GraphBased-Features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "025dbde1",
   "metadata": {},
   "source": [
    "## Fraud Detection inotech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3def0510",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import math\n",
    "from datetime import datetime\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "#Plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.pyplot import figure\n",
    "#Preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.manifold import TSNE\n",
    "#Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "#MLP\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.layers import BatchNormalization, Dense, Dropout, Input\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "#Feature selection\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.inspection import permutation_importance\n",
    "#Evaluation\n",
    "from statistics import mean, stdev\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "#Anomaly Detection\n",
    "from sklearn.ensemble import IsolationForest\n",
    "#Time Series\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "#Ignore warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bbe391",
   "metadata": {},
   "source": [
    "## Outline\n",
    "- [Methods](#0)\n",
    "- [Dataset](#1)\n",
    "- [Features](#2)\n",
    "- [Graph Based Features](#3)\n",
    "- [Train the models](#4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d154faf2",
   "metadata": {},
   "source": [
    "<a name=\"0\"></a>\n",
    "**Methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a2a1bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def featureAnalysis(feature,dataset):\n",
    "    a = dataset.describe()\n",
    "    b = a[feature].to_frame().T\n",
    "    fig, ax = plt.subplots(2, 1, figsize=(20, 18))\n",
    "    sns.histplot(x=dataset[feature], data=dataset, kde=True, element=\"step\",ax=ax[0])\n",
    "    sns.boxplot(data=dataset , x = feature ,ax=ax[1])\n",
    "    return(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7404c446",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusionMat(Y_test, y_pred,y_prob,modelName):\n",
    "    cf_matrix = confusion_matrix(Y_test, y_pred)\n",
    "    group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
    "    group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                cf_matrix.flatten()]\n",
    "    group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     cf_matrix.flatten()/np.sum(cf_matrix)]\n",
    "\n",
    "    labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
    "          zip(group_names,group_counts,group_percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "    fpr, tpr, thresh = roc_curve(Y_test, y_prob, pos_label=1)\n",
    "    auc = roc_auc_score(Y_test, y_prob)\n",
    "    \n",
    "    accuracy = accuracy_score(Y_test, y_pred)\n",
    "    f1 = f1_score(Y_test, y_pred)\n",
    "\n",
    "    print(\"\\n\",modelName,\"...................................\\n\") \n",
    "    print(\"Accuracy:\", accuracy)\n",
    "    print(\"F1-Score:\", f1)\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(20, 8))\n",
    "    sns.heatmap(cf_matrix, annot=labels, fmt=\"\", cmap='Blues',ax=ax[0])\n",
    "    plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.legend(loc=4)\n",
    "    plt.show()\n",
    "    print(classification_report(Y_test, y_pred,zero_division=1))\n",
    "    return accuracy, f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1563412c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def KmeansMethod(dataset,minK,maxK): \n",
    "    inertia = []\n",
    "    silhouette_scores = []\n",
    "    silhouette_dict = {}  # Dictionary to store silhouette scores\n",
    "    k_values = range(minK, maxK)  # Test values of k from 2 to 10\n",
    "\n",
    "    for k in k_values:\n",
    "        kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "        kmeans.fit(dataset)\n",
    "        inertia.append(kmeans.inertia_)\n",
    "        silhouette_score_k = silhouette_score(dataset, kmeans.labels_)\n",
    "        silhouette_scores.append(silhouette_score_k)\n",
    "        silhouette_dict[k] = silhouette_score_k\n",
    "        \n",
    "    sorted_scores = sorted(silhouette_dict.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Print all silhouette scores in sorted order\n",
    "    print(\"Silhouette scores (sorted in descending order):\")\n",
    "    for k, score in sorted_scores:\n",
    "        print(f\"Silhouette score for k={k}: {score}\")\n",
    "        \n",
    "    # Plotting inertia and silhouette score\n",
    "    plt.figure(figsize=(20, 6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(k_values, inertia, marker='o')\n",
    "    plt.xlabel('Number of clusters (k)')\n",
    "    plt.ylabel('Inertia')\n",
    "    plt.title('Inertia vs. Number of Clusters')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(k_values, silhouette_scores, marker='o')\n",
    "    plt.xlabel('Number of clusters (k)')\n",
    "    plt.ylabel('Silhouette Score')\n",
    "    plt.title('Silhouette Score vs. Number of Clusters')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431a43ed",
   "metadata": {},
   "source": [
    "<a name=\"1\"></a>\n",
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca186d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/InnoTech-Data/Trainset-Evaluation/Train_Set.csv\")\n",
    "evaluation   = pd.read_csv(\"data/InnoTech-Data/Trainset-Evaluation/Evaluation_Set.csv\")\n",
    "transactions = pd.read_csv(\"data/InnoTech-Data/Transaction/InnoTech_Trans.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564c412d",
   "metadata": {},
   "source": [
    "**Train and evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b8e869b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CARD</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>009e24d27d670b24a73d</td>\n",
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       "      <td>64e272dfcf2db4b4d4af</td>\n",
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       "      <td>77787842a915e581e2b6</td>\n",
       "      <td>2023/04/12 00:00:00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>28185 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                       CARD                 DATE  LABEL\n",
       "0      009e24d27d670b24a73d  2023/05/09 00:00:00    1.0\n",
       "1      009e24d27d670b24a73d  2023/05/03 00:00:00    1.0\n",
       "2      009e24d27d670b24a73d  2023/04/26 00:00:00    1.0\n",
       "3      034ae47f44e914cb7ee6  2023/05/30 00:00:00    1.0\n",
       "4      0b2c8a4c5e9a0b1546ee  2023/03/16 00:00:00    1.0\n",
       "...                     ...                  ...    ...\n",
       "28180  ad0b2864c4544f27c83d  2023/03/06 00:00:00    2.0\n",
       "28181  64e272dfcf2db4b4d4af  2023/05/03 00:00:00    0.0\n",
       "28182  77787842a915e581e2b6  2023/04/12 00:00:00    0.0\n",
       "28183  c618a52c75a225593871  2023/05/05 00:00:00    0.0\n",
       "28184  705da72519aee5e63432  2023/05/05 00:00:00    0.0\n",
       "\n",
       "[28185 rows x 3 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b93b8104",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = train.groupby('CARD')['LABEL'].nunique() != 1\n",
    "result = train[train['CARD'].isin(mask[mask].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b33bb338",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CARD</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LABEL</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CARD, DATE, LABEL]\n",
       "Index: []"
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   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1dc0ccb",
   "metadata": {},
   "source": [
    "The label is not related to date or time it's about each Card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb989768",
   "metadata": {},
   "outputs": [
    {
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       "3235"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train['CARD'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a9094d9",
   "metadata": {},
   "outputs": [
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       "      <th>CARD</th>\n",
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       "      <th>6</th>\n",
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       "      <td>705da72519aee5e63432</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3235 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CARD  LABEL\n",
       "0      009e24d27d670b24a73d    1.0\n",
       "3      034ae47f44e914cb7ee6    1.0\n",
       "4      0b2c8a4c5e9a0b1546ee    1.0\n",
       "5      0cdf22b047d079e06299    1.0\n",
       "6      0cf173146d0cdb62a209    1.0\n",
       "...                     ...    ...\n",
       "28178  1b30d7dd0f8cf938d7dc    0.0\n",
       "28181  64e272dfcf2db4b4d4af    0.0\n",
       "28182  77787842a915e581e2b6    0.0\n",
       "28183  c618a52c75a225593871    0.0\n",
       "28184  705da72519aee5e63432    0.0\n",
       "\n",
       "[3235 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('DATE',axis=1,inplace=True)\n",
    "train = train.drop_duplicates()\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ffe00f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1612\n",
       "2.0     909\n",
       "0.0     714\n",
       "Name: LABEL, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LABEL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fef154eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "5595"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation['CARD'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fe5f471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5595, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation.drop('DATE',axis=1,inplace=True)\n",
    "evaluation = evaluation.drop_duplicates()\n",
    "evaluation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49953aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation['LABEL'] = 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97d70b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cards = train['CARD'].unique().tolist()\n",
    "evaluation_cards = evaluation['CARD'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6bae4bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8830"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels = pd.concat([evaluation, train], ignore_index=True)\n",
    "df_labels= df_labels.drop_duplicates()\n",
    "all_cards = df_labels['CARD'].unique().tolist()\n",
    "len(all_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fdc04814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown    5595\n",
       "1.0        1612\n",
       "2.0         909\n",
       "0.0         714\n",
       "Name: LABEL, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels['LABEL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a499b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df_labels['LABEL'].value_counts()\n",
    "# Plot a pie chart\n",
    "plt.figure(figsize=(20, 12))\n",
    "plt.pie(value_counts, labels=value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Pie Chart of Unique Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "361bdf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set1 = set(evaluation['CARD'].tolist())\n",
    "set2 = set(train['CARD'].tolist())\n",
    "# Find the common elements\n",
    "common_elements = set1.intersection(set2)\n",
    "common_elements_list = list(common_elements)\n",
    "len(common_elements_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c1d166",
   "metadata": {},
   "source": [
    "**Dataset Overview:** <br/>\n",
    "The dataset comprises 3,235 unique credit cards in the training set and 5,595 unique credit cards in the test set, bringing the total to 8,830 credit cards. Notably, these credit cards are not associated with specific dates or any other identifiable information. The objective is to develop a classification model to categorize these credit cards effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78d7ad2",
   "metadata": {},
   "source": [
    "# \n",
    "**Transactions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e5a3f217",
   "metadata": {},
   "outputs": [
    {
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       "      <td>10</td>\n",
       "      <td>2023/03/15 00:00:00</td>\n",
       "      <td>204954592</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>10</td>\n",
