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"nltk.download('stopwords') # ADDED\n", "nltk.download('punkt') # ADDED\n", "nltk.download('punkt_tab') # ADDED" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OLXDXRDOFjKu", "outputId": "31909659-c214-4c24-ad76-a889fc998114" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n", "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n", "[nltk_data] Package omw-1.4 is already up-to-date!\n", "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Package punkt_tab is already up-to-date!\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "import os\n", "import re\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import seaborn as sns\n", "from collections import Counter\n", "from tqdm import tqdm\n", "\n", "# NLP\n", "from wordcloud import WordCloud\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk.corpus import stopwords\n", "\n", "# Sklearn\n", "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import Ridge\n", "from sklearn.metrics import (\n", " mean_absolute_error, mean_squared_error, r2_score\n", ")\n", "from sklearn.pipeline import Pipeline\n", "\n", "# Deep Learning\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.utils.data import Dataset, DataLoader\n", "from transformers import BertTokenizer, BertModel, get_linear_schedule_with_warmup\n", "\n", "# Reproducibility\n", "SEED = 42\n", "np.random.seed(SEED)\n", "torch.manual_seed(SEED)\n", "\n", "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Using device: {DEVICE}\")\n", "print(f\"PyTorch version: {torch.__version__}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1r0jTak17ow", "outputId": "a6fa772a-12f2-4dae-8d9c-ed3fcfc5abc5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using device: cuda\n", "PyTorch version: 2.11.0+cu128\n" ] } ] }, { "cell_type": "code", "source": [ "begin = time.time()" ], "metadata": { "id": "LyeIWAORdMOc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 1. Data Loading" ], "metadata": { "id": "qIV9oC132Izk" } }, { "cell_type": "markdown", "source": [ "## 1.1 Take Data" ], "metadata": { "id": "iHZH-Cfu2SJf" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Es7jvORJ1_3h", "outputId": "e00bf332-e7b4-456e-82d5-680ffb0f6f61" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "zip_path = \"/content/drive/MyDrive/dataset/daic-woz.zip\"\n", "extract_path = \"./dataset\"\n", "\n", "if os.path.exists(zip_path):\n", " print(f\"📦 Unzipping {zip_path}...\")\n", " !unzip -q -o {zip_path} -d {extract_path}\n", " print(\"✅ Dataset berhasil diekstrak!\")\n", "else:\n", " print(f\"❌ File tidak ditemukan: '{zip_path}'\")\n", " print(\" Pastikan file sudah diunggah ke Google Drive.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bktrhzei2B3m", "outputId": "95f72a93-3ebc-4864-b81b-47c6c006060e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "📦 Unzipping /content/drive/MyDrive/dataset/daic-woz.zip...\n", "✅ Dataset berhasil diekstrak!\n" ] } ] }, { "cell_type": "code", "source": [ "DATA_DIR = \"/content/dataset\"\n", "LABELS_PATH = os.path.join(DATA_DIR, \"labels.csv\")\n", "\n", "IGNORE_IDS = set() # Kosongkan dulu, biarkan fungsi load yang handle missing files\n", "\n", "print(f\"Dataset path: {DATA_DIR}\")\n", "print(f\"Labels path: {LABELS_PATH}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQsoCJlV2E2-", "outputId": "feb6e0e7-5dc1-43bf-a0b3-d83ba10a7915" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset path: /content/dataset\n", "Labels path: /content/dataset/labels.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 1.2 Load Label (PHQ-8)" ], "metadata": { "id": "J86cCRmP2Wf2" } }, { "cell_type": "code", "source": [ "label_df = pd.read_csv(LABELS_PATH)\n", "label_df.head(5)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 226 }, "id": "keTtFr1P2xYl", "outputId": "405d9ffa-abb2-431b-a451-23356171c8bf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Participant PHQ8_1_NoInterest PHQ8_2_Depressed PHQ8_3_Sleep \\\n", "0 300 NaN NaN NaN \n", "1 301 NaN NaN NaN \n", "2 302 1.0 1.0 0.0 \n", "3 303 0.0 0.0 0.0 \n", "4 304 0.0 1.0 1.0 \n", "\n", " PHQ8_4_Tired PHQ8_5_Appetite PHQ8_6_Failure PHQ8_7_Concentration \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 1.0 0.0 1.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 2.0 2.0 0.0 0.0 \n", "\n", " PHQ8_8_Psychomotor Depression_severity gender Depression_label split \n", "0 NaN 2 male 0 test \n", "1 NaN 3 male 0 test \n", "2 0.0 4 male 0 dev \n", "3 0.0 0 female 0 train \n", "4 0.0 6 female 0 train " ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "label_df", "summary": "{\n \"name\": \"label_df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_1_NoInterest\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8263769184261883,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_2_Depressed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.888359105266613,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 2.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_3_Sleep\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0695520857674088,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_4_Tired\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9580398611680276,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_5_Appetite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0374083873873179,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 2.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_6_Failure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.040389037675481,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_7_Concentration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9669392593723531,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_8_Psychomotor\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6933045951183561,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Depression_severity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21,\n 12,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"female\",\n \"male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Depression_label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"split\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\",\n \"dev\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "print(\"Shape:\", label_df.shape)\n", "print(\"Columns:\", label_df.columns.tolist())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "szTbFcdO2pN0", "outputId": "b2c4baf9-e0c2-4ab8-a770-882bdb3dd3dd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Shape: (189, 13)\n", "Columns: ['Participant', 'PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor', 'Depression_severity', 'gender', 'Depression_label', 'split']\n" ] } ] }, { "cell_type": "code", "source": [ "# cek column PHQ8\n", "phq8_items = [col for col in label_df.columns if col.startswith('PHQ8_')]\n", "print(\"PHQ-8 item columns:\", phq8_items)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0N9Th_As3VQA", "outputId": "42b4f465-84e1-4e04-8d86-ba4ddfe75d8d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "PHQ-8 item columns: ['PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor']\n" ] } ] }, { "cell_type": "code", "source": [ "# Rename kolom agar konsisten di seluruh notebook\n", "label_df = label_df.rename(columns={\n", " 'Participant': 'Participant_ID',\n", " 'Depression_severity': 'PHQ8_Score',\n", " 'Depression_label': 'label'\n", "})" ], "metadata": { "id": "43oekgEW37dI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#define target\n", "label_df['phq8_target'] = label_df['PHQ8_Score']" ], "metadata": { "id": "4G_l8pde4A3g" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 1.3 Load & Merge All Transcript" ], "metadata": { "id": "T8OlGbEk7-Z7" } }, { "cell_type": "code", "source": [ "# NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "# def extract_nonverbal_features(raw_utterances):\n", "# features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", "# features['nv_total'] = 0\n", "# for utt in raw_utterances:\n", "# utt_str = str(utt).lower()\n", "# for tag in re.findall(r'<([^>]+)>', utt_str):\n", "# tag_clean = tag.strip().split()[0]\n", "# for known_tag in NONVERBAL_TAGS:\n", "# if known_tag in tag_clean:\n", "# features[f'nv_{known_tag}'] += 1\n", "# features['nv_total'] += 1\n", "# break\n", "# all_text = ' '.join(str(u) for u in raw_utterances)\n", "# features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", "# return features\n", "\n", "# def load_participant_transcripts(data_dir, ignore_ids=set()):\n", "# records = []\n", "# participant_folders = sorted([\n", "# f for f in os.listdir(data_dir)\n", "# if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", "# ], key=lambda x: int(x))\n", "# print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", "# for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", "# pid = int(pid_str)\n", "# if pid in ignore_ids:\n", "# continue\n", "# transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", "# if not os.path.exists(transcript_path):\n", "# continue\n", "# try:\n", "# df = pd.read_csv(transcript_path, sep='\\t')\n", "# except Exception:\n", "# try:\n", "# df = pd.read_csv(transcript_path, sep=',')\n", "# except Exception as e:\n", "# print(f\"⚠️ Skip {pid_str}: {e}\")\n", "# continue\n", "\n", "# part_df = df[df['speaker'] == 'Participant'].copy()\n", "# raw_utts = part_df['value'].dropna().tolist()\n", "# nv_feats = extract_nonverbal_features(raw_utts)\n", "\n", "# noise_patterns = ['', '', 'scrubbed_entry', '',\n", "# '', '[', ']', '(', ')']\n", "# def is_valid_utterance(text):\n", "# return not any(p in str(text).lower() for p in noise_patterns)\n", "\n", "# part_df = part_df[part_df['value'].apply(is_valid_utterance)]\n", "# if len(part_df) == 0:\n", "# continue\n", "\n", "# full_text = ' '.join(part_df['value'].dropna().astype(str).tolist())\n", "# record = {\n", "# 'Participant_ID': pid,\n", "# 'text': full_text,\n", "# 'utterance_count': len(part_df),\n", "# 'word_count': len(full_text.split())\n", "# }\n", "# record.update(nv_feats)\n", "# records.append(record)\n", "\n", "# return pd.DataFrame(records)" ], "metadata": { "id": "Uhg9dHSE8Cv1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "def extract_nonverbal_features(raw_utterances):\n", " features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", " features['nv_total'] = 0\n", " for utt in raw_utterances:\n", " utt_str = str(utt).lower()\n", " for tag in re.findall(r'<([^>]+)>', utt_str):\n", " tag_clean = tag.strip().split()[0]\n", " for known_tag in NONVERBAL_TAGS:\n", " if known_tag in tag_clean:\n", " features[f'nv_{known_tag}'] += 1\n", " features['nv_total'] += 1\n", " break\n", " all_text = ' '.join(str(u) for u in raw_utterances)\n", " features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", " return features\n", "\n", "def load_participant_transcripts(data_dir, ignore_ids=set()):\n", " records = []\n", " participant_folders = sorted([\n", " f for f in os.listdir(data_dir)\n", " if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", " ], key=lambda x: int(x))\n", " print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", " for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", " pid = int(pid_str)\n", " if pid in ignore_ids:\n", " continue\n", " transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", " if not os.path.exists(transcript_path):\n", " continue\n", " try:\n", " df = pd.read_csv(transcript_path, sep='\\t')\n", " except Exception:\n", " try:\n", " df = pd.read_csv(transcript_path, sep=',')\n", " except Exception as e:\n", " print(f\"⚠️ Skip {pid_str}: {e}\")\n", " continue\n", "\n", " part_df = df[df['speaker'] == 'Participant'].copy()\n", " raw_utts = part_df['value'].dropna().tolist()\n", " nv_feats = extract_nonverbal_features(raw_utts)\n", "\n", " # DIGANTI: buang hanya utterance yang seluruhnya tag, bukan yang mengandung tag\n", " verbal_utts = [\n", " u for u in raw_utts\n", " if not re.match(r'^\\s*<.*>\\s*$', str(u), re.IGNORECASE)\n", " and 'scrubbed_entry' not in str(u).lower()\n", " ]\n", " # DIGANTI: strip inline tags dari utterance yang tersisa\n", " verbal_utts = [re.sub(r'<[^>]+>', '', str(u)).strip() for u in verbal_utts]\n", " verbal_utts = [u for u in verbal_utts if len(u) > 0]\n", "\n", " if len(verbal_utts) == 0:\n", " continue\n", "\n", " full_text = ' '.join(verbal_utts)\n", " record = {\n", " 'Participant_ID': pid,\n", " 'text': full_text,\n", " 'utterance_count': len(verbal_utts),\n", " 'word_count': len(full_text.split())\n", " }\n", " record.update(nv_feats)\n", " records.append(record)\n", "\n", " return pd.DataFrame(records)" ], "metadata": { "id": "3EQMJqctPVJm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "transcripts_df = load_participant_transcripts(DATA_DIR, ignore_ids=IGNORE_IDS)\n", "print(transcripts_df.columns.tolist()) # harus sudah ada nv_*" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "C9_RH0dKyEaC", "outputId": "2fb5e0d0-6455-42ae-cbe5-657f3f11e4e2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total folder ditemukan: 189\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Loading transcripts: 100%|██████████| 189/189 [00:00<00:00, 371.98it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count']\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "# Load semua transcript\n", "transcripts_df = load_participant_transcripts(DATA_DIR, ignore_ids=IGNORE_IDS)\n", "print(f\"Berhasil load {len(transcripts_df)} participant transcripts\")\n", "transcripts_df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kFy7-14Z9HKF", "outputId": "3a4d4605-8621-4081-f263-9d282988bbc0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total folder ditemukan: 189\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Loading transcripts: 100%|██████████| 189/189 [00:00<00:00, 385.58it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Berhasil load 189 participant transcripts\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Participant_ID text \\\n", "0 300 good atlanta georgia um my parents are from he... \n", "1 301 thank you mmm k i'm doing good thank you i'm f... \n", "2 302 i'm fine how about yourself i'm from los angel... \n", "3 303 okay how 'bout yourself here in california yea... \n", "4 304 i'm doing 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Participant_IDtextutterance_countword_countnv_laughnv_sighnv_coughnv_breathnv_sniffnv_groannv_pausenv_umnv_uhnv_totalfiller_count
0300good atlanta georgia um my parents are from he...87352000000000031
1301thank you mmm k i'm doing good thank you i'm f...1041465300000000320
2302i'm fine how about yourself i'm from los angel...96611100000000125
3303okay how 'bout yourself here in california yea...1031960300000000333
4304i'm doing good um from los angeles california ...10497611000000001128
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "transcripts_df", "summary": "{\n \"name\": \"transcripts_df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant_ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 189,\n \"samples\": [\n \"yes fine oh san fernando valley uh well i really like the culture i love going to museums and being able to go at night and having lots of people around it's spread out a lot and it's a pain in the ass to have to drive everywhere and not have a good public transportation system uh not really i like to go camping but i don't really travel um i think it might've been around six months ago i went on a trip with a geology class yeah it was fun hmm i think it's tough to say um well i think the first time i went camping was probably really memorable i went to death valley and i got to see a lot of really cool things and hang out with my friends at night philosophy yes um hmm i don't know i think it just sort of happened i wanted to be a journalist for a really long time and then i kind of grew disillusioned with that and i liked all the different ways or all the different things i could do with philosophy huh i don't know but i really like children and i really like studying their behavior so i think something along those lines might be it i think i'm generally pretty outgoing i tend to be shy around my family but when i'm around friends and stuff i i think i'm pretty friendly um a lot of times i'll watch t_v or read um yeah i think i'm pretty good i don't really get mad i try to think about other things if i start getting mad or um i just start trying to focus on my breathing i think the last time i argued with someone it was probably with my boyfriend um but i'm having trouble remembering what the last thing we argued about was um i think it was i wasn't feeling very well and i was having trouble deciding whether i wanted to go to his department's end of year party and he was getting frustrated which i thought was very inconsiderate 'cause i wasn't feeling well to begin with well i felt like he wasn't really taking my feelings into consideration and i really hate when he gets stressed out or gets angry easily because i just see that as unnecessary and it stresses me out hmm i think a lot of times when my oldest youngest brother or my oldest little brother uh does something wrong i tend to be harsh with him um we're not as close as my sister and i or my youngest brother and i and so he tends to be hostile back and i think that if i were a little bit more gentle it might be easier to communicate with him hmm um when i was nineteen i came home from a party i think around four a_m on a sunday morning and my dad got really mad at me and yelled at me said a lot of not very nice things and then he didn't speak to me for about a year uh well my relationship with my mom is a lot better now that i don't live at home um i used to be really frustrated with her all the time 'cause she has a really quick temper but i think now that i've got a little bit more distance i can see that she did the best that she could um and i love my siblings i again my little sister and i get along really well she's four years younger than me and when when she was around twelve and i was around sixteen it was really difficult 'cause she had a really harsh temper and she was always moody and i wasn't in a place to be patient with that sort of stuff so we fought all the time but we don't anymore and that's really great and we get along okay with my uh my older little brother who's fifteen and i think i get along pretty well with my youngest brother who's twelve one of the first not the first but one of my first philosophy professors and i really loved her classes and it was really good uh being in a class with a professor who really cared and who really engaged people that was really motivating since not all my classes were like that i think it's pretty easy i mean sometimes i'll have trouble going to sleep but generally it's not that bad it's i think it's usually harder for me to wake up in the morning um well i'm really tired i can get grumpy but i think usually i'm just groggy pretty good i'm a little anxious about um about getting a job and i'm gonna be taking summer school about it's only one class so i'll have a lot of free time um yeah well i mean it's not the end of the world if i don't get a job but i know that i will eventually so mostly i think i just try not to think about it too much and i try not to be pessimistic no no hmm well i think the last time i felt really happy i went to a party with my boyfriend the end of department party and um i was the you know i knew a couple people there so i felt comfortable but i was also able to meet new people and there were a couple kids there splashing around in the pool and i had a couple drinks and ate some food and it was fun um she would probably say that i'm smart and uh pretty intellectual i love to read and to watch really like serious films and t_v um and she would probably also say that i'm fun 'cause we like to go out and have fun a lot well i'm kinda i can be really disorganized and that gets me in trouble sometimes so um so that's a major one that and procrastinating hmm um i got into a little bit of trouble a few months back and had to go to court and was uh uh given eh sixty some hours of community service and of course i procrastinated on doing that so i had to show up to court again to get an extension and the judge was very mean and he didn't seem i mean he yelled at me but it didn't seem like he gave it much thought it seemed like it was just sort of a stock speech he gave everyone that he thought needed some yelling at so i understand 'cause that's his job but it was was not great for me 'cause i wasn't i i was really anxious in that situation well i would've been twelve and two um but i think i think i would've told myself at twelve not to be so hard on myself and not to be so hard on my parents 'cause i think i was a little selfish and self-involved back then i can't really think of anything i mean i regret how i treated my mother when i was a kid but everyone does that and um i do regret a couple of semesters ago i didn't do so well in school and i got a a few ws which i now have to work to get off on my transcript i think so i'm i think i i wasn't focusing in school at that time and i didn't see any counselors at school or anything so i definitely could've done that hmm i think i'd probably spend my ideal weekend sleeping in saturday and then going hiking or maybe just going to the duck pond by my house and feeding ducks and picking some oranges and then maybe going out to breakfast sunday and just like window shopping and maybe watching a little bit of t_v huh well my youngest brother is autistic and my mom and dad were always working when he was growing up i'm ten years older than him so and being the oldest i always i always took care of him which made me really really patient because he was a handful but i'm really proud of how he turned out and um and how close we are\",\n \"yeah i'm okay with it uh i feel i feel pretty good little little nervous because i've never done anything like this before that's why yeah from ohio cincinnati yeah uh two months ago two and a half months uh was just looking for just wanted to be in a new environment oh yeah i'm ecstatic very happy uh it took a few weeks just to get adjusted 'cause i didn't know anyone the weather is probably the best thing yeah and uh i mean other than that it's pretty much the same as everywhere else i've been there's nothing i don't really really like about l_a it's just you know just the weather is the best part of it yeah uh i like to travel somewhat uh i haven't traveled a lot in my life but over the past uh year and a half i've done done quite a bit of traveling just seeing different places different people new people new faces new places uh let's see last well last summer i spent the uh summer in cleveland doing uh urban farming uh in the city yeah and uh i stayed at a hostel the first hostel in cleveland and i was uh doing a program with the uh and it was basically we would go to different urban farms throughout the city and volunteer about twenty twenty five hours a week so yeah really a spur of the moment type of thing um wanted to try something different something i'd never done before and farming is one of those things that you know most people would never try 'cause of a you know 'cause of a certain stigma that comes along with it uh and you know i just thought it would be fun and interesting to do something like that yeah what in the uh uh mm urban farming or in general in urban farming uh i don't know i guess uh i don't know just just the overall experience just going you know eating uh going to the farmers markets and eating all the fresh food and just seeing the difference in you know how fresh natural food taste eh you know as opposed to the food you buy in a grocery store yeah yeah it is really is uh i was i took two semesters of uh college and i was undecided so i was just taking general classes my dream job huh i guess my dream job would to be anything that involves uh i don't have like a specific dream job but anything that involves nature just being outside uh i would like to uh i wouldn't mind being like a like a forest ranger or something like that just as long as i you know yeah consider myself more shy uh i don't i don't know why it's just the way it is uh huh i read um listen to music uh i draw watch movies stuff like that yeah i think i'm pretty good at it i think yeah wow uh yeah i can't remember the last time i had an argument with someone i don't know again i don't know because i don't dwell on things that happened in the past yeah mm maybe whether or not to come to california i guess that was a hard decision well i don't know i've been thinking about it you know moving to a new place for a long time and i guess the hardest part of the decision was thinking whether whether or not i would be secure or not you know what i mean um just going to a new place where you don't know anyone uh it's kinda uh you really don't know what's gonna happen so uh there was a lot of uh you know fear in that in that decision of should i go to a place where i don't know anyone i don't i'm not familiar with the area you know only only perception ever had that i have of this area is you know what i've seen on in media media and t_v so you know eh yeah guess that was hardest hardest decision decision that i can think of yeah eh guess so uh nothing absolutely nothing yeah uh nothing i don't feel guilty about anything uh relationship with my family uh i'm not really close to my family um i guess the person i'm closest to is my mom but you know i don't you know i don't i never really talk to her much uh i don't really know my dad that well so you know uh i guess my grandmother yeah she yeah she's uh she's very um xxx how should i say it uh she's very she's a thinker i put it like that like she's very insightful so um yeah i look to her for a lot of advice because she's been through a lot through a lot in her life and i feel like she you know she's been through a lot so she understands a lot so eh you know she's a very positive influence very easy uh maybe unsure i would say because right now i really don't know what direction i wanna go in with my life and there's been a lot of confusion with that um but you know it's not it's not something that overly affects me but you know it's always a thought in the back of my mind it feels feels like somehow maybe i'm running out of time and i know that's not like that's not xxx that's not a realistic thought because i don't see how you could ever run out of time but yeah i guess yeah just general not knowing what i want but i know like how do i cope with that how do i cope with that i uh i don't know i just do a lot of thinking a lot of soul searching it's not hard i mean i mean i'm it's xxx like i can't say it's hard because it's one of those things that i feel like it's necessary and you know certain things in life you just you're forced to face so it's harder to i think it's harder to avoid those type of things yeah no no no uh xxx today when i talked when i uh talked to my friend earlier yeah how would my best friend describe me uh i guess he would say i'm quiet uh uh guess he would describe as a nice guy uh guess insightful mm uh goofy and yeah oh i don't really think like that no ten or twenty years ago uh guess i would tell myself to stick with what what stick with what i love hmm uh i did uh acting workshop in um yeah i don't know it's always always been an interest of mine and i decided why not you know just try it see how you like it what am i most proud of like an event or a an accomplishment i guess i pride myself on being independent mm yeah bye\",\n \"yes i'm alright los angeles california yep um the lights big city it's always something going on the traffic and that's it uh business administration and business management uh not that i'm doing something else i'm doing networking at the moment hmm uh to open up a big clothing line and just supply the whole world with clothing yeah definitely uh 'cause i'm all about myself it's all about me not often but more times than not yeah being lied to and i guess when people think you're dumber than you than what you are uh try to get away from everything go into my own space very well i'm great at controlling my temper i um probably like two days ago and it was probably just over some sports it wasn't a real argument not a lot but i travel enough just being able to see to get a change of pace see somebody new see new faces new environment mm let me see the last trip i had was vegas you know how vegas goes it was pretty fun it was a lotta lotta drinking and a lot of partying going on um pretty open-minded i'm kind to pretty much everybody i'm a pretty even-keel guy it's alright it could be better no no ma'am nope no uh lately it's been pretty tough i guess because i've been staying up so late but uh that's my fault i don't know i guess i'm a night owl all of a sudden i'm turning into an owl sluggish and tired all day if i don't sleep well all day i'm just laying around no not too much mm no oh stay focused and listen to the adults yeah i mean it's self-explanatory i could've just listened to parents or other adults that tried to steer you in the right way and you know you're being a stubborn kid at that uh at those ages so it's like what what are you talking about i this is my life i can do what i want i don't have to listen to you but now i see that what they were telling me was correct um hilarious pretty close uh both my parents mother and father mm recently what did i do um new year's eve party i had i had the time of my life it was so fun uh this new year's eve i went to a party at a friend's house in baldwin hills area he had it at a a pretty nice house it's a three story house and it was like three hundred and fifty people in there it was pretty fun but it was also not that fun because there was so many people and you couldn't really move so that was the only downfall but besides that i brought my new year in great with tons of friends and that's how it's supposed to be done um i'm generally in a good mood every day i mean waking up puts me in a good mood i i guess you could say that but um i don't know a good a good meal ooh one of my most memorable experiences were probably uh when i was younger i was in the boy scouts and we used to go camping all the time and one year we went to yellowstone or yosemite one of those places and we had to work on a mountain biking merit badge and of course being from the city i'm like a mountain biking i can ride a mountain bike but once we got in the real mountains on got on real mountain bikes i gained a new respect for the mountain bikers it was pretty it was fun and pretty intense going downhill doing about forty five miles an hour in dirt on a bicycle was pretty intense and that was i'll i'll never forget that mm i don't know that's a tough one to to answer what am i most proud of i don't know honestly bye\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"utterance_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 73,\n \"min\": 42,\n \"max\": 385,\n \"num_unique_values\": 128,\n \"samples\": [\n 155,\n 116,\n 161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 801,\n \"min\": 165,\n \"max\": 4551,\n \"num_unique_values\": 183,\n \"samples\": [\n 755,\n 1912,\n 2640\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_laugh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7,\n \"min\": 0,\n \"max\": 38,\n \"num_unique_values\": 29,\n \"samples\": [\n 17,\n 27,\n 18\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sigh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 31,\n \"num_unique_values\": 22,\n \"samples\": [\n 0,\n 21,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_cough\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_breath\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sniff\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 12,\n \"samples\": [\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_groan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_pause\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_um\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_uh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_total\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 0,\n \"max\": 61,\n \"num_unique_values\": 42,\n \"samples\": [\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"filler_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49,\n \"min\": 1,\n \"max\": 379,\n \"num_unique_values\": 107,\n \"samples\": [\n 47\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "code", "source": [ "df = pd.merge(\n", " transcripts_df,\n", " label_df[['Participant_ID', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split']],\n", " on='Participant_ID',\n", " how='inner'\n", ")\n", "\n", "print(f\"Dataset setelah merge: {df.shape}\")\n", "print(f\"\\nDistribusi label:\")\n", "print(df['label'].value_counts())\n", "print(f\"\\nDistribusi split:\")\n", "print(df['split'].value_counts())\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zGz3NjAf9L3s", "outputId": "34bc7a02-a4f2-4957-fd73-4322a59c404f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset setelah merge: (189, 20)\n", "\n", "Distribusi label:\n", "label\n", "0 132\n", "1 57\n", "Name: count, dtype: int64\n", "\n", "Distribusi split:\n", "split\n", "train 107\n", "test 47\n", "dev 35\n", "Name: count, dtype: int64\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Participant_ID text \\\n", "0 300 good atlanta georgia um my parents are from he... \n", "1 301 thank you mmm k i'm doing good thank you i'm f... \n", "2 302 i'm fine how about yourself i'm from los angel... \n", "3 303 okay how 'bout yourself here in california yea... \n", "4 304 i'm doing good um from los angeles california ... \n", "\n", " utterance_count word_count nv_laugh nv_sigh nv_cough nv_breath \\\n", "0 87 352 0 0 0 0 \n", "1 104 1465 3 0 0 0 \n", "2 96 611 1 0 0 0 \n", "3 103 1960 3 0 0 0 \n", "4 104 976 11 0 0 0 \n", "\n", " nv_sniff nv_groan nv_pause nv_um nv_uh nv_total filler_count \\\n", "0 0 0 0 0 0 0 31 \n", "1 0 0 0 0 0 3 20 \n", "2 0 0 0 0 0 1 25 \n", "3 0 0 0 0 0 3 33 \n", "4 0 0 0 0 0 11 28 \n", "\n", " PHQ8_Score phq8_target label gender split \n", "0 2 2 0 male test \n", "1 3 3 0 male test \n", "2 4 4 0 male dev \n", "3 0 0 0 female train \n", "4 6 6 0 female train " ], "text/html": [ "\n", "
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Participant_IDtextutterance_countword_countnv_laughnv_sighnv_coughnv_breathnv_sniffnv_groannv_pausenv_umnv_uhnv_totalfiller_countPHQ8_Scorephq8_targetlabelgendersplit
0300good atlanta georgia um my parents are from he...87352000000000031220maletest
1301thank you mmm k i'm doing good thank you i'm f...1041465300000000320330maletest
2302i'm fine how about yourself i'm from los angel...96611100000000125440maledev
3303okay how 'bout yourself here in california yea...1031960300000000333000femaletrain
4304i'm doing good um from los angeles california ...10497611000000001128660femaletrain
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant_ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 189,\n \"samples\": [\n \"yes fine oh san fernando valley uh well i really like the culture i love going to museums and being able to go at night and having lots of people around it's spread out a lot and it's a pain in the ass to have to drive everywhere and not have a good public transportation system uh not really i like to go camping but i don't really travel um i think it might've been around six months ago i went on a trip with a geology class yeah it was fun hmm i think it's tough to say um well i think the first time i went camping was probably really memorable i went to death valley and i got to see a lot of really cool things and hang out with my friends at night philosophy yes um hmm i don't know i think it just sort of happened i wanted to be a journalist for a really long time and then i kind of grew disillusioned with that and i liked all the different ways or all the different things i could do with philosophy huh i don't know but i really like children and i really like studying their behavior so i think something along those lines might be it i think i'm generally pretty outgoing i tend to be shy around my family but when i'm around friends and stuff i i think i'm pretty friendly um a lot of times i'll watch t_v or read um yeah i think i'm pretty good i don't really get mad i try to think about other things if i start getting mad or um i just start trying to focus on my breathing i think the last time i argued with someone it was probably with my boyfriend um but i'm having trouble remembering what the last thing we argued about was um i think it was i wasn't feeling very well and i was having trouble deciding whether i wanted to go to his department's end of year party and he was getting frustrated which i thought was very inconsiderate 'cause i wasn't feeling well to begin with well i felt like he wasn't really taking my feelings into consideration and i really hate when he gets stressed out or gets angry easily because i just see that as unnecessary and it stresses me out hmm i think a lot of times when my oldest youngest brother or my oldest little brother uh does something wrong i tend to be harsh with him um we're not as close as my sister and i or my youngest brother and i and so he tends to be hostile back and i think that if i were a little bit more gentle it might be easier to communicate with him hmm um when i was nineteen i came home from a party i think around four a_m on a sunday morning and my dad got really mad at me and yelled at me said a lot of not very nice things and then he didn't speak to me for about a year uh well my relationship with my mom is a lot better now that i don't live at home um i used to be really frustrated with her all the time 'cause she has a really quick temper but i think now that i've got a little bit more distance i can see that she did the best that she could um and i love my siblings i again my little sister and i get along really well she's four years younger than me and when when she was around twelve and i was around sixteen it was really difficult 'cause she had a really harsh temper and she was always moody and i wasn't in a place to be patient with that sort of stuff so we fought all the time but we don't anymore and that's really great and we get along okay with my uh my older little brother who's fifteen and i think i get along pretty well with my youngest brother who's twelve one of the first not the first but one of my first philosophy professors and i really loved her classes and it was really good uh being in a class with a professor who really cared and who really engaged people that was really motivating since not all my classes were like that i think it's pretty easy i mean sometimes i'll have trouble going to sleep but generally it's not that bad it's i think it's usually harder for me to wake up in the morning um well i'm really tired i can get grumpy but i think usually i'm just groggy pretty good i'm a little anxious about um about getting a job and i'm gonna be taking summer school about it's only one class so i'll have a lot of free time um yeah well i mean it's not the end of the world if i don't get a job but i know that i will eventually so mostly i think i just try not to think about it too much and i try not to be pessimistic no no hmm well i think the last time i felt really happy i went to a party with my boyfriend the end of department party and um i was the you know i knew a couple people there so i felt comfortable but i was also able to meet new people and there were a couple kids there splashing around in the pool and i had a couple drinks and ate some food and it was fun um she would probably say that i'm smart and uh pretty intellectual i love to read and to watch really like serious films and t_v um and she would probably also say that i'm fun 'cause we like to go out and have fun a lot well i'm kinda i can be really disorganized and that gets me in trouble sometimes so um so that's a major one that and procrastinating hmm um i got into a little bit of trouble a few months back and had to go to court and was uh uh given eh sixty some hours of community service and of course i procrastinated on doing that so i had to show up to court again to get an extension and the judge was very mean and he didn't seem i mean he yelled at me but it didn't seem like he gave it much thought it seemed like it was just sort of a stock speech he gave everyone that he thought needed some yelling at so i understand 'cause that's his job but it was was not great for me 'cause i wasn't i i was really anxious in that situation well i would've been twelve and two um but i think i think i would've told myself at twelve not to be so hard on myself and not to be so hard on my parents 'cause i think i was a little selfish and self-involved back then i can't really think of anything i mean i regret how i treated my mother when i was a kid but everyone does that and um i do regret a couple of semesters ago i didn't do so well in school and i got a a few ws which i now have to work to get off on my transcript i think so i'm i think i i wasn't focusing in school at that time and i didn't see any counselors at school or anything so i definitely could've done that hmm i think i'd probably spend my ideal weekend sleeping in saturday and then going hiking or maybe just going to the duck pond by my house and feeding ducks and picking some oranges and then maybe going out to breakfast sunday and just like window shopping and maybe watching a little bit of t_v huh well my youngest brother is autistic and my mom and dad were always working when he was growing up i'm ten years older than him so and being the oldest i always i always took care of him which made me really really patient because he was a handful but i'm really proud of how he turned out and um and how close we are\",\n \"yeah i'm okay with it uh i feel i feel pretty good little little nervous because i've never done anything like this before that's why yeah from ohio cincinnati yeah uh two months ago two and a half months uh was just looking for just wanted to be in a new environment oh yeah i'm ecstatic very happy uh it took a few weeks just to get adjusted 'cause i didn't know anyone the weather is probably the best thing yeah and uh i mean other than that it's pretty much the same as everywhere else i've been there's nothing i don't really really like about l_a it's just you know just the weather is the best part of it yeah uh i like to travel somewhat uh i haven't traveled a lot in my life but over the past uh year and a half i've done done quite a bit of traveling just seeing different places different people new people new faces new places uh let's see last well last summer i spent the uh summer in cleveland doing uh urban farming uh in the city yeah and uh i stayed at a hostel the first hostel in cleveland and i was uh doing a program with the uh and it was basically we would go to different urban farms throughout the city and volunteer about twenty twenty five hours a week so yeah really a spur of the moment type of thing um wanted to try something different something i'd never done before and farming is one of those things that you know most people would never try 'cause of a you know 'cause of a certain stigma that comes along with it uh and you know i just thought it would be fun and interesting to do something like that yeah what in the uh uh mm urban farming or in general in urban farming uh i don't know i guess uh i don't know just just the overall experience just going you know eating uh going to the farmers markets and eating all the fresh food and just seeing the difference in you know how fresh natural food taste eh you know as opposed to the food you buy in a grocery store yeah yeah it is really is uh i was i took two semesters of uh college and i was undecided so i was just taking general classes my dream job huh i guess my dream job would to be anything that involves uh i don't have like a specific dream job but anything that involves nature just being outside uh i would like to uh i wouldn't mind being like a like a forest ranger or something like that just as long as i you know yeah consider myself more shy uh i don't i don't know why it's just the way it is uh huh i read um listen to music uh i draw watch movies stuff like that yeah i think i'm pretty good at it i think yeah wow uh yeah i can't remember the last time i had an argument with someone i don't know again i don't know because i don't dwell on things that happened in the past yeah mm maybe whether or not to come to california i guess that was a hard decision well i don't know i've been thinking about it you know moving to a new place for a long time and i guess the hardest part of the decision was thinking whether whether or not i would be secure or not you know what i mean um just going to a new place where you don't know anyone uh it's kinda uh you really don't know what's gonna happen so uh there was a lot of uh you know fear in that in that decision of should i go to a place where i don't know anyone i don't i'm not familiar with the area you know only only perception ever had that i have of this area is you know what i've seen on in media media and t_v so you know eh yeah guess that was hardest hardest decision decision that i can think of yeah eh guess so uh nothing absolutely nothing yeah uh nothing i don't feel guilty about anything uh relationship with my family uh i'm not really close to my family um i guess the person i'm closest to is my mom but you know i don't you know i don't i never really talk to her much uh i don't really know my dad that well so you know uh i guess my grandmother yeah she yeah she's uh she's very um xxx how should i say it uh she's very she's a thinker i put it like that like she's very insightful so um yeah i look to her for a lot of advice because she's been through a lot through a lot in her life and i feel like she you know she's been through a lot so she understands a lot so eh you know she's a very positive influence very easy uh maybe unsure i would say because right now i really don't know what direction i wanna go in with my life and there's been a lot of confusion with that um but you know it's not it's not something that overly affects me but you know it's always a thought in the back of my mind it feels feels like somehow maybe i'm running out of time and i know that's not like that's not xxx that's not a realistic thought because i don't see how you could ever run out of time but yeah i guess yeah just general not knowing what i want but i know like how do i cope with that how do i cope with that i uh i don't know i just do a lot of thinking a lot of soul searching it's not hard i mean i mean i'm it's xxx like i can't say it's hard because it's one of those things that i feel like it's necessary and you know certain things in life you just you're forced to face so it's harder to i think it's harder to avoid those type of things yeah no no no uh xxx today when i talked when i uh talked to my friend earlier yeah how would my best friend describe me uh i guess he would say i'm quiet uh uh guess he would describe as a nice guy uh guess insightful mm uh goofy and yeah oh i don't really think like that no ten or twenty years ago uh guess i would tell myself to stick with what what stick with what i love hmm uh i did uh acting workshop in um yeah i don't know it's always always been an interest of mine and i decided why not you know just try it see how you like it what am i most proud of like an event or a an accomplishment i guess i pride myself on being independent mm yeah bye\",\n \"yes i'm alright los angeles california yep um the lights big city it's always something going on the traffic and that's it uh business administration and business management uh not that i'm doing something else i'm doing networking at the moment hmm uh to open up a big clothing line and just supply the whole world with clothing yeah definitely uh 'cause i'm all about myself it's all about me not often but more times than not yeah being lied to and i guess when people think you're dumber than you than what you are uh try to get away from everything go into my own space very well i'm great at controlling my temper i um probably like two days ago and it was probably just over some sports it wasn't a real argument not a lot but i travel enough just being able to see to get a change of pace see somebody new see new faces new environment mm let me see the last trip i had was vegas you know how vegas goes it was pretty fun it was a lotta lotta drinking and a lot of partying going on um pretty open-minded i'm kind to pretty much everybody i'm a pretty even-keel guy it's alright it could be better no no ma'am nope no uh lately it's been pretty tough i guess because i've been staying up so late but uh that's my fault i don't know i guess i'm a night owl all of a sudden i'm turning into an owl sluggish and tired all day if i don't sleep well all day i'm just laying around no not too much mm no oh stay focused and listen to the adults yeah i mean it's self-explanatory i could've just listened to parents or other adults that tried to steer you in the right way and you know you're being a stubborn kid at that uh at those ages so it's like what what are you talking about i this is my life i can do what i want i don't have to listen to you but now i see that what they were telling me was correct um hilarious pretty close uh both my parents mother and father mm recently what did i do um new year's eve party i had i had the time of my life it was so fun uh this new year's eve i went to a party at a friend's house in baldwin hills area he had it at a a pretty nice house it's a three story house and it was like three hundred and fifty people in there it was pretty fun but it was also not that fun because there was so many people and you couldn't really move so that was the only downfall but besides that i brought my new year in great with tons of friends and that's how it's supposed to be done um i'm generally in a good mood every day i mean waking up puts me in a good mood i i guess you could say that but um i don't know a good a good meal ooh one of my most memorable experiences were probably uh when i was younger i was in the boy scouts and we used to go camping all the time and one year we went to yellowstone or yosemite one of those places and we had to work on a mountain biking merit badge and of course being from the city i'm like a mountain biking i can ride a mountain bike but once we got in the real mountains on got on real mountain bikes i gained a new respect for the mountain bikers it was pretty it was fun and pretty intense going downhill doing about forty five miles an hour in dirt on a bicycle was pretty intense and that was i'll i'll never forget that mm i don't know that's a tough one to to answer what am i most proud of i don't know honestly bye\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"utterance_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 73,\n \"min\": 42,\n \"max\": 385,\n \"num_unique_values\": 128,\n \"samples\": [\n 155,\n 116,\n 161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 801,\n \"min\": 165,\n \"max\": 4551,\n \"num_unique_values\": 183,\n \"samples\": [\n 755,\n 1912,\n 2640\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_laugh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7,\n \"min\": 0,\n \"max\": 38,\n \"num_unique_values\": 29,\n \"samples\": [\n 17,\n 27,\n 18\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sigh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 31,\n \"num_unique_values\": 22,\n \"samples\": [\n 0,\n 21,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_cough\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_breath\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sniff\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 12,\n \"samples\": [\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_groan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_pause\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_um\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_uh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_total\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 0,\n \"max\": 61,\n \"num_unique_values\": 42,\n \"samples\": [\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"filler_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49,\n \"min\": 1,\n \"max\": 379,\n \"num_unique_values\": 107,\n \"samples\": [\n 47\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_Score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"phq8_target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"split\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "def extract_nonverbal_features(raw_utterances):\n", " features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", " features['nv_total'] = 0\n", " for utt in raw_utterances:\n", " utt_str = str(utt).lower()\n", " for tag in re.findall(r'<([^>]+)>', utt_str):\n", " tag_clean = tag.strip().split()[0]\n", " for known_tag in NONVERBAL_TAGS:\n", " if known_tag in tag_clean:\n", " features[f'nv_{known_tag}'] += 1\n", " features['nv_total'] += 1\n", " break\n", " all_text = ' '.join(str(u) for u in raw_utterances)\n", " features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", " return features\n", "\n", "def load_participant_transcripts(data_dir, ignore_ids=set()):\n", " records = []\n", " participant_folders = sorted([\n", " f for f in os.listdir(data_dir)\n", " if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", " ], key=lambda x: int(x))\n", " print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", " for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", " pid = int(pid_str)\n", " if pid in ignore_ids:\n", " continue\n", "\n", " transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", " if not os.path.exists(transcript_path):\n", " continue\n", "\n", " try:\n", " df = pd.read_csv(transcript_path, sep='\\t')\n", " except Exception:\n", " try:\n", " df = pd.read_csv(transcript_path, sep=',')\n", " except Exception as e:\n", " print(f\"⚠️ Skip {pid_str}: {e}\")\n", " continue\n", "\n", " part_df = df[df['speaker'] == 'Participant'].copy()\n", "\n", " # --- TAMBAHAN: extract non-verbal dari raw utterances dulu ---\n", " raw_utts = part_df['value'].dropna().tolist()\n", " nv_feats = extract_nonverbal_features(raw_utts)\n", " # ------------------------------------------------------------\n", "\n", " # filter noise seperti sebelumnya\n", " noise_patterns = ['', '', 'scrubbed_entry', '',\n", " '', '[', ']', '(', ')']\n", " def is_valid_utterance(text):\n", " text_lower = str(text).lower()\n", " return not any(p in text_lower for p in noise_patterns)\n", "\n", " part_df = part_df[part_df['value'].apply(is_valid_utterance)]\n", " if len(part_df) == 0:\n", " continue\n", "\n", " full_text = ' '.join(part_df['value'].dropna().astype(str).tolist())\n", "\n", " record = {\n", " 'Participant_ID': pid,\n", " 'text': full_text,\n", " 'utterance_count': len(part_df),\n", " 'word_count': len(full_text.split())\n", " }\n", " record.update(nv_feats) # gabungkan non-verbal ke record\n", " records.append(record)\n", "\n", " return pd.DataFrame(records)" ], "metadata": { "id": "NSUZMyHYwkOy" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 2. Exploritory Data Analysis" ], "metadata": { "id": "qH4fOB6P_GJ9" } }, { "cell_type": "markdown", "source": [ "# 3. Pre-Processing For Baseline Model" ], "metadata": { "id": "eHtr4Unz_Jeq" } }, { "cell_type": "markdown", "source": [ "## 3.1 SLANG WORD DICT" ], "metadata": { "id": "pWuyZ0gD_rK4" } }, { "cell_type": "code", "source": [ "SLANG_DICT = {\n", " # ── yang sudah ada ──────────────────────────────────────────────\n", " \"gonna\": \"going to\",\n", " \"wanna\": \"want to\",\n", " \"gotta\": \"got to\",\n", " \"kinda\": \"kind of\",\n", " \"sorta\": \"sort of\",\n", " \"lotta\": \"a lot of\",\n", " \"hafta\": \"have to\",\n", " \"dunno\": \"do not know\",\n", " \"ain't\": \"is not\",\n", " \"cannot\": \"can not\",\n", " \"wouldn't\": \"would not\",\n", " \"couldn't\": \"could not\",\n", " \"shouldn't\": \"should not\",\n", " \"didn't\": \"did not\",\n", " \"doesn't\": \"does not\",\n", " \"don't\": \"do not\",\n", " \"wasn't\": \"was not\",\n", " \"weren't\": \"were not\",\n", " \"haven't\": \"have not\",\n", " \"hasn't\": \"has not\",\n", " \"hadn't\": \"had not\",\n", " \"won't\": \"will not\",\n", " \"i'm\": \"i am\",\n", " \"i've\": \"i have\",\n", " \"i'll\": \"i will\",\n", " \"i'd\": \"i would\",\n", " \"it's\": \"it is\",\n", " \"that's\": \"that is\",\n", " \"there's\": \"there is\",\n", " \"they're\": \"they are\",\n", " \"they've\": \"they have\",\n", " \"we're\": \"we are\",\n", " \"we've\": \"we have\",\n", " \"you're\": \"you are\",\n", " \"you've\": \"you have\",\n", " \"he's\": \"he is\",\n", " \"she's\": \"she is\",\n", "\n", " # ── contractions yang belum ada ──────────────────────────────────\n", " \"it'd\": \"it would\",\n", " \"it'll\": \"it will\",\n", " \"that'd\": \"that would\",\n", " \"that'll\": \"that will\",\n", " \"who's\": \"who is\",\n", " \"who'd\": \"who would\",\n", " \"who'll\": \"who will\",\n", " \"what's\": \"what is\",\n", " \"what'd\": \"what did\",\n", " \"what'll\": \"what will\",\n", " \"where's\": \"where is\",\n", " \"when's\": \"when is\",\n", " \"how's\": \"how is\",\n", " \"how'd\": \"how did\",\n", " \"why's\": \"why is\",\n", " \"you'd\": \"you would\",\n", " \"you'll\": \"you will\",\n", " \"we'd\": \"we would\",\n", " \"we'll\": \"we will\",\n", " \"he'd\": \"he would\",\n", " \"he'll\": \"he will\",\n", " \"she'd\": \"she would\",\n", " \"she'll\": \"she will\",\n", " \"they'd\": \"they would\",\n", " \"they'll\": \"they will\",\n", " \"let's\": \"let us\",\n", " \"there'd\": \"there would\",\n", " \"there'll\": \"there will\",\n", " \"here's\": \"here is\",\n", " \"could've\": \"could have\",\n", " \"would've\": \"would have\",\n", " \"should've\": \"should have\",\n", " \"might've\": \"might have\",\n", " \"must've\": \"must have\",\n", " \"i'm not\": \"i am not\",\n", " \"aren't\": \"are not\",\n", " \"needn't\": \"need not\",\n", " \"mightn't\": \"might not\",\n", " \"mustn't\": \"must not\",\n", " \"daren't\": \"dare not\",\n", "\n", " # ── casual speech / reduction (khas speech transcript) ───────────\n", " \"tryna\": \"trying to\",\n", " \"finna\": \"fixing to\", # Southern US, \"about to\"\n", " \"lemme\": \"let me\",\n", " \"gimme\": \"give me\",\n", " \"gotcha\": \"got you\",\n", " \"getcha\": \"get you\",\n", " \"betcha\": \"bet you\",\n", " \"whatcha\": \"what are you\",\n", " \"watcha\": \"what are you\",\n", " \"ya\": \"you\",\n", " \"yep\": \"yes\",\n", " \"yup\": \"yes\",\n", " \"nope\": \"no\",\n", " \"nah\": \"no\",\n", " \"yeah\": \"yes\",\n", " \"yea\": \"yes\",\n", " \"cause\": \"because\",\n", " \"'cause\": \"because\",\n", " \"cuz\": \"because\",\n", " \"cos\": \"because\",\n", " \"'bout\": \"about\",\n", " \"bout\": \"about\",\n", " \"'til\": \"until\",\n", " \"til\": \"until\",\n", " \"em\": \"them\",\n", " \"'em\": \"them\",\n", " \"an'\": \"and\",\n", " \"n'\": \"and\",\n", " \"o'\": \"of\",\n", " \"ma\": \"my\", # \"ma family\"\n", " \"hafta\": \"have to\",\n", " \"oughta\": \"ought to\",\n", " \"useta\": \"used to\",\n", " \"supposta\": \"supposed to\",\n", " \"sposta\": \"supposed to\",\n", " \"woulda\": \"would have\",\n", " \"coulda\": \"could have\",\n", " \"shoulda\": \"should have\",\n", " \"mighta\": \"might have\",\n", " \"musta\": \"must have\",\n", "\n", " # ── filler / disfluency yang sering muncul di clinical interview ─\n", " \"y'know\": \"you know\",\n", " \"ya know\": \"you know\",\n", " \"y'all\": \"you all\",\n", " \"c'mon\": \"come on\",\n", " \"c'mere\": \"come here\",\n", " \"somethin'\": \"something\",\n", " \"nothin'\": \"nothing\",\n", " \"everythin'\": \"everything\",\n", " \"anythin'\": \"anything\",\n", " \"somethin\": \"something\",\n", " \"nothin\": \"nothing\",\n", " \"everythin\": \"everything\",\n", " \"anythin\": \"anything\",\n", " \"doin'\": \"doing\",\n", " \"goin'\": \"going\",\n", " \"feelin'\": \"feeling\",\n", " \"thinkin'\": \"thinking\",\n", " \"talkin'\": \"talking\",\n", " \"havin'\": \"having\",\n", " \"bein'\": \"being\",\n", " \"gettin'\": \"getting\",\n", " \"puttin'\": \"putting\",\n", " \"tryin'\": \"trying\",\n", " \"comin'\": \"coming\",\n", " \"workin'\": \"working\",\n", " \"sleepin'\": \"sleeping\",\n", " \"eatin'\": \"eating\",\n", " \"drinkin'\": \"drinking\",\n", " \"livin'\": \"living\",\n", " \"lookin'\": \"looking\",\n", " \"runnin'\": \"running\",\n", " \"walkin'\": \"walking\",\n", "\n", " # ── mental health / mood context (khas DAIC-WoZ) ─────────────────\n", " \"prolly\": \"probably\",\n", " \"prob\": \"probably\",\n", " \"probs\": \"probably\",\n", " \"def\": \"definitely\",\n", " \"defo\": \"definitely\",\n", " \"rly\": \"really\",\n", " \"rlly\": \"really\",\n", " \"tbh\": \"to be honest\",\n", " \"idk\": \"i do not know\",\n", " \"idc\": \"i do not care\",\n", " \"imo\": \"in my opinion\",\n", " \"imho\": \"in my honest opinion\",\n", " \"lol\": \"laughing\", # sering muncul di transcript sebagai marker\n", " \"haha\": \"laughing\",\n", " \"hm\": \"hmm\",\n", " \"uh-huh\": \"yes\",\n", " \"mm-hmm\": \"yes\",\n", " \"uh\": \"\", # disfluency filler → hapus\n", " \"um\": \"\",\n", " \"uh huh\": \"yes\",\n", "}\n", "\n", "print(f\"Total slang entries: {len(SLANG_DICT)}\")\n", "print(\"Contoh:\", list(SLANG_DICT.items())[:5])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KwTQLKkF_LVc", "outputId": "7586ea69-b83b-4522-aafa-d65b9f95c38f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total slang entries: 168\n", "Contoh: [('gonna', 'going to'), ('wanna', 'want to'), ('gotta', 'got to'), ('kinda', 'kind of'), ('sorta', 'sort of')]\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.2 Normalisasi & Teks Cleaning" ], "metadata": { "id": "6F2pR0QyAOsc" } }, { "cell_type": "code", "source": [ "# # ADDED: Inisialisasi lemmatizer dan stopword list\n", "# lemmatizer = WordNetLemmatizer()\n", "\n", "# STOP_WORDS = set(stopwords.words('english'))\n", "\n", "# # Kata klinis depresi — keluarkan dari stopwords supaya tidak ikut kebuang\n", "# DEPRESSION_KEYWORDS = {\n", "# 'no', 'not', 'never', 'nothing', 'nobody', 'none', 'nor',\n", "# 'neither', 'cannot', 'cant', 'dont', 'doesnt', 'didnt',\n", "# 'wont', 'wouldnt', 'shouldnt', 'couldnt', 'havent', 'hasnt',\n", "# 'hadnt', 'wasnt', 'werent', 'isnt', 'arent',\n", "# 'empty', 'hopeless', 'worthless', 'tired', 'sad', 'lonely',\n", "# 'anxious', 'numb', 'fail', 'useless', 'burden'\n", "# }\n", "# STOP_WORDS -= DEPRESSION_KEYWORDS\n", "\n", "# # Filler words transcript — tambahkan ke stopwords karena noise\n", "# FILLER_WORDS = {'uh', 'um', 'uhm', 'hmm', 'hm', 'like', 'okay', 'ok', 'yeah', 'yep'}\n", "# STOP_WORDS |= FILLER_WORDS\n", "\n", "# print(f\"Total stopwords aktif: {len(STOP_WORDS)}\")\n", "# print(f\"Depression keywords dikecualikan: {DEPRESSION_KEYWORDS}\")" ], "metadata": { "id": "WJzs65j_Kl_5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Inisialisasi lemmatizer dan stopword list\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "STOP_WORDS = set(stopwords.words('english'))\n", "\n", "# Filler words transcript — tambahkan ke stopwords karena noise\n", "FILLER_WORDS = {'uh', 'um', 'uhm', 'hmm', 'hm', 'like', 'okay', 'ok', 'yeah', 'yep'}\n", "STOP_WORDS |= FILLER_WORDS\n", "\n", "print(f\"Total stopwords aktif: {len(STOP_WORDS)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bRBvpkJJOgGF", "outputId": "fbba6b3d-5d15-4661-92c3-252785a7889f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total stopwords aktif: 208\n" ] } ] }, { "cell_type": "code", "source": [ "#fungsi untuk ganti kata slang dengan salng_dict yang sudah dibuat\n", "def normalize_slang(text, slang_dict):\n", "\n", " # Buat regex pattern dari keys slang dict (escape karakter khusus)\n", " pattern = re.compile(\n", " r'\\b(' + '|'.join(map(re.escape, slang_dict.keys())) + r')\\b',\n", " re.IGNORECASE\n", " )\n", " return pattern.sub(lambda m: slang_dict.get(m.group().lower(), m.group()), text)" ], "metadata": { "id": "F-T0XGr3ARD6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def clean_text(text, slang_dict=None, remove_stopwords=True, lemmatize=True):\n", "\n", " text = str(text).lower()\n", "\n", " # Hapus marker sinkronisasi dan tags\n", " text = re.sub(r'<[^>]+>', ' ', text)\n", " text = re.sub(r'\\[.*?\\]', ' ', text)\n", " text = re.sub(r'\\(.*?\\)', ' ', text)\n", " text = re.sub(r'scrubbed_entry', ' ', text)\n", "\n", " # Normalisasi slang\n", " if slang_dict:\n", " text = normalize_slang(text, slang_dict)\n", "\n", " # Hapus karakter khusus\n", " text = re.sub(r\"[^a-z0-9\\s']\", ' ', text)\n", "\n", " # Hapus standalone apostrophe\n", " text = re.sub(r\"(? 1]\n", "\n", " # ADDED: Lemmatisasi\n", " if lemmatize:\n", " tokens = [lemmatizer.lemmatize(t) for t in tokens]\n", "\n", " return ' '.join(tokens)" ], "metadata": { "id": "pVszUOLcaSB4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df['text_clean'] = df['text'].apply(lambda x: clean_text(x, SLANG_DICT))\n", "\n", "print(\"Contoh teks sebelum preprocessing:\")\n", "print(df['text'].iloc[0][:200])\n", "print(\"\\nSetelah preprocessing (slang + stopwords + lemma):\")\n", "print(df['text_clean'].iloc[0][:200])\n", "\n", "# ADDED: cek reduksi kata\n", "df['word_count_raw'] = df['text'].apply(lambda x: len(str(x).split()))\n", "df['word_count_clean_temp'] = df['text_clean'].apply(lambda x: len(x.split()))\n", "reduction = (1 - df['word_count_clean_temp'] / df['word_count_raw']).mean() * 100\n", "print(f\"\\nRata-rata reduksi kata: {reduction:.1f}%\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eH8OQMcHaw_f", "outputId": "a61338ff-8217-480e-ccf3-8f481b490fbd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Contoh teks sebelum preprocessing:\n", "good atlanta georgia um my parents are from here um i love it i like the weather i like the opportunities um yes um it took a minute somewhat easy congestion that's it um i took up business and admini\n", "\n", "Setelah preprocessing (slang + stopwords + lemma):\n", "good atlanta georgia parent love weather opportunity yes took minute somewhat easy congestion took business administration yes break right plan going back next semester probably open business specific\n", "\n", "Rata-rata reduksi kata: 58.7%\n" ] } ] }, { "cell_type": "code", "source": [ "# Cek data setelah cleaning\n", "df['word_count_clean'] = df['text_clean'].apply(lambda x: len(x.split()))\n", "\n", "# Filter participant dengan teks terlalu pendek (mungkin ada masalah data)\n", "MIN_WORDS = 20\n", "df_filtered = df[df['word_count_clean'] >= MIN_WORDS].copy()\n", "print(f\"Data setelah filter min {MIN_WORDS} kata: {len(df_filtered)} dari {len(df)} participant\")\n", "print(f\"Distribusi label: {df_filtered['label'].value_counts().to_dict()}\")\n", "\n", "# Update df utama\n", "df = df_filtered.reset_index(drop=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pK6iPVRTa26I", "outputId": "2093bd94-1ca4-471c-f5eb-a1ce74bd359f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Data setelah filter min 20 kata: 189 dari 189 participant\n", "Distribusi label: {0: 132, 1: 57}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.3 Future Eng" ], "metadata": { "id": "3ibiRnCW8hG7" } }, { "cell_type": "code", "source": [ "# ── Length Features ──────────────────────────────────────────────────\n", "df['avg_utt_len'] = df['word_count'] / (df['utterance_count'] + 1)\n", "df['response_brevity'] = (df['utterance_count'] < 50).astype(int)\n", "\n", "LENGTH_COLS = ['word_count', 'utterance_count', 'avg_utt_len', 'response_brevity']\n", "print(df[LENGTH_COLS].head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5oufGktt8kPs", "outputId": "29004216-6219-4cf2-c982-44115500bb54" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " word_count utterance_count avg_utt_len response_brevity\n", "0 352 87 4.000000 0\n", "1 1465 104 13.952381 0\n", "2 611 96 6.298969 0\n", "3 1960 103 18.846154 0\n", "4 976 104 9.295238 0\n" ] } ] }, { "cell_type": "code", "source": [ "# ── Lexical Sentiment Features ───────────────────────────────────────\n", "NEGATIVE_WORDS = {'hopeless','worthless','empty','tired','sad','lonely',\n", " 'fail','useless','burden','numb','anxious','hate'}\n", "POSITIVE_WORDS = {'happy','good','fine','great','better','enjoy',\n", " 'love','hope','okay','well','improving'}\n", "\n", "def lexical_features(text):\n", " words = set(str(text).lower().split())\n", " neg = len(words & NEGATIVE_WORDS)\n", " pos = len(words & POSITIVE_WORDS)\n", " total = len(words) + 1\n", " return {\n", " 'neg_ratio' : neg / total,\n", " 'pos_ratio' : pos / total,\n", " 'sentiment_gap': (neg - pos) / total\n", " }\n", "\n", "lex = df['text_clean'].apply(lexical_features).apply(pd.Series)\n", "df = pd.concat([df, lex], axis=1)\n", "\n", "LEXICAL_COLS = ['neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "print(df[LEXICAL_COLS].head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ny5nB7PU8nyy", "outputId": "372c343a-f61e-4d92-9d2b-3f644d507ee4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " neg_ratio pos_ratio sentiment_gap\n", "0 0.000000 0.055046 -0.055046\n", "1 0.003759 0.022556 -0.018797\n", "2 0.000000 0.037433 -0.037433\n", "3 0.005935 0.014837 -0.008902\n", "4 0.008547 0.025641 -0.017094\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.4 split data" ], "metadata": { "id": "gPSF1Ua6oCFH" } }, { "cell_type": "code", "source": [ "NV_FEATURE_COLS = [f'nv_{tag}' for tag in NONVERBAL_TAGS] + ['nv_total', 'filler_count']\n", "\n", "train_df = df[df['split'] == 'train'].copy().reset_index(drop=True)\n", "dev_df = df[df['split'] == 'dev'].copy().reset_index(drop=True)\n", "\n", "X_train = train_df['text_clean'].values\n", "y_train = train_df['phq8_target'].values\n", "X_test = dev_df['text_clean'].values\n", "y_test = dev_df['phq8_target'].values\n", "\n", "nv_train = train_df[NV_FEATURE_COLS].fillna(0).values.astype(np.float32)\n", "nv_test = dev_df[NV_FEATURE_COLS].fillna(0).values.astype(np.float32)\n", "\n", "# normalize non-verbal features\n", "from sklearn.preprocessing import StandardScaler\n", "nv_scaler = StandardScaler()\n", "nv_train = nv_scaler.fit_transform(nv_train)\n", "nv_test = nv_scaler.transform(nv_test)\n", "\n", "print(f\"Train size: {len(X_train)} | Dev size: {len(X_test)}\")\n", "print(f\"Train target — mean: {y_train.mean():.2f}, std: {y_train.std():.2f}\")\n", "print(f\"Dev target — mean: {y_test.mean():.2f}, std: {y_test.std():.2f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2DH0O630oEiq", "outputId": "b71ef2a7-1678-4207-94b3-1820ccb629b1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train size: 107 | Dev size: 35\n", "Train target — mean: 6.42, std: 5.44\n", "Dev target — mean: 7.43, std: 6.50\n" ] } ] }, { "cell_type": "code", "source": [ "# ── Apply extra features ke train_df & dev_df ────────────────────────\n", "extra_cols = LENGTH_COLS + LEXICAL_COLS\n", "\n", "for col in extra_cols:\n", " train_df[col] = df.loc[df['split'] == 'train', col].values\n", " dev_df[col] = df.loc[df['split'] == 'dev', col].values\n", "\n", "ALL_EXTRA_COLS = NV_FEATURE_COLS + LENGTH_COLS + LEXICAL_COLS\n", "\n", "extra_scaler = StandardScaler()\n", "nv_train = extra_scaler.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0))\n", "nv_test = extra_scaler.transform(dev_df[ALL_EXTRA_COLS].fillna(0))\n", "\n", "NV_SIZE = len(ALL_EXTRA_COLS)\n", "print(f\"Total extra features: {NV_SIZE}\")\n", "print(ALL_EXTRA_COLS)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NkusPLb-9fll", "outputId": "7c25598c-c93c-4220-bea9-cc97a513636b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total extra features: 18\n", "['nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'word_count', 'utterance_count', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"transcripts_df:\", transcripts_df.columns.tolist())\n", "print(\"df:\", df.columns.tolist())\n", "print(\"train_df:\", train_df.columns.tolist())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o8YHloURxhoc", "outputId": "e4959523-adc7-42d5-dcd6-02b04b4ae986" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "transcripts_df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count']\n", "df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split', 'text_clean', 'word_count_raw', 'word_count_clean_temp', 'word_count_clean', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "train_df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split', 'text_clean', 'word_count_raw', 'word_count_clean_temp', 'word_count_clean', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 5. Modeling" ], "metadata": { "id": "BkFpICUbmT9w" } }, { "cell_type": "markdown", "source": [ "## 5.1 Regulasi Max Min" ], "metadata": { "id": "zITbjz6kxbaC" } }, { "cell_type": "code", "source": [ "class DAICWOZDataset(Dataset):\n", " def __init__(self, texts, labels, nv_features, tokenizer, max_len=512, head=256, tail=256):\n", " self.texts = texts\n", " self.labels = labels\n", " self.nv_features = nv_features # shape (N, len(NV_FEATURE_COLS))\n", " self.tokenizer = tokenizer\n", " self.max_len = max_len\n", " self.head = head\n", " self.tail = tail\n", "\n", " def __len__(self):\n", " return len(self.texts)\n", "\n", " def __getitem__(self, idx):\n", " text = str(self.texts[idx])\n", " label = float(self.labels[idx])\n", "\n", " tokens = self.tokenizer(\n", " text,\n", " add_special_tokens=False,\n", " return_attention_mask=False,\n", " return_token_type_ids=False\n", " )['input_ids']\n", "\n", " max_body = self.max_len - 2\n", " if len(tokens) > max_body:\n", " head_len = min(self.head, max_body // 2)\n", " tail_len = max_body - head_len\n", " tokens = tokens[:head_len] + tokens[-tail_len:]\n", "\n", " tokens = [self.tokenizer.cls_token_id] + tokens + [self.tokenizer.sep_token_id]\n", " if len(tokens) > self.max_len:\n", " tokens = tokens[:self.max_len]\n", "\n", " padding_len = self.max_len - len(tokens)\n", " attention_mask = [1] * len(tokens) + [0] * padding_len\n", " tokens = tokens + [self.tokenizer.pad_token_id] * padding_len\n", "\n", " return {\n", " 'input_ids': torch.tensor(tokens, dtype=torch.long),\n", " 'attention_mask': torch.tensor(attention_mask, dtype=torch.long),\n", " 'nv_features': torch.tensor(self.nv_features[idx], dtype=torch.float),\n", " 'labels': torch.tensor(label, dtype=torch.float)\n", " }" ], "metadata": { "id": "ZxfMkQtmr3aR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 5.2 Baseline TF-IDF\n" ], "metadata": { "id": "wcsVKwXEEgly" } }, { "cell_type": "markdown", "source": [ "### import & severity helper" ], "metadata": { "id": "0IlU8cgWMB1e" } }, { "cell_type": "code", "source": [ "from sklearn.svm import SVR\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.decomposition import TruncatedSVD\n", "from sklearn.model_selection import GridSearchCV\n", "import scipy.sparse as sp\n", "\n", "# severity category helper (untuk post-processing prediksi)\n", "def severity_category(score):\n", " if score <= 4: return 'Minimal (0-4)'\n", " elif score <= 9: return 'Mild (5-9)'\n", " elif score <= 14: return 'Moderate (10-14)'\n", " elif score <= 19: return 'Mod-Severe (15-19)'\n", " else: return 'Severe (20-27)'\n", "\n", "all_results = []" ], "metadata": { "id": "K-c-3YyxMBJb" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Persiapan data: buat text_baseline & combined feature matrix" ], "metadata": { "id": "lbJMqzMbMFm1" } }, { "cell_type": "code", "source": [ "# Persiapan data\n", "train_df['text_baseline'] = train_df['text'].apply(lambda x: clean_text(x, slang_dict=None))\n", "dev_df['text_baseline'] = dev_df['text'].apply(lambda x: clean_text(x, slang_dict=None))\n", "\n", "y_train_base = train_df['phq8_target'].values\n", "y_dev_base = dev_df['phq8_target'].values\n", "\n", "# TF-IDF features\n", "tfidf_base = TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)\n", "X_train_tfidf = tfidf_base.fit_transform(train_df['text_baseline'])\n", "X_dev_tfidf = tfidf_base.transform(dev_df['text_baseline'])\n", "print(f\"TF-IDF shape: {X_train_tfidf.shape}\")\n", "\n", "# Non-verbal + extra features\n", "nv_scaler_base = StandardScaler()\n", "X_train_nv = nv_scaler_base.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0))\n", "X_dev_nv = nv_scaler_base.transform(dev_df[ALL_EXTRA_COLS].fillna(0))\n", "\n", "# Combined matrix\n", "X_train_combined = sp.hstack([X_train_tfidf, sp.csr_matrix(X_train_nv)])\n", "X_dev_combined = sp.hstack([X_dev_tfidf, sp.csr_matrix(X_dev_nv)])\n", "print(f\"Combined shape: {X_train_combined.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NB57EYTZH9Op", "outputId": "4943b77e-e01f-4765-f774-ad8fee50f451" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF shape: (107, 5000)\n", "Combined shape: (107, 5021)\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Helper Eval" ], "metadata": { "id": "Awh7vDyFMIyQ" } }, { "cell_type": "code", "source": [ "def evaluate_regressor(model, X_train, X_dev, y_train, y_dev, model_name='Model'):\n", " model.fit(X_train, y_train)\n", " preds = model.predict(X_dev)\n", " preds_clipped = np.clip(preds, 0, 27)\n", "\n", " mae = mean_absolute_error(y_dev, preds_clipped)\n", " rmse = mean_squared_error(y_dev, preds_clipped) ** 0.5\n", "\n", " pred_categories = [severity_category(round(p)) for p in preds_clipped]\n", " true_categories = [severity_category(int(s)) for s in y_dev]\n", " category_accuracy = sum(p == t for p, t in zip(pred_categories, true_categories)) / len(y_dev)\n", "\n", " print(f\"\\n{'='*60}\")\n", " print(f\" {model_name}\")\n", " print(f\"{'='*60}\")\n", " print(f\" MAE : {mae:.4f}\")\n", " print(f\" RMSE : {rmse:.4f}\")\n", " print(f\" Category Accuracy: {category_accuracy:.4f}\")\n", " print(f\" Pred range: [{preds_clipped.min():.1f}, {preds_clipped.max():.1f}]\")\n", " print(f\" True range: [{y_dev.min()}, {y_dev.max()}]\")\n", "\n", " return {'model': model_name, 'mae': mae, 'rmse': rmse, 'category_accuracy': category_accuracy}, model, preds_clipped" ], "metadata": { "id": "kT0BN0b3MLBN" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Load Model" ], "metadata": { "id": "s64TOcePMMlf" } }, { "cell_type": "code", "source": [ "### Load Model\n", "ALPHA_GRID = [0.1, 0.5, 1.0, 5.0, 10.0, 50.0]\n", "\n", "pipeline_ridge = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)),\n", " ('ridge', Ridge(alpha=1.0))\n", "])" ], "metadata": { "id": "1gqwPsIDMNke" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Optimizer" ], "metadata": { "id": "4lfe6Ox2Mybh" } }, { "cell_type": "code", "source": [ "### Optimizer\n", "param_grid = {'ridge__alpha': ALPHA_GRID}\n", "gs_ridge = GridSearchCV(\n", " pipeline_ridge, param_grid,\n", " cv=5, scoring='neg_mean_absolute_error',\n", " n_jobs=-1, verbose=0\n", ")\n", "gs_ridge.fit(train_df['text_baseline'], y_train_base)\n", "print(f\"Best alpha : {gs_ridge.best_params_['ridge__alpha']}\")\n", "print(f\"Best CV MAE: {-gs_ridge.best_score_:.4f}\")\n", "best_ridge = gs_ridge.best_estimator_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TztX-e4HMP6v", "outputId": "85d5607e-f357-4577-9f53-aa7501ffd86c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Best alpha : 0.1\n", "Best CV MAE: 4.3641\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Ridge" ], "metadata": { "id": "L1lRt-CIMRSh" } }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_ridge, _, pred_ridge = evaluate_regressor(\n", " best_ridge,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + Ridge (tuned)'\n", ")\n", "all_results.append(m_ridge)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WZ3B0vbVMSSk", "outputId": "385ecc07-3a29-4f65-beb5-62b9cb2b9007" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + Ridge (tuned)\n", "============================================================\n", " MAE : 5.3618\n", " RMSE : 6.4914\n", " Category Accuracy: 0.1429\n", " Pred range: [4.3, 8.2]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### SVR" ], "metadata": { "id": "Ug63gTDwMUmw" } }, { "cell_type": "code", "source": [ "### Load Model — SVR\n", "pipeline_svr = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)),\n", " ('svd', TruncatedSVD(n_components=100, random_state=SEED)),\n", " ('scaler', StandardScaler()),\n", " ('svr', SVR(kernel='rbf', C=1.0, epsilon=1.0))\n", "])" ], "metadata": { "id": "9O0UJJWFMVeO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_svr, _, pred_svr = evaluate_regressor(\n", " pipeline_svr,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + SVD + SVR'\n", ")\n", "all_results.append(m_svr)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ebOFmscxMYUh", "outputId": "85391541-b146-477f-bde9-9e8593e9ffa0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + SVD + SVR\n", "============================================================\n", " MAE : 5.4685\n", " RMSE : 6.8659\n", " Category Accuracy: 0.1714\n", " Pred range: [4.9, 5.5]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### GBR" ], "metadata": { "id": "fTiE7FvHMZ6X" } }, { "cell_type": "code", "source": [ "### Load Model — Gradient Boosting\n", "pipeline_gbr = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=3000, min_df=2, sublinear_tf=True)),\n", " ('svd', TruncatedSVD(n_components=100, random_state=SEED)),\n", " ('gbr', GradientBoostingRegressor(n_estimators=200, learning_rate=0.05, max_depth=3, random_state=SEED))\n", "])" ], "metadata": { "id": "b1O5EbWHMa2Q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_gbr, _, pred_gbr = evaluate_regressor(\n", " pipeline_gbr,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + SVD + GBR'\n", ")\n", "all_results.append(m_gbr)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5NfcVC46MdNu", "outputId": "920e2ecb-ee6e-4380-ae06-48e01372aca7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + SVD + GBR\n", "============================================================\n", " MAE : 5.5499\n", " RMSE : 7.0963\n", " Category Accuracy: 0.2857\n", " Pred range: [3.4, 6.9]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Ensamble" ], "metadata": { "id": "vBVhNC4vM5nA" } }, { "cell_type": "code", "source": [ "### Load Model — Combined Features\n", "ridge_combined = Ridge(alpha=5.0)" ], "metadata": { "id": "hwRU9OVkM-D1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_comb, model_comb, pred_comb = evaluate_regressor(\n", " ridge_combined,\n", " X_train_combined, X_dev_combined,\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + Non-Verbal + Ridge'\n", ")\n", "all_results.append(m_comb)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9x-Dj9W3M8-7", "outputId": "ec723b77-a7a3-4799-bcd5-ce87a5b06bad" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + Non-Verbal + Ridge\n", "============================================================\n", " MAE : 5.5879\n", " RMSE : 7.1236\n", " Category Accuracy: 0.1714\n", " Pred range: [0.6, 9.4]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Eval" ], "metadata": { "id": "k8jLd42HM_U3" } }, { "cell_type": "code", "source": [ "### Evaluation\n", "results_df = pd.DataFrame(all_results).sort_values('mae')\n", "\n", "print(\"\\n\" + \"=\"*65)\n", "print(\"MODEL COMPARISON — Dev Set\")\n", "print(\"=\"*65)\n", "print(results_df.to_string(index=False, float_format='{:.4f}'.format))\n", "\n", "# Plot\n", "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "fig.suptitle('5.2 Baseline Regression — Model Comparison (Dev Set)', fontweight='bold')\n", "\n", "axes[0].barh(results_df['model'], results_df['mae'], color='#2196F3', alpha=0.85)\n", "axes[0].set_title('MAE (lower = better)')\n", "axes[0].axvline(x=3, color='red', linestyle='--', label='Target MAE ≤ 3')\n", "axes[0].legend()\n", "\n", "axes[1].barh(results_df['model'], results_df['rmse'], color='#FF9800', alpha=0.85)\n", "axes[1].set_title('RMSE (lower = better)')\n", "\n", "axes[2].barh(results_df['model'], results_df['category_accuracy'], color='#4CAF50', alpha=0.85)\n", "axes[2].set_title('Severity Category Accuracy')\n", "axes[2].axvline(x=0.5, color='red', linestyle='--', label='Random baseline')\n", "axes[2].legend()\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"\\n🏆 Best model: {results_df.iloc[0]['model']}\")\n", "print(f\" MAE: {results_df.iloc[0]['mae']:.4f} | RMSE: {results_df.iloc[0]['rmse']:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 393 }, "id": "m086SVKoM_1t", "outputId": "fde1b52e-04b4-48d9-8334-7cc7249d1187" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "MODEL COMPARISON — Dev Set\n", "=================================================================\n", " model mae rmse category_accuracy\n", " TF-IDF + Ridge (tuned) 5.3618 6.4914 0.1429\n", " TF-IDF + SVD + SVR 5.4685 6.8659 0.1714\n", " TF-IDF + SVD + GBR 5.5499 7.0963 0.2857\n", "TF-IDF + Non-Verbal + Ridge 5.5879 7.1236 0.1714\n" ] }, { "output_type": "display_data", 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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "🏆 Best model: TF-IDF + Ridge (tuned)\n", " MAE: 5.3618 | RMSE: 6.4914\n" ] } ] }, { "cell_type": "code", "source": [ "print(f\"Train size: {len(train_df)} | Dev size: {len(dev_df)}\")\n", "print(f\"Train target — mean: {y_train_base.mean():.2f}, std: {y_train_base.std():.2f}, min: {y_train_base.min()}, max: {y_train_base.max()}\")\n", "print(f\"Dev target — mean: {y_dev_base.mean():.2f}, std: {y_dev_base.std():.2f}, min: {y_dev_base.min()}, max: {y_dev_base.max()}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZSMlJz7MOwBG", "outputId": "fe9743cf-8524-451e-cd08-dbf19261fac2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train size: 107 | Dev size: 35\n", "Train target — mean: 6.42, std: 5.44, min: 0, max: 20\n", "Dev target — mean: 7.43, std: 6.50, min: 0, max: 23\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 5.3 E-GCN (by paper)" ], "metadata": { "id": "OW9zxae_31GZ" } }, { "cell_type": "code", "source": [ "# !pip install torch_geometric -q" ], "metadata": { "id": "uRis_ky55DD4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Load Model" ], "metadata": { "id": "vj56w9Qj5Fq0" } }, { "cell_type": "code", "source": [ "EPOCHS_GCN = 200\n", "LR_GCN = 1e-3\n", "WEIGHT_DECAY_GCN = 1e-4\n", "DROPOUT_GCN = 0.2\n", "HIDDEN_GCN = 64\n", "TOP_K_WORDS = 250" ], "metadata": { "id": "6LJlLIBoFMhQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# from torch_geometric.nn import GCNConv\n", "\n", "# NV_SIZE = nv_train.shape[1]\n", "\n", "# class GCNRegressorModel(nn.Module):\n", "# def __init__(self, in_channels, hidden_channels=64, nv_size=NV_SIZE, dropout=0.1):\n", "# super().__init__()\n", "# self.conv1 = GCNConv(in_channels, hidden_channels)\n", "# self.conv2 = GCNConv(hidden_channels, hidden_channels)\n", "# self.conv3 = GCNConv(hidden_channels, hidden_channels)\n", "# self.bn1 = nn.BatchNorm1d(hidden_channels)\n", "# self.bn2 = nn.BatchNorm1d(hidden_channels)\n", "# self.bn3 = nn.BatchNorm1d(hidden_channels)\n", "# self.dropout = nn.Dropout(dropout)\n", "# self.shared = nn.Linear(hidden_channels + nv_size, 128)\n", "# self.regressor = nn.Linear(128, 1) # PHQ-8 score\n", "# self.classifier = nn.Linear(128, 1) # binary depression label\n", "\n", "# def forward(self, x, edge_index, edge_weight, interview_mask, nv_features):\n", "# x = F.relu(self.bn1(self.conv1(x, edge_index, edge_weight)))\n", "# x = self.dropout(x)\n", "# residual = x\n", "# x = F.relu(self.bn2(self.conv2(x, edge_index, edge_weight)))\n", "# x = self.dropout(x)\n", "# x = F.relu(self.bn3(self.conv3(x, edge_index, edge_weight))) + residual\n", "# x = x[interview_mask]\n", "# x = F.relu(self.shared(torch.cat([x, nv_features], dim=1)))\n", "# return self.regressor(x).squeeze(-1), self.classifier(x).squeeze(-1)\n", "\n", "# model_gcn = GCNRegressorModel(\n", "# in_channels=vocab_size_gcn,\n", "# hidden_channels=HIDDEN_GCN,\n", "# nv_size=NV_SIZE,\n", "# dropout=DROPOUT_GCN\n", "# ).to(DEVICE)\n", "\n", "# trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "# print(f\"NV_SIZE: {NV_SIZE}\")\n", "# print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "id": "Sf7pU6rKCG7u" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from torch_geometric.nn import GATConv\n", "\n", "NV_SIZE = nv_train.shape[1]\n", "\n", "class GATRegressorModel(nn.Module):\n", " def __init__(self, in_channels, hidden_channels=64, nv_size=NV_SIZE, dropout=0.1, heads=4):\n", " super().__init__()\n", " self.conv1 = GATConv(in_channels, hidden_channels, heads=heads, dropout=dropout)\n", " self.conv2 = GATConv(hidden_channels * heads, hidden_channels, heads=heads, dropout=dropout)\n", " self.conv3 = GATConv(hidden_channels * heads, hidden_channels, heads=1, dropout=dropout, concat=False)\n", " self.bn1 = nn.BatchNorm1d(hidden_channels * heads)\n", " self.bn2 = nn.BatchNorm1d(hidden_channels * heads)\n", " self.bn3 = nn.BatchNorm1d(hidden_channels)\n", " self.dropout = nn.Dropout(dropout)\n", " self.regressor = nn.Sequential(\n", " nn.Linear(hidden_channels + nv_size, 128),\n", " nn.ReLU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(128, 1)\n", " )\n", "\n", " def forward(self, x, edge_index, edge_weight, interview_mask, nv_features):\n", " x = F.relu(self.bn1(self.conv1(x, edge_index)))\n", " x = self.dropout(x)\n", " residual = x\n", " x = F.relu(self.bn2(self.conv2(x, edge_index)))\n", " x = self.dropout(x)\n", " x = F.relu(self.bn3(self.conv3(x, edge_index))) + residual[:, :64]\n", " x = x[interview_mask]\n", " x = torch.cat([x, nv_features], dim=1)\n", " return self.regressor(x).squeeze(-1)\n", "\n", "model_gcn = GATRegressorModel(\n", " in_channels=vocab_size_gcn,\n", " hidden_channels=HIDDEN_GCN,\n", " nv_size=NV_SIZE,\n", " dropout=DROPOUT_GCN\n", ").to(DEVICE)\n", "\n", "trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "print(f\"NV_SIZE: {NV_SIZE}\")\n", "print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7lKxa0EJIU1E", "outputId": "a3887268-27b2-4976-bb1a-fd67cde69f6b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NV_SIZE: 27\n", "Trainable params: 160,705\n" ] } ] }, { "cell_type": "code", "source": [ "# EPOCHS_GCN = 50\n", "# LR_GCN = 1e-3\n", "# WEIGHT_DECAY_GCN = 1e-4\n", "# DROPOUT_GCN = 0.2\n", "# HIDDEN_GCN = 64\n", "# TOP_K_WORDS = 250" ], "metadata": { "id": "uuL6xdxg5Kl7" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Build Graph" ], "metadata": { "id": "_SWgPgkN5PDV" } }, { "cell_type": "code", "source": [ "import torch.nn.functional as F\n", "from torch_geometric.utils import from_scipy_sparse_matrix\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_selection import SelectKBest, f_classif\n", "import scipy.sparse as sp\n", "\n", "# TF-IDF word-interview edges\n", "tfidf_gcn = TfidfVectorizer(\n", " max_features=5000,\n", " min_df=2,\n", " sublinear_tf=True,\n", " ngram_range=(1, 2)\n", " )\n", "\n", "X_tfidf_train_gcn = tfidf_gcn.fit_transform(train_df['text_clean'])\n", "X_tfidf_dev_gcn = tfidf_gcn.transform(dev_df['text_clean'])\n", "\n", "# Feature selection top-250 kata\n", "selector_gcn = SelectKBest(f_classif, k=TOP_K_WORDS)\n", "selector_gcn.fit(X_tfidf_train_gcn, train_df['label'].values)\n", "X_tfidf_train_gcn = selector_gcn.transform(X_tfidf_train_gcn)\n", "X_tfidf_dev_gcn = selector_gcn.transform(X_tfidf_dev_gcn)\n", "\n", "vocab_size_gcn = X_tfidf_train_gcn.shape[1] # 250\n", "n_train_gcn = len(train_df)\n", "n_nodes_gcn = vocab_size_gcn + n_train_gcn\n", "\n", "# Node features: eye(250) untuk word nodes, tfidf untuk interview nodes\n", "word_feats = torch.eye(vocab_size_gcn)\n", "interview_feats = torch.tensor(X_tfidf_train_gcn.toarray(), dtype=torch.float)\n", "x_gcn = torch.cat([word_feats, interview_feats], dim=0) # (n_nodes, 250)\n", "\n", "# Edges: interview-word dari TF-IDF\n", "cx = X_tfidf_train_gcn.tocoo()\n", "rows = np.concatenate([cx.row + vocab_size_gcn, cx.col])\n", "cols = np.concatenate([cx.col, cx.row + vocab_size_gcn])\n", "vals = np.concatenate([cx.data, cx.data])\n", "\n", "edge_index_gcn = torch.tensor([rows, cols], dtype=torch.long)\n", "edge_weight_gcn = torch.tensor(vals, dtype=torch.float)\n", "\n", "# Mask interview nodes\n", "interview_mask_gcn = torch.zeros(n_nodes_gcn, dtype=torch.bool)\n", "interview_mask_gcn[vocab_size_gcn:] = True\n", "\n", "print(f\"Nodes: {n_nodes_gcn} | Edges: {edge_index_gcn.shape[1]}\")\n", "print(f\"Node feature dim: {x_gcn.shape[1]}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q5ag9oqq5QO9", "outputId": "609fec76-d58b-48e2-f382-607f8ad46a2e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Nodes: 357 | Edges: 4506\n", "Node feature dim: 250\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Data Loader" ], "metadata": { "id": "lsxCoNkX5T12" } }, { "cell_type": "code", "source": [ "x_gcn = x_gcn.to(DEVICE)\n", "edge_index_gcn = edge_index_gcn.to(DEVICE)\n", "edge_weight_gcn = edge_weight_gcn.to(DEVICE)\n", "interview_mask_gcn = interview_mask_gcn.to(DEVICE)\n", "y_train_gcn = torch.tensor(y_train, dtype=torch.float).to(DEVICE)\n", "nv_train_tensor = torch.tensor(nv_train, dtype=torch.float).to(DEVICE)" ], "metadata": { "id": "KBXLd8dT5TFn" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Head Reggression" ], "metadata": { "id": "oESRelgY5W_M" } }, { "cell_type": "code", "source": [ "NV_SIZE = nv_train.shape[1] # tambah ini\n", "\n", "model_gcn = GCNRegressorModel(\n", " in_channels=vocab_size_gcn,\n", " hidden_channels=HIDDEN_GCN,\n", " nv_size=NV_SIZE,\n", " dropout=DROPOUT_GCN\n", ").to(DEVICE)\n", "\n", "trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "print(f\"NV_SIZE: {NV_SIZE}\")\n", "print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MOsgO99O5U-F", "outputId": "e57499a4-2785-41a4-dc04-2d567bde144d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NV_SIZE: 27\n", "Trainable params: 36,802\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Optimizer" ], "metadata": { "id": "7dqmre-B5axK" } }, { "cell_type": "code", "source": [ "optimizer_gcn = optim.AdamW(model_gcn.parameters(), lr=LR_GCN, weight_decay=WEIGHT_DECAY_GCN)\n", "loss_fn_reg = nn.MSELoss()\n", "loss_fn_cls = nn.BCEWithLogitsLoss()\n", "\n", "print(f\"Optimizer: AdamW | LR: {LR_GCN} | Epochs: {EPOCHS_GCN}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4LhMpeSz5cAO", "outputId": "8fe161e5-eddc-4120-ac88-e8fda9ed1422" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Optimizer: AdamW | LR: 0.001 | Epochs: 200\n" ] } ] }, { "cell_type": "code", "source": [ "# optimizer_gcn = optim.AdamW(model_gcn.parameters(), lr=LR_GCN, weight_decay=WEIGHT_DECAY_GCN)\n", "# loss_fn_gcn = nn.HuberLoss(delta=3.0) # ganti dari MSELoss\n", "\n", "# print(f\"Optimizer: AdamW | LR: {LR_GCN} | Epochs: {EPOCHS_GCN}\")" ], "metadata": { "id": "YdWPtMvbBzIh" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Train Loop" ], "metadata": { "id": "LwJeurT65eW_" } }, { "cell_type": "code", "source": [ "history_gcn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss_gcn = float('inf')\n", "best_epoch_gcn = -1\n", "patience = 20\n", "no_improve = 0\n", "\n", "y_train_label_gcn = torch.tensor(train_df['label'].values, dtype=torch.float).to(DEVICE)\n", "nv_test_tensor = torch.tensor(nv_test, dtype=torch.float).to(DEVICE)\n", "y_test_gcn = torch.tensor(y_test, dtype=torch.float).to(DEVICE)\n", "\n", "for epoch in range(1, EPOCHS_GCN + 1):\n", " # Train\n", " model_gcn.train()\n", " optimizer_gcn.zero_grad()\n", " pred_reg, pred_cls = model_gcn(x_gcn, edge_index_gcn, edge_weight_gcn, interview_mask_gcn, nv_train_tensor)\n", " loss_r = loss_fn_reg(pred_reg, y_train_gcn)\n", " loss_c = loss_fn_cls(pred_cls, y_train_label_gcn)\n", " loss = loss_r + 0.3 * loss_c # multitask: regression dominan\n", " loss.backward()\n", " nn.utils.clip_grad_norm_(model_gcn.parameters(), 1.0)\n", " optimizer_gcn.step()\n", "\n", " # Eval\n", " model_gcn.eval()\n", " with torch.no_grad():\n", " interview_feats_dev_gpu = torch.tensor(X_tfidf_dev_gcn.toarray(), dtype=torch.float).to(DEVICE)\n", " x_eval = torch.cat([word_feats.to(DEVICE), interview_feats_dev_gpu], dim=0)\n", " cx_dev = X_tfidf_dev_gcn.tocoo()\n", " n_word = vocab_size_gcn\n", " rows_d = np.concatenate([cx_dev.row + n_word, cx_dev.col])\n", " cols_d = np.concatenate([cx_dev.col, cx_dev.row + n_word])\n", " vals_d = np.concatenate([cx_dev.data, cx_dev.data])\n", " ei_dev = torch.tensor([rows_d, cols_d], dtype=torch.long).to(DEVICE)\n", " ew_dev = torch.tensor(vals_d, dtype=torch.float).to(DEVICE)\n", " mask_dev = torch.zeros(n_word + len(dev_df), dtype=torch.bool).to(DEVICE)\n", " mask_dev[n_word:] = True\n", " preds_dev, _ = model_gcn(x_eval, ei_dev, ew_dev, mask_dev, nv_test_tensor)\n", " preds_dev = np.clip(preds_dev.cpu().numpy(), 0, 27)\n", "\n", " val_mae = mean_absolute_error(y_test, preds_dev)\n", " val_rmse = mean_squared_error(y_test, preds_dev) ** 0.5\n", " val_r2 = r2_score(y_test, preds_dev)\n", " val_loss = mean_squared_error(y_test, preds_dev)\n", "\n", " history_gcn['train_loss'].append(loss.item())\n", " history_gcn['val_mae'].append(val_mae)\n", " history_gcn['val_rmse'].append(val_rmse)\n", " history_gcn['val_r2'].append(val_r2)\n", "\n", " print(f\"Epoch {epoch:02d}/{EPOCHS_GCN} | Train Loss: {loss.item():.4f} | Val Loss: {val_loss:.4f} | MAE: {val_mae:.4f} | RMSE: {val_rmse:.4f} | R²: {val_r2:.4f}\")\n", "\n", " if val_loss < best_val_loss_gcn:\n", " best_val_loss_gcn = val_loss\n", " best_epoch_gcn = epoch\n", " no_improve = 0\n", " torch.save(model_gcn.state_dict(), \"best_model_gcn.pt\")\n", " print(f\"Best model saved (epoch {epoch})\")\n", " else:\n", " no_improve += 1\n", " if no_improve >= patience:\n", " print(f\"Early stopping at epoch {epoch}\")\n", " break\n", "\n", "print(f\"\\nDone. Best epoch: {best_epoch_gcn} | Best Val Loss: {best_val_loss_gcn:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j3LDlkbY5fTO", "outputId": "4f7eea3f-32b4-4634-e317-df0f697a2c21" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 01/200 | Train Loss: 73.4761 | Val Loss: 96.8921 | MAE: 7.4063 | RMSE: 9.8434 | R²: -1.2967\n", "Best model saved (epoch 1)\n", "Epoch 02/200 | Train Loss: 72.2436 | Val Loss: 96.7431 | MAE: 7.3981 | RMSE: 9.8358 | R²: -1.2932\n", "Best model saved (epoch 2)\n", "Epoch 03/200 | Train Loss: 71.0436 | Val Loss: 96.5740 | MAE: 7.3895 | RMSE: 9.8272 | R²: -1.2891\n", "Best model saved (epoch 3)\n", "Epoch 04/200 | Train Loss: 69.9745 | Val Loss: 96.2557 | MAE: 7.3729 | RMSE: 9.8110 | R²: -1.2816\n", "Best model saved (epoch 4)\n", "Epoch 05/200 | Train Loss: 68.7990 | Val Loss: 95.7137 | MAE: 7.3448 | RMSE: 9.7833 | R²: -1.2688\n", "Best model saved (epoch 5)\n", "Epoch 06/200 | Train Loss: 67.6981 | Val Loss: 95.0270 | MAE: 7.3095 | RMSE: 9.7482 | R²: -1.2525\n", "Best model saved (epoch 6)\n", "Epoch 07/200 | Train Loss: 66.6059 | Val Loss: 94.2481 | MAE: 7.2702 | RMSE: 9.7081 | R²: -1.2340\n", "Best model saved (epoch 7)\n", "Epoch 08/200 | Train Loss: 65.4623 | Val Loss: 93.4237 | MAE: 7.2284 | RMSE: 9.6656 | R²: -1.2145\n", "Best model saved (epoch 8)\n", "Epoch 09/200 | Train Loss: 64.3200 | Val Loss: 92.5546 | MAE: 7.1836 | RMSE: 9.6205 | R²: -1.1939\n", "Best model saved (epoch 9)\n", "Epoch 10/200 | Train Loss: 63.2223 | Val Loss: 91.6357 | MAE: 7.1359 | RMSE: 9.5727 | R²: -1.1721\n", "Best model saved (epoch 10)\n", "Epoch 11/200 | Train Loss: 61.8682 | Val Loss: 90.6643 | MAE: 7.0854 | RMSE: 9.5218 | R²: -1.1491\n", "Best model saved (epoch 11)\n", "Epoch 12/200 | Train Loss: 60.6733 | Val Loss: 89.6412 | MAE: 7.0317 | RMSE: 9.4679 | R²: -1.1248\n", "Best model saved (epoch 12)\n", "Epoch 13/200 | Train Loss: 59.4385 | Val Loss: 88.5593 | MAE: 6.9745 | RMSE: 9.4106 | R²: -1.0992\n", "Best model saved (epoch 13)\n", "Epoch 14/200 | Train Loss: 58.0935 | Val Loss: 87.4055 | MAE: 6.9130 | RMSE: 9.3491 | R²: -1.0718\n", "Best model saved (epoch 14)\n", "Epoch 15/200 | Train Loss: 56.5298 | Val Loss: 86.1810 | MAE: 6.8470 | RMSE: 9.2834 | R²: -1.0428\n", "Best model saved (epoch 15)\n", "Epoch 16/200 | Train Loss: 55.1672 | Val Loss: 84.8961 | MAE: 6.7769 | RMSE: 9.2139 | R²: -1.0123\n", "Best model saved (epoch 16)\n", "Epoch 17/200 | Train Loss: 53.6248 | Val Loss: 83.5013 | MAE: 6.7000 | RMSE: 9.1379 | R²: -0.9793\n", "Best model saved (epoch 17)\n", "Epoch 18/200 | Train Loss: 52.3502 | Val Loss: 82.0682 | MAE: 6.6204 | RMSE: 9.0592 | R²: -0.9453\n", "Best model saved (epoch 18)\n", "Epoch 19/200 | Train Loss: 50.0876 | Val Loss: 80.4697 | MAE: 6.5403 | RMSE: 8.9705 | R²: -0.9074\n", "Best model saved (epoch 19)\n", "Epoch 20/200 | Train Loss: 48.7493 | Val Loss: 78.8052 | MAE: 6.4580 | RMSE: 8.8772 | R²: -0.8680\n", "Best model saved (epoch 20)\n", "Epoch 21/200 | Train Loss: 46.5983 | Val Loss: 77.0719 | MAE: 6.3701 | RMSE: 8.7791 | R²: -0.8269\n", "Best model saved (epoch 21)\n", "Epoch 22/200 | Train Loss: 44.3141 | Val Loss: 75.2063 | MAE: 6.2731 | RMSE: 8.6722 | R²: -0.7827\n", "Best model saved (epoch 22)\n", "Epoch 23/200 | Train Loss: 42.1936 | Val Loss: 73.2599 | MAE: 6.1692 | RMSE: 8.5592 | R²: -0.7365\n", "Best model saved (epoch 23)\n", "Epoch 24/200 | Train Loss: 40.5436 | Val Loss: 71.2539 | MAE: 6.0631 | RMSE: 8.4412 | R²: -0.6890\n", "Best model saved (epoch 24)\n", "Epoch 25/200 | Train Loss: 38.1232 | Val Loss: 69.1782 | MAE: 5.9644 | RMSE: 8.3173 | R²: -0.6398\n", "Best model saved (epoch 25)\n", "Epoch 26/200 | Train Loss: 36.2870 | Val Loss: 67.1269 | MAE: 5.8723 | RMSE: 8.1931 | R²: -0.5911\n", "Best model saved (epoch 26)\n", "Epoch 27/200 | Train Loss: 34.6331 | Val Loss: 65.0127 | MAE: 5.7779 | RMSE: 8.0630 | R²: -0.5410\n", "Best model saved (epoch 27)\n", "Epoch 28/200 | Train Loss: 31.9935 | Val Loss: 62.9847 | MAE: 5.6830 | RMSE: 7.9363 | R²: -0.4930\n", "Best model saved (epoch 28)\n", "Epoch 29/200 | Train Loss: 29.8969 | Val Loss: 61.0310 | MAE: 5.5976 | RMSE: 7.8122 | R²: -0.4467\n", "Best model saved (epoch 29)\n", "Epoch 30/200 | Train Loss: 27.5633 | Val Loss: 59.1063 | MAE: 5.5439 | RMSE: 7.6881 | R²: -0.4010\n", "Best model saved (epoch 30)\n", "Epoch 31/200 | Train Loss: 25.8460 | Val Loss: 57.3021 | MAE: 5.5028 | RMSE: 7.5698 | R²: -0.3583\n", "Best model saved (epoch 31)\n", "Epoch 32/200 | Train Loss: 22.6114 | Val Loss: 55.6159 | MAE: 5.4641 | RMSE: 7.4576 | R²: -0.3183\n", "Best model saved (epoch 32)\n", "Epoch 33/200 | Train Loss: 20.9884 | Val Loss: 54.0541 | MAE: 5.4252 | RMSE: 7.3521 | R²: -0.2813\n", "Best model saved (epoch 33)\n", "Epoch 34/200 | Train Loss: 19.2279 | Val Loss: 52.5960 | MAE: 5.3928 | RMSE: 7.2523 | R²: -0.2467\n", "Best model saved (epoch 34)\n", "Epoch 35/200 | Train Loss: 16.2423 | Val Loss: 51.3440 | MAE: 5.3677 | RMSE: 7.1655 | R²: -0.2170\n", "Best model saved (epoch 35)\n", "Epoch 36/200 | Train Loss: 14.9587 | Val Loss: 50.1539 | MAE: 5.3490 | RMSE: 7.0819 | R²: -0.1888\n", "Best model saved (epoch 36)\n", "Epoch 37/200 | Train Loss: 13.0558 | Val Loss: 49.1019 | MAE: 5.3282 | RMSE: 7.0073 | R²: -0.1639\n", "Best model saved (epoch 37)\n", "Epoch 38/200 | Train Loss: 11.1617 | Val Loss: 48.1405 | MAE: 5.3046 | RMSE: 6.9383 | R²: -0.1411\n", "Best model saved (epoch 38)\n", "Epoch 39/200 | Train Loss: 10.4343 | Val Loss: 47.3138 | MAE: 5.2779 | RMSE: 6.8785 | R²: -0.1215\n", "Best model saved (epoch 39)\n", "Epoch 40/200 | Train Loss: 8.4362 | Val Loss: 46.5266 | MAE: 5.2461 | RMSE: 6.8210 | R²: -0.1028\n", "Best model saved (epoch 40)\n", "Epoch 41/200 | Train Loss: 7.8759 | Val Loss: 45.8588 | MAE: 5.2099 | RMSE: 6.7719 | R²: -0.0870\n", "Best model saved (epoch 41)\n", "Epoch 42/200 | Train Loss: 6.3910 | Val Loss: 45.2842 | MAE: 5.1684 | RMSE: 6.7294 | R²: -0.0734\n", "Best model saved (epoch 42)\n", "Epoch 43/200 | Train Loss: 6.4473 | Val Loss: 44.8951 | MAE: 5.1250 | RMSE: 6.7004 | R²: -0.0642\n", "Best model saved (epoch 43)\n", "Epoch 44/200 | Train Loss: 6.0198 | Val Loss: 44.6449 | MAE: 5.0833 | RMSE: 6.6817 | R²: -0.0582\n", "Best model saved (epoch 44)\n", "Epoch 45/200 | Train Loss: 5.5848 | Val Loss: 44.5921 | MAE: 5.0438 | RMSE: 6.6777 | R²: -0.0570\n", "Best model saved (epoch 45)\n", "Epoch 46/200 | Train Loss: 4.9346 | Val Loss: 44.6310 | MAE: 5.0037 | RMSE: 6.6806 | R²: -0.0579\n", "Epoch 47/200 | Train Loss: 5.7220 | Val Loss: 44.8162 | MAE: 4.9695 | RMSE: 6.6945 | R²: -0.0623\n", "Epoch 48/200 | Train Loss: 5.8936 | Val Loss: 45.1502 | MAE: 4.9496 | RMSE: 6.7194 | R²: -0.0702\n", "Epoch 49/200 | Train Loss: 5.2026 | Val Loss: 45.6095 | MAE: 4.9309 | RMSE: 6.7535 | R²: -0.0811\n", "Epoch 50/200 | Train Loss: 5.7482 | Val Loss: 46.2056 | MAE: 4.9145 | RMSE: 6.7975 | R²: -0.0952\n", "Epoch 51/200 | Train Loss: 4.7854 | Val Loss: 46.9053 | MAE: 4.9031 | RMSE: 6.8487 | R²: -0.1118\n", "Epoch 52/200 | Train Loss: 4.2987 | Val Loss: 47.7000 | MAE: 4.9095 | RMSE: 6.9065 | R²: -0.1307\n", "Epoch 53/200 | Train Loss: 4.5246 | Val Loss: 48.5763 | MAE: 4.9240 | RMSE: 6.9697 | R²: -0.1514\n", "Epoch 54/200 | Train Loss: 4.8655 | Val Loss: 49.5219 | MAE: 4.9455 | RMSE: 7.0372 | R²: -0.1738\n", "Epoch 55/200 | Train Loss: 4.1584 | Val Loss: 50.3223 | MAE: 4.9719 | RMSE: 7.0938 | R²: -0.1928\n", "Epoch 56/200 | Train Loss: 4.1676 | Val Loss: 50.9609 | MAE: 4.9952 | RMSE: 7.1387 | R²: -0.2080\n", "Epoch 57/200 | Train Loss: 3.8288 | Val Loss: 51.4643 | MAE: 5.0214 | RMSE: 7.1739 | R²: -0.2199\n", "Epoch 58/200 | Train Loss: 4.0166 | Val Loss: 51.7538 | MAE: 5.0432 | RMSE: 7.1940 | R²: -0.2267\n", "Epoch 59/200 | Train Loss: 3.9768 | Val Loss: 51.8038 | MAE: 5.0598 | RMSE: 7.1975 | R²: -0.2279\n", "Epoch 60/200 | Train Loss: 3.5392 | Val Loss: 51.3765 | MAE: 5.0612 | RMSE: 7.1677 | R²: -0.2178\n", "Epoch 61/200 | Train Loss: 3.4859 | Val Loss: 50.6182 | MAE: 5.0506 | RMSE: 7.1146 | R²: -0.1998\n", "Epoch 62/200 | Train Loss: 3.1583 | Val Loss: 49.5041 | MAE: 5.0267 | RMSE: 7.0359 | R²: -0.1734\n", "Epoch 63/200 | Train Loss: 3.0095 | Val Loss: 48.1478 | MAE: 4.9900 | RMSE: 6.9389 | R²: -0.1413\n", "Epoch 64/200 | Train Loss: 2.8501 | Val Loss: 46.5221 | MAE: 4.9342 | RMSE: 6.8207 | R²: -0.1027\n", "Epoch 65/200 | Train Loss: 3.0698 | Val Loss: 44.9076 | MAE: 4.8699 | RMSE: 6.7013 | R²: -0.0645\n", "Early stopping at epoch 65\n", "\n", "Done. Best epoch: 45 | Best Val Loss: 44.5921\n" ] } ] }, { "cell_type": "code", "source": [ "print(f\"interview_mask_gcn sum: {interview_mask_gcn.cpu().sum()}\")\n", "print(f\"n_train_gcn : {n_train_gcn}\")\n", "print(f\"y_train_label_gcn shape: {y_train_label_gcn.shape}\")\n", "print(f\"nv_train_tensor shape : {nv_train_tensor.shape}\")\n", "print(f\"x_gcn shape : {x_gcn.