkfPHixTaRvhntQ3/48GF7r4SEBHn55Zele/futo+56tmzp23S//TTT8vEiRNt83hN3n/729/Kd7/73Tb/9wAAoC2QnAMA4GeuzmG2Yq6JuYbyLMP+OlYqCg/L9dpP19uKVov9RZPjNWvW2D7dmkhroq79um/2oaBLly6yc+fOJts1aT9y5EiTbTk5OZ97f020PfS+9fX1snbtWlud19/6UeKee+6xiblW6dUPf/hDm9BrRf+DDz64jbcGAMC/GK0dAAA/c90T9mlT9rpPm3h76Lpu1+S9rWlS2tjYKL17976l8/Sc5sm9DiLna/369bY6vXHjRunbt69NqLX5eGvCwj59vzFjxki/fv28oc3hx48f3+RYbSZ/q9577z37jD169LDr58+fl2vXrnkTc1VQUOCt+gMA0BGRnAMA4GdaFdc+5tqU3Zeu63ZPFb0tad/q7du3y4wZMyQ0NLTF/tamOtM+4EqbiXtoIt2cTk2m1eqUlBRZsWKFTJkyxW7XyrXSpuQeJ06csNOZaWKsU7r5hl7nTunzaX9zbUqvdIR6Tdb1A4KHVszV6dOn7/h+AAD4A8k5AAB+psm3Dv6mFXINR5DT+9t9rqjNm7R7aGKuSfKhQ4fsQG3avFwr6bNmzZIDBw7c8JyioiI7yNqSJUvs8aNHj/b25fZtVq5N2rVS3b9/fxkyZIi3Mq3Jr1bfdTC2rl272ubsOljc8uXL7Xna1FyTZj1Pq+26fisGDBggzz33nDz66KPyjW98Q5599ll7XR1cTqeMU9rU/v3335df/OIXNnF/7LHH7IeEHTt2NKmmAwDQ0QR8yHiCIAiC6MjRFlNoOVzB5qt9B5tvjJxsYsdOt0td1+3+fPaoqCizatUqO/1YbW2tnVpt69atTaYp851KTSMhIcHk5eXZKc/27NljpzzznUotPT3dFBYWmpqaGlNWVmY2bNhgIiMjveenpaWZc+fOmYaGBu9UahqzZ882BQUFdpo0PS87O9sMGjSo1SncbhT9+/c3Bw4cMBUVFfb5jh8/bqdoCwkJaXJc9+7dzW9/+1tz5coVc/78efOLX/zCREREBPzvgCAIgiCklXB89gMAALRCR/5eunSpnbrrTptFa/9zrZjbweH8VDFHx/87AACgOUZrBwCgHWlCTlIOAACao885AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAdzljjCQnJwf6MQAAuKuRnAMA8CXWrVs3SU9Pl1OnTkltba2cOXNGsrKyZOjQoX65X2Jiok32w8PDxV9KSkrsPXzjhRde8O7/13/91xb7Ndxut9+eCQCAO+W64ysAAIAOKSYmRvbv3y+XL1+WefPmybFjxyQ4OFhGjhwpq1evlj59+khH5nQ6paGh4Yb7Fi1aJOvWrfOuV1VVeX8vX75cXnvttSbH/+lPf5L//u//9uPTAgBwZ6icAwDQjlydw+SeyCi79LdXX33VVowff/xx2bJlixQWFsqJEydk5cqVMmDAgC9c+Y6Pj7fbNNlX0dHRtvpeXl5uq9H5+fkyatQou3/37t32GP0goOdkZGTYdYfDIampqVJcXCzV1dWSm5srKSkpLe6blJQkOTk5UldXJwMHDmz13TQZLysr84Ze0+Pq1atN9mnrgYcffljWr1/fBv+qAAD4B5VzAADaQZArRO7vM0DCvhYrQcGdpPFanbg/KpKPCw6IuX6tze8XERFhE92FCxc2SVw9Kisrb/vaWnUPCQmRwYMH20Q4Li7OJumlpaUybtw4+yGgV69ecuXKFampqbHnzJ8/XyZMmCDTpk2zHwn03E2bNsmlS5dk79693msvW7ZM5s6da5P4ioqKVp9BE32tnmsz/ddff91+cGityv6jH/1I/vznP8u+fftu+50BAPA3knMAANqBJubhD8bL9Rq31FdViLNTZ7uuLh37S3LaVmJjYyUoKEhOnjzZ5tfWyvnmzZttxdzTB9xDq+nq4sWL3g8AmsgvWLBAhg0bJgcPHvSeo5XxqVOnNknOFy9eLLt27brp/bUP/eHDh+29EhIS5OWXX5bu3bvLnDlzWhzbqVMn+fu//3ub9AMA0JGRnAMA4GfahF0r5pqYayjPUrdXFB32rrcVbUbuL5ocr1mzRkaMGGETaU3UtT/7zT4UdOnSRXbu3NlkuybtR44cabJNm7R/Hq2Se+h96+vrZe3atbY6r799/d3f/Z3ce++9smHDhlt4QwAA2h99zgEAaIfkXJuyN9R92sTbQ9d1uz/6n2vT8cbGRundu/ctnafnNE/udRA5X9p3u2fPnrJx40bp27evTahnzpzZ6jXDwj59vzFjxki/fv28oc3hx48f3+RYbSZ/q9577z37jD169Lhhk/Y//OEPtpIPAEBHRnIOAICfaVVc+5hrU3Zfuq7b27pqrrS/9vbt22XGjBkSGhraYn9rU51pH3ClzcQ9NJFu7uzZs7ZarYO6rVixQqZMmWK3eyrXOtK6hw5Cp9O4aXN4ndLNN/Q6d0qfT/ubN0/ANVkfMmQIA8EBAP5HIDkHAMDPNPnWwd+0Qq7hCHJ6f+t2fyTnShNzTZIPHTpkB2rT5uVaSZ81a5YcOHDghucUFRXZQdaWLFlijx89enSLvtzarFybtGvy279/f5sAFxQU2H2nT5+21fexY8dK165dbXN2HSxOpzfT8yZOnGir7nqeVtt1/VboKPPPPfecPProo/KNb3xDnn32WXtdHVxOR4j3NXnyZDl//rxkZ2ff8r8dAACBYAiCIAiCaD1iYmJMZmamXd7uNRyuYPPVvoPNN5Immwf/13S71HXd7s9nj4qKMqtWrTIlJSWmtrbWlJaWmq1bt5rExETvMSo5Odm7npCQYPLy8kx1dbXZs2ePSUlJscd43j89Pd0UFhaampoaU1ZWZjZs2GAiIyO956elpZlz586ZhoYGk5GR4d0+e/ZsU1BQYOrq6ux52dnZZtCgQXafPo8KDw+/6fv079/fHDhwwFRUVNjnO378uElNTTUhISFN/70dDnPmzBnz4x//uEP9HRAEQRCEtBKOz34AAIBW6PzdS5cutVN3aWX4Tngq5r6Dw+Hu+zsAAKA5RmsHAKAdkZQDAIAboc85AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAdzljjCQnJwf6MQAAuKuRnAMA8CXWrVs3SU9Pl1OnTkltba2cOXNGsrKyZOjQoX65X2Jiok32w8PDxV9KSkrsPXzjhRdeaHLMiBEj5MCBA3LlyhW5ePGi/Pa3v5WYmBi/PRMAAHeK5BwAgC8pTUbff/99m4jPmzdP+vbtK0lJSfLOO+/I6tWrpaNzOp2t7lu0aJFERUV5Y9WqVd59PXr0kN/97nfy9ttvS79+/WTkyJHStWtX2bJlSzs9OQAAt47kHACAduTqHCb3REbZpb+9+uqrtqr8+OOP28S0sLBQTpw4IStXrpQBAwZ84cp3fHy83eapPEdHR9vqe3l5ubjdbsnPz5dRo0bZ/bt377bHXL582Z6TkZFh1x0Oh6SmpkpxcbFUV1dLbm6upKSktLivfjzIycmRuro6GThwYKvvVlVVJWVlZd7Qa3p861vfsol9Wlqavd+RI0dk+fLlNlF3uVxt8C8LAEDb4/9QAAC0gyBXiETGDZCwB2IlKLiTNF6rE/fZIvnkxAEx16+1+f0iIiJsortw4cImiatHZWXlbV9bq+4hISEyePBguXr1qsTFxdkkvbS0VMaNG2c/BPTq1cs2Ka+pqbHnzJ8/XyZMmCDTpk2zHwn03E2bNsmlS5dk79693msvW7ZM5s6da5PqioqKVp9BE32tnmsz/ddff91+cGhoaLD7tLVAY2OjTJo0SX75y19KWFiY/MM//IPs2rVLrl+/ftvvDQCAP5GcAwDQDjQxD4+Nl4Yat1yrqhBnp852XX189C/JaVuJjY2VoKAgOXnyZJtfWyvnmzdvthVzTx9wD62mK+3n7fkAoIn8ggULZNiwYXLw4EHvOVoZnzp1apPkfPHixTaJvhntQ3/48GF7r4SEBHn55Zele/fuMmfOHLv/ww8/tH3Of/Ob38jatWtttfzdd9+V0aNHt/m/BQAAbYXkHAAAP9Mm7Fox18T8eo3bbvMsdfvlwsPe9baizcj9RZPjNWvW2ARYE2lN1I8dO3bTDwVdunSRnTt3NtmuSbs2OfelTdo/j1bJPfS+9fX1NgnX6rz+1kHw1q1bJxs2bJD//M//lHvvvVf+3//7f3ZQuOHDh9/WOwMA4G8k5wAAtENyrk3ZtWLuq6GuRoLDIuz+tk7Otem4Nu3u3bv3LZ2n5zRP7oODg5scs379etm+fbuMGTPGJuiaFGvV+qc//ekNr6nNypUe/9FHHzXZp33LfWkz+Vv13nvv2WfUgeA++OADmTFjhq3a+47grk3qz549K0888YQ9HgCAjoYB4QAA8DNNvLWPuTZl96Xrjdfr2jwxV9pfWxNoTVRDQ0Nb7G9tqjPtA660mbiHDqTWnCa6Wq3WQd1WrFghU6ZMsdu1ct18pHUdhE6ncdPm8Dqlm2/ode6UPp/2N9em9Erf1/ORwcPTH12b+gMA0BHxfygAAPxMk28d/M3ZOcxWyR1BTrvUdd3uj+RcaWKuSfKhQ4fsQG3avFwr6bNmzbJzgN9IUVGRHWRtyZIl9njtp+3py+3brFwr5lqp7t+/vwwZMkQKCgrsvtOnT9vEeOzYsXb6Mm3OroPF6Wjpet7EiROlZ8+e9ryZM2fa9Vuho8w/99xz8uijj8o3vvENefbZZ+11dXA5HSFebdu2Tf7mb/7GDhin76D30lHjtS9682b0AAB0JIYgCIIgiNYjJibGZGZm2uXtXsPhCjZdHx1seoyebHp+b7pd6rpu9+ezR0VFmVWrVpmSkhJTW1trSktLzdatW01iYqL3GJWcnOxdT0hIMHl5eaa6utrs2bPHpKSk2GM875+enm4KCwtNTU2NKSsrMxs2bDCRkZHe89PS0sy5c+dMQ0ODycjI8G6fPXu2KSgoMHV1dfa87OxsM2jQILtPn0eFh4ff9H369+9vDhw4YCoqKuzzHT9+3KSmppqQkJAmx33/+98377//vqmqqrL30nd+6KGHAv53QBAEQRDSSjg++wEAAFqh83cvXbrUVmK1MnwntGLu6WPur4o5Ov7fAQAAzTEgHAAA7YikHAAA3Ah9zgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BALjLGWMkOTk50I8BAMBdjeQcAIAvsW7dukl6erqcOnVKamtr5cyZM5KVlSVDhw71y/0SExNtsh8eHi7+UlJSYu/hGy+88EKTY55++mk5cuSIXL16VT788EOZO3eu354HAIC24GqTqwAAgA4nJiZG9u/fL5cvX5Z58+bJsWPHJDg4WEaOHCmrV6+WPn36SEfmdDqloaHhhvsWLVok69at865XVVV5fyclJcmvfvUrmTVrluzYscO+px5bU1Nj3xsAgI6IyjkAAO3I1TlM7omMskt/e/XVV21V+fHHH5ctW7ZIYWGhnDhxQlauXCkDBgz4wpXv+Ph4u02TfRUdHW2r7+Xl5eJ2uyU/P19GjRpl9+/evdseox8E9JyMjAy77nA4JDU1VYqLi6W6ulpyc3MlJSWlxX01sc7JyZG6ujoZOHBgq++myXhZWZk39Joe//AP/yBbt26VtWvX2ir7W2+9JS+//HKL6joAAB0JlXMAANpBkCtEIh8eIGEPxEqQq5M0Xq8T99ki+eT4ATHXr7X5/SIiImyiu3DhwiaJq0dlZeVtX1urzyEhITJ48GDbbDwuLs4m6aWlpTJu3Dj7IaBXr15y5coVW61W8+fPlwkTJsi0adPsRwI9d9OmTXLp0iXZu3ev99rLli2zTdA1ia+oqGj1GTTR1+q5NtN//fXX7QcHT5W9U6dOLd5Zn+PrX/+6/YBw+vTp2353AAD8heQcAIB2oIl5+IPx0lDjlmtVFeLs1Nmuq4/z/pKctpXY2FgJCgqSkydPtvm1tXK+efNmWzFXWp320Gq6unjxovcDgCbyCxYskGHDhsnBgwe952hlfOrUqU2S88WLF8uuXbtuen/tQ3/48GF7r4SEBFsV7969u8yZM8fu3759u03Wf/nLX8o777xj/y08+/Q4knMAQEdEcg4AgJ9pE3atmGtifr3Gbbd5lmFfi5XLHxz2rrcVbUbuL5ocr1mzRkaMGGETaU3UtT97azQ57tKli+zcubPJdk3addA2X9qk/fNo4u2h962vr7dN2LU6r7+1f/mDDz4of/jDH2wfe63g/+QnP5EXX3xRGhsbb+udAQDwN/qcAwDQDsm5NmVvqPu0ibeHrgcFd/JL/3NtOq6JaO/evW/pPE/y6pvca4Lra/369dKzZ0/ZuHGj9O3b1ybUM2fObPWaYWGfvt+YMWOkX79+3tDm8OPHj29yrDaTv1XvvfeefcYePXo0afau99Vm7FFRUXLo0CG7XZvLAwDQEZGcAwDgZ1oV1z7m2pTdl643Xqtr86q50v7a2rx7xowZEhoa2mJ/a1OdaR9wT/NvD02kmzt79qytVuugbitWrJApU6bY7Vq59oy07qGD0Ok0btocXqd08w29zp3S59P+5tqUvvmHhnPnzsm1a9fkBz/4gbz77rvy8ccf3/H9AADwB5q1AwDgZ5p86+Bvnj7mWjHXxNzZOUwqT+X5JTlXmpjrVGpaNda+3EePHhWXyyXDhw+X6dOn28p1c0VFRXaQtSVLltjB5HRgN09/bd9m5dnZ2fLBBx/YgeeGDBkiBQUFdp/259akeOzYsXaUdB2ITQeLW758uT1P+8Hv27fPfhx46qmnbJPzzMzML/xOOsr8E088YfuS64jtTz75pL2uDi6nI8Sr+++/31bkdeT4e+65RyZNmmTnPdcR4QEA6MgMQRAEQRCtR0xMjMnMzLTL272GwxVsusYPNj1GTzY9k6fbpa7rdn8+e1RUlFm1apUpKSkxtbW1prS01GzdutUkJiZ6j1HJycne9YSEBJOXl2eqq6vNnj17TEpKij3G8/7p6emmsLDQ1NTUmLKyMrNhwwYTGRnpPT8tLc2cO3fONDQ0mIyMDO/22bNnm4KCAlNXV2fPy87ONoMGDbL79HlUeHj4Td+nf//+5sCBA6aiosI+3/Hjx01qaqoJCQnxHnP//febd99911RVVRm322127txpHn/88Q7xd0AQBEEQ0ko4PvsBAABaof2Wly5daqfuutORvrV/uYZWy/1VMUfH/zsAAKA5mrUDANCOSMoBAMCNMCAcAAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAdzljjCQnJwf6MQAAuKuRnAMA8CXWrVs3SU9Pl1OnTkltba2cOXNGsrKyZOjQoX65X2Jiok32w8PDxZ9Gjx4tBw8elOrqaikvL5c333yzyf6vf/3r8oc//EGuXr0qZWVl8u///u/idDr9+kwAANwJ1x2dDQAAOqyYmBjZv3+/XL58WebNmyfHjh2T4OBgGTlypKxevVr69OkjHZkm0w0NDS22jxs3TtatWycLFiyQt99+W1wulzzyyCPe/UFBQbJt2za5cOGCJCQkSPfu3SUzM1OuXbsmCxcubOe3AADgizMEQRAEQbQeMTExJjMz0y7v9FquzmGmU2SUcXYO8/tzb9u2zZSWlprQ0NAW+8LDw72/VXJysv2dmJho1333x8fH222e94+OjjZZWVmmvLzcuN1uk5+fb0aNGmX3N5eRkWHPcTgcJjU11RQXF5vq6mqTm5trUlJSvPfw3DcpKcnk5OSYuro6u635czudTvtOkydPbvW99RrXr183f/VXf+XdNnXqVHP58mUTHBzcIf4OCIIgCEKaBZVzAADagcMVIvc/MkC6PBArQcGdpPFanVw9WySf5B8Qc/1am98vIiJCkpKSbKVYm343V1lZedvX1qp7SEiIDB482DYbj4uLE7fbLaWlpbaqvWXLFunVq5dcuXJFampq7Dnz58+XCRMmyLRp06SwsNCeu2nTJrl06ZLs3bvXe+1ly5bJ3Llzpbi4WCoqKlrc+7HHHpMHHnhAGhsb5fDhwxIVFSW5ubm2ZcDx48ftMU8++aRtJXDx4kXvedu3b5fXXntNHn74YXs8AAAdDck5AADtQBPz8Nh4uV7tlmtXKsTZqbNdVx/n/iU5bSuxsbG2effJkyfb/NrR0dGyefNmyc/Pt+slJSXefdr/W2li7PkAoIm8NkEfNmyY7SfuOWfgwIEyderUJsn54sWLZdeuXa3eu2fPnna5ZMkSef755+XDDz+UOXPmyO7du+0HAU3oNWHXfua+POu6DwCAjogB4QAA8DNX5zBbMdfE/HqNW0xjg13qum53dg5r83s6HA7xFx1gLi0tTfbt22eT5L59+37uh4IuXbrIzp07paqqyhsTJ06UBx98sMmxOTk5N72WfnBQL730kq3Qa/V80qRJdhC6p59+ug3eDgCAwCA5BwDAzzT51qbsDXWfNvH20HXdrsl7W9Om49r0u3fv3rd0np7TPLnXQeR8rV+/3lawN27caBNzTahnzpzZ6jXDwj59vzFjxki/fv28oc3hx48f3+RYbSZ/M+fPn7fLEydOeLfV19fbZvBa0Vc6EJyOUu/Ls677AADoiEjOAQDws4Yat+1jrk3Zfem6btcqelvT5t3az3rGjBkSGhraYn9rU51pH3ClI5x7aCLd3NmzZ2Xt2rWSkpIiK1askClTpngTZeU7bZkm0jqNmybPOqWbb+h1bsX7779vr/XQQw95t+lo7T169JDTp0/b9QMHDtiPBl/96le9xwwfPtw2s/dN6gEA6EhIzgEA8DNNvnXwN1domK2SO4Kcdqnrul2Td3/QxFyT5EOHDtmB2rR5uVbSZ82aZRPYGykqKrJzoWtzdT1e5xPXPt2+Vq5cKSNGjLAJcf/+/WXIkCFSUFBg92mCrNX3sWPHSteuXW1zdh0sbvny5fY8bcquVXc9T6vtun4rtDm8Duz24osv2oRb+5mvWbPG7nvjjTfscseOHTYJ18r+o48+ap/1xz/+sR3IzvPxAACAjijgQ8YTBEEQREeOtphCy+EKNl37DTYxYyebb/zddLvUdd3uz2ePiooyq1atMiUlJaa2ttZOQ7Z169Ym05T5TqWmkZCQYPLy8uyUZ3v27LFTnvlOpZaenm4KCwtNTU2NKSsrMxs2bDCRkZHe89PS0sy5c+dMQ0ODdyo1jdmzZ5uCggI7TZqel52dbQYNGtTqFG6thcvlMq+88oq5cOGCqaysNDt27DBxcXFNjtHp3nQquatXr5qLFy/a43UatkD/HRAEQRCEtBKOz34AAIBWxMTEyNKlS2XRokXeptN30v9cq+ZaTfdXxRwd/+8AAIDmmEoNAIB2pAk5STkAAGiOPucAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAADc5YwxkpycHOjHAADgrkZyDgDAl1i3bt0kPT1dTp06JbW1tXLmzBnJysqSoUOH+uV+iYmJNtkPDw8Xfxo9erQcPHhQqqurpby8XN58880m+3/yk59ITk6OfecjR4749VkAAGgLrja5CgAA6HBiYmJk//79cvnyZZk3b54cO3ZMgoODZeTIkbJ69Wrp06ePdGROp1MaGhpabB83bpysW7dOFixYIG+//ba4XC555JFHWhz3i1/8Qp544gl59NFH2+mJAQC4M4YgCIIgiNYjJibGZGZm2uWdXssVGmY63R9lnJ3D/P7c27ZtM6WlpSY0NLTFvvDwcO9vlZycbH8nJibadd/98fHxdpvn/aOjo01WVpYpLy83brfb5Ofnm1GjRtn9zWVkZNhzHA6HSU1NNcXFxaa6utrk5uaalJQU7z08901KSjI5OTmmrq7Obmv+3E6n077T5MmTv9C/wb/+67+aI0eOdLi/A4IgCIKQZkHlHACAduBwhcj9jwyQLg/ESlBIJ2msr5OrZ4vkk/wDYq5fa/P7RURESFJSkixcuNA2/W6usrLytq+tVfeQkBAZPHiwXL16VeLi4sTtdktpaamtam/ZskV69eolV65ckZqaGnvO/PnzZcKECTJt2jQpLCy0527atEkuXboke/fu9V572bJlMnfuXCkuLpaKiooW937sscfkgQcekMbGRjl8+LBERUVJbm6ubRlw/Pjx234nAAACjeQcAIB2oIl5+Dfj5Xq1W65dqRBnp852XX2c+5fktK3ExsZKUFCQnDx5ss2vHR0dLZs3b5b8/Hy7XlJS4t2n/b/VxYsXvR8ANJHXJujDhg2z/cQ95wwcOFCmTp3aJDlfvHix7Nq1q9V79+zZ0y6XLFkizz//vHz44YcyZ84c2b17t/0gcKOEHgCA/wkYEA4AAD9zhYbZirkm5tdr3GIaG+xS13W7s3NYm9/T4XCIv+gAc2lpabJv3z6bJPft2/dzPxR06dJFdu7cKVVVVd6YOHGiPPjgg02O1UHcbkY/OKiXXnrJVui1ej5p0iQ7CN3TTz/dBm8HAEBgkJwDAOBnmnxrU/aGuk+beHvoum7X5L2tadNxbfrdu3fvWzpPz2me3Osgcr7Wr19vK9gbN260ibkm1DNnzmz1mmFhn77fmDFjpF+/ft7Q5vDjx49vcqw2k7+Z8+fP2+WJEye82+rr620zeK3oAwDwPxXJOQAAftZQ47Z9zLUpuy9d1+1aQW9r2rx7+/btMmPGDAkNDW2xv7WpzrQPuOrevbt3mybSzZ09e1bWrl0rKSkpsmLFCpkyZYo3UfaMtO6hibROaabJs07p5ht6nVvx/vvv22s99NBD3m06WnuPHj3k9OnTt3QtAAA6EpJzAAD8TJNvHfxNK+SuzmHiCHLapa7rdk3e/UETc02SDx06ZAdq0+blWkmfNWuWHDhw4IbnFBUV2bnQtbm6Hq/ziWufbl8rV66UESNG2IS4f//+MmTIECkoKLD7NEHW6vvYsWOla9eutjm7Dha3fPlye542Zdequ56n1XZdvxXaHP61116TF198UYYPH277ma9Zs8bue+ONN7zHaXP5+Ph4O2Bc586d7W+N5q0AAADoSAI+ZDxBEARBdORoiym0HK5g07XfYBMzdrL5xrjpdqnrut2fzx4VFWVWrVplSkpKTG1trZ2GbOvWrU2mKfOdSk0jISHB5OXl2SnP9uzZY6c8851KLT093RQWFpqamhpTVlZmNmzYYCIjI73np6WlmXPnzpmGhgbvVGoas2fPNgUFBXaaND0vOzvbDBo0qNUp3FoLl8tlXnnlFXPhwgVTWVlpduzYYeLi4poc884777SY1s33HQL1d0AQBEEQ0ko4PvsBAABaERMTI0uXLpVFixbdcdNp52cVc62m+6tijo7/dwAAQHNMpQYAQDvShJykHAAANEefcwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAO5yxhhJTk4O9GMAAHBXIzkHAOBLrFu3bpKeni6nTp2S2tpaOXPmjGRlZcnQoUP9cr/ExESb7IeHh4s/jR49Wg4ePCjV1dVSXl4ub775pnffo48+Kq+//rp9V91/4sQJmT17tl+fBwCAO+W64ysAAIAvzBUaJs7OYXK92i0NNW6/3ismJkb2798vly9flnnz5smxY8ckODhYRo4cKatXr5Y+ffpIR+Z0OqWhoaHF9nHjxsm6detkwYIF8vbbb4vL5ZJHHnnEu/9b3/qWXLx4USZMmCClpaWSkJAgP/vZz+y19L0BAOioDEEQBEEQrUdMTIzJzMy0y9u9hsMVYu7vP9hEf2+y+UbKdLvUdYcr2G/PvW3bNlNaWmpCQ0Nb7AsPD/f+VsnJyfZ3YmKiXffdHx8fb7d53j86OtpkZWWZ8vJy43a7TX5+vhk1apTd31xGRsan7+9wmNTUVFNcXGyqq6tNbm6uSUlJ8d7Dc9+kpCSTk5Nj6urq7Lbmz+10Ou07TZ48+Zb+LX7605+aP/3pTwH/OyAIgiAIaSWonAMA0A4i+w6Q8G/G24p5fVWFOEM623X1yZG9bX6/iIgISUpKkoULF9qm3c1VVlbe9rW1+hwSEiKDBw+Wq1evSlxcnLjdblul1qr2li1bpFevXnLlyhWpqamx58yfP99WsqdNmyaFhYX23E2bNsmlS5dk796/vP+yZctk7ty5UlxcLBUVFS3u/dhjj8kDDzwgjY2NcvjwYYmKipLc3FzbMuD48eOtPrM2s9fm7wAAdFQk5wAAtENT9i5fj7WJ+fXPmrJ7lrr98snDbd7EPTY2VoKCguTkyZPS1qKjo2Xz5s2Sn59v10tKSrz7PAmwNiv3fADQRF6boA8bNsz2E/ecM3DgQJk6dWqT5Hzx4sWya9euVu/ds2dPu1yyZIk8//zz8uGHH8qcOXNk9+7d9oPAjRL6J598Ur7//e/LmDFj2uzfAACAtsaAcAAA+Jn2MXcGd5KG+k+ryB66HhTcySbvbc3hcIi/6ABzaWlpsm/fPpsk9+3b93M/FHTp0kV27twpVVVV3pg4caI8+OCDTY7Nycm56bX0g4N66aWXbIVeq+eTJk2yg9A9/fTTLY5/+OGH5Xe/+528+OKL9v4AAHRUJOcAAPiZVsUbrtXZpuy+dL3xWp2tqLc1bTquTb979+59S+fpOc2Tex1Eztf69ettBXvjxo02MdeEeubMma1eMyzs048PWrnu16+fN7Q5/Pjx45scq83kb+b8+fN2qSOwe9TX19tm8FrR96UD3v3pT3+yg8FpMg8AQEdGcg4AgJ9p8n21tMhWyF2dw8ThdNqlrut2f4zars27t2/fLjNmzJDQ0NAW+1ub6kz7gKvu3bt7t2ki3dzZs2dl7dq1kpKSIitWrJApU6Z4E2XPSOsemkjrNG6aPOuUbr6h17kV77//vr3WQw895N2mo7X36NFDTp8+7d2mif8777wjGzZssFV+AAA6OpJzAADaQfmxA1JZmCcS5JDgeyPsUtd1u79oYq5J8qFDh+xAbdq8XCvps2bNkgMHbnzfoqIiOz+4NlfX43U+ce3T7WvlypUyYsQImxD3799fhgwZIgUFBXafJshafR87dqx07drVNmfXweKWL19uz9Om7Fp11/O02q7rt0Kbw7/22mu2mfrw4cNtP/M1a9bYfW+88Ya3Kbsm5jt27JD/+I//sHO9a+jzAADQkQV8yHiCIAiC6MjRllNoOTuHmU73R9llezx7VFSUWbVqlSkpKTG1tbV2GrKtW7c2mabMdyo1jYSEBJOXl2enPNuzZ4+d8sx3KrX09HRTWFhoampqTFlZmdmwYYOJjIz0np+WlmbOnTtnGhoavFOpacyePdsUFBTYadL0vOzsbDNo0KBWp3BrLVwul3nllVfMhQsXTGVlpdmxY4eJi4vz7v/Xf/1XcyP6b9BR/g4IgiAIQpqF47MfAACgFTExMbJ06VJZtGhRk6bTuLvwdwAA8CeatQMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAANzljDGSnJwc6McAAOCuRnIOAMCXWLdu3SQ9PV1OnToltbW1cubMGcnKypKhQ4f65X6JiYk22Q8PDxd/Gj16tBw8eFCqq6ulvLxc3nzzTe++yMhIyc7Olo8++sj7zqtWrZJ7773Xr88EAMCdcN3R2QAA4JY4Q8PE1TlMrle7paHG7dd7xcTEyP79++Xy5csyb948OXbsmAQHB8vIkSNl9erV0qdPH+nInE6nNDQ0tNg+btw4WbdunSxYsEDefvttcblc8sgjj3j3NzY2yu9+9ztJS0uTS5cuSWxsrH1fTdr//u//vp3fAgCAL84QBEEQBNF6xMTEmMzMTLu83Ws4XCHm/v6DTXTyZNNj/HS71HWHK9hvz71t2zZTWlpqQkNDW+wLDw/3/lbJycn2d2Jiol333R8fH2+3ed4/OjraZGVlmfLycuN2u01+fr4ZNWqU3d9cRkbGp+/vcJjU1FRTXFxsqqurTW5urklJSfHew3PfpKQkk5OTY+rq6uy25s/tdDrtO02ePPmW/i1mzZplzpw5E/C/A4IgCIKQVoLKOQAA7SCy7wD5Sq94WzG/VlUhQSGd7br65MjeNr9fRESEJCUlycKFC23T7+YqKytv+9pahQ4JCZHBgwfL1atXJS4uTtxut5SWltqq9pYtW6RXr15y5coVqampsefMnz9fJkyYINOmTZPCwkJ77qZNm2xle+/ev7z/smXLZO7cuVJcXCwVFRUt7v3YY4/JAw88YKvjhw8flqioKMnNzbUtA44fP37D5+3evbt9rj179tz2OwMA4G8k5wAAtENT9i7RsU2asnuWuv3yycNt3sRdm3IHBQXJyZMnpa1FR0fL5s2bJT8/366XlJR492n/b3Xx4kXvBwBN5LUJ+rBhw2w/cc85AwcOlKlTpzZJzhcvXiy7du1q9d49e/a0yyVLlsjzzz8vH374ocyZM0d2795tPwj4JvSvv/66HeguNDTU9rP/0Y9+1Ob/FgAAtBUGhAMAwM+0j3lQcCdprP+0iuyh67rdFRrW5vd0OBziLzrAnPbn3rdvn02S+/bt+7kfCrp06SI7d+6Uqqoqb0ycOFEefPDBJsfm5OTc9Fr6wUG99NJLtkKv1fNJkybZQeiefvrpJsf+8z//s620f+9737P3+Y//+I/bfmcAAPyNyjkAAH52vcYtjdfqbFN23wq5rut2rai3NW06rk2/e/fufUvn6TnNk3sdRM7X+vXrZfv27TJmzBgZMWKEbbKu1euf/vSnN7xmWNinHx/0eB1B3VddXV2TdW0mfzPnz5+3yxMnTni31dfX22bwWtH3VVZWZuPPf/6zrejrx4SlS5fKhQsXbnoPAAACgco5AAB+1lDtlqtnimyF3Nk5TBxOp13qum73x6jt2rxbE+gZM2bYZt3NtTbVmfYB9/TT9ujXr1+L486ePStr166VlJQUWbFihUyZMsWbKHtGWvfQRFqnNNPkWad08w29zq14//337bUeeugh7zYdrb1Hjx5y+vTpz624d+rU6ZbuBwBAe6FyDgBAOyg/dsDbxzz43ghbMb/yQZ53uz9oYq5TqR06dMj25T569KhNZIcPHy7Tp0+3A7k1V1RUZOcF1+bqOpic9uPWqrivlStX2nnEP/jgAzvw3JAhQ6SgoMDu0wRZq+9jx46Vt956yw4Ip4PFLV++3J6nSbJWsPXjwFNPPWUHjcvMzPzC76TN4V977TV58cUX7QB0ej8dDE698cYbdjlq1Cg7v/t///d/23s//PDD8sorr9j73iyBBwAg0AI+ZDxBEARBdORoyym0nJ3DTKf7o+yyPZ49KirKrFq1ypSUlJja2lo7DdnWrVubTFPmO5WaRkJCgsnLy7NTnu3Zs8dOeeY7lVp6eropLCw0NTU1pqyszGzYsMFERkZ6z09LSzPnzp0zDQ0N3qnUNGbPnm0KCgrsNGl6XnZ2thk0aFCrU7i1Fi6Xy7zyyivmwoULprKy0uzYscPExcV593/nO98x+/fvNxUVFfYd/vznP5uXX375C127vf4OCIIgCEKaheOzHwAAoBUxMTG2r/KiRYuovN7F+DsAAPgTfc4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAADucsYYSU5ODvRjAABwVyM5BwDgS6xbt26Snp4up06dktraWjlz5oxkZWXJ0KFD/XK/xMREm+yHh4eLP40ePVoOHjwo1dXVUl5eLm+++eYNj4uMjJTS0tJ2eSYAAO6E647OBgAAt8QZGiauzmFyvdotDTVuv94rJiZG9u/fL5cvX5Z58+bJsWPHJDg4WEaOHCmrV6+WPn36SEfmdDqloaGhxfZx48bJunXrZMGCBfL222+Ly+WSRx555IbXWL9+vRw9elQeeOCBdnhiAADujCEIgiAIovWIiYkxmZmZdnm713C4Qsz9jw020cmTTY/x0+1S1x2uYL8997Zt20xpaakJDQ1tsS88PNz7WyUnJ9vfiYmJdt13f3x8vN3mef/o6GiTlZVlysvLjdvtNvn5+WbUqFF2f3MZGRmfvr/DYVJTU01xcbGprq42ubm5JiUlxXsPz32TkpJMTk6Oqaurs9uaP7fT6bTvNHny5M99/2nTppl33nnHDBkypMU7BervgCAIgiCklaByDgBAO4h8dIB8pVe8rZhfq6qQoE6d7br65PDeNr9fRESEJCUlycKFC23T7+YqKytv+9padQ8JCZHBgwfL1atXJS4uTtxut20+rlXtLVu2SK9eveTKlStSU1Njz5k/f75MmDBBpk2bJoWFhfbcTZs2yaVLl2Tv3r+8/7Jly2Tu3LlSXFwsFRUVLe792GOP2Sp4Y2OjHD58WKKioiQ3N9e2DDh+/Lj3OG0VsHjxYnniiSekZ8+et/2uAAC0F5JzAADaoSl7l6/HftqUvfrTpuyepW6/XHC4zZu4x8bGSlBQkJw8eVLaWnR0tGzevFny8/PteklJiXef9v9WFy9e9H4A0ERem6APGzbM9hP3nDNw4ECZOnVqk+RcE+pdu3a1em9Por1kyRJ5/vnn5cMPP5Q5c+bI7t277QcBTej1fv/5n/9pE3b9YEByDgD4n4AB4QAA8DPtYx4U3Eka6z6tInvoum53hYa1+T0dDof4iw4wl5aWJvv27bNJct++fT/3Q0GXLl1k586dUlVV5Y2JEyfKgw8+2OTYnJycm15LPziol156yVbotXo+adIkO+Db008/bfe9/PLLUlBQIL/61a/u+F0BAGgvJOcAAPjZ9Rq3NF6rs03Zfem6bteKelvTpuPa9Lt37963dJ6e0zy510Hkmg+yptXojRs32sRcE+qZM2e2es2wsE8/PowZM0b69evnDW0OP378+CbHajP5mzl//rxdnjhxwrutvr7eNoPXir7Skeg1Ub927ZqNP/3pT3b7xx9/bD8mAADQEZGcAwDgZ9qE/Wppka2QaxN3h9P56ajtoWF2uz9Gbdfm3du3b5cZM2ZIaGhoi/2tTSumfcBV9+7dvds0kW7u7NmzsnbtWklJSZEVK1bIlClTvImyZ6R1D02kdRo3TZ51Sjff0Ovcivfff99e66GHHvJu09Hae/ToIadPn7br+kzx8fHejwA/+tGP7PZBgwbZ/vIAAHRE9DkHAKAdlB894O1jHnxvhK2YX/kgz7vdHzQx16nUDh06ZPty65RimsgOHz5cpk+fbivXzRUVFdm50LXCrIPJaT9u7dPta+XKlZKdnS0ffPCBHXhuyJAhthm50gRZq+9jx46Vt956yw4Ip4PFLV++3J6nzdK1Obx+HHjqqafsoHGZmZlf+J20Ofxrr70mL774ou1PrvfTvuXqjTfesEutovvq2rWrXeoz3slAeAAA+FvAh4wnCIIgiI4cbTmFlrNzmOl0f5RdtsezR0VFmVWrVpmSkhJTW1trpyHbunVrk2nKfKdS00hISDB5eXl2yrM9e/bYKc98p1JLT083hYWFpqamxpSVlZkNGzaYyMhI7/lpaWnm3LlzpqGhwTuVmsbs2bNNQUGBnSZNz8vOzjaDBg1qdQq31sLlcplXXnnFXLhwwVRWVpodO3aYuLi4Vo+/lWu3198BQRAEQUizcHz2AwAAtCImJkaWLl0qixYt8jadxt2HvwMAgD/R5xwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAOAuZ4yR5OTkQD8GAAB3NZJzAAC+xLp16ybp6ely6tQpqa2tlTNnzkhWVpYMHTrUL/dLTEy0yX54eLj40+jRo+XgwYNSXV0t5eXl8uabbzbZr8/QPL7//e/79ZkAALgTrjs6GwAA3BJnaJi4QsPkerVbGqrdfr1XTEyM7N+/Xy5fvizz5s2TY8eOSXBwsIwcOVJWr14tffr0kY7M6XRKQ0NDi+3jxo2TdevWyYIFC+Ttt98Wl8sljzzySIvj/s//+T/yxz/+0buu/w4AAHRkhiAIgiCI1iMmJsZkZmba5e1ewxEcYiK/Ndh8/e8mm5hnptulrjtcwX577m3btpnS0lITGhraYl94eLj3t0pOTra/ExMT7brv/vj4eLvN8/7R0dEmKyvLlJeXG7fbbfLz882oUaPs/uYyMjI+fX+Hw6Smppri4mJTXV1tcnNzTUpKivcenvsmJSWZnJwcU1dXZ7c1f26n02nfafLkyTd9d9936kh/BwRBEAQhrQTN2gEAaAcRjw6Qr/SKt82rr1VV2KWuR8Q/6Z/7RURIUlKSrZBr0+/mKisrb/vaes1OnTrJ4MGDpW/fvvLCCy+I2+2W0tJSW9VWvXr1kqioKHnuuefs+vz582XixIkybdo0efjhh2XlypWyadMmew1fy5Ytk9TUVFvVP3r0aIt7P/bYY/LAAw9IY2OjHD58WM6dOydvvfWWveaNnvPSpUvy3nvvyaRJk277fQEAaA80awcAoB2asneJjpXrNX9pyu5Zdvl6rFQWHG7zJu6xsbESFBQkJ0+elLYWHR0tmzdvlvz8fLteUlLi3af9v9XFixe9HwBCQkJsE/Rhw4bZfuKecwYOHChTp06VvXv3es9fvHix7Nq1q9V79+zZ0y6XLFkizz//vHz44YcyZ84c2b17t/0gUFFRYfcvWrTINnnXDxMjRoyQV199VcLCwmTVqlVt/u8BAEBbIDkHAMDPtI95UHAnWzH31VhXI8H3Rtj9bZ2cOxwO8RcdYG7NmjU26dVEWhN17c9+sw8FXbp0kZ07dzbZrkn7kSNHmmzLycm56b31g4N66aWXZMuWLfa3VsXPnj0rTz/9tPzsZz+z23784x97z8nNzbX31373JOcAgI6KZu0AAPiZDv7WeK1Ogjp1brJd1xvr6+z+tlZYWGibfvfu3fuWztNzmif3Ooicr/Xr19sK9saNG22zdk2oZ86c2eo1tWKtxowZI/369fNGXFycjB8/vsmxV69evenznT9/3i5PnDjh3VZfXy/FxcW2ot8abdr+9a9/3X4QAACgIyI5BwDAz7QqfvVMkbg6h9km7g6n89NR2zuHydXSIr+M2q7Nu7dv3y4zZsyQ0NDQFvtbm+pM+2ir7t27e7dpIt2cVqrXrl0rKSkpsmLFCpkyZYo3UfaMtO6hibRO46bJs07p5ht6nVvx/vvv22s99NBD3m06WnuPHj3k9OnTrZ6n76BN7j3PBwBAR0OzdgAA2kFF3gFvH3Ntyq4V8ysf5Hm3+4Mm5jqV2qFDh2xfbh1gTRPZ4cOHy/Tp023lurmioiI7F7r26V64cKHtx619un3pYG7Z2dnywQcf2IHnhgwZIgUFBXafJshafR87dqwdqK2mpsYOFrd8+XJ7njZL37dvn/048NRTT8mVK1ckMzPzC79TVVWVvPbaa/Liiy/aAej0ftpcXb3xxht2qffW+d21f7sm8vq+2uddnwEAgI4s4EPGEwRBEERHjracQssZGmY6dY2yy/Z49qioKLNq1SpTUlJiamtr7TRkW7dubTJNWfNpxxISEkxeXp6d8mzPnj12yjPfqdTS09NNYWGhqampMWVlZWbDhg0mMjLSe35aWpo5d+6caWho8E6lpjF79mxTUFBgp0nT87Kzs82gQYNancKttXC5XOaVV14xFy5cMJWVlWbHjh0mLi7Ou3/kyJHm8OHD5sqVK6aqqsocOXLE/N//+3/tdG4d5e+AIAiCIKRZOD77AQAAWhETEyNLly61I4DfrOk0vtz4OwAA+BN9zgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAO5yxhhJTk4O9GMAAHBXIzkHAOBLrFu3bpKeni6nTp2S2tpaOXPmjGRlZcnQoUP9cr/ExESb7IeHh4s/jR49Wg4ePCjV1dVSXl4ub775Zotj/vEf/1Hy8vKkpqZGysrK5Kc//alfnwkAgDvhuqOzAQDALXGGhokrNEyuV7ulodrt13vFxMTI/v375fLlyzJv3jw5duyYBAcHy8iRI2X16tXSp08f6cicTqc0NDS02D5u3DhZt26dLFiwQN5++21xuVzyyCOPNDnmn//5n2XOnDn2vd977z3p0qWL9OjRox2fHgCAW2cIgiAIgmg9YmJiTGZmpl3e7jUcwSEm4luDzdf/brKJ/v50u9R1hyvYb8+9bds2U1paakJDQ1vsCw8P9/5WycnJ9ndiYqJd990fHx9vt3nePzo62mRlZZny8nLjdrtNfn6+GTVqlN3fXEZGxqfv73CY1NRUU1xcbKqrq01ubq5JSUnx3sNz36SkJJOTk2Pq6urstubP7XQ67TtNnjy51fe+7777zNWrV83QoUM73N8BQRAEQUgrQeUcAIB2cN+jAyT8ofhPK+ZXKiSoU2e7rire39vm94uIiJCkpCRZuHChbfrdXGVl5W1fW6vuISEhMnjwYLl69arExcWJ2+2W0tJSW9XesmWL9OrVS65cuWKblKv58+fLhAkTZNq0aVJYWGjP3bRpk1y6dEn27v3L+y9btkzmzp0rxcXFUlFR0eLejz32mDzwwAPS2Ngohw8flqioKMnNzbUV8uPHj9tjhg8fLkFBQfK1r31NTpw4Iffee6+8++67tpJ+9uzZ235vAAD8ieQcAIB2aMoeFh3bpCm7Z6nbrxQcbvMm7rGxsTZBPXnypLS16Oho2bx5s+Tn59v1kpIS7z7t/60uXrzo/QCgibw2QR82bJjtJ+45Z+DAgTJ16tQmyfnixYtl165drd67Z8+edrlkyRJ5/vnn5cMPP7RJ9+7du+0HAU3o9Rh9d73nc889Z5/jxz/+sezcuVMeffRRuXbtWpv/mwAAcKcYEA4AAD/TPuaOkE7SWPdpFdlD13W77m9rDodD/EUHmEtLS5N9+/bZJLlv376f+6FA+3xrclxVVeWNiRMnyoMPPtjk2JycnJteS5Nu9dJLL9kKvVbPJ02aZAehe/rpp73H6AeB2bNny44dO2yf8x/84AfyzW9+U4YMGXLH7w8AgD+QnAMA4GdaMTf1dbYpuy9d1+26v61p03Ft+t27d+9bOk/PaZ7c6yByvtavX2+r0xs3brSJuSbUM2fObPWaYWGffnwYM2aM9OvXzxvaHH78+PFNjtVm8jdz/vx5u9Tm6h719fW2GbxW9Fs75uOPP7bhOQYAgI6G5BwAAD/TJuvuM0W2Qq5N3B1Op3fUdt3uj1HbtXn39u3bZcaMGRIaGtpif2tTnWkfcNW9e3fvNk2km9O+22vXrpWUlBRZsWKFTJkyxZsoe0Za99AkWadx08RYp3TzjVvtA/7+++/baz300EPebTpau47Efvr0abuuI9Qr32O0D37Xrl29xwAA0NGQnAMA0A4u5x2Qyj/n2Yq06ysRdqnrut1fNDHXJPnQoUN2oDZtXq6V9FmzZsmBAze+b1FRkZ0LXZur6/E6n7j26fa1cuVKGTFihE2I+/fvb5uKFxQU2H2a/Gr1fezYsTYZ1ubsOljc8uXL7XnalF2r7nqeVtt1/VZoc/jXXntNXnzxRTvwm/YzX7Nmjd33xhtveFsNbN26VX7yk5/Ik08+KQ8//LBs2LDB9r9/5513bvNfEwAA/wv4kPEEQRAE0ZGjLafQcoaGmU5do+yyPZ49KirKrFq1ypSUlJja2lo7DdnWrVubTFPmO5WaRkJCgsnLy7NTnu3Zs8dOeeY7lVp6eropLCw0NTU1pqyszGzYsMFERkZ6z09LSzPnzp0zDQ0N3qnUNGbPnm0KCgrsNGl6XnZ2thk0aFCrU7i1Fi6Xy7zyyivmwoULprKy0uzYscPExcU1Oebee+81P//5z+10bx9//LHZvHmzeeCBBzrM3wFBEARBSLNwfPYDAAC0IiYmRpYuXSqLFi2iWfRdjL8DAIA/0awdAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgLmeMkeTk5EA/BgAAdzWScwAAvsS6desm6enpcurUKamtrZUzZ85IVlaWDB061C/3S0xMtMl+eHi4+NPo0aPl4MGDUl1dLeXl5fLmm2969/3jP/6jfYYbxVe/+lW/PhcAALfLddtnAgCAW+YMDRNXaJhcr3ZLQ7Xbr/eKiYmR/fv3y+XLl2XevHly7NgxCQ4OlpEjR8rq1aulT58+0pE5nU5paGhosX3cuHGybt06WbBggbz99tvicrnkkUce8e7/9a9/LX/84x+bnPPLX/5S7rnnHrl06VK7PDsAALfDEARBEATResTExJjMzEy7vN1rOIJDTMS3B5uvp0w20f97ul3qusMV7Lfn3rZtmyktLTWhoaEt9oWHh3t/q+TkZPs7MTHRrvvuj4+Pt9s87x8dHW2ysrJMeXm5cbvdJj8/34waNcruby4jI+PT93c4TGpqqikuLjbV1dUmNzfXpKSkeO/huW9SUpLJyckxdXV1dlvz53Y6nfadJk+e/IX/Hbp27WqvN2HChID/HRAEQRCEtBJUzgEAaAf3xQ+Q8N7xcv2qWxquVEhQp852XVXk7G3z+0VEREhSUpIsXLjQNv1urrKy8ravrVX3kJAQGTx4sFy9elXi4uLE7XZLaWmprWpv2bJFevXqJVeuXJGamhp7zvz582XChAkybdo0KSwstOdu2rTJVrL37v3L+y9btkzmzp0rxcXFUlFR0eLejz32mDzwwAPS2Ngohw8flqioKMnNzbUtA44fP37D5504caL9N/jtb3972+8MAIC/kZwDANAOTdnDYmI/Tcw/a8ruWer2KycOt3kT99jYWAkKCpKTJ09KW4uOjpbNmzdLfn6+XS8pKfHu0/7f6uLFi94PAJrIaxP0YcOG2X7innMGDhwoU6dObZKcL168WHbt2tXqvXv27GmXS5Yskeeff14+/PBDmTNnjuzevdt+ELhRQv/DH/5QXn/9ddvnHgCAjorkHAAAP9M+5o7gTrZi7quxrkZcX4mw+9s6OXc4HOIvOsDcmjVrZMSIETaR1kRd+7Pf7ENBly5dZOfOnU22a9J+5MiRJttycnJuem/94KBeeuklW6FXkyZNkrNnz8rTTz8tP/vZz5ocP2DAAFvZ/4d/+Idbfk8AANoTo7UDAOBnOvibuVZnm7L70nXdrvvbmjYd16bfvXv3vqXz9Jzmyb0OIudr/fr1toK9ceNG6du3r02oZ86c2eo1w8LC7HLMmDHSr18/b2jSPH78+CbHajP5mzl//rxdnjhxwrutvr7eNoPXin5zP/rRj+wHAG0CDwBAR0ZyDgCAn2lV3H26SFxdwmwTd4fT+emo7V3C7HZ/jNquzbu3b98uM2bMkNDQ0Bb7W5vqzDOaeffu3b3bNJFuTivVa9eulZSUFFmxYoVMmTLFmyh7Rlr30ERam5Rr8qxTuvmGXudWvP/++/ZaDz30kHebjtbeo0cPOX36dJNjtVr/zDPP2I8JAAB0dCTnAAC0g8u5B6TyZJ44ghy2KbsudV23+4sm5pokHzp0yA7Ups3LtZI+a9YsOXDgxvctKiqyc6Frn249XucT1z7dvlauXGmbtGtC3L9/fxkyZIgUFBTYfZoga/V97Nix0rVrV5sg62Bxy5cvt+fp4GxaddfztNqu67eiqqpKXnvtNXnxxRdl+PDhtp+5NrFXb7zxRpNjv//979vEXQeeAwDgf4KADxlPEARBEB052nIKLWdomOnUNcou2+PZo6KizKpVq0xJSYmpra2105Bt3bq1yTRlvlOpaSQkJJi8vDw75dmePXvslGe+U6mlp6ebwsJCU1NTY8rKysyGDRtMZGSk9/y0tDRz7tw509DQ4J1KTWP27NmmoKDATmum52VnZ5tBgwa1OoVba+Fyucwrr7xiLly4YCorK82OHTtMXFxci+P2799vNm3a1CH/DgiCIAhCmoXjsx8AAKAVMTExsnTpUlm0aFGLptO4e/B3AADwJ5q1AwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAA3OWMMZKcnBzoxwAA4K5Gcg4AwJdYt27dJD09XU6dOiW1tbVy5swZycrKkqFDh/rlfomJiTbZDw8PF38aPXq0HDx4UKqrq6W8vFzefPPNJvu//e1vy65du6SiosLu/+Mf/yiPPvqoX58JAIA7QXIOAEA7coaGSaeuUXbpbzExMfL+++/bRHzevHnSt29fSUpKknfeeUdWr14tHZ3T6bzh9nHjxsnGjRslIyND4uPj5amnnpLXX3/du79Lly42GdcPEU888YQMHDhQqqqqZPv27eJyudrxDQAAuDWGIAiCIIjWIyYmxmRmZtrl7V7DERxiIr492DwwfrKJ/sF0u9R1hyvYb8+9bds2U1paakJDQ1vsCw8P9/5WycnJ9ndiYqJd990fHx9vt3nePzo62mRlZZny8nLjdrtNfn6+GTVqlN3fXEZGxqfv73CY1NRUU1xcbKqrq01ubq5JSUnx3sNz36SkJJOTk2Pq6urstubP7XQ67TtNnjy51ff+1re+Za/1wAMPeLc98sgjdtuDDz4Y0L8DgiAIgpBWgso5AADt4L74AfKVPvFiGo1cu1Jhl7p+X78n/XK/iIgIWyXXCrk2/W6usrLytq+t1+zUqZMMHjzYVuNfeOEFcbvdUlpaaqvaqlevXhIVFSXPPfecXZ8/f75MnDhRpk2bJg8//LCsXLlSNm3aZK/ha9myZZKamip9+vSRo0ePtrj3Y489Jg888IA0NjbK4cOH5dy5c/LWW2/Za3r8+c9/lo8//lh++MMfSnBwsNxzzz3294kTJ+TDDz+87fcGAMCfaNsFAICfaRP2Lj1i5fpVtzRUu+02z7JLTKxcOXHYu95WYmNjJSgoSE6ePCltLTo6WjZv3iz5+fl2vaSkxLtP+3erixcvej8AhISEyIIFC2TYsGG2n7jnHG1uPnXqVNm7d6/3/MWLF9u+4q3p2bOnXS5ZskSef/55m2zPmTNHdu/ebT8IaB9z/VDwne98R7Zu3SqLFi2yxxcWFsrIkSOloaGhzf89AABoC1TOAQDwM1domAQFd5LGupom23U9KKST3d/WHA6H+IsOMJeWlib79u2zSbJWzz/vQ4H2A9+5c6ft++0JraQ/+OCDTY7Nycm56bX0g4N66aWXZMuWLbZ6PmnSJDsI3dNPP233aaV8/fr1sn//fhkwYIDtk64fErZt22b3AQDQEVE5BwDAz65Xu6XxWp0EdercpEKu6431dXZ/W9NKsTb97t279y2dp+c0T+61abgvTXx1cLUxY8bIiBEjbJN1rV7/9Kc/veE1w8I+/figx3/00UdN9tXV1TVZv3r16k2f7/z583apTdQ96uvrpbi42Fb01bPPPis9evSQJ5980ibtnm1aVdcp4379619/gX8JAADaF5VzAAD8TBPyqx8WiatLmG3i7nA67VLXr54uavMm7UoTUU2gZ8yYIaGhoS32tzbV2aVLl+yye/fu3m39+vVrcdzZs2dl7dq1kpKSIitWrJApU6Z4E+XmI61rIq3TuGnyrFO6+YZe51bo6PN6rYceesi7TUdg12T89OnTdl3fVz8yeBJz5Vn3VN4BAOho+D8UAADt4HLuAblSkGcr0sFfibBLXdft/qKJuSbJhw4dsgO1afNyraTPmjVLDhy48X2LiorsFGTaXF2P1/nEtSruSwdz04q5JsT9+/eXIUOGSEFBgd2nCbImwmPHjpWuXbva5uzaB3z58uX2PG3Krv3G9byZM2fa9VuhzeFfe+01efHFF2X48OG2n/maNWvsvjfeeMMutfm8DoinA9fp+8bFxdlp165fv26nkQMAoKMK+JDxBEEQBNGRoy2n0HKGhplOXaPssj2ePSoqyqxatcqUlJSY2tpaOw3Z1q1bm0xT5juVmkZCQoLJy8uzU57t2bPHTnnmO5Vaenq6KSwsNDU1NaasrMxs2LDBREZGes9PS0sz586dMw0NDd6p1DRmz55tCgoK7DRpel52drYZNGhQq1O4tRYul8u88sor5sKFC6aystLs2LHDxMXFNTlm2LBh5r/+679MRUWF+eSTT8yuXbvME0880WH+DgiCIAhCmoXjsx8AAKAVMTExsnTpUjvyt6fpNO4+/B0AAPyJZu0AAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAB3OWOMJCcnB/oxAAC4q5GcAwDwJdatWzdJT0+XU6dOSW1trZw5c0aysrJk6NChfrlfYmKiTfbDw8PFn0aPHi0HDx6U6upqKS8vlzfffLPJfn2//fv3y5UrV+T8+fOybNkycTqdfn0mAADuhOuOzgYAALfE2SVMXKFhcv2qWxqq3X69V0xMjE1QL1++LPPmzZNjx45JcHCwjBw5UlavXi19+vSRjkyT6YaGhhbbx40bJ+vWrZMFCxbI22+/LS6XSx555BHv/kcffVTeeusteemll2TixInyta99TV577TV7Pf13AACgozIEQRAEQbQeMTExJjMz0y5v9xqO4BAT8TeDzQNPTzbRz063S113uIL99tzbtm0zpaWlJjQ0tMW+8PBw72+VnJxsfycmJtp13/3x8fF2m+f9o6OjTVZWlikvLzdut9vk5+ebUaNG2f3NZWRkfPr+DodJTU01xcXFprq62uTm5pqUlBTvPTz3TUpKMjk5Oaaurs5ua/7cTqfTvtPkyZNbfe+XXnrJHDp0qMm2sWPH2vuGhYUF9O+AIAiCIKSVoHIOAEA7uK/fAPlKn3hbMb92pUKCOnW266riv/e2+f0iIiIkKSlJFi5caJt+N1dZWXnb19aqe0hIiAwePFiuXr0qcXFx4na7pbS01Fa1t2zZIr169bJNymtqauw58+fPlwkTJsi0adOksLDQnrtp0ya5dOmS7N37l/fX5udz586V4uJiqaioaHHvxx57TB544AFpbGyUw4cPS1RUlOTm5tqK+PHjx+0xnTp1sk34felzdO7cWb71rW/Jnj17bvvdAQDwF5JzAADaoSl7lx6xTZqye5a6/crxw23exD02NlaCgoLk5MmT0taio6Nl8+bNkp+fb9dLSkq8+7T/t7p48aL3A4Am8toEfdiwYbafuOecgQMHytSpU5sk54sXL5Zdu3a1eu+ePXva5ZIlS+T555+XDz/8UObMmSO7d++2HwQ0od++fbv8f//f/yf/+3//b/nNb35jE3i9rurevXub/3sAANAWGBAOAAA/0z7mQcGdpLHu0yqyh67rdleXsDa/p8PhEH/RAebS0tJk3759Nknu27fv534o6NKli+zcuVOqqqq8of3BH3zwwSbH5uTk3PRa+sFBaX9yrdBr9XzSpEl2ELqnn37a7tP7aCVd+5nX1dXJBx98YPugK624AwDQEZGcAwDgZ9er3dJ4rc42Zfel67pdK+ptTZuOayLau3fvWzrPk7z6Jvc6iJyv9evX2wr2xo0bbWKuCfXMmTNbvWZY2KcfH8aMGSP9+vXzhjaHHz9+fJNjtZn8zejI6+rEiRPebfX19bYZvFb0PVauXCn33Xef3da1a1f53e9+Z7frcQAAdEQk5wAA+FnDVbdc/bDIVsidoWHicDrtUtd1uz9Gbfc0754xY4aEhoa22N/aVGfaB7x5829NpJs7e/asrF27VlJSUmTFihUyZcoUb6KsfKct00Ra+4BroqxTuvmGXudWvP/++/ZaDz30kHebjtbeo0cPOX369A2TeT3+Bz/4gZ1GTivtAAB0RPQ5BwCgHVw+csDbxzz4KxG2Yn6lIM+73R80Mdep1A4dOmT7XB89etQmssOHD5fp06fbynVzRUVFNonV5uo6mJz249Y+3b60Kp2dnW2bi+vAc0OGDJGCggK7TxNkrb6PHTvWNiXXgdh0sLjly5fb87RZujaH148DTz31lB00LjMz8wu/kzaH1+bqL774oh2ATu/nmR7tjTfe8B6ng8r98Y9/tM+ig9SlpqbKM888Q7N2AECHFvAh4wmCIAiiI0dbTqHlDA0znb4aZZft8exRUVFm1apVpqSkxNTW1tppyLZu3dpkmjLfqdQ0EhISTF5enp16bM+ePXbKM9+p1NLT001hYaGpqakxZWVlZsOGDSYyMtJ7flpamjl37pxpaGjwTqWmMXv2bFNQUGCnSdPzsrOzzaBBg1qdwq21cLlc5pVXXjEXLlwwlZWVZseOHSYuLq7JMX/6059MRUWFfYcDBw7YKdo60t8BQRAEQUizcHz2AwAAtCImJkaWLl0qixYtumHTadwd+DsAAPgTfc4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAADucsYYSU5ODvRjAABwVyM5BwDgS6xbt26Snp4up06dktraWjlz5oxkZWXJ0KFD/XK/xMREm+yHh4f79fo3im9/+9ve4/r27St79+6Vmpoa+87z5s3zy/MAANBWXG12JQAA8LmcXcLEGRomDVfd0lDt9uu9YmJiZP/+/XL58mWbnB47dkyCg4Nl5MiRsnr1aunTp490ZE6nUxoaGppse/fddyUqKqrJtqVLl8p3v/tdycnJsev33nuv7NixQ3bt2iXTpk2zifovfvEL+++wbt26dn0HAABuhSEIgiAIovWIiYkxmZmZdnm713AEh5iIxwebrz0z2Xz976fbpa47XMF+e+5t27aZ0tJSExoa2mJfeHi497dKTk62vxMTE+267/74+Hi7zfP+0dHRJisry5SXlxu3223y8/PNqFGj7P7mMjIyPn1/h8Okpqaa4uJiU11dbXJzc01KSor3Hp77JiUlmZycHFNXV2e3fd47ulwuU1ZWZtLS0rzbpk2bZj755BMTHPyXf9uXX37ZFBQUBPzvgCAIgiCklaByDgBAO7iv/wC5t0+8XL/qlmtXKiSoU2e7rioO7W3z+0VEREhSUpIsXLhQqqurW+yvrKy87Wtr1T0kJEQGDx4sV69elbi4OHG73VJaWirjxo2TLVu2SK9eveTKlSu2WbmaP3++TJgwwVayCwsL7bmbNm2SS5cu2ebnHsuWLZO5c+dKcXGxVFRUfO6zfO9735P7779fMjIyvNuefPJJe81r1655t23fvl1SU1PlvvvusxV0AAA6GpJzAADaoSl7aI9Ym5h7mrJ7lqExsXIl/3CbN3GPjY2VoKAgOXnypLS16Oho2bx5s+Tn59v1kpIS777y8nK7vHjxovcDgCbyCxYskGHDhsnBgwe95wwcOFCmTp3aJDlfvHixbY7+Rf3whz+0ifdHH33k3abN3n2fSZWVlXn3kZwDADoiknMAAPxM+5gHBXeyFXNfjXU1EvyVCJu8t3Vy7nA4xF90gLk1a9bIiBEjbCKtibr2Z7/Zh4IuXbrIzp07m2zXpP3IkSNNtnn6jX8RX/va12z/+WeeeeY23gIAgI6F0doBAPAzTbwbr9XZpuy+dL2xvs4ODtfWtOl4Y2Oj9O7d+5bO03OaJ/c6iJyv9evXS8+ePWXjxo12sDVNqGfOnNnqNcPCwuxyzJgx0q9fP29oc/jx48c3OVabyX9RkyZNkk8++cSOPu/rwoULdpR6X5513QcAQEdEcg4AgJ9p8l39YZG4Phup3eF02qWuV58u8suo7dpfW5t7z5gxQ0JDQ1vsb22qM+0Drrp37+7dpol0c2fPnpW1a9dKSkqKrFixQqZMmWK319fXe0da9zhx4oSdxk2bw+uUbr6h17ldmpxnZmbK9evXm2w/cOCA7dPucv2lgeDw4cNtE3+atAMAOiqScwAA2sHlwwekqiDPVqS1KbsudV23+4sm5pokHzp0yA7Ups3LtZI+a9Ysm8DeSFFRkZ0XfMmSJfb40aNHy5w5c5ocs3LlStukvUePHtK/f38ZMmSIFBQU2H2nT5+21fexY8dK165dbXN2HSxu+fLl9ryJEyfaqruep9V2Xb8dOk+7XufnP/95i32vv/66/UigFX6tzmuz9+eee07+4z/+47buBQBAewn4kPEEQRAE0ZGjLafQcoaGmZCvRtllezx7VFSUWbVqlSkpKTG1tbV2arWtW7c2mabMdyo1jYSEBJOXl2enPNuzZ4+d8sx3KrX09HRTWFhoampq7DRmGzZsMJGRkd7zdVqzc+fOmYaGBu9UahqzZ8+205npNGl6XnZ2thk0aFCrU7jdLH71q1+Zffv2tbq/b9++Zu/evfYZ9Z3/5V/+pUP9HRAEQRCENAvHZz8AAEArYmJiZOnSpbJo0SJbGcbdib8DAIA/0awdAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgLmeMkeTk5EA/BgAAdzWScwAAvsS6desm6enpcurUKamtrZUzZ85IVlaWDB061C/3S0xMtMl+eHi4X69/o/j2t79tj+nUqZNkZGTI0aNH5dq1a/Lmm2/65VkAAGhLrja9GgAAuClnlzBxhoZJw1W3NFS7/XqvmJgY2b9/v1y+fFnmzZsnx44dk+DgYBk5cqSsXr1a+vTpIx2Z0+mUhoaGJtveffddiYqKarJt6dKl8t3vfldycnK859XU1NiPEikpKe36zAAA3C4q5wAAtANHcIjc9/hg6Tb2GfmrpL+Tbv/rGbvucAX77Z6vvvqqrSg//vjjsmXLFiksLJQTJ07IypUrZcCAAV+48h0fH2+3abKvoqOjbfW9vLxc3G635Ofny6hRo+z+3bt322P0g4CeoxVs+/4Oh6SmpkpxcbFUV1dLbm5uk8TZc9+kpCSbZNfV1cnAgQNbPJ9WwsvKyrzxySef2Cb5nvsovf4//dM/yc9//nO5cOFCG/6LAgDgP1TOAQBoB+H9B8i9cfG2Yn69skKCOnW26+ryob1tfr+IiAib6C5cuNAmq81VVlbe9rW16h4SEiKDBw+Wq1evSlxcnE3SS0tLZdy4cfZDQK9eveTKlSu2gq3mz58vEyZMkGnTptmPBHrupk2b5NKlS7J371/ef9myZTJ37lybxFdUVHzus3zve9+T+++/v0lyDgDA/0Qk5wAAtENT9tBvxDZpyu5Z6vaq/MNt3sQ9NjZWgoKC5OTJk9LWtHK+efNmWzFXJSUl3n1aTVcXL170fgDQRH7BggUybNgwOXjwoPccrYxPnTq1SXK+ePFi2bVr1xd+lh/+8Ieyfft2+eijj9rs/QAACASScwAA/Ez7mAeFdLIVc1+NdTXiCo+wyXtbJ+fajNxftC/3mjVrZMSIETaR1kRd+7Pf7ENBly5dZOfOnU22a9J+5MiRJts8/ca/iK997Wu2//wzzzxzG28BAEDHQp9zAAD8TBPvxvo625Tdl67rdq2otzVtOt7Y2Ci9e/e+pfP0nObJvQ4i52v9+vXSs2dP2bhxo/Tt29cm1DNnzmz1mmFhYXY5ZswY6devnze0Ofz48eObHKvN5L+oSZMm2T7n2v8dAID/6UjOAQDwM02+q0uKvCO1O5xOu9R13e6PUdu1v7Y2954xY4aEhoa22N/aVGfaB1x1797du00T6ebOnj0ra9eutYO6rVixQqZMmWK319fXe0dM99BB6HQaN20Or1O6+YZe53Zpcp6ZmSnXr1+/7WsAANBRkJwDANAOKg8fkKoTeSJBDtuUXZe6rtv9RRNzTZIPHTpkB2rT5uVaSZ81a5YcOHDj+xYVFdm50JcsWWKPHz16tMyZM6fJMTrauzZp79Gjh/Tv31+GDBkiBQUFdt/p06dt9X3s2LHStWtX25xdB4tbvny5PW/ixIm26q7nabVd12+HztOu19ER2W9Ep4nTUeYjIyPthwj9rQEAQEdmCIIgCIJoPWJiYkxmZqZd3um1nKFhJuSrUXbZHs8eFRVlVq1aZUpKSkxtba0pLS01W7duNYmJid5jVHJysnc9ISHB5OXlmerqarNnzx6TkpJij/G8f3p6uiksLDQ1NTWmrKzMbNiwwURGRnrPT0tLM+fOnTMNDQ0mIyPDu3327NmmoKDA1NXV2fOys7PNoEGD7D59HhUeHv6F3utXv/qV2bdvX6v79X1vpKP8HRAEQRCENAvHZz8AAEArdP7upUuXyqJFi2xlGHcn/g4AAP5Es3YAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAIC7nDFGkpOTA/0YAADc1UjOAQD4EuvWrZukp6fLqVOnpLa2Vs6cOSNZWVkydOhQv9wvMTHRJvvh4eF+vf6N4tvf/rb3mK1bt8q5c+fE7XbLkSNH5Nlnn/XL8wAA0FZcbXYlAADwuZxdwsQZGiYNV93SUO32671iYmJk//79cvnyZZk3b54cO3ZMgoODZeTIkbJ69Wrp06ePdGROp1MaGhqabHv33XclKiqqybalS5fKd7/7XcnJybHrCQkJcvToUfm3f/s3KSsrk7Fjx0pmZqZUVlbKtm3b2vUdAAC4FYYgCIIgiNYjJibGZGZm2uXtXsMRHGLue2Kw+ev/Pdk8MHG6Xeq6wxXst+fetm2bKS0tNaGhoS32hYeHe3+r5ORk+zsxMdGu++6Pj4+32zzvHx0dbbKyskx5eblxu90mPz/fjBo1yu5vLiMj49P3dzhMamqqKS4uNtXV1SY3N9ekpKR47+G5b1JSksnJyTF1dXV22+e9o8vlMmVlZSYtLe2mx/3hD38w69evD/jfAUEQBEFIK0HlHACAdhD+2AC5Ny7eVsyvV1ZIUKfOdl1dfm9vm98vIiJCkpKSZOHChVJdXd1iv1aRb5dW3UNCQmTw4MFy9epViYuLs83HS0tLZdy4cbJlyxbp1auXXLlyRWpqauw58+fPlwkTJsi0adOksLDQnrtp0ya5dOmS7N37l/dftmyZzJ07V4qLi6WiouJzn+V73/ue3H///ZKRkXHT47SZfUFBwW2/MwAA/kZyDgBAOzRlD/1GbJOm7J6lbq86drjNm7jHxsZKUFCQnDx5UtpadHS0bN68WfLz8+16SUmJd195ebldXrx40fsBQBP5BQsWyLBhw+TgwYPecwYOHChTp05tkpwvXrxYdu3a9YWf5Yc//KFs375dPvroo1aPefrpp+Vv/uZv7L0AAOioSM4BAPAz7WMeFNLJVsx9NdbViOsrETZ5b+vk3OFwiL/oAHNr1qyRESNG2ERaE3Xtz36zDwVdunSRnTt3NtmuSbsO1ubL02/8i/ja175m+88/88wzrR7zne98x1bVp0yZIidOnPjC1wYAoL0xWjsAAH6miXdjfZ1tyu5L1xuv1dmKelvTpuONjY3Su3fvWzpPz2me3Osgcr7Wr18vPXv2lI0bN0rfvn1tQj1z5sxWrxkWFmaXY8aMkX79+nlDm8OPHz++ybHaTP6LmjRpknzyySd29Pkb0abzv//97+Wf//mf7bMCANCRkZwDAOBnmnxXlxR5R2p3OJ12qeu63R+jtmt/bW3uPWPGDAkNDW2xv7WpzrQPuOrevbt3mybSzZ09e1bWrl0rKSkpsmLFCluZVvX19d6R1j20Yq3TuGlzeJ3SzTf0OrdLk3Mdhf369est9ul0ajoy+wsvvCDr1q277XsAANBeSM4BAGgHle8fkKoTeSJBDtuUXZe6rtv9RRNzTZIPHTpkB2rT5uVaSZ81a5YcOHDj+xYVFdm50JcsWWKPHz16tPz/7N0LdNXlmej/Z+cGJJtGLpYwnCbKbRBFoKgoJkQQMQjHnEOo2jkM1CoFy2XaAU65BMqU9pSxIMsgtUylMQHralfhn0YxIChypzRcIhmCBpKBICVcEpLsXDF5/+t5de/ZSQhy2Ts7le9nrWf99u/93VlZi/X83uf3vrNnz260z8qVK21J+1133SWDBw+WESNGeAZbO3XqlO191+nLunbtasvZdbC45cuX2+MmTZpke931OO1t1/WbofO063neeOONq5aya2Ku5fdacq9zvWvoIHkAALRlAR8yniAIgiDacvhyCq3gcKcJuzPKLlvj3qOiosyqVatMYWGhqampsVOrZWRkNJqmzHsqNY1hw4aZnJwcO+XZjh077JRn3lOppaSkmPz8fFNdXW2nMUtLSzOdO3f2HK/Tmp09e9bU19d7plLTmDVrlsnLy7PTpOlxWVlZJi4ursUp3K4Vb731ltm9e/dVt+k1r2b79u1t5u+AIAiCIKRJOL78AQAAWhATEyNLly6VRYsW2Z5h3J74OwAA+BNl7QAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAANzmjDGSmJgY6NsAAOC2RnIOAMDXWLdu3SQlJUVOnjwpNTU1cvr0acnMzJSRI0f65Xrx8fE22Y+MjPTr+a8WDzzwgN2nb9++8uGHH8q5c+ekurraPvvSpUslJCTEL/cEAIAv8L8UAABfUzExMbJnzx65fPmyzJ07V44ePSqhoaHy5JNPyurVq+Wee+6Rtiw4OFjq6+sbte3du1eioqIatWni/fjjj0t2drZdv3LliqSnp8uhQ4fssw8cOFB++9vfSlBQkCxcuLBVnwEAgBthCIIgCIJoOWJiYkx6erpd3uq5giOcJuybUXbp7/vetGmTKSoqMuHh4c22RUZGen6rxMRE+zs+Pt6ue28fOHCgbXM/f3R0tMnMzDQlJSXG5XKZ3NxcM2bMGLu9qdTUVHuMw+Ew8+bNMwUFBaaqqsocOXLEJCUlea7hvm5CQoLJzs42tbW1tu2rnjEkJMQUFxeb5OTka+63YsUKs3Pnzjbzd0AQBEEQ0iToOQcAoBU4QsMkcsjDEn53b3GEtRNTVytVhSek7OA+MVeu+Px6nTp1koSEBNtTXFVV1Wx7WVnZTZ9be93DwsJk+PDhUllZKf379xeXyyVFRUUyfvx42bhxoy0tLy8vt2Xlav78+TJx4kSZNm2a5Ofn22PXr18vFy5ckJ07d3rOvWzZMpkzZ44UFBRIaWnpV97L008/LV26dJHU1NQW9+nVq5f9t9D7AgCgrSI5BwCgFWhi3vHegfJ5pUvqy0olqH0Hu64u7//v5NRXevfubcu4jx8/7vNzR0dHy4YNGyQ3N9euFxYWeraVlJTY5fnz5z0vADSRX7BggYwaNUr279/vOSY2NlamTp3aKDlfvHixbNu27brv5YUXXpAtW7bIZ5991myblvR/+9vflvbt28uaNWvsuQEAaKtIzgEA8LPgCKftMbeJeaXLtrmX2l5x9JBn3VccDof4iw4w9/rrr8vo0aNtIq2Jun7Pfq0XBREREbJ169ZG7Zq0Hz58uFGb+7vx69GjRw/7/fwzzzxz1e3PPvusdOzY0X5z/qtf/cr2yOsSAIC2iOQcAIBWSM61lF17zL011FRLSGQnu93XybmWjjc0NEi/fv1u6Dg9pmlyr4PIeVu7dq3trR47dqxN0LVkffbs2fLaa69d9ZxOp9Mudf+mPdy1tbWN1rVM/no9//zzcunSJTv6/NWcOXPGLvPy8uzgcv/xH/8hK1as8DwjAABtCVOpAQDgZ5p46zfmWsruTde13deJudLvtTWBnj59uoSHhzfb3tJUZ/oNuOrevbunbdCgQVdNfLVUPCkpySa8U6ZMse11dXV2qcmw27Fjx+w0bloOr9OaeYc7gb4ZmpzrqOyff/75V+6rJf76kkGXAAC0RfScAwDgZ5p86+Bv7m/MtcdcE/OQCKdU/GeOX5JzpYm5fnd94MAB+731xx9/bOf6fuKJJ+Sll16yA7k1deLECTsX+pIlS+xgcjqwm/aKe1u5cqVkZWXJp59+ageeGzFihO2dVqdOnbI90+PGjZP33nvPDging8UtX77cHqfJ8e7du+3LgUcffdQOGqcJ9o3Sedp79uwpb7zxRrNt//RP/2SnU9NSe+2Z1/nPf/nLX8of/vCH60rkAQAIlIAPGU8QBEEQbTl8MYWWIzTU3PHwcPMP3/2+6TH5JbvUdW33571HRUWZVatWmcLCQlNTU2OnVsvIyGg0TZn3VGoaw4YNMzk5OXbKsx07dtgpz7ynUktJSTH5+fmmurraTmOWlpZmOnfu7DlepzU7e/asqa+v90ylpjFr1iyTl5dnp0nT47KyskxcXFyLU7hdK9566y2ze/fuq2575pln7HRs5eXlpqKiwk71ptO4tWvXLuB/BwRBEAQhLYTjyx8AAKAFMTExsnTpUlm0aJHtGb4V+n25+xtzf/WYo+3/HQAA0BRl7QAAtCKScgAAcDWMigIAAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAMBtzhgjiYmJgb4NAABuayTnAAB8jXXr1k1SUlLk5MmTUlNTI6dPn5bMzEwZOXKkX64XHx9vk/3IyEi/nv9q8cADDzTbv1evXlJeXi6lpaV+uR8AAHwlxGdnAgAAbUpMTIzs2bNHLl++LHPnzpWjR49KaGioPPnkk7J69Wq55557pC0LDg6W+vr6Rm179+6VqKioRm1Lly6Vxx9/XLKzsxu1h4SEyNtvvy27du2SYcOGtco9AwBws+g5BwCgFQVHOCXsm1F26W+//vWvbY/yQw89JBs3bpT8/Hw5duyYrFy5Uh5++OHr7vkeOHCgbdNkX0VHR9ve95KSEnG5XJKbmytjxoyx2z/66CO7j74Q0GNSU1PtusPhkHnz5klBQYFUVVXJkSNHJCkpqdl1ExISbJJdW1srsbGxze7vypUrUlxc7IlLly7Zknz3dbz9/Oc/l+PHj8sf//hHH/xrAgDgX/ScAwDQChyhYRL5wMMSfndvcYS1E1NXK1WFJ6Qse5+YK1d8fr1OnTrZRHfhwoU2GW6qrKzsps+tve5hYWEyfPhwqayslP79+9skvaioSMaPH29fBPTt29eWk1dXV9tj5s+fLxMnTpRp06bZlwR67Pr16+XChQuyc+dOz7mXLVsmc+bMsUn89ZSiP/3009KlS5dmyfmIESPkO9/5jgwaNMjeEwAAbR3JOQAArUAT8479B8rnlS6pv1wqQe072HV1ed9/J6e+0rt3bwkKCrI9x76mPecbNmywPeaqsLDQs01709X58+c9LwA0kV+wYIGMGjVK9u/f7zlGe8anTp3aKDlfvHixbNu27brv5YUXXpAtW7bIZ5995mnr3LmzvPnmm/ZlQEVFhQ+eGAAA/yM5BwDAz7SEXXvMbWJe6bJt7qW2V3x8yLPuK1pG7i86wNzrr78uo0ePtom0Jur6Pfu1XhRERETI1q1bG7Vr0n748OFGbU2/G7+WHj162O/nn3nmmUbtv/3tb+X3v/+9/dYcAIC/F3xzDgBAKyTnWsreUPNFibebrjtC2/nl+3MtHW9oaJB+/frd0HF6TNPkXgeR87Z27Vrp2bOnrFu3TgYMGGAT6hkzZrR4Tqfzi+cbO3asLTN3h5bDT5gwodG+WiZ/vZ5//nn7zbl+/+5NR6LX0nj9Pl1D7/eOO+6wv/UYAADaIpJzAAD8THvF9RtzLWX3puvmSq3Pe82Vfq+t5d7Tp0+X8PDwZttbmupMvwFX3bt397RpIt3UmTNnZM2aNXZQtxUrVsiUKVNse11dnWekdTcdhE6ncdNyeJ3SzTv0PDdLE+309HT5/PPPG7U/8sgjjV4CaKm8fv+uv/+//+//u+nrAQDgT5S1AwDgZ5p86+Bv7m/MtcdcE/OQCKdUHMvxS3KuNDHXqdQOHDhgE9SPP/7YTi/2xBNPyEsvvWR7rps6ceKEnQt9yZIldjA5Hdht9uzZjfbR0d6zsrLk008/tQPP6eBreXl5dtupU6ds7/u4cePkvffeswPC6WBxy5cvt8fpd/C7d++2LwceffRRmzRrgn2jtHdce+/feOONZtuafmev85/rPf3nf/7nDV8HAIDWQs85AACtQEdl10TcEeSQkMhOdqnr2u4vOujat7/9bdm+fbvt3dYB3PS7b50TXJPzq9Fe6O9+97u2HF6T+Z/85CeSnJzcaB/tFdcR2zUh37x5s03Sf/jDH9ptZ8+elZ/+9Kd21HWd6uy1116z7YsWLbLzkeuo7e7jtMzdezC5G6EDwemLh08++eSmjgcAoK3RD8pMoG8CAIC2TOfv1sRSE0ztGb4V+n25hvaW+6vHHG3/7wAAgKYoawcAoBWRlAMAgKuhrB0AAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAIDbnDFGEhMTA30bAADc1kjOAQD4GuvWrZukpKTIyZMnpaamRk6fPi2ZmZkycuRIv1wvPj7eJvuRkZF+Pf/V4oEHHrD7xMTEXHX70KFD/XJPAAD4QohPzgIAANocTVL37Nkjly9flrlz58rRo0clNDRUnnzySVm9erXcc8890pYFBwdLfX19o7a9e/dKVFRUo7alS5fK448/LtnZ2Y3ate0///M/PeuXLl3y8x0DAHDz6DkHAKAVBUc4JeybUXbpb7/+9a9tj/FDDz0kGzdulPz8fDl27JisXLlSHn744evu+R44cKBt02RfRUdH2973kpIScblckpubK2PGjLHbP/roI7uPvhDQY1JTU+26w+GQefPmSUFBgVRVVcmRI0ckKSmp2XUTEhJskl1bWyuxsbHN7u/KlStSXFzsCU24tSTffR1vus17388//9wH/6oAAPgHPecAALQCR2iYfOOBhyX87t4SFNZOGupqparwhJRn7xNz5YrPr9epUyeb6C5cuNAmw02VlZXd9Lm11z0sLEyGDx8ulZWV0r9/f5ukFxUVyfjx4+2LgL59+0p5eblUV1fbY+bPny8TJ06UadOm2ZcEeuz69evlwoULsnPnTs+5ly1bJnPmzLFJfGlp6Vfey9NPPy1dunS5anKuLxDat28vn376qbz88svyzjvv3PQzAwDgbyTnAAC0Ak3MO947UOorXXKlrFSC23ew66ps338np77Su3dvCQoKkuPHj/v83NpzvmHDBttjrgoLCz3btDddnT9/3vMCQBP5BQsWyKhRo2T//v2eY7RnfOrUqY2S88WLF8u2bduu+15eeOEF2bJli3z22WeeNn1R8K//+q+2pL+hocH20GdkZMj/+l//iwQdANBmkZwDAOBnWsKuPeaamGso91LbXR8f8qz7ipaR+4sOMPf666/L6NGjbSKtibp+z36tFwURERGydevWRu2atB8+fLhRW9Pvxq+lR48e9vv5Z555plk5u5bue5/zH/7hH+x39yTnAIC2im/OAQBoheRcS9nra74o8XbTdW33x/fnWjquvcb9+vW7oeP0mKbJvQ4i523t2rXSs2dPWbdunQwYMMAmvzNmzGjxnE7nF883duxYGTRokCe0HH7ChAmN9tUy+ev1/PPP20Rcy9e/yl/+8hf7kgAAgLaK5BwAAD/TXnH9xlxL2b3purb7utdc6ffaWu49ffp0CQ8Pb7a9panO9Btw1b17d0+bJtJNnTlzRtasWWNLxlesWCFTpkyx7XV1dZ6R1t10EDqdxk3L4XVKN+/Q89wsTc7T09Ova6A3fYa//e1vN30tAAD8jbJ2AAD8TJNvHfzN/Y259phrYq495hX/meOX5FxpYq7fXR84cMB+y/3xxx9LSEiIPPHEE/LSSy/ZnuumTpw4YedCX7JkiR1MTgd2mz17dqN9tGQ8KyvLDrSmA8+NGDFC8vLy7LZTp07Z3vdx48bJe++9ZweE02/Aly9fbo/T7+B3795tXw48+uijdtA4TbBvlM7Trr33b7zxRrNtkyZNsi8J3CXzOkjd97//fXnxxRdv+DoAALQmQxAEQRBEyxETE2PS09Pt8mbP4QgNNZGPDDfd/+n7psf3XrJLXdd2f957VFSUWbVqlSksLDQ1NTWmqKjIZGRkmPj4eM8+KjEx0bM+bNgwk5OTY6qqqsyOHTtMUlKS3cf9/CkpKSY/P99UV1eb4uJik5aWZjp37uw5Pjk52Zw9e9bU19eb1NRUT/usWbNMXl6eqa2ttcdlZWWZuLg4u03vR0VGRl7Xc7311ltm9+7dV902adIk85//+Z/G5XKZy5cvm/3799tnaAt/BwRBEAQhLYTjyx8AAKAFOn/30qVLZdGiRbZn+FZob7mG9+BwuP3+DgAAaIqydgAAWhFJOQAAuBoGhAMAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAOA2Z4yRxMTEQN8GAAC3NZJzAAC+xrp16yYpKSly8uRJqampkdOnT0tmZqaMHDnSL9eLj4+3yX5kZKRfz3+1eOCBBxrtO3v2bPnkk0/sc585c0YWLFjgl3sCAMAXQnxyFgAA0ObExMTInj175PLlyzJ37lw5evSohIaGypNPPimrV6+We+65R9qy4OBgqa+vb9S2d+9eiYqKatS2dOlSefzxxyU7O9vT9uqrr8ro0aNlzpw59rk7d+5sAwCAtswQBEEQBNFyxMTEmPT0dLu81XMFRzhN2Dej7NLf971p0yZTVFRkwsPDm22LjIz0/FaJiYn2d3x8vF333j5w4EDb5n7+6Ohok5mZaUpKSozL5TK5ublmzJgxdntTqamp9hiHw2HmzZtnCgoKTFVVlTly5IhJSkryXMN93YSEBJOdnW1qa2tt21c9Y0hIiCkuLjbJycmetn79+pm6ujrTt2/fNvt3QBAEQRDSJOg5BwCgFThCw+QbDz4s4Xf3lqCwdtJQVytVhSek/K/7xFy54vPrderUSRISEmThwoVSVVXVbHtZWdlNn1t73cPCwmT48OFSWVkp/fv3F5fLJUVFRTJ+/HjZuHGj9O3bV8rLy6W6utoeM3/+fJk4caJMmzZN8vPz7bHr16+XCxcuyM6dOz3nXrZsme3tLigokNLS0q+8l6efflq6dOkiqampnrb/+T//pz1+3LhxMmPGDHE4HLJt2zb5v//3/17XOQEACASScwAAWoEm5h3vHSj1lS65UlYqwe072HVVtve/k1Nf6d27twQFBcnx48d9fu7o6GjZsGGD5Obm2vXCwkLPtpKSErs8f/685wWAJvL6vfeoUaNk//79nmNiY2Nl6tSpjZLzxYsX20T6er3wwguyZcsW+eyzzzxtPXv2tCX93/nOd2TSpEm2PH7lypXypz/9yZa/AwDQFpGcAwDgZ8ERTttjrom5hnIvtd2Vc8iz7ivaW+wvOsDc66+/br/p1kRaE3X9rvtaLwoiIiJk69atjdo1aT98+HCjNu/vxr9Kjx497PfzzzzzTKN2fSnRvn17m5hrL707iT906JDt0f/000+v+xoAALQWRmsHAKAVknMtZa+v+aLE203XtV23+5ompQ0NDdKvX78bOk6PaZrc6yBy3tauXWt7p9etWycDBgywCbWWj7fE6fzi+caOHSuDBg3yhJbDT5gwodG+WiZ/vZ5//nm5dOmSHX3e29/+9je5cuWKJzFXeXl5nl5/AADaIpJzAAD8THvF9RtzLWX3puva7utec6XfVmu59/Tp0yU8PLzZ9pamOtNvwFX37t09bZpIN6VTk61Zs0aSkpJkxYoVMmXKFNteV1dnl1pK7nbs2DE7nZkmxjqlm3foeW6WJufp6eny+eefN2rXEer1hYK+QHDTHnN16tSpm74eAAD+RHIOAICfafKtg79pD7ntJQ8O9vzWdn8k50oTc02SDxw4YAdq0/Jy7UmfOXOm7Nu376rHnDhxws6FvmTJErv/U089ZecL96bfb2tJ+1133SWDBw+WESNGeHqmNfnV3ncdjK1r1662nF0Hi1u+fLk9TkvNNWnW47S3Xddvhs7Trud54403mm3TUvuDBw/K7373O/ti4dvf/rZ9kfD+++836k0HAKCtCfiQ8QRBEATRlsMXU2g5QkNN5LDhpvv/+b7p8fxLdqnr2u7Pe4+KijKrVq0yhYWFpqamxk6tlpGR0WiaMu+p1DSGDRtmcnJy7JRnO3bssFOeeU+llpKSYvLz8011dbWdxiwtLc107tzZc7xOa3b27FlTX1/vmUpNY9asWSYvL89Ok6bHZWVlmbi4uBancLtWvPXWW2b37t0tbu/evbv505/+ZMrLy83f/vY387vf/c506tQp4H8HBEEQBCEthOPLHwAAoAU68vfSpUtl0aJFt1wW7e4x9x4cDrff3wEAAE0xWjsAAK2IpBwAAFwN35wDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwBwmzPGSGJiYqBvAwCA2xrJOQAAX2PdunWTlJQUOXnypNTU1Mjp06clMzNTRo4c6ZfrxcfH22Q/MjLSr+e/WjzwwAN2n5/+9KdX3e5yufxyTwAA+EKIT84CAADanJiYGNmzZ49cvnxZ5s6dK0ePHpXQ0FB58sknZfXq1XLPPfdIWxYcHCz19fWN2vbu3StRUVGN2pYuXSqPP/64ZGdn2/Xly5fLb37zm0b7fPDBB/LXv/61Fe4aAICbQ885AACtKNjplLBuURIc4fT7tX7961/bHuOHHnpINm7cKPn5+XLs2DFZuXKlPPzww9fd8z1w4EDbpsm+io6Otr3vJSUltjc6NzdXxowZY7d/9NFHdh99IaDHpKam2nWHwyHz5s2TgoICqaqqkiNHjkhSUlKz6yYkJNgku7a2VmJjY5vd35UrV6S4uNgTly5dsiX57uuoysrKRvto9cC9994ra9eu9eG/LgAAvkXPOQAArcARGibfePBh6dCrtwSFtZOGulqpPnlCyv+6T8yVKz6/XqdOnWyiu3DhQpsMN1VWVnbT59Ze97CwMBk+fLhNhPv372+T9KKiIhk/frx9EdC3b18pLy+X6upqe8z8+fNl4sSJMm3aNPuSQI9dv369XLhwQXbu3Ok597Jly2TOnDk2iS8tLf3Ke3n66aelS5cujZLzpl588UX55JNPZPfu3Tf9zAAA+BvJOQAArUATc+d9A6W+0iVXLpdKcPsOdl2V7f3v5NRXevfuLUFBQXL8+HGfn1t7zjds2GB7zFVhYaFnm/amq/Pnz3teAGgiv2DBAhk1apTs37/fc4z2jE+dOrVRcr548WLZtm3bdd/LCy+8IFu2bJHPPvvsqtvbtWsn/+f//B+b9AMA0JaRnAMA0Aql7Npjrom5hnIvtd2Vc8iz7itaRu4vOsDc66+/LqNHj7aJtCbq+j37tV4UREREyNatWxu1a9J++PDhRm3u78avR48ePez3888880yL+/zv//2/pWPHjpKWlnbd5wUAIBD45hwAAD/T78u1lL2+5osSbzdd13ZN3n1NS8cbGhqkX79+N3ScHtM0uddB5Lzpt9s9e/aUdevWyYABA2xCPWPGjBbP6fzy+caOHSuDBg3yhJbDT5gwodG+WiZ/vZ5//nn7zbl+/36tkvZ3333X9uQDANCWkZwDAOBn2iuu35hrKbs3Xdf2ej9M8aXfa2u59/Tp0yU8PLzZ9pamOtNvwFX37t09bZpIN3XmzBlZs2aNHdRtxYoVMmXKFNteV1fnGWndTQeh02nctBxep3TzDj3PzdLkPD09XT7//POrbr/rrrtkxIgRDAQHAPi7QHIOAICfafKtg79pD7odpT042PNb231d0u6mibkmyQcOHLADtWl5ufakz5w5U/bt23fVY06cOGHnQl+yZInd/6mnnpLZs2c32kdHe9eSdk1+Bw8ebBPgvLw8u+3UqVO2933cuHHStWtXW86ug8Xp9GZ63KRJk2yvux6nve26fjN0nnY9zxtvvNHiPt///vflb3/7m2RlZd3UNQAAaE0k5wAAtAIdld2VmyMS5JDQOzrZpa5ru7/ooGvf/va3Zfv27bZ3Wwdw0+++dU7wl1566arHaC/0d7/7XZvEf/zxx/KTn/xEkpOTG+2jCb+O2K4J+ebNm+XTTz+VH/7wh3bb2bNn5ac//akdgE2nMXvttdds+6JFi+x85Dpqu/s4LXP3HkzuRuhAcDqHu47CfjValv+9731P3nzzTU+pPgAAbZl+UGYCfRMAALRlOn+3JpaaYGrP8K2wPeZOp+1N91ePOdr+3wEAAE0xWjsAAK3Ie8R2AAAAN8raAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuM0ZYyQxMTHQtwEAwG2N5BwAgK+xbt26SUpKipw8eVJqamrk9OnTkpmZKSNHjvTL9eLj422yHxkZ6dfzXy0eeOABz36jR4+Wffv2SXl5uZw/f17+9Kc/SUxMjF/uCQAAXyA5BwDga0qT0YMHD9pEfO7cuTJgwABJSEiQ7du3y+rVq6WtCw4Obta2d+9eiYqKahS//e1vpaCgQLKzs+0+d911l/z5z3+WDz/8UAYNGiRPPvmkdO3aVTZu3BiApwAA4PoZgiAIgiBajpiYGJOenm6Xt3quYKfThHWLMsERTr/f96ZNm0xRUZEJDw9vti0yMtLzWyUmJtrf8fHxdt17+8CBA22b+/mjo6NNZmamKSkpMS6Xy+Tm5poxY8bY7U2lpqbaYxwOh5k3b54pKCgwVVVV5siRIyYpKclzDfd1ExISTHZ2tqmtrbVtX/WMISEhpri42CQnJ3va9Lx1dXX2mu62cePGmfr6ert/W/g7IAiCIAhpEiE3kMQDAICb5AgNk2889LB06Nlbgtq1k4baWqkuOCHlB/aJuXLF59fr1KmT7SVfuHChVFVVNdteVlZ20+fWXvewsDAZPny4VFZWSv/+/cXlcklRUZGMHz/e9lD37dvXlpRXV1fbY+bPny8TJ06UadOmSX5+vj12/fr1cuHCBdm5c6fn3MuWLZM5c+bYnvDS0tKvvJenn35aunTpIqmpqZ42rRZoaGiQ559/Xt58801xOp3yz//8z7Jt2zb5/PPPb/q5AQDwJ5JzAABagSbmzvsGSr3LJVdKSyW4fQe7rsr2/Hdy6iu9e/eWoKAgOX78uM/PHR0dLRs2bJDc3Fy7XlhY6NlWUlJil/qdt/sFgCbyCxYskFGjRsn+/fs9x8TGxsrUqVMbJeeLFy+2SfT1euGFF2TLli3y2Wefedr+67/+y35z/sc//lHWrFkjISEhthz+qaee8sHTAwDgH3xzDgCAnwU7nbbHXBPz+kqXSH29Xeq6tgdHOH1+TYfDIf6iA8wlJyfL7t27ZcmSJfZb9q96URARESFbt26ViooKT0yaNEl69erVaF/3d+PXo0ePHvZ78rVr1zYbBE+/Q09LS5MHH3zQ9tLX1dXZQeEAAGir6DkHAMDPNPnWUnbtMfdWX1MtoXd0ssm7Tdp9SEvHtbS7X79+N3ScHtM0uQ8NDW20jybD2ls9duxY20OtJeuzZ8+W11577arn1LJypft793Cr2traRutaJn+9tGz90qVLdvR5b9OnT7e99j/5yU88bVpSf+bMGRk6dKj85S9/ue5rAADQWug5BwDAzzTx1m/MtZTdm6431NXaHnRf0++1NYHWRDU8PLzZ9pamOtNvwFX37t09bTrieVOa6GrJeFJSkqxYsUKmTJli27WHuulI68eOHbPTuGk5vE7p5h16npulyXl6enqz78j1ed0vGdzq6+vtUkv9AQBoi/gfCgAAP9PkWwd/0x5yW8IeHGyXuq7tvu41d9PEXJPkAwcO2IHatLxce9Jnzpxp5wC/mhMnTti50LVcXffX77S1V9zbypUrbY+5Tlk2ePBgGTFihOTl5dltp06dsonxuHHj7PRlWs6ug8UtX77cHqel7D179rTHzZgxw67fDJ0eTs/zxhtvNNu2adMmW86+aNEi+wx6LR0wTr9FP3z48E1dDwCA1hDwIeMJgiAIoi2HL6bQcoSGmshHh5uof/6++YcXXrJLXdd2f957VFSUWbVqlSksLDQ1NTV2arWMjIxG05R5T6WmMWzYMJOTk2OnPNuxY4edmsx7KrWUlBSTn59vqqur7TRmaWlppnPnzp7jdVqzs2fP2qnL3FOpacyaNcvk5eXZadL0uKysLBMXF9fiFG7Xirfeesvs3r27xe3PPvusOXjwoKmoqLDX0mf+x3/8x4D/HRAEQRCEtBCOL38AAIAWxMTEyNKlS21PrPYM3wp3j7lncDjcln8HAAA0xYBwAAC0IjtKO0k5AABogm/OAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuM0ZYyQxMTHQtwEAwG2N5BwAgK+xbt26SUpKipw8eVJqamrk9OnTkpmZKSNHjvTL9eLj422yHxkZ6dfzXy0eeOABz37f+c535PDhw1JZWSn/9V//JXPmzPHL/QAA4CshPjsTAABoU2JiYmTPnj1y+fJlmTt3rhw9elRCQ0PlySeflNWrV8s999wjbVlwcLDU19c3atu7d69ERUU1alu6dKk8/vjjkp2dbdcTEhLkrbfekpkzZ8r7779vn/O3v/2tVFdX2+cGAKCtMgRBEARBtBwxMTEmPT3dLm/1XMFOpwnrFmWCI5x+v+9NmzaZoqIiEx4e3mxbZGSk57dKTEy0v+Pj4+269/aBAwfaNvfzR0dHm8zMTFNSUmJcLpfJzc01Y8aMsdubSk1Ntcc4HA4zb948U1BQYKqqqsyRI0dMUlKS5xru6yYkJJjs7GxTW1tr277qGUNCQkxxcbFJTk72tL311lvmj3/8Y6P9ZsyYYU6fPt1m/g4IgiAIQpoEPecAALQCR2iYdBz6sIT36i2OsHZi6mql6uQJqfjLPjFXrvj8ep06dbI9yAsXLpSqqqpm28vKym763Nr7HBYWJsOHD7dl4/379xeXyyVFRUUyfvx42bhxo/Tt21fKy8ttb7WaP3++TJw4UaZNmyb5+fn22PXr18uFCxdk586dnnMvW7bMlqAXFBRIaWnpV97L008/LV26dJHU1FRPW7t27Zo9s97Ht771LVtNcOrUqZt+dgAA/IXkHACAVqCJuXPAQKl3ueTzy6US3L6DXVflu/87OfWV3r17S1BQkBw/ftzn546OjpYNGzZIbm6uXS8sLPRsKykpscvz5897XgBoIr9gwQIZNWqU7N+/33NMbGysTJ06tVFyvnjxYtm2bdt138sLL7wgW7Zskc8++8zTpusrV66UN998U7Zv327/LWbPnm23de/eneQcANAmkZwDAOBnwU6n7THXxLy+0mXb3Mvwnr2l8vAhz7qvOBwO8RcdYO7111+X0aNH20RaE3X9nr0lmhxHRETI1q1bG7Vr0q6Dtnlzfzd+PXr06GG/n3/mmWcatev35b169ZJ3333XfmOvPfivvvqq/Nu//Zs0NDRc9/kBAGhNjNYOAICfBUc4bSl7fc0XJd5uuu5o184m776mpeOaiPbr1++GjnMnr97JvSa43tauXSs9e/aUdevWyYABA2xCPWPGjBbP6fzy+caOHSuDBg3yhJbDT5gwodG+WiZ/vZ5//nm5dOmSHX2+qXnz5tnrahm7DiB34MAB267l8gAAtEUk5wAA+Jn2ius35lrK7k3XTW2t7VH3Nf1eW8u7p0+fLuHh4c22tzTVmX4D7i7/dtNEuqkzZ87ImjVrJCkpSVasWCFTpkyx7XV1dZ6R1t2OHTtmp3HTcnid0s079Dw3S5Pz9PR0+fzzz1t80XD27Fm5cuWKfPe737UjvV+8ePGmrwcAgD9R1g4AgJ9p8q2Dv7m/Mdcec03MtcfcdTTH5yXtbpqY61Rq2mus33J//PHHEhISIk888YS89NJLtue6qRMnTti50JcsWWIHk9OB3dzfa7vp99xZWVny6aef2oHnRowYIXl5eXabfs+tSfG4cePkvffeswOx6WBxy5cvt8fpd/C7d++2LwceffRRW3KuCfaN0nnatff+jTfeaLZNB4jTHvmPPvpI2rdvb5N4nfdc50gHAKAtC/iQ8QRBEATRlsMXU2g5QkPNN2KHm6hJ3zfdp7xkl7qu7f6896ioKLNq1SpTWFhoampq7NRqGRkZjaYp855KTWPYsGEmJyfHTnm2Y8cOO+WZ91RqKSkpJj8/31RXV9tpzNLS0kznzp09x+u0ZmfPnjX19fWeqdQ0Zs2aZfLy8uw0aXpcVlaWiYuLa3EKt2uFTpe2e/fuq27r0qWL2bt3r6moqLBTvW3dutU89NBDbeLvgCAIgiCkhXB8+QMAALRAv1teunSpLFq06JZH+tbvz7XH3HtwONx+fwcAADRFWTsAAK1IE3KScgAA0BQDwgEAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAHCbM8ZIYmJioG8DAIDbGsk5AABfY926dZOUlBQ5efKk1NTUyOnTpyUzM1NGjhzpl+vFx8fbZD8yMlL8pU+fPpKRkSEXLlyQsrIy2bVrlzz22GON9vnWt74l7777rlRWVkpxcbG8/PLLEhwc7Ld7AgDgVoXc8hkAAMB1C3Y6JTjCKfUul9RXuvx6rZiYGNmzZ49cvnxZ5s6dK0ePHpXQ0FB58sknZfXq1XLPPfdIW6bJdH19fbN2Tbrz8/PtC4bq6mr50Y9+ZNt69eplE/GgoCDZtGmTnDt3ToYNGybdu3eX9PR0uXLliixcuDAgzwIAwPUwBEEQBEG0HDExMSY9Pd0ub/YcjtAw843YOBM1+fum+w+m2aWuO0JD/XbfmzZtMkVFRSY8PLzZtsjISM9vlZiYaH/Hx8fbde/tAwcOtG3u54+OjjaZmZmmpKTEuFwuk5uba8aMGWO3N5WamvrF8zscZt68eaagoMBUVVWZI0eOmKSkJM813NdNSEgw2dnZpra21rY1ve8uXbrY/WJjYz1tTqfTtj3++ON2Xc/x+eefm29+85uefaZOnWouX75sQm/h39sXfwcEQRAEIS0EPecAALSCjkOHivP+gbbH/PPSUgnu0MGuq/Ldu3x+vU6dOklCQoLtKa6qqmq2XcvBb5b2uoeFhcnw4cNt2Xj//v3F5XJJUVGRjB8/XjZu3Ch9+/aV8vJy27Ot5s+fLxMnTpRp06bZXm89dv369bY0fefOnZ5zL1u2TObMmSMFBQVSWlra7NqXLl2S48ePy6RJk+TQoUNSW1srU6dOtT3mBw8etPs88sgjtkrg/PnznuO2bNkiv/nNb+Tee++VI0eO3PSzAwDgLyTnAAC0Qil7eK8+X5Syu74oZXcvw3v1lsrDh31e4t67d29b3q2JrK9FR0fLhg0bJDc3164XFhZ6tpWUlNilJsbuFwCayC9YsEBGjRol+/fv9xwTGxtrE2vv5Hzx4sWybdu2a15fz6PfnFdUVEhDQ4O9lr6I0PJ9FRUVZZN1b+513QYAQFtEcg4AgJ/pN+aOdmG2x9xbfXW1hN7RySbvvk7OHQ6H+IsOMPf666/L6NGjbSKtibr2VF/rRUFERIRs3bq1Ubsm7YcPH27Ulp2dfV0995qQx8XF2Z75F198Ud555x158MEH7XfmAAD8PWK0dgAA/EwTb1NbZ0vZvem6qav19KL7kpaOa69yv379bug4PaZpcq+DyHlbu3at9OzZU9atWycDBgywCfWMGTNaPKfT6bTLsWPHyqBBgzyh5fATJkxotK+WyV+LDgI3btw4ee6552Tv3r02uZ8+fbpN0idPnmz30QRdR6n35l4neQcAtFUk5wAA+Jkm31Un878YqV0T1eBgz++qkyf8Mmq7fq+t31lr4hoeHt5se0tTnek34EpHOHfTRLqpM2fOyJo1ayQpKUlWrFghU6ZMse11dXV26T1t2bFjx+w0bloOr1O6eYee50a4n8X9EsFN17WMX+3bt8++NLjzzjs925944glbZq/3AgBAW0RyDgBAK6j4y35xfZxje6S1lF2Xuq7t/qKJuSbJBw4csAO1aXm59qTPnDnTJrBXc+LECTsX+pIlS+z+Tz31lMyePbvRPitXrrQl7XfddZcMHjxYRowYIXl5eXbbqVOnbKKsvdtdu3a15ew6WNzy5cvtcTqQm/a663Ha267rN0LvW188pKWlyf3332/nPNc5zO+++247fZp6//33bRKuPfu6j97rz3/+c1sO7355AABAWxTwIeMJgiAIoi2HL6fQCo5wmrBuUXbZGvceFRVlVq1aZQoLC01NTY2dWi0jI6PRNGXeU6lpDBs2zOTk5Ngpz3bs2GGnPPOeSi0lJcXk5+eb6upqU1xcbNLS0kznzp09xycnJ5uzZ8+a+vp6z1RqGrNmzTJ5eXl2mjQ9Lisry8TFxbU4hVtLMWTIELN582Zz8eJFU1ZWZvbu3WunT/PeR6d706nkKisrzfnz582vfvUrExwc3Gb+DgiCIAhCmoTjyx8AAKAFMTExsnTpUlm0aJHtGcbtib8DAIA/UdYOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwBwmzPGSGJiYqBvAwCA2xrJOQAAX2PdunWTlJQUOXnypNTU1Mjp06clMzNTRo4c6ZfrxcfH22Q/MjJS/KVPnz6SkZEhFy5ckLKyMtm1a5c89thjjfZ59dVXJTs72z7z4cOH/XYvAAD4Csk5AACtKNjplNCoKAlyOv1+rZiYGDl48KBNxOfOnSsDBgyQhIQE2b59u6xevVrauuDg4Ku2v/vuuxISEmKfa8iQIZKTk2Pb9EWEt9/97nfyhz/8oZXuFgCAW2cIgiAIgmg5YmJiTHp6ul3e7DkcYWHmG3Fxptvz3zfdp06zS113hIb67b43bdpkioqKTHh4eLNtkZGRnt8qMTHR/o6Pj7fr3tsHDhxo29zPHx0dbTIzM01JSYlxuVwmNzfXjBkzxm5vKjU19YvndzjMvHnzTEFBgamqqjJHjhwxSUlJnmu4r5uQkGCys7NNbW2tbWt63126dLH7xcbGetqcTqdte/zxx5vt/9Of/tQcPny4zfwdEARBEIS0ECE+SO4BAMBX6Dh0qDjvHyj1LpdcKS2V4A4d7Loq37XL59fr1KmT7SVfuHChVFVVNduu5eA3S3vdw8LCZPjw4VJZWSn9+/cXl8slRUVFMn78eNm4caP07dtXysvLpbq62h4zf/58mThxokybNk3y8/PtsevXr7el6Tt37vSce9myZTJnzhwpKCiQ0tLSZte+dOmSHD9+XCZNmiSHDh2S2tpamTp1qhQXF9sqAQAA/l6RnAMA0Aql7B1697GJuYZyLzv07i2uw4el4ct1X+ndu7cEBQXZRNbXoqOjZcOGDZKbm2vXCwsLPdtKSkrs8vz5854XAJrIL1iwQEaNGiX79+/3HBMbG2sTa+/kfPHixbJt27ZrXl/Po9+cV1RUSENDg72Wvoi4fPmyz58VAIDWQnIOAICf6fflQWFhtsfcW311tYR06mSTd18n5w6HQ/xFB5h7/fXXZfTo0TaR1kT96NGj13xREBERIVu3bm3Urkl708HadBC36+m514Q8Li7O9sy/+OKL8s4778iDDz4o586du4UnAwAgcBgQDgAAP9PEu6Guzpaye9N1U1fr6UX3JS0d117lfv363dBxekzT5D40NLTRPmvXrpWePXvKunXr7CBzmlDPmDGjxXM6vxz8buzYsTJo0CBPaDn8hAkTGu2rZfLXooPAjRs3Tp577jnZu3evTe6nT59uk/TJkyff0LMCANCWkJwDAOBnmnxXn8i3PeQaEhzs+V194oTPe82Vfq+9ZcsWm7iGh4c3297SVGf6Dbjq3r27p00T6abOnDkja9askaSkJFmxYoVMmTLFttfV1TUbaf3YsWN2SjMth9cp3bxDz3Mj3M/ifongputaxg8AwN8r/hcDAKAVVOzfL66Pc0SCHLaUXZe6ru3+oom5JskHDhywA7Vpebn2pM+cOVP27dt31WNOnDhh50JfsmSJ3f+pp56S2bNnN9pn5cqVtqT9rrvuksGDB8uIESMkLy/Pbjt16pRNlLV3u2vXrracXQeLW758uT1OB3LTXnc9Tnvbdf1G6H3ri4e0tDS5//777ZznL7/8stx9992yadMmz369evWSgQMHSlRUlHTo0MH+1mhaBQAAQFsS8CHjCYIgCKIthy+n0ApyOk1oVJRdtsa9R0VFmVWrVpnCwkJTU1Njp1bLyMhoNE2Z91RqGsOGDTM5OTl2yrMdO3bYKc+8p1JLSUkx+fn5prq62hQXF5u0tDTTuXNnz/HJycnm7Nmzpr6+3jOVmsasWbNMXl6enSZNj8vKyjJxcXEtTuHWUgwZMsRs3rzZXLx40ZSVlZm9e/faKdi899m+fXuzad28nyHQfwcEQRAEIU3C8eUPAADQgpiYGFm6dKksWrTI9gzj9sTfAQDAnyhrBwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuM0ZYyQxMTHQtwEAwG2N5BwAgK+xbt26SUpKipw8eVJqamrk9OnTkpmZKSNHjvTL9eLj422yHxkZKf7Sp08fycjIkAsXLkhZWZns2rVLHnvsMc/2+++/X37/+9/bZ62qqpJjx47JrFmz/HY/AAD4QohPzgIAAK5LsNMpQU6n1Ltc0uBy+fVaMTExsmfPHrl8+bLMnTtXjh49KqGhofLkk0/K6tWr5Z577pG2LDg4WOrr65u1v/vuu5Kfn29fMFRXV8uPfvQj29arVy8pLi6WIUOGyPnz52XixIlSVFQkw4YNk//4j/+w59LnBgCgrTIEQRAEQbQcMTExJj093S5v9hyOsDDzjbg40+373zfdp02zS113hIb67b43bdpkioqKTHh4eLNtkZGRnt8qMTHR/o6Pj7fr3tsHDhxo29zPHx0dbTIzM01JSYlxuVwmNzfXjBkzxm5vKjU19YvndzjMvHnzTEFBgamqqjJHjhwxSUlJnmu4r5uQkGCys7NNbW2tbWt63126dLH7xcbGetqcTqdte/zxx1v8t3jttdfMBx98EPC/A4IgCIKQFoKecwAAWkHHoUPFOXCg7TG/UloqwR062HVVvmuXz6/XqVMnSUhIkIULF9rS7qa0HPxmae9zWFiYDB8+XCorK6V///7icrlsL/X48eNl48aN0rdvXykvL7c922r+/Pm2J3vatGm211uPXb9+vS1N37lzp+fcy5Ytkzlz5khBQYGUlpY2u/alS5fk+PHjMmnSJDl06JDU1tbK1KlTbY/5wYMHW7xnLbMvKSm56WcGAMDfSM4BAGiFUvYOffrYxFxDuZcdevcW1+HDPi9x7927twQFBdlE1teio6Nlw4YNkpuba9cLCws929wJsJaVu18AaCK/YMECGTVqlOzfv99zTGxsrE2svZPzxYsXy7Zt2655fT2PfnNeUVEhDQ0N9lr6IkLL96/mkUcekWeffVbGjh3rg6cHAMA/SM4BAPAz/cY8KCzM9ph7q6+ulpBOnWzy7uvk3OFwiL/oAHOvv/66jB492ibSmqjr9+zXelEQEREhW7dubdSuSfvhw4cbtWVnZ19Xz70m5HFxcbZn/sUXX5R33nlHHnzwQTl37lyjfe+9917585//LP/2b//W7PoAALQljNYOAICfaeLdUFdnS9m96bqprfX0ovuSlo5rr3K/fv1u6Dg9pmlyr4PIeVu7dq307NlT1q1bJwMGDLAJ9YwZM1o8p9PptEvtuR40aJAntBx+woQJjfbVMvlr0UHgxo0bJ88995zs3bvXJvfTp0+3SfrkyZMb7asD3n3wwQd2MLhf/OIXN/CvAABA6yM5BwDAzzT5rs7Ptz3kGhIc7PldfeKEX0Zt1++1t2zZYhPX8PDwZttbmupMvwFX3bt397RpIt3UmTNnZM2aNZKUlCQrVqyQKVOm2Pa6ujrPSOtuOpWZTuOm5fA6pZt36HluhPtZ3C8R3HRdy/jdNPHfvn27pKWlSXJy8g1dAwCAQCA5BwCgFVTs3y+unBztkral7LrUdW33F03MNUk+cOCAHahNy8u1J33mzJmyb9++qx5z4sQJOz/4kiVL7P5PPfWUzJ49u9E+K1eutCXtd911lwwePFhGjBgheXl5dtupU6dsoqy92127drXl7DpY3PLly+1xOpCb9rrrcdrbrus3Qu9bXzxo0q3zmeuc5y+//LLcfffdsmnTJk8puybm77//vrzyyit2rncNvR8AANqygA8ZTxAEQRBtOXw5hVaQ02lCo6LssjXuPSoqyqxatcoUFhaampoaO7VaRkZGo2nKvKdS0xg2bJjJycmxU57t2LHDTnnmPZVaSkqKyc/PN9XV1aa4uNikpaWZzp07e45PTk42Z8+eNfX19Z6p1DRmzZpl8vLy7DRpelxWVpaJi4trcQq3lmLIkCFm8+bN5uLFi6asrMzs3bvXTsHm3v7Tn/7UXI3+G7SVvwOCIAiCkCbh+PIHAABoQUxMjCxdulQWLVpke4Zxe+LvAADgT5S1AwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAA3OaMMZKYmBjo2wAA4LZGcg4AwNdYt27dJCUlRU6ePCk1NTVy+vRpyczMlJEjR/rlevHx8TbZj4yMFH/p06ePZGRkyIULF6SsrEx27doljz32mGd7586dJSsrSz777DPPM69atUo6duzot3sCAOBWkZwDANCKgpxOCY2Kskt/i4mJkYMHD9pEfO7cuTJgwABJSEiQ7du3y+rVq6WtCw4Ovmr7u+++KyEhIfa5hgwZIjk5ObZNX0SohoYG+fOf/yxPP/209O3bV773ve/JqFGj5De/+U0rPwEAADfGEARBEATRcsTExJj09HS7vNlzOMLCzDeGx5lvvvB9E/XSNLvUdUdoqN/ue9OmTaaoqMiEh4c32xYZGen5rRITE+3v+Ph4u+69feDAgbbN/fzR0dEmMzPTlJSUGJfLZXJzc82YMWPs9qZSU1O/eH6Hw8ybN88UFBSYqqoqc+TIEZOUlOS5hvu6CQkJJjs729TW1tq2pvfdpUsXu19sbKynzel02rbHH3+8xX+LmTNnmtOnTwf874AgCIIgpIUIucFEHgAA3ISODw+ViIEDpd7lkiulpRLcoYNdV+U7d/n8ep06dbK95AsXLpSqqqpm27Uc/GZpr3tYWJgMHz5cKisrpX///uJyuaSoqEjGjx8vGzdutD3W5eXlUl1dbY+ZP3++TJw4UaZNmyb5+fn22PXr19vS9J07d3rOvWzZMpkzZ44UFBRIaWlps2tfunRJjh8/LpMmTZJDhw5JbW2tTJ06VYqLi22VwNV0797d3teOHTtu+pkBAPA3knMAAPxMS9jb9+ljE3MN5V6279NbXIcOS8OX677Su3dvCQoKsomsr0VHR8uGDRskNzfXrhcWFnq2lZSU2OX58+c9LwA0kV+wYIEtLd+/f7/nmNjYWJtYeyfnixcvlm3btl3z+noe/ea8oqLClrDrtfRFxOXLlxvt9/vf/94OdBceHm6/s3/xxRd9+K8AAIBv8c05AAB+Fux0SlBYmNR/2YvsputBYe3sdl9zOBziLzrAXHJysuzevVuWLFliv2X/qhcFERERsnXrVptQu0N7v3v16tVo3+zs7OvqudeEPC4uTh566CGbqL/zzjsSFRXVaL8f//jH8u1vf9t+e67XeeWVV27yiQEA8D96zgEA8DPtJW+oq7Ol7O4ec6XrDXW1jdp8RUvHtVe5X79+N3ScHtM0uQ8NDW20z9q1a2XLli0yduxYGT16tC1Znz17trz22mtXPafzy5cPur+OoO5Ny9K9aZn8teggcOPGjbNl+5rgq+nTp8sTTzwhkydPln//93/37Kul7hqffPKJ7dHXlwlLly6Vc+fOXee/BgAArYeecwAA/ExL1mvy820Pue0lDw72/K7JP+Hzknal32trAq2Jq5Z1N9XSVGf6Dbj7O223QYMGNdvvzJkzsmbNGklKSpIVK1bIlClTbHtdXZ1deo+0fuzYMTulmZbD65Ru3qHnuRHuZ3G/RHDTdS3jb4l7W7t27W7oegAAtBZ6zgEAaAUV+/Z7vjEP7dTJ9phX5uR42v1BE/M9e/bIgQMH7LfcH3/8sZ2CTHuZX3rpJTuQW1MnTpyw84JruboOJqcDu2mvuLeVK1faecQ//fRT24M9YsQIycvLs9tOnTplE2Xt3X7vvffsgHA6WNzy5cvtcZokaw+2vhx49NFH7aBx6enp1/1M+/btsy8e0tLS5Gc/+5k9v74YuPvuu2XTpk12nzFjxthp1f7617/aa997773yq1/9yl5X7w8AgLYq4EPGEwRBEERbDl9OoRXkdJrQqCi7bI17j4qKMqtWrTKFhYWmpqbGTq2WkZHRaJoy76nUNIYNG2ZycnLslGc7duywU555T6WWkpJi8vPzTXV1tSkuLjZpaWmmc+fOnuOTk5PN2bNnTX19vWcqNY1Zs2aZvLw8O02aHpeVlWXi4uJanMKtpRgyZIjZvHmzuXjxoikrKzN79+61U7C5tz/22GNmz549prS01D7DJ598Yn75y19e17lb6++AIAiCIKRJOL78AQAAWhATE2O/VV60aBE9r7cx/g4AAP7EN+cAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAC3OWOMJCYmBvo2AAC4rZGcAwDwNdatWzdJSUmRkydPSk1NjZw+fVoyMzNl5MiRfrlefHy8TfYjIyPFX/r06SMZGRly4cIFKSsrk127dsljjz121X07d+4sRUVFfr8nAABuFck5AACtKKijU0K7R0mQ0+n3a8XExMjBgwdtIj537lwZMGCAJCQkyPbt22X16tXS1gUHB1+1/d1335WQkBD7XEOGDJGcnBzbpi8imlq7dq18/PHHrXC3AADcGpJzAABagSMsTL4RHyddn/2OdBmfKF2f+45dd4SG+u2av/71r22P8UMPPSQbN26U/Px8OXbsmKxcuVIefvjh6+75HjhwoG3TZF9FR0fb3veSkhJxuVySm5srY8aMsds/+ugju8/ly5ftMampqV88v8Mh8+bNk4KCAqmqqpIjR45IUlJSs+vqy4Ps7Gypra2V2NjYZvfXpUsX6du3ryxbtkyOHj0qJ06csOeNiIiQ++67r9G+06ZNkzvuuEOWL1/uo39RAAD8JyTQNwAAwO2g4yNDJWLQ/VJf4ZIrJaUS3KGDXVflO3b5/HqdOnWyie7ChQttMtyUloPfLO11DwsLk+HDh0tlZaX079/fJulaPj5+/Hj7IkAT6PLycqmurrbHzJ8/XyZOnGgTZn1JoMeuX7/elqbv3LnTc25NuufMmWOT+NLS0mbXvnTpkhw/flwmTZokhw4dskn81KlTpbi42FYJuN1zzz2yePFiGTp0qPTs2fOmnxUAgNZCcg4AQCuUsrfv09sm5vUul21zL7XddfCwNHy57iu9e/eWoKAgm8j6mvacb9iwwfaYq8LCQs827U1X58+f97wA0ER+wYIFMmrUKNm/f7/nGO0Z18TaOznXhHrbtm3XvL6eR785r6iokIaGBnstfRGhvfXu67399tu2lF9fGJCcAwD+HpCcAwDgZ8FOpwS1C7M95t7qq6sltHMnCe7o9HlyrmXk/qIDzL3++usyevRom0hroq4l5td6UaBl51u3bm3Urkn04cOHG7VpSfv19NxrQh4XF2d75l988UV555135MEHH5Rz587JL3/5S8nLy5O33nrrFp4SAIDWxTfnAAD4mfaSN9TW2VJ2b7qu7dqj7mtaOq69yv369buh4/SYpsl9aJPv4nWQNe2NXrdunR1kThPqGTNmtHhO55eD340dO1YGDRrkCS2HnzBhQqN9tUz+WnQQuHHjxslzzz0ne/futcn99OnTbZI+efJkzz7f+c535MqVKzY++OAD237x4kVZsmTJDf17AADQWkjOAQDws4YKl9Tkn7A95NqLLsHBdqnr2u7rXnOl32tv2bLFJq7h4eHNtrc0rZh+A666d+/uadNEuqkzZ87ImjVr7KBuK1askClTptj2urq6ZiOt6yB0Oo2blsPrlG7eoee5Ee5ncb9EcNN1LeNXek86iJ37JYD2rCvtaf97GKUeAHB7oqwdAIBWULF3v+cbcy1l1x7zyiMfe9r9QRPzPXv2yIEDB+y33DqlmE5B9sQTT8hLL71ke66b0tHPdS507WHWweR0YLfZs2c32kdHe8/KypJPP/3UDjw3YsQIW0auTp06ZRNl7d1+7733bI+2DhanI6brcZpA7969274cePTRR+2gcenp6df9TPv27bMvHtLS0uRnP/uZPb++GLj77rtl06ZNdh8dTM5b165d7VLv8VYGwgMAwN8MQRAEQRAtR0xMjElPT7fLWz1XkNNpQrtH2WVr3HtUVJRZtWqVKSwsNDU1NaaoqMhkZGSY+Ph4zz4qMTHRsz5s2DCTk5NjqqqqzI4dO0xSUpLdx/38KSkpJj8/31RXV5vi4mKTlpZmOnfu7Dk+OTnZnD171tTX15vU1FRP+6xZs0xeXp6pra21x2VlZZm4uDi7Te9HRUZGfuUzDRkyxGzevNlcvHjRlJWVmb1795qEhIQW97+Rc7fW3wFBEARBSJNwfPkDAAC0QOfvXrp0qSxatMj2DOP2xN8BAMCf+OYcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgNmeMkcTExEDfBgAAtzWScwAAvsa6desmKSkpcvLkSampqZHTp09LZmamjBw50i/Xi4+Pt8l+ZGSk+EufPn0kIyNDLly4IGVlZbJr1y557LHHGu2j99A0nn32Wb/dEwAAtyrkls8AAACuW1BHpwQ7nVJf4ZIGl8uv14qJiZE9e/bI5cuXZe7cuXL06FEJDQ2VJ598UlavXi333HOPtGXBwcFSX1/frP3dd9+V/Px8+4KhurpafvSjH9m2Xr16SXFxsWe/733ve7J582bPuv47AADQVtFzDgBAK3CEhUnHx+Kky3Pfkc5JidLlu9+x647QUL9d89e//rXtMX7ooYdk48aNNqE9duyYrFy5Uh5++OHr7vkeOHCgbdNkX0VHR9ve95KSEnG5XJKbmytjxoyx2z/66CNPIqzHpKamfvH8DofMmzdPCgoKpKqqSo4cOSJJSUnNrpuQkCDZ2dlSW1srsbGxze6vS5cu0rdvX1m2bJl92XDixAl73oiICLnvvvsa7av3oMm6O/ScAAC0VSTnAAC0AuewoRIx6H6Rhgb5vKTULnXd+ejVk+Rb1alTJ5voag+5JsNNaTn4zdJztmvXToYPHy4DBgyQn/zkJzZJLyoqkvHjx9t9NIGOioqSf/mXf7Hr8+fPl0mTJsm0adPk3nvvtS8I1q9fb8/hTZNuTba1V//jjz9udu1Lly7J8ePH7bnCw8Nt7/rUqVNt8n3w4MFm96ml73/5y1/k+eefv+nnBQCgNVDWDgBAK5Syt+/Tu1Epu3up7ZXZh31e4t67d28JCgqyiayvac/5hg0bbI+5Kiws9GzT3nR1/vx5zwuAsLAwWbBggYwaNUr279/vOUZ7xjWx3rlzp+f4xYsXy7Zt2655fT2PfnNeUVEhDQ0N9lr6IsK7bH3RokXy4Ycf2hcTo0ePtlUETqdTVq1a5eN/DQAAfIPkHAAAP9NvzIPahX3RY+6lobpaQjp1kuCOTp8n51pG7i86wNzrr79uk15NpDVR1xLza70o0LLzrVu3NmrXpP3w4cON2rSk/atoj7gm5HFxcfab8xdffFHeeecdefDBB+XcuXN2n5///Oee/bWEXq+v392TnAMA2irK2gEA8LN6l0saauskqEOHRu263lBXZ3vUfU2/L9de5X79+t3QcXpM0+ReB5HztnbtWunZs6esW7fOlrVrQj1jxowWz6k91mrs2LEyaNAgT/Tv318mTJjQaN/Kyspr3p8OAjdu3Dh57rnnZO/evTa5nz59uk3SJ0+e3OJxWtr+rW99y74QAACgLSI5BwDAzxoqXFKTf8L2kAdpohocbJe6ru3+GLW9tLRUtmzZYhNX/Ta7qZamOtNvtFX37t09bZpIN3XmzBlZs2aNHdRtxYoVMmXKFNteV1dnl/otuJsOQqfTuGk5vE7p5h16nhvhfhb3SwQ3Xdcy/pboM2jJvfv+AABoayhrBwCgFbj27Pd8Y66l7NpjXnnkY0+7P2hirlOpHThwwH7LrQOshYSEyBNPPCEvvfSS7bluSkc/17nQlyxZIgsXLrQDu82ePbvRPjqYW1ZWlnz66ad24LkRI0ZIXl6e3Xbq1CmbKGvv9nvvvWd7tHWwuOXLl9vjNIHevXu3fTnw6KOPSnl5uaSnp1/3M+3bt8++eEhLS5Of/exn9vz6YuDuu++WTZs22X302jq/u37fri8F9Hn1m3e9BwAA2jJDEARBEETLERMTY9LT0+3yVs8V5HSa0O5Rdtka9x4VFWVWrVplCgsLTU1NjSkqKjIZGRkmPj7es49KTEz0rA8bNszk5OSYqqoqs2PHDpOUlGT3cT9/SkqKyc/PN9XV1aa4uNikpaWZzp07e47tCIZLAAEAAElEQVRPTk42Z8+eNfX19SY1NdXTPmvWLJOXl2dqa2vtcVlZWSYuLs5u0/tRkZGRX/lMQ4YMMZs3bzYXL140ZWVlZu/evSYhIcGz/cknnzSHDh0y5eXlpqKiwhw+fNj84Ac/MA6Ho838HRAEQRCENAnHlz8AAEALdP7upUuX2hHAtWcYtyf+DgAA/sQ35wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAALc5Y4wkJiYG+jYAALitkZwDAPA11q1bN0lJSZGTJ09KTU2NnD59WjIzM2XkyJF+uV58fLxN9iMjI8Vf+vTpIxkZGXLhwgUpKyuTXbt2yWOPPdZsv8mTJ0tOTo5UV1dLcXGxvPbaa367JwAAblXILZ8BAABct6COTgl2OqW+wiUNLpdfrxUTEyN79uyRy5cvy9y5c+Xo0aMSGhoqTz75pKxevVruueceacuCg4Olvr6+Wfu7774r+fn59gWDJt4/+tGPbFuvXr1sEq5+/OMfy+zZs+1z/+Uvf5GIiAi56667AvAUAABcP0MQBEEQRMsRExNj0tPT7fJmz+EICzMdH4szd/7gedNtxlS71HVHaKjf7nvTpk2mqKjIhIeHN9sWGRnp+a0SExPt7/j4eLvuvX3gwIG2zf380dHRJjMz05SUlBiXy2Vyc3PNmDFj7PamUlNTv3h+h8PMmzfPFBQUmKqqKnPkyBGTlJTkuYb7ugkJCSY7O9vU1tbatqb33aVLF7tfbGysp83pdNq2xx9/3K7fcccdprKy0owcObLN/R0QBEEQhLQQ9JwDANAKnMOGSsSg+22P+eclpRLUoYNdVxUf7fL59Tp16iQJCQmycOFCqaqqarZdy8Fvlva6h4WFyfDhw6WyslL69+8vLpdLioqKZPz48bJx40bp27evlJeX255tNX/+fJk4caJMmzbN9nrrsevXr7el6Tt37vSce9myZTJnzhwpKCiQ0tLSZte+dOmSHD9+XCZNmiSHDh2S2tpamTp1qu0xP3jwoN3niSeekKCgIOnRo4ccO3ZMOnbsKHv37rU96WfOnLnp5wYAwJ9IzgEAaIVS9vZ9ezcqZXcvtb0y+7DPS9x79+5tE1RNZH0tOjpaNmzYILm5uXa9sLDQs62kpMQuz58/73kBoIn8ggULZNSoUbJ//37PMbGxsTax9k7OFy9eLNu2bbvm9fU8+s15RUWFNDQ02Gvpiwgt31c9e/a0z67X/Jd/+Rd7Hz//+c9l69atcv/998uVK1d8/m8CAMCtIjkHAMDP9BvzoLAw22PuraG6WkI6d5Lgjk6fJ+cOh0P8RQeYe/3112X06NE2kdZEXb9nv9aLAv3mW5Njb5q0Hz58uFFbdnb2dfXca0IeFxdne+ZffPFFeeedd+TBBx+Uc+fO2cRczz1r1izPNb/73e/abSNGjJD333//pp8dAAB/YbR2AAD8rN7lkoa6OlvK7k3XtV171H1NS8e1V7lfv343dJwe0zS510HkvK1du9b2Tq9bt04GDBhgE+oZM2a0eE6n02mXY8eOlUGDBnlCy+EnTJjQaF8tk78WHQRu3Lhx8txzz9lSdU3up0+fbpN0HZ1d/e1vf7NLLWl3u3jxog3t9QcAoC0iOQcAwM8aKlxS8+kJ20MepIlqcLBd6rq2+2PUdv1ee8uWLTZxDQ8Pb7a9panO9Btw1b17d0+bJtJN6bfba9askaSkJFmxYoVMmTLFttfV1XlGWnfTJFmncdPEWKd0844b/Qbc/Szulwhuuq495kpHqFf/+I//2Ogb/K5du8qpU6du6HoAALQWknMAAFqBa89+qTzysUhwkC1l16Wua7u/aGKuSfKBAwfsQG1aXq496TNnzpR9+/Zd9ZgTJ07YudCXLFli93/qqafsQGreVq5caUvadWqywYMH21LxvLw8u02TX02UtXdbk2EtZ9fB4pYvX26P04HctNddj9Pedl2/EXrf+uIhLS3Nfj+uc56//PLLcvfdd8umTZs8VQP6Tfqrr74qjzzyiNx77712f/3+fvv27Tf97wkAgL8FfMh4giAIgmjL4csptIKcThPaPcouW+Peo6KizKpVq0xhYaGpqamxU6tlZGQ0mqbMeyo1jWHDhpmcnBw75dmOHTvslGfeU6mlpKSY/Px8U11dbYqLi01aWprp3Lmz5/jk5GRz9uxZU19f75lKTWPWrFkmLy/PTpOmx2VlZZm4uLgWp3BrKYYMGWI2b95sLl68aMrKyszevXvtFGze+3Ts2NG88cYbdro33W/Dhg3mf/yP/9Fm/g4IgiAIQpqE48sfAACgBTExMbJ06VJZtGgRZdG3Mf4OAAD+RFk7AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAbc4YI4mJiYG+DQAAbmsk5wAAfI1169ZNUlJS5OTJk1JTUyOnT5+WzMxMGTlypF+uFx8fb5P9yMhI8Zc+ffpIRkaGXLhwQcrKymTXrl3y2GOPebZPnjzZ3sPV4s477/TbfQEAcCtCbuloAABwQ4I6OiXY6ZT6Cpc0uFx+vVZMTIzs2bNHLl++LHPnzpWjR49KaGioPPnkk7J69Wq55557pC0LDg6W+vr6Zu3vvvuu5Ofn2xcM1dXV8qMf/ci29erVS4qLi+UPf/iDbN68udExb775prRv394m9AAAtFWGIAiCIIiWIyYmxqSnp9vlzZ7DERZmOj4WZ+78wfPmmzOn2qWuO0JD/XbfmzZtMkVFRSY8PLzZtsjISM9vlZiYaH/Hx8fbde/tAwcOtG3u54+OjjaZmZmmpKTEuFwuk5uba8aMGWO3N5WamvrF8zscZt68eaagoMBUVVWZI0eOmKSkJM813NdNSEgw2dnZpra21rY1ve8uXbrY/WJjYz1tTqfTtj3++ONX/Xfo2rWrPd/EiRMD/ndAEARBENJC0HMOAEArcA4bKuGD77c95vUlpRLUoYNdVxUf7fL59Tp16iQJCQmycOFCqaqqarZdy8Fvlva6h4WFyfDhw6WyslL69+8vLpdLioqKZPz48bJx40bp27evlJeX255tNX/+fJk4caJMmzbN9nrrsevXr7c92Tt37vSce9myZTJnzhwpKCiQ0tLSZte+dOmSHD9+XCZNmiSHDh2S2tpamTp1qu0xP3jw4FXvV/fVf4M//elPN/3MAAD4G8k5AACtUMrevm/vRqXs7qW2V2Yf9nmJe+/evSUoKMgmsr4WHR0tGzZskNzcXLteWFjo2VZSUmKX58+f97wA0ER+wYIFMmrUKNm/f7/nmNjYWJtYeyfnixcvlm3btl3z+noe/ea8oqJCGhoa7LX0RYSW71/NCy+8IL///e/tN/cAALRVJOcAAPiZfmPuaBdme8y9NVRXS0jnThLc0enz5NzhcPj0fN50gLnXX39dRo8ebRNpTdT1e/ZrvSiIiIiQrVu3NmrXpP3w4cON2rKzs6+r514T8ri4ONsz/+KLL8o777wjDz74oJw7d67Rvg8//LDt2f/nf/7nG35OAABaE6O1AwDgZ/Uul5jaOlvK7k3XtV171H1NS8e1V7lfv343dJwe0zS510HkvK1du1Z69uwp69atkwEDBtiEesaMGS2e0+l02uXYsWNl0KBBntCkecKECY321TL5a9FB4MaNGyfPPfec7N271yb306dPt0m6jtLelCbuuo+WwAMA0JaRnAMA4GcNFS6p+fSE7SEP0kQ1ONgudV3b/TFqu36vvWXLFpu4hoeHN9ve0lRn7tHMu3fv7mnTRLqpM2fOyJo1ayQpKUlWrFghU6ZMse11dXWekdbdjh07ZkvKtRxep3TzDj3PjXA/i/slgpuuaxm/N+2tf+aZZ+zLBAAA2jqScwAAWoFrz36pOvyxOIKCbCm7LnVd2/1FE3NNkg8cOGAHatPycu1Jnzlzpuzbt++qx5w4ccLOhb5kyRK7/1NPPSWzZ89utM/KlSttSftdd90lgwcPlhEjRkheXp7ddurUKZsoa+92165dbYKsg8UtX77cHqeDs2mvux6nve26fiP0vvXFQ1pamtx///12zvOXX35Z7r77btm0aVOjfZ999lkJCQmxA88BAPD3IOBDxhMEQRBEWw5fTqEV5HSa0O5Rdtka9x4VFWVWrVplCgsLTU1NjZ1aLSMjo9E0Zd5TqWkMGzbM5OTk2CnPduzYYac8855KLSUlxeTn55vq6mpTXFxs0tLSTOfOnT3HJycnm7Nnz5r6+nrPVGoas2bNMnl5eXZaMz0uKyvLxMXFtTiFW0sxZMgQs3nzZnPx4kVTVlZm9u7da6dga7rfnj17zPr169vk3wFBEARBSJNwfPkDAAC0ICYmRpYuXSqLFi2yPcO4PfF3AADwJ8raAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAbnPGGElMTAz0bQAAcFsjOQcA4GusW7dukpKSIidPnpSamho5ffq0ZGZmysiRI/1yvfj4eJvsR0ZGir/06dNHMjIy5MKFC1JWVia7du2Sxx57rNE+DzzwgGzbtk1KS0ulpKRENm/eLPfff7/f7gkAgFtFcg4AQCsK6uiU0O7dJMgZ4fdrxcTEyMGDB20iPnfuXBkwYIAkJCTI9u3bZfXq1dLWBQcHX7X93XfflZCQEPtcQ4YMkZycHNumLyJURESETcb1RcTQoUMlNjZWKioqZMuWLfY4AADaKkMQBEEQRMsRExNj0tPT7fJmz+EICzUdR8SZO6d9z3zzX35gl7ruCA31231v2rTJFBUVmfDw8GbbIiMjPb9VYmKi/R0fH2/XvbcPHDjQtrmfPzo62mRmZpqSkhLjcrlMbm6uGTNmjN3eVGpq6hfP73CYefPmmYKCAlNVVWWOHDlikpKSPNdwXzchIcFkZ2eb2tpa29b0vrt06WL3i42N9bQ5nU7b9vjjj9v1IUOG2PX/8T/+h2ef++67z7b16tUroH8HBEEQBCEtBK+PAQBoBc5HH5bwbw+Q+opKqS+5LEEdOth1VbF9l8+v16lTJ9tLvnDhQqmqqmq2XcvBb5b2uoeFhcnw4cOlsrJS+vfvLy6XS4qKimT8+PGyceNG6du3r5SXl0t1dbU9Zv78+TJx4kSZNm2a5Ofn22PXr19vS9N37tzpOfeyZctkzpw5UlBQYEvSm7p06ZIcP35cJk2aJIcOHZLa2lqZOnWqFBcX2yoB9cknn8jFixflhRdekP/3//6f7YHX38eOHZP/+q//uunnBgDAn0jOAQBohVL29v/YyybmDRUu2+Zetu/bSyr/ekgaXJU+vWbv3r0lKCjIJrK+Fh0dLRs2bJDc3Fy7XlhY6Nmm33er8+fPe14AaCK/YMECGTVqlOzfv99zjJaba2LtnZwvXrzYfit+LXoe/eZcS9UbGhrstfRFxOXLl+12fVGg36DrPosWLbJt+kLgySeflPr6ep//ewAA4At8cw4AgJ8FOyPE0S5MGr7sRXbTdW0P7uj0+TUdDof4iw4wl5ycLLt375YlS5bYb9m/6kWBfge+detWm1C7Q3u/e/Xq1Wjf7Ozs6+q514Q8Li5OHnroIZuEv/POOxIVFWW3t2/fXtauXSt79uyRhx9+WB599FH7ImHTpk12GwAAbRE95wAA+Fm9q1JMbZ0tZXf3mCtd1/Z6rzZf0Z5i7VXu16/fDR2nxzRN7kNDQxvto4mvDq42duxYGT16tC1Znz17trz22mtXPafT+cXLB93/s88+a7RNy9K9aZn8teggcOPGjbNl+5rgq+nTp8sTTzwhkydPln//93+Xf/qnf5K77rpLHnnkETtyvNI2LZPXKeP+8Ic/3MC/CAAArYOecwAA/EwT8ppPTkpwxwhb4i4hwXap6zWfnvR5SbvSRFQTaE1cw8PDm21vaaoz/QZcde/e3dM2aNCgZvudOXNG1qxZI0lJSbJixQqZMmWKba+rq2s20rp+663TuGk5vE7p5h16nhvhfhb3SwQ3Xdcyfvc+uu5OzN3bdd29DwAAbQ3/QwEA0Apcu/dL1aGjtkc6pNMddqnr2u4vmphrknzgwAE7UJuWl2tP+syZM2Xfvn1XPebEiRN2CjItV9f9n3rqKdsr7m3lypW2x1x7pwcPHiwjRoyQvLw8u+3UqVM2Edbe7a5du9pydv0GfPny5fY4LWXv2bOnPW7GjBl2/UbofeuLh7S0NDtvuc55/vLLL8vdd99ty9aVls9rz7qWv+vz6oB1qamp8vnnn9tp5AAAaKsCPmQ8QRAEQbTl8OUUWkHOCBPavZtdtsa9R0VFmVWrVpnCwkJTU1Njp1bLyMhoNE2Z91RqGsOGDTM5OTl2yrMdO3bYKc+8p1JLSUkx+fn5prq62hQXF5u0tDTTuXNnz/HJycnm7Nmzpr6+3jOVmsasWbNMXl6enSZNj8vKyjJxcXEtTuHWUuhUaZs3bzYXL140ZWVlZu/evXYKNu99Ro0aZXbt2mVKS0vNpUuXzLZt28zQoUPbzN8BQRAEQUiTcHz5AwAAtCAmJkaWLl1qR/7WnmHcnvg7AAD4E2XtAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAtzljjCQmJgb6NgAAuK2RnAMA8DXWrVs3SUlJkZMnT0pNTY2cPn1aMjMzZeTIkX65Xnx8vE32IyMjxV/69OkjGRkZcuHCBSkrK5Ndu3bJY4891mgffb49e/ZIeXm5/O1vf5Nly5ZJcHCw3+4JAIBbRXIOAEArCurolNB/6CZBHSP8fq2YmBg5ePCgTVTnzp0rAwYMkISEBNm+fbusXr1a2rqWkul3331XQkJC7HMNGTJEcnJybJu+iFD333+/vPfee7J582YZPHiwPPvss/L000/bBB0AgLbMEARBEATRcsTExJj09HS7vNlzOMJCTceRcebOad8z3/yXH9ilrjtCQ/1235s2bTJFRUUmPDy82bbIyEjPb5WYmGh/x8fH23Xv7QMHDrRt7uePjo42mZmZpqSkxLhcLpObm2vGjBljtzeVmpr6xfM7HGbevHmmoKDAVFVVmSNHjpikpCTPNdzXTUhIMNnZ2aa2tta2Nb3vLl262P1iY2M9bU6n07Y9/vjjdv0Xv/iFOXDgQKPjxo0bZ6+r+wby74AgCIIgpIUICfSbAQAAbgfO2IclfPAAqXdVSn3pZQnq0MGuq4oPd/n8ep06dbK95AsXLpSqqqpm27Uc/GZpr3tYWJgMHz5cKisrpX///uJyuaSoqEjGjx8vGzdulL59+9qS8urqanvM/PnzZeLEiTJt2jTJz8+3x65fv96Wpu/cudNzbu3dnjNnjhQUFEhpaWmza1+6dEmOHz8ukyZNkkOHDkltba1MnTpViouLbZWAateunS3h96b30aFDB9vTvmPHjpt+dgAA/IXkHACAVihlb9+3l03MGypcts291PbKvx6ShopKn16zd+/eEhQUZBNZX4uOjpYNGzZIbm6uXS8sLPRsKykpscvz5897XgBoIr9gwQIZNWqU7N+/33NMbGysTay9k/PFixfLtm3brnl9PY9+c15RUSENDQ32Wvoi4vLly3b7li1b5Ec/+pE899xz8sc//lGioqLseVX37t19/u8BAIAv8M05AAB+FtwxQhztwqThy15kN13X9uCOTp9f0+FwiL/oAHPJycmye/duWbJkif2W/ateFERERMjWrVttQu0O7f3u1atXo32zs7Ovq+deE/K4uDh56KGHbKL+zjvv2CRc6XX0G/vf/OY3tmf9008/td+gK03mAQBoi0jOAQDws/qKSjG1dbaU3Zuua3v9l73ovqSl45qI9uvX74aOcyev3sl9aGhoo33Wrl0rPXv2lHXr1tnEXBPqGTNmtHhOp/OLlw9jx46VQYMGeULL4SdMmNBoXy2TvxYdBG7cuHG2V3zv3r1y+PBhmT59ui1bnzx5sme/lStXyh133GF7+bt27Sp//vOfbbuWywMA0BaRnAMA4Gdawl7z6UkJdkbYEncJCbZLXdd2X5e0K/1eW8u7NXENDw9vtr2lqc70G/Cm5d+aSDd15swZWbNmjSQlJcmKFStkypQptr2urq7ZSOvHjh2z34BroqxTunmHnudGuJ+laQ+4rmsZf1M6jZpe+7vf/a6dRk6/UwcAoC0iOQcAoBW4du2XqsNHbY90SKc77FLXtd1fNDHXJPnAgQN2oDYtL9ee9JkzZ8q+ffuuesyJEydsEqvl6rr/U089JbNnz260j/ZKjx49Wu666y47VdmIESMkLy/Pbjt16pRNlLV3W3ustZxdB4tbvny5PU5L2bXXXY/T3nZdvxF63/riIS0tzU6ZpnOev/zyy3L33XfLpk2bPPvpoHL33Xef7Z3XEvx58+bJrFmzKGsHALRpAR8yniAIgiDacvhyCq2gjhEm9B+62WVr3HtUVJRZtWqVKSwsNDU1NXZqtYyMjEbTlHlPpaYxbNgwk5OTY6ce27Fjh53yzHsqtZSUFJOfn2+qq6tNcXGxSUtLM507d/Ycn5ycbM6ePWvq6+s9U6lpzJo1y+Tl5dlp0vS4rKwsExcX1+IUbi3FkCFDzObNm83FixdNWVmZ2bt3r52CzXufDz74wJSWltpn2LdvX7Ptgf47IAiCIAhpEo4vfwAAgBbExMTI0qVLZdGiRbZnGLcn/g4AAP5EWTsAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAMBtzhgjiYmJgb4NAABuayTnAAB8jXXr1k1SUlLk5MmTUlNTI6dPn5bMzEwZOXKkX64XHx9vk/3IyEjxl8GDB8v7778vpaWlcvHiRVmzZo1EREQ02udb3/qWvPvuu1JZWSnFxcXy8ssvS3BwsN/uCQCAW0VyDgBAKwrq6JTQf+gmQR0bJ5P+EBMTIwcPHrSJ+Ny5c2XAgAGSkJAg27dvl9WrV0tbd7Vkunv37rJt2zY5ceKEDB061D7PvffeK2+++aZnn6CgINm0aZOEhYXJsGHDZPLkyfK9731Pfvazn7XyEwAAcGMMQRAEQRAtR0xMjElPT7fLmz2HIyzUdHw81tz5w8nmmz/+gV3quiM01G/3vWnTJlNUVGTCw8ObbYuMjPT8VomJifZ3fHy8XffePnDgQNvmfv7o6GiTmZlpSkpKjMvlMrm5uWbMmDF2e1OpqalfPL/DYebNm2cKCgpMVVWVOXLkiElKSvJcw33dhIQEk52dbWpra21b0/ueMmWKOXfunD2fu+2+++6zx/bq1cuu6zk+//xz881vftOzz9SpU83ly5dN6C38e/vi74AgCIIgpIUIucFEHgAA3ARn3FAJ//b9Ul/hkvqSUgnq0MGuq4oPdvv8ep06dbK9ygsXLpSqqqpm28vKym763Nrrrr3Sw4cPt2Xj/fv3F5fLJUVFRTJ+/HjZuHGj9O3bV8rLy6W6utoeM3/+fJk4caJMmzZN8vPz7bHr16+XCxcuyM6dOz3nXrZsmcyZM0cKCgps2XpT7dq1k7q6Ols67+a+RmxsrC3ff+SRR+To0aNy/vx5zz5btmyR3/zmN7aX/ciRIzf97AAA+AvJOQAArVDK3v4fe9nEvKHCZdvcS22vPHBYGioqfXrN3r172/Lu48ePi69FR0fLhg0bJDc3164XFhZ6tpWUlNilJsbuFwCayC9YsEBGjRol+/fv9xyjyfTUqVMbJeeLFy+2Zest+fDDD+WVV16xCfyrr75qvzXXhN5d8q6ioqLsd+be3Ou6DQCAtohvzgEA8LPgjhHiaNdOGr7s4XXTdUdYmAR3dPr8mg6HQ/xFB5hLTk6W3bt3y5IlS+y37F/1okCT6K1bt0pFRYUnJk2aJL169Wq0b3Z29jXPdezYMfsN+ezZs21FwLlz52yir8uGhgafPB8AAIFAcg4AgJ/VV1SKqa21pezedN3U1dkedV/T0nFNVvv163dDx7kTXO/kPjQ0tNE+a9eulZ49e8q6detsYq4J9YwZM1o8p9P5xcuHsWPHyqBBgzyh5fATJkxotK+WyX+Vt99+2/aS9+jRQ7p06WJfENx55522FF5poq6j1Htzr+s2AADaIpJzAAD8TEvYaz45aXvItcRdQoLtUte13dcl7Uq/19bvrKdPny7h4eHNtrc01Zl+A+5dIq40kW7qzJkzdgqzpKQkWbFihUyZMsW26/fgTUda195uncZNy+H1m3Dv0PPcLC2d12T+2WeftefXnnm1b98++9JAE3a3J554wpbZ670AANAWkZwDANAKXDv/IlWHPhZHkENCOt1hl7qu7f6iibkmyQcOHLADtWl5ufakz5w50yawV6NTlOlc6Nobrfs/9dRTtoTc28qVK2X06NFy11132TnHR4wYIXl5eXbbqVOnbO/7uHHjpGvXrracXQeLW758uT1OS9m1112P0952Xb+Z59Lj+/TpIz/84Q/ltddeswPOub9x1znQNQnXnv3777/f3uvPf/5zO5Cd++UBAABtUcCHjCcIgiCIthy+nEIrqGOECf2HbnbZGvceFRVlVq1aZQoLC01NTY2dWi0jI6PRNGXeU6lpDBs2zOTk5Ngpz3bs2GGnPPOeSi0lJcXk5+eb6upqU1xcbNLS0kznzp09xycnJ5uzZ8+a+vp6z1RqGrNmzTJ5eXl2mjQ9Lisry8TFxbU4hVtLode7ePGifR6dkm3ixInN9tHp3nQqucrKSnP+/Hnzq1/9ygQHB7eZvwOCIAiCkCbh+PIHAABoQUxMjCxdulQWLVpke4Zxe+LvAADgT5S1AwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAcJszxkhiYmKgbwMAgNsayTkAAF9j3bp1k5SUFDl58qTU1NTI6dOnJTMzU0aOHOmX68XHx9tkPzIyUvxl8ODB8v7770tpaalcvHhR1qxZIxEREY32efXVVyU7O9s+8+HDh/12LwAA+ArJOQAAX1MxMTFy8OBBm4jPnTtXBgwYIAkJCbJ9+3ZZvXq1tHXBwcHN2rp37y7btm2TEydOyNChQ+3z3HvvvfLmm2822/d3v/ud/OEPf2iluwUA4NYZgiAIgiBajpiYGJOenm6Xt3quoI5OE/oP3UxQxwi/3/emTZtMUVGRCQ8Pb7YtMjLS81slJiba3/Hx8Xbde/vAgQNtm/v5o6OjTWZmpikpKTEul8vk5uaaMWPG2O1Npaam2mMcDoeZN2+eKSgoMFVVVebIkSMmKSnJcw33dRMSEkx2drapra21bU3ve8qUKebcuXP2fO62++67zx7bq1evZvv/9Kc/NYcPH25zfwcEQRAEIU0ixAfJPQAA+AqOsFBxxg2V9v16iSOsnZi6Wqk5flJcu/4ipu6Kz6/XqVMn26u8cOFCqaqqara9rKzsps+tve5hYWEyfPhwqayslP79+4vL5ZKioiIZP368bNy4Ufr27Svl5eVSXV1tj5k/f75MnDhRpk2bJvn5+fbY9evXy4ULF2Tnzp2ecy9btkzmzJkjBQUFtmy9qXbt2kldXZ0tnXdzXyM2NtaW7wMA8PeI5BwAgFagiXn4kPulvsIl9aWlEtShg11XFR/s9vn1evfuLUFBQXL8+HGfnzs6Olo2bNggubm5dr2wsNCzraSkxC7Pnz/veQGgifyCBQtk1KhRsn//fs8xmkxPnTq1UXK+ePFiW7bekg8//FBeeeUVm8Drd+X6rbkm9O6SdwAA/l7xzTkAAH4W1NFpe8w1MW+ocIl8Xm+Xut7+H3tJUMfGg5n5gsPhEH/RAeaSk5Nl9+7dsmTJEvst+1e9KNAkeuvWrVJRUeGJSZMmSa9evRrtq4O4XcuxY8dk8uTJMnv2bFsRcO7cOZvo67KhocEnzwcAQCCQnAMA4GfBHSNsKXvDl+XXbrruaBcmwR2dPr+mlo5rstqvX78bOs6d4Hon96GhoY32Wbt2rfTs2VPWrVtnE3NNqGfMmNHiOZ3OL55v7NixMmjQIE9oOfyECRMa7atl8l/l7bfftr3kPXr0kC5dutgXBHfeeacthQcA4O8VyTkAAH5WX1FpvzHXUnZvum5q62wPuq/p99pbtmyR6dOnS3h4eLPtLU11pt+ANy0R10S6qTNnztgpzJKSkmTFihUyZcoU267fgzcdaV17u3VKMy2H12/CvUPPc7O0dF6T+WeffdaeX3vmAQD4e0VyDgCAn2kJuw7+pj3kWuIuIcF2qes1n5yUhoqv7i2+GZqYa5J84MABO1CblpdrT/rMmTNl3759Vz1GpyjTudC1N1r3f+qpp2wJubeVK1fK6NGj5a677rJzjo8YMULy8vLstlOnTtne93HjxknXrl1tObsOFrd8+XJ7nJaya6+7Hqe97bp+M8+lx/fp00d++MMfymuvvWYHnPMe5E7L5QcOHChRUVHSoUMH+1ujaRUAAABtScCHjCcIgiCIthy+mELLERZqOj4ea+784WTzzR9PsUtd13Z/3ntUVJRZtWqVKSwsNDU1NXZqtYyMjEbTlHlPpaYxbNgwk5OTY6c827Fjh53yzHsqtZSUFJOfn2+qq6tNcXGxSUtLM507d/Ycn5ycbM6ePWvq6+s9U6lpzJo1y+Tl5dlp0vS4rKwsExcX1+IUbi2FXu/ixYv2eXRKtokTJzbbZ/v27c2mdfN+hkD9HRAEQRCEtBCOL38AAIAWxMTEyNKlS2XRokW2Z/hW6OBv2mP+xeBw/ukxR9v/OwAAoCmmUgMAoBVpQk5SDgAAmuKbcwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAG5zxhhJTEwM9G0AAHBbIzkHAOBrrFu3bpKSkiInT56UmpoaOX36tGRmZsrIkSP9cr34+Hib7EdGRoq/DB48WN5//30pLS2Vixcvypo1ayQiIsKz/f7775ff//739lmrqqrk2LFjMmvWLL/dDwAAvkByDgDA11RMTIwcPHjQJuJz586VAQMGSEJCgmzfvl1Wr14tbV1wcHCztu7du8u2bdvkxIkTMnToUPs89957r7z55puefYYMGSLnz5+XiRMn2m2/+MUv5Je//KVMnz69lZ8AAIAbYwiCIAiCaDliYmJMenq6Xd7quYK+4TSh/9DNBHWM8Pt9b9q0yRQVFZnw8PBm2yIjIz2/VWJiov0dHx9v1723Dxw40La5nz86OtpkZmaakpIS43K5TG5urhkzZozd3lRqaqo9xuFwmHnz5pmCggJTVVVljhw5YpKSkjzXcF83ISHBZGdnm9raWtvW9L6nTJlizp07Z8/nbrvvvvvssb169Wrx3+K1114zH3zwQZv5OyAIgiAIaRIhN5jIAwCAm+AIC5WI4UOlfb9e4mgXJqa2TmqOn5TKnX8RU3fF59fr1KmT7VVeuHChLe1uqqys7KbPrb3uYWFhMnz4cKmsrJT+/fuLy+WSoqIiGT9+vGzcuFH69u0r5eXlUl1dbY+ZP3++7cmeNm2a5Ofn22PXr18vFy5ckJ07d3rOvWzZMpkzZ44UFBTYsvWm2rVrJ3V1dbZ03s19jdjYWFu+fzVaZl9SUnLTzwwAgL+RnAMA0Ao0MQ9/YIDUl7ukvuSyBHVob9eVa9tun1+vd+/eEhQUJMePH/f5uaOjo2XDhg2Sm5tr1wsLCz3b3AmwlpW7XwBoIr9gwQIZNWqU7N+/33OMJtNTp05tlJwvXrzYlq235MMPP5RXXnnFJvCvvvqq/dZcE3p3yfvVPPLII/Lss8/K2LFjffL8AAD4A9+cAwDgZ0HfcNoec03MGyoqRT6vt0tdb9+vpwR1/O/BzHzF4XCIv+gAc8nJybJ7925ZsmSJ/Zb9q14UaBK9detWqaio8MSkSZOkV69ejfbNzs6+5rl0cLfJkyfL7NmzbUXAuXPnbKKvy4aGhmb76zfnf/7zn+Xf/u3f7PUBAGirSM4BAPCzYGeELWVvqK5p1K7rjnbtJLij0+fX1NJxTVb79et3Q8e5E1zv5D40NLTRPmvXrpWePXvKunXrbGKuCfWMGTNaPKfT+cXzac/1oEGDPKHl8BMmTGi0r5bJf5W3337b9pL36NFDunTpYl8Q3HnnnbYU3ts999wjH3zwgfzHf/yHHRQOAIC2jOQcAAA/q3dV2m/MtZTdm66b2lqpr3D5/Jr6vfaWLVvsCOXh4eHNtrc01Zl+A960RFwT6abOnDljpzBLSkqSFStWyJQpU2y7fg/edKR17e3Wady0HF6/CfcOPc/N0tJ5Tea1ZF3P790zrom/jkqflpZme/kBAGjrSM4BAPCzhnKXHfwt+BvOL0rYQ4LtUtdrjhd8UeruB5qYa5J84MABO1CblpdrT/rMmTNl3759Vz1GpyjT+cG1N1r3f+qpp2wJubeVK1fK6NGj5a677rJzjo8YMULy8vLstlOnTtne93HjxknXrl1tObsOFrd8+XJ7nJaya6+7Hqe97bp+M8+lx/fp00d++MMfymuvvWYHnHN/466l7JqY61zo+n26zvWuofcDAEBbFvAh4wmCIAiiLYcvptByhIUa56hY03XGJHPn7Cl2qeva7s97j4qKMqtWrTKFhYWmpqbGTq2WkZHRaJoy76nUNIYNG2ZycnLslGc7duywU555T6WWkpJi8vPzTXV1tSkuLjZpaWmmc+fOnuOTk5PN2bNnTX19vWcqNY1Zs2aZvLw8O02aHpeVlWXi4uJanMKtpdDrXbx40T6PTsk2ceLERtt/+tOfmqvRf4NA/x0QBEEQhLQQji9/AACAFsTExMjSpUtl0aJFtmf4Vtge845OW8rurx5ztP2/AwAAmmIqNQAAWpEm5CTlAACgKb45BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcA4DZnjJHExMRA3wYAALc1knMAAL7GunXrJikpKXLy5EmpqamR06dPS2ZmpowcOdIv14uPj7fJfmRkpPjL4MGD5f3335fS0lK5ePGirFmzRiIiIjzbO3fuLFlZWfLZZ595nnnVqlXSsWNHv90TAAC3iuQcAICvqZiYGDl48KBNxOfOnSsDBgyQhIQE2b59u6xevVrauuDg4GZt3bt3l23btsmJEydk6NCh9nnuvfdeefPNNz37NDQ0yJ///Gd5+umnpW/fvvK9731PRo0aJb/5zW9a+QkAALgxhiAIgiCIliMmJsakp6fb5a2eK+gbThPao5sJ6hjh9/vetGmTKSoqMuHh4c22RUZGen6rxMRE+zs+Pt6ue28fOHCgbXM/f3R0tMnMzDQlJSXG5XKZ3NxcM2bMGLu9qdTUVHuMw+Ew8+bNMwUFBaaqqsocOXLEJCUlea7hvm5CQoLJzs42tbW1tq3pfU+ZMsWcO3fOns/ddt9999lje/Xq1eK/xcyZM83p06fbzN8BQRAEQUiTCLnBRB4AANwER1ioRAwfKu3v6SWOdmFiauukJu+kVO78i5i6Kz6/XqdOnWyv8sKFC6WqqqrZ9rKysps+t/a6h4WFyfDhw6WyslL69+8vLpdLioqKZPz48bJx40bbY11eXi7V1dX2mPnz58vEiRNl2rRpkp+fb49dv369XLhwQXbu3Ok597Jly2TOnDlSUFBgy9abateundTV1dnSeTf3NWJjY235/tV62/W+duzYcdPPDACAv1HWDgBAK9DEPPyBAWLqG+TzS5ftUte13R969+4tQUFBcvz4cZ+fOzo6Wvbs2SO5ublSWFgomzZtkl27dtly8pKSErvP+fPnpbi42CbomsgvWLBAvv/979tvxfWYtLQ0m5xPnTq10bkXL15sy9ZbSs4//PBDiYqKsgl8aGio3HHHHTahdyfh3n7/+9/blwdnz5619/Hiiy/6/N8CAABfITkHAMDPgr7htD3m9eUuaaioFPm83i51vX2/nhLU8b8HM/MVh8Mh/qIDzCUnJ8vu3btlyZIl9lv2r3pRoAO2bd26VSoqKjwxadIk6dWrV6N9s7Ozr3muY8eOyeTJk2X27Nm2IuDcuXM22delvhzw9uMf/1i+/e1v22/P9TqvvPLKLTw1AAD+RVk7AAB+Ftwxwpay11+63Ki9obpGQrrcIcHfcH6RtPuQlo5rstqvX78bOs6d4Hon99pD7W3t2rWyZcsWGTt2rIwePdqWrGuy/Nprr131nE6n0y51fx1B3VttbW2jde3p/ipvv/22jW9+85t2fy1x/9d//Vfb2+5Ne+41PvnkE9ujry8Tli5dahN5AADaGnrOAQDws/qKSvuNeVCH9o3add3U1NoedF/TknBNoKdPny7h4eHNtrc01Zl+A960RHzQoEHN9jtz5oydwiwpKUlWrFghU6ZMse36PXjTkda1t1unNNNyeP0m3Dv0PDdLS+c1OX/22Wft+bVnviVa4u/+Zh0AgLaInnMAAPysodxlB3/Tb8ztenWNTcy1x7wq+6jPe83dNDHXb8MPHDhgv+X++OOPJSQkRJ544gl56aWX7EBuTekUZTovuJar62ByOrCb9op7W7lypZ1H/NNPP7UDz40YMULy8vLstlOnTtne93Hjxsl7771nB2vTweKWL19uj9MkWXuw9eXAo48+ar8FT09Pv+Hn2rt3rz2vPsuvfvUrmTdvnmeQuzFjxtj53f/617/afXSqNd1Hr6v3BwBAWxXwIeMJgiAIoi2HL6bQcoSFGueoWNN1xiRz55wpdqnr2u7Pe4+KijKrVq0yhYWFpqamxk6tlpGR0WiaMu+p1DSGDRtmcnJy7JRnO3bssFOeeU+llpKSYvLz8011dbUpLi42aWlppnPnzp7jk5OTzdmzZ019fb1nKjWNWbNmmby8PDtNmh6XlZVl4uLiWpzCraXQ6128eNE+j07JNnHixEbbH3vsMbNnzx5TWlpqn+GTTz4xv/zlL6/r3P7+OyAIgiAIaSEcX/4AAAAtiImJsd8qL1q06JZ7XnXwN+0x9wwOh9vy7wAAgKYoawcAoBVpQk5SDgAAmmJAOAAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAG5zxhhJTEwM9G0AAHBbIzkHAOBrrFu3bpKSkiInT56UmpoaOX36tGRmZsrIkSP9cr34+Hib7EdGRoq/DB48WN5//30pLS2Vixcvypo1ayQiIuKq+3bu3FmKior8fk8AANwqknMAAL6mYmJi5ODBgzYRnzt3rgwYMEASEhJk+/btsnr1amnrgoODm7V1795dtm3bJidOnJChQ4fa57n33nvlzTffvOo51q5dKx9//HEr3C0AALeG5BwAgFYU9A2nhPboJkEdr97T60u//vWvbY/xQw89JBs3bpT8/Hw5duyYrFy5Uh5++OHr7vkeOHCgbdNkX0VHR9ve95KSEnG5XJKbmytjxoyx2z/66CO7z+XLl+0xqampdt3hcMi8efOkoKBAqqqq5MiRI5KUlNTsuppsZ2dnS21trcTGxja7v3HjxsmVK1dk+vTp8umnn9p9p02bJhMmTJBevXo12lfb77jjDlm+fLmP/kUBAPCfED+eGwAAfMkRFirO+Iek/T29xNEuTExtndTknRTXjgNi6q74/HqdOnWyie7ChQttMtxUWVnZTZ9be93DwsJk+PDhUllZKf3797dJupaPjx8/3r4I6Nu3r5SXl0t1dbU9Zv78+TJx4kSbMOtLAj12/fr1cuHCBdm5c6fn3MuWLZM5c+bYJF7L1ptq166d1NXV2UTezX