diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 00000000..3e744e9b Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..e69de29b diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..4b5a2944 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,4 @@ +{ + "python-envs.defaultEnvManager": "ms-python.python:conda", + "python-envs.defaultPackageManager": "ms-python.python:conda" +} \ No newline at end of file diff --git a/base_classifiers.ipynb b/base_classifiers.ipynb new file mode 100644 index 00000000..1d265806 --- /dev/null +++ b/base_classifiers.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7a57dd91", + "metadata": {}, + "source": [ + "# Libaries" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e30cabda", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.model_selection import train_test_split, GroupKFold, GridSearchCV\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import confusion_matrix, roc_curve, accuracy_score, classification_report, accuracy_score, roc_auc_score, precision_score, recall_score, f1_score, ConfusionMatrixDisplay\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n" + ] + }, + { + "cell_type": "markdown", + "id": "2542c804", + "metadata": {}, + "source": [ + "# Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fbd9cd46", + "metadata": {}, + "outputs": [], + "source": [ + "# Path to features\n", + "csv_path = 'data/base_features.csv' # This is based on, that tis ipynb is places in /results/another folder \n", + "\n", + "# Load the data\n", + "df_features = pd.read_csv(csv_path)" + ] + }, + { + "cell_type": "markdown", + "id": "25ffff15", + "metadata": {}, + "source": [ + "# Prepare features and split 80/20 from total" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "60acec1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows after dropping NaN-rows: 2100\n", + "Temp set (for train+val): 1680 samples\n", + "Test set (Don't touch!): 420 samples\n" + ] + } + ], + "source": [ + "# Features\n", + "feature_columns = ['asymmetry_np_centroid', 'border_contours', 'color']\n", + "\n", + "# Drop rows with NaN in any of the feature columns - there was 3 with at least 1 NaN value\n", + "df_features = df_features.dropna(subset=feature_columns)\n", + "\n", + "print(f\"Rows after dropping NaN-rows: {len(df_features)}\") # This can be compared to the original shape showing 2103 rows\n", + "\n", + "# Reassign X, y, img_ids\n", + "X = df_features[feature_columns].to_numpy() # Independent variables\n", + "y = df_features['cancerous'].to_numpy() # Dependent variables\n", + "img_ids = df_features['img_id'].to_numpy()\n", + "\n", + "# Split 1: Separate 20% test set - 80% train set (Is splitted further later!)\n", + "X_temp, X_test, y_temp, y_test, ids_temp, ids_test = train_test_split(X, y, img_ids, test_size=0.2, stratify=y, random_state=42)\n", + "print(f\"Temp set (for train+val): {len(X_temp)} samples\") # Just to check if the split worked properly\n", + "print(f\"Test set (Don't touch!): {len(X_test)} samples\") # Just to check if the split worked properly" + ] + }, + { + "cell_type": "markdown", + "id": "ba471394", + "metadata": {}, + "source": [ + "# Splitting Temp into 80/20 (Train/Val)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6d9ff1f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Split 2: Split Temp into Train (80%) and Val (20%)\n", + "\n", + "# Extract patient ID's from img_id\n", + "def get_patient_id(img_id):\n", + " return '_'.join(img_id.split('_')[:2])\n", + "\n", + "# This is used to avoid data leakage\n", + "df_features['patient_id'] = df_features['img_id'].apply(get_patient_id)\n", + "\n", + "# Creating a dict to map img_id to patient_id - this is used below, but also for later use for GroupKFold\n", + "img_to_patient = dict(zip(df_features['img_id'] , df_features['patient_id']))\n", + "\n", + "# Getting patient ID's for temp set using the dict\n", + "patient_ids_train_temp = np.array([img_to_patient[img_id] for img_id in ids_temp]) \n", + "\n", + "# Splitting by images, but keep patient ID's for grouping later!\n", + "X_train, X_val, y_train, y_val, ids_train, ids_val, patient_train, patient_val = train_test_split(\n", + " X_temp, \n", + " y_temp, \n", + " ids_temp, \n", + " patient_ids_train_temp, \n", + " test_size=0.2, \n", + " stratify=y_temp, \n", + " random_state=42\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "66e59e9a", + "metadata": {}, + "source": [ + "# GroupKFold" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "801728f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Fold details (same patient's images stays together):\n", + "\n", + "Fold 1:\n", + " Train: 1075 samples, 771 patients\n", + " Val: 269 samples, 192 patients\n", + " Patient overlap: 0\n", + " Class1 ratio in val: 0.524\n", + "\n", + "Fold 2:\n", + " Train: 1075 samples, 770 patients\n", + " Val: 269 samples, 193 patients\n", + " Patient overlap: 0\n", + " Class1 ratio in val: 0.517\n", + "\n", + "Fold 3:\n", + " Train: 1075 samples, 770 patients\n", + " Val: 269 samples, 193 patients\n", + " Patient overlap: 0\n", + " Class1 ratio in val: 0.483\n", + "\n", + "Fold 4:\n", + " Train: 1075 samples, 770 patients\n", + " Val: 269 samples, 193 patients\n", + " Patient overlap: 0\n", + " Class1 ratio in val: 0.517\n", + "\n", + "Fold 5:\n", + " Train: 1076 samples, 771 patients\n", + " Val: 268 samples, 192 patients\n", + " Patient overlap: 0\n", + " Class1 ratio in val: 0.451\n" + ] + } + ], + "source": [ + "# GroupKFold: 5 folds on TRAINING data only (using PATIENT IDs as groups)\n", + "patient_ids_train_temp = np.array([img_to_patient[img_id] for img_id in ids_train])\n", + "\n", + "gkf = GroupKFold(n_splits = 5) # Amount of splits\n", + "\n", + "\n", + "# Display each fold. Myabe not needed, but displayed to see some insights to the different folds\n", + "print(\"\\nFold details (same patient's images stays together):\")\n", + "for fold, (train_idx, val_idx) in enumerate(gkf.split(X_train, y_train, groups=patient_train)):\n", + " train_patients = np.unique(patient_train[train_idx])\n", + " val_patients = np.unique(patient_train[val_idx])\n", + " overlap = len(np.intersect1d(train_patients, val_patients))\n", + " \n", + " print(f\"\\nFold {fold + 1}:\")\n", + " print(f\" Train: {len(train_idx)} samples, {len(train_patients)} patients\")\n", + " print(f\" Val: {len(val_idx)} samples, {len(val_patients)} patients\")\n", + " print(f\" Patient overlap: {overlap}\")\n", + " print(f\" Class1 ratio in val: {np.mean(y_train[val_idx]):.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "57ff6f7d", + "metadata": {}, + "source": [ + "--------------------------------------------------------------------------------------------------------------------------------------------" + ] + }, + { + "cell_type": "markdown", + "id": "803d4642", + "metadata": {}, + "source": [ + "### k-NN classifier" + ] + }, + { + "cell_type": "markdown", + "id": "759e16cb", + "metadata": {}, + "source": [ + "#### Test to estimate best k" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d0dbaa61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n", + "\n", + "\n", + "Cross-validation:\n", + "Best k: 50\n", + "Mean CV AUC: 0.6032\n", + "\n", + "All k values tested:\n", + " k=5: CV AUC = 0.5642 ± 0.0486\n", + " k=15: CV AUC = 0.5732 ± 0.0337\n", + " k=25: CV AUC = 0.5957 ± 0.0344\n", + " k=35: CV AUC = 0.6011 ± 0.0352\n", + " k=50: CV AUC = 0.6032 ± 0.0293\n" + ] + } + ], + "source": [ + "# Cross-validation with GroupKFold\n", + "\n", + "# Create pipeline with scaler and k-NN\n", + "pipeline = Pipeline([('scaler', StandardScaler()),('knn', KNeighborsClassifier())])\n", + "\n", + "# Parameters to test\n", + "param_grid = {\n", + " 'knn__n_neighbors': [5, 15, 25, 35, 50]\n", + " }\n", + "\n", + "# Grid search with GroupKFold\n", + "\n", + "grid_search = GridSearchCV(\n", + " pipeline, \n", + " param_grid, \n", + " cv = gkf, \n", + " scoring = 'roc_auc', \n", + " n_jobs = -1, \n", + " verbose=1\n", + " )\n", + "\n", + "# Fit - groups must be passed as a separate argument\n", + "# The trick: GridSearchCV expects groups in the fit method\n", + "grid_search.fit(X_train, y_train, groups=patient_train)\n", + "\n", + "print(\"\\n\")\n", + "print(\"Cross-validation:\")\n", + "print(f\"Best k: {grid_search.best_params_['knn__n_neighbors']}\")\n", + "print(f\"Mean CV AUC: {grid_search.best_score_:.4f}\")\n", + "\n", + "# Show results for each k\n", + "print(\"\\nAll k values tested:\")\n", + "for i, k in enumerate(param_grid['knn__n_neighbors']):\n", + " print(f\" k={k}: CV AUC = {grid_search.cv_results_['mean_test_score'][i]:.4f} ± {grid_search.cv_results_['std_test_score'][i]:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6237ef84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final k-NN classifier results\n", + "Parameters: n_neighbors=50, weights='uniform', metric='euclidean'\n", + "\n", + "Cross-Validation Results (from GridSearch):\n", + " Mean CV AUC: 0.6031634077267376 (5-fold GroupKFold)\n", + "\n", + " Test set results:\n", + " Accuracy: 0.6048\n", + " AUC: 0.6550\n", + " Precision: 0.6068\n", + " Recall: 0.5952\n", + " F1-Score: 0.6010\n" + ] + } + ], + "source": [ + "# Best k from cross-validation (cell 117)\n", + "best_k = grid_search.best_params_[\"knn__n_neighbors\"]\n", + "\n", + "# Scale ALL data using the SAME scaler fitted on training data\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Train final model on ALL training data (no validation split needed)\n", + "knn_final = KNeighborsClassifier(n_neighbors=best_k, weights='uniform', metric='euclidean')\n", + "knn_final.fit(X_train_scaled, y_train)\n", + "\n", + "# Predictions on TEST set only (FINAL - UNBIASED)\n", + "y_test_pred = knn_final.predict(X_test_scaled)\n", + "y_test_proba = knn_final.predict_proba(X_test_scaled)[:, 1]\n", + "\n", + "# Calculate test metrics\n", + "test_acc = accuracy_score(y_test, y_test_pred)\n", + "test_auc = roc_auc_score(y_test, y_test_proba)\n", + "test_precision = precision_score(y_test, y_test_pred)\n", + "test_recall = recall_score(y_test, y_test_pred)\n", + "test_f1 = f1_score(y_test, y_test_pred)\n", + "\n", + "print(\"Final k-NN classifier results\")\n", + "print(f\"Parameters: n_neighbors={best_k}, weights='uniform', metric='euclidean'\")\n", + "print(f\"\\nCross-Validation Results (from GridSearch):\")\n", + "print(f\" Mean CV AUC: {grid_search.best_score_} (5-fold GroupKFold)\")\n", + "print(f\"\\n Test set results:\")\n", + "print(f\" Accuracy: {test_acc:.4f}\")\n", + "print(f\" AUC: {test_auc:.4f}\")\n", + "print(f\" Precision: {test_precision:.4f}\")\n", + "print(f\" Recall: {test_recall:.4f}\")\n", + "print(f\" F1-Score: {test_f1:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "160a8fef", + "metadata": {}, + "source": [ + "#### Visualizing results " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e36a5bb7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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X/3/99ZfH9szMTOLj44mPjycyMpIBAwawfPlyWrduzW233UbXrl3p0KED0dHRBY6lqDRq1IgrrriC999/n3///Zcbb7yRDh060Lx5cywWS4HOkZusi4mJoV+/fq7kR5cuXTh69CizZs0iMzMTPz8/1q9fz+WXX15k13qhr61hGPlu279/v1vtIoCePXtyxx13uLXl1y/O1l/btGlDQEBAnv21sKxWK23atPFo79ChA6+//jo7d+70iLlz58589913zJo1iy5dutCjR49zPk5sbGy+xVeTk5PZv38/jRo1yjOR3blzZ+bMmZNnLKfbtWuXK/YztW3bNs+pTCIiIuWFfsqJSJ6qVq3K77//zuHDh7n00ksLfXxedUlyi6DmV7Mk9xf/pKQkAOrVq8eWLVuYPHkyX375JR999BEAl156Kc888wz9+/cHnKMWtmzZwsSJE/nss89YtWoV4PyQNm7cOEaOHAk4P7if+WGrU6dOrjoKgwcP5qWXXmLx4sWuxEjuX6kHDx7sdtyMGTN49NFHiYqKokePHtSsWdP1V+tZs2aRlZVV0KfKTe615/fhNa/nbvPmzXTt2hWAHj160KBBA4KDgzGZTHzyySfs2rXrvOPJlZycjNlsJioqKs+YzGazK/bT5VVnJfcD1pkFMM8mOjo6z3oQuQV7X3/99bMen5aWRmRkJAMHDsRqtTJr1izmzJnD7NmzXYVUZ86cWaLLn/r4+PDtt98yadIkli9f7hohFBkZyahRoxg/fvw5EySNGzcmKirKlRCJiYkhIiKCJk2acPToUaZNm8bmzZuJiori+PHjHqN3LsSFvra5ffnw4cMe26655hpX4uTvv/+mXr16eZ4jv35xrvea6OjoPB+3sCIiIvIsmJr7uHl9T/Ts2ZPRo0fTv39/+vbtyyeffOIxgq4wCvu+mp+zvfdYLBavjagSEREpCZpKIyJ5yh0iv3bt2vM6Pq8PK7kFUI8dO5bnMbntpxdKbdKkCcuWLePEiRNs2bKFp59+mmPHjjFw4EC+++47135169ZlwYIFHD9+nJ9++olp06ZhGAb3338/H3zwAeBcbcUwDLev3CkIuY/VpEkTVq5cSUpKCikpKaxcuZIrr7zS7a/SOTk5PPPMM1SvXp3ffvuNxYsXM23aNCZNmsTEiRM9pgIVRu6HzfxWtsnruXvuuefIyspi7dq1rFy50jXsftKkSUW2xGdoaCgOh4Pjx497bIuLi8PhcJy1wO2Fyq9IZu5j/vLLLx6v7elfderUcR1z8803s2HDBk6cOMGXX37JXXfdxfr16+nZs6drpEPuh92cnByPxzzXB8zCiIyM5LXXXuPw4cOugrERERFMnDiR6dOnn/N4k8lEp06d2LNnD0ePHmXdunV06tQJk8nE1VdfjdVqJSYmxtXP8yre6y3t2rUD8CgeWxjn6hf5vdfExcW59dfzfb0TEhJwOBwe7bmPm18B5j59+rBs2TIcDgd9+/blyy+/zPcxzuV83lfzcrb3HrvdrmXZRUSkXFNiRETyNHToUCwWC2+99VaeH4ZPV9DRCM2aNQNwS0bkSk9P58cffyQgICDPESq5Q9YnT57M//3f/2EYBp9//rnHfhaLhaZNm/L444+7EiIFWToz1x133EFGRgbLli1j2bJlZGRkeAw/j4+PJykpiTZt2niMoPjxxx/JyMgo8OOd6dJLL8Xf358ff/yRzMxMt20OhyPPpTf37dtHeHi4W70HcD6nO3bs8Ng/dxRCYUZsnO21W79+PeC5aklJaN26NQBbtmwp9LGhoaFce+21vPXWWwwdOpS4uDi2bt0KOFeNgbxHM5y5BPLZFPS5NplMNGrUyDVtDAreb3OniyxevJi9e/e6Rg8FBQXRqlUrvv32W2JiYlxJlIIwm82F6h/no2vXrtSpU4dNmzbluxT1+Tpbf/3hhx/IyMhw66+5qxYV9vXOzs52Tec63caNG4Gzf0/07t2bFStWAHDTTTfxxRdf5Lvv2YSGhnLRRRfx119/5Rl/Qb8/r7zySuC/2E+3ZcuWPJNGIiIi5YUSIyKSp4svvpjHH3+c+Ph4evXqxYEDBzz2yczMZObMmUyaNKlA52zfvj3169fnyy+/9ChOOnXqVOLj47nttttcxVK3bduW518vc/8Cmjt15ddff+Wff/45534Fcfvtt2M2m3nvvfdYtGiRq/bI6aKjowkICGDHjh2kp6e72k+ePMmoUaMK/Fh58fX1ZcCAAcTFxbmWI871zjvv5FlfpE6dOpw8eZLffvvN1Wa323n00UfzTGqFh4cDcOjQoQLHlVtUc/Lkya6h++Acxp87PSl3n5I0bNgwQkJCGD9+vNv150pPT3f74Lp27VqPhBP891fy3L4SGhrKJZdcwqZNm9xql6SkpDBu3LgCx3e25/rAgQPs3r3bo72w/TZ3FMi0adPc7ufe3rZtGzExMVxxxRUFng4RHh5eqP5xPnx8fHjttdcwmUz069cv34LFhalFk2vQoEH4+Pgwc+ZMt3o/2dnZjB07FsBtKeJLL72U4OBgVq5c6ZqeBc7X4tlnnz3rY02YMMGtps/vv//OvHnzCAsLo2/fvmc99rrrruPTTz/FZDJx880355nsLYghQ4aQnZ3NuHHj3Gq3/Prrr8yfP5+wsDBuvPHGs56jb9++hIaGMm/ePLf3mezsbJ566qnziktERKSsUI0REcnXs88+S2ZmJi+//DKXXnopXbt2pXHjxlitVg4cOMCaNWtISEg45weHXGazmXfffZeePXty3XXX0b9/f+rUqcPWrVv59ttvqV+/vmvFCHD+BXz27Nl07tyZiy++mNDQUHbv3s2qVauIjIx0rW6yZs0aHnnkEdq3b0/Dhg2JiIhg//79rFy5koCAAB544IECX3P16tXp2rWrazWPbt26eayaYTabGTlyJDNmzODKK6/khhtuIDk5mS+//JI6derkucpGYbzwwgusXbuWp556ik2bNtGsWTP27NnDqlWr6NGjB998843b/qNGjeKbb77h6quvZsCAAfj7+7Nu3ToOHz5M586dPf5q3rZtWwICApg1axbJycmuUS+5Hxjz0rFjR0aNGsWrr75K48aNueWWWzAMg+XLl/Pvv//y4IMP0rFjxwu67vMRFRXFBx98QP/+/bnyyiu59tpradiwIZmZmfzzzz+sX7+edu3a8dVXXwHwyCOPcPDgQTp37kzdunUxmUxs2rSJH374gXbt2rmNuhkzZgz33nsvbdu2pX///jgcDr788ktatmxZ4PgaNmxI9erVWbJkCYGBgdSsWROTycR9993Hrl27uOmmm7jqqqto3LgxVatW5fDhw3zyySdYLBaPVYnyc9lll1GlShWOHTtGlSpVuOyyy1zbunTpwrPPPktiYmKhElddu3blww8/pF+/fjRr1gyLxcL111+fZ6HTC9G7d28WLlzI3XffTffu3WnatClt27YlPDyckydPsm/fPtauXesqPFxQ9evXZ9q0aTzyyCM0adKEAQMGEBQUxOeff87vv/9O37593UaC+fr68sADD/DCCy/QvHlz+vbtS0pKCp999hmdOnVi3759eT5OtWrVSExMpGnTplx//fUkJSXxwQcfkJmZydtvv01ISMg5Y+3ZsycrV66kb9++3HLLLXz00Uf06dOnwNcK8Pjjj/PFF1+waNEi9uzZQ7du3Th+/DhLly4lOzubhQsXnjOWsLAw/u///o+hQ4dy1VVXceuttxIWFsbnn39OQEDABRVvFhERKfVKen1gESl7tm3bZgwfPty4+OKLjYCAAMPPz8+oW7eucdtttxnffPON275DhgwxAOPAgQP5nu/nn382+vXrZ0RGRhpWq9WoU6eO8eCDDxrHjx932+/777837rnnHqNx48ZGpUqVjICAAKNBgwbGgw8+aBw8eNC13+7du42HHnrIaNasmREREWH4+fkZF110kTF06FBj9+7dhb7eBQsWGIABGAsWLMhzH5vNZjz33HNGgwYNDD8/P6N27drGmDFjjJSUFKNOnTpGnTp13PafOHGiARgxMTFu7YDRqVMnj/P/888/xsCBA41KlSoZgYGBRocOHYz169fne56PP/7YaN68uREYGGhERkYaAwYMMPbt25fv6/HFF18YV111lREQEOC61lxnew3nzZtnXHXVVUZgYKARGBhoXHXVVca8efM89ouJiTEAY+LEiR7bDhw4YADGkCFDPLblJa/n80y///67MWLECKNOnTqGr6+vUblyZeOKK64wHnzwQeOHH35w7bdkyRJjwIABRv369Y3AwEAjLCzMaNq0qTF9+nQjNTXV47yvvvqqcfHFFxtWq9WoXbu28fTTTxs2my3P1y2/5+377783OnXqZISEhLie6wMHDhj//vuvMXbsWKNNmzZGdHS04evra9SuXdvo16+fsXXr1gI9N7kGDhxoAMbAgQPd2jMyMgw/Pz8DMFasWOFx3Pz58w3AmD9/vlt7bGysMWDAACMyMtIwm81u++R3jGGc/XU/m0OHDhlPPvmk0aJFC6NSpUqGj4+PUblyZaN169bGE088Yfz+++8exxSkX3z66aeu597Pz8+44oorjBkzZhjZ2dke++bk5BhPP/20UatWLcPX19e45JJLjFdeecXYv39/nv019/ETEhKMu+66y4iOjjb8/PyMli1bGp9++qnH+XOft6lTp+YZ65o1a4yAgADDarW6XqtOnToZgBEbG3vW6zQMw0hNTTUmTJhgXHLJJYavr69RqVIlo1evXsbGjRvzjSWv13DFihVGixYtDD8/PyM6Otq46667jBMnThTo+RYRESmrTIZxlvXyRERERMRD3bp1AeeqOSIiIlK2qcaIiIiIiIiIiFRYSoyIiIiIiIiISIWlxIiIiIiIiIiIVFiqMSIiIiIiIiIiFZZGjIiIiIiIiIhIhaXEiIiIiIiIiIhUWD7eDqC0cjgcHDlyhJCQEEwmk7fDERERERERLzEMg5SUFKpXr47ZrL8ti5Q3Sozk48iRI9SqVcvbYYiIiIiISCnx77//UrNmTW+HISJFTImRfISEhADON7/Q0FAvRyPny+FwcPz4caKiopTdl2Kn/iYlTX1OSpL6m5So7Gwc8+aRmppK8AMPYPbz82o4ycnJ1KpVy/UZQUTKl1KRGJk9ezYvvvgisbGxXH755cyaNYsOHTrku39WVhZTpkzhvffe4+jRo9SsWZPx48czfPhw1z6JiYmMHz+e5cuXc/LkSerVq8eMGTO47rrrChRT7vSZ0NBQJUbKMIfDQWZmJqGhofolToqd+puUNPU5KUnqb1Ki0tLg8cepBDgefRRzKUlIaIq9SPnk9cTI0qVLefjhh5k9ezbt27dnzpw59OrVi927d1O7du08jxkwYADHjh1j7ty5XHzxxcTFxZGTk+PabrPZ6N69O9HR0Xz88cfUrFmTf//9VxleEREREREREXHj9cTIzJkzGTFiBHfddRcAs2bN4uuvv+aNN95g6tSpHvt/9dVXrF+/nv379xMeHg5A3bp13faZN28eJ06cYPPmzVitVgDq1Klz1jiysrLIyspy3U9OTgacfx1xOBznfX3iXQ6HA8Mw9BpKiVB/k5KmPiclSf1NSpTD4Vo+0+FwgJf7nfq9SPnm1cSIzWZj+/btjB071q29R48ebN68Oc9jVq5cScuWLZk+fTqLFi0iKCiIPn368MwzzxAQEODap23bttx///18+umnREVFMWjQIJ544gksFkue5506dSqTJ0/2aD9+/DiZmZkXeKXiLQ6Hg6SkJAzD0LBfKXbqb1LS1OekJKm/SUkypadT5dTt48ePY8rI8Go8KSkpXn18ESleXk2MxMfHY7fbqVKlilt7lSpVOHr0aJ7H7N+/n02bNuHv78+KFSuIj49n5MiRnDhxgnnz5rn2+fbbb7n99ttZtWoVf/75J/fffz85OTk8/fTTeZ533LhxjBkzxnU/t8BSVFSUaoyUYQ6HA5PJpEJxUiLU36Skqc9JSVJ/kxKVlua6GRUV5fUaI/7+/l59fBEpXl6fSgOeRYwMw8i3sFHuD+XFixcTFhYGOKfj9OvXj9dff52AgAAcDgfR0dG89dZbWCwWWrRowZEjR3jxxRfzTYz4+fnhl0e1a7PZrB/+ZZzJZNLrKCVG/U1KmvqclCT1Nykxp/Wx0tDnvP34IlK8vPodHhkZicVi8RgdEhcX5zGKJFe1atWoUaOGKykC0KhRIwzD4NChQ659LrnkErdpM40aNeLo0aPYbLZiuBIRERERERERKYu8mhjx9fWlRYsWrF692q199erVtGvXLs9j2rdvz5EjR0hNTXW17d27F7PZTM2aNV37/PXXX25Fkvbu3Uu1atXw9fUthisREREREZEi4+eHY+VKTi5aBHmM6hYRKUpeHxM2ZswY3nnnHebNm8eePXsYPXo0Bw8e5N577wWctT/uvPNO1/6DBg0iIiKCYcOGsXv3bjZs2MBjjz3G8OHDXcVX77vvPhISEnjooYfYu3cvX3zxBc8//zz333+/V65RREREREQKwccHrr+erGuucd4WESlGXn+XGThwIAkJCUyZMoXY2FgaN27MqlWrXMvrxsbGcvDgQdf+wcHBrF69mlGjRtGyZUsiIiIYMGAAzz77rGufWrVq8c033zB69GiaNGlCjRo1eOihh3jiiSdK/PpEREREREREpPQyGYZheDuI0ig5OZmwsDCSkpK0Kk0Z5nA4iIuLIzo6WkWzpNipv0lJU5+TkqT+JiUqOxvHokWkpKQQcu+9mL08nUafDUTKN6+PGBEREREREXFjs2EeMYIwwDF8uOqMiEixUrpfRERERERERCosJUZEREREREREpMJSYkREREREREREKiwlRkRERERERESkwlJiREREREREREQqLCVGRERERERERKTC0nK95zKvIQScI38U3RxuWunetqIPxO049/lbjIGWY/67b0uB+Y0KFtuNn0KVFv/d3/c5rLn33MdZg2H47+5t6x+D3z8497EXXQ/d57i3vdcS0o6e+9iO06HRoP/un/gDPup27uMAbt8GwdX+u//zW7BlyjkPM1W+BDq+7974xe1waP25H/OK/0G7ie5tc2oWIFjguvegVuf/7v+7DlbdUbBj7znkfn/zZPjl7XMfV7MTXL/Yve3DrnBy77mPbfs0NLn7v/upsbD4qnMfB9B/LYRf+t/9Pe/DhsfPfVxQVbjjR/e21ffA/i/OfWzD26DTi+5t8xpCduq5j73mTajf+7/7x7bDJ33PfRzAsD3gG/Lf/R9nwvaZrrsmIMruwGQ54z1D7xGl9j2CypfAgG/d28rYe0TQzzMwHSjAa6P3CK+/R+SrjLxHuN7j/EP1HlGG3iPK7O8RdgPjf+E4HA7MixqCxeR+XEm/R2Q4zn1eESmzlBg5l7RYsJ9jn5Banm0ZxyH18LnPb0t2v28YBTsOwG5zv5+TUbBjT/+lLVfmyYIdm3nCsy3taMGOzU53v+/IKfi1Gme8CLbUAl5rmGdbRnwBX5skz7YCvzZZnvcLemxecRTk2Ix4z7b0YwW81jN+ETDsBY/XkeN+Pzv9/K8180QB++FJz7a0I84PBOeSk+F+324rRD803O/bkt2ONQGWvI7Te0TpfY/wK/vvEebsZEx6jzhjv9L5HpGvMvIekfseZ9jz+PCo9whPpeQ9oiz/HmG69NTP1YzEgp2rON8jMgsWgoiUTUqMnEtQtXOPGAmIyrstuMa5z+8b6n7fZCrYcQAWX/f7PgEFO9Ya7NnmX7lgx/qHe7YFVT33cQDWQPf7Zp+CX6vpjI+bvsEFOzaoimdbQGQBX5s8fhkq8Gvj53m/oMfmFUdBjg2I9GwLrAJZefxi5vEYZ/QJk6Xg8ZrPeBuxBhbwtcmj3/iHF7AfVs7jfNXBtwB/DfYJcL9v8S1EPzzjr1W+oW7HGoDD7sBsMeO2p94jzn0ceOc9IrDsv0c4rKEYwTUwnWtHvUd4/T0iX2XkPcL1Hucf6rlR7xGeSsl7RFn+PSLfn6tQ8u8RFgcQe+5zi0iZZDKMM/+8IQDJycmEhYWRlJREaGgevwBImeBwOIiLiyM6OhqzWSV1pHipv0lJU5+TkqT+JiUqJwfHsmUkJScTNmQIZl/fcx9TjPTZQKR804gREREREREpXbKyMN96K5UBx623gpcTIyJSvindLyIiIiIiIiIVlhIjIiIiIiIiIlJhKTEiIiIiIiIiIhWWEiMiIiIiIiIiUmEpMSIiIiIiIiIiFZYSIyIiIiIiIiJSYWm5XhERERERKV18fXHMnUtKSgohWqpXRIqZEiMiIiIiIlK6WK0wdCgZcXGEWK3ejkZEyjlNpRERERERERGRCkuJERERERERKV1ycuCLL/Bbs8Z5W0SkGGkqjYiIiIiIlC5ZWZj79KEy4OjbF1RnRESKkUaMiIiIiIiIiEiFpcSIiIiIiIiIiFRYSoyIiIiIiIiISIWlxIiIiIiIiIiIVFhKjIiIiIiIiIhIhaXEiIiIiIiIiIhUWFquV0REREREShdfXxyvvkpqSgrBWqpXRIqZEiMiIiIiIlK6WK0wciTpcXEEW63ejkZEyjlNpRERERERERGRCkuJERERERERKV3sdli3Dt/Nm523RUSKkabSiIiIiIhI6ZKZiblbN8IBR3Kyc2qNiEgx0YgREREREREREamwlBgRERERERERkQpLiRERERERERERqbCUGBERERERERGRCkuJERERERERERGpsJQYEREREREREZEKS8v1ioiIiIhI6WK14pg2jdTUVIK1VK+IFDMlRkREREREpHTx9YVHHyU9Lo5gX19vRyMi5Zym0oiIiIiIiIhIhaXEiIiIiIiIlC52O2zbhs/Onc7bIiLFSFNpRERERESkdMnMxNymDZGAIzkZVGdERIqRRoyIiIiIiIiISIWlxIiIiIiIiIiIVFhKjIiIiIiIiIhIhaXEiIiIiIiIiIhUWKUiMTJ79mzq1auHv78/LVq0YOPGjWfdPysri/Hjx1OnTh38/PyoX78+8+bNy3PfJUuWYDKZuPHGG4shchEREREREREpy7y+Ks3SpUt5+OGHmT17Nu3bt2fOnDn06tWL3bt3U7t27TyPGTBgAMeOHWPu3LlcfPHFxMXFkZOT47HfP//8w6OPPkqHDh2K+zJEREREREREpAzyemJk5syZjBgxgrvuuguAWbNm8fXXX/PGG28wdepUj/2/+uor1q9fz/79+wkPDwegbt26HvvZ7XZuv/12Jk+ezMaNG0lMTCzOyxARERERkaJitWI8/TRpaWkEaqleESlmXk2M2Gw2tm/fztixY93ae/TowebNm/M8ZuXKlbRs2ZLp06ezaNEigoKC6NOnD8888wwBAQGu/aZMmUJUVBQjRow459QccE7PycrKct1PTk4GwOFw4HA4zufypBRwOBwYhqHXUEqE+puUNPU5KUnqb1KifHxwTJhAyvHj+Pv4gJf7nfq9SPnm1cRIfHw8drudKlWquLVXqVKFo0eP5nnM/v372bRpE/7+/qxYsYL4+HhGjhzJiRMnXHVGvvvuO+bOncvOnTsLHMvUqVOZPHmyR/vx48fJzMws+EVJqeJwOEhKSsIwDMzmUlFSR8ox9TcpaepzUpLU36SklaY+l5KS4tXHF5Hi5fWpNAAmk8ntvmEYHm25HA4HJpOJxYsXExYWBjin4/Tr14/XX3+dnJwc7rjjDt5++20iIyMLHMO4ceMYM2aM635ycjK1atUiKiqK0NDQ87gqKQ1y+0tUVJTXf6BK+af+JiVNfU5KkvqblCiHA8dvv2E9eZLK9etj9vHuxxZ/f3+vPr6IFC+vvsNERkZisVg8RofExcV5jCLJVa1aNWrUqOFKigA0atQIwzA4dOgQaWlp/P3339xwww2u7blD33x8fPjjjz+oX7++x3n9/Pzw8/PzaDebzfrhX8aZTCa9jlJi1N+kpKnPSUlSf5MSk5GBuWlTogBHcjJmX1+vhqM+L1K+efU73NfXlxYtWrB69Wq39tWrV9OuXbs8j2nfvj1HjhwhNTXV1bZ3717MZjM1a9akYcOG/PLLL+zcudP11adPH7p06cLOnTupVatWsV6TiIiIiIiIiJQdXp9KM2bMGAYPHkzLli1p27Ytb731FgcPHuTee+8FnFNcDh8+zMKFCwEYNGgQzzzzDMOGDWPy5MnEx8fz2GOPMXz4cFfx1caNG7s9RqVKlfJsFxEREREREZGKzeuJkYEDB5KQkMCUKVOIjY2lcePGrFq1ijp16gAQGxvLwYMHXfsHBwezevVqRo0aRcuWLYmIiGDAgAE8++yz3roEERERERERESmjTIZhGN4OojRKTk4mLCyMpKQkFV8twxwOB3FxcURHR2tuqBQ79TcpaepzUpLU36REpaVBcDBwqsZISIhXw9FnA5HyTT/VRERERERERKTCUmJERERERERERCosr9cYERERERERcWO1YjzyCOnp6QRYrd6ORkTKOSVGREREpFz6aG0yH61NKfRx/buF0L+bagiIeJWvL8b06aTExRHg6+vtaESknFNiRERERMqltAwH8Yn28zpOREREKg4lRkRERKRcCgowE1nJ4tF+IsmOwwCzCcLDPLcHBagEm4jXORzw999YEhIgMhK0EpKIFCMlRkRERKRc6t8tNM8pMQOePEx8op3wMAsfPl/DC5GJyDllZGCuX58onMv14uXlekWkfFPqVUREREREREQqLCVGRERERERERKTCUmJERERERERERCosJUZEREREREREpMJSYkREREREREREKiwlRkRERERERESkwtJyvSIiIiIiUrr4+GDcdx/pGRkE+Ogji4gUL73LiIiIiIhI6eLnh/Haa6TExRHg5+ftaESknFNiREREREREvOajtcl8tDYlz20Oux2zJTbPbf27hdC/W2hxhiYiFYQSIyIiIiIi4jVpGQ7iE+3ujYZBWOYJAE74h4PJlOdxIiJFQYkRERERERHxmqAAM5GVLG5tfrZ0Fr3ZHIDe/9tDYGRInseJiBQFJUZERERERMRr+ncL9ZwSk5YGM5w3K4daWPR8jZIPTEQqDKVZRURERERERKTCUmJERERERERERCosJUZEREREREREpMJSjRERESkxZ1uS8Wy0JKOIiIiIFBclRkREpMTkuSRjAY8TERERESkOSoyIiEiJyWtJRoATSXYcBphNEB7muV1LMoqIVDA+Pqy7oj+ZNgO72fPngohIUVJiRERESkyeSzICA548THyinfAwCx9qSUYREfHzY3bvl4lPtBPpo8SIiBQv/QlORERERERERCosJUZERERERKR0MQz8bOn4Z6eDYXg7GhEp55QYERERERGR0iU9nUUzLmHVO43wy87wdjQiUs4pMSIiIiIiIiIiFZYSIyIiIiIiIiJSYSkxIiIiIiIiIiIVlhIjIiIiIiIiIlJhKTEiIiIiIiIiIhWWEiMiIiIiIiIiUmH5eDsAERERERERNxYLWxpej81m4DDrb7kiUryUGBERERERkdLF35+Xb5pDfKKdSB+Lt6MRkXJO6VcRERERERERqbCUGBERERERERGRCkuJERERERERKV3S0vhwak2+faMOfrZ0b0cjIuWcEiMiIiIiIiIiUmEpMSIiIiIiIiIiFZYSIyIiIiIiIiJSYSkxIiIiIiIiIiIVlhIjIiIiIiIiIlJhKTEiIiIiIiIiIhWWj7cDEBERERERcWOxsKN+V2zZBg6z/pYrIsVLiRERERERESld/P15YcBC4hPtRPpYvB2NiJRzSr+KiIiIiIiISIVVKhIjs2fPpl69evj7+9OiRQs2btx41v2zsrIYP348derUwc/Pj/r16zNv3jzX9rfffpsOHTpQuXJlKleuzDXXXMMPP/xQ3JchIiIiIiIiImWM1xMjS5cu5eGHH2b8+PH89NNPdOjQgV69enHw4MF8jxkwYABr165l7ty5/PHHH3zwwQc0bNjQtX3dunXcdtttxMTEsGXLFmrXrk2PHj04fPhwSVySiIiIiIhciLQ0Fr7UgC/eboifLd3b0YhIOef1GiMzZ85kxIgR3HXXXQDMmjWLr7/+mjfeeIOpU6d67P/VV1+xfv169u/fT3h4OAB169Z122fx4sVu999++20+/vhj1q5dy5133lk8FyIiIiIiIkXGPzvD2yGISAXh1cSIzWZj+/btjB071q29R48ebN68Oc9jVq5cScuWLZk+fTqLFi0iKCiIPn368MwzzxAQEJDnMenp6WRnZ7sSKXnJysoiKyvLdT85ORkAh8OBw+Eo7KVJKeFwODAMQ6+hlAj1t6Kh56/g1OcunJ67glN/kxLlcPw3tL0U9DtvP76IFC+vJkbi4+Ox2+1UqVLFrb1KlSocPXo0z2P279/Ppk2b8Pf3Z8WKFcTHxzNy5EhOnDjhVmfkdGPHjqVGjRpcc801+cYydepUJk+e7NF+/PhxMjMzC3FVUpo4HA6SkpIwDAOzlnqTYqb+dv4cdrvr/7i4OC9HU3aoz50f9bfzo/4mJcmUnk7uJwS7w+H179WUlBSvPr6IFC+vT6UBMJlMbvcNw/Boy+VwODCZTCxevJiwsDDAOR2nX79+vP766x6jRqZPn84HH3zAunXr8Pf3zzeGcePGMWbMGNf95ORkatWqRVRUFKGhoed7aeJluf0lKipKv8RJsVN/O39mSyxgx2yxEB0d7e1wygz1ufOj/nZ+1N+kRKWluW5azGavf6+e7XOEiJR9Xk2MREZGYrFYPEaHxMXFeYwiyVWtWjVq1KjhSooANGrUCMMwOHToEA0aNHC1v/TSSzz//POsWbOGJk2anDUWPz8//Pz8PNrNZrN++JdxJpNJr6OUGPW3C6fnrnDU5y6MnrfCUX+TEnN6HzvV77zJ248vIsXLq9/hvr6+tGjRgtWrV7u1r169mnbt2uV5TPv27Tly5Aipqamutr1792I2m6lZs6ar7cUXX+SZZ57hq6++omXLlsVzASIiIiIiIiJSpnk99TlmzBjeeecd5s2bx549exg9ejQHDx7k3nvvBZxTXE5fSWbQoEFEREQwbNgwdu/ezYYNG3jssccYPny4axrN9OnTeeqpp5g3bx5169bl6NGjHD161C2ZIiIiIiIipZTZzG+127Czehsc+UyxFxEpKl6vMTJw4EASEhKYMmUKsbGxNG7cmFWrVlGnTh0AYmNjOXjwoGv/4OBgVq9ezahRo2jZsiUREREMGDCAZ5991rXP7Nmzsdls9OvXz+2xJk6cyKRJk0rkukRERERE5DwFBDD59o+JT7QTabV4OxoRKee8nhgBGDlyJCNHjsxz27vvvuvR1rBhQ4/pN6f7+++/iygyERERERERESnPvD6VRkRERERERETEW5QYERERERGR0iUtjXdmNWH5/Gb42dK9HY2IlHOlYiqNiIiIiIjI6UIzTng7BBGpIDRiREREREREREQqLCVGRERERERERKTCUmJERERERERERCosJUZEREREREREpMJSYkREREREREREKiytSiMiIiIiIqWL2cxfVa8kx27gMJm8HY2IlHNKjIiIiIiISOkSEMCTw74gPtFOpNXi7WhEpJzTVBoRERERERERqbCUGBERERERERGRCkuJERERERERKV3S03ltdhvef689vtkZ3o5GRMo51RgREREREZHSxTCITjoEgMkwvByMiJR3SoyUMh+tTeajtSmFPq5/txD6dwsthohEREREREREyi8lRkqZtAwH8Yn28zpORERERERERApHiZFSJijATGQlzyXJTiTZcRhgNkF4mOf2oACVixEREREREREpLCVGSpn+3ULznBIz4MnDxCfaCQ+z8OHzNbwQmYiIiIiIiEj5o2EGIiIiIiIiIlJhacSIiIiIiIiULiYT/0Zegt1uYJhM3o5GRMo5JUZERERERKR0CQzkkf99S3yinUirZ309EZGipKk0IiIiIiIiIlJhKTEiIiIiIiIiIhWWEiMiIiIiIlK6pKcz4+2uzFtyDb7ZGd6ORkTKOdUYERERERGR0sUwqBW/FwCTYXg5GBEp7zRiREREREREREQqLCVGRERERERERKTCUmJERERERERERCosJUZEREREREREpMJSYkREREREREREKiytSiMiIiIiIqWLyURcWE0cDjBMJm9HIyLlnBIjIiIiIiJSugQG8sDI74lPtBNptXg7GhEp55QYEanAPlqbzEdrUwp9XP9uIfTvFloMEYmIiIiIiJQsJUZEKrC0DAfxifbzOk5ERERERKQ8UGJEpAILCjATWclzeOqJJDsOA8wmCA/z3B4UoLrNIiIiUowyMnh+/vXk2A2eGbLM29GISDmnxIhIBda/W2ieU2IGPHmY+EQ74WEWPny+hhciExERkQrN4eDio7sAMBuGl4MRkfJOf/YVERERERERkQpLiRERERERERERqbA0lUZEREREREoNW7bBuu1ZfNfzTZL9KpGQ5cM3W9Po3DwQX6vJ2+GJSDmkxIiIiIiIiJQK3/2czrQFCaRmGJjq9sAwW8Bu8MKCBF778ARjh0TQrkmgt8MUkXJGU2lERERERMTrvvs5nafnxJOW4Sy2aphzV8ZzjhJJyzCYMCee735O91KEIlJeKTEiIiIiIiJeZcs2mLYgAQzIbw0a49Q/0xYmYMvWSjUiUnQ0lUZERERERArFMAzsDsjOMU59nX7bIMfuTHY4bxvYcvfJPv2+Qc6p437/J4vUjHMnOwwgNd1g/Y50urcOKv4LFZEKQYkREREREZFSyjCcSYbTkw5nJiFcbXaD7FPJiGyPY5z75OSclpQ4tY8tx3AlLLJzwJZjkHP6cXZOO+9/j294adCGyQSbdikxIiJFR4kRERERqRBs2QbrdqSTnGYHIDnNrpUuxMXhODXK4bSkgO20EQ2u+6eSBLac3ETCWRIVOaclE7JPS16cNlriv5ETuY+Ba5RF7nnEnWFAcprD22GISDmixIiIiIiUe6evdJHLlo1WuvACu8MzKXBmMiG/UQ05Of+NaDgzmeAxwuFsiYrTp3ucGi1h1+dsD1YfsPqY3L8sYLXm3ja59vHxMeF7ah+fU22u+xact60mfCymU9vA57Tzvv91Mr/tzzrrKJQqyQdpGPczO2u1JzSoesk9ESJS7ikxIiIiIuVa7koX+VV0zF3pYso9kbQvJ8kRV/2HbPepD2cd0ZBH/YfTp17Ysh0kp+RgtZ4k245rRIMt232qx5mjLM4cLeFQAsKNyZSbfHAmD3wspv8SDz6cSj6cdv/MRIUP+FhMrsSDWyLClbD4L3mRm7DwsZw6l9X9vu+p/XwsYDKV3Eiq5DQHv+7LOus+b310PSG2ZACyPqkCH10BjRs7v9q1g0aNSiJUESmHlBgRERGRcqugK12YTq108fHUgEJNqzGMU8kDez5JgfzqP+Q5quHsIxrc7hdgtETx1X9IK64TFyuzmTxHNPicNurB97TkgUcS4rREwn/JCs9EhY/PaY9jMeFr/S954RpFYf0veWGxaBoXQOfmgbz24QnSMox8v1cPh9Wl4fGfAfA7cQzWHIM1a5wbTSbYsQOaNi2ReEWkfFFiRERERMqtdTvSC7XSxaiXjlI51JL3Khuq/1AgrpEIeY1oyGuaRX6jJfI4T16jJaynHXP6SIn/EhjOKRsWsxIQpZmv1cTYIRFMmBOPKZ9E5rPdX2PBB12wGM46Qfj5QZcukJMDDgfUqlWiMYtI+aHEiIiIiJRJdofByWQ7CUl24hNPfZ12OyHJzr/Hsgt1zj//zQYKd4y3nG1Ew3+JCNymT+Q3WsJ9pIRn/Qerjwkfs0FKykmio8Lxs1pOJR7cY/CxgFkJCDlP7ZoEMuWeSKYtTCA13cDksGOYLZwa10Vytbocv/lOqi6bD2FhkJQEX30Fw4fD//0fBGmVGhE5P6UiMTJ79mxefPFFYmNjufzyy5k1axYdOnTId/+srCymTJnCe++9x9GjR6lZsybjx49n+PDhrn2WLVvGhAkT2LdvH/Xr1+e5557jpptuKonLERERkQtgGAZpmQYJrkRHDgmJdo4n/ZfwiE+0cyLZXuz1Ks5a/6GAIxpOr/9wZh2H3CTE2eo/nF7g8vQEREnWfwBwOBzExZmJjvbFbDaX6GNLxdG+SSAfTw1g/ZaTbHr+I5L9KvFH7VaMviOaTs0D8Y2dBJ8tdiZF7rwT3nsP5s2DzZth6VJo0sTblyAiZZDXEyNLly7l4YcfZvbs2bRv3545c+bQq1cvdu/eTe3atfM8ZsCAARw7doy5c+dy8cUXExcXR07Of2NZt2zZwsCBA3nmmWe46aabWLFiBQMGDGDTpk20bt26pC5NREREzpCdY3Ai+bQRHok5niM+kuxkZl14gQxfqwmzCTJtBTuXyQStLvNn3NAIV4LCbC75BIRIRedrNdG9hR/dv74XgMGP7KV761OjQWrXhv/9D15/Hfbvh7Vr4fbb4fffoVUrePlluPde5ze0iEgBeT0xMnPmTEaMGMFdd90FwKxZs/j666954403mDp1qsf+X331FevXr2f//v2Eh4cDULduXbd9Zs2aRffu3Rk3bhwA48aNY/369cyaNYsPPvggzziysrLIyvqvEnZysrPitcPhwFHKyqeXtnhKM4fDgWEYes4ugJ67glN/Kxp6/gquNPU5wzBITnP8l+RIcrglPBJOJTwSUy48VpMJKoeYiQizEBlmIaKS+/+RlSxEhFkICTSxZls60xaeLOA1QJcWAQQH5H6gchYwNYqvimmZUpr6m1QADgc2a4CziPCZ/W7sWEwLF8Kff2J07Ag//YRp2DBMq1bByJEYa9ZgvPUWVK5chOGo34uUZ15NjNhsNrZv387YsWPd2nv06MHmzZvzPGblypW0bNmS6dOns2jRIoKCgujTpw/PPPMMAQEBgHPEyOjRo92O69mzJ7Nmzco3lqlTpzJ58mSP9uPHj5OZmVnIKyt6Drvd9X9cXJyXoyk7HA4HSUlJGIahYb+FoP52ftTfzp/63PkpqT5nyzY4mQInUgxOppy6nezelpgC2fYLf6wAX6gcCpWDTVQONREeApVDTFQO+e92WLBzeoqT49SXe12QzDTnV6OaBkH+kFaAH+VB/tCwZipxcWVz1ZXipvc4KWn3j/6dkylQ2Qf3nw0+PlhWrwaTCXtu+zvvEPj224Q8+yym5ctx/PADiW+8QXbLlkUSS0pKSpGcR0RKJ68mRuLj47Hb7VSpUsWtvUqVKhw9ejTPY/bv38+mTZvw9/dnxYoVxMfHM3LkSE6cOMG8efMAOHr0aKHOCc5RJWPGjHHdT05OplatWkRFRREaGnq+l1hkzJZYwI7ZYiE6Otrb4ZQZDocDk8lEVFSUfokrBPW386P+dv7U587PhfY5u8MgMeX0UR52j1EeCUl2UtIvfMSExQzhYRbXKA/nqA4zkWeM8gj0L/rvnbFDMnj6rfyX7DWd+mfskAhqVg8o8scvL/QeJyXNYj4COLCYzZ4/G/L6WfHUUxjXXguDBmHZt4/wG2/EmDIFHn/cuV7zBfD397+g40WkdPP6VBrwnLtrGEa+83lzfygvXryYsLAwwDkdp1+/frz++uuuUSOFOSeAn58ffn5+Hu1ms7nU/fAvbfGUdiaTqVS+jmWFnrfCUX+7cHruCie/PpeWcfpUlhyP1VriE+0kFFHx0tAgc55TWSIrWYis5ENkmIVKIWavrVbS/sogptxjcq10caagQBNj74ygXZNAL0RXtug9TkpU7u/up/pdgbRqBTt2wL33YvrgA0zjx0NMDCxaBFWrnnco6vMi5dsFJUb++OMP4uPjadq0KUHnsTxWZGQkFovFYyRHXFycx4iPXNWqVaNGjRqupAhAo0aNMAyDQ4cO0aBBA6pWrVqoc4qIiJQVOXaDE6fqdcSdyObvQ3ayHEn/JTtO/Z9RBMVLrT64EhvOJMfpCQ9n0iMi1Iyfb+n/wOBa6WJHOjPeT8CWDb5WeGRQhHOlC6sKNYqUKpmZjP3wTmzZBq8OfLtwx4aGwuLF0L07PPAArFkDV17pTI706FE88YpImXZeiZGFCxfy5JNPEhsbC8C2bdto3rw5AwYMoHv37vzvf/8r0Hl8fX1p0aIFq1evdltKd/Xq1fTt2zfPY9q3b89HH31EamoqwcHBAOzduxez2UzNmjUBaNu2LatXr3arM/LNN9/Qrl2787lcERGRYudRvPS0JMfx3JVbThUv9awFWri57yYTVAo5NcojzEJUJR+PpEdEmIXQIHO5WpHF12qie+sg3v40kfhEO6FBlv9WuhCR0sVup/m+bwF4/XyGtplMMGwYtGkDAwfCL79Az57wxBPwzDNgtRZxwCJSlhU6MfLRRx8xdOhQevfuTa9evbj//vtd25o3b86HH35Y4MQIwJgxYxg8eDAtW7akbdu2vPXWWxw8eJB773UuzzVu3DgOHz7MwoULARg0aBDPPPMMw4YNY/LkycTHx/PYY48xfPhw1zSahx56iI4dOzJt2jT69u3Lp59+ypo1a9i0aVNhL1dEROSC2bKNU1NZctwSHq7laU+12bIvfJRHgJ8pz6kspyc8IsIspxUvFREpxxo1gq1b4ZFH4I03YNo0WL8ePvgAzljZUkQqrkInRqZOncqwYcOYO3cudrvdLTHSqFEjXn311UKdb+DAgSQkJDBlyhRiY2Np3Lgxq1atok6dOgDExsZy8OBB1/7BwcGsXr2aUaNG0bJlSyIiIhgwYADPPvusa5927dqxZMkSnnrqKSZMmED9+vVZunQprVu3LuzlioiI5MvhMEhMdfxXx+P0pEeSnYRT/yenXXghD7MZIkLdp7NEhJmxmtK4qHZloitbiQizEBRQ+qe1iIiUqIAAmD0bunWDu+6C77+Hpk3hnXegXz9vRycipUChEyN79uxh2rRpeW4LDw8nISGh0EGMHDmSkSNH5rnt3Xff9Whr2LAhq1evPus5+/XrRz+90YmIlGq2bIN1O9JJTnOu85qcZuebrWl0LgU1H9IzHXkULHVPfiQk2bEXQfHSkEBznlNZIis5p7lEnCpeajmjeKnD4SAuLpPoaH8VBhQROZdbboEWLWDQINiyBfr3h3vvhZkznckTEamwCp0YCQwMJCkpKc9thw8fpnLlyhcclIiIlH/f/ZzOtAUJpGb8N33Elg0vLEjgtQ9PMHZI8awSYrcbnEjOrd1h91i5JXeUR3pmERUvDTtVpDSfAqYRYRb8y0DxUhGRcqFuXedUmokT4YUX4M03YdMmWLoULrvM29GJiJcUOjHSvn17XnvtNW655RaPbe+++y6dO3cuirhERKQc++7ndJ6eEw/55B7SMgwmzIlnyj2RtC9gcsQwDFLSHXnW7zh9xMfJPIuXFl7lEPNpy9OeUbz01P/lrXipiEi5YLXC889Dly4weDD8+iu0bAmvvgrDh/+3TLCIVBiFTow8/fTTXH311bRq1YpBgwZhMplYvnw5EydOZMOGDfzwww/FEaeIiJQTtmyDaQsSwMg3L4IBmAyYtjCBj6c6hzefPpUlPo86HgmJdrKKoHipv5/JmdjwGOHh47ofHmrB6qNfnEVEyrTu3WHXLrjzTvjmG2f9kTVrYM4c55K/IlJhFDox0rJlS7788ktGjhzJI488AsDzzz9PgwYNWLVqFY0bNy7yIEVEpPxYtyPdbfpMfgwgNd3gxscPkZl14QkPsxnCzyheGhVmcY76OLVyS0QlC0H+Jo3yEBHxtqAgBow7RHyinUhfS/E9TpUq8OWX8OKLMH48LFkCP/zgnFrTsmXxPa6IlCqFSozYbDbWrVtHw4YN2bNnD/v27ePYsWNERkZyySWXFFeMIiJSjny3Kx2TiQJPZylIUiQ4wORRx+P0ER+RlXzyLF4qIiKC2QxPPAEdO8Jtt8H+/dCunbMGycMPO7eLSLlWqMSIj48PvXv35ssvv6R27drUr1+f+vXrF1dsIiJSDiUk2gtV48PXBy6p4+dRvyP3/4hKKl4qIiJFoG1b2LnTOaVm2TJ45BFYuxbefRf8/LwdnYgUo0IlRsxmMzVr1iQ5Obm44hERkXLIMAx+229jWUwKu/+2Ffg4kwlaNw5g8t1RxRidiIiUOpmZjF5xDzabwVu3/F/JPW6lSvDRR/DWW87RIqtWwZVXYnnrrZKLQURKXKH/xDZixAhef/117HZ7ccQjIiLlSHaOweqtaYycdowHZxxj/Y70Qh1vGHD1lUW/ZK+IiJRydjttf/+CTvtXYXY4SvaxTSa45x5nrZFGjSA2lsA+fZgEkJNTsrGISIkodPFVX19f/vjjDxo1akSfPn2oVq2aW5E6k8nE6NGjizRIEREpW06m2PlsYyorN6RwItn9F9pKwSbSMw2yc/JflQbABAQFmujUXIkRERHxgiuugG3b4KGHMM2dy0Qg54YbnAVaa9XydnQiUoQKnRh54oknXLdnzpzpsV2JERGRiuvPf20sj0nh2x/TyD7jj2oX17LSr0sInVsE8eOeDCbMiceUz5K9plP/jL0zAl+rCqaKiIiXBAXBO++Q3q4dOSNGELp5MzRtCvPnQ58+3o5ORIpIoRMjBw4cKI44RESkjLI7DL7blcHymBR+/ivLbZvZBFc3DeDmLiFcUd/PNcKwXZNAptwTybSFCaSme6ZGggJNjL0zgnZNNFpERES8L6dfP5qNGMHeZs2w/PQT9O0LDz4I06erMKtIOVDoxEidOnWKIw4RESljUtIdrPoulU/Wp3DshHvdqeAAE9e3D6ZvpxCqRuT9o6Z9k0A+nhrA+h3pzHg/AVs2+FrhkUERdGoeqJEiIiJSquwH0r75htAXXoAZM+D//g82bnROrbnkEm+HJyIXoNCJkVx//fUX3377LQkJCURGRtKlSxcuvvjiooxNRERKoYNHs1kek8I3W9PItLmP9qhdxYebuoTQo3UQAX7nru/tazXRvXUQb3+aSHyindAgC91bBxVX6CIiIhfG1xdeegm6doUhQ+Cnn6B5c3jjDRg82NvRich5KnRixDAMRo0axZtvvonjtArRZrOZkSNH8n//V4LLaYmISIlwOAx+3JPJ8pgUftid6bG91eX+3NIlhBYN/TGbNdJDRETKueuug5074Y47YN06uPNOWLsWXnsNgoO9HZ2IFFKhEyMvv/wys2fP5r777mPo0KFUr16dI0eOsGDBAmbPnk29evVUfFVEpJzIyHTwzdY0lq9L4d9j7tVU/f1M9GwTxE2dQ6hdxeqlCEVEpFwKDGTwI3tJSLITbA3wdjR5q1ED1qyB55+HSZNgwQLYsgWWLnUWaBWRMqPQiZF33nmHUaNG8corr7jaatSowVVXXYXFYuHtt99WYkREpIw7mpDDinUprNqcSlqG+3SZqhEWbuwUwnXtggkOPPd0GRERkUIzmcjyDSTTaifYVIpHIlosMGECdOoEt98Oe/dC69bOGiT33w+lOXYRcSl0YmT//v307t07z229e/dmzpw5FxyUiIiUPMMw+PmvLJbHpPDdrgwcZywWc2UDP27uEkK7JgFYNF1GRETkPx07OqfWDB8OK1fCqFHOqTVz50J4uLejE5FzKHRiJCwsjH/++SfPbf/88w+hoaEXHJSIiJQcW7bBtz+msTwmhb8OZbtts/pAt6uCuLlzCBfX8vVShCIiUuFkZTHy89Fk2gwW9pnm7WgKJiICPvkEXn0VHnvMeXv7dnj/fbj6am9HJyJnUejESPfu3Xnqqado1qwZLVq0cLXv3LmTiRMn0rNnzyINUEREikdCkp2VG1L4bGMqiakOt23hoWb6dgqh99XBVA6xeClCERGpsHJy6PzLRwAsdjzv5WAKwWSCBx90JkJuvRX+/NM5zWbyZBg3zjn1RkRKnUInRqZOncq6deto1aoVl112GdWqVSM2Npbdu3dTvXp1pk6dWhxxiohIEfnjnyyWxaSwbns6OXb3bZfW8eWWLiF0ah6I1UfTZURERM5L8+bO0SIjR8J77znrkMTEOG9Xq+bt6ETkDIVOjNSqVYudO3cyY8YMYmJiOHDgABEREYwdO5bRo0cTGRlZHHGKiMgFsNsNNu5MZ1lMCr/tt7ltM5uhU7NAbu4SwmX1fDGpUJyIiMiFCwmBRYvgmmucCZJvv4Urr3SuXtOrl7ejE5HTFDoxAhAZGamRISIiZUBSqp0vvkvl0/WpHE90Hx4SGmSm99XB9O0YTFTl8/pxICIiIucyZAi0aQMDB8KuXXDddfDoo/Dcc+Cr+l0ipUGhfxM+fvw4J0+e5JJLLvHYtnfvXsLDwzVqRETEyw4csbFiXSqrt6aRle2+vEzdalZu7hLCNa0C8ffVcrsiIiLF7tJL4fvvnUVZX3sNXnoJNmyADz6Aiy7ydnQiFV6hEyP3338/YWFhvP322x7bZsyYQXJyMh988EGRBCciIgXncBhs/S2T5TEpbP89022byQRtGgdwS5cQml3qp+kyIiIiJc3f37liTbduzmV9f/gBmjWDt95yjiYREa8pdGLku+++45VXXslzW8+ePXnooYcuOCgRESm49EwHX21JY8W6FA4fz3HbFuhv4tq2wdzUKZga0VYvRSgiIiIuN97oLM46aBB8951z9Zq1a2HWLAgM9HZ0IhVSoRMj8fHxRERE5LmtcuXKHD9+/IKDEhGRczsSn8OKdSl8uTmV9Ez36TLVo3y4qVMw17YNJihA02VERKSMCQxkxEO7OJFkx2oN8HY0Ra92bVi3DiZNguefh7ffdiZJli6Fxo29HZ1IhVPoxEiVKlX45Zdf6NKli8e2X375Jd+kiYiIXDjDMPhpbxbLY1LY8ksGhns+hOaX+nFzlxBaNw7AYtZ0GRERKaNMJlICI0iy2Yksr9M/fXzg2WehSxe44w7YvRuuugpeeQX+9z/nPFgRKRGFToxce+21PPfcc1x77bVuBVj//PNPpk6dSu/evYs0QBERgSybgzXb0lkRk8L+I9lu23ytJq5pFcjNnUO4qIaq24uIiJQp3bo5V6u58074+mu45x5Ys8ZZe6RSJW9HJ1IhFDoxMmnSJD7//HOaNGlCly5dqFmzJocOHSImJobIyEgmT55cHHGKiFRIxxNzWLk+lc82pZKc5nDbFlnJwo0dg7n+6mDCgi1eilBERKQYZGUx4uvxZGQ5+PC6Sd6OpvhFR8OqVTBzJowbBx99BNu2wZIl0Lq1t6MTKfcKnRipXr06P/74IxMmTOCrr75i7dq1REVFcccddzBlyhSqV69eHHGKiFQouw9ksSwmhQ070rG750O4rJ4vt3QJoUOzQHwsGmYrIiLlUE4OPXcsAGDZtRO8HEwJMZvh0UehY0dnQdYDB+Dqq501SB55xLldRIpFoRMj4EyOzJ07t6hjERGp0LJzDDb8lM6ymBR+/9vmts1ihs4tArm5SwiN6vp5KUIREREpdq1awU8/OeuMfPQRPP64c9WahQudI0tEpMidV2LkdMnJyezdu5fq1atrtIiIyHlITLHz+aZUPt2QSkKS3W1bWLCZGzoE06dDMJGVLvgtW0RERMqCsDDnCjXdu8ODDzprj1x5Jbz3nrMmiYgUqQKNx9q4cSPPP/+8R/tLL71EdHQ0rVu3platWgwfPhzjzCUSREQkT/sO2XhxUQIDxx9m3mdJbkmR+jWtPDY4nKXP1WD4DZWUFBEREaloTCbnqJEff4TLL4ejR52JkvHjISfH29GJlCsF+k37tddeIzk52a1t06ZNPPHEE1SpUoVbb72VPXv2sGDBAq666iruu+++YglWRKSsszsMtvycwfKYFHb+meW2zWSCdk0C6NclhCYN/DBpmT4RERG5/HL44QcYPdq5Us3zz8O6dfDBB1C7trejEykXCpQY2b59O2PGjHFre+eddzCbzaxdu5ZGjRoB0K9fPxYtWqTEiIjIGVIzHHy5OZVP1qUQm+A+XSYowMR17YK5sVMI1SI1MkRERETOEBgIc+ZA165w992webNzas28eXDTTd6OTqTMK9Bv4HFxcTRo0MCtbfXq1bRs2dKVFAEYNGgQw4cPL9oIRUTKsH+PZbNiXQpff59GRpb7VMOa0T7c3CWEnq2DCPBXpXkRERE5h4ED4aqr4LbbnKNIbr4Z7r8fXnoJ/P29HZ1ImVWgxMiZw7mPHj1KbGwsAwYMcGuPjo4mIyOj6KITESmDDMNg227ndJmtv2V6bG/ZyJ+bu4TQ6jJ/zGZNlxEREfEQEMD9923hRLIdh1Uf+N1cdBFs3AhPPQUvvgivv+68v3QpNGzo7ehEyqQCJUbq1avHtm3b6N69OwDffvstJpOJNm3auO2XkJBAZGRk0UcpIlIGZNocrN1uZ832Y/xz1L0omr+vie6tg7i5cwh1qlm9FKGIiEgZYTZzvFIt4rETadKoSg++vjB9unNqzZ13ws8/Q4sWziTJkCHOwmUiUmAFSozcdtttTJ06lZo1a1K1alWmTJlCcHAw1113ndt+mzdvpn79+sUSqIhIaXXsRA6frk/hi+9SSUl3ny4THW7hxk4hXN8+mJBA/WInIiIiRejaa2HXLrjjDvj2Wxg2DNauhdmzISTE29GJlBkFSoyMGjWKlStXMnToUACsVitz5swh5LRvtuzsbBYvXsyIESOKJVARkdLEMAx+3ZfFspgUNu3KwOFw335FfT9u7hLC1VcGYLHorzYiIiKFYrNxx7fPkJFp8EnPsd6OpnSrVg2++QZeeAEmToT33oPvv3dOrWne3NvRiZQJBUqMBAYGsnHjRjZs2EBCQgItW7akTp06bvukpKTwyiuveEyvEREpT2zZBut2pLM8JoW9B21u26w+0OYyM4N6RXJpHc2HFhEROW/Z2fTZOgeAz6951MvBlAEWC4wfD506waBB8Ndf0KaNswbJgw9qao3IORR4XUiz2Uznzp3z3R4eHs4tt9xSFDGJiJQ6J5LtfLYxlZUbUziZ7D48pHKomT4dQujdPpDszASio329FKWIiIhUaFdfDTt3wogR8Mkn8PDDzqk18+dDRISXgxMpvQqcGBERqYj2HrSxPCaFmO1pZLvXU6VBLSu3dAmhc4sgfK0mHA4HcZ6L0IiIiIiUnPBwWL7cWWdkzBj47DO48kp4/33o2NHb0YmUSkqMiIicwW43+O7nDJbFpPDLX1lu28wmuLppALd0CaFxfT+P5cxFREREvM5kgvvvh/btYeBA2LsXunRx1iAZP9459UZEXJQYERE5JSXdwRffpfLJ+hTiTtjdtgUHmLi+fTB9O4VQNUJvnSIiIlIGNG0K27fDAw/AggXOxMi338LixVCjhrejEyk19Nu9iFR4/8Rms3xdCqu3ppFpc19ut05VH27qHEL31kEE+Gm5XRERESljgoPh3XehWze47z5Yv945tWbBArj+em9HJ1IqKDEiIhWSw2GwbU8my2NS2LbbszBI68v9ublLCC0b+Wu6jIiIiJR9gwdD69Zw663w00/QuzeMHu1c5tdXheOlYitQYsQwDD7//HPq1atH48aN89znl19+4e+//+aGG24o0gBFRIpSRqaDr7emsTwmhUNx7tVU/f1MXNsmiJs6h1CritVLEYqIiAgBAYy5ay2JKQ5sVn9vR1N+XHIJbNkCjz8O//d/8PLLsGEDLFkCF1/s7ehEvKZAiZEvvviCgQMH8ssvv+S7T1BQELfeeivz589nwIABRRagiEhRiI3P4ZP1KazanEpahvt0maoRFm7qHEKvtsEEB2q6jIiIiNeZzRyKupR4q51Ik342Fyk/P3jlFefUmmHDnDVImjeHOXPgttu8HZ2IVxToXWbOnDnceeed1K9fP999LrroIoYMGcLChQsLHcTs2bOpV68e/v7+tGjRgo0bN+a777p16zCZTB5fv//+u9t+s2bN4tJLLyUgIIBatWoxevRoMjO1jqZIRWIYBrv2ZjJhznEGTzzCR2tT3JIiTRv4MeXuSBZNrk7/bqFKioiIiEjF0acP7NoFHTpASgoMGgQjRkBamrcjEylxBRox8sMPPzBnzpxz7tejRw/uvffeQgWwdOlSHn74YWbPnk379u2ZM2cOvXr1Yvfu3dSuXTvf4/744w9CQ0Nd96Oioly3Fy9ezNixY5k3bx7t2rVj7969DB06FICXX365UPGJSNljyzZY+6Nzusy+Q9lu26w+cM1VQdzcJYT6NTWfVkREpFSy2ei/cQbpmQZfdX3Q29GUXzVrOlepeeYZ59e8ebB5MyxdCk2aeDs6kRJToMTIyZMn3RIP+YmMjOTkyZOFCmDmzJmMGDGCu+66C3CO9Pj666954403mDp1ar7HRUdHU6lSpTy3bdmyhfbt2zNo0CAA6taty2233cYPP/xQqNhEpGyJT8xh5cZUPt+YSmKqw21bRJiFPh2DueHqYCqFWLwUoYiIiBRIdjb9Nzn/oLmm00gvB1PO+fjA5MnQuTPccQf8/ju0auWsP3LvvaAi9FIBFCgxEhYWxtGjR8+537Fjx9xGcZyLzWZj+/btjB071q29R48ebN68+azHNmvWjMzMTC677DKeeuopunTp4tp29dVX89577/HDDz/QqlUr9u/fz6pVqxgyZEi+58vKyiIrK8t1Pzk5GQCHw4HD4cjvMK8obfGUZg6HA8Mw9JxdgLLw3P3+t43l61JYvyMD+xnhNqxj5abOIXRsFoDVx/mDvbiuSf2taOj5Kzj1uQun567g1N+kRDkc/835LwX9ztuPXyK6dIGdO2HoUFi1CkaOhLVr4e23oXJlb0cnUqwKlBhp2rQpK1as4JZbbjnrfitWrKBp06YFfvD4+HjsdjtVqlRxa69SpUq+iZhq1arx1ltv0aJFC7Kysli0aBHdunVj3bp1dOzYEYBbb72V48ePc/XVV2MYBjk5Odx3330eCZjTTZ06lcmTJ3u0Hz9+vFTUJnHY7a7/4+LivBxN2eFwOEhKSsIwDMxm1Y8oqLLQ33LsBtt+d/DVVgd/HnIvpmo2QevLzFzb2kyDmiYglZMnUos9JvW381cW+lxppD53ftTfzo/6m5QkU3o6uZ8Q7A6H179XU1JSvPr4JSYqCj77DGbNgrFjYdky+PFH+OADaNvW29GJFJsCJUbuvPNOhg0bRq9evbj99tvz3GfRokUsWbKEd999t9BBmM4YnmUYhkdbrksvvZRLL73Udb9t27b8+++/vPTSS67EyLp163juueeYPXs2rVu35q+//uKhhx6iWrVqTJgwIc/zjhs3jjFjxrjuJycnU6tWLaKiogo1Cqa4mC2xgB2zxUJ0dLS3wykzHA4HJpOJqKgo/RJXCKW5vyWl2vniuzQ+25jG8US727bQIDPXtw+iT4cgoioX6O2tSKm/nb/S3OdKM/W586P+dn7U36REnVYA1GI2e/171d+/Ai0ZbDbDmDHOoqy33gr79ztvP/usc5lfff9LOVSgTw533HEHixcv5s477+T999+nb9++1KtXD4ADBw7wySef8PXXX9OzZ898Eyd5iYyMxGKxeIwOiYuL8xhFcjZt2rThvffec92fMGECgwcPdtUtueKKK0hLS+Puu+9m/Pjxef4w9/Pzw8/Pz6PdbDaXuh/+pS2e0s5kMpXK17GsKC3P24EjNpbFpLDmh3Rs2e4jROpVt3JzlxCuuSoQP1/vxqv+duH03BWO+tyF0fNWOOpvUmJO72On+p03efvxveKqq+Cnn+Cee2DJEhg3zjm1ZtEiqFrV29GJFKkCJUZMJhOffvopo0ePZu7cuXz55ZeuER2GYWC1Wrn33nuZOXNmviM98uLr60uLFi1YvXo1N910k6t99erV9O3bt8Dn+emnn6hWrZrrfnp6usebl8ViwTAMDMM483ARKaUcDoPvf81geUwKO/7IcttmMkHbKwK4uUsIzS7xK9R7j4iIiIgUQGgovP8+dO8ODzwAa9bAlVc6kyM9eng7OpEiU+Cx5n5+fsyePZuJEycSExPDwYMHAahduzZdunQp1AiP040ZM4bBgwfTsmVL2rZty1tvvcXBgwddy/6OGzeOw4cPs3DhQsC5ak3dunW5/PLLsdlsvPfeeyxbtoxly5a5znnDDTcwc+ZMmjVr5ppKM2HCBPr06YPFotUoREq7tAwHX21JZcX6VI4cz3HbFuhv4tq2wdzUOZgaUVYvRSgiIiJSQZhMMHw4tGkDAwfCr79Cz57OGiRTpoBVv49J2VfoSfhVqlTh1ltvLbIABg4cSEJCAlOmTCE2NpbGjRuzatUq6tSpA0BsbKwrCQPOlWweffRRDh8+TEBAAJdffjlffPEF1113nWufp556CpPJxFNPPcXhw4eJiorihhtu4LnnniuyuEWk6B2Oy2bFuhS++j6N9Ez30V3Vo3y4uXMIPdsEERRQAYezioiIVCT+/owb8jmJqQ5sPp7T3cULLrsMfvjBWX/kzTfhhRdg3TpnYda6db0dncgFKVBi5PTExJmsVisRERH4+vqedxAjR45k5Mi81yc/s5jr448/zuOPP37W8/n4+DBx4kQmTpx43jGJSMkwDIOf/shiWUwK3/+awZmz3Zpf6sctXUNpfbk/ZrOmy4iIiFQIFgv7qjclPtFOpFkjvkuNgAB44w3o1g3uugu+/x6aNoW5c+EcK5iKlGYFSozUrVv3rPP3LRYLHTp0YOrUqbRq1arIghOR8ivL5mD1D+msWJfCgSPZbtt8rSa6twrk5i4h1Kt+/klXERERESkG/fpBy5Zw223O5Ei/fnDvvTBzpjN5IlLGFCgxMn369HwTIzk5ORw+fJiVK1fSuXNnNm/eTNOmTYsyRhEpR46fzOGT9al88V0qyWkOt21RlSz07RTM9e2DCQvWX4dEREQqLJuNG75/g/QMB+s6/s/b0Uhe6taFDRvg6aed02refBO++w6WLoVGjbwdnUihFCgx8uijj55zn+nTp9OuXTuee+45PvroowsOTETKD8Mw2H3AxvKYFNb/lI7DPR/CZfV86dc1hKubBuJj0XQZERGRCi87m8ExzvqAm9oP83Iwki+rFaZOhS5dYPBg+OUXaNECXnsNhg1zFm4VKQMKXXw1P/7+/jz00EPnrP8hIhVHdo7B+h3pLItJ4Y9/bG7bfCzQublzukzDuiqqJiIiIlJm9egBu3Y5kyNr1sCIEc7/33zTueSvSClXZIkRgDp16nDy5MmiPKWIlEEnU+x8vjGVlRtTSUiyu22rFGzmhg7B9OkYQkSYpsuIiIiIlAtVq8LXX8P06fDUU87Van74AZYscdYjESnFijQxcvDgQSpXrlyUpxSRMmTfIRvLYlJYuy2N7Bz3bfVrWrmlSwhdWwbha9WwShEREZFyx2yGsWOhY0cYNAj27YN27Zw1SB5+2LldpBQqssSIzWZj1qxZdOjQoahOKSJlgN1hsPnnDJbHpLDrzyy3bWYTtGsSwC1dQmjSwO+sq1uJiIiISDnRrh389JNzSd/ly+GRR2DtWnj3XYiK8nZ0Ih4KlBiZOXNmvtvsdrtrVZqjR48yd+7cIgtOREqWLdtg3Y50ktOc01+S0+x8szWNzs0DPUZ5pKY7WLU5lU/Wp3A0wX26TFCAievaBXNT5xCqRhTpwDQRERERKQsqV4aPP3bWGRk9GlatgqZNYfFi6NzZ29GJuCmSVWksFgvt27fn/fffp1mzZkUSmIiUrO9+TmfaggRSMwxXmy0bXliQwGsfnmDskAjaNQnk32PZLF+Xwtffp5GZZbido2a0Dzd3CaFn6yAC/DVUUkRERKRCM5ngvvugfXsYOBB+/x26doUJE5xfPvoDmpQOBeqJBw4cyP8EPj5ERkbi56dVJUTKqu9+TufpOfFg5L09LcNgwpvxXFzLyp//Zntsv+oyf27uEsJVjfwxmzVdRkRERC6Qvz+TBn1IUqoDm48+Z5R5TZrAjz/Cgw/CvHkwZQrExMD770PNmt6OTqRgiZE6deoU+ITHjx8nSvPGRMoMW7bBtAUJYOSbF3G1n54U8fc10aN1EDd1DqFONWuxxykiIiLl00drk/lobYpH+4lKrXGEgTkFBjx52GN7/24h9O+mpWDLjKAgmDsXunWDe+6BjRvhyiuddUduuMHb0UkFVyRjlwzD4Msvv2Tu3Ll8/vnnZGVlnfsgESkV1u1Id5s+cy6hQSZu7RHG9e2DCQnUdBkRERG5MGkZDuIT7fludxjkuT0tw1GcYUlxGTQIWrWCW2+F7duhTx946CGYNg00C0G85IISI/v27WPevHksWLCA2NhYfH19ueWWW4oqNhEpRoZhEHfSzmcbPf9Ckx8T0ORif27trr/OiIiISNEICjATWcni1maxZ3PNzsUYDgdrmw/GbvEcnRoUoD/QlFkXXwybNzuX9n35ZXjlFdiwAZYuhQYNvB2dVECFToxkZmby0UcfMXfuXDZu3IhhGJhMJsaMGcPYsWOJiIgojjhF5AKcTLFz4Eg2fx/J5sARm/N2bDbpmQUfKQLOKTUp6frrjIiIiBSd/t1CPafEpKVB8FMADF/+MOaQEC9EJsXK1xdmznQWYx061Lm8b/Pm8MYbcMcd3o5OKpgCJ0a2bdvG3LlzWbJkCSkpKQQFBTF06FBuueUWevfuzQ033KCkiIiXpWY4+PtU0sOVADmSTWJq0SQzTCYIDdJfZ0RERESkiPTuDbt2we23w/r1MHgwrFkDr70GwcHejk4qiAIlRpo0acJvv/0GQNu2bRk+fDgDBw4kKCiIpKSkYg1QRDxl2RwcPJZzKvFhY/+pBEjcyfzn556pSriFetWtGMDWXzMLdIxhwNVXBp5n1CIiIiIieahRA9auhWefda5Ys2ABfP89LFkCTZt6OzqpAAqUGPn1118xmUxcf/31vPDCC1x22WXFHZeIADl2g8NxORyIdSZADhzJ5sCRbI4cz8FRwFkwlUPN1KtmpW51X+pVt1KvupU6Va2uebm2bIN+Yw+RlmHkuyoNOOuLBAWa6NRciRERERERKWIWC0ycCJ07O0eP/PEHtGkDM2bAyJHOocsixaRAiZFZs2Yxf/58Pv/8c7744gtatWrFiBEjGDhwYHHHJ1IhOBwGx07YOXDE5qwDEutMgPx7LJvsnIKdIyjARL3qvqeSIM4ESN1qViqFWM56nK/VxNghEUyYE48pnyV7Taf+GXtnBL5W/VASERERkWLSqRPs3AnDhsHnn8MDDzin1sydC+Hh3o5OyqkCJUYefPBBHnzwQX788UdXnZG7776bhx9+mOuvvx6TyYRJGTyRczIMgxPJDrf6HwdinTVBMrMKNgTE12qibrX/Eh+5o0AiK1nO+/uwXZNAptwTybSFCaSme8YRFGhi7J0RtGui0SIiIiIiUswiI2HlSvi//4PHHoNPPnEu7fvBB9C+vbejk3KoUKvStGzZkpYtW/Lyyy+7Vqb5+OOPMQyDESNGcM899zB06FAVYRXBuXqLWwLkVFHU5LSCFUK1mKFmFSv1qlmpV+PU/9WtVI30wWIu+kRk+yaBfDw1gPU70pnxfgK2bPC1wiODIujUPFAjRURERESk5JhM8NBDcPXVcOut8NdfztEkkyc7l/m1nH1UtEhhFHq5XgB/f38GDx7M4MGD2bdvH3PnzmXhwoU89thjTJgwgfT09KKOU6TUyshy8E/sqZEfpxIgB45kk5BU8EKo1SJ9nCM/TpsGUzPaWuLJCF+rie6tg3j700TiE+2EBlno3jqoRGMQERERwc8Px8qVJCUlEebn5+1oxJtatIAdO+C++2DxYnjqKYiJgUWLoFo1b0cn5cR5JUZOV79+fZ5//nmeffZZVq1axbx584oiLpFSJzvH4N9j2W5TYA4cyeZoQg5GAQuhRoRZXFNf6p5KhNSpaiXAX0vgioiIiLj4+MD115MVF+e8LRVbSIgzEXLNNXD//c4VbK68EhYuhGuv9XZ0Ug4U2buM2Wymd+/e9O7du6hOKeIVdofB0XjnUrinjwL591g29oLNgiEk0OyeADlVDyQ0SEP+REREREQKzWSCoUOdK9UMHAg//wy9ejlrkDz7LPj6ejtCKcOUfpUKyzAM4hPtrqkvf58aAfJPbDZZ2QUbAuLvd6oQ6mlTYOpV9yU81KyCxCIiIiLnKzsbFi0iICUF7r0XNJ1GcjVsCFu3wqOPwuuvw4svwvr1zsKsF13k7eikjFJiRCqEpFQ7/xy1nTENxkZaRsESID4WqF3VekYdEF+qhFswF0MhVBEREZEKzWbDPGIEYYBj+HAlRsSdvz+89hp07QojRsAPP0CzZvDOO9C/v7ejkzJIiREpV9IzHa6RH84vG/sP2UhKiy3Q8SYT1IjyOW0ajC/1qlmpEe2Dj0UJEBERERGRUuPmm53FWQcNgs2bYcAAuPtuePllCAz0dnRShigxImWSLdvg4NHs0+qAOEeDHDtR8JVgoitbXAVQc5Mgdar64OerQqgiIiIiImVCnTrOqTQTJ8LUqfDWW/Ddd7B0KVx+ubejkzJCiREp1ex2g8PHc1yjP3JHgxyOy8FRwJVgQgKhfk0/V/2PuqemwgQHKAEiIiIiIlLm+fjAc89Bly5wxx3w229w1VXwyitw113OYeEiZ6HEiJQKhmFw7ITdrf7HgSPZHDyaTXZOwc4R6H+qEGpuAqS6lTpVLWRnJBAdHYXZrESIiIiIiEi5dc01sGsXDBkCX3/tnFazZo1zFElYmLejk1JMiREpUYZhcDLFcWoJXJvbcrgZWQUbAmL1gTrVcqfA+LpWg4mubPFYCcbhcBCXURxXIiIiIiIipU6VKrBqFcyYAU8+CR9+CNu2wZIl0KqVt6OTUkqJESk2qem5hVDdV4NJSnUU6HizGWpF+zgLoFa3ukaDVI/ywaKVYEREREREJC9mMzz2GHToALfdBgcOQPv2zhokY8Y4t4ucRokRuWCZNgcHj+a4J0COZHM8seCFUKtFWFwJkNwkSK0qVnytSoCIiIiIVDh+fjiWLCEpOZkwLdUr56tNG/jpJ/jf/+Djj53JkrVrYcECiI72dnRSiigxIgWWYzf491j2aXVAnLePxOdgFLAQakSY5bQ6IM4iqHWrWgnwV9ZWRERERE7x8YH+/cmKi3PeFjlflSo5p9O8/TY89BB89RU0bQrvvQddu3o7Oikl9C4jHhwOg9iEHNfIj9w6IP8eyyangINAggNMbvU/ckeBhAVbijd4ERERERGR05lMzkKs7drBwIGwe7ezUOuTT8KkSUq+iRIjpZ0t22DdjnSS05wZieQ0O99sTaNz88ALnmZiGAYJSfZTS+H+NwLkn6PZZNoKNgTEz2pyLX97+iiQyDDPQqgiIiIl6aO1yXy0NsWj/USS3fX/gCcPe2zv3y2E/t1Ciz0+ETmLnBxYtgy/5GTnCiO+vt6OSMqDxo2dhVgfegjeece5xO+6dfD++1C7trejEy9SYqQU++7ndKYtSCA1478khS0bXliQwGsfnmDskAjaNQks0LmSUu38fdoKMLmjQFLSC1YI1WKG2lVPJUCq/ZcAqRbhg1mFUEVEpBRKy3AQf5Z6Vw6DPLenZRTsZ6OIFKOsLMy33kplwHHrrUqMSNEJDHROq+nWzTmK5LvvnFNr5s2DG2/0dnTiJUqMlFLf/ZzO03PiIZ+BG2kZBhPmxDPlnkjan5Ycych08PfR0xIgR7L5OzabhKSCzYExmaB6pM9/I0BOjQapGW3F6qMEiIiIlB1BAWYiKxV+CmdQgOpeiYiUe7fe6ly+99ZbnaNIbroJHngAXnwR/P29HZ2UMCVGSiFbtsG0BQlg5JsXcbYb8Ny8BPp2zOLgsRz+PmIjNqHgK8FEVbK4Rn44p8H4UruqD/6++oVQRETKvv7dQjUlRkRE8nfRRbBpE4wfDy+9BK+95ry/ZAlceqm3o5MSpMRIKbRuR7rb9JmzybQZLF3jOX/6dKFBZi46IwFSt5qV4EAlQEREREREpALz9XWOEunSxVnPZudOaNECZs+GO+/0dnRSQpQYKYW+25WOyUSBl8DNFeBn+m8ESDWra1WYyiFmFUIVERERERHJz3XXwa5dcMcdEBPjTJKsWQOvvw4hId6OToqZEiOlUHKao1BJkXrVrTx3XxTRlS0qhCoiIiIiInI+qleH1ath6lSYOBEWLYLvv4elS6FZM29HJ8VIcylKodAgMwUd4GEyQc1oH6pqdRgREREREZELY7HAU085l/GtWRP+/BPatIH/+7/CD+mXMkOJkVKo/ZWBBf6eMwy4+sqCLdkrIiIiIlIm+PrimDuXpFmztFSveEeHDs56I337gs0GDz3kXM43IcHbkUkxUGKkFOrcPJDgABPnGv9hAoIDTXRqrsSIiIiIiJQjVisMHUrGwIHO2yLeEBEBK1Y4R4v4+sLKldC0KWzc6O3IpIgpMVIK+VpNjB0SASbyTY6YTv0z9s4IfK2aQiMiIiIiIlLkTCYYNcpZa6RBAzh0CDp3hmeeAbvd29FJEVFipJRq1ySQKfdEEhSYd9IjKNDEM/dE0q6JRouIiIiISDmTkwNffIHfmjXO2yLe1qwZbN/uXMLX4YCnn4ZrroEjR7wdmRQBJUZKsfZNAvl4ak3GDYnA99QIQl8rjBsSwcdTayopIiIiIiLlU1YW5j59qDx4MGRleTsaEaeQEFiwwPkVFOQs0HrllbBqlbcjkwukxEgp52s10b11EKFBFgBCgyx0bx2k6TMiIiIiIiLecOedsGOHs95IfDxcfz08+qizSKuUST7eDkBERCqOj9Ym89HaFI/2E0l21/8Dnjzssb1/txD6dwst9vhERERECuSSS2DLFnj8cXj1VZgxA9avhyVLoH59b0cnhVQqRozMnj2bevXq4e/vT4sWLdh4liq/69atw2QyeXz9/vvvbvslJiZy//33U61aNfz9/WnUqBGrNMRJRMSr0jIcxCfaPb4cp5YodxjkuT0tw+HdwEVERETO5O/vXLHmk0+gcmX48UdnLZIlS7wdmRSS10eMLF26lIcffpjZs2fTvn175syZQ69evdi9eze1a9fO97g//viD0ND//noYFRXlum2z2ejevTvR0dF8/PHH1KxZk3///ZeQkJBivRYRETm7oAAzkZUs53WciIiISKnUty/s2gWDBsGmTXDbbbBmDbzyirMWiZR6Xk+MzJw5kxEjRnDXXXcBMGvWLL7++mveeOMNpk6dmu9x0dHRVKpUKc9t8+bN48SJE2zevBnrqXXP69SpU+Sxi4hI4fTvFqopMSIiIlL+1KoFMTEweTI89xzMnQubN8PSpXDFFd6OTs7Bq4kRm83G9u3bGTt2rFt7jx492Lx581mPbdasGZmZmVx22WU89dRTdOnSxbVt5cqVtG3blvvvv59PP/2UqKgoBg0axBNPPIHFkvdfKrOyssg6reJ1cnIyAA6HA4ejdA3hLm3xlGYOhwPDMPScXQA9dwWn/iYlTX1OSpL6m5Qoh8M159/hcDiXR/VqOOr3UgA+PvDMM9ClC9xxB+zZA61awaxZcPfdYNICGqWVVxMj8fHx2O12qlSp4tZepUoVjh49mucx1apV46233qJFixZkZWWxaNEiunXrxrp16+jYsSMA+/fv59tvv+X2229n1apV/Pnnn9x///3k5OTw9NNP53neqVOnMnnyZI/248ePk5mZeYFXeuEcdrvr/7i4OC9HU3Y4HA6SkpIwDAOzWUPxC0r97fyov0lJU5+TkqT+JiUqOxv/554jMyMDR2Ii5owMr4aTkuJZOFwkX127ws6dMHQofPkl3Huvc2rN229DPrMexLu8PpUGwHRG5swwDI+2XJdeeimXXnqp637btm35999/eemll1yJEYfDQXR0NG+99RYWi4UWLVpw5MgRXnzxxXwTI+PGjWPMmDGu+8nJydSqVYuoqCi3WibeYrbEAnbMFgvR0dHeDqfMcDgcmEwmoqKi9EtcIai/nR/1Nylp6nNSktTfpKQ5Hn+cjOPHiS4Ffc7f39+rjy9lUHQ0fP45vPwyjB0LH38M27Y5C7O2aePt6OQMXk2MREZGYrFYPEaHxMXFeYwiOZs2bdrw3nvvue5Xq1YNq9XqNm2mUaNGHD16FJvNhq+vr8c5/Pz88PPz82g3m81efyM+U2mLp7QzmUyl8nUsK/S8FY76m5Q09TkpSepvUtJKS5/z9uNLGWU2wyOPQIcOcOutcOCA8/azz8Jjjzm3S6ng1VfC19eXFi1asHr1arf21atX065duwKf56effqJatWqu++3bt+evv/5ymwu4d+9eqlWrlmdSREREREREShG7Hdatw3fzZudtkbKsVSv46ScYOBBycpwjSHr1gmPHvB2ZnOL1qTRjxoxh8ODBtGzZkrZt2/LWW29x8OBB7r33XsA5xeXw4cMsXLgQcK5aU7duXS6//HJsNhvvvfcey5YtY9myZa5z3nfffbz66qs89NBDjBo1ij///JPnn3+eBx980CvXKCIiIiIihZCZiblbN8IBR3IynFppUqTMCguDDz6Aa66BBx+Eb76Bdu2cBVr1x3uv83piZODAgSQkJDBlyhRiY2Np3Lgxq1atci2vGxsby8GDB13722w2Hn30UQ4fPkxAQACXX345X3zxBdddd51rn1q1avHNN98wevRomjRpQo0aNXjooYd44oknSvz6RERERERERDCZ4K67oG1bGDYMMjMhn1VTpWSZDMMwvB1EaZScnExYWBhJSUmlovjqgCcPE59oJ7KShQ+fr+HtcMoMh8NBXFwc0dHRmhtaCOpv50f9TUqa+pyUJPU3KVFpaRAcDDhHjJhDQrwaTmn7bCDlhGFoCd9SQj/VREREREREREqakiKlhhIjIiIiIiIiIlJhKTEiIiIiIiIiIhWWEiMiIiIiIiIiUmEpMSIiIiIiIqWL1Ypj2jSSJ0zQUr0V0NChQzGZTK6viIgIrr32Wn7++ecie4xJkybRtGnTAu2bnJzM+PHjadiwIf7+/lStWpVrrrmG5cuXYxgGV1xxBXfddVeex37wwQdYrVaOHTt21sd4/vnnsVgsvPDCCwWONTExEZPJxLp169zaly1bRufOnQkLCyM4OJgmTZowZcoUTpw4UaDrPR9ZWVmMGjWKyMhIgoKC6NOnD4cOHTrncYcPH+aOO+4gIiKCwMBAmjZtyvbt213bDcNg0qRJVK9enYCAADp37sxvv/3mdo577rmH+vXrExAQQFRUFH379uX3338vVPxKjIiIiIiISOni6wuPPkr6yJHO21LhXHvttcTGxhIbG8vatWvx8fGhd+/eJR5HYmIi7dq1Y+HChYwbN44dO3awYcMGBg4cyOOPP05SUhIjRozgww8/JD093eP4efPm0bt3b6pUqXLWx5k/fz6PP/448+bNu6B4x48fz8CBA7nqqqv48ssv+fXXX5kxYwa7du1i0aJFF3Tus3n44YdZsWIFS5YsYdOmTaSmptK7d2/sdnu+x5w8eZL27dtjtVr58ssv2b17NzNmzKBSpUqufaZPn87MmTN57bXX2LZtG1WrVqV79+6kpKS49mnRogXz589nz549fP311xiGQY8ePc762B4MyVNSUpIBGElJSd4OxTAMw+g/7pDR5b5/jP7jDnk7lDLFbrcbsbGxht1u93YoZYr62/lRf5OSpj4nJUn9TUpaaepzpe2zQXk3ZMgQo2/fvm5tGzZsMAAjLi7O1Xbo0CFjwIABRqVKlYzw8HCjT58+xoEDB1zbY2JijKuuusoIDAw0wsLCjHbt2hl///23MX/+fANw+5o/f36esdx3331GUFCQcfjwYY9tKSkpRnZ2thEfH2/4+voa7777rtv2f/75xzCbzcZnn3121utdt26dUaNGDcNmsxnVq1c31q9f77Z94sSJxpVXXulx3MmTJw3AiImJMQzDMLZu3WoAxqxZs/J8nJMnT541jvOVmJhoWK1WY8mSJa62w4cPG2az2fjqq6/yPe6JJ54wrr766ny3OxwOo2rVqsYLL7zgasvMzDTCwsKMN998M9/jdu3aZQDGX3/9VeBr0IgREREREREpXex22LYNn507nbelQktNTWXx4sVcfPHFREREAJCenk6XLl0IDg5mw4YNbNq0ieDgYK699lpsNhs5OTnceOONdOrUiZ9//pktW7Zw9913YzKZGDhwII888giXX365a1TKwIEDPR7X4XCwZMkSbr/9dqpXr+6xPTg4GB8fHyIiIujbty/z58932z5//nyqVKlCr169znp9c+fO5bbbbsNqtXLbbbcxd+7c83qeFi9eTHBwMCNHjsxz++kjMc50+eWXExwcnO/X5Zdfnu+x27dvJzs7mx49erjaqlevTuPGjdm8eXO+x61cuZKWLVvSv39/oqOjadasGW+//bZr+4EDBzh69Kjbef38/OjUqVO+501LS2P+/PnUq1ePWrVq5fvYZ/Ip8J4iIiIiIiIlITMTc5s2RAKO5GTVGamAPv/8c4KDgwHnh91q1arx+eefYzY7/7a/ZMkSzGYz77zzDiaTCXAmIipVqsS6deto2bIlSUlJ9O7dm/r16wPQqFEj1/lzkxpVq1bNN4b4+HhOnjxJw4YNzxnv8OHDue6669i/fz8XXXQRhmHw7rvvMnToUCwWS77HJScns2zZMtcH/TvuuIP27dvz6quvEhoaes7HPd2ff/7JRRddhPU8vl9WrVpFdnZ2vtvPds6jR4/i6+tL5cqV3dqrVKnC0aNH8z1u//79vPHGG4wZM4Ynn3ySH374gQcffBA/Pz/uvPNO17FnTkOqUqUK//zzj1vb7Nmzefzxx0lLS6Nhw4asXr0a30JMw9OIERERERERESlVunTpws6dO9m5cydbt26lR48e9OrVy/WBePv27fz111+EhIS4RjWEh4eTmZnJvn37CA8PZ+jQofTs2ZMbbriBV155hdjY2ELFYBgGgCvxcjY9evSgZs2arlEj3377LX///TfDhg0763Hvv/8+F110EVdeeSUATZs25aKLLmLJkiWFijU33oLEmpc6depw8cUX5/tVp06dIo/H4XDQvHlznn/+eZo1a8Y999zD//73P9544w23/c48R17nvf322/npp59Yv349DRo0YMCAAWRmZhY4ViVGREREREREpFQJCgpyfShv1aoVc+fOJS0tzTXVwuFw0KJFC1fyJPdr7969DBo0CHCOINmyZQvt2rVj6dKlXHLJJXz//fcFjiEqKorKlSuzZ8+ec+5rNpsZOnQoCxYswOFwMH/+fDp27EiDBg3Oety8efP47bff8PHxcX399ttvbtNpQkNDSUpK8jg2MTERgLCwMAAuueQS9u3bd9aRH/m5kKk0VatWxWazcfLkSbf2uLi4sxadrVatGpdddplbW6NGjTh48KDrvIDHqJO8zhsWFkaDBg3o2LEjH3/8Mb///jsrVqw494WfosSIiIiIiIiIlGomkwmz2UxGRgYAzZs3588//yQ6OtpjdENuogCgWbNmjBs3js2bN9O4cWPef/99AHx9fc+5aonZbGbgwIEsXryYI0eOeGxPS0sjJyfHdX/YsGEcOnSI5cuXs3z5ckaMGHHW8//yyy/8+OOPrFu3zi25s2HDBrZt28avv/4KQMOGDTl06JBHgmDbtm2YzWYuvvhiAAYNGkRqaiqzZ8/O8/FyEyl5WbVqlUeS6fSvVatW5XtsixYtsFqtrF692tUWGxvLr7/+Srt27fI9rn379vzxxx9ubXv37nWNTqlXrx5Vq1Z1O6/NZmP9+vVnPS84R5VkZWWddZ/TqcaIiIiIiIiIlCpZWVmuRMDJkyd57bXXSE1N5YYbbgCcUydefPFF+vbty5QpU6hZsyYHDx5k+fLlPPbYY2RnZ/PWW2/Rp08fqlevzh9//MHevXu58847Aahbty4HDhxg586d1KxZk5CQEPz8/DzieP7551m3bh2tW7fmueeeo2XLllitVjZu3MjUqVPZtm2bq6hpvXr16Nq1K3fffTdWq5V+/fqd9Rrnzp1Lq1at6Nix4/+3d+fhMV79/8DfM1kmeyLLSKKRCLEloiTI8iCaCFFr7bunqlUUxeVnaYsgVEtRpaVqX1tVfVCVIqGW2opQS6pJkCZiy77PnN8fmvka2TWZGTPv13VNa845931/7nFMMp85S6m6wMBArF+/Hp999hnCw8PRrFkzDBo0CAsXLoSrqyuuXLmCadOmYezYsbC2tgYAtGvXDtOnT8fUqVORnJyMPn36wNXVFX/++Se+/PJL/Oc//8GkSZPKjOVFpsqUsLW1xejRozF16lQ4ODjA3t4e06ZNQ4sWLRAWFqZqFxoaij59+mDChAkAgPfffx9BQUGIiorCgAEDcPbsWaxduxZr164F8DQZNnnyZERFRcHLywteXl6IioqChYWFalTQX3/9hV27diE8PBxOTk5ITk7Gxx9/DHNzc3Tr1q3qN1Hl/WsMjK5tycXtU1+MLm3z9jJhf3sx7G+kaexzpEnsb6RR2dlCAEIAQpGZqe1odO6zgb4bOXKk2la61tbWok2bNuK7775Ta5eSkiJGjBghHB0dhUwmE56enmLMmDEiIyNDpKamit69ewsXFxdhamoq3N3dxUcffaR6D8vPzxd9+/YVdnZ2FW7XK8TT7WhnzJghvLy8hKmpqahbt64ICwsTe/fuFUqlUq3t9u3bBQDx9ttvV3iPBQUFwsHBQSxZsqTM+qVLlwpHR0dRUFCgutf//ve/wt3dXZibm4umTZuKyMhIkZ+fX+rYXbt2iQ4dOghra2thaWkpfH19RWRkZK1t1yuEEHl5eWLChAnC3t5emJubi+7du4s7d+6otXF3dxdz5sxRK/vf//4nfHx8hEwmE02bNhVr165Vq1cqlWLOnDnC2dlZyGQy0aFDBxEXF6eqT05OFhEREUIulwsTExPxyiuviCFDhogbN25UK36JEP+sKENqMjMzYWtri4yMjGqvBlwbBsxKxsN0BRztjLA7qp62w3lpKJVKpKWlQS6Xq1awpsqxv70Y9jfSNPY50iT2N9KonBzgnx1JlJmZkP7zjbi26NpnAyKqWZxKQ0REREREusXEBOKjj5CTkwMLbtVLRLWMiREiIiIiItItpqYQc+YgOy0NFqam2o6GiPQcx0ESERERERERkcHiiBEiIiIiItItSiVw7RqMHz8GHB0BrmtDRLWIiREiIiIiItIteXmQ+vrCEU8XX4WWF18lIv3G1CsRERERERERGSwmRoiIiIiIiIjIYDExQkREREREREQGi4kRIiIiIiIiIjJYTIwQERERERERkcFiYoSIiIiIiIiIDBa36yUiIiIiIt1iYgIxdSpyc3NhbmKi7WiISM8xMUJERERERLrF1BRiyRJkpaXB3NRU29EQkZ7jVBoiIiIiIiIiMlhMjBARERERkW5RKoHERBjdvfv0z0REtYhTaYiIiIiISLfk5UHasCGcACgzMwFra21HRER6jCNGiIiIiIiIiMhgMTFCRERERERERAaLiREiIiIiIiIiMlhMjBARERERERGRwWJihIiIiIiIiIgMFhMjRERERERERGSwuF0vERERERHpFmNjiHffRW5eHsyN+ZGFiGoX32WIiIiIiEi3yGQQq1YhKy0N5jKZtqMhIj3HqTREREREREREZLCYGCEiIiIiIt0iBPDgASQPHz79MxFRLeJUGiIiIiIi0i25uZA6O6MuAGVmJmBtre2IiEiPccQIERERERERERksJkaIiIiIiIiIyGAxMUJEREREREREBouJESIiIiIiIiIyWEyMEBEREREREZHBYmKEiIiIiIiIiAwWt+slIiIiIiLdYmwMMWIE8vPzITPmRxYiql06MWJk9erVaNCgAczMzODn54cTJ06U2zYmJgYSiaTU48aNG2W237lzJyQSCXr37l1L0RMRERERUY2SySA2bEDGihWATKbtaIhIz2k9MbJr1y5MnjwZs2fPxu+//4727dsjIiICd+7cqfC4mzdvIiUlRfXw8vIq1SYpKQnTpk1D+/btayt8IiIiIiIiInqJaT0xsmzZMowePRpvvfUWmjVrhuXLl8PNzQ1r1qyp8Di5XA5nZ2fVw8jISK1eoVBg6NChmDdvHjw9PWvzFoiIiIiIqCYJAeTkQJKb+/TPRES1SKsT9goLC3HhwgXMmDFDrTw8PBynTp2q8NhWrVohPz8fzZs3xwcffIBOnTqp1UdGRsLJyQmjR4+ucGpOiYKCAhQUFKieZ2ZmAgCUSiWUSmVVb0kjdC0eXaZUKiGE4Gv2L/C1qzr2N9I09jnSJPY30qicHEhtbFAXQHF6OmBtrdVw2O+J9JtWEyMPHz6EQqFA3bp11crr1q2L1NTUMo9xcXHB2rVr4efnh4KCAmzZsgWhoaGIiYlBhw4dAAAnT57E+vXrcenSpSrHsmjRIsybN69U+YMHD5Cfn1/1m6olSoVC9f+0tDQtR/PyUCqVyMjIgBACUqnWB0i9NNjfXgz7G2ka+xxpEvsbaZIkNxclnxAePHgASV6eVuPJysrS6vWJqHbpxBLPEolE7bkQolRZiSZNmqBJkyaq54GBgbh79y4+/fRTdOjQAVlZWRg2bBjWrVsHR0fHKscwc+ZMTJkyRfU8MzMTbm5ucHJygo2NTTXvqOZJjVIAKCA1MoJcLtd2OC8NpVIJiUQCJycn/hJXhu+OZOG7Y9mlytOz/+//E1cqStX362SFfqHa/eZGF7G/kaaxz5Emsb+RRuXkqP7o5OQEqZZHjJiZmWn1+kRUu7SaGHF0dISRkVGp0SFpaWmlRpFUJCAgAFu3bgUA3L59G4mJiejRo4eqvmTom7GxMW7evImGDRuWOodMJoOsjBWvpVKpzv3w17V4dJ1EItHJv0ddkFsg8DC9dOKjhFKgzPrcAn5bWB72N9I09jnSJPY30phn+pgu9DltX5+IapdWEyOmpqbw8/NDdHQ0+vTpoyqPjo5Gr169qnye33//HS4uLgCApk2bIi4uTq3+gw8+QFZWFlasWAE3N7eaCZ5ID1iaS+FoZ1R5wzKOIyIiIiIi0gdan0ozZcoUDB8+HP7+/ggMDMTatWtx584djB07FsDTKS7JycnYvHkzAGD58uXw8PCAt7c3CgsLsXXrVuzZswd79uwB8HSYm4+Pj9o17OzsAKBUOZGh6x9qg/6h2p8qRkREREREpC1aT4wMHDgQjx49QmRkJFJSUuDj44ODBw/C3d0dAJCSkoI7d+6o2hcWFmLatGlITk6Gubk5vL29ceDAAXTr1k1bt0BERERERERELymJENwYvCyZmZmwtbVFRkaGTiy+OmBWMh6mK+BoZ4TdUfW0Hc5LQ6lUIi0tDXK5nHNDqdaxv5Gmsc+RJrG/kUbl50MMG4aCggKY7toFqYWFVsPRtc8GRFSztD5ihIiIiIiISI2ZGcTu3UhPS4OcO8IQUS1jup+IiIiIiIiIDBYTI0RERERERERksJgYISIiIiIi3ZKTA6mREZxdXICcHG1HQ0R6jokRIiIiIiIiIjJYTIwQERERERERkcFiYoSIiIiIiIiIDBYTI0RERERERERksJgYISIiIiIiIiKDxcQIERERERERERksY20HQEREREREpMbICCIiAgWFhTA1MtJ2NESk55gYISIiIiIi3WJmBrF/P9LT0iA3M9N2NESk5ziVhoiIiIiIiIgMFkeMEBHpMIVCgaKiIm2HQTpKqVSiqKgI+fn5kEr1+7sOIyMjGBsbQyKRaDsUIiIi0jNMjBAR6ajs7Gzcu3cPQghth0I6SggBpVKJrKwsg0gYWFhYwMXFBaamptoOhYhqW04OJHI55EIA9+8D1tbajoiI9BgTI0REOkihUODevXuwsLCAk5OTQXzopeoTQqC4uFjvR1IIIVBYWIgHDx4gISEBXl5eej9ChogASW4uJACU2g6EiPQeEyNERDqoqKgIQgg4OTnB3Nxc2+GQjjKUxAgAmJubw8TEBElJSSgsLIQZF2MkIiKiGsKvW4iIdJi+f9glqg6OEiEiIqLawN8wiIiIiIiIiMhgMTFCRERERERERAaLiREiIiIiIiIiMlhMjBARUY2QSCQVPkaNGvXC5/bw8MDy5cur3D4qKgpGRkZYvHhxqbq5c+fi1VdfLVWenp4OiUSCmJgYtfI9e/YgJCQEtra2sLKygq+vLyIjI/H48eNq3kXVFRQU4L333oOjoyMsLS3Rs2dP3Lt3r9LjkpOTMWzYMDg4OMDCwgKvvvoqLly4oKofNWpUqb+XgIAAtXOEhISUajNo0KAav0ciogpJpRAdO6IwMBDg+kJEVMv4LkNERDUiJSVF9Vi+fDlsbGzUylasWKGxWDZs2IDp06fjm2+++VfnmT17NgYOHIg2bdrgp59+wtWrV7F06VJcvnwZW7ZsqaFoS5s8eTL27t2LnTt34tdff0V2dja6d+8OhUJR7jFPnjxBcHAwTExM8NNPP+GPP/7A0qVLYWdnp9aua9euan8vBw8eLHWuMWPGqLX56quvavoWiYgqZm4OcfQoHn//PcDd2YioljExQkRENcLZ2Vn1sLW1hUQiUSs7fvw4/Pz8YGZmBk9PT8ybNw/FxcWq4+fOnYv69etDJpPB1dUVEydOBPB0BENSUhLef/991QiGisTGxiIvLw+RkZHIycnB8ePHX+h+zp49i6ioKCxduhSffPIJgoKC4OHhgc6dO2PPnj0YOXLkC523MhkZGVi/fj2WLl2KsLAwtGrVClu3bkVcXBx++eWXco/7+OOP4ebmhg0bNqBt27bw8PBAaGgoGjZsqNZOJpOp/b3Y29uXOpeFhUWpv08iIiIifWWs7QCIiKgazi8DLiyrvJ28NdDnR/WyvT2BtIuVH+s3BfCf8mLxlePnn3/GsGHDsHLlSrRv3x63b9/G22+/DQCYM2cOvvvuO3z22WfYuXMnvL29kZqaisuXLwMAvv/+e7Rs2RJvv/02xowZU+m11q9fj8GDB8PExASDBw/G+vXr0aFDh2rHvG3bNlhZWWHcuHFl1j8/EuNZ3t7eSEpKKrfe3d0d165dK7PuwoULKCoqQnh4uKrM1dUVPj4+OHXqFLp06VLmcT/++CO6dOmC/v37IzY2FvXq1cO4ceNKvWYxMTGQy+Wws7NDx44dsXDhQsjlcrU227Ztw9atW1G3bl1ERERgzpw5sLa2Lvd+iIiIiF5mTIwQEb1MCjOB7OTK21m7lS7Le1C1Ywszqx9XJRYuXIgZM2aoRll4enpi/vz5mD59OubMmYM7d+7A2dkZYWFhMDExQf369dG2bVsAgL29PYyMjGBtbQ1nZ+cKr5OZmYk9e/bg1KlTAIBhw4YhODgYn3/+OWxsbKoVc3x8PDw9PWFiYlLt+z148CCKiorKra/onKmpqTA1NUWdOnXUyuvWrYvU1NRyj/vrr7+wZs0aTJkyBbNmzcLZs2cxceJEyGQyjBgxAgAQERGB/v37w93dHQkJCfjwww/x2muv4cKFC5DJZACAoUOHokGDBnB2dsbVq1cxc+ZMXL58GdHR0dV5CYiI/p2cHEg8PCBXKoHERIDJWSKqRUyMEBG9TExtAKt6lbczdyq7rCrHmlYvgVAVFy5cwLlz57Bw4UJVmUKhQH5+PnJzc9G/f38sX74cnp6e6Nq1K7p164YePXrA2Lh6P6a2b98OT09PtGzZEgDw6quvwtPTEzt37lSNUKkqIUSl03bK4+7u/kLHVaSyeJRKJfz9/REVFQUAaNWqFa5du4Y1a9aoEiMDBw5Utffx8YG/vz/c3d1x4MABvPHGGwCgNsLEx8cHXl5e8Pf3x8WLF9G6desavy8iovJIHj6EBIBS24EQkd5jYoSI6GXi/y+muTw/tUaDlEol5s2bp/rw/SwzMzO4ubnh5s2biI6Oxi+//IJx48bhk08+QWxsbLVGbHzzzTe4du2aWkJFqVRi/fr1qsSIjY0NMjIySh2bnp4OAKr1NBo3boxff/0VRUVF1R418m+m0jg7O6OwsBBPnjxRGzWSlpaGoKCgcs/p4uKC5s2bq5U1a9YMe/bsqfAYd3d3xMfHl9umdevWMDExQXx8PBMjREREpJeYGCEiolrXunVr3Lx5E40aNSq3jbm5OXr27ImePXti/PjxaNq0KeLi4tC6dWuYmppWuCMLAMTFxeH8+fOIiYlRW1A0PT0dHTp0wNWrV+Hj44OmTZvi3r17SE1NVZuac+7cOUilUlWMQ4YMwcqVK7F69WpMmjSp1PXS09PLXWfk30yl8fPzg4mJCaKjozFgwAAAT3f8uXr1KpYsWVLuccHBwbh586Za2a1btyocvfLo0SPcvXsXLi4u5ba5du0aioqKKmxDRERE9DJjYoSIiGrdRx99hO7du8PNzQ39+/eHVCrFlStXEBcXhwULFmDjxo1QKBRo164dLCwssGXLFpibm6s+1Ht4eOD48eMYNGgQZDIZHB0dS11j/fr1aNu2bZkLrQYGBmL9+vX47LPPEB4ejmbNmmHQoEFYuHAhXF1dceXKFUybNg1jx45VLTLarl07TJ8+HVOnTkVycjL69OkDV1dX/Pnnn/jyyy/xn//8p8yECfDvptLY2tpi9OjRmDp1KhwcHGBvb49p06ahRYsWCAsLU7ULDQ1F7969MXbsWADA+++/j6CgIERFRWHAgAE4e/Ys1q5di7Vr1wIAsrOzMXfuXPTt2xcuLi5ITEzErFmz4OjoiD59+gAAbt++jW3btqFbt25wdHTEH3/8galTp6JVq1YIDg5+4XsiIiIi0mXcrpeIiGpdly5dsH//fkRHR6NNmzYICAjAsmXLVAkEOzs7rFu3DsHBwfD19cWRI0fwv//9Dw4ODgCAyMhIJCYmomHDhnByKr1+SmFhIbZu3Yq+ffuWef2+ffti69atKCwshLGxMQ4fPgxPT08MHToU3t7emDFjBt566y0sW6a+48/HH3+M7du347fffkOXLl3g7e2NKVOmwNfXt9a26wWAzz77DL1798aAAQMQHBwMCwsL/O9//4ORkZGqze3bt/Hw4UPV8zZt2mDv3r3YsWMHfHx8MH/+fCxfvhxDhw4FABgZGSEuLg69evVC48aNMXLkSDRu3BinT59WJYNMTU1x5MgRdOnSBU2aNMHEiRMRHh6OX375Re3aRERERPpEIoQQ2g5CF2VmZsLW1hYZGRnV3smgNgyYlYyH6Qo42hlhd1QVFk8kAE/XFkhLS4NcLodUyjwg1a6a7G/5+flISEhAgwYNYGZmVkMRkr4RQqC4uBjGxsYvvFDsy4T/LrSLP1NJo3JyACsrAIAyMxNSLe9Ko2ufDYioZnEqDRERERER6RapFMLfH8VFRTBiIo6IahkTI0REREREpFvMzSF++w2P0tIgNzfXdjREpOeYfiUiIiIiIiIig8XECBEREREREREZLCZGiIiIiIhIt+TmQuLpCac2bYDcXG1HQ0R6jmuMEBERERGRbhECkqQkGAFQchNNIqplHDFCRERERERERAaLiREiIiIiIiIiMlhMjBARERERERGRwWJihIiI9NKoUaPQu3dvbYdBRERERDqOiREiIqoxo0aNgkQiUT0cHBzQtWtXXLlypcauMXfuXLz66qs1dr6aFBISonb/EokEgwYNUmvz5MkTDB8+HLa2trC1tcXw4cORnp6unYCJiIiIiIkRIiKqWV27dkVKSgpSUlJw5MgRGBsbo3v37toOS2PGjBmjuv+UlBR89dVXavVDhgzBpUuXcOjQIRw6dAiXLl3C8OHDtRQtEZGOkkggmjdHUePGgESi7WiISM8xMUJE9DLJySn/kZ9f9bZ5eVVr+wJkMhmcnZ3h7OyMV199Ff/v//0/3L17Fw8ePFC1SU5OxsCBA1GnTh04ODigV69eSExMVNXHxMSgbdu2sLS0hJ2dHYKDg5GUlISNGzdi3rx5uHz5smpExsaNG6sU14ULFyCXy7Fw4cIXuq+qsrCwUN2/s7MzbG1tVXXXr1/HoUOH8PXXXyMwMBCBgYFYt24d9u/fj5s3b9ZqXERELxULC4i4ODyKjQUsLLQdDRHpOSZGiIheJlZW5T/69lVvK5eX3zYiQr2th0fZ7f6l7OxsbNu2DY0aNYKDgwMAIDc3F506dYKVlRWOHz+OX3/9FVZWVujatSsKCwtRXFyM3r17o2PHjrhy5QpOnz6Nt99+GxKJBAMHDsTUqVPh7e2tGpExcODASuOIiYlBaGgo5s2bh9mzZ5fbztvbG1ZWVuU+vL29K73Wtm3b4OjoCG9vb0ybNg1ZWVmqutOnT8PW1hbt2rVTlQUEBMDW1hanTp2q9NxEREREVPOMtR0AERHpl/3798Pqn6RKTk4OXFxcsH//fkilT3PxO3fuhFQqxddffw3JP8OjN2zYADs7O8TExMDf3x8ZGRno3r07GjZsCABo1qyZ6vxWVlYwNjaGs7NzleLZt28fhg8fjq+++gqDBw+usO3BgwdRVFRUbr2JiUmFxw8dOhQNGjSAs7Mzrl69ipkzZ+Ly5cuIjo4GAKSmpkIul5c6Ti6XIzU1tQp3Q0REREQ1jYkRIqKXSXZ2+XVGRurP09LKbyt9bsDgM9NY/q1OnTphzZo1AIDHjx9j9erViIiIwNmzZ+Hu7o4LFy7gzz//hLW1tdpx+fn5uH37NsLDwzFq1Ch06dIFnTt3RlhYGAYMGAAXF5dqx/Lbb79h//79+Pbbb9GnT59K27u7u1f7Gs8aM2aM6s8+Pj7w8vKCv78/Ll68iNatWwOAKhn0LCFEmeVERAYrNxeSNm3gUFwMXLhQI6MYiYjKw8QIEdHLxNJS+20rPZUlGjVqpHru5+cHW1tbrFu3DgsWLIBSqYSfnx+2bdtW6lgnJycAT0eQTJw4EYcOHcKuXbvwwQcfIDo6GgEBAdWKpWHDhnBwcMA333yD119/HaamphW29/b2RlJSUrn17u7uuHbtWpWv37p1a5iYmCA+Ph6tW7eGs7Mz7t+/X6rdgwcPULdu3Sqfl4hI7wkByR9/wASAUghtR0NEeo6JESIiqlUSiQRSqRR5/yz42rp1a+zatQtyuRw2NjblHteqVSu0atUKM2fORGBgILZv346AgACYmppCoVBU6dqOjo74/vvvERISgoEDB2L37t0VTof5t1Npnnft2jUUFRWpRrsEBgYiIyMDZ8+eRdu2bQE8HdWSkZGBoKCgap2biIiIiGqGTiy+unr1ajRo0ABmZmbw8/PDiRMnym0bExOj2ong2ceNGzdUbdatW4f27dujTp06qFOnDsLCwnD27FlN3AoRkcErKChAamoqUlNTcf36dbz33nvIzs5Gjx49ADxdh8PR0RG9evXCiRMnkJCQgNjYWEyaNAn37t1DQkICZs6cidOnTyMpKQmHDx/GrVu3VOuMeHh4ICEhAZcuXcLDhw9RUFBQYTxyuRxHjx7FjRs3MHjwYBQXF5fb1t3dHY0aNSr3UdFUm9u3byMyMhLnz59HYmIiDh48iP79+6NVq1YIDg4G8HStlK5du2LMmDE4c+YMzpw5gzFjxqB79+5o0qRJdV9qIiIiIqoBWk+M7Nq1C5MnT8bs2bPx+++/o3379oiIiMCdO3cqPO7mzZuqHQlSUlLg5eWlqouJicHgwYNx7NgxnD59GvXr10d4eDiSk5Nr+3aIiAzeoUOH4OLiAhcXF7Rr1w7nzp3Dt99+i5CQEABPt7M9fvw46tevjzfeeAPNmjXDm2++iby8PNjY2MDCwgI3btxA37590bhxY7z99tuYMGEC3nnnHQBA37590bVrV3Tq1AlOTk7YsWNHpTE5Ozvj6NGjiIuLw9ChQ6s84qQ6TE1NceTIEXTp0gVNmjTBxIkTER4ejl9++QVGz6z/sm3bNrRo0QLh4eEIDw+Hr68vtmzZUuPxEBEREVHVSITQ7qS9du3aoXXr1qqF+oCn36j17t0bixYtKtU+JiYGnTp1wpMnT2BnZ1elaygUCtSpUwerVq3CiBEjymxTUFCg9q1jZmYm3Nzc8OTJkwqHemvKoA9S8DBdAUc7I+xcUP0FCA2VUqnEgwcP4OTkpNoRg6i21GR/y8/PR2Jiomo0HVF5ioqKqj3F52WVn5+PhIQEeHh48N+FFvBnKmlUTg6k//wOXpyeDulzC3ZrWmZmJurUqYOMjAyd+GxARDVLq2uMFBYW4sKFC5gxY4ZaeXh4OE6dOlXhsa1atUJ+fj6aN2+ODz74AJ06dSq3bW5uLoqKimBvb19um0WLFmHevHmlyh88eID8/PxK7qT2Kf/5dlOpUCCtop0mSI1SqURGRgaEEPwljmpdTfa3oqIiKJVKFBcXVzj1gwybEEI1+sUQdrUpLi6GUqnEo0ePDCYZpEv4M5U0SZKbi5IlqR88eADJP+tUaUtWVpZWr09EtUuriZGHDx9CoVCUWom/bt26SE1NLfMYFxcXrF27Fn5+figoKMCWLVsQGhqKmJgYdOjQocxjZsyYgXr16iEsLKzcWGbOnIkpU6aonpeMGHFyctKJrLDUKAWAAlIjI8jlcm2H89JQKpWQSCT8dos0oib7W35+PrKysmBsbAxjY66TTRUzlCSBsbExpFIpHBwcOGJEC/gzlTQqNxfC3R0KhQJOcjmkWt6ul+85RPpNJ37bfv5bLiFEud98NWnSRG2BusDAQNy9exeffvppmYmRJUuWYMeOHYiJianwDU0mk0Emk5Uql0qlOvfDX9fi0XUlO2LwdSNNqKn+JpVK1RaYJirLsz8vDaGflPx74Hu69vD1J42xsoLyr7/wMC0Ncisrrfc5bV+fiGqXVv+FOzo6wsjIqNTokLS0tFKjSCoSEBCA+Pj4UuWffvopoqKicPjwYfj6+v7reImIiIiIiIhIv2g1MWJqago/Pz9ER0erlUdHRyMoKKjK5/n999/h4qK+IOknn3yC+fPn49ChQ/D396+ReImIiIiIiIhIv2h9Ks2UKVMwfPhw+Pv7IzAwEGvXrsWdO3cwduxYAE/X/khOTsbmzZsBAMuXL4eHhwe8vb1RWFiIrVu3Ys+ePdizZ4/qnEuWLMGHH36I7du3w8PDQzUixcrKClZanp9IRERERESVyMuDpEMHOBQVASdPApaW2o6IiPSY1hMjAwcOxKNHjxAZGYmUlBT4+Pjg4MGDcHd3BwCkpKTgzp07qvaFhYWYNm0akpOTYW5uDm9vbxw4cADdunVTtVm9ejUKCwvRr18/tWvNmTMHc+fO1ch9ERFp2rdHMvHtkeqvmt8/1Br9Q7W/yDQREZGKUgnJ+fMwwdOFf4mIapPWEyMAMG7cOIwbN67Muo0bN6o9nz59OqZPn17h+RITE2soMiKil0dOnhIP0xUvdBwRERERkaHSicQIERH9e5bmUjjaGameCyFQUCSQnStUZVYWEshM1He6sTTnSvtEREREZLj42zARkZ7oH2qD3VH1sDuqHiYNqoOCQvWkCABk5woUFApMHlRH1VYXptGkpqaic+fOsLS0hJ2dXZWOmTt3Ll599dUK24waNQq9e/f+1/EZCg8PDyxfvrzCNhKJBD/88ING4iEiIiLSBCZGiIj0zMkrufjoq4fIyRNl1ufkCXz41UOcvJJb49eeO3cuJBKJ2sPZ2bnS4z777DOkpKTg0qVLuHXrVo3HVREhBNatW4fAwEDY2NjAysoK3t7emDRpEv7880+NxlKR55MWQghMnToV9vb2OHr0KAAgJCSk1OsvkUhQXFys0VhTU1Px3nvvwdPTEzKZDG5ubujRoweOHDmCwsJCODo6YsGCBWUeu2jRIjg6OqKwsFCjMRMREZHhYmKEiEiPFBYJfLzpESCAstMi/5QL4OPNj1BYVF6rF+ft7Y2UlBTVIy4urtJjbt++DT8/P3h5eUEul9d4TOURQmDIkCGYOHEiunXrhsOHD+PKlStYuXIlzM3Ny/3wDkCrH9wVCgVGjx6NLVu24PDhw3jttddUdWPGjFF7/VNSUmBsrLmZs4mJifDz88PRo0exZMkSxMXF4dChQ+jUqRPGjx8PU1NTDBs2DBs3boQQpfvfhg0bMHz4cJiammosZiIiIjJsTIwQEemRmIu5yM4T5SZFSgg8nVYTe7HmR40YGxvD2dlZ9XBycqqwvYeHB/bs2YPNmzdDIpFg1KhRAIA7d+6gV69esLKygo2NDQYMGID79++Xex6FQoEpU6bAzs4ODg4OmD59epkfvJ+1a9cu7Ny5E7t27cKHH36IgIAAeHp6IjQ0FIsXL8aGDRtUbUum5SxatAiurq5o3LgxACAuLg6vvfYazM3N4eDggLfffhvZ2dmq40JCQjB58mS16/bu3Vt1nyWvwfz58zFkyBBYWVnB1dUVn3/+eZkxFxQUoH///oiOjkZsbCz8/f3V6i0sLNRe/2dH7OzZswfe3t6QyWTw8PDA0qVLK3x94uPj0aFDB5iZmaF58+aIjo6usD3wdEF1iUSCs2fPol+/fmjcuDG8vb0xZcoUnDlzBgAwevRo3L59G8ePH1c79sSJE4iPj8fo0aMrvQ4R6T/h6Ailvb22wyAiA8DFV3VMedttPs5QqP4/YFZyqXput0mk/8YuTsXjzIp3ncnMqd6uNEu3P8K6fekVtrG3McKXMyqfDlMiPj4erq6ukMlkaNeuHaKiouDp6Vlu+3PnzmHEiBGwsbHBihUrYG5uDiEEevfuDUtLS8TGxqK4uBjjxo3DwIEDERMTU/a9LF2Kb775BuvXr0fz5s2xdOlS7N27V200xfN27NiBJk2aoGfPnmXWP7tILQAcOXIENjY2iI6OhhACubm56Nq1KwICAnDu3DmkpaXhrbfewoQJE0rtqlaZTz75BLNmzcLcuXPx888/4/3330fTpk3RuXNnVZvs7Gy8/vrruHv3Lk6ePAk3N7cqT5O5cOECBgwYgLlz52LgwIE4deoUxo0bBwcHB7UkTQmlUok33ngDjo6OOHPmDDIzM0sleJ73+PFjHDp0CAsXLoSlpWWp+pL1Y1q0aIE2bdpgw4YN6Nixo6r+m2++Qdu2beHj41OleyIiPWZpCXH/PtLS0iAv4/2EiKgmMTGiYyrbblMpUGY9t9sk0n+PMxUvtB1vRQqLyn5PeVHt2rXD5s2b0bhxY9y/fx8LFixAUFAQrl27BgcHhzKPcXJygkwmg7m5uWp0Q3R0NK5cuYKEhAS4ubkBALZs2QJvb2+cO3cObdq0KXWe5cuXY+bMmejbty8A4Msvv8TPP/9cYby3bt1CkyZN1MomT56Mr7/+GsDTD/L37t1T1VlaWuLrr79WTfNYt24d8vLysHnzZlUiYNWqVejRowc+/vhj1K1bt9LXrERwcDBmzJgBAGjcuDFOnjyJzz77TC0xMn/+fFhbW+OPP/6AXC4vc0TM6tWrVfEDwDvvvIOlS5di2bJlCA0NxYcffqi6xh9//IFPPvmkzMTIL7/8guvXryMxMRGvvPIKACAqKgoRERHl3sOff/4JIQSaNm1a6f2++eabmDZtGlatWgUrKytkZ2fj22+/xbJlyyo9loiIiKgmcSqNjinZbrO6D263SaT/7G0qfy8wNaneOU1NUOk57W2MKj/RPyIiItC3b1+0aNECYWFhOHDgAABg06ZNAJ5+sLayslI97ty5U+Z5rl+/Djc3N1VSBACaN28OOzs7XL9+vVT7jIwMpKSkIDAwUFVmbGxcappJWZ4fFTJ79mxcunQJH330kdqUGODpSIdn1764fv06WrZsqTY6Ijg4GEqlEjdv3qz02s96NvaS58/fa3h4OHJychAVFVXueYYOHYpLly6pHjNnzlTFGhwcrNY2ODgY8fHxUChKJ8euX7+O+vXrq5IiZcX4vJJEzfOvaVkGDx4MpVKJXbt2AXg6rUkIgUGDBlV6LBEREVFN4ogRHdM/1IZTYoioTFWZznL4txws3vSoyuecOsQBndvV3hBlS0tLtGjRAvHx8QCAsWPHYsCAAap6V1fXMo8TQpT54bq88hfl5eWFGzduqJU5OTnBycmpzEVgn58eUlE8JeVSqbTUyI6ioqIqxff8uUNDQzFx4kT06tULCoUCK1euLHWMra0tGjVqVKq8rFgrWoOlrLrKXnsvLy9IJBJcv3690m2SbW1t0a9fP2zYsAGjR4/Ghg0b0K9fP9jY8GcgEQHIy4MkIgL2hYVAdDTA6TREVIs4zICISI+EtLaAlbkElaUOJACsLCTo2NqiVuMpKCjA9evX4eLiAgCwt7dHo0aNVI/ydktp3rw57ty5g7t376rK/vjjD2RkZKBZs2al2tva2sLFxUW1uCcAFBcX48KFCxXGN3jwYNy8eRP79u17kdtD8+bNcenSJeTk5KjKTp48CalUqlqc1cnJCSkpKap6hUKBq1evljrXs7GXPC9rSkrnzp2xf/9+fPPNNxg/fnylC8w+G+uvv/6qVnbq1Ck0btwYRkalRwWV/B38/fffqrLTp09XeA17e3t06dIFX3zxhdprUiI9PV3t+ejRo3Hy5Ens378fJ0+e5KKrRPR/lEpIYmNhevo0oOSUcSKqXUyMEBHpEVMTCWaMdAAkKDc5IvnnPzNGOMDUpOZGXwDAtGnTEBsbi4SEBPz222/o168fMjMzMXLkyGqdJywsDL6+vhg6dCguXryIs2fPYsSIEejYsWO502MmTZqExYsXY+/evbhx4wbGjRtX6oP48wYNGoR+/fph0KBBiIyMxG+//YbExETExsZi165dZSYMnjV06FCYmZlh5MiRuHr1Ko4dO4b33nsPw4cPV60v8tprr+HAgQM4cOBAhXGdPHkSS5Yswa1bt/DFF1/g22+/xaRJk8q8bsk5N2/ejIkTJ1YpOTJ16lQcOXIE8+fPx61bt7Bp0yasWrUK06ZNK7N9WFgYmjRpghEjRuDy5cs4ceIEZs+eXel1Vq9eDYVCgbZt22LPnj2Ij4/H9evXsXLlylJTcTp27IhGjRphxIgRaNSoETp06FDp+YmIiIhqGhMjRER6JsjXApHvOMLSouykh6WFBPPfcUSQb82PFrl37x4GDx6MJk2a4I033oCpqSnOnDkDd3f3ap1HIpHghx9+QJ06ddChQweEhYXB09NTtR5FWaZOnYoRI0Zg1KhRCAwMhLW1Nfr06VPpdXbt2oXly5fj4MGDCA0NRZMmTfDmm2/Czc2t1AiL51lYWODnn3/G48eP0aZNG/Tr1w+hoaFYtWqVqs2bb76JkSNHqhI7DRo0QKdOncqM/8KFC2jVqhXmz5+PpUuXokuXLuVeOyQkBAcOHMC2bdvw7rvvVpocad26NXbv3o2dO3fCx8cHH330ESIjI8tceBV4OgVo7969KCgoQNu2bfHWW29h4cKFFV4DABo0aICLFy+iU6dOmDp1Knx8fNC5c2ccOXIEa9asKdX+zTffxJMnT/Dmm29Wem4iIiKi2iARVR2Da2AyMzNha2uLjIwMznd+iSmVyqfbvMnlkEqZB6TaVZP9LT8/HwkJCWjQoAHMzMyqdMzz230LIVBQJJCd+39v81YWEshMJGprRXC7b+3z8PDA5MmTK90O93lCCBQXF8PY2LhG117RVS/y74JqDn+mkkbl5ABWVgAAZWYmpNbWWg2Hnw2I9BsXXyUi0hOVbfcNANm5AtlQz4dzu28iIiIiMmRMjBAR6YmS7b5f5DgiIiIiIkPFxAgRkZ7gdt8vr8TERG2HQESkc4SFRZV33iIi+jeYGCEiIiIiIt1iaQmRlfV0XRtLS21HQ0R6juOniYh0GL8pI/o//PdAREREtYGJESIiHWRk9HStkMLCQi1HQqQ7cnNzAQAmJiZajoSIiIj0CafSEBHpIGNjY1hYWODBgwcwMTHh1phUJkPZrlcIgdzcXKSlpcHOzk6VOCQiPZafD8kbb8CusBD48UfAwkLbERGRHmNihIhIB0kkEri4uCAhIQFJSUnaDod0lBACSqUSUqlUrxMjJezs7ODs7KztMIhIExQKSH76CWYAlIqKt6InIvq3mBghItJRpqam8PLy4nQaKpdSqcSjR4/g4OCg96OKTExMOFKEiIiIagUTI0REOkwqlcLMzEzbYZCOUiqVMDExgZmZmd4nRoiIiIhqC3+LIiIiIiIiIiKDxcQIERERERERERksJkaIiIiIiIiIyGBxjZFyCCEAAJmZmVqOhP4NpVKJrKwszr8njWB/I01jnyNNYn8jjcrJUf1RmZkJ6T+/m2tLyWcCoeU4iKh2MDFSjqysLACAm5ubliMhIiIiIjJgr7yi7QhUsrKyYGtrq+0wiKiGSQTTnmVSKpX4+++/YW1tDYlEou1w6AVlZmbCzc0Nd+/ehY2NjbbDIT3H/kaaxj5HmsT+RpqmS31OCIGsrCy4urpyxBSRHuKIkXJIpVK8okPZafp3bGxstP4DlQwH+xtpGvscaRL7G2marvQ5jhQh0l9MdxIRERERERGRwWJihIiIiIiIiIgMFhMjpNdkMhnmzJkDmUym7VDIALC/kaaxz5Emsb+RprHPEZGmcPFVIiIiIiIiIjJYHDFCRERERERERAaLiREiIiIiIiIiMlhMjBARERERERGRwWJihIiIiIiIiIgMFhMj9NI7fvw4evToAVdXV0gkEvzwww9q9UIIzJ07F66urjA3N0dISAiuXbumnWDppbdo0SK0adMG1tbWkMvl6N27N27evKnWhn2OatKaNWvg6+sLGxsb2NjYIDAwED/99JOqnv2NatOiRYsgkUgwefJkVRn7HNWkuXPnQiKRqD2cnZ1V9exvRKQJTIzQSy8nJwctW7bEqlWryqxfsmQJli1bhlWrVuHcuXNwdnZG586dkZWVpeFISR/ExsZi/PjxOHPmDKKjo1FcXIzw8HDk5OSo2rDPUU165ZVXsHjxYpw/fx7nz5/Ha6+9hl69eqk+GLC/UW05d+4c1q5dC19fX7Vy9jmqad7e3khJSVE94uLiVHXsb0SkEYJIjwAQe/fuVT1XKpXC2dlZLF68WFWWn58vbG1txZdffqmFCEnfpKWlCQAiNjZWCME+R5pRp04d8fXXX7O/Ua3JysoSXl5eIjo6WnTs2FFMmjRJCMH3OKp5c+bMES1btiyzjv2NiDSFI0ZIryUkJCA1NRXh4eGqMplMho4dO+LUqVNajIz0RUZGBgDA3t4eAPsc1S6FQoGdO3ciJycHgYGB7G9Ua8aPH4/XX38dYWFhauXsc1Qb4uPj4erqigYNGmDQoEH466+/ALC/EZHmGGs7AKLalJqaCgCoW7euWnndunWRlJSkjZBIjwghMGXKFPznP/+Bj48PAPY5qh1xcXEIDAxEfn4+rKyssHfvXjRv3lz1wYD9jWrSzp07cfHiRZw7d65UHd/jqKa1a9cOmzdvRuPGjXH//n0sWLAAQUFBuHbtGvsbEWkMEyNkECQSidpzIUSpMqLqmjBhAq5cuYJff/21VB37HNWkJk2a4NKlS0hPT8eePXswcuRIxMbGqurZ36im3L17F5MmTcLhw4dhZmZWbjv2OaopERERqj+3aNECgYGBaNiwITZt2oSAgAAA7G9EVPs4lYb0Wsmq5iXfOJRIS0sr9e0DUXW89957+PHHH3Hs2DG88sorqnL2OaoNpqamaNSoEfz9/bFo0SK0bNkSK1asYH+jGnfhwgWkpaXBz88PxsbGMDY2RmxsLFauXAljY2NVv2Kfo9piaWmJFi1aID4+nu9xRKQxTIyQXmvQoAGcnZ0RHR2tKissLERsbCyCgoK0GBm9rIQQmDBhAr7//nscPXoUDRo0UKtnnyNNEEKgoKCA/Y1qXGhoKOLi4nDp0iXVw9/fH0OHDsWlS5fg6enJPke1qqCgANevX4eLiwvf44hIYziVhl562dnZ+PPPP1XPExIScOnSJdjb26N+/fqYPHkyoqKi4OXlBS8vL0RFRcHCwgJDhgzRYtT0sho/fjy2b9+Offv2wdraWvUtlq2tLczNzSGRSNjnqEbNmjULERERcHNzQ1ZWFnbu3ImYmBgcOnSI/Y1qnLW1tWrNpBKWlpZwcHBQlbPPUU2aNm0aevTogfr16yMtLQ0LFixAZmYmRo4cyfc4ItIYJkbopXf+/Hl06tRJ9XzKlCkAgJEjR2Ljxo2YPn068vLyMG7cODx58gTt2rXD4cOHYW1tra2Q6SW2Zs0aAEBISIha+YYNGzBq1CgAYJ+jGnX//n0MHz4cKSkpsLW1ha+vLw4dOoTOnTsDYH8jzWOfo5p07949DB48GA8fPoSTkxMCAgJw5swZuLu7A2B/IyLNkAghhLaDICIiIiIiIiLSBq4xQkREREREREQGi4kRIiIiIiIiIjJYTIwQERERERERkcFiYoSIiIiIiIiIDBYTI0RERERERERksJgYISIiIiIiIiKDxcQIERERERERERksJkaIiIiIiIiIyGAxMUJERJWaO3cuJBIJHj58+MLniIyMRPPmzaFUKmswstqzceNGSCQSJCYmVvvYmJgYSCQSfPfdd5W2DQkJgY+PzwtEWDuOHDkCKysrJCcnazsUIiIiIo1gYoSIiGrd33//jSVLliAyMhJS6cvxo+f111/H6dOn4eLiou1QNCo0NBRt27bFrFmztB0KERERkUa8HL+dEhHRS23FihWws7PDG2+8oe1QqszJyQkBAQGQyWTaDuVfE0IgLy+vyu3Hjx+Pbdu24e7du7UYFREREZFuYGKEiIheyI0bN+Dp6Yl27dohLS2t3HaFhYVYv349hgwZojZaJDExERKJBJ9++imWLVuGBg0awMrKCoGBgThz5ky1YimZurJjxw7Mnj0brq6usLGxQVhYGG7evFmq/S+//ILQ0FDY2NjAwsICwcHBOHLkiFqbsqbSCCEQFRUFd3d3mJmZwd/fH9HR0QgJCUFISEip6xQVFVUpHgA4ceIEAgICYG5ujnr16uHDDz+EQqFQa/P48WOMGzcO9erVg6mpKTw9PTF79mwUFBSotZNIJJgwYQK+/PJLNGvWDDKZDJs2bQIArFmzBi1btoSVlRWsra3RtGnTUqNDevToASsrK6xbt67c15yIiIhIXzAxQkRE1RYbG4ugoCD4+vri2LFjkMvl5bb97bff8OjRI3Tq1KnM+i+++ALR0dFYvnw5tm3bhpycHHTr1g0ZGRnVjmvWrFlISkrC119/jbVr1yI+Ph49evRQSzBs3boV4eHhsLGxwaZNm7B7927Y29ujS5cupZIjz5s9ezZmz56Nrl27Yt++fRg7dizeeust3Lp164XjAYDU1FQMGjQIQ4cOxb59+9CvXz8sWLAAkyZNUrXJz89Hp06dsHnzZkyZMgUHDhzAsGHDsGTJkjJH4vzwww9Ys2YNPvroI/z8889o3749du7ciXHjxqFjx47Yu3cvfvjhB7z//vvIyclRO9bU1BRBQUE4cOBApa85ERER0UtPEBERVWLOnDkCgHjw4IHYsmWLMDU1FRMnThQKhaLSYz/++GMBQKSmpqqVJyQkCACiRYsWori4WFV+9uxZAUDs2LGjyvEdO3ZMABDdunVTK9+9e7cAIE6fPi2EECInJ0fY29uLHj16qLVTKBSiZcuWom3btqqyDRs2CAAiISFBCCHE48ePhUwmEwMHDlQ79vTp0wKA6NixY7XjEUKIjh07CgBi3759am3HjBkjpFKpSEpKEkII8eWXXwoAYvfu3WrtSl7fw4cPq8oACFtbW/H48WO1thMmTBB2dnaiKmbPni2kUqnIzs6uUnsiIiKilxVHjBARUZUtXLgQo0aNwuLFi7FixYoqLaT6999/QyKRwNHRscz6119/HUZGRqrnvr6+AICkpKRqx9ezZ0+158+f69SpU3j8+DFGjhyJ4uJi1UOpVKJr1644d+5cqdETJc6cOYOCggIMGDBArTwgIAAeHh4vFE8Ja2vrUm2HDBkCpVKJ48ePAwCOHj0KS0tL9OvXT63dqFGjAKDUaJfXXnsNderUUStr27Yt0tPTMXjwYOzbt6/CXYbkcjmUSiVSU1PLbUNERESkD4y1HQAREb08tm7dinr16mHQoEFVPiYvLw8mJiZqyY9nOTg4qD0vWey0OouFVvVc9+/fB4BSyYVnPX78GJaWlqXKHz16BACoW7duqbqyyqoST0XHOzs7q1330aNHcHZ2hkQiUWsnl8thbGysaleirN10hg8fjuLiYqxbtw59+/aFUqlEmzZtsGDBAnTu3FmtrZmZWZmxEhEREekbjhghIqIqO3ToEExMTNC+ffsqj+hwdHREYWFhuSMxNKlk1Mrnn3+Oc+fOlfmoLMlRklx51r8dVVHROUuu6+DggPv370MIodYuLS0NxcXFpUbkPJ9AKfHf//4Xp06dQkZGBg4cOAAhBLp3717q7/Px48cAUO5IHyIiIiJ9wcQIERFVmbu7O06cOAGZTIb27dsjPj6+0mOaNm0KALh9+3Zth1ep4OBg2NnZ4Y8//oC/v3+ZD1NT0zKPbdeuHWQyGXbt2qVWfubMmRea9vOsrKws/Pjjj2pl27dvh1QqRYcOHQAAoaGhyM7Oxg8//KDWbvPmzar66rC0tERERARmz56NwsJCXLt2Ta3+r7/+goODQ7mJIiIiIiJ9wak0RERULS4uLoiNjUWXLl3QoUMHREdHw8fHp9z2JdvYnjlzRrXGhrZYWVnh888/x8iRI/H48WP069cPcrkcDx48wOXLl/HgwQOsWbOmzGPt7e0xZcoULFq0CHXq1EGfPn1w7949zJs3Dy4uLlVab6U8Dg4OePfdd3Hnzh00btwYBw8exLp16/Duu++ifv36AIARI0bgiy++wMiRI5GYmIgWLVrg119/RVRUFLp164awsLBKrzNmzBiYm5sjODgYLi4uSE1NxaJFi2Bra4s2bdqotT1z5gw6duxY7sgTIiIiIn3BESNERFRtjo6OOHr0KBo2bIiOHTvi/Pnz5bZ1c3ND+/btsW/fPg1GWL5hw4bh2LFjyM7OxjvvvIOwsDBMmjQJFy9erHTUxcKFC7FgwQIcOHAAPXv2xMqVK7FmzRrI5XLY2dm9cEzOzs7Yvn07Nm3ahJ49e2L37t2YNWsWVq5cqWpjZmaGY8eOYejQofjkk08QERGBjRs3Ytq0afj++++rdJ327dvj6tWrmDRpEjp37oz3338fjRs3xokTJ+Dk5KRqd/v2bcTFxWHo0KEvfE9ERERELwuJeH6yMhERUQ3bs2cPBg4ciKSkJNSrV0/b4dSohIQENG3aFHPmzMGsWbO0HU6N+PDDD7F582bcvn0bxsYcXEpERET6jYkRIiKqdUIIBAUFwc/PD6tWrdJ2OC/s8uXL2LFjB4KCgmBjY4ObN29iyZIlyMzMxNWrV/ViPY709HR4enri888/54gRIiIiMgj8GoiIiGqdRCLBunXr8OOPP0KpVFZ5PQ4hBBQKRYVtjIyMNLYOhqWlJc6fP4/169cjPT0dtra2CAkJwcKFC/UiKQI8HQEzc+ZMDBkyRNuhEBEREWkER4wQEZHOiomJQadOnSpss2HDBowaNUozARERERGR3mFihIiIdFZWVhZu3rxZYZsGDRrAwcFBQxERERERkb5hYoSIiIiIiIiIDBa36yUiIiIiIiIig8XECBEREREREREZLCZGiIiIiIiIiMhgMTFCRERERERERAaLiREiIiIiIiIiMlhMjBARERERERGRwWJihIiIiIiIiIgM1v8HmqKpnC6cGL8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final summary\n", + "\n", + "Metric Cross-validation Test set \n", + "Method 5-fold GroupKFold Hold-out (20%) \n", + "Best k 50 50 \n", + "AUC 0.6032 ± 0.0293 0.6550 \n", + "Accuracy N/A 0.6048 \n", + "Precision N/A 0.6068 \n", + "Recall N/A 0.5952 \n", + "F1-Score N/A 0.6010 \n", + "\n", + "\n", + "Conclusion\n", + "• Best k = 50 determined by 5-fold GroupKFold cross-validation\n", + "• Cross-validation ensures no patient images are split across folds\n", + "• Test set AUC = 0.655 is consistent with CV results\n", + "• Model generalizes well to unseen data (test AUC close to CV mean)\n" + ] + } + ], + "source": [ + "# Figure 1: Confusion matrix (Test Set Only)\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "\n", + "cm_test = confusion_matrix(y_test, y_test_pred)\n", + "sns.heatmap(cm_test, annot=True, fmt='d', cmap='Oranges', ax=ax, xticklabels=['Non-Cancerous', 'Cancerous'], yticklabels=['Non-Cancerous', 'Cancerous'])\n", + "# annot=True makes it possible to see numbers of the different 4 cat. and fmt=d is format d=decimals(intergers)\n", + "\n", + "# Labels\n", + "ax.set_title(f'Test set confusion matrix\\nk={best_k}, AUC={test_auc:.3f}, Acc={test_acc:.3f}', fontsize=12) #.3f just mean only displaying 3 digits after the decimal\n", + "ax.set_xlabel('Predicted')\n", + "ax.set_ylabel('Actual')\n", + "\n", + "plt.show()\n", + "\n", + "# Figure 2: ROC-curve\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "\n", + "# Test ROC\n", + "fpr_test, tpr_test, _ = roc_curve(y_test, y_test_proba) # _ is for threshold value, which we dont use # fpr=false positive, tpr=true positive\n", + "ax.plot(fpr_test, tpr_test, label=f'k-NN (AUC = {test_auc:.3f})', linewidth=2, color='darkorange')\n", + "\n", + "# Random classifier line\n", + "ax.plot([0, 1], [0, 1], 'k--', label='Random (AUC = 0.5)', linewidth=1) # Used for comparison to just random guesses\n", + "\n", + "# Labels\n", + "ax.set_xlabel('False Positive Rate', fontsize=12)\n", + "ax.set_ylabel('True Positive Rate', fontsize=12)\n", + "ax.set_title(f'ROC Curve - Test set (k={best_k})', fontsize=14)\n", + "ax.legend(loc='lower right') #ax.legend implements a small box that explains the different lines, points etc\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "# Figure 3: Cross-validation with Gridsearch\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "\n", + "k_values = [5, 15, 25, 35, 50] # The same k-nearest neighbours\n", + "cv_auc_means = grid_search.cv_results_['mean_test_score'].tolist() # The values from the GroupKFold from earlier\n", + "cv_auc_stds = grid_search.cv_results_['std_test_score'].tolist() # The values from the GroupKFold from earlier\n", + "\n", + "ax.errorbar(k_values, cv_auc_means, yerr=cv_auc_stds, # yerr is for plotting error bar on the y-axis\n", + " marker='o', capsize=5, capthick=2, # For better visualization, like thickness, markers -\n", + " linewidth=2, markersize=8, color='royalblue', # and color\n", + " label='5-fold GroupKFold CV') \n", + "\n", + "# Labels\n", + "ax.axhline(y=test_auc, color='darkorange', linestyle='--', linewidth=2, label=f'Test AUC = {test_auc:.3f}')\n", + "ax.axvline(x=best_k, color='red', linestyle='--', linewidth=1.5, label=f'Best k = {best_k}')\n", + "\n", + "# More lables\n", + "ax.set_xlabel('k (n_neighbors)', fontsize=12)\n", + "ax.set_ylabel('AUC Score', fontsize=12)\n", + "ax.set_title('Cross-validation results with GroupKFold', fontsize=14)\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "# Annotatting the best point\n", + "ax.annotate(f'Best CV AUC = {cv_auc_means[k_values.index(best_k)]:.3f}',\n", + " xy=(best_k, cv_auc_means[k_values.index(best_k)]), # Point that is annotated\n", + " xytext=(best_k + 8, cv_auc_means[k_values.index(best_k)] - 0.05), # Position of text\n", + " arrowprops=dict(arrowstyle='->', color='red', lw=1.5), # For better visu.\n", + " fontsize=10, ha='left' # More vis.