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9kVZU7LwG9oc", "outputId": "53ede25a-c30f-4a3b-f994-df659a2ca396" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "interview_mask_gcn sum: 107\n", "n_train_gcn : 107\n", "y_train_label_gcn shape: torch.Size([107])\n", "nv_train_tensor shape : torch.Size([107, 27])\n", "x_gcn shape : torch.Size([357, 250])\n" ] } ] }, { "cell_type": "code", "source": [ "print(nv_train_tensor.shape)\n", "print(nv_test_tensor.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NhdL1WNpAmB6", "outputId": "70744dcd-79a5-443f-f5e3-01bc918eaa9f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "torch.Size([107, 27])\n", "torch.Size([35, 27])\n" ] } ] }, { "cell_type": "code", "source": [ "epochs_range = range(1, len(history_gcn['train_loss']) + 1)\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "\n", "# Plot Training Loss vs. Validation Loss\n", "axes[0].plot(epochs_range, history_gcn['train_loss'], label='Train Loss', marker='o', markersize=4, alpha=0.8)\n", "axes[0].axvline(best_epoch_gcn, color='red', linestyle='--', label=f'Best Epoch ({best_epoch_gcn})')\n", "axes[0].set_title('Loss — GCN')\n", "axes[0].set_xlabel('Epoch')\n", "axes[0].set_ylabel('Loss')\n", "axes[0].legend()\n", "axes[0].grid(True, linestyle='--', alpha=0.6)\n", "\n", "# Plot MAE & RMSE\n", "axes[1].plot(epochs_range, history_gcn['val_mae'], label='MAE', marker='s', markersize=4, color='orange', alpha=0.8)\n", "axes[1].plot(epochs_range, history_gcn['val_rmse'], label='RMSE', marker='s', markersize=4, color='purple', alpha=0.8)\n", "axes[1].axvline(best_epoch_gcn, color='red', linestyle='--')\n", "axes[1].set_title('MAE & RMSE — GCN')\n", "axes[1].set_xlabel('Epoch')\n", "axes[1].set_ylabel('Metric Value')\n", "axes[1].legend()\n", "axes[1].grid(True, linestyle='--', alpha=0.6)\n", "\n", "# Plot R²\n", "axes[2].plot(epochs_range, history_gcn['val_r2'], label='R²', marker='^', markersize=4, color='green', alpha=0.8)\n", "axes[2].axvline(best_epoch_gcn, color='red', linestyle='--')\n", "axes[2].set_title('R² — GCN')\n", "axes[2].set_xlabel('Epoch')\n", "axes[2].set_ylabel('R² Score')\n", "axes[2].legend()\n", "axes[2].grid(True, linestyle='--', alpha=0.6)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Save history to CSV\n", "history_df_gcn = pd.DataFrame(history_gcn, index=epochs_range)\n", "history_df_gcn.index.name = 'epoch'\n", "history_df_gcn.to_csv('training_history_gcn.csv')\n", "print(\"Training history saved to training_history_gcn.csv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 185 }, "id": "SvrTQmuqLkNp", "outputId": "14a92a09-9d73-483e-d44e-8cbb162852e4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Training history saved to training_history_gcn.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 6. Pre-processing Transformer Model" ], "metadata": { "id": "ozQbNk4CDWlA" } }, { "cell_type": "markdown", "source": [ "## 6.1 Split Data" ], "metadata": { "id": "CoDZhWgkAHbx" } }, { "cell_type": "code", "source": [ "# ── CELL: Split train/dev untuk transformer ───────────────────────────────────\n", "\n", "train_df = df[df['split'] == 'train'].copy().reset_index(drop=True)\n", "dev_df = df[df['split'] == 'dev'].copy().reset_index(drop=True)\n", "\n", "# Pastiin kolom extra ada (dibuat di section baseline, carry over ke sini)\n", "LENGTH_COLS = ['word_count', 'utterance_count', 'avg_utt_len', 'response_brevity']\n", "LEXICAL_COLS = ['neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "\n", "# Kalau kolom ini belum ada (misal baseline section di-skip), bikin ulang\n", "if 'avg_utt_len' not in train_df.columns:\n", " for d in [train_df, dev_df]:\n", " d['avg_utt_len'] = d['word_count'] / (d['utterance_count'] + 1)\n", " d['response_brevity'] = (d['utterance_count'] < 50).astype(int)\n", "\n", "if 'neg_ratio' not in train_df.columns:\n", " NEGATIVE_WORDS = {'hopeless','worthless','empty','tired','sad','lonely',\n", " 'fail','useless','burden','numb','anxious','hate'}\n", " POSITIVE_WORDS = {'happy','good','fine','great','better','enjoy',\n", " 'love','hope','okay','well','improving'}\n", " def lexical_features(text):\n", " words = set(str(text).lower().split())\n", " neg = len(words & NEGATIVE_WORDS)\n", " pos = len(words & POSITIVE_WORDS)\n", " total = len(words) + 1\n", " return {'neg_ratio': neg/total, 'pos_ratio': pos/total, 'sentiment_gap': (neg-pos)/total}\n", " for d in [train_df, dev_df]:\n", " lex = d['text'].apply(lexical_features).apply(pd.Series)\n", " d[LEXICAL_COLS] = lex.values\n", "\n", "NV_FEATURE_COLS = [f'nv_{tag}' for tag in NONVERBAL_TAGS] + ['nv_total', 'filler_count']\n", "ALL_EXTRA_COLS = NV_FEATURE_COLS + LENGTH_COLS + LEXICAL_COLS\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "extra_scaler = StandardScaler()\n", "nv_train = extra_scaler.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "nv_dev = extra_scaler.transform(dev_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "\n", "y_train = train_df['phq8_target'].values\n", "y_dev = dev_df['phq8_target'].values\n", "\n", "NV_SIZE = len(ALL_EXTRA_COLS)\n", "print(f\"Train: {len(train_df)} | Dev: {len(dev_df)}\")\n", "print(f\"NV feature size: {NV_SIZE}\")\n", "print(f\"Columns check: {ALL_EXTRA_COLS}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Y5WYY34AFpc", "outputId": "c70a7a4b-5b80-4a13-a1f3-6c1afe7b59d3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 107 | Dev: 35\n", "NV feature size: 18\n", "Columns check: ['nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'word_count', 'utterance_count', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 6.2 Text Cleaning" ], "metadata": { "id": "HJNBwNFPAJCY" } }, { "cell_type": "code", "source": [ "# ── CELL: clean_text_transformer ─────────────────────────────────────────────\n", "\n", "def normalize_slang(text, slang_dict):\n", " pattern = re.compile(\n", " r'\\b(' + '|'.join(map(re.escape, slang_dict.keys())) + r')\\b',\n", " re.IGNORECASE\n", " )\n", " return pattern.sub(lambda m: slang_dict.get(m.group().lower(), m.group()), text)\n", "\n", "def clean_text_transformer(text, slang_dict=None):\n", " text = str(text).lower()\n", " text = re.sub(r'<[^>]+>', ' ', text)\n", " text = re.sub(r'\\[.*?\\]', ' ', text)\n", " text = re.sub(r'\\(.*?\\)', ' ', text)\n", " text = re.sub(r'scrubbed_entry', ' ', text)\n", " if slang_dict:\n", " text = normalize_slang(text, slang_dict)\n", " text = re.sub(r\"[^a-z0-9\\s']\", ' ', text)\n", " text = re.sub(r\"(? inner:\n", " h = min(head, inner // 2)\n", " t = inner - h\n", " ids = ids[:h] + ids[-t:]\n", "\n", " ids = [bos] + ids + [eos]\n", " pad = max_len - len(ids)\n", " mask = [1] * len(ids) + [0] * pad\n", " ids = ids + [tokenizer.pad_token_id] * pad\n", "\n", " return ids, mask\n", "\n", "# Quick check kedua tokenizer\n", "for name, tok in [(\"BERT\", tokenizer_bert), (\"MPNet\", tokenizer_mpnet)]:\n", " ids, mask = tokenize_head_tail(train_df['text_bert'].iloc[0], tok)\n", " print(f\"[{name}] ids len: {len(ids)} | non-pad tokens: {sum(mask)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vY-jcEqfAg9l", "outputId": "638f4ec4-e896-46ca-be20-34b676b10081" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (928 > 512). Running this sequence through the model will result in indexing errors\n", "[transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (928 > 512). Running this sequence through the model will result in indexing errors\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[BERT] ids len: 512 | non-pad tokens: 512\n", "[MPNet] ids len: 512 | non-pad tokens: 512\n" ] } ] }, { "cell_type": "markdown", "source": [ "### MiniLM" ], "metadata": { "id": "b95y9x4gFRkp" } }, { "cell_type": "code", "source": [ "# ── CELL: Tokenizer MiniLM ────────────────────────────────────────────────────\n", "\n", "from transformers import AutoTokenizer\n", "\n", "MINILM_MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n", "tokenizer_minilm = AutoTokenizer.from_pretrained(MINILM_MODEL_NAME)\n", "\n", "sample = train_df['text_bert'].iloc[0]\n", "tokens = tokenizer_minilm(\n", " sample,\n", " truncation=True,\n", " padding=\"max_length\",\n", " max_length=512,\n", " return_tensors=\"pt\"\n", ")\n", "print(\"[MiniLM] input_ids shape :\", tokens['input_ids'].shape)\n", "print(\"[MiniLM] attention_mask shape:\", tokens['attention_mask'].shape)\n", "print(\"[MiniLM] CLS token id :\", tokens['input_ids'][0][0].item())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yo9Tgiw7FT4X", "outputId": "fc176599-0063-407d-a4f4-3617777fbeb8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM] input_ids shape : torch.Size([1, 512])\n", "[MiniLM] attention_mask shape: torch.Size([1, 512])\n", "[MiniLM] CLS token id : 101\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 7. Modeling" ], "metadata": { "id": "CVXmA4eaBT4a" } }, { "cell_type": "code", "source": [ "# ── CELL: Dataset Class ───────────────────────────────────────────────────────\n", "\n", "import torch\n", "from torch.utils.data import Dataset\n", "\n", "class DepressionDataset(Dataset):\n", " def __init__(self, texts, labels, nv_features, tokenizer, max_len=MAX_LEN):\n", " self.labels = labels\n", " self.nv = nv_features # shape (N, NV_SIZE), sudah di-scale\n", " self.tokenizer = tokenizer\n", " self.max_len = max_len\n", " self.data = []\n", "\n", " for text in texts:\n", " ids, mask = tokenize_head_tail(text, tokenizer, max_len)\n", " self.data.append({\n", " 'input_ids': torch.tensor(ids, dtype=torch.long),\n", " 'attention_mask': torch.tensor(mask, dtype=torch.long),\n", " })\n", "\n", " def __len__(self):\n", " return len(self.labels)\n", "\n", " def __getitem__(self, idx):\n", " return {\n", " 'input_ids': self.data[idx]['input_ids'],\n", " 'attention_mask': self.data[idx]['attention_mask'],\n", " 'nv_features': torch.tensor(self.nv[idx], dtype=torch.float),\n", " 'labels': torch.tensor(self.labels[idx], dtype=torch.float),\n", " }" ], "metadata": { "id": "s9FbwWvOBVfT" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 7.1 Data Loader BERT" ], "metadata": { "id": "BM_s88_xBiJZ" } }, { "cell_type": "code", "source": [ "# ── CELL: DataLoader BERT ─────────────────────────────────────────────────────\n", "\n", "from torch.utils.data import DataLoader\n", "\n", "BATCH_SIZE = 8\n", "\n", "train_dataset_bert = DepressionDataset(\n", " texts = train_df['text_bert'].tolist(),\n", " labels = y_train,\n", " nv_features = nv_train,\n", " tokenizer = tokenizer_bert,\n", ")\n", "dev_dataset_bert = DepressionDataset(\n", " texts = dev_df['text_bert'].tolist(),\n", " labels = y_dev,\n", " nv_features = nv_dev,\n", " tokenizer = tokenizer_bert,\n", ")\n", "\n", "train_loader_bert = DataLoader(train_dataset_bert, batch_size=BATCH_SIZE, shuffle=True)\n", "dev_loader_bert = DataLoader(dev_dataset_bert, batch_size=BATCH_SIZE, shuffle=False)\n", "\n", "print(f\"[BERT] Train batches: {len(train_loader_bert)} | Dev batches: {len(dev_loader_bert)}\")\n", "\n", "# Cek shape satu batch\n", "batch = next(iter(train_loader_bert))\n", "print(f\" input_ids: {batch['input_ids'].shape}\")\n", "print(f\" attention_mask: {batch['attention_mask'].shape}\")\n", "print(f\" nv_features: {batch['nv_features'].shape}\")\n", "print(f\" labels: {batch['labels'].shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JTy_uWuzBjex", "outputId": "49d6732c-d4e6-444b-e374-2546885890b1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT] Train batches: 14 | Dev batches: 5\n", " input_ids: torch.Size([8, 512])\n", " attention_mask: torch.Size([8, 512])\n", " nv_features: torch.Size([8, 18])\n", " labels: torch.Size([8])\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 7.2 Data Loader MPNet" ], "metadata": { "id": "Lpl9xI2WBkJO" } }, { "cell_type": "code", "source": [ "# ── CELL: DataLoader MPNet ────────────────────────────────────────────────────\n", "\n", "from torch.utils.data import DataLoader\n", "\n", "BATCH_SIZE = 8\n", "\n", "train_dataset_mpnet = DepressionDataset(\n", " texts = train_df['text_bert'].tolist(),\n", " labels = y_train,\n", " nv_features = nv_train,\n", " tokenizer = tokenizer_mpnet,\n", ")\n", "dev_dataset_mpnet = DepressionDataset(\n", " texts = dev_df['text_bert'].tolist(),\n", " labels = y_dev,\n", " nv_features = nv_dev,\n", " tokenizer = tokenizer_mpnet,\n", ")\n", "\n", "train_loader_mpnet = DataLoader(train_dataset_mpnet, batch_size=BATCH_SIZE, shuffle=True)\n", "dev_loader_mpnet = DataLoader(dev_dataset_mpnet, batch_size=BATCH_SIZE, shuffle=False)\n", "\n", "print(f\"[MPNet] Train batches: {len(train_loader_mpnet)} | Dev batches: {len(dev_loader_mpnet)}\")\n", "\n", "batch = next(iter(train_loader_mpnet))\n", "print(f\" input_ids: {batch['input_ids'].shape}\")\n", "print(f\" attention_mask: {batch['attention_mask'].shape}\")\n", "print(f\" nv_features: {batch['nv_features'].shape}\")\n", "print(f\" labels: {batch['labels'].shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "P9QHNPofBqPl", "outputId": "b5d0e071-479b-4f43-edef-21682fcfb45b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet] Train batches: 14 | Dev batches: 5\n", " input_ids: torch.Size([8, 512])\n", " attention_mask: torch.Size([8, 512])\n", " nv_features: torch.Size([8, 18])\n", " labels: torch.Size([8])\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 7.3 Data Loader MiniLM" ], "metadata": { "id": "Vk2fiQyYFfEY" } }, { "cell_type": "code", "source": [ "# ── CELL: DataLoader MiniLM ───────────────────────────────────────────────────\n", "\n", "from torch.utils.data import DataLoader\n", "\n", "BATCH_SIZE = 8\n", "\n", "train_dataset_minilm = DepressionDataset(\n", " texts = train_df['text_bert'].tolist(),\n", " labels = y_train,\n", " nv_features = nv_train,\n", " tokenizer = tokenizer_minilm,\n", ")\n", "dev_dataset_minilm = DepressionDataset(\n", " texts = dev_df['text_bert'].tolist(),\n", " labels = y_dev,\n", " nv_features = nv_dev,\n", " tokenizer = tokenizer_minilm,\n", ")\n", "\n", "train_loader_minilm = DataLoader(train_dataset_minilm, batch_size=BATCH_SIZE, shuffle=True)\n", "dev_loader_minilm = DataLoader(dev_dataset_minilm, batch_size=BATCH_SIZE, shuffle=False)\n", "\n", "print(f\"[MiniLM] Train batches: {len(train_loader_minilm)} | Dev batches: {len(dev_loader_minilm)}\")\n", "\n", "batch = next(iter(train_loader_minilm))\n", "print(f\" input_ids: {batch['input_ids'].shape}\")\n", "print(f\" attention_mask: {batch['attention_mask'].shape}\")\n", "print(f\" nv_features: {batch['nv_features'].shape}\")\n", "print(f\" labels: {batch['labels'].shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2QdY4KvIFjrc", "outputId": "050151e8-be8b-4ff0-c09f-ed4f53546530" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (928 > 512). Running this sequence through the model will result in indexing errors\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM] Train batches: 14 | Dev batches: 5\n", " input_ids: torch.Size([8, 512])\n", " attention_mask: torch.Size([8, 512])\n", " nv_features: torch.Size([8, 18])\n", " labels: torch.Size([8])\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 7.4 Model Definition" ], "metadata": { "id": "J_aMTTHMBvsU" } }, { "cell_type": "code", "source": [ "# ── CELL: Model Definition ────────────────────────────────────────────────────\n", "\n", "import torch.nn as nn\n", "from transformers import AutoModel\n", "\n", "class TransformerRegressor(nn.Module):\n", " \"\"\"\n", " Transformer encoder + regression head.\n", " head_type:\n", " 'linear' — CLS → Linear(1)\n", " 'mlp' — CLS → MLP → scalar\n", " 'attn' — weighted mean pool semua token → MLP → scalar\n", " nv_features digabung ke head input.\n", " \"\"\"\n", " def __init__(self, model_name, head_type='mlp', nv_size=NV_SIZE, dropout=0.1):\n", " super().__init__()\n", " self.encoder = AutoModel.from_pretrained(model_name)\n", " hidden = self.encoder.config.hidden_size\n", " self.head_type = head_type\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " in_features = hidden + nv_size\n", "\n", " if head_type == 'linear':\n", " self.head = nn.Linear(in_features, 1)\n", "\n", " elif head_type == 'mlp':\n", " self.head = nn.Sequential(\n", " nn.Linear(in_features, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(256, 64),\n", " nn.GELU(),\n", " nn.Linear(64, 1),\n", " )\n", "\n", " elif head_type == 'attn':\n", " # Belajar bobot tiap token sebelum pooling\n", " self.token_attn = nn.Linear(hidden, 1)\n", " self.head = nn.Sequential(\n", " nn.Linear(in_features, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(256, 1),\n", " )\n", "\n", " def forward(self, input_ids, attention_mask, nv_features):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)\n", " hidden = out.last_hidden_state # (B, L, H)\n", "\n", " if self.head_type == 'attn':\n", " # Attention pool atas semua token (bukan cuma CLS)\n", " scores = self.token_attn(hidden).squeeze(-1) # (B, L)\n", " scores = scores.masked_fill(attention_mask == 0, -1e9)\n", " weights = torch.softmax(scores, dim=1).unsqueeze(-1) # (B, L, 1)\n", " pooled = (hidden * weights).sum(dim=1) # (B, H)\n", " else:\n", " pooled = hidden[:, 0, :] # CLS token\n", "\n", " pooled = self.dropout(pooled)\n", " x = torch.cat([pooled, nv_features], dim=-1)\n", " return self.head(x).squeeze(-1)" ], "metadata": { "id": "Ogb08joAB0vg" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 7.5 Inisiate BERT" ], "metadata": { "id": "7C5NrOS6B2tf" } }, { "cell_type": "code", "source": [ "# ── CELL: Instantiate BERT Models ────────────────────────────────────────────\n", "\n", "model_bert_mlp = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'mlp',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "model_bert_attn = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'attn',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "model_bert_linear = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'linear',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "def count_params(m):\n", " return sum(p.numel() for p in m.parameters() if p.requires_grad)\n", "\n", "print(f\"[BERT MLP] trainable params: {count_params(model_bert_mlp):,}\")\n", "print(f\"[BERT Attn] trainable params: {count_params(model_bert_attn):,}\")\n", "print(f\"[BERT Linear] trainable params: {count_params(model_bert_linear):,}\")\n", "print(f\"\\nEncoder hidden size: {model_bert_mlp.encoder.config.hidden_size}\")\n", "print(model_bert_mlp)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b7fdc88463d1455593860596fb025d65", "121a8af2c53a456e964a868e790e61be", "599fb36ee9344a618a3031d3b4f0bd17", "238d8d81b6c74939a2c576551ce3b7d3", "3b6ab54876034477abdd8c061a064bee", "7260cc490af348d0bff1008d4b8cabf7", "81ca062d3a0f425284ebd23e4edeb643", "368a99f7bbaf43a3999901b4e3ea47d2", "bab7c0d8d646468f936acf7a94b30996", "3a42af2b70124b1ea1fb6122e54684ab", "2f5c664efcdd473cb78f0e706595bcd1", "7bfff7b4b42740e79c0bdd177bdfaf5b", "d142ba8ae9364468bb4043729f793680", "1cbb8be31afb4a5989f273ed0edd1066", "5343b0e71ec541d2876508615a9d676d", "01c7e46639554592a40b8fd3e7f242fc", "e909e0c48bf2459395aafafdd940d588", "d3a5d7536fed4b139a2e2145f491338b", "d4d77ae19e3e45c19f72b6fab47c82fd", "544a474818a441dcab4ac2e574bbbecc", "30a08f128521468d9bd7c2bad5367869", "993058f1da1a4d649ba622adf98ee41e", "1e33a2e95af945bb8e38f32eea2fe604", "b8d5bc481e5547e59251effebfa62b2c", "8137b3382f584040b285f1318ecd481e", "d97577b6c49a4142b257ead4b25158cc", "29a1df882ef4420093b0ac4ea86533ec", "42a8938df3854b478c1f2d5f6d788293", "ff2157d3a2c147d6a2e989701cd51a47", "83e2e2f11cd94e1aa832c3edf5e3d565", "1eff594801cb463abd7983f726f95259", "d53860d921c54172b5676faff95502a0", "8797314606f64e1c8b7f89cd30f8bb53" ] }, "id": "qQCc5h9pB6XH", "outputId": "bee57757-26fc-4453-dd62-99e891b8beea" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 345 }, "id": "a2G4nkXICP1T", "outputId": "a798b031-3db7-4bc6-d748-4e2d35648694" }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_23788/1119985505.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEPOCHS_TF\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mtr_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader_bert\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDEVICE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_loader_bert\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/tmp/ipykernel_23788/450440906.py\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, loader, optimizer, scheduler, device, loss_fn)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mtotal_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "markdown", "source": [ "## 8.2 BERT Attn" ], "metadata": { "id": "LjZK_A7PCSqQ" } }, { "cell_type": "code", "source": [ "# ── CELL: Train BERT + Attn ───────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_bert_attn.pt\"\n", "\n", "model = model_bert_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_bert) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_bert_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_bert, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_bert, DEVICE)\n", "\n", " history_bert_attn['train_loss'].append(tr_loss)\n", " history_bert_attn['val_mae'].append(metrics['mae'])\n", " history_bert_attn['val_rmse'].append(metrics['rmse'])\n", " history_bert_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[BERT-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LRr5VRWWCUkp", "outputId": "de8c5689-5fac-4fd5-e569-691ebec382ca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT-Attn] Ep 01/20 | Loss: 13.1076 | MAE: 5.6561 | RMSE: 7.6411 | R²: -0.3840\n", " ✅ Saved (epoch 1)\n", "[BERT-Attn] Ep 02/20 | Loss: 9.4665 | MAE: 5.5095 | RMSE: 7.3085 | R²: -0.2661\n", " ✅ Saved (epoch 2)\n", "[BERT-Attn] Ep 03/20 | Loss: 10.0801 | MAE: 5.4750 | RMSE: 6.9699 | R²: -0.1515\n", " ✅ Saved (epoch 3)\n", "[BERT-Attn] Ep 04/20 | Loss: 9.2671 | MAE: 5.4852 | RMSE: 6.9262 | R²: -0.1371\n", " ✅ Saved (epoch 4)\n", "[BERT-Attn] Ep 05/20 | Loss: 9.0958 | MAE: 5.4655 | RMSE: 6.9172 | R²: -0.1342\n", " ✅ Saved (epoch 5)\n", "[BERT-Attn] Ep 06/20 | Loss: 9.2013 | MAE: 5.4573 | RMSE: 7.0037 | R²: -0.1627\n", "[BERT-Attn] Ep 07/20 | Loss: 9.7616 | MAE: 5.4615 | RMSE: 7.1136 | R²: -0.1995\n", "[BERT-Attn] Ep 08/20 | Loss: 9.0789 | MAE: 5.4661 | RMSE: 6.8139 | R²: -0.1005\n", " ✅ Saved (epoch 8)\n", "[BERT-Attn] Ep 09/20 | Loss: 9.0335 | MAE: 5.3594 | RMSE: 6.8620 | R²: -0.1161\n", "[BERT-Attn] Ep 10/20 | Loss: 8.9311 | MAE: 5.3045 | RMSE: 6.8396 | R²: -0.1088\n", "[BERT-Attn] Ep 11/20 | Loss: 8.9504 | MAE: 5.3592 | RMSE: 6.6684 | R²: -0.0540\n", " ✅ Saved (epoch 11)\n", "[BERT-Attn] Ep 12/20 | Loss: 8.0654 | MAE: 5.2418 | RMSE: 6.6829 | R²: -0.0586\n", "[BERT-Attn] Ep 13/20 | Loss: 7.4423 | MAE: 5.1908 | RMSE: 6.7737 | R²: -0.0876\n", "[BERT-Attn] Ep 14/20 | Loss: 7.0828 | MAE: 5.2814 | RMSE: 7.1670 | R²: -0.2175\n", "[BERT-Attn] Ep 15/20 | Loss: 6.9949 | MAE: 5.3753 | RMSE: 6.8474 | R²: -0.1114\n", "[BERT-Attn] Ep 16/20 | Loss: 6.4702 | MAE: 5.4243 | RMSE: 6.8524 | R²: -0.1130\n", "[BERT-Attn] Ep 17/20 | Loss: 6.3050 | MAE: 5.4868 | RMSE: 6.8059 | R²: -0.0980\n", "[BERT-Attn] Ep 18/20 | Loss: 5.8155 | MAE: 5.4229 | RMSE: 6.9186 | R²: -0.1346\n", " Early stopping epoch 18\n", "Best epoch: 11 | Best MSE: 44.4680\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.3 BERT Linear" ], "metadata": { "id": "Uup02-DKCXG7" } }, { "cell_type": "code", "source": [ "# ── CELL: Train BERT + Linear ─────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_bert_linear.pt\"\n", "\n", "model = model_bert_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_bert) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_bert_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_bert, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_bert, DEVICE)\n", "\n", " history_bert_linear['train_loss'].append(tr_loss)\n", " history_bert_linear['val_mae'].append(metrics['mae'])\n", " history_bert_linear['val_rmse'].append(metrics['rmse'])\n", " history_bert_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[BERT-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6qzkAC7qCddc", "outputId": "a792e10a-a552-40d9-a9ae-1989edf24e2b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT-Lin] Ep 01/20 | Loss: 13.7975 | MAE: 5.6904 | RMSE: 7.8305 | R²: -0.4534\n", " ✅ Saved (epoch 1)\n", "[BERT-Lin] Ep 02/20 | Loss: 9.9173 | MAE: 5.4901 | RMSE: 6.6530 | R²: -0.0492\n", " ✅ Saved (epoch 2)\n", "[BERT-Lin] Ep 03/20 | Loss: 9.2854 | MAE: 5.4706 | RMSE: 7.0815 | R²: -0.1887\n", "[BERT-Lin] Ep 04/20 | Loss: 9.5571 | MAE: 5.4936 | RMSE: 6.6290 | R²: -0.0416\n", " ✅ Saved (epoch 4)\n", "[BERT-Lin] Ep 05/20 | Loss: 9.1200 | MAE: 5.4676 | RMSE: 7.0652 | R²: -0.1832\n", "[BERT-Lin] Ep 06/20 | Loss: 9.8015 | MAE: 5.4843 | RMSE: 6.5608 | R²: -0.0203\n", " ✅ Saved (epoch 6)\n", "[BERT-Lin] Ep 07/20 | Loss: 9.3973 | MAE: 5.4778 | RMSE: 6.7020 | R²: -0.0647\n", "[BERT-Lin] Ep 08/20 | Loss: 8.6733 | MAE: 5.4527 | RMSE: 6.7332 | R²: -0.0746\n", "[BERT-Lin] Ep 09/20 | Loss: 7.3596 | MAE: 5.7488 | RMSE: 7.6653 | R²: -0.3927\n", "[BERT-Lin] Ep 10/20 | Loss: 6.4353 | MAE: 5.7444 | RMSE: 6.7294 | R²: -0.0734\n", "[BERT-Lin] Ep 11/20 | Loss: 5.7332 | MAE: 5.0542 | RMSE: 6.8191 | R²: -0.1022\n", "[BERT-Lin] Ep 12/20 | Loss: 4.1695 | MAE: 4.9854 | RMSE: 6.3415 | R²: 0.0468\n", " ✅ Saved (epoch 12)\n", "[BERT-Lin] Ep 13/20 | Loss: 3.6465 | MAE: 5.4932 | RMSE: 7.7772 | R²: -0.4337\n", "[BERT-Lin] Ep 14/20 | Loss: 3.6767 | MAE: 4.7533 | RMSE: 6.2750 | R²: 0.0667\n", " ✅ Saved (epoch 14)\n", "[BERT-Lin] Ep 15/20 | Loss: 3.3533 | MAE: 4.7473 | RMSE: 6.1033 | R²: 0.1170\n", " ✅ Saved (epoch 15)\n", "[BERT-Lin] Ep 16/20 | Loss: 3.0074 | MAE: 4.7842 | RMSE: 6.0946 | R²: 0.1195\n", " ✅ Saved (epoch 16)\n", "[BERT-Lin] Ep 17/20 | Loss: 2.6339 | MAE: 4.6768 | RMSE: 6.3421 | R²: 0.0466\n", "[BERT-Lin] Ep 18/20 | Loss: 2.6619 | MAE: 4.6951 | RMSE: 6.2502 | R²: 0.0740\n", "[BERT-Lin] Ep 19/20 | Loss: 2.7600 | MAE: 4.6871 | RMSE: 6.3850 | R²: 0.0337\n", "[BERT-Lin] Ep 20/20 | Loss: 3.2858 | MAE: 4.6855 | RMSE: 6.3419 | R²: 0.0466\n", "Best epoch: 16 | Best MSE: 37.1447\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.4 MPNet MLP" ], "metadata": { "id": "6WbScWcICexB" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + MLP ───────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_mlp.pt\"\n", "\n", "model = model_mpnet_mlp\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_mlp = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_mlp['train_loss'].append(tr_loss)\n", " history_mpnet_mlp['val_mae'].append(metrics['mae'])\n", " history_mpnet_mlp['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_mlp['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-MLP] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hyLh5M93CjeC", "outputId": "5b3c3ff9-69bb-4bd9-b4ab-001c3971a54d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-MLP] Ep 01/20 | Loss: 15.4173 | MAE: 6.8488 | RMSE: 9.2865 | R²: -1.0442\n", " ✅ Saved (epoch 1)\n", "[MPNet-MLP] Ep 02/20 | Loss: 13.6117 | MAE: 6.1534 | RMSE: 8.5040 | R²: -0.7142\n", " ✅ Saved (epoch 2)\n", "[MPNet-MLP] Ep 03/20 | Loss: 10.8982 | MAE: 5.7902 | RMSE: 8.0037 | R²: -0.5184\n", " ✅ Saved (epoch 3)\n", "[MPNet-MLP] Ep 04/20 | Loss: 10.3227 | MAE: 5.6525 | RMSE: 7.6617 | R²: -0.3914\n", " ✅ Saved (epoch 4)\n", "[MPNet-MLP] Ep 05/20 | Loss: 9.7791 | MAE: 5.5700 | RMSE: 7.4405 | R²: -0.3123\n", " ✅ Saved (epoch 5)\n", "[MPNet-MLP] Ep 06/20 | Loss: 9.6639 | MAE: 5.5582 | RMSE: 7.2738 | R²: -0.2541\n", " ✅ Saved (epoch 6)\n", "[MPNet-MLP] Ep 07/20 | Loss: 9.3450 | MAE: 5.5388 | RMSE: 7.1718 | R²: -0.2192\n", " ✅ Saved (epoch 7)\n", "[MPNet-MLP] Ep 08/20 | Loss: 9.1953 | MAE: 5.5272 | RMSE: 7.1496 | R²: -0.2117\n", " ✅ Saved (epoch 8)\n", "[MPNet-MLP] Ep 09/20 | Loss: 9.2097 | MAE: 5.4480 | RMSE: 7.0855 | R²: -0.1900\n", " ✅ Saved (epoch 9)\n", "[MPNet-MLP] Ep 10/20 | Loss: 8.7609 | MAE: 5.2925 | RMSE: 6.8712 | R²: -0.1191\n", " ✅ Saved (epoch 10)\n", "[MPNet-MLP] Ep 11/20 | Loss: 7.5642 | MAE: 5.1195 | RMSE: 6.8194 | R²: -0.1023\n", " ✅ Saved (epoch 11)\n", "[MPNet-MLP] Ep 12/20 | Loss: 7.6355 | MAE: 5.0965 | RMSE: 6.8391 | R²: -0.1087\n", "[MPNet-MLP] Ep 13/20 | Loss: 7.3855 | MAE: 5.1097 | RMSE: 6.6452 | R²: -0.0467\n", " ✅ Saved (epoch 13)\n", "[MPNet-MLP] Ep 14/20 | Loss: 7.1569 | MAE: 5.2026 | RMSE: 6.6011 | R²: -0.0329\n", " ✅ Saved (epoch 14)\n", "[MPNet-MLP] Ep 15/20 | Loss: 6.3654 | MAE: 5.2529 | RMSE: 6.5867 | R²: -0.0284\n", " ✅ Saved (epoch 15)\n", "[MPNet-MLP] Ep 16/20 | Loss: 6.2147 | MAE: 4.8788 | RMSE: 6.4651 | R²: 0.0092\n", " ✅ Saved (epoch 16)\n", "[MPNet-MLP] Ep 17/20 | Loss: 6.1935 | MAE: 5.0155 | RMSE: 6.4753 | R²: 0.0061\n", "[MPNet-MLP] Ep 18/20 | Loss: 6.4584 | MAE: 4.9271 | RMSE: 6.4375 | R²: 0.0177\n", " ✅ Saved (epoch 18)\n", "[MPNet-MLP] Ep 19/20 | Loss: 6.3179 | MAE: 4.9708 | RMSE: 6.4499 | R²: 0.0139\n", "[MPNet-MLP] Ep 20/20 | Loss: 6.1787 | MAE: 4.9595 | RMSE: 6.4466 | R²: 0.0149\n", "Best epoch: 18 | Best MSE: 41.4412\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.5 MPNet Attn" ], "metadata": { "id": "2Oc36f9hCm7q" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xOpqqDElCptk", "outputId": "80e8d44f-6f34-451a-e021-17f01bb94620" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Attn] Ep 01/20 | Loss: 14.1513 | MAE: 5.9861 | RMSE: 8.3324 | R²: -0.6457\n", " ✅ Saved (epoch 1)\n", "[MPNet-Attn] Ep 02/20 | Loss: 10.4418 | MAE: 5.6609 | RMSE: 7.4468 | R²: -0.3145\n", " ✅ Saved (epoch 2)\n", "[MPNet-Attn] Ep 03/20 | Loss: 9.3593 | MAE: 5.6050 | RMSE: 7.2275 | R²: -0.2382\n", " ✅ Saved (epoch 3)\n", "[MPNet-Attn] Ep 04/20 | Loss: 9.5098 | MAE: 5.5596 | RMSE: 7.2310 | R²: -0.2394\n", "[MPNet-Attn] Ep 05/20 | Loss: 9.1264 | MAE: 5.4631 | RMSE: 7.0019 | R²: -0.1621\n", " ✅ Saved (epoch 5)\n", "[MPNet-Attn] Ep 06/20 | Loss: 8.3093 | MAE: 5.1073 | RMSE: 7.2729 | R²: -0.2538\n", "[MPNet-Attn] Ep 07/20 | Loss: 6.9213 | MAE: 5.1072 | RMSE: 6.7137 | R²: -0.0684\n", " ✅ Saved (epoch 7)\n", "[MPNet-Attn] Ep 08/20 | Loss: 6.3202 | MAE: 5.0374 | RMSE: 6.6316 | R²: -0.0424\n", " ✅ Saved (epoch 8)\n", "[MPNet-Attn] Ep 09/20 | Loss: 6.0526 | MAE: 5.1318 | RMSE: 7.1771 | R²: -0.2210\n", "[MPNet-Attn] Ep 10/20 | Loss: 5.4939 | MAE: 4.9005 | RMSE: 6.7235 | R²: -0.0715\n", "[MPNet-Attn] Ep 11/20 | Loss: 5.2951 | MAE: 4.8738 | RMSE: 6.6495 | R²: -0.0481\n", "[MPNet-Attn] Ep 12/20 | Loss: 4.9208 | MAE: 4.7648 | RMSE: 6.5548 | R²: -0.0184\n", " ✅ Saved (epoch 12)\n", "[MPNet-Attn] Ep 13/20 | Loss: 5.1644 | MAE: 4.6495 | RMSE: 6.3551 | R²: 0.0427\n", " ✅ Saved (epoch 13)\n", "[MPNet-Attn] Ep 14/20 | Loss: 4.3820 | MAE: 4.6261 | RMSE: 6.4032 | R²: 0.0281\n", "[MPNet-Attn] Ep 15/20 | Loss: 4.2711 | MAE: 4.7504 | RMSE: 6.2403 | R²: 0.0770\n", " ✅ Saved (epoch 15)\n", "[MPNet-Attn] Ep 16/20 | Loss: 4.1884 | MAE: 4.6491 | RMSE: 6.2341 | R²: 0.0788\n", " ✅ Saved (epoch 16)\n", "[MPNet-Attn] Ep 17/20 | Loss: 4.3819 | MAE: 4.5364 | RMSE: 6.3738 | R²: 0.0370\n", "[MPNet-Attn] Ep 18/20 | Loss: 4.6374 | MAE: 4.5777 | RMSE: 6.2623 | R²: 0.0704\n", "[MPNet-Attn] Ep 19/20 | Loss: 4.6382 | MAE: 4.5927 | RMSE: 6.2412 | R²: 0.0767\n", "[MPNet-Attn] Ep 20/20 | Loss: 4.4754 | MAE: 4.5940 | RMSE: 6.2394 | R²: 0.0772\n", "Best epoch: 16 | Best MSE: 38.8634\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 35\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t7_InX1YRcCq", "outputId": "d3033615-b378-4c3b-e86b-972bda8b2b86" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Attn] Ep 01/35 | Loss: 4.0665 | MAE: 4.5709 | RMSE: 6.1834 | R²: 0.0937\n", " ✅ Saved (epoch 1)\n", "[MPNet-Attn] Ep 02/35 | Loss: 3.9874 | MAE: 4.6293 | RMSE: 6.0513 | R²: 0.1320\n", " ✅ Saved (epoch 2)\n", "[MPNet-Attn] Ep 03/35 | Loss: 4.0860 | MAE: 4.6451 | RMSE: 5.9864 | R²: 0.1505\n", " ✅ Saved (epoch 3)\n", "[MPNet-Attn] Ep 04/35 | Loss: 3.7560 | MAE: 4.4960 | RMSE: 6.3831 | R²: 0.0342\n", "[MPNet-Attn] Ep 05/35 | Loss: 4.3299 | MAE: 4.7105 | RMSE: 6.1615 | R²: 0.1001\n", "[MPNet-Attn] Ep 06/35 | Loss: 3.7349 | MAE: 4.4975 | RMSE: 5.8318 | R²: 0.1938\n", " ✅ Saved (epoch 6)\n", "[MPNet-Attn] Ep 07/35 | Loss: 3.6111 | MAE: 4.4156 | RMSE: 6.1222 | R²: 0.1116\n", "[MPNet-Attn] Ep 08/35 | Loss: 3.3290 | MAE: 4.6306 | RMSE: 6.5524 | R²: -0.0177\n", "[MPNet-Attn] Ep 09/35 | Loss: 3.2196 | MAE: 4.2107 | RMSE: 5.6247 | R²: 0.2501\n", " ✅ Saved (epoch 9)\n", "[MPNet-Attn] Ep 10/35 | Loss: 3.0814 | MAE: 4.2159 | RMSE: 5.8189 | R²: 0.1974\n", "[MPNet-Attn] Ep 11/35 | Loss: 3.2135 | MAE: 4.1161 | RMSE: 5.6859 | R²: 0.2337\n", "[MPNet-Attn] Ep 12/35 | Loss: 2.9465 | MAE: 4.1310 | RMSE: 5.4887 | R²: 0.2859\n", " ✅ Saved (epoch 12)\n", "[MPNet-Attn] Ep 13/35 | Loss: 2.4240 | MAE: 4.2650 | RMSE: 5.5337 | R²: 0.2742\n", "[MPNet-Attn] Ep 14/35 | Loss: 2.3282 | MAE: 4.3893 | RMSE: 5.5736 | R²: 0.2637\n", "[MPNet-Attn] Ep 15/35 | Loss: 2.0639 | MAE: 4.2656 | RMSE: 5.6752 | R²: 0.2366\n", "[MPNet-Attn] Ep 16/35 | Loss: 1.9674 | MAE: 4.3419 | RMSE: 5.5516 | R²: 0.2694\n", "[MPNet-Attn] Ep 17/35 | Loss: 2.2346 | MAE: 4.1913 | RMSE: 5.6050 | R²: 0.2553\n", "[MPNet-Attn] Ep 18/35 | Loss: 2.0205 | MAE: 4.1673 | RMSE: 5.6637 | R²: 0.2397\n", "[MPNet-Attn] Ep 19/35 | Loss: 1.9343 | MAE: 4.2988 | RMSE: 5.6059 | R²: 0.2551\n", " Early stopping epoch 19\n", "Best epoch: 12 | Best MSE: 30.1253\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 35\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JNxck9LcTRT-", "outputId": "eaeffffa-2192-46d1-aef0-b412f3103666" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Attn] Ep 01/35 | Loss: 1.9564 | MAE: 4.1842 | RMSE: 5.5263 | R²: 0.2761\n", " ✅ Saved (epoch 1)\n", "[MPNet-Attn] Ep 02/35 | Loss: 1.6960 | MAE: 4.2419 | RMSE: 5.5978 | R²: 0.2572\n", "[MPNet-Attn] Ep 03/35 | Loss: 1.6080 | MAE: 4.2845 | RMSE: 5.6463 | R²: 0.2443\n", "[MPNet-Attn] Ep 04/35 | Loss: 1.6767 | MAE: 4.2495 | RMSE: 5.6054 | R²: 0.2552\n", "[MPNet-Attn] Ep 05/35 | Loss: 1.5503 | MAE: 4.3124 | RMSE: 5.6889 | R²: 0.2329\n", "[MPNet-Attn] Ep 06/35 | Loss: 1.6376 | MAE: 4.0964 | RMSE: 5.3180 | R²: 0.3296\n", " ✅ Saved (epoch 6)\n", "[MPNet-Attn] Ep 07/35 | Loss: 1.7667 | MAE: 4.3226 | RMSE: 5.5254 | R²: 0.2763\n", "[MPNet-Attn] Ep 08/35 | Loss: 1.6526 | MAE: 4.1940 | RMSE: 5.4926 | R²: 0.2849\n", "[MPNet-Attn] Ep 09/35 | Loss: 1.3705 | MAE: 4.3609 | RMSE: 5.6432 | R²: 0.2452\n", "[MPNet-Attn] Ep 10/35 | Loss: 1.3278 | MAE: 4.4806 | RMSE: 5.8994 | R²: 0.1750\n", "[MPNet-Attn] Ep 11/35 | Loss: 1.2774 | MAE: 4.3279 | RMSE: 5.4689 | R²: 0.2910\n", "[MPNet-Attn] Ep 12/35 | Loss: 1.2606 | MAE: 4.4014 | RMSE: 5.5643 | R²: 0.2661\n", "[MPNet-Attn] Ep 13/35 | Loss: 1.3467 | MAE: 4.3065 | RMSE: 5.5024 | R²: 0.2823\n", " Early stopping epoch 13\n", "Best epoch: 6 | Best MSE: 28.2812\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.6 MPNet Linear" ], "metadata": { "id": "PE5flHWDCpGO" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ApHFuSQ1Cvvx", "outputId": "13320c12-26f4-4ae1-aeca-dad9cf919e1a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/20 | Loss: 15.8707 | MAE: 7.0251 | RMSE: 9.4458 | R²: -1.1149\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/20 | Loss: 13.8954 | MAE: 6.3127 | RMSE: 8.6907 | R²: -0.7903\n", " ✅ Saved (epoch 2)\n", "[MPNet-Lin] Ep 03/20 | Loss: 11.7451 | MAE: 5.9891 | RMSE: 8.2932 | R²: -0.6303\n", " ✅ Saved (epoch 3)\n", "[MPNet-Lin] Ep 04/20 | Loss: 11.7000 | MAE: 5.8378 | RMSE: 8.0813 | R²: -0.5480\n", " ✅ Saved (epoch 4)\n", "[MPNet-Lin] Ep 05/20 | Loss: 10.8954 | MAE: 5.7119 | RMSE: 7.9109 | R²: -0.4834\n", " ✅ Saved (epoch 5)\n", "[MPNet-Lin] Ep 06/20 | Loss: 10.3758 | MAE: 5.6585 | RMSE: 7.7770 | R²: -0.4336\n", " ✅ Saved (epoch 6)\n", "[MPNet-Lin] Ep 07/20 | Loss: 10.6218 | MAE: 5.6164 | RMSE: 7.6651 | R²: -0.3927\n", " ✅ Saved (epoch 7)\n", "[MPNet-Lin] Ep 08/20 | Loss: 10.7724 | MAE: 5.5884 | RMSE: 7.5917 | R²: -0.3661\n", " ✅ Saved (epoch 8)\n", "[MPNet-Lin] Ep 09/20 | Loss: 10.2815 | MAE: 5.5586 | RMSE: 7.5157 | R²: -0.3389\n", " ✅ Saved (epoch 9)\n", "[MPNet-Lin] Ep 10/20 | Loss: 9.7618 | MAE: 5.5347 | RMSE: 7.4568 | R²: -0.3180\n", " ✅ Saved (epoch 10)\n", "[MPNet-Lin] Ep 11/20 | Loss: 10.1253 | MAE: 5.5189 | RMSE: 7.4190 | R²: -0.3047\n", " ✅ Saved (epoch 11)\n", "[MPNet-Lin] Ep 12/20 | Loss: 9.9847 | MAE: 5.5060 | RMSE: 7.3841 | R²: -0.2925\n", " ✅ Saved (epoch 12)\n", "[MPNet-Lin] Ep 13/20 | Loss: 10.2070 | MAE: 5.4971 | RMSE: 7.3560 | R²: -0.2826\n", " ✅ Saved (epoch 13)\n", "[MPNet-Lin] Ep 14/20 | Loss: 9.8208 | MAE: 5.4897 | RMSE: 7.3319 | R²: -0.2742\n", " ✅ Saved (epoch 14)\n", "[MPNet-Lin] Ep 15/20 | Loss: 10.2326 | MAE: 5.4868 | RMSE: 7.3191 | R²: -0.2698\n", " ✅ Saved (epoch 15)\n", "[MPNet-Lin] Ep 16/20 | Loss: 9.7495 | MAE: 5.4858 | RMSE: 7.3145 | R²: -0.2682\n", " ✅ Saved (epoch 16)\n", "[MPNet-Lin] Ep 17/20 | Loss: 9.5602 | MAE: 5.4847 | RMSE: 7.3085 | R²: -0.2661\n", " ✅ Saved (epoch 17)\n", "[MPNet-Lin] Ep 18/20 | Loss: 9.7387 | MAE: 5.4851 | RMSE: 7.3076 | R²: -0.2658\n", " ✅ Saved (epoch 18)\n", "[MPNet-Lin] Ep 19/20 | Loss: 9.4710 | MAE: 5.4847 | RMSE: 7.3055 | R²: -0.2651\n", " ✅ Saved (epoch 19)\n", "[MPNet-Lin] Ep 20/20 | Loss: 10.3428 | MAE: 5.4846 | RMSE: 7.3051 | R²: -0.2649\n", " ✅ Saved (epoch 20)\n", "Best epoch: 20 | Best MSE: 53.3646\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "frpTg7T6VWtF", "outputId": "698ac36e-d9fd-4a6e-a788-0dab7103dbed" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/20 | Loss: 4.6994 | MAE: 5.2016 | RMSE: 7.0877 | R²: -0.1907\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/20 | Loss: 4.6164 | MAE: 5.0909 | RMSE: 6.9341 | R²: -0.1397\n", " ✅ Saved (epoch 2)\n", "[MPNet-Lin] Ep 03/20 | Loss: 4.2127 | MAE: 5.3394 | RMSE: 7.3259 | R²: -0.2721\n", "[MPNet-Lin] Ep 04/20 | Loss: 4.5884 | MAE: 5.1297 | RMSE: 6.9579 | R²: -0.1475\n", "[MPNet-Lin] Ep 05/20 | Loss: 4.5195 | MAE: 5.1132 | RMSE: 6.8753 | R²: -0.1205\n", " ✅ Saved (epoch 5)\n", "[MPNet-Lin] Ep 06/20 | Loss: 3.9080 | MAE: 4.9806 | RMSE: 6.4383 | R²: 0.0174\n", " ✅ Saved (epoch 6)\n", "[MPNet-Lin] Ep 07/20 | Loss: 4.2840 | MAE: 5.0947 | RMSE: 6.8834 | R²: -0.1231\n", "[MPNet-Lin] Ep 08/20 | Loss: 4.5836 | MAE: 5.0285 | RMSE: 6.7535 | R²: -0.0811\n", "[MPNet-Lin] Ep 09/20 | Loss: 3.6392 | MAE: 5.0045 | RMSE: 6.6685 | R²: -0.0541\n", "[MPNet-Lin] Ep 10/20 | Loss: 3.5563 | MAE: 5.0163 | RMSE: 6.7870 | R²: -0.0919\n", "[MPNet-Lin] Ep 11/20 | Loss: 3.3936 | MAE: 5.0025 | RMSE: 6.4396 | R²: 0.0171\n", "[MPNet-Lin] Ep 12/20 | Loss: 3.3596 | MAE: 4.9557 | RMSE: 6.6041 | R²: -0.0338\n", "[MPNet-Lin] Ep 13/20 | Loss: 3.3306 | MAE: 5.0146 | RMSE: 6.6348 | R²: -0.0434\n", " Early stopping epoch 13\n", "Best epoch: 6 | Best MSE: 41.4517\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 40\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dT9BTADtTqHb", "outputId": "e49eaeee-5d97-4c2d-811d-db196e4ff3d3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/40 | Loss: 10.1257 | MAE: 5.4835 | RMSE: 7.2984 | R²: -0.2626\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/40 | Loss: 9.4538 | MAE: 5.4819 | RMSE: 7.2772 | R²: -0.2553\n", " ✅ Saved (epoch 2)\n", "[MPNet-Lin] Ep 03/40 | Loss: 9.5258 | MAE: 5.4740 | RMSE: 7.2620 | R²: -0.2500\n", " ✅ Saved (epoch 3)\n", "[MPNet-Lin] Ep 04/40 | Loss: 9.3209 | MAE: 5.3897 | RMSE: 7.3633 | R²: -0.2852\n", "[MPNet-Lin] Ep 05/40 | Loss: 9.1453 | MAE: 5.4075 | RMSE: 7.1503 | R²: -0.2119\n", " ✅ Saved (epoch 5)\n", "[MPNet-Lin] Ep 06/40 | Loss: 8.7849 | MAE: 5.3074 | RMSE: 7.0615 | R²: -0.1820\n", " ✅ Saved (epoch 6)\n", "[MPNet-Lin] Ep 07/40 | Loss: 8.3618 | MAE: 5.1668 | RMSE: 6.9971 | R²: -0.1605\n", " ✅ Saved (epoch 7)\n", "[MPNet-Lin] Ep 08/40 | Loss: 7.7231 | MAE: 5.3969 | RMSE: 7.2130 | R²: -0.2332\n", "[MPNet-Lin] Ep 09/40 | Loss: 7.4031 | MAE: 5.5344 | RMSE: 7.4269 | R²: -0.3075\n", "[MPNet-Lin] Ep 10/40 | Loss: 6.5324 | MAE: 5.6053 | RMSE: 7.4975 | R²: -0.3324\n", "[MPNet-Lin] Ep 11/40 | Loss: 6.4039 | MAE: 5.4003 | RMSE: 7.1151 | R²: -0.2000\n", "[MPNet-Lin] Ep 12/40 | Loss: 6.1688 | MAE: 5.1237 | RMSE: 6.7185 | R²: -0.0699\n", " ✅ Saved (epoch 12)\n", "[MPNet-Lin] Ep 13/40 | Loss: 6.1448 | MAE: 5.2975 | RMSE: 6.5642 | R²: -0.0213\n", " ✅ Saved (epoch 13)\n", "[MPNet-Lin] Ep 14/40 | Loss: 5.7310 | MAE: 5.3695 | RMSE: 6.9988 | R²: -0.1611\n", "[MPNet-Lin] Ep 15/40 | Loss: 5.5887 | MAE: 5.1678 | RMSE: 6.6078 | R²: -0.0350\n", "[MPNet-Lin] Ep 16/40 | Loss: 5.3204 | MAE: 5.1951 | RMSE: 6.7964 | R²: -0.0949\n", "[MPNet-Lin] Ep 17/40 | Loss: 5.2083 | MAE: 5.3900 | RMSE: 7.2128 | R²: -0.2332\n", "[MPNet-Lin] Ep 18/40 | Loss: 4.8174 | MAE: 5.5490 | RMSE: 7.4718 | R²: -0.3233\n", "[MPNet-Lin] Ep 19/40 | Loss: 4.8778 | MAE: 5.1006 | RMSE: 6.9450 | R²: -0.1433\n", "[MPNet-Lin] Ep 20/40 | Loss: 4.5415 | MAE: 5.1822 | RMSE: 6.6292 | R²: -0.0417\n", " Early stopping epoch 20\n", "Best epoch: 13 | Best MSE: 43.0884\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.7 XGBOOST with BERT" ], "metadata": { "id": "HJflsvpYCweW" } }, { "cell_type": "code", "source": [ "# ── CELL: XGBoost on Frozen BERT Embeddings ───────────────────────────────────\n", "\n", "import joblib\n", "from xgboost import XGBRegressor\n", "from transformers import AutoModel\n", "\n", "@torch.no_grad()\n", "def extract_cls_embeddings(loader, model_name, device):\n", " encoder = AutoModel.from_pretrained(model_name).to(device)\n", " encoder.eval()\n", " embs, labels = [], []\n", " for batch in loader:\n", " ids = batch['input_ids'].to(device)\n", " mask = batch['attention_mask'].to(device)\n", " out = encoder(input_ids=ids, attention_mask=mask)\n", " cls = out.last_hidden_state[:, 0, :].cpu().numpy()\n", " nv = batch['nv_features'].numpy()\n", " embs.append(np.hstack([cls, nv]))\n", " labels.append(batch['labels'].numpy())\n", " del encoder\n", " return np.concatenate(embs), np.concatenate(labels)\n", "\n", "print(\"Extracting BERT embeddings...\")\n", "X_train_xgb_bert, y_train_xgb = extract_cls_embeddings(train_loader_bert, \"bert-base-uncased\", DEVICE)\n", "X_dev_xgb_bert, y_dev_xgb = extract_cls_embeddings(dev_loader_bert, \"bert-base-uncased\", DEVICE)\n", "print(f\"Train: {X_train_xgb_bert.shape} | Dev: {X_dev_xgb_bert.shape}\")\n", "\n", "xgb_bert = XGBRegressor(\n", " n_estimators=300, max_depth=4, learning_rate=0.05,\n", " subsample=0.8, colsample_bytree=0.8,\n", " reg_alpha=1.0, reg_lambda=2.0,\n", " random_state=SEED, n_jobs=-1\n", ")\n", "xgb_bert.fit(X_train_xgb_bert, y_train_xgb,\n", " eval_set=[(X_dev_xgb_bert, y_dev_xgb)], verbose=50)\n", "\n", "preds = np.clip(xgb_bert.predict(X_dev_xgb_bert), 0, 27)\n", "print(f\"\\n[XGB-BERT] MAE: {mean_absolute_error(y_dev_xgb, preds):.4f} | \"\n", " f\"RMSE: {mean_squared_error(y_dev_xgb, preds)**0.5:.4f} | \"\n", " f\"R²: {r2_score(y_dev_xgb, preds):.4f}\")\n", "\n", "joblib.dump(xgb_bert, \"best_xgb_bert.pkl\")\n", "print(\"Saved: best_xgb_bert.pkl\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 780, "referenced_widgets": [ "e535fa1057044bdca9a902e03c36f1aa", "370b091db53744b19d6ed87e7031d8a4", "0bca46a07e124ca588294052dfd09e9a", "e4acc782baef42af8b707030d4a7f9d6", "280aff8760f24e058a5f91720ff56b4d", "47510542985a4c029fd476bb22c2e4c3", "716672a27e774c34baefc98750b3dd85", "abad2c605c37441e9a62a1b53249fc3c", "df1c5460a60f4ac2b70a851d64322e3f", "edd8ece504454be6a194b65c008d3283", "0b3831a4f78b4c90b4f4cffb45eb0a4e", "824b572d37284b96bebe7ba679af9f68", "845222cc81724a888967af20af091cb6", "22448ee8bcf14a2d9fc04bf93b19edd3", "8040e587fd444b23a0ffc74023c39cfe", "1cb05a936e3441ab8f71edfc180a7ab6", "ca5e66967e564c63bc88d07b6d8b1d63", "a722985e957243d9b1a76ded8823530c", "2e908da1909440e9ba74c0b01c79f30e", "ca9e791240654d75b2e72e7440c9cef4", "2275b145a55c4a859d0cf142d5328da9", "546171091aea43b0873e0dc890396dff" ] }, "id": "85FcqX1DC0Li", "outputId": "fd49db7c-0861-4620-a4fb-b1538cc4e655" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Extracting BERT embeddings...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fT7RJbxYGFJl", "outputId": "3864dad1-caca-4fa6-acb8-a3e604fcee25" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-MLP] Ep 01/20 | Loss: 15.2035 | MAE: 7.1227 | RMSE: 9.5541 | R²: -1.1637\n", " ✅ Saved (epoch 1)\n", "[MiniLM-MLP] Ep 02/20 | Loss: 14.6265 | MAE: 6.3820 | RMSE: 8.7736 | R²: -0.8246\n", " ✅ Saved (epoch 2)\n", "[MiniLM-MLP] Ep 03/20 | Loss: 11.8044 | MAE: 5.9298 | RMSE: 8.1991 | R²: -0.5935\n", " ✅ Saved (epoch 3)\n", "[MiniLM-MLP] Ep 04/20 | Loss: 11.3852 | MAE: 5.7025 | RMSE: 7.8440 | R²: -0.4585\n", " ✅ Saved (epoch 4)\n", "[MiniLM-MLP] Ep 05/20 | Loss: 10.5904 | MAE: 5.5938 | RMSE: 7.5582 | R²: -0.3541\n", " ✅ Saved (epoch 5)\n", "[MiniLM-MLP] Ep 06/20 | Loss: 9.7585 | MAE: 5.5224 | RMSE: 7.3319 | R²: -0.2742\n", " ✅ Saved (epoch 6)\n", "[MiniLM-MLP] Ep 07/20 | Loss: 10.3133 | MAE: 5.5109 | RMSE: 7.1950 | R²: -0.2271\n", " ✅ Saved (epoch 7)\n", "[MiniLM-MLP] Ep 08/20 | Loss: 9.3336 | MAE: 5.5060 | RMSE: 7.0942 | R²: -0.1929\n", " ✅ Saved (epoch 8)\n", "[MiniLM-MLP] Ep 09/20 | Loss: 9.2833 | MAE: 5.4926 | RMSE: 7.0359 | R²: -0.1734\n", " ✅ Saved (epoch 9)\n", "[MiniLM-MLP] Ep 10/20 | Loss: 9.3208 | MAE: 5.4886 | RMSE: 7.0130 | R²: -0.1658\n", " ✅ Saved (epoch 10)\n", "[MiniLM-MLP] Ep 11/20 | Loss: 9.2460 | MAE: 5.4887 | RMSE: 6.9782 | R²: -0.1542\n", " ✅ Saved (epoch 11)\n", "[MiniLM-MLP] Ep 12/20 | Loss: 9.7569 | MAE: 5.4889 | RMSE: 6.9658 | R²: -0.1502\n", " ✅ Saved (epoch 12)\n", "[MiniLM-MLP] Ep 13/20 | Loss: 9.2931 | MAE: 5.4829 | RMSE: 6.9977 | R²: -0.1607\n", "[MiniLM-MLP] Ep 14/20 | Loss: 9.4656 | MAE: 5.4817 | RMSE: 7.0202 | R²: -0.1682\n", "[MiniLM-MLP] Ep 15/20 | Loss: 9.2308 | MAE: 5.4804 | RMSE: 7.0346 | R²: -0.1730\n", "[MiniLM-MLP] Ep 16/20 | Loss: 9.2572 | MAE: 5.4790 | RMSE: 7.0845 | R²: -0.1897\n", "[MiniLM-MLP] Ep 17/20 | Loss: 9.6097 | MAE: 5.4710 | RMSE: 7.1093 | R²: -0.1980\n", "[MiniLM-MLP] Ep 18/20 | Loss: 8.9204 | MAE: 5.4657 | RMSE: 7.0917 | R²: -0.1921\n", "[MiniLM-MLP] Ep 19/20 | Loss: 8.7709 | MAE: 5.4632 | RMSE: 7.0844 | R²: -0.1897\n", " Early stopping epoch 19\n", "Best epoch: 12 | Best MSE: 48.5228\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.10 MiniLM Attn" ], "metadata": { "id": "9HC3ZjrLGJiC" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MiniLM + Attn ─────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_minilm_attn.pt\"\n", "\n", "model = model_minilm_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_minilm) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_minilm_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_minilm, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_minilm, DEVICE)\n", "\n", " history_minilm_attn['train_loss'].append(tr_loss)\n", " history_minilm_attn['val_mae'].append(metrics['mae'])\n", " history_minilm_attn['val_rmse'].append(metrics['rmse'])\n", " history_minilm_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MiniLM-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cvdusdJjGMb-", "outputId": "1b2ee085-09c7-4d8d-a9a8-e14dd51cdf5b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-Attn] Ep 01/20 | Loss: 14.8206 | MAE: 6.3989 | RMSE: 8.7779 | R²: -0.8264\n", " ✅ Saved (epoch 1)\n", "[MiniLM-Attn] Ep 02/20 | Loss: 10.9587 | MAE: 5.5899 | RMSE: 7.3555 | R²: -0.2824\n", " ✅ Saved (epoch 2)\n", "[MiniLM-Attn] Ep 03/20 | Loss: 9.4370 | MAE: 5.5238 | RMSE: 7.0515 | R²: -0.1786\n", " ✅ Saved (epoch 3)\n", "[MiniLM-Attn] Ep 04/20 | Loss: 9.5072 | MAE: 5.5312 | RMSE: 6.9232 | R²: -0.1361\n", " ✅ Saved (epoch 4)\n", "[MiniLM-Attn] Ep 05/20 | Loss: 9.1637 | MAE: 5.5030 | RMSE: 7.0136 | R²: -0.1660\n", "[MiniLM-Attn] Ep 06/20 | Loss: 9.4833 | MAE: 5.4885 | RMSE: 6.9550 | R²: -0.1466\n", "[MiniLM-Attn] Ep 07/20 | Loss: 8.3836 | MAE: 5.3564 | RMSE: 7.1746 | R²: -0.2201\n", "[MiniLM-Attn] Ep 08/20 | Loss: 7.6389 | MAE: 5.4079 | RMSE: 7.1252 | R²: -0.2034\n", "[MiniLM-Attn] Ep 09/20 | Loss: 7.3527 | MAE: 5.4707 | RMSE: 6.9549 | R²: -0.1466\n", "[MiniLM-Attn] Ep 10/20 | Loss: 6.2460 | MAE: 5.4801 | RMSE: 6.8353 | R²: -0.1075\n", " ✅ Saved (epoch 10)\n", "[MiniLM-Attn] Ep 11/20 | Loss: 6.1733 | MAE: 5.2910 | RMSE: 7.1320 | R²: -0.2057\n", "[MiniLM-Attn] Ep 12/20 | Loss: 5.5764 | MAE: 5.2919 | RMSE: 7.1588 | R²: -0.2148\n", "[MiniLM-Attn] Ep 13/20 | Loss: 5.5141 | MAE: 5.3176 | RMSE: 7.0464 | R²: -0.1769\n", "[MiniLM-Attn] Ep 14/20 | Loss: 5.3164 | MAE: 5.3504 | RMSE: 6.9135 | R²: -0.1329\n", "[MiniLM-Attn] Ep 15/20 | Loss: 5.2534 | MAE: 5.3158 | RMSE: 6.9331 | R²: -0.1394\n", "[MiniLM-Attn] Ep 16/20 | Loss: 5.1622 | MAE: 5.2478 | RMSE: 7.2375 | R²: -0.2416\n", "[MiniLM-Attn] Ep 17/20 | Loss: 4.8994 | MAE: 5.3054 | RMSE: 6.9523 | R²: -0.1457\n", " Early stopping epoch 17\n", "Best epoch: 10 | Best MSE: 46.7211\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.11 MiniLM Linear" ], "metadata": { "id": "JeNNffIrGPfD" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MiniLM + Linear ───────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_minilm_linear.pt\"\n", "\n", "model = model_minilm_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_minilm) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_minilm_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_minilm, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_minilm, DEVICE)\n", "\n", " history_minilm_linear['train_loss'].append(tr_loss)\n", " history_minilm_linear['val_mae'].append(metrics['mae'])\n", " history_minilm_linear['val_rmse'].append(metrics['rmse'])\n", " history_minilm_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MiniLM-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t-ZiqPdOGS4P", "outputId": "870ea74d-fe5b-4872-dd37-de4468cea609" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-Lin] Ep 01/20 | Loss: 16.2683 | MAE: 6.9767 | RMSE: 9.3879 | R²: -1.0890\n", " ✅ Saved (epoch 1)\n", "[MiniLM-Lin] Ep 02/20 | Loss: 12.6676 | MAE: 5.6913 | RMSE: 7.8359 | R²: -0.4554\n", " ✅ Saved (epoch 2)\n", "[MiniLM-Lin] Ep 03/20 | Loss: 10.0399 | MAE: 5.4390 | RMSE: 6.8797 | R²: -0.1219\n", " ✅ Saved (epoch 3)\n", "[MiniLM-Lin] Ep 04/20 | Loss: 9.6464 | MAE: 5.4394 | RMSE: 6.6904 | R²: -0.0610\n", " ✅ Saved (epoch 4)\n", "[MiniLM-Lin] Ep 05/20 | Loss: 9.6974 | MAE: 5.4832 | RMSE: 6.6409 | R²: -0.0454\n", " ✅ Saved (epoch 5)\n", "[MiniLM-Lin] Ep 06/20 | Loss: 9.3710 | MAE: 5.4361 | RMSE: 6.6631 | R²: -0.0524\n", "[MiniLM-Lin] Ep 07/20 | Loss: 9.2467 | MAE: 5.4162 | RMSE: 6.6792 | R²: -0.0575\n", "[MiniLM-Lin] Ep 08/20 | Loss: 9.0975 | MAE: 5.3998 | RMSE: 6.6838 | R²: -0.0589\n", "[MiniLM-Lin] Ep 09/20 | Loss: 9.0357 | MAE: 5.3475 | RMSE: 6.7639 | R²: -0.0845\n", "[MiniLM-Lin] Ep 10/20 | Loss: 8.9361 | MAE: 5.4385 | RMSE: 6.6210 | R²: -0.0391\n", " ✅ Saved (epoch 10)\n", "[MiniLM-Lin] Ep 11/20 | Loss: 8.8315 | MAE: 5.1866 | RMSE: 7.0776 | R²: -0.1874\n", "[MiniLM-Lin] Ep 12/20 | Loss: 8.0381 | MAE: 5.1700 | RMSE: 6.5457 | R²: -0.0156\n", " ✅ Saved (epoch 12)\n", "[MiniLM-Lin] Ep 13/20 | Loss: 7.6144 | MAE: 5.0521 | RMSE: 6.6836 | R²: -0.0589\n", "[MiniLM-Lin] Ep 14/20 | Loss: 6.9453 | MAE: 5.0581 | RMSE: 6.5916 | R²: -0.0299\n", "[MiniLM-Lin] Ep 15/20 | Loss: 6.7577 | MAE: 5.0361 | RMSE: 6.5188 | R²: -0.0073\n", " ✅ Saved (epoch 15)\n", "[MiniLM-Lin] Ep 16/20 | Loss: 6.5294 | MAE: 4.8992 | RMSE: 6.6068 | R²: -0.0347\n", "[MiniLM-Lin] Ep 17/20 | Loss: 6.3750 | MAE: 4.9193 | RMSE: 6.5964 | R²: -0.0314\n", "[MiniLM-Lin] Ep 18/20 | Loss: 5.8879 | MAE: 4.9695 | RMSE: 6.5243 | R²: -0.0090\n", "[MiniLM-Lin] Ep 19/20 | Loss: 6.2652 | MAE: 4.9904 | RMSE: 6.5042 | R²: -0.0028\n", " ✅ Saved (epoch 19)\n", "[MiniLM-Lin] Ep 20/20 | Loss: 6.4879 | MAE: 4.9878 | RMSE: 6.5059 | R²: -0.0033\n", "Best epoch: 19 | Best MSE: 42.3042\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 9. Eval" ], "metadata": { "id": "SIanU7A1C5xY" } }, { "cell_type": "code", "source": [ "# ── CELL: Plot & Save All Histories ──────────────────────────────────────────\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "HISTORIES = {\n", " 'BERT-MLP': history_bert_mlp,\n", " 'BERT-Attn': history_bert_attn,\n", " 'BERT-Linear': history_bert_linear,\n", " 'MPNet-MLP': history_mpnet_mlp,\n", " 'MPNet-Attn': history_mpnet_attn,\n", " 'MPNet-Linear': history_mpnet_linear,\n", " 'MiniLM-MLP': history_minilm_mlp,\n", " 'MiniLM-Attn': history_minilm_attn,\n", " 'MiniLM-Linear': history_minilm_linear,\n", "}\n", "\n", "for name, h in HISTORIES.items():\n", " fname = f\"history_{name.lower().replace('-','_')}.csv\"\n", " pd.DataFrame(h).assign(epoch=range(1, len(h['train_loss']) + 1)).to_csv(fname, index=False)\n", " print(f\"Saved: {fname}\")\n", "\n", "metrics_keys = ['train_loss', 'val_mae', 'val_rmse', 'val_r2']\n", "titles = ['Train Loss (Huber)', 'Val MAE', 'Val RMSE', 'Val R²']\n", "colors = ['steelblue','cornflowerblue','lightblue',\n", " 'darkorange','sandybrown','moccasin',\n", " 'green','mediumseagreen','lightgreen']\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(20, 12))\n", "for ax, mkey, title in zip(axes.flatten(), metrics_keys, titles):\n", " for (name, h), color in zip(HISTORIES.items(), colors):\n", " if len(h[mkey]) > 0:\n", " ax.plot(h[mkey], label=name, color=color, marker='o', markersize=3, alpha=0.85)\n", " ax.set_title(title, fontsize=13)\n", " ax.set_xlabel('Epoch')\n", " ax.legend(fontsize=7)\n", " ax.grid(True, linestyle='--', alpha=0.5)\n", "\n", "plt.suptitle('BERT & MPNet & MiniLM — Training History Comparison', fontsize=15)\n", "plt.tight_layout()\n", "plt.savefig('history_all_transformer.png', dpi=150, bbox_inches='tight')\n", "plt.show()\n", "print(\"Saved: history_all_transformer.png\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 524 }, "id": "srI4cZECGeKZ", "outputId": "87c02a04-4c24-49c1-9098-096c6936abca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saved: history_bert_mlp.csv\n", "Saved: history_bert_attn.csv\n", "Saved: history_bert_linear.csv\n", "Saved: history_mpnet_mlp.csv\n", "Saved: history_mpnet_attn.csv\n", "Saved: history_mpnet_linear.csv\n", "Saved: history_minilm_mlp.csv\n", "Saved: history_minilm_attn.csv\n", "Saved: history_minilm_linear.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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40ATM999/P9+2vPfpme+Dxx9/3F2PV155Jd+2//u//zMB86abbjpr55o9e7YJmM2aNcv3foiPjzejo6OLfI2KUtznyOmK+hzPKz/9enmfBxMnTixwnr1795o7d+50f5/3finqszolJcWsW7euCZivv/56vm0vvviiCZjh4eFmenq6uzzv9QbMa665Jt820zTNnJwcs1GjRqaHh4d59OjRAtfs3r27CZjr1q0r6uVw++abb0zAbNGiRYn7nmnfvn2mj4+PabfbzUWLFrnLnU6nO/9OnTrlOyYvL8CcPHmy6XK5TNPM/RwePXq0CZht2rQx27VrZx47dsx93PLly03AbNKkSb7znf551b59e/P48ePubTt37jQbNGhgAubChQvzHffdd9+ZR44cyVfmdDrNJ554osDn6ZnX6dq1q3ny5MkCr0de207/PNq9e7e73vHx8fn2T09PN9esWZOvLO/9fOONN5qZmZnu8tWrV5u+vr6m3W43N27cmO+YvHq1bt3aPHz4cL5rBwcHm0C+n1kRERERkX8KzRQXERERkX+805e4DQgIoEePHmzbto3XXnuNl19+ucjjzlxC+/SvvOVTixMfH8+HH37I/fffT1hYGK+88krlNaoQ27dv5//+7/8477zzCAkJYcqUKbz++uuVfp0RI0a4ZyYD7lnLgwYNIjAwsEznSk1N5dtvv+Xaa6/F6XTSu3fvImfue3t78/DDD+NyuXj88cdLPPdTTz0FwMcff5zvnHnL3UZHRxMTE0N8fHyZ6lycf//732zcuJGePXuyf/9++vTpU6nnB4iIiODCCy90z86H3NmbUPzS6SWx2Wy88847+TLs1KkTAwYMIC0tjQ0bNlSs4qUwYsQIUlJSWLRoEZC7TPOuXbu44YYbsNlqXvf2lltuoWfPnoVuu+qqq/Dx8clXFhAQ4J45mvcalVb37t0LrCDx73//G4Dvv//+rJ0rb2bs1KlTCQ8Pd5eHhITw3HPPlem6p3v//feL/Hw2DINVq1aV+lzHjx8HoHfv3gW2NW7c2L2CRGnMnz+fo0eP0rVrVyZMmJBv27333ssFF1zAgQMHWLBgQYFjvby8eO211wo8JsBut3PrrbeSnZ3tfsRDnj///JMff/yR9u3bc+GFF5ZYv7zHRoSFhZW6TXmmT59Oeno6w4cPZ/Dgwe5ym83GtGnTaNCgARs2bODHH38scGzz5s2ZOnWq+5ELhmFw7733ArlLzb/88sv56nT55ZfTsWNH9u7dW+TS+s8//3y+53m3aNHCvarFmb/7Lr300gJLxdtsNh577DEaNmzI4sWLi2z3q6++mu/RBsXJ+1nq2LFjgZVhvL296dq1q/v73bt388UXX+Dv789rr72Gp6ene9vFF1/MHXfcgdPpLHQZ+bx61atXz/19s2bN3L8DilqNQERERESkJqt5fzUQERERESmj05e4vf766+natSupqalMnTqVpUuXFnncmUton/5V1ODDmDFj3IMyYWFhjBw5koCAADZu3EibNm3OVhM5deqUe/By+fLlLF26lMDAQO6+++4Cy1GvWbMGwzDKvRx1nz59qFOnjvu8H330ES6Xq9QDsqcPZvn7+9O7d2+2bdtGp06d3M/FLsqtt95KeHg4n376Kb///nuR+x07dozffvuNyMjIfEtS5zEMg+7du+N0Ovnll19KVe+SvPbaazz11FM88cQTrFixgpEjR/Lnn3/Sv3//As/57du3L4ZhsGPHjnJda+TIke7nuGdlZfHJJ5/QsWPHCv2MNWnShJYtWxYoz3vW++HDh8t97tIaPnw4Hh4e7kH+yhjsr85OH1gszI4dO3jllVe46667GDt2LKNHj+Y///mPe1tZ9O3bt0BZSEgItWvXLnO2pT1XdnY2P//8M4ZhMHTo0ALH9O7dm9q1a5fp2nlatGhR5OfzzTffXOKzsk93wQUXAPCvf/2LL774wn2zSXnkDUYW9fla3KDl+eefT8OGDQs9bty4cTgcDqZPn56vPG958dtuu63cdS6t4trm5eXFsGHD8u13ul69euHh4ZGvrHnz5gB4eHjQq1evAsfkbS/s57N27dr06dOnQPkNN9wA5P6eO/PZ4gkJCcycOZP77ruPW265hdGjRzN69Giys7NJSEjgxIkTBc5Xv359OnXqVKC8KK1atcLPz48lS5bw3HPPFbksO8APP/wAQP/+/Qt9H9x0001A4a+nh4cHl156aYHyc/l5LSIiIiJS3TiqugIiIiIiIlVt1qxZBcryZvMOHjyYTZs2FToYOHny5EL/UF+c7t27ExERgcvl4uDBg6xatYq9e/dy88038/XXX2O328vZiuK98sor7N69mzfffJM6depQp04dlixZQr9+/Rg9ejSBgYEMGjQIgK1btwLkm7FWFg6Hg+uvv55XX32V2NhY5syZQ0hICAMGDCjV8S1atHA/f9fDw4O6devSo0cP+vTpU+JsYC8vL/71r38xfvx4Hn/8cRYuXFjofnkzC3fs2OGemViUypjJnZyczL///W8aNGjAww8/jGEYzJw5k9TUVBYuXMigQYNYunSpewbo1q1bCQsLK9MM1NNdf/31TJo0iQ8//BCHw8HJkyfdM3XL6/RZvKfLe0ZxZmZmhc5fGnk/R19++SWHDh1i/vz5nHfeebRv375c57v//vsrbaZ+YZ8jFdW4ceNCy03T5P777+ell17CNM1C9znzRouSFJdvYYOBlXGuhIQEsrKyCAsLKzD7OU/jxo3LfH3InUlbXCa9evXi6NGjpTrX6NGjWbZsGfPnz2fQoEF4e3vTuXNn+vfvz9ixY/PNxi1J3iBo06ZNC92eV37w4MEC24r6eYDcwdnBgwfz6aefsmrVKnr27ElWVhYffPABPj4+pb7JKW/2ct6M5rKoSNsKG+z39/cHoF69eoX+bszbXthnT5MmTQqtQ1BQEMHBwZw6dYqTJ0+62/vxxx9z2223kZKSUuhxkPueOnNwurhMChMYGMh7773HbbfdxoMPPsiDDz5IVFQUl156KTfddBPdu3d371uR17Oo1+xcfl6LiIiIiFQ3GhQXERERESlEx44duf3223n++ed56623il1GvSzGjRvH6NGj3d9v2bKFnj178u233/LSSy9x//33V8p1zvTVV18B5Js5dvHFF/PZZ58xaNAghg8fztKlS+nZsydz587NN6uvPEaMGMGrr77Kww8/zO+//86dd95ZYBZgUUoazCrJLbfcwrRp0/jss8/YuHFjofvkzRCsV68e/fr1K/Z8RQ2ulMWPP/5IUlISAwYMwOHI7YY5HA7mzp3L4MGD+frrrxk+fDiffvop69at48CBA9xzzz3lXhI8LCyMPn368NVXX5GcnIzdbnfPkCyv6rI8+YgRI1i8eDG33HIL8fHxPPDAA+U+V0xMDHv37q2Uep2NQfGiBornzZvHiy++SKNGjXjppZfo2rUrYWFheHh4kJWVhZeXV5GD5UWpzHyry89KZbHb7cybN4/JkyezaNEivvvuO9atW8fq1auZNm0aS5cupVu3bpVyreJu0inq5yHPHXfcwaeffsp7771Hz549+eyzz4iPj2fUqFGlXt67Q4cOQO7S3adOnSr1caVRXNuK+5k52z9Pe/fudf9efvnll7nyyitp2LCh+/EE3bp1Y+3atYW+p0rKpDA33HADvXv3ZtGiRSxbtoxVq1bxzjvv8M477zBp0iReeOGFUp2nvK+niIiIiMg/lf4vWURERESkCM2aNQPKvgxxWbRp04ZXX30VgP/+978kJiaelevknTcnJydfeZ8+fZg7dy45OTkMGjSI5557juXLlzN+/PgyLS98pgsvvJDIyEj38vPncnlrT09PHnnkEYAiny2eN5M1NDSUWbNmFfuVN2u9Iop6/T09PVm4cCGXXHIJn3/+OaNHj+bhhx/Gz8+PBx98sELXHDlyJFlZWXz33Xdcdtll1K9fv0Lnqy4GDx5MYGAgS5cuxWazceONN5b7XHFxcZimWSlf51LeCghvvfUWQ4YMoUGDBu6bTnbv3n1O61IRISEheHh4EB8fX+SS5Pv37z/HtSpax44dmTJlCt9//z3Hjx/n3nvvJTk5mf/7v/8r9TkaNGgAUOTNGHmrWBS1THpxevfuTUREBAsWLODkyZPlWjo9LCyMCy+8ENM0mTt3bpmufzbbVlb79u0rtDwpKYlTp07h4+PjHvD/8ssvycrK4u677+aee+4hIiLCPSAOZ+c9FRYWxrhx45g/fz5Hjhzhq6++IjAwkBdffJHNmzcD1ev1FBERERGpCTQoLiIiIiJShLw/hOct0Xq2XH/99URHR3Py5EneeOONs3KNtm3bAvDpp58W2Hb11Vcza9YsUlNTefDBB2nRooX7ucQVMXbsWEJCQoiOjq60WZSlNWbMGJo0acLnn3/Ohg0bCmwPDw+nVatWbNmyhe3bt5f6vJ6enkDBwe2S5L3+3333XYEbH3x8fPjiiy/o3LkzH374IatXr+b55593D4iU19VXX014eDghISH5ViewOm9vb0aOHElISAhXXnllkUt112QnT54ECl+mfP78+ee6OuXm4eHhHoAt7LPpu+++IyEhoQpqVrLAwECefvppDMNg06ZN7vKSPiN69OgB5C7XXZg5c+bk268sDMPgtttuIyMjg6lTp/Ltt9/SunXrfEtyl0beiiVTpkzh2LFjxe67Zs0a97+La1tWVhaffPJJvv3OpoSEBL799tsC5XkD/V27dnUvL17c++n7778v9TL75WUYBv379+fKK68EcA+K592QtXTpUk6dOlXguIr8rIiIiIiI/BNpUFxEREREpBAbN27k3XffBeCKK644q9cyDIMpU6YAuUu3pqWlVfo1Jk6cCMBTTz3FjBkz8s1sNU0TLy8v97NGjx8/zrZt2yp8zcmTJxMfH1/kEuZnk4eHh/sZ2m+++Wah+zz66KO4XC6GDBlCbGxsge0JCQnumZZ58gaqy/r6tGvXjl69enHy5EmuvvrqAjP/UlJSaNSokfv7NWvWVHj2sa+vL/v37yc+Pr5Cs6mrozfeeIP4+HgWL15c1VWpElFRUQC8++67+X5OVq9ezXPPPVdV1SqXO+64A4DHHnss37ORT5w4UaGl8SvT7Nmz8w185/nqq68wTTPfezc0NBQPDw927dqF0+kscMzw4cOpW7cuP/zwg/t3TJ5XX32VDRs20LBhQ4YMGVKuuo4ZMwYvLy9efvllTNPk1ltvLfM5hg0bxvXXX8/Ro0e55JJLWLt2bYF9jhw5wsSJE/OtAnLLLbfg4+PD3LlzWbJkibvc5XLxr3/9i4MHD3LBBReUeZC+vO6///58N1Xs2bOHqVOnAjBhwgR3ed77ac6cOaSmprrLDx486P75rCwbN27k008/JSsrK1/5iRMnWLduHYD756l58+ZceeWVJCcnc88995Cdne3ef+3atbz11lvY7fZ8bRERERERkaLpmeIiIiIi8o93+izarKws9u7dy08//YTL5WLQoEHcdNNNhR43bdq0Yp8j/Oabb+Lr61uqOlx11VWcf/75/Prrr7z33nvcc889ZWlCiXr16sWLL77I/fffz7hx43jqqafo0KEDNpuNDRs2sG/fPqKiorj33nuZMmUKAwYMYPXq1e7BAisaPXo0Tz/9dJFL3954441s3ryZ//73v1xwwQVER0fTokULTNNk165d/P777/j7++cbVBo0aBBTp07l/vvv55tvviE0NBSAZ555hpCQkGLr89FHH9G7d29WrlxJq1at6NSpE40aNeLgwYP89NNP5OTk8MADD7Bs2TJmz55N7dq1K+1Z9tVRly5ditw2btw4xo0bdw5rYy133303s2bN4s0332TlypW0b9+egwcP8sMPP3Dffffx/PPPV3UVS23EiBF8+umnLFy4kFatWnH55Zdjt9tZsWIFLVq0oEuXLvz000/uGdhVYcGCBYwaNYoWLVpw3nnn4ePjw549e1i3bh02m40nn3zSva+npyf9+/fn888/p0OHDpx//vl4enrSvXt3xowZg5+fHx9++CGDBg3i9ttv59133yUqKoo///yTjRs34u/vz8cff1yuZ1VD7qD8kCFD+Oijj/Dy8mLUqFHlOs8HH3yAr68v//vf/+jWrRutWrWiTZs2eHh4EBcXx4YNG3A6nfTp08d9TOPGjXnnnXcYPXo0gwYNonv37jRq1Ihff/2Vbdu2UbduXffs5rOtS5cuZGVlERERwWWXXUZ2djbffvstaWlpjBw5kmuvvda97+DBg2nbti0bNmwgIiKC7t27k5GRwYoVK9wrnZw+I74i9u7dy5AhQwgKCqJTp07Uq1ePU6dO8f3335OcnMygQYPo2rWre/933nmHHj168MEHH7Bq1Sq6du3K8ePHWblyJU6nkxdeeIHo6OhKqZuIiIiISE2nmeIiIiIi8o/3/vvvu7/mzZvHn3/+ySWXXMKMGTP47LPPsNkK/9/mr7/+Ot+xZ36dOROsJHmzxZ9//vkyH1sa9957Lz/99BM33HADWVlZLFmyhBUrVtC0aVPeeustfv/9dx5//HEefvhhjh07Rp8+fThw4ECl1+NccTgc7tniRXnqqadYtWoVQ4YM4ciRI3z22WesWLECp9PJnXfeWWAm8gUXXMCcOXNo06YNy5YtY8aMGcyYMYPk5OQS61O/fn02bNjAc889R7t27YiNjeXTTz/lwIED3HzzzcTGxvLss8+yePFi6tatyyuvvFIpy9hXV+vWrSvyy8o/d+dCVFQUGzZsYNCgQe4Z8ykpKbzzzjuWmyluGAbz5s3j6aefpn79+nz11VesW7eOkSNH8t133xEfH49hGNSqVavK6jhp0iQmTJhAQEAAq1evZuHChRw7dozrrruOdevWMWzYsHz7T58+nZtuuomEhAQ++ugjZsyYwapVq9zbL7/8cn7++WduuOEGDhw4QExMDEeOHGHkyJFs2LChwsthX3bZZQBce+21Jd6sUxQPDw9mzJjBmjVrGDt2LDk5OSxdupTPPvuMI0eOMGTIEBYtWsTXX3+d77ibbrqJ1atXM3DgQLZu3UpMTAzp6enceeed/PLLL7Rq1apCbSstLy8vvvvuO2688UZ++uknvv76axo1asTzzz9f4GY2T09PVq9ezZ133om3tzdffPEFW7du5a677uKbb77Bw8Oj0urVpUsXnnzySS644AK2bdvGJ598woYNG2jfvj3/+9//WLBgQb79GzZsyM8//8x9992Hw+Hg008/5ZdffuHyyy/n66+/ZtKkSZVWNxERERGRms4wK7omn4iIiIiIiIhIJTtw4ADNmjUjIiKCrVu3VnV1LKNfv34sW7aMFStW0KtXr6quzjkVFxdHs2bN6NmzJytXrqzq6oiIiIiISDWimeIiIiIiIiIiUmV+//33fM9LBjh69CijR48mJycn33OrpXjr16/nm2++oW3btv+4AXEREREREZHi6JniIiIiIiIiIlJlHnzwQdavX090dDR169bl8OHD/PLLL6SkpNC5c2fuu+++qq5itTd58mT27dvHkiVLME2Tp556qqqrJCIiIiIiUq1oUFxEREREREREqszo0aMxTZM//viDNWvWYLfbiYqKYujQodx77714e3tXdRWrvblz57J//36aNGnC008/zVVXXVXVVRIREREREalW9ExxERERERERERERERERERGpsfRMcRERERERERERERERERERqbE0KC4iIiIiIiIiIiIiIiIiIjWWBsVFRERERERERERERERERKTG0qC4iIiIiIiIiIiIiIiIiIjUWBoUFxERERERERERERERERGRGkuD4iIiIsWIi4vDMAymTJlS1VWpVq677jq6d+9+zq9rGAajR48+p9c0TZPzzz+fMWPGnNPrioiIiIiIiDXobwciIiLVnwbFRUTEUgzDKPVXXFxcVVe3WE2bNqVdu3ZVXY0y+/HHH5k/fz5PPvlkvvJevXrh7+9f5HFTpkzBMAw2bNhwtqtYqfL+sPHBBx8QGxtb1dURERERERGRchg2bBiGYRTbrzNNk2bNmhEcHEx6evpZqcfKlSvdf7eYOHFiofscO3YMT09PDMOgV69eRZ7roYcewjAMIiMji9xn1qxZxf7tJCIioqJNEhERsQRHVVdARESkLGbPnp3v+9WrV/Puu+9y22230aNHj3zbwsLCKny9Jk2akJ6ejsOhX5l5pk6dSnR0NJdeemlVV+WcGTx4ME2bNuWpp57ik08+qerqiIiIiIiISBndcsstxMTEMHPmTF555ZVC91mxYgVxcXHcfvvt+Pj4nNX6eHt789FHH/HCCy/g5eWVb9vs2bMxTbPYv0Xk5OTwwQcf0KJFC3bu3MmqVavo2bNnkfvffffddO7cuUB5QEBA+RshIiJiIfoLv4iIWMrIkSPzfZ+Tk8O7775L165dC2w7U3Jycpk7e4Zh4O3tXeZ61lQ7d+7km2++4YUXXqjqqpx1TqeTzMxMfH19gdyfvaeffpojR45Qr169Kq6diIiIiIiIlEXfvn1p1KgRH374Ic899xyenp4F9pk5cyaQO4B+tl1zzTV8/PHHLFq0iOHDhxeoxxVXXMG3335b5PFLlizhyJEjfPvtt9xwww3873//K3ZQvEePHgwdOrTS6i8iImI1Wj5dRERqpKZNm9KrVy82btxIv379CAoKon379kDu4Pi///1vLrroIkJDQ/Hy8iIiIoLJkyeTlpaW7zyFPRfs9LIvvviCzp074+3tTf369XnggQfIycmp1LbEx8czYcIEGjVqhKenJ40aNWLChAkkJCTk2y8jI4MpU6bQsmVLfH19CQ4O5rzzzuOBBx7It9+SJUvo2bMnoaGh+Pj40LhxY6699lq2b99eYl1iYmIwTZMrrriiUtqWt6R6YUvd52VYmOXLl9OlSxd8fX2pV68e99xzDykpKQX2S0xM5KGHHiIiIgIvLy/CwsK44YYb2L17d7798paTW758Of/5z39o0aIF3t7ezJ8/373PgAEDyM7O5rPPPqtIk0VERERERKQK2Gw2Ro8eTUJCAosXLy6wPSkpiQULFtCuXTs6d+5cpr8dlMf5559P+/bt3QPxedavX8/mzZsZM2ZMscfPmDGD5s2bc+mllzJixAhiYmJISkqqcL1ERERqKs0UFxGRGmvfvn1cdtllDBs2jCFDhrgHTQ8ePMj06dMZMmQIN954Iw6Hg1WrVvHss8+yceNGvv7661Kd/8svv+TNN9/kjjvuYOzYsSxatIjnn3+eWrVq8a9//atS2pCYmEi3bt3YuXMnY8eO5fzzz2fjxo289dZbfPfdd6xfv949+33ChAn873//Y9SoUUyaNImcnBx27NjBd9995z7fqlWrGDx4MO3atePhhx8mODiYQ4cOsXz5cnbu3ElUVFSx9Vm1ahXBwcHF7hcfH19oeWX80QDg119/JSYmhltvvZVRo0axYsUKXn31VTZt2sQ333yDzZZ7z1/ea7dv3z7Gjh1L27ZtOXz4MG+++SYXXXQRGzZsoEmTJvnOff/995Odnc2tt95KYGAgLVu2dG87//zz8fLyYuXKldxxxx2V0hYRERERERE5d8aMGcOTTz7JzJkzC8yanjt3Lunp6e5Z4pX1t4PijB07lkmTJnHw4EEaNmwIwP/+9z/q1KnDwIEDizzuyJEjfPXVV/z73//GMAxGjx7NSy+9xNy5c7ntttsKPSY5ObnQ/rqPjw9+fn4VbouIiEh1p0FxERGpsfbs2cN7773HuHHj8pU3b96c/fv34+Hh4S6bMGECjz76KE8++STr16/nwgsvLPH8mzdvZvPmzTRt2hSAO+64g/POO4/XXnut0gbFn332WXbs2MEbb7zB+PHj3eXR0dFMnDiRZ599lv/85z8ALFy4kAEDBvD+++8Xeb5Fixbhcrn45ptvqFOnjrv80UcfLVV9tmzZQrNmzTAMo9DtqamplfIs9+L88ccfLFy4kKuvvhqA8ePHc8899/Dqq68yf/58rr/+egAee+wxdu/ezU8//USHDh3cx48ePZrzzjuPxx9/nFmzZuU7d3p6Ohs3bnQvmX46T09PwsPD2bx581lrm4iIiIiIiJw9zZo149JLL+Xrr7/m8OHD1K9f371t5syZeHp6uh/NVll/OyjOyJEjefDBB3n//ff517/+RXp6OnPnzmXcuHHFPk/8/fffx+l0MmrUKADat29PdHQ0M2bMKHJQfOzYsYWWT5gwgddff71C7RAREbECLZ8uIiI1Vu3atQtdbszT09Pdqc3JyeHkyZPEx8fTu3dvANatW1eq81999dXuAXHIff74pZdeypEjRwpdyrs8Fi5cSFhYWIFO7e23305YWBgLFy50lwUFBbF582Y2bdpU5PmCgoIAWLBgQbmWeT9+/Di1a9cucru3tzfffPNNoV833XRTma9XmJYtW7oHxPNMnjwZwP16mKbJhx9+yCWXXELDhg2Jj493f/n5+dGlSxeWLVtW4Nx33nlnoQPieUJCQjh27FiltENERERERETOvVtuuQWn08kHH3zgLvvzzz/56aefGDx4MKGhoUDl/e2gOCEhIQwePNh9w/ann35KYmJikQPYef73v//Ro0cPmjVr5i4bPXq0e+n1wjz22GOF9tXvuuuuCrdDRETECjRTXEREaqwWLVpgt9sL3fbmm2/y9ttvs3nzZlwuV75tJ0+eLNX5mzdvXqAsJCQEgISEBPz9/ctY44L27NlDp06dCtwh7nA4iIqK4tdff3WXvfzyy9x0002cd9557ueKDRo0iEGDBrmXFJ84cSKLFi1i/PjxPPTQQ1x88cX079+fG264oVQzvA3DwDTNIrfb7Xb3HwjO9MMPP5SmySVq3bp1gbL69esTHBzsflb48ePHSUhIYNmyZUW2K+81OV1Jy8ebplnkLHkRERERERGp/q699lqCg4OZOXMmDz30EJA7yAwFZ1NXxt8OSjJmzBiuvPJKfvjhB/73v/9x4YUX0qZNmyL3X716Ndu3b2fkyJHs3LnTXX7RRRdhs9mYMWMGL774YoHjzjvvvCL76yIiIv8EmikuIiI1VlEzfl988UUmTJhA/fr1eeedd1iyZAnffPON+87sMzu6RSlqwB0oduD4bLnqqquIi4tj9uzZXHbZZXz77bdcffXV9OrVi6ysLCB30P7nn39mxYoV3HXXXSQnJ3PvvfcSFRXF2rVrS7xGWFgYJ06cqLQ6FzfAXJ6Z7HnyXv/evXsXOXO9sOe/FTdLHODEiRNnfXl4EREREREROXu8vb258cYb2bZtG2vWrMHpdDJ79mzCw8Pp16+fe7/K+ttBSfr160fDhg154oknWLFiRYmzxGfMmAHkzvyOjIx0f3Xt2hWXy8WcOXPIzs6ulLqJiIjUJJopLiIi/zizZ8+madOmfPXVV/lmCy9durQKa1W45s2bs23bNnJycvLNFs/JyWH79u0FZqvXrl2bkSNHMnLkSEzTZPLkyTz77LMsWrSIYcOGAbmD+b169aJXr14A/P7771xwwQU8+eSTLFmypNj6tGvXju+//x6Xy1XoTOuyyluK/cSJE/mWos/IyODw4cNEREQUOGbr1q0Fyg4fPsypU6fcr0dYWBjBwcEkJSVV2p3wmZmZ7N+/n2uvvbZSziciIiIiIiJV45ZbbuHNN99k5syZnDhxgiNHjvDII4/k6+eeq78d2O12Ro0axdNPP42Pjw833HBDkfsmJycTExNDnz59Cn12+O+//85//vMfFi9ezJAhQyq1niIiIlanmeIiIvKPY7fbCywDnpOTw7Rp06qwVoW7+uqrOX78ONOnT89X/t5773H8+HGuueYaAJxOJ6dOncq3j2EYdOzYEcA9uzs+Pr7ANVq1aoWPj0+pZoD36tWL5ORktmzZUp7mFJC3XPny5cvzlb/00ktF3nW/bds2Pvvss3xlzzzzDID7WeM2m40RI0awfv16YmJiCj1PWZ8NvnHjRrKysujZs2eZjhMREREREZHq5fzzzyc6Opp58+bxxhtvYBhGgRna5/JvB3fccQePP/44b7/9NoGBgUXuN3fuXFJTU7njjjsYOnRoga/Jkyfj6+vrXg5eRERE/qaZ4iIi8o8zdOhQHn74YQYMGMC1115LUlISH330ER4eHue8LsePH+fJJ58sdNuYMWN48MEH+eSTT5gwYQK//vorHTt2ZOPGjcyYMYOWLVvy4IMPArl3i9evX5/BgwfTsWNH6tSpw549e3jrrbeoVasWgwYNAuDWW2/lwIED9O3blyZNmpCens68efNITk5m1KhRJdZ3yJAhPPTQQ3z55Ze0a9euwu3v3bs3LVu25LHHHiMhIYFmzZrxww8/8NNPPxEaGlroMeeddx4jR47k1ltvJTIykhUrVhATE0PPnj257rrr3Ps99dRT/PjjjwwfPpzhw4fTpUsXPD092bt3L19++SUXXHCBe9m70vjyyy/x8PBwD7yLiIiIiIiIdd1yyy3cddddLF26lF69ehVYie1c/u2gcePGTJkypcT9ZsyYga+vL/379y90u6+vLwMGDOCzzz7j4MGDNGzY0L1t9erVZGRkFHrciBEjin28mYiISE2gQXEREfnHeeCBBzBNkxkzZnDPPfdQr149rrvuOsaMGUObNm3OaV2OHTvGo48+Wui23r1706VLF3788Ucef/xxFi9ezMyZM6lbty533HEHTzzxBAEBAUBux/f//u//+Pbbb1m+fDkpKSnuQfKHH36YBg0aAHDTTTcxa9Ys3n//fY4fP05gYCBt2rQhJiamVEurNWvWjH79+jF79mz3gHxF2O12Fi9ezN13381rr72Gp6cnffv2ZdWqVXTv3r3QY84//3xefPFFHnnkEfdd9BMnTuS///1vviXtgoKC+PHHH3nhhReYP38+ixYtwuFwEB4ezsUXX8y4cePKVNc5c+Zw1VVXUa9evQq1WURERERERKreiBEjeOCBB8jIyCj0Od7V6W8HAJs3b2bdunVce+21+Pr6FrnfkCFDWLBgAe+//z7/+te/3OWvvvpqkcdcf/31+R7ZJiIiUhMZ5unrv4iIiIiUYO3atXTr1o1vvvmm0p7XXd0tWrSIa6+9ll9++YXo6Oiqro6IiIiIiIiIiIiIlIEGxUVERKTMrr/+evbt28eaNWuquipnnWma7ufNzZw5s6qrIyIiIiIiIiIiIiJlpEFxERERERERERERERERERGpsWwl7yIiIiIiIiIiIiIiIiIiImJNGhQXEREREREREREREREREZEaS4PiIiIiIiIiIiIiIiIiIiJSYzmqugKl4XK5OHToEAEBARiGUdXVEREREREREQHANE2Sk5Np0KABNlvNve9c/XIRERERERGpjkrbL7fEoPihQ4do1KhRVVdDREREREREpFD79+8nPDy8qqtx1qhfLiIiIiIiItVZSf1ySwyKBwQEALmNCQwMrOLaiIiIiIiIiORKSkqiUaNG7n5rTaV+uYiIiIiIiFRHpe2XW2JQPG9ptsDAwGrd+TZNk8TERIKCgrScnEUpQ2tTftanDK1N+VmfMrQ25Wd9ytDaanpmVumXg95LVqf8rE8ZWpvysz5laG3Kz9qUn/UpQ2srKbOa+8CzKuByuThy5Agul6uqqyLlpAytTflZnzK0NuVnfcrQ2pSf9SlDkcqh95K1KT/rU4bWpvysTxlam/KzNuVnfcqwZrPETHEREREREZF/GqfTSXZ2dlVXQ07j4eGB3W6v6mqIiIiIiIjIOaB+efVTkX65BsVFRERERESqmZSUFA4cOIBpmlVdFTmNYRiEh4fj7+9f1VURERERERGRs0j98uqpIv1yDYpXIsMw8PPz03MGLEwZWpvysz5laG3Kz/qUobUpP+vLy9DlcnHgwAF8fX0JCwtTptWEaZocP36cAwcOEBkZqRnj1Zg+D61N+VmfMrQ25Wd9ytDalJ+1KT/rU7+8eqtov9wwLXCLQ1JSEkFBQSQmJhIYGFjV1RERERERETlrMjIy2LNnD02bNsXHx6eqqyOnSU9PJy4ujmbNmuHt7Q38c/qr/5R2ioiIiIiIqF9efVWkX247V5X8J3C5XMTHx+Nyuaq6KlJOytDalJ/1KUNrU37WpwytTflZ35kZ6k706keZWIM+D61N+VmfMrQ25Wd9ytDalJ+1KT/rU7+8+qtIJhoUr0SmaRIfH6/nC1iYMrQ25Wd9ytDalJ/1KUNrU37WpwxFKofeS9am/KxPGVqb8rM+ZWhtys/alJ/1KcOaTYPiIiIiIiIiko/D4SA6Opro6Gg6d+5MbGwsACtXriQ4ONi9LTo6mi1bthAXF4evry/R0dG0bt2aUaNGkZmZ6d6nXr16hIeHEx0dTbdu3fJda8qUKdjtdo4cOeIuu/POO/Pd/R0aGlqgjlOmTHGfs127dixevPjsvBgiIiIiIiIi55j65ZXPUdUVEBERERERkYo5kZLBscR06gT5UNvfu8LnCw4Odne4FyxYwNSpU/n0008B6N27NzExMfn2j4uLo02bNmzYsIGcnBz3PnnnmDJlCqGhoUycOLHQ67Vp04aYmBgmTpyI0+lk/fr11K5du8R6Tp48mYkTJ7J161Z69OjBsWPHsNl077eIiIiIiIicW+qXV/9+uQbFK5FhGAQFBekZAxamDK1N+VmfMrQ25Wd9ytDalJ/1nZmhaZpkZjtLPO7HP4/wv+/+JDPbiZeHnbGXtaJ7q3rFHuPpsJX6ZyUpKYng4OBS7Qu5d7N36dKFAwcOlPqYIUOG8OmnnzJx4kRWrlxJjx492Lt3b6mPb926NQ6Hg/j4eOrUqVPq46Rm0uehtSk/61OG1qb8rE8ZWpvyszblZ32FZWiaJlk5xT8nXv1ya/TLNSheiWw2G/Xr16/qakgFKENrU37WpwytTflZnzK0NuVnfXkZZmRkAJCV42LS/1YXe4zT5eJYUgYul4nNgOT0bJ5f/BsfrNyGvZg7s9+8rQdeHvYit586dYro6GjS0tJISEhgzZo17m3Lly8nOjra/f3q1fnrmJ6ezpo1a3j11VeLrfvpQkND8fT05NChQ8yfP5+bb76ZOXPmlPr4devWYbPZCAsLK/UxUnPp89DalJ/1KUNrU37WpwytTflZm/KzvjP75ZDbNx//btF9c/XLc1mhX149569blMvl4vDhw7hcxd8xItWXMrQ25Wd9ytDalJ/1KUNrU37WV54Mc1wmppnb8TYMA5uRexe702VWqC55y7Rt376dd955hwkTJri39e7dm9jYWPdXQEAAAFu2bCE6Opq6detSt27dfB300hgyZAjz5s3j559/pmvXrqU6Ztq0aURHR3P//fczb948zcgQQJ+HVqf8rE8ZWpvysz5laG3Kz9qUn/WpX16z++WaKV6JTNMkMTGx2i4LICVThtam/KxPGVqb8rM+ZWhtys/68jIMDAwEcpdSe/O2HsUeczIlk/s/WEtGVg6+Xg7SMnPw9nTw3Kiu1PL3KvI4T0fp748eOHAgo0aNKnG/vGeXHT9+nK5du7J48WIGDx5cYL833niD9957D4C1a9e6y6+55hratGnDiBEjSt2Jznt2mcjp9HlobcrP+pShtSk/61OG1qb8rE35Wd+Z/XIouW+ufrl1+uWaKS4iIiIiIlINGYaBl4e92K96tXy5vU8bfLwcpGe78PFycHufNtSr5VvscWW5c3vNmjU0b9681PuHhYUxbdo0pk2bVuj2CRMmuO9m9/HxcZeHhobyzDPPMH78+FJfS0RERERERORsKqlvrn65dWimuIiIiIiIiIX1aFOfto1rcSwxgzpB3tT2967wOfOeXWaaJg6Hg3fffde97cxnl7322ms0atQo3/FDhgxh6tSprFmzhm7dupX6umPGjCm0/OTJk4SHh7u/f+6550p9ThEREREREZGzSf1yazBM06zYovbnQFJSEkFBQQWWLKhuXC4XJ06coHbt2thsmoRvRcrQ2pSf9SlDa1N+1qcMrU35WV9ehr6+vuzdu5dmzZrh7V3xjrRUnoyMDPbs2ZMvG6v0VyvKSu3U56G1KT/rU4bWpvysTxlam/KzNuVnfeqXV38V6ZdrpnglstlshIaGVnU1pAKUobUpP+tThtam/KxPGVqb8rO+vAwzMjKquioilqbPQ2tTftanDK1N+VmfMrQ25Wdtys/61C+v2XSrSiVKzknmt4O/kZyTXNVVkXJyuVzs378fl8tV1VWRclB+1qcMrU35WZ8ytDblZ33KUKRyHEo6xOJfFnMo6VBVV0XKQZ+F1qcMrU35WZ8ytDblZ23Kz/qUYc2mQfFKsiNrB/NT5rPaezXzUuaxI2tHVVdJysE0TVJTU7HAUwWkEMrP+pShtSk/61OG1qb8rE8ZilTcJ5s/4YL3LmDk1yPpOqMrMVtiqrpKUkb6LLQ+ZWhtys/6lKG1KT9rU37WpwxrNg2KV4JUVyqr01aTZqbhNJxkmpmsTltNqiu1qqsmIiIiIiIi8o9wJOUI45eM51jKMXJcOaRkpXDfsvs4knKkqqsmIiIiIiIiVUyD4pUg2ZVMNtl44omBgYlJNtkku7SMuoiIiIiIiMi5sPfUXly4sBk2clw5+Hn4kZadxr7EfVVdNREREREREaliGhSvBAG2ADzwwMQEA5w4sWEjwBZQ1VWTMrLZbNSrVw+bTW8NK1J+1qcMrU35WZ8ytDblZ33KUKRimgQ3wd/TH5thw8TkRMYJfD18aRzUuKqrJmWgz0LrU4bWpvysTxlam/KzNuVnfcqwZlOqlcDP5kcP3x54Gp7YsGFg0MDRAD+bX1VXTcrIMAyCg4MxDKOqqyLloPysTxlam/KzPmVobcrP+qpThg6Hg+joaKKjo+ncuTOxsbEArFy5kuDgYPe26OhotmzZQlxcHL6+vkRHR9O6dWtGjRpFZmame5969eoRHh5OdHQ03bp1K3C9uLg4DMNgyZIl7rLY2FiWLVvm/n7lypWsX7/+rLddrKuefz1e6PsCgV6BmJi4TBcv9H2Bev71qrpqUgbV6bNQykcZWpvysz5laG3Kz9qUn/VVpwzVL698GhSvJJGekQzzH0bblLYEGAEkuZLIMXOqulpSRi6Xi927d+Nyuaq6KlIOys/6lKG1KT/rU4bWpvysryIZJqa52H00h8S0ysk/ODiY2NhYYmNjmTx5MlOnTnVv6927t3tbbGwsbdq0AaBNmzbExsbyxx9/sG/fPmJiYtz73HHHHUyePJnY2FjWrFlT4Hrz58+nW7duzJs3z11W0zrfcm4MbTOU70d/T33f+tT3r88F9S+o6ipJGen3mfUpQ2tTftanDK1N+Vmb8rM+9ctrdr/cUdUVqEl8DV/qpdYjPiieNDON/Tn7aebRrKqrJWVgmiZZWVmYplnVVZFyUH7WpwytTflZnzK0NuVnfWdmaJomWTkl5/nr7iw+/SmNrBzwdMC1XXw5v7lnscd42Cn1ne9JSUkEBweXal/IvZu9S5cuHDhwoNTHzJs3j9mzZ9OvXz8yMzNxOBw89thjZGRksHz5cu69917efvttHA4H06dPZ9asWbz88ssEBQWxbt06EhISmD59Oj179iz1NaXmigqJomvdrvx26je+2P4Fd110V1VXScpAv8+sTxlam/KzPmVobcrP2pSf9RWWoWmaZDuLP079cmv0yys8KP7999/z3HPP8csvv3D48GEWLlzI1VdfnW+frVu38tBDD7Fq1SpycnJo06YNCxYsoHHjmvdcLwODFo4W/JH9BzuzdmpQXEREREREyiXbCU8tTCx2H6fL5ESyiWmCYUBaBsz8NpVF69Ow24ruXD82PAjPYnqDp06dIjo6mrS0NBISEvLdRb58+XKio6Pd369evTrfsenp6axZs4ZXX321+Ab+ZefOnRiGQZs2bejVqxdfffUVV199NVOnTmXTpk08//zzAOzZs4fQ0FAmTpzoPvbEiRP89NNPfPfdd0ydOpVvv/22VNeUmu/SBpfy26nf+Hz750y8cGK1WP5QRERERESsJ9sJU+cX3TdXv9w6/fIKL5+emppKhw4deOONNwrdvmvXLi6++GJatWrFypUr+f3333n00Ufx9vau6KWrreYezQE4nHOYVFdqFddGRERERERqKpcLTHI73nlf5l/lFZG3TNv27dt55513mDBhgnvbmcu0BQQEALBlyxaio6OpW7cudevWzddBL868efMYNmwYAMOHD8+3VFtJ8m7IvuCCC4iLiyv1cVLzdanbBW+HN3sT97Ll+Jaqro6IiIiIiNRQ6pdfDVijX17hmeIDBgxgwIABRW5/5JFHuOKKK3j22WfdZS1atKjoZaslm81GeHg4fg4/6jrqcjTnKLuyd9Heq31VV01KKS9Dm63C94tIFVB+1qcMrU35WZ8ytDblZ31nZuhhz71zvDiJaS6e+yyJzGwTH0+D9CwTLw+D+68OJMi36J8FD3vp6zVw4EBGjRpV4n5t2rRhw4YNHD9+nK5du7J48WIGDx5cYL833niD9957D4C1a9cyb948Tpw4wVtvvYVpmpw6dYq0tLRS1c3LywsAu92O01nCenbyj2Gz2YhsGsmlBy7lq51f8fn2z2lbp21VV0tKSb/PrE8ZWpvysz5laG3Kz9qUn/UVlmFJfXP1y63TLz+rzxR3uVwsWbKEBx98kH79+rFx40aaNWvGww8/XGCJ9dNlZmaSmZnp/j4pKQkAp9PpfkENw8Bms+FyufKt7V9Uuc1mwzCMIsvPDCrvB951xq0cRZXb7bk/vT4+PrhcLprbm+cOimftop1Hu1LVsTq2yTTNfOV5dSmqvCa0KS/DmtSm8tbdim0qb37VuU3lLbdqm3x8fDBNs9i6W61NxZXXtDb5+Pi4t9eUNp1e939Cm/z8/Gpcm2piTkW1yc/PD6DUbbVCm2piTsW1yc/Pj7S0NPd58p4xVtgz6QzDIDTAxvBuvnyyJo3MbPD2MBje3Y/QgIId7zPPk/fvos6ft8+PP/5I8+bNMU0z3/POz9wv77+hoaE8/fTTTJs2jUGDBrnL877Gjx/PhAkTME2TrVu34nA42L9/v7suo0aN4osvvsDf35+kpCT3/xec/v2Z1z6zXsW9ZmUpL+z1yPtvXq7VvdP/T5T38zIoahBf7fyKL3d8yYPdH8Rm6I+TVpCXn1iXMrQ25Wd9ytDalJ+1KT/ry8swIyMjX1lxy5yHBdq5rrsf839MIzPbxNvDxvDuvoQFlmHUuwRr1qyhefPmpd4/LCyMadOmMW3atEIHxSdMmOCeeZ7XLz/9+eOjRo1iyZIlBAQEkJyc7C4/83urOauD4seOHSMlJYVp06bx5JNP8swzz7B06VKuvfZaVqxYUeTD1p9++mmeeOKJAuW7du1yf6AEBQVRv359jh49SmLi32v5h4aGEhoaysGDB0lN/Xvp8nr16hEcHExcXBxZWVnu8vDwcPz9/dm1a1e+P0o1a9YMh8PBjh078tUhMjKSnJwc9uzZ4y6z2WxERUWRnJzMH3/8Qa1atTBtJq4QF0meSexO3E3O0Rz3/n5+fjRq1IgTJ04QHx/vLq+ObUpNTc33RvD09KR58+YkJiZy5MiRGtemQ4cOcfLkSWrVqkVAQECNaFNNzKmoNh07dsydX61atWpEm2piTsW1yeVycfLkSSIiIqhbt26NaFNNzKmoNuXl17p1a2rXrl0j2lQTcyquTS6XC5fLRevWrWtMm6Dm5VRUm/L+GxERwe7du2tEm6Dm5VRcm5o3b87OnTvd78XMzEwMw8Db2xuXy5Xv3DabDS8vL5xOJ20bumh0pRcnU01CAu2EBXuSnZ1NTs7ffRC73Y6nZ2756YO4DocDDw+PAuV5zy5zuVzY7XZee+01MjIycLlc7meX5Q0Qv/jii+7Oed4fDq688kqmTp3KmjVr6Nq1Kzk5OWRnZ5ORkZGvTR9++CFXXHEFGRkZ7jYNHjyYDz/8kNdff52nn36ajh078t///pcBAwZw/fXXM3fuXN599133a5p3XtM0ycjIKLJNHh4eOBwOsrKy8uXh6emJ3W4nMzMz38C4l5cXhmHk+2NI3j7Z2dns3bsXgJSUFKR6cTqd7Nq1i25NuxHoFcjR1KNsOLSBCxteWNVVk1LIy69FixbuCQhiLcrQ2pSf9SlDa1N+1qb8rC8vw4YNG5bpuAtaeBJR38GJZBe1A2zFzhAvrbx+uWmaOBwO3n33Xfe2M58p/tprr9GoUaN8xw8ZMsTdL+/WrVuR15k3bx5XXXVVvrJrrrmGDz/8kHfffZdp06bRsWNHnnrqKQYNGsTQoUOZN28es2bNqnAbzzXDLM3t8KU9mWGwcOFC9yzwQ4cO0bBhQ2644QY++ugj936DBw/Gz8+Pjz/+uNDzFDZTPO+PVYGBge5rVbcZKTmZqeyP207DJhHYPXxZm7mWXdm7iPCIoItXlxLrWB3bZOVZNuVpU3Z2Njt37iQiIgKHw1Ej2lQTcyqqTTk5OeXOr7q2qSbmVFzdnU4nO3fuJDIyEg8PjxrRppLKa1Kb8vKLiorC4XDUiDadWfea3qa8//GPioriTFZt0+nlNSWnotqUl19kZCSGYdSINhVX95rYJtM02bFjBw0bNuTAgQM0a9YMb2/vSpv1fLbLy6K61b20bcrIyCAuLo6mTZvi6ekJ5PZXa9euTWJioru/WhMlJSURFBRkiXY6nU527NhBZGQkj618jJitMVzX9jqmXjq1qqsmpXB6fvpjsjUpQ2tTftanDK1N+Vmb8rO+vAwbNWrEvn373P1yqT4yMjLYs2dPvmxK2189qzPFQ0NDcTgctGnTJl9569at+eGHH4o8zsvLy70G/ensdnuBD5K8PyidqazlRX1Albo8/TD2U1tp7JeJ7eRJjODWRHpGsit7F3E5cVzocyEehsdZqftZaxO5fxwqS3lNaJPNZnP/tzLrrpzOfpvOZn7K6dy1yWazlTu/6tqmipRbrU15A0DF7W+1NpWmvCa1KS+/mtSm4upY1vLq3ibDMIqsS2H75x1TndtUnnKrtsnpdLrrmJdl3nvyzBsd8lS38rKobnUvTZtOzyMvP/2xq3ob1HIQMVtjWLpzKY9e8igedo+SDxIREREREZEa56w+UMvT05POnTuzbdu2fOXbt2+nSZMmZ/PS55YzExK3gisTAxNcWZC4lToEEWgLJMfMYW/23qqupYiIiIiIiMg/SucGnQnzDSMxM5Ef9hV9c76IiIiIiIjUbBUeFE9JSSE2NpbY2FgA9uzZQ2xsLPv27QPggQceYN68ebz33nvs3LmT119/nc8//5zx48dX9NLVhzMdTCcYHn/NHHCBy4nhyqCFZwsAdmbvrNo6SqnYbDaaNWtW5Cwfqd6Un/UpQ2tTftanDK1N+VmfMhSpHKe/l+w2OwMiBgCwZMeSKq6ZlIY+C61PGVqb8rM+ZWhtys/alJ/1KcOarcKpbtiwgY4dO9KxY0cAJk2aRMeOHXnssceA3Iexv/322zz77LOcd955TJ8+nQULFnDxxRdX9NLVh90HDDvgwv2Smk6wedHCowUGBkdzjpLkSqrKWkopORxn9akCcpYpP+tThtam/KxPGVqb8rM+ZShSCZyZOFwpuau6kbuEOsDy3ctJz06vyppJKemz0PqUobUpP+tThtam/KxN+VmfMqy5Kjwo3qtXL0zTLPA1a9Ys9z5jx45lx44dpKenExsby1VXXVXRy1Yvdi8Iag2GA6fLBAyweUDGUfxsftR31AdgV9auqq2nlMjlcrFjxw5cLldVV0XKQflZnzK0NuVnfcrQ2pSf9SlDkUqQdgiO/wAJ6+H4Gkg/zHl1zqNxUGPSc9L5bs93VV1DKYE+C61PGVqb8rM+ZWhtys/alJ/1KcOaTfP/K4tPfVwhXTiQXg9XUDuwOSB1H2QnEeEZAcCu7F24TL2RRERERERERCqdMxNObQZXFmCCmQOJWzFcWQyMHAjAF9u/qNo6ioiIiIiISJXQoHhlsnuR4fIGnwbgXQcwIfFPGtkb4ml4kupK5YjzSFXXUkREREREapiMHCcn0rPIyHFWyvkcDgfR0dFER0fTuXNnYmNjAVi5ciXBwcHubdHR0WzZsoW4uDh8fX2Jjo6mdevWjBo1iszMTPc+9erVIzw8nOjoaLp165bvWlOmTOH111/PV3bo0CFGjBhRKW2RfxDn30ujG5D7mDPTCc50roy6EoDv931PYkZi1dRPRERERERqLPXLqz8tjH+2BERA1ilwpuNI2Uszj2Zsy9rGzqydNHA0qOraiYiIiIhINWea5l+PZyreoeQMNsUn4XSZ2G0G7UIDaRDgXewxNgMMwyhye3BwsLvDvWDBAqZOncqnn34KQO/evYmJicm3f1xcHG3atGHDhg3k5OS498k7x5QpUwgNDWXixIkltgegQYMGfPjhh6XatzxcLhc2m3XvEW/atCl79+4tUD5+/HjeeOONQo/55JNPePTRR4mLiyMyMpJnnnmGK6644mxX9dyy+4DNDs4cwARXNtg9we5DRO0IWoW24s/4P1m2axnD2g6r6tqKiIiIiIgFmKZJSV1z9cvLrir65RoUr0Q2m43IyMjcEA07BLaEU39A+iEiPMLZxjb25ewj08zEy/Cq6upKIfJlKJaj/KxPGVqb8rM+ZWhtys/68jLMysoCwGXCt3HHiz3GZZqk57gw/y5gw5FT+MTbsBXTub68aRj2ojfnk5SURHBwcOl2Jvdu9i5dunDgwIFSH3OmuLg4hg4dyoYNG5g1axZffvklJ0+eZM+ePdx5553cd999AMyePZtXX32VrKwsLr/8cl588UUABg4cyOHDh8nMzOThhx9mxIgRxMXFMWjQINq2bUtsbCwbN27Ex8en3HWsSj///DNO59+zDzZt2kSfPn0YNqzwgd41a9Zwww038PTTTzNw4EA++ugjrr76an799VfatWt3rqp99tm9IKg1nNqM4cp9HxHYKrccGBg5kD/j/+Tz7Z9rULwa0+8z61OG1qb8rE8ZWpvyszblZ31n9suh5L65+uXW6ZdrULyS5eTk4OnpmfuNV+3cpdTTDxGScpRg7yBOuRKJy46jpWfLqq2oFClfhmI5ys/6lKG1KT/rU4bWpvysLycnp0z7mybujrdB7r/Nv8opZee6MKdOnSI6Opq0tDQSEhJYs2aNe9vy5cuJjo52f7969ep8x6anp7NmzRpeffXV8lfgDL///rv7bveWLVty1113sWvXLhYtWsTatWtxOByMGjWKJUuWcOWVV/LBBx9Qu3ZtUlNT6dy5M0OHDgVg69atfPjhh7Rv377S6lYVwsLC8n0/bdo0WrRoQc+ePQvd/5VXXqF///488MADAPznP//hm2++4fXXX+ftt98u9JjMzEwyMzPd3yclJQHgdDrdA/KGYWCz2XC5XJjm31Mniiq32WwYhlFk+ekD/XnlkDuDoDTldrsd07sezlp+2E7+ggG47L7YyJ3d0b9Ff55f8zzrD67nSPIR6gXUK3Xdq7RNppmvPK8uRZVbvU1Op5Ps7Gw8PDyw2Ww1ok01Mafi2mSaJtnZ2Xh6emK322tEm8pbbsU2lZSfFdtUUnlNa5NpmuTk5ODl5VVj2nR63Wt6m07Pr6a06fTymt6mvPw8PT1rTJuKq3tNbJNhGGRnZ+N0Ot2/F08/X2HK2y/PPfff1z3zOmf2y3/88Ud3fc7sl3///ff5Pkfy+uWvvPJKvvLT23P6NU/flld+5rbff/+dn3/+mZycHFq1asXEiRPd/fI1a9bgcDi4+eab+eKLL9z98lq1apGamsqFF17IkCFDME2TrVu3MmfOHHe/vLD6FP96/12vvFzPzLcoGhSvRC6Xiz179hAZGYndbs8tDGgOWScxnOlEOP3ZYCSyM2unBsWrqUIzFMtQftanDK1N+VmfMrQ25Wd9eRk2atQIyF1K7fKmYcUek5Hj5Pv9CeS4TBw2w/3fSxqF4O0o+ufAVsKA+enLtMXExDBhwgSWL18OFL5MW0JCAlu2bCE6Oprdu3fTr1+/fB30iurTpw/+/v5A7hJuR48e5dtvv+Wnn36iU6dOAKSlpXHBBRcA8NJLL7F48WIA9u3bx759+/Dw8CAqKsryA+JnysrKYs6cOUyaNKnIpffWrl3LpEmT8pX169ePzz77rMjzPv300zzxxBMFynft2uXOIigoiPr163P06FESE/9+TndoaCihoaEcPHiQ1NRUd3m9evUIDg4mLi4u38yL8PBw/P392bVrV74/SjVr1gyHw8GOHTvy1SEyMpKcnBz27NnjLrPZbERFRZGamsq+fQcINJ2EBbhIO7KNWk0uIjExkdQjqUT4R7Dl5BY++vkjJl02iRMnThAfH+8+T3Vt0+kzPDw9PWnevDmJiYkcOXLEXe7n50ejRo0s36ZDhw5x4sQJateuTUBAQI1oU03Mqbg2uVwuTpw4QbNmzWjYsGGNaFNNzKmoNuXlFxkZSd26dWtEm2piTsW1yeVykZWVxXnnncfevXtrRJug5uVUVJtcLhc5OTm0bdu2xrQJal5ORbXJ5XLhcrmIiopi9+7dNaJNUPNyKq5NzZs3Z+fOnUBunnk3Cl/eNAyn05nv3DabDS8vL1Iysvjx0El3f9zpMnHYbXSrF4Sdv69pt9vx9PQkKysr91yZGRiGgcPhwMPDwz0YnyevX56ZmcmCBQsYP348S5YsweVy0bt3b+bMmZNvENk0TbZs2UKHDh2Ii4ujd+/etGrVyr0tJyeH7OxsMjJyr+vt7e3+nZG3LSsry31TTmZmJi6Xi4yMDJxOJ3369MHb25ucnBzq1avHvn37WLZsmbtfnjcY3759e/r168dLL73EokWLgNx++Y4dO/Dx8SEqKopWrVqRkZGR7+fDbreTmZmZr01eXl4YhpFv37x9srOz3Y8VS0lJoTQMszTD7lUsKSmJoKAgEhMTCQwMrOrqFMnpdLJjx46Cf4jMToITsaSTTYzXPlyGjav8ryLYHlxldZXCFZmhWILysz5laG3Kz/qUobUpP+vLy7BRo0bs27ePZs2a4e1d/DPIAA4mp/P7sSScpondMGhfJ5CGARVbfiw0NNT9B4mMjAxq165NWloaK1eu5PXXXy/02WV5y6odP36crl278uKLLzJ48GAg/7PL3njjDd577z0gd7D2mWeeKfBcszOXadu0aRPPP/88AJ06dSImJobFixdz8uRJHn/88Xx1WbFiBf/5z3/48ssv8fb2plOnTsyaNQt/f3/3OcsrIyODPXv25Mumqvur8+fP58Ybb2Tfvn00aNCg0H08PT15//33ueGGG9xlb775Jk888QRHjx4t9JjCZorn/bEqr53VdUZKYmYiuw/GEuWXjZ/NByOsGya51/3oj494cvWTtKvTjgXXLag2M1Jq4iyb8rYpOzubnTt3EhERgcPhqBFtqok5Fdcmp9PJzp07iYyMxMPDo0a0qbzlVmxTSflZsU0llde0NjmdTnbt2kVUVFSBWXdWbdPpda8pORXVptPzO5NV23R6eU3Jqag25eUXGRlZ4IZVq7apuLrXxDaZpsmOHTto2LAhBw4ccPf9iprFnFd+MDmDP47n75c38C/Yny/pPKcLCwsjPj4e0zTJyMggJCSE1NRUVq5cyRtvvMEnn3ySb/+4uDiGDRvGzz//zPHjx+nWrRsvvPBCkf3y6dOnA7mP2zq9X55Xl9PPN2vWLDZv3sxzzz0HQOfOnfnkk09YvHgxp06d4rHHHstXlxUrVvDkk0+yZMkSvL296dy5MzNnzsTf3999ztK+NmfKyMggLi6Opk2buldLTEpKonbt2iX2yzVT/FzwCAS/Rvik7qNhjhf7PbLYmb2TTvZOVV0zERERERGxuIYBPoT4eJKW7cTXw17sDPHyWLNmDc2bNy/1/mFhYUybNo1p06a5O9+nmzBhAhMmTKhwvS6//HKGDRvGxIkTCQkJ4dixYzidTpKSkggJCcHb25vY2Fh+++23Cl+rOpsxYwYDBgwockC8vLy8vPDy8ipQbrfbC9x4k/cHpTOVtbyoG3rKUr4zeyerM1aTEZxBrGHQI6cBkZkJGN5h2O12roi6gqd/fJpNxzex99RemgQ3qZS6n802GYZRpvLKqntVtslms7n/W5l1V07nrk02m63S86vqNp2N8uraporkV13bVJFyK7YpbzCuJrWppPKa1Ka8/GpSm4qrY1nLq3ubDMMosi6F7Z93THVuU3nKrdomp9PprmNelnnvyTNvdMhjGAbhgT6E+pauX17ceYoqX7t2Lc2bNy+2PqeX16lTh2nTpvHMM89w1VVXucvzviZOnJjvxvTC2lrUv08/pnfv3oX2y5OTkwkJCcHHx8fdLy/ta1mS08+Rl19pJ4cU/tMh5VbUGw6/JuDwJ8IVCK4sdmftxmW6Ct9XqlSRGYolKD/rU4bWpvysTxlam/KzvvJm6O2wU9vHs9IGxPOeXdahQwceeOAB3n33Xfe2vGeX5X2d+UxxgCFDhpCSkpLvWeTFefTRRwkPDyc8PJzOnTuX6pi2bdvyyCOPcPnll9O+fXuuvPJKTpw4Qf/+/UlOTqZNmzY89dRT7iXVa6K9e/eyfPlyxo0bV+x+9erVKzAj/OjRo9SrV+9sVu+cS3WlsjJtJSlmCi5cZBkmqx0HSU3f794nxDeEruFdAViyY0lVVVVKoN9n1qcMrU35WZ8ytDblZ23Kz/rUL6+5/XItn16ZUo9A0l4IbAJ+hfxxIScVV8IvfOLYRobdxmV+fWnk0ejc11NERERERKqtwpboluqhui2fPmXKFN555x3279+Pw1H0QnDXXXcdaWlpfP755+6ybt260b59e95+++1SXcsK/fIjOUf4LPkzssjCjh0ffMg207gqpwX1QnuDzQOAz/78jIeWP0TzWs358sYvSzUbQURERERE/jnUL6++KtIv1y0rlWV7DOaHF2F+0gfzw4tge0zBfRx+2AKa09wVBK5sdmb+ee7rKcUyTZOUlJRSPbdAqh/lZ33K0NqUn/UpQ2tTftanDKUsXC4XM2fO5Oabby4wID5q1Cgefvhh9/f33HMPS5cu5YUXXuDPP/9kypQpbNiwId9yeTVBgC0AD8MDExMnTrLIwgMHAaYHZBx379e7eW+87F7sPrmbP+PVL69u9FlofcrQ2pSf9SlDa1N+1qb8rE8Z1mwaFK8MqUdg5X2QehQzOw3S4//6/kjBfX0aEuHInR1+IGsPGc70c1xZKY7L5eLAgQO4XFra3oqUn/UpQ2tTftanDK1N+VmfMpSyWL58Ofv27WPs2LEFtu3bt4/Dhw+7v+/WrRsfffQR7777Lh06dCAmJobPPvuMdu3ancsqn3V+Nj8u8b0EO3ZM08Rm2OjhEY0fHpDx9/Lx/p7+9GraC4Avtn9RRbWVouiz0PqUobUpP+tThtam/KxN+VmfMqzZNCheGZL2Qk4aeAWCYYAzG7KSIGlfwX0Ng1qBHQkxfXDhYnfqhnNfXxEREREREbG0vn37YpomUVFRBbatXLmSWbNm5SsbNmwY27ZtIzMzk02bNnHFFVeco5qeW5GekbT2aI2X04toz2gifTvmbshOgpy/b0ofGDUQyH2uuMvUH7xERERERERqOg2KV4bAJuDwBdOJy+4DpjO3s+3hV/j+dm8ivFoBsDN7J2ZW0jmsrIiIiIiIiEjNVc9eDzt2kl3JYPcCz1q5G06bLd6zSU/8Pf05nHKYjYc3VlFNRURERERE5FzRoHhl8KsHvV4AD38MmwPsnuBdG35+BlzOQg9p5tsBu+HgpJHJiaSNuQPpUuUMw8DT0xPDMKq6KlIOys/6lKG1KT/rU4bWpvysTxmKVI4QRwiGYXDCdSK3wLtu7n8zjsJfzwb0cnjRt3lfAD7f/nlVVFOKoM9C61OG1qb8rE8ZWpvyszblZ33KsGbToHhliRqKMWId9qFLMYavBN86cHgdxL5e6O5eNm8aebYADHaaRyBlzzmtrhTOZrPRvHlzbDa9NaxI+VmfMrQ25Wd9ytDalJ/1KUORyhHqCMXTw5M0M410Vzp4hYJhA2dG7jLqf7ky6koAlu5cSo4rp6qqK2fQZ6H1KUNrU37WpwytTflZm/KzPmVYsynVSnTYZbI8MYnDAY2h+39yC2PfggM/FLp/hGcU2DzYY0vCmbYfsk6du8pKoUzT5NSpU5h/zR4Qa1F+1qcMrU35WZ8ytDblZ30VyjD1SO5NualHKqUuhmEwfvx49/eHDx/GbrczZcoUAKZMmUJ4eDjR0dG0a9eOxYsXu8sDAwM5efIkACkpKTRt2rTYa8XFxTF//vwit/fq1Ys2bdrkK2vTpg29evUCcp+fPXTo0EKPa9WqFR06dKB79+5s27atpGZLDWHHjp8r93FmCc4EsNlzB8Yh3xLqXcO7EuITwsmMk6zZv6YqqiqF0O8z61OG1qb8rE8ZWpvyszblZ33qlxeupvTLNSheSWK2xHDhexcy5NMhdJnehZjMTGh5HWDC9/dD6tECx9R31MfXHkCmzWC/kQKJf4LuTq9SLpeLI0eO4HK5qroqUg7Kz/qUobUpP+tThtam/KyvQIamCdnpJX9t+Qg+vBA+vTL3v1s+KvmYEjr4tWvX5qeffsLpzH3MU0xMDG3bts23z+TJk4mNjeWTTz5h7Nix7noHBQXx+uuFr5hVmJI63wB2u51NmzYB8Mcff+BwOEp17piYGH777TduvvlmHnjggVLXSazN5XLhSM39GUlwJuQWupdQPw5m7s+q3WZnQMQAAL7Y/sU5r6cUTr/PrE8ZWpvysz5laG3Kz9qUn/UVmmFp+ubqlxepOvXLS1djKdaRlCPct+w+TqSfIDMnk3RnOuOXjOfCsWtofPx3OLEVVk6CAR/k3qH+F5tho4VHC/5w/cFOewpNcwIheScEtarC1oiIiIiISLXgzICPuxS/jysHkvblDvIZNsg4CUtvhjWPgq2Y7t5NseDhU+RmwzDo0aMHq1at4rLLLmPhwoVce+21he7bunVrHA4H8fHxANx2223MnDmTSZMmFdj3mWeeISYmhszMTEaNGsX999/PI488wubNm4mOjmbixImMGzeuwHHDhw9n/vz5tGvXjvnz5zN8+HCWL19e/GtzmksuuYSXX3651PuL9QXkBHCKU8Q7c38u8awFNk9wZUHmCfDOnTk+MGogc/6Ywze7vyE9Ox2fYt4XIiIiIiLyD5STAbOji96ufnmpVId+uWaKV4K9p/aSlp2Gl90Lm82GaZqcSD/BlfOv4eXg9hwxPOHoBvj15QLHRnhEAHDInkkq2blLuWUcP8ctEBERERERS3JlA2Zux9swcv+L+Vd5xeR1eA8dOoSnpyehoaGF7rdu3TpsNhthYWEA1KpViyFDhvDee+/l22/ZsmUcOHCA9evXs3HjRr788ks2bdrEU089Re/evYmNjS204w0wYMAAli5dCsDXX39N//79y9SWxYsXc95555XpGLE2/xx/4LSZ4oYB3nVy/33aEurR9aJpGNCQtOw0Vu1dda6rKSIiIiIiVqd+ealUh365ZopXgibBTfD18CXFlUKYTxinsk7hdDlJy07jrU3zeDcnnd7OZEb+/Aqd65yP0fhS97GB9kDq2OtwzHmM3d52zssAknaARxDYPauuUf9QhmHg5+eHYRhVXRUpB+VnfcrQ2pSf9SlDa1N+1lcgQ7t37p3jxUk9AvN7QlYqeAZCVhJ4+sHwVeBXr+jjHN4l1qdbt27cddddzJ07l6FDh5KRkZFv+7Rp05g+fToBAQHMmzcv38/epEmT6NGjB6NGjXKXLVu2jCVLlrB69WoAkpOT2b59O7Vr1y6xLt7e3kRGRjJ79myioqLw9i65/gBDhw7F29ubZs2a8dprr5XqGLE+wzCo41WHP/mTdDOdNFcavjbf3CXU0w5AZkLuH6hsHhiGwZWRV/Lur+/y+bbP6R9Rtj/sSOXT7zPrU4bWpvysTxlam/KzNuVnfYVm6Cihb65+ebGqU79cM8UrQT3/erzQ9wX8vfzJMrMI9Q3lg6s/4M0r3uTChhfidPjwtcvOTSeOMGjeNczd8Bbp2enu4yM8c2eL77SdwnT4gpkNSdtKfJ6AVD6bzUajRo2w2fTWsCLlZ33K0NqUn/UpQ2tTftZXIEPDyF1Krbiv4GbQ60Xw9IeclNz/9noxt7y440rxRxrDMLjkkkuYNm0a11xzTYHtec8uW716NT169Mi3rX79+vTu3ZvZs2e7y1wuF48//jixsbHExsaya9euQpd+GzNmDNHR0YwYMSJf+bBhw7jnnnsYNmxYaV5OIPfZZbGxsSxcuJDw8PBSHyfWZrPZaNqoKcH2YOC02eIe/uDwA8x8K7QNajkIgFV7V5GUmXSOaytn0u8z61OG1qb8rE8ZWpvyszblZ32FZlhS31z98mJVp365ZopXkqFthtItvBub9m+iXaN2NAhsAEC/iH5sT9jOh799wKINr7MjK43Hlz/E879OZ0jrodx43o00DWrK+oz1JLmSOO4XRp3EfZB1AjKOgE/9Km7ZP4vL5eLEiRPUrl1bv7gsSPlZnzK0NuVnfcrQ2pSf9eVl6OvrW7YDo4ZCw4tzn2EW2Lj4O9HLaMKECXTo0IGQkJAyH/vggw9y+eWXu7/v27cvTz31FMOGDcPX15e4uDhq1apFQEAAycnJ7v1mzpxZ6PkGDBjAQw89RP/+/dmxY0fZGyP/GO7PQ9/anHSeJMGZQCOPRrkbvetCyu7cJdR9c/vtUSFRRNaOZMeJHXyz6xuGtBlShbUX/T6zPmVobcrP+pShtSk/a1N+1qd+ea6a2i/Xu7IS1fWrS2N7Y+r61c1XHhUSxROXPcn3Y9fyr1oNaYKT5MR9zPptFv3m9GPCFxOwpeVGsdM8BP7Ncg9M3gU56WdeRs4i0zSJj4/H1Cx9S1J+1qcMrU35WZ8ytDblZ30VytCvHtS/sFI73gCRkZGMGTOmXMc2b96cbt26ub/v378/11xzDV26dKFdu3aMHDmSjIwM2rdvT3Z2NtHR0UyfPr3I83l5efHQQw/h5eVVYNuXX35JeHi4+ysuLq5cdZaaIe+9VNuWuwSge6Y4/P1c8eykfP3tgVEDAfhixxfnrJ5SOP0+sz5laG3Kz/qUobUpP2tTftanfnnN7pcbpgXenUlJSQQFBZGYmEhgYGBVV6dITqeTHTt2EBkZid1uL3ynvctxLR/PD5npzKnVnlUJOwGoE1KH/hf3J9AjkBFBN1I7fT9kJ4JHINSKLtUyClJxpcpQqi3lZ33K0NqUn/UpQ2tTftaXl2GjRo3Yt28fzZo1K/UzuuTcyMjIYM+ePfmysUp/taKs1M6891Jw82C+Tv8aH8OHYQHD/n6+3snfIOsU+DUB/6YA7E/cT+/ZvbEZNlaPWU2ob2iV1f+fTr/PrE8ZWpvysz5laG3Kz9qUn/WpX179VaRfrpni51qT3tjajeUSb1/edR5l2dWzGN1hNOnJ6ZxKOUVCZgKjvxnD47Hz2Z54MPfu9bQDVV1rEREREREREUupZauFDRvpZjppZtrfG7z/Wt0t4xj8NU+gUVAjoutF4zJdfLnjyyqorYiIiIiIiJxNGhSvRIZhEBQU9Pfd50XpfD+ERUNWMk1+eY6Hu93H6jGrae3dGk+bJ+ENw5m7ZQGDlj7KTd8+xddb5+HMTDwnbfinK3WGUi0pP+tThtam/KxPGVqb8rM+ZShSOfLeSx42D4LsQcAZS6h7hYFhA2c6ZP/93LwrI68EYMmOJee0vpKfPgutTxlam/KzPmVobcrP2pSf9SnDmk2D4pXIZrNRv359bLYSXlabAy59CbyCIGEzrH8GHw8fhjcZTnhgOB2aduCK1ldgt3ux/vgO7v7hVS774FLe+vkNEtISij+3VEipM5RqSflZnzK0NuVnfcrQ2pSf9SlDkcpx+nspxB4CnDEobrOD11/Lo2ccdRdfEXkFNsNG7JFY9ifuP5dVltPos9D6lKG1KT/rU4bWpvysTflZnzKs2ZRqJXK5XBw+fBiXy1Xyzv4N4JLncv+9dQ7sWYq/zZ96jnp4O7y5ufvNfHfzd9zZeQK1vQM5khrPy2tfoOesnjz4zYP8fvT3s9uYf6gyZSjVjvKzPmVobcrP+pShtSk/61OGIpXDlXyI+D+W4Eo+VPigOJyxhHruey7UN5Qu4V0AzRavSvostD5laG3Kz/qUobUpP2tTftanDGs2DYpXoviMRDYe3UF8RimXOm/UE9rflvvvH/4FSXuJ8IgAYFf2Lur61eX/ut3PqpuW8VzXO+hQqwnZzkwWbVvEsE+GMXT+UBb9uYgsZ9ZZatE/j2maJCYmYv71XDmxFuVnfcrQ2pSf9SlDa1N+1qcMRSrB9hiMj7sSvPImjI+7ErJ/I5A7KJ7vveVZC2yeYOZA5gl38cDIgQB8sf2Lc1pt+Zs+C61PGVqb8rM+ZWhtys/alJ/1KcOaTYPilWTFod+49ceXeeHg59z24yusOPRb6Q48//+gbifIToXv7qGxUQdPw5MUVwpHnEcA8PRrwOBW1zK/7xRi+k7h6qjBeNg8+OPYHzy4/EF6zurJS2tf4nDy4bPXQBEREREREZHqLPUIfHcPpP01+zs7hVorHsHmcpFhZpBmpv29r2GAd53cf5+2hHqfFn3wsHmw48QOtidsP8cNEBERERERkbNFg+KVICEjiTe2LiY5K40c00VKdjpvbF1MQkZSyQfb7NDrRfCuDSe24lj/LE09mgKwM2vn3/sFRIDNi/OCw3mmyzi+H/M993a5l3r+9TiRfoK3f3mbyz+4nLu/upv1B9frLhYRERERkX8QV3oizvhduNJLuWpVCQzDYPz48e7vDx8+jN1uZ8qUKQBMmTKF8PBwoqOjadeuHYsXL3aXBwYGcvLkSQBSUlJo2rRpsdeKi4tj/vz5xe7z/vvv4+np6T4vwGeffcb27X8PWs6aNYtjx46VpZlS0yTthewUcOVgc2aARyCOrGSCnXYA4p3x+ffPW0I9MwFc2QAEegXSs0lPQLPFRURERESk9NQvr/79cg2KV4Ij6SfJcGYR4OGLYRiYpkmGM4uj6SdLPhjAry70fB4wYNs8Io4eBGBfzj6yzL+WRrc5IKhV7r/Tj1Db5uKOTnfw3ajveG3Aa1zU8CKcppOvd33NTQtvYtDHg5i7aS5p2WmFX1MKZRgGoaGhGIZR1VWRclB+1qcMrU35WZ8ytDblZ31nZmiaJmZOVolf2XvWkrF0KhkrXiZj6VSy96wt+bgSbqKtXbs2P/30E06nE4CYmBjatm2bb5/JkycTGxvLJ598wtixY93PXAsKCuL1118vdbtL0/meN28enTt3ZuHChe4yq3W+5RwIbAJewYCJYTpzl0V3+BLiUR+AE84T+fd3+IHdFzAh47i7eFDLQUDuoLhuOD/39PvM+pShtSk/61OG1qb8rE35WV9hGZamb65+ea7q3i93VHUFaoJ6PrXwtnuSlpOBgUG26cTfsFHXp1bpT9KwO0TfCbFvEvr9kwQNepBEWzZx2XFEeUbl7uMZDL7hkHYAkrZDSCB2myd9W/Slb4u+7EjYwYd/fMhnf37GjhM7eHzl4zy/5nmubX0tI84bQZPgJmel/TWJzWYjNDS0qqsh5aT8rE8ZWpvysz5laG3Kz/ryMszIyMgtcGWT/uXUYo8xXU5ISwDTBAzMrFSy1k4n67eFGDZ7kcf5XPEEODyL3G4YBj169GDVqlVcdtllLFy4kGuvvbbQfVu3bo3D4SA+PncW7m233cbMmTOZNGlSgX2feeYZYmJiyMzMZNSoUdx///088sgjbN68mejoaCZOnMi4cePyHXPixAm2b9/O+++/z9SpUxk7dizr1q1j8eLFfP/99/z73//m4YcfZsOGDQwdOhR/f382bNhA06ZNGT16NJ999hkeHh4sXryY+vXrF/t6isX51YNeL2AsGwdZKYAJvV4gxKcxO9IPFZwpbhjgUw9Sducuoe7bAIBeTXvh6+HLweSDxB6JpWP9jue+Lf9g+n1mfcrQ2pSf9SlDa1N+1qb8rK9AvxzAmU36l48XeYz65dbpl2umeCUI8Q5kQuvB+Nq9sGFgMwzOq9WMEO/Asp0oeiLUvwgjJ52IbavANNmVtSv/Pv5Nc+9kd2VD0o6/3mS5IkMimdJrCt+P+Z5HejxCk6AmJGcl8/5v79N3Tl/GLR7HqrhVuExXxRtdQ7lcLvbv3+++m0asRflZnzK0NuVnfcrQ2pSf9ZUrQ9Pp7nhjGLn/Nc3c8goaPnw48+fP59ChQ3h6ehb5x51169Zhs9kICwsDoFatWgwZMoT33nsv337Lli3jwIEDrF+/no0bN/Lll1+yadMmnnrqKXr37k1sbGyBjjfAp59+ylVXXUW3bt3YsWMH8fHxXHTRRQwePJhXX32V2NhYrrvuOjp16kRMTAwbNmxwHxseHk5sbCwDBgxg+vTpFX5NxAKihuIa9Ck5PnUx/RtC036E2EOA3JniBWZj5D1XPDsJnOm5RQ5v+jTvA2gJ9aqg32fWpwytTflZnzK0NuVnbcrP+tQvr9n9cs0UrySXNuhA2+DGLNq0iuXJmzmafpJsVw4etjK8xDY79HwRFl1F850/sbFJFMd8DRKdiQTZg3L3Mey5y6if2AiZ8bl3s/vUy3eaQK9ARnUYxcj2I/lx34/M+X0Oq/auYvW+1azet5rGQY25sd2NDGkzhLTsNPae2kuT4CbU869XSKX+WUzTJDU1VUvkWZTysz5laG3Kz/qUobUpP+vLy7B27dq5BTaP3DvHizsmPZGM5dMwczLBwwey0zEcXnj3nozhE1T0gXaPEuvTrVs37rrrLubOncvQoUPz3ykPTJs2jenTpxMQEMC8efPyLS83adIkevTowahRo9xly5YtY8mSJaxevRqA5ORktm/f/nd7izBv3jyefPJJDMPgmmuuYcGCBdx+++0l1h/gmmuuAeCCCy5wP19Naj6z4SVk+EXgl3ME9nxFragh2LCRYWaQaqbib/j/vbPdK3dVtqxTkH4M/HNXWBsYNZBF2xbx1c6v+FePf2EvZoaHVC79PrM+ZWhtys/6lKG1KT9rU37WV6BfDmAvvm+ufnnxqlO/XDPFK1GIVyDdA6II8Q4kNSeD307sLvtJfEOh14v4ZmfR4PA2yEphZ/bO/Pt4BLg76iTvBGdGwfMANsNGjyY9eGfQOyy7aRljoscQ6BXIvsR9TPtxGu3fak+r11sx4MMBXDT9ImK2xJS9viIiIiIiclYYhoHh8Cz2yxYQhucF12M4vCEnA8PhjecF12MLCCv+2FI8484wDC655BKmTZvm7sSeLu/ZZatXr6ZHjx75ttWvX5/evXsze/Zsd5nL5eLxxx8nNjaW2NhYdu3aVejSb2PGjCE6OpoRI0Zw7NgxfvjhB6677jqaNm3Kxx9/zLx580r9Gnp5eQFgt9vdz2GTfwDDILle79x/71iI3bBTy577eLMEZ0LB/b3r5v4346h7NbZujboR7B1MQnoCaw+sPRe1FhERERGRaqikvrn65cWrTv1yzRSvZDbDRpewViw9uIE1RzfTKTSq7CepfxF0vIuIA59yICycXY7NdPTqiM047R4G38aQeSJ3ibfEbVCr/V/LMhSucVBjJl88mXsuuofPt3/Oe7++x8q4lbhcLmyGjRxXDvctu4+LG1+sGeMiIiIiIhbiaNwZW1gUZmoChl8ItuLuRC+jCRMm0KFDB0JCQsp87IMPPsjll1/u/r5v37489dRTDBs2DF9fX+Li4qhVqxYBAQEkJye795s5c6b732+99RZ33HEHL730krusefPmHDlypMBxZ34v/2wpdS+DAx/BsV8haS8hHiEkOBNIcCbQxKNJ/p29QoEducun5ySDRyAOm4MBEQP4eNPHLNm+hIsbX1wl7RARERERkepP/fJc1b1frpnilchms1GvXj0urtcOgF/id5Kek1m+k3W4g3DPJnhlZZCeeoBDmXvybzcMCGwFhg2yT0HawVKd1sfDh+Fth/OfXv/B39MfP08/DMMg25VNWnYa+xL3la++NURehjab3hpWpPysTxlam/KzPmVobcrP+iqSoc0nCHto80rteANERkYyZsyYch3bvHlzunXr5v6+f//+XHPNNXTp0oV27doxcuRIMjIyaN++PdnZ2URHRxd4vti8efO4+uqr85UNGjSImJgYrr/+ev7zn/8QHR3NsWPHGD16NKNHj6ZTp07lqq/UHDabjbAmbaHhXz9/Oz9zP1e80JniNgd4//VsvvSj7uKBUQMBWLZ7GZnl7dtLmen3mfUpQ2tTftanDK1N+Vmb8rM+9ctrdr/cMC3wcIOkpCSCgoJITEwkMDCwqqtTItM0uX/9uxxOO8EdrQdySb3zynei9BOs/+MhttZrSJMMB70iHyk4GzztECTvAAwIuQAcfqU69ZGUI1w0/SJSMlNIyUohx5VDiG8Iv9/5u2aKi4iIiIhUoYyMDPbs2UOzZs3w9vau6urIaQrLxmr91fKyZDt3L4GVk8C/AQnXzueL1C/xMry4LuC6gksVZp6AU3+A4QFhXcCw4TJdXPb+ZRxOOcxrA16jb4u+VdMOERERERE5p9Qvr74q0i/X7SqVyOVysXv3bkzTpHvdtgCsObq5/Cf0qU1Ek1GAwQGvLDK3zy1kn/rgWRswIfFPMF2lOnU9/3q80PcF/L388bB7YLPZaBTUiDp+dcpf3xogL0OXq3Svo1Qvys/6lKG1KT/rU4bWpvysTxmKVA73eyn8UvAMgJRDBB/fiR07mWYmKWZKwYM8a4HNE8xsyDoB5D4e7crIKwH4YvsX57IJ/2j6LLQ+ZWhtys/6lKG1KT9rU37WpwxrNg2KVyLTNMnKysI0TbrVaQPAppNxnMoqpMNdSrXr9qC2PQSnYWP3gfmQsDX/DoYBgVFgOCAnBVL3lvrcQ9sMZd24dXx+w+e0CmlFSlYKK+NWlruuNcHpGYr1KD/rU4bWpvysTxlam/KzPmUoUjnc7yW7FzQbAIB9xyKC7cEAnHCeKHiQYYB3WO6/04+5i/OWUF8Rt4KUCvTtpfT0WWh9ytDalJ/1KUNrU37WpvysTxnWbBoUP0vq+damRWADXKbJT8f+rNC5IkIvBQ9fdtVrDCvugTM74nav3IFxgNR9kJVU+nr61+PSZpcypmPu8wje/PlNvdlFRERERESkZoi4Nve/cUsJIffZfvHO+ML39a6b+9/MeHBlA9AqtBXNazUny5nF8t3Lz3ZtRURERERE5CzRoPhZ1L1u7mzxCi2hDjTzaIHNpw4JQWGccCXCD4/AmQPX3mHg/dfS50l/guks0zVGR4/G2+HNH8f+4Mf9P1aoviIiIiIiIiLVQp1oCGwCOemEJOSurJbgTCh8X4c/2H0BM3dgHDAMg0FRgwD4fNvn56DCIiIiIiIicjZoULwS2Ww2wsPDsdlyX9YudVpjMwx2Jh3iaPrJcp/X2+ZNI8+m4FuHnQ0jIG4pbP2w4I4BEbnPQHOmQ/LuMl2jtk9trmt7HQBvb3i73HW1ujMzFGtRftanDK1N+VmfMrQ25Wd9ylCkcuR7LxkGRFwNQMie74HcQfFCV0gzDPD5a7Z4+lF3cd5zxdceWEtCWhED6lJp9FlofcrQ2pSf9SlDa1N+1qb8rE8Z1mxKtRKdTM3kQGI2J1MzAQj29KddraYArDm6pULnjvCIALsXeyK64jRssP5piN+UfyebBwS2yv13+iHILOQ5acW4peMteNg8+PnQz/x88OcK1deqDMPA398fwzCquipSDsrP+pShtSk/61OG1qb8rK86ZWgYBuPHj3d/f/jwYex2O1OmTAFgypQphIeHEx0dTbt27Vi8eLG7PDAwkJMnc28KTklJoWnTpsVeKy4ujvnz5xe5vVevXmzalL/vs3jxYl566aVytEz+CQq8lyKuBgyC96zG7nKRZWaRYhbxfPC8JdSzE8GZAUCT4Ca0q9MOp+lk6c6lZ73+/3TV6bNQykcZWpvysz5laG3Kz9qUn/VVpwzVL698GhSvJKu3HObuGT/y0OyfuHvGj6zechiAbnVyl1D/4eimCj2ru4GjAT6GDxneARxoOwhcOfDd3ZB5xvPDvWqBT8Pcfydtcz8HrTTq+tdlaJuhwD93trjT6WT79u04nWVbfl6qB+VnfcrQ2pSf9SlDa1N+1lehDJ2ZkHUq97+VoHbt2vz000/uusTExNC2bdt8+0yePJnY2Fg++eQTxo4di8vlAiAoKIjXX3+91NcqqfNdmMGDB3PvvfeW6Ziy0PvI2gq8l/wbQP2LsJsuaqUmA8UsoW73Ao/g3H+fNls8bwn1L3Z8cbaqLX/R7zPrU4bWpvysTxlam/KzNuVnfeqXl54V++UaFK8EJ1IyeHf5Fk6lZpKV7SI5PZt3l2/hREoGncJa4mFzcDjtBHEpR0s+WRFsho0Wni0A2Nn6MggIh5SDsHpyweeLBzQDuw+4siBpR5muc+v5t2I37Pyw/wf+OPpHuetrZXkfGmJNys/6lKG1KT/rU4bWpvysL1+Gpgmms+SvtINw/EeI/zn3v2kHS3Fc8TfsGoZBjx49WLVqFQALFy7k2muvLXTf1q1b43A4iI/PfQbzbbfdxsyZM0lNTS2w7zPPPEPnzp1p3749zz//PACPPPIIy5cvJzo6munTp5fqdZo1axb3338/AKNHj+aee+6hS5cuREZGuuvsdDq577776Ny5Mx06dODDD3MfQbVr1y569OjB+eefz4UXXkhsbKz7nNdeey29evVi2LBhpaqHVF8FPg8jrgEg5Og2oJhBcfh7CfWMo+73yhWRV2Bg8OvhXzmUfKjS6yv56feZ9SlDa1N+1qcMrU35WZvys74CGZamb65+uSX65Y5KP+M/0LHEdDKyXfh4OkjNyCbH6SIjy8mxxAxa+QdzfkgE647/yZqjm2kWUK/c14nwiGBT5iYOuY6Tfunz+HxxE+z7Fja/D+1G/72jYYegVnAiFjKPQ8Yx8K5Tqms0DGzI4JaDWfjnQt7a8BZvXvlmuesrIiIiIiIV4YJjPxS/i+n6a4nnvzrTzmw4+TskbQejmHug61wM2Is99fDhw5k9ezatWrXC09OT0NBQdwf7dOvWrcNmsxEWFgZArVq1GDJkCO+99x7jxo1z77ds2TIOHDjA+vXrcblc9OnTh/79+/PUU0/x+uuvExMTU3xbi3HixAl++uknvvvuO6ZOncq3337LjBkzqF+/Pj///DPp6el06dKF/v37U79+fZYvX46Xlxe///479913H9988w0Av/32Gxs3biQwMLDcdZFqqmlfWDuFkOO7oVmH4gfFvUKBHeBMh5xk8Aikjl8dLmx4IesOrmPJ9iXcesGt56zqIiIiIiJSlUrom6tfDlijX66Z4pWgTpAP3h42TNPEZoDTZeI0TeoEeQPQvW7ucgZrjm3BaZb/LqEgexBh9jBcuNgV4AEXPZy74edn4Vhs/p09AsGvce6/k3aUabmG2y+4HQODb/d8y7b4beWur4iIiIiInG0m7o43RiFl5detWzfWr1/P3LlzGTp0aIHt06ZNIzo6mvvvv5958+ble+bapEmTeP3118nKynKXLVu2jCVLltCxY0cuuOAC9u7dy/bt2ytcT4Crr74agAsuuIC4uDj39aZPn050dDRdu3YlMTGR3bt3k5mZydixYznvvPMYNWoUW7ZscZ+nX79+GhCvqTx8odkAQpJOQFYyCc6Eoh9xZnOAd2juv9OPuYsHRg0E4PPtn5/t2oqIiIiIiGWoXw7W6JdrpnglqO3vzW292/DuN1twmuDKduLj4SA7J3cAvENIc/wc3pzMTOHPU/tpW6tJua8V4RnB8fTj7MzaSduWN2Ac+Rn2fAUr/g+u+gy8g//e2a8xZCZATkru88WDz4PT3hBFaVarGQMiBvDlzi95e8PbvNT/pXLX12psNhvNmjXDZtP9Ilak/KxPGVqb8rM+ZWhtys/68jL8e6k22193jhfDmQnxP4ErBwwHmDm5A3qhXXKfjVz01Uqsj2EYXHLJJUybNo2tW7fy8ccf59s+efJkJk6cWOix9evXp3fv3syePdtd5nK5ePzxx7n55pvz7bty5cp8348ZM4aNGzfStm1b99JqJfHyym2r3W53P3fM5XLxzjvv0LNnz3z7Pv744zRt2pQ5c+aQmppK06ZN3dt8fX1LdT2p3or8PIy4huCdC7FnJpPlnUmyK5lAexF/bPGum7vqWsYxCGgOho1+LfrxxKon2JawjZ0ndhJRO+LsN+YfSL/PrE8ZWpvysz5laG3Kz9qUn/UV7JdDiX1z9csBa/TL9c6sJD3a1OflG9vx3JVh9GsVgLeHnU/W7gbAw+bgwrCWAKw5urlC12nq0RSH4SDRlUi8KwG6PwmBTSD1MHz/YO4yDXkMGwS1BmyQdRLSS//cszs63QHAVzu/Ys/JPRWqs9U4HLpXxMqUn/UpQ2tTftanDK1N+VlfvgwNI/fRSMV9OXxz/5/f5gCcuf8Nap1bXuyxJd8sCzBhwgSeeeYZQkJCytyWBx98kJdfftn9fd++fZk+fTppaWkAxMXFkZiYSEBAAMnJye79Zs6cSWxsbKk73kXp27cvb775prszvmnTJpxOJ0lJSTRo0ADDMJg1a1aFriHVV6Gfh/U6YfOrT+2kBMhJI8FVzBLqnrXA5glmdm5/GgjyDuKSxpcAsGT7krNRbfmLfp9ZnzK0NuVnfcrQ2pSftSk/6yuQYUl9c/XLi1Td+uUaFK8kOft+xvv7p6n7+9uMNRfQ0XMvG3fHs/VAbuc5bwn19ce3ke3KKfd1PA1PGjtyl0Xfmb0TPP3h0lfB7gkHVsEfM/If4PDNvasdIHk35KSV6jotQ1tyWdPLMDF555d3yl1fq3G5XOzYseOMu4DEKpSf9SlDa1N+1qcMrU35WV+5M/SpD2HdIaRz7n996ldanSIjIxkzZky5jm3evDndunVzf9+/f3+uueYaunTpQrt27Rg5ciQZGRm0b9+e7OxsoqOjmT59eqHn6t27N+Hh4YSHh3P33XeX6vq33norTZs2pWPHjrRr1457770X0zQZP34877zzDtHR0SQkFDMoKpZV5HvJsEHE1dROPgFZKcU/V9wwwDv3eXykH3UXXxl1JZC7hHqRy69Lhej3mfUpQ2tTftanDK1N+Vmb8rM+9ctrdr/cMC3Qi0tKSiIoKIjExMRq+Xw3V3oiGUv/g5mRiOlyYtg9SMObp070J6h2KI8N74RhmNy99k1OZCYzqd0QOoVFlft6h3MOsyx1GZ6GJ8MChuEwHLBtPvz4aO7dJQM+gHqd/j7ANOHU75B1ChwBULtjqe5A+f3o7wz7ZBh2w843N31Dw8CG5a6zVTidTnbs2EFkZCR2u72qqyNlpPys7//Zu+/4Oop77+Of3T1NvTdbtiXZcm+yDRgbCKYXE0qMQzokARK4yU0vNxcuCankcpMnQEIICUkICWB6DYGAwcbYuMnGXZIly7J676ftPn+sJOtY7ajY1ojfm5deOt4zu2dGX59jRrMzIxmqTfJTn2SoNslPfd0ZTpkyhdLSUrKzs/F4PKe7WqKXzs5OiouLQ7IZ7/3VsaJSOwf9PGwupWDTrWyau5z0uEVcGnfNwBfyt0D9DkCDlBWgO+jwd3D2H8+mI9DBuuvXsTBt4clsyoeS/HumPslQbZKf+iRDtUl+apP81Cf98vFvNP1ymSk+Bqy2WqygF5yR9mCzZRJhmGS4Oymra+PdA5Xoms7ZqXMB2Fi1Z1Svl26kE61H47N8lPpL7YMzr4fpHwUrCOu/Dh297qzQNIidbQ+YB1qgrTSs11mYtpBzppxD0Aryhx1/GFWdhRBCCCGEEEKI0y52KsnODMCivrN08NnejmgwIgELvDUARDgjuDD7QgBePPjiya+vEEIIIYQQQogxMepB8XfeeYerrrqqZ+335557bsCyX/rSl9A0LWT9+olAi0pGM9xgBsACzCCarnPWotkAPLPlMB2+ACvS7EHxnXWFtAe8I389TWO6czoARf6i7oOw4ocQlwPt1fb+4mbw+EmGG2Jy7cdtR+w73sPw5TO+DMBT+56iqrVqiNJCCCGEEEIIIcT4Fpd5GYZp4gu00hJsHrigpkFEmv241xLqq2euBuCVwlcI9u53CyGEEEIIIYQYt0Y9KN7W1saiRYt44IEHBi337LPPsnnzZiZNmjTalxx39Ig4XEs/juaMQDMcoGkYyTmcu2QWaQmRtLT7eWn7EbKi05gcmYTfDLKt5uCoXnO6yx4UrwhU0Gq22gedkXDBb8DhgWMbYfcJe4F7UsGdAljQdMCeVT6EZZOWsWzSMvymnz/u/OOQ5VWn6zq5ubnouiyioCLJT32SodokP/VJhmqT/NQnGQoxNoZ6L+nZl5PU2gRBH7WN2we/mCfV/u5vgmAnAOdMPYdYdyy17bW8f+z9say6QD4LJwLJUG2Sn/okQ7VJfmqT/NQnGU5so0718ssv58c//jHXXnvtgGWOHTvGV77yFR577DGcTudoX3Jcckw9A8+l/42e90mITMRqq0M3/axdkQPA67vKqGnuZEXaPADerd43qteL0WNId6RjYVHkKzr+REIunH2X/XjnfVC++fhzmgaxuaC7INgOrcVhvdaXl9mzxZ/Y+wT1HfWjqrcKAoHA6a6CGAXJT32SodokP/VJhmqT/NQnGQoxNgZ9L7liSNSTAKirG2JQ2/CAM95+3GnPFncaTi6fcTkALx16abRVFf2Qz0L1SYZqk/zUJxmqTfJTm+SnPslw4jrptzqYpslnPvMZvv3tbzNv3rywzvF6vTQ3N4d8gb3BffeXaZo91w/nePc+YQMd732s+7hlWWEfBzBd0RQHktGiUrACPgLle1g0LYm5UxIIBE3WbSrkrORZAOxtKKG+s2VUbZruOL6EeiAQOF7HGddA7sewLBNr/TcItlYdr7vmIBg9w37cVkawo27QNlmWxfJJy5mfMp9OfyeP7Hyk5/ho6n46cxqo7pZl4ff7KSoqwu/3T5g2TcScBqrjaPIbr22aiDkNdrw7w+7/8ZgIbZqIOQ2VX/frToQ2TcScBmuT3+/n8OHDfcqq3KaJmNNAberO78R6q9ymiZjTYHUPBoMcPnwYv9/fU6b7Gr3/PF6PD+drvNV9OG3q7++SGF9M06S4uLjnPdufpIQ8AOp9FRD0DX7BiK7Z4h3V0PV3oHsJ9deKXsM31PliWMLJT4xvkqHaJD/1SYZqk/zUJvmpTzKc2Bwn+wV+8Ytf4HA4+OpXvxr2OT/72c/44Q9/2Od4UVER0dHRAMTFxZGRkUFVVRVNTU09ZZKTk0lOTubYsWO0tbX1HE9PTyc+Pp6SkhJ8vuMd1szMTKKjoykqKgr5S56dnY3D4aCgoCCkDrm5uQQCAYqLj8+y1nWdmTNn0t7eTn1DA9GuScQ2VdF+YAPJU5dyxcJUdh2uYvOBCmbEBZnsSOBYoIF/F29jgZbRc53htik5LRmn4aTeW8/2qu3EBeKOt2n5HbSVvIezuYTOl26mYtHPyM6ZYbfpSB3JLhcxzjaC1fm4UxYQ0DwUHynv06a2tjbKysq4Kv0qdhzbwSPbH+HmpTdjdphUVlb2lI+KimLKlCnU19dTW1s74jadipy629TN5XKRk5NDU1MT5eXl1NfXU1hYSExMzIRo00TMaaA2VVdX9+SXkJAwIdo0EXMarE2maVJfX099fT1paWkTok0TMaeB2tSdX3NzM4mJiROiTRMxp8HaZJpmz00pE6VNMPFyGqhNpmn2fB0+fHhCtAkmXk6DtSknJ4dgMEhpaSmmaeL1etE0DY/Hg2maIdfWdR23291zQ0Q3wzBwuVwEAoGQu9u7j/v9/pBBXIfDgdPp7HPc6XTicDjw+XwhdXe5XBiGgdfr7RkgBnC73WiaRmdnZ0ibPB4PlmXh9Xp7jqncpu4yfr+fI0eOANDa2opQT1LyCqjaRV10DNbRN9GyLhu4sDsFKLRXXAu0gjOGpRlLSYtKo6qtig1HNnBhzoWnrO5CCCGEEEIIIYZPs3r3+kd7MU3j2Wef5ZprrgFg+/btXHnllezYsaNnL/GsrCy+9rWv8bWvfW3A63i93pBfmjQ3N/f8sio2NrbntXRdxzTNkF9cDHRc13U0TRvw+Il393fvF3Di3SADHTcMg0AgwKFDh5g+KYngO78CNDwXfw/NE8tf1x/k7X0VTE2O5ozlFo8d/jc5MRn8MO8zQ9Z9sDa91/keBb4Cchw5rPCsCG1TfQH6i9dDsANr4ZfQln7teN2tIHrdZgi0ARoYLsyYWeBJD2mTZVn2L1ctk2ufvJaCugK+etZXue2M20J+BsOt++nMqbtNJ9al+5dbhYWFzJhh30AwEdo0EXMaqE2BQGDE+Y3XNk3EnAarezAYpLCwkNzcXJxO54Ro01DHJ1KbuvObOXMmDodjQrTpxLpP9DYFg0GKioqYOXMmJ1K1Tb2PT5ScBmpTd365ublomjYh2jRY3SdimyzLoqCggMmTJ1NWVkZ2djYejwdN00Ku27s+J+u4rut8+ctf5oEHHgCgoqKCKVOm8N///d/88Ic/5I477uDiiy/m3HPP7XO9bnfeeWdPmVWrVnHfffcxf/78ntd86623uOCCC3j88cdZu3YtAE8++SQ33HADb775Jueff35IHcMp3/06CxYs6GnT+vXrufbaa8nKysLr9fLxj3+c//mf/wnrZ3Oizs5OSkpKyMrKwuVyAfTcDNbU1NTTX52ImpubiYuLU6KdwWCQgoICcnNzMQyj3zKmZfKPqt8Q8DVyTXEdcR+5b/CLNu4Dbw1EToaYGQD8fOPPeST/Ea6YcQW/uuxXY92MD61w8hPjm2SoNslPfZKh2iQ/tUl+6uvOcMqUKZSWlvb0y08HTdP48pe/zG9/+1vA7pdnZmZyxx13cNddd4X0uQfSu8z555/P/fff39MvB7u/vGrVKp544omefvYTTzzBDTfcwFtvvRXSLw+3/ECvc8011/T0y2+44YY+/fJwdXZ2UlxcHJJNuP3VkzpTfMOGDVRXVzN16tSeY8FgkG9+85v8+te/pqSkpN/z3G43bre7z3HDMPp8kHT/QulEwz0+0AfUcI5rmobD4cARk4yVlI1ZV0KwbCfO3PO5dnkOW4tqOVrbxvKWKeiazuGWCmq8TaRHJo647jOcMyjwFVAaLGW5vhyndnzPdiMxF865G97+Ftru30P6GZB5jl33YABMP2DZX0E/RstB8CSDcfxnr2ma/XPH4LYzbuPrr32dv+z6CzcuvpEoV1RYdRzL42OV02DHHQ4HhmH01GEitGm0dVSlTSczP8np1LWp+4aGcMsPVcfhHpecRld3h8PRMxg3UdoUzvGJ1Kbu15pIbRqsjsM9Pt7bZBjGgHXprzyM/zaN5LiqbQoGgz19Hk3Ter66696f7uOVrZUcaTzCtPhppEenD1l+qOOJiYls3rwZ0zQxDIOnn36aefPm9ZS7++67+71ObyeW6d2e7j/Pnz+fp556io9//OMArFu3jgULFvQpO5zy/X2/6KKLeOqpp2hra2Px4sV89KMfZcmSJWH9bPor0/vvkvyya3wa6P3Y87ymk+ieTLWvkbr2QuLaayEyeeATItLsQfGOaojOAU1n9czVPJL/CG+WvEm7v51IZ+QYt+LDa6j8xPgnGapN8lOfZKg2yU9tkp/6Rprhif3y0erul3f/ruCpp54K2ab6Rz/60ZDXCKfM/PnzWbduXc8gd3c/e6zKdzuxX37VVVf16ZefbCf13fmZz3yG3bt3k5+f3/M1adIkvv3tb/Paa6+dzJc+LfwWJE/Jwm+BY4odZLBsB5ZlERvh4qpl0wB4bWsVc+LsGwU2Ve8b1WumGCnE6rEErAAl/pK+BaZfBbNuACx451vQ1rVcZLADMEFzApr92Ax2He/fpdMvJSs+iyZvE//Y849R1Xu8MgyDmTNnyi+2FCX5qU8yVJvkpz7JUG2Sn/pOzNCyLDr8HUN+/X333znzD2dy5d+v5Mw/nMnfd/99yHOGmhWtaRrnnnsub7/9NgDPPvss1113Xc/zN954Iy+99BJgrwZ21113sXjxYs444wwqKir6lBnIrFmzKCsro62tjdbWVsrLy/tdrWKk5U8UFRXF0qVLKSwsDPscoZ5wPw+TPNPAcFMXEw+HB/+7iisRdCdYfvA1ADAvZR7T4qbRGejk34f/PUa1F/LvmfokQ7VJfuqTDNUm+alN8lNffxmG0zeXfrka/fJRzxRvbW0NqXhxcTH5+fkkJiYydepUkpKSQso7nU7S09OZNWvWaF96XDnW0sHu6mYCQROHobMwcQYJhgOzpQar6RhafCYXLpjM+r3lVDd2MKk5BShhY9Verp22MqxZCf3RNI0Zrhns6NxBkb+IXFdu30Jn/RfU7IL6/bD+G3D5o2BEgGaAFaBnUBzLPj4AQzf40tIv8b1/f48/7fwTn174aTyO07NsxMliWRZtbW1ERUWNOBNx+kh+6pMM1Sb5qU8yVJvkp77uDLs7352BTpb/cfmg5wTMAKVN9h7kuqbT0NHA557/HHesvwOHPnB3L//WfCKcA/+/P8DatWt59NFHmT17Ni6Xi+Tk5JC913vLzMwkPz+fO++8k4cffpg77rhjiNYet3r1al588UUsy2L16tXs2LFjTMv3VldXx+bNm4dVP6GecD8Pk4wkcMVQF5sIhc/C/BsHvqimgScV2o9BRxW4k9A0jdUzV/PA1gd48dCLXDXrqrFvzIeQ/HumPslQbZKf+iRDtUl+apP81Hdivxzsvvni3y8e8Bzpl6vTLx/1TPFt27aRl5dHXl4eAN/4xjfIy8vjzjvvHHXlVNEZCLK7uhlf0MRvWviCJrvrfZA6B4DAUfsvg8PQWbtiOgCHDugYGFS211PSWjWq189x5qChURWoojnY3LeAww0X/D9wRkHVdtj+K3uJ9Lg5oDtA0wHNHiQPdg76WlfNuorJMZOp66hj3d51o6r3eGSaJmVlZX32dxRqkPzUJxmqTfJTn2SoNslPfSPJ0B/0Y1kWumbvX65r9t7p/qB/1PVZsWIF77//Po8//jhr1qwZtOy1114LwNKlSwfcJmsga9euZd26dSHLr41leYA33niDvLw8LrnkEr7//e+HLDknJp5w30tJRhI4o6iLTcKsPwh1+we/sCfN/u6tAzMAwOqZqwHYWLqRho6GUdddyL9nE4FkqDbJT32SodokP7VJfuqTfvnE7pePeqb4+eefP+QU/96GG4QK2v1BgpaFDgSx510HLQtv+kLcFR8QPLYLa94VaLqDxVlJzM6M50BZI+6OJNojqnm3ai/ZMSPfXyBKj2KSYxLHAsco8heRZ+T1LRQ7Dc79Gbz5VfjgD5C2FKauspeAC3ZA2zHw1ULzIUha2jVQ3pdDd3Dzkpu56+27eHjnw9ww/wachrPfskIIIYQQQoiR8zg85N+aP2iZytZKPvLnj9DmayPWHUuzt5koVxRv3/j2oHuYhbPik6ZpnHfeefz85z9n//79/OMfA2+h5Ha7AXupuWAwOOS1e8vNzaW8vNxeBWvGjJ7jW7Zs4dZbbwXgvvvuG7L8YLr3LhOit1g9FofuIuCOoTkqhvjCZyFpzsAnOKLBiIRgu72/eEQGOQk5zE2Zy76affyz8J98YsEnTl0DhBBCCCGEECfdUH1z6Zer0y8f9aC4gEingaFp+LFvDghYEKFpeNJmgTsay9uKWX0II30umqZxw8oZ/PDJbbRVxOKfWsmmqn18YvoqjAEGosMxwzWjZ1B8kXsRen/XyroU5n4G9j0K73wHrnkeoifZs8YdUVDXZHfu20ohOmvA17puznX8dttvqWyt5LkDz3H9vOtHXG8hhBBCCCFE/zRNw+McvJOcnZDN/136f3zzX9+k1d9KtDuaey+5l+yE7DGpw+23386iRYv6bIs11n784x/3OXbWWWeRn5/f8+f169cPWl6I4dI1nUQ9kWpXM3WxicQXvQhnfMdeUa0/mgYRadBabC+hHpEBwOrc1eyr2cfLBS/LoLgQQgghhBATjKZpgy5zLv1ydcig+BjwOAwWpsbae4pbQTRgSmwEES4nvszFBIo2EijbiZE+F4ApydGcOzeDt/eaNLdbNDpaOdBYyryErBHXIdORiUtz0Wa2URGsYLJjcv8Fz/gOVOdD7Qfw1tfgisfAcILuhJgZ0LTfHhT3pNgD5f1wO9x8Me+L/HTjT/n99t9z3ZzrMHSj37Kq0TQNl8sl+30oSvJTn2SoNslPfZKh2iQ/9Y00wzVz13DO1HMobSplatzUQe9EH67c3Fxyc3PH5FoXXXQRDofdBb3uuuu47rrrep678MILh3WtwcoP9jriw2E476UkI4lqRzV1iVOYXrEByt6BqRcMfIIn1R4U9zfZ248ZHq7IvYJ7Nt3D1vKtVLRUkBGTMYat+fCRf8/UJxmqTfJTn2SoNslPbZKf+qRfHj4V++WaNZy1z0+T5uZm4uLiaGpqIjY29nRXZ0CdgSCH6toob+0gLcpDXnocZnMFnet/A7pBxCX/heaKBKC53cf3H9tCWcRe9JQaLpu2hFtnXzmq19/SsYUDvgNkO7M5L/K8gQu2lMHz14KvGXI/BjOutpdXj0yDxj3gqwdnHCQssu+E70eHv4NVf1lFQ2cDv7z4l3x01kdHVXchhBBCCCGErbOzk+LiYrKzs/F4hl5OTZw6/WWjSn91tCZqOw/7DrOhYwOpjbVc/u+HYNolcOF9g59Un28PikdnQ9RUAD71zKfYVr6N76z4Dl9Y8oWTX3EhhBBCCCHESSP98vFrNP3yka/XLfpwGzrJRgBd06jt8OELmuixGehx6WAGCZbv7ikbG+li9bJpRHWk0drhZ0v1QXzBwKhef4bLXre/1F+K1/IOXDAmE879OfhaYOsv4alL4bGzoOBpiM219xP3N0FH5YCXiHBGcNPimwB4cNuDmJY5qrqPF5Zl0djYiAL3ioh+SH7qkwzVJvmpTzJUm+SnPslQiLExnPdSkmEvQVgfk4CJBkffhM7GwU+KSLO/d1RB12uszl0NwEsFL4243sImn4XqkwzVJvmpTzJUm+SnNslPfZLhxCaD4mPINE2aa6uJdhlYlkV1mz0wbWQuASBwdEdI+YsWZpIZkY4WcFPb1kp+fdGoXj9RTyTBSCBIkBJ/yeCFk+dBoAMs0/7ua4b137Q7/1Fd+xy0FkFw4MH1Ty38FLHuWIoaini96PVR1X28ME2TyspKTHNiDPJ/2Eh+6pMM1Sb5qU8yVJvkpz7JUIixMZz3Uqwei1NzEjAMmibngRmA4pcHP8mdAugQbIdAKwCXzbgMQzPYV7OP4obiMWjFh5d8FqpPMlSb5Kc+yVBtkp/aJD/1SYYTmwyKnwTpkW4AKroGxR2TF4OmYTYcxWyt7SnnNHQ+fvYMojrSaOsM8GbZ7v4uFzZN05jhtGeLF/oKBy/cfAR0BzgiAKtrcLwdmkshcjI4osEKQsvAA/XRrmg+s/AzAPxu2+/kzhkhhBBCCCGEEErRNI1EIxGA+hlde+IVPDP4SboD3PYMczqrAEiISOCcqecA8NIhmS0uhBBCCCGEEOONDIqfBKlRLgAaOn10BIJonhiMlFwAgmWhs8WX5CQzPyYXC4t3yw/QHugc1WvnOHPQ0akN1tIQbBi4YOw0cETanXnLAl8r6C6InWrvIx47C9DAWwPe2gEv89lFnyXCEcH+2v28feTtUdVdCCGEEEIIIYQ41ZJ0e4C7LnUGaAbU7oGGIW40715CvbP6+BLqM+0l1F889KLcNC6EEEIIIYQQ44wMio8hTdOIiooi0ukgweMEC6pau5ZQn9K1hHrZTqxe+29rmsYXVuThCkTR5vPxwqGdo6qDR/eQ6cwEoMg/yHLsUelw/r3gjrMHxjUdUvPs4wDOaIi0r0Nzob2EXD/iPfF8asGngIkxW7w7Q03TTndVxAhIfuqTDNUm+alPMlSb5Kc+yVCIsTHc91L3vuJ1WgdM+Yh9sPDZwU9yJYDmBNMPvnoALsy+EI/Dw5GmI+yr2Tfi+n/YyWeh+iRDtUl+6pMM1Sb5qU3yU59kOLHJoPgY0nWdKVOmoOs66dEeACra7JnfRvpcNKcbq70Rs64k5Lys1FgWxc4E4NlD2zFHObDcvYT6Yd9hTGuQfQ9mroFPbYHL/2rPEG8shIaC489HTwPDA6YXWksGvMxNeTfhNtzkV+az5diWUdX9dOudoVCP5Kc+yVBtkp/6JEO1SX7qkwyFGBvDfS91D4rXm/WYM66xDxa9AGZw4JM0HSJS7ccd9hLqUa4oVmWtAuzZ4mJk5LNQfZKh2iQ/9UmGapP81Cb5qU8ynNgk1TFkmia1tbWYpklalBtN02jxBmj1BdAMJ0bGAqDvEuoAt5xxLpqmUWtV8+99JaOqx2THZCK0CDqsDo4Fjg1eOCodZl0POVcCFuQ/cPw5zYAYe9l3Oo6Bv7nfSyRHJrN23loAfrv1t6Oq++nWO0OhHslPfZKh2iQ/9UmGapP81DeeMtQ0jdtuu63nzxUVFRiGwV133QXAnXfeyYYNGwa9Ru8y559/Pnv27Al5fv369WiaxpNPPtlz7IknnkDTNNavXz/gdXNycvj2t7/d8+fGxkYeeuihnj+XlJSEXFN8+Az3vRSrx+LUnASsAE2TloA7HtqroXzT4Cd6upZQ99b1rK7WvYT6ywUvExxsUF0MaDx9FoqRkQzVJvmpTzJUm+SnNslPfeMpQ+mXjz0ZFB9DlmVRW1uLZVm4DJ2kCHtv8cq20CXUg+UfYAV8IedOT0xhevQkLCwe/WAzXv/IO8+6ppPjygGg0D/EPmjdFt9ufy/+Z+hscXfi8Y5+8yEYYOb5F/K+gEN3sOXYFnZU9B30V0XvDIV6JD/1SYZqk/zUJxmqTfJT32gyrOtsZm/DEeo6+7+RdbgSExPZvHkzwaDdL3nqqaeYN29ez/M/+tGPOPfccwe9Rjhl5s+fz7p163r+vG7dOhYsWDBg+ffff5+UlBSeeeaZnp+TKp1vceoM972kadrxJdRpghx7YHvIJdQd0WBEAiZ4awA4b9p5xLhiqG6rZnvF9pE24UNN/j1Tn2SoNslPfZKh2iQ/tUl+6pN++cTul8ug+EmUEe0GoLK1E8uy0BOnoUUmYAV8BCv77i92zcylGLpOjVbGP/OPjuq1pzunA1DmL6PD7Bj6hMRZkHUpYMHO+0Kfi5lu75UWaIP2sn5Pz4jJ4NrZ1wLwu62/G03VhRBCCCGEENidcW/QN+TX62XbuWXjr/nu+3/glo2/5vWy7UOeM1QHX9M0zj33XN5++20Ann32Wa677rqe52+88UZeeuklALKysrjrrrtYvHgxZ5xxBhUVFX3KDGTWrFmUlZXR1tZGa2sr5eXlzJw5c8DyTzzxBLfeeivTp09n8+bNAPzgBz9g3759LF68mB/96Ef84Ac/4I033mDx4sU8/PDD/PnPf2bt2rVcfPHFzJgxg3vvvXfoH7740OkZFA/WQa7dt+XIG+Ad5BdamtZrCfVqAFyGi0umXwLAS4cG//svhBBCCCGEGP/C6ZtLv1yNfrnjtLzqh0RKpBtD12j3B2n2BojzOHFk5uE/9CbBsh04MheHlF+ZPpc/RfyLerOZ53ft59w56SR27U0+XAlGAslGMrXBWor9xcx1zx36pLyvQMm/oOQ1qD8EiV1/6XWnPTDefMDeW9ydAo6IPqffsvQWnt7/NO+UvsPe6r3MS53Xp4wQQgghhBAiPD4zwJff+fWgZYKmSVVnA6ZloaPR7O/gJ7v+wcMH/4kxyB5oj5z3TdyGa9Brr127lkcffZTZs2fjcrlITk6mtra237KZmZnk5+dz55138vDDD3PHHXcM2b5uq1ev5sUXX8SyLFavXs2OHf2vPGVZFs899xw/+MEPsCyLJ554grPPPpuf/OQnHDx4kG3btgFw3nnncf/99/PUU08B8Oc//5ndu3ezbds2AoEAs2bN4itf+Qou1+DtFx8uiXoi0DUonnQmJOTaq6gVvwqzPz7wiZ40u5/sb4RgJxgerpp5FU/vf5pXC1/ljvPuwGk4T0kbhBBCCCGEEGPPZ/q56Z2BB3GlX65Ov1xmio8hTdOIi4tD0zQAHLpGaqQ9W7yirRPotYR6TSHWCUsoxLmiOCNtBi6HQaOrgqc3F4+qPjNcMwB7CfWwlnpIyIXsy+zH+feHPudJBVcCYHUto973elPjprI6115m7sFtD46m6qfNiRkKtUh+6pMM1Sb5qU8yVJvkp76RZBiwgvaqVGhomoaOhmVZBKzR72W8YsUK3n//fR5//HHWrFkzaNlrr7Vn1i5dupSSkpJhvc7atWtZt24d69atY+3atQOW27RpEzNnziQxMZFrr72W5557Lux93i6++GKio6OJj49n0qRJVFVVDauOQi0jeS8lG8kA1Jv1mFgw/Rr7iaGWUDc84IyzH3fas8XPnHwmyZHJNHub2Vi6cbjV/9CTf8/UJxmqTfJTn2SoNslPbZKf+qRfPrH75TJTfAzpuk5GRkbIsfRoNxWtnVS2epmVGI0elYSeOA2z/giBsp04Z3wkpPzKtHlsqymkubOK9w5WcsH8yUxPjx1RfbKcWWzt2EpDsIF6s75nObhBLb7d3le85DWoOwBJs+3jmgYxuVC3zb4DvrMKItL7nH7rslt58dCL/OvwvyioKyA3KXdEdT9d+stQqEPyU59kqDbJT32SodokP/V1Z9jZad9Q69IdPHLeNwc9p97bwn9seoCOoJcoh4e2QCcRhpv7V9xOojtmwPNc+tAzVzVN47zzzuPnP/85+/fv5x//+MeAZd1u+2ZgwzB69jsLV25uLuXl5WiaxowZM3qOb9myhVtvvRWA++67j3Xr1rF161aysrIAqK6uZuPGjUydOnXI1+iu30jrKNQyks/DGD0Gl+bCZ/loMptImHE1bL8XqndC8xGInTbwyRFp4G+CjiqInIKhG1wx4wr+uvuvvFzwMquyV42yRR8u8u+Z+iRDtUl+6pMM1Sb5qU3yU9+J/XKw+8+D9c2lX65Ov1xmio8h0zSpqKgIuSsiKcKF09DxBU3qO/0AODLzAAge3dlnBveylJlEOV04In34nC08/m6Ys7z74dbcTHXafxELfYXhnZSQC9mX24/zHwh9zhEB0Vn245YiCPr6nD4jcUbP/mm/3/77kVT7tOovQ6EOyU99kqHaJD/1SYZqk/zUd2KGmqbhNlyDfmVEJvEfc68m0uGhI+gj0uHhP+ZeTUZk0qDnhXvX++23384vfvELkpLCuMF2FH784x9z9913hxw766yzyM/PJz8/n5UrV/Lcc89x6NAhSkpKKCkp4f777+eJJ54gJiaGlpaWnvNO/LP48BnJ56GmaSQa9hLqtcFaiEyBSSvtJwufG/xkdwqgQ7AdAq0ArJ5pr6L2xuE36PB3DLcJH2ry75n6JEO1SX7qkwzVJvmpTfJTX38ZDtU3l365