├── model.bst ├── output_df_water.csv ├── requirement.txt ├── image └── Screenshot 2024-09-22 134306.png ├── feature_importance.csv ├── README.md ├── output_df_water_utf8.csv └── Load_pretrain_model.ipynb /model.bst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yuntech-bdrc/WaterQuality/HEAD/model.bst -------------------------------------------------------------------------------- /output_df_water.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yuntech-bdrc/WaterQuality/HEAD/output_df_water.csv -------------------------------------------------------------------------------- /requirement.txt: -------------------------------------------------------------------------------- 1 | pandas==2.1.1numpy==1.26.2scikit-learn ==1.3.1xgboostshap==0.46.0seabornscipy==1.11.3matplotlib==3.8.1 -------------------------------------------------------------------------------- /image/Screenshot 2024-09-22 134306.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yuntech-bdrc/WaterQuality/HEAD/image/Screenshot 2024-09-22 134306.png -------------------------------------------------------------------------------- /feature_importance.csv: -------------------------------------------------------------------------------- 1 | Feature,Importance 2 | Sulfate,1537.7611 3 | ph,983.16156 4 | Chloramines,347.69135 5 | Trihalomethanes,345.25415 6 | Solids,333.57013 7 | Hardness,304.05408 8 | Turbidity,278.58853 9 | Organic_carbon,240.79886 10 | Conductivity,237.85881 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # WaterQuality 2 | 3 | This study aims to classify water resources, predict their safety, and explain the trained model. 4 | XGBoost is used to construct the model and predict the water datasets. 5 | SHAP is used to explain the model. 6 | 7 | 8 | ![image](https://github.com/yuntech-bdrc/WaterQuality/blob/main/image/Screenshot%202024-09-22%20134306.png) 9 | 10 | ## Requirement 11 | 12 | ``` shell 13 | pip install -r requirement.txt 14 | ``` 15 | 16 | ## Performance 17 | | Dataset | Accuracy | Precision | Recall | F1-score | 18 | | :-- | :--: | :--: | :--: | :--: | 19 | | water-potability | 0.77 | 0.73 | 0.66 | 0.7 | 20 | | water-quality | 0.96 | 0.93 | 0.78 | 0.85 | 21 | 22 | ## Datasets 23 | [water-potability](https://www.kaggle.com/datasets/adityakadiwal/water-potability "kaggle_water_potability") 24 | [water-quality](https://www.kaggle.com/datasets/mssmartypants/water-quality "kaggle_water_quality") 25 | 26 | ## References 27 | 28 | [SHAP](https://arxiv.org/abs/1705.07874 "A Unified Approach to Interpreting Model Predictions") 29 | [TreeSHAP](https://arxiv.org/abs/1802.03888 "Consistent Individualized Feature Attribution for Tree Ensembles") 30 | [XGBoost](https://arxiv.org/abs/1603.02754 "XGBoost: A Scalable Tree Boosting System") 31 | -------------------------------------------------------------------------------- /output_df_water_utf8.csv: -------------------------------------------------------------------------------- 1 | ,Feature,正數正相關,正數正相關%,正數負相關,正數負相關%,正數無相關,正數無相關%,負數負相關,負數負相關%,負數正相關,負數正相關%,負數無相關,負數無相關%,正數,負數,0的值,global右左相抵消,正負相關值相抵消,正負相關相抵消,Correlation,特徵屬性最大值,特徵屬性最小值 2 | 0,ph,173,0.176,317,0.322,0,0.000,314,0.319,179,0.182,0,0.000,490,493,0,-187.13909866241738,0.33489023884521885,-279,負相關,4.139712386571702,-4.146796365176485 3 | 1,Hardness,216,0.220,237,0.241,0,0.000,279,0.284,251,0.255,0,0.000,453,530,0,19.67445285152644,0.9235899973204482,-49,負相關,3.904700326885872,-4.58766141671768 4 | 2,Solids,254,0.258,220,0.224,0,0.000,200,0.203,309,0.314,0,0.000,474,509,0,-18.334633926249808,-7.10865766331176,143,正相關,3.9978231093310255,-2.4454365602568813 5 | 3,Chloramines,226,0.230,205,0.209,0,0.000,269,0.274,283,0.288,0,0.000,431,552,0,-61.953886051662266,34.783944301358815,35,正相關,3.6464199860569195,-4.124173599843703 6 | 4,Sulfate,176,0.179,232,0.236,0,0.000,197,0.200,378,0.385,0,0.000,408,575,0,-262.8857945492491,98.81119482059762,125,正相關,3.9729849964631656,-3.9061175490060496 7 | 5,Conductivity,159,0.162,222,0.226,0,0.000,300,0.305,302,0.307,0,0.000,381,602,0,-25.615331323700957,115.24517133758468,-61,負相關,4.056189461068857,-2.7761815670824146 8 | 6,Organic_carbon,214,0.218,256,0.260,0,0.000,277,0.282,236,0.240,0,0.000,470,513,0,-32.956991428975016,185.6339330869066,-83,負相關,2.9615396672124557,-3.653769211085762 9 | 7,Trihalomethanes,202,0.205,280,0.285,0,0.000,310,0.315,191,0.194,0,0.000,482,501,0,-41.335897111101076,146.85424083291446,-197,負相關,3.3597763074886053,-3.7895243498302293 10 | 8,Turbidity,262,0.267,169,0.172,0,0.000,228,0.232,324,0.330,0,0.000,431,552,0,-31.788177231210284,91.1816617474783,189,正相關,2.9421765857957234,-3.1451615659950036 11 | -------------------------------------------------------------------------------- /Load_pretrain_model.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "f3deeea3-7145-4ea5-924c-f6fe555df924", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd \n", 12 | "import shap\n", 13 | "import seaborn as sns\n", 14 | "import matplotlib.pyplot as plt\n", 15 | "import xgboost as xgb\n", 16 | "from sklearn.model_selection import train_test_split\n", 17 | "from sklearn.metrics import precision_score, confusion_matrix, accuracy_score, recall_score, f1_score\n", 18 | "from sklearn.metrics import classification_report, confusion_matrix\n", 19 | "from sklearn.preprocessing import StandardScaler\n", 20 | "from scipy.stats import pearsonr" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "id": "c0502151-39ae-4537-8e48-9897059e71c7", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "# you can select water_quality or water_potability\n", 31 | "Dataset = 'water_potability'" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 3, 37 | "id": "7bce5c08-956f-4ddc-8d10-006ec313677c", 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "def Load_Preprocess_WQ():\n", 42 | " # 水質資料集\n", 43 | " data = './dataset/waterQuality1.csv'\n", 44 | " df = pd.read_csv(data)\n", 45 | " \n", 46 | " # 刪除缺失值\n", 47 | " df.drop(df[df['is_safe'] == \"#NUM!\"].index, inplace=True)\n", 48 | " # 分割特徵與標籤\n", 49 | " X = df.drop(columns=['is_safe']) # Features\n", 50 | " y = df['is_safe'] # Target\n", 51 | " #分割訓練集與測試集\n", 52 | " X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42,shuffle=True)\n", 53 | " # 資料類型轉換 使用pd.to_numeric轉換成數字類型,\n", 54 | " y_train = pd.to_numeric(y_train, errors='coerce')\n", 55 | " y_test = pd.to_numeric(y_test, errors='coerce')\n", 56 | " X_train['ammonia'] = pd.to_numeric(X_train['ammonia'], errors='coerce')\n", 57 | " X_test['ammonia'] = pd.to_numeric(X_test['ammonia'], errors='coerce')\n", 58 | " # 設定特徵名稱\n", 59 | " \n", 60 | " feature_names = X.columns\n", 61 | " X_train = pd.DataFrame(X_train, columns=feature_names)\n", 62 | " X_test = pd.DataFrame(X_test, columns=feature_names) \n", 63 | " return df, X_train, X_test, y_train, y_test\n", 64 | "\n", 65 | "\n", 66 | "def Load_Preprocess_PT():\n", 67 | " # 水質資料集\n", 68 | " # 可飲用性資料集\n", 69 | " df = pd.read_csv(\"./dataset/water_potability.csv\")\n", 70 | " \n", 71 | " # 以分群特徵平均值處理缺失值\n", 72 | " df['ph']=df['ph'].fillna(df.groupby(['Potability'])['ph'].transform('mean'))\n", 73 | " df['Sulfate']=df['Sulfate'].fillna(df.groupby(['Potability'])['Sulfate'].transform('mean'))\n", 74 | " df['Trihalomethanes']=df['Trihalomethanes'].fillna(df.groupby(['Potability'])['Trihalomethanes'].transform('mean'))\n", 75 | " \n", 76 | " # 將資料分成特徵(X)與標籤(y)\n", 77 | " # Potability為目標(y)欄位故從X中移除\n", 78 | " X = df.drop([\"Potability\"], axis=1).values\n", 79 | " y = df[\"Potability\"]\n", 80 | " \n", 81 | " # 將資料分為訓練集 (70%)、驗證集和測試集 (30%)。\n", 82 | " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)\n", 83 | " \n", 84 | " # 標準化\n", 85 | " # StandardScaler 將每個特徵的均值設為 0,標準差設為 1,確保不同單位的特徵不會對模型造成不均衡的影響。\n", 86 | " sc = StandardScaler()\n", 87 | " X_train = sc.fit_transform(X_train)\n", 88 | " X_test = sc.transform(X_test)\n", 89 | "\n", 90 | " # 定義特徵名稱\n", 91 | " feature_names = ['ph', \n", 92 | " 'Hardness', \n", 93 | " 'Solids', \n", 94 | " 'Chloramines', \n", 95 | " 'Sulfate', \n", 96 | " 'Conductivity',\n", 97 | " 'Organic_carbon', \n", 98 | " 'Trihalomethanes', \n", 99 | " 'Turbidity']\n", 100 | " # 將資料給予特徵名稱\n", 101 | " \n", 102 | " X_train = pd.DataFrame(X_train, columns=feature_names)\n", 103 | " X_test = pd.DataFrame(X_test, columns=feature_names)\n", 104 | " \n", 105 | " return df, X_train, X_test, y_train, y_test\n", 106 | "\n", 107 | "df, X_train, X_test, y_train, y_test = Load_Preprocess_PT()\n", 108 | "\n", 109 | "if Dataset == 'water_potability':\n", 110 | " df, X_train, X_test, y_train, y_test = Load_Preprocess_PT() # 去除空值\n", 111 | "elif Dataset == 'water_quality':\n", 112 | " df, X_train, X_test, y_train, y_test = Load_Preprocess_WQ() # 去除空值" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 4, 118 | "id": "ba832e58-f2b0-4be2-a7e5-b6c92197b923", 119 | "metadata": {}, 120 | "outputs": [], 121 | "source": [ 122 | "model = xgb.XGBClassifier()\n", 123 | "model.load_model(\"model.bst\")\n", 124 | "model_result = model.predict(X_test)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 5, 130 | "id": "e26b7bd4-5b87-4ffc-b7ce-54cffcc692f1", 131 | "metadata": {}, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": [ 137 | " precision recall f1-score support\n", 138 | "\n", 139 | " 0 0.79 0.84 0.82 595\n", 140 | " 1 0.73 0.66 0.70 388\n", 141 | "\n", 142 | " accuracy 0.77 983\n", 143 | " macro avg 0.76 0.75 0.76 983\n", 144 | "weighted avg 0.77 0.77 0.77 983\n", 145 | "\n", 146 | "final_results: [('XGBoost Classifier_precision', '0.729'), ('XGBoost Classifier_accuracy', '0.770'), ('XGBoost Classifier_recall', '0.665'), ('XGBoost Classifier_f1', '0.695')]\n" 147 | ] 148 | } 149 | ], 150 | "source": [ 151 | "final_results = [] # 存放最終的評估指標\n", 152 | "confusion_matrix_list = [] # 存放預測結果的混淆矩陣\n", 153 | "\n", 154 | "# 評估指標\n", 155 | "precision = precision_score(y_test, model_result)\n", 156 | "accuracy = accuracy_score(y_test, model_result)\n", 157 | "recall = recall_score(y_test, model_result)\n", 158 | "f1 = f1_score(y_test, model_result)\n", 159 | "# 混淆矩陣\n", 160 | "cm = confusion_matrix(y_test, model_result)\n", 161 | "# 各種評估結果加入到 final_results,並輸出統整後的結果\n", 162 | "name = \"XGBoost Classifier\"\n", 163 | "final_results.append((name+\"_precision\", f\"{precision:.3f}\"))\n", 164 | "final_results.append((name+\"_accuracy\", f\"{accuracy:.3f}\"))\n", 165 | "final_results.append((name+\"_recall\", f\"{recall:.3f}\"))\n", 166 | "final_results.append((name +\"_f1\", f\"{f1:.3f}\"))\n", 167 | "\n", 168 | "print(classification_report(y_test, model_result))\n", 169 | "print('final_results:', final_results)" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 6, 175 | "id": "c13c5831-8c0b-4456-8b8f-0a2522eb42e2", 176 | "metadata": {}, 177 | "outputs": [ 178 | { 179 | "name": "stderr", 180 | "output_type": "stream", 181 | "text": [ 182 | "[15:37:13] WARNING: /workspace/src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.\n" 183 | ] 184 | }, 185 | { 186 | "data": { 187 | "image/png": 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8EKaEg7TH2LD0+NCiEkaiqKaLT6dq+Sgjk5Dfx/C9fY1yQzrNgGVbOyyHjycP7f3cnBjD8KZWUj0eABRJwmE2E9BpMYbC6BQFraKQV9/MqD1VaBSVkCSzbXAWtUNiCWk0nH/9//HYwtcYV1vD1rRMdPVWsmhHRUNYktiVmUVzfDxpPe34pP7lCmgi0zSKAh0O6OggNPVXuObPQh6Shv6iobz2i10stCRj8Qe5rnsFQxMdfFEV5p8TJrN0aiEXbl3LnAdXs7y0lHzVzQ0//A3Wei8SCsZzC5Cvnwbvb0JNi+P7Y+fxcrlEWJJRZQmtRuHlOTKXFg18zvZTVZWgAnrNtxcoBsLqt7q9k1nrDge73qlDUVRKFuSQPLSvMXg4qCBrpN5a/GW/2caO12v7baN+VTuf/2wzk+4swpJk7J3eXeti66vVVHzahLcjcj23ZZhIGR6HKd5AyYJsEgr6v/X6rhJddh6eyOk/gdTX1/PCCy+wceNGmpub0ev12O12SkpKmDdvHqWlpUe8zYGC/lAoxEUXXUQoFOLqq6/GZrMxePBgBg8e/I2363Q6eeWVVxg7dux/Va7vuuZNnXSUdZMyKh5vZ4DNz5TTsrkTNayihNRITeU32M43Xe7bIuslxt4yhNE3ForX0ScpV4uXmi/bMCcZyJmSjKyRaCxz8MNHHBz4azL6Avz8zZfJDdUjoVJmz2N51lgWDismtc3J6L0N+BMsVGcn49RqWCtpmVlWj1+r4ZOSLJpjLcxu7eTHry1Hc8AgXD+6eRab89N6P5+/ZifzN5TTHWOlPTmWG5d/Qozf2zu/U2/lZ/MW0GSPizqOxB43uW4XqiTRYzIS1EYeRu0uN8ZQGJPXywVL1yArKkE0hIgExyFzkKk/uxm/vu/txDWLNjOhqZkLtq8jhIYPR42jMTWxd74S9vLbMyagyH0N5H/x2UIe/Hzhvk8qEDnIHpLwkgCoNMfY+M3sM3hj7EjMfj+r/voog9vbALj28isJaEI8+sGL5HW1sTyviF/OuISXXngDQzgMKMTQhoYQHxUOw22QuXDHOrqNZn474zwWloxjVvUOHv3zBMzpkXOjhsIEPtyDUtmKASfLiec67Rhq/DqKE+C+cXDFMM0hGys3beyko7yHtNEJ2AtjAPiyXmV7u8rpGRJ6DfzklVYSl25DSY1j3u2jmJaj4d2yEPrNlZwXasR83lhIGrgXpLVNKuubVSakSYxNPbbXj5A/TPXSFoKeMLnTUzAdpj1T+54emjd3YR9sI+0QqY2NGzpY+IPVURUxY36QjzFOT8OaDmpWt7ErN4XYMzKYV6Jjwx2rv3aflhQDk380FGezl6olzbRscXzt8rJOZtbDo/B2BUgosJE+5rudghmUbuw3Tac+fRxKcuISVXYniJ07d3LjjTei1WqZM2cOgwYNwu/3U1dXx+rVqzGbzd9acN3Q0EBDQwN33nknl1566X+1DafTyTPPPAMggv4DqIpKZ4UTs92Ayd5/wJ+eBg+f3LKarnLnAGv3+aa3w2MddisBlXWPl9G4pp1zn51Md7ULnUWLNdV0yHUCriA9dR5issz01HmwZZox2Pqngxyou9aNJEFMluXbPoSTmqvJS9AbIn6QDVVVWftUBTVftWGI0VF0bjpJxTEk5Edq/gLuEN31HuJzLWgNGrqqXDRvdbD019tRgpG6npTBFs5P2k3lolY4/cqofZ1Ws5VEuQOvzoAt6KGko5KWmHhues+FzeWjrDiNykGR4F2WJPYmxfBUcknUNkZsq6U7To/Rq6ANKmwdlBwV8AMsnDCUs7ZWYnF7iKlwsSqpkGJHPdaQjy69lSpzKrrQQUP3AloUTP4AdUl9bVE04TD6fcuW7Knl3dFDWZOXTX5LB9N2VpHV0YPWo+fTR5/hoQtm0Wa2cdbmCs5aX8HCcycwf/tadphzogJ+AFlj4vSqNnalxKFRAtz15Yfc9eWnByzRV3cWwsT+ZsdpPU7+8ua7bMtOpyw1madOK+Wv775AGCN3LPuccc2b0Ycj5Z1aVcbv/S/yydDRmINB/nH6ZMbU1nDLihWMqe4gIdCOFoUEj4s/fvgqe+yp/P2Np/G+8zxXXXMHIxYM5/s/fxlpXTmx1CCjMh3YpjNwxfduJ6jRkverd6ggQP5t0/Ds6kK7fi/GiXlID57P0n/UUPZOpIvUdquJEWM06JqqyV6xkaSkdK6cuYARfgevPP0EhnBkjIZ/r5zIoIuvpktnAgrI6Yxh8cif8bPb7mRvTi7Xlki0uOHDvSrtXqjugWvWLWXYuiVUJGgp+Ok5UFaP8s5anEkJSPdfSMwB6VGqqrKhJkTj65X4N7ZhHxxD6c2DicmI7qTgYMGwys4OyImBOKNE514nb1+9koAzUm6dWcP8ZyaSMMhKd62b2BwrPfVuVj++G2eTF1OCnoa1Hb3bSx0Vx2k/LgFVxVHtZstLVTibvATcwaiAH2Djs5VAZHJjnJWN9gTM65wE32wmQa/DFohOSzuQu8XPZz/Z9LXHdiAlqPDpPRt7Pxvj9Uy5dygJBTacDR52vFWLvztIwex0RlyRiyRJeDr8eDv9NG/tYs8HjaiKSuGcDIZdkvON9yucvETQf4J45pln8Pl8vPLKKxQWFvab397e/q3tq6MjcjGLjRV9Un+buip6+OTWtfTUupG1EsOuHMSk+4YBkdSd5f9vM7vf7v9q92TUsLqd1875nJ46D0hQMDeTGb8djayN7ip0xytVrP7jDkLecOQJRQWtScOEu4Yy7MpB/bYbcAf57I511K+M1IZmTk5i1l/Gobd8/UPCqS4cVFh8/0YqP2kEFeILY3B2h/F1BZAUBUlVaVwe6S7TXhRDwfws1j5dSdATRm/TYorRRb6rg7SUu/EtXMYgc/TDlS4UwtSt8GrxHADyHPXMql3F0OYqtvkjNcAtKXG9yxtVlcE+P3tMfakJWV0uiru76Yq3orcEkVVotw8crCV4PXi0kXU9OgMbkiLdbsphlY6keFLcHurUBNR9ucySqpLb5cDm82HzeHCazWhDYS5dt4RB7c1sSSlg2aBcNgzK4P4Pl5Lf1okCeLQ6pJBEXIfKX579FBkVAwE+z8+hLhhmTXwB+tDA6TLZDjfxQRV9KMDE2gaCkhatGsJpMBLnjzzEq0iEMEatJ6swd8sOylKTeXbiDB767DUSPU6SvY29Af9+Yxqr+SB/Cmglfv3RQkoaOgEVPW5AQxArMhIg8dq/nwNVgyUY4PovPuKMQaW4Ddn8lJXIBzyEWIN+3n7xj0hEBlBTAfXHrVj3vflgaw3NXzVQphmJX6vhy2I7Exp2smmPnp1Jg3im+zNKWhoY0dhAfVwKvztjDi69gcs3rebdkjH7Av6ImoRkRt76MC69CZoiNfsHunbdEp5//cnIhypQLtrGP8efQWljiNHr1+L+YgtPv/5nbjwvlbIOlfkLw5R3ScgMYppFx4L3ymhY38Hl700/ZLfEy+tULv0gTLMbjFr4xQTY9VQdNUUFDG7pYlRtpLZ/yT1rcDkh4Aqhs2gIeRVUZeDEh+bNDt66YsWA8w5FBjIdLq5YvfOI1vtf+LoCfH7/5n7TW7Y6CLpD+J1Btr5SjRqOPs7mzV2Egwojr8g7RiU9OkR6z+GJoP8EUVtbS2xs7IABP0BiYqTmqbGxkfnz5w+Yg3+o/P0D3XjjjWzcGKkZeOihh3jooYcAeO+990hNTeX5559n9erV1NbW0t3djd1uZ8qUKdxyyy3ExcUBsH79em6++WYg8rCyv8Y/LS2N999/v3dfixYt4rXXXqO8vJxwOExBQQFXXXUVM2fO/C/P0olt+S+20FMb6Y1ECals/VclmaclkzUlmYoP60+ZgH+/3iBShYr360kvtVN8SW7vfGeDhxW/2drXkHjffSbkDbPit9vImppCbHZ0sLnp6fLegB+gfmUbm58pZ/ydQ/ku2/VmDZUf9+W+t1d5UCUJORzuu83JEqqi0lHWQ3v5LsL7cs4D3UGC3dG1ixXpKXTFWMlpbkOrhtB4NIzYVcO2IdmossSghhZcOmvv8lVxmWzzDEbRqtSbE+hJNBHncxPs0dIVE1nutB43kiSx22pCQuKmnbtx2yMBYcivIa3Fwei9zST2uGmP6fveC5vaGVnXzKrcnH09UPVpSU2gOSPSAHZIZxdujQZUyHZ0E+v3AzC0oY4Er4vSml2k9nQC4DDW8+WMGdy8dDX5bZFpMmANBfGjJYiWIJGg0YceSZJJ7vHSFWch3uMmzunEYevLlTb6/LjiZVDCPPjpcxS17v9b3l+nr6IA1dZUDB4ZnRJd/Tumvj5yrA2dVBtzifWUketo6fc9O/VGLt+ylsweJ0F0yIQw0oGGMCoSCmb2v98zhMMoWJDoZnBHpFHov8eN5udfvYAK+LQ6fjP1fFrNMdyz8kMKO5v3lThy1Cp934Gr3gM50BEf5D//+QM6JfIw0mWy8OTEs/jJ0ndZmZvP3fOvpdMS+b4fO/0sshx9teG92zIc+q3fDWu+iPosAzF+L+PueJj3n3+Ec3Zvpvofy9l52sXc8nmY8q7Icooss6Q4h6KmdoY1tFO/up2c05P7bV9RVa7+OBLwA/hC8ODSEP5hkfvqsqIcpu+q4eL1ZXQ2Bnt/b0F3/zdJp5qtr1bj6zp0O69db9ed9EG/cHgi6D9BZGZmUlNTw+LFiznjjDOO2n6uv/56Ro4cyfPPP88FF1zA6NGjAYiPjycYDPLSSy9xxhlnMG3aNIxGIzt37uTdd99l8+bNvPzyy+h0OvLy8rjrrrt49NFHmTFjBjNmzADAbO6rxfv73//Oc889x+TJk7n55puRZZklS5bwk5/8hHvvvZdLLrnkqB3j8aCqKs0bO/tNb1rfQdaU5AHnnWqaN3ZGBf0tmzsP2XMQKrRs7OgX9DdvGOgcnvrn7nAOPi+qJCGpA9RrSaCqQFgBzaEbeNq8PhqT7ZTlZFC5MwvJaWLE7nrya1rpsZlApxI+qFFtgzWZytQsQnIkF1oGUhzdFO1qwKs3sKUkk3ajAQw6plTWk9zpIqzXEO/wkt7UgwzocfHHFxfx8vTh7MxIZnR1E/d8tIJOi5mQNtJAVVJBE1aQVHDEx/Tu3xIKk9PagQa1N1hTgNXWWK7ZWoPXZ8GLGxN+lhYUIqsqRU1tHExGISjrexsdK8ikdPmYojSCBJkuB0UbWlk3pJCWuDgSe3pI6ewiqIfztn5CUetuQAPoAAlL0E+3QU+s38MgVx2daAlhYn94LaGQ7HTy+38tYsaOGiCVbSSRqW5nZ56d6VW7esu2KnMoU/c2IRHCggMJHxIqHSYr7xWO5+otG6OORUKi3RjLZwXDAdCFwwSwoug8fFowgoeWvIlmgGZ7EmFUAkDku0x3t6NBYd6eFb0BP0C8101eZysAmzPzegN+gLBG09uO4psKDvCbDMoawhoNvz3jAs7ZvZmgRsuXDSpfNfRfvzI5nmEN7cjagWt063qg5qBhIfy66DIuH5LFOdsqsfoPnWZz1O3/To5hLzxK+FAX44hDndOTy6lwDEeXCPpPEN///vdZs2YN9957L9nZ2YwcOZKSkhLGjh1LXt639/Q9ceJEtFotzz//PCNGjODcc8/tnaeqKp988glGY/Tr6REjRvDrX/+apUuXMmvWLOx2O9OnT+fRRx+loKAgahsAZWVlPPfcc1x33XXceuutvdMvu+wy7r77bp544gnmzJmDxXL887U7OzuxWCwY9vW/7XK5UFUV275avkAggNPpxG7vyxtuamoiLS2t3+eEwTY6D8rVtxfF0NnZSUzeoWu/ThWx+RY6Ojp6z1XC4JivXd5eFEtzczMpKSm9XdBZcvSwIXq5mEF95+5Ivo/9Dt7Ht/mdH6t9JAyJgY8PiIIO0/+Cephgwm3a195EktiRMZjJO3YAYPEFsPgCdCRa8B4U9Jel5hHWaqNG1wVAA8VlDeg7XTx7TQazy6q4aGs5yKAJhXGadfh0OvRBhRAyQ2va+eHHa8nt6EQfDtMaZ2N7bkZvyoEKKLKE22jEZ45ci+xtXaQ1taEJKwS1GnoSY+g2GfkoM5X19nhWpaTx7KtfoKDBmQh/nziRc9bWUGOPI/mgsQAaEmKoTkogzuUjt9GBRlXRhvrOpz4cxurzM2PLtt7ybMjI4fal/yQ+0L1vKQUFBQkjOsWH3t8XKBtx4kOHgrQvEUehwZywL+Dfv7aGJnUIV192OSuf+CmZPV0sGjQMnzYWaEKLD4kgEpFG/bOv+gkdphiu2LopKoj3anWcf8WtlKVFxgnI7O6mQ5dBq93DuXs2Dxjw7ycRRN0X9NfmZ3DmDelof97db7ncrsiDU3liWr955oCfC7atYWHJODSKgk4J49X3b8u0319PO4dpe/seckKyzJOTzgKgzRpDp8nCy2NO5/W4ECXxKts6o3+DGV1O4gdZyRifOODfYGpiMokmaPdySIosE9SooCpEukM7xo5DwA+QOy2FPe8P8CS1T/GCvrEmvo1r4vEg0nsO7zj84oWBjBgxgpdffpm5c+ficrl4//33+d3vfsfFF1/MDTfcQP2+18NHkyRJvQF/OBzG6XTicDgYN24cANu3b/9G2/n444+RJIk5c+bgcDii/k2dOhW32822bduO2nEciYSEhN7ADMBqtfZe6IDeHpQOdPCFbf/n0x4YEdWdZc6MVPJmppGQkMCwS/P7DX51KkkeGc/wy/OjzlVCYQwjrssfcPlhVw3CXhRLampqVJ/Tk344nNicvofB2BwL42/rS+05ku9jv4P38W1+58dqH8O+l0vSsLjez2arjMasPXh4hsi8BD1DL8nprfSStFLU77LHbKLJHt/7eV1GEUGdRGywq3earccX9WAR0sh0JsShDtDrizYYCXjzW7sZV9XCvO2VUfNVWaInZn9PKRJBNOh8YTanpbG0uJCVw4f0dlfbS5KoTktEBQw+Pxn1LWj21VTqQmH8Lj+/GT6E9fuOozwljo40SKKHQe09fPbX55i+vZzdGel0m/q+h44YKzvzs0CScNhMNCVaUYHW+L4a7BZrdBeILRYbJrX7gIA/QiaMikJAE30bNdKFBh8aFGQUXHojFmf/8xbEyK3LlpPZEznv/5h4Fm+NHA2oyCjsbyG6OnMwG9IHUR2fyG+mnoOy74sNSTI/mXU+K3MK6bTYQCPx2bAinvngpxQ8dTXGfY1tD2VbShafFpbw2DlzkT++m/zbS4k/b3i/5fI7Wug2mqiK7z/68HnhRl57+THaHvoBHQ9+n5f+8zf0oUPXoL81YiJzrv8Jbw0bz6ujJjP95gdZmRcZ+6OgvZlJt/2G+VMSOD1HzxNn6Yk5oIOdUnc3104zc/4/JyJrpAH/Bg1amb+eKaM/4FlBf1CUU9jSwj2bXmT8VGPf34gMJvshevP5NuPIY9xZoi3DRM60ZGY+PIozHhxB7rT+KVF6q5YzfzOS4Rf3tbH6Nq6JwolJ1PSfQAoKCnjwwQeByFPzhg0bePfdd9m0aRN33303L7/88lEvw2effcbLL7/M7t27CYWibxo9PT2HWCtaVVUVqqqyYMGCQy6zvzHxqSR9fCJXLDmLhlVtWFKMpIzs6z5Na9Qw78XTaN7QiavJw+Zny+nc8/U9+JzoBp+XyaCz0tFbdaSNsw84YMyke4dRdFEOneU9xGRbcNZ5iM+3El8w8FsAS7KJSz44g4bVkYbrGRMT+zUO/i7SW3Vc+OrpNK7rIOgJkTk5CSWosvH5Sio/bcTX5Se5JI4h8zPJOzMVnUnLyKsH0bHHScqwWEzxeurWtLOtPMC/vwz31jKagwFUrZU/zP4BZ3+2BU1QwWnT05FqQ5UlFEnCZ9DjtFlRZRmP0UiMy90bB2mCYZKa+n7H9364jp5UA+GDvrODa+DcBn1kexYjutDAwWlYlmm2WSjpcPSLu9JdHqyBIN3GvoDeJDmJpYUgBgjGkCp72GPQsHj8SFK6uglqZNriYghpNRjdXmRFpctmYm1xPg6rkbFle5GABlscpmCAnO5OJKDGnohe7V8DDrAqK5dhrdUYwn2BrkyYGGppsQ5mU+xgauKTsXiCQPT104CPKzZ/RXVcIn+aOpd3Rk9EFwqR7HLy0KI3MYaDSAQIH9BN6C9nzOWVEeMZ2VzP2oxc6uIS0Oll4m0ywTBcN0ziZ1ON6P1F+GwWjAe85VB6yweqzYT3lR8Rysng/3IkjPtSO2KevQkl2Yrj6aV0a/S8OXwCK3OLWJI/lG5z5MFIAqx6uGG4xG+njufZnF8w+o8vUdxaj0YJk+zpQUq3MywJtu7LrrpppMSoJFjbrFJTPIbbS8fgDYEsQZIECwolps4ppSRRZnhSpCynZ0rU3aThi1qVdKvEhDQ7cPiKk8uKZGZkSXxZrzIkQcIZgHuWhdnRDtNivfwtswlDzROUpsQxuM5N264ekktisaWZaFjXgavFS9WSFhrWdhCbY2HSD4swxOhY8cedtG53YIwzUHReBuufqjhkGSRt5EXCwT37fFOSBtSvaWagNcuEPIfYuATDv5dL0fxMkoqjO+s49y/jaNnaRU9jpD2WKd5Axji76H75O0QE/SeotLQ05s6dy5w5c/jBD37Ali1b2LFjBykpKYdcJxz+3xojLV68mPvvv5+SkhLuueceUlJS0Ov1KIrC7bffzpEM6SBJEn/5y1+Q5YEDtvz8gWuAT3YGm45BZw3ciFqSpH21/XbyZqXzxT3rqd43KqPOoiXo/vqauf20Jg0qKmHv4e8oB39j38alffD8THKmpzJodvo3ulnE59uI39eNZNLQuMMuL2tlsqb0r5H6rpNkiYwJB3QlaYCJtw9h4u1DBlw+PsdC/AFvTfKmpZA3DQqn+Vi/wUt8vIZpUyxYLZFq0ZYtGTx82y4MwRCZwUh+hAwYAiE8ikIICOp1OGJsGAIBsve2kdLQjT4Que5oCVPobaehO4Z6e1+wISkqMU7/vk8qbrMWt0lPvNfN+K3VWAJ+1mfm0GLtexAMaDQ0Jsbj1+vwq/1/Yy69DtcB/eyPbWvGaNRQnp7O0KZKdKqPoDELAEUj05QYeSMgAUgSikaDrITYm5HM1sGR5TRhhVnrdqINK2S5Gvls+FBm7qokydXNmuwcSjwpFHT0Nb5dkl9CXZyd0+p29RsvQzUZSWl8kPpnGwi+UIbLYqAh2UZ6aw8SEjoCpNJCgqeJSnMScWcW8+Did7ly+WdsLCzm8R/9gJv/8i9sASeT6/YwrKWW7SnZAFTYk6mwR/4+BnV1sOCCFB6ZdtCt3GzA+MoPCV7zN3SdPfisZvz3LSDW5wGDFumaGUzI7l9zj06L/IdrsP3uaj4oU2lqVbk8XeKhBPhPmYpFJ3FNiUSmre9ob7l9GB+c+zAP1aoU2yX2FEuYdANfF+YVDDj5kGIMEhcMPvKrVopFYsGQvvVWXr7//NiAvi6mY7MsxB7QLXDmvr+vovlZ/bZ5/j8nRX2WtTIbnqkgHFAOmi6x4NUpWFOMfPm7Hez9vJlwQMEYp6NofiZbXtg74Bs6iFQOTbhjCPkz0yh7rw5/d5Cdb9UQPCDAj822cPl702gv62Hxz7fQUeEEFaypRnKmJlM0L5OUEfGH2AOkjIj/2vnCqU0E/Sc4SZIYNmwYW7ZsobW1tXcArYFq3RsaDp2v90189NFHGAwGnnrqqai8/urq6gHLdShZWVmsXLmS1NTUb7U9wqlEa9Qw+28TcDV5URUVa5qJbS/upeqzRkx2A8OvycfX5WfZA5vx7+t5RaOXyZuVxvgfDUVv0/LWgmU4B+iGsR+JSM3uIbqj2y//nHSsGSa2PFs54HxJIzHl/41g6AGNdYWTT9FgI0WDjf2mp4yM50dvT+D377tof2Mnia09oIIGhVi7lvaeMIpGQ1irIRzUYG9yIRNC0UiEtDK2gAtJVcno7KE+JZ6gLKPKMiZfGL9Jj9ciE5IU3FYdkqQyrL0B7b6KhDH1tZQlp7AnJZUek4n6pAT8Oi2GQJCzVu9id7Yd475KDRWozUplfFc3Zo8bnd7IME+QD0ZEOkDI6WjgphWvk9HRiqQU9fZixL61NaEwmmAIn07Lnqy+lARzMExqZ6Rm3E08YZOf4l/eQ35zB5et3Mn3Lr2bGzZ8yoimWlbkDuEP0+ax4bGfAJE/Mb9Ghy43CUoHIf/mcrCZuOFHBTw9JYf3v3Sii9fzq80rSXtzDYbGBow40BCi8LJR/OqOwXBzLpSPIz83GSxGgpdk4bnqFdhdzzsvvcAjU2exOSODsfV1XL5pI954MxVPXsstUw/RYHtuKbrGZ6CiCWNeCkbzoXPtD6bTSFxdInH1AcMujBjgGaF3V/kyc0/NepxDKr1xMMMuy8Xd6qVxfQcVnzZjiNEx6ppBJO4b2GzWw6MJ/r8wzgYPsdlmkCTqVrbRUe6MSvUpnJvJmOvzsaYa0VsjD7OlN0Tu9SULsln/TAUde3rInJDIhNuGIEkSScWxXPrmVDztPoKecL9OEb6LRE7/4Ymg/wSxevVqSktL0R7UG4LP52P16sgofoMGDcJisWC321m3bh2qqvYG3/X19SxduvR/KsP+WnnlgIZ6qqryz3/+s9+yJlOkceVADx/nnnsur732Gk888QSPPPIImoN6bDiwsed3nTWtr5HqiGvzGXFt9J0zfUISNUua0Ro0ZE9PQWvoO5eXfngmr571Ge5m34DbNsbrGXltPrUrWmla3zFg7ZJGL6OqKvnnZHD6/xuJzqIleXgCa/64g556D+njE8k/NwONXiZneirGuK8fxVI4ueUkaXji+li4fhL16zvprvOQOT6BF1/spHtdD0N31xPT7SWhvYdUWunKtBGSZCzdfoI9evQBPz6Dju44GyFD5LfiBFozIqluhZvr6EqOJb+uEa2q4tdqqUxJwW0wkt7VhSEQJMPpI6O1i8KaJuqS7BiDYX5z0VQsgQC5bT0UuoKMqt3L7Us+Qaso+HQ6Phozjr1pkTdsNfYMtqYXYm8IoA0rhA+4ptq8bgbVVdNktFOXlIBfpwNVxRgO05KWQENWLBl1Drr1Zh6YOpdACLL8YbK1Ln7/3lvcd94FrMsdRGFrI8+9/iRlJaexzqliSzUx5OFZZI7rHxnfOE7HjeP2p/qdBY+fBTtqYeVuGJUL4/aNhK7XQUl273q6cZnoyu4lvLuV7OV7eXJoCvKQJMIfOpBuPhvNOcXM1h0i4N/PEL1N4dtljNFhjNFhL4hh+GUDV3DpTBoSCvry4y95YypLfrGVikWNKOHIwFhT7x+G1jjwdxmXa2Xmb0YdsgzmxP4P8N9dIug/HBH0nyAeffRRuru7mTp1KgUFBRiNRlpaWvjkk0+ora1lzpw5FBRE3o1ecsklPPnkk9xxxx1MmzaN9vZ23nrrLfLz89m5878fCOTMM89k8eLF3HzzzcyZM4dQKMSyZcvw+foHlXFxcWRlZbFo0SIyMzNJSEjAZDIxdepUSkpKuPHGG3n66ae5/PLLmTlzJklJSbS3t7Nr1y5WrFjR+yAjfD2DTUfhAK+aATQ6mVmPj2PhZV9G5fGYk42MuqGA4otz0Ro0jLqxEFVRqfi4gZW/3YavM9JXc8roBM55cgJ6my4qTWfQWekMOisdVVFFrud3WGZpApmlkWB14rQQO7Z42Dk0i/TmVsZ4t7I8cxypVQ5M7gBtMTb0MX4s7QG8Bn3U4FD7SaEwDbnxKLJEt9VCsE3m8+EjcO2rQNibmorN4QIiNfrxLg/7E3gaE2wUN7ZiDvro1MLszRuR99WUGoNBzt60nqeS5/TW6rdaEvHFq/gN0Q+pTpOVsR3lVBu7kVQwAA1pSb2hws78TPLq2liVm8389ib+7weDGVOYjapmUV3yB1Y98SuQQoSRWF8wkXFfXI3WqDnyv5OS7G8cjGuGJKMZ0pfuJl8z7sj2JZxQJFnijF+N5IxfjRTXWOGYE0H/CeKuu+5i2bJlbN68mcWLF+NyubBarRQUFHDNNdcwb9683mWvueYaXC4XH330ERs2bCAvL4+f//zn7Nq1638K+mfPno3H4+GVV17h8ccfx2azMXXqVG677TbOPPPMfsv/6le/4tFHH+WJJ57A5/ORlpbG1KlTgcggYEOHDuU///kPr776Kl6vl4SEBPLz87nnnnv+6zIK0VJGJHDhG9NY+fA23M0+sqYmM/7OoRhiokewlWSJwXMyGTwnk/Zd3Ugy2Id8/YjM4mYk7Dducgy7nqtgS7OMooe9Cdnk7Grv7VHH2uMnqJdpslhJ8LjReQOEtFqUfUG4JhjE4AuARsKh1+NLTsCo5vYG/Pu5YszoPf7eINzo89MZZ+Zv/3qfM8v2RrZFsN9DhTkQIN7lpD02DgB7ix/lELc3jRJmeNdevsoZSWNqYlTdoDYcJn5sHDc/OxHDqNTe6ZIkkb7qR6z75Vq61zTA2FzG/XAoOrO4hQr/PXGN/XaJ9J7Dk9QjaZ0pCIIgfCf5O/ys/uEaOj6qQ42RsNZFNzwPayUCZjnSfkRVcdjNdCbGoEqgC4ZwGA38c+xQauNsyIrCabXNzK+ojb5Nqyr2pkivOdpwmBh/AJ2ikNveRmT0WwktIWxE973v1el4evYcQpJMWmML521djiWo8u74KbTF9DVazG+r5aItX6ACC1Nn8umMUb3dkEqKwlVffMm4P03AeuWwo3IOBUE4erzSbf2mmdS/HYeSnLhENYUgCIJwWAa7gWkvT2X1+A/oLG8jMipthAoETHLfgEOSRFyHh4BBi8+kRwLeKsmnNi6S26zIMl/mppPb7WRkW9/4AJpACIlIX/xFTW3o9r1JCO5L9JGAMBoaEmMo7GhGVlWCssyfZs7AEWsjt7UDe3s3ZlXFjJ8LNy5nc3YBHVYrGd2tjKmLDAzVSTwZzd3M+2IDewuSkFWVMRVVFBRbsFxafJTPpCAIR4eo6T8cEfQLgiAI35ikldE6ZPwmTe9ItKqGSKfrUQtKZDQ5qByUjAqU2/unk1XF2BhX10pIK2PyBIjtdIGqEhsK9Ab8+zbGgQ1XzD0KP774EjIcDiqSU3AajRQ6eoj3exjU04IlFOki1BAKMmHvLmRCOM16AlodDtlKnS+ST5/U6iKl1UlClkzSvaOx31OKIAgnJ5Hec3gi6BcEQRC+saxbhtC9qg29N4Qqg6KV+g3GBYCqktjjQt6l4Eo0ktbjZq89LmqRgsZ2khu7+t2qdYcZc8QcCNJtstAa27c9RZIYt7eMkq66fv3mK2hZnTWSmeVrsCsurLH1OGZNQlOaTdKVBRgyRHeHgiCc+kTQLwiCIHxj6VflIxs1NDxXgcYok3lbEaZBNhoW1rLz11t6K+QtgSB+RYvOE6awtpVfvLuc/7v8bNzGSI86xY3tTN9dh7qvEn9/kB7QaWmIjye189AjgO9JT8J/wOBcVr+fK1csprixFpBQkJFRerfplYyU7q4meMNsLPeOw5AZT6xRN+C2BUEQTlWiIa8gCILwrXBV9tC5voPYYXGYU010LGok4PDTeftipLBCj0HPyoJMYr1+7E5vbx/6iiShUVW8JgOt6Qk0x8eQ2dDGlG3l6MNhYpQOghgIYcRKJw0ZGv5w5jW49XrivD6KW9r4/rJPsAb7uheWp+ShKUjE/XEVIaeC+dJi4v4yG9kqxpoQhFORR7qj3zSz+pfjUJITl6jpFwRBEL4V1vwYrPkxvZ9TvzcIgPgCC9ULPiamJ8DsnXtptVrptJp7l4t3eHHFWQgYDSh+BVOHl9g2H1/m5aCL0VHStovT61agV0I4dUbeKriU3G4nCW5PZMDpcAi30Yg5GOnuUzVoiX35EjQ58dgQBOG7QOT0H56o6RcEQRCOOsUXwre9g44aLyvvXIsajtx69IEQWYYQckYs/kYPXrMezcRkCs7NJHFaCrc91op5fROWoI8mM9jOLiamQyE+TsPs6RZizRINKnh+/BQ525wkDs/D8v/OQDdh4EHtBEE4NbmlO/tNs6iPHfNynMhE0C8IgiAcU+56N3VvVaPs7SZlvJ34i/KRjQO/eFYUlUW7g5S3KZxZqGNoqqbfMsFgkOeffx6A6667Dp1O5OsLwneNCPoPT6T3CIIgCMeUJdNC0Q9LvtGysixxdrGes0X3+YIgfA2R3nN4A/SzJgiCIAiCIAjCqUTU9AuCIAiCIAgnNVHTf3iipl8QBEEQBEEQTnEi6BcEQRAEQRCEU5xI7xEEQRAEQRBOaiK95/BETb8gCIIgCIIgnOJETb8gCILwnbOlKczKWoWRaTKTs/v3/S8IwslG1PQfjgj6BUEQhFOSxxGgdn0X1iQDmSPjCLhDrH5sF38p1/FWZt+IvdeM0vCvBcbjWFJBEP5XIr3n8ETQLwiCIJz0lA4dqltDwB1CF6ejem0nb929jbAfuuMsGFP0ZCQpNK7qoHlEMXMaO/BpJMotRl7YbGZSio+bTheBvyAIpy5JVVX1eBdCEARBEAB27vax7JkaetAwem4qZ0239ltGVVU6tjnQGGTiBsfw6QNbqPi4CUlRkDUSI28czN6/lZO4qx0JUCSJHSOzqE+NIanbSVeMlWZ7PEgSmnCYVq1MZWosux5OQCNHagtDQYUv32phz7J2Euxapt+cR1KmeCgQhBOVQ7q337Q49ffHoSQnLhH0C4IgCCeE3a9V03z15xgCIbSEaUm2sv3607nyvBhSR8ahMWnxtHj5+MLF9DT5AIjNteBucBKSNUhBSOpwowkpaN0KYakvVz+kkVl6Vgk5jS0ANCQm0BJjY69ex57EOACG6ALcSDtDp9jZs8PDllVOUFW0gSDoZLJGxpI51MbpFyRjtokX5YJwIhFB/+GJoF8QBEE47oJtXtamv4wU2n9LUrHiRUuYVrONrsxEhv1hHBXPlNG8owcAa8DLhLYKYgJePBioIYMDG/N5JS1BqS84X3V6IXanA0kFORRmzPY6dKrKnuR4fjV3Cg6LkQlNrZze0MyO+Fi22uM5u6wKndWEz2xEVhQUWSYt38TV9+VgsRvQGkQjYEE4ETik+/pNi1MfOQ4lOXGJqgpBEAThuGt/tTIq4NehEMBAABWbJ4h+bys7r1+G06RD1WnpsZoZ0VSDOejHp9XRE7JxcO8dejXcG/QHtTJhvYwUVrA4fWjCCuWZdmK8AZQYE98r28u7xXk0xlhRG6Cg28m/CnLZWTqM62obyOlyoAuFUVUItcHzF9RjsOmYcttghp+fcWxPliAI/Yga7MMTQb8gCIJw3KihMJJWw+flIfL3TZN7++FQkfb9tzEUJrOjm+15aSwZX4LfaCR3TSevTT6DgFbHGSu2kNTiGnAfAa3Mu9OH4rAZyauoR6MoAAR1GhrTbQxt20NSuwHDXoUdCYn0mE3YOx08vXAx6Z1eeqxG2lOthLX7bplqpF2BvyfAF4/sImN0HAk5lqN8pgRBEP43IugXBEEQjrngunpcN79HcGMj66eN4mcTZvCyvA6tEqmv8+m1VGUno8gyeXUtWL1+JGB0XTUX13zGTd+7hyUlo3u3t7l4EDNbtkbV9RuNTt6bNhrFqMeg07A3w468XumdnyB1Mm/tJ2jUyLRJ1Wv5x6Rr0Xhh0pa9dCsWQCLO66M1PbZ3vZSmbpKbnciKQqfdwpqnKjjntyOP5ukSBOEwRJedhyeCfkEQBOGoC/nCrH54G5Uf1mOwaSnctYfclgauvfpalgzJY1xFPUY1gIqM26zjozPH4TUZANhYkse5yzaR1NmDHIIQRkbWV1OXmNa7/Q57DF9NGsLENXuQkPBrNGwdNhqMemTAEgwzs7qF9hgzyT0eAMa3bOwN+AFsATcTajewV83BoxjZny6kaCTUfb36xHZ5SG/o7l0nsd1N+7O7+PyLOorvH0HG+dlH+UwKgjAwEfQfjgj6BUEQhCOjKLB4G7Q44OwxYLcddpU1v99O/bNl5Ha6UIFVgwr4z/BhfDx8MAC3fLqOF6eP4t3SIYxod5Lr9veuG9Jp2TQ0j7O+2kIM3YDMsPpqYtuD5Fc3E5ZlygZnUJVmx2k09a7XlhzXrxxbBmdxxsY9aFQVq9/db74p5GXj6CKM3t34zDqMHj9WR5AwMiG9FquzE59Ow+rBGWjCChMrGrF1+1gem89XTzQzvUti+nVZ/bYrCIJwvImgXxAEQfjGwh4/jaf9kqzNuwDwGmNR7r0MqSgdbXYsuslZ7KwP0eRQmFCgw2qU+LIefl9tpOLiaYDEkNZurli+mTWDkgFIc3r5ang+T00bSViWGebwAv6o/brNelKpJ452XCSRX9WCXQn2zp+4sZy0pPaodcw+H05ddK79njQ7adnJjKloYG9MDqM6tkfNX5MzgtzmNppzEyITFCv+Aj1uW2Q7W0cN4s38THamRuZntvfwuzeWEdLJWN1eVj+3l6ZtDs7/RRGmWN3/dK4FQfjmRHrP4YmgX/jWzZs3j7S0NJ5++unjXRThOGjd3Mnmp/bQU+Mie0YqpXeVIGuO7sW4fYcDJaiQNDIeSYrsy98doGNnN3EFNsxJYlCl/0ZgRxtKlw/DpAw2t0v8do3Ctu1OrkwoIjytmPcHjyfd4eYn//iQ0tYyFDTsyijm0jlX49fp0KtBlFwzZVhgaKSZbnabg1mbduPTapixo4Z4WYeq14PNxjXbalhYmE6bUceg7uiylDZuI416wsg0kQpK/746MtoddJr6gvxZZatYOPYMFFkGwO7qYt7Oetrt2dQHwnR5rWgkhSGd5QQ0OlZlj6PDmEps8IA3ALKEVgn3flRlmXHtjt6gvz4xhs+HZ7Bg61Ys3QF8mNjb2sMMJQEK45ll9XFGqsLfm0zUdyvMylC5Lk8he0QMGq38bX1VgiAIhyWCfkEQvjWOKicfXP4lSjCSJ+0od7LnjRrOeWkKu17aS1dFDya7ASWkojFqKLwwG4vfSYxdS7tqY8dLewn7wxRemEPuWem92+2pdRP2h4kfHNM7TVVUtv2znE1P7CboDgEQO8jKnJdPZ+e/K9n89z2ggqSB8fcOY/j1g4/tyfi2NXdBUxeMyAGNhooulZACRfb+D1R1DoUuHwxPiczb1Rgm3iyRFt/Xp/yiaoWntqgQDPP9mC7OnhzLnuVtfLhVocto5vK3F2P+sgqZELtzUphz27VY3D5+sHQbizLH82VBDgBldh/2rhkkLQljUZ0E/W1cv2EF/xw/ldwOB5tjjcQbfHRZImk3l3+5jRhvAIC2pIRIwL+PJRjmzk83MGPdbrYPzWFXQQaqLDG+Zhvn7PwSACexqMio9OXi71eZkUhrrI3CvS3IqIxp3EWms4UdaflY/R5GNuzhq/xRfBKfQ116EunNsCxrCsuyptAdayNg1GP0+vptV6MooKqw74Ey0dv3FiKj28Gtm5fSlJBBfWIi6R1tjCp3sy4lkX+ZYlmDnr9vCjC1voahPj9LbGa2uH2cg4uL/jSSpAIrZcvbKfuoCTWoUHhmMiXnpvUrg3DyCbiCdFe5iC+woTVpo6bF5dvQmUUI9m0SNf2HJ35xgiD8V0L+MLv+vZeGFW3Yi2PIPSeDD7/XF/Dv5+sK8M7cxQNuo/rDOuY2fIrW145qTKY55XS8OjO1XzQz6cERZE5JYdVDW6j/shUArUlD5tQUir6XR+W7dZS/Uxu1ve69Ll47YxFh3wE1s2FY8/B2wgGFYdcVDDiYUrNbRVUhzdp302hyqUgSpFoGvpH4Qir1TsiNBa0s0exSUYE0fNDSDYNSeoPEr6OoKnsdkG4Fs+4Qy9/zL3qe/ILHJp3NxsGdrCgaTnswchzjTR4+3vAqCXKIQGoSN3Tm81LcEFRJIk8X4IFP30XyhHhx5GmMmJ7Kn04LsEiyM/e9/f1ay7yjJvDUj19nUmUNtaNG02iN58NOHTdSj4VOXsnLI6jCc8+8w+CWDh7/yXi0oSDDa6spi03llWFFLM/M4PEPFqNoNCzPyuflF/9NkttNUJZ5ZfxoHrjoLJJ6XOS091Xh72+ou5/J62fWmjI0qkrptr2M3VaJKkmMUNehI4yTGBrIBeCzwhxOL29Gu298Sb9WwycTSmhJiMFv1DC4tpX2UBwpzk5SnJ29+yhPyo4ctyTRmJaM1eNm9tYVfJE8GYCQVsvBqUVBrSbqu6yJ6Xub8MCST/lyyEQ8hshDTY09nQJzHedvquBfpw0HoMugozMulimbdzJhtwsJUCSJj366BW9SLO5Nrb3hStWqDryOIKWXZ9PsjPwtpdoOeCNQ1QLxVoj7hl2ENndFHljSEr7Z8ichJaTgbPBgSTV9o8HSuip62PrPcvzdQYouziV7RipBTwhPm4+YLAuSHP136O30Ew5EHvy0Bg3GhL7fbcgfZseLlTSuaiN5dAJDFuSghFSa1rSx8ldbCXkj1yJbjpnsGWns+ncVSlBB1slMvH8YJVdG3oA5690EPSHK36nF2egle1oKObPS8HcFickW3cEK3w4xIq/wrRPpPaeO5nXtVH5Qj86ipeiyvKibz0fXfkXjirb/eR+5zirMYT+7YgtRpaOb7iBpJErvHsrIGwoJOINs+k8NP26N5ytD5A3CaUEnF3U28Xh2PtX+SJ1IiR1KUyXmDpJYMCRSvrsWh/n7JgV7Zwc/XvspKbowvyqewVl7tvDbz1/nneJSXp80nY6RhYzINXLrGA2DYuGf21Q2tqrEGsAVUAkpEp/XqNS7IEYPv58mc9PIvnOwok7h/je6qKj10hIfj2LWD/ggcfuXH/HQe4t5edgk7pg/N2qeTlJ44YMXmbdjCy7ZRmzAzwU33syng4uiz40/xCVLN3Hrii3Ee/20WUw0Joc5r2oba7Py+P30Ofz1pQ8JyDLTfnQddy1exENTZ0VtY3RjCy+88QmdNi1Zzuj8nOqYBNJ7XGxPSqU2Lg5rIEhHYjw1eX1vdDKaOjhjxXZM+DASQAJCyATQoiFMAD0gocXPLZfMoEsXx1Wb9qDTyKwryqU91oI+FEYfDPDAwvcIyhLDnBXoCBGWJJYVjuWTksn9zuH4qm2UJ+X2/v6MXh9GX6SLUFVRccTaULRaJEAOh/gsKZGVQzKRFYUPXnqJjXkjoranCYcpXbmHu6+aRa7XR6dehy2kcP6aLVF1kWGNTEeKHVMgEP1dyPDReSP5oj3yGzxvqJZXJjgxXfZH2FKDatDC2EKUgmyki0uR5x7UXWhTJ/z1IzpfW89KQw6t1njOyXSS9tINYDm5Ut1URaX83Tqa1rUTn2+j6NJc9Na+thINq9pYdu963C0+ZJ1M5pQkJj0wkpis6EC5aV07lR/WE3IHqfigATXcF/rYssy4m70oQRWdVUvuzDTG3F5M154eVv9uKz01nr4NSZA2LpHUcXY8rT4aVrbhavDw3xp6dT6Nq9pwlPf0nykBKuhjdKRPSqTwwhxyZhz6LZCnzceuV6twt3jJnZlO9ozU/7pcJ6N26Wf9piWqvzkOJTlxiaBf+Ebef/99HnroIZ544gk2b97M+++/T0dHBzk5OVx33XXMnj27d9n9Qf9Pf/pT/vznP7Np0yYkSWLChAnce++9JCYmHscjEb6pvR83sPiHa3uHOdTbdJz/znRicqys+d02tv2z4vgW8H9Qes9Qqj5u5HljMu+XDomaV9DUQUWafcD1fjZRIhiC36/pe5uR397M5r/ci6womENB7jjvOv465Zy+lVQVgwwjUyTWNn99uSSg/Aca8uMkFtcqzHzagxo44M2JQQOxhn7rWdwe3nryJf49ehQvjRnVb6MGi0ztr+8lzuMnjJ7zr7+RRcVDo5cLKVgdHm7/aiU3rthIGA2xRAa7UoEenRkpqEECPLKev08YzbPjo4NdSVXZ8pcXMeJFc1D6TQgNG1LTuGf