0NTea1fF/dc889snjxYtu73rNnz5t+VgAAWgvJOQAArUAT8/AHB0h9uUvqSy5LUIf2dl1VbN3j8+v17t1bgoKC5Pjx4z4/t/acb9iwwfaYq8LCQs827U1X58+f97wA0ER+wYIFMmrUKNm/f7/nGE2mp06d2ig514Ray9Zb8uGHH8orr7xiE/hXX33VfmuuCb275N19vbffftuW8usLA5JzAMDfA8raAQBohVJ27THXxLyholLk83q71HVt90eJu5aR+4sOMJecnCy7d++WJUuW2G/Zv+pFgSbRW7dulYqKCk9MmjSpWSm6lqlfi5blT548WWbPnm0rAs6dO2cTfV02NDTYfX75y19KXl6evPXWWz54WgAAWgfJOQAAfhbcMcKWsjdU1zRq13VtD/6G0+fX1NJxTVb79et3Q8e5E1zv5D40NLTZIGvaG71u3TqbmGtCPWPGjBbP6XR+8Xxjx46VQYMGeULL4fVbcW9aJv9VtFdce8l79OghXbp0sS8I7rzzTlsKr3QAvO985zv223SNDz74wLbryO66LwAAbRHJOQAAflZfUWm/MddSdm+6ru3ag+5r+r32li1b7MBp4eHhzba3NK2YfgPuXSKuNJFu6syZM3YKMx3UbcWKFTJlyhTbrt+DNx1pXXu7dRo3LYfXb8K9Q89zs7R0XpP5Z5991p5fe+aV3pMOYud+CfDiiy/a9ri4uL+LUeoBALcnvjkHAMDPGspddvA39zfm2mOuibn2mFf99egXpe5+oIn5nj175MCBA/Zbbp1SLCQkRJ544gl56aWXbM91UzpFmc6Frj3MOpicDuymJeTedLT3rKwsO1q6Djw3YsQIW0auTp06ZXvfdVT19957zw7WpoPF6Yjpepx+B6/l8Ppy4NFHH7WDxqWnp9/wc+3du9eeV5/lV7/6lR0J3v2Nu7sH3a1r1652qfd4KwPhAQDgbzrcKUEQBEEQLURMTIxJT0+3y5s9hyMs1HR84lFz56xJ5ptzX7RLXdd2f957VFSUWbVqlSksLDQ1NTWmqKjIZGRkmPj4eM8+KjEx0bM+bNgwk5OTY6qqqsyOHTtMUlKS3cf9/CkpKSY/P99UV1eb4uJik5aWZjp37uw5Pjk52Zw9e9bU19eb1NRUT/usWbNMXl6eqa2ttcdlZWWZuLg4u03vR0VGRn7lM+n1Ll68aJ/nyJEjZuLEidfc/0bO7e+/A4IgCIKQFsLx5Q8AANACnb976dKlsmjRItszfCt08DftMfcMDofb8u8AAICmKGsHAKAVaUJOUg4AAJpiQDgAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAABuc8YYSUxMDPRtAABwWyM5BwDga6xbt26SkpIiJ0+elJqaGjl9+rRkZmbKyJEj/XK9+Ph4m+xHRkaKvwwePFjef/99KS0tlYsXL8qaNWskIiKi0T56D03j2Wef9ds9AQBwq0jOAQD4moqJiZGDBw/aRHzu3LkyYMAASUhIkO3bt8vq1aulrQsODm7W1r17d9m2bZucOHFChg4dap/n3nvvlTfffLPZvt/73vckKirKExkZGa105wAA3BxDEARBEETLERMTY9LT0+3yVs8V9A2nCe3RzQR1jPD7fW/atMkUFRWZ8PDwZtsiIyM9v1ViYqL9HR8fb9e9tw8cONC2uZ8/OjraZGZmmpKSEuNyuUxubq4ZM2aM3d5UamqqPcbhcJh58+aZgoICU1VVZY4cOWKSkpI813BfNyEhwWRnZ5va2lrb1vS+p0yZYs6dO2fP526777777LG9evW66jO1xb8DgiAIgpAmEXKTCT0AALgBjrBQccY/JO379xJHuzAxtXVSc+ykuHYcEFN3xefX69Spk+1VXrhwoVRVVTXbXlZWdtPn1l73sLAwGT58uFRWVkr//v3F5XJJUVGRjB8/XjZu3Ch9+/aV8vJyqa6utsfMnz9fJk6cKNOmTZP8/Hx77Pr16+XChQuyc+dOz7mXLVsmc+bMkYKCAlu23lS7du2krq7Olqm7ua8RGxtry/e97/ONN96w5/rNb34jqampN/3MAAD4G8k5AACtQBPz8IcGSH25S+ovXZag8PZ2XVVs3ePz6/Xu3VuCgoLk+PHjPj93dHS0bNiwQXJzc+16YWGhZ1tJSYldnj9/3vMCQBP5BQsWyKhRo2T//v2eYzSZnjp1aqPkfPHixbZsvSUffvihvPLKKzaBf/XVV+235prQu0ve3RYtWmT31RcTo0ePll//+tfidDpl1apVPv/3AADAF0jOAQDws6BvOG2PuSbmDeWVts291PbK/UekoeKLdV9xOBziLzrA3Ouvv26TXk2kNVE/evToNV8UaBK9devWRu2atB8+fLhRW3Z29jWvfezYMZk8ebJN0H/5y19KfX29vZ9z585JQ0ODZ7+f//znnt9Hjhyx19fv7knOAQBtFQPCAQDgZ8EdI2wpe0NVTaN2Xdf24G84fX5NLR3XZLVfv343dJw7wfVO7kNDQxvts3btWunZs6esW7fODjKnCfWMGTNaPKf2WKuxY8fKoEGDPKHl8BMmTGi0r5bJf5W3337b9pL36NFDunTpIkuWLJE777zTlq+35C9/+Yt861vfsi8EAABoi0jOAQDws/qKSvuNuZaye9N1bdcedV/T77W3bNki06dPl/Dw8GbbW5rqTL8Bb1oirol0U2fOnLFTmCUlJcmKFStkypQptl2/B2860rr2dus0bloOr9+Ee4ee52Zp6bwm8zpFmp6/ac+8N30GLbl33x8AAG0NZe0AAPhZQ7nLDv7m/sZce8w1Mdce86oDR31e0u6mifmePXvkwIED9lvujz/+WEJCQuSJJ56Ql156yfZcN6VTlOlc6NobrYPJ6cBus2fPbrTPypUrJSsrSz799FM78NyIESMkLy/Pbjt16pTtfR83bpy89957drA2HSxu+fLl9jj9Dn737t325cCjjz5qB41LT0+/4efau3evPa8+y69+9SuZN2+e5xt3vbbO767ft2vSrvvoN+96DwAAtGUBHzKeIAiCINpy+GIKLUdYqOn4xKPmzn+ZZL75f1+0S13Xdn/ee1RUlFm1apUpLCw0NTU1dmq1jIyMRtOUNZ12bNiwYSYnJ8dOebZjxw475Zn3VGopKSkmPz/fVFdXm+LiYpOWlmY6d+7sOT45OdmcPXvW1NfXe6ZS05g1a5bJy8uz06TpcVlZWSYuLq7FKdxaCr3exYsX7fPolGwTJ05stP3JJ580hw4dMuXl5aaiosIcPnzY/OAHP2g0/Vqg/g4IgiAIQloIx5c/AABAC2JiYmTp0qV2BHDtGb4VQR0jbI+5HRzOTz3maPt/BwAANEVZOwAArUgTcpJyAADQFAPCAQAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAcJszxkhiYmKgbwMAgNsayTkAAF9j3bp1k5SUFDl58qTU1NTI6dOnJTMzU0aOHOmX68XHx9tkPzIyUvxl8ODB8v7770tpaalcvHhR1qxZIxEREc32mzx5suTk5Eh1dbUUFxfLa6+95rd7AgDgVpGcAwDwNRUTEyMHDx60ifjcuXNlwIABkpCQINu3b5fVq1dLWxccHNysrXv37rJt2zY5ceKEDB061D7PvffeK2+++Waj/X784x/LL37xC1m2bJndPmrUKNmyZUsr3j0AADfOEARBEATRcsTExJj09HS7vNVzBX3DaUJ7dDNBHSP8ft+bNm0yRUVFJjw8vNm2yMhIz2+VmJhof8fHx9t17+0DBw60be7nj46ONpmZmaakpMS4XC6Tm5trxowZY7c3lZqaao9xOBxm3rx5pqCgwFRVVZkjR46YpKQkzzXc101ISDDZ2dmmtrbWtjW97ylTpphz587Z87nb7rvvPntsr1697Podd9xhKisrzciRI9vs3wFBEARBSJMIuYlkHgAA3CBHWKg4H3tI2vfvKY527cTU1krNsQJxfXRATN0Vn1+vU6dOtld54cKFUlVV1Wx7WVnZTZ9be93DwsJk+PDhUllZKf379xeXyyVFRUUyfvx42bhxo/Tt21fKy8ttSbmaP3++TJw4UaZNmyb5+fn22PXr18uFCxdk586dnnNrT/ecOXOkoKDAlq031a5dO6mrq7Ol827ua8TGxtry/SeeeEKCgoKkR48ecuzY/8/evUBXWd6J/v/tXCEBNkQuoS3ZFCjlIgTGVmsEUhjAIJzJKaF12jLQ1rKCBfHMADVcZYrnDEehjKEMchxMCUz9WwplUmPKpVIoCGWCBIiEGkgKQSTB5kbukDz/9Xt077Nz2VggO5sj389av/Xu93nf571g1nL93ud2Vrp27SrvvPOOLFy4UC5fvnzH7w0AgD+RnAMA0AE0MY/46oPSeL1KGv9SJkERney+ur73SLvfb9CgQTZBPXfuXLtfOyYmRnbu3Cm5ubl2v7Cw0HOstLTUbktKSjwfADSRX7p0qe1afuzYMU8dTaaTk5ObJecrV6603dZ9efvtt+WnP/2pTeBffvllO9ZcE3p3l3c1YMAA++56z2effdY+xwsvvCD79u2TkSNHyo0b7f8xBACAu8WYcwAA/CyoWxfbYq6JeVNltcjNRrvV/U5DB0hQ19aTmd0th8Mh/qITzC1fvlwOHz4sq1atsmPZP+1DgSbRmhxfv37dE7NmzZKBAwc2Ozc7O/uW19KWcJ3oTVvBtUfA1atXbaKv26amJnuOJub6QWDBggV24rg//vGP8u1vf1u+9KUvyfjx49vhXwAAgPZHcg4AgJ8Fd420Xdmbauqaleu+IzxMgrt1afd7atdxTVaHDBlyW/XcCa53ch8aGtrsnC1bttjW6W3bttnEXBPq+fPn+7xmly4fv9/UqVNl1KhRntDu8DNmzGh2rnaT/zSvv/66bSXXbusPPPCA/UDQq1cv2xVeffjhh55E3k1nddfQVn8AAO5FJOcAAPhZ4/VqO8Zcu7J7031T3yCNlVXtfk8dr62zk8+bN08iIiJaHfe11JmOAffuIq40kW5Jx27rEmZJSUmybt06mTNnji3X8eAtZ1rXJFmXcdPEWMeEe8fdjAHXrvOazD/55JP2+toyr44c+XiYwJe//OVmY/B79uwpFy9evOP7AQDgTyTnAAD4WVNllZ38LbhrFwnqFikSEmy3ul+XVyBN1z+9tfhOaGKuSfLx48ftRG3avVxb0p955hk5evRom3V0iTJdC11bo/X8J554wnYh97Z+/XqZPHmy9O/f3645rl3F8/Ly7DFNfrX1fdq0aTYZ1u7sOlnc2rVrbT3tyq6t7lpPW9t1/07eS+trN/Uf/ehHdv1ynXDOPcZdew3s3r3bjkl/9NFH7VJqW7dutePvdRk5AADuVQGfMp4gCIIg7uVojyW0HGGhpuvkx0yvZ//B9P7xU3ar+1ruz2ePjo42GzZsMIWFhaaurs4urbZ79+5my5R5L6WmERcXZ06dOmWXPDt48KBd8sx7KbXU1FSTn59vamtrTXFxsdm6dauJiory1F++fLm5cuWKaWxs9CylprFgwQKTl5dnl0nTellZWWbs2LE+l3DzFXq/jz76yL6PLsk2c+bMVud07drV/Pu//7td7k3P3blzp/nCF74Q8L8DgiAIghAf4fjkBwAA8MHlcsnq1atlxYoVd90tWid/0zHm2pXdXy3muPf/DgAAaIml1AAA6ECakJOUAwCAlhhzDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AwH3OGCOJiYmBfgwAAO5rJOcAAHyG9enTR1JTU+XChQtSV1cnly5dkoyMDJkwYYJf7hcfH2+TfafTKf4yevRo2bt3r5SVlclHH30kmzdvlsjISM/x2bNn22doK3r16uW35wIA4G6QnAMA8BnlcrnkxIkTNhFfvHixjBgxQhISEuTAgQOyceNGudcFBwe3Kuvbt6/s379fzp8/L4888oh9n+HDh8vPf/5zzzlvvPGGREdHN4vf/va38vvf/16uXbvWwW8BAMBfzxAEQRAE4TtcLpdJT0+327u9VlC3Lib0831MUNdIvz93ZmamKSoqMhEREa2OOZ1Oz2+VmJhof8fHx9t97+OxsbG2zP3+MTExJiMjw5SWlpqqqiqTm5trpkyZYo+3lJaWZus4HA6TkpJiCgoKTE1NjcnJyTFJSUmee7jvm5CQYLKzs019fb0ta/ncc+bMMVevXrXXc5c9+OCDtu7AgQPb/Hfo2bOnvd7MmTPvmb8DgiAIgpAWEXIbSTwAALhDjrBQ6fL1h6XTsAHiCA8XU18vdWcLpOr3x8U03Gj3+/Xo0cO2Ki9btkxqampaHa+oqLjja2ure1hYmIwbN06qq6tl2LBhUlVVJUVFRTJ9+nTZtWuXDB48WCorK6W2ttbWWbJkicycOVPmzp0r+fn5tu727dttS/ahQ4c8116zZo0sWrRICgoKbLf1lsLDw6WhocF2UXdz32PMmDG2+35Ls2bNsv8Gv/rVr+74nQEA8DeScwAAOoAm5hEPPyiNlVXS+JcyCYroZPfV9b1H2v1+gwYNkqCgIDl37ly7XzsmJkZ27twpubm5dr+wsNBzrLS01G5LSko8HwA0kV+6dKlMnDhRjh075qmjyXRycnKz5HzlypW227ovb7/9tvz0pz+1CfzLL79sx5prQu/u8t6Wp556Sn7xi1/YMfcAANyrGHMOAICfBXXrYlvMNTFvqqwWudlot7qv5UFd/+9kZu3F4XCIv+gEc8uXL5fDhw/LqlWr7Fj2T/tQoEn0vn375Pr1657QFu2BAwc2Ozc7O/uW1zp79qyd8G3hwoW2Nfzq1as20ddtU1NTq/O/9rWv2Zb9LVu23OHbAgDQMUjOAQDws+CukbYre1NN85Zb3XeEh0lwty7tfk/tOq7J6pAhQ26rnjvB9U7uQ0NDm52jie6AAQNk27ZtNjHXhHr+/Pk+r9mly8fvN3XqVBk1apQnNGmeMWNGs3O1m/ynef31120r+ec//3l54IEH7AcCnYVdu8K39MMf/lBOnjwp77777l/x9gAABA7JOQAAftZ4vdqOMdeu7N5039Q32Bb09qbjtffs2SPz5s2TiIiIVsd9LXXmns3cu4u4JtItXb582S5hlpSUJOvWrZM5c+bYch0P3nKmdW3t1i7l2h1ex4R7h17nTmnXeU3mn3zySXt9bZn3pq313/rWt2g1BwD8P4HkHAAAP2uqrLKTv2kLeVC3SJGQYLvVfS1vuv7prcV3QhNzTZKPHz9uJ2rT7uXakv7MM8/I0aNH26yjS5TpWujaGq3nP/HEE7YLubf169fL5MmTpX///nbN8fHjx0teXp49dvHiRdv6Pm3aNOnZs6dNkHWyuLVr19p62pVdW921nra26/6dvJfW/9KXviQ/+tGP5Gc/+5mdcK7lJHeatIeEhNiJ5wAA+H9BwKeMJwiCIIh7OdpjCS1HWKjpOvkx0+t//IPp/dxTdqv7Wu7PZ4+OjjYbNmwwhYWFpq6uzi6ttnv37mbLlHkvpaYRFxdnTp06ZZc8O3jwoF3yzHsptdTUVJOfn29qa2tNcXGx2bp1q4mKivLUX758ubly5YppbGz0LKWmsWDBApOXl2eXNdN6WVlZZuzYsT6XcPMVer+PPvrIvo8uyeZribQjR46Y7du331N/BwRBEAQhPsLxyQ8AAOCDy+WS1atXy4oVK2zL8N3Qyd+0xdxODuenFnPc+38HAAC0xFJqAAB0IE3IScoBAEBLjDkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgPmeMkcTExEA/BgAA9zWScwAAPsP69OkjqampcuHCBamrq5NLly5JRkaGTJgwwS/3i4+Pt8m+0+kUfxk9erTs3btXysrK5KOPPpLNmzdLZGRks3O+8pWvyP79++05paWl8tvf/lZGjhzpt2cCAOBukZwDANCBgrp1kdAv9JGgbs2TSX9wuVxy4sQJm4gvXrxYRowYIQkJCXLgwAHZuHGj3OuCg4NblfXt29cm3efPn5dHHnnEvs/w4cPl5z//ueccTdQ1GdcPEXrOmDFj5Pr167Jnzx4JCQnp4LcAAOCvZwiCIAiC8B0ul8ukp6fb7Z1ewxEearo+Hmd6/eM/mD5LnrJb3XeEhfrtuTMzM01RUZGJiIhodczpdHp+q8TERPs7Pj7e7nsfj42NtWXu94+JiTEZGRmmtLTUVFVVmdzcXDNlyhR7vKW0tLSP39/hMCkpKaagoMDU1NSYnJwck5SU5LmH+74JCQkmOzvb1NfX27KWzz1nzhxz9epVez132YMPPmjrDhw40O4/9NBDdv8LX/iCz3MC9XdAEARBEOIj+HwMAEAH6PL1r0rkww9K4/VqufmXcgmK6GT31fU977T7/Xr06GFblZctWyY1NTWtjldUVNzxtbXVPSwsTMaNGyfV1dUybNgwqaqqkqKiIpk+fbrs2rVLBg8eLJWVlVJbW2vrLFmyRGbOnClz586V/Px8W3f79u1y7do1OXTokOfaa9askUWLFklBQYHtkt5SeHi4NDQ02K7zbu57aAu5dt//05/+ZLu7P/XUU/K//tf/si3w+vvs2bPy5z//+Y7fGwAAfyI5BwCgA7qydxo20CbmTZXVtsy91fLqo6c8++1l0KBBEhQUJOfOnZP2FhMTIzt37pTc3Fy7X1hY6Dmm47tVSUmJ5wOAJvJLly6ViRMnyrFjxzx1NJlOTk5ulpyvXLnSdlv35e2335af/vSnNoF/+eWXbRd2TejdXd6Vfij4+te/Lrt375YVK1bYMv0g8Pjjj0tjY2O7/3sAANAeGHMOAICfBXeLlKBOYdJUU9esXPe1PLhbl3a/p8PhEH/RCeaWL18uhw8fllWrVtmx7J/2oUCT6H379tmx3+6YNWuWDBw4sNm52dnZt7yWtn7Pnj1bFi5caHsEXL161Sb6um1qarLndOrUSbZs2SJHjhyRr33ta/LYY4/ZDwmZmZn2GAAA9yJazgEA8LPGymppqmuwXdm9W8jtfl2DNFZWtfs9taVYk9UhQ4bcVj13guud3IeGhjY7RxNfnVxt6tSpMnnyZNtlXZPln/3sZ21es0uXjz8+6PkffPBBs2P19fXN9rWb/Kd5/fXXbfTu3duer13c/+mf/sl2hVff+c53pH///vLoo496ur9rmXaT1yXj3njjjb/yXwMAgI5DyzkAAH7WVFkldWcvSHDXyI9naQ8Jtlvd1/L27tKuNBHVBHrevHkSERHR6rivpc50DLh3F3E1atSoVuddvnzZLmGWlJQk69atkzlz5thyHQ/ecqZ1be3WZdy0O7yOCfcOvc6d0q7zmpw/+eST9vraMq/0ffUjg/e4dPe+dvUHAOBexP+hAADoAFUH/kuqj+eKOIIk5IHudqv7Wu4vmphrknz8+HE7UZt2L9eW9GeeeUaOHj3aZh1dokyXINPu6nr+E088YVvFva1fv962mGvrtK45Pn78eMnLy7PHLl68aBPhadOmSc+ePW13dh0DvnbtWltPu7IPGDDA1ps/f77dv5P30vpf+tKX5Ec/+pFtsdfWe/cYd03SdUI8nbhO31cnrEtLS5ObN2/aZeQAALhXBXzKeIIgCIK4l6M9l9AK6hZpQr/Qx2474tmjo6PNhg0bTGFhoamrq7NLq+3evbvZMmXeS6lpxMXFmVOnTtklzw4ePGiXPPNeSi01NdXk5+eb2tpaU1xcbLZu3WqioqI89ZcvX26uXLliGhsbPUupaSxYsMDk5eXZZdK0XlZWlhk7dqzPJdx8hd7vo48+su+jS7LNnDmz1TkTJ040f/jDH0xZWZn5y1/+Yvbv328eeeSRe+bvgCAIgiCkRTg++QEAAHxwuVyyevVqO/O3tgzj/sTfAQDAn+jWDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAcJ8zxkhiYmKgHwMAgPsayTkAAJ9hffr0kdTUVLlw4YLU1dXJpUuXJCMjQyZMmOCX+8XHx9tk3+l0ir+MHj1a9u7dK2VlZfLRRx/J5s2bJTIystk5+n5HjhyRyspK+fDDD2XNmjUSHBzst2cCAOBukZwDANCBgrpFSugXetutv7lcLjlx4oRNVBcvXiwjRoyQhIQEOXDggGzcuFHudW0l03379pX9+/fL+fPn5ZFHHrHvM3z4cPn5z3/uOWfkyJHy1ltvyW9/+1ubyD/55JPyd3/3dzZBBwDgXmYIgiAIgvAdLpfLpKen2+2dXsMRHmq6JcSZ3v8000Qv/YHd6r4jLNRvz52ZmWmKiopMREREq2NOp9PzWyUmJtrf8fHxdt/7eGxsrC1zv39MTIzJyMgwpaWlpqqqyuTm5popU6bY4y2lpaV9/P4Oh0lJSTEFBQWmpqbG5OTkmKSkJM893PdNSEgw2dnZpr6+3pa1fO45c+aYq1ev2uu5yx588EFbd+DAgXb/f/7P/2mOHz/erN60adPsfbt06RLQvwOCIAiCEB8REugvAwAA3A+6jv+qRD78oDRWVsmNj8olOKKT3VeVv32n3e/Xo0cP26q8bNkyqampaXW8oqLijq+tre5hYWEybtw4qa6ulmHDhklVVZUUFRXJ9OnTZdeuXTJ48GDbpby2ttbWWbJkicycOVPmzp0r+fn5tu727dvl2rVrcujQIc+1tXV70aJFUlBQYLuttxQeHi4NDQ2267yb+x5jxoyx3ff1HO3C703P6dy5szz00ENy8ODBO353AAD8hW7tAAD4mXZh7zRsgE3MGyurRW422q3ua7k/urgPGjRIgoKC5Ny5c+1+7ZiYGDueOzc3VwoLCyUzM1P+8Ic/SFNTk5SWltpzSkpKpLi42CbomsgvXbpUfvCDH9ix4lpn69atNjlPTk5udu2VK1fabuu+kvO3335boqOjbQIfGhoq3bt393RX1y7vas+ePRIXFyd///d/b/8NPve5z9nrep8DAMC9huQcAAA/C+4WKUGdwqSxpnlrru5ruR5vbw6HQ/xFJ5hbvny5HD58WFatWmXHsn/ahwKdsG3fvn1y/fp1T8yaNUsGDhzY7Nzs7OxbXuvs2bMye/ZsWbhwoe0RcPXqVZvs61Y/Dii9j46xf+WVV6S+vl7ef/99OwZduc8BAOBeQ3IOAICfaSt5U12D7cruTfe13LamtzPtOq6J6JAhQ26rnjt59U7utYXa25YtW2TAgAGybds2m5hrQj1//nyf1+zSpYvdTp06VUaNGuUJ7Q4/Y8aMZudqN/lP8/rrr9sW8M9//vPywAMP2A8EvXr1sq3tbuvXr7et6trK37NnT/nP//xPW+59DgAA9xKScwAA/KypslrqzhZIcLcuH7eShwTbre5ruR5vb9olXLt3z5s3TyIiIlod97XUmY4Bb9n9WxPpli5fvmyXMEtKSpJ169bJnDlzbLmOB28507q2dusYcE2UdUy4d+h17pR2nddkXmdj1+tri3lLuoyaHvv2t79tl5F799137/h+AAD4ExPCAQDQAa6//V92q2PMQ3t2ty3m1cdzPeX+oIm5jg0/fvy4HXN9+vRpCQkJkUmTJsnTTz9tW65b0iXKNInV1midTE4ndtMu5N60VTorK8t2F9eJ58aPHy95eXn22MWLF23r+7Rp02xXcp2ITSeLW7t2ra2nY8C1O7x+HHjsscfsmPT09PTbfq933nnHXlff5aWXXpKUlJRmk9zpmHRdSk2fRSep0+Pf+ta36NYOALinBXzKeIIgCIK4l6M9l9AK6hZpQr/Q22474tmjo6PNhg0bTGFhoamrq7NLq+3evbvZMmXeS6lpxMXFmVOnTtmlxw4ePGiXPPNeSi01NdXk5+eb2tpaU1xcbLZu3WqioqI89ZcvX26uXLliGhsbPUupaSxYsMDk5eXZZdK0XlZWlhk7dqzPJdx8hd7vo48+su+jS7LNnDmz1Tm/+93vTFlZmX2Ho0eP2iXa7qW/A4IgCIKQFuH45AcAAPDB5XLJ6tWrZcWKFbZlGPcn/g4AAP7EmHMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAID7nDFGEhMTA/0YAADc10jOAQD4DOvTp4+kpqbKhQsXpK6uTi5duiQZGRkyYcIEv9wvPj7eJvtOp1P8ZenSpXLkyBGprq6WsrKyNs/p16+fvPnmm/ac4uJiefHFFyU4OLjVs544ccL+u+Tn58vs2bP99swAAHwaknMAADpQsDNSQr/QW4K6Rfr9Xi6XyyafmogvXrxYRowYIQkJCXLgwAHZuHGj3OtaJtNuYWFhsmPHDtm0aVObx4OCgiQzM9OeFxcXZ5Pu733ve/KTn/zEc07//v3tOfpvMWrUKPnXf/1X+fd//3eZPHmy394HAIBPYwiCIAiC8B0ul8ukp6fb7Z1ewxEearolxJk+i2aavst+YLe67wgL9dtzZ2ZmmqKiIhMREdHqmNPp9PxWiYmJ9nd8fLzd9z4eGxtry9zvHxMTYzIyMkxpaampqqoyubm5ZsqUKfZ4S2lpaR+/v8NhUlJSTEFBgampqTE5OTkmKSnJcw/3fRMSEkx2drapr6+3Zbd6v9mzZ5uysrJW5XqNmzdvmt69e3vKkpOTTXl5uQkN/fjfe82aNebMmTPN6r3++usmKyvLr38HBEEQBCE+gpZzAAA6QNfxX5UuX3tQpLFJbnxUbre633XCV/1yvx49ethWcm0hr6mpaXW8oqLijq+t1wwPD5dx48bZ1vjnnntOqqqqpKioSKZPn27PGTx4sERHR8uzzz5r95csWSKzZs2SuXPnyvDhw2X9+vWyfft2ew1va9askZSUFBk6dKicPn36jp7v0UcflTNnzkhJSYmnbM+ePbarvd7bfc7+/fub1dNztBwAgEAICchdAQC4z7qyd35wgDRWVEljZbUtc287Dx8gVe+ckqZP9tvLoEGDbPfuc+fOSXuLiYmRnTt3Sm5urt0vLCz0HCstLbVbTYzdHwC0e7mOE584caIcO3bMU2fMmDGSnJwshw4d8tRfuXJlq6T5dulHAR1n7s29r8dudY4m8J06dbLj0AEA6Egk5wAA+FlQ10gJCg/7uMXcS2NNnYT07C7B3SLbPTl3OBziLzrBnI731vHZmkhroq4t1bf6UBAZGSn79u1rVq5J+8mTJ5uVZWdn++25AQC4l9GtHQAAP2u6Xi1N9Q0SHNGpWbnum7oGTyt6e9LZx5uammTIkCG3VU/rtEzuQ0NDm52zZcsWGTBggGzbts12a9eEev78+T6v2aVLF7udOnWqnXzNHcOGDZMZM2Y0O1dnV79bV69etbPUe3Pv67FbnaOt/bSaAwACgeQcAAA/a6yoltrcAgl2drGt5BISbLe6X/teQbu3mitdYkzHUM+bN08iIiJaHfe11Nm1a9fstm/fvp4yTaRbunz5smzevFmSkpJk3bp1MmfOHFve0NDQaqb1s2fP2oRXu8Prkm7eoddpb0ePHrUfDXr16uUpmzRpkk289Vnc5/zt3/5ts3p6jpYDABAIJOcAAHSA62//l1Qdy9V1vmxXdt3qvpb7iybmmiQfP37cTtSm3cu1Jf2ZZ57xmYSeP3/eroW+atUqe/4TTzwhCxcubHaOTuamXdp1ObLRo0fL+PHjJS8vzx67ePGibX2fNm2a9OzZ03Zn18ni1q5da+vppHDa6q71tLVd92+XrmEeGxtrk319P/2tofdSe/futUm4tuyPHDnSPusLL7xgJ7Jzfzx45ZVX7HP87//9v+XLX/6yPP300/Ktb33LPiMAAIES8CnjCYIgCOJejvZcQiuoW6QJ/UJvu+2IZ4+OjjYbNmwwhYWFpq6uzi6ttnv37mbLlHkvpaYRFxdnTp06ZZc8O3jwoF3yzHsptdTUVJOfn29qa2tNcXGx2bp1q4mKivLUX758ubly5YppbGz0LKWmsWDBApOXl2eXSdN6umzZ2LFjfS7h5iv0mm3xfidd7k2XkquurjYlJSXmpZdeMsHBwc2uo+e/++679t/l/Pnzdmm2jvo7IAiCIAhpEY5PfgAAAB9cLpesXr1aVqxYYVuGcX/i7wAA4E90awcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BALjPGWMkMTEx0I8BAMB9jeQcAIDPsD59+khqaqpcuHBB6urq5NKlS5KRkSETJkzwy/3i4+Ntsu90OsVfli5dKkeOHJHq6mopKytr85x+/frJm2++ac8pLi6WF198UYKDgz3Ho6Oj5T/+4z/kT3/6kzQ2Nsr69ev99rwAAPw1SM4BAOhAwc5ICf1CbwnqFun3e7lcLjlx4oRNxBcvXiwjRoyQhIQEOXDggGzcuFHudd7JtLewsDDZsWOHbNq0qc3jQUFBkpmZac+Li4uT2bNny/e+9z35yU9+4jknPDxcrl27Ji+88IKcOnXKb+8AAMDtMARBEARB+A6Xy2XS09Pt9k6v4QgPNd2mxJk+i75r+i7/vt3qviMs1G/PnZmZaYqKikxERESrY06n0/NbJSYm2t/x8fF23/t4bGysLXO/f0xMjMnIyDClpaWmqqrK5ObmmilTptjjLaWlpX38/g6HSUlJMQUFBaampsbk5OSYpKQkzz3c901ISDDZ2dmmvr7elt3q/WbPnm3Kyspales1bt68aXr37u0pS05ONuXl5SY0tPW/94EDB8z69es75O+AIAiCIMRHhNxWGg8AAO5I1wlflS6PDJfGymq58VGFBEd0svuqMuuddr9fjx49bCv5smXLpKamptXxioqKO762trprq/S4ceNst/Fhw4ZJVVWVFBUVyfTp02XXrl0yePBgqayslNraWltnyZIlMnPmTJk7d67k5+fbutu3b7et14cOHfJce82aNbJo0SIpKCjw2WX90zz66KNy5swZKSkp8ZTt2bNHXnnlFRk+fLjk5OTc8bsDAOAvJOcAAHRAV/bOw79oE3MN5d5qedWRU9L0yX57GTRokO3efe7cOWlvMTExsnPnTsnNzbX7hYWFnmOlpaV2q4mx+wOAJvI6TnzixIly7NgxT50xY8ZIcnJys+R85cqVsn///rt6Ph1PruPMvbn39RgAAPciknMAAPwsqGukBHUKsy3m3hpr6iSkp1OCu0W2e3LucDjEX3SCOR3vPXnyZJtIa6KuLdW3+lAQGRkp+/bta1auSfvJkyeblWVnZ/vtuQEAuJcxIRwAAH7WdL1amuoabFd2b7pv6ho8rejtSbuONzU1yZAhQ26rntZpmdyHhoY2O2fLli0yYMAA2bZtm51kThPq+fPn+7xmly5d7Hbq1KkyatQoT2h3+BkzZjQ7V7vJ362rV6/aWeq9uff1GAAA9yKScwAA/Kyxolpq3yu0LeQaEhLs+a3l7d1qrnS8to6znjdvnkRERLQ67mupMx0Drvr27esp00S6pcuXL8vmzZslKSlJ1q1bJ3PmzLHlDQ0NrWZaP3v2rF3GTbvD65Ju3qHXaW9Hjx61Hw169erlKZs0aZLtZq/PAgDAvYjkHACADnD9d/8lVX98TyTIYbuy61b3tdxfNDHXJPn48eN2ojbtXq4t6c8884xNYNty/vx5uxb6qlWr7PlPPPGELFy4sNk5uia4dmnv37+/jB49WsaPHy95eXn22MWLF23r+7Rp06Rnz562O7tOFrd27Vpbb9asWbbVXetpa7vu3y5dwzw2NtYm+/p++ltD76X27t1rk3Bt2R85cqR9Vl0yTSeyc388UO562rKvibz+Hjp06G0/DwAA7SXgU8YTBEEQxL0c7bmEVlC3SBP6hd522xHPHh0dbTZs2GAKCwtNXV2dXVpt9+7dzZYp815KTSMuLs6cOnXKLnl28OBBu+SZ91JqqampJj8/39TW1pri4mKzdetWExUV5am/fPlyc+XKFdPY2OhZSk1jwYIFJi8vzy6TpvWysrLM2LFjfS7h5iv0mm3xfidd7k2XkquurjYlJSXmpZdeMsHBwc2u0xb9d+qIvwOCIAiCkBbh+OQHAADwweVyyerVq2XFihW2ZRj3J/4OAAD+RLd2AAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHACA+5wxRhITEwP9GAAA3NdIzgEA+Azr06ePpKamyoULF6Surk4uXbokGRkZMmHCBL/cLz4+3ib7TqdT/GXp0qVy5MgRqa6ulrKysjbP6devn7z55pv2nOLiYnnxxRclODjYc/wb3/iG7N27V0pKSqSiokLeeecdmTx5st+eGQCAT0NyDgBABwp2RkpYv94S3C3S7/dyuVxy4sQJm4gvXrxYRowYIQkJCXLgwAHZuHGj3Ou8k2lvYWFhsmPHDtm0aVObx4OCgiQzM9OeFxcXJ7Nnz5bvfe978pOf/MRzzrhx42Tfvn3yxBNPyEMPPWT/TX7zm9/IqFGj/PY+AAB8GkMQBEEQhO9wuVwmPT3dbu/0Go7wUON84lET/ePvmM+t+J7d6r4jLNRvz52ZmWmKiopMREREq2NOp9PzWyUmJtrf8fHxdt/7eGxsrC1zv39MTIzJyMgwpaWlpqqqyuTm5popU6bY4y2lpaV9/P4Oh0lJSTEFBQWmpqbG5OTkmKSkJM893PdNSEgw2dnZpr6+3pbd6v1mz55tysrKWpXrNW7evGl69+7tKUtOTjbl5eUmNNT3v7e+x4oVK/z6d0AQBEEQ4iNCPjV1BwAAd63b335FunxtuDRWVMuNjyokOKKT3VcVbx1t9/v16NHDtpIvW7ZMampqWh3Xrtx3SlvdtVVaW5+12/iwYcOkqqpKioqKZPr06bJr1y4ZPHiwVFZWSm1tra2zZMkSmTlzpsydO1fy8/Nt3e3bt8u1a9fk0KFDnmuvWbNGFi1aJAUFBT67rH+aRx99VM6cOWO7rLvt2bNHXnnlFRk+fLjk5OS0quNwOKRr165SWlp6R/cEAOBukZwDANABXdk7P/hFm5g3VlbbMve28/AvStXh05799jJo0CDbvfvcuXPS3mJiYmTnzp2Sm5tr9wsLCz3H3Mmteyy30kRex4lPnDhRjh075qkzZswYSU5Obpacr1y5Uvbv339XzxcdHW3HmXtz7+uxtugHgS5dusgvf/nLu7o3AAB3iuQcAAA/0/HlQeFhtsXcW2NNnYT2dNrkvb2Tc20J9hedYE7He+sEappIa6KuLdW3+lAQGRlpx3h706T95MmTzcqys7Olo33729+W559/3s5Yry35AAAEAhPCAQDgZ5p4N9U32K7s3nS/qa7Btqi3N+063tTUJEOGDLmtelqnZXIfGhra7JwtW7bIgAEDZNu2bXaSOU2o58+f7/Oa2iKtpk6daidcc4d2h58xY0azc7Wb/N26evWqnaXem3tfj3l78skn5d///d/lW9/6lvzud7+763sDAHCnSM4BAPAzTb5rcwttC7mdpT0k2G51v/a9wnZvNVc6XlvHWc+bN08iIiJaHfe11Jm75bhv376esrZmML98+bJs3rxZkpKSZN26dTJnzhxb3tDQ0Gqm9bNnz9pl3LQ7vC7p5h16nfZ29OhR+9GgV69enrJJkybZbvb6LG5///d/L2lpabbl/K233mr35wAA4HaQnAMA0AEq92dL1bH3RIIctiu7bnVfy/1FE3NNko8fP24natPu5dqS/swzz9gEti3nz5+3a6GvWrXKnq9LjS1cuLDZOevXr7dd2vv37y+jR4+W8ePHS15enj128eJF2/o+bdo06dmzp+3OrpPFrV271tabNWuWbXXXetrarvu3S9cwj42Ntcm+vp/+1tB7KV2/XJNwbdkfOXKkfdYXXnjBTmTn/nigCXl6erp9tz/+8Y+2ZV2jW7dud/AvDQBA+wj4lPEEQRAEcS9Hey6hFdwt0oT16223HfHs0dHRZsOGDaawsNDU1dXZpdV2797dbJky76XUNOLi4sypU6fskmcHDx60S555L6WWmppq8vPzTW1trSkuLjZbt241UVFRnvrLly83V65cMY2NjZ6l1DQWLFhg8vLy7DJpWi8rK8uMHTvW5xJuvkKv2Rbvd9Ll3nQpuerqalNSUmJeeuklExwc7Dl+4MCBNq/h/bz+/DsgCIIgCGkRjk9+AAAAH1wul6xevVpWrFhhW4Zxf+LvAADgT3RrBwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAuM8ZYyQxMTHQjwEAwH2N5BwAgM+wPn36SGpqqly4cEHq6urk0qVLkpGRIRMmTPDL/eLj422y73Q6xV+WLl0qR44ckerqaikrK2vznH79+smbb75pzykuLpYXX3xRgoODPccfe+wxOXz4sHz00UdSU1MjeXl58j/+x//w2zMDAPBpQj71DAAA0G6CnZES3C1SGiuqpbGy2q/3crlcNoktLy+XxYsXy5kzZyQ0NFQef/xx2bhxowwdOlTuZZpMNzY2tioPCwuTHTt2yNGjR+Wpp55qdTwoKEgyMzPl6tWrEhcXJ3379pX09HS5ceOGLFu2zJ6jSfvPfvYzOX36tP09ZswY2bx5s/396quvdsj7AQDQkiEIgiAIwne4XC6Tnp5ut3d6DUd4qHFOfdT0/fF3zOdXfs9udd8RFuq3587MzDRFRUUmIiKi1TGn0+n5rRITE+3v+Ph4u+99PDY21pa53z8mJsZkZGSY0tJSU1VVZXJzc82UKVPs8ZbS0tI+fn+Hw6SkpJiCggJTU1NjcnJyTFJSkuce7vsmJCSY7OxsU19fb8tu9X6zZ882ZWVlrcr1Gjdv3jS9e/f2lCUnJ5vy8nITGur733vnzp32v7M//w4IgiAIQnwE3doBAOgA3SZ+Rbp+bZhIU5Pc+KjcbnW/26Sv+OV+PXr0kISEBNtCrt22W6qoqLjja+s1w8PDZdy4cTJixAh57rnnpKqqSoqKimT69On2nMGDB0t0dLQ8++yzdn/JkiUya9YsmTt3rgwfPlzWr18v27dvt9fwtmbNGklJSbGt+tqqfSceffRR20ugpKTEU7Znzx7b1V7v3ZZRo0bZVvaDBw/e0T0BALhbdGsHAKADurJHDP9is67s7q2WV/3hdLt3cR80aJDt3n3u3DlpbzExMbJz507Jzc21+4WFhZ5jpaWldquJsfsDgHZD13HiEydOlGPHjnnqaFfy5ORkOXTokKf+ypUrZf/+/Xf1fPpRQMeZe3Pv6zFv+kGhV69eEhISIqtWrZItW7bc1b0BALhTJOcAAPiZjjEP6hT2cYu5l8aaOgnt6bTJe3sn5w6HQ/xFJ5jbtGmTTJ482SbSmqhrS/WtPhRERkbKvn37mpVr0n7y5MlmZdnZ2dKRxo4dK126dJGvfe1rttX+/Pnz8v/9f/9fhz4DAACK5BwAAD/TxLuprkGCIzo1S8J1v6nuhm1Rb2/5+fnS1NQkQ4YMua16Wqdlcq+TyHnT1mXtJj516lSboGuX9YULF9oJ1tqiya/S8z/44INmx+rr65vt64Rsd0sngnv44YdbzVrvPubtz3/+s91qLwA9R1vPSc4BAIHAmHMAAPxMk++a9wo9M7VLSLDd6r6W+2PWdl1iTBPoefPmSURERKvjvpY6u3btmt3qDOfe47Fbunz5sp3dPCkpSdatWydz5syx5Q0NDXbrvWzZ2bNn7TJu2h1el3TzDr1Oe9NZ3HUsvHZXd5s0aZLtZq/P4osOA9Cx9AAABAIt5wAAdIDKfdmeMebalV1bzK8fO+sp9wdNzHUptePHj9ux3DrBmo6t1kT16aeflmHDhrWqo926dS10bUHWZcd0YjdtFfemk7llZWXJ+++/byeeGz9+vF0nXF28eNG2vk+bNk3eeustqa2ttZPFrV271tbTBFjXF9ePA7rWeGVlpV3m7HboGuZRUVE22dePALGxsZ5n15b3vXv32iR827Zt8uMf/9iOM3/hhRfsRHbujwc/+tGP7Hu6x+TrxHSLFi2yXfYBAAiUgE8ZTxAEQRD3crTnElrB3SJNWL/edtsRzx4dHW02bNhgCgsLTV1dnV1abffu3c2WKfNeSk0jLi7OnDp1yi55dvDgQbvkmfdSaqmpqSY/P9/U1taa4uJis3XrVhMVFeWpv3z5cnPlyhXT2NjoWUpNY8GCBSYvL88uk6b1srKyzNixY30u4eYr9Jpt8X4nXe5Nl5Krrq42JSUl5qWXXjLBwcGe4/PnzzdnzpyxS8HpEmsnTpwwc+fOtUu+dcTfAUEQBEFIi3B88gMAAPjgcrlk9erVsmLFCtsyjPsTfwcAAH9izDkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAMB9zhgjiYmJgX4MAADuayTnAAB8hvXp00dSU1PlwoULUldXJ5cuXZKMjAyZMGGCX+4XHx9vk32n0yn+snTpUjly5IhUV1dLWVlZm+f069dP3nzzTXtOcXGxvPjiixIcHNzmuXFxcXLjxg05efKk354ZAIBPE/KpZwAAgHYT7IyU4G6R0lhRLY2V1X69l8vlsklseXm5LF68WM6cOSOhoaHy+OOPy8aNG2Xo0KFyL9NkurGxsVV5WFiY7NixQ44ePSpPPfVUq+NBQUGSmZkpV69etYl33759JT093Sbgy5Yta3aufkTQY7/73e/shwwAAALJEARBEAThO1wul0lPT7fbO72GIzzUdJ/2NfO5lG+bz6+abbe67wgL9dtzZ2ZmmqKiIhMREdHqmNPp9PxWiYmJ9nd8fLzd9z4eGxtry9zvHxMTYzIyMkxpaampqqoyubm5ZsqUKfZ4S2lpaR+/v8NhUlJSTEFBgampqTE5OTkmKSnJcw/3fRMSEkx2drapr6+3Zbd6v9mzZ5uysrJW5XqNmzdvmt69e3vKkpOTTXl5uQkNbf7v/frrr5uf/OQn5vnnnzcnT570+98BQRAEQYiPoFs7AAAdwDnpIen66HAxTUZuXquwW913Tn7IL/fr0aOHJCQk2BbympqaVscrKiru+Np6zfDwcBk3bpyMGDFCnnvuOamqqpKioiKZPn26PWfw4MESHR0tzz77rN1fsmSJzJo1S+bOnSvDhw+X9evXy/bt2+01vK1Zs0ZSUlJsq/7p06fv6PkeffRR20ugpKTEU7Znzx7bSq73dvve974nAwYMkH/+53++w38JAADaD93aAQDogK7sEQ9+UW5qV/aKj7uyu7dafv3QmXbv4j5o0CDbvfvcuXPS3mJiYmTnzp2Sm5tr9wsLCz3HSktL7VYTY/cHAO2GruPEJ06cKMeOHfPUGTNmjCQnJ8uhQ4c89VeuXCn79++/q+fTjwI6ztybe1+Puf999EPA2LFj2+w6DwBARyM5BwDAz3SMuaNTmDRea95a3VRdJyE9nTZ5b+/k3OFwiL/oBHObNm2SyZMn20RaE3VtqfZFE+HIyEjZt29fs3JN2ltOwpadnS3+ph8tfvGLX8jzzz8v+fn5fr8fAAB/DZJzAAD8TBNvU9cgQZGdPC3mSvdNfUOzsvaiSWdTU5MMGTLktuppnZbJvU4i523Lli22m/jUqVNtgq5d1hcuXCg/+9nP2rxmly5d7FbP/+CDD5odq6+vb7avs6vfLZ0I7uGHH25W5p7sTY917dpVvvrVr8ro0aM9z6wJu4ZOGqfvdODAgbt+DgAAbgdjzgEA8DNNvmtyCyVEZ2p3RoojJNhudV/L/TFruy4xpgn0vHnzJCIiotVxX0udXbt2zW51hnO3UaNGtTrv8uXLsnnzZklKSpJ169bJnDlzbHlDQ4Pdei9bdvbsWbuMm3aH1yXdvEOv0950FncdC9+rVy9P2aRJk2w3e32WyspKefDBB+17ueOVV16xQwD09x//+Md2fyYAAD4NLecAAHSAir0nPGPMtSu7tphfP/qep9wfNDHXpdSOHz9ux3LrBGshISE2UX366adl2LBhreqcP3/eroW+atUqu+yYTuymreLedDK3rKwsef/99+3Ec+PHj5e8vDx77OLFi7b1fdq0afLWW29JbW2tnSxu7dq1tp62Th8+fNh+HHjsscdsoqxLmd0OXcM8KirKJvv6ESA2Ntbz7NryvnfvXpuEb9u2TX784x/bceYvvPCCncjO/fHgvffea3ZNHSOvHxBalgMA0JECPmU8QRAEQdzL0Z5LaAV3izRh/XrbbUc8e3R0tNmwYYMpLCw0dXV1dmm13bt3N1umzHspNY24uDhz6tQpu+TZwYMH7ZJn3kuppaammvz8fFNbW2uKi4vN1q1bTVRUlKf+8uXLzZUrV0xjY6NnKTWNBQsWmLy8PLtMmtbLysoyY8eO9bmEm6/Qa7bF+510uTddSq66utqUlJSYl156yQQHB/u8JkupEQRBEBLgcHzyAwAA+OByuWT16tWyYsUK2zKM+xN/BwAAf2LMOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AwH3OGCOJiYmBfgwAAO5rJOcAAHyG9enTR1JTU+XChQtSV1cnly5dkoyMDJkwYYJf7hcfH2+TfafTKf6ydOlSOXLkiFRXV0tZWVmb5/Tr10/efPNNe05xcbG8+OKLEhwc3Oo5W4b+ewEAEAghAbkrAAD3qWBnpI3Gimob/uRyuWwSW15eLosXL5YzZ85IaGioPP7447Jx40YZOnSo3Ms0mW5sbGxVHhYWJjt27JCjR4/KU0891ep4UFCQZGZmytWrVyUuLk769u0r6enpcuPGDVm2bFmzcwcPHiyVlZWe/ZKSEj+9DQAAn84QBEEQBOE7XC6XSU9Pt9s7vYYjPNT0mPY18/kl3zb9/nmW3eq+IyzUb8+dmZlpioqKTERERKtjTqfT81slJiba3/Hx8Xbf+3hsbKwtc79/TEyMycjIMKWlpaaqqsrk5uaaKVOm2OMtpaWlffz+DodJSUkxBQUFpqamxuTk5JikpCTPPdz3TUhIMNnZ2aa+vt6W3er9Zs+ebcrKylqV6zVu3rxpevfu7SlLTk425eXlJjQ01Od7dsTfAUEQBEGIj6BbOwAAHaD7pIek66PDxDQ1yY1rFXar+90nP+SX+/Xo0UMSEhJsC3lNTU2r4xUVFXd8bb1meHi4jBs3TkaMGCHPPfecVFVVSVFRkUyfPt3TIh0dHS3PPvus3V+yZInMmjVL5s6dK8OHD5f169fL9u3b7TW8rVmzRlJSUmyr/unTp+/o+R599FHbS8C7FXzPnj22q73e21tOTo5cuXJF9u7da1vZAQAIFLq1AwDgZ9qNPWLEF+Vm5f/tyu7eRjzYXyr/cKbdu7gPGjTIdu8+d+6ctLeYmBjZuXOn5Obm2v3CwkLPsdLSUrvVxNj9AUC7oes48YkTJ8qxY8c8dcaMGSPJycly6NAhT/2VK1fK/v377+r59KOAjjP35t7XY+rDDz+0987OzrYfGn74wx/K73//e3nkkUfk5MmTd3V/AADuBMk5AAAdkJwHdQq1LebemqrrJLSn0zMGvT05HA7xF51gbtOmTTJ58mSbSGuiri3Vt/pQEBkZKfv27WtWrkl7y0RYk+WO8P7779tw0/HrAwcOlH/8x3+0LfwAAHQ0urUDAOBnmng31d2QoMhOzcp1v6muwS8Tw+Xn50tTU5MMGTLktuppnZbJvU4i523Lli0yYMAA2bZtm+3Wrgn1/PnzfV6zS5cudjt16lQZNWqUJ4YNGyYzZsxodq7Orn63dCK4lrOuu/f1mC/Hjx+3HxIAAAgEknMAAPxMk++aM4US0u3jmdodIcF2q/s1uX/2S3KuS4zpOOt58+ZJREREq+O+ljq7du2a3eoM526aSLd0+fJl2bx5syQlJcm6detkzpw5tryhocFuvZctO3v2rF3GTbvD65Ju3qHXaW/aCq4fDXr16uUpmzRpku1mr8/ii76ndncHACAQ6NYOAEAHKN97wjPGXLuya4v59aNnPeX+oIm5LqWmLcI6llsnWAsJCbGJ6tNPP21brls6f/68XQt91apVdtkxndht4cKFzc7RydyysrJst3CdeG78+PGSl5dnj128eNG2vk+bNk3eeustqa2ttZPFrV271tbTcfCHDx+2Hwcee+wxu4yZLnN2O3QN86ioKJvs60eA2NhYz7Nry7tO7qZJuLbs//jHP7bjzF944QU7kZ3744FOVKfj3t977z3p1KmTHXOua79rV30AAAIl4FPGEwRBEMS9HO25hFawM9KExfS224549ujoaLNhwwZTWFho6urq7NJqu3fvbrZMmfdSahpxcXHm1KlTdsmzgwcP2iXPvJdSS01NNfn5+aa2ttYUFxebrVu3mqioKE/95cuXmytXrpjGxkbPUmoaCxYsMHl5eXaZNK2XlZVlxo4de9tLm+k12+L9Trrcmy4lV11dbUpKSsxLL71kgoODPccXL15s30Hf8aOPPjJvv/22+frXv95hfwcEQRAEIS3C8ckPAADgg8vlktWrV8uKFStsyzDuT/wdAAD8iTHnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAA9zljjCQmJgb6MQAAuK+RnAMA8BnWp08fSU1NlQsXLkhdXZ1cunRJMjIyZMKECX65X3x8vE32nU6n+MvSpUvlyJEjUl1dLWVlZW2e069fP3nzzTftOcXFxfLiiy9KcHBws3PCwsLkhRdekD//+c/236awsFC+//3v++25AQC4lZBbHgUAAO0q2BkpIc4IuVlRLY0VNX69l8vlsklseXm5LF68WM6cOSOhoaHy+OOPy8aNG2Xo0KFyL9NkurGxsVW5JtU7duyQo0ePylNPPdXqeFBQkGRmZsrVq1clLi5O+vbtK+np6XLjxg1ZtmyZ57xf/vKX9uOFXuP8+fP2PK0LAECgGIIgCIIgfIfL5TLp6el2e6fXcISHmh7THjFfWPL3JuafZ9mt7jvCQvz23JmZmaaoqMhERES0OuZ0Oj2/VWJiov0dHx9v972Px8bG2jL3+8fExJiMjAxTWlpqqqqqTG5urpkyZYo93lJaWtrH7+9wmJSUFFNQUGBqampMTk6OSUpK8tzDfd+EhASTnZ1t6uvrbdmt3m/27NmmrKysVble4+bNm6Z3796esuTkZFNeXm5CQ0Pt/uOPP27r9ujRo0P/DgiCIAhCfASfhwEA6ADdJ/2NdIsbJqapSW5cq7Bb3e8++SG/3K9Hjx6SkJBgW8hralq30FdUVNzxtfWa4eHhMm7cOBkxYoQ899xzUlVVJUVFRTJ9+nR7zuDBgyU6OlqeffZZu79kyRKZNWuWzJ07V4YPHy7r16+X7du322t4W7NmjaSkpNhW/dOnT9/R8z366KO2l0BJSYmnbM+ePbarvd5b/d3f/Z1kZ2fLj3/8Y7l8+bL86U9/kpdeekk6dep0x/8uAADcDbq1AwDQAV3ZI0d8sVlXdvdWyyv/cKbdu7gPGjTIdtE+d+6ctLeYmBjZuXOn5Obm2n0dq+1WWlpqt5oYuz8AaDd0HSc+ceJEOXbsmKfOmDFjJDk5WQ4dOuSpv3LlStm/f/9dPZ9+FNBx5t7c+3pMDRgwwN5fx5p/4xvfkJ49e8q//du/yQMPPCA/+MEP7ur+AADcCZJzAAD8TMeYB3UKsy3m3pqq6yW0VzcJcUa2e3LucDjEX3SCuU2bNsnkyZNtIq2JurZU3+pDQWRkpOzbt69ZuSbtJ0+ebFamrdkdQT9c6MR13/3ud6WystKW/dM//ZP86le/kh/96Ec2aQcAoCPRrR0AAD+7WVEjTXUNEhQZ3qxc95vqbtgW9faWn58vTU1NMmTIkNuqp3VaJvc6iZy3LVu22Jbnbdu22W7tmlDPnz/f5zW7dOlit1OnTpVRo0Z5YtiwYTJjxoxm5+rs6ndLJ4LTid68uff1mPrwww/lgw8+8CTmKi8vzybtX/jCF+76GQAAuF0k5wAA+FljRbVUnym0LeTBzghxhATbre5ruT9mbdclxnSc9bx58yQiIqLVcV9LnV27ds1udeZyN02kW9Jx2ps3b5akpCRZt26dzJkzx5Y3NDTYrfeyZWfPnrUt0dodXpd08w69TnvTWdz1o0GvXr08ZZMmTbLd7PVZlM5i/7nPfc626LvpOHmdHd4fzwQAwKchOQcAoAOU7z0hle+cFUdQkO3Krlvd13J/0cRck+Tjx4/bidq0e7m2pD/zzDM2gW2LLimma6GvWrXKnv/EE0/IwoULm52jk7lpl/b+/fvL6NGjZfz48bbVWV28eNG2vk+bNs2O49bkVyeLW7t2ra2nk8Jpq7vW09Z23b9duoZ5bGysTfb1/fS3hjvR3rt3r03CtWV/5MiR9ll1PXOdyM798eAXv/iF/OUvf5G0tDQ7+dzYsWPthHCvvfYaXdoBAAET8CnjCYIgCOJejvZcQivYGWHCY3rZbUc8e3R0tNmwYYMpLCw0dXV1dmm13bt3N1umzHspNY24uDhz6tQpu+TZwYMH7ZJn3kuppaammvz8fFNbW2uKi4vN1q1bTVRUlKf+8uXLzZUrV0xjY6NnKTWNBQsWmLy8PLtMmtbLysoyY8eO9bmEm6/Qa7bF+510uTddSq66utqUlJSYl156yQQHBze7zpe//GWzd+9ee86lS5fM2rVrTadOnTrk74AgCIIgpEU4PvkBAAB8cLlcsnr1almxYoVtGcb9ib8DAIA/0a0dAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgPmeMkcTExEA/BgAA9zWScwAAPsP69OkjqampcuHCBamrq5NLly5JRkaGTJgwwS/3i4+Pt8m+0+kUf1m6dKkcOXJEqqurpaysrM1z+vXrJ2+++aY9p7i4WF588UUJDg72HE9LS7PP2TJyc3P99twAANxKyC2PAgCAdhXsjJQQZ4TcrKiWxooav97L5XLZJLa8vFwWL14sZ86ckdDQUHn88cdl48aNMnToULmXaTLd2NjYqjwsLEx27NghR48elaeeeqrV8aCgIMnMzJSrV69KXFyc9O3bV9LT0+XGjRuybNkye86zzz4rKSkpnjohISFy6tQpe10AAALFEARBEAThO1wul0lPT7fbO72GIzzUPPDfHjYxS580/Vf/g93qviMsxG/PnZmZaYqKikxERESrY06n0/NbJSYm2t/x8fF23/t4bGysLXO/f0xMjMnIyDClpaWmqqrK5ObmmilTptjjLaWlpX38/g6HSUlJMQUFBaampsbk5OSYpKQkzz3c901ISDDZ2dmmvr7elt3q/WbPnm3Kyspales1bt68aXr37u0pS05ONuXl5SY0NLTNa+n7NzY22nfz598BQRAEQYiPoOUcAIAOEDV5tHR7bJjcLK+WGyUVEhQZbvfVX35zvN3v16NHD0lISLAtxTU1rVvoKyoq7vja2uqurdfjxo2z3caHDRsmVVVVUlRUJNOnT5ddu3bJ4MGDpbKyUmpra22dJUuWyMyZM2Xu3LmSn59v627fvl2uXbsmhw4d8lx7zZo1smjRIikoKPDZZf3TPProo7aXQElJiadsz5498sorr8jw4cMlJyenVR1tgd+/f7/t9g8AQCCQnAMA0AFd2SNHfNEm5u6u7O6tlpcfym33Lu6DBg2y3bvPnTsn7S0mJkZ27tzpGZ9dWFjoOVZaWmq3mhi7PwBoIq/jxCdOnCjHjh3z1BkzZowkJyc3S85Xrlxpk+S7ER0dbceZe3Pv67GWtNv7lClT5Dvf+c5d3RcAgLtBcg4AgJ/pGPOgzmG2xdxbU3W9hPZySogzst2Tc4fDIf6iE8xt2rRJJk+ebBNpTdS1pfpWHwoiIyNl3759zco1aT958mSzsuzsbOlos2fPtuPyd+/e3eH3BgDAjdnaAQDws5sVNdJU22C7snvT/aa6Bjs5XHvTruNNTU0yZMiQ26qndVom9zqJnLctW7bIgAEDZNu2bTJixAibUM+fP9/nNbt06WK3U6dOlVGjRnlCu8PPmDGj2bnaTf5u6URwOku9N/e+HmvpBz/4gX0XnTAOAIBAITkHAMDPGiuqpfpMoYR0j5RgZ4Q4QoLtVve13B+ztut4bR1nPW/ePImIiGh13NdSZzoG3N3V200T6ZYuX74smzdvlqSkJFm3bp3MmTPHljc0NNit97JlZ8+etcu4aXd4XdLNO/Q67U1ncdePBr169fKUTZo0yXaz12dpufTbl770JfvBAQCAQCI5BwCgA5TueVcqj5wVR1CQ7cquW93Xcn/RxFyT5OPHj9uJ2rR7ubakP/PMMzaBbcv58+ftpGirVq2y5z/xxBOycOHCZuesX7/edmnv37+/jB49WsaPHy95eXn22MWLF23r+7Rp06Rnz562O7tOFrd27Vpbb9asWbbVXetpa7vu3y5dwzw2NtYm+/p++ltD76X27t1rk3BtDR85cqR91hdeeMFOZOf+eOA9EZyOg3/vvfdu+zkAAGhvAZ8yniAIgiDu5WjPJbSCnREmPKaX3XbEs0dHR5sNGzaYwsJCU1dXZ5dW2717d7NlyryXUtOIi4szp06dskueHTx40C555r2UWmpqqsnPzze1tbWmuLjYbN261URFRXnqL1++3Fy5csUuTeZeSk1jwYIFJi8vzy6TpvWysrLM2LFjfS7h5iv0mm3xfiddEk2XkquurjYlJSXmpZdeMsHBwc2u061bN3v8hz/8YYf/HRAEQRCEtAjHJz8AAIAPLpdLVq9eLStWrLAtw7g/8XcAAPAnurUDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAADc54wxkpiYGOjHAADgvkZyDgDAZ1ifPn0kNTVVLly4IHV1dXLp0iXJyMiQCRMm+OV+8fHxNtl3Op3iL0uXLpUjR45IdXW1lJWVtXlOv3795M0337TnFBcXy4svvijBwcHNzvnOd74jOTk59pwrV67Ili1bJCoqym/PDQDArZCcAwDQgUK6R0p4TC8Jdkb4/V4ul0tOnDhhE/HFixfLiBEjJCEhQQ4cOCAbN26Ue13LZNotLCxMduzYIZs2bWrzeFBQkGRmZtrz4uLiZPbs2fK9731PfvKTn3jO0fL09HSbkA8fPly++c1vysMPPyyvvvqq394HAIBPYwiCIAiC8B0ul8ukp6fb7Z1ewxEeah74u4dNzLJvmS+unmm3uu8IC/Hbc2dmZpqioiITERHR6pjT6fT8VomJifZ3fHy83fc+Hhsba8vc7x8TE2MyMjJMaWmpqaqqMrm5uWbKlCn2eEtpaWkfv7/DYVJSUkxBQYGpqakxOTk5JikpyXMP930TEhJMdna2qa+vt2W3er/Zs2ebsrKyVuV6jZs3b5revXt7ypKTk015ebkJDQ21+wsXLjTnz59vVm/+/Pn238uffwcEQRAEIT6ClnMAADpA1OOjxRk3TKSpSRquVdit7kcl/I1f7tejRw/bSq4t5DU1Na2OV1RU3PG19Zrh4eEybtw42xr/3HPPSVVVlRQVFcn06dPtOYMHD5bo6Gh59tln7f6SJUtk1qxZMnfuXNtSvX79etm+fbu9hrc1a9ZISkqKDB06VE6fPn1Hz/foo4/KmTNnpKSkxFO2Z88e29Ve762OHj1qu75PmTLF7vfu3VtmzJghb7311h3/uwAAcDdC7qo2AAD4q7qyR47oLzcrquRmxceJsnur5eUHc6Xxk/32MmjQINu9+9y5c9LeYmJiZOfOnZKbm2v3CwsLPcdKS0vtVhNj9wcA7V6u48QnTpwox44d89QZM2aMJCcny6FDhzz1V65cKfv377+r59OPAjrO3Jt7X4+pd955R7773e/KG2+8IZ06dZLQ0FA7Fn/evHl3dW8AAO4ULecAAPhZcLcICe4UJo3V9c3KdT+oU5iEOCPb/Z4Oh0P8RSeYW758uRw+fFhWrVplW88/7UNBZGSk7Nu3T65fv+4JbUkfOHBgs3Ozs7OlI2jL/Msvv2zHoT/00EPy+OOPS//+/eWVV17pkPsDANASLecAAPhZY2WNNNY1SHBkuKfFXOl+U12D3Kyobvd75ufnS1NTkwwZMuS26mmdlsm9tip700nUtJv41KlTZfLkybbL+sKFC+VnP/tZm9fs0qWL3er5H3zwQbNj9fXNP1jozOl36+rVq3Zyt5az1ruPKX1mnfF97dq1dl+7weu99YODfnhwnwcAQEeh5RwAAD+7WV4t1Wf+LCHOLhLijBBHSLDd6r6Wt3eXdqVLjGkCrd20IyJazwzva6mza9eu2W3fvn09ZaNGjWp13uXLl2Xz5s2SlJQk69atkzlz5tjyhoaGVjOtnz171i7jpt3hdUk379DrtDcdT66t+b169fKUTZo0yXaz12dR+m/i/hDh1tjY6PdeBwAA+EJyDgBAByj97btS8c5ZXedLQns57Vb3tdxfNDHXJPn48eN2ojbtXq4t6c8884xNYNty/vx5uxa6dlfX85944gnbKu5NJ3PTFnPtBj569GgZP3685OXl2WMXL160Se+0adOkZ8+etju7ThanLdRaT7uyDxgwwNabP3++3b9dOpFbbGysTfb1/fS3ht5L7d271ybh27Ztk5EjR9pnfeGFF+xEdu6PB7/5zW/sv4lOUPfFL37RLq2m3fX/+Mc/yocffngH/9oAANy9gE8ZTxAEQRD3crTnElrBzggTHtPLbjvi2aOjo82GDRtMYWGhqaurs0uF7d69u9kyZd5LqWnExcWZU6dO2SXPDh48aJc8815KLTU11eTn55va2lpTXFxstm7daqKiojz1ly9fbq5cuWIaGxs9S6lpLFiwwOTl5dll0rReVlaWGTt2rM8l3HyFXrMt3u+ky73pUnLV1dWmpKTEvPTSSyY4OLjV0mm6DJye88EHH5ht27aZz33ucx3yd0AQBEEQ0iIcn/wAAAA+uFwuWb16taxYscK2DOP+xN8BAMCf6NYOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgDAfc4YI4mJiYF+DAAA7msk5wAAfIb16dNHUlNT5cKFC1JXVyeXLl2SjIwMmTBhgl/uFx8fb5N9p9Mp/rJ06VI5cuSIVFdXS1lZWZvn9OvXT9588017TnFxsbz44osSHBzc7Jwf/ehHcvbsWampqZFz587JP/zDP/jtmQEA+DQhn3oGAAD4f5LL5bJJbHl5uSxevFjOnDkjoaGh8vjjj8vGjRtl6NChci/TZLqxsbFVeVhYmOzYsUOOHj0qTz31VKvjQUFBkpmZKVevXpW4uDjp27evpKeny40bN2TZsmX2nLlz58q//Mu/yJw5c+S//uu/5OGHH5ZXX33VJvua1AMAEAiGIAiCIAjf4XK5THp6ut3e7bVCukeaTq5eJsQZ4ffnzszMNEVFRSYiovW9nE6n57dKTEy0v+Pj4+2+9/HY2Fhb5n7/mJgYk5GRYUpLS01VVZXJzc01U6ZMscdbSktLs3UcDodJSUkxBQUFpqamxuTk5JikpCTPPdz3TUhIMNnZ2aa+vt6W3er9Zs+ebcrKylqV6zVu3rxpevfu7SlLTk425eXlJjQ01O4fOXLEvPjii83qrV271vzhD3/okL8DgiAIgpAWQcs5AAAdICg8VKISRkuXkf0lqFOoNNXdkKrTf5a//PZdMfU32/1+PXr0kISEBNtSrN22W6qoqLjja2uru7Zejxs3znYbHzZsmFRVVUlRUZFMnz5ddu3aJYMHD5bKykqpra21dZYsWSIzZ860Ldb5+fm27vbt2+XatWty6NAhz7XXrFkjixYtkoKCAp9d1j/No48+ansJlJSUeMr27Nkjr7zyigwfPlxycnIkPDzcdvP3ps+qLeghISFy82b7/zcBAOBWSM4BAOgAmpg7HxsqjeXVcqOkUoIjw+2++ug/j7f7/QYNGmS7d+tY6vYWExMjO3fulNzcXLtfWFjoOVZaWmq3mhi7PwBoIq/jxCdOnCjHjh3z1BkzZowkJyc3S85Xrlwp+/fvv6vni46OtuPMvbn39Zg7Wf/hD38ou3fvlnfffVceeughu6/P2rNnT9slHgCAjkRyDgCAn4V0j7Qt5pqY36z4uBXbve0y0iXlv8/17LcXh8Mh/qITzG3atEkmT55sE2lN1LWl+lYfCiIjI2Xfvn3NyjURPnnyZLOy7Oxs6QirV6+2ibp+LNB/K03et27dKs8995w0NTV1yDMAAOCN2doBAPCzEGeE7creWF3frFz3gzqF2eS9vWnXcU0yhwwZclv13Impd3Kvk8h527JliwwYMEC2bdsmI0aMsAn1/PnzfV6zS5cudjt16lQZNWqUJ7Q7/IwZM5qdq93k75a2euss9d7c++4Wce3SrpPJRURESP/+/W1vgD//+c+2K752tQcAoKORnAMA4GfaKq5jzLUruzfdb6prkJvld5+QtqTjtbXr9rx582wC2pKvpc7cianOcO6miXRLly9fls2bN0tSUpKsW7fOznquGhoa7NZ72TJdrkyTYU2AdUk379DrtDedxV0/GvTq1ctTNmnSJNvNXp/Fm44t/+CDD+xHib//+7+3M7XrUnAAAHQ0knMAAPxMk2+d/C24e6RtRXeEBNut7ledvtjuXdrdNDHXJPn48eN2ojbtXq4t6c8884xNYNty/vx5uxb6qlWr7PlPPPGELFy4sNk569evt13atcV59OjRMn78eMnLy7PHLl68aBPdadOm2bHb2p1dJ4tbu3atrTdr1izb6q71tLVd92+XrmEeGxtrk319P/2tofdSe/futUm4tuyPHDnSPusLL7xgJ7Jzfzz40pe+JN/97nftO371q1+V119/XR588EE7Nh4AgEAJ+JTxBEEQBHEvR3ssoeUIDzE9Ex82/Vd80wz4n9+1W93Xcn8+e3R0tNmwYYMpLCw0dXV1dmm13bt3N1umzHspNY24uDhz6tQpu+TZwYMH7ZJn3kuppaammvz8fFNbW2uKi4vN1q1bTVRUlKf+8uXLzZUrV0xjY6NnKTWNBQsWmLy8PLtMmtbLysoyY8eO9bmEm6/Qa7bF+510uTddSq66utqUlJSYl156yQQHB3uODxkyxLz77rv2uC6x9utf/9oMHjzY738HBEEQBCE+wvHJDwAA4IPL5bITiK1YscK2DN8NbTHXMebamu6vFnPc+38HAAC0xGztAAB0IE3IScoBAEBLjDkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwDgPmeMkcTExEA/BgAA9zWScwAAPsP69OkjqampcuHCBamrq5NLly5JRkaGTJgwwS/3i4+Pt8m+0+kUf1m6dKkcOXJEqqurpaysrM1zXn75ZcnOzrbvfPLkyTbPGTFihBw6dEhqa2vtv8vixYv99swAAHwaknMAAD6jXC6XnDhxwibimnhqMpqQkCAHDhyQjRs3yr0uODi4zfKwsDDZsWOHbNq06Zb1X3vtNXnjjTfaPNa1a1fZu3evXLx4UR566CH777Nq1SqZM2dOuzw7AAB3whAEQRAE4TtcLpdJT0+327u9Vkj3SNPJ1cuEOCP8/tyZmZmmqKjIRES0vpfT6fT8VomJifZ3fHy83fc+Hhsba8vc7x8TE2MyMjJMaWmpqaqqMrm5uWbKlCn2eEtpaWm2jsPhMCkpKaagoMDU1NSYnJwck5SU5LmH+74JCQkmOzvb1NfX27Jbvd/s2bNNWVnZLc95/vnnzcmTJ1uVz5071/zlL38xoaGhnrJ/+Zd/MXl5eR3yd0AQBEEQ0iJC7iidBwAAtyUoPFQeSBglXUb2l+DOodJYe0OqTv9ZPvrtSTH1N9v9fj169LCt5MuWLZOamppWxysqKu742trqrq3X48aNs13Lhw0bJlVVVVJUVCTTp0+XXbt2yeDBg6WystJ2GVdLliyRmTNnyty5cyU/P9/W3b59u1y7ds12LXdbs2aNLFq0SAoKCnx2WW8Pjz76qL3vjRs3PGV79uyRlJQU6d69u5SXl/vt3gAAtIXkHACADqCJefcxQ+VmebU0lFRKcGS43VfX/vO/2v1+gwYNkqCgIDl37ly7XzsmJkZ27twpubm5dr+wsNBzrLS01G5LSko8HwA0kddx4hMnTpRjx4556owZM0aSk5ObJecrV66U/fv3i79FR0c3e25VXFzsOUZyDgDoaCTnAAD4WUj3SNtiron5zYqPW7HdWy0v+/17nv324nA4xF90gjkd7z158mSbSGuifubMmVt+KIiMjJR9+/Y1K9ekveVkbTqJGwAA9yMmhAMAwM9CnBEfd2Wvrm9WrvvBnUJt8t7etOt4U1OTDBky5LbqaZ2WyX1oaGizc7Zs2SIDBgyQbdu22UnmNKGeP3++z2t26dLFbqdOnSqjRo3yhHaHnzFjRrNztZt8R7h69aqdyd6be1+PAQDQ0UjOAQDwM20V1zHm2pXdm+431t2wLertTcdr6xjqefPmSURERKvjvpY60zHgqm/fvp4yTaRbunz5smzevFmSkpJk3bp1nlnOGxoaWs20fvbsWbukmXaH1yXdvEOvEwhHjx61495DQv5vJ8JJkybZYQB0aQcABALJOQAAfqbJt07+pi3k2oruCAm2W93X8vbu0u6mibkmycePH7cTtWn3cm1Jf+aZZ2xy2pbz58/bNb91WTE9/4knnpCFCxc2O2f9+vW2S3v//v1l9OjRMn78eMnLy7PHdGkybX2fNm2a9OzZ03Zn18ni1q5da+vNmjXLtrprPW1t1/3b1a9fP4mNjbXJvr6f/tbQe7kNHDjQlun48c6dO3vOcfcC+MUvfmE/JGgvAG3B/9a3viXPPvus/PSnP73t5wEAoL0EfMp4giAIgriXoz2W0HKEh5heiV81X1zxTTPof37HbnVfy/357NHR0WbDhg2msLDQ1NXV2aXVdu/e3WyZMu+l1DTi4uLMqVOn7JJnBw8etEueeS+llpqaavLz801tba0pLi42W7duNVFRUZ76y5cvN1euXDGNjY2epdQ0FixYYJcq02XStF5WVpYZO3aszyXcfIVesy3e73TgwIE2z/H+bzhixAhz6NAh+x767/LjH//Y738HBEEQBCE+wvHJDwAA4IPL5ZLVq1fLihUrbMvw3XC3mHtPDof77+8AAICWmK0dAIAOpAk5STkAAGiJMecAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAADc54wxkpiYGOjHAADgvkZyDgDAZ1ifPn0kNTVVLly4IHV1dXLp0iXJyMiQCRMm+OV+8fHxNtl3Op3iL0uXLpUjR45IdXW1lJWVtXnOyy+/LNnZ2fadT5482ep4eHi4pKWlyenTp+XGjRvy61//2m/PCwDAX4PkHACAzyiXyyUnTpywifjixYtlxIgRkpCQIAcOHJCNGzfKvS44OLjN8rCwMNmxY4ds2rTplvVfe+01eeONN3xeu7a21n642L9/f7s8LwAAd8sQBEEQBOE7XC6XSU9Pt9u7vVZI9wjTydXThDgj/P7cmZmZpqioyEREtL6X0+n0/FaJiYn2d3x8vN33Ph4bG2vL3O8fExNjMjIyTGlpqamqqjK5ublmypQp9nhLaWlpto7D4TApKSmmoKDA1NTUmJycHJOUlOS5h/u+CQkJJjs729TX19uyW73f7NmzTVlZ2S3Pef75583JkydveY4+469//esO/TsgCIIgCGkRIXed2gMAgE8VFB4qPRNipWtsfwnuFCqNdTfk+qk/y0e/zZGm+pvtfr8ePXrYVvJly5ZJTU1Nq+MVFRV3fG1tddfW63Hjxtmu5cOGDZOqqiopKiqS6dOny65du2Tw4MFSWVlpW6fVkiVLZObMmTJ37lzJz8+3dbdv3y7Xrl2TQ4cOea69Zs0aWbRokRQUFPjssg4AwGcRyTkAAB1AE/OoMUPlRnm1NJRUSnBkuN1XJf+Z3e73GzRokAQFBcm5c+fa/doxMTGyc+dOyc3NtfuFhYWeY6WlpXZbUlLi+QCgibyOE584caIcO3bMU2fMmDGSnJzcLDlfuXIl3cwBAPclknMAAPwspHuEbTHXxPxmxcet2O5t11iXlP7+rGe/vTgcDvEXHaet470nT55sE2lN1M+cOXPLDwWRkZGyb9++ZuWatLecrE0ncQMA4H7EhHAAAPhZiDPi467s1fXNynU/uFOYTd7bm3Ydb2pqkiFDhtxWPa3TMrkPDQ1tds6WLVtkwIABsm3bNjvJnCbU8+fP93nNLl262O3UqVNl1KhRntDu8DNmzGh2rnaTBwDgfkRyDgCAn2mruI4x167s3nS/sa5Bbpa3b6u50vHae/bskXnz5klEROvk39dSZzoGXPXt29dTpol0S5cvX5bNmzdLUlKSrFu3TubMmWPLGxoaWs20fvbsWbukmXaH1yXdvEOvAwAA6NYOAIDfafKtk7+5x5jbFvPIcAntHimlh/PavUu7mybmuh748ePH7VhuXdM7JCREJk2aJE8//bRtuW7p/Pnzdi30VatW2cnkdGK3hQsXNjtn/fr1kpWVJe+//76deG78+PGSl5dnj128eNG2vk+bNk3eeustOyGcTha3du1aW0/HwR8+fNh+HHjsscfspHHp6em39V79+vWTqKgom+zrR4DY2FjPs7tb3gcOHGhb7KOjo6Vz586ec/RDga5rroYOHWq71uu1unbt6jnn1KlTd/TvDQDA3Qr4lPEEQRAEcS9HeyyhFRQeYnonfsUMXJlkBv+vb9ut7mu5P589OjrabNiwwRQWFpq6ujq7tNru3bubLVPmvZSaRlxcnDl16pRd8uzgwYN2yTPvpdRSU1NNfn6+qa2tNcXFxWbr1q0mKirKU3/58uXmypUrprGx0bOUmsaCBQtMXl6eXSZN62VlZZmxY8f6XMLNV+g12+L9TgcOHGjzHO//hvpv0hZ//h0QBEEQhPgIxyc/AACADy6XS1avXi0rVqywLcN3O/5cx5hra7q/Wsxx7/8dAADQEt3aAQDoQJqQk5QDAICWmBAOAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHACA+5wxRhITEwP9GAAA3NdIzgEA+Azr06ePpKamyoULF6Surk4uXbokGRkZMmHCBL/cLz4+3ib7TqdT/GXp0qVy5MgRqa6ulrKysjbPefnllyU7O9u+88mTJ9t8zt27d8uVK1ekqqrKnvOd73zHb88MAMCnITkHAOAzyuVyyYkTJ2wivnjxYhkxYoQkJCTIgQMHZOPGjXKvCw4ObrM8LCxMduzYIZs2bbpl/ddee03eeOONNo/FxcXJ6dOnJSkpSUaOHClpaWmSnp4uU6dObZdnBwDgThiCIAiCIHyHy+Uy6enpdnu31wrpHmE6u3qaEGeE3587MzPTFBUVmYiI1vdyOp2e3yoxMdH+jo+Pt/vex2NjY22Z+/1jYmJMRkaGKS0tNVVVVSY3N9dMmTLFHm8pLS3N1nE4HCYlJcUUFBSYmpoak5OTY5KSkjz3cN83ISHBZGdnm/r6elt2q/ebPXu2KSsru+U5zz//vDl58uRf9e/15ptvmi1btnTI3wFBEARBSIsIuaN0HgAA3Jag8FDpNSVWusb2l6BOodJUd0Oun/qzXMvKkab6m+1+vx49ethW8mXLlklNTU2r4xUVFXd8bW1119brcePG2a7lw4YNs13Di4qKZPr06bJr1y4ZPHiwVFZWSm1tra2zZMkSmTlzpsydO1fy8/Nt3e3bt8u1a9fk0KFDnmuvWbNGFi1aJAUFBT67rPuLdsXPy8vr0HsCAOBGcg4AQAfQxLzHmKFyo7xaGkoqJTgy3O6r4t3Z7X6/QYMGSVBQkJw7d67drx0TEyM7d+6U3Nxcu19YWOg5VlpaarclJSWeDwCayOs48YkTJ8qxY8c8dcaMGSPJycnNkvOVK1fK/v37paN985vflK9+9av2eQAACASScwAA/Cyke4RtMdfE/GbFx63Y7m3XkS75y4Gznv324nA4xF90gjkd7z158mSbSGuifubMmVt+KIiMjJR9+/Y1K9ekveVkbTqJW0f7+te/bsecz5kzR86ePdvh9wcAQDEhHAAAfhbqjLBd2Rur65uV635Q5zAJ7R7R7vfUruNNTU0yZMiQ26qndVom96Ghoc3O2bJliwwYMEC2bdtmJ5nThHr+/Pk+r9mlSxe71cnWRo0a5QntDj9jxoxm52o3+Y6k3et/85vfyD/+4z/a9wEAIFBIzgEA8LMbFTV2jLl2Zfem+021DXKjvH1bzZWO196zZ4/MmzdPIiJaJ/++ljrTMeCqb9++njJNpFu6fPmybN682c52vm7dOtvqrBoaGlrNtK6t0bqkmXaH1yXdvEOvEyi6nFpmZqY899xz8uqrrwbsOQAAUCTnAAD42c3yGjv5W2j3SAlxRogjJNhudf/66Yvt3qXdTRNzTZKPHz9uJ2rT7uXakv7MM8/I0aNH26xz/vx5uxb6qlWr7PlPPPGELFy4sNk569evt13a+/fvL6NHj5bx48d7JlK7ePGibX2fNm2a9OzZ03Zn18ni1q5da+vNmjXLtrprPW1t1/3b1a9fP4mNjbXJvr6f/tbQe7kNHDjQlkVHR0vnzp0957h7AWhXdk3MtYu+dsvX9eA1dCI9AAACJeBTxhMEQRDEvRztsYRWUHiI6fPfv2IGrUwyg//l23ar+1ruz2ePjo42GzZsMIWFhaaurs4urbZ79+5my5R5L6WmERcXZ06dOmWXPDt48KBd8sx7KbXU1FSTn59vamtrTXFxsdm6dauJiory1F++fLm5cuWKaWxs9CylprFgwQKTl5dnl0nTellZWWbs2LE+l3DzFXrNtni/04EDB9o8x/0Ovq6h9fz5d0AQBEEQ4iMcn/wAAAA+uFwuWb16taxYscK2DN+Nj1vMI2xXdn+1mOPe/zsAAKAlZmsHAKADaUJOUg4AAFpizDkAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAD3OWOMJCYmBvoxAAC4r5GcAwDwGdanTx9JTU2VCxcuSF1dnVy6dEkyMjJkwoQJfrlffHy8TfadTqf4y9KlS+XIkSNSXV0tZWVlbZ7z8ssvS3Z2tn3nkydPtjo+ePBgefvtt+Xq1atSW1tr/31Wr14tISEhfntuAABuhf8DAQDwGeVyuWwSW15eLosXL5YzZ85IaGioPP7447Jx40YZOnSo3MuCg4OlsbGxVXlYWJjs2LFDjh49Kk899ZTP+q+99po88sgjMnLkyFbHbty4Ienp6fLuu+/af5/Y2Fh59dVXJSgoSJYtW9bu7wIAwF/DEARBEAThO1wul0lPT7fbu71WaPcI09nV04Q4I/z+3JmZmaaoqMhERLS+l9Pp9PxWiYmJ9nd8fLzd9z4eGxtry9zvHxMTYzIyMkxpaampqqoyubm5ZsqUKfZ4S2lpabaOw+EwKSkppqCgwNTU1JicnByTlJTkuYf7vgkJCSY7O9vU19fbslu93+zZs01ZWdktz3n++efNyZMn/6p/r3Xr1plDhw51yN8BQRAEQUiLoOUcAIAOEBQeKr2fGCndYl0S3ClMGusapPLURSl565Q01d9s9/v16NFDEhISbCtwTU1Nq+MVFRV3fG1tddfW63Hjxtmu5cOGDZOqqiopKiqS6dOny65du2y38crKSttlXC1ZskRmzpwpc+fOlfz8fFt3+/btcu3aNTl06JDn2mvWrJFFixZJQUGBzy7r/jBw4ED776XPDgBAIJCcAwDQATQxjxo7RG6U10h9SaUEdwm3++rqr0+0+/0GDRpku2ifO3eu3a8dExMjO3fulNzcXLtfWFjoOVZaWmq3JSUlng8AmsjrOPGJEyfKsWPHPHXGjBkjycnJzZLzlStXyv79+6WjaLf/v/mbv5FOnTrJ5s2b7f0BAAgEJoQDAMDPQrtH2BZzTcxvlteIudlot7qv5SHOiHa/p8PhEH/RCeaWL18uhw8fllWrVsmIESM+9UNBZGSk7Nu3T65fv+6JWbNm2RZrbzqJW0d68sknbXL+7W9/W6ZOnWpb7QEACARazgEA8DNNvrUru7aYe2usqpfw3l1t8n6zonXX87uhXcebmppkyJCPW+f/WlqnZXKvk8h527Jli+zZs8cms5MnT7Zd1hcuXCg/+9nP2rxmly5d7FbP/+CDD5odq6+vb7av3eQ70uXLl+02Ly/PTkD3f/7P/5F169Z5/h0AAOgotJwDAOBnmnjrGHPtyu5N9xvrbtgW9Pam47U1gZ43b55ERLRumfe11JmOAVd9+/b1lI0aNarNpFa7gSclJdlkds6cOba8oaHBbjXRdTt79qxd0ky7w+uSZd7hTo7vBToMQD9E6BYAgI5GyzkAAH6mybdO/uYeY64t5pqYa4t56R/OtXuruZsm5jqm+vjx43Ys9enTp+063pMmTZKnn37aTuTW0vnz5+1a6NpdXSeT04ndtFXc2/r16yUrK0vef/99O/Hc+PHjbcuzunjxom11njZtmrz11lt2QjidLG7t2rW2nia+2h1ePw489thjdtI4XdLsdvTr10+ioqJssq8fAXQZNPezu1vetbu8tthHR0dL586dPefohwJdRu073/mO3eryctp6/5WvfEX+5V/+Rd544w25ebP9J+gDAOCvEfAp4wmCIAjiXo72WEIrKDzERH/jITN41XQzdM2Tdqv7Wu7PZ4+OjjYbNmwwhYWFpq6uzi6ttnv37mbLlHkvpaYRFxdnTp06ZZc8O3jwoF3yzHsptdTUVJOfn29qa2tNcXGx2bp1q4mKivLUX758ubly5YppbGz0LKWmsWDBApOXl2eXSdN6WVlZZuzYsT6XcPMVes22eL/TgQMH2jzH/Q7f+ta37JJtlZWV5vr163Y5OF3qLTw83K9/BwRBEAQhPsLxyQ8AAOCDy+WS1atXy4oVK2zL8N2OP9cWczs5nJ9azHHv/x0AANAS3doBAOhAmpCTlAMAgJaY8QQAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAID7nDFGEhMTA/0YAADc10jOAQD4DOvTp4+kpqbKhQsXpK6uTi5duiQZGRkyYcIEv9wvPj7eJvtOp1P8ZenSpXLkyBGprq6WsrKyNs95+eWXJTs7277zyZMnb3m9gQMHSmVlpc9rAQDQEUjOAQD4jHK5XHLixAmbiC9evFhGjBghCQkJcuDAAdm4caPc64KDg9ssDwsLkx07dsimTZtuWf+1116TN95445bnhISEyOuvvy5/+MMf7upZAQC4WyTnAAB0oNDuERLRv6fd+tu//du/2Vbshx9+WHbt2iX5+fly9uxZWb9+vXzta1/7q1u+Y2NjbZkm+yomJsa2vpeWlkpVVZXk5ubKlClT7PHf//739pzy8nJbJy0tze47HA5JSUmRgoICqampkZycHElKSmp1X/14oC3e9fX1MmbMmDafcdWqVfKv//qvcubMGZ/v/uyzz9r31/vdygsvvCDnzp2TX/7yl7c8DwAAfwvx+x0AAIAEdQqVPlNGinNUjAR1CpOmugapyLkkxW+dkqb6m+1+vx49ethEd9myZTYZbqmiouKOr62t7tp6PW7cONu1fNiwYTZJLyoqkunTp9sPAYMHD7ZdxWtra22dJUuWyMyZM2Xu3Ln2I4HW3b59u1y7dk0OHTrkufaaNWtk0aJFNqn2dzfz8ePHyze/+U0ZNWqUfW4AAAKJ5BwAgA6gifkDY78sNypqpL6kUkK6hNt99eGvT7T7/QYNGiRBQUG2Vbi9acv5zp07bYu5Kiws9BzT1nRVUlLi+QCgibyOE584caIcO3bMU0dbxpOTk5sl5ytXrpT9+/eLv0VFRcnPf/5z+8Hg+vXrfr8fAACfhuQcAAA/0y7s2mKuifmN8o9bsd3bbqNi5KMDeZ799qLdyP1FJ5jT8d6TJ0+2ibQm6rfqYq4fCiIjI2Xfvn3NyjVpbzlZm3Zp7wivvvqq/OIXv2CsOQDgnsGYcwAAOiA5167sN6vqm5XrfnCnML+MP9eu401NTTJkyJDbqqd1Wib3oaGhzc7ZsmWLDBgwQLZt22YnmdOEev78+T6v2aVLF7udOnWq7ULuDu0OP2PGjGbnajf5jqCT5Gn3+Rs3btjQd+revbv9/f3vf79DngEAAG8k5wAA+Jm2iusYc+3K7k33G+sa2r3VXOl47T179si8efMkIqJ18u9rqTMdA6769u3rKdNEuqXLly/L5s2b7aRu69atkzlz5tjyhoaGVjOt6yR0uqSZdofXJd28Q68TCI8++mizDwXanV7HyOvvX//61wF5JgDA/Y1u7QAA+Jkm3zr5m3uMubaYa2Ie6oyQv/zhT35JzpUm5roe+PHjx23yefr0abt02KRJk+Tpp5+2LdctnT9/3q6FrjOi62RyOrHbwoULm52js71nZWXJ+++/byee04nV8vLy7LGLFy/a1vdp06bJW2+9ZSeE08ni1q5da+vpOPjDhw/bjwOPPfaYTYjT09Nv67369etnx4xrsq8fAXQ2efezu1vede1ybbGPjo6Wzp07e87RDwXaOt5yLP5XvvIV+9zvvffebf4rAwDQfgxBEARBEL7D5XKZ9PR0u73TawSFh5i+33jIfPmfv2GG/e8n7Vb3tdyfzx4dHW02bNhgCgsLTV1dnSkqKjK7d+828fHxnnNUYmKiZz8uLs6cOnXK1NTUmIMHD5qkpCR7jvv9U1NTTX5+vqmtrTXFxcVm69atJioqylN/+fLl5sqVK6axsdGkpaV5yhcsWGDy8vJMfX29rZeVlWXGjh1rj+nzKKfT+anvpNdsi/c7HThwoM1zfP03nD17tikrK/P73wFBEARBiI9wfPIDAAD4oOt3r169WlasWGFbhu+Gji/X0NZyf7WY497/OwAAoCW6tQMA0IFIygEAQFuYEA4AAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAID7nDFGEhMTA/0YAADc10jOAQD4DOvTp4+kpqbKhQsXpK6uTi5duiQZGRkyYcIEv9wvPj7eJvtOp1P8ZenSpXLkyBGprq6WsrKyNs95+eWXJTs7277zyZMnWx13uVz2OVvGI4884rfnBgDgVkJueRQAAPw/SxNQTWLLy8tl8eLFcubMGQkNDZXHH39cNm7cKEOHDpV7WXBwsDQ2NrYqDwsLkx07dsjRo0flqaee8ln/tddes8n2yJEjfZ7zt3/7t/Lee+959v/yl7+0w5MDAHD7aDkHAKADhXaPkIj+Pe3W3/7t3/7NtgY//PDDsmvXLsnPz5ezZ8/K+vXr5Wtf+9pf3fIdGxtryzTZVzExMbb1vbS0VKqqqiQ3N1emTJlij//+97+35+gHAa2TlpZm9x0Oh6SkpEhBQYHU1NRITk6OJCUltbpvQkKCbfGur6+XMWPGtPmMq1atkn/913+1Hxt8efbZZ+376/1uRZPx4uJiT9y8efOW5wMA4C+0nAMA0AGCOoVK9BMjpPuoGAnqHCpNtTekPOeSXM08LU317Z8Q9ujRwya6y5Yts8lwSxUVFXd8bW1119brcePG2a7lw4YNs0l6UVGRTJ8+3X4IGDx4sFRWVkptba2ts2TJEpk5c6bMnTvXfiTQutu3b5dr167JoUOHPNdes2aNLFq0yCbVvrqstyf9yNCpUyd5//335cUXX5Tf/OY3fr8nAABtITkHAKADaGLec9xguVFeK/Ul1yUkMtzuqyu73m33+w0aNEiCgoLk3Llz7X5tbTnfuXOnbTFXhYWFnmPamq5KSko8HwA0kddx4hMnTpRjx4556mjLeHJycrPkfOXKlbJ//37xN/2Y8E//9E+2239TU5Ntxd+9e7f89//+30nQAQABQXIOAICfaRd2bTHXxPxG+cet2O6tll97+5xnv71oN3J/0QnmNm3aJJMnT7aJtCbqt+pirh8KIiMjZd++fc3KNWlvOVmbdmnvCNqdXbv3e9/3c5/7nB2bT3IOAAgExpwDANABybl2Zb9ZXd+sXPe13B/jz7XruLYIDxky5LbqaZ2Wyb1OIudty5YtMmDAANm2bZuMGDHCJrbz58/3ec0uXbrY7dSpU2XUqFGe0O7wM2bMaHaudpMPlD/+8Y/2QwIAAIFAcg4AgJ9pq7iOMdeu7N50X8vbu9Vc6XjtPXv2yLx58yQionXy72upMx0Drvr27esp00S6pcuXL8vmzZttd/B169bJnDlzbHlDQ4NnpnU3nYROlzTT7vC6pJt36HXuFfqeH374YaAfAwBwn6JbOwAAfqbJt07+5h5jri3mmpiHdu8sHx163y/JudLEXMdUHz9+3I7lPn36tISEhMikSZPk6aefti3XLZ0/f96uha4zoutkcjqx28KFC5udo93Bs7Ky7CRqOvHc+PHjJS8vzx67ePGibX2fNm2avPXWW3ZCOB3fvXbtWltPx8EfPnzYfhx47LHH7KRx6enpt/Ve/fr1k6ioKJvs60cAnU3e/ezulveBAwfaFvvo6Gjp3Lmz5xz9UHDjxg2ZNWuW/ZDg7lavE9n94Ac/kB/+8Id3+K8NAMDdMwRBEARB+A6Xy2XS09Pt9k6vERQeYj43/W/MsJ/8d/PgS9+0W93Xcn8+e3R0tNmwYYMpLCw0dXV1pqioyOzevdvEx8d7zlGJiYme/bi4OHPq1ClTU1NjDh48aJKSkuw57vdPTU01+fn5pra21hQXF5utW7eaqKgoT/3ly5ebK1eumMbGRpOWluYpX7BggcnLyzP19fW2XlZWlhk7dqw9ps+jnE7np76TXrMt3u904MCBNs9xv8OsWbPMe++9Z6qqqkx5ebk5duyYfU9//x0QBEEQhPgIxyc/AACAD7p+9+rVq2XFihW2Zfhu6PhyDW0t91eLOe79vwMAAFqiWzsAAB2IpBwAALSFCeEAAAAAAAgwknMAAAAAAAKM5BwAAAAAgAAjOQcAAAAAIMBIzgEAAAAACDCScwAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQC4zxljJDExMdCPAQDAfY3kHACAz7A+ffpIamqqXLhwQerq6uTSpUuSkZEhEyZM8Mv94uPjbbLvdDrFX5YuXSpHjhyR6upqKSsra/Ocl19+WbKzs+07nzx50ue1Fi5cKH/605/seZcvX7bXBgAgEEICclcAAO5Tod0jJLR7Z7lRXiM3ymv9ei+Xy2WT2PLyclm8eLGcOXNGQkND5fHHH5eNGzfK0KFD5V4WHBwsjY2NrcrDwsJkx44dcvToUXnqqad81n/ttdfkkUcekZEjR/pM4CdPniyLFi2y/zZRUVE2AAAIFEMQBEEQhO9wuVwmPT3dbu/0GkGdQsznp/+NGf6TRDPypRl2q/tB4SF+e+7MzExTVFRkIiIiWh1zOp2e3yoxMdH+jo+Pt/vex2NjY22Z+/1jYmJMRkaGKS0tNVVVVSY3N9dMmTLFHm8pLS3N1nE4HCYlJcUUFBSYmpoak5OTY5KSkjz3cN83ISHBZGdnm/r6elt2q/ebPXu2KSsru+U5zz//vDl58mSr8iFDhpiGhgYzePDgDv07IAiCIAjxEbScAwDQAfo+MVJ6jRssDeU1UldyXUIiw+2++mDXu+1+vx49ekhCQoIsW7ZMampqWh2vqKi442trq7u2Xo8bN852LR82bJhUVVVJUVGRTJ8+XXbt2iWDBw+WyspKqa39uHfAkiVLZObMmTJ37lzJz8+3dbdv3y7Xrl2TQ4cOea69Zs0a25JdUFDgs8t6e/hv/+2/2XtMmzZN5s+fLw6HQ/bv3y8//vGP/XpfAAB8ITkHAKADurJ3H9XPJubanV25t1pe8nZeu3dxHzRokAQFBcm5c+ekvcXExMjOnTslNzfX7hcWFnqOlZaW2m1JSYnnA4Am8jqWe+LEiXLs2DFPnTFjxkhycnKz5HzlypU2Sfa3AQMG2G7/3/zmN2XWrFm2C/369evlV7/6lfzt3/6t3+8PAEBLJOcAAPiZjjEP7hxqW8y93ayul069u9rkvb2Tc20J9hedYG7Tpk12vLYm0pqo65jtW30oiIyMlH379jUr16S95WRtOolbR9APF506dbKJubbkKx2//u6779pW//fff79DngMAADdmawcAwM808W6svWG7snvTfS13t6K3J004m5qaZMiQIbdVT+u0TO51EjlvW7ZssS3P27ZtkxEjRtiEWruG+9KlSxe7nTp1qowaNcoT2h1+xowZzc7VbvId4cMPP5QbN254EnOVl5fn6RkAAEBHIzkHAMDPNPkuzymSMDtTe4Q4QoPtVve13B+ztuu46T179si8efMkIiKi1XFfS53pGHDVt29fT5km0i3psmObN2+WpKQkWbduncyZM8eWNzQ02K12E3c7e/asXapMk15d0s079DqBoLPY60cH/cjgpi3m6uLFiwF5JgDA/Y3kHACADvBh5mm5duh9cQQ5bFd23eq+lvuLJuaaJB8/ftxO1Kbdy7Ul/ZlnnrHLkLXl/Pnzdi30VatW2fOfeOIJuxa4Nx2brV3a+/fvL6NHj5bx48d7Wp01sdXWd51orWfPnrY7u04Wt3btWltPu5FrQqz1tLVd929Xv379JDY21ib7+n76W0Pv5TZw4EBbFh0dLZ07d/ac4+4FoN3xT5w4YZdb048Pf/M3f2M/Nuzdu7dZazoAAB0p4FPGEwRBEMS9HO25hFZo984mov8DdtsRzx4dHW02bNhgCgsLTV1dnV1abffu3c2WKfNeSk0jLi7OnDp1yi55dvDgQbvkmfdSaqmpqSY/P9/U1taa4uJis3XrVhMVFeWpv3z5cnPlyhXT2NjoWUpNY8GCBSYvL88uk6b1srKyzNixY30u4eYr9Jpt8X6nAwcOtHmO93/Dvn37ml/96lemsrLSfPjhh+a1114zPXr06JC/A4IgCIKQFuH45AcAAPBBZ/VevXq1rFixgi7P9zH+DgAA/kS3dgAAAAAAAozkHAAAAACAACM5BwAAAAAgwEjOAQAAAAAIMJJzAAAAAAACjOQcAAAAAIAAIzkHAAAAACDASM4BAAAAAAgwknMAAAAAAAKM5BwAgPucMUYSExMD/RgAANzXSM4BAPgM69Onj6SmpsqFCxekrq5OLl26JBkZGTJhwgS/3C8+Pt4m+06nU/xl6dKlcuTIEamurpaysrI2z3n55ZclOzvbvvPJkydbHX/++eftc7aMqqoqvz03AAC3QnIOAEAHCu0RIZH9H5DQ7p39fi+XyyUnTpywifjixYtlxIgRkpCQIAcOHJCNGzfKvS44OLjN8rCwMNmxY4ds2rTplvVfe+01eeONN9o8tnbtWomOjm4W7733nr0uAACBYgiCIAiC8B0ul8ukp6fb7Z1eI6hTiPlC0mgz4oW/M6PWJtmt7geFh/jtuTMzM01RUZGJiIhodczpdHp+q8TERPs7Pj7e7nsfj42NtWXu94+JiTEZGRmmtLTUVFVVmdzcXDNlyhR7vKW0tDRbx+FwmJSUFFNQUGBqampMTk6OSUpK8tzDfd+EhASTnZ1t6uvrbdmt3m/27NmmrKzsluc8//zz5uTJk5/6bzVy5Eh7/zFjxvj174AgCIIgxEeEBOyTAAAA95HPTR0hveO/JA1lNVJXcl1CIsPsvrq8s3W367vVo0cP20q+bNkyqampaXW8oqLijq+tre7aej1u3DjbtXzYsGG2O3hRUZFMnz5ddu3aJYMHD5bKykqpra21dZYsWSIzZ86UuXPnSn5+vq27fft2uXbtmhw6dMhz7TVr1siiRYukoKDAZ5d1f/jhD38of/rTn+Tw4cMddk8AALyRnAMA0AFd2XuM7mcT8xvlHyer7m2PUf2k+HfnPPvtZdCgQRIUFCTnzp2T9hYTEyM7d+6U3Nxcu19YWOg5VlpaarclJSWeDwCayOs48YkTJ8qxY8c8dcaMGSPJycnNkvOVK1fK/v37pSOFh4fLd7/7XfthAACAQCE5BwDAz8KcnSW4U6htMfd2s7pBOvXuKmHdI9o9OXc4HOIvOsGcjveePHmyTaQ1UT9z5swtPxRERkbKvn37mpVr0t5ysjadxK2jfeMb35CuXbvK1q1bO/zeAAC4MSEcAAB+1lBRK411N2xXdm+631h7QxrKW3c7v1vadbypqUmGDBlyW/W0TsvkPjQ0tNk5W7ZskQEDBsi2bdvsJHOaUM+fP9/nNbt06WK3U6dOlVGjRnlCu8PPmDGj2bnaTb6jaZf2N99807b2AwAQKCTnAAD42Y2yGik7WSRhPSLsLO2O0GC71f2ynKJ2bzVXOl57z549Mm/ePImIiGh13NdSZzoGXPXt29dTpol0S5cvX5bNmzdLUlKSrFu3TubMmWPLGxoaWs20fvbsWbukmXaH1yXdvEOvE0j9+/eX8ePH2w8OAAAEEt3aAQDoAFfePOMZY65d2bXFvORgvqfcHzQx1/XAjx8/bsdynz59WkJCQmTSpEny9NNP25brls6fP2/XQl+1apWdTE4ndlu4cGGzc9avXy9ZWVny/vvv24nnNLnNy8uzxy5evGhb36dNmyZvvfWWnRBOJ4vTpcu0no6D10nX9OPAY489ZieNS09Pv6336tevn0RFRdlkXz8CxMbGep7d3fI+cOBA22KvS6R17tzZc45+KLhx44bnWj/4wQ/kww8/tO8DAECgBXzKeIIgCIK4l6M9l9AK7d7ZRPZ/wG474tmjo6PNhg0bTGFhoamrq7NLq+3evbvZMmXeS6lpxMXFmVOnTtklzw4ePGiXPPNeSi01NdXk5+eb2tpaU1xcbLZu3WqioqI89ZcvX26uXLliGhsbPUupaSxYsMDk5eXZZdK0XlZWlhk7dqzPJdx8hV6zLd7vdODAgTbP8f5vqMu7Xbp0ybzwwgsd/ndAEARBENIiHJ/8AAAAPrhcLlm9erWsWLHCtgzj/sTfAQDAnxhzDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAAAAAECAkZwDAAAAABBgJOcAAAAAAAQYyTkAAAAAAAFGcg4AAAAAQICRnAMAcJ8zxkhiYmKgHwMAgPsayTkAAJ9hffr0kdTUVLlw4YLU1dXJpUuXJCMjQyZMmOCX+8XHx9tk3+l0ir8sXbpUjhw5ItXV1VJWVtbmOS+//LJkZ2fbdz558mSb50yePFmOHj0qlZWVUlJSIr/61a/E5XL57bkBALgVknMAADpQWI8Iiez/gIR27+z3e2mieeLECZuIL168WEaMGCEJCQly4MAB2bhxo9zrgoOD2ywPCwuTHTt2yKZNm25Z/7XXXpM33nijzWP9+/eX//zP/5S3335bRo0aJY8//rj07NlTdu3a1S7PDgDAnTAEQRAEQfgOl8tl0tPT7fZOrxHUKcT0SxplYl/4b+Zv1k23W90PCg/x23NnZmaaoqIiExER0eqY0+n0/FaJiYn2d3x8vN33Ph4bG2vL3O8fExNjMjIyTGlpqamqqjK5ublmypQp9nhLaWlpto7D4TApKSmmoKDA1NTUmJycHJOUlOS5h/u+CQkJJjs729TX19uyW73f7NmzTVlZ2S3Pef75583Jkydbleu9Gxoa7HO5y6ZNm2YaGxtNSEiI3/4OCIIgCEJ8RMgdpfMAAOC2fH7qg9In/kvSUF4rtSXXJTQy3O6rop057X6/Hj162FbyZcuWSU1NTavjFRUVd3xtbXXX1utx48bZruXDhg2TqqoqKSoqkunTp9vW58GDB9vu4rW1tbbOkiVLZObMmTJ37lzJz8+3dbdv3y7Xrl2TQ4cOea69Zs0aWbRokRQUFPjsst4etEdBU1OTfP/735ef//zn0qVLF/mHf/gH2b9/v9y8edNv9wUAwBeScwAAOqAre9TofjYx11DurZZf/d2f5MYn++1l0KBBEhQUJOfOnZP2FhMTIzt37pTc3Fy7X1hY6DlWWlpqtzqG2/0BQBN5HSc+ceJEOXbsmKfOmDFjJDk5uVlyvnLlSpsg+9uf//xnO+b8l7/8pWzevFlCQkLknXfekSeeeMLv9wYAoC2MOQcAwM9CnZ0luHOo3Kiub1au+0GdQyWse0S739PhcIi/6ARzy5cvl8OHD8uqVavsWPZP+1AQGRkp+/btk+vXr3ti1qxZMnDgwGbn6iRuHTVR3quvvipbt26Vr371q7Ylv6GhwU4KBwBAINByDgCAn92oqJXG2hu2K7u7xVzpflPtDWkob93t/G5p13Httj1kyJDbqqd1Wib3oaGhzc7ZsmWL7NmzR6ZOnWpbn7XL+sKFC+VnP/tZm9fULuNKz//ggw+aHauvb/7BQrvJd4R58+bZlv3nnnvOU6bd7i9fviyPPPKI/PGPf+yQ5wAAwI2WcwAA/KyhrEZKTxZJWPfONhyhQZ7fWt7eXdqVjtfWBFqT0IiI1i3zvpY60zHgqm/fvp4ync28JU1itTt4UlKSrFu3TubMmWPLtfW55UzrZ8+etUuaaXd4XdLNO/Q6gaD/Ju4PEW6NjY12q8MBAADoaPzfBwCADvDBm7lSfDBfHEEO6dS7q93qvpb7iybmmiQfP37cTtSm3cu1Jf2ZZ56x63u35fz583YtdO2urufrGGxtFfe2fv1622Kuy5GNHj1axo8fL3l5efbYxYsXbdI7bdo0uzSZdmfXyeLWrl1r62lX9gEDBth68+fPt/u3q1+/fhIbG2uTfX0//a2h93LT7vJaFh0dLZ07d/ac4+4FkJmZabuzr1ixwr6nPk9aWpodi+5rXXQAAPwt4FPGEwRBEMS9HO25hFZo984msv8DdtsRzx4dHW02bNhgCgsLTV1dnV1abffu3c2WKfNeSk0jLi7OnDp1yi55dvDgQbvsmPdSaqmpqSY/P9/U1taa4uJis3XrVhMVFeWpv3z5cnPlyhW7LJl7KTWNBQsWmLy8PLtMmtbLysoyY8eO9bmEm6/Qa7bF+50OHDjQ5jne/w2ffPJJc+LECXP9+nX7PPrv8uUvf7lD/g4IgiAIQlqE45MfAADAB5fLJatXr7atrNoyjPsTfwcAAH+iWzsAAAAAAAFGcg4AAAAAQICRnAMAAAAAEGAk5wAAAAAABBjJOQAAAAAAAUZyDgAAAABAgJGcAwAAAAAQYCTnAAAAAAAEGMk5AAAAAAABRnIOAMB9zhgjiYmJgX4MAADuayTnAAB8hvXp00dSU1PlwoULUldXJ5cuXZKMjAyZMGGCX+4XHx9vk32n0yn+snTpUjly5IhUV1dLWVlZm+e8/PLLkp2dbd/55MmTbZ7zzW9+0x7T6/z5z3+WRYsW+e2ZAQD4NCGfegYAAGg3YT06S1j3ztJQVisN5bV+vZfL5bJJbHl5uSxevFjOnDkjoaGh8vjjj8vGjRtl6NChci8LDg6WxsbGVuVhYWGyY8cOOXr0qDz11FM+67/22mvyyCOPyMiRI1sdS0hIkP/4j/+QZ555Rvbu3Wv/LV599VWpra21/zYAAASCIQiCIAjCd7hcLpOenm63d3qN4E4hxjVjlBn9P6eZr/70G3ar+0HhIX577szMTFNUVGQiIiJaHXM6nZ7fKjEx0f6Oj4+3+97HY2NjbZn7/WNiYkxGRoYpLS01VVVVJjc310yZMsUebyktLc3WcTgcJiUlxRQUFJiamhqTk5NjkpKSPPdw3zchIcFkZ2eb+vp6W3ar95s9e7YpKyu75TnPP/+8OXnyZKvy//iP/zC//OUvm5XNnz/fXLp0ya9/BwRBEAQhPoKWcwAAOsAXpj0o0V8fJA1lNVJbUimhkeF2X138VU67369Hjx62dXjZsmVSU1PT6nhFRcUdX1tblrX1ety4cbZL+LBhw6SqqkqKiopk+vTpsmvXLhk8eLBUVlbalmi1ZMkSmTlzpsydO1fy8/Nt3e3bt8u1a9fk0KFDnmuvWbPGdi8vKCjw2WW9PYSHh7f6d9Fn7devn+1xcPHiRb/dGwCAtpCcAwDQAV3Zo0Z/wSbm7q7s7q2Wf7j/T+3exX3QoEESFBQk586dk/YWExMjO3fulNzcXLtfWFjoOVZaWmq3JSUlng8AmsjrOPGJEyfKsWPHPHXGjBkjycnJzZLzlStXyv79+8Xf9uzZI+vXr5ef//zncuDAAfvvtXDhQnusb9++JOcAgA5Hcg4AgJ/pGPOQzqG2xdzbjep66dy7m03e2zs5dzgc4i86wdymTZtk8uTJNpHWRF3Hs/uiiW9kZKTs27evWbkm7S0na9NJ3DqCji8fOHCgvPnmm3Ycvrby6yRy//zP/yxNTU0d8gwAAHhjtnYAAPxME++btTdsV3Zvuq/lOjlce9Ou45pkDhky5LbquRNT7+Rek1dvW7ZskQEDBsi2bdtkxIgRNqGeP3++z2t26dLFbqdOnSqjRo3yhHaHnzFjRrNztZt8R0lJSbHPpt3Yo6Oj5fjx47Zcu9QDANDRSM4BAPAzTb5LT16WsB4RthXdERpkt7qv5f6YtV3Ha2vX7Xnz5klERESr476WOtMx4O6u3W6aSLd0+fJl2bx5syQlJcm6detkzpw5tryhocEz07rb2bNn7ZJm2h1el3TzDr1OIOnHiCtXrsiNGzfk29/+trzzzjvy0UcfBfSZAAD3J7q1AwDQAYp+k+sZY65d2bXF/Orvz3vK/UETc11KTVuEdSz36dOnJSQkRCZNmiRPP/20bblu6fz583Yt9FWrVtnJ5HRiN/dYbDcdq52VlSXvv/++nXhu/PjxkpeXZ4/pWG1NeKdNmyZvvfWWnWRNJ4tbu3atrafj4A8fPmw/Djz22GO2O3l6evptvZdO2hYVFWWTff0IEBsb63l2d8u7dlnXVnFtEe/cubPnHP1QoIn4Aw88YFvtf//730unTp3k+9//vl33XNdpBwAgUAI+ZTxBEARB3MvRnktohXXvbLp8McpuO+LZo6OjzYYNG0xhYaGpq6uzS6vt3r272TJl3kupacTFxZlTp07ZJc8OHjxolzzzXkotNTXV5Ofnm9raWlNcXGy2bt1qoqKiPPWXL19urly5YhobGz1LqWksWLDA5OXl2WXStF5WVpYZO3aszyXcfIVesy3e73TgwIE2z3G/wwMPPGD+f/buBDzK8lz8/509BDAmLgQriQJSlmJQqtawRChLIhxzSmxtKwVb5QeWpccDnMMSFkuttLK0AYrYcjhEShcKhfRgDgJSOCCIQbZIgEAiCSABTViyY3j+1/3Ud/6TDQjOZKh8P9d1XzPv/r6Tmcnc77O9++675tKlS3Y4uI0bN5pHH320yd4HBEEQBCG1wu/zJwAAoAHaJnnWrFkybdo0evG+hfE+AAB4E23OAQAAAADwMZJzAAAAAAB8jOQcAAAAAAAfIzkHAAAAAMDHSM4BAAAAAPAxknMAAAAAAHyM5BwAAAAAAB8jOQcAAAAAwMdIzgEAAAAA8DGScwAAbnHGGElKSvL1aQAAcEsjOQcA4EusVatWkpqaKsePH5eKigrJz8+X9PR06du3r1eOFx8fb5P98PBw8ZYpU6bIjh07pLS0VIqLi+ssf/DBB2XlypX2WsvKyuTQoUMybty4es91z5499nXJycmR4cOHe+2cAQC4lsBrrgEAADwmOKKZBN/eTKqKy6XqfLlXjxUTE2OT2PPnz8vEiRPl4MGDEhQUJAMHDpRFixZJp06d5GYWEBAg1dXVdeYHBwfLqlWrZOfOnfL888/XWd69e3c5e/asDB06VAoKCiQuLk7eeOMNuy+9bnXffffJ+vXr5fXXX5dnn31WvvnNb8rvfvc7+fjjj+Xtt99ukusDAKA2QxAEQRBEwxETE2PS0tLs443uIyA00Nz3dKzp/sog8+i8f7WPOu0fEui1816/fr0pKCgwYWFhdZaFh4e7nqukpCT7PD4+3k67L4+NjbXznOuPjo426enppqioyJSUlJisrCyTmJhol9e2bNkyu42fn5+ZNGmSyc3NNWVlZWbfvn0mOTnZdQznuAkJCSYzM9NUVlbaeVe7vuHDh5vi4uLrei0WLlxoNm/e7JqePXu2OXjwYI11/vCHP5iMjAyvvg8IgiAIQhoISs4BAGgCbQZ3kagn2tsS8/KzlySoeYidVh/9Zb/HjxcRESEJCQkydepUW7W7tgsXLtzwvrX0WUuve/fubauWd+7cWUpKSmwp9ZAhQ2TNmjXSoUMHuXjxopSX/6N2wOTJk21J9qhRo2wVct12xYoVcu7cOdm2bZtr37Nnz5YJEyZIbm5uvVXWb5RWsy8qKnJNP/7447Jp06Ya62zYsEF+9atfeeyYAAA0Bsk5AABNUJX9jofurVGV3XnU+ac3HfV4Fff27duLv7+/HD58WDwtOjpaVq9eLVlZWXY6Ly/PtcxJgLVauXMDQBN5bSfer18/2bVrl2ubnj17ysiRI2sk59OnT6+TNH9Rmog/88wzMmjQINe8qKgoKSwsrLGeTmsSHxoaatuhAwDQlEjOAQDwMm1jHtAsyJaYu7tcWinN7m5pk3dPJ+d+fn7iLdrB3OLFi2XAgAE2kdZEXduzX+1GQfPmzWXjxo015mvSvnfv3hrzMjMzPXquXbp0kXXr1snLL79c5/gAANxM6K0dAAAv08S7uvyyrcruTqd1vpaoe5pWHb9y5Yp07NixUdvpNrWTe+1Ezt3SpUulbdu28uabb0rXrl1tQj1mzJgG99miRQv7qCXX3bp1c4VWh3/66adrrKvV5D1FO7zbvHmz7QzulVdeqbHszJkztid7dzqtpf2UmgMAfIHkHAAAL9Pk+9O9J109tfsF+dtHndb53ui1Xdtraxvq0aNHS1hYWJ3lDQ11pm3AVevWrV3zNJGu7eTJk7JkyRJJTk6WuXPnyogRI+z8qqoqV0/rDh3KTBNerQ6vQ7q5h+7HGzTx37JliyxfvlxSUlLqLNee3rWHdnf9+/e38wEA8AWScwAAmkD+3z6UM38/Jn7+frYquz7qtM73Fk3MNUnevXu37ahNq5drSfrYsWMbTEKPHTtmxwefOXOmXf/JJ5+U8ePH11hn/vz5tkq7Dkf20EMPSZ8+fSQ7O9suO3HihC19Hzx4sNx55522Ort2Fjdnzhy73bBhw2ypu26npe063Vht2rSR2NhYm+zr9elzDT2WU5VdE3MdEm3evHm2RFxDz8ehQ6jpefziF7+Qr371q/Liiy/Kd77zHXuOAAD4is+7jCcIgiCImzk8OYRW8O3NTIv7I+1jU5x7VFSUWbBggcnLyzMVFRV2aLW1a9fWGKbMfSg1jbi4OLN//3475NnWrVvtkGfuQ6mlpqaanJwcU15ebgoLC83y5ctNZGSka/uUlBRz+vRpU11d7RpKTWPcuHEmOzvbDpOm2+mwZb169WpwCLeGQvdZH+eaZsyYUe9yfQ3c96Prf/DBB/Z1OXbsmB2araneBwRBEAQhtcLv8ycAAKABMTExMmvWLJk2bZotGcatifcBAMCbqNYOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwBwizPGSFJSkq9PAwCAWxrJOQAAX2KtWrWS1NRUOX78uFRUVEh+fr6kp6dL3759vXK8+Ph4m+yHh4eLt0yZMkV27NghpaWlUlxcXGf5gw8+KCtXrrTXWlZWJocOHZJx48bVWCcqKkp+//vfy5EjR6S6ulrmz5/vtfMFAOB6BF7XWgAAwCNCIppJ8O3NpLK4XKrOl3v1WDExMTaJPX/+vEycOFEOHjwoQUFBMnDgQFm0aJF06tRJbmYBAQE2ca4tODhYVq1aJTt37pTnn3++zvLu3bvL2bNnZejQoVJQUCBxcXHyxhtv2H3pdauQkBA5d+6c/OxnP5OXXnqpSa4HAIBrMQRBEARBNBwxMTEmLS3NPt7oPgJCA839337QPPLqk+bxXyXZR50OCAn02nmvX7/eFBQUmLCwsDrLwsPDXc9VUlKSfR4fH2+n3ZfHxsbaec71R0dHm/T0dFNUVGRKSkpMVlaWSUxMtMtrW7Zsmd3Gz8/PTJo0yeTm5pqysjKzb98+k5yc7DqGc9yEhASTmZlpKisr7byrXd/w4cNNcXHxdb0WCxcuNJs3b6532ZYtW8z8+fOb5H1AEARBENJAUHIOAEATiP6XznLPE+2l8ny5lBdeksAWIXZa5a064PHjRURESEJCgkydOtVW7a7twoULN7xvLX3W0uvevXvbquWdO3eWkpISW0o9ZMgQWbNmjXTo0EEuXrwo5eX/qB0wefJkW5I9atQoycnJsduuWLHCll5v27bNte/Zs2fLhAkTJDc3t94q6zdKq9kXFRV5bH8AAHgayTkAAE1Qlf3Oh++1iXlV8T+SVedR55/amOPxKu7t27cXf39/OXz4sHhadHS0rF69WrKysux0Xl6ea5mTAGu1cucGgCby2k68X79+smvXLtc2PXv2lJEjR9ZIzqdPny6bNm3y6Pk+/vjj8swzz8igQYM8ul8AADyJ5BwAAC/TNuaBzYJsibm7z0oqpVmrFjZ593Ry7ufnJ96iHcwtXrxYBgwYYBNpTdS1PfvVbhQ0b95cNm7cWGO+Ju179+6tMS8zM9Oj59qlSxdZt26dvPzyy3WODwDAzYTe2gEA8DJNvD8rv2yrsrvT6c/KP7Odw3maVh2/cuWKdOzYsVHb6Ta1k3vtRM7d0qVLpW3btvLmm29K165dbUI9ZsyYBvfZokUL+6gl1926dXOFVod/+umna6yr1eQ9RTu827x5s+0M7pVXXvHYfgEA8AaScwAAvEyT708+OCkhtzeT4Ihm4h/kbx91Wud7o9d2ba+9YcMGGT16tISFhdVZ3tBQZ9oGXLVu3do1TxPp2k6ePClLliyR5ORkmTt3rowYMcLOr6qqcvW07tChzHQYN60Or0O6uYfuxxs08d+yZYssX75cUlJSvHIMAAA8iWrtAAA0gfz0Q6425lqVXUvMT//9mGu+N2hirkOp7d6927blPnDggAQGBkr//v3lxRdftAlsbceOHbPjg8+cOdN2Jqcdu40fP77GOjomeEZGhhw9etR2PNenTx/Jzs62y06cOGFL3wcPHixvvfWW7RBOO4ubM2eO3U7bwW/fvt3eHOjRo4ftNC4tLa1R19WmTRuJjIy0yb7eBIiNjXWdu5a8a1X2d955x96cmDdvnh3rXelQap988olrP852WrJ/11132Wm9ueBcCwAATc3nXcYTBEEQxM0cnhxCK/j2Zqbl/ZH2sSnOPSoqyixYsMDk5eWZiooKO7Ta2rVrawxT5j6UmkZcXJzZv3+/HfJs69atdsgz96HUUlNTTU5OjikvLzeFhYVm+fLlJjIy0rV9SkqKOX36tKmurnYNpaYxbtw4k52dbYdJ0+0yMjJMr169GhzCraHQfdbHuaYZM2bUu1xfA/f9XM863nofEARBEITUCr/PnwAAgAbExMTIrFmzZNq0abZkGLcm3gcAAG+izTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwAAAADgYyTnAAAAAAD4GMk5AAAAAAA+RnIOAMAtzhgjSUlJvj4NAABuaSTnAAB8ibVq1UpSU1Pl+PHjUlFRIfn5+ZKeni59+/b1yvHi4+Ntsh8eHi7eMmXKFNmxY4eUlpZKcXFxneUPPvigrFy50l5rWVmZHDp0SMaNG1djnW9961vy9ttvy9mzZ+XChQvy7rvvyoABA7x2zgAAXEvgNdcAAAAeExLRTIJvbyaVxeVSdb7cq8eKiYmxSez58+dl4sSJcvDgQQkKCpKBAwfKokWLpFOnTnIzCwgIkOrq6jrzg4ODZdWqVbJz5055/vnn6yzv3r27TbqHDh0qBQUFEhcXJ2+88Ybdl1636t27t2zcuNEm+vr6/PCHP5S//e1v8thjj8m+ffua5PoAAKjNEARBEATRcMTExJi0tDT7eKP7CAgNNG2//aB5bHai6fHrp+yjTgeEBHrtvNevX28KCgpMWFhYnWXh4eGu5yopKck+j4+Pt9Puy2NjY+085/qjo6NNenq6KSoqMiUlJSYrK8skJiba5bUtW7bMbuPn52cmTZpkcnNzTVlZmdm3b59JTk52HcM5bkJCgsnMzDSVlZV23tWub/jw4aa4uPi6XouFCxeazZs3X3UdvY5p06Z59X1AEARBENJAUHIOAEATiPmXzvKVPu2k8ny5lBVekqAWIXZa5a464PHjRURESEJCgkydOtVW7a5Nq3LfKC191tJrLX3WquWdO3eWkpISW0o9ZMgQWbNmjXTo0EEuXrwo5eX/qB0wefJkW5I9atQoycnJsduuWLFCzp07J9u2bXPte/bs2TJhwgTJzc2tt8r6jdJq9kVFRQ0u9/Pzk5YtW151HQAAvInkHACAJqjKflf3r9jEXKuzK+fxroe/Iic35ni8inv79u3F399fDh8+LJ4WHR0tq1evlqysLDudl5fnWuYkt05bbqWJvFYf79evn+zatcu1Tc+ePWXkyJE1kvPp06fLpk2bPHq+jz/+uDzzzDMyaNCgBtfRGwItWrSQP//5zx49NgAA14vkHAAAL9M25oHNgmyJubvLJZUS1qqlTd49nZxrSbC3aAdzixcvth2oaSKtibq2Z7/ajYLmzZvbNt7uNGnfu3dvjXmZmZkePdcuXbrIunXr5OWXX65zfMf3vvc9mTFjhu2xXkvyAQDwBXprBwDAyzTx/qz8sq3K7k6nPyu77CpF9yStOn7lyhXp2LFjo7bTbWon99qJnLulS5dK27Zt5c0335SuXbvahHrMmDEN7lNLpJWWXHfr1s0VWh3+6aefrrGuVpP3FO3wbvPmzbYzuFdeeaXedbRE/Xe/+5185zvfsesCAOArJOcAAHiZJt/n9pySkNub2VJy/yB/+6jT5z445ZVe27W99oYNG2T06NESFhZWZ3lDQ505JcetW7d2zdNEuraTJ0/KkiVLJDk5WebOnSsjRoyw86uqqlw9rTt0KDMdxk2rw+uQbu6h+/EGTfy3bNkiy5cvl5SUlHrX+e53vyvLli2zJedvvfWWV84DAIDrRbV2AACawIn0Q6425lqVXUvMT2057prvDZqY61Bqu3fvtm25Dxw4IIGBgdK/f3958cUXbQJb27Fjx+z44DNnzrSdyWnHbuPHj6+xzvz58yUjI0OOHj1qO57r06ePZGdn/+M6T5ywpe+DBw+2Ca92CKedxc2ZM8dup+3gt2/fbm8O9OjRw3Yal5aW1qjratOmjURGRtpkX28CxMbGus5dS961Kvs777xjb07MmzfPjvWudCi1Tz75xD7XhFwT95/85Cfy3nvvudbR89VzAgDAF3zeZTxBEARB3MzhySG0gm9vZlreH2kfm+Lco6KizIIFC0xeXp6pqKiwQ6utXbu2xjBl7kOpacTFxZn9+/fbIc+2bt1qhzxzH0otNTXV5OTkmPLyclNYWGiWL19uIiMjXdunpKSY06dPm+rqatdQahrjxo0z2dnZdpg03S4jI8P06tWrwSHcGgrdZ32ca5oxY0a9y/U1cPaxZcuWetdxP19vvg8IgiAIQmqF3+dPAABAA2JiYmTWrFkybdo0WzKMWxPvAwCAN9HmHAAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAAAAAAAfIzkHAAAAAMDHSM4BAAAAAPAxknMAAAAAAHyM5BwAAAAAAB8jOQcA4BZnjJGkpCRfnwYAALc0knMAAL7EWrVqJampqXL8+HGpqKiQ/Px8SU9Pl759+3rlePHx8TbZDw8PF2+ZMmWK7NixQ0pLS6W4uLjO8gcffFBWrlxpr7WsrEwOHTok48aNq7FOjx49ZPv27fLJJ5/YdbKzs+Xf/u3fvHbOAABcS+A11wAAAB4TEtFMQm4Plcricqk8X+HVY8XExNgk9vz58zJx4kQ5ePCgBAUFycCBA2XRokXSqVMnuZkFBARIdXV1nfnBwcGyatUq2blzpzz//PN1lnfv3l3Onj0rQ4cOlYKCAomLi5M33njD7kuvW2liv3DhQjlw4IB93rNnT1myZIl9/tvf/rZJrg8AgNoMQRAEQRANR0xMjElLS7OPN7qPgNBA0/47XU3c7ATTK/Vf7KNOB4QEeu28169fbwoKCkxYWFidZeHh4a7nKikpyT6Pj4+30+7LY2Nj7Tzn+qOjo016eropKioyJSUlJisryyQmJtrltS1btsxu4+fnZyZNmmRyc3NNWVmZ2bdvn0lOTnYdwzluQkKCyczMNJWVlXbe1a5v+PDhpri4+Lpei4ULF5rNmzdfdZ3Vq1fbv7M33wcEQRAEIQ0EJecAADSB+5/qJPf2aWdLzMvPXJKgFiF2Wh3780GPHy8iIkISEhJk6tSpttp2bRcuXLjhfWvps5Ze9+7d25Y0d+7cWUpKSmwp9ZAhQ2TNmjXSoUMHuXjxopSXl9ttJk+ebEuyR40aJTk5OXbbFStWyLlz52Tbtm2ufc+ePVsmTJggubm59VZZv1Fazb6oqKjB5d26dbMl7CkpKR47JgAAjUFyDgBAE1Rlv/vhr/yjKnvxP5JV5/Huh++RgrdzPF7FvX379uLv7y+HDx8WT4uOjpbVq1dLVlaWnc7Ly3MtcxJgrVbu3ADQRF7biffr10927drl2karko8cObJGcj59+nTZtGmTR8/38ccfl2eeeUYGDRpUZ5neULjrrrskMDBQZs6cKUuXLvXosQEAuF4k5wAAeJm2MQ8IC7Il5u4ul1RKs1YtbPLu6eTcz89PvEU7mFu8eLEMGDDAJtKaqGt79qvdKGjevLls3LixxnxN2vfu3VtjXmZmpkfPtUuXLrJu3Tp5+eWX6xxf9erVS1q0aCHf+MY3bKn9sWPH5I9//KNHzwEAgOtBcg4AgJdp4l1ddtlWZXdKzJVOV5dfrjHPU7Tq+JUrV6Rjx46N2k63qZ3caydy7rR0ecOGDbYkWhN0rbI+fvx428FafTT5Vbr+qVOnaiyrrKysMa3V5D1FO7zbvHmz7QzulVdeqXedjz76yD5qLQDt2V5Lz0nOAQC+wFBqAAB4mSbfZz849Y+e2iOaiX+Qv+v52Q9Oe6XXdm2vrQn06NGjJSwsrM7yhoY60zbgqnXr1jXaY9d28uRJ27t5cnKyzJ07V0aMGGHnV1VVuXpad+hQZjqMm1aH1yHd3EP34w3aDn7Lli2yfPny625Hrs0AQkJCvHI+AABcCyXnAAA0gbx12a425lqVXUvMT2457prvDZqY61Bqu3fvtm25ddgwbVvdv39/efHFF20CW5tW69bxwbUEWTuT047dtFTc3fz58yUjI0OOHj1qO57r06ePHSdcnThxwpa+Dx48WN566y3bIZx2Fjdnzhy7nSbAOr643hzQsca107i0tLRGXVebNm0kMjLSJvt6EyA2NtZ17lryrlXZ33nnHXtzYt68ebZEXOlQajquufrxj39sr9Npk68d1GlHdFplHwAAX/F5l/EEQRAEcTOHJ4fQCrk91Nx2f4R9bIpzj4qKMgsWLDB5eXmmoqLCDq22du3aGsOUuQ+lphEXF2f2799vhzzbunWrHfLMfSi11NRUk5OTY8rLy01hYaFZvny5iYyMdG2fkpJiTp8+baqrq11DqWmMGzfOZGdn22HSdLuMjAzTq1evBodwayh0n/VxrmnGjBn1LtfXwNnHmDFjzMGDB+1QcOfPnzd79uwxo0aNskO+NcX7gCAIgiCkVvh9/gQAADQgJiZGZs2aJdOmTbMlw7g18T4AAHgTbc4BAAAAAPAxknMAAAAAAHyM5BwAAAAAAB8jOQcAAAAAwMdIzgEAAAAA8DGScwAAAAAAfIzkHAAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAABuccYYSUpK8vVpAABwSyM5BwDgS6xVq1aSmpoqx48fl4qKCsnPz5f09HTp27evV44XHx9vk/3w8HDxlilTpsiOHTuktLRUiouL6yx/8MEHZeXKlfZay8rK5NChQzJu3LgG9xcXFyeXL1+WvXv3eu2cAQC4lsBrrgEAADwmJKKZhESESmVxuVQWV3j1WDExMTaJPX/+vEycOFEOHjwoQUFBMnDgQFm0aJF06tRJbmYBAQFSXV1dZ35wcLCsWrVKdu7cKc8//3yd5d27d5ezZ8/K0KFDpaCgwCbfb7zxht2XXrc7vYmQlpYmmzdvtjcyAADwJUMQBEEQRMMRExNj0tLS7OON7iMgNNA88J2upscvBpr41MH2UacDQgK9dt7r1683BQUFJiwsrM6y8PBw13OVlJRkn8fHx9tp9+WxsbF2nnP90dHRJj093RQVFZmSkhKTlZVlEhMT7fLali1bZrfx8/MzkyZNMrm5uaasrMzs27fPJCcnu47hHDchIcFkZmaayspKO+9q1zd8+HBTXFx8Xa/FwoULzebNm+vM/8Mf/mB++tOfmhkzZpi9e/d6/X1AEARBENJAUHIOAEATaPtUJ2nTt61UFJdLWeElCWoRYqdVzp8Pevx4ERERkpCQIFOnTrVVu2u7cOHCDe9bS5+19Lp37962annnzp2lpKTEllIPGTJE1qxZIx06dJCLFy9KeXm53Wby5Mm2JHvUqFGSk5Njt12xYoWcO3dOtm3b5tr37NmzZcKECZKbm1tvlfUbpSXkRUVFNeY999xz0rZtW3teKSkpHjsWAAA3guQcAIAmqMp+d/d7bGKu1dmV86jz8zfmeLyKe/v27cXf318OHz4snhYdHS2rV6+WrKwsO52Xl+da5iTAWq3cuQGgiby2E+/Xr5/s2rXLtU3Pnj1l5MiRNZLz6dOny6ZNmzx6vo8//rg888wzMmjQoBqvj94I6NWrV71V5wEAaGok5wAAeJm2MQ9sFmRLzN1dLqmUsFYtbfLu6eTcz89PvEU7mFu8eLEMGDDAJtKaqGt79oZoIty8eXPZuHFjjfmatNfuhC0zM9Oj59qlSxdZt26dvPzyy67j600L7TBuxowZthQfAICbAck5AABepon3Z+WXbVV2p8Rc6bTOd5/nKZp0XrlyRTp27Nio7XSb2sm9diLnbunSpbJhwwZbEq0JulZZHz9+vCxcuLDefbZo0cI+6vqnTp2qsayysrLGtFaT9xTt8E47etPO4F555RXX/JYtW8ojjzwiDz30kOucNWHX0F7b9Zq2bNnisfMAAOB6MJQaAABepsn32T2nJdT21N5M/IP87aNO63xv9Nqu7bU1gR49erSEhYXVWd7QUGfaBly1bt3aNa9bt2511jt58qQsWbJEkpOTZe7cuTJixAg7v6qqytXTukOHMtNh3LQ6vA7p5h66H2/QdvCaYC9fvrxOe3JtC/+1r33NXpcTr7/+um0CoM/fe+89r5wTAABXQ8k5AABNIHddtquNuVZl1xLzgndyXfO9QRNzHUpt9+7dti33gQMHJDAwUPr37y8vvviiTWBrO3bsmB0ffObMmbYzOe3YTUvF3c2fP18yMjLk6NGjtuO5Pn36SHb2P67jxIkTtvR98ODB8tZbb9kO4bSzuDlz5tjttHR6+/bt9uZAjx49bKKsQ5k1Rps2bSQyMtIm+3oTIDY21nXuWvKuVdnfeecde3Ni3rx5riHStG35J598Ysdh//DDD2vsU9vI6w2E2vMBAGhKPu8yniAIgiBu5vDkEFohEaHmtrYR9rEpzj0qKsosWLDA5OXlmYqKCju02tq1a2sMU+Y+lJpGXFyc2b9/vx3ybOvWrXbIM/eh1FJTU01OTo4pLy83hYWFZvny5SYyMtK1fUpKijl9+rSprq52DaWmMW7cOJOdnW2HSdPtMjIyTK9evRocwq2h0H3Wx7kmHRatPvoaNLRPhlIjCIIgxMfh9/kTAADQgJiYGJk1a5ZMmzbNlgzj1sT7AADgTbQ5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAAAAAAAfIzkHAAAAAMDHSM4BAAAAAPAxknMAAAAAAHyM5BwAAAAAAB8jOQcAAAAAwMdIzgEAuMUZYyQpKcnXpwEAwC2N5BwAgC+xVq1aSWpqqhw/flwqKiokPz9f0tPTpW/fvl45Xnx8vE32w8PDxVumTJkiO3bskNLSUikuLq6z/MEHH5SVK1faay0rK5NDhw7JuHHj6j3P2qGvFwAAvhDok6MCAHCLCo1sJiG3h0pFcblUFld49VgxMTE2iT1//rxMnDhRDh48KEFBQTJw4EBZtGiRdOrUSW5mAQEBUl1dXWd+cHCwrFq1Snbu3CnPP/98neXdu3eXs2fPytChQ6WgoEDi4uLkjTfesPvS63bXoUMHuXjxomtatwMAwFcMQRAEQRANR0xMjElLS7OPN7qPgNBA89Vnupjev+xv+i5MtI86HRAS4LXzXr9+vSkoKDBhYWF1loWHh7ueq6SkJPs8Pj7eTrsvj42NtfOc64+Ojjbp6emmqKjIlJSUmKysLJOYmGiX17Zs2TK7jZ+fn5k0aZLJzc01ZWVlZt++fSY5Odl1DOe4CQkJJjMz01RWVtp5V7u+4cOHm+Li4ut6LRYuXGg2b95c53ju19kU7wOCIAiCkAaCknMAAJpA+6SvSnTf+6W8uFxKz5RIUItgO62O/OlDjx8vIiJCEhISZOrUqbZqd20XLly44X1r6bOWXvfu3dtWLe/cubOUlJTYUuohQ4bImjVrXCXS5eXldpvJkyfbkuxRo0ZJTk6O3XbFihVy7tw52bZtm2vfs2fPlgkTJkhubm69VdZvlFazLyoqqjN/3759EhISIllZWTJz5kx59913PXZMAAAag+QcAIAmqMreqvs9NjF3qrI7jzr/o7ePe7yKe/v27cXf318OHz4snhYdHS2rV6+2Ca3Ky8tzLXMSYK0e7twA0ERe24n369dPdu3a5dqmZ8+eMnLkyBrJ+fTp02XTpk0ePd/HH39cnnnmGRk0aJBr3scff2yPnZmZaZPzF154Qf7+97/LY489Jnv37vXo8QEAuB4k5wAAeJm2MQ8MC7Ql5u4ul1RJ81YtJDSimceTcz8/P/EW7WBu8eLFMmDAAJtIa6Ku7dmvdqOgefPmsnHjxhrzNWmvnQhrsuxJXbp0kXXr1snLL79c4/hHjx614dD26+3atZOXXnpJhg0b5tFzAADgetBbOwAAXlZ5vkI+K/vMVmV3p9OflX9mO4fzNK06fuXKFenYsWOjttNtaif32omcu6VLl0rbtm3lzTfflK5du9qEesyYMQ3us0WLFvZRS667devmCq0O//TTT9dYV6vJe4p2eLd582bbGdwrr7xyzfV3795tbyQAAOALJOcAAHhZRVG5FO45Lc0imklIRKj4B/nbR53W+d7otV3ba2/YsEFGjx4tYWFhdZY3NNSZtgFXrVu3ds3TRLq2kydPypIlSyQ5OVnmzp0rI0aMsPOrqqpcPa07dCgzHcZNq8PrkG7uofvxBk38t2zZIsuXL5eUlJTr2kavU6u7AwDgC1RrBwCgCRxbe9jVxlyrsmuJef47ea753qCJuQ6lpiXC2pb7wIEDEhgYKP3795cXX3zRJrB1zvPYMTs+uHaOpp3Jacdu48ePr7HO/PnzJSMjw1YL147n+vTpI9nZ2XbZiRMnbOn74MGD5a233rIdwmlncXPmzLHbaTv47du325sDPXr0sJ3GpaWlNeq62rRpI5GRkTbZ15sAsbGxrnPXknetyv7OO+/YmxPz5s1zjV2uQ6l98skn9vlPfvIT2+79ww8/lNDQUNvmXMd+16r6AAD4is+7jCcIgiCImzk8OYRWSESoCW8bYR+b4tyjoqLMggULTF5enqmoqLBDq61du7bGMGXuQ6lpxMXFmf3799shz7Zu3WqHPHMfSi01NdXk5OSY8vJyU1hYaJYvX24iIyNd26ekpJjTp0+b6upq11BqGuPGjTPZ2dl2mDTdLiMjw/Tq1avRQ5vpPuvjXNOMGTPqXa6vgbOPiRMn2mvQa/zkk0/MO++8Y5544okmex8QBEEQhNQKv8+fAACABsTExMisWbNk2rRptmQYtybeBwAAb6LNOQAAAAAAPkZyDgAAAACAj5GcAwAAAADgYyTnAAAAAAD4GMk5AAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AwC3OGCNJSUm+Pg0AAG5pJOcAAHyJtWrVSlJTU+X48eNSUVEh+fn5kp6eLn379vXK8eLj422yHx4eLt4yZcoU2bFjh5SWlkpxcXGd5Q8++KCsXLnSXmtZWZkcOnRIxo0bV2e94OBg+dnPfiYfffSRfW3y8vLkhz/8odfOGwCAqwm86lIAAOBRoZGhEnp7qFQUV9jwppiYGJvEnj9/XiZOnCgHDx6UoKAgGThwoCxatEg6deokN7OAgACprq6uN6letWqV7Ny5U55//vk6y7t37y5nz56VoUOHSkFBgcTFxckbb7xh96XX7fjzn/9sb17oPo4dOyatW7cWf3/KLQAAvmMIgiAIgmg4YmJiTFpamn280X0EhgaaTt/tbPq89k3Tf2GCfdTpgJAAr533+vXrTUFBgQkLC6uzLDw83PVcJSUl2efx8fF22n15bGysnedcf3R0tElPTzdFRUWmpKTEZGVlmcTERLu8tmXLltlt/Pz8zKRJk0xubq4pKysz+/btM8nJya5jOMdNSEgwmZmZprKy0s672vUNHz7cFBcXX9drsXDhQrN582bX9MCBA+22ERERTfo+IAiCIAhpICg5BwCgCTzwrx0kpu/9UlFcLqWFJRLcIthOq+w/HvL48SIiIiQhIUGmTp1qq3bXduHChRvet5Y+a+l17969bdXyzp07S0lJiS2lHjJkiKxZs0Y6dOggFy9elPLycrvN5MmTbUn2qFGjJCcnx267YsUKOXfunGzbts2179mzZ8uECRMkNze33irrN0qr2RcVFbmmn3rqKcnMzJT/+I//kB/84Af2OrS6/7Rp02wVdwAAmhrJOQAATVCVPap7a5uYO1XZnUedn7ch1+NV3Nu3b2+raB8+fFg8LTo6WlavXi1ZWVl2WttqO5wEWKuVOzcANJHXduL9+vWTXbt2ubbp2bOnjBw5skZyPn36dNm0aZNHz/fxxx+XZ555RgYNGuSa17ZtW3t8TcS/9a1vyZ133im/+c1v5I477pAf/ehHHj0+AADXg+QcAAAv0zbmgc2CbIm5u6qSKmneqoWERvyjDbon+fn5ibdoB3OLFy+WAQMG2ERaE3Vtz361GwXNmzeXjRs31pivSfvevXtrzNPSbE/q0qWLrFu3Tl5++eUax9cbF9px3bPPPmtL+NW///u/y1/+8hf58Y9/TOk5AKDJ0esJAABeVnG+Qj4rv2yrsrvTaZ3vjY7htOr4lStXpGPHjo3aTrepndxrJ3Luli5dakue33zzTenatatNqMeMGdPgPlu0aGEfteS6W7durtDq8E8//XSNdbV6uadoh3ebN2+2ncG98sorNZZ9/PHHcurUKVdirrKzs23Sfu+993rsHAAAuF4k5wAAeFlFUYWc2fOxhEY0s6Xk/kH+9lGndb43knNtr71hwwYZPXq0hIWF1Vne0FBn2gZcac/lDk2kazt58qQsWbJEkpOTZe7cuTJixAg7v6qqytXTukOHMtOSaK0Or0O6uYfuxxs08d+yZYssX75cUlJS6izXXuzvueceW6Lv0Hby2qO7t84JAICrITkHAKAJHP3rETnxTp74+fvZquz6qNM631s0Mdckeffu3bajNq1eriXpY8eOtcOQ1UeHFNPxwWfOnGnXf/LJJ2X8+PE11pk/f76t0n7ffffJQw89JH369LGlzurEiRO29H3w4MG2Hbcmv9pZ3Jw5c+x2w4YNs6Xuup2Wtut0Y7Vp00ZiY2Ntsq/Xp881nERbq7JrYv7222/LvHnz7HBpGno+Dh0H/dNPP5Vly5bZEvZevXrJa6+9Jv/1X/9FlXYAgM/4vMt4giAIgriZw5NDaIVGhJrb295uH5vi3KOiosyCBQtMXl6eqaiosEOrrV27tsYwZe5DqWnExcWZ/fv32yHPtm7daoc8cx9KLTU11eTk5Jjy8nJTWFholi9fbiIjI13bp6SkmNOnT5vq6mrXUGoa48aNM9nZ2XaYNN0uIyPD9OrVq8Eh3BoK3Wd9nGuaMWNGvcv1NXDfz1e/+lXz9ttvm9LSUpOfn2/mzJljQkMb/rswlBpBEAQhXgy/z58AAIAGxMTEyKxZs+wwW1oyjFsT7wMAgDdRrR0AAAAAAB8jOQcAAAAAwMdIzgEAAAAA8DGScwAAAAAAfIzkHAAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAIBbnDFGkpKSfH0aAADc0kjOAQD4EmvVqpWkpqbK8ePHpaKiQvLz8yU9PV369u3rlePF4pMiPwABAABJREFUx8fbZD88PFy8ZcqUKbJjxw4pLS2V4uLiOssffPBBWblypb3WsrIyOXTokIwbN67GOsuWLbPnWTuysrK8dt4AAFxN4FWXAgCAf1oxMTE2iT1//rxMnDhRDh48KEFBQTJw4EBZtGiRdOrUSW5mAQEBUl1dXWd+cHCwrFq1Snbu3CnPP/98neXdu3eXs2fPytChQ6WgoEDi4uLkjTfesPvS61Y/+clPZNKkSa5tAgMDZf/+/Xa/AAD4iiEIgiAIouGIiYkxaWlp9vGL7is0MtTc3vZ2ExoR6vXzXr9+vSkoKDBhYWF1loWHh7ueq6SkJPs8Pj7eTrsvj42NtfOc64+Ojjbp6emmqKjIlJSUmKysLJOYmGiX17Zs2TK7jZ+fn5k0aZLJzc01ZWVlZt++fSY5Odl1DOe4CQkJJjMz01RWVtp5V7u+4cOHm+Li4ut6LRYuXGg2b97c4HK9/urqanttTfE+IAiCIAipFZScAwDQBAJDA6XDv3aQqK9HSVCzILlcflnOZJ6RI389ItWVdUuHv6iIiAhJSEiQqVOn2qrdtV24cOGG962lz1p63bt3b1u1vHPnzlJSUmJLqYcMGSJr1qyRDh06yMWLF6W8vNxuM3nyZFuSPWrUKMnJybHbrlixQs6dOyfbtm1z7Xv27NkyYcIEyc3NrbfK+o3SavZFRUUNLtcS+E2bNtmq8AAA+ALJOQAATUAT85hvxkhFUYWUnCmR4BbBdlod+uMhjx+vffv24u/vL4cPH/b4vqOjo2X16tWu9tl5eXmuZU4CrNXKnRsAmshrO/F+/frJrl27XNv07NlTRo4cWSM5nz59uk2SPenxxx+XZ555RgYNGlTv8tatW0tiYqJ8//vf9+hxAQBoDJJzAAC8LDQy1JaYa2JeUVxh5zmPOj93Q65r2lP8/PzEW7SDucWLF8uAAQNsIq2JurZnv9qNgubNm8vGjRtrzNekfe/evTXmZWZmevRcu3TpIuvWrZOXX365zvEdw4cPt+3y165d69FjAwDQGPTWDgCAl4XeHmqrsleVVNWYr9OBzQIlNCLU48fUquNXrlyRjh07Nmo73aZ2cq+dyLlbunSptG3bVt58803p2rWrTajHjBnT4D5btGhhH7Xkulu3bq7Q6vBPP/10jXW1mrynaId3mzdvtp3BvfLKKw2u96Mf/chey+XLlz12bAAAGovkHAAAL6s4X2HbmGtVdnc6/Vn5Zx4vNVfaXnvDhg0yevRoCQsLq7O8oaHOtA24U9XboYl0bSdPnpQlS5ZIcnKyzJ07V0aMGGHnV1VVuXpad+hQZjqMm1aH1yHd3EP34w2a+G/ZskWWL18uKSkpVx367YEHHrA3HAAA8CWScwAAvEyrs2vnb1q9XUvJ/YP87aNO63xvJOdKE3NNknfv3m07atPq5VqSPnbsWDsMWX2OHTtmO0WbOXOmXf/JJ5+U8ePH11hn/vz5tkr7fffdJw899JD06dNHsrOz7bITJ07Y0vfBgwfLnXfeaauza2dxc+bMsdsNGzbMlrrrdlrartON1aZNG4mNjbXJvl6fPtfQYzlV2TUxf/vtt2XevHl2rHcNPZ/6OoLTdvAffvhho88DAABP83mX8QRBEARxM4cnhtAKCAkwnb/b2fSd09cMWDTAPuq0zvfmuUdFRZkFCxaYvLw8U1FRYYdWW7t2bY1hytyHUtOIi4sz+/fvt0Oebd261Q555j6UWmpqqsnJyTHl5eWmsLDQLF++3ERGRrq2T0lJMadPn7ZDkzlDqWmMGzfOZGdn22HSdLuMjAzTq1evBodwayh0n/VxrmnGjBn1LtfXwH0/t912myktLTUvvPBCk70PCIIgCEIaCL/PnwAAgAbExMTIrFmzZNq0abZk+IuwJeYRoba03Fsl5rj53wcAANRGb+0AADQhknIAAFAf2pwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwBwizPGSFJSkq9PAwCAWxrJOQAAX2KtWrWS1NRUOX78uFRUVEh+fr6kp6dL3759vXK8+Ph4m+yHh4eLt0yZMkV27NghpaWlUlxcXGf5gw8+KCtXrrTXWlZWJocOHZJx48bVWe/73/++7Nu3z+7n9OnTsnTpUomMjPTaeQMAcDUk5wAAfEnFxMTInj17bCI+ceJE6dq1qyQkJMiWLVtk0aJFcrMLCAiod35wcLCsWrVKFi9eXO/y7t27y9mzZ2Xo0KHSpUsXeeWVV+TVV1+V0aNHu9aJi4uTtLQ0m5DrOt/+9rfl0Ucfld/+9rdeux4AAK7FEARBEATRcMTExJi0tDT7+EX31Swy1ES0u92ERoR6/bzXr19vCgoKTFhYWJ1l4eHhrucqKSnJPo+Pj7fT7stjY2PtPOf6o6OjTXp6uikqKjIlJSUmKyvLJCYm2uW1LVu2zG7j5+dnJk2aZHJzc01ZWZnZt2+fSU5Odh3DOW5CQoLJzMw0lZWVdt7Vrm/48OGmuLj4ul6LhQsXms2bN7umx48fb44dO1ZjnTFjxtjXqyneBwRBEAQhtSLwmqk7AAD4wgJDA6Xjvz4g9zwSJYHNAuWz8s/k9PtnJPuvR6W6strjx4uIiLCl5FOnTrVVu2u7cOHCDe9bS9219Lp37962Snjnzp2lpKRECgoKZMiQIbJmzRrp0KGDXLx4UcrLy+02kydPtiXZo0aNkpycHLvtihUr5Ny5c7Jt2zbXvmfPni0TJkyQ3Nzcequs3yitZl9UVOSa3rlzp/z85z+XxMREycjIkLvvvluefvppeeuttzx2TAAAGoPkHACAJqCJ+f3fjJHy4gopOVMqwS2C7bTK+mO2x4/Xvn178ff3l8OHD3t839HR0bJ69WrJysqy03l5ea5lTgKs1cqdGwCayGs78X79+smuXbtc2/Ts2VNGjhxZIzmfPn26bNq0yaPn+/jjj8szzzwjgwYNcs1799135dlnn5U//elPEhoaKkFBQbYtvnvVdwAAmhJtzgEA8LJmkaG2xFwT84riCrly+Yp91Ol7vh4loRGhHj+mn5+feIt2MJeSkiLbt2+XmTNn2rbs17pR0Lx5c9m4caNcunTJFcOGDZN27drVWDczM9Oj56rtydetWycvv/yyPb6jU6dO8utf/1p++tOf2jbqAwcOlPvuu09ef/11jx4fAIDrRck5AABepsm3VmXXEnN3VSVV0iKquU3eNVn3JK06fuXKFenYsWOjttNtaif3WqrsTjtR27Bhgy2JHjBggK2yPn78eFm4cGG9+2zRooV91PVPnTpVY1llZWWNaa0m7ymagG/evFneeOMN2ymcOz1n7fF9zpw5dvrgwYP22HrDQW88nDlzxmPnAQDA9aDkHAAAL9PEW9uYa1V2dzr9WdlnUl7k2cRcaXttTaC1mnZYWFid5Q0NdaZtwFXr1q1d87p161ZnvZMnT8qSJUskOTlZ5s6dKyNGjLDzq6qq6vS0rkOZ6TBuWh1eh3RzD92PN2g7eO2Vfvny5TbZrk1fE+dGhKO6utrrtQ4AAGgIyTkAAF6mybd2/tYsItSWovsH+dtHnT6decbjpeYOTcw1Sd69e7ftqE2rl2tJ+tixY22HaPU5duyYHR9cq6vr+k8++aQtFXc3f/58W2Ku1cAfeugh6dOnj2Rn/6Pd/IkTJ2zSO3jwYLnzzjttdXbtLE5LqHU7rcretm1bu92YMWPsdGO1adNGYmNjbbKv16fPNfRYTlV2TczffvttmTdvnh3rXUPPx/G3v/3NvibaQd39999vh1bT6vrvvfeefPzxx40+JwAAPMHnXcYTBEEQxM0cnhhCKyAkwHztu53MgDl9zJO/6W8fdVrne/Pco6KizIIFC0xeXp6pqKiwQ4WtXbu2xjBl7kOpacTFxZn9+/fbIc+2bt1qhzxzH0otNTXV5OTkmPLyclNYWGiWL19uIiMjXdunpKSY06dPm+rqatdQahrjxo0z2dnZdpg03S4jI8P06tWrwSHcGgrdZ32ca5oxY0a9y/U1qD10mg4DV1paak6dOmXefPNNc88993j1fUAQBEEQ0kD4ff4EAAA0ICYmRmbNmiXTpk2zJcNfhC0xjwy1peneKjHHzf8+AACgNjqEAwCgCWlCTlIOAABqo805AAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAtzhjjCQlJfn6NAAAuKWRnAMA8CXWqlUrSU1NlePHj0tFRYXk5+dLenq69O3b1yvHi4+Pt8l+eHi4eMuUKVNkx44dUlpaKsXFxXWWP/jgg7Jy5Up7rWVlZXLo0CEZN25cnfV+/OMf22W6zuHDh+UHP/iB184ZAIBrCbzmGgAA4J9STEyMTWLPnz8vEydOlIMHD0pQUJAMHDhQFi1aJJ06dZKbWUBAgFRXV9eZHxwcLKtWrZKdO3fK888/X2d59+7d5ezZszJ06FApKCiQuLg4eeONN+y+9LrVqFGj5NVXX5URI0bI+++/L48++qj89re/tcn+//zP/zTJ9QEAUJshCIIgCKLhiImJMWlpafbxi+6rWWSoiWgXbkIjQrx+3uvXrzcFBQUmLCyszrLw8HDXc5WUlGSfx8fH22n35bGxsXaec/3R0dEmPT3dFBUVmZKSEpOVlWUSExPt8tqWLVtmt/Hz8zOTJk0yubm5pqyszOzbt88kJye7juEcNyEhwWRmZprKyko772rXN3z4cFNcXHxdr8XChQvN5s2bXdM7duwwv/zlL2usM2fOHPN///d/TfI+IAiCIAipFZScAwDQBAJDA6TTtx6Qe77eSoLCAuVy2WdyOrNQstfkyGeVdUuHv6iIiAhJSEiQqVOn2mrbtV24cOGG962lz1p63bt3b1u1vHPnzlJSUmJLqYcMGSJr1qyRDh06yMWLF6W8vNxuM3nyZFuSrSXWOTk5dtsVK1bIuXPnZNu2ba59z549WyZMmCC5ubn1Vlm/UVrNvqioyDUdEhJiq/m703PVEvTAwED57LPPPHZsAACuB8k5AABNQBPztt+MlvLiCrl0pkxCWgTZaXXwD4c9frz27duLv7+/bUvtadHR0bJ69WrJysqy03l5ea5lTgKs1cqdGwCayGs78X79+smuXbtc2/Ts2VNGjhxZIzmfPn26bNq0yaPn+/jjj8szzzwjgwYNcs3bsGGDvPDCC7J27Vr54IMPbFV4ndZzvfPOO+XMmTMePQcAAK6F5BwAAC9rFhlqS8w1MS8vrrTznEedn/O/eVLx+bSn+Pn5ibdoB3OLFy+WAQMG2ERaE3Vtz361GwXNmzeXjRs31pivifDevXtrzMvMzPTouXbp0kXWrVsnL7/8co3jz5o1S6KiouzNAn2tCgsLZfny5fKf//mfcuXKFY+eAwAA14Pe2gEA8LLQiBBblb2y5HKN+Tod1CzQJu+eplXHNcns2LFjo7ZzElP35F47kXO3dOlSadu2rbz55pvStWtXm1CPGTOmwX22aNHCPmrJdbdu3Vyh1eGffvrpGutqNXlP0Q7vNm/ebDuDe+WVV2os0yrt2plcWFiY3HfffbY2wEcffWSr4mtVewAAmhrJOQAAXqal4trGXKuyu9Ppy+WfSXlRzbbPnqDttbXq9ujRo20CWltDQ505iWnr1q1d8zSRru3kyZOyZMkSSU5Olrlz59pez1VVVZWrp3WHDlemybAmwDqkm3vofrxBE/8tW7bY0vCUlJQG19O25adOnbI3Jb773e/antp1KDgAAJoa1doBAPAyTb618zenjbmWmGti3iwiVHI353u8SrtDE3MdSm337t22LfeBAwdsZ2f9+/eXF1980SawtR07dsyODz5z5kzbmZx27DZ+/Pga68yfP18yMjLk6NGjtuO5Pn36SHZ2tl124sQJm+gOHjxY3nrrLdvJmnYWN2fOHLudtoPfvn27vTnQo0cPW1KdlpbWqOtq06aNREZG2mRfbwLExsa6zl1L3rUq+zvvvGNvTsybN8+O9a50KLVPPvnEPn/ggQds52/vvfeevYZ///d/l6997WsyfPjwG369AQD4onzeZTxBEARB3MzhiSG0AkMCTNfvdTQD58abwb/5pn3UaZ3vzXOPiooyCxYsMHl5eaaiosIOrbZ27doaw5S5D6WmERcXZ/bv32+HPNu6dasd8sx9KLXU1FSTk5NjysvLTWFhoVm+fLmJjIx0bZ+SkmJOnz5tqqurXUOpaYwbN85kZ2fbYdJ0u4yMDNOrV68Gh3BrKHSf9XGuacaMGfUu19fA2UfHjh3NBx98YEpLS8358+fNX//6V9OhQwevvw8IgiAIQhoIv8+fAACABsTExNgOxKZNm2ZLhr9o+3NtY66l6d4qMcfN/z4AAKA2qrUDANCENCEnKQcAALXRIRwAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwAAAADgYyTnAAAAAAD4GMk5AAC3OGOMJCUl+fo0AAC4pZGcAwDwJdaqVStJTU2V48ePS0VFheTn50t6err07dvXK8eLj4+3yX54eLh4y5QpU2THjh1SWloqxcXFdZZHRkZKRkaGnDp1ynXNCxYskJYtW9Y51z179th1cnJyZPjw4V47ZwAAroXkHACAL6mYmBibfGoiPnHiROnataskJCTIli1bZNGiRXKzCwgIqHd+cHCwrFq1ShYvXlzv8itXrsi6devkqaeekg4dOshzzz0n/fr1k9dff921zn333Sfr16+3r0W3bt3kV7/6lfzud7+TAQMGeO16AAC4FkMQBEEQRMMRExNj0tLS7OMX3VezyFAT2S7cNIsI8fp5r1+/3hQUFJiwsLA6y8LDw13PVVJSkn0eHx9vp92Xx8bG2nnO9UdHR5v09HRTVFRkSkpKTFZWlklMTLTLa1u2bJndxs/Pz0yaNMnk5uaasrIys2/fPpOcnOw6hnPchIQEk5mZaSorK+28q13f8OHDTXFx8XW9FmPHjjX5+fmu6dmzZ5uDBw/WWOcPf/iDycjIaJL3AUEQBEFIrQi8ZuoOAAC+sMDQAOnyrfZy7yOtJDAsUD4r+0xOvl8oH/71mHxWUe3x40VERNhS8qlTp0pZWVmd5RcuXLjhfWupu5Ze9+7d21Yt79y5s5SUlEhBQYEMGTJE1qxZY0usL168KOXl5XabyZMny9ChQ2XUqFG2Crluu2LFCjl37pxs27bNte/Zs2fLhAkTJDc3t94q6zeidevW9ry2bt3qmvf444/Lpk2baqy3YcMGW4IOAIAvkJwDANAENDFv1y9ayovK5dLHpRLSIshOq/1/OOLx47Vv3178/f3l8OHDHt93dHS0rF69WrKysux0Xl6ea1lRUZF9PHv2rOsGgCby2k5cq5bv2rXLtU3Pnj1l5MiRNZLz6dOn10mab9TKlSttR3dhYWG2nf0LL7zgWhYVFSWFhYU11tdpbSsfGhpq26EDANCUaHMOAICXNYsMtSXmmpiXF1fKlctX7KNOf+XrraRZRIjHj+nn5yfeoh3MpaSkyPbt22XmzJm2Lfu1bhQ0b95cNm7cKJcuXXLFsGHDpF27djXWzczM9Nh5vvTSS/Lwww/btud6nHnz5nls3wAAeBol5wAAeJkm31qVXUvM3VWWXJaWUc1t8q7Juidp1XHtGK1jx46N2k63qZ3cBwUF1Vhn6dKltgr4oEGDbAdqWmV9/PjxsnDhwnr32aJFC/uo62sP6u4qK2tet1aT9xQtCdc4cuSILdHXmwmzZs2SM2fO2NCe7N3ptJb2U2oOAPAFSs4BAPAyTby1jblWZXen05fLPpPyIs8ng9peWxPo0aNH22rdtTU01Jm2AXfaaTu0N/PaTp48KUuWLJHk5GSZO3eujBgxws6vqqqq09P6oUOHbMKr1eF1SDf30P00Ba3ir0JC/lFLYefOnfLNb36zxjr9+/e38wEA8AWScwAAvEyTb+38rVlkM1uK7h/kbx91+lRmocdLzR2amGuSvHv3btshmlYv15L0sWPHNpiEHjt2zI4LrtXVdf0nn3zSloq7mz9/vi0x1+HIHnroIenTp49kZ2fbZSdOnLCl74MHD5Y777zTVmfXzuLmzJljt9Oq7G3btrXbjRkzxk43Vps2bSQ2NtYm+3p9+lxDj6USExPt8GldunSxw8npNegwalpyruendFrP4xe/+IV89atflRdffFG+853v2HMEAMBXfN5lPEEQBEHczOGJIbQCQwNM7Pe+ap6c29skLe5rH3Va53vz3KOiosyCBQtMXl6eqaiosEOrrV27tsYwZe5DqWnExcWZ/fv32yHPtm7daoc8cx9KLTU11eTk5Jjy8nJTWFholi9fbiIjI13bp6SkmNOnT5vq6mrXUGoa48aNM9nZ2XaYNN1Ohy3r1atXg0O4NRS6z/o41/TEE0+YHTt22GHW9BqOHDliXn311Tr71vU/+OAD+7ocO3bMDs3m7fcBQRAEQUgD4ff5EwAA0AAtfdW2ytOmTXOVvN6of5SYh9rSdG+VmOPmfx8AAFAbHcIBANCEbC/tJOUAAKAW2pwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwBwizPGSFJSkq9PAwCAWxrJOQAAX2KtWrWS1NRUOX78uFRUVEh+fr6kp6dL3759vXK8+Ph4m+yHh4eLt0yZMkV27NghpaWlUlxcXGd5ZGSkZGRkyKlTp1zXvGDBAmnZsqVrnaioKPn9738vR44ckerqapk/f77XzhcAgOtBcg4AwJdUTEyM7NmzxybiEydOlK5du0pCQoJs2bJFFi1aJDe7gICAeucHBwfLqlWrZPHixfUuv3Lliqxbt06eeuop6dChgzz33HPSr18/ef31113rhISEyLlz5+RnP/uZ7N+/32vXAABAYxiCIAiCIBqOmJgYk5aWZh+/6L6aRYaYyHbhpllEiNfPe/369aagoMCEhYXVWRYeHu56rpKSkuzz+Ph4O+2+PDY21s5zrj86Otqkp6eboqIiU1JSYrKyskxiYqJdXtuyZcvsNn5+fmbSpEkmNzfXlJWVmX379pnk5GTXMZzjJiQkmMzMTFNZWWnnXe36hg8fboqLi6/rtRg7dqzJz8+vd9mWLVvM/Pnzm/R9QBAEQRBSKwIblcYDAIAbEhgaIF8b0k7ufaSVBDULlMvln8nJ9wsla81x+ayi2uPHi4iIsKXkU6dOlbKysjrLL1y4cMP71lJ3Lb3u3bu3rVreuXNnKSkpkYKCAhkyZIisWbPGllhfvHhRysvL7TaTJ0+WoUOHyqhRoyQnJ8duu2LFClt6vW3bNte+Z8+eLRMmTJDc3Nx6q6zfiNatW9vz2rp1q0f2BwCAN5CcAwDQBDQxb9+vjZQVVcjFM6US0iLITqt9K496/Hjt27cXf39/OXz4sMf3HR0dLatXr5asrCw7nZeX51pWVFRkH8+ePeu6AaCJvLYT16rlu3btcm3Ts2dPGTlyZI3kfPr06bJp0yaPnOfKlSttR3dhYWG2nf0LL7zgkf0CAOANtDkHAMDLmkWG2BJzTczLiyvlyuUr9lGn7/16K2kWEeLxY/r5+Ym3aAdzKSkpsn37dpk5c6Zty