Ov1yGRQfQ5Zl0dTUFDKIrWsaaVFdS6i3di2hPnkh6AZmSxVWc3nINTyGi6XJM4nxOOmIruZwZTNbCqpHXKfprukAHPYfJmAFwjtp8e2ABkf+BXX7Q5+LzARHNFgBaC3q9/QvLfsSAC8XvMyRxiMjrfpp0V+GQh2Sn/okQ7VJfuqTDNUm+alvpBmumrSI36/8T+4544v8fuV/smrSojGrU25uLjfddNOYXOuiiy4iMzOTzMxMvvrVr4Y8d+GFF3LhhRcOeO4777zD9OnTSUxM7Dn20Y9+lOeee474+HiWLFnCggUL+NGPfsTChQvx+/0sXryYhx9+eEzqLtQy0vdS9xLqdcE6+0Dudfb3wufAGuQXm7oD3F2/oOq0lwBcmLaQKbFT6Ah08Gbxm8Oqx4ed/HumPslQbZKf+iRDtUl+apP81Cf98ondL9csBd6dzc3NxMXF0dTURGzsyJYSPxWCwSAFBQXk5uZiGEbP8YZOH1vLG3HoGh+Zmoyha3i3PUawfA+OnJW45q8Ouc6O2gL+94OnMH0OIguXkRTj4SefPBOXwzjxJYdkWibPtD5Dm9nGRyI/QpYzK7wT138DDr8MUy+Ci06YMe5vgfod9uP4BeBO7HP6rS/eyvoj61kzZw0/ufAnw6736TJQhkINkp/6JEO1SX7qkwzVJvmprzvDKVOmUFpaSnZ2Nh6P53RXS/TS2dlJcXFxSDaq9FdHS6V2jvTzsNhfzDvt75BsJHNl9JUQ8MLjK8HXApf9BSYtH/hkbx007gHdBcnLQdP49eZf87ttv+OCrAv43erfjUHLPhzk3zP1SYZqk/zUJxmqTfJTm+SnPumXj3+j6ZfLTPFTIN7txOMwCJgWtR32kuOOzCUABI/lY5mh6+YvTMwh2uFBcwZwJbRS3+LltfyjI3ptXdOZ7rRni4e9hDocny1e+gbU7Qt9zhkDkZPtx80FYPVd9797tvizB56lvKW8z/NCCCGEEEIIIcR4kmTYs70bgg2YlgkON2RfYT9Z+OzgJ7sSQHOC6QNfAwBX5l4JwDul79DU2XTS6i2EEEIIIYQQYmgyKD6Gmjss2rVkmjtCJ99rmkZ6tL2EemXXEup66kw0dxSWtw2zpiCkvEM3WJ46B02DtCx777FXdhylsc07onp1D4qXB8ppM9vCOyl+OuR0zWDfeX/f56OyQXeD2QmtJX2ezsvIY3nmcoJWkId3jJ+lEYaiaRrJyclh7+UgxhfJT32SodokP/VJhmqT/NQnGQoxNkb6XorRYnBpLoIEaTQb7YMzrrW/l7wG/vZBXlQHT6r9uGsJ9dykXGYlzSJgBnit6LVhtuLDSz4L1ScZqk3yU59kqDbJT22Sn/okw4lNBsXHyPYiHz9/ppW/bDT42TMtbC/yhTyfEWVP4a9p9+E3TTTdwJi8GIDA0e19rrcibR4AZYEystKj8PmDPL25eER1izViSXOkYWFx2H84/BMX32Z37Ev/DbV7Q5/TDYjNtR+3l9lLqp/gtmW3AbBu3zpq2mpGVPdTTdd1kpOT0XV5a6hI8lOfZKg2yU99kqHaJD/1SYZCjI2Rvpc0TeuZLd6zr3jqYoidBoEOKPnn4BeI6B4UrwUzAMDqmfbN5i8demlYdfkwk89C9UmGapP81CcZqk3yU5vkpz7JcGKTVMdAU7vJk++209xh0uE1ae+0ePLddprazZ4y0S6DKJcD07Ko7prx7ZiSB0Cwcj+WvyPkmjPjJpPkjqUj6GXhAnvviU0HKimp7jv4HI7eS6iHvY18fA5k28u9kd/PbHF3ErhT7MfNh+CE6545+Uzy0vPwBX08kv/IiOp9qpmmydGjRzFNc+jCYtyR/NQnGapN8lOfZKg2yU99kqEQY2M076U+g+Kadny2eMEQS6g7YsCIAEzw1gLHl1B//9j7VLVWDbs+H0byWag+yVBtkp/6JEO1SX5qk/zUJxlObDIoPgbqWkx8AQu3w+4vW4AvYFHfcvxNo2kaGVHdS6jbg+Ja7CT0mDQwgwSP7Q65pq7prEibC8BhbzFnz0oD4B8bhzGo3UuWMwuH5qDZbKYmOIxZ23n/0TVb/M2+s8UBYmaA5oBAqz1jvBdN07jtDHu2+N8/+DsNHQ3DrvepZlkWbW1tI/oZi9NP8lOfZKg2yU99kqHaJD/1SYZCjI3RvJe6B8Vrg7XHD864GtCg8n1oOTbwyZoGHrvvToc9AD45djJ56XlYWLxa+Oqw6/NhJJ+F6pMM1Sb5qU8yVJvkpzbJT32S4cQmg+JjIClGx+XQMC17srQ/AA4DEmNCf7zp0fYS6vWdPrwBE03TMLpmiwfKdva57squJdR31hdx2bIMXE6DwoomthUNfylyp+Yky5kFQKG/MPwT47Ig5yr78c77+j5vuCAmx37cWgLB0Bnv5049l3kp8+gIdPDXXX8ddr2FEEIIIYQQQoQ6duwYn/70p0lKSiIiIoIFCxawbdu2AcuvX78eTdP6fFVWVp7CWquhe1C8MdhI0AraB6MnQcZZ9uPC5wa/QETXoLi/EYL2DfFXzbT71LKEuhBCCCGEEEKcPjIoPgbiInXWrowkwqWha/bN4fOmOImLDP3xRjoN4jxOLAsq2zoBcGTmgaZh1h/BbKsLKT81OpXMqBQCZpCC9iNcnjcFgCc3FeELBIddz+4l1Ev8JQSsQPgndu8tfvQtqPmg7/OedHDGASY0F4Qso65pGl9a9iUAHt39KC3ekS3/LoQQQgghhDh1NE3jtttu6/lzRUUFhmFw1113AXDnnXeyYcOGQa/Ru8z555/Pnj17Qp7vHqh98skne4498cQTaJrG+vXr+1xv/fr1rFmzps/xK664go6Ojj7HJ6qGhgZWrlyJ0+nk1VdfZd++fdx7770kJCQMee7BgwepqKjo+UpNTT0FNVZLtBaNS3MRJEij2Xj8idzr7O+Fz/XZOiyE4enqHwOd9mzxy2ZchqEZfFD9AUcaj5yUegshhBBCCCEmFumXjz3H6a7ARLF0uovp6QZv72pic5FGbYuJaVnomhZSLiPKTVOnn8o2L9PiItE8sRgpMwhWFxAs24k+66KQ8ivT5vLE4bfZVL2Xby3+OO/sq6C+xcvru8q4cum0YdUxzUgjRo+hxWzhiP8I013TwzsxLgumf9Tu/O+8Dy55KPR5TYPYWVC3FXwN0Fl9/O544KKci5iROIPC+kIe++CxnkHy8UjXddLT09F1uV9ERZKf+iRDtUl+6pMM1Sb5qW80GbaZbbSYLcToMUTpUaOuS2JiIps3byYYDGIYBk899RTz5s3ref5HP/rRkNcIp8z8+fNZt24da9euBWDdunUsWLBgWHV95ZVXhlV+uLp/BuPFL37xC6ZMmcIjjzzScyw7Ozusc1NTU4mPjw+rrNfrxev19vy5ubkZsH8ewaB9k7amaei6jmmaIcsLDnRc13U0TRvwePd1ex8H+uznN9BxwzCwLAvLskhNTcWyLEzTRNf1nsdD1VHTNJKNZMoD5dT4a4jH/nlpUy9Cd0RitZRiVmyFtKUDt8mdgu5rQuuoIuieRLw7nuWTl/Pu0Xd56dBL3HbGbcNuU391H06bxmNOA7Wpv/xUb9NEzGmwNnVn2G0itGmkx1Vs01D5qdimoY5PtDZZlkVaWtqgbVWtTb3rPtHb1Du/idKm3scnepu68xtOW8d7mwar+0Rsk6ZppKWl9fyb2P2laVq/S6r3Pt67Xx5tRA9Zfqjj3f3yQCCAYRisW7eOefPm9ZT74Q9/CDDoUu8nluluT+/X7O6XX3/99cDxfnnvsifW8cTjL7/8cr/HR/sz6Na7X967Dt25npjvQGRQfAzFRxlcfmYCu8uaaWwzOVIdJDst9EecFuXhYH0rTZ1+2v1BIp0GRuYSgtUFBI7uwDHzQrReA+krUu1B8X0NpbSb7aw5O4c/vL6fl7eXsnJ2OvFd+5SHQ9M0pjunk+/Np9BfGP6gONizxYtehLK3oWY3pCwMfd4RAdHT7CXUW4rAnQi6E7D3R//S0i/xrde/xSP5j/C5RZ8jwhkR/mufQpqmhf1LIjH+SH7qkwzVJvmpTzJUm+Snvu4MOzvtVaUsywprhadCXyHvdrxLgAAOHKyMWMkM14xBzzEwQvod/dXl3HPP5e233+aCCy7g2Wef5brrrut5/sYbb2TNmjWsXr2arKwsbrzxRp577jmcTicvvPACGRkZIWUGMmvWLMrKynr2bCsvL2fmzJlDtrm3rKws9uzZQ21tLVdffTWLFy/m/fffZ+HChTz++ONomsa2bdv45je/SWtrK5MmTeIvf/kLiYmJ/M///A+vvPIKHR0dXHzxxfzqV7/queYNN9zAa6+9xj333MPFF188rDqdTC+88AKXXnop119/PW+//TaTJ0/mtttu4+abbx7y3MWLF+P1epk/fz533XUXK1euHLDsz372s55foPRWVFREdHQ0AHFxcWRkZFBVVUVTU1NPmeTkZJKTkzl27BhtbW09x9PT04mPj6ekpASfz9dzPDMzk+joaIqKikJ+KZWdnY3D4aCgoCCkDrm5uQQCAYqLi3uO6brOzJkzaWtro6ysDIDq6mpcLhc5OTk0NTWFLBcfFRXFlClTqK+vp7b2+P7hcXFxJCUkcbTzKAXNBdDWq03ZlxHY/yRNW/9E7azYAdukYzI9zsIItnO0+ACdQQd50Xm85X+LFw6+wK1LbqWwMHRbs3DbBIyoTeM5p4HaVF1dPeHaBBMvp8Ha5PV6J1ybJmJOA7XJNM0J16aJmNNgbdI0jeLi4gnVpomY00Bt0jRtwrVpIuY0UJssy5pwbZqIOQ3UpsjISIqKiggEAj03Cjs9ToLBYMi1dV3H7XYTCAQ41HmI9wPvEyCAU3OyMmIl07RpBALH+/SGYeByufD5fPYgb1e/3OFw4HQ68fv9IYO73f3yN954g4985CM888wzXHXVVT2Dwp/97Ge55ppruOKKK5g9ezY33ngjzz//fM8AekZGBrfccgtr167lyiuvxDRNvF4vnZ2daJqGx+PBNE1mzJhBaWkpdXV1aJpGeXk5ubm5+Hy+nt9PdNe9+ybpE49nZ2ezdetW6urqWLt2LYsWLWLbtm3Mnz+fv/zlL2iaxvbt2/mv//ov2traSE9P56GHHiIxMZG7776b119/nY6ODlatWsU999wDwOzZs7nhhhv417/+xd13382FF14I2P+PaVkWfr+fI0fslbhaW1sJh2YNdgvBONHc3ExcXBxNTU3Exsae7uoMyDRNSkpKyK9OY8dhP0uyXVx3dmSfctsrG6lr9zE9IYrpCVFYAR+d//oJVsCHe+UtGEmhd/nfteNRDjWV8akZF3J55hn89OmdFFc1s3JOOp+/YPaw6thmtvF0y9NYWFwXcx0xekz4J7/zPSh8FjLPg0v+0Pd5y4S67RBsB08axB2vW9AMctljl1HaVMr3Vn6Pm/JuGla9T5XuDLOysnruFBLqkPzUJxmqTfJTn2SoNslPfd0Zpqenc+TIEaZkTeFp39ODn2OZtFgtWFhoaD3fY7QYdG3gvwefiv0UDm3ge6STk5N5/vnnefTRR7nzzjv5/Oc/z+rVq6mtreWuu+7qMyj+3//933zxi1/kzjvvxOl0cscdd4SUOf/887n//vuZP39+z2usX7+e+++/n7y8PKZPn45lWRQXF7Njxw7+4z/+g/PPPz+kTt3ln3rqqZDjvQfFZ86cSX5+PnPmzGHVqlXcfffdLF++nIsuuohnn32WxMRE/vSnP7F//35++ctfUl9fT2JiIpZlsWbNGr7xjW+wcuVKsrKy+Pa3v83tt98e8lqdnZ0UFxeTnZ2Nx+MBTn1/tft1v/GNb3D99dezdetW/vM//5MHH3yQz33uc/2ec/DgQdavX8+yZcvwer08/PDDPProo2zZsoUlS5b0e05/M8W7f1nV3c7xOiMlEAhw5MgRpk2bhmEYw56RcjR4lPXt60nUE7ki8orj5au2Yb36GXBEYX78HXBEDNymlgNo3lpMzySs6Bxafa2c88g5+Ewfz659lllJs4bVJhVn2Yy0Tf3lp3qbJmJOg7XJNE2OHDlCVlYWDodjQrRppMdVbNNQ+anYpqGOT7Q2maZJaWlpz0oyE6FNves+UXIaqE298ztx6EbVNvU+PlFyGqhN3fllZWVxIlXbNFjdJ2KbAEpKSkhNTeXo0aP24Lnbwd9b/t6nXLeR9ss/GfPJnn55f7OkU1JSeP755/nrX//KnXfeyRe+8AWuvPJKamtr+eEPf8iNN97Ixz72MVavXk12djY/+MEP+vTLb7rppp4yq1at4r777uvpl2uaxltvvcUDDzzA4sWLe/rlJSUl7Nixg9tvvz2kX967/Lp160KOZ2Vl8cEHH1BbW8usWbPYuXMnc+fOZdWqVfzoRz9i+fLlXHzxxTzzzDMkJSXxxz/+sU+/HGDNmjV8/etfZ+XKlWRnZ/Otb32r33559++/XC4XYPdXExMTh+yXy0zxMWRZFj6fj7wsJzsO+/mg1M+VyyzcztAZGOlRHurafVS2ecmJj0RzuDAmLSBQup3g0R19BsXPSZvHoaYyNlXt5copZ/KJc6bz06d38u6BSi6YP5ms1PAHtqP0KDIcGZQHyinyFbHYszj8Bi6+DYpegLJ3oDofUk84V9PtZdQbdtp7p3nSwG3va2foBrcuvZUfvPkDHt75MJ9c8EncjvBnuZ8q3RkqcK+I6Ifkpz7JUG2Sn/okQ7VJfuobSYYmdgde6/qv93Gd0d0csWLFCr7yla/w+OOPs2bNmp47wftz7bXXArB06VJeeOGFYb3O2rVr+d73vodlWdxzzz3s2LFjxHWeNWsWc+fOBSAvL4+SkhISEhLYtWsXF1xwAQCBQKBnKfh///vf/PKXv6Szs5Pq6mouu+yyntnT3UvHjTemabJs2TJ++tOfAnY79+zZM+ig+KxZs5g16/gg7IoVKygqKuJXv/oVjz76aL/nuN1u3O6+fTbDMPosJz/QjTjDPT7QMvXDOd79S7JAIICu6z2vpWlav+X7q0sSSQD2nuI6GFrXeenL0KInQ+sxjLK3YPpVA18nIg28tei+WtBnEBcRx6rsVbxW9BovF7zM3NS5w2rTcI6PVR4nO6fh5Kd6myZiTkO1KRAI9KxIMlHadDKOj9c2jSa/8dqm0RxXsU1+vx/Lssak7gMdl5xOXpvGMr/x0qbB6jjc4+O9TX6/v9/jA5WH8d+mkRxXtU3BYBC/398zmN79NZiR9stPvHZ/r9PdL3/iiSd6+uUnntP95+uuuw5N01i2bBkvvPBCz/HeZQZ6zY9//ON9+uX9tb33dQZqz6xZs3r63Hl5eRw5coTExER27drVM9u7u1+uaRpvvvlmn375OeecA9i/LxisDt35hbvlmQyKnwRTknWSonXqWk32HvWzJMcV8nxalIv9tRptvgAtvgCxbidG5hJ7ULziA6wFH0UznD3lz0qZzZ8LXqe4pZLy9jqmpydx1sxUthyq5ol3C/nONYuHfFP2Nt053R4U9xexyL0o/HNjp8KMa6DgaXtv8Uv/2LeMKxYiJkFHObQcAtcy6PoFwtWzrub+9++norWCp/c/zScXfDLsOgshhBBCCPFhY2DwqdhPDVqmzWzjqZan8Ft+XJoLn+XDqTlZE7Nm0L3FDYbuMGqaxnnnncfPf/5z9u/fzz/+8Y8By3YPnhqGEfZeXt1yc3MpLy9H0zRmzDi+7PuWLVu49dZbAbjvvvvCulbvQdzuupimSV5eHm+99VZI2c7OTr72ta+xbds2MjIy+Na3vhUyMzoysu+qX+NBRkZGz8B/tzlz5vD004OvKnCiM888k40bN45l1caN+lYvJXWdJGV4SYkbfo5RWhRuzY3X8tJoNpJk2IPkaLrdJ85/AAqfCxkU78OVCJoTTB/4GsCdyJW5V/YMin9zxTcHnTUihBBCCCGEGB+G6ptLv7z/evSuy3jpl0sP7CTQNI28roHwnYd9fZ536DopkfbzlW12uHpSFlpkPJbfS7ByX0j5GFckCxPt2eObqvYC8LHlOTgdOofKm9h+uJbhmOqciktz0Wq2UhmsHPqE3hZ92R7kPrYRqnb2XyY6G3QXBDuh9UjPYafh5OYl9j53f9jxBwLm0PsjCiGEEEII8WGlaRoOzTHoV5wRx3mR5+HSXPixO+DnRZ5HnBE36Hnh3hh7++2384tf/IKkpKST2tYf//jH3H333SHHzjrrLPLz88nPz+fcc88d8bVnz57N0aNH2b59O2AvC37gwIGeO+yTkpJoamriueeeG00TTpmVK1dy8ODBkGOHDh1i2rRpw7pOfn4+GRkZY1m1cWHDvgq+/uf3+P1G+/uGfRXDvoamaT0D4bXBE/rbM66xv5dvgraqQS6igyfFftxpl/tI1keIdkVT0VrBjoqRr4gghBBCCCGEOHWG6ptLv3xo46VfLoPiY0jXdTIzM9F1ncXZLjSguDpAfWvfPQnSo+194Cpb7Q3hNU3HkZkHQLCs72DzyjR7qYF3q/ZhWRZJMR4uy5sCwLpNRfiDfV9jIA7NQbbTHmQv9BUOq43ETjn+S4D8+/svozsgNtd+3F4G/uMb3H9s7sdIjkymvKWc5w88P7zXPgV6ZyjUI/mpTzJUm+SnPslQbZKf+kaaYa4rl4/Hfpyro6/m47EfJ9eVO2Z1ys3N5aabbhqTa1100UVkZmaSmZnJV7/61ZDnLrzwwp5l1Abzyiuv9FwjMzOTkpKSIc9xuVw88cQT/Od//ieLFi1i6dKl7Nq1i/j4eD73uc8xd+5crrrqKpYvXz7Spp1SX//619m8eTM//elPKSws5O9//zsPPfRQyD5r3//+9/nsZz/b8+df//rXPP/88xQWFrJnzx6+9rWv8eabb/bZm0119a2d3P/qHupaOrHQ6PQFeeiNfdS3Drz0/0C6B8XrgnWhT8ROhbRlYJlQNESfNiLN/t5ZC2YAj8PDxTkXA/DSoZeGXacPC/n3TH2SodokP/VJhmqT/NQm+alP+uUTu1+uWQpsOtjc3ExcXNyQG6SPN4+82UpRZYALFni4YIEn5LmgafF2aR0B02RZRjyJES7M1lo637wXNJ2Ii7+H5jm+V3hn0MeX3/0N3qCfHy39HDNiJ+H1B/n+Y1toavOxZkUOl+dNDbtuNYEaXml7BUMzWBuzFpfmGvqkbi1l8NQlYAXhyschLa//co17wVsLjhhIzIOuu17+uOOP3LPpHqbFTePVT72KoYe31r8QQgghhBAfBp2dnRQXF5OdnY3H4xn6BHHK9JfN6eivvvTSS3z/+9+noKCA7OxsvvGNb3DzzTf3PH/jjTdSUlLC+vXrAbjnnnt46KGHOHbsGJGRkSxcuJA777yTVatWhf2aKvTLDxxr4Ad/30qH14+mayRGuekMmNx9wxnMnhw/rGsd8R9hfft6kowkVkevDn3y0FOw8QcQlwPXvdLT1+3DsqBuKwQ7IHYWRKSzsXQjX3jhCyR4Etj4+Y04dNnVTgghhBBCiPFG+uXj12j65XK7yhgKBoMcOnSoZ63+Jb2WUDdPuPfA0DVSo05YQj06GT1hClgmgWP5IeU9hotlyTOB40uou50Ga5bnAPDStiM0tfddqn0gyUYy8Xo8ftNPfmc+bWZb+A2NyYTc6+zHOwfZQyBmhr3UeqAFOo71HP7Egk8Q547jSNMRXi18NfzXPQVOzFCoRfJTn2SoNslPfZKh2iQ/9UmGIlyrV6/mgw8+oLOzk/3794cMiAP8+c9/7hkQB/jOd75DYWEhHR0d1NXV8dZbbw1rQFwVqXERRLoM0DTMoEVzhw+PUyc1bvi/xOqeKd4QbCBgnbD1V9Zl4PBA02Go/WDgi2gaeLpni9tLqJ+deTZJEUk0dDaw6eimYdfrw0A+C9UnGapN8lOfZKg2yU9tkp/6JMOJTQbFx5hpHl/GfE6mE7dDo6HN5Eh13zdQRtcS6lVt3p5Bc8eUJQAEy/ruL7YidS4A71XvJ2jZr7N8VhrTUmPo9AV5bktx2PXUNI1oPZoWq4UtnVt4ovkJCnwFYZ/fs7d4+btQNcBeaIYbou1Be1qL7T3GgUhnJDcuvhGAB7c9iGmFv/T7qdA7Q6EeyU99kqHaJD/1SYZqk/zUJxkKMXKJ0R5uuXgukW4HFvZE7Vsumkti9PAHxaO0KDyaBxOTRrMx9ElXNEy1l0Gn4JnBL9S9hLqvEYJeDN3gshmXAbKE+mDks1B9kqHaJD/1SYZqk/zUJvmpTzKcuGRQ/CRyOTTmT3MC9mzxEyV6nLgMHX/QpK7Dft6YtBB0HbOpErOpIqT8gsRsop0RNPna2NtwBABd0/jEOTMAeGd/BaU1LWHVrc1so8RfgtX1n9fysqF9Q/gzxmMmw8yP2Y93/mbgchEZ4Iy191trLrB/KwF8euGniXZFU1BfwJvFb4b3mkIIIYQQQnyIKLDT1YeOZDL+nTs3g//9zFkkRBkkRLuZlhI9outomjbwvuIAudfa3w+/DMFBVm0zPOCMsx93VgNw1cyrAHj98Ot0+DtGVD8hhBBCCCHEySd9wPFnNJnI5lUn2ZIcF9uLfHxQ6ufKZRZu5/G9xjRNIz3aQ2lTOxWtXlIi3WiuSIy0OQQr9hIo24Er7sqe8g7dYHnKbN4o38mmqr0sTMwGIDcjjjNyU9laUM3j7xbx7asXoQ20p1mXFrOFAAGcOAliz2L346fFbCFKjwqvcYu+bN8VX/4eVG6D9GV9y2gaxM6Euu3gq7f3GPekEOuO5dMLPs2D2x/kt1t/y4XZFw5ZZyGEEEIIIT4MnE4nmqZRU1NDSkqK/H/yOGFZFjU1NWiahtPpPN3VEYOYkhzN/Ixoiur9bNhfyadSYkZ0nSQjiWOBY/0Pimcsh8g0aK+Co29B1qUDX8iTBv4m6KiEyEwWpy9mUswkylvKefvI2z0zx4UQQgghhBDjg/TLx6fR9stlUHwM6bpOdnY2un58Av7UZIOkaJ26VpO9R/09+4x3y4h2U9rUTk27l4Bp4dA1HFOWEKzYS7AsH2vO5Wi9rrcyfT5vlO/k/ZqD3DTzUtyGHfr1Z+ew83AtB481srO4jiU5yYPWNUaPwYkTExMLCx8+oogiRh/GLwuiJ0HuGjj4uL23+OV/6b+cIwqipkLbEWgpBFc86E4+t/hz/GXXX9hbs5eNpRs5d9q54b/2SdJfhkIdkp/6JEO1SX7qkwzVJvmprztDp9NJZmYmZWVllJSUnO5qiV40TSMzMxPDME53VcQgdF3nijNzue+f+9l8qIrrV+Tgcgw/s0FniusGzLgadj8EBc8OMSieDC0FEGyHQBuaM5rVuat5aMdDvHjwRRkUP4H8e6Y+yVBtkp/6JEO1SX5qk/zUJ/3y8W80/XIZFB9jDkfoj1TTNBbnuPj37k52Hvb1GRSPdTmIdBq0+4PUtHvJiPagp85Ec0VieVsxawsxUmf2lM+NnUSyJ5bazmby6wo5K3UOAEkxHi7Nm8LL247w5KYiFkxLxGkM/MEbpUdxbuS5bGjfQNAKYmKSZqSFP0u826JboeApqNgMlVsh/YwBXnAqdNbYvwRoLYbYmSRGJHLD/Bt4JP8Rfrvtt5wz9ZxxcbfNiRkKtUh+6pMM1Sb5qU8yVJvkp77uDKOjo8nNzcXv95/mGonenE6nDIgrYt7UJBKj3TS0etlZXMtZuWnDvkb3oHhjsJGAFcChnfAZO+Nae1C87B1or4XIAW5O153gTrJXTuusAmc0q2fag+JvH3mbZm8zse7YYddvIpN/z9QnGapN8lOfZKg2yU9tkp/6pF8+vo2mXy7vzjFkmiYFBQXk5uaGBJKX7eLN3Z0UVwdoaDVJiD4+WN29hPrhhjYqWu1BcU13YExeRKD4PQJHt4cMiuuazorUebxQ+h7vVu3rGRQHuGLJVDbsq6CmqYM3PzjGpYunDFrfXFcukxyTOBo4ynvt79FgNtAYbCTeiA+/0dGTYOb1cOAfsOM3cMWj/ZfTdHsZ9YZ86KgATyq44vl83uf52+6/saNiB1vLt3Lm5DPDf+2TYKAMhRokP/VJhmqT/NQnGapN8lPfiRl2fwkhhsc0TQ4XFXL2zFRe2XGUjfsrRzQoHqlFEqFF0GF10BBsIMWRElogPgdSFkHNLjj8Isy/aeCLRaR1DYpXQ3QOM5NmkpuYS0F9Aa8Xvc7H5n5s2PWbqOTfM/VJhmqT/NQnGapN8lOb5Kc+6ZdPbLKGwykQH6WTnWbff5Bf7OvzfEaUG4C6Dh++oAmAY8oSAIKV+7D8HSHlV6bNs69VX0Sbv7PnuMdpcN1ye5/xF7aW0NzR97VOFKVHMds1myxXFhYW+d78YbYOWHgr6A6ofB8q3h+4nCsOIjLsx82HwAqSGpXKmrlrAPjdtt8N/7WFEEIIIYQQQoh+rJxtD4TvK2ugrqVziNJ9aZp2fAl1s58l1MGeLQ5Q8AxY1sAXcyWC5gDTB74GNE3jytwrAXip4KVh100IIYQQQgghxPDIoPgp0r1s+o7DPswTOspRLgexbgeWZVHV5gVAi5uMHpMCwQDBij0h5adEpzAlKoWAGeT9mgMhz62cnc7UlGg6fUGef78k7Potdi9GQ+OI/0j/+6UNJjrDni0O9t7ig5bNBt0FwQ5oKwXg5iU3Y2gGm45uYlflruG9thBCCCGEEEII0Uudt5nDndXobj+zM+PBgo0HKkd0rUQj0b7mQP3knCvs5dEbDkH9/oEvpOn2imlgL6EOXDnTHhTfXLaZ2vbaEdVPCCGEEEIIIUR4ZFD8FJk7xYnbodHQZnKkOtjn+fRoDwCVrfbd65qmYWTas8UDR3f0Kd89W3xj1d6Q47qmccM5MwB4e285ZXWtYdUvwUgg22nPMt/ZuTOsc0Is+pL9i4DK96Fiy8DldCfE2PWj7SgE2pgcO5lrZl8DwIPbHhz+awshhBBCCCGEEMDLpVu4acO9/OrYy9y26T7iMpsA2Li/os8N6uFINux9wgccFHfHwdQL7ccFzw5+sYiuJdy9tWAGmRo3lUVpizAtk1cKXhl23YQQQgghhBBChE8GxceQruvk5uai631/rC6HxvypTgB29rOEenqUGzRo6PTTEbAHzY3MxaCBWVeC2V4fUn5F2lwADjQepa6zOeS5WZPiWTo9BcuCxzcWYYXZ8V/kXoSOzrHAMaoD1WGd0yMqHWattR/v+M3gy8a5k8GdBFhdy6hb3LL0FnRN582SN9lfM8jd9SfZYBmK8U/yU59kqDbJT32SodokP/VJhkKMTl1nMw8eeIkWfwemBh1BH681vI0jIkB9i5f9ZQ3Dvmb3TPGmYBMBK9B/odyuJdQPvwTmAGUAHDFgRIBlgrcGgNUzVwPwcsHLw67bRCWfheqTDNUm+alPMlSb5Kc2yU99kuHEJqmOsUBg4A5w9xLqHxzx4/WHDhp7HAYJHvv5ylZ7CXU9Ih4jeToAwbL8kPLJnjhmx03BwmJzdd9B5OtX5GAYGvvLGthVEt5y6LFGLDNc9izund6dYQ+m91h4qz0TvGrb4LPFNc2eLa4Z4G+Gjgqy4rO4fMblwOmfLT5YhmL8k/zUJxmqTfJTn2SoNslPfZKhECNX2dGAaVnoaJiWiVt34jX9zMxyA7Bx//CXUI/UIonQIjAxqQ/W919o8rkQkQyd9VD2zsAX0zTwdM0W71pC/fIZl6NrOtvKt/HCgReobB3ZMu8TjXwWqk8yVJvkpz7JUG2Sn9okP/VJhhOXDIqPIdM0KS4uxjTNfp+fmmKQGK3jD1rsO+rv83xGlN1Rr2zr7DnWewn1Ewepu2eLv3vCEuoAKbERXLJoCgBPbCoiEOy/Tida6F6IgUFloJKKYEVY5/SISoNZH7cf7xxitrjhsfcXB2g9DEEvX1r2JQBeK3qNovqi4b32GBkqQzG+SX7qkwzVJvmpTzJUm+SnPslQiNFJj0ggwuFG1+xB8RZ/Ox7DxYUz7ZvNdxyupa2zb198MJqmkWQkAQw8KK4bkHOV/bjgmcEvGNG1r7ivEYJeUqJSSItKo7SplE89+ynOevgsntr31LDqONHIZ6H6JEO1SX7qkwzVJvmpTfJTn2Q4scmg+CmkaRp5XbPFdxzuu4R6WpQbTdNo8QZo9dl3ohgZ89AcTqy2OsyG0pDyZ6XOQdd0SlqrONZW2+d6Vy6dSkykk+rGDt7aUx5WHaP0KGa6ZgL23uLDni2+6FYwXFC1HcrfG7xsxCRwxoAVhJZCZibN5KLsi7Cw+P323w/vdYUQQgghhBBCfKgleWK5fc5HiXS4sQALuH3OR1k0OYPM5GgCQZPNh6qGf92uQfHaYN9+d4/uJdSPvgWdjQOXMyLAGWs/7qymsrWSvTV7MU0Tf9BPq7eVb/7rmzJjXAghhBBCCCHGmAyKn2J52S40oLg6QENr6J0mTkMnOSJ0CXXN4cbImA9AsGxHSPkYZwSLE3MA2FS1r89rRbgcXHeW/fwLW0to6QjvjvgF7gU4NAe1wVqOBo6G3ziAyFSYdYP9eOd9g88W1zSImQlo4K2Fzhq+fMaXAXjp0EscbRrmawshhBBCCCGE+FBbNWkRvz7zyyQYkSS5Y1mYmI2maZwzOx2ADSNYQr17ULzOHGRrssRZkDTX3lP88EuDX7DXEupHGo9gWRZOw4lpmWiaRru/ndKm0sGvIYQQQgghhBBiWGRQfIzp+uA/0vgonew0BwD5xX1ni2dE20uoV7R19szS7l5CPXhsN1YwdGD77O4l1Kv39jure+XsdDKTo2n3Bnh+a0lYbYjQI5jjmmPX0Zs/gr3FbwbDDdU7oHzT4GWd0RBlL/NOSyHzk2dzzpRzCFpB/rDjD8N73TEyVIZifJP81CcZqk3yU59kqDbJT32SoRCjlx2bzvTIdAxdZ1vtIQDOnpWGYWgcrW3lSE3LsK7XPSjeFGwiYA2yv+CMrtnihc8OfkFPCqBBoI1pUclEuaJwGS4sy6KxsxGP4WFq3NRh1XGikc9C9UmGapP81CcZqk3yU5vkpz7JcOKSZMeQYRjMnDkTwzAGLbek1xLq5gkDzimRbgxdo8MfpMlrd7b1pBy0iDgsfyfBqgMh5Zcl5+I2nFR3NFLY3HeJdEPX+MQ59v5pb+8t51hdW1htmeeeh0tz0RBsoNhfHNY5PSJTe+0tPsRscYCoqfYScqYPWou57YzbAHh6/9OnfMm4cDMU45Pkpz7JUG2Sn/okQ7VJfuqTDIUYG4ZhcGH2MoCeQfFoj5O87GRg+LPFI/VIIvVILKyB9xUHyFkNmgG1e6ChYOByuhPc9kB7ugPuveReEiISMHQDTdNYkrGE9Oj0YdVxIpHPQvVJhmqT/NQnGapN8lOb5Kc+yXBik0HxMWRZFq2trUPOrJ47xYnLodHQZnKkOhjynKFrpEbas8Ur2zoB0HQdR+ZiAIJlO0PKuw0Xy5LtPcA3VfddQh1g9uQE8nKSMU2LJzYVhjXz2625meuyZ6Hv8u7CtMwhzjjBwlu6ZovvhGPvDl5WMyDWbgMd5SxNnsGZk88kYAb4444/Du91RyncDMX4JPmpTzJUm+SnPslQbZKf+iRDIcaGZVnMjcwEYH/jUVp87QCcOycDgC2HqvAFggOe358kvWsJ9eAgS6hHJMKU8+3Hhc8NfsGeJdSrWTPnY2z54hYeufoRpsZN5UDdAXZV7hpW/SYS+SxUn2SoNslPfZKh2iQ/tUl+6pMMJzYZFB9DpmlSVlaGaQ4+gOxyaMyf6gRg5yBLqFe2evsuoV51EMvbGlJ+Zdo8ADZX7yc4wOD12hXT0XWNvaUNfFA6yJ3tvcx1z8WjeWg2mynyF4V1To/IFJj9CftxOLPFXfHg6boTvvkQX1pyCwBP7nuS2vba4b32KISboRifJD/1SYZqk/zUJxmqTfJTn2QoxNgwTRNvbQtTo1IxLZPtdfas7TmZCSTGuGn3BthxeHj9zJ59xQcbFIdeS6g/D+YgA+/uRNAc9oppvgbSo9P5xIJPcP3c6wH46caffmh/ESefheqTDNUm+alPMlSb5Kc2yU99kuHEJoPip0n3Eup7Sv34AqEd3cQIF05Dxxc0qeuw9xDXY1LR4zPBMgkcC71jfH5CFjHOCJp8bextKOn39VLjIrhkkX2n/OPvFhEM4w3t1JzMd88HYLd3N0FreHfSs+BmcHigJh+ObRy6fEwOaE4ItrMicQoL0xbSGejkz/l/Ht7rCiGEEEIIIYT40Duja1W1bTX2EuqGrrFytn0z9sYDw1tCPexB8SnngzseOmqgfJBV0zS9a29xoLOq5/A3zv4GEY4I8ivzeenQS8OqoxBCCCGEEEKIgcmg+GkyLcUgMVrHF7DYd9Qf8pyuaaRHhS6hDuCYkgdAsGxHSHmHbrA8dQ4A71btHfA1r1w6jZgIJ1UN7by1p+/+4/2Z5ZpFpB5Jq9lKgW+QPdH6E5kMs7pni/9m6NniuhNi7P3PtfZSvpx3EwCPffAYTZ1Nw3ttIYQQQgghhBAfasuScwHY3VBMR8AL0DMovv9oA7XNnQOee6LuQfEmswm/5R+4oOGE6VfZjwueHfyi3Uuoe2t7ZpWnRqVy69JbAfjf9/6XDn9H2HUUQgghhBBCCDEwGRQfQ5qm4XK50DQtrLJ5XbPFdxzuu4R6erQHgKo2L0Gzawn1SYtA0zEbyzGbQ+9q715CfWvNIbzBvtcDiHQ7uObMbACef7+Ets5BOvJdHJqDhe6FgD1bPGAFhjwnxIIvds0W3w3HNgxd3pMKrgTAYlVCBrOTZtPub+evu/46vNcdoeFkKMYfyU99kqHaJD/1SYZqk/zUJxkKMTa630uZUSmkRyYSMIPk19lbgqXERjAnMwGAjQcqwr5mhB5BpB6JhUV9cIgtybqXUC99A7zNA5dzxoIRAZZpD4x3uSnvJibFTKKytZI/7vxj2HWcKOSzUH2SodokP/VJhmqT/NQm+alPMpzYRj0o/s4773DVVVcxadIkNE3jueee63nO7/fz3e9+lwULFhAVFcWkSZP47Gc/S3l5eLOUVaPrOjk5Oeh6eD/WvGx7UPxwVYDGttDlzOPdDjwOg6BpUdthD3Jr7iiMtFkABMp2hpTPjZ1MiieOzqCPnXUD7/997twMJidF0e4N8PzWkrDqOcM5g2g9mg6rgwO+A2Gd0yMyGWZ/0n68I4zZ4poGsTNB09ECzXxp4VoA/rr7r7T6Wgc/dwwMN0Mxvkh+6pMM1Sb5qU8yVJvkpz7JUIix0f1eMgyDM5PtPvTW2oM9z58zx54t/u6Byp6b0MMR9hLqSXMhIReCPih+deBymmbfGA4hS6h7HB6+s+I7APxhxx+obB3eUu+qk89C9UmGapP81CcZqk3yU5vkpz7JcGIbdaptbW0sWrSIBx54oM9z7e3t7NixgzvuuIMdO3bwzDPPcPDgQT760Y+O9mXHJcuyaGxsxBpq4LdLfJROTpoDgJ3FobO7NU0jI9peQr2i9fiSbsaUJQAEy3Zi9doXXNM0VnTNFt84yBLqhq5xwzkzAHhrTznl9W1D1tPQDBa5FwGwx7sHn9X/TPQBLfgiOCKg9gMoe3vo8oYHorIAuCRpCtlx02j2NvOPD/4xvNcdgeFmKMYXyU99kqHaJD/1SYZqk/zUJxkKMTZ6v5fOSLH3Fd9ZV4QvaK98tiQnmUi3g/oWL/vLGsK+bpIe5qC4psH0a+zHheEuoV4HHdUQtJd5v2zGZSzNWEpnoJP/3fS/YddxIpDPQvVJhmqT/NQnGapN8lOb5Kc+yXBiG/Wg+OWXX86Pf/xjrr322j7PxcXF8frrr7N27VpmzZrF8uXLuf/++9m+fTulpaWjfelxxzRNKisrMU1z6MJdlnQtob7zsK/Pm6x7CfXadh/+oH1NI3U2mtOD1dmCWRs6I3xl2lwAdtUV0TrIvmNzMxNYlJ2EaVo8uWngWeW95ThziNPj8Fpe9nn3hde4bhFJMOdT9uOd9w09WxwgMhMc0RiaxZfm2vux/Sn/Tyd9P7WRZCjGD8lPfZKh2iQ/9UmGapP81CcZCjE2er+XcmIySHTH4A36+aChGACXw+CsmfZg9MYD4c/CTjaSgTAGxQFmXA2aDtU7oalk4HKOCNCdEOyEhp1Q8y50VKBpGj849wdoaLx46EXyK/PDrqfq5LNQfZKh2iQ/9UmGapP81Cb5qU8ynNgcp/oFm5qa0DSN+Pj4Act4vV68Xm/Pn5ub7T24gsEgwWAQsGdG67qOaZohg8kDHdd1HU3TBjzefd3ex4E+f/EHOm4YBpZlYZpmnzp2H++vjrMm6bgMqG8xKa7yk5Pu6qljpKER5TRo8wepbveSEeXGQkOftJDgkS0Ejm7HSM3tKZ/hSWRqVAqlbTVsqTnA+WkLB6z7muXZfHCknp3Ftby0rYSzZ6UTH+kcsE0AC10L2dC5gX2+fcx2zsbJ8fJD5jHvJrR9j0HtHswj/0abesHgOVkWVvQM9IZ8rsyYy33RaZS1VvHEnif4zMLPjElOJ9bRsiyCwWBPhsP9OzYe/u7116aB/u5NxDaNJr/x2qaJmNNgde/O0DRNDMOYEG0a6vhEalN3ft1lJkKbTqz7RG9TMBgMeTwR2tT7+ERvU3d+3e/HidCmweo+Eds0UH4qt2ki5nTi8RPPE+OLpmmckTKL18q2sbXmIEuTcwE4Z3Y6b31wjB2Ha2jt9BPtcQ5xJUg0EgFoNpvxW36c2iDnRKbA5HOg7B0ofA6Wfq3/ckEv+NsAy95b3AxA035wJTIvdR7XzbmOp/c/zU83/JTH1zyOrskyjkIIIYQQQggxEqd0ULyzs5Pvfve7fOITnyA2NnbAcj/72c/44Q9/2Od4UVER0dHRgD0LPSMjg6qqKpqamnrKJCcnk5yczLFjx2hrO740eHp6OvHx8ZSUlODzHV/+OzMzk+joaIqKikJ+2ZGdnY3D4aCgoCCkDrm5uQQCAYqLi3uO6brOzJkzaW9vp76+nsLCQnRdx+VykZOTQ1NTE5WVx+8+j4qKYsqUKdTX11NbW0tGdBQFNW427vGRk54a0iYdF0EjgopWL2ZjLW1tbTiDSST5/DiOfYBr0bWUlB7radN0K5kSs4pNVfuY0ho1aJuSIyH/aAe/e20fj79bxOp5cSzOjA5pU1tbG2VlZQBYWLgT3Hg9XrY1byO5KnnANnXryanJj5F8MXFHn8K38Rd4L11EckrKkDklOt3EuXx8YcaF/HDn37n/vftZ4lqCy3CNOKfebQJCciovL+/JMCYmZvA2jaO/e4O1abC/exOtTdXV1T35JSQkTIg2TcScBmuTaZrU19dTX19PWlrahGjTRMxpoDZ159fc3ExiYuKEaNNEzGmwNpmmSSBgLy07UdoEEy+ngdrUfVORaZocPnx4QrQJJl5Og7UpJyeHYDDY05+YCG2aiDmd2KbW1lbE+HZG8kxeK9vGjtoCgpaJoelkpcYwJTmao7WtbD5UxUULM4e8ToQeQZQeRZvZRl2wjnRH+uAnzLj2+KD4kq/aM8dPFOxeDU3n+MB40D5uuPn68q/zauGr7KraxcuHXuaqWVcNt/lCCCGEEEIIIQDNGsOF8TVN49lnn+Waa67p85zf7+djH/sYZWVlrF+/ftBB8f5minf/EqT7vPEyK6CbYRgEg0HKysqYNGlSz3XDmelwpCbAn/7dgcuh8b2PxeLQrZ46dgSCbDrWCBqcOzkRl6FhWRb+t3+N1VaLa/Ea9MwlPeXrOpv52vsPAvDrM79Ekuf4z7l33etbvXztkfeob+1EAxyGTmyki1/deDaJXXuZ9zd741jwGG91vIUDB1dHXk2EHhF+Hh316E9dBMEOrAseQM+6aOicrCB6ww58vlYuevX7VHc0c9dH7mLtvLUjzmmwGSmBQIDy8nImTZqEYRjjekZKuG1SaZbNaNsUDAZHnN94bdNEzGmwupumSXl5OZMnT8bhcEyINg11fCK1qTu/zMzMQWf6q9SmE+s+0dtkmiYVFRVkZmaGlFW5Tb2PT5ScBmpTd36TJ0/mRKq2abC6T8Q2ARw7doyMjIyeMqq3aSLmdOLx7pvBmpqaBu3nqq65uZm4uDgl2mmaJseOHWPy5Mnouk7QMrnt3d/Q4u/gB4s/wbyELADe2F3GPzYUkpkczV1rl6Jp2pDXfqv9LUr9pZzhOYO57rmDFw544fGV4GuBy/4Mk87uWybotZdMNwNgBQELNAekngcOe0u132/7Pf+3+f9Ii0rjtU+/RoQzYng/EMWcmJ9Qj2SoNslPfZKh2iQ/tUl+6pMM1RRuf/WUDIr7/X7Wrl3L4cOHefPNN0lKShrWdVXqfI+EZVn86sUW6ltN1pwdyeJsV8jz75c30NjpZ1ZSNNPiIgHwH3oT/4HXMZJzcK+4OaT8j3b+jQONR/nE9FVcNXV5v6954FgDdzy+DR1o7fRjWRZul4OffeosZk+OH7Su/2z7J9XBama7ZnNWxFnDa+y2e2H3Q5A0Fz76DITxSwe89dD4AX89+Bo/2bWOybFT+Ndn/oVDP+Wr/wshhBBCCCFEiIneX+2mejsfOvAK6yt2cfHkJdw081IA2jr9fOPP7xEImtxx/VKyUmOGvM5u7252du4kx5nDuZHnDv3C7/4PHHwcpl8NH7mn/zIdFfaS6d0D47oL4uZA5CQAvAEvlz92OcdajvEfZ/wHXznrK2G3WwghhBBCCCEmunD7qyf9NofuAfGCggLeeOONYQ+Iq8Q0TWpra/ud8TEYTdPIy7EHwncc9vV5Pj3avju8ovX47HkjMw+AYO1hzPaGkPIr0+YBsKlq34CvmRoXgcepEzQtPC4D0wKvP0hbp3/ounrs1z7kO0SrOcylAud/HhyRULcPSt8M7xx3InhSuX76+SQ6IzjSdIT/e+//qGytHPrcYRpphmJ8kPzUJxmqTfJTn2SoNslPfZKhEGOjv/fSGSkzAdhWewjTso9HeZwsybG3Bdu4vyKsaycZ9u806oJ14VVmxjX29yP/6to7vB8RGZCyEpLPhNhZoDugpRB89rYAboeb7678LgAP73yYipbw6qoq+SxUn2SoNslPfZKh2iQ/tUl+6pMMJ7ZRD4q3traSn59Pfn4+AMXFxeTn51NaWorf72fNmjVs27aNxx57jGAwSGVlJZWVlSF7uE0UlmVRW1vbZ7nRcCzOsgfFD1cFaGwLfbOlRbnRNI1mr592v73Ppx6ZgJ6UDUCwLD+k/FkpszE0nSOtVZS11fT7eonRHm65aC4RbgNd03C7DOIiXTz69iFqmjv6PadbuiOdDEcGJia7vLuG11BPAsz9jP14530Q7s8qZjoRzigWJmZR2ljKf7/535z18Fk8te+p4b3+EEaToTj9JD/1SYZqk/zUJxmqTfJTn2QoxNjo7700PyELj+GiwdtKUfPxQeVz5tj7gm8uqMYXCPa51omSdHtQvMlswmeF8XuN1MUQOw0CHVD8z4HLGW5wxUN0NrhTAAua9tnLqwOXTL+EZZOW0Rno5N737h36dRUmn4XqkwzVJvmpTzJUm+SnNslPfZLhxDbqQfFt27aRl5dHXp49e/gb3/gGeXl53HnnnRw7dowXXniBsrIyFi9eTEZGRs/Xpk2bRl35iSQhWic71V4OfGdxaMfabegkRjiB0NnijilLAAiW7Qh5g0Y7I1iUNB0YfLb4uXMz+H+fX8ndnziTB285j1mT4mnp8POblz+g3RsYtL55bjvvw77DNAebw22mrXu2eP1+OPJGeOfoLiq1GF4t3YyFvQdgRUsFt7x4C+8ceUc+oIQQQgghhBBCDMipO1iSPAOArbUHe47PyUwgMcZNhzfAjsO1Q17Ho3uI1qMBqA/WD/3Cmga519mPC58Lr3zcLHBEgemDxr1gBdE0jf8657/Q0Hjx0IvsrNg59LWEEEIIIYQQQvQY9aD4+eefj2VZfb7+/Oc/k5WV1e9zlmVx/vnnj0H1J5YlXUuo7zzs6zPImxF1fAn17ueMSQvQDCdmay1m49GQ8itT5wLwbtW+QQeME6M9zJ4cT0ZCJF+9cj5xUS7K69t58F97CZoDn5fiSCHTkYmJSb43f3gN9cTDvM/aj/PvByu8ZSiOdLbTEfST6I5B18CyTJq9TXz6mU9zwV8u4O6372bT0U0EzMEH9E+LoBd8jT13+QshhBBCCCGEOLXOSJ4FwNaaQz39ZF3TOGe2PVt8w8laQn361YAGle9DS9nQ5TUD4uaB5oBACzQXgmUxL3Ue182xB9h/uvGnPcvACyGEEEIIIYQY2knfU/zDRNM04uLi0DRtROfPm+rE5dCobzUprQldti01yoWuabT7A7T47EFfzeFGz7AHv4NHd4SUX5I8A4/hoqazkYLmY2G9fmK0h69esQCX02BvaQN/31Aw6IB6997ixf5iGoINA5br1/zPgzMK6g+EPVt8WmwGkQ4XQcskPSIBj+EkwnARrWuUNxbxt51/4Kanb2D5gwv45nOf5OVtv6Kl4h2o3wkNu6DhA/su+6b90HzI/sVCy2FoLYG2UmgrQ+usIDXWQvPWgrcWvPX2YLa/GQJt9pJ3QS+YfrCC4S3/3lEBNe9C3Vb7e8fE3v/tdBrte1CcfpKh2iQ/9UmGapP81CcZCjE2BnovLUrKwak7qOpo4GivrcbOmZMBGhwoaxxyOzEYwaB4dAZMWm4/Lnw+vHMcERA3x37cWdnTj/zG2d8g0hnJ7qrdvHjwxfCupRj5LFSfZKg2yU99kqHaJD+1SX7qkwwnNhkUH0O6rpORkYGuj+zH6nJozJ9qL5O+43DoEuoOXSclyp5JHrKEembXEurlu7GCx2dIuw0Xy5JnAvBu1d6w65CVGsMtF88BDdbvKeeN3QMPqCcaiWQ5swDY2TnMpdvccTDvc/bjnfeFNVs8PSKOe5ffQrQzgvagl0RPDI985Ots/9iD/O68r/OxnPNI9MTQ4mvjpZKNfGPjr1i+7nN8/l8/4G97nqS84ZA90N1Zbf9CoeMYtB+FtiPQWgytReitRSQa1egtB+wB9MYP7AH1+p1Qtw3q3ofazVCzCao3QvU7UPWO/bhmE9Rshtr3oW571zk7oGF310C6BWbAHpSXGeMnxWjfg+L0kwzVJvmpTzJUm+SnPslQiLEx0HvJY7hYmJgNwPs1x5dQT4rxMCczAYCN+yuHvH6ikQgMY1AcYMa19vfCZ8O7uRrAnQjROfbjlkLwNZEcmcyXl30ZgP9973/p8A89iK8a+SxUn2SoNslPfZKh2iQ/tUl+6pMMJzZJdQyZpklFRQWmOfIlzPKy7YHvPaV+fIH+l1CvbOs8vtRb8gw0TwyWrwOz+mBI+ZVp8wDYUn2AgBk683zwOiSzdoW9J/nj7xaSXzzwvmqL3YvR0DgaOEpNoGbAcv2adyM4o6HhEBx5fejyRgRrpq9iy9W/4eXLf8aWq+9jzYyLiEg7jwsW3c5Pr3yYjZ/fzOMf+wc3L72FnKSZBDSDd2sOcffOJ1j10ne49o2fcd+ht9nX6ceKnAKRmRAxCTzp4EnFciXRHvBgOePAGWPv42ZEgO4GzWkvY8eJdwhZ9qxx0w+mF4IdEGi1Z5f7G+3nsIAgWAG7nL91eD8rEZaxeA+K00syVJvkpz7JUG2Sn/okQyHGxmDvpTO6bh7fWhPafz53jr2E+qaDlYNuJQaQrCcD0Gw247N8g5btMe0ScERCy1Go2h7eOWD3Wd0pgAVN+yDo5XOLPkdmbCbVbdU8tP2h8K+lCPksVJ9kqDbJT32SodokP7VJfuqTDCc2GRQfQ5Zl0dTUNOiS40OZlmqQGK3jDVjsO+oPeS450oXD0PEGTBo67ec0XcfItJcxD5ywhPr8xCxinZE0+9vZ01AyrHpcsiiT8+ZlgAW/f30/pTUt/ZaLM+KY7rIH0Hd6RzJbvGtv8XBmixtuiJtDenQKZ6bkkh6dYi8n54wCwwWGB8MVQ96UlXzr3P/m1c+8wWufeYPvrPw+SyefiW642NdQzP27HuPaF29n1dOf50c7HuPdxmr80TkQNwczdg6lLXGYcQsgcQkkLYPkMyFlOaSugNRzIO08SD0PUs6BlBWQfBYknQmJSyExDxIWQfwCew+42FmguwCDnrebZULzfnu2+ij+roi+xuI9KE4vyVBtkp/6JEO1SX7qkwyFGBuDvZeWJOeiazpH22qobK/vOZ6XnUyk20F9i5f9ZYNvD+bW3UTr0QDUB+sHLdvDGQHZl9uPC58N7xwATYO4WfYN26YPGvfiNhx8Z8V3APjjzj9S3lIe/vUUIJ+F6pMM1Sb5qU8yVJvkpzbJT32S4cQmg+LjjK5pPbPFT1xCXdc00iLdAFS09V5C3R4UD1YfwPK29Rw3NJ3lqfYeZMNZQh3sfRM+fV4uc6Yk4PMH+X+v7KGxrf8lvxe5F6GjUxGooDIw9FJzIebdBK4YaCiAkn8NXT4iA1JWQtIZ9veIjEGLZ8Vn8YUlX+DvH/s7737+XX524c+4KPsiIhwRVLRW8NgHj/H5Fz7P8j8u5+v//DovFbxEazizuDUNdAN0Jxgee783ZzQ4Y8EVby9z50mGyMkQPw8MJ+i6PUDuiLZnjzcfgoad9oxyIYQQQgghhBAnXbQzgrnxUwHYWnuo57jLYbB8ZhoAG/ZXDHmd7n3Fa4MDr6zWx4xr7O/Fr8Jwlj3XDPuma80BgRZoLuSSnIs5c/KZeINe/nfT/4Z/LSGEEEIIIYT4kJJB8XFocdegeHFVgMa20NnTGdH2oHhVmxezewn12HT0+ElgmgTLd4WU715CfVvtIbzBMJd162LoOl++dC7pCZE0tnr5zcsf4PX3XYY9Wo8m15UL2HuLD+sOGncszO3aWzz//rD2Fsdw2wPPhjv81wESIxK5bs51PHDlA2z+4mYevPJBrp97PcmRybT6Wnml8BW+8/p3uOGNG/j8C5/n0V2Pjs0d970H8lPPhdSV9r5wmgH+Fnvv8aaDMMx8hBBCCCGEEEIM35kpswDYVnMo5Hj3Euo7DtfS0uHvc15v3YPiw9pXPH0ZxGSCvw1K3xhGjbFvxI6zb3qnsxKts5Lvn/N9NDReLniZHRU7Bj9fCCGEEEIIIT7kZFB8DGmaRnJyMpp24p7Tw5MQrZOd6sACdhaHDpQmeJy4HTqBoElt+/HnjMwlAASOhi5hPiN2EqkR8XiDfrbXFg67LlFuJ/955QKiI5wcqWnlD2/s7xmM722heyGGZlAdrOZY4NjwXmTe53rNFn9t2HUcCY/Dw6rsVfz4gh+z4aYNPLHmCW5ZcgvTE6eDDpvLNvPjDT9m1V9WcfXjV/ObLb9hb/XekS+Z0XsgX9Mhaoo9SO6xZyLQWQl170N7mSypPgpj9R4Up49kqDbJT32SodokP/VJhkKMjaHeS0uTZ6KhUdB8jHrv8a3CpqbEMDUlGtO02HyoatDXGNGguKbD9GvsxwXDWEK9mzvRvsEaoKWQuXGTWTN3DQA/2fATzHBuMleAfBaqTzJUm+SnPslQbZKf2iQ/9UmGE5sMio8hXddJTk5G10f/Y12SY88W33nYFzIQq2ka6VEeACp7L6E+eRFoOmZjGWZLdUj5lan2bPFN1ftGVJfUuAj+4/L5GIbGzsO1PP3e4T5lIvVIZrtmA5DvzR/+bPF5N9qPd94PZt/Z6CeTruksTl/MN1d8k1c+9Qr//ty/+e453+WMSWegazoHag/wwNYHuO7J6zj/L+fzw/U/ZGPpRvzBwWcODMlwQ9xsSFh8fEn1liKo2wa+xjFo2YfPWL4HxekhGapN8lOfZKg2yU99kqEQY2Oo91KCO5oZcZOA/maL21t0bdhfMWi/tntQvMVswWv1v9VYv7qXUC/fBG3D3H4MIDIT3CmABU37+NoZXybKGcWe6j08f+D54V9vHJLPQvVJhmqT/NQnGapN8lOb5Kc+yXBik1THkGmaHD16FNMc/d3Z86Y6cTk06ltNSmtCB4m7l1CvafcS6HotzR2NkToTgGBZ6GzxFWlzAcivK6JlOPuW9ZKbEcfnL7AHvf+58yjv7Ou7x9p813ycmpO6YB2lgdLhvcC8z4ErFhoLoeSfI6rjWDBNE71F58ZFN/K36/7Gps9v4ucX/pxLci4hwhFBZWslf9/zd77wwhc46+Gz+No/v8aLB1+k2TuKfcFdcZC4BGJy7T3igu3QsAsa90Gwc+wa9yEwlu9BcXpIhmqT/NQnGapN8lOfZCjE2AjnvXRmsr2E+tbagyHHz5qZisPQOVbXxpGa1gHPd2tuYvQYAOqD9eFXLnYKpC0DLCgcwSC2pkHcLHBEgekj2VfFbcu+BMC9791Lu799+NccZ+SzUH2SodokP/VJhmqT/NQm+alPMpzYZFB8DFmWRVtb28iX2O7F5dCYP9UJwI7DoUuox7gcRDoNgqZFde8l1Kd0LaFethOr17Jpk6OSyYpOw7RMtlQfGHGdls9M46NnTAPg0bcPsa+sIeR5j+5hrssegN/ZuXN4S7e5YmD+Tfbj0zBbvNuJGSZEJHDtnGu574r72PLFLfx+9e/5+LyPkxyZTJu/jVcLX+Vbr3+L5Q8v53PPfY6/7vorx5qHuXw82L/YiJwEyWdChD1jAW8N1G2F1iP2LHIxpLF8D4rTQzJUm+SnPslQbZKf+iRDIcZGOO+lZSn2TeX7G4/S4js+kBzldrJkejJgzxYfzIiWUAfIvc7+XvjsyLbP0gyIm2ffVB1o4bPZZzMldgo17TU8tP2h4V9vnJHPQvVJhmqT/NQnGapN8lOb5Kc+yXBik0HxcSwv215CfU+pH18gdAn1jOiuJdRbj88kNtJmozk9WB1NmHXFIdfqni2+qWrvqOr00TOyOHNmKqZp8bt/7qWiIfQu9Lnuubg0F01mE8X+4gGuMoC5n7FnizcdhuJXR1XPk8HtcHN+1vn8aNWP2HDTBp5c8yRfWvolchNzCVpBNpdt5icbfsIFf72Aj/7jo/y/zf+PPdV7sCyLytZKtpRtobJ1iOXxdCfE5kLiUnDGgWVCW4m9pLq3VvYbF0IIIYQQQogxkBaRwLSum8e31xWEPHfO7HQAthRU4wsMfIPyiAfFsy4FhweaiqFm9/DO7eaIgLg5ALj89Xx32RcB+OPOP1LeUj6yawohhBBCCCHEBCaD4uPYtFSDhCgdb8Bi39HQ/avTu5ZQr+vw4wt2LaFuODEmLwQgeHR7SPmzU+eioXGg6Si1nU0jrpOmady0ahbT02Np9wb49cu7aek4XjeX5mKBewEAu7y7Rj5bPP+B0zZbPBy6prMofRFfP/vrvPTJl3j9M6/z/XO+z5mTz0TXdA7WHeS3237Lx578GHN/O5fZ98/m0r9dylkPn8VT+54a+gWc0ZCwCGJng+6yl1Fv3AuNH0BA/eXwhBBCCCGEEOJ0OzPFXkL9xH3F52QmkBjjpsMbYMfh2gHP7x4Urw0OXKZfrmiYerH9uPDZ4Z3bmzsRonMAuCgxgzPTF+ML+rjn3XtGfk0hhBBCCCGEmKBkUHwM6bpOeno6uj42P1Zd08jLsWeL7ywOXUI9yukg1u3Esiyq2rw9x41Mewn1YMUerMDx40meWGbHTwFgU9W+UdXL5TD4jyvmkxzrobapk/tf3YM/eHzwe7ZrNhFaBC1mCwX+gkGu1I+5n+01W/yVUdVzJEaa4dS4qdy4+EYevfZR3vvCe9xz0T1cOv1SHJqDwvpCWrwttPnaqGyp5KuvfnXoGeNgL6kekQZJZ0LkFEADX4M9a7zlMJiBkTVyAhvr96A49SRDtUl+6pMM1Sb5qU8yFGJshPteWpZsL6G+u6GYjl79Z13TOGdOBgDv7Bt4CfVEIxGAVrMVr+UdsFy/cq+1vx9+GQLDPLe3yExwp6Bp8IOF16ABrxa+yvby7UOeOl7JZ6H6JEO1SX7qkwzVJvmpTfJTn2Q4sUmqY0jTNOLj49E0bcyuubhrCfXDlQEa20JnXWd0zRav6LWEup4wFS0qESvgJ1gRulT6yrR5AGyqHt2gOEBshIuvXrmACLeDwoom/vzWwZ49Fhyao2e2+G7vbgLWMAZvXdEw//P249Owt/hYZBjviefq2Vfzm8t/w4OrHyTKGUWsOxZDNzAtk+q2ar73xveobQ9zNoFuQEwOJC0DVyJgQftRe7/xjipZUr2Xk/EeFKeWZKg2yU99kqHaJD/1SYZCjI1w30uZUcmkRyQQMIPk1xWFPHfO7HTQ4OCxRqqbOvo93625idVjgREsoZ6xHKLSwdcMR98a3rm9aRrEzQJHFLPjJnF9zrkA/GTDT4a3cts4Ip+F6pMM1Sb5qU8yVJvkpzbJT32S4cQmg+JjyDRNDh8+jGmOXcczMVonO9WBBeSfMFs8LcoNGjR2+unw24PHmqbh6J4tXrYzpPxZKbMxNJ3S1mqOttaMum6TE6P48qVz0XWNzQereGn7kZ7nZrpmEqVH0W62c8h3aJCr9GPuZ8AdB80l9l3zp9BYZ5iblEucJw5d10mLSsOhO9A0jQ2lG7j0b5fyp51/wh/0D30hAEckxM+H+HlgeMD0QfMBaMgHf+uY1Fd1J+M9KE4tyVBtkp/6JEO1SX7qkwyFGBvhvpc0TeOMriXUt9aG9luTYjzMm5IAwLsHBl7pq3u2+LAHxXUDpl9tPx7NEuoAmgFx80Bz8LV5HyVKd7C3Zi/PHXhudNc9TeSzUH2SodokP/VJhmqT/NQm+alPMpzYZFB8DFmWhc/n65kxPVZ6llA/HHptj8Mg0WM/VxmyhPpiAIK1hZgdx/cPj3J6WJw0HYB3q0NnkY/UvCmJfOq8XACe21LCloIquw6awSL3IgA+8H6A3wpz4Be6Zot/wX6cf2pni491hunR6dx7yb1Eu6JpD7STGp3KTy/4KYvTF9Pqa+UX7/6Cq/5xFe8ceSe8C2oauJPtWePRWaDp4G+G+u3QfAjMYfycJ6CT9R4Up45kqDbJT32SodokP/VJhkKMjeG8l7oHxfPrivAFQ1c5O2e2vYT6uwcqCZr9X6t7X/FhD4rD8SXUyzZAuCuJDcQRAXFzSPLEcdvcK8AMcO9799LmaxvddU8D+SxUn2SoNslPfZKh2iQ/tUl+6pMMJzYZFFfAvClOnIZGXatJaW3oAHG/S6hHJaEnZYEFwWP5IeXP6V5CvWrvmL2pz583iUsW2/uV/+nfBymssAfipzunE6vH0ml1st+3f3gXnftpcMdD8xEoenFM6nm6rJm7hi1f3MLLn3yZLV/cwrdXfpt116/jpxf8lKSIJIobi7n5xZu59cVbOdJ4ZOgLgj0TIGoaJJ0B7hT7WEcF1L4P7eWypLoQQgghhBBChCEnJp1EdwydQR97GopDnsvLSSbS46Ch1cu+soZ+z082koERDorHZbPikAYAAQAASURBVEPKYrCCUPTC8M8/kTsRorP57MxLmRqZQG1bNQ9tf2j01xVCCCGEEEKICUAGxRXgdmosmOoEYMfh0CXUUyPd6JpGqy9Ai+/4Xe2OKV1LqB/dETL4vThpBh7DRW1nM4eaj41ZHdecncOi7CQCQZP7X91DTXMHuqb3zBbf692L1/IOcZVenFGwoGu2+K7fnvK9xcdaenQ6Z04+k/TodAB0Tedjcz/Ga59+jc8v/jyGZrD+yHqu+PsV/PLdX4Z/N7/hgfi5kLAIjEiwAtBSAPU7wNc09PlCCCGEEEII8SGma/qAS6g7DZ2zZ6YBsGFfRb/ndy+f3mq24jWH0eftNuMa+/uBf0D5ZmgbeKn2sEROwRU5ie/mfQJMH3/a+UfKmstGd00hhBBCCCGEmABkUHwM6bpOZmYmuj72P9buJdT3HPHjCxwf5HYaOsmRXUuo95otbmTMB8OB2VKN1XR88NttODmzq8O/qWpsllAHMHSNWy6ey9SUaFo6/Pzm5Q9o9wbIdmYTb8Tjs3zs9Q7z9eZ8GjwJXbPFx+Cu+TCczAz7E+OO4bvnfJeXP/ky5049l4AZ4OGdD3PJ3y7h2f3PYlph7lvhireXVI+ZAZoDAq32XuNN+yE4gl/MKOpU5yfGnmSoNslPfZKh2iQ/9UmGQoyN4b6XzkieCcD2mkMET+iDnTPHXkJ9Z3EtLR19t6tyaS5i9VgAas0RLIGecyUEOuDoenjmCnjsLDj01PCv003TIG4WF047j+Wpc/D5W/nlu/eM/HqngXwWqk8yVJvkpz7JUG2Sn9okP/VJhhObpDqGNE0jOjoaTdPG/NrTUg0SonS8AYv9ZaEd8YxoDwAVrd6eWeGaMwIjfS4AgaM7QsqvSLOPb67eT2AMZ2B7nAZfvWIBcVEuyuvb+d1rezEtizx3HgD7ffvpMDvCv6AzEuZ/0X6cf2pmi5/MDAeTnZDNH676Aw9e+SDT4qZR217L9/79PT7+1MfZXbU7vItoGkROhuQzIMKekU5nNdRthbajEO4Au8JOV35i7EiGapP81CcZqk3yU59kKMTYGO57aVb8FGKcEbQGOjnQWBry3NTkaKalRGOaFpsPVfV7/qj2FQ+0Q2ej3V+zLPC3wvpvjm7GuGagxc/nv5Z8Dh2Lfxa8yNay90d+vVNMPgvVJxmqTfJTn2SoNslPbZKf+iTDiU0GxcdQMBjk0KFDBINjP3ira1rPbPETl1BPjnBh6BqdgSBN3l5LqGd2LaF+bBeWefz4vIQs4lxRtPg7+OCEPdNGKyHazVevWIDLabDvaAN/31BIppFJspFMwArwgfeD4V1wzift2eItpVD0/JjWtT8nM8OhaJrGquxVvPTJl/j2im8T6Yxkd9Vurl93Pd9743vUtNWEdyHdBbGzIDEPnDH2/nSth6FuG3jrT24jTrPTmZ8YG5Kh2iQ/9UmGapP81CcZCjE2hvteMjSdpV2zxd+vOdjn+e7Z4u/srwjZnqxb96B4fXAE/a3mI6Dp9legHRwe+3tz6dDnDsYRwaysS1k7fRWYQX769v8QVGRbMvksVJ9kqDbJT32SodokP7VJfuqTDCc2GRQfY6Z58mbjLs62B8UPVwZobDv+OoaukRblBqCi1xLqekoumjsay9eOWX18bzRD0zk7dQ4Am6r2jXk9s1JjuOXiOaDB+j3l/PuDchZ7FgNwyHeINjPM/bKhn9nigcHLj4GTmWE4XIaLLy75Iv/69L+4dva1ADx74Fku+dslPLzjYfzBvkv29csZCwl59gC57oRgBzR+AI177OX5JqjTnZ8YPclQbZKf+iRDtUl+6pMMhRgbw30vnZFiD4pvqz3UZxurs2am4jB0yuvaKKlu6XPuqGaKx04Ddxzohj1bvK0aHBEQO3X41zqRO5GvLv860c4I9tXs5bk9j43+mqeIfBaqTzJUm+SnPslQbZKf2iQ/9UmGE5cMiiskMVonO9WBBeQXh84WT4+yl1CvavNidi+hrusYmYsBCJTtDCnfvYT6ttpDeIOh1xoLednJrF0xHYDH3y2k+qiLNEcaQYLs9oa5HHi3OZ8CTxK0HIXCkz9bfLxIiUrh5xf9nCfXPMnCtIW0+9v55aZfcuXfr2R9yfrwLqJp9lLqSWfaS6ujgbfOXlK9tdieRS6EEEIIIYQQH3LzE7LwGC4avK0UNVeEPBfldrJ0ejIAG/b3XdY80UhEQ6PVbKXT7Ozz/KCi0uH8eyEy1e6/AaQts4+PgaSkhdy++DMA/N97/0tbx8RePUwIIYQQQgghBiKD4orJ65otvvOwL2TZtqQIJy5Dxxc0qe84PpO4Zwn1yv1Yvvae49NjJpEWkYA36GdbbcFJqeslizI5b14GWPDQ6wfIaJ8FQKGvkBaz7931A3JGwIKu2eK7Ts1s8fFkUfoinljzBD+/8OckRyZzpOkIt750Kze/cDPF4S5/rzsgZgYkLQVXPGBBWynUboXOGnvvOiGEEEIIIYT4kHLqDvKSZgCwtbbvEurndi2hvqWgCq8/9OZil+YiVo8FRjhbfOYa+NT7cNHv7ZnjdXuh5LXhX6c/msZnzvoW02InUdvRyIPv3m3PSBdCCCGEEEKIDxkZFB9Duq6TnZ2Nrp+8H+u8qU6chkZdq0lp7fGOuKZppEf3s4R6XAZ6XDqYQYLlu0PKr0idS9A0ebl0M3WdzWNeV03T+PR5ucyZkoDPH+Svr1SSQjomJrs6dw3vYrM/0TVbvAwKnxvzunY7FRmOhK7pXDvnWl779Gt8Me+LOHQH75S+w5V/v5JfbPwFLd4wbzJwREH8QoibC7obTC807YOG3RAYxrL249R4zU+ETzJUm+SnPslQbZKf+iRDIcbGSN9LZ3Ytob615lCfvcNnTY4nOdZDpy/I9sM1fc7tXkK9Nlg7skpHpcOCm2DxbfafN/43tPWdlT4SToeH7533PwA8su85yo5tGNc3RstnofokQ7VJfuqTDNUm+alN8lOfZDixSapjzOFwnNTru50a86c6AXu2eG/dS6hXt3sJmsc7uEbXbPHA0R0h5Q1Np6qzgXer9nHzxl/xVvkwB6rDYOg6X750LhmJkTS2etmx0YNlwWH/YRqDjeFfyBkBC2+2H+f/FsLdV3sETnaGoxHtiubbK7/Ny598mfOnnU/QCvKn/D9x6d8u5el9T/fZ+65fmgaeFEg+A6KmARr4G6FuO7QUKj8TfzznJ8IjGapN8lOfZKg2yU99kqEQY2Mk76VFSdNx6gZVHQ0cbQsd+NY1jXPm2Euab+xnCfXuQfF6c5TLk+d9BZLng68Z3vkOmGOz5dWq6Zdx9pSV+M0g92z5DXRUDH3SaSSfheqTDNUm+alPMlSb5Kc2yU99kuHEJYPiY8g0TQoKCjDNk7sU2ZIcewn1D0r9+ALHB7/j3A4inAZB06Km3dtz3DF5MWgaZsNRzFb7rvW6zmb+fvgtNEBDo9nXzgP7XzgpM8aj3E6+esUCoiOcFJdYNFdEY2GR780f3oVm3QARydB8BLbeM2Z3zfd2qjIcraz4LH5/1e95aPVDZMVnUddRx3+9+V+sXbeW/Mr88C6iGRCdZQ+Ou5MAC9qPQd379i9IAp3ga4Sgd4gLjR+q5CcGJhmqTfJTn2SoNslPfZKhEGNjpO8lj+FiQWIOAFtr+i6hvmJWOmhw8Fgj1U0dIc91D4qPaPn03gwnfORecERAxRbY88jortdF0zT+6/wfohsuXju6lfcLXwRf05hce6zJZ6H6JEO1SX7qkwzVJvmpTfJTn2Q4scmguIKmpRrER+l4/Rb7y47PmNY0jYyu2eIVbccHMjVPDEZKLgDBMnu2eGVHA53/n73zjo+jOvf3M2X7SqteLNmybEvuxjbYYGx6ryFgWsgl+d30hFTSyE0uyU3hJgQS0ki9IaQQOqGG3mxwxb1J7updWq22z5zfH0fVkmyVlaUV83w+45VnZ8+cs+/M2Tnne973NaKk2jwoikLcNGiPBqkLtYxJnXN8Lj5/2QJ0TWXPRi+BUIwjsSPDmzCwuSB7MfiPwrvfg7+fDmWPjUl9k4Vzpp/Dszc/y9fP/Doem4cd9Tu48bEb+frLX6e+o35ohWguSFsAaQvl32YMWndD3RvQuAEa1k54LwILCwsLCwsLCwsLC4tEsDyrM4R6Y1m/9zJTnMyfmg7Amr19F2lnaBkoKHSYHYTMUL/PDgvfdDjj2/LvzfdC467RlddJaWYpNy64BRSNH235K0bLjqRaBG1hYWFhYWFhYWFhYTEaLFE8CVEVhSXF0lu8Xwj1zrzijcEoMaNnJYs2tTOEeuUWhDDJc6Xj1OzEhYFbd2AiiJgxqjpGuar9OMzK9/H/zp9NtN1BzUE3oajBlvCWoRfQUQsHnwGEzH8WqIaXPw2tB8eszsmATbPxsaUf46X/eInr5l4HwL/2/YuL/3oxv9/8e6JG9AQldOLIgMzTwD0VzChggojLSZLWnRALjl0jLCwsLCwsLCwsLCwsJgBLs0pQFZWjgXpqg/1DoZ81Nx+AtXtr+6Qtsyk2UtVUIAHe4gAl10HRxSAMeOMrEBul0N7JF874IimuTPa0VvDkgVehbRcMJQ2XhYWFhYWFhYWFhYVFkmOJ4knKkmKZV/xAbZzWjp4BrNeuk+LQEUJQ1yuEupY3D8XmQARbMZsOk+lM5XNzr8alO1AUBbfuIM3u5a8HXmF3y5Exq/cZpblcvXw6jfuyaOuIcihcQX18iB7N/iMQD4ErG1RN7ou0whOXwbvff9+L41nuLH50wY947PrHWJy3mFA8xD3v3sMV/7iC1w69hhDixIUoqhTHFR3QAQUQ0nu8aQO07ZMh9oZSloWFhYWFhYWFhYWFRZLhtbmYlzYNGNhbfHFxFm6nTmsgwq6KvqJ5wkKoAygKrPoBuHPBfxg23DX6MoEMVwa3Lb8NVAf3bn+MQLAO/OXWGM/CwsLCwsLCwsLCYtJjieIJRFVVSkpKUNWx/1ozUjSKc3QEsPXQMd7inSHUawO9QqhrNrT8hUBPCPXzppzC71Z+kZ8s+zgPnvN1zslfRNw0uHfn4xwNDFGoHgFXn1bE0sJC2ip8tHZEWOvfODTBNrUIdLf0Ynbnge4A1QZmHPb8TYrj//5/cORVMI0R1e1k2nCsWJi7kIeue4ifXPgTst3ZHG07ymee+wwff/rjHGg+cOICNJdcdKAooDqQ3YQKCAjXQstWaNoIHUcnXKi9yWC/pMCIjFm+ecuGyY1lv+THsmFyY9kv+bFsaGGRGEZ7Ly3Png3Apob+orhNU1lRmgvAmj19Q6hnaVlAgkRxAIcPzv4JMpH5w3D45YQUe8vCW5ieXkxTLMRvdz0tx3kTKGWW1RcmP5YNkxvLfsmPZcPkxrJfcmPZL/mxbDi5sayaYOLx+Ek7V+8Q6r1F5a4Q6s3hKOF4jzjcFULdqN6BiEshPdOZyrz0InJcaXx27lXM9hUSjEf48faHaQy3jUm9FUXho+fNJqW1GNNQONhRyYFgxYk/6MmDc+8BmxfiHeDMhMsehMv/BtMulF7O1e/Aq5+Fxy6E7X+A8PBzpJ9MG44VqqLygTkf4MUPv8gnln4Cm2pjTcUarnroKu56+y78Ef/gH9Yc4Jvb6Y1vgGaH9EWQsRScefJ7NkIQOASN66BlB4QbJkzIvclgvwlNqEbmmW/cOGb55i0bJjeW/ZIfy4bJjWW/5MeyoYVFYhjNvXRqVikKCuX+Kpoj7f3e7wqhvuVQI/5QzyL1bk9xM4FpyaacAQs/Jv9e+18QHP0Cdptm45srvwmKxgPlr3G0vQ7a98uoYBMEqy9MfiwbJjeW/ZIfy4bJjWW/5MayX/Jj2XDyYoniCcQ0TQ4dOoRpnhxxcP40GzZNoSlgUtHYI367dI10pw0E1Hb0eHKqGUUo7nREPIpRu7tfeQ7NxlcXXk+hJ5uWSIAfb3uYQILylh2LXdf43IVLidZmYZiCf1W+TTQ+BO/u0tVwy3r44HPydfb1MGUFXPhruP5VWPgJcKTJfOObfgoPnw1vfRMadw6pXifbhmONx+7hq2d+lec+9BznTz8fQxg8sO0BLvnbJTy661GMwTzqXfmQvRIyl8lX9xSw+8A3G7LOhNTZYPPJY6PN0LYbGt4F/36IBU5eA49hstlvXBCi0xPcLxc7dFRC+wFo3Q1Nm6B5GxhhELHOfPO7IJ64fPOWDZMby37Jj2XD5MayX/Jj2dDCIjGM9l5Kd3iZ5ZsCDOwtPjXLS1FOCqYpWFdW170/Q8tAQSFoBgmZCRxLL/0SZM6HSBu89fWELEg+d/q5rJy6khhw985/AUKO6yZANDCrL0x+LBsmN5b9kh/LhsmNZb/kxrJf8mPZcHJjieJJjMOmsGCazC3+3sFjQqh7u0Koh7v3KYqKXrgE6Amhfiwem5OvL7qBDEcKVcEm7t7xKBEjNhbVJ9Vl58OzzwVTxXC286dN7wwtjLonD/KXy9feeKfAsq/CjW/CWXfJSQMjCvufhKevg2dugP1Py33vM4rSirj/yvv509V/ojitmOZQM99+/dtc/+j1vFcz8LWA5gB7mnztjaqBKw8yFkPmcvBMA9UOIg6hKmjeDE2bIVglc5FbTByEKQXtaBuE66GjQnqEtO6C5i1yYUP9W50RALbISbHAAQhWQqQBYn6g62GgK998VHqNt+2TIdWtXIQWFhYWFhYWFhaTgGVZpQBsbNw34PtnzZXj0bf31HaPY3VFx6fJxcMJC6EOoNngnJ+C7oTqd2HnA6MuUlEU7lh1B6qi8lLFJjY0HZbP9m27JkwUMAsLCwsLCwsLCwsLi0RiieJJztIZMoT6jqMxovEeMSrX40BRFPyROB3RnlAPWpco3rAfER44hHaWM5VvLLoRj+6kvK2KX+3+F8YYDYqLMzJZ4JoHQKN3P09vOjz6QnUnlFwLVz8OVz4MM64CVYeGbfDW1+Dhc2Dzz6Gj9oRFTTZWTVvFsx96ljtW3YHX7mVXwy5ufvxmvvrSV6kL1J24gGPRXeAthqwzIG0hOLIBBeIBKbY2vCs9jCPNllg61hxP8G56r1Pwfhsa18u88G17IHBQLl6INErB2+xaMNKZT96WKm3qLgTvTEidI/crulwI0Z1v3uzMN79Nlt9+cFwjBlhYWFhYWFhYWFiMlmWdecX3tFbQPkAEteUlOdh0leqmDg7V94RYz1Q7Q6gnUhQHSJsBy78l/958DzTtGXWRJZkl3DT/JlDgh1sfxRAKxNrBX26N3ywsLCwsLCwsLCwsJh2WKJ5gVPXkfqVFORppHpVITLCnsscr166pZLqkYN4nhLo3CzVjGghBvHLroOVO9WZz+8LV2FSNzY3lPFD20tC8uEfABbnLSHO6caREeL1yR5/wc6NCUSBnMZz7U7jhTVj6RXDnQrgZtt0Pj5wPr30Bajb0GfCfbBuebHRV56OLP8pLH36J1XNXo6DwTNkzXPK3S7h/4/1E4iMIl6co4MiAtHmQvQJSZoHuBYT0MG7dIb2P2w9CfGxC8neRdPYzItLD+nhhCoUp87hHWyFUJwVvf5fgvfnEgne8/RjB2ykFb2cOuKdCykzwzYOMJXKBQ85ZkH2G/H/aPPm+p1CG0U+bJxeZYPbKN79EhtxXNDAjEKzojBiwSdZ1mCEYk86GFn2w7Jf8WDZMbiz7JT+WDS0sEsNo76VcVzpF3hxMYfJeY3m/9z0OG0tnZAHw9p6a7v1decUbjcZRnX9AZt8A0y4AMw5vfAUSkO7sC6d/gVRHKnuby3m8dr/cGa6FUM3xPzjGWH1h8mPZMLmx7Jf8WDZMbiz7JTeW/ZIfy4aTF0WMldKZQPx+Pz6fj7a2NlJTU8e7OhOOV7eHeX1nmFl5Oh8939u9vyYQZke9H7dNY2VhBoqiABA/vJ7o9qdQU3JxnPvF7v0DsaFhH/ftfBKB4Pris/ng9JVj0oZt4W283bqR1maFirdn8bWrFzMr35f4E5lxOPoq7P4b1G7o2Z9eAnM/DDOvBps78eedwOyq38X33/o+W2q3AFCYWsgdq+5gfvZ8jrYdpSitiDxv3glKGYRYoHNCpU6GV+/ClipDsDtyZDj29yuhGilgmwYoKrgLQHdLEdmM9LwOOQy9IsPdq47BX1WbXMQwGoyIFOk1V9/w+sKQUQHC9RBpAnr9vNjTwJkLjqxOUd3CwsLCwsLCYnLwfhmvvl/a2ZsnDq/hsUNvszSrhK8uXN3v/b1VLdz91Dacdo17P3omDptGfbyeFzpewKW4uCH1hsRXKtwCT14FoQaYczOc+d1RF/mXrX/hR2t+RIYrg5eu/RMp0QZAgfRTwD4G43ILCwsLCwsLCwsLC4sEMtTxqrXcIYEIIQgEAmPmUT0Yi4tlXvEDtXFaO3rCnGe7HWiqQjBm4O8dQr1gEagaZnsdwl993LKXZ8/mo6UXAfDoobd4vXpr4hsAzHPMI8vtJSXNxJ3fwq9e2EmDfww8ilUdpl8Cl/8VPvisnETQXdBSDu/ciXj4bCJv3YloO5z4c09Q5ufM56HrHuKnF/2UHE8Olf5Kbn3yVkp+WcJlf7+M0/94Oo/tfmxkhdu80ms8e4X0RLZnyP0xP/jLoPGdhOaiHq97cMiYMYi2QEcltOyE5m0y5LmISfE7cAjaD0CoWorK8UAvQVyRIrTN18vDexakzYeMpZC1Qnp4Z50u87375kLKDCm0O7PAliI9u0criMPg+eYVDZzZsk7ZKyClRNYXpI39+3qF1G8aMFfhhLehxXGx7Jf8WDZMbiz7JT+WDS0sEkOi7qVlWTKE+o7mg4QGiKhVOiWNrFQn4ajB5gMNAGRoGSgohESIoBkc1fkHxJkOZ/9Y/r33ITj62qiLvGXRLRSnFdMcaua3u5/tTIsloG33sKM+JQKrL0x+LBsmN5b9kh/LhsmNZb/kxrJf8mPZcHJjieIJxDRNKisrMc2xyb89GJkpGtOzdQSw7XC0e7+uKmS7pWhVG+gZyCo2F1reXADiFVtOWP5FBadyTdGZAPyp7N9sHiB03GixKTYWOhbic9spmN9CIBLhvud20BEZqofsCEgvkavqb3wLTv8vSC2CaDvsfhAevwRe/BgcfV168U5yFEXhqtlX8eKHX+RDCz5EY7CRUCxEIBqgoaOBr7z4FWoDo8jBrqhSLE1fKMNze4ulwCt65aJu2ggdR0Y16TJe92A/hCm95EN1MmR8y3YpBje8I/8OHIBIPdBVTwXZHStgTwfPNCkopy2AjFMh+8xOwXt5f8HbkWDBO1GoNhluPWOxFOq900FzA2ZnSP2d0LBO5iuM+rsXRUwYG1qMCMt+yY9lw+TGsl/yY9nQwiIxJOpeKvRkkedKJ2YabGs+2O99VVFYNVdG1eoKoa4rOj5NLgxNeF7xLgpWwvz/J/9e8y0INoyqOF3V+eaqbwLwwLa/cMR0gu6RKZjadg24mHUssfrC5MeyYXJj2S/5sWyY3Fj2S24s+yU/lg0nN5YoPklYMkPmD3/vYLTPCpZ8b6co3hHus18vXAqAUbUVMQTR9/riszknfxGmEPxy11OUtVUmsvoAzLbPxqO5ycxQyC8JUNMc5Lcv7sYY687HkQrzb4Xr/o154e8JZi4HFKhaA698Gh6/GHb8CSJtY1uPCYDb5ubSWZeSYk/BY/cAEDNi1ARqeGTnI4lZHaU5pOibuQzSF4MzT3oYGyEIHJa5x1u2yxDcJ3nyZdgIIUX8SLPMnd22R+bRrl8jc2r798r82tGWnpzemlMK2e5poNpB0WWOb0WTwrZvrlw04J4CjkzpbZ+IkOfjieYETxFknia92t0Fsu0iJr3iW7bIRRGBw/I6sLCwsLCwsLCwsJgAKIrCsmzpLb6hYd+Ax5w5Ow8UKKtuo65VeoZnaTLX+JiJ4gCnfQUy5shw6m9/c9Rjp3OKzmHV1FXEzTg/efce8M2XY5VYO7SXJySyl4WFhYWFhYWFhYWFxXhiieKThAXTbNg0haZ2k4rGHpE702XHpqlE4iYt4R6vazWnFMXhQUQ6MBtO7PmtKAofK72UxZkziZpxfrr9Uao7EjvA1xWdRY5FqKpC6dIOHA6F3RUt/P2t/ScnVIWiQsEq6hZ+D/ODL8CC/wR7KrRXwsafwD/PgjX/BU17xr4u40hRWhFehxebZiPLnSWdmIH7NtzHx5/+OEdajyTmRIoi89P5ZsvQ36mze4XabpECc8O70pM41p6Yc44GYch6hGqhfb/0bm94V4r4rTsgcFAK+fEOQEiR2+YD1xTp9Z2+BLJXSa/ptPmQ2hn6XNWBuMyt7pvbPyT5ZEJRpFd7yiwZMSBtoQwFr6hSDO84gtq8iSnOOpRQdc9CAgsLCwsLCwsLC4txoksU39p0gKgR7/d+ZoqTBVNlmqg1e2V0rUw1ExhjUVyzwzn3yPFD1RrY/ddRFacoCnecdQeaovHKoVdYV7tNjk9AjoFCNQmotIWFhYWFhYWFhYWFxfhhieIJRFEU7HY7yjh4dDpsCvOnydziWw71CEmqopDrkSJbTSDcvV9RNbSCUxCmQazsdczQib2gdVXji/OvYWbqFALxMP+7/Z+0RBIrVs6yzcKrejG1KFdd5gAF3txVzcvbE++ZPhDdNvQVwfJvyNDqq34oV+AbESh7DP51DTx7Mxx8DowxDO8+TuR587jn4nvw2r3EzBj53nz+Y9F/4NJdrKlYw5UPXckv1/+SyAA59UaMqoErT4bazlwuPclVB4i49CRufk96YAcre+XY7k9C7kEhZJ7vSBN0HJX5rxs3Qv1aWQ//PghWdeZB76yL5pZ59zzTpdCddTpkr5TtSS2RXt/2VNnO3rjy5XGZy+SrK3/k9U42FAUcGXKiLetMSJ0jQ8ej4NRiKIEDMrx6y47OqAGTP43BZGA8fwctEoNlw+TGsl/yY9nQwiIxJPJempGSR4YjhbARZWfLoQGPOWuefI5fu7cWwxRkaj2i+Jgu8E6fBcvvkH9vvBuaB/ZmHyqzMmZx84KbAfjh2z/EsPlkFCuQC4OjJyd6mtUXJj+WDZMby37Jj2XD5MayX3Jj2S/5sWw4uVFEEmSL9/v9+Hw+2traSE1NHe/qTFgO1sX5v1cDOGwK3/xgKjZd3rTNoSibalrRVZVzizJRO2/m2J4XiW55BADFlYb91JvQpy074Xn80Q6++95fqQ21MM2bw38vuQW37kxYO/ZH97M2tBaH4iD10HIeX3MUFPj8ZQtYXJyVsPMMCyGgfgvs+Rsc+nePQOfKgtk3wZwbwZ0zPnUbI2oDtRxtO8o03zTyvHkcaT3C9978Hmsr1gIwzTeN/z77vzmr6KyxqYAQ0mM8VAuRRqCrq1JkWHFXHtgzRhdWXBjSszveAbEOiAfk36K/B4g8tQ66F2wemWNP94Lull7hFonBiEoRPFwn7dGFosmw865csKUldzh5CwsLCwsLi0nF+2W8+n5p50D8pfxlXqzcxDn5i/jUnCv6vR8zTG5/4F06wjG+cMVC5hf5eMj/ECYmq1NW41E9Y1c5IeCVz0DF65A2C65+HEYxPm8Nt3LRXy/CH/HzvXO/x03zb5RRvCINMgVSxtLJHd3KwsLCwsLCwsLCwiLpGOp41fIUTyBCCFpbW09OqO8BmJ6jkeZWicQEuyt7vGnTnTacukbcNGkMSi9yM9RGbO/LgAICRLid6OZ/DsljPNXu4Zun3ITP7uFooJ57dzxBzBxERBwBM2wz8Kk+IiLClNl+zpk/BQT87uU9HGkY2zDag9pQUSB3KZx7L9z4Jiz5vBTEQ42w9Vfw8Lnw+pehbvOkybWW581jecFy8rx5gAyr/qer/8R9l95HjieHo21H+fgzH+cLL3yB2kBt4ivQ5UmcNg+yV8iQ27oXEFIkb90pQ5e3H4S4zN0n4mHamyoQ8XDfsoSQ4bnDjTJvdesuaNzQmft7C/jLIFQFsbZOQVyR3t/OHOkZkbZQhvvOPhMyTpF1ceXLUOCWIJ5QhGqjNeZFZCyVOci7owYYUihv2d5j91jgxAVanFTG+3fQYvRYNkxuLPslP5YNLYZKVVUVH/7wh8nMzMTlcrFw4UI2bdp03M+88cYbLF26FIfDwaxZs3jggQdOTmXHgUTfS8uySgHY3FCGMUDubpumsmJ2LgBr9tagKzo+TaaGGtMQ6iDHTat+KMenrfulx/goSHOm8fnlnwfg5+t+Tns0IFNd6R6Z3qht96jzl58Iqy9MfiwbJjeW/ZIfy4bJjWW/5MayX/Jj2XByY4niCcQ0TWprazHNsR0gDoaqKCyZYQdgy8GeEOqKopDn7RtCXXQ0IowI2D2gKmAaiLAfs/nwkM6V40rj64tuwKnZ2d16hPv3PIOZoIGxqqgsdi4GYHd0N6vPmsq8qelEYwa/eH4nLYEEhu0+hiHZ0J0NS26DG96Ac38GuadKwe7Q8/Dch2R49X2PQiw0ZvUcLxRF4dJZl/LCLS/w0VM+iqZovHjgRS7926X835b/I57AxRF9UG3gLoDMUyHjVPm3ostJmWAFNG2Ehneg/m1cwV3QsEaGPfeXSdG7Ya0Uwdt2QccRKaobnfZR7TJst7tQTvZknAo5qyBrmQzt7ZkmxXnNYXknnwT63IO6Ry5KyDod0hfLhQi97d68WYbV76iQ6Q0sxp3x/h20GD2WDZMby37Jj2XDXhgRma7G+o3vR0tLCytXrsRms/HCCy+we/du7rnnHtLT0wf9zKFDh7jiiis477zz2Lp1K1/60pf4+Mc/zosvvngSa37ySPS9NDttKl6bi0A8zN7WowMec9ZcuZh466Em/MFonxDqY44rE876X/n3nr9BxZujKu5DCz/EjPQZtIRb+M3G38g0UL758lk85of28jFdDG71hcmPZcPkxrJf8mPZMLmx7JfcWPZLfiwbTm4sUXySsbhY5hU/UBunLdhz0+Z15hVvCEaJmyaKJwtF6/S+tHd63wqT6PYnMRoPDulcxSl5fHnBtWiKyrr6vfxt/6sJWz1TpBeRoWUQEzH2xHbz6UvmkZ/hpjUQ4adPb2X7kSaaA+ETFzSWaDaYcTlc8Q/4wFNQer0MU9e8F9Z+Gx4+Gzb8GPxHoaMWatbL10mA1+7ljrPu4Ikbn2BJ3hJC8RA/XvtjrvnnNWyu3jy2J7d5pad29grwzZMh1IUp89uJGKpiStG044jM/R3zd4a7V6SnuTMXvDMgbRFkrZDlpC+ClJkyJLvNC8rJ6xprA7Wsr1w/Nt72kwVFAbsPUks77T5fhlJHkeHuAwel93jzNgjVwFgtzrCwsLCwsLA4OYRqOhc1bpSvoZrxrtGE4sc//jFTp07lz3/+M8uXL6e4uJiLL76YmTNnDvqZ3/72txQXF3PPPfcwd+5cbrvtNlavXs3Pfvazk1jz5EVTVE7NKgFgY0PZgMcUZnqZnpOCaQreLas7uaI4QOFZMO9W+ffbd0CwccRF6arOHatkrvIHtz/IkdYjoLvkomGQKa6s+9LCwsLCwsLCwsLCIsnQx7sCFoklM0VjerbO4YY4Ww9FOWe+zCWWYtfx2HU6onHqOqIUpPiwn3oj0c0PS49xlw/VlYaIhYm8+yds8y9HLz4T5QSesQszivn03Cv59e6n+XflJtIdKVw17YxRt0NRFJY4lvBq8FX2Rvcy1zuXL16xkG/8dR1bDjWx/UgzPredT100j7Pm5Y/6fKMmcy6s+gEs+xqUPQ57/w7tlbDz/+C9X0DU35kT2Qfn3gOlq8e7xglhTtYc/nHdP3hyz5P85J2fUN5czoee+BDXzrmWr638GhmujLE7uaKCM1tuoXpo2dIZxk8AitycOVI8tXlBc51UsXsoPLb7MW5/6XaCsSBum5t7Lr6H1fMmx7UxZigqOLPkZsZkSPxwnQx9H2uVG+Wd+cdzOvPOq9LLzAjJ68DKgWhhYWFhYTFxMSIy4o8ZBRQwkfmM7RnWb3gnTz/9NJdccgnXX389b775JgUFBXz2s5/lE5/4xKCfeffdd7nwwgv77Lvkkkv40pe+NOhnIpEIkUiPp77f7wfAMAwMwwDkuE1VVUzT7LNAerD9qqqiKMqg+7vK7b0f6OelMdh+TdMQQhDvaEZvryTekYnizURVVYQQfY4fbt1PyyzhzZrtbGzYxy0zzkPtHFv0btOZs3M4VO/n7d01LJk/HYBGo5F4PN49th5pmwaqe7/9S7+CWrMO0VKGePubiAt+C4oyIjudXXQ2q6auYs3RNdz19l38+vJfo9rTUbzFiPaD0F6OiQaKjmrzgOZIWJsMw8A0TQzDGHbdx/vaG5Kd3gdt6rKhaZpomjYp2jTS/cnYphPZLxnbdKL9k61NhmF0/z1Z2tS77pO9Tb3tN1na1Hv/ZG9Tl/26+tPJ0Kbj1X0ytmkw+yVzmyajnY7df+znBsMSxROIoih4PJ4TCsljzZIZdg43xHnvYJSz5zlQOgfBeR4HB6JxajvCFKQ40actQ80uRXQ0oXgyUWwuotufwKjcRmzns5itFdgXXYui2497vpW582mNBPj7gdd46MDrpNu9rMpbMOp2FOgF5Gg51Bv17IjsoEQ9hWDUwBQCDEFTe5h7ntlGRyTGyjn5pLhsoz7nqG3o8MHC/4T5H4Gqt2Hb76HsUSnWKirEOuDlT0HGHMga/Xc0EVAVlevmXccFMy7gnnfu4ZHdj/DE3id45dAr3L7idm6Yf0P3hNGYYffJEOtmHEMo6IoAVZce5RN08rTKX8Vtz99Ga7gVRVGIxCPc/tLtrJq2qjuP+/uRYd2Dqg3c+XIzwhCuh1AdGEGINMhN0aUQHm2R96GqSQ8X1wRYTDMJmSi/gxYjx7JhcmPZL/l539tQGODfD2aXEKvIZ2hhdC5um5jPdSebgwcPcv/99/OVr3yFb33rW2zcuJEvfOEL2O12PvKRjwz4mdraWnJzc/vsy83Nxe/3EwqFcLlc/T5z11138b3vfa/f/gMHDuD1egHw+Xzk5+dTV1dHW1tb9zFZWVlkZWVRVVVFR0dH9/68vDzS0tI4fPgw0WhPyq/CwkK8Xi8HDhzoM9lRXFyMruuUl5f3qUNJSQnxeJxDhw5171NVldLSUjrK12BsfZRsI0b48DM0Tr+IgmVX0tbWRm1tT3Qmj8fD1KlTaW5uprGxx6t6sDZNyUjDqdlpCLbyxq4NTHVk9mtTphLGNOIcqWulpiKGmqkSiATYVbsLh+kYeZs6OqisrOzeb7fbmTFjxsBtOvdejCc+gHHoVZreuAd/4QdGbKcPT/8wbx58kxfLX+SRdx/hqsVX4fVMpbWhAq8WQGvejClU0O2YqbMpr2hPSJtqampob2/vvtaGY6dxvfaGY6dJ3iYhBO3t7TQ0NDBlypRJ0abJaKfB2tRlv5aWFnJyciZFmyajnY7XJiFEt7gwWdoEk89Og7VJCIGu6yiKwv79+ydFm2Dy2WmwNgkhcDgcCCHYv3//pGgTTD47Ha9NM2fOxOFwcODAgT4LW5O5TZPRTse2KRAIMBQUkQTZ4v1+Pz6fj7a2NlJTU8e7OhOeSEzwv0/4iRmCT17kZVq2XPsQjBmsqWhCUeDsqVk49P5CpRCC+KG1xHa9AMJE9eVjX/ZhVPeJPX7/tv9Vnq/YgKqofH3R9SzKmDHqttTGa3mx40VUVBZ2nM8PH9qNpkBHJI5hmAggK8WJw6YxK9/HkuIsFk/PJDfNPepzJ4Sa9fDE5WAKKdSZBiAgZSrMuAJm3whTzpRC3SRhS80Wvvvmd9nbuBeARbmL+O4532V+zvyxPXGoRnoRCUN65U9Q4bMx2Mijux7lj1v+yLbabSjIRStCCOy6nRdueYGzi84e72omL0JAPCAF8nC9FMuNMH0iCKg2mTfemli3sLA4HlaECQuLk0u0BfxlEA92/nYDame6J1WD7JUT9l482eNVu93OaaedxjvvvNO97wtf+AIbN27k3XffHfAzpaWl/L//9/+44447uvc9//zzXHHFFQSDwQFF8YE8xbsmQbraOVG8ArrPGw0QeuF7EPaDzQUI0B24Lv1vFGfqqD0dfr3nGd6t380Vhcu5aca5A7bpT6/uZV15PWfPm0LmsjKajWbOdp7NNH3aiNo0Iu+NnX9B2fAjUO2YVz6KklE6Yjv94K0f8Pcdf2dWxiz+ddO/0DUdI9KO2rgWGcpBAVRQbZiZZ/S5T98PHilWm6w2WW2y2mS1yWqT1SarTVabrDZZbRr/Nvn9fjIyMk44Lrc8xROIaZo0NzeTkZHRbZDxwGFTmD/VxtbDUbYcinaL4m6bhs9poy0co64jzDRff+FYURRsM1ahpk4huvkfmG01RN76FfalN6HllB73vB+aeR6t0QDv1O3m5zuf5DtLbqE4ZXQer3l6Hvl6PjXxGpo8+3HaVEIRg3SPg0A4hqIoTM3yUtcaory6jfLqNh5Ze4ApmR6WTM9kcXEW03NSUIfobZNwG6YWydDdsQA48mWo5y6v8SMvy807ReYjL7kOPLknLnOCsyR/CU/c8AR/3/F3fr7u52yv287qR1dzy8Jb+OLpXyTFkTI2J3blY+pp+FvqSE3PRbX1n9gbL4QQrK9az0M7HuLlgy9jCIO4GUdTNTRFw6k7aQu3ETWifPeN7/Lry3/N3Oy5413tcWHU96CigC1Fbt4Z0HFULpag6wfWlN5nLTvAOw0cmXIRhUVCmCi/g5OeMRRrLRt2EqqR4ZuTLMKEZb/k531pQzMG7Qch3LkCXXOCuwCClT2CuG/uhBXEx4P8/HzmzZvXZ9/cuXN5/PHHB/1MXl4edXV1ffbV1dWRmpo6oCAO4HA4cDj6f++apqFpfZ+fBrteh7v/2HKHu9/oaIRYCIRAxEIoDi8YUURHE6rLN2A5w6nj8uxS3q3fzeamcj406/w+UR26jj97fgHryxvYuL+eG5el00wzraKVYq14xG1VFGVY+9X5t0L1Wqh8E+3tr8FVj4HqGJGdvnjGF3mm/Bn2t+zn0d2PcvPCm9GUzoXIAuQ/BpgmWrwV7FNG3SZFUfr1hYm6xsbq2oMR2GkSt6n379lI6j4R2zRW+ydim0Zrv4nYptHuT7Y2maZJU1PTcZ8pk61NQ9k/WdrU236TpU0nquNw90/kNo3UfhO5TSPdn6xtOt64PFnbdLz9k6VNg73f77xDOspiSAghusNEjTdLZsiQ59uPxIjFe+qT55GTGjUdkQE/14WWNQPH2behphUioiEi6x8gVv7GcdumKiqfnnMl89OnEzai/GT7I9SFWkbfFscSAKo5ykcuKcLl0AjFDDxOnS9dsZAf3XI6P7n1DG4+axZzCtNQVYXqpg6e23yUHz72Hl/9y7s8+EYZO442EzPM454r4Tb05Mkc4jav9F51ZsBlf4Hr/g3zbgV7KgSq4b374JHz4JXPQsWbnR7lyYumatx6yq38+8P/5oqSKzCFyV+3/5VL/34pz+x7ZszuEaHaqW0OI9Tjh/w/WbSGW/nzlj9z6d8v5SNPfYR/H/g3hjBYkreEey++lz9/4M9ke7LRVI1sTzYz02dS1V7F9Y9ezwNbH8AUx79eJyMJvQcVBVx5oNllGHXFRre3eLxdiuUN68BfDrF26WVuMSom0u/gpEQY0H4I6t+Gxg1Qv1aKt4k8xfvZhkJAtBXa9kHzNrmARsTkIoSWnRBpHe8anpD3tf0mCe8rGwoho7o0bewRxF1TIHMZpJZKz/DMZfI1CRalnExWrlzJvn37+uwrKyujqKho0M+sWLGCV199tc++l19+mRUrVoxJHccLxZOFYveAqsqFTeF2UG0onsyElH9K5kxsqkZtqIWKjoYBj5k9xUe2z0U4atDaKFN8NRqNAx47ZigKrPoRODOhpQw2/XTERfmcPr6w/AsA3Lf+PvwRv1yYp2pysbfSy9fCv0/+jhrRQUobGu+rvnCSYtkwubHsl/xYNkxuLPslN5b9kh/LhpMby1N8klKcq5HmVmkNmuypjLFouhQJ8zxOypoDtIVjBGMGbtvgqydUVxqOlZ8ktuNfxI9uJrbnRcy2KuyLV6PoA3tq6KrGlxdcy/e3/J0jgTp+vO1h7lz6H/jsnhG3JVvPZqo+lYp4Bfap1dz3nyupbwuT43OS4XUCkJni5MJFhVy4qJCOSIwdR5rZcqiRHUeaaeuI8uauat7cVY3TrrFgWgZLirNYWJSBxzH6POQnpHQ1FKwC/1FInSaFcoAz/gtOux0OvwT7Hoa6TXD0Vbl58qX3eOl1PccnITmeHO695F5Wz1vN9978HodbD/PVl7/Ko7sf5c5z7mRmxszxrmLCEUKwtXYrD+18iBf2v0C0c0LIY/Pwgdkf4KYFNzE7a3b38ecXn8/RtqNM803DoTn41qvf4rXDr3HXmrt4+8jb/PiiH5Plzhqv5iQ/mkN6l3WF1lcd4J0JmDL/uBmBULXcdA8488CVAxNkYUXSYURwqmEpImoTJI1FMmIaMuVGvKMzjHBQvsY7eqUDAERcirfednnd2lLl5LTF0DHjEG2GSBNEmuV3KgxkOFiQC2mEFMdb3gN7OjhzwJEtF9xYWFiMDCMsF6VFm+X/NbcUwu2+nmM0h+UdPghf/vKXOfPMM/nRj37EDTfcwIYNG/j973/P73//++5j7rjjDqqqqnjwwQcB+PSnP82vfvUrvv71r/Of//mfvPbaazzyyCM899xz49WMMUF1+bCfeiORzf+EUKvc505DsSfmucSp2VmYMYP3GsvZ2LCPad6cfscoisLKOXk8tf4Qe8pi5J0OzUYzQog+nuVjjjsLzroLXv4k7H4QCs+GwrNGVNTNC2/moZ0PcaDlAL/Z+Bu+ueqbfZ+xFadc8B3vkItcIg3gmQ7uKdaziYWFhYWFhYWFhYXFhMESxScpqqKwuNjOG7vCvHcw2i2KO3SVDKedplCU2kCYGenHF6sVzYbtlOtQ0wqJ7nwGo3onkfZ67Mv+A9U7sFDn1h1845QbuHPzg9SGWrh7+yN8e8ktOEcxebzYuZiKQAWHYodY6F3IHG/6oMd6HDbOKM3ljNJcYobJ3qpWth5qZMuhRto6omza38Cm/Q2oqkLpFJmHfElxFpkpzhHX74R48gYWt3UnzLpabq0HYN8jsP9J6KiBLb+Arb+CqedC6Q1yEiNJc4+fOfVMnrn5Gf703p+4f9P9rK9az9X/vJqPLfkYnzntM7gmUKjzkRKIBnh639M8tPMhyprKuvfPy57HzQtu5srSK3Hb+k/G5XnzyPP2XBu/ueI3/HPnP7lrzV2sqVjDVQ9dxV0X3MW50889Gc2YnLjywZ7RP9y0Z7rMXxqug3CDnMQLHIDAQRlW3ZUnP3cyJy+TmVANauseCl1R1KZmSEuOcNPjihnrL3rHg3KxxoB0rVBV5XXZJd6GKiFcI8OY2tPBkSGvXUtMGhgj1CmCN0G0jZ7vFentZs+Q/YIw5f/NmNTGUSDml1v7AbCngTNX9heq9UhtYTEkhJAL0QKHOvswBTzT5GYJZ0Nm2bJlPPnkk9xxxx38z//8D8XFxfz85z/nlltu6T6mpqaGo0ePdv+/uLiY5557ji9/+cvcd999FBYW8sc//pFLLrlkPJowpujTlkHGTCp2vkNO0wZENERs+1PYFq9OiCi9PKtUiuKNZVxXPLDIvHJOLv/acIjyA1FylkFYDdMhOvAq3lGff1hMPQfmfhj2/A3e/iZc8zS4hu81r6s6d6y6g48/83Ee3PYgNy24ielp0/s/Y0fboH2/jJIWOCAj2qTMlM8mFhYWFhYWFhYWFhYW44wikiAGgN/vx+fznTBB+nhjmiZ1dXXk5uZOiByATe0GP3umHQX42jWppLplnaraQ+xqaMdj1zmzIH3IEwNG8xGim/6OCLej2BzYl9yAljdv0ONrgs18970HaY+FWJQxg68uXI0+ClH3zeCbHI4dJpdCSsPnkJmi4nMP/Xs2heBwfbsUyA83Ud3U0ef9qVleTpmewdQUWDK7aMg5CBJOPAJHXpICee2Gnv2ePChZLb3Hvf3ztCULlf5Kvv/m93njyBsATEmZwnfO/g7nF58/6rLH4x7c3bCbf+78J0/ve5pQPASAU3dyRckV3LzgZhbkLBjR5Nv+5v185cWvsK9Jhsb8j0X/wdfO/BqOQaI0TBbGrR81YzKEa6hWTuJ1odql6OXKA93yfB6UWAAa1yHMOAKBgoKiqJA6G2we6aGv2uX2flxkYEY7Re8ub+/Ov83jhBZVbdJzUvfIa093Azo0b5Se5GqXWKvKazTml17OvdHcchLakQE235AEp4n2LJMQhJDfT5cQbgT7vq+5wJHV63tS5CR+t/dbZz5je4b0fAvVyxQM3aidC2lyOhfSjN/3Nint9z5jUtsw3gH+Mnk/goxukVoq+7kkJ1nGq6MlmdrZdS9lae3E1j8AQmBfeDV68ejDxQdiIT699heYwuTe0z9Fnntgwfdnz25n55Fmll9Vh9MX5lz3uRTZBg9xP2bEw/DMamgph6nnwYX3j/h57JPPfJI3j7zJ+dPP5/4r7x/4ICGkt3j7IRlpBeTvpHcm6ENbDD2p+8L3CZYNkxvLfsmPZcPkxrJfcmPZL/mxbJicDHW8aonik5w/vBzgSEOci09xcvZ86QkdM03ePNKEKQQrCjJIcQzdu0mE/UQ2/QOz+QgAttLz0WdfIMWPAdjvr+KHWx8iYsQ4K28Bn55z5YhX57cZbTzc8hTt4TjNu0/BbJnC6tOyOHXmyDzQ61qDbD3cxJZDjeyvaeuTTjgjxcHi6VksmZHF7Ck+tPHq/FoPQtmjUP5ETy5TRZVe47NvhMJzktJ7XAjBa4de4wdv/4Dq9moAzp9+Pt8++9sUpBaMc+1OTCgW4vny5/nnrn+yvW579/6Z6TO5acFNXDPnGlIdo++rIvEIP33npzy4XYa9LM0s5d6L76Uks2TUZVsch1hATuSF6nsm8kBO3rvyZNjk97tXqDAg6pee9tEWiLV1hvWG7nDTAJpTCordKFLsVR0y9HSXWK4d86roySeeC9EpfvcSvbtE8GPF6t6ojh7Ru7cIrg6S3mMgsdaVL88fb5chwKMtPaJTF4oqvcjtnSK5NobRUSYCpnFMWPRe9zKKvJ8dmVIMH2yC3oj0jzDRRTzUGWWiXh7TXbQOzmwZYr1LYLeweL8jTOg4KjeE7Lu8xTJ/+CS5R94v49VkbWfswFvEdr0AiorjzE+gZU4fdZk/3PoQu1oOc/PM87hq2hkDHrPpQAP3/3sX006tZ/rcEAsdC1nqXDrqc4+I5n3w9HVyQd2K78Lcm0dUzMGWg1z5jysxhMH/Xf1/rJy2cvCDzRh0HIFgNfLZUAHPVHBPS8oxrIWFhYWFhYWFhYXFxOWkieJvvfUWd999N5s3b6ampoYnn3ySa665pvt9IQR33nknf/jDH2htbWXlypXcf//9lJQMXdRJlsH3RFxBsvlAlCfXB8lK0fjild5uQXpbXRt1HRGmp7kpzRheCDdhxonteo74oXUAaLlzsC+9AWWQENhbGvdzz87HMYXJ1UUruGnGuSNqS3VznD9XP40nvxIhFOJhF/5dp/HVsxYNy2N8IPyhKNs7BfJthxowUemaonM7dBYWyTzkC6Zl4LKPgxhmROHIyzL3eM36nv3uHJmzvPT6pPQeD8VC/Gbjb/jTlj9hCAOn7uSzp32W/1zyn9i04ed7H+t7cH/zfh7e+TBP7n2S9qj0EtRVnUtnXspNC27itCmnjUmewLeOvMU3X/kmTaEmHJqDb676JjcvuPnk5iQ8SUyoflSYUlAL1UqRsUvoVVQpjLvy3j+ilxDSgz7a2imEt9GTcxn5XRkRBGCioCLkYilnrhSEzejxvaL7oR5fNO8S1ZVxmFAVAsxwL9E7CEZn2HNhDP45zdnf81tzj2yBxfHE2i7MmLRVl0h+7PevuXrCrNt93d/lhLoHh4sR7hUWvZW+YdG1zgUBmbLdgy06GC5d90ZXGobe37PqkOK4MwdsJydcblLbzwKYhDaMtknv8K4IDY5MSCmZdOkdkmW8OlqSqZ297yVFUYi+90+Mqu0oDi/Os29DcflOXMhxeLlqM38ue4mS1AK+d+qtAx4TN0xu/8u7aNn1zFnRQrFrKhd5LhrVeUfFrr/A+h/J++/qJyB91oiK+eFbP+TB7Q9SklHCv276F9qJBO54hwypHm2V/1ft4J0hfx8HeY6edH3h+xDLhsmNZb/kx7JhcmPZL7mx7Jf8WDZMTk6aKP7CCy+wdu1aTj31VK699tp+oviPf/xj7rrrLv7yl79QXFzMd77zHXbs2MHu3btxOofmpZQsg2/DMCgvL6ekpGT8Qm8fQyQm+N8n/MQMwScv9jItS06+13VE2FbXhlPXOGtqxojEtXjFZqLbngTTQPFk4lj2YdTUAfJmA2/UbOP3e58H4NaSi7i08LQhn6ehzWDt3gibjraRduYz6M4QQiiAghmzkXH4Sm46tQCXffTClGEY7Nq7j5gzi21Hmtl+uIn2UI93maYpzC1IZ3FxJkuKs0jzjMOEXtvhTu/xxyHc0rlTgcKzpPf41HOTzot1f/N+vvfm99hQJcPFz0ifwZ3n3MkZhQN7XQzGWNyDUSPKSwde4p87/8nG6o3d+6emTuWmBTfxwTkfJNM9/Lx8w6Ux2Mg3X/kmbx99G5Ce9T+64Eeku9LH/Nwnk4nYjwJShOwKr9479LLmBGceuHInn+etEZIRKrq8wY/1dlbtnZ7H6TK3crQZ0boHIx5F0+0ox+YU7/KkNiNyoY8Zkf/v+tvo/P/xvKqPRdGPL5p3ierH+40bTGAWQu7vLXp35f8W5iCFKfI66BK+NXePAD4eAn4X3YsamiHS5UXe+/FPlTZ0pGPoaZQfrKCkpHRi3YMD0e0d3+kN3jv1AUhbODLlNsTw8aOuT7RVCuSRxr6LJDS37CecOWPaV0zYPtRiyEwaG5pxCByU0S1A9sUps2R0hkm4mCxZxqujJZnaeey9JOJRImt+g+mvQ02fiuPMT6JoIx8ztUTa+dw7vwLg12feRrojZcDj/rlmP28d3s/s8yvI9aZwY8qN47ewVZjw0iegag1kzIGrHpXPS8OkLdzGRX+9iLZIG188/Yucmn8qRWlF5HkHnguQ5xby97r9gFxcCDJqS8ossPX/7iZNX/g+xrJhcmPZL/mxbJjcWPZLbiz7JT+WDZOToY5XR62cXXbZZVx22WUDvieE4Oc//znf/va3+cAHPgDAgw8+SG5uLk899RQ33XTTgJ+LRCJEIpHu//v9MgSoYRgYhpxgVBQFVVUxTZPeuv5g+1VVRVGUQfd3ldt7P8hVIUPZr2kaQghM0+xXx679J6rjWLRJV2Fuocb2w3G2HIgyNVPDNE3SHRqaqhCKG7SEY6Q7bcdt07F1EUKgTFmMzZNNfPM/EB2NRNb8Bv2U1ah58/sdf3buQppDfh47soYHy1/GZ3OzInfecet+qD7OO3tjlFVLgUT1taNoJkbMjmaLI4RAs0dpnvZvfnMwixJPIctyCsnWsro7q+G2yTAMdAXmTE1jSXEWAoUDNa1sOdzE1kON1LWF2HG0mZ1Hm/nrm+VMz/ayeHomi4szKcjwoGna2F973qmw9Ctwym1w9FW0/Y8hqt+Fyrfk5spClKxGnX09wlswbtfecNpU7Cvmz1f9mWfLn+XH7/yYgy0H+chTH+GKkiv4+oqvk+3JHtL9ZBhG9+to21TRVsHDu6VXeEu4BQRoqsa5Redy4/wbWVW0ClVR+9zzx2vria69E9kp3ZHOb6/4LX/f8XfufuduXjv0Glf+40r+94L/ZeW0lePe742kTQPt77KhaZon534aaps0B8JdiOnIh3g7SrgOJdKIYoQRgUMQOIywpyGcuSiOLFRNn5C/T8fWpc9+M4YSb0ONtSEiLX1DQgOKqiNsvs4tDTQ3iqr2tMmeg5HupfLIfqZNL0V3eAZokx1Fc2Cox2mTMLo9y1URAzOKiIe79ymdmxCmDIltxqQHUme75Ol6C74KimZHKHZEV17zTsFcNToQHUc7xUsV4chCUW0oRhBhhI4RvxUURT7boKiguRCaC8XmRdE9GIpDCuudwmufNpkARmLsxAj7clsKpuZBOAulWBVrRY21okSbEUak28NaBaa6TJSAimHrXPDQKehPhD5Chu5vRY219NS9l42wp6I4MjFt6QjV1S2+qSgoMLb3E2DqqeBNBc8MlFgLaqQBEWnu9JI7KDebD8WVi2nPRCh6v3JG00cYhoEQors/HXWbxvEZdih1n4xtGsx+SdWmUANqYH9P5ARXPniLMdGgVz2Tqk0nuPaO/ZzFxEPR7diX/QeRt36F2VJBbOcz2E/54IjLS3ekUOIroLytik2NZVxUcOqAx501N59XdhwlHDEJucMERIAUZWABfcxRVDjrf+Gpq6B5L2y6F07/5rCL8Tl9fPH0L/K1l7/Gl1/8Min2FDx2D/dcfA+r560e5NwKOLPAkQ4dlRA8KhfpNb/X2UdMl89oFhYWFhYWFhYWFhYWY8iYupMeOnSI2tpaLrzwwu59Pp+P008/nXfffXdQUfyuu+7ie9/7Xr/9Bw4cwOv1dpeTn59PXV0dbW1t3cdkZWWRlZVFVVUVHR0d3fvz8vJIS0vj8OHDRKM9oS0LCwvxer0cOHCgz2RHcXExuq5TXl7epw4lJSXE43EOHTrUvU9VVUpLSwkGgzQ3N7N//35UVcVutzNjxgza2tqora3tPt7j8TB16lSam5tpbGzs892MRZuydJ1oLJVtR+CSxXYOH9oPgI6DIDYOtgQo8bmoqTjSr00dHR1UVlZ27x+oTWr+JWTVvIUzVEt4w9/wp8+nPXcZKGqfNs2NZLHYMY0N7fv55c6n8Nk9pATUPm3Kzsml0u/hpc3NNLT3eHTNm2pnxcJcXhYqUWEQDzvQ7FFUBUAh7mmmjGYONmzDq+mUphaTp+QRqYhgE7Yht6m6urrbhikpKUydOpUMh8EpmXFOyUyjMeChoh0ONscoq2qmvHN7dG05OWkulpfmk+82yHKaqJ0T8WN77ZVSeukDBGt20/HeA3hrX0Zrq0bZ/Evs239HNHsZdWnnEsw8HVT9pF97w23THGUOvz7j1zzb+Cx/2/Y3ntz1JC/ue5FbS2/l2lnXUjKr5Lj3U319fbf90tPTh92mlNQU/vHuP3jqwFO81/geALquk5+Sz7nZ53JRwUVku7IhAvFYfNh9xFDup2PbdKydPrL4IxQ7ivn229+moq2CW5+4lVsX3Mq3L/g2tdW1J73fS0SbetvJNE2am5tpbm4mNzd3XPryobbJ65lKYZaTSOthlLgfYvXQUY+i2rCnFNAU0GjyR6AzGcN4/j4N2KbiIgLN1YRaq3BpYexqDFVVUG02uQgtbhA27YQMJ5ori+zcYmpr62irawOCA7ZJ2q+DtOwIGQ5P4tpUcQjQABeq6qG0pISOQBt11RXoqoGmGDh0yM5IJRpqJxJqR1fkflVVsClgGKE+ooWmKqjEAIEQ0kpKqBpDcaDrOvFYDMOEmKkTFTZc3kzcKVlUVDcSjIpuuxYWZuF1ejlQVjbh7qcujnvtZZVScXgfNtOPWwvjUCPoikAJ1RD3H0UICJkOQnEnGVNK0O0plO/ff3LblJNBW90BiDTh0iIoikDTNHRNIxY3CcTsBA0XwbiTnLwC0jxpHD54cNz7CNmmBTQ31BFuq8CrB3GqETSjGT3ux4jvJRC1EYh7CBpOMrNyRt1HdC0qMk2TgwcPnlw7TYDniMnQphkzZmAYRvd4IqnaVFSALXyEeEB6h8dNnYZIOlOzZ3XaqaetSdOmIV57gcAxkSosJiSqJxP7qTcTWf9n4kc2oPqmoE8/fcTlLcsqpbytig0Ng4viBZkepmf7iLQ7CHkMmo1mUtRxEsUB3Nmw6kfwymdg159ltLGC4+QFH4Rzp59LS7iFuBEnZsQIRALc/tLtrJq26vge44oG3iIZPaX9IEQaZESJcIMUxl1TJmU0CQsLCwsLCwsLCwuLicGow6f3KUxR+oRPf+edd1i5ciXV1dXk5/eEUL3hhhtQFIWHH354wHIG8hTvmgTpcnufKF4BXWiahmEYNDU1kZ6e3l3uRPB0MIXg58920NYhuH6lmwVTpcfXvuYO9jXLSSKnrrIgy8sUb09Iz+F4byAExr6XiB94S9Yhayb6kptQHZ6+XqDC5Fd7nmZTYxlu3cF3TrmFQk8W4ahg88EY68tjtAVNEKBrsLjYxpmz7WT7dBRFYV94H2+H1hATcWyKztnus8hVpvBmxRH2tleiptWjaHGcdgWPQ0FVFLLULKboUyjQCsiyZfWzX+82xeNxWlpaSE9PR9O049qjJRBm66FGth1uYndVK3FDoCD9Ez1OnVOKMlg8PZMF0zIJRuPUtgTJTnWS4XUMaKcTXWND8oYyYlDxGmr5oyjV7/T4SjozESXXQun1qL6iCetl07tNO+p2cOcbd7KrfhcAc7Pn8r1zv8ei3EXH9RQfqv16768N1PL43sd5dPej1Afqu49bOXUlNy+4mfOKz+vreDrCNiXSGyoYDfKTd37Cw7tkPzo/Zz53X3g309Om96lLsnnimaZJS0sLGRkZ6Hp/b+sJ26Z4ECVcjxKuAzPS6bEsQPcgnLkIRzaK5hhfj0nDgHgHSqwFJdaGEvNLb+veF7fuQXGkY+o+hJ7anYphuPbLzMw8rqf/SbOTECjEUUUMMx7uCdFuRlHi7SiRxs7W90y+Cs90VFc2pupCKD2h1yfstZfgvtw0YnS0VOFzxmXYfCPcq5YKaA6ELQ1hTwdbGqh64ttkmmB0oESaUWLNKPFAn3qjORD2TFRnFqaeiuhlvwlvJyOCEm2UHuSx9l5frQaOTBRXnrz/jqnLUNtkmiatra1kZGRwLBP92husTcer+2RsE0BzczNpaWndx0z4NgkBkTrUjkNgyn3CXYhwTwVFm5R2Ona/3+8nIyMjKcKKj4ZkCp/etdAyIyOjz70EECt/ndiel0BVcZz5KbSMaSM6R12ohS+v+y2qonL/yi+QYnMNeNwbu6p5qektMme0cW7maZzqGlhAP6m8813Y+xC4suGDz4BzeCmZ1leu55K/XUJHtANFUXDqTuyanedveZ7lBcuHXlC0VeYb74z6g+aGlFmYNt+g9rNIDo53D1pMfCz7JT+WDZMby37JjWW/5MeyYXJy0nKK9yksQaL4sSTT4Hui8sq2MG/sCjMrT+ej53sJxw1eP9JIKC4nclQFHJrKeUVZOPWR50mIV20jtu1xRDyG4k6TecZ9BX2OiRgx/nfbP9nXVkmK7uV8xw2UHXYSictL0etUOb3UzvJZdjzO/p1Oh9lBu9lOipqCR/V07/cHTf69Jcju1hpsGXU4s+rwprXj7JVr3KW4KLAVUKgXkq/nY1cSE6ItHDPYebS5WyQPRnpy4kbjBv5wDBXwOG186qJ5nDUvf/DCEoW/oif3eKjHo4YpK2HOjTD1fNBsY1+PUWCYBo/seoR7192LP+JHQeGG+Tdw+4rb8Tl9oyrbFCZrj67loZ0P8frh1zE7wyRnuDJYPXc1N8y/gam+qYloxpjyysFX+Nar36It0oZLd/Hts7/NdXOvQ7E8LMYHIaSQGKqVOYW7pS1F5jV25YE94+R5wMRDnTnBW4eWF7x3Tu3JjhGBhrVSwFF1GVJc1SB75fvrezgeQnTmlm+W+cijrRwbmh5bKjgy5HWte0Z+bQsDom3yvok0yzzzvdFTevKDj+Y8E4l4B4Tr5dZ78YFiA2c2OHNlntXJ0FaLyUs8CP4yiHV6aeteSJ0NNu/41usk834Zr06WdgohiG7+B0b1ThRnCs6zP4/iHJn39h0b/8SRQD2fmnMF5+QvGvCYYCTOna88R/aCGma6p3Jd9hWjqX5iiIXgmeug9QBMuwAu+PWwfm9qA7Wc/sfTaQm1EI6HMUwDp83Jxo9vZF7OvOHVRQjpLR441POs6siClJmgOY//WQsLCwsLCwsLCwsLCyaIKH7w4EFmzpzJli1bWLx4cfdx55xzDosXL+a+++4bUrnJMvg2TZOqqioKCgom3AqSpnaDnz3TjgJ87ZpU4kqcd6qaQUDMFHQFgp3uczEnKwXXKIRx019LZONfER3NoOnYT7kWvXBJn2PK6wJ8b+vfqI804TLSmRu6limpHlbOdbBoug2bNvIJ4EN1cZ7dFKKuzUB1BMmZ2sC0mQ202+qI9xKEVFRy9BwK9AIK9ALS1DSEEKO2oWEKymva2HKokQ3l9eyrbsU0BaoipQS3Q+cn/3EGM/NGJ+oOGTMOR1+DfQ9D1Vq6BQ1nJpRcC7Ovh9Sik1OXEdIUbOLud+7myb1PApDuTOfrK7/ONXOuQVV67DSUe7Ap2MTjex7n4V0PU+nvCde5vGA5N82/iYtmXoRdS658dnWBOr7+ytdZV7kOgEtmXsIPzv8BqY6J218OxkTuR4eNGZNiV6gW4r3Cqqp2KXa58kB3J/6cXQJ4Py9fpDeqPa2XCO5OqOCWdPYL1UDbHinIKhr45sq8lu9jjmvDbuG6UyQ/Ju+8XGSRIfOF2tNB7bXwyojI4zVXz6IDIwrRpp7yeudwV1RZhiMT7JkyD/xkRQiZVzVcL8PHiljPe5oTnDmyzxhCf5F096BFP5LGhsKEYAUEjgBC3rOeYnAXvC8XciTLeHW0JFM7T3QviXiEyNu/wWyvR80ownHmx1HU4WeXe+LwGh479DZLs0r46sJB8mkDv1+zkdapm3HrDj435aMTYwFr0x54ZrUcL575P3Lh9DB4bPdj3P7S7fgjfoKxIJmuTOZlz+OPV/+xT/SqIWPGIHAYQtUIIYjFTXTfdFRvkXxOs0gqkub3zGJALPslP5YNkxvLfsmNZb/kx7JhcjLU8eqY5hQvLi4mLy+PV199tVsU9/v9rF+/ns985jNjeepxQQhBR0cHCVxnkDAyUzSKsnWONMTZeijK8tk2NEUhLgQ2TSFmyDrXdURoDMXI8ziYnuYmxT78S0RNzcN59m1EN/8To76M6HuPYLZWos29jLIawdo9EQ43xMlXrqDV9ThxewvRnH/zydM/hFMf/aR3ca7O5y7zsq4syqs7FGr3u6ndX8SSGRqLF7bRrNZQFa+izWyjNl5LbbyWzWzGo3qYok5BRAXZZjZOdWSr0jVVYU5BGnMK0lg8PZNvP7QRRYFo3CQeNwhG4nzvkc2smJ3LRYsKmZmXOraTIqoO0y+WW3sllD0mt1AD7PiD3KasgNk3wrQLJ6T3eKY7k/+98H+5bu51fPfN77K/eT93vHoHj+1+jO+e+11KM0sBCMXiNHSEyYjF8Th6riUhBBurN/LPnf/kxQMvEjfl4ohURyrXzL6GmxbcxMyMmePStkSQ683lzx/4M/+35f+49917efHAi2yr28bdF909vPCFE4CJ3I8OG9UmxQF3AcQCEK6FUL0M3x2skJstVYrjjuzuUOXDQhgQ9feI4PFjc5p2evJ2CeG2FClcjBFJZz9XvhRxjxVr38cc14aKJr3CHZ2hueMhKWZHmiHWKq/tcK3coPPay5DXacdh6ZWvqFLkFXEpBPdGtfd4g9vT3j8T4IoCdp/cUmbKhS3hOuk1b4Sh46jcdG+nQJ4z6LWadPegRT+SwoYxv/QO7wp3bE+H1BLZj1pYTBBOdC8pugP7sv8g8vavMJuPENv1HPaFHxj2eZZlzeaxQ2+zo/kgoXgElz5w/7yqeCbPRN8jZERoiraR5Ugb9rkSTuZcOPV22Phj2PAjyFsGaTOG/PHV81azatoqjrYdRUHhjlfvoMJfwY2P3chvr/gtS/KXnLiQ3qg22Ze48hH+/YhYA0rHUbloLGWm9B6fCIsJLIZEUvyeWQyKZb/kx7JhcmPZL7mx7Jf8WDac3IxaFA8EAuzfv7/7/4cOHWLr1q1kZGQwbdo0vvSlL/GDH/yAkpISiouL+c53vsOUKVO6vcktTh5LZ9g50hDnvYMxzprnYFFOKtvr/RhC4NRVin1uAjGD5lCUmkCYmkCYLLed6T436U7bsIRbxebCvvwjxMteIVr2GoF9azm6+wjPxa4hpHhRFTi9KJObi27i/oMPURut4dd7nuZLC65FS4Bgo6oKZ85xsLDIxstbw7x3KMqWgwZ7KlK4cFE2V5ecRoB2qmPVVMYrqTVq6TA7KDfLiaZGOdhxkDw9r9uLPFUdmXCdm+bC7dAIRQzS3Hb8oShCyHD1m/Y3sGl/A9NzUrjolEJOm5mNro3xyqOUQjj1S7DkNqh4Q3qPV74N1e/KzZnR6T1+g5xw9x+RXuSevLGt1xBZVrCMf930Lx7Y+gC/2vArNtds5pp/XsNHTvkI1877GBuqa6huO8yeeJhzi2aRYo/x1N6neGjnQxxsOdhdzim5p3DTgpu4bNZluAbJ/XeyaAuaNLWbZKao+Nwjt7+qqHx86cc5veB0bn/pdo60HeHWJ2/l06d9mtuW34Y+EsHVInHYvGCbBd4ZEGmS3uPRZiksxPyg7JfCuCsPbD454TeQZ60QUvjuEsGjbfRLeK+5ezx1bWkyJLjFoNSGWjjSeoSitCLyvBOjr0sadBfonQs/urzIoy1SJDeC8tqOtnZGLOiMSSMEBCulB7SiSqG3Oyy615rsVtSehQfCgHCjFAO6Fr0EAhA4KO9tV44UCNSJt5jNYpJiGtBxCIJV8v+KDimz5EKN9/u9a5GUqN4s7EtuJLLhQeKH1qH6CtCnnTasMgo9WeS50qkNtbCt+SBn5Mwd8Li5U9J5Zo8b4elgXeVBrpy5NBFNGD0LPgpVb0P1O/Dm7XDlI8NaJJ3nzet+fnp49cN86tlPsaN+Bx956iP89OKfcvHMi4dfJ5sX4VtAffN2Cp0RmVKlbbd8Rk6Z9b5Lz2BhYWFhYWFhYWFhkThGHT79jTfe4Lzzzuu3/yMf+QgPPPAAQgjuvPNOfv/739Pa2sqqVav4zW9+Q2lp6ZDPkSxh2gzDoLy8nJKSEjRt4okQ4Zjgx0/4iRmCT13sZWqWTjhuEIwZuG1ady5xfyTG4bYQdR1huq4On8PG9DQ3OW77kMXh9pDJ+rIoNfu2c2bsaWxECSkpNBTfxKJTZpLaKQDuba3grm0PETMNzp+ymI+VXppwz+mjDXGe2RSipsUAIC9N46plLoqypVAYF3Hq4nVUxCoo95djusw+n09RUyjQZS7yXD0XXRm6wPj27hp+/8puwjETp03lkxfOozg3hZe3VbKurJ64Ic/l89g5f2EB58ybQorrJE5wB6pl7vGyxyBYL/dF26X3qaKCzQNnfAfmfhjsEye3aXV7NXe9fRcvHXxJpqGLC9ojLZjCRFd1SrOW0h6uJC6iALh0F1fPvpqbFtzEvOxh5rkbIzYfiPLI2iDRuMCuK9yw0s2pM0cfLSEYC/KDt37A43seB+QigHsuvicpcqRP9H40oRiRzvDqNX3DUGtOKYSHGwATUDtDegspME7gvODJZr+usJ/BWBC3zc09F9/D6nmDhz59P5AwGxphKY6HauR13p2oRQCq9PjyTLO884eKGZN9QrjuGA97RS4ocOaAIwMjFqHicBlTp5ei2ROcnsGih4EWLSWq6Inaj0aapXe4GZH/d+bI+1idxKkNhkGyjFdHSzK1czj3Umzfq8T2vQKqhnPVp1HTCod1rocOvM4zR9dxRs5cvjD/mkGPe/Dgv6nVDqPUT+Fry64e1jnGlGA9PHklRNpg4cdh2ddGXFQoFuIrL36F1w6/hoLCt876Freecuuwy+m236wZaOFq6KhAPhcDringnW4tCpvgTNjfM4shYdkv+bFsmNxY9ktuLPslP5YNk5NxySk+ViTL4FsIQVtbGz6fb2LkCBuAR98Jsu1wlOWz7Fy9/PiTpcGYwZG2IFXtYczOy8Rt05juc5PvdaKpA7exvs1g7d4I2w7FiJvyc9NczVylP0GqaERRdewLr0Yv6gnrvKFhH/ftfBKBYHXxWVw7fVWCWtyDaQo2Hojy8tYw4Zis1+Lpdi5Z4iTFJQV6IQStra0oKQrVhvQir4/XY2B0l6MpGnlaHoV6IQW2AlLUlBOeuzkQpr4tTI7PSYa3Jyy7PxTlzV3VvLajGn9Qirc2XWVFaS4XLiqkINOTyK/g+JgGVL4JO/8M+/5Jh8NDuzeTlEATnnAHpE4D3Sm9yZ0ZMie5MwNcvf/O7HnflSnzn47hvSCE4OmyV/nv177DroZN3YJ43IyjKirZ7gKKM2Zz7ZwbWT3vGqampg963Z5s2oImP3i0jWBYSEdeAW6HwrdW+0blMd6bF8pf4Duvf4f2aDsem4fvnvtdrp49gSbgBiAZ+tGE051PuFaKXmasr2dt12uXZ22fvODpUpSZIN9VMtmvNlDLsj8sozXUikN3EDfj+Jw+1n98/fvaYzzhNjQi0LBWXteKJvMQqzpkr7QE8ZFihDsX1NRJj/wuhIkwYwghUFQdxTdHevFbJJZQDbTtkZ78iga+uZ0LlxLDhOtHzSi0H+hc3AKoDkgt7UmjYAEkz3h1tCRTO4dzLwlhEt34N4zaPSguH86zb0NxDN0beb+/iv/e/CBOzc5vV34RuzbwAuqt/t281PIWHY1uPjNjNXnpE2jx0pFX4NXPyb8vfUCm1xohhmnw/be+z0M7HwLgo6d8lG+s+gbqMCLC9bOfEYb2gxBpkAcoOniLZf87EfpKi35MuN8zi2Fh2S/5sWyY3Fj2S24s+yU/lg2TE0sUtxiQA7Ux/vxaB06bwjeuTcWmnfimjhgmFf4QR/2hbq9mu6YyzediaooLm6YihOBgnRTDy6pj3Z8tzNRZNdfOvEIbihEhuvVRjJrdAOhFy7EtuAqlc9Lg5arN/LnsJQA+MfsyzpuyOMGtl3SETV7eFmbzgSgCcOgK5y9yckapfUDBNCZi1MZrqYxXUhWvosPs6PO+T/XJMOu2AnK1XLQR5ECNGyYb9zfw8rYKjjT05ASeNzWdi04pZMG0DNST1QHXrKd84+28veQiYroNWzzKWRuepqSpUYbKHQ6avUcw7xbNe4vpnYK6K1OGex5iKHMhBE2hKAdagrRFYmyrfYfvvHYLcTOKgoIANFXji2f8lAuKV3f/eGmqQobLTrbbTpbL3h0d4WRiCsH+mjivbAuz9XAMBTmP0xVW/+az3Jw1z5GwH9zq9mq++tJX2VyzGYCrSq/iznPuJMVx4sUcFuOAMCBwWHrjHSuKe6aDe4rM02w9kI0YwzR4++jb/Gbjb3hizxMoKCiKIoVEReGzyz7Lp0/7NLMzZ1sPvolijEXE9zWxQKdAXguxY9MpKDLUrO6Wi2d0V6dnc2c0CiutxvERRqdHeFh6hRthiHVAqBrprdjZPygapC3oXKTknDz9sxDy2mrf3xOhxF0AnmIrLccAvF/Gq5O5nSIWIvL2rzEDTaiZxThWfBxFHZqIawqTL7z7G5oj7Xxt4fUsyZo14HHNRjN/rXucYFAws+F8blgx8HHjxtr/lum13LlwzdPgTBtxUUII/rTlT9z9zt0AXDLzEu6+6G4cg+RcHzLRVvCX9ywK0z0ypLp95HW1sLCwsLCwsLCwsEh+LFF8HDBNk8OHDzN9+nTUIQ6gTzamENzzr3bagiY3rnSzsGjoIQ/jpqCqPcSRthDhuPSc1lQFLW5nz0GoapSXkgLMLbSxcq6DaVlaH1FBCJN4+ZvE9r0EAtT0qThOuwXF5QPgkYNv8tSRd1AVhS8vuI5Ts0oS1/hjqGyK88zGEFXNsi05Po0rljpQQpWD2lAIQavZSlW8iqp4FfXxekx6Qq3rik6+lk+BTeYi96rSw6DD7KDdbCdFTcGjDu79LYRgf62fl7dV8t7Bhu7w9blpLi5cVMjKOXk4bEObiBRCYGAQF3HixDGEMeBrXPTdF4zUszX8HoaqoRkGQlWwx6Lc5F6N154GoSYIN8st1AThJgi3dL4297wfD52wjv3QXf3F814e6MKZQSteDoVdNOFFqHIhg0P1c+Nj5+EPt2JXNKLC7Pb4tGkZNIaiNAQjROJ9w+KnOnSy3A6y3XZS7fqYCmAdYZP3DkbZuD9Kc8DEMAXN7QJFAbsO4aicR89IUSjOsXH+Qgcz8xJTJ8M0+N3m3/GrDb/CEAYFKQXcc/E9LMlfkoCWJZZk6EfHnG7P2rj0DE8iz9qJbL/ypnKe2PMET5c9TWOwkbgZ52jbURQUnLqTjlgHqqIyzTcNXdUpTivm8pLLubzkcmZlTLBJ6zFkzGw4huGmLYBICzRtRAgQwkBRkEvENKcUbQdCsXUK5Z0ieR/B3DZ5xN3BEEJ6QXcJ3t1b5//N6ACfMTojeUDPoiV6vmdFA90LtpSe12FG85gQ/agRkqJTtEX+X/dI73DbxB2HjTfJMl4dLcnUzpHcS2Z7PZG3f42IR9FnrMS+4Mohn++Bspd4qWoz5+Qv4lNzrhi4fGHyp8YHaQmGqX93Nj++4dwJE8UKgFgInv4gtB2Coovh/F+M+rfg2bJn+eYr3yRmxliav5T7r7iftCGI7ce1nxBygVLgcM+iHUc2pMyQ/bHFhGBC/J5ZjBjLfsmPZcPkxrJfcmPZL/mxbJicDHW8armIJBAhBNFolIm8zkBVFJYU23ljV5j3DsaGJYrrqkKRz83UVBdHW8PsrOsgEI1jihCpOaA4Naa4Xayc7SIzZeAJWEVRsZWeh5pWQHTzQ5gtFYTf+hX20z6EllnM9cVn0xIN8GbNdn656ym+tfhmSn3Dy+k2VAozdT51iZf3DsR4aWuI+jaDP78WpDDVzo3ZBukp/Ts8RVFI19JJ19JZ4FhAVESpiddIL/JYFSERoiJeQUW8AoA0LQ2n4uRo7CgmJjZsnOk6k2J7cR8xOi7i3QK2Lcvg4vMdLD8jix2VjZTXNWMIg5caD/PmBoWpuS4Ks9zYbPQXtzvLMITRJ+T7cIiLODGbE4SBUFSEAnHdwVPqJgqVQnJTcslJn0e6mn78EHixUKd43lss7xTPewvrXe8bUSmkB6rk1gsBGKYgZgrsQjC7c7/mSMHuyUJ1ZXKfL53bOyoJCkGGonLPglspSJWeiDkeB0J4aY/GaQhGaQxFaYvE8Efi+CNxDrZ0YNdUstx2st0OMl029AT84AkhqGg0WF8eZdfRnnQCTpvCkmIHLofCi1vCROOCNA/MyrdR3WxwtDHOA6/HmZalJ0Qc11SNzy77LCsKV3D7S7dT1V7FLU/cwm3Lb+NTp34KbQJ5fCVDPzrmaA7pSdvlWavq8v9JICRONPu1hlt5tuxZntjzBLsadnXvz3BlcFXpVbhtbn627mcEY0HSnGncsvAW/BE/bx19i0Oth/j1xl/z642/piSjhMtLLueKkisoSisaxxaNPWNmQ82RFNdw0qK7ZV9hxomZGnYVGVo2bVGnkBvqJfiGZDh7EYNY7Jgc5Z0oWl+RvLeXuepIqGBeG6jlSOsRitKKEp+6oCsdxbGCd9fGCa5zRev8Djo3Re/0nDble2Zcfhd6iixbGNJjP9bWqwy1r1Cue6XIPMh3OK79qBAQrISOw7KNKDJvr7tQtsPCIokYyb2kpuRgX3IDkY1/I35wLWpaIXrh4iF9dln2bF6q2szmxnIMYaINcM+oikq+M5vWUAVxRzs7jjSxuDhryPUbc2wuOOceePYGOPISlD8OpatHVeSVpVeS48nhc89/jvdq3uPGx27kj1f9kam+qcf93HHtpygycoUzBwKHZESaSANEmsAzDTyFgy8IszhpTLRxgcXwsOyX/Fg2TG4s+yU3lv2SH8uGkxvLUzyBGIZBeXk5JSUlaNrEHYQ1tRv87Jl2FOBr16SSOoz8xS0Bk3f2Rdh8IEo0buL1muTlGqT5BE6bgqpAttvB9DQ36U7bccsyO5qIbvwrpr8OFBXb/CvQi1dgCJN7dz7O1qYDeHUn3116K1M8maNs9fEJRkxe2R5hY3mESDSKx2Xn/IVOVsx2oA8hxDzIzrLZbJZe5LEqGowGDGHQLtoRiM6w3vI1RUkZck41ISAUjROMxDHMntvVYdPwOHRs+onL0RQNDQ1d0dHR0RRt0FdTmOyI7MAgjiYgpshJUa/i7VNnu2InW8smR88hV8slS8saUej47kbGOjoF8+ZuwVyEGgm21+Nva8AMNWGLNmOPteKKt2FTBN2OFWYc/EepMQyOCIUiRZCvaTDtfJhyJuSeKjdvT7jeSNykMRShsVMk7/3dKopChtPWLZK7h+id3112TLD1cJSN5VFqW3sWJxRkaCwvcbCwyIZdl5VvC5o0t5tkpKj43CrtIZO3dkfYWB7tFtETJY4DtEfa+e4b3+XZ8mcBOG3Kadx90d1MSZkyqnITRbL0oyeFJPSsnQj2i5tx3jryFk/tfYpXD71K3JQeRJqicX7x+Vw791rOLjobvTN0dG2glqNtR5nmm9YtyAWiAV49+CrPlz/Pmoo13WUAzMuex+WzpAd5Qerky9c8EWxoMUJCNYjWPRjxKJpuR0k7Tph6M95XJDdCcmGaEQYzcoITKb2E8t5e5p191TDE08d2P8btL91OMBbEbXNzz8X3sHreMAQYYfb0lQMJ3yJ+ggKUzgUbzl6LAJw9/1f0/uL1YOkAhAnxIMTbZVj7eEC+Yg5wXhVsnmPEcg8o6vjdg7EA+PfJeoMMvZ86e/ipc96nJMt4dbQkUztHcy/F9r5ErOx1FM2GY9WnUX0nfk42hMln1v6CQCzEfy3+EPPTB15Ety60jk1tO6ncnUpBeC6fv3zhsOp2Utj+R9h0t7z/P/AU+KaPusjypnI++ewnqW6vJtOVye+u/B0Lcwdv+7DsFwvIBUtdC5JUp/Qad2RN/qgnExjrmTK5seyX/Fg2TG4s+yU3lv2SH8uGyYkVPn0cSKab5Q8vBzjSEOfixU7OnnfiEGOVjXHW7I2w62is26cmx6exco6DU6bbCMTiHG4LUh+MdDvdpDltTPe5yXbbBxXyRDxKdNsTGFXbANALl2A75YNEEfxg60Mc8FeT5Uzle0tvJf0k5ECuaIzy8BuNtEbcoEBWisaVpzmZlX98gX8gIiLCrsgu3g292x1iXXR+OR7Fg12xoyt6t1jdJUz32dfrVUWjriXC3go/1Q0hTENBmCo5KR6WzchjQWEWdk0fsIzhCqnl0XLeDr5NjBg2bKx0rSRNS6PeqKcuXkeD0UBU9A0rqqGRpWeRo+WQq+eSrWVjV4YeiaA3QggaO3OG+yMyR72mKhSmuJjuc+PQFIi293idV78Da76NUO2YZhzVjKCYMfBO6TuZ650COadC7lIpkqfNAlXDFIKWcEx6kQcjBGN9vew9dr07D3m60zbo91nXKr3Ctx2KEolLW+uqwqLpNpaX2CnMHHpwjvaQyVu7Imzcn3hxXAjB0/ue5rtvfpdgLEiKPYXvn/d9Liu5bMRlJopk6kct+jOe9itrKpPh0fc9TVOoqXv/vOx5fHDOB7my9EoyXBnDLtcf8fPygZd5vvx53q18F0P09A+Lchdx+azLuazkssR7uI4T1j2Y3BjRIBWHy5g6vRTN7h5ZIV0hwo0QxHsL50Pxru4SmY8RzLsE9F6L57bUbOHiv11MIBLA2ZljNrUz9Un3/dQd4nwQT+8TCvjIUPADCd6j8Xof6qIlIWTe21igr1guBormo4DuwdQ81DQGyCucheZIGXtvR2FA4Ij0EEfIhQApM8CZZ4lJwyBZxqujJZnaOZrfM2GaRDf8BaO+DMWdhvPsz6MMoU/93d7neLNmOxcXnMpHSy8e8JjyaDlvBdZScVilan0RP/3ICnzukY2Zxgxhwr8/CjXrIXM+XPkwaMMfDx9LfUc9n3zmk+xp3INLd/GzS37GecXnDXjssO0nhPQWbz/Qk/7CnibzjeuDpy+zGDusZ8rkxrJf8mPZMLmx7JfcWPZLfiwbJieWKD4OCCHo6OjA4/GMaW7iRLD5QJQn1wfJTtX4whXeAetrmoK9VXHW7o1wpKHH02ZWns7KuQ5mDSDMdUTjHG4LURMIY3ZeWh67znSfi3yvE3WA8wghiB9cS2z3CyBMVN8U7Ms+TEC38933/kptqIVp3hz+e8ktuPWxzREmhKA9EGB/g52XtkYIhKWYPX+qjcuWukjzDC90ZIfZwT/a/knYjKIIDaHEcah2bk69kRRt5CJ/ZVOAl7dVsq6snrgh6+jz2Dl/YQHnzJtCimv0kxbHy4NuCpMWs4X6eD11Rh318XpCom8OcQWFDC2jWyTP0XJwqcf3NhpMDJ+a4qLI58YxmFd8Ry38/XRELICwpaDE2lF0N5zzE5kTr24zNO3pPwltT4GcJT2e5FkLEZqDYMygIRSlIRilNRzrEypF11SyXHay3XYyXXZUFHZVxNhQHu1zn2SmqJxe4mDJDDsu+8j7g7EUx4+2HeX2l25ne912AK6bex3fPvvbuG0jFFISQDL1oxb9Odn2awm18EzZMzy196l+4dGvLr2aD879IHOy5iTsfM2hZl468BLPlz/PhqoN3QudAE7NP5XLSy7n0lmXkuWeQKFQh4l1DyY3Y24/IY4Rp4/xMh/QK1rSEGplXUMZ6+r3sa5uF+WtR6kO1KKgyLoKga7pPHT5z/jA9FW9RPjBywSkZ7rq7OW5fozwPdFC6HZ9h90ieedrp1e7zAtvoiiq/F50d//w64lKexJtkbnDjc5nOEe2FJC0CSbQJQHJMl4dLcnUztH2hyIWIvzWrxAdzWjZs7Cf/v9QTpBeaUvjfu7e8SgZjhR+seKzA0YGazFaeDrwNE2tcXY9O4PVZ87ksiXThl2/MaejFp68CqJ+WPQpOO0riSk22sEXXvgCayrWoCoq/332f3Pzwpv7HTdi+wkDOo5CRwVyEZcC7ingKZKLpCxOGtYzZXJj2S/5sWyY3Fj2S24s+yU/lg2TE0sUtzgu4Zjgx0/4iRmCT13sZWpWjwdrNC7YcijKO3siNAXkRKSqSG/XlXMc5KefeCIuHDc46g9R6Q91C3kOXaPI56IwxTlgvmaj8QDRTf9ARIModjf2U2+iyZvNne89SFu0g3lpRXzjlBuwqUP3th0N4ajg1R1h1pdFMAXYNIVz5jtYOdeB7QQh1WOGiT8apykYZXP7Hho9mxHEUdDJDp3K5bmLSXWMflDuD0V5c1c1r+2oxh+UK+JtusqK0lwuXFRIQebJWRUvhKDdbKfOqKMuXkedUUfADPQ7zqf6usOt5+q5eBT5wzJiMbw3ZY/R9Na3qEUjD4PMs3/UNwdeLAgN26RAXrcZ6rfKMKe9UXXIXNAjkucuJWbz0dQpkDeGosQ6FyEYQl4jbX6FljaVQLtKLKowt9DO8hIHM3KH76F/PPxBk7d3J14cj5txfrXhV/x2028RCIp8Rdx7yb0syFmQsLpbWCSSrvDoT+x5gtcPv94d2lxXdc6ffj4fnPvBPuHRx4rGYCP/3v9vni9/ns01m7v3q4rKsinLuLzkci6ZeQnprvQxrYeFxYSh26tbCuVtwXo2VG5kXc1m1tVsZX9b1TGHC6qCDSBAU1U6YmFURaU4JZ/VM8/hM/M/wFRvDqBIj+4usVt39ojgqlOKHMk+SBVCer3H2jvDrncJ5bGBj9fcYPP2FcuH0+eZMWg/COFa+X/VDikl4EzeBT3jzftlvPp+aWcXpr+WyJrfIOIx9FlnY593/KhKUSPOp9feR9iI8j+n3sqsAdKsmMLkH+3/IBCJsPW5qWTY0/jhzcsm5mTb4RfhtS8AClz2IOQvT0ixcTPOna/fyWN7HgPgk0s/yZdXfHnI6cWGdpIQBA5CpFH+X7GBd7pMd9H1W5VEKYosLCwsLCwsLCwsLI6PJYqPA4ZhcODAAWbOnJkUYRUefSfIewcjzJli45oz3KgKrC+LsqE8QjAqLwunTWF5iZ0zSh3Dyj3eRcw0qfSHOeoPEolLMVFXVaamOpmW2l/sNIMtRDf9HbO1ChQF29xLqMwp5ftb/0HYiHJGzhxum/eBxA6YezGQDWtbDZ7dGOJwpxdwhlflilNdzC6QonaXAO6PxGkNR2gMh/BHQsSMEEa4laMN69goatEdGoQNznctYvrUS8ly28nxOMhx23Hqo7te4obJxv0NvLytgiMNPWL0vKnpXHRKIQumZQzopT+WdJgd3eHW64w6Wo3Wfse4FTepZBMLpkLYh830yusjxUVRmhuHNnQ7v169jV/veoJgNITb7uJz86/lvCmnDP4B04DmvT0ied1mCDX0P843ozvcupG1hO3+XPbWhwmbMRxOeZ+oCjjtCj6nRp7XQbbbQbrThqYm/jsfK3F8Q9UGvvby16gN1KIpGl9Z8RX+c8l/jtm9NhjJ1o9a9GUs7bevcZ8Mj172NM2h5u7987Pnd4dHHy8BujZQ2y2Qb6vb1r1fUzRWFK7g8pLLuWjmRaQ6Ju4zTBdjZcPaQC1HWo9QlFY0aULNT0TGsw8NxoJsrt7Mu5Xvsq5yHbsbdveJpqAAczNLOSP/FFbkLeLU9Km8UP4st6//A8F4BJuiMy99GhXBVlBUNFXn2jnX8Jllt1Hgm3pS2zKedNtwxgw0xTgmR3l7T1jgY9FcnSJ5L7G8t1ekEZGLAY0gdByRwjiAawp4i4cnqlv0I1nGq6MlmdqZqP4wXrWd6OaHALCfejN6waLjHv/LXf/i3frdXDntdD408/wBj3k+8Dz18QZ2vp1Jy1Evd1y7hFn5vhHXcUxZ819Q9hh48uCaZyBBzzJCCO7fdD/3rb8PgKtKr+JHF/wIe2ekioT9nkWaZUh1o3MxtKL3RCFRNPDNlUK5RcKxxnXJjWW/5MeyYXJj2S+5seyX/Fg2TE4sUXwcSLZcA89uCvLMxjAAdhu47Cr2zvmwdI/KmXMcLJ1hx2EbvbBnCkFNIMyh1hDBmBSXVUVhSoqT6T4XblvPRJwwYsR2/Iv4Uel5p01ZyL6i07h715MYwuTsvIWclbuAPHcGmc7RXQ9CCKJmjJARJRyP0hENU3Z4P9lTcomIOOHO/aF4hINNIfbWBIkRQdHCOO1BVDVM1AgTNyJERQxDGCjCBGGiIDCARgQmoCIDgOrA2VoG03yzyUxfiMuZic9pI8ftINfjwG0b+bUjhGB/rZ+Xt1Xy3sEGuu7u3DQXFy4qZOWcPByjKH80RESE+ng99UY9tfFa6uNNRA2jO8w+gEt1UGDPZ4pNhlvP1DKHJMrWh1r55Jqf0xEPowsFRVVx25z8buUXh36NCAGBqr4ieet+AMxOr/BwTNBOBtW2xVTbFqMXLCG3dA42N7SEY33aoqkKGZ1h1rNco1/4cCwDieNF2VIcn5E7MnG8LdzGd17/Di8eeBGAMwrP4CcX/oRcb25C6348kq0ftehLou3XHGrm2bJneXLvk+xu2N29P9OVyQdmf4APzv0gpZmloz5PIqnyV/HC/hd4vvz5PiHddVVn1dRVXF5yORfMuACv3TuOtRycsbgHH9v9GLe/dDvBWBC3zc09F9/D6nmrT/xBi2FzMvvQSDzCtrptvFvxLuuq1rGtdhvGMWlKZqbP5IzCMzij8AyWFywnzZnWq7IRaFhLbaCBo8EmprkzyfNms9Xw8stNv2NNxRpALi5ZPW81nz7t00xJmTKmbZoInNCGRrRHII8HpGBuhgcuTHVIcVwYEK4HMw4I6RluS4XUUrBPUCEuyUiW8epoSaZ2JrI/jO5+gfj+t1B0G45Vn0VNHXxx1/r6Pdy36ynyXOncc/qnBnwmXx9az97oXoIV2Wx6083KuXn85/mJS/eSUGJB+Nc14D8C0y+F836e0OgcT+55kv967b8whMHyguX8+vJfk+pITezvmTAhVC0jZMQ7kGHVNdkOVYfslZbH+BhgjeuSG8t+yY9lw+TGsl9yY9kv+bFsmJxYovg4kEw3S1vQ5EePtdHaIc0vhBwTLiqycc4CB/MKbahj4OUqhKAhGOVQW5C2cKeHigK5bgfT09z4OkOKCyEwjqwnuvMZME3UlFw2FZ/OT8tfpDUawK7qeG0ubpl5PqdkziAUjxI2pHgdMjr/7hS0j7vfiGKKvrkqo9EINl0D08Q0TYQwoPO1W/A+9rZRpAdUFzoKTsBEcAiBJg8hhhyCZ6Jg7/w7Q3VQ6MglJ2UWWWlzyfWkk+txkONx4LWNPAR3oz/MqzuqeGt3NeGonKR2O3TOnpfP+QsLyEwZ2/zsA9E7THprJEREayVia0Z1thHWWjDoO5muKzrZWnZ3uPUsLQtd0YkYMQ6217CvrZJ9rRVsbz5IZUcjSqcVNEXFodv46fJPMi+9aMR1PVrZxMHt64lWbyE/uoXc+G504jhsCi67gqYiw7dmn4KZs5Q23yJqPXOpj9mIxPu2JdWhk+12kOW2k2rvEa3DcYNgzMBt00YknCdaHBdC8Njux/jh2z8kFA+R5kzjR+f/iAtmXDDsuo2EZOpHLfqTCPvFzThvHn6zOzx6l8imqzoXFF/AB+d8kLOKzhrz8OiJ4EjrEZ4vf57n9z9PWVNZ9367ZufsaWdzecnlnFd8Hm6bexxr2ZfR2DAUC1Hpr6SqvUq++qsoayrjoZ0PETWiaJ25nV02F98/7/ssL1hOSWYJGa6MsWjK+5Kx7EMN02Bn/U7WVa7j3cp3ea/mPSJGpM8xU1KmsKJwBSsKV3BG4Rlke7KPX2ioBtr2SNH2GI+9LTVb+MWGX/BOxTuA7AOun3c9nz7t05M62sCIbGjG+nqTxwM9ecKF2ekV2evZVbFBzir5DGOREJJlvDpakqmdiewPhWkSXf9njIb9KJ4MnGffhmIb+P4JG1E+tebnxEyD/132MaZ5c/odsz+6n7WhtbijGfz7kRTsNo2f/b8zcY7T4uUT0rADnr1R9tVn3QUl1ya0+LVH1/L5Fz5PR6yDWRmz+MNVfyDXnZv437NQAzRvRi5VV5D9ogrpC8HdP9S9xeiwxnXJjWW/5MeyYXJj2S+5seyX/Fg2TE4sUXwcSKab5WBdnF8+146CIBQFTQNNUfjSVSkU54690CCEoDUS41BriMZgz4RqustOsc9NpsuGoigYzUeIbvwbIhKgRbfzSSVKuxFFRcFEoCoKuc50tAFylA9yYjk52C109/xtFwKHaeA0TRyKglOAA3AKcNLzt6vzPcXUiRke4qYXTUtl5pRMpuZl4nZnYvNkoLh8NAaa+PSaewkhcKPQgUAHrkidypFgE0fiYczOSUoBCBQyNA8FrnyyfbOZnj6LGb4MctwOfI6Ref+Go3HW7q3llR1V1LfKCVJVVVg6I4uLFhUyMy91zHPYDZozPLUzZ7imYgqTJqOJOqOO+ng9dUYdUSFDhJpCEDPjRI04wbBJvT9IIGjQETQxTDBMk/pwK06bisuu0R6JEY/DdxbfwvkFi4dV13BUsPVQlA3lUer9PcL21Eyd02cI5rv3YWt6r9Ob/D2I+vsWoKiI9DlEshbTkrqIau98mpS0PnPRdk0ly21HQeGoP4ghBJqisCgnlYKUkU1QJ1ocP9RyiK+89JVu79ybF9zMN1Z+A9cgE4CJIpn6UYv+jMZ+exv38sSeJ3im7Jl+4dGvnXstV5Ze2dfLNMk40HygWyA/2HKwe79Td3Ju0blcXnI550w/B6d+8hcs9eZ4NgzHw1S3V1Pll6J3HwG8vaqP3boIxUJUt1ejoKAoCkIIBIIpKVO6+5NMVyYlmSWUZpRSkllCSUYJJZklE9abfiKTyD7UFCZlTWWsq1zHusp1bKjaQEeso88xWe6sbgH8jMIzKEwtHEGlI8fN7bqpehO/WP8L1letB8Cm2rhx/o186rRPkePpLzYlOwmzoRmX4nioXubVBVBUufgAEzKXgT0tEVW2IHnGq6MlmdqZ6GdKEQ0SfuuXiGArWk4p9uUfQRlkDPrTHY/xXmM5q4vP4trpq/q932K08HTgaWyKjR3PzKC+NcxHz5/NWXMncBjvbb+DzfeC7oZrnoLUkS08Hoy9jXv5xDOfoL6jnmx3Nr+94rfoLXpixwSdEUow451jsziggOYERwZ4iqx+MYFY47rkxrJf8mPZMLmx7JfcWPZLfiwbJieWKD4OCCGIRqPY7fYxFxlHi/QU9xOJmbjsCqGowGFT+dbqVHwjyB0+GgLROIfbgtQEInRdjl67TnGamzyPAyLtRDb9nd3Nh/mOEkOoOiFMGRpcgZkp+WQ6U3GpuhSwhcApDJxGHKcRw2lEccTCOKMhHEYcBwK7AIcAu5B/24RA7fQyFoCpOYjaUhCOVFS3D92VhtObjislA5vbh+Lyge5kV0WMF94L0xaU3uYl+TauPM1JZkpPZ/nq9sf5zZG1hBE4Ufhs0UouWHSdbLu/lr0VG9nVsI89gXqOiHi3dioECFUjXU9hinsqUzLmcEpmMbPSMkh32oZ9jZlCsP1wEy9vr2RvZWv3/uk5KVx0SiGnzcxGH0YO76EwFDF8oM/Uh1vZ21bBvsAhKqNVRLUgHreKTe9ps6qoOFQdj5JCob2ApkgbVeIIigJx0+RgZZiOdpVrp6/k2umrThiGvbrZYEN5hG2HY8QMaQWbprC42MbyWQ7yMwb4ARQmtB7oG3I9UNXvMNNTSEfGKTSnLqDKPZ+AuwgThVDcxB6uJyVcSchViOnJ47yirFGFWk+kOB41ovx83c/505Y/ATIM7r2X3IswnGyvLmfRlBLm5k4fcV0HIpn6UYv+DNd+TcEmni17lif2PsHexr3d+7PcWTI8+pwPUpJZMpZVPukIIShrKusWyI+2He1+z21zc0HxBVxecjmrpq3qzqt5sogaUar91RxqPkRdqK5HAG+XAnhjsPGEZaQ6UilIKaAwtZCClAI8dg8/fPuHROIRUuwptEXa0FWdq2dfTVV7FRVtFX3yTvdmSsoUKZBnlDA7azYlGSXMSJ+BQ7dCnA7GaPpQIQRH2o7IcOiV61hftZ6WcEufY1IdqZxecDpnFJ7BisIVzEifcdL66g1VG/jF+l+wsXojICMu3DT/Jj556idP7JGeRCT8d7BbBDJkmGAzDqpmhQtOMMkyXh0tydTOsXimNNuqiaz5LcKIYSs9D9uciwc87s2a7fxu73MUeXO4a9nH+pcjTB5qf4i4iJN2ZDlPv13HrHwfd1y7JCH1HBNMA/79UajdANmL4IqHZJ+SQGraa/jEM5+gvLlcplu54B7Om3leYn9nekcoQZUieHdIdcDmA880sKcnNEz8+xFrXJfcWPZLfiwbJjeW/ZIby37Jj2XD5MQSxccBIQSmaaKqalLcLJsPRHlkbZBoXGDXFW5Y6ebUmSd3Ar43objB0bYQle0hjE4xz6lrFPlcFHhsNO54ks9VvktICNxAUFFwKRq/dOWQHgkg4rF+ZQoEppCCsNwgpjqI2bzE7ClEbSlE7SnEbCmoLh92TzpubxqpHi+pDhu2IYjE0bjgzV0R1uyJYJgCTVVYNdfBOfMd2DtF3IbWKupaj5KbNo3stIFDswnToL1hP3sqN7O7+QB7w60c7ZQK5AIABVO1kWHzkZ8ynblZpSzLmcH01DTUYV5vFY0BXt5eyfqyeuKGFPR9HjvnLyzgnHlTSHHZaA6EqW8LkeNzkeEdnuficMRwQ5gcDdSzr62Cfa2V7GurpDUa6FdmgTuDkox88nxeXE4IKG34TemlbQqTdtGOQKAIGctemFDdGCUaFRS6crmq8EzS9FRcqguX4kJXdGJxwY6jMTaUR6lsinefKydVY3mJncXFdpz2Yd7LHXVQ/16PSN68V1am67sBDD2FFt8iAtE4eXUvoppxDN3Fznl3MmXphylMHb03tj9o8tbuCJv6ieNOZuQOLyz/6wff4huvfIPGYCNtoQCBaBAAXXHy9dP/hzsv+dSo69tFsvWjY8meusNjtvhgrBiK/WJGjDePyPDobxx+ozs8uk21cX7x+Vw791rOmnYWmjr5V2IKIdjdsLtbIK9ur+5+L8WewoUzLuTykss5c+qZCQkXHzfj1LTX9AlvXuGvoKq9iip/FfUd9fJXp3PR2UC4bW4KUwspTCmUwndqjwBekFpAqqP/c9rxcoqHYiEOtBygrKmM8qZy+dpcTl1H3YDnVxWVIl8RpZmllGSUyNfMEop8Re+La+ZEDLcPrW6v7vYEX1e5rt/37tJdLJuyrNsTfE7WnHH9noUQrK9azy/W/4LNNZsBcGgObl5wM5849RNkubPGrW6JYkx+B48Tpt4iMSTLeHW0JFM7x+qZMl65leh7DwPgWPZhtPz5/Y5pj4X4zNpfYAqTn53xaXJd6f2OeSHwAvVGPUvVM7jvrzUIAT/40HLy0ydOSpV+BGrgqatlpKzFn4WlX0z4Kdoj7dz2wm2sq1yHpmj8z3n/0/3MkDCOjVBihKHjKIRq6RHHUzo9xzMscXyEWOO65MayX/Jj2TC5seyX3Fj2S34sGyYnlig+DiRjWIW2oElzu0lGinrSPcQHI2aYHPWHqPCHiHYKtjZNpcgRo+ydu/iDrhJGhjT/RDjEWXYfiqohEBi6k5gthbCeQlDzENK9RG0pxOydr7YUTN2OW9fxOXRSHDqpdvlqU9VR2bCp3eC5TWHKaqQI7HOrXLbUyfypw/fqBhDhdtpqd7GnZhu7Wo6wz4jQ7U/X6UVuqjYy7BnMzCjhlKxiTs+dSbrDM+Rz+INR3txdzWs7qvEHZahym65SkOFhT2ULMVPgtKl88sJ5nDXvxJOnPWJ4B/6IFJm7xPDpPjd2TSVsRNnvr2ZfawVl/irK26oIG9E+5WiKyozUfGb7Cpntm0ppagEp9v4TRCEzRL1Rz/7ofnZHdyMQUn9WpZ6jm3baYyGEAF3VyHCkoCkqhgnRiEZHwE484kDEHIiYk1yPm5JsL9N8Hime9xLQR0w0AA3bekTyhm0QD2EacWg/iiJMBCogMFU7NYXXYMuYSXrmNBypU8CTB+48cOeAZhv26QcTx89d4CArxcAfiuEPRmkNRvEHo7Qds/mDUcJRg4jp593ALzkUfR2BiYodEKjYOCvrQ5TmTqUwI41pGel47C5cNhdO3YlDc+Cyubpfnbqze3Nojn7iylj1o7WBWo60HqEorWhC5aI1TIOoEe233fPGX/nTjvswRBRdcfD5pXdw5yWfwaW7JvTD2GD2E0Kwp3EPT+55kmfKnunjfbogZwHXzrmWK0qvSOrw6KNFCMG2um08X/48L+x/gfqO+u73fA4fF8+8mMtLLuf0gtNpCDYMeD0bpkFdR11PaHN/T2jzqvYqagO1mL0W6QyES3fhU33MzptNoa+w2+u7a0t1jCzlRm2glqNtR5nmmzake9Af8fcRycuayihrKqMt0jbg8TbVxsyMmT1Ceedrfkr+CSOFTCZO1Ic2BZtYX7W+WwQ/0nakz/s21caS/CXdecEX5i5MyIKMRCOE4J2Kd/jlhl+ypXYLIFMR3LLwFj625GNkujPHuYYjZ8zGEycIU28xOpJlvDpakqmdYzk2j+58lvjBtSi6HcdZn0NN6Z/K4YdbH2JXy2E+NPM8rpx2Rr/3N4Q2sCe6h3n2ebzzmpPth5u4dMlUrj9zZkLrmnAOvQCvf0mmY7jsr5B3WsJPETNi3PHqHTy+83HsNjufW/45Pr/882P/DGxEIFgBwRpk3nFA90rPcUeWJY4Pk2ScH7PowbJf8mPZMLmx7JfcWPZLfiwbJieWKD4OWDdLYjFMQXUgzOG2IKGYgbf9KHP3/pkmRadJMck2TbKEQUPJB6lPKaZD8WAeKxoq4LFJ4TvVoZPS+aoPkv9ttDYUQrC3Ks5zm0O0dsiB9IxcnXPmO1AUhcwRLj4QwkS0VdNWs4vdtTvY3V7HHkyqlC6PPgVT1TFVGzmuHOZnzeLU7BksyCgiZQg5oGOGycb99by8rZJDdX7q/WFMU2C3qZimQNNUVs3Jw2WXE+Ndnuld8wKKomB36LhSHWi2zu9NQDQYxR/w02Q00CgaaTIbaDNb+4XKtSl2srRMsrUcsrVsMrWMbiFaURSU7nMpKEqnA6NCZ45aMLUwVYVvESeGEVfQ7Sa6olMYnUOH6KA8fAChxrHrGl4tBSF6bKup4LQrOGwK6iDzHDbFhlNx4lJcOFX52vtvp+IcuoBuxqFpD+x7BHPTTwk43AQ8aaQEWvCG2gi78jE16Zmvqwo2Ve2plysL3LngyZdiedfmzgVPHsKVQ8jUu0XutmAEfzBGazBCoz9ORYOdlg4XhimjJiCCCJqA4IkuEWy6SmN8N081fgWDKApKVxwD3EoWuion2BXkIhabrmLXVey6dtz5I5tq6yeUx8NxstKy+onoLt2FQ3d0vw60z6V3ivG99v17/7/5r9f+S3qp6m5+eMEPuaLkigHF6IRt5vHfjxkxoka020s6bghihkksbhCOxWiL1yLoXOGBiYJKhiMfj91JmiuFDJcPj92D1+4d0uax9T3WbXOPibdnVVsVa3auYdWCVRT4CmgKNvFM2TM8secJ9jXt6z4uy53FNbOv4Zo510y68OiJwBQmm6s383z587x44EWaQk09bwqo66jDFCa6qnPu9HPx2r1UtVdR017TfU0Nhl2z9xG6u0Odd3p8p9pS2b9//4R8lhFC0Bhs7COUlzeXU95UTigeGvAzbpu7Xwj2kswSMl2ZE3qByUg59h70R/xsrNrIusp1vFv5LuXN5X2O1xSNhbkLOaNAeoIvyV8y7nnth4MQgv/P3nvH2XGV9//vM+X2sk3bpN1V710usiU3cME2timmO2AglISEZlLIFxL4kYQkxHRCcxJIbDDEgMG94CJbtiVbvWu1kna1vd/eZub8/pjd1a7qStrV7kjzfr3mNfeeKfec+9wzd858zvM864+s57sbvsu2jm2APbHjrqV38dEVH6X4BN6Zkx13POFMnDJePVec1M7x7EvSMsm9+p9YPYdQQmV4r/pzxDFjrmdaNvHf+59mTnQqX135wePO0ZBv4OXMy1RoFUzpWMV/PLGLgFfjkzctpKo4cMaRus4r6/4WDvwOQtVw/U8g12vnGA+O3cRTwzD48mNf5rdNvwUB75j/Dr72pq+dn4laZh7SzZBpORrxSw3Y4riv3BXHR4n7f+ZsXPs5H9eGzsa1n7Nx7ed8XBs6E1cUnwDczjI+SCnpSOVo7Ghj1ubvopo5DNWHZmYxVS/bFn8K0xs5TgAfFMFPJoCfiLGyYcGQrNud46XdOZJZi0RGoqng1QW3rPSzZoEX/5mG5R6GLGQwuw7Q376LnR172ZtPsk9IWgfEZikULEUHzcO0UBVLy2aytHQ6C6K1BPWTP2CRUvLMtma+98ROTNNCCIGUtvRZFvbhOUGu69JiP9NrigkFvUgpiRX6qe9qpDnZSlrrp6AdL7hqpg9frghvPoovX4RuBBEni9U7CkzLQpR1Me/KblTdwiwo7HulDNk9BVXRMRUfnSWbyHq6EAhKUysoMabhC/TjD6bxByw8PhPdb6J5TVSPgdALoBdAsVDEoBg/sBa2gC6OKYeRArpf8R8npg8J6OkYh168m5eWXkVB86Abea7a+hzTKt5OTzKOkWjHk+vEl+0kWOhGlQaWNZAGYGA9KG4Pf58iRFyUEBMlxLDXw9/HxRQKogpBFIQtuPv0PBVFGSqLoCjkIeL3UBT0EAl4iA4sPl1lb2cjl/74cnIyhYKKSQ4Fndtn3kUmBx2JGJl8FkPmMcljDqw9uoWmmaiaAaKAIY9PdXD0Rwj5Qh6P7jlp+OYzwbAMmmJNdsgbIbCkRFEUaqO1E+b9aElJwbDsxbQXS9rNVdAxZYGE1QooA5MPLCTWiMkHqgBdU/Foij0JQVXO+PsK6IHjBXR9dCL7kNjuCQ59jw/tfoh7nrqHRDaBruksq1xGc7x5RHj0N894M+9Y8A7W1q51Q12PEtMy2diykcfrH+fR/Y+ys2vnwO9ZwZLWcb9nTdFG5PQeHt68JlpDib/klJ7TTryXsaRFa6J1KAT7oGDe0NeAYRknPKbYV8yc0jnMLbHDr88tncvsktkjwr9P1ggTYP9fm9KkYBYwLAPDMvjdnt/x9y/8PfFsHFVVmVc6j1gudlx0gPll84dE8EunXkrIE5qgVowdUkrWNa7juxu/y87OnYB9jfuTpX/CR1Z8xFFRKJzYB12cM149V5zUzvHuSzKXJLvu+8hMDLVyAZ5L70IM+3/tyyX41CvfB+AHV/4Fxd7wiOP7zX5+n/w9mtB4d/A9fOJHL9Hen8Grq4R82qgjdU0IhRQ8fAf07IZC0o4+oQXg2nth7tiEOh+035b8Fr720tcwpcmamjV89+bvnr//Latgi+PploEc5NjRNoI14KuwveVdTor7f+ZsXPs5H9eGzsa1n7Nx7ed8XBs6E1cUnwDczjK+9KRz1O9Yx+ymx1GtPKbiYX/NzRTNWk1V2D8ggJ+bijbWNmzsLPCN3yfIF+wJ5VLa65KwIOhVKAoqFAcH1iF7GSwbbS5rKSUy0YHZuY+u1t3s6TvMXmlSLyzaBCDAEhpS9aBoHmZEp7K4pI6FRXXMj9YcJ5L3JrN85r/Wk84aqKpCrmDi0RQ+dN08Ql59KE+7ISAtLLoKPXTkOujIddCV7SBrZY+rY4laTKWnnEptCuV6OSElxGC+Njng7C6lZPBqJAc2yKHtcii92/B9B193xTP8cUcrgRD4Q5J0QlDIBJlWOpWCEUBKsKRJV2Q9scBuwCKYmUJZ/0IUeSo7S4RqoXlNNK+B6jNs0dxroHlMNJ+B6hko8xmoqhzm2W4L5UOC+kD5oJiOkOSVFEKaqKaBpSggNYLdi8gmvBRyOn5fBL8eRFqgZHuJNR8i29VE2OohKnuJyF6ispcofURkLzq22Dz4uaoiUIRAGVqDKgR4o1j+SjqNcg6nyoiLchJKBeHSKpYtrqWmdiriJA+bvvb0T/m31/4OU2ZQhZ+/Xv3PfPnGjw39FrviWfa39rO/Lcb+1hhdseO9NyuLfcyoCFBb4WVqqYeAD7JGlqyRJZVPceDwAcoqyyhYhaHyjJEhZ+RGrLNG9miZmSNTyAztnzWy5MwcfZk+WuPNCCRi8PeDoDoyjZAnhEf1oKs6HtVzwsWrek+67USLrow8lyo0+hImnbE87X15OvoK9CVMVHQUoaGgowoNr+Zh+pQiZldFUT1xPvj4WyjIDF4RJCdT6MLP1y77JT1xaOjqJGekMWSGwsAi1BxFYUk4aBH0S3RPnoyRJplPkswnSeQT9jqXOK0n8Zni03x4VA87O3diWiZIMKU5JNauqFzBOxa8g1vn3ErUFx3Tz77YWH9kPbc+cCtgXxcFAsMy+Mq1X+G66dcxLTKNKcEp5xQu/EK6lzEsg8b+Rup769nXvW9ILG+KNR0XsWSQylAlc0rmkDWyPN3wNHkzj0/z8YlLPsGV067ElCaGZYwQow3LoGAd83602+WJ9z/dscf24+ETgAYjeQz2wTklc1g9bTVXTLuCy6Ze5kjv6dEipeT5w8/zvY3fY3fXbgCCepAPLfsQH17x4RPmvJ9sXEh98GLCKePVc8VJ7TwffcnqO0J2/Y/BMtHn34A+900jtv/D5v+hPtbCh+feyA1TV408Vlr8MvFLDGlwrfIWPvfDraRzxlC0pYBX4zsfWTN5PcYb/wi/fQtYFvhKAAv0EHxgw5h4jA+338tHXuYzT36GjJFhXuk8fnrbT6kIVZx7G0aLVYB0qy2Qy4HJdorPFsf9la44fhLc/zNn49rP+bg2dDau/ZyNaz/n49rQmbii+AQgpbS9txTlggzJOdFkDZPnG7tRsnGCRoyUFsXyRbiurgzfCTyYz4axtuHBDoPvPZZAUSSGITAsiWFCUVCga6c+v08XI0TyIeE8qFAUUvDpJz5eGjms7oNk2/fS3b6X/Zke9gnJfkXSge1FLlUdoXrQdC/Tw1UsKq5lYVEd84qmEdB8vLS7jf94fjNp0gQI8OfXreSqhVWkClne6GxkY+chDiVb6Mh2YkrTDvOtKnbobEVlVqSaedEa5kWnMScy9ZTe6eeKJSXtfVm++MA+NFGJECogkBiUR72oiqBuisZlczwsqtF5oX0LP9v/DIY0qfSW8sG6WwkqIbJ5k2zBGFib5Ar2eqh88HV+4PWwfQYTvSuaheo5gYDuNYY80G0h3UTxGHj8hj1RYlDeEpBPa2Ad/T0rioJH+NCkH9XyIQwvZkpFL3gJa0GK9TDFvjDFPi9FniwR2Ysn2wWpdki3Q6oDUm32+1QbHBNi2LQgk5dkC0cnJegq+ENhPNEqxLDw7AQroXcvrZt+wOFChjpPkKlXfQ1m3mKHFpTmwCwG035vmcTTWRo7YzR2xmjqitMdS6Fg2d8XFgJJcUClpjRAbUmAqSU+on4NBYnAGnm+oc845rMsEzjxfq2xI6xe/22SliQsBAkpCSkqr37sdaZWrhjT36KUkt5kjob2OAc77KWxK4lhHp/DuaI4wMzyMLMqI8woj1BTFkQdFtXia0//lH997UsUZA5dePmb1f84NPkgb5gc7kxQ3xZjf1uMA20xsvmRApmiCGaUh5lTHWVOlb0EvTpSSvJmfkgsT+aTpAqpEe9PtwzunzWOToDJFDK0JloHoibYi67o/PxtP+edC985pt/zxUx7sp3L77ucZD5J1BsllosR8oTY8KcbxsyT+WK4l8kaWRp6G4ZCrw+GYW9LtgHHRpg4sUf+ZGOwDypCQVM0PIoHieTBOx/k5jk3T3T1zjtSSp479Bzf3fhd9nbvBSDkCXH3srv50PIPTWpx/GLogxciThmvnitOauf56ktG0xvkt/4GBHgv/SBq5YKhbY82vcYvGp5nUfF0/t/y9x137BPJJ+g0O5mRWc53/7eDTN4WXBVF4NVV/vF9lzF/atG41f2caNsA//dmMHL2jFyhguqBdz4N1Zef8+mPtd+uzl18/NGP053upjJUyU9v+ylzS+eOQUPOAMuETKudd9waiHyleAbE8Sr7O3AZwv0/czau/ZyPa0Nn49rP2bj2cz6uDZ2JK4pPAFJK8vk8Ho/H7SzjREsiw/bOOKaUqEKwtDzC1PDpc2aPlrG2YSxt8c8PxckVLPweQSYv8eoKX3hbGCmhL2nRn7KXvqRF38DrVO703XJQNB8Syo8Rzr0DormV7Cbbvo9M+15iPQ3st/LsF5J6RdIpQAoNNNuLXFU9zAhX4FV1NnTspWAZaIrKJVPmkSjkaUx0YAxcMgR2zusib4D5RbYAPi86jRnhSvSzFA2klOQKkMpZpHNyxHKisnROks5bFExJT9wOQT2QZB1VgTcv83P1Qi+VRSMfUOyLNfPtnb8llk8R0nx8evHbWVw8/azqbElJ3rCOiuX5QTH9GPF82PZMwaAz14t3+TZU3cIyFFSPhbQEen8l4ZACeh5LyyGEnffclFCwrCHhWgjwKAqqIhAIfMJHQAkQVIIExNF1QBlYRAAN1Q5xmBoQy9PtA6/bycfa6OtsxUh04LESgC2OB7wKujYQmdsyIN5ki85CObqO1MIobT74feUNO392wTz+ty4EeDV1yFNGG5hwcVYYGR7qa+GenEUaCAD3+lTuXPVnsORPYcrSs84LmM0bHOpMDAngBzsSxNP54/YL+DRmVkSYWRFhVkWEGRVhgl79tOff03GYHa0NLKmexYKK6Sfdz7QkLT1J9rfFqB9YYqlj6iFgakmQuVVRZldFmVsdPWfPI8MySOVtgfxg30He/dC7SeVThDwhckaOkHdsxVoXm4d2P8Q9T99DupAmoAe498Z7uXPh2IQqhYv7XiaRS3Cg9wCP1T/G11/++lBaD4nEsAyuqbuGilAFuqKjKdrQcuz7EdvUU+97Jtt1RUdV1BPu253u5sr/upJkPknEEyGej4/5hAknYkmLZw8+y/c2fo/9PfsBiHgj3L3sbj647IOEjwlnPBm4mPugk3HKePVccVI7z2dfym9/GOPwBoTuxXvVX6CEygDoyPTxudd+hCIUfrjm04SPyTu+MbORPfk9TGcO9/08QyprkDdMDFOiqQrfvPsK5lYXjWvdz5pUOzxwOWR6bIHYMuxxwfSbYPWXoHr1OZ3+RPZrjjfzsUc+xsG+g4Q8IX5wyw9YPe3cPufsKmdCpg1SR8AauOdWdAjUgL8a3BRBgPt/5nRc+zkf14bOxrWfs3Ht53xcGzoTVxSfANywCueHrGGSLpgEdHXMPMQHGQ8bbmrI8+v1afKGxKMJ3r0mwKpZnlMekzfkcUL5oIDel7LF4dPh9wiKgyNDshcFLIryjYi+fdC9n0Sqg3qFIZG8SwgMRadb5pFSogAWIIRCRI+iCIWoHmF+dBqXlE9nUXEtVYET56eVUpIzIJ0dELPzg+L2oKBtDRO8j5ZZZ3FFKhiS/pTEjp5v4dUUUASfuTXMjIoTi7U92Tjf2vlbDibaUITg/bPexM3TLj1vf3S9ySxfefZxpq5sQ9UlZkHQsrmKr1x/y5BYaUmLrMySlmlSVoqUlaI1E6MzmyBHGkPJItUsmjoQFv00DArnQ6L58NcDAno6o7J+Rx/765vxG+2EzU7qAt0sLO2lOLMTDj5Oyh8iESwinOwnmIlBZDp4QvaDMKEe9RYZeq8cv02xPfpNqZDMmySzJvGsSSJrPwhEUZGoSCFQFJVw0E806CMa8BEO+uzfnKICA+dWTvJ5hSRs+xHthSxNKNSaGSoV9aiQH6yyH95NfwuULztp+EPTkrT3pWkYEMAbOuK09qY4NgKzoghqykJDAvjMijDlUf95vYE6Noz9gbYYHf3Hh7EvjfiOiuRVUaqKA+dUz+E5xcO+MPfeNLZirctR2pPtNMWaqI3Wjrng6d7LnB+P/PHA7YMnx5IWzzQ8w/c2fo/63nrAFsc/svwjfHDZBwl6ghNcw6O4fdCZOGW8eq44qZ3nsy9JyyD3yn1YvY0o4XK8V/05QvMC8MXX/5PGZCefmH8r11QtHXHcwfxBXsq8RLlaTqhpOT95djeZnD2pNuzXqSwK8LnbllJbdp5yaJ8p+x+CF+6xc4xLC7wRO784QPUauOQeKFt0Vqc+mf1i2Rh//vif80brG2iKxtff/HVun3f7WLTmzJEWZNoHxPGBCEpCg8A0CFTbQvlFjPt/5mxc+zkf14bOxrWfs3Ht53xcGzoTVxSfANzO4nzGy4axtEVvwqIkrBANnHvOsVxBDgnk/ScQztP503frgEdQHUww03OAcqOBaPoQCSvLi4rFg5p9vAUo2F7Cbypby9VTr6TGX0quII7x2LaGhO1UTpIZWFtneXnRVUHQKwgMLEGvYr/2Db4XBLwKAY/9vmBK/u13CXIFC1UYmFLDqyv83Z2RU37fedPgv/Y/ybr2HQCsrVjMR+e9Ba96fh5gvLS7jf96eRt485Dz8JG1y7hqYdVpjzMsi8ZYhsOxNIZlYYk8Ab9JeVii6HlSVoq0lR4S09MyjTGY/+40eISHgBJANwN0dXtp6fBgZH1YOT8zfVlWZv+ajYuvpKB50I08V21/iTnX3j8muQMBsrkC6zbtIqtFOdARp74tTr4wMjS4R1eZVRlhXnWUOVVFzKwI4znVBJnBB3ZGGjQ/LPyg/brpuZHh5AMVAwL5TcRCizjUlRoSwQ93Jo4LUQ5QEvaO8AKvnRI6dV0miFg6b4dbb+3nQFuMpu4kx3bPkF8fCrU+pypK3ZTQiJDuo6El1sL6netZs3gNU6NTx7AFLucL917GZrw98scLtw+eGktaPHngSb6/8fs09DUAUOQr4qMrPspdS+8ioAcmuIZuH3QqThmvnitOauf57ksymyC77nvIbAK1ejGeVe9HCMFvDr3Ebw6/zMqyOXxhycj/kX6zn98nf48mNN4Xfh/9qTydsSx+j8p/PbeP5u4kfq/Gp29ZPLk9xuNN9oRTocG2H8K+B23PcYAZN8PKz0J0+hmd9lT2yxk5/ubZv+GJA08A8LnVn+MTqz4xcV480oJsJ6SawBwYWwgVAlNtgfwiFcfd/zNn49rP+bg2dDau/ZyNaz/n49rQmbii+ATgdhbnc6HYMFeQ9A0TyY8V0DMnEM0VTOoCzcxQX+X7ci9ZICAlaSHwAbfkb+Zw/szzww0K3P7hYrb32DK7fLBMV8/8gYbtkZ8ikzPxe1XevSZ4Wo98sL1qn2p5g/sPPIclLWaEK/nc4ndS5js/15reZJbOWJbyqO+Mw1nnTYtD/WmOxDNDExCmBL3MKQ4S8hz1kJdSkidvC+VWmpRMjVgPCuh5eXzob0tCJjeQcxwLzZ8ELBTTQCoqApUadS4B1YdHaHgUHY+ioQsdXehoQkNDQxN22Yleq6hDD7GO7YOmJWnqSrB/QNDd3xYjnR0p8KuqYEZ5hLnVUeZWFzG7MoLfMzJCQP2RPTR37mda+Vzm1AzkejSy0PIy5sHHKRz6I2YuScGwKJgW/VaY3eql7FAu5bCYhxQqHl1lRnl4SASfWRGmKOg9I5tNFrJ5gwPt8aFw6wc74hSMkbnPPbrKrIoIc6ptT/KZFRG8+qmvixfKNfRixrXhUcbTI3+8cO03OkzL5PH6x/nB6z/gUP8hAIp9xfzpyj/lA0s+gF8fu9Q8Z1w314aOxCnj1XPFSe2ciL5k9jaRe+XHYFnoC25Cn3MtTclO/vb1/0RXVH689rP41KPjE0taPJh4kIIscHvodorV4qFtqVyB7z2+k/rWGLqm8MkbF7J8Rtl5acc5Ez8CW74LDY8A0haI594Jyz8FwYpRneJ09rOkxb+/8u/855b/BOA9i97DP1zzD6gTGbpcymHieNouEwr4B8Rx9fRj0wsJ9//M2bj2cz6uDZ2Naz9n49rP+bg2dCauKD4BmKZJQ0MDs2bNcjuLQ7lYbJgtyCGB/FjhPB3vZa72bX6uS3IIfEj+NJNhoRFlq7iaBv8VeH0eW8j2jBS5A8ME7sHXHu38zdjvTRTYsfcIS+bXUBI+sxn5u/oa+c6u35EsZIjoAT67+B3ML6oZp5qOLRnD5GBfitZk1vb+FVAd8jGrKIj/NCLmcPIyP9LDfOB12koTM1K0pvswPDFGuBgLgZkNgDXyc4QYyEM+sB503BADBceWKaioUkORGkbOIuANoAkdHX1ARLcXTWjkspK+uEF3LEdbT5ZU2sAyBJapIA2BtBSmFkWYXVnMvOoi3ug9RLxyG4pqYpkqobalrIhOt8Ogt8dp6k6imDlmWztZbL3OAmszPjJoqkBXFYSvBDn9RkLz34pavfqCzBNYMC0aOxPUt8fY32qHXE/nRk4+UBRB3ZQQc6qKmFtte5OHfCP7WVcszZbdB1ixcDZTohPvcely5lws/4MXKq79zgzTMnms/jG+v/H7NMYaASj1l/KxlR/jvYvfOyHiuGtDZ+KU8eq54qR2TlRfMg5vIL/9YRDgvfzDKFPmcM+GH9Oe6eMzi97G5eULRuz/ZOpJOowO1vrXMssza8S2vGHyo6d3s+1QD4oi+PB187hyvjMmaQHQuw82fROOvGC/V72w8E9g6cfBGz3loaO13/3b7+cf1/0jEsk1ddfw7bd8e+KjfkgJuW5bHDeSA4UKBKrsvOOqMyfVninu/5mzGU/7jWdKxPHCiXV2+6Czce3nbFz7OR/Xhs7EFcVdXFzOilja4umnXmBZ4RF6KVCKgmlNYaovhaqACJTgWfxW1MqFE13VMacr0883d/6WxmQHilC4e84NvLl6xcSF4jtDknmDhr4UHakcAIoQTIv4mFEUxKuee9j+3R0xnjB+jdAKWIaGohWQlkZq3woUNFAMhGqAaiJUA6EYiIHXqEdfC2WgTDk+HPnZIqVEMqjXy4EykJbAG0khhMQyFYQikZZCT30VhYwHK69iFgQ6OuWhIHXFEeaUhpln1eNvfhaanoVc7OgHeYug7nqYfjNUr7bzkl+AWFLS2puyQ663xahvjdGXzB23X1VJgDlVUeZWFdGXyvLLlw+QLVj4dIWPX79wVOkAXFxcXCYa0zL5w74/8IPXf8CR+BEAygJlfHzlx3nP4vfg084skovLxcfFMl69WNp5ruS3/Raj8XWE7sN79V/wYPs2Hm3awBXlC/nLRXeM2Pf1zOvszu9mgWcBl/kvO+5cpmXx38/t49V9HQC8Z+0sblzmjIm7Q7S/AW/cC52b7feeMCz5mJ3SaAwmHz178Fk+/9TnyZk5Fk1ZxE9u+wllgUngVS8l5Hsh1QiFxEChAH8lBGtBdf9bXC4+mmJptnfGMSUoAmoifkp8OkIIlIFZ9crQJHqBwN5v8PWJykYeJ+wJ+gKUwdcDx50tLYnMQJ0lqhAsLY8wNTxxUYVcXFxcXFxcTowrik8AUkpSqRTBYNAxIprLSFwb2mxqyPP0hjaiSi8xq4QbL6tkWWA/hZ2PIrNxANSK+eiL34oSLJ3g2h5lLOyXM/P8ZO8TvNq5G4DrqpZx99wb0R0kfsZyBep7U/Rm7HDoqiKoiwaYHvWjnWGO6BHnTVt8c/12wgvfQFUNTFMjsfsSPr9mKdGAgmVJCiYY5tG1YULhpGuTvGWStwrkLYO8LGDIApl8FjQwMDCkgYmBIQwsCljCxBIFLGEghYlUBgT34QK8YiKRtkCumOj+9IBYLgCJEFBIB0CqA4Pkkb+VwTLF0lEsnWAhSSTbQVHqMP5cHK+Rw1vI45UqatFSfFVXEK66HJ8njI5+QV47pJT0JHLsb+sfCrne1pse2m5aFp3xLAKNgMePRZ6AF77zkTVnnBbAZWJx/wedjWu/c8OwDH6/9/f84PUf0JJoAWBKYAqfWPUJ3r3o3Xi18ffuc23oTJwyXj1XnNTOiexL0jTIvfITrL4jKJFKjiy9g3/Y9kt8qocfrfkMHvXouOJg/iAvZV6iXC3n5tDNJzyfJSX/90oDT29tBuCWVbW84/IZzrpGSGl7jG+6F/rq7TJ/GSz/C5j3ruMmmp6p/ba2b+WTj36SvmwfU8NTue/2+5hZPHPs23E2SAn5/gFxfHCyrQBfhS2OaxemuOb+nzmb8bBfumDwzKEuzOGB5wC/pqCM829EDBfMh4nlihgpnB8ruFsSOlJ2RD6PpmBaEk0RXFdXNuk9xt0+6Gxc+zkb137Ox7WhM3FF8QnAzTXgfFwbHiWWtuhNWJSEFaIBW0iVRg5j/3MUDr4MlgWKij77GrQ51yLUMwtXPh6Mlf2klDx6ZAO/OvgClpTMiU7ls4veQbE3NIa1HX96Mnnqe1PEcwUAdFVhZlGAaWE/qnJ2f+ibGvI89EYPlieJkg9x5yWlo8rdPlrOxobHi/EWWdMgZxZo6O9iu/8ZFM3ANFRU3USaCqHehaiaQl7mMUQOU+QwlQJoOVtYPyESnRy6lcYj0wjMYVsUDMWPIYJYMooifejSiy48eIUXv2IvQc1HSPcS1n1EPD6Cmhev8KKJ4yddtKUStKZiVAejVAXDZ/N1jivxTJ4DbXHq2/rZ1NDFgTaIBuoQQkVKk2TuCH/3zlmscEr+SxfA/R90Oq79xgbDMvjdnt/xH2/8B62JVgAqghV88pJPcufCO+nN9NLY30hdUd2Y55t3behMnDJePVec1M6J7ksyEyO77vvIXBJRvYQv5HvpyyX4qyXvYkXZ7KH9YmaMh5MPowmN94XfhyJOPIFVSskTW47wm1cPAnD1wiruumbuWd/TTxiWCQcfg83fhqQ9+YhwLaz6LMy42c7BzdnZr7G/kY898jEaY41EvBF+eOsPuaT6kvFpx9mS77fDquf7jpb5ygfE8eCEVWs8mOg+6HJujLX9pJS83tZPcyKLADRFIKXEAqqCXryaipRgDUxulwPHnLAMW6w+Gi1uZNlYYlqSjGnBQJ09isCQkiunllDiH7vnIOOB2wedjWs/Z+Paz/m4NnQmox2vOsf10cXF5bwSDRwVwwcRmhd94c2oNaso7HwEs+sAhf3PYTRvxrP4NpSKBRfE7CkhBLfVrqYuVM73dv2e+lgL/++N/+Zzi9/BnOjUia7eqCn1eyip1ulM2+J4umCwrydJYyzDrOIA1SHfGdtr1SwPs6sq6E1MGTFhYiJRFIFXAa8+2BYF++/Nx6yyMEe2rKJ3yhYU1cTMeyjpWsFHV5z4AVnBkCRzJrF8lnguR6KQJWXmSBtZMlaOrJUjL/NkyKBZ7XjpQFdjmLqCwEInhaSLggiQUwIkhR+JsEfw5sAyLAq5EAMz0aWKanlRpQcPHrIyS87fYYd7T6iEW+ZSp01HsbxgelEtD5g6SIFp2R5EUtoPAixrYC2l/WDAAlPaTiqWZT94GNrHGthnxLHDyiwwB89tDTRjcB8LpNSx5BRUpYSS0OADCAuJIOSt5UdP7edtl6d585KpeCb5THoXFxeXQTRF412L3sXb5r+N3+z5DT9640e0Jdv46otf5Z/W/ROdqU4QENAD3Hvjvdy58M6JrrKLi8skQ/ijeC55P7lX7kO27mBV+UyeJcHG7n0jRPGIEkEXOgVZIGbFKFaLT3w+IbhlZS1Br8b/vrifdbvbSOUMPnbDAvQxSJN03lBUmH07zHgL7PsVbP0PSDTBC5+H7T+FS+6BqWvP6tR1RXU8eOeD/Nljf8bW9q3c/fDdfOOGb3DznBN74E8IniJ7KcQh2WiHV8922ou3zBbH9ck3GdbF5Vyp77Mj2QlAFaArgoIFXkWwrCI6pl7XJxPOLQZSrA2K7AOvrVOI8DnDZHtnnLwlMSw5JOgHdHds6+Li4uLi4lRcUdzFxeWMUcLleFZ/BLNtJ4VdjyHT/eQ2/i9qxTz0xbdNqpDq58LSkpl8bdWH+ObO39Kc6uJrWx/gI3Nv4tqqZRNdtVEjhKAi6KU84KE1maWhL03WMNnVleBwLMPs4iDlAc8ZieMnmjAxmfnoikvY1zWDA729zC4pYd6Kk/8+dU1QrGkUB0PA6CIDWKZJrmUzuUNPYnW8SN6Mk9O8ZHUPGU+YnvBCuoPz6PVWkxcWhpLDUvIILQ96HlPairkh0iDSpIWF6k+hIJFSoOgGqdKd7MgcAjn8exfIgger4EEWvEhDxzK8yIIHaQyWe7AKR8ukoQNjP3HFsgSKkFhSAgIhBJqiYJgeHnrlIM/taOGdq2dy2ZzycQ+N5+Li4jJW6KrOexe/l3cseAe/2f0bvrPhO2xq24RlWaiKSn+2nw89/CH+ePCPzCyeSWWocsRS4i9BVdyHpi4uFytq6Qw8i99KfscfWN7VwLP+AJu66zGlhTrgES2EoFQtpd1op8fsOakoPsg1i6oJ+nR+8sxuNjV0kcoV+MubF+PzOOzRjuqBhX8Cc94Bu34OO+6D3j3w9J9C5WWw4rPAmXtOl/hL+NkdP+MLT3+BZw89y2ef+iytiVY+suIjk2vyth6B4iV2rvFUE+S6jy6eEgjV2fu4uFwAHIlnONyfRhGCWcVBWhIZDGmHIV9aHhnzMOTH5ho/V1RF8EZbP6a0zzYedXZxcXFxcXE5fzhs5DS5EULg8ZyZuOQyuXBtOHqEEGjVS1DL52HUP0eh4SXMjn2YXQfQZ1+NNvtahHZ+w0mNh/0qAyV8deWf8KM9j/J6935+svdxDic6uGv2m9Ec9KBbCMHUsJ/KoI8jiQyH+tOk8gbbOmJEvTpzSoKTIvzXePXBeVNKmTdlfCZrKKqKv/ZS/LWXgvx/0LUdDj8Fh5+0H+6177F31Hww7RqYfhNy2lvIESSVNYkX8sRzOZKFLEkjR3OujZhnM9JSEAiksBCqiW6E0DWBpeZAmPaI3JNHkAeSgO15PpzhzwHslYImPWjSgz4Q4l3DgwcvOl506cUjfAMlPryKDx0dTbFzrSmKnXdNGfBwVxTbZsmsxfcfS5AXaVR/nEwigpnzUxSYTs5spzfRzU+f2cPTW4/w7jWzmT+1aFxs4XLuuP+Dzsa13/jgUT28b8n7mBaZxq2/uJW8mceSdviMnJHjsfrH8OvH54RVhUpFqIKKYMVxgvngMiUwZYRw7trQxWVsmCx9SZ2+Gq2/mTlHNhHIJkgqCnv7j7CouG5on+Gi+Gxmn+JsNpfMmkLQu5TvPb6Tvc39/Nvvt/HZty4hMgnu5c8YPQjL/xzmvw+2/Rj23A/tG1GeeD9VRZchyr8MJXPP6JR+3c93b/4uX3/56/zv9v/l3175N1oTrfzdVX83+SYq6WEoWgRGyhbHs52293hvL3iKbc9xTxGYOTAzoPpB9U50rUfFZOmDLmfHWNmvK51jb08CgFnFQWYVB5lbEiRdMAnoqiPE5alhPznDYl9PkpBHozrkm+gqjQq3Dzob137OxrWf83FteGHj5hR3cXEZE6xklx1SvbMeABEouqBCqlvS4veNr/J/h9YBML+ohs8sejtRjzNzvxUsi8ZYhsZYGtOy/wZK/R5mlwSJeic+P/wFgZTQvdMWxw8/BYkjR7epHph6FUx/C9ReB56jYRLbUgl+0f8rhFpAGh6ElkeaOu8ves9QbnFDGuRkbsSSlVly1jHvh20vyMJZNUMg8Arv0OJTfCPee4UXn/CxuaOVBmUbKBbS1DAPXkb8SC1SgkfL0RE7RK6QBWDp9FLedcVMqkuc2X9cXFwuTtqT7Vx+3+Ukc0mCniDxXByP6uGLa79IxsjQnmynI9VBe7KdzlSnLZyfBkUoTAlMGRLJTySglwfL0VX3v3kyc7GMVy+Wdo410iyQW/8j7os1sV5VuGnWtdw972hI74P5g7yUeYkp6hRuCd0y6vMe7kzwrUe3k8wUqCgOcM9tSykNO0OsOSnJVtjyfTjwOzsHkFBg9ttgxV9CqPqMTiWl5Gdbf8a/rP8XAK6fcT3/fuO/n3AS06TBSEPqCGQ7sIM3A4rHFs2ltMPPRxeAv2pCq+niMhpiuYLtYW1JqsM+FpWFHftsqGBZvNjYgyUll08tdp+ZuLi4uLi4TEJGO151RfExREpJLBYjGo069kbvYse14bkhpcRs30Vh56PITAwAtXyuHVI9VHZePn+87bepu54f7P4DWTNPqTfC55e8kxnhynH5rPNBzrA4FEtxJJ5l8O+gIuhldnGQ4ASEYbxg+6CUttf4oSdtkTzeeHSbotu5E6ffBLVvBm+Ep5r2sFtdZwvjps5C82puql1wTlUwpXlCwTxrZY8X189QSLekRUImkEiQAlWo+IWPOV3v4PnNCgXTzr8W9seobzuMJSWKIrhqQRV3XDadaMCBnk0XKBdsH7xIcO03/jy0+yHuefoe0oX0KXOKm5ZJd7qb9mT7CLF8+NKR6sCwjOOOtUwL5ZgcwWWBspN6nFcEK6gIVeDTTi6GtSfbaexvpK6ojsqQc+9bJitOGa+eK05q52S7HlrpPja+8C2+Y6Yo9ob43vV/P+S1HDfj/C75O1Sh8v7w+1HE6NMUtfWl+eYj2+hN5CgOefn8bUsviEmHsree/Gv/hqd9nR3tSNFhwQdg6SfAX3JG53rywJP81TN/Rd7Ms6xiGV+99qvEc/HJfT00s7bneLrV9hDHTlEEgFCheDF4Sie11/hk64MuZ8a52i9TMNnQ2kfetCj1e1hRGXV8Gq0dnXHakllqIn4WlIVPf8AE4/ZBZ+Paz9m49nM+rg2diSuKTwCmaVJfX8+cOXNQ1ckfAsjleFwbjg3SyGPUP0+hYR1YFijqeQmpfr7s15Lq5t4dD9Ge6UNXND4+/xbWVCwat887H6QLJg19KdpSWZB2mJjqkI9ZxYHzGtLsouiDUkLfflscP/QkxA4e3SZUqL4SvEUkDj9CwusjnM8TvuJrMPd40WW8GS6kDxfNjxXSY2aMVrN1oHl2sjWB4Cr/VdQZS/j9xgwH2m3hpyQEptXG3pYOALy6ys0ra7hxWQ1e/QK1uYO4KPrgBYxrv/NDe7KdplgTtdHacxJULGnRm+kdIZS3xlvZ27yXvJ6nI20L6XkzP6rzFfmKbKE8OCCWhyqoClWxu2s3//HGf5AzcgQ9wZMK+S5nj1PGq+eKk9o5Ga+Hmc79/NmGH5MD/mHWm5m/0PYKl1LyYOJB8jLP7aHbT5tX/Fh6Elm+9eh22nrTBHwan711KbMqJ7d9TseQ/YrSqJu/Be0b7Q16EBZ/FBbfbb8eJZtaN/HJxz5JS7yFvmwffs1P2Bue/NfDTCf0bQFpYoviA4/vVJ89blA8dgh2LWyv9bA9gWASMBn7oMvoORf7FUyLjW39pPIGIY/GZdVFaMroJ/tMVnrSeTa196OpCtfWlk56kd/tg87GtZ+zce3nfFwbOpPRjlfdnOIuLi5jjtA86AtuQq1ZRWHnHzA76ynsfx6jeQueRW9FqVzo6FlWU4NlfG3V3fxgzx/Y2tPAD3b/gcOJdt476zrUM/DsmEwEdJUl5RGm5wMc6E3Rlc7RksgMzYSeURTAozqzbZMOIaBknr2s/Az0HRgIsf4k9NXDkech3kRYWoT1ICgavHCP7U0ePL/eLKpQCYgAAQKn3C9lpfhV/FfkZR5pSQpqAYlkS3YLHXoHt199KY1NIR7blKE3KVFEFW9aUklD+0EauxI8vOEwz+9s5e2Xz+DKeZWoinOvDy4uLhc+gx7a54oiFMoCZZQFylhcvhg4fvAtpaQ/23+ch/mxXucZI0N/tp/+bD97u/cOfYZhGTTFmrAsC0UoWNLinqfvYW3t2snrIeniMgZkDZM0KlnDJDhJHmT5y+eyvHQWG3oa2NDwAnMq5qOWzkQIQYlaQmuhlQP5Ayz0LiSojF7wLQ37+Nu3r+Dbj27nUEeCf//DNv7i5kUsqjkzj+pJyZRlcPP/QMt62HQv9OyGLd+1c48v+zOY/147LdFpWFW9iu/f/H2u+dk1FMwChmmQyqf488f+nNXTVjMtMu08NOYs8ERtkdsStghuGfZYQg2ClQUrD7keexlE9Y0UybWwHXbdxeU8YEnJ1s44qbyBV1NZWRm9IARxgBK/jk+z/1e60nkqgpM3UoOLi4uLi4vLyXFFcRcXl3FDCZXhufzDWO27ye96FJnuJ/f6/ajlc9AX335eQqqPF0Hdxz1L7uQ3h17i4cZXeOzIRppSXfzlwjsITeY8dach7NFYURmlP1ugvjdJX7ZAYyxNSyJDXTRAXTSAYVmkCyYBXT2vXuQXLMWzofgvYMVfQP9B2PYj2Pwde5uZBWvgIUK86byL4qMlqAS5KnAVL6VfIiuyBEWQWr2WDrODdqOdR41HmVs1l0/euoxnNlnsOlJgT7OgNDyHd6zO8OLuQ/TEs/zsuX08s62Zd185i8W1F8CDXBcXF5dzRAhBsb+YYn8xC6acOI2GlJJEPnGcUN6ebGdHxw6OxI6gKRoI25s8kU/QFGtyRXGXC5bmeIYtHTFMAnQ097G0PMLU8OS4P7981rVsiDWz2cjxrtcfwHfNp1H8USSShEywMbuRHbkdXBW4ijmeOaM+b8in84U7lvODJ3ay+0gf3350Bx+/YQGXzi4fx9acJ4SAaWth6pV2lKXN37ZTEW34J9j1M3uS6cy3jkr4DXqCGKZB1sxiWXa0jlsfuJU/XfmnvGfxeygPTrLvS/XaOcRje2xvcVU/mlPcMsFIQCE5sE7YodbNrL3kuoadJ3BUJNfDoIXsfO0uLmOIlJKdXQn6MnlURbCyMnpBPS8QQlAV8nKoP01LIuuK4i4uLi4uLg7FFcXHECEEwWDQ0R6wFzuuDcceIQRq1SJ8U+ZgHHiBwoF1mJ31mC98G33WVWhzrhuzkOrn236qUHj3zGuoC1Xwo72PsqP3EF/a9DM+v/id1IYm2QOVM6TIp3NJVRE9mTz1fSkSOYOGvhQNfSnypgUCVCHG/CHjRd8Hi2bCJZ+Hfb+CXHzgoVYeZAoy3RNdu1MyxzOHSqWSgx0HmVkxk7AWJmkl2ZTdxOHCYfbl93FIHGLFZStYOn0Gj7yeoydh8do+L2vmLUVTe3hySyMtPSm+9ch2FtYU864rZ1FbFpropl1UXPR90OG49nM+Z2NDIQQRb4SIN8Lc0rkjtrUn27n8vstJ5pNEPBFiuRghT4jaaO1YV93FZVKQNUy2dsYoWBKEoGBabO+MU+r3TApxZnnpLHR/lM5UDy35JDVv3I+x+i6aCk1IJBYWOZnjpfRLVGvVZ+Qx7tNVPn3rEu57dg9vHOjiR0/vJpktcN3iqePYovHhhNdCocDMW2D6jbD/IdjyfUi2wLq/hh0/hVWfh5rrbBH9BNQV1RHyhEjmk1T6KulOd2NKk2Qhyfdf/z4/fOOH3DjrRu5aeherqlZNnv9SfxV4SmzBW/UfzSOuqOApspdBrIItjg+K5YUEWDkw0/aS7RjYUYAWHOlNrgVP+t2dDe49ibM5G/sd6EvRnswihGB5eZSw58J75Fwd9nGoP01PJk/OMPFOgv+Vk+H2QWfj2s/ZuPZzPq4NL2zcnOIuLi7nFSvZPRRSHUAEitAX3YpaucjRfzRNyU7u3fEburL9eFWdT85/K5eXz5/oao0JUkraUzn29ybpzRSQgAIoikBXBNfVlU2Kh4wXFPsfskOmF1JgZMAbBV8xXPUvMOu2ia7dGdNutLMxu5E+sw+AIrWIZeqlbNlWxKaDdq7cooDCjct1dje38McdLViWnZt8zbxK3nb5dEpCvolsgouLi4tjeWj3Q9zz9D2kC2kCemDy59B1IBfLeNUJ7ezN5Fnf3ItpSSR2BmZVCNZOK6Y0MDm8+v59x0Ns7trLHdkMtxkWPbOX8Hh1AlOamJgAaGi8Pfx2KrUzj+hgWpJfvFTPCztbAXjb5dN566o6R4+1TkghY4dR3/4TyMftsvIVcMkXoPKSEx5y7PXwX6//V8KeMA/seIBNbZuG9ptbOpe7ltzF7fNux+/gKGCAPcHWiNsC+eAijeP3E4rtQT48R7nqH1Oh3OXCpTmeYXd3AoBFU8KTJjrHeLCxtY/+bIG5JSGmF506zZiLi4uLi4vL+WO041VXFB9DLMuit7eXkpISlAskZ87FhmvD84OUckRIdWBMQqpPtP0ShQzf3fUwu/oOA/C2uiu5c8ZVKBdIaLrudI5Xmvswh/1taIpgzbQSSvxj4+0/0TacVKTa7ZDpgQrY/C049IRdvupzsPQTk/IB1ansZ0mL+kI9W7JbyMkcAHV6HaWxZTy1UaU/ZQGwfIaHS2fDE1sO83p9JwC6pnDj8hpuXlGD/wL0OJhMuH3Q2bj2cz7jZcP2ZDtNsSZqo7Vu2PRxwCnj1XPFCe3MGibPN3ZjWBLLkpiAAKaG/ayoiKCrE39tfLFtOz/e+xi1epC/72snrQl+f9k0CorAwqJAAQWFW4O3MtMz86w+Q0rJwxsP8+gbjQC8eelU3rt2NsokvH88EWd0LczFbU/x3f8DRtYum3aN7Tleevwk5ZNdD/d27+WB7Q/wh/1/IDtwnrAnzDsWvIMPLPkAdUV1Y9a+CUVK23t8SCSPg5G0w7Mfi1BHiuR6GBTvqMYh7j2JszkT+3Wlc2ztiCOlZFZxkFnFo49w4UQGJwCEPBpXTC2etBOO3D7obFz7ORvXfs7HtaEzGe141bXoGCKlpLu7GwfMM3A5Ca4Nzw9DIdWv/Rz63OtAUTE768m+8C0Ke55CGvmzOu9E2y+s+/nbZe/h1prLAHi48RXu3fEQ6cGHMw4n5NHQVYGmCBRszxvDknZ4yjFiom04qQhWQtVlEK2Da78Jiz9ql2/6FrzyD3YewUnGqeynCIV5nnm8Pfx2FngWoKDQWGhkW+Axrr2hgcvnqwhg66E8v1xnsHb+bL74jhXMropSMCwee6ORL96/ged3tmBa1vlv3EWC2wedjWs/5zNeNqwMVXLZ1MtcQdzlgsenqSwtj6ApAqSFRxH4NIX+bJ7XWvuI5woTXUVWls1BEQpNhRS9s68iULC4fH8XuhSoEnQ0fMLHK5lXaCm0nNVnCCF4++UzeO/a2QD8cXsL9z27xzH3UGd0LfRG4JJ74M5nYN57bSG3+UX4/dvghS9A/MiI3U92PZxfNp+vvelrvPThl/ji2i9SG60lkU/w820/58b7b+Sjv/8ozx96HnMS3oOfEUKA6gPfFAjPhJLlMGUNlF4Kkfngnwp6BFBsoTzfD+kjENsN3Rug+1Xo2wHJw5DrsT3RT4A0siR7m5EXyFj4YmO0fTCeK7C90xbEq8M+Zl4EntMVIS+KECTzBvH8CaIuTBLccYGzce3nbFz7OR/Xhhc2riju4uIyYQjNgz7/RnzXfha1fC5YFoX6F8g+/02M1h2O/ONRhcIHZr+ZP19wG7qisaWngS9t+jmtqZ6Jrto5M/who6oIVAFeVWFnV5zezNlNZHAZJUKBy/4aVn8ZEHbO8Wf/DArpia7ZGeMVXi7zX8ZbQ2+lSqvCxGRXYTvZOU/y1hu7mBJRSOYsHnw5zYZ6jT+/aSmfunkx5UV+EpkC979Yz5cffIMth9ybUxcXFxcXF5fjmRr2s7y0iEAyx6opxayZVoJfV8kUTDa29tOSyExo/cK6nwVFtQBsCRSjVi9mRmsftz+/lRtfb+CdrzQyMxfGxOT59PMcKRw5zRlPzg3LpvGxGxagKIIN+zv53uM7yRUcLuqejEA5rPkqvPMJmHELIOHgI/Cbm+DV/w/S3fZ+qXZo22CvT0DEG+Hu5Xfz1F1Pcd9t93Ft3bUIBC8feZlPPvZJbvjfG7hv8330Z/vPW9PGHSFAC4C/AiKzoWQFlK+BklUQnmvnNddCgLDzlud7IdUI/TttkbzrNejfBakmyPdBqhml5zWm+dtRel6DTNtEt9BlHMgYJpvbY5iWpMTvYWFZeNJ6TY8luqJQHrTTcbQm3EkfLi4uLi4uTsMVxV1cXCYcJVSG5/K78V72J4hAETITI//GL8i/9l9Yya6Jrt5ZsbZyMV9Z+SeUeiO0p3v50qafsam7fqKrdc5MDfu5rq6MK6eVcP30KZQHvZiWZEtHjO60K4yPOwvvgjd/H1Sv7QHz+F1HH/A5jGK1mBsCN3Bt4FpCSoi0lWav/gpzrlnPFcvTKAL2NBf43uNJpIzwtfdewgeunkPIr9PRl+b7j+/k3x7eyqGO+EQ3xcXFxcXFxWUS8caBHP/yUIo/bI7w7T+kqW+WrK4uZkrAiyUlu7oS7OyKY45htKMz5bIpc+269uxHn3cDFFIEsjnKu/sJxXq4bN2L1CRVTCvH8+nnaSw0nvVnrZ5bwV/eshhdU9jR2Mu9f9hGahJ4zI8bkTq47ltw+29h6lrb23nPA/DQ9fDE3fDAZfC7t8IDl8P+h056GkUoXFV3FT++7cc88yfP8NEVHyXijdCSaOEbr3yDq//7av7uj3/Hrs5d569t5xOhgB6CQBVE5kLpKlsoL14O4dngqwB1wCvYykGuG5KHoHcr9O8AK4sQEqy8LZhne08cot3FkRRMi83tMfKmRcijsawi4pj0DGNBdcgHQFsqh+VO1HZxcXFxcXEUbk7xMcSyLDo6OqioqHBzDTgU14YTjzQLGPUvUDjwoh0eWlHQZ65Fm/smhOY95bGT0X6xfIrv7Pode/uPIBDcOeMq7qi74oLJM25akm2dcbrTORQhWFoeGZo1fTZMRhtOSjq3wrOfhGwfhKrhxvugaNZE1+qs7WdIg9353ezI7cCQBgJBlTWL+k1zae3UAZhZofG2y/z4PJIntjTxzLZmCoYdAvSyueW88/KZlEV849Kuiwm3Dzob137Ox7WhM3HKePVccUI7Y2mLrz4YI5mVCCSqKgj5Bf/vzigRv+BQLE1DXwopIezVWFYeJaCr572efbkEn3rl+wB8d/5b8b/8YzAL9vhHSkBiBYp5fXYpTaVehNC5IlXJzMAClKJpCH/RGXtk1rfF+O5jO0jnDKpLg9xz21KKzuG+fTwZ02th2wZ449+hYzPEmwAJnghIC7xR+MAGO23RKMgUMjxe/zj377if3V27h8qXVy7nriV3cdPsm/ConnOrr9OwDDsn+WCO8lwvGHHsB40CkAiwQ7YLFVS/7ZWuBuy1FrRfK+e/H7qcnFP1QUtKNrXH6Mvk8WoKl1UX49cuLvtJKVl3pJecYbKsIkrFJLyWuveUzsa1n7Nx7ed8XBs6k9GOV11R3MXFZVJipXoo7HwEs2MfAMIfRV90K2rVYseF5DIsk/898CzPtGwG4NIp8/jk/Fvxn0bkdwqWlOzojNORyiGEYEl5mMqgK06OO/FGePpj9toTgev/AyovnehanRMpK8Xm7GYOFg4C4BEe/L0L2baxloIh0FXB9ct8XDHXQ18qx+82HOLV/R0gQVUFb14yjbdeUkvQq09wS1xcXFxcLiYulvGqE9p5sMPgO48mMCyJOagvA2+91MetK/0oiqAnk2dHZ5y8aaGpCovLwuc0qfNs+YdN/0N9vIUPTb+Kq7Y+bOde1nyQT4GioFYtwUx08PpUjcOlPgRw6eEE03tyCG8IpbgWpbjGXoqmnXYCMUBzT5JvPrKdWCpPWcTH529bSsVFkAMYKWHL9+DFL9ivhTj641h0Nyz+MFRdBoo2ytNJtrZv5YEdD/DEgScwLDuvcIm/hHcvfDfvXfxeqsJV49SYSY6Zg871IAsM5SUHW/zmFDntFe+ASD5MKNcCoLj39ZMJKSU7uxK0JbOoiuDSqiIiF+nYq743yaH+NGUBLysroxNdHRcXFxcXl4seVxSfANwZJM7HteHkw2zfTX7nI8h0PwDqlNnoi29DCZcft+9kt9/zrdv47/qnMCyTacEp3LPknVT4iye6WmPC8MGxELBoSmQopNiZMNltOOnI9tm5xTu32A+Mrv5XmHnrhFVnrOzXaXSyMbuRHrMHAL+M0L93CU0HywCYVqrx9sv9VBSpNHYl+L9XDrKnuQ+AgFfjtkvruG7xVHTV/Q2dKW4fdDau/ZyPa0Nn4pTx6rnihHbG0hb//FCcXMFCIEnn7cm0JWFBVbHGzSt9zKnSyRom2zrjxLJ2GPEZRQFmFwfP6+TbR5pe45cNz7O4eDp/XTKb/KZfIc0cQvXiWfUetNpLkdLCSnbzWvpl6kUbwjRYdSjGzO5j8qILgRIuRymqGRLLRagccYLrSGcswzcf2U5XLEMk4OFzb11C7ZTweWr16BiXa2Gq3Q6Znu2zRfFCChAQqbXFcE8Eat8EdTfC1DX2BIVR0J3u5te7fs2DOx+kI9UB2KHXr59xPR9Y+gEun3q54yZ1nzOZNmT/HiyrgKLoiKIF4Ku085GbKTDSA0sKzIwdYv1kKJ5hXuXDPct1244u48LJ+uCBvhQH+1IIIVhREaUscJFFRhhGKm+wvrkXIQRX15TgnWTe8u49pbNx7edsXPs5H9eGzsQVxScA0zSpr69nzpw5qOrkuhlyGR2uDScn0ixgHHiRQv0LQyHVtZlr0Y8Jqe4E++2PNfPtnb+jP58kqPn44JzrKfFGqPQXU+qbvNe30SClZFd3gtZEFgQsLA0zLeI/o3M4wYaTDiNre700PmO/v+SvYMlHJ+Qh0VjaT0rJgcIBtmS3kJH2g19vupoDbywkmwiiCMF1i71ctdCLqsDOpl5+/epBWntSAJRFfdy5eiaXzJpy8T2IPAfcPuhsXPs5H9eGzsQp49VzxSnt3NSQ59frU2RyJj6vyorpHg52GmTy9qOPuVU6b1npoyyisL83SVPMvs8o8XtYUh7Be54m1bWne/n8hh+jCIUfrfk0ASOPTPUggqUo/pFeh1JKNmY3sje/F4DLzFnM7rOw+o5g9TcPTSAejtA8KEXTjnqUF9UgfLb4HUvn+eYj22nuTuLzqHz61iXMqy4a7yaPmnG7Fu5/CF64xxZktYB9z2zmoemPds7rQTQ/TLsa6m6AmmvBc/pJA6Zl8uzBZ3lgxwNsaNkwVD6reBYfWPIB3jb/bQQ9wbFryyTHzKc5cng/NdPnonpOE43AKhwVyoeL5lbu5McI7Xivci1ge5y79/7nzIn6YHMiw+6uBACLpoSZGj6zsf6FyMbWPvqzBeaWhJg+yaJuuPeUzsa1n7Nx7ed8XBs6k9GOV0cXG8rFxcVlAhGqjj7vetRpKyjsegyzfQ/GgXWYzVvtkOrVSxwjes2NTuOfLrmbb+/8Hdt6D/LlTT/Ho2hE9ACfWngH11Uvm+gqnjVCCBaVhVGF4Eg8w+7uBKaU1EUn1+DwgkPzwXXfgY3/Arv/B974BiRbYPWXHJ2bTwjBHM8c6vQ6tuW2sTe3l1yglZqr2sm3zqZxx2z+uEOyo6nAO1b7WVJXysKaEtbvbed3Gw7RHcvyo6d2M6MiwnvWzGJOlRvSzsXFxcXF5WJg1SwPM8oF2/Y0smxBHSVhnUxe8vyOLK/tz7G/rcCBxwtcOtvLm5YEiXp1dncn6M3kea2lj2XlEYp84x8OuDJQQm2onKZkJ5u767m6ain4T3y/IoTgMt9lKCjszu9mo9oA0y5jwayrAZDZ+IBAfmRoLY08ZvdBzO6DR88TKEIpqiFQXMNfX1PND1+R7GlL8a1HtvPJGxeyfEbZuLd7Qpl7J0xda+cWj9QezSVufRU6N8Php6HpWUi2wuGn7EXRoOoKmH4j1LwJAif+jlRF5abZN3HT7Juo76nnFzt+we/2/o6Gvgb+v3X/H/e+ei9vm/82PrDkA8wqmXUeGz1BqF6ylg/UUaQmUHTwRO1lOJYBZnqYYD7oXZ4FaUAhbi/DEeoxnuUBUIMDOc2d8cxgMtKdzrOnOwnAzOKgK4gPUB3y0Z8t0JrMUhf1O+a5lIuLi4uLy8WM6yk+hrgzSJyPa0NnYLbvIb/rUWTKns2vls1EX3IHltBo2reV2nnL0UIlE1zLU9OR6eNDL36DtJFDQSAERDxB/uuqey4Ij/H63hSHY2kA5pSEmDHKWdNuHzxHdv7MFseRdvjHa74J+vl7YDGe9ouZMV7Pvk6L0QKAzPvo3L2AeEsNAsGV8728eakPjybIFkye2nqEJ7ccIV+w8xiumFnGnVfMpHKSzeCfbLh90Nm49nM+rg2diVPGq+eKk9p5sr7UkzB5cnOWPS122HSfLrhusY9F0xV2didIFwyEEMwrCVITGX+B4zeHXuI3h19mZdkcvrDkztPuL6VkS24LO3I7AFjlW8Vi7+Lj97MsZLLTFsj7mux1sgOOefojheBwJsjueJBWWcoVl67i0mULJlzYmdBroZTQsxsan7YjMfU3DNsooGKV7UFedwOEp57yVMl8kof3Psz92+/nUP+hofLV01Zz15K7eNOMN6E6eBLrqRhXG0oTjMyAQD5cNM9w3I98CMWOAHCsZ7nqBzEQHcLM2edQ/aMT8y9ghtsvZVi83taPaUmqQj4WTwlP+DVislAwLV5s6sGSksunFhOdRPnV3XtKZ+Paz9m49nM+rg2diRs+fQKwLIve3l5KSkrcXAMOxbWhc7BDqq+jcOAFMA2kkQMjhwSE7sO76r1otZdOdDVPyq6+Rv7m9fsQQMrIYVkWEskV5Qv4s4W3UReqmOgqnhNSShr60xzss0NZzywOMqsocNrBs9sHx4DDT9nh1M08lC2BG34M/tLz8tHnw37NhWZez75O3IojJeRixbRuX4yZKKEkpPC2ywPMrLAD4fSncvx+42Fe2tOGlKAogmsXVXPbpXVE/Bdv/rtT4fZBZ+Paz/m4NnQmThmvnitOaufp+lJDu8ETmzO099uT50pDCjcs92F6M3Sk7JDNlSEfC8vCaMr4iT9NyU7+9vX/RFdUfrz2s/jU09+fSCnZltvGttw2AJZ7l7PMd/poU7KQxYq1HBXJ+44gc0lAEksXyOQNAALBEMVTZ9lh14tq7Pzkpwt/PcZMqmth/0FbHG98Grp3jtxWunBAIL8Rimad1BNZSslrza9x//b7ee7wc1jSAqAqVMV7F7+Xdy18F6WB83O/fr6YEBtKyxa1R3iWDyxYJzlI2CI4EvL99lqoEJ4Ngal2mPaLUAAetJ8/EuWN9hg5w6LE72FlZRTlIvw+TsX2zjjtySw1ET8Lyk6fauF8Mamuoy5njGs/Z+Paz/m4NnQmriju4uJyUWCle8lv/Q3m4dfsWf2KCqqO0AP43vLl4/LxTRZ6snE+sf47ZIwcftVLfz6JIU0qfMVoisraykW8a8bVlPkmZ/1Hy6H+NPW9dpi16UUB5hQH3Vnl54OOzfDsJyEXg3AN3HgfRKdPdK3GDFOa7MnvYXtuOwVZIG9AonkaffWLkHkfl8zy8JYVfnwe+7fW0pPi/15tYEejHV3C51G5dVUd1y+dSjJboDOWoTzqpyTkm8hmubi4uLg4lItlvHqhtdOyJFsOFXhmW5Zk1hbMZpSrXLJQ0JnLIKUk6NFYXh4h6BmfzHNSSu7Z8GPaM318ZtHbuLx8waiP3Z7bzpbsFgCWepey3Lv8jO6zpZTITD9W3xHMviYa6/dAog0Nk6BPI+zTAft8IliKWmwL5EpxLSJSiVA0rEwMmepGBMsm7bhrTEm2QuOztkje8YYtwg4SmW6HWK+7wZ6YehJbtCZaeXDng/x616/py/YBoCs6N8++mbuW3sXSiqXueGmskdIOuT4kkqeOiubSHBDTs4z0Mhd2yHVFs/OUK17be1z1guIZWA8u+gUpnBdM20M8mTcIeTQurSpCV11h4Fi603k2t/ejqQrX1pa6kwZcXFxcXFwmCFcUnwAsy6KlpYWpU6e6M0gcimtDZ2J2N5B97l7bY1yaCAToXnzX3YNaNnOiq3dSnm/dxg/2/IGsmcenerhr1ps4kurm1c7dAOiKyk3TLuGO2isJ6s4V6xpjafb12MJ4TcTP/NLQSR/0uH1wDIkdhqf/FBJHwBuF638EFSvH9SPPt/0yVobNuc0cyB9ASshkVXr2zyXbMouwT+f2S/0smHY0hN2e5j5+/UoDTV3271FRBLF0HonEp6t8/PqFXLWwatzrPZlx+6Czce3nfFwbOhOnjFfPFSe180z6Uq4gWbc7x/o9OQxLIoAVc1SKpuQwLAtVESyaEqYyOD73479oeI5HmzZwRflC/nLRHWd07M7cTjZlNwGw2LuYld6VZy2oSil5YvNh1m/cyjS1j8vK8ywMJ5GpnuN3VlSE7seKtQASNC+ele9Fn776rD77WBxxLcz0wpHnbIG8ZT1YhaPbgpVQe70tkldcYk/cPoackePJA09y/4772d6xfah80ZRF3LX0Lm6Zcws+zbljQEfYUEqw8pBph/hekMKeByItQNrCtxhNyFQxIJp7jornI9YD5Q4SSw3TZP2hdnKKjldTuKy6GL/mho89EVJK1h3pJWeYLKuIUhGcHKH3HdEHXU6Kaz9n49rP+bg2dCajHa+Oz3TnixQpJalUCgfMM3A5Ca4NnYkIliH0AJIM0pAIaYKRB8uc6Kqdkuuql7G0ZAYdmT4q/MVDucRvqbmUXzQ8z57+Jh5t2sDzrdt42/Q13Dh1JbrivMt2XTSAKgS7exIciWewpGRh2YnzkLl9cAyJToe3/gqe+QR074AnPwTX/DtMv2ncPvJ828+v+FnjX8NcfS6vZ1+ny9+FvngPyWmNxPYv5oF1lSyp9XDrJX5CPoUF04r58rtW8dr+Dn79SgP7W2NYlkTXFSxL8pNnd7Ootvii9hh3+6Czce3nfFwburiMDWfSl7y64IZlPi6d7eGprRl2NBbYXG/ia9RYvshE95ps74gTixrMKQmOuRfgZVPm8WjTBl7v2se2noNMC5YNjQtOx2LvYlRUNmY3sjO3E1OaXOq79KyEcSEEt6yaQcjv439e2McbLbBiZhkfu74OLdl2NOx6/xGsbBIZb7NFRQQUcuRfuQ+j/kWU4mkokSqUcIXtUR4oRogze5joiGuhvwTm3mkv+SQ0r7NDrB95AVLtsOd+e/EWQe2bbQ/y6itBswUzr+bljvl3cMf8O9jRsYMHdjzAY/WPsatrF1/84xf5l5f/hXctfBfvX/J+VEWlsb+RuqI6KkOVE9nqUeMIGwphC9f+Skg22M8PFA0sw57IULYaEGDl7HzjQ+v80fdWHpBgZe3lVCgnEs09I8XzUYnwwxiHPOhSSnZ3J0kYEr9XsKIi6grip0AIQXXIy6H+NC2J7KQRxR3RB11Oims/Z+Paz/m4NrywcZ664uLi4nIMij+KZ9V7yG16EGlZgAJ6gPyWX+G98mMo4cmbn7vUFznuodesSDVfWv5+tvY28MuGF2hOdfHAgT/ydPMbvHvmNVxRvgDlDB8sTTTTIn4UIdjVHaclkcWSsHjKiYVxlzHEXwo3/y+8+Hloeg6e+wxc9rew+O6JrtmYMkWbws3BmzlYOMim7Cb0kjTelRuId0xhV8MSDjwa5dZVfpZN11GE4Mp5lYR9Ol968HUMw8IyJTnLjjLRGcte1KK4i4uLi4vLxUpRUOE9a4JcMdfg8c1ZmnsMXtuiUDcNKisNGmNpYrkCS8sj+MZQIJoZrkIRgqZUJ3/z+n2EdD+fWnA711WfPk84wAKvPTZ4LfMae/J7sLC43Hf5Wd9nX72wiqBX48fP7GbLwW6+kzP4y1sW4y+fC9gPCc0jW8i99lNAGQg9XQBpYSU7kZl+zNajubeFpiNCFSgRWyRXwpUokUqEN3RW9ZuUeEIw8xZ7MXLQ+ortQd70R8j1Q/1v7EULQM01dg7ymmtADwKwpGIJ/1LxL/zNmr/hod0P8Yudv6A10cp9W+7j2699m3g+jiIUIt4I37rpW9y58M6Jbe+FhuqF6AKI7QE5IIhHF9j2AsB/8mOlNUwkzx8jng8T0RnwSrfyYCROfj6hn0AsPzZk+8Cj3EzbQJ1NW0yPLgD/uUe9auhP05bKAbB4SoiIVz/NES7VIR+H+tP0ZPLkDBOvO4nAxcXFxcVl0uKK4i4uLhcEWu2lUDKLpn1bqZk1H3P7b7D6W8m9ct+AMF4+0VU8I4QQrCidzdKSmbzUvoP/O7SOrmyMH+z+A481beD9s9/E4uLpE13NM6I67ENRYEdngrZkFktKlpRH3Jxb443uhzd9H177R9j7C9j4dUi22OL4CUI5OhUhBLM8s6jVa9mR28FusRvP1G58Jc+TOjKD37w+n+2NQW6/1E9RUGFqaZCIXyedM8gbFgXDIouBYVqn/zAXFxcXFxeXC5baKRofvzHIjsYCT2/N0tgs6I3B9DqDglngtZY+lpZHKPF7xuTz+nJJurNxLCkpWCaxXIrv7Podi4rrKPcXjeoc8zzzUFB4NfMq+/L7sLC4wnfFWQvjq2ZN4XPepXzv8Z3sa+nnGw9v5bNvXUok4EEIgTJlFkIPIo2sLQgXMgjVg+eSDyBzSWSiHSvegZXoQBoFZH8zVn/ziM8Q3qAtjocrUSIVKOFKRLgCoY3N9zphaF6ovc5eLNPOPd74DBx+GtIdcOgJe1F0mLrG9iCveRP4Syj2F/OxVR/jIys+wouNL/LTTT/loT0PYVkWilBI5pJ88HcfZGfnTm6ZcwsrKlegq65gOSb4q8BTcuZe10Kxc4+rp5hUK6UdXv9UHudmDrBAFsAonPxcYAvgQodCv31uodrn798Fig/0yFmP81oSGQ72pQCYQo6yMbrOXegEPRpFPp3+bIG2ZI7pRYHTH+Ti4uLi4uIyIbg5xccQKSWxWIxoNOp6PzoU14bOZrj9KGTIvfpTrFg7whfGe+XHUUJlE13FsyZn5nmy+Q1+3/gqWTMPwNKSmbx/1nXUhpwl+HemcmzvtB/8lQW8LCuPoCp2f3P74DgiJez4T3jjG/b7uhvscOpjmKtwMtkvbsZ5I/sGR4wjZHKSVMpD+tBCZHcdNy0PcOkcD+v3tPOTZ3eTyZvkCiYhn05Z2Mdnbl3C7KrohNZ/ophMNnQ5c1z7OR/Xhs7EKePVc8VJ7RyrvpQ3JOv35li3K4dQTabVFIiGIOgTzCsNMT3qP+e+uquvkS9s+AkZI4cQAiklEklNcAorymazqLiOhUV1TAuWnTZaVEO+gfWZ9Ugks/RZXOm/8pwiTB3uTPCtR7eTzBSoKPJzz+3LKA3b945G0+vkN/0KaeYQqhfPqvfYE5WHIS0Lmeq2xfF4G1aiAyvegUz3wImeRAkQgRKUcAU5PUqgfIYtnAfLEE7P5ygt6N55VCCPHz66TSh27vG6G+wlZHv7bmjewC0P3AJAzsyRM3JIJNXhavy6n4AeYPXU1aytXcva2rXURmsnzX+H+392hkhpe6mf0uM8Z3uFg702B8O1C4Y6lOobEM21YZ7mvpFe56rP9jw/5trQnc6zpSOGlJIZRQGmKIZrvzOgOZ5hd3eCkEfjiqnFE/69uX3Q2bj2czau/ZyPa0NnMtrxqiuKu7i4XLDIXMoWxuMdCF8