2dKrjY4n3+rhy625iLRa6Y60gSSS3dTFn2QZi8EatG0YmgG7f//rR4uX+s2bzfnEJV+yuoiorncQeFyNr6rEEgnh1Wibt3cb4tt2923BrDTx25mV0W2z9gv44dw9FTdWUpeX3TlNV8GtkJI2mbwIQ5+whbU8HGm8YjaJgt/WwrmhY1PZkRSGlto3q/HSQZVQgJEnkVNahOeiW25kcj06JPlcq8NNJowho+mqtKz7/M/mbdhx0XkyAluAFkzC+fUNkYnsPjLoLGvrebvxj4kV8MXgcj2VsJ+PhCzmZfPnzTZS9Vt37OXFYHOe/OR1JlggHFF6Z+jG+zuiHJp1Vy4ULz+itpNj7cQNf/HDtEe1XNsgo/v4pZMfbhPuGMeL7/VMVfY4Ab89fjLu5729nwk+GMeJkT2s8Am3SA/2mJam/Pg4lOXGJVkTCEfnrX//KokWLWLBgATfddBPBYJCf/exnvP/++1HLtbW1cdNNN5Gamsodd9zB2WefzZIlS/jFL35xnEouHKmtT++JGtc84Ayy69UqGle3ndQBP8D6P+2kY4eD3en9g/t6e8wAa0T8eb3CXzdEBwKViam8MXwS5lAQh9HMPyZG134jSfjDHDbgh8jpXlYXOekPLA5FB/wA/vCADVjn797A/zvzdIpbW/tvVCPh1+lYmZvP/i/0+2uX984e1VDFzz97k+vWLCYsw8NnzmBtbgYm/ASRcWPGSUxvwK8goVFgcHtXv10VtDuoSoijOaZ/OkJQkrltzplUx0cGueoyGXmydDjmHie5dY1k1zdhczrxWvv3eCPve4AIo8dJAl1kcO+i7Tz59ufcuGIFxkCA0r01WAKR3nxMwRDFjujUL0vIz5w1K4jtdvbbfljV4FaNnL5nPemOFvxaLU6TEb/B0PfoIkkgSUhBhfbceEJxenRaCQc29MHooNPa7aZmX8APkYc5narSmpEctZwiSwS1Wg7+RsMaDfP21kdNy9myq1+5JSKpI/I7awksq4pMfHFpVMAPcPHWz3AbzHy4W8/JxNvhZ/ebNVHT2rc7aFgZ+Z13VfT0C/gBgq4Qu/5T1ft5y9N7jnjfJ2LAD4c+lor36qIC/q9bVvjuEkG/cEQcDgcvvPAC1157Lddeey0vvPACqamp/PnPf8bn62sAV1dXxz333MP999/PggULev9/zZo1VFdXH78DOEhnZyd+f1/+rsvlwunsCwoCgQAdHR1R6zQ1NX3t5+bmZg58gXay7sPTFX0DAfB2+aj/sqXf9JPOvlOX1uXqNyvW3b8h536eYCTuPpjDZAbArTcQ1P5vTaWK9zXMbXcFB14grFLQ3vddDepo5ncfv8L8vesZV9fA+Lq6vmUlwKRFUhSKWptQ9jXjumj7V7zx4p+49cuP2PD4T/jlotd5buHTrHzuQUxBP8vy81CRCaEn3Nv0S0Klr7HczIoaplf2BdZWf4A7l24mGNbxq1lTo2qplw3OZU1mOs02KwC5Xd1ctWkn06vqqIyxoguFMQSCyICk9A+29iQm8KtZk3Hpox8Iihs68IcNzFm5DX04ej290v/8jXZs5d7Ff2f83o0AGHwB0hpaSW9sxas1YvZ6SXR34dPrUWUZJImgRoMcCPB5XgqPTSrid2eN46v8dNozYqkuTqY2NxU1rBLb3Y3J68XW2QPBcGT9g4QMegL7jkFSFRz2OLRSiJBWgyJLKJJESBP579LmNuQDzkVLZnq/7e2/hUsoBD4tJxAI4Glq77eUxR/5W3bbE06q61XQHYpKw9nP3xOkqakJW4b5kOMx9bS5evcRcB7ib+kkFHAG6enuSwXaf64CPf2PMeAMoarqcfnOjwcVqd8/IZpoyCsckQULFmC1Wns/W61WLrroIp544gk2bNjAaaedBkBSUhKzZkXXeJaWlvLGG29QV1dHbm7usSz2ISUkRDduO/DYAPR6PXZ7dG1wWlra135OTY3OozxZ9zHk/Fw2PbE7atrg83Jw1vUf0OhkpLNqOWdzBduzk+mwRYL2BKeXa5Zt4ZmZY+mymvqtM69ARofK23v6bpiGYIALt0dSB2riEplauZPl+Qekzuy7ucYboevQzxMAXFMiMSk9cqO6eYKRu+s90TX7Ggk0sOipX1NtTyao0TCjYgc6JUy6y0FOt5d3X3qVOy6ayxtjhoMu0gB1weZ1rMkqIKd9CxAJEhdsW8NZe7YgH3DzH9VSy6U7VpPT6du33MEBl7Sv1l1Fq8IfP1jO5uRU2i1GxtS3YQ5GelGqTEtl3M/+j3E1jdQkxFGWnszE3dXoQ2FmVdTw+0+/7E1zaY6xsm1QVm+QHA5rCEga9Grk6SqgkXm3eDCfFg8CFe77fF1vaXoMRpwGI4kON01WXVTaTmV8JkPbqw8ou4oWL3o1xPc2vUPQq0fn6osZQ1qZXcm5bBtU2LtGk16Hzx+iNTORDVlJAPh1WhaOzCfB46O4JfK2w+gLkNjQTU9s5DcT/poG3PY2H2a3D2Osg6rYHBSXi6LONpqS+v6mtYEgulAYnaLgl2Wy4yS0j10DV/wB9vV8pKJB3XcLD2BCU2BHr9ejv+ZMeOxjCPU9nS7LHwvAtO8NIi0t+tpxQl+v7JA8Mp7WLX1vlQyxOrKmpvTm9ScOjaN9h4ODlVyUj8EQSYXLn5vJpr/v7rfMyWjQnExiYvveRu4/V/LZOjb+vQw11Pc3mz83E0mSjsv94/gQQf7hiKBfOCIDBet5eXkANDQ09E7LyMjot1xsbOS1fnd3d795woln9G1FKIpK5Xv16KxaRvxgMBmTkwn5wux+vYa2rf3TO75NkkaK1EYdhbfsQ68axODzs1n3xx384YuV1E4ZhD5WR+ryChySRGlHB825mZyWIbGlTaW6G87Ok/jTdBlZggRjmIXlKundHfz+k1fISjWw6Yc38EDxLDwrKsjraKE6IQmNLJFuhXvGyZyZI/PjZQobW1QsOnCHIM0CN4yQMWsjNfzj0/puWj8qlalzGHl6hR/FE2R8fSVza3cwZ89mAlo9Myqj87vfKB7PwqFmtJLKqrwsblr7OW+PnESbLZY3Ro3jjVHj+MP0RhZs2sXQ9jIu2b4Mm7//U8i4+mrO2hZJDxmopkwC9AQJIePWGyhqjf4dBDUyzXYbDquJT4f1BdBrBufwg3e/4oqtu6Py2lN7XLQ6nDQnxGLwuWlIjKfFZ6XbouG02jp0YYUff7mW0U2tPDZjMpWZyWS0dGIMhnAZIukq2pCCtceHK7bvQa1Dn0CAJrQE0OBHgw+JSA9LMiqWHj9Bua99hDak4FGMvQ8Oi+xx7LKa+w7MHQBLX3rMzqQ4hte1YXIHMHr91A9K7qvdVxTkYBBFF/1mQu8LktjmQlZVvEV5vDt1CCNX7OGaNUv51BRDWKtBVhRkRcWUqOf5S8wYLFrOLdJi1I2Cmr/Dp1sIfrQL5dUNSCgEMREoLSbue/vaVwzLgXfuQ33odTx7O1iVWcKHp5/L3RfbGDMttt/3eaKb+bcJrH54276GvDGMv7ckqiHvjD+WsvCSpQSdkQdOSSMx/t5hpE9M6l1mzG1FBJxBdr5S1ffmQAaNUUNMloVAdxBZL6O3aPB2+DHGGxh2dT7bXqika09frbrOqkXWSiSPttO910VPzQFvCvc1uj1iB64ng6yVUQ5K6zPa9Wi0MtlnpjHhx8P6bQIgviCGs/4+kY1/LcPT6iXnzDTG3zvwssJ3lwj6haNCHuDV9n6i7fjJQdbKjLurhHF3lURN1xo1zPvPVGoWN9HwVSu736pBDR7wnf63N7/9q2skxt9XwvDrIg3QOsq62fN2LQ1fttBT40bWSYR9YdT/4mEgpdTOuLuHkloaaUx+7otTohe4P/IA+6PDbOeZc7Q8cw5AKvz8LgBGA4uBpZMK2doOp6VLjE2NDpo/vOjw3QnuJ0kSf56l48+z9gU4L3vh/60HpwPG5aEWDCH0+iq60fN51mhuxsWZ9+byzks7SSlv48vxk7n9tBhWtUssrVNJNqrMnJVK3OUZaI3TCT9jQ37yYwge0L0pcP6mWlTViAoY6SJISu98p1GHze+l3WbiL6dNZnFBAfd8sprTy/vyz/89dTiOAd6SxPq81OdlkbpyU795xa1VTGpv45nJ86hN1DNmRxWljU1Eztb+dKJqWnIy+WTqKHTBECXltSQ1OTAGIsFefKcHsztAWZadC8s2kO50EMBMCC0xeAADCgYi/QG5CErR+e3dVhNWn5echmbWZ2dEB/yAxuEl39nKntRMAFLanQxd14AK7BmdFp3OI8uYAkGCqhrJ2ZckYrs8FOxq7H2zUjgnnY0/tvLBmUNxbN/J2PJyKjJScRvMZA61Mesv4zAnHNRoOykWrpyK7sqphH/bReDD3WjTYzDPHYKkO+C3NbcUaW4pFmDmvn8nK0uKiTMfG3/I+XH5Nr63eDbVnzUiyRK5Z6VHPRRA5Fo2+ecjGXd3CdWLGgn7w+TOSo/qenMgQy7OpXl9O+07u0keGU/yyOgacVejh9olzZgSDSSPTqBuSTMao4acM9NoXt9B23YHTWvaaNnUiSleT8gfxu/oS8Mp/l4eY24bQu3iZjQGDbmz0gl4QnxxxxratjqwppuYeP9wcs74ZrXo2dNTyZ7+3eqx50Aisjg8EfQLR2SgfPyqqkiDqYFq94VTk6yTyZudQd7sDCb+dDh7P2rA0+ojZ2YalR/Us/nAV+kS5M5K4/RHxrLi55up+rgBWSNReEkOnnY/DctaCPnCxBfGMPiCbIZcnIMhpi8gsxfFMumnw4HhvdNURWXHS3vZ8tRuvG19uaiSDLlnp7NzeQcml58aewyrCjOZkRTmmgsSyZwS3ZDyaJieLTM9+yhs+MrTI//2kQDdc7eRCFx24GJjM7nysBvTwOPXw08vRLn+CfhkEy57HO13X0JKYhJKq5tguh7LjX/CFminnTxCGFiVncMvZs/kwEeX35w7iWGdXRS0dLF5UAob8/flnocUOGDE2VHVjeyJt7MzNZGhzdF55/mevezIyGJ3ag7ZDW1oVAVzKHTAEioG3OS1NlKbkkRQp2VbYQ5XVm6nU5vC/gcDvyRTnhpL+jpH75omXEQ3X9PSrbMT0shoFRVFklg7fDAtSfEAmL1+UiqaIbPvYccWCjHe4eTc2t3ARn479Qym76ju/R4Chv6Nj3ckxaNFJcfpJqbNy4hNNb3vTdQEI7l3FKPTSFw4zoSy/TIcr+2hsMVDzPn5GIsP36e+Jjce060TD7vcd4EhVs+QBbmHXU5n1jL4/CP740wtTeytJDiYNd3M0CsG9X4uujSv9797A/Db+rrGDfnDVH3cgKvJS/b0VOzFsf3W01m0zH912hGVURC+KRH0C0fkzTffjMrrd7lcvPXWW9hsNsaOHXucSyccD1qTlsKLcno/l/5oKClj7TSubCU2z0r+/Cx0+0ajPOPP41D/VApSpCZ7PyWsImu+eT6mJEsMuyafYdfk42nzUvFePf6uAHnnZJBYEsfpYZVXtofxdknckSkxv0D0WTCglDjkD38GoTAxWg39+i2a/GeUl5bhblLoXuHj3F2VNCXF8/TIUYQ0GgJaGWe8mRq9inJ2LhsbQS+rEFIIADnGEHNWbmTu2jXcPSvyaOIwRt4iRL5thZ44+PPo+WzPiARHDakJdNos+Lq1GMMhdHhJoww9Pq7cto1B7t38a8ICvHodK0uHYPV5yapt5+OCkTTlpJIYCOI0GHtTlzSEDj4qtmcMZf3ooRTtrsZhNfcG/AAek4FAUQaSqqJKElpF5bLGNmLCYRrjI7X89yzfiElVCWlltCEFozuAJza6//sRU+Jo8hnYu6WVMYFuto/LJqHdjX1oLNOfGY/O3re8bNGRcH30GzXh1KM1aI74oUP45kTD3cMTQb9wROLi4rjmmmuYN28eEOm/v7m5mQceeACj8eQa9EU4erKmppA1NWXAeQePdgkcUcB/MHOSqV+/1XqNxLUjxeXtG9MeIu1ocDryL7/H/pEMFIePBy06bn2pjH89W8/65GRyVR933p1L+lQtDl+kvYIsQbcfEkwS6v9NovWfMbA58h1ndjlRkIm8jNfw+MzZNCXEERsIoldUwhoNiycNo3uXmbO2bSdJrUWvRgJ4GZiydz0bM4pYVlDKutzI2x9LnofChgp84WTCGg0vT5nB91YtJ87jxi8ZMKnRPVFVJmUS0OvYOnzwQWPv7jtOjcw5Le0sSYwnyx8gJhzdZVN7XCJevYWW7GS0gSCqRkYbDCGrKgqQd1Yql/woCwCXOxGjoQQ1pKCqoDd98xQvQRC+ORH0H564KwpH5Pbbb2fz5s288cYbdHZ2kp2dza9//WvOPvvs4100QRCOMjku8mCffH0J91w+hFCTG11OTO+DXJyx76absC+tX5IlUm4YxlXvuPjnYi97Uu1kdDvZX9ef29pJU0IcLq2WmGAQrQpBnRazEkRLEIvav+F/dnstFJT2fnYbzPRY+1K3KlLT+fV5l2B3Oslrb+HizYuw+7pRgbU5JazJ7UsVQ9P/LZA+HKbE42PKxu1Ygg5q0wsGOBmRvo1COi0aVSVo0CEVxjP31hxGDu1r02C17Nv+oR6sBEEQjhER9AtHRKPRcNNNN3HTTTcdcpmDB+rar7S0lPXr1x+togmCcAzJRi36vG/eG8x951nISdKwNnUcY/7hILbeAcBpuytZPziHoEZDl15HXksHVyxeS7arHYfBRHcoDns4uo/w9Qn92w9ZfJE6+6T2buydTpxWE454Cy6DlXo5HxfdrCoazJcjRkZ17WnxBkhp6qIqO/LQoA0rJHh9aEJh9IqKvVWhK96J02TrXUcbDve2EkjodJLtdnPm6jlY4vrn9guCcKyImv7DEUG/IAiCcNTJssTlU0xcPiUD9afX4FvXAqEwg97ZyLraSgpWtZLg8pDeFeki0afX4TVo2a4dyUTPVxjUSP/0Dbo0Hht1OjM9gd4GxdpwmPM2rGFd2hASW/u6UfSZdAzqaSPG40VBz6iKeipSM2lKtoMkIYcVxm2qJKO+nb9dOoUcbwB9OIysKMQ4XciKQlpLDwntbqoHJ9JliyGg17N/x4mt3YzYXE3eI+NFwC8IwglPBP2CIAjCMSVJEqbx+7oWnJyB9aeVfKKmcva6HRjCCk1J8XQkWpixZQtL0ofgajsHe6gdr8bIw9NOxxYMMWfjJrbnZBHj83LOlo2kdneTJPegSn3pOkZvEKMn0PvZEAqR3Okgv7IZTRjsHU6M/iD/nlrCu4MySPL4+MWitUgyGAJBMhu6MHsDGJEYvqkRaKQpNY76LDuD9zQS3+0h7twsMu4ZcYzPoCAIBxM5/Ycngn5BEAThuNrYo8cRY+DdqWOI9/f1Y65VFJJaO/jTGWNQkNmZEk+71cSwbjejt9Qxfcsu4ulBAjyyPirg3y8o9+XSS6jkN7SyOycVkzdIWG9jc1YST0+NBO1tZiO1Oj0XbCyP2oZyQDBhcfsYUtlISpKWpLvGkvWzUVE9UQmCcHyIfvoPTwT9wjcyb9683h57BEEQvk32ZD2O2iA+jQaVYG+IvWXQIIL5gxjT7WS31cTYQQZGpsl8vlrHF8MKmba9ih6smPHRbrQQ0khowwfe+lW0hPHptLTYYkjs7iGzw0GCy02nzYLJH0Tv9qA7bSQhjQZLKMx7E0oosqgMWbWXsApyuK+DUYC6rESU+QWc+/AAjXsFQRBOYCLoFwRBEI6ry+fH8Ju/dxBGpsOoJyEYRKOoDCkxkXV2MnuCWu7JkJiWLeP1qxiBhdqRVA7N4Dyri4kXpbD8qVVodhqwuQLogwpBrUxHopX6nASSu7uRexRMuiBxYR9mfxCz3wGAIssQVMlxurBIMtNGGpn3uxnEG6bz5ve+QvtVMwldbiQVanKSqC5M5bpLBu6OVhCE40ek9xyepKqqeCMiCIIgHFfV9UG+Wu/BapY5Y7IZm0X+2rQZVVV75weDQZ77+7/g01jUKgMhWaYyIwWrJ8isNTsxhQK4UyzInhCJTlfUdpoS47jisnN56ftWZo40Re3T2erj01/upHqjg3C8mfQxCZx1YzYpWWJMEkE40dRLv+o3LVP9+XEoyYlLBP2CIAjCSS0YDPL8888DMOX073H/Ez10B2UkVcXu9TMqSeWBRwfR/GYFbZd8iGbfbU+RYOmYYuytTi6ruhBpgD77AQLuEFqDjKwVIzsLwomqXvp1v2mZ6gPHoSQnLpHeIwiCIJwyBhcYeesxK+996KCpzs+gvHhmnBkDQOqCAmp/eyYtT2wFJBqSE8iramf4DQWHDPgB9BZxqxSEE51I7zk8cSUTBEEQTilarcSF58UPOG/8T4bRfV4GlX/eSUGzE/stY0j6ftExLqEgCMKxJ4J+QRAE4TsltjieMU+fdryLIQjCt0jkqh+eSFAUBEEQBEEQhFOcqOkXBEEQBEEQTmoip//wRNAvCIIgCIIgnNRE0H94Ir1HEARBEARBEE5xoqZfEARBEARBOMmJmv7DETX9giAIgiAIgnCKEzX9giAIwinj/T0qr+wKYjFI3DZWZly6qNsShO8C0WXn4YmgXxAEQTjpqSp8tXsk/25TKWztxBoIMmtzAotvMDEmVQT+gnCqEw15D08E/YIgCMJJyb2zi7o/b8fX4oOQHdVm46H1q4n1+gEIyRL/3JPB5nwDI6clMHbGwKP0CoIgfBeIoF8QBEE46XirnCw8bznNiTEE9AY85hyKW7uweXwk9fSgC4bwSAYyarehfiGxYmEcXfePYOalqce76IIgHAWipv/wRNAvCIIgnBACQZVXl3tZWeYnWQ1S1NqJVVYZdV4aOaPjaN3UQcVLlQRcQTp2OOmINWNv7SYky3QkqWjUEJN37ybR5aLeGs9rw0tYnZNKjC/AnF3V7Hxyjwj6BUH4zhJBvyAIgnBCuOV5J03Lmxnb2Ias1bFBr2V4TR2vLm4lYDOR2tqOpKqEgqAJKCR291A+JBsFCXt7Dxa/hx7VCuh4ZuwoXhw/tHfbq3LS+PWiNXx15zoqrxpFcaqG8Rki118QThWipv/wRNAvCIIgHBM1bWH+tdhDc2eYGYUa8nfWs+6LNqpsNtxn5BD76g7GtXYBkZ449InxtMTGIQfCyE4f1UnJJHd3k+xz4PNCZX46WVXtpDV27duDij4UBuCjoXlR+/bqtazITkH9oJnPavbw2Yg8rhqt5V/n64/hGRAE4WgRvfccngj6BUEQhKOuvUfhsj904fCo5Le10/BEJS6vHz0whDbaNjcSGw6BFKmtk4DYrh60igKAM86G7A/gMZqoNpqQE8OkNHeR1ug4YC8SLo0eQyhEWOpf69eaFIe2o4PvbdrDTZ9s5vXTilmVM4TB2xvQZ1iwzUhHGmA9QRCEU4EI+gVBEISjKhhUeG+5i+agTEqoG4Pq7+1hZ78kj4+AVia7x0Gyy4lXp6cyMQmfXk9YIxM0RNfIKxoNqtQ/PScsy3j0WsbXN/PpkFwSnV4uWbeH7C4nGqsBv1WH36wDrcT1S7agKV1HlRKpI7RMTKFo+XxknebonQxBEI4S8cB+OCLoFwRBEI4KryfMX35Rg399Cx5ZpWHWWKq1MaR1xTFnQ2W/5fMcHQxtben9nOrsZlnBEMKagXPvm7LsxLd5sPX4UCVoyo4nbFOZWbmahe+v41ftsxm1LUCy09u7jtusZX1BCtuLMrlsQxmS2pcU4F7dQtNft2OZmoGrrJuEKSmYc63f4hkRBEE4fkQrJkEQBOFbpbiDBINh/nTtFor/vYbzl66jyawnqI3UoDfF26hIie4z32nQMbi9LWqaMRQirduBJqygDQSj5qmAKss05CWgStCeaqMrxcrsilVk9bSiUVVuXbkuKuAHMHrD/H7GOP46YxQ6VelX9o/+updNE96l/NqlfFn8NrXPl38LZ0QQhKNNRer3T4gmgn5BEAThW+FZXk9V8b+4Y/4a4n/t5RcjhvLk6SPoMeppibNFLfvXcyawrDiHLouJxvgYlg8tQFb7N8XzGw0E9DqckkxFnI0Oo56wLBHQaUGSCGtlHHY9jdkJhDSQ7ux7cFAPcYuTrTpIMHHBjy/m3dJCQnJfcNBos4As0ZyegMdmoPWW9wivr/qWzpAgCEeLCPoP7zsZ9M+bN48bb7zxGy37/vvvU1payvr1649yqY6NxsZGSktLeeqpp453UQRBOMkpioo/oLKrws+jf2tm71lv8642gSfOHI/boEeRZT4ZOZg7fnA+qskMBwT1LpOBsE6LGQV7wM8Fm3YSVqNz6cOSxM5BuawszGPh8ELWpaWwKC+bjSlJvQ1+47pcdCbG0phip8tiY5V9OCvsI6k1pZJAJ1qi3xB8NTQTp9UIGmhIjuU3l07l8rsuotNiJITEmdtq0YUUJEXlq9OKWTJ2DOFx98Ev/gOqyr/XeLnoOSc3veZiR0s4atuqP4gaip4mCIJwojhlcvp9Ph9vv/02ixcvZu/evbjdbmJjYykqKmLWrFmcc845aLWnzOEKgiAcU2pIwf1JFUqnF70U5KMOK6+XG0itbKYyI43vfbkcnT/E0qJcAPJau8jq6KEs3U6F3UZpWQNGZwCfWQcSJHW6mVRZD0C32YRfq6PREk+q24GOMAoSPlXPqC0VvH3RzN4gH6Ai1kZxcxu5De3EdbjYMTQHvT/IsB1V1Jsig281mpIZ6diN1tjF+tgRZHU4KctJ5HcXTIpUdx2wveqUOF4+fTg3frKpt24ws6ETp7memC4vyzmXlF+X8491HzCmoZJXtq9ElWBh8UTMS+4g1xSm9gfv0LC6lUx3BynTstFcOhbNnKFIJtElqCAcC6LLzsM7JaLguro6fvjDH1JbW8v48eO59tpriYuLo7Ozk7Vr1/LQQw+xd+9efvjDHx7voh53aWlprFixAo1G9E4hCEI0X52Llkc3wK4O9HYd3g43ZX4L7pDMkPUVmHx+pH231lSrld+4utg0KIcfLF+M3i+hoiHd0cP1Szcza3skJUYBPhozGNliYXZtG1+k2XHptGS1dSMDO3IyaUiyA7B8RDFzV29m/O7IujoC6GVfb1uAXpLE1vhYNtgTKK5rx2AxktzWhUaJztFfm1TMk7Mmkeb1M7SrmzJ7DF6DbsBOPqpT4lGJnhU06lg3JYuUhi5GrpeYs6aaczuX986/bPtXtAyq4q1hp7M6pQRGlaANhZi5eTkT336CYHo6sf+5kuDbG9CVNSJNHcLi+WewcFkn8957j7HuFuwXluK56gz+vF3DyjYNI5Ik7hknYzftK8naCpS/L0Lt8rD0/Nk8nTgURYWbRkjMyJYIKmDUSjgDKo+uV1nZoDAuTeKeUhmrHkIKGP1+sBhRVRVPEMw68IbArJPAH4RQCPQ60H19SOAOqFj0/13KxP+yriAI346TPuj3+XzceeedNDQ08Pvf/54zzjgjav61117Ljh072Llz53EqYX/hcJhgMIjRaDzm+5YkCYPBcMz3KwjCie13y4P86+0efvFuPeOrG/AD6/NzeGnaKBQkZgQMXLx2Q29QnOpyATKj9jYjA2bcaAlz3dq1VFoyercrA3M2llPY1sbKkUUk+wJsSoplSmMbPSZDb8APoMoSi0qHMbKyFmMogJ4QuR1d2Lw+nKa+66UKlKUkgiThsJo4vaUDSelfzyepElOb29ErCipQ0OWioKWLirT4foH/mIqmfuu3JcUA0JIRT1tNO2ntflpII4F2ukhEQmVXbByrk4uIdzuZv+Ersjvb6DaYWJOby+TaMkLTHkRSZVAkgh/tZOzPFjEprCLjQ08nLN/KxVuT+ah4DAAfVal8VBVm08wepEv+jLRyNzLwyZCRnNs2BLUjcpxv7lHRyRBW4bwCiXqnyrrmSLkX1aj8fVOYSXt28KfXnqGorRHH4Gyuu/BGFiYWopdB8gd465NnOferZZFzp9XAbefAn64FOTrz96t6lRsXhdnVCUPt8MxZGiZnfLMAfl2Tyg8WhdnaBoXx8OQsmTOyv5OZxcJRJnL4D++k/8tbuHAhNTU1XHnllf0C/v1KSkq4+OKL+02vrq7mhz/8IVOnTmXatGnce++9tLe3f6P9OhwOHnnkEebMmcPEiROZM2cOjzzyCA6HI2q5/W0C1qxZw7PPPst5553H5MmT+eyzzwBYvXo1999/P+eddx6nnXYa06dP59Zbb2XDhg399nnjjTcyb948Ghsbueeee5g+fTozZszgwQcfxOPxoCgKzz33HPPnz2fy5MlcccUVbN68OWobA+X0Hzjtyy+/5Oqrr2by5MnMnj2bxx9/nFAo1K8stbW1/PznP2f27NlMnDiRefPm8fjjj+P1RveU0dzczEMPPcTcuXOZNGkSs2bN4vrrr+eDDz74RudZEISj76MKhftXwC/f+YLx1Q0AaIBxe2uxO10oGhm72x11S1UBBS0KoCDhxkydJY4PCob0274ESKictWEzasBHY6yW2WU7SPH09FvWr9cRsijYcCEB+nCYHy9aQoLbDYAuFKI+0UJQp0FSVWZX1ZPU3cOWpHhCBw2stTshFpvbiwqEZBlVlplb08KcshpymyOj+MqKwlkbKjlv1W6C+96ABrUyW4fn4Iwx927LYzUSxMhmeQJLpHPYTinbGYfalI3BF+SCdcvJ7ow0Ik7yt3Dm3m2YQkH04TB6JYiKQggz+nAkaFcwEiCBCntKb8C/39Y2WPbjj5BW7u6d9tfTzkI9KBgPKqCo8E55X8C/X5cf5q1bQVFbIwBx5bU8+cQf0IVCBBT42RdvM2f50r6HpVAYHvsA/vFp1Ha8QZXzFkYCfoCdHXDewjC+0OGTKYJhlfPfjQT8AHu64IKFCs6ASMQQvn2iIe/hnfQ1/YsXLwbgggsuOKL12trauOmmm5g+fTp33HEH5eXlvP3227jdbp544omvXdflcnH99ddTV1fH/PnzKSoqYvfu3bz55pusW7eOF154AYvFErXO/uD5ggsuwGKxkJOTA0QeCrq7uzn33HNJSUmhtbWVd999l//7v//jH//4B6NHj47ajtfr5ZZbbmHMmDHcdttt7Ny5k/feew+/309cXBzbt2/nkksuIRQK8fLLL3PXXXfx/vvv9yvPQFasWMGbb77JRRddxPz581m2bBkvvfQSNpuN66+/vne5Xbt2cfPNN2Oz2bjwwgtJTk5mz549/Oc//2HLli08/fTTaLVaQqEQt956K21tbSxYsIDs7GxcLhcVFRVs2rSJuXPnftOvSxCEo+iDCpUYr4+xtdG13bKqUlLXSEtcLKld3f3WU5AIoQUk/BoN511+Pi69nr+8swztAY12teEwhlAIM17u/GQhz7yzmRZpMJ3mBN4fW4pyQDBrd3cxumcFrRSh7Nv26PpGHnv9XV45fTItiXYGV/upMRpQg2FswSCWHh+Vick8MnUMc3dXY/UHWZ2VwvbkBH736Wrqs5JpSU8EQKNCUZeXWquJv/7tI7Lbe4hz+1GAB684nYpMO+dUNhN7UIPchHZX73+rkkwYJXKMYS1Fu+rJcHT0ztfh63eupAEyjhUMOAwD5/zvqPEy44DPwf8iJXNjZh6s6fuc6uqmtL6SVblDmLtr48ArfbAB/u+c3o+rmlQ6Dzqcdi+saYJpWV+//02t0OiKntYTgOV1KnPyRUAmCMfaSR/0V1ZWYrFYyMzMPKL16urqePjhh5k1a1bvNFmWeeONN6iuriY3N/eQ677wwgvU1tZy3333Rb1BKCws5Pe//z0vvvgit9xyS9Q6Pp+PV155pV9KzwMPPIDJZIqadtFFF3HJJZfw/PPP9wv6HQ4HV199NVdffXXvNKfTyeeff05RURHPP/98b4PlvLw87r77bj755BMuuuiiw56TvXv38vrrr5Oent5bjksvvZTXXnstKuj/5S9/SWJiIi+++GLUw8T48eP58Y9/zMcff8y8efOoqqqipqaG22+/nWuuueaw+z8eOjs7sVgsvSlPLpcLVVWx2SLdCwYCAZxOJ3Z7XwpCU1MTaWlph/zc3NxMSkoK0r5aR7EPsY8TfR85seDR6+g0G0nwREd4ndbI3/j27AyGNjb11p1JQBgN+/NkluZnUhcXSYd5ZuIwrtqwC2sghEYJk+1wYCCIBMTvG4nXoPpIcLu5YuVy3ho3AY/BRJKrkzuXvQSySkOckcQu0CiRTPsPJ5TSlmhHBkwhhSKXl50mA2HA4AuS7PKyOi+VnckJvWUf2taFBASMOg4m6WTu/cEsZmytxuoNsHx4NnaXm4bEGN4z6liwrQaToqIJhcnf1YTVFX1eIrWIkUA+qbkbn1aHMRTpKUhhoAC9f5ArEcYSCgywLNgs0dv4/tqlfFY4YsBlD+X0vbuiPoclibq4yO+hJj6R0Y3V/dbxpcYg+f29vyu7xgtEp4RKqGQf0AProX5XmTbQSJEUpAPF04Pfbzpp/j7EPo58H8eDeH90eCd9eo/L5fpGtdgHS0pKigr4AUpLS4HIA8HXWbp0KfHx8f3eLlx44YXEx8ezZMmSfussWLBgwBz+AwN+j8eDw+FAo9EwbNgwduzY0W95jUbDpZdeGjVt1KhRqKrKRRddFNVD0f4HhsMdz37Tp0/vDfghkv9fWlpKR0cHHo8HgIqKCsrLyzn77LMJBoM4HI7ef6NGjcJkMrF69WoArNbISJYbNmygs7PzG5XhWEtISIhq42C1WnsvdAB6vT7qQgf0u7Ad/Dk1NbX3Yir2IfZxMuzjhlEyeYka/jxrctT8vUl2tuZEKlSWFw+mOSYmMigWEEYifEAgGzqgtn55fgb/d+EMfjR/CqlOB/aAEwORYF+VJcriBxPS+5AIM6W8jH++9nP+/uaveOT9R/lw2Bl8/3sP89SsC3lv8khUgx9Z8lGRlgqqiqwokVF0VZVcVzd7E+JwWw2cv7WKBHdfYG4MhsiUZEw9AQwHDewFcMXG9fh1Gj4aP5jXp5XgMeh44j+v86d33iano4Xy+BCTt65k3qIvGVzV/xp64JgCS4ZksceY2vvZTwwBue9a7NHpKUtMwxVnPmALKjp6KGxrJL07+vqokeCMK0tQDxg/YO7OTZy3dX3UCML76TVw88iDpsng1kcH63877Wzq4yJvPH45cwE9hugKJ+w2jA9cEvW7Gplh4aaR0Q8st4ySyYvrm3ao31W6VeLOsdHrXj1UYnJ+3En19yH2ceT7EE5MJ31Nv9Vqxb0v1/NIZGRk9JsWGxsLQHd3/9fYB2psbKS4uLhfF6BarZbs7GzKysr6rZOdnT3gturr63niiSdYvXo1Tqczap4k9a8ZSkxM7NcQNyYmUrt2YMB+4PTDHc9+hzsnZrOZqqpIrxpPPfXUIfv63x/gp6Wlcf311/Ovf/2Ls88+m8LCQsaNG8fMmTMpKSn5RmUSBOHoSzBJbLxeyxuTx7D4wnROf38Dts93Mai7mavWrKQsKZUtuXk8Pm8Ws7ZsJ66zmxFNrbTE2UjriuRvTK+sw+720mGJBJJBrYai1g7yPB1R+9oVn0t5WgoAk5vW4SKe4R2tpDnbeXzqNWzJKAYgoNOzIa+YitQcbl7xFrFeFx6DOfKYoaoowNj6JmKbvbhiDDRlxvOXt75kZW4qPWYDweQ4QmYDBlVh0upyNo0KU5OdjCpLJHR1YWxTOGvNHrbkpDB3WxXnri4n2+1kXNtX3LzyK2QC6PEgoeKS9fwn/RyyGz0ogKpV0AVUVKDdLjEhsJqxnk1o0bB40GkszR/GV/k5LGjeyvjBOtZOn8TwkliKU8OE39rC2jd2017eQLc+j8+mT+dx9wbuMk2kTm8jzgCPTpfJGj4Bxv4RzzsbWKXGsXPKeJ4Ya+JxNdKI16BRkSXwhCQuHCwxKE7i/0apfLhXJScGZuXAuzNv5s2lkzizfS8xUwvJyRnGrzpUNJIEFFB97V8Y8f4XsLUGxhfA9TPBHj2IGsCTM2UuGhxpNzA+DWbmfPP6wj9O1zB3kMLKRhjz/9m77/AqqryB49+Z20t6b5AGARJ6qCqKiCLNgr1hw762Xcu+uhZ0XV1314INK2JFBWmKCtIEKdJL6KT3ntzcfmfePy4kXBIEBQnB83me7HJnzpxzZhLv/ObMKTFwQbLo1iP8MUQf/qPr8EF/WloaGzZsoKio6Dd18ZHlI39pqW20pByvtlr57XY7kyZNwuFwcPXVV5Oeno7FYkGSJKZNm8Yvv/zS6phfq/eR9h3r+RzLNTn4/9dddx1DhgxpM+3Bhw2Au+66i/Hjx7NixQo2bdrEnDlz+Oijj7jhhhu49957j6legiD88ax6iZt6SdArHm6IB8bhy60lJcyIfVkR+95YT1B1PZGXJvDfnmez/H8bOG9fHrujQohrcqKoMHnRz3zStxvVJgND8kqJ8MHHAwZy+S8bkVApNYdSFBaOpPrfFqyK7c+sAX1x6y/h/xZ9xYbEHoGVUlX0Djffpp1JkMuBw9DSUi4Ddo2JSLWBsHoPe2MjmX3hQEwuNzaLkRCHg+778tnaLxlzk4sue0rokVNAaXQwsuqf2vOGtTlsLqpk2NYizE4PZUQTTj06vCjocaJBppEyJZrwOiea6HLOKt+G2e3ChQEJFc1jV+K4/Tomr/KxY0MlQemR3Ndfx4NWiA5PAWDoIbXWXpPN0GuyaXCpVDvg+gMt5pcoKrn1kGAFk+5A8JKZhDkziRHAiEMuy18HtB3c9IyS6BnVsu+WXkCvfoB/oPDFwMVdDj0iAvpccdS/DUmSGJksMTL5qEnbdE4nmXPabvcShBNGBP1H1+GD/nPPPZcNGzYwZ84c7r777pNSZkJCAvn5+Xi93oDWfq/XS0FBQZst5m1Zu3YtlZWVPPHEE4wfPz5g35tvvnlC63yiHHxjIcsygwYNOqZjEhMTueqqq7jqqqtwuVz85S9/Yfr06Vx33XWEh4cfPQNBENqFJiUMAOtFXeh9UUu0+DSwou8wft4/lMx4Df3TNeQvLSdmRj53Lt3G+bnr+CG1PwaPQoUpmFpCUCTISYrDd2DOfX+PeJnMqlocFhOzBoxHkVv6sUuKQtbuXEIb/W9y82NiWtXPe8jg1vSyKtwGPXs6J6D3KWSUlDfPPGm3GNjZPZ6BG3fRp7oSWVUpskRQZQyhd0kl32V15tJ1e3FhYCvdCKeOePIIoYICUlg3YQxjJmeht/hoHP8y5i170Ou8SHeMhPtHY5Uknh+hhRHH3vAUbJAIPuSlrUaWSA875sMFQRB+sw7fp//iiy+mc+fOfPTRRyxdurTNNDt27ODLL788YWWeffbZ1NbWMnv27IDts2fPpra2luHDh7d94GEOLpB1eEv86tWr2bZt2wmp64mWkZFBWloaM2fOpKioqNV+r9fb3J3IZrO1mu7TYDA0D5JuaGg9XZ8gCB3DmWk6Hh5pYkymHq1BQ9oF8Ux4fwi9h4exKSqV4pgghhXmENHkHw9kMxuaA/6DJGhudQcweH1w4PswqqauOeAHCLLZW9Uh5JD9KtCltIKo0mKuWb0gYPYgALdBR5ixgThHLTHOOvpX7yPG4Z+2c+agDP4zOhut1oGFeqLII5QyJBTmTRjBVV+eQXCPUIydI4ja/Azkv4ZUNhVevTFgZV9BENqP2saPEKjDt/QbjUZefvll7rvvPv72t78xePBgBg0aREhICLW1taxfv55Vq1YFzHZzvCZOnMiPP/7Iv//9b3bt2kVGRga7du1izpw5dO7c+ZjL6tOnDxEREbz88suUlpY2T3357bffkp6ezt69e09YnU8USZKYPHkyd955J1dffTXjx48nNTUVp9NJUVERixcv5p577mHcuHGsW7eOf/7zn5x77rl07twZs9nMjh07mDNnDllZWb86Q5IgCB1Tz/dG0nVhCsmXfU5ZcDBlMcGkFVeh97Re70MFPPqWmXU0qorR42bkrhX0y8/BgZnN4ZnYdWaSiitw63Q0Wc1IqorZ6cLscOI0G5BUcJv0xNbV868fv0DVKeyL7h5QlqSqWF2BM/B0slWyvHMqdVYTmggLy87N4uq1M4iqK8Oj17H/pnHc+cYFrcdXdYo8YddLEAThZOnwQT9AUlISn376KTNnzmTx4sW8//772O12QkJC6N69O0899RSjRo06YeVZrVbee+89pk6dyvLly5k7dy4RERFMmDCB22+//ZhnEwoKCuK1117j1VdfZcaMGfh8Prp168Yrr7zCnDlzTsmgH/yt/Z988gkffPABy5cvZ+bMmVgsFuLi4hg3bhwDBgwAoEuXLgwfPpz169fz3Xff4fP5iI2N5aabbuK6665r57MQBOGPYhiZTqeHhuD4x/f0K9nD7LOz6bO7EGuTC5ulpU9LY5AFt/6QeepVlVvWfsmQvI3Nm5JtBXyRcjF4YfjmDfSv3svqxBQ2xXdH1Wv9MwlpZJKqauiZW0QYNSxKG4pbpw140Mgq3ofZ4wqop08rU5ocy6Td+WiAKlMY7jcehiFmdGEWMkJ++8xwgiC0D9Gn/+gk9Y8YtSoIgiD86XlX5+Ndup/6lBjWhCbyyS9u+s1Zw6D8AqLsjbhNMl8MHkatJRSz24HkaeLFb/4bMB0mwA/x51CvD2NY8VaCPE62RsXyU/IA9D4FVZZQNDIJVTV0KyjDEeNmZv/zQVXRebxofV6ibDX0K9xJt8IyDp0vf01SN5b37IXboMPg9JBdXsEla89H0nT4nq+C8KezSXqt1bY+6j3tUJNT12nR0i8IgiCcerSDO6Md3BkjMB7oNcDHmbbBfFDbh/8s/JixuT/T+5tVVJvD2BPRidcGX9wq4AcYVL4Dna+lG5BLY0HvU1A0MuqBAL04KoKaICsazYExApKER6/Dg44CYzy5MYmcGbSRXnvz8Gk05IbG0eeFs+i8vIKy9aXE9gwh9e2zRMAvCMJpS7T0C4IgCCdNTZPCF5vcuH1wbf1uwpZuwtugQmoszy0L4uJt39CnuGUl2SatidzgTJJrypCBgqAolnXuAwrYg0ytBtIG253sSkkK2KYCyoGFrrpvyyeswUGvm1Lp8q/+f/DZCoJwsmxso6W/r2jpDyCCfkEQBOGUsO7qb/jAk8TYnCUM2LeNKms4qxMHUGcKxWpvIq26BnOFExWJihAL+9LjWgX9yUXlbMzsgtPoHzugAopEc7obrggh88JYtCF6BEE4fYig/+hE9x5BEAThlNDvpWGEXzWLTWokv8QNRW7UoauWidM00HtSGt0ev4jGxUU0rS0nKcpM0au5uMwtA4Mln0Kt3kCnvUU0hFqpiI3Ao9M2B/wmh5OMUV1FwC8IpyExkPfoRNAvCIIgnBLk2CBSl06kc2EdkkWPHG7GntuIPtKINsjfpz/o3ESCzvUvgtXpkwJKy+y4jHpUwGBzIKmgURXCahrQ252s65GKTpaIq6pjzIUhGEMNv1IDQRA6LhH0H43o3iMIgiB0SO4GDz9eu4yaTTXURAThMhsJraxD41PwaWRqo8IoVyTGjo2gx6hYorPF/PqCcLraIL3Rals/9a52qMmpS7T0C4IgCB2SPljHhfPOo76ogY/fnYtzZTplZiMarw+fVoPR4ebGc6xkPZ7V3lUVBOEPJrr3HJ0I+gVBEIQOzRxjQhdvx9qrBNfuRDxOiWCLzKg7U+k6IenoGQiCIPwJiKBfEARBOC2YM6qY9ORo3E0QFmds7+oIgnASib7qRyeCfkEQBOG0oTdpsATrjp5QEITTiujec3Ri6UFBEARBEARBOM2Jln5BEARBEAShQxPde45OtPQLgiAIgiAIwmlOtPQLgiAIgiAIHZoi+vQflQj6BUEQBEEQhA5NDOQ9OtG9RxAEQehwlP1VKLlVuL0qm8sUHIq/Daup1EHd/sZ2rp0gCMKpR7T0C4IgCB2G2ujEMexVpE0FAPyU1oVHho5lQEE3BpSX8tW/F6HKEmGRWi54ewhBWZHtXGNBEE4GMZD36ERLvyAIgtBhNJz/HsqmIpqw4MKApVbiiW/XcuG2fCIr3YQ2uJAUldoqLysunIdnR2V7V1kQBOGUIIJ+QRAE4ZSnenx4t5fTtLqUYjpRRQxlJKCzaUFtaePTKGB0eQGo1Rio6PcKG8+exbZP8tqp5oIgnAwqUqsfIZDo3iMIgiCccuwelc+3KxTWq1xQsJfgR7+lusJMGBFoD3mRH+x2UqKHWqsFa4MDrU8hxOHA6PCCJPNt3ADWG2NxflqNY7GLp/7Vhe7Ror1LEE43Isg/OhH0C4IgCKcUh0dlxJs2Lvn4R8bt3ktxSDBbjElkemsDAv6DJJ1EdUwwtREWEvKr8dl1SIqEyePB1OjBYbawtUciLo+O/962lWG+BhxBRjJHRjP0hk7IGhEsCIJw+hNBvyAIgnBK+SJH4aZ3vmH8th00YcJSX08PqqkhGA8yOpSA9DXBVgAUrYayuBAWdUri9u/XowK5KTHkZHVCA4S4vWA04VpbhE1u5OtqFUmGMyZ2PvknKQjCCSUG8h6deMcpCIIgnFJKqjyM2b6DakKxYcGJERtWDHgoMwfh1GgAUCSJPYkxVIcGNR8b5yzjou3bKE2yUpJkpSbK1Cr/4qRIuhdVU6rIfL204aSdlyAIwtEUFxfz2Wef8corr1BUVASAz+ejpqYGn893XHmLoF8QBEFoNx6bh/VPbmD+mfNYfsNSanfWkyXZsWlMeA97Ga2gwYuGep+VKoLYGpfIrs4J/oG8B34kh4Iq6fwHSBBXVoXFZg/IpzbUgCPWwZh9m1jhgJmb3Vz7sY2ez1Rx+7RqKhoD3yQIgnDq6+gDeVVV5cEHHyQlJYVrr72WBx98kN27dwNgs9lITk5mypQpx1WG6N4jCIIgtJsVNyymZJ2/tb2+wEn5z9+jJlvRetvouw+Y7V5AxoeG6NJGGoJN2EKNgISk+KjThrQ6LqS+kSarGQAPMCu9M69mdQGgR3k5N79bS4PBAGjZVg8bN+xj7ctpIIt2MUHoKDpakH+4F198kVdeeYVHHnmEESNGMHLkyOZ9ISEhXHrppcycOZP777//d5chgn5BEAShXTgr7LiWlhJq0GF0uYltKiPOU0ZhYSd0KCiSSnWIv+tOeH0TXllG61MABQUJBRmnWQ8HbvaqrEHRyOjdPkwOLxpFxauR0EX60Pl8oKpsDg+m0qhvrkNOTAz4fMTYXVh9Pkr0On7RR7E19VGyCvfBkG5I0+6B9Lh2uEKCIPxZvPPOO9xwww0899xzVFdXt9rfq1cvFixYcFxliKBfEARBaBc/PbyOzb3SUA60qKdVGUnensv8QUn8mNqbUTsLMfr8XW10bi/ddpZiONCnVYOKw6jBq9cE5Okx6AmvbWhu89P5VBJKamkIC8ar1VBmMrSqR5CicmFNPagqqgrLwoIw1dX7F/naUIL23H8RVPDqH3chBEE4bh19IG9hYSFDhw494n6LxUJDw/GNQTqp7y7XrVtHdnY28+bN+9VtJ8pTTz1Fdnb2Cc+3vWRnZ/PUU0+1dzUEQRB+t7q9DWyevIE9o75kx253c8APsC+yE/887zq+6zGYuEZXc8AP4NFrKY0LDcjL5PSg8QYObNMqrV/ymx0uUvYVkLovn8S6xlZ1inO40Hu8mN1egj1exlTU8t6ZtzLlzElMOesOXs24ku/vXXHc5y4IgnAk0dHRFBYWHnH/+vXr6dSp03GVccwt/b8leJ47dy7x8fG/q0J/dlOnTiUjI4NzzjmnvasiCMIpwuf2UfFzJVqzhsgBkUjSqd131dPooeyHIvTFtYRs3UXTDzsoD41i3Yj+/FNKpSK4C8+U5tIU0XpmHbNPy/n7SzAeNvgWwGnUBXw2qG7ScsvZkxaHKkugqmg83lbHqYAqSUgqjN+8k10RoVQeaPEPdnsZVFXL6K2/0DdvD5sTU/ipW2/sBjN54fFoFAWTR2LNLpUlY9cTFqHn6jtiqHxlPa71ZaRFqYQ+PwJDtxBYtRu6J0JXcf8ThJOto/fpv/TSS3nrrbe48cYbCQnxj006+F3/ww8/MG3aNB5++OHjKuOYg/7JkycHfN64cSNff/01l1xyCX379g3YFxYW1mYe/fr1Y+XKlWi1olfRkbzzzjuMHTtWBP2CcJpTFRVXjQtDhKHNIN7d4EbWythL7fx4+VKcFU4AjNEGzp52FmFZYSgeBa/di9asxWv3Yggz4HP58FQ5MFg1SCGtg+qj1svlAZsL1aeA04vcKRy1yYXaYEfSafB9vgHfj7uRIi3IV/fHkRLPrrd3s3tnE+tT4lDDDXT+ehtLuybi1ei57BeJ9KY4vsvow+baKEbUlZJaUcpZ+/cxOyIG/WE3avXAtVA1GjgsgA9ptKPHgw8ZHxLxVBBcbcPl1VAZZ0H2KWi8PrwaCa2v5WW/w6T1PxQAQR4vN2/by/aEaBQk4hxORm9bx/Adm8gPj2Zx1gBUScKp1eIyGkCSUIBcjUxaTTlXL5rJnp/DKbSmkNLkYakujDNHTyXevQfJ40MFpDO7o8x7DLdPxhhh/M2/g2NRMLeAgtn56E0yyTd0oamgicJ5hRhjTGTc2hVjlBFDeOuuTIJwuuro3XuefvpplixZQp8+fTjrrLOQJIkXXniBf/zjH6xatYq+ffvyf//3f8dVxjFH36NHjw747PP5+Prrr+nVq1erfYdramrCYrEgyzIGg/gSEgThz6V+TwMli0rQWnQoXoWGvQ0UfVOEs9qFxqwh9coUej6YiaSRyP0yj/0zcqnbWY+EhKqqAXczZ4WL70cvQmPU4FNVcLV0gTFEGPDUOFFUCZPXQZ8MN52+vBK+3woLtkJsMNLNw5CyEvmpSGVloY8R27fSr7wYSQZl+R7UxbtQ3AoqWkACLVSadeyNjMCuNXPm3nwMihtQcL23koUxQ3BpDEhA9i/l2E0qd95yMR6tv6/9/D7dePPDOSRUVdOrqBCjx01BZDTllnAWpcVzTn45Zq8CqopPI8OBoN9l1GPTyoTbHMiqSnhdE53LqtGgosGHmSasOKgiFKPTi8594AFBkrAbNWh8KtGeCipNUdgPGbgLsDM8FIeswaQqeGSZnoX7AdiWmIIqSfgkCZeu5fYoA4mKysepXYhsGsR/lnxIlTmHxZ3OZ9iOHAzYkPB3M5KA/RucbMz6Gjc6gjOCOefaICw5e8DmhB6J4FPZj5mvramEl1VweaYG60V9QRM4PgHA5/JR+E0RjlI78UPC8c7ZwJYfbJTnt/ze988sCJhpaP8n/vMxxZpIuiiRuLPjkPUy+bPyUb0qSeOTiBsed8q/MRKEP5OQkBBWr17Nf//7X7766iuMRiPLli0jLS2NJ598koceegiT6bc35BzqhDe5jxs3jri4OB588EFee+01tm7dSkhICHPnzmXdunXccccdPPnkk4wbN67VsXPnzuXjjz+msLCQiIgILr/8ciZOnBiQZvXq1cyZM4ecnByqqqrQ6XRkZmZy8803079//2Oq4549e5g6dSobN27E4XCQkJDA2LFjue6669Ac8qX71FNPMX/+fBYtWsTLL7/MTz/9hMfjYcCAAfz9738nMjKSWbNm8emnn1JSUkJcXBx/+ctf2myl/+GHH5gxYwZ79uzB5/ORnp7O9ddfz3nnnQdASUkJ48ePB2D+/PnMnz+/+dh169YF5LVlyxZee+01cnJyMBgMnHPOOfz1r3/FbDY3p8nLy+Pzzz9nw4YNlJWV4fP5SElJ4bLLLuPiiy8OyG/q1Km88847fPXVV3zzzTd888031NbWkpyczN13382ZZ575m8/noBUrVjB9+nT27duH0+kkNDSUHj16cM8999C5s1gFUzj9Fcwr5Od7VqP62m6H8tl97PlgLwVzCpA0Es5KV/M+9VfarnzO1ou0uKpdHJzJxqE1sWqficbeb5FZtr0lz5d+4Mv/u4Urw4bwxYdv0W/bRgCUAy/HJcCHsTkfvBDd4CCpIQdFksiJSCajqhIAvephcM0Wlkf2R5X8QafOLePV+P8d7HDQq7SI2f0zeP2TOQR7HQAM3LsTj87DIz86iat2YnbDroR4tqamtpyMJJEXHETGjmK61VQgewKHoDkwoQBudJhcXqwNbmzBLcG9otXiMekYWLaOZfFDm+u3IyaSXZH+t9FaVSXU5camNxIDmDxu/7WVDwuGVZUQj4frSyspjOnOyuT+nJG3HnuQ78C1a+l25EWm0JiI+8C2bqsXYVm8PyC7H1KyGHfFX3FX64AY/rWwlNXTXyZ89l8D0nkdXn68ZDG1W2oB2Ox/j0ArR5ha1FHmYPfUPeyeuidge+4XeaRckcyglwe1eZwgdEQdvXsPgMlk4vHHH+fxxx//Q/L/Q/rZlJeXc+edd3Leeedx7rnnYre37pt5uJkzZ1JTU8P48eMJCgpiwYIFTJkyhZiYGEaNGtWcbt68edTX1zN69GhiYmKoqKhgzpw53HXXXbz11lutuhodLicnh9tuuw2tVsvll19OREQEP/30E1OmTGHPnj08++yzrY659957iY6O5o477qCwsJAZM2bw0EMPMXz4cL7++msuuugi9Ho9M2bM4JFHHmHWrFkkJCQ0H//GG2/w/vvvM3ToUO644w5kWWbJkiU8+uijPPzww1xxxRWEhYUxefJknnjiCfr27csll1zSZv13797NAw88wLhx47jgggtYv349c+bMQZZlHnvsseZ069atY8OGDZx55pnEx8fjdDpZtGgRzz77LLW1tdx0002t8n7qqafQarVcd911eDwePvvsM/72t78xa9asgDEax3I+4B908uCDD5KWlsZNN92E1WqlqqqKtWvXUlhYKIJ+4U9h8/NbjhjwH8pV4/5Dys+Rk+gi7UKvHuwqozJkykwG3RDNpQcC/oMkVHwHW/gPoaJFRUJSVboeCPgPinLXEeespMQUczAxkqpy2eb1vPXlJ1jdbtyyBo9iwosJDS7CKUDj8ZCYuwcHwdSQTN/9+/FqteyNj0fj85FVkMvIPBdafBjx4kZ/WJ0kyojFghc9NqxVTjy1Mg6jlhq9kU/6duMMRyg+vcr4/Quo1ofz1PAr2R7X8t3slSS09XaWZfSic3U52bm7+CWlGz6T2b/Y14GWcLPHg96nYARCfD5m9hlHt4p9nLn/FwzImKhtzlOLwlk1K/g+6nx8koY0e2DAD/Bq9vm4tS0PCnsi4nhncxiPLN8OwzKbtxfOK2wO+A/+hk6U3C/y6HZXN0K6tl7XQBCE09MfEvQXFxfz+OOPt2pR/jVlZWV89dVXWK1WAC666CLGjh3LjBkzAoL+xx9/vNXrjQkTJnDFFVfwwQcfHDXo/89//oPH4+GDDz6gSxf/4ixXXnklf//73/nuu+8YP348AwcODDgmMzOTRx55JGDbp59+SkVFBTNmzGiu84ABA7j66qv5+uuvueeeewDYuXMn77//PjfddBN333138/FXXXUVf/3rX3n99dcZM2YMFouF0aNH88QTT5CQkHDELlN79uzhgw8+ICsrq/ncm5qamDt3Lg888EBza/+YMWO47LLLAo695ppruOOOO5g2bRrXX399q7EVoaGhvPTSS82vfLOzs5k4cSKzZs36XeezbNkyFEXh9ddfJzw8vDntrbfeesTfz8lWU1ODxWJp7nZms9lQVZWgIP/c4G63m8bGRiIiIpqPKS0tJS4u7oify8rKiImJab6Ooow/dxlNRUdv9PgjKZIGl8aA3tvSPz6hsY70qoo20/t7sLfKhYN9jNoKOy0+Z/O/V3RNwuRx8/rMz7C6/Q8yesWHDhuN6LFQjgYPAI16K/sjOqG4jITW+Biwaw/Zu/aiImHCRj6JqKh40Rwov6V0DQog40aD50CrutYHQU0eQptchLuc7AoNojRmKIu6nw3AzojA8WY+YGNECGs6xbIyLpYBebvw2CpYFp9FgttLtFdBAnS+wGuiyBo2J2Ry7p6VB65O4FWRUUl25FNmiGnzGrd1EfNDIqnbvJfQQ4L+8h3lbR9/gjQV2XEEO077/wZFGSe/jPbQ0Vv6b7755qOmkSSJ995773eX8YcE/SEhIW123/k148aNaw6eAYxGIz179mTLli0B6Q4N+O12O263G41GQ1ZWFtu2bfvVMmpqatiyZQvDhw9vDvjBfxFvvvlmFi1axJIlS1oF/VdffXXA5759+/Lpp58yZsyYgDp36dIFi8VCQUFB87YFCxYgSRJjxoyhrq4uIJ9hw4axbNkytm7dyuDBg3+17gf17NmzOeA/aMCAAaxcuZKSkhLS09OBwOvkcrlwOPyv1QcPHsyGDRvIy8trTnvQVVddFdDHMzMzE7PZ/LvP5+C1Wbx4MRdffPEpOYD70IcRIOD3CaDX6wO+6IBWX2yHf46NjRVliDKa/x1/bhzFC0toL1avDau3KWDbim49+L5bFg6tDpPXE7BPQkHCh8rBro4qWhzNt1OXRsZwSBDskyQWJ2fgU/Vs7BTLd1nJfPbhu4Q4nYflCxq86PB/F+0NT+HrrHH4ZP/3QmRdPeds3oZG9T9cyMCGfiGk7XUQ0eigSWNAr3jRKgoevUx+XDTdi0vweA//XpFwazRkVtSwILErgysbUIPBYncRb9JTaPUHMk4JCrQalAh/S3deQiL7ImLYZjHgkSS2AkmNtVy1az2NkT0wqIFva/Q+zyEltn6T45U0VOqjcEs69GrgNY611bdKPzpvG6GX3RmwrctFXcibmt8q7YmgtWqJGhiJzho4I9Lp+N+gKOPkl9Ee2mqu6EgWL17capyNz+ejtLQUn89HVFQUFovluMr4Q6KwhISEgL7xx3rM4UJCQqivD/xyLCoq4vXXX2f16tU0NgbOt3y0QUklJf4bb+qh/UYPSElJQZZliouLj1q3g0+/bU1LGhwcHFDn3NxcVFVt1ep+qLZWXjuSI10nIKBcu93O22+/zcKFCykvb91a1NYCD4mJiW3m/XvP54orrmDZsmU8//zzTJkyhd69ezN06FAuuOCCI87wJAinmwEv9MfnVihbVobGqEHxKaCC1qLFU+8PBmWdTJcb05H1Ens+2IfX5cUca8JZ5UJxtX0r88oSWqV1sCnJgNeHKmsITrYwdFQ40r+N0HggCB/albDXbiFhSxBXXX8br8/9nMTqaiSdBsXjRpJktKoL34Ehs40GDbKsJdzhYmXnLqCoDCws5mBb+6MjL+KVM0Y0l3//yiU4NDpsen1zSz/42+m9aPFiRE8Ti9PPbg74AapCQyiIjiKl3P8GQouHAcW57LGm4PPoUKSWfuuy5MNp1GH2unHRenKI3E7RyBEWhhWX03tXAQlVjcgqjDRomT6iF6uSU6mVJZRD7hkOWaYI8ByyrTAojJzwKKrCghhc0/KdGeJooF9hYIPUoew6Iz69Su+GzbglHbogLVKD/2EHrYYXl31BtSWYuel9CXI7eGTHUsb+awTEBX4vRvSJIPv5/mx9cRuuahcR0SrOsiaa5EOCsoMPI23c/yRZQlVUzPFmFJ+Cs9z/N2CKNTHolUGtAn5BENpPXl5em9s9Hg9Tp07l5ZdfZuHChcdVxh8S9BuNv32KsmN5SLDb7UyaNAmHw8HVV19Neno6FosFSZKYNm0av/zyy++p7u+u25G2q4e1CEmSxKuvvop8hMFWaWlpx12Xw8t97LHHWLFiBZdccgn9+vUjJCQEWZZZuXIln376KYrSOpA4Uv1+7/mEhoYyffp0Nm7cyJo1a9i4cSP/+9//mDp1Kq+88gq9evU66vkKQkdnijYx/JNheB1eZL3s79+vgMaowWv3d7mRtBKaAyvL9vpbT1Sf2vyAULu1luU3r8BZ4R/gG5wexJnvnYE+SMeeaXup3tmA5FGQdJAwIp7Uq1LwOrxIkoTO4g/q1L+fi9rkQtLISBYDPYFNfcF2dV/M7/ZBsrvBrAe7G0x61A35yB+ugjoH4Rf3Qb6kD00NbgYXVyFP+Q4WNqLmVqJF5ZHV3+DqFMbCrj3pkaSnPnUoE/cP4/0Z07k0ZxMS/oDfJlmwqxY8JKE3FlNjav3g32j2v6F0GVVKDSEUhEaw0xLNwLqygHQ+VYPF4fK/fZBVUFoCXp8sURPmbw2LrbeRVNnSOBTk8nL/t6v5l3YWA2+4g/qQwDq422g32hnZGS0uFsRGkGpzoPM6ePe7dzF7Wt5kVAzozUIyyCzOwWkyE/ngufQq3Qc7i5FHjoBbRoDDDRoZdFrCFIWvVXDIWnQOGW3QZW0G7QDpN6STem0qiktBa9aCx0vFz+Xsn12E4lbQGGU8jV7CMsPwObw07G0kZlgMyZd0RmPW4HP6mv8OPE0eVFVFb9W3WZYgdGTq4QPwTxM6nY577rmHnJwc7rnnHr755pvfndep19/iV6xdu5bKykqeeOKJ5pluDnrzzTePevzBlvn9+1sPrMrLy0NRlDZb0o9HUlISP//8M7GxsaSkpJzQvI+ksbGRFStWMHr06FZzuq5du/a48v6t56PRaMjOzm5e3G3Pnj1cd911vPfee7zyyivHVRdB6Ei0pgNft4c8t2vNrb+CZZ3MwclgZI1MRJ8Ixq8eS9Uv1RjC9YR2D21O2+vhnm2WdXhQJ2lkpODWU71Z9Qfm67EeaKg58P/SgBTkAYH/fVvCTBCWBFMn+dPsK4f9FcQN6cKb1kMbegy85FLJ6XUBDf+yo+4oY1lqF2ZdOorH5y/GtL6Eu8Y/QJdGOxGOwMaH5NpC9sWHsifC/9bRJ4FH13bjQp3ZxNpOnXBqdcRV2QhucqFTvWzPSMKj1+KTJCoNrc/Zgx6dV2V4QS7TegYG/d3LalmdFthNIQQt55UU81pGBKVGPV1qfTTqjARpDRi8buTenYlZ+ihjbRKVRaPp1tWMyaoFAruJYj20Lv4/AhOAzszRyBoZ2XzgOui0RJ+dQPTZx3avki0t1+9g8C8IQsfTu3dvPvroo+PKo+1v01PUwVbuw1ueV69efdT+/ODvt9arVy+WL1/O3r17m7erqsoHH3wAwPDhw09gjVvWN3j99dfx+VpPsXd41x6z2dyqS9NvdbAF/vDrVFVVxezZs48r799yPof3+QdITk7GaDS22b1IEIS2afQaYs6IDgj4211aDIzs2fLAcIgQg8SQi1OJWvNXohte5PJNt/HZE53IXHsjcT9dwzk1lXzQP4vCEH+LvKQo9N6/n+1BnZoDfgCNCl1rqzAoroD8vUYZq91Bk8GATyNTFBNMTmoUa3p0ojHEH0TnhYeyJy6qdb0lBQmFJ1cspn+Zv8un1udjzPY8HlyyibP3FCOpKpKqkmZ30rvRTm1QFF9nNLBguI//Xmgl+tXrMD12CfJ3/4BN/wOzgdBoPV36BR8I+AVBONlUqfXP6WThwoUBU7P/Hh3q26lPnz5ERETw8ssvU1paSnR0NLt37+bbb78lPT09IJA/kr/97W/cdtttTJo0qXnKzhUrVrBq1SpGjRrVahDv8crMzOS2227j7bff5pprruG8884jKiqKqqoqduzYwcqVK1m9enVz+qysLNauXcu0adOIjY1FkiQuuOCC31SmxWJh8ODBLFiwAIPBQGZmJqWlpc1TiR7PQ8VvOZ9nn32WiooKBg0aRFxcHC6Xi4ULF9LU1MSYMWN+dx0EQei4TEOTuX9hMj23OflfnoZ8cwgjdhehItHW2/l+1TmkuwrZr02lSTajqjpCatzkGKLhsMUeXRotdoOBIJeLBqORfVHhrOnaiUG7/ZMReDQyi3p3o0tRJKG6Bj6ZO4vNhhRUVYf5wOq/d67YRopJg0/WYDzQcKKVVM6+IQmD8WA7WSww5I+6RIIg/A4dvXvP5MmT29xeV1fH8uXL2bBhA48++uhxldGhgv6goCBee+01Xn31VWbMmIHP56Nbt2688sorzJkz55iC/h49evD+++8zdepUvvrqq+bFuf7yl79w3XXX/SH1vu222+jRoweff/45n332GQ6Hg/DwcNLS0vjb3/4WkPbRRx/lhRde4IMPPqCpyT/jxm8N+gGeeeYZpkyZwk8//cQ333xDUlISd911F1qtlqeffvqknM/o0aOZN29e82JfFouF1NRUXnjhBUaMGPErJQiCcLobkWXEvXMrfdbtxoUBDzoaDXrWBFsC+rd3aizFoHpI8lTSQCgO2YyClhCHixpr4EwWFaHBVFmC6J9fSN+qAmx9tHx8bjZLe6YRVd9EWaiVqvAQoqsbCap1EWJ3cqZ9F/sMCTRozDgserZlpWBUVHxSy5vSCy6OOCTgFwRBOPGeeuqpNreHhYWRlpbGW2+9xaRJk46rDEk9vA+IIAiCIJwEG36ppe6iT+lRWoFPkvh0YF/047MwfJaDKklofV4u37kQrxJMaVAE65PSsBlMBDnsZOSXURIaTPWBwN/kcbM6NZ1xi7c1Ty/q1ci8d9FAyqJD0Hm8qBoNEjB+xRbSSqrRKR56ObazpXMP8iNjqQjxT12oAN0vSUQyaune00yvftY26y8IwqnjW2Pr/u6jnde3Q01OXR2qpV8QBEE4ffQbEMbi1Xfy2OxKKnQGxgwJ5o4+Miv+tYitKQlE2my4lFC8Gi0rU7rjPTCuq9FkZmtqEmds34NHK6NKKpJHQ+fCBiTAgQ4VCa1PYfC2AuafkwUHAn6D20NiRR0Aob5qtPpauk3sSlKvzqz/oQrZaiB7TDQ9zgo/Yr0FQRA6IhH0C4IgCO3m3E4y594buHKtVfXSpaiC/JhwKsyh+HSa5oD/IJdeh81kINTRRCNWFGSMByYX0KDQiH+AscXtQauo+GSJpJoq7lw5i/iGBkyKnWhvORvGX0r/x4cC0HP8iZ29TRCEk0fVdKw+/YcufPpbdOrU6XeXKYJ+QRAE4ZSy+9aB9HxlBfG1DchIGEyuVmkkVcXi8S8iphw2EZ0WlTDqqSOIstgQepWX8OT8z9CpTiyU4pZ15IZ1Ykn8GWQ/cuHJOi1BEP5ASgcbyJucnHzURWXb0tbMicdKBP2CIAjCKeXc57J5slbDRQvWowJOVUd8TTUl4REAJNZW0bW0FElS2R8fR3RJU6s8UihGQWK7M5qrlm8iNyKWxgYT1QnZlGV1wRxrJvuyBNKHRpzksxMEQYD333//dwX9x0MM5BUEQRBOOTa3yhe7VOpccG5+CUXXLcFp0RKqNpFVU9iczgcUSnFoDrmTWbCRSCkAlYQye8hQDG4voarCyC+HEZNqQRCE08vckE9abRtff2071OTUJVr6BUEQhFOOVS9xc88DrWDZiSRFjmT7PatI3B64oroGMOjt1KqhRLobCMJGGHXN+/WSBy0qZ58fQeaTfZENgWMDBEEQ/ixE0C8IgiCc8iLOjuWsLRdTaNoJh3Xxdxu1lJstRNZUEeGqDdjniwvhzs/6o+8cdhJrKwjCydbRF+c6aOXKlWzYsIH6+noURQnYJ0kS//jHP3533iLoFwRBEDoESZIIemQorsmLm7cpwM6kJOqNJjYkJhOnmjCsy0MFpBEZdJ53E5JJ1251FgTh5FA7eMxfU1PDmDFjWLt2LaqqIkkSB3vgH/y3CPoFQRCEP42wp8/GkRFG05vrkEMMmO8dzIVdovh6wQzcWpXQm+5AU2kHSUKOC27v6gqCIByThx56iC1btvDpp58yaNAgUlNT+f7770lJSeGll15i1apVLFiw4LjKEOuKC4IgCB2K6ZpeRP50M+Hzr8V4fhphiSYkbctIXjk+RAT8gvAno8pSq5+O5Ntvv+X222/nyiuvJCgoCABZlklPT+f1118nOTmZ+++//7jKEEG/IAiCIAiCILSjuro6MjMzAbBarQDYbLbm/eeffz7ff//9cZUhgn5BEARBEAShQ1Ok1j8dSXx8PGVlZQAYDAaio6PZvHlz8/7i4uLjntdf9OkXBEEQBEEQOrSO1p3ncMOGDWPhwoU89thjAFx55ZX8+9//RqPRoCgKL7/8MhdccMFxlSGCfkEQBEEQBEFoRw8++CALFy7E5XJhMBh46qmn2L59e/NsPcOGDWPKlCnHVYYI+gVBEARBEIQOraNP2dmzZ0969uzZ/DksLIxFixZRV1eHRqNpHtx7PESffkEQBEEQBEFoRzk5OW1uDw0NPSEBP4igXxAEQThNOEuD+e7NQn6eWYqzydve1REE4SRSJanVT0eSlZVFr169eO6559i7d+8fUoYI+gVBEIQOr3FHDHUrktk6u5Slr+fy2g2bcNl97V0tQRBOko4+e8+bb75JVFQUTzzxBBkZGfTv358XX3yR/Pz8E1aGCPoFQRCEDs3rUXBtDUejqqiyjCpLuOo9zH1mZ3tXTRAE4Zjcfvvt/PjjjxQXF/PKK69gsVh49NFHSU1NZciQIbzyyiuUlJQcVxmSqqrq0ZMJgiAIQvsqrlf4fpeXpFCJ87pocTf52L+yCqfby7LJu2gKMQekt0gK9y88o51qKwjCyfRZ4hettl1ddEU71OTEKS4u5ssvv+SLL75g7dq1SJKEx+P53fmJ2XsEQRCEU5KiqhQ3QowFvlvbxLIH1pJVVMmimHD+PbYn56/ajbbRgwoYPD5U4NA3+gav6NcvCELHFRcXR2ZmJt27d2fbtm00NTUdV34i6BcEQRBOOSuLVa6d7yW/0odFVnn7jfncWFwBQHZxOYPzS3lm1AAeKNqIYjSzM6kzqCpBTXY0ioJPlklL0LK7RmX2XpUYM1yeIWHWdbCOvoIgHJOOPmXnQaqqsnTpUmbMmMHXX39NVVUVYWFhXHXVVVx55ZXHlbcI+gVBEIRThqfcjrPQxhWrQqgqcoIkE1NVR88DAf9B6dV1zP/kU0y4AcjO3cUPGf3hwIwdslehMjiYpRctIm1/A1VWI1dO6MsnT8YRbDhNogNBEJp1tNl6DvfTTz/xxRdf8NVXX1FRUUFwcDAXX3wxV155Jeeddx5a7fGH7CLoFwRBEE4JxY/+jP3FZQQrNfyrW1fuvWA8bqMZ6QhDzw69xcfX1ZBSXUZuZBwAilZG80Met+3LoTAulJ/CenPOvO181F3L3ddHn4SzEQRBOHZnn302VquVcePGceWVVzJq1Cj0ev0JLUME/YIgCEK7sy0vxvfCt6Tjn57u+p1lDKjYxYCJj7AvKoz1iTH0LypvTq/Fg+FAK/9BVpcTVLX5IaEyJJSPBg1nVc/05jShC+q55YpIjHUNcO978N0G6BQFr0+CYZkn4UwFQfgjdLQpOg/35ZdfMmbMGIxG4x9WhpiyUxAEQWh3Vf9ZQZRcGrCtW005V+1YD8C9E0bwSXYmO+KiWJiZjiMssKVfBYpCI5BUFQn/PkUrY9AoSErLm4I6i4nlvzhwj/0XfLESGhyo2wpQhz8Be0po+KWSnbetYOftK2lYV/WHn7cgCALAhAkT/tCAH0RLvyAIgnAKyMl1kKS0noouztaApKoMKCmik7uKnSkR6H0a1iR3Q6/4SKivpklvYE1qd6rNwWhVBfA/BADovT4iG2xUhrYsY7/42wrOX7eHyQPH8lLfkTi1Oibu+Jkn+/+bckcKZq8HLV6qPviBnx4dR+LwBLIK8pAGJiN3jzsZl0MQhN+oo/fpPxnEPP2CIAjCSfXLinpWL6nDZJA4d7iF7Z+tY81KLTfn/ECKrWXxGa8ksS08ivS6amSfiZXRfSkIigT8N3h7sBlFr8Vp0OOTZWILqmiKMKMe2H9wUK9PktiQnEidxT8+4Jw1GzH46rnv/DEB9UpwNHBGcTXn7y0grr6RIGcTa3p2ZnlqN2JrK3hp3nS0T4zB/bdRBFk1J+16CYJwdB+mfNVq28Tcy9qhJqcu0dIvADBu3Dji4uJ4++23m7fddtttlJaWMm/evKMev27dOu644w6efPJJxo0b90dWVRCEU1itU+W1tT42F3sZmSwR0+BkSalEWpqBiQP0zH6vlF+W1Ten3/iLjcUh3bnRuZ31IT0IdjUR4rWRHxxGYmMZfatzAbBr3NTrDOi9CirgkyVMjQ4aokLItZiY3i2Vyz27ubhsH4Xh0c0BP4BGVckorWB7fCwZBYXctG0pt46+pFXdi80hfNE7ii3x0dy6eRfrMlLwarQYgNrwOP5vzFWMmr6Bj3eloksK4bxhVswmmaG9jcRGitupIAinNvEt1QEVFRXx4YcfsmHDBsrKytDr9URERJCZmcm4cePIzs5u7yoKgnCaqbKr5FSrRJmgW4TEjmqINoNRC4tzfSRY4D/L3WxY08RugwEkiZmbAEkGgwbjHjfPzG3CZg5G6RPOqG37uXXpRoKcLrLiotA7XXR2VCN7rPzQuRdbYmT+vja/ufy14QNwaC0AuIx6GsOC8GplkCR2BwfhkmQ+y+7KZdO3kKCtpCg0JqD+wQ4nFbLKW79MJ5x6km0lQODA3e4l1QzfX8ZHZ/UgLyocrybwFtlgjWRWr4FI7ib214Xw2txGPBoZZtqIjoDKCjed6xo4T6mg+73ZZGdbsZr8Q+f2lnkJs8hEBImhdILwR1BE956jEkF/B5OTk8Ntt92GVqtlzJgxpKam4nK5KCwsZPXq1ZjN5hMW9L/++uuI3l+C0DEpqsrsPSqrS1X0koqj1ktwaRMZoSp1sVZyJR3DEiXGpLUOQn0/78c3dytbNMHM7tWHFS4Ly0rxd5RXoFt1ORdv38SyLhms7pSCKknovD76FdWRa7IEtLKjQniTi642O6URFqqCjHQrrmTyjEXoFH//++jaBnZ0iiO5thKAQfn7yQ3vHFCnEmMsAB6dlsqEyIAyLAe+pyRZ4v4rxnPpjj1YJYlQp6s5jdnRQJ/CjYS7/G8Z7t+4iM8yBlFhDgb8K/resnIHWaW1pFQ0UNQ9qvV1kWWGF+1kZs8RaFQFp06HW5aokyRK6yUwaCmJMbPHHUHkxzakjxqJitRSLcvk1oOESo8kLS9ODGbOVg9Oj8qV/Q10jfZ3FdpS5GXuZjehZolrBhoIt4gHBEEQThwR9Hcw77zzDk6nk08//ZSuXbu22l9VdeJmm9DpdCcsL0EQTow5exWeWaVQYYfLuko8d5aMUdsSALu8Kv9Y6uOH70p4ev6X3FuSz7K0HizoOZI6n0oB4JPqWJgSw7+DTaSFKiy4TEOXMImfixT+/lYROd5gdE3Z+FSI+bGSrelWQAV/jM7OiBj+e+Z5eA3a5sFzHq2GrfHheOpbD8atMehZbTWCxX/LuWj9zuaAH6AwNIgqvZaikDCS62rIDQtD8VlwyVoMiheAIK+NWn0YTUHmwIcKoFddAwvjY+judKOTJJb16IqsKIzbthOr10e4vZ7eeTlYnabmY5Ibq9n28RO8lTGS/fo0zt5dQlyDHYCeRVX0rKlg+sWhuLUt82Sft3sV5UGR6IFIr49Qr49tFlOr+tTotIS7PSDJVFd6sXp99EKlXqNhawEMmlyJXefP98UfHXx7ZzBrtzr5/LsmdKqKQ5Z4ZbbMP68K4oohJgRBOLrTYUXehoYG3njjDZYsWUJFRQVTp05l4MCB1NTUMG3aNMaPH096evrRMzoCEfR3MAUFBYSEhLQZ8ANERkYGfJ49ezZffvkleXl5aLVasrKymDRpEn369DlqWUfq07906VLefvtt8vLyCAsLY+zYsfTt27fV8S6Xi2nTpvH9999TXl6OTqcjJiaGoUOHct999x37SQuCAMDGcpUJcxR8B17AvbRexe1TeO28lkGlf1/s49Wf3ex+72WS66oBKArvhMHX8tZOo0Kf8jpKQkzsq4eRX/pYdbXMBZ+4sVliod4JZn9QWpEUBbIEnsC3fh6tplWwa9drQecDjxKwHY0/XXJ1OXVmKx5NS33/OeoMPhuQiSpJGDxeBubl8VNyCgCze3TjnyvnEdvUAGYFndtDWmMRm8K7B2TvkySivD4ObaZQZJmd0RH88+t5NGFCJZg+jQp1RBGK/41ClMPGnZtWsF5u/UYz3Obg1iXzWdclnTpzEAPzt5JcXcRz59/enEYLWHw+muTAFnnpwP/KqkqYx9s8tWik1wdON8XWlkDe5YXn5tho2uXEfGCbTlGR3QqPfWojI05L72TRACMIR9PRZ+8pKiri7LPPprCwkC5durBz505sNhsA4eHhTJ06lfz8fF555ZXfXYZ4d9jBJCYmUl9fz+LFi4+a9tVXX+XZZ59Fq9Vy1113cd1115Gbm8vtt9/OihUrflf5S5Ys4aGHHqKpqYlbb72VK664gh9//JEpU6a0SvvCCy/wzjvv0LNnTx588EHuuusuBg4cyC+//PK7yhaEP7sZu1oC/oM+2RG44ZNtPs4o2Nsc8APsiElslZfJ62v+d34DvLJBxaZq/AH7wSxlQHOE20QbPf9kn+J/QDj0EJ0MskRqTTmvzfmAOrOVmQO749DJlEYpVIUrzYtpuXRafkpPQ1ZUOjc52RWbxi0T7uWlc25ja6csOisVDCjKQe8NXJRrVWQ4uja6ItaazUhIGPAcqLJMMRl40aICTZgJoRqrWh9wnF71okGlU3EDDy6dRq/iXWyLS+ef59+BIgfO2hPs9cFhZVt9/oceg6JweBgS4fWhUwLT15V7W6UzKyoqMG+9C0EQTn8PPfQQjY2NbNq0iWXLlrXqXn3xxRezaNGi4ypDBP0dzC233IJWq+Xhhx/m0ksv5emnn+arr74iNzc3IF1eXh4fffQRvXv35t133+Xaa69l0qRJfPjhh5hMJl544QV8Pt8RSmmbz+fjP//5D8HBwXz44YfcfPPNTJw4kWnTplFfX98q/dKlSxk6dChPP/00EyZM4IorruCvf/0rH3/88XFdgxOppqYGl6vlpmqz2WhsbGz+7Ha7qa6uDjimtLT0Vz+XlZUF/McqyhBlnKgygttYkT3UEFhGqEGi3mgOSGNyNbQ6Lj8kME30wY+HRp+y3BLQHh6VqoC75TtE41PIKCz3B/0GLRg1/h+t/zZzx7ofSWioAUCHkwTDJkZUruD7T19g09t/J9x+4PpIEpeUVHJeZS0XlteQH2KmIMRCg9lEtK2GEGcT16xbQK/i3aRUFdE/fys6SSK2tq7VOQ7an+evGwom7ERTTiTV1JBOFRls1JzFYu1YbLIVSVLQq14siotg1em/tlSgIjNy58/UG6z45Na3TJtWi1aSkA9cIg2gyBKgtvVchKqqrVYO7RHfevrPg+9Kgk0tiTvy364o489VRntQJanVT0fyww8/cO+999KjRw+kNuqemppKYWHhcZUhgv4OplevXnz88ceMHTsWm83GvHnzeP7557n88suZNGkSRUVFAM1PiTfccENA3/yoqCjGjRtHaWkpu3bt+k1l79ixg/LycsaPH09oaGjzdqvVyoQJE1qlt1qt7N+/n7179/6+kz0JwsPDMRgMzZ+tVitBQS2L+BycGelQcXFxv/o5NjY24D9YUYYo40SVcXNPmZjAWJ1HB8kBZTx6hobNcUl8l94yM81ffprHhphg7FoNHlliR4SVjXGhzfsHxsDdfWV6WD3+IP1g675PAe+B8FNzsAVfxeD10auiHpw+aPKQua+EJf96jyfnLkV38A2CJCEBA3OLMbk9RDU10qckn1E7NzL5hxnE2uqay+9ZUcQDqxcAEOLyEOL14ZIkfogIwW73MCcyjOndk1memgaAR6NlT2wSm5IzWNm1L+cWl/Hi9z/ywLdLGbVxJ3G19YzcvpOr1/pX85VQiKUcC3asNGHGjoxKN18BdoMOVdVg0NvorJZixcm2hFDm94tkfkI2C7XjWOc7h8xN+5B8Pv8aADSPaUaRWoJ9Lf5L5AO8SNhlGW/gr4tLti3myVWzCXM2Iasq56Vq+Of1wYQeNqtPo0YmzCpx+ZCWFTo78t+uKOPPVYbw2zkcDqKiWk8gcNChD2K/l+jT3wGlp6fz1FNPAf6n6/Xr1zNnzhw2btzY3JJeUuJf4CYtLa3V8Qe3FRcX06NHj2Mut7i4GIDOnTu32peSktJq24MPPsiTTz7JVVddRUJCAtnZ2Zx11lkMGzYMuY0WM0EQfl2sRWLd9Rre2twykPf85MD/lm7qo6FTiMT0Lvew5LNldM/LZ23nFKoSrTA8nCYv5Oz24nOpoFUYGg8/XqXFoJVYcauJqd/Xsn19BV6vl72R8ZQb9BTWePzddFTVH+HqJFStTLjHS42kIycqkp+TE5mweScz3/2Cjwb0YnF6Z3qUVHHL2u3+ujv8jQ9ff/gfmvSGw0+NnhWFGL0+hpdWYXW5+D46nEJTS7pSScND54+mV0kBi7LOpsYaAoBLZ8CLBpfbQEp9Hd3yq7lk9TaCsaHDhw8IwhZQlgRo8eJAh9nuBUkiwVFHNNX859wBvJk9uDntZb/s4eYVOURXgL3ShumQhyWAcI+PWm3grVQG6nUaNIqPlMYq6sxRaBSV7uX7CLdVUhsZzzVpWi4eHcSwTP85vv1YJHOX2dlV5MEuS6Qk6rjmTBNxYWIRMEE4Fh19IG+PHj1Yvnw5t99+e5v7Z8+e3eb4yd9CBP0dXFxcHGPHjmXMmDHceuutbN68me3bt7d3tQA455xzmDt3LitXrmTDhg2sXbuWOXPm0LdvX9544w0xO5Ag/A6JQRLPnvnrgeCIFJkRKWaUK0axulChvx7eims5xunRsLpIITFYIj2i5aEhzCjx6EXhcFF4QH5fbPXy1GI39Y0K54R5uHColciQMOJMKg9/72F9scpTF5/Lu8Oz6VNWjUnVEexRqTW1tFKXmWJYHjmUzIYcgpyBQThAgyWO23bkNt+UCg459qC4Wgfz+5yP3Ri4LzmvhPzIYBSNjM7jo1dRCRqvCsi4dRo8Pk1Lf5lmKqVSRPNg5AbJij1E4a3+AwNSzeqfxvhN+4m0OTlrXxmr4kIDBgyHOl3EKwpNJh16txuDz4fPaCA+zsB1Z1sZPzSWPcVe/vNpA2ujsvBe1p+/XmolOjTwdxgdpuHWi4MQBOH3UeWOHfXff//9TJw4kV69enH55ZcDoCgKe/fu5emnn2bVqlXMnDnzuMoQQf9pQpIksrKy2Lx5MxUVFSQkJACwb98+EhMDB/Ht378foDnNsTqYPj8/v9W+w8cUHBQSEsLo0aMZPXo0qqoyZcoUpk+fzrJlyzjvvPN+U/mCIPw2siwxtHPrBwSjTuKclGNvQb6ip5YrerZ9u/h+oj+fOqfK9BwTuysicX1bwvkVdQDUdIsjdE8Zsk8lN7k7sxNGEF1ZycTNs4hrqkABlqb1ZXtCX7Sq2hyEh3i9OLT+vLtVl/Ps8u8YWFpEtTmMeX2G0Wj2B8hatwfZ7UE50CVJp3oJ89qbhyAYPT68kh4ZO8qB+XFU4PlB53DWujo0B/o2VxJGaUgYqhT45kSRZcpCzETanGiMMhJQptXgkSSSwiQuzw7irCEWEhPaGHBxQPdOOt57NOKI+wVBEK677jry8/N5/PHHeeyxxwAYNWoUqqoiyzLPPfccF1988XGVIYL+Dmb16tVkZ2ejPex1stPpZPXq1YB/sEfXrl2ZMmUKH330EWeccUZz+qqqKubNm0dcXBwZGRm/qezu3bsTExPD3LlzmThxYnO/fpvN1urp0+fzYbfbA/oFSpLUXGZbA38FQei4Qo0S9/aTABlGdaa+1ovTqRAT1xVXnRtHpZOQtCB8Cryx3MVlReeQtTeX3l47xetzadLp0B8yuPDM6jq+jotGq3hZMOMd4pv8/VljmhoIX/0Nr517FQAmuyNgkHGYw9FqzLGi6tHQyP6wGGpNYczoPYDPOydTqRRw+fp9/kSShNRowuT24NC3tOUHOdx0Kauj3mpka2Znwi0Sj94dSVKsjsRQ0U1REE4VHW3gblsee+wxrr/+embOnMnevXtRFIW0tDQuvfRSUlNTjzt/EfR3MP/73/+or69n2LBhpKenYzQaKS8v57vvvqOgoIAxY8Y0L9xw/fXXM336dCZNmsTIkSOx2+18/fXX2O12nnnmGTSa39ZXVKPR8MADD/D3v/+diRMncvHFF6PRaJg7dy4hISGUlZU1p7Xb7YwaNYphw4aRkZFBWFgYJSUlfPXVVwQHBzNs2LATel0EQTi1hIRpCTnwb0OoHkOovyVcK8O95xq5FwB/I4CzJJ3d6a+xKLsfxZHxAHRyuLh9334KjN7mgP+g2MYa0ooLsKvGVrMKNelbt7jLeNDhZltCFM+cezWhznq+nPkWy2L6sydVxuqU+b53TyJsDv72wwbeGtaTymAziXX13LN6HRv7d2Z3SjwRcQbuuiWCHhmtxyQIgiD8Xna7nbPOOotJkyZxxx138MADD/wh5Yigv4N58MEHWbZsGZs2bWLx4sXYbDasVivp6elMnDiRcePGNae99957SUpK4ssvv+S1115Dp9ORmZnJs88++7sHg5x33nnIssy7777L22+/TXh4ePPiXPfcc09zOqPRyNVXX83atWtZu3YtdrudyMhIhg0bxk033fSrI9QFQfhz0YSYKAqN5i8/vcvq1GxyYrsSbq9j+J4V/Ovsi9s8RlJg6Ma9yCrkxYZQF+Tv519rMlMcGkpCXd2BlCrhFCChUhAWRHGEgdc+/46z63bTv7KIfNLxoCfTUUjSU4OR7vuBG3eswG1ykT4yHM13k1DCrTQ6ZUKC5Tan0hMEof115D79ZrOZ3NzcP/z7RVIPn/1fEARBEE6yjfctoe+rrRf5u+Hyu7h75Rr6lrTMT707shNronrRa0dR8za7QYtTr6UwIQyHxUif4hz6luVgpAEtHhwaPZPG3ctfFy+j943xyMO7ofx1GlJuBbZBmVg/uxspOQZKa+DHrZAaA0O7nZRzFwTh+L2VNa/Vtju2jWsj5anpmmuuwel0MmvWrD+sDBH0C4IgCO1OVVRsyfcRVHhIIK81kPCPN/HKWt7/7EsyKssoCY5ie0wqfXbmE1zTsqCQgoQDLQ1mI03BBirjLHSqLSCjfj8ujYEZPc5kZo+e7N7wMnz/BIRaDhyo+BchEwShQ+voQf+OHTu4/PLL6du3L7fffjspKSmYTKZW6cLDw9s4+tiIoF8QBEE4NZTU4LvnXVi8jY1h8dwz5nrWJPv7/cs+hfuXbuT8DTuIq6lH7/Vhx4APDSrQgAHlkPUmqyLMVMeamNmnG3VGI7VmI+eF2nhvcnL7nJsgCH+oN3vNb7Xtzi1j26Emv8+h6xf9Wjcfn893xH1HI/r0C4IgCKeG+HA0sx4GwFOssG+WDxwqGhmyCyswI7MzJYmoukb0+NBKHgojg5HdMob6wLVvI6vt2CL05IWHoCLRNUri2XuS2uOsBEEQjuqJJ54QffoFQRCEPyePT2VrJXQKhtJFJXzz3314NBr0Hg8pRg92vY7tigVLrYuha/a1Or58aAznzhuJzamSES/auAThdPZG729abbtr85h2qMmpS3wLCoIgCKcknUaiX6z/35HjE4jPCiF3RSXWaCPpw6NRvCq7fyyn+t2NqLhx0zJdp8cs0eWsMBLCf9vUxIIgdEyHL6wntCaCfkEQBKFDiEi1EpFqbdlggKzxCVQ2VuObt4ZSIrBjwCw5SbRXYr3+wvarrCAIwm8wefLko6aRJIl//OMfv7sM0b1HEARB6NDcLhcFnZ8nvNzZsvHCboR/O7H9KiUIwkn1Wr/vWm27Z8OodqjJ7yP/yixikiShqiqSJImBvIIgCMKflyTLLP97DOmL6+ktx2M4JxXDHYPau1qCIJxEagdfOE9RlDa35efn8/rrr7N8+XIWLFhwXGWIDlCCIAhCh+c1yewcE4b5i6sw3ncGkkG0aQmC0LHJskxKSgr/+c9/6NKlC3/5y1+OL78TVC9BEARBEARBaB9SGz+nkWHDhvHtt98eVx4i6BcEQRAEQRCEU9i6det+td//sRDvPwVBEARBEIQOraP36Z8+fXqb2+vq6li+fDmzZs3i1ltvPa4yRNAvCIIgCIIgdGiq3LGD/htvvPGI+yIjI3n00Ud54oknjqsMEfQLgiAIgiAIQjvKzc1ttU2SJMLCwggKCjohZYigXxAEQRAEQejQOnr3HkmSiIqKwmQytbnf4XBQWVlJp06dfncZYiCvIAiC0KGoisrBdSV9ilhfUhCEji8lJYWvv/76iPvnzp1LSkrKcZUhWvoFQRCEU5q9wcvudfU46tzk/1BM1bpKkGRyO0UyLasLplAt1xbF0K1zSXtXVRCEdtLRW/oPNmQcicfjEbP3CIIgCKev3K2NfHnvVhJ2liErKrmdw0mschLc4CBldxnXf7OEunAdK9KTyZpVBmcVQ9/k9q62IAgnWUcM+hsaGqirq2v+XF1dTUFBQat0dXV1fP7558TFxR1XeZJ6tEcLQRAEQWgHe/c5ee6JQuxaHRqPjw1xIeyMCyWtuJr33ptNfGM9AEacWLFRaTXTdFY6jknn0W1QKOHxxnY+A0EQTpb/DV3catuDP5/bDjU5dk8//TSTJ08+prSqqvLss8/yf//3f7+7PNHSLwiCIJxS1Do7nv3VPPOqB5dWB4BPp6FnlY38SCsTNm4nvrGBg0tuOjGxPrUrJTFhpOwuwvjATCb3HkRjagxnnRVEryHB9IiS0KOy8f197J9fiCnKQL+7uhHfP6Idz1QQhBOlI7b0n3/++VitVlRV5eGHH+bqq6+mX79+AWkkScJisdC/f3+ys7OPqzwR9AuCIAinDM9/f6Txye/5oPc5uNJ7BOyTgWibizN357c6ztrkoCCyG3uiY8kLDcGn0UADzPqmkf+u8FKcGsrrezZRt6TMf0CeneKbfuayT4YS0VME/oIgnHxDhgxhyJAhADQ1NTFhwgSysrL+sPLE7D2CIAjCKUHZXkr9I9/ybrfhWCqdeNtouLPpteRFhgKgAj4kVKDBYgagwWj0B/yHSGpwolY3UbKiEgCNTyHUZkfn9vLQ47nM2OT6A89KEISTQZWkVj8dyZNPPvmHBvwgWvoFQRCEU4DP6WPd7SvxGdK5eH0OAFq9jkX9ezanKQg2UW0x8NrIIQzLyUf2SXAg6HfL/m5AUhvD1LSKQnKtnbWpcVz6yy4yCsvRKioqoPpUnv4gGevtGsb0ELdEQeioOlqQfyQrV65kw4YN1NfXoyhKwD5JkvjHP/7xu/MW33CCIAhCuyp5cSMbn9mOoveRYbc3bx+2fTfWBjuruiTj9SmcXVzBGdGhpFTUoioa/G39/p79PfYWsS8xFq+qUqfX49brmvPpVFPHWXtqKQoy0qW4Eq3SctygvYWkllXz7y80jHkq7CSetSAIQouamhrGjBnD2rVrUVUVSZKap/E8+G8R9AuCIAgdkurxUTx1B2v+vQOvWUu0wxGwv8po4oUz+pIT4+9zb8xM5ckf1jBgbwkaAlv0JWDwyp3YgoyMwM2q3ukURkUSYWsipsEGQEKjE7mNxbx0jY0U1vvLcNm9aDQyWoOM4lNx2n2Yg8StUhBOdarcsVv6H3roIbZs2cKnn37KoEGDSE1N5fvvvyclJYWXXnqJVatWsWDBguMqQ3yTCYIgCCeF0uDEPSsHvD7qKmHbm3toaFIx2hS8skpeYiz1YUF0KSpD6/OxLTqKrNIKzirdQZNez+z0Xnw6OIviLklctWgVRo+3OW8V8Kky5iYXaa5y9DkKG7r4ALDr9Tj1OkxuNw06IyEuR/NDwyujBjP37F4ossTAf1Ry86w1qBYD9IyioknCZfMRFmvgusdSiEsxt8dlEwThT+Dbb7/l9ttv58orr6S6uhoAWZZJT0/n9ddf59JLL+X+++/ns88++91liKBfEARB+MPUOFSqHdC5rpaqAVPR19io0FvZEpWK5NNi9HhoCDLy89mZeHT+W9LughISSysxaWSu3bqPpJoqurCdp1d9wzPn30plRAg/DO7FmBUb0aj+vvn1sgmfpEGRQIuH+JoatniTKY4Moy7I2lyf9d3T6L9pP1acLOqTwrsj+jfv+yUkjMSsTjw8fyW5lQ2Ud00ivq6W0KI6is+Zhevp84i4ZjBB+o7doigIp6OO3qe/rq6OzMxMAKxW/3eWzWZr3n/++ecf1xz9IIL+00ZJSQnjx49n0qRJ3H777e1dHUEQ/uSc+Tbu/6yRD6Rw3Mgk1yt8oLFQHtcZySdjcPoHqCl6GZ2sIB3odmNyOLHabNQF+1vVGy1GFFkipCqORFs+l+as4JPs0ezqHI/W5mXAlv14JA2K5J+MzqcDi7MJn0dDr9xcticnBtQrJyORbrtLsKsGpo8Y2Krey7qnMLisDoPTwxk/7yDI5kSRID8+g7pn1vLOF41cEFNPr16hzE7ph89iZEy2kdgwTau8BEE4eTp60B8fH09ZmX9KYYPBQHR0NJs3b+aiiy4CoLi4GOk4z1FM2fk7rFu3juzsbD766KMjpsnOzub+++8/eZUSBEE4BdTub+T7K5byz4t/YaoUifvAbSYvJIR7Lp+AW6tF6wvsV69RFLrtKOScRVs5Z9E2wioccMgsPOXhQbjxr64b01DTvH1Ht0R2d47FdyDgd+tlYqhCg0II9TTpDXDYTVKVJWwWA0anG6/cOlA3H+gylLavjCCbEwBZhYTiWso0nanVRzMp7Cx6lWYxf8YOHljg4Oz/K2fLwnIaVpWjepVWeR6q1KayvFClyd16bIEgCH9ew4YNY+HChc2fr7zySv7973/zz3/+k2eeeYaXX36Z4cOHH1cZoqVfEARBOCE8Th8f3LWJPcYolp4X12r/zoTINo+TvQqpeyo4GJ4bnf7AuzbG39ovqRBEHQD1anDzcYpGZmdmAkHeJoIcTSQ5K0hpLPfniUp4Yz1arw+vtiW417s8RNTYqA8xE1pRC5FBcHAAoKpisfnn7A+pb5lF6KCuuUXcds15OIx6kCQKwyNIqqjirtVL+HmpStNOG0qIDt2gMLqeHUPQHf3QhJtw/VyI/bPtvGmKxbk5n+iGBv7Tvy93PtiDC1NF25sgnAgdvaX/wQcfZOHChbhcLgwGA0899RTbt29vnq1n2LBhTJky5bjKEEF/B9HU1ITFYmnvagiC8GdRUAlr90CfFEiPw72nBveWSowD4/DGBfFjvorh21xM/16Pq8pN6aAEXurZmUEmFyZvI733+dgXlkF0kwu7XsO161Zx7/JlRNjr2WLKoFYf3lyU5FE5/HYdVOdqDvrTq4sIp4IqovBVWhm1cCO5qdG4LTosThclnaLpXbiHb3oPpCAimqSaSkZuXc/SzL7EVtdSHBWOKstYGu2cuyIHJIWEuhqe/2wJNqOej87qyRdDeoBPhQMN9XazgZCGwNmEdsdF4DAbWh4SkPCZNFy2YxNWl0qdFExtUyjyIhtf7nLz16I0Qh2V3LliE1n15VztWkxCQz0At61ewW2NN7Lvzv4YDDIjUjSkhkpQUYfn3Z9QLEHobh6CHGRoLl9VVX4qgnq3ynmdJEwaFZZuB6cbRvQCg45jsr8MrnsZNuRCeiw8fz2MzW6d7r9z4PlZ4PTATcPhPzeC/hjL+K027IO8SjgnE8KD/pgyBOEU1rNnT3r2bFmXJCwsjEWLFlFXV4dGoyEo6Pj/uxBB/0myevVq5syZQ05ODlVVVeh0OjIzM7n55pvp379/QNrbbruN0tJS3nzzTV599VXWrVtHQ0MD69atA2DTpk28+uqr7Nq1C4vFwogRI5gwYUKrMtetW8cdd9zBk08+iaqqfPzxxxQWFhIREcHll1/OxIkTWx2Tk5PD+++/z8aNG7Hb7cTFxTFmzBgmTpyIVtvy57Jv3z7efvtttmzZQl1dHcHBwSQnJ3P99ddz5plnAuByuZg2bRrff/895eXl6HQ6YmJiGDp0KPfdd9+JvLyCIJxIU76B+z8ARQFJwj5sMKXLVVBhX2w4Nz54DXKlg3f/uxLHgb74/ZctZe2ybcioLA8/g8XdB3LJrhKkA+F8RKORuIYirNRzdmMRe0xd+DFpIE7JRNaeCpTDept6tRqcej1JxdXoamRySSWCarK8Obi93dDvdlPWKQKHxYAKfDH4LLwHvqP2R8fxzjkXEuRw0mDUU241Y/B4uWDtTmLqGvz5o8Hs9mJxe/nbN2soCbayIiOJgXml/jxSY8jaVoDuQHcdPR5yOkeALDEiZy8TftlGvdnIh2f0wxZsJ6zSQZOagMHrQUXDGfllTNyQw2vnZPPYdefx/MJ5ZG2ReP7MCykJCqXUbGZubDc+nH1gNWCrjpms45InX0Z3YEEe+8OfoV32D/SDE2lyq1ww08fKYn/yLtjYPP0ZTFtz/RsSI2DJZEhv/YYlwLq9MPTv4PHPbMT2QrjkBdg1BVJjW9K9uxD+9uEhfxMLQKOBl24+pj+hY6YocO3L8PkK/2ezAWY+BKP6ndhyhNNeR2/pP5LQ0NATlpcI+o+D0+mkrq7umNLOmzeP+vp6Ro8eTUxMDBUVFcyZM4e77rqLt956i759+wakt9vt3H777fTq1Yu77rqLmhp/P9Zt27Zx1113YTabueGGGwgKCuKHH37gySefPGLZM2fOpKamhvHjxxMUFMSCBQuYMmUKMTExjBo1qjndihUreOihh0hKSuK6664jODiYrVu3MnXqVHbv3s0LL7wA+EeY33nnnQBMmDCB2NhY6urq2LFjB9u2bWsO+l944QXmzp3LmDFjuPbaa/H5fBQWFvLLL78c8zUWBOEkq26Eh6b7gzEAVcW0bBVa+uDFyH9HDqVU1XFZzt7mRa60uOnCdmRUFCTWde7J7tTE5oAfYGfnRHYkJTGgsB4J6OrYQ2RhCd8Ej0Wn+HBJUkD/+4ZwM702F2Hw+IihlGT2Ne87t7qK2YkjSdtWij3UwMoh3ZsD/oMUjYbi0BAcWhkkiT67CkmqqDskhYSCysGOPxds3Ue38gqeWTyDteGDefXcvkzJSmfWx7PQoGLAQ/eKSu79YSUPLPy5OZdL1ufQEB2MmVK6sBodHhxYKSGDKI8bIkyokkSjycjVl91OlaWltS7c46XmYMt5k4f+b3yIdMgKnGZ3JXW3fIp++8O8vUVtDvgBrv3+25aAH6CoGh7/FD7/66//fu99ryXgP8jrg7m/wP3jWrY9P6v1sR8uPfFB/3cbWwJ+ALsL7n4H9r7RajyGIPya0yHoLygo4LnnnmPJkiVUVlYye/Zshg0bRlVVFZMnT+amm25qFS/+FiLoPw5Tp05l6tSpx5T28ccfx2QyBWybMGECV1xxBR988EGrX2J9fT0TJkzgrrvuCtj+v//9D0VReO+99+jcuTMAl19+ObfccssRyy4rK+Orr75qngLqoosuYuzYscyYMaM56He5XDzzzDNkZWXx5ptvNrfqT5gwgS5duvDSSy81D2DevHkzNTU1/Otf/2LkyJFHLHfp0qUMHTqUp59++piuUXuoqanBYrFgMPhfodtsNlRVbX6N5na7aWxsJCIiovmY0tJS4uLijvi5rKyMmJiY5lH2ogxRRocqY38dksvDoSRAjwMvRnbGRgFQZzU27zdjQ3OgX4yESlF8FEobN+CCsHgGFG5r/hzuaMIb5UMGDKoXHzIqEj4dRNTUYzgQnMZTEJCPHg9pTQVUGWOZOXwwob7AIDbI7uDMbTuJqG9gX0wUP/TpQUJ1Q6v6HGr0tr1Eakr457nnsjwmky3xUdy3ZgVGPAdqBWfsKWDw3qKA4ww+H8ml/gG/OvzXzYSNRHKYNej65sB1Vo9+uBRjwLEhHh81Oq0/jUqbA4ulPSUAbK0KHPjbs6ygVVp1cx411dW/+jtXthe0PYNHTChwyN9Vo7ONROqJ/9vdkt+6mP3lVBeUENE54cSUQQf7b/A0KEP47XJycjjrrLNQFIVBgwaxd+9evF7/+KbIyEhWrFhBU1MT77333u8uQwT9x+GSSy7hvPPOa3Pf3XffHfD50IDfbrfjdrvRaDRkZWWxbdu2ww8H4Prrrw/4XFNTw5YtWxgxYkRzwA+g0+m45pprePzxx9vMZ9y4cc0BP4DRaKRnz55s2bKleduaNWuorq7m7rvvDpgXFuCMM87gpZdeYs2aNWRnZzfn9fPPPzNkyJCAvA9ltVrZv38/e/fuJT09vc007S08PDzg8+HnotfrA77ogFZfbId/jo2NDfgsyhBldKgygkIh2AwNLQNZVSRc+PPPziumIDKU5T2TuGJZDill9dgIxoMWHd4DDwgeZEVBkQPDy64V+wM+N2lMNBosKJIDWQVZ9aHHi9atIrnBh4QLDTKtZ8SRVR/z+3ZjbXQ4WkWhf70NHSApCtct+omIRv/3WHJFNWnllaxMzyBrb3FAHgcfSzxamfLEYOrtLl7r2zI7xpKUFO5fsRkV/8OMVvUhtzHpjg9Dq20GHJSGtCzmVRwSRmRt4BiBgMciGRRt6wcltb//u/OMBIkPtrUUvjI5g0u3rQ3M76weR/2dy2d2h283BBbSKRIuGQQc8nc1ph98sCQw3bgBJ/5v98zutNI7OSDgP+4y6GD/DZ4GZbSHjt7S//DDDxMaGsrq1auRJIno6OiA/WPGjGHGjBnHVYYI+o9Dp06dGDRo0DGlLSoq4vXXX2f16tU0NjYG7Gtr3tWwsLBWgzaKi/03rOTk5FbpU1NTj1h2QkJCq20hISHU19c3f87N9b8mnjx58hHzObhCXP/+/RkzZgzz5s1jwYIF9OjRg0GDBjFy5MiAejz44IM8+eSTXHXVVSQkJJCdnc1ZZ53FsGHDkOU225oEQWhvFiNMuwdueQNqbRBkwnnZ+Shf1oDNwyOrfyH/zHTWY+C+u87n/rlrGLdxF5+mj+Ki/MWEOu2cvW8VX/Udjdfjw2XQISkKWTnF6MuM+JDRoOCUDKyMGEKMw0Z3aR02NRYnQThpaSDRoKJDoZw4EmhpYfchszU6A5PVzBllVayNDmdNiJWB9TbqNHDT9ePQKArXr97MpZt2klxRzcyhQeSkxtN9fwkSUBtkxhGkx2XQUxgXhlevRaN05u+bc9gbHMR3CbGk1jZg0NZj9Kp4MOBqI7gHMFPTaptdq8fVsmAwNrOe8DpHwENDk+ZAlyYJzGF69v/vbtLu+Bey3YGKRFNUKtbpVwMwMVNiaaHEJzn+xch+Gn8BdnZjnr/Gn9ngrvDs1Uf//U65FfL+BTmF/qeOIRmw4HEw6gPT/fcmyCmCNXv8n8/sDm/+AWvAnNkd/m8CvDgHPF7/A8j7dx/9OEE4zSxfvpwnnniCqKio5njrUJ06dWqOA38vEfSfBHa7nUmTJuFwOLj66qtJT0/HYrEgSRLTpk1rs4+70WhsI6ffR6M5+qIx6oE5se+77z66du3aZpqoqKjmfz/99NNcf/31/Pzzz2zcuJGPP/6Y999/nwcffJArr7wSgHPOOYe5c+eycuVKNmzYwNq1a5kzZw59+/bljTfeQKf7g2aBEATh+FwyGC7oC7uKIT0OU5CJ5JddePbVkdItnHUmHTuqVUyyTNy4HmgTBtG4B25ZPBZ7QSk44ap129iW2InoigaSCqpwaGVyQruxQ5dOaGMDZSHhuI16supz0CgGXAThwNhqFh9JUtlDV+waA1FUYtcY2RieRWlMHEEeF0H1LmIcDmZ3TmBtdBj7wltmOXtkwvmY3R5G5exDlWUWDOvFiv5d0Lu9ZFQV4tZaUCUZFIWYijrC6uw0WQyExbhJr6/n0R9b+rVr8LfS29FwaBu9hI8GEtDhwURLQ8onF13CuxcZ2KnAnF9sUGtj1O6d5OlCKAsKYmhuHtf0hOoHzkOrk+kRKWHVZ8FVH+JbshPFbMZyRkpzo5BWlvhotIbnzlRpcENmpBkmPQJ5Ff7Ze7oFLkJ2RKmxsO1l2F4AYVZIiGg7XZgVVr8Au0tAKwcO8j3R/nkt3D8WSmogq5N/wLAg/EZqx27oR1EUzGbzEfdXVlY2d8H6vUTQfxKsXbuWyspKnnjiCcaPHx+w78033zzmfOLj4wHIy8trtW///v2ttv0WnTp1AvzdkI717UV6ejrp6enccMMNNDY2MnHiRF577TWuuOKK5htVSEgIo0ePZvTo0aiqypQpU5g+fTrLli07YtcoQRBOAWYD9G15cycHGzD0jWn+3D1CAjQQ5t92fhScP9SMqkbxn0d3831aMrf+sB63T49XK7FyQCp9C8sIcso4PMHIPv/bPrPXTgUJlBOKAS8GAvvnuzUaGowmSoJ7Nk+VWR7nf2A4KMjtJrO2nqUprbsYzO7TnT6lVSQ0VFFrtWAzGehTVUieSccPaekYFJWLN+6mZ07Lm4SY8no29kmm2hxEhL3lzayRJt4YMpiisAji6+30LComO78ICYkSS0+Sp/RHU14N5/Zk0sAuzcc9PiwUCAUScS3Jxb2mGN1NXTCcl9r6Ta9Oi+b8LI4U9iYFH5Y+ObrthL9GkiCr89HTAXSN/+35/x5RIf4fQfidOnr3nn79+vHNN9+0GssJ4PV6+fzzzxk8ePBxlSGC/pPgYEu7qgZ2Bl29evUR+/O3JSIigp49e7Js2TLy8/Ob+/V7PB4+/fTT46rjkCFDCA8PZ9q0aYwcOZKQkMAvX6fTic/nw2KxUF9fT1BQUEAXnaCgIBISEigsLMTlcqHT6bDb7QFdlCRJIiMjAyCga5EgCKcPSZJ46IUMbplSzY1JcYzbsI8uJTWMLSqgR8lKdoR1xSeHYK1z4zbIFJkTsNq0gIQbLVoUNPi/K32SRJNWj0YBWVFRJECScJlbt3al1DWwq95KnSmw5brRbGLe0AE8+v3HeGX/kNzJZ49iXrdeANiBDwdlkplbRudq//dSaIOd0Do7OsV3eDFcvHULnZvKaIiLxHv2QGydQgjuH0nnyQPQBOlbpT+cYXgKhuEpv+2iCoJw2vv73//O2LFjufPOO7nqqqsAKC8vZ9GiRTz33HPs2LGD11577bjKEEH/SdCnTx8iIiJ4+eWXKS0tJTo6mt27d/Ptt9+Snp7O3r17jzmvBx54gNtvv51bbrmFyy+/vHnKTp+v9c3ptzCZTDz99NP87W9/Y8KECYwfP56kpCQaGxvJy8tjyZIlvPjii2RnZ/PNN9/w6aefMnz4cBITE9FqtWzYsIFVq1YxcuRIjEYjjY2NjBo1imHDhpGRkUFYWBglJSV89dVXBAcHM2zYsOOqryAIp7b3/hLBmn0e9leEcmZXHQnBElt7lHHm/jXUK2FsZQCRJQ6aTGEY8A8aVpFoQo8WBZdBxmY0oHMr6NxeTE4Zn0ZG4/Wgd7jxHLZIVIjdyUVbdrAremjzAGKNTyFc1eAwGSkJjSSlyj8H/3ddegQcq8gya9ISmoN+gMjqeqzOwFV5cyM70+XyThjHjif0gh5IGjE2SRBOFR29pf/CCy9k2rRp3Hfffbz99tsAXHfddaiqSnBwMNOnTz/u2EkE/SdBUFAQr732Gq+++iozZszA5/PRrVs3XnnlFebMmfObgv5evXrx+uuv89prr/Hhhx9itVqbF+c6+GT4ew0ZMoQPP/yQDz/8kAULFlBbW0twcDCJiYlce+21dOnif13dv39/du3axU8//URVVRUajYb4+Hjuv/9+rrjiCsA/JuHqq69m7dq1rF27FrvdTmRkJMOGDeOmm24KGB8gCMLpaVCajkFpLcF5tzV3sffdXTTuriPMrlL8TQkOk44Gi4HEyoOzhkl40VARakGSVDQ+N/vTo1H00L90IwmuEnpt07MwaxjlIf7uPJLipm/hHobv3865+duY3i8bh2rk7PUl5PXshMeiZ17vM7hpxTcEuRyEOhxUWgMnSghyupr/rUjQZ3Qk8yrHkbwuB53PS/mAbpz/8bmYgsRtUxCEP8b111/PpZdeyg8//MDevXtRFIW0tDQuuOCCE7Iir6Qe3udEEARBEE4Cr81DxWVvs6LAyyprP87fnIsEFHYKJzfD35c8tNaG3uvi4l3zSa4vbDkWLd93GsEPvQdSHhZGVGM9N6/4nvTKMsA/3ed6KZO9aXFs75MMgNbnJbWyhAVJndgW2dLwIPkU3p71PeGNLoydLCT9byghozqj+FTytzai0UokZVrbnGlNEIRTw5MXtp4U5ekFA9qhJsfu//7v/7jqqqvo1avXSSlPvJsUBEEQ2oXWqiP+o+u4LKiUz4Z3Z/TjV/LCxHPI7ZZwYCpLidrwIHw6lWh7ZeCxeOlRUMKt85Zxx+J5uLRapp49Gt+BwNy/iq6bTrlVxBb7p9T0arTsiYxmeXws5UYdjVoNtToNhUFGYgcFk227laycqwkZ5R8vJWskUvoE0ykrSAT8gnCKU5Fa/Zzqnn/++YCxndXV1Wg0GhYvXvyHlCeCfkEQBKH9RIUgr3meGWc5SLE1kV7bMluOV5ZpMhjIj0viiTGPsDwtcGYx/y1dplNRPX9d8jl1ZiulIf6FhlxosWNEVlV6rs/l53Ar5Uo116z+GJtOi0sjU2vQ0qjXkllWQWKEuB0KgtD+/sgOOKJzoiAIgtDuzh6bxNqhXj65N5dyp4wKuHQ6f4s/4NbqmdlnLJmlu4iw1+HCSCNhAHjQk161n07VZYQ3NVJpCWLq0PNQ7Xq0Xh+zB6WzIyKIzVHhbDWHcv3Gn5nVoz9NBgNdqqr534KFWGaPa8ezFwTheHX0gbwngwj6BUEQhFNCZLiWq/+Vyet3bMOp0ba6iauSzJbYTHrt308FnTn4stpKAwBhjU3MyzyDfZGRFEZEUJZsZmdkEJUmPcgy/zbl8V+DmY+6DeCiHbsYkVtAiNdD7/fOxzDkGBe3EgRB6KBE0C8IgiCcMqKTjPz1rR58c9s61mjCUbSBy1Rti8jE5oglpbQSUAmhlmhKybPGEVbjpNFiIrqxidAaG/EaHQNUaAg1cduLPejbpztnr6vmpi8dzMnIYOvQ7rx+mZmo7kefX18QhFNbR23pz8vLY8OGDUDLGkZ79uwhNDS0zfT9+vX73WWJ2XsEQRCEU9LP31cz490KDt6k+u3ax6VbF4GsolW8yHjRoFCjDWJx7EAUSYM9PYJBD2cS6XNTOCMXfbiBtNszCO4eGpC3zaVi0SMG6ArCaeKxsRtbbfvn/L7tUJNjJ8tyq+8gVVXb/F46uP141mUSLf2CIAjCKWnoBRGkdjXx2Z0bSNlRQI/CEhwYCVZsgISKDi+QZ01EkTSAylWfDiQo1gpA/JikI+ZtNYhgXxCE9vXBBx+c1PJE0C8IgiCcsmJTzNz28QC2fR1HSWkTk8s0PPDlLHpUlQNQYgpnvyUeVVZxj7RjjDC0c40FQWgPagd8jp84ceJJLU8E/YIgCMIpzRJpYNCkNAD6OlX+fkE6BQtzCbZqmXRFHGc7Gpi/bj6YRG9VQRCEIxFBvyAIgtBhhBgl3rjIABd1a97m8ehhuwj4BeHPTBHjc45KBP2CIAiCIAhCh9ZRZ+85mcQShIIgCIIgCIJwmhMt/YIgCIIgCEKHJlr6j0609AuCIAiCIAjCaU609AuCIAiCIAgdmhjIe3Qi6BcEQRAEQRA6tI44T//JJrr3CIIgCB2Cr6IJ+/TNOH/Yh6q0TNFZvrkOZYcZtUHTjrUTBEE4tYmWfkEQBOGUpKoqW7+rYM/qGtz5DfSctYJqo5UqaxBhURsZPPtCNty4DM/PJaTJMtVBVjY4tjPoH33au+qCIJxkKqKp/2hE0C8IgiCckpa+nce6j/LQub2YbG7mdeuNTiNhdHko9UDeqMXEFlSzPzkOSVGIqm0kf8oOwuJMdL01o72rLwiCcEoRQb8gCIJwylF8Kus/LyCkzk76rgq0XgWA8phgNvZNJraihlqDkb1De6LIMpnFuSSpVVQajKz993YR9AvCn4wYyHt0ok+/IAiCcMpRVRXJ7SMprwaj100M1XSmlD7l+0krKKM+2EJDaDCKRkP2vh240bI5MR2LDL0qcqifvZPGjdW4Su0B+SrVTXjXF6G6ve10ZoIg/BFUSWr1IwQSLf2CIAjCKUdV/Ddxc5OLWKox4A/Stbg4L2czc4IGUhIUjdXeRG5EQvNxGzplkJ2volzyLXWEggSx16Uij0um/ovtVP5SxNaYGBJUF5c8n43l3LR2OkNBEISTSwT9giAIwimh3qXy0FIfq0tVhuWW0M3twmuWMNgDW+W1KFy4YT3vn3c++jZa7PdEJ5JWU0KDXo/Op1D06X5qvithaVYKP4wb2Jzuy/fK+eJMHwa9mPVHEDo60bJ/dCLoFwRBENrdjmqVQdO9NPokHluwiHuWr8Do9VJsCkeF1vNySBJmjxuvrEHn8wXs8qkSbo2exLoGJFXFKelIqGpgbef4gHTbw2P44etSxl2Z+IeemyAIwqlA9OkXBEEQ2t3Fn7jos7OY/331HX9bvBSj19+Cn+CowWkIbJ+yGY0YPB767dvLFevnovO6QT0wb7+qYq1qwqWXKQwPpg4zLlVPo2TioZmrCbE5A/L6+f3c5n97vSqCIHRMitT6RwgkWvoFQRCEdlXe6KPnmv2cua+EAfn5rfYHu20s6d6P6Lo6IhsbiLDZAOibm4tTF4re6UYveQhz1ZJdsokVoYOoCTYTVeZgZ1w4L4/qx76YUFIr6hiyv5zVWZ0BkBWFfusL+P7/tvKZPZy6RoXMLgb+ensE4WGiy48gdCSie8/RiaBfEARBaBeKR2HHu3u4Zk8oVxVUUG+1UB4aRkp1KR/16YfTbaJLWS3pjRWck/cTdeYQgmwaDu3sY/R4iWlsoCQ0nCpTJPmWRFTZv19V4B+XDaXOYgRgf3QoFcFmoj0+omxNjNq0B6vbg+Xfq7BfeS6K0cDW3U4ef7GcN56Lb6vKgiAIHZYI+gVBEISTTvX4yO81jSkJ6VT0jKAuLBQkifWpGTw/NJvLl+1lQFEViiSxNTGNVeGZRNrrGF69o1VektrSLafKEsmBiX7YnBzZHPAfZDPqeefrLzk/fw95lnDyLDGg0fDA/B+ItDWiIlFrtrDG2Y9Gm0rlz+VYogx0vyWD9GtTkQ5pTVRzq1DLGpCyOyPpxJsBQWhPiliR96hE0N9BlZSUMH78eCZNmsTtt99+1PRTp07lnXfeYe7cucTHH70FKzs7m7Fjx/LUU0/96jZBEIQjqmuCD5dAcQ1YDWBz0dCjCztdkdjn7KbGBu/3zGJ4VR0cCKYLgq00SWYyi6oBKE8MoTYmCIDSkCiKQ8tIqKttLsKh01EWEtr8OajJg0OnR5FlNDoFSVVbvfYviI7j8/BYuucX0aOiHAPegHAhxlbPxk/30GA0YXT68JQ72fLAWtY8uxX3HdnEp5o566v5aD9eBSoQbkb73V/QDEj+Ay6iIAjCiSGC/hMkOzv7mNMea+DdEUydOpWMjAzOOeec9q6KIAjtqKRRRZL8MbCiqmzZ3UT38Y+RUF7Kjsh4Xj3jQrbHpHLVa/vRKvWABmdsEtF2JwZFac7HpZGxuDzNnxvCzQHlrMzoTlhNJdH19ZQHWfGYgvHJ/lZ2j0aD5IWelWXkhoehyBJn5hXzU0rL7DxdGppY36MHRrebiKZG4hvqUL3qgaD/YOgvYdE1sTUygbh6O/tT4vBoZGIra9F+sJ2GUJXhy1e1VKrGjmfkq8g7n0SKDTmh11UQhGMj+vQfnQj6T5DJkycHfN64cSNff/01l1xyCX379g3YFxYWdjKrBsAtt9zCjTfeiF6v/915rFy5Eo0m8BX2O++8w9ixY0XQLwink8IqePoLqG7E1jWRr5J7Y+vThUt76ok3q6jfrKd8zjZ2+6zUZSTwAqn8rIaDqpJSU4PJ5yEnJhbLjU8jS9Bo8AfusqKg66nluvVb6VK3HwmVi/Ya+L5LF7rb/CvndrLZ+TGjMzUWI+FNTjReBd8hXWe8Wi0f9+3DooRYACIdTsYUltLZ5abOYubCtVtQkOhRVoVTp8XsctOnuJL8sGCS6m1UpCQBcP2y5XQrKWnO14eEEz16fBQHhVBFGLGNNtb17Up6bikJJVXIqkqTyUB5qJllaZkMyduJ3ucFVOT6JtzxD+Me3Qf13RvZsMWFVlbJrtmPvqga6fxMpKyWRcQEQTixxGw9RyeC/hNk9OjRAZ99Ph9ff/01vXr1arXveDidTrTa3/5r02q1v+u4QxkMhuM6XhCEk2f2boV3NitoZLirr8yo1LZnaN5YrvKPFT7WV0CQDob7Knj17vsx+Pwd46tD9vLMpGFM/Oor9ubmoK8uwSZb6VxXR8yBPFaMuoyfzxkNSORGRvDwwsX0Dyvkoz7Z/qZ/WQIJFFlDSaiWK/bMJMzpn4GnX/kGlnT6OzuNerrbHJh8Cvcs38KXQzIYtKeUuCob1Ymhzd1/HLLMmqgIwD+It06vo9FooFGnw+xwoRwyE7XR46VrWRV2g56e5dVUhIdQCQQ5HAEBP4AGlRqrBbPDQ4k1FJ8ko0gaBq3fhcXuQgWQwOJwITdaefHcS4lorOP2n2ZzVvE+ABRVxvDNJuoSniLaFIZZbkDbWIECKMA3Iy5kc0p3wmsqOVPNp9/dfWBEr2P7hbo98NI8WLAR0mLh0UvA5YEXZkNxNVw0EO65EDRibIEgCG0TQf9J9Gv98Nvqc//UU08xf/58Fi5cyKuvvsrKlSupra1lzpw5Acd+9913TJs2jYKCAsLCwhg/fjy33HJLQJB/pD79+/bt4+WXX2bjxo3o9XqGDh3Kgw8+2Gb9D+3Tf/BcAObPn8/8+fOb061atYoLL7yQTp068f7777fKZ/r06bz66qu8/fbb9OvX7zdeRUEQjuarnQqXz2lZsGr+Xh/fXwEjUwID//11Kmd+5uPggrdlwB6i0Y+9nilzPgCgc301373/HF1qygGwaw1EeANXwX3ix7m8OXQ4NoMJJIlXzhmGqpNBlsGnNgfsAI+smNsc8AOEuO08t2oueaEDmrdJiso9izdicriJoJ6K2lDKI0MI8jShyC5GW3T0K63jjD2FWO1OvGioM5qpirC0uhZmjxcFiLXVYcHJFtLwyjI+SUKjBs7LX2sykRcaQXxtPaXBoYTXNwXsVw+sEhZXXQ9AdVAodSERSMV7AZDxoQAmxYmlSUGHHgUZ+cCjyPCflrIorDvl2lh2EMtNt3zBwI+0cFaPNn+PAe58G97/0f/vZdthzlrw+KDB/4aEJdugqBpenHj0vAThNKSI7j1HJYL+DuDuu+8mIiKCW265BYfDgdlsxm73f9EvX76c4uJiLr/8ciIiIli+fDnvvPMOZWVlPPnkk7+ab3FxMZMmTcLtdnPFFVcQExPDTz/9xF/+8pej1iksLIzJkyfzxBNP0LdvXy655JLmfTqdjrFjx/Lxxx+Tl5dHcnJywLFz586lU6dOIuAXhD/Im5uUgM8qMHWT0iron75daQ74D/XuoBH8b950dIr/weFgwA+wMS6VoYUFAektHjcxDfXYov6/vfsOj6JaHzj+ne1JdtMrBEIIJaH3TihKUemgWBDQK4iogOWqeL1X9Ge52EC9YgcERVFEQESkVynSkQ4pBEggvbfdnd8fIQvLBkKTFN/P8+wDe+bMzJlJsvvOmfeccQOg0Kgv6d23Owf8AOEZyS77a5x6lljvCxn1qkYhJcBMvZNnKcKId3Y+HgV5LGsaijvutMy3onqb2dQ2igaxZ6h38ixeBfm4ny5yeXpvrlGPosmjWfp+1AyFou3wW8t27Kxbl3YnTlyopzOQ4m5GRUOipyeaS57T5VucRt28EyjY2RnYxFGe6Onr+L8CKNixogegGAOFmHCj5PPaXJSPW3EhuefTnTbUbUe7T1eUH/TnFsDc9c5lqdmu9T75Dd4a6XLOhRAC5Im8VUJERAQffvghw4cPZ/To0Xh7ezuWHTt2jE8++YTHH3+ce++9l48++oju3bvz888/s3///itud8aMGWRlZTF9+nQmTJjA8OHD+fDDDwkJCSm3TW5ubo60pZo1a3LnnXc6XoDjIuDSuxJ79uwhLi6OgQMHXssp+MukpaVRWFjoeJ+Tk0N29oUv06KiIlJTU53WSUxMvOL7pKQk1It6EGUfso9bvQ+1jAfLqmVsMysnx7Viaf3LxI1JZk+KLkkhOeoXxAm/wMtuCw2gLXl92KmPy+JYv7rkuZlcynVcuHh5ckgP6p8pxGZ2Hth7LCyEAoMO7fkJ+85ZLICKgh0VlcRAD8Yc/Ynwgjjq5scy8tCPDF+3mgdGDSTW3Z9z7hZivfzYGRKGev4rsfiSVEjfohR6pq4mIj+GuvlxDI3/hbYJBwBom3DUqa6Khhw8yzwN8T7BjoAfzp/ji35YV/yZl/VDvYSqqlX+d1f2UT32URFURXF5CWcS9FcBI0aMuOyy9u3bExkZ6XivKAojR44EYO3atZddz263s3HjRho1auQ089DF69+IsLAwWrVqxbJly7BelAqwePFitFot/fr1u+F93Ay+vr5OYxXMZjMWi8Xx3mAw4Ofn57TOpRdFl74PDg52mstb9iH7uNX7eKyl80e7AoxtrnHZ5uPtPXErIwW8/8Ed6G028nV63unaj18atHAs631iL+Pvup9TniUTEuwNCuWee8eVpPJcSqHkW0ajlPQ+Kwo/N2nLty16Y9VosSoadtRqwbp6nbFeFGgrdhV9dhGJXiXpOvtD/NEVawgsKHTpxVY1CrluJscc3UlB3qBV0WPDiBWvvEJKLnlKaLDTOvUAXQ6fIl3nyWHvEGJ8AinW6hxNVjWK06DA+nnH0F50AaJB5e59q3l4+1Janb5wt8AOpBKM/fxNdB1F6CkCIFvnwZz2dzm1PTpmO4zt7Xh/2Z+5hwlGRDufW18zWNycT/ejvfHz93cqq2q/u7KP6rGPimBXXF/CmaT3VAFhYWGXXXZp6gxA3bp1gZL0nctJS0sjLy+vzG2Xrn+jhgwZwksvvcSmTZvo3r07ubm5rFq1iq5du7p8gAghbp67IzUsUCgZyKvA46009CljIG+Et8LG+7S8tMnG7nNg1kPbYPjTrxPeEc3QF1tJtXhiLipk6tZF9Iw7SK6iY2Tibl7uP4QYd1+8srOomXoO/+x80t0sdDxxnO87tSTF4nU+QL+kh1pR+LJlH/6s2QZFhWJdSSrMH6Fe+GS6oWogODGTF0b3IepsGjO+X0G+XofJasMzOw+tzYbtojsNOqsNS04eVnQcrulHkzOJGGwXxjPUTM0iFx/MXJjbX9XYCMnOwK6AR46VAjcVq06DXQFVW3KeivQadDYVrc2OQS1yOXfNzxyl5Zmj2NGjYqBIoyNNF4gWDXqjgs7PgOpuIemgseSug1VLqK0ALFZ80lLoosbT5rP+0K3x1f1QPx0H9UNg+R6ICILJQ6GgCKb+VJLLP7AdTLyr3M0IIf6+JOi/hZQr3GqyXfQldSmTyfW2d1XQs2dPvLy8WLx4Md27d2flypXk5+czaNCgim6aENXe0IYahjYs/2Zu62CFX4e5fhXEZ3rx0xE7PiYYFmnGw/Cg0/Ju5/99c0Mx/7feimdGBqMPbMZizaNI05oaGak89vtq3ut+J+nuZqd168edRatXKTS5Ywf2BXrzp4cHOos3KtC1SGXS2j3M6NqMgWOG0G/fMY75e5Nt1NPkcAIHG4RSrNehtdqwpKZitmfhSS4BKaewFbrOk1+Ih1PQ71Go4YktWzlpDEBfqOKWb0PFRo67Dlvp9YSiYNUpNMk6jn9hvss2NY5/i4FiihVv/Ofdh2FQM5TzFw6qXSX/1xMUH0jB1L02Y9rdwPNZDHp4cVjJ62LfPHX92xSiGlHlibzlkqD/FvL0LMnzzMrKcll2pV75K4mLi3Mpi4mJAUpy7S/Hx8cHd3d34uPjL7v+jTIYDNx1113Mnz+f5ORkFi9eTGBgIB07drwp2xdC/HXCvBQmtSt/+sfJ0XrGt9ORlBNEA7+hKGk5PHY2j2NbT6P5Zh/5Bh1v3D7EUb9mXhavnlpCXJ43sWZ/PuzSiQSTO7rz2TPuRUXsDfFnZ3gQkzbvJjgzl46xh3ht2zySzF6cpB6RB+JJ87GwoZY/7x3e5Ni2pRDOaMwodud2z2vdh1wvDf32bSAgRSXN6EeiVyD76oQTfCYNn8xsjoUEsqdeKDVSM6hzLhWD1UrXU3/SMPskKkasWLBqrSRZvDniX5eeJ7ajV0seIqaiYB/eE+PQFk77VTQK7nfVg7vqXedPQQghbh4J+m8hDw8P/Pz8+OOPP1BV1dHzf+rUKdatW3dd29y2bRuHDx925PWrqsqcOXMArvjALK1WS5cuXVixYgU7duxw5PVfvP7VcHd3JzMz87LLBw8ezLx58/jggw/Yv38/Dz/8sMsDvoQQVZuXScHLdL6Xzc+Cr5+F9o2CSLynJeY/VR5IsaPTKrQOUhjV2AfP/7xBxuKTNB3xDZ/NX0Dfh8ahsdt5YcMqBh/Yj1a1s7tGbbZEtiIo8wzNbX+gACEZZ2nJUZY0voNjtWozbP9el7b429NI0IfgXmxFBXbUD2dDk8agKETvjcOLNMz5RbQ6EUOt5BQ+79MLFIV6CTFkWtxI8zJzPDSQu7dvoXFWnGO7dtxICwilxjPtqXE0mbT8NhhPx5fM1tOxGT6fD78l51oIUTaZsrN8EvTfYvfccw8ff/wxEyZMoFu3bqSkpPDjjz8SERHBwYMHr3l79evXZ9y4cdx99934+/uzfv16tm/fzp133kmzZld+6Mv48eP5/fffmTRpEsOHDycwMJCNGzeSnp5+xfUu1qRJE7Zv387s2bMdg4P69LkwO0d4eDgtWrTg119/RVEUx9z+QojqL8SsMLlD6WheZ/UH1mbvx/dy9NMDeBQVceeRAwzfv8exvM3peDCYCEs74nLTvnXCbnbWakFCYCCcPuC0zKqFF3t3oeWZZFJqBpPq5wuKQs3UNGqlpjnVDcjKIjQllVMB/rQ8dYqBe3ZyxtsbvV3B69Xb0Zz2wP7JBtRiO5qRHagx/W7HuoF2laI/zqB46DE0ucLMRUKIW0KC/vJJ0H+LjRo1ipycHJYtW8bOnTsJDw/n3//+N4cOHbquoD86OpqwsDBmz55NfHw8vr6+PPLIIzzyyCPlrhsaGsoXX3zBtGnTmD9/vuPhXK+++iq9e/cud32AF154galTpzJr1ixyc0seZHNx0A8lvf179uyhTZs2hIaGXvMxCiGqp+Yjwmk+IhzTrkKK7451WR6WnES+hxEuuZloV0ouIs56+XHSLYDa+SVz/1sVhfF9B7Assj4bwmszdeVa7lp2nDyDga31y06xURUFnc1KvXNn8CgsxNdXh/fux9EEWYCG8Er/MtdTNArG9pdPoRRCiMpGUdWrmPxXiBuwcuVKJk+ezGuvvUbfvn0rujlCiEro7OiFGL/a6lR2LKgmv9ePJCwjAXNRLk1P7+P7Fu1Z3LgrHsUGev9xlMDkTCzFuXjYCnjxtq5sCq8NwNu/reK+P507Uk55+1Ij48IVRKKPD9/cHs3DE2sSlXEWdBr0t0Wg6CQFUYiq5v5Rrh0H874Kr4CWVF7S0y/+cj/88APe3t707NmzopsihKikAt64jayVB+FMyUQH+Xo9axq1IsvdzDmvkvSZ3xpG83H7SHKNJdN8pqvw5K9/kK33IFvvQYbpwoOvep9wnZDAszCdYwHeWHUGTvl6kmR2Z+oH4ZiCPQCfv/4ghRCiAsnDucRfIi0tjeXLl/Pf//6XXbt2MWLECAwGQ0U3SwhRSWlqeOF19Dl45U5iPEI5UVybAq3RqY7BDs2TLow52tSoNinnH1Cls9qonXphZrSzZudpQgEWNmvDZ9Fd2FKvFisjI9ENb30+4BdCVHV2FJeXcCY9/eIvERMTw0svvYTFYmHo0KFXfKqwEEIAKB4GvP/TnZb/7sbe35Ipfj/BpY7JeuGZJqqikGU2EZaZjk96IS+s3klgbgGbwkNYHl6PyORUNOcfDpbjZmJ5oygy3N05EFyTELPKI48E3LJjE0L8tVQZyFsuyekXQghR6RSuj+OzicdJCr3w9G478FWrCBI9S9J4/PIKuX9fPArQc+M+apzLcNS1aTXU/+k29LtOofFzw31EM2LydazcXYiXh8JdbU14ucvNbiGqi+GjXZ87NH92