\n", + " )\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "# Printing results\n", + "print(\"Final summary\")\n", + "print(f\"\\n{'Metric':<25} {'Cross-validation':<20} {'Test set':<15}\") #<25, <20 and <15 is used to align the printing statements\n", + "\n", + "print(f\"{'Method':<25} {'5-fold GroupKFold':<20} {'Hold-out (20%)':<15}\")\n", + "print(f\"{'Best k':<25} {best_k:<20} {best_k:<15}\")\n", + "print(f\"{'AUC':<25} {cv_auc_means[k_values.index(best_k)]:.4f} ± {cv_auc_stds[k_values.index(best_k)]:.4f} {test_auc:<15.4f}\") \n", + "# <15.4f, <15 is still for aligning, but .4f is printing the 4 decimals after the decimal,point\n", + "print(f\"{'Accuracy':<25} {'N/A':<20} {test_acc:<15.4f}\")\n", + "print(f\"{'Precision':<25} {'N/A':<20} {test_precision:<15.4f}\")\n", + "print(f\"{'Recall':<25} {'N/A':<20} {test_recall:<15.4f}\")\n", + "print(f\"{'F1-Score':<25} {'N/A':<20} {test_f1:<15.4f}\")\n", + "\n", + "print(\"\\n\")\n", + "print(\"Conclusion\")\n", + "print(f\"• Best k = {best_k} determined by 5-fold GroupKFold cross-validation\")\n", + "print(f\"• Cross-validation ensures no patient images are split across folds\")\n", + "print(f\"• Test set AUC = {test_auc:.3f} is consistent with CV results\")\n", + "print(f\"• Model generalizes well to unseen data (test AUC close to CV mean)\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3275859", + "metadata": {}, + "source": [ + "### Descicion Tree Classifier" + ] + }, + { + "cell_type": "markdown", + "id": "e36a5fcf", + "metadata": {}, + "source": [ + "#### Estimate best depth" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "44095f32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Cross-validation:\n", + "Best depth: 3\n", + "Mean CV AUC: 0.6120\n", + "\n", + "All depth values tested:\n", + " depth = 2: CV AUC = 0.5996 ± 0.0244\n", + " depth = 3: CV AUC = 0.6120 ± 0.0209\n", + " depth = 4: CV AUC = 0.6035 ± 0.0094\n", + " depth = 6: CV AUC = 0.6001 ± 0.0240\n", + " depth = 8: CV AUC = 0.5742 ± 0.0265\n" + ] + } + ], + "source": [ + "# Although a Decision Tree doesn't call for scaling, to ensure fair comparison to kNN\n", + "# we also scale for the Decision Tree\n", + "pipeline_dt = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('dt', DecisionTreeClassifier(random_state=42))\n", + "])\n", + "\n", + "param_grid_dt = {\n", + " \"dt__max_depth\": [2 , 3 , 4 , 6 , 8]\n", + "}\n", + "\n", + "\n", + "grid_search_dt = GridSearchCV(\n", + " estimator = pipeline_dt,\n", + " param_grid = param_grid_dt,\n", + " cv = gkf,\n", + " scoring = \"roc_auc\",\n", + " n_jobs = -1\n", + ")\n", + "\n", + "grid_search_dt.fit(X_train, y_train, groups=patient_train)\n", + "best_depth_dt = grid_search_dt.best_params_[\"dt__max_depth\"]\n", + "\n", + "print(\"\\n\")\n", + "print(\"Cross-validation:\")\n", + "print(f\"Best depth: {best_depth_dt}\")\n", + "print(f\"Mean CV AUC: {grid_search_dt.best_score_:.4f}\")\n", + "\n", + "# Show results for each k\n", + "print(\"\\nAll depth values tested:\")\n", + "for i, k in enumerate(param_grid_dt['dt__max_depth']):\n", + " print(f\" depth = {k}: CV AUC = {grid_search_dt.cv_results_['mean_test_score'][i]:.4f} ± {grid_search_dt.cv_results_['std_test_score'][i]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "169878c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Descision Tree classifier results\n", + "Parameters: max_depth = 3\n", + "\n", + "Cross-Validation Results (from GridSearch):\n", + " Mean CV AUC: 0.612 (5-fold GroupKFold)\n", + "\n", + " Test set results:\n", + " Accuracy: 0.6048\n", + " AUC: 0.6249\n", + " Precision: 0.6667\n", + " Recall: 0.4190\n", + " F1-Score: 0.5146\n" + ] + } + ], + "source": [ + "# Train final model on ALL training data (no validation split needed)\n", + "dt_final = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('dt', DecisionTreeClassifier(max_depth=best_depth_dt))\n", + "])\n", + "dt_final.fit(X_train, y_train)\n", + "\n", + "# Predictions on TEST set only (FINAL - UNBIASED)\n", + "y_test_pred_dt = dt_final.predict(X_test)\n", + "y_test_proba_dt = dt_final.predict_proba(X_test)[:, 1]\n", + "\n", + "# Calculate test metrics\n", + "test_acc_dt = accuracy_score(y_test, y_test_pred_dt)\n", + "test_auc_dt = roc_auc_score(y_test, y_test_proba_dt)\n", + "test_precision_dt = precision_score(y_test, y_test_pred_dt)\n", + "test_recall_dt = recall_score(y_test, y_test_pred_dt)\n", + "test_f1_dt = f1_score(y_test, y_test_pred_dt)\n", + "\n", + "print(\"Final Descision Tree classifier results\")\n", + "print(f\"Parameters: max_depth = {best_depth_dt}\")\n", + "print(f\"\\nCross-Validation Results (from GridSearch):\")\n", + "print(f\" Mean CV AUC: {round(grid_search_dt.best_score_ , 4)} (5-fold GroupKFold)\")\n", + "print(f\"\\n Test set results:\")\n", + "print(f\" Accuracy: {test_acc_dt:.4f}\")\n", + "print(f\" AUC: {test_auc_dt:.4f}\")\n", + "print(f\" Precision: {test_precision_dt:.4f}\")\n", + "print(f\" Recall: {test_recall_dt:.4f}\")\n", + "print(f\" F1-Score: {test_f1_dt:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a61bd5ae", + "metadata": {}, + "source": [ + "#### Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f58500ac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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F0qVLo1OnTrhw4UK+jD98+HBIkoSWLVvmuH///v2QJAl//PFHjvsHDRqU48LIaWlpmD9/Pj766CMULVoUJiYmKFmyJDp06IADBw7kS+xJSUkYOnQonJycYGpqCm9vb6xduzZPY/z555/w8/ODlZUVLCwsULlyZfz666/q/U+fPsWUKVPQsGFDODo6wtLSEp6enpg+fbrsDSdxcXGQJCnHLa9x5eT+/ftQqVSQJOmNb3BR2qlTp9C4cWNYWlrCxsYGbdu2zdNrIpOTkzF+/HiUL18eKpUKdnZ28Pf3x5UrVzT6ZWRkYMKECShTpgxUKhUqVqyIefPmycZ78Tv66vbqW2uISFlcJ5Eojx48eIBmzZqhatWqKFq0KK5fv45p06ahTp06iIqKynEx7dzKyMhQL+C8Y8cO3L59W+Odvu/q/v37+PTTT/HPP/+gZ8+eGDlyJGxtbXH79m31Go1RUVGoWrXqe52nbdu2OHnyJKZNm4by5ctj9erV+OKLL5CdnY3OnTu/9fhp06ZhzJgx6N+/P4KCgmBsbIyLFy9qLNJ848YNzJkzB926dcPw4cNhaWmJQ4cOISQkBLt378bu3btlSfLXX38tO//r1kjMixUrVqhjW7JkCWrWrPneY+a3ixcvomHDhvD29sb69evx7NkzjB8/HvXr18eZM2dk60q+KikpCf7+/rhz5w6+/fZbeHl54cmTJzh69ChSUlI0+n711VdYsWIFJk2ahFq1amHnzp0YMmQI/vvvP3z33XeysXfs2AFra2v1ZwMD1i2IdIrSa/AQ5bcX69Pdu3evwM4ZHR0tAIhx48a91zi///67ACBatGghAIgpU6bI+uzbt08AEL///nuOYwwcOFC2Pl+zZs2EkZGRiIiIyPGYv//+W8THx79X7H/99ZcAIFavXq3R3qRJE+Hk5CQyMzPfeHxkZKQwMDAQ06dPf2O/pKQkkZSUJGt/sU7foUOH1G1vWo8xP1SpUkUUL15c1KpVS1hbW4uUlBStnOd9tG/fXhQrVkw8efJE3RYXFyeMjY3FqFGj3nr8kCFDhIWFhbh27dob+50/f15IkiSmTp2q0d6nTx9hZmYmHjx4oG5T4neUiPKOf20jvXDx4kWULVsWderUQWJiYr6P/6IaY2T0fsX5JUuWwMTEBEuXLoWzszOWLl0K8Z5LmUZFRWH79u3o1asXPv744xz71KpVC6VLl36v84SHh8PS0hLt27fXaO/Rowfu3LmDEydOvPH4+fPnQ6VS4euvv35jPwsLC1hYWMjaa9euDQC4efNmHiN/NydOnMD58+fRrVs39OnTB0+ePMGGDRtk/bKzszFv3jx4e3vDzMwMNjY28PHxwebNmzX6rV69GnXr1oWlpSUsLS3h7e2NJUuWvFeMmZmZ2Lp1Kz7//HNYWVmp211cXODv74/w8PA3Hp+SkoLFixejffv2b30v86ZNmyCEQI8ePTTae/TogdTU1Ne+65yIdBeTRPrgHThwAL6+vvDy8sK+fftQvHhxAM//552ZmfnW7dV34L6QlZWFtLQ0XLx4Eb1790bx4sVl/4PMi1u3bmHXrl0ICAiAvb09AgMDcfXqVRw8ePCdxwSAXbt2AQDatGmT62Nyc10yMzM1Etjz58/Dw8NDlih7eXmp97/JwYMH4eHhgQ0bNqBChQowNDREqVKl8O233+bqncAvXjdYuXJl2b5p06bBxMQE5ubm+Oijj2QJ2rt4kcD17NkTnTp1grm5eY5JXffu3TFkyBDUqlUL69atw9q1a9G6dWuN90aPHz8eXbp0gZOTE8LCwhAeHo7AwEDEx8er+7zLn9dr164hNTVV/TN4mZeXF65evSq7j/NlUVFRSE5Ohru7OwYMGKC+l7VmzZr466+/NPqeP38e9vb2cHR0lJ3nxf5XeXp6wtDQEA4ODvjyyy9x48aN18ZCRApQuJJJlO9enspasWKFMDExEYMHD9Z4LdzL/d62ubi45HieF6/RAyDKly8voqOj3yvuiRMnCgBix44dQgghrl+/LiRJEt26ddPol9fp5v79+wsA4uLFi7mK48UUbW62ffv2qY9zd3cXTZs2lY13584dAUA2DfkqlUolihQpIooWLSrmz58v9u7dK8aMGSMMDQ1F586d33js2bNnhZmZmfjss89k5+7Tp49Yv369OHTokFi1apXw8fERAMSiRYtydT1ykpycLKysrISPj4+6LTAwUEiSJK5evapuO3jwoAAgxowZ89qxrl+/LgwNDUWXLl3eeM53+fN65MgRAUCsWbNGNt7UqVMFAHHnzp3XnnPNmjUCgLCyshL16tUTmzdvFlu3bhX+/v5CkiT1n1Uhnt9WUKFChRzHMTExEX379lV/Xr58uZgyZYrYtm2b2Lt3r5g2bZqwtbUVDg4O4tatW2+8DkRUcPjgCn2wpkyZgnnz5uH777/HsGHDZPv79u372ieIX6ZSqXJsP3r0KNLT03Ht2jXMnj0b/v7+iIiIyLGS9TZCCPUUc5MmTQAArq6uaNiwITZs2ID58+drTBdqk5OTE06ePJmrvq8+pJPTU9W52Qc8r5T9999/WLNmDTp16gQA8Pf3R3JyMubMmYMJEybAzc1NdlxcXBxatmwJZ2dnLF68WGNfiRIlNJ6MBoD27dujTp06+Pbbb9G9e/d3ukVg/fr1ePr0KXr27Klu69mzJ5YtW4alS5di8uTJAIDt27cDAAYOHPjasXbv3o2srKw39gHe78/ru/5csrOzAQAmJibYvn07ihQpAuD5z8Xd3R2TJk1C06ZN83yebt26aezz9/eHv78/6tatixkzZuDHH3987ThEVHCYJNIHa+XKlShZsqQ64XiVo6Ojeur5TV73P77q1asDAHx8fNC6dWu4ubnhu+++w59//pnnWPfu3YvY2FgMHz4cT58+Vbd36NAB+/btw5o1a9CvXz8A/3/f4+umwTMzMzUSnxf3GsbGxubqyWsTExN4e3vnKm5DQ0P1v9vZ2eHBgweyPg8fPgQA2NravnEsOzs7JCQkaCQdANCsWTPMmTMHp06dkiWJ8fHx8Pf3h5GRESIiIt56DgAwNjZGx44d8e233+LKlSvw8PB46zGvWrJkCUxNTfHpp5/i8ePHAJ5Pq5YpUwZhYWGYMGECDA0Nce/ePRgaGsqmYF927949AECpUqXeeM53+fNqZ2cHAK/9uUiSBBsbm9eO9eJ4X19fdYIIAObm5vDz88OmTZs0+p45c0Y2RnJyMtLT09/6s6lduzbKly+P48ePv7EfERUc3pNIH6wdO3bA2NgY9evX17i364WJEyfC2Nj4rVu5cuXeeq4iRYqgYsWKuHz58jvF+uJetlmzZqFo0aLqbcCAARr7AcDBwQEAcPv27RzHun37troPAHXS9fL/0N8kLi4uV9fF2NhYY31FT09PxMTEIDMzU2O8c+fOAQCqVKnyxvPmdN8cAPV9j68ujxIfH4+GDRtCCIF9+/a9NcnKzZi5cfnyZRw+fBjPnj1D6dKlNX5ecXFxuH37Nnbu3Ang+QNNWVlZSEhIeO14Lx56unXr1hvP+y5/XsuVKwczMzP1z+Bl586dg5ub2xvXJnzdzwR4fg1fvn6enp64d++e7Lvm9uef05hEpCxWEumD5eLigkOHDqFx48aoX78+IiIiNNbGe9/p5pfdv38f586dQ7169fIc56NHjxAeHo569eqppylftnjxYqxatQrnz59HlSpV4O7uDhcXF/z+++8YNmyYRuXo3r172LdvH9q1a6duq169Opo1a4YlS5agQ4cOOT7hHBkZieLFi6N06dLvPN382WefYdGiRdiwYQM6duyobl+2bBmcnJxQp06dN471+eefY9euXdi+fbvGmobbtm2DgYEBatWqpW67ceMGGjZsiKysLOzfvx8uLi65ihd4vhblunXrUKxYsRynr9/mRcK+aNEi2fGpqakICAjAb7/9hubNm6NZs2YIDQ3FwoULMXHixBzH++STT2BoaIiFCxeibt26rz3vu/x5NTIyQqtWrbBx40bMmDFDXQ28ceMG9u3bl+NtGC8rUaIE6tatiyNHjuDp06fqWx5SUlJw4MAB+Pj4qPsGBARg7NixWLZsGUaPHq1uDwsLg5mZGT799NM3nuv48eO4cuUKBg8e/NbvSEQFRMkbIom04dU12O7duyeqV68uHB0dxblz595r7MePH4tatWqJ2bNni61bt4qIiAixcOFCUbFiRWFubi5Onjyp0d/Pz0+2ZuGr5s2bJwCIdevW5bj/n3/+EQDE0KFD1W2///67kCRJ+Pv7i9WrV4u9e/eKX375Rbi6uoqiRYtqPDwhxPNrUKNGDWFiYiL69+8v/vzzT3Hw4EGxbt060bVrV2FoaCjOnDnzjlfl/zVp0kQULVpU/Prrr2Lv3r2iT58+AoBYuXKlRr+ePXsKQ0NDERcXp25LT08X1atXF9bW1uLHH38Uu3fvFqNHjxaGhoZi0KBB6n7//vuvKFu2rFCpVGLlypXi2LFjGtvNmzfVfYcNGyYGDRok1qxZI/bt2yeWL18uatWqJQCIpUuXasT04oGg4ODg136/jIwM4ejoKDw8PF7bp23btsLY2FgkJiYKIYTo1q2bkCRJ9O3bV2zevFns3LlTTJs2TcydO1d9zLhx4wQA0a5dO7FhwwaxZ88eMXfuXDF+/Pg3Xu/ciImJEZaWlqJBgwZi27ZtYuPGjaJKlSrCyclJHeMLhoaG4uOPP9ZoO3LkiDAxMRE+Pj4iPDxcbNq0SdSvX18YGxuLo0ePavTt3bu3UKlU4vvvvxf79+8X3333nZAkSbbep5eXl5gxY4bYsmWL2L17t5gyZYqwsbERTk5Ob3yQhogKFpNE+uDktFDv48ePRb169YStra0skcuLZ8+eid69ewsPDw9haWkpjIyMRKlSpUTXrl3FhQsXZP1r1KghHB0d3zimt7e3KF68uEhLS3ttHx8fH1GsWDGNPnv27BGffPKJsLGxEUZGRqJEiRKia9eu4sqVKzmOkZqaKubOnSvq1q0rrKyshJGRkXBychJt27YVf/31Vy6vwJv9999/YvDgwcLR0VGYmJgILy+vHJ+sDQwMFABEbGysRvuDBw9Ev379hIODgzA2Nhbly5cX33//vcaT6S+SuddtLyd5S5YsEbVr1xa2trbCyMhIFC1aVDRt2lTs3LlTFtOWLVsEAPHzzz+/9vtt2rRJABBz5sx5bZ8dO3YIAGLmzJlCCCGysrLE7NmzRZUqVYSJiYmwtrYWdevWFVu2bNE47kUCa2pqKiwtLUW1atVkiey7ioyMFI0aNRLm5ubCyspKtGnTRvYXCSGEACD8/Pxk7YcOHRJ+fn7C3NxcmJubi48//lgcOXJE1i89PV0EBweL0qVLCxMTE1G+fHmNZPiFTp06CTc3N2FhYSGMjY2Fi4uL6N+/PxNEIh0jCfGeK/USUY7+++8/2NraYs6cOW99cpWUN2rUKKxZswZXrlzhO4SJiMAHV4i05uDBgyhZsiT69OmjdCiUC/v27cO4ceOYIBIR/Q8riUREREQkw0oiEREREckwSSQiIiIiGSaJRERERCTDJJGIiIiIZJgkEhEREZEMk0QiIiIikvkg390sNSmldAhEpCWpO24oHQIRaYmpoXK1K23mDmL3La2NrU2sJBIRERGRzAdZSSQiIiLKE0lSOgKdwySRiIiIiHOrMrwkRERERCTDSiIRERERp5tlWEkkIiIiIhlWEomIiIhYSJRhJZGIiIiIZFhJJCIiIuI9iTKsJBIRERGRDCuJRERERCybyTBJJCIiIuJ0swzzZiIiIiKSYSWRiIiIiIVEGVYSiYiIiEiGlUQiIiIiA5YSX8VKIhERERHJMEkkIiIikrS45dHBgwfRqlUrODk5QZIkbNq0SdYnJiYGrVu3hrW1NYoUKQIfHx/cuHFDvT8tLQ1ff/01ihUrBgsLC7Ru3Rq3bt3KUxxMEomIiIh0SHJyMqpWrYr58+fnuP/atWv46KOPULFiRezfvx9nz57FuHHjYGpqqu4zdOhQhIeHY+3atTh8+DCSkpLQsmVLZGVl5ToOSQgh3vvb6BipSSmlQyAiLUndcePtnYioUDI1VK52JX1eVmtjiw3X3/lYSZIQHh6ONm3aqNs6deoEY2NjrFixIsdjnjx5Ant7e6xYsQIdO3YEANy5cwfOzs7Ytm0bmjZtmqtzs5JIREREpMXp5rS0NDx9+lRjS0tLe6cws7Oz8ddff6F8+fJo2rQpihcvjjp16mhMSUdFRSEjIwOffPKJus3JyQlVqlTB0aNHc30uJolEREREWhQaGgpra2uNLTQ09J3GSkxMRFJSEqZNm4ZPP/0Uu3btwmeffYa2bdviwIEDAICEhASYmJigaNGiGsc6ODggISEh1+fiEjhEREREWlwCJygoCMOHD9doU6lU7zRWdnY2ACAgIADDhg0DAHh7e+Po0aP4+eef4efn99pjhRCQ8vD6QVYSiYiIiLRIpVLByspKY3vXJLFYsWIwMjJCpUqVNNo9PDzUTzc7OjoiPT0djx490uiTmJgIBweHXJ+LSSIRERGRDi2B8yYmJiaoVasWLl26pNF++fJluLi4AABq1KgBY2Nj7N69W73/7t27OH/+PHx9fXN9Lk43ExEREemQpKQkXL16Vf05NjYWZ86cga2tLUqXLo2RI0eiY8eOaNCgAfz9/bFjxw5s2bIF+/fvBwBYW1ujV69eGDFiBOzs7GBra4tvvvkGnp6eaNy4ca7j4BI4RFSocAkcog+XokvgdHLT2thi7dW3d3rJ/v374e/vL2sPDAxEWFgYAOC3335DaGgobt26hQoVKmDChAkICAhQ93327BlGjhyJ1atXIzU1FY0aNcKCBQvg7Oyc6ziYJBJRocIkkejDxSRRt3C6mYiIiEiLTzcXVkwSiYiIiJgjyvDpZiIiIiKSYSWRiIiIKA+LTOsLVhKJiIiISIaVRCIiIiIWEmVYSSQiIiIiGVYSiYiIiLgEjgwriUREREQkw0oiEREREQuJMkwSiYiIiLgEjgynm4mIiIhIhpVEIiIiIpbNZHhJiIiIiEiGlUQiIiIi3pMow0oiEREREcmwkkhERETEQqIMK4lEREREJMNKIhERERHvSZRhkkhERETEuVUZXhIiIiIikmElkYiIiIjTzTKsJBIRERGRDCuJRERERCwkyrCSSEREREQyrCQSERERGbCU+CpWEomIiIhIhpVEIiIiIj7dLMMkkYiIiIg5ogynm4mIiIhIhpVEIiIi0nsSp5tlWEkkIiIiIhlWEomIiEjvsZIox0oiEREREcmwkkhERER6j4VEOVYSiYiIiEiGlUQiIiLSewYsJcroRCVx2bJl+Ouvv9SfR40aBRsbG/j6+iI+Pl7ByIiIiEgfSJKkta2w0okkcerUqTAzMwMAHDt2DPPnz8eMGTNQrFgxDBs2TOHoiIiIiPSPTkw337x5E25ubgCATZs2oV27dujbty/q1auHhg0bKhscERERffAKc8VPW3SikmhpaYkHDx4AAHbt2oXGjRsDAExNTZGamqpkaERERER6SScqiU2aNEHv3r1RrVo1XL58GS1atAAAXLhwAWXKlFE2OCIiIvrgsZIopxOVxJ9++gl169bFvXv3sGHDBtjZ2QEAoqKi8MUXXygcHREREZH+kYQQQukg8pvUpJTSIRCRlqTuuKF0CESkJaaGytWuzEfV0NrYKTOitDa2NunEdPPBgwffuL9BgwYFFAkRERERATqSJOb0BPPL9wZkZWUVYDRERESkb3hPopxO3JP46NEjjS0xMRE7duxArVq1sGvXLqXDIyIiItI7OlFJtLa2lrU1adIEKpUKw4YNQ1RU4ZzLJyIiosKBlUQ5nUgSX8fe3h6XLl1SOgwiIiL6wElgkvgqnUgS//nnH43PQgjcvXsX06ZNQ9WqVRWKioiIiEh/6USS6O3tDUmS8OpqPD4+Pvjtt98UioqIiIj0Baeb5XQiSYyNjdX4bGBgAHt7e5iamioUEREREZF+04kk0cXFRekQiIiISI+xkCinE0vgAMCBAwfQqlUruLm5wd3dHa1bt8ahQ4eUDouIiIhIL+lEkrhy5Uo0btwY5ubmGDx4MAYNGgQzMzM0atQIq1evVjo8IiIi+sAZSJLWtsJKJ97d7OHhgb59+2LYsGEa7bNmzcKiRYsQExOTp/H47maiDxff3Uz04VLy3c1Fx/hobexHU45rbWxt0olK4vXr19GqVStZe+vWrWUPtRARERHlN0mStLYVVjqRJDo7OyMiIkLWHhERAWdnZwUiIiIiIn3CJFFOJ55uHjFiBAYPHowzZ87A19cXkiTh8OHDCAsLw48//qh0eERERER6RyeSxAEDBsDR0REzZ87E+vXrATy/T3HdunUICAhQODoiIiL60BXigp/WKJ4kZmZmYsqUKejZsycOHz6sdDhEREREBB24J9HIyAjff/89srKylA6FiIiI9BTvSZRTPEkEgMaNG2P//v1Kh0FERERE/6P4dDMANGvWDEFBQTh//jxq1KgBCwsLjf2tW7dWKDIiIiLSB4W54qctOrGYtoHB6wuakiTleSqai2kTfbi4mDbRh0vJxbSLh3yktbETQwrnMxc6Md2cnZ392o33KhIREZG26dI9iQcPHkSrVq3g5OQESZKwadOm1/bt168fJEnCnDlzNNrT0tLw9ddfo1ixYrCwsEDr1q1x69atPMWhE0niy549e6Z0CERERKRndClJTE5ORtWqVTF//vw39tu0aRNOnDgBJycn2b6hQ4ciPDwca9euxeHDh5GUlISWLVvmqfimE0liVlYWJk2ahJIlS8LS0hLXr18HAIwbNw5LlixRODoiIiKigtOsWTNMnjwZbdu2fW2f27dvY9CgQVi1ahWMjY019j158gRLlizBzJkz0bhxY1SrVg0rV67EuXPnsGfPnlzHoRNJ4pQpUxAWFoYZM2bAxMRE3e7p6YnFixcrGBkRERHpA0nS3paWloanT59qbGlpae8ca3Z2Nrp164aRI0eicuXKsv1RUVHIyMjAJ598om5zcnJClSpVcPTo0VyfRyeSxOXLl+PXX39Fly5dYGhoqG738vLCxYsXFYyMiIiI6P2EhobC2tpaYwsNDX3n8aZPnw4jIyMMHjw4x/0JCQkwMTFB0aJFNdodHByQkJCQ6/PoxBI4t2/fhpubm6w9OzsbGRkZCkRERERE+kSbS+AEBQVh+PDhGm0qleqdxoqKisKPP/6IU6dO5TlmIUSejtGJSmLlypVx6NAhWfvvv/+OatWqKRARERERUf5QqVSwsrLS2N41STx06BASExNRunRpGBkZwcjICPHx8RgxYgTKlCkDAHB0dER6ejoePXqkcWxiYiIcHBxyfS6dqCQGBwejW7duuH37NrKzs7Fx40ZcunQJy5cvx9atW5UOj4iIiD5whWUx7W7duqFx48YabU2bNkW3bt3Qo0cPAECNGjVgbGyM3bt3o0OHDgCAu3fv4vz585gxY0auz6UTSWKrVq2wbt06TJ06FZIkYfz48ahevTq2bNmCJk2aKB0eERERUYFJSkrC1atX1Z9jY2Nx5swZ2NraonTp0rCzs9Pob2xsDEdHR1SoUAEAYG1tjV69emHEiBGws7ODra0tvvnmG3h6esoSzDfRiSQReJ4FN23aVOkwiIiISA8Z6FAlMTIyEv7+/urPL+5nDAwMRFhYWK7GmD17NoyMjNChQwekpqaiUaNGCAsL03hA+G104rV8J0+eRHZ2NurUqaPRfuLECRgaGqJmzZp5Go+v5SP6cPG1fEQfLiVfy1c61P/tnd7RjaB9Whtbm3TiwZWBAwfi5s2bsvbbt29j4MCBCkREREREpN90Yro5Ojoa1atXl7VXq1YN0dHRCkRERERE+qSwPLhSkHSikqhSqfDvv//K2u/evQsjI53IY4mIiIj0ik4kiU2aNEFQUBCePHmibnv8+DG+++47Pt1MREREWidp8Z/CSieSxJkzZ+LmzZtwcXGBv78//P394erqioSEBMycOVPp8EgB9T3rYPPEpbi9NhJi9y0E+MqffK9Y2g1/TvwNjzdF4+mfF3Fs7mY42ztp9PHxqI6IGeuQtPkyHoVfwL4ffoepiWlBfQ0iyqMlv/6KqpU8MCN0ao77JwYHo2olD6xcvqyAIyPSPzoxl1uyZEn8888/WLVqFc6ePQszMzP06NEDX3zxBYyNjZUOjxRgYWqOs9ejsXTXemwMXiTbX7aECw7PDseS7WsRvGwmniT/B4/SbniW8f8vTPfxqI4doSsRuuYnfP3TOKRnZqBq2UrIFtkF+VWIKJfOnzuHP35fj/L/W+vtVXv37MH5f/6BffHiBRwZ6QPekyinE0kiAFhYWKBv375Kh0E6YsfJfdhx8vVLBkzpMQrb/t6L0YunqNtiEzSXRpk9IARzw3/D9HU/qduu3o7N/2CJ6L2lJCcjaNRIBE+YiEW//Czb/++//yJ0ymQs/HURvh7QX4EIifSPziSJly9fxv79+5GYmIjsbM1Kz/jx4xWKinSRJEloUacRZqxfiB2hK1GtXBXEJtxE6Nr5+PPoTgCAvY0dfDyqY1VEOI7M2YRyTi64ePMaxvw2HUcunFT4GxDRq6ZOnoQGfn7w8fWVJYnZ2dkY8+1odO/ZE27u7gpFSB86VhLldCJJXLRoEQYMGIBixYrB0dFR4wf14jV9RC8UtymGIuaW+LbjQIwNm4HRi6fi05r+2Bi8CP4jO+DgP8dRtoQLACDky+H45tdJOHP1Ar5s0g4RM9aiSt/GrCgS6ZDt2/5CTHQ0Vq//Pcf9SxcvhqGhITp37VbAkZE+YY4opxNJ4uTJkzFlyhSMHj06z8empaUhLS1NszFbAAb8aX+oDAyeP2/157FdmLNxMQDg7LVo+Faugf4tu+LgP8fVr1f65a+VCNu5HgBw5toFNKr2EXo27YjvfpumTPBEpCHh7l3MCA3Fz4sWQ6VSyfZHX7iAVStWYO2GDaz0EBUwnUgSHz16hPbt27/TsaGhoZgwYYJmo2sRoJxVPkRGuuj+k4fIyMxAdPxljfaYG1fxUZVaAIC7DxMBANHxV17pcwWli5csmECJ6K2iL1zAwwcP8EX7duq2rKwsREVGYu3q1RgyfAQePnyATxt9rLF/5owZWLV8ObbviVAibPoA8S8hcjqRJLZv3x67du1C//55vxk5KChI/eLrF6w/88iv0EgHZWRm4OSls6jgXE6jvXzJsoj/9zYAIC7hJm7fT0CFUmU1+5Qqi+1veCCGiApWnbp18ceff2q0BY8ZgzKurujRuzfs7e3h+1E9jf0D+vRBy9at0eaztgUZKpHe0Ykk0c3NDePGjcPx48fh6ekpW/Zm8ODBrz1WpVLJpyg41VzoWZiaw61kGfVnV0dnVC1XCQ+fPsbNe3fw/e8/Y92YBTj4zwnsO3sUn9ZqiFZ1G6PhiP+vSH+/fiEmBI7A2esxOHPtAgKbtENFZze0m9hPgW9ERDmxsLCAu3t5jTYzMzPY2Nio221simrsNzYyQrFixVDG1bXA4qQPnyTpxNLROkUnksRff/0VlpaWOHDgAA4cOKCxT5KkNyaJ9GGqWb4q9s/8/5vYZw8IAQCE7VqPHt8Px6YjO9D/xyAEfTEIcwdOxKVb1/D5hL4aTy7/GL4EpiammN0/GLZFbHD2ejSajP4C1+/GF/TXISIiKnQkIYRQOoj8JjUppXQIRKQlqTtuvL0TERVKpobKVfPKz2qmtbEvD9+utbG1ibVVIiIiIpLRielmALh16xY2b96MGzduID09XWPfrFmzFIqKiIiI9IFkwLrZq3QiSYyIiEDr1q3h6uqKS5cuoUqVKoiLi4MQAtWrV1c6PCIiIvrA8cEVOZ24IkFBQRgxYgTOnz8PU1NTbNiwATdv3oSfn987r59IRERERO9OJ5LEmJgYBAYGAgCMjIyQmpoKS0tLTJw4EdOnT1c4OiIiIvrQSZKkta2w0okk0cLCQv1qPScnJ1y7dk297/79+0qFRURERKS3dOKeRB8fHxw5cgSVKlVCixYtMGLECJw7dw4bN26Ej4+P0uERERHRB473JMrpRJI4a9YsJCUlAQBCQkKQlJSEdevWwc3NDbNnz1Y4OiIiIiL9oxNJYtmy//9+XXNzcyxYsEDBaIiIiEjfFOZ7B7VF0drqo0ePMG/ePDx9+lS278mTJ6/dR0RERETapWiSOH/+fBw8eBBWVlayfdbW1jh06BDmzZunQGRERESkTyTJQGtbYaVo5Bs2bED//v1fu79fv374448/CjAiIiIi0kdcAkdO0STx2rVrcHd3f+1+d3d3jeVwiIiIiKhgKJokGhoa4s6dO6/df+fOHRjwXYpERESkZZxullM08mrVqmHTpk2v3R8eHo5q1aoVXEBEREREBEDhJXAGDRqETp06oVSpUhgwYAAMDQ0BAFlZWViwYAFmz56N1atXKxkiERER6YHCfO+gtiiaJH7++ecYNWoUBg8ejDFjxqBs2bKQJAnXrl1DUlISRo4ciXbt2ikZIhEREZFeUnwx7SlTpiAgIACrVq3C1atXIYRAgwYN0LlzZ9SuXVvp8IiIiEgPFOZ7B7VF8SQRAGrXrs2EkIiIiEiH6Fza7OnpiZs3byodBhEREekTA0l7WyGlE5XEl8XFxSEjI0PpMIiIiEiPcLpZjleEiIiIiGR0rpJYv359mJmZKR0GERER6REugSOnc0nitm3blA6BiIiISO/pTJJ4+fJl7N+/H4mJicjOztbYN378eIWiIiIiIn3AexLldCJJXLRoEQYMGIBixYrB0dFRo+QrSRKTRCIiIqICphNJ4uTJkzFlyhSMHj1a6VCIiIhID7GSKKcTV+TRo0do37690mEQERER0f/oRJLYvn177Nq1S+kwiIiISE9JkqS1rbDSielmNzc3jBs3DsePH4enpyeMjY019g8ePFihyIiIiIj0kySEEEoH4erq+tp9kiTh+vXreRpPalLqfUMiIh2VuuOG0iEQkZaYGio3wVl7WW+tjf134GKtja1NOlFJjI2NVToEIiIi0mOFeVpYW3TinsSXCSGgA8VNIiIiIr2mM0ni8uXL4enpCTMzM5iZmcHLywsrVqxQOiwiIiLSA5JkoLWtsNKJ6eZZs2Zh3LhxGDRoEOrVqwchBI4cOYL+/fvj/v37GDZsmNIhEhEREekVnUgS582bh4ULF+LLL79UtwUEBKBy5coICQlhkkhERERaxXsS5XSiBnr37l34+vrK2n19fXH37l0FIiIiIiLSbzqRJLq5uWH9+vWy9nXr1sHd3V2BiIiIiEifSAYGWtsKK52Ybp4wYQI6duyIgwcPol69epAkCYcPH0ZERESOySMRERERaZdOJImff/45Tpw4gVmzZmHTpk0QQqBSpUr4+++/Ua1aNaXDIyIiog8c70mU04kkEQBq1KiBVatWKR0GERER6aHCvFSNtiiaJBoYGLw1c5ckCZmZmQUUEREREREBCieJ4eHhr9139OhRzJs3j29fISIiIq3jdLOcokliQECArO3ixYsICgrCli1b0KVLF0yaNEmByIiIiIj0m85MwN+5cwd9+vSBl5cXMjMzcebMGSxbtgylS5dWOjQiIiL6wPG1fHKKR/7kyROMHj0abm5uuHDhAiIiIrBlyxZUqVJF6dCIiIiI9Jai080zZszA9OnT4ejoiDVr1uQ4/UxERESkbbwnUU4SCj4ZYmBgADMzMzRu3BiGhoav7bdx48Y8jSs1KfW+oRGRjkrdcUPpEIhIS0wNlZvg9Pt9mNbGPtB+ttbG1iZFK4lffvklM3ciIiJSXGG+d1BbFE0Sw8LClDw9ERER0XM6lCQePHgQ33//PaKionD37l2Eh4ejTZs2AICMjAyMHTsW27Ztw/Xr12FtbY3GjRtj2rRpcHJyUo+RlpaGb775BmvWrEFqaioaNWqEBQsWoFSp3M+26s4VISIiIiIkJyejatWqmD9/vmxfSkoKTp06hXHjxuHUqVPYuHEjLl++jNatW2v0Gzp0KMLDw7F27VocPnwYSUlJaNmyJbKysnIdh868lo+IiIhIKbp0+1uzZs3QrFmzHPdZW1tj9+7dGm3z5s1D7dq1cePGDZQuXRpPnjzBkiVLsGLFCjRu3BgAsHLlSjg7O2PPnj1o2rRpruJgJZGIiIioEHvy5AkkSYKNjQ0AICoqChkZGfjkk0/UfZycnFClShUcPXo01+OykkhERER6T5sPrqSlpSEtLU2jTaVSQaVSvffYz549w7fffovOnTvDysoKAJCQkAATExMULVpUo6+DgwMSEhJyPTYriURERERaFBoaCmtra40tNDT0vcfNyMhAp06dkJ2djQULFry1vxAiT9PqrCQSERGR3jPQ4j2JQUFBGD58uEbb+1YRMzIy0KFDB8TGxmLv3r3qKiIAODo6Ij09HY8ePdKoJiYmJsLX1zfX52AlkYiIiEiLVCoVrKysNLb3SRJfJIhXrlzBnj17YGdnp7G/Ro0aMDY21njA5e7duzh//nyekkRWEomIiEjvSdCdp5uTkpJw9epV9efY2FicOXMGtra2cHJyQrt27XDq1Cls3boVWVlZ6vsMbW1tYWJiAmtra/Tq1QsjRoyAnZ0dbG1t8c0338DT01P9tHNuMEkkIiIivadLb1yJjIyEv7+/+vOLqerAwECEhIRg8+bNAABvb2+N4/bt24eGDRsCAGbPng0jIyN06NBBvZh2WFjYG1+D/CpF392sLXx3M9GHi+9uJvpwKfnu5k/+HKu1sXcFTNba2NrESiIRERHpPV1aTFtX6E5tlYiIiIh0BiuJREREpPck1s1keEWIiIiISIaVRCIiItJ7vCdRjpVEIiIiIpJhJZGIiIj0noEOrZOoK5gkEhERkd7TpTeu6AqmzUREREQkw0oiERER6T1dei2fruAVISIiIiIZVhKJiIhI73EJHDlWEomIiIhIhpVEIiIi0nt8ulmOlUQiIiIikmElkYiIiPQen26WY5JIREREes+A080yTJuJiIiISIaVRCIiItJ7nG6Wy1WSOHfu3FwPOHjw4HcOhoiIiIh0Q66SxNmzZ+dqMEmSmCQSERFRocPFtOVylSTGxsZqOw4iIiIi0iHvPAGfnp6OS5cuITMzMz/jISIiIipwEgy0thVWeY48JSUFvXr1grm5OSpXrowbN24AeH4v4rRp0/I9QCIiIiIqeHlOEoOCgnD27Fns378fpqam6vbGjRtj3bp1+RocERERUUGQJElrW2GV5yVwNm3ahHXr1sHHx0fji1eqVAnXrl3L1+CIiIiICgKXwJHL8xW5d+8eihcvLmtPTk4u1NkyEREREf2/PCeJtWrVwl9//aX+/CIxXLRoEerWrZt/kREREREVEEmL/xRWeZ5uDg0Nxaefforo6GhkZmbixx9/xIULF3Ds2DEcOHBAGzESERERUQHLcyXR19cXR44cQUpKCsqVK4ddu3bBwcEBx44dQ40aNbQRIxEREZFWGUiS1rbC6p3e3ezp6Ylly5bldyxEREREpCPeKUnMyspCeHg4YmJiIEkSPDw8EBAQACOjdxqOiIiISFGFedFrbclzVnf+/HkEBAQgISEBFSpUAABcvnwZ9vb22Lx5Mzw9PfM9SCIiIiIqWHlOm3v37o3KlSvj1q1bOHXqFE6dOoWbN2/Cy8sLffv21UaMRERERFrFxbTl8lxJPHv2LCIjI1G0aFF1W9GiRTFlyhTUqlUrX4MjIiIiKghcTFsuz1ekQoUK+Pfff2XtiYmJcHNzy5egiIiIiEhZuaokPn36VP3vU6dOxeDBgxESEgIfHx8AwPHjxzFx4kRMnz5dO1ESERERaVFhXvRaW3KVJNrY2GjMqQsh0KFDB3WbEAIA0KpVK2RlZWkhTCIiIiIqSLlKEvft26ftOIiIiIgUw3sS5XKVJPr5+Wk7DiIiIiLSIe+8+nVKSgpu3LiB9PR0jXYvL6/3DoqIiIioIBXm1+dpS56TxHv37qFHjx7Yvn17jvt5TyIRERFR4ZfnCfihQ4fi0aNHOH78OMzMzLBjxw4sW7YM7u7u2Lx5szZiJCIiItIqCQZa2wqrPFcS9+7diz///BO1atWCgYEBXFxc0KRJE1hZWSE0NBQtWrTQRpxEREREVIDynN4mJyejePHiAABbW1vcu3cPAODp6YlTp07lb3REREREBYCv5ZN7pzeuXLp0CQDg7e2NX375Bbdv38bPP/+MEiVK5HuARERERNomafGfwirP081Dhw7F3bt3AQDBwcFo2rQpVq1aBRMTE4SFheV3fERERESkgDwniV26dFH/e7Vq1RAXF4eLFy+idOnSKFasWL4GR0RERFQQuJi23Duvk/iCubk5qlevnh+xEBEREZGOyFWSOHz48FwPOGvWrHcOhoiIiEgJhfkBE23JVZJ4+vTpXA3GC0xERET0YchVkrhv3z5tx5GvGvRtr3QIRKQla6/n7i+tRFT4dHevodi5DQrxotfawitCRERERDLv/eAKERERUWHHW+bkmCQSERGR3jNgkijD6WYiIiIikmElkYiIiPSeQSF+fZ62vFMlccWKFahXrx6cnJwQHx8PAJgzZw7+/PPPfA2OiIiIiJSR5yRx4cKFGD58OJo3b47Hjx8jKysLAGBjY4M5c+bkd3xEREREWidJkta2wirPSeK8efOwaNEijBkzBoaGhur2mjVr4ty5c/kaHBEREREpI8/3JMbGxqJatWqydpVKheTk5HwJioiIiKgg8elmuTxXEl1dXXHmzBlZ+/bt21GpUqX8iImIiIiIFJbnSuLIkSMxcOBAPHv2DEII/P3331izZg1CQ0OxePFibcRIREREpFUSVwWUyXOS2KNHD2RmZmLUqFFISUlB586dUbJkSfz444/o1KmTNmIkIiIi0ipON8u90zqJffr0QZ8+fXD//n1kZ2ejePHi+R0XERERESnovWqrxYoVY4JIREREhZ6BJGlty6uDBw+iVatWcHJygiRJ2LRpk8Z+IQRCQkLg5OQEMzMzNGzYEBcuXNDok5aWhq+//hrFihWDhYUFWrdujVu3buXtmuQ1cFdXV5QtW/a1GxERERG9u+TkZFStWhXz58/Pcf+MGTMwa9YszJ8/HydPnoSjoyOaNGmC//77T91n6NChCA8Px9q1a3H48GEkJSWhZcuW6vWtcyPP081Dhw7V+JyRkYHTp09jx44dGDlyZF6HIyIiIlKcLi163axZMzRr1izHfUIIzJkzB2PGjEHbtm0BAMuWLYODgwNWr16Nfv364cmTJ1iyZAlWrFiBxo0bAwBWrlwJZ2dn7NmzB02bNs1VHHlOEocMGZJj+08//YTIyMi8DkdEREREuRQbG4uEhAR88skn6jaVSgU/Pz8cPXoU/fr1Q1RUFDIyMjT6ODk5oUqVKjh69Giuk8R8e967WbNm2LBhQ34NR0RERFRgDCBpbUtLS8PTp081trS0tHeKMyEhAQDg4OCg0e7g4KDel5CQABMTExQtWvS1fXJ3TfLJH3/8AVtb2/wajoiIiOiDEBoaCmtra40tNDT0vcZ8dXpcCPHWKfPc9HlZnqebq1WrpnECIQQSEhJw7949LFiwIK/DERERESlOm/ckBgUFYfjw4RptKpXqncZydHQE8LxaWKJECXV7YmKiurro6OiI9PR0PHr0SKOamJiYCF9f31yfK89JYps2bTQ+GxgYwN7eHg0bNkTFihXzOhwRERGR4gwk7b1xRaVSvXNS+CpXV1c4Ojpi9+7dqFatGgAgPT0dBw4cwPTp0wEANWrUgLGxMXbv3o0OHToAAO7evYvz589jxowZuT5XnpLEzMxMlClTBk2bNlVnskRERESUf5KSknD16lX159jYWJw5cwa2trYoXbo0hg4diqlTp8Ld3R3u7u6YOnUqzM3N0blzZwCAtbU1evXqhREjRsDOzg62trb45ptv4OnpqX7aOTfylCQaGRlhwIABiImJycthRERERDrNALqzBE5kZCT8/f3Vn19MVQcGBiIsLAyjRo1CamoqvvrqKzx69Ah16tTBrl27UKRIEfUxs2fPhpGRETp06IDU1FQ0atQIYWFhMDQ0zHUckhBC5CVwf39/DBkyRDbtrEv8fh+mdAhEpCU9vLsqHQIRaUl39xqKnXtc5F9aG3tSzRZaG1ub8nxP4ldffYURI0bg1q1bqFGjBiwsLDT2e3l55VtwRERERAVBlxbT1hW5ThJ79uyJOXPmoGPHjgCAwYMHq/dJkqR+rDovr3shIiIiIt2U6yRx2bJlmDZtGmJjY7UZDxEREVGBM2AlUSbXSeKLWxddXFy0FgwRERER6YY83ZPI+XoiIiL6EEk69HSzrshTkli+fPm3JooPHz58r4CIiIiIChqnm+XylCROmDAB1tbW2oqFiIiIiHREnpLETp06oXjx4tqKhYiIiEgRrCTK5fpFhbwfkYiIiEh/5PnpZiIiIqIPjZT7upneyHWSmJ2drc04iIiIiEiH5Pm1fEREREQfGt6TKMfaKhERERHJsJJIREREeo8P6MoxSSQiIiK9x+lmOU43ExEREZEMK4lERESk9wz47mYZVhKJiIiISIaVRCIiItJ7fHBFjpVEIiIiIpJhJZGIiIj0noHEutmreEWIiIiISIaVRCIiItJ7Ep9ulmGSSERERHqPi2nLcbqZiIiIiGRYSSQiIiK9x0qiHCuJRERERCTDSiIRERHpPT64IsdKIhERERHJsJJIREREeo/3JMqxkkhEREREMqwkEhERkd6T+Fo+GSaJREREpPcM+OCKDNNmIiIiIpJhJZGIiIj0ngELiTKsJBIRERGRDCuJREREpPckLoEjw0oiEREREcmwkkhERER6j083y7GSSEREREQyrCQSERGR3uM9iXJMEomIiEjv8d3NcpxuJiIiIiIZVhKJiIhI7/HBFTlWEomIiIhIhpVEIiIi0nt8cEWOlUQiIiIikmElkYiIiPQe70mUYyWRiIiIiGRYSSQiIiK9x3sS5VhJJCIiIiIZVhKJiIhI7/GNK3JMEomIiEjv8cEVOU43ExEREZEMK4lERESk9zjbLMdKIhERERHJKF5JTE1NhRAC5ubmAID4+HiEh4ejUqVK+OSTTxSOjoiIiPQBH1yRU7ySGBAQgOXLlwMAHj9+jDp16mDmzJkICAjAwoULFY6OiIiISD8pniSeOnUK9evXBwD88ccfcHBwQHx8PJYvX465c+cqHB0RERHpA0mL/xRWiieJKSkpKFKkCABg165daNu2LQwMDODj44P4+HiFoyMiIiLST4oniW5ubti0aRNu3ryJnTt3qu9DTExMhJWVlcLRERERkT4wkCStbYWV4kni+PHj8c0336BMmTKoU6cO6tatC+B5VbFatWoKR0dERET6wACS1rbCSvGnm9u1a4ePPvoId+/eRdWqVdXtjRo1wmeffaZgZERERET6S/EkEQAcHR3h6Oio0Va7dm2FoiEiIiJ9IxXiaWFtUTxJ9Pf3f+MPZu/evQUYDREREREBOnBPore3N6pWrareKlWqhPT0dJw6dQqenp5Kh0dERER6QFceXMnMzMTYsWPh6uoKMzMzlC1bFhMnTkR2dra6jxACISEhcHJygpmZGRo2bIgLFy7k9yVRvpI4e/bsHNtDQkKQlJRUwNEQERERKWf69On4+eefsWzZMlSuXBmRkZHo0aMHrK2tMWTIEADAjBkzMGvWLISFhaF8+fKYPHkymjRpgkuXLqmXFcwPilcSX6dr16747bfflA6DiIiI9ICuLKZ97NgxBAQEoEWLFihTpgzatWuHTz75BJGRkQCeVxHnzJmDMWPGoG3btqhSpQqWLVuGlJQUrF69Ol+vic4miceOHYOpqanSYRARERG9l7S0NDx9+lRjS0tLy7HvRx99hIiICFy+fBkAcPbsWRw+fBjNmzcHAMTGxiIhIUG9rjQAqFQq+Pn54ejRo/kat+LTzW3bttX4LITA3bt3ERkZiXHjxikUFREREekTbS56HRoaigkTJmi0BQcHIyQkRNZ39OjRePLkCSpWrAhDQ0NkZWVhypQp+OKLLwAACQkJAAAHBweN41681jg/KZ4kWltba3w2MDBAhQoVMHHiRI0smYiIiEhbtJkkBgUFYfjw4RptKpUqx77r1q3DypUrsXr1alSuXBlnzpzB0KFD4eTkhMDAQHW/V1eGEULk+zI+iieJS5cuVToEIiIiIq1RqVSvTQpfNXLkSHz77bfo1KkTAMDT0xPx8fEIDQ1FYGCgel3phIQElChRQn1cYmKirLr4vhRPEl+IiopCTEwMJElCpUqV+Eo+IiIiKjB5fcBEW1JSUmBgoPnIiKGhoXoJHFdXVzg6OmL37t3qXCk9PR0HDhzA9OnT8zUWxZPExMREdOrUCfv374eNjQ2EEHjy5An8/f2xdu1a2NvbKx0iERERUYFo1aoVpkyZgtKlS6Ny5co4ffo0Zs2ahZ49ewJ4Ps08dOhQTJ06Fe7u7nB3d8fUqVNhbm6Ozp0752ssiieJX3/9NZ4+fYoLFy7Aw8MDABAdHY3AwEAMHjwYa9asUThCIiIi+tAZ6EYhEfPmzcO4cePw1VdfITExEU5OTujXrx/Gjx+v7jNq1Cikpqbiq6++wqNHj1CnTh3s2rUrX9dIBABJCCHydcQ8sra2xp49e1CrVi2N9r///huffPIJHj9+nOcx/X4flk/REZGu6eHdVekQiEhLurvXUOzc+xNitTZ2Q0dXrY2tTYpXErOzs2FsbCxrNzY21ngFDREREZG26Mo9ibpE8cW0P/74YwwZMgR37txRt92+fRvDhg1Do0aNFIyMiIiISH8pniTOnz8f//33H8qUKYNy5crBzc0Nrq6u+O+//zBv3jylwyMiIiI9YCBJWtsKK8Wnm52dnXHq1Cns3r0bFy9ehBAClSpVQuPGjZUOjYiIiPREYU7mtEXRJDEzMxOmpqY4c+YMmjRpgiZNmigZDhERERH9j6JJopGREVxcXJCVlaVkGERERKTn+OCKnOL3JI4dOxZBQUF4+PCh0qEQERER0f8ofk/i3LlzcfXqVTg5OcHFxQUWFhYa+0+dOqVQZERERKQveE+inOJJYps2bZQOgXSQV7Gy+KLCxyhftBSKmVljzJElOHznPADAUDJA7yrN4VPCAyUs7JCc8QxR/17GL+e24sGzpwCAIsbm6Fn5U9R0rIDiZjZ4kp6Mw7fPYcn57UjOfKbkVyOil2RnZeHQ6g24sP8Ikh89hmVRG3g29kO9jm0g/e/9tempz7AvbA2uHI9C6n//wbq4PWq2borqzXkfO5E2KZ4kBgcHKx0C6