E75qPowRLJ7pa50Q8n+Lhxld4pmUzprQQCK6qXMydM66mbJS5BycD3ek82zpjmJakxO9hRUV0SBh3GWcOPgbr/sb2JihfAdf/EHzFE12rcaPFaOH1zOv0GjGSGUk2FiHdsIyp3grefrmfVC7PwfYsVcUav91QT0N7HI+u8hc3L2JRTclEV9/FxcXFxQFcLOPVi6WdJyKetnh2e5ath3JUVhsUF5n4PYKaIh9LK8Lo5yDY9mTjfGL9d0gVMqiKSrqQRQLlviLUYecN634WFtUNieRVgZITPqQ7VDjEy+mXsbCYoc9grX/tOQnj7X1p7n1kG72JHEUhL5+/bSlTS+yw31Ymhkz1IIKlKP7RTyiURh6Z6BgQydsHPMvbkbnUiQ9QVJRwue1NHqkYylmOL+LMB5VSQn+DnYO88Vno2TVye9liqLuR9rLlXP5/7yaZSxDV/fTnU2iqh3cueCdbO7bSm+kdcdi0yDTW1tgC+eppqwl7w+exUS7nBcu0xfFCAvp32PnPUQATEAMh30f5mFfxDAjkXnJS50DcIGNpRAIh5pSVIBSPnXfdZVQUTIsXm3qwpOTyqcVE3bDzLi4uLi4u5xVXFJ8ALMvi8OHDTJ8+HcXps5gvUlwbOpsT2U/mkuRe+SlWohPhj9rCeMD5QldHpo9fHXyR1zr3AKArKjdNu4Q7aq8kqDsjH3Jvxp6JblqSYp/OisooCrh98HzQ/jo8++eQj0OkDm78qb0+RybrNdSSFnvze9mW20Y8lyOVk+Q6pxGvX0S+AFowiZIPc8fyYl7Zv49dTX2oquATNy5k1cwpE13988pktaHL6HDt53xcGzoTp4xXzxUntXO8+lJLr8GTmzP0F3JUVhmoCkR8Gmtqo0R9Zy+APN+6jR/s+QNZM49P9fBnC97K9FAFu/oa2d3fyL5YMzlzZEjlIk9oSCBfWFxLua9oSCBuLDSyLr0OC4s6vY6r/VefkzDem8zyzUe209abJuDT+OytS5hVOfZRdWQuiRVvx4y303dkLxElC8lO5EnCSQvdN5CnvML2KB94LfST54C2hfxuRLDsjIT8cSXRYnuQNz4DHZsYLmo+lMtzT3cjaSkJCIV7F7+TO6/4KyzVx95EO+s7tvNS6xtsat+OIY2h41ShsqxyGVfVXsWamjUsLl+Meh5TJ7n/Z+eBk+UUt4yj3uVm9sSvh/3GLCnJGhZSShRF4FNVQFIomOi+EGLQ01z12uHZh14Py2/uAsD2zjjtySw1ET8LyiZ2UorbB52Naz9n49rP+bg2dCaTRhQ3TZOvfOUr3H///bS3t1NdXc3dd9/Nl770pVHP6HXK4Ns0Terr65kzZw6qeuHkab2YcG3obE5mP5lNkHvlJ1jJbkSgyA6lHrgwPGMPxFv5ZcPz7OlvAiCk+Xjb9DXcMHUlugMGqP3ZApvbYxiWRdSrs6w8xOGGBrcPng/6G+DpP4Vkq+0pfv2PoXzZOZ1ysl9Ds1aWLbkt7M3uJ5GxyJkmil4ABNLQSOy5hL9cvZhfr9/HpoYuhIAPv2k+a+ZXTnTVzxuT3YYup8a1n/NxbehMnDJePVec1M7x7EtSSvY0G/xxV4pIWQ5dl6hCMLckxIppwbM+b082Tkemjwp/MaXHRIAyLJOGRBu7+xrZ1ddIfbyZgmWO2KfMFxkQyOtYVFRHWo3xYvpFTExqtBquCVyDKs7+u0hmC3z70R0c6rCj6nzqLYuoLgnQGctQHvVTEhq7ibnD7acoApnuQ8bbBzzL25DxDqxUjx2S/ASIQNFRoXwgZ7kITcFs3nLakO8TTrobjjxnh1hveQn6D9JumjShUItFpapCpPY4MTJtWWw0DF4uFHg5l+ZQIW97+QoFEER1H1cW1bG2dBZrpsynKlQFesD2LB6x9oMePPpe9Z1Vfmoz3sKRXS9Ts2gtamTqGH05Lsdh5s48D/pQjvMshpHlUE8f0swSUk0q/aBYOaSZJ1/I49H1Uz+7FeqQt/lJhfNjJ+ScTZ0dQnc6z+b2fjRV4draUpQJ9LR37ymdjWs/Z+Paz/m4NnQmox2vjrti8q//+q/88Ic/5Oc//zmLFi3ijTfe4MMf/jDRaJRPf/rT4/3xLi4uLgM5xT9Gdv1PkKkecq/eh/fKj6H4iya6aufM7Eg1X1r+frb0HOCXB1+gJdXN/Qf+yFPNb/DumddwRfmCc/IKGW+KfDqXVEXZ1B4jliuwuSNOFDdE23mhaBa89f/gmY/bIRuf/CBccy/UXT/RNRs3fIqPK/xXMNczl2fM9bSLw4BEWgqoBpFFG1hXX82H37QAn0dl/Z52/uuPe0nnDG5YNm2iq+/i4uLi4uIyCRBCsLBGZ251lFf2Z6nvT+APWOzpSbC/I8ub5kSZEjnzRy2lvshxYvggmqIyLzqNedFpvH36GvKmQX28xRbJ+xs5EG+lOxtnXfsO1rXvAKDSX8y88imIaD+Nsonn089zbeBaNHF2j4FCPp0v3LGM/3hyJ7ua+vj6b7dQMC0k4NMVPn79Qq5aWHVW5z4VQiiIYCkES1GrFg2VS7OATHZhDYjlMt5m5yvPxpHpfsx0P2bHvqP7SwmZXlsQ1P1II0t+069QpsydPB7jAIEymPdue2l6Dn7/dip1qEQM5JwuQGCKHfraSEMhDdIkoChc6/FwrccDwSAtRoGX81nW5zK8kssQM9I8kenlibYtAMzSdNZ6/az1+LnU48N/Ki8ozWeL5Jp/QDgPgu4fKAsMiOj+o+J69w6Uvb+gppBF2RqCK74M898P3sjxAqnLuTEoPp8JQoDqwVJ0tnZb9OaL8agKl1cWo2j2g3/LKHCkYR8z6qpRZWHAwzw7Mse5NGwvdSMFnCT1Adi/1UHR3MpDthuQtqAengWBqXYO9AsgVHupX8erqeQMk650norghSX6u7i4uLi4XAiMuyj+yiuvcMcdd3DrrbcCMH36dH75y1+ycePGkx6Ty+XI5XJD7+PxOGDP0DBNeza0EAJFUbAsO8TPICcrVxQFIcRJywfPO7wc7FAJoylXVRUpJZZlHVfHwfLT1XEyt+nYulyobTJNc8iGF0qbLkQ7naxNp7SfHkS//CMUXrsPmeol98pP0Vd/FOGLTuo2jdZOK8vmsKR4BuvadvCbxpfpysb4we4/8PiRjbx3xrUsLKqdtG2KeHVWVUTY1BEjkTdI4KeuYBBSVcf89hzbn/ylWDf9HPHi5xEt6+CPf4FY/f+w5n/grNo02AcH95msdiqiiBWelTyebQZhIhSQ0kL15jhU9hg/2F3DzOoarlIrWLeznV++XE86X+D2S6YjpZyUbTqb8hP99kzTHPH6QmjT8PILvU2D9hvsjxdCm05V9wuxTSezn5PbdCHa6djyY49zuTjQVMHVC/yszHh4dl+CtMhiagUe2d1LpTfIdYsC+D3jI/J4VI1FxXZ+8XcBOTPPvljzQLj1Jg4l2mjP9NHe2EcooDC9xkOPGmd/vJkV2mUsLppB+BQhxk+GT1f59C1L+P4TO3l6WzOWJQl4NBIFkx8+vYuFNcWUhs9PKieh6ohoNUq0ekS5zKexEu22N/lgrvJEBzKbANMABOTT9r5mHrNlO2LWGsRkFGtLF4K3CApJ8EQhHwNfKdzxMAQHIhkNev0W0kdFciPN1EKa9xhp3lNIY+aTbO/Zz8udO3m5ez/b4y00SIuGvMnPcwk8IsEl/ihrfEGu8vqZi0SYmaOe+EbWXkaDZUC8CaSFQNh1eu4z8Ma9oOjgjdpt8hWPXA8uviLwFo8sV93czGONlJLd3Ql6M3lURbCyMopfG+YJJxQMqYEehZN5yI0I054DKzvs9YCIjrSFcCsPhdjRMrBF9dgeSB6yBXLFM5Dn3GP/Vgbfj1h0e99JKqALIagOeTnUn6YlkXVFcRcXFxcXl0nIuIdP/+d//md+8pOf8PTTTzN37ly2bdvGjTfeyDe/+U0+8IEPnPCYr3zlK3z1q189rvz1118nFAoBEI1Gqaqqoq2tjVgsNrRPWVkZZWVlHDlyhFTq6EzFyspKioqKOHjwIPl8fqh82rRphEIh9u/fP+Jhx4wZM9A0jfr6+hF1mDNnDoZhcOjQoaEyRVGYO3cuiUSCQ4cOoQ+EF/J4PMycOZP+/n7a29uH9g8Gg9TU1NDd3U13d/dQ+WRsUzKZpLm5eaj8Qm9TW1sbhUIBXdcJhUIXRJsuRDudrE1dXV1D9isqKjpxm0I6wb0PUYh3kdfD9E6/DUsPTNo2nY2d8pbBK4n9vJo+QAGTQqHAHF8lNxYtpdJTNGnb1BWL00IAQ0LQo3NF3RS621od8dtzfH+SJmX7v0+042l0TSM14z0cqXzvkCfHaNskpaRQKFBTU0NxcfGktpMn6uFp/XnSZh5pKih6HkUBK+vHsuw8etJSMPsjtBwUpLoC3LBwFpdWCQqFo3ktJ1ObxuK3J6WktLSUKVOmUF9ff0G0CS48O52sTVJKKioqiEQiHDhw4IJoE1x4djpVm2bPnk08Hqejo2MoXKnT23Qh2unYNiWTSS699FJHhBU/F5wUPl1KSSqVIhgMjjpt27lS35Vha3ucgiUxTehu93DF7CCXzfagKOdXwEkbWfb2H2FXfyO7+5rosbqYXuNBEZBKWxw+UmBasJyFxbUsKqpjflENAW30YvauI3188YHXMAwLIYQ9oQeomxJm+Ywy5lZFmVMVZWpp8KzCB4+1/aSUWH1NZJ//JhSyA0KyYQtrgVKUUCnatJWoNStRgqXn/Hljyv6H4IV7bHFZC8C198LcO8/plPFcnFePvMrLTS/z8pGXaU20jtheFihjbc0a1k5dzZVVyyjVfCME9+PWRgYKKft9fwPs/z+komELoibCKkCo2vYmPxu0wIlF9OME9WFiuuY/c+E01Q7xRojUHZ10cIHS0JeioS+FELC8IsqUwEjxdkz64FCY9gGBPN9nC+ASEAPbkbYH+ZmkdhDK8UL5seK5OkxAP8+k8gbrm3sRQnB1TQlebWLC7k7E/6DL2OHaz9m49nM+rg2dyaTJKW5ZFn/3d3/Hv/3bv6GqKqZp8k//9E988YtfPOkxJ/IUr6mpobe3d6gxk8UrYJALydPBbZPbpgu9TWRj5Nb/GJnpR4SmoK/+KKo/6ug2nag8aWR5uOkVnmnZhDVQflXFYu6ccTVT/NFJ2aZMwWRzZ5ysYeHXVVaUR/BrR702nP7bm9T9SUrEjp+gbPkOEpB1b0Gu/TpoXue26RR1FELQYDTwYmodBWmgC421vjVElSib+5poyDZRUJMDX43EtCyy/V4qqOTOBSsp1oomZZsuRDu5bXLb5LbJbdNkb1M8HqekpMQRYvG54CRRfKLIGCbrG/vpSRcwLejq1CDn4eaVfuZWT5y3a6KQ4fX+HeySm8nJPLFUgUNH8gz+tBUhmB6qHPI+nxedhlf1nPR8vcksn/nP9SSzBVRFIZM3kEB5xIeqHL1393s1ZldGmFtdxJyqKNPLw+jqxHlkG02v077pQTqsAuUoTCmbhUz3IAtHnz8ppdPRalaiVi1B6OfH6/20pNpt7+tI7ZiLtVJKDvUfsgXyppfZ2LKRjJEZsc+iKYtYU7OGq+quYkXlCvRTeW6n2uGBy0d6t+sheO/LtlCd64NcP2T77XWu75jXA9tz/ZCLnTRv/GlR9JN7pJ9ITG9eB+u/PKaTDyYrLYkMu7oSACwsCzMtcpaTFc4UMwdd68EyQdHsiSmKCmWrbaF70KPcHFhbhaNlg8uZ/h6Ge6APF9HVE3mgn+LadIZ50De29tGfLTC3JMT0osCZ1dnFxcXFxcXlrJg0oviDDz7IX/3VX/GNb3yDRYsWsXXrVj772c/yzW9+kw996EOjOodTBt+madLQ0MCsWbNQTxZeyGVS49rQ2ZyJ/axUD7lXforMxFDCFXiv/FOEN3Seanp+aU/38utDL/Ja514AdEXj5mmXclvtaoKT5UHPAKZpsvfAQXr9xWQMC6+mcklllKBn3LN9uAxy4A/w8hfthxQVq+D6H9phDkeBE6+hKStFwkoQVsIEleBQuWVZ7Orq5bWOw/RpzSjh3oEHxxa6BnXhUuo8tdRoNUxRp1wwM0edaEOXo7j2cz6uDZ2JU8ar54qT2jmRfcmSkj3dCRp6M6RykkRCoaVZZ8YUnZtX+qkomri+3WV08Wz6WbJWDtXwkesNsae3mfZM34j9VKEwK1LNoqI6FhbXMScyFY868n78pd1t/OTZ3WQLFj5d4cPXzae6JEh9Wz/7W2McaI+TL4ycaKKpCjMrwsypijKnuojZlRH8J7jPHy/7Pd+6jR/sfpiskcOnefnUwrdxbfkCzPbdmM2bMbvqhyI7C1VHqVqIVrMKpXQWQpk4Mf98kjfzbG7bzEuNL7H+yHr2dO8Zsd2v+Vk9bTVra9eytnYtddG64++D9z+EfOEezFwC1RtGnK3ALC3IJ0eK5ceJ6YPlw8R0M3+Kk56AYSHfUTQ7T7aiw7SrbcF8MGe6FjiaN13zD+RRPyafuh4Y+X5wP2Wc+v0Zerf3ZPJsbo8hpWRGUYA5JSd+BjJu19BMmx0yXZq2YB1dAP6q0R9vmccL5cMFdHNYGWf4uFtoJw7hbqQg3WL/PoQKkbkQqD6lF3pzPMPu7gQhj8YVU4snZKzo3lM6G9d+zsa1n/NxbehMJo0oXlNTw9/+7d/yqU99aqjsH//xH7n//vvZu3fvqM7hlMG3aZrU19czZ84ct7M4FNeGzuZM7Wclu8m98hNkNoESqcR75ccQngt3Fu+BeAu/aHievf1HAAhpPt4+fS3XT12BrkwO0XnQhrUzZ7GlM0Eqb+BRFVZVFRF2hfHzR9sG+OOnIJ+A6Ay48T4ITzvtYRfqNbSj32RdfT87Y4egqJVAaQ+KahHxq/g8Ar/wU6PXUKPVUKlVognn/lYvVBteLLj2cz6uDZ2JU8ar54qT2jkZ+lJrIsuurgTJnEU8BUeadHJZhUtne3jzUh9B38SIrD1mD8+kniEnc5SqpdwQvIFkLsfu/iZ29R1md38j3dn4iGN0RWVOZBqLim2RfFa4Ck1RaejpYl9PB/NKK5hVOmXEMaYlOdKdpL4txv62fupbYyQyhRH7CAE1ZSHmVEWHvMmjAQ+GYbCnfh/TZ07HElCwDAzLpGCZFCyDgjQxLIP8seWWiSEH1sPKCpZBopDm0SMbMCwTj6JhWCY+zcMXl72PGeFKSrwh9HwGs3kL5pFNWMmjqRqEP4o2bQVqzSqUUNn4GWcS0p3u5pUjrwyJ5D2ZnhHbp4anDgnkV0y7grA3DEBL2xY2bnucy5bdwtSqFeevwlLa+c+P8zrvPyqiDxfTc/2QaLbDviPsH+VgWO9zCfl+LKp3mGAePF5U106x7VjBfXDboSdg3d+M2rs9kTfY2NqHaUkqQz6WTAmfVKwd12voGXpdnxVS2jnLj/U2N0/kgV7gpAK6tEbmQQdAgOob8ELXbDFdaAOv7TJTqtT35yhIhZklEYJe7zH7aeOeG30y/A+6nD2u/ZyNaz/n49rQmYx2vDruT23T6fRQeLlBVFU9LgSdi4uLy/lGCZXhvfJj5Nb/BCveTu7V/7Q9xvXzFD7sPDM7MpUvL/8Am3sO8GDD87Ske/jfA8/yZPMbvHvm1VxRvgDlVCHDziNeVeHSqiI2tfeTyBm80dbPqsooEe/EhZ28qKi6HG79JTz9MYgdgkfeDTf+BMoWT3TNJoSKIpV3XVrKTeliHt44k/WbE/jLekhWdxKt6ibgT5O29rM/vx9NaEzVplKr1TJVn4pXjNODHhcXFxcXF5dJTXXYR9ijsa0zhk83CfoLNDapbDwg2dZY4NpFXq6Y50VTz68HYalayk3Bm3g69TQ9Zg9PpZ7ixsCNXFW5mKsqFyOlpDPbz+6+Jnb3N7Krr5H+fJLd/Y3s7m+EQ+BVdSJ6gAPxVgzLRDukcl3VMmZFqkcI0YY0KXgMzBqDmmqTeDZHbzpDfzpLPJsjZxQ4IizWd0hkp4XcaqFqEk0TKFj4WzxoYxRuPWcWyBp5BIKsLCClJJ9Pc++Oh/AOhAMPaj5KvGFKSqdRXDKVolQP0VgbxZk+iuufo7j+eYJFNei1l6BOXXrBjhuHUxYo4/Z5t3P7vNuxpMW+7n1Ducg3tW6iJdHCr3b9il/t+hWqUFlasZSQHuKxA4+RyWcI7/kR995wL+9e/O7zU2EhQPfbS6h6dMcMhnzPJ8ATglzcPv6679lrI3NM/vThOdUzJ86vbmTsZTDct5mzF/pOWZVRM+jdjmUL4oUMPP9ZmLr2hB7jWcNkc3sM05IU+z0sPoUgPu6o3vETwwcRAoRue3tzGseHIQH9GE9zqwCFOGTasZOggy2OD1usAlA47pQqMEOzMCwLLdkOmZNcx4Q6UigfIbKrx4voima3a+j1qUO++5Ss/btTL1znDxcXFxcXlzNl3D3F7777bp599ll+/OMfs2jRIrZs2cLHP/5xPvKRj/Cv//qvozqHU2akuzNInI9rQ2dztvazEh12KPVcCqVoKt4rPnrBP+AwpcW6tu3836GX6M/beZNnhCt5/6w3sai4buLqdYwNC6bF5vYYsVwBTVFYWRmlyOcK4+eNdCc8/XHo3WN7JFz7Lai97qS7XyzX0N1H+vne40coGFE0TaNiWgJ/eTvFU9vx+LMMzgVUUKjQKqjRaqjRawgpkz9Fw8ViwwsV137Ox7WhM3HKePVccVI7J1NfKpgWO7sTdKVyFEzo6VWoP6gipaAkpHDjch+LanTiGUlPwqI0rBANjP9E1T6zj2dSz5CRGYqUIm4M3ohfOX4MJKWkLdPL7j5bIN/d30h/LkVHtg9LShQEFhJFCCp8xSNyip8Oy5LkDYu8aVIwLArmUecJKSVCCBQh8Gk6AV0n6PUQ9HjxKCqaoqErKpqiogv7ta5oaIqKZ2CtD+yjC42cWeCXB58nbxnoikrOLKAIhQVFtSQKaXLm8cLWQE3AyCONLBgFvEARUCIUSoJTKCuZQWnpTEp9EYq9YUq9YcJ64IJJrXMqMoUMG1o2sL5pPS81vcSh/kMYlkFTrAnLshAIJBJFUZhVPIuIN0JADxDQA/h1v/1aO/o+qAfx6378mp+gJ4hf8x+3bej4gW0e1TM23/X+h2j/42dpzCep84SofPO3zz2nuJS2IDkqIf0UovuQyD7smHwCkq0c591etdqud+2b7AnNQsGwLDa29pPMGwQ9GpdVFaGfZrLJZLqGTigny4Neerm9tgxbUJeGvY80hsrS+RxdyTS6YlEV0BBD+xmccWj3k6KM8FAfEtTNLDLXjbRMhKIhglPBWzYgwqvDPNXVgdD+yrh6rbucOW4fdDau/ZyPa0NnMmnCpycSCb785S/zu9/9js7OTqqrq3nf+97H3//93+PxeEZ1DqcMvu2Zxnk8njG6KXc577g2dDbnYj8r3m4L4/k0SnEN3tUfQUyyfNvjQc7M8/iR13mk6TWyA7nXlpfO4r0zr6U2VH7e63MiGxqWLYz3ZwuoimBFRZQS/+j+P1zGgEIKnvs0tLxsz0Rf/few4H0n3PViuoYe6U5y7x+2k8h48elTiATCqAro4X5qZncSqmgno/aPOKZELaFWq6VGr6FYmZjccqfjYrLhhYhrP+fj2tCZTMR49Stf+Qpf/epXR5TNmzfvpCnKfvazn/HhD394RJnX6yWbzY76M52OyHGEAAEAAElEQVQyLofJ15eklByOZTjQl7R1MkNh1z6N/oS9PexT6IqbmBZ4NMG71wRYNWv873djZoyn00+TttJElSg3BG8gqARPeYwlLV5o286/bPsVAlCELYqblsWbp66gyl9yVIweEqdVNKGNELP1IVFbs0VsoVIwJM1daRo7UzS0xmnpyWKaIDhqQ59HZXZllDnVUeZWRZleHsajje5h5fOt2/jBnj+QNfP4VA+fWnA711UvQ0pJxszRk03Ql0/Qmzu69GTj9OWT9GbjJAtppJGDQtYWwAYRCuhehOaFgXYVe8OUeMOUeiMUe0O2B7o3MlAWJuIJoo4ySldPNk57po9KfzGlvsnb91oTrfz3lv/mH1/6Ryxp2Y600kIiqQ5X4x+HieeKUEaI5EOC+4DYfiJR/dh9g3qQdY3r+Mb6fyFTSBPQg3ztzf/MOxe8E03RhpbJcC0ZItEMv7yS9myMRktQZ6apVCSEa21xFMA/BVlzHfvDq2kKLEX3+LmsupiAfvr+MtmuoRPKWeZBl1Ky7kgvOcNkWUWUiuAw73hpHhXRh4R08xiR/Zhtw8ukeYoPtkO+y2HCuxgK+X6ya46wtw0XywcF85MJ6UNlytiI7OcjrL6DcPugs3Ht53xcGzqTSSOKjwVOGXxLKbEsC0VR3M7iUFwbOptztZ8VayP36k+R+QxKSa0tjGsXx814LJ/id4fX82zrFixpz+q/umoJd06/6rw+eDmZDU1LsqUjRm8mjyIEyyuilAVcYfy8YRmw/u+h/jf2+6Ufh1WfO25QfbFdQzv60/z7H7bRm8gRCRQxt3IGLX1Hb6umT8syc14X+WALnWbniAcTISVEjVZDrV5LuVo+aVIXXGw2vNBw7ed8XBs6k4kSxR966CGeffbZoTJN0ygrO3G+45/97Gd85jOfYd++fUNlQggqKipG/ZlOGZfD5O1LvZk82zvj5E0LRQiMpI/1O0w6YxZSgq6CJUHX4O2r/Uwr1SgJ2Z7j4xVmPW7FeSb1DEkrSVgJc2PwxtNGt+nJxvnE+u+QMXIENR8pI4tf8/LjNZ8Zk7HDoP0MS3K4K8n+1hgH2mIcaI+RzY8Ug1RVMKM8MpCXPMqsygjBU6Rc6snG6cj0UXEWAnPOzNOXS9KTjdPb10hXxx56+pvoNfP0AX1I4qpmjyE13ylFIUUoFHmCw0TzCKUDQvrgUuwN81L7zhMK+ZOV9mQ7l993Ocl8kqgnSiwXI+AJ8Mj7HiHkCZEpZEgX0kNLxjj6/pTbjJHb8gMTuseC4d7tilCwpH3tqI3WoilHs06qQh0SyFVFRVf0EaK5rupD++jqsG1CO26/4e9HbFN0VEUd8f5k+76y9T5+vOe35KTELwRfXvYnvGfWDQTbXsHTsh7MDHnT7keW6sNTezXeGTdAzbXgKz7ldzJZr6ETxlkKtvW9SQ71pykLeFlZGR27+gyGfD+RkF6IQ/IQUg5GEbAQSPCW2KK1NEcu48IJRPYRAvsxIrsRh3SrLegLFUIzwFc+cA7FLmPw9ST6PY6jkO/2QWfj2s/5uDZ0Jq4oPgG4YRWcj2tDZzMW9rNiLeReuQ9ZyKKU1OFd/eGLRhgHaE/38quDL7Khy/Yy0hWNm2su5fba1QS08fecP5UNTUuyrTNOdzqHIgRLyyOUBy8e20w4UsLW/4At37Xfz7wNrvpnUI9OTrgYr6G9ySz3/mE77X1pwn6dP7lmCQfbNXY05bEG7rDKoyqXL4SiqnZarWZajVYMaQydwyM8QyHWq7VqdDFxKQIuRhteSLj2cz6uDZ3JRIniDz/8MFu3bh3V/j/72c/47Gc/S39//1l/plPG5TC5+1LWMNneGac/a4fr9lpefvVHA9MSQ1GQJVAcFOia/RBOANGAQnFIoSR0dD34OuAV5/TALmkleSr1FEkrSUgJcWPwRsJK+JTHnMzreiw4mf1MS9LcY4vk9W32Ek8fI44KmFYaskXyAaG8aNiYoS2Wpi2WoSrqpyp67nl2pWVgdezDOLIJs2MfhjSJAb2KIFY0jVhRNf2eEL35JL35BL3ZBH35pO1JfbrvwbLoyvYjkeiKhmFZeFSNt9VdSZkvgl/14te8BDTvwGvPiDKfqk/IxMuHdj/EPU/dQyKbIOwLc+9N93LnwnMMRX4MpmUOCeWZQoZUITVCVD9WRD/VtvZkO1vat6CggBh4EC6tcfNuHwtGCvnCTmUwTMhXhYLHtPAZGaJGgiIMQopCUCiEFJVgaCrBsgWEypcTjNQR9AQJ6kFCnhBBTxCf4qOjuYOl85cS8obGVBBoT7bT2N9IXVEdlaHj859fSKTyBuubexFCcHVNCd5RRrU4JwZCvkvLIG+CRwWhaDBlzfHCrZTHi+RDXuknKjeHea6f4JhRXNeOY8CzfWRI+VN5toujYjkDgvmI98pIMf1E5ajH7Dd8n5MdcwyZNmT/HqQ0EUJFFI0ugsBomcz3MS6nx7Wf83Ft6ExGO17VTrrFxcXF5SJEido5xXOv/idWbyO5DT/He/ndCO3i8EquDJTwmcVvpz7Wwi8bnmdv7Ah/aHyV51q38o7pa7i+eiWxfGpCwvepimB5RYQdnXE6Ujm2dcZZUh6mMnjhh7mfFAgBKz4FoWp4+f/BwUcg3QFv/gF4J/eD8VOSaod4I0TqIHjmD2VKQj7+5u3L+fYj22nsSvJfz23js29dyg3LI7y6L8frB/J0xkweeRUi/gqumFfDHbNU+pR2mowmmgvNZGWWhkIDDYUGVFSqtCpq9VqmadNOmNvTxcXFxcUFoL6+nurqanw+H1dccQVf//rXqa2tPen+yWSSuro6LMti5cqV/PM//zOLFi066f65XI5cLjf0Ph6PA/ZDItO0vcuEECiKgmVZDJ9vf7LyQW+Lk5UPnnd4OYBlWaMqV1UVKSWmaWJZFqZpDtVl0OPjdHUc7zZ5VYVVlVH29yRpSmRJk2P5YsnBwypeD8STAtMUzK3WSGUlfSlJwZD0pyz6UxaHOgYrxJCG4NWheEAgLw2rFAUExUFBcUihKCDQNOWUbQoQ4AbfDTybeZaEleCp5FO2x7gInbRNV1csZlFRLZ2ZfioDJZT6IqO236CdTmSPk9nPsiyQkmklAaaVBHjzkmqEELT3p9nf2s+B9jj1bTE6Y1mau5Mc6U7y3I5mAKZE/MytjqJ6NbSQF1VV2N2bolxXuWFh9Tn+9gSUz8dbtQgrm8Bo3orevJnSeBv0HoHeZoQ3iFK9DGXGm1AiVVhIkkaG7kyc3lx8KFR7Xz5JX37AEz2XICcLmANRvAqWaYd5N3I807IZrzq6SZQ+1UNgQDAPaD58qge/6sGveYfWQd2HX/XiU/QhYT2geQl5/PhUDzrqkCg6mv709nlvZ27JIp7bu543L1jLkor549Kf/Kod+nx0drI50W+vI9XBFf91xQjv9pAnxIt3v0h5qJy8kadgFjAsY2ixsCiYBfJmfqjMlCamNCmYhaH9C1YBS1qY0iT//7P33vFxZNed7/dW6ozuRg4EQYAEM4ec4eSkGWXLSla2d23vOul5tY7S2rve5LTrt17Jtmxr31rOScmjNMqjGU3gcEaTAzNBAkTOQKNzV7rvj2o0ABIkQRIk0GR9+SkWurrCvXWqquve3z3n2OaSfdjSxnHL67vWkv3Yrn3OMRevP54dZyg9hK7pCCG87crfaUKj5DpkHBeEwWSgHk1aCznJnRzk0zBxDI5+BRQd9DBokfKAZy8XvGVa6Af0Soj6sB72RPNF4nlEjxDWw0T0CBEjQiwQI2pECarBJevNL//Oye/wm4/9Jnk7T1gL88m3fpIP7PzAJT8jrvWzfDw3Tu9MLx3xBSF/Jc+IoCqIBzRSJZvRbImOeOjq1wkNYtuYGHuW3tQQXYkNNDXf7S0v3z+V+8l18X5MvFzkinp5v7mV5VIicFGE9AYMOVZFNBe4KLi4rr1EWBdOHuGUGM3P0p+bpCPSQHM4CagIoSCle47YLnCQrg1nLRXlQS1nLy8XbkXLhRB4u5CLFyKEivSShnhbWXMgXRwEChZi5nUITYCieyK5UHARVET8ssiuKCquxNuXWFgmxKLlKDiuRME7V44s26n8HL7cdyPXdSve7UILo+ihdfNudME6OSVcK7fEI3+9PSPOrpNj5QkoBaRdxGFpn+Xlvhtd7TrNZfJks3mi0TCJmsiqvpdftTpJC9fKIZVg5dpYrWtPSll5J13VOjklhFtE0SO4Ql9X7afz1skqVKJiCC24/p4Ri5afvd358D3FVxF/BEn149uwullN+zmzA5jP/jXSNlEbNmPc/tOIFXY8XC9IKXl5+hRfOP04w/lpAHRFZaaURSKvSvi+ldhQSsnhyQyj2SJCwK76GlpjvjB+TRk+CD/4JS/feGILvPUvIdpafc/Qkw/BEx/3Ooi0MDzwKdh6eV4s+ZLNn377ED0jc+iawsd+ZDd7NtZSMCUvnCrx7HGTTNF7SQtogtu6De7aFiAWgklnkkF7kAFrgIybqexTIGhQG2jX22nX2omrXsi9nJsj42aIKbGL5v28VKrOhj5L8O1X/fg2rE7Wor36ne98h2w2y7Zt2xgdHeV3fud3GB4e5vDhw8Ri53r4Pvvss/T09HDTTTcxNzfHJz/5SZ566imOHDnChg0blj3GcnnLAV544QWiUS+8djwep6WlhdHRUebm5irr1NfXU19fz+DgILlcrrK8ubmZRCJBb28vprng4bthwwai0SgnT55c0tnR2dmJpmn09PQsKUN3dze2bdPX11dZpigKW7duJZvNMjAwwMzMDLW1tQSDQbq6ukilUoyNjVXWj0QitLe3MzU1xdTUVGX5taxTBo1JghRdgSsUyrovRsliU6SEJqC7q4uZVI6eMxNkigqZkkre0nG1GiZTJqncgjCgCAVN15YOXABqwgrNtUEM8hgiTyzgEgs6bGpNsqGljqGhIXK5HCVR4nD8MGbAJKJG2DGzA72oX1KdLsVOQ0NDleWGYVTsNDIyUrFfLBY7r52am5sZGh1jNp3GQcFBQCBC2hIMTc2RKTk4CAxDIxBQiUWCZdFEgJC4riQ1W0ABzFIRHAdFSjRcGmuTxIIGs1PjhDSFoK4Q0AW7tnWjKzDQf+aCddKK00Qzp4nnB3AKaWzbs4cVrMNt2k3jTW9kOlM877WXSqUYM+f41Mg3EZpLPBxkNlfAseGN8V0gBFrIwNUEk6lp8naJkmtRdC0cVSIFS65HAF33BNSzlxuGgZQSy7LOWe66Lo7tEFB0gopGSA1QV5NEcwRO0SKo6AQVnVgwQmtDE3be5PBkLwfSx7Fdh5AW4Ff2vI/tonFdPSPOvvZeLr7Mr33318iVcoS0EL+x7zd43/b3rfkz4nx1mixM8pFHP0LOzlEbriVVTBFSQnzuTZ9D0Wvod8AWFmGRw0mPkbc9D3lHdQgaRWYGXyA7fZRSYZSc65BHkgcyUiGtRsgqBmlHouk6qqZiW/aS6Aaa6gmolmUt6czWNA1FUZa99hzXoX+uHyklCiouDpqqcVPjTSiOgiKUSvj5mmgNuGAWTe+5pmhoqkayJolt2ZSKJVShogqVoBEkEU9gFs0lyyOhCMl4klw2h1kyK8vjNXESsQSp2RSO5Xj7Fxp1tXXURGuYnJjEtV1UxVu/p9TD3xz+RxxUcE0+sun93NF0B62traiqyuDg4JK6btiwAcdxGB0dBSAjVVLCoCYUYHNEY3JycuF86Rqtra1kMhlmZmYqy4PBIE1NTaRSqSXXWCQaob6unqnpKXLZXCU9VzweJxFPMD4xTrFY5PmJ5/lcz+cw3RIhPcxPbPkJbqm7BQCJpLGhkVAoxODQ4JJrr7m5GVVVGR4e9tYt27ZtQxuO4zA2OlY5phCC9vZ2CoXC0jppXp2y2SwzMzOV9YPBII0N3nMgNZeqrB+LBDk09h3+4ug3K5FH/t3O93B35wepr2tlenIaq2RhCBVdUWlpbKIunmR8dBThuOhCRVMVGuvrCQUNRkaGPbFdSASS+rpaFAEz05MIZGV5PF6DdG3y2QxCeENBFCEJBQM4jo1tL1zDAoFh6DiOg+14Ar8mHMbyM/RnJ+mINtAcrsWWKhIFVVHQNA3btssDDzxUVUVTVSzLwl1836gqqqpimtZCyrXy94ZhePeZZzwkAiMQRAiVfKGIxPtNkwiisRqkhHQmi1dLT2Cvq6unZNkU0pPkrCH6s+N0RJtoa7iJnG0wNzdXXhcCgSD19fWk0xnS6XRleTgSoa6ujunpGbK5HOANHkgkEiQSCcbGJygUCpX16+sbqKmpYWBgENOymB+E0NLSSiQS4dTp3vK1J5DApk2b0DSdnlOnKmcdoLs9VhngsTHajKJuIefWXPQ9Yi3f92JaFsfpYTA7TldiA67cRMZeSEtzue9GV7NOL796mE3RMQzFxXQVxt0udmztuurve1dSp9akQo0cwrEtXCmYKNWSsaOr9pvb1eXVX1GUivB6pXWKaVkaAzMoikRVdWbdZsbTC5Eo1tu7UVdXF7mpUxiFXgQuEoU5Wqlv370u343AGwR+2223+eHTryV+J1b149uwullt+zkz/Zg//BtPGG/sxrjtJ284YRzAkS5Pjr7O507/gFPpEVzphe8TQEgL8Ed3fJQtNa2rElJtpTaUUnJ0KsNwpggCdtbF2FDje9ReU2ZOwCM/73mLh+rhLZ/FSW5f+2eodD0PCCvrifZmeW7lFpZZOciNwMufBrvkjSh1bTBi8OEnoXbrZR26ZDn8f987wqH+GRRF8Atv2cFtWxoBsB3Ja2csDh4rMZEuj9AXcNMmg3t3BGhOeCMzU26KQXuQQWuQKWdqyf7jSpyQEmLAGsDFRUfnvvB9dBvdV3bOFuH/DlY3vv2qH9+G1cl6aK+mUik6Ojr4oz/6I372Z3/2outblsWOHTv48R//cX7v935v2XWW8xRvb29nZmamUs/14hUwz7yng2VZnDp1ii1btlREmvXgObRc2WeKJgeHU5W0K+B1BQc1BUWIii+bKgS6KtAVBV0VBFQVTREICZYFBRPyRUGmAHMZl5mMZDbrYtliYafL9P4EdEEyKkhEvHDssViJwdonKapzRJQgbw69mYSaWLZOc3mXmYxLfVwjHlZWxSPFdV3yJZNTfWdo2dCOjYItJUXboWQ7lGxJ0XExHYnlut5AAmQl7DzluazMJbYjsV2JbohzIvQWijaycvIXz73z5rgupulgmjYly8E0HUqmg3RdykF4MVSFkKERNjRChkrY0AgHvCkSUEnm+4nOHiY4e6rsRQkIFaVxK0rbzShN2xCKds415kqXr049Qr9yEoQXzbhb7uI9jW9aYo/lrj3LdciaeQqOScEuUXBMiq73d84qVpYtTCXyVrGyft4pUXSsC4Z5l1IuOWNIsKXLVDGFi4uCiioUonqI39v3/5A0Yt41LbzrWVWVsmep914sEGiqgqoIpFzwyRTCW66U67qSa+xyrr3Xhnt5daSHm5q3cFPrpnXzjDjf8i8f+zIff+QT5Kw8ET3Mp976Sd62+T28OJ7GcSXN0SC766MXLqOZgeEDKENPIIaeQprpeeNStMHZeC/FtnvI1O8jo+jkzBxZM0vB9kLWZ8xMZVnOzJG3vZD0mVKGnJXzvrOylWXD6WG82AeeN7qCpK2mjZB2Vlv+0px7z/t8u6Tly+zbdm2yis7G1ntRFR3HtRgYeZqoa6Gp2orKKJEUbM9mQU1BQVxw/Sutk+3aDKS90PqV86wobKzxQutf9JhXe/lZyyqpAKQ3QMGVLopQvVQAqraifStCQVd1dFVHUzR0RfcmVcdQDQzVqCyvfK/plc+GYnjbKzoBLeANwhAquqJ5k6oRUHV0RUNDYNkOrwx8l78+/m1KrkVQNfjFHe/m/s3vJKTpgERVQLqeNF32LUcRZT9zKb1nn5AoUiLml+Mi5LxMLHEsC6M8mEkgynMQKAt/l5+d8+dBlD3my1sAoCres/bhvif5ry/9AwW7RFgL8D9v/Te8t+tBhFAWHQNUoS451cr890Is44nPeZaf33N/xR79UvLQ6Uf5xHN/Sb5c5k/e8fN8YMvbEIriCfBSljedTzcjlrk9ysec371YvHw+MsAydVpcHDG/93NiC3iRBCjXSTo8dOJhr8xWibDulfn9298LQq0ck/n1Fz0PFEWpvLcsKmGlLHJRWSrLF/0Oz5fbi7AgL7K+d1zbFcykS8QZRVVcLEdBV10cVyGntZKM6Iv6euVZZV963IXli37nhFJ+R1tcp/nze/byhWtj8e6FWLjGKutLB1EcRUi3fIe5IBRksKXyPnXufrz9u65cfNoXlZ2lSJiZnSGZSCKURXYSy9hp0f0hF534JWV3bERxuBz5QvFEZqEgQ20w/2xGVFKiLD6P8/fn+ZefFU1jRXZaYDk7gUCRDjI/WCmz94DSEY33rNjL/Vq/G6XTaWpra31R/Foy/2I9bzyf6sO3YXVzNeznTPdhPve3SNtCbdxaFsZvzMwTr06f4jee/yss1/th8l6mJPXBOA3BOJtrWthc00pXrIXNsRZixqXn6LsUG0opOT6dZTBdAGBbXZSOVcgL6HMJ5MbgkZ+D2R7QQsg7/xtuuBkl0YmIXkI+Ldc5V7Q2s2BfQNQ+n+htF1Z2TLsA2RHmGzHMv1xGWyHeCcltULt9YarpAOXiApXtuPzVY8d5oWcCIeCnHtjG/TsXzoUrJT0jNk8fK9E3seDZ1d2ic+8Og64mrXLt59wcQ/YQA9YAY/YYtrTJyAwSiYqKQBAUQT5S85FV8xj3fwerG99+1Y9vw+pkvbRXb7vtNt785jfzB3/wByta/4Mf/CCapvH5z39+Reuvl3quhGq6l2YKJs8MeR6C851sroR4QAcBtusu22G7EhThda5JV+A4YFmCogn5ImTzkmzBi6brON73ti1wXRC6SWzPQdTIHIobIDpwP3V6bSWHeV1UoX/S5juv5lFUiesovPvWMDd1GFiOxLQllgNWeW5aLkXHpWi7mI6LVRa0beniSIkjXSQSV0ivw22RyH222L0cruuV3ZvAWfS37Xh/m6bL1q0Wqgq2DZrubdd3RkVTQdckmr4wN3SJqs13vs53Ii/uMFw6XxDPHUqmjWkt/F0qL1fMHDvUIW4yBmhTU54QIgSmEuBMTSdjyXbMeBARLuAaOSwjS1HNVjqxPZFYoVvdToPaSJ2WpEGvI6yEKp2Cl4LjeoMFbNfFWjKXWI6L5bjkHZOcVSJnl8jZRfK2Sd4uUXSKlMphv0uuiVmeZkop+nJ9LDpjCAT31d/NPQ13VcSWy2VeVPeubSoDR5Z8nv8bbz7/eV5AktLr3/XuNS9iQO9UhpIQKIrAcVwMy2VfWwKl8vxYeI7ML1r8ZFnJc2a57Vi0nVh+cUXYWrozePHUBH934AVS1hhxvZmfvPdWonURLCkJKQotAW9Av+uWQ8BKiet67REpJY7r3Wfz58B1LKJzr5KYeprkzEGM4tiSQ85FdzBRcxcT8TtJBzpwpajsy3W955a76Jy65WfZ/N/DmUH+5NhPITQIG/XkzSmk7fIL2z5JXaQOIVyEcMvPAAehSCSexy/CxRM9vO8QLhIH8L5zcaAcmtsVLg5eKHsbB7cc1t4V0nvm4GBLF1dKbNcpL5M4bnkuXa/cSHJ2CUevQQiBZZe8jn3HwsicJhmInmWS818DGdOhaDuENJVY4Nz+pAtte7Fr6+xtZwozPDv0LJqioXgBy7Fdm7s23EVtqPa8+zxfGa503QutDzCdn+apgafQFQ1FKDjSK+8tLbcQ0SOV1AKmYy5JNbCWWI7NwFw/UroVIV8IhY3xDvTV7C+U5/1w1md51p/nrms7FgOZ0fKgA6Uy3xhrWRgssexxlqKIBbHde/YuEugXC/JCVAaAzP99ofXmrx1P2Pe+M12L5ye8qCOaouK4Dqqick/zLsJaaJlt54cCiMqz+5yBAkvKsrRMXl0WBhSI8jvUwqADsWQ98H6T5/ehCIWsmePvTz6C6doEVZ2SYxFQdT6x90M0hGoJqDqGomOUB1oEFMObqzqGqhOofFdernjLDUVDXUF/1MVw3fL7mSOxbLBdiSZc4gGTkUWRD1rDtcyVDBypoGvlQZkqaKpgBT93Vx/pgFMsf1g0VEENlgcfrMIhFo2iWJU6X4Myrzpnl1no3u9w3W1gJNayZOdlpe1VXxRfRaSUmKaJYRjrvuHtszy+Daubq2U/Z6oX87m/QzoWavMOjFt/AqHceML4dDHNRw9+mrxVRFVU8nYRgaAhGF/2fDeGEp5AXtPKllgLm2JNBNQL52a/VBtKKemZzXEmlQeguzZKZ8IXxq8pZsYLpX7mEWRh0vO61kKIbR+Gup1nCdfLCdxZsIsXP86lIlQwoqBHQJ+fL/pbuvD6Z8ExvZdQO+ttF108SnMRWhAS3UuF8tptnnf5WTiu5J+eOslTR7yQeR+6ZzNv29d+znpD0544fmTAqjQ9W5Mq9+wIsGejjrJoNKopTQ6XDvNM4ZklnYwAtwVu47bQbWjiyp9L/u9gdePbr/rxbVidrIf2ajabZePGjfz2b/82v/zLv3zR9R3HYdeuXbzjHe/gj/7oj1Z0jPVQz5VSTfdS0XZ4vH/K82RWBJYr0RTBgx31BDXPq9V2PRF53jt6fm45nohpOS7mvJDpeqKzu4KuHoknDDsuFcHMcT3RuGibZBuexzXmwNGRZ+7EzSZxHIFpSYTm0rHBQVW9fYyMKwhXwdBB0ySq5s01TXI5gS+cRYL2UqFbYDuAo4AEVShoikDXBIYm0FQwNK/zdr4j19DAcuClgSxbumwU1RuT2dOrsaEmhO1I8iVvKlqLvVu88uvlOmm6RFPLc21+AlVdEK2Xnl1vPu9940qJLQqYpFEYJaoMoxppimEFR/E6SG1USlKjJDWkKjFCdsXfTwpPxDTzGrgLJ9W1NJx8CAphhBlFMSMYTgxd1dB1BU3zwgtrqoKqChRVQVEWxAAqgvG8gFEWsRYJyWd/npe5FEWgK14EA0NVmDMz/MmJv8F0SgihUHK99/xaI07SSHBX/X52xLYipfCuubI4WxFUmRdqqUQAgAWvpsURABY+y7OiBCz9bmH58teaEJJQ0IB5p8NyX3WhaGJZrjeooTzIwbS8QQ6W6ZQjB9iYpoPjXtuuVcd1mUgXQWroagCJyQN3bKA+ESJfsHnl0Ai2c34v/4shpaTJHWSHfIWd7stskH1Lvp8WjRxXbuaYcgtnxFbkCjr0S7bDceV7qG1pL6+y6yKnomzkVlQVpHCQwi1PC3+7lb+Xzt2z1qv8fRFRbzGLr2tYem0jwJYmplKoXFOOayOk5L/e9FO8Y/OdKz7OVN7k5bEUmqrwwMa6RYMtVp+x7Bh3/NUdZM0sNUYNaTNN1Ijy3M89V8mHvp5YXN54IM5cae6i5fV+F20s16oI5YtF8wvNTcdc9jvbtS/4/eL5UHqYx888jhBe2H/X9QZm3NF2OzWBmsozf7HnpVse/DW/fL4eEi8qyDnLpcRxHe+5dJ71gXOONe8xevaxClaBkczwWR7DktZYGyF9nURdPEvQL9gFRjIjy5S59dzoEufu6GoXdFkKVoGR7Ni5ZY42L3+eL1jUpV9qiieWG6pOQA0s+tsgoBneZ8WoLDdUAwUD29KwbY1SScN1y1EUFANDeBES4gGNWftJPnviG5Qci5Bq8Ou7P0yz8W4sJ4AqVBShowkvckJjjUFDjUFDTCMRFahCnPUCdLHn2+U8/87axrUgc4qx3CT9uSk6IvU0RxogtgWUsyO8Xs714L1/2I6NpqrLtCXOHohy4X15ZbYh23dumaOdy/dBrgfKZUY6ZeFeemVtuKeSw3294Yvia4Af7rD68W1Y3VxN+zmTpyg9//fg2KgtOzH2/wRiFUbpVRuPj7zGZ449XMn19LEd7+aepl0M5MY5lR7ldHqE05lRxvIz52yrCMGGSAObYy101bSwOdZKe7QBVSx4NVyODaWUnE7l6Z31cpN0JSNsToTXfQfodUV6EP5+N9LMln1BJEIoULPx0l7uFH1ByNYiFxa19Yt8rxpcdDjncjnFN70dZk944eFnjnvT7Mnze6BHW88SyrdDrB2J4KFne/nuK16OuXfe2sF7b9+07HU5k3F45oTJS6dNLMd7LUuEFe7eHmD/ZoOAvuA5/sX0FzGliUBQwgtpGxNebvGdgZ1sM7ahi8tP8+D/DlY3vv2qH9+G1clatFc/8YlP8K53vYuOjg5GRkb47//9v/Pqq69y9OhRGhoa+Kmf+ina2toqXuO/+7u/y5133smWLVtIpVL87//9v/na177GSy+9xM6dO1d0zGppl0P13UvDmQKvT6RxpEQVgpsaa2iLXX4ntecVSkUkP1tENxeJ6Yu/P1vYs6XFaOR5SuosQmokU7ejmglsR6IbC1638/NSiYrQWvGKXSy4Sm9SpOd9paKgKQqaEBiqgqEoBDQFXUgmxkfo7NhA0FAXvJQ0Kt5KiwcPrpSXTpt8+bksQnGRrsL774iyf/PSQbuOKymYCyL5wuSSP2t5ruRSKEmKllsWyufFcxctWEQNZ1DCaQhmIJDB1bNIcbaHo0RxbQynRDxXIJa3iOZtYnmHjFLP4Z0CKWx028HUVaQwYKYbYZhII4ur5c9TW4Fqh1GsGKoZRbViKFYU4RpneW1KbNvFdlxvvuhvq/LZOee7+enswReBgELjjhLPpZ/Gci10RWdjoJO+2SFsxXt31e0wiUwnkULTBT1I51GUsreeUvYALwv6i+cVj/Al35U9yM9Zv7xcVdBUgWGoNDXEkG7ZS1B4QnnJtFfcl+66Est2sCzvXFmWU55cLNsT1S3LWz5vn8VzV3q+it7xFr6rBEqd/x6QUlCyHdJ5g0igEUXA7h2SRNzCdWz6zkzjWO5CfYVAXXR+FCFQF5+fRedLVeY9LCVzqRR1tbWoqkLYnqE1+zwt2WdpyL6KIq1K6W0tylT8TqaS9zCbuB30SGXfIJm2Zxi1xjmV7+dw7oTnZVYZRSKIKzHUcthgr34Lgxko139h0MPCgAdvvmCgSlSJRYMphFQRrgJSQUgFIVWURX8vnSsoZy0zpcVU8hBSsSvhcZEK/679J/iJm29d2cVRLudTgzOUbIe9TXGaIldXTHjo6EN8/HsfJ1PMEAvG+NTbPsUHdn7gqh7zSnjo6EN8/JGPk7fyhPUwn3rr+i7vs4O9vPeLb6Bo5YgFakiX0kSMCK989IVVG3iwmu8xUkpGs6Pc9Ve3ky2liRth5sw8ESPGUz9zkKZIE3CucH+22D5//y0W8c+eny3Yn2/dxcc6+9jzwv5kfpKf/upPkjezRPUQWatA2Ijwl+/6G2rDtZX1Fh+jMijgrGMsd9xLXbZ4/+c75mxhlv918A8oWgWCqkHRMTG0AD97y89jqAamY1KyS5Sc8mSXvGVOael35fmFUpgsh+MuROmxHG+w49loKks8v11s+lMDyEUpDIRQ6UhsBKlhORK7HP1n8WuiIlRUoRHQdMKGRiRgEA0Y6Kq2NF2BupCqoJK+YFGqg2XXWeb7s7d9tu8RPvvq31NyvBQGv3zrR3nzth9DIFAVtTJXyoNXFKGgCk/g9oT+heXLLUNC7+letnZvRVO1yr4WogVc+nvoQ6/+JR9/7L9U0gF86k2/zwf2/fwl7+eaUhiFuWMLwnh8B4QuITLoNcYXxdeAamt4+5yLb8Pq5mrbz5k4Sen5fwDXQW3djXHLjyMuI0xdtTNdTDNemKUplKQuuPwzOWcV6c2McjozSm96lFPpEVJm9pz1DEVjU6y57FHeQmekibnBSbZu3XrJNuxL5emZ8Y6xKR6muzbiC+PXitHn4Kvv9Lw67IL3giht2P7jkNy6VKw2lhO454XsyxdzL5vcGKQHPAE/cp4GrOtAZnBBJJ+fcqPLr6+FPS/y2u28MlfHw6eCjIl27r1pMz9+35bzegbkSy7P9Zj88ESJXMl7PQvqgju2Brhzq0EspNBj9nAgfwALCx2dLcYWJp1Jsq537QdEgJ2BnWw3tmOIC0dmWA7/d7C68e1X/fg2rE7Wor36kY98hKeeeorp6WkaGhq49957+R//43+wefNmAB544AE2bdrE3/3d3wHwa7/2a3zlK19hbGyMZDLJ/v37+f3f/31uvvnmFR+zWtrlUJ33UtF2yFsOYV0lqK1NmV15tmAuydslXnKfYlZOoaKy2bobtxBnumAtCFUSFAWaIwEaowYB1fManp8bqoKuiBW/m19N+83nQa+NKcTDV96WK7pFZuxZpqwUU9Yss06KtExhYXrezvNez3I+bLVAFqO4hSjCjKFaUTSnBk2GCIsSbaUTtGaPkCiMIKTLWJ3k+R0t2KqC5khu6ZmlL/LTlHTvHpTCxlKy2GoGW8vg6GlcI4NUPNFykbboYRlQiOEWYri5GG4ugsxHPEFRSlxcpOviShfXdTzvdsfBkS6O6+C4ZZHccbAcB9ddmvOxZDvU19azc2eYvMwQFjGOHs0zOTtOqWaMuVg/rmKCEASdCM2lbuqcVgxVQVNVNEVBVRU0RUXTvEETquLNFUWU594yb/Lyp6pCqQi+ilAQSjnMraIshNQVIMod2vMCs+PCVK6IEy6gqmDZoGteWgFrNkgkqJU9kiVyPry/4oXxFkp5Ekt9kxf3si52fJz3Vq+E9rcFtrUQDcE66/OFvOks22U6IzEMl21bHGoTEseBO9qi7Gg5N5LVpXLBe9DKw/BBGHgMBh+HUmrhK8Wgt/lOjiV2c1yv4WRuhqJjAlByLCaLc0hXMp9BWVVgb91m6oM1GIpWCRNsKAs5nJcu90ILn/N9ZXl5PdXLCb34meO45RDprhedwHbcpXPXC/tuOy52ed3ZXIk/fOr7pOqP4ioWQgFsHYMAe/Wb+cnd97B3U/2KPL97ZrL0pfLUhwPc0hy/UhNdlOG5YQ4ePsg9u++hLd521Y93pYxlxxiYG2BjfOO69Gifx3RcDgxO80Tfw/zdq79D0SqgKEF+Yf9v85/u/WnC+up4fF6N30FvsMSvLww+eNsfrevBB1DdZc6WMkQDsSsqs+M6S0TyeeHcdEwKVpGRVJ6B6QJDMwWGZwvkzRKOLOFIC1uWcDGJhmyiYYtI0CIYsHCkiemYFTF+JDPCwcGDKPOZ7aWLi8vm5GYCWgDLsXCk91vvzIdeX0YkB+9XS1sUqUdTL+6rcjnYrs3A3ICXKmk+HYCisDG+8ax0AFeABNMyMXRj2Z/jeXFcEcp5RfjFk+VYvDz6Mo500Mq/W4lQct1G8ViCUwKnAGpo3XqIz+OL4mtANTa8fZbi27C6uRb2c8aPU3rhnzxhvO0mjJs/fEMK45fDTClT8SQ/nR6lNzNKwS6ds55qw86GTWypafM8ymtaSBjRZfZ4Lv1zeU5Me+Jge02I7XVRXxi/FuTG4J/vQFpZTBHCkAWEHoV/9dz5hebrgVL6XKE81eOFZF9EvmSTLlhMi0ZIbmPHTXeh1O3wvMqjree0Eixb8kqfycFjJaaz3tBeVRHs69S5d3sAW8szkkvTGqmhJRLDlS69Vi+HSodIu2kADGGww9jBjsAOAmLlL63+72B149uv+vFtWJ1US3v1Sqmmevr30upiS5vH8o8xZo+hCY37Ag/yyhmVouViO14HaFBXeNvm+lUR9Nej/UqyRMpJkXJTlfmcM0dBLh9NSCCIKTESaoKEkqjMa5QaVOGFxS9a3qDIQkmSK3ke6rmyV7qTG6dh4gdszR0kb2hkwwaGpRMplvha4OcYcTuwyjngpTw3rKcSKKBG5lAjadToHFokjRLKsrzrs8DJx3CyNTi5OHYujpONI60AFw1zWvb4VRVQBdiuy8iMi2FIQkFJoSgwTcGGOgVDV7GlxbDyOkPqy9iihJQQcmtpM28n6XStyHN8tbFsiRG22b5lIbT+8VMaZl5D1y5eHiEk+uJIAfOh9suf9fLni13KYtEfAnAcgVuepCtwXQGu93ehCLNFi+7NDpoGSDh9RoFCkAf2BNm3aSHa1OWw0nuwZBU52f84x4d+yPG5QU5JBYtF/SNqgIgRZVttFxuSW/hC35OU7BJBFEq4hI0wf3HPr5x3sP164MDRUf7P4y+TJ4fqaihtw8yICQAihUa2uTfz1j0d3LO9mUjg/IO8c6bNwaEZhBDc315L4CoPflqPz9HrgZMzWc6k8sQCGptqSgymB5ktxREk2ZyMsDkZWZXjXC37Vcvgg8VUY5mvxqAU25GMzDicmbA5M+HQP2VTspb+pmuKoL1eZVOjxqZGjfZ6FeMiv2MrSWEwn7JgSdoCx2I8bdI7UaB/qsjApEm2ZOJIGxcbV1pIbOpiksaEpLbGIRl1QTiVdAWLUxecvf8LfR7NjvJ43+MVMdotD9zb37qfmkCNJ+yfNTnSKUdScpYsX26ZV+kLi+KXipfCYCEdQG2oFkc6fOsnvsXtbbdf+QF8AF8UXxMcx+H06dNs3rzZf+GoUnwbVjfXyn7O2FFKL/4zuC7qhr0Y+z7kC+OXgStdxgqznC6HXe/NjNKXGaNQKmIYSz1c64M1lfzkm2MtdMaaCWnLC31D6QJHpzMgoS0WZGd9zBfGrwUnH0I+8XGcUgY1EEM88CnYur5H8F4VXAfm+pYK5bMnKKRGmct7YnlAV0iEA54WbsQguW1p+PXEFtBDuFJyfMjLOz4w5YXaLJperktFQMgQfOieSCXspytdzlhneL30OnPuHAC60NlubGensZOgErxo8f3fwerGt1/149uwOqmW9uqVUk319O+l1ceWNo/nH2fEHkETGru5m/5JQVHkCMoI+xubrijk+2Kupv1ybo6MmyGmeOlnzsaUJnPOHCk3xawzy5zr/Z13zxemHE/8XiR8J1RP/NbElXkrzc3Mkv/u7xKRmXIgbciJGkJv/2/Ea5NeyG7H6yi33fLcWZjPe3N53rBQdCxyzJFTUuTVFAVllpI2h6uYFS9mWBSG2jYgn0Dma7BzcexsHCsbw7YV7PPk0LZsyWx2aVh1RQiSUWWJwOxgMqa/xpjxKg7eO3LYrafNvJ2Es6nsgTUf/pxy2HTKob/LnwWV0Onz66hnfZ7/fmGbc/dn2pInjxRRVEkoJCkUBNJV+NFbgkRCihd6XPHEBrFoX+oyn5Xy4AChLJRFLR9HCIkjXWzpYstyNAbpYjpeCoOS41JyvM8X66p1XEnOciupC1wXLAuOHw8gXYWAJtjbaXBHt0FT4tLvofPdgzmryMm5IY7NDXAiNcTpzOjS8L6uRdwpsd2cYvtcDzvsFBucnCeTR1t5PNTBZ5wARaESlA4fa7+ZB/f/P5dcvmvNcGaGwew07dE6WqIJvnDyIF84/QR500KxAzTM7KaGWu7e1sQb97TRVru8MPr8yCyposXW2iibEuGrWmb/d3D1KdkuTw9N47iSm5vjNIS9PqnhTIEjkxkihsbdbclV6X/y7VfdrIb9TFsyOOUJ4GcmbAannHN+ewO6oKNeY1OjJ4S31qpo6mWE9V6FFAZSSqYzLn0TNmfGHfombNKFpfHbFQFttRqdTV55Oxq0yxrAtRIh/0pwpYtt25w8dZLOrk6EIpYV2ufFeInEcZ3zruNKl4ncBP/qK/+KvJUnYkQoWAVigVh1eIpXEb4o7uPj43MVcUaPUHrxcyBdtPZb0Pe938uh7HNF2K7DQHaC05kRTyzPjDKSm0ae5dEgELRG6tgca2FLTStdsRY2RhvRynneRzJFjkylkRJaokG2JMMUbHdNw1HeEKwkFPmNSmGGk0ee49lnn6TR6WdLcJTNgUkUeXZeSbxetJqOJUL5kNzCIyfjPNdjEXPHqZODTIt2inoT77k9xO4Og6aEFx5SSkm/3c/rpdeZdWYB0IRWEcdDyup0WPv4+Pj4eNwo7dUbpZ4+58eWNk/mn2TIHsKWtudZg0QXOveH76Pb6F7rIl6QxWloNDRuDt5MjVJT8f6ec+cqKWmWI6pEiSvxc7y/r1T8vhAnn3+WWM8XiZABIJe8iZZ3/Mqq7V9KSV7mmXFmmHVnmXW8Ke2mz2mDASgoxNU4CSVBXCSpoZaoTKC5IWzHE8Q/+/0slpInGM1SzEbR3TA//5Yo8YhSEYm9XNdQcos8OvoC3x99gZJjgYDNsRY+2HkfN9V2XbPBzS+dNvnSwTymLTE0wYfuCZ+Tb/5aIaXEdCWm7ZaFcqcilpfKy7KmTapk47pg254oHgoKkjLGKz0O05kFIaKjQeP2boNd7foliyVzZo7jqUFOzA1yLDXIQHbinOuiLlDD9kQ7OxIb2Z5opyVU69mtMA2DT8DAD2DkaTCzkB5gWgkyHqyjyS1RpxnrPrrY2emr7is/606nR/j04a8xkJ6mUHKIpjqIZ70BHTvak7x5Txt7OupQlYVzPpQucHQqQ9TQuGuVxFOfa8eJ6Sz9c3niAZ3bWxMV+1muy5P907hScmdbkpoLRAzw8TkfBVMyMGlXPMGHZ+xzwpOHA8LzAm/wPMGbE17qkNVgtT3ypZTMZF3OTDj0jXv1SuXPFclba1U6y57tHQ0aQWNl9VkNIf9aU41lrjZ8UXwNkFKSy+WIRPw8ttWKb8Pq5lrbzx45hPnSFzxhfOOt6Ht/zBfGr5DlbJi3S5zJjJXDrnse5VPF9DnbaorKpmgTm8sieY1Wy3haw3YlKTND1kmTNOLc3dq6al4sPkvxn6EX5/hwij/91iFKlsPmxhC/em+EcO70Us/y4syy2xaVGgZTBm3OYQQuJSI8ZPwOPfEPoWuCcECwuUmjq1ljc7NOMiIYtAd5vfQ608404InjW/Wt7ArsIqyc653g27C68e1X/fg2rE6qpb16pVRTPf176erhSIdH849ywjyBRGJg4OCgoLDN2IYu9GXF1LO50DpSSizbQtf0JSErl2xzns3Pt19LWvRavTg4CERlHhMxlLPacGEl7Im+SpykmvT+VuMYYm1E0rmZWXJnXqOm/9soqoqx/yNobXuv6jFtaVe85WedWWbcGWadWUxpLrt+SIRIqAlq1VrOzOQY4AQoDtLR2e3cw9s27rjg8TJmnm8OPscjwy954jiwNb6BD3bez65kx6rXbzlWO9/81aRoOzzeP4XlSBSENzhFFTzYUU9AVegdd3i+p8SxIasiqkQDCrds1rm9O0Aisnz9poppTwCfHeDITB/jpblz1mkO17Ij3s72xEZ2JNqpD64gP7ZdhEN/A0/9B3BtvGT3ElQd3vN12PSWKzgbV4+Mk+ELmS9gSpOQCGFKE0MYfLjmw0SUCHm7xN+e/B4Hx49g2g5BM4k2uAXV8TyI62uCvHFPG/fu8EKrW47LkwOeeHpHW5L4VRRP/d/B1aVoOzw9OIMrJbc0J6gPL/09eHV8jolciU2JMFtrV5b+70L49qtuVmK/TMGlf7IcCn3SZmzWOecNJh5W2NSg0VH2BG+oUar6epjNuhWBvG/CZja3VCQXQEtSpbNJq4SAD11AJL+aofWv1j1YjekAqglfFF8D/Hwt1Y9vw+pmLexnD7+G+fIXQUq0Tbej73lvVb+grDUrtWHKzHp5ydOjFa/ynF08Zz1N6Fi2wqQ5CUBACfCmpgf5d3vu9T3GrwL+M3Rl9I2n+aNvvk6+aNNaF+Hj77qJRGRROoD81Fnh149DqhfHKkFmAIGLi4KCi0RhMrSfEWUbE0oXM2oX01oXM1on0Wi0LJCrhOrHOSkPMeVMAaCi0m10szuwe0noUN+G1Y1vv+rHt2F1Ui3t1Sulmurp30tXlxFrhK9kv4KLi0BUhOiIiKya17RpmuekVLoSbGmTkzmAJWXeqG2kRWtZ4v0dEMunaVprrOOPYJ18HKEHCLzhl1HCtdf0+FJKcjLnCeXn8Sp3pUtGZrzP5XzjCgotagsRJUJABAgoAQIiQFAEvc9i4XPRtvne4Ms8NvwqlutFVNqZ6OCDnfexLdF+Teu73hnOFHh9Io0jJaoQ3NRYc87g73Te5cXTJi+cMsmUw9gKYGurzu3dOrFEhpPpIY6nPE/wyWKqsu38Pdgeaah4gm+Lt5MMXKbglxuDf74DzAxI15sLBep2we2/Cds+BMraP6+llMy4M/RavZw0T1YGFysohEQIG5v3RN9Ds7aQa/epsUP8Xc8jlByLkBJkN/vp7VHIl7xr2NBV7traxJv2tDHtOIxli7TXhNhRH7tq9fB/B1eXY1MZBtMFEkGd21oS5/T7jedKvDY+R1BTua+99or7BX37VTczGYvXjvazd2cHtTFv8Esq55a9wD0hfCrjnLNdXVSpiMGbGjUSEXFd9zHPn5N5oXw6e65I3pxU2dSglYVylXBgYVDXXN5lOuNSdxUGs/n3YHWy0vbq1Yvx5OPj43MDoLXtBSkxX/ki9pnnQSjou999Xb+0rAcSRpT99d3sr/dCNEopGS/Mlr3JPaH8TGacnF1kojiLRCIQ2K7No+M/4D2du9mcqFvjWvjcqHQ21fAf33szn/rGa4xM5/iDr77CJ969l4aacidWuB7C98KGexc2skuoPV/BeeSjFG3PH0SjhIZNszZCkzaL7fwQ05RYeS9vZHqmhemRLkbULqa1zZDYTE33Zqzm0+T1KY6bxzlpnmSLsYU9gT1ElSsf0e7j4+Pj4+Nz/RNX44RFmKIsoqJiY6MJjf3B/csKyoJl2kbi/N+7rsvY7BjNsWYURTnvPpbd73kouSUOFg/iSAcDAxsbQxi8JfKWZXOLr0e0rW/GmTqNOzOA+dIXCNzzUcQ1FBGFEERFlKgSpZ0FgXqxV/mANcAx8xiy/A887/05d64yKOGix2kQvKWhlbligYl8mrw9wt+MfZGGmSS31G6lPdx8jphuCOMcj/9L5WL55tcbbbEQdSGDvOWcN01YTVjhjXuCvGFXgKODJo/1jHFkZoCe6RG+kBrBVfMEDUFAn8/hrrAp2sS2mg1Es4I37ryDeHCV2giRZnjgU/DEx8HOQ6QRohvATMOzvw0n/wXu+u/QeHWjIJyPtJOmz+qj1+ol7XqR6Vy5MPDHxSUnc4RFmJiyIGYLIXhDy010x9v4syNfpz87zgsc5C337WejtYMnD48xPJ3jySMjPHlkhH3dDXR01DKaLbKtLori9x2tewq2w3DGc8LYklzea7Q+ZKAqgqLtkCrZJIN+CPUbFS8dR458McHDRzJ0NeuUTHlO6HCAprhaFsC9eSy0vqOUrDaJiMK+ToN9nd4gyHS+nJN8wqZv3Bs4MDrrTc+eLAHeOets0rBsybMnSlgOa572xKf68EVxHx8fnytE27APpIP56kPYfT/0hPFd7/SF8WuIEILmcC3N4VruadoFePnJvz/8Kp889C8gvc4aF5ecnefFiTN01NSirVLuHR+fS6WtLsJ//DFPGJ+aK/IHX3mFj797L2215+mA0wKw8UHUUJKgmcXWalHtOYQRhrd8FlGcRU+dQk/1QOo0bn6KGmeMRnsMq3QQOw+kgQHBnNrCQONuRnZ1kq8LctQ8RE/pJJsDW9ip7byWp8HHx8fHx8enCokoEe4L31fJsxsiVMmzuxo4joMoCrr11fXOCSiBSpkNDO4L31cV4uc8QlEwbvkIpSf/FHd2EPvEo+g73rbWxUITGvVqPfVqPa1aK/1WP6Y0EY5AahJNaLwx/EYUoVCSJUqyRFEWKbmlyuf5ZaY0kUgcbKJBnVAgSdYqkLdL2KR5vvAir5s6UT2MvmhAgECgC52gCFZE8qASXCKcX0hIP1/e6PVOUFteDAdwpMtAdoJjqQGOpQY4kRokqxZx6qBoSmxLgqug5JsIua3sq9/Ij2zbRHdTCNd16enpIaqvctqxrR+AtnshPQA1GyHUAMc/Dy//CUwfgW9+CLZ+EG79OASTq3vsZci7ec5YZ+i1eise4eBd0xu0DXTpXeTdPE8XniYv80gkutAxpUmEpc+O1nAdv7v/p/j86cf57tCLfH/kJTZFh/j373oP6ZTKY4eGeKV3ild7JgnWBMkEdb49N8SD25qJ+ALquqYvlceVkmTIOK/YrSqCpkiAkUyR0WzRF8VvIGxHMjnnMjLrcGbc5geHi1gOIAUlGw73W9TGBLoqaK1VK7mzOxqWej37eAO59m4y2LvJE7czhcWe5A4TaYfxOYeRWZuZjERK0FSwHMmXDubZ0qKt+/QnPusDXxRfRYQQGIbhC2FVjG/D6mYt7ae17wfpYr76FezeZzxhfOc7/GvpEllNG2qKyu0N3dToYbJWkZASIu/kAclXBn4ABHnrxm6SQX8k4WrhP0MvjcZ4iP/0YzfzqW+8zsh0jv/11Vf59XfdxKbG84TRK3tXKE98HMPOgBH1vC06337OqkpxFiN1CmP2FKR6sKZOYk6dwi3MoNojxEdH2DMK03WN9GzbzWRjC8eVIU6Ip2m2XJr6tpBM7oJ4F2jBq3wmfFYL/x6sfnwb+visDv69dPXpNrpp1Vqvimft1bLf1SzztUIJJ9H3vg/zxc9hnXoCpWELav3mtS5WhcUDJkpKiYAIcF/4PjqNzhVt70p3qXBe/nuqNMsrsz0MFsZRlCIzaonaUIRkKALCRSIxpYkpTdKkV1xeQxjo6Iw747i4aGjkyfNY7rHKdWIIg4AIoAu9MjdYH8+X6WKascIszaEkNUaY3swYx1MDHEsNcnJuiKKzNAd8QNXpTraxI7GRzdEN5GfreeW0y/CMw+Qw/MOwSXPC4bYtOgn1KtUx0uxN8+z817Dp7fDiJ+HUVz2P8TPf84TxrR9c9ZDqJVliwBqgz+pjzB6rRDRQUGjRWujSu2jX29HFgqi5Qd/AtD3Ny6WXmXPn+F7ue7wl8hbq1KXR53RF46e638Ku5Cb+4vi3OJMd5z+/9Lf8261v42Nv38N0psgPDg/TN5sn0FRDz2yOb/7wWS+0+k1tbKhbvchd/u/g6lCwFrzENyfCFzyfzZEgI5ki47kS268wCoBvv/VJ0ZQVz+WxWYfRlMN4ysUtZye2bInleMFwhID5cX0/cnOIO7YGCOi+PS+FWEhhT4fBng6v3zZb9ETyl3stnjnueY67EoIamLZkJuOumiju34PXN35OcR8fH59VxD7zHObrXwNA33I/2o63+z+ga8zjI6/x50e/TtEx0RWNuB4lbRYRQuENjffy9g23sCUZQfW9xn3WiGzR4k+++Tp94xmChsovvWMP29sS598gN7bgXbG4Q2klFGYgdYrs6Anmhk9gTZ9Gz5wiV6NxcvseJppaAa8R1z7az+7eo9RpNeh13ZDshsQWSHRDvNPzXvfx8fHxuWHaqzdKPX18qgHzta9g97+ACMYIvuFXEIH1JfBfrVDkY/kZvnzmaZ4ZP1oRM+9o3Ma7Ou6gNhQ5R0yvCOxu8RyP9HnOl28+IiJoYnlfonnPdEMY504ss+ysSUO74n6C7w29wF/1fAs0h0LJIUCQgLrUOzWsBdgeb2dbop0diXY2RZvRlhGZh6Ztnu8xef2Mhe169Q9ogpu7DG7vNmiMX6Mw/eMvw7O/AzPHvc/1u72Q6g03XdFubWkzZA/RZ/UxbA3jsJDLt1FtpNPoZJO2iaBy4cHAJVni0dyjTDlTGMLgzeE306A1LLvuTCnDZ44+zLHUAAD3NO3iZ7a+jZAWIFUwebJ/mlzJ4sBz/ZiWV55tbQnetKeNfZ31fv/EOuHIZJrhTJHakMGtLYkLriul5MmBaUzH5ZbmOPVhv71crUgpyRQkI7POEhF8JntuCHSAoC5oSaokIgpPHyvhuJJIQFAwJQFd4bc+UON7MK8ic3mX//lQmqLpoqlguxD0z7MPK2+v+qL4KiKlZG5ujng87otgVYpvw+pmvdjP7nsW89DDAOhbH0Tf/tY1K0u1cbVsOF1MM16YpSmUJKwF+Myxb/Ds+AlsV3JTfDdvab2XvQ1x4n6IqytivdyD1UjRtPnTbx/mxHAKTVX4xbfvZN+m+mtybFdKJkYnGTtzgom5Y0zUT5FvFKjSQuDSMjLA9pOHacin0FXQNeHl94y1ewL5vFieLIvl6jLRF3JjkO6Hmo5LF/J9Vox/D1Y/vg2rk2ppr14p1VRP/16qbnz7XRxpm5QO/DluZhK1aTvG7T+1rs7V1bbhcG6KL585wA8nPPFUEYK7Gnfy/k330hyuvej2rnQxpUlRFkk5KR7JP4ItbVRUbGwUFLYZ2yoe6CVZwpQmFha2tK+4/ArK+UX1ZQR2x4GJwhzD2Rn601OcmhtmWhlnd0cCTRXYjuRwf4qQmWRP7Sa2J9rZHt9Ie7QB9RJyredLLq/0WTx3ssTUnO298wvobNS4vdtgxwYdTb3K15nrwPHPwUt/AlYWELDtg7D/1y8ppLorXUadUfrMPgbsASxpVb5LqAm69C469U6iyqV5Z5vS5LHcY0w4E+hC583hN9OoNS67riNdHu5/li+fOYArJc2hJL+06710xpp5fmSWVNGiRlF4+fg4r/RN4ZYHJNTGAjy4u437drQQC11eH4X/HL1y8pbNwaFZpJTc3poksYL+omNTGQbTBVqiQfY0Xv67km+/a4frSqYy7oL396zDyKxDvrS8ZBYPK7Qk1SVTIiIqdprPKV6yJAFd8KF7In6u66uAd57zmLa8KjnF/XuwOvFF8TXAcRx6enro7l7dvFc+1w7fhtXNerKf1XsQ6/A3AdC3vRl925vWtDzVwrWyoStdvnrmIF/qO0DJdmkNtfD2lrews7aOrmT4isJc3cisp3uwGjFth//7yFFe65tGUQQ/+6bt3Lm16ZqWwXEcjp/owa5N8Lp5mJTWj3RMVGlRM56l68RxOmYOExEZDE14IrkqqNwyQvU82BPdkNziieWp0/Dip8DOgxb2Qr5v/cA1rdeNgn8PVj++DauTammvXinVVE//XqpufPutDHdulOKBz4DroO9+J3rXPWtdpArXyoYD2Qm+3HeAF6ZOAqAIhfuad/NjHffQGEqseD+XklPckU4lVLspTUzMpZ8vMrks72k4j5QSS9pYroPlenPbdc5ZJxBQEIDjSoQQOI7LA8ZbuT25e8X1Ph+WbfPUS2cYKzZzYsSmrNUSDSrcutng1i0GichV9sbLT8GL/xtOfc37HIjD/o977YjzhFSXUjLlTNFn9XHGOkNBFirfRZUonXonnXonSfXK8pVb0uIH+R8wZo+hCY03hd9Es3b+gb8nUoP8+dGHmS6lUYXCRzY/yJ6aXRyfzhE1NO5qSzKTLfHEkRGePDJKrugJ+LqmcHt3I2/e08bGhvOk2DoP/nP0yjk8mWYkU6Q+bHBLc2JF26SKFs+PzKIqggc2Xr7Hv2+/q4NpS8ZTC97fo7Mu4ykHyzlXHlME1NeotCRUWmpVWpKeGL6SPOAzGYvXjvWzd0cHtTHf+eZqMZd3mcm41MaUVfcQ9+/B6mSl7VU/p7iPj4/PVUDvugekg3XkO1gnHvVyjG99cK2L5VNGEQrv77yPjmgTnzn6MOOlMf5l4Cv8iPU2JvJN7GmoIRbwfyJ9ri2GpvKxt+/irx87znMnJ/jLR49RMG0e3N12TcuhKrC9oYG96puZdWZ5tXCI06U+MhtreLGthQOTH4YzzSRnp6iz+6i3T9Ou9tEg+wiRRZvrQ8z1Qf8j4NpeqHfpghYCx4QnPg5t9/oe4z4+Pj4+Pj4+V4ASb8HY9Q7MQ9/AOvod1LpOlHjrWhfrmrIx2siv7Xk/fZkxHup7ilemT/Pk6OscGDvMAy038d6Oe6gPXnwQz6Xkm1eFSkiECBG65PJKKXFwKt7nc1aWgfwYI4VJxoszTFkp8k4BVRGoKqiqQFW8eUDTCGgquqIhhMQURaSkIrqpusJr/JChzEnq1Drq1Drq1Xpq1VoMcWnec4oQtMVtHrg1RK4keOGUyYunTDJFlyeOFHnySJFtbTq3dxtsadGuzqDycD3c/79g24fgmd+B2RPwzH+Dk1+Cu34bGvZUVk05KfqsPnqtXrJutrI8KIJ06B106V00qA2r5u2nC503hd/E4/nHGbFHeCz/GA+GH6RVW/7+25Zo5w9u+xn+8vi3eWHqJP986jH2JPvYFbkTzBBp06YuFuT9d3bxrls7eL5ngscODTMwmeXgsTEOHhujuzXOm/a0cUtXgx9a/RqQM21Gs+Vc4smVp4CIBzRCukrBcpjMl2iOXjgkv8/VI1d0F4U+dxmZdZhKOyznHaqrXvjz5oRSFsBVmuIqunZ591o8rNBSY/uhvK8y8fDqi+E+NwZ+j7+Pj4/PVULffD9IiXX0u1jHHwFFQd/yhrUuls8ibm3Yyu/u/2k+dfghRnOzfHX467yx8QFy1ma6EmG6EmE/TI7PNUVVFH7uzTsIB3QePzTMPz3ZQ8F0eMctG9ekPEk1yYPR+7kltJdDpUP0WX3Y4SmstkmUbBP9p97KqxPlEJVSEnEnaaaPreF+Nmq9NOZexEj3g1A8QdyRCMeE8Zeg60fXpE4+Pj4+Pj4+PtcL6qa7UCdP4Ywdw3zp8wTu/yWEduOFae2MNfMfbvoQp9LDPNR3gNdn+vjByKs8NXaIN7bs4z0dd5EMXNjTNqJEVjX/+dlkrQK9mTHOZMbozYzSlxlnsphadt2GYILOWBNdsRY2xZrpjDUT00OeFzkWKSfFv6S/TFGWcByJpglUFDShknEzZNwMZ6wzlf3VKDUVoXx+0sXKvBdrwgpvuinIA7sDHBuyeL7HpHfc5viwxfFhi9qowm1bDG7pMogEr4I40bQf3vNVOPbP8PKnYeowfOODZHd9mDO7fpQ+xplxZiqra0Jjo7aRLr2LFq0F5RLCx18KmtB4MPwgT+afZMge4ge5H/BA+AE26BuWXT+qh/jV3e/jsZFX+MdTj3JotpfjqRHeUP8A7ZkQ8YBnD0NTuXdHC/dsb+bUWJrHDg3z0ulJekbm6BmZIxkN8MDuVu7f2UJN6Ma7168Vp1N5pISGcKBim5UghKA5EqAvlWcs54viq8Fc3mU641J3Hm9gKSWzWZfR2UUieMphLr98VI5oQKE5uSgEeq1KXVRB8Qeb+PjcMPjh01cR13UZHh6mra3Ny7vjU3X4Nqxu1qv9rJM/wDr+fQD0Xe9A33zfGpdo/bJWNsxaBf7s6Nd5faYP03HZF9/H7bW3Eg8a7G6IETX8MWQrYb3eg9WIlJKvPn+Gb73YD8Dbb2nnA3d2XfVBGhezYdpNc7h0mNPm6Urox6RsIjy7k4mhWvrGHQrmwqtl1Bnno1NvxnAzAITIAAp2zWaKOz4KN/0CwXAYXcUfgLIK+Pdg9ePbsDqplvbqlVJN9fTvperGt9+lIc08xSc+jSym0Tbux9i39mlq1tqGJ1KDfKnvKY6lBgDQFY03t93MuzfeRdy4esL3PBmrQF9mlL7MWGWaLM4tu25jKEFn1BO+56eofnEv9B6zhydzT2JiYaDzhsgb2KhvZNqZZsaZYcqZYtqZXuI5PY9AVITyerWeOrWOWrUWTXjt3ovZb3LO4fkek1f6TIqW9+6vKYLdG3Vu32rQXqdelXf7Ym6Y/lOfpU9MMZ5o8FI3BZMoRpw2rY1Oo5N2rb1Sj2uBIx2eLDzJoDWIgsIbwp4dLsRAdoI/O/p1BrKTlGyXW2tv5jdufhuGuny5Zyuh1UfIFLzQ6pqqcEd3I2/c08amxnMHfKz1PVjNZE2bZ4ZnQMKdbUlqLkEUB8iYNs8OzaAIwRs21qGrl37+fft5nJ03+v13hWiv1xidWRQCPeVQspaXt+qiCs2Lcn+31qpEg2LN+1V81j++DasTP6e4j4+PzzrCOvEo1onHANZdvjcfD0e6fP7043x78HlsV9IWaudNjQ8SVANsSUboiId80c7nmvPdVwb4l2d6AXjDrlb+1f3d6yJcXtbNcrh0mB6zpyKON6qN7AncBJlG+sYcTo/Z9I7btE19jQ+Y/xWDAjY6k0onSTHp7Udt5OnIL3Eq9HZChkLAgJChEDIEQQNCuiBoiPLn8lwXhALlefnz1RjVfbER6T4+Pj7z3Cjt1Rulnj4+1Ygz1Uvp2b8ECcb+j6C17V3rIq0Ljsz28y99T3FybgiAgKrz1rb9vLP9DmJGeFWOkTHz9GXH6K0I4KNMFdPLrtsUStIZa6Iz1uIJ4NFmIvrle5Lm3NxFQ74X3SLT7jTTzsKUc3PnrCcQxNU4dcqCUJ5UkxcUmE1bcqjf4rmTJUZmF/KetyRVbu822LvJwLjM8MPzWNJi0Bqkz+pjxB7x2h52EVGcpmlqiM6xM3TYMQK3/5clIdWvJa50OVA4wBnrDAoK94XvY5O+6YLblByTf+x5jG8NvIQLbE+08Yk9P0ZDKHHebUzb4cVTkzz6+hD9kwuDHba0eKHV92+uR/XFmyvmtfE5xnMlmiIB9jbFL2sfzwzNkDVtdjXEaItdeqoFH689/j8fmiNfkggBpu0tT0bFOX0iqiJoii8I4K1JlaakSlBf+74THx+fa4cviq8BrusyMzNDbW2tP4KkSvFtWN2sZ/tJKbFPfB/r5OMAGHvejdZ51xqXav2xHmz41Ngh/vrEdzAdh5gW582NbyFhxEkEdXY3xAjrvtf4+VgP9rseefLICP/w5EmQcHt3I++7s5PpTJHGeIjaVQ7Hdqk2zLk5jpSOcNI8iYPXEVav1rM3sJc2rY1TozZ/9u0scXeMhDvMlGhj1m3kDdEnuXvuj4nYY0hgVL+JJ6KfYELfcVnlDujiXAF9OUF9meXLeamfPSL9Q/eE2b95/Ycn9O/B6se3YXVSLe3VK6Wa6unfS9WNb7/Lwzr+CNbJxxF6gMAbfhklXLtmZVlPNpRScmi2j3/pO8Dp9AgAQdXgRzbcxjvab78kUTpt5ujLjNObGeVMZoy+7Nh5BfDmULIS+rwr1symKxTAV5OCW1jiTT7tTpN380vWcRwHTdVIqklq1doFoVxJogp1ybpSSoanPe/x1/stbNfrag7ogps7DW7vNmiMe9usZOCpIx1G7BH6rD4G7UFsaVe+q1Pr6NQ72aS2EznxNXj5T8DKAQK2fRj2/xoEE6t1qlaMK10OFg7Sa/UiENwbupcuo+ui233l9Ct8oe/7ONIiGQzx89t+hDsaL9wmklJyeizNY4eHefHUJG75fMcjBg/ubuUNO1uZztgc6p1mT1cdnU2rMwDkRiBdsvjh8CwIuLut9rKjBvamcpyayVEbMri1JXHJ26+nZ+i1RkrJwKTD44eLPHvCBEAIkBIk0BRX2NSoLRHA62sUNHX9COA3sv2uF3wbVie+KL4GOI5DT08P3d3dqKp68Q181h2+Daub9W4/KSXWse9in3oKAOOm96JtumONS7W+WC82PJ0e4Y8Pf4WZUgZdMXhD/YNsCG1AVQTdtVHaY0Hfa3wZ1ov9rkdeODXBZ79/jGzRIm/aGKpC0FD5hTfv5L6dLat2nMu1Yd7Nc8T0xPH5Tqs6tY7N7ObvvhLHVvJE4jlycxE0N8JvfaCGuGEiD/018vW/RFoFJIJs+/uY2PLLZKmlaEkKpqRoluflz4uXWc6Vv8YqQhA2RMVLXRGex4vjQkADV0LQULwyr3OPcf8erH58G1Yn1dJevVKqqZ7+vVTd+Pa7PKTrUnrmL3BnBlCS7QTu+ShCWZvztx5tKKXklelTPNR3gDPZcQDCWoB3tN/O2zfcRsEuMVaYpTmUpC5Yw5yZWxL+vC8zxnTp/AK45/3dVJmHtfUhgK+UvJuveJJP2VMM54ZhmSooKCTV5JL85AklURHK8yWXV3otnu8pMZ1dyOnb2ahRG1X44ckSps05A0+llIw74/RZffRb/ZRkqbJtTInRpXfRqXcSV8/y3M1Pwgt/CKcf9j4HEnDrJ2Dr++Eq5RM/H650ebb4LKfMUwgEd4fuZoux5YLb5Eyb7/T18+j4D5izpxAC3ti6j5/c8iYC6sUH5aZy86HVR0nnPQHRcmJoSisCBYTLg7sU/vUDTatSx+udV8bnmCznAr+p8fLfdfKWw9OD0wgB97fXEdAu7Tm4Hp+hV5vpjMMrvRavnTGZzbk4rmQm47X3g/p8u1zwnz9QQyKyvs/JjWi/6w3fhtXJSturvrubj4+PzzVCCIG+4+0gXezTT2O+/jWkXURJbEBE6lFClxeWyWf12VzTyu/f+m/448NfoWdumEcnHuHu+tvpDu/i+FSGiVyJXQ0xQpfYsPHxuVxu29KIaTv8jy+/gutKpC5xXMlnHz3Kro3JVfcYv1TCSpjbgrex29jNUfMox83jXqcaT9L9do20mwUkcUdjt3MP8XACCCJu/hhi6/vhhU9C7zdIDH+ZxMT34OZ/Dzv+NagXzt9mO5KSVRbMS5KCtUhEP0tMX7K8vJ4rwZWSbEmSLQG4WLakZIMACpY3It1yXF46ZfKG3YF1Eb7ex8fHx8fHx+dshKJg3PIRSk/+Ke7sIPaJR9F3vG2ti7VuEEJwS303N9dt4YWpkzzUd4Ch3CQP9R3gS71Pkrby2K4DCBpDcdzz+BA1h2vpKoc+74w1s6kKBfDlCCthwkqYdr0dx3E4OXSSts1tpEgtCb1elMXK3/OoqCTURMWbfMfWOu7YFufMuMtzPSbHhyxOjVnMZCRqsEA4nqWQjfL5Ay7JBodZY4A+q29JWPeQCLFJ30SX0UWdUnf+QenhBnjD/4ZtH4Jnfwdme+Dgf4GTX4K7fhvqd13lM7eAIhTuDt6NgsJJ8yQHCwdxcdlqbD3vNhFDY2Oslvdq76KveIinJ17iByOvcmJuiF/a+R42RhsveMxEJMB7b+/kR27eyOOHp/j+azOksnUIoeBKGyEVHj/ict+uAh0NfhjvCzFXspjMlRACNieuzLs+rKvEgzpzRYuxXImO+Prw1p8uppcM/llrckWXQwMWr/ZZDE0vRIQwNMGudoOgAU8d8QbSBMsDada7IO7j47P+8UVxHx8fn2uIEAJ95ztAulgnfoD5/D+AZiD0CMb+D6NtvG2ti+hTJmFE+S/7foK/PfkIT4y+xjNTz5FJzHJz/G5mCibPDM2wvS5Ka9T3Gve5NtTFggR1lZLl4DgS27GxXcnAVHbNRfF5QkqI/cH97DJ2ccw8xpHSEeb0aVQkQioIYdHLE7xUzFOv1hNTYsTCdegPfBJ2/AT88Pdh+gg8///CiS/CHb8FG+4/7/E0VaCpgkgQiF1aWaWUmDYLwnnJm09nHB56plDxQrdscFz4/usFnu8x2delc0vXQghIHx8fHx8fH5/1ghJOou99H+aLn8M69QRKwxbU+s1rXax1hRCC2xu2cWt9N89NHOfzvU/w+kwvrpQoCFwkQ7kpmoNJ2qL1dMVavDDo0SY2xZoJa4G1rsI1QSCIKBFq1Bo26hsB7/05J3NLRPIpZwpTmucK5UKlNlbLltvq2H1zksPHorwyMUbTvpdRNAsAKxfhq3MOmgq6KggoOhv1DnZGumjRmlEuxdO7+TZ4z9fg2D/Dy5+Gydfh4ffD9o94IdUD18YJQQjBncE7UVE5Zh7j2cKzuNJle2D7ebdpjQZJFS32xvdzb8sW/s+xbzCcm+K/vvT3/OSWN/Gm1puX9DlIKZnLS4ambYanHYbKk+UEkG4TQkhAopTzwUsU/vHJAX75HZ3UhNd/Sqi14vSsNyijORIkcplh0xfTEgmsK1H80eGX+czRhyk6FiHN4GM7382bWm++5uWwHMmJYU8IPzliUY7+jwC2tOjs69TZsUHH0Lxr/t4dQWYyLrUXSLng4+Pjcyn44dNXEdd1GR8fp6mpyc81UKX4Nqxuqsl+bj5F4Zu/BVbRS44jFDAihH7kt1HCibUu3pqxHm0opeT7wy/zD6cexZUuGyNNvLnpzbiOJ0LWhw121scI+l7j69J+1xMz2SK/8jcHyRVtXCkpmg6KIuhsjPHBuzZz386WK/ZiXm0bDlgDfDP7zUq+cYn32hkRETSx0NEQEiFPIFeixKZ6iZ38BtG5UWryGQJNdyLu+C2Ib7ri8qyUxTnFNQW2b9CZTLtkiwshIDfWa+zfbLB7o05AXx8DY/x7sPrxbVidVEt79Uqppnr691J149vvyjFf+wp2/wuIYIzgG34ZEYhe0+NXkw0PzfTxiec/i5QSTVFRhILjOvzPW3+Gm+svHPr6euVS7CelJCuzS4TyaWcaU5pL1rNdl4ybWbwlILBzMazpVsyJdqzZJnBVgrpgQ53KhjqNtjqVDXUqsdAlXEf5SXj+f0HvN7zPgQTc9h+g+33XLKS6lJKXSi9xpHQEgFuDt7IrsLzXuuW4PDkwjSsld7QlQZj832Pf5LWZXgBuqd3KW+vewkzKqAjgi9sl8xiaIB6WHOo38SRGAAXvXJdQlSz37wryvjvbCK2C6Hs9kSpaPD8yixCCezYkCetXfn5KtstTg1NICfe21xHWV95ndDnPUEe6TBfTTBbnmCymmCzMMVFMMVWcYyQ/zYm5oSWDfxQh2JPcRFuknqZQksZggsZQeQomCK3iACBXSvonHF49Y3JkwKJoLchRLUmVfZ0GN3Xol3afr2Oq6TfQZ3l8G1Ynfk5xHx8fn3WMM3Wa4hN/AtIFq+TF50WiNG5F3/5W1LabEIrfSFlPHJ3t59NHvkrGKlCjh/nwpnfgWAlcKdFUhR11UZojAd9r3OeqcuDoKJ999ChFy0URUBsNYlqe4NxWF+HH793Cjg3JNS7lAjk3xxfTX8SUJhoaJiYKCluNrRRlkbSbXpIvsIJ0oZQCM41uW8QKOWKhDcQa7iJmlD3MlRhhEb40D5JLYC7vLhmR7riSkyM2L502l4xo11XBno06+zcbbGxQ/WeAj88NyI3SXr1R6unjcz0gbZPSgT/HzUyiNm3DuP2n/XeU8zBdTPPRg5+mYJeIaEFydpGQFuAv7vmVdRFeuBqRUpJxM0w5U8y4M0w704zZY6ScOa/rA4F0FHRV8Eb9nTizzQxNOwxOOYzMONjuuV3VibBSFsg1NtSptNaqFx+YOvo8/PB3vZDqAA174a7/fs1CqkspebX0Kq+XXgfg5uDN3BS4adl1X59IM5Yt0hYLERch+qcsHh19keeyT2O7LoaMsrn4FmJuKwCKgOaEN2CgrXxOGmoUFEXwT0+M88QRF0/+hEQEciWJZXvnVVML3L8rwPvvbCKwCuLv9cBLoymmCyatsSC7G1bvvp/f7+ZkhM3JyBXty5Eus6WsJ3gX55gslOdlEXymlMWV5w6WACg5FlPFORQEqqJiuw4SSX0wTuA8Kctq9DCNoURFMG8IxWkMep+TgeiK2uGTaYfX+ixe7TNJ5RfKFg8r7N2ks3eTQVPCdzDx8fFZHXxRfA3wR5BUP74Nq5tqsp9bmKP43d9D2kVQDShlPREoXItQVEQgitZ5F1rHHYjAlb04VxPr3YaThRR/dPjL9GcnUIXCR7reRJ3aRbrk5T5qjATYUR8joK6/sl8L1rv9rhdmskUm5oo0xoPEwwaPHx7h68+fIV++Dm/uqueDd3XRdBl50K6GDXvMHg7kD2BhoaNzX/g+uo3uyvemNMm4mWWnvD2HLE6DlfdWFioEa8HwvJ1UVC+so1JTEcqjSrQyX+yNvppkCi6v9pm8eNpkOrPQuK+LKezfbHBzp7Emo9z9e7D68W1YnVRLe/VKqaZ6+vdSdePbb3Vw06MUn/oMuA767neid91z7Y5dZTZ8fOQ1PnPsYYqOSVA1+NiOd/Ng6961LtaacTXsl3WyfCHzBUrSRJMBXGEREAYfrvkwEWWhz8NxJRMpl8Fpu+IVPTnncHbntQCaEmrFk7y9TqMx7onCSytjw9F/glf+FKyct+U1Dqn+WvE1Xi29CsDewF72BvYihMCVkum0y9C0w0CqREnPYVqCEycMpPTqkVMmOB18BEudQ1cV3lB3F+/vvJu2Wh1dO/+ggL7xPK/1jLO3u4nOpjADkxZffW6OY0M2djlNlK6Z3LsjyAfvbsDQ1v99erWYLZq8MJJCCMG9G2oJXYJH98UYzhQ4MpkhYmjc3Za84OAkKSUpM8dkMcV4fpbeyUFKumSylGaqOMdUcQ7nPKL3PJqiUh+M0xiM0xBM0BCM0xCMY6ga/+PVz1NyrMrgn4Cq84k9H6TkWIwXZ5kopLypmCJrFS56nIZg/BwP86ZgkrCo4cQgvHbGZHjGqWwT0AS7Nurs6zTY1KiiXMcDtartN9DnXHwbVie+KL4GOI5DT08P3d3dqKo/yqka8W1Y3VSb/eyBFzBf+iLSKSHUAPre94JjYfc9iyyWw4qpGtqGm9G67kGJNa1pea8F1WDDkmPyF8e/xQ8njgPwptabubf+bgbSJaSU6KrCzvoYTZEbI9fcYqrBftcr2aLFwy+c4fHDI7iuRFEEb927gR/d30E4sHJh+GrZMOfmyLgZYkpsSafXxbClTc7NkR5/mmzfl8lQJBOKkom3kElswNXOnxNPIAgr4YpYfvZkiAvn01tJmaWUDEw5vHTa5FC/VclDrgjY2urlHt/Wpl1xWPuV4t+D1Y9vw+qkWtqrV0o11dO/l6ob336rh933LOahh0FRCd73iyjxtmty3Gq04XQxzXhhlqZQ8ob3EL9a9rvYYNnzUbIkIzMOQ9M2g9MOw9MOc/lzxUFdFbTWqrTXq5Xw6/Gw8ITI/AQ8/4cLIdWDSbj1P0D3j12TkOqHS4d5ofAStgOxue3kz+xkZNpdFEZa0r3VRNclk2MGyUCgXAeV+oTN5898n4PjXij2HYmNfGznu6kNxM57vPPZcHLO4UvPTPN6v43jeG0UQ3O4e3uA991VR9i48cSfF0ZTzBZMNtSE2Fl//nN6OViuy5P9Xmj8O1uToJhMlAXuiYIX2nyi7Pk9VUxjuXZlW9M0MYylbVZFKNQHa8pityd6N4YSFSE8bkTO68F9KYN/claRieKCSD4/Hy/MMlVMn+ORLiWYtqRkSUwHdDdEQMYJyhrao0l2NddyS1s9bdHkBct4vVCNv4E+S/FtWJ34ovga4N8s1Y9vw+qmGu3nFuaQuWlEpA4l5I1Slq6NM3IYu/cAbmqksq7a2I3WdS9KQ/d1G/quWmwopeThgWf5Uu9TSCTbE+38zJZ3MjDnkjW9RkxLNMj2uij6DeQ1Xi32u54ZmcnxxYOnOTwwA0AspPPeOzq5b8fK8o2vaxs6Fhz7J3jlz8HK4iLIb303mb0/RSZgnONlbknrgrsLiAAxJUaNUlPxLp+fhqwhni48fUkddiVLcnjA4qXTJgNTC50Z0YDCvi5PIG+MX91zuq7t57MifBtWJ9XSXr1Sqqme/r1U3fj2Wz2klJgv/CPO2DGUaB2B+38JsYp5Ys+Hb8Pq5mra73IHy55NOu+WPck9j/LhGYeSdW4XdzSolEOMlz3KrZcJvvi7kDrlrdCwD+76b6seUn05Ib+YPEm465D3/fBm8r170FWF1lqvbJG4SY4STRGDW5oT5+zzqbFD/O3J71FyLKJ6iI9u/1H21y/fRrmYDVM5m88fmOTVPgfH9b43NLhja4D33B4nEbkx7tuZgsmLoykUIbinvZaQtvJ6TxfTjBVmaS4PpJFSkrdLFZF7Prz58dkJJgpzFN0cCOeC+1SEoDYQoz4QR807bG3tpCmUoCHkCeDJQAz1CgTl1Rj8M5+7fKwwy5GxaQ6NzdCfniEv05SUNDYlNBUCuiCgC87uhtAVrexVvjSHeWMoQUMwcU4497PPczXg/wZWP74Nq5OVtlf9xCE+Pj4+a4gSikNoacguoWhoG/ahtu3FnenH7n0aZ+wozkQPzkQPSqwBrfMe1PZbEOfJ/eNzdRFC8J6Ou9kYaeTPjz3M8dQg/+vQP/Oru99Hgx3jzFye0WyRmaLJzvoYDeEbz2vcZ21orY3wa++6idf7p/nCwdOMz+b5xydO8vjhEX783i1sb0usdREvH1WH3f8WNr8bXvpjlJMPET35daK9j9By0y/A7p8BLQh4HcAlWSLtpsm62XME84IsUJIlSk6JKWdqyWFc6ZKVWSQSRapYwuLx/ONERIQmrQlVLN8gCuiC/ZsN9m82mJxzeLnX5JU+i2zR5eljJZ4+VmJjvcb+zQa7N+oXz4Ho4+Pj4+Pj43OFCCEw9n2A4hOfxs1OYx3+Bsa+D6x1sXxuYCJK5IrE8Hlqwgo7wwo7270+EVdKpsqhyIemPKF8LOWQLbocH3Y5Pjw/YHYb9dF/5J7aL7Fz8v9ijL2K9vAHEJWQ6pcuujmuZLwc8n34QiHf81uIBzXUTa8S3dzL7Vt1HozfgVoeSJ8zdQ4OlZguWJRsh8BZAu39zXvormnjz458jTPZcT516CHevuFWfnzzg+jKpXXxJyIav/j2FtIFi39+apxXeh1M2+DA0RLP90yxr9PgnbfW0JK8fsUgKSWnZnMAtMWCFxTEpZQUnBKzpSwpM8uBscN8Y+CHmK6FQKEj2oALFOzSOds6rqTouChASFdJBqILXt7Bspd3KE59ME5doAZNURcEuY7VFeTqgjVXLCxPpyWv9gV4/UwdqXwSBegEEmGFvZsMujc4SCPDeGF2iYf5RCHFdMnzhh/OTTGcm1p2/8lAtCKSz5VyHJw4gi1dImqAj+18zw2d2sLHx2d18D3FVxHXdZmZmaG2ttbPNVCl+Dasbq5n+7n5GezeZ3AGXkDaJgDCCKN13I7WeReiSkZLXoxqtOFIbppPHn6IsfwMhqLxC9t/lJ3xbg5PZshbnrdoa8zzGteqpE6XSzXa73rGcV1+cGiEh19Ymm/8Q3dvpjEeWnabqrLh1BH44e/DxMve52gb3P6b0PFWuEg0DUta581jPufMkXEzuIsiwikKRJUohjCIKTESaoKEkiChJogrcWqUmmXFcseVnByxeem0yckRC7f81q2rgj0bdfZvNtjYoK5a9I+qsp/Psvg2rE6qpb16pVRTPf17qbrx7bf6OFO9lJ79S5Bg7P8IWtvVFRV8G1Y314v9LEcyOuswNOWUxWqb6ezCS37EmeS+3KfZXvoemgIykCS18+PE9ryP2hqtkvN4Lu8ynXGpiynUhAQzWbeS73x42mFkxsF2z+1eT4SVsoe6xoY6ldZalYAu6DF7eLbwLBJJt9HNXcG7Ku2B50dmSRUtttZG2ZQIL18v1+bzp5/gu0MvANARbeKXdr2H1nBdZZ1LtWEqX+JzT43ySq+DlN5xA7rCrvYAb90XYXOzdt1FLJzKm7w0OoslTbY36BScArNmllRZ+J4XwFNmjtlSBrMc2txxXcaLs7hSoiBwkShC0BRMoioKcSNCQzBeDmmeoDZQw9AcRNUoD2xspzG8fFt8MevtHswUXA71W7zaZzIyuyhPuC7Y3e7lCe9YQZ5w23WYLqWXCOYThRTjBU84LzpmZd2zz7NEYqg6P7f17dzRuION0YZ1G4Z9vdlvJVSjR/7VpBpt6OOHT/fx8fG5LpFWEXvwRezeZ5D5WW+hoqC13uSFVk9cmxxxPkvJWUX+/OjXeW2mF4B3bbyT92+6n75Ugf50HiQENZVdDTHqQhfOY+zjs9pkChZff+EMTx7x8o2rquAtN23gnbd2EDKqPGiQlND3bXjhDyE35i1ruQPu+M9Qu+2Sd1eyHB49dprDye+j6g6uI9AMFxDUaUlQzs1dCKCgXFQszxRcXu0zefG0yXRmYT91MYX9mw1u7jSIhfzGlo9PNXKjtFdvlHr6+FyvWMcfwTr5OEIPEHjDL6OEa9e6SD4+15x8ya2I2YNlobw28wIPZv+QWrsPgFF9D88kf5Ng8y6khEP9FqbtdZ/Hw8qyKciDuqjkMZ8P1X6hd/vT5mkOFg4ikWzWN3N36G4UoTCULnB0KkPU0LirLXlBIfqVqVP83+PfJGMVCKg6/6b7rdzfvOeKxOvpTJHPHRjktTMuyBgIQUhX2dRo8KabwuzeqKOp618cl1KStcoi9yKBe/G8P5MibeVQhIuxwrR3YS2AJlROpocxFBWt7KFvuw7/ce+HuatxBwH13D6fw5NpRjJF2mtC7FjlvOVXC9OWHBuyeK3P4tTYwgBvRcDWVp19nTrbWnV0bXWuByklWbtYEcxfmznNl/ueRgiBK10c10UiqQ/GCag6MT3EzkQHu2s3sTu5icZg4robuHGt+N7Qi/yfY9+gYJeI6CF+eed7fY98n6rEF8XXANd1GR4epq2tzR9BUqX4NqxubiT7SdfFGT+Kffpp3Jn+ynKlbhN6170oTTsQVXgOqtmGjnT5l96neHjgWQD21nbx73e+B9NRODyZoWB5o2nba0JsrY2uKL9ztVHN9rsRGJ7O8YWDpzg66A2oiYV13ndHF/dsb65cj1VrQ6sAh/4SDv0VOCUQCmz7CNzyKxBMXHBTKSVnJjI8fXyMH54cZy5vojVOsf3uKVTdxbEVep9r5NfufjMbWwxSboo5Z46UmyLlpEi5qfPmL1dQqFFqKmJ5XI0TF3FSsxFeOW1zqN/CcrxX8fnOhVu6DLa1aZf1jKha+/lU8G1YnVRLe/VKqaZ6+vdSdePb7+ogXZfSM3+BOzOAkmwncM9HEcrVCY3s27C6uZHsJ6VkNicZmijC0X+gtf//AzuPKxVeC76Pb7ofRZcF6uQg06KdjNJEQ1ypeH/PT7Ux5aJesmfTZ/XxdP5pXFw69U7uDd2L48KTA9O4UnJHW5J44MIp82ZLGT5z9BscTXn9Qvc07eLfbn0bOavA4f6T7O7YSkMoccnnZXQ2zxcP9nOoXyKII4RCyNBoTurctyPIbVsCBI3V69NYqZeqK10yVmGRF/e5gvf8345cfkAzgCMlRdtFACFNJaoHSQZiJIwICSNKMlCejCgJI0oiECVhRAioBtPFNB89+GlPQNSC5OwiIS3AX9zzK+ct+1Te5OWxFIaqcP/GuoteK2t1D7pS0jfu8FqfyZFBi5K9IBttqFPZt8lgT4dOJHj1y3T2ec5YeRShcEfjdvoyY5ScpW3w+mANu5KeQL4r2UHCiF71Mp6P9fwMlVIyWpihZ26YnvQwR2b6eWXmVMUj31A1aozIBa/nG4H1bEOf8+OL4mtAJd9H9+rm+/C5dvg2rG5uVPu5qSGs3qdxhg9B+aVfRGrROu9G23grQquefNbXgw2fGT/KZ49/C9O1aQ7X8ondH6AxVMvJmSxD6QIAYV1lV0MNyeD1lRP+erDf9Y6Uktf6p/nSwdOMp7zrsb0+ykfK+car3oaZYc9r/Mx3vc9GjSeMb/9xOKvTN1Ow+OHJcQ4cG2V4OldZXhM2GJjKogUtAhGT1KyCXdT5rx+4hbu2NZ9zSCkleZmvCOSLRfMLiuVqDTES5FJRhoYijI6FcQtRkArRgMK+Lk8gb4yv3A5Vbz8f34ZVSrW0V6+Uaqqnfy9VN779rh5ufpbSk3+KtIro3Q+g73jbVTmOb8Pq5oa2X24c9/n/F/fUtylaklzRISznEAJMQnwt9Hvc/95/xZaW1WnL91v9PJV/CheXDr2D+0L3cWQyx1h25V7FjnT5xsAPeajvAK500RWNOTNLybKIGMErysN8ZiLDl57p4/iwRJBECI1wQKU2qnPbFoM7tgaIRwRSgkTCokzqEq+tJMuf5hUIiVxYS0oOjB3ir05+l5JjoSsa7954F5tiTRVxe3FI85SZw72A2H02MT1UEbnnxe2kHmU0KxFugO219dzc2IihXloEtcdHXuMzxx6m6JgEVYOP7Xj3Bc+xlJInB6YxHZdbmhPUhy8cQfBa34PjKYdX+0xe77eYyy+c30REYd8mg72dOg011/5ZcL7zbLsOpzOjHJ7p48hsPz3p4XMGQWyINLA72cHu5Ca2JzYSvoZ9o+vpGVqwS5zOjFZE8FNzw2TtYuX7kmMxVZxDEyoBTccQGjYuf3jbz7Ez2bGGJV9b1pMNfVaOL4qvAf7NUv34NqxubnT7ycIc9pkfYvc/hzQ9sUvoAdSNt6F13o0STq5xCS/O9WLDvswYf3z4y0wV04S0AB/b8S5uqe9mOm9yZCpD0XZAQEdNmC3JyHXjNX692O9GwHZcfnB4mIdf6KdQzje+f3MD779jE7Pjg9Vvw9HnvXzjsye8z8luuP23cFru4ujgDAeOjfFK3xRuOQacpirs31zPvTta2N6W4OCxMT776FGKpoPluIQMjXjY4FffeRPb2xIrKoKUkpzMLXiVlz3L59y5ZcVyx4WSKSikI5QyMZx8DU4+RlMgya0batmzMUBAv/Czwr8Hqx/fhtVJtbRXr5Rqqqd/L1U3vv2uLvbIIcwXPwcCAnf+LGrDllU/hm/D6sa3HzD6HNaT/wll+AACF4sQDholJYb6k89RU9+6aocatAZ5Mv8kDg7tWju7uIvXxjNoqsIDK/Aqnufk3BB/fPjLHEsNLsnDrAiF9kgDmqJW5OglYnV5+4Vl5eVy4VvTdsgULLwAeCoCgRACRQgCuiBkCLTLuFQulp97OQSCGiPsidxGhEQgSq0RIxGILBXAjSjaMtEwxnMlXhufQ1UE97XXrTh0+tlMF73c2E0rzMF8bCrDYLpAayzI7oYLr3+17sG5vMt0xqUupqAIeP2MxatnTEYX5QkP6oLdG7084RsbLp4n/GqzkvNcckyOp4Y4PNvH4dl++rPjS75XhMLmmhZ2Jzwv8u54G7py9VLJrdUzVErJWGGWnrkhetIj9KSHGcpN4p4l/+mKRlesme54G03BJJ859jAlx1px5IMbAf93sDpZaXu1yhNJ+vj4+PjMI0Jx9B1vQ+t+EGfoFezeg7jZSezTT2P3HkRt2YXWdQ9KssPPs3OV6Yw183v7/w2fPvJVjqcG+dShL/PBrvt5z8a7uGtDkhPTWUYyRfrn8kwVTHbXx4hfZ17jPusbTVV469527trazNee7+PJIyO8dHqSV/um2NcaoL3DJhKq4hf/ltvhPV+FE1+Cl/8Ee/okha//a16XN/NV+WFmRSMAHQ1R7t3Rwh1bG4ksCk14384Wdm1MMjFXpDYa4PNPn+L1M9N8+luH+PV33UR3S/yiRRBCEBVRokqUNtoqy+fF8nnP8jl3zvtbpFCDNuFgFrM2S9EawbIlOQlPSoUn+qIk1QRd8Vo6a5Ik1SQxJYayKKlhzs2R0lLk3Bw16o3bgPXx8fHx8fE5P1rrHtyO27H7n8d85UsE3/DLiMDahZn18VmXtNyBfu/v4Tz0dlynhIqJiklAcVHNAWD1RPF2vZ0Hww/yeOFxBu1BHM1B1/Zi2S6TeZOmyMo8XLfGN/BzW9/Bb7zwlwtRBPHyMefsIgH18vscDE2lLqZSsjxx3HZBShUpBUVTULIEugYhQ2BcQo5pWzrIsiAuhEDD23ZzTQtdNS2LwpkvhDaPG5Flxe6VIKXk9KwXJayjJnzZgjhAXbDmkkTD5miQwXSB8VyJHXXymjtHvHTa5IsHcxRK3oCHkCEIlEPgK0KwrVVjb6fOtjYdfR3ljV/JeQ6oBnvruthb1wVAxsxzNNXPodkzHJntZ7ww63lKzw3z1f6DGIrG9kR7Jdx6R7RxSbu6Wig6JqfTI5ycG+ZUepie9AhZq3DOevXBGrprNtAdb6W7po2OaNOSe0gRCp859jB5p1R27Hn3DS2I+1z/+J7iq4iUkrm5OeLxuC84VSm+Dasb335LkdLFnejB7n0aZ/JUZbmS2IDWdQ9q656rlkPucrnebGi7Dv946lG+P/wyAHc2buej23+UgGowmS9xZDKD6bgIIeiMh+lKhtd8FO6VcL3Z70ZieDrH58v5xl3XIRkN8b47O7lne3PVXpMly+Gl3kmeP3KKjYN/x53Ooyi4uEJjrO3DNNz/a7Q3N65oX6bt8OffOcyRgVmChsqvv2svm5tX9530bLE85aSYslJMlFLkLQtnUTQ4VYGgIQjpKkktTkJJUJIl+qw+HOlgCIP7w/fTbXSvahl9rj7+c3SBnJsj42aIKTEiSmSti3NBqqW9eqVUUz39e6m68e139ZG2SenAn+NmJlGbtmHc/tOreq59G1Y3vv3K5Mbgn+/ALaXBsRBOASEU6P4xeOCPIdZ28X1cAqP2KD/I/wBb2kSceqKpm2kMR7il+eIDcueZz8Ocs4oEFY2iaxFUDf7gtp8lWR78IhAIWGTbpZ/n/5//urI+AoTAdSUvnprkWy8PMJORKCRR1RqiAYOgrtEYV7lrW4A9HTqaKspe5fNHWXqc6WKaXzz4ZxSclefnvhLGckVeH0+jlb3E9SsQxS8VKSUHBmco2g57m+IXHOywmveg7Uhe7TP5+8dzlCwQAqT05ns26tzW7eUJDweqTxReKVPFOQ7PnuFwWSSfM3NLvo9qQXaWQ63vTm6iKZS8ovN+NZ6hUkrGC7OcLIdA70kPM7isF7hKZ6yF7po2uuNtdNe0kgxcPA3DpUY+uN7xfwerEz98uo+Pj49PBTc9ht17EHvoFXC9sEgiWIPWeRdax+0II7zGJby++cHIq/ztye/hSJeOaCO/vvv9NIQSmI7L8eksY1kvn0/U0NjTUEMs4Ady8bn2SCl57cw0X3zmNBPlfOMbG6L8+L1b2NqaWNvCrRApJX0TGZ4+NsZzPeMUzXIYOAH3NGV4l/s56jMve90xoQa47T/A5nfBCkaFm7bDn3zzECeGU4QCGp949142NV68cXmlSCnJuBlOzs5wdHKG0cIsIpRGDWcQqoOuCQK6pKhkvPCGUoCQKCi0aq3ElTgRJUJYhIkoEW8SEcJKGE34z5rLIjcG6X6o6YDIuXnmfa6cHrOHA/kDWFjo6NwXvm9dD/K4UdqrN0o9fXxuFNz0KMWnPgOug777nehd96x1kXx81h8nH4InPg523lMSjQioQdDCcNtvwPaPwCoKJmP2GD/I/wDTtZDFOK2FO3igvZHAJcQmv9R815eL5bg8eWSEb77UTyYvgSSGVks0aBDQVGIhhbu2GdzWHSBknP8cXavySil5ZniWnGmzORlhc/LaD7o8OZPlTCpPYyTAvqaVD3a4VFwp6Z9weL3f5FC/RabgMpuTCEBTQVdBIvjVd8bobLqx2oRSSoZyUxxJneHwbD9HZ/spOuaSdeoCNewqi+S7kpsqA0quJZ4X+GglD3hPepjMebzAt9S0sTXexpaaNjad5QXu43Mj4Yvia4Drupw5c4ZNmzahnCfvic/6xrdhdePb7+LIUha7/3nsvmeRpSwAQtNRN9zihVaPNqxp+a5nG56YG+JPDn+FOTNHTA/xK7t+jJ3JDsDLaXV0KoNV9hrfnAjTEg1QsF3CukrwcpJzrQHXs/1uFFzX5XRvH6fSGt98aaAiKt+6pYEP3rWZ+prgGpdwedIFkx+eHOfAsTFGphdGfdfXBLl3RzN3b2umLhb0OrIGH4fn/gAyA95KDfvgzv8MDTdd9Dgly+GPv/k6PSNzhAMa/+G9+9hYf20byCVLcnjA4sXTJYazadRwGr1uhHDnMS9KoiIRQiIERJXIBYXvgAgsEcnP/jsswqiiOp4/14zFHaNaGB74FGz9wKrt/kZ9js5HSphz5hh3xnmu8Bw2NgKBhoYhDD5c8+F16zFeLe3VK6Wa6nmj3kvXC779rh1237OYhx4GRSV43y+ixFfH89W3YXXj2+8scmOQHoCajeCU4MBvwfiL3netd8G9/xOiqxdOfcKe4LH8Y6StIoad4F7jQbqTiUvax2Qhxau9x9jXtYOG0KVte6kULYfvvzbEd18ZoGhKIEHIqCccCGJoCoYm2L/Z4O5tAZLR5a+na+GlOpotcmgijaYq3Ndei74G13bGtHl2aAZFCN6w8fye6pdzD0opGZl1eP2MxaF+i3RhIdRYUBdMzHlt+0hAUDAlAV3htz5QQzx8Y9/jjnQ5nR7hyGw/h2fP0JMexnadJeu0RerLAnkHOxMbCWsX7he5VPvNe4H3pEcq+cAHcxPLeoFvijWztcYTwLvjbdSuwAvc59LxfwerE18UXwMcx6Gnp4fu7m5U1e9ArEZ8G1Y3vv1WjnRsnJHXsHufxp0bqyxXm7Z54nj9ljUJD3O923CqmOaPD3+ZvswYilD4qS1v5i1ttyCEoGS7HJvOMJErYbsSy3VRhEBTBDc11tAWC6118S/K9W6/G4HFNsyZDl977gxPHR1BSi8P+dv2beAd+zsI6mtvX8eVHBmc4cCxUV7tm8Z1vVdaXVPYv7mB+3Y0s7U1sXz4d8eEI38Pr/4fT9wE6H4f7P91CF94cFDRtPnUN16ndyxNNKTzG+/ZR1vd2gh1k3MOL/eavDiQwtj/LRTNwjF1VMNCOiqcvpt4VBKIFNBDBYRRQOoFLDWPFPZF9y8QBEWw4mG+2Nt8/u+QCFVl/rVLwi5BqgeGDsKB3/SuGUXzIq9oQdj/cUh2Q7gRIk3ePFgHlzFC/3p/jrrSJetmmXPnSLkp5pzy3J3Dlt41aUubnPQGtwgEERHBwuI90ffQrK1Pz/xqaa9eKdVUz+v9Xrre8e137ZBSYr7wjzhjx1CidQTu/yWEtrL8xRfCt2F149vvIkgXjv4TvPQpsIveQMnbfxO2fXjVvMannCm+nX6EnFMkIpN8qO5HCCorH6C8FjbMFi2+8/IAjx0axrJdoIaaUBOGFkLXFBQBuzbq3LsjQFvttfVOllJycGiGvOWwpTZCV2Jt2m+LvdV3NcTO289zKfabTHtC+Ov9JtOZpUL4znadvZt0Ohs1Xumz+NLBPKYtMTTBh+4Js3+zsar1uypc4yhdJcfkxNxQJdT6mcy4F5GtjCIEXbGWcj7yDrprNmCoS6/ni9mvtMgLvCc9wqm5YdJW/pz1agMxumva2Br38oF3RJvQlRvLs3+t8H8HqxNfFF8D/Jul+vFtWN349rt0pJS4031e3vHxY8y/5yk1TWhd96K27UWo+jUrz41gw5Jj8VcnvsPB8SMAPNCyl3+79a3oioaUkv50gVfG5pBQztsFhqbwxo76de8xfiPY73pnORsOTmX5wsFTHB9KAVATNnj/nZ3cvUb5xsdTeQ4eH+Pp42PM5RbCnHU0xrhvRzO3dzcSCazwuZWfgBf/CE591fushWHfx2DXT0Fx5ryN71zJ4lMPv07/RIaasMFvvncfzcm1S0NxatTir189TN2elxCqjetojL+yH3V2E7q2nI0kqm4RqykSjZcIRQsEwkXUYB6MAq5awFTyuDjLbLsUBYWQEjqvt3lERAiK4HkHWq27vNFWDmaOw/RRb5o6AqlTIB2wC5AdgXI+RaQEpOcZpJ3VoSUUCNV7Anm4EcJNi+ZNCwK6UbOk8/R6eY460iHjZjzx20lVRPC0k8Y5z3WloFCj1hAREU6Zp3BxCYgAlrR8T/F1QjXV83q5l25UfPtdW6SZp/jEp5HFNNrG/Rj7rjwCim/D6sa33wpJ98OB/wTjL3mfW++Be39/1bzGx80pvj73XRxh0mTU847oW1csjK+lDWezJb7xYj8Hjo2WBy6HaahpRRBBVb333s5GjXt3BOhu1a5Jm3I4U+TIZBq97CWuraHnZ+9sjlOzOWpDBre2JJZd52L2m8u7HOq3eP2Mycjswru1pgi2t2nctMlga6uGpopztpvJuNTGlOrwED/xJXji18DKe+2mVY7StRKyVoEjs/0cmT3D4VQ/Y/mZJd/risa2+IaKJ/mmWDPThTlePHmIW7fuoSGUYKKYoqccAr1nbpiB3CSudJfsR1NUOqPNbIm3VjzB/dzea4f/O1id+KL4GuDfLNWPb8PqxrffleFmp7D7nsEZfBFpWwCIQASt4w60TXciglc/JM+NYkMpJd8efJ7P9z6OKyXd8TZ+ddf7SAaizBRMDg7N4EqJK/9/9t47vo7rvNN/zrRbgYveC0EQ7GIRRapQ1bZkyZIs22tbtrPrbOyfk02c2OmbZDcbe7OJ42SdTXPiOM1pjuU4tmTLlmTJVqEaSYm9gwUgegduv3fK+f0xwAVAgiRIAAQuOA8+9zNzz507c8595wzmzPe870tOHG+KBFlfXoBxifBaS4EbxX7LmUvZUErJgbYhnnjtDANjbh6rxvIwH72rhZbqhcuFNkHGtHn7zAC7jvdwqnssVx7y69y+ppI711ZRP5cw5v0HYff/gYFD7nvVD8le1wvkEiGyE2mTP3rqIB2DcSIhVxivLFocYXws6fD734piKQmMUJRMohDVDvJf7g1hWTCadBhNTH3Ji0KxXYwkHDIpKEoTLkjjD6fQA2kUXwpHT2IpSTKkcHCusB9QUaeFZJ/wNh+xRziSOYKNvTh5ozNjk8L38LgIPtYGzPDb+IqgsAnan3M9xI0wZGOgGrDhp1zv8WQ/JPsgNeieO7NBNaaJ5o6/nL6YpKJpI2q4xhXOAxWgL81oIZa0iDrRnPA9IYLHnNglzw1NaESUiPtSIxQpRUSUCAVKQS7qgJdTfGmST+307knyG89+1x978CyZN/4GJBg3P45Wt2Vu+/NsmNd49rsKHBuO/wu8/ceu17gegh2/Aas/NC9e42/2d3BIvIJQs1QYJTwQfICAcuX7wqVgw77RJE/uaWNPaz8AivBTW1qHaYVyE2bLC1XuXOdjRYXKWFJSugBirTPuJZ4ybVpKwjQt0phtgqRp82rHEELA3fVl+LSL2zuT/ZIZh6PnTQ61m7T1W7kRiyJgVZXOphU66+p0fPr1n7g+Lzg2jJ6BoaPuuKzvbTj7tFsuFHfcFCiDn9h9XTzGL8VgOuoK5OOe5KPZ+LTPbekwkonjODaKolDmn/l5SYmvgFWFNbRE6mgprKGpoMrzAl9CLIVrqMfV44nii4CUkkQiQSgUWpSwwx5zx7NhfuPZb36QZgqrfS9W2xvI5KhbqKhotZvRVt4JRhCZGESEylAC8yuG3Wg2PDR8lj8/+hQJK02Jr4Bf2vgBaoOVvNg+iOVIFAFZ2/03HdAUdFWhMRKksTBwydxTi8mNZr/lyJVsaNoOLxzq5Om32qflG//wHc1uzu55rsvZvhivnuhhT2t/7nhCwIaGEu5aV83mFZfOw3b1B3Tg9Hdh9+9B/4FxQdw/ubzj81Cyzh2Ah6ogUEosbfOHTx2geyhBSYGPX3/fFsoLF0e8fPtMlm++liBjSny64MM7Q5cMh+dISTwlJ0XypMNo3GE0OVmWta48RPDpkkjE9TgPFqTxBZOofjdMu62lyIgEaZmeFm5usg4OMRlzP5MCRSj4hMFHCj5CoboA9/vJgckHLEPH3PV498zbBiuhbINr79INULretbkQs8sp7thupIFknyuUJ/ogNb6cKEv2Q2b0okNLQDoOQlGY1gONggs8zmdaL3fDus/EHMMOmtJ0RW97etjzuBOf0b4AutCJKBGKVFf0LlKKiKgRwiI8q/8RSy6KwGXIl/HqXMmndnr3JPmNZ7/FwTzxQ8xTLyJ0H767fwElVHrN+/JsmN949rsGou3wym9A/z73fe2dsPN35+w1PpjM8mZ/Jz2hNzEMk4gS4YHQAwSVywu7S8mG7QMxvr37HEfaXQ9bTTVormwglQ1j2pDOShJpiaq6Ib8/tDPIrS1zT+MwQWcsxbGBGIaqcFd9Kaqy+Of07u4RxtIma0rDNEYutuWE/XRfkBNdFofaTFp7rGkTmxvLNTY16mxs0An5l97zoctiZWDk1Pj47Li7HDnppjnLbTMepWsiVZcEjBD8p+egeseiVPtCpJR0JQc5Mu5Jfnj4HG3xPhwpUQAHN9x6daCElojr/b16fFnmeYEvaZbSNdRj9niiuIeHh4fHnJCOg91zxM07PtLhlplpMFOgKAjdj7Hto2gN2xe5pvlNb3KYLx35D7oSg+iKyifXPERzaBWH+qPYUqIKwYpIkLGMSTTj5lvVFIXGSIDGSGBRw3553LiMJbM8ufscrxzvAenm8X73lnoeurlhzvnGo8ksb5zq49UTvXQPJXLlZRE/d66tZufaSkrC8yvAT6PjJfjOI2Bl3TANlwqRLVQIVZL1VbC3R6E7U4gdqOQ9d26nsKwBwtXXnFf6WpmvcHhSSlJZyWhCTvcwz4nnDsnMlYcQmiKIhCSFRRlC4TSBgjS6P4XiS5HUh+h0zuE4uL8zEiGgRC2mxWhhpb6ScrX86gegUroPTy4UwFODM29fUO+K3qUbxl/rIHAFESDRC9HzUNgwNy8FKwOpgUkP8wmxPCeej5dZqVnuULh1v1A0Hz0LJ77uPmTSQ66Qv+bDM+4hIzOM2WPTcn6POWPEnfiM2wMYwqBIKcqJ3xPe30ERvGEeINwo49UbpZ0eHjcq0nHIvP5VnOF2lOJ6fDt/BnEd72M8PPIex4Zj/wRv/z+wM/PiNS6l5JWOYeJOlLGivZgiRaFSyAOhB5b8pMELOdU9yn+8eY7TPW7kL5+u01LdxKkuP/aUAENCwLo6jYZyjdoSleoSleoi9RJpoS6PIyWvdgyTtuxLCtCLQftYkpNDcSJ+nVtriqd9ZtmS072uEH6808S0J8ddVUUqm1bobGo0KArlybOgifRUg1PGZxPpqS5ED42PzdZDqAZe/R9gp8GxIBt3vcX/6zEoarr+7ZgFh4fP8et7/xYFgQQUBLZ0+OL2T7KpdOViV8/DY9njieKLgG3bnDlzhubmZi+sQp7i2TC/8ey3cNjD5zFbf4x95tXxkLACkKCoqI07UEubUCI1KJFa8Bde80PwG9WGKSvDXx7/Hm8PtgLwnvod3FO1lc74MI0FpdSEipFS0p/McmYkQTzriuO6qtAUCVJfGFgSs51vVPstJ67WhucHYnzjtTOc7BoFIBIy+OBtK7ltTeVV5YazHcmR88PsOt7Dwbah8dxzrth+S3M5d66rZnVN5PrkME/0wr/eCtkoqL7xwbcOLR9ww2Unel0hc0p4bNuRDMcz2I5EVQQlYZ/bJ4U6njt63Ls8OL4MV7uCZagKAuXzJpxfzz6YtS4QzBMOYwnJSMJhLOkQTTqX8B92kXqSott/gKKZSFtFNbIIISlQw7nJPmElzEp9JU16E0Vq0cU7cWzXK2eqh8HQMdd2FyIUiKycfMhSugFK1oJvaY0tLrKhlO6DpKmi+VQP9GT/pBf6TA+WHAui50n4Q8TCpRTEhwhlEqTrdjJa1sxYpILRcBFjgSCjPp2UiuttLtRJr4xxAiIwGe58Stjzy+WMv1HIl/HqXMmndnr3JPmNZ7/Fw0mOkHn5z5BmGr3lXvR1776m/Xg2zG88+82RsXNurvH+/e772jth5/9xxwDXQOtwnHOjSQqCFt2hN4g7ccJKmAdCD1CgzJzmbqnaUErJofZhvv3mWTqHEkAAQRNSOiAUBAqKEBSHlWkiuCKgrFB1RfJildpSlaoi9YqhwjuiKY4PxvBpCnfWLQ0vcYCM5fBKxyBSwp31pfg1hfZ+m0PtWY6cN0llJFkzi6EblBQobGo02LRCpyKydGw5I+nRSeF7eDxNVbSdGdNT+YvHx2Tr3ChdpevdCctTxyATUbrMBJhx8JfA5v8Gt/+v69Wiq2IoHeVnXvtTUlYGQ6pkhU1A8/HXOz/r5QfPM5bqNdTj8sx2vOolKphnHGeW+QM9liyeDfMbz34Lg1rSAM13YZ9/yy2wTfdBu2Nj953AGTyb21b4wq5AXlSLGBfKRaBo1g/Mb0QbBjQfv7TxA3y77VW+3fYa3zq3i39sfR5D0QhoPj697r3cV7OZypCPiqBBbyLDmZEESdPm1HCctrEkK4uC1BUGro9oeBluRPstN67Ghg3lBfzaY5vZd26Qb75+hsGxNH/3oxP86HAXH71zFauukG+8bzTJqyd6ee1EL2OJyVBpKyoKuGtdNTtaKgj6rvPtaqjK9aSdCJEdKL04RLZju8J4vAeSfaiJXoIjHew/fAQjM0AsNUZjKIkqbUj0uK9LIVQ37HWwys0fHaq+WEQPVsxOOE/0Ygzth6oQFNbO/be4DIYmqIiol3wwYzuSsaRkLOG4Qvn4cnR8vT8apG//Niq3vo2iWlgZP30HbqalNELFqi6iRgdxJ86hzCEOZQ5RqpTQlA3SNDxEcPDk+IOW4zN7USsaFK+eIn6vcwXwJZqX+0Km9UEh3PzlRhiKmi/9JelAeuRir/O+fRy2z/D6ze/G0nSQklAyOu75N/H/IgUyBWn3XSiTJBKPEkklKDIFRdJHRBTg85ePe6BnISggFIAAkK95Cz2WPd49SX7j2W9xUILF6Js/QPatr2OefgmlrBm1fNU17cuzYX7j2W8ORJrgPf8KR/8R9v0JdL3qRqK69Teh5T9dtdd4TdjPudEk8ZTOfSXv4qXMj4g5MZ5LPMcDwQcumXpoKdpQCMHmFaXc1FjCntZ+vvFaG2NxGyEUpGOhKCqOlLx7axjH0egatuketkhkJP1jNv1jNvvPje8LVyivKVapKXWX1SUq/vF7U9uRnB1NAtBUFFoygjiAT1Mo9hv0J7L86HiM42cUoqlJe4X8glWlae67uYiGcn3pTUCV0h0PTwjgg0ddEfxS6alCVe7YrGT9ZJqqifRUl2P1B91JJdHz7uT0V34Njv8r1L8D6u6c/3bNkVJ/IZ9e916+fOwpEtk0IcPPp9e91xPE85SleA31mB88UdzDw8PDY1aIUBlC8yOtNPgLx8Ooa+jrHoTUKM5YF05sAJmJY/efwu4/NfldI+CK45GanEe5CJUgRJ6Ee7oOKELhg013U2QU8HsHvo4tHWzHJmml+f2D/8br/UcpMsIENR8B1Ydf1Uk7CmNpiXRUzsYMwrqf5qJCVkQKCGo+fKqO4v3GHguMEIJtK8vZ1Fiayzfe1h/jC9/ez47VFXzwtpUIAf1jKSoiAUI+nbfODLDreA+t3WO5/YQDOrevruTOdVXUlYYXsUVMH3zPFCJbUSeF63FCwNrNab745H6GYxlqA35+7YFaCuxhSPa6nryJHncwn+idLJP2ZNnAJeojFAiUjR9zipf5VAG9cxfKK79OXSaGcrhg5lzX1xHXY15QElaYKbjdSMLmC99awdiPBKXBdvpjjcQyDbSNCnrOhqjXBCvqT2KWDdIfdBiSbQxJyds+qAr0slIdoUGaGJp/PPf3+slXUYvr3X8jIRR3AkegFLNkFb1WL91WNx0riul2NiIRCCQSQbSghELpp1AaRLI2RakkkfgYkbF+ImNdGNHumb3tL4UenhKyfUro9lAlBCbel7vhDq+GOeZB9/Dw8PC4NrSam3Aad2C17yG77wn8934W4VvkezMPj3xDUeGmT0DDfW6u8YEDbijoc8/Cnf/nqu5tQoZGkV9nNG0STeo8WPggP0z8kDFnjGcTz/Lu0LuJqJefjLzUUITgttWVFAR0Pv/NVkK+BoRQcRyHlNlJTUkLa2vd646UkmhK0j1sT3vF0g4DUZuBqM3B9sl9l4YVakpVSkossppNyFCoK1jA9FtXyWDU5lC7SeugQ0GJQyadIZry4dcF6+t1NjXqNJYJzpzpoa5UXXxBXEqId12QnurYZdJTNbhjsrINrgheuh4CJdd+/Knj7oGDcPxf4NXfhPc/Db6ld97fV7OZDUUNvH3qMNtW30RFsPjKX/Lw8LiueKK4h4eHh8esUAIRjG2Pk337CaSVRmh+jG2PT8spLm0TJ9qDHOvGGe3CiXbjRHuR2RT2wGkYOJ3bVug+RGFNzqtcidQg/XO4UV4mVAdLCGgGGcvEQSIlZG2TfYOn8V1C5LEciWk7OADtbmgxXVHQFYFf8xFQDfel+fCPLwOXWPqnbBtQDfyaQUD1YSjaFQdjQ5koZ9P9lGQqvRv/GxBdVXhoawN3rKni22+e5dUTvew51c+rx3pIZm2klEigMKDnwmMLARsbSrhzXTVbVpSiqUtoEscFovdsKCv082uPbeEPvrOfruE0//eFPn79sc2EKjbP/AXHhvTwuCg+Lpgn+y5+71iTnr8Dh2bYjxsiGySK0MCKwzMfh6P/5HoYC3UyJHZuOVPZlLDZs9l+Yl1RQWjTt7lwv8r0smJF49M1P6DoyBfRB5NIoRAvvQNp2mixc0jHhnEHe9vvo7++kY7G1QyWVNFTvYaeuk28qfqpN1ay0mimRqtBFTdmWDMpJcPOMD1WD11WF/1WP+P/EbCEBUJFszLotgVS4vgKuD/yGLX6ZaIJWGlIDrih2aeGap/mid7neuqbcRiLw9jZS+8PwFd0GdF8/BUoc8+ViVCJVhK04KJP8vDw8PC40dA3PIwzfA4nNkD2wLcwdvzk4gszHh75SKQJHv46HP3auNf4Lvj2w3Drb7npmWbZr2rCfkbTJt3xNI2RYt4dejc/TP6QUXuU5xLPcX/oforV/BuDVxcH8elxYuljOI5OOptGUSwU0ZLbRghBJCiIBBXW1U0+E4mlHFcgH5kUyseSDkNxh+GETYs/i6ZJTpxW2Hcw5oZdH89RXlOsEvJfv7FnNOlwuN3kYFuW7hE35ZGiCNYWQ2EYPrjTz4Y6H7o67uVuz5AWaT640qRTx4Zo2xTxezxN1aXSUxU1TwrfpeuhdB0YM4f0nxdu+VXofs1NUfDG5+HeP164Y82BUl8hTf4KSpdYqi4PDw8XL6f4PCKlJJvNYhiGN1jIUzwb5jee/a4PTmoMmRhChEpRAleelSltCxnrwxnrdr3Jx7pxoj1gWxdvrOoQrkQrrhsXymsRBRXjYV5vDCZyECWtND5FJ2llMFSNT655CE0opO0sSStLys6QHl+m7CxJM8NYNk00myZjZ3GQOXFcm4cwYYpQxsVyA7/qIzAulk8I6D3JIXYPnMB2HMJ6gJ9f/xj31VxCCPRYsszndbR9IMY/vniSN1v7cRz3fHSkO/hfV1vMfTfVcMeaSkrCS2fW/nzRO5Lki08eIJrM0lge5lce20zId42ey9KB1IS3+QWvibLRsxA7j0S4D9akRCAhXAPaEg0ZPi7kS+kACuC40UMKG5CKRkoppkdZy+lMCz3aWga0NYwptdTXZqhu7sIsPE+cyYczPuFjhb6ClfpKytXyvL0PmG0fTDmpnAjeY/WQktPDyIeVMLVaLcVKMa+mXsWUWQwHsgoYwsfjhY8TUkJzr7CZuIxo3j8pqtvZK+8LAOE+SBs66p77asB94GaE4Sd2L1mP8XwZr86VfGqnNy7Ibzz7LQ2caC/pV/4CHBt94yPoK3fO+rueDfMbz34LxOhZ2PUbrrcrQN3dsPN3Z3V/Y9oOL58fwpGSW2uLifh00k6a55PPM2wP4xM+Hgg9QInqOhrkkw13Hevhqy8cI5W1yZg2Yb9OQ1mY3/zAVopCvqvaVyLt0D1ic3Y4SYw0mQwcP2EwmTZokqKgQs24SD6Rq7wgMH9CeTLjcLTD5FCbSVu/lcuqrQhornI9wu1AiuFUhqaiIC0lkxE5FsR+F046vfuLUL55ivh9DIZPXCI9lT49PVXpevf9YqSnGjgMTz/uRly7949h5cPXvw5XIJ/6n8fMeDbMT2Y7XvVE8XlESonjOCiK4nWWPMWzYX7j2S9/kI6NjA9MiuSjXchoN9Ka+tB83IaKilJYhRKpncxVXlCJWMahcV/sPsiXj3+XtJ3Frxq5nOKzwXYk7WNJTo9GSZoZso6JpjpUhlV8mkPazo4L666YnrazpKyM+7Kz7svK5LZJz0LIsB2HvvQIjpQo4wF6g5qPP73t51hdVDfXn8PjOjLf19HjnSP81tf3YNkOUrre5ELA739sB2vr8s+T4WroGk7wR08eIJYyaaos5Ffeu4mAsUBBmuI98K87kNkY6EEwEwgtAO/8CzAK3QcGjuXO/J9Yn7a0QVoXLC+x3YVl0/Z9ue9OPZYN6VH3oYtQJj3MpQN3/6GbazFYAUKQNiXHzpscaMtyru+Ch0kr45Q2dpIInic9RRQOK2FW6itp0psoUosW5jdfIC7VB21pM2AP0GV10W11M2wPT/ueJjSq1CpqtVpqtBoKlILc91uzrexK7sLEREfnruBdtBgtXDekhMzYzKL51PepQffcsFLj+QiFe36Ea8GMwfu/D9U7rl+9r4J8Ga/OlXxqpzcuyG88+y0drHNvkD38XVBU/Hf9LErkMlFGpuDZML/x7LeAODYc+QfY/6fupEGjAHb85qy8xg/1R+mNp6kvDLCuzPXGzcgMzyeeZ8gewhAG94fup0wtyzsbDsfT9I+l8esKX/nhcQbGUtSUhviN920h5L+6Zz+WI9nVMYRpO2woL6DY8NE9bNMzYtM1bNMzbDMUnzlXcGHAFcprxkXy2hKVgoCY9W+YtSQnOk0OtZuc6rZwpsguDWUam1fobGjQCY97qfcm0hzqi+LXVO6qL8kdZ97tl+iFf70V0iPu/bXl5lqnoN4dh01FC0DJ2knxu3Q9FK1aWump9v05HBgf677/aTcC1RIi3/qfx8V4NsxPPFF8EbBtm9bWVlpaWlDVG8ercTnh2TC/8eyX30jHwYr103X8LarDQLQHZ6wbaaYv3lgoKIUV40L5eK7ywmqEdpX5SpcwQ+kofakRKgPFlPqv/n+f5Ti0j6VoH0tiOe6/+kKfzqriEKUBfdY3dY50yNimK6BbWZJ2Jieap6wMSTvLuVgP3257DU0oZG0LWzpIJBWBInZWbuChuu2sjtR5N5J5wHxfR4fjaT7796+RytgEfRrJjEXAp/Knn9i5LD3EL6RjMM4fPnWAZNpiVXWEX3p0E359gf4/nfoW8qVfwc7EUH0FiKUebnriwYwZByMC2TE3N/VlvIGjSYeDbW7Ywd7RyZCCfl2yavUoBbWdjBkdmNLMfVaqltKkN9GkNxFUggverLky0QdXrVpFUiRzIniv3Yslp0dYKVVLqdFqqNFqKFfLLxs+PuEkiDkxCpSC+fEQXwgc231QN3QUnv4wmElQ/YBzxXNjscmX8epcyad2euOC/Maz39JBSkl27z9j9x5HCZfiu/sXENqVPTc9G+Y3nv2uA6Nnxr3Gx1Mj1d0z7jV+aXFvMJllX+8omqpwb0Mpyvj4OiuzvJB4gQF7AEMYvDP4TvzSz7H2Y6xvXE+hvrT/Z1/IQDTFF769n7FElpVVhfzqezfju4ox1LnRJK3DcYK6ys66khmfQ6Sykt4pInnXsM1QzGYmkSTsHxfKi9WcYB4JCqIpyVDMoSgkGBhzONRmcqzTxLQn91JVpLJphc6mRoOi0MVe6LYjeen8ILYj2VFTTNH4BIB564NSQv8BOPBlOPqPbtl4ZDGQUNwCVTvGBfB17rKw0Z20vJRxLNdbfPAI1OyEd/+tK/YvEbxraP7j2TA/8UTxRcDrLPmPZ8P8xrNf/nOhDaV0kMkRVxwf63LzlI91I7PJi78sBEq4HCVSg4jUohTVoBTWIHT/eMj3QUSobFYh35cTpu3QNpbkfDSFPS6OF/ldcbwkMD+TCCZCvqesDIZUSUo3fHuJUYA6nju6qaCKh+q2c1vFOrSlPsC6gVmI6+hEOLy06eDXFX76Xeu5a331vOw7H2jrj/Gl7x4kmbFYU1vEZx++6aoe6lwNdrSLjmOvUb9+J2rh7Dy5FpU55I3uHbU5eC7LoXaTseSkp0ck7LBy9QD+yk6Gle5cbm2BoEqrYqW+kga9AUMsvUlUWZmlK9vF0f6jpCNpEjIx7fOACORE8GqtmoCyREPjzwd5llM8X8arcyWf2umNC/Ibz35LC5lNkn7pT5HpKFrDNowtV74eezbMbzz7XSccG478Pez7U3BM12v81v8Bq943o9e4lJJXOobJWDabKyNUTgktbkqTHyV/RJ/Vh4ODJS1M28Sv+q9/lKB5oGsowR98Zz/JjMWGhmJ+4T03oatXFj0tx2FXxzCm7bCxopCaq5iInTEnhfKJPOUDYzML5VLCWMLBkW6KsLBf4DdcmxWHFDavMLipUaey6Mr953B/lJ4LIgDMuQ+OnYMz33Vfsc5c6iqQrne1Y4GvEH5iL4TzdGw+ehaeeh/YGbjtt2H9f17sGuXwrqH5j2fD/GRJieJdXV389//+33nmmWdIJpOsWrWKf/iHf+CWW26Z1ffzZfDtdZb8x7NhfuPZL/+ZjQ2llMjUKHJqjvLRLmQmPvNOVQ0ZHwQkwghibPsoWsP2hWvEEiVjO7SNJumIpnIhvEoCBquKQ7nZyHPhxe6DfPnYUySyaUKGn0+vf4xVhTU81/UWu3qPYDquh2PECPFA7TbeUbOFiLFEvRVvYBbqOjoRDq8i4r8hPMQv5GxflC999yDprM36+mJ+4T0bMbT5/z+Vj/8HR/rbGOs9TaRqFcUVK676+46UtPfbHGjLcvS8SdqcHNpUllo0tPQiSs8zwkCuXBUq9Vo9K/WV1Gg1l/WwXkgc6TBkD9FtddNtdTNoD+Lg5HKXqahUaBU5IbxYKb6xIm4ket2Hd4UNS9ZDfIJ8Ga/OlXxqZz5eDz0m8ey39LAHz5J5429BSoybH0er23L57T0b5jWe/a4zI6ddr/HBw+77+ntdr/FgxUWbtg7HOTeapCzo4+aq6RP+LWnxw8QPOW2eBkBxFFBARWWrfythJYwhjOkvJteVJeRpC3Cmd4z/+91DZE2bW1aV89P3r0dVLn8vfGYkwZmRBCFD447aud87Zy1XKJ8Qyd0w7BaDUYmUk07XqgLv2uRn+2qD+lL1qo47EQHAUBXuaShFCHFtfTA5COd+AGeecj2oJ9CCsOLd7qSLQ1/Nm0mns+LYv8CbvwuaHx57EiJNi10jwLuGLgc8G+Ynsx2vLlBiw0lGRkbYuXMn9913H8888wzl5eW0trZSXLy880h6eHh4eCwMQghEsBiCxajVG3LlMh3NCeQ5sTwxDPH+8dBQAmmNknnj70HzoVbfdEOJCz5VYU1pmMZIgHOjSTpjaYZTWfakspQFXXG80Hft4vh9NZvZUNTA26cOs231TVQE3f/z/9+ah3i86R5+1HOA57veZiQT59/PvcKT7a9xx3ho9YbwxYN9j+VFSfjGFMMnWFlZyC89sokvfe8QxzpG+Mtnj/LphzbOytthOeNGEThD2pT49TP89Lt8Vx1FQBGCpkqNpkqNR26RnOwyOdhmcrLLom9Io2+oDkEdjXUZqlZ2kS08T0yO0Wa20Wa24RM+VugraNKbqFArFvz/QsJJ5ETwHquHjMxM+7xQFGKkDTYWbqTWqEUTCz5cW7qEqpa8GO7h4eFxo6CWrURvuQ/z1I8xD30HpbgeJVS62NXy8FgeFK+CR56AI38H+/4MOl6Cbz887jX+2DSv8Zqwn3OjSYZSWTKWjW/KRFtNaGzybeKseRYHB0e4EZMcHI5mjl7xvlIXOoYw0IWOT/imCeZXeunMPkXbTMyU6qe5KsLPP7SBP3n6MG+dHiDoO8XH71l9yeOYtptCDqC5KDgv9/WGJmgo12gon/ztTnWbfPkHcVRFIhGoisR2BDc3GzSUXf29e0lAR1cVsrbDUMqkLHgVEa3MFJx/AU4/Bd2vgxxPMSVUqLsbmh+F+neAPh5h6qZP5s2k01mx7mNw/sfQ/Rq8/GvwyDcuzpHu4eHhcQEL7in+G7/xG7z22mvs2rVr1t/JZDJkMpMPiKLRKPX19QwPD+cUfiEEiqLgOA5Tm3CpckVREEJcsty2J/MSTpQDOI4zq3JVVXEcB8uycvucqIuUctr2V1v3xWzTpeq+XNtk2zaO46AoSu6V721ajna6VJsmXtdiv6XapuVop8vVfeJ7qqrmrqtzaZPsO0bm1b9yBwSOCZYJSAgWoxTVoTZsR6nditD9C9amK5Uvlp0yjuTMSILuWDoXDqwiaNBSEiaoKdfUponjaJo24/YOkj2DJ/nB+d2ci/flytcXNfJg3S1sLm6aNjvd60/Xv00Ty4n+txzaNLV8se10snuUP/vBUUzLYfOKUv7bA2tzKQbmo00Tn0/U53q06VrKFUVhOJ7h5/5mF/GUm//bkRJNUdjYUEzQp6NrCrqqYGgKhqaiqwq6KnLluqbg01R0TUVTQNcUDFV1yw0N24G2PjjdAz0jEiHc+uiqpLkpTkljF/HAedKkcvUKiRBNWhMr9BUUq8WXbNNoMkvvSJLyQj8lYd9l7eQIh36nn65sF912N2PO2LTfX0enSq1yQ6Kr1RSoBbnffOqDPK8/Le02RaNRSkpK8sKDei7kk6f4xDk0YVuP/MKz39JEOg6Z17+KM9yOUlyHb+d/Q1wiLZJnw/zGs98iMtI67jU+7ulbfx/s/N/TvMb3dI8wmjZZXRJmRVFw2tcTToInok+Qlmk0NExMVFQ2+jYCbrqeLFl3Of4ypTnnagvE7AT0GYT28+Z5Xk+9jomJjn5RuPe3zgzwleeOIiU8dHMDH7x95Yx1OD2S4OxIgrChcfs8eIlfirGkw+9/K0rGdAgYglRW4tMVfuuDhUSC1zbp+fhgjI5oipoCPxvLCy/fBx0but9wPcLbnwdrcjxD+RZY9V5Y8RAESq69kflEog++8whko7D1F2Drzy92jbxr6DLAs2F+smTCp69fv553v/vddHZ28vLLL1NbW8vP/dzP8alPfeqS3/nc5z7H5z//+YvK9+7dSzgcBiASiVBdXU1PTw9jY5MPl8rKyigrK6Ojo4NEYjIPX1VVFUVFRZw9e5ZsNpsrr6urIxwOc+rUqWkPO5qamtA0jdbW1ml1aGlpwbIszp07lytTFIXVq1cTi8Vob29HVd0wKYZhsHLlSkZHR+nt7c1tHwqFqK+vZ3BwkMHBwVz5UmxTPB6ns7MzV77c29TT04Nt26iqSjgcXhZtWo52ulSbBgYGcvYrKipaFm1ajna6XJsmJqdUVlZSXl4+5zatqC7B/vEXsTNJHNWHOj5YUMJl4NiYpolUNFKRZtJlG1m5+Y4bzk7ne/sYwUcMV8jWdI2IIgmkRjHG5fLZtmnCfrW1tRQXF1+yTSdPnqQ9PcDr0VMcS3ai6hpCCAocg9sKWrg53IRP0b3+tAhtklJSWVlJcXExra2ty6JNsLTs1J9W+dc3e0hns6wu9/P+LaUoQsxLm6SU1NbWEgwGOX369JK0k5SSKCG+81Y3B9qGEEyGHZQCygv8rofDlBGKrusgwMxOf2CnGzpIMM0p5QIM3cCRDpZpTWyJqhahqcXApOeFUCwKyrsoaezBXz6EUO2JXSBSQfyJGuz+EE4SNEWgqYLRjMLecyOYlo1fV3jf5lK21IVzdjpz9gyj9iij+igjxgjJQBKpSMysietLIghbYVYVrKLOqGPk7AiCyUH2qlWrSCaTdHV15QbfXn9a+m2Kx+Ns3749L8TiuZBvovhEKgLvQVb+4dlv6eIkR8i8/GdIM43ecg/6ugdn3M6zYX7j2W+RcWw4/Dew/8/d3M9GIdz2P6H5vSAEndEUxwZjlxR/W7Ot7EruwpQmurhYZL7ocNLBlCYZMtPE8tm+bOxL7vuKTZUOMRlDIlFxJ9kERICPFH4k5zEO8PLRbv7ppVMAfPCOlTy0tWHafrK2w66OIWxHXpRvfSF4+0yWb76WJGtJDE3w4Z1BtjVfhYf3BYykTfZ2j6AqgnsbylAE0/uglDB01M0Rfvb7kJq8H6aw0T03mh91129EzjwNL/+K6xDzyBNQftOiVse7huY/ng3zkyUjivv9rvfdL//yL/OhD32IvXv38tnPfpavfOUr/ORP/uSM38lXT3HLsjh16hSrVq3KCeP56OkwtU3LxXtjtm0yTZPTp0+zatWqS3o65lublqOdLtUmy7Ku2X5LtU3L0U6Xq7tt25w+fZqWlhZ0XZ+XNlnn3yK77xtgZ0E10Ld+GK3mJuzO/VhtbyLjk3lmleIG1MYdiKqNCFWflzZdqXyp2CmetTg3lqI/OSkSVId9NEUCBPXZ9acJ+61evRpN02bVpsH0GD/qOcCLPQdJWGkAAqrBPVWbeLB+O+X+iNefrmObbNvmzJkzrF69mgvJ1zZNLV8qdjraOcqf/+Awlu2wY1UFn3jHGjR15ggnV9OmCfu1tLRcNHBbCnY62T3KU3vbae2J4jgO/dE0qiIoDOgksxY+TeMzD2/EpymYlkPWsjFtB8sB07LJmO5703LGyyXZ8e2ylltu2e5nWcvBtCfLHemK3RIfgkIEhYAK44K0UFKEKs9TUNdFuCIGYnx7CcmhINHOQka7Q/QNm2h+k1ChRSqmY0g/X/rENqxQlG67m26zm6RMTvuNQ0qIKqWKGrWGKq0Kn/Bd0h5SSlpbW2lubp6Wu8zrT0u7TZ6n+NLDywOY33j2W9pY3YfJvvV1EOC77ROo5ReLbZ4N8xvPfkuEkVZ45b+7YihAwzvgjv+N6Svl5fNDOFJya20xkRnSoEXNKMfaj7G+cT2F+sL+z7akdWnRnEuL6RmZIeWkiMs44Hqby/GZsWv0NewI7KBYnUy/+sz+83zr9bMA/Nf71kxLuzSRa73Ap3FbzcJ5iU9lLOkwHHMoKVCu2UN8AikluzqGSVs2mysjlPndSZktVQHUtu+7YvjY5ART/MXQ9LArhpdvcmcZ3+i8+EtuTvVIE7z3O5Mh4xcB7xqa/3g2zE+WjChuGAa33HILr7/+eq7sM5/5DHv37uWNN96Y1T7yZfDtdZb8x7NhfuPZL/9ZKBs6qTFkYggRKkUJRHLlUkqcoXNY7buxe47A+MNtoftR67ehrbjN9Sq/gYhmTM6MJBlIupPThBDUFvhZWRTEr13eJnOxX8bOsqv3CD/o3EtvchgARQhuKVvNQ3XbWR2p82ZnXge86+j148C5Qb787FEcR3LH2ip+6h1rUOZ4ji9V+53sHuWpPW2c7BoFQFMV7tlQTUnYx7/uaiVtOvh1hZ9+1/qrzik+W2xHYlo22XFRPZO1Odtvc6LL5myvxLQlE6OiSGGa8sZO1PJOTN8IElcctywYHJQUlmdQVHf7xIhOKAThgE7A0BACVKHmQqLXaDVElMisr19L1YYelydfxqtzJZ/a6fWl/Maz39Ine/A7WO17EL4w/ns/i/CFp33u2TC/8ey3hHAsOPQ3cOAvJr3Gb/9fHArfTW8iQ31hgHVlBRd9LV9sGLfjPBF7AlO6Yd7TpJFICkQBilCo0WrY4NtAtVqNEIJ/f/0Mz+7vQAj42XdvYFtzORnb4dVxL/EtlREqFthLfKE4NRynbTRJtZ5iQ/RV4oe+TkHq9GRMKc0PDe9yPcJr78TLnX0BmTE3jHqyH9b/Fze6wiKRL/3P49J4NsxPZjteXfCrZ3V1NevXr59Wtm7dOv7jP/5joQ/t4eHh4eEB4ArhU8TwCYQQqGUrUctWItMxrI63sNr3IJOjWGdfwzr7Gmp5M2rjrahV6y+ZM285UejT2VoVYTRtcmYkwVAqS2c0RXcsTV1hgKZIEJ82t1nQM+FTDd5VezPvqNnCoeFzPNu5l0PD59gzcJI9AydpKqjiwbrt3F6xDu0GsIPH8mdLUxk/88B6/vqHx3j9RC+qIvj4vavnLIwvJU73jPHknjaOd44AoKqCu9fX8PDNDRSP5+Le0VJB/1iaioifkrB/weqiKgLV0Jh6hJpSuHMdZC3J8U6Tg20mp3tMMukQnSfXoJxaw8r6LBUrO8mEzzNsD0MgBrgiuRAQqcyQTegM9guywyE2RFbw7lUbKA4tnmeCh4eHh4fHQqNveBhnuA0n1k/2wLcwdnwcIeZ/jODhccOjaLDlZ6HhnW6u8aGj8PKv0lJ9D0MrfomeRDlrSsN5O4YIq2HuDt7thnvHJESILf4txJwY7WY73VY33VY3JWoJG4wNfOC2FSQyFruO9fDXzx/js8ZNaEED25EU+nTKg9cewnxRsTLUDb5C4Mi/Uzz4BqgOPtMEww81t7se4SvuBz105X3dqPgicOcX4IefhGP/DPX3Qe3Oxa6Vh4fHEmTBRfGdO3dy8uTJaWWnTp2isXF55riYCKXnkb94NsxvPPvlP4tlQ+EvQG+5D635HpyBU1htb2L3n8QeOIM9cAbhL0BruAW1cQdKoGhR6ng9KfLrbKsuYjiV5cxIgpG0yfmxJF2xFPWFAVZEghjqxbaaq/0UobCltJktpc10xAd4rustdvUe4Vysl786/j2+fubHPFC7jXfUbCFieAPChcC7jl4/bmkux37XWv7m+ePsOtaDpgh+4u6LQ59fDUvBfmd6ozy19xxHz7tiuKII7lpXzcPbGigtmC58l4QXVgyfDYYm2LzCYPMKg3ja4XC7ycG2LJ1DNqfPG5w+vxJdXUnT+vOI6ldwJtINWAqaBuuyt/LGQYv+0RTdxHnpzb3cubaKB7bUUxG5enF8KdjQw2M54PWl/Maz39JGaAbGto+SfuUvsPtOYp17HX3lndO28WyY33j2W2KUrIZHvwmHvgoHvoy/52W29ezlzJpfZqD0Q1TOcD+dLzZsMVqo0WqIOTEKlIJcLvGYE+N45jitZivD9jC7UrvYr+znltvXkjBL2Nc6zF+/cJwH72xGUxVWFYfyK7qcdKD3LTjzFJx7loAZp8yycSSkI+uIFt1B+a3/FbVgYSJpLUvq7oS1H4MTX4ddvwnvfxp8ixPdKF/6n8el8Wy4fFnw8Ol79+7ljjvu4POf/zwf/vCH2bNnD5/61Kf46le/yk/8xE/Mah/5FKbNw8PDw2P54CRHsNv3YJ3fi8wk3EIhUCvXuqHVy1fdEB4RUkqGUyanRxKMZUzA9bpcEQnSEAmgL/CNYiyb5Ec9B3i+621GMm6+MV1RuaNyAw/VbachXLGgx/fwWGheP9HL3/34BEh41+Y6PrKzOb8e6IzT1h/jyT3nONw+ngJBEexcW8Uj2xopK1xc4ftaGIrZHDjnCuTDcQepJym6/QcomoljGqh6FmnrfKzocSoCYfafG+SZfedp63e9yYWAW1ZV8NDWehrLLw5r6bF8uFHGqzdKOz08PGaPde4Nsoe/C4qC/86fQymqXewqeSxx3NRmg4hQ2bTUZh5XwfBJ2PUbZPuPYDqSZPV9lL/rCxBcnqnfMk6GE9kTnMyeJCVTAOgY9J4Okxmqpr68nLqiIPesKMuPMdTwKVcIP/s0JHony8M1DFa/mxORewlUrGVbVdGiVTGvMVPw1Psg2gYrH4F7v7TYNfLw8LhOLJmc4gBPP/00v/mbv0lraytNTU388i//Mp/61Kdm/f18GXxLKUkkEoRCeTYzzSOHZ8P8xrNf/rNUbSgdC7vnKFbbbpyhc7lyESpBa9yBVn8Lwrf8vZallAymspweSRDLWICbG3hFJEBDYRDTthmKJSgtCBHQ5z8YjeXY7B44wTMdezgbmxw8bihu5MG67WwtbUa5ASYpLCRLtQ/eCOw61sPXXnSjKz24tZ4P3r7yqm2wWPZrH4jx1N42Dp4bAlwx/PY1lTyyrfGaPKWXGlJKOoZsXjqc4VD8FJVb30ZRLXA0hg/fwv+3dSNNlVpu25Pdo/xg3/mcpzzA+vpiHtxaz/q64svaxuuD+Um+jFfnSj610+tL+Y1nv/xBSkl2779g9x5DCZfiu/sXEJrPs2Ges1D2M9t2k33730DaCNWHse1xtIbt87b/GwrHIrPvLzH3fRkhbQKhYpTbfwdWPgxCLMs+aEmLs+ZZjmWOMeaM4UhJIuOgJ6vpPVbAp++6ZelOxE30wpmn4cx3YWRKRF2jAJoecsOjV24jaTm82jEMAm4p8VNcWLBs7HddGTgET38EpA33/j9Y+Z7revjl2P9uNDwb5idLShSfK/ky+LZtm9bWVlpaWlBVL99pPuLZML/x7Jf/5IMNnVg/Vvtu7I59SDPtFioqas1G13u8uHHZ3zBJKelPuuJ4ImvlyjO2g2PbGLrGpopCagsWRgyTUtIa7eKZjr3sHTyZC2VcFSjmgbpbuKfqJgKab0GOvdzJhz64nHnxSBf/8nIrAI/c0sj7b226qu9fb/t1DMZ5am8b+88OAq5n9G2rK3n0lkYqi4ILfvzrzVjS4fe/NUaGJL5wAisZQnNC/NYHC4kEL56Qc34wzrP7z7P39ACO416nGsvDPLi1gW3N5ajKxf8rvD6Yn+TLeHWu5FM7vb6U33j2yy9kNkn65T9DpsbQ6m/G2Pohz4Z5znzaT0qJM3Ie69wbWKdeAMcBVQdVQ2gB/A/+tucxPgcOndhN1b7PUZw8g64IaLwfbv8ctpWh4+ir1G+4E7VweUVwkFLSaXXyevQAQ3IAy3JIZyzs0Qgfar6T5lDd0ngmk41B2w9dIbxnNzAuwSg61N/rCuF198AFzy52d40wmjGJZGNsb1nhXUOvlX1/Bge+DEahG0Y9VHndDu39D8x/PBvmJ7Mdry54TnEPDw8PD4/lhFJQgbHxUeTad2N3H8RqexNntBu78yB250GUwkq0Fbeh1m5B6Et0lvIcEUJQGfJRETToTWQ4NRxnOGWOD/EEpu1wqD9KacDAr83/zaMQgtWROlZH6hhMj/HDrrd5sfsgvakR/qn1ef793CvcV72ZB2q3UXED5H/3WD7ct7EWy5Z849XTPP1WO5oqePSWFYtdrYvoGkrw3bfaeOv0gFsg4NaWCt57ywqqipefGD5BJKjw4Z0hvvmaIDsaxNAEH94ZnFEQB2goC/PT96/nA7em+eHBDl451kP7QJy//uExyiJ+HtxSz861VRgLcJ308PDw8PC4XggjiLH1w2Te+Fusjn0o5S2I6psWu1oei4yTGMLu3I/VuR+ZGEbaWVcQR4BjgwRJGpkYAk8Uv2ZKajZxUPs7mjv+maa2f0S0Pw9tz6FkxqizTJTDhW746NUfXOyqzhtCCMpEDSVRPwFlGFF4npPmWSga47ujP2ClU83mwE00ao3XP5KcbULXLjj9FHT8GOzs5GdV22Hlo9D0IPgufc5XhX2MZkzinmwzN7b8HHS+DINH4NXfggf+1p3F7eHhccPjXV09PDw8PDyuAaEZaA3b0Rq244x0uN7jXYdwon1kDz2FOPYMat0WtMbbUCLVi13dBUEIQXXYj64IXu8cwZYSCTgSbEeSNO0FEcWnUuaP8LHmd/CBFXeyq/cwz3S+RW9ymB907OHZzr1sK1vNQ3XbWRNZIrPFPTyuwP2b67Ach2+9fpYnd7ehqQoPbW1Y7GoB0DOS5Lt729hzuj/n6LC9pYJHb2mktmT5p5AA2NZssKpaYzjmUFKgXFIQn0pZoZ+P3dXCo7es4MdHuvjRoS4Gx9L8y8utPLWnjXduquUdG2sJ+fXr0AIPDw8PD4/5Ry1bid5yH+apH2Me+g6aUYiR6EamKiBcstjVmxVeruu5I80UdtchrM79OMPtuXKh6ajV67HOv+WKhrYJjgmW4wrkHtdMZcjHCVXndONPUb72IQpe+zU4/2OQDgoCrBj84D/D0X+CgjoIlEGgFPylk+uBMve9UZA3ouHZkSRSSmqMSm4pXsMG+vin46/grxrmXKyXUWeEAjXMemM9q4xV6GIB7rMTvRBth4KG8fDoT8G5H0BmbHKbomZofgyaH4Vwzax2WxXycXIoThqVlGUT9rxUrw1Fg7v/yM0v3vUqnPg3WPexxa6Vh4fHEsATxecRIQSGYXgP3fMYz4b5jWe//CdfbagU12MU1yM3PIzVsQ+77U2c+CBW2x6stj0oJQ1ojbei1tyEUJef6BE2NHRVIBwwHYkD2FLiU6/frGy/anB/7TbeWbOVQ8PneLZzL4eGz7F34CR7B07SVFDFg3Xbub1iHZriDSovRb72weXGQ1sbsGzJk7vP8a3Xz6IpCvdvrrvi9xbKfn2jSb77Vju7T/UxkXhpW3M5793eSF1peF6PlQ9EgrMTwy+kIKDz2PYVPLilnl3He3juQAfDsQxP7m7jB/s6uGd9Ne+6qcbrgx4e84D3/yy/8eyXn2ir34k9eAa77yTZH3+RcqGSOf995M0fQWu45aLtl5J9rfN7yb79BNLOeLmuubo+KB0Lp/8UVud+7N7jkyK3EKhlzaj1N6NWbUBoBmrlWvd3FmmwsmCEyL79dYwdH0ctvbq0QR4uuqpQEfLRG0/TqTex7rb/Bd2vgW0ipAQkOBYMHJiey3omFP0CwbwM/CXTxfOJMl8RLNK4OmnadMfdVHarit2JuSuLK/nk6of4v99/C1/1IGJtDBGJsye9hwOZA6wx1rDOWEdAucoUb44FZgKycXc58Wp7Fg79NVgp95z3F7uTCsD9jVY+CqveCyXrrnqigU9TKfbr9JsmfcksYZ9xdXX2mKRoJdzyq7D792DvF6Hmdogs/LXGu4/JfzwbLm+8nOIeHh4eHh7zjJQSZ+gsVtub2D3HQDqAG1pQrd+G1rgDJVy2yLWcX7piKQ71R7EcieW4gnhtgZ9NFYWLdhPZER/gua632NV7BNNxc59HjBAP1G7jHTVbiBghhtJRelMjVAWKKfV79xgeS4vv7D7H02+5Xjb/+Z4W7tt4ffMB9o+l+N5b7bxxsjcnhm9dWcZ7t6+goezGE8PnG9tx2Ht6gGf2d9A5GAdAUQS3tlTw0NYGaktvDO/75cCNMl69Udrp4eFx7djD7aSf+93JMNlIVxAKliLmU0C77PDiSmOP6Z9Lx4bEoDtmm5jArAcIPPjbKKHSudRy2SKlRI52ukJ410FkNpn7TCmsRK3bila7BTGDx73rkT+EMAJkDz2JM3weFBXj5sfRaryw+9fCYDLLvt5RNFXh3lIT5eu3gRkHvQCyY6D54d7/B1JCahDSQ5Aamr5uxq/uoEJxxfEJAT0nnk/1Pp8iqCtX8Mub8LoubIRQ1WU3PTIQpTuWpjRosK2qaNpnrd2j/Nl396A6CTZtsylalSJGEnBQHcnKtM76qKQok5oUuC8UvK0p61PDn0/gWBA9714zhOIuFRU2fQrWfgyqb5vzhIGuWIqjAzHChsYddfkRbWPJIh147hPQ/QaUb4KHv7FoEzo8PDwWltmOVz1RfB6RUjI2NkYkEvFmkeQpng3zG89++c9ytKFMx7DO78U6vxeZHM2Vq+Wr0FbchlK5DqFc5zxXC0TKtBgYjaIHghwdiOFISWMkyJpF9iSNZZP8qOcAz3e9zUjGHezrikptqIxjI+expI1fNfj0uvdyX83mRa3rYrMc+2A+I6XkW2+e5dl9HQD81/vWcNf6S6djmC/7DUQnxPA+HMcdKmxaUcpj21ewoqLgmvfrMTNSSo52jPCDfec50TWK49goisrmFaU8tLWelmqvPy518mW8OlfyqZ3e/7P8xrNf/mIPniH94y+BlUEiJj1Vg8UIdWl6O0o7C8kRQLgC/nidRUE5auU6lPIW1PJViHDFDXM+XqoPOskR7M792J37cOJDuXLhC7upw+puvqrUYdI2yb79DezeYyDA2PgoWtMd89qWGwEpJa90DJGxHDZXRqjs+h7ypV9BZhMII4SYTU5xKw3pYVcoTw2Ni+UzrKcGITN69ZX0RSbDtE8N2R4og6EjcOhvwM6A6oebf9H16LWmCtbuMpuO0T0yhGolqTQsDCc5XdA2k2RMi5GEK2b7fRpjDU0cW7GO/sikY0L9QBfr209QOdp/xWk0AKgG6CHQw65Q3rsHFMMV+1XDFco/8AOo3nH1v80MZC2bF9sGEIrC7XUlFBhesN85keiF7zwK2Shs/Qxs/fSCHs67j8l/PBvmJ54ovgjYtk1raystLS2oXr6PvMSzYX7j2S//Wc42lI6D038Cq2039sCpXD5e4S9Ea3Rzk880kz6fmGq//pTJ4f4oAGtLwzREgotcO7Acm90DJ3imYw+tY930pUdwpMRQNBzpoCkaD9TeTLGvAL+q41MNfKpOQDVy635Vx59bn1waioYirs/khoX0bl/OfTBfkVLyxGtneP5gJwj45DvWcsfamb0n5mq/oViap99u59XjvTkxfGNjCY9tX8HKyqV7D76cON0zyhMvHeHssJUra64q5MGtDWxpKkXxBuRLknwZr86VfGqn9/8sv/Hsl784qTHSz/4u0kxhChVd2gjdh/8dv4aYdt86h0eR1/wYc+bvyXSU9I//GGllXO9BM+V+ECie5t0u/IWo5atckbysGeFfvhMFp/ZBxcli9xzB6tiPM3Qut41QdZTq9a4QXrbqmid6S8fBPPJdrLbdAOgt96KtfcATIa6S1uE450aTlAV93FwVwY520XHsNerX70QtnOdoU44F6ZHpQvlM3uepQVdol5fJGz+T17VQoLBhRu/yjO1gORJVCPzapc+5lCnpSwgy+IkUFVNVXk5/pIyjlVV0FIaQQgGhUGYqbMiEaHCKUPQwaCFX/DbC4yL4+GtqXRK98K+3ukK9EXG98fUw/MTuK3q5zxbbtnmltYOsEWRlUZCWEi9K15w58z14+VdBqPDoN6Fs44IdyruPyX88G+Ynsx2vetOMPDw8PDw8rgNCUVCr1qNWrcdJDmO37cbqeBuZjmKe/BHmqRdRq9aiNd6GUt6MTMeQiUFEqAwlD8Xy6rCftOXQOhznxHAcv6ZSEfItap00RWVn5QbuqFjPc51v8UeH/x2kgy0dpJRk7Cxv9B/Hd4153y8UyieWfkXHuKS4buQE+AsFd7+qYyj6tAdCL3Yf5MvHv0vaznre7TcIQgge39mM5UhePNzF3//4BKoquLWlct6OMRxP8/23z/PKsZ6cGL6hoZj33rKCVdX5d/3JZ5oqCvhPW8sorKjj+UNdvH6ijzO9Ub78zBGqioM8uLWe21ZXoqvLI8KIh4eHh8fyQglEMLY9Tubtb6BkU2AEMLZ95Kq8h683wl+IcctHJ3OK+yPoN38YtagOe6AVZ+A09nAbMh3F6tgHHfsAUAqrUMpXoZa3oJSsQGhL0xP+WpCOjS92HnPf2zj9J8Aen6wnQC1diVq3FbXmJoQ29/GdUBT0mx5D+AsxTzyP2foSMj2Gvvk/zW/I/WVOTdjPudEkQ6ksGctGC1WRLto0byLtNBQNguXu60pIBzJjkwL5hUL60DGIdU6Gs5YKICFcBwV104TpjBLgbFJgK0FWVpRDIDJduDYmRe2A5ufc0W7+5eVWSMHjDc08sLmeCiBqRzmWPcZp8zSDfpuXC6BAEaw3GlllrEITV5BLQlVw75fgpV8BM+YK4vd+ad5/6zAWw0BvIsOq4pA3UWSurHwEzv8Izj0DL/8aPPYdN7WAh4fHDYcnint4eHh4eFxnlGAJyvqH0Nbc7866b9+NM9SG3XPMzUGuasjkCABC82NsexytYfsi1/rqWREJkLJsOqMpDg9EuUUtIuK/NsF5PhFCsL18DcW+AhJmGkPRSNkZDEXnw013oykqadskY2dzy4xjkrKzZGyTjG2StrOkx99PMPHZvNYVgU/V8ak6qlA4MdqBLR10RSVtZfnS4W9hqBprInWU+Aqum7e6x/VFCMHH7lqFbTu8cqyHv33hBKqicEvzLB5EXYbRRIYf7DvPS0e7sW1XDF9bV8T7djTR4onhi0plJMBP3ruG9+1o4oVDnbx4pJvekSRf+/FJvrP7HPdvruPeDTUEvFCKHh4eHh5LDK1hO5Q0c/7kARrWbEELL/18uFrDdpTy1W6u61BpblKyEqmBVfcgbRNnuA174DTOQCvOWA9OtBcn2ot15lVQVNSSxvFQ6y2ISDUiz+7LpZTIsW6szn1YnQcpjo/gGDogUArKUetuRq3bghIomvdjCyHQV78D4S8ke/A7WB37kZk4xi0/MS/C+41AyNCI+HXG0iY98Qz1BUvkdxMK+Ivd10xcyuv60ScuEplP9I3Rl8hQEfIRrLzyWOW+jbUkMhbfefMcT7x6hpBPZ+faKgrVQm4L3MZm32ZOZk9yInuCmBNjd3o3BzIHWGOsYa2xloASuPTOV38Qau90vdwLGxZk8kEIi6gQpEybsYxF0RJ4lpLXCAF3fB5634Kxs/DWl+C2/7HYtfLw8FgEvPDp84jjOHR1dVFbW4uyTPLD3mh4NsxvPPvlPzeyDZ1oL1b7bqz2vchoz3hYQAECUDS05rtRiutQQmWIUKn7ukaP5oViJvtJKdnfN8ZgMouhKuyoKSaoL40Z//Phde1IB9OxLimYT4rrUwR2e4rA7oxvb7nC+8T3ZxLXM7bJYHoMgUAI4T64QlLmj+BTdXRFozJQTFWgmKpgMdWBEioDxVQHSygywrOaWX4j98F8wJGSf/jxSV4/0YuiCD794Aa2NE3mxput/caSWX6w7zwvH+3GtBwAVtdEeGxHE2trixa6GR6X4VI2TGUtXjnWww8PdjIazwDgN1Tu3VjD/ZvqKFrkSBw3OvkyXp0r+dRO7/9ZfuPZL/9Z7jaUmbgrkA+exh44jUyNTftcGEGUsmbXi7x8FUrwEoLgEsBJjWF3HcDu2IcT6x8vlZjo+Ju2o9dvQ0RqrpuXqt13guzbX0daJkpRDb4d/3VZh6qfTzqiKY4PxggbGrdWR+ju7s6PPnjqW67XtZUELeh6XV+QAz2WtXijcxgE3FFbQniWE0OllHzz9TP88EAniiL4uQc3sHXK+AnAkhanzdMcyxwj5sQAUFFpNprZYGygUL3+9zwT19ARo5DeRIaGSIC1pV4/mBc6d8EP/z93/d3/ALV3zPshlvv/wBsBz4b5iZdT3MPDw8PDIw+x+k6QeelP3TBjjj0ujksIFiPUKSH5BIhA0aRIHi4bXy9DBIuXVKg5y5Hs7RkhlrEI6hq31hQtmdC/Q+kofakRKhcgP/dccKRD1rGmCeZ9qVH+z4Gvk7GyGKpGysqiCMGaonpGMnEc6Vxyfz5VpypQ7IrmwRKqAuOiebCYiO6FYssnbEfydz86zu5T/aiq4Bce2shNjaWz+m40meWZ/R28eKQrJ4avqo7wvh0rWFtb5J0HeYBlO7xxqo/nDnTQM5wEQFUFO9dU8e6t9VQVBRe5hjcmN8p49UZpp4eHh8fVIKVExgdyIrkzeAZpZadtI0KlqBOh1stWIvTLeKBeB6SVwe45gt2xD3vo7GS6dUVFrVqPVn8zSnnLoo0pnZEOMru/hswmEaESfLf+FEq47MpfvMExbYeXzw/hSMmttcVEfEtrEv1lSfRe1uv6QN8Y/YkMVWE/myqu7h5ESsk/vHiS1473oqkKv/ToTaytvXiiiiMdOqwOjmaOMmAPAG7ktjq9jo3GRiq0imtr2xwYTGbY1zuGoSrc01A65/HacDxN/1iKikiAkvANHDr89c/BiX9zz7X3fQ983n2th8dywBPFFwHHcRgeHqakpMSbQZKneDbMbzz75T+eDd2Z+ulnfxdppUH3QzYJioa+5l3IbBKZGEAmhpBm5tI7EQoiWIwyTSx314W/CLFAv+3l7Je2bPZ0j5K2bIr8OtuqilAVT4C7Wi7l3W5Lh8H0GD3JYXpTw/QmR9xlaoTB9BjOZW73AprP9S4PlFDpLyJk66yqqKcmVEbBIj+w85gZ25H89Q+P8faZATRV4TMPb2RDfckl+2AsZfLsgfP8+HA3WdMGYGVVIe/bsYL1dcWeGL6EmO3/QUdKDrUN8YN95znTG3ULBdy8soyHtjawsnLpjpmWI/kyXp0r+dRO754yv/Hsl//cyDaUjo0z0oEz0Io9eBpnpNOd8DyBUFCK61DLVqGUt6AU118X8Vk6Ds7gaazOfTg9x5BTIlMppSvQ6m9Grd6I0ANLwn5OfJDMm3+PTI4gfCF8O34Spbh+UeqSTxzqj9IbT1MVMgg7WapKiwkaeSSOz0A0Y/Jm1whCwO1X4SU+FduR/NVzR9l/dhCfrvLr79vCioqZPa+llPTb/RzNHKXD6siVV6gVrPetp16rX/C0ZRN9sKi4mF2dI5i2w7aqIkqDxpW/fAl2Hevh7189CP4MpH184s7N3LW+eh5rnUeYKXjqMYi2w8pH4d7/O6+7XwrXUI+54dkwP/FE8UXAtm1aW1tpaWlBVZeOh57H7PFsmN949st/PBu6WOf3kn37CaSdQai+i3KKSykhm8BJDCLjg8jE0OR6cghpXSavtaKihEpcj/JQ2TThHH/hnMSxK9kvnrXY0z2K5ThUhf3cVF7giXHXwNV6t1uOTX96lN6kK5LnlqlhhtIxJNNvBbPZLIbhDrZDmj/nWV41xcu8KlBCSL+BZ5YvAWzH4S+fPcqBc0PomsIvPrKJlqqCaX0wkTZ57kAHLxzqIjMuhq+oKOB9O1awsaHE639LkGv5P9jaM8Yz+89z8NxQrmxNbREPba3P2dnzCllY8mW8OlfyqZ3ePWV+49kv//FsOIk0UziDZ7EHWt1Q64mhaZ8LzUApa0YpW4VavgoRLp/XezQn2oPVsQ+76yAyHcuVK+HS8TzhWy8K775U7CfTMTJ7voYz2o3QdIxtP4FauWbR6pMPDCaz7OkeIWM7KNLB0DU2VRRSW5C/k5339Y4xmMxQHfZz01V6iU8la9n86fcPc6JzlJBf5zfev4WaktBlvzNmj3Ese4wz2TPYuOOpQqWQ9b71VKlVpGSKAqWAkHL5/VwtU/vgiZEEndEkVQUGa8tCONIh9zdl3Zb2tHIbO7ceTWd44vDblDaNoqgSx1boO1HCz22/l8pQBL/wo4ob7FrdfwC+/1F30tJ9fwJND83brpfKNdTj2vFsmJ/Mdrx69VOrPDw8PDw8PBYUrWE7SvlqZGIIESpFCUSmfS6EAF8Y1ReGkhXTPpNSQjqaE8mdxBAyMYhMDOIkhsGxcWIDEBu46LhC0xHBC7zLQ2VuqDpj7iG2w4bGlspC3u4dozeexq8prC4Jz2mfNyKl/sKrCvWuKSo1wVJqgheH2M7aFn2pEfpSI/SkhulJDNHa305SsxnJxklYac5EuzkT7b7ou4V6kMoLcpdPLP3q9BnsQ+kovakRqpZYmPp8RlUU/tu7N/DlZ45wuH2YP/3+YT5xXwtDQ2mCpQn2nRvi+YOdpLPuw5uG8jCP7VjB5sa5h93zWFq0VEdoqb6JruEEz+3v4I1TfZzsGuVk1yi1pSEaysK8dLSbjOXg1xV++l3rb1yvEA8PDw8Pj+uM0AOo1RtQqzcA4CRHcAZOYw+0uqHWs0ns3uPYvccxARGIoJa7XuRqWTPCd/XjJZmOYnUewO7cjxPtnayLEUCt3ewK4UX1S/6eUPgL8N3x02Tf+lfs/lYye/4JY/P70RpuWeyqLVlCukLWdpCAgxtS/VB/lNKAgV/LP2FnNG0ymMwghKC5eG5pggxN5Rce2sgfPXWQtv4Yf/y9Q/zmB7ZSWnDpCaMRNcLtgdvZ4tvCiewJTmRPEHWivJJ8hbRMo6CgCY1V+irK1LJZidWzKpcOyZIke+N7sTSHdKHNWeBA9OptmLUcYukMlWsyCOFm6FM1m9pNA3xn7LuE0gZ+XcWnGPiFH7/w41N8BERg8r3wEVCmv19ob/kFp2ILbP5vcOAv4bX/BZXbIHj9Q+R7eHhcfzxR3MPDw8PDYwmiBCJwgRg+G4QQEIigBiJQ1jztM+k4yPQYMj4wzbvcSQwhkyNIy0RGeyHaOz4Hesp+dd+4SF4+PSx7uCyXE0+mxjAS3chUBYRLZqxfScBgQ1kBRwaitI0mCWgq9YX5O2s93zFUjfpwOfXhcmB8NqxwZ8Na2PSlRulJDudE8wkv87FsgqiZJDqWpHWs66L9RoxQLmd5NJtkV+9hTGnjV3R+avW7eWfNVnyqjrZIeQqXC7qq8HMPbuDPf3CEt84M8Llv7kMR4LzUTWHQIGho1JWFeWz7CrY2eWL4cqe2JMQn3rmW9926gucPdvHy0W7OD8R468wAUkpCPp1o0uTPnzmCT1dpKAtTENQJGpp3bnh4eHh4eFwnlGAxSuN2tMbtSOkgx3pcgXygFXu4HZkawzr/Npx/290+UoNS7nqRKyUrEOpkKGwnNYZMDLpRwPQAdu8x7M592AOnXeULxvOEr0Or24pSsRqh5NejYKH5MHZ8HPPAt7E695M98B/IdBSt5T7v/mUGUpbj/i5SIhFYEmzL4a3eMWpCPkoCBhFf/tz7nRlNAFAd9hHU537u+g2NX3xkE198cj89w0m+9N2D/Mb7t1J4hbDkASXAVv9WbvLdxJHMEV5NvYqDg0RiSYuj2aMUiIJ5FYptYaOioghyYrbtSFRFoKCgCAUFZXwbd32iHCkYjZn0jqRJpGwUHQpqstiWmydd4qBqkM2AZWWJKeDXswR8aXRV4aIHQjPgEz5XJFf8ObF84n1ABHKfB0QAQxjXfM4lnAQxJ7YgHvls+TR0vAxDR2HXb8IDf+v+2B4eHssaL3z6POI4Dn19fVRWVnq5BvIUz4b5jWe//Mez4eIhHdsVxhODF4dlT43CZe4WhBEEIXBGu5DSQegBjFs+ht6445LfOTOS4MxIAiFgS2WE8qBv/hvlcdXMtg8mrQx9F4RinwjNHjNTue1sx6EvPYIjJQoCB4kiBJX+YlRFQVNU/IqOXzPwqQY+RcevGvg1A7/qrvtUHb8yvlTdcp9qENDc7X3jZe53DAxFm5eHEfnk3d47kuRn/voVMqade2Ciawq//Ogm7tlQg+IN7POG+fw/mMiY/Nurp/n2m+dASoQQSOkmTCgr8GOMewtpqkJhUKcwYBAJGe4yaFAY0ImEfO4yaFAYdL1I8uUh6vUkX8arcyWf2undU+Y3nv3yH8+G14a0sjjDbeMi+elpXt4AqBpqyQqU8lVgZjBPPIc0U4BEGGGYIngrJQ1odTej1m7KTWKeLUvRflJKrBPPYba+DIC24lb0je9FLJH6LRXSls2L7YNkbQek6y0OENCU3JhAUxSKAzqlfp3SgEFwid7fjaRN9naPIIRgZ10JQX3+JlQPx9N84dv7GY5laCgP82uPbSHom53o3mv18lT8KYQUOMJBSomDw2pjNYVKIYoYF6qnCNcT67MtF45gaHCIyvJKNEXj7GiKjtE0laEAWyqKLmmv0USGF4908/LRbmIpN62eoavctqGIWMsbZOwMZlZFN2z8qo+arnt48+gww4kkqs9C89lUlOmsWxGiocoPmklapidfTpqMzFyUgu1KKCiuSD6DgD7t/XiZjo4QgtZsK7uSuzAx0dG5K3gXLUbLVR37ioyegafeD3YGbv8crPvonHe5FK+hHleHZ8P8xMsp7uHh4eHh4TEvSNtEJoYmRfLEEE7cDcku0zGkY0NyaNwbQQDS9UhYuROteiNqeQviAq93KSVHB2N0x9KoimB7dRGFPn3G43vkFwkznRPJ9w+e5sn213OfOdLBlg5l/gg+deHs7ZsqqI8vA+pU4X1SRM+J6lO2Pzx8jm+efZmsY+FXDX5+/Xu5r2bLgtV3rpzoGuG3/20vpu0gJQQMFVtKfvcjO1hbW7TY1fNYRIbjaT7zd68RS5loiiBj2SiKwprqCMmslQuvP1t0TaEwOCGc6xQGJwR0IyecTxXQbxRulPHqjdJODw8Pj6WCTMewB8+4XuQDrbmc4DOOv4RAlK1Cb9zuhkcPXZw6aTlgnXud7JHvgQS1ej3GzR+Z5j3vAV2xFIf6o9hSogrBmpIwuqownMoylDaxbGfa9n5NpSTgCuQlfgOftjQEoLd6RhlOZakrDLC+rGDe9987muQPvr2fWMpkdU2EX3p0U27S6OVIOAmeiD5BVmYxhJFbPl74+Px7Mo8TzZi82TWCIgT3NpaiXSDStfXHeP5QJ3ta+3EcV+opKfDxjptquXtdNSG/Tmu2lZcTr5CVJobQuSd0Ny1GC46UnOoeY9fxHt4+M4BpueeHogi2NpVx17oq1teXoCquEO9Ih6zMkpIpMjKTE8unvk85qZyQnpXZq26viooudAbtQRwctPFgxz7hW5jf+eg/we7fAy0A73sKChvnd/8eHh7XBU8UXwS8GST5j2fD/MazX/7j2TD/kFYWu/MAmT1fA6EiHQthmyAdCBYjxnNLKwWVbti/itVu2D/NwJGS/b1jDKWyGKrCrbXFBPIwz9lyYr774FA6ys+89qekrAwhzU/CShPQfPzlHT9PUPOTtrOk7SwZ2yRtm9PeZ6a8n9wmS9o2yUxZpqZsf7Uz1mfiUt7tzQXVRIwQYT1AUPMT1v2EtCkv/cL1AGHdj0/RF9zzYjie5rN//xqpjI1PF2RMScCn8qef2ElJ+NI58jyWHgvxf3DXsR6++sIx0ubFOcWzlk00aTKWzBJNZd3l+GsslSWaNN3yRJaMeXUCuqGrFAZ1IoHpYrnreT7ugT7ume6bQUAfjqfpH0tREQks+fM4X8arcyWf2undU+Y3nv3yH8+G84+UEhnvxx44jd3xNnbXQTeal6KAqgMC332/jFbefKVdXZGlbj+r+zDZfU+AY6OUNOLb8XE3eplHjmTWpLN/kLqKMoLG5KQBKSXRrMVwymQolWU0beJcIA2EDY2SgEFpQKfYr18kwF4PhlNZ3uoZRRGCnfUlC/acoH0gxh8+eYB01mZzUymffnAD6izau9AezBf2QSklr3eNkMhabCgvpLbAj+1I9p0d4IVDXZzuGct9d1V1hHdtquXmleU5IXuCK4UiT2RM9rT2s+t4L+39sVx5UdjHzrVV3Lm2iorI1UWesKU9KZ6PC+gXep9PfW9K18PdkhYJ6YbPd0O+SxQUHgw9yCpj1VXV4YpIB577BHS/AeWb4eF/gzmkelvq11CPK+PZMD+Z7Xg1vxLJLHGklIyNjVFRUbHYVfG4Rjwb5jee/fIfz4b5h9AMlMo1CD2ItNJYig9dUUHR0FfdjTPSiTPWiRPrw4n1YZ19zfUiL21CKV/FTWUtvGX5iJs2+3rH2FFd5Oaw8rgkU3MHKteQd/5yzHcfLPUX8ul17+XLx79L0s4Q0Hx8et17qQgUAxC+ylCOl0NKSdaZFNcvJaJfWDZ124xt0p8epS89gjYRgl26+x7NJkjZWUiNXFW9FKEQ1vwEdb+7vISgHtYCBDXfNNF9toJ6SdjPT79rPX/5431EnRjhQAE//Y6blryQ6HExC/F/8K711WxoKKZ/LE1FxD/tvDA0lbJClbLCK58rGdPOieauYD7+SpkXlJtkTZusaTM4ZjM4lr7ivv2GOsUD3WAknmb/uSEsxyHs1/mZ+yeFfA+P2eDdU+Y3nv3yH8+G848QAlFQiVJQiVq9kfSz/9sNnW6EwEwhND9KuGxejrXU7afV3IQwQmT3/jPOcDuZ176CcetPoQSLF7tqSwafquDEx/BVT7ehEIKITyfi02kqCmI7ktGMK5APp7JEsxbx8df5sYntNdeLfDwf+UKnZpJScnrEFUNrC/wLOnG+sbyAzz58E3/8vUMcPDfE3//4JJ9859ortrHFaKFGq1mwXNcX9kEhBFUhH2eyFp3RJIdO9/Pjw10MxzKA69W9o6WC+zfVsaLi0l71ISV02bqGfDr3bazlvo21dAzG2XW8hzdO9TEaz/D9t9r5/lvtrKkt4q511WxrLpuVZ70qVIIiSJDZTVyxpEVaphm1R3km8QymNBEIsmRxcHgt+Rr9dj+bfZvn73cXCtz5BXjyURg4CIf+Grb83DXvbqlfQ2ck0QvRdtdLPlS12LVZdPLShh6zxhPFPTw8PDw8POaEEohgbHuczNvfQMmmwAjg2/YRtIbtAMhsEnvg9GTYv9SY6+EwcBp4ls1GiN5APSOhRg7bq9jSUOvlQL4EZvtesm//G9hZhObH2PZ47ndeqtxXs5lNJU30pUaoXMD83EIINzy6ahDh2gfHF3q3x60UftXgc1v/C7qqEbdSJMw0CSs9uZz6Gi+Lmyls6eBIh6iZJGomr7ouMwnqBfq4eK4FCOp+QuPrbfp5Eqv3kDGz6IYfq6gO8ERED5eSsH/OkyR8ukpFJDAr75C0aTOWyEwXzMe90d11M7duWg7prE06m6J/NIXtOPRH0ziORBGQVhS++sIxNjQUexM9PDw8PDw8mBh/fYTs208grXRuXDDfE2aXMmrZSnw7f4bMm/+AExsg8+pf4bvtEyiFnphzNaiKoDRgUBpwI7xlbYeR9KRInjRtRtMmo2mTMyMJVEVQ4jdy4dZDC5CPfDjlHk8RgqaihY8AsLqmiJ999wb+4pkjvHmyj5BP46N3rrpiu64kMM83iuUQTZn0jqZ4/a12TNMhHNC5d0MN922soSjkm9fj1ZeF+dhdLXzojmYOnBtk1/EejnaMcLJrlJNdo/zrLo1bWyq4a101jeXheTsPNKERFmHCSph7g/fmPPJDhChVS4k6UVqzrZzNnmWNbw03GTfhV+ZhjBCuhtv+F7zya7D/L6DuHijbMPf95gOnvgUv/QpYSdCCcO+XYPUHF7tWHh4LhieKe3h4eHh4eMwZrWE7lDRz/uQBGtZsQQuX5D4TRhCtdhPUbhoP+zeAPdCKM9CKM3QWsgkqM8eJDB6Bdhg8WU1R7drxUOuNN2yOOGllcGJ9yGgvTrQXe/g8TvcBcBwQAmllyb79byjlq5f8A7BSf+GCieHzzYXe7UHNz6fXvZebSpuuaj8TnuvxC4XzKYJ63EqRtDLEzfHllPdXI6hPDfkugGzG5AsHv0FHvJ8NJStYEa6kxFew4GHcPTwm8Osq/qIglUWX305K6QroU4TzE50jfOvNc6i6QAhByKeRNG36x9KeKD6Fz33uc3z+85+fVrZmzRpOnDhxye/8+7//O7/9279NW1sbLS0tfPGLX+Q973nPQlfVw8PDw2MB0Bq2o5SvRiaGEKHSJT8eWAiUwip8d/0s2Tf/ASfW73qMb/84atnKxa5a3mKoCpUhH5XjAmvStBlOZ3Ph1k3bYSCZYSCZyW3vepG7Irl/jl7dUkpOj7pe4nWFgTnvb7ZsXlHKJ9+5lr95/jg/OtRFyK/z2PYV1+XYl8ORkmPnh3n+YAdHz49w8001FIR9rGks4ZaGUm5tqZiVt/Zc0FWF7asq2L6qgqFYmtdO9PLqiV6GomleOtLNS0e6qSsNcee6am5fU0nYP3/Pb2byyO+3+tmf2U+v1cuxzDFas61sMDaw3rceXczx2M2PwvkXoO05ePlX4bHvgLbMxx+JXvjxZyA15L7PxOCFn4PyLVA8z2HqPTyWCF5O8XnEcRyGh4cpKSnxcg3kKZ4N8xvPfvmPZ8P85lrsJ20LZ6Qdp/8Uqd5TZEc6AXfgpSsKQtVRSptQKlpQy1sQ4YplJ+xJx0Emh3GiPa4AHnNFcJkYnr6dnYXkCAgBCJASBGgbHsG46VGEMve5jl4fnGQoHV1w7/ZLcaGgPlU0T1gZkhcI6j3JYQ4On0UADtIN+Y6kzB/BNz6ppEAPsKKgihXhSvdVUEVloAhF3Nh2Xmp4fdDNJf7Zv3+NVMYm5NNIZCwCPpU//cTOJSuKL8Z49XOf+xzf+ta3eOGFF3JlmqZRVjZz6NzXX3+du+++my984Qs88sgjfP3rX+eLX/wi+/btY+PGjbM6Zr6My8HrS/mOZ7/8x7NhfpNv9pNmisyef8IZagNFxdj6YXdS9g3MQthQSklsIh952s1HbjvTZYWgrlE6LpAX+/WrTo02kMywv3cMVRHcWVeKT7u+59+PDnXy9V2nAfjoXat416a663r8CVIZk+f3neHNs2P0jabcQgF33FRDZWUh5WEft9YsXroAR0pOdI2y63gP+84MYtkOAKoq2NpUxl3rqllXV3xRTvP5QkpJt93N/vR+hmxXzPULP5t8m1htrEYVc5gokB6F7zwCqQFY/3G47X9c9S7y5ho60gqv/CaceRIQ7rMmKQEJBQ3Q9CA0vQca3gH6wkdtWErkjQ09pjHb8aonint4eHh4eHgsGbqHBug6d5TCWBtVqQ50a7qHrAhEUMtXoZSPi+RGft2Yy0xiUvQe9wCXsT6kbc64vfCFUSLViIIqhL+A7MFvg22CokEmDkgIlqIWVqBveASlYs2ymzTgcWWmhnwPqj5iVgpVKLyzZgt9qVG6kkM40rnoe37VoDFcMSmWF1RSGyxDU66PR4aHx6XYdayHr75wjLTp4NcVfvpdSzun+GKJ4k8++SQHDhyY1faPP/44iUSCp59+Old22223sWXLFr7yla/Mah/euNzDw8PDY6kibZPs/m9idx8BAfqGR9BX7lzsai1rHCkZTZs5kTyaMZmqMggBhT6d0vFw60V+/bJp0qSU7O4eIZqxWFEUZHVJ+Dq04mK+u7eNp/a0AfCp+9dx2+rK63bsoViaHx/u4pVjPSQzFgB+Q+XOddW886ZaCkMGr3QMgYS76ksJ6Is/bktkTHaf6mfX8R7OD8Rz5SUFPnaureLOtdWUFS7MxFYpJe1WO/vT+4k6UQDCSphNvk00683XPgG88xX44afc9Qe/BjW3z0+FlwqxLtj/53DmKbCzEDsPQgN/CWRGQToQrnGfO4HrLV93L6x8jxtWfrl7zy8gCScxLfKBx/ziieKLgOM4dHV1UVtb680gyVM8G+Y3nv3yH8+G+c182e/0SIKzIwkEcHMoQ2HsnJuPfOgcOPbkhgKUSK0bZr18FUpxw7x4S88H0raQ8QGcaM94CPQeVwBPx2b+gqqhFFSiFFYhCqtQCqtRCioRvukPAqzze93cgXYGoRqoDdtxBk8jM26IObWiBX3DwygF1zZw9/pg/vJi90G+fOwpkmaGoO7j0+sf476azQBkbJPOxABt8T7aYn20xfs4H+/HdKyL9qMpKvWh8pxIviJcRUO4HJ9qXO8m3ZB4fXCS4Xia/rE0FZG550NfaBZLFP+jP/ojIpEIfr+f22+/nS984Qs0NDTMuH1DQwO//Mu/zC/+4i/myn7nd36HJ598koMHD874nUwmQyaTyb2PRqPU19czPDyca6cQAkVRcByHqY8WLlWuKApCiEuW2/aU//Pj5eD2jdmUq6qKlBLLsuju7qampgZVVVEUBSnltO2vtu6L3aaZ6r5c2zST/fK9TcvRTpdrk+M4dHd3U1tbi6Zpy6JN11qej226kv2Wapsc28Y+9n3s9jcB0JrvRl37ACBm3D4f2nSt557jOPT09FBX53o6X482mY7DaMZiJG0xlMqSMCf3L3Dzl0d8GiV+nRK/Tlh3r++KopDMmnTF0pweTaIrCnfWFePXF+fccxyHf3v1ND863IUiBJ9+cANbV5Yv2DVCCMGZvhg/PNDB/nODOOOfFfoU3nPLSu5YU0HAmHzOsa8/ykjKpLkoyIpI4KrtNLUu892m8wNxXj3Zy+7WAVIZC4lr+7V1RexcU8m25nJ8ujbv554jHc5aZzlkHiLpuI4VESXCZmMzDVpDzq5X0yb52u/AyW9AsBLnvU+hBIpmfe5N9L/a2louZFHtlOiHg19BnHwCpPssQDTej1O8GvHWl8BMuh7h9/xfRMUWnNPfQ7Q944rmE2hBZP19yKaHoOZOUI0lce5daI+5XiMAurq6qK6unjYun0ubTpuneS39GiYmOjp3Bu6kWWu+bm26VjvFrFhOyA+r4SVlpwvLo9EoJSUlVxyXL40nx8sEKSWJRGKaIT3yC8+G+Y1nv/zHs2F+M1/2ay4KkjJteuJpDqT8bK+/ncJVdyNtE2fonJuPvL8VJ9aHM9qFM9oFp15EaAZKWTNqeQtKeQsiVLrgXtNSSmR6LOf1nfMAjw+4s2tnQIRKpgjg1e4yWIqYhQA2U+5AaaYxW1/EOvsqdn8r9sCfoa24FX3Nu67ak97rg/nLfTWb2VDUwNunDrNt9U1UBCfD6flUnebCGpoLa3JltnToTgy5Qnm8l7ZYH+3xPpJWhnOxXs7FeqHH3VYRgupAyTSP8hXhKkL60hYq8xGvD05SEl76Yvhicuutt/K1r32NNWvW0NPTw+c//3nuuusujhw5QkFBwUXb9/b2Ulk5fcJUZWUlvb29lzzGF77whYvylgOcOXOGcNidtBWJRKiurqavr4+xsbHcNmVlZZSVldHV1UUikciVV1VVUVRURFtbG9lsNldeV1dHOBzmzJkz0x52NDU1oWkara2t0+rQ0tKCZVmcO3cuV6YoCqtXryaRSHD+/HmGh4dJJBL4/X5WrlzJ2NjYtPaGQqGcyD84OJgrX6pt6uzszJUbhrGs29Td3Z2zX0FBwbJo03K00+Xa5Dhu2FFN06itrV0WbVqOdrpUmybs5/f7qayszK82GWsIFaeJDL4NZ3YRH+qhv/w2GA+nvJzsNMFMbXIch2w2S21tLe3t7de1TetWrmR0dJSO3kFSqCTRyKoGNhoD8TS9Y+MTupEU+TRCoSDnR5Nkx0OxG6SIjwn8i2SnZDLJ1nKbznKDI10J/vz7B/nv/+kWKoPMq51sR3KsN8nBniw9oxmypglSsqLUzy0NIRqLNW7aWHNRm8orahlOmZzpH8bsn4yst1TOve2V8ODG9ZwZMnl2bytn+pMcOtvPobP9FIT83LG2mvpAmsqCyfzf83Xuvb/p/ewf3c8R8wgDYoAX0i9QTDE7i3eij+pX16aa/0zx6RfQRjuIP/erqPd9adbnnuM4OI5DVVUVZ8+enVOb5sNO5RE/pd1PYh34G6TpnjPp4i2I7b9C4co7aTt7FnvHVvR0D6a/murarYTDYU5HHsHZ+BBG/DTh/lcoi+1BJHsxT34HTn4HRwuTKLuD4ps/hlW+nXPtk+J5vv/PXblyJdFolHg8nhNeZ9Omnt4ebGFjChMtpFFUUcTA2AB9yT7Ohs5iCxsVlazI8nzieQ6lDqFJDYEgGAgSDAaJRWOYWRMxPqGrIFxAwB9gZHgEZzxVgUBQFCnC7/Mz0D+AlBIx/ldWVoamaPT19eX2IaSguroaaUv6+/vdcum2qa62jnQqzdCgG4VCINB1nZqaGk7HTnPMPoYjHFSpsiW7hdtqblsydrqwP8XjcWaD5yk+j9i2TWtrKy0tLajq4ocv8bh6PBvmN5798h/PhvnNfNrPkZJ9vWMMp7L4NIUdNcUEtOn7lOko9kArdn8rzkArMntBqPVgEWr5ajcfeVkzQg8wF6SVwYmOe31PCYEuzfSM2wvdP+71XYVSUJVbF5pvTvW4FE58EPPYM9i9x3LH19bcj7biVsQsw2F7fTC/mav9pJT0p0dz3uRtsV7a4n2MZRMzbl/uj+Tyk0+I5UVG2AvhPwe8PpifLIXx6ujoKI2NjfzxH/8xn/zkJy/63DAM/vEf/5GPfvSjubK//Mu/5POf/zx9fX0z7jOfPcVN0+T06dOsWrUKTdPywiNlOXrZXGubZrJfvrdpOdrpcm2ybZvTp0/T0tKCruvLok3XWp6PbbqS/fKhTU7XfsyD3wFpo5Q1o938MXd8tozsdLly27Y5c+YMq1evRgix6G0CSNmSwWSGoVSWkbSFLSVSSlKWw1Rxwq8q3NtQQtBY3HPPdhz+6rnjHGwfJmCo/Npjm6kvnQx3fK12Go2neflYDy8e7SaazAICXVW4bXUF79hYQ11paJr9LsSW8HLHMI6U3FYdITzuRb5Uzr2p9nAch4FoitdO9PL6yT6G41kEIJE0lIXZubaK21oqCAeMa7bTTG1K22mOZ49z3DyOhesRXaVWscXYQplaNvs29e1HeeY/Aw7c+yeIlQ/N6tybsF9LS8tFY+PraicrhTjxdcThv0Fko24/K92Ic/MvQs0dV9+fhICBg8izTyPanoXUpCiKrxjZeD+y6T1QsQ0UddHPvblcI6SUtLa20rSyCUuxSMs0GZkhq2RJO2lSToqMzJCRGfczMmQcd11Ou6K5WNIiId3nKgKR2yYkQmhiafotO9IhJmNIXMFdQ8Mv/Dxe+DgBAkvCTheWe57iHh4eHh4eHnmLIgSbKwrZ0zNKImuxv3eM7TVF6MqkN7XwF6LVb0Or34aUDnKsZ1wkP4Uz0o5MjmK174H2PSAESnF9zotcKapHKApOagyZGESEylACEQCk4yCTwzjRnvGw5304sV5kYnjmygoFJVw+LnpXohRWIwqrEP7IdRUHlXAZvh3/BXvwDOaR7+FE+zCPfA+7/U30DY+gVlw8oPbwmIoQgspAMZWBYm6tWJsrH8nEp3mTn4v1MZAeZSA9xkB6jL2Dp3LbRowQjeMCeVO4ihUFlVT4izyh3MNjgSkqKmL16tWcPn16xs+rqqouEr/7+vqoqqq65D59Ph8+38UTuVRVvWjShqLMHO3kassvNRnkasqFELmQ2xPLqeVzreNitmm25cuhTRfabzm0aa51zLc2KYoy7/Zb7DYtRPlSbdNc7LcU2qQ23ILiLyT71r/gDJ7F2v13+G79KYS/4LJ1X8ptutryifvvpdKmsAphQ2NFUQhHSsYyFh3RJGdHkrkw27oicJCkbUnwMnW/Hm1SVZWffXADf/L0YU52jfInTx/mNz6wlaqi6dHYZlvH84NxXjjUye5T/Vjj3p5FIR/3bazlng3VFAamp6uasN+FdVSBsoDBQDJDf8okEph+r7YUzr2p5ZVFIT5wWzOP7VjJ8c4Rdh3vYf+5QToGE3zj1TN8641z3Nxcxp1rq1hXVzxj3vmrbVNAC3CzdjPrnHUczhzmZPYkvXYvz6aepVFvZItvC0VK0ZXbVL0NNv8MHPwreON3oOoWlGD5jNtPrctwPEP7cIayRJbyyMzR+xbUTjjQ+i048GVI9ruFRc2Im38RGu9HvYRQP5s6UrkVUbkVbv0t6Hsbzv0A2p6F9Aji1Dfh1DchUAZND0HTw4iKzQt27l0uP/dM20spcRQnJ3CnnTRpO50TttMyTcZxhe6Uk2KsdIw96T0z1udK6ELHJ3z4hR+/8KOgcDJ7EhsLTQosIdGEzjb/NgxhIC/8GxeKHdxrxYWfXfJv/HuX+mymP3AF8Au/l3JSxO04CgoCgYGBiUnMiRHSZs6Hvtj3RrN1LPA8xecRKSVjY2NEItf3IbjH/OHZML/x7Jf/eDbMbxbCfinLZnfXCFnboSRgcHNVZMZB0kV1sTJTQq2fwokPTvtc6H7whXGG29w85UJ1RWMhkLF+pG3OuF/hL3C9vQvGPcALqxHhcoS6tOYZSsfBPr8H88TzOQ96tXKNm288PPMgDrw+mO9cT/slzLQrkI97lLfH++lOTubAm0pA87EiXDlNLK8JlaIKhaF0lN7UCFWBYkr9S/c+/3rh9cH8ZCmMV+PxOA0NDXzuc5/jM5/5zEWfP/744ySTSb73ve/lyu644w42bdrEV77ylVkdYym0c7Z4fSm/8eyX/3g2zG+Wk/2c0U4yu7+GzCQQwSJ8t33isuOh5UK+2DBt2bzYPojlSFQhsKVEUwT3NZbh12Ynbiw0qazFHz15gPaBOCUFPn7zA1tnneLHdiQH24Z44VAnJ7tGc+VNlQW8a1MdtzSXo6kzi3eXs19vPM2h/igBXeXOupIlbeOZiKdN3jjZx6vHe+gcmoxKVlro5861VexcW0VpwfylUYo7cQ6kD3DWPJvzem02mtns20xYCV/+y7YJT38Yho5B3d1w/1fhMr/3rmM9fPX5Y6SyFgFD46fvX89d66vnrS2XRTpw9gew/88g2u6WhWtg6y9A82MwywiCV41jQ8+b4wL5DyEbnfwsVA1N74GV74HSDZf97a6G1mwrLydeIStNNKFys38r5Vr5NME758U9RfS2pX3lnU80y3Fcz2UEPuFzRW7FFbmnCt4+4cOnTH8/k/d367l/ZJfejqlq6LbFXfFCWmreD1pg/BVcOBtdAwknwRPRJ8jKLIYwcsvHCx+/aBLCUmG241VPFPfw8PDw8PBY0kQzJnt7RrEdSU2Bnw1lBVc96HOSIzgDp7EHTuEMnMbJJCA5BHJiTrp0b86DpQhFRag6oqASpbAyl/dbKahC+Jbmjd+lkGYK89SPsc6+7g6QhILWdDv6mnfOOZy8h8eFZOwsHYmBaeHXOxIDmM7FA09d0fCrOu3xfqSUhPUAP7/+Me6r2bwINffwmBuLMV791V/9VR599FEaGxvp7u7md37ndzhw4ADHjh2jvLycj3/849TW1vKFL3wBgNdff5177rmHP/iDP+Dhhx/mG9/4Br//+7/Pvn372Lhx46yO6Y3LPTw8PDzyEScxRObNv0cmhhFGEGPHT6KWNCx2tTzG6YqlONQfxZauML6popDagqU1Vo2msvzBdw7QN5KkqjjIb7x/KwUB/ZLbJzMWr57o4UeHuhiMuunWFEWwrbmc+zfV0lwVmVN9bEfy0vlBbEeyo6aYIv+l67KUkVLSPhBn1/Eedrf2k8q4oc4RsKG+mLvWVdNYXsBwPE1FJDDryQgzHceRksHsMAeyB+m2O9zoBFKh1llJnbkaxTEwbQdr/GVaDqbtvozYWbYc/mmEk+Vo3Wc4W/zw+LYy9x3TdoinTPae6ceyJaoicByJpircvrqScEDH0BR0VcHQVPTc+vj78XV9/GVoKoY6ua7n1t2XOtXDVkrofBne/n8wfMIt85fA5p+FtR8B1Zjxd5nKcDxN/1hqTr8z4E4i6H4Nzn4fzv8IzCmp2AoaXHG86T1QvPqqBHIpJTEnxrAzTI/Vw1vJt7GxkeOP8oQQFCoFKGJmz+OpqKgXiduXE70NYcxqvzkcG2IdMNIKo60wehoGDkHnKyR8QWLhEgriw4TSCShsAGWKgK4akwL5hFiuByfL9OB0EX3aNsHp21+47TUI7q3ZVnYlXsKUWXRhcFfoXlqMlqvez/XCE8UXAcdxaGtrY8WKFZd0/fdY2ng2zG88++U/ng3zm4W030Ayw4G+KFJKmotDNBdfuzgtHQer/U2ye/5pXBQHhAJI9M0fQKu7GREsQSyjc9CJD2Ie/T52nztAEkYQfc27UBtvndZOrw/mN0vRfpZj050cyuUnd8XyPhJmmr70CI6UKAgCmo+Q7uevd372hvYYX4o29LgyizFe/chHPsIrr7zC0NAQ5eXl3Hnnnfze7/0ezc3NANx7772sWLGCr33ta7nv/Pu//zv/83/+T9ra2mhpaeEP//APec973jPrY+bLuBy8vpTvePbLfzwb5jfL0X4yEyez+2s4o10IVcfY9lHUqnWLXa0FI99smLZskqZNUFeXjIf4hQzF0nzh2/sZiWdorCjg1x/bjN+Y7g3aN5rkR4e7ePV4LxnTnRgc9Gncs6GG+zbWzNr7eTb2O9wfpSeepiESYG1pwdwatwTImDb7zg6y63hPzqs+mbWIprKoQqCpCjevLKOmJDRFvJbTxGtrikBt2TK3btoOU9M8+4tSlK8bIFjqRtVzLMHw2RJGzpbgWDOff3fYz/Kw9XWyGPyF8X8YEhenIMpYNkOxNFOlXgmUFfgx5vm8VhQ3F/1KTvHO7BPUWScRAkwlyOHC93Oq+L0oRoErpKsXiO1TxHlDUzjRNcoz+89jWg6GpvDILY1srC/BdtzJBLYjcRyJPXV9/DNnprLxdew0ZWN7qRl9iarYbhSZydlh1KijLXQXZ4N3MqLVXvBdBwIpCCZQQ0mUUAo1nESo4/mkhY0WMHOP8yaEcWukEM0MozkGOgY+XHE7oPoJKgHCWpCwFiBk+AgaGkGfht/Q8GnKRY43s7qGSgdina7oPdI6KYKPnQU7O31bKwXxbhAqqLornDsWFK10n0dKZ+ZjzCfXIrgPHCJ27kli/gBh06Hwjt+D1R9c+LpeI7Mdry6tWJ95jpSSbDZLHswz8LgEng3zG89++Y9nw/xmIe1XHvSxtjTM8cEYZ0YS+DWV2msMpyUUBbVqA8III6006AEwUwjNj9Z4ay63+HJCCZfhu/UnsftPYR79Pk6sn+zh76K07Ubf+DBquTvT0+uD+c1StJ+mqDSEK2gIV3D3eJkjHV7rPcrvHfw3FCGQEgr0AEk7Q19q5IYWxZeiDT2WJt/4xjcu+/lLL710UdmHPvQhPvShDy1QjZYWXl/Kbzz75T+eDfOb5Wg/4Qvju+NTZN/6Onb/KTJ7/xlj0/vRGrcvdtUWhHyzoV9bumL4BKUFfn7lvZv5g+/sp70/xh89dZD37VhBTUmIvrEUzx/s5FD7UE70qykJ8s5Nddy+uhKffnVtm439qsI+euJpeuMZ1pSE8y6E+oX4dJXb11Ry+5pK+sdS/PBgJ9949TS2LZFCkrUcXj/ZR0Whf7qX9DWQjQbpe2sFBeVJilf3YRSmqVw3TGVLFKurBjFYiaaoaKorHmuqgqr8JInzJylPHOQzoX9m3/o/Q9d1NEVBU12BOpW1+dsXjpOxbDQhsaTA0FR+4u4WfLpKdkLAtxx33bLJ5t7bTKdRmAABAABJREFUmLZbPn07e1L4tyaF00qrjfsz/84a5xAAaXTeVN/Fy+JRUvEwxNNA+oq/he049EfTOI5EEZDMwNd3nZ6X39mlCWjCkB9lrbOfm5zdrHYO4TfbWZtop0X5N85F1nKiZBvdxU3IIhVfQQZFnX7uS8C2BJmoj1RMo3TlCEKRmBkVTXewTYWTP65GsS58Tpgdf0W5FEJAwNDwGyoBQ3PXdYV0MkZNe5agoVLEMCXmeSLZ8xRk2gkmz+FLtKE4GYQARYhpkyHQ/BBphuIWKGpxPfdf/AyOmcLUCtCtGIoRhsdfhmClK6JbKbCSYCbH18ffW6mLy8wrlFtTyicEdzvrvjJjszOdY+FEzxOSNgEUTAzSz/8i/to7IXTxpJB8whPFPTw8PDw8PPKC+sIAacvm3GiSY4Mx/JpCaeDKYaBmQglEMLY9TvbtJ5BWGqH5MbY9viwF8amoFatRylZht+/GPPk8TqyPzBt/j1q1Dn39eyBQvNhV9LgBUITC+uJGwnqAlJUhrPtJWGkCmo9K7xz08PDw8PDw8PBYAITmw9jxccyD38bq2Ef24LeR6TG01e/Me0HR4/pQXRzklx7ZxO88sZc3TvWxu7UfISDs1wmOe41vWlHKuzbVsr6ueEHPq9KAga4qZG2H4bR5zc9GliIVkQC3NJfx1J5zaKqG7YDjuKHK795QQ11pKCdWTywnhOnJ966HtDbuJa2pYlzAVlCVSbtIKTlvnWd/ej9jzhgU9xNSEmz2baZZb54eNjv+ZfjOoxSap6kzfghbfvaiuvt1lb9+/hjJdJawX+dn5jGnuJQSc+QcYt+fobT9AAlIgsQbHiPe/EnW6WU0TxHYzYvWnSnCu1s+MJZiMJZG1RQUIXLe35XFQYqCPlRFoCgCVYwvFYEixCXLZ9pmYl2oa4jrj7BXG0DYJzHVPpKGRIr/n707D5OrqvPH/z733tqrunpLL+nsSQcSIIRAAgkgoCigDqKo6DCCgOMG/nTGmS/OfEVAnS84zqizKDIoYRQVXAfGBQUkbGEJgbAFQvakt3R6q6qu/d57fn/c6uol3Z1eqrvqVL9fz9NPd9+6VX1O3n2rc+pT5xwBA8AiHIbU3bD1AIQeQgA1CMoqVKASFahESIRhBDX0p7L43nOPYeHpHXC5JayshkMvNOCac9fA0DQkMyYSGQupjIlkxkIyayKZzn2dMZHMmEjlvpbSmWmeSJtIpLKoQC/8sgVhuwXLzUNobGtHvWyDG+nBHADEcx8mDBwV89EpmnBUX4iIexFiviXIeBrglW74+nX4Mk6hPRT8Aja2fxNu9CIKL7bP+zyMvSakbB3y2BJSegB4IGU4v8DB0PfHSCkhDUDqADwyf7vMfSGHnC+lhGannQ8rCc1KQLfS0O0UdDsJzUrDsBPQ7MFjupWEbqfhSxzAIrsFJgwAAgn44U71I31kN8LLWBQnIiIimhUrqgJImjY6+lN4+UgEG+ZXIeie2n9njEXroc1bCRnvhgjUlH1BfIDQnH3F9aZTkX3rUZj7n4XV8Qaszl3Ql2yEEAuL3USaA2q8Fbh+1aX47hsPImGl4TM8uH7VpXN6ljgRERERzSyh6XCt/SCEL4zsW48hu+tRyFQUrlMuK6vts2jmVPhdSGdt2Llqmi2BaCKDd5zShPeesRgNlf5ZaYcmBOoDHrREk2jvT5VVURxwCuNet45k2kLAYyCeNlHhd+GyDUumt+f1CEIILHYtxkJjIfZm9+Ll9MuI23FsTW7F6+nXcZr3NCwyFjlvcAjOBzZ+BXji/wAv/Qew4G1A7UnDHu/c1Y04sSmMF19/C+tOWol54QL9PsSPQOz4Htxv/QKQFqAJYNl7gHWfR1XFYkz1reU9/Sl8/u6nh/07+zw6brxs7bT+ndMyjR6rB91WN3qsHvRYPYjaUch8mdcPYCkgbXgzSdT0dqC68y1UR7tRE+1BKNkPUbfO6eOS1YC/dtjjXxHbhLsfehnwZIC0G9eec+rE33wgJZDsguzZhWzPbtjduyB790CL7AEy/ZDIvQlDM6FpujNLHW5EjCZ0GYtwVGtCO5rQas9Hu1mDtDnksU0AMQCxBIBE/rAzI381fmb/K+q1Thyx6xDZU426zrcKNCN/onQ4//bH/730Zo/iK/J5+EQKcelHUEsgBR/67Dqo/uop9xQvICkl4vE4AoEA32GoKGaoNuanPmaottnKz5YS29v70JvKwmvo2DC/suSXWitldqwT2dd/C6tzNwAJ2/DBs/piGIvW84Uhxaj4HNqdiuJIshf1vioWxKFmhqTOeHW6VOonryW1MT/1MUO1zZX8zAPPIvPqA4AE9IZVcK/7CIRRHoXFuZJhMbzZ2oub7nsBhgakMjYMXUAC+PpHN+DEpsqC/IyJ5teTzOCF9j4YmobzFtUMmwFdDp7c2Y7/emQnUlkbXpeGT15YuFnXYzGliV2ZXXg1/SrS0pkdXKPXYJ13HeYb852C6mOfBw78EahcDlz6a2ep7CEKev2l+oBX7wJ2/hiwcrOVF7wNOP1vgJrV03vsnOn+OyfsxGAB3HYK4P12/6jn+jU/qrVq1Og1qNarUa1XIyBy/06JLuDgn4D9vwc6XkB+LwKhAQ3rgaXvAZa8C/A6bwHo7TyASMcehBtWoKpuyeiNS3bn9vx+C+jd4+z53bsbyIyxnLrQgfASyPByZAJL4K4/CaKqGahYDGijT8ixbIlU1pl9nkibSGWdz/lZ6VkLBzpj+MNLh6ELAaEhv+/6xpX1qAp6IAaWXxfIL8MuhizJPvT2gU/D7yMghtyG3G1Df/3EMcec+wz5Nv84AkAiY6H9uR/hanEnvEgjBS/uMz6Nv/rEPxT0jSmFNNHxKoviREREpJysZeO5tj4ksiZCHgPrGythsIA7LdaRXci+/lvY/V0AAK2iAa6T3wu9dnmRW0ZEVNrmynh1rvSTiIjmDqv9daS3/wywLWhVC+HZcDWEJ1DsZlEJG2tm7b9de/asF4qklHjycA9SpoVT68OoD3hm9efPhp7+FDojKdSFvbP675uRGexM78TOzE5kZRYA0Gg04jTPaZhnGsBv3gsku4CTrgbO/MfCNyCbAF6/B3j1h0A2V2CuWwec8UWg4YyC/7iJ/DtLKRGX8WGzv7utbiRlctTzQ1oI1XquAK45BXCf5ptYg+JHgAMPAft+DxzdMXhc6MD8TU5hfNf9zp7Zhh/Y9FWgdrVT+O59yymE9+0GUr2jP77QgNAiZ8/vgX2/q5qBiiWA7ppYGyehlJ43JuPJne24/+GnEDI7EDMacMU7z5nxN6ZMB4viRWBZFvbu3Yvly5dD1zljTUXMUG3MT33MUG2znV8ia+H5tl5kLBs1fjdOqw9D47vgp8XMZtC+7X9RE3kNyKYAAHrjarhWvxtaoKbIraPj4XPo7LCTEch4F0SgtuDbLsxUhjPZZlJnvDpdKvWTz4dqY37qY4Zqm2v5Wd0HkHn+vyGzKWjBWrjPuhaaf6qLEZeGuZbhbJvpGcyTye+t7n4ciCRQH/Dg1Hr+P7/QUnYKr6RfwVuZt2DBAgAsci3CaT0ZVP7p/3NOuvi/gfln5e8zrevPygBv3ge8/H0g1e0cqzoBOONvgQXnDZ/6W0BxO46YHUNICyGgBWBLGzE7Nmz2d7fVjYzMHHNfAYGwFs7P/K7Ra1ClV8EjCvQmjVgrsP8PwP7fAd07AdsEoocAaTuz9K00AAFULBplNrcAQguBqhWDhe/KZiC8FDDGbt9MPIcWY+WDQijWG1OmYqLjVe4pXmC2bRe7CTRNzFBtzE99zFBts5mf36XjtIYwXmjvQ3cigze6+7G6Jsjl4aZBaDri1Seh8bR3wt79Z5gHn4fVvhPWkV1wLTsbRvMFEK7S/k/wXMfn0JllHtqGzPb7Ia00hO6B+/QrYCxaX9CfUegMZ6PNRKWIz4dqY37qY4Zqm0v56TVL4Dnn00g/uxl2fxfST34PnrOugRaeX+ymTctcynC2nbu6ESctqprRQtFE82sIenAgksDRRAambXMFvQLzal5s8G3Aas9qvJx+GXsze3EoewiHQwLLz70Gp267H8EnvwRc9iDgGSzCTfr6sy1g74PAS/8O9Lc5x0KLgHWfB5a925nZXGCmNJGWaezK7ML21HZkZRYCAjV6DbLIwpTmMffRoKFKrxosgGtOAdwQM1hmDDUBaz7hfEQOADu+C7z4785tdhbOYt8S8FQBDacDlUMK4OFlgGuCs9NHKPRz6Gw8b8yE6qA6bZ0oFsWJiIhIWWGPC6fMq8DLnRG0RpPwGRqWVXK5u+kS7gDcay6DseQsZF//Hayje5Dd8wTMwy/Ctepd0Beczv3Gac6QVhYy1gmraw8yL94PmBlAaJCZBNJP34Xsrj9D5JdYG2sRrrEX5xq2bpeUmJdIINPiP84sgIkt9iUtE7J7PyAtQDMgs6ncC77d0MLzIfyV0HxVgCcAMQMvtBARERGpQAvVw3vOZ5B+bjPs6BGkn74T7vUfgz5vRbGbRiWqVApFIbcBv8tAImuiM57B/FDx21SOgloQZ/vOxknuk7AjvQMHswexZ14D9p/7Aaw8+AZO2fb/4Dvn9sk/sJTAwUeAF7/jLPkNAL55wGk3ACs/OOY+1iMNFLgHPlIyhbQ94vsRt5vSdGaEyxgkJAQEJCQ6rA6ERAhuzZ1f9nzgo1KrhC6KuPpEeImzhPyunwPpqDPb20w5b0j4wG+BQEPx2jYBpfK8MdexKE5ERERKqwt4cEJ1EG9292NPTxw+Q0cj/5NZEFpFA9xnXQv7yBvIvP57yHg3Mjt+DW3/s85+4zVLi91EooKSmQTsSBvsaBtkpB12tB127CggbUgrk9tWQACwnbq0NGH3d0Lo7kK1AHomC4lk7udM89GsjLO8HIQz+0BKwE4hu+vh4W3WdAhfJTRfGMJfBeGrPPZD59CRiIiIypfwheE5+1NIP/9j2N37kX5uM9xrPwStZim3oaGSJYRAY9CDvb0m2uMpFsVnWKVeifP95+OoeRQvpV9CeyCDNxbZ2G2ZWN32M5zUeDlSdgp9Rh/idhwV+jhbDrU9C2z/V+DoK8737gpgzadgnfgRpA0NaRlD2hwsaKfs1KhF7oEC93QYMKBh8E3S5/jPwXLXcmil+MbpQANw/r8CW74ImAnAE3a+L/GCOJUO7ileQFJKZDIZuN1uLt2qKGaoNuanPmaotmLnt6u7HwcjCWhCYF1DGNW+QhWp5o7xMpS2CXP/Vphv/RkymwYA6PNPdvYbV3zPvXJR7GtQJVLakIle2JE2yGg77FwBXCYjo54v3D4Ifw2sjp3OrGvDC5hpwPDAc9a1EJ7gkJPH+qnjZTJwm0Q2m4XL5XKOjRvj8TOW6RjSW3/g7E2nu52ivq5DX7QByCYgkxHIVHTEdPUxfponmCuYhyF8TuFc8+cK5v4qwPDO2d87Vcar06VSP/l8qDbmpz5mqLa5np+0TGRe+jmstlchsylneV4hlNqGZq5nqLrJ5pfImnjqcA+EEDhvUQ3cegkWMstUu9mOF9vvR5fsA4QOGZyPNJwCtUu4cI7vHCx0LRxexI7sRvrQQ0glWpF2e5B2B5CuaUaqYj7SwkRWZqfUFg0aPMIDj+aBV3idr8UoX2uDX6ftNH4e+zkyMgO3cOc/X1FxBQJaia/CGO9w9havWFTwgjifQ9U00fEqi+IFJKWEbdvQNI0Xi6KYodqYn/qYodqKnZ+UEq90RnEknoaha9jQWImgmzMbJ2MiGcp0P7K7HoZ5cJtTSNN0uJafC6P5fAjDM8stpqGKfQ2WqoHlz+1om1P8zhXCpZkZ9XwRqIYWng+tohFaRSNEeD6EtwJCiBnfn3smMjxem6VtQaaikIleyGSf85HohUxFcscikNbxX5gRhtsplvtHzDD3VzrHPaGy3XZBlfHqdKnUTz4fqo35qY8Zqo35OW+gzOz4Fcw3HnLGPEIDIAHNgL7kTGjBec7/D71hCG9o8HOJjIeYodqmkt+zrb2IprNYVRvCwoqp7aFMUyPNDA5t/TReqK9Ga+0CSE1AgwaZ2/IqJELOjGs7C6R6gWzcuaMQgCsEeCuBEUuSC4jBQrbmPaa4Pez7XBHcBdeUrvfdmd14MvEkssjCBRfO9Z+LZnfzdP9ZlMbnUDWxKF4ElmVh9+7daG5uhq4XcW8FmjJmqDbmpz5mqLZSyM+yJbZ39KEvlYXX0HHm/Cp4jPIswsyEyWRoR9uRfe23sLr2AQCENwTXqougLziNexMXSSlcg8Um0/F88Ts/A7zfWf78GJoOraIeWsV8aOFGiIr50CoaIFzjLzloJyOQ8W6IQE3Bl9CcqQyn02YpJZCJQyYjsJO5wnmiD3Lo15nE8R9IaBC+itwy7aMUz32VEMbgCh9Om9VYqlSV8ep0qdRPPh+qjfmpjxmqjfk5zKN7kP7zvwBWblliKQFIwF815tY5wuWB8FQMK5RjZOHcG4KY4D7BU8UM1TaV/A5GEtjV3Y9Krwsb5nMlt1nXuxttT3wa/7PxYti6G7YYXPUrCB8CiT54Yh3wZNLwZNPwBhfD07AJHm/dsAL3wExuN2Z3hnLcjiNmxxDSQqU/Q3wW8DlUTRMdr3L6FBEREZUNXRNYWx/G8229SGQtvHSkD+sbq6BrfGdnoWkVjXBv/ATsjp3I7Pw9ZLwHmZd+CW3/M85+49VLit1EKmNS2pDxHmfJ86gz+9uOtDtLgY9CuP25wndjfha4CM6D0CY/wNV8YaDEi7QjTafNQgjAE4TwBKFVNo16jjQzg7PM87PN+waL6Mmosy97InccB0b/WW4/hL8S0szA7toLSBvCHYD79I8osVQpERERlQ8tOA/CHYQ0U4DhAbJJQDPgWnUxYJtAKuqstjPwYWYgs2nI7FGg/+i4jy08gdxM87E/4PbzzcY0YfUBD3b19KMvlUXStOAzWMibVVXNCDf/JbzZdmSkDSG9sN063GYWH3r0LoQGtuladCFw+v8BqkprJnZAC7AYTnMGi+JERERUVty6hnUNYTzX1odo2sQrnVGsra/gkkczQAgBvfEkeOtOgLn/aZhv/Rl2XyvST90JvWkNXKsu5n7jNG3O8udHcvt+D84CH3v58xpo4cZRlz+nmSEMN0SoDgjVjXq7tG3IdMyZXZ44tnguk73OC8mZBOxUDEh055YqFZBmGpnt90Obt7LkZ4wTERFR+dB8YbhPv2JwGxqXf9ytc6SZdradSccGP48snKeigG1DpuOQ6TgQaR+nAdroBXNPCMIXHvw8ypLtMhmBO94GmawDgtWF+iehEuY1dFR73ehJZtDRn8bSSn+xmzTnBFZdg3MfvxJPLl+JrGHCE8/g3JefcArijWcCp38RqDu12M0kmvNYFCciIqKy43cZOK0+jBfa+3A0kcab3f04sSbIotgMEboB14rzYCxYh+ybf4J5+AVYra/A6tgJ1/JzoTedBpmOKrEMMs2ukUtky3R/rvjtFMBlpOM4y583OMXv8HyIcCO00PGXP6fZJzQNYmC2+iivy0opATMFmeiD1fE6Mjt+BWgaAAG4fJBmCjLerdwMfSIiIlKbsWg9tHkrJ7QNjTA8475JEHBWO0ImMWqxfNhHOu4Uz3Or7IxHGO7B2eXeEGQyCqvtFdRaFtKHfgdwxZ05oyHgcYri8RSL4sWQ6ETzrifR+MZDiPorURHvRTCbAd5zH7D8L5w9xImo6LineAFJKWHbNjRN44vuimKGamN+6mOGaivF/I7E03i5MwJIYGV1EEs4MBxXoTK0I63IvPZb2N0HILMpINMPaDqge+BadRGMxRsgPAHA8JbM70o5KMVrcCwym4S5+3Fkdv4eMDOAAIS/ChhjZOIsfz5/yPLnDRDBOgitvJaUVCnDmWInI0g99DVnqVKXD8gmIQwvvBffVLJvqlFlvDpdKvWT15LamJ/6mKHamF/xSduETMVyHxHIVAzIfR74XqajkNn0iPtZw1bcgdAAdwC+S26B5q8sTmdo0qZ6DWYtG1sOdUNKiU0LqhF0cz7krGp/DvjNeyE1F5BNOK93SAt4/++Axg3Fbh1NAv8Oqol7iheJaZpwu93FbgZNAzNUG/NTHzNUW6nlVx/w4ITqIHZ19+Otnn54DQ0NQc4iHU8hMtTCTfBs+iTMA88h8+wPAdsCbBswM8i+/Ctkd29x9nIWmrOHsDvgFMlzn4Xb+Rj1+zIrghZaKV2DMpOAHe+CjPdAxruGfN09fIlsCAASMtYJ+GugheoGlz/P7f+NObT8eSllWAzDlio1UxCGF+7TryjZgjiVrrl+LamO+amPGaqN+RWX0AznDaPH2YpKmulhhXKraw/MNx92bhwYg6WiSD/5PbhOeDv0BaeNutw6lZ6pXIMuXUOtz42jiTQ64mmsYFF8dlUsBgw/kO2H9FRBZKOAKwhULCp2y2gK+HewfPGZsYBs28b+/fvR3NwMXdeL3RyaAmaoNuanPmaotlLNb1GFD0nTwqFIEq8djcFj6KjyuordrJJUyAyFENBC8wCX15n9K63cCzPWYGFb2pDpfsh0PxCb4OO6fRBuP+AO5ovpwh0AhhTPh32vTy3rkct6q2C2r0EpJZAvfHc7xe7cZxnvclYJGPPOlvNZdzkf0ABpwbPxWhj1J85420tVqT6PzrbJLFVKNBpeS2pjfupjhmpjfuoQhgci6AGCtQAArWYprP3PQpopmJoHhpUCpA072YfMKw9AvPEQ9IWnw1hyFrTgvCK3nsYynWuwIejB0UQa7f0pLK/0z5k3FpeEQANw/r8CW74IOx2B7gk53wcait0ymiT+HSxvLIoTERFRWRNC4ITqIFKmjc54GjuORLBhfiUCLv43aKaJQC2E7nGWQXYHnGWQPSF43/kPEG4/ZCYBZPoh03HITAIyEwcy8dz3/c7t6ThkJg6ZTQASkJkkZCYJoHtibTDcgNsP4Q4OFtCHzUDPFdgHbjM8sA6/4MxStdIQugfu06+Ys/vwOYXv+CiF79ys7/EK3wCEN+S8sSBQA5H70AK1gKYj9cg/D18i2+VzZoUTwZkxzj3EiYiIiCZuYMWd9Pb7IDJJwBuC+9QPAADM/c9Axrth7tsKc99W6HXNTnG87kSuxlVG6vwe6JpAMmshmjYR5oSA2bXyg7AbNqJl59NYuPps6BVNxW4REY3AV4OJiIio7AkhcMq8Crxg9iGSzuLFjgg2zK+CR+fgfyYdbxlkMYmil7RtIJtwZpYfU0CPH/t9Ou7MRDczgJmBTPRN7OcAQLzLWdZb1yGzKaSfvRtW90FnHz6Xz5mR4fIBLi+Ey+vsFebyOZ8VfEFJSgmk+8ee8W1mxr2/8IWPKXqLQA2Ev9p5U8IYuEQ2EREREVFhGYvWA9XLcWjXDiw6YS2MYLVzfOlG2Ef3wDzwLKwjb8Dq3A2rczeEvxLGkrNgLFrvvGGYlKZrAnV+D9r7U2iPp1gUL4ZAA1KVazhDnKhEsSheYJqCL4TScMxQbcxPfcxQbaWcn64JrG0I4/m2XiSzFnYcieCMhkroGpcTG6rQGRZqGWShaYAnCOEJTuh8KSVgpvJFcmQGi+UyEx+cgT60oG5mASvjLPMOAViWUxy30zAPPAOhH38/KWG4B4vkLs9gsXyggJ4rqjufvfnC+sB50F1TXuJOJiPwJNohk3VA7sWvYf8e6dgYM767nb6P2SlA+CoHC9/+GmjBWqfoHaiZ8hL1XCJ7dKX8PEqkEl5LamN+6mOGamN+ahO+MMzQAudNyAPHhAa9biX0upWwEz0wDzwH69A2yEQfsjsfQnbXIzCaToWxZCO0Ss5uLbbpXIMNQaco3tGfxgnVQS6hXgR8DlUfMyxfQkopi92I44lGowiHw4hEIqioqCh2c4iIiEhh8YyJ59v7kLVsVPvcWBr2IeA24DW4T9BcJ80M7Gg7Uo99GzDTzl7XZgbQDRgr3w4hhLNceDbpfDZTg5/HKypPhtByxfNcsTxfOB8sriN3PD9b3fDC6tyF7Ou/dWZ1awaMJWdC+CoHZ3sneo5T+Ba5Gd+1Q2Z81zhL4Purplz4JpoL5sp4da70k4iIaK6QVhZW68sw9z8DO9KWP65VLYSxdBP0xpMhdM6pU40tJR4/1I2sZeP0xkrU+I7/5m4iItVNdLzKongBSSkRj8cRCAT4DixFMUO1MT/1MUO1qZRfbyqDZ1t7kTJtaAJw6xrW1FWgKeQrdtOKSqUMZ5J5aNuk9xSXtgVkU5DZJGCmIbNJZ7/sbGqwkD7k+6Hn5YvrU/xvubQtINENSAkJAQEJCAH4ayC0IW/2EALCXzV8qXN/DUSwFsJXxRe8SgCvQTWpMl6dLpX6yWtJbcxPfcxQbcxPfZPNUEoJu/cQzAPPwGp7FbBtAIDwBGAs3gB98Zlc1WkWFeIa3NkVQ0s0ifkhL06eV9r/bys3fA5VHzNU00THq3zlq4Bs20ZLSwuam5uh65xtpiJmqDbmpz5mqDaV8vMZOmwpIQFYEkiaNrZ3RODSNMzzu+fsf3pVynAmTWVZb6HpgCcA4QlM6WdKKQErM2oB3SmaJ53vzaFF9jSQTcJO9Dl7oUMAAoAwAEjojauhz2seMuO7EkLjf/9LGa9BosLgtaQ25qc+Zqg25qe+yWYohIBevRh69WLI1e+BeWgbzAPPQaaiyL71GLK7H4feuNpZWr1m6ZwdL8+WQlyDDQEPWqJJdMYzsGslNGY2a/gcqj5mWN74qhgRERHNOYmsBQnArQlYUsKSgGlLbG/vQ9jrQlPIi8agF26dewjNVZovDMzibAghBGB4nP3GJ/lz7WQEqYe+BmmmYEKHCxaE4YV7zfs5o4OIiIiIiCZMeENwrXw7jBXnwerY6Syt3r0fVttrsNpegxaqh7H0LOgLTnPGLlSSqrwueAwdadNCVyKDugCzIiICWBQnIiKiOcjv0qELAVNKeHQNGcsGIKBrAv0ZE7u6+/FWTxzz/G40hbyo9c3d2eNU+jRfGO7Tr0B6+33QMknA7YP79CtYECciIiIioikRmg5j/ikw5p8CO9oB88CzsFpehB07gswrD0C88RD0hafDWHIWtOC8YjeXRhBCoCHgwcFIAu3xFIviREQ5sz796fbbb4cQAl/4whdm+0fPOCEE3G6+aK4yZqg25qc+Zqg2lfLzGjrW1FXA0JzCuEvXcHpjGBcsrsWq2hAqPC5IKdEZT+OljgieONyN3T39iGfNYjd9RqmUIQ1nLFoP77u+jL4TPwrvu7583D3QqTTxGiQqDF5LamN+6mOGamN+6it0hlpFA9xrLoP3nf8A18nvhQjUQGbTMPdtRerP30L62bthdeyEzO1FTtNTqPwag04h/Gg8A5PZzBo+h6qPGZY3IaWUs/XDtm3bhg9/+MOoqKjABRdcgO985zsTut9EN0gnIiIimoyUaSGRteB36fAaw/cJimVMtMZSaO9PIWsNDiCrvC7MD3lRH/DC0PgfZCKiuW6ujFfnSj+JiIhofFLasI/ugbn/GVidbwK56oLwV8JYchaMResh3P7iNpIgpcTTLT1IZC2cXFeB+UFvsZtERDRjJjpenbXl0/v7+3HllVfirrvuwte//vVxz02n00in0/nvo9EoAMCyLFiWBcB5t4amabBtG0Pr+mMd1zQNQogxjw887tDjAGCPeBfVWMd1XYdt2+jr60NFRQWEEPm2SCmHnT/ZthezT2O1vVz7ZFkWotEoKioqoGlaWfSpHHMaq0+2bU85v1LtUznmNF7bpZT5P2ADz6uq9+l4x8upTwP5VVZWjtvXUuqTWxNwuZ1iuGVZw8736wLNlT4sD3vRnTLR1p9CVyKDnlQWPaks3uzqR33QgwUhH4KGGPYO0lLOaWhbRv7uSSkRi8VQWVk54baXep+GHi+l372Z6NNAfuFweNi5KvdpvLaXY5+EEIhEIgiFQsOeU1TuUznmNPL4yPtR8UkpEYlEEA6HOcNDQcxPfcxQbcxPfTOdoRAa9LqV0OtWwk70wDzwHKyDz0Mm+pDd+RCyux6B0XQqjCUboVU2Ffznl7tC5SeEQGPQi729cXT0p1gUnyXJrInOvijqKivgc3H3YhXx72B5m7Wr8vrrr8d73vMeXHjhhcctit9222249dZbjzm+d+9eBINBAEA4HEZjYyOOHDmCSCSSP6e2tha1tbVobW1FPB7PH29oaEBlZSUOHDiATCaTP75gwQIEg0Hs3bt32IsdS5cuhWEY2L1797A2NDc3wzRN7N+/P39M0zSsXLkS/f392LlzJ6qrq50X2t1uLFu2DJFIBB0dHfnzA4EAFi5ciJ6eHnR1deWPl2Kf4vE4Wlpa8sfLvU9tbW3o6elBdXU1QqFQWfSpHHMaq0+dnZ35/KqqqsqiT+WY03h9sm0bPT09aG5uRn19fVn0qRxzGqtPA/mtXr0a1dXVZdGnoTmta6jErr370JMFojCQhIYW20JbLAVk0wjJDEIwYUAq06eRv3u2bcM0TVRUVCibU7lcT1Pp08AbxAKBAPbt21cWfQLKL6fx+rRs2TK0trbm39xXDn0qx5xG9qm/vx9UWmzbRkdHB0KhEHRdP/4dqKQwP/UxQ7UxP/XNZoaavxru1ZdArnwHrLZXYO5/BnakDeah7TAPbYdWtRDG0k3QG0+G0FkgnIhC5tcQ9GBvbxzdySwylg23Puu76c4prbEkXumMIpM14Y5msaauAk0hX7GbRZPEv4PlbVaWT7/vvvvwT//0T9i2bRu8Xi/OP/98rF27dszl00ebKT7wIsjAtPdSmRUwQNd1mKaJt956CytWrICu68rOdBjap3KZvTHRPmWzWezZswcrVqyAYRhl0adyzGmsPpmmOeX8SrVP5ZjTeG23LAt79uxBc3MzXC5XWfTpeMfLqU8D+a1cuRKGYZRFn0a2feC4lBJ9aRPt8Qw6E2mYtnOuAFDrc6OpwotanxtQbLauZVnYu3cvVq5ciZFUzGnk8XL43RuvTwP5NTc3H/NuZlX7NF7by7FPUkrs3r0by5cvHzb4VrlP5ZjTyOPRaBTV1dVlv6y4SsunW5aF3bt3o7m5mS9kKYj5qY8Zqo35qa+YGUopYfcegnngGVhtrwK5/y8JTwDG4g3QF58JzRee1TapptD5Pdvag2jaxKraEBZWsEBbSJYtkchtyxdJZfFmTz8s23nNSNME3LqGty+uPWa7Pipt/DuoppJZPv3w4cP4/Oc/j4cffhhe78SW6PB4PPB4PMcc13X9mF/CgRckRprs8bF+uSdzfODFl5HtFEKMen6h2j7TfZrM8XLo00CGA20ohz5Nt42q9Gkm82NOs9cnTdOmnF+p9mk6x1Xr00BhYbzzVevTWMdrDQO1AS9M20ZHPI22WAp9qSy6khl0JTNw6xrmB72YH/Ii6B7+X65S7dPAzxurjZM9Xip9Gq+Nkz1e6n0SQozZltHOH7hPKfdpKsdV7dPANg6jjXtU7dN4x8ulT3yhhIiIiMghhIBevRh69WLI1e+BeWgbzAPPQqZiyL71GLK7H4feuNpZWr1m6TFv5qXCawh6EU33o6M/xaL4FFi2RCJr5YvfiayZ/z5t2sPOG5gwAQC2BFKmjVc6o2iuDqDS4+LvO1EJmPGi+Pbt29HZ2Yl169blj1mWhSeeeAL/+Z//iXQ6XTYvIgghEAgE+OSmMGaoNuanPmaotrman6FpWBDyYUHIh/6MibZYCm39KWQsGwciCRyIJBD2utAU9KIh6IExRjGmFMzVDMsF81MfMyQqDF5LamN+6mOGamN+6iuVDIU3BNfKt8NYcR6sjp3O0urd+2G1vQar7TVooXoYS8+CVrMMMh2DCNRyFjkKn19DwIO3evrRm8oiaVrwcdbyMSZa+B6NoWsIGDrcukBLLAVbSggAA2tQ9SQz2NaWRdBtYGGFD40l/roQlc5zKM2MGV8+PRaL4eDBg8OOXXPNNTjxxBNx44034uSTTz7uY6i0TBsRERGRLSW6Ehm0xlLoSmbyy/TqmkB9wIP5QS+qvHyXMBFROZgr49W50k8iIiKaOXa0w1laveUlSDMLmU0BmRggdMDth+eMv4SxaH2xm1l2trX3oTeZQXN1EEsr/cVuTlGYtkRySOE7njXz3x+v8O3SNfgNHX7X4IfP0BFw6XAN2ad9YE9xS0roQmBFVQAZS6IjnoJlD74uND/oxcIK3zGrChLR1JXM8umhUOiYwncgEEBNTc2ECuIqsW0bPT09qK6uHnNJPiptzFBtzE99zFBtzG+QJgTqAh7UBTxImzba+lNojaWQyOZmksdS8Lt0zA95MT/oLZn9pZih2pif+pghUWHwWlIb81MfM1Qb81NfKWeoVTTAveb9kKsuRnbvU8i+9IvcvuMSSEaQfuaHgOGF3njynH0T+Uzk1xjwoDuRxqFoAo1BT8m8BnE8djICGe+a8CoCpi2RyJpImjbiudnekyp8u/Thxe/c10ML3+NpCvlQ5THQ0d2Lhpoq+N0uAMDK6gDa+lM4HE0ikbVwOJrE4WgSVT43FlZ4Uef3QJujv++lqJSfQ2n6+FaUApJSoqurC1VVVcVuCk0RM1Qb81MfM1Qb8xudx9CwtNKPJWEfImkTrbEkOuJpJLIW9vTEsbc3jhqfG00hH+b53UUdCDFDtTE/9TFDosLgtaQ25qc+Zqg25qc+FTIULh/02mXIujyA8ANWBsimATOD9HP3QK9ZCmPZOdDnnwKhqVHALZSZyM+UEknTRsK08fD+o1hY4UONzw0hAA3C+SwENAEI5D7nvh96+8D5Q28XwIy8gcE8tA3p7fdBmhkIww3P6R+BsWg9TNsesdS5U/iOZy1krJkvfB+PR9eQifTAU1cz7OcuDvuxqMKHnlQWh6NJHE2k0ZvMoDeZgcfQ0BTyYUGodCZNzGUqPIfS1BWlKL5ly5Zi/FgiIiKiohFCoNLrQqXXhRNqJI7EnRnjvaksuhIZdCUycOkaGoNeNIW8CHEZLSIiIiIiIipTIlALoXsgzRTgDgzeYHhg97Ui8+L9EG88BGPpRhiLN0C4fMVrrMJSpoU3umIQACSArC2xvy+Bjv5Uwd6Uny+QCwENQwroQkBgSMF9tEI7MPw2IWCkY6h+7l5o2Xiu0SnEnv0xXk/VIGEEx23LqIXv3PeFKnxPlRACNT43anxuJE0LrbEUWqJJpE0b+3rj2N+XQJ3fjYUVPm65RzRD+GorERER0SwzNIGmkA9NIR/iA0uq96eQNm0ciiRwKJJAhceFppAXDQFP0Qdu05XKvYPb79L5rmciIiIiIiKC5gvDffoVyGy/H9JMQbj8cJ9+BfT6VTAPPg9z/1bIZATZnQ/BfOtR6IvWw1i6CVqg5vgPTnmJrAVLSrgNDbYtYUsJG0CV1wW3rsOGhJSALXOfIWFLZ7asLQGZ+z5/u5TH/AwpJSwJOBXs6QnEW7Hg8CMwMjFICEAIQNpwZfuxfOc96KzfgOi8U+D2V5Rk4XuifIaOFVUBLKv0ozOexuFoEr2pLI7E0zgSTyPgNrAgt+WeKn0iUgGL4gUkhEA4HOY7eBTGDNXG/NTHDNXG/KYm4DLQXB3EiqoAupIZtMVS6ExkEE1nEU1nsau7H/UBD5pCXlR5XUhb9owVmKeToZQSEhg2eLYh0R5L4c3ufthSQtcE1tRVoCnEd/jPBF6D6mOGRIXBa0ltzE99zFBtzE99KmVoLFoPbd5KyHg3RKAmv2+0a+XbYSw/F1bryzD3PgU7dgTmvq0w92+F3nASjOXnQKtarEQfJ6vQ+fldOnQhYNoSLk0gawMeTeDU+vCUXlOQuaJ4vlAO53P+tQAMeU1gyO0DBfWhtw+cb9s2jN798B18Eq6+g5DSgoSAFBpMwwfDTAKwEbATWHH0KYiuZ6A3rnZ+f8IrIEpo3+fJ5qcJgYagFw1BL/ozJg5Hk2jrTyGeMbGrux97euNoDHqxMORDyMNy3mxQ6TmUJk9IOcpbe0pMNBpFOBxGJBJBRUVFsZtDRERENKMylo32/hRaYyn0Z8z8cU0IJE0LAKALgRVVAdT63ce+mxvHDjLHfOf3yHNzt8uhA9QhA9iRhW855LFGsnP7lg3cInLtPnleCHUBDwIunYMMIlLeXBmvzpV+EhERUWmRUsI+ugfmvidhde7OH9eqFjj7jjeeUlJF0VLUGkvilc4oLCmhi9J5s7qUNuwjbyK7+zHYvS3OQU0D5q/FW0kXFrQ9Dt3OwNLcOLDwIpxUXwXRsh127+H8YwhfGMaiM6AvPB2avzz2gDZtG+39zuzxoa8JVXpdWFjhQ33AU7Cl74nKxUTHqyyKF5Bt2zhy5Ajq6+uh8Q+xkpih2pif+pih2phf4UkpEc2YaI2l0BZLIpaxhi1GJgD4DK2ggyHTsmDohZmBbkvki/jA4EJqPl2Drgm4dA2VXheqvC5UeVwIeQwO7KaB16D6mKGaVBmvTpdK/eS1pDbmpz5mqDbmp75yztCOdsDc9zTMlpcA2xlrCn+ls+/4ovVlse/4TOVXStuaSduG1f4qzN2PwY4ecQ7qBoxF62GseBs0XyVaY0m80dIKV7oXWU8VVi1oyhfy7WgHzEPbYB1+ETKbcu4vAH1eM/RF66E3rILQijOrupD5SSnRm8qiJZbCkXg6PxnBrWtoCnmxoMIHH7eoK7hyfg4tZxMdr3K9hQKSUiISiaCurq7YTaEpYoZqY37qY4ZqY36FJ4RA2ONC2OPCPL8bz7T25pcph3SKzF5Dh8fQICCgCec+mkD+e00ICDjbcGkjbhNCQMPgfSAljnS0o7FuPgxdy983f7+RjysADSL/2APfa7m6dtqy8djBLpi2hKEJZC0bQghU+dzoz5jIWjaOxtM4Gk8DAHTN6W+V14VKr9NvQ2ORfKJ4DaqPGRIVBq8ltTE/9TFDtTE/9ZVzhlpFA9xrL4dr1UUwDzwL88CzkIk+ZF//A8xduX3Hl22C5q8udlOnbKby8xqlUAw3YbXsQHb3Fsh4NwBAGG7nTQ3LzoHwBPPnNoV8qGleikR20TGFfK2iAe6T/wJy1cWw2l+HdWgbrK59sDp3w+rcDeEJQF+wDsaiM6CFZvc6KGR+QghU+9yo9rmRNi20xFJoiaWQNi3s70vgQCSBWp8HCyt8qPG5uBJfgZTzcyixKE5ERESkhJDbgEsTMG3k9gFzCs1nNVUVbGBrWRaSHSbmBz3QCzBb3GvoWFNXkV+mzaVr+WXabCkRTZvoTWXRl8qiN52FadnoSWbQk8wAcAaAIbeRL5JXeV1w63yXLhERERER0VwmPEG4TrgQxorzYLXuyO073unMIt+/FXrjSc7S6tWLi91UAiCtLMyD22DufQIyGQEACLcPxrJzYCzdOOYM/+MV8oXugrFgLYwFa2HHu2EdegHm4e2QqRjMvU/C3PsktOrFMBavd5bZN9wz0r/Z4DF0LK8KYFmlH52JDA5Hk+hJZnA0kcbRRBp+l44FFT40Bb1w8XUTojGxKE5ERESkgKEFZlM6BfE1dRVFf6f38TSFfKjxuY9Zpk0TApW5YjfgvBM3nrUGi+SpLFKmhWg6i2g6i4POuBl+l1MkHyiU+wyN74YmIiIiIiKag4TugrFoPfSFZ8A+uhvmvqec2cJtr8Fqew1a1UIYy8+F3nAS9x0vAplNwTz4HMy9T0Km4wCcNzQYK86FsfhMCMNTsJ+lBWqgrboIxgnvhN35JsxDL8A68ibsnoPI9ByEeO1/oTethbFoPbTKpoL93NkmhEB9wIP6gAfxjInDsSTaYmkkshbe6u7Hnp44GoLO7PGwx1Xs5hKVHBbFC0gIgdraWr4wqzBmqDbmpz5mqDbmN/PGKjAXykxlOJFl2oQQCLoNBN0GFlY47xJPmla+QN6XyqI/YyKRdT5aY0kAgMfQUOV1OzPJPS4E3fqc/R3kNag+ZkhUGLyW1Mb81McM1cb81DcXMxRCQK9bCb1uZW7f8adgtuyA3XsYmRd+mtt3/GwYi86AcHmL3dxxlUN+MpPIz9of2PNb+CvhWnEe9IWnQ+gzV6wVmga9YTX0htWQyQjMwy/CPPwCZLwH5oHnYB54Dlp4PoxFZ0BfsLbg+9DPZn4Bt4ETa0JorgqiPZ7C4WgSsbSJtlgKbbEUKjwuLKzwoiHghV5mW9OlTEu518aoNAgppSx2I45nohukExEREVH5ylg2+lJZ9KWdQnk0bWLkf2UNTUOl18gtt+5G2GNA40CGiGbQXBmvzpV+EhERUXmQqdjgvuOZBABn/2p98QYYSzdB81cVuYXlR6ZiyO59EtbBZyHNLABAC9bCaD4fetNaCK04K91JacPu3g/z4DZY7a8BtgXAWWlAn38y9EXroVUvUb4IKqVEJG3icDSJI/E07NzrJYauoSnoxcIKH/yu0l5tcDRSStgSyNo2srZEayyJvb0JWLaEpgHLwgHUBTzQhLMqoSYAXYjc12LY8WJmPJOF/JmiUpsnOl5lUbyAbNtGa2srmpqaoHE5FiUxQ7UxP/UxQ7UxP/WplqFlS0TSgzPJ+9JZWPbw/9pqQiDsMfJ7koe9LrgU6NtUqJYfHYsZqkmV8ep0qdRPXktqY37qY4ZqY37qY4bDSSsLq+UlZ9/x/qPOQaE5+44vPwd61aLiNnAEFfOzE70w9zwO89AL+YKzFm6E0XxByS1dLzMJmC0vwTq4DXbsSP64FqyBvmg9jAXrILyhKT9+qeSXsWy0xpzZ4ynTyh+v8buxMOTDPL971gvEUkpYUiJrS2Qtp8CdtW1kLeezaUlkbBvmKLcPFPhtKZE0bQx95UcA8BnahCZEDC+SD36tD/laCCARj6MiFISuadBHOd+5z/GODx7r6E/j9a4oLCmhC2c7xKZQYVcpKLTWWBKvdKrT5omOV7l8egFJKRGPx4+ZsUTqYIZqY37qY4ZqY37qUy1DXROo9rlR7XMDcNofy5jD9iXPWDZ6c1/vBwABhNwGKj2D+5KX+rtdJ0q1/OhYzJCoMHgtqY35qY8Zqo35qY8ZDid0F4zFG6AvOgN2Z27f8aN7YLW9CqvtVWjVi53ieP3qkijeqpSf3X8U5u4tMFt2ANIGAGjVi+BqvgBa3QklOfNauP1wLTsbxtJNsPsOwzq4DVbby7D7u2HvfAjZN/4EvXG1s/d47YpJ/06USn5uXcPSSj+WhH3oSmZwOJpEVzKD7oTz4TV0LKjwoinkg5RyUrOBpZRO4XpkUduWyFi5ovaQ40OL4NP5dxFCwMj9SukAhCYgpYSUgM+lw9A02NIpoFtysAg/tIJu52adDzs4sn8AslmJeH8ahfgNPraQL/F8Wx8Crli+CJ/vo9PR/NdDf75zWOSPDdxv+DlilGNDz8vdX4w4J3fiwDHLluiIp2BLQBOADeCVzihqfG7lX0NjUZyIiIiIyoIQAhUeFyo8LiwOOwOgxIh9yRNZC7G0iVhuSTHAGTwNFMirPC74XTrSlq3MElFEREREREQ0PiE06PUnQK8/AXak3dl3vHUH7J6DyPQchPBXwViW23fc8BS7uSXNjrQhu/sxZynyXKVPn7cCRvMF0GqWlmQxfCQhBPSqRdCrFkGe/F5Yra/APLQNdu9hWG2vwWp7DcIXdvYeX3i6ssvtCyEwz+/BPL8HiayFlmgSrf0ppEwLe3ri2NXdj6wlIXKzmheHfahwG8cUvIcWtk3bxnRq/poQcOkCLk2DSxMwdOezS9MGjw+53ZW7XRcCacvGYwe7YNoSLk0gawOGLnDm/KpRX7uRUkJisBhuSZkrliNfQLdtwMbg7aZloa09hrqqSkgIyNy5Vu4x8vfLnW+Pcnzo+dKW+YK4gHPJSDiFZ2gYpT5f/DfEWLbzbwQAtgTcutPXRNZS/jUyFsWJiIiIqCwJIRBwGQi4jPwST2nTQm96cCZ5LGMimbWQzFpoi6Xy902ZzrvcDc1ZImpBRekuEUVEREREREQTp4Ub4T7tQ3Ctuii37/hzkIleZF/7LcxdjzjLaC/dqGwhdKZYPQdh7n4M1pFd+WN6wypnz/ASW4Z+MoThgbF4PYzF62FHO2Ae2gar5SXIZATZXY8i+9aj0Oc1Q1+0HnrDKghNzbKa36VjZU0Qy6sC6IincbAvgSOJNAYnTkvs7olPeClyPVfINoYUrscqaA+9fTp7e3sNHWvqKvBKZxSmlPnXbMYq1Irc7OeJ9GeAZVlIIIvFFT7o+vQLwMmsiS2HumHaTntNW0LXBDY2VcFj6MCwyewyn4fMH8l9lkPPGu0cOezNCoP3G/HYQ44NLb8PPL6Es/z+q0ejsGxn6XRbOq+Pqbgn/UjcU7yApJSIRCIIh8NKvBuKjsUM1cb81McM1cb81DcXM8zaNiIpE72pTL5QHs9ax+xPtbDChzq/s1R7wKWX5L/PXMyv3DDDQSnTUma1BlXGq9OlUj95LamN+amPGaqN+amPGU6eNDPOvuP7noLd3+UcFBr0+afAtfwcaJULZq8tJZaflBJ2116nGN61zzkohPNv03w+tIrG4jZwhkgrC6tjJ6yDzw/2G4DwBKAvWAdj0RnQQnXH3q/E8htPTzKDp1t68rOppQQggMaAByGPa9zZ24amQdeK17+ZHC/ORIaq7c8NqNfmiY5XWRQnIiIiIsrpSqTxTGsvBJw9k6zcMlc+fXDA59Y1VHldzn7mXme59VIf7BKppDWWxMtHnHf+u7TyGXyrbq70k4iIiOYuKW3YnW/B3PvksEKoVrMErmXnQKtfVRL7js8GKSXsI28gu3sL7N7DzkGhwVh4GowV50ML1ha3gbPIjnfDOvQCzMPbIVOx/HGtejGMxeuhN54CYbiL2MKpSZnWiKXInZnMFyyuLfk3JqtIpTd+D1CpzSyKF4Ft2zhw4ACWLFkCbY78cSw3zFBtzE99zFBtzE99zHD0QaEmBFZU+dGfsdCXzjr7Pg3hMTRUeZ0CebXPBZ9RnCI581PfXM8wa9k4HE06y7RJZ5UGQxMl/8KMKuPV6VKpn3P9WlId81MfM1Qb81MfMywMO9KW23f8ZcB2ttcSgWq4lp0NrXYFZDoGEaiF5gsX9ucWOT9p27DaX4W5ewvsaIdzUDdgLFoPY/m5c3pJeWnbsDvfhHnoBWcJeZn7vXB5oDethbFoPaTLj9Y9r6FpxcnQA6X/b6XabODZUOxrkKZmouNVNTc/KFFSSmQyGSjwPgMaAzNUG/NTHzNUG/NTHzMce3+qgUGhLSUiaRM9yQx6U87e5GnTRkd/Ch39qfxjVOUK5FVe96ztucT81DcXM5RSojeVRWsshSPxNLKWDSvX/YGCuCUlElmrZIviVHrm4rVUTpif+pih2pif+phhYWjh+XCf9mG4Vl0Mc/8zMA88CxnvQfrFXwCZGCB0wPDAdcKFMBath/AEAXdg2jPJi5WftC1nCfk9W2D3dwMAhOGGseQsGMvOgfCGZrU9pUhoGvSG1dAbVkOmojAPbYd5+AXIeA/MA88hu/txIBtHlRRI7QvAc/pHYSxaX+xmj6sp5EONz63MbODZwOfQ8saiOBERERHREOMNCjUhUOV1ocrrAuAsrx5JZ9GTyqI3mUFf2kTKtNDeb6F9SJG82udCtdeNqtxMcqK5LmVaaI2l0NafQjJr5Y8H3QasdBYSGLaE32y9uYSIiIiIaCjhrYBr1UUwmi+Aue8pZLbfl5s5LgEri+yrDyC79ykITQcEINwBCHcQwhsEPEEITwjCE8x9hCA8AQhPCHAHS2IpdmllYR3ahuzeJyETfQAA4fbBWHo2jGWbIFxze9bwWIS3Aq6VF8BoPg92935k9z4Fa++TgLQhACDRh/SzmyGC86BXLylya8fnNVgMp7mDRXEiIiIiohEmOijUNeHsLe5zA1UBWLZEXzqbn0keyRXJ22IW2mJOkdzv0vN7kld5XRx80pxhS4mjiQxaY0l0JzMYeOO9rgk0Br1oCnpR4THQ1p86ZrUGXidEREREVEzCcEOrXgy4PIDwAbYF2CZgm85+0tIGpIRMxyHTcSB25DgPCAiXP18oR654Lt0B+HqjsDslhK/CmYHuCTpF9wKSZhrmgWdh7n0KMt3vNMkThLH8XBhLzoQwPAX9eeVKCA167XIAgHVoGwABaaYhbAvIppB64j9hzD8VxrKzodUsLcpWa0Q0iEXxAtI0DQsWLOA+AwpjhmpjfupjhmpjfupjhtOnawI1PjdqfG4AgGlL9KWy6Ell0JvMIprJIpG1kMg6s2QBp0he7XP2JK/yuuExpvbvz/zUV64Z9mdMtMRSaO9PIWvZ+eNVXheaQj7UBzzQtcEXh7iEH01XuV5LcwXzUx8zVBvzUx8znDkiUAuheyDNFODyAdkkhDsI7zv+3pn9nYlDZvohU/25zzGn4JzOfZ+O5W6LOwX0TAIykwBiQ3+KRI0tkT36DLIY/D+ycPvzM86Rn3k+dCZ6KHc8AKEdW/axkxHIeBeE2w+r/XWY+56GzDpjUuGvhGv526AvOgNCd83wv2J5EoFaCMMLaaYgPBVANg5AAtBgdeyE1bETWrgBxtKzoTedyn/nEsbn0PImpAIL4090g3QiIiIiolJm2jZ6U1n0JLPoTWUQzZjOOHmIgNtA9ZCZ5G6dAzFST9a20dGfRlsshUg6mz/uMTTMD3rRFPLC7yqP92jPlfHqXOknERER0XjMQ9uQ2X4/pJWG0D1wn37FpPeNlrYNZBOQ6VyhPB3Pfc4V0HNfOx9xZxb6JAiXd7BI7g1BJiOw2l8DrKwzw90TgnB5oQVrYKw4H/qC0wo+E30uGu13Q6taDHP/VlgtL0KazrhIeAIwFm+AseQsCC//X01UCBMdr7IoXkCWZWHv3r1Yvnw5dJ1/RFTEDNXG/NTHDNXG/NTHDGdf1soVyVNOkTyWNo85J+g2Bvck97rgGqNIzvzUp3qGUkr0prJo60/hSDwNy3aGmkIIzPO70RTyotbnLrslA1UZr06XSv1U/Vqa65if+pih2pif+pjhzHNmXXdDBGqg+cIFfeyR+UlpA5mkUyjPxIfMPh9SOB+YnZ7uP6aALm0LSHTD2btIAJCAbsB95rUwFm8oiX3Ny4nZ34PDb72ChSvXwAhW54/LTALmoW0w9z8DmYw4BzVtcGn1yqYitZhG4nOomiY6Xi2Pt+aXENue3Lu2qPQwQ7UxP/UxQ7UxP/Uxw9nl0jXUBTyoCzj7tWUGiuS5Pcn7M2b+41AkCQgg5DZQ7XWj2udCpdcFV+5FjJRpod8WSJkWAhy4KUvFazBlWmjrT6EtlkIia+WPB9wGmkJeNAa8U94WgGiqVLyWaBDzUx8zVBvzUx8znFmaLwwUuBg+1ND8hNAAT8DZd/w4pLSBbGrYTHOrax/MN/4IaBogBKC5AGlBC9WxID4DhC+MtL8BYsTvh3D74VpxHoxl58LqeB3mvqdg9xyC2fISzJaXoFUvhrHsbOgNJzGXEsDn0PLFojgRERERUYlw6xrqAx7U54rkactGbzKDntyS64msiVja+TgYcV7TCLld0DWgM56GCT+OtPRiTV0FmkK+IveGypktJY4mMmiNpdCdTGNg/TFdE2gIOMujhz1G2c0KJyIiIiIqVUJogNsP4fYDoXoAgFa9BNa+p4fvg254IQI1RW7t3CQ0Dcb8U2DMPwV2XwvMfU/DbHsFds9BZHoOQvgrYSw5y5nF7+KYnqjQWBQnIiIiIipRHl1DQ9CLhqAXgDMjN7/cejKDRNZCXyqDpGnntiYXSJk2XuyIwKVpmOcvv6Wqqbj6MyZaYym096eQsQbfPV/pdaEp5EV9wAtD4+8cEREREVEp0HxhuE+/wtnr2kxBGF5nr+sZnOlOE6NVLoB73RVwrb4E5oFnYR54DjLRh+zOh2C+9Sj0BetgLN0ELVRX7KZSATjbLnRBBGqVuf5UbPPxcE/xApJSIpPJwO3mi4+qYoZqY37qY4ZqY37qY4bqSZoWDkWSeLM7BikBCWDgP/c+XYPXpaPG50aNz4Uanxteg8uql7JSvQZN20ZHPI3WWAqRVDZ/3K1rmB/yoinoRcA9d99vrcp4dbpU6mepXks0McxPfcxQbcxPfcxQbTOV30zug06DppOftLKwWl+Gue9p2NGO/HG9rhnGsnOgzWvmNT0LZuIaNA9tc96YYqUhdA/cp18BY9H6gjz2TFGtzdxTvEgMg/+kqmOGamN+6mOGamN+6mOGavEZOhaHfdjXF4dpS+gCyNoSAgKGLpC1bHT0p9DRnwLg7PFc43Oj1udCldcNnTN6S06pXINSSvSlTbTGkjgST8Oyc2sRCIFavxtNQS9q/W5ofFGGSlSpXEs0NcxPfcxQbcxPfcxQbTOR30zvg06Dppqf0F0wFp0BfeHpsLv3wdz3NKwjb8Dq3A2rcze04DwYyzZBX7AOwnAXuNU01GQzlLYNmClIMw1kU5BmMvc5AxnvRuaV3wBWFtAMyGwU6Wc3wzq6F8LlgTPDYWB6w7Ffy4GpD3Lo7QPHcl9LjHJs5HlDHnOsn5c7V1oZ2B1vALYFaDqkZSKz/X5o81Yq/6Ya/nUsINu2sXv3bjQ3N0PXOQtHRcxQbcxPfcxQbcxPfcxQTV5Dx5q6CrzSGUUma8LtMrCmrgKNQS8iaRPdyQy6kxlE01nEMybiGROHIoAmBCq9rvxM8pCb+z8XWylcg2nTRlt/Cq2xJBJZK3/c79LRFPJhftALj6EVpW1EE1UK1xJNHfNTHzNUG/NTHzNUG/NTWyHyE0JAr10OvXY57Hg3zP3PwDq0DXb/UWReeQDijT9CX7wBxpKzoPmrCtwDNRRyWW9pZXMF7DRgpmClE2g5uBdNdTUQ9sBtqcHPZhoyO/T7FKSZHefxM0AmAUA4RWYpAduEeWgbhF6ab26QVsYp4g+02eOFtNKQ8W7l31zDojgRERERkeKaQj5UunW8tf8gVi5YjIDHGVhVeV2o8rqwoiqArGWjJ5VFVzKD7kQGKdNCTzKDnmQGu+EshV3jc6M6VyTnUutzhy0luhIZtMZS6Epm8u8a1zWB+oDH+f3y8E0TREREREREs0kL1MB98nshT7gQ5uHtMPdvhYz3wNzzBMy9T0FvXA1j2dnQqhbPmfHa8GW93XCdcin0uhNyRe00ZDY5rMgts7nCdTb3ff5YrrhtWyN+gkRVJguzwwVgkv+mugFheJ0Z4IYPwuWFBGAdfsH5ObrbKTbrBlzN50O4/EA+N+F8PTRHIXJtEMPOc74Ux94XItdkMfz++cfBKPdxjokh38t0P9Iv/AQwM4DhAawshMsHEaiZ3L9HCWJRnIiIiIioDHgNHT5YYxazXbqG+oAH9QEPpJRImBa6Exl0J7PoSWWQsWy096fQnltqPZhbar3G50aV18Wl1stQPGOitT+FtlgKGcvOH6/0utAU8qI+4IGhcVY4ERERERFRMQmXF65lZ8NYshF255vO0upd+2C1vQar7TVolU0wlp0Nff4pEFp5lf1kNgk71gnZfxRWzyGYu/6Um8UMSBlH5vkfAf4aCG16b+wXhhtw+QDDg2wyA29VLYTbD2F4AZcXwvAAhhfC5XzAyB1zeXPneMb8tzeb1gwW8j2hkt+fGwAgxGCbXT64T79C+aXTARbFiYiIiIjmHCEEAi4DgbCBRWFnpnBfKouepDOTPJrJoj9joj9j4mAkAU0IVA0ste53I+jS58y70MtByrSQyFrwu3QYmoYj8RRaYyn0pQaXeHPrGuYHvZgf8iLo5jCRiIiIiIio1AhNg96wGnrDatjRDpj7nobZ8hLsvlZkXvw5xOu/h7F0I4zFGyA8wWI3d8KklEAqCru/E3bsKGR/Z74QLtP9g+dZGWf28rBZ0hJCd0H4woPFatfIgvXwonb+uOHJ3ccDIZw3hFuWhe7du1FdwC0MjEXroc1bCRnvhgjUKFFcVrHNEyHk4I7qJSsajSIcDiMSiaCioqLYzRmTlBK2bUPTNL5IqChmqDbmpz5mqDbmpz5mqLZC5pexbPTk9iLvTmaRMocvJzaw1HqN340ar5t7TBfITFyDrbEkXumMwrQlJAC3JqDlHlsIoNbnQVPIi1q/O3+cJkeV8ep0qdRP/j1TG/NTHzNUG/NTHzNUG/NTWzHyk+l+mAefh3ngWchUzDmo6TAWrIWx9Gxo4cZZacdESNuGTPQMK3rb/bnidzY95v2EtwJaqA7whGDuexKwTMDtB8w0hOGF9+KbCla05TWopomOVzkFoMBM04Tb7S52M2gamKHamJ/6mKHamJ/6mKHaCpWfW9fQEPSiIeiFlBLxrJUrkGfQm8oes9R6yDO41Hqlh0utT8fxMpRSwpISWUsia9vIWhIZ24ZpS2QsG1lbwrRtZCyJtGnhSCINe8jboC1botrnwsIKH+YHvdw7nsoW/56pjfmpjxmqjfmpjxmqjfmpbbbzE54gXCvfDmPF22C1vQZz39Ow+1pgHtoO89B2aDVL4Vp+DrS6EyFmaXssaWWdgnes0ymA9x+FjB2FHe8aZQ/vgY5o0AJVEME6aKE653NwHkRwnjObO0evXTq4rLfhnZFlvXkNli8WxQvItm3s378fzQVcVoFmFzNUG/NTHzNUG/NTHzNU20zlJ4RA0G0g6DawOOzPL7U+UCSPpk3Ech8H+hLQNYFKrwu1uSJ5oMyWWh+6FPl0CspOcRu5wrZTzE5nTRxua0dNXR0sKfJF76zt3D5w3kQX+7JsmS+IawLQ4OR5cm0INX7PlNtOVOr490xtzE99zFBtzE99zFBtzE9txcxPaAaMBWuhN50Ku/cQzP1bYbW9Brt7P9Ld+yEC1c7S6gvPGFZkng6ZSTgzvWO5Gd+xTudzsg8YY9gqdJdT6A7OyxW/5znF70AthH78kuVML+vNa7C8sShOREREREQTpgmBap8b1T43mgGkhy21nkHatNGdyKA7kQEAeAwtP4u8xueGWx98Z3qhCsyzZWApcktK6EJgTV0F5ge9sCWc2dojCtgDxzK5Y6ZlI5O7zbQl7BHFbQkgCw/6+pI43tsIdE3A0DS4NAGXPuKzpsGtC9gS+fa6NAHTljA0gQD3DCciIiIiIipLQgjo1YuhVy+GvboP1v5nYB58HjLeg+xrv4P55sPQF50BY8lGaMFa2MkIZLwLIlA7aoFZSgmZiuQK3keHzQCX6fjY7XD7Bmd7h+qgBesgQnXO3t9iejPWNV8YKJM9rml28dUQIiIiIiKaMo+uoTHoReOQpda7BpZaT2aRNm20xVJoizlLrVfkllq3pMT+vsSwAnNTyDfmz5HS2RPblhJSArYEJJyZ0ENvc47n9gEb4xwpAXvoOUNvG+UcKZ191ltjKVhSQgOQhcS2tj74XdMr5mtCwKU7hWxDAPFMArXBEDyGDtc4Re+JLlGvicHCuKE5/84qvAGBiIiIiIiIpkfzVUJbfQmMle+A1fISzP1Pw44dhblvK8z9WyH8NbD7DgPShtDdcJ30bmihhuGzv/uPQpqZMX+G8IWHzPiuy88AhztQVqvGUXlgUbzAtFnak4FmDjNUG/NTHzNUG/NTHzNUW7HzG7rU+pKwH5Yt0ZceXGo9ljYRTZvoS2WRNG1IYLDA3N6HPb1xCIhjitMSzudismwJM9cIG7nCe+64rgkIIfKFa7cmYOQK2G5Ng6E7n4cWtQeOaQL5Fwosy8LeeBeW1wQLtkxbU8iHGp9bqRn5RIVQ7OdDmh7mpz5mqDbmpz5mqDbmp7ZSzE8YbhhLzoS+eAPso3tg7n8aZvtOyCNvAFICmg6ZjiPzwk8Bfw2ENmLcKDRogeoh+30PLn0ujPLbmqsUM6TCEHKiG9IVUTQaRTgcRiQSQUVFRbGbQ0REREREU5A2bXSnMmiNJtESS0ECEBjcasynT3wGNOAUkweKytrQ74d9PfKcwa81IZzv8+cAGpxjmhDDHse0Jd7oijkz2zUB25YwNA2bFlQh6HZBH1LcprllroxX50o/iYiIiIjmAvPQdqS3/hdgW84BKQEBaPOaodUsyS93rgXrIALVEBrn2FLpmuh4lb/FBSSlRDweRyDAZSFUxQzVxvzUxwzVxvzUxwzVpkJ+HkPD/KAX1V4XupIZZG0JXQzMttawrrESPkODyBWkRxa8B4rWA3Xz2e6n19AG9+jWNaypq0Cl112wx1chQyIV8FpSG/NTHzNUG/NTHzNUG/NTm0r5afNWQHhCkGYS0FyAlYVweeE55zOj7i0+V6iUIU0e1wAoINu20dLSAtu2i90UmiJmqDbmpz5mqDbmpz5mqDaV8vMaOtbUVcClCUgALl3D2voK1Ac8qPC4EMotwe53GfAZOjyGDreu5ffSdgrksz84bQr5cMHiWmxqqsYFi2vH3QN9KlTKkKiU8VpSG/NTHzNUG/NTHzNUG/NTm0r5ab4w3KdfAWH4AGlBuHxwn/6ROV0QB9TKkCaPRXEiIiIiIpp1M11gnileQ0e1z829ualk3H777RBC4Atf+MKY59xzzz35N5MMfHi93tlrJBERERERlRxj0Xp4L74J3vP/Bt6Lb4KxaH2xm0Q0o7h8OhERERERFYXX0FlcJpqGbdu24c4778SaNWuOe25FRQV27dqV/55LARIRERERkeYLA3N8djjNHZwpXkBCCLjdbr64oDBmqDbmpz5mqDbmpz5mqDbmpz5mSJPR39+PK6+8EnfddReqqqqOe74QAg0NDfmP+vr6WWhlcfBaUhvzUx8zVBvzUx8zVBvzUxvzUx8zLG9CSimL3YjjiUajCIfDiEQiqKioKHZziIiIiIiIiAAUb7x69dVXo7q6Gt/+9rdx/vnnY+3atfjOd74z6rn33HMPPvGJT6CpqQm2bWPdunX4f//v/+Gkk04a8/HT6TTS6XT++2g0ioULF6KnpyffTyEENE2DbdsY+tLCWMc1TYMQYszjlmUNa4OmOe/jH7mf31jHdV2HlHLY8YG2jHV8om1nn9gn9ol9Yp/YJ/aJfWKf2Cf2iX1in0qzT9FoFNXV1ccdl3P59AKSUiISiSAcDvNdJIpihmpjfupjhmpjfupjhmpjfupjhjRR9913H1588UVs27ZtQuefcMIJuPvuu7FmzRpEIhH8y7/8CzZt2oTXX38dCxYsGPU+t912G2699dZjju/duxfBYBAAEA6H0djYiCNHjiASieTPqa2tRW1tLVpbWxGPx/PHGxoaUFlZiQMHDiCTyeSPL1iwAMFgEHv37h32YsfSpUthGAZ27949rA3Nzc0wTRP79+/PH9M0DStXrkQ8Hsfhw4eRTqfh8Xjg8XiwbNkyRCIRdHR05M8PBAL5In9XV1f+eKn2qaWlJX/c7XaXdZ/a29vz+QWDwbLoUznmNF6fpJRIp9Oor68vmz6VY05j9Wkgv6amJsybN68s+lSOOY3XJyklgsEgFixYUDZ9Asovp7H6JKVEZWUlGhoayqZPQPnlNFafpJSoqalBdXU19uzZUxZ9Asovp/H6tGLFCvT09KC7uzs/Lle9T+WY08g+9ff3YyI4U7yALMvC7t270dzcDF3n3ogqYoZqY37qY4ZqY37qY4ZqY37qY4Zqmu3x6uHDh3HGGWfg4Ycfzu8lfryZ4iNls1msWrUKH/3oR/G1r31t1HNUnimezWaxZ88erFixAoZhKDnTYWSfymH2xkT7NFp+qvepHHMar0+WZWHPnj1obm6Gy+Uqiz5N9biKfTpefir26XjHy61PlmVh7969WLlyJYQQZdGnoW0vl5zG6tPQ/EZStU9Dj5dLTmP1aSC/5ubmY97orGqfxmt7OfZJSondu3dj+fLlw8blKvepHHMaeZwzxYmIiIiIiIjKzPbt29HZ2Yl169blj1mWhSeeeAL/+Z//iXQ6fdw3VbhcLpx22mnHzF4ZamCW9Ui6rh/z+AMvSIw02eNjtXsyx4UQ0HUdmqblPw89Pt02FrNPEz1eDn0amV859Gm6bVStT5qmFTy/YvdpJo6Xap+mk1+p9mk6x1Xs09DZjRNt42SPM6eZ69NAfuXUp/HaONnjpd4nIcSYbRnt/IH7lHKfpnJc1T5ZlpVv42jZqtin8Y6XS58mOrGARXEiIiIiIiIiRbzjHe/Aq6++OuzYNddcgxNPPBE33njjhF4MsCwLr776Kt797nfPVDOJiIiIiIiISgqL4gUkhEAgEDhmWQxSBzNUG/NTHzNUG/NTHzNUG/NTHzOkiQiFQjj55JOHHQsEAqipqckfv+qqq9DU1ITbbrsNAPDVr34VZ511FlasWIG+vj5885vfxMGDB/GJT3xi1ts/G3gtqY35qY8Zqo35qY8Zqo35qY35qY8ZljcWxQtI0zQsXLiw2M2gaWCGamN+6mOGamN+6mOGamN+6mOGVCiHDh0athxdb28v/vqv/xodHR2oqqrC6aefjq1bt2L16tVFbOXM4bWkNuanPmaoNuanPmaoNuanNuanPmZY3oQcupN5iYpGowiHw8fdIL3YbNtGT08Pqqurx1wPn0obM1Qb81MfM1Qb81MfM1Qb81MfM1STKuPV6VKpn7yW1Mb81McM1cb81McM1cb81Mb81McM1TTR8SoTLSApJbq6uqDA+wxoDMxQbcxPfcxQbcxPfcxQbcxPfcyQqDB4LamN+amPGaqN+amPGaqN+amN+amPGZY3FsWJiIiIiIiIiIiIiIiIiKhssShORERERERERERERERERERli0XxAhJCIBwOQwhR7KbQFDFDtTE/9TFDtTE/9TFDtTE/9TFDosLgtaQ25qc+Zqg25qc+Zqg25qc25qc+ZljehJzhhfFvu+02/PrXv8abb74Jn8+HTZs24Rvf+AZOOOGECT/GRDdIJyIiIiIiIppNc2W8Olf6SURERERERGqZ6Hh1xmeKP/7447j++uvx7LPP4uGHH0Y2m8W73vUuxOPxmf7Rs862bbS3t8O27WI3haaIGaqN+amPGaqN+amPGaqN+amPGRIVBq8ltTE/9TFDtTE/9TFDtTE/tTE/9THD8mbM9A946KGHhn1/zz33oK6uDtu3b8fb3va2Ue+TTqeRTqfz30ejUQCAZVmwLAuAs4SBpmmwbRtDJ7uPdVzTNAghxjw+8LhDjwM45hd/rOO6rsO2bfT29qKmpga6rufbIqUcdv5k217MPo3V9nLtk2ma+QwNwyiLPpVjTmP1aTr5lWqfyjGn8dpuWRZ6e3tRW1tbNn063vFy6tNAfvPmzRu3ryr1aWTby71PlmWhr68PdXV1ZdOnocfLvU8D+c2bN2/YuSr3aby2l2OfpJTo6+vLjyfKoU/lmNPI4yPvR8UnpUQkEkFdXV2xm0JTwPzUxwzVxvzUxwzVxvzUxvzUxwzL24wXxUeKRCIAgOrq6jHPue2223Drrbcec3zv3r0IBoMAgHA4jMbGRhw5ciT/mABQW1uL2tpatLa2DpuN3tDQgMrKShw4cACZTCZ/fMGCBQgGg9i7d++wFzuWLl0KwzCwe/fuYW1obm6GaZrYv39//pimaVi5ciUSiQR6enqwZ88eaJoGt9uNZcuWIRKJoKOjI39+IBDAwoUL0dPTg66urvzxUuxTPB5HS0tL/ni596mtrS2fYSgUKos+lWNOY/Wps7Mzn19VVVVZ9KkccxqvT7Zto6enBz09Paivry+LPpVjTmP1aSC/aDSK6urqsuhTOeY0Xp8G3mAEoGz6BJRfTmP1ybbt/Me+ffvKok9A+eU0Xp+WLVsGy7Ly44ly6FM55jSyT/39/SAiIiIiIiKi0jbje4oPZds2Lr30UvT19eGpp54a87yRM8UjkQgWLVqEAwcO5NeCL5VZAQN0XYdpmtizZw+WLVvGmeKK9imbzWLfvn1YtmwZZ4or2CfTNKecX6n2qRxzOt5M43379mH58uVwuVxl0afjHS+nPg3kt2LFChiGURZ9Gtn2cu+TZVnYv38/VqxYgZFU7dPQ4+WS01h9Gshv+fLlEEKURZ/Ga3s59klKib1792Lp0qWcKa5Qn6LRKJYsWYK+vj6Ew2GUq0gkgsrKShw+fLjk9xS3LAt79+7F8uXLh11LpAbmpz5mqDbmpz5mqDbmpzbmpz5mqKZoNIqFCxced1w+qzPFr7/+erz22mvjFsQBwOPxwOPx5L8fWD59yZIlM9k8IiIiIiIioimJxWJlXRSPxWIAgIULFxa5JURERERERETHOt64fNZmit9www144IEH8MQTT2Dp0qWTuq9t22hra0MoFDpm1kspGXgnggrvnKfRMUO1MT/1MUO1MT/1MUO1MT/1MUM1SSkRi8Uwf/78/CzycqTKuBzgtaQ65qc+Zqg25qc+Zqg25qc25qc+ZqimiY7LZ3ymuJQSn/vc5/Cb3/wGW7ZsmXRBHHCWp1uwYMEMtG5mVFRU8GJRHDNUG/NTHzNUG/NTHzNUG/NTHzNUTznPEB+g2rgc4LWkOuanPmaoNuanPmaoNuanNuanPmaonomMy2e8KH799dfjpz/9KR544AGEQiF0dHTkG+fz+Wb6xxMRERERERERERERERER0Rw242u73XHHHYhEIjj//PPR2NiY/7j//vtn+kcTEREREREREREREREREdEcNyvLp88VHo8HN998MzweT7GbQlPEDNXG/NTHDNXG/NTHDNXG/NTHDIkKg9eS2pif+pih2pif+pih2pif2pif+phheRNyLlWtiYiIiIiIiIiIiIiIiIhoTpnx5dOJiIiIiIiIiIiIiIiIiIiKhUVxIiIiIiIiIiIiIiIiIiIqWyyKExERERERERERERERERFR2WJRnIiIiIiIiIiIiIiIiIiIyhaL4pP03e9+F0uWLIHX68WZZ56J559/ftzzf/GLX+DEE0+E1+vFKaecgt///vez1FIa6bbbbsP69esRCoVQV1eHyy67DLt27Rr3Pvfccw+EEMM+vF7vLLWYhrrllluOyeLEE08c9z68/krLkiVLjslQCIHrr79+1PN5/RXXE088gb/4i7/A/PnzIYTA//zP/wy7XUqJr3zlK2hsbITP58OFF16I3bt3H/dxJ/t3lKZuvAyz2SxuvPFGnHLKKQgEApg/fz6uuuoqtLW1jfuYU3kupqk53jX48Y9//JgsLr744uM+Lq/B2XO8DEf7myiEwDe/+c0xH5PXINEgjs3VxHG5+jg2VxvH5erh2FxtHJerj2NztXFcTiOxKD4J999/P/72b/8WN998M1588UWceuqpuOiii9DZ2Tnq+Vu3bsVHP/pRXHfddXjppZdw2WWX4bLLLsNrr702yy0nAHj88cdx/fXX49lnn8XDDz+MbDaLd73rXYjH4+Per6KiAu3t7fmPgwcPzlKLaaSTTjppWBZPPfXUmOfy+is927ZtG5bfww8/DAD40Ic+NOZ9eP0VTzwex6mnnorvfve7o97+z//8z/j3f/93fP/738dzzz2HQCCAiy66CKlUaszHnOzfUZqe8TJMJBJ48cUXcdNNN+HFF1/Er3/9a+zatQuXXnrpcR93Ms/FNHXHuwYB4OKLLx6Wxc9+9rNxH5PX4Ow6XoZDs2tvb8fdd98NIQQuv/zycR+X1yARx+Yq47i8PHBsri6Oy9XDsbnaOC5XH8fmauO4nI4hacI2bNggr7/++vz3lmXJ+fPny9tuu23U8z/84Q/L97znPcOOnXnmmfJTn/rUjLaTJqazs1MCkI8//viY52zevFmGw+HZaxSN6eabb5annnrqhM/n9Vf6Pv/5z8vly5dL27ZHvZ3XX+kAIH/zm9/kv7dtWzY0NMhvfvOb+WN9fX3S4/HIn/3sZ2M+zmT/jlLhjMxwNM8//7wEIA8ePDjmOZN9LqbCGC2/q6++Wr7vfe+b1OPwGiyeiVyD73vf++Tb3/72cc/hNUjk4Ni8fHBcrh6OzcsLx+Vq4dhcbRyXq49jc7VxXE5SSsmZ4hOUyWSwfft2XHjhhfljmqbhwgsvxDPPPDPqfZ555plh5wPARRddNOb5NLsikQgAoLq6etzz+vv7sXjxYixcuBDve9/78Prrr89G82gUu3fvxvz587Fs2TJceeWVOHTo0Jjn8vorbZlMBvfeey+uvfZaCCHGPI/XX2nav38/Ojo6hl1j4XAYZ5555pjX2FT+jtLsikQiEEKgsrJy3PMm81xMM2vLli2oq6vDCSecgM985jPo7u4e81xeg6XtyJEj+N3vfofrrrvuuOfyGqS5jmPz8sJxuZo4Ni8PHJerj2Pz8sNxuZo4Ni8PHJfPDSyKT1BXVxcsy0J9ff2w4/X19ejo6Bj1Ph0dHZM6n2aPbdv4whe+gLPPPhsnn3zymOedcMIJuPvuu/HAAw/g3nvvhW3b2LRpE1paWmaxtQQAZ555Ju655x489NBDuOOOO7B//36ce+65iMVio57P66+0/c///A/6+vrw8Y9/fMxzeP2VroHraDLX2FT+jtLsSaVSuPHGG/HRj34UFRUVY5432edimjkXX3wxfvSjH+HRRx/FN77xDTz++OO45JJLYFnWqOfzGixt//3f/41QKIQPfOAD457Ha5CIY/NywnG5mjg2Lx8cl6uPY/PywnG5mjg2Lx8cl88NRrEbQFQM119/PV577bXj7vWwceNGbNy4Mf/9pk2bsGrVKtx555342te+NtPNpCEuueSS/Ndr1qzBmWeeicWLF+PnP//5hN69RaXlhz/8IS655BLMnz9/zHN4/RHNjmw2iw9/+MOQUuKOO+4Y91w+F5eOj3zkI/mvTznlFKxZswbLly/Hli1b8I53vKOILaOpuPvuu3HllVfC6/WOex6vQSIqJxyXq4l/i8oHx+VEpYPjcnVxbF4+OC6fGzhTfIJqa2uh6zqOHDky7PiRI0fQ0NAw6n0aGhomdT7NjhtuuAG//e1v8dhjj2HBggWTuq/L5cJpp52GPXv2zFDraKIqKyuxcuXKMbPg9Ve6Dh48iEceeQSf+MQnJnU/Xn+lY+A6msw1NpW/ozTzBgbeBw8exMMPPzzuu9FHc7znYpo9y5YtQ21t7ZhZ8BosXU8++SR27do16b+LAK9Bmps4Ni8PHJeXD47N1cRxeXng2Lw8cFxeXjg2VxPH5XMHi+IT5Ha7cfrpp+PRRx/NH7NtG48++uiwd0wOtXHjxmHnA8DDDz885vk0s6SUuOGGG/Cb3/wGf/7zn7F06dJJP4ZlWXj11VfR2Ng4Ay2kyejv78fevXvHzILXX+navHkz6urq8J73vGdS9+P1VzqWLl2KhoaGYddYNBrFc889N+Y1NpW/ozSzBgbeu3fvxiOPPIKamppJP8bxnotp9rS0tKC7u3vMLHgNlq4f/vCHOP3003HqqadO+r68Bmku4thcbRyXlx+OzdXEcXl54NhcfRyXlx+OzdXEcfkcImnC7rvvPunxeOQ999wjd+7cKT/5yU/KyspK2dHRIaWU8mMf+5j80pe+lD//6aefloZhyH/5l3+Rb7zxhrz55puly+WSr776arG6MKd95jOfkeFwWG7ZskW2t7fnPxKJRP6ckRneeuut8o9//KPcu3ev3L59u/zIRz4ivV6vfP3114vRhTnti1/8otyyZYvcv3+/fPrpp+WFF14oa2trZWdnp5SS158qLMuSixYtkjfeeOMxt/H6Ky2xWEy+9NJL8qWXXpIA5Le+9S350ksvyYMHD0oppbz99ttlZWWlfOCBB+Qrr7wi3/e+98mlS5fKZDKZf4y3v/3t8j/+4z/y3x/v7ygV1ngZZjIZeemll8oFCxbIHTt2DPu7mE6n848xMsPjPRdT4YyXXywWk3/3d38nn3nmGbl//375yCOPyHXr1snm5maZSqXyj8FrsLiO9zwqpZSRSET6/X55xx13jPoYvAaJRsexubo4Llcfx+bq47hcLRybq43jcvVxbK42jstpJBbFJ+k//uM/5KJFi6Tb7ZYbNmyQzz77bP628847T1599dXDzv/5z38uV65cKd1utzzppJPk7373u1luMQ0AMOrH5s2b8+eMzPALX/hCPu/6+nr57ne/W7744ouz33iSV1xxhWxsbJRut1s2NTXJK664Qu7Zsyd/O68/Nfzxj3+UAOSuXbuOuY3XX2l57LHHRn3OHMjItm150003yfr6eunxeOQ73vGOY3JdvHixvPnmm4cdG+/vKBXWeBnu379/zL+Ljz32WP4xRmZ4vOdiKpzx8kskEvJd73qXnDdvnnS5XHLx4sXyr//6r48ZQPMaLK7jPY9KKeWdd94pfT6f7OvrG/UxeA0SjY1jczVxXK4+js3Vx3G5Wjg2VxvH5erj2FxtHJfTSEJKKac6y5yIiIiIiIiIiIiIiIiIiKiUcU9xIiIiIiIiIiIiIiIiIiIqWyyKExERERERERERERERERFR2WJRnIiIiIiIiIiIiIiIiIiIyhaL4kREREREREREREREREREVLZYFCciIiIiIiIiIiIiIiIiorLFojgREREREREREREREREREZUtFsWJiIiIiIiIiIiIiIiIiKhssShORERERERERERERERERERli0VxIiIimhAhBP7nf/6n2M0gIiIiIiIimpM4LiciIpo6FsWJiIgU8PGPfxxCiGM+Lr744mI3jYiIiIiIiKjscVxORESkNqPYDSAiIqKJufjii7F58+ZhxzweT5FaQ0RERERERDS3cFxORESkLs4UJyIiUoTH40FDQ8Owj6qqKgDOEmp33HEHLrnkEvh8Pixbtgy//OUvh93/1Vdfxdvf/nb4fD7U1NTgk5/8JPr7+4edc/fdd+Okk06Cx+NBY2MjbrjhhmG3d3V14f3vfz/8fj+am5vx4IMPzmyniYiIiIiIiEoEx+VERETqYlGciIioTNx00024/PLL8fLLL+PKK6/ERz7yEbzxxhsAgHg8josuughVVVXYtm0bfvGLX+CRRx4ZNri+4447cP311+OTn/wkXn31VTz44INYsWLFsJ9x66234sMf/jBeeeUVvPvd78aVV16Jnp6eWe0nERERERERUSniuJyIiKh0CSmlLHYjiIiIaHwf//jHce+998Lr9Q47/o//+I/4x3/8Rwgh8OlPfxp33HFH/razzjoL69atw/e+9z3cdddduPHGG3H48GEEAgEAwO9//3v8xV/8Bdra2lBfX4+mpiZcc801+PrXvz5qG4QQ+PKXv4yvfe1rAJwBfTAYxB/+8AfuoUZERERERERljeNyIiIitXFPcSIiIkVccMEFwwbXAFBdXZ3/euPGjcNu27hxI3bs2AEAeOONN3DqqafmB94AcPbZZ8O2bezatQtCCLS1teEd73jHuG1Ys2ZN/utAIICKigp0dnZOtUtEREREREREyuC4nIiISF0sihMRESkiEAgcs2xaofh8vgmd53K5hn0vhIBt2zPRJCIiIiIiIqKSwnE5ERGRurinOBERUZl49tlnj/l+1apVAIBVq1bh5ZdfRjwez9/+9NNPQ9M0nHDCCQiFQliyZAkeffTRWW0zERERERERUbnguJyIiKh0caY4ERGRItLpNDo6OoYdMwwDtbW1AIBf/OIXOOOMM3DOOefgJz/5CZ5//nn88Ic/BABceeWVuPnmm3H11VfjlltuwdGjR/G5z30OH/vYx1BfXw8AuOWWW/DpT38adXV1uOSSSxCLxfD000/jc5/73Ox2lIiIiIiIiKgEcVxORESkLhbFiYiIFPHQQw+hsbFx2LETTjgBb775JgDg1ltvxX333YfPfvazaGxsxM9+9jOsXr0aAOD3+/HHP/4Rn//857F+/Xr4/X5cfvnl+Na3vpV/rKuvvhqpVArf/va38Xd/93eora3FBz/4wdnrIBEREREREVEJ47iciIhIXUJKKYvdCCIiIpoeIQR+85vf4LLLLit2U4iIiIiIiIjmHI7LiYiIShv3FCciIiIiIiIiIiIiIiIiorLFojgREREREREREREREREREZUtLp9ORERERERERERERERERERlizPFiYiIiIiIiIiIiIiIiIiobLEoTkREREREREREREREREREZYtFcSIiIiIiIiIiIiIiIiIiKlssihMRERERERERERERERERUdliUZyIiIiIiIiIiIiIiIiIiMoWi+JERERERERERERERERERFS2WBQnIiIiIiIiIiIiIiIiIqKyxaI4ERERERERERERERERERGVLRbFiYiIiIiIiIiIiIiIiIiobLEoTkREREREREREREREREREZYtFcSIiIiIiIiIiIiIiIiIiKlssihMRERERERERERERERERUdliUZyIiIiIiIiIiIiIiIiIiMoWi+JERERERERERERERERERFS2WBQnIiJSxIEDByCEwC233FLsphARERERERHRJM3kuH7z5s047bTTsG7dOjQ3N+NLX/oSbNsu+M8hIiJSFYviREREBfShD30IQgjs2LFjzHOklFi6dCkqKyuRTCZnpB1btmyBEGLYRzAYxLp16/Dtb38bpmkec5/zzz8fQgi4XC50dHSM+rif//zn84+3ZcuWYbft27cPn/zkJ3HiiSfC7/ejqqoKq1atwtVXX43HHnts2LlLliw5pn1DP+69996C/VsQERERERERTZSq4/pVq1bhsccew4svvogtW7bgzjvvxH/913/NSNuIiIhUZBS7AUREROXkuuuuwy9/+Uts3rwZ//Zv/zbqOY899hgOHDiAT33qU/D5fDPano9+9KN497vfDSklOjo68KMf/Qh/+7d/izfeeGPUwbFhOP81+PGPf4y///u/H3ZbJpPBT37yE3i9XqRSqWG3vfDCCzjvvPPgcrlw1VVX4aSTTkIymcTu3bvxpz/9CaFQCBdccMGw+yxYsAC33XbbqO0+++yzp9NtIiIiIiIioilRdVx/1lln5b9uampCY2Mjdu3aNaNtIyIiUgmL4kRERAX0rne9CwsXLsRPfvITfPOb34Tb7T7mnM2bNwNwBtozbd26dfirv/qr/Pef/exnceKJJ+IHP/gB/umf/gnz5s0bdr7H48Hb3/52bN68+Zii+AMPPIDu7m785V/+JX76058Ou+3WW29FIpHAjh07cOqppx7TjtFmnofD4WFtIyIiIiIiIio21cf1APCrX/0Kb775Zr6dRERExOXTiYiICkrTNHz84x9Hd3c3HnzwwWNuj0aj+NWvfoWTTz4Z69evRywWw5e//GWceeaZqK2thcfjwYoVK/ClL30JiUSi4O0LBAI466yzIKXE3r17Rz3nmmuuwRtvvIHnnntu2PHNmzfj1FNPxWmnnXbMfXbv3o2amppRC+IA0NDQMP3GExEREREREc0w1cf1u3btwnXXXYd//Md/xJlnnlnwn09ERKQqFsWJiIgK7JprroEQYtR3ZN93331IJpP5d5O3trbiBz/4Ac444wzcdNNN+Na3voV169bhn//5n/H+979/Rto3MGiurq4e9fb3vve9qKurw913350/1traij/96U+49tprR73P8uXL0d3djV//+tcTbodlWejq6hr1Q0o5iR4RERERERERFY6q4/rW1lZcfPHFuPTSS/G1r31tRn42ERGRqrh8OhERUYEtXboUF1xwAf74xz+ivb0djY2N+ds2b94Mt9udX/ps2bJlOHz4MFwuV/6c66+/HjfddBO+/vWv4/nnn8eGDRum3JZEIpEvMnd0dOD73/8+XnrpJWzYsAErV64c9T4ulwt/9Vd/hR/+8If4zne+A5/Ph//+7/+Gruu48sorR31R4Mtf/jIefvhhXH755WhubsY555yD9evX4/zzz8eqVatG/TlvvvnmqMu8AcDRo0dRW1s75X4TERERERERTZWK4/qjR4/ine98J84991zcc889EEJM+WcSERGVI84UJyIimgHXXXcdLMvCj370o/yxN998E88++ywuvfTSfMHX7XbnB86maaK3txddXV248MILAeCYJcwn6+abb8a8efNQV1eHNWvW4Hvf+x4+8IEP4IEHHhj3ftdeey0ikUh+5vc999yD973vfaipqRn1/I0bN2L79u24+uqrEYlEsHnzZnz2s5/F6tWr8ba3vQ379u075j5LlizBww8/POpHOByeVr+JiIiIiIiIpkO1cf2Xv/xlvPnmm3j99dexYcMGnHHGGbj11lun9bOJiIjKCWeKExERzYAPfOADqKysxObNm3HjjTcCQH458pFLkH/ve9/D97//fbz++uuwbXvYbb29vdNqxyc/+Ul86EMfQjabxauvvopvfOMbaGlpgdfrHfd+J510EtavX4/Nmzdj0aJF2L17N/7t3/5t3PuccsopuOeeewAABw8exOOPP44f/OAHePLJJ/G+970P27dvh9vtzp8fCATyLxIQERERERERlRLVxvV33nkn7rzzzmn9LCIionLGmeJEREQzwOv14i//8i+xa9cubN26FZZl4cc//jEWLFiAiy66KH/et771LVx//fVobGzEnXfeid/97nd4+OGH88XlkYPpyWpubsaFF16ISy65BP/n//wf/O///i+2bduGT3/608e977XXXos///nPuPXWW9HU1DSs3cezePFiXHXVVXj88cdx9tln47XXXsPzzz8/na4QERERERERzZpyGNcTERHRIBbFiYiIZsh1110HwNlv7A9/+AM6Ojpw9dVXQ9MG//z++Mc/xpIlS/CHP/wBn/jEJ/Dud78bF154Ierr62ekTZs2bcLHPvYx3H///di6deu45370ox+F1+vFo48+eky7J0oIgTPPPBMA0NraOqU2ExERERERERWD6uN6IiIiGsSiOBER0QxZt24d1q5di/vvvx/f/e53IYQ4Zok1XdchhICUMn/MNE3cfvvtM9aum266Cbqu4ytf+cq454XDYXz/+9/HzTffjE996lPjnvvwww/DNM1jjieTSfzpT38CAKxevXrqjSYiIiIiIiKaZaqP64mIiGgQ9xQnIiKaQddddx0+97nP4aGHHsL555+PZcuWDbv9gx/8IP7hH/4Bl1xyCT7wgQ8gGo3ipz/9KVwu14y1acWKFfjIRz6Cn/zkJ3jyySdx7rnnjnnuVVddNaHH/Ju/+Rt0d3fj0ksvxSmnnAK/34/Dhw/jpz/9Kd566y1cddVVOOWUU4bdJxKJ4N577x318U455RSceuqpE+8UERERERER0QxQfVxPREREDhbFiYiIZtCVV16Jv//7v0cqlTrm3eQA8Pd///eQUuKHP/whPv/5z6OhoQFXXHEFrrnmmhmdWf1//+//xc9+9jN85StfwWOPPTbtx/vWt76FBx54AE899RR+9atfoa+vD+FwGGvWrMGNN96Ij3/848fcp6WlBR/72MfGbB+L4kRERERERFRsc2VcT0REVO6EHLquCxERERERERERERERERERURnhnuJERERERERERERERERERFS2WBQnIiIiIiIiIiIiIiIiIqKyxaI4ERERERERERERERERERGVLRbFiYiIiIiIiIiIiIiIiIiobLEoTkREREREREREREREREREZcsodgMmwrZttLW1IRQKQQhR7OYQERERERERAQCklIjFYpg/fz40rXzfd85xOREREREREZWiiY7LlSiKt7W1YeHChcVuBhEREREREdGoDh8+jAULFhS7GTOG43IiIiIiIiIqZccblytRFA+FQgCczlRUVBS5NURERERERESOaDSKhQsX5set5YrjciIiIiIiIipFEx2XK1EUH1iaraKioqQH31JKRCIRhMNhLienKGaoNuanPmaoNuanPmaoNuanPmaotnLPTJVxOcBrSXXMT33MUG3MT33MUG3MT23MT33MUG3Hy6x8NzwrAtu20dHRAdu2i90UmiJmqDbmpz5mqDbmpz5mqDbmpz5mSFQYvJbUxvzUxwzVxvzUxwzVxvzUxvzUxwzLmxIzxYmIiIiIiOYay7KQzWaL3QwawuVyQdf1YjeDiIiIiIiIiCaJRXEiIiIiIqIS09/fj5aWFkgpi90UGkIIgQULFiAYDBa7KUREREREREQ0CSyKF5AQAoFAgPsMKIwZqo35qY8Zqo35qY8Zqo35qW8gQ9u20dLSAr/fj3nz5jHTEiGlxNGjR9HS0oLm5mbOGC9hfD5UG/NTHzNUG/NTHzNUG/NTG/NTHzMsb0IqMPUgGo0iHA4jEomgoqKi2M0hIiIiIiKaMalUCvv378eSJUvg8/mK3RwaIplM4sCBA1i6dCm8Xi+AuTNenSv9JCIiIiIiIrVMdLyqzWKbyp5t2+jq6oJt28VuCk0RM1Qb81MfM1Qb81MfM1Qb81PfyAz5zvTSw0zUwOdDtTE/9TFDtTE/9TFDtTE/tTE/9THD8saieAFJKdHV1cV9/xTGDNXG/NTHDNXG/NTHDNXG/NTHDIkKg9eS2pif+pih2pif+pih2pif2pif+phheWNRnIiIiIiIiIYxDANr167F2rVrsX79euzYsQMAsGXLFlRWVuZvW7t2LXbu3IkDBw7A7/dj7dq1WLVqFa666iqk0+n8OQ0NDViwYAHWrl2LTZs2DftZt9xyC3RdR0dHR/7YZz7zmWGzsmtra49p4y233JJ/zJNPPhkPPvjgzPxjEBEREREREZHyjGI3gIiIiIiIiKanpz+FzkgSdWEfqoPeaT9eZWVlvhD+q1/9Cl/96lfx61//GgBw4YUX4pe//OWw8w8cOIDVq1fjhRdegGma+XMGHuOWW25BbW0tbrjhhlF/3urVq/HLX/4SN9xwAyzLwvPPP4/q6urjtvNLX/oSbrjhBrzxxhs499xz0dnZCU3je7+JiIiIiIiIaDgWxQtICIFwOMx95hTGDNXG/NTHDNXG/NTHDNXG/NQ3MkMpJdJZ67j3e/rNDtz95zeRzlrwuHRc+/YTcfaJDePex21oE/5diUajqKysnNC5gDPL/KyzzkJLS8uE73P55Zfj17/+NW644QZs2bIF5557Lg4ePDjh+69atQqGYaCrqwt1dXUTvh+VJz4fqo35qY8Zqo35qY8Zqo35qY35qY8ZljcWxQtI0zQ0NjYWuxk0DcxQbcxPfcxQbcxPfcxQbcxPfQMZplIpAEDGtPG3dz857n0s20ZnNAXbltAEEEtm8S8PvowfbdkFfZwZ09/75LnwuPQxb+/r68PatWuRSCTQ3d2NrVu35m975JFHsHbt2vz3Tz45vI3JZBJbt27Fv//7v4/b9qFqa2vhdrvR1taGn//857j66qtx7733Tvj+zz33HDRNw7x58yZ8HypffD5UG/NTHzNUG/NTHzNUG/NTG/NTHzMsb1xXroBs20Z7ezts2y52U2iKmKHamJ/6mKHamJ/6mKHamJ/6ppKhaUtI6RTEhRDQhDPD3LLltNoysHz6W2+9hTvvvBPXX399/rYLL7wQO3bsyH+EQiEAwM6dO7F27VrU19ejvr5+WOF8Ii6//HLcf//92LZtGzZu3Dih+9x+++1Yu3Yt/u7v/g73338/381PAPh8qDrmpz5mqDbmpz5mqDbmpzbmpz5mWN44U7yApJSIRCJcrk9hzFBtzE99zFBtzE99zFBtzE99AxlWVFQAcJY4/94nzx33Pr39afzdj55BKmPC7zGQSJvwug1886qNqAp6xryf25j4+6Pf+9734qqrrjrueQN7ih89ehQbN27Egw8+iEsvvfSY87773e/irrvuAgA888wz+ePvf//7sXr1alx55ZUTLm4P7ClONBSfD9XG/NTHDNXG/NTHDNXG/NTG/NTHDMsbZ4oTERERERGVICEEPC593I+GKj8+9c7V8HkMJLM2fB4Dn3rnajRU+ce932RmVG/duhXLli2b8Pnz5s3D7bffjttvv33U26+//vr8LHOfz5c/Xltbi2984xv47Gc/O+GfRUREREREREQ0EZwpTkREREREpLBzVzfipEVV6IykUBf2ojronfZjDuwpLqWEYRj4r//6r/xtI/cU/4//+A8sXLhw2P0vv/xyfPWrX8XWrVuxadOmCf/ca665ZtTjvb29WLBgQf77b37zmxN+TCIiIiIiIiIiFsULSAiB2tpa7mOnMGaoNuanPmaoNuanPmaoNuanvulkWB0sTDF8gGmaox4///zz0dfXN+ptL7zwQv5rIQReeeWV/Pe33HLLmD9rrNu6urryX1uWNXZjiUbg86HamJ/6mKHamJ/6mKHamJ/amJ/6mGF5Y1G8gDRNQ21tbbGbQdPADNXG/NTHDNXG/NTHDNXG/NQ3kGEqlSp2U4iUxudDtTE/9TFDtTE/9TFDtTE/tTE/9THD8sY9xQuoLdqGB7c/iLZoW7GbQlNk2zYOHz4M27aL3RSaAuanPmaoNuanPmaoNuanPmZIVBi8ltTG/NTHDNXG/NTHDNXG/NSWyGTxxoHDSGSyxW4KTRGvwfLGmeIF8sudv8Tf/vFv0ZPoQdUTVfj2Rd/GB1d/sNjNokmSUiIej0NKWeym0BQwP/UxQ7UxP/UxQ7UxP/UNZFhdXV3sphApjc+HamN+6mOGamN+6mOGamN+g1KmhUTWgt+lw2voRWuHlBISgC2dr+2BYxKQkLnjQHs8hd09/ciaGg4c7sGaugo0hXxFazdNDa/B8saieAF09Hfgi3/6Io7Ej8CyLPQme/HFP30R5yw6Bw3BhmI3j4iIiIiIiIiIiIiISAmtsSRe7ozCtCU0ASwM+VDldQ0rTjtfy9GPSQz72kbu2NDz8wXt8YveE6mN2lIiadpwThVImTZePhJFjc9d1II+EQ3HongBHOw7iEQ2gbAnjN5kL0zbRCKbwKHIIRbFiYiIiIiIiIiIiIiIjkNKidZYCts7IrCGVKP39SXgMzRoQhSxdYOEENAEIHJfW7mltjU4xXgJIGXZePVoFKfMq2BhnKhEsCheAIsrF8Pv8qM/3Q9NaMhaWQgILAovKnbTaJI0TUNDQwM0TSt2U2gKmJ/6mKHamJ/6mKHamJ/6mCFRYfBaUhvzUx8zVBvzUx8zVNtczS+RtdAaS6KtP4VExsoXxHUh4MzXBqq8LvhdBoQABASEQK4wnStQCwEBQBMid87QrwfOATSMPDZwv1yxG7ljAiO+HnxcMaI4nzItPHawC6YtIQCYtlMY705k8HRLD5aE/VgS9kPXSqOoT2Obq9fgXMGieAE0BBvwr+/6V3zxT19E2krDho0V1Ss4S1xBQghUVlYWuxk0RcxPfcxQbcxPfcxQbcxPfQMZplKpYjcFhmHg5JNPBgC4XC7cddddWLt2LbZs2YLLLrsMS5YsyZ/705/+FH6/H6tXr8bKlSuRTqexfv163HXXXTjzzDMBAB0dHTAMA7W1tfD7/di6deuwn3fgwAEsXboUv/3tb/Ge97wHALBjxw50dnbiXe96FwBgy5Yt8Pv92LBhwyz8C5DK+HyoNuanPmaoNuanPmaotrmUny0ljsTTaI2l0JPM5I97DA1mbklztyaQtQFDEzi1PlyyM669ho41dRV4pTMKSwIeQ2B5ZQC96SwiqSz29sbRGkuhuTqAhoDnmKI6lY65dA3ORZN+q0MsFsMXvvAFLF68GD6fD5s2bcK2bdvGPH/Lli25d9IM/+jo6JhWw0vNB1d/EM9c+wy++7bvYnnVcnQnu/FS+0vFbhZNkm3b2LdvH+zcciekFuanPmaoNuanPmaoNuanvulkGEnY2HfERCRRmPwrKyuxY8cO7NixA1/60pfw1a9+NX/bhRdemL9tx44dWL16NQBg9erV2LFjB1599VUcOnQIv/zlL/PnfPrTn8aXvvQl7Nix45iCOAD8/Oc/x6ZNm3D//ffnj+3YsQN/+tOf8t9v2bIFzz//fEH6R+WNz4dqY37qY4ZqY37qY4Zqmwv59WdMvNndj8cPdePVzqhTEBdAjd+NU+vDOH9xLdY1hOHSBEwpYWgCa+pKfwnyppAP5y2sxmIkcd7CaqysCWJDYyVOybU9ZVp4tTOK59v70JfKFru5NIa5cA3OZZOeKf6JT3wCr732Gn784x9j/vz5uPfee3HhhRdi586daGpqGvN+u3btQkVFRf77urq6qbW4hNUH6nFm7Zm47ITL8Jtdv8HmHZtxWuNpxW4WTYKUEplMBnLIfiWkDuanPmaoNuanPmaoNuanvpEZSimRMY+f54v7Mvj1swlkTMBtAB84y491y9zj3selH7vk31ii0eik3ilvGAbOOusstLS0TPg+999/P3784x/joosuQjqdhmEY+MpXvoJUKoVHHnkEf/M3f4Pvf//7MAwDP/jBD3DPPffgO9/5DsLhMJ577jl0d3fjBz/4Ac4777wJ/0wqX3w+VBvzUx8zVBvzUx8zVFu55mfZEh3xFFpjqWEFYa+hY37Ii6agFz7XYNG7KeRDjc+NRNaC36WXfEF8gEfX4DLT8OjOfFQhBBqDXtT5PTgQSeBAJIFIKovn23rRGPRiRXUAPkX6NleU6zVIjkkVxZPJJH71q1/hgQcewNve9jYAwC233IL//d//xR133IGvf/3rY963rq5uziw58PG1H8dvdv0GD+97GC3/P3tvHh9Xdd/9v8+9d/YZjfbV8i6DF4zB7MGBJCQhGyHBQNO0BPJq0uYJafqQpcmT0hDykJCnLckvoVmAFijNAnGW0pIQtgBmj8HGYBvvsiVZ+zKjWe92fn9caSRZki3bsq2Rz9sMM3PuuXfOmc+dO5r7ud/vN9nKnJI5J3tICoVCoVAoFAqFosiwHLjtN4lD9nFcSd+gREqvPl4mB/c+mea/Xskcsl7dP14Tx3+IX4MDAwOsWrWKTCZDb2/vmOjuJ554glWrVhWer1+/fsy62WyWF154ge9///uHnuAQu3btQgjBsmXLuPTSS/n973/PlVdeya233sqbb77JP//zPwOwd+9eKisrufHGGwvr9vX18dJLL/HUU09x66238uSTT07pNRUKhUKhUCgUCsXxRUpJ0rRpG8zRnsrhuJ7JKISgKuynIRakMuSf9GLdoFE8Zvjh0DXBorIIDbEgu/rTHEh570lXJs+8oXrjhqo3rlAcd47IFLdtG8dxCAaDY9pDoRDPPffcIdddtWoV+XyeFStWcMstt/C2t71t0r75fJ58Pl94nkwmAXAcB8dxAO/AqWkaruuOuWJjsnZN0xBCTNo+vN3R7cC4FAmTteu6jpQS13VZHF/MRXMu4oWWF3jg9Qf4ysVfGdP/SMc+E+Z08Fgmay/2OTmOg+u6OI4za+Y0G3WabE7Hot9MndNs1OlQYx/W0HVddF2fFXM6XPtsmtOwfsN9ZsOcDh77bJ+T4zhjHs+GOY1un+1zGtZv+PM4G+Z0qLHPxjmN1m/4MRIQePcHI8B1hroI78ZQV9cB/eBzKqO2421/ZF4HXwU/nD5dSsm6dev47Gc/y+OPP46Ukssuu4xf/vKXY/r39PSwdetWVq1axZ49e3jve9/LmWeeOSbqvTCng17zF7/4BWvXrkVKyTXXXMPPfvYzPvzhD49ZZ7j/6G0AXHnllUgpOfvss2lubp5w+2PegiNsP5jR8xnW9WB9FQqFQqFQKBSKUxnLcWlP52kbzDKYtwvtYZ9OQyxEfTRIwDjiyr6zgqChs6KqhLklIbb3pujPWezpT9M2mGVxWZT6qKo3rlAcT47IFI/FYlx44YV885vfZOnSpdTU1PDzn/+cF198kcWLF0+4Tl1dHT/+8Y8555xzyOfz3HPPPVx66aW8/PLLnH322ROu8+1vf5tvfOMb49p3795NNBoFIB6PU1dXR2dnJ4nESPREZWUllZWVtLW1kU6nC+21tbWUlpbS3NyMaZqF9jlz5hCNRtm9e/eYk1ILFizAMAx27tw5ZgxNTU3Yts3evXsLbZqmsWTJErLZLLZts3v3bt5V8S6e2fsMv9z6S/7ytL8k1Zcq9I9EIjQ2NtLX10dPT0+hfSbOKZ1Oj0l76Pf7WbhwIYlEYkxd+Nkyp/b29oKG0Wh0VsxpNuo02Zy6u7sL+pWWls6KOc1GnQ41Jykltm0zMDBAVVXVrJjTbNRpsjkN6zc4OEhZWdmsmNNs1OlQc5JSUlFRgaZp7Ny5c1bMCWafTpPNSUpJTU0NwKyZE8w+nQ41p8WLF1NTU0NLS4t3oZGd58sfDhIMBnEcZ8y2NU0jEAjQlzT5l/9OY1oQ9EPOhIBf8HcfCBH2j7ymruv4/X5M0/QuNrZy5GyBYRj4fD4sy5rQ3DVNk8suu4xPfOIT5HK5wjzy+fwYE1lKybJly1i/fj3d3d284x3vYN26dQWz27ZtLMsil8vxk5/8hPvuuw+Ap556igcffJC+vj5+9KMfAV6Uel9fX2FMlmXh9/txXbewDfAuKggEAliWhWVZ2LZNLpebdE4+nw/DMDBNc4wefr8fXdfHzSkQ8E5IDb/e6HlblsW+ffsASKVGfuspZgaapjFnzpzCBSiK4kLpV/woDYsbpV/xozQsbopVPykl/TmLtsEcnek87tDf1ZoQ1EQCNMSClAV9s97wnap+JQEf59SV0pUx2dGXIms5bOlO0pI0OK0iSlnw0OWwFMePYv0MKqaGkFO5HH4Uu3fv5pOf/CTPPvssuq5z9tlns2TJEl599VW2bds2pW1ccsklzJ07lwceeGDC5RNFig+frBquSz7TI1KklHzowQ+xp38PX3nbV7hu5XWHHeNMn9Poscz0KBs1JzUnNSc1JzUnNSc1JzUnNSc1p2KdUyaTYd++fSxYsIBgMHjY6OZXd5v88oWRmuLXvC3C2Qt9k/afSntVVRU9PT1IKXnqqaf4/Oc/zxtvvMHTTz/Nv/7rv46LFG9ububqq6/mT3/6EwDr1q3ju9/9Ls8//zzgld0anfp8+DW3bdvGX/zFX/Dqq68W2q+77jo++MEPomkajz32GD/5yU8QQvDP//zPmKbJV7/6VQBuuOEG1q5dywc+8AFSqRRnnHFG4SKJ4xUpnsvlaG5uZv78+fj93omqZDJJeXk5iUSi8Ht1NpJMJonH47N+ngqFQqFQKBSKqZO3XQ6kcrQNZslYI797on6DhliQ+mgQn67MxUPhSsm+RJY9A+lCivmaSICm8ihh3+xIH69QHG+m+nv1iCLFARYtWsQzzzxDOp0mmUxSV1fHtddey8KFC6e8jfPOO++Q6dYDgQCBQGBcu67r6PrYg8BkV2scafvB2z2adtd12b17N4sWLcIwDG5YdQM3//Fm/mPzf3Ddmdeha8dn7MdzTkKII2ov9jkBBQ2Hx1Dsc5qNOk02J8dxjpt+SqcTM6fRGk6l/7GMfbJ2pdPRj320frquz4o5TbV9tszJcRx27txZ0HCqY5+sfSbM6XBjPNL2mTyno9VvJs/paNuLdU7Dx9GGhgaEEIXb8NgnQgjBOYsDNNX76Bt0KY9pxMOTn3Q61HZGM1xTXEqJYRjcddddhfE88cQTnHXWWYW+P/jBD2hsbByznbVr1/LNb36TF198kYsuumjcfIb7PvTQQ3z4wx8e0/6Rj3yEn/70p9x111185zvf4eyzz+a2227jiiuuYO3atTz00EOFSPPh7Uz0Pk11rodrn6jP6H1pMn0VJ4+D/yZRFBdKv+JHaVjcKP2KH6VhcVMM+kkp6c1atA1m6cqYhYtLdU1QGwnSEAsSDxhT+vt6tnE0+mlCsKA0TH00yO4BL5V6ZzpPd8ZkbjzEwtIwxiS/NRXTTzF8BhVHzxGb4sNEIhEikQj9/f384Q9/4P/9v/835XU3bdpEXV3d0b70jGZ0tMcVp13BHS/ewYHBAzyx5wneu/i9J3FkiqlycMSOorhQ+hU/SsPiRulX/CgNixulX/FztBrGw4c2w48U27YnbL/00ksZGBiYcNmGDRsKj4UQbN68ufD8lltumXCdido/8pGP8JGPfASgEHk+zOhtjjbGo9Eozc3NE76G4tREHQ+LG6Vf8aM0LG6UfsWP0rC4man6ZW2HA4M52gZz5OyRqPB40EdDLEhtJKDMW45ev4ChsawyRuNQvfG+rEnzQIYDgzkWl0VoiAVPyQsNTgYz9TOoOHaO2BT/wx/+gJSS0047jV27dvGlL32J008/nRtuuAGAr371q7S1tfEf//EfAHzve99jwYIFLF++nFwuxz333MNTTz3FY489Nr0zmQk4eYJaDpw86GGCRpCPrfgYP9zwQ+7ddK8yxRUKhUKhUCgUCoVCoVAoFAqFQqFQFAWulHRnTFoHs/RmTRiqOGToGvXRAA2xEDH/UcdezjpkNoE/fQCZrYZo+VFtI+Y3WF0bp3uo3njGctjaM8j+ZJbTK6KUh1S9cYXiaDnio1UikeCrX/0qra2tlJeXc9VVV3Hbbbfh83k169rb29m/f3+hv2mafOELX6CtrY1wOMzKlSt54okneMc73jF9s5gJZNvRBrbRGM6j9fZB6VII1fHxlR/n7tfuZmPHRjZ1bGJV7aqTPVKFQqFQKBQKhUKhUCgUCoVCoVAoFIoJSVs2bYM5DgzmMJ2RqNnykJ+GWJDqcABdO75Ry242gUz3ICKVaKH4cX2t6cDe/yfyr/6CSjNLfv8jsPrPMOaee1TbEkJQHQlQGfbTksyyeyBDyrTZ0D5AVTjAaRURwj51MUIxUWz7MxTnmA+HkMMFH2YwUy2QftJw8tD9PNLJ410qpSF0P1S9DfQAX33iq/z6rV/zvsXv43uXf+8kD1ZxKKSUmKaJ3+9XqUiKEKVf8aM0LG6UfsWP0rC4UfoVP8Mauq5Lc3MzCxYsIBgMnuxhKUaRy+XYu3fvGG1m/O/VaaKY5qmOh8WN0q/4URoWN0q/4kdpWNycTP0cV9KZydM2mKM/axba/bpGQ8yrFX6ijFh7/58wX/050sqBZmAsWoNeuQikC64DSKTres8nuUkpvb6FNgnSGVo2/NyFwnacQps8eHvuyPpy9PaGx2LnkYl2r11oIAT4QoTe+w9osepjfj9Mx2XPQJqWZA4pJUII5pZ49cZ9+qmXsj5nO2Qsh7BPJ2hMb93v6foMSinBMcE2vf35zYfBtkD34Tv93ei1S4cyL8jhFbzHo9tGtx/8eOi5RA61T9a/sGDU84Mfj29zevZgN78Ijg2+IIFz/vyoL/I4EUz196q6lGQ6cLLeAVMYIG1g6GDoZEEPcP2q6/n1W7/msd2PcWDwAPWx+pM9YsUhMAz1sShmlH7Fj9KwuFH6FT9Kw+JG6Vf8GIaBaZqH76hQKA6JOh4WN0q/4kdpWNwo/YofpWFxc6L1G8zbtA5maU/nsYeiwoWAilCAObEglWE/2gky6KXr4rS+Rv7Ffwc7Dwggh73tUexwBUKbXgN0upCO6RniDL1Prgv5FNmn/gWjbgV6/Qr0mtMRvtBRbd+va5xeEWNOLMSOvhQ9GZN9iQwHUjkWlUVoPIXqjbcms2xra8Of68MKlrN0TgMNsaN7X0cjXQfsPK6VQ7NyuGmrYGpLOw/O0L2dR9qmd+8M3Q8vt/JD6wwtk0PbzfQOGc4CrCzW67/G2jmD9+eDxwyYrz6IVrWk6CPG1bfjdKCHQOgg7aGrdPC+NXTvg3ha5WlcOOdCXmx9kQdef4C/v/jvT+54FZPiui47d+6kqakJXZ+ZByTF5Cj9ih+lYXGj9Ct+lIbFjdKv+BnWsLGx8WQPRaEoatTxsLhR+hU/SsPiRulX/CgNi5vjpd/BkbW269KRytM6mCOZtwr9goZOQyxIfSxIaJojcA+FO9iF3fIqTutG3HTviCGu+0BIcF300gZEqNTzXjS9EJEthDb0ePxNaAe3iaF7b30x/FzTh5bphT7jtqtphT7ioG3KfJrc+ju9qGA0DDfvRd9KidOxFadjK2gaeuVi9Poz0GuXIfzhI36fon6Ds2tL6cmYbO9LkTZt3uoZpCWZ5bTyKJXh2Vdv3HRcEnmLRN6mL2ti7/sTZ7X+Ht0xcTSDfT3vQC44gyo/GNIaZWB75vQY09rOj5jctullfx5+7jpDrygxTQu/30fhIodjQQ5td3ifHcpGoAVjCH+EIUPR6yOG/jd8X3h88PPh/uKgxyPLChdJTNgHbyzD/cdcUCGQuQRObqDwOcHwI508Mt0LyhRXoAcgvhQGtgFDO7geAm3kAHTDqht4sfVFHtr6EDeedyMRf+TkjFWhUCgUCoVCoVAoFAqFQqFQKBQKxSlB22CWzV1JHCkRCMpDPjKWg+N6qZKFEFSH/TTEQlSEfCcs4lhaOZwDm7FbXsXt219oF8EY0sp6T/wRsLIIfxD/+dfP6CjVwDl/Tv7VXyDMLARi+M/+M/TSOTjtb+C0v4k72I3TtQOnaweI36BXLkCvW4FeuxwRjB3Ra1WG/VSEymgdzLGrP03atHmtY4DKsJ8l5VGi/uK0/hxXMmjaBRM8kbfIWp7nZlgZSvrfYlnzb9Fd70IOnyNZsv+/yXWuZ1BoaJrAEAJdE4ijNbQ1A9fQEKE4whcAI4AwAqD7vXvDX3heWGYEvJLKRgBh+Ef1DSDNDLk//F+knQNfyNufjSCBS/52xu7PbjZB7tFvjhuziFSc7KEdM8X5yZiJhOpw9ThtzW/RWJL00qhbCfCXArBm3hoWlC5g78BefrXtV1x35nUnd7wKhUKhUCgUCoVi1jDdNdUMw2DFihUA+Hw+7r77blatWsXTTz/NlVdeyfz58wt9f/aznxEOh1m2bBlLliwhn89z7rnncvfdd3P++ecD0NHRgWEYVFZWEg6HeeGFFwrr33LLLVRWVnLjjTcW2g4cOMCXvvQlfvrTnx7zXBQKhUKhUCgUilOVnO2wuSuJ5UhcKXGRdKTyhAyNqN/HnJIgddEggRNUl1pKF7d3L87+V3Ha30Q6Q1HqQkOvOQ2jcTVazWk4rRsxX30QaecQRhD/6mtnrIE4jDH3XChfxP7tm5h72iqMaDkAWrwO3+nvwR3swml/0zPIE+043btxunfDG/+FVj7fM8jrlqOFSqf0ekIIGktC1EYC7BnIsD+ZpSdj0pvtZ04syKKyCP4ZXG9cSknGchjI2yTzFom8xaDpeLW4Ac3JE0u1UjG4j9JMC6FsD5probsmcii6WQ7VwpZGkLwRwdX9OJofV/dj+IKEgiEioRB+f2jEzJ7A5B7d5krYP43ZGoThx7/62qLan7VQvOjGPFWUKT6NpIXNAc2kLFhOLN8P6ZaCKa4JjetXXc/Xn/46979+Px8/4+PoM7RegEKhUCgUCoVCoTj5SCkL0RuH4sBgjjd7kjiuRNcEKypLqI8FD7mOJjhkBEhpaSmbNm0C4Fe/+hW33norv/71rwG47LLLWLdu3Zj+zc3NLFu2jA0bNmDbdqHP8DYmMr4PRX19/XE1xF3XRdNm7gkihUKhUCgUCoViOshYDo6UgGS4OrAmYGlljLkloRMWFe5m+nFaXsNueRWZ6S+0a7Eq9MbVGHPOHhMtbcw9F61qCTLdi4hUFI0ZJ0JxzEg9YoLxarFqtNg78S15J266d8Qg72/F7W3G7W3GevN/0Moa0euWo9etQJtCZK5P1zitIkpjSZAdfWm60nlakl6N+EWlYRpLQiesJvyhyDsuiZxFMm8zkPfubdctLBeuTSTdRlm6hbJ0C6FMBxrS++2KAF1DlNRj9+RwXRfLCGE4eXR/iMr3fpmsHqErY9KVyZPIWWNeO+I3qA77qY4EKPEbh97vHWfyZUdJMe7PxTjmqaBM8Wlip7mT9Zn1WJUWWzFYIyppMgErBb4oAB8+7cPc8eIdtCZbeXLvk7xn0XtO7qAV49A0jaamJnWCrEhR+hU/SsPiRulX/CgNixulX/EzrKFpmgC4Ep5s7j7kOq6UZG0XOdLAho4BQj3aIU98vGt+FfoUz4skk0lKS0un1hkvyvyCCy6gtbV1yuscTHNzM2vXrmXDhg3cd999/O53v6O/v5+9e/fymc98hi984QsAPPDAA3z/+9/HNE3e9a53cccddwDwwQ9+kPb2dvL5PF/96lf5+Mc/TnNzMx/60IdYvnw5mzZtYuPGjYRCoaMeo2Lmoo6HxY3Sr/hRGhY3Sr/iR2lY3Ey3fmGfji4E5lD0rSEEPl1QEwkcd0NcOhZO+xaclldxenYx/KNF+ALo9WeiN65GK2ucdBxaKF509Yunqp8WqUBbfAm+xZd4Fwx0bME58CZu/z7c/hbc/hasrY+ixetHDPJY9SG3GfYZrKqJ05f16o0P5m2296ZoSWZZUh6lKuw/YRdBOK4kWUiDbpHI2eTsg8xm6RLLdlKZbSWe2k9wsBUdZ7gitvdfpBK9chFa5SL0yoWIQBR7/5/Iv/oLdNtEBEIEVv8ZWihOBFjgN1hQGiZnO3QPGeR9WYu0abPXtNk7kCFg6AWDvCzoG/e7+XgdQ4tyfy7CMR8OZYpPA2k3zfrMenIyB4CDw3pfB/VmhEimFeKnAxDyhfjzM/6cH234EfduuleZ4jMU27bx+/2H76iYkSj9ih+lYXGj9Ct+lIbFjdKv+LFt+4j6S1k4t4TAeyyH2o+2hBrAwMAAq1atIpPJ0NvbOybd+RNPPMGqVasKz9evXz9m3Ww2ywsvvMD3v//9ox/AQWzevLkQhX7aaafxuc99jt27d/Nf//VfvPjiixiGwXXXXccjjzzCBz7wAf7jP/6D8vJy0uk05557LmvXrgVg27Zt/PSnP2XlypXTNjbFzEQdD4sbpV/xozQsbpR+xY/SsLiZTv2Chs6i0ghbegYB8OmCldUl01JyaSKklMiBVuyWV3HaXkdaucIyvXIh+txzvBraxuzdP49UPy1chrbwYnwLL0bmkjgdW3EOvIHT24ybOICbOID11uNosWrPIK8/AxGrndTgLg/5uaC+jLZUjl19aTKWw6bOBOUhP6dVRIlNc71xKSVpyxlTBzw1Kg36CC7l9gCVmVZiqX0EEvvQHJPRP1xFsKRggmuVi9DCZeNebyoRzEFDp7EkRGNJCMtx6cmadKXz9GRN8rZDSzJLSzKLoWtUhTyDvDLkR9e8sahj6OxFmeLTwKA7iIWFQGBJC5/wYSEZFCaRXBdEF4AeAODjZ3ycu1+7m9faX2Nz52ZW1qiTMTMJ13XZu3fvtNWLUJxYlH7Fj9KwuFH6FT9Kw+JG6Vf8DGvY2NgIeGkN3zW/6pDr5GyHZ1t6sV2JoYnC/dsbKw55oks7jGE+On36unXr+OxnP8sTTzwBTJw+vbe3l61bt7Jq1Sr27NnDe9/73jHG+bHy7ne/m2jUy8BVX19PZ2cnTz75JC+99BLnnHMOAJlMhtWrVwPw3e9+l4cffhiA/fv3s3//fnw+H0uWLFGG+CmAOh4WN0q/4kdpWNwo/YofpWFxczz08+kaIUOjNOhjVU38uBjiMjeI3bYRZ/9ruIOdhXYRLsVoXI3eeDZauHzaX3emcaz6iWAJxvwLMOZfgMyncTq34hx4E6dnF+5gF+5gF9aOPyIiFRhDEeSidM44g1wIwZyYV29870CGfYksfVmTl9r6qI+FWFwaIWAcXSR03nYK5vfw/URlv/yaoIIU5ZkWosl9+AaawcyMHacvNBQF7pngIlo1pWj2I4lg9ukaddEgddEgjivpy5l0pU26M3lMx6U9laM9lUMTgoqQn8qQQbK9hdPVMXRWokzxaSCmxfDhI0sWicTGJiIixIxKsLKQaYXYIgCqIlV8sOmD/Hb7b7lv033c8d47TvLoFQqFQqFQKBQKxUxECFG4Un0yIn6DldUlbO5K4kjPEF9ZXUJkGq/+/+AHP8h111132H7DNcW7u7u58MILefjhh7niiivG9fvXf/1X7r77bgBefPHFKY0hEAgUHuu6juM4uK7Lpz71Kb7+9a+P6fvHP/6R559/npdffplgMMg555xDPp/H5/MRDoen9HoKhUKhUCgUihNDznbIWA5hn37copdPdfpyJpoQ1EWD0/oeS9fB7dqOvX8DTud2kEP1oXUDvW45RuM5aJULEUKl8j8aRCCCMfdcjLnnIq0sTsc2rw551w5kuhdr17NYu55FhEvRa5ej169AK5s75v02NI2m8igNsRA7+1J0pvO0JbN0pHIsLI0wLx7CdNxJP4OOK0kM1f8eNsHHpUEHdE1Q4jcoI0dppoVQshmtdy8yOzB2ToYPrXz+kBG+GFFShziBpR50TVAVDlAVDiBllIG8TVc6T1cmT9Zy6M54jy2ipDsT1ESCVEcChE7RY9NsPD4rU3waiGgR1oTX8EzmGWzppTu8OHQxESpg4E3ItkNkHmje2339quv57fbf8uiuR/nSRV+iLlZ3MoevUCgUCoVCoVAoipiGWIiKkP+4/Vh94YUXWLhw4ZT7V1VVcfvtt3P77bdPaIp/9rOf5bOf/ewxj+td73oXV199NTfeeCMVFRV0dXXhOA7JZJKKigqCwSCbNm3i9ddfP+bXUigUCoVCoVBMP22D2cLFnbrwLu5siIVO9rBmFa6UDOQsACpC05MO2k12eOnRWzci8+lCu1bW6EWFN6xE+JSO04nwhTAaz8ZoPBtp53E6t+O0v4HbtR2ZGcDe8zz2nucRwdhIDfLyBQXDOezTObMmTn/OYntvimTeYmdfit39afKOi8T7DDaVRwkZGgN5m+RkadAFRH0G8YBBXLOJp1oIJJpxe3bjproL3SSApqGVzR1JiV7WiNBmhi0phKAs6KMs6GNJeYSU5dCVztOZztFnQn/OZiCXYntviljAoDocoDoSIOrTT1ht9pPJbD0+z4y9bxbQ5G+immp+mfgluk+n3CgHrRz0MDiZIWPcS4O4tGop5zecz8ttL/Ofm/+TL73tSyd59IrRaCfwyiTF9KP0K36UhsWN0q/4URoWN0q/4udoNQwa02uGD9cUl1JiGAZ33XVXYdnBNcV/8IMfFFK+D3PVVVdx66238sILL3DRRRcd9vVuvvlmbr/9dgDq6ur45S9/edh1li9fzte+9jXe9a534bougUCA++67j8svv5wf/ehHLFu2jOXLlxdSqitOLdTxsLhR+hU/SsPiRulX/BSDhjnbYXNXEsuRgMQVsLkrSUXIP+MjEt1sApnuQUQqJ6xnfKxMp37Dqa39ukbEd/Tvq7SyOG2vY7e8itvfWmgXgSj6nLMw5q5Gi9VMx5CLnuP9+RNGAKNhJUbDSqRj4Xbt8CLIO7d5aez3voS99yWEPzxikFcuQmg6ZUEf59eX0p7Ks71vkIGcjQQ0wETyRleSkKGhjTJ9A4ZGPODzboZLNNUKfXtxe3bhJg6ABLswONDi9WiViz0jvHx+UdSPF0IQ8xvE/AbzS4K8tauPcFkpPTmbgZzJYN5mMG+zuz9N2KdTNWSQlwaMWWGQO64k77hDN4dU3mZbbwrHlQgBLsVzfD4cQo6vdj/jSCaTxONxEokEJSUlJ3s4h+SJ9BO02W2cFzyPpYGlkO2A5HbQ/FB5Pgylrvjj3j/yN4/8DTF/jGdveJawT6XxUygUCoVCoVAoFJDL5di7dy8LFiwgGAye7OEoRjGRNsX0e/VYOFXmqVAoFAqF4sTQlzV5vrUPe6gWsS5A0wQXNZRTPk0RzccDe/+fMF/9BdLKgu7Hf+ZHMRZdPGNThO/uT7O7P01NNMCZ1Udm4Evp4vbs9qLC27eAM2R9Cg295nTPCK8+DaEVt0k2W5COjduzyzPIO7YizWxhmfAF0WuXegZ5VRNC99GdyfNCaz/ukEUoAQFURQJUh/3EAz5KfOAfbMPt2YPTvQt3oAVcd8zrarGqERO8cuGsyxJgOq6XVj1t0ps1C+8XgF/XhgxyPxUh/5iLCWYCrhwyu+0hw9t2RpnfI+2WM1ZTx5VkR7X5BIgZfnye6u9VFSk+jUgpKbFLaKONbqebpSyFYDWk9oJrQq4TQl6q9EvmX8L80vk0DzTzq62/4i/P/MuTPHoFeBqm02kikcisuMLnVEPpV/woDYsbpV/xozQsbpR+xc+whrquTiopFMeCOh4WN0q/4kdpWNwo/YqfYtHQ0ASOlAzbS44EnxCEjyGa+XjjJtvJv3wfmFlAgJXDfOV+zK2/RwuXIUJxRCiOFipFBONDz0sRoVLwhaakx3Tr1zeUOr08OHUjy8304ex/Fbv1NWRmoNCuxWrQ567GmHMWIhA95rHNRk7m50/oBnrN6eg1p3v13nv34LRvwWnfgsynsFs2YrdsRBh+tJrTiVYvIygqwMoSsvrJ+MqQgSirQymMvr1eOvS+ZvK2NfZ1wqVDBvhi9MqFiODsumD2YA39ukZDLERDLITtSnqzJl3pPN1ZE9NxaRvM0jaYRdcElWE/1eEAlWE/voMyBvSlcnQlslTHQ5RHj+3idynlOGM777jkbAdzVLt5kNk9ZhtjtgdCgF8TGJpAANmMg+24WLaLqWsEDX1GH5+nijLFpxHXdXF6HKiEbmeodoLQIDwHUnsg3QrBWhACTWhcf+b13PLMLdz/+v38+Rl/jq6uqDrpuK5La2srTU1N6mRkEaL0K36UhsWN0q/4URoWN0q/4mdYw4NTkSsUiiNDHQ+LG6Vf8aM0LG6UfsVPMWjouJK3elP4NQ3TdRkOvDytPDojU/PKXBJr93rs3c+OGOK67jlJ0gHHRGb6kZl+AJwJtiEMHyJY4pnkBxnmhcdGYFr1c1xJYtgUD/kOPUfbxGl/E3v/BtzevSPj9gXRG87EmHsOIt4woy+0mAnMlM+f0HT0qib0qibkiitw+/d5EeTtW5DZBE7bZmjbzIW2icynRtYLliB1g9E2uAhE0CoXFeqCi3D5rN4PDqWhoQlqIgFqIgFcKenPWXSl83Rl8uRtl85Uns5UHiEE5SGfV4c8HOC5rQd4YP1ODJ9OLm9z3sIqFtaW4LoSZ+jmyqHHQw61y5BTrXk3oQk0TSB0DU0bef8lwKgLjIaeDi2TSAmm6ZA3bUzTIWfa5PM2edMZ026PMtAd1yUcC3L+ynoMQ8O0HDZuOcDb55QRjM68Y/SRoEzxaSZmxwBIuSkyboawFvaiw9P7vNriZh8EKgC48vQrueOlO2hJtvDH5j9y2cLLTubQFQqFQqFQKBQKhUKhUCgUCoVCoThuSCl5syfJQM4i6NO5oKqMrX0pUnkLnz6zUpC76V7s3c9i738VXAfpStANEDoEomBlEUaAwCV/65lS2QFkLoHMJsY+zqeRtoVM9UKqd9LXE74gBEsoM8HKbcEtRJ+PMtL1Q5vboxnIW7hSEjB0whNcbCClxO3fj9PyKk7b60jbHBoI6JWL0eeeg1677IheczqZzsjaE0VfKk9zb46KujxV8ZlRMldoGnrFAvSKBcjlH8AdaMVp34LdshE9tXvIUBUIJGT7IF6PXtWEVrkQvXIxIlYzq03wo0UTgoqQlzb9dBklmbfpyuTpSudJ5m1aE1n29qbJWw6JdJ73rFmMEOA4kl37++hr6SMYMAj4Dfw+Hb9fx3+IGuWSoQtuXAmuREqJaXnGtmna5K0Rkzs/3GY62Pbk0eLj5qQJNCGQSJpbB+juTRGNBJCOSzpn05XIFc1ncTKUKT7NGNKgVCtlwB2g2+lmnjYPNANC9ZBpgXRLwRQP+UJ8bMXH+MmrP+HeTfcqU1yhUCgUCoVCoVAoFAqFQqFQKBSzll396UIk5arqEspDfurNALtMm96sSWPJya9H7CY7sHY97UXTDoVcauXz8DW9AzeXxHrtQaSdQxhB/KuvRS+fd8jtScfyzPGJDPPsgHdv5ZBWDqwsAdPCzXfiMt4cE/7wqOjy+PjI82BJocZ3X9YzucuDPmQuiZvuQUQqEUJgt27E2f8qbqp7ZNuRcozG1ehzzkILl03X23lUrN/azk8e30o6b+PTNd53ViMr51WgD0XLamLkXh9zD7qmDS3zHgtBYT1dE+hCIIb7D607nWPO5EzCG3r563cvY82yumnZ9nQhhIZeNhe9bC5aVRP5p78LCISUXtZj6eI/7xMYVYtP9lCLBttx2dc9yK6OJLs7kuxsT2BJSWV5mMryMCWxANXlERDehSg+n2BFUzW25WCMuhBoeDcUANK716T09m0JugBdaBgCfJrAEAJd1zBG7ceGPvTZEAJD1wqfgTH7/6jPy+h2behzAd4FKZ//9+fJ5h1c0yGdtwkFdKrjxW2IgzLFpxUhBH6/nyq9asQU9w19IYYbINMKVgKsJPi8OgsfP+Pj3PPaPWw4sIE3u95kRfWKkzgDxbCG6sqn4kTpV/woDYsbpV/xozQsbpR+xY/SUKGYHtRnqbhR+hU/SsPiRulX/MxkDVsHs+wdyACwvDJGecirc10R9LOLNH1ZCynlSRu7078fe+fTOB3bCm16dRNG0zvQKxZ4zwG9egky3YuIVKCF4ofdrtB9iGglRCsn7SPtvJfWOtNP//6dhGMBKBjnCWRuwIs2NzNIMwOJA5O8GIhADBGKE3CCzNEi1OpZsu0bwbU8k98XRhgBr7vhQ687A71xNVrFfIQ4+dH6fakcP3l8K/2pPFJKshJ++eIentlyAF07DuMTjDfXxxjvI0a7rgk0GNNH1wSm7bJhdze266ILSGUtfvL4VpbPLZuxkbVatAphhJB2DvxhL/OBL4QWrTrZQzupHO4Ymsya7O5Isqs9wa6OJM1dg2NSj4MXcW3YLpWaTpnfx8BQmQhdEziuiyYEjWVhaqJBArrm3Qyt8PhkH7/Lo0E+fdky7npiKxnLIRTQ+fRly2bsvnwkCCmlPHy3k0symSQej5NIJCgpKTnZwzksu8xdPJ99nmq9mvdF3zeyIPEW5DohUAmlywvNX3rsSzy842E+tORD/PN7/vkkjFihUCgUCoVCoVDMFHK5HHv37mXBggUEg8X/o3M2MZE2xfZ79Wg5VeapUCgUCoXi+NCbMXmtM4GUkkVlERaVRQrLpJT8cX8vtuNyXn0ZpcETl65bSonbsxt75x9xevZ4jQL0uhX4Fl+KVtpwwsZyKKSUYOdGIstH3bujotBxhyuaSzKWA9IlZA4MRbwLGKpTrDWciW/Bhej1KwsG+UxhW2s/X37gJRzHRRuKcnVcyRlzy4mGfF795aHay3KoDrM7qiazO6bNq48sJbju8bPC8rZD72AOgWeqyqEaz2uW1fP+sxpZMbd8TFTwTMHe/yfMVx9EOnmEHsC/+lqMueee7GHNGFwpae/LsKvDM8B3dSToGsiO6xcJ+lhcV8Li2jiLa0uYXx3DP1SyIGc7PLqri6xlY1oOfp9OyGdw+eJqghOUNZhJeCUMclTHgzPeEJ/q71UVKT6NyFQ7qQNbqKqbD0Cv04sjHXQxtGNHGj1TPN8DdhYMLxXM9auu5+EdD/O7nb/jixd9kdpo7UmagUJKSSKRIB6Pn/SrcRRHjtKv+FEaFjdKv+JHaVjcKP2Kn2ENA4GjOCmV7oDkPiiZB5Fj/z0hhOAzn/kMP/zhDwFob29nzpw53Hzzzdxyyy3ccsst3HPPPVRWVmLbNt/61re44ooruOWWW7jjjjvYt28fZWVlpFIpVqxYQXNz86Sv1dzczCuvvMI111wz4fJLL72Urq4utm7dWmhbtmwZ1dXVPP300zz99NPceeedrFu3btx6HR0dBAIBotEo//7v/85pp512zO+NYuajjofFjdKv+FEaFjdKv+JnJmqYMm1e7/IM8bpokIWlY2stCyEoD/roSufpy5onxBSX0sXt2Ia162nc/tahgWgYjWdhLL7kpEXLTqafEAJ8IYQvBCUTp+SW0oV8GplL0D/QTUtnB6XpVkJdf8ILIRdeqVfAv/yD6JULT8SUjpgtLX2eqQ3EQwaWLSkJ63zpyjOPyZiTcsQkd4dMcvewpvqQAT/8uNBnaDuuZ5wm0iY/eXwrecvB0ARZy0EAO9r62d2eIBwwOGdxFRcsqaGpLj5tKduPFWPuuWhVR5b5YEYwzb9/h8lZDns6Eryxt5MDCYs9nUkyeXtcv/ryMItq4yyqLaGpLk5NPDTp8TZo6JzbUMqmzgS238DQBatq4jPeEAcvYnymm+FHijLFp4sd6+CpzxPJ9kGogsBH/w/5QIQ+p48qY+gL1IiAvxzMPq++eMkSAJZXL+e8hvN4pe0V/nPzf/LFi754EidyauO6Lh0dHcRiMXR95h+UFGNR+hU/SsPiRulX/CgNixulX/EzrGFjY6PXICVY469CH8fO38BzXwErA74wXHw7NH3k0OsYwZGiZRNQXl7OSy+9hOM46LrOunXrWL58+Zg+X/nKV7jxxhvZtm0ba9asoaurC4B4PM6dd97JzTfffPix45niDz300KSmOICu67z55pusWLGCN954A8OY2k/ZdevWsWLFCu666y6+9KUv8fDDD09pPUVxo46HxY3Sr/hRGhY3Sr/iZ6ZpmLcdXutIYLuSsqCP5VWxCc2jipCfrnSe3pzF8bRqpeviHHgde+fTuIPe369C96HPPQdj0ZqTXkv7WPQTQoNgDBGM0eOU0u02EGpYiUhs91Jk+0JeimwjiIhUHKcZHBubmnv4/cYW4mE/tuNiu0xb6mYhhFeXWTs+nwu/oRVqipdHA3zk/AXYjsvLO7tIpE2e3dLOs1vaKYsGOL+pmvOX1NBYETnpF69ooTgUixkOnhf39BfAzoARhkv/BZasPeLNSCnpS+XZ2Z5gd0eS3R0JWnrTOK6LZVr4/D4EAr9PZ0F1jMV1XhT4otoSIoEju3CnIRaiIuQnYzmEfXpRGOLAcbv44GSiTPHpIN3hfQizPeA6iFwfVftfpXXR2+h2ukdMcfCixc0+yHZCZD7oXt2U68+8nlfaXuHBLQ/yv879X4R94YlfS6FQKBQKhUKhUJwaODn4+QWH7uPakNwP0gWhQa4fHv0EvHBzIQpkQv5yk3dSbBKEEKxZs4ZnnnmGd77znfzmN7/hox/96IR9ly5dimEY9PT0APDpT3+ae++9l5tuumlc3+985zusW7eOfD7Pddddxxe/+EW+9rWvsWXLFlatWsWNN97IX/3VX41b75prruGhhx5ixYoVBQP9iSeeOPR7M4q3v/3tfO9735tyf4VCoVAoFArFseO4ko2dCXK2Q9hnsKpm8gjZipBnMg3kLBxXomvTaxRKx8JpeRVr17PITD8AwhfAmH8hxsK3IQLRaX29k01/zgQgXlqBf/W1XopsO4cwgvhXXzsjI4LbetPc/fg2kPC+s+bygdVz6U4WR+pmgDXL6ji9Ic5rW3Zw9vIlVMU9j2fthYvYcWCAF3d08tqeHvpTeR7d2MKjG1uoLw9z/pIazm+qpqpk8t9npyxSeuZ3tgeyvdC/A578nNemByDbDY99GgZ2Q7ja89v0AGh+MIbuh9ocYdCecNjbZ7KnJ8fu7hy9aRcbHza+wkXj5dEAFQEfq0+fy5L6Mhoro9NyPArmuwkOG8xGERjM03TxwUxDmeLTQXKft2MEy70Pp3So6m2ldYFFt9M9tq8vDkYM7EHIHoDofAAunX8p8+Lz2JfYx2+2/YaPr/z4iZ+HQqFQKBQKhUKhKC5cC68uoDb0I17znrvWoU3xKXDNNdfwwAMPcPrpp+P3+6msrCwY36N5+eWX0TSNqirvYuCysjKuuuoq7r777jEG92OPPUZrayuvvPIKruvy7ne/m8svv5zbbrttwvTno3nf+97H//pf/4tbb72VP/zhD/zwhz88IlP84Ycf5owzzjiC2SsUCoVCoVAojgUpJZu7kiTzNj5d4+zaOL5D1FQOGV70ZM526M+ZVIanp861tPPYzS9h734OmU8BIAIRjIVvw5h/gZeOfJZhOS5J00v5XB70FUWK7FTO4vu/f4Oc6XBaQykff/tidE2jIjbzzfDRlIt+ThPbKRdVgGeK65pg6Zwyls4p4y/e7vDGvj5e3tnF6829HOjL8JuX9vKbl/ayqLaEC5bUcM7iKkpC/pM7keOJlGClPJM72wO5ofth47tw3+0ts3Mj69pZyPcDwvvNKyU4Jmz610K54mFcKTFtF8t2MR3v3gCahm7DGLrAb+gYPj/+QBjd8JPNuoR2xhF7AxOY7ENtun/UbVQffbQZP7TswIvw+o/AyYMehLP/Fhov9S5ulxJwhx4PPZfuUJscaWfU43HPR2/j4H5HsW0zCZt/Arbpjd9JeAZ5w8VFHzGuTPHpoGSed6WElQKhg2tR1d8Bmm+8KS6EFy2e2AqZNu+x0NE1nU+c+QluffZW7n/9fj52xsfQxOR/JCiOD0IIIpGTn7JEcXQo/YofpWFxo/QrfpSGxY3Sr/gZp6Ee9CK6D0W6Ax66BMw0+Eu8H6/+CFzzzKF/rBqHP7l00UUX8bnPfY5f/OIXrF27llwuN2b57bffzj333EMsFuPBBx8cs+/ddNNNrFmzhuuuu67Q9thjj/HII4+wfv16AAYHB9mxYwfl5eWHHUswGKSpqYkHHniAJUuWEAxO7eTY2rVrCQaDLFiwgB/84AdTWkdR/KjjYXGj9Ct+lIbFjdKv+JkpGm7vS9OdyaMJwVk1ccK+Q6cLFkJQEfLTNpilN2sdsykuzQz2nuex976AtLy/Y0UojrHo7Rhzz0EYM9N0nA79+nIWSIj4DQJDaZpncopsx3X54aNb6EnkqCwJ8pn3LkfXitAb2bEO7ekv0JhPob0RnTCy1m/orF5UxepFVaTzFq/t6eHlHV1sa+sfSuGd5OfP7WJZYxkXLqlh1YJKgof57MwIpARzcMTczvWONbkPNr6d/JFt3whBqBJ8Ecj1I10bRw+iO3mE4UcuupK0rZEYHCSZTpNJp7HyWQxsDCwMTAxh4xc2EZ8kpDsENO+CnZEgcAlOGumk8dk2DCam5705OLubTMLz/wglc4/5Qvbjhp0FMwUIkDYEK7zA4OR+ZYor8HaCS/8F8fQXEK4FlkOlBUIzSLtp0m6aiBYZ6R+o9E5wOTnIdkC4AYCPLP0I33v5e+xL7OPp5qd554J3nqQJnbpomjZSw1FRdCj9ih+lYXGj9Ct+lIbFjdKv+BnWsGA+CwG+w5i/pQvg0juG0pqlwD908qV0wTGPRwjB29/+dm6//Xa2bdvGz3/+8zHLh2uKT0RdXR2XXXYZDzzwQKHNdV2+/vWv84lPfGJM36effnrM8xtuuIGNGzeyfPlyfvrTnxbar776aj75yU9y7733TnkOwzXFFacW6nhY3Cj9ih+lYXGj9Ct+ZoKG+xMZ9icyAKyojlEanFr93YqQb8gUN4/6tWU2gbXnOZx9LyNtCwAtWomx+BL0OasQM9WEGmI69Osbev/Kp/i+n2x+tn4X29sGCPh0/vb9ZxALFce4x5DugD/+HSKfQBcGZLrhsU9B92YIVYDmG4ke1nyg+4hoPtYE/axZ5WNwmWTrgRSbW1K09ufp3WvwX3sN/tsX4PS5VaxaVMeyudXoxlCEstAL6b6nZewT1Y2WEvKJiU3uiYxu1zqy1/VFPKM7WOHdhyoOej7qNiqjw7Yn76Zq0z/gs3OYxPhD9O94uXkNmZw9dvt+qC0Ls6i2hKbaOIvr4tSUhkZKOEjpGdZO3os2d/LgmgjHxDe6bcxjc0xf7PzI49HLRq+TaofBVtB9eCazC9KBSJ2XfVpoI5nfhAaMeiw0bx2hjWhe6D962SEeH822zRS8+l1v/EbI8zL9JZ6RX+TM7G+AYmLJWty6i0juf5X4q/+Iz8pQlrfoCxj0OD1jTXEhIDwHBndBphVC9SAEYV+Ya5dfy92v3c29m+5VpvhJwHVd+vr6KC8vRyvGq9FOcZR+xY/SsLhR+hU/SsPiRulX/AxrGA6Hj2zFJWu9NGbJ/d6P1Gm8cvuzn/0sZ555JhUVFUe87pe//GXe9a53FZ6/5z3v4bbbbuPqq68mHA7T3NxMWVkZsViMwcHBQr/JTO/3ve99/P3f/z2XX345O3fuPPLJzDL+9V//lX/6p3+io6ODM888kx/84Aecd955k/b/5S9/yc0330xzczNNTU185zvf4f3vf/8JHPGJQx0PixulX/GjNCxulH7Fz8nWsDuTZ3ufl6a8qTxKbWTq6a/Lh1JGp0ybvO0SMKY+fjfVg737WeyW18B1ANDi9RhNl6LXLkcUyf48Hfr15zxzsrwIUnA/9UYbT795AAR8+t1LaaiIHH6lmYLreKZ369Ow8zeQ7kAiPP9HSoRpwpb7xqX1nogYcP7QzQ5KcpZN1nRwLAlvAW9Bj4CgTyfkN/AZGkLzDRnsI0b7yPOJ2nwjt+Hn/dth7+89Q1voUHsu+GMjRrd0juw98UVHmdmjzO2JjO8plC6QUtKXytPS2kNLb4qd7QmeemMOMedfqNG66HSrSeTLqS4xCfoNFtaUsKjWuy2ujRM91IUhQnjvjz62T+EzWDVNx9B0B/z0fC/TtD8OZsJ7n65YN7OjriuWjtQU95V4F9/P5PFOEWWKTyMyXEOH3kTJ0r9EvPETqtrfom/+CrqcLub55o3tHKqF1D7vCot8NwSrAfiLlX/Bv2/8d15pe4UtXVtYXr38JMzk1EVKSU9PD2VlZSd7KIqjQOlX/CgNixulX/GjNCxulH7Fz7CGRxUZEqk9Lj9Qm5qaaGpqOnzHCVi4cCEXXXQRzz//PACXX345W7du5YILLsB1XUpLS/nVr37FypUrsSyLVatWceONN46pQz6aQCDA3//930+47He/+x1z5swpPH/uueeOaszFwoMPPshNN93Ej3/8Y84//3y+973v8d73vpft27dTXV09rv8LL7zAxz72Mb797W/zwQ9+kJ/97GdceeWVvPbaa7Mykl4dD4sbpV/xozQsbpR+xc/J1DCZt9jclURKaIiFmB8/snrdfl0jFjAYzNv05Uzqooc31N1kB9bOp3EObB6qqwtaxXx8Te9Aq2o66Wnkj5Rj1S9vu6SG6omXzfBI8W2t/fz8uV0AXHXBQlYtqDzJI5oC+QS0roeWp6FtPeQHvHbXhiFD3NHD6NLyjOelf+nVmXYtcKyhe9O7H/14VJvhmERdi4hjYZo5stksZj6LKyFrOmRNB00ThHwWQb+OTz9K43ZcWm8XWp4Zn9bbXzLWzA5XjTe5h5cZR1/2wHZcDvRnaOlJ0dKTYn9Pipbe1JgI8Lzt4LguCVFGSqvAMAR+4LpLT+OS5XXTknZ/2o+hQ5mmefoLYA16hngxGMzH8eL7k4kyxY8D8vQ/hy3/RtWBN9je2ESP3TO+k9AhXA/pfZBugUAVCEFttJb3N72f/97x39y36T7+6T3/dOInoFAoFAqFQqFQKE5penrG/4YZnSr9lltumXC9g9tHpz8Hr9b4TTfdNG69p556atKxHJxeHWDFihWF9ksvvZRMJjOl9WYLd9xxB5/61Ke44YYbAPjxj3/MI488wr//+7/zla98ZVz//+//+/+4/PLL+dKXvgTAN7/5TR5//HHuvPNOfvzjH5/QsSsUCoVCoZidZG2HjZ0JHFdSEfKztDJ6VIZ0RcjPYN6mN3toU9zp24+962mcjm2FNr3mNIzFl6JXzD+aKcwK+nJe6vRYwMB/tGbpCaBzIMMPH92C60ouOK2G9501Q8s2SAn9O71o8JanoWujZx4P4y/xjMPGSyHXDy98HfKDEKycsKb4kSCAwNDNcVzeau1lw442Nu/twDbz6NJGz9s0lPo4a16cMxtLKA1pQ+a7OWLCD5vuB5vx/TvhjbuGosr1oXTiJpzzRZizZsj4rvDM/Wkmnbdo6UmPmN89Kdr60riuHNdX0wR1ZWHmVkYpjwZ46IU9WI5DNOAjnbcJBXRWLaiY2XXol6wlXb+awdQeYtGFRKLHXu7sRJAOxRgMzCWmxSiiHA6HRJnix4NwFSz8IFVtT0A+Sa/eiyMddKEf1K/BM8TtFFgJ8JcCcP2q6/nvHf/NIzsf4YsXfZGaaM2Jn4NCoVAoFAqFQqFQKGYcpmny6quv8tWvfrXQpmkal112GS+++OKE67z44ovjLkZ473vfy29/+9tJXyefz5PP5wvPk8kkAI7j4DheGkUhBJqm4bouUo6cwJqsXdM0hBCTtg9vd3Q7eCkMp9Ku6zpSShzHwXVdHMcpjEVKOab/kY79ZM9porHP1jlNpF+xz2k26nSoOQ1r6Louuq7PijkdbXsxzulw+hXjnA7XPtvm5DhO4fGJmpPturzWmSRvu0T8BssrI0jXxTmKOZUFDJqB3qyJbdsFY314LHbXDuxdzyB79wxvCL3uDLSFa9Di9YX3YKbrNDyWg/e90fodjU69mTwSKPV71s9MmNPodsdxyORtvv+7N0jnLRbUlHDdJU0TzintpknYCc+QGypPe0J0MtPIAy8iWp9FtD4DmQ4EUOhZuhjZ8HbE3HcgalbjjPJxnYZ30vbWizQuexsiWgejNDzWfe/0OeUsbazAds/g9b09vLSzizf29dGbcNm8WSA2p1lcV8J5ixtYvbCKWMh3aJ0yncidv/LSevtiXlrvQBmi6SM4wapRkzr6z5PtOPQO5jwDvDdNa59nhPcO5kb1FoX3N+TXaayM0lgRobEyyryqGLWlIQxt5AKbiliAe558i4xpE/Tr/NU7Tyce8iGlnJZjuZSy8H14JDo5joOLi4WFg4MjHEzXxHIt9tn7eNN6EyfgoNu7OC19GrV67dDMh7YjvO0M72gCgdAEAjFhuya0wliGt6MJrTCHMe2ahsB7bwqviSjMacz7JTQ0obHf2s/G/EZsaeMXftaE1rAksGTGfucevN5kKFN8GhFCEI/HvS/q5dcT2/Vbgpk+csFy+pw+qoyqsStoPi+NevYApPcXTPEV1Ss4p/4cNhzYwE/f+Ck3XTg+kkJxfBijoaLoUPoVP0rD4kbpV/woDYsbpV/xozRUTIWenh4cx6GmZuzF0zU1Nbz11lsTrtPR0TFh/46Ojklf59vf/jbf+MY3xrXv3r2baDQKQDwep66ujs7OThKJRKFPZWUllZWVtLW1kU6nC+21tbWUlpbS3NyMaZqF9jlz5hCNRtm9e/eYkx0LFizAMIxxNeSbmpqwbZu9e/cW2jRNY8mSJaTTaVpaWkin0+zevZtAIMDChQtJJBJj5huJRGhsbKSvr29MZoSZOqfW1tZCu9/vn9Vzam9vL+gXjUZnxZxmo06HmpOUknQ6TXd3N/X19bNiTrNRp8nmNKxff38/1dXVs2JOs1GnQ81JSonP55lhJ2JOEmgnRFYYRIIBFocEzbt3H/Wcyisq0IQgnTPZums3fiRISZ2RJND+J/Lde70s6UKQLW0idsblhKvnsWPHDtyukfHPdJ1g4n1PSkkwGEQIwa5du45YpzbCWGhk+wahqmRGzAlG9r2dO3fxiw2d7O/OEQvq/PW7lmBoYtycxHzBc7nnyDt5dKlz2uBp1Fv1x00nI9dJjbmN2MCr2PufQ9ojpq3hD6PPeRtdgWUMxs/GHiqFOyc2h6ims3vHjoJOUkoiDRciwzXsOo773oIyndhCH2saq3irM8P2bov9/Xneau1j674e7n9qG4uqQlx4Wi2XnLmQro4DE+hUS9fSL1O++VuI3ABSC2FfcBuhSO2YOcHU9j3LcelOWXSnbGxfCXs6BmjuHMC0h91cgd/nw3EdHNshHjaojflorIyysqmRqGZiZxJDv4dd4nFBXVWM9vb2Mfve8ppKbr/hLF7fv5MSx6DSGGTnzsEjPpbPmz8PDNixZweO8ExsB4eahhqSJUmeb3u+0C41SbwyTsbMkEglRtp1iS/kI2fnyNk55JBzrWkahmHgOA62Y5PTR5ZZWGzOb2aHs6NgUOu6jq7r2LY9ZoyGYaBpGpZljTGQfYYPoQks0ypsFygc+0fPf3h/klJiWVahTSDw+X1IV2LZo9qFQDd0BuXgyAU6rsPTqadp8DWQ7cvOyO/cVCrFVBBy9Ds5Q0kmk8TjcRKJBCUlJSd7OFPnd3/JUzVBWuYs59zS97AssGx8HzsLva94j8tXe/UEgMd3P86Nv7+ReCDOM9c/Q8h3ZPVXFAqFQqFQKBQKRXGSy+XYu3cvCxYsIBg8fB1FxYljIm1O9O/VAwcO0NDQwAsvvMCFF15YaP/yl7/MM888w8svvzxuHb/fz/3338/HPvaxQtsPf/hDvvGNb9DZ2Tnh60wUKT58UnF4njMlKmCYmRLhpeak5qTmpOak5qTmdKrMyXEctvelaU3l0YXgvPpSYn7jmOf0WmeSvqzJktIg9YM7cHY/g0x1AwJ0A73xHPSFFyNCpUqnUe0Z0+K5tgEEcEljGQGfb8bN6RfP7eSx11vxGzpf/vBKFtTEC3NypUtKpmh32nk2+ywWFhoaEokPH+8PvZ+GQEMh4vWY5oSL27EBWp7xosET3oUchYjwSB2y4e3IOZeg1V+I8Idn/DFiIGPy8o5OXt7Zxf6eYYNQEPDprJpfwfmLq1jWWIquaWN1SrXD4H6IzUWL1U9pTsmMORT5naG1N8X+7hQdAxncURHK3iOJoWs0lEVorIwwtypGY0WE+rIw4YBxeJ0maN9l7eK53HOY0kQXOst8y6jSq3CEg42N6ZrY0saSFvbwP2ljyqF2LGxp4zC1yOKjQUfHJ3wYGDg4dDqd6OhD74vExWWePo+wFh4xtcVQlPook3vIMx/fPrRs9L4hkZP3H9qxJSPtElkYz/D7O7wNU5p0O91eVDoCP35c4fLh6Iep1qpn1HGvsE8mk5SXlx/2d7mKFJ9GXNels7OTmpoaT5AVN1C16we0VCfpsg5MbIobIa+eeL4bMq0QPx2Ady54J40ljbQkW/jtW7/lY2d8bPy6imlnnIaKokLpV/woDYsbpV/xozQsbpR+xc+whvF4/GQPRTGDqaysRNf1cWZ2Z2cntbW1E65TW1t7RP0BAoEAgUBgXPtwFMNoJjvmHGn7wds9mnYhBEKIccdDIcSE/adr7Md7TkfSXuxzmki/Yp/TbNTpUHMa/TfJ0Yx9Js7peLXPxDkdq34zcU7H2l5sc3Jdt5Al5njPqTVl0pbKIwSsrC6hJOCbtP9kY8mSZVAOjkmRXeEXaPs3EnlrA7btmXvCF8JYcCHGgosQgehxm9PJ3vdG63ekc0qYLgKIB3wEfL5Djv1kfJ6ef6uDx19vQwB/+a4FBCszbDXbGHAH6Hf6SbgJbDlkYOJFkLq4SCR58jyWe4ygGaRCr6BSr6RKr6JCryAiIlObU7YPWp/16oO3PYdmDo56Q3SoPgsaL0U0Xgqli8dl8JrK5+lo9ZsOncqjQd539jzed/Y8DvSleXlnFy/t7KQnkeOVnV28srOLaMjHuYuquGBJDYtqS9A0jT6tgi7CVGshysVI5DKA40q6Etkxtb9belMk0ua41weIhfw0VkaZO3RrrIhSWxaaUr3vieZkStPbP9x+BpwBup1u9lp7cfHSgJvS5DXzNWIiVkgdfqRoaBjCwCd8+PCMbCtvURIq8dqEz1uOr9DPwDjkstFjSbtpHkw+iClN/MKPKU2CIsg7o+8sHPNmGhON2Y+fmBabsd+5ky0/GGWKTyNSShKJBNXVXvoMGi+l6q0fgXTpTm2H6GUTrxiZ45niuS6Izgc9iK7pXHfmddy2/jbue/0+rl1x7VF/qBVTZ5yGiqJC6Vf8KA2LG6Vf8aM0LG6UfsXPsIZFlR1LccLx+/2sXr2aJ598kiuvvBLwTr49+eST3HjjjROuc+GFF/Lkk0/yd3/3d4W2xx9/fEyk+WxCHQ+LG6Vf8aM0LG6UfsXPidKwM51nR59nWJ9WHqU6Mv5iusOx09zJ+vQzWNLCJ3xcHLyIBW3dVO1aTyztpeEV0TjGwosx5p+POAWyqR6Lfn05z6gsD/mme1hHjS1t+p1+3uo9wO8732LOBXlq62BXZD+70uP760InrsUxbRMXFx8+LLzUzgERwJY2nXYnnfbIBZ8hEaLSqBxjlPuFH6SE3q3Q+gy0PA3dm2F09GygFOa8HRovhYY1EDj232Ez5RhaXx7hI+cv4Mrz5rOnc5CXd3byyq4uBjMWf3zzAH988wAVJUGqS4K8tqcHy5UEDI0rzp1PVUlwyABP09qbwrLd8S8goCYe8up/jzLB42H/UZUDc6RD0k3S7/QXDPABd4CUOzYlti3tQoSzjmeCSiSVeiUlesmUDOvhZcPtGtqYMTuOw862nTQ1NU3ZaD0UES3CmvAa1mfWY2F59bnDa2asIQ7FOeapokzx44nQqGz8MJrcScYeIO0MEtFj4/v5SsAXBysBmTaILQLgqqVX8f2Xv0/zQDPPND/DOxa84wRPQKFQKBQKhUKhUCgUM42bbrqJT3ziE5xzzjmcd955fO973yOdTnPDDTcAcN1119HQ0MC3v/1tAD7/+c9zySWX8C//8i984AMf4Be/+AUbNmzgrrvuOpnTUCgUs5Sc7ZBBJ2c7RKbhZLJCoZh5DOQs3uhKAtBYEmJuyZGb1Wk3zbODT5J3s7iAJXI8nXyU0l29RE0HKxCno+pcFqxYQygSnuYZzD6klPRlPfO4POg/4a/vSpdBd5B+t59+p78Q/Z1yU9iuS28+R+l8CPh0whE/AkFMi1Gml1GqlVKml1GmlRHVomhC8y6YGDLkQoRYE17DIt8iEm6CHqeHHqeHXqeXfqefrMzSYrXQYrWAlAg7S0lmkMruvVT17qcy0UtZagANCeVLPRN8ziVQtRK02f09JYRgUW0Ji2pLuPZti9nW2s9LOzp5bU8PXQMZ3tzfh+tKDF0wmJHc98ftVJcEx0R2+306cyoiNFZEvRTolVHmVEQJ+I78vZNSkpKpMfvIgDNA0k3iMoH5DoS1cGEfCRHixdyL2NIeiWAWft4ZmblR1wBN/ibqjXoG3bFZMWYyxTjmqaBM8eOMsfijlO26md5IjO6Op4g0fHjijpFGGEhAth0ic0HzEfFHuGb5Nfzbxn/jvtfvU6a4QqFQKBQKhUKhmBA3m0CmexCRSrTQsadfF0Lwmc98hh/+8IcAtLe3M2fOHG6++WZuueUWbrnlFu655x4qKyuxbZtvfetbXHHFFdxyyy3ccccd7Nu3j7KyMlKpFCtWrKC5uXnS12pubuaVV17hmmuumbTP/fffz6c+9Sk6OzspKysD4Le//S3Lli1jyZIlANx33328//3vP+lRGSeCa6+9lu7ubv7xH/+Rjo4OVq1axaOPPlpIdbt///4x6eguuugifvazn/EP//AP/J//839oamrit7/9LStWrDhZU1AoFLOUtsEsmzqT2ITpbO1nZXUJDbHZH9mpUJxKZCyHTZ0JXCmpCgc4vSJ6xJGh0jbp734NU8tg6wIhvPjdnKHx+5WVLNLn4sjl5DKllJiS0tnhxRxXsrZLznYQQlAaPH6R4lJKsjJbiOYdNjcHnIEJazRLCf0DLqm+MEEnxrvPWEa1v4JSrRRDTG5PTWbIlelllOllNNEEeJHDfcktdPe+TE9uHz2GRSoYJgEkqmrYXV0HRgjdCFPhr6fC30CVUUWlXklUaBx5THPxomuCFXPLWTG3nLzl8Mir+7j3j9sBiZSgDb0Z86pjrJxXUUh/XhUPoWtH/k7l3NzIfjLq3pb2hP39wl8wv0u1Ukp173FAjM1C4df8RRnBHNEiRTHO0RTjmA+HMsWnESEElZWVY/8I8IWp9NXTyyBdPc8xfzJT3F8OehiczIgxDvzFyr/gvk338VLrS2zr3sbSqqUnYCanLhNqqCgalH7Fj9KwuFH6FT9Kw+JG6Vf8HKyhlBJpT1yrbTR2y6tYm9YhbRNh+PGtWovRuPrQK+m+Q+4r5eXlvPTSSziOg67rrFu3juXLl4/p85WvfIUbb7yRbdu2sWbNGrq6ugCIx+Pceeed3HzzzYcdO3im+EMPPXRIU/zBBx/k3HPP5Te/+Q2f/OQnAc8UNwxjjCl+zjnnnBKmOMCNN944abr0p59+elzb1VdfzdVXX32cRzUzUMfD4kbpV7zkbIfXOhJYrgShkbddNnUmKA34iPjVKchiQX0Gi5/jqaHluGzsTGA6LrGAwRnVJVN6HZlL4vTtwx2+JQ6g6ybOefVITaA7LlIIpJC4wQi79RS2+xK2bpCy6gnbS6nRa06J8qJHq19f1vvdUBowjsrAnAhLWuMMzX6nn7zMT9jfEMaImamVEddK+e0znWzdniAW8vG3V6+mIhSc8utPasg5FnT8CVqfxmh5murkPkb/AsiWL6JnwdvpqWmiJxqlx+3DlCZdJOkyk2wztwEQFEEqdS/t+nD69YMN2COlWI6hAZ/OpSvq+c0re8nkbQI+nbzlEA4YfP4DZ1AenbpOwynyC5HfQxdJZGV2wv46OnE9PsYAL9PLCIvwlN634x3BXCwaKo4O9RfpNKJpGpWVlePaq2suZXv//9Cj5bwaFhXLxq8shBctntzupVAPzwGhUR+r5/LFl/PIzke4b9N9fOfd3zkBMzl1mUxDRXGg9Ct+lIbFjdKv+FEaFjdKv+JnWMNcLuc1uBbZ3916yHWk60Cm1wvBQCDNNOaL92C+/hvEIVIBht7/DTAmT6sohGDNmjU888wzvPOd7+Q3v/kNH/3oRyfsu3TpUgzDoKenB4BPf/rT3Hvvvdx0003j+n7nO99h3bp15PN5rrvuOr74xS/yta99jS1btrBq1SpuvPFG/uqv/mrMOn19fezYsYP777+fW2+9lU9+8pO8/PLLPPzwwzz77LP8wz/8A1/96lfZsGEDa9euJRqNsmHDBubPn8/111/Pb3/7W3w+Hw8//DB1dXWHfD8VswN1PCxulH7Fy4HBnGeIAwJwgbwjea61j/pokNpogIqQH02dZJ7RqM9g8XO8NHSl5PWuJGnTJmjonFUTx5jAfJXSRQ524fY14/btw+nbh8z0j+u3q7GKgOWQ8+voLvgch/P2JKg460Ps13rYazYzIDJ0iWYeS7cQFEHm+eYx3zefar161hrkR6tfX24odXpo4r/x0256UhPRlS5JN1kwNYfN74PrOQ8zOvV5mVZWMMGHU58P81+v7OW17Qk0TfDZ962gIjZ1o9UbdAck90HJPBD6SG3wtufBzowakA6153pp0RsvJVQyn0YhaBxaLKVk0B2k2+kupF7vc/rIyRytdiutdisM+fwlWsmIUa5XUq6Xo4uppwkvpmNoeTTIpy9bxl1PbCVnuYQDBp++bNmkhvhE+8mA49X9lqNrtY8ipsUK+8ewAV6ilRzz5/d4RjAXk4aKI0eZ4tOI67q0tbXR0NAwJlVdZaQJUhF6S1zsLfdhvP3/TbyBYDWk9oJrQq4TQt4Jm+tXXc8jOx/hf3b+D1+86ItURapOxHROSSbTUFEcKP2KH6VhcaP0K36UhsWN0q/4GdawoqJi6itJp2CIF/JOSum1c2z18a655hoeeOABTj/9dPx+P5WVlQXjezQvv/wymqZRVeX9TikrK+Oqq67i7rvvHmNwP/bYY7S2tvLKK6/gui7vfve7ufzyy7ntttu48847Wbdu3YTj+PWvf82HP/xhLrroInbu3ElPTw/nn38+V1xxBWvXruWDH/wgAD/60Y+48847x6QEnzNnDps2beIf//Efueeee6Ycva4obtTxsLhR+hUnGcthTyKDwDPEdSTOqMwn7akc7akchq5REw5QGw1QHjx01hLFyUF9Bouf46GhlJKtPYP0ZU10TXBWbZyg4f2tKW0Td6B1yARvxu1vQVq5sRsQoJXUopXNQyufz/64zh73NfxWnrdt2U8olyNmaZSecTVGZDH1LOa84Hn84cAO+kQr0ugmR47t5na2m9sJiRDzfPOY55s36wzyo9HPqyfuRYqXTZA6fae5k/Xpp7GkiSF8rAieSUSLFKK/E05iwtTnACERKqSxHjY241r8kKnPAf60q4uH/7QPgOsuXUJT3VCJJ9cGO+fdnDw4ox7bOe+5Y8L+p+DNe8HJgnQhUAr+2KiBVXp1wRsvgfq3gT866ViEEJToJZToJSxiETAS3Txskvc4PSTdZOG2x9oDeFHNZXoZlXolVbqXdj2mxSb9/iq2Y+iaZXUsbAzQkuqlMVpBQ6wcKSUZmRkX/T2V/eTg9Oc+cfxS+R8vik1DxZGhTPFpREpJOp1GyrFXxcREjFCggqyVoq/3JaozXRCeIKWf0LwI8dQeSLdCsBaEYGXNSs6uO5vX2l/jp2/8lL+74O9OzIROQSbTUFEcKP2KH6VhcaP0K36UhsWN0q/4GdawvLzca9B8XkT3odbJJsg9cTvSzoMvBFYWYQQIXvYVxKFqi+uHPzlx0UUX8bnPfY5f/OIXrF27diSCfYjbb7+de+65h1gsxoMPPjjmxNBNN93EmjVruO666wptjz32GI888gjr168HYHBwkB07dozMdxIefPBB/u///b8IIfjIRz7Cr371K/76r//6sOMH+MhHPgLA6tWrefjhh6e0jqL4UcfD4kbpV3w4ruT1rgSuKykL+sjaDqZlEzB0zqgqIeo36Ejn6EjlMR2XtsEsbYNZ/LpGbTRAbSRIPGAog3yGoD6Dxc/x0HDPQIYDgzmEEJwZ1wj1bMMclQod6Y7pLwwfWtncIRN8HlrZXITPiz5NOkleSv8PAGdEz+H0c69FpnsRkQq0UX+/akJjXqABPVnOnECQeHyQfdY+9ln7yMosb5lv8Zb5FiERYr5vPvN986nSq4r+WHI0+qUtB9Nx0Q6qJ25JixarhScHf48lLUCS00xezr5EbDhaV0pAYqBRKsOUySBljp9Sx6DMFgRte8iwbgbnLbCHjOyCiT3azPYep9MpfK1d/J3MUx3RqNko4E9Dy+XEpuoYXBuS+739SmjevdMN1WfDgsu9iPCKZd6yo8QQBlVGFVXGSABi3s2PMcm7nW7ycqTtLd4CvNrXw5Hkw0Z5UPP275SToi3fRtyJU6KVHPX4jgYpJRKJg4OLiyO9e1e649ocHFzp0mK38KbzJnbQZqMtqEhW4OBgyonLeBnCGJMdYNgID2mhEzrX44n6HpzdKFP8BCCEoDI4j5ZcJ90lZVRv+yms/t8Tdw7VQXqfV1vc7IOAFyVy/ZnX81r7a/z8zZ/z16v/mpBv9hxkFAqFQqFQKBQKxXiEEIhDpDgHELEq/Kv/DPPVB5F2DmEE8a++Fi127NmlhBC8/e1v5/bbb2fbtm38/Oc/H7N8uKb4RNTV1XHZZZfxwAMPFNpc1+XrX/86n/jEJ8b0Pbj+9Q033MDGjRtZvnw53/3ud3nuuee49tprATBNk9NPP33Kpngg4NUE1HUdx5nCCTiFQqFQHBHD0aODeRu/rnF+Qxmu47Bj7z6WzJlHJOB9j5UGfZxWHqU/Z9GeztOZ9gzy/Yks+xNZgoZObTRAXSRI1K8XvamlmD0cKuX0qYCULl2dLST2b2d+qo1qqxN/PsHBdpkIlnjmd/k89PL5iJI6xAQRlra0eSb7DJa0qDFqWBVY5Rmzk1zMWR7y0ZrMMpC1WVbRQIPRwAXBC2i322m2m9lv7Scrs2wzt7HN3EZYCzPPmDdrDPKp0pc1kUhCgSx7BzfQbbbS7fbSTxZLmpiGp4VAIFwXgUtN26s09BygLJWgNDVALJtiOt4tx5WkU3lqXYnf0CgzApCbpLMe8G5GEPQgGAHv3kpBqg30EGg+EAZIGy78R6g7bxpGOTEBLUCD1kCDrwHwvuNSMkW37aVd73V66XV6MaXJAfsAB+wDhXWjWhQDgw67A7vM5o30G6wMrKTOqJvcmB42rScwqydqG15/wsdD90eCK10G5SASiUAgkXQ4HcREDEMYlGglY7IElOqlREX0lPlcKWYnyhQ/QVTr1bT443THK+Gtn8PKv/EiOQ5GMyBUD5kWSLcUTPHLFl7GnJI5tCZb+a/t/8WfrfizEzwDhUKhUCgUCoVCMRMx5p6LVrVkwgibY+Wzn/0sZ5555pGldB/iy1/+Mu9617sKz9/znvdw2223cfXVVxMOh2lubqasrIxYLMbg4GCh37333lt4/KMf/Yi/+Zu/4bvf/W6hbeHChXR0dIxb7+DnCoVCcTA52yFjOYR9eiHtr+LYaBn00qILIVhZXULI0HEEhHDGvcdCCMpDfspDfpZWROnNmnSk8nRl8uRsh+aBDM0DGSJ+g9qIl2I94lOnLhUnj53mTtZn1mNh4cPHmvAamvxNJ3tYx5WDU6GbvfvQcxnmAoau4de0canQtfJ5iFDplIyyDbkN9Dl9BEWQNaE1h017Xh70g4CUaZO3HQKGjiY0GnyecVkwyK1mWuwWMm6mYJBHtaiXYt2YR6VeWfxGnpWFTKd3S3dgZzrotbvpFmladZu+cIB222DXwNjVYvk0+ZJKpBAE8llsw4ffznPJa08QcUZFwmrGkDEdBN0/6vEo03oiA3tUHws/v97QRkvGIVYa47p3noEIRSburwe88k8Tke6An57vmeP+OJgJ8EWhZO5xe3snQghBTMSI+WMsZCEAjnQYcAe8+uS2F0GecBMknWTBYEZ4F4C8nHuZmIidtPT+AoEudDQ0dLzPzvA/XeiYrknaSaOheRdkS88Yvzh8MQt9C4+olrpCUSyovyynEU3TqK2tnbDOQKVeCb4wXRUNyPxziN3/BadPYmyHGyDTClYCrCT4StA1netWXse3nvsW979+P9csv2ZW1UqZKRxKQ8XMR+lX/CgNixulX/GjNCxulH7Fz7FoqIXik0bYHAtNTU00NR3dyd+FCxdy0UUX8fzzzwNw+eWXs3XrVi644AJc16W0tJRf/epXrFy5EsuyWLVqFTfeeOOYOuQPPvgg3/jG2BTyH/rQh1i3bh1/9md/xqc+9Sm+853v8Nhjj3H99ddz/fXXE4vF2LBhw9FPWlH0qONhcXO89GsbzLK5K4kjJfqQgdsQU1n4joX+nMX23hQATeURykNeVPhUNNSEoCocoCocwHElPVmT9lSOnoxJ2rTZbdrs7k9TEjCoiQSpjQYIqQsZxuBmE8h0DyJSOa0XxKljqMeAM8BTmaewpEVIhDClyfrMeuqN+hkfMX4kGspcEmc4DfpBqdAlkrzt4mg+3PgcSuoXo5fPH5MK/UhotprZbm4H4OLQxVN6H/26RonfIJm36c1a1MfGHgd0oTPHN4c5vjnY0h5jkKfcFFvyW9iS30JUixZSrJdr5TPLIJcSzEHPCM50oqXbmdvbjNaTgHQnZDog3UlaWHTHK+kqraK7tJK+eDmuEEAAKYeyI0lJxWA/VZk81ZZGpYwQIcjObf/D+rPehWUE8NsmazY/S+R9v4DY3BGTWju2Y6yUkvuffIsXk1WEwwb/cOXZhErDR7exSC1c+i/w9BfAGvQM8Uv/xWs/yehCp0KvoEKvgKGkXqY02W5uZ33GKxPlipHPUFyLE9EjY8zoMfejzOqJHk9p+ajtjW47nH+UdtM8mHwQU5r48WNiEhAB6o36U9oQV9+DsxshiyAxfjKZJB6Pk0gkKCk5sXUYpgtb2vw8+XNcc4CrHv93ooFa+Ogjk9e9SLwFuU4IVELpcgBSZopL7ruElJnirg/exSXzLzmBM1AoFAqFQqFQKBQnglwux969e1mwYAHB4JGfcFQcPybSZjb8Xp0Kp8o8FbOTnO3w1L4eTNvF0AVSgqEJ3jGvcsZHjM/U1M152+HFtn5Mx6UmGmBlVcm0mEy269KVNulI5+jNWmPqeZYGfdRGg9REAgT0U/NEtZQSmUtg73gKa8dTAIXSKcbcc0/y6IofW9q02W3stfay19zLoPQy0GhohEUYC4sPRz9MrXHyjbmjQUoXOdg1FAW+D6dvHzLTP66fCJZA2Tz2iEr6grX4yxo4p64cXTv6z3jSTfI/qf/BkhZnBM7g7ODZU153Z1+KvQMZ6mNBVlRN7W8QW9ocsA8UDHJb2oVlMS1WMMjLtLKjO3alOyC5D0rmHdqolS5kewvR3YX70Y8znV5N7lG4QtAXK6M7XkVXaSXd8UrSwbDnJQjdi+rWDEJSp8wOYqbCBKnh7XNX4QtXjfccdqwj/eLNDAb8xPImkQu/CUvWHvm8D8HvN+5n3Qt70DTB333wDJY3lh/7RtMdXm3xkrkzwhA/FGMMZuEv3F9bcu2M+v4+mFMxI4ZidjLV36sqUnwacV2X5uZm5s+fP+4qEkMYlOll9PocuivmEG3bAa3roXESYzvS6Jni+R6ws2CEiPqjXL3sau7ddC/3vX6fMsWPA4fSUDHzUfoVP0rD4kbpV/woDYsbpV/xM6xhbe3MPuGjUMx0ivF4qNJ6j3A89EvkbfK2iwQsR+LTBY6UZKzxKb5PNq50ycosKTfFTnMnW8wtONJBFzpnBM5gnm8ePnz4hA+/8OMTPgyME5pN0JWS17uSmI5L1G+wvHKsIX4sGhqaRn0sSH0siOm4dKbzdKTz9OdMBnIWAzmL7b2DlAf91EYDVIcD+GaxQS5zSS+V9UAbbqLNe5xNQqbXiy7VfUjAfPVBtKol0xIxXozH0GPBlS6dTid7rD3st/ZjypFq2Rpaod5vSqaIiAgxLXYSRzs1nHQ/bbvepH7BEoSZLqRCd/tbkNZBxZ2FQCupGZMK3Q3Eea0zwUDOIuTTWVVbdkyGuCMdns08iyUtqvVqVgVWHdH65SE/ewcy9GZNpJRTMrENYTDXN5e5vrmFix2arWZa7VYG3UHeyL/BG/k3KNFKCgZ5qTa1FPDsWIfzx5uQZhrhC6Kf/XmoXDHe6M50epHe8vB1nnO+AN1V8+mqnOsZ4aEQ0hcYMr89E1wIgzK9giqjimq9miqjiqiIsi+ZZYeVoiwcwBeZ5BiwZC2RhouJHCeD+fXmXta9uAeAP3vboukxxMEb5ww3w4eJaBHWhNewPrOevJMnoAdYE14zow1xgCZ/E/VG/Yy8AO9kcap9D55qKFN8GpFSYpomkwXfV+lV9Dq9dC+8hAVtO2DLfZOb4kYE/OVg9nn1xUuWAHDdmddx/+v380LLC2zv2c5placdp9mcmhxOQ8XMRulX/CgNixulX/GjNCxulH7Fj9JQoZgeiu2zpNJ6j2W69XNcyZ7+9Mj2AdORBHSNsO/EG+KOdEjLNGk3TcpNkXbTpKX3OOWmyLgZz4KTbqE2qUBgSYsNuQ1sz2+f0AD3Cc8oHzbMR5vmo9uGnxeWDffFjyGMKaVL3dGXYiBnYWiCVTUlGAeZZSknRZfbRaVTSYl29Jkl/LpGY0mIxpIQOduhM52nPZUnmbfozZr0Zk22iRQVYT+1Ec8gPxbj7mQjc4NDxveIAS5zgxN0lEN1eDVwHdANpJNHpnunpZRKsR1DjwYpJX1uH3vMPey19pKV2cKyiBZhgW8BC3wL6LF7WJ9dT1p6x5C4HicoZmYWH+nayMFurN3rsXc9Tbltkt8iIVAyJtW5MHxoZXOHTPB541KhSynZ2j3ofcZ1jbNq4secmWFDbgO9Ti8BEeDt4bcf8UU8pQEfmhDkbZe05RD1H5mlYQjDqy3um4ctbVrt1oJBnnSTbM5vZnN+M3EtznzffOb55lGml028sXQH+cduROT7ELhoZj/uc19DK5nrGdgTIiBc5Zm74RrcSC2Jkmq6YjG6gwbdhk1S5MfU1zZNk6g/SrVRTaVeWbj3Cd+4rfdlLQDKQ+OXjeE4GcxtvWnuenwrSHj78jreeUbDtL9GsdDkb6JG1LB131aWzVtGia84sitFtIgyw0dxKnwPnsooU/wEUqVX8RZv0V0130thcuAF6NsB5UsmXiHS6Jni2Q6IzAfdT32snvcsfA+P7n6U+1+/n2+961sncgoKhUKhUCgUCoVCoVAopomc7fB6Z5Kc46ILkAI2dyWpCPlnXARzMSKl5M3uJIOmTcjQcaXEdF0vwFaA6bjT/j7b0i6Y3SmZGmN+p2SKrJtFcuiTrBoaPs1H2kmjoyOEwJVetGpUi6ILHUtamNLExatbakkLS1rHPH5d6PjxjzPRhx9nTUFX1kHzGyyKl9BDFr89Yr632C28lH2JfFmeremt05aGNWjozIuHmRcPk7EcOlI5OtJ5UqZNdzpPdzqPrnl1ymsjASrDfrSZVDP4IGQ+PRL5PWSCy2xifEch0GLVaKVz0OINiNIGhC9M7vHbkWYabBPMLARjiEjFiZ9IkZF0kl5qdGsvCXfk/fYLP/N981ngW0CNXlOIFi7Xy2nwNdBmt/FK9hXSbpqXci9xUfCik1qTWuYGcZPtuMkOZLLdezzYjXQsyPR6afaH+gozhTbnTPSqJejl8xAldYhDRD7u6k/TkcohhGBVdckRG9AHs8/ax1vmW8DU64gfjK4JyoK+wsUwxzImQxiFyHBLWrRYLeyz99FmtZFwE7yef53X869Tqpcy3/D6xfWhi026Xie//usY+W5cKQCBg4ZAYscW4q85A8JDxnOkBsI1EKnFDMbpln102910O930OD1DGQmGbwCCuBan2qimQlSQ7kqzcsFKDOPQc3WlpD/nbaM8eBhT/DiQyll8//dvkDMdTmso5eNrmmZWvfaTQESLUGqXKpNZoZihKFP8BFJlVAHQJ7LY89+DsfdRL1p8zSTGti8ORgzsQci2QXQBADecdQOP7n6Uh7c/zE0X3kRluPIEzUChUCgUCoVCoVAoFArFdJGxHCzXMzVdCQKJI5mRab2LkZ39aTrTeYQQnFcXJ+zTSZk2u/ozJPMWr3UkOK++7IgixvMy70V3D5ndBxvfOZk77DZ0oRMVUSJahKg2dD/qeUiEyMrsSG1S/JiYBEWQ90ffP+ZEuyMdzyDHLBjjhRtWwTw/XNtwvV1HOmTJjomcHcaV3oUcBMGna7zhCsiMXj4S3Y7wLhB4NP0oG3Mb8QsvEt0QBgbevY4+5n70MgMvan343id8I/10g7mlARaUhklbDu1DBnl22CxP5TB0jeqwn7pokPKg77AmzfGs3S6t7LgU6DIzML6jAC1aXTC/tdI5aCV1CMM/rqt/9bUMvPFLBjWTaDpLxBdCnMAU+sVE1s3SbDWzx9pDj9NTaNeFTqPRyELfQuqN+kmzJES0CEv8SwiLME9lnmKXuYuoiHJm8MzjPnbpOshUt2d6J9qRgx3efT41YX+hGzho5KSBJf24aEQ0i77YRcjQAvyWwBiQ+HQHnyEwdPDpAp8OmiZoG8yyd8D7UC+rjFEeGr/vHQmD7iAvZF8AYEVgBXN8cybsl8i49A66VMQ04uGJ9+PykJ/erElf1mLesSdEALwMGwv9C1noX4gpTVotL4L8gH2AAWeATc4mNuU3UWYJ5u/fzPztfySQ6ENKQToUpz3SQFm6By1r077iu5y5fAVSSgbdQbqcLrqdbrqcl0mkEuMuhjKEQZVeRZXupUKvNCoJiAAAjuOw09k5JXM5mbdxXImha8SO8QKGI8VxXX746BZ6EjkqS4J85r3LMWZxOQuFQjE7UKb4NKJpGnPmzJm0zkBUjPyw6V1+FTV7H4XdD8Pqm2AiY1sIL1o8sRUyByAyF4TOqtpVrKpdxaaOTfx080/5/AWfP84zO3U4nIaKmY3Sr/hRGhY3Sr/iR2lY3Cj9ih+loUIxPRTTZylkaIXT5BKwJRjSaz9VmS79WpJZmofMneVVnrmTdtPY/kGW1kTY2ikZzNu81jHAuXVlBAwNKSU5mRuTzrxggA+Z36PrDU+GX/i9VKRilOmtRQvPgyJ4WLMjIkZqk1pY+IV/wtqkutDRhU6QY0vp7Eq3YJiPvg2b7TnHZGciQcA1Cfok5X5tbH8sMm4Gzw8XCCGQQ/8G3UEMcXxOQepCxzAMjLiBlBq2o2HaIF2NFltHGzAwhE6JL0DcHyTmG2/Ot9vtvJF/o1C7/ezA2czzz0Mg0PD2Qw2tMK8J/w23W3lksh05cAB3oA2ZOADpvgnHrkUrEKVz0OJz0Eob0OL1CCMwpXnvrS1lfWwJljQxTJPz9wzQtOmX+M+//pjN8WI6hk6GKU32W/vZa+2l3W4vGJICQZ1Rx0LfQub65k6Yinoy5vjmcH7ofF7KvsSm/CZiWoyF/oXTNmaZT00Q/d0FQxdOjUGAFqlAlNR5F07EammzqnlhW56LnX8hQB5LGoREjpQd4OevBchp6fHbGUU06jB3noUmIDngY9tWC59uYegCnzFinvv0ITPdEGMejzbYDV2g6w6b/E+TESbloooF9pmkcu4YEx7g1d0mDz2fwbQlfkNwzdvCrF403oyvCPnYCfTlTFwppz0ThF/4CwZ5XuZpNVtoTrzEAauFfidPf2WUjZVXEHcCtLTnCczVcDUd3bHpf1Ojt207r0V3EyzNYpIft/2YFvNM8KF64KVa6aRp5I/kM9g3Kkr8REdo/2z9Lra3DRDw6fzt+88gdrj07acIs+EYeqqjNJzdKFN8GhFCEI1GD7m8yqhiv7Wf7pIyaqrOhO7X4a2fw9mfm3ilQCXoQXByXhr1sFeT44ZVN/D5Rz/Pz9/8OX99zl8TNGZmPZti43AaKmY2Sr/iR2lY3Cj9ih+lYXGj9Ct+hjXM5Q4fZXgixvKZz3yGH/7whwC0t7czZ84cbr75Zm655RZuueUW7rnnHiorK7Ftm29961tcccUV3HLLLdxxxx3s27ePsrIyUqkUK1asoLm5edLXam5u5pVXXuGaa66ZcPmll17KnXfeyYoVKwptDz/8MLt37+Z//+//Pa3zVswOiul4aLkSv6aRd1x0TXjRXkKwP5ljSXnklExBOh36daXzvNXr1WJeXBahPhpkp7mzYDBraCwpPZ1U0qBbptnfnyMUtMjITCFi+lAERXDSKO+IFilE+x0rTf4m6o364xbBPBpNaAQITDh2KSWv9SWIZaqp9ulcUFOGb4JowLSbHoluF37yMo9P+Hh3+N34NT+2tLGxsaWNg+M9H/14aJmNjSOdQy4bxpEODg75YRNK925SSixXYrueGToggTwIEwyhoWsCTTCudruUkudzz7M5v/nwdY+lBNcG10Y69tDjobGFhm51GoJKhNAQmg+h+9F0v3cvdAQCOIAmOhDZ1yY22kf909Cwpc1+ez8uLoYwyAcMXlxYQvXG3ZTtfRHfwrcdsf6jKaZj6Ggc6dBmt7HH2kOr1YrDyH5SpVexwLeA+b75hLTQUb/Gaf7TSLkp3sy/yfPZ5wlrYWqNI6vRPBL93YGbbB8ywDsmrh8PCF8AEatFi3sGuGeC1yAMP6Yt2bTX5MXXTLoSDpat05f/EB8O/jdBLY8pA/xP/kMEK0sp8WnYjsRywLIltkPh8xEIuMyZ45Vf6O/XaWvTYNT7dzSEF20mUN+DtH3se20Vrx4U3a4J0DTo7HdxJega5CzJfz6TpiQkWFRrFIxzgJjfwKdrWI5LIm9TdrxShUuXwL5nWPT6j1nUu4W84WN/zXz2LXk77aWVdDoudkkaB5AOiAD4z4OKdBfdUqAlBfFggDmhmpF64Hr1Ee13R/IZLNQTP8Gp0596o42n3zwAAj797qU0VKhU4cMU6zFUMYLScHajTPFpxHEcdu/ezaJFi9D1iVPuVOlDprjbA8uvh6f/N7z1M1j5aZjoalAhINwIgzsh3QqhehCCdy98N/Wxeg4MHuDh7Q9zzfKJTyApjoypaKiYuSj9ih+lYXGj9Ct+lIbFjdKv+BnWsKGh4ShWzoOTBT0E+rGbMeXl5bz00ks4joOu66xbt47ly5eP6fOVr3yFG2+8kW3btrFmzRq6uroAiMfj3Hnnndx8881Teq3m5mYeeuihSU3xibjiiiumPpmjYHjeiuKkmI6HXRkTQxNUR0IsLA2TyNvs7EuxL5HBr2ssKA2f7CGecI5Vv0Te4o3uJFJCQyzEgtIw3XY3T6afxByq3SqRvG5uJBqMYTpeDOmgKQgaXjRwSAsdMr358Yp6noiIFjnpdUl3D2TozZjommBVdXxCQxy8sQ5Ht+ecHEE9yJrwGub6507reKSUE5rlthwy0xm5t6RNwswxkM+TsPK40sYVDhIHTZf4fFkGScFQfWAv1F0S0AL4hG+oNrPElQ7SMXEdE+laSMdCSmfi6vCaBpoPoRugeTcpRKGvZzVaMLoG/KHLzI9h+GIBABsbicT26zxyZjkV6eeoGshQGZ5PhV5xyGjUySimY6grXbqcLvZYe9hn7RuTySGuxVngW8AC/wJKtJJpe82zA2cz6A6yz9rHHzN/5P2R94/UnD4IaWZGUp8Pmd/uYOfIxROjESDCFaPM71qv/neodNwFUn0pl1d2ZtmwyyRneTuPTxesnOfjlZ2r+LG5gHKtlz63Astfyv95T2zCtOSulGRMlw0d/eRsjajPYGFjCc5ysB0wbYnleAa65Ugs2zPSLZtxBrs19Nx2JLlwK3bjbqQEq/kc/G4EWxsx4b3XhnzeKxkihp5LCem85J4nUkSDGnMqdeZWGjRW6jRWGpSHfHSm8vRlzek3xV0H9v4OXv8xDOzy2owQgdP+jKYzPklTuJquVJI73/wDZUszCCHRDA2EN6c6Xw07txn0tvvIJwP01ZZx7dsWM686dsRDmepn0JWSgdyQKX6Mqe6PhG2t/fz8Oe89uuqChaxaoEq7jqaYjqGKiVEazm6UKT7NuBOltBlFle7VFe+2u5HzPoqI1EG6Hfb8NyxZO/FKoRpINYObg3w3BKvRNZ3rVl7H7c/fzn2b7uPqZVefkleQHw8Op6FiZqP0K36UhsWN0q/4URoWN0q/4meMhlJ6ISiHI9sBye3eyTxNh5LTIHS4qCXNuwB3EoQQrFmzhmeeeYZ3vvOd/OY3v+GjH/3ohH2XLl2KYRj09Hh1Oj/96U9z7733ctNNN43r+53vfId169aRz+e57rrr+OIXv8jXvvY1tmzZwqpVq7jxxhv5q7/6q8NO+b777uPNN9/kn//5n7n++uuJx+O8/PLL9Pb2cs8993DJJZfgOA5f/vKXefbZZzFNky9/+ct8/OMfZ/fu3Vx//fWk02kMw+Cuu+5i1apV3HfffTz88MP09fVRXl7Or3/968OOQzFzKZbjYVfai26tjwYpD/kpD/kRAnb0ptjZl8KnC+bEjj6qsVg5Wv0ylsPGjgSOK6kI+2kod3kx9yLb89sLhvjolNgNvnrivkoODIDuhKgNxVldWYOhqdNlw3Sl8+zp99IuL6uMEQsc+r1p8jdRI2rYum8ry+Yto8Q3fWbkMEKIQurzKREAYuC4kp6sSUcqR3fGS8FskqGv5HEkFoYjsA2J5hqcM7iC8nQ/vlQ7+uABRKobz9Ye+90pQnFEaT0i3uDdl9QjAyEvcbyUTPhv2GjH28+H213pHrL/8L+sm+WZ7DPY0kYgsLFBCKTuoy8Mfdkt7KANhEBHp0wvo0KvKNymYpTP5GOolJI+t4+91l72Wnu9tP1DhLWwZ4T7FlCulR+Xc6VCCC4OXUzGzdDtdPNE5gneF7qcYDY9EvmdGI7+Tk68DcOPKKkdifwuqUMrqTlk+nwpJXs6bV7cbrK9zSpcR1Ee1Th/SYCzF/oJ+QWLan089Dw056OEAjrXvC08aZ1uKeHNniSm4xL165xXX4r/GGtCp9wU/53aiCk1lgeWc847Ti8sc+VQhPqQgd6fcrjzdylMW+LTIWd5n7CgX5C3Jbs7bHZ3eNk7BDC3XlJRLdnZnaNED1IZ045dY8eCXb+FzXfB4H6vzReFZX8Jyz8BwbLC2P/zj3vp7CqldFEv/hAY6Dg4+IWfj9a8F6MyyKMb9/P7jS3sOJDgm+te5W2n13LVBQuJh4/MtJ7KZ3AgZ+FKiV/XiPhOjHHXOZDhh49uwXUlF5xWw/vOajwhr1tszORjqGJqKA1nL+qv/BNMhV6BhkZWZkmJLLGlfwEb/gm23AdNV018YkroEK6H9D5It0CgCoRg7bK1/OCVH7C7fzfP7X+ONfPWnPD5KBQKhUKhUCgUiuOFC13PHbqLdL1SS8OnRh0L+jdDcgcc6oR39cV4OWYn55prruGBBx7g9NNPx+/3U1lZWTC+R/Pyyy+jaRpVVd4FwGVlZVx11VXcfffdYwzuxx57jNbWVl555RVc1+Xd7343l19+Obfddht33nkn69atO/RcD0FfXx8vvfQSTz31FLfeeitPPvkk//Zv/0ZdXR1/+tOfyGazXHDBBVx++eXU1dXxxBNPEAgE2Lx5M1/4whd4/PHHAXj99dfZuHEjJSXTb+IoFAeTsWxSprgr11UAAQAASURBVO2VWht1snx+PIzluOwdyLCtZxCfplETmZ503LMZy3HZ2JnAdFyMYJpkbCsPp/YXagnreKmqgyJYSO99cfhiIlqEXs3ktc4EiZRkp5bl9IqoCjwA0pbNm92eqTc3HqIuOrXSfREtQqldetIj3A9G1wQ1kQA1kQC269KVMdk3YCA7Suks34etC0J5l7N39FPWuxMAa+gGYPqi5CK15KN12NE6nFgderDEq5Gsafh1DZ8j8OfAp+v4NYFf16a99jF46e6HywFEiHBx6GJqgiW0b76fPl+WRHWEgZIopjTpcXrocUa+v4eN8nK9fIxRrouZHQ2XdJPsNT0jPOEmCu1+4Weebx4LfQup1quPODL+cGQH+8gluwmWVBGKlSOtLCLRzprBCH8I7GdQG+CJjn/l0u39GBOV/46Uo40xwGsR4bIp137PW5JNzSYvbzfpSo5cLLm41uCC0wIsqRubZnz1Ij8LqgWvb9vHmUvnUR6bOKJaSskb3UmSeQufrnF2bfyYDXFHOjyTeQZTmlTpVZwdOHvMck0I/Ab4DW+88bDGx9ZECjXFo0GvpvhZC3x0Jlz299i0dDvs77HpS7kc6IZImSRrWvzgd0kChlaIJJ9XZdBQoRe2fVjsHOz4Jbzxb17AGkCg1MvsuvTjEBj7t+CTm9vYsr8fnxHgbcGL2SJewcLCj5814TXe8U6DD5+3gIuX1vGrl/bw8o4unt/WwYZd3XzwnHlctrIBvzF9n7P+UVHiJ+I7K5O3+cHv3ySTt1lQE+MTly5R35UKhaLoUKb4CcYQBuV6OT1OD912N7HTroFNd0L/TjjwAjRMUvcn3OAZ4nYKrAHwlxELxFi7bC33v34/9226T5niCoVCoVAoFArFKYdkJOeqGPX8CPKwTsJFF13E5z73OX7xi1+wdu3acbXOb7/9du655x5isRgPPvjgmJNiN910E2vWrOG6664rtD322GM88sgjrF+/HoDBwUF27NhBeXn5MY/1yiuvBGD16tWF+uWPPfYYb775Jv/5n/8JQCKRYM+ePSxevJgbb7yRzZs3o+s63d3dhe28973vVYa44oTRmfYil8uCvnHpqBeXRTBdSVsyyxtdSXy18ROaGrXYcKVkU2eCHqeTwcge7EAPYqg0+BxjDmcEziDhJgomol+MMjGAirCfFVUx3uhK0pLMEjA0FpbOLEP3RGO7kk2dSWxXUhb0saR8dtXWNDSN+miQMpnBfO4p8rpDKuQnmjUJmQ65aD252BzS4VpSoRoGgzWYvoP2CRtI5Sbc/mj0IXPcp2n4dIFf0/DpGn5NePe6hu+gPocz0pv8TVRQQ4+VoNIXp9zvfXdFF15F/qV/g9Y9+M/9OJnqufS6vfQ5ffQ6vfQ6vZMa5aV6KRV6BWWijJSewpEO+mEuoDveZN0szVYze629dDsj39e60Gk0GlngW0CD0XDcDP2ut57D9/ov0FwLE7DC5Qx7rjqwJqDz5NI4fWGd/5+9946T66rv/t+3zJ2+M9urykpa9S7bslzkJhdMtTGCQADbEAMBEghP8oQnDyUEnhDyg5BgSCAYG+zgjo0Bg4vcZFtyUZdWZSWtVrva3qbP3HZ+f9zdlVbaKq2kXfm+X695zcyZc++cc79zz71zPuf7/b45K8LlXWGUSMVx7+9wKZJnbItJTqYrYbH5gM62w8dDpGuqxIpqjUvnahRHhu9zJCBTnmcO6yEOcKA7RXsqhyxJLC+NEPCcuUywNbeVTqsTTdJYG1g7pgUKq2ZrzClX6U7YFITlgTaX5yuU5yusrnHqJbM2jZ0W++Mmhm0RDgliccH+ZoP9zY44LEtQFlWYXuwI5dOLVKJBabBwa6Rg74Ow517I9J0D/mJY8imY9xHwnBqd5VhXisc2HQbgQ2tmsbqgisV2NQk7QVgOn7IAqDDs467rF3Lt4koeeu0g9W0JHt90mJf3NPOhy2azalbRhIjJ3RnnPuJc5BO3bMHPnqulpTtNNOTlC+9aPKECv4uLi8u5whXFJxBZlqmurkaWR77gFyvFjihudTDLP8sJm157v+MtPpwoLnucEIiZZkcc15zQLZ9Y9gnu33k/rza+Sl1XHTWFNRPcq3cWY7Why+TEtd/Ux7Xh1Ma139THteHUxrXf1KffhsdDtcl9Ht0jYOWgczPYJkgqCNPJX1p06Si5xUf/nUiSxNq1a/nud7/L3r17efDBBwd93p9TfCjKy8tZt24d999//0CZbdt84xvf4JOf/OSgui+99NKg93fccQfbtm1j0aJF/M///M+o7QTwep2+KoqCZVkD3/fTn/6Uq666alDdb3zjG8ycOZMHHniAVCrFzJkzBz4LBN55+ZsvRKbKeNiedkKnD+UFLkkSCwtDGJZNeyrHtrYYF5VHiXjP/sT3+Wa89rNtm41dBzms7CGr9eBTFRRJYqZnJku8S8hXnPmTEkqoUCuGFTHKQz50y2Z/V5KD3Sk0RX5Hhq4Hx4N0T0eclG7iVWWWlkTG5e08Vc5BALV1J5aVw29JeGyBkHzgsci/7HbU4tkD9YQQTi5l20a3Bj8b1kmvbYFh2Ri2jRCOmJSxLTKMISVKf7v6BfM+kXzAE71PVI/lTI7EMgg8HJIyLC3xUBn2oxTPQZ29FvPgKxg7fkPoqr8mz++EE+/vR0IkBonk/UJ5/2sAUSzYm9o7EHq936s8X84/6x7lutBpNBo5bBymxWwZiPYgIVGullPtqWa6ZzqadHYXCqXjnXi3PYBi6wgkJAQi2Y4IFiCHipDzyinIK+NqT5ANvjqOhST2zFrERb6LTvs7beGEDN+8X+dA8/EQ6YX9IdJna/g8o5+Lo52DR+MZGmJO2PlFxeEJyc/daDRSm6sF4HL/5YTksS+kiQTkEQV8gJBPZkGVjOj00RTP8O7VHvIkP0c7HU/yxk6LWNqmuceiucdi8wFnu7BPZnqxwsxImnmxX5Pf8ACS3hfWPlQBS+6CmlthmND1umnxs+dqMS2bxTMKuHZJJeBExBgtGsac8gj/54MreeNAO49tPkxnPMt//mkPcysifOSKOcwoHjrf+FjGUMsW9Oac1V/nYtHc45sPs6uhG48q88V3LSbqRrAZlql0DXQZGteGFzauKD7BqOroh7RYLWavvvf4CseFn4DaB6DpFeg9BNHZQ28YqHJEcb0HjCR4QlTlVXH9rOt55tAz3Lf9Pr5z3XcmsDfvTMZiQ5fJi2u/qY9rw6mNa7+pj2vDqY1rv6mPqqrouuP1gSQ5qZRG3CAAkQUQ2+vkH5dV5706MeLu5z//eZYtW0ZhYeG4t/27v/s7rrvuuoH3N9xwA9/5znf40Ic+RCAQ4MiRI+Tn5xMOh0kkEgP17r333glp+w033MBPfvITrrjiChRFYffu3SxYsIB4PM6cOXOQJIn77rtvQr7LZfIx2cfDrGkR6wt7WjxMnlFJklhaksfW1hjdGZ2trTEuKY8S1CZ33yaCsdjPFjYNZgObE9vplXpAAb+qMs9bwyJtEXnKqVEfRhMxZkQC6CeErtdkmZJ34MR/QyxDWyqHJEksK4ngVcc/KTzZz0EAYWQwDm1EkiQkSUHVAshmBkn1I4eKBtWVJMkRpxWZwBj1w5OFdL1PKDcsgX6KgH78c4TjqW8OI6TbQpAxbQTgkcEUsLM9TqFfw6cqeOZfj915CLv3GMa2R9Au/RRS38S+JEnkSXnkyXnM9MwcaGdSJAeJ5F1mFzqDhXIAGXnAo7xQKaRALiBfyR97fvdhsITFMfMY9UY9jWYjljje7yKliFmeWcz0zMQvn7uFKsbePw4I4qbiRUgysrCoq76NQNVCIl4PUZ+HckXmcqOcjZmN7MntISSFmO+dP/oXnEDOEGw7rLPpQI6uxPEY7HPLPVw6z/GkHm8Y/uHOwY50jv1dzn3XnILgmNMijETSTvJqxkn5s0BbwHTP9DPe53AU+jWa4hl6cgYLq8JUFalchjNO96ZsGjtNjvaFXG/psTDTnUT3/g8zMo9hiTRdEmR8M+iY+Sm88z7A9FIv4RHGuN9srqepK0XY7+HOa+eP28NbliTWzCtl5awi/rjtKH/qyzf+rUdHzjc+2hjamzUQQuBTFfynMUaPh9f2tfLMtkYA7rx2PjNLhhbzXY4zFa6BLiPj2vDCZdyWTSQSfO1rX+OJJ56gvb2dFStW8O///u9cfPHFw27z0ksv8Td/8zfs2bOHadOm8X//7//l9ttvP5N2T0ps26auro6amhoUZfjJq2LFybXXY/VgChM1bzpMvxaOboA9v4TLvzX0hqrfySee64B0E0ScG6w7lt/BM4ee4akDT/HlNV+mKFA09PYuozJWG7pMTlz7TX1cG05tXPtNfVwbTm1c+019+m04bdq08W3oLwetAKwMKP5RPMTHR01NDTU1pxeNatasWVx22WW89tprANx0003U1tZy6aWXYts20WiUxx9/nKVLl2IYBsuXL+cLX/jCoDzk/axbt25gYuLWW29l5cqVp9Q5mb/4i7+gvr6eFStWYNs25eXl/PGPf+Qv//IvB3Ke33LLLafVN5fJzVQYDzvSzuKXiM+Db4Two05Y2zzebokRzxlsaY1xcUUU/wUcsnQ0+1nC4pBxiD25PXSbMXTbRhIKNdo81oSXEpDPbFHQnPwgOcumOZFlZ3ucVeXRCfGinCp0Z3TqepIAzC8MET2Nvk+FcxBA3/lb0NPIkQqEnka2skiqD23Vh5H9kTPe/+kK6YYtjovnfUL5ce90m0TOJGM6kSYsAV5FwhKCtGHhUxUkWUVb+RFyr/wHVudhzEOv4Km5esR2hqUwYTnMTM9MLMviQN0BKmZX0EvvILE8J3J0W910W93U4eRd7xfKB3KUy4XDCuUpOzUQrSEgBWiz2qg36jliHEEX+kC9iByh2uN4uA+1wOVsYx59G6V5JwIJW1YxFR+qlcVUfPRohXTGMkAGAK8qE/UWMt27iCPs4c3smwTlINM8o9/PdcaPh0jPmaJvfxIrZ2msnqtRlHd6589w52A8Z7CzPY4QUBn2UR0580WUtrB5Jf0KutApUopY5Vt1xvsciQKfByRI6SZZ0xp0DY0GZaJBjSUzgFQr1o6fY+17BNPOYirQJs9mk+9O6rzXIboUeD0H5IgGZWYUqUwrVphWpFAeVZBliT2N3Ty3owmA26+dN6R4PVa8HoUPXFLNlWPINz6WMbQ72xc63e85q3m9D7bE+OVL+wF4z0UzuKSm5Kx914XCVLkGugyPa8MLm3GL4p/+9KfZvXs3999/PxUVFTzwwAOsW7eO2tpaKisrT6lfX1/Pu9/9bj772c/yP//zP2zYsIFPf/rTlJeXc+ONN05IJ6YaQSmIX/KTERk6rU7K1DJYfIcjih98ElZ9GXz5w2w8zRHFs+0QmgmKj+Vly1laupSdbTt5cNeDfHH1F89ld1xcXFxcXFxcXFxczjeKd0LF8M7OzlPKTgyV/s1vfnPI7U4uPzn8+d/8zd/wN3/zN6ds98ILLwzblpPDq5/Mid7eoVBoIKe4oij8y7/8C//yL/8yqH5NTQ07d+4ceP+Nb3wD4IJcuO0yeWlLOYJWSWD081aVZVaWRXizuZe0YbK1NcbF5VE05Z0V0tEQBgf0A9TqtaTtNJYQmKZCvj6H5YFFzI8MM48yTiRJYlFRGMMSdKSd0PUXl0cJvwM89DOmxY4+wawi7KMqfOYepJMV89gOrGM7QJLxXnYX+PIQqS6kYOGECOKniyRJaIqTX5xhhPSsafFiQydZ08YWoFs2miIT8ByfuJdDRXgWvw99++MY+55DKZqNnD/2RXcSEiE5RESJMMMzA3AE+5RIDRLJu61usiI7IJQf5KDz/chElMiASF6oFNJldfF65nV0dBAQkAMDodHBeT/TM5NZnlkUyAVnVegbCavzEPqOJ7AkmY6i5UTjh1HtHLbiJbf4gyycVkUsZ9CbNUnoJjnTps3MIVLT8Ph6SGiN/Cn+Iiu4hmm+EiJedZBwawtBXbPJ5gM6dS3GQHlxnsLquRorqjW8YwiRPl6ypsW2thiWLSjwaywoCk/IMd6a20qH1TGQR/xsh9f3KDJ5mod4zqArY1AZPun74o2w62dQ9xsU20QBtMplsOxzhKuuoiAFjZ0WRztMjnZatPVa9KZselM6Oxr6v0OiLCqx62gbEOSKBVGWzxzshBZL23QlbArDo4d+P5Gx5BsfC90Z57dT4Dt7odO7Elnu/tNuLEuwYlYR779k5ln7LhcXF5dzxbju6DOZDI8//ji//e1vWbt2LeBMevzud7/jP//zP/n2t799yjb/9V//RXV1Nd///vcBWLBgAa+++ir/9m//9o4VxSVJokQtocFoOC6Kl14EhYugaw/sewiWf27ojT1h8ETB6IX0MQjPRpIk7lh+B19+5sv8evevuWvVXXiHyYPi4uLi4uLi4uLi4uLi4uJy/jAsm56+0OmlwbFNZmuKzKryCG8195LSTba1xlhVHkWVz49odC7JiRz7cvvYq+8lJ5zFBF78qKkZhHLTqAqHmBed2FCu/aHrt7T20ps12NrayyUV+Re0h75lC3a0xTEsm7BXZUHhxAhmkxGRiWHsfBIAT83Vx8Xi8yiGjwefqgykVjBsR1ReWpJ3StQJZdoqlI46rGM7yW19CN9Vf4V0BvOFkiQRkkKE5NCQQnl/nvJuq5uMyNBj9dBj9XCQg9jCJimSAyK4QKDbOgVyAbO0WczyzKJEKUGWzu9iHzvRjv7WAyAsuqPzOTztZuYGbSJWnEBeMYXhAoCBkOOWLYjlDGI5k96sgZZbRoOcIaN2slW8Qlv75XhEAJ+qEPKo9MQk9h6xae0WOEsPYG6FhzXzNGaXqWftnDNtm21tMXKmTVBTWVaSN+5w7EPRaDSyJ7cHgMv8lxGWz01Y7UK/I4p3Z3Qq+xfv9B6CHT+Fw793UgoBlF0Cyz4LFZeB5BzvwjAUhhWWVzvX36whaOrLSd7QYdLUZZHVBbuO5sjqUTxKPgdbfPzwdwmmFytML1KJpW2e3Z5BN0FTJdZfHmDV7PGJ0yPlG1+/ZtaI2xq2TVx37iPy/WcnkknOsPjR07tJpA2qCoN8et2CCfnNuLi4uJxvxiWKm6aJZVn4fINXivr9fl599dUht9m0aRPr1q0bVHbjjTfypS99adjvyeVy5HK5gffxeBwAy7KwLOeiJkkSsixj2zZCHF9VOFy5LMtIkjRsef9+TywHJ1TCWMoVRUEIgW3bp7Sxv/zENhYrxTQYDbQZbSxQFzjlCz+BvPF/I2ofwF54Oyja0H3yVyDrPZBuRgpOx0Zh3cx1lAXLaE228tT+p/jQog9NaJ9OPr7DlY/VHpPBTkP1ybKsARteKH26EO00XJ/OxH6TtU8Xop1Ganu/DW3bRlGUC6JPo5VfSH3qt19/nQuhTye3/ULvk2VZg15fCH06sfxC71O//frPxwuhTyO1/ULs04n2638thECSpEH7PbE9k6l8PEy2to+1T/11TjzPTravi8todKR1hBCENJWAZ+zTMn5VYWVZhLdaeonlDHa0xVhRFrlgJ6nTdpp9xj4O6AcwhQlAnpzHXHUhbe1RTEuiwK+xcIK8HU9GkSVWlDrHO6mbbGmJcUnFheuhv68rSTxn4FFklpdEUC7QBRdCCPQdjyOMLHK0EnXutee7SadFZdhPSFPZ1NQDiCEjGUiShLb0A2R7jiJS3Ri7nkJb8aEJbcdwQnlapAd5k7eYLcevs0JClVRkJK4NXEuFp2JC23S6iGyC3Bv3IowsZmQ6BytvRFFkqkqKUeXyIbdRZGccKvA7c7hCCGLGOp5NP0PM7qUj/DbFvWtIJgXHDAMBFJRDQRkEPSozCrxU5KlEfMpZE8SFEOxsj5PImWiKzMrSCJ4JGMdSdorXMk6KnAXaggH7nwsK/Rr1vWm6Mjqicw/Szp/CkWehP/JA5ZWOw1np6KHcfR6JOeUe5pQ74rItBH/c0sZv3mhBlvzMKC4llYPOhEVnwuKtgzm6E87yDk11FkY88lqaOeXquDzGYfh8499+fBvzSzx8qnIGBWH/Kds5+cQh4FHOymItWwju2bCPxs4kYb+Hv3r3EnyeC3dRmIuLyzuLcYni4XCYNWvW8E//9E8sWLCA0tJSHnzwQTZt2sScOXOG3Ka1tZXS0tJBZaWlpcTjcTKZDH7/qQP7P//zP/OP//iPp5QfOnSIUCgEQCQSoby8nLa2NmKx2ECdoqIiioqKOHbsGKlUaqC8rKyMaDTKkSNH0PXjOWqqqqoIhUIcOnRo0KRUdXU1qqpSV1c3qA01NTWYpkl9ff1AmSzLzJ07l0wmM9BOSZLQNI1Zs2YRi8VobW0dqB8MBikuL8ayLJqyTRxoOoCERCS8knJ/MVaihdbX7yFZtm6YPgmq/OD3GCiZFo60mei6zvWl13NPzz3cs/Ueblt424T0KZVK0dTUNFA+Up+mTZtGd3f3oFCLk9FOI/WppaVlwIahUOiC6NOFaKfh+tTR0QE49otGoxdEny5EO43Up/4/yb29vRQXF18QfernQrLTcH3qt18ikSA/P/+C6FM/F5KdRuqTEILKykpkWaauru6C6BNceHYark9CCGbMcCajLpQ+wYVnp5H6NGfOHGbMmEF9fT2WZZHL5ZAkCZ/Ph23bg/YtyzJerxfLsjCM46E3FUVB0zRM08Q0zVPKDcMYJOKqqorH4zml3OPxoKoquq4ParumaSiKQi6XGyQie71eJEkim80O6pPP50MIMWjR81TuU38dwzBoaHBibCaTSVwmF7IsU1NTM7AAZbLRnu4LnR4cv8dmSFNZWRrh7dZeujI6u9rjLC3Ju6A8epMk6ZrWxVvpt7BxztUCpYDF3sVUSNN4uzWOaZmENJVlpRPj7TgcHmVw6PptrTEuKo9ecIJxUzzDsUQGJFhSnIf/DMWPyXwOWkc2Y7XXgaKirViPJE9doSfi9VAR9tGazNKUyLLQe6rHqOTxo634MLnXf4bZuBW5uAa1avmI+z1T+0mSRFAKEpSDVCrTSGR0WnI9PCeexMDAyMqomoVP8SIbASxFnPdzSlgGubfuR6R7kYIFHJ1zKyInUx7yoY7jOEiSRFQLcJO8jid6nyZuJGnLbMM+vBp/QKIgIsiPCDQPSNi0ZzK0980nBzwKEa+HqM9DxKsS1k7fc7zfhpIksbcrSWdadxb6lEXO+PyG43nEcyJHoVJ41vOIn0zU5yES303loXsRvW8ycJhmXO94hhctPu19d8Qy/H5rHWBxy6WF3Lwyn3TOdkKud1rsOarTnTCRANMCCZAkQXfCHrco3s+J+cYf68s3vrdd5/8+9DbvWTU43zicEDrdf3ZCp//u7Qa2HOpAliU+/67FFF7AqTTOBpP5GugyNlwbXthIYpxL/A8dOsSdd97JK6+8gqIorFy5krlz57Jlyxb27t17Sv25c+dyxx138NWvfnWg7Omnn+bd73436XR6SFF8KE/x/smqvLw8p+GT0CPFtm1yuRwejwdJkkb0SLElmwfjD2Jj8/7A+wnLzspmedd/I7b8APLnY7/3cSe0y1B9yrYhJ+qQFA274GIEEvFcnGt/dS1pM80977uHNZVrzrhPU9XL5nT71D8B5/F4kGX5gujThWin4fpk2/Zp22+y9ulCtNNIbe+fZO6fnL4Q+jRa+YXUp377eb3eEfs6lfp0ctsv9D4JITBNE6/XO+a2T/Y+nVh+odhpuD7120/TtFM8Xqdqn0Zq+4XYJ0mS0HUd0zQ5evQo1dXV+Hy+Sec9/U72FM9msxw5coSZM2eiac5EZDwep6CggFgsNvB/9UIkHo8TiUSmRD+FEOi6jqZpk04stmzBS0c7sWzBpZX55A0hYo2FrozO1lZnUWlVnp8FhaFJ19fx0m11szu3myPGEWxhI0kSJUoJS71LqVArEMCW1hg9GR2vqrC6InpKuOizRVI3eaulF8OyKQxorCi9cDz0Y1mDt1p6sYVgTkGQWdHgGe9zsp6DdrKD3Ms/QlgGnsXvxTPrsvPdpDOmO6Pzdksviixx1fTCYQVcY//zGPs3IHm8eK/6K+RAwbD7HIv9LFuQzBr0pnL0pnR6Uzliad15nc7Rm8zRm9aJp3WEAMu2kYo6mHdZJ4rHxjJk9r9ehOgsRlFkgl4PYb+HkM95Dvc/+7WBsoHP/J5BAuGZIoSNvuVBrObdSB4f8mWf5ZVu575xvON0RhdsOaTzxoEccamLvGUbQbYIp6u5NnI5s0sd/7S0YdHbl5e8N2eQ0s1T9qXI0iCRPOr1jNnDu9+GzRmTuu4USLC8JHJai7GGYkt2C7tzu9EkjfeE3nPOwqYjBLS8ATt+QrZxE5YQaIqKZ857YNlnIL/mjHZv2Tb//Jtt1LclmFcZ5SvvW3bKgo1Y2ub/PRYjowssGwwTFBn+9gNhZpVNTCjzupZefv1KHQ2dKSSgKM83kG9ckiQ2HesmkTNZUpI3EMp/onjrYDv/9UwtALdfO48rFwwdJcFleCbrNXAkYmmbroRNYVg+7cUdFxJT0YYuY/+/Oi5PcYDZs2fz8ssvk0qliMfjlJeX8+EPf5hZs4bOdVFWVkZbW9ugsra2NvLy8oYUxMFZke/1nnqRVhQFRRl80zPcao3xlp+839MpF0LQ0NBATU3NoM8lSTq13cgUKoV0WB10i26iStT5YP5HkHb8BHr2oXRshfJLhm57oAzSDWDryHoH+MvJD+Rz28Lb+NXOX3Hf9vu4YvoVZ9ynodo+UvlE2eNs2mmkPkmSNGDD/jZM9T5diHYark8nnoMTbT/XTuemT5ZlDdhwLPXPpO3Dlbt2Ov22j9V+U6lPYy2/UPpkWRZHjhw55V5mtLYPVz4Z+jRaG8dbPpn7dLr2m8x9Ot3yqdqnfhtOmzZt4N60/0/4cH/GJ1v5eJhsbR9Ln060R7/9hrOvy/nDtm3q6+tHHA/PF50ZHcsW+D3KkOGOx0qhX2NJSZid7XGa4hk0RWZO/pmLmeeDdrOdXbldNJnHI3yEU2EuK76Mcs2ZjBdCsKcjQU/G8XZcWRo5Z4I4OB76K0ojbGntpSuts6cjweLiqZ9zO2fZ7GiPYwtBSdBLdSQwIfudjOegsC30rY8gLAOleA5q9aXnu0lnTCxt0xOX0BQF3bJoSeaYljf0XKtacy1WRx1291H0rQ/jvewzSEPct9hCEEtm2bG3jmhROfGsSSztCN89qRyxlD4gdtv22BbISRL4NJXG+jDJjiC+kEE6rpJJKhSGBYoMqaxBKmuMvrM+NI9CyKcS9msDAnpoCCG9X2D3e9VhF7KY+57Fat4Nsox28cc5KsIIkSTP6yHP66E7maU9lqEk4qdgGAGyrddi84Ec2+sNDMs5Lj5PAdXpy4mXvYaU10DaV4AkLQUgqKkENZXKPi3ZsGwnL3nOIJY16M0ZWLagO6PTnTkeVSeoqY5A7vMQ9XoIeoYOu57WDbbXHyPhCSJLEvMKQhMmiDcZTezO7QbOYR5xIaDpZdjxX9C+DQBZUWkuuZH0gjtZPGfphHzNU281UN+WIOBV+dR184eMYBAJyKy/PMgjr6XJGY5zQMAr8ejrGT61TqYo78zHvFklYT60NEQ3lfzmjSMD+cbnVUb54JpZJHLOIooC38R6ijd0JLhnwz4Arl9W5Qrip8lkvAaOxJZDOo+8lkI3QVMl1l8eYNXssxOFYKow1WzoMj5O+x9YMBgkGAzS09PDM888w/e+970h661Zs4ann356UNlzzz3HmjVrhqz/TqJIKaLD6qDdamcWfYsKvBGYcwvsexD23DtIFB+EJEOgCpKHIdUEvjKQJD6x7BPcv/N+Nh7dyMHug8wpGDqsvYuLi4uLi4uLi4uLi4uLy7mlPdUXOj3gPWNBtSzowygU7O1McLgnhUeWmDFBoubZRghBs9nMLn0XbabjSCEhMcMzg4XqQrpauigpKxmof6g3TUsyiyRJLCuJEPae/oKC0yXq87CsJI9tbXFaklm8iszcwtA5b8dEIYSTZzhrWgQ86gUh8o+EWfcidm8TkseHtvw2JGlqe8JtOaTz4MYkOQOmVdrMny1oSmSoCvuGtKOQJPSFtyJe+zF2ez1HNj5BXXDVKV7esbSOZdsYuoFH60JihN+EBHl+jfygRiToJRrQiA567SUa1Aj7NWLpHH/9i9fI5CxkAkiGSWGewg9uvxxNlUlmDBJZg2TWIJHRSWT6XzvliczxctsW6IZFt2HRncgN374TkGWJ4Akier9oPtusY273i8iyRO+s96KIYo70phBAVZ6PjbUt/Oz5WrKGjc8jc9e6hVy50BEKbVuw75jJpv056tuPe3qXRRUunetl6UwPmhphn27wRuYNtmW3EZJCzNJOdSzzKDJFAY2iwPHc5EnDojdrEMsZ9GYN0oZFSjdJ6SbNCSe1iyrLRHyOF3nU6yHiU2lL5djbcBAl0YAdmEZ+0XSmD7NYYryk7BSvZl4FYL42/+znERc2NDwH2/8Tuvsi1CoazF2PPveTHIz5UGSJhUKccfSOupYYf9jipMb5+FVzRwwZvmq2xpxyle6Ejc8Dj23K0BazuGdDik+vC1IYPnMRTZYk1tSUcvGc0oF84/uP9fKzF/dx0eIKSsI+vOrEjWOxtM6Pnt6NYdosnl7Ahy6bPWH7djn/6KYglraJpWxiaUFvynndHrfYecRwUgFITrSLX72UIuiFuZWeCyYqjovLiYz7X8QzzzyDEIJ58+Zx8OBB/vZv/5b58+dzxx13APDVr36VY8eO8atf/QqAz372s9x999383d/9HXfeeScvvPACjzzyCH/4wx8mtidTkBK1hL36XjqtzsEfLPqkI4offRHiDZA3zA2GvxxSDWClQe8CbxHTItNYN2sdzx1+jl9u/yX/dO0/nf2OuLi4uLi4uLi4uLhcUEiSxOc+9zl+8pOfANDS0kJVVRVf+9rX+OY3v8nXv/51rr/+eq688sph93Finauvvpq7776bxYuP53h86aWXuOaaa3j44YdZv349AA8//DAf+chHePHFF7n66qsH7W8s9Yf7ng984APMnDmTXC7HRz7yEb7xjW9M1KFycRkzthB09Hn8TZTH3rQ8P4Zlc7Anxf6uJB5FpmKCQ6lOJLawOWoeZXduN11WF+BE0putzWaxtpg8JQ/Lsuiia2CbpkSGwz0pABYWhQZEo/NBUcDLoqIwuzviHIml0RSZmdGpsRDhZOq6UwOe98tL88aVN3mqYfc0Yhx4EQDPkvcj+SPnuUVnRmfc4p7nEzgRtwWHj0qUlgrSus7Ofe0YuoVt5chZOdK5LIlMhlifZ/dCdS63+N/Ck9zIlrSg0So6Zf8SEkGvQllhqE/YdsTtSEAj/4T3Yb825lzgBSEfd61byM+eryVtWPi9CnetW0hxnjNeRcZ4XgshyBpWn0hukMjqxwX1EwT05AlCela3sG1BIm2QSB/3Rq9W2rks8DpxBBtz83ml1SYa2cOyheVYls2Tz++jsTOFpWTRggaG7uNnz9cyqyzK4VaJNw7o9KadVDiyBAuqPFw618vMksHe2/O1+STsBLW5Wl7PvE5QDlKqlo7YT0mSCGtOXvFpOIK2btkDAnlvziSeMzBtm660TncyiTfTipZppqjpKdY2PYZsG1iyl+bpH8ZY/G40Xx6oAfAEnUf/a9XnqGGjcE7ziNsWHP694xkeO+yUqX5Y8DFYdAcEiggKgZbsQrdserPGGeXXTudM/vv5vQgBa+aVcklNyajbRALHw0zfeV2QezakaI9Z3PN8ik9NkDAOp+Yb7zEsMrrFm/taSXWlT8k3fjropsXdf9xNTzJHaX6Au25YMOZz2+X8YwtBKtsndKdtelOCWMqmt08E703bpHNDR/YwTIFpO0OAJDlBGTK64N4XUkSDMnPKPcytUKkpVwl4L9z7BJd3FuMWxWOxGF/96ldpamqioKCAD37wg3znO9/B43FyZrS0tHD06NGB+tXV1fzhD3/gy1/+Mv/+7/9OVVUVP//5z7nxxhsnrheTiOFCJg5FsVIMQI/VgylMVKnPHJFqmHY1NL4Ee34Ja74+zJep4K+AdKPjLe51bmTvWH4Hzx1+jif3P8mX13yZAv/weYJcTmU8NnSZfLj2m/q4NpzauPab+rg2nNq49pv6nK4NW5OtNPQ2MCM6g7JQ2Rm3o6CggM2bN2NZFoqi8Nhjj7Fo0aKBz7/1rW+Nuo+x1Fm8eDGPPvrogMj96KOPsmTJkgmr38+6det47LHHSKVSLF++nPe+972sXLly1O1cpi6TcTzsyRiYlo2myEQn0NO5OhpAtwVHY2n2dCTwyBLFgYkR3ScKS1gcNg6zO7ebuB0HQJVU5nrmstC7kKA8OPR7v/060zp7O5MAzMoPUhmeGG/HM6Ei7EO3bA50JznQnURTZCpG8CicjLQmsxyJpQFYXJxH6AxC+Q/HZDkHhamjb3sYhI1SuRS1avn5btIpWLYga5ikc84jo5ukTnjdX57OmfSmBUdaI+imD3BEDtOC9k6J0mKbmCE4cFgCfH2PCGADJhIGh6QKDkgpFskH+PPoLraVf5SCghAFIce7OxLUCHkVjtTXM3v27AkNG3vlwnIWTc+nPZalJOIbNhT5SEiShF9T8WsqJZGxjQemZZPMGsQzxz3Rzd5WZjU8i2QpHPXMoi18KRU5k5JSJxR4W2eSRMYgF24hV3mQlGwj2Qqi81J+8Nskfs0DEgQ0iYvmaFxS4yUaHP43f5H3IlJ2igajgRfTL/Ku4LuIKONYnCFstFwnxYkmihONkGhCJI5ixBqx442QbsMWNsI08WePIQkbgYxiZZlx+OeIjmdAGSbftSQPFss9JwrnwYHX2wt9tIclPMisTeWhdm88YZsTtlX9zj7HQ6rVcQwLlkPzJtj135BodD7T8mDhx2HhJ8AXPd5sSaLAr9GazNJ9hqL4rzfW0RXPUpTn42Nrx5+XPOiTuePaIL/YkKIjbvGLDSk+dV2QgjMQxk8eQwvDPj5z/UKeO9RGZ0qnszvN4wc7eXlP86B84+NFCMGvXjrA4dY4Aa/KX9+8mKB3YnKjv5OZyGugbp4oeNvEUmJA8O4XwW0xejoLTZWIBmWiAZlIUCISkPGo8JtNGQxL4NckUlkBEs7rnGDHEZ0dR3QkoKpQdQTyCpWKAuWC9yKfLPcxLhPPuO96169fPzABMRT33XffKWVXX30127ZtG+9XTTkURWHu3Lljrh+UgwTkAGk7TafVSZl6wuTVotsdUbzuN7DyS+AdJjF8oBLSTWDEQI+DlsfK8pUsLlnM7vbdPLT7If7y4r88g169sxivDV0mF679pj6uDac2rv2mPq4Npzau/aY+/TbMZp2QmEIIMkZm1O2e2PsEf7/h70kbaQKeAN+97rvcsuCWEbfxqUOHV+1HkiSuvPJKXn75Za699lqeeOIJbr311oHPb7/9dm677Tbe8573MHPmTG6//XaefPJJPB4PTz31FOXl5YPqDMe8efNoamoilUo54ZSbm0f8HY+3/skEg0FWrVrFwYMHXVH8AmayjodtaSfMb3HwzEOnn4gkScwrCGJYNi3JLDva4qwqj5LvO/+T2qYwOaAfYI++h7TtiLCapLFAW8ACbQFe+VTxvt9+8ZzBjvYYQgjKQz5mTyKP7JnRADnLpiGWZk9nAo8y+RYiDEdSN9nTmQCcfpROUNSCE5lM56Cx90/YyS4kXxhtyfuHrTeWvNHDMR5RO62bZE54nc6ZZHVrjN8URKIcgYSEQAgbW5jIksqxVpvyEouKUol0TGDbKqalYFgysiQhyxKyJCFJ8Ib4AFX8gjyrh+Km13nh2AfICyrkB2UiQZtoAPJDM6lvt8kPOR6xqjIxY1ZB6PTE8DNBVeQBj3cAkU2QbfgTQhPIhXMpvfRTXKKo5EybVxq7sIVg7fQi3qwQ/PjIcwjJQrH9CMUkXryFrtb5+HWNpTNlblldRFl0dHFekiSu8F9B2k7TYXWwIb2Bm4M345NPOBZ6EhJNjhic7HseeH8MrMGh4iVgQAZWJFBDmEoQkWvFUAJIkgLCRLF1iNaAFgAjdfxh9t1jChuMpPMYhmOFZewquwaycNmu18hrOzpsXeegB04Q10MnCe0nve7cDft+7bTHNsBXAFrYeV58B8z/KGhDp6oo9HtoTWbpyujMyQ8OWWc03qhrY9P+NiQJPr1uAf7TXCQU9svced1Jwvi6EPmh8Qtrw42hOdNCSBKFIS/vXTmN32yqH5Rv/MOXz2ZG8fhyvD+zvZFN+9uQZYnP3riQ0kl0rQWIpW26EjaF4eOe+ZOdZE5CjcwimZMYLauNbQuS2ZNE7/Tg9xl9dMFbAvL6ohdEgzKRgCOARwZEcBmfhyHvP30emUdeS6ObgqBPZv3lAZbN9NDYaXGg2eBAs0lbzKKxy6Sxy2TDLgh6pQvai3wy3ce4TDznPgnTBYwQglQqRTAYHPMf3GKlmAa7gQ6rY7AoXn4p5M+Dnv2w/2FY+hdD70Dxgq8Usq2Ox7i2CEmSuGP5HXzl2a/wwM4H+NSKT+FVp8afs/PN6djQZfLg2m/q49pwauPab+rj2nBq49pv6tNvw36vrKyZ5dJ7Lh1xG9M2ORo7im3byJJMT6aHT/72k3ztpa+hysP/3dv+me34PSNP4q5fv57777+f+fPno2kaRUVFdHZ2Dlm3qqqK7du38/Wvf52f//znfO1rXxult8d5z3vew+9+9zuEELznPe9h69atE1r/RLq6uti8efO42ucy9ZiM46EQgo4+Ubz0LIinkiSxqDiMYQs60zm2tca4uCJK+Cx4AI+FnMixL7ePvfpecsLpt1/ys8i7iLnaXDzS8IK9EILueILdMR3LFhT4NRZNwnzXcwuC6H0LEXa2x1lVFiU6CRYijIRh22xviw0c15rTFJFGY7Kcg1b7Acz6TQBOHnFtaHViY20LP32ulqxu4VFl3n/xDGoqomdB1B4ZjyoT8KrOQ1Px9732e1TaY0GOdXmRJIgGbQ40H0VTy1FkBVtY9MSbqcqfgW7brL0pxPQ+Jca0xIAnYW+q38tQY1/vB1nd8wtmib00ilnsTy2jN+WEAkc4obJlSaY/pXjIJ5MflIkG+zwNT3p4PWOz89kUuGwh0A3IGoJc3yNrCLL68ddGTqf66H0Ec92k5AJeTd9C4tkMOUOgBXMY4Sbqkw3cHW8kI3Vhy064dUtJI9saQjaQfA3oaZW3D8Lbhw6xeFoBaxeWs7y6EGUEz0JVwDVWDU8brSREMy/2/CfXHz6KGj/qiN+53pE7KMmOJ3W4CsLTINT33P/eV4CabsN84BKkXBJDDeExk4hAFPXmX0LwpEhCwnaEaCM9WCwfEM2d8pSV5NViE2TBvJ4kM71zoLy8r156cH3R9xsy084jM/R943GjmRA/6mwnyc5zthsu+XtY8hcwyr1qv3d4PGdg2DaecXp2diWy3P9yHQDvXjWDmvIzS63QL4z//PkkXQmbezYk+fS60IhRBIZiuDG0O+v8HvO8KpfOKuGiWSX8cetRntnu5Bv/1qNbuHJBObesrh5TSoIdR7p4dJMTnv7Dl89m0bTJFW12yyGdh19LkdEFHlniyoVe5lZ4kGUn1Lfc95Ak6fhrub/MycveXy73DWfOAqHj2w/Uk08tO902P/Jaipwh8HokblkdoLpUPVXw7gtrHk/b2KNr3vg80oiCd57fWfx0OqyarTGnXKU7YVNwwthcXapSXapy4wpn7K5rNjnQYnCoxTzFi7yyz4t87gXiRT5Z7mNczg6uKD6B2LZNU1MTNTU1Yw4vVKKU0GA00GF2wIn/iyXJyS3+6v+B2vudlXHDTWoFqxxRPNfp3HCoAW6acxP/+vq/0pps5fcHfs8HF37wzDv4DuB0bOgyeXDtN/VxbTi1ce039XFtOLVx7Tf16bfhtGnTxryNYRkIIZAl2ZkQQkYIgWEZI4riY+Gyyy7ji1/8Ig899BC33XbbgAf7UNxyi+OZvmrVKp566qlxfc/69ev5+7//e4QQfO973xtV5B5vfYDnn3+eFStWIMsyX/3qVweFgne58JiM42EsZ5IzbVRZosB/doRTWZJYVpLHltZeerMGW1p6uaQin4Dn3B2DtJ2mVq/lgH4AQziT92E5zGLvYmZ7ZqNIo7clZ5psaelFaD7CmsqykrxJObnavxBB78vpu60txsXl0bMSinwiEEKwuz1B2rDwqQpLS/LO2kTvZDgHhZ5G3/4YAGr1pSgljseXLQSd8SxHO5M0diapa43xyp4WTMtGlsDOwq9erqMkzzeiwDkUw4naAU0dKPdrKsG+55PrepRTvy+RsXnktTS9KZOgDy6d6+WmFT427bf42fMHMEwVj2py17q5zMgPsK8rSVMiy7Q8P5IkoSoShWGFwlOcR+diHHwXRu0fuUXeQM/SeXTbBfSmbHoSFg0tvQg1TCwtMCxBMmuTzNo0dg3dd59HIj/kCDTRoEy0/3VIIhqQCXglth42BrwRNVVi/eUBVs3W+u5bGBCucwNC9hAC9wmf6yZOPVMMPI/8o7C5gSdQaCKGnyf4EN0dgpi6j16lnqxxBKM7h2lK2JKEjAdJqCBZCGxsOYssPNx+1Xx8lodXalvY19TL7qPd7D7aTdivcs1sL1dUmRTSearXd6oVv7BZF8jj6UtuoF318GoozlWHdzNwJvryTxW7+9+HyoefG+4nWIZ6zQ+wX/oKUi6B4gsjX/39UwVxcNTDfo9tiofcnS1sNqafJWu2UaAUcFHpu2DBMG0QAszsIEH9lNdGus8rva+spw6SzU6/JNlx/hLCcRYbRRAH8KsKAY9C2rDoyRiUjCPyhWUL7tmwj0zOpLo0j/ddPGPM245E2C/zqetC3PN8kq6kzT3PJ/nUOIXx4cbQ7oxzXc33OYK3z6Nwy+pq1i4s57FNh3mzrp2NtS28dbCdd6+aMWK+8WPdKX72XC0IWLuonOuWVJ5BryeeWNrmoY0p4hnnvM4JwZ+2ZXnzYO6c5TuX+8TxQWK5fGqZ0ifMW7bgUKuJbYOETVqXufeFFAVhacQ2yxLk+U8UuaWB144ILuPTzm6fI4GRFypFAjIXzdG4aI6GZQuOdjhe5HUtJq29Fk1dJk1dJi+c5EU+p0wl6Jt6XuST4T7G5ewxOe/U30H05xXvsDoQQgz+QzL7vbDlB5Bug/o/wexhwg6qQdAKQO92QqnnzUWVVT6+9OP86+v/yn077uPWBbe6q1pcXFxcXFxcXFxcphA+1cf2z2wfsU5rspWr7ruKlJ4iz5tHPBcnqAV5+faXR8wt7lNHD10qSRJr167lu9/9Lnv37uXBBx8ctq7X60xCKoqCZY3PY66mpobm5mYkSWLOnDkD5W+88Qaf+cxnAPjRj340av2R6M8p7uJyvmjv8xIvCnjPqsCryBIrSiO81dJLUjfZ0trLJeVRvMNMip8JKTtFwk4QlsNYWOzJ7eGQfggLZwzIV/JZ4l3CDHWG43E6Bmwh2NWRREcmpMisKIsMKRROFmRJYnlJhLdbeonlDLa2xrikIorvLBzvM+Vwb5qOdM5ZPFGahzaJj+tEoO/6LXY2jq7ls0VaQcMrdTR2JmnqSg7y7M6ZFpZt93kSysgIhICy/ABl0cAgUds/hMA9mqh9JjS0mzz0appE1sajSNyy2s/SmY4YNlR+bsOyqetJkdRNenPmqCkU1NlXYrcfgM7DFB95lKorPocka1iWRV1dgpqaMmRZJp1zPBt7+nPZ9nmc96ZtepL2gFjd0mPR0jP0PYAsQWfc8YhUZMjkBD9/Psnv35axbMbkKTlWZEnCpzlCvdcjDTwvSD3PtOQBemSFF0svolVsojHXiMBG4AjAIcXP5VXzuaR4DjMDM/npkz9le6iWlOwhJ6t4heDBo7/mb8pr+Ns5CZKRw7QfqyPTdYRwbxvaFh1jC3SrMn5NxedRGDTkK14iWjHXtCZ5btYMGqYvZ2vl9azyrnBEcM8ERG+YexuibA3Hal9j2sLLIe/0xc4duR20mW14JA9X+a9ClUaQEiTJEbLHIGYPkGqFYxsdoVyLgB5zwq3nTR/zLgr9GmkjQ1dGH5co3u9drXkU/mLd/HEvghmJvIDMnescYbw7afOLDY4wfqbREbqzOsApi+sKwz4+c8NCrltSya9fPUhDe4LHNx3m5T3NrL9sNitPyjeezBr8x9O7yOoWcysifOzKmkmnG2w7rA8I4kqfF7dtQ3GeQsDrLAK2hbOGwu4bQwaVDTwEto1zjtv9nznj/GjDTv/2pzL0loYpMG3HI91Jc+HUVGWJ0qgyIHBHgyd4fQdlwr7T9/I+HyiyNLwXeesIXuTlKhWFU9+L3GXq44ri55kCpQAFhazIkrAT5Ckn5A5XNCdvyrb/gD33wqx3w3CDRnCaI4pnWiE4ExSN9YvWc/ebd3Og6wCbmjZx2bTLzkmfXFxcXFxcXFxcXFzOHEmS8HlGFq+r86v5wY0/4CvPfoWkkSTkDfH9G75PdX71hLTh85//PMuWLaOwsHBC9jcc3/72t08pW716Ndu3bx94/9JLL41Y38VlsiKEoD3VFzr9LORvPhmPIrOqLMqbLT1kDIstrY4H80QKdnV6HRvTG9HRsYWNJmkDIdFLlBKWeJdQqVaOa5JdCEFtZ4LurIEELCsJ45+E4vLJKLLEirIIbzb3kjZMtp6F432mdKZzHOpNAbCgKETEO7nDvJ8O8bROY1eSo51JrGM7WBp7A8OCe1NzaTl0eFBdVZGpLAgwrShEQcjLg68ewrAsQl4PqZyJ36vwdx9Yfs5zX/cjhOD1/TrPbMtgC0cE+rMrA5REBp8PJ+fn9igypUEvzYksTfHMqKK4JMloK9aTffnfsXubMfY9h7bwXSfVkQj6JII+mcphbgVyxglCed+jJ3U8ZHsya5MzjotF1gkCVTon8KjOOCEB3j4Bu1/MdgRt8GnHy72qhE87LnafKIB7PRKek/Kf28Lm4P5neevAqzzkFxzz+cHc7RwzFSoCRZR5qyj2VHFRyQwWFfeF0E618vex79LTlaXVEwEMfll0Ka2il2/11vGx9CFuyh5jFiBCkDMgrSt0mBG6rRK6s8WkrDJKp9WwYP5SSqpqwF8EkkQZcLl+mI2ZjewmR8gP8yZCEO8nWEY2unRoD/Ex0mw2syu3C4BLfZcOnrOeKIJlcPX34aWvgJFwBPHhPNuHodCv0RjP0NXnRT0WjrQneOKNegA+euWcs5JHOxKQufO6E4TxPo/xvNMUxjOmRcawkCRp2HN7TnmE/3vbSjbvb+OxzYfpjGf5yQn5xsN+Dy09aX6zuZ7OWJbCPB9/edNi1El0veof/57d7uS8lyQI+yUnHLlPnpDFBSd+12ABvV9kP1Vc7xfcjwvrg0V4y4ZE1uaXL6TQTYEi2dgo+DSJ//WBvCmTD/10GI8XecArUVPmYW7l1PUid5n6uKL4BCJJEpqmjetPnyIpFCqFtFvtdFgdp95gzP8I7Pwv6NwN7VuhdNXQO/JEwBN2biAyxyBUTZ43jw8u+CAP7HqA+7bf54riY+B0bOgyeXDtN/VxbTi1ce039XFtOLVx7Tf1OV0b3rbwNq6YfgVHY0eZHpk+oof4eKmpqaGmpmZC9rVu3TpU1fkLeuutt3LrrbcOfHbdddeNa18j1R/pe1zeGUy28TBpWKQNC1mSKPSPnuNzIvCqfcJ4cw9J3WRbW4xVZdEJCTmaslO8kn6FjMhgYyMQmMKkRq1hhW8FpUrpaR37w71pmhNZZAmmqeaUEm41RWZVeeSsHO8zJW1Y7OxIgICqPD+V4XF4cZ4mZ/MctGxBRyzjhD/vE8EbO5PEUo4HZVjKcFfwZUxJ8GpuPglvCQuKQkzve0wrDFGWHxhkm4KQj589X0vasPB7Fe5at/C8CeJZQ/DE5jR7Gh2Bb8kMDx+4JDDmnN3Twn6aE1naUjnmWfaoEQEkfwRt2QfJvfUA5sFXUIrnIBXOHpf9vB7HC7I0OvQiFsMSHOsy+dEfkk7ubg8YprPdXTeEKMpT8HkkPOrp5/E9mZxlUNvTwJauOra07KI30eao7loAxeNjbqSKVYU1rCyqoUCL8EpjN0IIpkf6hOl4A7z8t2h6ByVIlORSgOA7zX/gvytvZrO/kvsjy9hfehl3lc0nEJmJL28avmA5Im2zf18rG/e20J3IQSPQqFNd2shVC00urinB51GYpc0iYSfYntvOG5k3CEpBqjxVE9L/Mz0H03aajemNCARztbnM0mZNSLuGZO5tUHmFk1s8b/q4hfx8nwdJgrRhkjGtURdT5QyLnz2/F9sWrJxdxBXzJ+6++WSiQUfE/flzTij1X2xI8al1QcL+Uc7LIezXnXHGuDyvijqCV7ssSVw2v4yVs4sH5Rv/+wfeIGtYmJaNZQuKwj7++uYlhM9SSpfTwbQET72VYethHVmWWDZTpaHDRDfB65FZf3lgQsVlqT8M+qmfnPY+P7oWHnktRSYn4fdKrL88eEEL4idzshd5PG1T12JyoNngYKtJOifY0aCzo2Fye5FPtv8SLhOLJMSQMSAmFfF4nEgkQiwWIy/vLKxKO8+8lX2L2lwt87R5XOq/9NQKr/4DHHgMZtwA1/3o1M/7yXZArBYkFYovBUmhobeBGx+4EYHg6Y8+zeyC2WevIy4uLi4uLi4uLi4uZ0w2m6W+vp7q6mp8vvMzKe8yNEPZ5kL/v9rPO6WfZ4NDPSkO9aQoDnhZURY5p9+d0E3eau7FtG2KAl6Wl555fu5Ws5XfJH6DiYmEhIqKJEl8IPQBytTTExeOJTLs6UgAsLAoTFXe2RduzwZJ3eTNll5My6a473ifzwlVyxa82dJDImcS8Xm4uDw6aSacx0LOsGgaEL5TNHYlaepKoRtDhOiWoDTi48Pe16gQLdjhCryXf5ZIyD8mG3Qns4NCkZ8PWnstHtyYoithI0sSN6/0sXru+CblhRBsbnZsPq8wxIzI2Dxg9Z1PYh55A8kXxnfVXyF5Q6fbjWHZckgfMqf4RBHTU2zrOsjWzoPs6qknZxlgm4hML14BS0OlXDT/XawomkP4hBDfh3tSHOxJEfV5uKQA2P5j2PtrsHKQOOrMs3qjYGbAm4f46Bs823uMBw5uwBI2Zf58/nrxLcwIlQ5qj2ULapt62Fjbwrb6Tuy+GPFej8LquSWsXVDOjOIQm3KbOKgfRJVUbgreRKFydqPzjIYtbJ5LP0er2Uq+ks/NwZtHDps+CXijuYdY1mBRcXjUhT/3v3yAl3Y3EwlqfOsjFxMaJaLCRNDvKd6btinOU7jzutGF8ZPZ1R6nJZmlOhqgpmDs52dXIssDLx/gmR1N2LZAlhwv50hQ4z/vuvK8jXcnk8zaPLgxTUOHiQS8a6WfNfM04hlBd8KmIDxyzuvJRCxtT7k2nwuG8iI/EdeL3OVMGev/1cl9RZtiCCGIxWJEIpFx3bCWKCXUUkuH1TF0hUW3O6L40ech0eTkmBkKbxEofrAyThj1QCUzojO4tvpaNtRv4Fc7fsU/XvOP4+/YO4jTtaHL5MC139THteHUxrXf1Me14dTGtd/Up9+G/fm5XVxcTo/JNh725xMvCZ4bL/ETCWsqK8oibGnppTOdY09HgsXF4TM6LiEpNOAh7sWLQKChEZbDp7W/roxObWcSgOpogMqwj97e3kljv/EQ0lRWlOaxpSVGRzrHns4Ei4rO7HifLv3h6BM5E02RWVZy5gsixvPd4zkHhRDE0jqNnY4A7niBp2jrTQ+ZutWjylQV9nl+FwWZVhiiqiiE2vQG+q5OJCWA94qPI4fGHhL55FDk55rt9Tq/fTODYQkiAZkPXxFgetH4p20lSaIq7GdvLkFjPMv0vLEtCvAsvBm76zB2ogN9++Nk5r6PSDQ6ob/dVbM15pSrEyYWCSFoTnexpbOOLV11HIw1I074wRR4AixNdLLcVllQMIvQZZ9CktVT9tGUyCJbWWqaHoMN9zj5rQGmXwdFS+Dt/w/MNHgjcPX3kULl3BgqZ3ZeOf+++0laMz18fcuvuGPuDVxdvmxg34ossWR6AUumFxBL67y+v5VXalto783wyp4WXtnTQlVRiLULp1M8I0GHaOOF9AvcHLyZoHxmodTP5Dq4M7eTVrMVVVJHzyM+SSj0a8SyBl0ZY0RRfHt9Jy/tbgbgU9fNPyeCOEBBSObO64LcsyFFR9zi3g0p7lwXJDSM6Hey/YQQdGed6BEF44w4Uxj2ceOKaby8t4WclMFUMgQJYdvQHstOClG8pcfif15O0Zu28XkkPnxFgJpyxzaRgDTlhOU8v4TQk+T5z+1CyMnO+L3IFWrKPcytUKksUM5p3vXJ9l/CZWKZ/Fe1KYRt27S2thIOh1GUsee9KlKKAOixejCEMZCHa4D8GieMzLFXofZXsPr/DL0jSYJAFSTqINUE/gqQJO5Yfgcb6jfwxL4n+NKlXyLfn3+6XbzgOV0bukwOXPtNfVwbTm1c+019XBtObVz7TX36bTht2rTz3RQXlynNZBoP04ZFImciSRLFgfOz4CXf52FZaR7b2xwvM48iMa8gdPphdUUar+QlK7IggYbGlYErT0vESegm29tiCCEoD/mYkx88q/brysZpzfRQ5s+n0Hd2Ih7k+zSWluSxoz1GcyKLV5HH5dV3MrG0TVfCpnCcImJjPENLMoskSSwtycN3DvOzd8YzbNlzhFWL51J8kqeyZQtae9I0diUHRPDGziSJYfIB5wW0PvE7NPBcEvGfEpreTrSTq/0TAJ6FNyGHis9O5yYY0xI8vSXDmwed0MhzylQ+dFngjDzkykNeDnQnSRsmPVljTCKapGpoK/+M7MYfY7XtJW6FCa9+/4Sfg5HAmYnhlrDZ39vI1q6DbO2sozXTM+jz6nAZKwvnsDJ/JmU7nkAYJnKoBO8lHz9FEAfoSmcJN/6BmQd/StTqdAoLFsAl/xsq1jjv531oyNDec/Iq+X8X38mPa59iZ/dhfrbvafbHmri95ga8yuC53UhA410rpnPT8mnsb47xSm0zWw510tSZ5NevJPH6vCy8TsUIJ9nABm4K3YQmnf5CqtMdR1vMFnbmdgKwxreGiDI1RL0Cn4fDOCHGhRBDXt9iaZ17X9wPwA3Lq1g0reDctjHseIjf83yK9rjlhFK/LjjkuX6y/TKmRc500rBETyO1SEnEDwUdJPNrQbHJWjKFPQspiZx/Qby20eDR19MYlqAwJPPnVwcpzpva/2Un033oZCYvILNqtsaq2cdzkde1GBxo7s9F7jxe3D20F/np3h+NBdeGFzauKD4JCMpBgnKQlJ2i0+qkXC0/tdKi2x1RfP+jsOKLoA2zAttfCskjYGch1wG+Ei6quIhFxYvY07GHh3Y/xOcu/tzZ7I6Li4uLi4uLi4uLi4uLiwvHvcTzfZ5Rc/ueTYoDXhYVh9ndHudoLIMmy8zKPz1PxDqjDk3SqNFqmK/NJyyHT0sQz5oWW1tjWLYg36+x6Aw92EfjxeYd/GjPb8laBj7FwxcXvZ9rKpaNvuFpUBL0srAozJ6OBPW9aRRJpiLox7IFlk3fw3lt9pdZYIvBn+8/ZvLq3hy2DT4N1l8eHFO46Z6swf7uFABzC4Lj9iw8EzbWtvDT52pJZ3X8b3fx/otnUhjy0tiV4mhnkmNdKUzLPmU7SYLy/ABVJ+T+nlYUIhIYve3CttC3PYKwDJSSGpSZQ6QmnIT0JG0eejXFsW4LCbh6sY9rFnvP2BtOlWXKQz6a4hmaEtkx21+OlONZ+C6M3b8j3LoZO3EpSrTijNoyEaTNHLu6D/N2Zx3buw6RMrMDn6mywsLoDC4qqmFF4RwKfXkI20Z/+wGsWAuSFkBbfTuSNkTUgObNqK9+h7k9e1FlCSmvElZ9GWa/F6QTxutg2bB5rsMeP3+39EP8tmETjx/ZyMstO6lPtPKlRbdQFjhVdJUkifmVUeZXRkldafD6/jZe2dtCc1eKXS+UMOOKI8T8LXT3PM0Him4iGjh3omXGzgzkEa/Ras5uHvEJJurzoMgSumWTNCzC2qkRAX6xYR/JjEFVYZBbLz0/fSsMK3xqXZCfP5eiPWZx7wsp7rh2aGH8RLr6Fg1F+vo5XmwlR7ZiP7bhLL7BI4iV7eLnhzTyvSG8ioZf0fCpzrNf9eJVPE6ZouFXvPhUDZ/iwatoKNKZ388IIXh5T47ndzrn8+wylY9cEcSvDe7fuVjM5nL+OdGL/Iblo3uRB7wSrb02iPHdH7m4gCuKTyjdyRxHurIUludOWQk7GsVKMSk7RYfVMbQoXnkFRGdD7yEnlPriO4bekaRAoAJSDZBqBG8xUp+3+P967n/xwK4H+NTKT6Ep7iDh4uLi4uLi4uLi4uLi4nI2aU/1hU4fg7B3tqkI+TAsm/1dSQ72pPAoMtPGmbvbEAZHjCMALNQWUqKWnFZbTNtma2uMnGkR1FSWn8XQ3rYt2N/VwXe3P0bGyiLZHlKSwfe2PYHeXUlADmH2idKWLbDFCaK1xWDh+gQxe9Br64QycVzkjhbIlJSadCbiNDVmiMfH7m1k2YLuhEAIRzDO6PCLDUka2n3MrVSZVqQO6RmVNS129Hnfl4V8TD9H+dlNy2ZnQzf//vQusrqJEIJ0PMt9L+6nJM+HIh9vq9ejMK0oxLTC4IAHeGVhEO00vdnNuhexe48heXxoyz6INAGCzdlm/zHHOzJrCPyaxIcuCzC3YuJCOVeFHVG8LZUjZ9l4x7goR62+DKttP9KxPZjbHkZd+wUk5dyEmD6RzmyMrZ0H2dpVx56eBixxfCFFyONnReFsVhbWsLSgGr86OAqHUfs0VutekBW0Sz6BHDwpR3dPHbz1r9iNL+MxLSw1iLLis7D0DlDHL0LLkswtMy+nJlLJ3Xt+y9FkO/+w5T4+M//dXFI8b9jtgj4P1y+rYt3SSg63JXiltpntWwXll9TTrrTyr1ufpDK1mKsWVrCgKv+spj+whc3GzEYyIkNUiXKJ75Kz9l1nA1mSyPd56EzrdGX0U0TxF3YdY/fRbjyqzF3XL8RzHhepFYYV7lzneIy39lrc96IjjAe8w7epO+uI2QXjCPcuhKAx1cHr7bVsOLaNlJVGliUkCYSQMIXJGx37TolqMBa8iqdPLHdEc1+foO47QVz3yYNFdqees52Kxgs7LA40SsioXDbXz7tW+k5ZEPRi8w5+vPcpspaOT9H4/IL3nbXFbC6Ti5G8yI91mxxptwfuj0wbHnktzZzyoe+LXFxOxhXFJ4iNtS3817N7SGR0Qm918rkbFnHlwiHE7WEoUUo4Yhyhw+yAoSKqSZLjLf7a15wQ6gs/AfIwfxYClZBuBDMJei9487lpzk187/Xv0Z5q5+m6p/nA/A+cRi8vfCRJIhgMurkipiiu/aY+rg2nNq79pj6uDac2rv2mPq4NXVwmhslyLuVMm96c491VEjw/odNPZkYkgGELDvek2NuVwKNIlAXHLgIdMY5gCpOIHKFYOb3w1LYQ7GiPk9SdXNcrSyODBIrx2M+2BcmsoDdtE0/bxNKCWMp2QmqmshzOHuKYXUeXcoicnAZAyDogSNoG/37wV+Rb1YStckJWOV6Rh8TE/W46OxVUVVBQaFFZZWA1QCatosigyKDKEopC33tpoFyRJdI5m96UiSyD6BPcDQveqMuxtd4RSCIBmWmFCtOKVaYVKZRFZXa2x9Etm5CmntV85umcyaG2OHUtMQ62xDjcFieZNUhlDecIStCvb1SX5rF0RuGAEF4c8U+YwGf3NGIceBEAz9IPIE3yHK62LXhhV46X9jjekZUFCn92ZZBocGIn8vO8HvK8HuI5g+ZEluro2Jx3JEnCs/yDZDuPIBJtGLV/RFvyvglt21DYwuZIoo0tnXVs7aqjIdk+6POyQAEXFdWwqrCGOZHKYT1VzfpNmIdfA0Bb8SGUghnHP0x3wLb/cJyNhI2JRPO024gvuItV1WfuObw4fyb/7+I7+dGeJ9kfa+KHu3/Du6ZdzJ/NugZ1uDlcnGM+uyyP2WV5ZPQ5PN+wmzrtTSLTeqnfu58tT3VSmOfjygVlXDG/nPzQ6NeT8V4Hd+V20WK2TKk84idT6NcGRPGZJzirHetK8cjrhwG4bc0sKgvPLF/7RFCc54RS/8WGFC09jsf4ndeFBrykT7SfEIKezNjziTenutjUXsum9r00p7sAsGwbWZKRJYmg6idn6XgUlT+bfTWKpJC1dLKWTsbUyVk6mf73lk7WPP7a7luckrMMcpZBjNS4+24LxwvYtIAQhLwSzWmN328aLLIjwWutezCFhVfRSBtZfrz3KZYWVE96j/HJch96oXCyF/muBp2fPZsEHEE84JXQTUF3wp4wUdy14YXN1LvCTUK6k1l+9nwtvSknb0kyY/Cz52tZND2fgtDY/lz25xXvsDqGzX3C7PfBlh9AshmOPg8zbxx6Z7IHfGWQaXbEcW8+HsXDx5d+nO9v+j73br+X9897v3tSD4Esy24OxymMa7+pj2vDqY1rv6mPa8OpjWu/qU+/DbPZ7OiVXVxchmWyjIcd6RwIR5w6l/mcR2N2NIBu2TTFM+xqT+AplSkcoyf7Qf0gAHO0Oac1pyCEoLYzQVdaR5ElVpRF8HsGH5t++9lCkMg4AncsJYilbeIZu0/0FsT73tvi+LYmOXrVerrVQ8SUowjJBgVAQkIBARIytmQ67VFTJLS9JKS9TjhOOUC5t5JKrZIqXwVl3hI8ijKkaC3LoCqnlp/8WpYE+3uSdGRylC2zubg8QN4YcsLG0jb/77E4OcPG75NI5QSKLLFqjpeOmEVbr+Ucm7TN7kZHMKmoMCkqMvHIMsWyn2RWEAlMzNxPVyJLXUtsQARv6k6BGFwn7PeQyBpYShYtYGBnvYTVIF+8efGY58fGgzB19G0Pg7BRKpehVk5uD8JU1ubR19McbHV+f5fM0bh5lR9VOTvzc9PyfOzpMDiWyDAz4h/zOav4I4RX/zm5zfdi1m9CKZmLUjp/Qtp0YijksCfAnt4jAx7hPbnkQD1ZkpibV8XKojmsLKqhIlA4wl4drLZ96Lt/B4BnwQ3Hfw9GGnb/Anb9HMwMAGLG9Wyr+DQxbwWLiydOYCvwhvmH5R/l4cMv84fGN/hj41scjDfzVws/MCYhz6+pvLdmOXtzXl5PvYF/eQ+S6aOrAZ584wi/ffMIS2cWsnZBOUtmFA4bSns818FWs5UduR0AXOq7lKgSHXN/JxP9gnFv1sAWAlmSMCybnz1Xi2nZLJ5ewHVLKs9zK49TEunPMZ50hPENSe7oE8ZPtF9SN9EtG0WWiHiHlnLaM71sbt/LpvbaQQtKPLLC8oLZrCldSEJP89P9T5O1dMJaYNxe10IIDNsia+XIWDo5yzhBNM8dF9L7RPShRPaeTI6j3RkQBrKkEw6AR5GGFNlzlkHW0pGQyIgcMhJCgrZMz6QXxSfLfeh4mEph6qcXqwS8MjnDJuSTyOgCr0emIDxxi8umog1dxo4rik8A7bEMWcMm5PeQyDgX3qxu0R7Ljvmmv0ApQJEUciJHwk6Qpwwx+Kg+mP9nsP0nsPve4UVxgGCVI4rrPWAkwRPiw4s+zE/e+gn7OvfxxrE3uLRqauRYOpfYtk13dzcFBQXIshtuY6rh2m/q49pwauPab+rj2nBq49pv6tNvw0BgfKmYzgaSJPG5z32On/zkJwC0tLRQVVXF1772Nb75zW/y9a9/neuvv54rr7xy2H2cWOfqq6/m7rvvZvHixQOfv/TSS1xzzTU8/PDDrF+/HoCHH36Yj3zkI7z44otcffXVQ+531qxZfPCDH+Rf//VfAejt7eWRRx7hrrvuAuDIkSO8+eabA/t0eecxWcbDtr584qWTxEu8H0mSWFAYwrBt2pI5trfHuKgsSmSUsKy9Vi/tVjsyMrM9s0/ruw/3pmlOZEGCmmiYZEqiucPoE75t4mlBb8qiM66TMZRBgvdw2FKOTKCeXs8hujkKskCWICxBWaCI1cXzWVEwj397/hBHAs9hSyaq7WNa5iruuKKcpmwzB2JNHE60YIkMreIgrbmDbMk5IWLn5FUwN6+KedEq5uRVElDHb8/lZXlsbY3RndHZ2hrjkoooAc/IU3KRgMz6ywM88lp/iG3nfX/OTN0UHOuyONpp0thp0ZPLEs03MS04fFhhSzIH5E7xJq/IV0YVYS1bcKwr6QjgrY43eE8yd0q94oifmvIINeUR5pTlUZYf4L/eeplHjr2CLVnIQuGGyhvOiiAOYOz9I3ayC8mXh7b0/WflOyaKo50mD7+aJpa28SgS77/Ez/Lqs5tWoSzoY39XkrRh0Z01KBxjbnHbtumRCwhXX4ZV/zr6tkfxXf3XSGcolLzYvIMf1T5JyswiBES0AB75+HngVTwsK5jFyqIaVhTMJjxUHvDh2hxrRt/yIAiBOn0V6pyrwbag7nHY+u+Q6XQqFi+HS/43neFFxFpjeBSZ0sDEjtGqrPCxOdcyL1LFf+77PXWxY/yft3/B5xe+n6UF1WPaxwLvAhJ2gr3KXpZc2U3Z/Gq27MpR1xxjR30XO+q7iAQ1rlhQzpULyig+KU3CWK+DGTvDK+lXEAjmaHOYrZ3e2H6mtCZbaehtYEZ0BmWhofO3j0bIo6ApMrpl05s1KPBr/GZzPU1dKUJ+D3deN3/SOYg5wniIezYkae6xuO+FJHdcG0JTxYD9ujJOZJCozzMowkZ3LsHm9r1sbt/LwXjzQLksySwtqGZNyUJWFdUMumatLKqhLdND6WkIn5IkoSkqmqKSx/i97Xcc0Xlic4aILSiJKHxsbYBwQAwS1LOWMSCyt2dj/Nfe36NbBjaOIG+bNocSLSzMnzH6F55HJst96FjpH5sTRoaIJ8DnF75/UoepP/n+yOtx3k9k6PSpZkOX8eGK4hNAScSPzyOTyZlIQmDazorGksjYb/oVSaFQLqTdaqfdah9aFAeY/1HY+TNo3wbtO6BkmAFK8YO3GHIdjrd4ZAERX4Rb5t/Cr3f/mvu23+eK4kMghKCzs5P8/Pzz3RSX08C139THteHUxrXf1Me14dTGtd/Up9+Gp7MqfaJX9xcUFLB582Ysy0JRFB577DEWLVo08Pm3vvWtUfcxljqLFy/m0UcfHRCwH330UZYsWTJs/TfffJPi4mJ+85vf8L3vfQ9Jkujt7eVnP/vZIFH8kUcecUXxdzCTYTw0bJvuvnCnkyGf+MlIksSS4jxMK0ZXRmdrW4yLy6OEtOGniQ4ajpd4pVqJX/Y7IcoTNoVhedBEpBCCjC6cUOZ9nszxtCCm5xD+DLaAlmYPr3QNExVDgG5YaB4FSYKwXyYvIBMJSEQCMpGgjObN0aAfYm9yP/sTDQMhXYNAZbCY1cXzWV08n2mh4yHe//KSAh54fQZJ0UtIivLnl5WwqkoDHA/YnGVwONHCgVgT+2NN1MWOkTKz7OlpYE9PAzQ4cz3TgiXMjVQyLzKNuZEqisYw5sqSxPLSPN5u6SWeM9nSGuOS8ijeUSIIrJqtMadcpTthU3DScdbU4+FE4zmDN5vTGJZMAC+hco3GTnNIb3JFlqgsUJhW5DymF6l4PYLD/aHQW2Mcao2T1a3BfZAlpheFHAG8PMLssjCWotOS7qI108zzXbs5fKSVjZ27sD0WHhQ8CryQ2MiHsysn3PPMaj+AWb/ZORbLb0PynJvc6eNFCMHmAzp/3JrFFoLCsMxHrwxSGj370SMUWaI85KMxnqEpnhmzKN4/hkbn3YDoPowda0Xf/hja6ttPK1+7EIK3Ow/wzzseImvpyEjYCPSswbxIFauL57OqaC4LotPRlPFPVYtMjNwbv0SYOkrRLDxL3o/U9Aq8/a9O/nCA8DS46H85DkaSRFNrDICKkG9Yb+sz5aLiufy/0B38cPcTNCTb+JcdD/PB6iv4wIzLkMdwHC/yXURSJGk0Gukq2clfvu9mUnGVV2pbeH1/K7GUzh/ebuAPbzewcFo+axeWs7y6CI8i05XIsmV/E6sWByiODL24YFAecTnKat/qiT4EY+Kx2sf4yrNfIW2kCXgCfP+G73PbwtvGvR9Jkij0a7Qks3RnDFo6kzy7vRGAO66ZR2QSXosBSqMKd14b4hcbkhzrtvjliyn+/CrfwH1Md7YvdLpPI6aneLNjH5va97K/twnRF65DliQWRGdwWckCLiqeR3iY8bDQl3fOvYBtIdiwM8fLfSkj5ld6uO2yAD6Pc96NJLKHVD8/3vsUGTOHIlkEVC8PHnqRzmyMj89ZN2JagvPJZLgPHStd2Tg/3PMbYjnHSz8mUlMiTP1I90cTwVSyocv4cUXxCaAg5OOudQv56XO15AwLIWB6UWjcK2GL1WLarXY6rA7mMGfoSoFimPVeOPgE7LkPSv5t+B0GpzmieLYDQtWg+Pjk8k/y692/5sUjL1LfU091/thWKLq4uLi4uLi4uLi4nFuEEOQsfdR6r7Ts4r/2/YGcpeNVND47/92sLR9eWAbQZM+I3jKSJHHllVfy8ssvc+211/LEE09w6623Dnx+++23c9ttt/Ge97yHmTNncvvtt/Pkk0/i8Xh46qmnKC8vH1RnOObNm0dTUxOpVAohBM3NzcydO3fY+g8//DCf+cxneOihh9i8eTNr1qzhH/7hH6itrWX58uXceuutPPPMM+zZs4fly5fzhS98AVVVefrpp+np6aG+vp7Pfe5zfOUrXxnx+Li4nCmdaSe9WlBTCY4gNJ9PZEliWWmELS29xHIGW1tjXFwRxT+EUGsLm8O6k5O1Rqvh7YM5Hno1Tc4QyLLE3HIVv1caEMANa7CLdyBoM2OGjmQ5ebY7uxQkIOjrE7uDMpE+8TvsF8Q6O1k8bwbRkDogWCX0NG937mdjxz52tx4XwgGqgsVcWjKfS4rnURUcOte5M4FaQXeibMgJVK/iYUF0Ogui0wf6fCzVxf5YE/tjjRyIHaMj20tDso2GZBvPHdsKQJEvj7mRKuZFqpgbqaIqWDxkvmNVlllRGuWtlh7ShsXWthgXlUfxjOKBFAmMPNmrWzY72uPYQlAa0lhRmjcwvp/sTX600ySdExxpNzjQnMMwbXTLwrR0hMgiyAAZwManKcwuy2N6qZ9ogY3i1+nM9dKSOcqO7m5aj/WQs4xBbclZBqZtISNhYWNZOpZu05Bsm9DJdaGn0bc/BoBavQalpGbC9j2R5AzBk2+k2XXUOU6Lpnm45dLjYtC5oCrPT2M8Q3taJ2faeNWxCweSoqKt/DNyr9yN1V6Hefg1PLOHjxBzMpawebN9H083vUVtTwO5PkFcU1RUWQUEf7XoFhadgdenMHPk3vwlIhtHDhejzVqF9OynocVZMIE3Asu/4ETeVJxoGBnTojPjRD6oCp+dKAb9lPrz+ebKj3P/wed5oXk7j9Vv5ECsic8veN+onvCyJLPWv5Zn7GfotDrZkN7AuyLv4sOXz+bWS6vZXt/JK7Ut1Db2DDxCfg/l+QE2H66jVz9G4Zt1fOnGK7hyYfkp+x+URzwweh5xy7bIWTlyZm7gWbd0smZ2ULlu6QOvs2Z20PuT63Rnunm09lEMy8CjeEjkEnzqqU+xs3UnM6IzKPAXnPLwj7AApsDvoSWZpS2V5fEN+wG4alEFy6uLxmCt80dZvsId14b4xQtJGrtMHng5w2VVzn+BllSSA/FDbOppoi5xFFscv77Oi1RxackCVpfMJ6qFzmMPhiZnCB57Pc3eY84YuHahl3VLfchjXIhyTcUylhZU05bpodgX4dW2PTxWv5Hnjm2lIdnOlxbfMin7PVWwhc2j9S/Tm0siIeFRFMJqgKylT4kw9aPdH7m4DMfk/Gc2BblyYTnzKyO8vGUvv98TozuZo7U3TVl07KF+ihXnj1uH1TFyxUW3O6L4kWec/OKhiqHrecLgiYLRC+kmCM9hZnQm1868lheOvMAvd/ySb179zTG3z8XFxcXFxcXFxcXl3KHbJp975Ycj1rFsm7Zsj5M7EYm4keE7Ox7k5/v/hDKC0HLv2q/gVUb2mFm/fj33338/8+fPR9M0ioqK6OzsHLJuVVUV27dv5+tf/zo///nP+drXvjZq//p5z3vew+9+9zuEELznPe9h69atQ9YTQvDkk0/yD//wDwghePjhh1mzZg3f+c532L9/P2+//TYAa9eu5e677+axxxzB5L777mPnzp28/fbbmKbJvHnz+OIXv4imTU6PIZcLg/ZUX+j0CQ7LO9GofXm932rpJaWbbGlxQntryuDxo8lsIiMy+CU/gWwZv3opQVYHSXLOzZ0NBgVhaZDHZdDreHZHwwJ/gY4sS4RUjTVlIaKrFcJ+acgw3pZlUZcziQZlUmaatzoP8GbHfvb0DBbCp4dKBjzCK4Kj5xuG8U2gypLMtFAx00LFrKtcAUBPLjHgSb4/1kRDsp3ObJzObC2vt9UC4Fe91ORVDHiSz8krHxhvvarMyrIobzb3kMiZbG+Ls7I0ctqeqkIIdnXEyRgWAY/CkuK8QQueNFViRomC5skhiJHRY+xvSdGTkJHwAz7Ai0DB9tjY3hy2lsTyxNH9cd5Se3mtN40nKTFUE2VJptQfpTxQSLm/gLDHzz0H/kTONDBtE11YGLbJj2t/x0fnXMPV5cuGXDAwXvRdv0VkE8ihIjwLbjrj/Z0N2mMWD25M0xG3kCW4aYWfNfO0cx6+OaypRH0eerMGx5IZZkXHF/ZYDpfgWfRu9J1PYuz9E0rhLOToyHmZU0aWF1q282zTFrpycQB8ige/6kUCwp4AKTOLX/VR5j99Lzxh2+hbHsSOtSCpHjS7CenpP+truAcWfRKWfga8g4Wd5oQTvj3fr52TRUtexcOn572LuXmV/OLAM+zsruerb/+Cv1r0AeZGqkbcVpVUrg1cy9Opp4nbcV5Mv8j1wevxKCoXzynh4jkldMQzvFzbzAt76miMH+WPx56nwX4KGx1Z97D7t1dzV886VMV2xGgrh6VZBKcHEQjaj7TzfMfzp4jd/a/7hW1LWCO29XTIGBmyZhYJCd1yFpOl9BT37bhvWPHbp/qOi+S+wYJ5yBulMSaDHaItFac8Usz6y2ZNeLvPBuUFCrdfG+TeDSmOdGaoS3cQyW5na/dhhLAJeJwFa7PCZawpWcjqkgVjilRyvuhJ2jzwcoq2mIUqS3xg9emljDjRu/2WmZczM1TKj/c+xYFYE//w9r18adGt1EQmT674qUJMT/Gfe3/Hts5DSJKEIslEtRBpM4df9VJ6BmOzi8tkxxXFJ5DCsI/LF1TQlPawq6Gbl/c08+HLh/H4HoJ+UbzX6kUXOpo0zIWicD6Ur4aWN6D2Abjk74bfaXAa9PZCphWCM0D2cPvy23nhyAs8se8JvnTpl4j6omPv5AWOJElEIpFJl2PGZWy49pv6uDac2rj2m/q4NpzauPab+pyODU1hIfoEcUmSkIUjkpjCQuHMhIfLLruML37xizz00EPcdtttZLPDhDoGbrnlFgBWrVrFU089Na7vWb9+PX//93+PEILvfe97w4rir7/+OnPnzqWgoIBbbrmFf/qnf+IHP/jBmL7j+uuvJxRyPEkqKipoa2s7rTD1LlOD8z0eWragsy8HaElw8i++0BSZVWUR3mzuJW2YbG3t5aLyKOoJC2sO6k7o9JnqLB7ZmCVrOIK41yMBAlvA1Yt8zK3wkBeQyAvIeBSJrGnxZnMvWROiPo1VZdFRBeC4kWa31czjO7eyN9Y4SAifESpldYkjhJcHCs7K8RiJfG+Y1SULWF2yAICspXMw3jwo5HrGzLGzu56d3fWAIxzPDJUyL1o1kJt8Zd9ChJ6Mzu6OOEtL8ob9vdqZGCLViRQsQvZHBn12sCdFV1pHkR2vf48io5sWDR1OPvC6lhgHW2KkcyYCgS3r6GoaM5gmkGehBXVMNU3CTmHaAsMUmBZOLnez7wF4hI88uYAyfwEzI4XMKyxkcUkxZcHoKeFrw54AP679LWkzR0D2UejLI2PluGf/n3j+2FY+PmfdGeWDNZu2Yx3bCZKMtmI9kjr5zrGdR3SeeCODYQnCfpmPXBFgRvH5m4KtCvvpzRo0xbNURwKjjo0nj6HKjEtQOg5gtdSib30Q79q/GvK4t2V6+FPT27zUsmMgikBEC7KuYgXrKleytfMgP977FGnLEV0+v+B9Z+SJaOz5PVbrXtBjaD1bkS0nJDqz3gurvgzhU4UyIQRNiWzfcTm7XuIns7Z8KTPDZfxwzxO0prv51rb/4c/nXMuNlRcNaRPLtujJ9tCZ7iSaidLubadO1FHbWktbfRtd6S46M510pjvpyfRg2RYxPUO73YzABmQgS53+B374xk4CfYsBvZqXd131LoQhqG+sZ/P2zePui0f24FW9eBUvXtWLpmh4FS8+1ee8l7VBnw/3nDbSfP3Fr5M1swQ8AVJ6Ck3R+NiSj2HYBj2ZHrqz3XRnnEe/Z3pzopnmRPOQbUvpJpYNumESMTT+9HPvKd7m+b58CvwFFAYKT/ksrIXHdP8wEXnQTyRn6TSZh5Cqa9neXIcpTLxtoKhQ4S/ixulLWVOyYEqIlUfaTX69MUU6Jwj5ZD62NsC0ookZA1cUzeHbq27n+7sf51iqk29te4Db597AdRUrJmT/E8H5vg8djdqeBu6ufYpePYlf1bit+kpeat5JxtInZGy+EJjsNnQ5M1xRfAKRZZny8nKuWayxq6GbV/e2csvqarRRckT1E5ADhOQQSTtJp9VJhTqMBzg43uItb8CBR2DFF8AzjEe6lg9qEMwUZFogOJ1LKi9hQdEC9nbu5eHdD/OZiz4z/s5eoPTb0GVq4tpv6uPacGrj2m/q49pwauPab+rTb8N+8VmTVe5dO3KY7+5cgi+8/mMyVo6g6nM8rxQvd1/2eQq84WG302TPqO2RJIm1a9fy3e9+l7179/Lggw8OW9frdbxhFUXBssbnSVRTU0NzczOSJDFnzvFFxW+88Qaf+YzzX+VHP/oRjz76KG+99RYzZ84EoL29nVdffZXp06eP+h397TvdNrpMLc73eNiV0bFsgU9VCE/S0Okn41MVRxjvy3m9vS3Oij4P5rSd5ph5DCFg21sVHO0wkSVQFQh6IaODX5O5dJ53kBe2adtsa4uRNR0v5uUjeET36kne7jjA5vZ97IsNDg9bHS5jdbETGr3sPAjhI+FTNBbnz2Rx/kzACRd9NNk+IJIfiDXRnUtwONHC4UQLf+QtwAmpPD1Yjmzl062XosiwqOhUYdw8+hb6locRVg5J8aKt+jDq9IsBJxpBfW8aW0DQEvzp7QbqWmIc7uglK6Uw1DSmmsbwpzEjaWRfDlm18SgymiqjSxL9CTo8MoQUjTJ/AeWBAiJyPrIRQU9GSPSG6epVncy1SejogI6DsFmWqCjIDMpNnheQuaZiGdX+GdR1d1NTUEBVJMzzzVt5vP5VGpLtfHv7r7m4aC4fnXPtuMUdOxPD2PVbp81zr0HOn1yLm0xL8KdtWTYfcCJFzCpVWX95gJDv/IZ3LQ162dctkzUtujI6RaNEsDh5DJUkCW3ZB8n2NGEnuzB2/w5t+QcBR2TeF2vkj41vsaWzbiDHcVWwmJunXcxlJYsG8oSfGAq51J9/ZoL4wVcw9z8DuR40/RCKiEHZJXDJ/4aixcNu15HWyZkWHkWmNHjuI3lUBAr4q3k3c2/ds+zsaeAne57k8QN/YrqqEMt205l2RO6uTBc9mZ6B4wlQUljCtZdei+SROCIdYUfjjsE7lyDqD9KekVDwIiEjEAgsCqVFrJu9nOJwGG+lFykgoZgKsyOzee+178Wr9AnbowjZmqKhKRrKBOZyLgoUDeQULwgUDJtTXAhBykgNCOTdmW5HND/h/bFYOzuamzDlJFk1jkc1MWyDtlQbbam2MbVHkRTy/fnDeqMX+AvY2rqVu9+8m5yZI6gFTzsPumGb7OyuZ3P7Xt7uPDCwmCQcEOipfKq91SwsqObaGTOYXTD2aLTnky2HdH77ZgZbCCryFT52VXDCQ1yXBQr41spP8NN9f+DNjv3cs/9P1Cda+WTN9Xjk83/fdb7vQ4fDEjZPNWzi8SMbsYWgMlDIXy26hWmhYv5s1jUTMjZfKExWG7pMDOd/lLiAsG2btrY2FlaVUBD20p3I8dbBDi6fP/bVYsVKMUk7SYfZMbIoPu1qyJsB8QaoexwWfnzoepIEgWkQ3wfpYxCoQpJkbl9+O//7+f/N/Tvv584Vd+JRRp8UeyfQb8PS0lLkUfJ6uUw+XPtNfVwbTm1c+019XBtObVz7TX36bRiJOJ6AkiSNGuK8PFDIFxa+nx/vfYqMpRNQfXx+wfsoD4wtlPBofP7zn2fZsmUUFk7M/obj29/+9illq1evZvv27YBzbD72sY9x4MABCgocUewXv/gFDz/8MN/61rdIJBID24XD4UHvXd55nO/xsD3tCGIlwXMfKvlMCGoqq/o8mLszOrs64iwryeOQcQjTtol35NNxLEDQK3HlAo2X9+TIGgKvR2b95YFBk95CCHa0x0nkTDTFCRl+ckj2nlyStzr280bHPvb1Ng4Sf8o9Ua6atpzVJfOnhFdcP4okUx0uozpcxo1VFyGEoCsXd8Kt9zZyIH6MxmQHbZke2jI9WLYga9n4mrzMi1ZxUfFM5kWqqA6Xo+opcm8/SJeeolX1UWZkKNryMN1aFbU9Noezcbr1Huo7WmnqbXcEcDWDWZpFliQ0VcajKnhVmZAiAwqypFLki1DeJ35XBAopDzge4AXe4b0jc4bgWLfF0Q4nN3ljp0laFxztNDnaaQ7UiwRkNBXq2wSWHSHgFay/3OKm2RdzeckiHjuykQ3N23mr8wDbug9xU9VFfGDG5QTU0cVJIWyM7Y8hjCxyfhVqzTUTZbYJoTdl89CraZq6nONx1SIf1y3xjjl37tlEkSUqQl6OxjI0JrKjiuJDjaGSFkBbuZ7cpp9jHn0bu3gObysKf2x6i/pE68C2ywpmcfO0S1icP3PI39OJoZBPCyGwdtyPUfssWCYe6xhqXgFc/M8w7RpnHnQEmhIZACpCPuQxjs+jeQTrlu4I2emuAUH7xPedmeOvY7nYwHa+YCWByGw6JImdZoZE1x4sMzVo3xLSgEdzUaAIu8OmYHoBVyy5gvfNfh8z5BkDnxX4C+hMd7L8Py+iJx1HJYBBGk0Ks0C9k9iRQhZfbUF+Ix5Z5ebgzeRXnv/x9baFt3HF9Cs4GjvK9Mj0Yb2uJUkipIUIaSGmR05dEGnZNv/8m+1UFJmsXFxOZX6Ai8oC9GQHC+fdmW660l1DlqeMFJawBhYnDIVpmxyNHcW2bWRJpifTw0cf/yj/8tq/DHiah71hwlp4oL0nlgU8QbpNnfp0N/vjrejCREZGkiSKfHlOaPTi+XQ3CfbrMpIEG3cZTL9c4FHP/3gyHLYt+OPWLJv6FgUtnu7h1ksDaGepzX7Vy18vuoXfHd3Mw4df5oXm7TQmO/jS4lvIH2Fh8LngfN+HDkWvnuQntb9jd88RANaWLeGOuTcM/Nc847H5AmMy2tBl4nBF8QmkMxtjW1sdF0V8XLWogic21/PSnuZxi+L1Rv3oecUl2fEW3/SPsOdXMP+jMNwqPV8xJOvBzkG2DfzlvLvm3fx/r/9/tCRb+OEbP+TjSz8+IaFepjpCCGKxGCUlJee7KS6ngWu/qY9rw6mNa7+pj2vDqY1rv6lPvw3z8sY3ITGRnlcnU1NTQ01NzYTsa926daiq8xf01ltv5dZbbx347Lrrrhtx21deeYXZs2cPCOIA73vf+/ja177Gf/zHf7By5UqWLFnChz70Ib761a9iGAbLly/nC1/4wsB3urxzOJ/joRCCjnRf6PRJnk98KPK8HlaURtjaGqM9lWNPZ4I9Sh29GZtE4wzy/DKfvCZIaVTh8gVeuhM2BWH5FEG8tis5ENZ7RWlkIBdqTy7BGx37ebN9H/tjTYOE8Nl5FawunseqghpiTR3UVNWgKBPnjXg+cESOCEW+CJeXLgKcfMsH483sjzWyP9bEvt5jJI0cO7oPsS9WjypLqJLMDMtEFjm2BPzokoQsNGpMnaZX/xsjIIEksG1B1jBRw473d1CV0VQ/UW+Asn7Ru08ALw8UUOrPPy0vOq9HYlapyqxSZ1shBN1Jm6MdjkDe2GXR2mPRnbToTgj6Hf11U3Dfi0lsO8iSmX7umHsj6ypW8sChDezqruf3R99gY+tu1ldfxdryJSPmGzfrN2N1HERSPGgrPow0gZ6qZ0pdi8Gjr6VJ6wKfR+K2ywLMr5xczidVYT9HYxk60zmypoVvhKiWw42hStFsctWX8/zhV3hh2wPEAlGQZDyyytqyJdxUdRGVwaKz14m2bdibv4cetwAZVU6hXvSXMG/98HOiJ5AxrYHUFlV5Q4dOt2yLpJ4koSdI5BI8uf9Jfrj5h2SMDKqscv2s6ykNlQ4SvhP6+BbiKZJCgb+AIn8eQQV61TCWN0JpuIq1xXNYUzyfokARhYFC8n35p3hl78juYHtuO8InmBWYRaXneJj4slAZd7/7h3z5T39DPJugwBflW1d9l1T7XPb1HqXNfxQlIbFcvpj8vPMviPdTFio743np373dQH1bnFDAQzSokTEsQKMiXEFFeATHsxPImblBYvnJnujdmW7quutojDWiyioCgYyMLWzakm3Ec/Fh961qEbyBEjR/MdIJEZtsS0fPtGNluwlIsPdgmEc8IfSsDVo+kh0i2Rvh2aMRrl5QQNSfN1hw7xPbw94wftU/4mK8iQ753k9GFzz8aoqDrc6ioOuW+rh6kfesLwyUJIn3zVjDjFApd9f+lrr4Mf7P2/fypUW3MC96/iKJTLb/5Xt6Gri79rfE9BRexcMdNTewtnzp+W7WpGay2dBlYpGEOCEm1SQlHo8TiUROa4LoXPFi8w7+fc8TJPUMIc3PX9S8lyf/mMS2Bd9Yv4rpxWNbodRpdfKH5B/QJI2PhD8y8sXDyMDDa0GPw3U/hhnrhq+baoTkYVACUHgRSBJ3/e4u7t1+L7IkUxYqO+1QLxcSlmVRV1dHTc3U//P9TsS139THteHUxrXf1Me14dTGtd/Up9+G06ZN4+jRo1RXV+Pzndt8ly4jk81mqa+vH2SbqfB/dSKYSv08n+Nhd0bn7ZZePIrM1dMLp5Sn+Im0pXLsbI8Rp4sm3yYM3YOn9j18cm2EaHBkj5nDvSkOdqdAguUlERQlx1ud+9ncvo+62LFBQnhNXiWXFM9jdcl8inxOlIx32vXMtC02Nh/hrfZ6WrOtxLNNpDJdGJZFp3Q8M3D/c0AKoKg+FEmm1BtldrSU6XlFVPgLBoTwkMd/zvuRMwRvHszx8KtpBGCZAoGEAPKDEl6PxIxilbkVKjXlKsfMev7n0AZaMz0AzAyV8vGadSyInuoBaifayb7yI7BMtCXvQ61ec247Nwy2ELy8O8cLu7IIoCJf4SNXBikITU6vsreae+jJGszODzI7PzhsvaHOwZZ0N39sfJONrbvIpjrBMslTvdw0/2bWVa4krI0ttPNpiXLxBnj7+9hHXiDnmYeQNJRoBdr130DyHr8e2cImpacGBO3+536R+2BPB42xbiBLWDOO1zmhftpID+zvZI9gW9jIssz0yHTUkxaXqLLqCNl+x2v7xNf9ntz97yO+CPIJC0ASepof732Knd31AFxdvoxP1lyPd5ionkIIXsu8xiHjEB7Jw03BmyhQBqeWOBY7xmu7X+PyxZdTGakka2d5qOs3dGYT9B4N07K9nFWzS/jolXOInocw8hNNXUuMf3liG0LAXTcsQApq9GYNFhWHqQxP7HjYmmxl9c9Xk9STRLQIvbleAp4AD37wQXyqj4Tu/Obi2ThH053Up7pp0TPkbAtb2NjCRtgGtt5LJtVCItU66LoIgICMYWBLMjIS6SwIAZoKYb88bEAERVIIasFTvNNDWoijsaM8f/h5DNvAq3j55LJPcvXMq/GpvlMefo9/0PuTf+8n0hm3eODlNJ0JC48i8cE1fhZPHznS1XiP91jGjLZMDz/Y9TiNqQ5kSeaTNetYV7HyvNyHTZb7GEvYPHnkNZ5oeA1bCKqCxfzVovdTFSw+b22aKkwWG7qMj7H+X3WXzE8AXdk4P977FHE9jSUsUkaGew89zWXVN7HnUIqX9rTwiavHJooXyAWokooudOJ2nIgSGb6yxw/zPgy7/hv23DeyKO4vh1QDWGnQu2g1TP548I/YwgYBvdlevvLsV7hi+hWux7iLi4uLi4uLi4uLi4uLy2nQlnLChhYHplbo9JMpDXoJCj/1ciOKAj69kk9cF8WvjdynlmSWg90pEkaStNTMj/cfpi52bFCdmkjlQI7wfiH8nYwqK1xdOYsKyYu8r568nk6aDYsNsszv/eCRBIoAGYElSfyZaVBRfBnLl1xHYWDyLJzyeiSWztD4/VtZcoaNV7MwbBVJgqKwTCwjqG83qW83eWY7RAKlXF72MXoKdrMptokjyTb+adv/cEnxPD46+1pK/FEAhG2hb3sYLBOlpAZl5qXntZ/9pHM2j76eoa7FyQF80WyNd1/kx6NM3vO+Ks9PT9bgWCLLrGhg1DFKCMGeniM83fgm27oODZTPzK/muu6jXGzYBAwdzxgF8cdqHxvIHR3wBAY55wghSBvpwUJ2spn43odINL5IwrLokQpJyPUktRDpcIDk7z87SNBO6alTxcUTyBgWAvAqMsooYe39qh8hBBISfo9/QBQ0bZOPL/04qytXD4jdRYEiwtrw6QdGI6wF+Lul6/ltwyYeq9/ISy07qE+08NeLbqEsUHBKfUmSWONfQ0qkaDVb2ZDewM3BmwnKxxc6lIXKWFq4lLJQGUIIXs28iqQZVHtLMKRFtMktbDnUwZ7Gbm5dXc3ViytHPSaTlYxu8t/P70UIuHReKatrSjnYk6I3a9CVMSZcFO93LPvKs18hYSQIe8N8/4bvc+WMKxFCcCTZxqb2Wt5MN9KZzYDiJ8/vJ6B6ubh4HmtKFrAof+ZAZAxb2M5v/4QFHL2ZXjYdOUJC6BT4bOKpNK/VdZM1E/i9GcoKs6SMvvp921nCwhIW8Vz8FI/1kxd4pPU0//Hmf/Dk/idHFLz7USRlSLHcMDSOdapIwktQ87N8RognjwT5U9NJIrvqx6t68av+IQV4r+IdeD4xMsJIY8bJlPrz+daqT/DTfU+zuX0v9x54lsOJVu6ouRFNeedJYL16kh/XPsWengYAripfyu0114+amsvF5Z3AO29EOAu0ZnrIWjoBj5ekkUGSJLKWzoJZAfYcSrHpQBsfumwWfm30wy1LMoVKIW1mG+1W+8iiOMDCP4fdv4DWt6BzDxQtGmbHKvgrIN0IqUYa0jpZM0uelkfKSGHaJmkjzdHY0Xe0KC5JEkVFRVN68uKdjGu/qY9rw6mNa7+pj2vDqY1rv6mPa0MXl4nhfJ1LQgja+0Knl05xz7vX9uX4444sRVe04pGgTKugO5elUjsuLnRl47RmeijrS9tQ19vJk4e3czB5mC69Y1AO8bmRKi4tmc/FRfNGTfHwThsLhW2Sq9tI/u5nMU0dW5bJ5a+guaME4XkFUzGRbBlDsclDsNqyKWt5Ba/Si1h2K9J58AofjkjAyS//yGspsrpCwCux/vIgq2ZrdCUsDjSb1DWbHG4ziaVtth0GWECJVE0i+ibN8h42te1na9dBbp52Ce+fvgb14CvYvc1IHh/a8tsmxe+iqcvkoY1petM2qizxvov9rJw9+YWG0qCXfYpM1rToSOuUDDNOmcKiTu7k3q2v0phy0jtKSKwomsPNVRezIDodq2kb+rZHMfZvQC6ag1Jwqoc/QNbM0tDbwJaWLXzh6S+QMTJ4FA/xbJxPPPEJ/m3Tv6HbOindyeUMOC6xehxyvSBsp0j2g5QDSUHyR6Gxbdh+emQPed68gdDSed48FClA0lAJa2GWlZYd/1wLD6rb712ryupgj2BvhFguRsQX4bMXfXbC505lSeaWmZczJ+//Z++946O6zvz/9y3TNRr1LiEBKiAw1aYbcO/GNm6xkzi7iZOs03ad7C/7TeLYqZviVCebZLNJ3BJ34xJ3G4zBGEw1HSEJ1FCXRtNnbvn9cSWBQBKSkJAG3zeveYk5995zz5nn1vM5z/Pk8Lt9L3LU38y3tv2NL5RdzfnppaesLwkSK5wreNX/Kl7NyzvBd7jCdQUWwfAuP/E6uje6l3qlHkmQuMi1guTFySwuyeGRdYeoburi7+8dZuPBJj61vITCjPHNxTwSHl9fQVtXmNREO3csM1L+pDqsVHUEaAtFjckNo3zdWD19NeWZs9nTVsGM1GLcdg9PV61nU/O+3ugXADbJwvy0EhZmTOO8lKJ+01eIgtgbCr2HmKIS1aYhihJL8lJwWWWqZyk8si5ATNUpTbVw+zIncvckHF3XCSvhPpNE/FF/r2i+q2kXv/rgV1gtxoQ9RVOIalHK0spwW92ElfApn5AS6m2P4QQYIBA7nvM+HNXxh41JKBYJ3KJIY/WZ/7YW0YLD4kBAYHfzblRNxSpZ8Uf83P3S3XSEOpiRMYN8Tz7ZCdl9RHSbZOXL069nsjuLJ6rW8e6xj6j1N/O1GTeRdhbzZY/3c8yejiP8bt+LveHS/7XkCpZmzRiXtsQr421Dk7HFDJ8+CrSFu/j8xl8TiIUIKVF0dNLtHv6w5Kv88vmDNHYEuePCYi6amXv6yoBt4W3sieyh2FrMYsfi02+w7utQ9RJMuQ6W/2zg9dQItG4GdBot2Sx45FJ8EWMmpaIpJDuS2fNvez7WoriJiYmJiYmJiYnJeNNfiG6TiYEZPv3c7+eZ4A3H2NzQgSQKrChIi0uvO03XeX1HmI0HIlizjpBWvhOPJYG0zmWIgsisjEQyXDbWNuzid/tfJKhEEIAcZxotIT86IIsCdkmk1JPPBRmGR3iKLf6ElrNBoOEAbVueRfO1oOk69WoqNYWX4syahMdhYV/zXp6pfwNd1HBYrFyUvpJbpTDp9e+BriM4PFjn3IKUNnm8u9IHb1DrN998DzHF8Bo3RPIYbX5D+AyKbdRYN+C31GGVBVItMreF2liiC9jn3Y6cO+tsd6UPuq6z5XCUV7aFUTWd1ASR25a5yE6On9Cqh9r8HPEGSXNamZuV1GdZVzTA2w07eaN+G96oIX7ZJAsXZs3kyrzz+3gt67pOdPuTqPW70B0eOufdwhH/Mao7q6nuqDb+dlZzzHcMHZ1QLESDrwEBAUEQ0HUdHZ0cdw6OEyZ2SEoId6QTt67iFgXctmScrkJcMQW3bCd56goSkwr6hIVOtCXitrl7RW6bfKrYv62xk7ZglMIkJyUpCacsH4jheKqOFu0RH7/Zu4ZD3joArsq/gNsmr0DuJ2+6X/Pziv8VQnqIXDmXi5wX9QnN3qw083rgdTQ0FjkWUWIt6V2majrr9zXw7AfVhCIKggAXn5fHDRcUYh+CY9dEYEtFM398Yx+CAP/fDXMozjacyzRdZ+3RVlRNZ2FuMom2/kPRj5S1DTv5zd41BJQwmq6TYDE8wQEsosyc1CksypjO7NTJI/LM7UnFYpNFLsw/noqlsjHGo+uCKJpOWa6F25YeF8YHo78JHgnWBDZ/dvOAOoCu60TV6CliuS8S5O09XvbU+lH0MAUZKjMLVRQ9SigW6ldcjygRQsrAyyJq5JT9D+WaIQkSWQlZ5HvyyXPnkZdofPI9+fh1eKRyHf5YiESLk6+Ur2J68qRh2yKeUHWN549s4Pkj76Ojk+9K56vlN5DjSh3vppmYnBWG+r5qiuKjxNqGXfxu3wu0hL0IgsDny67m9ikreXNXHU9sOExuqosHbp0/pNklNbEa1gbXkiQlcX3C9affeeseePEmECS4ZS24Mgde13sQwo1gS+OZhv3c+8a9dIQ6CCkhChIL2PK5LaQ6P74XSk3TqK+vJzc3F1GcmDmgTAbGtF/8Y9owvjHtF/+YNoxvTPvFPz02TE1N5ejRo6YoPgExRfH46Od4XQ8r2v1UdwbJTLAxKyP+woIrqs5zH4T46Kjh7V684j1wtTPXNhfBN4kGXxhREChKFrn3w9/ijQbQdR0NI8Swx5LIZHc+V+TPZEFGGcm2oQtPJ/JxuJ9521up3fg0CZ0H0HQI6jY2i3MomrWMBWWZ7Gr24Y8qOC0SbkuUrc2NuK1uCtwpnJ+djOCtI7L9SfRAGwhgmboCufQShH5Es/FguDZs86kcrFeoOKZQ1RSjVaimzvoesliLhEq64GFp/p1cVFRIhkccF8+tqKLzwpYQu44Y58e0PAs3LXRiP01KgYlGMKawobYdBFiWn4pDlqgLtPBa3Vbea9xDTFMAcGHlmqJFXJw7pzdHvT/q50jnEao7qqnqqKKqvYLKync4GmwlIskI9v4nvyTaEslOyObdo++iaAoui4uwEsZpcfLYjY9RlFSEu/MQ7l1/wN6+37CvMxPm/TsxIZXYvldBANv8O5GyB4iQOWifVTbUtYEOS/NTcFqGJ/g2+hup8dZQ4Ck4a45EiqbyZNU6/lm7BTCibXylfFW/E4xalBbeCL6BoiuUWktZYF+ArutU11ez3bOdoB6kyFLEMseyfs+dzkCEJzZW8mFFMwDJCTZuXzaVuUUT20uyzRfm/ie3EowoXDN/EjcsKOqzfHujl9ZghOKUBIqShhbifzCiqsK+zqNsbNrLs0c2oGgqIgIaOqIgcEnOHFbmzGZu6lQc/UzMGA4VbX4q2rrI87g4L7Pv88ThYzEee9cQxqfnWbh1qXNIk/BGY4JHIKzxj/eCHGlREIDL5zhYUnbm6WI0XSOiRPoI5TVdNdz2zG0Eo0EcFgf+qB9ZlLm6+Graw+3UddURVaOD1uu0ekhKn41ocWIRZeZ7clmRNdMQ0RPzcFldg24/4v6Mw3NMR8THQ/teZH9nDQAX5czmk1MvwSaN7oSQjwsfh2fRcxEzp/hZZmXOLMqTCvjh5sdo1Lp6HxgXl2Xy7AdV1LcFONzY1TtjbTDSpXQAvKqXqB7FKpxmRlnaDMicB03bYP/jMP8/Bl7XlWeI4pFWVpdcxdKCpVR3VPP99d+nsqOSn7//c358yY+H3O9zDV3XCQSMF3uT+MO0X/xj2jC+Me0X/5g2jG9M+8U/PTZMSTk1f6SJicnQGY/roa7rvfnEM53xFzo9HNX5+3sBqpoURAGuXBzjkKsdEZGp1qnY0+zEVJ2WYIQnDm+mI+Lr9d6yIIEgsCrvSu4sm9cnbPpIOJfvZ80dfg5sepXM1g9woqAicEguJWX2VdxVVtArbMzN8vDhsU66IgotQUi2ZSEAGS6bsU5yPvblXya25yWUmm3EKtahthzGOvdWxIS08e0kw7dhqlticZnE4jIbUUXnSPMMQrsq2O2v5VWrxGFdpKLmCV6omkq5tITzclIoybEwOVPGZhl70a6lS+Uf7wVp9qqIAlw6y8HSaWcuBI0HTotMisNKWzDC2rqDfOT9iI/aj8c8LkrIZG7SJHwNdbS27een1a9R3WmI4K3B1lMr1BR0XUVSNQrsqUzOmsXk5MkUJRdRlFREUXIRyfZkBEHoI8olO5J58LIHuSS5ELb+DGrXGvVZE+C8u2H6p1Fbq4htfQwAy/QrRySIA9T7QqBDisM6bEEcjBzSZzuqpixK3DH1Yoo9efzxwD855K3j/334F+6Zfh0zU/qKv+lyOsscy1gXXMfB6EESxATyhXw2y5uJaBGSpCQWORYNeLwmuWx84bLpLC3L4tH1h2j1hvn9q3uZVZTKHcuKSXVPvAmaqqbzf28fIBhRKMp0c935p3oApzostAYjtIejFDEyUbwt3MXO9kp2tFWyt+MIETVGRI2haioSAjbZikWU0XSNGwuXjponcns4iqZpJNtPPV6nZlu4Y7mTx98Nsq8uxlMbg9yy5PTC+Orpq1lasHTEEzwaO1UeezdAZ0DDZhG4ZbGT0tzREVxFQcRhceCwOEgmGYCi5CJ+e+Vve68Zqc7UPkK+pmu0Blup66qj1ltLXVed8fEZ3xv9jQSjXoIN75GQVIrVmcHbzV28cmQdgc5DoGsk25OPe5cn5vfxNM9x5wwp33p/nO3nmI/aq/j9vpfoigWxS1b+tfQKlmSO7HppYnAuP4uamKL4qJJqS+Q8VwEtgX0c7Kzl2oKFuGwWLijOYOP+RtbuqR+SKO4QHSSICfg1Py1qC7nyEMKul3/GEMUPPgGzvggD5ZSSXWBLhUgbBOvISiwhKyGLH1/8Y2555haeO/AcN5ffzNzsucPsvYmJiYmJiYmJiYmJiYnJx5NATCUYUxEFgTTnxM8tfCJdQY1H1gVo7FSxygKfWOakM3kXRCDXkotDNMYXZqQn8Jvdm/igbSt0C+IJsouwGsUmWbls0tQzFsTPVera/Gze/AH5zevIE30AdFoysZ13PZdPn454klhllyXK09y8W9NGz3CsJMDBNj85CXbssoQg27DOXo2YUUps13NonXVE1v8Gy4zrkPLnxaVgC2CVBabI1URiOyi2ySwtv42/NdaytfMjOoTDbNSrqayZTfbheVhFK5PSJUpyLJTkyKQnjr4X+Z6aKM99ECKq6CTYRW5d4qQoM36HU6OqQm24guePbqQt0oYoqiiqgk2LEPLVsKvjEM+oCtFYFKvFCif9nGnONEPs7ha8i5KKyO9sJLNmJ7LFjm35VxAHCNW7evpqlqaVUHNsKwVJU8iqexs2f8fIGy5IUHYbzL4HHKlonXVEtz8JOsiFC5AnLxtRfzVdp94XBiA/cYCx0gnMBemlFLjS+fXeNRz1N/Hfu55kddEyrp+0qE+Y9AJLAefbz2dLeAubQptYr68nZosh6iKzLbN7c40PxoyCFL5/2/m8vPUor+6oZVd1G/vrOll1QSGXnJc3oVKCvLGrloP1nVgtEp+7ZBpSP56cqQ7jXtwRiqFq+pDar+oalV0N7GyrZEfbYY76m/ssT7YlUObJ58367Si6SoLsIKCEccg2Mh3Jo9I3RdPxRoyIDcn2/u1WnG3kFP/7e0H21sZ4ulsYF0/Tx5FO8NhfF+Pp94NEFZ2UBJE7l7vI8Ix9ZJLBhHxREMlwZZDhyuhXx4ipMRp8DdR21VLrrWVTawX7Ax3Y3AU4bEm0t+ygI9xBR7iD3c27T9leFESyErL6COYnCudpzoEjKTT6G9nVtgt3tptcz9DS6Y4EVdd4tvo9Xji6CR2dSQkZfKX8BrKd5gTriUqjv5GjnUeZlDTJTGE8jsTvU9wEpdCWDgE44K1F1TUkQWRleQ4b9zeytbKF25fGcDtO/yCSIWXg1/y0Kq1DE8ULLgJ3Pvhq4fAamHb7wOs68wxRPNQIrkKQrMzKmsXN02/m6X1Pc/+6+3n+1ueRJkjYLRMTExMTExMTExOTs4sgCHzxi1/k97//PQDHjh0jLy+P73znO9x///3cd999XHrppSxbNvAg9YnrrFixgoceeogZM2b0Ll+3bh0rV67kySef5JZbbgHgySef5LbbbmPt2rWsWLGiT33r1q3joYce4plnnulTftVVV/Hss8/icMTfYLfJuUNzt5d4isOCHEdhFpu9Ko+sDdAZ1Eiwi3xqhYvMZHjGVwlAsaUYgKAS4ff7X2JbRwWyKFKWWEZtsJaIGsMiWvlE0ZXkJYyOIHAuUXHMyztbD5DTvJ45ljoQAasTqewKZp+3FPE04y6yKKBpOhZJRBRA0XWCMRW7fHw7OWcmYnI+0e1PobVVE935LFLzQayzbkQYyGFiAqNHg0R3Gtd5efJiCqfM4v4ps6jxX8DfDr3JR21HaVe202HbT3ZoEUpTKVVNCq/tgCSnSEmOTPEoeJGrms5rO8JsOmic24XpMrcudeJ2TJzz+3SD61E1So23huqOava3V7CtrZraaIiIpqBqGpqmEg0eIxKoR1PDvdvZZBu59lxm5M5gSsoUCpMKewVwdz/hu3VNIxLsQms7QnT7k9iWfL7/UP6HniFr3X+QFW4HNQqONLC6oeASOP/r4DE8oLVgB5HND6OrMaSMYiwzrhvxZIeWYJSoqmGVRNLjbMJSD1nOFO6f+0keqXiTtcd28XT1eg56a7ln2nW4rcc9oKfZptGmtrE9sh29ezqNiMiO8A5KrCW4xNOHirbKEjcunMyCkkweffcQFQ1entpYyfsHm/jU8hKmZI1/+pQjzT6e+8CIbvCJpVPJHCA0ussiYZNFIoqGNxIjxdG//QOxMB+1V7GjrZJd7ZX4YqHeZQICUxNzmJ06hTmpU5mUkIEgCMxILuJ3+18kqEZwyDbumXYdqfbR+W06wzF0QEbHIQ98jyjN7RbG1wfZUxtD2BTk5kWnF8aHg67rvLcvwpu7wujA5EyZ25Y6cdrO3nVwpEK+RbIwKWkSk5IM7/3bgX0dR/nN3jV0xYJMyziPG/LnY9NjfTzNa7tqqffVE1bCNPgaaPA1sKV+yyn122U7ue7cUzzN97bs5cFNDxIIB3BvcfPg5cMPUz8U2iM+Htr7Age8tQBckjOHO6deglX6+Mh98SYwj0YKA5PR4eNzlpwFRFFkdkEZdu8mgkqEWn8Lhe5MijITmZSewNEWPxsOHOPKOQWnrStdSqcqVkWz2nzadY2dSzD9U7D5h7D3b1B2KwgD3KAsHrC4IeaDUD0kGA+d9y66lzcq3+Bg20Ee3/04n5r1qSH2/NxBFEWysrLMXBFximm/+Me0YXxj2i/+MW0Y35j2i3/OxIYBLYBP8+EW3UMa9DwdKSkpfPDBB6iqiiRJPPPMM5SXHw/D973vfe+0dQxlnRkzZvD000/3iuJPP/00M2fOHFZbX3nllWGtP1x6fgOT+GE8rofNwe7Q6a74CZ1+tEXh0XUBwjGdVLfIp1cmkJIgcjR2lLAexiE4yJVzORZs58Hdz9AQbMMqStxVeiWhcCa+mB+f4iPJ4iZZSiSs9BVrR0q83890XWdPTTuvbDtCctt2llsPYLUo2K0y8qQFpM+/dkhitdMiIYsCCiAKENN0ZFHAaTn1NxYdSdgWfRalcj2xA2+iNuwh3FGLdc4tSGmTx6CXgzNSG+q6TvSjNehhH2JCOpZpV/QuK0jI4DtzPsG21goer3yHplAHHc61COJepqjL8Ldm0hnU2HI4ypbDUSRRoDBDoiTbQvEwvci7ghpPbAhS02p4ay6bZuPSWfZRFZvOlBMH1+2ynX+b/28UJhUaOb87q6nurKauqw5BcmBPyMPmzITu/mtKhICvFikWY1JiAfOnL+z1/J6cPJlMVya+Lh8ej2dIv5kgiljn3ELk3d+gddSiHHobS9lloKngq4GOCiO65ZYfQywICIZ3eMQLl/8Fio7bWY+FiW5+GD3iR0zMxDrvEwhncC2o6zIEzly3/ZSIDPGETbLwubKrKPHk8ddDr/NRezX/tfUvfLX8BopP8ESdbJnMzshONDQsggWbYCNGDJ/mG9bzYW6Ki/9cNZuNBxp56v1K6lr9/Oi57awoz+HGhUW4bOOTpzgSU/nTW/vRNJ25U9JYOm1gIUwQBFLsVo75w7SFor2iuK7rNATb2NF2mB1tlRz01qHpWu92TtnGeSlFzE6dyuyUyST2k3d6Zc4szkspoinUQaYjedQEcTBCpwtAmst+2mtoWbcw/o/3Auw+GkMkxE2LHKNyrYqpOms+CLHrqJG3+4JiG1fPs0+oiAHDZXryJH4w/y5+uec5qn2N/OPoJm6fvJK7Zt/V51qn6zptobZesby2q+/fRn8jYSVMZUcllR2VvdspmkKNtwZN05BFGZ/fx6ee/xRP73uaTFcmHpuHRFtin4/HfmrZ6cK272qr4vf7X8QXC2GXrNxddhULM6aN2e82EXlq71N8/Y2v994Dv3Phd7h86uXE1BiqrqJoCjE1hqIpvZ+Ydvy7qql9vp+8fkyL4Q/6kVvlAdftsx+9/3p6PsFYkB2NO4zUC4JERIlw7xv3srRgaVwI+ucapig+igiCQGpyCiWePD5qr+KAt4ZCdyYAK2bk8vDag7y79xiXz84/7YNYumzkFW9VW9F1fWgP7iU3wfZfQ9cRqFsP+SsGaig488G7D4IN4CwAUSLZkcy9i+7lvnX38evNv+aq4qtIc45/LqqziSAIJCUljXczTEaIab/4x7RhfGPaL/4xbRjfmPaLf3psGA4b3lq6rqPoymm3Oxw9zMbQRhQUZGSWOJYw1Tp10G0kpEHfMQRBYNmyZbz77rtcdNFFPP/889x44429y++66y5Wr17NNddcQ2FhIXfddRdr1qzBYrHw4osvkp2d3WedgSgtLaWurq43Z1tDQwMlJSWn7fOJFBYWsmfPHlpbW7n++uuZPXs2W7Zs4bzzzuOJJ55AEAS2bt3Kvffei9/vJycnh4cffpiUlBS++93v8sorrxAKhbj00kv55S9/2Vvnbbfdxuuvv85Pf/pTLr300mG1aSxpb2/ny1/+Mi+99BKiKHLTTTfx61//moSEhAHX/+53v8sbb7xBTU0N6enprFq1iu9///t4PKdP7xWPnO3rYUhR6YooCAKkx0k+8X21Rh5SRdPJT5W5c7kTl90YfD8cPQzAFOsUdrVX8bt9LxJUIqTY3PzHjJtIsqSysa4djyWBVHsiIv17MI+UeL2fqZrOtsoWXtleg9BxhCvsu8iwdeGwytgzppA49ybEpKGHUbXLEudlJPJRcxeKbgji52UkDvgbC6KIpXgFUtoUItufRA+0Edn0v1imrkAuvaR/z90xYqQ2VOt3oTbsBkHEOvcWBKmv8CYIAvPTS5iVOpnX6rby/JGNeNVmtkvPckH5NBa5l9Hc4uRQQ4yOgEZlo0Jlo8KrJ3mRT8mSscr93wMrGxWe3hjEHzHy5t600Mn0/PERAPsjFAvxRuUbfO6lzxGIBhAQaNfauW/dfRR4CnrFFIstBVfqDGy2VCySBYtoIcOWwKL0YlbmzCUrYRLbmoKgw9L81FMmWwzLfpqKqHRhySoiengTsV1PIu75A1KgwvAIB1BCxwVxyWp4iOsq2I+H+dU1lei2v6P5mhDsbqwL7kKwjDyfdTCm0haKggC57viLmtAfy7PPo8idxa/2PEdjqIPv7XiMO6ZexOW58xEEAY/kwSk4iegRbIKNqB7FKlhxi6d6+J8OURBYNi2bWYWpPP1+Fe8faGTdnga2V7Vy29IpXDA146ynaXjq/UqaOoJ4XFY+vaL0tPtPdRiieFMgTIhmdnSHRW8OdfZZL8eZypzUqcxJnUKJJw95CNfLVHviqIrhPbSHYgBkJ7uH9PtOy7Nw21IXT2wIsOtoFEGAGxeemTDuC2k8vj5AXZuKKMDV8x0sKI6P55vTkWb3cN+cO/nroddZ37ibxyvfocrXyN1lV2KTjIkTgiCQ5kwjzZnGnOw5p9ShaAoNvobj3uXd4vmupl3UeGsQBdGI1qAbETs21W7CMYzILQ7Z0Vcstxp/3VY3jTocCQcQBZFMeyI3588nVZJp8jeRaEvELttHdF6Olde1rutE1SjBWLD3E4gFCMVCfb4HY8Hesp7vA328YS+H2g+haRqiINKhd/ClV7/U5x440QjFQqiaioCAjo7T4iQYC1LjrTFF8XFgYh4lcYqmaRw5coSyHlG8s5Yr8s4HYEFxBk+9X0mLN8S+2g5mFAye2yFZTEYWZKJ6FK/mJUlKOn0DLC4ovQX2/MXwFh9IFAewpYHkACUA/kpwTQLJxurpq3l639Psbt7NTzb8hJ9d9rMh9/9coMeGhYWFcTsr/eOMab/4x7RhfGPaL/4xbRjfmPaLf3psmJVlvBirqDzV9dTg2+gaPt2Hjo6AQJgwbwTfYFNoU59ckydzR+IdyKd5Hbzlllt49NFHKSsrw2q1kpaWRmtra7/r5uXlsXPnTu677z7+/Oc/853vfOc0vT3ONddcw0svvYSu61xzzTVs3759yNuezP79+/nHP/7BtGnTWLlyJRs2bGDhwoXce++9PP/886SkpPCXv/yFH//4x/zsZz/jq1/9Kg888AC6rrN69Wo2btzIkiVLAMjPz2fHjh0jbstYcccdd3Ds2DHefPNNYrEYn/nMZ7j77rv5+9//3u/6DQ0NNDQ08POf/5zp06dz9OhRvvCFL9DQ0HBKKPpzhbN9PewJnZ5ks8RFTu3NFRFe/jCEDpTmWLh1qbNXIAxqQeqVeqA7RG3lm+jolHjy+NqMG0iyJhBWVMODWTOigQ/mwTwS4u1+FlM1Nh5o5LUdtQS7OrjEtoeZrlqcVhlnQgqOmVd15/gefl9y3Q5SHVaCMRWnRRrSpAMxOR/78i8T2/MSSs02YhXrUFsOY517K2LC2XF8GIkNtVAnsd0vAGApvRgxKW/AdS2izLUFC1mWNYNnqt9jbcMutrTuZ0d7BdfkL+CeOQvxBSUqGhQONcSoblb79yLPsVCSLZOWKNIZ1Hh7V5jtVVFEUSArSeL2ZU5S3eMbKUTVVPY07+H92vd5v+59dhzbQVekC1/Eh4BgaMzdAt6MjPMoSD+Pdl0kpGtYRAuyKLMgo5Qr8y7o41EMkOpQaAtGqfOFKEk5PrFqQPvpGvgbDM/vzsPQccj421kJagQZ0KQCFDGVKGBXVQTZDp4pkJANB54ATTGE8GgXWBIg0Yimqes6sT0vojZXIMgWbBd8GtGRdEa/XZ0v1N1P66hdnyYCBQkZ/GD+Z/jTgX+ypeUgj1S8xcHOOj5XdhUu2cUy5zLeC75HRI1gk2wscy47oyhCiQ4r/3pxGUvKsnjk3UM0dQT50xv72XigkTsvLCHDc3YmHOw80sq6PQ0A/OvFZSQMkG+7h46Ij93eCl47to/aUB0WSadHKpRFielJBczuFsJHKx/4mRJTNXxRQxT3NTWgOYd2DZ2eb+GWJU6e2hhk55EoogCrFjpGFB2hvl3h8XeDdIU07BaB25e5mJJ1bklINsnC58uuZrI7m0cOv8Wm5n3UB1v59xk3DulYkEWZAk8BBZ6+0YAb/Y0s+PMCfBEfNtFGUA3ikB38YOUPEEWRrkhX78cb9tIV7epT5o/6AQgpIUL+EI3+xt66RclGQvI0ZJsxoTXsr+egt4r1u/9ySts8Ng9um9v4a3Wf4o1+ssf6ezXv8aP3fkRICeGQHdy3/D4uKrpoUGG6j8gd7Ra1lVNF7VAshKqrZ2qyPoRiIXRdRxSMCDCSIKF13/M8dg+yKPfe//r7WKTuZcLx75Ig9VkmCRI+r4+M1Axk6YRtT6q3v21PXlcSJbxhL5949hMEY0HcVjeBWIAEW8Ipx5DJ2eHcuqKNM7quE41GKcvIB+BAZ22vl7fNIrG4NJO3P6pn3d6G04rioiCSJqXRqDTSorYMTRQHmP5JQxBv2ATtByGltP/1BAHkBCO3uK8SgnXgmYbkyOa7y7/LzU/fzIuHXuSW8ls4P/f8of8IcU6PDXVdH++mmIwA037xj2nD+Ma0X/xj2jC+Me0X/4zEhhpGuEeh+9+J5SJnJiYtXryYL3/5yzzxxBOsXr2614O9P2644QYA5s2bx4svvjis/dxyyy1885vfRNd1fvrTn56RKF5aWsr06dMBmDNnDkeOHCE5OZldu3Zx0UUXAaAoSm8o+Lfffpuf/exnhMNhmpubueKKK3pF8ZtvvnnE7Rgr9u/fz2uvvcaHH37I/PnzAfjtb3/LVVddxc9//nNycnJO2WbGjBk8++yzvd+nTJnCD3/4Q+68804URUGWz71hgbN9PewRxTMmeOh0Xdd5+6MI6/Ya5/L8KVauO7+vN1llrBINDa8/xuuVmwC4OGcOny6+tNdzbrgezCNpZzzcz8JRhXV7G3hjVx1dgTDzLdWsdO8n2QZOmwNr0UIsZZchWPvPdztU7PLQxPATEWQb1tmrETNKie16Dq2zjsj632CZcV23QD+23p3DtaGua8R2PoMeCyMm5yNPXTGk7ZKsCXy29EouyZnLo4ffYn9nDc8f3ci7jR9x2+QVLC6dzuIyG1FFp6pJ4VC3SN55ohc5IInQ2qURU0EAziuUufvSBCwDeJOPJbquc9R7lPdr32djzUY212/GF/X1WScvMQ9vxIsk2Ul2ZRGM+ElNKiQ5exFtmiE+JMs2LsqexeV580mz9x8VJN/toC0YpcEXZmqyq1dA0zUNrasOvfYodFV1i+AVhvithPqtC8kKnilYEqegdqnomk408zKsiz53PEpB/kpYdy/E/IYgvuJBcBkTAZXK91CObAEBrHNvG1ZUhf7QdJ0Gn3GtyztHvMRPxCnb+Gr5Dbxev5XHDr/D5pYDHA0087XyGyhOKCZTyGTf0X1MnzSdRMvoeDOX5SbxwK3zeW1HDS9vrWFvTQf3PfEh18yfxBWz85HHcFKYNxjlb+8cBODSWXmU5586rq7pGlW+Rna0HmZneyXVPkNQDCkqmg7JNjcXpBczJ3UqM1IKsUsTL8d8RziGroNDltCDw7sPziiwouvw9PtBtlcbHuPXLxieML77aJTnPggRU3XSEyXuXD7+E4PGCkEQuCxvHgUJGfx67/PU+Jv59ta/8eXy6zkvZWRpR7ISsnjwsge59/V78YV9JNmThpVTXNVUfFFfH6G8K9LF3o5a3muvJqIqCGhkoSKIWXhtjj7r9YTzbgu10RZqG9I+Twz5Lgoi7Xo7X/znF8fE69ou23FanKd+ZOOvy+rCITv6LDu5zGV14Y/6uf6J6wlGg3jsHrwRLwnWBNbdtW7UvK5VVaWiooLi4uJRS6P16yt/3Zv2JMGWwIOXPWh6iY8T597b7wSgyJ2FRZTpigVpCLaR6zJm4i6fnsPbH9Wzs7qVdn+YlITBQwClS+m9ongxxUPbeUIOTLoMjrxmiOPLftz/emoEwo1A981VjYF3P1hTmJk5k1vLb+WJvU/wwLsPsOa2NRM29ISJiYmJiYmJiYnJuYqExB2Jdwy6TkAL8IzvGWJ6DKtgJapHsQgWVrtXD+oVJHH6l3tBELjwwgv57//+714P7IGw2QwxUJIkVHV43gDFxcU0NDQgCAJTpx4P+75582Y+//nPA4bwOxR62nFiWzRNY86cOaxdu7bPuuFwmK997Wts3bqV7Oxsvv71rxOJRHqXO51nJmSNBZs2bSIpKalXEAe45JJLEEWRzZs3905OOB1er5fExMRBBfFIJNLn9+jq6gKMQaIeGwuCgCiKaJrWZ+B2oHJRNDw6Bio/+djp8Y7SNG1I5ZIkoet6r91VVe1ti67rfdYfbtsHKld06IwYXl2pdrm3D6Pdp/7aPpw+6Qis2RxkR5XR1pUzrKyYYUMUj9tD13X2hvbTFumiqimMKAh8asqlXJwzG3R6J/2rqkqW00pSXjIhRcNllbHLp577I+1Tf/Y7UzuN5rHnC0V5c1cda/c2EIwo5Ett3JG4m0n2AA6rjJiUizzjOqTkfIQxPPaG0ichczqWZTmG4NxeTXTnM4hNB5BnrurNaz4Wx16PDTVNQ5Kk0/ZJrX4fteUwSBasc25BB7QTbHI6O+U70/ivmbeytfUQ/6heR0vYy+/3v8TrdVu5Y8pFFCfmUpwlUpbrQNNsNHtVKo4pHD5mCOONHTq6bviPWCSoaVHwh1WSE+SzYqfWYCub6jbxQd0HfFD/Acf8x3qH6wASbYksyFvA4vzFLMxZSIGngP/e+if+2bgbUbQgCCIuyUZUU0m3e7g8dz4rc2Zhl6y951J/bUyxirhirVhaK/G1HCMxWI3eeRih8zD5wU5EiwUEgT6ynGgBTxFCcjGaZwp60lRIKoaEPETZgiAIyO01xDb+EbXtCLGjW7EUXmAcY1NugKxFRp5xdwGiOwd0nVj9Ryj7XgFAnnYVUtb0M77uNQejRFUNqySSYpNOe986W/enM72Wn1x+afZcCl1ZPLT/BRqD7dy37WHumnopZYn5tPkihGMqCdLo9UkErpqTz7zJafz9vUr21XXw3AdVfHCwiTsunEppTtKo33M1TeMvb++nKxQlLzWBGxcW9a4fVCLs6TjCzo5KPmqvxhsN9Gn7FHc2OfZ83GIWs1NzmZbm7u3TyW0cSzsNVn7isdcajKADSTYJPWCsMxw7zSiwoKp2nv0gzLbKKKBz/QInwknrn9wnTddZuyfK+r1GuoHibJnVC+3YrYzoXhxP51NJYi7fm/MpfrNvDZW+Y/xk11PcOnk5V+ddcEpbhtKnG0pv4IKsC9i0bxNLZiwh253dp7+D9UkURNwWN26Lm9yEXFRd45mj77HN34rT6mZ6QiZfmnYdWc6UU/qk6zphNYwv6sMb9tIZ7uyNKtIV7TLE9nAXnZFOoyxilNV11fXxuhYxfkOrZCXDmdErRjtkBy6bC6fs7Fe47lnnxGWJ9kQcsgObZMMhO5BEadTOpwcvfZBvvPUNfFEfCZYEfnbJz0h3pPc+q57psdfzXHzy+mdy7N1QegOLchdR21VLYXIhGc6MPvWfC+fTeF8jhjoOYSqdY4AsShQn5rKv8yj7O2t7RfHcVBclOR4ONXh5b98xrr+gaNB60iUjr3iz0jy8BpTfZYjilS/BvHuhv7zgasjI3SNYQFcAFTTBKJds/Mei/+C1yteoaK/gsY8e467Zdw2vDSYmJiYmJiYmJiYmZ4QgCMjC4K9sHsnDhc4LeS/4HjEMYXyZcxkeaXRyRd9zzz3MmjWL1NTUUalvIH7wgx+cUrZgwQJ27tzZ+33dunUjqrusrIza2lq2bdvGvHnziEQiVFdXk5WVhSAIpKam4vV6WbNmDV/72tdG1oGzRGNjIxkZGX3KZFkmJSWFxsbGAbbqS2trK9///ve5++67B13vxz/+MQ888MAp5ZWVlb35yz0eD9nZ2TQ1NeH1envXSUtLIy0tjfr6egKB4wPUWVlZJCUlceTIEaLRaG95Xl4eCQkJVFZW9hnsKCoqQpZlKioq+rShuLgYRVGorq7uLRNFkZKSEgKBADU1NbS3t3P48GHsdjuTJ0/G6/X2+Y1cLhf5+fm0t7f3SQsw3D6JSenoOkhKlLrqqjHrU11dXW+51WodVp8cLg/rDieyvzaErmksnhwg1xaho6Nvn/Zr1RxNrkfVdPSwjc+kLyA34Opt60B9chcVoWnCqPWpoaGh135ut3tU7DQax543pHDYZ2X9vmMEQxFcQoTVnoPMc9TjdtrQJBetybMJJZdCSwir98iYHnvD6lPqMialTkE//A7hoztQGw7hzVtB1JUzJseepmm0t7fT3NxMbm7uoH0Kt9eRVvkSgqYiTLsUZ0IaVVVVI7JTEiKfTVpOpb2TNTXvs7/tKN9u+yuzXJO4PGUWF0yfQzAYpLOpjnQR0nOhyOPguZ2GUGYRVUAnFBE5WNXEwvNyx8ROISVEk9TEjtYdvHngTap9x397i8WCVbIyxTWVGamzKEoqxWVLxpbmpinYwR/2vE5LrIuqcDMW2YGAoZ8rus6VCecxL6EIu2LHIdvo7Ow07KTrSLFOEtUmMq1dhBt2obTsxxKsYa4SREVAFkVUNNTegWWdqKcAW9YMOvU0/JYsoq5JxBw5ZGXnGn2qqiIaiUJTFJqqeu1U3RrGkXwe7sYtRHY8hyMxF0tS9gnnUzL4fBQXa8RajxDa8jiCphJMmY5fzaQEzvi612L1ABKJeozKw4dHZKfhHHs9jOW1fLBj78fz/4Wfbv0H+311/PyjZ4hoMQTA2bqWa5PnMt9ZhFWQEQRh1Pp073Xnse6jozyx8TBHmzr50dNbmTMpkbuvmocSDozade/dfY1sP9yBJArcvjCPtqiX1w+8zz5fLUfCLWjoyLKMKIqIis4UeyaljhyKHdmcN3UaHVGNrfVt1Hf4kDsax9VOpzv26qIyMURC4U7sijGJbbh2skePsLDAyvrDCWw6YKSLWDlNpb6+vt8+1dY3sr4ygaPtVkRBZMV5LubmBqg9evw3mKjPe6Npp1td5/NSZDs7Q0d5omodHx2r4NrEudi6HQWH0ydN0yhPKifDmTHiPnUqAZ5p30KD2ommacx3FHFFwiy66lpRXaFB+4QfCEASSSBBWr5hp9ra2lPsFJbDzPvDPALRAC6Li0AsQKI9kbWfXktXQ9fw7RQFokafJmVNwu/3U3d09M+ni7IuYvNnN/PhoQ9JEpJIl9OpqKgYtWNv8uTJqKrK4cOHe4XX0Tr2JnmMvO3Hjh07Z8+n8eqT3+9nKAj6RI9HhTEj3ePx9M5mn6jouk4gEMDlcvHskQ08d2QDSzLLuWf6db3rbK5o4k9v7MfjsvKzTy1EGiQvSEgL8ZTPyCF4W+Jt2IRhhGJ76VZo2Qmz74G5Xzl1uRqBlo2gqaDrgAIIkLrAyO0DPLPvGb71zrdwWVy8dudrZLgyTq3nHONEG451ODGT0ce0X/xj2jC+Me0X/5g2jG9M+8U/PTaUJIkjR45QVFSE3T54dKkeAloAn+bDLbrPKG9kD/3lD3/ooYdobW3l/vvv56677mL16tVcc801FBYWsmfPHhISEnj55Zd55pln+Nvf/tZnnRUrVnDgwIFez+Qbb7yRG2+8kYceeuiUvNarV6/mS1/6EitWrOhTvm7dOq666ipSUo6HzNywYQMrVqxgz549tLa2snr1arZu3QrA17/+dWbMmMFdd93Ftm3b+OpXv4rP50NVVb7zne9w66238q1vfYsnn3ySnJwcCgoKWLhwIV/60pf69OlEwuEw1dXVfWwzGu+r3/zmN/nJT34y6Dr79+/nueee4+GHH+bgwYN9lmVkZPDAAw/wxS9+cdA6urq6uPTSS0lJSeHFF1/EYhk4H2d/nuI9gyA9/ZwoXgE9nOgpHgwGcTqdxiD5GHo67Grx0RqMMjnJSdEJeVUniveGP6zx+PowDR0qFgluXuSgNFfus76qqrxRv413g+tJ8kioQTufz72DFGvCuHik9Ge/8fRIOdYR5LWdtXxwqBlNBwGNy1OOcZF9Pw5JQUBAnnQ+ctll6PLxY2AietlonXVEtz2BHmwDBKSpF2IpvRRBlEb12NN1nWAwiMvlGtRTXFViRDf+D7q3ATGtGOvCzwza1+Ece51RP09Wvsv6pt0AWEWZawoWck3+AizC8WgpXSGd/37OTySm4bAKhKI6NovAN290j5qnuKZr7Dy2k011m9hUt4mdjTtRdAUEEUm0I8oO8pMnk5tUhMeZAaKVtkgXUU2hPyJqjNawFxEBhyRhk2zEdJ3/nv8vTLMnIHgrETsPo3dUoHdUIHRWGDm8oVdE70FHpN2aQ8BVRGZuOZaUYnTPFIKWDFzupH7tN5RjT9c1Ypv/ht5WiejJwbrki+jCSWOgYS/RDb9Hj/gR00uR59+JIIpnfN0LxlQ2NXSCAEtyk7GfENZ7vO9PY+mJF1MV/l75Dn+teANN1xEBDRAFgUx7MhZRIsHiIMHiwGWx45LsJMh247tsx211kiDbcUg2Yz3Zjku247LaEQVxwD75Q1Ge/aCa9fuPAQJuh4WbF01mYXF67zvJSPtU1+rje89sxSe3M7VEx29tpSnU0acdWY5k5qROZW7aVKYm5PSm+uixh6JprD3ahg4syUnCYZEmpMdkOKbwbm07AMtyk1CjERISEoZ8jJ1cvvNIjOc3GykEFhRbuXKO9RR7dPhVHns3QFOnhiTCdRfYmTfFPmG9QHsYSzutPbaLhw+/iapr5DrT+Fr5DWQ5kofVJ13XCYVCuFyuPusOtU/b2w7zx4P/JKhEcMo2Pld6FfNTj0cRHu1j76k9T/GNN79BUAnilJ38/LKfc3P5zRPaTmN97AmCQCAQwOFw9Blbiec+nYt2Orm8q6uLlJSU076Xm6L4GLG34yg/3Pl3Umxufrvont6TJ6ZqfOPhTfhCMf7tinLmTUkftJ7nfM/h03xc4ryEXMsw8ulUvwprv2YI3LesBbmfwbTQMSNkuqaCroEog+yElLkg2dB0jdueuY1dTbu4pvgaHrz8wWH8AiYmJiYmJiYmJiYmI6E/4dVkYjBWonhLSwttbYPn/ps8eTKPPfYY9957Lx0dxweEFUXBbrfz9NNPDxo+3efzcfnll+N0Onn55ZeHfWzF43v5WKNoGuuOtqHpOovzUkiwTqxgfG0+lYfXBmj3azhtAp9c7iI/rW8bo6rCXyte572mj5hebMdlsbHas4pc66n56T9uVDd18cqOGrZXtfaqiEuyolzr+ghXtBUQEJNysMy8Him5YFzbOhx0JUJsz0soNdsAEJPysM69FTGhnyiDY0zswBvEDq1FsDqwr/gagn30ry3VvkYerXiLA95aAFJsbm6fspLFGdN7x+q2VUZ5amOQqKJjlQVuWeJk3pSR5xvWdZ2qjio21Gxkfd0HfNSyn4iuIcoOJMmOKNtxWD3YrQk4ZAd2ix1JODWtiYBAsi2BDHsS6Q4PGXYP6bINm6bwk71riMSCuNQIAcmKQxD4Y2A3qaGm/hsliODON0KdJxdD0lTjr6eIHS0hWoIRJnmclKYm9L/9SH6HcBfhdb9GjwaRpyzFWn718WWxEJENf0DzNSN6srAt+QKCPAxnoEE41ObniDdImtPK3KykUakzXtjbcZSvb/4jqq6hA6qmoeoqaXYPNmngiXCDIQoCzm7x3C13C+qyHXe3mO6yOHBbHHR4Vd7e2Uhbu4KoWZiWk8qnlpeRnXz6dDRt4S4aQx1kOZJJtSfSGfWzreUwf97yAS16IxYLJCd0p+oRRKYlFTA3bSqzU6aQ5Tw1v/jJbGnooDMcY3qam7zEiZljvjEQ5qOmLhKsMovzTt+nobC9Msrzm4PowKISG1fNs/de92paFP6+Pog/opFgE/nEhU4K0ifWc8x4cchbx6/2PE9n1I9TtnHPtOuYkzb19BueIYqm8kTVOl6p3QLAZHc2XylfRYYjacz33ehvpMZbQ4GnwMxzbRK3DPV91bzSjSKqqlJZWcmUKVOYmpiNJIi0R3w0hzvJdCQDYJFElk7L5tXtNazb23BaUTxdSsen+WhRW4Ynik+6zMgv7m8wwqiX3nzqOo5ssKYYIdMF2RDI1SB07obk2YiizHeXf5fVT6/m5YqXuaX8FhbkLRjOTxJ3nGhDSTp9nkWTiYVpv/jHtGF8Y9ov/jFtGN+Y9ot/emyYmzuM536Tc4L09HTS0wd/NwRYtGgRnZ2dvaHgAd555x00TWPBgoHf1bq6urj88sux2Wy8+OKL5/xki7N1PWwNRtF0HadFwmWZWNfdulaFR98NEIjoJLtEPr3SRVpi3zZ2RHz8cs9zHO5qIC1ZJsnmJMeaSY4le5xabTCe9zNd19lf38kr22rYX3d88smCSU6uT6rA3b4HoiBYHFimXY5UcAHCIBEAJyKCbMM6ezViRimxXc+hddYRWf8bLDOuQ8qfNyrRZoZiQ7W9hljFuwBYZq4aE0EcoMidxXfm3MGWlgM8XvkOreEufrfvRd6o28Yniy9mamIu86ZYKfa04m+qJiGziMS0pCHVres63liAlpCXis6jfNi4iwPtldQFmonqIEo2EATkpGKsgoij2wvXIVmRBRF0DZcokiFopAsaGahk6DEytDDpip+0WBeWTj9EfcYn1h16VFP4smbhd+lLCYoWHFqUe1o2kCrGjJzf7rzjwnePCO4p6t9hBshL1GkJRmjwh5ma7AJdG5VzULAnYp19E5Etj6JUbkBKL0bKKEHXVKJbH0fzNSPY3VgvuGvUBHFN16n3G96xee6JKX6OJVmOZFwWByElglWXiMoqDtnGbxfdg02y4I+FjI8SJtD91x8LEej+64+FCSjGX78SIqLG0HS9d7vTJmpJh4BbwR+OUaPDW2tlMlxuJqUkkWh19HqlJ8gO3FYHLtlBhbeeF2reJ6oqCALku9LxxUL4QjECxBBFgfykVOalT2VO6lRmJhfiGObxkuqw0hmO0RaKTlhRvD1khEtPcVhG7T44d4oVHXh+c5BNhyJEYjqziiwca1d586MIqqaTlSRx53IXSa74upeNJSWePH44/zP8au9zVHjr+fnuZ1hdtIzrJy1CPDniRT+MxH4toU5+u+8FDnc1AHBF3vl8YsrKPpEPxpKshCxTDD8Bc2zl3MYUxUeZHld9m2RlcmI2Fd56DnTW9oriAMvLs3l1Rw37ajto6gySmTTwjLl0OZ2qWBUtasvwGiJKMO1O+PCnsO9hKFkN/b3YSDbjA5A8E9p3gBIA7z5ImkF5Rjm3z7idx3c/zgPvPsCLt7+ILJ7bh83JYRhM4gvTfvGPacP4xrRf/GPaML4x7Rf/mDY0GYxp06ZxxRVX8LnPfY4//OEPxGIxvvSlL3HbbbeRk2N49tbX13PxxRfzyCOPcMEFF9DV1cVll11GMBjkscceo6uri64uI4xuenr6OTvQczbOpeagkQMvw2WbUGkrDtbHeGJDkJiqk5Ms8ckVLtyOvoO4h7x1/HLPc3ijAVyynYUFecSkIMXW4gnRl7N9LdR0nR3VrbyyrYYjzT4ARFFg4dQ0rs1qwV33Knq7IbTJBfOwTLsCwTZ6HrXjgZwzEzE5n+j2p9DaqonufBap+SDWWTciWM5cNBrMhroSJbrjKdA1pLxZyLnnnfH+BkMQBBZkTGNOajGv1G3hhaPvU9FVz33bHmFJZjm340d//346kLChwoU/MsbRgJASoSXYTrO/kZZAM83BVlpCnRwLd1IT7KRLCRNWo31DnQsWJAHs6LjQydXCFKphMsNhMrQQ6VqYdDVMhhbGpfcfIv10rPRXcl6kmSZrCplaiFTFD5f8EaZeD8O0X5rDil2WCCsqTcEImQ7LqJ2DUtZ05MIFKEc2E93xNLaFnyG69xXU5kOIVju2BZ9GdHhGZV8ATYEIMVXDJkukO0fu7R+vpNoTuWfadfxu3wsEomFcVjv3TLuO7G5v6hSbe1j1RVWlVzAPKIZQ7o+FCMTCvcK6r4/IHsIuhbFbI3SFokRiMY4F22kJd5LotGCV+z5zqJpGU7ijO9y7gIbOQW8dyZZEFJ+DpEg+nz1/AVeUlQ1JjBzwd3FYqewI0B6Ooev6hLjPnUyvKG43jtvROgfnTbGiaTpPbgzy+s4wb+4Ko+ngdgjMm2LlpkVOrPLE+z3Gm2RbAt+ZfQePVLzJWw07eLp6PdW+Rr4w7RqcQ5iUMRz7bW05xB8P/JOAEsYl2/n8tKuZn1ZyJs03GQXM9/Jzl3Nb3RxnpiUVUOGtZ39nDcuzjz/gpyc6mFGQwp6j7azbe4xbl0wZsI50yfAWaFVbh3/TLrkZdjwEHRVQvxHylg6+vmSHpBnQsROiHdBVAYklfG3h13j18KtUdlTy8M6H+de5/zr0NpiYmJiYmJiYmJiYjIg4yHT1sWMi2OTxxx/nS1/6EhdffDGiKHLTTTfxm9/8pnd5LBbj4MGDBINBALZv387mzZsBmDq1b+jH6upqCgsLz1rbzyVUzfCsBMh0jo6H42iwrTLKC1uCaDpMzZK5fZkLm6XvOMLahl38teJ1FE0lz5XO3eWXslFZh4jIZMvkcWr5+KBqGpsONvHqjloaO4xzxiKLLJuWzZWTwVn1Glp1AzogerKNUOkpk8a30aOI6EjCtuizKJXriR14E7VhD+GOWqxzbkFKG7tjIbbvFfRAG4LDg3Xm9WO2n5OxSjKrJi1medZ5PFm1jvWNu9nYsIN1vjoC6ZegCiKirjFzxz+w7nuFFkHGhwDd1/6wrhPWdUK6RkTXe3Nza0oQLeYnRQ0xVY8wB4UFokqeFsGjR+kj5Uk2sLrBnmb87flYEgb/fmJZuB0eX0BqzE+qqIESBlsSFKwYtiAOxqSBXLedyo4AdV0hMh0jC7M9EJbyq9HajqC2HyX0yneNNI6CgFR0LaJndKPj1PmMySu5bvuEFD7PBitzZlGeVMC2Q7uZVzKTDGfy6TcaAKskY5USSB7mJCBFU/HHQnxQ1cDzWyvoioTQxBj5OQ5mFCWiCDH8sTB1gRZawp1I3RE3bN1pBFJaZkJnMheWZ3PVtNIRt7+HRJuMJArEVA1fVCHRNrrH+JkSVlSCMcNTPtluoTdnxyhRkmtB1YxLmYbxN6bCFXPtpiA+CLIo8S+lV1DkzuavFa+ztfUQ9217mP+YcRM5rtQzrl/RVP5e+Q6v1W0FYGpiDl8pX0WaffQmCpmYmJyKKYqPIdM8BbzIpt68RSeysjyHPUfb2XDgGDcsKDxlplwPyWIysiAT1aN0ap0kS8N4kLElGjNb9z0Ce/92elEcwOIGz3To3APhRpDsJCZM4huLv8F/vf1f/HbLb7m65GoznIaJiYmJiYmJiYnJGGGxWBAEgZaWFtLT0z+2g7oTDV3XaWlpQRAELJbxG0xNSUnh73//+4DLCwsL+4j3K1asmBBi/rlGeziKqunYZIlE2/gPrei6zrq9Ed7+yBCEZhdaWbXAgSwdv34omsqjh9/izfrtAJyfXsoXp13D7tguUCDPkodDnJhhZUeLdn+YZm+IJKeN3TXtvLGrlnafMbnBYZO5aEYOl5QlYz/yDsr2rWiAYLFjKbsMadKCuAuVPhQEUcRSvAIpbQqR7U+iB9qIbPpfLFNXIJdegjDKoVvVpoMoR4yJOtbZq0fFK314DYiS3L6XLwQOclnXTv43ApscWWgICLqOLspsceaRqYRQdZ2wrhFVI/iiXURjfrRYEDUWQFOCZAkCixJSWJoymQV580lyZXQL2AnG+FqvoH3C9xHmde6DKwtWPAjr7oWYzxDLVzxolI+QXLedqs4gneEY/ujIvNcHQpAsWMqvRn3rJ6BrgACiBaVqA3LJylHzFA9EFTpCUQTB6M/HmVRbIkX2DFJtY5OW4HTIokSSLYErppWwfMpknt9czdu762mthC11MjctKuPC6dl0RHx8fuOvCSkRXLKdgBJGiQkoXQ5ykxzctmR08jiLgkCK3UpLMEJbKDbhRPH2sOEl7rZasEgiqqqOav1tPsPjNcEOUQWsFkMY7/TrJLtGdVfnJCtzZpGfkM6v9jxHQ7CNb2/7G/dMv455acUjrrM51Mlv9q6hyncMgKvzL+DWySvOWrh0E5OPM+P/5nYOIYoiRUVFiN0vScWeXERBoDnUSVu4i9QT8iPNnJRKittGuy/C1soWFpf2/+AqCiJpUhqNSiMtasvwRHGA6Z+EfY9C/XvQcRiSh/AwYUsF91TwHYbAEZDsrCpbxVN7n2JH4w7+e8N/86srfjW8dsQJJ9vQJL4w7Rf/mDaMb0z7xT+mDeMb037xT48NLRYLeXl51NXVceTIkfFulskJCIJAXl7eORty/FzhbFwPmwOGkJrhtI77xBVN03l5a4gth41w7hdOt3HprL5ekl3RAL/eu4b9nTUA3Fx0IddPWoSOTlW0CoCpltERH86UsbLfe/uO8cc39+ELxVA0jUSHFadVJtFp5dJZeawoz8J6bAexTY+ixLpDpefPxTL9yrgPlT4UxOR87Mu/TGzPSyg124hVrENtOYx17q2ICWnDq2sAG+rRINGdzwAgT16MlH4Wjjldh87D0PA+1G+Axg9BCQEwGbhTcrPFlkase1UdHVkQqfPV0OGvR1MjGD7hAsmOFBblL2Jx/mIW5S8iLzFv7Ns/ECWrIXcpdNVAYsEZCeIA9u5w482BCA2BKJNH+xwURWNCgBoDqxNkG7oSRg+0wSiJ4j1e4mkOG44BnI8+Lkyk9wKHVeYTy4pZXJrFI+sOcrTFz6PrDvH+gUY+taLECPe+/0WCagRRl/G0lGDFwd2XTsdmGT07pjos3aJ4lKJBUpmOB+0h4/6d0h2lYbTtl+oWscoCkZiO2yEQiurYLAIp7vE/PuKFqYk5/GD+XfxmzxoOeGt5cPcz3Fi4hBsLl54S2v909tvScpA/HfgnQSVCgmznC9OuYe4ZCOwmo89EuoaajD6mKD7KyPLxn9Qp2yhMyKTK18gBby1L7OW9yyRR4MLp2azZfIR1exoGFMXBCKHeI4qXMMx8EokFUHAx1Lxl5BZf8v2hbefMBTUMwTroOogo2bh/xf3c8OQNvHr4VW6pvYXF+YuH15Y44UQbmsQfpv3iH9OG8Y1pv/jHtGF8Y9ov/umxYUJCAsXFxcRisXFukcmJWCwWUxCPE8byeqjrem8+8UzX+IZOjyo6T28Msr8+hgBcPc/BwtK+bar2NfLLPc/SGu7CLln7eDcdjR0lrIdxCA5y5dENY3wmjLb92v1hfvvqHnyhKAKg6eALxfjEsqlcPjsf2ddAdPOfiHbWAyAmZmE573qklMJRbcdER5BtWGevRswoJbbrObTOOiLrf4NlxnVI+fOGNQHkZBvquk70o+fRI35EdzqWaVeMdvOPE2ozRPAeITzY3He5Iw1ylkDuEpo1Gf+eNQiijKpGkCQbESVMh68Gp2Rhfu4yFucvZnH+YkrTSs8or/Go48o6YzH8RPLcDpoDEY75w0zxjK4Hv+BKQ7C60JUwyDaIhRBkO8IohCAGI6VFg98QxfMSP95e4j1MtPeCwgw331o9j3d21/P85moqG7t44KltXDY7j+/PuJuPGht4/YNmBMXGqoWFFGYML//56UhxGLm6O8MxVE1HEidOJKaOcN984jC69vM4RW5Z4uSpjUHCMR2bxfjucU6g61kckGRN4P/Nvp3HK9/h9bqtPHdkI0d8TXxx2rW4LH2vO/3ZL6YpPH74Hd6o3wYYDpVfnn69GS59gjLRrqEmo4dp2VFE0zQqKiooLi7uHSgpSyowRPHOWpZklvdZ/8LpObz44VEqG7uoafVTkNb/zOOevOItSsvIGjbjM4YofvgFmPcfYB+it3nCZFAjEGmBzj2Upczhzpl38shHj/C9d7/HS7e/hGU0wj5NIPqzoUn8YNov/jFtGN+Y9ot/TBvGN6b94p+TbdjzMTExGR5jfT3sCMeIqRoWSezO/Tk+BCMaj70bpKZVQRYFVi92MKPA2medjU17+d8DrxDVFLIcydw7czW5ruNev4ejhwGYap06YcS+0bafpus8tbGyVxC3yBJ2q4Sm6ZSk2WDvi4RrtoAOgsWGXHopcuGiczJU+lCRc2YiJucT3f4UWls10Z3PIjUfxDrrxiGFO+/Phmr9LtSGPSCIWOfcijCa40lKBJq3GwJ4w/vQtq/vcskGWef3CuFdzixeq3ydNbueZEPNBsKSk8LcpUiiDU1TaGjczPcWf507Zt6BTR7fiS9nk1SHBYdFIhhT2Vl5hHnFRaN2DRUdHqzzbiW67Ul0JYwg27HOu3XUQqc3ByPEVA27LJHmsJ5+g3OcifpeIIkCl87KY96UNP6x4TDbK1t57oNqHl4bQ9eNmAzTcq1cMadg1PftskjYZImIotIZjpHqnBjHSTCmEoqpCIJAUvczxVjYb94UK1OzZdp9Gilu0RTER4gsSny6+FKK3Fn838HX2N52mO9s+xv/MfMm8lyGhtOf/ZpCHfxm7xqqfY0AXFOwgFuKlpvh0icoE/UaajI6mKL4GFOWlM8rtVs40B2i7EQ8TitzJ6ex9XAL7+5t4JPL+/cC7xHFvZqXiB7BJgzzgTxzHqSWQ9teOPAPmP1vQ9tOEMBTCh0RiHVBx26+PP/zvHL4Fao7q/nrzr9y97y7h9cWExMTExMTExMTExMTE5M4pjlohE5PH8fQ6R1+jYfXBmj1qdgtAncud1GYcXyIR9U1nqxax8s1Ru7m81Im8+Xp1/fxZApoAeoVwzN6JKHTtZAXPdCK4EobNWFrtAlFFf73rf1sr2xBEAQkUcDjtBKKxFjiridn7/soqmFPOW+OESrdPrreifGK6EjCtuizKJXriR14E7VhD+GOWqxzbkFKmzysurRQJ7HdLwBgKb0YMekMoxL0hESv3wD1G6HpQ1DCfddJKTPCi+cshsx5KKLMhpoNrNnyP7xd/TZR1Yj2YJWstPvrqKh6kfSEXDqDzTgkmauKr/pYCeLQnSLE7eBQu58uRn/Cj1xwPmJ6CXqgDcGVOqrXjbouIyR+rts+7iktTE5PSoKde66YwXv7GvjJml0oqoYogA40dobwBiOkJIyux78gCKQ6LDT4VNrC0QkjineEjWuRxyYjj7H3usdpiuGjxYVZM8l3pfPLPc/SGOrgvm2P8IVp13BBeukp625u3s+fDr5KSImQYHHwxWnXMCd1YqSsMTH5OGKK4mNMmScfgPpgG95oAI/V1Wf5ivIcth5uYdPBJm5eNBm79VST2EU7iWIiXVoXLUoLeZZh5iwSBMNb/N2vw/7HYeZnQRrijV+QIGkGtO8ANURi6Cj/uejr/Ofb3+T3H/6ea0quIcedM7z2mJiYmJiYmJiYmJiYmJjEIbqu0xwwBrAznOMjmB1rV3l4XQB/WMPjFPnUCheZSce9WAKxML/d9wIftRu5wq8tWMgtk5cjneQJXhmrREcnU84kUUocVhuUmg8Nj081giDZsM67Fbng/DPv3CjS1BnkoVf30NAexGaRuXFBETv2V1IkVrI49SiliREkVUZ0Z2I57zqk1OEJvR8HBFHEUrwCKW0Kke1PogfaiGz6XyxTVyCXXoIwBA83XdeI7XgaPRZGTM5HnrpiZI0JtsKxTceF8NBJ0RQd6ZC7xBDCsxeBMw1d19nfup81m37By4depi3U1rv61JSp3FB2A9eUXMOGmg3c+8a9dPjrcFqcPHjZg2QljF5Y8ngiJ8FORbufMBK+qEKSY3Q95ESHZ9RyiPfgjyp0hGMIgkCu2wydHk+kexw4rBK6LhFVVJw2maiq0ewNj7ooDpDqsNLgC/fm8J4ItIe6Q6ebEQ7ijiJ3Fj+Ydxe/2fsC+zqP8qs9z3HdpEWsyDyPqnAz7mAabxzbxpv12wEo9eTxpenXk2of3jOXiYnJ6GKK4mNMgsVBviud2kALBzprWZBR1md5WW4SmclOmjqCbDrUxMoZ/c+WTZfSDVFcHYEoDlB4BXz4UyOPUtU/ofiGoW8rWiBppiGMK36uy5zCU9nz2XpsK/+94b/5zZW/GX57TExMTExMTExMTExMTEziDF9UIayoSKJA6jgMYFc2xvj7+iARRSfDI/Hpla4+Xl91gRZ+sdvwWrKKMneXXc3izOmn1KPr+vHQ6cP0EtcCHUS2PALRICCg60EiG/9EdO8/ESQrIEBP6HFBBATDc7P7//T8XwAQu78fX64DKT4fsfYkFFHss+zkdYWTvvf8v9kb4sPKVmYoOvPcMotKs/EoB7gtY4cRalsQEOUULDOu7g6VbobGHAwxOR/78i8T2/MSSs02YhXrUFsOY517K2JC2qDbKtWbUFurEGQL1jm3DD0svRKBpm3QsNEQwtsP9F0u2yHz/ONCeNJUw/5Ac6CZF7f/mTUH1lDRXtG7SYojhWuKr2FV2Sqmp0/v9ShePX01SwuWUuOtocBT8LEVxAFsskiG00pdNEq9L0ySY+J7y9f5DC/xNKcVu2yey/FEhseB3SoRiqgku2wEIgoOm0SGZ2wmN/Tk7O6KKkRVDas0vl7Tuq7T3u0pnjKO6VhMRk6i1cV/zb6Nf1Su5ZXaLTxRuY7/O/gaog5qwz9JsDhwyjaum7SIm4suPGWCoomJydnHFMVHEVEUKS4uRjzpAb8sKZ/aQAsHvaeK4oIgsLI8hyc2HGbdngZWlOf0G+YnXU6nMlZJizrCvOKSBaZ/ErY+CHv/BlNX9b4sDAnZAUnl0PERQqyD787/NKte3sHrla+zoWYDSwuWjqxdE4yBbGgSH5j2i39MG8Y3pv3iH9OG8Y1pv/jHtKGJyegwludSj5d4msOKNMZhTk9m15Eoz24Koek6RRkyn7jQhcN6vA1bWw/x+30vEVajpNkT+fcZN1Hk7l/ca1Kb8Gk+LIKFQkvhkNugdtQQ3foPiAQwROjuBZqKHvYNGJVOH/IejLUTAK21heM7GPq2gYhCNBRjlghWp0iSy4Zw7AhqsA1B17vbLYKmImXPNAXxISLINqyzVyNmlBLb9RxaZx2R9b/BMuM6pPx5fcayes5BAi3E9r0KgGX6VYML6LoOHYcML/CGjdD4IXSHtu8ldXpvXnAy5sIJ4c1DsRBvVb3FmgNreL/ufTRdM/YrWri46GJWla1i2aRlyGL/Q6FZCVkfazH8RPITHTQFozQGo5Rq+piHdD4TVE2nwW8cJ/mml3gv8fJMmZJg5+5LpvOnt/YRjKk4bBJ3XzJ9TLzEwZj0kWCV8UcV2kNRssZoP0MlGFOJKBqiIOCxHRfF48V+JgaSIHLn1ItJsyfyk11Pouo6IgIaOl3RAPfOvIllWTPHu5kmw8A8B89tTFF8lFEUBau170vgtKQC3qzfzv7O2n63WVyaybMfVFHXFqCysYup2aeGEerJK96qtqLpGuJIZhWV3go7f2/Mrm3cAtkLhre91QOeMvDuo8Ru45PTruVv+9bwvXe/x8ufeBnrUEOyT3D6s6FJ/GDaL/4xbRjfmPaLf0wbxjem/eIf04YmJqPDWJ1LTd35xDNcZ897Utd1NuyP8vpOwxtyRoGF1YucyJIhVGm6xpqj7/NM9XuAMQbx1fJVJJ6Uvu1EerzEiyxFyMLph4b0iJ/Y/tdQaraha6rhCS7IYHWBEgLZhn3JFxBsCaBrgI6ua4bQiW6U6XT/PbGs5/8nrK9rxGIxZFky/MZ1vbe8z7p96jBsvvlQE0c6vQjA5Cw3509ORRJADbShVr4Hkowg29EFEV0JowfaRj2U87mOnDMTMTmf6Pan0Nqqie58Fqn5INZZNyJYHL3rxaIR2PG0MfkgowRpUj9jUMHW457gDe9DqLXvcmemIYDnLIGcReBI7bNY0zU+rP+QNQfW8FrlawRjwd5lc7Pnsqp0FVcWX0mizQxVOxyS7RYckkBY1Wn0h8lLdJx+o3GiKRBBUTXssjQu0TsmMvHyTLlsejblBck0e8NkeOxjJoj3kOqw4o8qtIVi4y6Kt4eN0OlJdsspE+3ixX4mx8l3ZeCQbUSUGBo6NkHGKsmkmveguMQ8B89dTFF8FNE0jerqaoqLi5Gk4zONy5KMvOI1/mYCsTAuS98brstu4YKpGWw80Mi6vQ39iuJJYhIWwUJMj+HVvCRLycNvoM1jeIgf+Afs+evwRXEAezqok8FfxZdLVvBy1VqOeo/yf9v/jy+e/8Xh1zfBGMiGJvGBab/4x7RhfGPaL/4xbRjfmPaLf0wbmpiMDmN1LgWiCoGogiAIpDnPziCZpuu8uj3MpoOGGL+o1MaVc+2I3V65ISXCH/a/zIethwC4LHced069GHkQ7+eoHuWochQ4feh0XdNQjnyAcvBN9FgYAMuk8xHcmcR2v2jkFLc4sc67FSmj+Iz7C6CqKkcrKiguGLr9OvwRHnp1D0eaJUQxl1uXTGHpzNxe72Up5CVct9MQwgURYiEE2Y7gSj1NzSb9ITqSsC36LErlemIH3kRt2EO4oxbrnFuQ0iajaRqtW54jxVuPYHVinX2TYQslDI1bjwvhHYf6Viw7IOuC40J40pR+oxxWdVTxwoEXeOHgCxzzH+stz0vMY1XpKq4vu54CT8FY/wznLLquYwl1EbK6qfOFJrQo3hM6Pc9t7zfy5seVeHumTEkYezG8d18OC0e90BaKouv6uB43PbnNk08KnR5v9jMxyHIk45TtoIOogyaAQ7aR6RiBjmMyrpjn4LmNKYqfBZKsCWQ5U2gMtnPQW8vctFNfFJeX57DxQCMfHm7htqVTSTjpZigKImlSGseUYzSrzSMTxQHKP22I4rVrwXsEPIXDr8OZB2qYBBr45swb+PqWv/CHbX/gutLryE3sPye6iYmJiYmJiYmJiYmJiUk80xzszvvpsGA5C+EUY6rOs5uC7KkxPMkun+1g6TRr7wB+Y7CdX+x5jrpAC7Io8S8ll7Mie9Zp662OVaPoCkliEmnSwOGs1bYqYrtfROtqAkBMyjFCZadMAkDKnYUeaENwpSKOo7f14WNefvfaXrqCUZx2mX+7vJxpeX3HTESHB+u8W4luexJdCSPIdqzzbh3Xdsc7gihiKV6BlDaFyPYn0QNtRDb9L5apK9AtNtzHNqJbHVhLLkQ49JQhhDdtBTV6Yi1GSPTcpd0h0ecMGIK/I9TBKxWvsObgGj5q+qi33G11c+XUK1lVtoq52XNNYXSUSCSGX4CuiII3EusT2nmi4I8qdIZjCIJArhk63WSIJNuN+2hYUQkpGk7L+AheRj5x4/5uRjk4N0i1J3LPtOv43b4XCEQNx8h7pl1Hqt30FDcxmUiYovhZYponn8ZgOwcGEMUnZ7opSE+gpsXPxgONXD47/5R10qV0jinHaFFaKLWWjqwhniLIX2mI4nsfhsXfHX4dggDuqaBFuGbSIp6qepctrVX86L0f8burfzeydpmYmJiYmJiYmJiYmJiYTGCaAt2h051jHzo9FNX5+/oA1c0KoiBw0yIHswqPD5p/1F7Fb/e+QEAJk2RN4N9n3EixZ2iT1HtCp0+1Tu1XQNTDXUT3vYJatwsAwerAUnY5UsH5CCdMBhAdnnEPPf7e/mM8+u4hVFUnN9XFl66cQYanf69WueB8xPSSCSHkn0uIyfnYl3+Z2J6XUWq2Ev3oOYiFjQHHWBds+BaIx8Oa48rqzgu+1AiJbh/Y6SOqRnn3yLusObCGdUfXoWgKAJIgsaxgGavKVrGyaCV22RRERxsJyHBaaQpEqesK40mfeKJ4bZfhJZ7utGKTTU8+k6EhiwJJNpmOcIy2UBSnZXwiIfhjKjFVQxIFEm2mRHOusDJnFuVJBWw7tJt5JTPJcJpe4iYmEw3zijvKiAPMFp+WVMDaY7sGzCsuCAIrynN4ZN0h1u1t4NJZeb3h0HroySveoracWSPL7zJE8cPPw7yvGWHVh4sggGcagrqT++Z+kutf/zZvVb3Ju0feZXnh8jNr3zgzkA1N4gPTfvGPacP4xrRf/GPaML4x7Rf/mDY0MRkdRvtcCisqXZEYCGMvinuDGg+vDdDsVbHJAp+40MmULEOQ0nWdf9Zu5omqdWi6ztTEHP59xo0k29xDqrtD7aBVbUVEZLJlcp9luqagVL2PcuhtdCUKAsiTLsBSdjmC1Tnq/RyM09lP1XSe3HiYtz+qB2DO5DQ+e8k07Kfx+JsIQv65iCDbsJYsR2jeTLQrBAiAkfc9ap2KmGJHLLrUEMI9Rf2GRO9B13V2Ne3ihQMv8M+Kf+KNeHuXTU+fzqrSVVxdcjVpzoGjHJicOaIokptgpykQpTEQpiTVdVYiZAwVVdM55jcmKuW5J2549/HEfKYcmBSHlY5wjPZQlPxxSg/QEzo9yW45RQMA037xTKotkSnOLDOXeJxjnoPnLqYoPopIkkRJSUm/y3ryilf7GgmrUez9hINaUJLJU+9X0twZYn9dB+X5KX2Wp8uGKN6ldRHWwtjFEc6EzV4AyaXQcRAOPgnn3T2yegQJkmZSrCncVXI5/3fwNb6//nsszHsFmzz2M+fHgsFsaDLxMe0X/5g2jG9M+8U/pg3jG9N+8Y9pQxOT0WEszqXmbi/xJJsFmzx2g2RNnSqPrAvgDWok2EU+vcJFdooh9EbUGH8++Cobm/YCsDz7PD5TfDlWaehDOxXRCgDyLfk4xONCgNpSQWz3S2h+YxK+mJyPdeb1iElnP0Xa6eznD8f4n9f3cqCuE4DrLyjkmvmT+hUVTM4CnVWw969weA1iTAfbeQAIqOiALljQi2+B0msHraa+q54XDr7AmgNrOOo92lue7kzn+tLrub7sekpSzXvk2aDnHNR1HZdVJhBVaPRHxk087I/GQBhF03BYJFIdE8+LfbwxnykHJ9VhpbIjQFs4Nm55xdtDRuj0FPupGoFpv/jGtF/8Y9rwBNQIqCGQHCDFp+Z3MqYoPorouk4gEMDlcp1yM02ze0izJ9Ia7uKQt57zUopO2d5ukVhUmsXa3fWs29twiihuE2x4RA9ezUuL2kK+eGqI9SEhCDDjLnjvv2DfYzDjX0Ac4aEgWSF5BvfMXM1LR9+ntqOKP2//X+654Esjq2+cGcyGJhMf037xj2nD+Ma0X/xj2jC+Me0X/5g2NDEZHcbiXOrJJ57hGpvBIG9QY8/RKG99FCamQppb4tMrXSQnGAJ8a9jLL/c8R7WvEVEQ+eTUi7ksd96w+qfqKlWxKgCmWqYCoAU7iO17BbVhDwCCzYVl2hVI+XMRhPHxkBnMfnVtfn77yh5au8LYLBKfvWQacyebHsNnHV038oPv+QvUvNNbLKSUIgQ0dCR0BEBCQEVIK+u3Gn/Uz+uHX2fNwTVsqd/SW+6QHVw6+VKuL7ueRXmLkEQzNPbZ5MRzMM9t52Cbn9quEHlu+4R5PqnzhQHDS3yitGkiYT5TDo7HJiOLIoqq0RVV8NjO7sQKXdfpCBvPFf1N6jDtF9/Epf3iUfgcwzaPmQ3j7XcOHQPvftBVw0HWMw0c2ePdqjNmWEqoqqrcf//9PPbYYzQ2NpKTk8Ndd93Ft7/97QEPjnXr1rFy5cpTyo8dO0ZWVtbIWj1B0TSNuro6iouLkaRTH9inJRXwXuMeDnbW9iuKA6wsz2Ht7np2VrfR4Y+QnND35EiX0/FGu0VxywhFcYDJ18DWB8FfD9t+BdPvNPI6jQTZhSt9Pv9v7p18beND/PHDh7iu9HryPWfQvnHidDY0mdiY9ot/TBvGN6b94h/ThvGNab/4x7ShicnoMNrnUlTV6AgbHl1jETp9W2WUx9cHCIR1AIoyZe6+zIXTZojSBzpr+dWe5+iKBUmwOPhq+Q2UJ08a9n7qlDoiegSn6CRbSCd26B2UinXoagwEEbloIZbSSxDGKb9qDwPZb1tVC39+6wDRmEpaop2vXDWT3FTXOLb0Y4imwtE3YPf/Qevu7kIBCi6Cmf+KmDEX6+aHiFZsREdEQMVavAQxtbi3ClVTeb/2fdYcWMObVW8SUSPdtQgsyFvAqtJVXDblMlxW07bjxYnnYE6CnYr2AP6ogjeikGQff69sX0TBG44hCAI5CWY++f4wnykHRxAEUhwWmgMR2kLRsy6Kd0UVFE1HlkTc1lPlGdN+JxBvIiKgxUK0NhzGUViCdJbTz/SLrgPdH/2kv+gQagLfYdA1EERImAz29P4qGmwngxSPYLv+1jlx1UgrBI4eb7NrEthSjq/f73576tFPLTtxXV1H1zS8jY04szIResOoD7XefurXdYh1Qbi5u0wAewZYEvupbyiMZJvBNu+nPk2BYK3xGyOCoBgCuTUlbs7FgRiWKP6Tn/yE//mf/+Hhhx+mvLycrVu38pnPfAaPx8NXvvKVQbc9ePAgiYnH8yhkZGSMrMVxTJnHEMX3d9YMuE5uqoup2R4OH/Oyfv8xrj+/sM/ydCmdwxw+87zikhVSpsGxLbDhW7Djt7DiQShZPbL6rElcUf4pFh1+h01N+/jh2v/HH1Y9emZtNDExMTExMTExMTExMTEZZ1qCEXRdx22TcZ4mZ/Vw8QY1HnvXEMQFAUQBOvwqMdXwUnm7YQd/q3gTTdeYlJDJf8y4kXRH0oj21RM6fXLYTXTLb9AD7QCIqYVYZ16HmDgxPT80XeelrUd5ccsRAKblJfOFy6eTMAHEuY8NsSAcegb2/s1wrgBjQHTqDUYkQs9xxw954Zdh8qU07ltP1vQLkTMML/GDrQdZc2ANLx56kdZga+/6RUlF3FB2A9eWXkuOO+csdspkKFgkkawEGw2+MHW+0IQQxWt9IQAynNYxTWdhcm6T4rDSHIjQHooxOens7rsndHqy3RI/nsTjwXC8VPsIkieKvvT9/8mCcJ/lJ4qeJ9Zzcr2D1BPtQAw2kO9UEFtbwJF1gvB5siit9d2/rvXT/gH+P5zlg6FroIZP6APQdQACRwyxeSLSX5t9hyFoH7U2C7pOmi2G4PcbUZfPlJPbDMbxHe2YwL+zCrrS8wWwGGVq6OMlir///vtcf/31XH311QAUFhbyj3/8gy1btpxmS0MET0pKGlEjzxWmdecVP+xrIKoqA+b9WjkjxxDF9x3jmnmTkMTjJ166ZMzSaVVb0XQNcaQnTaARjrwOaMYxHemEdfdC7tIRe4wLzizuu/BbXPvsp1h75F3eOfg8F5XeMLL2mZiYmJiYmJiYmJiYmJhMAJoD3aHTR9lLXNd13tgRIhAxBHGHVcBhhXAMmr1Rnq5dyzsNOwFYmDGNz5ddhU06NffoUAhoARqitehRP3k7D6FHNAS7G8v0q5ByZ03YQflwVOHPbx9gR5Uhol4yK49bFk/pM05iMoYEW2Dfo3DgHxDtMsrsyVB2B0z7BDhS+92syeZmo8XDdC3G1h1/Zc3BNRxoPdC7PMmexDXF17CqbBUzMmZM2OPPxCDP7aDBF6bRH6E0RcMijd8AvqLpHPN3h06fQDnOTeKPnrDlneEYqqaf1ftKe3fo9JSzPclktL2uTxRzdQ1Qj/9f1wwBDe2ksv7W62ddLQaRNqMMAYhB+06QD3eLlCcL2BOAE4RPAUCPQbAOpNETa0cXoftz4v+7f0tBPk263cHOF2GQVYaw3WAIgnFsqGEM72Xh+HEoOUC0dldzYl0n/X+w5d3PI7quEwx5sSQk9U0nJAinbtOnv/2VC6CEINTQvf8T2mxPN9o9lL4PlzN9ttIU8B85PikFzTgupPi/9w5LFF+8eDF/+tOfOHToECUlJezatYsNGzbwi1/84rTbzp49m0gkwowZM7j//vtZsmTJgOtGIhEikUjv964u48FbVVVUVQWMMCeiKKJpGvoJIQ8GKhdFEUEQBizvqffEcjDClQylvCeUiSzLvct62qLrOpqmkWZNxGNx4o0FqfQ1UJqY12/b5xSl4rLLdPjD7KhqZu7k9N4+JegJWLAQ02N0qB2kyqkj61NHFaISBosblCCoUYgF0Dqre8NjSJLU2/aT2zhQeWHWYu4qv5k/736CH6z/PgvzFuN0ZU4oO52uTz02HO4xNpH7NNK2x1ufzsR+E7VP56KdBmt7jw171jkX+nS68nOpTz326+Fc6NPJbT/X+6RpGhaL5Zzq04nl53qfeuwHDLmvE71Pg7X9XOwTgMViOaf6dC7a6eTyk7czGX8EQcBqtY6K0KZoOm2h0c8nruk6r2wLs706igDIIjisEIrqiNYwf619iSOBegQEbp28nGsLFo64P7oS5VD9S2iWdtK7YrhjIE+9EEvJRQjyxPP06LFfqy/MQ6/to6EtgCQJfGp5CUunTUxv9nOOjgojX3jli8agKEDiJCj/DExdBYOE2H9qz1N86dUv4Q170d7RSHem47a5kUWZiwovYlXZKi6cdCEWafw9jk365+RrqMcmk2CV8UcVGvxhJnnGLxxwYyCMquk4LdLZFxTjiNG8D56rOGUJuywRVlQ6wjHSnCObdDZcNF2nszslS4qj/32Omv103fD01BTDK9VfRW+4aUcOWBOHIFj3J2yfUDZW9Oyjb6EhNDPSqD3dwq9wwv97RMqe5cJJQvGJ34UTtjn5uyB0TzqIACI6OgKi0QdbqiEknrz+yf8f0vJugbZXqBWHuf1JfVYj0LLRSI8iysaxIkqQdsHE9QYeqM0pc0avzZpGR8cRPJ5CEEdhQoMagUiL0WZBNs5LUQZ38cT9nQEsCSdEa5CNaA0Tub1DZFii+De/+U26urooKytDkiRUVeWHP/whd9xxx4DbZGdn84c//IH58+cTiUT485//zIoVK9i8eTNz587td5sf//jHPPDAA6eUV1ZWkpCQAIDH4yE7O5umpia8Xm/vOmlpaaSlpVFfX08gEOgtz8rKIikpiSNHjhCNRnvL8/LySEhIoLKyss9gR1FREbIsU1FR0acNxcXFKIpCdXV1b5koipSUlBAKhVAUhcrKSgCsViuTJ0/G6/XS2NhotEN306Z0caCzlnTFSWvr8ZBRPX1qb22hNE3ig6ogL246SGGS1KdP1kQrAUuAo9pRUpNTR9QnKaxSiBVRiyIigBpFEyxUtSiovorePgUCAerq6nrr6K9PAC6Xi/z8fNo7Org083ZeqHiTOn8Lf9jwQ/7jkp/S1NwxYew0lD5VVlYe71N7e792mkjH3rDtdI73qbKy8pzrE5x7dhqsT52dnedcn85FOw3UJ5/Pd8716Vy002B9EkWRQ4cOnVN9OhftNFCfgHOuT+einQbqU15eXu/7xLnSp3PRTif2ye/3YzKxEEWRyZMnj0pdbaEomm4IMAmjFDpdUXWe+yDER0ejSKLA8nIru47ECMd0FHsLvqzXiQUCOGUb90y/jjmpU0e0H13XUY/tIbrvFQ5PFkCWmBJLxr78XxDdEzelnSiKhK3J/PLZHQTDColOK1+6spwpWZ7xbtq5ja7DsQ+MfOH17x0vz5wHM/4VClZyOk+3d4+8y10v3EVEiSAKIpqu4Y14+c6F3+H2mbeTZE8a2z6YjAonX0MFQSA/0cH+Vh91vjAFiY5xE1vrugwv8Vz3+LUhHhjN++C5Sk9e8QafSnsoetZEcW9EQdV0rJI44HNFH/vpmiH66YrhIdsjcvf7N2aIVz3r6erxOk4ONx04CuHR9mAWjfoEEQSpu+4Ty7rLh1KmK9C577gY1+OtmtwjfJ4kZg8obp8oYI8x3WKtoKlIUo9Ya4XE0okrJEo2Q+j07u8WaqWJL3yehTaP+jU0Hn9nMNIVWFNGN8LEBEDQT5wifxqeeOIJvvGNb/Czn/2M8vJydu7cyde+9jV+8Ytf8OlPf3rIO12+fDkFBQU8+mj/Oaf78xTvGQTpyUs+UbwCepAkCU3T6OzsJDExEUEQ+vV0eKthBw8ffpMZyYV887xbB2x7szfIt/6+FR2dH33iArKSXb1t3xXZxe7YbiZbJrPMuWzkfap4FnH9NyDihVgAnOloVz8JOYt7+zRS743XD7/Kv792D1ZR4uWrf0V+wRXogjTg+mfTToP1SVVVurq6SExMRBTFCe2RMtQ+xZOXzZn2SdO0EdtvovbpXLTTYG3XdZ2uri48Hk/vdTXe+3S68nOpTz32S0pKGrSv8dSnk9t+rvdJ1/XeSQ1DbftE79OJ5eeKnQbqU4/9PB5Pn3XjuU+Dtf1c7JMgCHi9Xtxud5+B3nju07lop5PLu7q6SElJwev19r6vnov0PKPFQz91Xcfr9eLxeM5YNNnd3MUxf5hCj5OS1IQzbltU0fn7+gCHGxVEAW5a5GRWoZUjHZ28dPRD3m/biiBCjjOVe2euJtuZMqL9aL5mYnteQm05TLPbwrqyZKxWDzcnfxKLOHG9K3Vd562P6vjHexUgiEzOdHPPFTNITjg3BuEmJJoC1a8aYnj7fqNMEGHSpTDjXyBj9mmrCEQD/OqDX/G/2/+Xuq46JEEi0ZaIXbYTVsP88xP/5ILcC8a2HyajRn/X0Jimsb6mDVXTOT8niWT72REQT6QrEuOD+g4EQeDCglRs4xjGfaIzmvfBc5lj/jC7m7tw22QW5Q7zfjvUUOS63u1dHQNNoc7ro9EXIM0hUui29iNqK+iaghoLI4kgnOItPQJ03Whrb+hmMEI3Z4LsGKaIPcB6vR7Lo8hwcopPFELH0L370VUFQZIR4qHNMPqh9c8GY9jmMbuGxuPvHEcM9X11WJ7i3/jGN/jmN7/JbbfdBsDMmTM5evQoP/7xj4clil9wwQVs2LBhwOU2mw2b7dSDQpKk3jDlPfQMSJzMcMtPrnck5bqu09zc3Cvm9CAIQu/36cmTAKjoqkdDR+6nHlEUyUpOYEZBCntq2nlvfyM3L57S2/ZMSya7Y7tpVVvPrE9lt0D+hdBVAwefgqqXkN7/Nqx6CWzHJx/0V89A5T1tubLkap7d/wwbjq7lBx/+iT958hFTZp0y+2w87DRYnwRB6LVhTxtG6xgbrz4NZqexKh+vPp14Do62/Uw7nZ0+qaraa8OhrH8mbR+o3LTTyNs+VPvFU5+GWn6u9ElVVZqamkhMTDxn+nS6Ng63fCL3aaT2m8h9Gml5vPZpMBvGa58GKz9X+jTQcpPxQ9M0GhsbcbvdZ2QfTddpCRoT9kcjdHowovHouiC1bQoWSeD2ZU5KciysbdjJTz96iqASQRAE5qZO5XvzPo1zBKHNdSVC7ODbKFUbjUF4UeJocQmCQ6HIVjqhBfGYqvHou4fYsP8YsZjChTPy+PTKUqyyeY6NCVG/MRa072EIdEfnkB1QfBOUfxoSC05bha7rvFn1Jj9Y/wOaAk1IooTT4kQWZWyijWAsSIItgQLP6esymTj0dw21iCJZLhv1vjB1XeFxEcXrfIaXeKbLZgrip2G07oPnOj3hy30RhYiqDX5c9Xpsx7pDkR/p9sQWjVSksnNgD+4Tcl6nKirJso5VE8E/wP50HVWJIVksx4VmQaY3z/PJf3v+L8ggWk76Lhtie3/hppOmT2xhLh69VB3ZaJKH2iOHyC8sQbKOX7qJYSHZ4uP3PZExbPOYXUPj8Xc+BxmWKB4MBk8ZQOjx5BsOO3fuJDs7DmbIjAG5rlQSLA78sRDVvkaKPbkDrrtiRk6vKH79BYW9L4JpchoAXVoXYS2MXbSPvEGuLOOTNhNaP4Kuo7Dpe7Di5yOvE2Mg6zsr7ueax7ew/thHvH3kHS6R7ZBYNvqzxkxMTExMTExMTExMTExMRpn2UAxF07HJIh7bsIZPTqErqPG3dwI0d6nYLQKfWuGiIF2mLdzFL/c8R0CJICIgCxI1/mZCSmRYoriu66j1u4jtewU97ANAypqGPv1SarW3QReYahlZGPazQWcgwu9e20tVYxeiIHDptCQ+sbIE2RTERx//Mdj3KBx8EmLd6R8caTDtTii7HYYY4rzB18D33/0+7xx5B4D8xHzuX3E/jf5G7n39XnxhH267mwcve5CshKwx6ozJ2SQv0UG9L0xTIEKpqmE9i8K0omkc8xuieJ77DMZBTUygV+C26TGybWGi0QhBbwibDUMw1mLdXtvdf3vFbU4NRY5qiOTS6UKRC+iCTFCTiOkSqTYnSCcI2CeI2ZouUl9bz6TMKUiytVvgPoPx9HgN3QzxKSJKNsKaPf7abWLyMWFYb3XXXnstP/zhDykoKKC8vJwdO3bwi1/8gn/5l3/pXee//uu/qK+v55FHHgHgV7/6FUVFRZSXlxMOh/nzn//MO++8wxtvvDG6PYkTREGkzJPP1tZDHPDWDiqKnzcpleQEGx3+CNurWllYkgmATbDhET14NS8tagv5Yv6ZN8zigAt/Cv+8HapegkmXQNEVZ1RlYVIhn513N//z4UP8cPtjLMmaiUOyQ0LRmbfXxMTExMTExMTExMTExGQMae72Ek932s4odGJrl8rf3gnQGdRwO0TuWukiM8kQextDHYTUKCICLosdl2wnqEZoCnWQah9amHqt6xjR3S+itR0BQHClYJ1xLVJmGQejB1FDKklSEmlS2oj7MJZUNXXx0Kt78AaiOG0yn7ukDGu41Qz5O9q07YPdf4HqV47nmU2aYoRIn3wtDHEShqIpPLzzYX675beElBCyKPPZOZ/li+d/EbtsiJWLchexcc9GlsxYQu4g414m8YXHZiHRJtMVUWjoTitxtmj0R1A1HadFItk+cSNemIyQMwkprGsniNgnCNq9onY/Ird+PGVOqaARkzXkkAixoUz0EI//FURDGxd0sGeB1d3Xi/tEsRuRjnCMbf5ObLLEhSkpAwvdqkpUazGEdnGUJofFo9e1iYmJyRgwLFH8t7/9Ld/5znf4t3/7N5qbm8nJyeHzn/889913X+86x44do6ampvd7NBrl3nvvpb6+HqfTyXnnncdbb73FypUrR68XEwRBEHC5XKd9cStL6hbFO2u4tmDhgOtJosDy8mzWbD7C2j0NvaI4QLqcjjfaLYpbRkEUByNP1KwvwM7fw/v3QeZccGacUZWfn/d5Xjj4Ag3eGv6w90X+fdbNxg19gubSGKoNTSYmpv3iH9OG8Y1pv/jHtGF8Y9ov/jFtaGIyOozGuaTrOs0BQxTPdI584Li+XeHhtQGCEZ3UBJG7LkogOeH4oHuWIxld19HQkQSJgBLGIdvIdCSfvo2xELEDb6Ic+QB0HUGyIBevRJ6yFEEyRKOKaAUAxZbiCXltef9AIw+vO4SiauSkOPnSlTNIT7RTXx+akO2NO3Qd6jfAnv+Dhk3Hy7MXwIx/hbxlp/Fs7MvOxp3ct/Y+DrYdBGBe9jy+t/J7TE3pG4Ug253NooJFZLsn5tiPyeAMdg3NczvYF/FR1xViUqLjrJ2ndb6Qsf+zuM94Jq6eKU/OG+2eaoi3/QrcsVMF8BME7mEjyCDJdCkaumYh256AIFpP8Nq2dP+/+yPIoEX7CUUug3vyaYXm9lAMgBS7ZVDbjJn94tHrOg6Jq/PPpF9MG57bCLqu66dfbXwZaoL0eKHa18i3tv4Vp2zjj0u/hjTIC0hnIMI3HvkATdN54Lb55KUmAMaL7fuh98mSs7jcdfnoNU5T4KVboG0v5C6Fy/58xuHO36p6i3teuQcZnX9e/j0KE3MgaQbYUkap0SYmJiYmJiYmJiYmJuPDufa+OhAfl3720BGO8WFDB7IksqIgFXEE78WVjQqPrw8QVXRykiU+tdJFgr3v+39EjXLb2h/TEfHhku04ZBv3TLuOlTmzBqxX1zXU2u3E9r+GHgkAIOXMwDL9KkTncTG9Q+3gRf+LiIjc7L75zFKvjTKqpvPMpkre2FkHwOyiVD53yTTs1jMLU2/SjRqFqpdhz1+gw5gYgSBB0ZWGZ3ha+bCq64p08YtNv+CJPU+go+OxefjPJf/JjdNuRByGqG4S/yiaxrs1baiazvzspN6czGOJNxJjc30HoiBwYUHqWQ3bbjIK6Hq3kB3t/kSM72oUlCCEGjgeilwHhCGEIu+HXgG72zu7j6DdT1l3SHJV01l7tBVN11mSn4LLMoT70MlCvmfakBzAtjR00BmOUZ7uJtftGF7/TExMTEwGZajvq+bbxiiiaRrt7e2kpKScknv9RAoSMrBLVoJKhBp/M0XugXMrJblszClKY1tlC+v2NHDn8hKA3rBnrWormq6N3kuIKBth1F+8wZhNfOAJmHb7GVV5cdHFXFhwIetr1vP9XU/z56VfRvDug+TZYEkYnXaPEkO1ocnExLRf/GPaML4x7Rf/mDaMb0z7xT+mDU1MRofROJd6vMTTHdYRCeJ7aqI8/X4IVdOZnCnziQtd2C2n1nO4qwGHZCXTk89XyleR6UgeNGy61llHdPcLaB2GmCy607HMuA4p/dR84T1e4vmW/AkliAciMf7w+j721XYAcM38SVx/QWHv72xeC8+ASBccfAL2PgKhFqNMdkLpLVD+aUjIGVZ1uq7zSsUr/GjDj2gNtgKwqnQV/9/S/48Ux8CODqYN45vB7CeLItkJduq6QtT6QmdFFK/rMnKJZ7pspiA+RM7KOairhrCtnfTpr2ywOtC6v/TcI3VAMkJ8D1PgHgmSKOCxW+gIRWkPxYYmio8gFLmiaXgjRl7y05035jU0vjHtF/+YNjy3MUXxUUTXdVpbW0lOHjzMmSSIlHry2NVexUFv7aCiOMCKGTlsq2xh06EmVi+egt0ikSQmYRWsRPUoHVoHqVLq6HUkeSrM/zps/hF8+BPIXQyJk0ZcnSAIfPvCb3PNP65hQ+Me3ji2j8tzyqFzN6TMnVBhW4ZqQ5OJiWm/+Me0YXxj2i/+MW0Y35j2i39MG5qYjA5nei7put6bTzzDNfz31Q8PR3hxSwgdmJ5v4ebFTixS/4P1BzprAZiRUsj05IHfu/VIgNiB11FqPgQdBNmKXHoJctFihH7yjaq6SlWsCjBCp08U6tsDPPTqHpo7Q1gtEv96cRnzp6T3Wce8Fo4AXx3sfRgOPQ2KEWYaZwZM/7QhiNuGH92hxlvDA+seYEPtBgAKkwr53orvsSBvwWm3NW0Y35zOfnluQxRvDkSJKBo2eewEA0XTaAwYoniue+JM7pno6EoYf3sdyYlOsA4j9/spXt2DiN3DDVveG5bcevwjCOCr7Pa4lo2/ogzpC87qeHGqwxDF20JR8hOH6ME9zFDkneEYuq7jsEg45MHzhJvX0PjGtF/8Y9rw3MYUxceJaUkF7GqvYn9nLVfknT/oumW5SWQkOWjuDPHBwSZWzMhBEATSpDQalAZalJbRFcUBpn8Sat6BYx/Au9+Aq/8B/bxoD5VJSZP43NzP8bsPf8ePdj7Bsqwf4iRqCOPJs40HHhMTExMTExMTExMTExOTccYXVQjFVCRRIG0YXpC6rvPu3ghvfWQIOPOnWLnufAeiOLD32kGv4fFd5snvv05NQz26mdiBN9BjRr1y3hws069AGMSjvFapJaJHcIpOsuWJkdd5Z3Urf3pzP5GYSmqinS9dOYOCtIkVPS7uaNlt5As/8jro3d6WyaVGiPTJV0N3bvnhEFNj/N+O/+P3H/6eiBrBIlr4wvwvcPe8u7FKY+8VbDLxSbRZ8NgteMMxGvxhipKGIboOk2P+CKqm47LKJNuHfzx/LAkdQ+zcT54jitjWDknTwJ4xRK/uGMdDmQ8FESRrX6FbtPZfNpAnt2Q/HopclI1Q5GfZgSrVbuUwAdpDhnA9FnmET8wnbmJiYmIyfphK5DhRlmS88B7orDntzVYUBFaU5/DUxkrW7m1geXk2giCQIWUYorjaQhllo9tAQYRlP4bnr4WWXfDRH2H2v51RlXfPu5s1B9ZQ76vn95Ub+HrpClAC4N1n5Bg381CZmJiYmJiYmJiYmJiYjDPNQSPUa6rDijSIoH0imq7z6vYwmw4aHubLy+1ccp5t0Hd9Vdeo6KoHoMSTd+ry9iPEdr+I5j0GgJiYhWXm9UiphadtT0/o9KmWqeOe81nXdV7edpQ1m48AUJqbxBcvL8ftMIWBEaFrULvOyBfe+OHx8tylhhies3jEYYS3Nmzlu+u+y+H2wwAszFvIAyseoDCp8IybbXJukee24w3HqPOFKPQ4xkRE1HWdOl+od39jsY+4pzeEeQTUiDHO6jsMuoog6KCFoX3n8HN0n+zRPaDQLY34etPLCEKRjzaJNhlZElFUI8R50hgI1+1h49nibKQcMDExMTEZGFMUH0UEQcDj8QzpIW2yOxurKOOLhagPtpLnSh90/aVlWTz3QTV1rX6qmrqYkuUhTU6DCLSoLaPVhb4k5MCi78L6b8COhyBvOaSVj7g6u2zn2xd+my/+84v8Zdej3FByFVMEBaId0FUBiSVn/iB1hgzHhiYTD9N+8Y9pw/jGtF/8Y9owvjHtF/+YNjQxGR3O9FzqySee4RzawLyq6Tz3QYhdR4wB7yvnOlhSdvptj/qaiKgxXLKdPFdab7ke9hHb/ypK7Q4ABIsdS9llSJMWIAwhr2FAC3BMMYT0KdYpQ+rDWBGJqfzf2wfYVmmMW1w0M5fblk5BGqQf5rXwJAKN0HUUnFnQuNkQw73VxjJBginXQvlnIHXkzhKd4U5+tvFnPLP/GQBSHCn819L/4tqSa0dkB9OG8c1Q7JflsnOwLUAoptL2/7N35vF1lPX+f8/M2c/JvjdpurfpwtaWlqXsyKYoXCsqKIIgKhe9qBcFr6LggqiIwkV/F1FwQRREZBVUdgq0pbSlW9p0TZM2e3KSc3LWmfn9MUna0qbNOZm0mfT77quvJHNmnnmefPLMOTOf5/v9xlIUB+w3+sKJND2JNKqiMC50lKVON82+yO3EHtPbSFrG98DXxP4pzE0dTKtutaooWFHf/f9tiOoeKTJMRW43iqJQ6HPTEk3QHkvaboqndIPuZF898SG0LddQZyP6OR/RcGwjpriNqKpKRcXQ0pK5VI1peZWs69xBbdfOQ5riQZ+bE6eW8NbGZl5eu4sp5XmUaCUoKPQYPcSMGH51iDVPMmHKxVD/bysV12s3wYf/Bq7sP4iePelszpqAIDs+AAEAAElEQVR4Fi9vf5nvvXk3D17wU5TwOog3WasWQ9nXLreDTDQURh+in/MRDZ2N6Od8RENnI/o5H9FQEOxhOHOpN5UmkkyjKAolQzB5kmmTv7zRy8ZdKVQFLl0Y4ITJQzOH+lOnT8+rhHgP6Z4WjPZt6NuWYKaToIBr/HzcM89H8Q49zfjm5GZMTMpd5eSqmdeStou27jj3PreGhvYoqqrw6TOmc/qsQ+si18K92PRXePkrkOgEPQX+IvDkgDsENZ+wSt8Fy7Nu3jRNntz4JD9640d0xjsB+Nisj3HTKTeR58vLul3R0NkMRT9NVRiX46U+HKOhJzYipnh/lHhZ0ItbGyPZJU3TMq33NrYPZHobyaG3qaigevdEbsd2o5iGVaObvlrdRQvBFTjiwUijmSK/h5Zogo54CruXk3XGU2BC0OPCe4h64iDXUKcj+jkf0XBsI6a4jRiGQXNzM2VlZahDWL09M7+adZ072NC1k3Mr5x5y/7PmjOOtjc0s39zKJxdNJejzkKfl0aV30aq3Uq1W2zGMfVEUOOV2aF4BXVvgnbvgpP8ZVpPfOv1bvLnzTd5qeIt/7FzGRZXHWal9otstY9xfZk/fsyBTDYXRhejnfERDZyP6OR/R0NmIfs5HNBQEexjOXGqOWkZEgc99SBMmljT5wytR6tvSuFSFT5wWoKZy6NFlteGdAEzVdWLP3QqJKGCCNxetZCqeYz6MWnDgWuODYZomm1NW6utp7mkZHWsntY1d/PL5dUTjKXIDHq6/YDbTKoZmssq1sI/unfCvz0MiDChW2vR4B5z4DTjmGvAMrx77ts5tfOeV77C0cSkA0wqncduZtzFv3Lxhd100dDZD1a8qx099OEZrb5J4Wsc3BLNvqKR0g+a+rB1VuSMQBJQJemJoqb33TmW+X1T3XlHfGEM8sbJXNLd336+qty/C27t/CnNfMWbXBgwjhaq6UfJngjs4nN/AUUFRX0mPrniKtGHiGmL5lKEwkDp9iBHocg11NqKf8xENxzaiqI2Ypkk4HMY0zSHtX5PXV1c8XD+kYyaX5TK+OERaN3ijtgmAEs2KMB+xFOoAvnyrvjjA+t/DrreG1VxVbhWfn/d5AO544w6irnwI9NVP694Iya5htT8cMtVQGF2Ifs5HNHQ2op/zEQ2djejnfERDQbCH4cyl1l7LhCkLHjyNa3evwa//FaG+LY3PrXD12cGMDHHTNNkUbgDTYNK2pRDv6YsgBMw0nvmXZ2yIAzTpTUSMCB7FQ7V7BBbOHwLTNHlpTSN3PbWaaDzFhJIQ31o8d8iGeH8bR/21cPcyeObjewxxlw8CJVaU+LiTh2WIJ9IJ7l16Lxc/cjFLG5fi1bx89aSv8sTHn7DFEAfR0OkMVb+Qx0W+z41pmjT2xG3tw+5IHN0wCXpc5HuPYExXbDe0LoG25dDyBnRvgt5dENkG4Y3Q+V7fa0us19uXQedqCG+AyFbobYBEK6TCoMcZMMQVF7iC4CkAXzkEqyFnGuTPhsK5UHwylJ4GJSdB0Vxre+40az9/OXgLrONV1/7R3/4KjKKT2Bktwyg6yarZLRwSv0vD59IwTZOueAaR+kOgI5YChl5PXK6hzkb0cz6i4dhGIsWPIFNyx+FSNToTEVriXZT5Cw66v6IonDl7HH94dROvrNvFB46rokQroY66kTXFAapOhxmfgI1/htdvhkueBm/2adiunXstT9Q+wc7undy3/D6+fspN1srJRCt0rYXCE6wPd4IgCIIgCIIgCIJwGIindbri1oPrg6VOb+/RefClKF1RgxyfymfOClJekFmEZFOsk3Ayiss0mZjsBRRwe8EdAD2B2dsJgYM/IzgQm5NWlPgk9yRcyuF95JPWDf74Wh2vr7fqmS+cXspVZ83AY2P06Jgn1gHLfwybnwAjbUWAal7wF0MybKVNz81+scPbDW9z68u3siO8A4DTqk/jO2d8h/F5mS/AEASwori74ikae+JMzg/YUn/VNE0a+kz2qhzf4avpauqQjkE6CnovJHsgtosBI9sEerZYWS6VweLM1D0R3AeK6u6v4a2M8HVR8xI3fEe0TrfTUBSFIr+Hxp4Y7bEUxQF7fneJtEGkr554gc21ygVBEITMEVP8COLV3EzJqWBjuIENXfWHNMUBTppeymNvbaGlK0ZtYxfjKqxI8Xa9HcM0UAf9UGYDC74Bu96Ennp4+3twxk+ybsrr8vLt07/Ndc9cx0OrHuLSmkuZVjgDOhOQ6obONdbKSM3+mkSCIAiCIAiCIAiC8H5ae63IsDyfe9A0wLs6dH7/cpRIwqAwpHLV2SEKQ5nfh2/sS50+JVSGu3k7YFrRwOk4isuHEizKuM2EmWBHyjI7p3qmZnz8cAj3Jvnl8+vYvDsMCiw+eTIXHD/+8JlZTsc0YNPjliGe7AYUq1543mRY8m1I9ViG+Jl3ZVVDvL23nR+98SOe2vQUAMWBYr512re4YOoFopEwLMoCXjZqKvG0TlssSYkNRmI4kSaSTKMqCuNCPht6+T6MtGV6p/v+6719Rvj7ot1NnT2pzveaJ+5c6/+B0porB4jcFhxDkd/dZ4rbFyne2Rd1nuN14TlEWRZBEARh5BFT3EYURaG4uDijG4qa/Go2hhuo7drJmRXHHXJ/n8fFSdPLeGXtLl5Zu4svVs7Co3hImkk6jA6KteLhDOHguANw+o/hucthy1Mw4VyYeH7WzZ0x8QzOnXQu/972b25/7XZ+f8nvUfLnQMdKq1ZP1xooOB7Uw7eqPBsNhdGD6Od8RENnI/o5H9HQ2Yh+zkc0FAR7yHYutfTVry0dxNTZ1pzmj69FSaRMKgo0rjwzSI4/uwfctV2WKT5NdYM3B1JRMFIoLh+eeR9H9Q893fhA/1Lb0NEp0AooUjM31bNle0sP//uPtXRGEvi9Lj5/3iyOqS7Mur2j7lrYsQnevBVaVlo/F9bAKbdB6fHWzxPPg+56K0I8Q0PcMA0eX/84P37zx3QnulFQuPyYy/nKSV8hx5tj7zj24qjTcIyRiX6aahnXO8K9NHTHbTHFG3piAJSHvLiHYyIaqb2M7+ie743E4McoLnAFrOyVihsiW6xFK4obzLT1jDJ/zqiPwJY5mB396c0jyTSJtIHXNXwTu6MvA02hb+iBX6KfsxH9nI9oOLYRU9xGVFWluDgzU3pm/nie3AEb+m6Ih8JZc8bxytpdrNzWRrg3SYlWQmO6kdZ068ia4gBlJ8Cx18Hq/2etVi6da9W1ypJvnvZN3tj5Bssal/HMpme4eMbFkH+MZYynIxBeb33YPEwXoGw0FEYPop/zEQ2djejnfERDZyP6OR/RUBDsIZu5lNKNgQfXZcH9H1yv35ni0SW9pA2TSaUurjg9iM+T/X3qpnADAFO6m1HcPlyzP4hWPBklWJSVIQ57UqdPdU89LA/xOiJxXlrTyPMrd2KaUFYQ4MsXzaE8PzCsdo+aa2EqBqv+F9Y+aEWkuvww90YrQnzv4IBgeVbR4XXtdXznle+wYvcKAGYWz+T2s27n2LJjbRrA4Bw1Go5RMtWvKscyxdtiCWJpHf8wSiakdIOmSKKvXf+hDzBNy/zuj/beO/rbOEi0r+oBLdBngPeZ4FoAVPe+zyBdPqs+eL8hnjdz1BviIHMwWzyaSo7XRU8iTUc8SYUNmQo6+qLOC/1DT50u+jkb0c/5iIZjG8nZYSN6tJNd695Ej3YO+ZhpuZWoikprvIu2ePeQjqkqCjG1Ig/DMHl9w25KNMuUHvG64v0cfwMUzYJEGN74pvUBNEsqcyv5wrwvAPCjJT8ikoxYN4L5swEVkh3Qs3lY58gEwzDYuXMnhmEcemdh1CH6OR/R0NmIfs5HNHQ2op/zEQ0FwR6ymUutvUlM0yTkcRFw7xs/sGJLkkdej5I2TGZWurnyrOEZ4l3JCE2xThQjzeRoJ6ga7smnoBVPztoQ79A7aNfbUVGZ7J6cdd+Gyqvrd/G5X73Kb16sZVdnLyV5fr710bnDNsThKLkW1r8ET1wEax6wDPEJ58F//APmXDXsbHmxVIy73ryLj/z5I6zYvQK/y8/Np97M45c9flgMcThKNBzDZKpf0OOiwO/BNKGxJ37oAw7Crkgco+9anOfd61psmlZ680QHRBuge5MVUNP6JrS9BZ2rreeHsV2Q6tpjiKte8BRAoBJyp1sZKUtOgZKTofA4yJ1mvebJ76v1/b5ru78CSk6FohOtr/6KYY3vcCFzMHuK+qLF7UihHkvr9KZ0FAXyM6gnLvo5G9HP+YiGYxuJFLeJdP1yEssfJicRJV4bwnvi5biqTzzkcX6Xl4mhMrb27GZjeCfFvtlDOt+Zs8exeXeYV9ft5sZjJwCH0RTX3HD6T+CpS6HhNaj9M8z8ZNbNXTP3Gp6ofYId4R3cu/RebjntFvDkQV6NFSke2wWaD4LjbRzEgTFNk2g0inmYTHjBXkQ/5yMaOhvRz/mIhs5G9HM+oqEg2EM2c6mlty91enDfCMDX18d5YZVl8syd7OGSBX5UdXhR2Bu7rCjxShSCKGgVc1A8wzOT+6PEq93V+NQRqMG7Fx2ROPc+t5ZoPI2qgEtVaGiPEE+lCXiH/5hpTF8LI7vg7e9D/YvWz6FKOPlWGH+mLc2/vuN1vvvqd2notv7Gzpl0Dt86/VuMyxlnS/tDZUxreBSQjX5VOT46Y0kae2JMyQ8Mnq1CT1glEzX/fhHXpmnS0BPDR4IpgTRKb8Oe6G+9t6/G9yBovj3R3v3R31oAVBsefWteR0SH743Mwewp8nnYTi/tsRSmaQ4r80pnzMpAk+tx41aHHpso+jkb0c/5iIZjGzHFbcCIhUmu+AskoyimDskoyRV/QS2ZPqRV3jPzx7O1Zzcbuuo5tWxopvi8KcU88oabzkiC5kYFpVAhYkSIGTH86hDSCw2Xgqkw72uw7A5YfidUngK5E7JqyqN5uPWMW7nmqWv4w3t/4KOzPsr0oungKwF9MkS2Wv81n7VNEARBEARBEARBEGxCN8yBiLDSgBUhZpomL6yK88YGyyw/baaX84732ZKWfGN4J5gm0+JRAFzV84fVnm7qbE1tBazU6SPN2vpOovEUqgJ5QS8eTaU3pdMSjlNoQ6rZMYmRhrUPWenS0zFQNDjmWjjui+Ae/jOc1mgrP3j9B/xj8z8AKA+V8+3Tv825k88ddtuCMBTKgl42aiqJtEFrb3K/BUYAxHb3pSLXrTkQmmwZ2X2mdyoR4VgzguoxCSRVSL3/eqtYZvreprcraGWcVIaXYUEQ+sn3uVEVhURflHfQk7190hHPPHW6IAiCMLJI+nQbMKNtmHoC3AErzY5pYuoJzGj7kI6vya8GoDaDuuIel8ZpM62aUq+tbSVfywegRW/JrPPDYfaVULHQuqF79etgHGTF5iFYVL2I86ecj27qfPeV7+5ZhROoAn/fiubwBkiGbei4IAiCIAiCIAiCIFi0xZLohonfrZHjcWEYJn97OzZgiJ9/vJ/zT/DbVqd7Y7gB9ARTdR0lUIBaPLx05/XpehJmgqAapMI1sql9dcPgn6vqURQFVVVwayrRRBqfW6U0TwzxA9L8Ljx5CbzzE+v5Sdl8uORJmP/VYRviuqHzpzV/4oKHL+Afm/+BqqhcddxVPHf5c2KIC4cVVVEYl2NdA3b2xPZ90UhDvAU611qR4oZupUIPb4CutRDdDvEWzFQEFQNNVVHcIfCVQnAi5M2CovlQugiKT7RKLoYmgb8M3CExxAVb0VRlINX5cFKom6ZJR1+keKHPY0vfBEEQhOEjprgNKMFiFM3bl8ZH7fuqoASLhnR8Td54FBR29bbTlYwM+bynz7Judtfu7CCQKgAOYwp1AEWF035kfQBtXQXv3T+s5m5edDN+l58Vu1fw1Man+s6hQM5U8BYBpvVhOR07aDvDQVVVysvLUTNIaSOMHkQ/5yMaOhvRz/mIhs5G9HM+oqEg2EOmc6kl2pc6PeAlrcOfXu9l5bYkqgKXLgxw2iz70ubG0gl2RJoxUwmmoeKqno+iDG/O96dOn+KegjrMtg7FMyvq2d0ZozTXT17ASyyl4/dqXHfuLNuixMfMtTDeBW98C579JHTWgTcfTrsDLvojFEwbdvMbWjfwicc/wW2v3kYkGeGY0mN4/LLHueW0Wwh6gsNufziMGQ2PUrLVryrHh4JJPB4m0dMA4Y3Qthxal1jP88wUYPb9x/qq+cFXRjowkbWpalakppMoOMUywfNmQmiClTXSFbSeQwpDQubg8NhTVzyVdRuxtEE8raMoSkb1xEH0czqin/MRDcc2kj7dBlR/Hp55H7dSqBtp0A3UvMohpU4HCLp9jA+VUB9pYWNXAwtLa4Z0XFl+gNnVBayr72RXgwLV0Jo+jKY4QGgcnPRteP0bsPJeqDodioeWAv79jMsZx/UnXs9db93FnUvu5KxJZ5HrzbWM8byZ0LEK0hHoXG0Z5e4c22v6KIpCfn6+rW0Khw/Rz/mIhs5G9HM+oqGzEf2cj2goCPaQyVwyTJPWvkiwPI+Hh16OsqM1jUtV+PiiADOr7E15WtfdiKGnKdJTFCpetPHzhtVexIiwO70bgKmekU2dvr2lh2fe2QHAFy+YxbSKPFrCcUrzfLamTXf8tdA0YfMTsPzHEO+0tk1fDPNvAl/+sJvvTfVyz9J7+P3q36ObOkF3kK+e/FU+OeeTaOroiJh1vIZHOUPWzzStiO9UN6R7CKR6ONUbxjR0lB4VtL3MBNUHRso6RnWBaVhfC08AzUtjuJd2I0KO10VuhgaisD8yB4dHf7rzjngy67ri/anT870uNDWz40U/ZyP6OR/RcGwjSx1swlV9It7z/ofOKZdAoBAz1oXR2zHk42vyxgNQGx56CnWAM2dXArB6nfVG2663o5vZpzHPiqkfgQnnWRHyr30d0omsm7r6+KuZlD+J9lg79yy9Z88Ligb5xwAKJDuhfTm0LLHqEdmIYRhs3boVwzBsbVc4PIh+zkc0dDain/MRDZ2N6Od8RENBsIdM5lJnLEVaN9AUlb++HmdHaxqvW+EzZwVtN8QBNnY1YKbjTEdFK5k25MX0g7EluQUTk3JXOTlqjk293J9kWuc3L9ViGCbzp5awYGophSEfNZX5ttcRd/S1sHMz/OPT8PotliFeMA0++Ags+oEthvhL217ioocv4sFVD6KbOhdMuYDnP/U8nzr2U6PGEAeHaygMrp+RgkQ7RLZD5xpofRPal0F3LfQ2QqobjwppNNr1IEag2kp1XnwylJ4CBceC5gH6jPG8maB5MU2Tnd1xAKpy7CtVcTQjc3B45HpcuDUV3TAJJ9JZtTGQOt2feep00c/ZiH7ORzQc20ikuJ34comEJlJcOh2jbSt6/TuoNecN6dCZ+dX8s3EFG7rqMzrlcROLyA956WqPk0qo4NXp0DsocZVkM4LsUBQ49XZoeRe6NsOKn8HCW7Jqyq25ufWMW7n6yat5eM3DfHTmR5lZMrPvVdP6AN6fZsmIQ9c6cBeAy56bcNM0SSaTe2qaC45C9HM+oqGzEf2cj2jobEQ/5yMaCoI9ZDKXmnsT6AY0NENTl0HIp/KZM4NUFI6MwbgxvBNSCaaaClr1/GG1ZZomm1NW6vRp7uGn4z4YTy7bzq72KDkBN586ffqImlaOvBamYrD6V7DmAStgwOWH42+AOVdZ5t8waIo0sWLXCv66/q+82fAmAJU5lXznjO9wxsQzbOi8/ThSQ2EA0zRJJeOYyTDo0YFIcPT4AfZWwBUCdy64c1BdIVbu6iWpmyhqLuXevZ7X+SvAUwh6zEqb3pf9sTOeojeVRlMVKkL2ZoQ8WpE5ODwURaHQ56Y5mqA9lsw4/blVT9wKYCvIIvOB6OdsRD/nIxqObSRSfARQx1s3tun6FZhDXE0yI78KgJ2RViKpodfM1lSlr7a4QrjVWnnWprdl1mE78BVYK58B1j0Eu97OuqlTxp/ChVMvxDANbnv1Ngyz73eoxwADVA8Df7pGcsTrjAuCIAiCIAiCIAhjD9M0aexOEO41aGlXKAiqfO4DI2eIpw2dzR3bwDSY7gmilc889EEHoUlvImJE8Cgeqt3VNvVyfzbvDvP8Kiur3VVnziDHL6mN92HnK/DEB+G9/7MM8eqz4T+eg2OvHbYh/ujaR5nzyzl85M8f4Y9r/kg0GeVzcz/Hs5c/O2oNccGBmCako1Y2xu5NqJ0rmRhoRO1aDZEtkGjdY4j31QAnZ6qV+rx0ERTNhdyp4C9DdQepygkA0NB9ABNd84Inf59yiA091n7lIR8uqd8qjBL664r3m9uZEE3pJHUDNYt64oIgCMLIIp80RgC1bBaKx48Z78Zo3TSkY/I9ISoChZiYbAw3ZHS+02dWoKoKzY0aad2kRW/JptvDZ/yZMOPj1vev3wyJ7qybunnRzfhdflY2reTvtX+3Nmp+K426ae5ljCvWatX2dyCyw6pJJAiCIAiCIAiCIAiHYMPuOG2RNOk0hFxurjsvRFHOyKWg3h5pIpGMEgTGV81DGaZhWpesA2CSexIuZWQSASZSVtp0TDilppzjJxWPyHkcSbQJXvwS/OvzEGmEYAWc80s491cQGjfs5je3b+aap6+hM9aJgoKKionJp479FH6334YBCEclpgl6AuKt0LMVOlZB6xLruVr3JssYT0dAwXr25i2C0ETIPxZKToHiBZBXA4FKKzpc2f/RcmWuDxTLTOxNHTz1dFI3aIlaZRjH59hbikEQhkN/2vOuRJp0himUO+JW6vR8nxtVygEIgiCMKsQUtxFVVamqqkJze9Cq5gKQrl8+5ONn5lsru2u7MqsrXhDyctzEImIdfnqT6SMTKd7Pgpshpxqiu+Ht72fdTHmonBsW3ADAT978Cd2JbmsVad5MUDVAt+oQ5c20Ui9hQHS79SE+2Zn1efs1VGVlqiMR/ZyPaOhsRD/nIxo6G9HP+YiGgmAPQ5lLGxpSvFwbxTRB0V1cc24OOf6RnXsbWusgnWQqCu4JC4bVVsJMUJ+yyq9N84xc6vS/vrWVlq4YBSEvn1g0ZcTOszej/lpo6LD2IXj8QtjxT2vx/jHXWtHhE86x5RTbOrfxqSc+RSwVQ1M1SoIlVOZWktST1IczK7t3JBj1Gh5NGGlIdEK03sq02Pa29T+8Hnp3QipsZThQVHDnQWA85M0inTcXik+C/DkQnADeAlCHFvHqd2kU9xmKB4wW34tdPXEM0yTX6yLXKxG1diFzcPgE3Bp+t4ZpmnT2mdxDpT+6vDDLzCqin7MR/ZyPaDi2EVVtRFEUQqEQiqLg6qsNpjfVYsZ7hnR8vymeaV1xgLPmjCPW5SeWSNOjR+g1ejNuwxbcATj9x9aH6S1PwvYXsm7qquOvYmrhVDpiHdz91t3WRn8FlJwKRSdaX0MToeBYyK2xVrDqMeh8D8IbQM88vc3eGgrOQ/RzPqKhsxH9nI9o6GxEP+cjGgqCPRxqLr27Nckjr0cI5uh4XApnzAjh94z8vKvd/R4AMwLFqDmlw2prW3IbOjoFWgGFaqEd3duP9Q2dvLSmEYCrzppB8DAZVqP6WtiyGp76D1h2B6R7ofQE+MgTcOJN1vMQG3htx2t87LGP0drbikt1EXAH8Ll8hBNhAu4A1XkjlyrfLka1hmMJPQHJLusrWNkTUz3Q2wjhWmhbbkWBd70HkW2QaLfKEPbXAfdXQO50KJoPJYug8HjImYziLyWYW4QyDDOgKsfKZtAYsUzvA2GaJg09sX32F+xB5qA99KdQb88ghfreJnqhz5PVeUU/ZyP6OR/RcGwjpriN6LrOpk2b0HUdNbcctbAaTIN0w7tDOn5G3ngAtkeaiaUTGZ17ZlUBJTlB4t1e4qk0rXprxv23jbIT4JjPWd8vuRV6s+uLS3Vx6+m3AvDI2kdY17LOeuH99YcUBfxl1od4f1+KsngLtC+zbgQG+fB9IPbWUHAeop/zEQ2djejnfERDZyP6OR/RUBDs4WBz6Y0NCf72di9uj0lOEPICKuUh7wFasblPhs7GsGUwz6yaN+z2Nqc2AzDNPW1EHtj1JtI8+FItAGfOGcec6pEx3g/EqLwWJrrhze/CMx+Hjlrw5MKp34cP/gkKZ9hyCtM0eeDdB7ju6evoSfZw4rgT+eUHf0meL4+eZA8hT4i7zruL8lC5LecbSUalhmON3l3Q8ga0LYPmV6H1TevnjnehZzPEm0HvC5jRfOAtgdBkKDgeSk+FonmWIe6vAFfQerbWhx36lQQ8eF0aKd2gOXrgZ5yd8RS9KR1NVSgPSep0O5E5aA/9kd4dsaFHivck06R0A01VyPNmV9pE9HM2op/zEQ3HNiNTdOooxtirxoir+kSSHfWkdyzHNeX0Q96oFvtyKfHl0RoPU9fdyLGFk4d8XlVROGNWBa91bqO3IExLuoUJ7glZj2PYnPAlaHgNOjbAG/8DH/i/fT5gD5WFVQv50LQP8UzdM9z26m38efGfUQ9Qrwiw0jjlTgN/uVUHKR2xbgRiTdZ2d+6QzmlkWCdGGF2Ifs5HNHQ2op/zEQ2djejnfERDQbCH988l0zT556o4r2+wzJnjp6l4fQrFfg8udeSjQBp3v0fUSONRFKZMOnVYbbXr7bTr7WhoTHYP/blBJvx5yWY6ehIU5/n42CmHJ2363oyaa6FpwtZnYOkdEG+3tk29FE78OvjtWygQS8X41kvf4pm6ZwD42KyPcesZt+LRPFw07SLqw/VU51U7whDvZ9RoOBYwTSvCO9VtRYKnuiDWDOwVBJIMW+a36gF3jvUMzJ1j/Vczj1Ydrn6KolCV42NLZ5SG7hgVBzC9G3qs1OoVId9huQ4fbcgcHD6FPg8oEEmmiad1fC7tkMf01xMv8HmGtWhN9HM2op/zEQ3HLhIpPoJo445BcXkwo+0Y7duGdMyeFOqZ1RUHWDSzgmQ4QFo32R7dnfHxtqK54YyfWEZ1w6uw8dGsm/rGom8QdAdZ3byax9c/fugD3DlQOBdyplq1vdIR6FhpGeVGZjVgBEEQBEEQBEEQBGdjGCZ/XxobMMTPO95HcaH1oKs0mF1q00yp3f42AJMDRbg9w0uzvTlpRYmPd4/Hq9of5b5qextLNjSBAtecXYPPfWgTYEwS3gbPXwWv/rdliOdNhgv/AKf/yFZDfFfPLq742xU8U/cMmqLxnTO+w/fO+h4ezfrbLA+Vs6BygaMMcWGYGClIdEBkO3Su2b8OeLKLAUNc0UBxWV/zZkHJKVaZwdBE8BZlZYjbRWWOD0WxIsIjyfQ+ryX2iiCvypEocWF04tFUcj1WTOFQo8X798u2nrggCIIwsogpPoIoLi9a5XEA6PXLh3RMTb6VQr02i7riIZ+b6XlVADQlW9HNI5zeoWAazP+a9f2yO6B7R1bNlAZL+fLCLwPw07d+Sle869AHKQoEKqFoAfj6arXFdkP7cms1bQYp1QVBEARBEARBEARnktJNHnmjlxVbkyjAJQsCzJ/mpieZRlEUSgIjnzrdTMWo7dwOQE3pzGG1lTbTbE1tBWCqZ+pwu7YfkXiK3728CYDzjhvP9HH5tp9j1JOOw7u/gCcuht1vW6Xb5n0FLnkKKhbYeqoVu1aw+NHFrGtdR4GvgIcueYjLj7lcalgeTRhpy+SO7oSu9dC61EqF3rUGojsg2bFXHfCglR0xNAVUr2WGqx7rNdVllRocRX87PpdGsd+6xvZHhfezqyeOaZrket3kesU8FEYvhX11xTvih64rbtUTt/Yr9MnftSAIwmhETHEbUVWVSZMmoap7fq2u6hMBSO9ei5mKHbKNmr664lt6dpPQM49qPmvaZNJJjVgqRWOsJePjbWf2Z6B8AaRj8No3wMjOqP/UsZ9iWuE0uuJd3P3W3UM/UPNA3kxrlazmt1bbdtdC52pIR/fb/UAaCs5B9HM+oqGzEf2cj2jobEQ/5yMaCoI99M+lpK7w+5ejbGhIoakKn1gUZP5UDy29VnRigc+NRxv5+aY3vkedmQZVo6bimGG1tTO9k6SZJKgGqdAqbOrhHh5+vY7u3iQVhQEuXTjR9vaHwhG9Fja8YZnhq35pPT+oOh0ufRaO+4KVEc9G/rL2L1z59ytpj7VTU1zD3z7+NxZU2mu6Hynk/WwQTB2S3dDbCOFaaFsOrUusZ1SRrZBoBaPPPNb8VpBHzpS96oDPh9wZEJoA+bMsI7zv2kLeTGsBhw3YqV9VrhUFvisSRzesABXTNGnssZ6Rjs+VKPGRQOagfRT1meLtsSTmIYKswok0umHi0lRyPNlXrRX9nI3o53xEw7GN1BS3GZdr31+pkl+FmluG0d2M3rAK16STD3p8mb+AAm+IzkSELd27mFWQWV3wqeV5aGtywNPF0l1bqJ5q/01yRigqnH6ndVPZshLW/Nq6mcwQl+riO2d8h0898SkeXvMw04umc87kc4aePsxTYN089O6ESD2kwtC+AgJV1s2Esicd3Ps1FJyF6Od8RENnI/o5H9HQ2Yh+zkc0FIThE+41qG8xeWlthLYeA69L4Yozgkwus+ZXS1/K3tLA4Ukr3Lr9LdoA1e1jWl92t2zpT50+1T0VVbH3Qd3yzS0s29SCqipcc3YNniHUTh0pDvu1sLfFqhu+7Tnr50ApnPQtmHCe7ZG3KT3FD17/AY+sfQSAC6deyB3n3IHf7bf1PEeao/79zDStYIx0T18d8J6+4IwDmGqqd0/9b1cOuENWOcKD4a8ATyHoMctAt8kQ78cu/Yr9HnwujXhapzmaYFyOj454it6UjktVKA+KKT5SHPVz0CbyvW5URSGRNoimdEIHMbs79ooSH27GD9HP2Yh+zkc0HLvIUgcbMQyDuro6DMMY2KYoClp/tHj9skOuKFMUhZo8q654bTjzuuKKojAlNA6AzT2NhzzfYSE0zrqZBHj3Hmhfn1UzJ1aeyIyiGewI7+CzT32WhQ8s5K/r/zr0BhQVghOgeL5144BpmeRt70CiHTiwhoJzEP2cj2jobEQ/5yMaOhvRz/mIhoIwfFZsSfKDv3Zz3/NRNjSkMU347Ll7DPFE2qArYWVlKwmOfOp0o3s3G7t3gQIT8scTcGV/zogRYXd6NwBTPFPs6iIA4d4kf3i1DoCL5lYzqSzX1vYz4bBeCw0d1v8RHr/QMsQV1cp499HnYeL5thvi7b3tXP3k1Tyy9hEUFL560le5+/y7x5whftS9n5kmpHutcn09m6FjJbS8AR0roHuTVc4vHQFMUNzWM6ngBMifA8UnQ8lJkD8bgtXgLTi0Id6P5rVSpttsiNupn6IoAzXDG/qiwxu6ra8VIR+aOnrSvY8ljro5OIJoqkJBXyr09tjBU6h39tcTH2bqdNHP2Yh+zkc0HNuIKX4YcFWdAKqGEW7CDO865P4z++qKb+jK3BQHOLFiMooChr+H2saurNqwnamXwIQPWKmiXr0J0omMm2iKNLGyaSWmaWIYBm3RNr72z6/RFGnKrCHNb9145M2yVuMacehaa/3X44c+XhAEQRAEQRAEQRh1hHsNHnk9SnevSf/68GTaJOTb8+ijtTcBJuR63fgPQyR0uv4dNmGC5qGmYOKw2tqc3IyJSYWrghw1x54OYqUy/t0rG4nGU4wvDnHx/Mwy1jmWtrXw9Mfg7e9BKgIlx8KHH4eF3wR30PbTrW9dz+LHFrN813KC7iC/+uCv+Pz8z0v9cKdhmtazo3gr9GyFjtVWCvT25Va5vt5GSHUDhpWV0J0PgfHWM6jihVByMhQcA6GJ4C2yyv6NcSpzfCiKQlc8RXssSUuvZSxW5Y6txSDC2GWgrnhs8FKnhmkOLLrr318QBEEYfUgOgMOA4gmgVcxBb1xNun45nvzKg+5f02eK14UbSBs6LjWzG/VxvjJ8bhemP83Lm7Yxs6og677bhqLAKbdbKdS7NsOKu2HhzRk1saNrB/F0nCJ/EZ3xTpJ6kvbedurD9UNPo753f3wl1urc6A7obYBEO2qikzy3F0wDOHKp4gRBEARBEARBEITMaO8xSKQtN9ylmoQCCqk0dPQY5AUsY7y5r554aXDkH1ibegq9YSV1ioni9jEjb3z2bZkmW1JbACt1up28tbGZ1dvaUVWFa8+twXUY6qwfMaJNVva6bc/DlqcAEzw5MO9rMOMyqzbzCPBc3XPc8uItxNNxJuRN4Fcf/BVTCu2N9heGiZ44cCpyI7kn/Xmqx0qHbhzIGFOttOeunD2p0DW/7dkGnIjXpVEa8NAcTbCqOUxaN8jzuYdVc1kQDidFfjd1WOnRDdNEPcC87oqn0A0Tj6YSdMszZUEQhNGKfPo4TLgmnIjeuBq9cRXmrItQXIPfgFcGislx++lJxdjas5vpGdYccytuyrzFbE82URdupCs6m/zDkBbukPgL4dTvw7+/AOseguqzoWLBkA+fkD+BgDtAJBkhz5tHR6yDhJ7gte2vsaBy6O3sg6pBzmTwl1kprZJhCj1h1K5VkDvNSkMlCIIgCIIgCIIgjHqKclQCHhXTNPCoBsmUitetUJhjmbwpwxiI8ioLjPw9st60nkiylwZNQdE8Gd/b781ufTcRI4JH8VDtrratj+09cf70hlWn/NKFk6gqCtnW9qhj42Pw4n9CvNP6OVACNZfDgpshUDwip9QNnV8s/QX/t+L/AFg0fhF3X3A3ud4jl55eOACx3RDeYKXTV1TwlVlmdqoHjANlOlTAFeyrAR7q+xq0jhUOSFWOn8aeONGUjgno8SSNPTEqcyRaXBj95HhcuDWVlG4QTqQH0qnvTWd8T5S4ZAARBEEYvcinNRtRVZVp06ahqvv/WtWiSSjBQsxUAn33moO2Y9UVt1aQ12aZQr3aX45bU/Hm9/L6ht1ZtTEiVJ9lrb7GhNe/AcmeIR9aHirnrvPuIuQJgQL5vnxKAiX8euWveWjVQ8PrlysIBcdD7gw83oBVC6pzNYRrrVXBgiM42BwUnIFo6GxEP+cjGjob0c/5iIbCUOjo6OCKK64gNzeX/Px8rrnmGiKRyJCONU2TCy+8EEVR+Pvf/z6yHT0C5AVULjs1QNCrYCpuvG6Fy04NDESJt/UmMU2ToMdF8DBEKOr177AVE1xeyvwFFHizN5w3Jy3jepJ7Ei7Fnr6bpslDL28klkgzuTyX84/PPpLdTkbkWhjZDf/6PMTa92wzDTjxv0fMEO9J9HD9s9cPGOLXnnAt9198/1FhiDvm/cw0rTTonWusSHEzZZngvTsh3rLHENf84CuFnCnWs6PSU6FoHuROh8A4yxQfQ4b4SOgXcKskDQMTULB+9e+1dBNP67adQ9iDY+agQ1AUhUK/ZYR3DFJXvH/7cOuJg+jndEQ/5yMajm1EVZtJp9MH3K4oKq7x8619diw/ZDs1+dbK79pwdqZ4qVZKwOvCXxDjtfW70Q0zq3ZGhAW3QM54iOyCt7+f0aGLZy1m6bVLefbyZ1n3n+u4ZdEtANzxxh08/N7Dw+uXooC/nGTOceDvS8ceb4a25dC7i4GidMKoZrA5KDgH0dDZiH7ORzR0NqKf8xENhUNxxRVXsG7dOv71r3/xzDPP8Nprr3HdddcN6dif//znYz56ad4UD7d8NJcvnOfllo/mMm/KnixtLdG+1OmBkU+dbvR2ordupg6jL3V69lHiCTNBfaoegGmeaXZ1kVfW7WL9zk7cLpVrzqlBU0fP34at10LThKU/hGR3XxRwAYSqwNShu96+8+zF9q7tfOyxj/HKjlfwal5++oGfctOpN6GNUHr20ciofT8zDUh0QncdtL0NXWvATMOAXdv3318BBcdCyalQvADyZkKgCjx5Vq3wMY7d+sXSBirWNcatKbhVBd006U2JKT5SjNo56FCK+uqEtx/AFNcNk66E9fu2q5646OdsRD/nIxqOXcQUtxHDMNi2bRuGYRzwdVf1PFBUjI4dGD0tB22rv674xnADunng9g5GsasYr1vDX5CgMxpjzY72Qx90uHAH4PQfWzejm/8O2/+Z0eHloXIWVC6gPFTODQtu4Lq51gOg21+7nUfXPTqsrhmGwbYdDRihqVBwghVBbqahpw46V0FqaBEYwpHhUHNQGP2Ihs5G9HM+oqGzEf2cj2goHIoNGzbw/PPP88ADD7Bw4UIWLVrEvffey5///Gd27dp10GNXrVrFXXfdxW9/+9vD1NsjR44PzEg9Ob4923TDpK3vQXbpYSgvpte/A0CdLwiKNqx64tuS29DRKdQKKdKKbOlfSzjGo29uBeCjJ02mPD9gS7t2YOu10DRh+Y9h23NYKa+9Vt3nVDe4ApBrXyr6fl7b8RqLH13Mtq5tlIfK+dNH/8TFMy62/TyjmVH3fmbqEG+zsgG2vgVd70Fsl5UZUHH1mdwaqF7rZ80DoUngKQD16Ks8ORL6Bdwabk3BpSq4FIWUYaIpCgGpvTwijLo5OAYo8llmdziRJv2+32tXIoVpmvhcGn7X8O0W0c/ZiH7ORzQc2xx9n+yOIIovF61sBnrTBtL17+CZfdGg+1aHSvG7vMTSCeojLUzKKc/oXDlKDn7VR8CbwpsX5+V1uzh+0sikBMuKsrlwzLXw3v2w5NtQOjerlGWKovDVk79Kykjx4KoHufXlW3Grbi6deenw++jJhcJ50NsI0e3WTXPHCghUQnDiUXljJAiCIAiCIAjCkeOtt94iPz+f+fPnD2w799xzUVWVpUuXcumlB74P6u3t5fLLL+e+++6jvHxo95aJRIJEYk8t3e7ubgB0XUfXrcg+RVFQVRXDMDD3yqw12HZVVVEUZdDt/e3uvR3Y74HUYNs1TcM0TXRdxzAMdF0f6Et7LEHaMPFpKkHN6kMmfc9kTKZhkN75DilMtvX1dVrOOHRdz2pMdck6ACZrk/cZk2ma++w/1L7rhslvXtxAIqVTU5nPWXMq9vndHy6dDtT3wfTLSifDQFn+I5QNfwDVhXL89Zib/26VcXOHME7/CWqgDAVsGZNhGDzw7gP87O2fYZomx5cfz30fvI8if9E+7Q9rTEdgPg2m08H+9vo1NAxj4Hdz2MdkpDHibSjJdpRkJ5jW35NpAqoL01OE6S1C8RSgJlsxB2qKaxg5M1AUN2pf22NVp8G2H0q/bMbk1VTmFIdY2xYhbZi4VIVjSnLxaup++4/EmA613Yk6HWy7rusD34+VMe3d9yMxJp9Lxe9S6U0btEUTlAa9A31siyYwgXyvC9M0D9jHTMa0t36ik/PG1K9f//V0LIzpYH0fi2MaTD8nj2ks6vT+7e8/bjDE1TvMuKrnozdtQG94F3PmeSiDGKuaojIjr4pV7Vuo7dqZsSmuKAolWglRbwx/QYy12zpo7Y5Rkuu3Yxj2cMKXoeE16KiFJf8D5/4/K4V5hiiKwjdO/QYpPcUf1/yRW168BZfqsmcltqJAsAp8JdCzBRKtlkkeb7VqSXlLsuqzIAiCIAiCIAhCpjQ1NVFaWrrPNpfLRWFhIU1NTYMe95WvfIVTTjmFj3zkI0M+1x133MFtt9223/YtW7YQClm1sfPy8qioqKC5uZlwODywT3FxMcXFxTQ2NhKNRge2l5eXk5+fz/bt20km96QfraqqIhQKsWXLln0edkyaNAmXy0VdXd0+fZg2bRrpdJpt27YNbFNVlenTpxONRqmvr6ejo4PNmzfj8/mYPHkyOzt6SCWTBEixeXMbwWCQ8ePH09HRQVtb20A7dozJ07OT0mgXOzxeenWTECo9DW1ElPaMx1Q0pYj2dDt6WsdoMqgz6/B4PEyePJlwOLyP7kMd09vbulm/owu/z81nz65h965dR0SnhoaGge17j2nXrl0D+uXk5GSnU0MDvtU/I7fxSQDSJ36L4LxrqC+/HLN7BylfBTolVEWjtoypenI1X3/+6zy96WkAzh9/PjcefyPFgWK6urqy0mm/MR2h+TSYTgcbk2EYdHR00NLSQmVl5WEbk4pO0BWjotANyS7SqT1t6KZGoKCamB6gvqm/L+14PD1MnjyZ7phGZ9suUqYLPdxDMGiMeZ0GG1O/fh0dHZSVldk2pujunYxHIYWKWzeoCBaTTCYPy5jGok4HG5NhGAPjGCtjgiOvk9nbQwo3dY1NpIKugTHtbIuSQiOe7KbZ6B32mAzDGEjdLDo5b0z9i4oMw2Dr1q1jYkww9nQ62JgmT7YWom7evHnAeHX6mMaiTu8fUyQytCzPirm3FT9K6e7uJi8vj3A4TG5u7pHuzqDous6WLVuYMmUKmnbg9D+mYRD/948w4z145l+Oa9wxg7b3dP3bPLLlZeYXT+erx3w04/6sSazh3fi7tO8Msu7VYi6cW83ikydn3M6I0lkHT14KRgpO/R7MuCzrpgzT4LuvfJe/rPsLmqJx9/l3c/7U8zNq45AaJjqsVOp63PrZUwA508A1ihYbHMUMZQ4KoxvR0NmIfs5HNHQ2op/zEQ2diR33qzfffDN33nnnQffZsGEDf/vb3/jd737Hxo0b93mttLSU2267jS9+8Yv7HffUU0/xta99jZUrVw6Y2Yqi8MQTT3DJJZcMer4DRYr3PwTpH+doiQropz/SIZVKsXXrViZPnozL5UJRFF7Z0U7SMJhXlkuBzz2ikQ6pFX/CaFrHC8UTeSzRyfzi6fzXrEuyGtM7yXeoTdYywTWB03yn7dOXbKI3dnVEuf2v75LWDT5z5nTOmF056iJS3q9fxjopCsZbt6PU/gkA8+TbUGo+PmJj2t2zmy+/8GXWta5DUzS+ueibfGL2J1BV1ZFRNv0MN1J869atTJkyBbfbPbJj0uOY8VZItKOkutlTGxzQfJjeYkxPEbhCaC7XmImG6mekIsUPpp8Tx3So7WNtTLqus23bNqZOndqXIcH5Y9q770dKp6aeGO+1RQi6NU6pLEBVVZLpNK/Ud2ACiyrz8bsze9860Jj21u/9iE6jf0z9+k2ZMgXlfcF0Th3Twfo+FsdkmiZbtmxh0qRJ+9yXO3lMY1Gn92/v7u6msLDwkPflYoofAVIbXiBV9wpa6TS8J3120P3qwo18593fE3L7+X+nfhlVyawmSVO6iReiL2Ak3LzxWAU5fg8//czJuLRRVkp+zW+sGl8uP1zy1LBqehmmwf+8+D/8rfZvaIrGvRfeyzmTz7Gxs1i1qKI7IVrPwM1WsBqC47HqUAmCIAiCIAiCcLRgx/1qa2sr7e3tB91n8uTJ/PGPf+RrX/sanZ2dA9vT6TQ+n4/HHnvsgOnTb7zxRu65556BhwbAQBrv0047jVdeeWVIfXTyfXlHLMk7u7twaypnVhft94DSTsxEhNi/7gDD4H/HzWBVz24+NfUcLhq/IOO20maax3oeI2km+UDwA4xzjRtW33TD5AePv8uOlh7mTCjkxg8eM6K/iyOCacBbt0PtI4ACi34A0zMPMhgq7+5+lxueu4H2WDsFvgLuufAeFlRmrrWQIekoJNqsOuHp90UFuULgKwZvMbiCR6Z/giCMSVK6wcv1bWDC6dVF+Fwarb0JVjaFCbg1Fo0vOtJdFARBOGoZ6v3qKHNHnY1pmkQiEQ61zkCrtuq/6a11GL2dg+43Kaccr+YmkorRGD34A5IDUawVo6KielMUFin0xFKs2NqacTsjzuyroPxESMfgta9bNZyyRFVUvn/297l4+sXops6Xn/8yr25/dcjHD0lDRYPQRCg60YoUx4ToDmh/x4okF44YQ52DwuhFNHQ2op/zEQ2djejnfETDo5eSkhJqamoO+t/j8XDyySfT1dXFihUrBo596aWXMAyDhQsXHrDtm2++mffee49Vq1YN/Ae4++67efDBBw/H8A47759LzVEr4r0k4BlxEzjdsBIMA/LGURez7vdr8sZn1VZ9qp6kmSSkhqjQKobdt+fe3cGOlh4CXhdXnzVj1BriWV8LTQPe/O4eQ/y0O0bUEH903aN8+olP0x5rp6a4hscve1wM8T5sfz8zTUj1QGQbtC23nr9Etu8xxN15Vom74oVQNA+CE8QQHwbyecT5iIYjg1tTyfW4AWiPpQDo6Pta6PfYdh7Rz9mIfs5HNBzbiCluI4Zh0NDQsF8Y//tRg0VoxZPBBH3nikH3c6ka03IrAdgY3plxf1yKiwKtAIC5c3wAvLJ2V8btjDiqBqfdCe4gtKyENQ8MqzlN1bjz3Du5YMoFpI00N/zjBt6of2NIxw5VQ8CKbM8/BvJmguqxUqp3rYGu9aAnDn28YDsZ6SeMSkRDZyP6OR/R0NmIfs5HNBQOxcyZM7ngggv43Oc+x7Jly1iyZAk33HADn/jEJxg3zooibmxspKamhmXLlgFWzbY5c+bs8x+gurqaSZMmHbGxjCR7zyXTNGnptWrVlQW9I3pe0zTR698BoKW8hmg6jldzMyGnLKv2Nqc2AzDFvX/6zUypb+3hqeU7ALji9Gnkj/DvYjhkdS00DVhyK2z8CygqnH4nTNs/c4IdpI00t796O99++dukjTQXTLmAP3/0z1T2Pb8RbHo/M01IdkHPZmhbCh3vWhn79F5AAU8h5E6H4pOh8HgIVIHms2kERzfyecT5iIYjR5HfMsU7YtZni4649bXA57btHKKfsxH9nI9oOLYRU/wIoVWfCEC6/h3Mg0yumflWKvH1XfVZnadEKwFg3HgDVVXYtCtMY0f0EEcdAXIq4aRvWd+vvAfaNwyrOU3VuOv8uzh30rkk9STXP3s9SxuW2tDR96Eo4Cu1osYDfTfAiVZoXw7RBusmThAEQRAEQRAEwSYefvhhampqOOecc7joootYtGgR999//8DrqVSKjRs30tvbewR7OXroTqRJpHU0VaHQZ18U14EwOndi9LSgaG7q/DkATM2tRMuwFBpAxIiwO70bBYWpnv1rimZCSjd44MVaDMNk3pQSFk4rHVZ7ow5Dhze+BZse6zPEfwxTPzIip+qIdXD1k1fz8JqHUVD4yklf4ecX/By/2z8i5zvqMA1ItEP3Rmh9CzpXQ28jGAlLW28x5NZAySlQcAz4K0Ab2XktCIKwN0V9EeHtsSRJ3aAnmQYY8c8YgiAIgj24jnQHjla0itkobh9mLIzRthmtdPoB95uRVwVAbddOTNPMeHV4iVZCLbVEtE6OmziBlVvbeHXdLi4/bdqwx2A7Uy+FHS9C/b/htZvg4sfBlf3qdZfq4u4L7uZLz32JV3a8wuef+Ty/+fBvmDduno2d7kN1Qc5U8JVDTx2kuiGyBeJNkDPNWq2sx0DzgzZ6V+QLgiAIgiAIgjC6KSws5E9/+tOgr0+cOPGQqf6OplSALb1WFq/igAdNHdl04Xr9cgC0ijlsirQAUNN3T58pm5NWlHi5q5yQGhpWv55avp3G9ig5fjefOmPaqE2bnhWGDm98Ezb/3TJNz/gpTP7giJxqQ+sGrn/uenb17CLoDvLT837K2ZPOHpFzHVUYOiQ7rBrhiXYw9yqpp7jAW2TVCPcUWOXsBEEQjiD5PjeaqpDUDXZ2x8CEkMeF1yWxh4IgCE5ArtY2oigKHs/QapQpmhtt/FwA0n03zgdiSu44XKpGVzJCc2zw+uODUeKyIsU79A5Om22lbFtS20QilX3d7hFDUeDU74GvCDrr4N2fD7tJj+bhngvvYdH4RcTSMT739OdY1bTqIF0YuoYHxB2CguMtI1xxQTpq1bpqftWqe9W6BGK7s2tbOCTD1k844oiGzkb0cz6iobMR/ZyPaCgI9tA/l2BPPfHSwAinTk8n0He9B4A2YT61XVYJtBn5mdcTN0xjIHX6NM/wFrRvaQrzj3etzHNXnjmDXBtrno4UQ74WGjq8fnOfIa7BmT8bMUP8+c3P88nHP8munl1MyJvAYx97TAzxg3BIDY0UxJqga631nCS8HuItliGuesA/DvKPhZKTIa/GihAXQ/ywIZ9HnI9oOHKoikJ+X6r07WErK4+dqdNB9HM6op/zEQ3HNmKK24iqqkyePBlVHdqv1VU9HwB993rMROSA+3g1N1NzrNpwG7oyryseUkL4FT8GBmUVBiV5fuJJnWV1LRm3dVjwF8Ki71vfr30Qdi8bdpNel5f7PngfCysXEk1Fufapa1nbsvaA+2aq4QFRFAiMg+ITwVMERhLMtPVfT0J4g9QcHyFs0U84ooiGzkb0cz6iobMR/ZyPaCgI9tA/l2K6SW9KR1UUSgIjawbru9ZgppMowSI6AoW0J7pRFZVpueMybmu3vpuoEcWjeBjvytxU7yeR0vnNi7WYJpw8o4y5k4uzbutwMqRroaHDa1+HLU9ZZulZd8OkC23vi2Ea/Pztn/Nfz/8XsXSMReMX8dfL/sqUwim2n2sscUAN9YSVCr1ztZUavXujFRmOaWXXC4yHghOg+CTInQbeAiv6XzjsyOcR5yMajixFfanSdcPKvFNo84Iz0c/ZiH7ORzQc24iqNmKaJl1dXUNORafmVqAWVIFpkN757qD71fStLK8NZ15XXFGUgWjxNqOVM2ZXAPDy2saM2zpsVJ8N0xcDJrz+DUgeeMFAJvhcPv7vQ//H/HHz6Un2cPWTV7Ohdf+65ZlqeFBUDwSr+lYz9081w7oRjGwDIz38cwj7YKt+whFBNHQ2op/zEQ2djejnfERDQbCH/rnUHyVe6HfjGuGHWv0Z4FzV89nUbd1vTwyV4c2i3nF/6vTJ7sm4lOyr3j3+9laau2Lkh7x88rTh1SU/nBzyWmjo8Op/w9Zn+gzxX8DE823vRyQZ4T+f/U9+9c6vALj2hGu5/+L7yfXm2n6usYaZjtPTvhMz3gnRndCxEtrehp7NkOwCTHAFITgBCudB0QLImQyeXCvQQDiiyOcR5yMajixFfg+GaaIbJgYmhTZHiot+zkb0cz6i4dhGTHEbMQyDpqYmDMMY8jGu6hMBq/bYYJNswBTPIlIcrLriAK16K6fWlKNpCjtaI2xr7s6qvcPCwm9CThVEdsHSH9jSpN/t5/4P3c/x5cfTnejm6ievpq69bp99stHwoGh+q964ooKy1wek2G5oWwqRHWKO24jt+gmHHdHQ2Yh+zkc0dDain/MRDQXBHvrnUkt/6vTgyKZON3paMDrqQVFxjZ/Lxr5795osUqcnjAQ7U9bxUz3ZG9m1jZ28+J5lzl991gyCXnsf2I8kB70WGml45auw7TnrXvvse2HiB2zvw46uHVz22GW8tP0lPJqHn3zgJ9x06k1oqqTwPiSRHdDyGsHYGmh/G7o3Qarv+ZM7F0KTLRO8aD6EJlpl6MQIH1XI5xHnIxqOLOFEinjaIKYbJNIGLb32ZgQV/ZyN6Od8RMOxjZjiRxit8jgUlxsj0obRseOA+0zPq0JVVFrjYdri4YzPMWCKp1vJ8bmZP8X6+eV1u7Lv+EjjDsLpPwYUqPsbbP+XLc0GPUF+ffGvmVM6h854J5/5+2fY2rnVlrYPiOaFvJl9xnhfSrDQJGtFtJmG6PY+c3y7VVNLEARBEARBEARBGDYpFLqTaRQFSka4nni6/h0AtLIZKL5casMNAMzIq8q4ra2prejoFGlFFGlFWfUnnkzzmxdrAThj9jjmVBdm1c6oo98Q3/48qG7LEJ9wju2neaP+DRY/tpgtnVsoC5bxyEcf4cMzPmz7ecYUpg6xZmh/16oPbvYv/jet74MTrbTohSdAcDy4/Eeyt4IgCFkTT+usad0TaGYC77V0E0/rR65TgiAIwpARU/wIo7i8aOOOA6xo8QPh0zxMyikHsosWL9KKUFGJmTGiZpSz5lQCsKyuhWhiFBuxZfPgmGut79/8NvS22dJsrjeX3374t8wsnkl7rJ0rn7iSHV0HXpBgC/4KKDkVik60vubVWN/n1oAW6DPHd/SZ49vEHBcEQRAEQRAEQRgmUay04/leN15t5B59mEYavcEqh+aqnk8kFaMh2gpYC9wzass0qUtZ2cymurOPEv/Lki109CQozvVx2SmTs25nVKGn4KX/gu0vWIb4OfdZpddsxDRNfvPub/jc05+jO9HNCeUn8LeP/405pXNsPc+YItUD3XXQ+jZ010KqC8si0tBNDVSflTnPk2cFDQiCIDic3pSObpp4XSoeTcWrqeimSW9KTHFBEAQnIKa4jSiKQjAYRMkw7ZPWn0J91xrMVOyA+9Tk9dcVz9wUdykuCjVrZXhLuoWp5bmMKwqSShu8WduccXuHlblfhoIZEO+EJd8Cm+o45PnyePAjDzKtcBqtva1c+fcraehuyFrDQ6J5wZO/5yZQUcBfZqULy5vVFzmuQ7TeMsd7toKRtLcPRwEjpp9w2BANnY3o53xEQ2cj+jkf0VAQ7EFRFBIuHwojnzpdb67FTERRvCHU0ho29UWJVwQKyfMEM2qrw+igU+9EQ2OSZ1JW/Vmzo53X1u8GBa4+uwafJ/ua5EeK/a6FehJe/jLU/xs0D5z7Kxh/hq3njKfj3PSvm/jxmz/GMA0Wz1zM7y/9PcWBYlvPMyYwUtDbCO0roONdiO2yFvurXghUW18VxTLDzbRV912TyHAnIZ9HnI9oOHIE3BqaopA2TFwKpA0TTVEIuO0rryH6ORvRz/mIhmMbMcVtRFVVxo8fj6pm9mtVC8aj5pRh6in0xtUH3GdmXy2yDV31WfWtP4V6m96GoiicNXscAK+s2zVoLfNRgeaBM35qrQTf+TJs+qttTRf4C3jokoeYXDCZpkgTn37i0zRFm7LSMGsUBXwlUDivzxwPWeZ4784+c3yL9QBAGBLZzkFh9CAaOhvRz/mIhs5G9HM+oqEg2EPahKRm1dAuHeHU6Xpf6nTX+LkoqjqwkH1GXub1xOuSVpR4tbsar5J5v6PxFA++vBGADxxbRU1lfsZtjAb2uRbqSXjpS1D/krXI/JxfQdVptp6vKdLE5Y9fztObnkZTNG49/Va+f/b38WgeW8/jaEwTkl0Q3mBFhfdshnQEUMBbAvnHQPFCyJ0K+bNQVBduDRTVZZWUkyhxRyGfR5yPaDhy+Fwax5bm4lIV0qaJS1U4tjQXn8s+U1z0czain/MRDcc2oqqNGIZBW1sbhmFkdJyiKGgT5gOQHiSF+oy88Sgo7O7toCsZybhvJS7LFG/RWwA4eUYZHrdGU2cvG3dlXqf8sFI4HebeaH2/9IfQnXm0/GAUB4r53SW/Y0LeBHb17OLKJ65kff36jDUcNgPm+FzIn91njhvQ29Bnjm8GPXF4++RAsp2DwuhBNHQ2op/zEQ2djejnfERDQbCHhnAvyZROwKXhtzFy6/0YsTB6yyZgTwa4jX2R4jUZpk5Pm2m2pbYBMNWTXer0h1+vIxxNUlYQ4D9Oyi7SfDQwcC1MxuDF/4Sdr1im6rn/D6oW2Xqud3e/y0cf/SjrWteR78vnoUse4opjr5DIoH70hFXurX0ZdK6GeAtgWKXgcqZAycmQPwu8hdZzDQB/BUbRyXRp0zCKTrZKygmOQj6POB/RcGSpzPFz1oRiTqks5KwJxVTm2JsNQ/RzNqKf8xENxzYZmeK6rvPtb3+bSZMm4ff7mTJlCt/73vcOGWn8yiuvMHfuXLxeL1OnTuWhhx4aTp9HLaZp0tbWllXktavyBFA1jK5dGF2N+70edPuoDlnG9sYs6or3R4p36p2kzTR+j4uTppeiGwaPv72Vjkg84zYPK3OuhrL5kO61Vok3vgnRJluaLg2W8rtLfkdVbhU7wzu57h/X0dpXA+6woyjgLe4zx48Bdy5gWKnJ2pZatbr0Ua7VEWQ4c1AYHYiGzkb0cz6iobMR/ZyPaCgIw6exJ8aath4ShklnIkVjz4FLlNmBvnMFmCZq0UTUUDEJPcXW7t0AzMjPLFJ8U2ITvUYvfsVPhZa5ifjOllaWbmpBUeDac2rw2BixdrgxTZP25l3w0n9Cw2vg8sEH7ofKU2w9z2PrHuPTT3yatt42ZhTN4G+X/Y0FlQtsPYcjMQ2It0HnGmh7GyLbrecQimYZ3IUnWKXgAlVWVr8DNaF6aOqIY6oSbe9E5POI8xENRx6fS6PQ77E1Qrwf0c/ZiH7ORzQc22Rkit9555386le/4n//93/ZsGEDd955Jz/+8Y+59957Bz1m27ZtfPCDH+Sss85i1apV3HjjjVx77bW88MILw+78WELxBtEqZgODR4vX5FcDsCELUzyoBPErfgwM2vQ2AHJ8blq647yxYTdfemAJr6/fnWXvDwOqBqffaaVO2/osPH4+PLzQtnTqFTkV/P6S31ORU0FjtJGrn7qajliHLW1nhaJYK60Ljof8Y/vMcdOq1dW2DLo3iTkuCIIgCIIgCIKwF/G0znst3eh9z69M0+S9lm7iad32c5mmQbo/dXpflPjWnt3opkG+J0SpL3/IbdUl63g59jJRM0qH3sHm1OaM+tLdm+QPr1oR6xfNm8DkstyMjh91pOOUrb0NZdcScPnhA7+GcSfZ0nRTpIklO5dw0z9v4lsvf4u0keb8Kefzl8V/oTK30pZzOJZ0L/RstRbkh9dBsu+ZiDsXcmdYUeG5062fJZJeEARBEARBcCAZmeJvvvkmH/nIR/jgBz/IxIkTWbx4Meeddx7Lli0b9Jj/9//+H5MmTeKuu+5i5syZ3HDDDSxevJi777572J0fa7iqrRTqeuNqzPT+daRr8rKvK64oCqWuUsCqK94RifPEsm0ogAJ0x5Lc/+/1oztiXHVBKmKtWk4nIBGGV75mW8R4ZW4lD374QYp8RWzp2MJVf7+KrniXLW1njaKAt8AyxwuOA3c+ljm+2zLHwxshPXKRD4IgCIIgCIIgCE6hN6WTMixHXAHcmopumvSm7DfFjfZtmL2dKG4vWsUcYE/q9Bl5VUNOvx01orza+ypp0gPbXu99nagRHdLxpmny+1c3EYmlqCoK8uH5EzIcySgjFUN58Xr8ne+C5ofzfg0V9kRv/3X9Xznx1ydyzu/O4edLf05PoocbF97ILy74BX63valvHYOpQ6wJOlZC+3Lo3QlG0ooAD4yHohOtyHB/uRUpLgiCIAiCIAgOxpXJzqeccgr3338/mzZtYvr06axevZo33niDn/3sZ4Me89Zbb3Huuefus+3888/nxhtvHPSYRCJBIrGnfnJ3dzdgpW/XdetmVlEUVFXFMIx90hgMtl1VVRRFGXR7f7t7bwf2qxsw2HZNs24OcnJyBl7r74tpmvvsP1gfKZyEEsjH7O0k1fgeWtUJ++w/vW/V8s5oK+F4hFxvMKMxFavF7GAHzalmlI4K4kmd3ICb7t4Uum4Qjadp6oyS53cPjGmwvg91TLbq1L0D0wTcQStKOh2zTOPwDgxfyZB1OtiYqnKquPfse/nakq+xsW0jn33ys/z2w78lx5MzMmPK5G/PnQcFx2IkOlF6d6IkOyG2GyXejOkrwfCNt1bRH2mdbJpP2fztGYYxMAfHypjGok4H63u/hv37jIUxHWr7WBpTv379jIUxvb/vY31MhmGQm5s7psa09/axPqZ+/YAhj3W0j+lgfR+LYwLIzc0dU2Maizq9f/v7jxOOHAG3hktVMEwTFYW0YeJSFQIjUFe8P8ObVnk8istKEV3bl9VtRv7Q64n3GD0kzSQKCi5ceBQPKVL0GD0E1eAhj19a18LKrW2oqsI1587EpWUU+zC6SMXg359HaVqK6glinvdrlPITbWm6KdLEjc/fSHOkGQATE93UuXTmpUdf/XDThHSPZYbHWyxjvB9PoZUi3VsISvZ/S4qikJeXd/T9bscIop/zEQ2djejnbEQ/5yMajm0yMsVvvvlmuru7qampQdM0dF3nBz/4AVdcccWgxzQ1NVFWVrbPtrKyMrq7u4nFYvj9+6/GveOOO7jtttv2275lyxZCoRAAeXl5VFRU0NzcTDgcHtinuLiY4uJiGhsbiUb3rKwuLy8nPz+f7du3k0zuicKuqqoiFAqxZcuWfR52TJo0CZfLRV1d3T59mDZtGul0mm3btg1sU1WV6dOnE4vF6OnpoaenBwCPx8PkyZMJh8M0Ne2JZg4Gg4wfP56Ojg7a2toGtufl5VFcfSKxdf8gsv5lOmKhfcYUae0iz/TRmurmpfVLOWvyvIzGlD8hH4CGaAOhjlI0xSSe0PG6NHqTaRKpNB3NjdRFWwfGFI1GaWhoGGgjmzHZplPuBNKKB9VMAS5UIw6pKEa8MyOdDjWmAgr4zrHf4ZvLv8m61nVc+fiVfOe47xB0B+0fUzZ/e9tbAC9etYBCTw95fh2zt4l0uIGIHqArmYviDh45nWyaT8P52+vp6RlzY4Kxp9PBxuT1esfcmMaiToONKRgMjrkxjUWdDjYmVVXZtGnTmBrTWNRpsDEBY25MY1GnwcZUVFTEli1bxtSYxqJOe48pEokgjA58Lo3jSnP7UqibaIrCsaW5ttf7NFMx9F1rgb0yvpkGdd2NAMzIG3o98Rw1BwUFExMVlaSZxKN4yFFzDnlsZyTBw69Zf58fWTCR6uJQpkMZPaR64V+fh6ZlKO4g7vN+A2Un2Nb8O7veoSXaAoDH5aHQV0hcj1Mfrqc8VG7beUY1RgrizZYZnt4rE4HmsyLBfeWgeW05laqqVFRU2NKWcPgR/ZyPaOhsRD9nI/o5H9FwbKOYGVSL//Of/8xNN93ET37yE2bPnj1QI/xnP/sZn/nMZw54zPTp07n66qu55ZZbBrY999xzfPCDH6S3t/eApviBIsX7H4L0R7+MlqiAfvoXCTQ1NVFaWjrQbqaRDiR6iP/7TjAN3Gd8BTVUvM/+v930PC/tXs0FlfP59LRzMxqToRj8uefPGBh8JPARVm+M8usXa0mkdGJJnRy/i4tOqOby06YOjGnURW/UPor62k2QilqR4t48CJRhfOB+KJ07JJ0ONqZ0Ok1LSwulpaVs7tzMZ578DOF4mOPLj+eBix8g4A6Mvr89I4oZ2QGJ9v6egLcYJTQB0xV0VJTNwJiy/NvTdX1AP03TxsSYxqJOh4o0bmlpoaysDJfLNSbGdKjtY2lM/fqVl5ejadqYGNP7+z7Wx2QYBq2trZSXl++zr5PHtPf2saLTYGPq1+/9C1KdPKaD9X0sjgmgubmZkpKSgX2cPqaxqNP7t3d3d1NYWEg4HB64Xx2LdHd3k5eX54hx9iZTNLS0UVVaTMDjtr399LY3Sa55GjW3HO8ZX0ZRFLb3NPPNd36LT/Pw69O+gpZBlO2fu/9Mi96CGzdexctpgdOY5pl20GNM0+Tnz6xhbX0Hk8pyuOU/5qKpDo1mSUXhn9dB8zvgDmF84Nc0mxWUlZXtcy3MltZoK4sfXcybDW8CMC40jkgqQsgTYum1S8e2KW6akOy0jPBEG9B/7VPBV2xFhbvzwOZIKMMwaG5utk1D4fAi+jkf0dDZiH7ORvRzPqKhMxnq/WpGkeI33XQTN998M5/4xCcAOOaYY9ixYwd33HHHoKZ4eXk5zc3N+2xrbm4mNzf3gIY4WBGCXu/+q1M1TRtIU97PYH+UmW5/f7vZbu/p6RkwA/pRFOWA+x+wL/48tNLp6M0bMRvfRZt14T77zyyYwEu7V7Opu2EgfcNQx6ShUagVWjXFzQ7OmDOZYyYW0RKO0xmJ88C/a3ll3W7mTS1lVlXBQfue0Zhs3K7VXAbjT4fuevCXwFvfgd1L0f71OTjvASifv+/+GfS9/+FZv4YzS2fy4Ece5Konr2JV8yqu/8f13P+h+wdqjY2avz0tF6XgGEj1QLTeutFNtkFHG4q3GC04Adz7RgyMuE42zKds/vZM0xzQr79vTh9TJn0cK2Pq13Co+x+qj5luF52G1/eh6Oe0MQ1l+1gaU3d3N2VlZWNqTAfrY6bbR/uYstFvtI8pm+1OHZOu64Nq6NQxHWz7WBnTYK8LRw6vpmJEwngrSkek/XT9OwBo1fMH7ss3hq3U6dPzKjMyxHVTJ2WmyFFyODNwJqWu0iGlTX9t/W7W1nfgdqlcc85M5xriyQj883PQ8q5133r+bzGL5hCuq6O0dPj6dcY6ufrJq2mKNlFTXENXrIvedC8hT4i7zrtr7BrietwywmNNYOwJOsEV6osKLwXV/gUj/ZimSTgctkVD4fAj+jkf0dDZiH7ORvRzPqLh2CajZQ69vb37PUDojwQbjJNPPpkXX3xxn23/+te/OPnkkzM59VGFq9qqmaXvXIFp7ButUNOXhm17pJlYOrHfsYeiRLNqb7fqrQAUhnzUVOZz8oxyzpwzDoAHX6ollkxn3f8RJ1gOFQsgfxKc+38w7hQravyf18LuZbaeanbpbH7z4d8QdAdZ1riM65+7nng6bus5bMOdA/mzoXAeePtqrCfaoGMFdK21THNBEARBEARBEAQha4yuRozwblA1XFV70nv3m+KZpE4H6DQ6MTAIqAEmuicOyRBv7Y7xlyVWmYX/OGkSFQWBjM45akhGrPv4lnfBkwsXPASlx9nWfE+ih2ueuoa6jjpKg6X844p/sPy65Tx7+bMsvXYpi2cttu1cowLTgHgrdL4HbUshusMyxBUX+MdB4VwomgeByhE1xAVBEARBEARhtJKRKX7xxRfzgx/8gGeffZbt27fzxBNP8LOf/YxLL710YJ9bbrmFK6+8cuDnL3zhC2zdupWvf/3r1NbW8stf/pJHH32Ur3zlK/aNYoyhltWgeEOYiShGc+0+rxX5cin152OYJpvCjRm3XeqyVrf0m+J787FTplCU66OjJ8Gjb27Z7/VRidsP5/4KKhdZxvi/Pge73rb1FMeWHctvPvwb/C4/b+58kxueu4Gknjz0gUcKdwjyZ0HRfGv1N1ip1Tvehc41kOw+sv0TBEEQBEEQBEFwKOn65QBoFbNRPJYZbZomG8MNAMzIq8qovXbdKoNVqBUORJ0fDMM0efCljSRSOtPH5XHusZmdb9SQ7IEXroGWlXsM8ZJjbGs+lopx3TPXsa51HYX+Qn53ye+oyq2iPFTOgsoFYytCPB2Fns3Q+haE11vp0gHc+ZBbAyUnQe40ayG9IAiCIAiCIBzFZGSK33vvvSxevJjrr7+emTNn8t///d98/vOf53vf+97APrt376a+vn7g50mTJvHss8/yr3/9i+OOO4677rqLBx54gPPPP9++UYwSFEWhuLh4SDeyB21H1XBVzwMgvWP/yOf+aPEN4fr9XjsU/ZHinXonaXPfaHCfW+OzZ9cA8No6KxWbI3D54JxfQuVpkI7Dv66DxjezamowDU+oOIFfX/xrfC4fr9e/zn/9479I6Sk7ej9yuIKQNxOKTgRfX23RZAd0rrRWjifDR7Z/I4Bdc1A4coiGzkb0cz6iobMR/ZyPaCgI9jBSc8nUU+iNqwFwVe8p3dUS76IzEUFTVKbkVmTUZr8pXqwVD2n/F99rZGNjFx63xtVn16A68XqR6IYXPgutq8CbBxf+DopnD7w8XP0S6QT/+dx/8u7ud8n15vLbD/+WyQWTber8KMFIQ+9ua/F7+zvQ2whmGlQPBKuhaAEUHgf+MlAOf4kHeT9zNqKf8xENnY3o52xEP+cjGo5tFNM0zSPdiUMx1ALpYwkj0kb8pbtAAd+530D15w+89sru1dxf+xzT86r47txPZ9z2Yz2P0Wv0cn7wfMpd+6+O/tPrdbz4XiP5IS+3f2I+Qa9D0mqlE/DSl6DhVdC8llFetcjWU7y18y0+/8znSegJzpt8HndfcDcu1WXrOUaMdMyqOR5vBvqmvTsfQhPAk38EOyYIgiAIgiAIzuVouV89WsZ5MNINK0m++yhKIB/fOTeh9NUOf61pDf9vwzNMy63ktnlXHqKVfXk68jQdegdnBc6i2l190H2bOnv57qPvkEobfPqM6QMl0BxFohteuBra1oI3Hy74HRTV2NZ82kjzpee+xEvbX8Lv8vPQJQ9xfPnxtrV/RNAToMdA9Vnp0GNNkGix0qUDoIC3yKoV7ikEeYArCIIgCIIgHGUM9X41o0hx4eAYhsHOnTsPWmN9qKihYtSiSWCCXr9in9dm5ls3ylu6d5HIIlr5/XXF389HT5pMab6frkhioE6ZI3B54Zz/heqzrZvGF78IDa9n1MShNDx5/Mncd9F9uFU3/9z6T77+r6+jv6/u+6jF5Ye8GVB8IvgrAAVSXdC5GjpWQaITRv8amYNi5xwUjgyiobMR/ZyPaOhsRD/nIxoKgj2M1FxK77BSp7uqTxwwxAE29adOz88slXnaTNOldwFQpBUddF/dMHngxVpSaYPZ1QWcMTuziPRRQSIMz19lGeK+Arjw9wc0xLPVTzd0vvGvb/DS9pfwal7+70P/53xDPLYbWpZYNcKbX7GiwuNNliGu+SE0GYpPgvzZljE+SgxxeT9zNqKf8xENnY3o52xEP+cjGo5txBS3EdM0iUaj2BV875qwAID0zncw95qApb58Cr056KbB5u5dGbc7YIqnD2yKe/vTqCuwZEMTq7a3ZdH7I4TmgbPugepzQU/Cv78IO18Z8uFD0fC0Cadxz4X3oCkaz9Y9yy0v3uIcYxysG+fc6VC8APzjsMzxMHS9B52rINFhmeN6ApJd1leHYPccFA4/oqGzEf2cj2jobEQ/5yMaCoI9jMRcMiJtGO3bQAFt/Nx9Xqvt2gnAjL5SZ0OlU+/EwMCv+AkogYPu+/zKerY1d+P3urjqrBnOS+cY74J/fAba14GvEC78AxTOOOCu2ehnmibfeeU7PFP3DC7Vxb0X3svCqoU2df4IkQxD5xow4mDqgAFGEjzFUHC8VSotON56DjLKkPczZyP6OR/R0NmIfs5G9HM+ouHYRkzxUYxWMRvF7cPs7cJo2zywXVEUavKtm+3aruzrirfqrYNO7GkVeZx3nLXK/fevbCIaH+X1s/dGc8PZv4AJ54GRghf/E+pfsvUUZ086m59f8HM0RePJjU9y68u3YpgOWzmk+SB3GhQvhEAloEKqG7rWQOtb0PI6tC2H1iXW6nRBEARBEARBEISjFH2nlcFNK5m2T3mz7mSUXb1WXfDpeZlFivfXEy/Sig5qcu9si/D3ZdsBuPy0qRSGfBmd54gT74R/XAkdG8BXZEWIF0yzrXnTNPnh6z/ksfWPoSoqPzvvZ5wx8Qzb2j/s6HHo3mTVCzfT1jZFA8UDiguCleDJGzVR4YIgCIIgCILgFMQUH8Uomhut6gQA0vXL93mtpm8Fem14Z8btFmlFaGjEzTg9Rs+g+126cBJlBQHC0SR/emPzoPuNSlQXnHU3TLwAjLRVa3zHi7ae4rwp5/HT836Kqqj8dcNfuf3V2525ekjzQs5UK3I8UGWVG0+FrRXopg56Cro2OCpiXBAEQRAEQRAEwS5MwyDdb4pXn7jPa5vCjQBUBovJcfszardNt7KyHSx1elo3eODFWgzD5ITJxZw8vSyjcxxxYh2WId65EfzFcNEfbDXEAe5Zeg+/f+/3APzw7B9y/tTzbW3/sKEnoLsO2pbttTBdswxx1QOYoGpW9jdBEARBEARBEDJGTHEbUVWV8vJyVNW+X6ur74Zbb1qPmYgMbO+vK14XbiSdYepuTdEo1AqBweuKA3hcGtecPQNFgbc3NvPuVgelUQfLGD/zZzDpoj3G+PZ/HvyQDDW8aNpF3HnunSgoPLL2EX74+g+daYxDnzk+BfJmwUB9PBPQrXRt4VorfdsoHt9IzEHh8CIaOhvRz/mIhs5G9HM+oqEg2IPdc8lo2YgZ70HxBNDKZ+7z2sa+heo1GaZOB2g39kSKD8bT7+ygoS1CyO/m02dMd1ba9N42eP5K6NwE/hK46I+QP+WQh2Wi369X/JpfvvNLAG49/VYunXnpsLt92Bkww5dCbBdggjsfCk+AwmNBdVsR46oGeTOte/dRjryfORvRz/mIhs5G9HM2op/zEQ3HNqKqjSiKQn5+vq03qWpeBWp+JRgG6YaVA9vHBYrIcftJGmm29mSe2rrEtSeF+sGYUp7H+SdYN/e/f3UjPTEHpVEH66bxjJ/C5IutqOeXb4Rtzw+6ezYafnjGh/nB2T8A4Pfv/Z6fvvlT5xrjAJ5c66ZbcVn/Uaz/yQ6r5nj7OxDdadVsH2WMxBwUDi+iobMR/ZyPaOhsRD/nIxoKgj3YPZf6M7dp4+eiqK59XqsNNwAwI8PU6WkzTVgPA4Ob4lubu3nuXatk2qfPmE5eYPTVjh6UAUO8DgKlliGeN2lIhw5Vv4ffe5ifvvVTAP775P/mimOvGHa3Dyt6Aro3v88Mz4OC46DwOPDkg78CSk616oeXnGr97ADk/czZiH7ORzR0NqKfsxH9nI9oOLYRU9xGDMNg69atGIa9taUHosXrlw+YrVZdcStavLYr8xTqpVopcGhTHOCSBZMYVxSkpzfFw6/XZXyuI46qwel3wpQPW8b4K1+Frc8dcNdsNfzorI/y3TO+C8ADKx/gnqX3DLfXRw7Na60+V12WH655IXe6dfOtqKD3QmQrtL0NXWsh0T5qosdHag4Khw/R0NmIfs5HNHQ2op/zEQ0FwR7snEtmvAe9eSMArur5+7yW0JNs62kCoCY/s0jxDr0DE5OAGiCgBvZ7PZnW+U1f2vSF00uZP6UkyxEcAXpb4R+fhq4tECzvM8QnDvnwoej3xIYnuP212wG4fv71fG7e54bb68OHnoCefjO8kX3N8OMtM3xvNK+1zQER4v3I+5mzEf2cj2jobEQ/ZyP6OR/RcGwjpriNmKZJMpm0PUpYqzwOxeXG6GnF6Kwf2D6z76Z7fVf9YIcOSrFWDECn3knKPHj0t1tTuebsGlRVYXldC8s3t2R8viOOqsFpP4Kpl1rG+Ktfgy1P77fbcDT85DGf5H9O+x8AfvnOL/nl8l8Ou9tHjPevRs+ZAnk1UHwy5EwHdw5gWoZ411rLIO/ZCunYEe32SM1B4fAhGjob0c/5iIbORvRzPqKhINiDnXMp3fAumAZqwXjUnH3reW/u3o1hGhR6cyjy5mbUbrvelzpdPXCU+BNLt9HU2Ute0MMVp9lbg3tEiTbDc5+C8FYIVsCFf4DcCRk1cSj9nt/8PN986ZsAXHnslXx54ZeH3e3DwoAZvgx6+83wXCg41jLE32+GOxh5P3M2op/zEQ2djejnbEQ/5yMajm3EFHcAituHVnEMYEWL91OTZ0WKbwo3oJuZrVoJqkGCahATc+Bm/GBMLM3hornW+f7wah3h3tGXOvuQqBqc9kOY9lEwDXjt67D5KVtPceVxV/KNU78BwC+W/oJfr/i1re0fVg60Gl11QaACCudC0XwIVILiBiMJvTuhfRl0rIJYs7X4QBAEQRAEQRAEwaGYpjlwD+6acOJ+r/fXE5+RNz7j9Iptehtw4NTpG3d18c/VVlr2q86aQdDnzqjtI0a0yYoQ794OoXFWhHhuta2neGX7K3z1ha9imAYfm/UxvnnaN0d/aks9+T4z3LDM8PxjoeB48BTAaB+DIAiCIAiCIIwBxBR3CFrfDbi+6z3MVByA8aESAi4vcT1JfSTz6O0SzUq/1qIP7diL50+gqihINJ7iD69ucuZKGUWFRd+HGZftMcbrnrD1FJ894bN85aSvAPDTt37KQ6sesrX9UYMrCDlToeQkyJtl3cgDpMLQXQutb0H3Jkj1jJr06oIgCIIgCIIgCEPF6NiOEWlHcbnRxh273+sb++qJ1+RnVk8c9kSK92dx6yee0vnti7VgwmmzKjh2woEjyUcdkd3w3KeheweEKuHCP0JO5r+Xg7G0YSlf+seX0E2dD037ELededvoNsT1JPRssdKk72OGH2OZ4V4xwwVBEARBEAThcCKmuI2oqkpVVRWqav+vVS2YgJpTgplOoTeuBkBTVGbkWTeZG7JIod5viremD11XHMClqVxzjpVGfeXWNpbWOTCNOljG+Cm3Qc0nARNevwU2PQ7Yp+EX5n+BG068AYA73riD+5bdx9KGpTRFmobb+9GHooKvxEr5VrwQghNB9VmR4rHd0PEudKywHgIYB0/VP1xGcg4KhwfR0NmIfs5HNHQ2op/zEQ0FwR7smkt6/TsAaOOORXHtW89ZNw3qwo2AFSmeCUkzSbfRDUChVrjPa4+9uYW27jiFOV4+fuqUbLt+eInssiLEe+otI/yiP0JOZdbNHUi/lbtX8vlnPk9ST3L2xLO58wN3oqmaHb23n33M8AYsMzxnLzO8cMyb4fJ+5mxEP+cjGjob0c/ZiH7ORzQc24iqNqIoCqFQaERWKiuKglZtRYun906hnm+lIsvWFDdMg13pXUT0yJCOqS7J4eL5Vj2wh1+royuayPi8owJFhZO/AzWXAya88U3Y+JitGt6w4Aaum3sdPYkebnzhRj7whw+w8IGF/HX9X4ff/9GK5oPQBCheYKWC85YACqSjVrq41regaz0kOkckenwk56BweBANnY3o53xEQ2cj+jkf0VAQ7MGOuWSm4ui71wAM3IvvTX2khbieJODyUhUs3u/1g9Ghd2BiElJD+FX/wPa19R28snYXANecU4Pf48q6/4eFaBNseRqe/hj07IScaitCPDRuWM2+X78NrRv43NOfI5aOccr4U/j5BT/HpY7C342RhJ6t0L6XGe7qN8NPOCrM8H7k/czZiH7ORzR0NqKfsxH9nI9oOLYRU9xGookkqzZuJpoYmXrbrqoTQFUxuhoxwtaN8sx8a0X6xq6dGBnWFe/QO4iYETqNTv7c82fqknVDOu6D86qZUBKiN5Hm905Now7WzejJt8KsT1s/L/kWxvo/sWnTJnR9+PWwFUXh8mMuJ5aOYRgGsVSM9t52vvbPr43NiPG9URQrFVz+LCg52Uqz7goCJiRaoes9q55aZDvocdtOq+u6bfoJRwbR0NmIfs5HNHQ2op/zEQ0FwR7smEt642rMdAo1pwS1YP+62LVdVj3x6XlVqEpmj1b6U6fvXU+8oT3Cfc+vQzcMzjm2kprKgqz7fljY9Ff443x46qOwe5m1QPqiP0CoYthN763flo4tfPapz9KT7GFuxVx+edEv8b4vav+IY6QsM7xtKfTutEq19ZvhhUeXGd6PvJ85G9HP+YiGzkb0czain/MRDcc2YorbRGNPjFd3drLdDPDqzk4ae2K2n0PxhtDKZwGQ7kvjNjFUjldzE0nHaYy2D7mtqBFlSWzJwM9JM8nrva8TNaKHPFZTVa45ZyaqqrB6WztvbmzOcCSjCEWBhf8Dsz9j/fj2bYR2PmVb8/XhelyqizxvHoqikEgnaI22sqVji23nGPWobghUQuE8KJwL/gpQNDDiEN1hPTjofA/irdbDg2FiGMNvQziyiIbORvRzPqKhsxH9nI9oKAj2MNy5lN7Zlzq9ev4Bo0Q2hi1TvCbD1Omwvyn++vrdfOmBJWxv6aY9kqA8P5Bttw8P0SZ4+SsQbd6TASzeYespDMNgZ3gnVz15FR2xDmaXzOb+D92P3+0/9MGHiwEz/O29zPAQ5M85as3wvZH3M2cj+jkf0dDZiH7ORvRzPqLh2EVMcRuIp3Xea+kmoRsYQMoweK+lm3ja/pUkrr60bXrDSkw9hUvVmJ5FXfEeo4cUKdy4UbBu0lKk6DF6hnR8ZVGQSxZMBOCRNzbTEbEv2vewoyiw4BaY81kAiuruQ9nwR1uanpA/gYA7gKIq5PnyMDBIGSl+vOTHtPW22XIOx6AoVh213OlW9HhuDbjzrNeSnRBeb6VX79lspVsXBEEQBEEQBEE4AhjdTRidDaCouKrm7ve6aZpsDDcAMKPvfjwT+k3xQq2Qjkic+55fRyyZRgFcmsqDL9eO7nvs8HaI9d3Pah4rXboeh+7My7oNRmuslc8+/Vlaoi1MK5zGbz78G3K8Oba1PywOGBneb4bPBW/RUW2GC4IgCIIgCMJoRUxxG+hN6eimiVvtu+kxQTdNelP2m+JqyVSUQH5ffbN1wJ6V6bXhod+A5qg5uHFj9v1LkcKFixx16DeZF5xQzaSyHGKJNL972cFp1MG6YT3x65izLWNcWfZDWPe7YTdbHirnrvPuIuQJAVASKKE6t5ra9loWP7qYDa0bhn0OR6Jo4C+DwuOhaAEEq0H1gJmG3kZofwfa34XeXWCkj3RvBUEQBEEQBEE4ikjXLwdAK5+J4g3t93pzrJNwMopL1ZiUk1m68ISZoNvoBqBYLaYlHCOaSKEqEPC5yfW5iacMWsKj2BTv3GTdp5kG+AohFQFXAHL3TzOfDe297Xxz2Tdp7G5kQt4EHvzIgxT4R0E6eSMFkW17meF6nxk+W8xwQRAEQRAEQXAAYorbQMCtoSkKBoCioAOaohBwa7afS1FUXOPnA5DesQyAmr664hu6dg7ZmA6qQU4LnIZX8aL0/ZvinkJQDQ65L5qq8Nmza3BpKmvrO3h9g8PrZCsKyok3oZ1wvfXz0h/Cmt8Ou9nFsxaz9NqlPHv5s6z6wir+feW/mZg/kd2R3Xzy8U/ywuYXhn0OR+PyQ2gSFJ9kraz3FgMKpHugpw7a3oJwLSS79qTmGwRVVZk0aRKqKpc2pyIaOhvRz/mIhs5G9HM+oqEg2MNw5pKpp9EbVgJ7MrW9n9q+1OlTcirwaK6M2u/QrTTjITWEV/WS4/OgGyaGCV6XSjSRxudWKc3zZdz3w0JvG6z6XwiUgC/fihB3h+DMuyBYPuzmuxPdfO6Zz9GcbKYip4KHLnmIkmDJ8Ps9HPY2w6P1e8zwvH4zvFjM8Pch72fORvRzPqKhsxH9nI3o53xEw7GNqGoDPpfGsaW5uFUrEbkCjM/143PZb4oDaNXzQAGjfRtGpI0pOeNwqxrhZJSmWOeQ25nmmcbHcz/OfO98cpQcombmKavHFQa5dOFEAP6yZDPtPaN4NftQUBSUeV+B475o/bz8TnjvgWE3Wx4qZ0HlAspD5UwqmMRjH3uMReMXEUvH+PLzX+a+Zfc5O9LeDhTFWlmfP9syyEOTQQtY0QfxZuhcDe3LrYcQenLQZlyuzB5KCaMP0dDZiH7ORzR0NqKf8xENBcEesp1LetN6zGQMxZ+HWjLtgPvsSZ2efT3xYq0YgLqmMHkBD26XSlI38Hs1rjt3FoWhUWqKv307JMJQsRCuXA2XPgtXLIXpi4fddDQZ5dqnrqW2vZbiQDEPfeQhxuWMs6HTWWKkILL9fWZ4cI8Z7hMz/GDI+5mzEf2cj2jobEQ/ZyP6OR/RcOwiprhNVOb4Ob2qgHwzjs+lHiqgdVio/ny0kukA6DvfwaO5mJJr3SjWZlBXHKyI8eN9x+NSXLTpbXTr3Rn35wPHjWdKeS7xpM6DL210tLlrGAZ1mzdjHP8lOOFL1sZ3fgKr/8/W8+R6c7n/4vv5zHGfAeCeZfdw4/M3EkvFbD2PY9E8EBwPRfOh4HjwlYOigh7rW6H/NnSthUTbPtHjhmFQV1eHYRhHru/CsBANnY3o53xEQ2cj+jkf0VAQ7GE4c0nvS53uGj8XZZDokI1dVqT4jPzs64kXaUUALKltIuBxcfVZM/jeJxbwi8+eymmzMkvJftjY/oL1X9Fg0Q8hpwoqFtgSIR5Px/nCs19gdfNqcr25fPvYbzM+N/NFB7awjxm+Yy8zfBYUzhMzfAjI+5mzEf2cj2jobEQ/ZyP6OR/RcGwjpriN+Fwa+aRRFYW2WHJEzWFtgpXGLV2/AtPQmZln1e7a0Hdzngl+1U+Fy7rp3pLaknlfVIXPnlOD26WyoaGTV9btyriNUckJN8Dc/7K+X/EzWPVLW5vXVI1vnvZNfnD2D3CpLp7f8jyX/+1ydvfstvU8jkZRwJMHeTOg+GTInQ7uXMCERDt0rbMM8p6tkI6BnsCnxkFPHOmeC4IgjBjxtE5HLEk8rR/prgiCIAjCmMLo7URv2wyANn7eAffpSkZoinWioDA9N3NTvE1vAyxTfFdHlK1N3aiqwjnHVlFTmT96I8TjXfDmbdb3x30eimba1nRKT/Gl577EssZlBN1BHvjQA0zKmWRb+0PGSO9vhmuBvczwEjHDBUEQBEEQBMHBiCluMz503KpCSjfoSqRH7DxaWQ2KN4iZiGC0bByoK14bzixSvJ/J7skAbE1tzcrML88P8NGTrDYefXMrLeExEvF8/PUw7yvW9+/+Alb+r+2nWDxrMb+75HcU+ApY37qexY8tZlXTKtvP43hUF/groPAEK4I8UAWKG4wk9O6E1jdRW19jfGA3avvbEJPFBYIgjD0ae2K8tKONJQ0dvLyjjcaeMfJ+KwiCIAijAH3nCjBBK56MGiw64D4bu6zU6eNDJQTdmRnYCSNBxIgAUKgVsqS2CYBjJhSSF/AMo+eHgaU/gHg7FEzbU27MBnRD52v//Bqv1b+Gz+Xj/ovvZ07pHNvaHxJGGiI7DmyGF80XM1wQBEEQBEEQxghiituMAhT53QC09Y5ctKqiugZWrqd3LGd6XiWqotIW76YtHs64vWp3NW7FTcSI0Kq3ZtWnc46tZPq4PJIpnYde3ojh4DTq+3DcF2D+f1vfr7wX3r0Hu/Pjzx83n8cve5zpRdNp623jU3/7FH+v/but5xhTuIKQMwVKTrIeVLjzLHMcAwUTjDh0vmfVfTNSR7q3giAIthBP67zX0k0ibZAyTFKGyXst3RIxLgiCIAg2YBoG6Z0rANCqTxx0v43hvtTpeVlEiRtWlHiumovL9PDmxmYAFtWM0nTp/dS/DFueskpaLfqhVe7KBgzT4JsvfpMXtryAW3Vz30X3MX/cfFvaHloH9jbDt4OZ7jPDZ4oZLgiCIAiCIAhjEDHFbURVVaZNm0ZJwAtAa29yRM/nqrZuFvWWjXiSMSbnWHW8skmh7lJcVLusFOzZpFAHUBWFq8+uwePW2NjYxYvvNWbVzpGkX0P1/bXjjv0cnPh16/tV91lR4zYb45W5lfxl8V84Z9I5pIwU3/j3N/jJkp+gG2J2DIqiWg8qQhNBcYHiRlE06zVTh57N0PomdKyG3kZJq+4ABp2DgiMQ/UaW3pRO2jAxsRbhqYBumvSm7HufEA2djejnfERDQbCHbOaS0bYZs7cLxe1Dq5g96H4bw1ak+PQsTPEOvQOwUqevqW+nuzdJjt/NcRMLM27rsJHohjdvtb6ffTWUHGtLs6Zp8r1Xv8ffN/4dTdH4+QU/Z1H1ImCEr4V6AuJt1r3ioGZ4qZjhw0Tez5yN6Od8RENnI/o5G9HP+YiGYxtR1WbS6TRFfg+KApFkmtgIRm+poRLUoolgmqR3rtiTQr0ryxTqHiv9+fbUdnQzu36X5vn52MlWO4+/vZXmrt6s2jmSpNODpL0/5hpYcIv1/epfWXXGbTbGA+4A/3vR//LF+VY6ugdWPsD1z15PJBmx9TxjDs0PqmWGm6p7wCDHFbJeT3X1PfR4GzpWQnQn6PEj11/hoAw6BwVHIPqNHAG3NvB81gTShommKATcmq3nEQ2djejnfERDQbCHTOdSun45AFrVCSia+4D7xNIJdkSs6O6avPEZ96ldbwcsU7w/dfpJ08vQRvMDt+U/ht4WyJ0Ac79sS5OmafLTN3/Kn9b+CQWFO8+9k3Mnn7vPPiNyLexthObXoH05dNdZ94RaAHJrxAwfAeT9zNmIfs5HNHQ2op+zEf2cj2g4dhnFd17OwzAMtm3bhkuBPG9/CvWRjha30rql69+hpm+l+oYsTfFyrZyAGiBpJmlIN2TdpzPnjGNmVQGptMFvX9qIbjgnjXq/hoZhHHiHOVfBwv+xvn/vflj+E9uNcVVRufGkG7nrvLvwal5e2fEKH//rx6nPsl78UYHmtVb1KxpGOmmZ4gVzoHgBFC2A0GRw51r7prohstWKCmhfYdWMSztv8cZY5ZBzUBjViH4ji8+lWQvv+jcocGxpLj6Xfaa4aOhsRD/nIxoKgj1kOpfMRBS9aT2wJyPbgajr3oVhmhT7ciny5Wbcr35T3J/OY9U26/tFM8szbuew0bgENj1mfb/oh+DKrIb6YPzqnV/xwMoHALj9rNu5eMbF+7w+ItfCeAt0rgEzhbW8EOtr/jHgLxMz3Gbk/czZiH7ORzR0NqKfsxH9nI9oOLYRU3yEKA5YNbZG2hTXKuaguH2YvZ1MTaVQUGiKddKZyDyyWFVUJrknAbA1tTXrPllp1GfgdWts3h3m3+9lb7CPSmZfCSf3pY9b+xtY9iPbjXGAD03/EA//x8OUBkvZ3LGZxY8uZmnDUtvPM2bwV2AUnURDrByj6CTw99Xlc/khOB4KT4DikyBnqlWDHCAdgch2K1KgbTlEtkEqMiJ6CoIg2IFumPhdKn5NxaepFPvtqekpCIIgCEcz6caVYBio+eNQ88YNut+mgXrimUeJx4wYESOCgkLdljSGYTKhNIeqolDW/R5RUr2w5FvW9zM/BeX21Pr+3arf8YulvwDglkW3cNnsy2xpd1CMNHRvgq61gAEooHpB9QEmGJJBTBAEQRAEQRCOFsQUHyH664p3xJMjGimtuDxolccB4N31HtWhUgA2hjOvKw4wxT0FgIZUAwkz+/rLRTk+Pn6q1dbf3t7G7s4xFok78wo4+bvW9+segqU/HBEj9ZiyY3j8ssc5pvQYwokwVz95NY+secT284wZNC9xw2dFjg/yOoFKKDweSk6G3OngKQAU0HshWg8dK6B9GfRstaLKxSAXBGGUEE/r9KZ0NFUh5HWhKgpdCUnnJAiCIAjDwTRN9B3vAHsysQ1GbZe14DsbU7y/nniumsubG9oAWFQziqPE37kLIrsgNA7mf82WJh9d9yg/fOOHAPzXwv/iquOvsqXdQYm3WQugY7sBpa/MlgaKatURVzSrFJcgCIIgCIIgCEcFYorbjNpXCyzk1vC5NHTDpDM+winUJ1g37vruddSEyoDsU6gXaAUUaAUYGGxPbR9Wv06fVcHs6gLSusFvXtzgmDTq6lDruc38JJxyu/X9+t/D298fEQO1NFjKH//jj1w8/WJ0U+e7r36X2165jbQhRsiBGLJ+qseKJi84ts8grwFvEaBateV6d1r1x9uWWvXIk11ikB8mhqyhMCoR/UaOzngKgByPi6K+CPH+bXYiGjob0c/5iIaCYA9DnUtG106MnmbQXAMLzg9E2tDZ3N0IQE1+Vcb9adMtI9ydzKGhLYJLU1k4vTTjdg4LTe/Ahj9a35/6A3AHht3k0xuf5taXrYxr155wLV+c/8WD7j+sa6GegK51EF4HRtIyvgtPgIJjQHVZhriqWSW4BltQLQwbeT9zNqKf8xENnY3o52xEP+cjGo5dRFkb0TSN6dOno2kaiqIMpFBvHeEU6mpeJWr+ODB0pqWs1F8burKLFAeY7J4MwNZk9inUARRF4aqzZuD3utjW3MMLq7Lv0+Fibw2HRM3HYdEPAMV6aPDWbWDaX2vC5/Lxkw/8hK+d/DUUFP609k989snP0hXvsv1cTiZj/fpR3VYdufw5UHIK5M0Cb4kVOWAkoLcROldD21tW6r1Ex4joLAxDQ2FUIPqNLF19Bni+z0OBz73PNrsQDZ2N6Od8RENBsIdM5pJe3xclXjEHxT141PD2SBNJI03I5WNcoCjjPvXXE29qtB7DnDC5mKDXnXE7I046Dm980/p++seg8pRhN/ni1hf5xr+/gYnJJ+d8kv8+5b9RDlLDO+troWlaUeHtyyHRBigQrIaieeDJtxZFl5wKRSdaX/tLbgm2I+9nzkb0cz6iobMR/ZyN6Od8RMOxjZjiNmKaJpFIBLMvmrRkL1PcHOEI0/40b1PbtgPQEG2lJxXLqq1J7kkoKLToLfQYPcPqV2HIxyf60qj/fdk2Gtujw2pvpHm/hkNi+mI47YeAArWPwJvfHRHDVFEUrpt3Hb/84C8JuAMsbVzKxx77GJs7Ntt+LqeSlX7vR9XAVwL5s6wI8vzZ4CuzUu0ZKeshS9caaH0LwrXWwxZTt28QRzm2aCgcMUS/kaU/KrzA5ya/7yF6TzJtayYW0dDZiH7ORzQUBHsY6lwy0wn0xtUAaIdInb6xL3X69PzxqErmj1LaDcsU31BnvZ+P2tTpK++F7h0QKIMF3xh2c0vql/Bfz/8XuqlzyYxLuPWMWw9qiEOW18J0zFrI3L3Juj9zhaBwLoQmWYud+9G8lkEuEeIjiryfORvRz/mIhs5G9HM2op/zEQ3HNmKK24hhGDQ0NGAYliFa6POgKgrxtE40NbKmmVZ5HIrmJhRpo9IdBLKvKx5Ug5S7rBv04UaLA5xaU86xE4vQdbMvjfrojbB9v4ZDZtp/wGk/AhTY+BdYcuuIRRKfPels/rL4L1TmVFIfrueyxy7j1e2vjsi5nEbW+g2GooG3GPJq+gzyY6xoAtVtpdyLN1tp+Vrfgq71EG8BQwzy4WC7hsJhRfQbOZK6QSRplc0o8LnxuVS8LhXTNAkn7IsWFw2djejnfERDQbCHoc4lfdcazHQSJViEWjTpoPtuDPfXE888dXqv0Uuv0UsiadDV6qIg5GVmVUHG7Yw4re/B2t9a359yG3hyhtXcO7ve4frnridlpDh/yvn88JwfDmlBQUbXQtOAaD20vwOpsFUvPDTFMsTdoWH1X8geeT9zNqKf8xENnY3o52xEP+cjGo5txBQfQTRVodBvRXKNdAp1xe1HGzcHgOm6ZcplW1ccYIrbiu7emto67BUxiqLwmTOnE/C62NEa4bl3s+/XqGbaJXDGT6yb8E2PwRv/M2IG6fSi6fz1sr9y4rgTiaaifP6Zz/Obd38jq5dGEkUFbyHkTofik6HgOAhUguq1IhESrRDeAK1vQtdaiDVbkeWCIAg20J8mPehx4dFUFEUZiBa3O4W6IAiCIBwtpPtTp1fPO2j0smmaA4vOszHF+1On93a7MXWVU2aUoakHj5Y+7OhJK226acCUD0P1WcNqbm3LWq57+jri6TinV5/OXefdhabanIIy1QMdKyGyDTDAUwBF8yFYBYeIRhcEQRAEQRAE4ehDTPERpiRgpeRq602M+Ln6071NjbSCaVI7jLri1e5qXIqLbqObNr1t2H3LD3q54vRpADy1fAf1rcNLyz5qmXIxnPFTK8K47m/w+i0jZowX+gt58CMPctmsyzAx+fGbP+bmf9/M/2fvvMPjqM6+fc/M9l3tqhfLVbbcbYyNMWDTTEKH0AmQhJbypick+ULeJG9CGslHyptAvpCEAIEkNFMScAi92Qbb2ODeJcu2el1p+7Tvj5Fky1bXytLI576uvbSanTlzzv52Zmfnd57nSWrD/1k74ZEkK+VexjTIXQLZJ4NvAihewIBkI7TutCLImzdDrBqM4Z0YIxAIxjZHpk7vILOjrngaI8UFAoFAIDhRMNrqMJoqQJJwTFjU67rVsSba1DhO2UFJxsDrUDfqjRiGSX2NdQtm6WhMnb7pD9C8Bzw5sOS7Q2pqT+MebvvnbUTVKKcWn8q9F9+LU0lj/XRTh7Z90LQRtIhV6io408rspfRcF14gEAgEAoFAIBCc2AhTPI1IkoTL5eoywzy3va54S1JD1Yc33YKcPRk5kMN03QAtSUWkltggDVKn5GSiYyJgRYungyWl+ZxckothmPzl9V1ow/x+DIbuNBwwJZfAOb+2jPF9/4S3vz1sxrhTcfKjc3/E9878Hoqk8Nyu5/jUc5+iITb0iQx2JC36DXyn4AxCRgnkLIbsReCfBIoPMCHVDG27LYO86UOIVYJ+xHGpJyHV0nXZCcxwaZjQdJriKRKaSG8/nIzIMXiC0LsprqUtU4jQ0N4I/eyP0FAgSA/9OZa0gxsAUApmInmCvba3sz1KfFpwHI5BRDs36o3EUzqJFi+l40IUZPoG3Maw0rgDNv3Ren7GD8CTOeimKloquOWftxBOhjmp4CTuv+R+PA7PgNroVb9ks5UqPWals8eTb/0O8xaI6PBRhPg+szdCP/sjNLQ3Qj97I/SzP0LDsY0wxdOILMuUlJQgy4ffVq9DIeByYJomDfFhTqEuSSgTF5OFRJ6hYZgmu9vrng2GEmcJAOVqOUYa6mNLksQnz56O3+PkUEOE59+vGHKb6aY7DQfFlAvh3N9YxnjZ8/DaF6ByDURr0tPRI5AkiU+e9En+fNmfCbqDfFjzIVc/eTXb67enfV+jnbTpN1gkyapbF5gMuYutmzOBKeBor2WnhqFtLzS8Z6X5a94KdaugcT3Ur4Z49cj0exQxHBpWtsV5o6KBNZVNvFHRQGVbPG1tC7oy4sfgGEUzTNra64lnHmGKZ7gcKLKEphtE1fRM+BAa2huhn/0RGgoE6aGvY8k0dPR2U9wx8ZQ+2+tInT4zNGHAfTFNs90U10i0eEZflLihtadN12HyBdZjkFS1VXHzczfTEGtgZu5MHrj8Afwu/4Db6VY/Q4XwTmjZDHrCKmOVORdCs0BxDbrPguFBfJ/ZG6Gf/REa2huhn70R+tkfoeHYRqiaRkzTpKWl5ZhorY5o8YZhrisO4Bi/ECSZUjUFhjakFOpFjiK8kpekmaRSq0xL/0I+F58820qj/u+NB9hfN7rSqPek4aCYfAGc+1tQo7DlQXjqPPj7Eti9Yuhtd8PSiUt56tqnmJI5hZpIDTc8fQP/2fufYdnXaCWt+qUDhw/8EyFnkZVmPTDViioHKzo8dhCMpJVJQE9ByzYr8mG09H8ESLeGCU1nc10rKd1AN0w0w2RzXauIGB8mRt0xOEYIJ1RM08TrVPA6DkenyZJEKM11xYWG9kboZ3+EhgJBeujrWDJqd2Imo0juAHL+jD7b29liTTafkTnweuIxM0arGkXTTcy4j8XT8gfcxrCy5QFo3A7uEJz2P4Nupj5azy3P3UJ1pJopmVN48PIHCbp7j8DviS76mSYk6qyJxIlaawVvsVU73J0z6P4KhhfxfWZvhH72R2hob4R+9kboZ3+EhmMbYYqnkeaIxge7GmiOaF2W53WY4vHUsB9IkicDpWg2000JU02wI3xg0G3Jksxk52QgfSnUARZPy+eUaXntadR3Dnta+YFgGAY1NTUYRpr6lDfPmtFuGtYM/Gg1vP7lYYkYB5icOZknr32SMyeeSUJL8NX/fJV7196blkh/O5B2/dKJ4gH/eKv+eO5p4BsPdKRgMQHDqjve/IEVNd60CdrKIFF/QqVWT7eGMVVHN0wMEwzTesd10ySWpqhaQVdG9TFoY5rba4Znuo+txZnuuuJCQ3sj9LM/QkOBID30dSxpB9YDoExYiNRHOvSmZBv1iRZkSWJasHjAfemIEk+2uTllSgEe58DTrw8bzXvhg/us50u+B77cQTXTkmjh1n/eSkW4guKMYh6+4mFyfIM3rDv1U+PQshXCO6zf1YoPsk6G4DSQHYNuXzD8iO8zeyP0sz9CQ3sj9LM3Qj/7IzQc2whTPE1s2JfiF89GeHZzJr94NsKGfYejwjPdThyKjKobhJNaL62kB8fExcxABi1JWWsVSX3wEepTXVMBOKgeJGWmL9L9E2dNJ8PnpKoxyj/X7U9bu6OO1grLEPcXWD/aTRNi9bD6B1YE+TAQdAf546V/5NYFtwJw3/r7+Np/vkZcFSmjRw2K24ogV1wgOUByYp2OFSvlvqmD2mJFkoe3W+nW69+1IsmjB6w65cbwn0vGAj6nApI17cAEVMNEkSRruUBgE5rby69kebsxxdMcKS4QCAQCwVjHjIfR63YD1m/nvugoSTbRn4/P4R7w/mpT9SRU3UqdPmsUpU43dCttuqHChHNg6mWDaqYt2cZt/7yNPU17yPfn8/AVD1MYGOI4TZOgow25eQOkmgAJ/JOtDFyuwUWfCwQCgUAgEAgEAoEwxdNAOGbw5OoYsaRJSpOIp0yeXB0jHLNmkkiSRK7Xihavjw1/xKecO408T4gs00RXE+xtrRp0W9lyNiE5hI5OhZq+GuAZXiefPHs6AP/54AD7alrT1vaoIjjJSqGtJ8HXbowjwYFX4ZmL4cAbw7JbRVa4c9md/Gz5z3DIDl7a9xI3PH0DVW2D/ywI0oziturfyQ6QTMsgz54P+WdB9iIITgdvETjaa/AZKUg2QKQcmjdb0eQN6626erEqUNusCRiCLngcCkGXszMmH2Bqlh+PQ5jiAntgmGbnhLosz7GmeMjjAMnKipDUxDlAIBAIBIK+0A5uANNEzpmMHOg7MrqjJNmMzIHXEwfYHa7ENMGlBpleFBpUG8PC9kegfhM4A3DGXSBJfW9zFOXN5Vz95NVsqt1ElieLhz/2MBNDE4fWLy2KFN5MjrvFmizsDFpmeGASSOIWlkAgEAgEAoFAIBg84hdFGmhsM0hpVnpeE0hpkFBNmtoO35zuqCtefxzqikuyjHPSYqYjWynUh1BXXJKkzmjxdKZQB1hUkseS6fmYJjz4+k5So6DGryRJ+P1+pEHcEOgWfyGc8yvrRoMWBV8+nPEDCE22Uqi/+l/wxtcg1pCe/R3F1bOv5tErHyXHm8OOhh1c8+Q1fFD9wbDsazSQdv2GG28R5C2FnMXWX2+RdTPKGbCeB6dbtfLyl0HWSRAoAXceyO0RKnrMqqvXtgeaNkLdamj6ANr2QrwWtLjt6pOnW8O4ppPQdbwOmXyfC69DRjfs9Z7YCdsdgzYgnNQwTBOXIuPrZjKHU5YJOK30oelIoS40tDdCP/sjNBT0h6amJm666SaCwSCZmZncfvvtRCKRPrd79913Wb58OX6/n2AwyFlnnUU8PjazSfV0LJmmYZnigGPiKf1qa1d7pPjM0MDriZumSZ1q/dablzth9BzbrRWw4TfW81PvtH63DpDHtz7OvD/M4/Xy1zkUPsT1c69navbUwffJNCCyHxo3IKmtSJKCmTENshYcnigssA3i+8zeCP3sj9DQ3gj97I3Qz/4IDcc2whRPAzkZMi6HhEMBhyJhGJBUTeKpI0xxrwtJgkhKI34czF9lwiKmI4GusqNh75DamuKcAkCNVkPE6Ptmy0C46cxSgj4XNc0xnl27P61tDwZZlpkwYQKynMZDY/o1cNNauHKl9fe078EVL8C8T1sz3ctfhGcugt1PD4uBubBoISuuW8HM3Jk0xhv55LOf5Jkdz6R9P6OBYdFvuFHc4Mq0/vaEpFjr+CdA5mzIOw1yT4fMOVYadleWlYYdA9RWiFVC605oXAf1a6B5i3WDKdloRZyPYtKtYXUkASbkeF3MzMlAliRqokkMm00WsAu2PAZHOc2J9tTpHmePF+OddcXTkEJdaGhvhH72R2go6A833XQT27Zt45VXXuGFF17g7bff5rOf/Wyv27z77rtceOGFnH/++axbt47169fzpS99acx+1no6lozGcsxoE5LDhVI0r892YlqCA5E6AKaHBh4pXhFuRCWFacKZk4dgGKcT04BV37WymY073fq9OkBqIjV88d9fJK7GUSQFr9PL/773v9REagbXp1QYGjdAtAIwkTy5uIrOQPaPH1QEu2DkEd9n9kboZ3+EhvZG6GdvhH72R2g4thGqpoGQT+a6pT48TgmHbOJ0QMAj