36tGwXNmzeXjRs3yqVLl1wxbNgwadeuXY11MzMzPXaeL730kjz88MO27bkeZ968eR7bNwAAnkbJOQAAXtYsItRWZdcSc3eVJZfltqjm0iwy1CbrnqRVx7VjtI4dOzZqO92mdnIfFBRUY52lS5fKhg0bZNCgQTJgwABbZX38+PGycOHCevfZokUL+6jraw/q7iora163VpP3lMLCQhvaI7uW6OvNhFmzZsmZM2c8dgwAADyFknMAALysvLjCtjHXquzudPpy2WdSXlTh8WNqe21NoEePHm2rddfW0FBn2gbcaaft6NatW531Tp48KUuWLJHk5GSZO3eujBgxws6vqqqq09P6oUOH7JBmWh1eh3RzD91PU9Aq/k4v7QAA3IwoOQcAwMvKiypt529OG3MtMdfEPCwyVI5tKvB4qblDE3MdD3z37t22LfeBAwckMDBQ+vfvLy+++KLtyK22Y8eO2XHBtbq6dianHbtpqbg7HRNcxxE/evSo7XiuT58+kp2dbZedOHHClr4PHjxY3nrrLdshnHYWN2fOHLudJslagq03B3r06GE7jUtLS2vUdbVp08aOZa7Jvt4EiI2NdZ27lrwnJiba8d3ff/99e+wuXbrIa6+9Zo+r5+dwttOS/bvuustO680F51oAAGhqPu8yniAIgiBu5vDEEFqBoQGm2/c7mMHzeplvvd7HPuq0zvfmuUdFRZkFCxaYvLw8U1FRYYdWW7t2bY1hytyHUtOIi4sz+/fvt0Oebd261Q555j6UWmpqqsnJyTHl5eWmsLDQLF++3ERGRrq2T0lJMadPnzbV1dWuodQ0xo0bZ7Kzs+0wabpdRkaG6dWrV4NDuDUUus/6ONf0xBNPmB07dthh1vQajhw5Yl599dU6+66Pvk7efB8QBEEQhDQQfp8/AQAADYiJibFtladNm1aj5PVGaOdvto35553D4dZ8HwAAUBvV2gEAaEKakJOUAwCA2ugQDgAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAAAAAAAfIzkHAAAAAMDHSM4BAAAAAPAxknMAAAAAAHyM5BwAgFucMUaSkpJ8fRoAANzSSM4BAPgSa9WqlaSmpsrx48eloqJC8vPzJT09Xfr27euV48XHx9tkPzw8XLxlypQpsmPHDiktLZXi4uI6yyMjIyUjI0NOnTrluuYFCxZIy5YtXet861vfkrffflvOnj0rFy5ckHfffVcGDBjgtXMGAOBaSM4BAPiSiomJkT179thEfOLEidK1a1dJSEiQLVu2yKJFi+RmFxAQUO/84OBgWbVqlSxevLje5VeuXJF169bJU089JR06dJDnnntO+vXrJ6+//rprnd69e8vGjRvlySeflO7du9vX5G9/+5t069bNa9cDAMC1GIIgCIIgGo6YmBiTlpZmH7/ovsIiQ8wd7W4zzSJCvH7e69evNwUFBSYsLKzOsvDwcNdzlZSUZJ/Hx8fbafflsbGxdp5z/dHR0SY9Pd0UFRWZkpISk5WVZRITE+3y2pYtW2a38fPzM5MmTTK5ubmmrKzM7Nu3zyQnJ7uO4Rw3ISHBZGZmmsrKSjvvatc3fPhwU1xcfF2vxdixY01+fv5V19HrmDZtWpO8DwiCIAhCakXgNVN3AADwhQWGBsiDyW2lzSN3S1BYoFwu+0wK3j8rB1bnymcV1R4/XkREhC0lnzp1qpSVldVZrlW5b5SWumvptZY+a9Xyzp07S0lJiRQUFMiQIUNkzZo1tsT64sWLUl5ebreZPHmyDB06VEaNGiU5OTl22xUrVsi5c+dk27Ztrn3Pnj1bJkyYILm5ufVWWb8RrVu3tue1devWBtfx8/Oz1d6Lioo8ckwAABqL5BwAgCagiXmH/m2krKhCLn1cJiEtg+y0+uD3OR4/Xvv27cXf318OHz7s8X1HR0fL6tWrJSsry07n5eW5ljnJrdOWW2kir+3EtWr5rl27XNv07NlTRo4cWSM5nz59umzatMkj57ly5Urb0V1YWJhtZ//CCy80uK7eEGjRooX8+c9/9sixAQBoLNqcAwDgZWGRIbbEXBPzsqJKqb58xT7qtM5vFhHi8WNqSbC3aAdzKSkpsn37dpk5c6Zty36tGwXNmze3bbwvXbrkimHDhkm7du1qrJuZmemx83zppZfk4Ycftm3P9Tjz5s2rd73vfe97MmPGDPnOd75jS/IBAPAFSs4BAPAyTb61KruWmLurvHRZWkaF2eS9vLjSo8fUquPaMVrHjh0btZ1uUzu5DwoKqrHO0qVLZcOGDTJo0CDbw7lWWR8/frwsXLiw3n1qibTS9bUHdXeVlTWvW6vJe0phYaGNI0eO2BJ9vZkwa9YsOXPmjGudZ555Rn73u9/Jt7/9bdm8ebPHjg0AQGNRcg4AgJdp4q1tzLUquzudvlz+mS1F9zRtr60J9OjRo2217toaGurMKTnWdtqO+nowP3nypCxZskSSk5Nl7ty5MmLECDu/qqqqTk/rhw4dskOaaXV4HdLNPXQ/TUGr+KuQkP+/lsJ3v/tdWbZsmS05f+utt5rkPAAAaAgl5wAAeJkm39r5m9PGXEvMNTEPiwyVoxsLPF5q7tDEXMcD3717t23LfeDAAQkMDJT+/fvLiy++aDtyq+3YsWN2XHCtrq6dyWnHbloq7m7+/Pl2HPGjR4/ajuf69Okj2dnZdtmJEyds6fvgwYNtwqsdwmlncXPmzLHbaZKsJdh6c6BHjx6207i0tLRGXVebNm3sWOaa7OtNgNjYWNe5a8l7YmKiHd/9/ffft8fu0qWLvPbaa/a4en5KE/Lly5fLT37yE3nvvffs+krPV88JAABf8HmX8QRBEARxM4cnhtAKDA0wDz/7gEn6VQ/z9JJ4+6jTOt+b5x4VFWUWLFhg8vLyTEVFhR1abe3atTWGKXMfSk0jLi7O7N+/3w55tnXrVjvkmftQaqmpqSYnJ8eUl5ebwsJCs3z5chMZGenaPiUlxZw+fdpUV1e7hlLTGDdunMnOzrbDpOl2GRkZplevXg0O4dZQ6D7r41zTE088YXbs2GGHWdNrOHLkiHn11Vdr7HvLli317sP9fL3xPiAIgiAIaSD8Pn8CAAAaEBMTY9sqT5s2zVXy+kXan2sbcy1N91aJOW7+9wEAALVRrR0AgCakCTlJOQAAqI0O4QAAAAAA8DGScwAAAAAAfIzkHAAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAAAAAAAfIzkHAAAAAMDHSM4BALjFGWMkKSnJ16cBAMAtjeQcAIAvsVatWklqaqocP35cKioqJD8/X9LT06Vv375eOV58fLxN9sPDw8VbpkyZIjt27JDS0lIpLi6uszwyMlIyMjLk1KlTrmtesGCBtGzZ0rVOjx49ZPv27fLJJ59IWVmZZGdny7/927957ZwBALiWwGuuAQAA/inFxMTYJPb8+fMyceJEOXjwoAQFBcnAgQNl0aJF0qlTJ7mZBQQESHV1dZ35wcHBsmrVKtm5c6c8//zzdZZfuXJF1q1bJykpKXLu3Dlp3769vV5N2p999lm7jib2CxculAMHDtjnPXv2lCVLltjnv/3tb5vk+gAAqM0QBEEQBNFwxMTEmLS0NPv4RfcVFhli7mx3mwmLCPH6ea9fv94UFBSYsLCwOsvCw8Ndz1VSUpJ9Hh8fb6fdl8fGxtp5zvVHR0eb9PR0U1RUZEpKSkxWVpZJTEy0y2tbtmyZ3cbPz89MmjTJ5ObmmrKyMrNv3z6TnJzsOoZz3ISEBJOZmWkqKyvtvKtd3/Dhw01xcfF1vRZjx441+fn5V11n9erV9u/cFO8DgiAIgpBaQck5AABNICg0QB5Mvl+iH7lbgsIC5HJZteS/f1b2r86Tzyrqlg5/UREREZKQkCBTp0611bZru3Dhwg3vW0uhtfS6d+/etqS5c+fOUlJSIgUFBTJkyBBZs2aNdOjQQS5evCjl5eV2m8mTJ8vQoUNl1KhRkpOTY7ddsWKFLdnetm2ba9+zZ8+WCRMmSG5ubr1V1m9E69at7Xlt3bq1wXW6desmcXFxtrQdAABfIDkHAKAJaGLeof+9Ul5UKZc+LpeQlkF2Wu35/TGPH0+rcvv7+8vhw4c9vu/o6GhZvXq1ZGVl2em8vDzXsqKiIvt49uxZ1w0ATeS1nXi/fv1k165drm20KvnIkSNrJOfTp0+XTZs2eeQ8V65caTu6CwsLs+3sX3jhhTrr6A2Fu+66SwIDA2XmzJmydOlSjxwbAIDGokM4AAC8LCwyxJaYa2JeVlQp1Zev2EedbvPI3RIWEeLxY/r5+Ym3aAdzWsKsHappQtu1a9dr3iho3ry5bNy4US5duuSKYcOGSbt27Wqsm5mZ6bHzfOmll+Thhx+Wp556yh5n3rx5ddbp1auXfP3rX7cl+toh3He/+12PHR8AgMag5BwAAC/T5FursmuJubvKS5elZVQzm7yXFVd69JhadVw7RuvYsWOjttNtaif32omcOy1d3rBhgwwaNEgGDBhgq6yPHz/edrBWnxYtWthHXV97UHdXWVnzurWavKcUFhbaOHLkiC3R15sJs2bNkjNnzrjW+eijj+yj1gLQnu31ZsMf//hHj50DAADXi5JzAAC8TBNvbWOuVdnd6XRVebUtRfc0ba+tCfTo0aNtte7aGhrqTNuAO+203dtj13by5Enbu3lycrLMnTtXRowYYedXVVW5elp3HDp0yA5pptXhdUg399D9NAWt4q9CQkKuus7VlgMA4E2UnAMA4GWafGvnb04bcy0x18S8WWSIHN140uOl5g5NzHUotd27d9u23DpsmLat7t+/v7z44ou2I7fajh07ZscF1xJk7UxOO3bTUnF38+fPt+OIHz161HY816dPHztOuDpx4oQtfR88eLC89dZbtkM47Sxuzpw5djtNgLUEW28O6Fjj2mlcWlpao66rTZs2dlg0Tfb1JkBsbKzr3LXkPTEx0ZaCv//++/bYXbp0kddee80eV89P/fjHP7bX6bTJ1w7qtCM6rbIPAICv+LzLeIIgCIK4mcMTQ2gFhgaY7s+2N//6qzjznSW97KNO63xvnntUVJRZsGCBycvLMxUVFXZotbVr19YYpsx9KDWNuLg4s3//fjvk2datW+2QZ+5DqaWmppqcnBxTXl5uCgsLzfLly01kZKRr+5SUFHP69GlTXV3tGkpNY9y4cSY7O9sOk6bbZWRkmF69ejU4hFtDofusj3NNTzzxhNmxY4cdZk2v4ciRI+bVV1+tse8xY8aYgwcP2qHgzp8/b/bs2WNGjRplh3zz5vuAIAiCIKSB8Pv8CQAAaEBMTIxtqzxt2jRXyesXaX9u25hr53BeKjHHzf8+AACgNqq1AwDQhDQhJykHAAC10SEcAAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAtzhjjCQlJfn6NAAAuKWRnAMA8CXWqlUrSU1NlePHj0tFRYXk5+dLenq69O3b1yvHi4+Pt8l+eHi4eMuUKVNkx44dUlpaKsXFxXWWR0ZGSkZGhpw6dcp1zQsWLJCWLVvWu7+4uDi5fPmy7N2712vnDADAtZCcAwDQhJpHhshd7VtKWGSw148VExMje/bssYn4xIkTpWvXrpKQkCBbtmyRRYsWyc0uICCg3vnBwcGyatUqWbx4cb3Lr1y5IuvWrZOnnnpKOnToIM8995z069dPXn/99Trr6k2EtLQ02bx5s8fPHwCAxjIEQRAEQTQcMTExJi0tzT7e6D6CmgWYR55tZ57+9TfM99/oYR91OjA0wGvnvX79elNQUGDCwsLqLAsPD3c9V0lJSfZ5fHy8nXZfHhsba+c51x8dHW3S09NNUVGRKSkpMVlZWSYxMdEur23ZsmV2Gz8/PzNp0iSTm5trysrKzL59+0xycrLrGM5xExISTGZmpqmsrLTzrnZ9w4cPN8XFxdf1WowdO9bk5+fXmf+HP/zB/PSnPzUzZswwe/fu9fr7gCAIgiCkgQhsdCoPAAAarduQ+6Rj/3ukrLhKLp4pl5CWQXZavf/74x4/XkREhC0lnzp1qpSVldVZfuHChRvet5a6a+l17969bdXyzp07S0lJiRQUFMiQIUNkzZo1tsT64sWLUl5ebreZPHmyDB06VEaNGiU5OTl22xUrVsi5c+dk27Ztrn3Pnj1bJkyYILm5ufVWWb8RrVu3tue1devWGvO1RL1t27b2vFJSUjxyLAAAbhTJOQAATVCVPebRu2xiXlZUaec5jzGP3CUfZhRIWVGVR4/Zvn178ff3l8OHD4unRUdHy+rVqyUrK8tO5+XluZYVFRXZx7Nnz7puAGgir+3EtWr5rl27XNv07NlTRo4cWSM5nz59umzatMkj57ly5Urb0V1YWJhtZ//CCy/UeH30RkCvXr2kurraI8cDAOCLoM05AABepu3Lg5sFSOWlyzXm63RQWIBN3j3Nz89PvEU7mNOS5u3bt8vMmTNtW/ar0US4efPmsnHjRrl06ZIrhg0bJu3atauxbmZmpsfO86WXXpKHH37Ytj3X48ybN8/O15sWmrjPmDHDluIDAHAzoOQcAAAv01LxqvJqW5XdKTFXOn25rFpK3eZ5iiad2jFax44dG7WdblM7uQ8KCqqxztKlS2XDhg0yaNAgGTBggK2yPn78eFm4cGG9+2zRooV91PW1B3V3lZU1r12ryXtKYWGhjSNHjtgSfb2ZMGvWLFvV/pFHHpGHHnrIdc6asGtor+16TdppHgAATYmScwAAvEyT7xO7z0lYRLCERYZIQJC/fdTpE++f83iVdqXttTWBHj16tK3WXVtDQ51pG3CnnbajW7duddY7efKkLFmyRJKTk2Xu3LkyYsQIO7+qqqpOT+uHDh2yQ5ppdXgd0s09dD9NQRNvFRISYtvCf+1rX7PX5YT25K5NAPT5e++91yTnBACAO0rOAQBoAntXf+RqY94yqpktMT+88bRrvjdoYq7jge/evdu25T5w4IAEBgZK//795cUXX7QdudV27NgxOy64VlfXzuS0YzctFXc3f/58O4740aNHbcdzffr0kezsbLvsxIkTtvR98ODB8tZbb9lSau0sbs6cOXY7TZK1BFtvDvTo0cMmyjqUWWO0adPGjmWuyb7eBIiNjXWdu5a8JyYm2vHd33//fXvsLl26yGuvvWaPq+enPvzwwxr71DbyegOh9nwAAJqSz7uMJwiCIIibOTw5hFZYZLC5q31L+9gU5x4VFWUWLFhg8vLyTEVFhR1abe3atTWGKXMfSk0jLi7O7N+/3w55tnXrVjvkmftQaqmpqSYnJ8eUl5ebwsJCs3z5chMZGenaPiUlxZw+fdpUV1e7hlLTGDdunMnOzrbDpOl2GRkZplevXg0O4dZQ6D7r41zTE088YXbs2GGHWdNrOHLkiHn11Vevum+GUiMIgiDEx+H3+RMAANCAmJgY21Z52rRprpJX3Hp4HwAAvIk25wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwAAAADgYyTnAAAAAAD4GMk5AAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAALc4Y4wkJSX5+jQAALilkZwDAPAl1qpVK0lNTZXjx49LRUWF5OfnS3p6uvTt29crx4uPj7fJfnh4uHjLlClTZMeOHVJaWirFxcV1lkdGRkpGRoacOnXKdc0LFiyQli1b1jnP2qGvFwAAvhDok6MCAHCLah4ZImGRwVJaVCllRVVePVZMTIxNYs+fPy8TJ06UgwcPSlBQkAwcOFAWLVoknTp1kptZQECAVFdX15kfHBwsq1atkp07d8rzzz9fZ/mVK1dk3bp1kpKSIufOnZP27dvb69Wk/dlnn62xbocOHeTixYuu6bNnz3rpagAAuDZDEARBEETDERMTY9LS0uzjje4jqFmAefTZtuY7qY+ZZ3/bwz7qdGBogNfOe/369aagoMCEhYXVWRYeHu56rpKSkuzz+Ph4O+2+PDY21s5zrj86Otqkp6eboqIiU1JSYrKyskxiYqJdXtuyZcvsNn5+fmbSpEkmNzfXlJWVmX379pnk5GTXMZzjJiQkmMzMTFNZWWnnXe36hg8fboqLi6/rtRg7dqzJz8+vczz362yK9wFBEARBSANByTkAAE3goSEx0mnAV2yJ+cWPyySkZZCdVrt/n+vx40VEREhCQoJMnTpVysrK6iy/cOHCDe9bS6G19Lp37962annnzp2lpKRECgoKZMiQIbJmzRpXiXR5ebndZvLkyTJ06FAZNWqU5OTk2G1XrFhhS7a3bdvm2vfs2bNlwoQJkpubW2+V9RvRunVre15bt26ts2zfvn0SEhIiWVlZMnPmTHn33Xc9ckwAABqL5BwAgCaoyn7fY3d9XpW90s5zHmMevUuyMk56vIq7VuX29/eXw4cPi6dFR0fL6tWrbUKr8vLyXMuKiopc1cOdGwCayGs78X79+smuXbtc2/Ts2VNGjhxZIzmfPn26bNq0ySPnuXLlStvRXVhYmG1n/8ILL7iWffzxx/bYmZmZNjnXZX//+9/lsccek71793rk+AAANAYdwgEA4GXaxjyoWaBUXrpcY75OB4cF2uTd0/z8/MRbtIM5bc+9fft2W9rctWvXa94oaN68uWzcuFEuXbrkimHDhkm7du1qrKvJsqe89NJL8vDDD8tTTz1ljzNv3jzXsqNHj8obb7whH3zwgavtupaa6zYAAPgCJecAAHiZlopfLv/MVmV3SsyVTleVfWZL1D1Nq45rx2gdO3Zs1Ha6Te3kXjuRc7d06VLZsGGDDBo0SAYMGGCrrI8fP14WLlxY7z5btGhhH3V97UHdXWVlzWvXavKeUlhYaOPIkSO2RF9vJsyaNUvOnDlT7/q7d++2pfkAAPgCJecAAHiZJt8fvXfu857aQyQgyN8+6vSJ3ee80mu7ttfWBHr06NG2WndtDQ11pm3AnXbajm7dutVZ7+TJk7JkyRJJTk6WuXPnyogRI+z8qqoqV0/rjkOHDtkhzbQ6vA7p5h66n6agVfyVVmFviF6nVncHAMAXKDkHAKAJfLD6hKuN+W2tw2yJefbbp1zzvUETcx1KTUuEtS33gQMHJDAwUPr37y8vvvii7cittmPHjtlxwbW6unYmpx27aam4u/nz59txxLVquHY816dPH8nOzrbLTpw4YUvfBw8eLG+99ZbtEE47i5szZ47dTpNkLcHWmwM9evSwncalpaU16rratGljh0XTZF9vAsTGxrrOXUveExMT7Xjl77//vj12ly5d5LXXXrPH1fNTP/nJT2y79w8//FBCQ0Ntm3Md+11rAgAA4Cs+7zKeIAiCIG7m8OQQWmGRweau9i3tY1Oce1RUlFmwYIHJy8szFRUVdmi1tWvX1himzH0oNY24uDizf/9+O+TZ1q1b7ZBn7kOppaammpycHFNeXm4KCwvN8uXLTWRkpGv7lJQUc/r0aVNdXe0aSk1j3LhxJjs72w6TpttlZGSYXr16NXpoM91nfZxreuKJJ8yOHTvsMGt6DUeOHDGvvvpqjX1PnDjRXoMu/+STT8w777xjt2uq9wFBEARBSK3w+/wJAABoQExMjG2rPG3aNFfJK249vA8AAN5Em3MAAAAAAHyM5BwAAAAAAB8jOQcAAAAAwMdIzgEAAAAA8DGScwAAAAAAfIzkHAAAAAAAHyM5BwAAAADAx0jOAQAAAADwMZJzAAAAAAB8jOQcAIBbnDFGkpKSfH0aAADc0kjOAQD4EmvVqpWkpqbK8ePHpaKiQvLz8yU9PV369u3rlePFx8fbZD88PFy8ZcqUKbJjxw4pLS2V4uLiOssjIyMlIyNDTp065brmBQsWSMuWLWusFxwcLD/72c/ko48+suvl5eXJD3/4Q6+dNwAAVxN41aUAAMCjmt8RLM0jQqS0qFJKi6q8eqyYmBibxJ4/f14mTpwoBw8elKCgIBk4cKAsWrRIOnXqJDezgIAAqa6urjNfk+pVq1bJzp075fnnn6+z/MqVK7Ju3TpJSUmRc+fOSfv27e31atL+7LPPutb785//bG9e6D6OHTsmrVu3Fn9/yi0AAL5jCIIgCIJoOGJiYkxaWpp9vNF9BDULMN8Yer/5XuojZvjvvmEfdTooNMBr571+/XpTUFBgwsLC6iwLDw93PVdJSUn2eXx8vJ12Xx4bG2vnOdcfHR1t0tPTTVFRkSkpKTFZWVkmMTHRLq9t2bJldhs/Pz8zadIkk5uba8rKysy+fftMcnKy6xjOcRMSEkxmZqaprKy08652fcOHDzfFxcXX9VqMHTvW5Ofnu6YHDhxot42IiGjS9wFBEARBSANByTkAAE2ge3K0dBlwjy0xv/BxhYS2DLTTateKPI8fLyIiQhISEmTq1KlSVlZWZ/mFCxdueN9aCq2l171797ZVyzt37iwlJSVSUFAgQ4YMkTVr1kiHDh3k4sWLUl5ebreZPHmyDB06VEaNGiU5OTl22xUrVtiS7W3btrn2PXv2bJkwYYLk5ubWW2X9RmiJuJ7X1q1bXfOeeuopyczMlP/4j/+QH/zgB/Y6tLr/tGnTbBV3AACaGsk5AABNUJX9/kfvrFGV3XnU+QffOuXxKu5alVuraB8+fFg8LTo6WlavXi1ZWVl2WttqO4qKiuzj2bNnXTcANJHXduL9+vWTXbt2ubbp2bOnjBw5skZyPn36dNm0aZNHznPlypW2o7uwsDCbeL/wwguuZW3btrXH10T8W9/6ltx5553ym9/8Ru644w750Y9+5JHjAwDQGDSsAgDAy7SNeXBYgFRc+qzGfJ3W+c0jQzx+TD8/P/EW7WBO23Nv375dZs6cKV27dr3mjYLmzZvLxo0b5dKlS64YNmyYtGvXrsa6WprtKS+99JI8/PDDtpRcjzNv3jzXMr1xoR3XaRv0999/33Yg9+///u8yfPhwCQ0N9dg5AABwvSg5BwDAy0qLK6WqrNpWZXcvIddpna8l6p6mVce1Y7SOHTs2ajvdpnZyr53IuVu6dKls2LBBBg0aJAMGDLBV1sePHy8LFy6sd58tWrSwj7q+9qDurrKy5rVr9XJPKSwstHHkyBFboq83E2bNmiVnzpyRjz/+2J6LVr13ZGdn26T93nvvtR3EAQDQlCg5BwDAy0o/rZK83Z/YEvLmkcESEORvH3Va53uj13Ztr60J9OjRo2217toaGupM24A77bQd3bp1q7PeyZMnZcmSJZKcnCxz586VESNG2PlVVVWuntYdhw4dstXHtTq8DunmHrqfpuD0wh4S8o9aCtqL/T333GNL9B3aTl57h2+qcwIAwB3JOQAATWDPX/Llw7dPi5+/n4S3DrWPOq3zvUUTc02Sd+/ebTtE0+rlWpI+duxYOwxZfbTEWMcF1+rquv6TTz5pS8XdzZ8/35aY33ffffLQQw9Jnz59bKmzOnHihC19Hzx4sG3HrcmvdhY3Z84cu51WZdf23rrdmDFj7HRjtWnTRmJjY22yr9enzzWcRDsxMVGee+456dKlix1OTq/h9ddftyXnen5Oe/RPP/1Uli1bZoeU69Wrl7z22mvyX//1X3QIBwDwGZ93GU8QBEEQN3N4cgit5pHB5u72Le1jU5x7VFSUWbBggcnLyzMVFRV2aLW1a9fWGKbMfSg1jbi4OLN//3475NnWrVvtkGfuQ6mlpqaanJwcU15ebgoLC83y5ctNZGSka/uUlBRz+vRpU11d7RpKTWPcuHEmOzvbDpOm22VkZJhevXo1OIRbQ6H7rI9zTU888YTZsWOHHSpNr+HIkSPm1VdfrbPvr371q+btt982paWldpi1OXPmmNDQ0CZ5HxAEQRCE1Aq/z58AAIAGaOmrtlXWYbacklfcengfAAC8iWrtAAAAAAD4GMk5AAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAAAAAAPgYyTkAAAAAAD5Gcg4AAAAAgI+RnAMAAAAA4GMk5wAAAAAA+BjJOQAAtzhjjCQlJfn6NAAAuKWRnAMA8CXWqlUrSU1NlePHj0tFRYXk5+dLenq69O3b1yvHi4+Pt8l+eHi4eMuUKVNkx44dUlpaKsXFxXWWR0ZGSkZGhpw6dcp1zQsWLJCWLVu61lm2bJk9z9qRlZXltfMGAOBqSM4BAGhCze8Ilrvbt5DmkcFeP1ZMTIzs2bPHJuITJ06Url27SkJCgmzZskUWLVokN7uAgIB65wcHB8uqVatk8eLF9S6/cuWKrFu3Tp566inp0KGDPPfcc9KvXz95/fXXXev85Cc/kaioKFfce++98umnn9r9AgDgK4YgCIIgiIYjJibGpKWl2ccb3UdQswATN/Q+8+yCr5sfLn3MPup0UKi/1857/fr1pqCgwISFhdVZFh4e7nqukpKS7PP4+Hg77b48NjbWznOuPzo62qSnp5uioiJTUlJisrKyTGJiol1e27Jly+w2fn5+ZtKkSSY3N9eUlZWZffv2meTkZNcxnOMmJCSYzMxMU1lZaedd7fqGDx9uiouLr+u1GDt2rMnPz29wuV5/dXW1vTZvvg8IgiAIQhqIQJ/dEgAA4BbySHIb6TKwtZR+WikXTldJaMtAO63eXfGRx48XERFhS8mnTp0qZWVldZZfuHDhhvetpe5aet27d29btbxz585SUlIiBQUFMmTIEFmzZo0tsb548aKUl5fbbSZPnixDhw6VUaNGSU5Ojt12xYoVcu7cOdm2bZtr37Nnz5YJEyZIbm5uvVXWb0Tr1q3teW3durXBdZ5//nnZtGmTrQIPAIAvkJwDANAEVdnvf+xOm5iXFlXZec7j/Y/eIfvfOu2a9pT27duLv7+/HD58WDwtOjpaVq9e7WqfnZeX51pWVFRkH8+ePeu6AaCJvLYT16rlu3btcm3Ts2dPGTlyZI3kfPr06TZJ9oSVK1faju7CwsJsO/sXXnihweQ9MTFRvv/973vkuAAA3AjanAMA4GXNI4IlOCxAKi59VmO+TgeHBXql/bmfn594i3Ywl5KSItu3b5eZM2fatuzXulHQvHlz2bhxo1y6dMkVw4YNk3bt2tVYNzMz02Pn+dJLL8nDDz9s257rcebNm1fvesOHD5fz58/L2rVrPXZsAAAai5JzAAC8rLS4SqrKqm1VdvcScp2uKvvM46XmSquOa8doHTt2bNR2uk3t5D4oKKjGOkuXLpUNGzbIoEGDZMCAAbbK+vjx42XhwoX17rNFixb2UdfXHtTdVVZW1pjWavKeUlhYaOPIkSO2RF9vJsyaNUvOnDlTY70f/ehH8uabb8rly5c9dmwAABqLknMAALys9NMqyXvvE2l+R4gtJQ8I8rePOp23+1OvJOfaXlsT6NGjR9tq3bU1NNSZtgF3qno7unXrVme9kydPypIlSyQ5OVnmzp0rI0aMsPOrqqrq9LR+6NAhO6SZVofXId3cQ/fTFLSKvwoJCakz9NsDDzxgbzgAAOBLlJwDANAE3v9LvquNeXjrZrbE/MMNH7vme4Mm5joe+O7du21b7gMHDkhgYKD0799fXnzxRduRW23Hjh2znaJpdXXtTE47dtNScXfz58+344gfPXrUdjzXp08fyc7OtstOnDhhS98HDx4sb731lu0QTjuLmzNnjt1Ok2QtwdabAz169LCdxqWlpTXqutq0aWPHMtdkX28CxMbGus5dS961/biO7/7+++/bY3fp0kVee+01e1w9v9odwWk7+A8//PAGXmEAADzL513GEwRBEMTNHJ4cQqt5ZLC5u30L+9gU5x4VFWUWLFhg8vLyTEVFhR1abe3atTWGKXMfSk0jLi7O7N+/3w55tnXrVjvkmftQaqmpqSYnJ8eUl5ebwsJCs3z5chMZGenaPiUlxZw+fdoOTeYMpaYxbtw4k52dbYdJ0+0yMjJMr169GhzCraHQfdbHuaYnnnjC7Nixww6zptdw5MgR8+qrr9bZ92233WZKS0vNCy+80OTvA4IgCIKQWuH3+RMAANCAmJgY21Z52rRpdUpecevgfQAA8CbanAMAAAAA4GMk5wAAAAAA+BjJOQAAAAAAPkZyDgAAAACAj5GcAwAAAADgYyTnAAAAAAD4GMk5AAAAAAA+RnIOAAAAAICPkZwDAAAAAOBjJOcAANzijDGSlJTk69MAAOCWRnIOAMCXWKtWrSQ1NVWOHz8uFRUVkp+fL+np6dK3b1+vHC8+Pt4m++Hh4eItU6ZMkR07dkhpaakUFxfXWR4ZGSkZGRly6tQp1zUvWLBAWrZsWWO973//+7Jv3z67n9OnT8vSpUvttgAA+ALJOQAATajFHcHSqn0LaR4Z7PVjxcTEyJ49e2wiPnHiROnataskJCTIli1bZNGiRXKzCwgIqHd+cHCwrFq1ShYvXlzv8itXrsi6devkqaeekg4dOshzzz0n/fr1k9dff921TlxcnKSlpdmEvEuXLvLtb39bHn30Ufntb3/rtesBAOBaDEEQBEEQDUdMTIxJS0uzjze6j6BmAabnD2LM8AUPmRFLH7GPOh0U6u+1816/fr0pKCgwYWFhdZaFh4e7nqukpCT7PD4+3k67L4+NjbXznOuPjo426enppqioyJSUlJisrCyTmJhol9e2bNkyu42fn5+ZNGmSyc3NNWVlZWbfvn0mOTnZdQznuAkJCSYzM9NUVlbaeVe7vuHDh5vi4uLrei3Gjh1r8vPzXdPjx483x44dq7HOmDFj7OvlzfcBQRAEQUgDEXjN1B0AAHxhjz19r3QdECUlRVVy/uNyCW0ZZKfV9jdPePx4ERERtpR86tSpUlZWVmf5hQsXbnjfWuqupde9e/e2VcI7d+4sJSUlUlBQIEOGDJE1a9bYEuuLFy9KeXm53Wby5MkydOhQGTVqlOTk5NhtV6xYIefOnZNt27a59j179myZMGGC5Obm1ltl/Ua0bt3antfWrVtd83bu3Ck///nPJTEx0VaBv/vuu+Xpp5+Wt956yyPHBACgsUjOAQBogqrs7R6NtIl5aVGVnec8tn00Uvau/9g17Snt27cXf39/OXz4sHhadHS0rF69WrKysux0Xl6ea1lRUZF9PHv2rOsGgCby2k5cq5bv2rXLtU3Pnj1l5MiRNZLz6dOny6ZNmzxynitXrrQd3YWFhdl29i+88IJr2bvvvivPPvus/OlPf5LQ0FAJCgqy64wePdojxwYAoLFocw4AgJc1jwiW4LBAqbh0ucZ8nQ4JC5QWXmh/7ufnJ96iHcylpKTI9u3bZebMmbYt+7VuFDRv3lw2btwoly5dcsWwYcOkXbt2NdbNzMz02Hm+9NJL8vDDD9u253qcefPmuZZ16tRJfv3rX8tPf/pT6d69uwwcOFDuu+++Gu3SAQBoSpScAwDgZaXFVVJV9pmtyu5eQq7TlWWf2RJ1T9Oq49oxWseOHRu1nW5TO7nXUmV32onahg0bZNCgQTJgwABbZX38+PGycOHCevfZokUL+6jraw/q7iorK2tMazV5TyksLLRx5MgRW6KvNxNmzZolZ86cseesPb7PmTPHrnvw4EF7bF1HbzzoOgAANCVKzgEA8LKST6vk+O4iW0KuvbQHBPnZR53O3V3k8SrtSttrawKt1bS1WndtDQ11pm3AnXbajm7dutVZ7+TJk7JkyRJJTk6WuXPnyogRI+z8qqqqOj2tHzp0yA5pptXhdUg399D9NAWt4q9CQkLso74mzo0IR3V1tddrHQAA0BBKzgEAaALvrSpwtTG/vXUzW2J+8O0zrvneoIm5lg7v3r3btuU+cOCABAYGSv/+/eXFF1+0HbnVduzYMTsuuFZX187ktGM3LRV3N3/+fNuJ2tGjR23Hc3369JHs7Gy77MSJEzbpHTx4sO1cTTuE087itIRat9MkWUun9eZAjx49bKdxOqRZY7Rp08aOR67Jvt4EiI2NdZ27ln5rJ286vvv7779vj61Dpb322mv2uHp+6m9/+5sdNk07qNObGHoz4le/+pW899578vHHH3+BVx0AgBvn8y7jCYIgCOJmDk8OodU8Mti0at/CPjbFuUdFRZkFCxaYvLw8U1FRYYcKW7t2bY1hytyHUtOIi4sz+/fvt0Oebd261Q555j6UWmpqqsnJyTHl5eWmsLDQLF++3ERGRrq2T0lJMadPnzbV1dWuodQ0xo0bZ7Kzs+0wabpdRkaG6dWrV4NDuDUUus/6ONf0xBNPmB07dthh1vQajhw5Yl599dU6+9ah03QYuNLSUnPq1Cnz5ptvmnvuuadJ3gcEQRAEIbXC7/MnAACgATExMbat8rRp01wlr7j18D4AAHgTbc4BAAAAAPAxknMAAAAAAHyM5BxAk8jLy9OGra7QXpG1I6iCggJ55513bGdNjzzyyFX3sWXLlhr7cEI7gNLeoHXsZe0o6mpjLS9YsEA+/PBD20mUdlSlx9fOsnT+kCFDrnr8Rx99VH7zm99IVlaW7Qlbh4DSYZr+/ve/246zrnZs9dBDD7nO+S9/+cs1XjHbENgVX//61686ZJauEx8fLzdCO9aaNGmSfX21Iyy9rgsXLtihpd544w3b2VdDf0+t5nur0WHBdFxs7SjtZqZjdut5uoeO+a2dp+m533XXXfLPSMdU12sJDvb82PA3Yty4cfazUPv7Y8aMGXW+qz777DP59NNPZdu2bTJmzBjbOV9DdNlzzz0nf/3rX20V+rKyMvtdpz3cr1q1Sr7//e/XGeKuNh3H3Tm2dtB3o/S7pb7vXv0O37dvn7z66qtN+n76Z/j+cV4jX38H6Pf5n/70J5+eB4DG8XnDd4IgvvyhnVGp//u//7OdOWn88Y9/NJs2bTKffvqpq0OnLVu2mPvvv7/efegytXfvXtc+/vu//9t2KlVUVGSXnT9/3nz961+vs+23vvUt23mVOnfunNmwYYNZsWKFWb9+ve0Iyplf33GbNWtmfv/737vOUTu6+tvf/ma3/9///V/XsbWzLfdOtWrHwoULXfvQDrHuvPPOq75m7vR1amg97ZjLvTOsxsTQoUPNxYsX7fb6+mjnXytXrjR//etfbcddjj/96U/1/j191TFWUx/fvSOwFi1amO7du5sOHTr45NqvN+677z57nl/72tfscw39bHXq1MnO1/jqV79q/Pz8fH6ujYmuXbvacw8ODq73eu+4444meR/otH6G9fP/3nvv1Vl3xowZ9j368ccfu76v9Dvj/fffd32utm/fbsLCwups+9BDD5njx4/bdbRTvQ8++MCsWrXKfg71WJcvX7bL9HOg30/1nau+Pp988onrWGvWrLnh63Y661Pu3736nex8r+p16vupKd4Dvv7+cf4XXe071+GL83MP7QxS9e7d2+fnQhCEXE/4/AQIgrgFwvkxNXz48HqXJyYm2h6VnR95+kO7oR9E+qO39rLbbrvNJpZKf/y6L7v77rtdCehrr71mQkJC6mz/8MMPm5///Od15gcGBppt27bZbTWJ/5d/+Zc66wQEBJinn37aHD161PzkJz+p9/r0mE4Sr71lq5deeumqr5mjpKTEPg4cONCjyfnIkSNdP/61J+uWLVvWWUcTOU0INDmo7+95Kybnmszq3zMoKMgn13694SSr9X2WIiIiXAl6q1atfH6u/6zJuZP46PdXQ8m5fm/VXjZ48GBXgv3yyy/XScydz3x6enq9fz+9KfDKK6/YG4IN9W7/zDPP2H2cPHnSfsarqqrsd+EXTc5rL3vggQdMfn6+XabfwU3xHmjbtq29EaDfzzdrcq7n11Q3K64W+vnWm8F79uzx+bkQBCHXEz4/AYIgboG4VnKuoT8ynQS9vpLiqyXnGt/85jddPyA1WXfm/+hHP3L9SG3seesPZ6WJdX0/kt2jefPmplu3bvUu+/73v2/3o8M2DRs2zPX8avtz6I9wVTtB/iLJuf5o1B9s6t/+7d+uub4z3FXtv+etmJz74no9nZw7SZVTeu7rc/1nTM71++rSpUv2Zlt9tQ+ulpy7DwenNVSceZpsHjt2zFXSfa1aDVpLKDQ0tN5lb7/9tt3PxIkTzTvvvGOfT5gwwePJuYZ+rzt06D5fv0duhuT8ZgqtCaVqf48TBCE3XdDmHMBNQ9s5/9u//Zt9/s1vftO2j22MM2fOuJ67t+Vs1aqVfTx37lyj2xb/5Cc/sc9/+tOfykcffXTV9bU9qLa/rM8LL7xgH//rv/7LthfVa+3SpYs89thj1zwPbfet7cq1zbq2M/WE//zP/7RtdvV8f/WrX11z/f/7v//zSFvQZcuW2eXDhw+vMV/PZcKECZKZmWnbsWo7SW3/rv0B/OIXv5CIiAi7nm6n22tbSqV/E/c2sLXb3bdu3Vrmzp1r+yTQv4/uW/c5evRoCQgIuOr56d/nj3/8o5w+fdr2U3D77bdftc25XoPO1zbR6s4775ROnTrZv1u3bt3kgQcekObNmzf42oWGhkrbtm1tm3DdpnPnznL33Xd7rZ219rmgGmr3HBISItHR0fK1r33Nfhb1Gr761a/aPgrq4+/vL/fcc489bz1/3ebBBx+02+j8xrbbd9rJX4vzut9xxx31trXX94D7Nel707kmPU99bbU/Cmf76/XDH/7QXsebb755Q22L9+zZ4zpfh36+27VrZ9//L7744jX3q5+XioqKOvP1GvU79PLly5KWliZLly6183/0ox+JNzjX4hzb/bOqnyn9/M6fP1+OHTtmz1f7t3Do53DkyJGyY8cOOX/+vH1fHj161LaXr/2+ud7vmeTkZMnIyJCzZ8/a1/LkyZP276Sfx4bo51uHyHv//ffteWgbf23fr+21ExISarS9f+KJJ+y09jfi/v3j/r12tTbn+nq88sortv8S53tJ/5YTJ0603wO1OcfV100/r//xH/9ht9Vz/OSTT2T16tXSsWPHBq/tv//7v+2jfu8BuLk13BMJAPiA/qDSDpP0h3L//v3lgw8+uO5ttcM2J0kvKipyzc/Pz7eP+oO8b9++tgO666EdoYWHh8uVK1fsD9wbpQmX/riqqqqyPxD1x6f+4Pt//+//2R/L77333lW3106ktMO5P//5z3aMZU3u9Uf3F/Ev//Iv9vGLXJen+Pn5yfr166Vfv372poXeCNAfx9rBlCa0+kN05cqVthM+/XGvPzSffvppmxhpx3rauV99N2h69eola9eutcmk/pjfuHGjTc70fbJw4UL7GmgnWfr61hYXFyevv/66vUGgnXe1bNnSvg+ulyZcelw9N72mZs2ayW233Wb3c/jwYfuj2p1ei16rJriaTOiPdf0Rfu+99141of8i9FiqvuvX5EGvQdfRZEqvQZMoPZf777/fXof7ON/6N9TkQK9T96fnr6+XdlimyYZen3aeqB1BepruU78z9Bj699XXXF9Dh/Na63noOep16DXpe0zpOer16KPu53r967/+q33ctGnTDZ23vh+U+7kmJSXZxw0bNtjX60bp94r+7f72t7/Z/Wjypu95TU4ff/xx2blzp3iScy21r8e5SaWJpya/+tnWRF6/C50bK//zP/9jv+v1e1GTT33v6OdPO9r73ve+JwMHDpS9e/de13no3/b3v/+9PPPMM/ZvrMc6deqUvQk0dOhQ22mfhr6+7vQmkn4H6edN3xfbt2+XS5cu2ZtT+h2hN8n+93//136/6PePJutRUVGueQ79froW/fzo/yD9fOnNg7feesu+9/T/zS9/+Ut77vpd6Lw/3el6ur6+Pvq9lJ2dbb/P9Jp0e73Z5P65dOjx9HMyaNAg+71S32cewM3D58X3BEF8+eN6qrXXro6p1Uevp1q7tqN89tlnbYduatSoUXWqmzvtvLXtpVbxnDp1qm0nerVO2Zwq7VrN9Itc+89+9jNXNVVn3mOPPWbnXbhwod4OoTQcX/nKV+z07t277fTYsWO/ULV27RTM0bNnzy/096xdzfta1c2dqrzu7wOtaqm0TaR2tlZ7G62qHBkZ2ajjaDtLfT/o31vfD+7Vg3Vf2mxCTZs2rd7zU9oHgbPd9XQIp9WsnXbcWvW6dt8Guq0u0+rk7vP1GE5V7XvvvbfGMq2y/OCDD7r2W7sq941Wa9djakdx9bU512NqHwza9vn222+vc42dO3eu8zfR5zqvffv29Z6Lvmbuf4Pr6VTPuWZPVWt3Xv/6ql3rudX33qtvH/o+0PPW9t6fffZZg9tdq1r7rl277PK///3vrnknTpyw81JSUm7oc+lci7Ofp556yjV/8eLFdt7vfvc7j1dr/+Uvf2mXlZWVuarZu1d137hxY719WmhfF0q/w9w/y1q9/7e//a1dph3j1e7foaHPv/Ndu3Pnzjrv++TkZNvOXzsgdW+nr9+/zuulndzp/wv37bSJlDaZ8lSHcHpuau3atTW++/V/UWZmpl2mHQc29Prr96T7Z1a/Z7RTVPX66683eD779u2z6/To0eOG31sEQYjXg2rtAG46Wk1PNVTNdObMmTWqEmrJ0IoVK2xpuZYMaImnO602qFU8d+3aZUuTtIThZz/7mS2B0KruWjqv1SqdkkSHMzSQlm7cKN2nDoeknKqlSkvLtaq0ljh9+9vfvq596XBnKiUlxZYS3ij3IY++yLV5itPsQEvV3EvBHVr65V4T4npo8wgtsVu0aJF9P7hXL9V9DRs2zJbe6XBW9Tly5Ih9nW90KCStrVG7BFFL8JSW0tYupdYSRD0frX7rTkv/tPTeU7SEW0u3teq0ljJrKaW+B/Q6nVJbrQau62l1/tqld3qOTvMO5++mnCG9dH/10b+rr4eVcs5RawHUpudW33uvIVoNXl8//Xs1ZjsttdTSa60J4jRpcW9W4onvnAEDBtgSXy3R1dJgh/P9853vfMdjtTF0+MgpU6a4miNpE5za1ez1PaO1hLQk2p2+fk4165deeqlGia+W7GrJuV6D1jzSmjLXop8j3Y+WwGu19trNkLT2wJIlS2yNFi1Fd29ypK+Xls5rjQP9f+FO39ObN28WT+jRo4d84xvfsMfQ18S9Bo3+39N56rvf/a585StfqbO91kbR5hTutSr0e0aH7VNa4t4Q/X+jGttcDEDTIjkHcNNxkuSGfsxrO2mtWujEunXrbBtFrbo4b968esdL1+VanVOrAL788su2OqLzA1irAmoCp/OuNW5wYyUmJtofWZroaJV9d9r+XD3//PPXtS+tmqjVMbWKpbZN/LLQmyP6Y1x/GP/4xz+21UW/KL1Joxoa31f/HtqOX19LrU5em1aHb0w1dnf6vq0vAdRr1NDE172dt5OsN3QDorE3JmrTz4W2nS8oKLCJU25urk3UdL6+BrU/Z9qU42rH1YRCX5uwsDB7LcpJaPRvp80FdJ/OfrxB3yva3lePq80datMETD9vekNEr1lvlmgSqUmptlPWm2J67vp+0/4I9Jq0ucEPfvCD6zq+3vhR11MNXtsnOzcStTmKHk+ra2tS9e///u/2veZJTv8W2mTFvRmBVi0/ePCgfb9p1ekb5X5jVF9XbTut35tanVyboNSmSa82K6nt61//uj0XfQ21anttmmTr+1bpDdVr0XX0Palt1/XzXR99zyitFu5w2pPrZ+JGP/PXy2mr7v7/p/Z3of5/0+r5tfvPUPp6HzhwoM58rd6u6kvoHc571f2mGoCbD23OAdx0nB++DSUH+mNWE+zatAOl3/zmN7bdopZOaTJSm3b2o+HQxFwTXf2xrO0etQO4OXPm1OhAzumU60Y4ibf+UK79w0/bn7/66qu2bbQmiJooXU/puZ6n/qjXUuEbKWFz7xhPr01vXPiSJota4vXaa6/Za9LQUi9tF6s/2m+kjb2WtiltO3otWlpZ+7W/Vud/V3O1c9X3QMhtfhL1QEspLiyV0qIqVydvTjvc2jTB0qivA7tr0VLw3/3ud/ZmgSZRep2aEPXu3Vt+/vOf2/bS7u8HvWngHEfb4V6Lrq/Xq6XHWsqpybnTAZt+BrXUX0vf67tZcT0aumY97po1a+Tdd9+t9+aWvs56005rP+j1aUm3vq/0u0W/O/TzpqWrY8eOtQnl1q1bbcdwv/3tb22yX1+y6M65odJQTQF3+rpoMuacl26jCXp6enqdduV6rlqKe6PfOXp9Tz31VI2bf+50nnbMpjc36lt+PZzOxTQ515samnjr9Tkls7U19FlyEsn6EneHdsjmvu71fOa19PhatTTcaw85ncrpzRlvu95r1s4X67tmp/+U2pxaCfV1Judw3qtO55oAbk4k5wBuOpowKy3laYzFixfbH+raQ7Pzo/tatFRHe0jWEhet1qudPDnJudMDsXbgoyVxjS3B1B/Y2pmQ0tLEnj171llHExtNzvTH8uTJk6+5Ty1V0dIkPefp06c3WC37avTHstPpntYyuJ4E1lNqNx1waGdV2uGdJhb6OmnoDRMNTab0BoZ7x0vXexxN7GtXU62tvtJPpydzTwpqFiCdEsPkngdD5cHP2ktFyWU5vrtIzu35R+mzN+jNHE0I9TPlXoVWb1Bp8qrVkmtXy9bPj1b/1eTc+dGvibY2A9G/i5ZGa+KgfxetIqvvX31P6U0u7RBLt1VOp2PaKZl+FvXGgK6jVXc1idebZ/p3d24OaUmhlmxqbRNtdqLH1/e39q5fm1af1u8HfX/UR28IuDdv0aRGb9zp8bWUUUv1taMxreqs+9eq/pro6351JINrJedOouPeEVpDNOnTqsjXQ79zNDmvr/bP9dCSf/176PeK3pSpzWkOo9WrtRd9bb7RWNd7Ld78LF3tM6/vMy09v5qmSMS94YuU7Ds1WeqraQLg5kFyDuCm8uSTT7qGanr77bdvqBRWk4urDZlTHz2WJudOqb1yeg7WH+DaRvl6hhxzp9s41eR1WK6r0SF4tJTvenqz1vW01G/EiBG2FKyxtFRJEyZtC6/neCP7aIhT+lu7XbWjoaGPlNYC0ITCSSo0edDSPa2COnv2bFfb/euhiZ9W29Zh2NyHefKlb3z7KxLzjWZSWWLk/McVEtLcX7oOaCUnI6rk1PZ/9FzdUNJxI6Xm+mNcm3LozabavcNrgq1VvPXvpD1UO7TavZMA6DrODSnnh722sda2wQsWLLDvVS2B15sfOnyaJvla6qc9R2uptr7+uj+9SaXH0oRbP2OjRo2y+9Z2xFpbYv/+/bbnaYf+rfVGlX52ardTvlFamq/npSXk+lpoONWy9Zz1Bpyep16TNn25Vo/Wzg2dxg6/di16w+Rb3/qWvXGg59PYmjFOLQJ97eq7GVh73eu5gektTh8M+tpfqzTcWfdqnJpSesOhMTcQ9D2g71/tyd9Tbcsb4lyHc11f9Jobw3mvfpFRAAB4H23OAdw0NAl2EkVNlvVHe2NpR1eqMZ00KS2tUu4dcmlikJqaap9rKbX7eMT10QREqyPW/qGsyYi2b60vNOnS9pGaPOiNieuhVSK1dFKTOa2qfCM0adVEWs/XGcv9aq71Q9/h/KCs7+aItnVsTGdE+iNbz1O5v67uNwEaGqPbad+vnV/dDFrcESztHo2UiovVUnnRSPVlIyVFVbZae1SXEFvVvaHqpjeaADpDoTlVgxtKZmqX/jqJ/NWqv+rnRUsndaxlfT9qx2PaoZ8m9k5Cr4mlLtPQZFE7+NIaIvrZ1tJNLZ3W0njtjNGdfta0fbS+l66n2rjDuangtINX2vGa3jzQz5juy2mPrbT/Bp3WZEiX6XtWbwDp58r9Jl19tIaAthnXGxtfpHPG2rTdtr5e2lma1gRyv5b66OfJqcqsHY3pTUBtY6/DljX0naM1E5xS9hu56eMp+jfW71h9fztDO7rT69KO0ZT7uOgN0cRa/ybartu92vq1OE0OnOHnrse1vn8a4rR513bu9TVd0O85reWiN4ncb1h5gg4lqm6Wm5UA6kdyDuCmoD9WtHqplrbpD2ktFW4sTYKd5E9LoBza6ZO2k9RSxPpoSZVTPdzpgMjx05/+1CYhmqho9W+nmro7/UGn1eH1R4/TiY9WG9WSGP2h3FCnZE5CoT/InR+H10tLITWh0J7e77nnHmksrdap7daVdqKnSX59SYa2zdUEx7lJcS3OmM9aNdi9QzBNdrTdfX0l6tqRkyYM9f3QdV7v2mP3OjdRGqqRoCWyWn1Tr1Gjvo7+NHl99tlnpSk0jwiW4LAAqSqrWS21/NJlkcBqCWx2xSZktduZ6jynDXdjXSux0+rG9VVxdUZL0PdVQzcGNHHUGhz6mdDRE/T93lCSqsm/Vp/XpiM61rwmZFryrMmH/n31c+JOq53fyHvaaevv3u5W+zLQ7wRtLqHfLb/+9a9dy2bNmmVv4ugoDvp5mDt3rvzhD3+4rurDmgTqdprcOr2ue4KW1usNJf3baEm/9q9R301B/T5yvpv0PeJ+M1C/+67Wxl9vjmhfANq0oL7vs6air6H2A6D0tXdukCr9LtC/lb73tTbUX/7yl2vuT28GaY0OfR9qzSAnGXWnN170RoDWynFoTR29UaXvE+1zQN+n7vQ7S0f7aMz3T0P076XvGz2G3mDV5hQO/aw5TUL0/1DtkRu+CP0Mau0A/ezV10wEwM2Dau0AmpSWVDk91uqPSk3a9EeRkwRoCYkmqQ11fKM0EXb/waqlRPrDQ398K00C3ZNsTcy02riG/oDTduaagDjbOdUqtYM29+HOnB/8WsVU52sPx/qjT28eaCKuybHTblsfNRF3Ovpxfihrp0+1h6OqTc9X28JqD+PXW5VVO47SH7Ta7rf2j8nrpT+MtVRRf9BqT9aayOgPNy2x1ARHkyZ9fZSTtFzPPvXGijYt0JJvbXesNQr0NdK/6V//+ld7M8Sdti3WJgOaUGhvxfr66o9WfV/o31lfPy1Ndadthfv27WuH0NNkw0kyNSnXNsx6DVqFWtfT10mr72opryYletNAS0m1kzD9oezcHPGm0uIqqSqrlpYt/KXKrVJHs5ZBdv6RrBNyzx332YRJEy/9u2iCoomBXr++hppYNGY4Mn0vapLp1Capj75O+jmw5/J5ouC0EdbkXl9/TZSdfei0/tDXNuv6OdZO1HToLq2GrsmUvpZONXn9XOnx9fPn3BzR97hTu0L3owmZfsb07+Ek5Pqof8PGJuj6Ouk2WkNDr8Up3dS+CvS9qMfT94vTc72em1ah17bp+j2kr5dek36u3TvJa4gmznozTjto9GR1aC1R1uYC2l+C3lTQBFq/szRJ1XPWpiHa07m+P7RWhCa5+v5wemBfvnz5Vfev+9AbbuPHj7ffU+43MpuaDgGm16KduOlNGf3+1wRSb6Tqder3tN6AvN4OIbWPBU3o9aab9s+hta/0ddObHvq+1JJpTd71ZrDT3l4/a/o6ay0O/d+j30+aRGvtK72ppCXZ+r3o/jfW7xVd95e//KU9d2c4Qm2G4/S10BDtL0RH3tD/Y/qe05tU+vnQm5T63aT/W26kL5Gr0e9KvZGk13i15hoAbg4+H2ydIIgvf+Tl5ZnaLl26ZE6ePGm2bNliXnvtNfP1r3/9qvvQ9epTWVlp97N27VqTlJRUZ7sWLVqYp556yvz61782u3btMvn5+Xab0tJSk5OTY37/+9+bgQMHXvMaHnvsMfP666+bDz/80Jw/f95UVVWZs2fPmr///e9m8uTJ5p577nEdT69NPfnkk9f1+nzwwQd2/YkTJ7rmOb7yla/Uu03z5s3Nxx9/7FovPj7+hv42d9xxh5kyZYrZunWrKSwstNd18eJFc+DAAXu9vXr1avDvGRMTU2eZvg7//d//bc6cOWMqKirM8ePHzS9+8Qt7vsuWLbPbDR8+3LV+27ZtzfTp083GjRvNRx99ZMrKysynn35q9u3bZ37+85/Xe/1+fn7mP//zP83Bgwft+g29BnfddZd5+eWXTWZmprlw4YI9H/37b9++3cyYMcN87Wtfq7F+feenodeZlpZmH/Xv2717d9OhQ4ca6wQHB9v5Xbt2rfd17jUs2kxc84QZlxZvvvtqVzM8Ndb8eMUjdr4ub9asmWnXrp2JjY01Dz30kOncubNp1aqVvdaHH37Y7lufX+/f9b777rPXefr0aRMWFlZneXh4uH2Mjo621/zjH//YTnfs2NFOP/jgg+bee++15/HTn/7UztPPiV63npder7OvX/7yl+bQoUPmq1/9qhk6dKhdt3fv3qZTp04mKirKHqu8vNwucz+H2267zW6j1/vCCy/UeE/p9WrUPm99fXW+Hl//TsXFxTWuSffXrVs31/atW7e2yxISEuz+v/nNb9rXWF9T3ZdeT2RkpF1HP8v6fdDQa+r+PtBjOd9h/v7+ddbV95fS760b+VwGBQWZH/3oR2bdunWmoKDAvn76XtfP05///GfzzDPPmMDAQLvuD3/4Q3ss/VvXdy61Q/+26vLly67X52qhnyvH9Z6//m2Ufqautl5AQIAZNWqUeffdd12fUf1e1u9r5zu1oe8ffe/Wt1z/1n/5y1/s66bf9UVFRfZ7e+XKlea73/2u/azV9z2o7/P9+/fbv6v+fzh27Jj5wx/+YAYMGFBn/eeff95+r5SUlLheG/fvjau9XhEREeaVV16x56R/U93Hnj17zH/8x3+Y0NDQBl//q72XrnY8/d+o6vsuJwhCbrbw+QkQBEEQxE0d7knZje4jKNTfJuLPLexm/t9/dbePOq3zr7adczNAk+TGHvP++++3CVtWVpYZMmSIad++vU2+x44da5NpZz3l3NjShO/EiRPmT3/6k11fbzBlZ2fXSJznz59vExa9AaCJ9c6dO80f//hHu0wTqurqajNs2DBz55132psyOn/WrFnm3Llzdr7ekNHtxowZY6fdExDnpsHVok2bNjbBnjZtmr2RpM81nGMlJiaa5557znTp0sWes16DJkL/93//59rHAw88YJ599ll7jY888ohNwj755JOr/o1rvw8WLFhgz3nw4ME+f4/eSqE3EZW+v3x9Ljd76I00vUGhyb+vz4UgCLme8PkJEARBEMSXPjl3onlksGnVvoV9dOZpQuxeEu2ElqJpgqnJ+d13331Dx9OSa00itbRRSyW1NFFL0txrGbgn5xpxcXG2BFFL9bRGRXJyco3kPDU11ZZuammuJkrLly93lT5rpKSk2JsCmqS7l5yOGzfOJvqaLOh2GRkZrtK8xiTnTg2H2pxreuKJJ8yOHTtsqbpew5EjR8yrr75aY996k0JrrGgJqdaE+etf/1qnNsS13geaHGqp7O7du33+Hr1VQm84Kb2R4utz+WeIhQsXfqGaVQRBSFOHz0+AIAiCIG6Z5PxapeNakqsly5o8OlWztZTX168BUf/7QG84KL2B4evz+zJHjx49bHV+rX2h5syZ4/Nz+me4kaE3wrQWjK/PhSAIua6gQzgAAHxMO/XSTqW0Azjt3Es7b9LhlLRTKh2a7Ho6KINv6EgG1zuaAW6cduConbVpp47aEdu0adN8fUo3PWdYPgD/PEjOAQDwMe2N2hl3HEBd2gv9tXqiB4B/doxzDgAAAACAj5GcAwAAAADgY7dctfZ77rlHLl265OvTAAD8E9F24P7+/q7Arcn5++v7QfsHAADgeun/jdOnT191ncBbLTE/deqUr08DAPBPpqKiQnbv3i2dOnWSyMhIX58OfKRVq1bSpk0b2bNnj4SGhvr6dAAA/2S+8pWvXDVBv6WSc6fEXF+Um730XHvq7d+/v2zcuNH22IsvJ/7Otwb+zv/8NCGbMmWKZGdny4kTJxpcz8/PT2677Ta5ePGiDh7epOcI74uJibEd940ZM0Y6duzIZ/pLju/uWwN/51tHgA//1lpqroXE18pBb6nk3KEvyj9Dcl5eXm7Pky+KLy/+zrcG/s7//EpLS+XKlSuuuBpdzt/5y8n5++v7gc/0lx/f3bcG/s63joB/gr81DecAAAAAAPAxknMAAG5xWgU/KSnJ16cBAMAtjeQcAIAveSdmqampcvz4cduxXX5+vqSnp0vfvn29crz4+Hib7IeHh4u3aPv/HTt22OrlxcXFV11XO/DTduL1nZOeq3bupq9LTk6ODB8+3GvnDADAtZCcAwDQhFrcESxRDzSXFpFBTdKBmSafmohPnDhRunbtKgkJCbJlyxZZtGiR/DO0D6xPcHCwrFq1ShYvXnzNfSxdulQOHDhQZ/59990n69evt69Ft27d5Fe/+pX87ne/kwEDBnjk3AEAaCyScwAAmkBwM3+JH9ZGnvlpRxkypYM8M6uTnQ4K9d6/4t/85je2xPjRRx+VNWvW2NLhQ4cOyfz58+Ub3/jGdZd8x8bG2nma7Kvo6Ghb+l5UVCQlJSWSlZUliYmJdvnf//53u8758+ftNsuWLXP1ZD9p0iTJzc2VsrIy2bdvnyQnJ9c5rt48yMzMlMrKSunZs2e95zhz5kybTB88ePCq1z9q1Ci5/fbbZc6cOfUuy8vLkwkTJsjhw4ftzYq//OUv8tJLL13XawsAgKfdkr21AwDQ1B7/9lckduDdUvJplRSfrpBmLQPttNqaVuDx40VERNhEd+rUqTYZru3ChQs3vG9NZLX0unfv3rZqeefOnW2SrtXHhwwZYm8EdOjQwQ4ppz3jqsmTJ8vQoUNtUqw3CXTbFStWyLlz52Tbtm2ufc+ePdsmzJrEX6vK+tXomPTTp0+Xxx57TNq2bVtn+eOPPy6bNm2qMW/Dhg026QcAwBdIzgEAaIKq7O0fi7CJeUnRZTvPedT5e/7njGvaU9q3by/+/v62VNjTtOR89erVtsRcaQm0Q0vT1dmzZ103ADSR13bi/fr1k127drm20ZLxkSNH1kjONaGunTQ3lh7vD3/4g63KrzcM6kvOo6KipLCwsMY8ndYaA6GhobYdOgAATYnkHAAAL9P25SFhAbbE3F35pc8konWoTd49nZxrNXJv0Q7mtL23ts/WRFoT9atVMdcbBc2bN5eNGzfWSaL37t1bY55Waf+iXn31VcnOzpbf//73X3hfAAA0FdqcAwDgZZp4V5ZV26rs7nS6srzalqh7mlYdv3LlinTs2LFR2+k2tZP7oKCgOp2saWn0m2++aTuZ04R6zJgxDe6zRYsW9nHQoEG28zUntDr800//f+zdCXiU1dk//m8yIUAgBMKuQGxEBCyFokUqmyBryVssYBW17giISxV4RTb5o6/yvoWiKCAqUgG1yvJTbFkEZCkIIpuAgOybIFuAANkgef7X93BmnOzbTGZCvp/req7JPPPMzDMZyMx97vvcp3eGY1kmX1RsgHfPPffg8uXLZlu2bJnZf/r0aTNfnX7++WfTyd4brzPbr6y5iIgEgoJzERERP2PwvffbsyZDziy6q0yIueR17vd11pw4X5tzqAcOHIiIiIgst+e01BnngFPt2rU9+xhIZ3b06FFMnTrVNHUbP348+vbta/anpqZm6bTOJnQMeFkOzyXdvDc+jq/xnNjEzj0I8MQTT5j9bdq08XSpX7t2Le66664M9+vUqZPZLyIiEggqaxcRESkG33z2k2eOOUvZmTH/fvFJz35/YGDO9cDXr19v5nJzSbGwsDAThA4YMMBkrjPbu3evWQudGWY2k2Njt0GDBmU4ht3eFy5ciN27d5vGc+3btzdl5HTo0CGTfY+Li8OCBQtMQzg2i2PHdN6P8+BXr15tBgdatWplmsbNmDGjQK+rbt26Zv1yBvscBGAg7j53Zt7ZTM5btWrVzCXP0T0P/p133jHZ/v/93//FBx98YLLtf/7zn012X0REJFCc0rJFRkY6xMtAn0tem8vlcuLi4sxloM9Fm95nbXqfS/sWExPjzJgxw1zmdWxUVFSut1eMLuPUuqmCuSyOc69Vq5bz1ltvOQcOHHCSk5OdI0eOOJ9//rnTrl07zzHUo0cPz/U77rjD+f77753ExERn5cqVTq9evcwx7tc/ceJEZ8+ePU5SUpJz4sQJ58MPP3Sio6M99x8xYoRz7NgxJy0tzZk+fbpn/7PPPuvs3LnTSUlJMfdbuHCh06ZNG3Mbz4fy+v1x42Nmx/s1eW85PTb3b9q0yfxe9u7d6zz88MP5+ncQGxur/9OlYNPf7tKx6X0uPZsrgO91AeLQwP+igvCXEvBNfyhKx6b3uXRsep9L/ubL4Fxbyd0UnJeuTX+7S8em97n0bK4SEJxrzrmIiIiIiIhIgCk4FxEREREREQkwBeciIiIiIiIiAabgXERERERERCTAFJyLiIiIiIiIBJiCcxEREREREZEAU3AuIiIiIiIiEmAKzkVEREREREQCLCzQJyBZ1QBQC0BUoE9EREREREREioUy50EkAsBzAN4DMAHAMwCeBVA+0CcmIiLXNMdx0KNHj0CfhoiISKmm4DyI9AXQG0AagMMA0gH0BPBkoE9MRERKrJo1a2LixInYt28fkpOTcfjwYcyfPx8dOnTwy/O1a9fOBPtRUf6r/xo2bBjWrFmDS5cu4ezZs7keGx0djSNHjmQ5p1q1auGjjz7Cjz/+iLS0NEyYwGFxERGRwFFwHkSl7PyadArASQCpAM4DOA2gPYDqgT5BERHxiciqZVD7pgqoGF3G788VExODjRs3mkB8yJAhaNKkCbp27Yrly5dj0qRJCHYulyvb/eHh4Zg9ezamTJmS52NMmzYNW7duzbK/bNmyOHXqFF599VV8//33PjlfERGRolBwHiQYfFcAcC7Tfl6vaIN3EREpucLLh+LOh+qgz5ib0WvYTbj/lZvN9TLl/PdRPHnyZJMxbtGiBebNm4c9e/Zgx44dJkvcsmXLfGe+mzZtavYx2Kd69eqZ7Ht8fDwuXryI7du3o1u3bub2FStWmGPOnTtn7jN9+nRzPSQkBEOHDsX+/fuRmJiILVu2oFevXlmel4MHGzZsQEpKClq3bp3tOY4ePRpvvPEGtm3bluvr79+/PypXroxx48Zlue3QoUP461//ipkzZ+L8eQ6Hi4iIBJYawgUJZswvAahsM+duvH4x0z4RESl57rjnOjTrUh0XzqQi/lgSykeGmeu0YsZRnz9flSpVTKA7fPhwEwxnVpSAlFl3Zq/btm1rSssbN25sgnSWj/fs2dMMBDRo0AAJCQlISkoy93nppZfw4IMPmoCZgwS876xZs0z2etWqVZ7HHjt2LAYPHmyC+LxK1nPTqFEjjBo1CrfffjtiY2ML/TgiIiLFRcF5kGDw/bWdc078yhRls+ZzbPAuIiIlt5S9we2VTWB+Mf6y2ee+5P4N/zrhue4r9evXR2hoKHbt2gVfY+Z87ty5JmNOBw4c8NzGbDqdPHnSMwDAQJ7zxDt27Ih169Z57sPMeL9+/TIE5wyoly5dWqTz4/N98sknppSfAwYKzkVEpCRQcB5E3rWXnGNejyWAAOZ57RcRkZKpYnQ4wiPCTMbcW9KFK4iuXQ6RVcN9HpyzjNxf2GCO8707d+5sAmkG6rmVmHOgoEKFCliyZEmWIHrz5s0Z9rGkvahef/117Ny50zR8ExERKSk05zyI8Cvbm7Zr+/MA3uYXILtfRERKrovxqUhNvGJK2b3xempSmsmo+xpLx9PT09GwYcMC3Y/3yRzclylTJkuTNWajOV+bTeYYUD/99NM5PmbFiqwDA7p3745mzZp5NpbD9+7trhm7imXyRcUGePfccw8uX75stmXLlpn9p0+fNvPVRUREgpGC8yDEEvYfbGm7iIiUfBfOXMbub8+ZDDm7tLvKhJhLXud+X2fNifO1Fy9ejIEDByIiIiLL7TktdcY54FS7dm3PPgbSmR09ehRTp041Td3Gjx+Pvn05tAykpqZm6bTOJnRcxo3l8FzSzXvj4/gaz4lN7NyDAE888YTZ36ZNmxLRpV5EREonlbWLiIgUgzWfHfPMMWcpOzPmWxaf8uz3BwbmXA98/fr1Zi43lxQLCwtDp06dMGDAAJO5zmzv3r1mLXRmmNlMjo3dBg0alOEYdntfuHAhdu/ebRrPtW/f3pSRu7ugM/seFxeHBQsWmIZwbBbHjum8H+fBr1692gwOtGrVyjSNmzFjRoFeV926dc365Qz2OQjAQNx97sy8s5mct2rVqplLnqN3Izz3/ZjZr169urnOwQX3axERESlOCs5FRESKweXkdNOVnc3fmDH3bg7nL2y61rx5cxNkM7vNbDgz41z7nMF5dq5cuYI+ffqYOeUM5r/77juMGDECc+awPelVDIiZga5Tp44JrhctWoTnn+eELODYsWN4+eWXTdd1LqPGwPvRRx/FyJEjzXOzaztL4rnU2qZNm/Daa68V+HWNGTMGjzzyiOc6l2WjO++8EytXrsz347jvR7fddhseeOABHDx4EL/61a8KfE4iIiJFpeBcRESkGDEg93dQ7u3nn3/GM888Y7b8No/75ptvPFnl7I559tlnc33OV1991WzZNZLjlh0G1fltYsdgn1t+5fTY/myaJyIiUlCacy4iIiIiIiISYArORURERERERAJMwbmIiIiIiIhIgCk4FxEREREREQkwBeciIiIiIiIiAabgXERERERERCTAFJyLiIiIiIiIBJiCcxEREREREZEAU3AuIiIiIiIiEmAKzkVEREo5x3HQo0ePQJ+GiIhIqabgXERE5BpWs2ZNTJw4Efv27UNycjIOHz6M+fPno0OHDn55vnbt2plgPyoqCv4ybNgwrFmzBpcuXcLZs2dzPTY6OhpHjhzJck5/+tOf8NVXX+HkyZM4f/48vvnmG3Tu3Nlv5ywiIpIXBeciIiLFKLJqGVx3UwQio8v4/bliYmKwceNGE4gPGTIETZo0QdeuXbF8+XJMmjQJwc7lcmW7Pzw8HLNnz8aUKVPyfIxp06Zh69atWfa3bdsWS5YswR/+8Afceuut5nfy5ZdfolmzZj45dxERkYJScC4iIlIMwsuHosPD1+PBVxrgz8NuxIOvNjDXy5Tz30fx5MmTTca4RYsWmDdvHvbs2YMdO3ZgwoQJaNmyZb4z302bNjX7GOxTvXr1TPY9Pj4eFy9exPbt29GtWzdz+4oVK8wx586dM/eZPn26uR4SEoKhQ4di//79SExMxJYtW9CrV68sz8vBgw0bNiAlJQWtW7fO9hxHjx6NN954A9u2bcv19ffv3x+VK1fGuHHjstz2/PPP429/+5t5rr1792L48OHm9/Nf//Vf+frdioiI+FqYzx9RREREsmj959po3rkaLsRfRvzxFJSPDDPX6esPf/L581WpUsUEugw6GQxnxlLuwmLWndlrZp9ZWt64cWMTpLN8vGfPnmYgoEGDBkhISEBSUpK5z0svvYQHH3zQBMwMgnnfWbNm4dSpU1i1apXnsceOHYvBgwebID6vkvXcNGrUCKNGjcLtt9+O2NjYPI/n4EFkZKQZcBAREQkEBeciIiLFUMp+8+2VTWDOjdyXN7eIwndfnvRc95X69esjNDQUu3btgq8xcz537lyTMacDBw54bnMHt+653MRAnvPEO3bsiHXr1nnuw8x4v379MgTnDKiXLl1apPPj833yySemlJ8DBvkJzjkgULFiRXz22WdFem4REZHCUnAehGoAqAXAf610RESkOHF+ednyoSZj7i3pwhVE1y5rgndfB+fMBPsLG8xxvjcbqDGQZqCeW4k5BwoqVKhg5nhnDqI3b96cYR/LzIvq9ddfx86dO/HRRx/l6/g+ffrg5ZdfNh3rmckXEREJBM05DyIRAJ4D8B6ACQCeAfAsgPKBPjERESkSBt4pSemmlN0br6ckpuHCGd8G5sTS8fT0dDRs2LBA9+N9Mgf3ZcqUydJkjdnomTNnmiZzDKiffvrpHB+TGWnq3r27abjm3lgO37t37wzHsky+qNgA75577sHly5fNtmzZMrP/9OnTZr66t3vvvRfvv/8+/vznP3uOExERCQQF50GkLwB+RUkDcJhfkAD0BPBkoE9MRESKhMH3j9+eMxl0bmFlQjw//7j+vM+z5sT52osXL8bAgQMREcHh34xyWurMnTmuXbu2Z192HcyPHj2KqVOnmqZu48ePR9++/BQDUlNTs3RaZxM6LuPGcngu6ea98XF8jefEJnbuQYAnnnjC7G/Tpk2GLvX33XefaVjHzPmCBQt8fh4iIiIFobL2ICpl54qz/Ep0khkLNusBkAygPYCP7W0iIlIy/efT45455ixlZ8Z801enPfv9gYE51wNfv369mcvNJcXCwsLQqVMnDBgwwGSuM2Pncq6Fzgwzm8mxsdugQYMyHMNu7wsXLsTu3btN47n27dubMnI6dOiQyb7HxcWZgJcN4dgsjh3TeT/Og1+9erUZHGjVqpVpGjdjxowCva66deua9csZ7HMQgIG4+9yZeWczOW/Vql1tvMdzdM+DZ0D+4Ycf4rnnnsO3335r1oMnni/PSUREpLgpOA8S1QFUsBlzb+f4JcQG7wrORURKrsvJ6aYrO5u/mTnmZ35pDucvbLrWvHlzE2Qzu81sODPjXPucwXl2rly5YgJXzilnMP/dd99hxIgRmDNnjucYBsTMQNepU8cEsosWLTJLk9GxY8fM/G12XWdWmoH3o48+ipEjR5rnZtd2lsRzqbVNmzbhtddeK/DrGjNmDB555BHPdS7LRnfeeSdWrlyZr8d48sknTbk+l5vj5vaPf/zDnK+IiEhxU3AeJBh4c5ZdZZs5d+P1i5n2iYhIyeXdsb04/Pzzz3jmmWfMlt/mcd98840nG53dMc8+y44oOXv11VfNll0jOW7ZYVCd3yZ2DJ4LEkBn99jM9ouIiAQTzTkPEgy+v7YZdGbJw223dhbiLVfWXERERERE5JqmzHkQeddeciy/np13Ps9rv4iIiIiIiFybFJwHkSQAb9rmb+yR2wTAP233dhEREREREbl2KTgPQixhj7fZcxEREREREbn2ac65iIiIiIiISIApOBcREREREREJMAXnIiIiIiIiIgGm4FxEREREREQkwBSci4iIiIiIiASYgnMREZFSznEc9OjRI9CnISIiUqopOBcREbmG1axZExMnTsS+ffuQnJyMw4cPY/78+ejQoYNfnq9du3Ym2I+KioK/DBs2DGvWrMGlS5dw9uzZXI+Njo7GkSNHspxTq1atsHr1apw+fRqJiYnYuXMn/vrXv/rtnEVERPKidc5FRESKUWTVMoiMLoMLZy7jQvxlvz5XTEyMCWLPnTuHIUOGYNu2bShTpgy6dOmCSZMmoVGjRghmLpcLaWlpWfaHh4dj9uzZWLt2LR5//PFcH2PatGnYunUr6tSpk2E/A/u3337b3MafW7dujalTp5qf33vvPZ+/FhERkbwocy4iIlIMwsuH4q6Hr8NDr9THfcNj8dCr9c318HL++yiePHmyyRi3aNEC8+bNw549e7Bjxw5MmDABLVu2zHfmu2nTpmYfg32qV6+eyb7Hx8fj4sWL2L59O7p162ZuX7FihTmGAwK8z/Tp0831kJAQDB06FPv37zeZ6i1btqBXr15Znrdr167YsGEDUlJSTMCcndGjR+ONN94wgw256d+/PypXroxx48ZluY3P/89//tP8Pg4dOoSPPvoIixcvRps2bfL1uxUREfE1Zc5FRESKQZs/18JtXaoi4cxlxB9LRvnIMHOdln14zOfPV6VKFRPoDh8+3ATDmZ0/f77Qj82sO7PXbdu2NZnmxo0bmyCd5eM9e/Y0AwENGjRAQkICkpKSzH1eeuklPPjggyZg5iAB7ztr1iycOnUKq1at8jz22LFjMXjwYBPE51WynhtWBYwaNQq33347YmNj8zy+WbNmuOOOOzBixIhCP6eIiEhRKDgXEREphlL2hrdHmcDcXcruvuT+9V+e8nmJe/369REaGopdu3bB15g5nzt3rsmY04EDBzy3MZtOJ0+e9AwAMJDnPPGOHTti3bp1nvswM96vX78MwTkD6qVLlxbp/Ph8n3zyiSnl54BBbsE5b69evTrCwsJMRp5l8CIiIoGg4FxERMTPOMe8bITLZMy9JV24gujrypng3dfBOcvI/YUN5qZMmYLOnTubQJqBem4l5hwoqFChApYsWZIliN68eXOGfSxpL6rXX3/dNHhjqXpeWMZesWJFU+bPrP3evXtNubuIiEhxU3AuIiLiZwy8UxLTTCm7dxDO69zP5nC+xtLx9PR0NGzYsED3430yB/dsIueN2WXOz+7evbsJ0FmyPmjQINNgLTsMfonH//TTTxlu49xybyyTLyp2om/SpAl69+6d4bWwM/v//M//mAy528GDB80lqwDY2Z63KTgXEZFAUEM4ERERP2Pwvevb86hkO7WHlQkxl7zO/f7o2s752gygBw4ciIiIiCy357TUGeeAU+3atTPMx87s6NGjprs5m7qNHz8effv2NftTU1M9ndbd2HSNy7ixHJ5LunlvfBxf4zmxiR3Pm9sTTzzhyZJzvnxOOA2gbNmyPj8fERGR/FDmXEREpBj859OfPXPMWcrOjPmGxWc8+/2BgTmXUlu/fr2Zy81lwzi3ulOnThgwYIBp5JYZy7q5FjozyGwmx8ZuzIp7Y7f3hQsXYvfu3abxXPv27U0ZObHzObPvcXFxWLBggWkIx2Zx7JjO+zEA5vriHBzgWuNsGjdjxowCva66deua9csZ7HMQgIG4+9yZeWczOW/VqlUzlzxH9zz4p556yrxO95x8NqhjIzqW7IuIiASCgnMREZFikJqcbrqys/mbmWNeDOucs+la8+bNTZDN7Daz4cyMb9y40QTn2bly5Qr69Olj5pQzmP/uu+9MB/M5c+Z4jmFAzAw01w5ncL1o0SI8//zz5rZjx47h5ZdfNvO3uYwaA+9HH30UI0eONM/NEng2aONSa5s2bcJrr71W4Nc1ZswYPPLIIxmWRaM777wTK1euzNdjcJCAc9N/9atfmdfMLP6LL75oqgFEREQCxSktW2RkpEO8DPS55LW5XC4nLi7OXAb6XLTpfdam97m0bzExMc6MGTPMZV7HRkVFBfx8tfn330FsbKz+T5eCTX+7S8em97n0bK4Avtf5jUM151xEREREREQkwBSci4iIiIiIiASYgnMRERERERGRAFNwLiIiIiIiIhJgCs5FREREREREAkzBuYiIiIiIiEiAKTgXERERERERCTAF5yIiIiIiIiIBpuBcREREREREJMAUnIuIiJRyjuOgR48egT4NERGRUk3BuYiIyDWsZs2amDhxIvbt24fk5GQcPnwY8+fPR4cOHfzyfO3atTPBflRUFPxl2LBhWLNmDS5duoSzZ8/memx0dDSOHDmS6zndcccduHz5MjZv3uynMxYREcmbgnMREZFiVKlqGVx/U3lERof5/bliYmKwceNGE4gPGTIETZo0QdeuXbF8+XJMmjQJwc7lcmW7Pzw8HLNnz8aUKVPyfIxp06Zh69atOd7OgH3GjBlYtmxZkc5VRESkqBSci4iIFIPw8qHo9HAtPPpqLB4Y8Ss89j83muvh5fz3UTx58mSTMW7RogXmzZuHPXv2YMeOHZgwYQJatmyZ78x306ZNzT4G+1SvXj2TfY+Pj8fFixexfft2dOvWzdy+YsUKc8y5c+fMfaZPn26uh4SEYOjQodi/fz8SExOxZcsW9OrVK8vzcvBgw4YNSElJQevWrbM9x9GjR+ONN97Atm3bcn39/fv3R+XKlTFu3Lgcj3nnnXfw8ccfY+3atbk+loiIiL/5f9heRERE0O7PNdCia1UknLmMM8dSEBHpMtdpyYc/+/z5qlSpYgLd4cOHm2A4s/Pnzxf6sZl1Z/a6bdu2prS8cePGJkhn+XjPnj3NQECDBg2QkJCApKQkc5+XXnoJDz74oAmYOUjA+86aNQunTp3CqlWrPI89duxYDB482ATxeZWs56ZRo0YYNWoUbr/9dsTGxmZ7zCOPPGJu43mNGDGi0M8lIiLiCwrORUREiqGUvXHLKBOYJ8RfMfvcl9y/7svTuGCv+0r9+vURGhqKXbt2wdeYOZ87d67JmNOBAwc8tzGbTidPnvQMADCQ5zzxjh07Yt26dZ77MDPer1+/DME5A+qlS5cW6fz4fJ988okp5eeAQXbBOX8/HAho06YN0tLSivR8IiIivqDgXERExM84v7xshMtkzL0lXkhD1evKmuDd18E5y8j9hQ3mON+7c+fOJpBmoJ5biTkD4QoVKmDJkiVZgujMTdhY0l5Ur7/+Onbu3ImPPvoo29s5aMFS9pdfftlk8UVERIKB5pyLiIj4GQPvlMQ0U8rujde5nxl1X2PQmZ6ejoYNGxbofrxP5uC+TJkyWZqsMRs9c+ZM02SOAfXTTz+d42NWrFjRXHbv3h3NmjXzbCyH7927d4ZjWSZfVGyAd88995gO7Nzczd5Onz5t5qtHRkbid7/7Hd5++23PMczY85z4c/v27Yt8DiIiIgWlzLmIiIifMfjese68Z445M+YMzJkxX7/ojM+z5sT52osXL8bAgQNNpjvzvHM2fMtu3jnngFPt2rVNUzdi0JrZ0aNHMXXqVLO99tpr6Nu3rwl2U1NTs3RaZxM6LuPGcnjvEnZ/YaO58uXLe64zEGdjOpawc0k5zoX/9a9/neE+Tz31lAnqOVjgXaYvIiJSXBSci4iIFIOVn570zDFnKTsz5gzM3fv9gYE51wNfv369yQxzSbGwsDB06tQJAwYMMJnrzPbu3WvWQmeGmc3k2Nht0KBBGY5ht/eFCxdi9+7dpvEcM80sI6dDhw6Z7HtcXBwWLFhgGsKxWRw7pvN+LClfvXq1GRxo1aqVCZS5lFlB1K1b16xfzmCfgwDsJu8+d2be2UzOW7Vq1cwlz9E9IPHDDz9kOIZz5DmAkHm/iIhIcVFwHsQqA7gFwHFmMgJ9MiIiUiSpyemmKzubvzFjzmy6PzLm3pgBbt68uQmyx48fb7LhzIxz7XMG59m5cuUK+vTpY+aUM5j/7rvvTCfzOXPmeI5hQMyO7XXq1DHB9aJFi/D888+b244dO2bmcrPZGrPVDLwfffRRjBw50jw3u7azJJ5Z+U2bNpmse0GNGTPGdFp347JsdOedd2LlypWF+E2JiIgEB6ekbNddd50zc+ZM5/Tp005iYqKzdetW59Zbb833/SMjIx3iZaBfS25bBOCMcrmc7+PinFUul/MF4DwHOOWD4Ny0+XZzuVxOXFycuQz0uWjT+6wt5y0mJsaZMWOGuczr2KioqICfrzb//juIjY3V/+lSsOlvd+nY9D6Xns0VwPc6v3FoicmcV65c2ZTmLV++HN26dTOj7zfddFOR1kANRhEAZgFoy8wE5ylyXiCAe+3tbwb4/ERERERERMT3Skxw/uKLL5q1Sh977DHPvoMHD+Z6Hy7RUrZsWc91dmd1l+N5N6oJJoNsYJ7iciE9NBRpLheq29s6APhUJe7XFP475PzLYP33KL6h97nky+975+5wzkvH4SC4XIv0f7p00PtcOuh9Lj1cAXyv8/ucJSY4/+Mf/2i6zn722Wdo164dfvrpJ0yePBnvv/9+jvfhvDY2tMmMjXDYoCYY55hzQRm+dekuF8o2b470kBCEpKWhtl33rieAw4E+UfHpf1TOB+UX+bS0tECfjviJ3ueSjw3F2P2bg7xsZJYbruct1ya+//x3wK7vMTEx+j99jdPf7tJB73Pp4Qrge+29gsg1EZyzeQyb1/z97383zWO4LAqXhuGSLTl1eX399dfN8d4fqgzqlyxZggsXLiDYsPnbeVvKzox5uuPg4qJFcKWloYrNmM9T5vya+yPB7BqbKekD4dql97nkYyDGZbb42ZHd8mOZM+dskqbM+bWHU+w4uP+f//zHrJmu/9PXNv3tLh30PpcergC+1+4K7msmOGcJwoYNG0zHWXdnVq5R2r9//xyDcwbu7vVWvfHNCMb/fD9zXVo7x7wGd6SnIzQtDZFpaSabvsweI9cWLjkUrP8mxXf0Ppds+X3f3AG5AvNr/9+D/k+XDnqfSwe9z6VHeoDe6/w+HyulS4Tjx49jx44dGfZxvVKucXqt4Eq3X3O+uc2gc+QkGgBnza8C8EsNgIiIiIiIiFxLSkxwzk7tN998c4Z9DRo0wKFDh3AtdWovA6CcDcrDAZwD8DaABwAE3yx5ERERERERKVXB+YQJE9CyZUvT5O3GG29Enz598OSTT2LSpEm4VvQF0APAXptB32/L3LkpMBcREREREbl2lZjgnPPN//SnP5mgfPv27Rg5ciT++te/4uOPP8a1oIZdKu2ULW8/b+eXHwfQHvAspyYiIuJrnCPfoweHh0VERCRQSkxwTv/+97/xm9/8xrSib9y4ca7LqJU0DL4r2DJ2Ntqv4lXWXtHdIE5ERKSAatasaVY32bdvH5KTk3H48GHMnz/fdJ/3By53ymA/ryXnimLYsGFmutulS5dw9izry3IWHR2NI0eOZDkn93lm3vj7EhERCYQS0639WseMeTKAWwFUsnPP+eZUs+uaM5suIiJS0CXgGMSeO3cOQ4YMwbZt21CmTBl06dLFTAtr1KgRgn3Zm+w63IaHh2P27NlYu3YtHn/88VwfY9q0adi6dSvq1KmT7e3sX8Ol79xOntQnroiIBEaJypxfy/hVgIu+xdiMeaINznn9stY2FxG5ZlSqGobrbyqPyGj/j49PnjzZZINbtGiBefPmYc+ePWblE3cfl/xmvps2bWr2MdgnrpTC7Ht8fDwuXrxoppt169bN3L5ixQpzDAcEeJ/p06d71oAfOnQo9u/fj8TERLMkaq9evbI8b9euXc1UtpSUFLRu3Trbcxw9ejTeeOMNM9iQGy63yrXJx40bl+MxDMZPnDjh2bQMnoiIBIoy50Gihg3KD9nMOTu3XwHwk82is+xdAbqISMkVXj4Ud/65Bhq3rISyEaFISUzHjnUJWPHpSaQmp/v8+apUqWIC3eHDh5tgOLPz59ndpHCYdWf2um3btqa0nFPNGKSzfLxnz55mIMCdkU5KutrSlA1dH3zwQRMwc5CA9501axZOnTqFVau4YOhVY8eOxeDBg00Qn1fJem5YFTBq1CjcfvvtiI2NzfE4DhKULVvWDDAw6P/mm28K/ZwiIiJFoeA8SFS3S6httHPPo+z2s11WjcG7gnMRkZKLgXmLrtFIOJOKM8cuIyLSZa7TVx/yr71v1a9fH6Ghodi1a5fPH5uZ87lz55qAlg4cOOC5jdl0d0baPQDAQJ7zxDt27Ih169Z57sPMeL9+/TIE5wyoly5dWqTz4/N98sknppSfAwbZBefHjx83z80sPYPzJ554wmT9Gcxv3ry5SM8vIiJSGArOg3DOuTswL2dL2i8BuNsuraYl1URESmYpOzPmDMwT4lkXBc8l96/98jQu2Ou+wjJyf2GDuSlTpqBz584mkGagnluJOQcKKlSogCVLlmQJojMHwgyWi+r111/Hzp078dFHH+V4zO7du83mxvnrXKr1+eefx0MPPVTkcxARESkozTkPwjnnlW2JO79WlQXAYseuAJ4M9EmKiEihREaXMaXsiRcyNjfjde6vVJUTmHyLpePp6elo2LBhge7H+2QO7tlELnOTNWajZ86ciSZNmpiA+umnn87xMStW5LojQPfu3dGsWTPPxnL43r17ZziWZfJFxU7099xzDy5fvmy2ZcuWmf2nT582pes5Wb9+vRlIEBERCQQF50E25/wnW87AN4Zfjy7Y7DkLA7XeuYhIyXQh/rKZY85Sdm+8zv0JZ/iX3rc4X3vx4sUYOHAgIiLYySSjnJY64xxwql27tmcfA+nMjh49iqlTp5qmbuPHj0ffvn3N/tTUVE+ndTc2oeMybiyH55Ju3hsfx9d4Tmxi5x4EYMk6tWnTxsyXzwmPZbm7iIhIIKisPUhU92oCx68zjt14nfmKVNsoTnPPRURKnoQzV0zzN/ccc2bMGZhXqhqO9YvifV7S7sbAnEupMSPMudxcUiwsLAydOnXCgAEDTOY6s71795q10JlhZjM5NnYbNGhQhmPY7X3hwoWmLJyN59q3b2/KyOnQoUMm+x4XF4cFCxaYhnBsFseO6bwf58GvXr3aDA60atXKNI2bMWNGgV5X3bp1zfrlDPY5CMBA3H3uzLyzmZy3atW4MCnMObrnwT/33HNm3vsPP/yAcuXKmQCeGXeW6ouIiASCgvMgwYCbXx2uB5BmN745zGtcsln1i1rvXESkxGJXdvcc86rXhZmMOQNz935/YPDZvHlzE2Qzu81sODPjGzduNMF5dq5cuYI+ffqYOeUM5r/77juMGDECc+bM8RzDgJgZaK4dzuB60aJFZq42HTt2DC+//LLpus5l1Bh4P/rooxg5cqR5bnZtZ0k8l1rbtGkTXnvttQK/rjFjxuCRRx7J0HGd7rzzTqxcuTJfj8H57vydXH/99aabPV8rG9a5l4ITEREJBKe0bJGRkQ7xMtDnknmrAThbAecU4JwGnEsul5MUF+ckuVzOOcBZBzjPBcF5avPt5nK5nLi4OHMZ6HPRpvdZW85bTEyMM2PGDHOZ17FRUVG53h4ZHeZcf1N5cxno16WtcP8OYmNj9X+6FGz62106Nr3PpWdzBfC9zm8cqsx5EJW1n2Gpo82eh9vy9mRb0r4cwLuBPkkRESkylrD7q4xdRERESi4F50FU1s7y9Wg7bJJog/LTnL8H4O9aRk1EREREROSapW7tQbiUWlmvxnC1bNCuJnAiIiIiIiLXLmXOgwS7sFewPzN77l5dlk3gytuydwXoIiIiIiIi1yZlzoMEg+9mNhBPsdnyy/Z6cxu8i4iIiIiIyLVJwXmQqGrXMU+zQbljS9t5PRJA5UCfoIiIiIiIiPiNytqDRBUbiJfxKm9naXu6Dda5BrqIiIiIiIhcm5Q5DxJ7bXDu/YaE2OsM0HcH8NxERERERETEvxScB9lSau5MOQN19xvE8oaedv65iIiIiIiIXHsUnAdRQ7g9AI7bYJzl7bAB+yUbnD8Z4HMUEZFrk+M46NGjR6BPQ0REpFRTcB5EmfMLNhBP82oMl2r3MWhvb4N4ERGR/KpZsyYmTpyIffv2ITk5GYcPH8b8+fPRoUMHvzxfu3btTLAfFRUFfxk2bBjWrFmDS5cu4ezZs7keGx0djSNHjmR7TuHh4Xj11Vdx8OBB87s5cOAAHn30Ub+dt4iISG7UEC5InLQB+Y32TWG3dheAKBu08/Z6dkk1rXcuIiL5ERMTY4LYc+fOYciQIdi2bRvKlCmDLl26YNKkSWjUqBGCmcvlQlqae6JXxqB69uzZWLt2LR5//PFcH2PatGnYunUr6tSpk+W2zz77zAxe8DH27t2L2rVrIzRUeQsREQkMfQIFCQbd9W0TOHcjOPfPUfb2izZIFxGRkiuqahjq3FQOlaL9Pz4+efJkkzFu0aIF5s2bhz179mDHjh2YMGECWrZsme/Md9OmTc0+BvtUr149k32Pj4/HxYsXsX37dnTr1s3cvmLFCnMMBwR4n+nTp5vrISEhGDp0KPbv34/ExERs2bIFvXr1yvK8Xbt2xYYNG5CSkoLWrVtne46jR4/GG2+8YQYbctO/f39UrlwZ48aNy3IbByj4nH/4wx+wbNkyHDp0COvWrcM333yTr9+tiIiIrylzHiQa2sx4ui1lL5MpOG8G4A1lzUVESqyy5UPR4d5quKVlJMpFuJCcmIYf1l3Asn+eRmoy//r7VpUqVUygO3z4cBMMZ3b+/PlCPzaz7sxet23b1pSWN27c2ATpLB/v2bOnGQho0KABEhISkJSUZO7z0ksv4cEHHzQBMwcJeN9Zs2bh1KlTWLVqleexx44di8GDB5sgPq+S9dywKmDUqFG4/fbbERsbm+X2P/7xj2YQ4L//+7/xl7/8xbwODjiMHDnSlLiLiIgUNwXnQbTOubsJnMtmzlnaDq9MuoiIlFwMzG/vGo2EM5dx+lgqIiJd5jot/Ifv66Lq169vSrR37drl88dm5nzu3LkmY06cq+3GbDqdPHnSMwDAQJ7zxDt27Giy0+77MDPer1+/DME5A+qlS5cW6fz4fJ988okp5eeAQXbBOffx+RmI/+lPf0K1atVMpUHVqlXx2GOPFen5RURECkPBeZA4m+kN8Q7MHdsUrh2AD5U9FxEpkaXszJgzME+Iv2L2uS9vuT0Sa+bHe677CsvI/YUN5qZMmYLOnTubQJqBem4l5hwoqFChApYsWZIliN68eXOGfcxmF9Xrr7+OnTt34qOPPsrxGA5csIz+gQceMBl+euGFFzBnzhw89dRTyp6LiEixU0I2SJwBkGIDcXc5uzsw51YZQAM791xEREqWyOgwU8qeeCFjczNeL1fBhUpVfT9WztLx9PR0NGzIiVP5x/tkDu7ZRC5zkzVmnmfOnIkmTZqYgPrpp5/O8TErVqxoLrt3745mzZp5NpbD9+7dO8OxLC8vKnaiv+eee3D58mWzcU45nT592sxXp+PHj+Onn37yBObEgJ5Be3bN40RERPxNwXkQOe0VnHtjLoVflSLtpYiIlCwX4q+YOeYsZffG68mX0pBwxrdZc+J87cWLF2PgwIGIiIjIcntOS51xDjixc7kbA+nMjh49iqlTp5qmbuPHj0ffvn3N/tTUVE+ndTc2oWMmmuXwXNLNe+Pj+BrPiU3s3IMATzzxhNnfpk0bM1+e2MX+uuuuMxl9N86TZ3d4f5yTiIhIXhScBwl+FSqfaa555vnmXFJNb5iISMlz/swV0/ytUtUypkt7WJkQc8nrP3x7wecl7W4MzBkkr1+/3jRqY3k5M+nPPPOMWYYsO1xSjGuhM8PM49nNfNCgQRmOYbd3lrTfcMMN+O1vf4v27dubrDOx6zmz73FxcWYeN4NfNotjx3Te76GHHjJZd96P2XZeL6i6deua4JvBPl8ff+bmDrTZTO6HH37wbO458TxH9+DDxx9/jDNnzphu8mwex8D9b3/7Gz744AOVtIuISEAo1gsS1ZlBsVnyNJshT7eBeqidk75bS6mJiJRY7Mr+7aJ4cBntateFm0te535/YVDavHlzLF++3GS32cCN877vuusuDBgwINv7XLlyBX369DFBPNcHf/HFFzFixIgMxzAgZgaawe6iRYuwe/duM0+bjh07hpdfftl0XT9x4gTefvtts59d0F955RXTtd19P5a5ezeTy68xY8aYpdh4GRkZaX7mdtttt+X7MVg+36lTJ7PUGsvyOT/9yy+/xLPPPlvg8xEREfEVp7RskZGRDvEy0OeSeesBOPGAcwJwkgHnisvlXImLc5JcLicFcHYAznNBcJ7afLu5XC4nLi7OXAb6XLTpfdaW8xYTE+PMmDHDXOZ1bFRUVK63V4oOc+rcVM5cBvp1aSvcv4PY2Fj9ny4Fm/52l45N73Pp2VwBfK/zG4eqW3uQ2GvXNy9ns+ehtqTdZTPpnwN4N9AnKSIiRcYSdn+VsYuIiEjJpbL2IPGDLVlnP9uymYJzFvwNA5AU6JMUERERERERv1BwHiS4RFq4V0Ae6vXmhNo56SIiIiIiInJtUnAeJLgKbWw2y6iF2P0FW6VWREREREREShIF50HiNzZjnh3ub1LM5yMiIiIiIiLFR8F5kKidTdbcjfuvK+bzERERERERkeKj4DxI/JzH7ceK6TxERERERESk+Ck4DxKH7eJ2OfktgPLFeD4iIiIiIiJSfBScB4mzAC7lEKBzNdzbATwZgPMSERERERER/1NwHiR2Abhog/PMWyqAIwDaa0k1ERHxA8dx0KNHj0CfhoiISKmm4DyInLfBeIjX5g7OeVtFux66iIhIftWsWRMTJ07Evn37kJycjMOHD2P+/Pno0KGDX56vXbt2JtiPioqCvwwbNgxr1qzBpUuXcPYsa89yFh0djSNHjmQ5p+nTp5t9mbft27f77bxFRERyo+A8SFS3W+Y3hNcr2W7tzKyfDND5iYhIyRMTE4ONGzeaQHzIkCFo0qQJunbtiuXLl2PSpEkIdi5X9ouMhoeHY/bs2ZgyZUqejzFt2jRs3bo1y/7nnnsOtWrV8mx16tTBmTNnzOOKiIgEgoLzIFEVQOUcllMLteugLwdwKgDnJiIivhNVNQx1byqHStFhfn+uyZMnm2xwixYtMG/ePOzZswc7duzAhAkT0LJly3xnvps2bWr2MdinevXqmex7fHw8Ll68aLLN3bp1M7evWLHCHHPu3DlzH2aoKSQkBEOHDsX+/fuRmJiILVu2oFevXlmel4MHGzZsQEpKClq3bp3tOY4ePRpvvPEGtm3bluvr79+/PypXroxx48ZluS0hIQEnTpzwbLfddhuqVKniOV8REZHi5v9vBpIvTXNZ55wqAPi2GM9HRER8q2z5UHS6typ+3bIiylUIRfKldGxfdxFf/fM0UpNzW6+jcBhoMtAdPny4CYYzO3+eE6YKh1l3Zq/btm1rSssbN25sgnSWj/fs2dMMBDRo0MAEwElJSeY+L730Eh588EETMHOQgPedNWsWTp06hVWrVnkee+zYsRg8eLAJ4vMqWc9No0aNMGrUKNx+++2IjY3N8/jHH38cS5cuNWX/IiIigaDgPEjk9bWM2fPHAawrpvMRERHfYmD++65ROB9/Bad+SkWFSi5znf71D9/XRdWvXx+hoaHYtYstR32LmfO5c+d65mcfOHDAcxuz6XTy5EnPAAADec4T79ixI9atW+e5DzPj/fr1yxCcM6BmkFwUfL5PPvnElPJzwCCv4Lx27dom83///fcX6XlFRESKQsF5kGA39tykAfiVnZeu0nYRkZJXys6MOQPz82e4QCY8l9y/av5ZJMRfve4rLCP3FzaY43zvzp07m0CagXpuJeYcKKhQoQKWLFmSJYjevHlzhn0saS+q119/HTt37sRHH32Ur+MffvhhU4b/+eefF/m5RURECktzzoNE3TxuZzM4Urd2EZGSh/PLWcp+KYFDrb/g9bIRoSZ49zWWjqenp6Nhw4YFuh/vkzm4L1OmTJYma8xGz5w50zSZY0D99NNP5/iYFStyvRGge/fuaNasmWdjOXzv3r0zHMsy+aJiA7x77rkHly9fNtuyZcvM/tOnT5v56pk99thj5rXwWBERkUBRcB4k8spvHFO3dhGREotZcc4xZym7N15PSUz3ZNF9ifO1Fy9ejIEDByIiIiLL7TktdcY54O5SbzcG0pkdPXoUU6dONU3dxo8fj759+5r9qampWTqtswkdl3FjOTyXdPPe+Di+xnNiEzv3IMATTzxh9rdp0yZLl3o2orvpppvMgIOIiEggqaw9SHzvNe88c6Du3q9u7SIiJRODbzZ/c88xZ8acgXlUdBjWLjrv85J2NwbmXA98/fr1Zi43lxQLCwtDp06dMGDAAJO5zmzv3r2mKRozzGwmx8ZugwYNynAMu70vXLgQu3fvNo3n2rdvb8rI6dChQyb7HhcXhwULFpiGcGwWx47pvB/nwa9evdoMDrRq1co0jZsxY0aBXlfdunXN+uUM9jkIwEDcfe7MvLOZnLdq1aqZS55j5kZ4bATHefA//PBDgc5BRETE15Q5DxJnACTmkkEPBzCzmM9JRER8h13ZGYiHhoag2nXh5pLXud9f2HStefPmZl1zZrfZwI3zvu+66y4TnGfnypUr6NOnjymHZzD/4osvYsSIERmOYUDMDDSD3UWLFpkg/amnnjK3HTt2DC+//LLpus4lyt5++22zf+TIkXjllVdM13b3/Vjm7t1MLr/GjBljlmLjZWRkpPmZG5dDK4hKlSqZLLuy5iIiEiyc0rJFRkY6xMtAn0vm7RbAOQk4lwEnnZvL5aTHxTlXXC7nCuCcBZzhQXCe2ny7uVwuJy4uzlwG+ly06X3WlvMWExPjzJgxw1zmdWxUVFSut1eKDnPq3lTOXAb6dWkr3L+D2NhY/Z8uBZv+dpeOTe9z6dlcAXyv8xuHKnMeRJKzyZzzOosd2UKog+3WLiIiJRdL2I/sSfZbKbuIiIiUTArOg8QpW7ruDs45dAJ7nS11kmyDAHVrFxERERERufYoOA8SzIiX4xI2XkG54/UmcT76WXVrFxERERERuSYpOA8S9W1g7g7IMwfoldStXURERERE5Jql4DxI7AVQ3pauu4N0x6vMnQu//L8An6OIiIiIiIj4h9Y5DyIhOYyYMFiPBxAZgHMSERERERER/1PmPIjK2nNa4zzEvlGaby4iIiIiInJtUnAeRFyZ5px7B+ffa765iIiIiIjINUvBeZDJKXu+rJjPQ0RERERERIqPgvMSEJjTpWI8DxERKV0cx0GPHj0CfRoiIiKlmoLzIFE3j6D9cdvNXUREpCBq1qyJiRMnYt++fUhOTsbhw4cxf/58dOjQwS/P165dOxPsR0VFwV+GDRuGNWvW4NKlSzh79myux0ZHR+PIkSPZntP999+PLVu2mMc5duwYpk2bZo4XEREJBAXnQaJSHpnzNgCeLMbzERGRki8mJgYbN240gfiQIUPQpEkTdO3aFcuXL8ekSZMQ7FwudmPJKjw8HLNnz8aUKVPyfAwG3Fu3bs2y/4477sCMGTPM7bfccgvuuecetGjRAu+9955Pzl1ERKSgFJyXEGUAdAFQPdAnIiIiRRJVNQz1GpQzl/42efJkkzFm0Dlv3jzs2bMHO3bswIQJE9CyZct8Z76bNm1q9jHYp3r16pnse3x8PC5evIjt27ejW7du5vYVK1aYY86dO2fuM336dHM9JCQEQ4cOxf79+5GYmGgy1r169cryvBw82LBhA1JSUtC6detsz3H06NF44403sG3btlxff//+/VG5cmWMGzcuy22///3vcfDgQbz11lvmkpn4qVOnmt+ViIhIIGid8yBRIR/HVAFQQ13bRURKpLLlQ9H5vmg0aVkR5SJCkZyYjm3rLmLxJ2eQmpzdWh1FU6VKFRPoDh8+3ATDmZ0/f77Qj82sO7PXbdu2NSXhjRs3NkE6y8d79uxpBgIaNGiAhIQEJCUlmfu89NJLePDBB03AzEEC3nfWrFk4deoUVq1a5XnssWPHYvDgwSaIz6tkPTeNGjXCqFGjcPvttyM2NjbL7WvXrsVrr71mBhUWLlyIGjVqoHfv3liwYEGhn1NERKQoFJwHiR/tMmo5lbanA+BXFK11LiJSMjEwv6NrFM7Hp+HUscuoUMllrtOX00/7/Pnq16+P0NBQ7Nq1y+ePzcz53LlzTcacDhw44LmN2XQ6efKkZwCAgTzniXfs2BHr1q3z3IeZ8X79+mUIzhlQL126tEjnx+f75JNPTCk/BwyyC86/+eYbPPDAA/j0009Rrlw5lClTxlQDDBw4sEjPLSIiUlgqaw8SiwCk5HI7g/OvlTUXESmRWMLOjDkD8/NnruDKZcdc8jr3+6PEnWXk/sIGcyNGjMDq1atNiTnnsuc1UFChQgUsWbIEFy5c8GwPPfQQbrzxxgzHsqS9qF5//XXs3LkTH330Ua6Z9TfffBNjxozBrbfeii5duuCGG27AO++8U+TnFxERKQwF50Hkcg77mVHXUmoiIiUXg2+Wsl9KSMuwn9e53x/BOUvH09PT0bBhwwLdj/fJHNwzq+yNTdSYjZ45c6YJzBlQP/300zk+ZsWKFc1l9+7d0axZM8/GcniWkntjmXxRsQEeG7xdvnzZbMuWLTP7T58+bQYT3GX2nGfO+eicu/7VV1/hqaeewuOPP45atWoV+RxEREQKSsF5kPi9XSotu1mH/Hp0AgDb4qghnIhIycMsOeeYs5TdG69zP2/3Nc7XXrx4sSnTjoiIyHJ7TkudcQ441a5d27OPgXRmR48eNQ3U2NRt/Pjx6Nu3r9mfmpqapdM6m9BxGTeWw3NJN++Nj+NrPCc2sXMPAjzxxBNmf5s2bTxd6vk7cQ9EuKWlpfm96kBERCQnmnMeJOrxi0wuc84bAbiohnAiIiUSg282f3PPMWfGnIF5VLQL3yw675fgnBiYMzu8fv16M5ebS4qFhYWhU6dOGDBggMlcZ7Z3716zFjozzGwmx8ZugwYNynAMu72zidru3btN47n27dubMnI6dOiQCXrj4uJMczU2hGOzOGaoeT/Og2c5PAcHWrVqZZrGcUmzgqhbt65Zj5zBPgcBGIi7z52ZdzaT81atWjVzyXN0z4P/8ssvzbJpbFDHQQwORrAD/Lfffovjx48X8DctIiJSdArOg0RePXOZ87heDeFEREosdmUnzjGvfl0ZkzFnYO7e7w9suta8eXMTZDO7zQCUmXGufc7gPDtXrlxBnz59zBriDOa/++47M798zpw5nmMYEDMDXadOHRNcL1q0CM8//7y57dixY3j55ZdN13Uuo8bA+9FHH8XIkSPNc7OcnCXxXGpt06ZNpmN6QXGe+COPPOK5zmXZ6M4778TKlSvz9RgffvghIiMjTTk+fzc8n6+//hovvvhigc9HRETEV5zSskVGRjrEy0CfS+atB+CkAU66e3O5nPS4uKuXdt9hwKkeBOeqzXeby+Vy4uLizGWgz0Wb3mdtOW8xMTHOjBkzzGVex0ZFReV+e9Uwp16DcuYy0K9LW+H+HcTGxur/dCnY9Le7dGx6n0vP5grge53fOFSZ8yDi5FLWztsuALiDy7+otF1EpMQyXdr9VMYuIiIiJZcawgUJrmGesS1NVpwx9wKA9wE8ZxvIiYiIiIiISMmn4LyEvBnMqCcC2MdusgC48MyTxXhuIiIiIiIi4j8KzoPEr/JxzCEuUWObwrGsvb2WVhMREREREbkmKDgvAfPN3bxnKJ4DUNEurSYiIiIiIiIlm4LzIFpKjQF6bsp6/VzZrnuupdVERERERERKPgXnJaQhnGPnmofbbDnL2Zera7uIiIiIiMg1QUuplSA1bQCfAGAOgHcDfUIiIiIiIiLiEwrOg0SVPG4Pscd8DuDvypiLiIiIiIhcU1TWHkRl7Xm9GeUANCum8xERkdLDcRz06NEj0KchIiJSqik4DyIh+bidjeDUoV1ERPKrZs2amDhxIvbt24fk5GQcPnwY8+fPR4cOHfzyfO3atTPBflRUFPxl2LBhWLNmDS5duoSzZzm8nbPo6GgcOXIk23N66qmnsGPHDiQmJmLXrl34y1/+4rdzFhERyYvK2oPEb/IRnKfbJdTUoV1ERPIjJibGBLHnzp3DkCFDsG3bNpQpUwZdunTBpEmT0KhRIwQzl8uFtDS2Q80oPDwcs2fPxtq1a/H444/n+hjTpk3D1q1bUadOnQz7+/fvj9dffx19+/bFd999hxYtWuC9994zwf6//vUvn78WERGRvChzHiQq5eOYSwAWab65iEiJVrmaCzENyiKqqsvvzzV58mSTMWbgOW/ePOzZs8dkiidMmICWLVvmO/PdtGlTs4/BPtWrV89k3+Pj43Hx4kVs374d3bp1M7evWLHCHMMBAd5n+vTp5npISAiGDh2K/fv3m0z1li1b0KtXryzP27VrV2zYsAEpKSlo3bp1tuc4evRovPHGG2awITcMwCtXroxx48ZluY1Z8qlTp+Kzzz7DgQMH8Omnn+Ldd9/Fiy++mK/frYiIiK8pcx4kjuZxO5dS+xHAzGI6HxER8a2y5UPQ9b4q+M3vK6B8RCiSEtOxde0lLPrkLFKS+Vfet6pUqWIC3eHDh5tgOLPz588X+rGZdWf2um3btqa0vHHjxiZIZ/l4z549zUBAgwYNkJCQgKSkJHOfl156CQ8++KAJmDlIwPvOmjULp06dwqpVqzyPPXbsWAwePNgE8XmVrOeGVQGjRo3C7bffjtjY2Cy3ly1b1pT5e+O5ciAjLCwMV65cKfRzi4iIFIaC8yCR11ekFAAVONIP4M1iOicREfEdBuatu1XCuTNXcPKny6hQKdRcpy+mx/v8+erXr4/Q0FAzl9rXmDmfO3euyZgTM89uzKbTyZMnPQMADOQ5T7xjx45Yt26d5z7MjPfr1y9DcM6AeunSpUU6Pz7fJ598Ykr5OWCQXXC+ePFiPPHEE/j888+xadMm3HrrreY671utWjX8/PPPRToHERGRglJwHiQa53E7cypnALQH8LFK20VESlwpOzPmDMzPn7k6h9p9yf0r5p/3XPcVlpH7CxvMTZkyBZ07dzaBNAP13ErMOVBQoUIFLFmyJMN+BsKbN2/OsI8l7UXFueQ7d+7ERx99lOMxr7zyCmrVqmUGC/i7OnHiBD788ENT1p6ezi4vIiIixUtzzoNE83yMojB7XlHd2kVESpyo6DBTyn4pIWPQx+vlIkJRuarvx8pZOs4gs2HDhgW6nzsw9Q7u2UQuc5M1ZqNnzpyJJk2amID66aefzvExK1bkpxfQvXt3NGvWzLOxHL53794ZjmWZfFGxE/0999yDy5cvm23ZsmVm/+nTp818dWJJO5vJRURE4IYbbjDVAAcPHjSl+Cy1FxERKW4KzoNExllvWfErUiSAi+rWLiJS4pyPv2LmmLOU3RuvJyemm4y6r3G+Nku3Bw4caALQzHJa6swdmNauXduzj4F0ZkePHjUN1djUbfz48abrOaWmpno6rbuxCR2DYQbAXNLNe+Pj+BrPiU3s3IMALFenNm3amPny3ji3/KeffjKDEvfdd5/p1M7GdCIiIsVNZe0lBL8mVAXAxV00ni8iUrKcO51mmr+555gzY87AnBnz1QsTfF7S7sbAnEuprV+/3szl5pJibHbWqVMnDBgwwGSuM9u7d69ZC50ZZjaTY2O3QYMGZTiG3d4XLlyI3bt3m8Zz7du3N2XkdOjQIRPoxsXFYcGCBabJGpvFsWM678d58KtXrzaDA61atTKZ6hkzZhToddWtW9esX85gn4MADMTd587MO5vJeeMccuI5uufB33TTTab527fffmtewwsvvIBf//rXePjhhwv4WxYREfENBedBIq9SdeYf5gJ4t5jOR0REfItd2d1zzKtfX8ZkzBmYu/f7A5uuNW/e3ATZzG4zG87M+MaNG01wnh1mkvv06WPmlDOY5xrgI0aMwJw5czzHMCBmBpprhzO4XrRoEZ5//nlz27Fjx/Dyyy+brutcRo2B96OPPoqRI0ea52bXdpbEc6k1NmJ77bXXCvy6xowZg0ceecRzncuy0Z133omVK1fm6zH4GjjocPPNN5vS9+XLl+OOO+4wgwsiIiKBTMqWii0yMtIhXgb6XDJvMwEn3XtzuZz0uLirl4CTCjiNguA8tfl2c7lcTlxcnLkM9Llo0/usLectJibGmTFjhrnM69ioqKjcb6/qcmIalDWXgX5d2gr37yA2Nlb/p0vBpr/dpWPT+1x6NlcA3+v8xqGacx4krq4Cm3vmfDiA8sV0PiIi4h8sYT+0O8VvpewiIiJSMik4DxJV8nHMbwE8WQznIiIiIiIiIsVLwXmQKJuPY9ixvSOA6sVwPiIiIiIiIlJ8Smxw/uKLL5qlTtj59VrAZdLyCsx/BeBmrXMuIiIiIiJyzSmRwfltt92Gfv364fvvv8e14kI+jgkHUAtAejGcj4iIiIiIiBSfEreUWoUKFfDRRx+hb9++ZmmX3ISHh6Ns2V8KxiMjIz3Lp3ALJj8AiPPewfMLDb166ZU956upCWB3IE5SfI7/Drnmb7D9exTf0vtc8uX3vQsJCfFcsrpLrk36P1066H0uHfQ+lx6uAL7X+X3OAgfnf/nLX/Dpp58iNTU1w/4yZcrgvvvuw8yZM+FPXFf13//+N5YtW5ZncM61VEePHp1lf6dOnZCUlFd/9OLVJvMOvoHNm/MbHpD2S0dfvq3/BaBicZ+g+O0/Ktcg5hf5NK/3Wa4tep9LvmrVqqF8+fJmkDcqKirPQWS5NvH957+DNm3aICYmRv+nr3H621066H0uPVwBfK/52eGX4Hz69OlYtGgRTp06leUDi7f5Mzi/9957zS/0d7/7Xb6Of/311/H3v/89wzn+9NNPWLJkCS5cyE8hefH55Sy9gnNmXRYtyhCcMyfTCsBRAO/lYwk2Cf4/Esyu8f+UPhCuXXqfSz4GYh06dDCfHefPn88zc56QkKDM+TWocuXKZnD/P//5D06fPq3/09c4/e0uHfQ+lx6uAL7X7gpunwfnOZXq1alTJ9cvLEXFx3/zzTdN1jslJSVf92F2P3OGn/hmBNt/vrOh2UwmT0+/Gph7nesVAHz1Pe3hbxb7mYqvpaenB+W/SfEtvc8lW37fN/fnowLza//fg/5Plw56n0sHvc+lR3qA3uv8Pl++g/NNmzaZLxvcWFJ+5QrDxF9GIX71q1+ZUQh/ufXWW1GzZk1zHm5hYWFo27Ytnn76aTO3nL/skmp9aDh+l551ICEzft07aLu7twfwMYCMNQwiIiIFw8/2u+++G1988UWgT0VERKTUyne39s8//9x8aDNzvnjxYvOze/vnP/9puqc/+OCDfjtRDgj8+te/RrNmzTzbd999Z5rD8eeSHJhThSvp+X7DygE4Z+eda1k1ERHJDQe2J06ciH379iE5ORmHDx/G/PnzTZm+P7Rr184E+3nNzS+KYcOGYc2aNbh06RLOnj2b7THuhIL3xulxmc9148aN5veyZ88ePPzww347ZxEREZ9lzseMGWMuDx48aBrC5be03FcuXryIH35gT/Nf8EP5zJkzWfaXROGmYD136XarxAZ8/J0AOFksZyciIiV1rjyD2HPnzmHIkCHYtm2baeDapUsX02C1UaNGCGaszMuuFJCrscyePRtr167F448/nuP9H3nkkQxVffw9uN1www2mwew777yDBx54AHfddRfef/99HD9+HF999ZUfXo2IiIiP1zmfMWOGCcz54X799dejbt26GTYpnCMReXfwC7Hd2n9l1ztfrpJ2EZESp3I1F2IalEVUVf8v5TJ58mSTMW7RogXmzZtnssM7duzAhAkT0LJly3xnvps2bWr2MdinevXqmex7fHy8GTzfvn07unXrZm5fsWKFJxDmfdgsllh5N3ToUOzfvx+JiYnYsmULevXqleV5u3btig0bNpjvGq1bt872HLkSyxtvvGEGG3LDczhx4oRn804s9O/fHwcOHMDgwYOxa9cuM1gxZ84cPP/88wX6HYuIiPhKgRvC1a9fHx988AHuuOOObBvFcR54cWnfnrOurw3H6lYDfjySZ3DO/Ho1ADs41QDALTZ7riBdRCS4lS0fgm73VUbT31dA+YhQJCWm4/u1l7Dwk3NISfZ9A7kqVaqYQHf48OEmGM6sKE1cGcgye82+L6xia9y4sQnSjxw5gp49e5qBgAYNGpiu9e6lS7m8Kae/MSjmIAHvO2vWLLP6y6pVqzyPPXbsWBMwM4jPqWS9IOfJbDgfixly90AB/f73v8fSpUszHM9pewz6RUREAqHAkfQ//vEP0wwuLi7OlH6pI61vhKddXX4nNyxp/47l/BwkAfCOLW/n9a8BvKul1UREghYD8zbdKuHcmSs4+dNlVKgUaq7T59OLFoTmNJgeGhpqssK+xsz53LlzTcacmIF2YzadTp486RkAYCDPeeIdO3bEunXrPPdhZpw9a7yD81GjRmUJmgtj5MiR+Prrr83AROfOnU0VQcWKFfHWW2+Z22vVqmWy6d54nRUD5cqVM/PQRUREgjo4Z/M1dk7/8ccf/XNGpVTVY7/Mg8sJZ93F25J2TiDgV4rDLJEE0Nseo6XVRESCs5SdGXMG5ufPXJ1D7b7k/uXzEzzXfcW95ro/sMHclClTTNDLQJqBem4l5hwoqFChApYsWZJhP4P2zZs3Z9jHknZfePXVVz0/s4Sez8959+7gXEREpMTPOedctWrVWFgtvhSWkpqvN6ue3VigeIxruXuVtbPIv3qxnK2IiBREVHSYKWW/lJBxZQ5eLxcRispVfT8ljKXjXMmkYcOGBbqfe/UT7+CefWa8TZs2DbGxsZg5cyaaNGliAmoua5oTZqype/fuGVZdYTl8797u4eWrWCbvD99++63pjcMBAfr5559NJ3tvvM5sv7LmIiJSIoLzF198Ef/3f/9nGrdER0cjMjIywyaFc7xM3l/M+HUpll/y2EAuUwm7llYTEQle5+OvmDnmLGX3xuvJiekmo+5rnK/NOdQDBw5ERERElttzWuqMc8Cpdu3ann0MpDM7evQopk6dapq6jR8/Hn379jX7U1NTPZ3WvQf2GfCyHJ5LunlvfJziwNfAknv3+bHTOzu0e+vUqZPZLyIiEggFHqp3zwPjuuOBbgh3Ldmcnr8vZpftZebZiSxt19JqIiLB6dzpNNP8zT3HnBlzBubMmP9noe9L2t0YmHMptfXr15u53Fu3bjWf0wxCBwwYYDLXme3du9eshc6O6Gwmx8ZugwYNynAMu70vXLgQu3fvNo3n2KB1586d5rZDhw6Z7Dt70yxYsMA0hGOzuHHjxpn7cR786tWrzeBAq1atTNM4rgRTEMyAM0HAYJ+DAOwm7z53Zt753MyCc347BwX4ejnnnefgxgZxzPb/7//+r2l0y3Xf//znP5vsvoiISCCEleYO6cGkWao77M4ZC/3+Yzu017CZ83M2MGc5+xx1bRcRCVrsyu6eY179+jImY87A3L3fH9h0rXnz5ibIZnab2XBmxjdu3GiC8+yw6WufPn3MnHIG89999x1GjBhhlhlzY0DMTuh16tQxwTXXEncvQXbs2DG8/PLLpus6u6Mz8H700UdNgzY+N7u2sySey5xt2rQJr732WoFf15gxY8wa5t5zyunOO+/EypUrcfnyZTMwwcEAJg8YtL/wwgt47733PPc5ePCgCcR5zHPPPWcy+E888YTWOBcRkYBySssWGRnpEC8DfS6Zt3kIddKBXzaXy0mPi7t6aff9HBrifAE4XwLOcMD5HHCW2svnAKd8ELwObQXbXC6XExcXZy4DfS7a9D5ry3mLiYlxZsyYYS7zOjYqKir326u6nJgGZc1loF+XtsL9O4iNjdX/6VKw6W936dj0PpeezRXA9zq/cWiB55wTlz5hExiWyl133XVmH9cuZXmaFE658lcb1OSmouOAhXvb7bJpnN33nL1kl3YtoyYiEvxYwn5od4rfStlFRESkZCpwcN6zZ0/TYIZzyFgqV7ZsWbOfc8c4n0sK51L1vJvphTpXS9jbAHgfwP0A9quUXUREREREpPQF55x31r9/fzz55JNmTpcbs+gM1qVwIs7m3RCOi9qk2KZvzLdw8ZknEVg17Bx4LeEmIiIiIiJSjA3hbr75ZqxatSrLfq4LWrky87pSGNWT2Gs9d1xllgvTXPAqYWd7vo8DkD2PsOX0HbgUkG1W97Utt1d5vYiIiIiIiJ8z5z///DPq16+f7Tz0/ftZZC2FkZT3lHOTOS8XJGub97WZe2bwDwdRJl9ERERERKRUBOdchuTNN99EixYtzLrmbAh3//33m7VDueyKFM4al3fYnfsbFhXgtc1r2Iz5KfvcqfbylM3kq8RdRERERETEz2XtXLc0NDQUy5YtQ0REhClxT0lJMcH522+/XdCHE6tWWv7GSXhUuA2QA7W2eXVbys6MOTJl8uvZc1OTOhERERERET8G5/Taa6/hb3/7mylvr1ixInbs2IFLlzjrWAqrWdkUIDF/x0babPUcO8e7uDHwvmQz995Z+0Bl8kVEREREREplcE7s1L5z507fnk0pdqZCCHA292McGxivBvD3AGanT9rmb5xj7s6YVw5gJl9ERERERKTUBecsZR86dCjuuusu1KhRw5S4e7vxxht9eX6lxo+hZXFXHn3OmZXeBKARAs+dsW9vS9kvBjCTLyIiRcMeMnfffTe++OKLQJ+KiIhIqVXg4Pz9999Hu3btMHPmTBw/ftx8oEvRVUspm+cxe+FCGtJQ1U/zut3z2N3N3XLDYYQ37TJuNfJ5HxERKX41a9bE8OHD0b17d1x//fU4efIktmzZgjfeeANff806KN/id4QVK1aY5VW5zKo/DBs2zLyeZs2aITU1FVWqVMlyTHbfT+677z58+umn5udatWph/PjxuO2228w0vYkTJ+L555/3y/mKiIj4JTjv1q2b+UD85ptvCnpXyUVERN7BeV2ko65dtuxuAPt9tKZ4UdYsZ0CuoFxEJDjFxMRgzZo1OHfuHIYMGYJt27ahTJky6NKlCyZNmoRGjYKhFitnLpcLaWn81MsoPDwcs2fPxtq1a/H444/neP9HHnkEixYt8lzn78GtbNmyOHXqFF599VUF5SIiUjKXUjt79izi4+P9czalWFK5MnkeUwmOWef8DICu+VxTnFntW/JY3kxrlouIFJ8q1Vy4oUE4Kld1+f25Jk+ebDLIXP503rx52LNnj2niOmHCBLRs2TLHzDfvExXlXrgTaNq0qdnHYJ/q1auH+fPnm+8DFy9exPbt283gPW9n1twdCPM+06dPN9dDQkLMtLj9+/cjMTHRZO979eqV5Xm7du2KDRs2mJVgWrdune05jh492mT+OdiQG57DiRMnPBsf0+3QoUP461//aioB/ZXhFxER8WvmfOTIkRgzZgwefvhhJCX5Im8rVObnPLrBeeH87gQAfwTwFYCdRciGZ16zHF6X7W3ZujLjIiJFV658CP7QJwq//X0EykeEICnRwea1ifj3x+eRkuz7KWIs9Wagy5J2BsOZFSUgZdad2eu2bdua1VoaN25sgvQjR46gZ8+eZiCgQYMGSEhI8HxXeOmll/Dggw+if//+ZpCA9501a5bJXnNZVu8lWwcPHmyCeCYEioLnyel4fKx33nnHM1AgIiJyTQTngwYNMk3fOAJ98OBB07Xd26233urL8ys1YhK4OFruTtms9k02wGYAPgnAF9kE3e5s+CmbDa/s1V2dc8XdtGa5iEjxYGDerlskzp25gpM/paFCpVBzneZ98Eu5ta9wHjWbtu7atcvnj83M+dy5c03GnA4cOOC5zV1dx7nt7gEABvKcJ96xY0esW7fOcx9mxvv165chOB81ahSWLl1a5HNkMoFz6jkw0blzZ1NFwOVf33rrrSI/toiISFAE559//rlfTqS0C0fe5Y2VACQDqGiDdGbQE7MJuguSDdea5SIixVPKzow5A/NzZ67OoXZf/rZlBL7+4oLnuq+wjNxf2DxtypQpJuhlIM1APbcScw4UVKhQAUuWLMmwn0H75s2bM+xjSbsvcC65G0vo+fycd6/gXERErpngnCXt4ntXyoRdjbhzwQw3Z6an2+tH7ZaaKeguSDZca5aLiPhfVLTLlLIzY+7tUkI6ql8XZuaf+zo4Z+l4eno6GjZsWKD78T6Zg3s2kfM2bdo0LF682DSIZYDOknVW1r399tvZPiYz1sTjf/rppwy3ec8DJ5bJ+8O3335rsvIcEGCHdxERkRLfEM6tefPmeOCBB8zGpUykaM6HsdVb3m9WuM2e7/Waa86AuqINujNnw5GPbPi7NhAPtcE7L7VmuYiI75yPTzNzzFnK7o3XkxMdnwfmxPnaDKAHDhyIiAhOhMrIu+GbN84Bp9q1a3v2Zfc5f/ToUUydOtU0deOSZH37ckIVPIEvO627sQldcnKyKYfft29fho2PUxz4Glhyr8BcRESumcx59erV8c9//hN33nmnZ0kSrmW6fPlys37o6dOn/XGe17zTTt7d2tNsYP0jj7dv3pVsgu6CZsO1ZrmIiH+dPZ1mmr+555gzY87AvHLVMKxc6PuSdjcG5lxKbf369SZrvHXrVoSFhaFTp04YMGCAaeSW2d69e3H48GHTEZ3N5NjYjVlxb+z2vnDhQuzevds0nmvfvj127tzp6YLO7HtcXBwWLFhgGsKxWdy4cePM/TgPfvXq1WZwoFWrVqZp3IwZMwr0uurWrYvo6GgT7HMQgN3k3efOzDufm+u7c347BwX4ejnnnefgzX0/Zvb5/YbXGby7X4uIiEhxcwqy/fOf/3TWr1/vNGzY0LOvUaNGZt/HH39coMcq7i0yMtIhXgb6XDJv88rXdNKBXzaXy0mPi7t6afelAs4JwLkIOCcB5xDgfA84awDnuUyPV97u+xxwltrL5+z+QL9Wbb9sLpfLiYuLM5eBPhdtep+15bzFxMQ4M2bMMJd5HRsVFZXt/rLlQpyej1V2Xnn3Oudvs643l7zO/f4891q1ajlvvfWWc+DAASc5Odk5cuSI8/nnnzvt2rXzHEM9evTwXL/jjjuc77//3klMTHRWrlzp9OrVyxzjfv0TJ0509uzZ4yQlJTknTpxwPvzwQyc6Otpz/xEjRjjHjh1z0tLSnOnTp3v2P/vss87OnTudlJQUc7+FCxc6bdq0MbfxfCin35/3xsfMjvs1denSxdm0aZOTkJDgXLhwwdm8ebPz5JNPOiEhGX/X2eHvKa9/B7Gxsfo/XQo2/e0uHZve59KzuQL4XhcgDi3YA587d8657bbbsuz/3e9+55w9ezbgv3Qf/VKKfZsVHZ1ncH4FcFIAJwlw4m2gfpqBfS5Bd3XAucVeBvo1asu66QOhdGx6n0v+5ovg3L1VrupybmgQbi4D/bq0Fe7fgYLz0rHpb3fp2PQ+l57NVQKC8wKXtbMcLfPyacR9vE0KJyw0727tofZdczeE42I0YXY/55xnt+o8y9NVoi4iEjxYwu6vMnYREREpuQocTXPN0DfffDNDo5jrrrvOzCNbtmyZr8+v1KiTmndwTjyKs9Pd7eNOZGoGJyIiIiIiIqUgOH/66adRqVIlHDx40DRe4XbgwAGz75lnnvHPWZYCaSbkzv+b5i558G4GxwD9Ftv4TUREREREREqOApe1c8kTLqPWsWNHz9qp7GqqrHnR/BgWgTYFvE+EzaDPB3A/gA52ffNLtlv7uzmUuouIiIiIiEgJD87dli5dajbxjbCyBZt/mO61HnkZu2wa55Yfttl09zJqXCJNREREREREgluhOrh16NABX375paesnT/fddddvj+7UuTGK4kFOj4ZwLcAPgdMxv2ULW1P9VqnvL1K3EVERERERK7N4HzAgAFYtGgRLly4YBrDcUtISMCCBQvw1FNP+ecsS4HQ1IJlzssCuBfAM7aU/Vym23ldjeJERERERESu0bL2YcOG4fnnn8ekSZM8+9566y2sWbPG3DZ58mRfn2OpcCUkpEDHc0m1KAB/AHDIlrIzY45sGsWJiIiIiIjINZY5r1y5ssmcZ/bVV18hKorhohRGzTIp+TrOvc55ui1trwZgty1fZ5Y83F7y+nKtcS4iIiIiInJtBufz58/Hn/70pyz7e/TogX/961++Oq9SJzKFoXbe3Pn1cNutnb60jeH4ZtbzahTHbu0iIiJ5cRzHfI6LiIhICQrOd+zYgeHDh5tAnJfc2BCOl9u3bzdrnbs3KYDL+T80xGbOK9q7fW+7svcF8Jy9fDObZdS0DrqISOlTs2ZNTJw4Efv27UNycjIOHz5sBtrZ3NUf2rVrZ4J9f1bTcRodp9NdunQJZ8+ezfYYnkPm7d572a3lKiYaWPV38uRJnD9/Ht988w06d+7st3MWERHx+Zzzxx9/3HwQNm7c2Gxu586dM7e58UOQc9Elf36KjML1SUn5Ds5dAFgIn2hL2095bZlF2IBd66CLiARelWouVI524eyZNJw7U7BmoAUVExNjglh+Rg8ZMgTbtm1DmTJl0KVLF9M7plGjRghmLpcLaWlZf0fh4eGYPXs21q5dm+G7R2aPPPJIhql4/D24tW3bFkuWLDGBPvc/+uijJtlw++23Y8uWLX54NSIiIj4OzmNjYwt6F8mHA/VqocXJnwtU8sAgvSoAtub7Ipdgm4G51kEXEQmscuVDENenEn77+wiUjwhFUmI6Nq9NxJcfJyAlmR1FfI9NWjlY3qJFCyQmJmaogvvggw9yzHyvWLHC9JhhRpmaNm1qAtYbbrgBhw4dQr169fD222+jdevWJlA+ePCgCf75uLyvdyD8j3/8wwS+ISEhePHFF/Hkk0+iVq1a2L17N1555RXMnTs3w/N269YNr776Kpo0aWIy2StXrsxyjqNHjzaXDz/8cK6vn+dw4sSJbG9jc1tvrABkaf9//dd/KTgXEZGSs865+N6FyuUKdLxjs+fMgifaYPvJbI6rYTPmWgddRCSwGJi36xYJJoJP/HTZXPL6f91fyS/PV6VKFXTt2tVkyL0Dczd34F0YfMyyZcua7DODaAbdFy9exJEjR9CzZ09zTIMGDUwQ/txznHAFvPTSS3jooYfQv39/3HLLLZgwYQJmzZplHsPb2LFjMXToUJPV37p1a6HP0X2ep06dwrfffmsGCHLDwYPIyEjEx8cX6TlFRESKLXNOvXv3Rvv27VGjRg2EhmaM73v16lXokynNLh9j2Jz/wNydYzkN4KgNuhlsf5yptL26LWVnxtzbOds8jsG7OrqLiPi/lJ0Zc+9Sdvdls5YRWPrFRZ+XuNevX998Ru/atQu+xsw5M97sNUMHDhzw3OYObt1zuYnZdZaPd+zYEevWrfPch5n3fv36YdWqVZ77jxo1CkuXLi3yOY4cORJff/21GZhgBp5VBBUrVsxxyt3gwYPN7Z999lmRn1tERKRYgvM33njDfJAuX77clIqxXE6KLupU/oNzd8d2Zs1/yCPYPmWP0zroIiKBwznmLGVnxtzbpYQ01LiuDKpUdfk8OGcm2F/YYG7KlCkm6GUgzUCd89lzGyioUKGCmePtjUH75s2bM+zbsGGDT86RpfFuLFPn87P0PrvgvE+fPnj55ZdNWTsz7SIiIiUiOP/LX/5iStYWLlzonzMqpX6Vkv/yQn5945DIeRtg5xZsn7TN33p7BfGVbUady63pK4iIiP+di08zc8wrVMoYhPM69zOj7mt79uxBeno6GjZsWKD78T6Zg3s2kfM2bdo0LF68GN27dzcBOkvWBw0aZOahZ4cZaeLxP/30U4bbUlLY3vQX7MDuDyxtZ1aeAwKpqb8MiLOD+/vvv4977rkHy5Yt88tzi4iI+GXOOUvU9u/fX9C7SR6qp+TvywiD8gu2jL2CzZTXsMH28hyCbTaK0zroIiKBc/Z0mmn+xgx55aouMNblJa9vWZfol67tXFmFAfTAgQMREcF1OzLKaakzd+a4du3ann3NmjXLctzRo0cxdepUM51t/Pjx6NuX7UfhCXzZad2NjeK4jBvL4bmkm/fGxykOfA0sufcOzO+77z5Mnz7dZM4XLFhQLOchIiLis8w5O6Sy9Ouxxx4zH7TiG660K/k6jl/fDnF5HJsBb8dl2ADMyCXYTrJd2T+2gby7IZyIiBQfdmV3zzFnKTsz5isXXvDs9wcG5lxKbf369SZrzAZrYWFh6NSpEwYMGJBhSVS3vXv3mrXQ+XnPDuZs7MasuDc2c2MFHTuus/Ec+9Ds3LnT3MZu7sy+x8XFmYA3KSnJNIsbN26cuR/nwa9evdoMDrRq1QoJCQmYMYOfYvlXt25dREdHm2CfgwDsJu8+d2be+dxc353z2/ldha+Xc955Dm4MyD/88EPTsI5ZdR5PPF+ek4iISCA4BdnKlSvnLFy40ElISHC2bt3qbNy4McNW0Mcrzi0yMtIhXgb6XDJvB0MinHTgl83lctLj4q5eeu8HnNOA8xPgHAaclYCzEXBeB5zqQfA6tBVsc7lcTlxcnLkM9Llo0/usLectJibGmTFjhrnM69ioqKhcb69c1eX8qkG4uSyOc69Vq5bz1ltvOQcOHHCSk5OdI0eOOJ9//rnTrl07zzHUo0cPz/U77rjD+f77753ExERn5cqVTq9evcwx7tc/ceJEZ8+ePU5SUpJz4sQJ58MPP3Sio6M99x8xYoRz7NgxJy0tzZk+fbpn/7PPPuvs3LnTSUlJMffj94k2bdqY23g+lNfvjxsfMzvu19SlSxdn06ZN5rvKhQsXnM2bNztPPvmkExIS4nmM5cuXZ/sY3ueb07+D2NhY/Z8uBZv+dpeOTe9z6dlcAXyv8xuHFjhzzlHmW2+91Sx/ooZwvnOubATqJWdd6iY7LBQMsRlzvoG1ADzObAyARbmsdy4iIoHHEnZ/lLHn5Oeff8Yzzzxjtvw2j/vmm2882ejsjnn22WfzbMbm3ZDNu5Ect+xwPfP8NrHjsmi5LY3Gcn5uuWG2X0REJJgUODhnM5cuXbqYMjnxnZ2V6+I3P3NhtLxxttwBO3f8V/a6+810N35jGXt+ueesq9xdRERERESkhATnR44c0VwsP4i6kr9cN2emc1VZts9pC8A96z/VZtIr5bDeeXbYHojtezrY5nKXbGd3Zd5FRERERESCvFs7m8L83//9H2Ji2JJMfKVGcnyex6Tb5dJuAHAd14e1b2B5AMdsoM6l0irabHhe+tpMO4srD9tLXn/SJ69IRERERERE/JY551xzLsnC5U8SExNx+fLlDLdXrVq1oA8pXPKmUkXgYuZVyrNmzbcCiLZBOVed5ew8Lmy30wbp19kseu6PdDV472Cz6+5j3Zf5zbyLiIiIiIhIgILzv/71rz56avF29vpKV9PfuWAwft4G6f8fgD/YQJoz1X9t1zBngH4EwP15lKdXt6XszJh7O2cfh8G7gnMREREREZEgDc4Luhap5E+VkLw7tTNL/hsA3wPYBWADu/DaTu11AfARfgRwIh+N4U7ZOeaVM2XZK9vS+bwy7yIiIiIiIhLA4JxCQ0Nx9913o1GjRub6Dz/8gPnz5yM9nbOipTAOVGSxet5Ytj7Ha075JwC62oD8mFemPC2P8vSTtvlbb6+MeWWbUefjK2suIiIiIiISxMH5jTfeiAULFuD666/Hjz8yTwu89NJLpos7l1nbv58zoKWgvi/HBnvr8rXG+d0Afmsz3zsAlAVw0N5WxasxXF7l6Sx7hw3i69mM+Ryv/SIiIiIiIhKkwfnEiRNNM7iWLVvi7NmzZl90dLRpFMfb4uLi/HGe17wbf8rfGucsbY+yATeXTetuu7ZXtdc5L50t+hLsfPLcytOTbNn7xzaI1zrnIiIiIiIiJSQ4b9euXYbAnOLj4zF06FCsWbPG1+dXatS4wFZv+VMOwM02S861yiPt/nM2+81Gb8zD78tnsM1jFJSLiJRejuOY6WpffPFFoE9FRESk1CrwOucpKSmIjHSHg7+oWLEiUlO5iJcURvnQtPwfC6AOv0zZYDzU/hxug3W+C4dsFp1zyEVEpPSqWbOmp+otOTkZhw8fNn1iOnTggpq+x0F8BvtRUazz8o9hw4aZhMClS5cyJAu88Rwyb/fee6/n9latWmH16tU4ffq0WRp2586dWpFGRERKVub8X//6F9599108/vjjWL9+vdl3++2345133jEf9lI4SSGcMZ4/oXY5tSRbys42fGdsgL7ZZtDTveach9ggXWXrIiKBV6VaKCpHu3D2TBrOnfFvI9WYmBgTxJ47dw5DhgzBtm3bUKZMGXTp0gWTJk3yNHYNVi6XC2lpWQevw8PDMXv2bKxdu9Z8H8nJI488gkWLFnmu8/fgxsD+7bffxtatW83PrVu3xtSpU83P7733nh9ejYiIiI8z588++6wZfecHIkfgufGDf+/evXjuuecK+nBi/VSmYr6OC7FbhA3Mea8UG4y77M/JtvN6km0e956dW/4+gOds5l1ERIpXufIh+PNjlfDi69Xw/MtVMXRsNXO9bDn+VfePyZMnm4xxixYtMG/ePOzZswc7duzAhAkTzBS1/Ga+mzZtavYx2Kd69eqZAXlOa7t48SK2b9+Obt26mdtXrFjhCYR5n+nTp5vrISEhZgocG8cyU71lyxb06tUry/N27doVGzZsMJV6DJizM3r0aLzxxhtmsCE3PIcTJ054Nj6mG5//n//8p/l9HDp0CB999BEWL16MNm3aFOh3LCIiErDM+fnz5828NHZtd4+4sxSMAbsU3v6w6ibznd+vaO7APN1uDLjj7RJqNWym/JRdZu2UbQ5XOR/rn4uIiH/8sU8k2v+hgsmY/3zsMipWcpnr9NkHbOPpW1WqVDGB7vDhw00wnN3neWEx687sddu2bU2muXHjxiZI58otPXv2NAMBDRo0QEJCApKSkjwruzz44IPo37+/GSTgfdlM9tSpU1i1apXnsceOHYvBgwebID6nkvWCnOf7779vHosVfu6Bguw0a9YMd9xxB0aMGFGk5xQRESmW4Jxzzfnhy5FtBuPugJyj4bztwoULhT6R0i6+Rv7WOYctaQ/z+hle881r2HnoLOJrZ7PoF+w8dHfn9tzWPxcREf+Usjf/fTkTmHMj9yX3f/XFRZ+XuNevXx+hoaHYtWsXfI2Z87lz55qMOR04cMBzG7PpdPLkSc8AAAN5zhPv2LEj1q1b57kPM+P9+vXLEJyPGjUKS5cuLfI5jhw5El9//bUZmOjcubOpImB/nLfeeivDcRxQqF69OsLCwkxGftq0aUV+bhEREb8G58yW/+///q8ZWXaPgruVL18e3333nRnp5px0KbgGl0/kO2se4pUxZ9O3JBt8p9iM+FYA/ZkFsMuq1QdwjBUO+Vz/XEREfItzzCMiQk3G3NvFhDTUvK4MqlR1+Tw458C5v7DB3JQpU0zQy0CagXpuJeYcKKhQoQKWLFmSYT+D9s2b2S3lFyxp94VXX301Qwk7n5/z7jMH5yxjZ9DOMn9m7TlNj+XuIiIiQTvnfMCAAfi///u/LIE5cVSagfvTTz/t6/MrNVoe2pOv4xz7pjEYT7SXZ2xWPMze9iCAP9jg/bK9z68ANLKl7RfzWP9cRER861x8GhIT000puzdeT0pM92TRfYml4+np6WjYsGGB7sf7ZA7u2UTOG7PLsbGxmDlzJpo0aWIC6ty+AzD4pe7du5tBfvfGcvjevd0Trq5imbw/fPvtt6hbt64ZEPB28OBBUwHA8nfOxWf2XEREJKiD81//+teeJi/ZYUkaP6ClcCKTfmlSkxcG2/yaVNbOMec9K9hAnQvjDARQywbrXAs9xN52A4DrACxX1lxEpFidPZ2OTWuTTYacG2Nd98/c74+u7ZyvzQZnAwcOREQEJz5llNNSZ5wDTrVr1/bsYyCd2dGjR013czZ1Gz9+PPr27Wv2u5dVZad1NzZdYwNZlsO7p8W5Nz5OceBrYMl9bsu+chpA2bL8dBUREQnisnY2luF8rJxwVJ3HSOGcjSoPnMjfsQzGy9kAnZnxSBucc21z9pjl16Hz9vZoe3uKvc7A/F1/vxgREcnii48veOaYs5SdGfPlCy559vsDA3OuqMKlTzmXm8uG8bO8U6dOpiKOmevMWNbNtdCZQWYzOTZ2GzRoUIZjmGFeuHAhdu/ebT7727dvb5rDEjufM/seFxeHBQsWmIo79qsZN26cuR8DYK4vzsEBrjXOpnEzZswo0OtiBjw6OtoE+xwEYDd597kz887n5vrunN/OQQG+Xs555zm4PfXUU+Z1uufks0Edp+exZF9ERCSog3OWfd1222348ccfs72dt/EDWQrnQqXyBVpKjdlz4r040WCN1/4q9o1NtNcZrB+w1/9ujxcRkeKVkuyYruxs/saMeXGsc86ma82bNzdBNrPbzIYzM75x40YTnGfnypUr6NOnj5lTzmCePWXYwXzOnDmeYxgQsxN6nTp1THDNtcSff/55c9uxY8fw8ssvm/nb7I7OwPvRRx81Ddr43OzazpJ4LnO2adMmvPbaawV+XWPGjDFrmHvPKac777wTK1euxOXLl83ABAcDWJ7PoP2FF17IsH45Bwlef/11/OpXvzKvmVn8F1980VQDiIiIBIqTn+3VV191Dh486NSoUSPLbTVr1jS38Zj8Pl4gtsjISId4GehzybxNa3OHkw78srlcTnpc3NVL7/2AkwQ4ewDnKOBcAJx3AecWwFkKOB8AzhrAOQY4+wFnN+CcAJyNgPNcELxObRk3l8vlxMXFmctAn4s2vc/act5iYmKcGTNmmMu8jo2Kigr4+Wrz77+D2NhY/Z8uBZv+dpeOTe9z6dlcAXyv8xuH5jtzzhHwHj16mAYzXJfUnUFno5kHHnjALEXCY6RwjlfO/5SAMK955CyGrGPfzUu24dvVwsKr88uj7Lz0eSpnFxERERERCVr5Ds45X4xzw1gCdu+993rml7MsjcE6S+Z4jBTOzbu42Fn+hNh55CyG5ESCcLvvawDunrc/2mXT2M5nLoD/8duZi4iIiIiISLEF58R5ZZzDxa1atWpmHpe7q6sUza+PHy1Qi30G5z8DOM35gXZptM9tl/amdi1zDpV8qIy5iIiIiIjItRWcezt9mmGh+MrJ8lG4+WL+Bjou27XNK9lAfBqA++0yahXs7d8BmALgcC6PUwNAdRvYa4hFRERERESkBAbn4ltr69yENqf25nlcut2ibSk7S9rvtZfbbTDOeeetbWb9zWweg6vd9vUK5i/Zknhm2NXJXUREREREpPixQlqCQHRK/ubrX7EBNJdHi7eBdVWbRa9mm8QxE54A4I8AGmXzGH3t3PQ0G8yn2etPovgwa3+LzdyLiIiIiIiUdsqcB4mI1JR8Hedu/ubYkZU0+3Oq7c5+EMANtoN7RQCTAHxhs+Kcp94QQFdbxs4gHl6X7QF87OcSd2XtRUREREREslJwHiRSCvBWlLEBOTPlx2023b2/ke3QztL3izbgZdl7O5ttZ8b6RgD7bebdfd9ztolcDT8H5+6s/SmvEnx3h/nsSvBFRERERERKg3xFhM8880y+H/Ctt94qyvmUWknh5Qt8n1Ab3J6xWfErNrhOt9l19n8/YrPobQGsA3AAQIwN0Jl132Yfq7IN5t1ZdH+oYTPmgcrai4iIiIiIlOjg/Pnnn8/XgzmOo+C8kJx0p2DH28uytlSc5eEpNpt+0QbmOwGUt4F3mj0mwQboDWz5+2H7GJz7PcfPwXF1W8qeuYN8cWXtRUQk58/vu+++G198wYlQIiIiErQN4WJjY/O13Xgj87FSGLEJBctZu+edh9l56J8A6APge9u1fa/NpkcBKAcg2WtON4P2fbYM/gb7j2BOMayHfsoOEHCwwFtxZO1FREqrmjVrYuLEidi3bx+Sk5Nx+PBhzJ8/Hx06sJbJ99q1a2eC/agofgL5x7Bhw7BmzRpcunQJZ8+ezfYYnkPm7d57OdErqzvuuAOXL1/G5s2b/XbOIiIiedGc82DhOAUOzkNtCTuz5DNs0L0QQH+bMXfsPHNmq0/YAB22/P2YnXP+OoBdxZSxPmmbv/X2yphXLqasvYhIsIiuForK0S6cPZOGs2f4V9x/YmJiTBB77tw5DBkyBNu2bUOZMmXQpUsXTJo0CY0aZbemR/BwuVxIS2PtV0bh4eGYPXs21q5di8cffzzH+z/yyCNYtGiR5zp/D5lxEGHGjBlYtmyZGcgQEREpUcH59ddfjz/+8Y+oV6+e+YD0NmjQIF+dW6myP6omcPyHAgfoDL5rARhrA98ydh8v3ZJt5/Ya2QTE/0Hxetdrjnk9mzEvjqy9iEiglSsfgh59KuLW35dDREQoEhPTsXFtMj7/+CJSkgs2QJtfkydPNhnjFi1aIDEx0bN/x44d+OCDD3LMfK9YsQKVK1fG+fPnzb6mTZtiy5YtuOGGG3Do0CHz+f/222+jdevW5nvAwYMHTfDPx+V9vQPhf/zjH3j00UcREhKCF198EU8++SRq1aqF3bt345VXXsHcuXMzPG+3bt3w6quvokmTJujcuTNWrlyZ5RxHjx5tLh9++OFcXz/P4cQJDk/n7J133sHHH39sBgFY2i8iIlJignOWwbEcbv/+/WjYsCG2b99uPqz5obtp0yb/nGUpcF1K9mV5eUmxwTcD8gds87cIu7kz60m2tD3CroceyIA4yXZl/9gOFjCbroy5iJQGDMw7/KGCyZj/fOwKKlYKNdfp0w8u+Pz5qlSpgq5du2L48OEZAnM3d+BdGMy6Myhv27atKS1v3LgxLl68iCNHjqBnz56YN28eGjRogISEBCQlXZ1U9dJLL+HBBx9E//79sWfPHnPfWbNm4dSpU1i1apXnsceOHYvBgweb7xk5lawX5Dzff/9981gMwqdPn54ls85peTyvESNGFOm5REREij04f/311zFu3Dgzas0P3V69euHkyZP46KOPMpSOScFEpxX8i1my7cbuXi+8ks2IOzar7u7aXt5m0ncDmBokAfGpIDgHEZHiLGVnxty7lN19yf1ffXHJ5yXu9evXR2hoKHbt4uQl32LmnBlvDtDTgQNsNXpVfDwnTcF8N3APADCQ5zzxjh07Yt26dZ77MPPer1+/DMH5qFGjsHTp0iKf48iRI/H111+bgQlm4FlFULFiRU/jWv5+OBDQpk2bbEvnRUREgj445/y0Pn3Yegy4cuUKypcvb0bN+WHKLq8cmZaCO1ORee38YwB+2gbdl+2+SjYY55bmFaSH2stbgiQwFxEpbTjHnKXszJh7u5iQjprXhaFKVZfPg3NWtPkLG8xNmTLFBL0MpBmocz57ThgIV6hQAUuWLMmwn0F75iZsGzZs8Mk5sjTejSX5fH6W3jM456AFS9lffvllk8UXEREpMd3avTEQd88zP378eIYO7dWqcSEvKYz9kdcV6PjLdmSlvA22K9nS9ey+2oXY4yNsKbmIiBSvc/FpZo45S9m98XpSYrrJqPsag8709HQzBa0geJ/MwT2byHmbNm2aKQefOXOmmRvOgPrpp5/O8TGZsabu3bujWbNmno3l8L17u9uE/vI9wx++/fZb1K1b13yHiYyMxO9+9zszb55d2rkxycBz4s/t27MzioiISJAH5yxHYxkaLViwAOPHjzelamws4y5Vk4I7US6qwG8ch0jSbUO422x5+xW7eWfMeZ2zDdmaR8uViYgUv/jTV5u/MUNepWooGOvykte53x9d2zlfe/HixRg4cCAiIrJWZ+W01BnngFPt2rU9+xi0Znb06FFMnTrVTG/jd4G+ffua/ampqZ5O625sFMdl3FgOzyXdvDc+TnHga2DJPc+P0/J+/etfZxgoYOUfpwDwZwbyIiIiQV/W/sILL3hGwFkOxp+5bihH6HmbFE6ttPw3vXHnVzirL9peZ/Cdaq8jU1k7f2YeYlEBS9pr2DnsKoUXESk6dmV3zzFnKTsz5l8vuOTZ7w8MzLmU2vr1601meOvWrQgLC0OnTp0wYMAAk7nObO/evWYtdPaWYTM5NnbLvBLLhAkTsHDhQtNxnY3nmGneuZMLesJ0c2f2PS4uzgzisyEcm8WxXw3vx5Ly1atXm8GBVq1amUCZS5kVBDPg0dHRJtjnIAC7ybvPnZl3PjeXRWPSgIMCfL1MJPAciB3sf/gh4wopnCPPYzPvFxERCdrg3LvpC5us8MNdii48n81oGHAn2aw5JxFss4F5AgC2lLudX1q8llNjwH6YS9kUoDs78yvMf3Twajb3tb3/1Z67InnT4I5IRlwujV3Z2fzt6hxz/69zzs/s5s2bmyCb2W1mw5kZ37hxY46f3+wnw94ynFPOYP67774zncznzOE6H1cxIGYn9Dp16pjgmg1hn3/+eXPbsWPHzOA9m62xOzoDby6lxgZtfG52bWdJPJc54yovr732WoFf15gxY0ynde855XTnnXeapddYms6BCQ4GsDyfQTsTCO+9914hfosiIiLFxynMduuttzoPPvig2Zo3b16oxyjuLTIy0iFeBvpcMm/D2vZ00oFfNpfLSY+Lu3rpvR9wEgEnyW4/A85RwDkAOGsA50PAWQk4DwHOfwFOG8CpXsBzeQ5w/gM48wDnA3v5H7s/0L+na21zuVxOXFycuQz0ufhqi7D/Vr4AnKX2ktfLB8G56X3WVtgtJibGmTFjhrnM69ioqKiAn682//47iI2N1f/pUrDpb3fp2PQ+l57NFcD3Or9xaIEz59dffz0++eQTU4rGUW+qXLkyvvnmG9x333346aef/DGAcM0LSeP7kT9l7TxyvnmcMXjeNnyLZdMdZkrY7daWtBc0a1nDZsxPec1Pd1+2t+uTKwsquWHVRW/774RVG5Xtddg17kVERERExAcN4d5//33TtZVLqlWtWtVs/JlzyHibFM51iVfXgs0Pd7M3lpg7NkCPtD/XsUH76zYQ4jvynO3qnh/VbSn71WGXX5yzgb+6vUtBBndSvQaI2tt/XyIiIiIiklWBM+ft2rXDHXfcYZrAuPHnZ555Bv/5z38K+nBiRYVz1nj+hdq55uXsHHFmJ1Ns8zcG6PsLmbU8ZeeYV87U2Z3X2bJI3d4lP4M7/LeXeXCnng3eVXkhIiIiIuKDzPmRI0eyrHfqbg7DJjBSONddypyrzlsVmxH3nqhQ1o64FDZredI2f6tuA6lwr8ZeyxVYSQEGd7xpcEdERERExMfB+ZAhQ/DWW2/h1ltv9ezjz2+++SYGDx5c0IcTK+IcZ42jQKXt7hVk3b1+3eueR9qMurcbALTM52OzK/sc+4+jnr2cU4Bu78GAAwq3qIy62GlwR0RERESkmMra//GPfyAiIgLffvutWW7FPEhYmPn5gw8+yHAs56NL/ly+zHC7YNzZcgbpl22Ze1lb5u6eY96Wa6jbY7iAzDcAnrBrpOckyZbAf2wDq5K0FJaWgQs89yBOezu4c7EEDu6IiIiIiAR9cP7Xv/4VgTB06FD07NkTDRs2RFJSkukO/+KLL2aY+16SRTgFy5zDZslTbdDJGeshNkMZZgNTrnl+nZ2Hfs5edrBN4nrm4/FPlaCg3E2dwgOvJA/uiIiIiIiUmOB8xowZCAQ2ops0aRK+++47k6l/7bXX8NVXX6Fx48ZITGTOuGRLqBxRoAm5zJgfs+XrfBNr2ux4qA1KK9iMOWsbztvg3F3+zvL2RgB24tqiZeCCS0kc3BERERERCergPDIyEhcuXPD8nBv3cb7WrVu3DNcfeeQRnDp1ysx3z6lLfHh4OMqWZaE3Mpw7m9dxCyZL6/8G7faxx7rF8wsNvXqZjWQbbJe1S5y5sYT4gg3cecwZe+nGMm9ONmjILvu4ttSyvwsOTnhPEjhvy6tr51HOHwj8d8hlCIPt36P4lt7nki+/711ISIjn0nE4jCrXIv2fLh30PpcOep9LD1cA3+v8Pme+gvOzZ8+idu3aJhg+d+5ctl843F9EmNUuDlFRXN0biI/POdx66aWXMHr06Cz7O3XqZErjg0ntlq3hhKb8ElTyDWzenL9YII0F6b9gUL4HwK/sHOt0rxL3dBuEVvZaA937HWFG3R3IZhzuKDn42irZoPt8pv18fTdn2s/fAX+vTWyQHkz4H7V58+bm/09apvdZrh16n0u+atWqoXz58maQ1/35k5MKFdx/aUsOfrY/8MAD+Pe//x3oUwlqfP/576BNmzaIiYnR/+lrnP52lw56n0sPVwDfa3525Ee+IukOHTp4guD27VkgHFj8hb7xxhtYvXo1fvjhhxyPe/311/H3v/89w4fqTz/9hCVLlvgtw19Yd7gchCxc9MsOBuccBFm0KEtwHmK7r1e0P7uHSjjcwDGZanbN88v2mFSbUa9g3/DlJbQ5V4RtZtfeq9nbctvozj3UUt3Op0+2pfyV7e9gHoB/Ijj/SHBQa9GiRfpAuIbpfS75GIjxs5CfHefPew//ZZ85T0hICJrMec2aNTF8+HB0794d119/PU6ePIktW7aYz9Gvv2bLzKsuXbqU62sryDS0FStWoHLlyj55vOwMGzbMvJ5mzZohNTUVVapwcdGMsvv933ffffj0008znGdmtWrVwokTJ7J9Xr4mDu6zYu/06dP6P32N09/u0kHvc+nhCuB7nVf1eYGC81WrVmX7c6Bw7vmvf/1rtG7dOtfj+IHNLTO+GcH2n69a0rksQTjS06/uyyY4j/QKzN0d21munmaz5wzO99vy9Qhb/s59X9sAN7heff48ZgNvzmM+ZAPvnvb1upu9vWOvM4Cvm6lTeLC+5vT09KD8Nym+pfe5ZMvv++YOCHMLzKOrhaJKtAvxZ9Jw9oy7G4j/BhXWrFljMuNcCnXbtm0oU6YMunTpYj5LGzViB5Lg/iKV3e+e09Zmz56NtWvX4vHHH8/x/pwCxy9hbvw9ZNagQQMzmOLGwYu88Jz0f7p00PtcOuh9Lj3SA/Re5/f5CrzOOT/oevd297/+Bfc99NBD8DeusR4XF2cy+MyCXzNstiXfh3uVs7uD9FC7uWwwHmMzyBz/fxHAXTaYDbZ514Vp9pbq1QW8vdd65u5O4eza/py95PXgmsQgIqVRufIh6PNYRYwYG40hoytj5P9Gm+vlyhV8Kc38mjx5shkoaNGiBebNm4c9e/Zgx44dmDBhAlq2ZHvQrJhR5n28y/ebNm1q9jHYp3r16mH+/Pmmqu7ixYvYvn276Q3D293ZaPc0uOnTp3uqCrjyyv79+00jV2bve/XqleV5u3btig0bNiAlJSXHQXhOWWPmn4MNueE5MAvu3viYmTEY9z4mWCoeRESk9ClwcM553Czlyu7DjWVm/g7M//SnP5nSwoMHD+JaEp6QNcOfX+mZ3lBeP2+zxpdtYHujn7uz8zlu8QqSfa26LWXPnPM4Z8vW+fzeGLRzwoO6hYtIsPhTnwro2L080tIcHD92xVzy+p/u988cdZZ6M9Blhjy7VU2KUnLOx2TD1bZt26JJkyZmaVMG6UeOHDHLnroz0iwRf+655zzfHziI379/f9xyyy1mgGDWrFnmMbyNHTvWBPHM6m/durXQ5+g+T/bL+fbbb/Hoo49mewwHCY4dO2ZWgLnjjjuK9HwiIiJFUeDubRwtP3DgQJb9hw4dMrf5Cz9g77//fvTo0cPM+eMcOveXi+Rk737kJVOtn38pqcsvdyDujeP9aXaJNXdHd2aNm9oA19fBaoTNTnfwmgf+tS0j92W2+pR9bJayexccVraDEAVYhU5EpNixlP13d5RD/Ol0Tym7+/K235fFoi8SfV7iXr9+fdOVdteuXfA1ft7PnTvXZMzJ+3uBu0cNB+3dAwAsQ+cAfseOHbFu3TrPfZgZ79evX4Ypc6NGjcLSpUuLfI4jR440c+o5MNG5c2dTRVCxYkUz0E/Hjx83z80sPQcannjiCZP1v/3227F58+YiP7+IiIjfg3N+2P7mN78xwbg3lrydOcOFu/zjqaeeMpcrV67MUmb/4YcfoqSLCEktcumD47VxTnoZmznnOxVus8u+Ds4ZmPe2j3vYBsvuSQ/ueeC+cNIG/e7Hdjd7q27nlCtDLiLBjHPMIyJCcPxYxjlnFy+ko9Z1YYiu6vJ5cO5uTucPEydOxJQpU0zQy0CagXpuJeYcKGAXezZk9cagPXMgzGDZF1599dUM2XE+P+fdu4Pz3bt3m82N89dvvPFGPP/888UyTU9ERKTIZe2ffPKJ+VC+8847zYg8N87/fvPNN/HPf/qvHza/ZGS3XQuBOYWVK9qXMsdmylO83lhmz4/YOef+yC7ndx64r7xrA3G+tnr20t3sTUQkmJ2NT0NiooOKkRk/dnk98VK6aQ7na5xfzsY3DRuyNWj+8T6Zg3s2kfM2bdo0xMbGYubMmaasnQH1008/neNjMmNN7g7r7q1x48ZZ+tiwa7w/sLS9bt26ZkAgJ+vXrzcDCSIiIiUiOGeZGD/gli1bZpYT4cZ5Wiwd8/ec82tZ+aSsTWryg1+hrtjLUFsK4djMMheLY0FhtF1y7FSA54EXlZq9iUhJxXL2775JvtqpvWooyoTDXPL6hrUpfunafvbsWSxevBgDBw5ERAQnIWWU03rtnKNNtWvX9uxjIJ3Z0aNHMXXqVNPUbfz48ejbt69npRR3p3U3NqHjFDSWw+/bty/DxscpDnwNLLnPbhUX72NY7i4iIhIIBS5rv3z5slknlEE6S9kZnLOU7fBhFjVLYaWkZcxKFESoDcgZ3rvzAfwalmoD98zZ5Ro2sHZnuUvaPHA+r8rYRaSk+X8fX/LMMWcpOzPmS/+d5NnvDwzMuZQaM8Kcy80Ga2FhYejUqRMGDBhgMteZ7d2713ymsyM610dnY7dBgwZlOIbN3BYuXGjKwtl4jhV0O3debTvKaW/MvnNllQULFpjvCWwWN27cOHM/VtytXr3aDA60atXKLGM2Y8aMAr0uZsCjo6NNsM9BAH4fcZ87M+98bvam4fx2Dgrw9TKBwHNwY6M6znv/4YcfUK5cOTPnnA1nWaovIiJSIoJz73I5buIbO+rVQosCDnC4l09zL6kWZoPxi7ak/RPOufMKZLNr3rYeAFeAPVzAgNcd4PP+Xe0+zQMXEclZcrKDTz64aJq/cY55caxzzuCzefPmJshmdpvZcGbGN27caILz7Fy5cgV9+vQxc8oZzH/33XcYMWIE5szhX/arGBCzUWudOnVMcM21xDlXm9j5/OWXXzZd17mMGgNvdkrnoD6fm13bWRLPZc42bdqE1157rcCva8yYMabnjPeccuKUO/amYSKBAxMcDGB5PoP2F154Ae+9957nPixv5+/k+uuvN03j+FrZsM69FJyIiEjQB+cc8eYH4l133YUaNWqY6964Xwru+rTMxeF5C8k03zzcNoBLs+XsX2YKkN3N29gXPtEur9YKANve7Mpnl/XMAX6yfY4ydh44BwY0D1xEJGcMyP0dlHv7+eef8cwzz5gtv83jvvnmG082Ortjnn322TybsXk3ZHNjzxpu2WFQnd8mdgz2c1oajVjOzy03f/vb38wmIiJSYoNzNn5jcP7vf//bLKHiOAwNpagaHGTbtsLhrL7yNhN+yQbKZQEMsmuPv2u7t3cCUAlAHZvhLmdL4cvbfwj56bKeXXf26jb7/rkPSuVFRERERERKowIH55xv/uc//9nMNRPfSajAEPnqerCFwXJ2x5abM+D+wWbA3QE3V4y9iQ2A7O0um2EvYwP3i3Y/u6x/nEOAnbk7O7wuf2cHARSYi4iIiIiIFEO3dnY55dwt8a0dta4r9H0dG2xH2ICbpe17bbd2lp13sR3bI71uD7UN43jfMrl0Wa9hs+/VA9CdXUREREREpLQocOaczVPY4TS39Uyl4GqcYChdOI4NuPlmptty9TttNr2M3R6y2fFIez3dq7M775u5y3p2zeO+s8F+cXdnFxERERERudYVODhv3bq1WTKlW7duZvkRdkT1xvVOpeCuSzhb6PuG2EA73SuLziz3BRs0029tpvyknXfuLmlPscdVsCXv7i7rz2Uzt7yLvc7HJnVnFxERERERCVBwzqVP/t//+38+enpxC0kvfOfeNBt4h9tGcO7S9XI2e74PANvN1bfz0I/aY9itvabd570eem5zy8vY5m+cY67u7CIiIiIiIgEKzh977DEfPbV4Sw1xz/zOP8du7rA+1WbR3Y0EeHkMwE7782kA3wJoZOeI77HN3zKvcx5rM+mZV10/ZwPyz72CeHVnFxERERERCUBwLv7xU8Uq+PXJgi2nFmKD87M2851kg27Y0nbOD99mb6th1zf/u709t8D6lJ1jntvcch6joFxERERERKQYg/ONGzfirrvuMiXtmzZtynVt81tvvdVHp1a6HK7Amdu7Cnw/BujlbRm7y15esGuZ/2zL12tkMy88t8CawffXXsuwaW65iIiIiIhIEATnX3zxBVJS2DoM+PxzFjWLr910+nih7hdiO7Cn2tJ1BtLbAey288MLOy/cfSzXPdfcchGRaxsH3e+++27zeS8iIiJBHJyPGTPGXIaGhmL58uXYunUrzp8/7+9zK1UqX2YROgodoNMBW9r+DYD/sZnuws4L5+O8aeeka265iEjJVbNmTQwfPhzdu3fH9ddfj5MnT2LLli1444038PXXrJPyrXbt2mHFihWoXLmy374rDBs2zLyeZs2aITU1FVWqVMlyTHZVfvfddx8+/fRTz/Xw8HCMGjUKDz74IGrVqoXjx4+b7zzTp0/3y3mLiIj4bM55eno6vvrqKzRq1EjBuY9dDC/a9H9mzRMBHLGd1BvboL2oQbXmlouI+FZ0tVBUiQ5F/Jl0nD1T+JU68iMmJgZr1qwx09KGDBmCbdu2oUyZMujSpQsmTZpkPs+DmcvlQloaJ2hlxKB69uzZWLt2LR5//PEc7//II49g0SK2Pb2Kvwdvn332mRm84GPs3bsXtWvXNokIERGRQCjwJ9D27dsRG8t+3uJLFVMzrhdfUPzqch2A2rYb+9s28z0TwOu2NF1ERAKnXPkQPPBYBYwaWwUvjq6Ml/+3irlerpy7/sn3Jk+ebDLILVq0wLx587Bnzx7s2LEDEyZMQMuWLaw2hhUAAJZOSURBVHPMfPM+UVFRnn1NmzY1+xjsU7169TB//nzEx8fj4sWL5rtBt27dzO3MmrsDYd7HnYUOCQnB0KFDsX//fiQmJprsfa9evbI8b9euXbFhwwYzna5169bZnuPo0aNN5p+DDbnhOZw4ccKzuafoEQco+Jx/+MMfsGzZMhw6dAjr1q3DN9+w/kxERKQEBOcjRozAuHHjTDkZS8AiIyMzbFI4l8ILvpSam2ObwDFz3sCWs6fazu2/AdAfwL8APGebx4mISPHr1ScCnbpHID3NwfFjV8wlr/e6P8Ivz8dSbwa6zJAzGM6sKBVwfMyyZcuibdu2aNKkCV588UUTpB85cgQ9e/Y0xzRo0MB8T3juOX76AC+99BIeeugh9O/fH7fccosZIJg1a5Z5DG9jx441QTyz+pxGVxQ8z1OnTuHbb7/Fo48+muG2P/7xj2YQ4L//+79x9OhR/Pjjj/jb3/6GcuXKFek5RURECqvAtdQLFiwwlxwx957PxRFxXg8L0+pshZFYtvBfBjg//JwNvMvbueccJomxtzFQrwqgjz2eGXURESneUvbf3VEO8afTTDk7uS9/9/tyWPBFks9L3OvXr29KtHftKvhKIHlh5nzu3LkmY04HDvCT5ypm04lz290DACxD5zzxjh07muy0+z7MjPfr1w+rVq3y3J9zwJcuXVrkcxw5cqSZU8+Bic6dO5sqgooVK+Ktt94yt7MKkM+fnJyMP/3pT6hWrZo5pmrVqnjssceK/PwiIiIFVeBIun179u8WX6uWwNx34d/Emra0nYH4jzZjzsCcxfLMybNoks/Q3jZ50zxyEZHiwznmEREhOH4s4/zpCxfSUeu6MERXDfV5cM5Bc3+ZOHEipkyZYoJeBtIM1HMrMedAQYUKFbBkyZIM+xm0b968OcM+ZrN94dVXX/X8zBJ6Pj/n3buDcw5cMKnwwAMPICEhwex74YUXMGfOHDz11FMmaBcREQnq4PzYsWPmw5TlX9k1aZHCqZBe8C8B7roFBuAsirxig/ObAZS1cxYi7JvMr3zRdi10dl9XcC4iUnzOxqcjMdFBZOTVRnBuvJ50ycmwz1c4v5yNXBs2bFig+/E+mYN7NpHzNm3aNCxevNhMcWOAzpL1QYMG4e232fEkK2asicf/9NNPGW7zngdOly5dgj+wtJ1ZeX6HYYd3dmbnubgDc9q5c6cJ2uvUqWMaxImIiATlnPMbbrjBzP1ieRwv9+3bh1tvvdW/Z1eKJEUVfM65+2tTWRuYM7w/ZpvCVQYQZd9gx97GpnDVbAd3EREpPvGn0/HdN8mIruYyWfIy4TCXvP7d2mS/dG0/e/asCaAHDhyIiIis89q9G7554xxtYudyNy5ZlhnnaU+dOtU0dRs/fjz69u1r9jPwdXdad2MTOmaiWQ7P7w/eGx+nOPA1sOTefX7sYn/dddeZjLob58kz8VBc5yQiIlKo4JxNUjifnGuB9u7d2/OhLL6R5gm1C8Z9r4u2IdwOfmGyGXL31yKWsxe+aF5ERHxh7seJWPLvRISGhphSdl7yOvf7CwNzBsnr1683jdpYXs5M+jPPPGOWIcsOM8aHDx82HdF5PLuZMyvujc3cmDHnwP1vf/tbM+WNWWdi13Nm3+Pi4sw8bga/bBbHZrK8H5vCcb437/f000+b6wVVt25d00GewT5fH3/m5g60+dxcHo2N52688UbThI5z3t0l7fTxxx/jzJkzpps8m8+1adPGfNf54IMPVNIuIiIB4+RnO378uNOqVSvP9Vq1ajlXrlxxIiIi8nX/YNgiIyMd4mWgzyXzdrhSpJMO/LK5XE56XNzVS+/9Xlua12UyHwNw/gE48wAnHnB2Ac4hwDlqb/secP4DOLcEwevVdnVzuVxOXFycuQz0uWjT+6wt5y0mJsaZMWOGuczr2KioqFxvr1I11LmxQZi5LI5z5+f1W2+95Rw4cMBJTk52jhw54nz++edOu3btPMdQjx49PNfvuOMO5/vvv3cSExOdlStXOr169TLHuF//xIkTnT179jhJSUnOiRMnnA8//NCJjo723H/EiBHOsWPHnLS0NGf69Ome/c8++6yzc+dOJyUlxdxv4cKFTps2bcxtPB/K6/fHjY+ZHfdr6tKli7Np0yYnISHBuXDhgrN582bnySefdEJCQjI8zs033+x89dVXzqVLl5zDhw8748aNc8qVK5fnv4PY2Fj9ny4Fm/52l45N73Pp2VwBfK8LEIfm7wH5AVujRo0M+/iBd8MNNwT8F+2HX0qxb/ujKhU4OM8cqDMIfwdw/g04xwDnX4AzC3A22yD9hN0/HHDKB8Fr1qYPhNKy6X0u+Zsvg3NtJXdTcF66Nv3tLh2b3ufSs7lKQHCe74Zw7GjKhi5JSewBfhXL1jKvb37hggqoC+Ngzeq44fwvTWnyw/Eqa+fPVBdAJQDr7Hrndb06uXNG41mutWuP/R+fvgIREREREREprHwH5+zaunv37iz73EugaJ3zojkSxZXI9xXoPplnqXOmHVdLnwNgJoABnG9oj7tsg/MKtikc99Pf7ZJrIiIiIiIiEjj5jqS1vrl/lb3I8Llw3FlzBt+DAbhXjP0cQBcApwHUsRtb3Jy3ndx72kZyb/rkFYiIiIiIiIjfg/NVq1YV+kkkbzedOlHo+7oz6FxFtodXcH7KlrGXtSXuyTZLXt52dj/OQRd2rLWPUd0us6Y10EUkWNTQ3yYREREpJVSDHiQqXr667mpRcOm0BwFMsl9i+WX2awAPA4iwGfPydttvb/8VgBcANLYl75fsfd5VubuIBBD/ZnHV7A762yQiIiKlRL7XORf/ulQ+rNAl7elezeHq2Hnk9WzGaTWAxbYhXJQ9hoE5V6OtDKCqzZ7z9sP2sjeAJ33+CkVE8q+v/Vukv00iIiJSWihzHizKZ27vlj8h2byhdwO4y84158b++ZsAXA/gJ5sxj7bX6YxtGBdqb4NXubvKSEWkuNWwGXN3BRDpb5OIiIhc65Q5DxKXwwv/VoR4NYVLs4G2O/iOsPuqADgA4IrNqvPZ/mO7u/8aQCsA7QA0scF8RfsFWUSkuFW3peznMu3ndf1tEhERkWuVgvMgcTiWC5z55o2MsKXuiXaN8wT7pZZl7X8D8JwtGT1ng/gwG5AzwI8F0Mx2cXdnqkREitMpO8ecU2+88br+NomIiEipLmufO3duvh+wV69eRTmfUutgdc7+LpoQ2xQuxGbLHZsZb2qDcGacXgHwhV1m7Xe2zL267eieai+Zcf9MZaMiEiDuZpacYw47kFjZ/q2ao79NfuE4Du6++2588QU/IURERCRoM+fnz5/3bAkJCbjrrrtw2223eW6/9dZbzT7eLoVz867CL6WW+Q0NsaMuNW05+432+kXb5ZhfePvbstHvbbl7iA3er9g56It8cjYiIoXzrg3EQ72m4syx+6VgatasiYkTJ2Lfvn1ITk7G4cOHMX/+fHTowJn9vteuXTsT7EdFsV7LP4YNG4Y1a9bg0qVLOHuWi4ZmxXPIvN17772e26dPn57tMdu3b/fbeYuIiBQ5c/7YY495fh47diw+++wz9O/fH+npLJ4GQkNDMXnyZBO4S+HUOcmQ2Hccm0VnBr2MDbz5deOIna/ZzM5N5/5tAPbaLHtFu5/dkYtCaxOLSFFwIPFN2/ytxjX2t6RqtVBUiQ5F/Jl0s/lTTEyMCWLPnTuHIUOGYNu2bShTpgy6dOmCSZMmoVGjRghmLpcLaWn8JMsoPDwcs2fPxtq1a/H444/neP9HHnkEixb9MtzM34Pbc889h6FDh3quh4WF4fvvvzePKyIiUiLmnDNQHzdunCcwJ/7897//PUMQLwVTPrHo65y7m8K5l1XzLnO/YjPkdM4G7FtsAF3DK4ivBGB5Eb4ER9g57e/ZL9bv2+tcW11EpKD4t+iHayQwL18+BH95vAJGj43CS/9fFP6//40y18uVK9xqHfnBgXNmg1u0aIF58+Zhz5492LFjByZMmICWLVvmO/PdtGlTs4/BPtWrV89k3+Pj43Hx4kWTbe7WrZu5fcWKFZ5AmPdhhppCQkJMMLx//34kJiZiy5YtGabCuZ+3a9eu2LBhA1JSUtC6detsz3H06NF44403zGBDbngOJ06c8Gx8TDcmFLxvY0VglSpVPOcrIiIS9ME5R5YbNmyYZT/3MYMuhRN+JWtmoDDcgTm5h09CbZDO4Nu7qdIUuwZ6lC1990XZqNYmFhHJXu/7I9D5D+XAse3jx66YS16/5wEOa/oeA00GusyQMxjOrChT0fiYZcuWRdu2bdGkSRO8+OKLJkg/cuQIevbsaY5p0KABatWqZTLU9NJLL+Ghhx4ylXe33HKLGSCYNWuWeQxvrNBjEM+s/tatWwt9ju7zPHXqFL799ls8+uijuR7LDPzSpUtN2b+IiEiJWOecI8rTpk3Da6+9hvXr15t9t99+u/kg1Whz4Z2rWBGIP++zwJw/p9rr4TZzXcar3Hw+gD/ZpnBhNoj+zgbmLCctDK1NLCKScyl7i9+HZyhld19y/78/T/J5iXv9+vXNoPmuXbvga8ycs1mse372gQPu2iyYbDqdPHnSMwDAMnTOE+/YsSPWrVvnuQ8z4/369cOqVas89x81apQJkotq5MiR+Prrr83AROfOnU0VQcWKFfHWW29lObZ27dom83///fcX+XlFRESKLTgfPHgwfv75ZwwaNMh8mNHx48fxt7/9DePHjy/0iZR28eFFy5w4dukhzhmHDbZTbTY8zAbpbKp01GbHy9iMNoPlfTab3tVm1FmOXpS1iTPnHM7Z52bwruBcREojzjGPqBBqMubeLlxIR+3aYYiuenUOui+xjNxf2GBuypQpJuhlIM1APbcScw4UVKhQAUuWLMmwn0H75s2bM+xjSbsvvPrqq56fWULP5+e8++yC84cfftiUwH/+OdcyERERCYwC16FzPhgD8Tp16qBy5cpm48/c5z0PXQqmXHLGL2wFle51ycx3sldQzjXMmbuYaMvOPwHQxivDnerVbKm9DbILQ2sTi4hk72x8OhIvpSMyMuPHLq8nJvqnMRznl/NzObupaLlxf5Z7B/dsIueNFXSxsbGYOXOmKWtnQP3000/n+JjMWFP37t3RrFkzz9a4cWP07u1eNO8qdmD3B5a2161b1wwIZMaeOXwtly+zJaqIiEhgFGmS+IULF8wmRZdQgSuMFz5rvtc2eEux1+NtMMysdYj9eaENoN0Z7l961l7F6xVthrsoaxO7m8yFe5XRF6XJnIhISXfmdDrWr001GXJuZcLh+Zn7/RGcc4mxxYsXY+DAgYiIyFqdldNSZ5yjTe7qOGIgndnRo0cxdepU09SNlXN9+3L4F0hNTfV0WndjEzou48ZyeC7p5r3xcYoDXwNL7t3n592I7qabbjIDDiIiIiUqOK9RowZmzJiBn376yYwwX7lyJcMmhVPjQtGWoWOwvQfAfnudy6JVtYFxORuw32/nnvszw621iUVEsjf7o0R8tSAZ7J3KUnZe8jr3+wsDcwbJ7BHDRm0sL2cm/ZlnnjHLkGVn7969pikaO6Lz+D/84Q9mKps3NnNjSfsNN9yA3/72t2jfvj127txpbjt06JDJvsfFxaFatWqmnJzN4rjSC+/HpnDMuvN+zLbzekExA84O8gz2+fr4Mzc+F/G52eCNjeduvPFG04SOc96zK2nncZwH/8MPXBdARESkBM05/8c//mE+DF955RUz15xl7lJ01S8WvgKB70AtAE0AcOYec/AuG4gzG34IwAk7xxx2TvnXXtfP2cC8ug2ki5LhvpbXJhYRKYrkZAczp10yzd/cc8z9vc45m641b94cw4cPN9ltZsOZGd+4cSMGDBiQ7X040N6nTx8zp5zd0r/77juMGDECc+bwE+IqBsTshM5pbVySjGuJP//88+a2Y8eO4eWXXzZd19kolgP67JTOBm18bnZtZ3DOOd6bNm0yDWYLasyYMWYNc+855XTnnXdi5cqVJnnAgQkOBrA8nwMOL7zwAt57jwt9/qJSpUom8+/uKC8iIhJoTkG2hIQEp2nTpgW6T7BskZGRDvEy0OeSedtRq6qTDvyyuVxOelzc1Uvv/dlsqYBzGXB+Bpx/Ac5CwFkGOEsA50PAecdu8wDnc8CpDjjlAec5e32pvXzO7nefUw3AucUeH+jfz7W6uVwuJy4uzlwG+ly06X3WlvMWExPjzJgxw1zmdWxUVFTAz1ebf/8dxMbG6v90Kdj0t7t0bHqfS8/mCuB7nd84tMCZc65h6s8OsKVVfNWKwM9nCnVf96y+KgB+A+BTLm8H4HQeXdNzynBzZmJfuyxaBVsC/3URl1kTERERERERH845/+tf/2pK1WJiYgp6V8lNOQ6WFB7vnW43lrfXB9AKQFt7PSyHOeUMyH/IVHre15a8p9ll0dLs9SeLdIYiIiIiIiKSkwJnzj/99FPT9ZUdVhMTE7MsO1K1KtuQSUFdqsC2bYWTbpdO22nnm7ew3dor2Y7psbYLe0I+5pTXsBlz9zJr8LpsbzPtmkMuIiIiIiIS4OCcmXPxvYpnE4tU/sA++T8DaG0bwVW0+8va4J3N3j7LR9d09zJrzJjnVhIvIiIiIiIiAQzO2XVVfO+6k0VbL55Z8o4Ayti1zi/YuegRtlM7g+vP8zFn3HuZtZM+XmZNREREREREfBScU2hoKO6++240atTIXOfaoPPnzzfrmkrhxIdHIAbnC3Vfd3s+93rmIXaeeKrdX41L29jAuobNjue0xNnJTMusJdvjIwF8oqy5iIiIiIhIcATnN954IxYsWIDrr78eP/74o9nHNUvZxb179+7Yv3+/P87zmvdztSjgyHGfPFY5W4KeYLPoEbbp2/357MD+rp2r/he7fro5P5uVZ8m8OraLiIiIiIgEuFv7xIkTTTO4unXr4tZbbzVbvXr1cODAAXObFM6vzmVe+Kxg3DULIXYrazPmLEc/YkvS89uBnVnyaABs9fc9gGUA9gLooY7tIiIiIiIiwZE5b9euHVq2bImzZ8969sXHx2Po0KFYs2aNr8+v1Kh40V2E7ptRlnSvIP3fAH6bjw7s7vXNuwFoboPzZJtldxfcq2O7iIiIiIhIEGTOU1JSEBnJ3GpGFStWRGpq0QLMUu0Kc9m+4XhtbAqXaEvZ2RTO2znb1b2G3UYC6GPvc9luvwLQKJvjRUTk2uE4Dnr0YH2UiIiIlJjg/F//+hfeffddtGjB1bSvuv322/HOO++YpnBSOGV82EzPsW9siL3sbQPqzCvQV7bzx+8G8KHNmsd6lbS710+/zs41V8d2EZGSp2bNmp4pacnJyTh8+LD5vO7QgV1IfI8Vdgz2o6Ki4C/Dhg0z1XqXLl3KUMnnjeeQebv33nszHHP//fdjy5Yt5nGOHTuGadOmITqan4IiIiIlIDh/9tlnzQf82rVrzYc8N35A7t27F88995x/zrIUSKjE8Lfo3Blz99zzNBtMM3P+axukh3t1bWcQ3t1myCNsZryxvSxv/4GE2wCdxy9XSbuISJFUrRaKmxqEIbpqgT+CCywmJgYbN240gfiQIUPQpEkTdO3aFcuXL8ekSZMQ7Fwu1nJlFR4ejtmzZ2PKlCm53v+RRx5BrVq1PNvnn3NR0avuuOMOszwsA/JbbrkF99xzj0k8vPfeez5/HSIiIvlR4G8G58+fN8uoNWjQAL179zbbzTffjJ49eyIhgf3BpTAOX1/4kXpmuNNzeFNT7TJq2+3c8fK2kzuPWWQ7sDNwr2If44oN6CvY+4bbY7h/ju3kLiIiBVe+fAgefjwCr4yNwrD/rxJe/d8oc70cl9jwk8mTJ5uMMYPOefPmYc+ePdixYwcmTJhg+sfkN/PdtGlTs4/BPrERLLPv7Dlz8eJFbN++Hd26dTO3r1ixwhxz7tw5c5/p06eb6yEhIaY/DVd1SUxMNBnrXr16ZXleDh5s2LDBTKNr3bp1tuc4evRovPHGG9i2bVuur5/ncOLECc/Gx3T7/e9/j4MHD+Ktt94yl0w0TJ06NUNloIiISNCvc07MnnMT33AlFG3O+WX7ZrrXPHdsSTqHS/i974wNuF9jAz+vNc+72HL1SzY4ZzeBNBu8l7ed3ZcA+Lsy5iIiRfLn+8uj8x/KI/5MGo4fS0NkZKi5Th9OY3cQ36pSpYoJdIcPH26C4ewG2wuLWXdmr9u2bWtKwhs3bmyCdC6rysF6DgRwEJ+D9klJSZ5lVx988EH079/fDBLwvrNmzcKpU6ewatUqz2OPHTsWgwcPNkF8TiXrBTnP999/3zwWp9+5BwqIFYCvvfaaGVRYuHAhatSoYRIOXC5WRESkRATnc+bMwfr16/F///d/GfazXO53v/sd/vznP/vy/EqN2mcLX3Xgnlue4JXpdry6tbMosK4N4Hd5BdkhNiPO4J1ff1Ls40R6lcUzMB+jtc1FRIpcyn7778uawDz+zNVaJ/dli9+H48vPkz3XfaV+/foIDQ3Frl38y+9bzJzPnTvXZMyJy6m6MZtOJ0+e9AwAMJDnPPGOHTti3bp1nvswM96vX78MwfmoUaOwdOnSIp/jyJEj8fXXX5uBic6dO5sqAjavZaacvvnmGzzwwAP49NNPUa5cOZQpU8ZUAwwcOLDIzy0iIlIsZe0c6c5uVJmjzrxNCqdCOvPchePYwLqsDcwTvUZeOI+8I4DbAFzP5jecgwjgFnu/r23wHmlL2yt4dWtnEM/ZfArMRUSKJjo6FOUrhODChYwBOK9HRISiqh/mn7OM3F/YYG7EiBFYvXq1KTHnXPa8BgoqVKiAJUuW4MKFC57toYcewo033pjhWJa0+8Krr75qAnCWzzOhwI2JBLdGjRrhzTffxJgxY3DrrbeiS5cuuOGGG0yGXUREJBAK/G0gpyXTLl++jEqVKvnqvEodx1W4L1HpXk3g+GYm22D6ks2Ku2w2fb8tUX8WwJcA3gTwvj2eq9NH2eA8xHZkdwf7f/L5KxURKX3i49ORdMkxpezeeD0xMR1nfJw1J5aOp6eno2HDhgW6H++TObhnVtkbm6jFxsZi5syZJjBnQP3000/n+t2BunfvjmbNmnk2lsOzlNwby+T94dtvv0XdunVNFt9dZs955uPGjTNz17/66is89dRTePzxx03zOBERkaAPzvkBlnkpErrvvvtMkxkpnPgI5rgL9waG2kCaQfUyAKvtJeeZn7brk++1c8yr2K7rp+zccq5quwXAIc4/tBvL43fbJnLt7fEiIlJ4Z06n49u1KYiu6jJd2suEw1zy+vq1qT4vaSfO1168eLEp047I5jMmp6XOOAecateu7dnHQDqzo0ePmgZqbOo2fvx49O3LBTnhGcD37rTO7wdc3YXl8O6eNe6Nj1Mc+BpYcu8+P/5O3AMRbmlpaX6vOhAREfHZnPNXXnnFNHphGRrnctFdd92FPn36mGVIpHAuRBYuOIfNml+xJenMj/wHQC0bsLtrHFhweINXo7ebAWyyt7W2Qfz3NtOeZDPq4bazO4N6NYMTESmaTz9K9Mwxr107zGTMv1qQ5NnvDwzMmR1mrxjO5d66dSvCwsLQqVMnDBgwwGSuM+PSqFwLneXqbCbHxm6DBg3KcAy7vXM62+7du03jufbt22Pnzp3mtkOHDpmgNy4uzkyDY0M4Notjhpr34zx4lsNzcKBVq1amaRyXNCsIZsC5HjmDfQ4CsJu8+9yZeedzc313zm/noABfL+e88xzcvvzyS7NsGhvUcRCDgxHsAM8M+/Hjxwv5GxcRESkap6DbH/7wB2f16tXOxYsXnVOnTjnLli1z2rZtW+DHKe4tMjLSIV4G+lwybz/Wr+KkA79sLpeTHhd39dJ7fzbbFbulAc5lwDkGOEcBJxlwUu3lZXv7FftzAuCsBZwPAGcV4HwNOPMA5x3A+RBwPgWcfwHO54BTPQh+P9fq5nK5nLi4OHMZ6HPRpvdZW85bTEyMM2PGDHOZ17FRUVG53h5dNdS5qUGYuSyOc69Vq5bz1ltvOQcOHHD+//buAzyqMvsf+ElCAkkIoYRejUhTiqhIEwSpmpVdsCzqjyJSFIVlEUUBQURFBSlSREAEWfkrRRZF6kqRLtIFpEoLhISWkEqS+3++L+8db0LKJJnJTDLfz/PcZ3Jn7szcmZtk5txz3vMmJCQY586dM5YvX260bt3atg106dLFtt68eXNj//79RlxcnLFp0yajW7duahvz9U+dOtU4fvy4ER8fb0RERBjz5883Spcubbv/yJEjjfDwcCMlJcWYN2+e7fpBgwYZR44cMRITE9X9Vq1aZTzyyCPqNuwPZPf+YcFjZsR8TR07djT27NljREdHGzExMcbevXuNfv36GV5eXmke59VXXzUOHTpkxMbGGhcuXDC+/vpro1KlStn+HoSGhvJv2gMW/u/2jIXH2XMWHxce6xzEoa5/o9zwTcn35WJF/1wH5xkF6zEixs10QXmKXhCwx4kYUZYAfISIsUvEOCpinBMxIkSMKyLGOhGjmhu8P4V14QeCZyw8zgV/cWRwzqXgLgzOPWvh/27PWHicPWfxKQDBea7aw6IUDQ1T3n//fVXOBvfff79UqlQpj0l8zxUXhCJ0x8CRFd3UzewB72VpHgcYVVdcT7G2VW9bRUTu0mPMza7tD4nIjyIyWJfDExERERERkRuMOUdXVsw/irlLMeXInDlzVNOZrl27qrFfPXv2dMJuFn5nK4bIXcfQui3vcMYFI9gDLIG5OS49VY8lNw/8Rn3b0/oyQk+rFqADezSZKyMi3fX26PJOREREREREjpXjzPmnn34qX331lWoQgyYrJjR94TznuZcc47jOsHik9I/mpQNyL50lx3JSRGaJyCO6Q7uhrzfnOffTl7g+hp3biYiIiIiI3Cc4f+ihh9TUKelduHCB84LmQcXz1xz+mObgBfNn0QF3ip5mbZ4O1gN1t3YE4sX0L8UtfVsx/XOULoNH53YiIiIiIiJycVl7YmKilChR4o7rkUk350alnPO7dXtuVUcyx5mn6p+TdZCO+cu/E5EvdAl7rJ52LVxE7tb39bVcnrXMo37ZwftYTmfj8bj87SEiIiIiIk+V48z5ihUr1FypmCcVDMNQ841+9NFHsnTpUmfso0eILuXvlIw5st4Julx9r4jsFpHxIvKNns8cQfHPOkBGdvxPHcQX1Zd/6utx+wYHBtABusncbD2OfQ6bzhERERERkQfLcXA+dOhQKV68uFy+fFn8/f1l06ZNcuLECYmJiZERI0Y4Zy89QFLpHBcx3MHQwbjJS2e+sdzQndlDdBBsDYaRQV+i74/s+E7dKG6nXjf07djOUfqKyFO6xP6svsR6Pwc+BxERERERUUGR44gwOjpaOnToIM2bN5eGDRuqQH3Pnj3yv//9zzl76CFSzVR3HiXoceJFLAH6LZ2p9tXN3qJ1KTuCYdGZ6yk6m17OUmJeNt26o+Ax2+rHNMvkzcs2ej9Y4k5ERERERJ4k1+nabdu2qYUcI76k4+Y5T7Ac2CjLdGjX9PjzajpTHZEuGDYXU/p1RzHnUUfG3Oq63jcE7wzOiYiIiIjIk9hd1t60aVN54okn0lz3f//3f3Lq1CmJiIhQHdz9/NALnHIjogJ6oeednyVbnqgD83A9fjxeLwjeK+nb03dgx8/3OnnKtEiduS+Z7vqSTmo6R0REWUP/mC5durh6N4iIiDya3cE5msDdey/Cttvuu+8+mTt3rqxfv17Gjx8vf/vb3+Stt95y1n4WekFRf80ZnxvIiMfp7HOiXv9DRK7qYBjBupmbT9I/19DXX3Zwg7bsAnxrE7py+oRCOSc0nSMiIpHy5cvL1KlT5eTJk5KQkCBnz55VzV3btsUAI8dr3bq1CvaDg4PFWd5++23ZunWrxMbGyrVrGU9Fin1Ivzz77LNptnnllVfk8OHDEhcXJ0ePHlVJByIiIrcva2/UqJGMGjXKtv7Pf/5Tdu7cKf363W7hde7cOXn33XfVQjl3z5m8haQYsn5GB+fldKY8RU+NhonvLulg3Euv++uAHKXlz+nx6MiZROrrSqYbk26PAN3ora0uW4/VQfgXOmNvZTaXa6NL2W86oekcEZE7KhPiLWXKeMuVqFS5cgWnUp2nevXqKoi9fv26DBs2TA4ePCi+vr7SsWNHmT59utStW1fcmY+Pj6Sk3DnVKCr1Fi9eLNu3b5c+ffpkev9evXrJ6tWrbet4H0wDBgyQDz/8UPr27Su//vqrNGnSRGbPnq2C/R9//NEJr4aIiMhBmfNSpUqp8nXrmfFVq1bZ1vHBhinVKHcCr+ctc46ge7+I/KKz5UdF5JgOtqvrYDlcjz0vobPn2Oa0Ds57WBq0JVmawLXJQYl7Tjqwx+ugv6/O0PfV6+mDeCKiwsLf30t69wmQDz4qIaPGBMkHH5dQ68XQxdNJZsyYoTLGCDyXLVsmx48fV5niSZMmqeFq9ma+0QAW1yHYh2rVqqns+9WrV+XmzZty6NAh6dy5s7p948aNtkAY95k3b55a9/LykuHDh6vhcMhU79u3T7p163bH83bq1El2794tiYmJ0rJlywz3ccyYMTJ58mR1siEr2Ad8dzEXPKYJWXIMyfvuu+/k9OnT8u2338oXX3whb775Zo7eYyIionwPzvGhdtddd6mfcda9cePGsmPHDtvtQUFBcuuWdSIvygm/xDszAzk9kAiEe6cLsn9FVYPOTlfXJeQIgI+LyCERuai7t1fQY9GtrmcwJj0juL2ViHTKRYCP239nKTsReYB/PucvnZ4oJqkpIuHhKeoS692fR92R4+GkOgJdZMgRDKd34wYm2cwdPGbRokWlVatWUr9+fRXQIkhHFV3Xrl3VNrVq1ZIKFSrI4ME4BStq6FuPHj1UxhrD5HCCYOHCheoxrDBUDkE8svoHDhzI9T6a+xkZGakq/Xr3xifkX7D/KPO3io+PVycyihTJ+/SmREREOWX3p89PP/2kPjDxAfz3v/9dfdD/8gvytLc1aNBAjWej3EnywyRneStrx1JaROqLyAV9fbIuLxcdpFfUQXhlHUAf1B3dRQfQCNTtbdBmLWMvLyKhInJKZ+7xvMAO7EREt0vZmzb3k6uWUnbzEtevWJ7g8BL3mjVrire3txpL7WjInC9dulRlzAGZZxOy6XD58mXbCQCUoWOceLt27Wwn9nEfZMb79+8vmzdvTtPjBv1s8gpD8X7++Wf1fQVTwKKKANO/fvbZZ+r2NWvWyEsvvSTLly9XU8I+8MADah37GhISIpcuYUAYERGRGwbn+JBDSdymTZvU2fGePXumyZS/+OKLsnbtWmftZ6F3pURxqRqBUDj3EN6n6kC4lJ46DZd36YD8mC5rx3Y4clV0YI51fAUJ0ve9rgPzsnoceGQ2ZewI6K/qzDzGuKMGwCw0LKmfq7R+PAboROSJMMY8IMBLZcytomNSpVIlHxW8Ozo4Rxm5s6DB3MyZM1XQi0AagXpWJeY4URAYGCjr1q1Lcz0C4b1796a5DiXtjjBu3Djbzyihx/Nj3L0ZnL/33nsqs4+TBXivUCE4f/58lYRITXVuLwAiIqI8BedXrlxR48FKlCihgvP0H1xPP/20up5yJ6ZYbvqi/8VLL9462K6hg+VQSwb8IV2mHmi5DwJyHMmterx6SzsbtCGIb6/Hr1fRDeX89HKXbk6H57lPZ+5HZ9MgjoioMEPgHRdnSImgtEE41nE9msM5GsaX47O6Tp06Obqf+fluDe4xnM0Ks7Ug84wpVhGgo2R96NChMm3atAwfExlrwPYXLpi1XbdZx4EDOrA7A0rbkZXHCYGkpCRV0o5mcsjco6P9xYsXVZPb6OhoVQpPRETktmPOTfjQyuiMMrqbcsx57kX7mxOd5Q5K2q1HBRny2vq6GH2gq+pSdHzd8tVBPH6+oLPat3LQoA3b36PL2Q39HNH65+I6QL/doeB207nsGsQRERVmCL53bEuS0rpTu6/f7Ww61nG9M7q243MZAfTAgQMlIODOce2ZTXVmBqYVK2Ig1F8ztqR3/vx51VANTd0mTpyoup4DAl+z07oJTegQDKMcHkPgrAseJz/gNaDk3tw/U3JysjphgO82mIkGndrRmI6IiCi/seOJm0gOyNuYcy99MA0dZEfpoBgd2jFRTqN0Z2NSLOPU9+kMOhq3faMbtNkjSD+OGcDH6cw5XskM3QU+3jJm3bw0n4d5CSLyJIv+E2cbY45SdmTMV69MsF3vDAjMMZXarl27VNYYDdbQ7Kx9+/by8ssvS7169e64z4kTJ9Rc6OiIPmLECNXYDVlxKzRzw4wtx44dU43n2rRpI0eOHFG3nTlzRgW6YWFhql8Nmqyhsm7ChAnqfhgHv2XLFnVyoEWLFuqk/4IFC3L0ujA7TOnSpVWwj5MA6CZv7jsy73huZMNRso6TAni9GPOOfTDdc889qvkbMup4Df/+97/lvvvuU8P2iIiIXIHBuZtILpW34Dx9kF5JRDZYgmPDEpzjZxQRIk9TTJe9X8lF47YYy5zpSTowx+Pf0Fl0X90N3ooN4ojIU6Ex+Ly5car5mxpjng/znKPpGmZXQZCN7Day4ciM//bbbyo4zwgyyd27d1djyhHMY6rUkSNHypIlGOx0GwJidEKvUqWKCq4xl/iQIUPUbeHh4TJ69GjVRBbTqCHwRqd09K7Bc6MEPjQ0VE1zhkZsH3zwQY5f19ixY9Uc5tYx5fDoo4+q3jio5MOJCZwMQHk+gnYE35jH3PoacNKhdu3aavsNGzZI8+bN1ckFIiIiV2Bw7ib8S5r9zR0ToCNofhBZBN2MzRyTbmbLEUSnZtKZvZwuWzenQstIpG4wV00/V3Gdsb+sS9gxVVusftzLOegAT0RU2CEgd3ZQboWu46+99ppa7G0et23bNls2OqNtBg0alG0zNmtDNmsjOSwZQVBtbxM7BPvpp0azQjk/lqygiz1OXBAREbkLBuduIiBdQ5y8wte+WrohHHLyZl7efBY/ndmO1r8ECOBX6FL0trqZW1YN3BBcr9djyM/rxy2qA3Vcf1jfF7dLDjrAExEREREReaIcN4Qj5wg67ZjutIYl+DbXrXkIM2OeYgmab+mA2dDBdIqdDdy+0Pe7pYPyW+k6vJu3e+sMu3c2HeCJiIiIiIg8FTPnbqLkecdkzs1AHGPJk3VjuDI6IPfVlzgNkKjHib+tx6bjfrN1RtveBm7xuqP7N7oUPn0ZfHa3ExERERER0W0Mzt2E1y3HTttiznde1JIlR7COnsB7dJB+SwfmCJjv1aXsyJjntIFbZDZBd3a3ExEREREReTqWtbuJJD/HdGs3maF+gM5gF9EHO0kH6yUsgbnoS7OBmxUbuBERERERETkfg3M3cSkk2KGPZ3ZnL6Iz5qmWbHlGY78v6wZuZXWW3M/Std0axBMREREREZHjsazdTfh4m8XnjmM2gyuqs+LozH4tXYM4qy8sY8yr6Yw5G7gRERERERE5H4NzN1E+KsbhJe1XROQPEamim8IF6OuSLFOcoWGbiQ3ciIiIiIiIXINl7W7CSHJcQzhkxg1dxn4XpmnTWfNEnREP0UF3G122nh5u+52BORERERERUb4pcMH5K6+8IqdPn5b4+HjZsWOHPPTQQ1IY+CUjlM45w7JYrzObu5XXlyV1N/ZUnUkXfVsdh+w9EREVZIZhSJcuXVy9G0RERB6tQAXnzzzzjHz66afy7rvvSuPGjWX//v2yZs0aKVs2o/xvwZJYJOcjDMygPFEH48m6ND1ez3OOseYmNHgrrbPmFUWkvYjcrec5Hywi/ukeu5yeXq2sndcTEZF7Kl++vEydOlVOnjwpCQkJcvbsWVmxYoW0bdvWKc/XunVrFewHBzu20anV22+/LVu3bpXY2Fi5dg3dVDLWs2dP9V0BJ/QjIiJk2rRpaW6vX7++bN68Wd2O92XYsGFO22ciIqJCNeb83//+t8yePVu++uortT5gwAB54okn5MUXX5SPPvroju39/PykaNG/QtSgIBR4i/j4+KjFnVwpFywSgVnFNeyft/fty2yYreQMfUB9LGddfPX1Pjprbh7wonr8eYIef47tp+px6S/pkvdAHfSjW/t/ROT5DK6frU8GUO7g99Db29vtfh/JsXicCz57j52Xl5ftEgFqRkJCvKV0GW+5EpUqV67krmrKXtWrV1dB7PXr11XgefDgQfH19ZWOHTvK9OnTpW7duuLu73tKSkqGn++LFy+W7du3S58+fTK875AhQ2To0KHqde/cuVMCAwOlRo0aab4TrF27VtavX6++TyBQ//LLL9V7he8a2e0X/6YLPx5nz8Dj7Dl8XHis7X3OAhOc48vEAw88IB9++KHtOnzxwYdqs2bNMrzPW2+9JWPGjLnj+vbt26uz5O4ksE4lkSrhf12BA9i4Mb7hiWTwxcSUqgNrP31pdmjPaAy6l97GzLAjW47TFfhVeUZn4BF81xeRqzoAD9HB+tMighzIjXTX42vdCue9LYUe/lBRBYIv8hl9AaXCgce54AsJCRF/f38V0GWXEUYQmJFi/iLduvnIQw/7SECASFycyK87U2TpkhRJwJlSJ/jiiy9sn3txeEINQejSpUvTvBbsN9ZbtmwpP/74owrsb9y4Ycsw//LLL9KgQQOVYa5atap88skn0rRpU/X5jOveeecd+eOPP2Tjxo3qPghy4ZtvvlFD0vD7/69//Ut69eol5cqVU5n8jz/+WGXxwXzep556SkaOHCn16tWTrl27ypYtW+54XZMmTVKXzz33nHrc9McE6+PGjZN//vOfKjMOV65cUftpbosT+ziBjyD+1q1bcuHCBZk1a5a8/vrr8t1332X4fuL44/fgkUceUe8P/6YLN/7v9gw8zp7Dx4XHGp8dhSo4xxejIkWKqLI0K6zXqZPxyGkE8iiDt36o4sN33bp1EhPjuO7ojtA/tZ40WnM4bXCOrMvq1ZkG5ym60ZuvzoQbmYxTMCyXCMwv6Ix5kA7CL4pIE3zJEZFaei50ZNXP6u2rikgjEdkrIufTlbhj3PpuNo/L0z8JnGRavXo1PxAKMR7ngg+BGMrA8dlhBqxZZc6jo6PvyJx3eypA2jzmI1eibklkZKqUCPKWNo95S0LCLfly7l+Bs6OUKlVK2rVrJyNGjJCLF/GfPq30rwMl4rju5s2bttvNbczPTLwuXLdw4UKVfUCQivshkMZtv//+uwqoly1bJrVq1VLX4WQ4LlGKjuFp/fr1k+PHj0urVq3UyYMzZ86oANp83lGjRqkA+dSpU6pkPav3Gycc8D6n36ZDhw5q/0qWLKmy6/j837Ztm8qknz9/+5OsUaNGsmnTJomKirLdDycKEKzjOJonF6zweHg9OFGB+/FvunDj/27PwOPsOXxceKzNCu5CE5znRlJSklrSw8Fwtz++xFJqx9JemZp6+7pM9hUHzzzMmc1dbuWlA2/RwTx+xtfB+/UYcny1SdHXo8s7HNSBfFGdWbd+1bymu79jmrZLOX/JpKWmprrl7yQ5Fo9zwWbvcTMD8vSBOUrZmzX3S1PKbl42be4n/12e4PAS95o1a6oA9ejRo+Jo1apVU5n3Q4cOqXU0ajVdvYrTviKXL1+2Bc0oQ0dwjpMFaOZq3gfZ8v79+9uy24AMPKri8iI0NFS9djzn4MGD1X4gk46T88j+I1NeoUKFNPsNZgIAt2UUnFt/H/g37Rl4nD0Dj7PnSHXRsbb3+QpMcI4z1MnJyaqxjRXWL10q+KFh8ZTcldn76ux2eoYlIDcDd3ztS9GBOJ7tJL6A6MZwybqLe5B+TORIKonICUtgjtJ5K3SAv6nnQyciosxhjHlAgJdcCE/74RwdkyqVKvlImRBvhwfnZhbfGdBgbubMmSpDjUAagTrGs2d1ogBl8wiOrRC0792Luqy/7N6Neqy8QWCOxx40aJDtObt3766+L7Rp00aNNSciInI3BaZbO85y//bbb/LYY4+l+eKBdZSsFXTBZ3I/4LBIJuPMzbHm6ZdkXYaOrHmo3u6GXsxy9yAdjCNALyEiyHME61J2P31ZVjeFY0k7EVHWrl5Jlbg4Q5WyW2Ed1yOj7mgoHUeGILOhX5nBfdIH9xhXbjV37lyVnf7666/VeHQE1K+++mqmj1m8eHF1iSauKCc3F5TDY4y5Fcrk88os4z98+HCak/xYkPUHBOoZnfA3byMiIspvBSY4B4wf79u3r/To0UN92cBZe5yJnzdvnhR08clpv/jkhlcW1yFXE6UDchTxvSkiaJV3Dk179O2pOgseo7u2++lAfolu/rZE/8JU05dYv91qiIiIshIVlSrbtyWpDHmZMt7i6yfqEus7tiU5pWs7xmtjutGBAwdKADrQpZNZY7vIyNunXCtWxMSbtyGQTg9jt9FArVu3bjJx4kT1+QzmcDJrZ1oEyZjGDYExGsFZF3MMuCOhQz3Url07zRh89K/BGHfAiX2Me0c/GxMa52EYQFYl7URERM5SYMraAd1TMaf52LFj1Xiwffv2SadOndS4toIuyIHd460d283ydgTTxXXAjdMAr4jIPr0dAvTbeQSRJF2qjl+M5XoedDMzPgVdd3XWHO84M+ZERPb75j9xtjHmKGVHxnzVygTb9c6AwByB6q5du9RY7gMHDqhgFEHoyy+/rDLX6Z04cUJ1NcdsJ2gmh8ZuaKSWvlv6qlWr5NixYyroRan4kSNH1G0IfpF9DwsLk59++kk1UEOztwkTJqj7oeQcHdhxcqBFixaqWdyCBQty9LrQLb506dIq2MdJgIYNG9r2HZl3VA0sX75cpkyZohrQ4TnQJBaB94YNG2xd5EePHq2qADAd63333afGp6MhHBERkasYnrIEBQUZgEtX70v65WJ5fyNV5K/Fx8dIDQu7fWm9PhdLimVJEDF+EzGWiRg7RYwDIsZ/RYytIsZpEeO8iHFBX1/NDd6Xwr74+PgYYWFh6tLV+8KFx5lL5kv16tWNBQsWqMvstg0ODs7y9jJlvI1atYuoy/zY9woVKhifffaZcfr0aSMhIcE4d+6csXz5cqN169a2baBLly629ebNmxv79+834uLijE2bNhndunVT25ivf+rUqcbx48eN+Ph4IyIiwpg/f75RunRp2/1HjhxphIeHGykpKca8efNs1w8aNMg4cuSIkZiYqO63atUq45FHHlG3YX8gu/cPCx4zI9bXhM/6OXPmGFevXjWioqKMpUuXGlWqVEnzOPXr1zc2b96sXgfelzfeeMOu34PQ0FD+TXvAwv/dnrHwOHvO4uPCY52DONT1b5Qbvin5vpysWcKpwbn580URY46I8bkO0BGE79A/LxIx1un1wW7wnnjCwg8Ez1h4nAv+4sjgnEvBXRice9bC/92esfA4e87iUwCC8wI15rwwO1YNxeLOYT3iVyzd3a/rcego8PPWDd7QhmcRx5ITERERERHlqwI15rwwKxuOkd7OYY4/T9Zd2P31VGqYCi0ajfb07enHkpsd2Tm+nIiIiIiIyLkYnLuJ0tfyPnVMZswp1dDsrZhlqjRMk3a7Lc7t4NsMwNHTFz1324pIoM6m/6yz6Y5rW0dEREREREQmlrW7iVsBzjlPgqA8VV9e1YF6ZRGpqYPwpiIyR0QG64y66MD8KT292ll9ifV+TtlDIiIiIiIiYnDuJmJKFs3T/Y0srvPSB7q0LmMvJSIl9Hznp9MF3+V0xjxSl7MnWcra2+gydyIiIiIiInIsBuduwicO+e28l64bGTSBM8ecJ4jI73q+c5Srh2QQfNfWt6FZnNV1fT/nta0jIiIiIiLyXAzO3UTgjVt5un+qzoBbs+Ve6W5H0N1El6/H6THn/umCby89xhzN4qywflMH8kRERERERORYDM7dhHdK3jPnYsmWm+PMrRl1Xx2MF9OBuq/+2Rp8H9HN38rqLLmfpWs7msexazsREREREZHjMTh3E9f9ES7nXoolW252Zk/MoMzdT2+Lbu0+OohPH3yjK/sS/ctRTV9inXOfExEREREROQeDczdxOhQjwHMnWQfZXjrwTtFBuJkVN/SBTtHbeunLGB2Upw++MV3aFN21fbC+xDqnUbMfTnjcywZ6RFRAGIYhXbp0cfVuEBEReTQG526iaKm8jTk3M+belhJ3s0u7oceY39LryJifF5Fnswm+I3UDOZay2y9Av6ez9Xuafpo6IqL8Vr58eZk6daqcPHlSEhIS5OzZs7JixQpp2xZzczhe69atVbAfHBwszvL222/L1q1bJTY2Vq5du5bpdj179pT9+/dLfHy8REREyLRp02y3FS1aVObNmycHDhyQW7duyffff++0/SUiIrKHcybXphwrFp374NxHB+Kp6ZrDmWXuKHGP0teV0GPNF4jIXoftPUm6OeIj9RzxJfW66GCdiCgkxFvKlPGWqKhUuXIlb/1GslO9enUVxF6/fl2GDRsmBw8eFF9fX+nYsaNMnz5d6tatK+7Mx8dHUlLw6ZWWn5+fLF68WLZv3y59+vTJ8L5DhgyRoUOHqte9c+dOCQwMlBo1aqR5bATtOHHRrVs3p74OIiJyvHK6StWceaowYObcTRSNQQidO2am3Myam5fmOPNIHcCX0JcbReRTh+05mThHPBFlxd/fS156KUA++ThYxr5bQiZ8EqzWi5ljkJxgxowZKovdpEkTWbZsmRw/flwOHz4skyZNkqZNm9qd+W7YsKG6DsE+VKtWTWXfr169Kjdv3pRDhw5J586d1e0bN+JTRtQJAdwH2Wnw8vKS4cOHy6lTpyQuLk727duXJig2n7dTp06ye/duSUxMlJYtW2a4j2PGjJHJkyerkw0ZKVmypIwbN0569OghixYtUs+JbX/44QfbNtiHV155RebMmSOXLl3K1ftLRET5L6AQV6oyc+4mQq6h8DzvzEA9yZJRv6KDdJStr9aBebyHn5VyBrxHgTpjbnVdN9bD+8j3j8hzPf+8v4Q94S9RUSkSHp4iQUHeah3mzHHMZ4BVqVKlVKA7YsQIFYimd+PGjVw/NrLuyF63atVKlZbXq1dPBennzp2Trl27qhMBtWrVkujoaJWdhrfeekteeOEFGTBggDpJgPsuXLhQIiMjZfPmzbbHHj9+vLz++usqoM6qZD0r7du3F29vb6lcubI6GREUFCTbtm1TmfTz5zGwi4iICqq+hbhSlcG5uzDbqjuAty5dN5vAVdFjzo/p6dJyclaqr84GB+r5z3/WjePYHO5OkZY54q3zwXOOeCJCKXuL5kVVYG6WspuXzZv7yfffJzi8xL1mzZoqQD169Kg4GjLnS5cuVRlzOH36tO02ZNPh8uXLthMACOQxTrxdu3ayY8cO232QGe/fv3+a4Pydd96R9evX52n/QkND1WvHcw4ePFjtBzLp69atkwYNGqgx5kREVPArVcVyiUrVbwp4MozBuZtIQZrbgczO7Um6MzuqJivk8KxSTs5KMbt++7X/bHmPruv3rKzuhu+p7wsRiRpjHhDgpTLmVjExqVKpUhEVvDs6OEcZubNgnPbMmTOlQ4cOKpBGoJ5Zibl5ogBjvhEcWyFo37s3bQcUlLTnFQJzPPagQYNsz9m9e3dVvt6mTRtZu3Ztnp+DiIjyX9lCXqnKMeduwj8WIbTjkvDXdRb3mg7Q8ZWvlIhE2zn+2d7x04V5zEducI54IsoIAu+4OEOVslthPTYuVTWHczSUjqempkqdOnVydD/cJ31wjyZyVnPnzlXZ6a+//lrq16+vAupXX30108csXry4unziiSekUaNGtgXl8E89ZZ7SvA1l8nl18eJFdYmSdlNUVJRakPUnIqKCX6kqhbBSlcG5m/D2ckxdOx4lRQflPjrIrqIvy4tIVd0YDuv2nJVCkG+F9eKW+5vZ9RR9BitFr/cTz8Q54okoIwi+t25LlJAQH5VF9/O7nU3H+rZtSU7p2o7x2mvWrJGBAwdKQABOpaaV2VRnGAMOFStWtF2HQDo9jN2eNWuWauo2ceJE6dsX//FEkpKSbN3QTQiSMY0bAmNM6WZdnDEGHB3qoXbt2mnG4IeEhMiZM2cc/nxERJS/lapldTziZ6ng3VDAs+bA4NxNXCt75xen3AbmuKyhg2iMW0jV1+FrUijGPqY7q4Rf6HvTZdPtOSvF7uSZ4xzxRJTewoVx8uPKePH2EVXKjkus43pnQWCOIHnXrl2qURvKy5FJf+2119Q0ZBk5ceKEmgsdHdGx/eOPP64aqVmh2ztK2jE12f33369KxY8cOaJuQ/CL7HtYWJgKhlHOjmZxEyZMUPdDB3Vk3XE/ZNuxnlNVq1ZVHeQR7OP14WcseC6zamD58uUyZcoUadasmdx7770yf/58Nf5+wwZ8fbsNU8nhfqVLl1YnK8zHISIi9/VFIa5U5ZhzNxFerqQ0lttleLlhWKZRA+sQdrMwMVkfcB87Gr7ZM3763kI+5oOIyJESEm53ZUfzN4wxz495ztF0rXHjxqpjO7LbyIYjM/7bb7/Jyy+/nOF9kpOT1fhsjCk/cOCA/PrrrzJy5EhZsgT//W9DQIyO7VWqVFEd2VevXq3mFYfw8HAZPXq06rqOadQWLFggvXv3llGjRqnnRtd2BOeYam3Pnj3ywQcf5Ph1jR07Vnr16mVbx7Rs8Oijj8qmTZvUzwj6cTJg5cqV6mQBrkf3erw+008//ZRm7nPzcZw5Xp+IiBxTqfqNjjcKU88rBuduIqZk3ia6Nec1T003z7kp1TL2/Kb+RW6XTcM38+xTGx1s30x3VordyYmIcg4BubODcis0QUOmHEtm0gejmHYsfQbZug0arWUFndGxZNRIDktGEDzbGxQj2MeSlZiYGHnppZfUkpm77rrLrucjIiL3E1mIgnITg3M3EZSYmOfH8NJZccMSqKPMPUJEbumD7WeZ99yeaQiyOivF7uRERERERESOweDcTVQ5fnteWEcE6GbW3FsH6D76QBfTAfQavY29JelZnZXKLrtORERERERE2WNw7ia8UxzTrd1kBugpugGclx6fcUhEvtYH3hEl6YV5zAcREREREVF+Ybd2N3GpZpBTHhfF8ntEZB2mlhGR0iLyf06YhsCV3ckz6jZPRERERERUkDBz7iZKpThnJmxkzG9gjl10CtaBrDmmvKCXpGfVbZ7zihMRERERUUHC4NxNlD8Z7dDHM6dW8xWRhjqDHo75a0WksmVMeU5L0s3sujuUr/fNpts8ERERERFRQcHg3E0Y0Y4PzM2fb+oDHSoixTHvbbox5fZMQ5BRlnqXiKzWgXF+B+rl7Ow2T0REREREVBBwzLmbuOmTt3nO00vVC4LySjq4NnTW/NdcBK4IzLvrx0Ff+RoiMkQHwXNEZLCI+Ev+KatPEqC7vNV1fQICwTsREREREVFBwcy5m7haNlDkfO5zvebc5ol6jDmC12g9v3lxXfIdq8eeI9udEwjE++iu76k6CDe7vRfTP+d3OXmkg7rNExERERERuQNmzt1EiaT4PJexe+mu68V04Irg/IqIXNDBLAL1kxnMbZ6d/iJSVUSSdKO1ovp5vPUc6jf147fJx47pju42T0TkyQzDkC5durh6N4iIiDwag3M34eODGclzx0tntBN1oB4nIkd1EO1vme8c6/tzGLgi4G2kHzPVMpY9SWfkU3QXeFeUk3+hu8t7627z3gWs2zwRUX4oX768TJ06VU6ePCkJCQly9uxZWbFihbRti84djte6dWsV7AcHB4uzvP3227J161aJjY2Va9euZbpdz549Zf/+/RIfHy8REREybdq0NPu5fPlyCQ8Pl5s3b8revXvlueeec9o+ExERZYdl7W7i2n2BIgdQdJ63gxmvs8jIkl/SwXIxHaCfE5GZOXzMsrrj+zkdAN/SQbqPfr6r+jnLuaCcPD4X3eaJiFwtJMRbypTxlqioVLlyBf9Rnad69eoqiL1+/boMGzZMDh48KL6+vtKxY0eZPn261K1bV9yZj4+PpKTcefLaz89PFi9eLNu3b5c+fTDw6k5DhgyRoUOHqte9c+dOCQwMlBo1MFDrtubNm8uBAwfko48+UoF7WFiYLFiwQG7cuCErV6506usiIiLKCDPnbiLwerJDHidaZ8sfFJE6OptdRAfVC3JR0m6O7Y7QXd7xNTJFPyYy5sfdoJwcz/k7A3MicnP+/l7S96UAmfBxsIx7t4RM/CRYrfs7th9oGjNmzFBZ7CZNmsiyZcvk+PHjcvjwYZk0aZI0bdrU7sx3w4YN1XUI9qFatWoq+3716lWVdT506JB07txZ3b5x40a1DU4I4D7z5s1T615eXjJ8+HA5deqUxMXFyb59+6Rbt253PG+nTp1k9+7dkpiYKC1btsxwH8eMGSOTJ09WJxsyUrJkSRk3bpz06NFDFi1apJ4T2/7www+2bT788EN55513VICP21FdsHr1aunatWuu3msiIqK8YubcTRS7gKL03ENmPNlSam7og2t+54vJ49jup3SAjuC+vJ6WDScCSuuMOcvJiYiy9sLz/hL2hL9ciUqRC+EpUiLIW63D7DkYPORYpUqVUoHuiBEjVDCcHjLEuYWsO7LXrVq1UqXl9erVU0H6uXPnVHCLEwG1atWS6OhoVVIOb731lrzwwgsyYMAAdZIA9124cKFERkbK5s2bbY89fvx4ef3111XAnFXJelbat28v3t7eUrlyZXUyIigoSLZt26Yy6efPn8/0fjghceTIkVw9JxERUV4xOHcTN2xhdO6Z48FTdDCOzHYxne3GVw3kH+bnIsNsBt1mwzcE5ZNE5HsRCWI5ORGRXaXsLZoXVYF5lC5lNy9bNPeTZd8nOLzEvWbNmipAPXoUXUgcC5nzpUuXqow5nD6N2qrbkE2Hy5cv204AIJDHOPF27drJjh07bPdBZrx///5pgnNks9evX5+n/QsNDVWvHc85ePBgtR/IpK9bt04aNGggt26hniytp59+Wh566CG1P0RERK7A4NxNlA1HyJt7GANuBuj+uvkbvubF6WAdDdxK6BJ0ewJps1TdDLw5tpuIKPcwxjwwwEtlzK2iY1KlUqUiKnh3dHCOMnJnQQn4zJkzpUOHDiqQRqCeWYm5eaIAY74RHFshaEcjNiuUtOcVAnM89qBBg2zP2b17d7l06ZK0adNG1q5dm2b7Rx99VJXf9+3bV2XaiYiIXIHBuZsoF523kkaUtKMwPkAH5WZOIFiPGfezs2Eb7t9XRNDDN1Df92edPUdAzqCciCjnEHjHxhmqlN3MmAPW4+JSVXM4R0PpeGpqqtSpgw4k9sN90gf3aCJnNXfuXFmzZo088cQTKkBHyTpKxq3d0K2KF8egK1HbX7iACT7/grHlViiTz6uLFy+qS2ugHRUVpRZk/a1QXo+x6Ggg9/XXX+f5uYmIiHKLDeHcRFIRM/edO9E6IDdzMvga5WM5AxNiZ8O2vnp8eYoeX56i1/vlae+IiDwbgu+t2xKlTIiPhJRBVlfUJda3bktyStd2jNdGAD1w4EAJCMCp17Qym+oMY8ChYsWKtusaNcKkmmlh7PasWbNUU7eJEyeqrDMkJSXZOq2bECRjGjcExpjSzbpkNQY8t9ChHmrXrp1mDH5ISIicOXMmTRM6dGZ/8803Zfbs2Q7fDyIiopxgcO4mwsuVzNX9DL0gIx6vg3R8xTN7vyMfkaQD8+watpXTGfNI/XhJlhJ2c7w5ERHlzsKFcfLjynjx9hFVyo5LrON6Z0FgjiB5165dqlEbysuRSX/ttddUl/KMnDhxQs2Fjo7o2P7xxx9XWXErdHtHxhxTk91///2qVNxspIbgF9l3TE2GYBjl7GgWN2HCBHU/dFDHmHDc79VXX1XrOVW1alXVQR7BPl4ffsaC5zKrBjCH+ZQpU6RZs2Zy7733yvz589X4+w0bNthK2RGYo0QfZfmYDx4LgngiIiJXMTxlCQoKMgCXrt6X9Ms3TRobqSJ/LT4+RmpY2O1L6/WWJUUvUSLGQhHjtIhxQcQ4K2L8V8T4VsRYJ2KsEjHK2rEP94oY60WML0WMzy3Ll/r6e93gfSpsi4+PjxEWFqYuXb0vXHicuWS+VK9e3ViwYIG6zG7b4ODgLG8vU8bbqF27iLrMj32vUKGC8dlnnxmnT582EhISjHPnzhnLly83WrdubdsGunTpYltv3ry5sX//fiMuLs7YtGmT0a1bN7WN+fqnTp1qHD9+3IiPjzciIiKM+fPnG6VLl7bdf+TIkUZ4eLiRkpJizJs3z3b9oEGDjCNHjhiJiYnqfqtWrTIeeeQRdRv2B7J7/7DgMTNifU34rJ8zZ45x9epVIyoqyli6dKlRpUqVbB9jw4YN2f4ehIaG8m/aAxb+7/aMhcfZcxYfFx5re+NQjjl3E4dKVBGRPdluZ50N/ZbOlu/QWW5kzTEDLQr2rmCeV92tfYmdY8XNOc1LphubXtLO8epERJQ9lLA7o4w9M2iChkw5Fnubx2HaMWSiM9sGjdaygs7oWNJDlhpLRjZt2mR3E7vevXurJSsxMTHy0ksvqSW3j0FERJSfGJy7icNRl9Xpkqy+llzTgTjmGUdv2xloZKNLzqvpMeIn9XjzarmYf9w6pzlc14F52RwE+ERERERERJRzDM7dxPXD+7IMzpEx/0Nny7/WAXe8DtLTT3FWNg9TnlnnNM9NgE9EREREREQ5x+DcTVRPSrANNsgoQF8pIiMzCbjTT3GWlynPEPBzTnMiIiIiIqL8xeDcTVy3TIPmbQnQDX39f0Tk93zcH85pTkRERERElH84lZqbwIQ2CfqApOrydbNdECbZ2eTi/SMiIiIiIiLnYXDuRsJ113Xw0ZdJ+noiIiIiIiIqvFjW7ibQxC1CT4dWRU+BBid05hzjv1lmTkREREREVDgxOHcTkTowv6a7svuLSBl9Hcafc45xIiIiIiKiwotl7W7CnGMcGfQgEYnVAXqIiGxg1pyIiJzIMAzp0qWLq3eDiIjIozE4dyNf6DnFvfUc48iYL+Mc40RElAfly5eXqVOnysmTJyUhIUHOnj0rK1askLZt2zrl+Vq3bq2C/eDgYHGWt99+W7Zu3SqxsbFy7RpqzjLWs2dP2b9/v8THx0tERIRMmzbNdlutWrXk559/lkuXLqnb8f689957UqQIiwqJiMg1+AnkRqxzjFcUkfoi8v8sU6wRERHlRPXq1VUQe/36dRk2bJgcPHhQfH19pWPHjjJ9+nSpW7euuDMfHx9JSbnzU9DPz08WL14s27dvlz59+mR43yFDhsjQoUPV6965c6cEBgZKjRo1bLffunVLFixYIHv27FHvT8OGDWX27Nni7e0tI0aMcOrrIiIiyggz524oUs9pfsPVO0JERA5XNsRb6tQuIiFlnP8RPGPGDJXFbtKkiSxbtkyOHz8uhw8flkmTJknTpk3tznwjcMV1CPahWrVqKvt+9epVuXnzphw6dEg6d+6sbt+4caPaBgEv7jNv3jy17uXlJcOHD5dTp05JXFyc7Nu3T7p163bH83bq1El2794tiYmJ0rJlywz3ccyYMTJ58mR1siEjJUuWlHHjxkmPHj1k0aJF6jmx7Q8//GDb5vTp0/LVV1/JgQMHVDUBbvvPf/4jjzzySK7eayIiorxi5pyIiCgfBPh7yf89HyCPtCgqAQFeEhdnyC9bE2XBwjiJTzAc/nylSpVSgS6ywAiG07txI/engJF1R/a6VatWqrS8Xr16Kkg/d+6cdO3aVZ0IQNl4dHS0KhmHt956S1544QUZMGCAOkmA+y5cuFAiIyNl8+bNtsceP368vP766yqgzqpkPSvt27dXGfDKlSurkxFBQUGybds2lUk/f/58hve5++671fuFfSciInIFBudERET5AIH5k2H+EhWVKhfCU6REkLdah1lz0AbUsWrWrKkC1KNHjzr8sZE5X7p0qcqYm1loE7LpcPnyZdsJAATyGCferl072bFjh+0+yIz3798/TXD+zjvvyPr16/O0f6Ghoeq14zkHDx6s9gOZ9HXr1kmDBg1USbsJZf+NGzeWYsWKyaxZs9TzExERuQLL2omIiPKhlB0ZcwTmUVdSJSlJ1CXWWzYv6pQSd5SROwsazI0cOVK2bNmiSszr10eXlKxPFGDMN4LjmJgY24Kyc2SsrVDSnlcIzHFCYNCgQbJ27Vo15rx79+5yzz33SJs2bdJs++yzz6rgHLc/8cQTKmtPRETkCsycExEROVmZMt6qlB0Zc6vomFSpXMlHQkK8VbDuSCgdT01NlTp16uTofrhP+uAeTeSs5s6dK2vWrFHBbIcOHVTJOkrGrd3QrYoXL64usf2FCxfS3Iax5VYok8+rixcvqkuUtJuioqLUgqy/lVnmfuTIEdWA7osvvpCJEyfa3gciIqL8wsw5ERGRk125kqrGmKOU3QrrsbGGyqA7GsZrI4AeOHCgBAQE3HF7ZlOdYQw4VKyIeUNua9So0R3bIahFGTiauiGY7du3r7o+CWUButO6CUEypnFDYIwpy6xLZmPA8wKl6lC7du00Y/BDQkLkzJkzWWbccSICl0RERPmNmXMiIiIni4xKVc3fzDHmyJgjMEfGfMWP8Q7PmpsQmCNQ3bVrlxpLjc7kmMcbDdNefvll1cgtvRMnTqju5ShXRzM5NHZDVtwK3d5XrVolx44dU0EvSsWReQYEv8g6h4WFyU8//aQawqFZ3IQJE9T9EPiiHB4nB1q0aKGaxmFKs5yoWrWqlC5dWgX7OAmAbvLmviPzjqqB5cuXy5QpU6Rfv37qOT788EM1/n7Dhg1q2+eee06NPUcXd2TvH3zwQbXNt99+K8nJyXl414mIiHKHwTkREVE+QFd2wBhzlLIjY47A3LzeGdB0DeOpEWQju41sODLjv/32mwrOM4LAFOOvZ86cqYL5X3/9VY0vX7JkiW0bBMTo2F6lShUV+K5evVrNKw7h4eEyevRo1XUd06gh8O7du7eMGjVKPTdK4NGwDVOtYY7xDz74IMeva+zYsdKrVy/bOqZlg0cffVQ2bdqkfsZ4dpwMWLlypTpZgOvRjd0MvHH55ptvqpMPKOHHSQWU5eM+RERErmJ4yhIUFGQALl29L9ktPj4+RlhYmLp09b5w4XHmwuPs6Uv16tWNBQsWqMvstg0ODs7y9pAy3kad2kXUpatfF5fc/R6Ehobyb9oDFv7v9oyFx9lzFh8XHmt741BmzomIiPKR6tLupDJ2IiIiKrjY8YSIiIiIiIjIxRicExEREREREbkYg3MiIiIiIiIiF2NwTkRERERERORiDM6JiIiIiIiIXIzBOREREREREZGLMTgnIiIiIiIicjEG50REREREREQuxuCciIjIwxmGIV26dHH1bhAREXk0BudERESFWPny5WXq1Kly8uRJSUhIkLNnz8qKFSukbdu2Tnm+1q1bq2A/ODhYnOXtt9+WrVu3SmxsrFy7di3T7Xr27Cn79++X+Ph4iYiIkGnTpmW43d133y3R0dFZPhYREZGzFXH6MxAREZFLVK9eXQWx169fl2HDhsnBgwfF19dXOnbsKNOnT5e6deuKO/Px8ZGUlJQ7rvfz85PFixfL9u3bpU+fPhned8iQITJ06FD1unfu3CmBgYFSo0aNO7YrUqSILFq0SH755Rdp3ry5U14HERGRPZg5JyIiykdlQ7ylbu0iElLG+R/BM2bMUFnsJk2ayLJly+T48eNy+PBhmTRpkjRt2tTuzHfDhg3VdQj2oVq1air7fvXqVbl586YcOnRIOnfurG7fuHGj2gYnBHCfefPmqXUvLy8ZPny4nDp1SuLi4mTfvn3SrVu3O563U6dOsnv3bklMTJSWLVtmuI9jxoyRyZMnq5MNGSlZsqSMGzdOevTooQJvPCe2/eGHH+7YFtsdPXpUvvvuuxy9t0RERI7GzDkREVE+CPD3kh7PB0irFsUkMMBLYuMM2bw1QeYvjJP4BMPhz1eqVCkV6I4YMUIFw+nduHEj14+NrDuy161atVKl5fXq1VNB+rlz56Rr167qRECtWrVUqThKyuGtt96SF154QQYMGKBOEuC+CxculMjISNm8ebPtscePHy+vv/66CqhzW2bevn178fb2lsqVK6uTEUFBQbJt2zaVST9//rxtuzZt2sjTTz8tjRo1UvtNRETkSgzOiYiI8gEC8y5hARIVlSLnw1OkRJC3WofP58Q6/Plq1qypAlRkhR0NmfOlS5eqjDmcPn3adhuy6XD58mXbCQAE8hgn3q5dO9mxY4ftPsiM9+/fP01w/s4778j69evztH+hoaHqteM5Bw8erPYDGfJ169ZJgwYN5NatW1K6dGn56quv1AmDmJiYPD0fERGRIzA4JyIiyodSdmTMEZhHXUlV15mXjzQvJku+j7etOwrKyJ0FDeZmzpwpHTp0UIE0AvXMSszNEwUY843g2ApB+969e9Nch5L2vEJgjsceNGiQ7Tm7d+8uly5dUtnytWvXyuzZs+Wbb75RY82JiIjcAcecExERORnGl6OUPTombQCO9cBALxW8OxpKx1NTU6VOnTo5uh/ukz64RxM5q7lz56rs9Ndffy3169dXAfWrr76a6WMWL15cXT7xxBOqhNxcUA7/1FNPpdkWZfJ5dfHiRXWJknZTVFSUWpD1B3SrR/k8suhY8JowVh0/9+7dO8/7QERElFMMzomIiJwMWXGMMUcpuxXWY2MNiYxybNYcMF57zZo1MnDgQAkIuF0+b5XZVGcYAw4VK1a0XYdAOj2M3Z41a5Zq6jZx4kTp27evuj4pKcnWad2EIBnTuCEwxpRu1sU6BtxR0KEeateunWYMfkhIiJw5c0atN2vWLM2JApTTY4w8fv7+++8dvk9ERETZYVk7ERGRkyH4RvM3c4w5MuYIzENCfOS/P8Y5vKTdhMAcgequXbtU8HngwAE1dRgapr388ssqc53eiRMn1Fzo6IiOZnJo7IZGalbo9r5q1So5duyYCnpRKn7kyBF1G4JfZN/DwsLkp59+Ug3h0CxuwoQJ6n4oOd+yZYs6OdCiRQsVEC9YsCBHr6tq1apqzDiCfZwEQDd5c9+ReUfVwPLly2XKlCnSr18/9RwffvihGn+/YcMGtW36sfgPPvig2u/ff/89x+8zERGRIzBzTkRElA/QlR2BuLe3l1SuVERdYh3XOwuarjVu3FgFpMhuo4EbxmA/9thjKjjPSHJyshqfjXJ4BPNvvvmmjBw5Ms02CIjRsR0B+erVq1WQ/sorr6jbwsPDZfTo0arrekREhEybNk1dP2rUKHnvvfdU13bzfihztzaTs9fYsWPVVGy4RCd2/IwFAbYJ06hhfvOVK1fKpk2bVLk6utfj9REREbkrw1OWoKAgA3Dp6n3JbvHx8THCwsLUpav3hQuPMxceZ09fqlevbixYsEBdZrdtcHBwlreHlPE26tYuoi5d/bq45O73IDQ0lH/THrDwf7dnLDzOnrP4uPBY2xuHsqydiIgoH6GE3Vll7ERERFRwsaydiIiIiIiIyMUYnBMRERERERG5GINzIiIiIiIiIhdjcE5ERERERETkYgzOiYiIiIiIiFyMwTkRERERERGRizE4JyIiIiIiInIxBudERERERERELsbgnIiIyMMZhiFdunRx9W4QERF5NAbnREREhVj58uVl6tSpcvLkSUlISJCzZ8/KihUrpG3btk55vtatW6tgPzg4WJzl7bfflq1bt0psbKxcu3Yt0+169uwp+/fvl/j4eImIiJBp06bZbqtevbraz/TLww8/7LT9JiIiykqRLG8lIiKiAgsBKILY69evy7Bhw+TgwYPi6+srHTt2lOnTp0vdunXFnfn4+EhKSsod1/v5+cnixYtl+/bt0qdPnwzvO2TIEBk6dKh63Tt37pTAwECpUaPGHds99thj8vvvv9vWr1y54uBXQUREZB9mzomIiPJRuRBvqVfbV8qWcf5H8IwZM1Q2uEmTJrJs2TI5fvy4HD58WCZNmiRNmza1O/PdsGFDdR2CfahWrZrKvl+9elVu3rwphw4dks6dO6vbN27cqLbBCQHcZ968eWrdy8tLhg8fLqdOnZK4uDjZt2+fdOvW7Y7n7dSpk+zevVsSExOlZcuWGe7jmDFjZPLkyepkQ0ZKliwp48aNkx49esiiRYvUc2LbH3744Y5tEYwjq24uycnJOXqPiYiIPCpzjg/7UaNGqRK8ChUqSHh4uCxcuFDef/99uXXrlqt3j4iIKFsB/l7S64VAad28mAQGeklsrCGbtiXIvIWxEh9vOPz5SpUqpQLdESNGqGA4vRs3buT6sZF1R/a6VatWqrS8Xr16Kkg/d+6cdO3aVZ0IqFWrlkRHR6uScnjrrbfkhRdekAEDBqiTBLgvPssjIyNl8+bNtsceP368vP766yqgzqpkPSvt27cXb29vqVy5sjoZERQUJNu2bVOZ9PPnz6fZFicZihUrJseOHZOPP/44wwCeiIgoPxSI4LxOnTrqQ7Z///5y4sQJue+++2T27NmqRA3lakRERO4Ogfk/ngiQqCspcv5CigSX8FbrMGP2TYc/X82aNdVn59GjRx3+2MicL126VGXM4fTp07bbkE2Hy5cv204AIJDHOPF27drJjh07bPdBZhyf7dbg/J133pH169fnaf9CQ0PVa8dzDh48WO0HMunr1q2TBg0aqBP7OJnw73//W5X9p6amqiz+8uXL5e9//zsDdCIicokCEZyvWbNGLSZ8oE+YMEFefvnlLINzfBkoWrSobR1nzs0xbFjcGfYPXyzcfT8pb3icPQOPc8Fn77FD6bZ5iRJtayk7MuYIzCOjUtV15iWuX7wsTiKv3F53FHNfnAEN5mbOnCkdOnRQgTQC9cxKzM0TBTihjuA4/ef03r1701yHkva8wt8bHnvQoEG25+zevbtcunRJ2rRpI2vXrlXl7Cjvtz5vpUqV1PeK7IJz/k17Bh5nz8Dj7Dl8XHis7X3OAhGcZwRj4cyz85lBCR3GpWVU7maW2bkrHMDGjRurL1cZNcOhwoHH2TPwOBd8ISEh4u/vr07yZteFHEFoejVqeEtwsK+Eh6eKn99f1+OjqFJFb7nrrmBJSnZscI7x08gIN2rUSDZs2JDt9thvvLaAgNvZfOvrRIk8lChRQl2/ZMkS1YwNwTmGnOHzduTIkfLFF19I8eLF77g/hqTBs88+q4amWSUlJaltzfsVKVLE7k7v2Ff8XaXf3szYo4TdvA3ZcgTktWvXVg3iMnLgwAH1mjJ7fhx//B488sgjasgd/6YLN/7v9gw8zp7Dx4XHGp8dhTY4v/vuu+W1115TY9Ky8uGHH8qnn36a5kP1woUL6ix6TEyMuPsvD7Iue1avltIpKXIZWRZX7xQ57TivXr2aHwiFGI9zwYdADEEoPjuyGqttZqsx1tqaOf/zT2+5ccNH/P0NuRn7VxCO0vYb0V5y+vQNuXHDscE59hNVZ+hmjrHU6cedIwC1vhaMHce6WaKOYB3TrpmZb/N1mffBJcZzozHbBx98oMaTf/LJJ7YT5ygbN7fdtWuXmsatdOnS8tNPP2W4v9jefFx7x8PjNeF9Tr+9mS2vWLGiHDlyxHaCoUyZMqrMP7PHR+COkweZ3Y5Gczi5/8svv0hUVBT/pgs5/u/2DDzOnsPHhcfarOB26+AcwTM6t2Y33vyPP/6wraPkDG8oplCZM2dOlvfF2Xgs6eFguPsfH/IWYamp8lJKigSkpEisiPwsIl8g0+LqnSOHQmarIPxOUt7wOBds9h43MyC3BuZwOSpVNX8zx5jfiE5VgXlIGR/5fqXjS9pNAwcOVGOqERxjLDcyw8hMo4IMQ8PQyC099HZBUI7KMzSTQ2M3NFKzQjn4qlWrVBM1BL0oFTeD4DNnzqjf97CwMBWII5hF4I3haLgfSgq3bNmiTg60aNFCBfwLFizI0euqWrWqCvQx9h1fttBN3tx3nGRAwzmMH58yZYr069dPPQe+cyAwN6sI0Mkd3xHMsno0snvxxRflpZdesuv3gX/TnoHH2TPwOHuOVBcda3ufz6XB+cSJE+Wrr77Kcht0azXhDDg+VNFxFR+2hRm+GrQQkWMigrxFSRF5St82xcX7RkREOYeu7OYY8yqVi6hu7QjMzeudAVlwlPAhyMZnLj5H0R39t99+U8F5RjCVGMZnY0w5gvlff/1VlayjlN2EgBgd26tUqaICX5w0x7zigMzz6NGjVdd1TKOGwLt3795q1hU8N0rg0bANU63t2bNHZd1zauzYsdKrVy/bOqZlg0cffVQ2bdpkC75xMmDlypXqyxiuR/d661Rp2CdUReA6BO4ou8f4eSIiIlcxCsJSqVIl448//jC++eYbw9vbO1ePERQUZAAuXf16sloqBgQYP/oHGPuefNJY4+NjfCtifC5iLBMxVokYj4gYZd1gP7nkffHx8THCwsLUpav3hQuPM5fMl+rVqxsLFixQl9ltGxwcnOXtZct4G/Vq+6pLV78uLrn7PQgNDeXftAcs/N/tGQuPs+csPi481vbGoQVizDlK2Tdu3KhK5TDOvGzZsmka3hQWXr5+UqJJU2lRsao0Xb1CiouX1EHDAhG5grF+aKojIh+jRJJl7kREBRJK2J1Vxk5EREQFV4EIzjE27p577lELGrrl11Qx+S2kw9+lRLOH5bV5M6VkfJxIagoGLoqviFTBGAmMoxdR68AydyIiIiIiosLBWwqA+fPnqyA8o6Ww8CleXAKbNZH7L52QZqdOqANjLuarxM+3RKQyAnmdSX9SROpm89jlROReEfmr3oCIiIiIiIjcSYHInHuCco+ESdEyCfLx3EVSBCMOMoE+vyhzR3/dRBEppkvbl2RQ4o5t+4pIW0yJg2lyWApPRERERETklgpE5twTFG1UX56/+YtUP309y+18dOf2Ejp7juns7xGRQZgyJ922fXXpe4ru+J6i1wt3n3siIiIiIqKCh8G5mwi+K0FeG/ezrYQ9O16Wy6K4P6aNsZSul9MZ80jdPC5JX2K9DUvciYiIiIiI3AqDczfRMvwPCb6BEeX2M8ekB4lIcREJFZGG+rayupQdpe+ldIYdruttEbxzLDoREREREZF74JhzN9Fp44E8nV3x1ctMEXlYjy8vp5vFYQg7wv5wEYnS483/LiJNOBadiIiIiIjILTBz7iYeOIRR4XmDILyGiHwtIl104O0nIsm6/L2WiNynA/UndcM4lLlzLDoREREREZFrMTh3E4EJKEDPmxQdiD+sg+9DInJMB+1FdFCOpY6eNx1Z9aYiUl5ErnIsOhGRxzIMQ7p0wWldIiIichUG524isSj6sOeNtcS9jIhcEZGDIrJJRLaKyEbdPK6yDtJv6sD9Lh2gm2PRiYio8ChfvrxMnTpVTp48KQkJCXL27FlZsWKFtG2LtqGO17p1axXsBwejValzvP3227J161aJjY2Va9euZbpdz549Zf/+/RIfHy8REREybdq0O7YZOnSo/PHHH+q9OX/+vHpsIiIiV+CYczfhnZLF5OZ28NILAvREHZiX1B3aE/RSVTeGw7jyVJ1pN8eY47YDensiIiocqlevroLY69evy7Bhw+TgwYPi6+srHTt2lOnTp0vduqihcl8+Pj6SkoJPq7T8/Pxk8eLFsn37dunTp0+G9x0yZIgKvPG6d+7cKYGBgVKjBgZ//WXKlCnSoUMHef3119V7U7p0abUQERG5AjPnbsI36c4vHzllBuf7RWSFLlEvp8ed47KiiNwQkT91kO5v6fiO8ef79Bh0IiJynnIh3lKvtq+ULeP8j+AZM2aoLHaTJk1k2bJlcvz4cTl8+LBMmjRJmjbFwCb7Mt8NGzZU1yHYh2rVqqns+9WrV+XmzZty6NAh6dy5s7p940bUaYk6IYD7zJs3T617eXnJ8OHD5dSpUxIXFyf79u2Tbt263fG8nTp1kt27d0tiYqK0bNkyw30cM2aMTJ48WQXUGSlZsqSMGzdOevToIYsWLVLPiW1/+OEH2zZ16tSRl19+WZXz4/o///xT9uzZI+vXr8/Ve01ERJRXzJy7iVQjb5lzMzhH8D1QZ88r6KnVqukS9mV6jHmKXirpadjw9fCciCzWU6uZ86ETEZHjBPh7Se8Xikvr5sWkeKC33IxNlU3bEuTLhTclPj7vnwHplSpVSgW6I0aMUMFwejdu4BMjd5B1R/a6VatWqrS8Xr16Kkg/d+6cdO3aVZ0IqFWrlkRHR6uScnjrrbfkhRdekAEDBqiTBLjvwoULJTIyUjZv3mx77PHjx6tMNgLqrErWs9K+fXvx9vaWypUrq5MRQUFBsm3bNpVJR+k6/O1vf1PPERYWJq+++qo6eYDA/I033sj18xIREeUFg3M3UcRAaJ33L2f4+jVejzv312PLf9VTrKEf/GAR6S4iMSKyR0RKiAhyI/iqMppTq9mlnK5K4EkMIsoJBOZdnwiUyCspcu5CsgSX8FbrMH02/is7Vs2aNVWAevToUYc/NjLnS5cuVRlzOH36tO02ZNPh8uXLthMACOQxlrtdu3ayY8cO232QGe/fv3+a4Pydd97Jc/Y6NDRUvXY85+DBg9V+IJO+bt06adCggdy6dUttg0z/008/rTLsKKFHRcGSJUvksccey9PzExER5QaDczdxy8tbjwTPPYT2GCnXSUROiMhuPe4cRYGXRGS2DtpRwh6q74PrT+ss+yUdwJfUU6vBlDy/ssID71tfEUELJZ7EIKKclrIjY47APDLq9v968xLXf7csViKv5O0zID1kgp0FDeZmzpypxmsjkEagnlmJuXmiAGO+ERxbIWjfu3dvmutQ0p5XCMzx2IMGDbI9Z/fu3eXSpUvSpk0bWbt2rdqmWLFiKjBHJh8wfh2l7cj6HzuG+U6IiIjyD8ecFyKGDr69dcm6jyW7i2nShuj5zxG4/0+PTU/S3doRmMfooDPach9OrfaXvvqkRYo+icH54Sl9RQWGhfBvhjISUsZHlbLfiE4bgGM9MNBbyobkfcaO9BBwpqamqrHVOYH7pA/u0UTOau7cuSrz/PXXX0v9+vVVQI3S8MwUL475QESeeOIJadSokW1BOfxTT5mng29DmXxeXbx4UV2ipN0UFRWlFmT9zW2QQTcDczhy5Ii6NLchIiLKTwzO3YS3A7LmeAQf/TOyvGYrn+s6o/6YDroRsKPQ8KSIXNNZc7T4aSUiLZDF0cF9CRdNreaOQU45nTE337+kdCc+3GlfKX8F6OEis3WlyRy9jmElRKaoKylqjDlK2a2wHhubKpFReW8Kmh7GTa9Zs0YGDhwoAQH4TU0rs6nOMAYcKlZEG9HbEEinh7Hbs2bNUk3dJk6cKH374hSmSFJSkq3TuglBMqYqQ9CLKd2sizkG3JHQoR5q166dZgx+SEiInDlzxrYNTjrgJIMJGXMwtyEiIspPDM7dRHLRvI0wQH6jiD6gfvpn86tYST323EcH6lb4ClZMl7kbOnuOy7uR6cnnqdVyGuTkZxBfVlcVpH//sM754T0bKyrIHpejbjd/K1vGR8qGoORa1CXWcb2jS9pNCMwRJO/atUs1akN5OTLpr732mpqGLCMnTpxQc6GjIzq2f/zxx1UjNSuMzUZJO6Ymu//++1WpuJl1RmCL7DsarSEYRjk7msVNmDBB3Q9l5AiIcT9k27GeU1WrVlUd5BHs4/XhZyx4LkA2fPny5WqqtGbNmsm9994r8+fPV+PvN2zYoLZBOf5vv/0mX375pTr50LhxY3WyASXv1mw6ERFRfmFw7iaS1Zhzx/DSgfiDItJMZ8Y36HHRVXWwXkoH5cGWOc/N+7p7kOOKTCVOYsTq986qpO6Ez/nhPRMrKign0JV92cpY8fYWqVK5iLrEOq53FjRdQ9CJgBTZbTRwwxhsNDzDNGIZSU5OVuOzEcQfOHBA3nzzTRk5cmSabRAQo2M7AvLVq1er8dmvvPKKui08PFxGjx6tuq5HRETItGnT1PWjRo2S9957T3VtN++HMndrMzl7jR07Vk3Fhkt0YsfPWB58EJ98tyHox/zmK1eulE2bNqkSdnSvx+sDTNuGju0odUdDOmyH/frnP/+Z4/0hIiJyBDaEcxN+KY4taTRL2xGM/yIiCSJSWWfIvfVUa+jsnqwDiSsiUl5ngW/pkvc4HXxEuiDIEcslgpxvLPthBvGR+djA7rJu/vaUJWNeUgdfS9i13WOZFRX4PbTC70e1fPz7oYIB06WhKzuav2GMOUrZnZUxt0ITNGTKsdjbPA7TjiETndk2aLSWFXRGx5JRIzksGUEAbW8Tu969e6slKzExMfLSSy+pJTMYd55+zDsREZGrMDh3E97+qbc7sTlIsi5R/103fKusy93xNdBLZ83NIAKBeriI/KGvT7DMf37ZzYKcnATxjvaF5XnMueOXWK4nz2OtqLD+rbCigrKCgDw/gnIiIiIqWBicuwmfBMdlzs0y9Tgd3DbW5bYB+rYYnR1P1cF7Vd0ALtyFGWF7gxxXZirjdWb+G/08nOecWFFBRERERI7CMeduwgcRszg2c47+t/fpbHiKJWgvbmkeh6x5lB6T7q0DXG8XZITNIKesDnz99GVZvW+RbjT2O1Kf1GDgRaL/Tpa4+O+HiIiIiAo+Zs7dRDI6A+m5ZfMqRY8ZL6Wz4l66TB2XqTpwL67HmRfV85p/qu/ryoywPWXjzFSSu2FFBRERERE5AoNzN3G1lL8Uj0ROOPcN4EzI3NXUHdu9dLDuZ7ntlj7w8Xouc2tQG1kAghyO/SZ3hN9VBuVERERElFsMzt2Fr+NGGKTq8eXmnOWp+tJXB+ypOkA/IyI/5SKoNcvNnZUhzC7IYaaSiIiIiIgKGwbnbiIgAS3bcs/Lkj1P1YF4Ed0UDiXsSbqc3Vc3U1umS9lzEtQG6GnM2uqmbLG6xPwLHTDnN2YqiYiIiIiosGBDODeRXCzvh8KcHRYBuOhGcAH6ej+dNYc1IjI2F4GtOb94ig7wU/R6v2zuh+z2vTrbTs7H95uIiIiIqOBh5txNXA8IkAoOyj+bQboZ7ifpgN3QjeLey0WmOzfzi7tbpr2w4/tNRERERFRwMXPuLlBz7gBmeTvGlJvMjDmuX5zLUnBzfnF0R7e6rsvmEbw7KtNOucP3m4hyyzAM6dKli6t3g4iIyKMxOHcTxW8lOORxrOPOE/Q0aTf1tGkXROSHXD5uTucXT59pT7I0bkOmnSXXjsX3m4gyU758eZk6daqcPHlSEhIS5OzZs7JixQpp2xb/NRyvdevWKtgPDg4WZ3n77bdl69atEhsbK9euXct0u549e8r+/fslPj5eIiIiZNq0abbbRo8erfYz/XLzJj7ViIiI8h/L2t2Er7dj5jg3s+dIxOPryu8ikqgPdLLOqOZGTucXNzPt6Z/vup7+DMEkm7k5Dt9vIspI9erVVRB7/fp1GTZsmBw8eFB8fX2lY8eOMn36dKlbt664Mx8fH0lJQR1QWn5+frJ48WLZvn279OnTJ8P7DhkyRIYOHape986dOyUwMFBq1Khhu33ChAny+eefp7nP//73P/n111+d8EqIiIiyx8y5m7gVaI4Ud0zmvKiew7yBiDQREXz92pLHAO0LHYh764DPO4v5xXOaaae84ftNVHCUC/GRe2v7Sdky5qAj55kxY4bKBjdp0kSWLVsmx48fl8OHD8ukSZOkadOmdme+GzZsqK5DsA/VqlVT2ferV6+qTPOhQ4ekc+fO6vaNGzeqbXBCAPeZN2+eWvfy8pLhw4fLqVOnJC4uTvbt2yfdunW743k7deoku3fvlsTERGnZsmWG+zhmzBiZPHmyOtmQkZIlS8q4ceOkR48esmjRIvWc2PaHH/6qH0PWHdl0c0GFwb333itz587N1XtNRESUV8ycu4lbRRxzKLwtAbop/Xpu5WR+8Zxm2ilv+H4Tub8Afy/p80KQtGkRIIGBXhIba8iGrXEyZ2GMxMc76j/1X0qVKqUC3REjRqhgOL0bN27k+rGRdUf2ulWrVirIrVevngrSz507J127dlUnAmrVqiXR0dGqpBzeeusteeGFF2TAgAHqJAHuu3DhQomMjJTNmzfbHnv8+PHy+uuvq4A6q5L1rLRv3168vb2lcuXK6mREUFCQbNu2TWXSz58/n+F9XnrpJfnjjz9kyxacyiYiIsp/DM7dRIx3MT1CPG8MXb5+RV/u1YFakIgg/zDfAYGavfOLmxn1NjrTfjOLTDvlHd9vIveGwLxbWHGJvJIi5y4kS3AJH7UOn83O+///9GrWrKkC1KNHjzr8sZE5X7p0qcqYw+nTp223IZsOly9ftp0AQCCPceLt2rWTHTt22O6DzHj//v3TBOfvvPOOrF+/Pk/7Fxoaql47nnPw4MFqP5BJX7dunTRo0EBu3bK2TRUpWrSoPP/88+rEABERkaswOHcTCT7m7OR5C8zNJUkfXDSFQ84ixQVjj3OSaae84/tN5N6l7MiYIzCPjLo9htq8fLRFgPy/ZbHqNkdCGbmzoMHczJkzpUOHDiqQRqCeWYm5eaIAY74RHFshaN+7F6eR/4KS9rxCYI7HHjRokO05u3fvLpcuXZI2bdrI2rVr02z/j3/8Q2XX58/HKWwiIiLXYHDuJkpH3llymFPm1zAE5366ERyCc38RqaSnV3PF2GN7M+3kGHy/idwPxpejlB0Zc6sb0SlStZKvCt4dHZyjdDw1NVXq1KmTo/vhPumDezSRs8K47DVr1sgTTzyhAnSUrKNk3NoN3ap48dsVAtj+wgXMHfIXjC23Qpl8Xl28eFFdoqTdFBUVpRZk/TMqaf/xxx9Vtp+IiMhV2BDOTQTFOGYqNUARYYAua64tIu1E5EERqSwiz+lgPaeQib2XU3IREeUKAm+MMUcpuxXWb8YZclln0R0J47URQA8cOFACAvCpkFZmU51hDDhUrFjRdl2jRo3u2A5jt2fNmqWauk2cOFH69u2rrk9KSrJ1WjchSMY0bgiMMaWbdclsDHheoEM91K6NT8G/xuCHhITImTNn0myLDu7IprMRHBERuRqDczfhZTimGRCy48d0czDkPGrryz8wvk83DOuXg8fD17nBIjJbl0zP0eu5CfCJiDwVgm80f0MGvWyIj/j5ibrE+satcQ7PmpsQmCNI3rVrl2rUhvJyZNJfe+01NQ1ZRk6cOKHmQkdHdGz/+OOPq6y4Fbq9I2OOwPb+++9Xwe2RI0fUbQh+kX0PCwtTwTDK2dEsDlOX4X7ooI4x4bjfq6++qtZzqmrVqqqDPIJ9vD78jAXPZVYNLF++XKZMmSLNmjVTXdhRso7x9xs2bEjzWC+++KLKtK9atSrH+0FERORIDM7dRJFbeftiZugmcGiz019EBojIOYzdExGMrNuHMj9d7twmBxnwvjqgT9FzaKfkIsAnIiJRXdmX/nhTvL29VCk7LrGO650FTdcaN26sAlJkt9HADWOwH3vsMXn55ZczvE9ycrIan40g/sCBA/Lmm2/KyJEj02yDgBgd2xGQr169Wo4dOyavvPKKui08PFxGjx6tmqthijKz1H3UqFHy3nvvqRJ4834oc7c2k7PX2LFj1VRsuMRYcfyM5cEHUSd2G4J+zG++cuVK2bRpk2oCh+71eH0mlO736tVLvvrqK1s5PxERkatwzLmb8EtK2zk2p4E5gu6TIvK9iBzRJei+OqC+XWB42/UcNIbDNm31duYoPPOyjW48xrHNRET2wXRp6MqO5m8YY45surMy5lZogoZMORZ7m8dh2jFkojPbBo3WsoLO6FgyaiSHJSMIoO1tYte7d2+1ZCUmJkaNJceSGcyrntEYdCIiIldgcO4mUn2KiKSkbRRkLwTfR3Vgbk6bhaA5Vs91bW1vU1KPRben5Q2y64E6wLfKSYBPRERpqY7t+RCUExERUcHCsnY38XuNvxrv2ANhPHLt6HG7X0Re1mPCMZ2W6OD7Zx1gl9Pd28vp9Q12BtXWAF9yGeATERERERFR9hicu4l9lewvq4vXgbmXDpIX6VL29JBFX6IPcjV9ucSSXc+OIwJ8IiIiIiIiyh7L2t1EdEScXWPLk/RBM8eZT8si2I7X2fRvdFB9ORcB9ReWMebV9MmAnAT4RERERERElD0G525i45/n5A2dDc8MZkL/m4hUwfy1ujO7veXpuc1yOyLAJyIiIiIioqwxOHcT8fFRapqyrA7ILb0skPyXlwCfiIiIiIiIssYx524EJeOZQRl77nq5ExERERERkbtjcO4mMBVaFKZUy+R2XH9Vb0dERERERESFC4NzN3FZd11PSBegI2OeoqdMw7hvlpYTEREREREVPgzO3ch4EVkrIjcs06XhMkZf/5Grd5CIiCgfnT59WgYPHmxbNwxDunTp4tJ9IiIichYG524EgfjzIvKpiOwUkVMisktEJurrcTsREVFONW3aVJKTk+XHH3+UgqxChQqyatUqV+8GERGRUzA4dzMIwN8XkedE5DN9iXUG5kRElFt9+vSRzz77TFq1aiUVK1aUgioiIkKSkpJcvRtEREROweDcTWFs+VmOMSciKnTKici9IlI2n54vMDBQnn32WZk5c6asXLlSevXqZbutdevWqlS8bdu28uuvv0psbKxs3bpVatWqleYxBgwYICdOnJDExEQ5evSovPDCC2lux2P069dPfvjhB/UYhw8fVtn6u+++WzZs2CA3b95UjxsaGmq7D35evny5XLp0SWJiYmTXrl3y2GOPZfla0pe1V6lSRb799lu5du2aXLlyRT1e9erV07y+nTt3qufHNlu2bJFq1arl6f0kIiJyFgbnRERE+SBARDB6eraITBGROXrd38nP+8wzz6iA+tixY7Jw4UJ58cUX79jm/fffl6FDh8qDDz6oyt+//PJL221///vfZcqUKTJx4kS57777ZNasWTJv3jx59NFH0zzGqFGjZMGCBdKoUSP1fN98843a9sMPP1SP6+XlJdOmTbNtX7x4cfnpp59UQH7//ffL6tWrVXBftWpVu15XkSJFZM2aNSqwf+SRR6RFixYqCMfj+Pr6io+PjwrWN23aJA0aNJBmzZrJF198oQJ8IiIid2V4yhIUFGQALl29L9ktPj4+RlhYmLp09b5w4XHmwuPs6Uv16tWNBQsWqMvstg0ODs7w+sEixi8ixjIR40t9+Yu+3pn7vmXLFmPQoEHqZ/wOXr582WjdurVaxyW0bdvWtn3nzp3VdUWLFrXdf9asWWke89tvvzV+/PFH2zqMHTvWtv7www+r63r37m277tlnnzXi4uKy3NeDBw8aAwcOtK2fPn3aGDx4cJrn6dKli/r5+eefN44cOZLm/r6+vkZsbKzRvn17o1SpUmr7Vq1aOfz3IDQ0lH/THrDwf7dnLDzOnrP4uPBY2xuHMnNORESUD6XsbfVQJUydmaQvsd7GiSXuKE9v0qSJLFqEyTpFUlJSVBk4xqBbHThwwPbzxYsXb+9zOey1SN26dVVJuhXWcX1mj4Gx4XDw4ME01/n7+0tQUJCt3P6TTz5RJfAoOUcGHI9pb9l5w4YNpWbNmup+5nL16lUpVqyYKqfHYyLDj+z6ihUrZNCgQaqhHBERkbsq4uodICIiKuwQfAfqXiJW10UEoWg5J/UYQRCOEu/w8HDbdSgvx9jxV1991XbdrVuYvPM2s+zb2ztn5+8zeoysHnfChAnSvn17ef3119V49vj4eFmyZIn4+fnZ9Xwoi//tt9/k+ecxn0lakZG3302U8E+dOlU6deqkxt2PGzdOPSfGoRMREbkbBudEREROhlAxVkRK6oy5Ces3013nKBhz3aNHD/n3v/8ta9euTXMbxmJ3795djQ3PzpEjR9R4bownN2EdGe+8wGN89dVXal/MTHqNGjXsvv+ePXtUwH358mWVNc/Mvn371DJ+/HjZtm2bPPfccwzOiYjILbGsnYiIyMkQfP+sM+jIkvvpS6xvcFLWPCwsTEqVKiVz586V33//Pc2ydOnSO0rbM4PSc3R4R8d2lJEPGTJEunbtqjLfeXH8+HH1OChPR8M2NJDLSbb+P//5j0RFRcl///tfadmypQrs0Z0dzesqV66s1j/44APVNR6l8siY33PPPepkAxERkTticE5ERJQPvhCRJfqDt5q+XKKvdwYE3+vXr5fo6Og7bkNw/tBDD6mgODsIfgcPHqzKzxHY9+/fX3r37q26oOcFMvoYF45sNrq0Y2w4suH2Qhk85m0/e/asLFu2TAXdOBGBMed4zXFxcVKnTh31WtGpHp3ap0+frjrIExERuSOWtRMREeWDeD2F2jc6a242hHOWJ598MtPbMKc5xp7DZ599lua2/fv3224zff7552rJTPrtz5w5c8d1COat12Gb9POaz5gxI836XXfdleXzoMmcdd52K5S6IzNPRERUUDA4JyIiykeRTg7KiYiIqGBiWTsRERERERGRizE4JyIiIiIiInIxBudERERERERELsbgnIiIKBuGYdjmDifPVaTI7VY9qamprt4VIiIqhBicExERZQOdv6FcOfRZJ0+FqdngypUrrt4VIiIqhNitnYiIKBvXr1+Xo0ePyjPPPCNXr16VxMTETLcNCgqSkiVL5uv+kfMz5gjMcfw3btyo5lAnIiJyNAbnREREdpS1z549W95//30ZOXJkltv6+/tLfDxmNafCBoH5vHnzxNubhYdEROR4DM6JiIjsEBkZKa+88opUqFAh07HnuL5Vq1ayefNmSUlJyfd9JOednImKimLGnIiInIrBORERkZ2Sk5Pl/Pnzmd6O4BxB3JkzZxicExERUY6wLouIiIiIiIjIxRicExEREREREbkYg3MiIiIiIiIiF/PIMeeY5sbdYdwiOv5iXzlusfDicfYMPM6eg8faM/A4ewYeZ8/A4+w5fFx4rO2NPz0qODfflAsXLrh6V4iIiIiIiMjD4tGYmJhMb/fCDCHiQSpVqpTlG+JOBw4nESpXrlwg9pdyh8fZM/A4ew4ea8/A4+wZeJw9A4+z5why8bHG84eHh2e5jUdlziG7N8Td4BeH/ygKPx5nz8Dj7Dl4rD0Dj7Nn4HH2DDzOniPGRcfanudkQzgiIiIiIiIiF2NwTkRERERERORiDM7dVGJioowZM0ZdUuHF4+wZeJw9B4+1Z+Bx9gw8zp6Bx9lzJBaAY+1xDeGIiIiIiIiI3A0z50REREREREQuxuCciIiIiIiIyMUYnBMRERERERG5GINzIiIiIiIiIhdjcO6GXnnlFTl9+rTEx8fLjh075KGHHnL1LlEejB49WgzDSLMcOXLEdnvRokVl2rRpEhUVJTExMbJkyRIpV66cS/eZ7PPII4/IihUr5MKFC+q4dunS5Y5t3n33XQkPD5e4uDhZt26d1KxZM83tpUqVkoULF8qNGzfk2rVrMmfOHAkMDMzHV0F5Pc7z5s2742981apVabbhcXZ/w4cPl127dkl0dLRERETI999/L7Vq1UqzjT3/r6tWrSo//vijxMbGqsf5+OOPxcfHJ59fDeXlOG/YsOGOv+mZM2em2YbH2b0NGDBA9u/fr/7nYtm2bZt06tTJdjv/lj3nWG8ogH/P6NbOxU2WZ555xkhISDB69epl1K1b15g1a5Zx9epVo2zZsi7fNy65W0aPHm0cPHjQKF++vG0pU6aM7fYZM2YYZ86cMdq0aWM0btzY2LZtm7FlyxaX7zeX7JdOnToZ7733nvH3v//dgC5duqS5/Y033jCuXbtmPPnkk0b9+vWN5cuXGydPnjSKFi1q2+ann34y9u7dazRp0sRo0aKFcezYMeM///mPy18bF/uP87x589RxtP6NlyxZMs02PM7uv6xatcro2bOnUa9ePaNBgwbGjz/+aPz5559GQECA3f+vvb29jQMHDhhr1641GjZsqH53Ll++bLz//vsuf31c7D/OGzZsUN+/rH/TQUFBPM4FaAkLCzM6d+5s1KxZ07jnnnuMcePGGYmJieq443b+LXvOsd5Q8P6eXf+mcvlr2bFjh/HZZ5/Z1r28vIzz588bb775psv3jUvug3N8Kc/othIlSqh/IN26dbNdV7t2bRUAPPzwwy7fdy72LxkFbeHh4cbQoUPTHO/4+Hjj2WefVet16tRR93vggQds23Ts2NFISUkxKlas6PLXxMW+44zg/Pvvv8/0PjzOBXMJCQlRx+2RRx6x+/81vtQlJycb5cqVs23Tv39/4/r164avr6/LXxOX7I+z+WV+0qRJmd6Hx7lgLleuXDFefPFF/i170LGWAvj3zLJ2N+Lr6ysPPPCArF+/3nYdSi+w3qxZM5fuG+XNPffco0piT548qUpbUT4DON5+fn5pjvkff/whZ86c4TEv4O666y6pWLFimmOLMsqdO3faji0uUeL822+/2bbB9qmpqfLwww+7ZL8pdx599FFVCnf06FGZMWOGlC5d2nYbj3PBFBwcrC6vXr1q9/9rXB48eFAuX75s22bNmjXqse699958fw2U8+Nsev755yUyMlIdzw8++ED8/f1tt/E4Fyze3t7y7LPPqqFE27dv59+yBx3rgvj3XCTfn5EyFRISIkWKFFFf8KywXqdOHZftF+UNgrFevXqpf/wI1jAG/ZdffpH77rtPKlSoIImJiWqMTPpjjtuo4DKPX0Z/z+ZtuLR+GEBKSor6ksjjX3CsXr1ali1bpnqF3H333eqDH2PO8YGPAJzHueDx8vKSyZMny5YtW+T3339X19nz/xqXGf3Nm7eR+x9n+Oabb1Sghn4hDRo0kI8++khq164t3bp1U7fzOBcM+J6FAK1YsWJy8+ZN+cc//qF6/jRq1Ih/yx5yrAvi3zODc6J8+OJuwpk5BOv4J/HMM8+opn9EVLB9++23tp8PHTokBw4ckFOnTqls+s8//+zSfaPcmT59uvqy17JlS1fvCrngOM+ePTvN3/TFixfV33JoaKj626aCAUkRBOLIgD711FMyf/58ad26tat3i/LxWB85cqTA/T2zrN2NoGNkcnKylC9fPs31WL906ZLL9oscC2dqjx07prp247iiY6hZVmfiMS/4zOOX1d8zLtN3h0V3UJRE8/gXXMigo3zO7MzP41ywfPbZZxIWFiZt2rRRw5FM9vy/xmVGf/PmbeT+xzkjOKkO1r9pHmf3d+vWLTWccM+ePfL222+rjt6DBw/m37IHHeuC+PfM4NzNfrEwJvGxxx5LU3KFdeu4CSrYMA4Gpa84c4fjnZSUlOaYY0qX6tWr85gXggANx9h6bIOCgtQYY/PY4hJTbDVu3Ni2Tdu2bdWYKfPDgwqeypUrS5kyZdTxBx7nghWwoRwSx+fPP/9Mc5s9/69xWb9+fSlbtqxtm/bt26uTsocPH87HV0K5Pc4ZQUYOrH/TPM4FD/7nIijn37LnHOuC+vfs8o56XNJOpYZuzj169FAdfj///HM1lZq1gyCXgrV88sknRqtWrYzq1asbzZo1U1M1YIoGdIg1p/PANC6PPvqoms5j69atanH1fnPJfgkMDFTTbmCBf/3rX+rnqlWr2qZSw9/v3/72N+O+++5THb0zmkrtt99+Mx566CGjefPmxh9//MEptgrQccZtH3/8serwi7/xtm3bGrt371bH0c/Pj8e5AC3Tp09XUx/i/7V1yp1ixYrZtsnu/7U5Jc/q1avVNF0dOnQwIiIiOP1SATrOoaGhxsiRI9Xxxd80/n+fOHHC2LhxI49zAVo++OAD1YEfxxCfv1jHDBnt2rVTt/Nv2TOOdWjB/Ht2/ZvKJe0ycOBA9Q8D851jajXMi+vqfeKS+2XRokXGhQsX1PE8d+6cWsc/C/N2BGrTpk1T0z7cvHnTWLp0qfqi4Or95pL90rp1ayMjmFrL3Obdd981Ll68qE66rVu3Ts3BaX2MUqVKqSAtOjpaTdsxd+5cFfC5+rVxse844ws9PtDxQY6peU6fPq3mU01/QpXH2f2XzGBO7Jz8v65WrZqxcuVKIzY2Vp2IxQlaHx8fl78+LvYd5ypVqqgv7lFRUer/9rFjx4yPPvoozbzIPM7uv8yZM0f9P8Z3L/x/xuevGZhj4d+yZxzrKgXw79lL/0BERERERERELsIx50REREREREQuxuCciIiIiIiIyMUYnBMRERERERG5GINzIiIiIiIiIhdjcE5ERERERETkYgzOiYiIiIiIiFyMwTkRERERERGRizE4JyIiIiIiInIxBudEROQRNmzYIJMmTRJ31bp1azEMQ4KDgx32mHi8Ll26iCPNmzdPvv/+e4c+JhERETE4JyKiQgSBIwLS9Mvdd98tXbt2lVGjRuVLsGt97uvXr8uWLVukTZs2Wd5n27ZtUqFCBblx44Y4Ch5v1apV4gp9+/aVHTt2SExMjFy7dk1+/fVXGTx4sPj7+7tkfzzlhAwRERVcDM6JiKhQQTCKoNS6nD59WgWIN2/ezPR+vr6+Dt2PXr16qedu0aKFREVFyY8//ih33XVXhtsWKVJEbt26JREREQ7dBzxeUlKS5Levv/5aJk+eLP/973/VSYlGjRrJe++9p05sdOjQId/3h4iIqKAwuHDhwoULl8KwzJs3z/j+++8zvG3Dhg3GpEmTbOunT582Ro4cacyfP9+4ceOGuq+vr6/x2WefGeHh4UZ8fLzx559/GsOHD7dtb4X1zPYDunTpYluvWLGiuq5fv3622wcMGGD897//NW7evGmMHj3aaN26tbo+ODhYbdOzZ0/j2rVrRocOHYzDhw8bMTExxqpVq4wKFSqkea7evXsbhw4dMhISEtR+Y/8z2o/q1aur9WeffdbYunWren0HDx40WrVqZdve29vbmDNnjnHq1CkjLi7OOHr0qDFo0CC732MsTz/9tHqeJ598MsPbS5QooS69vLyMUaNGGefOnVP7vnfvXqNjx4627cz9xeNt3rxZ7c+uXbuMe+65x3jwwQeNX3/9Vb0nP/30kxESEnLH/r3zzjvG5cuX1bGdOXOmOrbmNn5+fsaUKVOMiIgI9T788ssv6jHN281j0bZtW/U8sbGx6j2rVatWmteC1/jbb7+pxzh58qR6Th8fnzTvf58+fYxly5apxzh27Jjxt7/9Lc3rs8K+u/pviAsXLly4iCsXl+8AFy5cuHDh4pLg/Pr168a///1vIzQ0VC1Dhw41zpw5Y7Rs2dKoVq2a0aJFC+Of//yn2h4BICBoLl++fJqAMLvgvGTJkuq6V1991Xb7pUuXjF69ehl33XWXUbVq1QyD88TERGPt2rXGAw88YNx///3G77//bixcuND2uAjwEbQigDaD1sGDB2e4H2YwePbsWaNr165GnTp1jC+++EIFr6VLl1bbFClSxBgzZox6vho1ahjPPfecOnmAANme9xjL8uXLjSNHjmR7rP71r3+p9x8nCxD0jh8/Xr3emjVrptlfnJjACQrs77Zt21Sw/PPPPxvNmzc3GjVqpALeGTNmpNm/6OhoY9GiRUa9evWMxx9/XAXh48aNs20zefJk4/z580anTp2MunXrqvtcuXLFKFWqlLrdPBbbt29XJy+wzaZNm4wtW7bYHgO/I9j/Hj16qGPYrl07dVIDAbr1/cf7jd+hu+++Wz0v9g3PgxMh//jHP9Q2OHb4nTJPXHDhwoULF/HUxeU7wIULFy5cuDhkQZB169YtlVE1l++++y7T4BwZTev9kU1dv3693UG3Pdv5+/sb06ZNU/tVv3592+2ffvppmvtkFJwDThqY27z88svGxYsXbesIMN977z279sMMdt944w3b7cjyIngcNmxYpo+BTPzixYvtDs5xAgEBenbvEfb9rbfeSnPdzp071Xtl3d8XX3zRdjsCeWjTpo3tujfffDPNyQDsX1RUlHrfzev69++vgmJk6wMCAtRJgO7du9tux0kJ7M/rr79+R+bc3KZz587quqJFi6r1devW2aoqzOX55583Lly4kOb9Hzt2rG0dzw1mhUD6Y86FCxcuXMSjlyKurqknIiJydFf2l19+2bYeGxub6ba7d+9Os/7VV1/JunXr5I8//pDVq1erceJYz41FixZJSkqKaoAWGRkpffr0kYMHD2b63BnBvp86dcq2fvHiRSlXrpz6uWzZslK5cmX53//+l6P92r59u+1n7B/2o27durbrXnnlFXnxxRelWrVqat/9/Pxk3759dj++l5dXttsEBQWpfd+6dWua67HesGHDNNcdOHDA9rM5Jt/6PuI68z0x7d+/X+Lj49O8Zjxn1apVVfM1vCbrcycnJ8uuXbvSvA/pnxvvPeC5zp07p/YT/QRGjBhh28bHx0e9Z1jM57c+RlxcnGr4l35/iYiIgME5EREVKghoT548afe2Vnv37lVN2zp37izt2rWT7777TtavXy9PP/10jvdjyJAh6r4IxtAQLrvnzgiaxFmhs7e39+1ertbg01GeffZZmTBhggwdOlQFtOi0PmzYMHn44Yftfoxjx45JnTp1HLZP1vcArz+j68z3xNEyem7zuYoXLy6jR4+WZcuW3XG/hISEDB/D2ftLREQFGz8diIiILBCQIijv16+fClafeuopKVWqlLoNnc+RHbXHpUuX1EmCjAJzR0DneXShf+yxx3J0v6ZNm9p+xmt54IEH5MiRI2odmWBM6TZz5kyVLcf+Yxq6nPjmm2+kdu3a8uSTT2Z4e4kSJdR7fOHCBfV8Vlg/fPiw5BWy2sWKFUvzmvGcyHjjNSUmJqZ5bnTLf+ihh3L03Hv27FGvE4+XfjED+eyYnfTt/Z0iIqLCjZlzIiIiS7Yb5cvIoKempqqMOdYxVzn8+eefKhhGSTQCPPN6VxkzZox8/vnncvnyZTWFHEq3EXROmzYt0/sMHDhQjh8/rgJyvF6cePjyyy/Vbbi+R48earozBP7/93//p4JW/GwvnNj4xz/+ocr6x40bJ2vXrlVl/fXr11fP99lnn6kp1j755BN59913VTCLEwG9e/dWU649//zzeX5fULY+d+5c9fw1atRQz4P3BEEzSstx8gHPf/XqVTl79qy88cYbEhAQoO5jr7Fjx6phD7j/kiVL1O8LTgrcd999MmrUKLse48yZM+p+YWFh8tNPP6lqCHsqKoiIqHBicE5ERKQhu4pA7Z577lHjsX/99Vd5/PHHbZlQlHt/+umn0rdvX5X5zWze8vyyYMEClSFG0ItydGTpEShmZfjw4WpBIHzixAmV4b5y5Yq6bdasWXL//ffLt99+q14zAuwZM2aoMv+ceO6551TlAcauY0w2xnQj8Mf+rlmzRm0zdepUNf574sSJagw2stbYF+xTXmEcPp5v8+bNUrRoUfU6cCLD+h6gtBzzseOEBsbdd+zYMUcnW3DSAUH1O++8I2+++aYqXz969KjMmTPH7scIDw9XpfHjx4+XefPmqfcHJymIiMgzoWuLfbVXREREVGBVr15dZf4RlKNhWmGFILdkyZIqe09ERFSQcMw5ERERERERkYsxOCciIiIiIiJyMZa1ExEREREREbkYM+dERERERERELsbgnIiIiIiIiMjFGJwTERERERERuRiDcyIiIiIiIiIXY3BORERERERE5GIMzomIiIiIiIhcjME5ERERERERkYsxOCciIiIiIiIS1/r/C5CAVKOTSQAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DBSCAN clustering for anomaly detection\n",
    "print(\"🔍 DBSCAN Clustering for Anomaly Detection...\")\n",
    "\n",
    "# Use PCA for dimensionality reduction\n",
    "pca = PCA(n_components=10)\n",
    "X_pca = pca.fit_transform(X_test_scaled)\n",
    "\n",
    "print(f\"📊 PCA explained variance ratio: {pca.explained_variance_ratio_.sum():.3f}\")\n",
    "\n",
    "# Apply DBSCAN\n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
    "clusters = dbscan.fit_predict(X_pca)\n",
    "\n",
    "# Consider noise points (-1) as anomalies\n",
    "dbscan_anomalies = (clusters == -1).astype(int)\n",
    "\n",
    "print(f\"🔍 DBSCAN Results:\")\n",
    "print(f\"   Number of clusters: {len(set(clusters)) - (1 if -1 in clusters else 0)}\")\n",
    "print(f\"   Noise points (anomalies): {sum(clusters == -1):,}\")\n",
    "print(f\"   Anomaly detection accuracy: {accuracy_score(y_test, dbscan_anomalies):.4f}\")\n",
    "\n",
    "# Visualize clusters in 2D\n",
    "plt.figure(figsize=(12, 8))\n",
    "unique_clusters = set(clusters)\n",
    "colors = plt.cm.viridis(np.linspace(0, 1, len(unique_clusters)))\n",
    "\n",
    "for cluster, color in zip(unique_clusters, colors):\n",
    "    if cluster == -1:\n",
    "        # Anomalies in red\n",
    "        mask = clusters == cluster\n",
    "        plt.scatter(X_pca[mask, 0], X_pca[mask, 1], c='red', s=20, \n",
    "                   alpha=0.6, label='Anomalies')\n",
    "    else:\n",
    "        mask = clusters == cluster\n",
    "        plt.scatter(X_pca[mask, 0], X_pca[mask, 1], c=[color], s=20, \n",
    "                   alpha=0.6, label=f'Cluster {cluster}')\n",
    "\n",
    "plt.xlabel('First Principal Component', color='white')\n",
    "plt.ylabel('Second Principal Component', color='white')\n",
    "plt.title('DBSCAN Clustering Results (PCA Projection)', fontsize=16, color='white')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🕸️ Network Traffic Visualization\n",
    "\n",
    "Create interactive visualizations for network analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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",
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         "xaxis": "x3",
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        "title": {
         "text": "🌐 Network Security Analysis Dashboard"
        },
        "xaxis": {
         "anchor": "y",
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          "text": "Hour of Day"
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     },
     "metadata": {},
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    {
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     "text": [
      "📊 Interactive network dashboard created!\n"
     ]
    }
   ],
   "source": [
    "# Create network traffic visualization\n",
    "def create_network_dashboard(df_sample):\n",
    "    \"\"\"\n",
    "    Create interactive network analysis dashboard\n",
    "    \"\"\"\n",
    "    # Sample data for visualization\n",
    "    sample_size = min(1000, len(df_sample))\n",
    "    df_viz = df_sample.sample(sample_size).copy()\n",
    "    \n",
    "    # Create subplots with proper specifications for different chart types\n",
    "    fig = make_subplots(\n",
    "        rows=2, cols=2,\n",
    "        subplot_titles=('Traffic Volume Over Time', 'Protocol Distribution', \n",
    "                       'Attack Types', 'Connection Duration vs Bytes'),\n",
    "        specs=[[{\"secondary_y\": True}, {\"type\": \"domain\"}],  # domain for pie chart\n",
    "               [{}, {\"type\": \"scatter\"}]]\n",
    "    )\n",
    "    \n",
    "    # 1. Traffic volume over time (simulated)\n",
    "    time_series = pd.date_range(start='2024-01-01', periods=len(df_viz), freq='1min')\n",
    "    df_viz['timestamp'] = time_series\n",
    "    \n",
    "    # Group by hour for better visualization\n",
    "    df_viz['hour'] = df_viz['timestamp'].dt.hour\n",
    "    hourly_traffic = df_viz.groupby('hour').agg({\n",
    "        'src_bytes': 'sum',\n",
    "        'dst_bytes': 'sum',\n",
    "        'attack_type': 'count'\n",
    "    }).reset_index()\n",
    "    \n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=hourly_traffic['hour'], y=hourly_traffic['src_bytes'],\n",
    "                  mode='lines+markers', name='Source Bytes', \n",
    "                  line=dict(color='lightblue', width=2)),\n",
    "        row=1, col=1\n",
    "    )\n",
    "    \n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=hourly_traffic['hour'], y=hourly_traffic['dst_bytes'],\n",
    "                  mode='lines+markers', name='Destination Bytes', \n",
    "                  line=dict(color='lightcoral', width=2)),\n",
    "        row=1, col=1\n",
    "    )\n",
    "    \n",
    "    # 2. Protocol distribution (pie chart)\n",
    "    if 'protocol_type' in df_viz.columns:\n",
    "        protocol_counts = df_viz['protocol_type'].value_counts()\n",
    "        fig.add_trace(\n",
    "            go.Pie(labels=protocol_counts.index, values=protocol_counts.values,\n",
    "                  name=\"Protocol Distribution\", hole=0.3,\n",
    "                  marker=dict(colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'])),\n",
    "            row=1, col=2\n",
    "        )\n",
    "    else:\n",
    "        # Create a dummy pie chart if no protocol data\n",
    "        fig.add_trace(\n",
    "            go.Pie(labels=['TCP', 'UDP', 'ICMP'], values=[60, 30, 10],\n",
    "                  name=\"Protocol Distribution (Simulated)\", hole=0.3,\n",
    "                  marker=dict(colors=['#1f77b4', '#ff7f0e', '#2ca02c'])),\n",
    "            row=1, col=2\n",
    "        )\n",
    "    \n",
    "    # 3. Attack types\n",
    "    attack_counts = df_viz['attack_type'].value_counts().head(10)\n",
    "    fig.add_trace(\n",
    "        go.Bar(x=attack_counts.index, y=attack_counts.values,\n",
    "              name=\"Attack Types\", marker_color='lightcoral',\n",
    "              text=attack_counts.values, textposition='auto'),\n",
    "        row=2, col=1\n",
    "    )\n",
    "    \n",
    "    # 4. Connection analysis\n",
    "    if 'duration' in df_viz.columns and 'src_bytes' in df_viz.columns:\n",
    "        # Create color mapping for attack types\n",
    "        color_map = df_viz['is_attack'].map({0: 'Normal', 1: 'Attack'})\n",
    "        \n",
    "        fig.add_trace(\n",
    "            go.Scatter(x=df_viz['duration'], y=df_viz['src_bytes'],\n",
    "                      mode='markers', name='Duration vs Bytes',\n",
    "                      marker=dict(\n",
    "                          color=df_viz['is_attack'], \n",
    "                          colorscale=['lightblue', 'red'],\n",
    "                          size=6,\n",
    "                          opacity=0.7,\n",
    "                          colorbar=dict(title=\"Attack (0=Normal, 1=Attack)\")\n",
    "                      ),\n",
    "                      text=df_viz['attack_type'],\n",
    "                      hovertemplate='<b>%{text}</b><br>' +\n",
    "                                  'Duration: %{x:.2f}s<br>' +\n",
    "                                  'Bytes: %{y}<br>' +\n",
    "                                  '<extra></extra>'),\n",
    "            row=2, col=2\n",
    "        )\n",
    "    else:\n",
    "        # Create dummy scatter plot if no duration data\n",
    "        dummy_duration = np.random.exponential(100, len(df_viz))\n",
    "        dummy_bytes = np.random.lognormal(8, 2, len(df_viz))\n",
    "        \n",
    "        fig.add_trace(\n",
    "            go.Scatter(x=dummy_duration, y=dummy_bytes,\n",
    "                      mode='markers', name='Duration vs Bytes (Simulated)',\n",
    "                      marker=dict(\n",
    "                          color=df_viz['is_attack'], \n",
    "                          colorscale=['lightblue', 'red'],\n",
    "                          size=6,\n",
    "                          opacity=0.7\n",
    "                      )),\n",
    "            row=2, col=2\n",
    "        )\n",
    "    \n",
    "    # Update layout for better appearance\n",
    "    fig.update_layout(\n",
    "        height=800,\n",
    "        showlegend=True,\n",
    "        title_text=\"🌐 Network Security Analysis Dashboard\",\n",
    "        template=\"plotly_dark\",\n",
    "        font=dict(color=\"white\")\n",
    "    )\n",
    "    \n",
    "    # Update x-axis labels\n",
    "    fig.update_xaxes(title_text=\"Hour of Day\", row=1, col=1)\n",
    "    fig.update_xaxes(title_text=\"Attack Type\", row=2, col=1)\n",
    "    fig.update_xaxes(title_text=\"Duration (seconds)\", row=2, col=2)\n",
    "    \n",
    "    # Update y-axis labels\n",
    "    fig.update_yaxes(title_text=\"Bytes\", row=1, col=1)\n",
    "    fig.update_yaxes(title_text=\"Count\", row=2, col=1)\n",
    "    fig.update_yaxes(title_text=\"Source Bytes\", row=2, col=2)\n",
    "    \n",
    "    return fig\n",
    "\n",
    "# Create and display dashboard\n",
    "if df_network is not None:\n",
    "    dashboard = create_network_dashboard(df_network)\n",
    "    dashboard.show()\n",
    "    print(\"📊 Interactive network dashboard created!\")\n",
    "else:\n",
    "    print(\"⚠️ No network data available for visualization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🚨 Real-time Network Monitoring\n",
    "\n",
    "Create a real-time network monitoring system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚨 Network Monitor initialized and ready!\n"
     ]
    }
   ],
   "source": [
    "class NetworkMonitor:\n",
    "    \"\"\"\n",
    "    Real-time network monitoring and threat detection\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, rf_model, nn_model, iso_forest, scaler):\n",
    "        self.rf_model = rf_model\n",
    "        self.nn_model = nn_model\n",
    "        self.iso_forest = iso_forest\n",
    "        self.scaler = scaler\n",
    "        self.alert_threshold = 0.7\n",
    "        self.anomaly_threshold = -0.3\n",
    "        \n",
    "    def process_network_packet(self, packet_features):\n",
    "        \"\"\"\n",
    "        Process a single network packet and detect threats\n",
    "        \"\"\"\n",
    "        try:\n",
    "            # Convert to DataFrame\n",
    "            packet_df = pd.DataFrame([packet_features])\n",
    "            \n",
    "            # Apply feature engineering\n",
    "            packet_enhanced = create_network_features(packet_df)\n",
    "            \n",
    "            # Select features and handle missing columns\n",
    "            for col in X.columns:\n",
    "                if col not in packet_enhanced.columns:\n",
    "                    packet_enhanced[col] = 0\n",
    "            \n",
    "            X_packet = packet_enhanced[X.columns].select_dtypes(include=[np.number])\n",
    "            X_packet_scaled = self.scaler.transform(X_packet.fillna(0))\n",
    "            \n",
    "            # Multiple model predictions\n",
    "            rf_prob = self.rf_model.predict_proba(X_packet_scaled)[0, 1]\n",
    "            nn_prob = self.nn_model.predict(X_packet_scaled)[0, 0]\n",
    "            iso_score = self.iso_forest.score_samples(X_packet_scaled)[0]\n",
    "            \n",
    "            # Ensemble prediction\n",
    "            ensemble_prob = (rf_prob + nn_prob) / 2\n",
    "            \n",
    "            # Determine threat level\n",
    "            if ensemble_prob > 0.8:\n",
    "                threat_level = \"CRITICAL\"\n",
    "            elif ensemble_prob > 0.6:\n",
    "                threat_level = \"HIGH\"\n",
    "            elif ensemble_prob > 0.4:\n",
    "                threat_level = \"MEDIUM\"\n",
    "            else:\n",
    "                threat_level = \"LOW\"\n",
    "            \n",
    "            # Check for anomalies\n",
    "            is_anomaly = iso_score < self.anomaly_threshold\n",
    "            \n",
    "            result = {\n",
    "                'timestamp': datetime.now().isoformat(),\n",
    "                'threat_probability': float(ensemble_prob),\n",
    "                'threat_level': threat_level,\n",
    "                'is_anomaly': bool(is_anomaly),\n",
    "                'anomaly_score': float(iso_score),\n",
    "                'model_scores': {\n",
    "                    'random_forest': float(rf_prob),\n",
    "                    'neural_network': float(nn_prob)\n",
    "                },\n",
    "                'alert_required': ensemble_prob > self.alert_threshold or is_anomaly\n",
    "            }\n",
    "            \n",
    "            return result\n",
    "            \n",
    "        except Exception as e:\n",
    "            return {\n",
    "                'error': str(e),\n",
    "                'timestamp': datetime.now().isoformat(),\n",
    "                'threat_level': 'UNKNOWN'\n",
    "            }\n",
    "    \n",
    "    def generate_alert(self, packet_result):\n",
    "        \"\"\"\n",
    "        Generate security alert based on analysis\n",
    "        \"\"\"\n",
    "        if packet_result.get('alert_required', False):\n",
    "            alert = {\n",
    "                'alert_id': f\"ALERT_{datetime.now().strftime('%Y%m%d_%H%M%S')}\",\n",
    "                'timestamp': packet_result['timestamp'],\n",
    "                'severity': packet_result['threat_level'],\n",
    "                'threat_probability': packet_result['threat_probability'],\n",
    "                'is_anomaly': packet_result.get('is_anomaly', False),\n",
    "                'description': self._generate_alert_description(packet_result),\n",
    "                'recommended_actions': self._get_recommended_actions(packet_result)\n",
    "            }\n",
    "            return alert\n",
    "        return None\n",
    "    \n",
    "    def _generate_alert_description(self, result):\n",
    "        \"\"\"\n",
    "        Generate human-readable alert description\n",
    "        \"\"\"\n",
    "        threat_level = result['threat_level']\n",
    "        prob = result['threat_probability']\n",
    "        \n",
    "        if result.get('is_anomaly', False):\n",
    "            return f\"Anomalous network traffic detected with {threat_level.lower()} threat level (confidence: {prob:.1%})\"\n",
    "        else:\n",
    "            return f\"Potential network intrusion detected with {threat_level.lower()} threat level (confidence: {prob:.1%})\"\n",
    "    \n",
    "    def _get_recommended_actions(self, result):\n",
    "        \"\"\"\n",
    "        Get recommended actions based on threat level\n",
    "        \"\"\"\n",
    "        threat_level = result['threat_level']\n",
    "        \n",
    "        if threat_level == \"CRITICAL\":\n",
    "            return [\n",
    "                \"Immediately block suspicious traffic\",\n",
    "                \"Isolate affected systems\",\n",
    "                \"Initiate incident response procedure\",\n",
    "                \"Contact security team\"\n",
    "            ]\n",
    "        elif threat_level == \"HIGH\":\n",
    "            return [\n",
    "                \"Monitor traffic closely\",\n",
    "                \"Increase logging detail\",\n",
    "                \"Prepare for potential blocking\",\n",
    "                \"Alert security team\"\n",
    "            ]\n",
    "        else:\n",
    "            return [\n",
    "                \"Continue monitoring\",\n",
    "                \"Log for analysis\",\n",
    "                \"Review patterns\"\n",
    "            ]\n",
    "\n",
    "# Initialize network monitor\n",
    "monitor = NetworkMonitor(rf_ids, ids_model, iso_forest, scaler)\n",
    "\n",
    "print(\"🚨 Network Monitor initialized and ready!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🧪 Testing Network Monitor with sample packets...\n",
      "\n",
      "📦 Testing Packet 1:\n",
      "🔧 Network feature engineering: 5 → 18 features\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step\n",
      "   Threat Level: LOW\n",
      "   Threat Probability: 0.082\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191117\n",
      "   Severity: LOW\n",
      "   Description: Anomalous network traffic detected with low threat level (confidence: 8.2%)\n",
      "   Actions: Continue monitoring, Log for analysis...\n",
      "\n",
      "📦 Testing Packet 2:\n",
      "🔧 Network feature engineering: 5 → 18 features\n",
      "   Threat Level: LOW\n",
      "   Threat Probability: 0.082\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191117\n",
      "   Severity: LOW\n",
      "   Description: Anomalous network traffic detected with low threat level (confidence: 8.2%)\n",
      "   Actions: Continue monitoring, Log for analysis...\n",
      "\n",
      "📦 Testing Packet 2:\n",
      "🔧 Network feature engineering: 5 → 18 features\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 945ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 945ms/step\n",
      "   Threat Level: CRITICAL\n",
      "   Threat Probability: 0.980\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191118\n",
      "   Severity: CRITICAL\n",
      "   Description: Anomalous network traffic detected with critical threat level (confidence: 98.0%)\n",
      "   Actions: Immediately block suspicious traffic, Isolate affected systems...\n",
      "\n",
      "📦 Testing Packet 3:\n",
      "   Threat Level: CRITICAL\n",
      "   Threat Probability: 0.980\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191118\n",
      "   Severity: CRITICAL\n",
      "   Description: Anomalous network traffic detected with critical threat level (confidence: 98.0%)\n",
      "   Actions: Immediately block suspicious traffic, Isolate affected systems...\n",
      "\n",
      "📦 Testing Packet 3:\n",
      "🔧 Network feature engineering: 5 → 18 features\n",
      "🔧 Network feature engineering: 5 → 18 features\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step\n",
      "   Threat Level: LOW\n",
      "   Threat Probability: 0.099\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191119\n",
      "   Severity: LOW\n",
      "   Description: Anomalous network traffic detected with low threat level (confidence: 9.9%)\n",
      "   Actions: Continue monitoring, Log for analysis...\n",
      "\n",
      "✅ Network Monitor testing completed!\n",
      "   Threat Level: LOW\n",
      "   Threat Probability: 0.099\n",
      "   Is Anomaly: True\n",
      "   Alert Required: True\n",
      "\n",
      "🚨 SECURITY ALERT:\n",
      "   Alert ID: ALERT_20250824_191119\n",
      "   Severity: LOW\n",
      "   Description: Anomalous network traffic detected with low threat level (confidence: 9.9%)\n",
      "   Actions: Continue monitoring, Log for analysis...\n",
      "\n",
      "✅ Network Monitor testing completed!\n"
     ]
    }
   ],
   "source": [
    "# Test the network monitor with sample packets\n",
    "print(\"🧪 Testing Network Monitor with sample packets...\")\n",
    "\n",
    "# Create test packets\n",
    "test_packets = [\n",
    "    {\n",
    "        'duration': 0.1,\n",
    "        'src_bytes': 100,\n",
    "        'dst_bytes': 50,\n",
    "        'land': 0,\n",
    "        'wrong_fragment': 0\n",
    "    },\n",
    "    {\n",
    "        'duration': 3600,  # Very long connection\n",
    "        'src_bytes': 1000000,  # Large transfer\n",
    "        'dst_bytes': 10,\n",
    "        'land': 1,  # Suspicious flag\n",
    "        'wrong_fragment': 5\n",
    "    },\n",
    "    {\n",
    "        'duration': 0.01,  # Very short\n",
    "        'src_bytes': 10000,\n",
    "        'dst_bytes': 10000,\n",
    "        'land': 0,\n",
    "        'wrong_fragment': 0\n",
    "    }\n",
    "]\n",
    "\n",
    "# Test each packet\n",
    "for i, packet in enumerate(test_packets, 1):\n",
    "    print(f\"\\n📦 Testing Packet {i}:\")\n",
    "    result = monitor.process_network_packet(packet)\n",
    "    \n",
    "    print(f\"   Threat Level: {result.get('threat_level', 'UNKNOWN')}\")\n",
    "    print(f\"   Threat Probability: {result.get('threat_probability', 0):.3f}\")\n",
    "    print(f\"   Is Anomaly: {result.get('is_anomaly', False)}\")\n",
    "    print(f\"   Alert Required: {result.get('alert_required', False)}\")\n",
    "    \n",
    "    # Generate alert if needed\n",
    "    alert = monitor.generate_alert(result)\n",
    "    if alert:\n",
    "        print(f\"\\n🚨 SECURITY ALERT:\")\n",
    "        print(f\"   Alert ID: {alert['alert_id']}\")\n",
    "        print(f\"   Severity: {alert['severity']}\")\n",
    "        print(f\"   Description: {alert['description']}\")\n",
    "        print(f\"   Actions: {', '.join(alert['recommended_actions'][:2])}...\")\n",
    "\n",
    "print(\"\\n✅ Network Monitor testing completed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 💾 Model Deployment and Integration\n",
    "\n",
    "Save models and create deployment-ready functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💾 Saving network security models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING\tTask(Task-3) absl:saving_api.py:save_model()- You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ All network security models saved!\n",
      "📁 Models location: ../models/network_security/\n",
      "\n",
      "📊 Deployment Summary:\n",
      "   Models trained: 3\n",
      "   Features: 19\n",
      "   Training samples: 100,778\n",
      "   Test samples: 25,195\n",
      "   Deployment ready: True\n",
      "\n",
      "📊 Deployment Summary:\n",
      "   Models trained: 3\n",
      "   Features: 19\n",
      "   Training samples: 100,778\n",
      "   Test samples: 25,195\n",
      "   Deployment ready: True\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import os\n",
    "\n",
    "# Create network models directory\n",
    "os.makedirs('../models/network_security', exist_ok=True)\n",
    "\n",
    "print(\"💾 Saving network security models...\")\n",
    "\n",
    "# Save models\n",
    "joblib.dump(rf_ids, '../models/network_security/random_forest_ids.pkl')\n",
    "ids_model.save('../models/network_security/neural_network_ids.h5')\n",
    "joblib.dump(iso_forest, '../models/network_security/isolation_forest.pkl')\n",
    "joblib.dump(scaler, '../models/network_security/feature_scaler.pkl')\n",
    "joblib.dump(X.columns.tolist(), '../models/network_security/feature_names.pkl')\n",
    "\n",
    "# Save network monitor configuration\n",
    "monitor_config = {\n",
    "    'alert_threshold': 0.7,\n",
    "    'anomaly_threshold': -0.3,\n",
    "    'model_weights': {\n",
    "        'random_forest': 0.5,\n",
    "        'neural_network': 0.5\n",
    "    },\n",
    "    'feature_engineering': 'create_network_features',\n",
    "    'created_at': datetime.now().isoformat()\n",
    "}\n",
    "\n",
    "joblib.dump(monitor_config, '../models/network_security/monitor_config.pkl')\n",
    "\n",
    "print(\"✅ All network security models saved!\")\n",
    "print(f\"📁 Models location: ../models/network_security/\")\n",
    "\n",
    "# Create deployment summary\n",
    "deployment_summary = {\n",
    "    'models': {\n",
    "        'intrusion_detection': {\n",
    "            'random_forest': {\n",
    "                'accuracy': accuracy_score(y_test, rf_pred),\n",
    "                'f1_score': f1_score(y_test, rf_pred),\n",
    "                'file': 'random_forest_ids.pkl'\n",
    "            },\n",
    "            'neural_network': {\n",
    "                'accuracy': accuracy_score(y_test, nn_pred),\n",
    "                'f1_score': f1_score(y_test, nn_pred),\n",
    "                'file': 'neural_network_ids.h5'\n",
    "            }\n",
    "        },\n",
    "        'anomaly_detection': {\n",
    "            'isolation_forest': {\n",
    "                'accuracy': accuracy_score(y_test, iso_pred_binary),\n",
    "                'f1_score': f1_score(y_test, iso_pred_binary),\n",
    "                'file': 'isolation_forest.pkl'\n",
    "            }\n",
    "        }\n",
    "    },\n",
    "    'features': len(X.columns),\n",
    "    'training_samples': len(X_train),\n",
    "    'test_samples': len(X_test),\n",
    "    'deployment_ready': True\n",
    "}\n",
    "\n",
    "joblib.dump(deployment_summary, '../models/network_security/deployment_summary.pkl')\n",
    "\n",
    "print(\"\\n📊 Deployment Summary:\")\n",
    "print(f\"   Models trained: {len(deployment_summary['models']['intrusion_detection']) + len(deployment_summary['models']['anomaly_detection'])}\")\n",
    "print(f\"   Features: {deployment_summary['features']}\")\n",
    "print(f\"   Training samples: {deployment_summary['training_samples']:,}\")\n",
    "print(f\"   Test samples: {deployment_summary['test_samples']:,}\")\n",
    "print(f\"   Deployment ready: {deployment_summary['deployment_ready']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📋 Network Security Summary\n",
    "\n",
    "### ✅ Accomplishments:\n",
    "\n",
    "1. **🔍 Intrusion Detection System**\n",
    "   - Random Forest classifier for known attack patterns\n",
    "   - Deep Neural Network for complex threat detection\n",
    "   - Feature engineering for network traffic analysis\n",
    "\n",
    "2. **🌟 Anomaly Detection**\n",
    "   - Isolation Forest for unknown threat detection\n",
    "   - DBSCAN clustering for traffic pattern analysis\n",
    "   - Unsupervised learning for zero-day threats\n",
    "\n",
    "3. **📊 Interactive Visualization**\n",
    "   - Real-time network traffic dashboard\n",
    "   - Attack pattern visualization\n",
    "   - Protocol and service analysis\n",
    "\n",
    "4. **🚨 Real-time Monitoring**\n",
    "   - NetworkMonitor class for live threat detection\n",
    "   - Automated alert generation\n",
    "   - Ensemble model predictions\n",
    "\n",
    "5. **💾 Production Deployment**\n",
    "   - All models saved for deployment\n",
    "   - Configuration management\n",
    "   - Integration-ready components\n",
    "\n",
    "### 🚀 Integration Points:\n",
    "\n",
    "- **Desktop App**: Real-time network monitoring dashboard\n",
    "- **Mobile App**: Network security alerts and status\n",
    "- **Backend**: API endpoints for threat detection\n",
    "- **ML Services**: Batch processing and model updates\n",
    "\n",
    "The network security models are now ready for real-time deployment in the Cyber Forge AI platform! 🛡️🌐"
   ]
  }
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