SAzIxNcfXwb2+JOYLJvT419poYmKF+0FJZH78bVx7dRxMQcg7zbYGq93ugXMQsAUMzMCnZmVlh4djPinibAwbwoRtRoDzszawQfC1PgGxFRTo79sQWnt+9By2EDUKx0KSRcuY6/fvwFKnMz1ApoBgDYs2gF4s9Fo9WIr2DtYI/Y0/9g54KlsLQtivI+NRX+BvShkFhJlFE8SQSAx48f448//sC1a9cwcuRI2Nra4tSpU3BwcEDJkiWVDo8UcCLhIk4kXMxxX3LmM4w4+LNG29zTG/FL4+EobmaDxNTHiH2agPEvJYN3kh9g8fltGFO7KwwlA2QJvs2HSBfcvngF7nVqwq1WNQCAjYM9og8exd2rsRp9PD+uDxevSgCAap82wpntEbh75TqTRCItUvzBlX/++Qfly5fH9OnT8cMPP6jf1RweHo6goCBlg6NCw8LYDNkiG0kZqW/oY4qUzGdMEIl0iHOlCog/ex4Pbt8FAPx7PR43oy+hXE1vjT5X/j6F/+4/hBAC8f9cwMM7CShb3UuhqOlDZABJa1thpXglcfjw4ejevTtmzJiBIkWKqNubNWuGzp07KxgZFRYmBkbo69kSe26cQkpmWo59rEzM8aXHJ9h87WgBR0dEb+LTrhXSklPwa/9vYGBggOzsbPh164DKfr7qPk36BWLbvEWY330QDAwNIUkSmg3uA+fKFRWMnD40fHBFTvEk8eTJk/jll19k7SVLlkRCQsJbj09LS0NammZikJ2RCQNjxb8aFQBDyQDjfb6EgSRh9qk/cuxjbqTCtI/6IP7pvwiL3lnAERLRm8QcPIbz+w8j4JuBKOZSCv9ej8eeRStgaVcUXo0aAAAit+zAnUtX0W7cCFgXt8eN8zHYtXApLG1t4OrtqfA3IPpwKT7dbGpqiqdPn8raL126BHt7+7ceHxoaCmtra43tRvhJbYRKOsZQMsCEuoEoYWGLEQcX5lhFNDNS4fv6/ZCamY6xR3/jVDORjtm7dDXqtmuNSn6+KF6mNDw/ro/aAc1w7Pc/AQAZaenYv3wdGvXuCvc6NVDctTRqtmoKj/o+OLHxL4Wjpw+JpMWtsFI8SQwICMDEiRORkZEB4PnTRTdu3MC3336Lzz///K3HBwUF4cmTJxpb6c9qaTtsUtiLBLGkpT2GH1iIp+kpsj7mRirMbNAfGdlZ+O7IYqRnZyoQKRG9SUZaOiQDzf+NSgYGENkCAJCdlYnszCzZk6eSgQGEEAUWJ5E+UnxO9ocffkDz5s1RvHhxpKamws/PDwkJCahbty6mTJny1uNVKhVUKpVGG6eaCz8zQxOUtCym/lzCwg5u1k54mp6CB8+eYmLd7ihftBS+PbwYhpIBbFXP72d9mp6CTJEFMyMVfmjQH6aGJph8YiUsjExhYWQKAHicloRs8H8uRLrAvXZ1HF33J6zsi6FY6VL491oc/t60DVWbNAQAqMzNUbqKB/b+thpGJiawLl4MN87H4PzeQ2jUu6uywdMHpjDX/LRDEjryV7G9e/fi1KlTyM7ORvXq1dG4ceN3Hsvv92H5GBkpwdu+HH5sOEjWvj3ub4Rd2IF1LcbneNyQ/fNx5t611x4PAB3/moiElEf5Gi8VnB7eTAw+JGkpqTi48ndcPhaJlCdPYGlbFJX8fPFRp7Yw/N9f+JMePcb+ZWsRe+ocniUlwap4MVRr+jFqtWnOte0+MN3dayh27lMP3v4cxLuqbueotbG1SWeSxPzEJJHow8UkkejDpWSSePrhv1obu5qtg9bG1ibF70kcPHgw5s6dK2ufP38+hg4dWvABEREREZHySeKGDRtQr149Wbuvry/++CPnJU2IiIiI8hOfbpZT/AmPBw8ewNraWtZuZWWF+/fvKxARERER6RupUKdz2qF4JdHNzQ07duyQtW/fvh1ly5ZVICIiIiIiUrySOHz4cAwaNAj37t3Dxx9/DACIiIjAzJkzMWfOHGWDIyIiIr3AB+XlFE8Se/bsibS0NEyZMgWTJk0CAJQpUwYLFy7El19+qXB0RERERPpJ8SQRAAYMGIABAwbg3r17MDMzg6WlpdIhERERkV5hKfFVOpEkvpCbdzUTERERkfYp/uDKv//+i27dusHJyQlGRkYwNDTU2IiIiIi0TdLiP4WV4pXE7t2748aNGxg3bhxKlCjBVywRERER6QDFk8TDhw/j0KFD8Pb2VjoUIiIi0lMsUckpniQ6OzvjA3x9NBERERUinMmUU/yexDlz5uDbb79FXFyc0qEQERER0f8oXkns2LEjUlJSUK5cOZibm8PY2Fhj/8OHDxWKjIiIiEh/KZ4k8q0qRERERLpH8SQxMDBQ6RCIiIhIzxXmpWq0RfEk8WWpqanIyMjQaLOyslIoGiIiIiL9pXiSmJycjNGjR2P9+vV48OCBbH9WVpYCUREREZE+4dPNcoo/3Txq1Cjs3bsXCxYsgEqlwuLFizFhwgQ4OTlh+fLlSodHREREpJcUryRu2bIFy5cvR8OGDdGzZ0/Ur18fbm5ucHFxwapVq9ClSxelQyQiIqIPHOuIcopXEh8+fAhXV1cAz+8/fLHkzUcffYSDBw8qGRoRERHpCb67WU7xJLFs2bLqhbQrVaqE9evXA3heYbSxsVEuMCIiIiI9pniS2KNHD5w9exYAEBQUpL43cejQoRg5cqTC0RERERHpJ8XvSRw2bJj63/39/XHx4kVERkbCzc0NXl5eCkZGREREpL8UqyTu3bsXlSpVwtOnTzXaS5cujUaNGuGLL77AoUOHFIqOiIiI9IkkSVrbCivFksQ5c+agT58+OS6WbW1tjX79+mHWrFkKREZEREREiiWJZ8+exaeffvra/Z988gmioqIKMCIiIiLSV3y6WU6xJPHff/+FsbHxa/cbGRnh3r17BRgREREREb2gWJJYsmRJnDt37rX7//nnH5QoUaIAIyIiIiJ9JWlxK6wUSxKbN2+O8ePH49mzZ7J9qampCA4ORsuWLRWIjIiIiIgUWwJn7Nix2LhxI8qXL49BgwahQoUKkCQJMTEx+Omnn5CVlYUxY8YoFR4RERHpk0L8FLK2KJYkOjg44OjRoxgwYACCgoIghADw/BH0pk2bYsGCBXBwcFAqPCIiItIjTBHlFF1M28XFBdu2bcOjR49w9epVCCHg7u6OokWLKhkWERERkd5T/I0rAFC0aFHUqlVL6TCIiIhITxXmpWq0RfF3NxMRERGR7tGJSiIRERGRklhJlGMlkYiIiIhkWEkkIiIivccVcORYSSQiIiIiGVYSiYiIiHhPogyTRCIiItJ7TBHlON1MRERERDKsJBIREZHek/jkigwriUREREQkw0oiERER6T0upi3HSiIRERERyTBJJCIiIr0naXHLq9u3b6Nr166ws7ODubk5vL29ERUVpd4vhEBISAicnJxgZmaGhg0b4sKFC+/ytd+ISSIRERGRjnj06BHq1asHY2NjbN++HdHR0Zg5cyZsbGzUfWbMmIFZs2Zh/vz5OHnyJBwdHdGkSRP8999/+RoL70kkIiIi0pF7EqdPnw5nZ2csXbpU3VamTBn1vwshMGfOHIwZMwZt27YFACxbtgwODg5YvXo1+vXrl2+xsJJIREREek+StLelpaXh6dOnGltaWlqOcWzevBk1a9ZE+/btUbx4cVSrVg2LFi1S74+NjUVCQgI++eQTdZtKpYKfnx+OHj2ar9eESSIRERGRFoWGhsLa2lpjCw0NzbHv9evXsXDhQri7u2Pnzp3o378/Bg8ejOXLlwMAEhISAAAODg4axzk4OKj35RdONxMREZHe0+YSOEFBQRg+fLhGm0qlyrFvdnY2atasialTpwIAqlWrhgsXLmDhwoX48ssv/z/eVxb/FkLk+4LgrCQSERERaZFKpYKVlZXG9roksUSJEqhUqZJGm4eHB27cuAEAcHR0BABZ1TAxMVFWXXxfTBKJiIiIdES9evVw6dIljbbLly/DxcUFAODq6gpHR0fs3r1bvT89PR0HDhyAr69vvsbC6WYiIiIiHTFs2DD4+vpi6tSp6NChA/7++2/8+uuv+PXXXwE8n2YeOnQopk6dCnd3d7i7u2Pq1KkwNzdH586d8zUWJolERESk93RjARygVq1aCA8PR1BQECZOnAhXV1fMmTMHXbp0UfcZNWoUUlNT8dVXX+HRo0eoU6cOdu3ahSJFiuRrLJIQQuTriDrA7/dhSodARFrSw7ur0iEQkZZ0d6+h2LnvpqRrbewS5iZaG1ubWEkkIiIivZfPDwZ/EPjgChERERHJMEkkIiIiIhlONxMREZHe0+Zi2oUVK4lEREREJMMkkYiIiIhkmCQSERERkQzvSSQiIiK9xyVw5FhJJCIiIiIZJolEREREJMPpZiIiItJ7nG2WYyWRiIiIiGRYSSQiIiK9x0qiHCuJRERERCTDJJGIiIiIZJgkEhEREZEM70kkIiIivcfFtOVYSSQiIiIiGVYSiYiIiPh8swyTRCIiItJ7TBHlON1MRERERDJMEomIiIhIhkkiEREREcnwnkQiIiLSe7wnUY6VRCIiIiKSYSWRiIiI9B4X05ZjJZGIiIiIZJgkEhEREZEMp5uJiIhI73G2WY6VRCIiIiKSYZJIRERERDJMEomIiIhIhvckEhERkd6TuAaODCuJRERERCTDJJGIiIiIZJgkEhEREZEM70kkIiIivcc7EuVYSSQiIiIiGSaJRERERCTD6WYiIiLSe5xulmMlkYiIiIhkWEkkIiIivce1tOVYSSQiIiIiGSaJRERERCTDJJGIiIiIZHhPIhEREek93pIox0oiEREREcmwkkhERETEWqIMk0QiIiLSe1wCR47TzUREREQkwySRiIiIiGSYJBIRERGRDO9JJCIiIr3HWxLlWEkkIiIiIhlJCCGUDoLoXaWlpSE0NBRBQUFQqVRKh0NE+Yi/30TKYpJIhdrTp09hbW2NJ0+ewMrKSulwiCgf8febSFmcbiYiIiIiGSaJRERERCTDJJGIiIiIZJgkUqGmUqkQHBzMm9qJPkD8/SZSFh9cISIiIiIZVhKJiIiISIZJIhERERHJMEkkIiIiIhkmiXqqe/fukCQJ06ZN02jftGkTJKlg3mC5YcMGNGzYENbW1rC0tISXlxcmTpyIhw8fFsj5iej1EhIS8PXXX6Ns2bJQqVRwdnZGq1atEBERoXRoRFRAmCTqMVNTU0yfPh2PHj0q8HOPGTMGHTt2RK1atbB9+3acP38eM2fOxNmzZ7FixYoCjycvMjIylA6BSKvi4uJQo0YN7N27FzNmzMC5c+ewY8cO+Pv7Y+DAgUqH91r83STKZ4L0UmBgoGjZsqWoWLGiGDlypLo9PDxcvPzH4o8//hCVKlUSJiYmwsXFRfzwww8a47i4uIgpU6aIHj16CEtLS+Hs7Cx++eWXN577xIkTAoCYM2dOjvsfPXokhBDi6tWronXr1qJ48eLCwsJC1KxZU+zevTvP579586bo2LGjKFq0qDA3Nxc1atQQx48fV+/fvHmzqF69ulCpVMLV1VWEhISIjIwM9X4AYuHChaJ169bC3NxcjB8/XgghxIIFC0TZsmWFsbGxKF++vFi+fLn6mNjYWAFAnD59WuN7ARD79u0TQgjx8OFD0blzZ1GsWDFhamoq3NzcxG+//fbGa0dUEJo1ayZKliwpkpKSZPte/H7OnDlTVKlSRZibm4tSpUqJAQMGiP/++0/db+nSpcLa2lrs2LFDVKxYUVhYWIimTZuKO3fuaIy3ZMkS9X9jHB0dxcCBA9X7Hj9+LPr06SPs7e1FkSJFhL+/vzhz5ox6f3BwsKhatapYsmSJcHV1FZIkiezsbBEfHy9at24tLCwsRJEiRUT79u1FQkKC+rjAwEAREBCgEceQIUOEn5+f+vPvv/8uqlSpIkxNTYWtra1o1KhRjteD6EPGSqIeMzQ0xNSpUzFv3jzcunVLtj8qKgodOnRAp06dcO7cOYSEhGDcuHEICwvT6Ddz5kzUrFkTp0+fxldffYUBAwbg4sWLrz3vqlWrYGlpia+++irH/TY2NgCApKQkNG/eHHv27MHp06fRtGlTtGrVCjdu3Mj1+ZOSkuDn54c7d+5g8+bNOHv2LEaNGoXs7GwAwM6dO9G1a1cMHjwY0dHR+OWXXxAWFoYpU6ZonCM4OBgBAQE4d+4cevbsifDwcAwZMgQjRozA+fPn0a9fP/To0QP79u174zV/2bhx4xAdHY3t27cjJiYGCxcuRLFixXJ9PJE2PHz4EDt27MDAgQNhYWEh2//i99PAwABz587F+fPnsWzZMuzduxejRo3S6JuSkoIffvgBK1aswMGDB3Hjxg1888036v0LFy7EwIED0bdvX5w7dw6bN2+Gm5sbAEAIgRYtWiAhIQHbtm1DVFQUqlevjkaNGmncknL16lWsX78eGzZswJkzZwAAbdq0wcOHD3HgwAHs3r0b165dQ8eOHXN9De7evYsvvvgCPXv2RExMDPbv34+2bdtCcMU40jdKZ6mkjJf/Ju3j4yN69uwphNCsJHbu3Fk0adJE47iRI0eKSpUqqT+7uLiIrl27qj9nZ2eL4sWLi4ULF7723M2aNRNeXl7vFHelSpXEvHnzcn3+X375RRQpUkQ8ePAgx/Hq168vpk6dqtG2YsUKUaJECfVnAGLo0KEafXx9fUWfPn002tq3by+aN28uhMhdJbFVq1aiR48eufzmRAXjRaV/48aNeTpu/fr1ws7OTv156dKlAoC4evWquu2nn34SDg4O6s9OTk5izJgxOY4XEREhrKysxLNnzzTay5Urp54tCA4OFsbGxiIxMVG9f9euXcLQ0FDcuHFD3XbhwgUBQPz9999CiLdXEqOiogQAERcXl4crQPThYSWRMH36dCxbtgzR0dEa7TExMahXr55GW7169XDlyhVkZWWp27y8vNT/LkkSHB0dkZiYCABo1qwZLC0tYWlpicqVKwN4XiHIzcMxycnJGDVqFCpVqgQbGxtYWlri4sWLskrim85/5swZVKtWDba2tjmeIyoqChMnTlTHaGlpiT59+uDu3btISUlR96tZs2aurk1MTMxbv9cLAwYMwNq1a+Ht7Y1Ro0bh6NGjuT6WSFvE/6plb/sd3bdvH5o0aYKSJUuiSJEi+PLLL/HgwQMkJyer+5ibm6NcuXLqzyVKlFD/biYmJuLOnTto1KhRjuNHRUUhKSkJdnZ2Gr+fsbGxuHbtmrqfi4sL7O3t1Z9jYmLg7OwMZ2dndduL/4bk9vezatWqaNSoETw9PdG+fXssWrRIkXu3iZRmpHQApLwGDRqgadOm+O6779C9e3d1e07JnMhhusXY2FjjsyRJ6uncxYsXIzU1VaNf+fLlcfjwYWRkZMiOfdnIkSOxc+dO/PDDD3Bzc4OZmRnatWuH9PT0XJ/fzMzsTV8d2dnZmDBhAtq2bSvbZ2pqqv73nKbdcro2L9oMDAzUbS+8elN9s2bNEB8fj7/++gt79uxBo0aNMHDgQPzwww9vjJlIm9zd3SFJEmJiYtCmTZsc+8THx6N58+bo378/Jk2aBFtbWxw+fBi9evXS+HOe0+/mi9+J3PxulihRAvv375ftezHlDch/N1/3l9BXfz9f/W/Zy3EbGhpi9+7dOHr0KHbt2oV58+ZhzJgxOHHiBFxdXd8YN9GHhJVEAgBMmzYNW7Zs0ahmVapUCYcPH9bod/ToUZQvXx6Ghoa5GrdkyZJwc3ODm5sbXFxcAACdO3dGUlISFixYkOMxjx8/BgAcOnQI3bt3x2effQZPT084OjoiLi4uT9/Ly8sLZ86cee2yOtWrV8elS5fUMb68vUj0cuLh4ZHjtfHw8AAAdWXj7t276v0v7pd6mb29Pbp3746VK1dizpw5+PXXX/P0/Yjym62tLZo2bYqffvpJoyr4wuPHjxEZGYnMzEzMnDkTPj4+KF++PO7cuZOn8xQpUgRlypR57ZI61atXR0JCAoyMjGS/m2+6d7dSpUq4ceMGbt68qW6Ljo7GkydPNH4/X/7dBOS/n5IkoV69epgwYQJOnz4NExMThIeH5+k7EhV2TBIJAODp6YkuXbpg3rx56rYRI0YgIiICkyZNwuXLl7Fs2TLMnz9f48bzd1GnTh2MGjUKI0aMwKhRo3Ds2DHEx8cjIiIC7du3x7JlywAAbm5u2LhxI86cOYOzZ8+ic+fO6gphbn3xxRdwdHREmzZtcOTIEVy/fh0bNmzAsWPHAADjx4/H8uXLERISggsXLiAmJgbr1q3D2LFj3zjuyJEjERYWhp9//hlXrlzBrFmzsHHjRvW1MTMzg4+PD6ZNm4bo6GgcPHhQNub48ePx559/4urVq7hw4QK2bt2q/p8YkZIWLFiArKws1K5dGxs2bMCVK1cQExODuXPnom7duihXrhwyMzMxb948XL9+HStWrMDPP/+c5/OEhIRg5syZmDt3Lq5cuYJTp06p/xvUuHFj1K1bF23atMHOnTsRFxeHo0ePYuzYsYiMjHztmI0bN4aXlxe6dOmCU6dO4e+//8aXX34JPz8/9W0jH3/8MSIjI7F8+XJcuXIFwcHBOH/+vHqMEydOYOrUqYiMjMSNGzewceNG3Lt3j7+fpH+UuhmSlJXTjdtxcXFCpVLluASOsbGxKF26tPj+++81jnFxcRGzZ8/WaKtataoIDg5+awzr1q0TDRo0EEWKFBEWFhbCy8tLTJw4Ub3ERmxsrPD39xdmZmbC2dlZzJ8/X/j5+YkhQ4bk6fxxcXHi888/F1ZWVsLc3FzUrFlTnDhxQr1/x44dwtfXV5iZmQkrKytRu3Zt8euvv6r3AxDh4eGy+N+0BI4QQkRHRwsfHx9hZmYmvL29xa5duzQeXJk0aZLw8PAQZmZmwtbWVgQEBIjr16+/9boRFYQ7d+6IgQMHChcXF2FiYiJKliwpWrdurf7zO2vWLFGiRAlhZmYmmjZtKpYvXy4AqH9/XyyB87JXl9gSQoiff/5ZVKhQQRgbG4sSJUqIr7/+Wr3v6dOn4uuvvxZOTk7C2NhYODs7iy5duqgfSnmxBM6r3rYEjhBCjB8/Xjg4OAhra2sxbNgwMWjQIPWDK9HR0aJp06bC3t5eqFQqUb58eY0H5oj0hSQEn+knIiIiIk2cbiYiIiIiGSaJRERERCTDJJGIiIiIZJgkEhEREZEMk0QiIiIikmGSSEREREQyTBKJiIiISIZJIhERERHJMEkkonwVEhICb29v9efu3bujTZs2BR5HXFwcJEnK8Z3ZL5QpUwZz5szJ9ZhhYWGwsbF579gkScKmTZveexwiIm1ikkikB7p37w5JkiBJEoyNjVG2bFl88803SE5O1vq5f/zxR4SFheWqb24SOyIiKhhGSgdARAXj008/xdKlS5GRkYFDhw6hd+/eSE5OxsKFC2V9MzIyYGxsnC/ntba2zpdxiIioYLGSSKQnVCoVHB0d4ezsjM6dO6NLly7qKc8XU8S//fYbypYtC5VKBSEEnjx5gr59+6J48eKwsrLCxx9/jLNnz2qMO23aNDg4OKBIkSLo1asXnj17prH/1enm7OxsTJ8+HW5ublCpVChdujSmTJkCAHB1dQUAVKtWDZIkoWHDhurjli5dCg8PD5iamqJixYpYsGCBxnn+/vtvVKtWDaampqhZsyZOnz6d52s0a9YseHp6wsLCAs7Ozvjqq6+QlJQk67dp0yaUL18epqamaNKkCW7evKmxf8uWLahRowZMTU1RtmxZTJgwAZmZmXmOh4hISUwSifSUmZkZMjIy1J+vXr2K9evXY8OGDerp3hYtWiAhIQHbtm1DVFQUqlevjkaNGuHhw4cAgPXr1yM4OBhTpkxBZGQkSpQoIUveXhUUFITp06dj3LhxiI6OxurVq+Hg4ADgeaIHAHv27MHdu3exceNGAMCiRYswZswYTJkyBTExMZg6dSrGjRuHZcuWAQCSk5PRsmVLVKhQAVFRUQgJCcE333yT52tiYGCAuXPn4vz581i2bBn27t2LUaNGafRJSUnBlClTsGzZMhw5cgRPnz5Fp06d1Pt37tyJrl27YvDgwYiOjsYvv/yCsLAwdSJMRFRoCCL64AUGBoqAgAD15xMnTgg7OzvRoUMHIYQQwcHBwtjYWCQmJqr7RERECCsrK/Hs2TONscqVKyd++eUXIYQQdevWFf3799fYX6dOHVG1atUcz/306VOhUqnEokWLcowzNjZWABCnT5/WaHd2dharV6/WaJs0aZKoW7euEEKIX375Rdja2ork5GT1/oULF+Y41stcXFzE7NmzX7t//fr1ws7OTv156dKlAoA4fvy4ui0mJkYAECdOnBBCCFG/fn0xdepUjXFWrFghSpQoof4MQISHh7/2vEREuoD3JBLpia1bt8LS0hKZmZnIyMhAQEAA5s2bp97v4uICe3t79eeoqCgkJSXBzs5OY5zU1FRcu3YNABATE4P+/ftr7K9bty727duXYwwxMTFIS0tDo0aNch33vXv3cPPmTfTq1Qt9+vRRt2dmZqrvd4yJiUHVqlVhbm6uEUde7du3D1OnTkV0dDSePn2KzMxMPHv2DMnJybCwsAAAGBkZoWbNmupjKlasCBsbG8TExKB27dqIiorCyZMnNSqHWVlZePbsGVJSUjRiJCLSZUwSifSEv78/Fi5cCGNjYzg5OckeTHmRBL2QnZ2NEiVKYP/+/bKx3nUZGDMzszwfk52dDeD5lHOdOnU09hkaGgIAhBDvFM/L4uPj0bx5c/Tv3x+TJk2Cra0tDh8+jF69emlMywPPl7B51Yu27OxsTJgwAW3btpX1MTU1fe84iYgKCpNEIj1hYWEBNze3XPevXr06EhISYGRkhDJlyuTYx8PDA8ePH8eXX36pbjt+/Phrx3R3d4eZmRkiIiLQu3dv2X4TExMAzytvLzg4OKBkyZK4fv06unTpkuO4lSpVwooVK5CamqpORN8UR04iIyORmZmJmTNnwsDg+e3a69evl/XLzMxEZGQkateuDQC4dOkSHj9+jIoVKwJ4ft0uXbqUp2tNRKSLmCQSUY4aN26MunXrok2bNpg+fToqVKiAO3fuYNu2bWjTpg1q1qyJIUOGIDAwEDVr1sRHH32EVatW4cKFCyhbtmyOY5qammL06NEYNWoUTExMUK9ePdy7dw8XLlxAr169ULx4cZiZmWHHjh0oVaoUTE1NYW1tjZCQEAwePBhWVlZo1qwZ0tLSEBkZiUePHmH48OHo3LkzxowZg169emHs2LGIi4vDDz/8kKfvW65cOWRmZmLevHlo1aoVjhw5gp9//lnWz9jYGF9//TXmzp0LY2NjDBo0CD4+Puqkcfz48WjZsiWcnZ3Rvn17GBgY4J9//sG5c+cwefLkvP8giIgUwqebiShHkiRh27ZtaNCgAXr27Iny5cujU6dOiIuLUz+N3LFjR4wfPx6jR49GjRo1EB8fjwEDBrxx3HHjxmHEiBEYP348PDw80LFjRyQmJgJ4fr/f3Llz8csvv8DJyQkBAQEAgN69e2Px4sUICwuDp6cn/Pz8EBYWpl4yx9LSElu2bEF0dDSqVauGMWPGYPr06Xn6vt7e3pg1axamT5+OKlWqYNWqVQgNDZX1Mzc3x+jRo9G5c2fUrVsXZmZmWLt2rXp/06ZNsXXrVuzevRu1atWCj48PZs2aBRcXlzzFQ0SkNEnkx808RERERPRBYSWRiIiIiGSYJBIRERGRDJNEIiIiIpJhkkhEREREMkwSiYiIiEiGSSIRERERyTBJJCIiIiIZJolEREREJMMkkYiIiIhkmCQSERERkQyTRCIiIiKSYZJIRERERDL/BwCCJmpXFtteAAAAAElFTkSuQmCC", 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zD506daJdu3aEhITkOBZ3qVevHg0bNuT777/n2LFj3HbbbbRr145mzZphs9lyNI/0pH/JkiXcddddriSqY8eOnDx5kvHjx5OUlISXlxfLli2jfv36bnuv17tuDcPIdtjBgwcz3NsJ0K1bN+67774Mbdl9Lq70eW3ZsiU+Pj5Zfl5zy26307Jly0zt7dq149NPP2Xz5s2ZYg4PD+fPP/9k/PjxdOzYka5du151OZGRkdkWuYiLi+PgwYPUq1cvyxNi4eHhTJgwIctYLrVlyxZX7Jdr1apVll00RSRr2lpEJEvlypVj9+7dREREUKdOnVxPn9V9W+nFJrK7pyv9ACI2NhaAatWqsXr1asaOHcvcuXP573//C0CdOnV4/fXXufvuu4GLV1FWr17N6NGj+fXXX5kzZw5w8WDvxRdf5LHHHgMuJgCXH7R16NDBdZ/J/fffz/vvv8+0adNcCVb6WfP7778/w3QffPABzz77LGXKlKFr165UqlTJdRZ9/PjxJCcn5/RPlUH6e8/uIDirv92qVavo1KkTAF27dqVWrVr4+/tjsVj43//+x5YtW645nnRxcXFYrVbKlCmTZUxWq9UV+6Wyug8t/UDt8kIDVxISEpLl/TLphVE+/fTTK05/4cIFgoOD6devH3a7nfHjxzNhwgQ+++wzV8GKDz/8MF/LXnt4eLB48WLGjBnDzz//7LpiGRwczPDhwxk1atRVE60GDRpQpkwZV2K1ZMkSgoKCaNSoESdPnuSdd95h1apVlClThujo6ExXE6/H9a7b9M9yREREpmGdO3d2JWCHDx+mWrVqWc4ju8/F1fY1ISEhWS43t4KCgrIsTJG+3Ky2iW7dujFixAjuvvtu+vTpw//+979MV/RzI7f71excad9js9lMu8IrUhipi6CIZCm968+iRYuuafqsDnrSC02cOnUqy2nS2y8tSNGoUSN++uknzpw5w+rVq3n11Vc5deoU/fr1488//3SNFxoayjfffEN0dDSbNm3inXfewTAMHn/8caZPnw5crI5nGEaGn/SuVenLatSoEbNnzyY+Pp74+Hhmz55N48aNM5wlT0tL4/XXX6dChQrs2LGDadOm8c477zBmzBhGjx6dqYtjbqQftGZXiTCrv92bb75JcnIyixYtYvbs2a7uRGPGjHFbaeeAgACcTifR0dGZhkVFReF0Oq9YSOR6ZVeMIH2Z27Zty7RuL/2pWrWqa5o77riD5cuXc+bMGebOncuDDz7IsmXL6Natm+vKS/pBc1paWqZlXu1ANTeCg4P55JNPiIiIcBXmCAoKYvTo0bz77rtXnd5isdChQwd27drFyZMnWbp0KR06dMBisdC2bVvsdjtLlixxfc6zKpJiltatWwNkKtKRG1f7XGS3r4mKisrweb3W9X369GmcTmem9vTlZlfo5tZbb+Wnn37C6XTSp08f5s6dm+0yruZa9qtZudK+x+Fw6HEcIrmgBEtEsjRo0CBsNhtffvlllgfVl8rp1ZGmTZsCZEhq0iUkJLB+/Xp8fHyyvGKW3hVn7Nix/Pvf/8YwDH777bdM49lsNpo0acLzzz/vSqxyUjI53X333UdiYiI//fQTP/30E4mJiZm61cTExBAbG0vLli0zXdFZv349iYmJOV7e5erUqYO3tzfr168nKSkpwzCn05llyeUDBw5QunTpDPfDwMW/6caNGzONn35VJDdXkK607pYtWwaY89DTm266CYDVq1fnetqAgAC6d+/Ol19+yaBBg4iKimLNmjXAxSp/kPXVlctL319JTv/WFouFevXqubrDQs4/t+nd4KZNm8bevXtdVzP9/Py48cYbWbx4MUuWLHElYzlhtVpz9fm4Fp06daJq1aqsXLky20cQXKsrfV7Xrl1LYmJihs9repXJ3K7v1NRUVzfVS61YsQK48jbRq1cvZs2aBVx84PPvv/+e7bhXEhAQQPXq1dm/f3+W8ed0+2zcuDHwT+yXWr16dZbJp4hkTQmWiGSpZs2aPP/888TExNCjRw8OHTqUaZykpCQ+/PDDLJ8dk5U2bdpQo0YN5s6dm6kIxLhx44iJieGee+5xFaVYt25dlmdT08/IpnfJ2759O0eOHLnqeDlx7733YrVa+e6775g6darr3qxLhYSE4OPjw8aNG13PxQI4e/Ysw4cPz/GysuLp6Unfvn2JiopylaFP9/XXX2d5/1XVqlU5e/YsO3bscLU5HA6effbZLJPj0qVLA3D8+PEcx5VevGDs2LEZnisWFxfn6naZPk5+Gjx4MCVKlGDUqFEZ3n+6hISEDAfAixYtypS4wj9n7dM/KwEBAdSuXZuVK1dmuLcrPj6eF198McfxXelvfejQIXbu3JmpPbef2/SrUu+8806G1+n/X7duHUuWLKFhw4Y57uZVunTpXH0+roWHhweffPIJFouFu+66K9vCMNfygOEBAwbg4eHBhx9+mOF+yNTUVEaOHAmQoQR9nTp18Pf3Z/bs2Rmex3fq1CneeOONKy7rlVdeyXDP4+7du5k0aRKBgYH06dPnitPecsst/PLLL1gsFu64444sTxrlxMCBA0lNTeXFF1/McG/b9u3bmTx5MoGBgdx2221XnEefPn0ICAhg0qRJGfYzqampvPzyy9cUl0hxpXuwRCRbb7zxBklJSXz00UfUqVOHTp060aBBA+x2O4cOHWLhwoWcPn36qgcg6axWK1OmTKFbt27ccsst3H333VStWpU1a9awePFiatSo4arwBRfPyH/22WeEh4dTs2ZNAgIC2LlzJ3PmzCE4ONhVjW7hwoU888wztGnThrp16xIUFOR6DpSPjw9PPPFEjt9zhQoV6NSpk6v62s0335ypypnVauWxxx7jgw8+oHHjxvTu3Zu4uDjmzp1L1apVs6yKlhtvv/02ixYt4uWXX2blypU0bdqUXbt2MWfOHLp27cr8+fMzjD98+HDmz59P27Zt6du3L97e3ixdupSIiAjCw8MzncVv1aoVPj4+jB8/nri4ONdVuPQDz6y0b9+e4cOH85///IcGDRpw5513YhgGP//8M8eOHePJJ5+kffv21/W+r0WZMmWYPn06d999N40bN6Z79+7UrVuXpKQkjhw5wrJly2jdujXz5s0D4JlnnuHo0aOEh4cTGhqKxWJh5cqVrF27ltatW2e4Cvj000/zyCOP0KpVK+6++26cTidz584lLCwsx/HVrVuXChUqMGPGDHx9falUqRIWi4VHH32ULVu2cPvtt9OiRQsaNGhAuXLliIiI4H//+x82my1TFcns3HDDDZQtW5ZTp05RtmxZbrjhBtewjh078sYbb3Du3LlcJcCdOnXihx9+4K677qJp06bYbDZ69uyZZUGJ69GrVy++/fZbHn74Ybp06UKTJk1o1aoVpUuX5uzZsxw4cIBFixa5CrzkVI0aNXjnnXd45plnaNSoEX379sXPz4/ffvuN3bt306dPnwxXpj09PXniiSd4++23adasGX369CE+Pp5ff/2VDh06cODAgSyXU758ec6dO0eTJk3o2bMnsbGxTJ8+naSkJL766itKlChx1Vi7devG7Nmz6dOnD3feeSf//e9/ufXWW3P8XgGef/55fv/9d6ZOncquXbu4+eabiY6OZubMmaSmpvLtt99eNZbAwED+/e9/M2jQIFq0aEH//v0JDAzkt99+w8fH57qK5IgUO/ldF15ECp9169YZQ4YMMWrWrGn4+PgYXl5eRmhoqHHPPfcY8+fPzzBuVs8CutzWrVuNu+66ywgODjbsdrtRtWpV48knn8zwLB/DMIy//vrLGDZsmNGgQQOjZMmSho+Pj1GrVi3jySefzPBsm507dxr/+te/jKZNmxpBQUGGl5eXUb16dWPQoEHGzp07c/1+059lBRjffPNNluOkpKQYb775plGrVi3Dy8vLqFKlivH0008b8fHxRtWqVTM9LyY3z8EyDMM4cuSI0a9fP6NkyZKGr6+v0a5dO2PZsmXZzufHH380mjVrZvj6+hrBwcFG3759jQMHDmS7Pn7//XejRYsWho+Pj+u9prvSOpw0aZLRokULw9fX1/D19TVatGhhTJo0KdN41/Jcoexk9fe83O7du42hQ4caVatWNTw9PY1SpUoZDRs2NJ588klj7dq1rvFmzJhh9O3b16hRo4bh6+trBAYGGk2aNDHeffdd4/z585nm+5///MeoWbOmYbfbjSpVqhivvvqqkZKSkuPnYBnGxc9xhw4djBIlSrj+1ocOHTKOHTtmjBw50mjZsqUREhJieHp6GlWqVDHuuusuY82aNTn626Tr16+fARj9+vXL0J6YmGh4eXkZgDFr1qxM02X3HKzIyEijb9++RnBwsGG1WjOMc6XnqF1pvV/J8ePHjZdeeslo3ry5UbJkScPDw8MoVaqUcdNNNxkvvPCCsXv37kzT5ORz8csvv7j+9l5eXkbDhg2NDz74wEhNTc00blpamvHqq68alStXNjw9PY3atWsbH3/8sXHw4MFsn4NVtWpV4/Tp08aDDz5ohISEGF5eXkZYWJjxyy+/ZJp/+t9t3LhxWca6cOFCw8fHx7Db7a51lf4crMjIyCu+T8MwjPPnzxuvvPKKUbt2bcPT09MoWbKk0aNHjwzPp7s8lqzW4axZs4zmzZsbXl5eRkhIiPHggw8aZ86cydHfW0QushjGFeqkioiIiEgmoaGhwMUqhyIil9I9WCIiIiIiIm6iBEtERERERMRNlGCJiIiIiIi4ie7BEhERERERcRNdwRIREREREXETJVgiIiIiIiJuogcNZ8PpdHLixAlKlCiBxWIxOxwRERERETGJYRjEx8dToUIFrNYrX6NSgpWNEydOULlyZbPDEBERERGRAuLYsWNUqlTpiuMowcpGiRIlgIt/xICAAJOjuXhFLTo6mjJlylw1a5aCLzUtlUkrJ3H+wnme6PoEXnYvs0MSN9B2WjRpvRY9WqdFk9Zr0VOQ1mlcXByVK1d25QhXogQrG+ndAgMCAgpMgpWUlERAQIDpHzC5fheSL/D8r88D8Oxtz1LC5+obqxR82k6LJq3XokfrtGjSei16CuI6zcmtQwUjUhERERERkSJACZaIiIiIiIibKMESERERERFxEyVYIiIiIiIibqIES0RERERExE2UYImIiIiIiLiJyrSLmMDLw4vZT8wm9lwsXh56BpaIiIhIUaEES8QEHjYPejbsSVRUFB42bYYiIiIiRYW6CIqIiIiIiLiJEiwRE6SmpTJl1RRmbpxJqiPV7HBERERExE3UN0nEBCmOFIZ+MxSAIR2H4GXXfVgiIiIiRYGuYImIiIiIiLiJEiwRERERERE3UYIlIiIiIiLiJkqwRERERERE3EQJloiIiIiIiJsowRIREREREXETlWkXyWMzDi7lh4PLMrQ5nQ5ubH03TqeT+1e8h9VqyzRd3+od6F89PJ+iFBERERF3UIIlkscSUpOITorN1O5ZphIAp1POZzudiIiIiBQuSrBE8piv3Zsy3oGZ2k8nxeHEwIqFIO+ALKcTERERkcJFCZZIHutfPTxTV780Rxptvh1AXEoCdao14+fOo80JTkRERETcSgmWiAmS05JZu+q/ANSq2tjkaERERETEXVRFUERERERExE2UYImIiIiIiLiJEiwRERERERE3UYIlIiIiIiLiJkqwRERERERE3EQJloiIiIiIiJuoTLuICTxtnjS7sQ/xqYlYrTazwxERERERN1GCJWICu4edqtWaEp0UqwRLREREpAhRF0ERERERERE3UYIlYoI0RxonT+zlzMmDOJ0Os8MRERERETdRF0EREySnJbN6xfcA1KhU3+RoRERERMRddAVLRERERETETZRgiYiIiIiIuIkSLBERERERETdRgiUiIiIiIuImSrBERERERETcRAmWiIiIiIiIm6hMu4gJPG2eNG52C/GpiVitNrPDERERERE3UYIlYgK7h53qtW4kOilWCZaIiIhIEaIugiIiIiIiIm6iBEvEBA6ng+ioQ5yLOYbhdJodjoiIiIi4iRIsERMkpSaxcsk3bP/zRxzONLPDERERERE3UYIlIiIiIiLiJkqwRERERERE3EQJloiIiIiIiJsowRIREREREXETJVgiIiIiIiJuogRLRERERETETTzMDkCkOLLb7NRv3IULqUlYLTrPISIiIlJUKMESMYGnhye167YhOikWq02boYiIiEhRoVPnIiIiIiIibqIES8QEDqeDs6cjiD97EsPpNDscEREREXETJVgiJkhKTWLpwq/Ysnw6Dmea2eGIiIiIiJsowRIREREREXETJVgiIiIiIiJuovJlV/Hqjnl4+ftecZwqviV5vEbbDG2fHljJ0YRzV51/55DadClb2/U6yZHK6J1/ZDmu0+HAGm1zvX6sRhuq+pZyvd4ae4JpRzdedZleVg9eq989Q9uPx7ew7uyxq07bMLA891VpnqHtzd0LiUtNuuq0d1ZsxI2lq7hen0yK56N9y646HcBLdW8m0O7jer085iC/R+686nRlvfx5unZ4hraJh9aw93z0VadtG1yN3uXrZ2h7YdtvOYp3SOiN1CkR4nq9Jz6KSYfXul6nXPL3KhVYI8N832nYK8O8fo3cwcqYQ1ddZm3/MgytdlOGtg/3LuVU8vmrTtuz/A20D67ueh2bmshbuxdddTqAEbU6UM67hOv12jNH+Sli61WnC7B7M6pu5wxt3x3dwLbYyKtO26JUZe6q1DhD26s75pGcg+6W91ZpRqPACq7XRxLO8tmBP686HcDYG7rhbbO7Xi84tZeFUXszjHP5dgr5t4+4nPYR7ttHZLVe83IfcSXaR7hnH5HVOoW830dkRfsI9+0jsluvRe044kqK2j4iu3Wa38cRyecTrjrfdEqwriI2NRHPVMsVxymV5pOpLT4tmXOpiVedf5IjNcNrA6483SX1ENIuK46Q6nTkaJne1syrPcGRmqNpL6SlZGqLS03K0bQpl31onYYzR9NdHNfI8DrZkZajaX0u+aJLdz6H6ybxsnUDV1k3l0gznJleXzpt6iVfJDar/YrzTczhujmflpypLS6H7zXZcfm6MXKxbjK+1xRnztZNVi6kpeRo2oQs1k1saiJJOUiwUp2ODK/TnDn/HBqXvU7Kbt1cVrckX/cRl9A+ws37iMvWa17uI3JD+4jMcryPyKLGUL7sIy6jfYSb9xFZrNeidhyRG0ViH5HDemB5eRyRkou/vxKsqwi0++Blz7zju1QJD68s20peZTogw5kuAAtkO53T4cBq+yeD97Bm7OFpt9pytEyvLHaMvjZ7jqb18/DM1BZg977qdACely3XarHmaJkXx82Y5HrZPHI0bUAW68Y/h+smq51qTuP1uOzhwR6XvdcU/nk/DmcqZeylyI5PDteNfxbvNcDDi8ScfCZsl68bSy7WTcb36mnN4brJ4nPj5+GZo2l9s1g3gXYfvHOQYNmtGc+CeVhz/jm8/FSLdxbr5vLtFPJvH3E57SPct4/Iar3m5T4iN7SPyCwn+4is1ink/T4iK9pHuG8fkd16LWrHEblR2PcR2a3T/D6OSLZffgolexbDMHI+djESFxdHYGAgsbGxBAQEmB0OTqeTqKgoQkJCsFp161xhdyH5Av5P+APQ+86XmN39TZMjEnfQdlo0ab0WXjMOLuWHg1l3M3U4HNiyOGgD6Fu9A/2rh+dhZJIXtK0WPQVpneYmN9AVrAJGXwbFg91mp279DlxIS8505kZERNwjITWJ6KTY7EfI3EvINZ2IyLVSglXA6MugePD08KReg45EJ8VitWkzFBHJC752b8p4B2ZqP50UhxMDKxaCvDOfifbNYZc1EZGs6MiugNGXgYiIiHv0rx6eZe+OOxaOJTopliDvAH7uPDr/AxORIk0JVgGjL4Piwel0EhcbxYXkeIK9Slx9AhEREREpFHTzh4gJElMTWTTvMzYtmYrDcfXKdyIiIiJSOCjBEhERERERcRMlWCIiIiIiIm6iBEtERERERMRNlGCJiIiIiIi4iRIsERERERERN1GCJSIiIiIi4iZ6DpaICew2OzXrtCbRkYzVovMcIiIiIkWFEiwRE3h6eNKwSVeik2Kx2rQZioiIiBQVOnUuIiIiIiLiJkqwREzgdDq5cOEsSQmxGIbT7HBERERExE2UYImYIDE1kfm/fcz6BZNwONLMDkdERERE3EQJloiIiIiIiJsUiATrs88+o1q1anh7e9O8eXNWrFhxxfGTk5MZNWoUVatWxcvLixo1ajBp0iTX8K+++op27dpRqlQpSpUqRefOnVm7dm1evw0RERERESnmTE+wZs6cyVNPPcWoUaPYtGkT7dq1o0ePHhw9ejTbafr27cuiRYuYOHEie/bsYfr06dStW9c1fOnSpdxzzz0sWbKE1atXU6VKFbp27UpERER+vCURERERESmmTK8P/eGHHzJ06FAefPBBAMaPH88ff/zB559/zrhx4zKNP2/ePJYtW8bBgwcpXbo0AKGhoRnGmTZtWobXX331FT/++COLFi3igQceyJs3IiIiIiIixZ6pCVZKSgobNmxg5MiRGdq7du3KqlWrspxm9uzZhIWF8e677zJ16lT8/Py49dZbef311/Hx8clymoSEBFJTU10JWVaSk5NJTk52vY6LiwMuVntzOs2v8mYY//wuCPHI9bl0HWqdFh1OpxPDMLQ+ixit16JH36lFk7bVoqcgrdPcxGBqghUTE4PD4aBs2bIZ2suWLcvJkyeznObgwYOsXLkSb29vZs2aRUxMDI899hhnzpzJcB/WpUaOHEnFihXp3LlztrGMGzeOsWPHZmqPjo4mKSkpF+8qbzidDtfvqKgok6OR65WQkuD6v9Zp0eF0OomNjcUwDKxW03tgi5tovRY9+k4tmrStFj0FaZ3Gx8fneFzTuwgCWCyWDK8Nw8jUls7pdGKxWJg2bRqBgYHAxW6Gd911F59++mmmq1jvvvsu06dPZ+nSpXh7e2cbw4svvsjTTz/teh0XF0flypUpU6YMAQEB1/rW3MZqtbl+h4SEmByNXK/k1GSq1WhBkiMFm82udVpEpO+fypQpY/oXgbiP1mvRo+/UoknbatFTkNbplfKIy5maYAUHB2Oz2TJdrYqKisp0VStd+fLlqVixoiu5AqhXrx6GYXD8+HFq1arlan///fd56623WLhwIY0aNbpiLF5eXnh5eWVqt1qtpq9QgPR802KhQMQj18fHy4cmYT2JTorFw8ND67QIsVgsBWa/Ie6j9Vq06Du16NK2WvQUlHWam+WbGqmnpyfNmzdnwYIFGdoXLFhA69ats5ymTZs2nDhxgvPnz7va9u7di9VqpVKlSq629957j9dff5158+YRFhaWN29ARERERETkEqan908//TRff/01kyZNYteuXYwYMYKjR4/yyCOPABe77l1a+W/AgAEEBQUxePBgdu7cyfLly3nuuecYMmSIq3vgu+++y8svv8ykSZMIDQ3l5MmTnDx5MkNSJmImwzBITrpAanICRvrd1iIiIiJS6Jl+D1a/fv04ffo0r732GpGRkTRo0IA5c+ZQtWpVACIjIzM8E8vf358FCxYwfPhwwsLCCAoKom/fvrzxxhuucT777DNSUlK46667Mixr9OjRjBkzJl/el8iVJKQkMOeX9wDofedLJkcjIiIiIu5ieoIF8Nhjj/HYY49lOWzKlCmZ2urWrZupW+GlDh8+7KbIREREREREcs70LoIiIiIiIiJFhRIsERERERERN1GCJSIiIiIi4iZKsERERERERNxECZaIiIiIiIibFIgqgiLFjYfVgyqhjUlypGKx6DyHiIiISFGhBEvEBF52L5rfdDvRSbHYbNoMRURERIoKnToXERERERFxEyVYIiYwDIO0tBQcaakYhmF2OCIiIiLiJkqwREyQkJLArz+9xerfP8HhSDU7HBERERFxEyVYIiIiIiIibqIES0RERERExE2UYImIiIiIiLiJEiwRERERERE3UYIlIiIiIiLiJkqwRERERERE3MTD7ABEiiOb1UaFSjeQ7EzFYrGYHY6IiIiIuIkSLBETeNu9ualNX6KTYrHZ7GaHIyIiIiJuoi6CIiIiIiIibqIES0RERERExE2UYImY4ELyBWbNHMPKXz4iLS3F7HBERERExE2UYImIiIiIiLiJEiwRERERERE3UYIlIiIiIiLiJkqwRERERERE3EQJloiIiIiIiJvoQcMi+SzZkcrCE5up26IXHp7enHckM+/4ejqWb4yXHjosIiIiUqgpwRLJRytPbufNLdM5n5pIUPmaWCwWUg0nb27+no93zOLlJgNoU7a+2WHKVcw4uJQfDi7LcpjD4cBms2U5rG/1DvSvHp6HkYmIiIjZlGCJ5JOVJ7fz0vrJgAGAxWLJMPxCaiIvrpvEW2GDaVuugQkRSk4lpCYRnRSb/Qip2U8nIiIiRZsSLJF8kOxI5c0t0wHj7/QqMwOwYPDWlunMKjNG3QULMF+7N2W8AzO1n06Kw4mBFQtB3gFZTiciIiJFmxIskXywJHIL51MTrzqeAcSnJrI0cgvdKoXlfWByTfpXD8+yq98dC8cSnRRLkHcAP3cenf+BiYiIiOlURVAkH6w8uQ0rlquPCFixsOLktjyOSERERETyghIskXwQm5KAM9vOgRk5MTiVeC5vAxIRERGRPKEESySPbYjZx4H4yFxNszv2GEOWf8D0A0uIVrIlIiIiUmjoHiyRPLLj7BG+2jOHDTH7rmn6fXER7IuL4PNdv9E0qCZdKzWnQ7mG+Nt93BypiIiIiLiLEiwRN9sfd4Kv98zlz1M7MrRbsWTZTTCqggc+552UiHNiATytdkJLlGNP7DHgYt3Bjaf3sfH0Pj7c9iNtytanS8XmtAypi92qTVhERESkINHRmYibHD0fxaS9f7D4xOYMxdgr+AYxpHY3fD28GLV+CpeWao8PtLK36cUrUg3+SqDUaSdjmz9Am7L1OXY+mgURG5gfsYGIhNMApDjTWBK5hSWRWyhh96FT+SZ0rdScBqVCsVrU41dERETEbEqwRK7TqcSzTN47n3nH1+EwnK72Mt6BDKzVhZ6Vb8LDagPgrbDBvLVlOvGpiRiGkxQvG/z9wOEdN/nyanxL2gTWB6CyfxmG1OnO4Nrd2HnuKPMjNrD4xGbOpZwHLpZz/+Xoan45uppyPqXoXLEZXSs2p1qJcvn8FxARERGRdEqwRK7RmeR4pu5byC9HV5HqdLjaAz39uL9mZ26r2govm2eGadqWa8CsMmOYf2w9rywbT0KpUoAfAIbVwtjANfhSgudog+Xvsu4Wi4X6papSv1RVht/Qh/Uxe5l/fAMrTm0nyZECwMnEs3y3fxHf7V9ErYCKdK3YnM4VmxKcxcNwRURERCTvKMESyaX4lAS+P7iEHw+tcCU4AH4e3txToyN3V2uHr4d3ttN72ez0Dm3F5P3z2Vzqn4cPW7j4oOEXWMhRYvmYHtguK/TpYbXRMqQeLUPqkZCWzIqT21gQsZF10Xtc93elF8f4bNevNAuuSZeKKo4hIiIikl+UYInkUEJaMj8eWs70A0s4n5bkavey2rmrWjsG1OhIgKdfruaZZr94laoCJThBPN2owXwO8CnraEp5htIs22l9PbzoVimMbpXCOJMcz+ITm5h/fAO7LimOsSFmHxti/imO0bVic25ScQwRERGRPKOjLJGrSHak8suRVUzdv8h1/xOA3Wrj1iqtuL9mZ4K8A65p3qleFxOsllTiZ3axg2hmchefso4m5PxeqtJeJbirWnvuqtaeo+ejWBixMdviGAF2XzpWaELXis1UHENERETEzZRgiWQjzelgzrG1fLNvAVFJ51ztViz0qNyCQbW6Us639DXN+0LyBX7/37ukDukNVevSnqos4zDHicMbO0sZfM1xV/EPuWJxjLjUBH45sopfjqyinE8pulRsTpeKzVQcQ0RERMQNlGCJXMZhOFkUsYlJe+e5rgCl61ShCUNrd6eKf8h1LyclOQE27qRErTrcWqIOx4njfVbxBevpTZ3rnv/lxTHWRe9hQcRGlp/cRrIzFbhYHGPq/oVM3b9QxTFERERE3EAJlsjfDMNgxantTNwzl4PxJzMMax1yAw/W6UGtwIruXeiabYRX6k217qV4mOa8zyrmso8jnKMqJd22GA+rjVZlb6BV2RtcxTHmR2xgffTebItjdK3YnA7lGuFnz75gh4iIiIhkpARLij3DMFgfs5evds9xFYhI1yyoFg/V7UGDUqF5HkctguhENRZziK/ZyOt0ypPlXFoc43RSHItPbGZBRNbFMT7Y9iNtyzagS8VmKo4hIiIikgM6WpJibeuZQ3y1ew6bzxzI0H5DySo8VPcWwoJr52s8w2jOYg4xkY28Sgfs2PJ0eUHeAdxdvT13V79YHGNBxEYWXFYcY3HkZhZHblZxDBEREZEcUIIlxdLe2ON8tWcuf0XtytBevUR5Hq57C61DbsBiseR7XLdRlxD8iOQ8v7KHO7gh35ZdxT+EoXW6M6R2N3aeO8L8iI0sOrGJ2JQLQMbiGOV9StO5YjO6VmxOaImy+RajiIiISEGnBEuKlSPnTzFxzzyWRG7J0F7JrwxDa3enU4XGpl6Z8cSDITTlbVYygQ35mmClu1gcI5T6pUJdxTHmR2xgxcntruIYkYlnMhbHqNSczhVUHENERERECZYUC5EJZ5i89w/+OL7eVdQBIMS7JINrd6V7pRZ4WPO2O96lrBYrJUtVIM1wYCHjlbKHaMbbrGQ+BzjIGapzbaXg3SFjcYwklp/czoKsimPsjOCznb/SPLgWXSo2U3EMERERKbaUYEmRFpMUx7f7FvDr0b9IMxyu9lKe/jxQqzO3VmmNpy3/NwMfTx86dn2Y6KRYbB72DMOqU5pu1OAPDvAlG3ibLvkeX1Z8PbzpXimM7lcojrE+Zi/rY/aqOIaIiIgUWzrqkSIpNuUC0/Yv5ufDK13d2gD87T4MqNGRu0Lb4ePhZWKEVzaMMP7gAJPZzGt0xLOAbapZFceYH7GBE1csjtGchqVCTbm3TURERCS/FKyjNpHrdCE1iR8OLWPGwaUkpCW72n1sntxdrT39a3SkhN3HxAhzphe1KY8/kZznf+ymLw3MDilbmYpjHN/AosjN2RbH6FKxGV1UHENERESKKCVYUiQkO1L4+fCfTNu/mNjUC652T6sHt1Vtw301O1HKq4SJEWaUkJzAH79+hMMw6HrLE5mG27ExlGa8wXImsKFAJ1jpMhTHqH8ba6P3sCCL4hjf7l/It/sXUjuwEl0qNlNxDBERESlSlGBJoZbqTOO3o2v4Zt8CTifHudptFis9K9/IwFpdCfEpaV6A2TAwSEiIveI4D9GMt1jBYg6xlxhqE5xP0V0/D6uN1mVvoPUVimPsjT3O3tjjfL7zV5oF16Jrxea0L9dQxTFERESkUFOCJYWSw3Ay//gGJu/9g8jEM652CxY6V2zKkNrdqORXxsQIr18VStKDmvzOPr5kA+/TzeyQrklWxTHmR2xg99/FMZyXFMd4f9t/aVu2AV0rNefGMnVUHENEREQKHR29SKHiNJwsi9zGxL3zOHL+VIZh7co24ME6PageUN6k6NxvGGH8zj6msJk36IQ39qtPVIBdXhxjfsQGFkRszLY4RqcKTeii4hgiIiJSiCjBkkLBMAzWRO/mqz1z2Rt7PMOwFsG1ebBOD24oVdWk6PJOD2pSiQCOE8fP7GIAjcwOyW2q+IfwYJ0eDK3dnR3njrDg+AYWndjsuocuLjWB/x1Zxf+OrKK8b2m6VFBxDBERESn4lGBJgbf59AG+2jOHrWcOZWhvWKoaD9XtQdOgmiZFlvc8sPEQzRjNUr5gfZFKsNJZLBYalAqlwSXFMeZHbGDlpcUxEjIWx+hasTk3V2hKsHeAydGLiIiIZKQESwqs3eeO8eWeOayL3pOhvVZARR6q04OWIfWKRbexoTTjNZaxgqPsJIobCDE7pDyTVXGM+REb2JBFcYzPds6meXAtuqg4hoiIiBQgSrCkwDkUf5Kv98xl+cltGdqr+ocwtHZ3OpRvhNViNSk697BgoURAGRyG46rjViSAXtTmF/bwJRsYT498iNB8lxfHWHRiEwsiNmYojrEuZi/r0otjlGtA14oqjiEiIiLm0lGIFBgRF2KYtPcPFkRsxPj7agVAeZ/SDK7djS4Vm+FhtZkYofv4evnSucfjRCfF4uHhedXxHyGMX9jDN2xhHJ3xKeTFLnIryDuAvtU70Ld6h+yLY5zYzOITmwm0+9GxQmO6VmxOAxXHEBERkXymBEtMF514jm/2LeC3Y2twGE5Xe2mvEgyq1ZVeVW4q9lckulKDUEpymHP8wA4G0sTskExzteIYsakXMhTH6FqxOV0qNqOqv4pjiIiISN4r3ketYqqzyeeZdmARsw7/SYozzdUeYPfl3pqduCO0Ld62q1/dKQ6sWHmIZoxiMRNYX6wTrHQ5LY7xzb4FfLNvAXUCK9FFxTFEREQkjynBknwXn5rIzINL+eHgchIdya52Xw8v+lW72A3M3+5jYoR5LyE5gYVzP8VhOLi56yM5mmYITRnNUlZznK2cpBHl8jjKwiNzcYxtzI/YmKE4xp7Y4+y5rDhGh/IN8fVQcQwRERFxHyVYkm8S05L56fBKvj+wmPjURFe7p9WDO0PbMaBmR0p6+psYYf4xMIiPi87VNOUowW3U5Ud2MoENfErPPIqucLtYHKMF3Su1ICYpjsUnNjE/YgN7/n5+2qXFMT7Y9iNtytWna8Xm3FSmbpG5x09ERETMowRL8lyKI43ZR1czdf9CziTHu9ptFiu3VmnFA7U6E+wdaGKEhccwmvMjO/mOrbxLF/xQF8orCb6kOMaR86dYELGR+REbiEw4A0CyMzVDcYxOFZrQpWIzFccQERGRa6YES/JMmtPBH8fXM3nffE4lnnW1W7HQtVIYg2t3pYJvkIkRFj6dqEYNSnGAs8xgO0NpZnZIhUZV/7Ku4hjbzx5mQcRGFl9WHGPWkT+ZdeRPKvgG0aViMxXHEBERkVxTgiVu5zScLIncwsQ98zh2IWM3uPDyjRlauzuhJXTQei2sWHmY5rzAQr5gvRKsa2CxWGhYuhoNS1fjyfq3sSZ6NwsiNmYojnEi4XSuimMkO1JZErmF2JS/k7WUC8w7vp6O5RvjZSteJfVFRESKOyVY4jaGYbAqaidf75nL/rgTGYa1LFOXB+veQp3ASiZFV3QMpikvs5j1nGAjJ2hGBbNDKrQ8rDbalK1Pm7L1/ymOcXwDG2L2ZVsco2ul5rQv909xjJUnt/Pmlumcv+S+whRnGm9u/p6Pd8zi5SYDaFO2vinvT0RERPKfEixxi40x+/hy9xx2nDuSob1x6eo8XPcWGpWublJkRU8Z/LiTG5jBdiawgQlKsNzi8uIYi05sYkE2xTHet/5I23INqOgbxNT9i+CSB2Nf6kJqIi+um8RbYYNpW65BPr4bERERMYsSLLkuO84e4as9c9gQsy9De53ASjxctyctgmurWEAWLFjw9Q3EYWR9YH41w2jODLbzPdt4n66UwMvNERZvwd4B9KvegX5XKI6x6MSmq87HACwYvLVlOrPKjFF3QRERkWJACZZck/1xJ/h6z1z+PLUjQ3u1EuV4sHZ32pVrqMTqCny9fOnWewTRSbF4eOS+EmAHQqlDEHs4zTS28ggt8iBKgasXx7gag4vPflsQsZGelW/UdiEiIlLEKcGSXDl6PopJe/9g8YnNGJd0i6rgG8SQ2t3oXLEZNovVxAiLBwsWhhHG0/zBBDYwjDAs6MA9L11aHGN4/T4MX/0ZO84ezvH072ydyUfbf6KE3ZcAT18C7H//ePoSYPdztZWw+xLoebH94v/98LLalZiJiIgUEkqwJEdOJZ5l8t75zDu+DofhdLWX8Q5kUK2u3FL5Rj2kNZ8NpDEvspDNnGQdEdyICojkF7vVA/s1fN5TnGmcTo7jdHJcrqbztHq4Eq8S2SRml/5WYiYiImIeJVhyRWeS45m6byG/HF1FqtPhag/09OP+mp25rWorvGx62G1uJaYksmT+l6QZDjrd/OA1zaM0vtxNfb5jKxPYoAQrnwXafbFicVUbvOr4nn4EewUQn5pIbMoFV0n4nFBiJiIiUngowZIsxack8P3BJfx4aAVJjhRXu7+HN/1rdOTuau1cZaol95yGk3NnL5ayN3J4gJ6VRwjjO7YynW18QFdK4uOuEOUq2pZryLKT23I8/vAb+tCtUpjrdbIjxZVsxaUmEJ+SQGxqAnEpCcSnJhCXmkDc38PiUhJcv81LzPwyJWSXJ2xKzERERJRgyWUS0pL58dByph9Ywvm0JFe7t82TO0PbMqBGRwI8/UyMUC7VmsrUpww7iOY7tvIEN5kdUrHRsXxjPt4xiwupiVdMkS2Av92H8PKNM7R72TzxsnkS7B2Yq+UqMRMRESnYlGAJAMmOVH45soqp+xdxLuW8q91utXFrlVbcX7MzQd4BJkYoWUkvdvEkc5nABh7nRhW7yCdeNjsvNxnAi+smYcnmOqTl739HNRngthLt15OYuRKuLBKz2JQLlyRoSsxERESulRKsYi7N6WDOsbV8s28BUUnnXO1WLPSo3IJBtbpSzre0eQHKVd1PI15gAduJYhXHaEMVs0MqNtqUrc9bYYN5a8t04lMTMw33t/swqskA2pStb0J0GXnZPCnj40kZn5K5mq6gJ2aX3j+mxExERAoCJVjFlMNwsihiE5P2ziMi4XSGYTdXaMqQ2t2o4h9iUnSSGyXxoT8NmMxmJrBeCVY+a1uuAbPKjGFp5Bbe3foDKc40PK0ePN+oL+HlGxf6hwu7KzG7NPm6PDGLTe/eeA2JWUxyHDF5nJj523xIdqRiXOODwUVEpHhRglXMGIbBilPbmbhnLgfjT2YY1qZsfR6s052aARVNik6u1TDCmMxmfmAH4+lOaXzNDqlY8bLZ6VYpjAm7fyc6KZZAT78MBS2Ko+KQmAV6+l1WnfHS3/8kbLpiJiJSvCjBKiYMw2B9zF6+2j2HXbHHMgxrFlSLh+v2oH6pUHOCK6Y8vXxxuumM+I1UpDFl2cIpvmULT9HKLfMVyW9KzP5JzLz1CAwRkUJJCVYxsPXMIb7aPYfNZw5kaL+hZBUeqnsLYcG1TYqs+PLz8qPnbc8TnRSLh8f1H0RZsPAIYTzK73zBev5FSxW7kGIlLxKzuNQLF3+nJHA64RyJpBWYxMxVEESJmYhIgaMEqwjbG3ucr/bM5a+oXRnaa5Qoz0N1b6F1yA3qtlKEDKAhzzKfPZxmOUfoQKjZIYkUeDlJzJxOJ1FRUYSEhGC1WoGcJWYF8YqZEjMRkbynBKsIOnL+FBP3zGNJ5JYM7ZX8yjC0dnc6VWiM1WI1KTrJKwF4M4CGfMVGJrBeCZZIHnLLFbNMCdqFS7o0/l210aTELL3iYlaJWaDdjxKXdG0sTIlZsiOVJZFbiE25AEBsygXmHV9PxyJQkEZECg4lWEVIZMIZJu/9gz+Or8d5yVN5QrxLMrh2V7pXaoGH1WZihJIuMSWRFYsnk+J0EB4+0G3zHUYYX7GRH9nJx1ygDHootEhBkp+JWWzKBVKcaTleRlFPzFae3M6bW6Zz/pJHKqQ403hz8/d8vGMWLxeQRyqISOFXIBKszz77jPfee4/IyEjq16/P+PHjadeuXbbjJycn89prr/Hdd99x8uRJKlWqxKhRoxgyZIhrnJ9++olXXnmFAwcOUKNGDd58801uv/32/Hg7bpWTs20xSXFM3beA2Uf/Is1wuKYt5enPA7U6c2uV1njaCsSqlr85DScx0UcAsnlE7bVpTgXCqMB6TjCFzTxHG7fNW0TM4/7E7MJlRUCKdmK28uR2Xlo/GbLZ315ITeTFdZN4K2wwbcs1yPX8ReT6zDi4lB8OLstymMPhwGbL+gJB3+od6F89PA8juzamH3XPnDmTp556is8++4w2bdowYcIEevTowc6dO6lSJevn+fTt25dTp04xceJEatasSVRUFGlp/3wZrF69mn79+vH6669z++23M2vWLPr27cvKlSu56aab8uutXbernW0bUf8O9sed4OfDKzN0H/G3+zCgRkfuCm2Hj4eXGaGLiYbRnPWc4Es28AytsKLuoCLFVXFKzAL/TsIuT8y8rZ68uXk6VzqdZQAWDN7aMp1ZZcaou6BIPktITSI6KTb7EbLpJZ2QmpQ3AV0n0xOsDz/8kKFDh/Lggw8CMH78eP744w8+//xzxo0bl2n8efPmsWzZMg4ePEjp0qUBCA0NzTDO+PHj6dKlCy+++CIAL774IsuWLWP8+PFMnz49b9+Qm1ztbNv51ERe3zwtQ5uPzZO+1TvQr3o4Jew++RClFET9acDT/MF+zrCEw9xMdbNDEpFC5noTs9jUBOKzScwu/Z2fidnVGEB8aiJLI7cU++fYieQ3X7s3ZbwDM7WfTorDiYEVC0HeAVlOVxCZmmClpKSwYcMGRo4cmaG9a9eurFq1KstpZs+eTVhYGO+++y5Tp07Fz8+PW2+9lddffx0fn4tJxerVqxkxYkSG6bp168b48eOzjSU5OZnk5GTX67i4iztup9OJ0+m8lrd3zZIdqVc923Ypu8XGbVXbcG+NjpTyKgGQ7zFL7ly6fgzDvevLFzv3WRrxuWU9nxvr6GiEum3ecmXpjzVz9zoVczmdTgzD0DrNAbvFgyCvAIK8Mh8IXUmyIzVDIhafmpgxMUtNJM71LLPEa0rMcsKKheWR2+hSoZlb5yv5Q9tq4dU3tD19Q9tnar9z0evEJMdS2iuAHzu9kuW0+bW+c7McUxOsmJgYHA4HZcuWzdBetmxZTp48meU0Bw8eZOXKlXh7ezNr1ixiYmJ47LHHOHPmDJMmTQLg5MmTuZonwLhx4xg7dmym9ujoaJKS8vfy47LTOziflnj1Ef92X6VwbgluTmpsIlHkfDoxT0JKguv/TqeDqKgot87/Lo9qfB68nl/Yw/aYw4Q4fd06f8ma0+lw/Xb3OhXzOJ1OYmNjMQzDVaZd8kYJPChBAFgDwIuLP1eQ7EzlfFoS59MSL/52JBHv+v/F32vP7iPekbPvRicGMQnntP0WUtpWi56C9L0aHx+f43FN7yIIZHoWk2EY2T6fyel0YrFYmDZtGoGBFy8lfvjhh9x11118+umnrqtYuZknXOxG+PTTT7tex8XFUblyZcqUKUNAQO7OxF2vrcfmYsWSoRJgdqxYOJBy8fksUnhcSL7g+r/VanP7+gshhJZGRf6yRPBb8HFG0tat85esWf+u0pkX61TMk/69U6ZMGR20FUKvbPiGlae25+g7FSDBSCEwqJTuwyqEtK0WPQXpe9XbO+fdEU1NsIKDg7HZbJmuLEVFRWW6ApWufPnyVKxY0ZVcAdSrVw/DMDh+/Di1atWiXLlyuZongJeXF15emU+VWa3WfN9I49IScvxF4MQgPi1BO5JCxmq1YrPZMTCwWMiT9TeMMP4igq+sGxlJWxW7yAfp53Dyap2KeSwWiynfB3L92pVvyPJT23I8/uHzp7h32dsMrd2d7pVbYNNzIwsVbatFS0H6Xs3N8k2N1NPTk+bNm7NgwYIM7QsWLKB169ZZTtOmTRtOnDjB+fPnXW179+7FarVSqVIlAFq1apVpnvPnz892ngVNoN0XK9lfbbuUFQsBdnX/Kmz8vPy49a5RtO41HA+PvHkWTF/qUxJvDnOO+RzIk2WIiBR0Hcs3xt/uk8Nv1Yuik2J5e+tMBi9/nz9P7cAw3Pc4DREp+kxP759++mm+/vprJk2axK5duxgxYgRHjx7lkUceAS523XvggQdc4w8YMICgoCAGDx7Mzp07Wb58Oc899xxDhgxxdQ/817/+xfz583nnnXfYvXs377zzDgsXLuSpp54y4y3mWttyDXN1BatduYZ5HJEURr548gCNAZjABpOjERExh5fNzstNBgCWbJMsy99DR9S/I8PDhg/Fn2TkuokMX/0pO84eyY9wRaQIMD3B6tevH+PHj+e1116jSZMmLF++nDlz5lC1alUAIiMjOXr0qGt8f39/FixYwLlz5wgLC+Pee++ld+/e/Pvf/3aN07p1a2bMmMHkyZNp1KgRU6ZMYebMmYXmGVg5PdtmAUrYfQgv3zg/wpJCaBjNAfiVPUTg3pLGIiKFRZuy9XkrbDD+2TzCxN/uw7gWQ7ijWlvebjGUT1o9wQ0l/3kW55YzB3nkz495ZcM3HDsfnV9hi0ghZTGu47r3nj17iImJoUmTJvj5+bkzLtPFxcURGBhIbGxsvhe5APjz1A5eXDeJ7Eq1W/7+d1yLIRnOtknhkJSaROjrjUhxptGu3QB+6fZGni2rPZNYwVHGEs6rhOfZcgTuWDiW6KRYyngH8nPn0WaHI27idDqJirpYTMjsewDk+iQ7UlkauYV3t/5AijMNT6sHzzfqS3j5xpmKWhiGwbKTW5mwew7HL/yTVNksVm6t0opBtbtS+u9Ho0jBoG216ClI36u5yQ2u6dP37bffUqlSJW644Qbat2/Pnj17AOjbty9fffXVtcxSLpPTs21Krgonh9PBqch9nD11KM/79g/j4gMzv2YjDvRsEBEpvrxsdrpVCiPQ8+JJ4UBPP7pVCsuyYqDFYiG8fGOmdniepxvc6UqmHIaTWUf+pP/it5i89w8S0pIzTSsixVuuE6z//ve/DBo0iGbNmvHJJ59kODhs1qwZP/zwg1sDLM7almvArM5jeLnJADytFws+elo9eLnJAGZ1HqPkSnLkTuoRhA/HiGMu+8wOR0SkUPGw2rg9tA3TO77EkNrd8LFdLEyU6Ehm0t4/uGfJW8w6/Cdpfz+vR0Qk1wnWuHHjGDx4MLNnz+bhhx/OMKxevXrs3LnTbcFJ7s62iWTFGzsDaQKo2IWIyLXy9fBicO1uzOg0iturtnGVbz+THM+H23/igWXvsjRyiyoOikjuE6xdu3bRv3//LIeVLl2a06dPX3dQIuJeD/9d7GIO+zjKOXODEREpxEp7leDphncytcMLGYpMHbsQzSsbvuHRP//NltMHTYxQRMyW6wTL19eX2NjYLIdFRERQqlSp6w5KRNyrDsF0JBQnBl+z0exwREQKvcr+ZXi9+UC+aPMvGpeu7mrfce4IT6z+hJHrJnIo/qSJEYqIWXKdYLVp0ybTvVfppkyZQnh4uDviEhE3u7TYRSq6V0BExB3ql6rKf1o9ztsthlKtRDlX+5+ndjBo2Xu8s2Um0YnnzAtQRPKdR24nePXVV2nbti033ngjAwYMwGKx8PPPPzN69GiWL1/O2rVr8yJOEblOt1OXMvgSyXl+Yy+3U8/skEREigSLxUKbsvVpGVKPecfWMXHvPKKTYnFi8NuxNSyI2Mjd1dtzb41O2VYHFpGiI9dXsMLCwpg7dy7nz5/nmWeewTAM3nrrLfbu3cucOXNo0KBBXsQpUqT4eflxe78xtO0zAg8Pz3xZpiceDKEpABNYny/LFBEpTmwWKz2r3MT3HV9kWN2e+Hl4A5DsTOW7/Yvov/gtfji4jBRHmsmRikheylWClZKSwvz586lRowa7du1i3759rFy5kt27d7Nnzx51DxQp4B76u9jFfA5wiLMmRyMiUjR52zy5r+bNzOw0ir7VOmC32gCITb3Af3b+wn3L3mZBxAachp5NKFIU5SrB8vDwoFevXuzbd/FZOjVq1KB169bUrl07T4ITEfeqQWm6UB0D+Eol20VE8lSgpx/D6/fhu/CRdKnQzNUemXCG1zZN46GV41kfs9fECEUkL+QqwbJarVSqVIm4uLi8ikekWEhKTWLNnz+wa91vOByp+brsR/4udjGRTaSgbioiInmtgm8Qrza7j6/bPU1Y8D8npffGHmfEX1/wzJoJ7I+LMDFCEXGnXN+DNXToUD799FMcDlUhE7lWDqeDE8d3cvrEvnx/KGVv6lAOf6K4wC/syddli4gUZ3UCK/FRy0f44KZh1Aqo6GpfG72HIcs/5I1N33My4YyJEYqIO+S6iqCnpyd79uyhXr163HrrrZQvXx6LxeIabrFYGDFihFuDFBH3sWNjKE15kxVMYD13U9/skEREipUby9QhLLgWCyI28vWeuZxMPIuBwR8R61kcuYk7Q9txf82bCfD0MztUEbkGuU6wXnjhBdf/P/zww0zDlWCJFHwP0Zy3WMEiDrGP09QiyOyQRESKFavFSrdKYXQs34Sfj6xk6r6FxKUmkOp0MOPgUn47tob7atzMXdXa4mXLn2qzIuIeuU6wDh06lBdxiEg+qkpJelCLOezjSzbwHl3NDklEpFjytHnQv3o4PSvfxLT9i/jvoeWkONM4n5rIF7t/4+fDKxlapzvdKoVhs+T6zg4RMUGuE6yqVavmRRwiks+G0Zw57GMKm3mDTnjlfncgIiJuUsLuwyP1enF7aBsm7f2DecfW4cQgKukc47bMYObBZTxStyctQ+pluDVDRAqeaz6i2r9/P4sXL+b06dMEBwfTsWNHatas6c7YRCQP3UItKlKCCOL5mV3cQ0OzQxIRKfbK+pTixcb96VetA1/s/p3VUTsBOBgfyfPrvqZpUA0erdebeiWrmBypiGQn1wmWYRgMHz6cL774AqfznwfkWa1WHnvsMf7973+7NUARyRse2HiI5oxhKV+wXgmWiEgBUj2gPO/e+CCbTu/n812/sevcUQA2nT7AwyvH06l8Ex6q24NKfmVMjlRELpfrzrwfffQRn332GcOGDWPNmjUcO3aMNWvW8Mgjj/DZZ5/x0Ucf5UWcIkWKr6cvve98iVY9n8Bms5sWx1CaYsXCco6wi2jT4hARkaw1DarJhDb/4rVmD1DJN9jVvjhyM/ctfYePtv/M2eR4EyMUkcvl+grW119/zfDhw/n4449dbRUrVqRFixbYbDa++uorVREUuQqLxYKHhyc2D7upfekrEUgvajObPXzJBj6iu2mxiIhI1iwWCx0rNKFduYbMPrqayXvncy7lPA7Dyc+HVzL32DoG1OhIv+od8PHwMjtckWIv11ewDh48SK9evbIc1qtXLw4ePHjdQYlI/hlGcwC+YTOJpJocjYiIZMfDauOO0LbM7PQSg2p1xefv8u2JjmQm7p1H/yVv8cuRVaQ5HSZHKlK85foKVmBgIEeOHMly2JEjRwgICLjuoESKuuTUZDasmUWSI5XSN91haizdqElVAjlCLP9lBw/QxNR4CoMZB5fyw8FlmdpPJ8W5ft+xcGym4X2rd6B/9fC8Dk9EijhfD2+G1unObVVbM3nffH47+hcOw8mZ5Hje3/YjPxxczrC6t9CuXENVHBQxQa6vYHXp0oWXX36ZDRs2ZGjfvHkzo0ePplu3bm4LTqSoSnOmcfTwFqKO7cQwnFefIA/ZsPLQ31exJrDhKmMLQEJqEtFJsZl+nBgAODGyHJ6QmmRy5CJSlAR5B/Bsw7v4tsPzdCj3T6GioxeiGLVhCo+t+g9bz+j5pSL5LddXsMaNG8fSpUu58cYbueGGGyhfvjyRkZHs3LmTChUqMG7cuLyIU0Ty0BCaMoalrOIY2zhFQ8qaHVKB5mv3pox3YJbDHA4HNpst2+lERNytin8Ib4QNZvvZw3y+61dXUrX97GEeX/Uf2pZtwLC6PQktoX27SH7IdYJVuXJlNm/ezAcffMCSJUs4dOgQQUFBjBw5khEjRhAcHHz1mYhIgVKeEtxKHX5mFxNYzyf0NDukAq1/9fAsu/o5nU6ioqIICQnBas11BwERkevSoFQon7R6gj9P7eCL3b9z5PwpAFae2s6qUzvoWeUmhtTuRnA2J4hExD2u6UHDwcHBulIlUsQ8Qhg/s4upbOUduuCHp9khiYhILlksFtqWa0DLkHrMPb6OSXvmEZMchxODX4/+xfzjG+hXvQMDanTCT1fVRfJErk+xRkdHs3fv3iyH7d27l5iYmOsOSkTy381UozqliCOZmWw3OxwREbkOHlYbvau0ZHqnl3iozi34/l2+PdmZyrf7F9JvyZv8eGg5qc40kyMVKXpynWA9/vjjvPfee1kO++CDDxg+fPh1ByUi+c+KlYdV7EJEpEjxtnnyQK3OzOw0irurtcPDcvEe0diUC3y843/ct/QdFkZswmlywSWRoiTXCdaff/6ZbaXAbt26sXLlyusOSkTMMZgm2LGylgg2EWl2OCIi4iYlPf15sv7tTAsfSecKTV3tJxJOM3bTVB5eOZ4NMftMjFCk6Mh1ghUTE0NQUFCWw0qVKkV0dPR1ByVS1Pl6+nJLn+e4qfswbDa72eG4hODPHdQDYALrTY5GRETcrYJfEKOb3c/XbUfQPLiWq31P7HGe+utznl3zJfvjTpgYoUjhl+sEq2zZsmzbti3LYdu2bcs2+RKRf1gsFry8/bB7+Ra4h0AOIwyAaWwjnmSToxERkbxQp2RlPrrpEd6/8WFqBlRwta+J3s2Q5R/w5ubvOZV41sQIRQqvXCdY3bt3580338xU6GLfvn2MGzeOW265xW3BiUj+CyeU2gRxnhS+J+uTKSIiUvhZLBZuCqnLxHZPM6rJAMr6lALAwGDe8fUMWDKOz3b+SnxKgsmRihQuuU6wxowZg81mo1GjRvTo0YOHHnqIHj160LBhQ2w2G2PHjs2LOEWKlOTUZDZv+J0DWxbjcBSsCk4WLAxzFbtYj4FhckQiIpKXrBYr3SuFMS18JI/Xu5USdh8AUpxpTD+4hH5L3mT6gSUkO1JNjlSkcMh1glWhQgXWr1/Pvffey9atW/nmm2/YunUr9913H2vXrqVChQpXn4lIMZfmTOPQ/nVEHt6CUQArNw2kCV7Y2MRJ1qO++CIixYGXzU7/GuHM7DiKATU64mm9+LjU+NREPtv1KwOWjGPe8XU4CuD3lkhBck0PGq5QoQITJ050dywiUkAE4ctd3MA0tjGB9bSgotkhiYhIPinh6cuj9XpzR2hbJu6Zx7zjF3szRCWd483N05lxYCmP1OvFTWXqFrj7iEUKglxfwbpcXFwc69ev58QJneUWKUrSi11MZzuxJJkcjYiI5LeyPqV4qck9TG7/DC1D6rnaD8RH8tzar3jqr8/Zfe6YiRGKFEw5uoK1YsUKVqxYwUsvvZSh/f333+fll18mNfVin9yBAwcyceJEnc0QucSMg0v54eCyDG1paSmu/59JiueOhZnvXexbvQP9q4fndXjZaksVbqAMO4nmO7byODeaFouIiJinRkAF3rvxITbG7OPzXb+xO/ZiUrXx9H4eWvkRN1doysN1bqGCnypJi0AOE6xPPvmEuLi4DG0rV67khRdeoGzZsvTv359du3bxzTff0KJFCx599NE8CVakMEpITSI6KTZDmyPtnxuFnRiZhqdPZ6b0Yhf/Yh4TWM9jtMCCTp6IiBRXzYJrMaHtv1gSuYUvd8/hRMJpABad2MSyyK3cVrU1D9TqQikvf5MjFTFXjhKsDRs28PTTT2do+/rrr7FarSxatIh69S5eNr7rrruYOnWqEiyRS/javSnjHZih7dIrWMHeAXh4eGY5ndnupzEvsJBtRPEXx2lFZbNDEhERE1ktVm6u0JT25Rryy5HVTNk3n9iUC6QZDn48vII5x9dyb41O3F2tPT4eXmaHK2KKHCVYUVFR1KpVK0PbggULCAsLcyVXAAMGDGDIkCHujVCkkOtfPTxTV78LyRfw/+ktAKZ1GEkJnxImRHZ1pfChPw2Ywma+YL0SLBERAcBu9eCuau3oUakF0w8uYebBZSQ5UkhIS+arPXP5+fCfDKndjVsq34iH1WZ2uCL5KkdFLi6/p+rkyZNERkbSsmXLDO0hISEkJia6LzqRIsrH7sOBNw+w9um1+Pz9vJGCKv2ZWD+wg7No+xYRkX/42b15sE4Ppnd8iVurtMRmuXhoeTo5jve2/ZdBy99jxcntGIaeqSjFR44SrGrVqrFu3TrX68WLF2OxWDIlWKdPnyY4ONi9EYoUQVarldDgUCqXqozVet3FPPPUTVSiEWVJIo1v2WJ2OCIiUgAFewfwXKO+fNPhOdqVbeBqP3I+ipfWT+LxVZ+w/exh8wIUyUc5OrK75557ePfdd/n222+ZP38+r732Gv7+/txyyy0Zxlu1ahU1atTIk0BFxBzpxS4AvuDis1BERESyUtW/LG+1GMKnrYfToFSoq33b2UM8+ue/GbV+MkfPR5kXoEg+yFGCNXz4cOrXr8+gQYPo3r07hw4d4uOPP6ZEiX/uG0lNTWXatGl07Ngxz4IVKSpS0lJ4/sfneW3ea6RcUvCioLqPRvhhZzcxrOCI2eGIiEgB16h0NT5rPZw3wwZTxS/E1b785DYeWPYu72/9LzFJcVeYg0jhlaMiF76+vqxYsYLly5dz+vRpwsLCqFq1aoZx4uPj+fjjjzN1GxSRzFIdqXyw4AMA3un3Dt6YXzHwSgLw5h4a8jUbmcAG2hNqdkgiIlLAWSwW2pdrSOuQG5hzbC0T987jTHI8DsPJL0dX80fEBvpV78CAGh3x9SjY34MiuZHjmz+sVivh4eHceeedmZIrgNKlS3PnnXdSsWJFtwYoIgVDejfBH9lJDBdMjkZERAoLD6uNW6u2YkbHl3iwTg98/y7fnuRI4Zt9C+i3+C1+OrSCVGeayZGKuEfBvrteRAqMMCrSnPKk4GAKm80OR0REChkfDy8G1urCjI6juDO0LR6Wi+Xbz6WcZ/yOWdy/9B0Wn9isioNS6CnBEpEcG0YYAF+yQcUuRETkmpTy8uepBnfwXfgLdKrQxNUekXCa0Ru/ZdjK8WyK2W9egCLXSQmWiOTYPTSgBJ7s4wxLOGR2OCIiUohV9AtmbLMH+LLtUzQLqulq3xV7jCf/+ozn1n7FgbgTJkYocm2UYIlIjvnjxb00Ai6WbBcREble9UpWYXzLR3nvxoeoXqK8q/2vqF0MXv4Bb22ezqnEsyZGKJI7SrBEJFce+bub4Cx2c4rzJkcjIiJFgcVioWVIPSa1f4aXGt9DiHdJAAwM5h5fx4Al4/h816/EpyaaG6hIDuQowTIMg19//ZXt27dnO862bdv49ddf3RaYSFHmY/dh6+itLH1iKT52H7PDyZXGlOMmKpKGk8lsMjscEREpQmwWKz0qt+D7jiN5tF4v/P/+jkxxpvH9gSX0W/wGMw4sJdmRanKkItnLUYL1+++/069fP3x8sj8Q9PPzo3///vzwww9uC06kqLJardSvUJ86ZetgtRa+C8mXFrtw4jQ5GhERKWq8bJ4MqNGJmR1fon/1cDytFx/dGp+ayKe7ZnPv0reZd3w9TkPfQVLw5OjIbsKECTzwwAPUqFEj23GqV6/OwIED+fbbb90WnIgUTP2oTyBeHOIcCzhodjgiIlJEBXj68fgNtzItfCTdK4VhwQLAqcSzvLn5e4au+JC10XtMjlIkoxwlWGvXrqV79+5XHa9r166sX68b30WuJiUthbG/juX9xe+TkpZidji55osnD9AYgAkqdiEiInmsnG9pRjUZwKT2T3NTmbqu9v1xJ3hmzQRG/PUFe2OPmxihyD9ylGCdPXuWMmXKXHW84OBgzp5VlReRq0l1pPLab6/xwZIPSC2k/cjTuwnOZg8niDM5GhERKQ5qBlTk/ZseZnzLR6kTWMnVvj5mLw/9OZ7xB38lMuG0iRGK5DDBCgwM5OTJk1cd79SpUwQEBFx3UCJS8NUnhDZUxoHBRBW7EBGRfNQ8uBZftn2K0U3vp7xvaVf7n2d2cd+yd/n3jlmcS1GlWzFHjhKsJk2aMGvWrKuON2vWLJo0aXK9MYlIIZFesv0rNuBQsQsREclHVouVzhWbMi18JP+qfxuBnn4ApBkO/ntoBf0Xv8W3+xaS5Ch8XfGlcMtRgvXAAw8wY8YMpk2blu04U6dOZcaMGQwcONBtwYlIwXYXN1AaH44Rxzz2mx2OiIgUQ3arB3dVa8/3HUZye7mWeFntAFxIS+KrPXO4Z/Fb/Hb0L9KcDpMjleIiRwnWfffdR+fOnXnggQfo2bMnX375JQsWLGDBggV8+eWX3HLLLQwaNIiuXbty77335nXMIlJAeGNnoIpdiIhIAeBv92FApfZMCx9Jr8o3Yf274mBMchzvbP2Bwcvf589TOzAMw+RIpajzyMlIFouFX375hREjRjBx4kTmzp2LxXLxQ2sYBna7nUceeYQPP/zQ1S4ixcPDNOcj/uJ39nGMWCoTaHZIIiJSjJXxDuSFxv3oW70DX+7+nZWndgBw+PwpRq6bSOPS1Xm0Xi/qlwo1N1ApsnKUYAF4eXnx2WefMXr0aJYsWcLRo0cBqFKlCh07dqRs2bJ5FqSIFFx1KUM4oSzlMF+zkbF0NDskERERqpUox7gWQ9l65iCf7/qN7WcPA7DlzEEe+fPfdCjXiIfr3kIV/xBzA5UiJ8cJVrqyZcvSv3//vIhFpNjwtnvz14t/cfbMWbzt3maHc92G0dyVYL1CezywmR2SiIgIAI1KV+ez1sNZcXIbX+z+nWMXogFYdnIrK09tp3eVlgyu3Y3SXiVMjlSKihwlWOlXq7Jit9sJCgrC09PTbUGJFHU2q40WoS2I8o3CZi38ycjt1CMYX04Qz+/sow91rz6RiIhIPrFYLLQv34jWZevz27E1TN77B2eS43EYTv53ZBV/HF9P/+rh9K8Rjq9H4T/xKebKUZGL0NBQqlWrluVPpUqV8Pf35+abb2bt2rV5Ha+IFEBeeDCEpgB8oWIXIiJSQHlYbdxWtTXTO77EkNrd8LF5AZDoSGHyvvn0X/wWsw7/qYqDcl1ydAXr3XffzbZ4RVpaGhEREcyePZvw8HBWrVqlZ2GJXEVKWgrjF4zn/IXzvHTrS3h7Fv6zZQ/RjHf5kz/Yz2HOEkops0MSERHJkq+HF4Nrd+O2qq2Zsnc+vxxdjcNwcjblPB9u/4kfDi3n4Tq3EF6+kQq4Sa7lKMF69tlnrzrOu+++S+vWrXnzzTf573//e92BiRRlqY5UXvj5BQCe6/kc3hT+BKsmQXSmOgs5yFds5E1uNjskERGRKyrlVYIRDe/krmrt+WrPHJZEbgHg+IVoXt34DTeUrMKj9XrTJKiGyZFKYZKjLoI54e3tzb/+9S+WL1/urlmKSCEzjOYATGQjqah7hYiIFA6V/cvwWvOBTGjzL5qU/ieZ2nnuKMNXf8oLa7/mUPxJEyOUwsRtCRZA1apVOXv2rDtnKSKFSB/qUg5/TnGBX9htdjgiIiK5ckOpqvy71WO80+JBqpUo52pfFbWTQcve4+0tM4hKPGdegFIouDXBOnr0KKVK6b4LkeLKjs1V7GICG0yORkREJPcsFguty97A5PbPMrJxf0K8SwLgxOD3Y2u5Z8lbfLHrN+JTE80NVAostyVYKSkpjB8/nnbt2rlrliJSCD1EMyzAQg6yn9NmhyMiInJNbBYrPSvfyPcdR/JI3Z74/12+PcWZxrQDi+m/+E1mHlxGiiPN5EiloMlRkYsPP/ww22EOh8NVRfDkyZNMnDjRbcGJSOETSim6UZN57OdLNvAuXc0OSURE5Jp52Ty5t+bN9KrSkqn7F/Lz4ZWkOh3EpSbwyc5f+OnQCh6s04POFZtitbi1c5gUUm6pImiz2WjTpg3ff/89TZs2dUtgIlJ4PUIY89jPZDbzOp3wytmuRkREpMAK9PTjiRv6cGdoO77eM5cFERsxMIhMPMPrm6cx4+BSHq3XixZl6pgdqpgsR0c9hw4dyn4GHh4EBwfj5eXltqBEijpvuzeLnl7EuXPn8LYX/hLtl+tJLSpSggjimcUu+tPQ7JBERETcorxvaV5pei/9q4fz+e7fWBe9B4B9cRE8vWYCLYJr80i9XtQOrGRypGKWHCVYVatWzfEMo6OjKVOmzDUHJFIc2Kw2wuuEExUVhc1qMzsct/PAxlCa8RrL+IL1SrBERKTIqRVYkQ9vGsb66L18tutX9sVFALAuZi/rVnxIl4rNeKjOLZT3LW1ypJLf3NJR1DAM5syZw5133kmlSsrWRQQepBlWLCzjCLuJNjscERGRPBFWpjZftxvBq03vpbzPP8nUgoiN3Lt0HP/Z8QuxKRdMjFDy23UlWAcOHGDUqFFUrlyZ3r17u5IsEbmy1LRUPlvyGZPXTCbVkWp2OHmiMoH0pBYAX6pku4iIFGFWi5UuFZvzXfhIht/Qh0C7HwCpTgc/HFpG/8Vv8t3+RSQ7UkyOVPJDrhOspKQkpk6dSnh4OLVr12bcuHFERkby9NNPc/z4cb7//vu8iFOkSElxpDB8xnBe+u0lUtKK7s52GGEAfMMWEimaiaSIiEg6T5sHfat3YEanl7iv5s14Wi/ejXM+LYkJu3/nniXj+P3oGhyG0+RIJS/lOMFat24djzzyCOXKlWPQoEFs3LiRQYMG8dtvv2EYBr179yYoKCgvYxWRQqY7NalCIGdI5Ed2mh2OiIhIvvC3+zCsbk9mdHyJnpVvwooFgOikWN7eOpPBy99n1amdGIZhcqSSF3JU5KJRo0bs2LEDgFatWjFkyBD69euHn58fsbGxeRqgiBReNqw8RDNeYQkTWM/9NDY7JBERkXxTxqckIxv3o1/1DkzY/Tt/nrp4PH0o/iQvrPuaJqVr8Ei9XtQvlfOCclLw5egK1vbt2wHo2bMnX375JUOGDMHPzy9PAxORomEITbFh4U+OsYMos8MRERHJd9VKlOPtFkP5T6vHuaFkFVf75jMHeOTPj3llwzccO6+CUEVFjhKs8ePH06hRI3777TcaNmxIq1at+Prrr4mPj8/r+ESkkKtAALdy8aGLE1hvcjQiIiLmaRJUgy/a/IvXmw+kkt8/jzVaGrmF+5e9w0fbfuJMso6vC7scJVhPPvkkmzZtYu3atTz88MPs3r2bhx9+mPLly/Pwww9jsViwWCx5HauIFFKP/F3s4lu2kEDRLeohIiJyNRaLhfDyjZna4XmebnAnpTz9AXAYTn4+8if9F7/F5L1/kJCWbHKkcq1yVUUwLCyMzz//nMjISL755hvCwsL48ccfMQyDoUOH8sEHH3D69Om8ilVECqnOVKc6pYglmZnsMDscERER03lYbdwe2oYZnV5iSO1u+Ng8AUh0JDNp7x/cs+QtZh3+kzSnw+RIJbeu6TlY3t7e3H///SxdupS9e/cycuRIEhISeO6556hcubK7YxQpcrw8vJj9xGym3jcVLw8vs8PJc9a/i12AugmKiIhcytfDm8G1uzGj0yhur9oGm+Xi4fmZ5Hg+3P4TDyx7l2WRW1VxsBC5rgcNA9SoUYO33nqLo0ePMnv2bLp37+6OuESKNA+bBz0b9qRznc542HJUzLPQG0xTPLCyhgg2E2l2OCIiIgVKaa8SPN3wTr7t8DwdyjVytR+7EM3LG6bw2Kr/sPXMQRMjlJy67gTLNSOrlV69evHzzz+7a5YiUoSUxZ87qAfABDaYHI2IiEjBVMU/hDfCBvFFmydpXLq6q3372cM8vuoTXlw3kUPxJ02MUK7GbQmWiORcaloqU1ZNYebGmaQ6Us0OJ98MozkA09jKeXTzroiISHbqlwrlP60e5+0WQwn1L+tqX3lqB4OWvcc7W2YSk6Tn0RZEBSLB+uyzz6hWrRre3t40b96cFStWZDvu0qVLXVULL/3ZvXt3hvHGjx9PnTp18PHxoXLlyowYMYKkpKS8fisiOZLiSGHoN0N5atZTpKQVn6p6HalGLUoTTwrT2W52OCIiIgWaxWKhTdn6TG7/LC806kuwVwAATgx+O7aG/ovf4svdczifmmhypHIp0xOsmTNn8tRTTzFq1Cg2bdpEu3bt6NGjB0ePHr3idHv27CEyMtL1U6tWLdewadOmMXLkSEaPHs2uXbuYOHEiM2fO5MUXX8zrtyMiV2DBwsN/X8X6QsUuREREcsTDaqNXlZZM7/QSD9e9BT8PbwCSnalM3b+Q/ovf4r8Hl5PiSDM5UoECkGB9+OGHDB06lAcffJB69eoxfvx4KleuzOeff37F6UJCQihXrpzrx2azuYatXr2aNm3aMGDAAEJDQ+natSv33HMP69frgE7EbINogic2NhLJeiLMDkdERKTQ8LZ5cn/Nzszo9BJ3V2uPh+Xi8W9s6gX+vfN/3LfsbRZGbMRpOE2OtHgztXxZSkoKGzZsYOTIkRnau3btyqpVq644bdOmTUlKSuKGG27g5ZdfpmPHjq5hbdu25bvvvmPt2rXceOONHDx4kDlz5jBw4MBs55ecnExy8j/3hMTFxQHgdDpxOs3/kKZX5jQMCkQ8cn0uXYcF5TOWX0rjw52Weky3bOcLYz1fGuXNDsltnE4nhmEUq/VZHGi9Fj36Ti2aitO2GuDhyxP1buXOqm34eu88Fp7YBEBkwhnGbvqOGQeWMqxuT5oH17rKnAq2grSt5mb5piZYMTExOBwOypYtm6G9bNmynDyZdXWU8uXL8+WXX9K8eXOSk5OZOnUqN998M0uXLqV9+/YA9O/fn+joaNq2bYthGKSlpfHoo49mSuQuNW7cOMaOHZupPTo6ukDcu+X8+yFzTqeDqKgok6OR65WQkuD6f3R0NInexavv9N326kwP2s73xjaej25CgFE0ngXmdDqJjY3FMAysVtM7CIibaL0WPfpOLZqK47ZqA4ZV6EKXwIZ8d3wZ2+KPALAn7jhPr51A44Bq3FepA6G+IeYGeo0K0rYaHx+f43ELxAN4LBZLhteGYWRqS1enTh3q1Knjet2qVSuOHTvG+++/70qwli5dyptvvslnn33GTTfdxP79+/nXv/5F+fLleeWVV7Kc74svvsjTTz/teh0XF0flypUpU6YMAQEB1/sWr5vVanP9DgkpnBuJ/ONC8gXX/8uUKUMJnxImRpP/bqUM9YxV7LLGsKDMSR6lhdkhuYXT6cRisVCmTJli8+VeHGi9Fj36Ti2aivO2GkIILas1Yl30Hr7Y8zv7404AsCXuEFt3HqZLxWYMrd2Ncj6lTY40dwrSturt7Z3jcU1NsIKDg7HZbJmuVkVFRWW6qnUlLVu25LvvvnO9fuWVV7j//vt58MEHAWjYsCEXLlzg4YcfZtSoUVludF5eXnh5ZT6LbrVaC8RGmp5vWiwUiHjk+ly6DgvKZyy/DSOMp5jHl9aNPMaNWMj6pEphY7FYiu06Lcq0XosWfacWXcV9W72pbD1ahNRhQcRGvt4zl5OJZzEwmB+xgSWRm7kztB3317yZAE8/s0PNkYK0reZm+aZG6unpSfPmzVmwYEGG9gULFtC6descz2fTpk2UL//PfRwJCQmZ/gg2mw3DMDDSO3OKmMjLw4sZD8/gy35f4uVRNLrH5dYDNMYbD7ZyijUcNzscERGRIsFqsdKtUhjfhY/k8RtupYTdB4BUp4MZB5fSb8lbTNu/mGRH8XlMTH4zvYvg008/zf33309YWBitWrXiyy+/5OjRozzyyCPAxa57ERERfPvtt8DF51uFhoZSv359UlJS+O677/jpp5/46aefXPPs3bs3H374IU2bNnV1EXzllVe49dZbM1QbFDGLh82Du5vfTVRUFB420zdDU5TCh77U51u28AXraUlls0MSEREpMrxsdvpXD6dn5ZuYtn8R/z20nBRnGudTE/li92/8fHglQ+t0p1ulMGyW4nnFL6+YfmTXr18/Tp8+zWuvvUZkZCQNGjRgzpw5VK1aFYDIyMgMz8RKSUnh2WefJSIiAh8fH+rXr8/vv//OLbfc4hrn5ZdfxmKx8PLLLxMREUGZMmXo3bs3b775Zr6/PxHJ3iOE8S1bmMkOPqI7pfAxOyQREZEipYTdh0fq9eL20DZM2vsHc4+tw8AgKukc47bMYObBZTxStyctQ+plWwNBcsdiqM9cluLi4ggMDCQ2NrZAFLm4Y+FYopNiKeMdyM+dR5sdjlynNEcaP238ibjYOAZ2GIin3dPskExhYNCYz9lGFB/TnSdpaXZI18XpdBIVFUVISIjpfcXFfbReix59pxZN2lZz5mBcJF/s/p3VUTsztDcNqsGj9XpTr2QVkyLLrCBtq7nJDfTpEzFBcloy/b/sz8MzHyY5LfnqExRRFiwMIwyAL1iPgc73iIiI5KXqAeV598YH+Xerx6gX+E/3/E2nD/DwyvGM3vAtERdiTIyw8FOCJSKmuo9G+GJnFzGs5OjVJxAREZHr1jSoJhPaPsVrzR6gkm+wq31x5GbuXfo2H23/mbPJOX/2k/xDCZaImCoQb+6hAQATWG9yNCIiIsWHxWKhY4UmTA1/gREN7qCkpz8ADsPJz4dX0n/JW0zZO5/EYtzb5loowRIR06V3E/yRncRw4Spji4iIiDt5WG3cEdqWmZ1eYlCtrnjbLt4bnpCWzMS98+i/5C1+ObKKNKfD5EgLByVYImK6MCrQlHIk4+AbtpgdjoiISLHk6+HN0DrdmdHxJfpUbe0q334mOZ73t/3IwGXvsTxyq54rexVKsETEdBYsPPL3Vawv2aBiFyIiIiYK8g7g2YZ38W2H5+lQrqGr/eiFKEZtmMJjq/7D1jOHTIywYFOCJSIFwj00xB9P9nKapRw2OxwREZFir4p/CG+EDebzNk/SqHQ1V/v2s4d5fNV/eGndJI6cP2VihAWTEiwRE3jaPJk4cCLjbx+Pp0fxfAbW5Urgxb1cPEumYhciIiIFR4NSoXzS6gnGhQ2hqn9ZV/uKU9sZuOw93tv6AzFJcSZGWLAowRIxgd3DzqDWg+jXrB92m93scAqM9GIXP7OLKM6bHI2IiIiks1gstC3XgCntn+X5Rn0J8rr4sF2H4WT20b+4Z8lbfL1nLhdSk0yO1HxKsESkwGhKeW6kIqk4mcxms8MRERGRy3hYbfSu0pLpHV/koTo98PXwAiDJkcI3+xbQb8mb/HhoBanONJMjNY8SLBETpDnS+H3b7yzcs5A0R/HdAWVlGM2Bi8UunDhNjkZERESy4uPhxQO1ujCj4yjuCm2Hh8UGQGzKBT7eMYv7lr7DohObcBrF77vcw+wARIqj5LRkbv3kVgD63NgHT7vuw0rXjwaM4A8OcpaFHKQrNc0OSUQKqRkHl/LDwWWZ2k//fa/I6aQ47lg4NtPwvtU70L96eF6HJ1IklPLy518Nbufuau35as8cFp7YBMCJhNOM2TiVGYFLebReL5oF1zI50vyjK1giUqD44ckDNAZgAhtMjkZECrOE1CSik2Iz/Tj/fhSEEyPL4Qm6h0Qk1yr4BTG62f183XYEzYL+SaZ2xx7jX399znNrvmR/3AkTI8w/uoIlIgXOMJrzCWv5hd1EEk95SpgdkogUQr52b8p4B2Y5zOFwYLPZsp1ORK5NnZKVGd/yEdZG7+bzXb9xID4SgL+id7Mmeg/dKoXxYJ3ulPUpZXKkeUcJlogUOA0oS2sqs4pjTGITo2hvdkgiUgj1rx6eZVc/p9NJVFQUISEhWK3qzCPibhaLhZtC6hFWpg4LIjbw9Z55nEo8i4HBvOPrWHxiE3eGtuP+mjdTwtPX7HDdTnsVESmQLi124VCxCxERkULHZrHSvVILpoWP5LF6vSlh9wEgxZnG9INL6LfkTaYfWEKyIzXDdMmOVOYdX09sygXgYuGMecfXZxqvoLIYhmGYHURBFBcXR2BgILGxsQQEBOTbcq90Q64TAysWgrwzx6MbcguXC8kX8H/CH4C4f8dRwkdd4C6XSCoV+YCzJPE7A7iF2maHdFU6K140ab0WPVqnRZPWa8EXn5LA1P2L+OnwClIuKeNe1qcUD9bpTpeKzVl9aidvbpnO+dTETNP72314uckA2pStn59hA7nLDdRFsIBJvyE3O+k35GY1nUhR4oOdgTRhPH8xgQ2FIsESERGR7JXw9OWxG3pzZ7W2fL1nHn8cX4+BwanEs7y5eToT/+5KmJ0LqYm8uG4Sb4UNpm25BvkYee4owSpgdENu8eBp8+Q//f9D/Pl4PD1Uoj07D9Oc8fzFb+zlGLFUJuttQ0RERAqPsj6lGNXkHvpV78CEXb/xV/RuAE5eIbkCMAALBm9tmc6sMmPwstnzIdrcU4JVwOiG3OLB7mHnsY6PERUVhb2A7hwKgnqUoT1VWc4RJrKRMXQ0OyQRERFxk5oBFXjvpofZGLOPt7fMJDLxzFWnMYD41ESWRm6hW6WwvA/yGuhIXUQKtEe4uPP8mo2k4TA5GhEREXG3ZsG1qBVYEUsOx7diYcXJbXka0/VQgiViAofTwdI9S1l1aBUOp5KGK7mDegTjSwTxzGGf2eGIiIhIHohLTSCnlfecGMSlJuRpPNdDCZaICZJSk7j5w5u5c9KdJKlAyRV54cEgmgDwBevNDUZERETyRKDdF2sOr2FZsRBgL7jPz1KCJSIF3sN/PxNrHvs5zJVvgBUREZHCp225hjhzeA3LiUG7cg3zOKJrpwRLRAq8WgRxM9UwuHgvloiIiBQtHcs3xt/uc9VrWBaghN2H8PKN8yOsa6IES0QKhWF/F7uYyCZSVexCRESkSPGy2Xm5yQDAkm2SZfn731FNBhTYEu2gBEtECok+1CEEP05yntnsMTscERERcbM2ZevzVthg/O0+WQ73t/swrsUQ2pStn8+R5Y4SLBEpFDzxYChNAZigYhciIiJFUttyDZjVeQwvNxmAp/XiI3s9rR683GQAszqPKfDJFSjBEpFC5CGaYwEWcJADXP1hhCIiIlL4eNnsdKsURqCnHwCBnn50qxRWoLsFXkoJlogJ7DY779zxDq90ewV7IdlZFATVKEVXagDwJRtMjkZEREQkMyVYIibw9PDk2W7P8ljbx/D08DQ7nELlkb+LXUxmEymkmRyNiIiISEZKsESkUOlFbSpQgmgSmMVus8MRERERyUAJlogJHE4H6w6vY/PxzTicKjmeGx7YVOxCRERECiwlWCImSEpNouW4lvSY0IOk1CSzwyl0HqQZViws4TB7iDE7HBEREREXJVgiUuhUoSS3UAtQsQsREREpWJRgiUihNIzmAExhM0mkmhyNiIiIyEVKsESkUOpBLSoTwBkS+ZGdZocjIiIiAijBEpFCyoaVB2kGwAR1ExQREZECQgmWiBRaQ2mGDQsrOcoOoswOR0REREQJlogUXhUJoDd1ABW7EBERkYJBCZaICew2O6/2epVnOj6D3WY3O5xCLb3YxTdsJoEUk6MRERGR4s7D7ABEiiNPD09G9x5NVFQUnh6eZodTqHWlBqGU5DDn+IEdDPr7IcQiIiIiZtAVLBEp1KxYefjvq1gqdiEiIiJmU4IlYgKn08mOEzvYc2oPTqfT7HAKvcE0wQMrf3GcLZw0OxwREREpxpRgiZggMTWRRmMbEf5JOImpiWaHU+iVowS3UReACaw3ORoREREpzpRgiUiR8AhhAHzHVs6TbHI0IiIiUlwpwRKRIqEjodSkNPGkMIPtZocjIiIixZQSLBEpEi4tdvGFugmKiIiISZRgiUiRMYgmeGJjA5Fs4ITZ4YiIiEgxpARLRIqMMvhxJ/UAFbsQERERcyjBEpEiZdjfxS6+ZxtxJJkcjYiIiBQ3HmYHIFIc2W12nunyDAkJCdhtdrPDKVLaU5W6BLObGKaxjUdpYXZIIiIiUozoCpaICTw9PHn3rnd5tfureHp4mh1OkWLBwrC/i11MYD0GhskRiYiISHGiBEtEipwHaIwXNrZwirVEmB2OiIiIFCNKsERM4HQ6ORxzmGNnj+F0Os0Op8gpjS99qQ+oZLuIiIjkLyVYIiZITE2kxqga3PjhjSSmJpodTpGUXuxiJts5h/7GIiIikj+UYIlIkdSayjQghETSmMpWs8MRERGRYkIJlogUSSp2ISIiImZQgiUiRdZ9NMIHD3YQzZ8cNTscERERKQaUYIlIkVUSH+6hIQAT2GByNCIiIlIcKMESkSItvZvgf9nBaRJMjkZERESKOiVYIlKktaAiTShHMg6+YbPZ4YiIiEgRpwRLxAQeVg8e7fAog24chIfVw+xwirRLi118yQYVuxAREZE8pQRLxARedi8+GfAJ43qPw8vuZXY4Rd69NMIfT/ZwmmUcNjscERERKcKUYIlIkVcCLwb8XexisroJioiISB5SgiViAsMwiI6PJuZCDIahLmv5YQQtKYk3IfiZHYqIiIgUYbr5Q8QECSkJlHu2HABx/46jhE8JkyMq+upShrOMNDsMERERKeJ0BUtERERERMRNlGCJiIiIiIi4iRIsERERERERN1GCJSIiIiIi4iZKsERERERERNxECZZIETZo0CAsFovrJygoiO7du7N161a3LWPMmDE0adIkR+PGxcUxatQo6tati7e3N+XKlaNz5878/PPPGIZBw4YNefDBB7Ocdvr06djtdk6dOnXFZbz11lvYbDbefvvtHMd67tw5LBYLS5cuzdD+008/ER4eTmBgIP7+/jRq1IjXXnuNM2fO5Oj9Xovk5GSGDx9OcHAwfn5+3HrrrRw/fvyq00VERHDfffcRFBSEr68vTZo0YcOGDa7hP//8M926dSM4OBiLxcLmzZszTH/mzBmGDx9OnTp18PX1pUqVKjz55JPExsa6+y2KiIgUaUqwREzgYfXggVYP0LdpXzysefu0hO7duxMZGUlkZCSLFi3Cw8ODXr165ekys3Lu3Dlat27Nt99+y4svvsjGjRtZvnw5/fr14/nnnyc2NpahQ4fyww8/kJCQkGn6SZMm0atXL8qWLXvF5UyePJnnn3+eSZMmXVe8o0aNol+/frRo0YK5c+eyfft2PvjgA7Zs2cLUqVOva95X8tRTTzFr1ixmzJjBypUrOX/+PL169cLhcGQ7zdmzZ2nTpg12u525c+eyc+dOPvjgA0qWLOka58KFC7Rp0ybLxBPgxIkTnDhxgvfff59t27YxZcoU5s2bx9ChQ939FkVERIo2Q7IUGxtrAEZsbKzZoRiGYRgOh8OIjIw0HA6H2aGIm+THOh04cKDRp0+fDG3Lly83ACMqKsrVdvz4caNv375GyZIljdKlSxu33nqrcejQIdfwJUuWGC1atDB8fX2NwMBAo3Xr1sbhw4eNyZMnG0CGn8mTJ2cZy6OPPmr4+fkZERERmYbFx8cbqampRkxMjOHp6WlMmTIlw/AjR44YVqvV+PXXX6/4fpcuXWpUrFjRSElJMSpUqGAsW7Ysw/DRo0cbjRs3zjTd2bNnDcBYsmSJYRiGsWbNGgMwxo8fn+Vyzp49m2X79a7Tc+fOGXa73ZgxY4arLSIiwrBarca8efOyne6FF14w2rZtm6NlHDp0yACMTZs2XXXcH374wfD09DRSU1NzNO+iSvvfokfrtGjSei16bl8wxmj76wjj9gVjzA4lV7mBrmCJFCPnz59n2rRp1KxZk6CgIAASEhLo2LEj/v7+LF++nJUrV+Lv70/37t1JSUkhLS2N2267jQ4dOrB161ZWr17Nww8/jMVioV+/fjzzzDPUr1/fdZWsX79+mZbrdDqZMWMG9957LxUqVMg03N/fHw8PD4KCgujTpw+TJ0/OMHzy5MmULVuWHj16XPH9TZw4kXvuuQe73c4999zDxIkTr+nvNG3aNPz9/XnssceyHH7plaHLdejQgYCAAPz9/bP8qV+/frbTbtiwgdTUVLp2/X979x0W1bG/AfzdpS0daQKCFEVpKigWJLGCLWqMBQuKqClesSI3tsSCgolelahXEhLEHklUEqNGgxo0ahKIxk4EK5qA2CjSYc/vD37sdaUKqwvr+3meTXbnzJzz3TOs7JczZ6afrMzKygpubm44c+ZMte32798PT09PjBo1Cubm5vDw8MCXX35Z+xutRXZ2NgwMDKCuzjXpiYiI6oq/NYmUQBAE5BXlIb84H4IgvNRjHThwAHp6egDKh4lZWlriwIEDEIvL/76ye/duiMVifPXVVxCJRADKExojIyMkJCTA09MT2dnZGDx4MFq1agUAcHZ2lu2/IjmysLCoNoaHDx/iyZMncHJyqjXeyZMnY9CgQbh58yYcHBwgCAK2bNmCwMBAqKmpVdsuJycHe/fulSUi48ePh7e3NzZs2AADA4Naj/us1NRUODg4QEND44XaAcCOHTtgaGgoO7/Pq2mfGRkZ0NTURLNmzeTKmzdvjoyMjGrb3bx5E5GRkQgODsbChQuRmJiImTNnQktLCwEBAS/8HgDg0aNHWL58OT744IN6tSciInpdMcEiUoL84nwYzCz/0p+zPgf62vov7Vi9e/dGZGQkgPKJDDZt2oSBAwciMTERtra2OHv2LK5fvw59ffkYCgsLcePGDfTr1w+BgYHo378/fH194ePjAz8/P1haWtY5hooksiKBq0m/fv1gbW2NmJgYLF++HMePH8ft27cxadKkGtvt2rULDg4O6NChAwDA3d0dDg4O2L17N95///06x1oRb11irYqNjQ3Mzc2rTbDqo7Z4pFIpPD09ER4eDgDw8PDAlStXEBkZWa8EKycnB2+99RZcXFywZMmSesdNRET0OuIQQSIVp6uri9atW6N169bo0qULoqOjkZeXJxtCJpVK0alTJ5w/f17ukZKSgnHjxgEov6L166+/onv37oiNjUWbNm3w22+/1TkGMzMzNGvWDMnJybXWFYvFCAwMxNatWyGVShETE4MePXrA0dGxxnabN2/GlStXoK6uLntcuXJFbpiggYFBlbPiZWVlAQAMDQ0BAG3atMGNGzdQUlJS5/dYoSFDBC0sLFBcXIwnT57IlWdmZtY4uYelpSVcXFzkypydnZGWlvbC8efm5mLAgAHQ09NDXFxcva7iERERvc6YYBG9ZkQiEcRiMQoKCgAAHTt2RGpqKszNzWWJWMWjIuEAyq+KLFiwAGfOnIGbmxt27doFANDU1KxxhjugPGkaPXo0du7ciX/++afS9ry8PJSWlspeT5o0Cffu3cO+ffuwb9++Wmeyu3TpEv744w8kJCTIJYknT55EUlISLl++DABwcnLCvXv3Kg23S0pKglgsRuvWrQEA48aNw9OnT7Fp06Yqj1eRkFVlx44dOHfuXKWEteJx6NChatt26tQJGhoaiI+Pl5Wlp6fj8uXL6N69e7XtvL29ce3aNbmylJQU2NraVtumKjk5OejXrx80NTWxf/9+SCSSF2pPREREHCJIpPKKiopkCcWTJ0+wceNGPH36FEOGDAEA+Pv7Y/Xq1Xj77bcRGhoKa2trpKWlYd++ffj3v/+NkpISREVFYejQobCyssK1a9eQkpIiG3pmZ2eHW7du4fz587C2toa+vj60tLQqxREeHo6EhAR07doVYWFh8PT0hIaGBn755ResXLkSSUlJsskj7O3t0adPH7z//vvQ0NDAyJEja3yP0dHR6NKlC3r06FFpm5eXF6Kjo7Fu3Tr069cPzs7OGDNmDMLCwmBlZYWLFy8iJCQEU6dOlQ2T7Nq1Kz788EPMnTsXf//9N9555x1YWVnh+vXr+Pzzz/HGG29g1qxZVcbSkCGChoaGmDJlCubOnQsTExMYGxsjJCQE7dq1g4+Pj6xe37598c4772D69OkAgDlz5qB79+4IDw+Hn58fEhMTERUVhaioKFmbx48fIy0tTZbgViRkFhYWsLCwQG5uLvr164f8/Hzs2LEDOTk5yMnJAVB+BbKm+9+IiIjoGS91PsMmjNO008v0tPCpgHch4F0IOfk5L+04EydOlJtCXV9fX+jcubOwZ88euXrp6elCQECAYGpqKmhpaQkODg7Ce++9J2RnZwsZGRnCsGHDBEtLS0FTU1OwtbUVFi9eLPtZLCwsFEaMGCEYGRnVOE27IJRPQz5//nzB0dFR0NTUFJo3by74+PgIcXFxglQqlau7a9cuAYDw/vvv1/gei4qKBBMTE2HVqlVVbl+zZo1gamoqFBUVyd7rpEmTBFtbW0FbW1twcnISQkNDhcLCwkptY2NjhR49egj6+vqCrq6u0L59eyE0NPSlTdMuCIJQUFAgTJ8+XTA2Nha0tbWFwYMHC2lpaXJ1bG1thSVLlsiV/fDDD4Kbm5ugpaUlODk5CVFRUXLbq5pSH4BsPz///HOV2wHITdn/OuK/v6qHfaqa2K+qp6lO0y4ShJc8hVkTlZOTA0NDQ9k0xcomlUqRmZmp8JvnSTnyivKgN718Zr+XPckFvTr8nKom9qvqYZ+qJvar6hl+dBkeFGbDTGKIfT7KnXTpRXID/vQREREREREpCO/BIlICNbEaRnQcgaKiIqiJeW8LERERkapggkWkBBINCb754BtkZmZCosGZ2oiIiIhUBYcIEhERERERKUijSLA2bdoEe3t7SCQSdOrUCb/88ku1dRMSEiASiSo9/vrrL7l6WVlZCAoKgqWlJSQSCZydnWtcf4aIiIiIiKihlD5EMDY2FrNnz8amTZvg7e2NL774AgMHDsTVq1fRsmXLattdu3ZNbgYPMzMz2fPi4mL4+vrC3Nwce/bsgbW1Ne7evStb44ZI2TiLIBEREZFqUnqCtXbtWkyZMgXvvvsuACAiIgJHjhxBZGQkVq5cWW07c3Nz2aKkz9u8eTMeP36MM2fOQENDAwBga2ur8NiJiIiIiIiepdQEq7i4GGfPnsX8+fPlyvv164czZ87U2NbDwwOFhYVwcXHBRx99hN69e8u27d+/H15eXggKCsL3338PMzMzjBs3DvPmzYOaWtUzthUVFaGoqEj2OicnB0D5mgpSqbS+b1FhpFIpBEFoFLFQwz3bj43lZ4wajp9T1cR+VT3sU9XEflU9Fav1CgKU3q8vcnylJlgPHz5EWVkZmjdvLlfevHlzZGRkVNnG0tISUVFR6NSpE4qKirB9+3b07dsXCQkJ6NGjBwDg5s2bOH78OPz9/XHo0CGkpqYiKCgIpaWlWLx4cZX7XblyJZYtW1ap/MGDBygsLGzgO204qVSK7OxsCILAxfNUQH5xvuz5gwcPUCApUGI0pCj8nKom9qvqYZ+qJvar6pFKy2T/z8zMVGosubm5da6r9CGCACASieReC4JQqaxC27Zt0bZtW9lrLy8v3L17F//5z39kCZZUKoW5uTmioqKgpqaGTp064Z9//sHq1aurTbAWLFiA4OBg2eucnBzY2NjAzMys1tWaXwWpVAqRSAQzMzP+o6EC8oryZM/NzMx4D5aK4OdUNbFfVQ/7VDWxX1WP+P/XChWL1WBubq7UWCSSui+ro9QEy9TUFGpqapWuVmVmZla6qlWTbt26YceOHbLXlpaW0NDQkBsO6OzsjIyMDBQXF0NTU7PSPrS0tKClpVWpXCwWN5oPqUgkalTxUP0924fsU9XCz6lqYr+qHvapamK/qpaK6y0iEZTepy9yfKVGqqmpiU6dOiE+Pl6uPD4+Ht27d6/zfv78809YWlrKXnt7e+P69etyYyVTUlJgaWlZZXJFRERERESkCEofIhgcHIwJEybA09MTXl5eiIqKQlpaGqZOnQqgfOje33//jW3btgEon2XQzs4Orq6uKC4uxo4dO7B3717s3btXts9//etf2LBhA2bNmoUZM2YgNTUV4eHhmDlzplLeI9Hz1MRqGOg2EMXFxVATVz3xChERERE1PUpPsEaPHo1Hjx4hNDQU6enpcHNzw6FDh2TTqqenpyMtLU1Wv7i4GCEhIfj777+hra0NV1dXHDx4EIMGDZLVsbGxwU8//YQ5c+agffv2aNGiBWbNmoV58+a98vdHVBWJhgQHZhxAZmYmJBp1H9NLRERERI2bSBAqJkCkZ+Xk5MDQ0BDZ2dmNZpKLzMxMmJubK30MKikG+1T1sE9VE/tV9bBPVRP7VfUMP7oMDwqzYSYxxD6fJUqN5UVyA6VfwSIioupJpVIUFxcrO4zXmlQqRUlJCQoLC/mlTYmen7yKiKixYoJFpAR5RXkwDzaHIAi4v+Y+p2mnKhUXF+PWrVtKX1zxdVexcGlubm61S4jQq2FkZAQLCwv2AxE1akywiJTk2cWGiZ4nCALS09OhpqYGGxsbXjlRIkEQUFpaCnV1dX6xVxJBEJCfny9baPTZmYOJiBobJlhERI1QaWkp8vPzYWVlBR0dHWWH81pjgtU4aGtrA4DsHhsOFySixop/EiUiaoTKysoAgGv3ET2j4o8NJSUlSo6EiKh6TLCIiBoxXjEh+h9+HoioKWCCRUREREREpCBMsIiIiIiIiBSECRaREohFYvRs0xNedl4Qi/gxJNUgEolqfAQGBtZ733Z2doiIiKhz/fDwcKipqeGTTz6ptG3p0qVwd3evVJ6VlQWRSISEhAS58r1798LHxwdGRkbQ09ND+/btERoaisePH7/gu6i7oqIizJgxA6amptDV1cXQoUNx7969Wtv9/fffGD9+PExMTKCjowN3d3ecPXsWQPl9S/PmzUO7du2gq6sLKysrBAQE4J9//pHbR69evSr13ZgxY17K+yQiUkX8ZkekBNqa2jg+9zj2TdkHbU1tZYdDpBDp6emyR0REBAwMDOTKPvvss1cWS0xMDD788ENs3ry5QftZtGgRxowZA09PTxw6dAiXL1/GmjVrcOHCBWzfvl1B0VY2e/ZsxMXFYffu3Th16hSePn2KwYMHyyY/qcqTJ0/g7e0NDQ0N/Pjjj7h69SrWrFkDIyMjAEB+fj7OnTuHjz/+GOfOncO+ffuQkpKCoUOHVtrXe++9J9d3X3zxxct6q0REKofTtBMRkUJYWFjInhsaGkIkEsmV/fDDD1i6dCmuXLkCKysrTJw4EYsWLYK6evmvoqVLl2Lz5s24f/8+TExMMHLkSKxfvx69evXCnTt3MGfOHMyZMwdA+dTp1Tlx4gQKCgoQGhqKbdu24eTJk+jRo8cLv5/ExESEh4dj3bp1CAoKkk3TbmdnB19fX2RlZb3wPusiOzsb0dHR2L59O3x8fAAAO3bsgI2NDY4ePYr+/ftX2e7TTz+FjY0NYmJiZGV2dnay54aGhoiPj5drs2HDBnTp0gVpaWlo2bKlrFxHR0eu74iIqO6YYBERNSHx91NwNDOl1notdYwQ1OoNubL/3jiFtPysWtv6mLeBb/M29Q2xSkeOHMH48eOxfv16vPnmm7hx4wbef/99AMCSJUuwZ88erFu3Drt374arqysyMjJw4cIFAMC+ffvQoUMHvP/++3jvvfdqPVZ0dDTGjh0LDQ0NjB07FtHR0fVKsHbu3Ak9PT1Mmzatyu0VV4aq4urqijt37lS73dbWFleuXKly29mzZ1FSUoJ+/frJyqysrODm5oYzZ85Um2Dt378f/fv3x6hRo3DixAm0aNEC06ZNq/GcZWdnQyQSVXovO3fuxI4dO9C8eXMMHDgQS5Ysgb6+frX7ISKi/2GCRaQEeUV5sJtvB6lUituf3Ia+Nr+4UN0UlpUgq6Sg1nrNSisPPc0tLapT28Iyxa8xFBYWhvnz52PixIkAAAcHByxfvhwffvghlixZgrS0NFhYWMDHxwcaGhpo2bIlunTpAgAwNjaGmpoa9PX1a72qkpOTg7179+LMmTMAgPHjx8Pb2xsbNmyAgYHBC8WcmpoKBwcHaGhooLS09IXaHjp0qMa1mjQ0NKrdlpGRAU1NTTRr1kyuvHnz5sjIyKi23c2bNxEZGYng4GAsXLgQiYmJmDlzJrS0tBAQEFCpfmFhIebPn49x48bJnRt/f3/Y29vDwsICly9fxoIFC3DhwoVKV7+IiKhqTLCIlOTh04fKDoGaIImaBow0ar9vT19dq8qyurSVqFX/5b++zp49i6SkJISFhcnKysrKUFhYiPz8fIwaNQoRERFwcHDAgAEDMGjQIAwZMkQ2fLCudu3aBQcHB3To0AEA4O7uDgcHB+zevVt2xayuBEGo97pLtra29WpXk9rikUql8PT0RHh4OADAw8MDV65cQWRkZKUEq6SkBGPGjIFUKsWmTZvktj17xcvNzQ2Ojo7w9PTEuXPn0LFjRwW+IyIi1cQEi4ioCfFtXv/he88PGXyVpFIpli1bhuHDh1faJpFIYGNjg2vXriE+Ph5Hjx7FtGnTsHr1apw4caLGqz3P27x5M65cuSKXmEmlUkRHR8sSLAMDA2RnZ1dqW3FPlaGhIQCgTZs2OHXqFEpKSl440WrIEEELCwsUFxfjyZMnclexMjMz0b1792r3aWlpCRcXF7kyZ2dn7N27V66spKQEfn5+uHXrFo4fP17rlb2OHTtCQ0MDqampTLCIiOqACRYREb10HTt2xLVr19C6detq62hra2Po0KEYOnQogoKC4OTkhEuXLqFjx47Q1NSscQY9ALh06RL++OMPJCQkwNjYWFaelZWFHj164PLly3Bzc4OTkxPu3buHjIwMuSGHSUlJEIvFshjHjRuH9evXY9OmTQgKCqp0vKysrGrvw2rIEMFOnTpBQ0MD8fHx8PPzA1A+Q+Ply5exatWqatt5e3vj2rVrcmUpKSlyV9MqkqvU1FT8/PPPMDExqXZ/Fa5cuYKSkhJYWlrWWpeIiJhgERHRK7B48WIMHjwYNjY2GDVqFMRiMS5evIhLly5hxYoV2LJlC8rKytC1a1fo6Ohg+/bt0NbWliUHdnZ2OHnyJMaMGQMtLS2YmppWOkZ0dDS6dOlS5YQWXl5eiI6Oxrp169CvXz84OztjzJgxCAsLg5WVFS5evIiQkBBMnTpVNplD165d8eGHHyIkJAR3797FiBEj0KJFC1y/fh2ff/453njjDcyaNavK99uQIYKGhoaYMmUK5s6dCxMTExgbGyMkJATt2rWTzSoIAH379sU777yD6dOnAwDmzJmD7t27Izw8HH5+fkhMTERUVBSioqIAAKWlpRg5ciTOnTuHAwcOoKysTHZPl7GxMTQ1NXHjxg3s3LkTgwYNgqmpKa5evYq5c+fCw8MD3t7e9X5PRESvE66DRUREL13//v1x4MABxMfHo3PnzujWrRvWrl0rS0SMjIzw5ZdfwtvbG+3bt8exY8fwww8/yK6whIaG4vbt22jVqhXMzMwq7b+4uBg7duzAiBEjqjz+iBEjsGPHDhQXF0NdXR0//fQTHBwc4O/vD1dXV8yfPx/vvvsu1q5dK9fu008/xc6dO5GUlIQBAwbA1dUVwcHBaN++vWzCjpdh3bp1GDZsGPz8/ODt7Q0dHR388MMPUFNTk9W5ceMGHj78372cnTt3RlxcHL7++mu4ublh+fLliIiIgL+/PwDg3r172L9/P+7duwd3d3dYWlrKHhWTgmhqauLYsWPo378/2rZti5kzZ6Jfv344evSo3LGJiKh6IqGmxUReYzk5OTA0NER2dvYLzzz1MkilUmRmZsLc3BxiMfPipi6vKA960/UAADnrcziLoIpQ5Oe0sLAQt27dgr29PSQSiYIipPoQBAGlpaWydbBIeRT1ueDvVNXEflU9w48uw4PCbJhJDLHPZ4lSY3mR3IBDBImUQCwSw9PWEyWlJRCL+EuAiIiISFUwwSJSAm1Nbfy+8HdkZmZCW7P2abOJiIiIqGngn86JiIiIiIgUhAkWERERERGRgjDBIlKC/KJ8OCx0QOc1nZFfnK/scIiIiIhIQXgPFpESCBBw59Gd8uecyJOIiIhIZfAKFhERERERkYIwwSIiIiIiIlIQJlhEREREREQKwgSLiIheG4GBgRg2bNgrOZadnR0iIiJeybGIiKjxYIJFREQKExgYCJFIJHuYmJhgwIABuHjxosKOsXTpUri7uytsfw21ZcsWGBkZKTsMAMC+ffvg6ekJIyMj6Orqwt3dHdu3b1d2WERErxUmWERKIIIILpYuaGPWBiKRSNnhECnUgAEDkJ6ejvT0dBw7dgzq6uoYPHiwssN6LRgbG2PRokX49ddfcfHiRUyaNAmTJk3CkSNHlB0aEdFrgwkWkRLoaOng0tJLODHzBHQ0dZQdDjUheUV51T4KSwrrXLeguKBOdetDS0sLFhYWsLCwgLu7O+bNm4e7d+/iwYMHsjp///03Ro8ejWbNmsHExARvv/02bt++LduekJCALl26QFdXF0ZGRvD29sadO3ewZcsWLFu2DBcuXJBdJduyZUuVcZSVlSE4OBhGRkYwMTHBhx9+WGlZBEEQsGrVKjg4OEBbWxsdOnTAnj175OIQi8U4dOgQ3N3dIZFI0LVrV1y6dEm2fdKkScjOzpbFs3TpUln7/Px8TJ48Gfr6+mjZsiWioqLqdU7rqlevXnjnnXfg7OyMVq1aYdasWWjfvj1OnTr1Uo9LRET/w3WwiIiaEL3petVuG9RuEA7OPCh7bR5sXu1C1j3b9ETCvxNkr+3m2+Hh04eV6glfNmydtqdPn2Lnzp1o3bo1TExMAJQnHb1798abb76JkydPQl1dHStWrJANJRSLxRg2bBjee+89fP311yguLkZiYiJEIhFGjx6Ny5cv4/Dhwzh69CgAwNDQsMpjr1mzBps3b0Z0dDRcXFywZs0axMXFoU+fPrI6H330Efbt24fIyEg4Ojri5MmTGD9+PMzMzNCzZ09Zvfnz5+Ozzz6DpaUlFi5ciKFDhyIlJQXdu3dHREQEFi9ejGvXrgEA9PT05GJYvnw5Fi5ciD179uBf//oXevToAScnpypjDg8PR3h4eI3n9Mcff8Sbb75Z67kXBAHHjx/HtWvX8Omnn9Zan4iIFIMJFhERKdSBAwdkSUZeXh4sLS1x4MABiMXlgyZ2794NsViMr776SjZENiYmBkZGRkhISICnpyeys7MxePBgtGrVCgDg7Ows27+enh7U1dVhYWFRYxwRERFYsGABRowYAQD4/PPP5YbK5eXlYe3atTh+/Di8vLwAAA4ODjh16hS++OILuQTro48+gq+vL0QiEbZu3Qpra2vExcXBz88PhoaGEIlEVcYzaNAgTJs2DQAwb948rFu3DgkJCdUmWFOnToWfn1+N76tFixY1bs/OzkaLFi1QVFQENTU1bNq0Cb6+vjW2ISIixWGCRaQE+UX56BzWGaWlpTi7+Cz0JNVflSB61tONT6vdpiZWk3uduTaz2rpikfwI8duf3G5QXM/q3bs3IiMjAQCPHz/Gpk2bMHDgQCQmJsLW1hZnz57F9evXoa+vL9eusLAQN27cQL9+/RAYGIj+/fvD19cXPj4+8PPzg6WlZZ1jyM7ORnp6uixxAgB1dXV4enrKhglevXoVhYWFlZKP4uJieHh4yJV169ZN9tzY2Bht27ZFcnJyrXG0b99e9rwiCcvMrL5fjI2NYWxsXOt+a6Kvr4/z58/j6dOnOHbsGIKDg+Hg4IBevXo1aL9ERFQ3TLCIlECAgKvpV8ufCw0bgkWvF10tXaXXrXVfurpo3bq17HWnTp1gaGiIL7/8EitWrIBUKkWnTp2wc+fOSm3NzMwAlF/RmjlzJg4fPozY2Fh89NFHiI+Pl0t0GkoqlQIADh48WOmqkJaWVq3t6zJBjYaGRqU2FcetiiKGCIrFYtn5d3d3R3JyMlauXMkEi4joFWGCRUREL5VIJIJYLEZBQfnEGh07dkRsbCzMzc1hYGBQbTsPDw94eHhgwYIF8PLywq5du9CtWzdoamqirKysxmMaGhrC0tISv/32G3r06AEA5VeMz55Fx44dAQAuLi7Q0tJCWlqa3HDAqvz+++9wcHAAADx58gQpKSmyYX51iaeuFDFE8HmCIKCoqKghYRER0QtggkVERApVVFSEjIwMAOXJyMaNG/H06VMMGTIEAODv74/Vq1fj7bffRmhoKKytrZGWloZ9+/bh3//+N0pKShAVFYWhQ4fCysoK165dQ0pKCgICAgCUL+B769YtnD9/HtbW1tDX16/yitOsWbPwySefwNHREc7Ozli7di2ysrJk2/X19RESEoI5c+ZAKpXijTfeQE5ODs6cOQM9PT1MnDhRVjcsLAzm5uawsLDAokWLYGpqKluw2M7OTjYcr0OHDtDR0YGOTv1mB23oEMGVK1fC09MTrVq1QnFxMQ4dOoRt27bJhmwSEdHLxwSLiIgU6vDhw7L7pfT19eHk5IRvv/1WNkRNR0cHJ0+exLx58zB8+HDk5uaiRYsW6Nu3LwwMDFBQUIC//voLW7duxaNHj2BpaYnp06fjgw8+AACMGDEC+/btQ+/evZGVlYWYmBgEBgZWimPu3LlIT09HYGAgxGIxJk+ejHfeeQfZ2dmyOsuXL4e5uTlWrlyJmzdvwsjICB07dsTChQvl9hUWFobZs2cjNTUVHTp0wP79+6GpqQkA6N69O6ZOnYrRo0fj0aNHWLJkidxU7a9SXl4epk2bhnv37kFbWxtOTk7YsWMHRo8erZR4iIheRyKBN4BUKScnB4aGhsjOzq5xCMurIpVKkZmZCXNzc9lMXNR05RXlyabbzlmfA31t/VpaUFOgyM9pYWEhbt26BXt7e0gkEgVFSC8qISEBvXv3RmZmJkxNTbkwuJIp6nPB36mqif2qeoYfXYYHhdkwkxhin88SpcbyIrkBf/qIiIiIiIgUhEMEiZRABBFsTWxRVlbGv4gTERERqRAmWERKoKOlg5vhN5GZmQkdzfrdDE9EL1+vXr0glUpRWlqq7FCIiKiJ4BBBIiIiIiIiBWGCRUREREREpCBMsIiUoKC4AF3Du2LA5wNQUFyg7HCIiIiISEF4DxaREkgFKf6484fsOZEi7L6ZgG9unnjhdn4OPTHGoZfiAyIiInoNMcEiIlIR+SWFeFCYXXvFKtoRERGRYjDBIiJSEToaEphJDGWvBUFAkbQEuSX/G4aqr6ENLbGG3PIAOhpcyJiIiEhReA8WEZGKGOPQC/t8lmCfzxIEu41A4XPJFQDklhSgUFqCue1Gyuo2huGBGRkZ8PX1ha6uLoyMjOrUZunSpXB3d6+xTmBgIIYNG9bg+F4XdnZ2iIiIqLGOSCTCd99990riISJqiphgERGpmFMZl7HwjxjklVQ9gUpeSQEWJG3GqYzLCj/20qVLIRKJ5B4WFha1tlu3bh3S09Nx/vx5pKSkKDyumgiCgC+//BJeXl4wMDCAnp4eXF1dMWvWLFy/fv2VxlKT55MfQRAwd+5c6Ovr4/jx4wDK1+16/vyLRKJXvo5XRkYGZsyYAQcHB2hpacHGxgZDhgzBsWPHUFxcDFNTU6xYsaLKtitXroSpqSmKi4tfacxERIrCBIuISIUUlZUg7MLXAAQI1dQR/v+/4Re+RlFZicJjcHV1RXp6uuxx6dKlWtvcuHEDnTp1gqOjI8zNzRUeU3UEQcC4ceMwc+ZMDBo0CD/99BMuXryI9evXQ1tbu9okAIBSE4CysjJMmTIF27Ztw/Hjx9GnTx/Ztvfee0/u/Kenp0Nd/dXdEXD79m106tQJx48fx6pVq3Dp0iUcPnwYvXv3RlBQEDQ1NTF+/Hhs2bIFglD5pzQmJgYTJkyApqbmK4uZiEiRmGARKYmpnimMdYyVHQapmJ/TL+BpSUG1yVUFAeXDBRPSLyg8BnV1dVhYWMgeZmZmNda3s7PD3r17sW3bNohEIgQGBgIA0tLS8Pbbb0NPTw8GBgbw8/PD/fv3q91PWVkZgoODYWRkBBMTE3z44YdVfoF/VmxsLHbv3o3Y2Fh8/PHH6NatGxwcHNC3b1988skniImJkdWdNGkShg0bhpUrV8LKygpt2rQBAFy6dAl9+vSBtrY2TExM8P777+Pp06eydr169cLs2bPljjts2DDZ+6w4B8uXL8e4ceOgp6cHKysrbNiwocqYi4qKMGrUKMTHx+PkyZPo3Lmz3HYdHR258//sFcS9e/fC1dUVWlpasLOzw5o1a2o8P6mpqejRowckEglcXFwQHx9fY30AmDZtGkQiERITEzFy5Ei0adMGrq6uCA4Oxm+//QYAmDJlCm7cuIGTJ0/Ktf3ll1+QmpqKKVOm1HocIqLGipNcECmBrpYu7q+5j8zMTOhq6So7HGoi3v1lLR4X5dZYJ7s474X2ueriN/jir4M11jHW0sdXbwbXeZ+pqamwsrKClpYWunbtivDwcDg4OFRbPykpCQEBATAwMMBnn30GbW1tCIKAYcOGQVdXFydOnEBpaSmmTZuG0aNHIyEhocr9rFmzBps3b0Z0dDRcXFywZs0axMXFyV3ded7XX3+Ntm3bYujQoVVuF4lEcknasWPHYGBggPj4eAiCgPz8fAwYMADdunVDUlISMjMz8e6772L69OnYsmVLnc5XhdWrV2PhwoVYunQpjhw5gjlz5sDJyQm+vr6yOk+fPsVbb72Fu3fv4vTp02jZsmWd93/27Fn4+flh6dKlGD16NM6cOYNp06bBxMRELtmrIJVKMXz4cJiamuK3335DTk5OpUTxeY8fP8bhw4cRFhYGXd3K/7ZV3F/Xrl07dO7cGTExMejZs6ds++bNm9GlSxe4ubnV+X0RUdNX3TIjjwpzZP8ffnRZpe2NdZkRJlhERE3E46Lcek3DXpNiaalC99m1a1ds27YNbdq0wf3797FixQp0794dV65cgYmJSZVtzMzMoKWlBW1tbdnVlvj4eFy8eBG3bt2CjY0NAGD79u1wdXVFUlJSpas2ABAREYEFCxZgxIgRAIDPP/8cR44cqTHelJQUtG3bVq5s9uzZ+OqrrwCUJwR3796VbdPV1cVXX30lG7725ZdfoqCgANu2bZMlFBs3bsSQIUPw6aefonnz5rWeswre3t6YP38+AKBNmzY4ffo01q1bJ5dgLV++HPr6+rh69Wq1Qyk3bdokix8APvjgA6xZswZr165F37598fHHH8uOcfXqVaxevbrKBOvo0aNITk7G7du3YW1tDQAIDw/HwIEDq30P169fhyAIcHJyqvX9Tp48GSEhIdi4cSP09PTw9OlTfPvtt1i7dm2tbYlItdS2zIgUQpXbG+syIxwiSETURBhr6cNMYljjQ1P8Yn830xSr17pPYy39Ou9v4MCBGDFiBNq1awcfHx8cPFh+dWzr1q0Ayr+g6+npyR5paWlV7ic5ORk2Njay5AoAXFxcYGRkhOTk5Er1s7OzkZ6eDi8vL1mZuro6PD09a4352SnrAWDRokU4f/48Fi9eLDfUDyi/8vLsvUHJycno0KGD3NUab29vSKVSXLt2rdZjP+vZ2CteP/9e+/Xrh7y8PISHh1e7H39/f5w/f172WLBggSxWb29vubre3t5ITU1FWVlZpf0kJyejZcuWsuSqqhifV3G17/lzWpWxY8dCKpUiNjYWQPlwTUEQMGbMmFrbEpFqqVhmpMrfQRp61W5rrMuM8AoWkRIUFBdg4GcDUVxcjPi58dCVcJgg1a4uw/QO3/sDYed31XmfH7b3Q3/r2pOQ+tLV1UW7du2QmpoKAJg6dSr8/Pxk262srKpsJwhClV/SqyuvL0dHR/z1119yZWZmZjAzM6vyCtHzw95qiqeiXCwWV7oXrKSkbpOLPL/vvn37YubMmXj77bdRVlZW5X1ahoaGaN26daXyqmKt6R61qrbVdu4dHR0hEomQnJxc6/T4hoaGGDlyJGJiYjBlyhTExMRg5MiRMDAwqLEdEameMQ69qhzqJ5VKkZmZCXNzc4jFTee6UNOJlEiFSAUpTqScwK+3f4VUkCo7HFIhvS07QE9DG7WlICKULzrcy7LDS42nqKgIycnJsLS0BAAYGxujdevWskd1s9u5uLggLS1Nbnje1atXkZ2dDWdn50r1DQ0NYWlpKZtEAQBKS0tx9uzZGuMbO3Ysrl27hu+//74+bw8uLi44f/488vL+d+/b6dOnIRaLZZNgmJmZIT09Xba9rKwMly9XniL/2dgrXlc11M7X1xcHDhzA5s2bERQUVOtEHs/GeurUKbmyM2fOoE2bNlBTU6uyflpaGv755x9Z2a+//lrjMYyNjdG/f3/897//lTsnFbKysuReT5kyBadPn8aBAwdw+vRpTm5BRCqBCRYRkQrRUtPAR+7jAIiqTbJE///fRe7joKWmodDjh4SE4MSJE7h16xZ+//13jBw5Ejk5OZg4ceIL7cfHxwft27eHv78/zp07h8TERAQEBKBnz57VDvubNWsWPvnkE8TFxeGvv/7CtGnTKn2hf96YMWMwcuRIjBkzBqGhofj9999x+/ZtnDhxArGxsVUmHs/y9/eHRCLBxIkTcfnyZfz888+YMWMGJkyYILv/qk+fPjh48CAOHjxYY1ynT5/GqlWrkJKSgv/+97/49ttvMWvWrCqPW7HPrVu31jnJmjt3Lo4dO4bly5cjJSUFW7duxcaNGxESElJlfR8fH7Rt2xYBAQG4cOECfvnlFyxatKjW42zatAllZWXo0qUL9u7di9TUVCQnJ2P9+vWVhhj27NkTrVu3RkBAAFq3bo0ePXrUun8iosaOCRYRkYrxbu6KcM9J0NPQrnK7noY2VnaeDO/mrgo/9r179zB27Fi0bdsWw4cPh6amJn777TfY2tq+0H5EIhG+++47NGvWDD169ICPjw8cHBxk9+tUZe7cuQgICEBgYCC8vLygr6+Pd955p9bjxMbGIiIiAocOHULfvn3Rtm1bTJ48GTY2NpWu+DxPR0cHR44cwePHj9G5c2eMHDkSffv2xcaNG2V1Jk+ejIkTJ8oSRHt7e/Tu3bvK+M+ePQsPDw8sX74ca9asQf/+/as9dq9evXDo0CFs374d//rXv2pNsjp27IhvvvkGu3fvhpubGxYvXozQ0NAqJ7gAyoc2xsXFoaioCF26dMG7776LsLCwGo8BAPb29jh37hx69+6NuXPnws3NDb6+vjh27BgiIyMr1Z88eTKePHmCyZMn17pvIqKmQCTUdWzBayYnJweGhobIzs5uFOPBm+oYVKpaXlEe9KbrAQBy1udAX7vukwhQ46XIz2lhYSFu3boFe3t7SCR1u4n3+WluBUFAkbQEuSUFsjJ9DW1oiTXk7qVprNPcNhaCIKC0tBTq6uoKvf/rWXZ2dpg9e3at06C/7urzuagKf6eqJvar6mlMffoiuQEnuSAiUhG1TXMLlC8unIsCubLGOs0tERFRU8QEi4hIRVRMc1ufdkRERKQYTLCIlERHU6fOs38R1UV109xS43f79m1lh0BERArCBItICXS1dJG7IReZmZnQ1eIaWERERESqgncAEhE1YrzKSfQ//DwQUVPABIuIqBGqWH+puLhYyZEQNR75+fkAAA0Nxa7fRkSkSBwiSKQEhSWFGL5pOIqLi7F/5n7oaOkoOyRqZNTV1aGjo4MHDx5AQ0ND6dPTvs5exTTtVDNBEJCfn4/MzEwYGRnVugA0EZEyMcEiUoIyaRl+vPyj7DnR80QiESwtLXHr1i3cuXNH2eG81gRBgFQqhVgsZoKlZEZGRrCwsFB2GERENWKCRUTUSGlqasLR0ZHDBJVMKpXi0aNHMDEx4ZVEJdLQ0OCVKyJqEphgERE1YmKxGBIJ16lSJqlUCg0NDUgkEiZYRERUK/6mICIiIiIiUhAmWERERERERArCBIuIiIiIiEhBeA9WNSoWM8zJyVFyJOWkUilyc3N5D4CKyCvKA/5/3oKcnBwIJVw8UxXwc6qa2K+qh32qmtivqqcx9WlFTlCXBc9FApdFr9K9e/dgY2Oj7DCIiIiIiKiRuHv3LqytrWuswwSrGlKpFP/88w/09fUbxbonOTk5sLGxwd27d2FgYKDscEgB2Keqh32qmtivqod9qprYr6qnMfWpIAjIzc2FlZVVrVfTOESwGmKxuNbsVBkMDAyU/gNGisU+VT3sU9XEflU97FPVxH5VPY2lTw0NDetUjwNUiYiIiIiIFIQJFhERERERkYIwwWoitLS0sGTJEmhpaSk7FFIQ9qnqYZ+qJvar6mGfqib2q+ppqn3KSS6IiIiIiIgUhFewiIiIiIiIFIQJFhERERERkYIwwSIiIiIiIlIQJlhEREREREQKwgSrEVu5ciU6d+4MfX19mJubY9iwYbh27Zqyw6IGioyMRPv27WWL5nl5eeHHH39UdlikQCtXroRIJMLs2bOVHQrV09KlSyESieQeFhYWyg6LFODvv//G+PHjYWJiAh0dHbi7u+Ps2bPKDovqyc7OrtJnVSQSISgoSNmhUQOUlpbio48+gr29PbS1teHg4IDQ0FBIpVJlh1Yn6soOgKp34sQJBAUFoXPnzigtLcWiRYvQr18/XL16Fbq6usoOj+rJ2toan3zyCVq3bg0A2Lp1K95++238+eefcHV1VXJ01FBJSUmIiopC+/btlR0KNZCrqyuOHj0qe62mpqbEaEgRnjx5Am9vb/Tu3Rs//vgjzM3NcePGDRgZGSk7NKqnpKQklJWVyV5fvnwZvr6+GDVqlBKjoob69NNP8fnnn2Pr1q1wdXXFH3/8gUmTJsHQ0BCzZs1Sdni14jTtTciDBw9gbm6OEydOoEePHsoOhxTI2NgYq1evxpQpU5QdCjXA06dP0bFjR2zatAkrVqyAu7s7IiIilB0W1cPSpUvx3Xff4fz588oOhRRo/vz5OH36NH755Rdlh0IvyezZs3HgwAGkpqZCJBIpOxyqp8GDB6N58+aIjo6WlY0YMQI6OjrYvn27EiOrGw4RbEKys7MBlH8ZJ9VQVlaG3bt3Iy8vD15eXsoOhxooKCgIb731Fnx8fJQdCilAamoqrKysYG9vjzFjxuDmzZvKDokaaP/+/fD09MSoUaNgbm4ODw8PfPnll8oOixSkuLgYO3bswOTJk5lcNXFvvPEGjh07hpSUFADAhQsXcOrUKQwaNEjJkdUNhwg2EYIgIDg4GG+88Qbc3NyUHQ410KVLl+Dl5YXCwkLo6ekhLi4OLi4uyg6LGmD37t04d+4ckpKSlB0KKUDXrl2xbds2tGnTBvfv38eKFSvQvXt3XLlyBSYmJsoOj+rp5s2biIyMRHBwMBYuXIjExETMnDkTWlpaCAgIUHZ41EDfffcdsrKyEBgYqOxQqIHmzZuH7OxsODk5QU1NDWVlZQgLC8PYsWOVHVqdMMFqIqZPn46LFy/i1KlTyg6FFKBt27Y4f/48srKysHfvXkycOBEnTpxgktVE3b17F7NmzcJPP/0EiUSi7HBIAQYOHCh73q5dO3h5eaFVq1bYunUrgoODlRgZNYRUKoWnpyfCw8MBAB4eHrhy5QoiIyOZYKmA6OhoDBw4EFZWVsoOhRooNjYWO3bswK5du+Dq6orz589j9uzZsLKywsSJE5UdXq2YYDUBM2bMwP79+3Hy5ElYW1srOxxSAE1NTdkkF56enkhKSsJnn32GL774QsmRUX2cPXsWmZmZ6NSpk6ysrKwMJ0+exMaNG1FUVMQJEpo4XV1dtGvXDqmpqcoOhRrA0tKy0h+ynJ2dsXfvXiVFRIpy584dHD16FPv27VN2KKQA//73vzF//nyMGTMGQPkfuu7cuYOVK1cywaKGEQQBM2bMQFxcHBISEmBvb6/skOglEQQBRUVFyg6D6qlv3764dOmSXNmkSZPg5OSEefPmMblSAUVFRUhOTsabb76p7FCoAby9vSstd5KSkgJbW1slRUSKEhMTA3Nzc7z11lvKDoUUID8/H2Kx/FQRampqnKadGi4oKAi7du3C999/D319fWRkZAAADA0Noa2treToqL4WLlyIgQMHwsbGBrm5udi9ezcSEhJw+PBhZYdG9aSvr1/p3khdXV2YmJjwnskmKiQkBEOGDEHLli2RmZmJFStWICcnp0n85ZSqN2fOHHTv3h3h4eHw8/NDYmIioqKiEBUVpezQqAGkUiliYmIwceJEqKvzq60qGDJkCMLCwtCyZUu4urrizz//xNq1azF58mRlh1Yn/ClsxCIjIwEAvXr1kiuPiYnhDZxN2P379zFhwgSkp6fD0NAQ7du3x+HDh+Hr66vs0Ijo/927dw9jx47Fw4cPYWZmhm7duuG3337jlY4mrnPnzoiLi8OCBQsQGhoKe3t7REREwN/fX9mhUQMcPXoUaWlpTebLN9Vuw4YN+PjjjzFt2jRkZmbCysoKH3zwARYvXqzs0OqE62AREREREREpCNfBIiIiIiIiUhAmWERERERERArCBIuIiIiIiEhBmGAREREREREpCBMsIiIiIiIiBWGCRUREREREpCBMsIiIiIiIiBSECRYREREREZGCMMEiIiKF2LJlC0QikewhkUhgYWGB3r17Y+XKlcjMzHypx1+6dClEItELtwsMDISdnZ3iA6rlmM+eq+oegYGBrzQuIiJqOJEgCIKygyAioqZvy5YtmDRpEmJiYuDk5ISSkhJkZmbi1KlTiImJgZqaGmJjY+Hj4/NSjn/v3j3cu3cP3bp1e6F2N27cQE5ODjw8PF5KXNUd88GDB7LX586dQ1BQEMLDw9G7d29ZuZmZGVq1avXK4iIiooZTV3YARESkWtzc3ODp6Sl7PWLECMyZMwdvvPEGhg8fjtTUVDRv3lzhx7W2toa1tfULt1NGAtOqVSu54xYWFgIAHB0da0wQCwoKIJFI6nWljoiIXg0OESQiopeuZcuWWLNmDXJzc/HFF1/Ibfvjjz8wdOhQGBsbQyKRwMPDA998841cnfz8fISEhMDe3h4SiQTGxsbw9PTE119/LatT3RDBXbt2wcvLC3p6etDT04O7uzuio6Nl26saIlhYWIgFCxbA3t4empqaaNGiBYKCgpCVlSVXz87ODoMHD8bhw4fRsWNHaGtrw8nJCZs3b67nmfqfiiGXP/30EyZPngwzMzPo6OigqKgIABAbGwsvLy/o6upCT08P/fv3x59//llpP3U5v0REpDhMsIiI6JUYNGgQ1NTUcPLkSVnZzz//DG9vb2RlZeHzzz/H999/D3d3d4wePRpbtmyR1QsODkZkZCRmzpyJw4cPY/v27Rg1ahQePXpU4zEXL14Mf39/WFlZYcuWLYiLi8PEiRNx586datsIgoBhw4bhP//5DyZMmICDBw8iODgYW7duRZ8+fWQJToULFy5g7ty5mDNnDr7//nu0b98eU6ZMkXufDTF58mRoaGhg+/bt2LNnDzQ0NBAeHo6xY8fCxcUF33zzDbZv347c3Fy8+eabuHr1qqxtXc8vEREpkEBERKQAMTExAgAhKSmp2jrNmzcXnJ2dZa+dnJwEDw8PoaSkRK7e4MGDBUtLS6GsrEwQBEFwc3MThg0bVuPxlyxZIjz7a+3mzZuCmpqa4O/vX2O7iRMnCra2trLXhw8fFgAIq1atkqsXGxsrABCioqJkZba2toJEIhHu3LkjKysoKBCMjY2FDz74oMbjPuvnn38WAAjffvutrKzifAYEBMjVTUtLE9TV1YUZM2bIlefm5goWFhaCn5+frKyu55eIiBSHV7CIiOiVEZ6ZV+n69ev466+/4O/vDwAoLS2VPQYNGoT09HRcu3YNANClSxf8+OOPmD9/PhISElBQUFDrseLj41FWVoagoKAXivH48eMAUGkGv1GjRkFXVxfHjh2TK3d3d0fLli1lryUSCdq0aVPjVbIXMWLECLnXR44cQWlpKQICAuTOmUQiQc+ePZGQkADgxc4vEREpDie5ICKiVyIvLw+PHj1Cu3btAAD3798HAISEhCAkJKTKNg8fPgQArF+/HtbW1oiNjcWnn34KiUSC/v37Y/Xq1XB0dKyybcUsfS868cWjR4+grq4OMzMzuXKRSAQLC4tKwxJNTEwq7UNLS6tOSWBdWFpayr2uOG+dO3eusr5YLJarV5fzS0REisMEi4iIXomDBw+irKwMvXr1AgCYmpoCABYsWIDhw4dX2aZt27YAAF1dXSxbtgzLli3D/fv3ZVezhgwZgr/++qvKthUJ0r1792BjY1PnOE1MTFBaWooHDx7IJVmCICAjI6PaxOZleX7ijorztmfPHtja2lbb7kXOLxERKQ4TLCIieunS0tIQEhICQ0NDfPDBBwDKv9w7OjriwoULCA8Pr/O+mjdvjsDAQFy4cAERERHIz8+Hjo5OpXr9+vWDmpoaIiMj4eXlVef99+3bF6tWrcKOHTswZ84cWfnevXuRl5eHvn371nlfL0P//v2hrq6OGzduVBo++Kz6nl8iImoYJlhERKRQly9flt3rk5mZiV9++UW20HBcXJzcVaEvvvgCAwcORP/+/REYGIgWLVrg8ePHSE5Oxrlz5/Dtt98CALp27YrBgwejffv2aNasGZKTk7F9+3Z4eXlVmVwB5VOoL1y4EMuXL0dBQQHGjh0LQ0NDXL16FQ8fPsSyZcuqbOfr64v+/ftj3rx5yMnJgbe3Ny5evIglS5bAw8MDEyZMUPxJewF2dnYIDQ3FokWLcPPmTQwYMADNmjXD/fv3kZiYKLvaB9T9/BIRkeIwwSIiIoWaNGkSAEBTUxNGRkZwdnbGvHnz8O6771a6r6l3795ITExEWFgYZs+ejSdPnsDExAQuLi7w8/OT1evTpw/279+PdevWIT8/Hy1atEBAQAAWLVpUYyyhoaFwdHTEhg0b4O/vD3V1dTg6OmLmzJnVthGJRPjuu++wdOlSxMTEICwsDKamppgwYQLCw8OhpaXVgLOjGAsWLICLiws+++wzfP311ygqKoKFhQU6d+6MqVOnyurV9fwSEZHiiIRnp3QiIiIiIiKieuM07URERERERArCBIuIiIiIiEhBmGAREREREREpCBMsIiIiIiIiBWGCRUREREREpCBMsIiIiIiIiBSECRYREREREZGCMMEiIiIiIiJSECZYRERERERECsIEi4iIiIiISEGYYBERERERESnI/wGlEoI+vU12ugAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final summary\n", + "\n", + "Metric Cross-validation Test set \n", + "Method 5-fold GroupKFold Hold-out (20%) \n", + "Best k 3 3 \n", + "AUC 0.6120 ± 0.0209 0.6249 \n", + "Accuracy N/A 0.6048 \n", + "Precision N/A 0.6667 \n", + "Recall N/A 0.4190 \n", + "F1-Score N/A 0.5146 \n", + "\n", + "\n", + "Conclusion\n", + "• Best k = 3 determined by 5-fold GroupKFold cross-validation\n", + "• Cross-validation ensures no patient images are split across folds\n", + "• Test set AUC = 0.625 is consistent with CV results\n", + "• Model generalizes well to unseen data (test AUC close to CV mean)\n" + ] + } + ], + "source": [ + "# Figure 1: Confusion matrix (Test Set Only)\n", + "fig_dt, ax_dt = plt.subplots(figsize=(8, 6))\n", + "\n", + "cm_test_dt = confusion_matrix(y_test, y_test_pred_dt)\n", + "sns.heatmap(cm_test_dt , annot=True, fmt='d', cmap='BuGn', ax=ax_dt , xticklabels=['Non-Cancerous', 'Cancerous'], yticklabels=['Non-Cancerous', 'Cancerous'])\n", + "# annot=True makes it possible to see numbers of the different 4 cat. and fmt=d is format d=decimals(intergers)\n", + "\n", + "# Labels\n", + "ax_dt.set_title(f'Test set confusion matrix\\nk={best_depth_dt}, AUC={test_auc_dt:.3f}, Acc={test_acc_dt:.3f}', fontsize=12) #.3f just mean only displaying 3 digits after the decimal\n", + "ax_dt.set_xlabel('Predicted label')\n", + "ax_dt.set_ylabel('True label')\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "# Figure 2: ROC-curve\n", + "fig_dt, ax_dt = plt.subplots(figsize=(8, 6))\n", + "\n", + "# Test ROC\n", + "fpr_test_dt, tpr_test_dt, _ = roc_curve(y_test, y_test_proba_dt) # _ is for threshold value, which we dont use # fpr=false positive, tpr=true positive\n", + "ax_dt.plot(fpr_test_dt, tpr_test_dt, label=f'Desicion Tree (AUC = {test_auc_dt:.3f})', linewidth=2, color='aquamarine')\n", + "\n", + "# Random classifier line\n", + "ax_dt.plot([0, 1], [0, 1], 'k--', label='Random (AUC = 0.5)', linewidth=1) # Used for comparison to just random guesses\n", + "\n", + "# Labels\n", + "ax_dt.set_xlabel('False Positive Rate', fontsize=12)\n", + "ax_dt.set_ylabel('True Positive Rate', fontsize=12)\n", + "ax_dt.set_title(f'ROC Curve - Test set (depth = {best_depth_dt})', fontsize=14)\n", + "ax_dt.legend(loc='lower right') #ax.legend implements a small box that explains the different lines, points etc\n", + "ax_dt.grid(True, alpha=0.3)\n", + "\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "# Figure 3: Cross-validation with Gridsearch\n", + "fig_dt, ax_dt = plt.subplots(figsize=(10, 6))\n", + "\n", + "k_values_dt = [2 , 3 , 4 , 6 , 8] # The same k-nearest neighbours\n", + "cv_auc_means_dt = grid_search_dt.cv_results_['mean_test_score'].tolist()\n", + "cv_auc_stds_dt = grid_search_dt.cv_results_['std_test_score'].tolist()\n", + "\n", + "ax_dt.errorbar(k_values_dt, cv_auc_means_dt, yerr=cv_auc_stds_dt, # yerr is for plotting error bar on the y-axis\n", + " marker='o', capsize=5, capthick=2, # For better visualization, like thickness, markers -\n", + " linewidth=2, markersize=8, color='mediumseagreen', # and color\n", + " label='5-fold GroupKFold CV') \n", + "\n", + "# Labels\n", + "ax_dt.axhline(y = test_auc_dt, color='mediumaquamarine', linestyle='--', linewidth=2, label=f'Test AUC = {test_auc_dt:.3f}')\n", + "ax_dt.axvline(x = best_depth_dt, color='darkgreen', linestyle='--', linewidth=1.5, label=f'Best depth = {best_depth_dt}')\n", + "\n", + "# More lables\n", + "ax_dt.set_xlabel('Desicion Tree', fontsize=12)\n", + "ax_dt.set_ylabel('AUC Score', fontsize=12)\n", + "ax_dt.set_title('Cross-validation results with GroupKFold', fontsize=14)\n", + "ax_dt.legend()\n", + "ax_dt.grid(True, alpha=0.3)\n", + "\n", + "# Annotatting the best point\n", + "ax_dt.annotate(f'Best CV AUC = {cv_auc_means_dt[k_values_dt.index(best_depth_dt)]:.3f}',\n", + " xy = (best_depth_dt , cv_auc_means_dt[k_values_dt.index(best_depth_dt)]), # Point that is annotated\n", + " xytext=(best_depth_dt + 0.2, cv_auc_means_dt[k_values_dt.index(best_depth_dt)] - 0.05), # Position of text\n", + " arrowprops=dict(arrowstyle='->', color='springgreen', lw=1.5), # For better visu.\n", + " fontsize=10, ha='left' # More vis.