       "      <td>2023/03/30 00:00:00</td>\n",
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       "      <td>1000000.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>10</td>\n",
       "      <td>2023/04/10 00:00:00</td>\n",
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       "      <td>1000000.0</td>\n",
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       "<p>3259426 rows × 8 columns</p>\n",
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       "                   Primary_ID  TRX_Type                 Date       Time  \\\n",
       "0        9c2594cdb4153de3f748        10  2023/06/02 00:00:00  203918290   \n",
       "1        a8ae04d3b8f155a154af        10  2023/04/03 00:00:00  191720253   \n",
       "2        739c869136df74852e10        10  2023/04/29 00:00:00  120418648   \n",
       "3        60366904cbdb0d067c20        10  2023/06/02 00:00:00  201139692   \n",
       "4        911abef1ee23db40b108        10  2023/03/22 00:00:00   81928260   \n",
       "...                       ...       ...                  ...        ...   \n",
       "3259421  6f743892923ae636c1ac        10  2023/03/15 00:00:00  204954592   \n",
       "3259422  6f743892923ae636c1ac        10  2023/03/25 00:00:00  203016269   \n",
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       "3259424  6f743892923ae636c1ac        10  2023/04/10 00:00:00  213227613   \n",
       "3259425  6f743892923ae636c1ac        10  2023/04/18 00:00:00  163935352   \n",
       "\n",
       "             Amount  Terminal_Type           Terminal_ID             Second_ID  \n",
       "0        11518977.0              7  d7b0230e4491fbd3563a  3de4bc60f38a51a82365  \n",
       "1         5000000.0              1  9a858e74cc2faefbc12c  b4980da1e29f59c92a53  \n",
       "2         2000000.0              1  7cc4db9e78fb590b06a6  69c4a29dd54b44de457e  \n",
       "3         1000000.0              1  342abaad6ca26a8aea1e  a59ad5aa2a2b5e221572  \n",
       "4           50000.0              7  3381cb32e729571bfdfe  8457468be95f8a197756  \n",
       "...             ...            ...                   ...                   ...  \n",
       "3259421   1000000.0              1  4ff68720afddc796c9fb  42057e17833b83f303f4  \n",
       "3259422   2000000.0              1  4ff68720afddc796c9fb  281b90d9fdcc285cca2e  \n",
       "3259423   1000000.0              1  4ff68720afddc796c9fb  e32ec72f1bb0e39f0ea3  \n",
       "3259424   1000000.0              1  4ff68720afddc796c9fb  071b8737ef9df9a13712  \n",
       "3259425   1000000.0              1  4ff68720afddc796c9fb  375d739376a7d17c7b94  \n",
       "\n",
       "[3259426 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e87de569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3259426, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = transactions.drop_duplicates()\n",
    "transactions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8bd64fc",
   "metadata": {},
   "source": [
    "No Duplicated Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91d3e883",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checkout transaction count for each of the 8830 cards.\n",
    "# Note: This operation may take some time due to the large number of cards.\n",
    "data = []\n",
    "for card in all_cards:\n",
    "    num_transaction = len(transactions[(transactions['Primary_ID'] == card) | (transactions['Second_ID'] == card)])\n",
    "    data.append({'Card': card, 'num_transaction': num_transaction})\n",
    "num_transaction = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "211a2292",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
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       "      <th>8827</th>\n",
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       "      <td>10</td>\n",
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       "      <th>8828</th>\n",
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       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>8829</th>\n",
       "      <td>705da72519aee5e63432</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8830 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Card  num_transaction\n",
       "0     8e408f075b6872605906              379\n",
       "1     2c8ab490002745919e9a               48\n",
       "2     03fe8857991d2994fbf9              472\n",
       "3     a41d87398962952dc6e2              301\n",
       "4     a5a2a8417f671a395c38              961\n",
       "...                    ...              ...\n",
       "8825  1b30d7dd0f8cf938d7dc               58\n",
       "8826  64e272dfcf2db4b4d4af               10\n",
       "8827  77787842a915e581e2b6               10\n",
       "8828  c618a52c75a225593871               12\n",
       "8829  705da72519aee5e63432               16\n",
       "\n",
       "[8830 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "34588caf",
   "metadata": {},
   "outputs": [
    {
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       "                  count        mean          std  min   25%   50%    75%  \\\n",
       "num_transaction  8830.0  370.303624  2374.859204  1.0  33.0  90.0  271.0   \n",
       "\n",
       "                      max  \n",
       "num_transaction  151451.0  "
      ]
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     "execution_count": 26,
     "metadata": {},
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureAnalysis('num_transaction',num_transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dec89d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_transaction.to_csv('num_transaction.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "86c82d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Primary_ID            0\n",
       "TRX_Type              0\n",
       "Date                  0\n",
       "Time                  0\n",
       "Amount                0\n",
       "Terminal_Type         0\n",
       "Terminal_ID           0\n",
       "Second_ID        127382\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7aab862",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dac07872",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Cards: 100%|██████████████████████████████████████████████████████████| 8830/8830 [1:49:28<00:00,  1.34it/s]\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for card in tqdm(all_cards, desc=\"Processing Cards\"):\n",
    "    num_transaction = len(transactions[(transactions['Primary_ID'] == card) | (transactions['Second_ID'] == card)])\n",
    "    data.append({'Card': card, 'num_transaction': num_transaction})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a182d2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_transaction = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "22c693f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_transaction</th>\n",
       "      <td>8830.0</td>\n",
       "      <td>355.877576</td>\n",
       "      <td>2373.031983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>151451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count        mean          std  min   25%   50%    75%  \\\n",
       "num_transaction  8830.0  355.877576  2373.031983  0.0  23.0  78.0  256.0   \n",
       "\n",
       "                      max  \n",
       "num_transaction  151451.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureAnalysis('num_transaction' , num_transaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9430239c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No transaction in  128  Cards !\n",
      "47  in train dataset and  81  in evaluation!\n"
     ]
    }
   ],
   "source": [
    "print(\"No transaction in \" , len(num_transaction[num_transaction['num_transaction'] == 0]['Card'].tolist()), \" Cards !\")\n",
    "# separately for both the training and evaluation sets.\n",
    "count_train = 0\n",
    "count_evaluation = 0\n",
    "for card in num_transaction[num_transaction['num_transaction'] == 0]['Card'].tolist():\n",
    "    if card in evaluation_cards:\n",
    "        count_evaluation = count_evaluation + 1 \n",
    "    elif card in train_cards:\n",
    "        count_train = count_train + 1 \n",
    "print(count_train , \" in train dataset and \" , count_evaluation , \" in evaluation!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae89c71b",
   "metadata": {},
   "source": [
    "We lack data for 47 cards in the training set and 81 cards in the test set. Consequently, these records cannot be utilized for model training and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7a9d232c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique primary ID  1165758\n",
      "Unique Second ID  36937\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique primary ID \" ,transactions['Primary_ID'].nunique())\n",
    "print(\"Unique Second ID \" ,transactions['Second_ID'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9f3f84b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown    5595\n",
       "1.0        1612\n",
       "2.0         909\n",
       "0.0         714\n",
       "Name: LABEL, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation and train dataset \n",
    "df_labels['LABEL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4c29613b",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = dict(zip(df_labels['CARD'], df_labels['LABEL']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2e889a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use them to label transactions based on primary and second id \n",
    "transactions['labelPrimary'] = transactions['Primary_ID'].map(my_dict)\n",
    "transactions['labelSecond']  = transactions['Second_ID'].map(my_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab59a0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Primary_ID             0\n",
       "TRX_Type               0\n",
       "Date                   0\n",
       "Time                   0\n",
       "Amount                 0\n",
       "Terminal_Type          0\n",
       "Terminal_ID            0\n",
       "Second_ID              0\n",
       "labelPrimary     3076150\n",
       "labelSecond        45539\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checkout null values\n",
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6e8e954b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null values of labelPrimary :  0.9821541459826235\n",
      "null values of labelSecond  :  0.014539706338735981\n"
     ]
    }
   ],
   "source": [
    "print('null values of labelPrimary : ',transactions.isnull().sum()['labelPrimary'] / len(transactions))\n",
    "print('null values of labelSecond  : ',transactions.isnull().sum()['labelSecond']   / len(transactions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98645b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Drop null value in labelSecond\n",
    "# transactions.dropna(subset=['labelSecond'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9ba32731",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill null values with no info\n",
    "transactions['labelSecond'] = transactions['labelSecond'].fillna('no_info')\n",
    "transactions['labelPrimary'] = transactions['labelPrimary'].fillna('no_info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6a169735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Primary_ID       0\n",
       "TRX_Type         0\n",
       "Date             0\n",
       "Time             0\n",
       "Amount           0\n",
       "Terminal_Type    0\n",
       "Terminal_ID      0\n",
       "Second_ID        0\n",
       "labelPrimary     0\n",
       "labelSecond      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8d21b9",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b1a27da",
   "metadata": {},
   "source": [
    "<a name=\"2\"></a>\n",
    "## Features\n",
    "- [TRX_Type](#2.1)\n",
    "- [Date](#2.2)\n",
    "- [Time](#2.3)\n",
    "- [Amount](#2.4)\n",
    "- [Terminal_Type](#2.5)\n",
    "- [Terminal_ID](#2.6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1e1835c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3132044 entries, 0 to 3259425\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Dtype  \n",
      "---  ------         -----  \n",
      " 0   Primary_ID     object \n",
      " 1   TRX_Type       int64  \n",
      " 2   Date           object \n",
      " 3   Time           int64  \n",
      " 4   Amount         float64\n",
      " 5   Terminal_Type  int64  \n",
      " 6   Terminal_ID    object \n",
      " 7   Second_ID      object \n",
      " 8   labelPrimary   object \n",