8dSaOGW1VspTlyITaq/72XAcosVlXxazsksA2NtcjjqE+sMBOUChw5o5Xq6Wp6V/Hfg9Tm4+x0qj/vKmg+ypDqe1/YFiGAYNDQ0YRppT0PoLoejUwzPwnV5Y/C24bAXkzIZUq1XP7T+3WLP208y4jHE8dvVjnF9yPqqh8p3XvsMvVv0C3Rj56Px0Mmz6jUYUF7hzITAFsuZD3hlWxHlwJniLwZGBVUxbs+rwRSugZatVm7xhLbRsh+gh6waUOXo+B+nU0DRNqtqskhXjMjzkeJ24HTKqbhyX8/CJyAl1DB4nmtuN7u5Sp3fQaYqnIVJcaGhvhH72R2go6IsdO3bwn//8hwceeIAlS5awbNky7r33Xh5//HGqqnoulfT1r3+dr3zlK9x5553MmTOHGTNmcN111+F2D7xGth3o6VjSDqwHQBm/AMnh6rOdPeEqTEwKvFlkuQMD7se7h/YB4FYzyA+OkmjnnY9DzXpweGHpTwZlOm+u3Uw4EUaWZHL9uWR7s4mpMQ6EDwysIUOD1j3Q/KGVBUt2QmgWRsYsGpoj4lxoY8T3mb0R+tkfoaG9EfrZG6Gf/REajm0cI92BscKiqS6m5Ets2lHB7NKJvPhBir01Go+8EeXGs3xMH+ck1+eiJaHSEEsxIegd9j6Nn3wGGU17aEvF2ReuYmbW4Gt7lThLqNFq2Jfax1zX3LSmjlgwJZczZhayZmcND76+kx9edwpu58jU+zVNk4aGBrKyso7PDnPnWMb41ofgg99B9Xvw7GVw8pdh7q3tNcjTg8/p47cX/Zb71t3H79f/ngc/fJC9TXv59QW/JsOdkbb9jCTHXb/RhCRZ9esdPvAWWMtMA7SIVW9cbbOiyPU46Anrkazv2NhKSejMaH8EQfGNSFRGOjUMJzViqoYiSxT43UiSRFHAw/6WGFWRBPn+sXkTeCQ5oY/BYcA0zc7o7yxPzzfuM9sn3bUmNXTDRJEHf+wKDe2N0M/+CA0FffHuu++SmZnJKaccTv39kY98BFmWWbt2LVdeeeUx29TV1bF27VpuuukmzjjjDPbt28fMmTP56U9/yrJly3rcVzKZJJlMdv7f2toKgK7r6Lo1qVKSJGRZxjAMzCMy8fS0XJZlJEnqcXlHu0cuB465IdXTckVRME0TTdOoq6sjGAzicDisvqRi6FVbAROpeCGGYfTZ9x3N1oTl0mBxj+v3NCYkid0tlXgyTIq9+Z1jG+yYjlze0ceelvc4ptZDSOvvAcBceAf4xyG373MgOr2892UkSUJCwiW7CCfDBFwBigPFXTTsdUyJBssQN9o/Y55CpOBUTMmBpqrH6tfPz9hIf/bSotMYGJOu69TV1REKhcbMmAa73I5j6ks/O46pr+VjbUy6rlNfX09WVtaYGdORfR/rYzpSv7EypiOXj/UxdeiXmZnZ9drQxmPqre9jcUymaVJfX08wGERRDvtUdh7TWNTp6OVHb9cTwhRPIyGfTFFQIzeocNPZfp5YFWNnpcrf3orx8WU+JuS72UuUpkRqyDet+4OjaDbTZRcbjBQ7Dq0fkik+yTmJtfG1hI0wTUYTOUpOGnsKH182le2HmqlrifP0e2XceGZpWtsf1cgKzP80TD4f1vwPVL0L7/8SylbCsp9axnm6diXJfGXJVyjNLuXO1+7k7QNvc92K67j/kvuZlDkpbfsRjBIk2TK4ncHDywwV1IhlkGvtZrmRssxzLQLx6vZtFXAE2rdvN8pll2WU60nLXFe8vad9H2GqIgkA8n1uHO1fkuPaTfH6WIqUbuBSRMIUweilLWWZ3A5ZJuDqebKY1yHjdsgkNYNwUiXb23fkm0AgEAjsSU1NDfn5+V2WORwOsrOzqanpPnV1WVkZAD/84Q/55S9/yYIFC3jkkUc477zz2Lp1K6Wl3f/2uvvuu7nrrruOWb5v3z4CAStqOhQKUVRURG1tLeHw4axfubm55ObmUllZSTQa7VxeWFhIZmYm+/fvJ5U6nLln/PjxBAIB9u3b1+Vmx5QpU3A4HOzZs6dLH0pLS9E0jfLyw5nMZFlm+vTpRKNRDhw4QFNTE3v37sXj8VBSUkLbnncxE3E0TxYNdTH8sUomTJhAU1MTDQ0Nne0cOab3D+0glUoRiik0NTUNaExROQPDE0ECspOHxzDYMR06dKhzucvloqSkhHA43EV3v9/f85gKC0m89i2UWAuJ0ByqlVPIHeCYxo8fT71az3PbnyPLlYWJSTgeJuAJcM9H7qGtuo022nodk0M2mZZvYsZrUTUNzXBQn8zCiLkoyXQSbmmhqqqqU7+MjIw+dRpNn70h6zRGxmQYBk1NTdTV1VFcXDwmxjQWdeppTB36NTU1UVBQMCbGNBZ16m1MhmF0jmOsjAnGnk49jckwDDTNyvo6VsYEY0+nnsZkGEbno+M63O5jgrGnU29jKikpQdd19u7d22m82n1MY1Gno8cUifSv9LNkHj1dZRTS2tpKKBQiHA4TDAb73mCE0HWdPXv2UFpaiqIoaLrJijUxth5UkSW4+jQvYSVCQtNZWBgi1zf8ZtLK9x7gb/U7mOvN4rsf+d6Q2nor9hb71f3Mcs3iVO+paerhYbYcaOJ/n98MwLeuWMDM4sy076MvjtbwuGOasPdZWHu3lVJdkmHOLXDyV6yU62lkW902Pr/y89RGawm6g/zuwt9x+oTT07qP482I62dHTNOKzjgymlxrs6LMj0Z2WWZ5KgyYVh3zzFngLUpbd9KloWGavHmgEU03WFiYSa7vsEn4XmUTrUmNmTkBJoZ86ei2oB1xDKaXinCMXY0Rcn1uFhaGel13U22Y2miSadl+SjIHn55VaGhvhH72R2hoT9Lxe/XOO+/kF7/4Ra/r7Nixg2eeeYa//vWv7Nq1q8tr+fn53HXXXXz+858/Zrs1a9awdOlSvvOd7/Czn/2sc/n8+fO55JJLuPvuu7vdX3eR4h03QTrGOVqiAjroiHRQVZW9e/cybdq0zkjjxFu/wwhX4Zh9CcqUM/rse1JL8dnV/4tq6PzfUz7NOH/OgMb0wOs7aZqymoBf4uM5V5CtZA9pTEOO3tj7LOaq/wbFjXHZsxCaPCidvrDyC7xe/jrnTj6X75/1fQ6EDzA5azKFgcLex2SakKxDjpYhmTomYHqLMX0TQVK6jKk7/UZrREradRojY+q4kVxaWorT6RwTYxrscjuOqS/97DimvpaPtTHpus6+ffuYPn06kiSNiTEd2fexolNPYzpSv6Ox65iOXD5WdOppTB36lZaWIkldgyLtOqbe+j4Wx2SaJnv27GHq1KkiUtxGY2ptbSU7O7vP3+UiUjyNSJJEKBTqPNk5FInrlvpwvBfnw/0pVrwb55xTFHDo1MdSx8UUnz1lKdTvYE+8GS3egsObOei2Spwl7Ff3s1/dzymeU5Cl9EZYzpuYzZmzi3hnezUPvb6Tuz6+GM9xTqN+tIbHHUmC0qug+CxY9zMrWnzrg1DxMpx+F4zvOb3hQJmTP4enr3uaL/77i2yq3cTt/7qd7575XW6cd+PIjX+IjLh+dkSSQPFYD0+etcw0QYsejiRXW0GLHU67TvuXlalB0ybwN4M7Ky1p19OlYX0shaYbuB0KOd6utZiLAh5akxGqIklhiqcZcQyml/7UE+8g0+OkNpqkJaENaZ9CQ3sj9LM/QsMTl2984xvccsstva5TUlJCYWEhdXV1XZZrmkZTUxOFhYXdbldUZE1gnD17dpfls2bN4sCBnmtAu93ubmuOK4pyzKSNjhsSRzPQ5T1NBhnIckmScDgcZGVlHTZUw5UY4WqQHTgnLkI6Yrue+lIRrUM1dIJOH8WB3M7jsj9jiiZVttRUM3G6gd/lJduZjSJ17etAxzSQ5cf0MVYH6+5GAlj4VZTsqb2v38PydZXreH3/6yiywv9Z9n8oDhVTHCrue0xmCtp2Q6rFWuAIIAWnIzmPLePVnX4D6eNgl6frszckndK8fCTHJEkSWVlZneuMhTEN1/LROKah6jcaxzTU5XYbkyRJZGZmdhoSQ+17T8uFTsMzpnTrNxrG1FcfB7p8NI+pQz9Zlgf0HozmMQ12uV3HZBgGmZmZXa5FO7DrmHpbPlbG1N/AApE3No3IskxRUVEXMWVZ4qrTvSye5sIE1u/SiasmDfFUl1kQw8XE/Bn4HC6SQNm+t4bUVrGjGI/kIW7Gqdaq09PBo7h+6VSyM9w0tCZ4as2+YdlHb3Sn4Yjgy4Vzfg0f/SP4i6DtELx8O7z9bUi0pG03ef48Hr3yUT4242Pops6P3v4RP3zzh2jG0EyVkWLU6Gd3JAmcASsCPDgdck6B/KWQUWplL0Dh8NeHAYkaaN0Nje9D/Wpo3gyR/ZBsstK1D4B0aVjVZqVOHxdwH2MsFAU8SJJEa1IlkrLnZ320Io7B9GGa5oBNcYCWpDqk6wuhob0R+tkfoeGJS15eHjNnzuz14XK5OP3002lpaWHDhg2d277++usYhsGSJUu6bXvy5MmMGzfumOjy3bt3M2nS2CyhdPSxpB14HwClaA6Sq3+TIneFrRSCM0LjBzxRZf2eehyBGIoiUeDOPcYQP66YJqz5AaTaIG++lYlsEBimwc9X/RyA6+dcT0lWSf/2HT1o/U5ItQAyBEog+2SrRFMPiHOh/REa2huhn/0RGtoboZ+9EfrZH6Hh2EaomkYMw6C6uvrYMH5J4vLFXk6f4SYakYnETZqiGlG1f4Xfh4IiycwMTQBgW+WHmN2lRO4nsiQz2TkZgDK1rPeVB4nX5eCWc2cA8ObWKrYdbBqW/fRETxqOGBPOgatWwuxPARLsfQ6euRD2PW/dYEgDboebX3zkF3zrjG8hIfH4tse59Z+3srN+J2sPraUm0n1twtHIqNNvLCEpViS57LSMccVtpU+XXeAdD84QIIOpQ6oZohXQsgXq10DDOgjvhFiVVc+8l89uOjRMagYNcaseybiA55jXXYrcmU69o+64ID2IYzB9RFUdVTdQZImgu+/EPhkuB4osoenGkK4vhIb2Ruhnf4SGgr6YNWsWF154IZ/5zGdYt24dq1ev5ktf+hIf//jHGTduHACVlZXMnDmTdevWAdYM/29961v87ne/Y8WKFezdu5fvf//77Ny5k9tvv30khzNsHHksmbqKfuhDABwTTul3G7taDgIwPXPCgPe/amcNnlACn8tBjpIz4O3TStlKOPA6yA5Y9jOQB2fQr9y9km312/A7/Xzp1C/1vYEagaaNECkDDHBmWpNt/RPaJ9r2jDgX2h+hob0R+tkfoaG9EfrZG6Gf/REajm2EKZ5GTNMkHA53G6ElSRIXL/Rw1mwv0YhMNGmyak/0uESLzxx3EkgSu1JtGI3lQ2qrxGnNBj+gHUA1BxYB2l/mTMjmnLnWDZ2H39hF/DhGcvam4Yjh9MNp34VLH4esUkg0w1vfhJc/A5GqtOxCkiQ+vfDT/OGSP+Bz+nit7DUW/HEBF/39IpY8sIQV21ekZT/DzajUbyyhuCE0y7qRZmrWjbXMORCaDtkLIH8ZZC+0Iso9BaB4re30OCRqoW0PNG2A+lXQ9CG0lUGiAfRU5y7SoWFNNIFpmoTcTvyu7s3EDrO8OpIQn5c0Io7B9NERJR5yO5H7EZ0mSxJBd3u0eGLw389CQ3sj9LM/QkNBf/j73//OzJkzOe+887j44otZtmwZf/rTnzpfV1WVXbt2EYvFOpd97Wtf4zvf+Q5f//rXOemkk3jttdd45ZVXmDp1ane7sD1HHkt69TZMNYHky0TO6994DdPojBSfGRo/oH1XNUUpr23Fk5nA41JG1hSPN8J7P7aeL/ii9XtyECS0BL9691cAfG7R58jx9TImU7eu85s2ghaxJtIGp0PWfHB4+7U/cS60P0JDeyP0sz9CQ3sj9LM3Qj/7IzQc24ia4scRSZL46EkeXtqWpFGLcyic5OUPE5y/wDOsdQNnZU8Bh5s9aoLU/nV4cwd/4yNXySUoB2k1WjmgHmCqa3huolx7xlS2HmiioTXBE6v3dUaPn9DkL4DLn4Utf4YP/x9UvgPPXAyL7oBZNw16xv+RnDvlXH5/0e8575HzUHWVqBlF1VW+8fI3WDZxGYWB7usUCk4gvEXgyraMbsVrGeUdSJKVBtGZAVgTWzBUqya52nq4Prmpgxq2Hh3IHnBmIDkCuOUkmAZWmvaB0xH9PS7j2CjxDvJ8LpyKTFIzaIyrnZHjAsFooWUAqdM7yHI7aY6naEmqjKd/N50FAoFAYD+ys7P5xz/+0ePrkydP7vYGzp133smdd945nF0blegH1gNWlLjUR4RyB5XRRqJaArfiZFKgYED7W72zBjDJytOQJYVcJXegXU4f7/0Yki2QPQvmf3bQzTyy6RGqI9UUBYq4ecHNPa+YarHKKulx6393HmRMA0VcawsEAoFAIBAIBIKRR0SKjwBnTQ/gd0t4fQardyVYuSGBMYyzTqZkFOJxB4gBB2q2YKZifW7TE5IkdUaL71OHr+a3x6lw6/KZALyzvZotB45vGvVRi+KEBV+AK/4FBaeAFoe1P4WVH4emXX1v359dyAp+lx+v04uJSUJLUBup5f2q99PSvmAMoLjBldnVEO8J2QnuHAhMsaJD8pZaaROD08FbCEp7TUcjAcl6pEgZ47x1yI3vQtMH0LYXEnWgJ/pVMqA1qdKW1JAliUJ/z/078vVqkUJdMMowTZOmQZjimR5rruNQIsUFAoFAIBhLmNFG9IYykECZsLDf2+0KW6nTpwWLcQxg8rFumKzZVYvTr+L1giIphOTQgPudFva/DOUvWmWQzvyZleVpEDTFm7j//fsBuOP0O/A4jph4qictI1yNQOsuaN5kGeKyC0JzIHO2MMQFAoFAIBAIBALBqEGY4mlEkiRyc3P7jPr2OhVyA04yvBKBgMF7u5P8c10cwxgeY1yRZKZnTQFZYbehoR36YEjtlbgsU7xGqyFqRNPRxW6ZWZzJefOLASuNejQ5/Df5+6vhiJNZAhc/CmfcBc4A1G+Gf14JG34DWnJITU/KnITf5cetuAm5QxgY6KbOt1/5Nn/b/DeMIdSlH25so9+JjCSBw29FnAdnQO5iyyjPnA/+yeDORlZcVqS42gqxSgjvgIa10PAetGyD6EHr5pt5bN3kqoj1+e+IBO+NjhTqdbEkmqgRkxbEMZge4ppBUtORJImQu/+meMjtBAliqk5SG9xnWmhob4R+9kdoKBCkh45jyTi0EQAlrxTZl9Xv7TtSp88YYOr0LQcaaY2lyCpQcTsVcuQc5H5Gp6eVZBjevct6Pv8zkDN70E3dt+4+omqUOXlzuHT6pYdfiFdD/WpoWAd170DUes/wFkHOYvAMPkJenAvtj9DQ3gj97I/Q0N4I/eyN0M/+CA3HNsIUTyOyLJObm4ss9/225vlceJwSC2fISMCGfSmefnf4jPGZmROQnB52SyZ6xfoh1UPIkDPIV/IxMSlXh1ajvC+uPq2E/EwvLZEkj68avsj0Dgai4YgjyTDz43D1izDpo5ZBuOl+eO5yqF436GYLA4X86vxfEXAHQLL+X1K8BM3U+PHbP+YTz3yC/S370zeONGIr/QSHkR3gzoLAJKSs+TiLzkLKXQLBmeAdB44AIIGRgmQDRMqsKJS61dC4AVr3QLwWQ41RE7FSNRYFek6d3kHQ7cDvcqAbJrXRoU0mEViIYzA9HK4n7kCR+38B7lRkAs72aPFBTiQTGtoboZ/9ERoKBOlBlmVysrPRO0zxiYsHtH1HpPhATfFVO2oAmDbFii4fsXria38G8QbInAonfWHQzZQ3l/P41scB+PbSbx82+PUktOyw/poaYFplk0KzrYxQg4xK70CcC+2P0NDeCP3sj9DQ3gj97I3Qz/4IDcc2QtU0YhgGBw8exOhHxGGu10ohprh0rj3DiyzBpooUT6yOoenpN8ZnZU4ER7sp3laD2XJoSO11RIuXqWXp6F6PuJ0Kty2fCRKs2VnDh+UNw7q/gWg4avDlw3n3wfJ7wZsHrfvhxU/C6u9DsnVQTV4z+xrWfnotK29cyYbPbuDtW9/mf876H7wOLxuqN3D5Y5fzwMYH0I1jI3VHElvqJ+iCYRgcPHQIQ3aDtwCCpZCzCPKXQtZJECgBd66VkhETtAjEq6B1J0bDOhbI25nrrCDXrIZkMxia1XBHakf9sPktSVJntHhVm0ihng7EMZgeWhIpADIHkDq9g45tBptCXWhob4R+9kdoKBCkB8MwqNm2CjPRiuTyoRTM6ve2DYkwDYlWZEmmNFTc7+1a4yk27W8EICvfugYdEVP84Fuw9zlAgmU/A0c/Sh71wP9d/X/RTZ3lk5ezZPySwy9oMWvCKqa1H8lppWmXB37t0h3iXGh/hIb2Ruhnf4SG9kboZ2+EfvZHaDi2GZApPnnyZCRJOubxxS9+sdv1H3744WPW9Xj6jt6zK6ZpEo1G+xWFneVx4lBkVN1gYqHMDWf6UWSJbQdVHnsnhppmY3xqsAin4qBVcVELaAfWD6m9yc7JKCg06800683p6WQPlBaFOP+kCQD89c3dRIaxVupANBx1TD4frvo3zPi49f+uJ+GZi2D/S/2qxXw0hYFCTi0+lcJAIbIkc9P8m1h540qWTlhKUk9yz5p7uG7Fdexu3J3mgQweW+snAHrRUFKsOub+CZA5B3JPg9wlEJoFvmJwBlENcKKRo0SQoxXQstlK6Vi7CmrfslKw1606nNoRKAq4QbIic2Pq6JrkYUfEMZgemgdRT7yDzPZ064ONFBca2huhn/0RGgoE6cE0TaTqTQAo409GUvofudyROn1yIB/PAOphr91dh2GYTMoPEHeEgREwxVNtsOZ/rOdzboH8BYNual3lOl7f/zqKpPCtpd/q+mKyEcsQ57ARLiugeAe9vyMR50L7IzS0N0I/+yM0tDdCP3sj9LM/QsOxzYBM8fXr11NdXd35eOWVVwC49tpre9wmGAx22aaiomJoPR4jSJLUGS1eH0sya7yTT5ztw6lI7KpSefTNKCktfQedU3YwLViM5PSwCwO9chPmEGpPuyU3xU5r1vw+dfjTml+5ZDKFWT5aYykefG0HOyubaYqIyM5jcAdh6V1w8d8gONlKmff6V+C1L0K0dsjNFweL+cvlf+Fny39G0B1ka91WrnziSu5dey+qPvw13wWCTiQJFA948iFjGmroJNakZrFJm4rhLwF3Hshuqza51maldTR1MJLQsgXq1kDzFjzxCkrcbfilODVtsZEelUBAUtOtCRrSYYN7IHREircmNfRhKskiEAgEAsFox0y24Wmz7j04Jp4yoG13tbSnTs+c0P/9mSardlQDsHhOBpqp4ZAcBOXggPY9ZNbfA9EayJgIC7866GYM0+Dnq34OwPVzrqckq+Twi8kGiFdamZtkJ2BahnhoFiiDj0oXCAQCgUAgEAgEguFmQKZ4Xl4ehYWFnY8XXniBqVOncvbZZ/e4jSRJXbYpKCgYcqfHCrk+yxRviFtpUkuLnHzqHD8uh0RZrcZf34iSUNN3Q3tmaAIoTna73JhaCr1qy5DaK3FaP4zL1XIMc3hTSbgcVhr1eErjPx8e4jt/X8dXH1zNO9urh3W/tqVwMVzxL1jwBSvC9sBr8MzFsOMxyyQcApIkcfXsq1l540rOm3IemqFx3/r7uOrJq9hSO7TPlEAwWGqiSXRTAmcQb2gSZM6GvNMgNMc6BiQnSA5Awqp5mIBUE8QOMlk6yALHXgpj6zEb1kLLVoiUQ6IOtOiQjxmBYCB0RIlnuBw4lYFXufE6ZFyKjGmatKbEZCWBQCAQnJho5WuQdBUyCpGDhQPadmd7pPiMUP9N8Yr6CIcaozgUmUmTJACy5ezDNbiPB1Xvwa4nrOfLfgrOwUdtr9y9km312/A7/Xzp1C8dfkGLQXin9dw/EfLPgpzFkLcUvEVD6LxAIBAIBAKBQCAQDD/9zyF2FKlUir/97W/ccccdSJLU43qRSIRJkyZhGAYLFy7kZz/7GXPmzOm17WQySTJ5OIq5tdWqi6zrOrpupbeVJAlZljEMo0sag56Wy7KMJEk9Lu9o98jlwDF1A3parigKkiSRn5+PaZrout7ZF9M0u6zfsTzbbb39rUmNaDKF1+lgSoGDm8/x8uhbcSrqNB58tY2bz/Hj9ypDHtPMzAlQAbudHsxUK2rFOqTik3sdU099N02TIqkIFy5iRoxqtZpiV3G/9RiMTlkBF0lNxzBMVE0nloQ/vbKd2ROyyPR1jaYb7JhM0+zU0DCMAfV9JD973Y7J4cY8+SsYE89HWvM/SA2b4d0fIpU9j3H6XZihkq7rD3BM+f587r3wXv6999/85J2fsLthN9etuI7bFtzGF075Ah6Hp8v6aRlTL8dTRx8Hq9+I6dSPMQ12uR3H1KFhB/0dU2VbHIBCv6vLuGRnEGSHVVtcav/KkxQIzQVDxdQiSGoMTWvFYWoYahxZSwANhwcqSUiKD9Phw5Stvyg+JIcPWTn23Hwi6NTT8g79Oq4LxsKYju77cI+pKZ7CBEIuR+d5bKBjynQ7qI2laIqlCDqVAY3JNE0KCgqQZVnoZMMxdeg3kLGO9jH11vexOCZJkigoKOj8PTEWxjQWdTp6+dHbCUYWtWI9+ubnUAwNs3Ef2oH1OCYu7te2UTXBoWg9ADNC4/u9z9U7awA4uSSXiNwCHOfU6WocVn/Xej7zRig6ddBNJbQEv37v1wB8btHnyPG1j8PQrMmjpg7OEGRMBUkeluhwWZYpLCzsPNYE9kNoaG+EfvZHaGhvhH72Ruhnf4SGY5tBm+LPPfccLS0t3HLLLT2uM2PGDB588EHmz59POBzml7/8JWeccQbbtm1j/Pief2Defffd3HXXXccs37dvH4FAAIBQKERRURG1tbWEw+HOdXJzc8nNzaWyspJoNNq5vLCwkMzMTPbv308qlepcPn78eAKBAPv27etys2PKlCk4HA727NnTpQ+lpaVomkZ5eXnnMlmWmT59OrFYjLq6Ourq6gBwuVyUlJQQDoepqanpXN/v9zNhwgQi4RbkVJwEClvLDzIx5KOoqAiX3si5JRH+syPI/lqJP76k8tkLs2iuH9qYJk0cj4xErZqiRtXIqd1H5Za1TJm3pMcxRaNRDh06XH/36DFl+DOo9lSzObGZ4oJimpqaaGg4bCalU6e6sIppGDhkMAyTREoDoLYlSn1lfb916mtMAHV1dZ06DeeY0vXZ63VMDRKU/pCg73lyKx7FVbsB49nLaSi+hpaJ14HsHPSYKioqKDVL+e3i3/LH7X9kVf0qHvjgAf659Z98bd7XmJs9d3jG1M3xdKROdXV19tOpjzGBDT97QxiTYRj9HlMKiRZnCEVWSNRVs6f28I3pKVOm4MiYgdG0DYkUJjJ1yRCF+TnWmA62AV5qCZGQFEqzHIxzJIi01OKSVVyyiiyByxXDSLWhHXHTW5JkXN4QCVWmNaqRMpykDCf+jGyKxo07IXTqaUyyLI+5MR03nRpbUJGJNbayv61hUGOSknHUlM6BuhhqXWJQY5IkSehk4zGZpjnmxjQWdeppTD6fj717946pMY1FnY4cUyQSQTA6MOJhUu//HQwNSZLBNElteAI5bzqyN9Tn9rvCVur0Ql82IZe/X/tUdYP39lilqpbNLOSgvhaAXCV3kKMYBBt+DW2HIDAOFn9zSE09sukRqtqqKAoUcfOCm62FpgnhHaDHrRJFodmWIT5MSJJEZmbmsLUvGH6EhvZG6Gd/hIb2Ruhnb4R+9kdoOLaRzEFWi7/gggtwuVw8//zz/d5GVVVmzZrFDTfcwI9//OMe1+suUrzjJkgwaNXkGi1RAR0oioKu65SXlzNp0qTOdvuKdChribGvJUae18WCgmCXvteGdf76RpxowiQvpHDzuV4yPFKXvgx0TD/Y+Ah7W6u4XfFzeksVypSluOZe2uOY+oreqNPreDn+Mg4cXB+8HtmUhy16ozma5Ct/WU0sqaFqOqpuoigSP7x2EQtLut5wGGxEiqZpVFRUMGnSJBRFGdURKf0dU5fl0Wrk936Eeegta0HmNIzT70IqWJiWMb1Z8SY/fOuH1EWsiSE3zL2BO067gwxPxvCN6Yg+6ro+aP1GlU6jPBpqOMdkGAYVFRVMnjwZh8PRrzHtbY5R0Ronz+dmfl6g+76rcesmnuIFxX3MmJoTKhtqW3HKEmdNyEGifZ+miWSmkI0EphrB1KJIWtRqy7QyghzzNSopSA4/puLFVHyYDr8VWa64u40st6NOfek3ZcoUlF7GaqcxHd334RxTStN580AjJnDm+Cw8DmVQY2qOJ1lXHbY+z+OzkGW532MyDIMDBw50GqtDHdNY1Gk0j6lDv8mTJ3M0dh1Tb30fi2MC2L9/PxMnTuxcx+5jGos6Hb28tbWV7OxswuFw5+/VsUhrayuhUGhUj1Nv2Efi9V+BrmHICrI7AFoCzzlfR8kt6XP7x/a9wfMH3uOcopP47MyL+7XP9XvruP+l7WQF3Nz9iVN5Mvo4mqlxReAKQkrfRvyQqd0IK28ETDj/LzB+2aCbaoo38ZFHPkJUjXLPR+/h8hmXWy9E9kO0ApAhewE4M9LQ8Z4xDIP9+/czefLkLudCgX0QGtoboZ/9ERraG6GfvRH62R+hoT3p7+/VQUWKV1RU8Oqrr/LMM88MaDun08nJJ5/cJfKhO9xuN273sSm4FEVBUZQuy3r6UA50+dHtDna5pmnIstzldUmSul1flmUK/B7KWmI0J1VMpC59HJet8NnzFR58LUp9q86Dr8W47bwAmX75mHb628dZmRPZ21rFHn8mp7dUY1R+CLMvQlIc3a7fU987lhfKhQSSASJGhAPqAUpc3d9oSIdO2QEPn/vobP706nbiKQlJ1fF7nPz5tZ3cZs7ktOnH1qsf6JhkWe7UsKMP6fqMDednry+dOgmOh4/+EalsJaz9KbTsRXnxEzDrJjjlDmRn99EQ/R3TeSXnsbh4Mb9Y9QtW7FjBY9se460Db/GTc3/C0olLh2dMR/SlY2LDcOh3XHUaZB/Hypg0TUOSpH6tb5omNTEr0mtchqfnvrt8gO/Y5e3r5/hkvA6FhKbTEE9RGPAcsZYD8CG5s+mclmSaoCdAjyJpMav2uBa16ixigNaGpLXRpbiIpIDDj+zwg8MHDj8ofmt5d2PVk6DHUdqN/J763p/lx/Ozp2lan+uP1s/eUJanY0zh9gwofqcDn+twWZCBjinkceGQJTTDJGFAwDGw86GqqpimKXTqYfloH5Oqqt0u72l9GP1jGsxyu45J13VUVT3m9wTYd0y9LR8rY+rpdcHxR/LnIjl9mFICHQVZjSM5PEj+/qUy39VZT7z/qdNXtadOP2NmIRFa0UwNp+QkKB+HiQNaAt75DmBC6dVDMsQB7lt3H1E1ypy8OVw63Zo8T6K+3RAHgtOH3RAH6xo/lUodO/lUYBuEhvZG6Gd/hIb2Ruhnb4R+9kdoOLYZ1DSHhx56iPz8fC655JIBbafrOlu2bKGoqGgwux2TBFwKHoeCbpg0J9RjXs/JUPj0RwJkB2SaIgZ/fiVCY9vga9bNzJwAwC41huQJYqZi6DXbB92eJEmUOC0jvEwtG3Q7/eXM2UX89ral/OSGU/nTf53N0pmF6LrJn1/ZwcoNFeJE1R8kCaZeCle9CKVXASbs+Bs8czEceGPIzQfdQX563k956GMPUZxRTFVbFbf96zb++7X/pjXZOvT+CwRH0BRXSWo6DkUmz+cadDuSJDEuwzLCqyKJ/mwADi+4c8E/EUKzIOcUyD8TchZbKSX9k8CdB4oPkKz6i2orxKuhbR80b4aGd6FuDTR9CK17IFYFqTBED0H9amhcb/2NVw96bAL70HEdkOV19rFm78iSRNBttdGSPPbaQiAQCASCsYrsDeFadD043MiGCg43rkXX9yt1ekrXKGuzrrn6a4o3R5JsO9AEwNIZBTToVnr/HCWnc5LnsPLBfdC6H7x5cOqdQ2qqvLmcx7c+DsC3l34bWZKtSZ+tu6wVfMXgPXYiukAgEAgEAoFAIBDYhQGb4oZh8NBDD3HzzTfjcHQNNP/Upz7Fd77znc7/f/SjH/Hyyy9TVlbGxo0b+cQnPkFFRQWf/vSnh97zMYIkSeS2Gzn1sWS362QFZD79kQC5GQrhmMEDr0SpCw/OGJ8RGo+ERG28mbZxczENHW336xjxcN8b98BU51QAqrQq4kZ80O30l+yAh5nFmRRm+fj8BXM4f4F1w+KZ98p5+I1d6N2koRR0gycTzrwbLngIMsZDtAZe/S944+sQa7D+r15r/R0EZ0w4gxdufIFPzv8kEhJP73iai/9+Ma+VvZbecQhOaDoM7EK/G3mINx7HBaxo7MZ4iqQ2yMlHkmRFgXvyIDAZMmdD7mLIXwbZiyA40zLR3TlWKncAUwU1DPEqaNsDTRuhZYsViW4YoKegeQtEKiDZCGoEDNWKVheMKTpNcffQTHGATLd1jdbSzYQ7gUAgEAjGMo6Ji3F/9Ls0TLsG90e/i2Pi4n5tV9ZWjWbohFx+CrxZ/dpmza4aTBNKx4UoyPTRqDcClik+7NRvga1/sZ4v/RG4hxaZfs+ae9BNneWTl7Nk/BLrerNlmzWx05kJgb7TzwsEAoFAIBAIBALBaGbApvirr77KgQMHuO2224557cCBA1RXH45ma25u5jOf+QyzZs3i4osvprW1lTVr1jB79uyh9XqUIssy48ePH3CdgVyvZYo3xHtOyRD0ydz+ET8FIYW2hMEDr0aobhq4aeNzeJgUyAesaHFijehVm4m/+EO0A+sH3B5AUAmSp+RhYlKulg+qjcGiyBLXL53GTWeVIkmwakcN//vCFuIpre+Nu2GwGtqa4jPgihdg3qdBkqH83/CPJfDXefDspfD3JbB7xaCa9jl9fO+s7/GPq//B5MzJ1Mfq+cK/v8DX//N1muJNaR7ICarfGGMgGmqGQV37ZKJxXdKdDw6f00Gmx4lpQnWk+0lKg0aSwRmwomsCUyBzLuSe2m6WL4TgDPBNAFc2SE7oqGmOYT1MDSL7oGUrNG2A+jVQ9w40rLWizMM7oK0MYpVWiku11Uq/fpyNc3EMDh7dMGlNWt9dQ40UB8j0tEeKD9AUFxraG6Gf/REaCgTpQfFnkT/jVBR//8xtgJ3hg0D7RPJ+TLY0TbMzdfrSmYUAx88U11VY9R0wDSi5DCYuH1Jz6yrX8Vr5ayiSwreWfsu6hgzvBD0OshsyZ1nXs8cJcS60P0JDeyP0sz9CQ3sj9LM3Qj/7IzQc2wxY1fPPPx/TNJk+ffoxr7355ps8/PDDnf//5je/oaKigmQySU1NDStXruTkk08eUodHM5IkEQgEBpwmLcfrQpYk4qpOVO3Z6M7wWsZ4cbZCLGnyl9ciHGwYuPk7M3MimAbbD6xt/2ErQaKV5LpHMWItA24POK4p1Ltj+bxivnTxXFxOhe0Hm7n7mQ9obOtHCuSjGKyGtsfphcXfgstWQKgEWg9AvMmqUZcMw5vfGHTEOMDCooX88+P/5DMLP4MiKfx777+5+O8X88LuF9Ka8v6E1W8MMRANa6NJdMPE53QQcjv6XL8/FAUOp1A/LuUYJMWqy+gthIwSyJpnRZbLHpAc7Qa5bD135YAjAHJHmvj2uuZqGBJ1EDsIbXshvB2aPoCG9yzjvP496/+W7Vba9ughyzhPha3tzfRl1xDH4OBpSVp1vD0OBa9j6LVpM9ujzWOqTlLvv8ZCQ3sj9LM/QkOBID0M5lja1WKZ4h0lx/pib00rdS1xXE6FxdPyMUyDZr0ZgBx5mE3xTfdD8x7wZMNp3x1SU4Zp8PNVPwfg+jnXU5JVAtH9kGoCZMicc8T15/FBnAvtj9DQ3gj97I/Q0N4I/eyN0M/+CA3HNmKqQxrRdZ3du3ej6wOL4FZkqTMqrD6W6nVdn1vm1uUBJuY6SKgmD70epbx2YMb4rMwJYOjsxgB3BigOKygxGSG1/hGMWPOA2gOY7JyMjEyj3kiL3jLg7dPBgsm5fPuKBQR9Liobo/z06Y1U1LcNqI3BajhmyJ0Dp30fHB6QFSvSVI1CrBbK/zOkpj0OD98845s8de1TzMiZQXOimW+8/A2+sPIL1EZq09L9E16/McBANKxqn/gyLsOdtouUjjTskZRG2yAzTgwZpT0aR3aAZILisszy7JMgZxHknW7VLs9dAlkLrHrmgRKrzqM71zLZZXd7YyYYSStyPFkPsUNWxHl4OzR/aEWa170D9e9C40YrRWbrXogehHgtpFpAi/fbONdTMQ7s3Yyeig3TmzN26Uyd7hl6lDiAU5EJuKzJIuEBRIuL86i9EfrZH6GhQJAeBnos6abB7tZKAGaE+meKr9phZclbPDUPj1OhxWhBR8cluciQMwbX8f7QtAs2/cF6fvr/gKf/0fDdsXL3SrbVb8Pv9POlU79kTZ6MHrBeDE63ri2PM+JcaH+EhvZG6Gd/hIb2Ruhnb4R+9kdoOLYRpniaMQZZzzqvI4V6D3XFj8Tjkrj5XD8lBQ5Smskjb0bZW93/G94zQxNAVqiSZdr0FHiCljEuyejNh0i++RvUfaswBzAWj+yh2FEMjFy0OMDk/Ay+d81CxuX4CUdT/PzZD9lc0TigNgar4Zghs8SKOHD6weG1zDBDh/d+DCtvgkOrhpSSeU7+HJ6+7mm+uuSrOGQHr+9/nUv+cQkrtq9IS2TuCa/fGKA/GsZU3TIRpfSkTu/Aqcjk+a3zcUe98hHBWwR5SyFnsfXXW9T1dUkGxQOuEHjywT8BMqZZkTzZCyHvNMg/C3JPg+yTITQbMqaCbzy488AZtKLRaZ9MYKRAa4NkA8QrIVIGrTuheRM0rrOM87o10LjBSuHeuhuiFRCvgWQzaDGIVSI3vsc4dxVy43sQrz5mWIKeSbcpDoNPoS7Oo/ZG6Gd/hIYCQXoYyLF0KFpPXEviUVxMbC831htJVWf93noAls46NnX6sEWVGDq88x2rzvekj8LkC4fUXEJL8Ov3fg3A5xZ9jhyXx7oGBOu60Vsw1B4PGnEutD9CQ3sj9LM/QkN7I/SzN0I/+yM0HLsIU3yUkOuzovpakhpqP9Kcup0Snzzbz/QiJ6pu8uhbMXYc6t9N7wyXj/GBAiR3BnucbtASSO4MXIs+jpI3FVNTUbetJLnq9xjhyn6PocRlpVAvV8uPT9rhHsjJ8PCdK09m1vgsUqrO71Zu4Y2t/R/HCY+/EM75FbiCVoRqoAimXWEZ5LXvw8u3w7+uhopXB5122ak4+cLiL/Dc9c8xL38ebak2vvv6d7ntX7dxqPVQescjGJNUtxvW2R4XnjSkmj6SDpO9JpLEGMFzGYobXJnW38EgSda2ziB48qwbmxlTIXO2ZZTnLWmPOD/dMtJDcyxj3TcBPAXgzATFS+elgqmCFoFko2V4R/ZD6y5o2WxFnDdvBiOBLBlWdHrzFgjvPlznfBjStY8VDNPsjOZOqynenkK9JTkwU1wgEAgEghONXS3Wb5DpoWKUftTO3rCvnqSqk5/pZXpRCIAGvQEY5nriW/8Cjdus32qn/8C63hsCj2x6hKq2KgoDhdw8/0Zr8qNpWNeggZL09FkgEAgEAoFAIBAIRgnpKcIqGDI+p4Lf5SCa0miMpyjsR+Sj0yFx41k+nlwdY/shlcfeiXLdUh9zJ/Zd72tmaDyHovWUzVjMGXmzkfw5yN4QjunL0Q+8j7r9RYyWKhJv/z8cJUtxzvgIkqP3dsc7xuOSXESMCLV6LYWOwn6PP9343A6+duk8HnlzN6t31vC3t/ZQ35rgmtNLkEUtiL6Zfg0UL7NqiwcnWkZ5tNa6CbPrCetGzGtfhKxSmP85mHKxlW59gJTmlPLENU/w8IcP89u1v2XNwTVc+o9L+cbp3+Cm+Tch9+OGlODEwzTNzijucRnpixLvINfrwqXIpHSDhliKfP8gTWk7IEnW5BfF1XNqTNMEU7PKKRjJI/6mDv+vRbHqcHRuZG0TPwSJbs4NktPap+yyUr13+9xlRcSfALQmNQzTxKXI+Jzpm+TRESnemtTQDRNFFt9/AoFAIBB0x86wVU+836nTd9YAcMaMws6o8I5I8Vwldxh6CLTsgw/utZ4v+S748obUXFO8ifvfvx+AO077Op5ouTWBUfZYWYbE72aBQCAQCAQCgUAwxjgx7jYfJ2RZZsqUKcjy4N7WPJ9lOvdVV/xIHIrEx5f5OGmSC8OEJ1bF+LC87+1nZk4EYHe0HiW3BNlrzW6XJBnHpFPxnPt1lHHzwDTQ9r1D4s3/Ra/b3XtfJAeTnJOAkU2h3oFDkbl1+QyuWDIZgJc+OMj9L20npfVcC2KoGo4p/IVQdKr1F8BfAEv+G659A076L3AGoHkPvPVNePpC2L0C9IFHIyqywu0Lb+f5G55n8bjFxLU4P3nnJ3zimU9Q3lw+oLaEfvanPxo2J1Tiqo4iSxT40m9YS5JEUfvEpOqRTKE+WpAkkJ3WMe/OAd84CEyB0AzImg+5i60657IHJAeS7AIUy/j2jGuvc35UunZTtYz0VDMkaqy6lW17IbwNmj44otZ5e8r25i0Q3gWRcohVWWne1VbLkB/JaP400ZE6PdPjTGu6Va9DxqXIGKZJa0rr1zbiPGpvhH72R2goEKSHgRxLpmmyq9MUH9/n+nXhOLsqW0CCM2ZY6cV1U6dZbwaGKVLc0GHVd61JiePPgmkfG3KT9627j6gaZU7eHC4bN9e6LkO2yvHI6ctcMxjEudD+CA3tjdDP/ggN7Y3Qz94I/eyP0HBsIyLF04zDMfi3NNfrYn9LjIZ4CtM0+31jXJYlrj7di0OBDWUpnn43hqqZLC7t2SyalWnNgK+I1BLTEvgcXaMtJU8Q9yk3otdsJ7XlX5ixZpLvPYQy/iRccy5Fcge6bbfEWcKe1B4q1ApO9ZyKQxrZj5gkSVx2ymRyM7w8+PpONuyrpyWa5EsXzyXo7T7yfSganhB4s2HR12Hu7bDjb7Dtr9B2wLpJ88F9MO92mH4tOAYWwTspcxKPXPkIj299nHvW3MOG6g1c/vjlfOXUr3Dbybeh9DMSXehnf/rSsCNKvMDvHrbI13EZHirCMepjKVTdwKmIi6BeUdyQOQvCO6ybtooTQrOOrYXeJeo81f7oiDo/6jkmGKr16Av5iOhyxd39c9nVfcSTngQ9bqWKH2yq+iHSnLAms6UzdTpY34GZHid10SQtCbXf7YvzqL0R+tkfoaFAkB76eyzVJ8I0JyMokszUYFGf669ujxKfMyGLnPasRc1GMwYGbsmNX/IPvtM9seNvUPcBOP1wxo+GHMVd3lzO41sfB+Dbiz+DHG8vNxaaYU2EHAWIc6H9ERraG6Gf/REa2huhn70R+tkfoeHYRdzlTyOGYbBnzx4MY3D1UjM9ThyKjKobhJP9i+jqQJYlPrbEy2nT3ZjAP9fHWbMz2eP6We4MCr1ZGKbJrnDPNZyVwtl4zv06jpIzQAL90CYSb/wa7cD73dYNL1AKCMgBUmaKQ9roqQ19+owCvnH5SfjcDvbVtPLTpzdS0xI7Zr2hanhC4Q7Cgi/AdW/AqXeCNxei1fDeT+DJ5bD5AVCjA2pSlmRunHcjK29cybIJy0jpKX757i+59qlr2dWwq8/thX72py8NdcOkNmqd28b1o8zEYMlwOchwOzBMk5poz+dSwRF4izCyT6MikoeRfdqxhjgcFXWeDd5C8E+CYKkVlZS9EPJOs2qd550O2Ysgcx4Ep4N/stWmOwccGVaq9Y7IcyNl1TtPNVn1zqMV0LbHqovZtBEa3oO6t6H+XSvyvGUrtO626qDXvQ0N66BuFUQqwNCOa/S5aZq0DEM98Q46Uqh37KMvxHnU3gj97I/QUCBIDwM5ljp+D5dkFOFWei8ZZpgma3ZZpvjSmYfLhXWkTs9RctKa9QWwSlpt+LX1fPG3IdC3cd8X96y5B93UWT7xTJYE2k1w3wTw5A+57XQgzoX2R2hob4R+9kdoaG+EfvZG6Gd/hIZjG2GKjyJkSSK3PXq5YQAp1I/c/pJFHs6cZUW6/XtjnLe29Zz6d0Z7tPjOloO9tis53LjmXoZn2ReQg4WYqTipD58m9e4DGJGGrutKElOcUwAoS418CvUjmVmcyXeuOpncoIeGcIKfPr2R3VUtI90t++P0wdxb4drX4fQfQmAcJBrh/XvgiXNg472QDA+oyXEZ43jg8gf4+Xk/J+gOsq1+G1c+cSW/W/s71EGkaBeMHepiSXTDxOtUhsVAPJIO071KpFDvP4qbhOEZesS1JFmR3Z3meREEJlnmeOZcyDnCPM893TLTM+e2m+eTjjDPA1Y7HXSY58lGiFVaD0O1oteNJIS3W2nb6945bKA3b7Yi4Nv2WqZ5rAoS9ZBqsVLAG+qQTPS2lIZmmDhkiQxX+mehZrrbTfGk2u1kNoFAIBAITnQ6UqdP70fq9B2HmmlqS+JzOzh5yuHa4Uea4mnFNGD190BLQNFpMOO6ITe5rnIdr5W/hiLJfHPORdY+XFlWeRyBQCAQCAQCgUAgGMMIU3yU0WGK18cHF5koSRLnL/CwfJ5l5ryyKcGrmxLd3giflTkR3TBYW7+TxkRrn23LWRNwn/UlnLMvBMWB3lBG4q3fou55A9M4XKe7xFkCQKVWScIYXWbSuGw/3716IVMKMoglNH75r028t7t2pLs1NnC4YdYNcM0rcObdEJwMqVb48D7LHF9/D8Qa+mqlE0mSuHLWlfz7xn/z0ZKPops6v1//e6584ko2124etmEIRjdVbdY5ZVzAk/4onKMo9Fv7CCdUov2sxyw4zkgSKC5wZlgmuLcIApOPMM8XWRHn+WdB7mnt5vkcyCgFTwFWpLkCktL+3Dz86Iw+b4ZEnWWgR/dbEejh7dC8CRrft+qe170NdWugYT00fQgt26xI9Eg5xA5BvBaSzaBG2uugH55pOqh64nrSMuX1vq8Vgm4HsiSh6gYxVe9zfYFAIBAITjR2tU8Sn9k+abw3OlKnn1qaj8txuLzTsJniu56E6rXg8MLSnww5bbphGvx81c8BuK7kbKYGckHxWGVvhvnaWiAQCAQCgUAgEAhGGpEYf5SR63OBBG1JjYSm43H0r47ykUiSxPJ5HpwKvPRhgje3JVB1kwtP7moiNSfaqE00UxNv4rOr/pcvzf4Y5447qfe2ZQXntLNRiuaibn4OvX4v6o6X0Ss34TzpKpSsiWQqmeQoOTTqjexX9zPTPXPAYxhOgj4X3/rYAv786g4+KGvgz6/soLEtwcULJ45018YGsgNKr4KpH4OKl+HDP0DzLtjyAGx/1IpumHt7v9P+5fnzuPeie3lp30vc9dZd7Gnaw/UrrufWBbfylSVfwTPA2uUC+5LQdBrbay8XDWPq9A7cDpkcr4uGWJKqSILS7NFRX1EwCCTJil5X3ECGtcydA4kaqwa67LDSpsuKZZ5L8uF65kbqiOcqmOpR/7dPmDBV0FXor+8sKSA7CaZglkMmQ/ZCW5OVXv7oh+Sy+iRJVnr48A4wdauN7mq3H4EsSYTcDpoTKi1JFf8wRKMLBAKBQGBX2lIxKmOWoT09WNzrutGkysYya5LvsiNSp2umRoveAkCuktvdpoMjUgXr/6/1fNEdEOzbtO+LlbtXsq1+G37FwZdnX2xdX4TmWNcbAoFAIBAIBAKBQDDGkUwb5NJsbW0lFAoRDocJBoMj3Z0eMU0TwzCQZXlIEYzrqpppSajMzs1gfNA7pD69tyvJCxviAJw6zcWli73IkkRjopXPrfotDQkrrbVbcZLh8vHHpV8lx9O/99g0TfRDH6BuW4mZioEEjsmn45x1ATv0faxPrCdPyePiwMVDGsNwoRsmK97dx8sfWjXkls0q5JNnlyLBkDUUHIFpwsE3YNP9UL/JWiY7YNoVMP+zEJzU76aa48389J2f8vzu5wGYFJrET5f/lMXFi9t3lZ5jUDBy9KZheUuMPU0RsjxOFo/LOi79qYkm2FzbisehcOaEbPG56gPbHYMDNJi7xTR6NsyNlGW2dxjrHa93bGpCXNMxTROPQ0GRe3vPZKuPaitWNLsCGNay4DRQfNa5VXK0G+mO9v9l9jRFKG+JMS7Dw9y83r/jbaehoAtCP/sjNLQndvm9OlTsNM7+HkvvN+zm11ueptiXwz1LPttrm29ureLRt3YzLsfPj64/pbPdeq2ef0f/jVfycm3Gtek5dk0TXv4MVL4D+Qvhkr9bBvYQSGgJLvr7RVSFD3LHvI/xudmXQ2g2ePKG3t80I86F9kdoaG+EfvZHaGhvhH72Ruhnf4SG9qS/v1dFuFCa0TQNl8vV94q9kOt10ZJQqY+lhmyKnzbDjdMh8dzaGOv2plB1uHKJl5p4MwkjhcfhIqmraIZOTEtQG2/utykuSRKOCQtR8qejbvs32qEP0MrfRa/ZzsR5F/K+V6Jer6dVbyWojL6bJooscf3SaeRmeHhs1V5W7aihKZLk9nOnEQr4Rrp7YwdJgonLYcK5UP0ebPqDlQJw9wrY8wxMuQRO+hxklfbZVJY3i1+e/0suKb2EH7z5AyrCFXzi2U9w49wb+eYZ38Tn9KXlGBSMLN1paJomlR2p0zOOX3aAPK8bhyKT0HSaEio5XvHZ6gtbHYPeInBlgx4HxTu4WuiSfEQUej8wzfYa5iqJZILtdY24ZZ05Gd5uTPUOE92wHp3PwQpJNy1TPlLengK+u/4pTDJlQg4wkw5o8R9hmjuPNdIlBU0zcbk8Io2qTbHVMSjoFqGhQJAe+nMs7WqxJkjP6Efq9FXtqdOXzSzscnPsyNTpabtptvc5yxBXXHDmz4ZsiAM8uulRqloPUegJcPP0C8A3YVQa4h2Ic6H9ERraG6Gf/REa2huhn70R+tkfoeHYRdQUTyOGYVBeXo5hGH2v3Au5Putga0qk0I2hB/IvmurimjN8yBJ8UJ7iqTUx8jyZeBQXEhKyJKOaOnEtRXMqMuD2JXcA18LrcJ92G5I/GzMeRl73BAUtMTANytSyIY9hODlv/ni+dNFcXE6FbQeb+NET62hojY10t8YekgTjToeLHoFLHofxZ1tmTtnz8Oyl8OoXoWFrv5o6d8q5rLxxJdfNvg6Af2z9B5c+dinP7niW595/jqrWquEciWAY6ek8Gk5qxFQNRZYo8A/CuBwkiixR2L6/6kjiuO3XrqTre/C4orjBlTk4Q3wwSJJlQDt8NOpemswQCVchcmASZEyzotWz5rfXQz8N8pdB3jLIPRUy54PswqqD7sCKHneAKxdcWeAIWHVBpSPmPJo6TjOFX4oTMNswE/VWhHzsIETKrNrnHTXSmzZAw1qoXw31q6D+3fY66R9Ay1YI74S2vRCpsGqsJ+og2WRFr2vx9mj4Hq5bBlAHXTB4bHkMCrogNBQI0kN/j6VdYaue+IxQ76Z4ZVOU8tpWZFni9OkFXV5rNNJcTzxWD2t/Zj0/+asQmjLkJpviTdz//h/ASHHH/Gvx+IogMPR2hwtxLrQ/QkN7I/SzP0JDeyP0szdCP/sjNBzbiEjxUUiGy4HboZDUdJoTaqdJPhROmuzCqUg8virGlgMqqu7k8zMu4w+7nkfWksiShN/h4Q87XsAwTc4snDvgfSj5pXjO/ira7tdQ961i4sEaqp1B9qkfMN81D1keeH3048WCKbl8+4oF/O/KzdS3RLn7mU189dJ5TMrLGOmujU0KTobz/wQN22DzH2H/y3DgVetRvAxO+jwUntJrExnuDH68/MdcXHox33vje+yo38H1T1+PQ3KQ/V42v73ot1wz+5rjNCDBcFPVbkjn+9w45OM7n2tcwMOh1ji10SQzc0wcvaa4Fgj6T3PCSqWe5emljqcktUeBe61o9sw5h1O+y66eU753RqRrSKZKWW0TmpaiJOAi5KTztc666IZ2uC46tKeFTwGp/tdJ7+xzR+r29kh0I2WZ55iAbPXXnd1eJ739gdL+XDlieftz5JGJWteTQ8siIBAIBIJRTVJPUdZmRX/PDI3vdd3V7VHi8ydlEzzq93mDbtUZT4spbpqw5geQaoXcuTD31qG3Cdy39l4iiSbmZE3isqnnQWimyAgjEAgEAoFAIBAITjiEKT4KkSSJPJ+LQ61x6mPJtJjiALMnOPnE2T7+8XaMnZUqmj6N+077Ck2pFnI8QZ4se5t367bzhx3P05AIc8WkMwac/k1yuHDOvgileAETNj/N+3qCNrOJyo1/pHjG1cgZBX03MkJMzs/gv69cwN0r1tESS/LzZz/k8xfMZv6kNM34FxxL7hxY/jto2Qeb/ghlL0DlKutRcIpljhcv7fWGzekTTueByx7g5D+ejGEYqKjURGq4+bmbiaViXD/3etwOYWbYGcM0qYla0aVFgeOXOr2DkNuBz6kQU3Vqo0mKj2P6dsHYpl+m+NH0N+W7JLWb0k7Ai+yRqG+N4zd9hAKBHps3NI39e3czrWQSimS2p3TvxjzvbpnZPoPW1EDXgIS1TE9gGeIAuhWlnqwfYCpY+VizvIuZ3o2hzgCXH2nAp6Pe/EigJ/HICcvQV0QpGIFAIOiNva3VGKZBtjuDXE+ox/V0w+DdXbUALJvV9btAMzXCehhIkyle/iIceM2aVLbsbkjDxPLy5nIe3/IomAbfPvkm5Kx57dcHAoFAIBAIBAKBQHBiIUzxNCOnKYIx12uZ4g3xFKZppq022fRxTj55jp+/vRVlb43Gs+8oLJ83Do9b4YuzLyPXE+T5A+/xVPnbNCTC3Dr9AhyD+CEuh4rwL/0Ck2qfosysZL+jmZy3fodz2jk4Ss9FUkbnRy8nw8Mtpxfy8p4EOyvD/G7lFm46q5Rz5xaPdNfGNplT4ez/Cyd/Gbb82ao1Xvs+vHy7FSFx0uetuuQ9GCh10TqcipPCjELaEm0ktARJLcn33vge966/lytmXMF1c66jNKfvuuWCkefo82h9LIWmG7gdCjne438DT5IkxgU87G2OUh1JCFO8D9L1PTjWiWs6CU1HkiQy3QP8XA+khnk7mW4nlcRpaTfie0SSkGSHlYZdGeD3v2l0Nc1NFVJhK+U6MkiAYQIGOEPWDX/TaH/o1vKO56bBYSOdI14bWJcGhQnosfYnEqBB0yZwHbD6DNZySWp/nSOeH7W8y/8dz7tZ3lsbPW1/dBupJuToASb4NOTGBghMBnf+4XW6a7/je1WSu9nHccKOEfnD3OdhOY+K91lwAtLXsbQ73F5PPDSh19/bWyqaaI2lyPA5mT8pu8trTXoTJiZeyYtPHuJkpMYd8Pb/sb5DT/4yZE8fWnvt3PP2Xeh6iuXFJ7Ok9Cpw+NPS7nAjrintj9DQ3gj97I/Q0N4I/eyN0M/+CA3HLpJp9lT8cfTQ2tpKKBQiHA4TDAZHujvHBd0weaOiAcM0OWN8NgFXek3kinqN+1+K0NRmIEuQ4ZO4fqmfRVNdvFK5gb/ueQXDNJmfXcLX5l6JRxlctHqVVsUrkZdwxqNc9n4liglyIBfnSVei5JSkdUzpRNMNHnlzd2eavAtOnsA1p5cgixRzx4doLWz9C+x6ArT2Os5ZpTD/v2DKRcdETNREaljywBIiqQghd4iWRAsAM3Nn0hhv7FxvQeECrpt9HReXXozX6T1eoxEMkQ9qwtTHkkzO9DE9u+cI1+Ekrum8c6ARJDhzQg5ex+gtByGwB1VtCbbWtxLyOFkyLmvY9xdTNVYdbEKWJM6dlItyvMoA6EmrRrmht6dS16xzeN7Svk0u0zzCID/CLD/SOO9iph+1Dj2s39leDwa8qbdHt4NlFLe/pnjaI81HIcdE5ANI7X0ezA+5o030o03zbv52Ppd7MOA7lrVPkEAGrQ3idYBh/e8tsCZMtHfhqCdd+9dX/7tdtbftenpN6vpyqhlildZ7LingK7ayN3SuLnXdruO5dOSyo17rst3R/R/oNkf2t/15og5a9x7OfBCcBp4C+n4fj+KY1dN5HjmqrXgNtO5uLxUx+rM1nCi/V8faOO/e9Dhbmsq5pfR8zh+/qMf17ntxKx+UNXD+gvFcv3Ral9d2JHewLrGOCY4JLPcvH3xndq+Al24DNQaKCy54EGZ+fPDttbOu4g0++ezNKLLM81c9zNTx5wy5TYFAIBAIBAKBQCAYbfT39+roDNe1KaZpEo1G8fv9Q47sVmSJLK+TxliK+lgq7aZ4pl9G1UxMrKCt1pjJE6uiTCty8NHiRWS7g9y77Tk2N5Xx4w/+zjfnXUuWe+BmVKFSiE8JEPPJNCy+kMJNqzAiDSRX/xnHpMU4Z1+ENIrMySM1vHX5DPJCHp5bu5+XPjhIQ2uCT39kJi5hhg0//gJY8t8w/3Ow7a+w42/QvAfe+gZ88DuY/1mY+jFQrOjKwkAhvzr/V3zj5W/Qmmwlw53Br87/FVfOvJI1B9fw5LYnea38NT6s+ZAPaz7kZ6t+xmXTL+Pa2dcyJ3/OCA9WcCRHn0eTmkFDPAVYtb1HCq9DIdvroimeorotQUmWPSJsjjfp/B4c6zQnB5E6fQh4HQouRSalG7SmtB73m3YNFbdlZoV3WGnVO8yt/kR9ShJIx+lS9UgDXo9D4/vtBqLc/tcBoTnt6WZN69E5r7TjeW/LwTLi6WY943AfOl87er0j2jlmX6ZVt11PYHLYAJcwrf5KjvZ1jW7a7PENOaJ/faw6WLpNrV8JSuMgjfzjwNF9NnWI7AelJm19Nk0wTQNJktMTtH9Mn1Vo2QbKPvu8zwbWOcSVLSLGBf2mr+8z3TTYE64EYGbmhB7baY2n2LTfmmS7bOaxEzMadeu1IaVOj9bAa1+GVNSauCK74O1vw4RzwF846GYNLcHP37oLgOtKL2Jq8dmD7+NxRlxT2h+hob0R+tkfoaG9EfrZG6Gf/REajm2EKZ5GDMPg0KFDlJaWogw05Wg35HldNMZSNMRTTMlMb13IxjYD3YCQTyKaMNENaI2bVDZqhHwuFuWW8r2Tb+KXm5+ivK2GH2z8K9+efz3F/twB7UeWZKY4p7AtuY39QYPJy+9A3f4iWsV6tIr16DU7cM69DGXcvFFxgjlaw8tOmUxuhpcHX9/Jhn31tESTfOniuQS96anzLugDbw6ccgfM+7RljG97GForYNV34YP7rOXTrwGHh2tmX8Ppxaezeutqls5dSnHISnl/5qQzOXPSmTTEGnhmxzM8tf0pDoQP8NjWx3hs62PMyZvDdXOu49LplxJwjUwUsuAwRx+DNdEEpmkSdDvTPjlooBQFPDTFU1RFEkzJ9I2Kc9ZoI93fg2OZ5rhlig84dfogkSSJTI+TumiSloTaoyk+LBr2tw76SHKkAa+4IHP24ZrisrM9SrVgZPvYG50R+RopHVwKVmR+zuLe3+/uDHazPcX9McuONv/bzXyMHl4/ar2jTXktBvFKMNujyDv268453OduzfieHPpenPseE2MNcLmetB4d0fAdY3RkWBP1zKO3P7KdIyc7HLVelwkIBvFYDJ/Pc0Sigu7ag2MnNxy1ngmdWgKHG+xrUsRIc2T/2o9NU2s/h4zC84dgVNLX99mBSB0JPYXP4e71N+7a3XUYhsnk/AyKc46dFNmgNwBDNMVbK6xyI5IM7gxwhUBtg9YDgzfFTYOVm/7ItqYy/E4fXz7zh8e/PMYQENeU9kdoaG+EfvZHaGhvhH72Ruhnf4SGYxthio9icn1uaIzQklBRdQOnkr5ojpwMGZdDIqkaBDwS4ZiJacKLHySYkOsgwyszLTiOuxZ9il9seoKaeDM/3Pgod8y7mlmZEwe0r6nOqWxLbqNSrSTllXGfdBXK+AWom57FiDSQ2vAYyqEPcM67HNk3/ClkB8rpMwrICrj5/Ytb2VfTyk+f3sjXL51PYZonKgh6wR2EBV+AObfAzset1OrRanjvx7DpDzDnVph1AwXJNs5INVOQbDumiVxfLp9d9Fk+vfDTrKtcx5PbnuTlfS+zrX4bP3jzB/x81c+5uPRirp9zPfML5gvDc5RQFbFSGI+GOt4Ffhc7GyViqk44qZF5nCJ8BWOPpGYQUzWQjl+kONDFFD/uDKIO+ohiByP/SDoi8lt2oJACydW/iPwutcuHvZdd0ZOQrDsqtb4LgjNG7/vdUzmArHlp67Oh6xxs3EPp+DT9+B5KCYMjGVLFrQFuqyeg4T2rrx2GuKxYx6JAkCZ2hQ8CUBosRukha4JpmryzoxqAZbOONadVU6XVaAWGaIq7Mq3Pu2mAw2sZ5M4ABAf2u/tIEi07+PXGhwH43OIvkBMYxRO7BAKBQCAQCAQCgeA4MUpz5gkAfE4Fv8uBaZo0tqcPThchn8x1S324nTKqDkGfRH5Ipjli8OBrUdriVlRPgTeLuxZ+itJQMVEtwd2bHmdN7fYB7StLySJLyUJHZ7+6HwAlpwT32V/FOeM8kGX02p0k3/wNatlqTMPovcERYGZxJt+56mRygh4awgl++vRGdle1jHS3TjycPph3G1z7Opz+QwiMg3gDvH8P2qPLSf3nB2QdeovUSz9Ee+/ebpuQJZnTxp/Gry/4Ne/c+g53Lr2TkqwS4lqcp3c8zXUrruPyxy/n0U2P0ppsPb7jE3ShLanRltSQJYlC/8gbJA5ZJt9n9aPDrBcIBkNLe+r0gNOR1glvfdERld6SVDGHZHCdIChuy6gYrQbt0XiLMHJO41C8ECPntFFdfxk4bOTLCgNOrT9SnMh9lqQhPOSBPRy+9j47AN0e77PAduxssUzx3lKnV9RHqGyM4lBkTi3NP+b1Rr0RExO/7McrD2HSRs1a8OaCw21NZHEG4JxfDT5KPF7Dox8+TFW0kcLgeG5e+NnB900gEAgEAoFAIBAIxhAiUjyNSJKEy+VKa4RpntdFNKVRH0tRmOZ6uoumuphW5KCpzSA7Q0bTTR58LUp9q84Dr0S47SMBQj6ZDJeP/z7pBv7fjudZX7+L+7b/k8ZkK5dOWNLvsZY4S9igb6AsVcYM1wwAJMWBc8ZHUMbNI7XpWYymCtStL6Af+hDXSVchh47/zdzeNByX7ee7Vy/k3n9voby2jV/+axO3LZ/JadPFrPvjjsMNs26AGdfCvn9hbPg9qVQ2pmTVejVRSO1ZhTzhVOTiJT02k+XN4taTb+WWBbewsXojT257khf3vsjuxt385J2fcM+ae7hg6gVcP/d6FhUtEtHjx4Ejj8GqSByAPJ/ruBqHvTEuw0N1JEFNNMnMnACy+Ex0YTi+B8cizQlrotvxjBIHCLodyJKEqhvEVB1/NyUJhIb2RnJ4MBxBJMfIZ9foF3aLyIdh7/OwHIPifRacgPR2LJmmya7wIQBmhHo2xVftrAFgYUku/m7KnaSlnrhpwu6nwJUBp30PCk6xIsQHa4irrTTVvc/92/8FsoM7zvg2Hrt8JxyBuB6xP0JDeyP0sz9CQ3sj9LM3Qj/7IzQc20imDUKFWltbCYVChMNhgsHgSHfnuNIUT/F+dQtOReaciTnDfiA2RQwefDVCS8wgOyBz23kBMv2WGaWbBn/f+zr/ObQegI8WL+RTpR/tMd3ckUSNKE+3PY2JyVUZV5EhZ3R53TQN9Ir1qDtexFSTIMk4py7DMf08JMfoqt+dVHX+/OoOPiiz6sddddoULl44UZwkRxBt61MkP3z+8AJTBww8yS0oedNhwjnWI3euFYHUC63JVv616188ue1JdjXu6lxeklXCtbOv5YqZV5DtzR6WcQgOY5gmbx9oJKUbLCgIkT8KIsWhPY3mwSYSms5JBSEKRkm/BPbi3com2pIa8/ODaZ/w1hfrq5ppTqjMycugOEOkIhYIBIJ0cKL8Xh0r46yJNXHH2j/ikBUeWHYHLuXYSWIpTecbD79LLKnx9cvmM3fisdf/b8feplwt52TPycx3zx9cZ+q3wPPXWBM/Pr7KKhs1WPQUNG3gR+v+zN/3vsGcwpNZcd0K5H78XhcIBAKBQCAQCAQCO9Pf36vi11EaMU2TlpaWtKYkzfQ4ccgyqm4QTmppa7cnsgMyn/5ogOyATFPE4IFXIjRFrHTmiiTzqdKP8MlpH0FC4pXKjfxm6zMk9b5Tu/tlP4UOa7Z7WarsmNclScYxeQmec+9AGTcXTAN179sk3votet2e9A6yF/qjodup8PkL5vDRk8YD8Mx75Tz8xi70UZj2/UTAaKkkdXArVjHU9odk3diS0KFxG3z4e3j+Wnj8THjnO7D/JUhFum0v6A7yifmf4J8f/ydPXfsU186+Fq/DS1lzGb9Y/QvOfOhMvvafr/HuwXcxTKF5uuk4BhtiSVK6gUuRyfWNnokxkiRRFGhPod4mUqgfzXB8D441VN2gLWV9nx/vSHGwrisAmnuoKy40tDdCP/sjNBQI0kNvx9LO9nriJRlF3RriAJv2NxJLamQF3Mwan9XtOmmJFN/9lPV38gVDM8RNA8LbKG+p4PF9b4Ls4ttLv21bQ1ycC+2P0NDeCP3sj9DQ3gj97I3Qz/4IDcc29vyFNEoxDIOamhqMNJqjsiSR47NuYDekua54T2T6ZW7/SICcgExLzOAvr0ZobNM7X79owmK+MucKnLKDjQ17+MmHjxFORftst8RZAkCZWtbjCUXyBHGfchPuUz+J5AliRptIvvcgqY1PYib73sdQ6a+Giizx8WXTuPHMaUgSrNpRw/++sIV4avgnLggOox14n8Tq+0FNIflCYKqApZ3kzUa69mU4827rJpMzYNUf3/MMvP4V+McS+M8tsPVhaK04pm1JkphfMJ+fLP8Jq29bzY/O+RFz8+eiGRov7n2RW/55C+c/ej5/fP+P1Efrj+u4xzIdx2Blu+FcGPCMuhTl49ojexviKZKamBhxJMPxPTjWaEmqYILPqeB2KMd9/x2meEui++8roaG9qY+3sGb/ZurjLSPdFcEgEcegQJAeejuWOlKnz+wtdfoOK3X6GTMLUeRjr0VTZopWoxWAHHmQprgah7IXrOfTrx1cGx207QO1lXs2PYEuOVg+ZTlLxvdcRmq0I86F9kdoaG+EfvZHaGhvhH72Ruhnf4SGYxthituAPK8VlVgfSx63fYZ8VsR4XlAhHDN44FWr1ngHS/Jn8t0FNxBweNjXWsUPNj5Cdayp1zYnOSfhkBy0Gq006A29rqsUzsZz7tdxTDkdJNAOfUDijV+jHdyIEWtBb9iHEQ+nZaxD4bz54/nSRXNxORW2H2zm7mc+oLGb6FEjHh41fR4LmLpGavOzpD58GnQNpWAm3st+gfuC/6G5eClSbim4gqjl66D0Klj+O7jxPbjwrzDnVghOBkODqndh3d2w4nzrsfZua5neNYLS7/Jz/dzrefq6p3nu+ue4ce6NBFwBDrYe5Nfv/ZqzHz6bL678Im9XvI1u6N13WtBvdA5PAhoXGH3pyf0uByG3E9M0qYmKaHHBwOiI0B6JKHGAzPaaqDFVI6WLi/uxxBtVm/ivNb/jd1Uv8oU19/JG1aaR7pJAIBCMSg7XEx/f7evNkSTbDlq/bZfOKOh2nSbdej0gB/DIgyyFsv9FUKMQnASFiwfXBkC8GuJVrKvdwWvVW1BkB98845uDb08gEAgEAoFAIBAIxijd5woTjCpyfS6QoC2pkdB0PMcpsizDK3PbeX4eej1KXVjnL69Gue08P/kha//TQ+O5a9HN/GLzE9TFW/jBxkf45rxrmN7DzQWn5GSiYyJlahllahl5jrxe9y85PbjmXY4yfgHqpmcwWmtJrnsUtDjIDiSHB9ei61EmnAKmCRjWX9O00sdxxPP2v2ZHuusjlnHEMkPXcMbrMZrdIMuH18MEo6PNw8tM02Cu2+T7S538a/0h4mGVZ57ZzaWLJpKX4QHTQG/Yh7b/XTA0JKcP16KP45g4hJseJzhGvIXU+3/HaD4EEjhnfBRH6TlIkoyUU0qkCApynKjvPoB24H2UiYtQsieD4oRxp1mPJXda0eEH37QeNeus/7c9bD2cfiheBuPPth6+3M79z8qbxQ/O+QH/Z+n/4cW9L/Lktif5oOYDXi1/lVfLX2VcxjiunnU1V8+6mqKMopF4i2xPBCeGCRkuB0H3yBiHfTEuw0M4qVIVSTAp5Bvp7ghsREu7KZ7pGZmyAE5Fxu9yEE1ptCRU8v2jb+KJYOA0Jlr5zbZnCCet0iCGavL7Hf9ifvYUcjz2rfsrEAgE6aYlFaEm1oSE1OPv1jW7ajBNKB0XoiCz++u8jkneQ0qdvqs9dfr0a2CwmZHUVmjdg2Ea/HzLcyApXDfnOqZmTx18vwQCgUAgEAgEAoFgjCJM8TQiSRJ+vx8pzal+XYpMyO0knFBpiKUYH/Smtf3eyPDK3N5ujNe06Pzl1Qi3Lg9QmGUZ40W+bO5a+Cnu2fwUZW3V/PTDx/ji7Ms5NW9Gt+2VOEsoU8vYr+5nsWdxv2qcKVkTkc/6MuqOl1A3PWOZ0pKKmYqRXHU/+HKQ5HRNFDDJ0zTUgw6s+tT9IxO4KdOgOZpC0w0SH6yjze/GJQOxxnYTXcLUUiTX/x05bzqyN5SmPp846A37SL3/D8xUzJo0segGlPzpna93HINKTjHmxFPQDryPuuk55LO/fOxnJDgJ5txsPdQoVK62DPJDb1lp1ve/ZD0AcufBhHNhwjmQMwskGa/Ty1WzruKqWVexp3EPT21/imd3PktVWxX3rruX36//PWdNPIvr5lzHOZPPQUnbZ3RsI0kSMYcHCct4Hq0U+t3saozQltRoS2lkuMTXKQzf9+Bw0hRJUBeOkx/ykh0Y3s+cbpiEkyNXT7yDTLfTMsWTx5ridtRQAP85tJ5wMoKEBBKkdI2WVISqWKMwxW2GOAYFgvTQ07G0O1wJwHh/Ln7nsd/7pmmyaqeVOn3ZzMIe2++oJ56r5Pa4Tq+0lEHdRpAUmHbl4NrQU9CyDTBZWbWdbc3l+J1+vnzqlwfX3ihCnAvtj9DQ3gj97I/Q0N4I/eyN0M/+CA3HNuIufhqRZZkJE3quSzYUcr0uwgmV+vjxNcUB/B4rYvzh16NUNev85TXLGB+XbZl8IZef7598I/du/xcbG/bw263P8snS87hw/LHR0EWOIrySl7gZp1KrZIKzf++XJCsoBTNQnR4wVNDbU1SbJpg60IfhKAHIIElIkvXXmo0vQef/MiDhlOWOnQId68tWGx3rckQb7espkkSOATsqW2iOqpgtEjPzHOTILdbNDlO3+p1sJbXlOdwLb0ByjEykoN0wTRNt39uoO14C00QOjcO1+CZkX3aX9Y48Bp2zL0Kv2Y7RVotWtgrntLN73oHTD5PPtx6mAQ3b2qPI34DGbdCwxXp88Dvw5sGEs2H8OVB8Bjj9lOaU8t9n/jffOP0bvLzvZZ7c/iTrKtfxZsWbvFnxJnm+PK6edTXXzrmW8cHuI1IEFnHNQJWdSJJEkX/0muJORSbP56I2mqSqLcGMnMBId2lUMJzfg8PBO9ur+dOr20moBh6nzGc/MpszZw9fhodwUsU0TTwOBa9j5CrYZHocVLYdjlo/ErtpeKJjmibPVazhXxXvIUkSiiTjUVy0qXE0Q+ep8neYklFIwHl8rx0Fg0ccgwJBeujpWNrZchDoOXX63ppW6lriuJwKp0zL77H9DlN80JHiu1dYfyecDb7eM6h1i2lAeBsYKRKmwq83W1Hnn1v0OXJ8Q4heHyWIc6H9ERraG6Gf/REa2huhn70R+tkfoeHYRpjiacQwDJqamsjOzkaW03uzO8/nYl9zlKZ4Ct0wUeTjO0vF55a59bwAf30jwqFGnQdfi3DLcj/jc6yPkFtx8fW5V/HX3S/zatUHPLLnVeriYT4xbXmXaHBZkpnsnMyO1A7K1LJ+m+IAkj8XyeHB1ABXBqhxJIcbz/JvIHkzuxjURz/v76yeoWroBhboBo+8uZvVO2vIOBjne9mH8EgamuLFYbYhmQZ61TYSbb/FddLVKLklA97PiYSpJkh9uAK9ehsAjomLcM77GJJybJRlF/1cPpxzLib1wQq0Xa+hjJuP7Mvqe4eSDHnzrMfCL0OsHg69bRnklashXm/dxNq9AmQnFJ5qRZBPOAd3cCKXzbiMy2Zcxv6W/Ty17Sme2fkM9bF67t9wP/dvuJ+lE5Zy3ZzrOG/KeTgVJzWRGipaKpiUOYnCQM/RKCcKh9ri6LpOQcCLewRNw/5QFPBQG01SHUkwPVvMHoTh/R5MN02RBH96ZTttCRWnIhNP6vzp1e3MmZg1bBHjzZ2p050j+nnpiFJvTWoYpol8RF/spOGJjmboPLT7Jd6o3oQiy3xk3EI+bNxLXEuS6fLjVpzsba3kfzb8lW/Mu4Zi/yCjGQXHFXEMCgTpoadjaXe43RTP7P536Ds7qgFYPC0Pj7P7iddJM0mb0QZAtpLd7Tq9oquw9znr+fRrB749QNteK3W6pPDogU1URaopDBRy84KbB9feKEOcC+2P0NDeCP3sj9DQ3gj97I3Qz/4IDcc2QtE0YpomDQ0NmKaZ9rYzXA7cDhndMDtvqh9vvC6JW84NMDHXQUI1eej1KAfqtc7XFUnm1ukXcMPUcwErlefvtj1HUu/a36kuq77ZQfUgKTPV7/3L3hCuRdcjOTygJ5GcXlyn3IAcGofk8iE5vUgON5LDhaQ4kWQFSZYHZDykQ0OHInPr8hlcsWQybaaXh5tOoiEO8ViMJtVNZcG5yP5szGgTyTV/JrXlX5hactD7G8sYbbUk37nPMsRlBdf8K3CedHW3hjgcq58yfiFyzhRMXUXd8s/B6erLg+lXw3n3wU1r4YIHYfanIGOilbWgajWs/Sms+Cg8fRGs+wVUr2NycDzfWvot3r7lbX534e9YNmEZEhKrD67mq//5Kmc9fBafeOYTLPrTIi597FKWPLCEFdtXDOXtsj2maVITSaLrOoX+0Z9FIdfnwqXIpHSDhnj/z2VjmeH8Hkw3ZbVttMRSpFSdWFJDkiQSqkFdODFs++z4/h7J1OkAXoeCS5ExTJPWpNblNTtpeCKT0FP8cssK3qjehCxJ3Db9Ar538o38vzO+zJcKL+CBZV/nV0s+R64nSE28me9v+CsfNO4d6W4L+oE4BgWC9NDdsZTQU+yP1AEwI3SsKZ5Udd7fWw/0L3V6UA7iltw9rtcjB9+ARKOVgWp8L9mseiJWBXHLvG9yFXH/Bw8CcMdpd+BxjN5MSwNBnAvtj9DQ3gj97I/Q0N4I/eyN0M/+CA3HNiJS3CZIkkSuz01la5yGeJJc38gYRh6XxM3n+nn0zSj76zUefiPKzef4mZTv6OznZRNPI9udwR93rmRd/S6aUxG+OfcaMlw+ALLlbEJyiLARpkKtoNRV2u/9OyYuRs6bjhltRPLnjNq63JIkcdkpk3E7HPzvyhjvtyynyBOnxQigtWTw209eSuDA62gV69HK30Wv3YlrwdUouVNHuuujBq1yM+qmFZiaiuQN4T7lJuSsgaUtkSQJ1/wrSLz1W/TaXeg123AUzR18pxQXFC+1Hkv+G1r3w4E34NCbUPM+hMusx9YHrWwGxctwjj+HC8afxQXTLuBQ6yFWbF/Biu0rqI5U837V+xiGgdfpJa7G+ep/vsrSCUspyhi+9M2jmaa4SkI3kDFH7Bw3EGRJojDg5kA4TnUkSZ5vEDdFBSPChn31PPT6TnTDxDBBxiSSSJEd8JAfGp6byYZpEk6ODlNckiQyPU7qokmaEyqZI9wfwcBoSUW4Z/NTlLfV4JIdfHnOFSzKta6lctxBpnjyyXEHURSFnyy6hf/d9iw7Ww7yy80ruGHqOVwyYYnIbCEQCE5I9oQrMUyDXE+QXE/wmNff31dPUtXJz/RSWtTz78yhp063Up1TehXIfZQBO5pU2IoSBwhM4b73HyaSijA7bzaXzbhscP0RCAQCgUAgEAgEghMEESluI/K8lklUH0uN6CwVt1PiU+f6KSlwkNJMHn4jSnlt10izpQVzuHP+9fgdHvaEK/nBxkeojTcD1s34jmjxMrVswPuXvSGU3JJRa4gfycQ8Px6nQpvpZXc8m9qkk3A0xZ6GJK6TrsJ92m1IvkzMWDPJNQ+Q2vzcCR81bhoGqW0rSW14DFNTUXJL8Jz1pQEb4h3IGfmd9cTVLc+n7/2VJAhNgXm3wUWPWFHk5/4Wpl0JnmxItUH5i/DOt+GxM+D56xlf9i++VnoBb938JnecdgcO2YHHGcDpzsRAoTZSy+WPX85P3v4J71S8Q/IE+yxURawI3QAaSpoNGyMeRm/YhxEPp7Xdce1ptuuiSVTdSGvbgvQTTao88OoO/t9/tpFI6ZQUBMkKuJFlq8zGxLwAWf7hmdzQmtTQDROnIuPvIR3r8STTbRnhLcmRyT4jGByV0Qb+Z8NfKW+rIej08b2Tb+o0xLsj6PLz3yfdwPJxCzAx+ce+N7h/5wukdK3HbQQCgWCssit8COg+Shxg9c4aAM6YUdjr5KEOU3xQqdMj1VC5yno+/eqBbasnIbwdMMGdS3lS4/GtjwNw59I7u5QtEwgEAoFAIBAIBALBsYhI8TQiSRKhUGjYom+yvS5kSSKu6kRVnYBr5ORzOSQ+cbaff7wdZW+NxiNvRrnpLB/Tig5Hm83OmsQPFn6C/7v5SWrizfxgwyN8c/61TAuOY4pzChsTG6nRaogYEQJyYMTGciTp1jA/5MXvsXQyTJNESkfD5E+vbGdfTSuXLJpE8JyvoW5/EW3/WrT9a9HrduGafxVKfv8j6McKZqKN5IbHMBrLAXBOOwvHzAuQ+lm7oyf9HKXnolVuwow2oe58BdfcS9Ped1wZMOVC62Ho0LDViiA/+CY0bof6D63Hxt+i+Aq4Ov8U7s2eQSj/FGTZiW6oHKhcRVO8iUc3P8qjmx/F6/By+vjTOWvSWZw9+WzGZYxLf79HCZphUBdLImEZzek8j2oH1pN8/zHQEkhOH65F1+OYuDgtbWe4HARcDiIpjdpokvFBb1ratSvD/T04FLYdbOKh13fRHEkiSXDxwolcvngyrfEU2w4289DrO6lribOxrIFFU/PSvv8jU6ePhvenIzo8nFAxTbOzT8OlYThm0NhmkJMhE/KJm/aDYUfLAX695WmiWoJCbxbfPul6CrxZXdbpTj+HrHD79AsZ78/jb3tf452ardTEmvna3KvIco+O6y/BYUbzeVQgsBPdHUu7OuqJh8Yfs35dOM6uyhaQ4IwZBb223WGK5yq5A+/Y3mfBNKDwVAhO6v92pmEZ4kYKFB8EZ3LPi19GN3WWT17OkvFLBt6XUYw4F9ofoaG9EfrZH6GhvRH62Ruhn/0RGo5tJNMGifFbW1sJhUKEw2GCwWPTnJ1IbKhpoTGWYnp2gMmZvpHuDqpu8tg7MXZXqThkiRvP8jF9XNc0rM3JCPdsfpL9kdouaT5fir5EjVbDQs9C5rnnjdAIhp93tlfzp1e3k1ANFFmiONtPS8SKAHY5Fc6bV8xFJ0/AEzlA6sOnMWNWRL1j4ik451yM5DwxTDa9qYLU+3/HTLQhOVw4T752aKnOj26/bjfJ9x4CScJz1heRQ8Vpa7tPorVw6C2rfmDVu6DFaUThpqxlRBQvJtaXrcPUWSCphFMR9rYepCneiK7FMU0dJIXS7GmcM+U8zp58LguLFqIMNN3iKKayLc62+jZ8TgdLx2el7aLDiIdJ/OdHmPEWMAHFgeTOwHPh99OWbWJ/S4zdTREyPU5OHZfV9waC40pS1Vnxbhmvb6kEID/Ty+3LZzKtPS1qY6KVmngzG3e28ebGBkJ+Fz+98VS8aZ54trEmTEMsyYycAJNCI//9bZgmr+9vwDBNlk7Ixu8cvol2G/aleHJ1lJRmTaq7bqmPRVNHf4mE0cS7tdv5w84X0Ayd0lBxl7I0A2FLUzm/2/YcUS1BtjuDb8y7hikZPdfNFQgE/eNE+b1q53Fqhs5nVv2GpK7yi8WfZkKg6wS4Z9eW88L7FcyZmMUdl53UYzsJI8ETbU8AcEPwBlzSAL7PTAOe+ghEKuHsX8LUfqY7N01o2w3xGpAckL2QdbVb+OSzn0SRFJ6/4XmmZosyXAKBQCAQCAQCgeDEpb+/V0WkeBoxDIPa2loKCgqQ+xnZOlDyvC4aYynq46lRYYo7FYkbz/TxxKoYOypV/v52jI8v8zFr/GFjPMsd4Psn38Rvtz3H5qYyfrP1aW4pPZ+SvBJqtBrKUmXMdc0dFTNvhkPDM2cXMWdiFnXhBPkhD1l+N9sPNfPs2nLKa9t4ceMB3thayYUnT+AjZ3wRx77X0MrfRTvwPnr9HlwnXYWSPz0tfRmNmKaJvv89UtteAMNAzsjDdconkDPyB9xWb/op+dNRiuejV24mtek53Ms+3+8I9CHjL4AZ11kPLUmq6l3+vnUFcd2BYppIWO+DKSkc1FO4FR8TsmZQkGkSM00SepJIspVqtY2/bX2Sv37wEF49zqkuF+dlFHJ2zlSyfXngDlkR664guIPW345Hx/9Ov5X6fShEa6C1wopu8afHTKlqs1KnFwVc1NTUpO0YNKMNmMkIfkOogwABAABJREFU7TMPwNAw1ThmtBHSZIoXBdzsaY7QklCJqRq+YTQXRzvH43twIOyrCfPAa1YEOMDyecVcc3oJ7vb05W9UbeL32/9JwlBxy06yc2cTbsjlmffKuems9GXrME2TlsToqCfegSxJBN0OWhIqLQm10xRPt4bhmMETq6O0xqxznaabPLk6xrQih4gY7wemabLy4Fr+se8NABbnTucLsy/HrXT/OepLv3nZU/jxopv55ZYVVMUauWvjo3xu5iWcXjB7WMch6D+j7TwqENiVo4+l/ZFakrqK3+Gh2N+1FrhumKzZZaVOXzazqNd2O6LEQ3JoYIY4QNV7liHuyoBJH+3/dvFqyxAHCM3CUNz8YvUvALhuznVj0hAX50L7IzS0N0I/+yM0tDdCP3sj9LM/QsOxzYl7534YME2TcDhMfv7Azbz+kutzQ6Nlvqi6gVMZ+YPSoUh8/EwfT66Ose2gymPvWMb47AmHb9h6HW6+Oe8aHtz9Em9Wb+LB3S9xSWIxSq5Ci9FCk9FEjpLTy16OD8OlYXbAQ3Z77WGAOROymT0+i037G3l2bTmHGqM8t3Y/r26u5OKFJ3P2ktmYW5/FjDaRfO8hHBMX4ZxzyZiLGje1FOrmZ9EOfQiAMm4urgXXIDkGV9O3L/1ccy4hUbcLo+UQesVaHFNOH2zXB832thoeqNpJpSsPKd6MjEmmniAuO3GaBtcEgkQNkxpNpcbQaTIBhwvD5SNu5BEzTeKmiW6abADeN1Tubmwiq+4QM8wkp6JyiqQyzkiQYaocY39L8mHjvIt5ngGudlP9SHO9c532ZWXPw5vfAC0GDh+c8yuYfs2Q3pOYqluppSUo9Lk4WJvGY1BLgaEBJsgO0FXQU4P+jHWH26GQ3T5hqSqSZFrWifvVejy+B/vaf0xL0pyM8MKmMt7eeQBNSuHJgYWlmcT8e/j9zs20qTGakm1saSpHMw0USSJGknjoQ3KaT+f1rZWcNr2AqYXpiYKLpHQ0w8oWkjGCpU+OJtPj7DTFizOs75d0a1jXotMWNzFNQAJVg7hk0NRmCFO8D3TT4NE9r/Jy5QYALhx/CjdNOw+ll5qx/dGv0JfNjxZ9inu3/ZNNTWXcu/2fHIo2cPWUZaIe7ShgpM+jAsFY4ehjqbOeeOaEY851OyubaWpL4nM7WDCl99+kDXoDwOB+u+5+yvo79XJweHpft4NUGNr2Ws8DJeDOZuWu59latxW/08+XT/3ywPthA8S50P4IDe2N0M/+CA3tjdDP3gj97I/QcGwzeu7MCvqFz6ngdzmIpjQa4ykKA/38MT3MKLLE9Ut9PPVujC0VKo+vinLtGT7mTTo8e94hK3xmxkXkeUI8Vf42Kw+s5zR3AaEMhbJUGTnekTfFjyeSJLFgSi7zJ+ewfm8dz63bT11LnCdX7+Mlv4vLF17LaXlbMCrWoB3YgF63G9f8K1EKZ41019OCEW0ktf5vGK01IMk4Z1+Io2TZsGYMkDxBnDMvILXlX6g7X0IpmoPkOT6pHyNqnH/se4M3qzcBkOPN5qO+DP5Tt4uE7MBvanxxwsmcu+i/umyX1FXq4i3UxpupjjVQG6mjOlJLWVs1NYkwcS1B0uGhFVhvwnpMFEnCJyuEJCgxNYqNGEVqmAI1QqERpyAVI5gMH2uY94WhQdtBK/Wj4oJUBF75HMguKF4KgzyGqyNWlHi2x4XHkb6U8KZhoO56xTL1tYRlimOC04+6/d+4Trs9bdkCxgU8linelmBqpm9UZL4YCRqTrZQl6shOFpDvG1oqedM0ietJ2tR4+yPW5XnkqOUd/6c0jXAshaobkA1elwPZ62RdqwSth9tP6iq6aSAjARKGaRDV4pj/n733Do+zvPL377dMH2lGvTdbkm25497Aphhw6CWEECDJZrObkLIJy2423y3J7v42hWVLEnZJXwIhoZsOAYwL7sa4ylaz1bs0ml7e9vtjLNmyJVmyZVsyc1+Xrhk985bnzJm3PZ/nnJO/C8mfymNbPPzwlutJtpx/VhZPJAbEReiJ9NtwW+KT1/qi6gXZvm4YbK+OouvxZA2yGBfFowq0elRKshK3ocMR1WI8Xvkae7qrAbiv9BrW5S8at9+PXbby13Pu5o91G3mjaScvN2ylOdjFVypuxiolUtsnSJDg8qOqb/h64h8ejUdhLynLxHyWe9EePR4pPmZRPNIHDe/G35ffPbp1tGi8jjgGWDLAnk9EjfAfO/4DgC8v+DJp9k/WM3SCBAkSJEiQIEGCBAkSnA+J0chJSIbNTDCm0j2BRHEAURS4e5kdSQizrz7Gc1tD6AbMLT45uCoIArcXryDNmswvjr5JZVsX5WYHtdSxwLrgExmhJAoCS8qyWDg1k21V7by6u55ef5SnttTzVnIG98y8ixmeDzCCPUR3/Q45fz6mWTchnEMt0YmC1n6E2MfPYSgRBIsD84LPIqVPuSj7loqWIDZ9hN7XQuzwG1gW3HtB92cYBts7j/BU7Xt4Y0EArs2dz2emrsYuW7m9t5aO3hqyUstISy09Y32LZKLAmXGi7uHgNPr9gvlRz3G2tuzmUHc1zaFOFNGMXxDxG9CCjMWUgd1WhN1kwyRIgI5NEMk2WciSJLIFyEYjS4+RrQVJVgIIMS/E/BDzQfTEe10BQ6dHctBuSiZb8ZEW9cKGr4FsA1sGpE6P/6XNiL8mF8MItc8Nw6D1hCiemzS+5zP12BZ0bxui3Y1l+Z9jKBEwDGK7n0TrPoZa/R6m6WvHZV+ZdguyKBBR41HvqbZPnqjUn4o8GIvg6N7IQxW3siY3XpPTMAwiWmxIEfuk2B06QwDXDX1MfQhGVQLhGIIuYxGsTElLpcDtJslkI8lkP/Eaf68bOj8+8BxRTcEimfArYQwMnBYTHrWHo0Y3D35wmCU5ZSzJmMaC9DKSzY5z+m48Eyx1ej/uE/0JxlRimo55nLPPvLsvQnWrSrJdQNdBN0AUDCwmgbf2RhAQWD59/LI2XC74YkEeO/giNb4WZFHiqzNuZmnm+E+IkwSR+0qvpsCRzq+q32Z3dzUde5/i4Vl3kmFzj/v+EiRIkOBSYRgGVd6hRfFgVOHjY/Ho75UzRk6dDifTp49ZFK97NX4vnTYzfp981k5r0HcY9BjIDnBNA0Hgqf1P0epvJduZzefnfX5sfUiQIEGCBAkSJEiQIEGCTzgJUXwcEQSB9PT0Cx4Flm43U+8N0R2KYRjGhIo6E0WBO5baEEXYeyzGC9tCaDpcMWWwQHRl9mxSzE7+89BLhGMxWoxOjsq1VDgube3si+XDoZBEgVUzclhansXmw628/lEj3b4Ij2+PkOdezucLmsn27kNt/jhea3zObUg5My96P88HQ9dRq99Hqd4AgJhSgHnhfYjjVNt5NP4TRBHznNuJbHkcreUAWsGCC1azvTvi5ddVb7O/9xgAefY0vjR93aDBuLTU0iHF8NFwqmB+XcFiABRNYU/rHt4//gGbm3fQGupGkm14ZRuibMNucWOzuLDLNkKIHFdPjRAVgSSspjRyXKlk2dxk21LJsqWQbXOTFfPx8Vtf4vHkOUREGauu8FDvTtY4U+N1xsNd0NIFLVtOblK2grtssFCeMg3MTiAuFoYVDUkUyLJbEDDG5RjUgz2oVe8DYJr5KURX7sBnpjl3ENv7LEr1B4gpRUhZ085rXxA/frMcVlr8YVoDkU+cKN4T8fH4kVfxK2EEAXqjAf5t/x94tWE7UV0hoIZRde2ctm2RTKeJ2oNfnSYbalTkrd1tNHdESdNNzCvJ4MHV03DZR/bDN2bezuNHXiWixXBbnDw042amJufyx0O72dB4CB9BPu6u40DvMX5d/TbTXYUszpjGooxyUixJo+q/YRiniOIT63dhlsSB7DN9EYVMh2XcroM7qqNsORIF4LOrHEzJlun167idAtuOxtheFeXNvWFCUYNr5lgm1L3MpaQ91MuPDzxHe9iDQ7by8Oy7mO4uGPX65+K/K3PmkG1P5T8PvURjoJO//+j/+NasO8e03wTjx6W8F02Q4HLi1GOpLdSLXwljEmVKkrIHLberphNF1clNc1CU4RxxmyE9REgPISCQKqWOvjOGAdXPxd+PJkrcMMBXC6ofBBncM0GQ6A338sRHTwDw7aXfxjraFOyTkMS5cPKT8OHkJuG/yU/Ch5ObhP8mNwn/TX4SPry8EQzDMC51J86Gz+fD5XLh9XpJTr44qY4nMrphsLGhB1XXWZybMhDpNZHQDYPXdofZXRtDAG5dbGdh6ZliQGOgk1+1/hF7kkYoKPKFrHvOGKj4pBJVNN4/2MJbexsJnUhtuyA9zF3O/Ti1PkBAypuLedbNCJZzi168mBixELG9f0TrrAFALlkar5MuXpq5ObFDr6Ee24bgSMW6+q8QpPE7jjRD553mPTx/fDNRTUEWJW4vWsFNhUswnWavN6TT49dJSxIvSH3dRm8jm+o3sbF+I7tadxHTYoCIJFuxmJMpy5hJnrsEuzUVvxqhJ+LHYOjLgqbrdIZ7wVCx6CoIAk5zEj9f/XekSSbwVEHPkfhr79H4nxoeumNJBZA6g1ZzMa2mIpw5s5leWA7jcLNhGAaxnb9F66xBSp+CedmXzriJiR1Yj1q/E8Fsw3Ll1xHPM9U3xAX+3a0eJFFgdWE6kvjJuXE63FvPt3b+nJimIAgChmFgYJBudWE55dgyiTLJ/aK22Y5Ttg2K3u4XuU99bxnh2DQMg02H23h2Wx0xRcNqlrh3ZSkrpmeP+sa1J+KjI+why5ZC2olyCoZh8Ogr+znU0YIrN0hSdpD6QPvAOgICpa5cFmdMZ0nGNNKtw0/sCSoqW5t6EQWBq4vTESfYDfXhLh8t/gjFbjvlqSOLAaPlSLPCM5uDGMA1c6ysmTV40D7utyjvHYhniVhcauamhTbET9AxMxS1vhYePfA8fiVMujWZv51zD3mO9Iu2/+6Ij/889CLH/e1IgsgXy69nTe68C77f9kA7DX0NFLmLyHYm7gETTE4+Kc+rk9XOD1r38cuqt5juLuAf539u0Gf/+sJHHO/w8+kVU7l+3siTgZqUJjaENuAW3dyadOvoO9B1AF67Oz5Z9J4t8dI+IxFqOVlH3D0HLPH71H/e9M/8/uDvqcio4MVPv/iJzLKWIEGCBAkSJEiQIEGCBEMx2ufVRKT4OKLrOi0tLeTl5SGOU53aoRAFgTS7iY5AlO5wbEKK4qIgcMsiG5IosKM6yvpdITTDYEnZ4DSphc5MHsi/hVf8r2K1a/zLvqf5ZsUdzE27OKm0T+di+XA0WEwS664oZPXMXP60v4k/7Wvmo24b+7sXcVv6cZaZazC37CfSXYtp9q3IubMvaX9HQve2EN39NEaoDyQZ85zbkQuuGP/9jMF/pulr0VoPYQR7UWs+GLc02sf97fyq6i2O++MC2nRXAV+adiO5jjNTLH5UF+O5rUGiClhMAp9eYWfB1PGNJC10FXL/3Pu5f+79hJUw25u3x0Xyho20B9rZ39DO/ob4sqWppawqvIo5uQtId+bRFfXREeqlPeyhPdxLa7AXHRAEmagsYxig6BofdVezNn8hZM6L//Vj6PEa5D1HTorkvUfiUeX+Jgx/Ey5FwwVYD4tgcWGkTMMr55JcvAgxrQLcpSCPLb2y1rIvPvlClDDNuX1IcdQ08yb0vib0vlZiH/0By4ovn/cEDbdFxmaSCCsanaEoOROovMWFRDN0NrYfIKYr6BhYBRlDBKtk4q9m3UG+I31A+LaMY61iTyDK/31QxaHGXgCm5bn5s2umkzbGNPxp1uQBMbwfQRB44Kpy/vFZL6FGB/dPr6B4lpndXVXs6q6ixtsy8Pf72veZkpTN4ozpLM6YRrZ9cORYf5S4yyJPOEEc4inUW/wR+k7083yvg03dKs9tDWEAC6aaWT3zzONXEARWz7Jiswi8vjvMrtoY4ZjBncvsyNLE+44uBnu6q/nZ4VeI6SolSdk8Mudu3OaxT1I4H/+lW5P5x/n38fOjb7Cj8yi/rHqLpmAX95Veg3SBhJcXKl/g4T89TEgJYTfZeWztY9xVcdcF2ddkYSLdiyZIMJk59Viq8jYD8fvyU2npDXK8w48oCiwrzzrrNs85dXr18/HXoutHFsS1KEQ6wV8XnyjqnDIgiB/3HOePh/4IwN+u+NvLXhBPnAsnPwkfTm4S/pv8JHw4uUn4b3KT8N/kJ+HDy5uEKD6OGIZBMBjkYgTfZ9gsdASidIWilKZMzChhQRD41AIrogDbqqK8tjuMpnFG/dCptiKK1DyaIm3YHAqPHnyePyu/YaAW7cXkYvpwtNgtMrctLuGa2fm89XEjGw628EJ3KVvFNO5NOUi+5sfY8wxa7izMs29FsIxPpN94oTZ9ROzAetBUBEcqloX3DUplPZ6MxX+CbME06yZie55Bqd2ElDcPMSnznPcd1WK8WP8hbzbtRjd0HLKVz05dw1U5c84YtIoqBh8fi/L7zSGiKgiAohk8tzVIaY58QSLGAWwmG1eXXM3VJVdjGAY1vTVsrN/IpoZNfNz2MbW9tdT21sK+X+M0O1lRsILVxau5pfw60u3pdIQ9/OWH/01QjaAbBjEjXmP811XvsLenltuLVw6u0yiIkFwU/yu54WR7pA96j+JtO4in5QDJwVrs0cZ4/fKO3dhiMYSWV05sQwL3FEg5Jf166nSwDT0YaUSDKIdeB8BUfg2ic+goS0GSMS+8j+imn6J7mlAq38I86+bz+n4FQSDXaaXOE6TVH/lEiOIhNcpPD69nf+8xUsxOYrqKruk4TFYeqriVVdmzLsh+d9Z08PSmGkJRFZMscufSKVwzJ29cRefsFDs3LSjilV31/OHDWv71s4v4VOESPlW4hN6oPy6Qd1VR5W3imL+dY/52/nhsIwWODBZnTGNxxnTyHel4widSp0/QlPr9E+t8URXdMM7rOtjj13hqUxBFMyjPMXHrItuIEftLyizYzQLPbwtzsFEhHAvy2SsdmOVPljD+p+aP+F3tu+iGwdzUKXxz1u1Yz3ECyfnex1gkM1+vuI0CxzaeP76Zt5v30BLq4RsVt+Ewnds5TdM1vFEv3oiXvkgffZE+vFEvDX0NfH/T94moESySha5YF19+7cs0e5vJTc7FYXLgMDuGfLXK1ss2jdlEvBdNkGAycuqxdHSgnvhgUXzr0fgk1jlFqSSfpeQKnKMoroSgLn5vyrQRUqeH26CvMl5DHMCaAfaT99WPbnsUzdBYU7yGpflLR7//SUriXDj5SfhwcpPw3+Qn4cPJTcJ/k5uE/yY/CR9e3iRE8UlKut0MAvijKhFVwypLl7pLQyIIAjdeYUUSYcuReP1Q3YCVMyyDlikzlxI0AkzPsrHd28kvq96kO+LlrpJVl+2g51hJspn49PKprJ2bz+t7GthUKfDfPStYZalijaOO5KYD6N3HMM2+BSl3ziX/3gxNRTn8Omr9TgCkrGmYr7gHwWS7pP06FSlnFlLWNLSOKmIH1mNZ/ufn9L0d6D3Gr6veoSvSB8DSzOk8UHbdoCi/YETnaItKZZNCXbtKOKYPCOKCAKoGgYhBt1e7YKL4qQiCQHlaOeVp5Xx5wZfxRX182PghG+s3srlhM56Ih3fq3uGduncAmJU5i9VFqykxyWwPeOJiNQblyXloosyB3uMc6D3OzJQibi9aQUVK0fA7t7ohdyl1wnR6Um5lSoqDtGQz9NWhd1firfmQdLEbwXMUol7w1MT/jr12chu2jMF1ylNnQHIRSuUbGLEQYlIWcumVI34Hoj0V8/y7ie56CvXYNsTU4vPOuJBzQhTvicQm9Ll5POiO+Hj0wHM0BbswizLfmncHRY5MPqo+yILy2WSOQ0r60wlEFJ7aVM2e2i4AijKT+NI108lNvTCTw268opCdNZ20e0K8uP04D6wuByDVksT1+Qu5Pn8h3liQPd3V7O6q4pCngaZgF03BLl6s/5BseyoZpgIKrEVcYR4+xfqlxC5LmCWRmKbji6okmc7t/BOM6PzugyChqEFuisQ9K+2jSoc+u8iM1SzwzOYQte0qv30/yP2r7dgtl/9MXN3Q+eOxjbzeGL9OrsmZyxen3XDBorJHiyAI3F68gjxHOv975DUO9h7nHz76P7416w6ckglv1Isn7BlS6O6L9MXbon0D7/0x/5D7CSthfFEfAgKqrsZLX2gx/mPHf2A7y72CJEjYTXacZueZwvkIYnr/6+nryWPMFJJI+Z4gweTBEw3QGe5DFATKXHkD7Zqus72qA4CVM3LOuh3DMM5NFD/+Fqih+CTRrIVDL6NFwXvkhCBuAAIovvj/koVdLbt4//j7SILEI8sfGf2+EyRIkCBBggQJEiRIkCDBIBKi+CTFLIm4LCa8EYXuUIz85IkjNJ6OIAisnWdFEgU2Ho7w9sdhNN3gqpknI46mmKewL7oPl9PELcWLebV+Fy83bKU76uXPp61DFk8KSxFVI6Ro2E3SZS04DYfbYeFzV5Vz/fwCXtvdwKYqiaNKDrfY9lIY9eDc/QzmvIPxqHFr0iXpox72Etvze3RPEwjxiF25/GqECZbmTxAETLNvQe/+L/Se42jNH48prbsvFuSp2vfZ2nEYgDRLMl8oX8sV6WUA9AV1KpsUjjQr1Heqgyp1pyeJRGIaugGyBMFIXBj/4FCU/HQZi+niTmpItiSzrmwd68rWoekahzoPDUSRH+46zKHOQ+xr30ejtxFZsuGyZxCJ+TguSjxz9wvUhX1saj/IYU8Dhz0NTHcXcEfxSma6i4acaBBRNXoi8UiYXKcVJAnSpoO7jF6jgrSyMhBFCHWcmX7d1wjhLmjpgpYtA9vU5DRUuQwkM6a8IoSu/ZA6DUyOeMp2X0N8QNJxUsCQsiswlV6JUrsZZd8LiMk5w0aXjwa7SSLFasITUWgLRClx2895WxOZ4/52Hj3wPH2xAC6zg7+efRdTk3PRNI0SayZpZ6uVeQ4caOjhtxuq8IViiKLAzQuL+NSCQqQLmMbIJIk8sLqcH7+8j02HW1k2LYuynMHitsvs4Jrc+VyTO5+AEmZvdw27uqs52HuMtmAvdWoXO9nLLm8qSzKmsShjGqXJuRMm7akgCLisJrqCUfoiCkmmsZUrAIipBk9tCtET0HE7RO5f7RjTOawsx8QXrnHwuw+CNPWo/Oq9IJ9f4yD5IkwQulQousoTR95ge2clAHeXXMltRcvPe0Jbe6Cd/T37ScpJIu8U8edUNF3DH/PjjXjxRDx4I94BQXtA1D7x3hML0isn0+Bt4O6WXQR6K1GinnPqm9PsxG1147K4cFvdmEQTzx5+FkVTsJlshJQQZsnMutJ1IEBQCRKMBQnEAgPvw2o4boMRt2E4wX2smCXz0CL6EEL6oc5DPHPwGaJaFKts5WuLvsZ1U69DFmVMoin+KpkGve//rP+9LMoX/RyQEPIvL3p7e/n617/Oa6+9hiiK3Hnnnfz3f/83TufwGaPa29t55JFHePfdd/H7/UybNo3/9//+H3feeedF7PnFp9oXT51e4MjEfkpJnoMNvfhCMZLsJuYUpQ63+gAhI0TYCCMikiqdffmTHTiROr387vhM2KHQwqArxAVxEUQzGBpoYXTRxI+2/giAT8/8NFNTp45+3wkSJEiQIEGCBAkSJEiQYBCCMQlyAIy2QPqlxjAMvF4vLpfrokTp1nmC1HmCZDgszM+amBFop/PBoQjvH4gAcM1sK2tmnxTG3wq8RafWyQLrArp6VH5d/Q66oTMrpZi/mnU7dtlKiz/MgU4fmmEgCQJzMpPJSxq/CQEX24fjQZsnxPpdx9lb28FKcxUrLVXYzRLOpCRsc29Dypt7UW3RuuuIffQHjGgQwWTFfMU9SFnTL8q+z9V/Ss1GlCPvIJjtWK9+GME8spBpGAZbOg7xdO37BJQwAgLX5y/g7pIr8QdlKpsUKpsUWj3aoPVyUiQq8k1UFJjIdInsPabw3NYQMdVAEMAkCZhkyHZLPLB64ghCXcEuNjds5tnDz/LSkZcQEBAEAcMwMDDITcolzZ5GSWo5tqRCfMjIJwb/Z7iLuLNkJbNTSgb55HhfiJreAClWE4tyT0YUj8qHSgh6q04Ryo9ieKqJCMUYWJD1Lsxa88nlJQt46wEDrCmw+j+g/GTNWkPXiW7/JXpPPWJyFpaVX0WQzz3VdYs/zOEuPw6zzPK8lElzLhkte7qrebzyVaKaQr4jg7+Zczfp1vg1KKAFaPO1kZOcg1Man1IOkZjKs1vr2FzZBkBOqp0vXTOD4syLN+nntxuO8uGRdnLTHPzT3QuQpbMfmyE1yobmI2xoPUxzuBlZ1Ac+S7E4WZQ+jcUZ05jmLrjkkcH9x2Omw8LczOQxnUd13eAPW0IcaVGwmgT+Yq2TDNe5TVjr6NP4vw1B/JG4uP6Fqx2kJV1+k9+CSoT/OPQiR/oaEQWRv5j+qfMuNRBWwvzio1/wb1v+jZASwiSZWDt1LfnJ+YOiuPsiffijfgxGf/sviCaSUmciW1xggBZsxqFHSLG6B4ncbqsbl9V1xv9uq5tkS/KQ0dhjrSmuGzohJUQwFhwQyod7PVVMDypDLxfTYmP6nlVdpdHbiK7riIKIbuiIokihq3DM0eaSIA0SzWVRxiyZkQUZdLBZbCfb+pcT5EGi+6nrnvr/6eL83ra9PHPoGaJqlBRbSqJ2+wXgYj+v3njjjbS1tfHzn/8cRVH4whe+wKJFi3jmmWeGXWft2rX09fXxs5/9jPT0dJ555hn+6Z/+iT179jB//vxR7XeyPJfDyXvK9Z27+FPLR1yfv5AHy64b+Pxnbx3i42PdrJ1XwD0rzi40NyqNfBD6gBQphVuct4yuE3118NK6eIalezaDfZiJl1oU2j8AdBDiZU0QJchYwWu1f+Kv3/1rHCYH797/Lmn2MdYzn6RMxufyBINJ+HByk/Df5Cfhw8lNwn+Tm4T/Jj8JH05ORvu8mhDFJzG+qMKOFg+SKLCmKH1ca6leSDYfjvCn/XFhfPVMK9fMsSAIAlWxKnaEdwwMNOzrqeO/D79MVFModGbyjYo72N8eI6bpGAaIQjyKb01R+icyYvx0Grr8rN9VT0djHTfb9pItebGZZRwFs3BecSeCdWzHzlgj8g3DQK3bjHLkHTAMRFcO5oX3ITom/sCNoWvx2tL+DuTChZjnDR8x0xH28OuqtznkqQegwJHBLVnXE/SkU9mk0u0/KYQLQGGGTEWBiRn5JlKdZwpf3pBOr18nNUnEH9Z5emOIQFQn2RaPtsxJmTi/7fZAO0t+uYS+aB8m0UQwFgQBCl2Fg5YTJQtWZwFWRw7iicH5FNnC4tQSVuXMpSytjBqPTEjRmJmRNC4TW5TDb6LUbECQRKzZWQh91fEI80BLPLLc0OM1zgFsqfDA/kER40bER2TTTzCiQeTCBZjnnbtYoOo6mxp70HSDJXkpuCym8zVvQmAYBm817+b3tRswMJiTWsI3Zt4+EHVVE6thU2gTiqFgESyssq+izFx2Xvusau3jN+8fpdsXAQHWzs3n9iUlmC/yOT8YUfh/z+zCH1a4fWkJNy0YoTzAKVR2+2n2hclxmggLnezuquKj7hoipwhxySY7CzPKWZwxjQp30aDMKBeLvojCrlYPZknkqsK0Ud/wG4bB63si7KyJIosCX7jaQVHm+SUh6g3oPLkhQE9Ax2kReXCNg5zUiXMePF+6I15+dOA5WoLdWCUz35p1B7NTS0a1bkSN0ORtor6vngZvw6DXVn/rmMVap9k5IGC7rC7clpMidr/Y7bK6SLGm4DA7eat1Pzu7qwGBK7Nn82fTbsA0RiF4KNoD7TR6Gyl0FV70CGZVV0cW0k97remp4YUjLyCLMgICmqGh6iqLchfhMDtQNAVVV1F1FUVXUDQFRY+3XSpOF/KTLck4LU52fmlnImJ8HLmYz6tHjhyhoqKC3bt3s3BhPB3322+/zbp162hubiY3N3fI9ZxOJ//7v//L/fffP9CWlpbGj370I770pS+Nat+T8bn873b/hoZAB9+YeRtLM2cA4AvFePjJ7ei6wT9/ZhF5aWcvw/Jx5GMORA9Qai5lhW3F6Ha+60dw6DdQeA1c+z/DL6f4oXtXPF26IMcFcdcMIqYUbvz9jbT6W/nW0m/xlwv/cnT7TZAgQYIECRIkSJAgQYJPGKN9Xk2kTx9HdF2nvr6e4uJixAuYzrWfJLOMRRaJqjq9YSVeZ3wScOVMK6Io8PbHYTYejqDpBmvnWSk2FbM7vBuP5sGjeZiXNpV/nP85fnzgORoDnXz/46dYlnINyXIqAqAZIOgGIWX86vZebB+OJ0UZSXzzU7OpbSvk5R2FpHfvYpVxlHDdPnytNbjm34ZjyqJRiR1jjcg3lAixfS+gtcXTiMsF8zHNvu28om3PhXP1nyBKmObcRnTrz1Eb9yAVLkBKLR60jKprvNm0ixfrP0TRVQxdYqZpKabW2bxVKwBRAERBoDRbZkaBzPQ8E0m2kfvhsosDNcRddpG/uN7J7zYG6fJp/PLdAJ9Zaac8d2KIqtnObB67/rGBqL5MZyaPrX2M26bfRn1fPbW9tdT01FDTW0Ntby0NHbsxO/OwOHJp16K82raflxq2EvIdR41GyEsqYXnBTMrTyihLLaMsrYwUSwoNDQ1j8qHubUU5thVEE+ZF9yNkV5z8sP5deP3T8QFGNQiaAuEeeP8huPqn4IwPHAvWZMwL7iW6/deojR/F64sXDlP38SzIokim3UJbIEKrP3JZiOKaofNk9Z94r/VjAK7Jnc/ny9cORDgH9SCbQpsIGSEMw0BHZ0toC7lyLg5x7PW+Y6rG+l31vLOvCQxITbLwZ9fMYHqeezzNGjUOq4nPrCzll+8e4bXdDSyamkHWKFLjeyIKAFkOO5mOeGS4oqsc6q1nV3cVH3VV41NCbGjdx4bWfThkK1ekl7I4YzqzU0owSxfnNi3ZIiMKAjFNJxhT6WxpGtUx+OGRKDtrogjAXctt5y2IA6Q6Rb50Xfw82ObR+NX7AT53pYOSrMl/y3pq2YFUSxJ/M+fTFDozBy0T02I0eZtOit59DdR746/tgfZhI7wVTYknwzBZkYV4am5FV/jsrM9yRc4VQ4rdY41sLkst5Z2WPTxd+z6b2w/SFu7lW7PuwG0+v6wQ2c7sSybOyqIcj263ji7jUnugnc2NmwnEArgsLrxRL06zkxc+/cKINhiGgW7oxLTYINFc1dVBwrmiKcTUGI0tjaRnpaMb+rDLnSq8j7S9Jm8Trf5WTLIJURBxWVz4FT+N3saEKD5J2b59O263e0AQB7j22msRRZGdO3dy++23D7ne8uXLefbZZ/nUpz6F2+3mueeeIxKJsHr16mH3FY1GiUajA//7fD4ANE1D0+ITQQVBQBRFdF3n1Pn2w7WLooggCMO292/31HaI3+ePpl2SpPhkXVXl6LEaGgPxuuHTXPnxY1HX2VbVhqbrFGcmkZfmGFXfu9VuANKl9NHZpCmINS/Hn1tL74BT7Dq970KwFUGQwJYHjgJ0wQKShSf3/opWXyvZSdk8MOeBQd9Nfx/7bTrb9z5R/TRU3/v919DQQFFREZIkXRY2XY5+GskmXdcHnutkWb4sbDrX9slo09n8NxltOlv75WaTrus0NjZSUhKfgHs52HRq3y8XPw1n06n+Oz2ecbLadGr75eKn4Wzq919xcTGnM1ltGqnvl6NNAPX19RQWFg4aG5vMNl2Ofjq9/fT1hmPyjzBOIAzDIBaLnXGxulAIgkC6zUKLP0x3ODppRHGAlTMsSCK88VGYLUeiqDqsu8JKnimPRqWRY8oxFkgLKEnK5p+veCAujAe7ea31dZamLsch20mSk0k2ObHK4ydeX2wfXghKc1w8cvsVVDaX8Pa2vVwR3Ex2qI/ubb+n9chO8q+8D3vy8HXwIqrGgU7fQES+LsKBTh9pNvOQkw90fyex3U+jB7pAFDHPuhmpaMklSS1yPv6T0uIiqNq4B2X/esSrvo5wImKz1tfKL468yTFfJzEFbEoeBaGriBhuIsTTnk/LlZlRYGJargmr+dxtT3GKfHmtk2c2BzneqfL0piA3L7SxqGzsdX4vBHdV3MXKwpVnRPWVppZSmlrKDaU3DCyraAr1ffXs7zzMhrYD1IR6iYomZJOTWMxPq6+B5ysPD9q+y+Ii25TNvPp5TEufRllaGaWppaTahv7NGrpObP9LYOhIubOQThXEATJmgzkZlADYsiDcGY/C6dwXT2c5/+sw80EQZaT0qZimXYty9F1iB19BdOUhunLO6XvKcVppC0RoD0aZluacNJk8hiKkRvnJ4fUc6D2GgMBnp65hXcHiQce4R/MQNuK1fgUEdHQUFPy6f8yieH2nn19vOEprTxCAlTOy+czKUmzmS3vLsqQsk61H26ls8vDU5hoevnnOiOe56AmBGcBtPTkxwiTKzE8vZX56KWr5DRzpa2R3VxW7u6vxxoJsaT/ElvZDWCUz89NKWZwxjXlpU7BIF+4aLwoCyRaZvoiCJxIb1Xn0QH2Md/bFs77cMN/GrMLx61+STeTPrnHy1KYgDV0qT34Q5J6VdmbkT94JJgd6j/Ffh14mosXIs6fx2eLl1HUdZkPtm4Mivtv8bSOmNk+2JFPkKqLYXUyhq5BidzFFriJsso3rnr6OQDSATbIR1sKk2dN4aPFD4yZ8CoLADfmLyLWn8dPDr1DjbeHv9/wfD8++i5KkT4a4mu3M5rG18clh/pgfp9nJY2sfO+t3LAgCkiBhE8+eGUXTNBx+B2X5ZUjS+U/6bA+0s7V56xlC/ulZXhJMHtrb28nMHDyhRpZlUlNTaW9vH3a95557jnvuuYe0tDRkWcZut/Pyyy9TWlo67Do/+MEP+P73v39Ge11d3UD9cpfLRU5ODh0dHXi93oFl0tPTSU9Pp6WlhWAwONCenZ2N2+2mvr6eWOxk5pT8/HycTid1dXWDBjtKSkqQZZmamppBfSgrK0NVVY4fPz7QJooi5eXlBINBGhsb2d9ZTTQWI82cRIolib6+Ptra2vjTR+0oMYWKzPi1q7e3l+7u7oHtnG6TgUFLSgu6SSdVTB2VTY6uD8kL9yA4sqiL5qGf0v9TbRLQKbK3IggGsisPFTvHjx2nL9rHT7b/hJga49tLv40e06lpPrkNs9nMlClT8Hq9g/zucDgoKCg4q00TxU/NzSdLHp1qU2trK729vcRiMZKSki4Lmy5HP41kk67r9Pb2YrPZyMvLuyxsuhz9NJxN/f5LSkoiKyvrsrDpcvTTSDbpuj7wXNfQ0HBZ2ASXn5+Gs0nXdVRVxTCMy8YmuPz8NJxNuq6j6zqapnHs2LHLwia4/Pw0kk1TpkwhHA5TU1MzILxOdpsuRz+dblMgEGA0JNKnjyOaplFTU0NZ2fgMYo2GzmCUfR1ebCaJlfmpk67Gwc6aKK/tjgspS8oszJ7bwabwJuyinTuddyKeiELsi4T4hz1/pDZQT0gNIQsyFsnKtZlruLHwCmakj09t2UvhwwuJYRjsP97FsR1vMlvdj4SOIpjxFV7DnOXXYjGdKTL1hmNsbe5F0Q0E4inAZUlgeV4qqbbBoofaehBl3wsYaiweabvwPqTUSzfIer7+M2IhIhsew4iFMFXcQCB3Cb84tJEPu/cSUw0kw0JhdCVp6jQcZpHpJ+qDT82WMUnje+ypmsH6XWH2HY9fGFbNsHDdPOukFlf9sRBvNO3iheM7CKshBDRsgoFF8dHhqabJ2xif2KDEMJvM8R/fCVJtqZSmlsYjyk9ElZellmFvPYhy6A0EkxXrmm8NXSag+gXY+DCoIZDtsPCvoW07dHwU/zxlGiz/PmTNxzB0YjufROusRnCkYb3yIQTT2NO7G4bB5qYeoqrO3CwXWY6JMalhrHRHfDx64Dmagl2YRZmvVdzKwozyM5bbFNrE/uh+AARdQBd1TJh4IPmBUdcW13SDN/c28OruBnTdINlu5sE15cwrHqb+5iWg0xvmH/+4G0XV+dK1M1g2LWvYZTuCUfZ3eHGaZZbnDz8RqR/N0Kn2NrO7q4pdXVX0Rv0Dn5lFmbmpU1icOZ35aVMJqzHawx6ybSmkjbE0xnBU9wao7wuR67Qg97aNeB493qHyfx8E0XSDZeUW1i2wXpD7D0U1eHZriKMtCqIAdyy1M69kckwAVHWVFl8L9X31bGzbz3ZPIzFNQYt56erYg6Yrw67rMDkGxO5idzFF7qKB926re9jv+oXKF3j4nYfxR/wkWZN47PoLVze6LdTLowefpz3Ui1mU+cqMm1hyIi3xJ4ELmfL9QtyLjrV2e4KxMx7Pq9/5znf40Y9+NOIyR44c4aWXXuLJJ5+kqqpq0GeZmZl8//vf5ytf+cqQ6379619n165d/Nu//Rvp6emsX7+e//zP/2TLli3Mnj17yHWGihTvHwTpt3OiRAX00x/poCgKT3y0nu3hWlZlz+KrFbdgGAbHO7z864sfI0sijz24lCSb5ax9D+gB1ofWxycHJn8W0RDPapPw7pcRWj9EmPuXaPO+MXzfI+2I/hqQbJC2CE5s51+3/CvPHHyGGRkzeOmel+KTDi+D6I3T/TScTYqiUFtbS2lpKbIsXxY2XY5+GskmTdOora2lrKwMk8l0Wdh0ru2T0aaz+W8y2nS29svNJk3TqKuro7y8HEEQLgubTu375eKn4Ww61X+nM1ltOrX9cvHTcDb1+6+srOyM5/fJatNIfb8cbTIMg5qaGqZOnTrouXwy23Q5+un0dp/PR2pqaiJ9+uVOqs2MKAiElXj9Z8cljqQbK0vKLMiiwPqdIXbWRNGMNMzTzYT0EB1aBzlyTnxWnCfGlRmrOOKrxsBANVQ0LcDbHX8ix5aF21pMjtN6qc2ZcAiCwLwpmcwpeZD9hyrRDrxEqtZNWsNb7Gz8GGHm7SybW44snYy2FwBVj5+MjP4/A+ymkxcAQ9dRjryNWrcFADGtBMvCzyJYzi+F6qVGMNtRS29E2f8CH378Fv9zaA8hIR4JmaaWM0taxbwpLioKTBRlSIjihROoZUngzqU20pwi7x+MsOVIFE9A587l9nEX4C8WSWY7a7KXYdenUOmvpDZYSUiNopuTmJsxh78rWESaIPJh5YcELUGO9R2jpreGZl8zveFedrXsYlfLrpMb1HVSlQhTzW6mFa2gvPatgcjyZMspF77yu2h3l9PQtoeinIVkZ84B4yGoeRl2/wg8VfDGvTDtHoSFD2O+4p54ffFgD7H9L2Fe8NkxC36CIJDjtFLfF6I1EJmUovipqZ5dZgePzL6bKclnRs4fix2jXqnHLtgHIsQBTIKJTq1zVKJ4uyfEr94/wvGOuBC8YGoG919VTpJtYkUGZ7ps3LywiJd2HOePW2uZXZSK0zp0H/tTp6cM8/npSILIDHchM9yFfK70Go7529h1QiDvDPexu7ua3d3VRDUFvxJCEAScso2vVdzKmty5522b+0Sa/76oykjTEDq9Gr/fHBfEKwpM3HjFhRHEAUyywL2r7Ly8I8y++hgvbA8Rihosn35xjqf2QDsNfQ0UuYuGFD41XaPV3zo41fmJqO9mXzOaoWFLKsaWXARANNRB0FMFGNhk2yCx+1QBPM02+rrup3JXxV0sy1vG1kNbWTFrBXmuvPP9CoYlx57Kv1zxID+tXM+B3uP89+H13BHs5o7iFQMTGi9nLmXK93NhuCwvCSYWDz/8MJ///OdHXGbKlClkZ2fT2dk5qF1VVXp7e8nOHtq3dXV1/OxnP+PQoUPMnDkTgLlz57JlyxYef/xxnnjiiSHXs1gsWCxnnnMlSTpj0kb/gMTpjLV9uMkgY2kXBAFJkmiKdSMIAjPchQPt26q7EBBYMCWDJJtlVH3s0/sASJVSkQV50MTNIW0KtELr1vj7sjtH7nu0EwQB7DlwYv1GXyPPHn4WBPjOyu8MnFdHsnXYvlyg9vH003DtoigOvI5n3y+lTefbx8lmkyiK4+6/S23ThWifqDadj/8mqk3n0z4Zbep/pricbDpb++VkU7//LiebRurjWNsnuk2CIAzbl6GW719nItt0Lu2T1SZN0wb6OJRvJ6NNI7VfLjaNNrBgcimoExxRFMnPzx/WmRcCWRRIsZnoCcXoCscmnSgOsGCqGVGAl3aE2FOrMTUlHzHzGHWxOnLkHKp7g/SEY4S0IBZJxixJhNV4CqCIFuGZxuf4qK+UO0oWsCSrbKDG7blwKXx4MRAFgfmzZ6LOmE719jewNmwkX28ndvCXPH34CkoXrGHZ9LjYdbQ3gEUSUXQ9PjNHN5BEgbCqY5UljIif6Ed/QO+Jp6WQS6/ENP16hAnwnZ2r/3r9GpXNKpVNCnXdOUgWK7VmHwJdOMU8PpV1HTdMLSU3Vbpg4s9QCILAmtlWUpwiL+0Ic6hJwfd+kPuutOOwXvrv+1xo9UewSlbuKF5JXtJq/tTyEW827aI11MOvqt4my5bCDVMWcXXhFZhO1FMOK2HqPHXU9MRrldf01lDTU0NL11F6tBg9usLuxg+g8YOB/WQ6MilPLac0tZTucDd/OPgHYloMh9lxMkKu/E4oWAN7HoWal6Dqj9DwLsKSv4vXF9/2C7TWQ6ip2zBNWTFmW3NPiOLdoRgxTccsTR6f7emu5vHKV4lqCvmODP5mzt2kD1Fzt0/rY3tkOwALrQspNZXSFeyi29RNZaySXZFd5Mg5w6YM1g2D9w+08OKOYyiqjt0ic9+VZSwpy7yox9pYuGF+ATtqOmntCfLctjq+ePX0IZfri8SzPLhHKYqfiiiIlCbnUZqcx71T1tAQ6GR3VxUfdhxif+8xdMNARCCixvjRgWdxmqwsTC8/r++sv58hRSMrN2/I86gvpPO7D4JEFIPCdJm7l9kv6OQgAEkUuGOZDZtFYHtVlDf3hglFDa6ZY7mgv5FTI2stkoW/XPiXTEmZQkNfw4AI3uRrQtXVYbYg4Eqdic2Zi0k0UWZ3cXX5dQPCd4Y944L0Pzc5l3Vz1+FwjK1swbngMFl5ZM6n+UPdB7zZtIuX6j+kOdjFV2bcdEHT/V/uXKh70ckm5H8SycjIICMj46zLLVu2jL6+Pj766CMWLFgAwIYNG9B1nSVLlgy5TigUAs4cdJAk6YzZ9pcLGjrtenyy3TRXAQAxVWNndbzG+Irpoz8eerQeANKktNGtUPMyYEDOEkgeIYuWGgTFBwhgPdmfR7c9imZorClew9L8paPu5+XE5fpc/kki4cPJTcJ/k5+EDyc3Cf9NbhL+m/wkfHh5k0iffhnQ4A1R1RMgxWZmUY77UnfnnNlfH+PF7SHEpB7SF24hxWbiSvFWjnTFB3EK3AL/8PEThNUoNsmMNxZCRydJTsJAQBQgy57MqqyZrMqeTaEz8yx7/OQS83bQtuVpjL4mdN3guJrBLstyFi6qwJBFzJLI/KxkNAMafSE6gzHsJonF9gDaR89gRHwIshnTvLuQc4dOtziRMQyDjj6dymaFyiaF9j4NA4MuuZJmy3ZEKUSS7mWtIfLphffhyJt3qbvM8Q6V32+Oi1FpTpEH1jhIS5pcKf6jqs7mph4Mw2B5firOE5N4QmqUd1s+4o2mXQSUeDmFTJubWwuXsyp7FrJ4pp1qy348u37HMcVH49Rl1IU746K5p5b2wMlaJ6qu0uhtRNd1REFEN3RkSWZR7iIyHZkkW5JJtiTjUgIkt27DFfOQLIq40mdiz7kWe3stybKVjOV/gSurAmmIvozEjpZefFGV6WlOCl328/j2Lg6GYfBW825+X7sBA4M5qSV8Y+bt2OUzo8RUQ+X1wOt4dS85cg7X2q8diGTSDZ03gm/Qq/VSbCrmKvtVZ6zf44/wmw1HOdrcB8DMwhQ+v2YaqZMg60dtm5cfvPQxAI/cNo/pee5Bnyu6zsaGbgwDrixMwyqPz7F62NPAI7t+gYBATFdQtPi5K93qojgpi9U5c1mVPQu3+dyydnzY1EtIUZmf7SLDPtjnUcXgV+8FaPNopCWJfPk655gm55wt6vpsGIbBpsNR3jsQz96xuNTMTQttA6K8pmtE1AhhNUxICRFRI4SUEGElTFgNj/g6aHk1TF+kjw/qP0DV1BOTwzREUaTQVYgsDp58aJbMFLoKB0V85yTl8V5XDbX+dkRB4Avl13NN7vwx2zyZ2Ni2n19XvY1m6BQ5M3l49l1DTqRJkOBy5WI/r9544410dHTwxBNPoCgKX/jCF1i4cCHPPPMMAC0tLVxzzTX87ne/Y/HixSiKQkVFBTk5Ofz7v/87aWlprF+/nkceeYTXX3+ddevWjWq/k+m5vNrbzPf2PkWSycYTK76JIAjsru3kiXcqSU2y8MPPLUUa5cSud4Pv0qq2ssy2jHLzmWlMB2Ho8Pw18Wjxq/4dpt48/LL+Wgi1gCUd3PEI/l0tu7j/5fuRBInX7n2NqalTR2tyggQJEiRIkCBBggQJEnxiGe3z6uQLK57A9NeLOL3WwIUmw26hqidAX0RB0XVMk3QGy9xiM5IIz201CPttIITYE6vFQS4lbjtlqU4emnELjx95lYgWw2Vx8NCMm8m1Z/LHmt0c8dXQFfbzRtMu3mjaRZEziyuzZ7M8qwKXeXQRU5fKhxcbsyuLwnXfIlK7Bd+Bt5ga7qZEeIv6lhDtKbOYmuygK+iluqeDqamZWCURe+segm0bsYggOjMwL/ocYtLEmngwkv90w6C5W+Nwk8KRZoXewMmomKjoodO1Cb/YSrJJYGpyAZ83zyKvfjfC4bcwsmYgDCEKXkxKsmS+vNbJ7zYG6QnoPPFOgM9d6aAoc/KcxtuDEQzDINliGhDEAeyyhVuLlnN9/kLeadrDS7Vb6Az38cuqN3m54UNuKVzGVTlzMJ0QowwljHLoNRySmSsq7mHJtGsH7ccf9VPbW0ttby0bjm/gt/t+G4+EMnRERDRdo9HbSFeoa3AHDQNiQLQXvJuhbguG7ABDgNpnEewpJPWL6FZXXFA3D/7fZXEN+l/XzfijIk0+Ycyi+PmKiGNFM3SerP4T77XGxd5rc+fzYPnaIbNvGIbBjvAOvLoXm2BjlW0VoiAOOgaX25bzZuBN6pV6SpQSCk2FA+tuq+rgmS01RGIaZpPEp5dPYfXM3AkbHX46pTkuVs/KZeOhVn63qZrv37MQ0ymZALwRZaDsxHgJ4gDZthTsspWwGiXFnIRPCWFgYJcttIV6+UPdBzx7bBPz0qayOmcO81KnDjmpZDjcVpmgolLT1EpqaeHAeVTTDf6wJUibR8NpEXlwtWNMgnh/1HUwFsRmsvHdVd/l6uKrzypWDyVaN8cCNHSH+ENLmO9sj+Cwx4ioYWJabMzf53CElTCKpiCcyI/bPxlmbtZcFuctpshVRJE7LoJnO7MHpQv3RP38+MBzNAQ6sUgmvlFxG/PTS8etb2fjUt3HrM6ZS449jf869BINgU7+fs//8a3ZdzLNlX/R+nC58Em5F01wfvz+97/na1/7Gtdccw2iKHLnnXfyk5/8ZOBzRVGoqqoaiBA3mUy8+eabfOc73+Hmm28mEAhQWlrKk08+OWpBfLJxxNNILBajPK104P5iy5E2AJZPyx61IG4YBt1aNzDKSPHW7XFB3JwMRWtH2LAG4XjUOrZ4xq5Wfyt/+97fouoq98659xMtiCfOhZOfhA8nNwn/TX4SPpzcJPw3uUn4b/KT8OHlzZjUlOLiYhoaGs5o/+pXv8rjjz8+5DrPP/88//AP/0B9fT1lZWX86Ec/umwfvOHMYu8XA7tJwmGWCcZUesIxsh0TP8puOGYVxmukv9JcgJF8lG6hiUJbMaUpcVF7Te5c5qSW0BH2kGVLIc0an/Hxjdnr2NnaQ0OwkU6lnmrfcRoCHTxV28Hv6zYwL20qq7JmcUV66YCwNhyXawrB0xFEEVv5VVhyZ+Ld8yxKdz0V3RtxdBziJ4F0WlLrQFIRdZk/t2VxXbAFDVCyZpO86J5LLhIPhTek0+IRSQ/ppCZJqJrB8c54WvQjzSqByEnfyqJASbZAn+Njdgd2oKPjkszcXXIl1+cvRNQ1Il11GMFelKPvYp510yW0LE6mS+Iv1jr5/eYgzT0av9kQ5M6lNuYUT440ta2BeIRnbtLQ5yirZOamgiWUhJJpdAR4o3kX3REfv6l+h/UN27i5cClrcubB4TcxokHEpAzk0tVnbCfJksT8nPnMz5nPqqJVvFn7JoFYgGRzMt6oF5vJxq9v+TVmyYwv6sMb9cZfIyde/c34WrfjDbTjMyJ4DAsRQ8eI+PALAv6YnxZ/y6hsNgwIqxoAabYkUmzuk9HplqGF9GRLMjtbdvJfO/6LqBodnPL9AhFSo/zk8HoO9B5DQOCzpVezLn/RsCJ1rVJLnVKHiMiV9isHpUfvP4emSWnMtMzkYPQgO8I7yJKziIYFntxYxb7j8TSkU7OT+bNrppPlnvhR9Kdzx9IS9h7rpsMT4s29jdy6qHjgs/564ueSOn0k0qzJA5PDQloUp8nGQzNuYWnmdHZ0HmVj+35qvC3s7a5hb3cNLrODVdmzuCp7DnmOkSqFx0mxmmjxRwgZJ0VewzB4ZVeY2nYVQVRYt0SjT+2msd03cNz4Y/5Br76oD18s/nlXsIvdrbtRNRVREPGEPXzjrW8MGXU9WqKSgT9sgALmKCTZRPp/qgICVtmK3WQfeLWZbPH3cvy9TbYN+2o32QkqQR568yHCSphkSzLBWBCnxcn/fOp/Rpyg0hTo4scHnqMn6sNldvDI7LuZkpxzTjaeD5fqPmaaK59/WfAgjx18kYZAB//68e/5s2k3sDrn/Gvef9L4pNyLJjh3UlNTB6LCh6K4uJjTE8KVlZXx4osvXuiuTRgO9B5D0VXy7PHrX28gwuEmDzC21OkBI0DMiCEh4RbdZ1+h+vn469RbYKTnpUg3GCqIFjCn8ELlC3z1ja/SG+5FFESKXEWj7uPlSuJcOPlJ+HByk/Df5Cfhw8lNwn+Tm4T/Jj8JH16+jGk0cvfu3WiaNvD/oUOHuO6667j77ruHXH7btm3ce++9/OAHP+Cmm27imWee4bbbbmPv3r3MmjXr/HqeYBDpNjPBmEp3aHKL4gDT82Xq1XyOCUeJyN0crlOZmwn9wXZp1uQBMbyfFKuJ6WnJiBQzVSjhi+U3cLivhs3tBznmbxsQCJyylWVZFazKns3UpJxJE5V4IYlaU9hffDdJ1j3QsYV9SicdWR3ogACoosovlQaqJIGU5HIcSdNYG/aR50w7r/rt481HdTGe2xokFHXz2uEAJVky3pBORDk5IGgxCUzPNTGjQAZHB0/WvU1LIB75MSd1Cn9Wfj0ZNnd8YUnEPPtWojt+i3p8G3LBfERX3iWwbDBJNpEvXuPkhW0hKpsVntsWwhPUubLiwtbXPV/8URV/NJ6KONsx8oQKsyhzY/4irstfwAdt+3mtcTu9UT9P1rzL+roPuMHbxZWIJM+5A0Ea+TKW7czmsbWP8fCfHiagBEiyJPHY2se4bup1I3fYMKD+bdjxr+iRAAGpDK9sITx1NaGiRYNF9BOi+kBbbPBnMS2AZhh4owHCaohWf+uIuz495bsn4uFzL32Op/Y/RaGrkExH5sBfhiODLEfWQCr4c/kNdEe8PHrgeZqCXZhFma9V3MrCjOFTg/ZqvewM7wRgvnU+2fLwA8tzLHNoVBrx6l5eb9vMlrft+MMKoihw+5ISrp9XMOpIrYmGw2Lis6tKeeKdSt74qIHFpZnkpMTF/X5RPGWcRXGITw7Ltjg41F3NrPRyZqSVDrSvyZ1LS7CbTe0H2NJ+CG8syOuNO3m9cSdlrjxWZ89htruQmBYZ+M2eKmx3Bns53NVJINLHs9UaASXA8a4+Wr19RHU/dluM59aPzV9hJRxPPy6ICIKAiIhhGDhMDjIcGWcI0gNCtmwbJGqfLmB39Zl4fz8IupXCNCefuzKVNIcDizQ+50Ld0AdqijstTh5b+9iIgnilp4H/OPQiITVKtj2Vv53zabJsKefdj8lGutXF9674HP975HV2dVXxi6Nv0hjo4r7SqyfUPUOCBAkubza0fsyGtv1ohs4fjm8ky55KsM0NBpTnush02c66jX7664mnSClIwlmiRCIeaHg3/r586DGSAcLxqHVsObQHO/j2O9/GE/YgIGCSTHxv0/e4sezGi5ItKEGCBAkSJEiQIEGCBMOjaRqKolzqbiQ4BZPJdM5R/GMSxTMyMgb9/8Mf/pCpU6dy1VVn1goF+O///m9uuOEGHnnkEQD+5V/+hXfffZef/exnPPHEE+fU4QRDk2E30+AN0R2KYRjGhBbHzsbRngCyYcWmu/FpXhqUBp7ZYuPeVXZM0vB2FSXb6IsodAaj1PUqrMmbz9r8BTQHu9jSfogPOw7hiQZ4t2Uv77bsJdeexpXZs1mRNfMMkf2TgGEYNAa6eL3+CDW+Rtoj7YhOB+GwimKoiMRFcQPQgK2YMEU6EZv/xPomA5skU5iUwRRXFnmOdAocGeQ70smwugalkr3QaLpBVYvCUxvj9bYNXSCqGBxqVEhNEnDZJWbky1QUmCjJlIkZUf5Yt4H36uIpol1mB/eXXsuyzBlnHDdSZjlS3hy0lgPE9q/HsvIrCBOgPIFZFvjMSjtvfxxhW1WUd/dH6PXr3LLYNmEFxv4o8Qy7GbM0uu/QIpm4IX8hV+fMY3P7AV5p2Ea3p4E/CjpvWuzcHGznWncuVmnkSPm7Ku5iZeFKGr2NFLoKRze4KAhQciPkrUT86L+wV72FrBVCzQYsooq08C9glL/zFn+Q7c3NqHqQ6aliPJr2FCH9dIG9vq+eJm8TsihjYCAaIqqu8nH7xxztOTrsfsySmQx7xiDRfJCAbs8gy5lFkjlp4Ld+3N/Ojw88hzcWHFVka8yIsSm0CQ2NfDmfmeaZI9ouCzLz5cW80vcGHUodmqOAfHsmf3btDArTz63u9URi4dQMZhelcrChl6c2VfPIrXPRDfBGVQBSrOOfxeH5w88PpCK3yBa+sugrLMhZcEbEtinsRVNCeHSIiCYavY2827AV3VCJhbqIhtpRY94zth9SNAzDwCpLKCoEIvGJRU6rgFkWEBBIsiQNZDlIsiTFX81JuKwnX/uzHsS0GA+uf5CwEsZlceGL+XCanfzp/j+d30B/ESzKU/ndB0H8foMXP5T4/BozVvv4nAPHct7Y2nGYnx99A1XXKHfl8/Dsu0gyjV5wudywSGa+MfM2Xq7fyov1H/J2825aQ918veI2HKbJPXEzQYIEE5+eiI+fHn4FzdARAUVTebzyVYo6VwFjixKHk6L4qFKn170KugppMyFt+vDLqSFQTlyDbdk0tO2jL9IHgEkykWnPJKAEaPQ2JkTxBAkSJEiQIEGCBAkuIYFAgObm5jMycSW4tAiCQH5+Pk7n2MeXBeMcvRmLxcjNzeXb3/423/3ud4dcprCwkG9/+9v81V/91UDbP/3TP7F+/Xr2798/7Laj0SjRaHTgf5/PR0FBAb29vQMF0gVBQBRFdF0f9IMcrl0U4xFKw7WfGgHf3w5npkkYrl2SJHRdJxqNYjKZEARhoC+GYQxafqx9H41Nmq6zudmDqhssyXXjtprHxabh+n6hbGoNRjnSHQAgOa2dQ+pePF0uvB+toTRH5p4VVszyyQHv0/uu6Dq727yEVZ10u5k56c4B8UczdI76mtjcdpA93dXE9LhoIQAzU4pZlT2LK1JKEfX4TBNRFMfdTxfqtzdaP/VEfVR6GznUW88hTz1dkQCqbiAANlnCYbKQh5Xdnmp0wGkYBAQRGYMpsXLaDAlriopP9RKKRdE0HVkUMcsn/kwSVslErj2NfHs6eY508uzpFCVlkmZJPuPicS42qZpOU7dKfadKfadGY7dGKGrgCcbtEIT4nyjAfVc5WDxVHkiru7urit/VvUdfLAjAlVmzuXfKapwm27B+EmIBohv/E0OJIM+8Bal4yQX301h+YztrYry1N4oBTM2S+fQKK1aTMNCXifDb0w2DrS19xDSduZnJpNtMZyzf30fDMFAUBbPZPHBe7e975Oif2FL3Aa9LAr22ZBBEnLKNdfmLWJu/ALvJeuFs6q0kuuH/Q4saCCiYkySEld9DTK84q590w2DLifPzFdluUq3yiOeI9kA7y3+znIAST/neF+nDarLy79f+O5qh0RXqoivURWegk45gB52hTnwRH/SfGk+/qp/WbpEtZDoySXLmETSnIggSKWYbn8qayRR3AdnObNJsaThNJ8+f/de0TaFNNKgNOAQH6+zrsIrWQba2+duo66mjLKOM3ORcDjX28NsNVZiKG0kt6SNJcvJg9t1YhMFz8ibK8TRS+3DHU28gxj8+u4eoovLg6nJmFqfxUYcPqyyyKj91VOc9wzCI6lG6gl10BjvpDnXTE+qhJ9xDd7ib7lA3XcEu2gJt7GndMxB5rRs6oihSmHxKKvL+2UynIIhmLI4sLPZsJNket1EQkQ2NJFTSJIlUSxJJ5iQCigXDsJFhTedgtQOLmMSyslRumJ2By+bCaXaesf2z+en5w8/zyLuPEFJD2E12Hlv7GHfOuHNc/NTWq/DkxhCBsIHbIfD5qx2kJ8sX5bxnGAZvNO/i2eObAFicMY0vl6/DIpnOy6bzuY8wDANVVTGbzeNyzT3f42l3dzU/r3qDqKaQbUvh2zPvJMeeOmGuT+di04W+3xMEgVgshizLgybrTWabLkc/nd7u8/lITU3F6/UOPK9ejvh8Plwu14S287CngW/vfIKoqmCRTLjMDvxKhJSmBbiMdP7zC8uxmkYfUfBO8B3a1XaW25ZTZi4bfkHDgPU3g6cGln0PZtw7/LL+Ogg1gzkVUmbTHmhn6k+mElEipNpS0dFxmp3s/NLOT6wobhgGsVgMs9k8qSf8f5JJ+HByk/Df5Cfhw8lNwn+Tm4T/Jj/9PpQkidraWux2OxkZGQl/ThAMw6Crq4tQKERZWdlAxPhon1fPrZgjsH79evr6+vj85z8/7DLt7e1kZWUNasvKyqK9vX3Ebf/gBz/g+9///hntdXV1A8q/y+UiJyeHjo4OvN6TkU7p6emkp6fT0tJCMBgcaM/OzsbtdlNfX08sFhto759NUFdXN2iwo6SkBFmWqampGdSHsrIyVFXl+PHjA22iKFJeXk4wGKSpqWlAPDCbzUyZMgWv1zvIZofDMSDyd3d3D7Sfr00SVsLItPQFcGenjptNzc3NA+0X0qYwIp1yMoIo4tbCWNuBVBVnSg8hh4/a9mSeeKOTa6f56B/HGMqmZEQiFjddwSi7+3pIQRmwaU55OVPMmawUpnA41MTHgXoaYt0c8tRzoOcYogYz7fnMdxQzM62EosLCcfdTP+P92xvKT83d7exoPEhdpINjkU769BCyLMdTfmg6CDJ5lmzmu/K4esocrAGDYE8bV7Qf5f/MIiFBxGYYfCFmMH36YiKinYaIRMRspyfiZfOBSkL4UcxBAqYQmjmMLEfp9AU4LDUiSwIC8UkGVsmMS7eQaXaRZXKRaXKxZNpckkUb9fX1w9qk69AVlOkOWvHrbo61x4jE1JPLCyIOq0wgoqPrBlZZR9VFLCaBmQUmuro6aOpp4zXPXo6EWpAkiTxnOje7ryCHZNrqm8/qJ+v06wnsfYHogVfpClnRTfYJczylAjfNS+ftgyaqmiP85BU/103347Tol/S3d6pNQSRCogO7xYwY9lPT3DOsTf3CeEZGBhkZGQPHkxzpJb3ufVZKImsWfJb1XR2833OQ3pCXp6vf47XGHdxUtJQpEReWUy5t42fTLMJr/hM2/iumSDfRPh/yy7dinvslegvvpbsvNKKfTFiISlbaAhHCPd6zniO+PevbPHbwMQJKALtk52/m/A0LLAviNs0406aYFsOd66bN18bBYwfpifbQG+mlN9aLZtFo9bXS4mkhoAaIKTF0cypBwQKxINFwNzWdB9h5MC60yqb4OUI2ZFKtqaRZ0shOzqa0rBRLhgXREBFbRLYKWynLLaMop4iWlhZeOvoSP/z4R4TUME6zg7tK/pbepulgGKSo6UwtjWJyqBxS9uNuck+q69NwfoL48XTroiJ+v+koT39whPtWF6PIVjJtdkKxEHsq99Ab7aUv2ocn6sHkMtEZ6ORYx7GBtr5YH7qko+s6qnry/CYIAiaTCV3TUTWVqBYdEMQlUYqnItcNcq25FDgLSHemk5OaA1GQVAmnyYnT5CQvI4+irCL8XX5agh4OhJs5GGzCkOLfZ0hRybNmM9dZQoa1EK+cTFuXRL7JYGp6lFUZQUIdIbJKssBgzH6aI8/h6dVP0xZqo8hdxJKKJfT19Y2Ln9RgO9dMCfPOkWQ6+0SeeFvji9e6iPQ1XNDzXmNTI6/37mVXoA5BELhlynI+lbaAxmP1F/W3d7pNhmFQVFSEruvU1taOyaYLcTyVpmfwT/Pv59/2/J5Gbwff2fFL7klfzqqSeRPi+nSp/DSSTaWlpei6Tk1NzcAD92S36XL00+k2BQIBEkwMsm0pJJnsiESwSiaCagRDEzGpNhZNyxiTIG4YBr1aLzCKSPGu/XFBXLbC1JtG2KgO4Y74e3s8O09EjeCyuIhpsQFB/GxlOz4JyPI5D1clmCAkfDi5Sfhv8pPw4eQm4b/JTcJ/kx9ZlonFYgPj1DbbJzcj4EQkIyOD+vp6FEUZcxr1c44Uv/766zGbzbz22mvDLmM2m3nyySe5996Ts6T/53/+h+9///t0dHQMu95kjRRXVZXq6mpKS0uRJOmiRzq0BqJU9gRItsgsy0udVNEbEVVnd7uXmKaT5bAw60Ra3Q/CH9CitZCvzGTrhjJiikFOisiaWWayUyRSnPKQfW8Lxqjs9gNwRVbyQF3XoWzqinjZ2nmYLe2H6Ah7UBQFk8lEhs3FldmzWZ5ZQZb1ZF3Qifjb67cppEap8jZxuK+RI95GGgOdg7chCExNzmWKIx9DTSXTmsW0lCRK3PZBfdea9tC291k6dYUs0UTOFZ9BLIgLcrphsKfDhz+q4rLIiKEYR1u9HG3po9UTQpPCRE0BFDmIag4iO6LopjCyLGCShDNmVNlly0Bkeb4jnRxbOmYlnc4eM/WdKo3dGkq/RnQiAtJmESjJlCjOkCjJkslKkfmoNsrz28NEohpWi8Tdy2wsKDXzbvNenj2+iYgWQ0Tg5sKl3Fa8ApMgjdpPGAaRLY9jeFsQc+dgmn/PhDue2vp0nvogSCBi4LQJ3LfKRn66aUJEeB3o8tMVilHoslOeYh/RJk3TqK2tpaysLC4G6jq6rqFs+wVGXxNSdgXmRffHoyF1jR2dR1jfuI32sAcAm2ThhrwFXJ+3EIfJOu42af4uYpv+C4LtyNHjmLVWDHs2+pLvQuG1w/rJF1XZ3e5FEgVW5acgnXIcDPfb6wx10uRrIs+ZN2hQ9HxsCiohflX1Jh92VqIZGkWWJPJE6Ap30RXsGohC90V8g7aR6k5l7cq1CKLA3oN7qTpeNfCZzWQj2ZLM7pY9xDQVCQsaMWTMzLXfz/zCYlZNK0B2Cnjc8VqZpZFS0sQ0rLIVi2TBbo7XkjaJpkE1oS/G8dTqa6W+r54iVxHZzuyznssjSoTuYDfd4Xj0tifqoTPYxYu7DtIR7MSdqhE1vETVPhQ9fNbI/VPb7SY76bZ00mxpZDgySLenk+HIIM2WRqo1FYAvvPqFeCpyqwtvxIvT7GTbF7eN2Peh2iNajF3d1WxuP0CV9xRBCgsZ8hQKzdOx+3O5/0o7kjSyPybCfUQgrPPU5jDtHh2LOX4OLMo4eVM8nue9iBrjp4fX83FvHQCfm3o16wqXTIjIVk3TqKuro6ys7Izr7aX0U2/Ez38dfolaXysCAveVXs2N+YsGJkKNZNNIfZ8Iv73T+36+11zDMKipqWHq1KmDHuwms02Xo58SkeIT284PWvfzeOUrBGMR7GYr1tZyrP5s/vb2eZTnuke9HZ/m4+XAy0iCxGeTPjtyiagP/x6qn4fS2+DKHw2/XKQTvEdANEP6UhAE/mP7f/Dzj37OwpyFfHPpN0df7ucyRtM0ampqBkV+JJhcJHw4uUn4b/KT8OHkJuG/yU3Cf5Offh8WFBTQ2NhISUkJVmuiHNxEIhKJcPz48UG+uaCR4g0NDbz33nu89NJLIy6XnZ19hvjd0dFBdvbID3gWiwWLxXJGuyRJZ5xI+gckTmes7cOdoMbS3j/4cno/BUEYcvnx6nt/e6bTypHeAP6oSkTVsMrjY9NY2s+l75pucLDbR0zTcZplZmYkD9RFLrWU0hJqoc/SwINXz+WJt4McaFA51KhiNQtcN9fKsmkWXHYR8ZRaynlJVvqiCq3+CIe6AyzLS8Fy4vs4ve/ZjlTuLFnFHcUrqfQ08OqRzdRoXXRHfLxUv5WX6rdS7srnyuzZLM2cjl2ynrOtQ3E+v72YpnLU00Clp4FDffXU+drQjcGDdkXOTCpSipjlLma6uwAREztae4mq8QkIU1IcZwhQUvESCrKmkx/sQXCkIdpcJ/cPzM5IZkeLB29UpSLbxaKy+DHdF4xytKWPymYPR5o99PZGoRcMdBQ5jGEN407TMDujRKUAXs1PSI1y1NPCwe4WFNVA0eLZB2Us2LQ0bHIKLlMaZSmZzMnKYmZuEhkuEfG0wf5FZVZSU0NsPVrFiukzMFv8fP/jt6jxtgBQlpzHl6bdSIEzY8jvtZ8h/SQIWObeQWTL4+itB6FwIUJm+aj9dHIzF+54yksV+csbkvjdxiCdXo3fbAjzmZUC0/JMF/y8N1K7oun0hOPZGnKdllHZJIriwP+iKKI37MToa0aQLZhm33oyG4cocmXuHFbkzGJn51FebthKS7Cblxu38VbLHm7IX8iNBYtJMtnoU4K0hz1k21JIs568KI7VJjk5ExbcS2zPM6jmJKSYDSlUh/TBN6Dwalj6D+DMPcMmt03EYZYJxlS6wgp5SWfOLjz9u8lJyiEnafj63mP1U9RQ+Z+qNznQewybbOOzpVezLn/RkKl/ImpkIJV3W6iNBkcDiqAQ7AuSoqYwNXUqncFO/DE/YTVMd6iXiBYFBAwiGBjECNHAq3i7HGzsim936byllBSUUB2o5q1Nb50hMvTTL5ZbZWv8vXzyvVU6s61/WZvJNux6Fslyxudv1LzB373/d4SUEFbZyreXfpsFuQvoCnbRE+6hK9hFd6h7IIV5d6gbX9Q3ZJ8VVadXidLrlZFFEZtJQhTi6er7xe10Wzrp9uH/bKOoQ/1fN/wXD//pYfwxP05LPJIsz5U3aJnRHGcOycaa3LmsyZ1La6iHTW0H2Nh2kMY+P93yQY4GDzIzpYAt3XNZllmBXT55XzZRznuntrucEl+6VuapTUEaulR+tzHMPSvtzMg3DVr+fM973liQfz/4AnW+VkyixFdn3MKSzOkXxKbRtp++z/5z5ETyU6o1iX+Yfx+/qX6HTW0HeLr2fZqCXXyx/HpM0pmPIxfj+jTavl/s+z1N0wb6OJRvJ6NNI7VfLjYlBrsmFmty5zLTXchH1QcxxBxerGoh022jLMd19pVPoVuLZyVIFVNHFsSVEBx7I/6+/O6RNxpui7/askEQ0HSN9UfXA3D/3PtZnLd4TH1MkCBBggQJEiRIkCBBggSj55xE8d/+9rdkZmbyqU99asTlli1bxvvvvz+opvi7777LsmXLzmW3Cc6CRRJxWUx4Iwrd4Rj5Q4guEw3DMDjS48cXVTBJIvOyXMiniNv5cj5mwUxAD6BYu1BUGwagGxCMGLyyK8yWIxFMkkCyXSTFIZLijL+6HBZEFCKqxv5OH4ty3CPWfRAEgemuAqS0xRROKeZjTx1b2g9yyFNPtbeZam8zT9a8y8L0cq7Mns2s1GKkkQZHLgCaoVPvb+ewp4FDnnqqvM0oujpomSxbCjNTipiVUkyFu5Bks2PgM90w2NPWR1TVcZhlZmYkDfudiDYX2IYeOHKaZUpTHVT3BKjqCZBqNWM3SbgdFpaWZ7G0PAvDMOj0hjnS3MeRFg9HmvsIBhyEAxDGCtixyzYwq6jmPlSTl6jUS1jqJSZ6keQohrUNRW7HI8IuYFcHuDwO8h3p5DsyyLOnU3Di/a6uqhMRIWH+sPtNrLIZm2TGKpn5zJTVXJM3/7z8JbrzkEuWoR7bRuzgK1hX/xWCZDr7ihcRt0Pky9c5+cOHQeraVZ7eFOSmhTaWlJ85yehi0R6MohsGTrNMsmXs35ce9qIceRsA04wbBk3Q6EcSRJZnVbA0czq7u6p5qf5DmoJdrG/YxlvNu5malMPenlpiuopVMvPQjFtYkzv3nG2Sc2ejT1mBemwrMesiLEVXIh79HTRugNbtMP/rMPNBEE9eYgVBINdppaY3QKs/MqQofiHpjnh59MDzNAW7MIsyX6u4lYUZ5cMub5WtFLgKyE/O54PQBzhVJ0liEjdl3YR5unlgubASZnd9Hb/ZupE/+P8elSgSJjSiiJi4tuhGMpIdRNUoES2C0q0g5oqkJ6ezYvYKKqsriagRImoEzTgZndff5o16h+reuKDqKo3eRnRdH6jP/Tfv/Q2FrlPqcw+DLMpk2DNIs6eRYc8YELVrWjRsjnSSTCncO3cmuclZOEyOEa89Y+WuirtYWbiSRm/juEWS5drTuLt4NYFjC5ADdcTSDtJlNHEs0Mqvq9p4quY9lmRO56rsOcxwF07YGkpWs8Dn1zh4dmuIoy0Kf9gS5PYlduZPMZ995VHQFurlRweepTPch1O28vCcu5nmyh+XbX8SMIkyX562jgJHBs/UbWBT2wHaQr18vuw6gmr0jElLCRIkSHA+pFmSKbFmsr4ynj1sxfTsMV+/evR4yZ+zpk4//haoIUguhqwFwy+nhiHWF39vi0983N68nY5gBy6Li6tLrh5T/xIkSJAgQYIECRIkSHB5I8sys2bNAuIlan/5y18yb948Nm7cyG233UZxcfHAss888wx2u52KigrKy8uJRqMsWrSIX/7ylyxZsgSIl7yWZZn09HTsdjvbtm0bWP973/se//Iv/0JLS8tAYPNXvvIVnnjiiYFMa+np6YNKmvWv96tf/Spe5lBV+bd/+zduueWWC/m1nBdjFsV1Xee3v/0tDz744Bm1ER544AHy8vL4wQ9+AMA3v/lNrrrqKh577DE+9alP8cc//pE9e/bwi1/8Ynx6n+AM0m3muCgemhyieKMvTKs/giDAnMxk7KfVeJMFmUJTIbWxWqojx9D0mbjtAjEVFN0gnuFQQDegL6jTF9Q5fkrGcLNZYMpUnd5AlAN1vYgxy4Bo3v/qdog4bcKgyGOLZGJF1kxWZM3EE/XzYcdhNrcfpCXYzfbOSrZ3VuI2O1mZPZNVWbPPGn18rhiGQUuom0OeBg576jnS10hIjQ5axmV2MCuleEAIT7cOHwFR1ROgL6IgiwLzspKRh4mSGQ1FyTa6glE8EYXDXT4WnjbpQBAEstx20pJsTMnKZHqeyuHGCPWdCuGYTkzV0VVABTGUhJk0UqwFlOeamVVkJyVdw6f7aAp20RLspinYTVekD28siDcW5LCnYWBfmq7TFe2LpyM2QNV1wlqUBXlX8JXpN43bILtp+lq01kMYwV7Umg8wTV87LtsdT6xmgQdWO3hlV5i9x2K8tidMb0Dn+vnWM6LrLwat/ggAuUnnlmJGOfgqhhpDTClAKloy4rKiILIkczqLMsrZ213LS/UfUudr473WjzEMA4dsJaCHebzyVeaklpzX78JUcQO6pxHd00RMScdy84sIO/8FOj6C3T+G2ldg+fcha/7AOjlOCzWeAJ6IQkjRzjjfXSiO+dp49ODzeGNBXGYHj8y+mynJw0egn8rh2GGa1CYkJK6yX4VZiIuLmm6w73g37+xroq7dB3oFFeJXOar/Cp0oFsHNQvtf8tN1f0eqc7DvG5QGNoY2UjCngB8v/zGpUuqJbWpE1AhhNRwX0U8I41HtlPdqdPSfayfb+j8/9S+shTEMA1GIp8yVBAkDg4LkAkpTSweJ3af+ZTgySDIPPaGousfP7iYPnT1BDtUKlC91nqf3hibbmT2uaVV1w+DFHSEaunSy5RKWZKUQtEKXWk+Nv5qWUA9b2g+xpf0QWbYUrsqZw5XZs0m1JI1bH8YLkyxw7yo7L+8Is68+xos7QoRjBsunn9/koGpvM/9+8AUCSpgMq5u/nftpcu1nEUkSnIEgCKwrWEyePZ2fVq5nX08dX2w/iFUyYZet5z1pKUGCBAlOpTekUN3mRRQElk/LGvP6PdooRfHq5+Ov5XfDSPfb/VHi5hQ4kX3sxcoXAbi5/GbM0vhM4kqQIEGCBAkSJEiQIMGloTcQodMbJtNlO2NM9Fxwu93s27cPgBdffJF//ud/Hsjgfe211/LCCy8MWr6+vp6Kigr27NmDqqoDy/Rv43vf+x7p6el87WtfG3J/FRUVvPDCC3zta19D0zR27dpFamrqWfv5ne98h6997WscOXKEVatW0dnZOWyGtkvNmEXx9957j8bGRr74xS+e8VljY+MgQ5cvX84zzzzD3//93/Pd736XsrIy1q9fPzCz4XJDFEXKysouqbMz7GbqPEF6wjF0w7gkItho6QnHqO4NAlCe6iTNNvQgwFTTVGpjtfTKTZjNM4hGBWxmAWLgtIj8ze1JSIKA54Qo3hvQ6QvoA/+3tRrk5SvYkxUaG+FYx5lClCwKuBxCXCS3F9BxVCHVqeF2iLgdDm4qWMJNBUs47m9nc/tBtnVW0hcL8HrjTl5v3ElJUjarsmexPLNiUGT2udAd8XLIUz8QDe6NBQd9bpctVLiLmJkS/8uzp48q6qHFH6HJFwZgdmYyDtM5JYoYQBAEZmYks72lF09EocEXpthlR9UMWns1jnWo1HeqNHRpKNrJmo1Ws4nUJIHiDAm7RSEU89PU3Uttu5dAxGDvMdh7LL5sTqqdGXn5XJ0/m2nTXEiyQUuwm+ZgN83BLpqC3bQEu2kN9aDpOgLxlLEyImbJxO1FK8Y16kyQLZhm3URszzMotZuQ8uYhJmWO2/bHC0kUuH2JjVSnyHsHImw9GsUT1LlrmR2zfPHOCcGYijeqIAgCOY7R3QSceh5V2w6htVeCIGKeewfCKM+toiCyMKOcBellvFy/lZ9VvoIOhLUYhmEQ1RSePbaR24tXkmM/+0V9KARRxrzws0Q3/RS9rxWltQrzuqeh5mXY/SPwVMEbn4Fp98DCvwZLMlZZIs1qpiccoy0QYWrK+Z0rRsOermp+VvkKMV2lwJHBI3PuHnHizKl0qB18HPkYgEW2RaRJaUQUjS2Vbbx3oJluX3zCgygKrJiey21Jf87TW+fjibWRYs7hm9evHPLmr8hURJGpiAalgW3hbaxzrEMURCRRwmF24DjPc+hoafG1sOzXywjEAiSZkwgoAZxmJ3+864/nLDgHFZ0km4lqX5jK6k6WlGWRl3Zx7Dkf3t0X4WCDgijAvascOJwWDnb5ybTP4c9nXEWtr5VN7QfY1lFJR9jDc8c28cLxzcxJncJV2XNYkF6GLE6c1MGSKHDHMhs2i8D2qihv7g0TihpcM8cypijBnoiP9rCH9lAvv6t9D0VXmZKUzV/PuRu3+cJMeDhfJsK96GiYmzaFb8+6k69vfxxV1wgbOrph8PiR85+0NNmZLD5MkGCiI4oibVE7IFBRkDLmASnd0OnVeoGziOKeWuj8GAQpXk98OAwdIidKy52IEvdFfbx3/D0A7phxx5j6d7mTOBdOfhI+nNwk/Df5SfhwcpPw3+Qm4b/JT78PY7HYQJthGMTUoctA9rP1aDu/2XCUqKJhMUl88erprJg+8hijWRZHPVbl8/lwu92jWhbiUeZLly6lubl51OvceeedvPTSS3zta19j48aNrFq1ioaGhrOveIIZM2YgyzLd3d1kZk483QTOQRRfu3btQKj86WzcuPGMtrvvvpu77z5LXa3LCFVVMZsv3QzvJLOMRRaJqjq9YYV0+8ScbR5SNA50+jAMgxynlcLk4aPas6QsHKKDoB7kuis9vLsxnYhiYDGJfHqFnRRHfCA+2S5SNETAtq4bHOjw0xKIMGuahjVqwxcAT1DHE9DxhnRU3aDHb9Dj0zEwEBDglHORSRJwO0RSHMmkOlfx2ZQVdFHP0VAlRwN1HPe3c9zfztO1G5ifNpUrs2czP610QCTwhnR6/DppSSIu++ALoi8WpLKvcUAI7wh7Bn1uEmWmuwuYeUIIL07KHnMacF9U4Uh3PHXg1BQHGfbxSadtN0mUpTg52Olnf5ufzftUjrcbg0RwALtFoCRTjv9lyafVBE8BCokqGjVtXo6cqEfe0B2grTdEW2+IDQdbEAQozEhiRr6bivx8lhbNxHIi0rY52MVD235GWI1iFk3oho7dZCXbljIudp6KlDMLKWsaWkcVsQPrsSz/8wmZSlgQBFbPspLiFHlpR5jKJoXfhIJ87io7TuvFuSlrDcRF0zSbGYs8+n2qqopJ0FAOvgqAqewqxOSxi5SCIHBVzhyeqfsAvxICDCJavL75praDfNhxmJKkbJZlzmBp5oxRi8X9iDY35ivuIbrzt6j1OxFTi5HL74zXFt/9Y6h5CaqehYb3YPF3YOrN5CZZ6QnHaA1EmOK2X7DfjmEYvNm8m2dqN2BgMCe1hG/MvH1QXeiRiOgRNoc3o6MzxTSF9Gghz++pY1NlG+FovGSD3Spz9aw81szKxe2Ib3fVjBxae3zkpiWTNkJ2gCXWJbSpbfRoPRyOHWa2Zfb5Gz1G8pLz+I/r/4OH//QwISWE0xyvz32ugrhhGHiiClaTRHaSleZWH09urOI7d8yf0BPUdlRH2XIknoHkjqV2pmbLBMJxHwdjKqpuUObKo8yVx/2l17Cz8ygb2w9wtK+JfT117OupI8lkY0XWLNbkzL1gmVPGiigIrLvCisMi8N6BCBsPRwhFdW5aaEMUz+6PD1r38/iRV/HFQsR0JZ4dJmsmX595K5YJHsl3qe9FR4sBWCUzkhD/vSWZbIS1GB1hzydaFIfJ48MECSYymm6wraodgJXTR5ch51R8ug/FUJAFGZc4wj1i9YnIjII1YE8ffrloL+gxEE1giYvsr1e/TkyLMS1tGhUZFWPu4+VO4lw4+Un4cHKT8N/kJ+HDyU3Cf5ObhP8mP6o6uGRtTNX56i+2DLu8put0+iLouoEogD+s8O+v7ud3G6uQRpgg8T9fXjWgcQxFX18f8+bNIxQK0dPTMyjd+Xvvvce8efMG/t+yZXD/wuEw27Zt4yc/+cmw2z+d9PR0zGYzra2tPPfcczz44IM8/fTTo15/586diKJIRsbEGB8civMLFU0wCF3XOX78OGVlZUjSpYmYEgSBdJuFFn+Y7nB0Qorimm6wr8OLoukkW0xUpA9f1xriNk0xTeFg9CBiWiPfvWsKvX6d1CEE5qEQRYE52UlE2zS8EQWnI8KamSlIJwbFNd3AFzLwBHV6fCo19R3I9jS8oXhKdl9IR9EMunwaXT7tlC3nAXnksxKvpYY+SxU+oZPN4Wo+bK3GabKxOG0GecIMNu8XCRpenIKbu5e6saW0c6gvLoI3BjoH91cQmZqcwyx3PCV6mSsP01nq245ETNPZ1+FDNwwy7BamuO2jWm84If/MSHCV7DxISjJQLWEUzYzdIo4ggg+NxSQxqzCVWYXxyN1gROFoS19cJG/po90ToqHTT0Onn7f3NiFJAqXZLmbku5mRl8I3Km7n8cpXCMQiOM3xFKwXYlBdEARMs29F7/5P9J7jaM0fIxdcMe77GS/mFptx2UWe3hSkuUfl5+8EeGC1gwzXhT1HGYZBayAutOUmjX4SRv95tDhSiRHxIzjSkMvWnHM/0qzJPFRxC48feZWIFsNpsnNN7jx8SphDnvqBCS3P1H1AuSuf5ZkVLM6cNuooUCmzHFPZGpTqD1AOvIToykFMyoJVP4DS22H796CvDjY/AjUvkrn0n5DEJMKKRl9UJcU6/nXpNUPnyeo/8V5rPMr72tz5PFi+dtSTaXRDZ0t4CyE9hFl1ULnHze+qd6Hr8ckuWW4b183NZ/m07DNu2tx2E10tvbjPklbaJtpYbF3Mh+EP2R/ZT6FciEsa26SE8WA863MHFA1V05FEgbsXl3DwWA917T42H25j9azccez1+HGkWeGNPfEMItfOsTKvxIymabQ0NmCzpxNWdfqiysBEKotk5sqcOVyZM4f2UC8b2w+wue0gfbEAbzfv5u3m3UxJymF1zhyWZ1Vgl88/TdT50D85yGYReH13mJ21EbzRMNddIREzYoTVKCE1SkiLElajhLUYITVKT8THK43biGkqhmGgYxDRYjxQdt2EF8Qnwr3oaMm2pWCTLaCCTbIQUqPYZAtZF2BC23gz0mTH82Uy+TBBgonMjup2Wrr9uJNszCsZe7mL/tTpqWIq4nD3UFoM6tbH3087SyBAf+p0azac2N6LR+Kp0++cceeEnGR7KUmcCyc/CR9ObhL+m/wkfDi5SfhvcpPw3+Sn34cFBQWjXkfVjRNlGuPjUSLx/zXdQDqPYYNT06e/8MILPPTQQ7z3Xjzb1FDp03t6eqisrGTevHkcO3aM66+/fpBwPhruvPNOnn32WXbv3s0TTzwxqnV++MMf8qtf/YqkpCSeffbZCf18kxDFL0My7Oa4KB6KYaQaE+oHaBgGh7p8BGIqZklkblbygDg9Ev2ieIvawvKkGCX2sQ20i4LAnMxkdrR48EVVqnoDVKTHa6FKokCKUyDFKVKULpCkhSkrsw1ctFTNwBuKR5X3BQ36TkSYe4LxP3/YRnp0DunROYTEHnrkKrrlKjoiIV7z70ExtqG5IvSHnu87bMUmm+k3WxAgRcogz1RAnrmAPEs+dsGC5BfoDEFPm4okashSvK/x15PvZVGI/3/iff9y8WXgULeXiBqvXzwrc+QJCP18VBfjua0hYqqBWYY1s61YTcKQ6dAB+rrNpLmjOFJg/mqZuTnO846MdFhNLJiawYKp8VlFnkCUIy0ejjTHhXJPIEpVSx9VLX2spx5V11Gi85DlEJKQDDnpcIF0KNGegjztGpTKt1EOv4GUNR3BPLrJBpeC4kyZv7jeyVMbg/QGdH7+pwD3XemgJOvCXQJ6wwpRVUOWRDJsY8tMYAq2o7fsAoR42nTp/ITjNblzmZNaQkfYQ5YtZWCyhC8WZFdXFds7j3C0r4lqbzPV3mZ+V/suM1OKWZY5g0Xp03CYRj7fyOXXonsa0brqiO35PZZVDyHIFshZDLe+Aod+Dfv+B9p2IL1yCzOmPMDhnHtp9UfGXRQPqVF+cvhlDvQeR0Dgs6VXsy5/0ZiuAweiBzkebiIY1qn+IImYPz4oPC3Pzdq5+cwpThuXyOcppikcV47TorawLbyNGxw3XJLr1XjV5/ZE4lkI3BYT6ck2bl9Swh8/rOWFHceYPyUd1wSbpNbUrfLc1hAGsHCqmatmDj5O3RYTYTVKX0QZMrtItj2Vz0xZzd0lV3Kg9xgb2w7wUXcNx/xtHPO38VTt+yzOmMbqnLnMcBfgiQZoD3vIPuUYHC2GYRDVFUJqdEDIDmv972MDovap7f0Cd/hEW29mhJ5QjN298OxGSLKJw5Z9jWoKETU2UJIjSbYiCALdES+ZNveY+p5geNKsyTw04+SkJZtsuWAT2saTwfdIAp9eYWfB1Il1fCdI8ElnS2Ubj712gEhMI6pH2FndyaqKsUWLj6qeeOMGiHjAngV5q4ZfTotALJ6KvT91ek1PDYc6DyEJEjdPu3lMfUuQIEGCBAkSJEiQIMHFxSyL/M+Xh7/n9wSi/PXvthOJqdgtMqGoitUs8+gDy0hxDj82bh5DdtWbbrqJBx544KzL9dcU7+rqYtmyZbz66qvccsstZyz3+OOP88tf/hKA7du3D7TffvvtVFRUcN999416rLa/pvhkICGKX4ak2syIgkBI0QgpGg7zxHHzcW+IjmAUQRCYm+XCJo9utpRbcpMmpdGj9VCv1DPdMn3M+7bJErMzktnb0UezL4zbaiJ3FLXlZEkgLUkiLWnoviqagfeEQO4J2OgL5tITWEWNv5HK8Me0mPYBOnFR3ECVNFDcuPRikrV8krU8TEZcUG0/8QfRMds3FFlZCmnpGroO9cdk3lP8J4X0E+L6yfdxIV03DA42KGgaiCIEI/DyjjCpScLABIah0qF3hqIc6PDRHY3gj1pxjbPYl+K0sHxaNsunZWMYBh3e8IlU630cbOyhrSuErssIJOMHHn1lP+8faGZaXgolmUmUZCWTk2IftzTG8pSVaE0fo/s7UCrfwjzvznHZ7oUiI1niL9Y6eXpTiKYeld9uCHLHUhvzSi7MQH5/6vRsh2VUE1/6MTQVV2s81YtcuAApfcq49CfNmnyG0JJsdnBt3hVcm3cFPREfO7uOsq2jkmP+Ng72Hudg73F+I77DnNQpLM+sYEF66ZBRooIoYr7iM0Q2/QTd30XswMuY598Tv2mQTDD3L6FkHWz/PrR8SFbNrzA3vEl9xSNoaevG9P2MRHfEy6MHnqcp2IVFMvHQjFtYmFE+6vWjisa7xw9TZduBphu07ctBDVpZUp7B2rkFFGcmjUs/+xEEgaW2pbwaeJVOrZOqWNU5ndsnCp5IvM6Q+8S57+rZeWyv7qCh088fPqzlL9dOnLSoPX6NpzYFUTSD8lwTtyyynXGT67LKtAXjovhISILI/LRS5qeV4osF+bDjMBvbDtAc7GJrx2G2dhzGJEp0RbwYgFmUublgKeXu/EGidViNDYjap7eHtSj6MKV7RosgQbJNwB82UFWJSNhCYYoNp9mCTbJgly3YZDN22Ypu6LxwfAuKruKQrcR0FatknhQRzJON4SYtTVRaelSe3hQkHIvP/jYMg+e2hijNkcc9YjxBggTnRm8gws/frSSiaCemJBv84r1KZhaOra54vyieLo2QEr36+fhr2R0gjvBcG44/4WFygxwvG9YfJX51ydWk2lJH3a8ECRIkSJAgQYIECRJcfARBGDHNeXaKnb+4roJfvFdJWNGxWWS+fG0F2SnjF0i3bds2pkwZ/Vh5RkYGP/zhD/nhD384pCj+0EMP8dBDD53Rnp6ezo9+9CNWrlx5Xv2dqEwctfQyQRyhPsDFQhYFUqwmesIxusKxCSOKd4Wi1HqCAExPc445QnKKaQo9Wg/HlGPnLJyk281McTs45glypNtPslnGedr3M1YfmiSB9GSJ9OTTT4oz2dVm5293H8EwBATBwDAEEHQenHIT5UlFqLqBpoGmx1NsqFo8nbumM/Be1UHTTrTp8ch1TT9zOe3EZ/HlweFUSUuPp3tvaTERiYpA/PN4Jc+hUVSDmHpCwtcZiKQrzpCZW2weNh16tsNKlzNGWyDCwS4/y/JSxk3sOx1BEMh228l221kzK4/KZg//8IfdCAKoqhb/XnWDhq4AbZ4wG0+sZzVLFGcmUZKZTElWElMyk0ecqTViH0QJ05zbiG79OWrjHqTCBUipxeNl4gXBYRX54jUOXtge4nCTwgvbQ3iCOqtnWsY1QlfVdTpDJ1Knj2HwEUA7thk51geOFEwV68atT2cjzZrMuoLFrCtYTEfYw7aOSrZ3HqE52MXe7hr2dtdgkUzMTytleVYFc1OnDCprIFicmBd8lui2X6I170dLLUYuXnpyB8mFsPZXUP824o7/D7uvmRl7vkm4912cK/5+5BqUo+CYr41HDz6PNxbEZXbwyOy7mZI8uoiovmCUDYda2VRdT9aSWmTdINiawvKMWVxzTd6YBpBhbOdQp+jkCusV7Azv5KPoR+Sb8nGKo0tdP5EwDGNAPO6/tkmiwIOry/mX5z9id00nK6ZlMbto7Olbx5tgROfJD4KEoga5KRL3rLCfUWNbFEXclvjv2xtV0Q1jVBOKks0O1hUs5sb8RdT529jUdoDN7QdpCHTEt4FAkAhP171PljVlxJpKQyEKInbJjE0+IWJLJ4Vs24l2m2zBLllOWcY86H13n8Qzm6JEggaZSHx+jYPkIcTMYmf2pItg7mci3IuOhaEmLU0kfCGdw00KhxsVatoUglEDgfh0R5MsEFMNev36uIrik82HCRJMJDq9YQIRBRGQJIEkq4mwotPpjYz6nkY3dHr1eGT3sJHigVZo2Rp/XzbC5FjDOCmK2+KZaVRd5dWqVwG4Y8Ydo+rTJ5HEuXDyk/Dh5Cbhv8lPwoeTm4T/JjcJ/01+zsWHqypymFmYQqc3QqbLOuYx1aHoryluGAayLPOLX/xi4LPTa4r/9Kc/PSPl+5133sk///M/s23bNpYvXz7q/X7hC18Yst3j8ZCfnz/w/6OPPjrqbU4UBMM4z9Cbi4DP58PlcuH1eklOnriDZhOJBm+Iqp4AqTYzC3Pcl7o7BGMqO1s9qLpBfrJtIHX5WAjrYZ73P4+Bwe3O20mWzu23YBgGH7V76Q3HsJtklua5kS/Qhaon4uMLG/8LXyyKpJvRxBjJZgu/Xf1XF3QA2B9T2XXi+y5MtlOUZD8hpJ8inA8jyPvCOs9vDaOcSJ2u6mAzC3z3LtdZB3wVTWdbSy9RVafQZWN62vhGlg5HbyDCN3+zlXBUw2GRCUZVLCaJL107nS5fhOMdPuq7AsQU7Yx13U4LJZlJTMlKpiQzieLMJGxjmEgS2/ciauMexKQsLFd9HWGkKJEJgm4YvLsvwpYjceH6ihIztyy2IUvjI4y3+MMc7vJjN0msyE8dteCu+zuJbPoJ6BrmBZ9Bzps7Lv05H5oCXWzvrGRbZyWd4b6BdrtsYVHGNJZlzmBmSvFAvW6ldjNK5VsgSlhX/iWiO//Mjcb89Gx9FGv1H5AEsNpdsOBhmH7PQI3JsbCnq5qfVb5CTFcpcGTwyJy7SbeevT53U3eAd/c3s6OmA03XKVjaRFJGGLfk5p7023Caz23CyFgxDIN3Qu/QoXaQK+dyrf3aCVX2YzSEFJUPm3oRBYE1RemDJgQ9u7WWP+1rJjXJwr/eu3jEWaUXmphq8Jv3gzT3qLgdIn+x1kmSbejfnGEYbGzsQdF0FuemDETAj5V9PbX87e5fo+kGuqGfyJlisDhjGrmOtFOitOOvVmmwyN0vfttlM2bRNC6/jY4+jf/bEMQf0XE7RL5wtWPIbDA9Ed+kiWBOML54AieF8KYedaBd0w18ofijk8MqEFMMLCaR796VnIgU/4TzSXlenQx29gYifOPXW/GHFWxmCVUzsFkk/vuLK0Y9KOXRPLwaeBWTYOLepHuHvvZ8/DP4+KeQsxRufHL4jUV7oO8QCDJkLAVB4v1j7/PVN79Kuj2dzZ/fjDQJnh8SJEiQIEGCBAkSJPikEYlEOH78OCUlJVit5y9wJxg/hvLNaJ9XJ0YI8WWCYRgEg0EcDsclH9DPsFuo6gngiSgouo7pEs5OUnSdjzt8qLqB22pietq5RQHaRBu5ci5NShP7ovtYYF2AQ3SMeTvCifri21s8hBSVyu4AszPitbbH24dp1mS+NedWfnr4FSKaglWy8PWZt17QwXVF09nX4UXTDdLtZqanjd0WiywO1Mu0meP1Mkcz2GuSRGamJ7O3vY9Gb5gMu4U024Wvs5nqtPLlayv4xbuVBGMqNrPEl6+rGFQ7UNMN2jxBjnX4Od7h41inn5aeAH2BKB8Honx8rDu+oAC5KfaBaPKSzGQK0h3DRjSaKm5Ea69E93egHvsQU+lVF9ze80UUBK6fb8PtFHl9d5i9x2P0hXTuXeXAZj7/332rP546PTfJOurfnmHoxA68DLqKnjoVMWf2efdjPChwZlDgvIq7S67kmL+N7Z1H2NF5hN6on01tB9jUdoBkk50lmdNZnllB6ZQVSL31aO1HiO75PdarvoFgsg3eqDkJ28p/ZF/KNUw98mPMoVrE7d+D2pdh+T9D2ugyYRiGwZtNu3im7gMMDOakTuEbM2/DLg8vZhuGwaEmD+/sa+JIk2egffqiAK5CFYfJyc3OG3FK5yaIn8s5VBAEllv/f/b+OzyO87z3xj/PlO276B0EAZIAq0hKpBolqlvFkm11O44jn9TzJrLPdU563ryOHf1+zqucnDQ7Ts6x7NixYieyJdmSLdmSVShRoiiJlNgbWAASRK/bd9rz/jEASIgACKKQWHI+uPbawezM7DN77zwzO9/n/t4beD75PO1WO0fMIyzxLZnW+18oRuqJx/zaGQ4Zn7qqgW1HeuhP5Hj+/RYe2rD4QjQRx5H86O00bX0WQZ/g8zeHxxXET49hoV+nJ51jMGdOWxRfEC7Hh58hM4OwdaRqUeAP8UerH75gQnNFocpv3x7h315L0pd0eOLlFI/cHKa6eKwoMd8zmMdjPl2L5hu9cXtUCG8fGDuIrq5UY2WdzsoFOkc6rdFrJL+uTPkaaap4MfTwmBnFkYBrW/jLfWRM2/1dcNuKaVmnl6gl4x+Hjg3Nrv05TQ9NvrFMh/scrHBreXDKOv1TSz/lCeIT4PWF+Y8Xw/zGi1/+48Uwv/Hil9948ct/RmKoqt61+sWIJ4rPIo7j0NbWRmNj4wU/YEK6SkjXSJsWfRmDyvCFGckipWRPd4K0aeHXVNaUF8yopnNABEjIBDtzOzlsHGZjaCONvsZz3o5PVVhdHmNbxyCdySxFAZ0FseCcxPB81suUUrK7J+7eANLdGurTOfmuW+xjSZVGf8KhOKqc083e0pCP2liQtribLXxtbdF5GZSxcUUVy2oK+GDvIa5Y2URZwdh6HaoiqC2JUFsS4YZhsTxn2rT2JDjWneBoV5yjXXH6Ezna+9O096d5+4BrdahrCnWlERqGs8kXVcQoi7mCr/CF0Fd+HOPDp7EOvopavRollB91Z69u9FMUVvjPt9Ic7bJ44uUkj9wcpjA8/XilTdsVCAVUncPNR/v4Npy+FlB9tMfWsniemZgIIVgcq2ZxrJrPLr6Zg0NtvNO1j609B4ibaX558gN+efIDSvwxrildwhWDJ6hLD2B8+CN8V34O8ZEM8JCuoZWvZmf0W1ze/zPKDvwL9OyE5++HFY/AFf8NjDjEWyG2EMKVY9a3pcN3D73Mq+0fAnBb9eV8vun20Yz1j2JYNu8e6ublnSdo708P7xOsW1zGurU6+wPvACobQhum7cIB0z8PxtQYawNr2Z7dzvvZ96nRaggqwbOvOE8Y+Ih1+ukEdJVfvaGRr7+wh5d3tnF1Yzl1ZefHRWMEKSUvbM+y/6SJpgg+d2OYsjNKfricHsPCwLAonjXh7OYD4yIsH1rbEkTxPqRiIWwV/eQShDX3A6Ymozii8Fsfi/C9TSk6Bmy+/WqSz90QpqEivy+N59O16HxHSkn30KmM8K6hU0K4AOrLXSF8Ra0+xmJ/JtdIU8GLoYfHzDnb74KzcbooPi7t77j26b4YLPzYxBuyc5BzbdgJur8/+tJ9bGrZBHjW6ZPh9YX5jxfD/MaLX/7jxTC/8eKX33jxy39GYvhRK3KPi4P8vvPnMSllIR+tQxa96Qsnih8ZTNOTzqEIwdqKGH5t+jcOU06Kg8ZBwLVezcosm9Obqdaqp5UxXhTQaSoOc7AvyYG+pFtfXJ8b8fZ8ZZsdGUzTmzZQFcGa8hg+dfr7UxCa/o3epcUR+jMGadPmYF+SVWXnJ9OuOOJnYXGA4inWCffrKk3VhTRVF47OG0obHOuOc6wrMfqczlkc6YxzpDM+ulw4oA/XJY/SUL6QRYULUQZbMXc/h++qz+fNSMCmap3fvi3C995I0R23+d8vJfm1G0PUlEzv9NCRdLPEiwM+gtrULvxkNu5ajgPa0ttwrPMrGJ4rilBYXljH8sI6Hmn8GHsHWnmnex/v9x6iLxfnhZMf8ILqUKZYXNW5m+v2/4L6ceqjV0cCDGZNmqseoHT5JxDv/b/Q8gvY+x3Y9z3I9oF0QAvBTX8LTQ8CkLZyfG3vj9nVfwyB4LNLbuHjtVeO+52LZwxe39PO67tPksi4wm3Ap7JxeRW3ra4lGLH5afKnIGGZbxkNesPcfniTsMK3ghazhT67j3ez73JT6KYL1pZz5ZQoPr7Qu7a+lHWLy9h+pIfvvXGIP7v/ijMyyueSt/bneLc5hwAe3BBkYdnUju/CgLvcYNZESjmtfm3v8QHM7mKCvevBlyMkIqhO4Jxqu84V0aDCb94a4ck3UrT2WPzb6yk+fX2I5bXTy4r3mP9IKekYOCWE9yZOCeGKgEUVp4TwcGDia6CZXCN5eHicH871d8HpnFUUP/Qj93nJp2AShx6ynYAEPQaa+3v1+YPPY0ub1RWrWVKcX844Hh4eHh4eHh4eHh4e+Y4nil/EuKK4K5JO92b2TOhK5Tg6kAJgRWmUAv/MbjInnAQWFjo61vBfWqY5bh5nuX/5tLZZFwsykDXpTuXY2R3nqsr8skk9ne6PfN6xGX7eM0FVBKvKYrzfMUB7IktZyE9F+PzUJ54pBSEfa+tLWVtfCrg30LuGMqMi+dGuOMd7k6SyJnta+9nT6mZ/lCg1/G70EIH0Lk6YmyhefAV1ZRF8UxSGLyRVxSr/1x0RntyUonPQ5luvpHj4unMXhqSUtCdPWadPFWPPT5FmFqWwBmXhtXDkyDm971QYSjv0JRxKZjmrT1NU1pQsYk3JIn7DttjZf4R3uvfxQe9henwWP8sl+dmRV6kbOMyG2vVcW76C8mAhABVhPwf6kiQNi4ReQuyWf4QTb8Bb/zd0vOcK4noIzDS88nsQKKG3sIm/OfBzTqR68Ks6jy7/JOvLms5oV3t/il/ubGPLwS4s2wGgOOrnttW1bFxeRcivYUubl1KvkZM5StQS1gfWz9rnMh0UobAhuIEXki/QarbSarayUF94Qds0FbKWTca0EeKUiDwen924hL0n+jnWlWDTnpPcunqcevNzwK4Wg5d2uMflnVcEWVU39QztmM+t4W3YDhnLIXQO9dBtR/LSjhM8s/UojgQsP4rlJw0UhQXlBfOjFlPAJ/gvN4d56u00B06a/MfmFPddHeLyRRc2k91j9pBS0tZ3yhp9IOWMvqYqgsYqjRULdJbX6rNSQsTDwyO/caRDvz1yfT+OKJ7ph+OvuNOTWadLCRnXdWokS1xKOWqdfv8yL0vcw8PDw8PDw8PDw8PjfOOJ4rOIEAKfzzdvMkQLAzqa4t7MjucsCqZZD3Q6JAyLPT1uVu3CgtA5CWQTEVWi6OgYGKPPDg5bM1vptDu5wn8FBeq5+bsKIVhZFiVpWKRNm719KQr1+RPDqZIyLHYPf951BcFzsq2eKwoDOvUFIY4Nptnfm6AwoOOfQeb6VJiLY1AIQWVhiMrCENcurQDcuu1tvclTtuvdCboGYHNmCdf7D+I/8gv+dqeFpfioLQmP1idfVB6jsig0JkO0P5mleyhDeUHwgmZNFoRcK+H/3JzicKfFD95McdcVQTYsm/pghsGcSca0URVBRWhq69md+7Db94BQ8K25H1R11mLoSEk8LdlyIMcvd2YxbYmuCjYu93NZvU5QFwR8gqDPfZ5p5q5P1biybClXli0lY+XY3tvM2/t+xq5UD8cHTnAil+Cpo2+wJFbNhooVXF22jPKwn85klvZk1h3IsuBGuP6v4Md3g2W4tptSgp3j6Ctf5G8K1zOkBSlQVP44GqWh401In4CCemRkAfs7U7y048ToYA2A+vIod6xdwLrFZWP28YPcB/TYPfiEjxtDN6KKmQ/gmOkxWKwWs8q/il25XbybeZdKrRK/mN8DakayxKM+DW2SUhGFYT8PXLOI77/ZzLPvHuOKRWUUTSN77Vw41mXxzNYMANcu9XPdFI7n02OoKIKYX2MoazKYNacsip/sS/Ht1w7Q2p0A4LK6Ytr6UsQzBjiS4oifyHm8Jjkbuib4lY0hfrw1w44Wg2e2pskYkpV1+pwMpplL4hlJTzpIRUZSFLnQrblwOI7keK/N3uMm+9pMhtKnhHBdFTRVu0L40hqdgD6/rvnm2+8JD498ZbrH0qAziI2NT/iIKuO4Fx15DhwLSi+D4qUTb8gYADvr1hEPlAGwt2cvzf3N+FQfdzfdfU7tutTw+sL8x4thfuPFL//xYpjfePHLb7z45T9eDC9uhJTzrHDrOMTjcQoKChgaGiIWy99M3gvBzq4hulI5FhWFWVJ07hbj08GwHd5tHyBj2hQHfayrLJi1DqTZaGZzejMmJhoalVolfXYfEomCQqOvkTX+NedcizaRs3i3fQBHSpYUh1lUeH4+q9nAchy2nhwkbVoUDX/eM6nbPps4UrL15ABJw6Is7Gdt+fRqnOcDqZxJa2c/0Q/+D0p2kPeNxfw0ufKM5QI+lYVlURoqoqSyFr/c2YZhOwR0hd+5bQUbh+udXyhsR/LT9zNsO2IAcG2Tn7uuCKBMQTDe0xOnPZGlOhqYkmW+tHJkX/97ZGYIbckN+FbcNa32DqbcLPD+pPs8cNpzznLoT0ikdGtojzwXR88UwXVVEPIL/LorlI+I5R8Vz4M+QUAXBP2npn0a4363pWXQ/+bXeT/ZxfuBMAd0PxL3tCsQNERrKFHrWFawmDsaatxjN9UJ378aJxfHQUO1k2wLLeAbFTdhSFhgp/ijxG5KnZz7HkDWsEnmbLqdEvpEJT2ikkhFEytWXE7tolWIcAWcVm+81WxlU3oTADeHbqZOrzvnz36usKVr6T7kDLFYX8z1oesvdJMmZV9vgrZ4hoUFIZaWTK5COlLy/z77IUc741yxuJRH71w1Z+3qHrL55stJsqZk5QKdT18fmta54WBfktahNLWxICtKJy9tYDsOL35wnOffb8VxJCG/xmeuX8KGpRUMpHIc607w3dcOks5Z3Lq6hs9ubJzu7s0JjpT8/IMs7xzMkTUkpu1aavs0wcPXhVi3eO6zx6V0+yvn9Idzap7tMDwtcZzTl5PsbTV5eWcW0wa/Dp++Lnxe2jxfcBzJsW5rWAi3SGZPCeE+TbCsRmfFAo2mah2fdnFei3hcOC6V36uXwn42G81syWyhSqvi9vDtY1+UEn58Dwwehg1/Ccs+M/GGBvdBrgeC1RBzz3d/uekv+cGeH3B349383R1/N4d74eHh4eHh4eHh4eExU7LZLMeOHaOhoYFA4MInIXqcYrzYTPX3qpcpPotIKRkaGqKgYPZE4JlSGvLTlcrRm86dF1FcSsmu7jgZ0yaoq6yeZRG00ddItVZNwkkQVaKElTCD9iAfZD/ghHWCg8ZBjphHWOVbxQr/CnQxtUy0qF9jeWmEvT0JDvUmKPBplEwx0/VCIqVkT0+CtGnh11RWl8XmjSAOoAjBZeUxtp4coCeVoz2Zo2YWXAMm4kIeg2G/zoqFFdjBz5Db+h1uj5zkho99nKOZCMe6XNv1lp4kWcPm4MlB9p3opzuexXEkmipIZeFvnt/JO81dlEYDhP064YBG2K8RCeiEA/qYab+mzMk+qorgU1cFKY4qvLwjyzuHcgymHB66LjSpiGA7kq6UK9JWTzHj3dz/EjIzhAgXozfdCowfQ9OS9Cdd0bs/4dCXsEf/H0w5rjXzBIyI4Lrm7pvtSGwHisIKihBkTEnOdDdg2pKh9PTGiSmCUQE96B8WzYdF9KLIQ6wbfIKrEhk6KteyvaCSPYkDtKbbORxvY4/Vyps9b/HewCJuqbmM9aWNtC/7a8SO/4c+TWNPZBUvVlyLHoyyunAB/616JaFkG7m+I5w8todkdzOFZjs+bErpZoGvn5D/EFp6M2zDfWhBiC2EggYSxQ1sqfaBqrLSt2JWBfHZOAZVobIhuIFfpH7BEfMIDWYDNXrNrLXxDFKdEG91P59w5TmvPjicKV44hcxnRQg+f1MTf/nD7XxwpJcdx3pZ21B6zu95NuJph++9niJrSupKNR68duqC+EdjWBjQaR06tZ8Tcbwnwb++dpATvUkA1jSU8MiNTRQOl84ojgQojgTQVYV//NluXt11kjX1JaxcUDyznZ1FFCH4+BUBFAE/eTeDlODTIWdKvvtakv0n/OjaiFB9SpCW0u1X3OlxBGtneJmR153hZca8fmredLAdeWoAEJDJwRO/TLLlgE5FoUpxRKEoolAcUSiOKkSDYl5dL0wXy5Yc7XKF8P1tJmnj1AcY0AXLa3VWLNBZUqWhq/mxv/Px94SHRz4y3WNp0nri3TtcQVwLwqJ7Jt6IbUCu150etk7PWTl+1vwzAO5f7lmnnw2vL8x/vBjmN1788h8vhvmNF7/8xotf/jMSQ7//wutDmqaxapWbVKPrOk888QRr165l06ZN3HvvvdTX148u+4Mf/IBQKMSKFStoamoil8tx5ZVX8sQTT3D11VcD0NnZiaZplJaWEgqF2LJly5j3a2lpoaGhgZ/97GfcfbfrbrVjxw66u7u5/XZ30PCmTZsIhUJcddVV5+ETmH08UXwWcRyHzs5OotEoqjo/6giXhnwgIJ6zyFk2/jmub3yoP0V/xkBVBGsrCvDNgV12WAkTVk4J/IVqIbeEb6HT6mR7dju9di87cjs4aBxkTWANjXojijh7O2qiQfozBq39CXb1xLm2ppjAPK8HfWwwTXcqhyIEa8tj+LX5Z+8a9WksKQrT3J/kYF+C4qBOcI4+1/lwDKrlTag1q7FP7iJ0+EXWXf+7rF/sWibajqRjIMXRrgTvH+7mtT0nUYazl5ES07LZ3dI3pTrkiiKIBHQiAY3QsIAe8Q+L5zMU04UQ3LAiQFFY4Zl3Muw/afLtV5J87sYw0aAyruV7dzqH7UiCukrRFMRBe+A4Vss7APguu5eco9PXb9E3ZLH/6CBaUGcwLelLOMQzzqTb0lVBUUShZFjsKY6OTKsIJI8/myBnOgR9kDEg7Ff4r3dER+2QHUeSNSUZQ5I1hp9NSSZ35vzxXhsRttI5STonIfnRFsY4Ku/kFp4j2vI2GT6NT3yKOhGnXzuMDB8krfTyXtcR3u04ioqGnY2QXfgwjjCwhUQ3NR6sW8t/XXkHvUNZnj0Q5u2DhRjmWlCgoEjn48uDXFeVJZg5AUPHIN7iPidOgJWB/gNYg4d4o/x2jEwR5UO9XLHt78BfBLF6KGhwHyPTsTpQzy3LdLaOwXKtnGW+Zew39rM1u5VPap+c8iCnMxtlg5kEIwFGEsyR5yS0vgr7nwTHBH8h3PR30PTglDdt2A5JwwKY0vceoLYkwu1ra/nFByf49zebWVZTSMA3e5djOVPy5BspBtOu7ffnbgyhn0NW7EdjWOh39ytpWJi2g/6R87plO/xseysvbD/uZocHNH51YyNXN5aP29esXljCzauqeX1PO99+9QCPfebKeWWlLoRgaY2OT8tgWGDZbh9tObD7uHFOn+Wst224fariDsQRAhTFFfMNUyKwUVVXXEcKHAltfRZdQ/YZ21IVQVF4uM88XTAfnp7PmdSmJTnc6QrhB06aZM1TQnjIL1hRq7OyTqehXEPLEyH8dObDtYyHx8XAdI+lXtsVs8cVxZufdp/r7wTfJO4w2S5AghYF3V3u1WOvEs/FqYxUcm3ttVNuz6WK1xfmP14M8xsvfvmPF8P8xotffuPFL/8ZieGCBQvOed2htDOrpfgKCwvZsWMHAM888wyPPfYYzz77LAC33XYbTz/99JjlW1paWLFiBdu2bcOyrNFlRrbxla98hdLSUr7whS+M+34//OEP2bBhA0899dQYUXzPnj1jRPHS0lJPFPeYn/hVhQKfzlDOpDdjUBM9N1vxc6E9kaV1KA3AqrIY0Vm8yT8VKrVKPh7+OK1WKx9kPyDhJNia2cr+3H6uCFzBAm3BWcXApUVhOvqHMGzJ7u4466sK5+2Irt50jsODKQCWl0bOa834c6W+IEhPOsdg1mRvT2JWLfXnI76Vd5PtPogz2Ibd+i5ag3vjS1UEtSURaksirKorYkdLL5mchV9XSecsfJrKp69bghCQylkksyaprEkqZ7n/Z0ySWRPHkTiOJJ42iKeNc2rbiJjuCueTi+mxkMb91+g8/77ByT6b//NSkuW1Kf5zyz6y5ljL9/ZEFoCqSGB8G3HpCsZ9CYf+hEnpnqfRsw6t2mW8srmcdG5oeEEwzBA+3XQVoGECuhjNcCyJqqPiTclwxuNk36eHrwvxw7fTZE2JX1d4+LrQmIsSRXFt06djDiGla7E8Kpgb44johiRjXk53z0lq0tu5g+d5wfdb9JoF+M0r8GfWUlbfTUvmCPt6j5EW/eT8PYCD+yFIHGxKMyv455/vZUdLH8MO7CwojXDH2gVcuaQMbaJBSI4FiTYYOsY2eYA+fwa/keGG5oMoUkK23310fzB2PaFApBpiw2J5Qf3wdL2bUT3OYKPc0EkY2ENuKESoeGYZ6Ff413LCbCVpDfHB4CtcbVSCmTolahuJU2K3mfrI/6fNtzITfy7x4yAdd1/MDLzye1B9HUSmVsZgJHs67NPOaRDYJ9fXs+1wD73xLD9+r4VfuX7JlNedDNuR/MfmFB0DNhG/wudvjhDyz+wC3K8phHSVtGkzmDMpO+1AaelO8K+vHeBkn3suWre4jF+9oZGC0OSDKR7asJh9JwfpGkjzvU2H+N07Vsyrc0JJVBn+3BxUBUzbHXxz25oAYb8yKkorinCF6ZHHsEAtBKjKsGgtxGmvjTwEyvDr7nJi9LWReSPbURRQh+dP9hkNpR3+6uk4OdNBFTaWo6FrgvuvCWJY0J90S0qMOGzYjqQ3YdObOFMwB4gExorkI/1vcUQhEpi8z50LDEtyqN0cFsItTPuUEB4NKKxY4Arh9WXqlMp9eHh4eIyHLW0G7AFgHFHcTMHRF9zppQ9NvBEpIdPhTodOXU88s+8ZAO5bdh+q4t0c9fDw8PDw8PDw8MgnRu4BT8YHRw2e3ZrGsMCnwf3XhLhi0eT3yHR18vs9pxOPxyksLJxii90s82uuuYa2trYpr/PUU0/x5JNPcscdd5DL5dA0jb/4i78gm83yyiuv8D/+x//gf//v/42maXzrW9/iu9/9Lv/wD/9AQUEB7777Ln19fXzrW9/ixhtvnPJ7nm88UfwSoDTkYyhn0pOeO1F8KGeyrzcBwKLCMBXhC2MtIYSgXq9ngbaAQ8YhduZ2MuQM8Xr6dcrVctYH1lOmlU24vqoIKsnQowQZyJo0D6RoKp68RuyFIG3a7OpJgITaWHBOBzvMBkIIVpVFeefkAP0Zg+PD9XcvVkQghr7sDozdz2MeeAm1aiUiMLaORXEkwO/ctoJvvuIKzJGgPqWa4lJKDMsZVzBP5cw5EtN1FFFL95CPPcdNkpkgqmITT+f4xxd3k7UcjKCKIgQhoXOsyxqu7227dufDtuc5yxUx1sotFNNJnCAvm7eQFe78iF+hKCLAzLFkQYTSAm1UlAn5py/CrFvsY0mVRn/CoXiWRumNIIRbT9ynCc72lZb2/eTe7sYZPMlvFv0U/3W/g4NK1pBs65TUmcXcv2AjH/Ts5wftzyIRCEBxAiAcntvRQ8AIARqr6wu4Y20tS6unMHBH0aCgnqNBh4OZ40CIjSW3Eb7/z90bvEMtw1nlLcMZ5sfcZzPliumJNji5eew2Vf+oHftIdvlQ1358O79Bg53F+TDIwFX/D0VNd53K0D5DtE6OI2Kfmq8ZSTYUl/PyFbdwgOPUb3uFisGe6YYKtICbraVHwBd1xfJku7svjgG26Q4OePFzcOPfQMUVZ93kwLAoPtUs8RH8usqv3djE3/90F6/uauPapgrqyyev1302pJQ8916Gw50Wuir4tZtCFEdm57teGNBdUTzriuKGZfPTba384sMTOI4kEtT53A2NXLmkfErb8+sqv3Pbcr76zAdsP9LDOwe72LDs3K3r54qCkDI6mMawJJHA+aspPl1OtTlFJqcQ9AsenqCmuONIBtOSgaTbR58umPcnHbKmJJl1SGYdjvee+V4j7hwfzTAvjigURpRZsynPGpKDw0J4c8dYIbwgpLByWAhfUKpeFFbwHh4eF55BZxAHB7/wExEf+Q149EX32qGgAconuUYwh8DOgFDB754XO5OdvH3ibcAVxT08PDw8PDw8PDw88gvThsd+ODTh62PK2glIZ+E7r6Z47r006iSD9//i4QImyy0dHBxk7dq1pNNp+vr6xtidv/LKK6xdu3b0/82bx96/zWQybNmyha997Wtn30Hg8OHDCCFYsWIFN910Ez//+c+59957eeyxx9izZw//63/9LwCOHTt2RrZ5f38/W7du5bXXXuOxxx7j1VdfndJ7Xgg8UXwWEUIQDofnVbYTQFnIx5GBFH0ZA0fKWb9xmLMcdnbFcaSkLORncdGFFztVobLcv5zFvsXsye1hn7GPbrubF1MvslBfyBX+K4ipsTPWE0JQFA5RVhhhd0+ClsE0hX6d8gsk8o+H7Uh2dA1h2Q4FAZ1lJfNPtB+PkK7RVBxhf2+C5v4UpUEf4Vl2E5hPx6C68GqUE9txBk9i7H0B/7pfOWOZjSuqWFlXRPdQlvKCwKgV+WQIIfDrKn5dpeQc6rPPTEw3ceRxHFmNECFKIk1IJFI6ZM1B3jqcY3G9n3hC8OzLfShCoCruQ1HGTtcFh7jWfgtdCAZqP869taWURFSKowp+XeA4DidPDlJTE0BRZk+8LgjNrhg+HYSq4Vv/WXJvfB1n4ATmvp+jr7wHIRyK/CoJw6QvmyJqhlFsHak4KI4PRzEQjo5uFxD2FxANVtFQ5qeycPys/PEYsod4J+va1a/2rz5Vo1sPQ+lK93E6UkKmb1ggbxkWzY+604njYOdg4BAMHEICtmUSTZ5ASAcHBcUcxP/mF3E+rENRp3+cV/V30dh+jOaaJWxZfSOf2LsfTQ+5orYWdp99wyL3iNithz/yf8RdRvlIO1Kd8P2rXSHeXwGZbtdGPd4CL/wK1N8B6//QtZGfgOmK4gCr6oq5qqmc9w51871NB/nzB9dNepF8Nl7fk+ODowYC+Mz1IWpKpve5j9ePFvp12hNZBnMWRzrjfOf1A3T0u84wVzaW89mNS4gFz00wri+P8skr6/nJu8f4/uZmmqoLKY1NvU+ba+ZyMM1csW6xj0UVCgePdrF0UQVFkfG/A4oiKI647huLxxmLkDEk/Ql7VCzvO000H0o5mLake8imexxbdgHEQoprzR5RKIqOFc3HG+B0urWYrsL+Not9J0wOd1rYpxVZL46cEsJritV5ca6fC+bTtYyHRz4znWPpdOv0M9Y7NGxL2Pige5drIkayxAPlMJwR/tyB55BI1levZ2Hhwim351LG6wvzHy+G+Y0Xv/zHi2F+48Uvv/Hil/9MJ4aO4xp7CnHq54Icnj+TCsOn26c//fTTPProo7zyyivA+PbpfX197Nu3j7Vr13L06FHuuOOOMcL5ZDz11FM89JDrivXwww/zgx/8gHvvvXdK644st27dOlpaWqa0zoXCE8VnEUVRplVnYK6J+jT8mkLOchjImJScxdb0XHCkZGf3EFnLJqRrrCqPzqsO3yd8XBG4gqW+pezI7eCIcYRWs5UT5gmafE2s9q8mqJzKsj49hnHDpnUozZ6eBNf4NEL6hbe5k1KytzdB0rDwqQprymN5lR1VGw3Qnc7RlzbY3ZPg6qlkuZ4D8+kYFIqCb/V9ZDd/A/vkLuwF61DLm85YrjgyNTF8xu2ZBTH9QFuK77zmWnq7tW01Qr5iaiotBNDV4y5vSwPbMZEYgAmnPd9hvYWhpWhVqtna66c4d4ziiJ/iiJ+SaIDiiJ+KqupZFcQvBDnTZmg4I38oY4xm5w+lTULGeq5OvYKz41We3ZpgT64aXVe5dp1b4uGt3f3ouUXkag5jK1mwFUKdDTx8TRHtfQFaeizeO2yw7YjJusW6W/99koxgS1psymzCkhaVWiVr/GvOvgNCQKjUfVReOeYlaVsk+ltJ9jaT6z+GHDxKpP8DSpMnkLg166VUQDo4QkUJV55FsB5nvi8Cuvu8ToGTqZ8Sd9LsvON+1gXWzTQ8LuFKuOlvYdMfgJWEYClc+2V3IMChp6HlJTj+Kiz/NVj7e+AfO5DKchwShiuKF06zfMVnrlvCntZ+WnuSvLq7jdvXTK//2n7E4LXdbgmDT14ZZGnN9MtpjNePFgZ0JHC8P8U332zGcSAW8vG5GxtZt2hi95Wz8fEr6thzvJ/DHUM88cp+/vjetTMaGDDbzIfBNOdKUUTjmtU1M9pG0CeoKdGoGaecrmVLhtKnssoHhp1ARv43LPf1obRDyzjGDn5tbJb5UNrh/WYDw3JHUwf9Ar9+6jtQFlNHhfDKQmVeXWPOFfPpWsbDI5+ZzrHUZ/cB41inDzRDzw43+3vJvRNvwDEhO9z5BV33Jyklz+x3rdPvX3b/ObXnUsbrC/MfL4b5jRe//MeLYX7jxS+/8eKX/4zEMJvNjs7TVTereyKG0g5/85M4OVMS9AkyhsSvC/7w3tik95bORXK65557eOSRR8663EhN8Z6eHq699lqef/55PvnJT56x3De+8Q2eeOIJAN555x2eeuop+vv7+Zd/+ReklAwODpJOp6fUNr/fTSpVVRXbPovP/AXGE8VnEcdx6O/vp7i4eF4JOkIISoM+Tiay9GRysyqKH+hLMpg10RTB5RUx9Hm036cTVsJcF7yOFb4VfJD9gDarjQPGAY6YR1jlX8UK3wo0oY2JYWNxmKGcyWDWZGf3EFdVFV3wm/Wt8QydySxCCNZUFBDQLrxQfy4IIVhZGmXLyQHiOZOjg2kWF4Vnbfvz7RhUCmvQGq7FOroFY/dzBG767wh1/tZ+H4/TxfSqYo2Ab4isYQFuvd3CmKC0UCGgC37v1iI01WIoZdCfzNGXzDKQzNGXUBhICirTR6lXejClyjNDqxgcHAQGx7yfBBzbpiASoCTip3hYKB8RzN1HgFjId96PR8t2GEobJDImg+kc8bTJ0Oiz4YrgwwJ41pjs5B8m51/Mtb5m7tK3027GSIsCMmmDwoIgVy2vpPedDP6jxeghE7J+IlqYa5oiFEcCHOuyeG13lmPdFu8fNth+xODyBh83rvRTHD2zT3g3+y6D9iBBEWRjcCPKOLXAJ0NKSdK06c8Y9GdMBrIGlhMF/xVQdQVUQcjoJvLanWhWClMNoVtpLD2M8tDraAUzE+j8wDWBa3gt/Rr7cvuo1+vPvFk9XZoehJrr3drisTpXKAdXCH//r+HkW7D3O3D4WVj7BVj+2dGM88Gs5Yp4ukpwmn1xQcjHQxsW82+vH+TH77awblHZOQ1aAWjuMHnuPfcC9caVAa5snJmzyXj9aHtvku7BNBJBOORnZU0hn71+CeFpDgYYQVUEv3XbMr78n9s43DHESztO8PErZlaL/lJnrs+DmiooiaqUjNPXSClJ5+SoQD4imo9kmcfTbhmNzkGbzkH7DGsxKcG0JctqVNYu8rFygU55QX5d58wG8+1axsMjX5nOsTShKH7oR+5z3S3ugMGJyHYBErSI+wA+7PyQ1qFWglqQuxrvOtfduGTx+sL8x4thfuPFL//xYpjfePHLb7z45T8jMQyFTjkij5TQnIiymMqnrwvzw7fT5ExJQHfL3JXFZu++xpYtW1i0aNGUly8rK+Pxxx/n8ccfH1cUf/TRR3n00UcB2L9/P5qmjak//sgjj/DCCy8QjUZJJBKj8z/6f77hieKziJSS3t5eioqKLnRTzqA05OdkIktv2kAWy1nJtDkRz9AWz4CAy8pjs26FPRcUqUXcGr6VDquD7dnt9Nl9fJj9kIPGQdb611Kv1I/GUFUULiuPsfXkAImcxcH+JCtKZ1bzdSb0Zwya+5MALC0OT8uudz4Q0FSWl0TY3R3n6GCaspCPmH929mU+HoP6stux2/cgU/1Yza+jL7v9Qjdp2pREFcJ+t3arrrq1VOqqbUJ+QXU0QFWRDuiUxc6scS9zSbKvv4hjBMnW38pvFK2jP5lzH4nsmOmkYZDIGCQzJq09yXHboiiColGR3BXKS6L+4cx7P8VRPyGfNtrX9SezdA9lKC8IjsnMtx1JMmsylMoRz5wmbg9neA+lXKF7KG2Qzlrn9HnpmkIs5KNg+BEL+igI6e684Apix36IP3mCv1zQTPCGR+nOSXZ2DVFdGKKpJMoTr+wjm3EI6Aq/c9uK0XY3VGj8ZkWElm6L1/dkOdJpsf2owYfHDNbW+7hxlX9UsGo2mjlsHEYguCF0AyHl7OUtpJRkLJv+jElf1mAgY2LYzphlNFWhOKBTHPRRHNAJ62X05/4nobf/BM12BfEDq77CIn85s+GDsEBfQL1eT4vZwpbMFu4O333O4v6EhCtPieEjFDfBHd+Gts2uOD7QDO9+Ffb/O1z5J1B3C4MzsE4/neuXV/L2gU4Odwzx7282898+vmrK5+j2fpv/2JzGkbC23sdtq2de6uP0fjRn2jz77jFe2dXGqqUVlJWEeOD6xWxcPLXa4VOhLBbksxuX8J3XDvLjd4+xorZoxvXVL2Uu5HlQCEE4IAgHFBaMoxmZtmQw5WaWDyQdDndabDmQA0BRQFfAQXDv1SEaKub/NeVcMR+vZTw88pFzPZYsaTFoDwIfEcVtAw4/5043PTTZG0J62Do9WDnqmfjMPjdL/K4ldxHSL3yZsXzB6wvzHy+G+Y0Xv/zHi2F+48Uvv/Hil/+MxPBcM/7nohTfSE1xKSWapvHNb35z9LWP1hT/+te/fkabH3jgAR577DG2bNnChg0bJnyfp556ik996lNj5t133318//vf55vf/CaPP/44l19+OV/96lf5xCc+wYMPPshTTz3Fd7/73Rnv4/nm0r3jdIlREtRRhCBt2qRNe8YC9kDW5ECfK1YtKQpTFpo/NbenQpVWxd3huzlmHuPD3IcknSRbMlvYq+ylXC9HSreGZVBTuawsxgddg7TFMxT6darPMYtvNshYNju740gJ1dEAC8YRHfOJyrCf7rCfrlSO3T0Jrqm+8Fn4c4XQ/Oir7sHY9gPMw2+g1qxFic6eoHQ+KQi5I9x++HYaw5KE/LCoVqIIqD6LBbyx92dII4NaUE3xqlspmWCkpGVZ7N5/kMLyWobSpptxnsjSn8rRn8jRn8wymDJwHElfPEtfPDvudoBhq3g/OcumuSOO7UhUIWisjhHUNeIZk0TGQMoJN3EGiiKGRW2dgrCfaFCnMOQbFb9PF8ED+uQ1b2XNI2Tf+Doy2Y2553nK1jyApipkLZt1DSX8429cN2m9+fpyjV+/JcLxHjdz/HCnxQfHDHa0GKyu93HFijTvifcAWONfQ6U2TvHgYbLDInh/1s0Gz1pjM91VRVAY0CkO+CgJ6kRPG3AwQsmaz5JecD3HDr1HtmwVvaKYVE+ca2qKZsVF5KrAVXRYHfTb/ezO7WZNYAo28DOldiNUb3Dt1D/4R4i3wqu/B5VXkWn4XfAvmbEorgjB529q4stPbWNXSx/bj/ayfvHkluRDaYfWboufvp/BsCSLKjTuvTo4q9bSB9sH+d4bh+kZygBQFvZTGg0Si8z++f66ZZXsbO3jgyO9PPHKfv7ioXX450HJEo/ZRVcFZTF1dJT08gU6u1tNcqYzai0W0AXFUW8kvYeHx/lnwB7AwSEgAoTFaU5Wx1+F3CCEKlx3mYkw42CnAQUCFQBkzAwvHn4RgPuXe9bpHh4eHh4eHh4eHhc7s12Kz7LGT9K66aabhl1Yz2Tbtm2j00IIdu3aNfr/V77ylXHXGW/+fffdx3333QfA+++/P+a107d5ujAeiUS8muIe8wNNUSgK6PRlDHoyxoxE8axls7NrCCklFWE/DQX5OeJdCMEi3yIW6gs5aBxkZ24nQ84QPbEe4tk464PrKVVLKQ35WFwY5shAiv19CaJ+jeh5zIq3HTd71LQdYn6N5SXzq277dBBCsLw0ykDWJGVYHB5IsbQkcqGbNWeoVatQK5Zidx3E2PUT/Bt+O29jePqIN6mZHB4y8WsqJcGJhUG76yB2204QAt+a+xGTCKRCCIK6Sl1pBFUdXxSzHclQ2hXJ+5LDWebDgvlIxnkyY5IzbU70JumOZ9ECJuEii1RcY89xm/JYAHWkHQKiQZ2CoI+C8EhG90eE7uHXQn4NZZZiJwIxfOs+Q+6db2Md345SXE9leClt8QztiSyXlcemVG++rkzjv9wS4XivxaY9OQ61m+w4nuJYySaCMZOF/mpW+1ePWcewHQayJn3Dluhpc+wFlhCCQr/mZoIHfRRMcb/9BTVQuIpldYtJdMbJmDYHepOsKpt5vxVUglwVuIrNmc3szu1mob6QQrVwRtucEooKyz4Ni+6G3U/Anu8gO99j0YmtRKrupOiGPwFqZ/QW1cVhPn5FHT/b1soPNjezoraIkH/888z2IwZPvZ0innZtp2tLVD67MYymzs73Mmva/GJfP7s6OhC4jgyfv3kpNWURtnUMMpA1kXJ2HGdGEELwyI1LOdwRp3MgzdPvHOVXb2icte17zE9OH2iVNSX+YWuxfKvj7uHhcXHQ55yyTh9zjjv0tPvc9IB7TTARmeEs8UDZaKmVl468RNpMU1dQx/rq9XPRbA8PDw8PDw8PDw8PD49zwBPFZxEhBAUFBfNW7CoN+ejLGPSmDeqnKWTbjmRHVxzDdoj4NFaVxebt/k4VVais8K9gsW8xu7O72WPtocvu4oXkCzToDVweuJxFhREGcyZ9aYOdXUNcU1OEdh5qgkgp2d+XIJ6z0FWFNRUFF01GtU9VWFkW5cPOIVrjro16cXBm9e7n6zEohEC/7FM4vX+P03cMu+1DtAVXXOhmTZuREW8fdqYAqIr4J/zMpWVg7P4JAFrDBpTCycXDqcRQVcSwTXqAJRSMu0zOtBlI5vjgWA8/Ofwei6/pRtEkjiU4srWcTy1eyWV1xRSE/UQC+gU7rtTSxehLb8M88EuM3c9RfeVv00aA7nQOy3HOqZ+pK9V45CaNE30mL/a9STKYJJMK8P7mNaSrU1zeqCJVm/6sQcKw3ALuwwgBUZ9OcVCnJOCjcJqfyUj8fKrrsvF+xyAdySwlQd+suGw06A0cM4/RZrWxJbOFO8N3zp6N+tnwRWDd/4Clnya79W/gyE+p6Pg5wefehMt+Ay77bZiBLerd6+p473A33YMZnn33GJ/7iCicyDjsPm7wH5vTGOap+fGMW6c54Jv5d3hf2wDffe0AXYNpVFXjhpVVPLxhMUGfhu24QrhhO2Qsh9AsZ3JHgzq/eesy/v6nu3ht90nW1Jewqq54Vt/jUmC+ngcnYi6sxfKdfIuhh8d85VyPpXHriSdOwsm33enGByZe2bEg2+NOB6tGZz+z37VOv2/Zfd4xfY54fWH+48Uwv/Hil/94McxvvPjlN1788h8vhhc33p2nWURRFKqqqlDOg1g6HcpCruA4kDUxHecsS5+JlJJ9vQniORNdVVh7EQm0AH7hZ31wPQ8WPchifTECwTHzGD9J/IT3s+/TVOonoKmkTZu9vYlRi/W55EQiS3siixCwpjxGULu47GTLQn5qokGQsLc3gTWN7+XpzOdjUAkVoS29FQBz7wtII32BWzQzcpZDb8YAJrdONw/+EpkeRIQK0Zd97Kzbna0Y+nWVisIgjUsETRu70IMWiuKg+W2WXNvD2qYodWVRCkK+C96PaY03oZY3gW3h3/1DIoqF7Ui6Usa0tpeOHiVQ0UZBWKEyeSV1tQIrnGRbxxB7OlMMZFxBPOLTqCsIsraigJvqSrmmpoim4gglM/hMTo9fYUBncaErEu/vS5yRjT4dhBBcE7wGXej02D0cNA7OeJvnTKSajnX/f3Ze9S1ypVcg7Czs+Gd4+mNw8Efg2Gffxjj4NJVHbmwC4PU9J9l3YpD9bSYvbMvwtRcS/PWP4zz3bobcsCCuKhALCSwb+hMz6zszhsW/bTrI3z63k75EjvLCMH/4qTV8/qalBIedUVRFEBvOXh+ppz7brKor5pbLagD49qsHSGTm5n0uZubzeXAiCkIKDRWaJ4gPk48x9PCYj5zrsTQiipeqpadmNj8DSLeUSnSSgZ3ZbsABNQR6DIATQyd47+R7CAT3Lbtvmntx6eL1hfmPF8P8xotf/uPFML/x4pffePHLf7wYXtx4UZ1FHMeho6MDZ4bC3lwR0jVCuoaUkv5p3Gg+Hs/QkcwihGB1eWzWs8TmA47jkOhKsCGwgXsi91CtVePgsN/Yz8/SPyFQfAIpHLqSOU7EM3PaloGsycHhuu2NRZEZZ1HPV5aWhAnqKhnT5mBfakbbmu/HoLboepRoBdJIY+77+YVuzozoTGWRUhLz60QmKCfgDJ7EOupm1/gu+xRCO3st4pnE0JEOPVYPe3N72ZTexI8SP+JN55fofhshQNEkiirxhy2OqfvIOHN7DE8VIRR8V3waESpEpvpobHsZpKQjOXG99I8ipWQwa7JzoI03E1tJmzaxdBMV0RjVZeDXBYYh6OxR2HNApbc9SEOogGUlUcrDfnR1di4HPhq/hsIQRUEftiPZ1R3HmYXBRGElzLrAOgA+yH1AwknMeJvnykDOJFmwgsSt34Fbvg7ROsj0wtv/Dzx3L7S9dc7bNG2JX4/SUN6AkHV8/YUs338jxTuHcnQP2QigqkgloINPcwVx05L4tJnVYN5zvJ+/+M/3eXOva/t606oqHr1pAcuqz3RhKPS7ZRIGc3MnVj947SKqikPE0wbfe+PgeRmAdjEx38+DHmfHi6GHx+xwLseSJS0G7UHgtExxx4bmZ93ppgcnXlnKU9bpwSrXfgf48YEfA7BhwQaqolUTre0xAV5fmP94McxvvPjlP14M8xsvfvmNF7/8x4vhxY0nis8iUkqGhobm9Q3ckWzxnnTunNbryxgc6ncF2qbiMCUXqUB7egyL1WI+Fv4Yt4Vvo1gtxpAG++2d9BS+QVw/wYH+xJxly51et70yEmBhQXBO3mc+oCkKK0ujIOBkInPO383Tme/HoFBU9NX3AmAd34bd33JB2zNdspZNy1AaR8oJLbGl42DsfBakRK1ZjVqxbErbPpcY5mSOk+ZJPsx+yEupl/iPxH/wYupFtmW30Wq2kpEZNDQ0oaGrKj7Fh6IKhIBmo5mnE0/zRvoNOq3OC/6dEb4QvnW/AopCqP8AZb0f0J8xyFjjZx1LKUnkLFqH0nzYOcRrrb28097Ne+ZbWNImZFZQ7iyhKhJgTUWUu5tKubOxlBI1RCKusu+4zTd+keD7b6Q42T/zDO7T23V6/IQQXFYWRVcV4jmLw/0zG/gyQpPeRKVWiSUt3sm8c17jNzIAAaAo5If62+H+F+Hq/xt8MRg4BC//Jrz82zDQPOF2HEfS1mfx5t4s33ktyVd/FOdfX02SzsVQlCCmLZEYXLXEx2euD/FnD8T475+I8fmbI4T8CjmTGdVgTuVM/vW1A/z9T3fRn8hRVhDkj+5dy2evX0I2nRz3My0MDIvic3TuA9fl4bduXY6iCD440svbBzrn7L0uRub7edDj7Hgx9PCYHc7lWOq3+5FIgiJISBkuhdK+BVId4C+AutsmXtlKug8EBCsAd6DmiCh+//L7Z7orlyReX5j/eDHMb7z45T9eDPMbL375jRe//MeL4cWNV1P8EqMs5KN1KE1v2kBKOaW6CGnTZld3HCmhOhqgLnbxCrTjUaPVUB2u5qh5lA9zH5IiRX94F0PmUay+Vdxa0YR/Fm3NHelmVI7UbV9ZGr3o61cUB30sjIVoHUqztyfBhlod3yxlrs431JJ6tLr1WMe3Ye78CcqNX0Qo+eO6cDKRYWdXnKztIGDCzF/r6Fs4Q+0IPYBv5T0zfl8pJUmZpMfqodvupsvuYsgeQjL2/f3CT5laRrlWTrlaTolawjHzGJvTmzGFSYAwS31LSTgJuu1uWswWWswWCtVClupLWeRbhE9cmEE/alEd+oq7Mff8lIXtb5AMVnC4309jcQS/qpCxbPoyJv0Zg/6siWmfGq0okfSGdyK1DDER5ePRmyn0Bcf0HcFC+MzGMF2DNpv2ZtnTarL/pPtYWq1z82V+aktm/7IgoKmsLI2yo2uIlqE0xUEfpaGZfcZCCK4NXMtPUz+lw+rgsHmYRl/j2VecBeKGa2+vqQqREccUVYeVn4cl98KOb8C+f4e2N+HkW7D003D5F5GBYnriDkc7LY50WRzrssiaY7+/kYDC4koN0zJ4dXczfUmHa5ddSVns1Oc1GzWYd7T08r1NhxhKGSDgttW13H91A35dxbYntn8fyRRPmham7cyaw8BHqS+Pct/VDTzzzlF+sPkwTdWFlF/Eg8M8PDw8PC4s49YTP/Qj93nxp2Ayt6ORLPFAGSjuefLdtndpT7QT9UW5bdEkgrqHh4eHh4eHh4eHh4fHecUTxS8xCgM6qiIwbId4zqJgOOtrIixHsqNrCNN2iPl1lpdc/ALteAghWOxbzEJ9IQeMA+zK7SYuE7Sq7/Ds4BE+VngNpVrp2Tc0BQ70JRnMmmiqwtqK2AWvd3y+WFIUpjdjkDIs9vcmWF0eu2i/a/qKu7A79+EkurCOvoW+5MYL3aQxSCmxpMSwneGHJGc7pAyL5oEUtjOcBQzs701QGfYTOG1giJPuxzr4CgD6yrsRgeg5t8GRDgP2wCkR3OoiI8+0O48psTEieIFScMb3ptHXSLVWTcJJEFWihJUw4GYFHTQOctQ8yqA9yLv2u2zPbWexvpilvqUUqUXn3O6ZojVci9N/DHFiFw1HfsyR7O1s6qlEDRWeMTpRVQSFAZ2SgI8+/QidVg8hdO6I3EyRGprwPSoKVT59XZhbVtls2pNjV6vBwXaTg+0mTVU6N13mp650di8PysN+FsSCnIhn2NMT59qaYvzazATVmBpjrX8t27Lb2JbdRo1Wcyq7aw4ZGMkS9+tn9lH+AjdjfPmvwvt/g33sl5i7f0Bm1094N/hf2Kp9BkucclcI6IKGco1FlRqLKzXKYgpCCKQM0tbfwcGTgzz5xiH+xz2rx7xXQWh6Yngqa/KDtw6z9WAXABWFQX79lmU0Vp1plT4efk0hpKukTZuhnElp6OwlEabLHWsXsKu1j+b2Ib796gH++N61l8z50MPDw8Pj/HJGPfFMHxx/1Z2ezDrdsYfrieNapw/zzP5nALi78W4C2viuSh4eHh4eHh4eHh4eFx9ZyyZt2oR0dcz98umiaRqrVq0CQNd1nnjiCdauXcumTZu49957qa+vH132Bz/4AaFQiBUrVtDU1EQul+PKK6/kiSee4Oqrrwags7MTTdMoLS0lFAqxZcuW0fW/8pWvUFpayhe+8IXRee3t7fzRH/0R3//+92e8L/MFTxSfRYQQlJaWzmshTxGC0qCPrlSOnowxqSgupWRvT5ykYeG7RATas8VQExqr/Kto9DWyLbWT3dl9DNLNTxI/Y6l/MZcHLieiRKb9/icTGdriGRCwuixKSL90DlFVcW2W320fpCuVozOVoypybjeR8uEYBNcqW1/5cYwPn8bc/zIiUIBS0oASnJowNR2klFiOK26PiN2nT48I3yP/j5cBbjvuNsAVxHVVYEtJ2rRHT/JSSsxdP0HaJkpJA+qCdVNqnyENeuweuswu2sra2J7ajsVYW28FhRK1hDKtjHLVFcGDytSyR8NKeFQMH6FYLeba4LWsC6zjiHGEg8ZBhpwhDhoHOWgcpFwtZ6lvKQv1haji/GTzCyFwVt6LcWIPBel2Lj/8b5hqkEMLPk6ich1FQR/FAZ3ioI8Cv4YiBN1WN5tTOwBYH1h/6obuWSgrUHnouhA3X+Zn094cu1oMDnWYHOowWVKpcctlAerKzq0PmuwYbCqOMJA1SRoWe3rjXFFx5gCGc2W5bzktZgu9di9bM1u5OXTznB//I6J44Tjnz3TO4WiXxdHOMo6k/4qAdj835P6BcnM/67LfYKn6DIdrvoiv6R4WV/qoLlZRxjmvCiF45KYmvvyf29h7fID3DndzdWPFjNr9wdFennzjEPG0gRBw+9oF3HtVPb6PXKCfrR8tDOikTZvBrDWnoriqCH7r1uV8+an3OdwxxIsfHOcT6xfO2ftdLOTLedBjYrwYenjMDudyLPXavcBpmeKHnwPHgrLVULx04hVz3SBtUIOgu9fxiVyCl4+8DHjW6TPB6wvzHy+G+Y0Xv/zHi2F+48Uvv/Hil/+MF0MpJc5Z3NTbE1n29MaxHYmqCFaVxiYsPTqCIpj0u1JYWMiOHTsAeOaZZ3jsscd49tlnAbjtttt4+umnxyzf0tLCihUr2LZtG5ZljS4zso3xhO/JqK6unlNB3HEcFOX8OgZfOorbeUBRFEpLZydbeC4pDbmieG86x5Ki8ITLHRtM05XKIYRgTUXBrIxsme9MNYZ+4ee6yFVUOIt4J7WdJCdpNo7QarayzL+My/yX4RfnJhYMZU3297p12xcXhudUbJivxPw6iwpDHBlIsb8vSVFAP6fvXb4cgwBq7RWI/S/j9B4mu/kbiEAM/7rPoNVdOeVtSCkxHTmuyP1RwXsioXvSNioCv6rgG34oAlqHMjhSoikCR4KmCEL6qRjZJ3didzeDouJbc/+EJ/Wkk6Tb6qbb7qbH7mHAHjhlhT58ZvIJH+Vq+agIXqqWoonZP235hI/l/uUs8y2jy+7ioHGQ4+Zxuu1uujPdvJ99n0ZfI02+phkNepkqmWwa3UwhkCDBb6VYdexpZHI3gYpGlKIFKGotwldOVuZ4M/MmDg71ej1LfZPcuJ2A0pjKg9eGuHmVnzf25thxzOBwp8XhziSLKlxxvL58ap/7ZMegqghWl8fYenKAvrTB8XiGhQUzy+xWhMKG4AZ+lvwZJ6wTtFgtNOgNM9rmZIypJx7QMSxJa4/lWqJ3WnQM2GMM/YXvCjZXPsnV6sss6/w6pUYXDYNfgqM/hLI/A2XiQSOVhSHuWV/HT95t4T/eOsyqumLC/sndXcYjkTH5weZm3mt2M9mqikP8+s3LWFwZG3f5s/WjhX6d9kSWgdzc1RUfoTQW4Fc3NvLtVw/w/PstXFZXTH35uTtPXErk03nQY3y8GHp4zA5TPZZMaRJ34sCwKC4lNA/fWGqcJEscID1snR6sguFr3hebXyRn51hctJjVFaun3f5LHa8vzH+8GOY3XvzyHy+G+Y0Xv/zGi1/+MxLDbDY7Os+R8GpLz4TrOFKSsZxT9wUdybbOQYK9Csokovet9WWoUxw/EY/HKSwsnNrCuFnm11xzDW1tbVNe56O0tLTw4IMPsm3bNr773e/y4osvMjAwwLFjx/jd3/1d/uAP/gCAJ598kq997WsYhsGtt97K3/3d3wFwzz330NHRQS6X48/+7M/41V/9VVpaWvjEJz7BypUr2bFjBx9++CHB4Pkrm+iJ4rOI4zicPHmSmpqa8z664VwoDfqBBPGcRc6yx62H3ZPOcXgwBcDykghFZ7FZv1g41xguiZWSy13L0WQXA8H9WP4B9ub20mw0c5n/Mpb5lk1JxMvZDju74zhSUhb2s6hw7i2A5ysNhSF60gbxnMne3sQ5ZZPmyzEIILNxrEQXQkqkdCA9QHbLt9Djncjy5ZjRagy0s4rdH7XUPhuaouBTx4rdPlUZ8//I9HjOECVBH7u649jDwvjq8tipLPFcCnPPTwHQm25BibgXgI50GHAGxojgKSd1xrajSpQypQw1rrKsdBlFWtF5HVUphKBSq6RSqyTtpGk2mjlkHiLtpNmd282e3B5qtBqW+pZSrVWjiLn5jgWNQbLSJqdH0BwTYZsoOCipLqzWAWh9z11Q87F5WRmpqEJURLnGt2pGn1dJVOX+a0LctNLPG/tyfHjUcLOeu5I0lLvieEPF5P3Z2Y7BiE9jaUmE/b0JDvWnKAroxKYh9J5OkVrEav9qduR28F7mParUKgLK3FiVxnMWacPBsuGZt7Ic73VGywmMUB5TR+3Q68s1gj4BPATWJ2Dvd2Hn/4He3fDCZ6H+Dlj/hxCrG/f97ry8jq2HuukcSPP0lqN8/uZzG/Tw/uFuvv9mM4mMiaII7rx8AZ9Yv/CM7PDTOVsMRzLk4zkTR8pJL+xng2uXVrCztY9th3v45iv7+fJD6/DrF/8gvemST+dBj/HxYujhMTtM9Vjqt/uRSMJK2HUg6voQBo+AFoRFd0/8BmYSrAQgIHDKzeXZA27Wxv3LJx4c6nF2vL4w//FimN948ct/vBjmN1788hsvfvnPSAxLSkqmvI50c5sA12FVjjzk8IxpMjg4yNq1a0mn0/T19Y2xO3/llVdYu3bt6P+bN28es24mk2HLli187Wtfm34DPsKuXbtGs9CXLl3KF7/4RY4cOcJzzz3HO++8g6ZpPPLII7zwwgvcfffdfO9736O4uJhUKsWVV17Jgw+6A4/379/P97//fVavPv8DiT1RfBaRUpJKpc5ZpDrf+DWFmF8nnjPpzRjURMeOwkgZFru74yChNhakNnb+RmlcaKYTw2UlUeKGhS91NaocIBU+yKAzyPbsdg4aB7ncfznlajlJmRxTz3gER0p2dcfJWjYhXeOyskuzbvsIinBt1N8ZziZtS2RZMMXvYL4cgwC5eDeWZSK1EJqdA+kgbJPk/leRB99ECoV0qIJkuJZkpJZkuAZbG/9z0MaI2qcE748K3z5lfKH7XKiJBikJ+satjWLuexFppLELKhisb6Qnu5Muu4teuxdTjs0qVVAoUosoV8up0CooU8sIKSFs26b5RDMF5TO31p4JISXEmsAaLvNfxgnrBIeMQ7Rb7bRZbbRZbUSUCEt9S1miL5l1AdYfK8fSA9hGBlMNoiEQuh//5Q8j0/04AyeQQ23sK1Xp8OdQc5Jr9rdjZ/aRCRagFNa62eSFC1AKaxDaublOFEdV7rs6xE0rA7y5L8v2IybHui2+/WqS+jKNmy/zs6hCGzc+UzkGa6MB+jIG3akcu7rjXFNTjDbD7+Uq/yparBYG7UHez77PxtDGGW1vBCklXYMOR7rcbPB+I0tZhUMqqdDabQNufe/FwyL4ogqNaHCCHzxaANb8X27W2Ydfg0M/gpaX3Jqlyz8Hax8F/9jsbV1V+PxNTfz1j3fw5r4ONiyrnFL976G0wfffbGb7EXcEa3VJmN+8ZdmUsqzPFsOwrqKpCpbtkDAsCmY4qOFsCCF45MYmmjuG6BpI86MtR/jcjU1z+p75TD6dBz3Gx4uhh8fsMNVjaaSe+Kh1+qEfuc8Nd4FvEoegzHCWuL8EVB8AR/qPsKNzB6pQuXfZvTNp/iWP1xfmP14M8xsvfvmPF8P8xotffuPFL/8ZiWFxcfHoPEW4Wd0TkbVs3jzRh+W4iWQjzzcsKJnUCfdst0RPt09/+umnefTRR3nllVeA8e3T+/r62LdvH2vXruXo0aPccccdY4TzmfKxj32MSMT9nVRdXU1XVxevvvoqW7duZf369QCk02nWrXPdMf/+7/+e559/HoDjx49z/PhxdF2nqanpggji4InilyxlIR/xnElPeqwobjoOH3bFsRxJYUBnWcncWwXnO6oiWDNsCWyli1mm34wa7ebD3IcknSSvpl/FkAYKCj7h44bQDTT6GkfXP9SfYiBjoCqCtRUxNG8EGWGfRmNxmIN9SQ71JykJ6hdNfXUpJYM5k2NpH3WKD0O1GCosoTCZJGBK+guWEs724LOSRNKdRDOdiN7tCMCJlCML6xDF9Wgl9fgixcO25udXPLaVLIaewK9EgTApJ0Vn/w46OEjvikLixVFk5rUx6/iEj1K1dFQEnysr9NlGEQoL9YUs1BcSt+McNA5y2DxM0kmyPbudHdkdLNQXstS3lDK1bFaEfCVYQHD9Z8ht/080y0D4g2dY63cY7exN/QJhm6zvD1Gs6zjZLmRmCDszhN2x111QCJRouSuQF7kPEalATKGfKYoofOqqEDeudHhzb5btR01aeiy+85pFXakrji+pHF8cnwwhBCtLo27WtWlzoC/BqrLxrbyniipUNgQ28PPUzzlqHqXBbKBWr53SukNph76EQ0lUoSCk0J90XDv0LpNjnTbJnDO6bE2tjRBuqYdPXBlkSaVGcUQ5t88gVArXPQYrfg3eexxOvuVmkDc/C5d/EZb9CqinhOam6kI2rqhi874OvrfpIF9+eD05kSHhJM4YaCWl5N3mbn6w+TCprJsdfve6Ou5ZtxBNnZ1zixCCQr9Gb9pgMGvOuSgOEA7o/Oaty/i753fx+p52VteXsHrh1Efrenh4eHh4TMQYUdxIwrEX3ReaHpp4JWlDtsudDlaNzn52v5slfsPCGygNeZaZHh4eHh4eHh4eHhcLQohJbc7DPo3V5bEzHFbDvtm7/33PPffwyCOPnHW5kZriPT09XHvttTz//PN88pOfPGO5b3zjGzzxxBMAvPPOO1Nqg99/KvlKVVVs28ZxHH77t3+bL3/5y2OWff3113n77bd59913CQQCrF+/nlwuh67rhEIXzil5/isSHnNCWcjHkYEUfRlj1P5USsnu7gRp0yKgqawpLzjvYlu+EtI1VpbF2Nk1ROtQljWBBdwXqWdHbgfvZN7BwUEgMKTBL1K/4KBxkCK1CGkG6U6p6EqYK0qriMxiJ5nv1MWC9KQN+jMGe3oSXFlVmNcZ9Kbj0JHI0pbIkjQsHBmgffl1dBS3Yqug2mXUDtSzYfEniPg0RHYQp68Fp7/FfU72QKbXfXR8AIAMFWIWL0QtrkcprkdEyxFCQUqJROLgIEf+5Ef+R+LIj/w/8vok67dZbezO7caSFgJBRIngSBtpDkJ5EPQgQtWIKBHK1XLKtXLK1XIKlII5sxs/X8TUGFcGr+TywOUcM49x0DhIn93HUfMoR82jFKvFLPUtpUFvQBczEwq1uitRypqQqT5EuAQleCo7OONk2Jx9C6moLPY3sazkOkSTQFo5nMGTOIMncAbb3IzyzBBOvAsn3gXHtwEgNB1RUINSVDecVV6HCMQmPL4KwwqfvCrEjascNu/Lse2wwfFei3973WJBiSuON1admziuqwqryqJs7xykPZGlJOijKjKzjPsyrYwV/hXsze3lnew7fEr7FD7hG7OMlBLbAdMGw5JsP2Lw4vYMOVOCgOKIgu2M3a6uCurLNRoqVHIBCwmsrwpTHBy77XOmqBHu+Da0vQXvPw4DzfDuV2H/v8OVfwx1t47WJ33o2kXsaOmlvT/Njw5tJVW1D0ta6EIfHWg1mMrxvTcOsfOYe3N/QWmE37hlKXVls1+DuzCgj4riC8+euD4rrFxQzK2ra3h110m+89pB/vIz64nNNAYeHh4eHpc8vXYvMCyKH3kRrAwULILyyydeKdvjCuNKAHxFANiOzU8O/gRwrdM9PDw8PDw8PDw8PC4tJnNYnQ22bNnCokWLprx8WVkZjz/+OI8//vi4ovijjz7Ko48+OuN23XrrrTz00EN84QtfoKSkhO7ubmzbJh6PU1JSQiAQYMeOHezcuXPG7zUbeArcLKIoCpWVlXlRKyLq0/CpCobtMJA1KQn6ODyQojedQxGCNRUx/Nr834/ZZiYxrAj7WVgQonUozd4e1xK4VqvFJ3w40sHGHhUZu6wueqw+spYNIdAVhVdsQTAeJKbGiCkxokqUmHJqOh+yamcTIQQry6K809bPYNakZShDw1lqrWfIoFaoZMgQ4fy5HDjSwcLCku7DxBydHjKzdKbT9Ocy2NLCERYEbXw+k46ibmx0hBRIXXKkuhPD/DmqpbqidJGDLFSQixqQzgIcM4NjZXCsLNI2cQRAG45oQ8bfQiYVpKojVB0UHdTZ/c440iEhE0gkAoFEMuAMELU0ipM5SrMK1Utuo9xfc0aZgKmSD/2oJjQafY00+hrptXs5aBzkmHmMfrufdzLvsD27ncX6Ypp8TRSqhdN+HyVYAMGxiqMjHTZnNpORGQqVQq4JXjMqRgvNj1q6CLX01MWRzMZxBk64QvmAK5ZLy0D2uYMtRhCB6JhscqWw9gzb9YKQwj3rg9ywws/mfTneP2xwos/ie5ssaopVbr4sQEWBwNAqSGSh8CxfgeKgj4bCMEcHUuzvTVDg1wnpKpYtMW0wLYlhSQwLTNudNkemTYkxLGybw/MNS2I4TaRqjzGkp3iidSvy+OVjtmFakpES4LYj6U9IpHS1Zymhc8ChNCZYWKaP2qEvKFXRVEHatHnrhIMQYnazo2uvh+rn4NDT8ME/QrwVXn0UKq+Eq/4USlcRDug8fF0DzzRvpbO0DUXK0YFWv0z9kl2dJ3n7HZOhXg1FUfjklQv5+BV1qNM4jqZyDBYO7/9gzkRKed4GLD147SL2tw3Q3p/me5sO8eidK/N6sNRckA99qMfkeDH08JgdpnIsGdIg7sQBKFFKTlmnNz04OjBtXEas04OVo8ttPr6Z3nQvRYEibq6/eVb24VLG6wvzHy+G+Y0Xv/zHi2F+48Uvv/Hil//MJIYBbXbF8JGa4lJKNE3jm9/85uhrH60p/vWvf50FCxaMWf+BBx7gscceY8uWLWzYsOGs7/elL32Jxx9/HICqqip+9KMfnXWdlStX8ud//ufceuutOI6D3+/nu9/9LnfeeSf/8i//wooVK1i5cuWopfqFRsg8KG4Qj8cpKChgaGiIWGxmFqsep9jTE6c9kaWuIEhhQGdXl3tDYFV5jOoZZuxdqjhSsq1jkMGsSdSvsbLCx9PJH2JIA5/wYUgDVahc49/AoaEh0jKBomdQfVlyMjfptkNKaIxYfvrzxSyYn0xk2NuTQAjBNTVFRCfIpm82mtmc3oyJiY7OxtDGMTb1MJwliu0K1liY0jxj+nRBe2TalOYZovfp61rSOqM9liOxHIlzWhcrhEBXhFtXRFqkZMqdPywwA4RFeGrxlBIcC2wTaZvu9Ee7cyFA0VyRXNXdaaGg4No9n/730XkKijslTv2fc3K02+0ow39uux1u39VF1ZCB/6pfQ61ccfa2X4TkZI7DxmEOGYdGb64CVGqVLPUtpU6rm5VM+R3ZHezM7UQTGneH7z5n0V06DjLZ7WaSjwjl8S6QH0mPFqBEKlCKakfFchGtHGO7nsg4vLU/x3vNBqYtyRqSdE6iKqCpgiuX+KguVkfFbMMeFrUtOTptWA4FZVl0v0Mmo3DsqI4jZyZyagU9RFe/5bZx1/VYQ+PX+7FsGEg6KAJUFVQBEnj0rihLa84UvUf6osKAzlXVRTNq44SYKdj1BOz5V7Ddc8LQsvtoXnk7h0U3nalB8LnzVaGODrQyUzrSUVClzrLYQhojdVRpVUSV2c8SB3dAwWutvUgp2bighKA+uyNfJ+N4T4L/39Mf4DiS/3LzUjauqDr7Sh4eHh5zzKXye/Vi289Oq5OXUi8RUSI8YF4GP/kECBU+sxmCE5TpsFLQtw0QUHo1qO4gwv/28//GS0de4pHVj/DnN/z5+dsJDw8PDw8PDw8PD485IZvNcuzYMRoaGggEPL1sPjFebKb6e/XiVdIuAI7j0NLSQn19fV6MBCoL+WmLZ2iLZzgRzwCwsCB0SQviM42hItxaEVtPDpDIWZwY0NlYsHFUrPUJH9cHrycxUE40XUa5rnJNeRG6qpCTOeJ2nISTIOEkiDvx0eeczJF20qSdNJ10nvG+F7NgXh0J0J026E5l+LC7h9WVIUwM90+6j4STYFtmGxYWwhEYisHLqZfZm3PrKp8udDs4Z3nH6SOlQDoKlq0gpIouNRQ0QpqPQt1PWPOjCx1NaDg4bM9ux5Y2GhoWFprQuCF4A0EliBCnCdOni9dCGVe4xnEg0YXsb4OB48iBE5DLIJAIifsQCmpBFUpxPUpJPWpxPcI/9Yz6lJPiqfhTYwZ5+AyTgrSFWr1qVgTxfOtHR/ALPyv9K1nhW0GH3cFB4yAnzBN0Wp10Wp0ERZBGXyNNvqZpZ9GftE6yK7cLgGsD104rC10oCiJWiRKrhLr1AEjLwBlqxxk8fiqbPD2Ik+jCSXTB8e3uuqqOKKgeziavI1xYy52XF7JxhZ9Xd2b5+YdZwjJOEf0MWMW8sTdGcVSgKpOL3H0pnUWLc/gDDiVlNj3dbp+lCPBpAl0V+DTQNeH+r4FPdae1kWldoKvDy2t1nNQa6fEdofLqXWyQdxPQdHcb6qltJLOSv3o6Ts50CPoEGUMS0BUqi8YXeAeyJgBFgTmsoa2HYd1/x2p6kOPN/4dmtY/OogDEN4OvgFJ/KV2GiZSQMQS6H5Aqqd4IFVUOoaCgl5P0Zk4CEFWiVGlV7kOtwq/4J39/pnYMqoog5tMYypkM5szzKorXlUW5/5oGnt5ylB+8dZilNYWUFwTP2/vPd/K1D/U4hRdDD4/ZYSrH0hjr9L3DGRALb5tYEIdTWeL+4lFBfDA7yGvHXgPggRUPzM4OXOJ4fWH+48Uwv/Hil/94McxvvPjlN1788p+RGFZWVl7opnjMAfmtls0zpJQYhkEeJN8DkLNsMpaDBARQEvTRVDw9seZiYTZiGNBULiuPsb1zkJOJDCsDtXw69mkSToKoEqV9UNKXTqMqgrUVBeiqe3L0Cz9lWhllnJnVeLpgfrpYHnfiGNKYE8E85aRG2zxdEe90pJSYmKNC9mSPnMy508PCdy5gkFRzSOBQXEFXx4pslrQwMNx/hl+ysemz+ybcR01oaGhoQkMXOjo6qlBd0RptVLzWcZ9Pnx5ZRhMaKhqDaYeOpMVgxq2zDRDUVWqjQaqjAfzq+BdABUrB6ICJAIFxs9unjAIUFUBR0/Dn7SCTPTh9Ldj9x3D6W12hc/AkzuBJOPq2u1qkdFQkV4rrEaHiCe2Iw0qYjaFTgzx0W3L1kQHC+PCt+sT02v0R8q0f/ShCCKq1aqq1alJOikPGIZqNZjIyw67cLvbk9lCr17LUt5QqtWrK1s8pJ8Vb6beQSJp8TSzyTb1+zFnbrPlQS+pRS+pH58lsYkxtcmfwBNLMIftbcfpbT63rj6AXLeAqrRon0M9V6jto7hAgfpr7BIGqKykvUNFPE7V96kcEbk2Qsv20JpMURxwevipIWdiHpk4/Y3yNvIrnk52knCR9/j2sD6w/Y5mCkODh60L88O00WVPi1xUevi5EQWj843XwPIjiQ/YQh8xDHJFHyC1eAnYdItNHTddRmtreoDye5Rv+O7GvKsSvm1g5lUNby/mDDbfRVFFAr91Lh9VBh9VBr93rDrIyEhwyDiEQFKvFoyJ5uVo+bv841WOwMKAzlDMZyJozrgd/rty+ZgG7W/s5eHKQJ17Zz5/ed/lZB19cKuR7H+rhxdDDY7aYyrHUZ/cBUCIK4chz7symByfZqAOZLnc6eMqp5KcHf4rpmKwoW8Gy0mUzbboHXl94MeDFML/x4pf/eDHMb7z45Tde/PIfL4YXN54ofomStWz29ibGzEuZFjnbmdWaB5cqJUEfiwvDHBlIcaAvyVX+Iip9YbpSOY4NDgGwojQ6oRX4R5lrwXxEJB8RzLusLrZktpxhRW5Le1wB28R0n6V5StAe5zFiET4dfKpCznawbYWoGiSkBvAJHz7hQyA4kDuAjY1wBFKV6ELnpuBNRNTIGeK2hjZjK+uMadMWz3AykcWw3exzRQhKQ34WRIOUBPWzCp6NvkaqtepZHXwwghAKIlqBEq1Aq78aACcziNPXgtPv1pN2El04yV6cZC8c3+auF4iilDSgFi9EKW5ARCvGWGY3+hqptCLEB5vx7/4loZyFvvpeRCD/LTRnm7AS5vLA5az2r+aEdYKDxkE6rU6Om8c5bh4npsRY6lvKYt9i/GLiDF5HOryZfpOszFKsFnNl4Mo5b7sIRFErV4xm/7uDLHpPq01+AmeoE5lLYnfuJ2rv5latH4HEQsOHwf2BHxNYvYKCsoopvKOG7LE4mchyaDBBSbiY0REu08AnfFwTuIZX06+yL7ePer2eUrX0jOXWLfaxpEqjP+FQHFUmFMSzlk3atEFAwSyL4pa0OG4e55B5iC6ra3R+WAmzxL+GxoIlhM134OjfkEv38F86/xHRDYPhUnypLE9nfgWh3I4qVCq0Ciq0CtayFkMadFldrkhudzBoD9Jn99Fn97EntwcVlXKtfFQkL1aKz6lfLAzotA6dGixwPlEVwW/euoy/+M/3OdoZ58UPWvnE+vrz3g4PDw8Pj/xmVBTvPgK5IQhXQvV1E6+Q6wVpgeIHX/Ho7GcPPAvA/cvun9P2enh4eHh4eHh4eHh4eMwMTxS/REmbNraU+FUFW0pURWBLSdq0PVF8llhUGGIwZ9KXNvigc5D6ghCH+pOAa1M/W5l1kwrmTm6MUD4VwdyRDgnpDphQUcmR4xepX7A1s3VWrMdV1FExe6KHX/jHnX+oN0NHwiCkq1xTU4x2WmZgjVbD5vRmsiJLUATZGNrIYt/iGbf3dKSU9KQN2hIZejMGIxq/X1OoiQapiQYInuPxE1bCsyqGT4YSLESpXQu1awGQRhqnvwW7v9UVygfbkNkE9sld2Cddm26hB1CKF7rZ5MX1OIku1A9/RGE2DtJGlCxCXTj3Im0+owqVer2eer2eQXuQg8ZBjphHiDtx3s++zwe5D2jQG1jqWzqucPth7kO67W58wseNwRsvSEkEd5BFOUq0HBasA0Da5rDt+gns9t1YmQFsBxQcQBIWScTbf0uuvBGlvAm1bAkiVoWYQHhdVhJlIGuRNi329iRYWxGbcib9eNTqtSzSF3HUPMrbmbe5J3wPqjjz+CwITSyGjzAi/EZ9GvosWV8N2oMcMg5x1DxKTrp1wgWCWq2WRl8jNVrNKZG6/mOw4Cas975OdMufoGRsotkhhHR4VPwv5Cu/hOIlEK2BSC1Ea/FFalgQrWVB+HJQr3L7equTdqudDruDtJMezSoHdyBBlVZFhVJBVsmePVPc7w4OSJoWpuPM2ucyVUqiAX7txiae+OV+nn+/lZULillU4Q3O8fDw8PCYGjmZI+G4v3lKDr3ozmy8H5RJruXTw9bpwUoYvkY50HuAfT370BWdTyydHeckDw8PDw8PDw8PDw8Pj7nBE8VnEUVRqK2tzYtaESFdRRUCy5HoisB0JJoiCJ3HuqDzkdmMoRCCy8pivHG8j/6MSV9m6Lzb1PsVP2XKmYK5lBJDGmcI5Qkn4WZMDGshIyK4xF1eExoCgS70UfFaF/ros0/48DGxqO0TvhkJesuKdQYyA6RNm0P9SVaURkdfa/Q1UqVW0ZPqoSxcRkSdeq3ss5GzbNoSWU4msmQte3R+SdBHbSxIWciHMgPx7kIhfKGx2cCW4dpl97fg9B3DGTiONLPYXQexuw4iHRvS/e7KUgISJ9WLzCYQwYJZaVM+9aPToVAt5Org1VwRuIJj5jEOGAcYsAc4bBzmsHGYErWEpb6lNOgN5GSOw8ZhdmV3oQiFDcENxNT5I/oJVUctXug+qi7D6TkCZgYbFdXOIoY7Erv3KHbvUUxA+MMoZUtQyxpRyxrHOAyoimB1eZT32gfpSedoS2RZEJtZregrA1fSbrUzaA+yJ7eHNYE109rObNUTt6RFq9nKIeMQ3Xb36PywEqZRb2SJb8nEg2RUnXD9dZjvR0mbFhomqrDQcRC5HuhJQs+OcVYUEK4gFKllUbSWRZEaZLSGeLSBjpBOu5qmy+7GkAatZiuttCLLJEcyR6jWqqnSqqhUKwkqY2Ph1xSCukrGtBnKWpSGfDP6bKbD1Y3l7Gjp4/3mbp54ZT9feXg9fu865qLuQy8FvBh6eMwOZzuWRrLEo9KH/8RbgIDGSazTrQyYg+508FRtwWf2PQPALQ23UBgonIWWe4DXF14MeDHMb7z45T9eDPMbL375jRe//MeL4cWNJ4rPIkIIIpHZE+LmkoCmsro8xq7uOJZ0BfHV5bFLPkt8tmPoSIlhO2NMw+eDTb0Qws0wH0cwT9pJnko85YrgaFhY6ELnnsg9FKqF6Ogzth6fLrqqsLIsyvaOQdriGcpD/jFCTESNEInNTvyklAxkTU7EM3SnT9UQ0VWFmkiA2liAkH5xdaFC86GWLkItdetVS8dBxjvcmuR9LdhdB5DSAYSbHaMHwbGQqT6YJVE8n/rRmaALnSZfE416Iz12DweNg7SarfTZfWzJbOGtzFtknSwmrhjbqDWyUF94gVs9MUqwAN+6T2Nsfwph5xB6DP2KT6OWNOD0HMLuOYzTewSZS2G37cRu2+muF61AKXcFcqW4npjfR2NxmIN9SQ72JSkM6FMuMzEeASXAVcGreDP9Jrtyu6jT6yhSi855O6dE8ekJvwP2AM1GM0fMIxjSAEBBoUavoUlvolqrnlq/GluIHoiiKklMrRjNSiB8YbjrSbCzkDgJyTZItEFyeNrKQqrTfXQNl0kACoYfy4SKE6mmr3wJ7aW1dMYK6A6opKRBsx2n2WgGOFWPXK2iQqtAExqFfp2MaTOYMy+IKC6E4NdubKS5Y4juwQw/3HKEX7ux6by3Yz5xqfShFzNeDD08ZoezHUuj1umDw4PUqje4jisTkRnOEvcVg+o6fpm2yU8P/RSAB5Y/MPNGe4zi9YX5jxfD/MaLX/7jxTC/8eKX33jxy39GYpjNZi90UzzmgItL0bnA2LbNkSNHWLx4Mao6/8XlmmiQkqCPtGkT0tVLXhCH2Y9h2rSRSHyqwJGgiflvUx9RI9wQuoHN6c2YmPjxszG0kQptKnWB556SoI+6giDHhzLs7Y2zoaYYXXXFpNmIn2k7tCeznIhn3BrCwxQGdGpjQSpCflQl/7LCp4NQFERhDUphDSy6Hic9SOYXfwlmBhQf4CBUPyJcMmvvmW/96EwRQlCulVOulXOlcyWHzcPsy+2j0+5EIhHDfx1WBykndd6s9qeDVnclFC/mxKFdLGhajRZxa20qkVK0hg1Ix8IZOI7Tcxi7uxlnqM2ta5/owjryFigqakkD1aVLSChVtMsCdnfHubq6aEbHXL1WzzHtGCesE2zJbOGu8F3nNLDHtB2ShgW4/cBUsaRFi9lCs9E8Jis8okRYoi+ZPCt8IsKVcNPfomz6A/xWCnwRuOlvYcGN4y8vJWT6TgnkI2J5os39P3kSHAslcYKyxAnKjsBqIGPb9JfX0llcSUdJNf2xEvqVLvqVZvYqGqqiUyaKCCg1ZJUyBrIacJZ9SXVCvBViC939mCXCfp3fvHUZf/vcTjbtaeeyhcWsrT+zDMGlQtyMc+DEAZYtWEZMnz/OEh5T51I7D3p4zBVnO5ZGRfG2D9wZTZNkiUsHsl3u9GlZ4q+3vM5AdoCyUBnX110/a2338PrCiwEvhvmNF7/8x4thfuPFL7/x4pf/jMSwpmaSQbMTMcv3v4QQ/O7v/i7//M//DEBHRwe1tbV86Utf4itf+Qpf+cpX+Na3vkVpaSmWZfFXf/VXfPKTn+QrX/kKf/d3f0draytFRUUkk0lWrVpFS0vLhO/V0tLCe++9x8MPPzzu6zfddBPd3d3s27dvdN6KFSsoLy9n06ZNbNq0iX/6p3/i6aefPmO9zs5O/H4/kUiEf/3Xf2Xp0qUz/mymiyeKzzKOM/Oay+eTgOaJ4R9lNmN4uk29L49s6ht9jVRr1SScBFElOu+EuMaiCL1pg7Rpc6AvyWXlp278Tyd+UkqGchZtiQydyRzOcFa4qgiqIgEWxIIzyla9WFBChfjXfxZj+1NIO4dQA/jWfRpllrLER8i3fnS2CCgBVvlXUawU81zyOeSwx0RABDAxSTiJeXcsfhQRLCAXqhzXTl8oGmrJItSSRejLbkcaaTeDvKcZu6cZmRnC7jmM3XOYBiSlBIhHFnI83kT9otWIQHScd5xCm4TgmuA1dCW76LV72W/sZ6V/5ZTXH8kSD/s0/OrZxfSJssJr9Vqa9CaqtKqZuW00PQg110P8OMTqJr/AFgJCpe6jfBzreMeGTI8rkg8L5jJ+HLNjPzXZOLWHd0Lzh2R8ATqKK+gorqSjuJJUIEQnXcABHEUwlIPEQYdqK0SVUkYstAARWeBm3PmicOhp2PQHYKVBC7lC/mTiwzmyoraIj62p5Zc72/ju6wd57NMxYhcgc/1Ccyh3iDcyb2AUGOxI7WCtfy31ej2KUFBH/sTYZ4FAXODyHyknNW+vNy4Ul+p50MNjtpnsWOqz+8DKUNpzFPwFsPC2iTeU6wPHcAeF+k8NBn12/7MA3LvsXtTJapF7TAuvL8x/vBjmN1788h8vhvmNF7/8xotf/nNGDKV03Rgno/nH8NafgpkGPQTXPw6N902+jhZw799NQHFxMVu3bsW2bVRV5emnn2blyrH3Nf/0T/+UL3zhC+zfv5+NGzfS3e0m5xQUFPBP//RPfOlLX5q8DcO0tLTwwx/+cEJRHEBVVfbs2cOqVavYvXs3mjY1zeTpp59m1apVfPOb3+SP/uiPeP7556e03lzgqTweHnNIPtvUh5XwvL05rSqCVWUx3u8YoCOZpSzsozIcOOftWI5DRzJHWyJDImeNzo/6NWqjQaoifjSvdsgYtLorUcqakKk+RLhk1gVxDyhQC/ALP4Y08Anf6HNUmZ4oPF8RvhBazWqoWY2UEpnswe5pxulpxuk7StjMog3sg4F9JJt/il5QhTJci1wpqUeoU8/aDikh1gfWsyWzhQ9zH7JAWzDl+uyDU6gnPllWeKOvkSX6EkJKaMrtPSvhytnJtlbUU9uqXA+AtG06mpuJNDaiCgnJDoLJkyxKtrEo0YZsbyNhHqVDN2mPhGgvqsDUfJwICk6QAY4Tzh2gqrmLqv4OqoaGCHbvJuUPkSisJJroIfzL34H4Cfd9taBbikELuT8EtJA7b3Q6AKp/0h8IAPdf08DetgHa+1J8d9NBvnjXqpmLvXOU3T5bONKhz+6jy+7ihHmCw+ZhHBwQ7ndya3Yre3N7Jx2EIRCoQkVBGVc0H31NqOMK61N+7bRlRkV6oXLMOMbbmbcxMdHR2RjaSKOv8Tx+ih4eHpciWSdL0kmCkaA4MQBLPwvqJIOpRqzTAxUw3Kf2pnt5s/VNwLNO9/Dw8PDw8PDw8LjosbLw5NqJX3csN4FFOu5vhuwA/OLzsOVLoEwiw/7aDve+2AQIIdi4cSNvvPEGt9xyCz/+8Y+5//77x112+fLlaJpGb28vAL/zO7/Dd77zHX7/93//jGX/+q//mqeffppcLscjjzzCH/7hH/Lnf/7n7N27l7Vr1/KFL3yB3/qt3zpjvYcffpgf/vCHrFq1alRAf+WVVybev49www038A//8A9TXn4u8ERxD485xrOpnxsKAzoNhWGODqTY35ukyK+jTVH/SBgWJ+IZOpJZbMfNxlWEoDLipzYapMCvXfDMufmMEiyYtRriHmcSVsJsDG0cLWHgEz42hjbO20Eqs4EQAhEtR4mWw6LrkLZrtZ5o3YPVfQgy3SjxDpx4J9aRzaBqqCUNKGVLUMuaENGKsx6zS/QlHDOP0WF18E72HW4P3T6l43wkU3w86/QBe4BDxiGOmkfHZIUv0Be4jhtqdX73JYoGsQXuYxgBxIYfS60cu1v20ztwGH+4j6QvRbduk1L9HA5EOVzdAEj03EaGoqUgwGfmuOHd52j84O9d8XsqCMWt3zoqoJ/58Gkh/rBM4ZfdfeSafRxS61haV+2K6nrotPWHp/Vh8V0NjD8qd46z26eDIx0GnAE6rU46rU667e7R750lLSQSBQUh3exvB4egEkQTGra0cXCwpY3NqdIgEoklTw0KGzaoOG/7k5CJ0XYbGLyaehVFus4KfsV//hrj4eFxSdFn94G0iQ114bNMaHpo4oXtLBgD7nSwanT2Tw78BFvaXF55OQ1FDXPcYg8PDw8PDw8PDw+PeY1jAtK9hyUEoLj/O+bkovgUePjhh3nyySdZtmwZPp+P0tLSUeH7dN59910URaGsrAyAoqIiHnjgAZ544okxAvfLL79MW1sb7733Ho7j8LGPfYw777yTr371q+Pan5/OXXfdxe/93u/x2GOP8dJLL/HP//zP5ySKP//881x22WXnsPezjyeKzyKKotDQ0IDiZZbmLXMVQ8+mfm5YVBiiJ50jkbPY25tkbXl0wvjZjqQr5WaFj2R+gmtxvyAWpDoSGK1N7nHh8PpRl/lewmAiZit+QtVQSxdRWdLAe+2DpJNDVOVOssjuwOlpRmbj2N3N2N3NmPwcEYiili1BKWtCLVuC8EfO3KYQXBu8lueTz9NpdXLIPMRS3+T1ayxHEh+uJz6SKW5Ji2PmMZqNZnrsntFlo0p0NCs8qExR7J2HnFMMNT/hkkY6lGqKI342lBdgSYtuu5sOq4MO4wS9Zie94TQSgZAOhubnpRs/S0t/kqq0QWkyTkliEN1IufZSdtZ9tjLDPyhwR9laafcxiVNVAXCXbpHImIi9YJ0IoE21Hv3pIjsKdGxx7eW1INiDrkBec/15zRiXUjLoDLoiuO0K4SMi+Ag+4aNCq6BYKWZbdhuWtPApPgwMgiLIJyKfOKP/kFJiD/850nGnhsXy0efJXhsR2Md5bXSd8Zb/6LK4VmQCN0YSiYHBpswmtKxGRIlQqpZSqpZSopZQopagi6k7ROQr3nnQw2N2mOxY6nP6wEhSGu+DsjVQNIlDxUiWuK9wdDCXlHLUOv2+ZWexQ/SYFl5fmP94McxvvPjlP14M8xsvfvmNF7/8ZySGYyzUtYCb1T0RqU744Y1gpMAXAyMOvjA8/Mbk95K0s7vfbtiwgS9+8Yv853/+Jw8++CDZ7NibY48//jjf+ta3iEajPPXUU2MSdH7/93+fjRs38sgjj4zOe/nll3nhhRfYvHkzAIlEgkOHDlFcXHzWtgQCARobG3nyySdpamoiEJiae++DDz5IIBCgoaGBr3/961NaZ67wRPFZZqoe+h7zFy+G+YMiBJeVxdh6coDedI72pI+q8Fjrw7RpcSKepT2ZxbSHb8ALQXnIx4JYkKKAnt+ZnBch3jHoMp9LGEzGbMZPEW7JiXdMixN6I76itSxa+wAy2e1arXc34/QdQ2YTWCc+hBMfuusVVLsieXkTStFChOq2KapEudx/Oe9n32d7dju1Wu2kn/FQ1kRKSUBTyYghdmYOccw8NiYrvE6vo9HXSJVaddH0JecSw5EM+sGshZQSTWhUa9VUa9UQWEer0cqLiefAzOAoCrYicBSd4+ULaBfu+wgEBWrBqABaqpZSqBSiSlxx3MoOi+KZsz6CZpode1uIx4codWBdbQjFzn1kubS7TTt3akdGXhuZtg1cH/K0WzfKTMNbX4I1/xUqrhi1z51NpJQMOUOjAniX1UVWjv2howudCrWCSq2SSq2SIqVo1B49rIRPOUwwscOEEAJt+I8L9JWVUpJwEvwo8SNMaaKhkSOHgkKBUkBKpkg6SZJOkhazxW33RN8TcfENOvTOgx4es8NEx1Kf1QtGgpJ4HzR9buINSAmZTnf6tCzxXV27ODJwhIAW4OONH5/NJnuchtcX5j9eDPMbL375jxfD/MaLX37jxS//0TQNwzgtMUGISW3OKWyAm/5u2HUwCb6I6zpYOHNXKSEEN9xwA48//jj79+/nP/7jP8a8PlJTfDyqqqq47bbbePLJJ0fnOY7Dl7/8ZT7/+c+PWXbTpk1j/v/1X/91PvzwQ1auXMn3v//90fkPPfQQv/Ebv8F3vvOdKe/DSE3x+YB3dM4ijuPQ3NxMY2Mjqnrx3aC7FPBimH9EfBpLisIc6k+yvzdBR3ucZfV1pG3JiXiG/sypk1dAU6mNBqiJBvB7mfvzEu8YzG/mIn4hXWV5aZQ93XGODqYoDvooilagRCtg0fXDVuutON2HsHsO4wy1jz44/CZC1VFKGlDK3XrkS8NLaTFb6LF72JrZyk1iHaT7EOFStzTAafTmMsT14/SG2jiWHBqdH1WiNPmaWKwvzuus8PE41xgW+N2BRTnLJms7BD/St5ZqpfjVCIaiE3TAUCSq0FkbWEvCSdBn95F0kgzagwzagxzmMAAqKsVqsSt+aqWU+kuJKgvOOvBAAZatyPLlp7aRyVl8sqSeT11ZP8HO2q4wPpKZPpKlnjwJL/06mBkQGphxQMCJ1+DkmxCugkV3w6JPQPHSs9Y7n4gRYXhEBO+0OsnIzJhlNKFRrpa7IrhaSYlaMmGN8EZfIxWign2t+1ixcAUxPTatdp0PhBDE1Bg3hG4YFfKDBEdriudkjj67jz67j167l167l7STHvd7UqQWjckoL1AK8nqAince9PCYHSY7lvqyreCYlKRSbn8+EUYfOAYIHfylo7NHssQ/tuhjRP3ROWn/pY7XF+Y/XgzzGy9++Y8Xw/zGi19+48Uv/xmJ4YIFC86+8Ok0Pei6DMaPQ6xuVt0GH330UdasWUNJSck5r/vHf/zH3HrrraP/33777Xz1q1/loYceIhQK0dLSQlFREdFolEQiMbrcRKL3XXfdxZ/8yZ9w55130tzcfO47c4HxRHEPD4+8Z2FBkNahNP1ZkwQhTrb24VMV1zZXQGnQzQovDfry+ka5h8elSnUkQF/aoCOZZXd3nGtrikbLHbhW64tRSxejAzKXdMXxnmbsnmZkNoHdfQi7+xAmIAIxrqxu4BdVBm1mM4f2vU5d9xBC9eNb92m0uivps/toNprZ4zRjBk18ioIPlTq9jiZfE5VqpdeXDKMqgqhPI54zGcyaBCNjf/CFlTAbQxtd4VM18aGPCp8jZJzMqPA58jCkQY/dM8ai3id8Y7KES9QSQkrojDaVRAN87oZGnvjlfn62rZVVC4pZXDmOQKyooITc+uKnU74Gbv3GqZri/hpY/dtgpqDlJUh1wO5vuY/CJbDoHvcRO/uPpaSTpMPqGLVETzvpsZ+nUClXy6lQK6jSqihRS84pEzqshCm0CvPGZWKiUhF+4T/lODBM2kmPEclHvicj0yP4hO/UgIrh70lYhL1j1sPDA3DPOSmzHwEUl1wN+iT95WiWeMWoQ0jWyvJC8wsAPLD8gTlurYeHh4eHh4eHh4dHXhGunJPSe42NjTQ2TlL2aRIWLVrEhg0bePvttwG488472bdvH9dccw2O41BYWMgzzzzD6tWrMU2TtWvX8oUvfGFMHfLT8fv9/Mmf/Mm4r7344ovU1taO/v/WW29Nq81ziSeKe3h45D052yFl2cjh/x3AsB3qC8M0FIQJ6t6oPA+PfGd5aYShnEnatNnXm2B1eWxckUv4I2i1a6F2LVJKZKLLtVrvacbuO4bMxgkf3cmydIC9VUG214VRzByxtEHv8edpKWijnzgAprTRnTCXB5ezIthEQJlanZxLjcKAPiqKV0XO/IwmEj5HCCpBFigLWKC7ovJIBvWI2DmSMWxIg3arnXarfXTdsBIeI5QXq8X4hI9rmirY1drHu4e6+dar+/nyw+sJnMu5YKLRvdd+GdregCM/gxOvw+Bh+OAf3EfZWlh8D9TfBSE3ozDlpMbUBE86yTFvo6JSqpVSqbp26KVqKZq4tC7Pc2mVwSE/vgKVcGTi5UJKiJASGvs9kQl6rd5Rsbzf6ceQxmjm/QhBEaRELRkjlHvHs0c+89WvfpUXXniBHTt24PP5GBwcPOs6Ukq+/OUv88QTTzA4OMh1113Hv/zLv0z7xkq+0pdrAzNFLBVHb/y1iRe0c5Drc6dPs07/5ZFfkjASVEerubr26jlurYeHh4eHh4eHh4fHpUxvb+8Z8063Sv/KV74y7nofnX+6/Tm4tcZ///d//4z1XnvttQnb8lF7dYBVq1aNzr/ppptIp9NnLDPeeheSS+uum4eHx0VJ2rRxpMSnCizLQVMUEFAZDniCuIfHRYKmKFxWHuO99kG6Ujnak1lqopNblwshELFKlFglLN6ItE2c/hbsnsOsaPuQQ2UWfbEQL691s1EDho3P7kNVA1QotaRTFcQo5fKyUi/LdBKKAjrHh2AwZ064TFgJTzl7ecRaO6bGWMQiABzpMOAMjMkSHrKHSDkpUk6KVrPVXRdBTIlRqpZy9YYijiVtujvSPPXWYT5/89Jz27HxRvdqAai/w33k4tD6Szj6M+jYCj07yAwdoKPl3+mqW0dn2ULifv8Ye3UFhVK1dLQmeJladsmJ4Kfz+p6T/MtL+zAsm6BP479+bDk3raqZ0rpCCGIiRsw39nsy5AyNGVAxYA+QkRnarDbarLbR9SNKZIxIXqKWoAt9TvbTw2O2MQyDhx56iGuvvZZvf/vbU1rnf/7P/8nXvvY1/u3f/o2Ghga+9KUvcccdd7Bv3z4CgUtnkEhf92YQDqWGA+VrJ15wJEtcLwDtlKPIswdc6/T7lt03YTkLDw8PDw8PDw8PDw8Pj/mJkFLKsy92YYnH4xQUFDA0NEQsNn/rI0opcRwHRVG8m+d5ihfD/CRr2bze2ovlSDRFjD7fvLCUgFc7PK/wjsH85nzE79hgmub+JKoiuKa6iLBveoJiIt3Bf8R/QManIqRECoHqSK5ql6ys+Thd6gIO96eoCPtZU1Fw9g1eJEwnhjnL5o3jfQgBNy8sdQcmnQdMaY5mCI88fzQT27Ac+uMG2SE/a8sXsqq0jlK1lJgyvtPAuZJ1sm4WeOYInakDDDkJtzb5MALx/7V35/F5lXX+/19nubfs+562SZvupYWyFShLqQJaUUqpzpf5SnXcwVHBr8IoUBwXGEedhzKiDgqO+oNCsYgCylr2LiyFlpauSdckbfb1Xs451++PO7mbtNmb5T53P0/No8m5z7nv6+KdcydXPue6DtmWSYG/nIKsReT5ise08OqW99CGtiBPvXWAh17bg20rdA0cBbquce60POaUZlJekMa0gnTSk7yn9FqWsmiwG3otvd7itJy0n4ZGupHea+WBDD2jz+Xr2532flc+OFVuyVD0NlHj1QcffJCvf/3rg84UV0pRVFTELbfcwje/+U0Ampubyc/P58EHH+RTn/rUkF7PLeNy6P9cen77rRxK8XNupJBZU7/Y38FQtxGcEKTNjC6fDhxpPcKS3y9BoXj+089TklbS9/HilMl7oftJhu4m+bmfZOhukp+7SX7u151hOBymqqqKsrKy0+pCYjcIBoNUVlb2ymao49XTd2rKGLEsC6/31P54JyaWZOg+ftPgjLw03jvagt1VED8jL00K4i4l56C7jXV+U9ID1HeGaegM896xFs4tzMTQhz/IaPdqKI8ffziI0jR0x0bpOnl1TWhVv8eTXoE3/2IysosGf7IEM9wMfaZBwGPQGbFpClrknGIRc6g8mic247pb9/3JYwVQrY5QwEbTg+yydtLQsR9d0/Bq3l7LaecYOSfdn7yvwmdIhaixaqi1aqm2q2mym44fkJSCRipZjp/8xloKKjeQf/hdvFbXDHpvKkz+cPT+44XnRe9pPgbi9T1UKcXu6mae33qYt/fV0Rm2cByFaWh4TINw16ove2uaOVh3/OKGnDQ/U7sK5OX5aZTmpAzrnDc1kzwzjzwzL7YtpEI02A29ZpS3O+002U002U3sYQ8QXd4+08jsNaP8qHWUVztfJUIEDx4WJy2mwju6y0/Ha4bCvSorK6mpqWHp0qWxbenp6Zx33nm88cYb/RbFQ6EQoVAo9nVLS/SCEtu2sW0biK7aoOs6juPQ83r7/rZ3/3Gwv+3dz9tzO4DjOEPabhgGSils2yYSieDxeNB1PdqW+h3UewE0MvMuif2x8qS2R5rQnRBKM3A8mdDVpnU71qFQnFN0DoXJhbG2jlefem7v/u/b3/ah5jHROfXXpz7zc3mfEjGngfqklCISieD1ejEMIyH6NNLtbuzTYPm5sU+DbU+0PimlsCwLn8+XMH3q2fZE71PP/BKlTz23J3qfuvPzer0J06eB2p6IfdI0jUgkgm3bsZ+LSik0Tev1vD33j6ftwxFvbR9qn3q+X3bnemK+/ZGi+ChyHIfKykoqKiowDCnGuZFk6F7FqQEyvAa7KvczfdJkkn3yx2Q3knPQ3cYjP03TmJebyuuHG2kNWexubGNmdgfzmw0AAIEFSURBVOqwnydVT8VjBAjrOl5lENZsvJqHjLzJ0P4WgbodzG7YTcC6BJW8BM08Pd5TRpphhs8TLYqHIuNWFO9LX/cnbwy08MuXN9CmN+EtdsjOtwmrMNVWNdVWdezYJD0pVvzsdDrZGtqKhYWGRqmnFFvZNNgNKHr/cp5pZMbuCZ5v5uPTfJAJlN0EDR9El1ff9zdor4Hdj0U/AjlQ9hGY+jHImddrifVTEY/voWHLZuPuozz33mEO9Sh2zyjKYNuBBmxHkewzadc0vB6df75kOjVNHeytaeFwQzt1LUHqWoJs3HUUAK/HoCwvlakFaUzNT2NqQTqpgeHNvvdpPgrNQgrN4/cJ7nA6es0mr7PrCKtw7HOILs/epqJ9MDHppJPnO56nw+mIvqdoHryaF6/mxaN5oh94hrXEcjxmKNyvpia6FHh+fn6v7fn5+bHH+vKjH/2Iu+6666Tte/fuJSUlBYgW1wsLC6mtraW5uTm2T05ODjk5ORw+fJj29vbY9oKCAjIyMqiqqiIcDse2l5SUkJKSwt69e3v9UaqsrAzTNNm9e3evNlRUVGBZFpWVlbFtuq4zffp02tvbOXDgAA0NDWRlZeH3+ykvL6dxx/9HR6kPpfuoP2wTST5MaWkpDQ0Nve7VV5raSrIJbVYyh/fsA6I/Tx7Z+ggAl+Zd2qs949WnQ4eO3wLC6/VSXl5Oc3NzrwyTk5P77FO85tRfn44cORLLLzU1NSH6lIg5DdQnx3FoaGigrKyM4uLihOhTIubUX5+686uoqCA/Pz8h+pSIOQ3UJ8eJznCcN28e+/fvT4g+QeLl1F+fHMfBsizmzJmTMH2CxMupvz45joPjOEyfPp19+/YlRJ8g8XIaqE/l5eXs2RO9WN5xHEKhEJqm4ff7Y++vPfvafQFLJHL8toKGYeD1erEsC8uyTtreXXTvZpomHo/npO0ejwfTNAmHw73a3n3hWigU6lVE9vl8aJpGMHh8FUMAv9+PUqrXRc9u7lP3PpFIhP37o7dUbGvrvXpkf4a9fPrhw4f59re/zdNPP01HRwfTpk3jgQce4Oyzz+5z//Xr13PZZZedtL26upqCgoI+jjiZW5Zps22b3bt3yx+xXEwydDfJz/0kQ3cbz/yOdYR4pyb6C/GZBenkJvmG/Ry7w7t5peOVk2Z8thw7QP3bj5Padogkj47mT8cz+yqM4vkJv/TVSDM82NLJjrpWsgJezi7MGLsGjtCh+jb+/dG3sWyHf75kGvNnJfUqfjbZTbFit6McWlUrCoWGFvs3VUtF13TS9XQKzAIKzULyjXz8+hCW0FIO1L4Fe/8GVU9D6PhgjtRJMHUZlH8MMspPqZ/x9B5a3xrkhW2HeWV7De3B6CDKY+osmp7PknnFlOak8Mr2an7z3HaCEQe/R+cLS2ezeHaPQnXIovJoC3truj5qW+gMWSe9Vl5GoKtAHp1RXpSVPKIVJHpSStGqWqOFciv6fVJr18aWXu/+3gBI1pIHvDd8d4HcS++CeexzjhfTDWVQc7CGqZOm4jf9scdO9d7FY7nk+1hxU5tHY7x66623cs899wy4z44dO5g5c2bs66Eun/76669z4YUXcuTIEQoLj59jK1euRNM01qxZ0+dxfc0U7/5jVXc/43VGSiQSYc+ePUybNg3TNNGdCAeevZYX55xNRmAyy7I+3XcbnTB6wyY0wMk8C2VEVxHZfGQzNzx+A8neZF5Z9Qp+09+rLTLLZnT7dFJ+CdCnRMxpoD7Zts2ePXuoqKjA4/EkRJ9Gut2NfRosPzf2abDtidYn27bZu3cv06dPP2nWnVv71LPtiZJTf33qmd+J3NqnntsTJaf++tSdX0VFxUl/Q3JrnwZqeyL2SSnF7t27KS4u5tChQ7EluuNt9vTpPFM8GAxSVVXFlClTYivttbS0kJWVNbrLpzc2NnLhhRdy2WWX8fTTT5Obm8vu3bvJzMwc9NidO3f2akheXt4AewshhBBC9C83ycek9AAHmjvZdqyVC4pNfMO8ZUKFt4Iis+ikokujN4fd0z7JpGAlk6tfQnU0EX57DXrVG3jnLEPPLB2LLrlahj86U7c5FEEpFXcXD5Rkp3DtojLWvLqXR16vZFbJQqZnTGc60T8yWMqKzRLeH9lPWyR6damGho6OhsYZvjOY5Zt10jLrQ6LpUHBO9OP878LhV6Ozxw88D60HYMsvox/Zs6PLq5d/FJKHdvFoPFFKsfNIM89vPcQ7++roHsdkpfpYMq+Yi2cVkuw/Pqt78exC5kzK5GhzkLx0P1kpvS8wSPKZzCnNYk5pFgCOUlQ3drCvpoXdNc3sq22huqGDo02dHG3q5I2dtQD4vQZled1F8jTKC9JI9g1vNrmmaaRpaaTpaZR5ygBotVtZ07qGsAqjo2NhYWDEViYIqzARFSGiIoRVGJvogLt7WwcdQ3rtcHqYnZ07e20zNTNWIO9ZUPdq3l7bT5yt7sXLQesgGzo3YGHhwcNFgYuY5p0WK+r3XP2g57ZeqyKok7f19XWv7b0OV4O+Xs9/94f383bobWxl49W8Y7JMfby55ZZbWLVq1YD7lJeP7MKZ7ovRa2trexXFa2trWbBgQb/H+Xw+fL6TLzozDOOkC2+6/6B0ouFu7++CnuFs1zQNwzDQdT32L1XPUZ8UAN0kJ7n3hUO92hI8Fv3Xk4buPb4SzV92/gU0uGraVST7+r5IYzz6NNTto5XHRPapV36j2HbJafz6pOv6qOc30X0ai+3x2qdTyS9e+3Qq293Yp+4xWSL1abDtidSn7vwSqU8DtXG42+O9T5qm9duWvvbvPiae+zSS7W7tk23bsTZ2Z9l9Tvb396542z4c8db2ofSpZx7d+Q11csiwiuL33HMPpaWlPPDAA7FtZWVlQzo2Ly+PjIyM4bycK/V3wgn3kAzdTfJzP8nQ3cYzv+lZKTQGI7SGLLYea2VhQfqwfxlM1pNPmoHYGIyApuErPgP/zIVY+17F2v0iTsMBgq/8ErP0LDyzrkDzx+/qNadiJBmmeAxMXcdyHFrDFmnDLECOh6VnlPBeVQM7DjXyP8/u4N+uPROjq6+mZpJv5pNv5jPFM4WalhrCKoxX88b+nembObKC+IkMD0y6LPoR6YwWxvf9DQ69DPXbox+bfxwtoJcvg7IrwZc+5KefiPfQUMRmw65ant96mMP1x5cum1mSweXzSpg/JbvfmdtZKScXw/ujaxrFWckUZyXHZpS3ByPsq21hT9ds8n21LQTDNjsONbLjUGPs2ILMpNhM8qkFaRRmJqEP8/0i1UjlkqRLYitMJJE0YLHWVnasUB4mHCuW9yycn7g95IRodprxaB4iRLBUdGa8pSwsLDpV57DafOLKB0GC/L3j76R2pp7y7POxcmKbUfBKxysUmUVxP2P8VOTm5pKbmzsmz11WVkZBQQHPP/98rAje0tLCxo0b+fKXvzwmrxkPer0f7nqU+vws8KSSbeb0fYBS0Nm1jGPg+IVJHZEOnt7zNADXzr52rJorTiBjAveTDN1N8nM/ydDdJD93k/zcb6QZOp3NqPY6tOQc9MDQ/5bTH03T+PKXv8wvf/lLILoCd0lJCbfffjurV69m9erV3H///eTk5GBZFj/84Q+5+uqrWb16NT/96U/Zv38/mZmZtLW1MXfuXKqqqvp9raqqKjZt2sTKlSv73ef3v/89n//856mtrY1Nln788ceZPXt2bHWLBx98kI985CNxOzF6WEXxJ554giuuuILrrruOl156ieLiYr7yla/w+c9/ftBjFyxYQCgUYu7cuaxevZoLL7yw3337WqYNoldodC9xEI/LQei6ztSpU2NtdctyEIm4xMVI+wTEMnQcJyH6lIg59dcnGHl+8dqnRMxpsLZPnTo1VthMlD4NtD3R+jR16tRebRnLPinHYU52Cpuqm2noDFPV3MmU9MAp9Qm6iuJAmlfHQUcvvxhfyVlYH/wD6+DbWAffwjqyFWPapXimXoRuel2XU3db+vre617ia7h9SvMZ1Hc6NHSESDb1uOqTruvomsYNl1Zw16NvUXm0hcc3VrL8/PKT+uTHz0WBi3i181UiRPBqXi70X4hfRQu3o9on3QtTroIpV6GFmtEPPIPa+1eofRNqNkU/NnwPreRinCkfQZVeBmagV19PzKmiIlqgHWp+p5JTXWuQ9e9X88qOGjpDFgrwmTrnT8/jsrlFlGSnxPa37ePPM5rve0k+k9klGcwuyYj221HUNgfZXd3M3ppm9ta2cLS5k+rGDmoaO3h1Rw2gSPKZ0XuT56dTUZTOlNwU/J7jVxX3l99UcypFaUU0W82kaCkk68nYtt1vn/y6n4AeiPZJI/oxSJ9IOb7dUQ4RIti6TcgJEXbCxwvrRLA0K7rNOV5gj3C86N6horPTta7/dXOi726MVM/n6v78xNeIbe+++EB1bdMG2BeNsBamzW6LrdLgxUuECK1OKwECcfkz98Tjxlr3/bIPHDiAbdts2bIFgGnTpsXu9T1z5kx+9KMfcc0116BpGl//+tf5/ve/T0VFBWVlZdx+++0UFRXxiU98YlzbPl4Mwzi+5GjLQdSRN6ivWA7eFLL17L4PijSD3QmaAf7jf8B5evfTdFqdTMmYwpkFZ45D60Wv/IQrSYbuJvm5n2TobpKfu0l+7tedYc97WCulwI4McBRYB98ismUtygqjmV48C1Zgli4c5MU8A07wycrKYsOGDdi2jWEYrF27ljlz5vTa59Zbb+Wmm25ix44dLF68mKNHjwLRe7Lfe++93H777YP0OKqqqopHHnlkwKL4mjVrOOecc1i3bh2f/exngWhR3DTNXkXxs88+OzGK4vv27eO+++7j5ptv5t/+7d/YvHkz//qv/4rX6+WGG27o85jCwkJ+9atfcfbZZxMKhbj//vu59NJL2bhxI2eddVafx/zoRz/irrvuOmn73r17Y4P89PR0CgsLqa2tpbn5+H0Zc3JyyMnJ4fDhw7S3H5+pUlBQQEZGBlVVVb1uGl9SUkJKSgp79+7t9ceOsrIyTNNk9+7dvdpQUVGBZVlUVlbGtum6zvTp02lra6OyshKPJ/qN7PV6KS8vp7m5mZqamtj+ycnJsXux1dXVxbbHY5/a29s5dOhQbHui96m6uppIJILH4yElJSUh+pSIOfXXp2PHjsXyy8jISIg+JWJOA/VJqeg9BAsLC8nNzU2IPiViTv31qTu/0tJSMjMzx61PaZgc0wLsaWzHEwnSVn90xH1KzswmYitsy+LogSqO9czpzJXUmCUEDr6Mp+MYbH2SSNVGAmdczd4WE6dHgSWec+rW1/eeUors7Gxyc3OHnVO4uZEIXqpqOwgfC8dNn+D4915j7SGWTE1i3ZZ6/rJxL7OK05hRnNVnn3L9uew8tJOAE0A5ir363rHv08xPcSj5QkL1lSQffYmUoy+RFNyPceAFrL3/wNF9tGcvoi3/UrLnXU1KWkavnJRS5Ofnk5aWxp49e/rM6VS/90pKSti4fT/PvXeI3Uc7UQp0Q6cgM4WFk1KYnqXj90BH/REatMFzslsO4+k8QiRQROG0M0ftPSLTD0WeVhaXZtAeTqW2zaFTT2bHgTr21TTT3BphS2sn71Yew2N6sB2bzIBOaYaP4gwvsyblMK9icr/few21DRxpPzLk772h9mnatGm0tLRQW1vba8nL6dOno3Vo1B06/r3k9/opLy+nqampz++9uro6DjccZkPWBiwtunS6ozvoSmd+3Xz8TvRCj+zsbHKyczh06BAd7ceL6Pn5+dE+VfbuU2lJKSkpKezatWtUxk8nfu/lT8nnoeaHCDkhTMekU+/Eg4dUPTVuf+a2tbUxnu644w5+//vfx74+88xoofbFF1/k0ksvBaK3L+v53+Rb3/oW7e3tfOELX6CpqYmLLrqIv//97/j9Q1upwW2UUrS3t5OcnIy258+0+5MIBjLRdS+ZRj+3f+voOqf9edHCeJc/f/BnAJbPXD4qyxOKwfXKT/6bu5Jk6G6Sn/tJhu4m+bmb5Od+3Rn2Wo7bjtD51J39H+PY0FEfXX0KDRVuJ/zG/YTfXYem97+sd+Ajd4Hp7fdxTdNYvHgxL730EkuWLGHdunUsX768z31nzZqFaZqxMfMXvvAFHnjgAW6++eaT9r3nnntYu3YtoVCIT3/603zzm9/kO9/5Du+//z4LFizgpptu4nOf+1yvYxoaGti1axe///3v+d73vsdnP/tZNm7cyBNPPMHLL7/Md7/7XW677TbefPNNVqxYQUpKCm+++SZTpkxh1apVPP7443g8Hp544olet/Uab5oaxp3YvV4vZ599Nq+//nps27/+67+yefNm3njjjSG/6CWXXMKkSZP4wx/+0Ofjfc0U7/6jYvd9yeNlVkA3wzCwLItdu3Yxbdq02P0GZHahu/oUiUTYs2cP06ZNwzTNhOhTIubUX58syxpxfvHap0TMaaC227bNnj17qKiowOPxJESfBtueSH3qzm/69OmYpjlufVJKsa2+jaPtYQIeg3ML0jC7jh1unw63hfigvo1Mv4ez8o8vj94zJ8excY68h7XjHxBqATS07DLM2R9FTysYlT71bON4fu/Zts3evXv7vKp5sD7VdYR4u7YFv6FzUUlm3PSp5/bu773fPv8BG3YfJTctwOqVC/GavWfMxlVOzfvQqp5C7fkrtB083hhfBlrZVdhTroK8s0DTsVsOc/iD1ymdfRFaSu8Bxqn2KRSx2bD7KC++X921RHq0jbNKMrl8XjHzp+SgoU7uk6bhRIIoOwR2COwwurLQnDDO3r+ivfkTsIPgSYZLfoo2c+XY/75n2Ryqb2NvbSv7alvZV9vCsZYgvW6CjUaK30N5firlealMLUhjSl4qAa9JU0eYmsYOctP8ZKX4Ym0ZjfdypRS7d+9m6tSpvQbgp/K9tyeyh9eCrxEhErun+FRzar/7n9j2ifj5tCu0K7ZaQ3ebp/umx+3P3JaWFrKysmhubo6NVxNRS0sL6enpruinbdvs3r2biqnlGI8tZX+Kl/XnLyfLX8rHUj528gFOBI69ASjIOgs80fuJ72/az4f/+GF0TWf9DevJT8kf346cpmL5VfS+/7twD8nQ3SQ/95MM3U3yczfJz/26MywtLeXAgQOUlZXhM/WBi+J2GDoaiS6NpnUVxxUkZaIZ/Re9Ax+5C22AonhOTg5/+ctf+MMf/sAdd9zBZz/7WZYtW0ZdXR2ru5ZPz8nJ4aabbmLjxo1cc801HD58mLvuuoucnBz2799PcXExn/vc52LLpz/zzDP89a9/5ec//zmO4/ChD32In//859TV1XHvvfeydu3aPtty//33s2PHDv7zP/+TqVOnsmnTJnJycli1ahUrVqxg2bJlAFx66aXce++9zJ07F4ApU6bw3e9+l8997nPccccdeDyeIc9e708wGKSyspKysrLYhd5DHa8Oa6Z4YWEhs2fP7rVt1qxZPPbYY8Nq8Lnnnsurr77a7+M+nw+fz3fSdsMwTnoj6f6DxImGu72/N6jhbO/+48uJ7dQ0rc/9R6vtY92n4WxPhD51Z9jdhkTo06m20S19Gsv8JKfx65Ou6yPOL177dCrb3dan7sLCQPuPRZ/m5KTREmqkM2Kzq7GTeXm9f/kZap+aQ9H792YFvP3+t9F1HSYtxFM0F2vPS0T2vIyqryTy6n9jTj4Xz4wPofmST7lPg20fq++97vyGm1NWwIeuaYRsh7CCwABtn+jz6Z8vmcHumhbqWoI8/NpePrNk5oD7D6XtY9anrArI+hramf8Kx96L3n+88inorIMPHsL44CFILoTUUvQDz1MaDmJsTUW75D9h2sfBDseK0ThhjNjXodhjmh3GiH0d3Q87hGaF6Ghvp7L6GIeONqKsEBdh4dUtStJNijM8JBk2bAvDuz2O7/HcOJG+F+l2LPSWA6Ac0PToBSZPXw/v/BdG6iRILoCkgui/yQWQlI+RXABJeXDCVdbDzcnrMSkvyKC8ICO2vbkjzJ6aZvbVRO9PXnW0lfZghK37G9i6v6HrWAh4TQ43tKMUJPlNvvSh2bH7mw+U61DPp+5bMPU17hnp994MYwYl3hJanVZS9dR+78sdTz+fpvumU+wpPqnN8fozV/7YFceOvAodtdRPOh88SWQb/Syd3lkLKDBTYgVxgHUfrAPgwtILpSAuhBBCCCHE6crwRGd190N1NhN87m6UFQJPACKdaKYP/9Jb0Qa6t7jhGfSlL7jgAr761a/y8MMPs2LFil7LugPcfffd3H///aSmprJmzZpeKxTcfPPNLF68mE9/+tOxbc888wxPPvkkr7zyCgCtra3s2rWLrKysAduxZs0avv/976NpGtdccw2PPfYYX/ziFwdtP8A111wDwMKFC3niiSeGdMxYGVZR/MILL2Tnzp29tu3atYvJkycP60W3bNkyodPjhRBCCJFYPIbOGXlpbK5uorotSHbAS1Hq8JaEVUrF7iee6R/8l1LN9OGZ+WGMSecQ2f4U9pFtWFUbsQ+/izljKeaU8wdcIinRGLpGqtekJRShKRghkBK/fU/ymfzL5bP48V+28OqOGuZPyeGs8pyJbtbANA3y5kc/zr0VqjdGC+RV/4DWg3D4NVAOumZAWzM8+X8gbRLow/p1H4jOlw5bNh0hi1DEIQ/II5pxks8k4DXRbaB+BP0wvGD4wApF+6R7o//aVrRA3noIOgd4Yk2HQE7vonlS/vHieXfhfIArsfuSnuRlYXkuC8tzAbBshwN1beytaYkWy2tbONbcyd7aFhxHoWvQGbb48V+2sL+ujXOn5TK1IA2jn2LrREvubCW5ZT+kTYbkvovi8SZZT+63gC/EUGm7oxfw1xedAWh9F8WVgs7q6OeB43+nsB07VhS/dta1Y91UIYQQQgghRJzSNG3gZc5Tc/Eu/BTht9agrCCa6ce78JPoqbmj8toXX3wxd999Nzt27OChhx7q9Xj3PcX7UlhYyNKlS3ut2u04DnfeeedJt8Rev359r68/85nP8M477zBnzhx+9rOf8eqrr/LJT34SgHA4zMyZM4dcFO+eBG0YxkkrtI23Yf2V7Bvf+AYXXHABP/zhD1m5ciWbNm3iN7/5Db/5zW9i+9x2220cPnyY//3f/wXgv/7rvygrK2POnDkEg0Huv/9+XnjhBZ555pnR7Ukc0LTofcTlXhHuJRm6m+TnfpKhu010fhl+D+UZSextbGdHfSsZfpMkz9B/1QlaDkErOksz3Td4UbybnpSJ7+zrsev2EXn/rzjNNUS2/Q27aiOeucsw8k5eijxenWqGGf7jRfHClPi+T+3M4gyuXFDK3985yO/X72RqQRrpScMrpE4Y3YDiC6Ifi+6E934NL38bHJuudRoAFV0OuLsobnijBWjD11WY9h4vUOteMH1Ympfq5gj7G8I0B8HCg2WY5GSkM604l7zcTHTT3/U8PY4/6V9fj9fr8ZjuiRbAAdpr4E/nQaQNvOkQbgYzAFf8DpQdfbznR0cNtNdGH+s4Gv3gvf7/G/mzu4rk+b1nnHd/BPKiV2/3wzR0yvPTKM9P40PzSwDYtLuWH63bgg5YTnQZ9ojt8I93DvDi1sP4vQazSjKZU5rJ3ElZ5Kb1//z9GZP30V1rYf0tYHWAmQSX/gSmrxi95x8r7TUQK+QXTHRrhMtomoZftaEdfBEF1KdHi+E5Rh8XQEVawO6IXnTjz4ttfuPQG9S01ZDmS2NJ2ZJxarmAif+dUpw6ydDdJD/3kwzdTfJzN8nP/UaaoTnpHPTc6aj2erTkbPSBZogP04033sj8+fPJzu5n5asBfOtb3+Lyyy+Pff3hD3+YH/zgB1x33XUkJSVRVVVFZmYmqamptLa2xvZ74IEHYp/fd999fOlLX+JnP/tZbFt5eTk1NTUnHXfi1/FmWEXxc845h3Xr1nHbbbfxve99j7KyMv7rv/6L66+/PrZPdXU1Bw4ciH0dDoe55ZZbOHz4MElJSZxxxhk899xzXHbZZaPXizih6zrl5eUT3QxxCiRDd5P83E8ydLd4yK88I4mGzjCNwQjvHW3l3KIM9CH+Ets9SzzNZ2Lowx+8GDnl6Iu/in1gM5EPnsFpO0ZowwMY+TPwzFmGnhLnM5E59Qwz/B4ONHfSFIqMYqvGzifOK+P9g40crGvjdy98wNc/Os99A1fTBzNWwps/QQu3gTcJwm3gSYHrXuiaLd6jGN2H2qYOXth2hNc+qKGz6xYCviSDC2cWcPm8Ygoyk0a/3ckF0eLs+lsg0hpt76U/gclL+z9GOdDZ0FUgP7FoXnu8eG6HIVgf/ah/v//n86WfXDA/cda55/hM5WmF6aT4TXyhY5T66zkQyaJVz+HsqbnsrY0ut/7Ovjre2VcHQF5GgLmlWcyZlMnM4kz8nj5WT1Dq+HL2Vie6HaI8IwJ1W2PbsIPRmfV2EKxgj39D0X+tzuOf28Eex4Ug1Aw1m8GxQDMg2AhPXg8bfwiGP1oE1PSuJem16D5a97/6CZ93f3Ttq/f8t799+zhG0084ro9967bCviejS/n7s91TyBdxQ9d1pkS2gLJpLT6XkK5hYJChZ5y8c/cscV9er9U1/rzjzwB8bPrH8Jkn395NjJ14+J1SnBrJ0N0kP/eTDN1N8nM3yc/9ujM8cZnyIR0bSIdRLIZ3q6iooKKiYkTHlpeXc8EFF/Daa68BcOWVV7J9+3bOP/98HMchIyODxx57jDPOOINIJMKCBQu46aab+NznPhd7jjVr1nDXXb2Xj//Yxz7G2rVr+dSnPsXnP/957rnnHp555hlWrVrFqlWrSE1N5c033xx5p8eIppRSE92IwQz1BukTTSlFc3Mz6enp7vuDqgAkQ7eT/NxPMnS3eMmv07J543Ajlu0wJSOJ6VkpQzru/WMtHG4NDuuY/qhIJ5FdL2Dtez1ayNN1zLIL8ExfgjbA7NSJdqoZBi2blw/Uo2lw2eQczDhdSrqnw/XtfO/Rt7Bsh+svrmDJvOKJbtLI7FqLWn8LKtyO5k1GG6SI6CjF9oONPL/1MO/tr4+umU60kLtkbjEXzSog4B3+0uvD1l4DLQeixfvRmA2sFISaehfLuwvmsY/qaNF4KDwpvQrmNYf3kXT4aUwsLDy0TrqW4hmLcKxOGpubOdbQRH1jE23trZgqgocwHhXGo4XJ8CnSfYpUj41Pi6B1F7B7Np/oUma6rjMq76JWJ7QdIVrw1qL/fVCQUhSdmR+PHCv6PdF9v3lfWjSH6zfG7Yxxt4xXT5Wb+qkcB+uRD2F2HGL/Zd/lpSyHbCObZSnLeu/oRODYBsCBzDPBG+1XS6iFC393IWE7zGMrH2Nu3tzx78RpLF5+pxQjJxm6m+TnfpKhu0l+7ib5uV93hj6fj6qqKsrKyvD743s1xNNNMBiksrKyVzZDHa+Ow1+6Th+O48SWCzCM+L2PpuifZOhukp/7SYbuFi/5BUyDOTmpvFvbTFVTB9l+L9lDWBZ7OPcTH4zmCeCd81HMyecSef9J7NqdWHtfxT70TvQ+5KVno8VhwfhUM/SbBgGPQWfEpjloDem/+0Qrzk5mxaJyHn51D4+8vpdZJZkUjsXM6LE2fQVOwSIObn+N0tkXYqT1XdwPhi1e+6CG57cdobaxI7Z93uQsLj+jhDmlmUNeXWFUdBecR4umgT8z+pE9q+99lIou297etST7iTPPu5dqD7dE92vaE/1wLApaDqBwUOhoBEk7+L/QvB5dN8kGsgF0cFKi92UPRWzCloPtKOgEuxOaAF0Dr8fAZxp4PTqGpkVnqBp+QhFFIDkdPEnRpedNf3RWt+mLFrIN//Htsce6tnV/3r093Ap/vyFaHPemRr82k+CjayCQHS08K5vocvt29Gucru1dH07X48ruvT32YUf/m47WczXvh/cfAN0Huh4tUkZao4XyOC2Ki/jjVP4dVb8DkjKpz5sG1q6+l04PHgUcMJLAkxrb/OSuJwnbYSqyKpiTO2f8Gi6A+PmdUoycZOhukp/7SYbuJvm5m+Tnft0ZlpaWTnRTxBiQorgQQgghEk5+so+StACHWjrZeqyFC0qy8Br9F6FDlkNHxAYNMoZxP/HB6Cm5+M5bhV27k8j7f8NpqyP87jr0qg3R+41nJ96SWhk+D50Rm6ZQxBVFcYDLzyjmvf31bD/YyP88u51/u/YszAG+X+JWcgHBjDP6LBzWNnXw/NbDvPZBDcGwDYDfe3yJ9PwMF14IMFKaFi0Qe1Mhc4DlxyIdvWeZH3kDtvw3mmZEr/hXXfdtL7oQMsq7CtUBMHzoph+/4cdv+lCGn8Yg7K0Ls/tYiD1HQ7RbJhZeIpaHiO2lKCeLOZNzmVWcgdNay8wZ00fvDyhLfn78nuLetOhS5MWLRue5x0J7Dez7a9f95tOi95v3pERXExBiKHatRf/Hv6CH28Bpp75lKyT5yDZOuP+eUseXTk8q6nWbicd2PAbAtbOulRk+QgghhBBCCJEgpCguhBBCiIQ0IyuFpmCEtrDFtmMtnJnf/9JVjaEwAKleE88YFEON/BnoudOwqt7A2vk8TnM1odf+B6NoLp7ZH0FPyhz115woGX4P1W3B2Mx7AKezGdVeh5acE72/UpzRNY1/uXwmtz+8mf3H2njizf0sP69sopt1yhyleP9AA8+9d5htBxpi2/Mzk1g6r5gLZuTjH48l0t3KkwTpZdEPgKJFsHNNV7E2PVqs9WfCJf8x4AxmDcjq+jgHiNgOe6qb2XaggW0HGzlU10ZVXSdVdQf421v70RyL+ZUR5k3OZk5pFnnpp7jM+fQVUHzR6C5TP5b6u998vLdbxIf2Glh/M0TaUGiARn1HJfgrTi6KW61gtQM6+PNim3fX72br0a0YmsHVM64e1+YLIYQQQgghhBg78lewUaRpGsnJyXIluYtJhu4m+bmfZOhu8ZafoWvMy0tj4+FG6jrCHGjpZHJ637NhGztHb+n0/mi6gaf8IsziBUR2Poe1fxP2kW3YtR/gmboYc9qlaObEzqwejQwzuv4bNociKKWwD75J+K01KDuEZvjwLvwk5qRzRqvJoyYj2ccNl87gvr+/z1Nv7WfepCwqCuOvgD+QxvYwtR0ayY0dfHC4mRe2HeZoU9e9szU4Y3I2l88rZvZ4L5GeKEapWOsxdGaVZDKrJJPrgOaOMO8faGDbwQa2HWigqc1iS1UD71ZFL2TIywgwpzSLuaWZzCzJxO8ZwQzy0V6mfqy5rZAv4kfLfgg1AToYXlqzJhM2PRiORYae0Xvf7lni/hzQj//8//OOPwNw6ZRLyU46oZAuxkW8/U4phk8ydDfJz/0kQ3eT/NxN8nM/yTCxaUopNdGNGMxQb5AuhBBCCHGigy2d7KhrRdc0zi3KIK2P5dHfONxAa8jijPw0CpL949Iup6WayLa/YdftA0Dzp+GZfRVG8XzX/uKtlIPT0cRb+/ZhdDYy3WhG3/M82BHQDdB0NG8S/ivviMsZ4wC/ff4DXv+ghvQkLzdcNp3SnBSyUsbne0IphaMUtnPiR/Se1I6jsLr+7d7uKLAdh7f21vHEm1V0hm1s2yEtyUuS1yTgM7loZgFL5hWf+oxjEdVeM2bFWkcpDhxrY9vBBt4/0MCemhYc5/hwTdc1KgrTmTspizmlmZTmpMgFDnHgdBmvuqKf7TXwp3Mh2AieZPYVTOGVhVeQmz6fj6QvP76fY0HdG9F72WfOB28GAJZjcfEDF1PfWc8vP/JLLi+/fGL6IYQQQgghhJhQwWCQyspKysrK8PvH5+9CYmj6ymao41WZKT6KHMehoaGBrKwsdN2F96EUkqHLSX7uJxm6W7zmV5Lqp64zzLH2EO8dbWVRcSaGfryIFLEdWsMWAJm+8ZupracV4l30Oeya94m8/ySqo4nw22vQK1/HO/dj6Jml49aWbkPJUDk2qqMR1dGAaq/Daa9HtTeg2utxOhrAsZlm2ziOQmGDFQI0cGxQFsqOYO15Cc/sq9CMsZuZP1L/Z/E0Nu6u5YMjTXzn/9uMz2NwXkUuk3PTokXonsVq1bto3f3h9PrciRayu/a17OOFbNvuXQTvWfwcDttxONoSxHEU3be6bgtG+D+Lp/Gh+aUjm1ks+jeWs66VIkUP8ZEzS1m2cDKdYYsPDjd1LbXeQF1zkJ2Hm9h5uInH3oC0JC+zSzOZWxotkqcl9f0eFrRsOiI2SR4DvynfDyKBJRfApT9Frb8FFW6jPqsEAnlke4t77xc8Gi2IGwHwHL9I6+X9L1PfWU92IJtLplwyzo0X3eL1d0oxdJKhu0l+7icZupvk526Sn/t1Z5iU1Pdqk+NJ0zS+/OUv88tf/hKA6upqSkpKuP3221m9ejWrV6/m/vvvJycnB8uy+OEPf8jVV1/N6tWr+elPf8r+/fvJzMykra2NuXPnUlVV1e9rVVVVsWnTJlauXNnn45deein33nsvc+fOjW174okn2Lt3L9/4xjdGtd9jSYrio0gpRV1dHZmZiXNf0NONZOhukp/7SYbuFq/5aZrGnJxU3ghZdEQsPqhvZU7u8SsGm0IRUJDkMfCZ4ztg0TQNs3AuRt4MrH2vYu1+EafxIMFXfolZehaeWVeg+cdvNl53hhmpyRBqRnU04LTXHS96t9ejOpujhYR+O6VDUgYteireQBqZ1RujM/J0A8KdgCKy91XsI1sxp16EOfk8NE/8XHHbGbZo6YhEC9Saoj3osP79avLSGjEmaECr6xqmoaNrYOg6hq71+ugMWxxrDeIxdQwNAj4Ty1FUFGbEfUE83u83P95OfB8NeE3OLMvhzLIcAGqbOnj/YCPbDjaw41ATLR1hNuysZcPOWgAm5aZEl1qflMW0gjRMQ+dAcwfvHm3BchSmrjEjO4Up6Ul4dM21q1IIMaDpK3AKFnFw+2vUz/SA1nLy/cS7l04PFEKP8+Cx7Y8BcPWMqzF1+XPJRInX3ynF0EmG7ib5uZ9k6G6Sn7tJfu7XnWFp6Qgmq9ghsDujF98avlNuS1ZWFhs2bMC2bQzDYO3atcyZM6fXPrfeeis33XQTO3bsYPHixRw9ehSA9PR07r33Xm6//fYhvVZVVRWPPPJIv0Xxvlx99dVD78wIdPd7NMkoTwghhBAJz2vozMtN5c2aJg63BskOeCnoWhK7Mdh9P/GJu5+3ZnjwVFyGWbqQyI6/Yx18B+vg29jVWzErLsMsv2jUZ1WrSCeqvaFrpnc9qqMeu62OvPrDhHdFgP6LZZrhQUvKQkvOQkvORk/ORkvKRkvJRvNnEAxZ7Kluwm8aXDBpxvF7iidlYJScidN0CNXZTGT737F2r8eccj5m+YVovpRR7eNIHG3uxFaKjGQvlqNQjiLiKM6ZlkdhZhJ6rBgdLU7rmnZCkfrkorV+wnZd0zCN6DZdo6vg3bW/oWFox4/RNQYtXDa0Bfna716jM2Th1SFsOQR8Jnnp8XOxQV+sA5tdcb/5eJKfkUR+RhJL5hVj2Q57alrYdqCB9w82cOBYW+zj6bcPkJOZxOyp2aR0LZvvKIWjNLYdbWVfYzseQ8dvGgTM7n+jnwdMA7/HwDvBRfOGtiBHmzvJSw+M2y0MRAJJLqAzYx712tsAvYvikVaw2gANAvmxzQ2dDbxY9SIA1866djxbK4QQQgghhIhnSgEDTBAB6KyBlp3RlRJ1A9JmQGCwVeb0XhfpnkjTNBYvXsxLL73EkiVLWLduHcuXL+9z31mzZmGaJnV1dQB84Qtf4IEHHuDmm28+ad977rmHtWvXEgqF+PSnP803v/lNvvOd7/D++++zYMECbrrpJj73uc8N0nZ48MEH2bZtG//5n//JqlWrSE9PZ+PGjdTX13P//fdzySWXYNs23/rWt3j55ZcJh8N861vf4vrrr2fv3r2sWrWK9vZ2TNPkN7/5DQsWLODBBx/kiSeeiK248Oc//3nQdgyHFMWFEEIIcVrICngpT09mX1M72+vaSPd5CHiMHkXxiV/KW/On4T1zJeaURYS3/RWn8SCRHc9g7d+Md85H0Qtmo4ItQ5pZq5SCcPvxond7PU7H8aXOVbijr6PQIxHwetA8/q6idw56UrT4rSVnoSfngC91wGJZus+DpmkELZvIpLPw505HtddHC+iBdJRjYR/agrXnZZy2Y0R2ryey71XMSWdjTr0YPWnirqjOSw/g9+h0hmySfSbtIYv0JJN/WjwtbgtzWSl+vrB0Nr9+djsdwTBJfi9fWDo7btsLYDfsJ7TxQYgEAQ2lBQlt+iN65hT01NyJbp4rmIbOzOIMZhZnsGJROc0dYbYdbGBvQzthDbxeE12Pjt27l/7Xui7IaGiLoNTxsbcGoGnHL4XRAAWO7aBsB+UocBQ4oCmFDpixCzmOX/BhnnBRiBl7TO+6EOT4BSJmjwtJui8S6T5u455afv/iLkIRG7/H4Asfms3i2YUT8Z9ZuFin0YmFhamZpOs9fl52zxL35YB+/IK4v+78K7aymZs3l4rsinFurRBCCCGEECJ+OXD01f4fVg7YQaDrtnh2BBrfg5Zd0RUV+5N3ETDwTOiVK1fyhz/8gZkzZ+L1esnJyYkVvnvauHEjuq6Tmxv9m0pmZibXXnst//M//9OrwP3MM89w6NAhNm3ahOM4fOhDH+LKK6/kBz/4Affeey9r164dsD0DaWhoYMOGDbzwwgt873vf4/nnn+e3v/0thYWFbN68mc7OTs4//3yuvPJKCgsLee655/D5fLz33nvccsstPPvsswC8++67vPPOOwPeG3ykpCg+ijRNIz09XZYhdDHJ0N0kP/eTDN3NDfmVZybREAzTFIyw9VgLZxak0xLqup94HBTFu+mZpfgu+hL24feIbH8K1dFIaPMf0QJpOK1HwbHQDB+es1Zi5E0/Xuhur8PpaIgVwZUVHvB1NF9yr5neJGXS2gmpk6aj+5JHnKWha6R6DVpCFk3BCIUp6dCjgK/pJuakszFKz8Kp2UFk93qcpkNYlRuwqjZhFp+BOe0S9LQxum/zALoLzL95bjsdEZuAz4j7AjPA4tmFzCrJYGflYWaUFZOTFpjoJp1EOQ7O0Q+wqjZiVW/rWk5fixZgHQdCrQRf/E+MojMwCuZg5M+Mq6X1x8NI3keVUtR3Rjjc2kmH16AgPxUA21E0NnUQxELXNcIRG6/HoD1s8eqmKnweE7/PxOcz8fs8+H0mfn/XNq9BbLUIXYcTxvA2EHIUwZBFqCNEZ8giFLIIdn8ELcIRe0T/DWzH4WhLEMdR6Fq0qP+b57YzZ1Jm3J+HIn5omoaTFp3JkaVnoXf/IUrZ0fuJQ3Tp9C5KKR7bEV06XWaJTzw3/E4pBiYZupvk536SobtJfu4m+bnfyDJUxAri3VeZ99o2chdccAFf/epXefjhh1mxYgXBYLDX43fffTf3338/qamprFmzple7b775ZhYvXsynP/3p2LZnnnmGJ598kldeeQWA1tZWdu3aRVZW1im39ROf+AQACxcujN2//JlnnmHbtm388Y9/BKC5uZl9+/Yxbdo0brrpJt577z0Mw+DYsWOx57niiivGpCAOUhQfVbquU1goMxjcTDJ0N8nP/SRDd3NDfrqmMS83jTcON9AUjPB2dTOW7RDwGPjH+X7ig9E0HbNkAUbBLKw9LxHe9SLq2J5ohcj0oMIdhF/7DSRloen9XFWqgeZP7yp6Z6Gl5KAlZUW/Ts5GM0++v1F+H08zEhl+T4+ieN+FLE3TMQrnoBfMxqnfh7V7PfaxPViHtmAd2oJRMAtz2iUYWZNHqVVDs3h2IXMmZXK0OUheut81hbictAA586dNdDNO4nQ2Yx/YjLV/MyrYEt2oGWB6AQ1l+NAiHUB0uXr7yDbsI9tANzByp2EUzsHIn43mS57IboyL4byPdkZsDrcFOdIaJGgdL0Cn+zwUp/opSPHR0hHmniffY+70fHxeg4jlsG3nUW66ch4pfg+OUli2g+0oLCf6r21HPw87CstRWMrBAmwFjgZK01BdY/yUgAcIRFemoGu4r0ChUAosyyEStohEbEJhm3DIIhg+Xjy3HRX76G5HyFIopUj2m6Sm+PBqGo3tYY42B11zLoqJp+t6tCgePmHp9OCxaGHc8IM3I7Z5+7Ht7Kzfidfwsmz6svFvsOjFDb9TioFJhu4m+bmfZOhukp+7SX7u151h7+Kz3jWrux92COo2gGOBZoKyQDch5/xB7i0++N8iNU3j4osv5u6772bHjh089NBDvR7vvqd4XwoLC1m6dCl/+MMfYtscx+HOO+/khhtu6LXv+vXre339mc98hnfeeYc5c+bwpz/9adB2Avh80b4ahoFt27HX+/Wvf80ll1zSa98777yTKVOm8Mc//pH29namTJkSeywpKWlIrzcSUhQfRY7jUFtbS35+ProeX39YF0MjGbqb5Od+kqG7uSW/gMdgdk4qb9c0c6QtiAIijuJIW5Di1PibXauZPjwzP4yWkkfo1V+CbUc/FETvZ6S6Znt3L3GejZ7UVfROyhzWvchHM8MMn4cDdNIUigy6r6ZpGDlTMXKm4jQdIrLnJezqbdg1O7BrdqBnT8FTcRl6bsW4XW2dleKeYni3eDoHlePgHNuFtX8Tdu0HXfffAs2bhDHpbMxJ51B3eCfGe2vR7TCOmYw971pyCsqi2Vdvw2mrw67diV27E7R1GNllGIVzMQpmow1w6wA3GyxDRymOtoc43BqkPhiOXXTuMXQKU3wUpwZI9R4f4mWl+Fk2r5Tfv7wTw2NiRyxuuHgG86dkn/Tcw2qnUgQth6Bl02nZdMY+d+iM2IRsuzvyfumahv+E+5n7PQbhkMX/vPgBM6fmYpo6luWwbVcteenuOh/FxHIch0Pth8BzQlG8e+n0QGGve/f9eUf0PnVLy5aS5hubGQli6OLp55kYGcnQ3SQ/95MM3U3yczfJz/26M0xP7/F3B01jwGXOzSRInwXNO6IX4epm9GtzdIq7N954I/Pnzyc7e/hj+W9961tcfvnlsa8//OEP84Mf/IDrrruOpKQkqqqqyMzMJDU1ldbW1th+DzzwwKi0/cMf/jC//OUvueiiizAMg23btjFr1ixaWlqYNm0amqbx4IMPjsprDYUUxUeRUorm5mby8vImuilihCRDd5P83E8ydDc35Zfh92ApFVvESKF472gL2QEvfnPge/lMFD27DM2Xhop0RK86dSLg8RP40L+hJ4/OPbhHM8OMruXo28IWluNgDnEwqGeU4Dv7epy2Y1h7XsY69DZOfRWh+gfQ0wujM8cL56HJ4PIk8XAOqmAL1oE3sQ5sRnU0xbbr2WWYk8/FKJyLZpgELZs3zWmoWV8mEGoi6MtAmelcHMgiddYVeGZdgdNai139frRA3lyNXbcPu24fbH0CPWtSdIn1wjnoyadW4I0n/WXYFrY43BrkSFuQiO3EtmcFvBSn+slL8mHofV8wMhYrH+iaRpLHIMnT9/uloxQhy+kqmNsEY59Hi+dBy8FRio6ITccJy6w7SrFwbjEOCsdWaCYsml9KUhzd4kLEP9uxaXKaMDCOF8Wtdoi0ABr4j9+eI2yH+euuvwJw7WxZOj0exMPPM3FqJEN3k/zcTzJ0N8nP3SQ/9+vOcNjLdwcKwZsFdicYgUFmiA9PRUUFFRUVIzq2vLycCy64gNdeew2AK6+8ku3bt3P++efjOA4ZGRk89thjnHHGGUQiERYsWMBNN93U6z7k3ZYuXYppRsvKy5cv56yzzhr09T//+c9TWVnJmWeeieM4FBYW8vTTT/OVr3wlds/za665ZkR9GwkpigshhBDitNMRsdE5vkiR19Cxuwo0cVsUD6TjXfhJwm+tQdkhNG8y3oWfHLWC+GjzmwZ+0yBo2TSHLLID3mEdr6fk4l1wLeaMpVh7X8E+sAmnuZrwWw+jJT+DZ+rFGKVnDWsmvBgbSjk4x/Zi7d+IXbMDVLRoq3n8GKULMSefi56a12N/xf7mTkKWg/KkEvakRi9QsR1ePdRApt9LdsBDViCTjGmX4Zm+BKe9Hrvmfezq93EaDsQ+ItufRk8vPD6DPDU/Ye7dZjkONV2zwpuDx1dc8JkGxal+ilL8/RalTzTeKx/omkbAYxDop31KKYJ2dFZ58ITieUsoQofloKNhmBpe3UBpxPX7s4g/LU4Ltmbjw0ea3vXHrO5Z4r5sMI7/THqh8gWaQ80UpBSwqGTRBLRWCCGEEEIIkRAM36gWw+vq6k7a1nOp9NWrV/d53InbT1z+/Oabb+bmm28+6bgXXnih37acuLz6iXrO9k5JSYndU9wwDO655x7uueeeXvtXVFTw3nvvxb6+8847AVi1atWAr3OqpCguhBBCiNNOksfA0DUUYOoalqMwdW3IBaaJYk46Bz13Oqq9PrpMepwvIZ3p91DdZtMUjAy7KN5ND6TjnbsMNX0JVuXrWJWvo9obCL/3ONrO5zCnXoQ5+Tw0jyytPN5UqA3r4FtY+zeh2hti2/WsSZiTz8MomnfSRQsR22FHfRtHWjsB0ACPET0Hu79uCUVoCUWobOpA1zQy/B6yAwGySxaRWr4YQq2xGeR2fRVOczVOczWRD55FT8mJFsgL56KlF7muQK6UIojO9vo2jnaEsbv/u2gauUnRWeE5Aa/r+nUiTdO6lkw/+T03aNm8uL8Oy1EYmoatFKYW/+/PIr7UO/UAZBlZ6JoeXcKwszb6YKD3PR4f2/4YAJ+Y8QkMXb7PhBBCCCGEECJRSVF8FGmaRk5Ojuv/SHU6kwzdTfJzP8nQ3dyUn980OCMvjfeOtkQLLrrGGXlprpiFqAfSYYyK4aOdYYbfQ3VbkKbg4PcVH4zmTcIzYynm1IuxDmzC2vsqqrOZyPa/Y+1ej1m2CLPsAjRfyii03J3G4xxUSuHU74veK7x6Gzjds8J9GCVnRWeFpxX0eWxjMMzWo60ELRtD15mc5qO2I4StFD5D54y8NLIDXho6IzQEw9R3hglZDg2dYRo6w+wmeu/sLL+HrOwFZJeci9/pxKn9IFogP7YHp60OZ/d6IrvXoyVldC2xPhc9c1JcL7kfth2q24IcaumkxUjFaAsB0Qt4ilMDFKX48Znx2/7R5Ob3ZxE/GpwGDMMgW+9aOj1YB8oC3Qfe4yus1LbV8urBVwFYPmv5RDRV9MFNv1OKvkmG7ib5uZ9k6G6Sn7tJfu4nGSY2KYqPIl3XycnJmehmiFMgGbqb5Od+kqG7uS2/4tQA2QEvHRGbJI8hBRdGP8MMX3SWcFMoglJqVAYUmunFU34R5pTzsQ9twdrzEk5bHZFdL2LtfRVj0tmYUxejJ8XnsvJjaSzPQRXuwDr4Fvb+TThtx5fv0jNLumaFn4Fm9r0agFKKvU0dVDa1oxQEPAbzctPI8HsIWvZJ52BRqkFRqh+lFO0Rm4bOMPVdhfKI7VDbHqK2PVo0DngMsgIzyJ4zl0zTxqjbjV3zPs7RnaiOJqx9r2Htew3Nl4JROAejYA56TjlaHMwGVUpR3xnhcGsnRzvCKBWdFe71mOQn+yhODZDhM0/Lgbi8P4tTddQ+itIVKXrXhVLdS6cHCqDHOfWXnX/BUQ4LCxcyOWPyBLRU9MVtv1OKk0mG7ib5uZ9k6G6Sn7tJfu7XnWEwGJzopogxIEXxUeQ4DocPH6a4uBg9jmeiiP5Jhu4m+bmfZOhubsyv+77XImq0M0zxGrHl6VvDFmm+0bv/t6abmJPOxig9C7tmO9bu9ThNh7Eq38Cq2ohZPB9z2sX9zlpORKOdn1IKp2E/1v4N2Ee2gWMD0QsTjJIF0Vnh6cUDPkdHxGbrsZbYPbGLUv3MzE7B7GrfQOegpmmkeE1SvCaT0qPtaQ5Z1HfNHG8KWXRGbA5HOjnctRx7qm8S2WXTyJoBaa1VUPM+du0H0aXeqzZiVW2M3uu8YDZG4Rz03Ipxvy99p2VzpDXI4dYgQcuObU/zeShK8eI0NzApO9s176NjRd6fxUjtCu1iv7UfRzm8Hnwdj3KoiDRHH+yxdLpSisd2RJdOl1ni8cWNv1OK3iRDd5P83E8ydDfJz90kP/frzjA7O3uimyLGgBTFR5FSivb29tgsD+E+kqG7SX7uJxm6m+TnfqOdoaZppPs91HdEC5ijWRQ//ho6ZuFcjII5OHV7sfa8hH1sD9ahd7AOvYNRMAtz2qUYWZNG/bXjzWjlpyKdWAffjs4Kbz0a266nF2FOPhejZAGa6Ru0LdVtIXbUt2I7ClPXmZ2TQkHKyO/9rnXdXzzD72FqZjKWo2gKRmeR13eGaQtbtIaiH1WAruWTXlhC9uSryO48hL9uB07tDlSoHevg21gH30Yzveh5M6L3Ic+fMWi/RspRimMdYQ61dlLfGYauiExDpzDZR3GqnzSfB9u22V0t76NCjFS7087LnS/j4KChYSmLV4KvU0Q5yd58MI6f41tqtlDVVEXADHDVtKsmsNXiRPI7pftJhu4m+bmfZOhukp+7SX7u151hVlbWRDdFjAEpigshhBBCiDGT4fNwrD1ETVuQvCTvmM381DQNI3caRu40nMaDRPa+HL3PdM0O7Jod6NlleCoujc4MPg2Xox6MUgqn8SD2/o3YR7ai7OjMbs30YBTNx5x8LlpGyZD+20Vshx31bdS0RZcay/B7mJeXRmCUszd1jZwkHzlJ0SJXyHJi9yJv6IwQtGwaO8M0dsIecjAzLiGrYAl5oRrSm3ahH92O6mzBPrIV+8hW0A2MvOnRZdbzZ6F5k065jW1hi8OtQarbgoRtJ7Y9K+ClONVPXpIPQ5fvRyFGS6vTSkRF0NDQlY5X8xJRnbRqYZJ7zBIHYrPEr5h6Bcne5IlorhBCCCGEEEKIcSTrNwghhBBCiDETcRw6LYfDrUFe3F8XW+Z6LOmZpfjOvh7/pd/AnLQQdB2nvpLQhgcIvXwv1uH3UI4z+BOdBlQkiFW1gdDLPyf06n1YB99G2RH0tHy8867G/6Hb8C64Fj2zdEgF8cZghDcON1LTFkTTNKZmJnNOYcaoF8T74jN1ClP8zM1NY3FpFheVZjErJ5W8ZB+moWM5Dkc7Imyzs3ktdRGbp3+eg3M/TeekCyApCxwbu2YH4XfW0vmPHxB647dYVRtQwdZer+N0NmPX7cXpbO6zHbajONzayaYjjbx+qIH9zR2EbQefqVOWkcRFpdmcXZhBYYpfCuJCjLJUPRWf5sODB9MxCRPCg0aqlgy+48sfdkY6eWr3UwBcO/vaiWquEEIIIYQQQvRL0zS+8pWvxL6urq7GMAxWr14NwB133MErr7wy4HP03OfSSy9l27ZtvR5fv349mqbxyCOPxLatWbMGTdNYv379Sc83lP37e52MjAwWLFjArFmzuOuuuwbt/1iQmeKjSNd1CgoK5F4RLiYZupvk536SobtJfu432hkGLZvKpo7ulaIJWQ7v1raQHRi7GeM96al5eBeswJyxFGvvq9gHNuE0HyH81kNoydl4pl2MUXIWmpEYvxIPJz+n6TDW/o3Yh7egrOiscAwTs2gexuTz0DMnDWtGvVKKvU0dVDa1oxQEPAbzctPI8I/v/bq7aZpGksckyWNSmhZAKUVL2IrNIm8KRgjaDvvJZn9aNqSeS5bdREHbHtIad2K2H8U+tgf72B7Y+hf0zMkYhXPBsYhs+xvKDqEZPrwLP4k56Zzo84csDrUFqWkLYjsq1o6cJC8lqX5yAt5B/5vK+6gQpyZZT2Zx0mJe6XiFsBnGqzQWW0UkJ02CHuffM3ufoT3STklaCWcXnT2BLRZ9kfdC95MM3U3ycz/J0N0kP3eT/NzvVDKsaathf9N+JmdMpiCl4JTbkpWVxYYNG7BtG8MwWLt2LXPmzIk9/r3vfW/Q5xjKPnPnzuXRRx9l5cqVADz66KPMmzdv1PbvtnTpUtauXUt7ezsLFizgYx/7GGedddagx42mxPgLYJzQNI2MjIyJboY4BZKhu0l+7icZupvk536jnWFHxMZWCo8OlgMOELQd3q1tZk5uGine8flVVA9k4J27DDV9CVbl61iVr6Pa6wm/uw5t53OY5RdhTjlvzO4nPV4Gy09ZIezD72Lt34jTdCS2XU/JxZhyLmbJWSNaMrwjYrP1WAvNwWhxvSjVz8zsFMw4+iOApmmk+zyk+zyUZ0RnczcFI9QHwzR0hmkJWzRomTRknAMZ5+APNVHQvpfs5t342o5AQxV23T7oqAc0lOkFq5XQxt9T19LCUZVMm+PBNn0Yuo+AP4nCjHSKUgP4hnEBiLyPCnHqKrwVFJlFtFp1pDbuJhkPBHr/Qap76fTlM5eja/HzXiWi5L3Q/SRDd5P83E8ydDfJz90kP/frzjAYDMa2KaUIWsEBjoJ1O9Zx6/O30hHpIMmTxN2X3801s64Z8Bi/6R/w4nlN01i8eDEvvfQSS5YsYd26dSxfvjz2+KpVq1ixYgXLli1jypQprFq1iscffxyPx8MTTzxBYWFhr336M2PGDA4dOkR7eztKKY4cOcL06dNHbf8TJScns3DhQvbs2SNFcTdzHIeqqiqmTJkiVwK5lGTobpKf+0mG7ib5ud9oZ5jkMTA0DUuBz9AI2w6K6BLbrx9qID/ZR3lmMqnjVBzXvEl4ZizFnLoYa/9mrL2voIItRLY/jbX7RcyyRZhlF6D5UnA6m1HtdWjJOeiB9HFp36nqLz+nuTo6K/zQOygrHN2oGxhFczEnn4ueVTbi+6wfaQuyo64V21GYus6snBQKU/yj0Z0xZega2UlespO8AITt6P3IGzoj1HeG6SSDKt9CqrIW4gm3kNWyh8L6d0lqr4uufGCFUUqh2RE8O/5Kke7pel4dU9cwNAANx+Oj0xOIXnDhCaD1+twPph/NEwCPH830o0wfh2rqKC2bhu5NGrVVDFz5/ezCNov4ESBAsPoYST4TfJlgBGKPHWo5xMbDG9HQBv0jlZgY8jul+0mG7ib5uZ9k6G6Sn7tJfu7XnWFBwfELa4NWkAW/XtDvMZZjcaD5AI7joGs6jZ2N3PCXG7h9/e2Yev/j+i1f3ELAE+j3cYCVK1fyhz/8gZkzZ+L1esnJyaGurq7PfUtKStiyZQt33HEH999/P7fffvvAne1h2bJl/PWvf0UpxbJly3j77bdHdf+e6uvr2bBhw7DaN1qkKD6KlFKEw9E/kAl3kgzdTfJzP8nQ3SQ/9xvtDP2mwRl5abx3tAVbKXymTkVWCu1hi9r2UOwjL9lHeUYSab7xWWpbM314pl6EWXY+9qEtWHvW47TVE9n1ItbeV9HSCrGP7QYn0muJ7HjndDRBYxVOXgaaPwX7yHtY+zfhNB6M7aMlZ2NOPhezdCGaL3nErxVxHD6oa6O6LXqldIbfw7y8tHG5d/hY8Bo6Bcl+CpKjBf2OiE1DZ7hrJnkGtd6zqEudxvzWX+CxgziajuFYOJpBR1oZyR4dP2G0SBBlBcG2AFCREERCDP2MUmSEI4T2eAANDBPN9EcL6P0U0k9+rMfXhhf74JuE31pz0pLv8cw6sNl1bRbxRTkOAZoBAwKFvR5bt2MdAOeXnE9RatEEtE4MRn6ndD/J0N0kP/eTDN1N8nM3yc/9RpJhxI6glELXdDRNQ0dHKUXEjgxYFB+KCy64gK9+9as8/PDDrFixotcM9hNdc030ot+FCxfyxBNPDOt1Vq5cya233opSiv/4j/8YtMg93P0BnnvuOc4880x0Xee2227rtRT8eJGiuBBCCCGEGDPFqQGyA146IjZJHiN2L/G2sMXepnZq20Mc7frITfYxdTyL47qJOelsjJKzsGvex9qzHrvhIBx8E5QC04uKBAm98Tus6vfRPQHQDdCM6L1pdQM0Ha3rXzQDdD36uW6gaUbs8+jj3dt7bOvxmNZrPzP2Glqv5z7+GlqPJX+tA5sJvfUwOaEOQnvXovlSovtGO4pROBtz8nnoOeW9jhuJxmCErUdbCFo2mqZRnpFEeUbSiGebx6Mkj0GSJ0BJ1/3IW8MWB1r87Jr0UaYffBrDCRP2pLBn0keYfsYlZAW8vY5XtgVWEBUJQqQT1V0sjwRRkc7ej1mh6Laux5TddPyJbAtlt6FCbSPqh1IKOuqi38+agQp3Enr9f4jsfgnN9AA6xGLr/lzrce/l3p/HMta0fvY7cVt/z6d1/V87aT8VCWJVvh69sMAwUSpI+K016LnTZca4GLpgLaZugeYFX3Zss6Mc/vzBnwG4dta1E9U6IYQQQgghhIv4TT9bvril38dr2mq45MFLaA+3k+ZLoyXUQrI3mZdWvTTgvcX95uAr7WmaxsUXX8zdd9/Njh07eOihh/rd1+eL3hbQMAxs2x70uXuqqKjgyJEjaJrGtGnTYts3btzIF7/4RQB+8YtfDLr/QLrvKT6RpCguhBBCCCHGlN88XgzvluI1mZ+XTlvYYl9TBzXtQY61hzjWHiI3ycfUzPEsjuuYRfMwCudi7XmJ8Jt/AuVEi3JKgRPCPrIVx/AO/mTjSdOiBXQUqrUWHIWOAjt6/3AtuxxP+QWYpWej+VNP+eWUUuxt6qCyqR2lIOAxmJebRoZ/fHKaKJqmkebzMC1T58Xc+byVWkay1Uy7mY7jTyPJc/LseM0wwUiJXpwwDLZts3/3bqZNnYqB1VU4711Ij37eo5Bu9Si89yjCoxxwIuA4RKvOTvT72bZwWqrR4u37uYuywxAJAlq0D0mZKCuIaq8HKYqLoeisRm/ehq7ZYHdCsDY2W3zT4U0caT1CijeFpeVLJ7ihQgghhBBCCDfQNG3AZc7LMsv46RU/5ZZnbqEt0kaKL4WffPgnlGWWjcrr33jjjcyfP5/s7OzBdz4F3//+90/adt5557Fly5bY1+vXrx9w/3gnRfFRpOs6JSUlcq8IF5MM3U3ycz/J0N0kP/ebiAxTvCZn5KVRHk6isrs43hHiWEeInK7iePp4Fcc1DaPkTLRtf0NFOqKzte0wGB48s65E8/iiBUblgGODclA9PkfZx7c73V93/du1n1K9j+9+vNfz9Hg+1f16fS3bpVR0Hzt8vPCp6WB4QNPwnvVJzNyhXa07mI6IzbZjLTQFIwAUpviZlZOCeRqd78dvBwBNKhVD0zgjL+2kCz5ORfc5aJgmmuaJLpM+AkopsCOotqN0vvATsEJgeKP/ml58Z//z8eXzlYLuBd6V0/NJUKjj33vd+/X8Xuze/4Tjju/X+9jood2P0+d+KtxBZPtTYEeibY50RpeJTx7bwb9IEHYImrYD9vFVEJp3gDcLDB9/3hGdJf7Rio8Oeu8+MXHkd0r3kwzdTfJzP8nQ3SQ/d5P83G+kGa6YvYKLJl3EgeYDTEqfNOAM8eGqqKigoqJiVJ5r6dKlmGa0NLx8+XKWL18ee+zyyy8f1nMNtP9ArzORNOWCmxu0tLSQnp5Oc3MzaWlpE90cIYQQQggxRtp7zBzv/i01J8lLeUbyuM1Ijsf7GR8vpvcutivloDoaCb7082jB05MEVhDN9OO/8vZRWW76SFuQHXWt2I7C1DVm5aRSmDL4El+JKmjZJ90OIJ7F4/fzYNzW5tNlvOqKfoaboG5j9H1SM0D3grIg+xzaMLnwdxcStIKsWbGGBQULJrq1QgghhBBCiDgVDAaprKykrKwMv//0/RtIPOorm6GOV2Wm+CiybZu9e/cydepUDCP+/0AmTiYZupvk536SobtJfu4XDxkme03m5aUxNZLEvqYOqttC1HWEqesIk53kZeo4FMfNSeeg505HtdejJWfHxX2Mtdi9xk/YDhDIwHf2/yH01sPY4Q4MbwDvwk+ecrsjjsMHdW1UtwUByPB7mJebRqCP5cJPJ33dDmC0jMU5GI/fz4NxY5tFnDACoHtQjoZlK0wiaLoJRoCnP3iCoBWkPLOc+fnzJ7qlYgDx8PuIODWSobtJfu4nGbqb5Odukp/7dWdYXFw80U0RY0CK4qPMcZzBdxJxTTJ0N8nP/SRDd5P83C9eMkzymMzNTaM8w4oVx+s7wtR3hMkKeJmamUzmGBbH9UC6q+5fbE46B7KmcmDnFibNWICZknVKz9cUjLD1WAudERtN0yjPSKI8IwlN0wY/WJySsTgH3fb9DO5ss4gDhg/SZ0HTDjTCoHmiXxs+HtvxGADXzrpW3stcIF5+HxEjJxm6m+TnfpKhu0l+7ib5uZ9kmLikKC6EEEIIIeLW8eK4TWVTO0faQjR0hmnojBbHyzOSyAp4J7qZcUELpBNOLkI7hUKiUop9TR3sa2pHKQh4DOblpo3b0vVCCHHKAoU4RjqHqnZROmU6hjeJysZK3ql5B0Mz+PiMj090C4UQQgghhBBCTAApigshhBBCiLiX5DGYk5tGWYZNZXMHR1qDseJ4ZsDLVCmOn7KOiM22Yy00BSMAFKb4mZWTgqnrgxwphBBxxvARdPzRmePAn3f8GYDFkxaTm5w7kS0TQgghhBBCCDFBpCg+inRdp6ysDF3+cOhakqG7SX7uJxm6m+Tnfm7IMMljMCcnlfKMJCqbOjjcGqSxM8ybPYrjmX7Pabk07qnkV90WZEddK5ajMHWNWTmpFKb4x6CVYiBuOAeFcIOe55Lt2Dy+83EAls9aPrENE0Mi74XuJxm6m+TnfpKhu0l+7ib5uV93hrKEemKSovgoM035T+p2kqG7SX7uJxm6m+Tnfm7JMGAazM5JpayrOH6kZ3Hc76E8M5ms07A4Ptz8Io7DB3VtVLcFAcjwe5iXm0bAY4xF88QQuOUcFCLedZ9Lrx18jaPtR8nwZ7CkbMkEt0oMlbwXup9k6G6Sn/tJhu4m+bmb5Od+pmkSDocnuhliDMjlKqPIcRx2794tV5C4mGTobpKf+0mG7ib5uZ8bM+wujl9UmkVpWgBd02gMRniruonN1U3UdYRRSk10M8fFcPNrCkbYcLiR6rYgmgZTM5M5pzBDCuITyI3noBDxqOe59Nj2xwC4evrVeAzPBLdMDIW8F7qfZOhukp/7SYbuJvm5m+TnfvGUoaZpfOUrX4l9XV1djWEYrF69GoA77riDV155ZcDn6LnPpZdeyrZt23o9vn79ejRN45FHHoltW7NmDZqmsX79+n6ft7y8nP/3//5f7OumpiZ+85vfxL6uqqrq9ZzxQoriQgghhBDC9fymwayu4vik9GhxvCkY4e2aJjadZsXxwSil2NvYzubqJjojNgGPwTmFmUzNTD7tZtYLIRJbc7CZ5yufB+CaWddMcGuEEEIIIYQQiaw+2ML7jfupD7aMyvNlZWWxYcMGbNsGYO3atcyZMyf2+Pe+9z0WL1484HMMZZ+5c+fy6KOPxr5+9NFHmTdvXr/7b9q0idzcXP785z/H/tbmlqK4rOMghBBCCCESht80mJmdypT0JKqaOzjUEqS5qzie7vdQnpFETsB72hZ/OyM2W4+10BSMAFCY4mdmTgoeud+ZECIB/W3334g4EWbmzGR27uyJbo4QQgghhBDChZRShJ3IgPu8XL2VX33wJCE7jM/w8qWZH+Xiwv4LywBefeDb/mmaxuLFi3nppZdYsmQJ69atY/ny5bHHV61axYoVK1i2bBlTpkxh1apVPP7443g8Hp544gkKCwt77dOfGTNmcOjQIdrb21FKceTIEaZPn97v/mvWrOGLX/wiDz/8MBs2bGDRokV85zvfYfv27SxYsIDly5fzj3/8g/fff58FCxZw0003YZomTz31FI2NjVRWVvLlL3+ZW265ZcD/PmNBiuJCCCGEECLhdBfHy9KTqGru5FBrJ83BCO/UNJPm8zA18/Qrjle3BdlR14rlKAxdY3ZOKoUp/olulhBCjJl1H6wD4NpZ105wS4QQQgghhBBuFXYifObln/T7uO041AYbcZRCR6Ml0skP3n2I+3f+HWOASQgPXHwLPsM74GuvXLmSP/zhD8ycOROv10tOTg51dXV97ltSUsKWLVu44447uP/++7n99tuH1kFg2bJl/PWvf0UpxbJly3j77bf73E8pxeOPP853vvMdlFKsWbOGRYsW8YMf/ICdO3fy5ptvAnDxxRdz7733snbtWgAefPBB3nvvPd58800sy2LGjBl89atfxesduP+jTaaEjCJd16moqECXmTauJRm6m+TnfpKhu0l+7peIGfpMgxnZKVxUks3k9CQMXaMlFC2ObzzSyNH2UMIsq95ffpbjsPVoC1uPtmA5igy/h0XFWVIQj0OJeA4KMRF0XcfJcthetx1TN/nY9I9NdJPEMMh7oftJhu4m+bmfZOhukp+7SX7uN5IMLWWjugrimqaho6GUwlL2KbfnggsuYNOmTTz88MOsWLFiwH2vuSZ6y6iFCxdSVVU1rNdZuXIljz76KI8++igrV67sd7/XX3+d6dOnk5WVxTXXXMPjjz8+5Puvf+hDHyIlJYWMjAyKioqora0dVhtHg8wUH2WWZY37lQ1idEmG7ib5uZ9k6G6Sn/slaoY+U2dGdgpTMpLY39zBwZZOWkIWW2qbSfWZTM1IJjfJ/TPHT8yvKRhh67EWOiM2mgblGcmUZyS5vp+JLFHPQSHG29r3ozMSlkxZQmYgc4JbI4ZL3gvdTzJ0N8nP/SRDd5P83E3ycz/Lsnp97dU9PHBx/0t9N4Rauen1/6bTDpFs+mm3ggQMH/decCNZvtR+j/PqnkHbomkaF198MXfffTc7duzgoYce6ndfn88HgGEYsfuQD1VFRQVHjhxB0zSmTZsW275x40a++MUvAvCLX/yCRx99lM2bNzNlyhQAjh49yquvvsqkSZMGfY3u9o20jaNBLlcZRY7jUFlZOeSrIkT8kQzdTfJzP8nQ3SQ/9zsdMvQZOtOzUlhcmk1ZRnTmeGtXcXzDkUZqu2aOBy2bhs4wQWv8f0EfqZ75KaXY29jO5uomOiM2AY/BOYWZTM1MloJ4HDsdzkEhxkMoEuKxbY+BguWzlg9+gIgr8l7ofpKhu0l+7icZupvk526Sn/v1laGmafgMb78fhUnZ3DT74ySZfjrtMEmmn5tmf5zCpOwBjxvq32duvPFG7rnnHrKzs8eq2wB8//vf59///d97bTvvvPPYsmULW7Zs4cILL+Txxx9n165dVFVVUVVVxb333suaNWtITU2ltbU1dtyJX8cLmSkuhBBCCCFOO15DpyIrhcnp0ZnjB1o6aQ1ZvFvbjKlrdFoOCoWhaZyRl0ZxamCimzyooGXTgUFTMMLe5k6aghEAClP8zMxJwSPLtwkhThN//uDPHAseoyi9iIsnXzzRzRFCCCGEEEIkuMuK5nNGVhm1nY3kBzLJ9qeN2nNXVFRQUVExKs+1dOlSTDNaGl6+fDnLlx+/iPjyyy8f8NiXX36ZqVOnkpWVFdt29dVXc/vtt/Pzn/+cs846i3nz5nHddddx2223EYlEWLBgATfddFPsNSdafLRCCCGEEEKICdBdHJ+SnsT+lk4qm9ppDlkoQAMiKDZXN7G3sQOPoaFrGjrRq4R1jejXmoZG9+fRf7Xux07at8fjaLFtsX044ete+9LvVcSHWzt572gLIZLZf6gRn6HjM3Vm5aRSJPcOF0KcRtZuX8sXn/wiHeEOOp1O1n2wjhWzB773nhBCCCGEEEKcqmx/2qgWw+vq6k7adtNNN8U+f/DBB2Of97yH+LJly1i2bNlJ+6xfv77P17n00ktP2rZ27do+9ztx35ycHA4fPgxw0tLuL7zwQp+vB/Dmm2/2+9hYkqL4KNNlBo7rSYbuJvm5n2TobpKf+52uGXoMnWmZyaR5Td443IijFAAKUArawxaGPvHLjms9i+hEi+YKRWMwglKgupoYcRwW5WeSGZD7mLnN6XoOCjEaatpq+MY/vkFHpAMNDaUUtzxzCxdNuoiClIKJbp4YBnkvdD/J0N0kP/eTDN1N8nM3yc/9JMPEJUXxUWQYBtOnT5/oZohTIBm6m+TnfpKhu0l+7icZQprPxGtoWA4YuoZlKwxdY0FBOh5dx1EKBThKdX1EP1cKHKJfqx7bHU74OvZ49LHYsf0co7qK892UUtgKouX6KNuJ7gugoeHRNdB67iHcQs5BIU7N/qb9NAeb0dHxe/1kBbJoDbdyoPmAFMVdRN4L3U8ydDfJz/0kQ3eT/NxN8nO/7gyDweBEN0WMASmKjyKlFO3t7SQnJ/e7tKWIb5Khu0l+7icZupvk536SIfhNgzPy0njvaAu2UniM6D3FCydoCXLVqwh/QmG9a3vQcnizugnbcTA0cABT00jyGBPSZjFycg4KcWomZ0wmM5CJhkaSJ4nmUDMp3hQmpU+a6KaJYZD3QveTDN1N8nM/ydDdJD93k/zcrztDw5C/qSQiWQNgFDmOw6FDh3AcZ6KbIkZIMnQ3yc/9JEN3k/zcTzKMKk4NcNnkHC4ozuKyyTkUpwYmrC1a1z3LTV3Ha+j4TYMkj0Gy1yTVa5Lu85Cf7GNBfhoeQ8eyHUw9Wsj3mzKAcxs5B4U4NQUpBfzkwz8hw59BKBIixZPCTz78E5kl7jLyXuh+kqG7SX7uJxm6m+TnbpKf+0mGiU1migshhBBCCHECv2m4qqhcnBogw2uwq3I/00smk+yTe4kLIU5PK2avYFHxIl7b9hoXzr2Q4vTiiW6SEEIIIYQQQog4IDPFhRBCCCGESAB+0yCA7apivhBCjIWClALOyD5DZogLIYQQQgghhIiRovgo0jQNr9cr94pwMcnQ3SQ/95MM3U3ycz/J0N0kP/eTDIUYHXIuuZvk536SobtJfu4nGbqb5Odukp/7xVOGmqbxla98JfZ1dXU1hmGwevVqAO644w5eeeWVAZ+j5z6XXnop27Zt6/X4+vXr0TSNRx55JLZtzZo1aJrG+vXrT3q+9evXs2LFipO2f+QjH6Gzs3OoXZswsnz6KNJ1nfLy8oluhjgFkqG7SX7uJxm6m+TnfpKhu0l+7icZCjE65FxyN8nP/SRDd5P83E8ydDfJz90kP/frzjAYDA772HannVanlVQ9lWQ9+ZTbkpWVxYYNG7BtG8MwWLt2LXPmzIk9/r3vfW/Q5xjKPnPnzuXRRx9l5cqVADz66KPMmzdvWG196qmnhrX/cHX/NzhVMlN8FCmlaGpqQik10U0RIyQZupvk536SobtJfu4nGbqb5Od+kqEQo0POJXeT/NxPMnQ3yc/9JEN3k/zcTfJzv74yVEphKWvAjw9CH/Bwy8P8pe0vPNzyMB+EPhj0mMG+TzRNY/Hixbz00ksArFu3juXLl8ceX7VqFX/7298AmDJlCqtXr2bBggWcc845VFdXn7RPf2bMmMGhQ4dob2+nra2NI0eOMH369GH9d5syZQptbW1UVVUxf/58brjhBmbNmsUnP/nJWD/ffPNNLrnkEhYuXMjHPvYxGhoaALjzzjs555xzmDt3Lt/4xjd6Peett97KmWeeyQsvvDCs9vRHZoqPIsdxqKmpITU1dVSuWBDjTzJ0N8nP/SRDd5P83E8ydDfJz/0kQyFGh5xL7ib5uZ9k6G6Sn/tJhu4m+bmb5Od+3RmWlpbGttnY/KnlT/0foxxaVSsKhYZGkCDPdDzDG51voGv9z02+Pu16zEHKtCtXruQPf/gDM2fOxOv1kpOTQ11dXZ/7lpSUsGXLFu644w7uv/9+br/99kF6e9yyZcv461//ilKKZcuW8fbbbw/52BPt2LGDhx56iFmzZnHZZZfx6quvcv7553PLLbewbt06srKy+N3vfsePfvQjfvzjH/O1r32Nu+66C6UUK1as4LXXXuPCCy8EoLS0lHfeeWfEbTnRsGeKHz58mH/+538mOzubQCDAvHnzePPNNwc8Zv369Zx11ln4fD6mTZvGgw8+ONL2CiGEEEIIIYQQp60f/OAHXHDBBSQlJZGRkTHo/pFIhG9/+9vMmzeP5ORkioqK+PSnP82RI0fGvrFCCCGEEEIIkeAcHAC0Hv/ruf1UXHDBBWzatImHH364z3t593TNNdcAsHDhQqqqqob1OitXruTRRx/ttYz6SM2YMYPZs2ejaRpnnnkmVVVV7Ny5k3fffZclS5awYMECfvrTn3LgwAEAnn/+ec4991zmz5/Pa6+9xvbt22PPdd11151SW040rJnijY2NXHjhhVx22WU8/fTT5Obmsnv3bjIzM/s9prKyko9+9KN86Utf4k9/+hPPP/88n/vc5ygsLOSKK6445Q4IIYQQQgghhBCni3A4zHXXXceiRYv47W9/O+j+HR0dvP3229x+++3Mnz+fxsZGvva1r3H11VcPeoG7EEIIIYQQQpzuDAyuT7u+38fbnXbWtq4loiJ4NS9hFcajeViRumLAe4sbDL6agKZpXHzxxdx9992xGdj98fl80ec1DGzbHvS5e6qoqODIkSNomsa0adNi2zdu3MgXv/hFAH7xi18M6bm629GzLY7jcOaZZ/Liiy/22jcYDPL1r3+dN998k8LCQr75zW8SCoVijyclJQ2rH4MZVlH8nnvuobS0lAceeCC2raysbMBjfvWrX1FWVsZPfvITAGbNmsWrr77Kz372s4QrimuaRnJyMpqmTXRTxAhJhu4m+bmfZOhukp/7SYbuJvm5n2QohuKuu+4CGPIKbOnp6Tz77LO9tt17772ce+65HDhwgEmTJo12EyecnEvuJvm5n2TobpKf+0mG7ib5uZvk5359Zahp2oDLnKcb6VycdDGvdLxChGhhfHHSYtKN9FFp04033sj8+fPJzs4elefrz/e///2Ttp133nls2bIl9vX69etH9NwzZ87k4MGDvPXWWyxcuJBQKERlZSUFBQVomkZ2djbNzc08/vjjfP3rXx9ZB4ZgWEXxJ554giuuuILrrruOl156ieLiYr7yla/w+c9/vt9j3njjDZYuXdpr2xVXXDFgp0KhUK8rAVpaWgCwbTt2dYOmaei6juM4vW5G3992XdfRNK3f7SdeNaHr0ZXlHccZ0nbDMNA0jaKiIpRS2LYda4tSqtf+w237RPapv7Ynap+UUrEMHcdJiD4lYk4D9Wmk+cVzn0a63a19Kioqij2eKH0aaHui9amoqCj2S2Oi9Kln20+HPpWUlCRcnxIxp/76VFJSAjDkvrqhT4mY00B9KikpwXGcXse4vU99bU+kPg33Cvx40NzcjKZpAy6/7tZxeX/jOrd9X53Yp0Q4V4baJxmXJ0afBhrXubVPI9nu1j7JuNz9feoe1yVSnxIxp/76JONyd/dJxuXu71NJSQkdHR2x302VUmia1ut5e7ZHKcU0zzQKUwtpdVpJ1VNJMVIG3H+o2wGmTZvGtGnTerWnW8+vT3z8xO0AS5cuxTSjpeHly5fHllxXSrFkyZJer3via3W38amnnop9nwO8+uqrfbale3+lFB6Ph4cffpivfe1rtLa2Yts23/3ud/nkJz/Jpz/9aWbPnk1RURHnn39+n306sV3d/3bnOtRxuab6+6/cB7/fD8DNN9/Mddddx+bNm/na177Gr371K2644YY+j5k+fTqf+cxnuO2222LbnnrqKT760Y/S0dFBIBA46ZjVq1fHrn7vafPmzaSkpADRq90LCwuprq6mubk5tk9OTg45OTkcPHiQ9vb22PaCggIyMjLYt28f4XA4tr2kpISUlBR27drV65u/rKwM0zTZvXt3rzZUVFRgWRaVlZWxbbquM336dFpbW9m9ezeBQABN0/B6vZSXl9PU1ERNTU1s/+TkZEpLS6mrq6Ouri62PR771NbWxqFDh2LbE71P1dXVdHZ2EggESElJSYg+JWJO/fXp2LFjsfwyMjISok+JmNNAfVJK0dnZSUlJCXl5eQnRp0TMqb8+dec3ZcoUsrKyEqJPiZjTQH1SSpGSkkJxcTF79uxJiD5B4uXUX5+UUqSnp5Obm8vevXsTok+QeDkN1KepU6dy7NixWMEyEfqUiDmd2Ke2tjbOOeccmpubSUtLY7w8+OCDfP3rX6epqWlYxwWDQS688EJmzpzJn/70p373c+u4vK2tjYMHD8bGBT6fz5XfVyf2KRHOlaH2Scbl7u9T97igoKCAoqKihOhTIubUX59kXO7+PimlCAQCTJo0iaqqqoToEyReTv31Scbl7u6TjMvd36fucXljYyO2bTNp0iT8fj9+vx/btns9t67r+Hw+LMsiEonEthuGgdfrJRKJYFnWSdvD4XCvIq5pmng8npO2ezweTNMkFAr1arvX68UwDILBYK+Csc/nQ9M0gsFgrz75/X6UUr0uetY0zbV9CoVCHDlyhJKSEo4cOQIMfVw+rKK41+vl7LPP5vXXX49t+9d//Vc2b97MG2+80ecxIymK93VFemlpKQ0NDbHOxOOVL5ZlsWvXLqZNmxabOe6WK1/665Obr+YZSZ8ikQh79uxh2rRpmKaZEH1KxJz665NlWSPOL177lIg5DdR227bZs2cPFRUVeDyehOjTYNsTqU/d+U2fPh3TNBOiTye2PdH7ZNs2e/fuZfr06ZzIrX3quT1RcuqvT935VVRUxAqqbu/TQG1PxD4ppdi9ezdTp07FMI7fW8zNfUrEnE7c3tLSQlZW1ikVxW+99VbuueeeAffZsWMHM2fOjH09kqJ4JBLh2muv5dChQ6xfv37A9rp1XN7fuM5t31cn9ikRzpWh9knG5e7v02DjOjf2aaTb3dgnGZe7v089x3WapiVEn3q2PVFy6q9PMi53d59kXO7+PnWPy4uLizl06BBlZWX4/f6T3k97tieetg9HvLV9qH0KBoNUVVUxZcoUvF4vMPRx+bCWTy8sLGT27Nm9ts2aNYvHHnus32MKCgqora3tta22tpa0tLQ+C+IQrfz3vBF7N8Mwev1xCI5/455ouNtPfN6RbO8+GU9sp6Zpfe4/Wm0f6z4NZ3si9Kk7w+42JEKfTrWNbunTWOYnOY1fn3RdH3F+8dqnU9nutj51/6I50P5u69NQtidSn7rzS6Q+DdTG4W6P9z5pmtZvW/rav/uYeO7TSLa7tU/dt2Dqa9zj1j4NtD1R+tTf48Nxyy23sGrVqgH3KS8vP6XXiEQirFy5kv379/PCCy8MWsB387i8r3GB276vekqUc6UnGZcnfp9OZVwXr30ai+3x2icZlw/exuFuH+8+ybh8ZNvjpU8yLh94e7z3ScblUW7tU89xeXeW3efkiRc6dIu37cMRb20fSp965jHccfmwiuIXXnghO3fu7LVt165dTJ48ud9jFi1axFNPPdVr27PPPsuiRYuG89JCCCGEEEIIIURCys3NJTc3d8yev7sgvnv3bl588UWys7PH7LWEEEIIIYQQIlGc6mxsMfpOJZNhFcW/8Y1vcMEFF/DDH/6QlStXsmnTJn7zm9/wm9/8JrbPbbfdxuHDh/nf//1fAL70pS9x77338q1vfYvPfvazvPDCCzzyyCM8+eSTI250vNI0jfT09FG5OkNMDMnQ3SQ/95MM3U3ycz/J0N0kP/eTDMVQHDhwgIaGBg4cOIBt22zZsgWAadOmxe71PXPmTH70ox9xzTXXEIlEWLFiBW+//TZ/+9vfsG07dg+/rKys2HJziUTOJXeT/NxPMnQ3yc/9JEN3k/zcTfJzv+4MvV4vmqZx7NgxcnNzJdM4oZTi2LFjaJqGx+MZ9vHDuqc4wN/+9jduu+02du/eTVlZGTfffDOf//znY4+vWrWKqqoq1q9fH9u2fv16vvGNb7B9+3ZKSkq4/fbbB10arqeWlhbS09NP6R5tQgghhBBCCCHEaBvv8eqqVav4/e9/f9L2F198kUsvvRSI/iHngQceiI3Py8rK+nyunscMRsblQgghhBBCiNNJW1sbhw4dktnicUbTNEpKSmIXhcPQx6vDLopPBLcMvh3Hoba2lvz8/H7vWyDim2TobpKf+0mG7ib5uZ9k6G6Sn/tJhu7klvHqqXJTP+VccjfJz/0kQ3eT/NxPMnQ3yc/dJD/3OzFD27aJRCIT3SzRg8fjOeke4kMdrw5r+XQxMKUUzc3N5OXlTXRTxAhJhu4m+bmfZOhukp/7SYbuJvm5n2QoxOiQc8ndJD/3kwzdTfJzP8nQ3SQ/d5P83O/EDA3DOKkAK9xLLlURQgghhBBCCCGEEEIIIYQQQgiRsFwxU7x7hfeWlpYJbsnAbNumra2NlpYWuXLEpSRDd5P83E8ydDfJz/0kQ3eT/NxPMnSn7nGqC+5MdkrcMi4HOZfcTvJzP8nQ3SQ/95MM3U3yczfJz/0kQ3ca6rjcFUXx1tZWAEpLSye4JUIIIYQQQgghxMlaW1tJT0+f6GaMGRmXCyGEEEIIIYSIZ4ONyzXlgsvZHcfhyJEjpKamomnaRDenXy0tLZSWlnLw4MEBb+Qu4pdk6G6Sn/tJhu4m+bmfZOhukp/7SYbupJSitbWVoqIidD1x71DmlnE5yLnkdpKf+0mG7ib5uZ9k6G6Sn7tJfu4nGbrTUMflrpgprus6JSUlE92MIUtLS5OTxeUkQ3eT/NxPMnQ3yc/9JEN3k/zcTzJ0n0SeId7NbeNykHPJ7SQ/95MM3U3ycz/J0N0kP3eT/NxPMnSfoYzLE/cydiGEEEIIIYQQQgghhBBCCCGEEKc9KYoLIYQQQgghhBBCCCGEEEIIIYRIWFIUH0U+n48777wTn8830U0RIyQZupvk536SobtJfu4nGbqb5Od+kqEQo0POJXeT/NxPMnQ3yc/9JEN3k/zcTfJzP8kwsWlKKTXRjRBCCCGEEEIIIYQQQgghhBBCCCHGgswUF0IIIYQQQgghhBBCCCGEEEIIkbCkKC6EEEIIIYQQQgghhBBCCCGEECJhSVFcCCGEEEIIIYQQQgghhBBCCCFEwpKiuBBCCCGEEEIIIYQQQgghhBBCiIQlRfFh+u///m+mTJmC3+/nvPPOY9OmTQPu/+ijjzJz5kz8fj/z5s3jqaeeGqeWihP96Ec/4pxzziE1NZW8vDw+8YlPsHPnzgGPefDBB9E0rdeH3+8fpxaLnlavXn1SFjNnzhzwGDn/4suUKVNOylDTNG688cY+95fzb2K9/PLLfOxjH6OoqAhN03j88cd7Pa6U4o477qCwsJBAIMDSpUvZvXv3oM873J+jYuQGyjASifDtb3+befPmkZycTFFREZ/+9Kc5cuTIgM85kvdiMTKDnYOrVq06KYsrr7xy0OeVc3D8DJZhXz8TNU3jxz/+cb/PKeegEMfJ2NydZFzufjI2dzcZl7uPjM3dTcbl7idjc3eTcbk4kRTFh2HNmjXcfPPN3Hnnnbz99tvMnz+fK664gqNHj/a5/+uvv84//dM/8S//8i+88847fOITn+ATn/gE27ZtG+eWC4CXXnqJG2+8kQ0bNvDss88SiUT48Ic/THt7+4DHpaWlUV1dHfvYv3//OLVYnGjOnDm9snj11Vf73VfOv/izefPmXvk9++yzAFx33XX9HiPn38Rpb29n/vz5/Pd//3efj//Hf/wHP//5z/nVr37Fxo0bSU5O5oorriAYDPb7nMP9OSpOzUAZdnR08Pbbb3P77bfz9ttv8+c//5mdO3dy9dVXD/q8w3kvFiM32DkIcOWVV/bK4qGHHhrwOeUcHF+DZdgzu+rqan73u9+haRrXXnvtgM8r56AQMjZ3MxmXJwYZm7uXjMvdR8bm7ibjcveTsbm7ybhcnESJITv33HPVjTfeGPvatm1VVFSkfvSjH/W5/8qVK9VHP/rRXtvOO+889cUvfnFM2ymG5ujRowpQL730Ur/7PPDAAyo9PX38GiX6deedd6r58+cPeX85/+Lf1772NTV16lTlOE6fj8v5Fz8AtW7dutjXjuOogoIC9eMf/zi2rampSfl8PvXQQw/1+zzD/TkqRs+JGfZl06ZNClD79+/vd5/hvheL0dFXfjfccIP6+Mc/PqznkXNw4gzlHPz4xz+ulixZMuA+cg4KESVj88Qh43L3kbF5YpFxubvI2NzdZFzufjI2dzcZlwullJKZ4kMUDod56623WLp0aWybrussXbqUN954o89j3njjjV77A1xxxRX97i/GV3NzMwBZWVkD7tfW1sbkyZMpLS3l4x//OO+///54NE/0Yffu3RQVFVFeXs7111/PgQMH+t1Xzr/4Fg6H+eMf/8hnP/tZNE3rdz85/+JTZWUlNTU1vc6x9PR0zjvvvH7PsZH8HBXjq7m5GU3TyMjIGHC/4bwXi7G1fv168vLymDFjBl/+8pepr6/vd185B+NbbW0tTz75JP/yL/8y6L5yDorTnYzNE4uMy91JxuaJQcbl7idj88Qj43J3krF5YpBx+elBiuJDVFdXh23b5Ofn99qen59PTU1Nn8fU1NQMa38xfhzH4etf/zoXXnghc+fO7Xe/GTNm8Lvf/Y6//OUv/PGPf8RxHC644AIOHTo0jq0VAOeddx4PPvggf//737nvvvuorKxk8eLFtLa29rm/nH/x7fHHH6epqYlVq1b1u4+cf/Gr+zwazjk2kp+jYvwEg0G+/e1v80//9E+kpaX1u99w34vF2Lnyyiv53//9X55//nnuueceXnrpJa666ips2+5zfzkH49vvf/97UlNTWb58+YD7yTkohIzNE4mMy91JxuaJQ8bl7idj88Qi43J3krF54pBx+enBnOgGCDERbrzxRrZt2zbovR4WLVrEokWLYl9fcMEFzJo1i1//+tf8+7//+1g3U/Rw1VVXxT4/44wzOO+885g8eTKPPPLIkK7eEvHlt7/9LVdddRVFRUX97iPnnxDjIxKJsHLlSpRS3HfffQPuK+/F8eNTn/pU7PN58+ZxxhlnMHXqVNavX8/ll18+gS0TI/G73/2O66+/Hr/fP+B+cg4KIRKJjMvdSX4WJQ4ZlwsRP2Rc7l4yNk8cMi4/PchM8SHKycnBMAxqa2t7ba+traWgoKDPYwoKCoa1vxgfN910E3/729948cUXKSkpGdaxHo+HM888kz179oxR68RQZWRkMH369H6zkPMvfu3fv5/nnnuOz33uc8M6Ts6/+NF9Hg3nHBvJz1Ex9roH3vv37+fZZ58d8Gr0vgz2XizGT3l5OTk5Of1mIedg/HrllVfYuXPnsH8ugpyD4vQkY/PEIOPyxCFjc3eScXlikLF5YpBxeWKRsbk7ybj89CFF8SHyer0sXLiQ559/PrbNcRyef/75XldM9rRo0aJe+wM8++yz/e4vxpZSiptuuol169bxwgsvUFZWNuznsG2brVu3UlhYOAYtFMPR1tbG3r17+81Czr/49cADD5CXl8dHP/rRYR0n51/8KCsro6CgoNc51tLSwsaNG/s9x0byc1SMre6B9+7du3nuuefIzs4e9nMM9l4sxs+hQ4eor6/vNws5B+PXb3/7WxYuXMj8+fOHfaycg+J0JGNzd5NxeeKRsbk7ybg8McjY3P1kXJ54ZGzuTjIuP40oMWQPP/yw8vl86sEHH1Tbt29XX/jCF1RGRoaqqalRSin1f//v/1W33nprbP/XXntNmaap/vM//1Pt2LFD3Xnnncrj8aitW7dOVBdOa1/+8pdVenq6Wr9+vaquro59dHR0xPY5McO77rpL/eMf/1B79+5Vb731lvrUpz6l/H6/ev/99yeiC6e1W265Ra1fv15VVlaq1157TS1dulTl5OSoo0ePKqXk/HML27bVpEmT1Le//e2THpPzL760traqd955R73zzjsKUD/96U/VO++8o/bv36+UUuruu+9WGRkZ6i9/+Yt677331Mc//nFVVlamOjs7Y8+xZMkS9Ytf/CL29WA/R8XoGijDcDisrr76alVSUqK2bNnS6+diKBSKPceJGQ72XixGz0D5tba2qm9+85vqjTfeUJWVleq5555TZ511lqqoqFDBYDD2HHIOTqzB3keVUqq5uVklJSWp++67r8/nkHNQiL7J2Ny9ZFzufjI2dz8Zl7uLjM3dTcbl7idjc3eTcbk4kRTFh+kXv/iFmjRpkvJ6vercc89VGzZsiD12ySWXqBtuuKHX/o888oiaPn268nq9as6cOerJJ58c5xaLbkCfHw888EBsnxMz/PrXvx7LOz8/X33kIx9Rb7/99vg3XqhPfvKTqrCwUHm9XlVcXKw++clPqj179sQel/PPHf7xj38oQO3cufOkx+T8iy8vvvhin++Z3Rk5jqNuv/12lZ+fr3w+n7r88stPynXy5Mnqzjvv7LVtoJ+jYnQNlGFlZWW/PxdffPHF2HOcmOFg78Vi9AyUX0dHh/rwhz+scnNzlcfjUZMnT1af//znTxpAyzk4sQZ7H1VKqV//+tcqEAiopqamPp9DzkEh+idjc3eScbn7ydjc/WRc7i4yNnc3GZe7n4zN3U3G5eJEmlJKjXSWuRBCCCGEEEIIIYQQQgghhBBCCBHP5J7iQgghhBBCCCGEEEIIIYQQQgghEpYUxYUQQgghhBBCCCGEEEIIIYQQQiQsKYoLIYQQQgghhBBCCCGEEEIIIYRIWFIUF0IIIYQQQgghhBBCCCGEEEIIkbCkKC6EEEIIIYQQQgghhBBCCCGEECJhSVFcCCGEEEIIIYQQQgghhBBCCCFEwpKiuBBCCCGEEEIIIYQQQgghhBBCiIQlRXEhhBBCCCGEEEIIIYQQQgghhBAJS4riQgghhBgSTdN4/PHHJ7oZQgghhBBCCCHEaUnG5UIIIcTISVFcCCGEcIFVq1ahadpJH1deeeVEN00IIYQQQgghhEh4Mi4XQggh3M2c6AYIIYQQYmiuvPJKHnjggV7bfD7fBLVGCCGEEEIIIYQ4vci4XAghhHAvmSkuhBBCuITP56OgoKDXR2ZmJhBdQu2+++7jqquuIhAIUF5eztq1a3sdv3XrVpYsWUIgECA7O5svfOELtLW19drnd7/7HXPmzMHn81FYWMhNN93U6/G6ujquueYakpKSqKio4IknnhjbTgshhBBCCCGEEHFCxuVCCCGEe0lRXAghhEgQt99+O9deey3vvvsu119/PZ/61KfYsWMHAO3t7VxxxRVkZmayefNmHn30UZ577rleg+v77ruPG2+8kS984Qts3bqVJ554gmnTpvV6jbvuuouVK1fy3nvv8ZGPfITrr7+ehoaGce2nEEIIIYQQQggRj2RcLoQQQsQvTSmlJroRQgghhBjYqlWr+OMf/4jf7++1/d/+7d/4t3/7NzRN40tf+hL33Xdf7LHzzz+fs846i1/+8pf8z//8D9/+9rc5ePAgycnJADz11FN87GMf48iRI+Tn51NcXMxnPvMZvv/97/fZBk3T+O53v8u///u/A9EBfUpKCk8//bTcQ00IIYQQQgghREKTcbkQQgjhbnJPcSGEEMIlLrvssl6Da4CsrKzY54sWLer12KJFi9iyZQsAO3bsYP78+bGBN8CFF16I4zjs3LkTTdM4cuQIl19++YBtOOOMM2KfJycnk5aWxtGjR0faJSGEEEIIIYQQwjVkXC6EEEK4lxTFhRBCCJdITk4+adm00RIIBIa0n8fj6fW1pmk4jjMWTRJCCCGEEEIIIeKKjMuFEEII95J7igshhBAJYsOGDSd9PWvWLABmzZrFu+++S3t7e+zx1157DV3XmTFjBqmpqUyZMoXnn39+XNsshBBCCCGEEEIkChmXCyGEEPFLZooLIYQQLhEKhaipqem1zTRNcnJyAHj00Uc5++yzueiii/jTn/7Epk2b+O1vfwvA9ddfz5133skNN9zA6tWrOXbsGF/96lf5v//3/5Kfnw/A6tWr+dKXvkReXh5XXXUVra2tvPbaa3z1q18d344KIYQQQgghhBBxSMblQgghhHtJUVwIIYRwib///e8UFhb22jZjxgw++OADAO666y4efvhhvvKVr1BYWMhDDz3E7NmzAUhKSuIf//gHX/va1zjnnHNISkri2muv5ac//WnsuW644QaCwSA/+9nP+OY3v0lOTg4rVqwYvw4KIYQQQgghhBBxTMblQgghhHtpSik10Y0QQgghxKnRNI1169bxiU98YqKbIoQQQgghhBBCnHZkXC6EEELEN7mnuBBCCCGEEEIIIYQQQgghhBBCiIQlRXEhhBBCCCGEEEIIIYQQQgghhBAJS5ZPF0IIIYQQQgghhBBCCCGEEEIIkbBkprgQQgghhBBCCCGEEEIIIYQQQoiEJUVxIYQQQgghhBBCCCGEEEIIIYQQCUuK4kIIIYQQQgghhBBCCCGEEEIIIRKWFMWFEEIIIYQQQgghhBBCCCGEEEIkLCmKCyGEEEIIIYQQQgghhBBCCCGESFhSFBdCCCGEEEIIIYQQQgghhBBCCJGwpCguhBBCCCGEEEIIIYQQQgghhBAiYUlRXAghhBBCCCGEEEIIIYQQQgghRML6/wE6V6V64w5gLwAAAABJRU5ErkJggg==\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: history_all_transformer.png\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Download ────────────────────────────────────────────────────────────\n", "\n", "from google.colab import files\n", "import os\n", "\n", "to_download = [\n", " \"best_bert_mlp.pt\", \"best_bert_attn.pt\", \"best_bert_linear.pt\",\n", " \"best_mpnet_mlp.pt\", \"best_mpnet_attn.pt\", \"best_mpnet_linear.pt\",\n", " \"best_minilm_mlp.pt\", \"best_minilm_attn.pt\", \"best_minilm_linear.pt\",\n", " \"best_xgb_bert.pkl\", \"best_xgb_mpnet.pkl\",\n", " \"history_bert_mlp.csv\", \"history_bert_attn.csv\", \"history_bert_linear.csv\",\n", " \"history_mpnet_mlp.csv\", \"history_mpnet_attn.csv\", \"history_mpnet_linear.csv\",\n", " \"history_minilm_mlp.csv\", \"history_minilm_attn.csv\", \"history_minilm_linear.csv\",\n", " \"history_all_transformer.png\",\n", "]\n", "\n", "for f in to_download:\n", " if os.path.exists(f):\n", " files.download(f)\n", " print(f\"⬇️ {f}\")\n", " else:\n", " print(f\"Not found: {f}\")" ], "metadata": { "id": "2hBpDKy1Ghdj" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 10. Final Model" ], "metadata": { "id": "3Q-b27XdWndi" } }, { "cell_type": "code", "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# 10. FINAL MODEL — MPNet Attention + Multi-Output + Ensemble Seed + KFold\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "# ── CELL 10.0: Setup Multi-Output Targets ────────────────────────────────────\n", "#\n", "# Output: PHQ8_1..8 (tiap item 0-3) + Depression Severity (PHQ8 total, 0-27)\n", "# Strategi: satu model dengan 9 output head (multi-task regression)\n", "# Data note: PHQ8 item-level HANYA tersedia di train+dev split (bukan test)\n", "# sehingga kita pakai train+dev untuk KFold, eval di dev.\n", "\n", "PHQ_ITEM_COLS = [\n", " 'PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep',\n", " 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure',\n", " 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor'\n", "]\n", "# TARGET_COLS hanya untuk training loss\n", "TARGET_COLS = PHQ_ITEM_COLS # 8 item saja untuk loss\n", "N_TARGETS = len(TARGET_COLS) # 8\n", "\n", "# Merge PHQ item labels ke train_df / dev_df dari label_df\n", "# (label_df sudah di-rename di section 1: Participant → Participant_ID, dll)\n", "phq_label_cols = ['Participant_ID'] + PHQ_ITEM_COLS\n", "train_df = train_df.merge(\n", " label_df[phq_label_cols].dropna(),\n", " on='Participant_ID', how='inner'\n", ").reset_index(drop=True)\n", "\n", "dev_df = dev_df.merge(\n", " label_df[phq_label_cols].dropna(),\n", " on='Participant_ID', how='inner'\n", ").reset_index(drop=True)\n", "\n", "# Rebuild nv features after merge (index reset)\n", "extra_scaler_final = StandardScaler()\n", "nv_train_final = extra_scaler_final.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "nv_dev_final = extra_scaler_final.transform(dev_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "\n", "# Multi-output targets (8 item saja untuk training)\n", "y_train_multi = train_df[TARGET_COLS].values.astype(np.float32) # (N_train, 8)\n", "y_dev_multi = dev_df[TARGET_COLS].values.astype(np.float32) # (N_dev, 8)\n", "\n", "# Severity untuk evaluasi saja (tidak masuk loss)\n", "TARGET_COLS_EVAL = PHQ_ITEM_COLS + ['PHQ8_Score']\n", "y_dev_multi_eval = dev_df[TARGET_COLS_EVAL].values.astype(np.float32) # (N_dev, 9)\n", "\n", "print(f\"Train after merge: {len(train_df)} | Dev after merge: {len(dev_df)}\")\n", "print(f\"Target shape — Train: {y_train_multi.shape} | Dev: {y_dev_multi.shape}\")\n", "print(f\"Targets: {TARGET_COLS}\")\n", "\n", "# Sanity check: pastiin tidak ada NaN di targets\n", "assert not np.isnan(y_train_multi).any(), \"NaN di y_train_multi!\"\n", "assert not np.isnan(y_dev_multi).any(), \"NaN di y_dev_multi!\"\n", "print(\"✅ No NaN in targets.\")" ], "metadata": { "id": "9ALdqSIpaXbL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "16963e91-8c50-4135-feec-5a22edf23325" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train after merge: 107 | Dev after merge: 35\n", "Target shape — Train: (107, 8) | Dev: (35, 8)\n", "Targets: ['PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor']\n", "✅ No NaN in targets.\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.1: Multi-Output Dataset ──────────────────────────────────────────\n", "\n", "class DepressionDatasetMulti(Dataset):\n", " \"\"\"\n", " Sama dengan DepressionDataset tapi labels shape (N_TARGETS,) bukan scalar.\n", " Reuse tokenize_head_tail dari section 6.\n", " \"\"\"\n", " def __init__(self, texts, labels_multi, nv_features, tokenizer, max_len=MAX_LEN):\n", " self.labels = labels_multi # (N, 9) np.float32\n", " self.nv = nv_features # (N, NV_SIZE)\n", " self.data = []\n", "\n", " for text in tqdm(texts, desc=\"Tokenizing\", leave=False):\n", " ids, mask = tokenize_head_tail(text, tokenizer, max_len)\n", " self.data.append({\n", " 'input_ids': torch.tensor(ids, dtype=torch.long),\n", " 'attention_mask': torch.tensor(mask, dtype=torch.long),\n", " })\n", "\n", " def __len__(self):\n", " return len(self.labels)\n", "\n", " def __getitem__(self, idx):\n", " return {\n", " 'input_ids': self.data[idx]['input_ids'],\n", " 'attention_mask': self.data[idx]['attention_mask'],\n", " 'nv_features': torch.tensor(self.nv[idx], dtype=torch.float),\n", " 'labels': torch.tensor(self.labels[idx], dtype=torch.float), # (9,)\n", " }\n", "\n", "print(\"✅ DepressionDatasetMulti defined.\")" ], "metadata": { "id": "FoLJqK_vaaPN", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "007572da-a817-4fc2-cd4d-45ae298875b9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ DepressionDatasetMulti defined.\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.2: Multi-Output Model (MPNet + Attention Head) ────────────────────\n", "\n", "class MPNetMultiOutputRegressor(nn.Module):\n", " \"\"\"\n", " MPNet encoder dengan attention pooling + 9-head multi-output regressor.\n", "\n", " Architecture:\n", " Encoder → last_hidden_state (B, L, 768)\n", " AttnPool → pooled (B, 768)\n", " Concat → [pooled, nv] (B, 768 + NV_SIZE)\n", " SharedMLP → shared_repr (B, 256)\n", " Heads → [h1..h9] 9 × Linear(256 → 1)\n", "\n", " Keuntungan shared MLP:\n", " - Parameter sharing antar PHQ item → regularisasi natural\n", " - Tiap head bisa belajar item-specific bias di atas shared repr\n", " \"\"\"\n", " def __init__(self, model_name=\"sentence-transformers/all-mpnet-base-v2\",\n", " nv_size=NV_SIZE, n_targets=N_TARGETS, dropout=0.1):\n", " super().__init__()\n", " self.encoder = AutoModel.from_pretrained(model_name)\n", " hidden = self.encoder.config.hidden_size # 768\n", "\n", " # Attention pooling (learnable per-token weight)\n", " self.token_attn = nn.Linear(hidden, 1)\n", "\n", " # Shared representation\n", " self.shared_mlp = nn.Sequential(\n", " nn.Linear(hidden + nv_size, 512),\n", " nn.LayerNorm(512),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(512, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " )\n", "\n", " # 9 independent heads — tiap item PHQ + total severity\n", " self.heads = nn.ModuleList([\n", " nn.Linear(256, 1) for _ in range(n_targets)\n", " ])\n", "\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, input_ids, attention_mask, nv_features):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)\n", " hidden = out.last_hidden_state # (B, L, H)\n", "\n", " # Attention pooling\n", " scores = self.token_attn(hidden).squeeze(-1) # (B, L)\n", " scores = scores.masked_fill(attention_mask == 0, -1e9)\n", " weights = torch.softmax(scores, dim=1).unsqueeze(-1) # (B, L, 1)\n", " pooled = (hidden * weights).sum(dim=1) # (B, H)\n", " pooled = self.dropout(pooled)\n", "\n", " # Concat nv features + shared MLP\n", " x = torch.cat([pooled, nv_features], dim=-1) # (B, H+NV)\n", " x = self.shared_mlp(x) # (B, 256)\n", "\n", " # Multi-output heads\n", " preds = torch.cat([head(x) for head in self.heads], dim=-1) # (B, 9)\n", " return preds\n", "\n", " def count_params(self):\n", " return sum(p.numel() for p in self.parameters() if p.requires_grad)\n", "\n", "\n", "# Quick sanity check\n", "_test_model = MPNetMultiOutputRegressor(nv_size=NV_SIZE, n_targets=N_TARGETS).to(DEVICE)\n", "print(f\"✅ MPNetMultiOutputRegressor | Params: {_test_model.count_params():,}\")\n", "print(f\" Hidden: {_test_model.encoder.config.hidden_size} | NV_SIZE: {NV_SIZE} | N_TARGETS: {N_TARGETS}\")\n", "del _test_model\n", "torch.cuda.empty_cache() if torch.cuda.is_available() else None" ], "metadata": { "id": "JsyFA6OLab-K", "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "fff62d85bd2e4e7b80e57b47f62192b0", "cf09785f39a54a95bec0ae39731528fa", "93a3089964594ca1849dd184b2f59601", "7f329ad4410242b089511f9fe3f52b01", "00d91e054fd34fd1ba1982eab26adf72", "031e51c654ee4832b030876135cbd83f", "97a5cc44029940ecb22017599348734e", "716d02d8efdc4f65a628576106ac4126", "3748a25a2dbe41f8ad61da0759750800", "98b6e0567f4349deace1ecd6e6f0d042", "6067039724ea487d868f359d9435c593" ] }, "outputId": "817cd110-1e7f-4370-82b4-f0b5ff048e8b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= PATIENCE_FINAL:\n", " print(f\" Early stopping at epoch {epoch}\")\n", " break\n", "\n", " print(f\" Best epoch: {best_epoch} | Best MSE: {best_val_mse:.4f}\")\n", " fold_histories.append({'fold': fold_idx+1, 'seed': seed, 'history': history})\n", " all_model_paths.append(save_path)\n", "\n", " # Load best & predict\n", " model_final.load_state_dict(torch.load(save_path, map_location=DEVICE))\n", " model_final.eval()\n", "\n", " # OOF predictions (val fold)\n", " val_metrics = evaluate_multi(model_final, val_loader, DEVICE)\n", " fold_seed_val_preds.append(val_metrics['preds']) # (n_val, 9)\n", "\n", " # Dev predictions\n", " dev_metrics = evaluate_multi(model_final, dev_loader, DEVICE)\n", " fold_seed_dev_preds.append(dev_metrics['preds']) # (n_dev, 9)\n", "\n", " del model_final\n", " torch.cuda.empty_cache() if torch.cuda.is_available() else None\n", "\n", " # Ensemble seeds → mean over seeds per fold\n", " oof_preds_fold = np.mean(fold_seed_val_preds, axis=0) # (n_val, 9)\n", " fold_oof_preds[val_idx] = oof_preds_fold\n", " fold_dev_preds.append(np.mean(fold_seed_dev_preds, axis=0)) # (n_dev, 9)\n", "\n", " print(f\"\\n Fold {fold_idx+1} OOF MAE (mean over seeds): \"\n", " f\"{mean_absolute_error(y_kfold[val_idx, -1], oof_preds_fold[:, -1]):.4f} [Severity]\")\n", "\n", "print(f\"\\n{'═'*65}\")\n", "print(\" KFold + Ensemble Seed training complete!\")\n", "print(f\" Total checkpoints saved: {len(all_model_paths)}\")" ], "metadata": { "id": "qnFogRykaepv", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "03e2ceb1bc9440c6b31e5c3fcf9f96f0", "f7f0b92d301f4e2181eccbdc428a6865", "8613d427dbf749e1862fbf34f3fccad7", "32d24042aeb84e68a6e075af01a75c1a", "0c5bcff3b362459998db4c7c4753c744", "913552898a9749bb90c73d51404df88e", "8abe31c9f56845c5a0768001ac599901", "20a6f235016d430aa3f89e15523cb3a4", "a00ddcaa6e4548f180b49d2b1f575d54", "3631f440b2614a08826b87e2acc65735", "81de99e21a1043cd80ce6a69c747a031", "2a265ba3005746399c8047423c4ad27e", "c7023f0da3a74aca89fa0aff19d5a1ce", "cb20d79d5b604c208a7abd0eeb98af8c", "df918ac4d4ab4e25a28aebcb640883e8", "be6678dc24be4a50ab21c4b18c1c61cb", "b43846a9e9b949e1bdfe9cc3c3f8bd89", "0731ca9c46c148cc8481805601461cd5", "94eb7e783bb84a0ca30d658ee61021df", "d6cd44a3f93b4afbb9d1056b18761ede", "8ec8be1fa98b4d97a4656490fa84153d", "8f081a0bf9c845549dc2d44a899b8007", "2c8fd208b43b4666a7ef090d49c2f969", "39ea30ababbc4134b8d8593ce13efcf3", "25ebc222b0004d44a4ca9dd764a28349", "ff3d19992a4b422fb473dc24d3049ef3", "0bc54e47971e4dc98a319e01139c90a4", "b298805e313c46259c7deb802241f980", "63a83713d8a3477387d6343411ab6352", "1c935264ba4c4f0a8312e6d9c3895d4d", "ec3ac74774504b81960a4bb7931f9266", "3a23b45b701642ea98efdba097f87abe", "4928082943aa447e8ff65ab04abe8cc7", "ce4c0b6170b346aa8df7ffdca4360b79", "af7e6c90ef49455582ef1a33d3af38a1", "67583411c40045ffac9898f9fbdf5cef", "12ee18d5254248e6b1a9088e5ba8de3c", "4156a74bdcec4f848ba84d4fba801514", "591af0d4f2604d1aaaef45a0fdeec341", "d4f67559f7e24a85b0ecec17e45167f7", "7fe75baff8c14876a5d408e0320e2eaa", "b6bc5cc917234599bc7c89bfd7a0c8c4", "e02242632bc94bd2acf784d096302d90", "5c341473adf648e3bf44170afc59e068", "8770b2cc149c4475a93b7da8c5e171e5", "69f2de0c773b4c7f8b0dfee8c6e8422d", "77b32d1795fc449ea9bb68b513103c5e", "6ad9c6954c4b4d0eba2741a1fc09fe9b", "85371c22454543cbb0c86e92fd114366", "2e62f23560cf469ca20d3f89a2e161b3", "0f16e3858da34bb9b38b824ff5775e4b", "72abcbaa36f1430b87436b4e55f8bcd4", "13b9dd30291c499a852485564d4cfdbb", "0ba7f24a1dcb4bafa297b0392b58640b", "3707b003161f44018dbd430e3f7d6864", "b77be11ff4be47e288d61a32e4f2f610", "d19b83ef6ad5434b94cbbcae7c779237", "a900c1ded155437d9f09196f14f565e7", "1749bcf4f17d43989c075fbe3fe45e94", "e0fa97c323c546dead21406867f15c56", "5b77831b5cde4cf3bc8b91b42f1e1342", "f69b88ab35604667b669270f1b87ed14", "295f283c26c44ddc8d0b9817eddd645e", "49419f537a324712a674c9f298f496ed", "59d8e685b054412f998bafe8c884504a", "e17010d3cc1e492cba54b08881eb949b", "6435a41fd19048ecbd9ce90c9b4549c1", "616c0e75790e4c1c90337bc6a4edf74d", "86c5049b7d4a465b8bcb2cbecf4156a0", "f3b9cc15729c48f382b040891aa9be7b", "8f5f06750bb84540a75d1a9afed0d786", "962dd0a9e465412b8f4eb8c08d5f1d47", "20da9443553c474b84a3b5b7bc18d17e", "439a82fa73ad477d94f584696abb845f", "570ab27959ec431991d490b8d7fd6b03", "144b581083924b8796bdd3575b9f9386", "378b046f5f13433aa1f4d2861e154a46", "16450ef175c24731b6e26dd0af299f38", "4821ffa48fac46c0a2200025f705ce40", "8c148ffdcfe2428389f031828ca7c554", "3780e757011a4c57b4077ad8d12ae6e3", "d489bb0989da464388008346eac8d4b1", "f91354b90cd249b79d92e508d16937c6", "9ad7380fafac4b38ad2b1ea2f19d84db", "0321e8fb8f284bdda17ff6a7b6343064", "51b1d7f93c9145dc9630e9a0db9f354d", "66212261c5744fd28d705e8297624e35", "4d7d6b82e2f6440ab2494c86ed0fbcb5", "b323271202544a50a6578ca07b72128e", "123bae2ff708417799f6805d743f50ec", "262c45036f084f08a83acb9d4e807303", "7daf6cba600642e5a1f0137ab1b39fa0", "3495854df1ed47eb9cd584b157ea505b", "5cace8cb9a8248dbac26fb7823ccd0ad", "df84b82627cc4713ac38ce5773261621", "66f723d5b9b7429691466e601c976157", "c7b49cf2653b47b7831bf24941a3b48f", "5901e68381324bf29f4630d44784e953", "50a9d438dc424d4d957226d933f55be0", "cb87aa7a7e6941f18d84430bf14b6128", "d1120cceb54649b5a97ff496e7c3b7d7", "8953c78c17aa44cb8c0999dc76644ad6", "44e12a1bce2a4d8096a7271b90098b2d", "b2dcb76839054094bdc8d660867bf10d", "8e8185035ed34635bee845ebaa953397", "810ad29bdd0e428f87fd82daecae872e", "a6f1e05894474400b1afe31476608891", "6141b366ee294082ab1316534d8136ab", "cd8609add42a47e3b0817803fe99fcbb", "b6a7eb77245d482692731e9293d37392", "332eb85938aa4203929b87f5807a14af", "14fe50f4483a4ff495c4e385d6dc30d4", "f70ddfab449e4395a116055625cfcc95", "23626ee2b67c48a38eb8fa77f64589c0", "bbc77003537348c8aeae3ebf4e153db5", "6ca19ef5eb474fc4af5f36ec26457912", "d421c92574074167bb1507022c2b0292", "d3e8467b7d3444c98fbfaed763c00ec8", "4f4c5b93576044d883ed204dbaeb3de9", "821d9207a0e24e069351283df96e18d4", "b35e5bd83afb41ee91969bf8eea28331", "df0a57c87e3942a6914041542a777f1f", "bd427fcc247a4f10a7f0cd0adaded206", "f6f039f0b53540c1ab6a277dac294ae0", "c56e1b7488e34989953257295f14d411", "f0cc8096157b4d5fa8c7f8debb1712db", "c14d2ff1d4c24609bf388984d7d5757c", "458d030744bc402b8b1da3f7382eab08", "c7820d28a812485ea53840d8526cfb8e", "158c3c90f0734bd5bd75352ee5a245ca", "2b44f261d1cc4a9cab0ffe4bbbfdb4d4", "eb64cc7ec6c84d6daa057b23e6325bb3", "9992b0294e1443398a3b6debbd43e440", "9763862f54124ac099a0a212790a5907", "e4c34ed2bcd04bc38985266fa8b83def", "556b90b2907f4625aebe342821b82a38", "660a24c2bc8c48a5973c781d305630a4", "87e54c5ab9694d3b9cc037bf5b5adb6d", "baa50b8e352241399e05550eac42c3fa", "7500b8eb39ee44998d5b85557417c6f9", "ad2a92b405c34e49a6d50434b35fa4b8", "83cdba540d4644729eddb53486cc0ea6", "44c49a2d4caa410aaac6ed6818e9f310", "9626eb521dba4cef9ce0ce21c639bf74", "19930c95874d4e86b8ae7452c7eca051", "b74bdf366c9c4e54818c1342979e27b6", "f6630ccf8faa4097ac553e05964650cb", "b67ae98a5b8544ecb6c53c4f87d826fa", "9477d1d914924423a18fba1f2d6e1493", "faa1622727db4ba2a7ee90483c179ffc", "0bb83c8d09a74c4983e1a50168367bd8", "b158ba8d8ca24274a6c2269361a16b5c", "2a9b3cd72ca54fea83171ea03b14ba82", "dbc1c32946024f62822a39ae94ffedc0", "fdc3e95567184012b499be4ef1c4686a", "a6a6be527db641aab50abf91547d16dd", "80eea16423094ffda17dcbbd070b6b72", "145e834e205d431e8eba53d2445b0108", "9f6de1524fd646709e974c43bcb3616b", "9a591bdae5da4dbaae33232953a9477e", "1b0cf40ed31c4a9087ea30e871b3386c", "35c6bc8c1d42490187016ebbcb0eca92", "05078213738c4cfa8b217a9f9d5f4156", "f3676e9b087d49b0857685119ec15637", "3d6eabccc7834587adf467f7fe66bdb5", "fb384d2310154097b129822b4a60a41b", "171581d386f04c2cb03b7c87912db02b", "e03a654106c14bfeadca4e57f21af92f", "a2cbd1b0b4cf4ee989d654cc82a8b0ac", "bf48e3367a494b9b9954a643ac5fcbb5", "4ba2fee44da04f33ba5eb7adc69e871a", "47c055faa6614b52af8a3987083ce8e4", "36f8ca3e990543caabcbe8f700aca5e7", "21fe5526dde04ba1b3ce78148c1125a8", "cf5e537a1aba461393cf3f7d8e15808a", "4462ce3680404e8d967edaa93430819c", "476ba9cb33324c6686c4772af700d3d0", "d370f978c6ec4a2c8e68c9d8caa445fd", "b067026965a24e30ac1322c30bd16caf", "fd096a748aa041dfa7d24248dc01e097", "2e53b57076084fbcbf91981b18d1fe19", "27ae72980eb24a1abcb2c0178ba196ee", "b81222e6fa8841a6aa3527e6dab0900e", "8e4cc847390c4abdb47e8728ce354a04", "67c642068161471793cdc4651713637c", "40dfd4bff0fe4b83b9e49c6245810e70", "17c1b35a7fcf4fc3ab2d21aa70c16f81", "e55cdb678be34baaacf3fbf6b10b67e2", "417a6a2db8a2436c92ba9842fa169661", "885904bc485d4c05b422c3b72dd85348", "7fed97375b774654a54612bf738906c1", "dcb0c2e911e34cc98f605bb3a9abc65f", "1f0d83f706f64ba38384a7346488b668", "976e5f1410934c878d1bd73055378969", "c723b3e1a56948c88c7ce6f2a17e71fe", "7b874356ace942fc9adc49b5c7ef7bdb", "37ea6e3bd8ee475281bb6d14f88f913b", "14db1bd9e36945ed8f8425ae74341eb1", "fefc755d62594b84ada4e4b98f8d2f7c", "4ae7282439114e22b5acd2a6193c54fe", "07acb4adf1814d5aa0b33e4eb81807dc", "1f311d6710d6432d9f846e3360622cd0", "ff1d7e23a39d40caa71a47925ff11514", "b2f2cc514034436c9bc7c867923602ee", "0708d57accd24e6295d35a47a99b079d", "f8c80d42e14c41ebbf3dd200c5863ae7", "4322defe53b548b1ae1a844c90149b1b", "2cc6def46b2742fd9b2f22fae1119db0", "4930cc1ebedd4339a9f3631c4553913a", "434f3450a8a44b0fbef1c3111ea96f2d", "d915cbd4f3c94757a2abefb26213b1ed", "dde0ab365980402b81069ce99ab782b2", "ba2c6a29b1584da7831f06caf7aef293", "9384d2011a334d14be3e21e3e55b90d5", "958436363a4c46a6bd65469ac31d22dc", "b1e94ab4e4594c3a9001a056b1975f64", "162b0af79d944e00b3f019528ee9448b", "897e9cb6459146c784ba7021f9e2e660", "f3e910b52c3e4bc3985fc67325316ad3", "a4342b7874b7437c817973d77e849fc2", "ecd6d77581ad4902b10c074b2cc52299", "d2ef8875188f438aaa499a7f53330cd9", "7c8d721f4fac4c7d9a8db372aca03c11", "71b4e84c307d4ac89933b99c33d2f9b5", "6eaee31d687a4be2bf7cb099b6838302", "3e9b836a0c334b4ab4ef2fd6bf3f7d20", "fd0b538219c34bb7b3ed452e2fcf8e48", "a258a759427940ee91c3bd83f119e686", "194cf33b335a4b63a73f46eb67a98008", "6317518d7cdb44f9b4a935c556fa85fc", "7f14a467282440869d88fb956834f22b", "da34980055554a2b8c053cdcc28cec3e", "01f0b407670f4fb6ac2f7e140277d9f7", "6d3833d553c14724bf5786b1c317bd7a", "e439ce82a9f742449e404a621bfbe380", "8d759a182779427bae33526ab400dc0b", "96d8cef281114a5ea59918ccb6e7d347", "866a84f1de2942f4bfe48692da5f71b6", "d993040c0596450b93e7dcf7112ca43f", "c759c008aebc4cba8ffa53fbc78fbd57", "a805d3fe88a54d0cb9a9a69928537ccf", "2d2c104b8b6b45999c4f81105b07dfb8", "159dfe9d2edd4439bcc9ae49c10d956c", "89757498b81e4931ac88c22baf71d7ce", "875bfe1153ef4f1094a2728a2a0d6dbb", "997d7a63012e43cd9cd45aca904b09cd", "821ac2a653d241f187d58eb4117111ce", "a07ec37ad62c4b339079bcd7057b977d", "ba3b2c5155cc4e36ab8c8d7e23304114", "c771ab05d67c420da608310bf1e94e92", "5b2be4a217e048a5b8621b97bff925c2", "92d3be73868b401a83c738778c5b7c96", "1d47b2644e86483e96936c131b29f10c", "a4179d8d7b0b43aa9e2de23eecdb7626", "545760d381994cda854b8daae591aeda", "a7bbb31cf9ef493086c4eb17d662df01", "71e20b270a71470083834f296284a7c0", "69b7d0fde60d4d818f72eab3cb387b78", "f702aa47aff64dd2ab6d78201011136a", "0476d95f71c0467da3a9ebeb0e0a9d4c", "d996be6cc964444a88b285beaa384dc5", "9da99bada15443c2b69e4057be100f0e", "011969982a7a455798756e1012104b9a", "f07001ed899a4b9db63a00d315309409", "29907ef042ee4921acfbba61ce2717fa", "b02ef2735b7746d1a67fe9d3c829500a", "6fd278b963c04e8588a8117e44c4c90d", "adb10a4bcaaf48eaa8428f808312d579", "e747e6d8f13c47fe8896315910519e51", "e27f9408922f42cd88eb2e1e330e1b0a", "06071a76c5b2421e93699cb933a5320c", "318d423614c9480596a074edebb8655f", "dba5a28720ef4ad49763dcac0d5cf0be", "754fc2924ec34488b32f2cb0d8036fea", "c4d507f79d904bd38fff506b4dbe4efb" ] }, "outputId": "4d46019a-c541-466d-e01b-c38bd1c75ce8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "KFold dataset size: 107\n", "Total models to train: 5 folds × 5 seeds = 25\n", "\n", "═════════════════════════════════════════════════════════════════\n", " FOLD 1/5 — 85 train | 22 val\n", "═════════════════════════════════════════════════════════════════\n", "\n", " ── Seed 42 ──────────────────────────────────────────\n" ] }, { "output_type": "stream", "name": "stderr", "text": [] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:007} {'RMSE':>8} {'R²':>8}\")\n", "print(\"─\"*55)\n", "\n", "final_metrics = {}\n", "for i, col in enumerate(TARGET_COLS_EVAL):\n", " mae = mean_absolute_error(y_dev_multi_eval[:, i], ensemble_dev_preds_full[:, i])\n", " rmse = mean_squared_error(y_dev_multi_eval[:, i], ensemble_dev_preds_full[:, i]) ** 0.5\n", " r2 = r2_score(y_dev_multi_eval[:, i], ensemble_dev_preds_full[:, i])\n", " final_metrics[col] = {'mae': mae, 'rmse': rmse, 'r2': r2}\n", " label = \"← PRIMARY\" if col == 'PHQ8_Score' else \"\"\n", " print(f\" {col:<26} {mae:>7.4f} {rmse:>7.4f} {r2:>7.4f} {label}\")\n", "\n", "print(\"─\"*55)\n", "macro_mae = np.mean([v['mae'] for v in final_metrics.values()])\n", "macro_rmse = np.mean([v['rmse'] for v in final_metrics.values()])\n", "macro_r2 = np.mean([v['r2'] for v in final_metrics.values()])\n", "print(f\" {'MACRO AVERAGE':<26} {macro_mae:>7.4f} {macro_rmse:>7.4f} {macro_r2:>7.4f}\")\n", "print(\"═\"*70)\n", "\n", "# OOF metrics (pada train saja, karena df_kfold sekarang = train_df)\n", "print(f\"\\n📊 OOF Metrics (PHQ8_Score / Depression Severity):\")\n", "# Derive OOF severity dari sum 8 item OOF predictions\n", "oof_severity = np.clip(fold_oof_preds.sum(axis=1), 0, 27)\n", "y_kfold_severity = y_kfold[:, -1] if y_kfold.shape[1] == 8 else train_df['PHQ8_Score'].values\n", "y_kfold_severity = train_df['PHQ8_Score'].values.astype(np.float32)\n", "oof_mae = mean_absolute_error(y_kfold_severity, oof_severity)\n", "oof_rmse = mean_squared_error(y_kfold_severity, oof_severity) ** 0.5\n", "oof_r2 = r2_score(y_kfold_severity, oof_severity)\n", "print(f\" OOF MAE: {oof_mae:.4f} | OOF RMSE: {oof_rmse:.4f} | OOF R²: {oof_r2:.4f}\")" ], "metadata": { "id": "HKENUV8vaf6F", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "67c67167-b6cb-430f-9f5c-9f450287647a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "══════════════════════════════════════════════════════════════════════\n", " FINAL ENSEMBLE — Dev Set Evaluation (MPNet Attention + KFold + Seed)\n", "══════════════════════════════════════════════════════════════════════\n", "\n", "Target MAE RMSE R²\n", "───────────────────────────────────────────────────────\n", " PHQ8_1_NoInterest 0.6176 0.7792 0.1103 \n", " PHQ8_2_Depressed 0.7814 0.9885 -0.1444 \n", " PHQ8_3_Sleep 1.1054 1.2561 -0.0302 \n", " PHQ8_4_Tired 0.8623 1.1236 -0.1174 \n", " PHQ8_5_Appetite 0.8650 1.1373 -0.0118 \n", " PHQ8_6_Failure 1.0146 1.2057 -0.0601 \n", " PHQ8_7_Concentration 0.8206 1.0787 0.0061 \n", " PHQ8_8_Psychomotor 0.5402 0.7473 0.0419 \n", " PHQ8_Score 5.0987 6.5188 -0.0073 ← PRIMARY\n", "───────────────────────────────────────────────────────\n", " MACRO AVERAGE 1.3006 1.6484 -0.0237\n", "══════════════════════════════════════════════════════════════════════\n", "\n", "📊 OOF Metrics (PHQ8_Score / Depression Severity):\n", " OOF MAE: 4.1803 | OOF RMSE: 5.0358 | OOF R²: 0.1424\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.6: Visualisasi Hasil ──────────────────────────────────────────────\n", "\n", "fig, axes = plt.subplots(3, 3, figsize=(18, 14))\n", "fig.suptitle(\"Final Model — MPNet Attention + KFold Ensemble\\nPer-Target Predictions vs Ground Truth (Dev Set)\",\n", " fontsize=14, fontweight='bold')\n", "axes = axes.flatten()\n", "\n", "for i, (col, ax) in enumerate(zip(TARGET_COLS_EVAL, axes)):\n", " true_vals = y_dev_multi_eval[:, i]\n", " pred_vals = ensemble_dev_preds_full[:, i]\n", " mae = final_metrics[col]['mae']\n", " r2 = final_metrics[col]['r2']\n", "\n", " ax.scatter(true_vals, pred_vals, alpha=0.7, s=60, color='steelblue', edgecolors='white', linewidth=0.5)\n", "\n", " # Perfect prediction line\n", " lo = min(true_vals.min(), pred_vals.min()) - 0.3\n", " hi = max(true_vals.max(), pred_vals.max()) + 0.3\n", " ax.plot([lo, hi], [lo, hi], 'r--', alpha=0.7, linewidth=1.5)\n", "\n", " # Trend line\n", " z = np.polyfit(true_vals, pred_vals, 1)\n", " p = np.poly1d(z)\n", " x_line = np.linspace(lo, hi, 100)\n", " ax.plot(x_line, p(x_line), 'orange', alpha=0.8, linewidth=1.5, linestyle='-')\n", "\n", " short_name = col.replace('PHQ8_', '').replace('_', ' ')\n", " ax.set_title(f\"{short_name}\\nMAE={mae:.3f} | R²={r2:.3f}\", fontsize=10, fontweight='bold')\n", " ax.set_xlabel(\"True\")\n", " ax.set_ylabel(\"Predicted\")\n", " ax.grid(True, linestyle='--', alpha=0.4)\n", "\n", "plt.tight_layout()\n", "plt.savefig('final_model_scatter_multi.png', dpi=150, bbox_inches='tight')\n", "plt.show()\n", "print(\"Saved: final_model_scatter_multi.png\")" ], "metadata": { "id": "DP3NcmIaailq", "colab": { "base_uri": "https://localhost:8080/", "height": 977 }, "outputId": "4bea55d9-32dc-4ea4-ab37-88c1a761d459" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: final_model_scatter_multi.png\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.7: Bar Chart — MAE per Target ────────────────────────────────────\n", "\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n", "fig.suptitle(\"Final Ensemble — Per-Target Metrics (Dev Set)\", fontsize=14, fontweight='bold')\n", "\n", "short_names = [c.replace('PHQ8_', '').replace('_', '\\n') for c in TARGET_COLS_EVAL]\n", "colors_bar = ['#2196F3'] * len(PHQ_ITEM_COLS) + ['#E91E63'] # PHQ items biru, severity merah\n", "\n", "# MAE\n", "mae_vals = [final_metrics[c]['mae'] for c in TARGET_COLS_EVAL]\n", "axes[0].bar(short_names, mae_vals, color=colors_bar, edgecolor='white', alpha=0.9)\n", "axes[0].set_title('MAE per Target (lower = better)', fontweight='bold')\n", "axes[0].set_ylabel('MAE')\n", "axes[0].axhline(macro_mae, color='black', linestyle='--', linewidth=1.5, label=f'Macro avg: {macro_mae:.3f}')\n", "axes[0].legend()\n", "for bar, v in zip(axes[0].patches, mae_vals):\n", " axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n", " f'{v:.2f}', ha='center', fontsize=9)\n", "\n", "# RMSE\n", "rmse_vals = [final_metrics[c]['rmse'] for c in TARGET_COLS_EVAL]\n", "axes[1].bar(short_names, rmse_vals, color=colors_bar, edgecolor='white', alpha=0.9)\n", "axes[1].set_title('RMSE per Target (lower = better)', fontweight='bold')\n", "axes[1].set_ylabel('RMSE')\n", "axes[1].axhline(macro_rmse, color='black', linestyle='--', linewidth=1.5, label=f'Macro avg: {macro_rmse:.3f}')\n", "axes[1].legend()\n", "\n", "# R²\n", "r2_vals = [final_metrics[c]['r2'] for c in TARGET_COLS_EVAL]\n", "axes[2].bar(short_names, r2_vals, color=colors_bar, edgecolor='white', alpha=0.9)\n", "axes[2].set_title('R² per Target (higher = better)', fontweight='bold')\n", "axes[2].set_ylabel('R²')\n", "axes[2].axhline(macro_r2, color='black', linestyle='--', linewidth=1.5, label=f'Macro avg: {macro_r2:.3f}')\n", "axes[2].axhline(0, color='red', linestyle=':', linewidth=1, alpha=0.5)\n", "axes[2].legend()\n", "\n", "plt.tight_layout()\n", "plt.savefig('final_model_metrics_bar.png', dpi=150, bbox_inches='tight')\n", "plt.show()\n", "print(\"Saved: final_model_metrics_bar.png\")" ], "metadata": { "id": "zZgRYS8Zajn7", "colab": { "base_uri": "https://localhost:8080/", "height": 440 }, "outputId": "23191593-137a-475a-a629-eaf86a7c4704" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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NPk440mvevHmJYpL7TI6NjXXa3/Lly2dOnjzptOyZM2es//HPPvvMin3xxRcTrefy5cvmxIkTaWpfx9GSjz32WJIxadlXo6KinGIaN25sjDHm7NmzxsPDw5o+adIkp+Vefvlla17FihWt6StWrLCme3p6WvvU8ePHnUayrl+/Pk3buX79euPl5ZXsscTHx8f06NHDREZGWsv8+eefTjG//fabU5mNGze25jVv3txpXnL/R0lxHA2Z8JgGAACAewMj/gAAAJCh3N3d9eKLL1qvw8PDneafP3/eer5lyxZ17NhRO3fuTLHMI0eOZGwlb0LTpk2VN29e67XjdjluU+3atTVq1ChJ0rvvvqv58+erVKlSCg8PV7Vq1VS7dm1rtNzu3budRlq+9957eu+995Jc/9mzZ/Xvv/+qVKlSiea1bdtWNptNkhKN7mjZsqX13HFElGOdE3ryySedRpK0b99e77zzjvV68+bNqlevXrLLR0VFWaNaJGnatGnWaJuErl+/rg0bNqhhw4bJlidJEyZM0MWLF1OMSU1KIzqTs2nTJmuEWUKDBg1SxYoVJUl//PGHNf38+fMKCQlJtsw1a9aoR48eiaZnz55dr7zySqLpCxcu1I4dOxJNb9SokcqUKZPqNkjSZ599poEDB+ry5cvJxqT0f9aoUSNVqlQp0XTHtmnUqJFy5sxpve7SpYsGDx6caJnM2D9uVnBwsDp27KixY8dq7dq1OnTokCTJ399fzz33XLKj/TLqfzctypYtq6ZNm6Y5fvfu3dYIPknq0aOHcuXK5RTjuH/WrFlTNptNxhh99dVX2rhxo+677z6Fh4erSpUqevjhh5U7d+40rdtxvUmNXkwrY0yS09evX6/r169br5999lk9++yzScZu27ZNUVFR8vPz00MPPaRixYpp7969unbtmr7//nt17dpVc+bMUWxsrCSpTJkyqlq1aprqV7VqVa1fv16DBg3SL7/8omvXrjnNv3r1qkaNGqWLFy9qypQpkpw/IyTpkUceSbb8NWvWpKkeSXF8bx3fDwAAANw7SPwBAABAkydPVufOnTOkrNy5cztdhs7b29tpflxcnCTpypUrevzxx63LUqYkOjo6Q+oWr1OnTtbJ2LRKmFBz3K74bZKkZ555Rm+88YZGjx6t6OhorV271unykIUKFdKCBQtUpkwZnTt3Ll11OH36dJLJA8eEZPxlVJOa5+Fxo/uf3Il1SYmSBAlP+l+4cCHFep4/fz7F8hNKy8npIUOG6ODBg2kuMykDBw5Md+LPkZeXl3Lnzq3q1avr5ZdfVt26da156Xkvk9veYsWKOb1H8WbNmqWpU6cmmh4aGpqmxN+8efPUu3fvVONiYmKSnZdc0spxX4i/7Glyr+Nlxv5xK1599VWNHTtWknT06FFJ9s+IlC6zmlH/u2mR3uUS1q1IkSIpxletWlWfffaZ+vfvr8uXL2vLli3asmWLNT80NFRz58512t8z27///uv0Ol++fJLS1+7GGJ09e1Z+fn6y2Wzq3Lmz+vfvL8l+ec+uXbs6XeazS5cu6apjhQoVNG/ePEVFRWnDhg1at26dFi1apBUrVlgxU6dO1WeffaYcOXJkyGdEWqTnfwsAAACuicQfAAAAMpSnp6fT6/iRaAmtWrXKKenXu3dvvfXWWwoNDVVUVJT8/f0ztZ7pldbtkqThw4fr3Xff1Zo1a7Rr1y79+++/mj9/vo4dO6aDBw/q5Zdf1sqVKxONiOnUqZPKli2bbLnJ3aspYd0cJZVISs2pU6ecXp88edLpdXBwcIrLJ5z/5JNPqnbt2snGJzWS7E6R1iSx43uZJ08e9erVK9nY5O6bmVn7/OzZs63n2bJl0w8//KDatWvLx8dHX375ZZKjDNNat+DgYJ09e1ZS4v3mxIkTyS7jKKv3j9KlS6t+/fpavHixJPv/9quvvpriMhn1v5sW6d0vEtZt//79qS7z+uuv64UXXtC6deu0c+dO7dmzRwsXLtSePXt05swZderUKU2J99DQUOt5SqOKU/P11187vY4fHZdw23r27On044aEHJO3nTp10sCBAxUXF6dVq1bp999/1/r16yXZPyfbt29/U3X18/NT3bp1VbduXb311lt6//33NWDAAGv+nj17VK1atUR1f++99zL03rXxHBOMjiNwAQAAcO8g8QcAAIAsEZ8siNeuXTvrpPGcOXOyokoZYv/+/cqePbuCg4PVqFEjNWrUSJJUv359PfXUU5JkjaYJDw9XSEiI1RZXrlzRG2+8kajMU6dO6Y8//kg2YZTR5s+fr4iICAUGBkqSvv32W6f5lStXTnF5f39/VahQwbqc49mzZ/Xaa68lSlBevHhRv/76a5pGrR04cCDtG5AFHnzwQWu/PX36tOrXr6/y5cs7xRhjtGzZMqdLrqbFlClT0j1C1ZHj/1rRokX12GOPSbKPVP3uu+9uulxJqlKlihYtWiRJWrRokc6fP6/s2bNLso8kTsqt7B+OMVFRUbdUd0evvfaalfh77LHHUh1ldyv/u5m1DY51y5kzpzVqbPTo0Xr22WcTJeXc3d0VGBioY8eOyd3dXblz59YjjzxiJdm2bt1qJV0PHTqks2fPpngJW8m+f8U7fPjwTdV/woQJ+uKLL6zXhQoVsj47q1WrJnd3d+vynJ6enkm2+4EDB7R7927rM0yyJ9zr1aunxYsXKy4uTh07drTmNWnSJM2XM5Xso0Sffvpp1alTJ9GPQLJly+b0Oj7RnXC0cWhoqLp165ao7J07dyZKmnp4eFiXOE1tn3Fsd8f3AwAAAPcOEn8AAADIEgnv/de+fXu1atVKBw4c0DfffJOp6965c6c++eSTJOe98MILTieL02v27NkaOHCg6tatqxIlSihPnjyKjIzUzJkzrZj4E8Fubm7q1auXdf+8OXPmaN++fXrssccUEBCgEydOaNOmTVq/fr1q1aql5s2b33S90uPMmTN64IEH1KJFCx05csTp/ShWrJgefvjhVMvo06eP2rVrJ8l+b6vy5cvriSeeUPbs2XX27Flt3bpVv//+u/LkyaPWrVtn2rbcLp07d9YHH3ygM2fO6Pr166pZs6ZatGih4sWLKzo6Wrt379aKFSt08uRJLV++PNXLL2ak8PBwLVmyRJL0119/qU2bNipdurR+/fVXrVu37pbK7tq1q5X4u3DhgqpVq6aWLVvq0KFDiRLGjm52/4i/5KNkv9fka6+9pgIFCsjLyyvJ+yamVaNGjfTTTz8pLi5O5cqVSzX+Vv53HbdhwYIF1kjn0NDQDLnkspubm/r06aM333xTkv3ejaVLl1bLli2VO3du7d+/X/PmzdPy5ctVoUIFrVq1Su3atVOtWrVUunRp5c2bV7Gxsfrhhx+sMr28vOTn55fqumvWrGk93717tyIjI1MdsfjJJ58oNjZWJ06c0NKlS53uZ+nt7a3p06dblzDOkSOHnn32WU2YMEGS9PHHH2vTpk168MEH5ePjo6NHj2rdunXaunWrOnXqpAYNGjitq0uXLlaC13EkZHov8/nzzz9rzJgxyps3r+rUqaMSJUrIy8tLu3fvdhphW6RIEZUsWVKSdP/99+uxxx6z/he7d++uX3/9VZUrV5abm5sOHjyoNWvW6J9//tHAgQNVq1Ytq5x8+fJZIy4//fRTnT17Vr6+vqpYsaIeffRRK+7SpUtOl0lNaSQtAAAAXBeJPwAAAGSJypUrq2HDhlq4cKEk6e+//9bAgQMl2S/JltQ9zTLKpk2btGnTpiTnPfPMM7eU+JPs90pbvHixdYI5ofgT8pL01ltvadeuXVZyLaW63S4PPvigNm7cqCFDhjhN9/Hx0aRJk+Tu7p5qGW3bttWOHTs0dOhQSdKuXbu0a9euTKnvnSAoKEg//fSTmjZtqjNnzujy5cvJjni73V577TVNnTpVly5dkmS/Z6BkH0XUrl07TZ8+/abLfvrpp9W6dWurzD179lj7TaNGjfTrr79asW5ubtbzm90/mjVrpvfff19xcXGKi4vTqFGjJNlHEd5K4s9ms+nJJ59M1zI3+7/71FNPWZ9vUVFR+uijjyRJZcqUybB7rb7xxhv6999/NXHiREn2ZP6XX36ZbHz85S9XrVqV5Pzu3bun6bKUJUqUUKFChXTw4EHFxcVpw4YNqf5QoE+fPklOL1SokGbOnKkaNWo4TR8xYoT279+vpUuXSpJ+++03/fbbb6nWTZKaN2+u7NmzO42oy507t5o0aZKm5RM6duyY0486HPn4+GjixIlOIwK//fZbNWjQQNu2bVNcXJx+/vln/fzzz6mu56mnntLnn38uSdq3b591KdFXXnnFKfG3bt066x5/xYoVu60/MAAAAMCdwy31EAAAACBzfP/993r99deVJ08eeXl5qXjx4vrwww8T3d/pbtKsWTMNGDBA9erVU+HCheXn5ycPDw/lyZNHTZo00fz5853uH+bm5qZp06ZpwYIFevrpp5U/f355eXnJ29tbhQoV0hNPPKERI0Yke3I5Mzz22GNatWqVNXrJ39/fmvbQQw+luZwPP/xQf/zxh9q3b68iRYrI29tbnp6eypcvn+rXr68PP/xQy5Yty8Qtub0efPBB7dy5U/3791flypUVGBgod3d3BQcHq3LlyurevbuWLFmSrjbMCMWLF9eqVatUv359+fn5KVu2bKpTp46WLVumevXq3XL533zzjYYNG6ZixYrJ09NThQsXVv/+/TV27FinuIT39ruZ/aNChQqaOXOmKlWqJB8fn1uu+6242f/dJ598UmPGjFHp0qWtkWwZzWazacKECVq8eLFatGhhjYrMli2bwsPD9cILLyh//vySpFq1amnIkCFq0qSJihUrpoCAAHl4eChnzpx69NFHNWXKFH366adpXvezzz5rPU/LpWRtNpu8vLwUEhKiMmXKqEWLFpo+fbr+/fffREk/yX5PvUWLFmnGjBlq3LixcufOLQ8PD/n6+qpYsWJ65plnNH78eH322WeJlvX29labNm2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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: final_model_metrics_bar.png\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.8: OOF Prediction Dataframe + Summary ─────────────────────────────\n", "\n", "# OOF predictions dataframe (pada train saja)\n", "oof_df = df_kfold[['Participant_ID', 'split', 'label']].copy()\n", "for i, col in enumerate(TARGET_COLS):\n", " oof_df[f'true_{col}'] = y_kfold[:, i]\n", " oof_df[f'pred_{col}'] = np.clip(fold_oof_preds[:, i], 0, 3)\n", "\n", "# Derive OOF PHQ8_Score dari sum item predictions\n", "oof_df['true_PHQ8_Score'] = train_df['PHQ8_Score'].values\n", "oof_df['pred_PHQ8_Score'] = np.clip(fold_oof_preds.sum(axis=1), 0, 27)\n", "\n", "# OOF per-target metrics\n", "print(\"\\n📊 OOF Per-Target Metrics (Train):\")\n", "print(f\"\\n{'Target':<28} {'MAE':>7} {'RMSE':>8} {'R²':>8}\")\n", "print(\"─\"*55)\n", "for col in TARGET_COLS_EVAL:\n", " mae = mean_absolute_error(oof_df[f'true_{col}'], oof_df[f'pred_{col}'])\n", " rmse = mean_squared_error(oof_df[f'true_{col}'], oof_df[f'pred_{col}']) ** 0.5\n", " r2 = r2_score(oof_df[f'true_{col}'], oof_df[f'pred_{col}'])\n", " print(f\" {col:<26} {mae:>7.4f} {rmse:>7.4f} {r2:>7.4f}\")\n", "\n", "oof_df.to_csv('oof_predictions_mpnet_multi.csv', index=False)\n", "print(\"\\n✅ OOF predictions saved: oof_predictions_mpnet_multi.csv\")\n", "\n", "# Dev predictions dataframe\n", "dev_pred_df = dev_df[['Participant_ID', 'split', 'label']].copy()\n", "for i, col in enumerate(TARGET_COLS_EVAL):\n", " dev_pred_df[f'true_{col}'] = y_dev_multi_eval[:, i]\n", " dev_pred_df[f'pred_{col}'] = ensemble_dev_preds_full[:, i]\n", "\n", "dev_pred_df.to_csv('dev_predictions_mpnet_multi.csv', index=False)\n", "print(\"✅ Dev predictions saved: dev_predictions_mpnet_multi.csv\")" ], "metadata": { "id": "LWeioH-_ak37", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6608c4c5-4b48-4573-84ef-de8d9e323925" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "📊 OOF Per-Target Metrics (Train):\n", "\n", "Target MAE RMSE R²\n", "───────────────────────────────────────────────────────\n", " PHQ8_1_NoInterest 0.6637 0.8182 0.0077\n", " PHQ8_2_Depressed 0.6541 0.8143 0.1229\n", " PHQ8_3_Sleep 0.7732 0.9572 0.0830\n", " PHQ8_4_Tired 0.6801 0.8734 0.0709\n", " PHQ8_5_Appetite 0.8088 0.9827 0.0275\n", " PHQ8_6_Failure 0.7739 0.9502 0.0725\n", " PHQ8_7_Concentration 0.6893 0.8664 0.1117\n", " PHQ8_8_Psychomotor 0.5033 0.6541 0.0299\n", " PHQ8_Score 4.1803 5.0358 0.1424\n", "\n", "✅ OOF predictions saved: oof_predictions_mpnet_multi.csv\n", "✅ Dev predictions saved: dev_predictions_mpnet_multi.csv\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.9: Severity Category Accuracy ────────────────────────────────────\n", "# Post-processing: evaluate berdasarkan severity category, bukan just continuous MAE\n", "\n", "def severity_category(score):\n", " if score <= 4: return 'Minimal (0-4)'\n", " elif score <= 9: return 'Mild (5-9)'\n", " elif score <= 14: return 'Moderate (10-14)'\n", " elif score <= 19: return 'Mod-Severe (15-19)'\n", " else: return 'Severe (20-27)'\n", "\n", "true_sev = y_dev_multi[:, -1]\n", "pred_sev = ensemble_dev_preds[:, -1]\n", "\n", "true_cats = [severity_category(s) for s in true_sev]\n", "pred_cats = [severity_category(round(p)) for p in pred_sev]\n", "\n", "cat_acc = sum(t == p for t, p in zip(true_cats, pred_cats)) / len(true_cats)\n", "print(f\"\\n🎯 Severity Category Accuracy (Dev): {cat_acc:.4f} ({cat_acc*100:.1f}%)\")\n", "\n", "# Confusion-style table\n", "from sklearn.metrics import classification_report\n", "cat_order = ['Minimal (0-4)', 'Mild (5-9)', 'Moderate (10-14)', 'Mod-Severe (15-19)', 'Severe (20-27)']\n", "print(\"\\nClassification Report (Severity Categories):\")\n", "print(classification_report(true_cats, pred_cats, labels=cat_order, zero_division=0))\n", "\n", "# Comparison terhadap single-target baseline dari section sebelumnya\n", "print(\"\\n📊 Summary vs Baseline (jika tersedia):\")\n", "print(f\" Final Ensemble MAE (Severity): {final_metrics['PHQ8_Score']['mae']:.4f}\")\n", "print(f\" Final Ensemble R² (Severity): {final_metrics['PHQ8_Score']['r2']:.4f}\")\n", "print(f\" Severity Category Accuracy: {cat_acc:.4f}\")" ], "metadata": { "id": "qcXRQph2al-4", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "66bffeb1-467a-4cad-a75a-b63eb1b08f8a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "🎯 Severity Category Accuracy (Dev): 1.0000 (100.0%)\n", "\n", "Classification Report (Severity Categories):\n", " precision recall f1-score support\n", "\n", " Minimal (0-4) 1.00 1.00 1.00 35\n", " Mild (5-9) 0.00 0.00 0.00 0\n", " Moderate (10-14) 0.00 0.00 0.00 0\n", "Mod-Severe (15-19) 0.00 0.00 0.00 0\n", " Severe (20-27) 0.00 0.00 0.00 0\n", "\n", " accuracy 1.00 35\n", " macro avg 0.20 0.20 0.20 35\n", " weighted avg 1.00 1.00 1.00 35\n", "\n", "\n", "📊 Summary vs Baseline (jika tersedia):\n", " Final Ensemble MAE (Severity): 5.0987\n", " Final Ensemble R² (Severity): -0.0073\n", " Severity Category Accuracy: 1.0000\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.10: Download Checkpoints & Results ────────────────────────────────\n", "\n", "from google.colab import files\n", "\n", "to_download = all_model_paths + [\n", " 'oof_predictions_mpnet_multi.csv',\n", " 'dev_predictions_mpnet_multi.csv',\n", " 'final_model_scatter_multi.png',\n", " 'final_model_metrics_bar.png',\n", "]\n", "\n", "for f in to_download:\n", " if os.path.exists(f):\n", " files.download(f)\n", " print(f\"⬇️ {f}\")\n", " else:\n", " print(f\"⚠️ Not found: {f}\")" ], "metadata": { "id": "EY4-36q9anSB", "colab": { "base_uri": "https://localhost:8080/", "height": 976 }, "outputId": "522c3f61-7f91-488d-b03e-0e646add0db8" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_3c7f1957-c45a-4df1-a24d-d547df842217\", \"best_mpnet_attn_multi_f1_s42.pt\", 440194800)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f1_s42.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_405c52a5-a55c-43f0-bdb6-d10579cd7018\", \"best_mpnet_attn_multi_f1_s123.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f1_s123.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_b8570d35-0661-41e6-a994-67b3b254a045\", \"best_mpnet_attn_multi_f1_s2024.pt\", 440195262)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f1_s2024.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_76cd42db-955b-4972-922e-b5f129ef71e3\", \"best_mpnet_attn_multi_f1_s7.pt\", 440194569)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f1_s7.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_8307513e-a38b-4cb0-82bd-2bf7f758056e\", \"best_mpnet_attn_multi_f1_s999.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f1_s999.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_bf4db918-8a91-434b-ace5-2b4e5d564597\", \"best_mpnet_attn_multi_f2_s42.pt\", 440194800)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f2_s42.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_bb63e900-0892-401d-a3ee-7bcb31c6d631\", \"best_mpnet_attn_multi_f2_s123.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f2_s123.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_9ff69389-ab56-44cc-a4cc-3654b00f9f98\", \"best_mpnet_attn_multi_f2_s2024.pt\", 440195262)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f2_s2024.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_3ac36811-18ef-402b-a7da-703b7392128b\", \"best_mpnet_attn_multi_f2_s7.pt\", 440194569)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f2_s7.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_6a84584e-e880-47e7-93ab-ad0b9668b027\", \"best_mpnet_attn_multi_f2_s999.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f2_s999.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_0a5bdf58-f6e0-4220-b889-5724b1b5bccd\", \"best_mpnet_attn_multi_f3_s42.pt\", 440194800)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f3_s42.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_c898ad24-ba6b-4945-afb7-74f7bacdf4c9\", \"best_mpnet_attn_multi_f3_s123.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f3_s123.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're 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"metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f5_s7.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_95129512-6203-41bf-8fe9-f85b914c42df\", \"best_mpnet_attn_multi_f5_s999.pt\", 440195031)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ best_mpnet_attn_multi_f5_s999.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_f136204d-7630-4a4b-a0d3-b4db5da704e8\", \"oof_predictions_mpnet_multi.csv\", 23512)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ oof_predictions_mpnet_multi.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_bfe2115b-a98c-44a3-91cf-b43de0e08787\", \"dev_predictions_mpnet_multi.csv\", 5251)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ dev_predictions_mpnet_multi.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_60831a6a-9db8-4ce0-b0b9-f7f550f9aa67\", \"final_model_scatter_multi.png\", 342455)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ final_model_scatter_multi.png\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_c8c0ced8-d47c-400c-b387-fb501a72bf1f\", \"final_model_metrics_bar.png\", 95590)" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "⬇️ final_model_metrics_bar.png\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL 10.10: Save Averaged Weights sebagai satu model ──────────────────────\n", "import torch\n", "import copy\n", "from google.colab import files\n", "\n", "# Load checkpoint pertama sebagai base\n", "base_ckpt = f'best_mpnet_attn_multi_f1_s{ENSEMBLE_SEEDS[0]}.pt'\n", "averaged_state = torch.load(base_ckpt, map_location='cpu')\n", "\n", "# Akumulasi weights dari semua 25 checkpoint\n", "total = 1\n", "for fold_idx in range(1, N_FOLDS + 1):\n", " for seed in ENSEMBLE_SEEDS:\n", " fname = f'best_mpnet_attn_multi_f{fold_idx}_s{seed}.pt'\n", " if fold_idx == 1 and seed == ENSEMBLE_SEEDS[0]:\n", " continue # sudah di-load sebagai base\n", " ckpt = torch.load(fname, map_location='cpu')\n", " for key in averaged_state:\n", " averaged_state[key] = averaged_state[key] + ckpt[key]\n", " total += 1\n", "\n", "# Average\n", "for key in averaged_state:\n", " averaged_state[key] = averaged_state[key] / total\n", "\n", "torch.save(averaged_state, 'final_ensemble_averaged_mpnet_multi.pt')\n", "print(f'✅ Averaged {total} checkpoints → final_ensemble_averaged_mpnet_multi.pt')\n", "files.download('final_ensemble_averaged_mpnet_multi.pt')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "id": "3K9nd5Ff4924", "outputId": "1615e22a-2382-40c1-a91a-97134472cc6c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Averaged 25 checkpoints → final_ensemble_averaged_mpnet_multi.pt\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_e91e8c17-73d7-4a4e-96ef-80709e28295b\", \"final_ensemble_averaged_mpnet_multi.pt\", 440193281)" ] }, "metadata": {} } ] } ] }