WAW0pPKSnn4hhBCVTt7svbT58zRbtZGkBXiSZ9Cyo6a3I+BHVel9KAG3vEIMxTZytCaK0aBBJd3Hg3azO+HXvy70j3Bss5431AuRrz0hqiO7dPSXSz79hBBCVDqKQUuILZ1GB04SU9+P/wzpRorF/aIKCsf9LTzy3RZHUQF67IrCyTAzd9wu02kKIcTF5N6mEEKISsdjXGsMRmhSeJKmJ09RIz3bpU7bTl7kujsP9k3x92BIm2y4pFwIUb3ZFcXlJZxJT78QQohKR98yBP9ND5H70R80yi3m0+hi+iSoZNlKvsjrW1ReHeTBhuQuHPnoIOacAs4GedGwh5GAtzpUcOuFELeazNZTPhnIK4QQokrIKFD5+YSK2QB31VUwaEu+5GOO5vDdN2vw8Mpm/JP3oNfrK7ilQohbbfDDrrN9/TSzVgW0pPKSnn4hhBBVgrdJ4cHGrr15tcKN+NdMqoAWCSEqC5t09JdLcvqFEEIIIYSo5qSnXwghhBBCVGkycLd8EvQLIYQQQogqTebpL5+k9wghhBBCCFHNSU+/EEIIIYSo0mTKzvJJT78QQgghhBDVnPT0CyGEqB7sKvue386Rleew2hXqDalNu5eaozNqK7plQoi/mE0G8pZLgn4hhBBVn6oS/r6NvUUX5us/PC8WnbuOdpObVWDDhBC3ggzkLZ+k9wghhKjyfA9bMSXa6XrmIL1P7qFpajwau53jC+IqumlCCFEpSE+/EEKIKqkop5ikfRm4Bxvwji0ipDiHozVCOe3lj12rITgtleycim6lEOJWsMlA3nJJ0C+EEKLKsGcVkvnfzWzelMM+mxmvnHxUwO4RxZ6meiyZOZBvR1Ft5JiMZBjdSdh0llpdgiq66UIIUaEk6BdCCFFl7Oj3Mx/5R3IqKhIAS14+nY7Hoer1eJ1Lx1Bgw1Rox6bTkOXpQVh2EkvGbSd8XCPufCKiglsvhPir2KSjv1yS0y+EEKLSs9tVtnxwmIVKMKcCfB3l2e5unPXxQmO14Z5XjH9KPubsQrzS86lxMpNzbv7UzMnB+Mo6fui5gp2/niU/MY+c9GIK820VeERCiJvJriguL+FMevqFEEJUStlFKu46lR178/lhejxNNx2kuE5NrDh/eSVbPAjIzcM9p9hpfY0KuhyokZqOT04entsL+eW/bnwUHIAdCC4s4LYBfvR6uNatPCwhhKgQEvQLIYSoVI6mqYz61cbWRDAXFNHnwEna9FM8FgAAVDFJREFUnzjF3C7N2FormFy0ND+Twn37j2DTaUmz6GmQchxVcXPZlmJX+aNhBDVT0vilfSuK9SVfeypwyN2d/CXphDY0E9XZ5xYfpRDiZpJ5+ssnQb8QQohKZfhSG8dOFvH6wg302neCDA8TX/RsxeqmdQGIPhjLJ18tQWdXAbCi4bhXEIlBnoScyXBsRwXOBXmxp0EYGlV1ymdVAHebjdOeFg5tTZOgXwhR7UnQL4QQokJkJRWw+r2jxOzNxj3ASNvu3mTbFfZkhTFlyWbu2n0MgMCsPF5ctIkTwX40Tkzi2V82OwJ+AB12AvKzOOYWRHxEIP5nM7ErCvnuRuxWBWNhMcUG1687japi02o5uvI0Of+ojdlbf8uOXQhxc1krugFVgKKqqlp+NSGEEOL6qHY79jVHyUktYEV4A0yp+bRPS2PZgnMkZtqom5yIJSWbZJM3HgWFFOsVGp9KRlHBjgLn59/eHBlKn8PHKNTo0NvtTvtIslhY0q09eSYjpoIiotcdxJxTCECxTsuK25qQ5u/ptE62Toe5sBCv3GyUuj6MeSGMJsHaW3JOhBA3V8vHz7mU7f4osAJaUnlJT/9VOnPmDAMGDGDMmDE8+uijFd2cm2LKlCksXbqUHTt2VHRThBDVjPVEGtbDKeSajNjv+YJ41cijfYZx376f6BEfx0mNlbpGC3dlJOFmLRmAm2jwpbDI4njEjoFCNNjIxQMVDfUTzwKQ7WbENzffaX9Jvp7kuJfk9NeKT3UE/AB6q43Wu+MwBWSzqlFr8vRG7Njpt/cPtFYth/xCSTyVyY6+/+NYjQgiY1MpNpoI+GdbQh6o+9efLCGEuAX+kqA/JyeH7777jrVr15KQkIDNZqNGjRp06dKFESNG4Ofn91fsVgghxK2WVwi7YiAiCEJKptKM6/k9GWuTcCOfYM6iQcWCmc++W42JIjS4A+CdXYjxopvypiI7RQCoBJFIsUnBqtERmJfEQVNdit0UyAQFlVM+XoRkZGHTaIgP8OV4aIhjO+Yc5wsCAL/0bKLP7eCOA86dHFuDGmJKtuFT048GcTaa7l2BniJQVPJHbiY562ECHmtRUrmgCHacgDoBEOpfUnY8EVKyoG090MpdAiEqilXG8Zbrpgf98fHxPPnkkyQmJtKjRw8GDhyITqdj//79fPvttyxZsoRp06bRrFmzm73rv1RISAibN29GKx/qQojK4shp8DBeCEBvgrRFcaTMPIDRnovfC50xd6lZsuDwKfB0hyIr2OwQEYz65Wps4z5FZ7WiAtZ/3MHRze7kH84EdARzBnfySKAWeXhgpNhpMG0xRnKw4EkWABpKUnbclFw21W1CglfJU3S987PoeXwnh92bEaYrxC83jyw3NzZG1aNYq8Ujp4jEgAvn4FyQN+GxyU7HFVycSq7WiIftwh0AFUjXWTgeFkzt+HPE1vTC5OZD86RToIJetZIz8TPOfRKOPTmNgKQYFFULKOQ3qUtOUhHnVD2z2nfA07qXewrOEmzSkOPlRc1RzdDW8kQ1GLB99jv2PQkotX1QMwtQktLQPNYd7chO2HfGQWouSs9IFF3Z3y9qVj5qXCpKZDBKGWMThBDiatzUnP6CggLuv/9+zpw5wzvvvEOXLl2clh88eJDx48ej1+v57rvvrtjjr6oq+fn5uLu736zmifNyc3Px8PCQ9B4hqqrENBjwZkmvs6LA/V1h9pNwmaDxap2bfYT0h+YTwZ/osGJFh+35uzH+thX2xDrVPdOzDfoth/HPz6EIE3pKgund9EBFg5uSg5taQC5mrOhRUNBfEvQDuJGHPykAWNFyilBSAw3sC3V+em5E2ikCzuUQ5xGIqbiYeH8zZ7wseOUU0T3mGEebB/N73ZYllVWVTtsOUvNkBioaPMnBQCEJnt4EGZNpnHyGIo2O/d512FWrAQci66DPzeO3xsEcmfpvUvWeHPUIQ6vaiMg7zdKGraidl0LPmF2Awraa9Wh++sJFhU1RMKl5fNe6J6e9fekcuwuTtZDwlJP45WdiUz2w4YGCFR05aLCjAjajF/bC82MW3PRol09AG13f6bhtH6/H+uxCyCuCQAv6bx5Cc3vUDf2chaiOop5Idik79L+ACmhJ5XVTuwwWLVrEyZMnefDBB10CfoBGjRrx+OOPM3XqVObOncukSZMA2LFjB+PGjePll18mPz+fH374gVOnTjF69GgeffRRCgoKmDFjBr/99hs5OTnUr1+f8ePHs2zZMpeg9c8//2TBggXs27ePs2fPotVqqVevHg8++CA9evRwak9p0Ltu3To+/PBD1qxZQ25uLpGRkTz99NM0adLEUfdKOf2rV69m/vz5HD16lOLiYoKCgujYsSOTJk1Cr7+22SDK25bdbmfWrFls3bqVkydPkpmZiZ+fH126dOGxxx7D29u7zDaHh4czZ84cYmNj6dWrF1OmTHHUS09PZ9q0aWzevJnCwkKaNm3KxIkTiYyMdGqb1Wrl66+/5pdffuH06dO4ubnRsmVLxo0bR7169crcb6NGjfj88885fvw4FouFO++8k8cffxydTnqrhLhu/5xTEvADqCp8swGiG8HY3je02XPTdhHFfrSUPKlWhxXtW9+V7OMSU7WRvJZ/nH10pQg3dBRRm8NYSCVLCaBQNVOIGVDRULK+igI4byvDXY9/XklpFp4UoyHV3ctlfylGb1RFjynfDmgJS8qnZkoBOe56Yr2DiN63i7qppzgaUIeQrGSiE3fyW2grOp86SQFGXo++k7m9mpFv0hOamsn45dvxLrTybXQzdoT6Y9NocCsq4qd6Hciy+aJqFFBVTphDqZMaT6ZbAKCQbTShYnBqm1ZV+SM0imSLJ/9c8zlateSOhU3RoKgqWnKwY0RHnuNuhooWe+FFl0D5xdiGfoLm3Dso5+cbV2NTsD4xH0pnKjqXTfGDszGcfANFL3edhbhYsaT3lOumRl5r1qwBYMiQIZet079/f959913WrFnjCPpLffvtt2RmZjJo0CD8/PwICiq5tfv888+zefNmunfvTrt27Thz5gz//Oc/qVGjhsv2161bR1xcHLfffjshISFkZmaydOlS/vnPf/Laa6/Rt29fl3WeeOIJfHx8eOSRR8jMzOSbb75h4sSJLFmyBA8Pjyse80cffcSsWbOoW7cu999/P/7+/pw6dYo1a9Ywbty4awr6r2ZbxcXFzJ07l549e9KtWzdMJhMHDx5k8eLF7Nmzh6+//tpln+vXr2f+/PkMHTqUoUOHuhzTk08+iaenJ2PGjCE1NZXvv/+esWPHMnPmTKdg/t///jcrV66kffv2DB06lNTUVH744QceeughPv/8c5eLhM2bN7NgwQKGDh3KgAEDWL9+PXPnzsVisfDwww9f9XkRQlxi40HXsg0HbzjoN2anOQL+Usplbgb/UrcZD3GSYkwAWDEQSxM0+nw0xZfWtgMarGhRsDp6+ws0WgbcO5SHd+xj2L4jhHGI2uzBmteS077OKUte6QXYlQuBbpabgWkD27MrIhhTkZV7toUza9lHdIrdC8Diuk0YMvA+Bhw6QbvEVD7r39qx7ik/Lz7t1ZYOp1LYVvvC7B75BgNT7riTp5ZtO3/wCoVGHQXZFgrUkkB/X0go+kLXc3IwJIxeRzY6An7A8f+SvvwiNBeNX1DL+vpNyYVz2RBUMsuQfUvMhYC/VFIW6rFzKI1CXNcXQogruKlB/4kTJ/Dw8KBWrcs/0txkMlGnTh2OHz9OXl6eU/pOUlISCxYswNfX11G2adMmNm/ezKBBg3jppZcc5W3atHG5aAD4xz/+wRNPPOFUdu+993L//ffz5Zdflhn0R0ZG8sILLzje161blxdeeIHly5czdOjQyx7Ln3/+yaxZs2jTpg3vv/8+RqPRsezJJ5+87Ho3si2DwcDy5csxmUxO6zdr1ozXXnuNdevW0atXL6dlJ06c4LvvviM8PLzMfYeEhPDWW285epd69uzJyJEjef/99/nwww8B2Lp1KytXrqRXr1688cYbjrq9evXiwQcf5J133uGLL75w2m5MTAzff/+94+Js6NChDB8+nPnz51eaoD8tLQ0PDw/H+c7JyUFVVSwWCwBFRUVkZ2c7paIlJiYSEhJy2fdJSUkEBQU5zpHsQ/Zx0/fRrA6cTOFihQ1DKMrOvqF9KEPrY39njaNnHkp64MvqQOsSH+8I+C/U1VCgmC4pVTBSQBFGVLQUo0NBRUFFa4eBB07QOi6NUI7iSRoALVL2k+BVk9OWkvEEHoUFeOXkkK0xO7b6eZ+W7KxX0vZ8o56volsTXjiMkTs2Mbdhc/7brhd3HInjvr0n+KWVc8oMQHygNxFn01zKD9e8JB1AUcjTuZNhsaACDZKT+L+eg3nr5yWOKjZFoUbWOYzWojLOVOm50WOnGM35iyrlkosrANVDD34XOmXSgo14XlrJ04QSVvIdWSV/d2Uff4t9VIRieSJvuS5Nr7whOTk5mM3mcuuV9jTn5OQ4ld91111OAT/Axo0bAXjggQecyrt06VJmEOvmduEx7AUFBWRkZFBQUEDbtm2JjY112SfA/fff7/S+TZs2ACQkJFzxOJYvXw6U3Cm4OEgHUBTF8Qd1Na52W4qiOAJ+m81GdnY2GRkZtG3bFii5eLjU5c5VqZEjRzq1NSoqivbt27N9+3by8vKAkjsoAA8//LBT3QYNGtC1a1f27NlDenq603a7d+/udDdGURTatGlDamqqY7sVzdfX1+l8m81mxwcdlFxkXTr25NIPtkvfBwcHO50j2Yfs46bv480HIMj7wsJ29TFOGnDD+6j3dm9yBvc+n4YDqqKgPHEH+LuEnjx1ehOFOtevkBP+vi5lPiTTiN3oKDof8EPppURQUQFaDXhx4SJGb7cy5NhSOh/ZTlBiOpbsXGID/bk4NWhnRLDLfn5s2Jovmw8AfS0ePpVMuJuJP6PCCcrMdanrnZtP7eQMl/LgDNfviDyDieO1a/JFx8Hk690Jystg4uAhbA2rw5p69fmpeRNuP7qLkz5ld3jZDGZUdFgxOY5AgxVFc9HjhBTQfXS/02DegJ7N0U7qeaGOToNu2t0oHiW/W1Xyd1f28bfYh6icbmpPv9lsLjOovlRubq6j/sVq167tUvfMmTNoNJoy7x6EhYURG+s8uCwtLY2PP/6Y9evXk5bm2otT1oVJzZo1nd6X5sVnZmZe8ThOnjyJoijUr+/ai3StrmVbK1eu5Ouvv+bIkSNYrc7PoMvKynKpX9Z5vVhZFwTh4eFs3bqVxMREIiIiHD+HsurWrVuXdevWcfr0aXx8LjzK/tLzCuDlVZKrm5mZKYO0hbheTcIg5mNYuRc83aB7k5IBvTeB58JHIWYg7DiB0qou1AuB/z54YV/nZ+9p3qs5J/61g9R39jruBLi39CM030Z6mgmvggKgJLEnhSAMFOFJOhlc3JOu0vXsTv7bvz89567ADefOALc8G+bzc/h7kEdNWwqpegs2RUtoehaH3Zx75WtmZBOQk8vhiDqompILkpQAb7wysml1IpFdESWBicZup/2pNDz1Ju48fIxlkSWfuzqbje7Hzzhts0CvY0uXcEaEFuNlCuaYoTW9MjNJ1uSxLzyEpmdOUT/YgNK1K7fd3oTkP8Lx+G4NJqOCpksUPH4Hmka10K85jFpQjN1qRdlyFOWOZug6NcD+yXrUlBw043ugCXEdy6CbdjeaRzqjHkhE0zkCpab3dfxUhaj+XLIKhYubGvRHRESwa9cuEhISLpviU1BQQFxcHDVq1HAJ+i5NWblWqqryxBNPEBsby7333kujRo0wm81oNBp+/vlnli9fjv2SpzgCl52G82omNrrWHv0b3daaNWuYPHkyjRs35tlnnyUoKAiDwYDdbufJJ58ss803el6vl0Zz+RtJ8iBoIW6QuxEGtvtrtl03uORVysMEg9q7VIt4uyM1HokiZ8MZ3Jr6Ye4QROiv8RwaspyzZjOW/CLyjVoWtIjknFc0D63Zg2dhIRpUtNiowXGanUkkeu5ujtOUeux3pBYVYCJEPUmNopOc1NYhQRuOTmcjKv80AP/5dRUPjbybQv35rzFVpeeRk2RaLI6Av1Smt4UJy7ZzLNiHEzX8UCxmzMU28t1N1MyFV5esINHgQ7OYJNKC/ThUPwxLTh5e2XmM/q49j0d6X9jYSx2ueOoCBrSC/xvkVKYASq9GFwoGXxhfoJ14+xW3B6BpXAMau45hE0JckCfpPeW6qUF/jx492LVrF4sWLbpsTvvSpUuxWq0uM+lcTkhICHa7nYSEBJde5vj4eKf3x44d4+jRo2XOsLNo0aKrP5CrFBYWxu+//87Ro0edZvr5K7e1bNkyjEYjn376qVMwHxcXd937jo2NpWnTpi5lWq3WccuuZs2a2O12YmNjXe5GlN5tKatnXwhRvbk19MatobfjvdcdYbQ8dB+Zi2PRBZiwZlsJen0X9j9yMTX2pjilkOJTudhQ8CYJ4PzDuzLZQ1d8OIeBAmoSh4kCUKGpdQ8qCjvrRNHzxEG0qkr3Y7GsfP9L3u8RjU9+Nv/YtJNsDzPb67neLdXYbGjsNmqnZpIb6EtusXM+faHej177jmG0FeAfYMR64CymEDdavdIM74sDfiGEqMJuak7/oEGDqFWrFt988w2///67y/LDhw/z0Ucf4ePjw4MPPnhV24yOjgZg3rx5TuWbNm1ySe0p7Vm+tBf5+PHjjpz0m6lPnz4AzJgxg+Ji1xtL19KbfbXbKj3Gi+9YqKrKl19+efUNv8ScOXOc2nr48GG2b99O27ZtHXdjunXrBsCsWbOc6h4/fpwNGzbQokULp9QeIcTfl7GOJ4ETm+N7f0MCH21Mi5MP0qpoHI1230vzhFG0UcfTSn0StceFhzSGEE8EB8hSAvHCNbXSz5TEgg6t+LlZK/6sUYs9tcKI8Q7hnws2MmbpXrwyiml8+hTdD/yJqaDQad0c1c5rg7ryz3tvI9nN6LJtvWrFu7aeTuuHMmzbndx78m4GbetH7YFXTo0UQlQe+YrrSzi7qT39bm5uvPfeezz55JNMmjSJnj170rp1a7RaLQcOHGDZsmW4u7vzzjvv4O9/dU+Q7Ny5Mx07duSnn34iIyPDMWXnwoULqV+/PseOHXPUDQ8Pp27dusyZM4eCggLCwsI4efIkCxcupF69ehw6dOhmHi5NmjRh1KhRfPXVVzzwwAP07t0bPz8/zpw5w+rVq/nqq6+cBsPcjG3ddtttjik877rrLqxWK+vXr6fgfP7s9UhMTOSJJ54gOjqalJQUvv/+e4xGIxMnTnTU6dChA7169WLFihVkZ2fTpUsXx5SdBoOBZ5999rr3L4T4e9LOfQJ12DsoW49gNxgovK8TS27rx+inXsec6jwxQIHBSIMzySxo3Zy6KWl4ZefTd/1h8tSSjolc3LCiISwlmQ7bD3AmJICjNfxZ1jiMBM8LqaTrwgK5MyHFMQZCa7MS2NqfDv9pj5tFnh8ihKi+bvonXHh4ON999x3ffvsta9euZfPmzdjtdoKDgxk+fDgjRoy46oAfSvLc33rrLcfDuX7//Xfq1avHO++8ww8//MDJkycddbVaLe+//z7Tp09n6dKl5OfnExERwZQpUzh69OhND/qhZDrN+vXr8/333zNnzhzsdjtBQUF07tz5mnPpr2Zbffr0IS8vj3nz5vH+++9jsViIjo7miSee4LbbbruuY/jwww957733+OyzzygoKHA8nOvSNJ7/+7//o2HDhixdupTp06fj5uZGq1ateOyxx5zm8xdCiKtS0w9ly5uQkoXGbCLQZODfABHjsHZ9CZ299EFWkJfrS+ddR2l85Az7ImoQeeIs2kvmsE/Hgi/Z6G12Qs+k8Hstf6eAH+C0j4Umy7eS4u+DpTiXeqkJDP6+ckwhLIS4fkVlTi4sLqaoVXhE5fDhw7Farfz4448V3RQhhBA3UezPhzj+3GJqJSajy3MnS+dHitYTt5zzT7tFQb0kQ1WLjVDtWXYElHRC7KwTzFv9OzrVqZ2Swas/bcBSnE14QQLmDkHUX+o8BkwIUfUoT7nO2KhOc51C+O+sStzLLCgocOk137RpEydOnOCee+6poFYJIYT4q4T3j6L2XZEciC8m81Que7akk7Qwlm5/xKG1q2hQsaM6Zv0H8Cabs6YLzxRoFZdEzz9jWdOkZBIIc0ERz6zbTFR2PGleJtx8ddR/+65bfmxCCFERqkTQ/8UXX3DkyBFat26N2Wzm6NGjLFmyBC8vL0aNGlXRzbuilJSUcuuYzeYKm1ZTCCEqK61GoVm4AcINtO/qw978LLZn5hEek0yR0UCuxUCtpBSwK3iSS46vnr2B4fhmlDwLRmNXmfzjZiz5RRysE8BTq3cRNa4xbds0QykohE4NQVf2lM1CiCpGpuwsV5UI+lu0aMHevXuZO3cuOTk5eHl50bNnTx577DGCgoIqunlX1Ldv33LrvPzyy/Tv3/8WtEYIIaqu5i+34OiSeOJqe9My/iTb6jTicINQwpLP4n8mkyzc0RQqaApUFLudWb2bs7teDbLd9Mza8zuD13VH4ysPBRRC/D1V6Zz+qmDbtm3l1omIiLimwc1CCPF3lZ9SwNaXdpG4OYmI4+coKijpuzJRQJFOR67GhFEtxteexfYGoSzo0IbJ/6xFdJShglsuhPgrKU+nu5Sp78lU4heToF8IIUSVpKoqGatOk70/lf1rN9JkaZbTcjswr3NX/rWpa8U0UAhxyyjPZLiUqe963/J2VGY39eFcQgghxK2iKAo+vUIJebIRSV315KN3Wp6JBzVbSE+fEEJAFcnpF0IIIa7E5q1wpLM3NTcXocdKPgaSawUx+LXIim6aEOJWkHG85ZKgXwghRLVwdoRKmzG9yNxwDnNDb9o+3hCth3zNCSEESNAvhBCiutBA4P3h1BzVoKJbIoS45aSrvzwS9AshhBBCiKpNYv5yyUBeIYQQQgghqjnp6RdCCCGEEFWb9PSXS3r6hRBCCCGEqOakp18IIYQQQlRx0tVfHgn6hRBCCCFE1SYxf7kkvUcIIUSVcTxNZeFhO6ey1IpuihBCVCnS0y+EEKJKePGnXP57QIdXbiH5Jh3T+hl4rLV8jQkhQLr6yyeflkIIISq9Q0dymbc2l48WbKXu2Qyy3AzM3d2U+75rhIe2olsnhBCVnwT9QgghKi3VZidlazLL5ybx3M/xZHsYeG7sbSR7udP+0GnW9ZpL1zMxNK6n5cCQwIpurhCiokhHf7kk6BdCCFEp5cRks3zQOrSnsvF0M6ErVHnqid4U6Uu+uhZ1jeRUrA8P5IUS8Gc6tY+n8HvHQhYcKMSoUXm4qxuNasjXnBB/CxL0l0s+DYUQQlRKax/ZQtDxRLzzC0ixejCrWyt0NjudD8ZiVxS2RdZkV1gAdx5PIMvPnbRzFnY++SeK1sZJdzdu21CDb/pAzwHBFX0oQghR4RRVVWUKBCGEEJVK/qE0Djb9FoPN5iizahQO+wfiUWAFINNdz2d3tuL2k8lolJJuvtRiG1vq1sC9oJDbD57gj9qhzP8qCqO3oUKOQwhxayiTc1zK1DfNFdCSykum7BRCCFGpFKcX8GfLH5wCfgCdXcVdtXKsXjDu2mJaJ53ms5k/0/f3PZhz88k16DlXK4QIKwRrDeQEmhh0eCeJa89U0JEIIUTlIUG/EEKISmX3gJVoCq3Yy1hWqNdQL+kUNdPTHSm8nnkFND8SyxlvLzjf468oCvFB4eR56Ih/fRlyU1uIak4p4yWcSNAvhBCiUkhZeYatEd9TtCmRAnQU4jwXZ6abkSw3Ix55Vpd1fTNysGovmbtTUXi3c196dr+ff350CtVqpzg2A7XY5rK+EKKKUxTXl3AiA3mFEEJUmN2HCth3pIjQU6koE9birhY7vphs6Eh0c8fqplBg0JPm4QaKQq7BNT//aIgfduxoLunLytDpsGs0fBhnZlToDLzPpoOXOz6z+2Me1OAWHKEQQlQOEvQLIYS45RZ/n8z22XFoC4sxWm1ojyQTrjr34CuAzqqS6aGQaXR3lGe56yk22tAXlvTsWzUKsb5e6DNOgyUEm7bkq+2YyUDa+ek9izRa3mvcmHV3hGGw2Rg1ZT+9ompj8TcSl6nSuaaCxSg9g0KI6kuC/lukTZs29OvXjylTplRYG8aOHUtiYiI///zzda1fGY5BCFH1rZmwA+8v9uDTMgK7RoNvcha1zqWh4Jp3r7GrBGRl4emezVlTID5FGTRP38+WwHZorDo0qkqWyUjzc/E8v281mSYzRwLDqZF5jpmN2rOjyxAAvPPzGHtgJbef9uODFt35V4/u/P7v41g9PdgeGkChm455h5dT71waqxu14/eAQLQhHtzdxsSgKHnkrxCVnlyzl6vaBP0FBQUsXLiQNWvWEBMTQ25uLl5eXkRGRtKrVy/uuOMOdLpqc7iXNW/ePCwWC/37978l+/v0009p2LAh3bt3vyX7E0JUbUVn87F8tJ2TNfywazWY8otocPwsOtV12K5dUUjxNJPv5k1I7jnaZ+wl1Wzh3W4P0ORYEvubhnPW3xut3U6Xo7sgCbwKcmh3cj8A9x/aystdhhCQm8Nny76j/dlYmqWeokVGMluC67CuRkNanz1L07PprK8dxAfaFtTyyCVq21kmHllHmtnCthpBzB3fnfHt9dxW9yYF/7tjYM1+iAqFvi1BI8PrhBB/vWoRBSckJDBx4kROnjxJu3btGD16NN7e3qSlpbF9+3ZeeeUVYmJimDhxYkU39S/37bffEhISUmbQ/9FHH93QDBabN29Ge8lAuc8//5x+/fpJ0C+EcLDbVBKPZFOYY+WnmEJ+SnLDM9+GpcCG+UgqYV2bke9mItvDDY/cQkICMqiZlA4o2FGxaTWcDfTmcP1QzgZ6437qHHs1AeR7tUdnV+mwN4bdUWGcDfABwKbVsj6qLS3OHaRT3B5HOwzFKr/NnEf99BQSLWb69Xua4/4Wup06wuubFzJ63yqO+EQwYeAj/BHs71ivnZ8XaWZPDnp7Ep6TS/A3exm+NwJVq8FmMdGjjpaXu2lJzVfQKhDuo3DmTD4hZ86QGBLC8UIj/9taRGYBjGmrY0y9Ig7sTiGqXRDeny2Df827cLJ6NIE1r17+ZOYXwp44qBcMAV439wclRLUiXf3lqfJBf0FBAZMmTeL06dO89dZb9OzZ02n56NGjOXDgAAcPHqygFlYeer3+htY3Go03qSVCiOokL6OY3T8nknW2EO8QE1tnxVJUBAEpWdTOzuNxvY5VLRqgUSAiJY10i5l0S8lDc4q89ayKbkq/Fbvwy8gBFHJMemKDLCg5uXwfGca5WiVP1O2QkMRrv27CkmtjdcemLu3YEdrYEfTbAfJ9qJebiVWjZXKPHnSNP03LM8n80LgJ/QeGsu271zHaM1kTHAyq6pjtY3uAL7s8LVjP98B7qTay3QzY822QbmNRuo0l+2Ds/nW0PhPL1yF1+appV6yakJJtqIV0iT/MhINbif/Znxpte5FrCMR9XQEfrjjFwxc3eu2f0OdVeOIO6BIFn62Ao4ngboCjZ2DDQSgoBo0CY3vDx49eWNduh+82ldw1aFQLxvQCi9tN/dkKIaqPKh/0L1q0iPj4eEaNGuUS8Jdq3LgxjRs3dipbt24dc+bM4ejRoyiKQv369Rk5cqRLj3X//v0JCQnhxRdfZNq0aezevRtFUWjfvj3PPfcc/v7+TvVPnDjB9OnT2b17NwaDgU6dOvH000+7tGnHjh2MGzeOl19+2aVXfsqUKSxdupQdO3Y4lSckJDBz5ky2bdtGWloa3t7eNGrUiDFjxhAVFUWbNm0ASExMdPwfYMmSJdSoUcMlp3/y5MmsXbuW5cuX4+3t7bSvuLg4hg0bxn333cczzzwDOOf0nzlzhgEDBgCwdOlSli5d6lh3y5Yt3HHHHdSuXZuZM2e6HPucOXP44IMP+Oyzz2jVqpXLciFE1VGQY+Wrx/aQmVToKFNsEBF/lronkx1l7jkFHIkKBSDL/UJgqrXZcC8s5HhEMOZ9cexuEc6ZGn6YCoo4YDJyzuNC3a21gtlSJ4jbDiXinZ1Httk5wM3WePJFkx5YtSq/1mlJq9MZPHB0Ez+17E3XQiv+bm50PHCYB/Ye4v67+3HQN4SG6UlgtYPVBnodKCqglgT8Ri3otWQCFNm5eMiB3QarQyL5tHE32iTG8ur6H3mx53AA7jm8nW8XfkSW0Y2aEz4gz1DSYZJnMDK+7yj6H9tNQF72hY2t2FPyCvCE5KyyT7RdhU9+g/oh8HTJZy8TvoSPfr1QZ95G2PZfuHTqUiH+DqSjv1xVPuhfs2YNAIMHD77qdX744QemTp1KnTp1eOSRR4CSwPXZZ5/lxRdfZMiQIU71k5OTefTRR+nevTsTJkzg2LFjLFy4kNzcXD766CNHvdOnTzNmzBiKioq45557CAoKYuPGjTz55JM3fJwHDx7ksccew2q1MnDgQCIiIsjKymLXrl3s3buXqKgoXn31Vd577z28vb15+OELfUk+Pj5lbvOuu+5i5cqV/PbbbwwfPtxp2S+//OKoUxYfHx9effVV/vOf/9CyZUun86/X6+nXrx9ff/01cXFx1KlTx2ndJUuWULt2bQn4hagGDq1Jdgr4S9U+ner0/uK0dY2qYgM88gsITs9AAYo9dKzp0ZTc80F+nruJcCC4sIgk44UpOnfUDeTh2HX0Ol7IrKD+qOc3bMXO7PoNOe7bzlF3wu63WNDmTnJMFgBSvL1Y2aYF96zdxMO79mOyFbM8/PwdA7sKBgUUDRi0Je8vHmZg1ILNDsUXCo/5BIOi8EeNCE56+hGRlsQJ32Amb16CBpW9QbUdAX+pQp2B3UFh9I7986LtBFE//ezlA/6LTf+5JOhPzYZPVzgv23kCVuyFO+SzVfwNSdBfrio/eujEiRN4eHgQGhp6VfWzsrL44IMPCA0NZfbs2YwePZrRo0cze/ZsatasyfTp08nOznZaJyEhgWeffZbJkyczbNgwx7/btm0jLi7OUW/GjBlkZWUxffp0JkyYwPDhw/nwww8JCQm5oWNUVZUpU6ZQXFzM7Nmzee655xg6dCgPPfQQH374Iffccw8Ad955J25ubvj6+nLnnXc6Xm5uZd/u7dixI35+fo4A/+L9/frrr9SrV4/IyMgy13Vzc+POO+8EoGbNmk77gwsXYYsXL3Zab8+ePcTFxTFw4MDrPyE3UVpaGoWFFwKWnJwcp59/UVERqanOwUtiYuIV3yclJTmNnZB9yD6q8z7SzrkGqhpVRWtzHphrySlw/N8rJw8rkK/VcNLHmwJdSc90rrvJZVsReflO7/vG76ZG3jn6xGzngx/fwT07iWE71/Dy4tkc972Q824pzKdudq4j4C9VYDRyztubeukppLj78FivUQBojVrnh/loFNdvSP0lBRdVP2v2dhyzd0EeAI2TT2G0FjmtorNZaZJ8yvG+UKvl+T73uxz35ahF56c1zckvuTtxieyTSU7vq+rvleyjau9DVE5VPujPycnBw8Pjqutv27aN/Px87r33Xsxms6PcbDZz7733kpeXx7Zt25zWCQgIoFevXk5lpekzCQkJANjtdjZu3EijRo2cUmsURWHkyJHXfFwXO3LkCDExMfTv35/69eu7LNdc58wPWq2WO+64g4MHDzpdvOzcuZOkpCT69et3vU0mLCyMVq1asWzZMqzWC3NvL168GK1We0Pbvpl8fX2dxiqYzWYslgtBgsFgwM/Pz2mdSy/iLn0fHByMclHwIPuQfVTnfbTsWxuNzrmLza5RSPZ1Drbd84tI9PBABaKSDpJmMnDKx5t4f1921q5FrkGP1u46g4/2oqD59vjDjD5w4fM5MC+TD5d8wYA/t1MrJx1T8YXPmnydHjuuT+4FcC8s5LdGkdw/9FkKTJ4oOoXAwgLXipf2HLo2z8lRv5Ke/2+bdATAPz+Hd1d9i852vh2qyr2HthKYV3KhVKTRMOGu0WwPjTh/sOV/liujz6exhgVCx4bOC73csQzv5lRUVX+vZB9Vex8VQynjJS5W5YN+s9lMbm7uVdc/ffo0AHXr1nVZVlpWWqdUzZo1Xep6eZX0KGVmZgIlV8p5eXmEhYVddrvXq/TComHDhuXUvHalwffFvf2//PILWq2Wvn373tC2hwwZQmpqKps2bQIgNzeXVatW0bVrV5cPECFE1eQb6saw1xsREmnG7GegXhtPauhzOFovkNNB3mS7GUkI8GZDmwYYUQnNjCXezw+r9kJ2qU2r4ZS3N+a8fKeZ+v1zUvnph9f57cf3+GPeq/y68H/EWGqzvHYX1tZsR6rRCzdrMQDu1mL+uW2dY12rVsfHzbrS8tRep/bWOJfMty2b8EvTknFebqiovu4E5JUR9Nsvao2qgmoHnQb0Cl62Aqc7A+biAowaFb0WpnQfxn879eekpx9dTx6m35FdJalBdpWG3irhT39AvxH/pPazH/FZ29vpmXQcxvaCnydD50jw94SwgJIc/4Y1IMSn5PXcIHj13gtt+vGfcG8XCPKG7k1gxcvgffWdYEKIv5cqn9MfERHBrl27OHXq1FWn+FyrK/WkX+8UmBdfZV/KZnO9ZftXqVevHg0aNODXX39l/PjxFBYWsmbNGtq3b+8ySPla9ezZEy8vLxYvXkz37t1ZuXIl+fn5DBo06OY0XghRKdRt50PddhePHWrm+N+5NBurt+dRw67SpTiX2hO/Z1dt1ymFi7Uamh2OJ87fD73VDtpitFaVH+sOocW5vZAXxLw6nci9aHaa4161GHB8E15FedjQ888tG+l8Kpbnu/fnQFAQ73a6i5aJcQyOOULL5LN80LoHMeEtnYL1IoMOvdWK1gY6mx2r5vyyYjvkWUGvoUnyKeItfmQb3fC0F/DfIRaaBZh4f3MxucUqD7bUc09TDzTn1z2dpfJNnxF8bR/BwEgNd8TbaJKh0j9KR5vQPiR+nsRnni2wKhqia8E7T3UFj+iS/d7R+upPfIgvfOs6UYQQf0vSsV+uKh/09+zZk127drF48WIef/zxcuuXXhjExMTQrl07p2WxsbFA2T375fHx8cHd3Z34+HiXZTExMS5ll94puNildxpq164NwNGjR8ttx5UuJi6nX79+vPfee+zYsYOUlBRyc3NvSvqNwWDgrrvuYv78+SQnJ7N48WICAwPp2LHjDW9bCFE1BPpqua9vaaqAJ+rPdei0fS9HAsOd6nXZc4w6p5LZXT+MBkdOkhzqS6FHSa7/Bq8u+CemU+DuPO2wVavnD58mdDl7APv5r7OI5BxSzBasOh2osDu4Di3PxfPvDauITNrHPUOfJ8tUktpp0yikeZlwyy1kv9mCNf98h4tdBZsKdjuhRdksz12L75+nSezRhhqTe2PyLGlH5zplf4XW9FR4ruuFtjYOcp5N56OxIbyWr5JbDKGeEqkIIW6NKp/eM2jQIMLCwpg7dy7r1q0rs86hQ4f44YcfAGjfvj1ubm7Mnz/fKS0oNzeX+fPn4+7uTocOHa65HVqtli5dunDw4EGnqTZVVWXOnDku9WvUqIFWq2X79u1O5Xv37mX//v1OZQ0aNKBu3bosWbKEEydOuGzr4rsNbm5uZGVdxQwQF+nbty9arZZffvmFX375BbPZTLdu3cpfEXB3dy/zwqXU4MGDsdlsfPDBB+zfv59+/fq5POBLCPH3oTzVj0FxWxi6bxXuhYV45BfSc+ch2h6JY3/dUJIDfMjy88Cuu+hzQqOQ7WumrPuqBo3VEfADfN28Jae9PJ3q7Aks6Tipl36O32c9z0ubFvLUtkUYdLnUyzxHsUaDG8WMOLSF+WdX831/lR/vM7DgfhNH3gim5leP4rbhVeq+MgCTp+tg4+vh46ZIwC+EuKWqfE+/yWRi+vTpTJw4kWeffZYOHTrQvn17vLy8SE9PZ+fOnWzZssUxmNZisTBhwgSmTp3K6NGjHT3aS5cuJSEhgRdffNFpgO+1GD9+PL///juTJk1i+PDhBAYGsnHjRtLT013quru7079/fxYtWsSLL75I69atSUhI4Oeff6Z+/fpOvfqKovDyyy8zfvx4Ro0a5ZiyMzs7m127dtGxY0fuvbckz7Np06YsXryYjz/+mPDwcBRFITo6+rIz+EDJIJ5OnTqxevVqioqKGDBgwFU/iKtJkyZs376d2bNnOwYH9enTx7E8PDycFi1a8Ouvv6IoimNufyHE31Tj2mh3v8tDM1cz5Nw6fjoUTEEh/NCtFcXuCordTnEZDxK0aTV4FFopcL8wfaeiqoSnn3Oqd8rT89JVifcsSVVUAZPNyoi9K/k1qg0fDHCnT7eAi2redlMOUQhRAeQaulxVPugHqFWrFvPmzePHH39kzZo1zJw5k7y8PLy8vIiKimLKlClOg1Lvvvtu/P39mTt3Lp9//jlQ0pv+zjvvuDyc61qEhobyxRdfMG3aNObPn+94ONerr75K7969Xeo//fTTqKrKunXrWL9+PVFRUbz33nv89NNPLqk8jRs35quvvuLLL79k1apV/Pjjj3h7e9O4cWNatGjhqDd+/HgyMzP54YcfyM7ORlVVlixZcsWgH0pSfDZu3Ahcfm7+srzwwgtMnTqVWbNmOe6cXBz0Q0lv/549e2jTps1fNu5CCFGFRATD6w/gBYxSVWJPFbNnbRrHPzuIX04xeSYDhkv69Y15RZjyivHOzyPfzYDeZqNucjJuBTZULnzf9z5xnC9bt3VaNyy7gHnNhnHPQDeUBVtRavjR7z8DUaIDEEKIvwtFvd6RqEJcpZUrVzJ58mRee+21G54RSAhRPVmL7fz0xRmyP9iLzVpEZqA3yvmvJ7/8FALzThOQWIB/9qUdeipu5KHDigKcc/PklW69WNKoHvl6HU2S0xgQm8DtY+vS/d4aFXBkQohbQXnFdQYu9eWbk45XXUjQL/5yY8eOJSYmhmXLlmEwGMpfQQjxt2UttrM28lv+9Akgx6dkALDBVkTbhN00jD9JcbFr7/yvbVrin5NJs9gYVjRuiaoo2BUFm0ZBb1fp8FYb2vXwvdWHIoS4hSToL1+1SO8RlU9aWhrbt29nz5497Nq1iyeeeEICfiFEuXR6DR3fb0/RI5s4aAwj391Evt7Eft+mGM64EWjPxHDRtMZFWi2xIcEc0dfihHcg7sVFoChoAI1dJd+gp3Fbr8vvUAgh/iYk6Bd/iZiYGF566SUsFgtDhw5lxIgRFd0kIUQVYe5Xj9tWe3Pq7q2YCi48kXdT8wbUOXuOZgkJuBcXk20ysaZlM4rOD/zN83Anz27EOzcPnd1OvkGPJcQND7PMGCZEtScDecslQb/4S7Rp08Zp6lIhhLgWpsb+RLXx5Jd4LToNFBiNqEVWbP7+pHl4kOntyTk/H9SLHp5oyc+nX148GzX+pHmYqWvNYuB/W1TcQQghRCUiQb8QQohKqcMHHbD9aw9zEkwcD/AhKCuXHRG1aBt7ksCcPKf5fXRWG1Gao0RsGUX4pjOoRTZMt3dBMUgvvxB/D9LVXx4J+oUQQlRKBm8DPT5qRw9g+eIUpvxWMi5oc4O6ABitNqKPxdHobCKh6hlO/EOPotVguj38ClsVQlRLEvOXq8o/kVcIIUT117GnDxq73amsUKel42PhDPqxK4eeMlLkKV9pQghxOfIJKYQQotLzsmgZ1N75SeG1zHZ6Dw1GV8+nglolhBBVh6T3CCGEqBL+OdqHZo0L+ONQEWHBOgZ3dUOnVSi2l7+uEEL83UnQL4QQokpQFIW+7dzo286topsihKhsJKe/XJLeI4QQQgghRDUnQb8QQgghhBDVnKT3CCGEEEKIqk2R/J7ySE+/EEIIIYQQ1Zz09AshhBBCiKpNOvrLJT39QgghhBBCVHMS9AshhBBCCFHNSXqPEEIIIYSo2iS9p1zS0y+EEEIIIUQ1Jz39QgghhBCiipOu/vJI0C+EEEIIIao2ifnLJek9QgghhBBCVHMS9AshhBBCCFHNSdAvhBBCCCFENSc5/UIIIYQQomqTnP5ySU+/EEIIIYQQ1ZwE/UIIIYQQQlRzkt4jhBBCCCGqNknvKZf09AshhBBCCFHNSdAvhBBCCCH+VqZMmYLZbK7oZtxSkt4jhBBCCCGqNkXye8ojPf1CCCGEEEJUcxL0CyGEEEKIqk0p43UD9u/fT58+ffDw8MDLy4thw4Zx8uRJx/J//OMfdO3a1fE+JSUFjUZD27ZtHWU5OTno9Xp++OGHG2vMTSLpPeJvS1VVsrOzK7oZQogbVFxcTH5+PgBZWVno9foKbpEQf28WiwWlCqfbJCQkEB0dTUREBF9//TUFBQX861//olu3buzbtw+LxUJ0dDTffPMNBQUFmEwmNmzYgNFoZPfu3WRnZ2OxWPj999+xWq1ER0dX9CEBEvSLv7Hs7Gy8vLwquhlCiJto0qRJFd0EIf72MjMz8fT0vKX7VJ+9eSHttGnTKC4uZsWKFfj6+gLQsmVLGjVqxOzZs3nyySeJjo6msLCQbdu20a1bNzZs2MDgwYNZsWIFmzdvpm/fvmzYsIEGDRoQFBR009p2IyToF39bFouF06dPc9ddd/HLL7/87Ubx3yw5OTlyDm+QnMMbJ+fw5pDzeOPkHJZ8v1ZlGzdupGfPno6AHyAyMpLmzZuzadMmnnzyScLDwwkNDWXDhg2OoH/cuHHk5+ezfv16R9BfWXr5QYJ+8TemKAqenp5otVo8PT3/th/ON0qj0cg5vEFyDm+cnMObQ87jjZNzWPWlp6fTokULl/KgoCDS0tIc70uD/aysLPbu3Ut0dDS5ubksWLCAwsJCtm/fzpgxY25hy69MBvIKIYQQQghxnq+vL+fOnXMpP3v2rFPvf3R0NFu2bGHdunX4+/sTGRlJdHQ0f/zxB2vXrqWwsNBpsG9Fk6BfCCGEEEKI87p06cLq1atJT093lB05coR9+/bRpUsXR1lpz/57773nSONp0aIFbm5u/Pe//6VWrVrUqVPnVjf/siS9R/ytGQwGxowZg8FgqOimVFlyDm+cnMMbJ+fw5pDzeOPkHFYdNpuNBQsWuJRPnDiRWbNm0bt3b/71r39RUFDASy+9RO3atRk9erSjXmRkJIGBgaxfv54PPvgAAK1WS+fOnfn111954IEHbtWhXBVFVVW1ohshhBBCCCHErTJlyhReeeWVMpfNnTuXZs2a8eyzz7J582a0Wi29evXivffeIywszKnu3XffzYIFC9izZw/NmzcHYOrUqbzwwgt8+umnjB079i8/lqslQb8QQgghhBDVnOT0CyGEEEIIUc1J0C+EEEIIIUQ1JwN5hbhIYWEhs2bNYtmyZSQnJ+Pr60vv3r2ZOHFiRTetyjl06BCjRo3CaDSycePGim5OlWGz2fj666/ZtGkTMTExqKpK/fr1GTduHC1btqzo5lVKcXFxvPXWW+zbtw8PDw/uvPNOxo8fj16vr+imVQmrVq1i2bJlHD58mKysLGrXrs3w4cMZMGAAiqJUdPOqpLy8PIYNG8a5c+eYM2cOjRo1qugmCSFBvxCl7HY7zzzzDKdPn2bMmDHUqFGDxMRE4uPjK7ppVY6qqrz11lv4+PiQl5dX0c2pUgoLC5k9ezb9+vVj1KhRaDQafvrpJ8aNG8f//vc/2rZtW9FNrFSysrIYN24ctWvX5u233+bcuXNMmzaNgoICnn/++YpuXpXwzTffEBISwqRJk/Dx8WHbtm28/vrrnD17tlINQqxKvvjiC2w2W0U3QwgnEvQLcd6SJUv4888/WbBgAf7+/hXdnCptyZIlZGRkMGDAAL777ruKbk6VYjQaWbx4MZ6eno6y9u3bM3z4cObNmydB/yV+/PFHcnNzefvtt/Hy8gJK7pZMnTqVhx9+mICAgApuYeU3bdo0vL29He/btm1LZmYm33zzDY888ggajWQCX4u4uDh++OEHJk2axJtvvlnRzRHCQf6ShThv0aJF3H777RLw36Ds7Gz+97//8fTTT6PTSb/CtdJqtU4Bf2lZ/fr1SU5OrqBWVV6///477dq1cwT8AL169cJut7N169YKbFnVcXHAX6phw4bk5uaSn59/6xtUxb311lsMHTrUZWpHISqaBP1CAFarlcOHDxMcHMx//vMfunTpQnR0NC+88AIpKSkV3bwqZcaMGURFRVWqR49XdVarlf379xMeHl7RTal04uLiXJ54abFY8Pf3Jy4urkLaVB3s2bOHwMBAPDw8KropVcqqVas4ceIEjzzySEU3RQgXEvQLAWRkZGC1WpkzZw6ZmZm88847TJ48mb179/Lcc89VdPOqjCNHjrBkyRKefvrpim5KtTJnzhySk5O5//77K7oplU5WVhYWi8Wl3GKxkJWVVQEtqvr27NnDihUrGDFiREU3pUopKChg2rRpjB8/HrPZXNHNEcKF3HsX1VZOTs5V9dLXrFmT0mfUubu78/bbbzsen+7r68vjjz/OH3/88bfMpb6Wc6jT6Zg6dSrDhg1z6Xn9u7uW83jpjDNbt27l008/5ZFHHiEqKuqvaqIQAJw9e5bJkyfTpk0b7r333opuTpXy5Zdf4ufnx4ABAyq6KUKUSYJ+UW2tWrWK1157rdx6CxYsIDg4GEVRaNasmSPgB2jdujVarZYTJ078LYP+azmHR44cIS4ujtdff53s7GwAioqKgJI8f4PBgNFo/EvbW1ldy3m8+ILp8OHDPP/88/Tt25cxY8b8hS2sujw9PcnJyXEpz87OdhkbIa4sOzubCRMm4OXlxVtvvSUDeK9BYmIiX3/9NW+//bbj97F0PEReXh55eXm4u7tXZBOFkKBfVF+DBg1i0KBBV12/Ro0al11WGrz+3VzLOfztt9/Iysqif//+Lst69OjBqFGjePLJJ29yC6uGa/1dBEhISGDChAk0a9aMf//7339Nw6qBOnXquOTul95ZkTtOV6+goIBJkyaRk5PDrFmzJD3lGp0+fZri4mImTZrksmzcuHE0adKE2bNn3/J2CXExCfqFOK9Lly6sWrWKwsJCR4/0jh07sNlsklZxFfr370/r1q2dypYuXcrKlSt5//33CQ4OrqCWVT0pKSk88cQTBAcHM3XqVJkF6Qo6derErFmzyM7OduT2r1q1Co1GQ4cOHSq4dVWD1Wpl8uTJxMXF8fnnnxMYGFjRTapyGjZsyCeffOJUdvToUd577z0mT55M48aNK6hlQlwg3yRCnPfggw+ybNkynnnmGe69914yMjL48MMPadGiBW3atKno5lV6NWrUcLlbsnPnTjQajZy/a1BQUMCECRPIyMjgmWee4cSJE45ler2eyMjICmxd5TN06FDmz5/PM888w8MPP8y5c+d4//33GTJkiMzRf5WmTp3Kxo0bmTRpErm5uezfv9+xrGHDhk4pj6JsFovlsp9zUVFR8ncrKgUJ+oU4Lzg4mE8++YR3332X559/HpPJRLdu3XjqqafkUfTilklLS+Po0aMALrMghYSE8PPPP1dEsyotT09PPv74Y95++22eeeYZPDw8GDRoEOPHj6/oplUZpc8zmD59usuyJUuWXDH1UQhRdShq6bQlQgghhBBCiGpJhuYLIYQQQghRzUnQL4QQQgghRDUnQb8QQgghhBDVnAT9QgghhBBCVHMS9AshhBBCCFHNSdAvhBBCCCFENSdBvxBCCCGEENWcBP1CCCGEEEJUcxL0CyHENRo9enSleUrzn3/+iU6nY+XKlY6ydevWoSgKs2fPrriGiUph9uzZKIrCunXrrmt9+V0q2549e9BoNKxfv76imyLEVZOgXwgBQExMDGPHjiUyMhJ3d3d8fHyIiopi1KhRrF271qlunTp1aNKkyWW3VRoUp6SklLn80KFDKIqCoihs3LjxstsprVP6MplM1K9fn6effpq0tLTrO9Bq5umnn6Zz58706tWroptyS8TFxTFlyhT27NlT0U0Rt0hGRgZTpky57guX63Wl37UWLVowaNAgnnnmGVRVvaXtEuJ66Sq6AUKIirdjxw66deuGXq9n5MiRNG7cmPz8fI4dO8aKFSuwWCz06NHjpu3vyy+/xGKx4ObmxsyZM+natetl67Zo0YJnnnkGgLS0NJYtW8a0adNYuXIlO3fuxGAw3LR2VTVbtmxh5cqVLFq0yKk8Ojqa/Px89Hp9xTTsLxQXF8crr7xCnTp1aNGiRUU3R9wCGRkZvPLKKwB07979lu23vN+1SZMm0a1bN5YtW8Zdd911y9olxPWSoF8IwSuvvEJeXh579uyhefPmLsuTkpJu2r6Ki4uZO3cud999N15eXnz22Wd88MEHWCyWMuvXrFmTESNGON5PmDCB/v37s3TpUhYvXszdd99909pW1cyYMQN/f3/uvPNOp3KNRoPJZKqgVgnx99C1a1fq1KnDJ598IkG/qBIkvUcIwbFjx/Dz8ysz4AcIDg6+afv6+eefOXfuHKNGjWL06NHk5uYyf/78a9pGnz59ADh+/Phl63z88ccoisKSJUtcltntdkJDQ51671asWMHw4cOpW7cubm5ueHt707t376vO2e3evTt16tRxKY+Li0NRFKZMmeJUrqoqH3/8Ma1bt8bd3R2z2UyPHj1cUqkux2q1smjRIm6//XaXHv2y8rAvLpsxYwYNGzbEZDLRtGlTli5dCsD+/fvp27cvnp6e+Pn5MWHCBIqLi8s8zpiYGAYOHIiXlxeenp4MHjyYmJgYp7p2u53XX3+d6OhogoODMRgM1K5dm8cee4zU1NQyj+vHH3+ke/fueHt74+7uTsOGDZkwYQJFRUXMnj3bccfpoYcecqR9XU3vb1xcHA8++CBBQUEYjUYiIiJ48cUXycvLc6o3ZcoUFEXhyJEjvPjii4SGhmI0GmnevDnLli0rdz9wIY9+9erVvPrqq4SFheHm5kb79u3ZunUrAOvXr6dLly54eHgQEhLC//3f/5W5rUWLFtG5c2c8PDwwm8107tyZxYsXl1n3888/JzIyEqPRSL169Zg+ffplU08yMzN5/vnnqVevHkajkYCAAO677z6Xn+G1utrzfKVxMYqiMHr0aKDk9zY8PBwo6Zwo/ZmX/q1d/Pf17bff0qxZM0wmE7Vr12bKlClYrVanbV/t3+nV/K4pikKfPn1Yvnw5OTk513imhLj1pKdfCEFERARHjhxh4cKFDBky5KrWsdlsl83ZLywsvOx6X375JeHh4XTt2hVFUWjZsiUzZ87kkUceuer2Hjt2DAB/f//L1rn33nt56qmnmDNnDgMGDHBatnr1ak6fPu1IG4KSL/m0tDRGjhxJaGgop0+f5osvvuC2225j7dq1V0xBuh4PPvgg3377LcOGDeOhhx6isLCQb775hl69erFw4UKXNl9q586d5OTk0K5du2va70cffUR6ejqPPPIIJpOJDz74gMGDB/PDDz8wZswY7rvvPgYNGsSKFSv48MMPCQwM5KWXXnLaRm5uLt27d6d9+/a8+eabHDt2jBkzZrB161Z2797tuEgsKiri7bffZujQoQwcOBAPDw/++OMPvvzySzZt2uSSnvWvf/2LN954g0aNGvHUU08REhLCiRMn+PHHH3n11VeJjo7mxRdf5I033mDs2LGOn0lQUNAVjzk+Pp527dqRmZnJ+PHjqV+/PuvWrePNN99k8+bNrF69Gp3O+etw1KhR6PV6nn32WYqKipg+fTqDBg3i6NGjZQaNZXnhhRew2WxMnDiRoqIi3n33XXr37s2cOXP4xz/+wdixY3nggQf4/vvv+c9//kN4eLjTXa0ZM2bw+OOPExkZyX/+8x+g5Pd00KBBfPrpp4wdO9ZRd/r06Tz11FM0b96cN954g7y8PN555x0CAwNd2pWZmUmnTp04efIkDz/8MI0bNyYxMZEZM2bQvn17duzYQVhY2FUd442e5/JERUUxbdo0nnrqKQYPHuz4fDKbzU71lixZQkxMDI8//jjBwcEsWbKEV155hfj4eGbNmnXNx3K1v2sdO3bk008/ZdOmTfTt2/ea9yPELaUKIf72fv/9d1Wv16uAWr9+ffWhhx5SZ8yYoR48eLDM+mFhYSpQ7is5OdlpvdOnT6tarVZ9+eWXHWXTp09XgTL3Bai9e/dWk5OT1eTkZPXo0aPqe++9p+r1etXLy0s9e/bsFY9r2LBhqtFoVNPS0pzKR4wYoep0Oqf1c3JyXNZPSkpS/fz81DvuuMOpfNSoUeqlH5/dunVTw8LCXLYRGxurAk7HvHDhQhVQP/30U6e6xcXFauvWrdU6deqodrv9isc2c+ZMFVAXL17ssmzt2rUqoM6aNculrEaNGmpGRoajfO/evSqgKoqi/vjjj07badWqlRocHOxynIA6ceJEp/LSY3r00UcdZXa7Xc3Ly3Np3xdffKEC6vz58x1l27ZtUwG1R48ean5+vlN9u93uOB9lHVt57r//fhVQf/nlF6fyZ599VgXUL774wlH28ssvq4B61113Of0Mtm/frgLqCy+8UO7+Zs2apQJqy5Yt1cLCQkf54sWLVUDV6XTqH3/84SgvLCxUg4OD1Q4dOjjK0tLSVA8PDzUiIkLNzMx0lGdmZqp169ZVzWazmp6erqqqqqanp6vu7u5qVFSUmpub66ibkJCgenh4qIC6du1aR/mECRNUk8mk7tmzx6ndcXFxqsViUUeNGuUou5bzfS3nuay/oVKAUxvK+hu6dJlGo1F37tzpKLfb7eqgQYNUQN2yZYuj/Fr+Tq/m2Ddu3KgC6jvvvHPZOkJUFpLeI4SgY8eO7Ny5k1GjRpGZmcmsWbMYP348jRo1Ijo6usxb/nXq1GHlypVlvnr37l3mfmbPno3dbmfkyJGOsgceeAC9Xs/MmTPLXGfFihUEBAQQEBBAgwYNePrpp2nUqBErVqwosxfzYqNGjaKwsNApfSgnJ4effvqJvn37Oq3v4eHhVCc1NRWtVkv79u3Ztm3bFfdzrb7++mssFguDBg0iJSXF8crIyKB///7ExcU57mZcTnJyMgC+vr7XtO/Ro0fj5eXleN+sWTM8PT2pUaOGy12eLl26kJSUVGbqwgsvvOD0fvDgwTRs2NBpULGiKLi5uQEld4YyMjJISUmhZ8+eAE7n9ZtvvgHgzTffdBmPUJpacT3sdjtLliyhZcuWLmMfJk+ejEaj4aeffnJZb+LEiU77bNu2LWazudyfy8Uee+wxpzsZpb3F7du3p02bNo5yg8FAu3btnLa9cuVKcnNzmTBhAp6eno5yT09PJkyYQE5ODqtWrQJK/kby8vJ4/PHHcXd3d9QNDQ3lgQcecGqTqqp88803REdHU7NmTaffPw8PDzp06MCKFSuu+hhLXe95vll69epFq1atHO8VReG5554D+Ev36+fnB8C5c+f+sn0IcbNIeo8QAoCmTZs6csDj4+NZv349X3zxBRs3bmTgwIEuqRgeHh7cfvvtZW7r66+/dilTVZWZM2fSrFkz7Ha7Uz5+586dmTt3Lm+++abL7f/27dvz2muvAWA0GgkLC6N27dpXdUylgf2cOXMYN24cUJIznpub63ThAXDixAn+9a9/8dtvv5GRkeG07GbPyX/o0CGys7OvmJZy9uxZGjRocNnlpW1Sr3G6wLp167qU+fj4UKtWrTLLAVJTU53SKby9vcsc5xEVFcWiRYvIzc11XER9//33vPvuu+zevdtlfEB6errj/8eOHUNRlMuOK7leycnJ5OTk0LhxY5dlvr6+hISElHlRW9Z58vPzu+xYhLJcuo3S81mao37psou3HRsbC1Bmu0vLSttd+m9kZKRL3UaNGjm9T05OJjU11XExXRaN5tr7A6/3PN8sUVFRLmWlx/5X7rf076+yPLdDiCuRoF8I4SIsLIyRI0fy4IMP0rVrVzZv3sz27dvp0qXLdW9z/fr1nDhxAoD69euXWWfp0qUMGjTIqczf3/+yFxfl0el03H///UyfPp3jx49Tr1495syZg4+Pj1POfE5ODtHR0eTm5jJp0iSaNm2KxWJBo9Hw5ptvsmbNmnL3dbkv/UsHEkJJoBAQEMC8efMuu70rPQcBcARs1/q8Aq1We03lcO0XFqUWLlzI8OHDadeuHe+//z61atXCZDJhs9no27cvdrvdqf6N9OjfbJc7H9dyLq7nXP/VStt/++238/zzz1dYO67l76Uy77f07+9yF1BCVCYS9AshLktRFNq3b8/mzZs5ffr0DW1r5syZGI1G5syZU2ZP4qOPPsqXX37pEvTfqFGjRjF9+nTmzJnDmDFjWLduHWPHjsVoNDrqrF69mjNnzjBz5kweeughp/UvHcR6Ob6+vuzcudOlvKxexvr163P06FE6dOjgMiDxapVeFFxLusnNkpGRQVJSkktv/6FDhwgMDHT08s+dOxeTycTatWud0k4OHz7sss0GDRrw66+/snfv3isOTr7Wi4KAgAAsFgsHDhxwWZaenk5iYmKlnO+/9C7BgQMHuO2225yWHTx40KlO6b+HDx++bN1SAQEBeHt7k5WVdd0X02W51vNcmpaWlpbmlKJW1t/L1fzMDx065FJ26Xkq3e/V/p1ezX5L71iWd5EuRGUgOf1CCFauXFlmT1d+fr4jv/fSNIFrkZmZyYIFC+jduzf33HMPw4YNc3kNGDCAX3/9lcTExOveT1latGhBs2bN+Prrr5k7dy52u51Ro0Y51Snteb20F3fFihVXnc/foEEDsrOz2b59u6PMbrczbdo0l7ojR47EbrczefLkMrd19uzZcvfXsmVLPD09HVNA3mr//e9/nd7/9NNPHDlyxOmiTavVoiiKU4++qqqOdK2L3X///QC8+OKLFBUVuSwv/dmUXiRd7R0OjUZD//792b17N8uXL3c5BrvdzuDBg69qW7dSr1698PDw4MMPPyQ7O9tRnp2dzYcffojZbHY8hblXr164ubnx0UcfOU2NeerUKZe7SRqNhgceeIDt27ezYMGCMvd9Pfnp13qeS1PXSscllHr33Xddtn01P/OVK1eya9cux3tVVXnrrbcAnH4nr+Xv9Gr2u3XrVnQ6HZ07d75sHSEqC+npF0Lw1FNPkZqayoABA2jatCnu7u4kJCQwb948jh49ysiRI2natOl1b//bb78lPz+foUOHXrbO0KFDmT17Nl999ZXLINEbNWrUKJ555hmmTp1KgwYN6NChg9PyLl26EBwczDPPPENcXByhoaHs2bOHuXPn0rRpU/bv31/uPsaOHcu7777L4MGDmThxIgaDgQULFpR5MVU6Tef//vc/du3aRb9+/fD39+fUqVNs2bKF48ePl5uHrNVqGTJkCIsWLaKwsNDpzsVfzd/fn4ULF3LmzBm6d+/umLIzKCjI6XkEw4YN48cff6Rnz56MHDmS4uJiFi1a5DJnO0C7du14/vnnmTp1Kq1atWL48OEEBwcTGxvLggUL2L59O97e3jRq1AiLxcKMGTNwd3fH29ubwMBAx+DgsrzxxhusXLmSQYMGMX78eOrVq8eGDRuYP38+0dHRLheBlYG3tzdvvfUWjz/+OO3bt3fMWz979myOHz/Op59+6hi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" 190 | ] 191 | }, 192 | "metadata": {}, 193 | "output_type": "display_data" 194 | }, 195 | { 196 | "name": "stdout", 197 | "output_type": "stream", 198 | "text": [ 199 | "特徵排名:\n", 200 | " Feature Importance\n", 201 | "4 Sulfate 1537.761108\n", 202 | "0 ph 983.161560\n", 203 | "3 Chloramines 347.691345\n", 204 | "7 Trihalomethanes 345.254150\n", 205 | "2 Solids 333.570129\n", 206 | "1 Hardness 304.054077\n", 207 | "8 Turbidity 278.588531\n", 208 | "6 Organic_carbon 240.798859\n", 209 | "5 Conductivity 237.858810\n" 210 | ] 211 | } 212 | ], 213 | "source": [ 214 | "# 使用SHAP解釋結果\n", 215 | "# TreeExplainer為專門用於基於樹的模型(如 XGBoost)的解釋器\n", 216 | "explainer = shap.TreeExplainer(model)\n", 217 | "# shap_values為特徵對模型預測貢獻的數值\n", 218 | "shap_values = explainer.shap_values(X_test)\n", 219 | "# 生成 SHAP 值的可視化,展示哪些特徵對模型預測最為重要。\n", 220 | "shap.summary_plot(shap_values, X_test)\n", 221 | "\n", 222 | "# 特徵重要性排序\n", 223 | "# 計算每個特徵的平均絕對 SHAP 值\n", 224 | "mean_abs_shap_values = np.sum(np.abs(shap_values), axis=0)\n", 225 | "# 建立特徵重要性表格\n", 226 | "feature_importance = pd.DataFrame({'Feature': X_train.columns, 'Importance': mean_abs_shap_values})\n", 227 | "# 依據特徵重要性進行排序,sort_values(by='Importance'):根據 Importance 欄進行排序,ascending=False為醬序排列\n", 228 | "feature_importance = feature_importance.sort_values(by='Importance', ascending=False)\n", 229 | "\n", 230 | "\n", 231 | "print(\"特徵排名:\")\n", 232 | "print(feature_importance)" 233 | ] 234 | } 235 | ], 236 | "metadata": { 237 | "kernelspec": { 238 | "display_name": "Python 3 (ipykernel)", 239 | "language": "python", 240 | "name": "python3" 241 | }, 242 | "language_info": { 243 | "codemirror_mode": { 244 | "name": "ipython", 245 | "version": 3 246 | }, 247 | "file_extension": ".py", 248 | "mimetype": "text/x-python", 249 | "name": "python", 250 | "nbconvert_exporter": "python", 251 | "pygments_lexer": "ipython3", 252 | "version": "3.10.12" 253 | } 254 | }, 255 | "nbformat": 4, 256 | "nbformat_minor": 5 257 | } 258 | --------------------------------------------------------------------------------