      " 9   labelSecond    object \n",
      "dtypes: float64(1), int64(3), object(6)\n",
      "memory usage: 262.9+ MB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "56d7d3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData = transactions.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd82a56a",
   "metadata": {},
   "source": [
    "<a name=\"2.1\"></a>\n",
    "- TRX_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0511393b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    3132044\n",
       "Name: TRX_Type, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['TRX_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0d574d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#since there's only one unique value\n",
    "encodeData = transactions.drop('TRX_Type',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d960da2",
   "metadata": {},
   "source": [
    "<a name=\"2.2\"></a>\n",
    "- Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d6446198",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Date to time stamp \n",
    "encodeData['timestamp'] = pd.to_datetime(encodeData['Date'])\n",
    "encodeData['year']   = encodeData['timestamp'].dt.year\n",
    "encodeData['month']  = encodeData['timestamp'].dt.month\n",
    "encodeData['day']    = encodeData['timestamp'].dt.day\n",
    "encodeData['hour']   = encodeData['timestamp'].dt.hour\n",
    "encodeData['minute'] = encodeData['timestamp'].dt.minute\n",
    "encodeData['second'] = encodeData['timestamp'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "33a6a623",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(encodeData['hour'].value_counts()[0]/len(encodeData))  \n",
    "print(encodeData['minute'].value_counts()[0]/len(encodeData)) \n",
    "print(encodeData['second'].value_counts()[0]/len(encodeData)) \n",
    "print(encodeData['year'].value_counts()[2023]/len(encodeData))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "36343b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These features cannot be extracted from this column (the time in the Date column is always 00:00:00).\n",
    "# Since all transactions are in the year 2023, we can drop the year column.\n",
    "encodeData.drop(['hour','minute','second','Date','year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d91719fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o51hgJiIiIiIiIrImD4C7lFJZAKYBuE1ExgHIBjBfKZUBYL7/a8sTkTcBrAAwVkT2i8gNInK5iOwHcCaAXBH5r96U+ojINwAcUEp9qTsLEVF3OHQHICIiIiIiIqLjKaWKABT5P68WkQIAQwBcCmCOfzM3gM8B3KshYrcopa7s4KH3QxrEgkQkDsAvAZyvOwsRUXdxBjMRERERERGRxYmIC8AkAKsADPQXn1uK0AM0RqPASAcwEsCXIrIbwFAAX4hImtZURERdwBnMRERERERERBYmIgkA3gVwh1KqSkR0R6IAU0rlo9WJAn+R+XSlVJm2UEREXcQZzEREREREREQWJSJOmMXl15VS7/nvLhaRQf7HBwEo0ZWPeqa9ftS6MxER9ZQoxYVJiYiIiIiIiKxGzKnKbgDlSqk7Wt3/RwCHlFK/F5FsAH2VUj/TFJOIiHo5FpiJiIiIiIiILEhEzgKwBEA+AJ//7l/A7MP8NoDhAPYC+I5SqlxLSCIi6vVYYCYiIiIiIiIiIiKiHmEPZiIiojZEJEZEVovIlyKySUR+qzsTERERERERkRVxBjMREVEb/n6H8UqpGv/COksB/FQptVJzNCIiIiIiIiJLcegOQEREZDXKPPta4//S6b/xjCwRERERUQdEZBiAVwGkwewZ/pxS6nEReQvAWP9mKQAqlFITtYQkoqBggZmIiKgdImIHsA7AaAB/U0qt0hyJiIiIiMjKPADuUkp9ISKJANaJyGdKqe+2bCAifwJQqS0hEQUFezATERG1Qynl9c+sGApgioicqjkSERERdZOIvCQiJSKysdV9b4lInv+2W0TyNEYkihhKqSKl1Bf+z6sBFAAY0vK4vw3dFQDe1JOQiIKFBWYiIqITUEpVAPgcwIV6kxAREVEPvII2/4crpb6rlJroP5H8LoD3NOQiimgi4gIwCUDrqwBnAihWSm3TEoqIgoYFZiIiojZEpL+IpPg/jwVwLoDNWkMRERFRtymlFgMob+8xzqYkCg4RSYB58uYOpVRVq4euBP+9EUUk9mAmIiI63iAAbn8fZhuAt5VS/9aciYiIiAKLsymJAkxEnDCLy68rpd5rdb8DwDcBnKYrGxEFDwvMREREbSilNsC8pI+IiIgiF2dTEgWQ/6qAFwEUKKX+3ObhcwFsVkrtD30yIgo2tsggIiIiIiKiXqXVbMq3dGchiiAzAHwfwNxWC2le7H/se+AJHaKIxRnMRERERERE1NtwNiVRgCmllgKQDh67NrRpiCiUOIOZiIiIiIiIIpKIvAlgBYCxIrJfRG7wP8TZlERERAEiSindGYiIiIiIiIiIiIgoDHEGMxERERERERERERH1CAvMRERERERERERERNQjLDATERERERERERERUY+wwExEREREREREREREPcICMxERERERERERERH1iEN3ACKiSGG4DSeA/gDiAcQCiPF/bP15249t74sBoAA0tbo1tvm6s/urAJQCKMmfl18Z3FdNRERERNQ1f/ruJYkA+gBIafUxBebxczSAKP/HlpsTZt3C7r+1fA4Azej8uLjl1gCgGkAFgMqWj3e99e/qIL1UIqJeRZRSujMQEVmW4TYcAAYASAMw0H9L6+BjHwCiJ2mHmgCUwSw4d+VWnj8vn/8xEBEREdEJ/em7l9hgHgMPbnNLA9AXxxaR+wBIxtHisDVI/JqYlJtHAjgE85j5UKvPDwLYB2C//2PRbc/M9eqKSkRkZSwwE1GvZriNNADpAEb7byNhHhS3FI1TYb2icTB5YR5M7wCwve0tf14+Z3kQERER9QL7s5c4AYwCkAHABWAYgOH+j8M2Hl66a1PFstn6Ep48sfVZHp183fQubu4FUASz2Nxy29/q8z23PTO3OChBiYgsjgVmIop4httIApDlv42FeZA8GmZhOUFjtHBUgo6Lz+U6gxERERFR9+3PXjIEwBj/bWyrz0fiBG0199Vu/nx5yYdzQhAxaMQ+eHF00vdmBXCXlQC2AdgKYEurj5tve2ZufQDHISKyFBaYiShiGG4jGcBEAONwtKCcBWCIxli9yWGYxecCAOv8t/X58/JrtaYiIiIiIuzPXjIYwASYx8sGgEyYEy96NOGivPHg0s8K3WcFLKAGNmf651EJl84JwVA+ALsBfNXqthFA/m3PzG0KwfhEREHFAjMRhSXDbUTDPDie4r+dAXOmRW9qZxEOfDBnbaxrdfuCRWciIiKi4NifvURgHhdP9t8mwSws9wvkOPWemnX/2ve30wK5z1CzRxmLnPHn6Wzz0QSz0Ly21W3jbc/MbdaYiYio21hgJiLLM9yGDeZM5DNwtKA8Huaq0hR+2is6r8+fl1+jNRURERFRGNqfvSQNwFkAzgRwOsyCcmKwx/X6mrf/c8+fRwd7nGByxJy51BF7ptVmYTcA2ACz2LzO/3ETFxgkIitjgZmILMdwG8NxdFbyFACnIQQHyaSVD2aPunUAVgBYkD8vv0BvJCIiImpNRF4CcAmAEqXUqa3uvx3AjwF4AOQqpX6mKWLE889OzoJZUJ7h/zhKRxalVOXbux9J1jF2oDjizl3tiB4/RXeOLqgDkAdgGYDFAJbe9szcCp2BIll7v+tEZCKAZwDEwPxd9yOl1GptIYkshgVmItLOcBtDAJwP4DwAZwNI05uILKIQwAL/bX7+vPy9mvMQERH1aiIyC0ANgFdbFV3OBvBLAF9TSjWKyAClVInOnJFkf/aSaJiTLloKytMB9NUaqpV/7v5zrVc1x+vO0VPO+Mu+tEeNmqA7Rw/4YLbWWBxfc+B/U9c+tDxrc0Gp7lCRooPfdZ8C+ItS6mMRuRjAz5RSczTGJLKUDleEJSIKFsNtxAOYA7OgfD7MWRhEbQ0GcI3/BsNtbAcwH/6ic/68/DKN2YiIiHodpdRiEXG1uftWAL9XSjX6t2Fx+STsz15ig9ni4jz/bQaAaK2hTiDBkVxS2Vw2UneOnhJbXJzuDD1kg9kycHxM42EDwPsFmVmbcHRyxqKszQUVGvOFtQ5+1ykASf7Pk2FOhiEiPxaYiSjo/D2UT8PRgvKZAKK0hqJwNNp/uxmAMtzGBhwtOC9iD2ciIiItxgCYKSK/g9k79m6l1BrNmcJKTk7OEFE4/7rGuefbIOcBSNWdqasSnX0rKpvD95y/SFzYt+EbWLwmAeZC56f6bz8B4CvIzPoCwCcA/g1gddbmAl6+fnLuAPBfEXkUZoF/ut44RNbCAjMRBYXhNkbgaEH5HFjoUj6KCAJzNfQJAO4E4DHcxmqYBef/AFiVPy+fB9FERETB5wDQB8A0mK0c3haRUYq9GDuUk5MTBbPlxYUALgJwqhKgWCq+GqT6hE1xGQCSnKl1ujOcFFtsUucbWZhSnn6H8se284gN5oKPpwP4FYDigsysj2EWmz/N2lxQHcKUkeJWAP+nlHpXRK4A8CKAczVnIrIMFpiJKCAMtyEwD5S/DfNgeYzeRNTLOGDOIpgO4D4ARYbb+BeAD2C202jSmI2IiCiS7Qfwnr+gvFpEfAD6AWA/2FZycnISAFwM4Jv+j8fNnN3sKCwd1Nwn1NFOSmJU32bdGU5Ck4gzXFtkAADs3oYtDm/jKV3YdCCAa/23poLMrMUwi80fZW0u2Bm8hBFlHoCf+j9/B8ALGrMQWQ4LzETUY/6i8gwAVwD4FsyeuURWMAhmK42bAVQZbuNjmMXm/+TPy6/SGYyIiCjCfABgLoDPRWQMzDZo4dszIYBycnJSAXwDZlH5XAAxJ9p+v60sJQSxAirB0cemO0PPSSWA/rpTnIzkyp09+bcWBfP9eC6AxwoyszbDX2wGsDRrc4EvgBEjSSGA2QA+h/k7b5vWNEQWwwIzEXVLq6Lyd2AWlYfoTUTUqSQA3/Xfmgy38RmAtwF8mD8vv1JrMiIiojAiIm/CXKi5n4jsB/AbAC8BeElENgJoAjCvN7fHyMnJGQrgMphF5VkA7F19biM845rgqY6CI2z6Asc6Ek5YNLc2WxXCvMCcVrwmPgC7yfTf7obZSuNdAG+hFxebO/hd90MAj4uIA2a/+Zv0JSSyHunF//cTURf5i8rTcXSmMovKFAmaAHwK8xI3FpuJiIioR3JycoYAuBLmBIwzYK4V0SMzm7NWj/UOnhKobMHm8TVtfnfPXzJ15+gRic6PSbnN0B2jx5Tyzlp6V53D2xisExKFAFqKzcu5SCARnQhnMBNRu/xF5TNxtKg8VG8iooCLAnCJ/9ZkuI1PAfwDwHv58/LrtSYjIiIiS8vJyUmGufbI1TAvmw9Iq4it9sKGsd7w6TpnF0dYLUp4LGdYH+/ZvY1bHN7GcUEcYjCA2/233QWZWW8CeD1rc8GmII5JRGGKBWYiOobhNgwA18GcgcGiMvUWrYvNlYbbeAPAC/nz8r/QG4uIiIisIicnxwFzgb7vA/g6gOhAj1EqVSMCvc/gkn422Jp88EXpTtJdItHhvEAhkit3hHIhTReAnwP4eUFm1gYAbwB4I2tzwb4QZiAiC2OBmYhguI04mP1pbwYwVXMcIt2SAdwK4FbDbawH8CKA1/Pn5VdoTUVERERa5OTkTAQwD8BVAAYEcyyfqBEVUrs3RcUPD+Y4gSIiEudILqnxHA6/iSm2WK/uCCdjYMnaQPRf7onx/ttDBZlZnwJ4HsC/sjYXeDTlISILYA9mol7McBvjYRaVr4ZZVCOi9jXA7EH3IoDP8+fl8z9PIiKiCJaTk5ME4AcwF/YaH8qxDc/wJVM9GTNDOebJWHzwnQ1F9TtD+j0KBJtzzOdRCZfM0Z2jR4Lff7m7DgJ4BcALWZsLdmjOQkQacAYzUS/jn638PZir3nK2MlHXxMA8EXM1gB2G23gJwCv58/IL9cYiIiKiQMrJyZkE80qmqwBomSG6y1YSNRUZOobukURn35qi+p26Y3Sb2OJ6vBijbiHov9xdaQCyAdxbkJm1AOas5vezNhc06Y1FRKHCAjNRL2G4jQk4Ols5SXMconCWDuB3AO433MbHAF4AkJs/L5+XBRIREYWhnJycGJjt4m6FBSZg1EhDlg8+jw22sPh7PcmZGpa9jEUSwuL7257kqp2h7L/cHQLgHP+trCAzyw3g+azNBVv0xiKiYAvbX6hE1DnDbcTj6GzlKZrjEEUaO44uDHjQcBuvAHgyf17+Aa2piIiIqEtycnIyANwC4FoAffWmaUWQdMBWvmGYr19YtJ1IcPYJy5nAYosPu4UJWwwsXhunO0MX9ANwF4C7CjKzlgB4CsA/2auZKDKxwEwUgQy3MQbAHeBsZaJQabks8C7DbbwB4NH8efkbNWciIiKiNnJycmwAvg7gxzBnWVqyOLrZXnh4mK+f7hhdEudICs9CrS0+RneEHlHK27/sy7G6Y3TTTP/tjwWZWX8D8FzW5oJyzZmIKIBYYCaKIIbbOANmkesyADa9aYh6JSfMVebnGW7jEwB/zJ+Xv0BzJiIiol4vJycnDuZM5TsA6zc4LrSVh0d1GUC0PTZBd4aeEInT0mP7ZNm9jVsd3oYs3Tl6aCiAhwHcV5CZ9SqAx9g+gygysMBMFAEMt3E+gHsBzNWdhYiOuBDAhYbbWAfgUQDv5M/L92rORERE1Kv87nf3Dmhujr0dZn/lVN15uqoZ3qwGNB2OQVQf3Vk645CosPm+tia2uETdGXoiuWpXCYBwLTC3iANwS3kCTjPcRjHMSRmLdYciop5jgZkoTBluwwbg2zALy5M1xyGijp0G4E0ADxtu4zEAL+TPy6/VG4mIiCiyzV+QPhrAPWdMcZy5fNmVhu483Sawbbcf3Hyqd/iZuqN0RiD9BeJVUHbdWbpFYpN1R+iJgSVrwqH/cpe8dL7NAf+aJobbWAXgjwDez5+X79ObjIi6i5fQE4UZw21EG27jJgBbALwFFpeJwoULwGMA9hlu43eG2xioNw4REVHkmb8g/fT5C9LfgXmsfJPd7jH69d+9TneunthmPxgWVz6JiCPWnlCqO0c31YrYw693tFK+fqUbxuiOEQgNTmxZPdY2qdVdUwH8E8AWw23cYriN8Pv5EPViLDAThQnDbSQZbuNeALsBPAtgtN5ERNRDfQD8AsAew208b7iNcFukhYiIyHLmL0ifMX9B+mcA1sC8yu/I37rp6WssuZBfZ8qlOl13hq5KcPY5pDtD90iV7gQ9Yfc2bnV668Ny5nVb786wlXXw0GgATwPYzkIzUfhggZnI4gy3MdBwGw8D2Avg9wDSNEciosCIBnAjgALDbbxnuI1xugMRERGFm/kL0qfNX5D+KYClAM5tb5uoqIbJiUklm0Ob7OQpwaAyqd6hO0dXJDr7VuvO0D32Gt0JeiKpanex7gyB4BUU/XuKTOlks2EwC83bDLdxs+E2nCGIRkQ9xAIzkUUZbiPNcBt/gzljORtARJypJqLjCIDLAeQbbsNtuI0RugMRERFZ3fwF6WfMX5D+HwArAJzX2fZjMlaG2Qxb0xZ74X7dGboiyZnapDtDt4gjLNfDiJT+y4sN2eq1S1cLxsMBPAOz0PxDFpqJrIkFZiKL8bfCeBDAdgA/AhCjORIRhYYNwA8AbDXcxhOG2xigOxAREZHVzF+QPmn+gvSPAKwGcFFXnxcbVzk1JqY6LIq1re2xl8TrztAVic6+SneG7hCJatSdoduU8vUv/TLs+y8roObVubaJPXjqCADPwTxWvtFwG47AJiOik8ECM5FFGG4jynAbPwWwA8AvAYTFwSQRBVwUgNsB7DDcxgOG2+DVC0RE1OvNX5A+Yf6C9PcBfAHgku4+XwSOjDErwqLdRGt1aBrnhc/yxdB4R1J4zSqV6GbdEbrL5muKiP7LXw3DutpYOZnX4QLwPMxC8/UsNBNZAwvMRJoZbkMMt3E1gM0AHgPQT28iIrKIBAC/ArDTcBv3GG4jVncgIiIKPhGJEZHVIvKliGwSkd/67/+O/2ufiJyuO2eozF+QPnr+gvS3AawHcNnJ7Cs5ufh0h6OxIhC5QkYQt9dW+pXuGJ2JtseH1eQYkViv7gzdlVy1K+z7LyvA++zF9lEB2t1IAC8C2Gy4jSsNtxGWi3kSRQoWmIk0MtzGeQDWAXgN5n+QRERt9QXwCMy+czdxlgYRUcRrBDBXKTUBwEQAF4rINAAbAXwTwGKN2UJm/oL0fvMXpP8VwFcAvgNzzYKTIoL4Uelr8k52P6G2xV5YqTtDZ5y2qD66M3SLLazq4QCAgcVrw36yQXEKVh/sK8MCvNt0AG8AWGW4jVkB3jcRdRELzEQaGG5jsuE2PgPwKYBJuvMQUVgYAuBZAF8ZbuN7nKVBRBSZlKnG/6XTf1NKqQKl1BaN0UJi/oL02PkL0n8Bs23cj2G+/oAZMGDXOBGv5VtOtFZkqxisO0NnBLaBujN0h0h8eB1HKeXrX5Y3VneMk/X8hbbEIO7+DACLDLfxoeE2MoM4DhG1gwVmohAy3MYow228CWAtgHN15yGisJQB4E0AXxhuo8uLGxERUfgQEbuI5AEoAfCZUmqV5khBN39BusxfkH4NgC0AfgcgKRjjiGDA8OH5q4Ox72DxwpdRi8YS3TlORESio21xh3Tn6CqxxYfVFWE2X9M2pye8+y/XRmNj/kjbqSEY6hsA8g238ZThNth+kihEWGAmCgHDbfQ33MYTAAoAfA8BuMSPiHq9iQD+Y7iNjwy3wRY7REQRRCnlVUpNBDAUwBQRCUVRRpv5C9KnA1gJ4O8AAn35/HGGDts0BFAq2OMEjEC22Qu36Y7RmQRnSpnuDF0ltvho3Rm6I7lq90HdGU7Wm7NtNZ1vFTAOALcC2G64jbsMtxFei1AShSEWmImCyHAbdsNt3AFgO4DbAUTpTUREEegSAJsMt/Frw22E1R9LRER0YkqpCgCfA7hQb5LgmL8gfcT8BelvAVgGYEqoxrXZfKMGpm1fE6rxAmG73frruyU6+1bpztBVIvFh1c94YEl491/22LD3s8kSsn/jrSQDeBTmsfKlGsYPKRF5SURKRGRjq/smiMgKEckXkY9EJChXhxCxwEwUJIbbmAJgDYC/IEiX+BER+cUC+C2AjYbbiMgiBBFRbyEi/UUkxf95LMy2apu1hgqw+QvSnfMXpP8c5gJ+V+jIMHLkFzE6xu2pCqkdqyw+6zrJmdqgO0OX2eLCZ5U/pXz9S/PG6I5xMj6dLLuUiM76UwaADwy38ZnhNsK+l/UJvILjT0i+ACBbKWUAeB/APaEORb0DC8xEAWa4jWTDbTwFYAW4gB8RhdZoAB8bbuNdw20E/RJjIiIKikEAForIBpiTFT5TSv1bRC4Xkf0AzgSQKyL/1Zqyh+YvSD8bwJcAHgIQpyuH09k0PiWlMF/X+N0m6FcslZZe5DHR2denO0NXicSGzQQgm69pu9NTl6I7R08poOLN2bbTdefwOxfABsNt3G+4jbA6ydQVSqnFAMrb3D0WwGL/558B+FZIQ1GvwQIzUQAZbuMqmAuT3Ar++yIifb4JoMBwG9nsOUdEFF6UUhuUUpOUUuOVUqcqpe733/++UmqoUipaKTVQKXWB7qzdMX9B+sD5C9JfA7AAQJbuPACQkbGqVneG7tjiKLR0H954R7Jdd4YuUpDYsFkwL6lqj6V/7p35Il3yGqPESjPGowDcB3MhwHN1hwmBjTAXPgSA7yAEfe6pd2IBjCgADLeRYbiNzwC8DmCg7jxERADiATwMc5bGXN1hiIiod5q/IN02f0H6bTAnYVytO09r0TE1U+LiKnbrztFVe21lKboznEisI0HbjPRuqhK97Rq6ZWDJ2rCdaauA5hcutGXqztGB0QA+M9zG64bbiOS/4a8HcJuIrAOQCKBJcx6KUGHzS5XIigy3EW24jRwA+TAvtyEisppMAPMNt/Gm4TYG6w5DRES9x/wF6acDWA3gSZiLbVmKCGwZY1bs1Z2jqxrRPK4ZHsvOunbaolN0Z+gaW7XuBF2mlBpQmpehO0ZP7euP1YeSJE13jk5cBWCz4TZuMdyG6A4TaEqpzUqp85VSpwF4E8AO3ZkoMrHATNRD/stp8gH8BkC05jhERJ35HsyD5zsNt+HQHYaIiCLX/AXpSfMXpP8NwCoAp+nOcyKJiWVTnM76Ut05ukQQtcte8pXuGB2xwT5Ad4ausYdNgdnma97m9NT20Z2jp565yN5Pd4YuSgHwNIDlhtsYrzlLQInIAP9HG4BfAXhGbyKKVCwwE3WT4TYGGm7jDZgN8sP2bDIR9UqJAP4EYJ3hNiboDkNERJFn/oL0iwFsAvAjhMHfmyKIGZ2xapPuHF211V5UpztDR0QkwWmLrtKdo1PiaNAdoauSqneHbf/lyjh8sX2IjNWdo5umwTxO/qPhNqzUN7pLRORNACsAjBWR/SJyA4ArRWQrgM0ACgG8rDMjRS7OYCLqIv/lMrfA7GlquUv8iIi6YTyANYbb+C2A3+fPy/fqDkREROFt/oL0PgAeA/ADzVG6LTV13wSbrbnW53NavqBUIpXDdWc4kXhHcmlFU0mS7hwnIhIdNgXmgcXrwvZK2VfPsfl0Z+ghB4C7AXzbcBvX58/LX6g7UFcppa7s4KHHQxqEeiXLn1EmsgLDbQyDOWP5KbC4TESRwQngQQBLDbfBqzGIiKjHnl5w8UUwZy2HXXEZAETQxzUyb63uHF3hEzWySur2687RkURn30rdGTolMR7dEbpEKdW/bH1YHqM12bFjySli6fY4XeCCuY7Jk+E4m5ko1FhgJuqE4TZ+ALPX8jm6sxARBcE0AHmG27gtEhc2ISKi4ElbmJectjDP/SDuf6YB0ZaetdqZQYO2pAO+sLiiZ4u9cJfuDB1JcqZatoVHC5HYsJhZa/M1b49qru2rO0dPfDRViiASCceVAuA2AF8abuMs3WGIrIwFZjppIpIiIv8Ukc0iUiAiZ+rOFAiG2+hvuI33ALjBWctEFNniADwJ4L+G2xiqOwwREVlf2sK8c2BOwviBVxzDH8GvwmIGcEdsNjV08JAtq3Tn6IqdthKn7gwdSXKmWr5IL7Y4pTtDVyRV7ynSnaEnfILSd2fYztCdI8DSASz6+vOZv0ZOcozuMERWxAIzBcLjAD5RSmUCmACgQHOek2a4jUsBbARwue4sREQhdB6AfMNtfF93ECIisqa0hXmxaQvz/gqzfdywlvu3IGvWOpyRpy1YALhceX10Z+iKaqnP8kFZspAb70y2fo3BFm/XHaErBpSEZ//lFZmyyeOQsMx+Qko