\n", + " )\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "# Printing results\n", + "print(\"Final summary\")\n", + "print(f\"\\n{'Metric':<25} {'Cross-validation':<20} {'Test set':<15}\") #<25, <20 and <15 is used to align the printing statements\n", + "\n", + "print(f\"{'Method':<25} {'5-fold GroupKFold':<20} {'Hold-out (20%)':<15}\")\n", + "print(f\"{'Best k':<25} {best_depth_dt:<20} {best_depth_dt:<15}\")\n", + "print(f\"{'AUC':<25} {cv_auc_means_dt[k_values_dt.index(best_depth_dt)]:.4f} ± {cv_auc_stds_dt[k_values_dt.index(best_depth_dt)]:.4f} {test_auc_dt:<15.4f}\") \n", + "# <15.4f, <15 is still for aligning, but .4f is printing the 4 decimals after the decimal,point\n", + "print(f\"{'Accuracy':<25} {'N/A':<20} {test_acc_dt:<15.4f}\")\n", + "print(f\"{'Precision':<25} {'N/A':<20} {test_precision_dt:<15.4f}\")\n", + "print(f\"{'Recall':<25} {'N/A':<20} {test_recall_dt:<15.4f}\")\n", + "print(f\"{'F1-Score':<25} {'N/A':<20} {test_f1_dt:<15.4f}\")\n", + "\n", + "print(\"\\n\")\n", + "print(\"Conclusion\")\n", + "print(f\"• Best k = {best_depth_dt} determined by 5-fold GroupKFold cross-validation\")\n", + "print(f\"• Cross-validation ensures no patient images are split across folds\")\n", + "print(f\"• Test set AUC = {test_auc_dt:.3f} is consistent with CV results\")\n", + "print(f\"• Model generalizes well to unseen data (test AUC close to CV mean)\")" + ] + }, + { + "cell_type": "markdown", + "id": "8c4bcc16", + "metadata": {}, + "source": [ + "### Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4c9e9c63", + "metadata": {}, + "outputs": [], + "source": [ + "#Making the model, and setting classweight=\"balanced\" to combat possible class imbalance\n", + "model_log_reg = LogisticRegression(class_weight=\"balanced\")\n", + "\n", + "#Fitting the model\n", + "model_log_reg.fit(X_temp, y_temp)\n", + "\n", + "#Making the predictions\n", + "predictions_log_reg = model_log_reg.predict(X_test)\n", + "\n", + "#Getting the probabilities\n", + "Y_probs = model_log_reg.predict_proba(X_test)[:, 1]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e8811634", + "metadata": {}, + "source": [ + "#### Visualizing the results" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f8f157fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Summary\n", + "AUC Score: 0.6528\n", + "Accuracy: 60.48%\n" + ] + } + ], + "source": [ + "#FalsePosetives, TruePosetives ROC curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, Y_probs)\n", + "\n", + "#Calculating AUC\n", + "auc_log_reg = roc_auc_score(y_test, Y_probs)\n", + "\n", + "#Calculating confusion matrix\n", + "cm_log_reg = confusion_matrix(y_test, predictions_log_reg)\n", + "\n", + "#Figure 1, ROC-curve for logistic regression.\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(fpr, tpr, color=\"blue\", label=f'ROC curve (AUC = {auc_log_reg:.2f})')\n", + "plt.plot([0, 1], [0, 1], color='navy', linestyle='--', label=\"Random\")\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate \")\n", + "plt.title(\"ROC Curve for logistic regression\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.grid(alpha=0.3)\n", + "plt.show()\n", + "\n", + "#Figure 2, Confusion matrix for logistic regression.\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm_log_reg, display_labels=['Healthy', 'Cancer'])\n", + "disp.plot(cmap='Blues')\n", + "plt.title('Cancer Prediction Confusion Matrix')\n", + "plt.show()\n", + "\n", + "#Final summary \n", + "print(\"Final Summary\")\n", + "print(f\"AUC Score: {auc_log_reg:.4f}\")\n", + "print(f\"Accuracy: {accuracy_score(y_test, predictions_log_reg):.2%}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2706da66", + "metadata": {}, + "source": [ + "#### Estimate best C" + ] + }, + { + "cell_type": "markdown", + "id": "77a4ba05", + "metadata": {}, + "source": [ + "#### Visualizing the results" + ] + }, + { + "cell_type": "markdown", + "id": "6a489c56", + "metadata": {}, + "source": [ + "# Needed comments for clarification!\n", + "\n", + "- The random state tested on is \"random_state=42\"\n", + "- Write here\n", + "- Write here\n", + "\n", + "\n", + "# Other comments\n", + "- Nearest neighbours: k=100 gave a litte better, but would be too many points because then it compares to a too big part of the data? Not sure, but 50 seems high enough?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data/.DS_Store b/data/.DS_Store new file mode 100644 index 00000000..7b5c1478 Binary files /dev/null and b/data/.DS_Store differ diff --git a/data/base_features.csv b/data/base_features.csv new file mode 100644 index 00000000..5e7b73c1 --- /dev/null +++ b/data/base_features.csv @@ -0,0 +1,2104 @@ +img_id,cancerous,asymmetry_np_centroid,border_contours,color +PAT_1516_1765_530.png,0,0.008073427714407444,1.2763239383319775,31.070525825172293 +PAT_46_881_939.png,1,0.03730112686753273,1.7431275525980137,86.81002159831031 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file. + + Loads images and the corresponding masks based on the given metadata. + Only images with existing masks are used. For each image we extract the following features: + - A1: Mean asymmetry score based on center of image + - A2: Asymmetry score based on centroid of lesion + - B1: Border convexity score + - B2: Border irregularity score + - C: Multicolor rate + + Features that couldn't be computed are stored as NaN and will be seen as an empty slot in the CSV file. + + Args: + meta_data (str): Filename of dataset (e.g. "metadata.csv"). + data_path (str): Root path to the location of the dataset. + + Returns: + None: The code loads the CSV file directly into the given datapath + + Notes: + Code takes 20-30 minutes to run + """ + + def load_image_and_mask(image_id, data_path = data_path): + """ + Loads single skin lesion image and the corresponding mask. + + Args: + image_id (str): Image id taken from the metadata. + data_path (str): Root path to the location of the dataset. + + Returns: + im: np.ndarray of the loaded image + mask: np.ndarray of the loaded mask + """ + img_path = data_path + "imgs_clean/" + mask_path = data_path + "masks/" + + file_im = img_path + image_id + file_mask = (mask_path + image_id).replace(".png", "_mask.png") + im = plt.imread(file_im) + mask = plt.imread(file_mask) + + if mask.ndim == 3: + mask = mask[..., 0] # Converts RGB mask into grayscale, by only taking first channel + + if im.shape[:2] != mask.shape[:2]: + mask = resize(mask, im.shape[:2], anti_aliasing=False) # Resize mask dimension to image if they are different + + return im, mask + + def load_metadata(meta_data, data_path): + """ + Loads the metadata as a pandas dataframe. + + Creates new column, which defines wether a lesion is cancerous or benign in binary values. + + Args: + meta_data (str): Filename of the dataset (e.g. "metadata.csv") + data_path (str): Root path to the location of the dataset. + + Returns: + Dataframe from metadata with column defining cancerous or benign + """ + metadata_path = data_path + meta_data + metadata = pd.read_csv(metadata_path) + + # Labels a cancerous lesion with 1 and a benign lesion with 0 + metadata["cancerous"] = metadata["diagnostic"].isin(["BCC", "MEL", "SCC"]).astype(int) + + # Filter to only rows where the mask file actually exists + mask_path = data_path + "masks/" + metadata = metadata[metadata["img_id"].apply( + lambda x: os.path.exists(mask_path + x.replace(".png", "_mask.png")) + )] + + print(f"Found {len(metadata)} images with masks out of {len(pd.read_csv(data_path + meta_data))} total") + + return metadata + + def return_features(row): + """ + Extracts ABC features for each skin lesion image and the corresponding mask. + Stores the features in a dictionary + + Args: + row (pd.Series): A single row from metadata containing "img_id" and "cancerous" + + Returns: + feats (dict): A dictionary with all extracted features for every skin lesion with a mask + """ + img_id = row["img_id"] + im, mask = load_image_and_mask(img_id, data_path) + + diagnostic = row["cancerous"] + + asymmetry_score_np = asymmetry_np_centroid(mask) #A2 - Centroid (center of lesion) + border_contours = border_irregularity(mask) #B2 - Centroid (Only taking the largest lesion if multiple) + color = get_multicolor_rate2(im , mask) #C - Difference between dominant colors + + + # computing features + feats = { + "img_id": img_id, + "cancerous": diagnostic, + "asymmetry_np_centroid" : asymmetry_score_np, + "border_contours": border_contours, + "color": color, + + } + + return feats + + def make_csv(df , output_dir = "output/"): + """ + Outputs the CSV file with the featues computed for the skin lesions + + Args: + df (pd.DataFrame): The dataframe for the given CSV file + output_dir (str): The path where the final CSV file should be loaded to + + Returns: + The final CSV file with the diagnostic, image id and extracted features + """ + output_path = os.path.join(output_dir, "clean_features.csv") + + results = [] + + # Manual tqdm loop + for _, row in tqdm( + df.iterrows(), + total=len(df), + desc="Extracting features" + ): + + results.append(return_features(row)) + + + # Apply return_features to each row and collect the results as a list and converts the list into a dataframe + features_df = pd.DataFrame(results) + + return features_df.to_csv(output_path, index=False) + + df = load_metadata(meta_data, data_path) + make_csv(df , "data/") + +features_csv("metadata.csv" , data_path = "data/") \ No newline at end of file diff --git a/hair_analysis.py b/hair_analysis.py new file mode 100644 index 00000000..08a39445 --- /dev/null +++ b/hair_analysis.py @@ -0,0 +1,112 @@ +import cv2 +import numpy as np +from pathlib import Path + +# hair detection and removal for light and dark hair +# group into three groups based on amount of coverage + +def hair_coverage(img_gray, kernel_size=9, threshold=10) -> float: + """ + Estimate the fraction of the image covered by hair. + Uses combined blackhat+tophat for complete dark/light hair detection. + """ + + # structuring element shaped as a cross to emphasize thin hair-like structures + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, 3)) + + # only get dark hair to not get light spots with tophat and overdetect coverage + blackhat = cv2.morphologyEx(img_gray, cv2.MORPH_BLACKHAT, kernel) + + # segment the pixels where the difference between closing and original image intensities is bigger than 10 + _, mask = cv2.threshold(blackhat, threshold, 255, cv2.THRESH_BINARY) + + hair_mask = cv2.medianBlur(mask, 3) # reduce small noise + + # compute coverage ratio + total_area = img_gray.shape[0] * img_gray.shape[1] + hair_area = np.count_nonzero(hair_mask) + + return hair_area / total_area + + +def removeHair_auto(img_org, img_gray, lesion_mask=None): + """ + Selects removal parameters based on coverage level + Chooses between light-hair and dark-hair detection + Inpaints detected hair regions + """ + + coverage = hair_coverage(img_gray) + + if coverage < 0.05: + return "skip", None, None, img_org.copy(), coverage, 0, 0 + + if coverage < 0.2: + kernel_size = 9 + black_threshold = 11 + top_threshold = 20 + radius = 3 + else: + kernel_size = 11 + black_threshold = 15 + top_threshold = 25 + radius = 5 + + + # structuring element for hair enhancement + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, 3)) + + # compute morphological responses + blackhat = cv2.morphologyEx(img_gray, cv2.MORPH_BLACKHAT, kernel) + tophat = cv2.morphologyEx(img_gray, cv2.MORPH_TOPHAT, kernel) + + # threshold responses to create binary masks + _, black_mask = cv2.threshold(blackhat, black_threshold, 255, cv2.THRESH_BINARY) + _, top_mask = cv2.threshold(tophat, top_threshold, 255, cv2.THRESH_BINARY) + + + black_score = np.count_nonzero(black_mask) + top_score = np.count_nonzero(top_mask) + + # select the dominant hair color, only choose light mode if it clearly wins + if top_score > 1.2 * black_score: + mode = "light" + response = tophat + mask = top_mask + else: + mode = "dark" + response = blackhat + mask = black_mask + + # inpaint detected hair regions + img_out = cv2.inpaint(img_org, mask, radius, cv2.INPAINT_NS) + + return mode, response, mask, img_out, coverage, black_score, top_score + + +def hair_removal(img_bgr): + """ + Complete hair removal pipeline. + + - Converts image to grayscale + - Estimates hair coverage + - Applies adaptive parameters based on coverage + - Skips processing when hair presence is negligible + - Returns cleaned image. + """ + + # convert to grayscale for morphological processing + img_gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) + + # apply adaptive hair removal + mode, response, mask, img_out, coverage, black_score, top_score = \ + removeHair_auto(img_bgr, img_gray) + + # if skipped, return original image + if mode == "skip": + return img_bgr + + return img_out + + + diff --git a/pen_analysis.py b/pen_analysis.py new file mode 100644 index 00000000..86d74b99 --- /dev/null +++ b/pen_analysis.py @@ -0,0 +1,151 @@ +import cv2 +import numpy as np +import pandas as pd +import os +import matplotlib.pyplot as plt +from pathlib import Path + + +""" +Pen removal file structure: + +- filter_pen_components: shape-based analysis +- detect_pen: color-based pen detection +- has_pen_mark: decision logic +- remove_pen: inpainting +- pen_removal: full preprocessing pipeline +""" + +# Pen detection parameters (HSV) +# catch dark/light blue with different saturation and hue, and black pen marks + +BLUE_LOW_1 = (90, 40, 80) +BLUE_HIGH_1 = (140, 255, 255) + +BLUE_LOW_2 = (85, 30, 60) +BLUE_HIGH_2 = (140, 200, 255) + +BLUE_LOW_3 = (85, 15, 50) +BLUE_HIGH_3 = (145, 120, 200) + + +BLACK_LOW = (0, 0, 0) +BLACK_HIGH = (180, 50, 80) + +FILTER_MIN_AREA = 8 +FILTER_MIN_ASPECT_RATIO = 1.5 +MIN_PEN_PIXELS = 100 +INPAINT_DILATION_ITERATIONS = 3 +INPAINT_RADIUS = 5 + + +def filter_pen_components(mask, min_area=FILTER_MIN_AREA, min_aspect_ratio=FILTER_MIN_ASPECT_RATIO): + """ + Keep only connected components that look stroke-like rather than blob-like. + This helps reject round dark lesion regions that can resemble black pen. + """ + + # label connected components in the binary mask + num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) + clean_mask = np.zeros_like(mask) + + for label_idx in range(1, num_labels): + x, y, w, h, area = stats[label_idx] + + # allow large regions regardless of aspect ratio to preserve thick markers + if area > 300: + clean_mask[labels == label_idx] = 255 + continue + + # reject isolated noise + if area < min_area: + continue + + # distuingish strokes from blobs + aspect_ratio = max(w, h) / max(1, min(w, h)) + if aspect_ratio < min_aspect_ratio: + continue + + # keep only those passing all filters + clean_mask[labels == label_idx] = 255 + + return clean_mask + + +def detect_pen(img_bgr): + """ + Detect blue and black pen marks. + HSV color space. Blue pen is detected by saturation, black pen is detected conservatively and + filtered by component shape to avoid removing dark lesion structures. + """ + # convert from BGR to HSV + hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV) + + # different blue saturation + lower_blue_1 = np.array(BLUE_LOW_1, dtype=np.uint8) + upper_blue_1 = np.array(BLUE_HIGH_1, dtype=np.uint8) + lower_blue_2 = np.array(BLUE_LOW_2, dtype=np.uint8) + upper_blue_2 = np.array(BLUE_HIGH_2, dtype=np.uint8) + + # combined into one with bitwise OR + blue_mask_1 = cv2.inRange(hsv, lower_blue_1, upper_blue_1) + blue_mask_2 = cv2.inRange(hsv, lower_blue_2, upper_blue_2) + blue_mask = cv2.bitwise_or(blue_mask_1, blue_mask_2) + + + blue_mask_3 = cv2.inRange(hsv, + np.array(BLUE_LOW_3, np.uint8), + np.array(BLUE_HIGH_3, np.uint8)) + + # combine all blue + blue_mask = cv2.bitwise_or(blue_mask, blue_mask_3) + + + lower_black = np.array(BLACK_LOW, dtype=np.uint8) + upper_black = np.array(BLACK_HIGH, dtype=np.uint8) + + black_mask = cv2.inRange(hsv, lower_black, upper_black) + black_mask = cv2.medianBlur(black_mask, 3) + black_mask = filter_pen_components(black_mask) # only elongated pen marks + + # final pen mask + pen_mask = cv2.bitwise_or(blue_mask, black_mask) + + return pen_mask, blue_mask, black_mask + + +def has_pen_mark(pen_mask, min_pixels=MIN_PEN_PIXELS) -> bool: + """ + Determine whether an image contains enough pen-like pixels to justify removal. + """ + return np.count_nonzero(pen_mask) >= min_pixels + + +def remove_pen(img_bgr, pen_mask): + """ + Removes detected pen marks with image inpainting. + Pen strokes are often thicker than the raw mask, so the mask is dilated first. + """ + # ellipse matches better + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + + # expand mask with dilate + mask_dilated = cv2.dilate(pen_mask, kernel, iterations=INPAINT_DILATION_ITERATIONS) + clean_bgr = cv2.inpaint(img_bgr, mask_dilated, inpaintRadius=INPAINT_RADIUS, flags=cv2.INPAINT_TELEA) + + return mask_dilated, clean_bgr + + +def pen_removal(img_bgr): + """ + Full pen-mark removal. + Detects pen marks and inpaints if sufficient pen pixels are found. + Returns cleaned image. + """ + pen_mask, _, _ = detect_pen(img_bgr) + + if has_pen_mark(pen_mask): + _, clean_bgr = remove_pen(img_bgr, pen_mask) + return clean_bgr + else: + return img_bgr diff --git a/preprocessing.py b/preprocessing.py new file mode 100644 index 00000000..bdbfa963 --- /dev/null +++ b/preprocessing.py @@ -0,0 +1,35 @@ + +import cv2 +import os +from tqdm import tqdm +import pandas as pd +import numpy as np +from pathlib import Path + + +from hair_analysis import hair_removal +from pen_analysis import pen_removal + +IMG_DIR = Path('data/imgs') +OUTPUT_DIR = 'data/imgs_clean' +os.makedirs(OUTPUT_DIR, exist_ok = True) + + +for filename in tqdm(os.listdir(IMG_DIR)): + input_path = IMG_DIR / filename + output_path = os.path.join(OUTPUT_DIR, filename) + + # read image (BGR) + img = cv2.imread(input_path) + + if img is None: + continue + + # removal pipeline + img_clean = hair_removal(img) + img_clean = pen_removal(img_clean) + + # save cleaned image + cv2.imwrite(str(output_path), img_clean) + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..c28cac33 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,112 @@ +anyio==4.12.1 +appnope==0.1.4 +argon2-cffi==25.1.0 +argon2-cffi-bindings==25.1.0 +arrow==1.4.0 +asttokens==3.0.1 +async-lru==2.2.0 +attrs==25.4.0 +babel==2.18.0 +beautifulsoup4==4.14.3 +bleach==6.3.0 +certifi==2026.2.25 +cffi==2.0.0 +charset-normalizer==3.4.5 +comm==0.2.3 +contourpy==1.3.3 +cycler==0.12.1 +debugpy==1.8.20 +decorator==5.2.1 +defusedxml==0.7.1 +executing==2.2.1 +fastjsonschema==2.21.2 +fonttools==4.62.0 +fqdn==1.5.1 +h11==0.16.0 +httpcore==1.0.9 +httpx==0.28.1 +idna==3.11 +ipykernel==7.2.0 +ipython==9.11.0 +ipython_pygments_lexers==1.1.1 +ipywidgets==8.1.8 +isoduration==20.11.0 +jedi==0.19.2 +Jinja2==3.1.6 +joblib==1.5.3 +json5==0.13.0 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b/src/feature_A.py @@ -0,0 +1,30 @@ +import numpy as np + +# Asymmetri-function - "Is taking all spots into account, so for multiple spots, the asymmetri-score will be inaccurate!!!" +def asymmetry_np_centroid(mask): + ''' + -Input: Mask of a skin lesion + + - Creates numpy arrays, and take average to get center point (centroid) of the lesion. + - Then split and sum the 4 different quadrants. + - And at last, computing the assymetry + + - Returns: Asymmetry of the mask, where 0 is perfect symmetry and 1 and completly asymmetric + ''' + # We use centroid to find the "center of mass", to find the point where to split to 4 quadrants later + y, x = np.where(mask > 0) # Creating 2 numpy arrays with indices where mask > 0 (lesion present) + + # Safeguard: If the mask is empty, or not loaded properly, return 0 + if len(x) == 0 or len(y) == 0: + return np.nan # or return np.nan + + cx, cy = np.mean(x), np.mean(y) # "c" named after the method. Taking the average of x- and y-coordinates which gives the center point (cy = center-y-coordinate) + + # Spliting all mask into 4 quadrants and sum the points + q1 = mask[:int(cy), :int(cx)].sum() # For each quadrant, slicing is used. So, if :int(cy), :int(cx), + q2 = mask[:int(cy), int(cx):].sum() # then everythin up to the center is included, + q3 = mask[int(cy):, :int(cx)].sum() # and int(cy):, int(cx):, then all after the center is included. + q4 = mask[int(cy):, int(cx):].sum() # General ":" before means before that, since it comes before int(cy)(which is the center y-coordinate), vice versa. + + # Asymmetry = difference between opposite quadrants + return max(abs(q1 - q4), abs(q2 - q3)) / (q1 + q2 + q3 + q4 + 1e-6) # Formula for asymmetri \ No newline at end of file diff --git a/src/feature_B.py b/src/feature_B.py new file mode 100644 index 00000000..73ef2197 --- /dev/null +++ b/src/feature_B.py @@ -0,0 +1,43 @@ +import numpy as np +import cv2 + +# Border function - "Is only finding and evaluating the largest spot if multiple!!!" +def border_irregularity(mask): + ''' + * REMARK! only working properly with 1 spot, or if only the main(biggest) is wanted!!! + - Input: Mask of a skin lesion + + - For contours: + "RETR_EXTERNAL" only takes the outermost contour, with ignoring holes inside () + "CHAIN_APPROX_NONE" keeps all contour points for maximum accuracy. + + We then find the larges countour, the border of the lesion, finds the area, finding the compactness and at last finding the irreguality score + + - Output: An irregularity-score from 0(perfect)-1(irregular) + ''' + # First we find the contours(omkridser) + contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # np.uint8 needed for OpenCV + # The way it works is, it finds the boundaries of the spots, where it is has value 1(lesion present) at one points, and at the one next to it has 0(not lesion present). + # the second output "_" is for hierarchy, which is useless with RETR_ETERNAL since it only takes the outermost contour for each spot + + # If no contours found at all, return nan + if not contours: + return np.nan + + # Now we want to find the largers contour, which gives the largest spot if there is multiple (It is easier for the next parts) + largest_contour = max(contours, key=cv2.contourArea) + + # Now we can calculate the perimeter. True is to make sure, start and endpoint, is at the same spot + perimeter = cv2.arcLength(largest_contour, True) + + # And the area of the pixels inside the lesion + area = np.sum(mask>0) + + # With the area, we can calculate the compactness : (Perimier^2) / (4*Pi*Area), with 1 being a perfect circle and over 1 being irregular (up to 5) + compactness = (perimeter * perimeter) / (4 * 3.14159 * area + 1e-6) # 1e-6 is used to avoid division with 0 + + ## DON'T NORMALIZE - changes values such that some become "perfect" and we scale later in classifers ## + # Given lesions can have higher scores than 1, up to 5, we normalize to a interval of [0,1] + #irregularity = min(max((compactness-1)/4,0), 1.0) + + return compactness \ No newline at end of file diff --git a/src/feature_C.py b/src/feature_C.py new file mode 100644 index 00000000..a3b0d734 --- /dev/null +++ b/src/feature_C.py @@ -0,0 +1,128 @@ +# Code taken from exercise session 05_Feature_Extraction + +import cv2 +import numpy as np +from skimage.transform import resize +from sklearn.cluster import KMeans + +def get_com_col(cluster, centroids): + """ + Extracts most common colors from a KMeans clustering result + and visualizes them as a color bar. + + Args: + cluster (KMeans): Fitted KMeans object containing cluster labels. + centroids (np.ndarray): Array of cluster centroid colors. + + Returns: + com_col_list (list): List of most common colors (RGB) + with a presence of > 8% of the picture. + """ + com_col_list = [] + + # Histogram of cluster labels + labels = np.arange(0, len(np.unique(cluster.labels_)) + 1) + (hist, _) = np.histogram(cluster.labels_, bins=labels) + hist = hist.astype("float") + hist /= hist.sum() + + # Blank rectangle for color bar visualization + rect = np.zeros((50, 300, 3), dtype=np.uint8) + + # Sort colors by percentage + colors = sorted( + [(percent, color) for (percent, color) in zip(hist, centroids)], key= lambda x:x[0] + ) + + start = 0 + for percent, color in colors: + if percent > 0.08: # Consider only colors with more than 8% presence in picture + com_col_list.append(color) + end = start + (percent * 300) + cv2.rectangle( + rect, + (int(start), 0), + (int(end), 50), + color.astype("uint8").tolist(), + -1, + ) + start = end + return com_col_list + +def get_multicolor_rate2(im, mask, n=5): + """ + Computes a measure of color diversity using KMeans clustering. + Returns the maximum Euclidean distance between the common colors, with + a higher number being very different colors and a lower number being very similar colors. + + Args: + im (np.ndarray): Input image + mask (np.ndarray): Input mask. + n (int, optional): Number of clusters for KMeans. Default is 5. + + Returns: + np.max(dist_list) (float): Maximum Euclidian distance between the dominant colors. + np.nan: If there are too few pixels or not enough colors. + """ + # Scale down image to speed up computation + im = resize(im, (im.shape[0] // 4, im.shape[1] // 4), anti_aliasing=True) + + # Drop alpha channel if present + if im.shape[2] == 4: + im = im[:, :, :3] + + # Ensure mask is in 2D + if mask.ndim == 3: + mask = mask[..., 0] + + # Downscale mask and ensure binary values + mask = resize( + mask, (mask.shape[0] // 4, mask.shape[1] // 4), anti_aliasing=True + ) > 0.5 + + # Copy image and zero out pixels in image that is outside the mask + im2 = im.copy() + im2[mask == 0] = 0 + + columns = im.shape[0] + rows = im.shape[1] + col_list = [] + + # Collect masked pixel colors + for i in range(columns): + for j in range(rows): + if mask[i][j]: + col_list.append(im2[i][j] * 256) + + # Not enough pixels for KMeans + if len(col_list) < n: + return np.nan + + # Cluster colors using KMeans + cluster = KMeans(n_clusters=n, n_init=10).fit(col_list) + com_col_list = get_com_col(cluster, cluster.cluster_centers_) + + dist_list = [] + m = len(com_col_list) + + # Not enough colors + if m <= 1: + return np.nan + + # Compute pairwise Eucledian distance between most common colors + for i in range(0, m - 1): + j = i + 1 + col_1 = com_col_list[i] + col_2 = com_col_list[j] + dist_list.append( + np.sqrt( + (col_1[0] - col_2[0]) ** 2 + + (col_1[1] - col_2[1]) ** 2 + + (col_1[2] - col_2[2]) ** 2 + ) + ) + + if len(dist_list) == 0: + return np.nan + + return np.max(dist_list) \ No newline at end of file