1/qmk7FoA65CTPEl3HCKrsf4vf7I0EUkCMAvAiwCglGpSSlVoDXUSCjKzEj6ZmfUHKPU+gAG68xARaZAC4FXDbbxruI1+usMQEZF1pC3MmwogD8CPYV46fpSIPI67UxsQXashWkDY7Z6s1NS9ebpzdEqQXGgr/0p3jPbE2hMtP7tTJMGyM8CPUEoNKP0i7PovK6D+5fNshu4cwXBVVc3qMc3NIwGMA7AKOck/R04YnFAhChH+Y6CTNQpAKYCXRWS9iLwgImHZAL8gM+sMAOtHlOJn1/7Pt1h3HiIizb4JYKPhNr6hOwgREemVtjDPmbYw73cAlgEY09F2XnEM+wPuWxe6ZIGXPnp1WCwAt8VeWK47Q3uibDGWby0otnjLz64N1/7L2wdjTVW8pOrOEWh9vN712eWHZ7W6ywngIQCLkZM8UlMsIkthgZlOlgPAZABPK6UmAagFkK03UvcUZGbZCjKzsmEeMI8GgIvWqikjitUOvcmIiLQbCOBDw228bLiNsF68iYiIeiZtYd5YAKsB/AJAp60FtiJzZji3yoiOrj89IaFsm+4cnTlgK7dk8dEujv66M3RGbPGxujN0JrF6b9j1X1aAeuYi+7DOtwwzSlW9UXhwgLS9asM0A8CXyEm+KtSxiKyGBWY6WfsB7FdKtSyI8U+YBeewUJCZNRTAfAAPwzwLCQAQIPaBv3ub7V7VrC0cEZF1XAtgg+E2ImIRVyIi6pq0hXlXAlgLYGKXnxQBrTLGjFlRrDtDZ5rgGdeI5krdOdoSkT42sTfoznFCEpeoO0JnBpasjdKdobsOJWLtvgEScbN5f1RRuWGoxzvkBJskAngdOcnPcQFA6s1YYKaTopQ6CGCfiIz133UOAEv2A2urIDPrmwC+BDCnvcdjmpF597u+ZSENRURkXSMALDLcxk90ByEiouBKW5gXnbYw7xkAbwBI6O7zw71VRlx8xdTo6BprzyAV2HfYiy25uHq8I6VEd4YTEVustQvMSqkBpevDrv/yS+fbrN/bupsGeTyrb62oOquLm/8QwErkJIfdz44oEFhgpkC4HcDrIrIB5uyGh/TGObGCzKyYgsysZwG8C+CEl5ZN3qFmTdru+zI0yYiILM8J4HHDbbxpuI2w7LdPREQnlrYwLx3ACgA3n8x+tiJz5lpMWR+YVKElAmdGxsqtunN0Zqu90JJXWyY6+xzWneEEPCLRlm77ZfM174hqrgmrPsYNThSsHWObqDtHIIlSh14vPNjdGdkTAKxDTvJ3g5GJyMpYYKaTppTKU0qdrpQar5S6TCll2QOKgsysYQCWAripK9sLYLvnXV9qbIOqCm4yIqKw8j0Aqw23kak7CBERBU7awrxvAlgHYNJJ70xEnsBd/eoRE5atMlL6FE2y25ss14KitUNSY8l2BEnOVCv/zC39MwWAxOq9hbozdNc7Z9ksuejkyfjFocPb+nt9PekpngjgH8hJfho5yZZfUJIoUFhgpl6jIDNrNswD5tO68zyHD0Pvf827ITipiIjC1jiYRebv6A5CREQnJ21hnjNtYd5jMK/wSw7UfsO5VYYIkkalr7X0DGwlami51OzSnaOtJGeqJWdWm2zVuhN0ZmDJurDqv+wVFOZOkam6cwRSelPT8u9V10w7yd3cAmAFcpLTA5GJyOpYYKZeoSAz63YA/wPQo1WNR5TirAvW+lYENhURUdhLBPC24Tb+YrgNh+4wRETUfWkL84YBWALgp8HY/zaMDdtWGQMH7swU8TbpznEiW+wH9unO0FaCM0V3hBNwWHl2NQBgQOn60bozdMfC8bLNZ5OIOQ60KVXkLio+JUC7mwTgC+QkfztA+yOyLBaYKaIVZGZFF2RmvQzgCQAn9Z/e9Z/5MlOr1MHAJCMiiih3AFhouI3BuoMQEVHXpS3MmwPzCr/gzT4UkcdxV/96xNQEbYwgEVFpQ4dtWq07x4nstpfG6s7QVqw9MUZ3hg6Js153hBOxeZt3RDVX99Odo6sUUP3a3Mjqvfz70kOFyT4VsCs5ACQBeAc5yU8iJzmsZqcTdQcLzBSxCjKzhgBYDODaQOxPgD4Pv+I9AKVUIPZHRBRhzgLwheE25ugOQkREnUtbmHcHgM/Qwyv8usMnjqF/wH1fBHucYBg2bGMaYN3j/1o0jvPCZ6lZ1tH22ETdGToiEm2p71VbiTV7D+jO0B2bRsgXdTESyGKsVhMaGhdfVFvXrZaa3XAbgM+Rk5wWpP0TacUCM0Wkgsyss2DOxpgSyP2m1OK06z7zLQ7kPomIIshAAP8z3MbPdAchIuoJEXlJREpEZGOr+3JE5ICI5PlvF+vMeLLSFubFpi3Mew3AX3CSV/h1R7i2yrDbvaMHDNi1VneODgni99nKNumO0ZpDnNadgSsxHt0RTmRAGPVfVoD32QttEdNf2KHUnucOlgSruNziTABrkJN8epDHIQo5Fpgp4hRkZt0KYAHMQkfAXbhOTXUdVDuCsW8ioghgB/AHw228Z7iNJN1hiIi66RUAF7Zz/1+UUhP9t/+EOFPApC3McwFYBuDqkA8exq0yRqWvderOcCJb7IVVujMcS/oJxJKFXLHF+XRnOJEBJV+ETcG2qA9WF/eVobpzBIRSvieLS6vilIoPwWhDASxBTnLofw8TBRELzBQxCjKzogoys54H8BSAoB0EChBz/2veZodXWfryKiIizS4HsNZwG2N0ByEi6iql1GIA5bpzBEPawrxzAayFueiUFmarjF+HXasMp7NxYnLywa905+hIke1wUCbW9JSI2OIcSSW6c7RHJE50Z+iIzde8I7q5OugtawLl+YtsETORYGZ9w+IZ9Q1GCIeMAfAacpIfRU4y63IUEfhGpohQkJk1GMAiADeGYryYZmTe/a5veSjGIiIKYxkAlhtuY4buIEREJ+nHIrLB30Kjj+4w3ZW2MO9uAJ8ASNWdZRvGzFyDqWHXKiNjzMpK3Rk64oFvbB0aS3XnaC3B2ceSJ2rEFm/XnaEjCdX7wqb/ck0MNmwaYTtFd45AiPb5tj1WXDpd0/B3AfgIOcmW7VtO1FUsMFPYK8jMmg5zNsa0UI47aYeaNXmb78tQjklEFIZSYfZlvkJ3ECKiHnoaQDqAiQCKAPxJa5puSFuY50hbmPcCgD/CbGGkn4g8gTvDrlVGTEz1lNjYyr26c7RLINvsB7fqjtFakjO1WneGdtkSLNvuZGDJOstma+uNObZ63RkCQqmmlw6W+KIAnb2vLwawHDnJLo0ZiE4aC8wU1goys24CsBDAoFCPLYDtnvd8qbENymI9z4iILCcGwD+4+B8RhSOlVLFSyquU8gF4HgFeRDpY0hbmJQD4CMANurO05RPH0N+HWasMEdgzxqzYrTtHR7bbi3RHOEaSs68l2wmKxMfqztCRAaVfjNadoSs8NuyZP1HO0J0jEC6prVs+vrFprO4cAE4FsBo5ybpmUhOdNBaYKSwVZGbZCjKzHgfwLDSebbT7MPSB17z5usYnIgojAnPxv6cMt2GNWXRERF0gIq0nMlwOYKOuLF2VtjBvEIDFaH/BQkvYjjEzV2NaWBWZk5JKT3c4GizZ+qFCajMUlNKdo0WCs6/uCO0SmzULzOJr3hndVBUW/Zc/OU32KJGwryUl+HwbHyw9NFN3jlb6A1iAnOTv6w5C1BNh/0uBep+CzKxoAP8A8BPdWQBgeClmXLjWt0J3DiKiMHErgA8NtxGKVbqJiLpFRN4EsALAWBHZLyI3AHhERPJFZAOAswH8n9aQnUhbmDcOwEpoXMyvS0Tkr7hzQDi1yhBB3OjRqzfoztEeJRhQKlXbdOdoEe9I0tlyoENii03QnaE9iWHSf1kBh/8x23a67hwnTam6vxcWx9ut0jroqGgAryIn+Ze6gxB1FwvMFFYKMrOSAfwXwHd0Z2ntus98mamVylrXpRERWdfXACw23EbI2xsREZ2IUupKpdQgpZRTKTVUKfWiUur7SilDKTVeKfUNpax7zJe2MG8OgGUAhmuO0iU+sYddq4x+/fecarN5LNl/dou9sFB3hhbRtjhLFnIhsSm6I7RnYOkXDt0ZumJthnzZ5JQ43TlO1jVV1WtGNzeP1J3jBB5ETvJfkZPMmh2FDb5ZKWwUZGYNBrAEwGzdWdoSoM/Dr3iLoKxzWRoRkcVNBrDScBsRsQI5EZFuaQvzroQ5ESNFc5RuCbdWGSLoN2LEl2t052jPHntpsu4MLRw2Zx/dGdrRKOK0ZIuMASXrLN9/WQFNL55vy9Kd42T19Xq/+Fl5xSzdObrgxwDeRE6yJa8GIGqLBWYKCwWZWVkwL1c0dGfpSEodJl/3mW+x7hxERGFkOIBlhtuYqzsIEVE4G/LikpsBvA6Na5P02JFWGbHVuqN01eAhm0cAyqc7R1sNaB7XDG+d7hwAILANBGCxyTdSqTtBe8TXvCsc+i/vGYA15UkyUHeOk6JU5RuFB9PEXBskHFwB4D/ISU7UHYSoMywwk+UVZGZNB7AUYXCp34Xr1NSRB9V23TmIiMJIMoBPDLfBBU2IiLrJlZ0rruzcRx3bqp6y761ZpTtPT/nEPvRh/Hq97hxdZbP5RgwatHW17hzHEUTvsZV+pTsGAIiIM8aeUKY7x7FsljyJkVizf7/uDF3x7EV2yxfBO/Pjw5Ubh3i8g3Xn6KZzACxCTnJ4F/cp4rHATJZWkJl1CYD/AbDmMsRtCBDz29e8XodXNenOQkQURpwAXjXcxn26gxARhQtXdq4DwEsA7hLA5iionGQrqc/THKvHdsiYWatwZti0ynCNXG/JGYVb7IW1ujO0SHSkWKvALA7LfG9aG1Bi/f7LFfFYt2OwjNGd42QMbvasurmyaobuHD00CcAy5CSn6w5C1BEWmMmyCjKzrgLwPgBL9snqSEwzxt7zT99y3TmIiMLQ/Ybb+JvhNsLlskUiIi1c2bkxAN4FcG3LfQJEO9eXj5TKpm3agp2kJ/F/A8OlVYbD0XxKn777v9Sdo60SW+VQ3RlaJEalWuxnGdWoO0F7Bpass3zR0H2OzWLtTrpHlCp7veig5b/PnUiHWWSepDsIUXtYYCZLKsjMuhXA3wFY/mxueybuVLMmb/NZ7oCTiCgM/AjAi4bb4DEKEVE7XNm5STAX8/tG28cESI5aWZoodZ4DoU928nxiH/IQfpOnO0dXZYxeZbmrFr3iS69GfZHuHACQ5OzboDtDa2KLtlyBWXyeXdFNlQN05ziRRge2LTvFdrruHCfjV4cOb+/n9fXTnSMABsJsl3GO7iBEbfGPN7KcgsysnwN4CmH8/hTAds97vtS4BmXJhSSIiCzuOgCvG24jLE8yEhEFiys71ywuALM62kaAtKhlxY1o8h4OXbLA2SkZM1di+jrdOboiKrru9Pj48h26c7S11VFoiTVhEp19LTXrVSTGqztDW+HQf/nDabZi3RlORkZT07Irqmum6c4RQIkwF/77tu4gRK2FbQGPIlNBZtbvATykO0cg2H0Y+sDfvRt15yAiClPfA/BPw21E6Q5CRGQFruxcF8yFryd2tq34MCp6SfF+eFV9sHMFw99wR1o4tMoQgYwZs6JQd462dtiKLXGCNt6RbIkcR0ic7gTHGVCyzq47w4n4BCUfTJcpunP0lF2pwleKik/VnSMIogD8AznJV+sO0h0i8pKIlIjIxlb39RWRz0Rkm/9jH50ZqedYYCZLKMjMshVkZj0D4F7dWQJpWBlmXLTGt0J3DiKiMHUpgH8ZbiOsevETEQWaKzs3A8AyAKO7+hzxKCN6aXE+lLLcrM3OhFOrjPiE8qlRUXWWmuFZJfWZCsqnO0eMPT5ed4bWxGapOACAASVfWLov8PIs+cpjl/A82a+UeqSk7GCSTyXrjhIkdgCvIid5nu4g3fAKgAvb3JcNYL5SKgPAfP/XFIZYYCbtCjKz7ABeA3Cz7izBcO3/fFmplcoSfdCIiMLQBQA+NtxGgu4gREQ6uLJzRwNYCGBwd58rDd4pUStLw3Lx6XBplSGCqNEZKwt05ziGoE+R7bD2TA5bdIruDK2JLd5SM6rF59kd01QxUHeOjiig7uXzbON15+ipSY2NS86vq5+sO0eQ2QC8jJzkH+oO0hVKqcUAytvcfSkAt/9zN4DLQpmJAocFZtKqIDPLBvMs1pWaowSNACm/f8VbBKUs1YOMiCiMzAbwHxaZiai3cWXnpsMsLg/p6T5sVc0znesPLQpcqtD5G+5Iq0Ncle4cnenb98Aku725RneO1jbbC0t1Z7DBZqnF60TiLTUTN6Fm/z7dGU5k6xCsrY6Tvrpz9IRTqd3PHiwN64UJu0EAPIuc5B/pDtJDA5UyJ+T5P1rq9wZ1HQvMpNvTAK7RHSLYkusw+fpPfYt15yAiCmMzwZnMRNSLuLJzR8IsLg892X3ZSxpmO7ZUht2xqE/sQx7Gb77UnaMzIkh2jfzCUrOt99sOperOICJxUbaYCt05jrAlxOiO0NqA0i8s239ZAb5nL7IP152jR5TyPnWwpCZWKes13Q4SpeC7v/maq13ZuT/WnYV6LxaYSZuCzKy/ALhJd45QueALNXVUkdqmOwcRURg7CywyE1Ev4MrOHQGzuDwsUPu07645y76vdmWg9hcqO2X0zBWYYanibXsGDdqWAfg8unO0aIInqwke7bO/Exx9tM+kbiESZ6njh4El60bpztCRsiSs2d9fXLpz9MTs+vol0xoaI3Fhv3YpBe9DnqtXveS9eDqAv7qyc8NtJnOxiAwCAP/HEs15qIdYYCYtCjKzHgBwh+4coSRAzG9f8yqHVzXpzkJEFMZYZCaiiObKzh0O4HMAIwK5XwFsjq8qJtpKGyw/I7itp/DTQVZvlSGiBg8d9tUq3TmOEDh22A9q78Oc6OxTqTtDC7FZp8AsPs+emMaKNN05OvLiBbawXGA5xufb8ufisum6c4SKUvA+7Llq5fPer7V+zU+6snNv1Raq+/4FoGWhwnkAPtSYhU4CC8wUcgWZWdkAfqU7hw7RHoz52Tu+sFxohYjIQs4C8AmLzEQUaVzZucNgzlx2BWP/AsQ4vzjkksqmsLqqzif2wQ+FQauM4cM39NedobVt9qJG3RmSolLrdWc4QmJSdEdokVBzYK/uDB2pj8JXX4wOw8X9lGp6uajEFgVYqtd2sCgF3x8831v5nPeSGW0eEgB/c2Xn3qIj14mIyJsAVgAYKyL7ReQGAL8HcJ6IbANwnv9rCkMsMFNIFWRm/QTAw7pz6DRhl5p12jZfnu4cRERhbgbMmcy9pr8eEUU2V3buIAALAAT1snkBkqNWliZIvacwmOME2q4waJVht3vH9Ou/2zIZS6V6pO4Mic6+Pt0Z/GpF7E7dIVpYuf/y2zNtFboz9MQ3amqXn9rUlKE7RygoBd+jniuWP+P9RtvicgsB8JQrO/fGUObqjFLqSqXUIKWUUyk1VCn1olLqkFLqHKVUhv9jue6c1DMsMFPIFGRm3QDgMd05dBPAdve7vv5xDcoyl4sREYWpswC8Y7gNh+4gREQnw5WdmwrgMwCjQzGeAIOilhbXo8l7OBTjBUo4tMpIT18jujO0UKKGHZbaPTozxDtSLFJIFUv97TWwZJ324n97vIIDH58uU3Tn6K4Ery///rLymbpzhIJS8P3F8+3lf/NedlYnmwqAZ13Zud8KRS4iFpgpJAoys64C8BzMX3K9nl1hyAN/927UnYOIKAJcDOBFw23w/xciCkuu7NxEAJ8AOCWU44oP6dFLivfDq6zTwqAT4dAqIyqqYXJiUslm3TlabLEf0FpgjrUnxOgc/yh7je4ELcTn2RvTeHiQ7hztmT9RtvtsEl4n7pWqfb3oYJIdsMjJjOBRCupx7zeXPeH9ZmfF5RY2AG+4snPPDWYuIoAFZgqBgsysywC4wffbMYaVYcbFq9mPmYgoAH4A4BHdIYiIusuVnRsL4N8ATtcxvniUEb20eAOU8uoYvyd2yeiZy3HWWt05TmTMmBWWucR7l71Ua4E3yhadonP8I8RhmRMpCbWFluy/rICq1+fYJunO0V3zqqrXjWr2BHRRVCtSCupv3kuXPub5dndnakcBeN+VnXtGMHIRtWDBj4KqIDPrQgBvAQivs6AhMm++b1y/SlWkOwcRUQS423Abd+sOQUTUVa7sXCeAdwHM0plDGrxTo1aVhtWkh6fxk8FWbpURG1s1JSamer/uHABQi4YsH3zNusa3iWOArrFbE4myTIF5QMkXlqzD5LtkfX2MJOnO0R2pHu+6u8orIr41hlJQT3m/sfRRz3d7+loTAHzsys7NDGQuotYs+YuNIkNBZtZsAO+hl6zi2hMCpDz8ivcglFK6sxARRYBHDLfxA90hiIg648rOtQN4A8BFurMAgK2yeaYz79DnunN0lU/sg3+HHMu2yhCBI2PMih26cwAABIn7beVfaRteJMkhTv3tKSRGW5G9rQEW7L+sAM9zF9lC0gM+YJSqfKPo4GCJ8DacSkE9671kyR893zvZQnoqgE9d2bnDApGLqC0WmCkoCjKzpsG83C9WdxarS67DpBv+61usOwcRUQQQmP2Yv6Y7CBFRR1zZuQLgeQDf1p2lNXtxwxzH1sqwOSbdLekzl2GmZVtlJCcXn+5wNFbozgEAm+0HKnSOH+9IKdU5PgCIxPp0ZwDM/suxjeWW679c2BerS1JkiO4c3fHTw5UbB3u8lvteBtrz3ouX/N5zVaCudBkGs8jcL0D7IzqCBWYKuILMrIkAPoZ5GQZ1wfnr1bRRRWqb7hxERBHAAeBtw22cqTsIEVEH/gTgOt0h2mPfVTPDvq92le4cXfUMbh9i1VYZIogflb4mT3cOACiyHe6vc/xEZ5/DOscHANjiLHHFaLxF+y8/d5E9RXeG7hja7Fl5Y2XVDN05gu0lz4WLHvJcE+g2Spkw22WwXkMBxQIzBVRBZtYImKtgp2iOElYEiP7ta17l8KhG3VmIiCJAHIB/G25jnO4gREStubJzbwfwf7pzdEQAu+Origm2soYNurN0hU/sg6zcKmPAgF3jRLzaj++b4c2sR9MhXeMnOVO19z8WibfrzgAAA0rXW66dQ3UMviwYLmFzzCRKlb5eeDBDd45gc3vOX3S/5wezg7T70wF84MrOjQ7S/qkXYoGZAqYgMysZQC6AgbqzhKNoD8bc+0/fSt05iIgiRF8A/zXcBvvMEZEluLJzLwHwF905OiNAjHPdoeFS1WSNHsKdsHKrDBEMGD48f7XuHBDYttsPbtE1fGJUqkfX2C3EZo0C88CStZbrv/zaXJv2kyDd8euy8p19fb5U3TmC6VXPeYt+47k2WMXlFucAeN2Vncu6IAUE30gUEAWZWQ4A7wA4RXeWcDZ+l5p5+lZfnu4cREQRYiiATw23EdF/hBCR9bmycycB+AcASxS5OiNAStTK0ljUe4p0Z+mKZ3D7kFrEVerO0Z6hwzYNBfQv6L3NXqStB3GCI0V73UFs8dpnaorPuy+2oXyw7hytNduwe+F4OUN3jq4a09i07Ns1tVN15wimNzxzF/3ac12wi8stvoUwOPFJ4UH7L3qKGE8DOE93iHAngO2u93wD4uuVJQ+QiYjCUCaAXMNtxOsOQkS9kys7dyjMxa/D6veQKAyOXlpciyZfhe4snfGJfdBD+K0l23rYbL6RaWnbtc9iPiw16brGjnUk6i/uSrz2xefjawv36M7Q1sdnyF6IWK5tR3vsSh14pajY0J0jmP7hmfP5Lzw3hqq43OInruzc20I8JkUgFpjppBVkZt0L4EbdOSKFXWHwA3/3btSdg4gogkyFufBfWMwcJKLI4V9E6d8ALDVrsavEh9HRSw7uhVc16M7Smd0yauZSzLJkqwzXyC+0FzeVYFCpVGlZVDzaFpOkY9xj2OK1n+CxWv9lH1D+9kxbeMxeVko9WlJWkqiU/vdSkLztmfV5tuemOZqGf9yVnXuRprEpQrDATCelIDPr2wAe1p0j0gw9hBlfW+1brjsHEVEEuRj8/4qIQsiVnWuH2RZjgu4sJ0M8anzU0uIvoZRXd5bOPIsfW7JVhtPZND4lpTBfd44t9sJCHePaxdlPx7iticRqL0wOLFnr0p2htbVjZEOTU7Sf/OiK0xoaF59bVz9Jd45gedc78/OfeW6ZozGCHcBbruzc8RozUJhjgZl6rCAzawqAvwOw1JnYSPGD+b5T+lcoLQeBREQR6h7DbVylOwQR9RqPAfia7hCBYGvwTo1aVbpMd47O+MQ+6He4X3shtz0ZGatqdWfYYy9N0DR0qg12nQvJKUhMssbxIT7v/tiGQ0N0ZmhNAY0vXGALi/WTnErteqa4dIruHMHyoXf653c13zpHdw4AiQD+7crOTdMdhMITC8zUI67s3GE/OP+XL9Q5onfqzhKpBEh+yO0thtK/KAgRUQR5wXAbk3WHIKLI5u9neRqAet1ZAsVW2TzL+WX5It05OrNHRp5lxVYZ0TE1U+LiKnbrzFCPplM88Ia83YmISJwjqSTU47ZSJSJaax/xtUW7dY7f1u6BWFORIP115+iUUt6nD5bUxSgVFjOtu+vf3mmLftr84zm6c7QyDMCHruzcGN1BKPywwEzd5srOjQXwQWlcH+PKi34z8kB8vxW6M0Wq5DpMuvG/vsW6cxARRZBYAB8YbmOA7iBEFJlc2bnnwJy9fCaAXQAOag0UQPaD9bMd2yqX6M7RGSu2yhCBLWPM8n16QyBmj61sk46hE519DusY12Sr0je2yWr9l5+9yD5Qd4auOLuufsnUhsawmGndXf/xTln04+afhHpBv66YAuAF3SEo/LDATD3xMoDJANBkj4q98dx7py0dbHyuN1LkOm+9mpZepLQsyEFEFKGGAfin4TacuoMQUWRxZeeOBvAOAIf/rnEAFIACbaECzL6zZrp9f+1q3TlOxKqtMhITD53hdNaX6sywxX6gRse4Sc5ULeOa7NrbkwwsWTtCd4YWh+OxducgydCdozMxPt+WP5WUzdCdIxj+6z398x8132HF4nKLq13Zudm6Q1B4YYGZusWVnftLAN895k4R+d2UeXOeP+WS5Qqw/ArT4UaA6JzXvHB4lM6+ZUREkWYmgCd0hyCiyOHKzk0G8BGAPm0eGgRgBICVIQ8VBALYHZsqDFtZg+UKuK3tkZFnLcHsNbpztCaCmNEZq7TMIG5RbKscrGPcxKjUZh3jAgDEWadtbADi8x6IbSgbqjNDa6+cZ7PUbOp2KdX4SlGJzQlE3GSAz7yTP7+5+c45unN0we9c2bnf0B2CwgcLzNRl/l8uD3T0+HsZc6b/YvpN230QrWflI1G0Bxn3vuOLiD9KiIgs5BbDbdykOwQRhT9Xdq4NwJsAMjvYJA7AVACW72HcFQLEOtcdGipVTTt0ZzmR53Db0FrEW6pVRmrqvgk2W7O2GbVe8WXUoCHkbVsSHX20FTVForRO1Imvs07/5UYHtq7Isp2mO0dnLq+pXXFKU5PlZ1l313zvpM9/2Hz3HN05usgG4HVXdu6puoNQeGCBmbrE/0vlNQAnPDDIGzDm1BvOy25qsDu3hiZZ7zF+t5p1xhbfet05iIgizJOG24jIyy+JKKTuB3BRJ9sIgNkAlgEI+yvTBOgTtbI0BvWeIt1ZOuIT+6AH8VtLzbQWQR/XyDytixButRdtD/WYcY7EqFCPeYTE6Js9DaB/qXX+hHt/uk3nYotdkuj1bcgpK5+lO0egLfRO+PyG5nvm6M7RTQkA3nVl5ybqDkLWxwIzdcqVnZsK4EMAXfqlcjA+dchVF+UMLolNsXRvtnAjgNz5vm9gfL2y1CwMIqIw5wTwruE2LHPpKhGFF1d27sUAftGNp8wAsBVA2F/1JwpDopcW16DZZ9nj070WbJUxaNCWdMDn1TX+DvvBkNcBouxx2gpUYov16RobsE7/ZZ+g+F/TZIruHCekVO3rRQeTbRFWq1riNRZd13zvHN05emgMgBd1hyDri6h/tBR4ruxcAfA6gFHdeV69Izrh2vN/cfraAWM/D0qwXsquMPjBv3u19m0jIopAAwF8YLiNGN1BiCi8uLJzhwP4Ozq5yq8dBsxZzGG/kLP4kBG95OBueJVl12J5FrcNs1KrDJtNDR08ZMsqXeNXSt1YBRXSoqtDnKmtv66oq8fTC1fgkY8/xx8/WYQlW3cd95z6pma8uGQN/vTfxfjjJ4uwetc+AEBNQyOeXLAcf/xkETYeONrt4+Wla1FZf/zbUCReX3sOn3d/XL01+i8vHScFHrvom0neBddVVq8b2eyxREE+UJZ5T1n0/eafW3lBv674jis796e6Q5C1scBMnbkHwAU9eaISm+2+6T+c8/rY85YoQOtlSZFkyCFMv2SVb7nuHEREEeY0AM/rDkFE4cOVnRsF4G0AfXu4i6EA0gBYanZtT0izmhC1rDgPKrRFy65SYk97EPdv1J2jNZcrr+1ikKEjSD0oFVtCO6T0F8iRWds2EXx94jj87KI5uP2cGVi2fQ8OVlYf85zl2/dgYFIC7rpgFm6dMw0ffVkAj9eH9XsLcfqIobj9nBn4fPNOAMCmwmIM6ZOE5Nh2zhXb4rXVPeLqDu7RNXZrCqh75VzbBN05TqSfx7v2zsMVEdUaY4U3a9HVzb8M9+Jyiz+6snOn6Q5B1sUCM3XIlZ07BcCDJ7uf17IumJkz7fqvfEB5AGIRgO8v8J3Sv0IV6s5BRBRhrjHcxp26QxBR2PgjzIX7TkYizBNcYb/4n63eOy1qVdlS3Tk6sldcM6zUKsNu92Slpu7V1px3i6OwOJTjiYg91p5wpP9vUmwMhvZJBgDEOB0YmJSAqrazjwVo9HiglEKjx4u4KCdsNoHdZkOz1wuvzwcRwOvzYcnWXZgzNr2DseOdLZ8frinB4x/diQfeug4Pvn09Fua/e9z29Y01eObjX+Lhd36IB9++His2fwIAqK6vwJ8//Cl+9/YN+HLX0bf6s5/ch4rasnbHHlC6XnX1exRMW4ZibU2c6Dup0RmlKt8oPDhEd4xAWuXLXHRl832RUlwGzLZyb7uyc/vpDkLWxAIztcuVnZsM4B8wf4mctNVp4ybcfM7PqptsDkuvNB0uBEh++BVvMZSyxAELEVEEecRwG2fpDkFE1ubKzv02gJ8EaHc2mIv/LUGYX/Vnq2ya5dxQbtliudVaZaSPXq2tD/M+W9kxxcbCqmJc8eZPcfbz1+CcF36AF9e+0+7zVuxdjwtevh7nvPADfPuN2wEAh+oq8M3XbsM5L87DJ1uXHNn2+nd/joPVRwuvCc4+7U44Kq+tw4GKSgxPTTnm/hmjXSipqsH9H83Hnz5djEsnjoNNBJOGD8aW4lI8v3g1zj9lDJZv34PTXEMR5bC3m1ls8UemNdvEjm9OuwX3ffdl3H3Zk1i86UMUHd59zPaLN32ItD4j8PPvPI+ffv3PeH/lM/B4m7Fu+wJMHXM+7rrsr5j/5dsAgPzdyzGsXwZS4tuvuVmh/7ICfM9cbNee40T+73DFpkFe7yDdOQJljW/M4u82/TqSissthgF4zZWdy1oiHYdvCurIswBGBnKH+xMHjLjqot/0K49O1LpqcqRIqsekH37iW6w7BxFRhLEDeN1wGym6gxCRNbmyczMQnAWPZgLYCOBwEPYdMvai+tmObVVLOt8y9PytMvJ152gRHV1/ekJCmZY+3I3wjGuC50hPCrvNjvvO/hEW/vA1fPj9Z+D+4n1sLdt9zHMqG6rxy0//jJe+9TDm3/gqnrn0fgDAh1/9D98+9UJ8eM3TeHb1mwCAz7YvgzFwDNISjxZek5ypx/bAANDY7IF7+TpcOnEcYpzHzm3acrAUg1OS8euvn4M7z5uJ99dvQkNzM2KjnLhx5hTccd5ZGNInGQVFJRg/JA3vrNkA9/J12F127D8hscXHtnyeHJ+KYf3HAABiouKQljLi+NnHImhsrjdnTjfXIy46ETabHXabA82eRni8zRAReH1eLMx/D+dOuKLd77H4vAfi6kuHdfAjCJmSZKwuTBXLFpiHNTevuL6yerruHIGyzpex+DtNv5mpO0cQXQDgPt0hyHpYYKbjuLJzbwTw3WDsu9YZm/z9C++btDF1pGVnNoSTc/PUtPRCtVV3DiKiCDMcwDO6QxCR9biyc2MB/BNAUpCGmASgEsDxK56FEfvO6un2A7Wrdedoz15xnbUIZ1sm25gxK0LaquIIgXOXvaSg5cuBCf1gpI0FACREx2F06ggcrC495ikffPU/XDhmFoYkDQQA9Is3J0E77A40eBrR5G2CiA0enwcvrn0Ht0y98pjnJzlTm1p/7fX54F6+DpOHD4Ex9PjJq2t274MxNA0ign6J8egbH4eSqtpjtvls0zackzUa6/cWYkifZHz3jPH4OH9zm9cal9Det+BQ9UHsP7QdrgFZx9w/+5TLcLBiD3752hV46J0b8e3pt8EmNpw+ei4K9q/F3/6TjYtPm4clmz7ElDHnIcrZ/hrBcXUHd7f7QIi9eIEtXneGjohSpa8VFo/RnSNQ1vvSF3+rKWcmINoWlgyRX7uyc8/THYKshQVmOoYrOzcLwOPBHMMnNvs9M2+b/e7o2YsV4AnmWJFOgOic173i8KhG3VmIiCLMdw23ca3uEERkOU8CGB/kMVwwFw78IsjjBI0AdsfGCsNW1mCZ2cKtPY9bh1ulVUZcfMXU6OiaIh1jb7UXNrR3/77KImwq3oZJg8cdc/+u8n2obKjGd974CS5+5Ub8c6PZm/iycedi0a7VuOade3DnjOvw6hcf4FunXIDYNoXXBGefI4tAKqXw9poNGJiUgNljR7Wbr09cLLYVm7OLqxsaUVpdg9SEuCOPl1bXoqqhAekDUtHs9cImAkDg8R271qTYYo87IdTYXI8XPs3Bt878EWKjjq2/Fuxfg6Gpo/G7a97Gz7/9HN5Z9lfUN9UiNjoBt170EO791tMY1j8DG/euxKRRs/DGoj/hhU9zsPPgpmP2078sT3s7w7oobMpLtxm6c3Tkt2Xlu/r6fKm6cwTCl75RSy5vur83FJcBs5b4his7d6juIGQdLDDTEa7s3BgAbwGI62zbQHjh1K/PeviMa75U5iwN6qFoDzKy3/Gt1J2DiCgC/dVwG6N1hyAia3Bl514H4PoQDZcMs5Adtu3QBIh1rjs0RKqbd+rO0pYSe9oDeMASxW8RODPGrNRyRWKpVB3XNqG2qQ43v38fcs65HYnRxxZePcqL/INb4f72H/DaFY/i8eVu7Czfh6ToBLi/8wj+M+95nJo2Bv/bsRwXj52Nn338CG5+/z6sO7ARABDvSIpq2dfussNYt+cAtpccwp8/XYI/f7oEBUUlWL59D5Zv3wMAOHdcBvYcOoxH/7sYz3y+El8bn4n46CO7wMf5W3Dhqeas64nDB2PN7n346/xlbQvWHpHoxNZ3eL0ePP9pDk7POAcTRx3fyWDllv9iwsizICLonzwEqYlpKK7Yd8w2H6/7Oy6YdDXWbl+AYf3H4Oo59+CjNcd2zkkrXju84+9+aLw1y1alO0NHMhubll5eUztFd45A2OhzLbm06YGzeklxuUU/AG+5snMDsm4XhT+H7gBkKX8GENKzm0uGTDxtf8KAnY9//niFU3kt2xfK6ozdataULb71q8faJunOQpGr6VATDjx/AJ5KDyBAnzl90O/8fqjfW49CdyF8jT5EpUZh6C1DYY9tf5EV5VPYkbMDzj5OjPg/85/8wbcPonpDNWKHx2LoTeZJ8MPLDsNb60W/87lIMWmVAOANw23MyJ+XH9YLbxHRyXFl554C4G8hHtYBYBaARQDOgtkjPqwI0DdqRUld46yBBxHjSNOdp7V9MuKsRers1bOxUHuBKyWlaLLd3lTp9UYlh3Jcn6gRlVK3L1nFDQOAZq8HN71/Hy4bdx4uGnv8+mSDEvujb2wy4qJiERcVi6lDJ+Crku0Y1fdom+HHlr2C28/8AT78aj6MtLG4bNy5uOG9X+DtKx9HtD3+SKuKkf374tErvnbCfMmxMbhp9tQOH//B9MlHPk+Micbt58xob7NKAEdmyCql8PqiR5GWMhznjP9Ou/vtkzAAWw6sx+hB41FVV47iin3ol3i0hUdJ5X5U1h5CxuAJ2H9oO5z2aAgEzZ5WHUCUtzCuvkRrgdljw/7/niba39/tsSu1/+Wi4gm6cwTCJt+IpV9verC3FZdbTAfwWwC/0B2E9OMMZgIAuLJzvwngVh1j70oePOrqC3+dVBUVl6dj/EgggPzf+760hHpVoTsLRS6xC9K+l4aMhzMw6r5RKJ9fjoYDDSh8uRBp30lDxoMZSDotCWX/KetwH4c+PYTowdFHvvbWeVG3vQ4ZD2ZA+RQa9jXA1+RDxdIKpM6NiKvlKPydAeAB3SGISB//7KzXAMR2tm2QzAawHoBlZyKeiCgMjV5SUoVmn+WuWnwePxpRg4QK3TlEkDgqfe16HWNvsRfuAszC6z0f/wEZqSNw05T2l+M5f/RZWL1/Azw+D+qbG7C+qACjU4/OEdpVvg/FNWU4c/hE1HsaYBOBQNDoL7w6bVF9QvCS2jh2Bu/Ogxuxettn2Fq4Hg//8yY8/M+bsGnvKiz56iMs+eojAMCFk6/BruJN+N07N+Kv/74Hl079IRJij9b+P1r9Ei6Zch0A4PTRc7Fq63/x6Ac/xjmtFvuLrys+0ke9qLkZ1+7di0t27cTXd+3E3w+XH5ey2uvFj/bvw+W7d+Hru3bivcoKAEC5x4Nr9u7BN3btxP+qj66ReNuB/SjxnPjc9/8myg6fTax3Ykop9eeSsrIEpRI739jaCnzDl17S9LvpCrbeWFxuca8rOzeSFzWkLhKltLcFIs1c2bnDAeQB0PAf/lF2n7f5L4ueWJlReYC/nHroQF8s/7+bHRGzAi9Z257H9yD1nFTsfXIvsp7Ogoig6VAT9vxpDzIeyjhu++byZux/fj/6f70/Dv33EEb83wh4673Y/cfdGHXfKOx7ch8GfmsgKtdUImZYDJImB2v9JKJu8wE4L39e/gLdQYgo9FzZub+DNWZn7QDghLkQadhRTlte4+y0LNgluvOtQ2eY2rP097jzLN05lJKDy5Ze2Vcpe1Tr+ysrK/HBBx+gpqYGIoLJkydj2rRpxz1/9+7d+OSTT+Dz+RAXF4drr70WtbW1eOutt9DQ0IC5c+ciMzMTAPCPf/wDX/va15CYmIgEX8yq7zXNmLp6/wZ86/UfI7P/KNjEnId276wf4kBVCQDg+5MuBQA8s+pNvJ3/H4jYcOX4r+HGM44WVW/94Df42awbMbLvMJTVHsaN7/0C1Y21uGvm9bh47BwopRrf3v1IiH/+UZti+vz4lNCOCbh2/2fpqN25ZwFAqceDUo8H42JiUOvz4tu7d+OvQ4ZidPTRb8Wzh8pQ4/Phrv4DUO7x4OJdO7F4dAbeqjiMGLHh4qRE3LRvP14fMQILa6rxVUMjbuvX8ZV+Cqi89k67rT5aLFfEPaO+YdFLB0uOnyIfZrb4hi67sOn3ZyrYOHET2ANgwu7ff81yJxIpdNgio5dzZec6ALwJzcVlAPDa7M6fnP1/M2/P++eii3avnCmcYd9tQ8ox/esrfcs+mmZr9/owokBpKm1Cw54GxKbHInpoNKrXVyNpchKq1lShubz92RRFbxQh7btp8NZ7j9xnj7Uj6fQk7Pj1DsSPi4ctzob6nfUYcOmAUL0Uoq6wAXjVcBsT8uflH9IdhohCx5WdeyaAe3Xn8EsHcAjAlwDC7tJyafZNjFpWvKJp5sCpELHMcb5VWmWIqLShwzYt3bd3/DHFbpvNhvPPPx+DBg1CY2MjnnvuOaSnp6N///5HtmloaEBubi6uueYaJCcno7a2FgCwceNGTJgwAaeeeipef/11ZGZmYsuWLUhLS0Nioll3rJGGLB98nilDxzv23dt5y+9bpl6JW6Ze2e5jT1/22yOf94vvgw++/3Sb1yjR0ba4Q42+utBdpibO+pCN1crA4rVH+ob0dzjQ32GWXuJtdoyKjkaJx3NMgVkgqPX5oJRCnc+HZLsdDgBOCBqUD00+BRHAoxRePXwYTw058dpqX46UvPposVwRN0qpnU8Vl1qybUd3bPUNYXH5WCNgtpG6RncQ0of/GCgHZt8cy/jrxG/P/sukK9YqoEZ3lnB0zULfqQMq1AHdOShyeRu82PvkXqRdlQZ7rB1Drx+KQ/MPYftvtsPX4IPYj79CrCqvCo4kB2Jdx19d3P/i/hj9wGgMunIQSt4rwYBvDkD5onLs/dtelPyrJBQviagrhgB4QXcIIgodV3ZuPIC/w1q9j1MBZAFYqjtIT9jqvWdGrS5bojtHW1ZplTFs2MY04NhLjBMTEzFokNn/Nzo6Gv3790dV1bHdUvLz85GVlYXkZLONQ3y8uTifzWaDx+OB1+uFiMDn82HVqlWYMaPVXBRB0gFbeUEwX1drCc6UjnupBYFIdGMoxwMAKG9RfH1xu+sLHWhuQkFDA8bHxBxz/9V9UrCzsQmzd2zHpbt34RcDBsImgq8lJWFZbS1u2r8ft6X2w5sVh3FpUjJiT1DXVIDn+Qttx19OqJtSnmcOljTEKKWr3VBAbPcNXn5h0x+msbh8nKtd2bntn32iXoH/IHoxV3buXAA/152jPZ+NmDLljtk/KfSKjYXSbhIg+aFXvKWilE93Foo8yqOw78l9SDkzBcmnm3/ERA+Oxsh7RmL0b0cjeVoyogZEHfe8um11qFpfhS13bcH+p/ejpqAG+549djXu+j3mBJPotGhULKvA8NuGo3F/IxoPhv7vAqIOXGa4jVt0hyCikPkTzFnDVhMFc9G/z2G28Akrtoqm2c4N5Yt052hNiW3gA7h/k+4cdrt39IABO9d29HhFRQWKioowdOixs1cPHTqE+vp6vPLKK3juuefw5ZdfAgAMw8COHTvw2muvYfbs2VizZg3Gjx8Pp9N5zPO32AtDdnVOkjM1tL3EJcbb+UaBFV9Xsqu9+2t9Pvz0wAH8fMBAJNiPPW+1tLYWmTHRWJQ+Gu+5RuLBkmLUeL1ItNvxzNBheMflwriYGCyqqcF5iYn49cEi3HHgAPLqj5+gfSAVq0pTZHBwXl3PnVNXv/SMhsZxunOcjJ2+QcvPb3pkqg82K514tAQF1HrSE69NW5h34un1FLFYYO6lXNm5/WEuVmLZ98DWPsPHfP+CX0XVOGLydWcJN0n1mPjDj32Wmx1C4U0phQMvHUD0oGj0u/BozzdPlcd83KdQ+q9S9D2773HPTftOGjL/komxfxqLobcORUJWAobdPOyYbUreK8GAywdAedTRP5dtgK8p7P52psj2Z8NtZOkOQUTB5crOvRjAzbpzdGIOgDUAajXn6DZ7Uf1s+44qS83C3i8jZnyOuat15xiVvs7Z3v1NTU14++23ceGFFyI6+tg2xj6fD0VFRbjqqqtwzTXXYPHixTh06BBiYmJw1VVX4aabbsKgQYOwdetWjBs3Dv/617/w9ttvY98+82T/AVt5//bGDIYkZ2pDqMYCALHFhXzRqf6leccdvDYrhTsOHMAlSck4L/H4tsjvV1bi3IREiAhGREVhqNOJnU1Nx2zz9KEy3JzaD/+pqsK4mBg8mJaGx0pLj9vXsxfZjz8Y1yzW5yv4Y0lZWLdx3OUbuOLcpj+yuNwO5ZD8phkDyjyjk84H8HLawrzevOhhr2XZ4iIF3dMABukO0ZnDMUn9r7ooZ8yexIHLdGcJN+d8qc5ML1RbdeegyFG3rQ4VyytQU1CD7fdtx/b7tqP6y2pUrKzA1nu3YtvPt8GR4kDKzBQAQPPhZuz+8+4u7btqXRViR8bC2ccJe7wdsaNjse1X2wAAscPD+io6ijyxAN403IalFqkiosBxZeemAnhRd44umgpgP4BC3UG6y7G9epqtsG6N7hytvYBbtbfKcDobJyYnH/yq9X1erxdvv/02DMNAVtbx5ziTkpIwevRoREVFIS4uDsOHD8fBgweP2WbRokWYOXMm8vPzMXjwYFx66aVYsMBcu7YZ3qwGNB0O4ss6IsHZJ6QzisUWH8rhAAADS9YcM4tCKYX7DhZhVHQUru3bfu13kMOJlXXmuaIyjwe7mpowrNVM891NTSjxeHBGXBwalA82CESAxjYXrVbFIm/LMLHWiXClGl8pKnY6zUVKw9Ie34CV5zY9egaLy8dSQLN3UOznjXMHjVMJzpa2MOcC+JHOXKQHC8y9kCs791IA39Kdo6ua7Y7oW865Z8aCoZM/V0DIz0CHKwGicl73isOj2F+AAiJ+TDxOfeVUZDyYgdEPjMboB0YjcUIi+p3fD2P+MAZj/jAGaVekQcQ8Ye3s44TrTtdx+0nISsCI/zu2LV3SaUkYcNnRhf0GfW8QMh7MwLBbhrV9OpEVTADwgO4QRBQ0zwBI0x2iG8bCXLxde4uH7hDA4cw/fIrtUINlcputMh7QnidjzMrKls+VUvjXv/6Ffv364cwzz2x3+7Fjx2Lv3r3w+Xxobm7GgQMHjlkE8NChQ6ipqYHL5UJzc/ORYzWPx7wKDQLbdnvx5iC+pCMSHCkhLTKKxDtCOR6Uryi+7tj+y1/U1+NfVVVYVVuHy3fvwuW7d2FRTQ3+UXEY/6gw6/q39ktFXn09Lt21C9fv24s7+/VHH8fR6I+XleIn/cyf6cWJSfigshLf27MH17UpWL8213bstGcL+FZ17cpxTc2jdefoqb2+ASvnNv3pdC/soX0vWZyyYUfTlH7bm8f3nQORtoX3R9IW5lmuD7iIvCQiJSKysdV9D4jIBhHJE5FPRazXXiZciFKs1/UmruzcJABfwVysKOx8fefSFbdu+GCCAHG6s4SL/BGy6IGr7JZbQZiIKMx5AZyRPy9/ve4gRBQ4ruzca2Au7BeOGgB8AYst4N0ZBZQ3TR9QqRKdI3VnaXGjemr12Zg/Rdf4SsG7bu03DtTXJw/fu3cvXn75ZQwYMOBIYficc85BZaVZgz799NMBAMuWLUNeXh5EBJMnT8a0adOO7O+dd97B3LlzkZqaitraWvzjH/9AY2Mj5syZg3HjzJa4qb7EJZc3TZkZ7NfW4K1d/+HeJycFe5wWjrjzVjuijZD9LONqi5ZPW/Ogln+DzXbsuvoeu+vIG8UCkrzeL5fsPWDYwnRy4z5fv1VnN/15sgeOsJ19HWgKUKpP1JKm0/qdAbuc6FLTFQBmHjx7Ysj7oHdERGYBqAHwqlLqVP99SUqpKv/nPwEwTinFNVd6gAXmXsaVnfs3hPnlCqeU7Sz4w9Kn+9ihwmlmiTYKUH++3LZ+VaZtsu4sREQRZh2Aqfnz8i1z4ExkZSISA2AxgGiYM27/qZT6jd5UR7myc4cByAeQrDvLSfocwGwAlikydUYJ9jfOSnMixj5QdxYAEOUrfgbXRSegJkVXhsrK/os3fHnhrFCNJwqFNzSeE/SZe17l2fXP3X8K2ckEZ8JlX9qdoyaEarwRez5Zkr7ro6AX6tvzwTRZ/MbZ9pC9ZzqlVM1H+4vKXR7PcN1ReuKASl09u/Evk1hcPkoBRc0T+hT60uJO6+JT7j149sRHghqqm0TEBeDfLQXmNo/9HMBwpdStIQ8WAcLyLBL1jCs7dzqAsP+HsqnfqKzrzv+FqrdHFejOEg4EkDs+8A1KqFcVurMQEUWY0wD8VHcIojDSCGCuUmoCgIkALhSRaSd+Ski9gPAvLgPm4n8rANRrztFlojA0eklxBZp9VbqzANZolZGUVHq6w9FQHqrxlGDwIaneEexxbLCHbEFBABCJD2kT5rTiNVr6u/mAQ/88y3aGjrE7cmNl1fpwLS4Xqr6r5zT+ZSKLy0f54hzLG88eFNuN4jIA5KQtzBsVtFABIiK/E5F9AK4G8GvdecIVC8y9hCs7NwrAcwijmQwnUhrXZ9CVF+WMKIxLXak7SziwKwx68FXvV51vSURE3XS/4TZGdL4ZESlTjf9Lp/9micspXdm5VwE4X3eOAJoOYCeAEt1Bukp8amz0kuId8ClL9JDdL8NnLMQ5q3WNL4K40Rmr80M55mZ74YFgjyEiCU5bdMhOJIgtLiFUY0H5DsbXHXSFbLxWVo+V/CbnCdsVhFR/j2ftTw9XapnJfbKKVN81sxsfm9gMR5TuLFaggMrmMUnLm2YOnI4oW0o3nx4Lc10DS1NK/VIpNQzA6wB+rDtPuGKBufe4F8ApukMEUqMjKu6G87Knrkg7ZZHuLOFgcDmmf2Olb5nuHEREESYewNO6QxCFCxGxi0gezMLnZ0qpVZojwZWd2wfAn3XnCIJTYPaL36I7SFdJs29S1LLidbBIH8cXcYurGomHdY3fr9+eU202T8hmou+xl4RknZt4R0ppKMYBAEhMyK5KiKsr2RmqsVpTQMOL59ss87e+KFXxRmHxUN05euKg6rN2duNfxrO4bFJRti8aZw2s845MPJm+4uelLcy7OmChgusNAN/SHSJcscDcC7iyc8cC+KXuHEEhIvdPu272S+MuXqbMyy7pBK5e6Dt1QIUK+swEIqJe5iLDbVypOwRROFBKeZVSEwEMBTBFRI7rgajBHwBYovdvEAyC+b3WXsjvKlud98yoNWWLdecAACW2AQ/ggQ6vAiwp8eCuOwtx/XX7cMP1+/Deu5XHbZOXV49vfGMXbr5pP26+aT/+/qpZr66o8OKnPz2AG2/Yh2VLa49sf999B1FW5gEAiCB1xIgv1wT6dXWkDk3jvPAF/W+qJGffimCP4dcg4gzZrN7+ZV9qWZNhZxrWVCZIfwBoOtSEXb/fhW0/34Ztv9iGsk/LjtveW+fFnr/swfb7tmPbL7bh8BLzPemp8mDn73Zi2y+3oWrd0Unmex7fg+bDzV3Oc1d5xVdpXm/YrZdUolLWzm78i9EEZ7TuLLopoMEzLH5x45y0SYh1DArALv+StjCvbwD2E3AiktHqy28A2KwrS7hjgTnCubJzBWZrjIj+JfnOmLkzfnXmD7f6IKE7Gx6GBEh++BVvqSjl052FiCjCPGa4DUseOBNZkVKqAuZidBfqzOHKzp0B4EadGUIgHsAUmN/vsGA73DTbkX/YElcpHpBhMxbi3HYL9HY7cMstqXjp5WH465ND8OGHVdiz+/gOH8apsXj2uaF49rmh+P4P+gAAFi6owfnnJ+KJvw7B229XAABWLK9FRkY0+vVzHHnu4CGbRwAhOnYXxO21lQW993Sis29dsMcwyfEV/yAaWLwm5LN2FaCevch+ZHFGsQvSvpeGjIczMOq+USifX46GAw3HPOfQ/EOIHhKN0Q+MxsjskTj4j4PweXyoXFWJlBkpGPWrUSj72CxMV62vQuyIWDj7dK0V8Yjm5hXzqqpPZrarFqUqed3MxsdObURURNdNukLZZXPTmf0PeMalzIJIoFqs9gfwaID21WMi8ibMNQrGish+EbkBwO9FZKOIbIDZKovrq/QQC8yR70YA1llJNoi+GDjWuOG8exsbbY5turNYWWI9Jt70sc8Ss0KIiCLIAFjgwJnIykSkv4ik+D+PBXAuNM4UcmXnOgE8iwhZo6QTAnPxv6UALNHjuDOOwrrZ9h1VS3XnAIAXcfPI9lplpKY6kDHGrEfFxdkwfITzyOzjztgdgqZGheZmBRGB16vw3nuVuOKKYzs62Gy+EYMGbQ1ZL+gt9gPVwR4jyZkaoskutprOtwkQ5StOqCsaGbLx/A4nYN3uNElv+dqZ4kSsy5y0bY+1I3pwNDyHj31Pigh8DT4opeBr9MEeb4fYBLADqllBeRRgA5RX4dCnh9Dvon5dymJTquTvhcVjA/jyQqJMJX0xs/GxcY2IitGdRScFeL39YxY1zh2UrpKi0jt/Rrddl7Ywb04Q9ttlSqkrlVKDlFJOpdRQpdSLSqlvKaVOVUqNV0p9XSle8d1TLDBHMFd2bhqAR3TnCKWD8f2GXnlRTlppTHLILiULR3O/VNNHH1Bh04+PiChMXGe4jbN1hyCysEEAFvpnCa2B2YP53xrz3I0IW6OkC86CWdQ/pDtIVzi2V0+zFdat1Z3D3yqj4ETbHDzYjO3bG5GZdXyN6quvGnDTD/fj59lF2O2f4Tx3bgLWrK3Dz7OL8IN5ffCvD6tw3nmJiIk5/k9018j1iQF6KZ06aKsIemuDeGdKaOoQ4qjtfKPAiKsr2RGqsVp7+Txbh9/LptImNOxpQGz6sV1C+p7TF42FjdhyxxZs/9V2DLpqEMQmSJmWgur8auz+024MuGwAyheUI2VGCmzRXftx3V9WvqePzxdWV3MdUonrz2p8PKsB0ZZZIFEHJdjbfFrqpubJqbNhk65NV++ZZ9MW5vX6WeKRigXmyPYEgBTdIUKt3hmTOO+CX562vv9oS1xWZ0UCROW84bU7Paqh862JiKgbnjXcRq+eAUPUEaXUBqXUJP8soVOVUvfryuLKzh0F4D5d42s2HkAdAC0Fse4QwOHMP5wl5Y0d9kEOlQMybHpHrTLq6334bU4xfvSjfoiPP/ZP7IyMaLzx5nA89/xQXHZ5Mn7z64MAgIQEGx56aBCeenooMjKisHJlHWbOisef/lSK3+YU46tNRw/THY7mU/r23b8hiC/vCA98Y2rRWBLMMWLtCSEq5kWF7G8dHf2XGx3YsirTNrm9x7wNXux9ci/SrkqDPdZ+zGM1G2sQMzwGYx8bi/T701H4WiG89V7Y4+xw3enC6JzRiB0Ri6q8KiSdnoQDLx3A3if3om57x51NxjU2Lr20pvaMwL7C4CpXiXlnNT4xtrcXl31JzqWNcwf19fWLGR+C4cYgUtcHIxaYI5UrO/frAL6jO4cuSmy2X8y4ZfabY85dooCur0jQi0R5MPrnb/vCZsEXIqIwkYHeW7QiCidPAejNRYVhMHtiap8d3BkB4qPWlA2QmubdurO01yrD41HIySnGOeckYObM+OOeEx9vQ2ys+Wf31Klx8HiAyspja5F///thXHV1ChYsqMGYMdG4+57+ePHF8mO2GT16VWiKpQLZZi8KasvBKFtMUjD330Js0SH7O3BgydqQ919+d4bt+BX8ACiPwr4n9yHlzBQkn5583OOHlxxG0mlJEBFED4xGVP8oNBYdu7ZjyYclGPD1AahcWYlYVyyG3DAExf8sbjeHXan9LxaVTAjASwqZwyohb0bjE2PqER2nO4suCihtPiVlddOZA86Cw5YQwqHvTVuYlxXC8ShEWGCOQK7s3ESYB8293qvjLpx5/9RrN/mA43qmEXDKHjVrWoHvC905iIgizD2G2zB0hyCi9rmyc78H4ALdOSwgCcAkAJZfm0OAflHLS+xo8AZ1Zm1nlNgG3I8HjsymVkrh0UdLMWK4E9/+Tkq7zykv90ApBQDYvLkBPqWQlHT0z/D9+5tx6JAXEybEorFBQQQQAZqa1DH7iYquOyM+vjwks8632w8GtS+5XRz9g7n/FiKxXWuIfbKUryShtjCk/Ze9gqKPpsqU46IohQMvHUD0oGj0u7D93slRqVGo+cpsT+2p9KCxqBFR/aOOPN54sBHNFc2Iz4yHr8l3pGrka26ndbZS6rGSsrIEpULWxuVkVaj4L2c0PpHRq4vLMfbVjXPS4B0af9x7KASiADyXtjCvN6x/0KuwwByZHgIQ8jOoVrVy0KkTbznnnqomm32X7ixWI4D89EPfoIR6VaE7CxFRBHECeN5wGzzOIrIYV3ZuCoC/6M5hIXaYC4IvBhCaYlwPicKw6KXFh9Hsq9KZo1CGzViA81YBwMaNjfjfZzVYv74BN9+0HzfftB+rVtXho4+q8NFHZszFi2tx4w37cdMP9+PJJw/hV78aCJGjdZWXXirHddeZbWvPnpuAT/9bjdt/fADfabPYnwhkzJgVhaF4jRVSm6GgVOdb9oyI9LGLoz5Y+z86UGzQXkNrcfWlIW83s+RU2eq1H98rt25bHSqWV6CmoAbb79uO7fdtR/WX1ShfUI7yBeas+P7f6I+67XXY9qtt2PXILqRdkQZHouPIPorfLcbAbw4EAKRMS8HhpYex84Gd7RaspzY0Lp5TVz8xSC8z4CpU/IYZjU+k1yHm+MsNegEF1HhGJS5tnJ02BdH2kJzo6cBZAK7XOD4Fgajg/b9BGriyc6cAWAGePDhOQlNd5XPzH9nRp7Gm3T5VvVlhH6y44xbHmbpzEBFFmOvz5+W/rDsEER3lys79G4Af6c5hUV8AGAWLr+GiomxfNM5OOxU2iep86+AQ5St9Ctfbk1Ad0gXNlELT6lXfOtzUFDcw2GN9vfG0zQNVSmaw9v/x/hf2VDUfGhGs/QOAPXrSYmfc2bOCOQYADN/76ZLROz+cGexxWiig9oY77M01sZISqjHbE+VTO5bv3TckWiEs1p6oVHH50xv/6qpFbNjMtg4k5ZD8pmn9k1W8c7juLH4lADIOnj1R60lDChwWISPPY+DPtV01UXHJ11xw3/iv+oyw/GWAoTb4MM68bLlvme4cREQR5kHDbfTayy+JrMaVnWsAuFl3DgubDLOt3G7NOU5ImnyTo5aVrIXGmVJKbP0fwIObQz2uCKJGZ6wsCMVYm+2F7TfcDZBEZ9+gtzAUW4K9861OXlrxmiGhGKdFwTCs1V1chlKe5w6WNIVLcbmqFxeXFdDsHRS7qHHuoHEWKi4DwABw3ZKIwkJkBHFl534LAGehnoDPZnfcNfv2WR+MOmuRAkK+0q+VXbnIZww8rPbrzkFEFEEGA7hbdwgiOuLPMFtCUMdGAkgGsF53kBOx1XmmO9eWaZ00UihDp8/H+StDPW7fvgcm2e3NNcEeZ5+9LKgL8SU6+9YGc/8AIBIf/FnuyleaUFs4KujjtAwH+J69yB7Sfs/tOa+ufulpjY1hsVBbtYrdNL3xiRG9srhsw46mKf12NI/vOxsiVvz/7ydpC/MydIegwGCBOUK4snMdAB7WnSNcPDv+stmPnHbVegXwcgw/AZIeesV7SJRqZ/UGIiLqoZ8ZbiNNdwii3s6VnfsNAOfqzhEm+gA4FcAS3UFOxF7eNNux8fDnOjO8jB+mVyGxPJRjiiB55Mgv1gV7nAY0n9IMT9CKwEnO1OD3/LbFxwZ7iNj60u3BHqO14hSsLkoVrbNQ43y+gkdKys7SmaGralTMV9Mbnxhag7ignjCxGgUob9+oxY1zBw9RfaKD1uomAKJgnvylCMACc+S4GQDP/HTD58Mmn/7jOf9X6hHbXt1ZrCKxARNu/o+PLUSIiAInHsADukMQ9Wau7NwoAI/qzhFmnABmAlgEwLKTD+wH6mbbd1Zra/Omq1VG2qBtGYAvuAVaQdQuW+lXwdp9gjNFOt/q5IgtLugLufUv2xDSxTGfv9Cmd3E6pRrcRcVRDsDR+cZ6+YvLQ6oRn9z51pFDAUXNE/qubz6j/yzYJRxamFyStjDvAt0h6OSxwBwBXNm5iQB+rTtHONqZMiT9mgt/HV/tjP1SdxarOHuDmp5xQG3RnYOIKIJcb7gNQ3cIol7sdnAiRk/NBrAOQLXuIO0RQBzbqqbYiurW6sqgo1WGiBo8dNhXq4I9zlZHYX2w9h1nTwp64UskLugtEQYWrxkc7DFa1EZjY/5Im9bjie9U16zKbGpO15mhK2pVTMH0xieGVCGhVxWXffGO5Y1zB8X50mIn687STX9KW5hnxRYe1A0sMEeGn8FskE49UBmdkHrVRb/J2pE0eKnuLFYgQNRvXvc6nB7VoDsLEVGEsAH4o+4QRL2RKzs3FcCvdOcIc2cAOAjAkmt1COB0bjicKeWNJ5xt6y05iPI7f4iya7+Jsuu+hbp33zhum4ZlC3Hoxitw6IffxaFbrkJTvtmK2ldRjvKfXIey67+NhqULj2xf8as74C0r0dIqY/jwDf2DPUaJVA4L1r6j7LEJwdr3ERKbEtT9K19pYu2BkBVb/zHLpvVET7LX++WvDh2eqTNDV9Sp6M3TG58Y1JuKywqoaB6btLzprIHT4bSF4+s+BcANukPQyWGBOcy5snMHAbhTd45w57E5on48986zPhkx5XNl4csAQyXKi/RfvOUL+qwIIqJe5ALDbfDyP6LQuw9Aiu4QESADQAyAfN1B2iNAQtSasv5S07ynw43sdiTecif6vfIe+v7tVdR9+BY8u3ccs0nU5Kno+/xbSH3+LSTdk4OqR+8HADQs+AQx538dfZ90o+4tNwCgcfkiODKyYO83AEps/e/H70LaKsNu947p1393UHsx+0SNrJK6oJxYcIijXzD220qNiC2obRxi68tC1n/ZY8O+zybLlFCNdxylql8vLO5rs3gNqU5Fb5ne+MTASiSk6M4SKirK9kXjrIH1XlfidN1ZTtL9aQvzet1CjJHE0r8cqEvuBxCnO0SkeHzSFXOemPjtNQoI+qrGVjdur5p1ZoEv6AuIEBH1In803AaPvYhCxJWdmw7gR7pzRJB+AMYA0Nbz+EQE6B+1vETQ6C1t73F7an84x2QBAGxx8XAMHwlv2bGb2mLjIGK2BlYN9YD/c9gdQFMD0NwE2GxQXg/q3n0D8d/9wZHnFsmQ6f/DBSFtlZGevibofYy32ot2BWfP0k9gaw7OvgFAgr6Ye7+yDUHMf6zPJslOn020tRC4qaIqb4THE7QZ7YFQr6K2zmh8vH8FEvvozhIKCqj3DI9f3DgnbRJiHYN05wmAgQDu1R2Ceo5/5IQxV3buOADX6c4RaT5xTZv6f7Nu3+8VW6HuLDoJID/50DckoU4d1p2FiChCGACu1x2CqBd5COZidRQ40QBmwFz8T2nOchxRGB69pLgMHt8JWwl4DxaiefsWOLNOPe6xhiULUDbvclT84idIuuc3AICYcy5C45oVOHzvbYifdzPqP3wbMed/DRITe8xzX8GN6VVIOhTAl3RCUVENkxOTSoI6c3qHrTgo/4ZExBbnSCoJxr5N9qC3k0grWTMk2GMAgAIq35xjOy0UY7VngMez5vaKyiOtMfZV+nC2uxZZf6vBKU/V4PGVjcc953C9wuVv1WH80zWY8nwNNpZ4AQCltT6c9VItTn2qBh9sPlqfv/QfdSis7vmFxPUqatuMxsdTDyOpb493EkaUXQqazuxf6MlKmXXkrFhkuDNtYd5Q3SGoZ1hgDm9/AMBG6EGwpe+IsT84/5eOWkf0Jt1ZdLIrpP3uVW/IV8YmIopg9xtuQ+8K8ES9gCs7dyqAK3TniGCzAawCUKc7SFviVVnRS4q3wafanV3qq69DxW/uRuKP7oYt/vg2wDEz56Kf+32k3P9n1L78FADAlpCIPg//FanPvAFnRhYaVyxB9KxzUfXo/ajIuRtNm8z1wpXY+v8WD4Z0sewxY1YEtfdztdRn+aC8wdh3gjMleNnFEbQFCgEAyleWWLM/JP2X16fL+oYoCX7P6naIUoffKCwe3vo+hw340/kxKLgtAStviMff1jTjq9Jj3yIPLWnExIF2bLg1Aa9eHouffmIu7/PmxmbMm+DEihvi8cflTQCAj7Y0Y3KaHYMTe1aealDO7Wc1Pt6nHMmpPdpBGFGA1zsgZlHj3EGjVVKU5Rdb7IFYAA/qDkE9wwJzmHJl584GcInuHJGsPDZ5wJUX5aTvS+i/XHcWnQYdxpmXL/dZ8lJIIqIwNAjm4rxEFFxcWDP4pgHYC6BId5C2pMk3OWp5yWoodcwsa+VpRuVv7kbMuRchZtY5J9xH1ITT4CncD1/lsRfz1b76HOKvuQEN8z+BY0wWku7JQc2LTx55/KAMmf4ZLgxZq4zY2KopMTHVwVuAUZBcZCsvCMauk5ypNcHYLwCIRB0/rTaAQtV/WQHNz19gGxuKsdrzs/KKzQO93oGt7xuUaMPkQeY8t8RoQVZ/Gw5UHXtBw1dlPpwzytwms58duyt8KK7xwWkT1HsUGr0KNgE8PoXHVjXhnhlRPcrXoJw7zmp8PPkQkoPd01s7JdjTfFrqV82TUmfDJpF8dc41aQvztL3nqedYYA5DruxcAQ+aQ6LZ7oy56dx7py8ePOFzZcHLAEPle4t8xsByZcmVw4mIwtDdhtsYrDsEUaRyZedeBGBmpxtSIGTC/JvyK91B2rLVemY41x1a3PK1UgpVf/wtHMNHIv4732/3OZ4De4/UpJu3FgDNzZCklKOP798D76FSRE04HWhsAGw2s09z07G1TDduCFmrDBE4Msas2NH5lj232V5YFoz9JjlTm4KxXwCAxARv3wD6HcoP6v5b7O+H1YeSRUt/XVdT8/JrqqrPPNE2uyt8WF/kxdShx15YPWGgDe8VeAAAqw94sadCYX+VwlWGE//d4cWFr9UhZ3Y0nlrThB+MdyLO2f0uD43KuWNW42NJZUjp3+0nhxlfknNJ49xBqb5+MYbuLCFgB/Ab3SGo+1hgDk9XADhDd4je5OH/Z+8+A+OorjYAv3dmi/qqN0uWZNlylSX3bsnGgE0voZlqwLSEAKkOCaAU4EsCBAi9G0JNAqGYkBAby3KVe7fc5CKrrdoWadvMnO/HUiystruzOyvpPn/A2pl7j2yV2TNnzpl6felL4y7aQEBwH7UKUwyIe2S53MyI/G+MxXEcx30jCvzxP44LpjKtAxhk0gDkAdigdSDfJza7SnR7W8sBwLNnB5xfroB7x2Y0L70KzUuvgmtjBTo++Ts6Pvk7AMC1ZiWab/4BmpdeBdvTj8L04B87tTe1v/osYm72zo2MmL8Qzi8+QcsPb0DUlTd02jfUrTJMpobJOp2rLVjrnxJaglIdGqsP3iw2xiKD+r4lvWFzSG4Uv3CeqEllrkDU8FZdw+iejrG7CZd/0IEnF0Ygztg5QbxsthGtTkLxC3b8tdKNCRkCdAJgimBYsTgKW26LwcQMEZ8dlHD5GD2WfuLADz7owIaTUp/ic5Gueq7rL7GNSBjQyWUCzJ6x8ZXuGalzoBM0aZOikavSv9oxVusgON+w7z01xIW53GUrDAD2AximdSyD0Xjz4b2PrnsxRQClah2LFr4qZKufv0As1ToOjuO4AUABMHb3jbt5n3uOU1HushXnAVihdRyDFME7/K9U4zg6IYCkgrj1cl7srFDvfRO9vPFsfDE9FHs1NOStPlg1uzQoixPk611z7UboTWoua/O0bPi85uUeK2T9JRjGrDZELywNxtogpWle+d1JDAjqcDVLFLYvvUc3IZh7dOfRxqYtF7R3TO7udY9MuODdDpybr8NPZhh7XIuIkPeUHbvujOmUiL7vCycuGaXDwWYFMgGLC/W4+L0OfHVjz6Mq3KSrnut6MqoeiWk9HtjPUYS4yTU9JR9GbW4yhIF/1M8rvkLrILi+4xXM/c9d4MllzexKGT52yTm/kp2iPqTDOwBAJsJlx6pxZ83JM1771GrBJdXVuKS6GouPH8cBp3eIQosk4boTx3FR9VH8z/bdIOUfnqpBo9Tl3JMele6mmQU1xJMhHMdxgRMA3K91EBw3APHHarXD4E0urwPg1DaU7zCA6Q5apwr1HVtDvXcoW2WkplaPYUwOTt9hBvGI2KB6H2ajEBW0ikzGooKW64h0Nh8OdnIZAN6aL/StnFdl41yuip6Sy0SEWz5xYnSy2G1yuc1JcMveYsZXtnkwN0fXKbl8qFlGrV1BSa4OHR5vP2YGwNnLZ+wm3bES118iB3JymQCblB+71lWSPm0QJ5cB4PL0r3YUaR0E13c8wdyP5C5bYQLwG63jGOwaoxIzrllUllUflbAplPu+1dqKfEPXv8Cz9HosHzoU/8rLwx1JSXiooR4AsMJmxcVxJrybk4PXW7xDmr+y2zDGGIFUne9zARhgePAdWa+XKGzeNHAcx/Vj1xQuL8zTOgiOGyi+rl6eqnUcHGYBOAzArHUg32CAXr+zdSRrdQVlWF13vK0yHj4Yir0YQ+rQnF2VwVr/kFjne3VKL3SCIVHtNb/BhJigDUFLbgp+/2W3iCNrxrFuk7zBoiM68UpdY49V0+tOynhrlwerqiUUv2BH8Qt2fH7Igxe2uPHCFu9fzX6zjLHPtWPUM3b8+7CEpxZGdFrj16tc+MM873vbawr1eGOHB9NfbcfPZnQ/7M9N4vFS1xPGOiSlB/p5hivSsV3u2Wlt0vC42VrHEgYYgN9qHQTXdzqtA+B88iMASVoHwQFOnTF6ydn3T/3txlfLpzYcKAn2fvUeD8rb7bg9MQnLW1vOeH1CZNS3/18UGYkGyXvrVw8GJylwKwTGAIkIb7a24rkhWX7HYpCRf/97cvlvr9MF/fPmOI4b4HQAlgG4XetAOG6AKNM6AO5b4wCcAnAQQIHGsQAAGBBjqGxKds9KPU4x+pxQ7VvPMmf8lxZtOAf/DkoriNNlZe3LOn6smLyTB9XVxGyq3xBlYKnwtoxSvfCNCdHdZyoDlNZQGfShe59NY7VgLD/Y+3RCpDzVYG6NJhra02Gzh+pAD8X1uNSMbB0O3d19gfoHV3z3/jU1WsD6W3pui+Eh8fg81xOGWiRrMvAw2AjwKJlR6z3j4ueAMV4I+p2L07/aMbF+XvE2rQPhese/cPuJ3GUrIgD8WOs4uNMwxh6acWvJ8tEL1xIQ1LvY/9fYiJ+lpELow6XiPy1tmBPt/QV9flwc1rW347aaGvwwKRnvtrXi4jgTIoXAvvXHnMTcmfuUkD9myHEcNwDdVLi8cIjWQXBcf5e7bMX54EOww80QAJkAglZV6ysGpBjWNwIuOaTV1W/i5hEWmJqCvY8gKHnp6YeD8vdNjLJamL1azTUZY/pIMSYofy+MRUcGY12Q0hxrPzk8KGt/TWEw/3OWEPKfZzOczoq5DmfYtSTwkHhinvsJ/SmkDMzksoDD7mkpRz2FCSU8udyl32kdANc3/Iu3/1gCYFAOlgt3741cMPvBGbccUICg9FdbbbcjUSdibEREr8du6mjHhxYLfpri/VKJFUW8kJWNv+fmYkxEBMrtdpwdG4sH6+tw76lT2OFw+BUTA9jdnyhDYjqo1a8FOI7juG8YAPxc6yA4bgDgvZfDUwyAyfAO/wsLjJBjrGgwQ1LsodqTmJD8W/zhUCj2ys3bFpzEKoAqsfaE2mvG6OODk3gPUn/nCGfzoWD3X944ku316Fjvb/5UZFSUw8/Um0MykNIXHhJPznc/LtZQSqbWsaiNAJITDeWu+ZlZFG8YqXU8Yez89K92TNM6CK53PMHcD+QuWyEC+JnWcXDd25I2evzSBb/scAm6w2qvvc3Rga/sdiw4chg/ra3Fpo4O/KK29ozjqpxOPFhfj2eGZCFeFM94/fnmJtyelIzPrVaMiYjAH9LT8aTZ/+INkZD+yHKZD/zjOI4L3NLC5YX8JjLH+Sl32YoLwKuXw5kAoARABQDV+/j6g8k0xljRUAWFQhZPA8uc8V8s2hDsffR69/j4+NrdwVj7mNgY1ftRvonVJwUl0c9YZM89HPyU3Bzc/ssEOF8/RxgXzD3O3JQ8L9U3egxA1wN/NCKRUHOW+zF2klIH3JNexFDrKU7c4ZmSUgIxtDcT+ilexdwP8ARz//ADAMO0DoLrWW1MSvbiRQ+lNkXEbVFz3Z+kpOKr/OH4X/5wPJ6ZiWlRUfhTZucbuLUeD35cewr/l5GBXMOZ7caOud1olCRMiYqCkxQIYGAMcJESUGzpbZhx2TplXUCLcBzHcVEAfqJ1EBzXj/Hq5f5hDoB9AM4cKKIB5lYmGTY0VoKIQrVnqFpljBixqT0Y67bDNUaGomqCNU6fFIzh4QQWEZQEc3rD5qC2aTiSgc2WaJYczD2+b2F7x7qJLvfoUO7ZG4mEU2e5H8MJSvN/eFCYUqJ1613zMqKVtMgehylynZyT/tWOWVoHwfWMJ5j7h19qHQDXNx36yLgbz/3NhJ3J+UF/DPC9tla81+btUPF8cxMssozfNTTg0mPVuOLYsU7HPtVkxo+TUwAA58XG4V8WC64+fhxLEgMf3HzVGqUwrYVqAl6I4zhucLurcHlhgtZBcFx/83X18mSt4+D6rAiADcCRvp4gWc2of/dXOPXyHah95S5Yt3x8xjH2vV+h9rUfofa1H6H+rZ/B3XgUACB3WFD/t1+g9tW70HHwu+Lhxn/+HpKtGYJdmqXf2rwm4M+qj7ytMh4OeqsMY4R9alRUm6r9kgEADNEnheZ9ai4Zq08MRoLfyoLRy5aoJdZ2Imj9lwmg588TQ5pQjVaUfY+am2eHcs/eSCTUnu3+k3yc0gdUcpmANs9I03r37LSZ0AsmrePph36jdQBcz3iCOczlLltxNgB+Z6sfUZggLpt9Z8kHI+atIUBSc+2pUdF4PisbAHB1fAKujvfmIn6fnoGNIwrwUW4ePsrNw99zczud95fMId9WNifpdHgnJwef5g3DObGB39hnQNyjy+UWRgGWQ3Mcxw1useDDfDnOH7x6uf/JAZAMoG8DowURCfNuwZClLyD9+sdg27YC7qbOrYB1pnSkLf4/ZN78DEwzr0bzF88AANr3lSN63HykX/cYrJUfAgA6Dm+CIS0futgkAIDY7CrR7WsLWY/oBpYx4z84L6itMhiDUFCwPigFIFXiqTY114vWxenUXM9LsKi/ZvD7L7fEYsvJVJYXrPXPQORcXtsQoQOC8G/gH4mEunPcf/JUU+ZQrWNRExmEra6SNKecGzNT61j6sYXpX+0o1DoIrns8wRz+ePVyP/X62PPnPjzlhl0EtGkdS7DFODH+rs+UCq3j4DiO6+d+XLi8MFbrIDiuv8hdtmIeePVyf2UCUAyg1+phXUwijOneolHBGAV9UjZkW+fZ2hFZoyFGeGe6GYaMgmzzdqFgog4kuUGyB2AMpMiwbfkYcdMu63S+eLJ9rnjMtj7wT6tv3sKSoLfKiIltnqLXO/wfuNKNOqEtXc31IsToaDXX8xKD0iIkuXm3KxjrfuO1s4WQJnqvttk3jfR4wqYVp0xC3bnuP7qPUmaO1rGohQCHNDR6jas0fSIidKp+7wxSv9A6AK57PMEcxnKXrZgE4Cyt4+D8t27I+Il3zv9Zq4eJx7SOJdjm7qGZI0/Sfq3j4DiO68cSAdypdRAc14/w3uX9mwhgLrxJ5j499SdZGuBuOApj5shuj7Hv/C8ih3nvO0SPKYGzehsa//4QTLMWw7ZtBaLHngVB33mmFgOYrso6Wah3bPP3k/FFKFplMIaI4SM27VV7XQnyyA64VEtc6wSj+u2hmL5D9TUBpDdsDlqC0KnHgc0jhZA9uRwvyzvub26dG6r9eiMTq1/kftR5hIYMnOSyyPa7Z6bWSaPj54KxoFW+DzJXp3+1Y0BVtw8kPMEc3nj18gBwPC4979qFD5oshujtWscSTAzQP/CubDB4yKF1LBzHcf3YTwqXF0ZqHQTHhbvcZSsKAJyvdRycKuYC2Amgx7YGitsB80ePIPGspRCMUV0e4zy+C/Zd/0V86U0AAMEYjdQrypBx45MwpA+H48hmRI2cieZ/Pw3zR4/Adeq72ggGGPQ7W0awVtcBtT6xnjSwjBlf4PygtspISjpZJAgedat5GdghsV615LgAIVWttb7BmEHVQYQAAKLWWNvxEaqv+7V/zBaaez9KJUS2d2obkoPZ7sMXMrGG89yPOg5SdujagwQRAbKcGlHump8xnGL1YVMhPkDowG8uhy2eYA5TuctWDAdwudZxcOqwGaMTrl344Liq+OwB3UbCICP//vflSq3j4DiO68fSACzVOgiO6wfuRZgkRzhVTALQDOB4Vy+SLMH80SOIHlOKqJFdtzB1N1aj+YunkXr5AxAjz5wzYln3LkwzrkT7vnIY0ocj6bx70brmzU7HMCDWUNmUxNo9J85YIAj+hpsKgtkqgzEk5Obt2KL2uofFOtVmrzDGIg1CRKta63kXjVB1Dg4ARDibDwYrISsz1H02lU0LxtpduaPNuiNbksJigJ5MrPF896PtVTR0YCSXGY57Jift90xIKoHA9FrHMyARXffLVT+M1zoM7kw8wRy+fgb+7zOgyIKov7f0njmf5s1cQ4CsdTzBMvok5s7aq6h+IctxHDeI/LxweSF/U8Jx3chdtiIRwI1ax8Gpbhi8A093nP5BIkLzv5+CPikbcVMv7fJEydoI80ePIOn8n0KfOOSM1z0tpyDbWxAxtBAkuQDmfZtF0pmFrgxIMaxrVOCSg9ojGQCICUnBbpWRkVGVDyiqvvdoY+0Faq4Xo0tQ9e+aCVGqv9dKbt4TtP7Lq8ezKkVgIem/nCZJlT9ss8wJxV69kYmZL3A/YjtAQwdEla9i0le45mckK0kR47SOZUAiap1IleXPYKl8Dr7gLeXCEE9ghqHcZSvSwC+aB6znii6b+9jEq7cRYNU6lmBgALv7UyU7toNatI6F4ziun8oCf4qJ43pyO4CueyRw/V0igLEAvn3qz3VqH9r3fgXniV2off1u1L5+NxxHNsO2/XPYtn8OALCsew+Kw4qWL59D7et3o275vZ0WbVvzFuLnXAcAiB5dgvbd/0P9Wz9D3NTOw/6+wQi5xoqGRkhKUIbFnS7YrTIEgbKGDDmwSc01iSG1kVkOqrVerCGxx/YovmJM/R8PaQ2b01RfFAAB9rfmh6b3MiNqeae2ITcUe/VGIWa+yP0Hy37Kydc6lkARYHaPi9/snp46BzohCEMrBzeRpBML6bM1r+Jaw0/xx5IEtKYC+NHKVfm8GCPMMCLSOgbue3KXrXgYwP1ax8EFV35bzeEny5+O0JESFo8nqa0+Hht/fKduutZxcBzH9VMVu2/cHTbDdzguXOQuW6EHcAxApsahcMFXDmAONCyKIqOwxTU3vRhBri5lpDQ/g6VKPNpSgrG+LOv2r193zWg11xwpZZbPkUaXqLHWvrYNFbtb16hWVStGTFurj5w1W631QNQ2r/xuEwOp3iJj71BW/ttrRVX+Hnvzq6aWDYtt9hmh2KsnCrGmi92/b91Nwzr1tJasZjSteAKyvRWMCYgpPhdxky/udK7stKP58ychtdWD6fRIWnQPDCm5kDssMH/4MBSXHfFzrkdUgffTbPzn75F4zl3QxSYF53OJFDe6p6WMgFEMzgaDWCR17L0S71gX4D9TBShiF4dcf9b8I38LeWBct3gFc5jJXbYiFsBdWsfBBd+R+Kzh15/7QIRNH7lL61iCIb0N0y9fq6zTOg6O47h+ak7h8kL+iCXHnelq8OTyYFECYAsAu1YBMJcy2bChcSOCXJX1dauMI8FaXxSl0UlJJ1QdOH5cNMeqtVasPlG1ns4AwIQYVW8IRLhaDgYjuUyA/MIiISQVvMPcnnVhklxuvsT9uzOSywAAQUTCvFswZOkLSL/+Mdi2rYC7qXM7dOuGD2BIHYbMm59B8vk/QevKlwAA7fvKET1uPtKvewzWyg8BAB2HN8GQlh+U5DIBNk9+7Fr33PTpPLmsIiIlmRorf05/2P0Krh97Dv49o5vkMgDcF9LYuF7xBHP4uQ1AvNZBcKHRFhGbfO3Ch0Yei00fkInYKyuU8ektdFLrODiO4/opfsOZ487E31AOLlMB1AI4pVUAgl2ard/WvCbY+zSy9On/xgXrg7V+/vBKVfsSO+EZK0F2qLFWjM6kal6CsWijmuslNe9xqrneN+oTUNmQyIL+NKtAVP9mXcPYYO/TG4VYy6Xu3zbvovwzk8sAdDGJMKYPBwAIxijok7Ih25o7HeNpOoGI3CIAgD4pG5KlEXJ7K5ioA0lukOwBGAMpMmxbPkbctK7b4ASC9Gyne3aaRR4ep16V/GBH5MyngxX/h/uOP4U7pxZje2Efzpq4clV+SKr/ub7hCeYw8vUjf/yieZDxiDrjnWf9bNb/sietJmBA9axhQOwjy+VWRqRqVQLHcdwgcV3h8kLVKsQ4rr/LXbaiFEBIepVyYaUAgAHAbq0CEJtcJbr9beXB3udt3DiyDfHmYKxtNDomx8Q0qTdQkMF4TDDvVWOpCDFG1abJTIhStQ9uepD6L7+8UIgLxrrf94i5ucakKPGh2Ks7CqH1cneZeScN79OASMnSAHfDURgzR3b6uD41Dx1V3vswrtoqSJZGSLZmRI8pgbN6Gxr//hBMsxbDtm0FoseeBUEfodrnQIBbGhJV7pqXUUjRugHZ5jLUGClN02h9+XO4xf47/GpONk7m+bgEz5+FEZ5gDi8XADhz7DE3KDw+6ZrSZ4su20RAh9axqCnGifF3faZU9H4kx3Ec9z2xAK7XOgiOCyM/0ToATjMp8CaaNXvqTzzRPlc8Zg9ahTHgbZVRFsRWGQUFGxrUXK9KrFVlCKJBiIhXY51vsWj1EsxEbXHWY11W3AbCbsSuPblC0KuKC52uivPbOyYHe5+eKIS2H7jLGrbTiJG9Hw0obgfMHz2CxLOWQjB2vvdgmn4FFGc7al+/G7Ztn8GQlg8mCBCM0Ui9ogwZNz4JQ/pwOI5sRtTImWj+99Mwf/QIXKf2B/Q5kIBD7mkp1dK4hBIwxvNoAdKRp/oC+qjiVVwb/WM8XmKCJdnPpS5YuSo/W9XgOL/xb4zwcovWAXDaWpE3c/pP5/zohMxYndaxqGnuHpo56iQF9lud4zhucLpT6wA4LhzkLlsxHN5iDG7wMgKYBWA1NHjqjwFMV2WZLDQ4VO1l/H1mlj79c1ywIRhrR0W3TTMa7aq9z2gULKpUcQpMVHW4IRMiTGqtZfT2X1Y9b/JuqaBKe5Ge6IiOv1LfODHY+/SECJYr3Q/VbaOCUX06XpZg/ugRRI8pRdTImWe8LhijkHz+vchc8lcknf8TyB0W6EzpnY6xrHsXphlXon1fOQzpw5F03r1oXfOmf/EDJCcZy13zM4dSvKFPCXKue9Fk330LPV/5Oq7JuQZ/m2OEOzLAJUXwPFrY4AnmMJG7bEUmgIVax8Fpb39S7qgbz/m10KEz7tM6FrUwQP/Au7LR4KGgX0hxHMcNMOMKlxfO1ToIjgsDtwFQfcgW1y+VAtgIIOTXlQww6He05LM2d1Uw93kHNxYEo1UGY9CPKNh4UK31ZKbk2+AIOGHNGIvTMb1awxwlxowxKq2F5Oa9qn+dSQKO/28Cm6L2up0QKU83mC1RRKq2C/EtBFiucj9waguNHN234wnN/34K+qRsxE29tMtjFKfd22cZgH3nfxCRPbZTlbOn5RRkewsihhaCJBfwdbExSW7f42eo9RQn7vBMTi6ByFTt6z2oEMlpVLfxfnpo70u4sXA+/jdVUPemzc0rV+V3NwiQCyGeYA4fS+C9+8JxaI6MT7tm0UN5p6KTg1K9oAW9jGG/fk+u1DoOjuO4fogP++MGta/nlNygdRxcWJkBoBpAfag3ZkCcYZM5gbVLNcHaI5itMuLj6yaKotui1noHdXWH1VgnWqdaQr1NpXUAAGkNlar3X/7PJHaMgtxmYZbDuWaOwzk+mHv0hAiWazy/rqmk0WP6eo7r1D607/0KzhO7UPv63ah9/W44jmyGbfvnsG3/HADgaT6J2lfvwqmX74CjeisSFtzeaY22NW8hfs51AIDo0SVo3/0/1L/1M8RN9W3YnxKjW+ealxGtpEXyvv/+ImofSfvWPIYfn3oCP5o+FnuC1RImG7xYMywwogE1U6xfyl22ggE4DGCY1rFwYYaIfl355prZdbsHxHRUAuivFwlb144VNO0DxnEc1894AGTvvnG3qr0zOa6/yF224lIAH2odBxeW6uBNKPapQlJNxFDtKkmPg1FMCtYe19Ib68/Dp2f2CQhQfX3+6kMHZ5aqsVacErnhSvfMGYGus67hX9trOqpUSOaJ1REJ9/g6KKxrRJZ55XfHqtkig4C2G34q6l0GFrTKYqOiHFp/vGaowdtWJuSIYL3Wc//J9cq4oPeYVhsBrdIo0wE5Jybgr+nBipHSOBMV+6/H6+NjYUsI0bafnDX/yMUh2qtbjLH7ANwKbxun3QCWEJFT26hCh1cwh4dS8OQy1xXG2MPTbix5eewF6wno9z+YGMB+9KmSHdtBLVrHwnEc14/oASzVOgiO09CtWgfAha0MADnwtswIKUbIM1Y01ENSVBl015V3cMOo1iC0ykhLOzqKMdn3ngFdsDLHKAIpga4TZ0hSZ9A5E1X79zC6WqvU7r+8dTjbEczkMog8r9Y3Khoml23Xe351vF8mlw3CFldJmosnl/2jJ/eRS+mDta/jGtNdeLokhMllADh/5ar8zBDudwbG2BAAPwYwmYjGwduh4GotYwo1nmAOD7wpOdejD0eUzrx/5m2HFTDVLzBDTSCkPbJcVq33G8dx3CBxW+HyQt5Kixt0cpetGALgXK3j4MJaFIBp8A7/Cykm01jj2oZ9UEgKxvrEhMTf4hHVW2UwRulZ2XvVaV3HkFAntAY8zDtWn+hRIxxAr1pRTpLK/ZcJcL9yrhDUavvz2jvWFbncmgyjI4L9Bs+yY2uVwkIt9vcXAQ4pJ2aNa17GZETo0ns/gztdLFl33EFPb34d1wz7Ad6frYekxc2NcBn2pwMQyRjTwfu7qVbjeEKKJ5g1lrtsRTyAy7WOgwt/O1ILxt1y9jK3U9T3++RsWhum/2CtslbrODiO4/qRbAAXah0Ex2mAzynh+oLB+1ToOgCukG7sUqYYNpqDVkFtZmnTV+Ci9Wqvm529Jx1Qp1/mAbG2KdA1YnTxqnyfM2ZUpTIbANIbKlPVWgsATqRgc0scU72n8zeiFWXvI+bmOcFavydEsC/x/OJohTK+fyWXRbbPPTO1Xhpl4gOVfUEkZVLN+gfoN/tfwJLiOSifwrQfxHvLylX5muU4iegUgMcAnIC3fZOFiP6rVTxa4Alm7S0GEKF1EFz/UB+dNGTxorLMxsj4fj8s74oKpSi9hU5qHQfHcVw/cqfWAXBcKH09p+RmrePg+pVZAKoABJzw9IVg88zWb2teHaz138X1qrfKEEV5eGrq0S1qrFUjNAf8KHyULlad98RCpDqV0EQWk7Va1UrgF84TU9RcrxMix5u1DVGiBjfkiNB+s+fnR1YrxZoNFfQVAbKcGlHump9RQLF6dXp2DwZEtrG0q/wvuKvhz7hn5ijsD3n/+x7kADhHq80ZYwkALgaQByATQDRj7Dqt4tECTzBrLxzK+Ll+xKEzxtx0zv2Tt6SOXK11LIFgQOwjb8itgkKy1rFwHMf1E2cXLi8crnUQHBdC8+F9o8ZxvhgP7+ySQ6HcVDQ7S3X728qDsTYxIbEMjx5Ve91h+Vv1aqzjhjTGDckayBoGISJOjVgYi1RjGRhdrQfV7L/cFoVtRzJZgVrrfd9iq72ywOMJ+c9LInTc6vnpoa+UCUWh3ttfxHDMMznpgGdCUgkEptM6nv5AILmuhFaWv4QblPvx25JUNA7ROqZu3Kbh3gsAVBORmYg88A4nVn1IazjjCWYN5S5bUQxgotZxcP0PMUF4YObS0rdHnl1BgEr9ykIvxoXxd33GW2VwHMf1EQNwo9ZBcFwI8eF+nL+yAKQD2BzKTcUT7XPE4/YNwVi7iaVOU7tVhl7vKjaZ6vcFvBCD7qjYEFAfZpHpkwKOAwAT1Jmfl9SyV52hg197c4EQ8CDE7iTI8vZlLa0hb/FAhI7bPfdVrVQmFYd6b38pJv0a1/yMFCUpot8NIdSCgVwHr6S3172Oa5Jvw3Ml0egwaR1TLy5cuSpfqz7aJwBMZ4xFMcYYgLMABNyfvj/hCWZt8YtmLiB/G33unLLpN+9TgBatY/HXnL00c9RJGlQ/eDmO4wJwrdYBcFwo5C5bkQjgUq3j4Pq1WACTAASlqrgrDBB0BywThUbHjmCsH4xWGSMKNlrUWOegWBto7+MkAWLA/bMZi1alRUR6w2bV+i+7dTi8dqwwWa31OiGyvlNbnxrq/rdEcNzhubfqv8qUCaHc118ENLoLEza7p6fOhU6luxADmIlat/2Intj2OhYXXIwPZ+kgq/K0QwjooFFrLSLaBOAfALYB2A1vvvUlLWLRCk8wayR32YoI8DeJnAoq08cU3X7WL2xuQaf6hOlQYID+gXdlo8FDqk5p5jiOG6DyCpcXztI6CI4LgesAaDGJnhtYBAAlACoQoqf+GGDUb2/JY21u1QdzB6NVRkSEbWpkpOVEoOs0MVtOIOczxli0Lq4x0DggRBsCXoPIYrIcVa2dxcfTWZ1aa33fD9ssu7IkOaTtCojguNNzz/7/KFP7RXJZiRQ3ukrTRSUzaorWsYQ1Ik8WHV/3O/rlwedw68QZWNdfn7bX7Gk/InqIiEYR0Tgiup6IQjp0Vms8waydywDEax0ENzDUxKbmLF70UHKLMXar1rH4Qy9j2G/elUP6CCPHcVw/xm9Qc4MBn1PCqWkOgD0AWkOxGQNMhk1mE+uQatReu4mlTvsMF69Taz3GII4o2HAs0HUURkPbWHtAieoYfWLA/z5MiAl4WKDR1XaQgVSphFYYzB/NEKaqsdb3ZUhS5R1t1tnBWLs7RHD+yPPjfV8o08I++UiA1TM8dq17bvp0GEVVWrAMSESWItq2+inc0fRH/GRWPg4HrVd4iBSsXJUfnCcGuB7xBLN2+EUzp6p2faTp+oUPFO9JygvZY4BqGnUKc+fsUVSZZM1xHDfAXVm4vLC/PKrIcT7LXbaiCN5BbRynpgkALACqQ7EZA9IMaxs8cMuqt7J7F9ePaUVC4NW+X4uLM0/W653Nga5TJdYeCygOQ5I90BgYi4oKdA01+y+vH832Sjqm+tMYjKj57dr6kA71I4Lrx54f7VmhTJ8Uyn39QXq20z07zSrnx4U0Ad+fCCSfWkBflL+C64Vf4OHSZDRlaB2TingxhgZ4glkDuctWDAMwT+s4uIFHYYL48zk/LPnn8JI1BEhax+OrH36mDI1rp4AvbjmO4wa4JACLtA6C44Loaq0D4AasXACJ8PbIDDpGyDNWNNRCVlQdGAfGEsrwiGqJcsYQlT+8cneg61SLjQFVD8fpEwPt4wwmRMUGukZaw+aUQNcAAAIcbywQCtVY6/t+3dx6KEVWVImzL4jgus9z1+5PlZlhXRlKgFsaElXumpdRSNG6LK3jCUdGcuxfTG+sfwNXpy3ByyWRcAT8PROGrlq5Kp/nO0OM/4VrYwlC3ISfG1xeGXfh3EenXLeTvFUa/YZASH14uXxI6zg4juP6AV6ZwQ1kPMHMBZMJ3gr5NaHYjEk0zljRsBcKqVr80cRSp32KS1RrlZGcfLxQEKSAZqLY4RytQPG713WMLiHw/ASLNAV0PpE13nJkZMBxADiUic3WaKZ6a4Z8t3vdVTb7dLXX7Q4R3D/z3LHrX8rs8E4uC+yQe1rKMWlcQgkY47mu0xFRAjVvvo/+uOM1XDf6fHw6U4Si0zqsIMoAMF/rIAYb/k2nDX7RzAVdxZDiST+c95NmDxOPax2LL9IsmH5FhbxW6zg4juPCFpEzz+1JQ5lpIFaccINc7rIV0+GtMuW4YNIBmAugHIAc7M2YS5li2GjeoPa67+E61VplMIaknJydgc1EYYitEVr2+3t6lC420AF9TsZ0AVVRG91tVWr0XyaAXjhPzA50ne8TiOqW1zWMU3vd7hDB/XPp9h3/VOaG7YA8AhQ5yVjump8xlOIN/b1/sLqIXLl0dO3D+PnRZ3DblMmoLNY6pBBarHUAgw1PMIdY7rIV4wAM1zoObnCoNmUOu3bhg3FWQ9QOrWPxxQ/WUnFGMwU8zZrjOG7AIJJSJGnLj1va1m04XuP55FRdCYCLtA6L44KAF2JwoVQCYDsAa7A3EmyeOfrtzerOSlG5VUbmkAM5ACmBrFElnvK757RRDLS9BQv46c3E5n2qtDNpisPmmhSmeo/k/zM315oUCqxKu4+I4PmltHTHP+SSoAwpVAMx1HgmJO7yTE4ugah+r+t+i6h1IlWWP4Ollofx89m5qM7XOiQNXLZyVX6gN604H/AEc+hdonUA3OBiM0YnLF740NhDpiEVWsfSVwyIeWS5bBEUCnpFCcdxXNgiIpMs71zSZl1TfuKUZdXJ2slLLdZZMUTfvAG/UtP4OE5luctWCOBf11zoTQZgBnAy2BuJjc4S3QGLqq051GyVIQhKTkbGwcpA1qgVWlP9PVdk+sRA9gYEW2DnA+kq9V9+7RxB9cRWkdO1ZlF7R0gG7BHBc790y7YP5Hlhm1xWYnTrXPMy4pTUyGKtYwkXIkknFtKn5a/iWsNP8ceSBPj//TgAmACcq3UQgwlPMIfeJVoHwA0+siDqfzzvvjmf504vJyCgqoRQiXah8IefKrxVBsdxg06Uouz/gdVW/p+a2vq1J04V/aS1bW6ionTVw/FclJniQh4gxwXJNuNtMz8yPHj4XGHzDgalX1yvcANGPoAoADuDvZF43D5bPGHfqOaaarbKyM3bHlAVsQfyKAfcflUxM7BUBuZ/gQnTBVZ9TGSLtxwOuP+yQ4/9W0cIxYGuczod0fGX6xtD0gOZCNID0pKt78pnTQvFfr4ioMUz2rTBPSttFvQCvw4CEEkde2+klze+gauzrscbJRFwRWsdU5jgN61DiCeYQyh32YpsACG548hxXflr8Q9K/jLhyi0E2LWOpS9m76NZo0/QPq3j4DiOCzYD0dGF9vbV/6qpPbbpeM3oh5pbSzIlOaOX04wALg5FfBwXConMfvUE4fCcFw1/KT5svL75n4aH1iwQtuzkyWYuRJIAjAYQ1AIHBgi6/ZYJQqNjh3qLsoSH8OgxNZbS6TxjExNr/E+0MwiHxfoqv05lTIwUY83+721w+n0uAIPbokr/5b/PEVoDXaMTIuWZBrM1kihK1XW73ArSQ9KNm/8mnx2yIYK+IKOwxVWS7pGHxszQOhbNESnJ1Fj5c/rD7ldw/dhz8MV0AcRzfJ1dtHJVPm+dEiIDeWpkOLpE6wA47sucqVOPx6UffGLNM9EiKUO0jqcnDND95j05csl9Yodbz4J+QcVxHBdKItGpGQ7noTvaLBlFLvdIAMP8WOZKAG+pHBrHhV6ZiQG47Js/ioxSJrFDKa8YnoBMrHErFex/Xroo4SuluBBgTMNIue+pef5mCIZIQBDABBEZNz7Z6XXZaUfz509CaqsH0+mRtOgeGFJyIXdYYP7wYSguO+LnXI+oAm++qPGfv0fiOXdBF9vVgxtBZwAwG8BqeIcABiVZwwCjfntLnnt6yiEyGUaosWYzS5n6CV267iJ8NCvQtYYP3+SqrMzy+/zDYp1cKA/169wYfUJzh2xN9+dcxoxuvzb9WlLLvvZAzgcAmaH28ylM1bYScxzONbMczlI11+wKEaQy6YbNb8rnhl3yloAOOTdmizTSNLe3Y+XGelj+7wEoLc0AY4i64HJEXd553ptis8L6pzLIdTWA3gDTL8qgyxsOpa0FbQ/+FIrdhpibf4iI2fMAAG2/uRex994PMTkMOk4QOfJxaMtSPJeVjZNh28IkTMQBWAjgY60DGQz43Y3QukTrADgOAA4mDC24/tzfGOy6iN1ax9IbvYy8B96Vt2gdB8dxnBoYkXmi01n+Qn3j7u3HTmY+32Au/Tq57K9zUGYKybAfjguyWQC6rNoXGaVOFapKXjf8efwR43UN7xl+v2ausHM3QBTiGLlupF3zCDKX/PWM5DIAWDd8AEPqMGTe/AySz/8JWle+BABo31eO6HHzkX7dY7BWfggA6Di8CYa0fK2Sy6crBbAZQMAJx+4wwGTYaI5hHdIptdZ8H9eObUFiQ6DrGIwdU6KjW474e34Ls/s9UCxOn+h3H2XGIgOa35LeUJkcyPkAsKqIHVIEplohn1FRDj3ZYJ6p1nrdIYL8e+n6yuXywvBLLotsr3tmakNfkssAAFFE7B0/QfIbHyLx2TfR8fH7kI51/nJuf/tV6IaPRNIrH8D0q9/D9syfAQDOVV8g4pwLkfjMcnS8vxwA4FpfDt2I0ZonlxkpTdNofflzuKX9d/jVnGycVH2I5ADF22SECE8wh0jushWJ8N4F57iw0BoRl7J4UVnBiZhUVYaCBNPIU5g7d7eyWes4OI7j/EJkGeVyr/1zY9PW7cdOJi6vayyZ5XAWMkCNKkwD+A1sbmD4QV8OEhmlTxf2z33T8MfCw8br69/R/6F8trB7D082hy9P0wlE5BYBAPRJ2ZAsjZDbW8FEHUhyg2QPwBhIkWHb8jHipl3Wy4ohMw1ADYDaYG3AgAzDugYX3LI6LRUYiy/DI8cDXwasoGCD3583MWQ0Methf86N0yf5X4XMovz/OUBkM1mOBNR/mQDb3+YJEwJZo/OC5H6tvlExeH/XBw0R5Eekaze9Ji8KeiLbFwRIclpkuWt+xkiK1fc5mSompUBfMBoAIERFQzc0D3JT584r0vGjMEz0Fv/qhuZBrq+F3NIMiDrA7QQ8bkAQQLKEjn++g+irblDxM/ONjjzVF9KHa17FtdE/xuMlJlgCvhEyyFy4clV+UL+HOC+eYA6dC8BbknBhxiPqjLcv+MWsr7ImlBMQ1m/M7lqh5MS1U7PWcXAcx/UJkSPH49nwYFPzpq3HTkb8vbZ+9sL2jkkiEHBvxy7wygxuIPA5q6hjSsZMcV/J3wyPjjtsvL72Lf0j5TOEvXuDERzXA8bQ+MGDqHvjHth2fHHGy/rUPHRUrQcAuGqrIFkaIdmaET2mBM7qbWj8+0MwzVoM27YViB57FgR9RKg/g56MhPc9XNC+rpiCYcaKhhrI5FBjvWaWMvUTXBpwAUl0TMs0g6HD72roA2KtX5XZMfoEv9+TMCHa7xu3BrelSiAloPfre3LYNkcEU23o3AXtHevHB/aUU6+IoPyfdM3Gl+Xzwyu5zFDtmZxc5SlOLEEAFeFyfS08h6ugHz2u08d1+QVwVawEAHj274HcUAelqQERZy2Ca/MGtP7yh4i+8XY4Pv4AEeecDxYRGdgn5Idosu26lZ6rfANX516Nt+ca4Q59EANDLIASrYMYDBjxm/0hkbtsxYcALtU6Do7rzoVH1264c9e/iph3gnZYajRh04/u0oXlNGOO4zgQedJkecdiq819ldVeFE0UE6Kd3QCSUGbpFwNcOe4MZabxAPwfKvY9HhJr1itjjzwnXZSyicaMUWtdrmuSrRm62CTI7W1oeP83SDz7DkRkf5fMUVwdaPnfS3A3HoEhJRee5hokLbobhtTv2s7LTjuaPv4jUi69H60rX4bitCNu6qUwDhmtxafUFSeAbQCCloSjCLHSNTdtEhgL/EYkUdtfcZsrES1pgSzT3Dxk9b6980v9OTeSDFuvdc3xecC91d28/t+nXvHr71kXOWe9LmKKX+dm1G0oH131N7+TUATIP75DrGtIYP43rz5NjKLsWXu8ZnSQbkwD8CaX/yxdteE5+eKA+3arSYk3rHFPTpoMUQjofani6EDrvbci+tpbEDH3rM6vtdthe+bPkA4fgC5vBKST1Yj72YPQ53+Xz1dsVlh+90uYfvc47M8+BsVuRdQV18MwtiiQsHpGJKehfvMteCF2LPaMDd5Gg85TZ80/cq/WQQx0vKI2BHKXrYgEcK7WcXBcTz4dNnvG0bjM/X9c+3yCCPJrsEawpVow7co18toP5oqztY6F4zgOAECkxCvKrh/Y7LYbLLaxCYoyRYMoDPD2C/1Mg705Tg3nq7mYnslZJeKurBJxFzwknlyrjDv6nHRx2mYaNUrNfTivb/oli9HxiCqYAVftwU4JZsEYheTz7wUAEBFOvXALdKbOl5qWde/CNONKtO8rhyF9OKLHlKLxw98j/ZpHQ/Z59CIC3uTyanh/3qqOOeWpho3mCveM1DmBL8biy+iRyqdxR0AJ5sTEUxNE0WOTZX2sr+c64B4jQXbqIPpUkm4Uo/y+OctYjN+PwacF2H+5LhGbGhKYOjcgiDreqm2IDnZy+XHpivXPyReHzfsqAho8hQknlcyogFuLkuSB5aGfIWLBojOSywAgRMfA9Mvfeo8lQtPi8yGmD+l0TPubLyH6ulvgXPkFdAWjEXHWIrQ9cB8Sn3g50PC6CJjaR2L/1qV4PjcDtdPV32DQOx/AvVoHMdDxFhmhcTbCuCqU476xN3nY6CXn3E8O0bBf61i6c/k6Ks5spoB7y3EcxwUiWlH2XWW1lX95srax4sSp4ntaLXMSFCVRw5D4jWyuP1M1wXw6PZOz54k7S/5u/N2og8brj7+if6x8Ajt0MFj7DTaK2wnF1fHt/zurt8OQktP5GKfd22cZgH3nfxCRPRaC8bu3Rp6WU5DtLYgYWgiSXADzvkUlyf9WvEFUCmA9vBXNqhOsnjn67c3laqzVzFKmfozLAmqVwRhMeXlbt/l3MiKPC037fD1NLxgS/NoPAIQo/1oIENnjLYcDakXx0kIxPpDzT3ed1bZ5uMcTtAFuRKAnpcvXPyNfGjbJZSVS3Oial65XMqMmB7oWEcH6599CNzQP0Vdc3/V+dhvI4/255FjxEQzjJ0KI/u7ehlRzHHKzGYaiyYDLCQgCwBjgdgUaXieMlMZZVF7+Apa4H8QDczNQO1TVDbhvDF+5Kr9A6yAGOl7BHBq8NQbXb5ijEjKuWVRmem7V4xszO5rD7u4pA2IeXi5X33KvKCuCCo8QchzH9ZFBoSMLOjpO3t5myRvmkcLtsftztA6A4/xSZkoEEJLrDQOTcxaI23IWiNvgJl31V0rRieekSzJ3Uv6IUOw/EMkdbTB/+AfvHxQF0WNKEDlsEmzbPwcAxE44D57mk2ha8QTAROiTs5G06J5Oa7SteQvxc71JoOjRJTB/+AfYtnwC05xrQ/q5+GAmvD2ZUwCkqr242OgsoSrLGmmkKeAqzg+weOwcrG4IpFVGesbhEYcPT5UAwefcwUGx1pav+LY1g5AK72wYn/spMyHar6Iug9taJZDiczuPb9gisHNfDlOlb0KiLG/7RUtbwP/23SECPS1fuvYp+fLAK+VVQIBVGhG3Wx4Wq1qbDs+eHXB+uQK6YSPQvPQqAEDMLT+C3FgPAIi66ApIx4/C+n8PAIIIXc4wxP38oU5r2F99FjG3/BAAEDF/IdoeuA8d/3wHMUvuVCVGPbmPXIB/1V2Mf07RQ+L9gUPjfAD8BnMQ8R7MQZa7bIUIoB4An/TJ9S9E9OCmN9bMqN8blr/w1o5h5U9fLIZlbBzHDRwiUc0sh/PIHa2WjEK3O9wrH/JQZjmmdRAc55My07UA/qZlCC7SHV2lTDj5rHTxkD00bLiWsXD9Si0AG7yDAFVFgCKNia+Us6MDvvmSSE2Vf8XtUwNZo7p6wrqak+N8TgCKJBxa4prn8w2cj0880+SU231+/2w03d7EhGifz0uv21g+puotv99XvLhQqFw5QQjo7xgAQGT5oqa2fYgkZwa8VpfLg56VL177mHRVeCSX9cIO9/SUZIrSqdK3uj+IJcuOa7HcMxvlk5kfN1G4gKw8a/6RBVoHMZDxFhnBNxs8ucz1R4yx301fUvLamPPWEaDus0AqmLWPZo05Tj4/dsdxHNcbgahxssNZ/nJdw54dx05mPdtgLukHyWWAt8ng+qegtcfoKyOThi0SN5d8ZvzN8APGG488q3+qfDQ7fkTruLiwlwkgC8AmtRdmgKDb11YsmJ0BD79sYclT/4XL1wayxtChu1L8OU9mygg7nPW+nheji2/yZz+wyHh/TktvqEzyaz8AkoDjq4pZwG0dAODuVsvuYCaXX5AvrAiH5DIBLikrqtw1L338oEguE0mZVLP+Qfr1/hdwc/EclE/hyWVNzF25Kt/nfvJc3/EEc/BdonUAHBeIvxfMn/WbGUsPKmBmrWM5HQN0v35fjjR4qEPrWDiOGwCILKNd7oonGszbth07mfR6fWPJdKdrXO8nhhXeJoPrX8pMIoCFWodxugjmyT9f3FTyb+Ov8g8Ybzz8tP6v5SPZiWqt4+LCVjSAqfAO/1MVAyL025pzmcV9KNC1/o5rCpuR5HOi9xuiKBckpxzb6s+5h8Q6n2/WxOoTrX5sZWPM9zYeIGqPtxzyewDovyez48RYwHmVTI+06TaLNWg9kV+Sz6/4o3RN0Fpv9BUJ7KB7esoJaWxCCVT4ewtrRLaxtKv8L7ir4c+4Z+ZIHBitdUiDnB7e+WhckAzsb+jwcInWAXBcoLaljSy85exfulyCLuALXDXpZeQ9+I7s18Uux3EciDpy3Z71vzU3V249djLyg9r6OWd3OCYGc2p7kJ2FMhOfr8H1JzMA+D/QK8gimGf4ReKGkv8Yl+XtN9508C/6Z8tHsJpjWsfFhR0G7/C/tQBUnUzIAJNhozmGOaTawBZipjI8ciKQJfLzN/uVOzgs1vtcqRmnT/Lj6UnmT1IaBrf1gECKX787CWh9f64QcPUyI2p6u64+P9B1uvOKdN6aR6VrNU0uE6DIycZy11kZuWQyDOi+9wLJdSW0svwl3KDcj9+WpKJxiNYxcd/S/KmpgYwnmIMod9mK8QBytY6D49RQH52cdc2isnRzhGmz1rGcrqAWc0p2K2EVE8dxYYzIky5JlT9tbl2/6XgNfXqqbuZl9vapBsCgdWgqMAGYpnUQHOeDfvNGL5K5Cy4V15V8afxF7j7jkqrH9c+XD2O1x7WOiwsrswEcANCs5qIMyDCsbXDArbQFsk6grTIMBueE2LjGA76eZ2EdIwm+DX6K1Scqvu4DiO2+nwMktu63+3MeAGwZwXa69cyvwYKne6C55UiyrASlreZr0sLyP0jXaZtcZqjxTEjc7ZmUXAKBDYTrrS4ZyHXwSnp73eu4Jvk2PFcSjQ6T1jFxZzhv5ap83p4kSHiCObjmaR0Ax6nJoY+IvfHcX0/akTy8XOtYTnfnCiXX1E7+9WrjOG7gI1ISZHn7ba2WirUnTrV/ebJ26k1W28woomitQwsC3oeZ60/6TYL5dFHMNfJysaJklfFnOXuNSw78SfdieS6rO6l1XFxYGA+gA4CqPbyZgnxjRf0JyOQIZJ1AW2UUFGxo8fkkhqQGZvEpMR2t9+NpHKb3K8Gc3rA50Z/zCHC9cq4wxp9zTzfC7V57ha09KDeHl0vnlP9OukHToehKrG6da15GnJIaWaRlHMFkotatP6Intr2OxQUX48NZOsh6rWPiupUOYKLWQQxUPMEcXJr3OOI4tREThF/NvqPkvYKzKgjwaB0PAAiElEfekPkwHo7jOolWlL3XWG3l/ztZa15z4tSEu9ssc0yKEq91XEHG+zBz/UOZKRVAodZhBCqauUZdqSsvWW38afYe4837/k/3cvlQ1lCjdVycprIBpADYouaiTKLxxrUNu0Ak+78IM5XhEb9vhkRGWqdGRNh8/vo+oDvV6MvxEWJ0pK97MGbwva0GUXt820G/+i8fT8Xm1liW6s+53xCJat+oawjKz8G3pAXlD0k3aZZcJqDZM9q00T0zbRb0QpxWcQQNkSebjq37Hf3y4HO4ddIMrONJy/6jX97c7g94gjm4eIKZG7CWj1k053fTbtqrAK1axwIAKVZMu6pcrtA6Do7jtGVUlEMX2NtXf3qy9sTG4zVj729uLUmT5TSt4wqhKSgz+VWNxXEhVqp1AGqLYc4xV+u+KlljvC9rt/GWvQ/rXinPYubAeudy/VUcgAkAVH3qjznlaYZN5vWBrNHCkqd8hB/41SqDMehGFGzwuajjpNDsU6sAvWD0vTc7M0q+nmLw2KoEUvyqNn3xPDGwawsi+lNjU32cQqq3UXhHml/+gHSzdsllo7DZVZIuy0NjpmsVQ9AQWYpoW/lTuKPp//DTWfk4XKB1SJzPeDFGkPAEc5DkLlsxBkBQ+ihxXLjYmDGu+I6zfm51C2JYTFe/bD1NyGwm3g+R4wYZHdGJ0vaO1e+dqj+05XjNiEfNzaW5kjRU67g0IgBYoHUQHNcHA7qVXCxzjL1Wt6qkwnBPxi7jLXt+p3u9PBNNdVrHxYWUCKAEwBoAPic/uyNYPHP0O1oCSlz/A1cXNiHZr1YZJlPDZJ3O1ebLOS54xnog9bnXsQAxxde4GIvy+e84sWW/zddzAKA1GluPZLCABtVNcLkqzulwqF71+r5Usvp+6VZNkssEdEi5MRWu0owpiBADqu4ONwLJNQvoi/JXcL3wCzxckoymDK1j4vw2deWq/IB7p3Nn4gnm4OHVy9ygcDI2LefahQ8lthpjtmkdCwNiHl4u2wQlgEcHOY7rFwSihikO55pX6hr2bj92cuhfG5tKx7rdA3oquQ94ZQbXHwzoBPM3GAOLY45xN+i+LFln/HH6TuPS3Q/plq/JQLPffXC5fmcugF0ALGotKDY4SnQHLf4/uRdAqwzGEJ2fv3mnbydBf1Rs3Nf3PViMXjBafdpDiPJ5cFean/2Xly8ILI2iJzr2Yr15ckCLdOEf8tzVv5RuL1V73b4gke11z0ptlEaa5mixf7AYybF/Mb2x/g1cnb4EL5dEwhGrdUxcwPQAZmkdxEDEE8zBwxPM3KBhN0SZrjv3gfH7EnLWaB1LtAvj7v5E8XtCNsdx4YsRtY51uSqebDBv337sZMpr9Y1zpzldY7WOKwzxQX9ceCszZQAYqXUYocYYmIm1Fy7R/WfueuPdqduNS3f9RvfWmjS0+NSfluuXJgJoAaDak3ZitX2mWNO+yd/zW1nSlA9xhV/XzCmp1WMYk33qeXxQrHX6cnyMLt6n7wvGon1LMBN1JLRV+dx/2aXDofVjhEm+nnfavvJz9Y32SCJVKyg/lGev/pnnjlI11+wLAiQ5PXK1a37GSIrR54Z6/6AgogRq3nwf/XHHa7hu9Pn4dKYIxffBk1w4GxQ3uUONJ5iDZ0DdueO43iiCqPtpyd1z/zVsdjkBmlYQz9xPs8YeV/ZqGQPHcSohah/m9qz7vbm5cuuxkzHv1TbMOavDMUHg1zA9yUKZKeDJ9hwXRKVaB6A1xiAksPbxt+r+PXej8UfJ24y37bxf9/aaZLSZtY6NC5o8eHszb1djMQaIur1t44Um5y5/1/gnripsQrLPrVsYQ8rQnF2VvpxjZlafWlfF6hN9qvhmQrRPvZT1HtsBf/ov/2uG0ODrOacrcTgqpjtd4wJZ4/s+lmeu/onnrlI11+wLYqh2T0k+6ClKLIXA+n8ClsiVS0cqHsbPjz6D26ZMRmWx1iFxQVOqdQADEX9zFgS5y1YMA5CldRwcp4UXx19S8qdJi7cT4NtjbSpigO7+95Uog4c6tIqB47gAELkzPNKmXzS3rq88XsM+PlU36xJ7+1S995E2rm94FTMXznjl0GkYg5DI7EW36VbM3Wy8K3Gr8fYdv9S9W5EES5PWsXGqSwAwDoAqg6kZEKnf2jyUWd0+D97zLsBMZXikxp9Ts7L2ZQFEfT1eYZRrYR19bssRp09y+BIPE6KNvhyf2HLA5/7LCkPjxzPYVF/P+0aEolQ90dA009/zu/KZPG31PZ4flaq5Zl8o8YY1rrMy0ynR2P9vaBO1TqJN5c/gVuvD+MWcXFTnax0SF3STV67Kj9Y6iIGGJ5iDg7fH4Aa11dkTJ/+o9D6zxIQTWsWgl5H30DvyVq325zjOR0Ryoixvu6PVUrH2RI3jvzW106632maq/QjpIML7MHPhjCeYu8EYxCRmK75T9+mcLcY7E7YY79j+c937FQmwtmgdG6caPbxPu5YDUAJdjAHxho3mSDgkv4ZI+tsqQxCUvPT0wz5VMVeJtX0eDB6rT/TpiUjGoiN9OT69oTLBl+MBYN0Ytk8SmcHX8wAARO7X6xoFA+Df+V34XJ66+keee0rVWq8vCGhwj0/Y4p6WMhci8+nvPNyIJJ1YSJ+ueQ3XGn+CP5UkoM3n4ZJc/6MorKbdHl95sGrGFK1jGWj6/2MM4YknmLlB72j8kPzrFj7Y/PL//rgz1uMo0iKGEbWYU7pLqVw9XvC70oDjuOCKkZXdF9vtLbdYrGNSZEX1aeqD2HSUmRjKLH2uLuO4kCgzDQEwXOsw+gPGICbDOuGHuo9xl/ixZIZp67vyfMfr0sKxbYj1OTnGhZ0SAJsBjAIQ0OAwRsg0rm047CrNsEAvmHw9/5+4qnAuvqpLRlOGL+fl5m2LrK/v+3zdo0KDYWofv/1j9PG+FcMJUX2vRiTqSGg76FP/ZQI6Xl8g+P2e5iJ7+/pxbnepv+d/33/kyavv8tyr2np9oUSKG9zTU0bBIKo+oDCUIqlj75V427YA/5kqgHxq3cL1P4oiVLe3J9Q0mYcKZnNunssVkwVvx4GzAKzWNrqBhSeYg4MnmDkOgMUYk7R40UOxT65+em2+tXa2FjHc8bmStz2fNVmiWbIW+3Mcd6YIRTl4TntH7e1t1uFDJalQ63gGqHgAIwAc1DgOjvs+Xr3sB8agS4Vl0j26j/Bj8SNPI+K3vC2d5XxDPrfQihifE4pc2JgC4BAACwJsscgUDDdW1O90lWSMhMgifDuZmcro0UPPYKlPCWa93j0+Pr52d1tbZp9+l9uZc7QCRRIg9JqHiBBjfKqOZSyyz98Heo+tSiB5gi/rHxyCLfYo5tf7/BhZ2f27phbVZjR9KU9cfbvnJ6VqrdcbAizSiLg98rDYWaHaU3VESjLMW5bgpchibOfXngMUERRFEQ/bbUl15qYcY5M5J9/jicyDtwf+92mSnxjIGPW9bRLXB7nLVmQCOKV1HBwXbu7d9kH5OScq5zANWvOY41D5wx/qeBUzx2lIR3R8boej+o42S/Zot4f3tguN61BmeVvrIDiukzLTiwBu0zqMgYIInnok7HhbWuBeLp8zzoZonmzun5oA1AEIOPGlRIob3XPSpoIxn6+5L6P31l6Ov/uUdHE6ozdtrrxsWl+PP9ddvCtbSRrf23GS4jn0z+NP9LU8WjHG38cYY6wvB6c1bC4fu/+Nkj6uDQKUnywVT55KZjl9Pee7k6n941N1TcM8ku/ndmGVXLz6Zs8vStVYqy9IL+xwT09JoSjdkFDtqSoiRz4ObVmK57KycbKrJCPXjxFBkmV9lc2a3GQ250Y1N2ePkCRjfB9P7wAQX1ZW5gliiIMKr2BWH69e5rguPDnxypIDiUM3/XjHP8YxIKQN9VOsmHp1uVzxXomoWuUAx3G9E4jqpzhdVbe3WVKmOF1jAKjy5orrsykAeIKZCzd9TkRxvWMM+gy0TvmZ/u/4qe7v7jokbX5LWuB+Sz670I6oOK3j4/osGd42GesABFQlKjjk6YZN5jXu6ak+vy/9EFcVzsXquhSY+1zJbDS2T4mKaqvu6IjvU/KuSjzVmq0k9XqcyHS+9MO1Msbi+3qwr/2XzSZsPpXM/PrZdaPVtnWYR1IlR1Aujw9ZcpkAl5wVtVEaEz8XfUzchxNGStNUbNhzI14dZ4KFvwccIIjgkiRDlcWS1mo258a2tgwpkGX9WD+XiwIwEcAmFUMc1HiCWX08wcxx3fgid/q06riMqscrno0VSckM5d6XrqeJFWPpuF+VBxzH9Rkjahnrcu9darHGlnY4xgtAutYxDWJ8eAkXXspMUQDGaR3GQMUYDJlonvJL/fv4he591ykkV74lnS39TV5Q2I7IgHr8ciFhhDe5XA7ve0q/k3qCxTNXv7Ol3FOU2OcqXQBft8p45NCzPrTKYAxCQcH6mh07zutTgvmU0NKntnWMsXiR6Tpkkvow7FewwtsaqndEjoTWKp/6L796juDXMLskSd7605Y2VZKbFXJh+Y2eZaVqrNUbEliVe2qyQCaDb18/YUBHnupF+LTmMnwwxQBPqdbxcIEhQrvHE1HV1pZuMzfmxbe2ZowiEnt9AsIHs8ETzKrhCWb18QQzx/WgKjFn5A3n/LrxpZV/2hstufy92+gzBkT/4U25+pZ7RVkRmBiqfTluUCCy53s8O2+2WA2L7B3FeoBXioSHCSgz6VBmkbQOhOO+NgkA/x0cAozBmIWmqb/Sv4tlunedNZSyabl8jvyuPH98OyJjtI6P61EJgI0AxsNbYecXsd5RQlHWCmlEnE+/k9tY4uR/0pVrL8cHfW6VERPbPEWvd5g9nsheq449kEc74WmLgD6+t2OjdaZGq6c5t/cIRHtf4gQAvcd+wJf+yw4D9m0fLvie0CKyvFNXn8kCuFHwjXXy2PLrPb8KerKXAEVJNlZ4JiTNgMAMwd5PTdFk23UN3nKWYuUU1nW/Xa4fIILF7Y462NqS2d5ozk2xtKWNBIRgDgGfBeDxIK4/qPAEs4pyl61IAjBG6zg4Lty1RJpSr1lUFvfsV0+sz7abZ4Zq32gXxv34Y6X8yUvFfnc3nuPCDpFriCRvv95qpctt7cURRP138MvAFQlvtegOjePguG/weQgaYAwR2cw87TfC2/i17m3HCUrd+Lq8kN6X5xU5YPQ7gckF1XQA++GtyvVp8N7pxKO2mRQpVspZ0T59732IKwvn4qs+t8pgDBHDR2zauH9faWnvB0M4ItbvHytnz+jt0Fh9YpvV09yHAPTOvsQJAImtB6x9PRYA3p8jtPly/DfubbXsyZTkgK+NNsijy6/1/Dr4yWWGGs+EpGYlJaL/vE8iktNQv/kWvBA7Fnv8rmp1uxXcd28dPB6CLBPmzo3GjTclfm8rwrPPNqNyUweMRgG/+EUKRhQY0dYm46GH6tFuV7BkSSJmzfZ2gnzggXrcc08ykpN5yq0nRGhyOmMOt7Rkuc2Nuek2W/JwgIXyCTx+XaIi/tWurjlQ4Q4lxw0GHlEfcduCX878VeVbq+fU7ixR4+5+X8w4QLO+PK7s3ZsjhKx6muMGDCI5WVZ2XGWzORZbbYVxCk3XOiSuV1PAE8xc+OD9lzXGGCJzWOP0MuFNPKR7s+MYpW94XV7IPpBLipww+tUGgAua0QDqAeyDn0VMDBB1e9sKKULcrSRH9H2AoB+tMpKSThYJgqddUfS9zlo5JNbJY+XsXteM0ye1n8KhXo9jzNjnBHNaw+b4vh4rC6j5YrLvvZezPNLGWyzWgJPLlcrI8ms8DwQ94avE6te6pyYXQSdkBXsvVRC1j8T+rUvxfG4GagO+FtXrGR57PAORkQIkiXDvPbWYMtWJMWMivj2mstKBUzUeLH8zG/v3u/DUU0145tkh+GqVHeecE4t582Lwq2V1mDU7GhvWt2PECCNPLneBiNU5OuKqm5uzZLM5d0h7e+IweHvQa2VIWVlZallZWaOGMQwY/CteXT5N3OU4Dnh06vWl+w/nrL9tzycTmLfaLqgYoLv/PSX65vtYu8vAQjpskOP6JSKKVWj3pXZ72xKLdXSyrEzSOiTOJ1MAvKx1EBz3NV4pFEYYQ1Qeq5/xO+EN/Fb3RvtRytjwmrxI+Ic8t8gFQ0TvK3AhkA7ABGADgF4rfrvCgEj91uYs94yUIxRnyO/reV+3yqi4HB/0qcUGY0jIzdux5uiRKb22jGxmtmF9WTNWn9i3Fk8som/HETkSfei//L8idkQRmE9JV0Zkfru2foQv53Rli1Kw5kr3Q0FNLhPQLI2JPyRnR/eLPAYjpXEmKvZdj9eLYmFTrTUpYwyRkd5aJ0kiSBLh+2MN169rx9nnxIIxhjFjImC3K2huliDqGNwugsdDYIxBlgkffmjB7//AR5AAgKIIJzo6TCeamoYyszl3qNMRl40AnsoIkgkA/qN1EAMBTzCri7/p5jg//Gv43JlHTZl7H133YooASg32fnoFuQ++I1f8+iYd7xPLcd2IUJSqRe0ddbe1WUZkSbKawzS40OKD/gY5xlg2gDfhTVQpAF4ioqdCHkiZKRUAH7QbphhDdD6rm/Gw8Br+oHvNdoQyt70qL9L9U55b5IbeqHV8g1wkvC0zyuHtz+wzBiQYNpo7XHPS6hCp63Ny50NcOd6XVhkZGVXDjh6ZJANCj73WiSGzmdmOJlFsj4nmGH1Cn55wZEIk9eU4vcdeJZBU3JdjCbC8M8/33q9lTS3ViYoS0M20bcrwNT9wPxTU9ylkFDa7ZqTmwiiG/dNoenIfuRAf1V2ED6foIZUGYw9ZJtx15ymcOuXBxRfHYfTozvfYmppkpKR8lz5LSRHR1CRj/vwYPPJIA7780oZblybhk4+tOPvsWERECMEIM6wRgRRFPNrennCqyZyjN5tz8tzu6KEAhmodWy8mgieYVcETzOoq0joAjuuvdqUMH7vknF/Vvbjyz1URsmdksPcbUYc5pTuVytVFAq+m4riv6YmOlXQ4jt3Rahk60uMZCSDo34tc0I1DmSkSZRaH1oFwmpEA/JSItjHGYgFsZYx9SUT7QhwHb4/RTzCG2OGsduajwqt4RPeq9RAN2fqyfL7uX/LsYg90/Wrw1wDC4E0ur4O3qMnnCnNGGGJc23DIVZphgV4w9e0k31plCAJlDRlyYP2pU2N6nbFSJdaenCmN7DHBHCXG9unzZH18KDGhtcrSpwMB7MpjOxxG5lNCf6TLvfYye3tA1cA7lPyKy9y/nYMzamjVQUC7nBezTSowhX2hTSxZdlyLN6TZWDOJAX2uvveHKDK8+FIW7HYZDz3YgOpqN/LyvvtxRzjzHgZjQEyMgEce8X572Gwy3n+vDWW/TcPjj5thtym44goTxowdmA+EEEFRFF2VzZrcaG7KiWhuGjrc44nIR5D/rYKgz0M/uZ7xBLNKcpetGAogQes4OK4/a4xKzLhmUVnc86se25Te0Rr0N6J3/FvJ257PzJYY1uvEa44bqASiumlO58HbW62pk1yu0QBytY6JU5UO3gvn9VoHwmmDiOoA1H39/zbG2H4AQ+Dt6xpK/IZuP8QY4grYqZl/Fl7Cn3QvWQ5Q9uaXpfMNnygziyXo9FrHNwjNArAHQBoAn69fmYIRxor6na6SjFEQWZ8q09tY4uR/0FUVP8D7fUpI5uTuTDh1qveW0cdEc/RMqef72AYxMrYve0KI7lO5aHpj3/ovEyC9tFDwqc2FSHTq9bqGgJ742qXkVVzi/t3soCWXdWyPe1pKLMXowze5TCRl4lTlrXg+YSQOFId6+5gYEUXFkdi8uaNTgjklWQez+btOLGazjKSkzoX6b73VisXXxmPVKjsKCoyYPz8GDz5Qj8efyAxZ/MFEBI8s66usltRmszk3urk5q0CWDaPh7Rffn/n8pALXtcFXtx88vHqZ41Tg1Bmjl5x9/9TKtFGrg72XQEh5dLlcHex9OC7cMKLmIqdrzTP1jbt2HDuZ/lK9ueTr5DI3MPE2GRwAgDGWC+8Nh00abM+/Dvs5xmAaLZyc9YThhSmHjDd0rDD8au0lwtotOkgerWMbZMYBcAM46M/JzENFhnUN20Gk9PWcj3BFkRkptX05VhSl0UlJJ7b3dlwHXGNkKK6ejtExXZ+GfzEhuvfKeiJnYsuBPvVfrk1CpTme9T0rSESPNTY1xhLF9fmc79mj5FZc5P5DUJLLBEhyemS5a37GaIrRh2erIiLbWNpV/hfc1fBn3DNzJA6E7Lq0rU2G3S4DAFwuBdu2OjA0u/P9sxkzo/Hlf20gIuzb50R0tICkpO/qNWtqPGhullFUFAmX09vDmTHA7e5T95awRASHx2PcYTYPLd+3t2T7+nXXeDasv3rc3r3zSxobh02WZYPfX+9hZlhZWVnfnurgesQrmNXDE8wcpxbG2EMzbi29uup/a2/Y/8VUBgTtccxkK6Zes1queLdUDN87+RynBiLbCI9n5y1t1ohz2zuKdYBqw1G4sMcTexwYYzEA/gngXiKyahACrxAaQBiDaSw7PvtJw3N4gp5r3Ue5e1+ULoj8XJlWJEPk7zGDbwgAO4BK+PF0gOCQpxsqm9a4p6X07VqAsbgyevTgs7i1T0nX/OGVcnNzL21XGaJOCE3b85TUHh5PZ8kMgoeg9Fgtz1h0r9XYek/7gb72X35xkejTk8mTnK6KBR0Ov6+r9ilD114YrOSygKPuSclOSjQGdWCgvwSS6+Zg9cFr8UZxNDo0ibGlWcIf/2SGIgNEhJKSGEyfEY1PP/X+qrzwwjhMmxaJyk0duOH6kzBGMPz8553HBr32WgtuvjkRADBvfgweerAeH31owY039Z+H3Ilgc7sjD7a1pdvNjXkJbW3po4jEYq3jCgEGoBjePvdcABhR/72jEk5yl634O4AfaB0Hxw00kxv27/rthleHCEBSsPYgoP2nt4rmmhSWG6w9OE4TRM4sSdpxo8WGS+32YiP53rORGxAOoszC+2kPYowxPYDPAPyHiJ4IeQBlpjQA9SHflws5hVjLHsrd+6J0YfS/lalFSi/D3riAKQAq4OfwPzkjcrVnfGJpX4+/lD7oc6uM7dsWHbLbk3tsM5ElJ61e6Cnucf/PTr54ql1qG9LTMYbYaw4KuoyCno5JbdxaPm7fa73+PdkiseOWe3XFvR33DT1R9frjNekRRJF9Ped0B5TstYvcj84kCKo+XU4AUYKhwj0peQpE5ldswWQgZ9Ul+GfT+fh4qg4yb7ejASK0ulzRB1tbhjgbzbmpVktKQW8DOgewn5SVlf1F6yD6O353WT3FWgfAcQPRlrTR45cu+OXJ51Y93mpUpOHB2IMB0X94Uz52832ipAiM/1zk+jciKUWWd1xttTuvsdrGxxKF/XRwLuhGoMxkQpmlz8ONuIGDMcYAvApgvybJZa+A+pJy/YfAKHE8q57zrOFpKMSadtGw/S9IF8b8V5k8niebg0KAN7lcAWA6AJ8SdWKdo5QirRXSiLg+JY0/whVFJVhVmwJzr5XMBSM3NG7bemGPCeZ6obXXdWL1CS29JZjBomJ6WyetYXOfHoF/a57g7stxAAAi+fn6xg5/k8tVSta6ICWX6z3jE04pGVFh97SaiVq33oDX2HSsnwg+TDqkiNDocMQeaWnJ8pgbczPs9qThAOMDeL34oD8V8ESKCnKXrYhB/5uUyXH9Rm1MSvbiRQ9ZX1z55y3JTuvkYOwR5cbYez5WVv/lUrE0GOtzXFARUZyi7LrM1m65yWIdk6QoQfk+4fotBmAygJVaB8JpYhaA6wHsZozt+Ppj9xPR5yGMgSeYByGBUXIxOzLnBcOTUIiZt9Pw/c9LF5pWKhML1U6ocZgDYAeAoQASfTlRPGqbSVFipTwkuvdWG4zFPUSPHnquD60yoqLaphqN9jqXKyaju2MkKCM64DJHwdjtwMI4fZK93tHzyBQmRPbcC5bIldi6v9eevh4R1avHsz63lZrX4aiY5nSV9vX40x1Uhqxb6P6/GWp/LyhR4gb3tJRRMIiT1Fw3IESebByvXIrnU/JxOHziGuAUhZ1yOEzHmpuyyWzOzeroiM8FkNrbeYMUb+OlAp5gVkchvG/eOI4Lkg59ZNyN5/5mwiPrXiwvajoSlP5c0w/Q7HHHlL17coWxwVif49QWqSj7z7N3NC61WEYMkWQ+C4DrCU8wD1JEtBbaX6fyBPMgJzBKmcQOpbxieAIyscZtVHDgeenC+K+U4nE82ayaYgDHAbQBGNbXkxgg6va0jaMIcY+SFDGut+MtLGHS3+nqiivwXo9Vz4xBP6Jg48E9uxd0m2AGAzso1h0slnO7TTDH6pN6qyj2MGbosYJZL7UfEBWp1+ukz6ewk2Asr7fjACBCUaoeb2ya1Zdjv++wkrl+ofuP09X82ifAIhXE7ZXzYmeqtWbAiCxF2L7jZrxYkAz//q64vlMUobq9PaGmqWmo0GTOyXU6Y4fA26+d692osrKyyLKyMkeoN2aMZQN4E0A6vG2PXiKipxhjZQCWAjB/fWioiwN8xhPM6uj1FzHHcYFTmCAum31nyZK9K9ZcceirmUzln2EM0N3/vhKz5D7W7jKwaDXX5ji16Imq57d3HL+jzZoz3OMZDSBkU7a5fo0/hsppiV8rc98SGaVOYVWpUwxVkInVb6GRB5+TLkooV4rGBWPI2SCTA8ACYCuAPleKMiBKv6U50z0ztZpi9b0mWP+FHxSVYmWvrTLi4+smiqLbIsuGbttTHBHrUSzndrtGrD6+t3DaAHSboAaAhNaDbb0togDNf58t9K16mcj1Rl2joPexJQkAHFXSN5zj/tM0NVvGkF7Y7p6RkkaRurBILgsk18zHl0euxt8mRsIRlsMF+zsikKKIh+32pNomc47R3DQ03+OOygPQpxsk3BlEeG+Gb9JgbwnAT4loG2MsFsBWxtiXX7/2FyJ6TIOY/MITzOrgb+45LoReH3v+3IPx2dt+vfnNYQyIV3NtnYKch96RK+6/SdenXnQcFwoiUe10h/PgHW2W9GKXexT4xSPnu6D0sOe4XpWZBPBrZa4bIqP0aexA+jTDAcgk1FUqow49J1+UWKEUjuXJZr+ZABQBWAOgzz14GZBo2NDY4ZqbVo8IXXrPB/etVQZjiB2Wv2X1oYMzS7s7ppW1FxCIWDf/3lG6OEPPkYt29JJg7kv/5c0FbLdbz7qN83SX2ts3jHW7+3Ts6aqVtA1nu/88Ra3kMgFOOTt6kzTaNBdM++8XIzn2/wDvt56LFVNFKFlaxzOQEEGWZV2VzZZiNjfmRjY3ZxdIknEEgB77nHM+GQsNEsxEVAeg7uv/tzHG9qOfVp7zBLM6xmgdAMcNNuuGjJ94Z+zPqv/61V/a9NRD2YMfhtdhTulOpXJ1kdB7LzqOCxJG1FTkcu+7rc0SP9vhLGRAr/0OOa4HPMHMaSUXgF8DsLjBRWRKxgxxX8YMcR8kEmo3KaMPPSdfnLxOGcdbl/lOB29yeQ28fdj7lNBkhCxjReNBV2l6JPRCj0lZC0uY9AFdU3El3u2xKCMt7eiow4emuYnErhPFDCmNzFKVRvFdPmljFKJiew5abO/xdSJXYuu+UT0eArhePbdvLfJiZWVXWVOLz8PzjiupGxe4H5siQ1QlB0Miq3JPTRYpzqBthTARJaJ5y014xTAJm3m7NpUQwSVJhiqrJbXVbM6NaWnJGinLep53Ci7Nn/ZjjOXCO3BwE7w/u3/EGLsBwBZ4q5xbNQyvVzzBrA5elcFxGjgel5537cIHW19e+aftJne7qpNf7/i3krc9n5ktMazHigiOUxWRtcDt2X2LxWo8p72jWOdD5RHH9SIDZaYolFk6tA6EG3T4G2LOZzqmZM4S92bOEvdCIqFmvTL2yHPyRSkblbH868k3c+FtlzEc3srmXjGFCowVDTtcJemjITJjT8d+jMuLS7HyVCoau622Y4zSs7P3rD1xomh2d8ccEGvr0qSuE8w6wdDL0EJDjz1TdX3ov1ydjsq2GNb704tE7W/X1ZsEwKfeySeUlI1nuR+bpEZymQBFSYmo8BQnzoDAeqnuDiIiVx6Obl6K5zJycKzPgxG5rhGhw+OJqLK0pVkbzXnxra0ZI0nR8fkFodXjjahgY4zFAPgngHuJyMoYex7A7wHQ1/99HMDNGobYK55gDlDushUxALK1joPjBiubMTrh2oUPxjyx5pmKgraTqrW1EAgpj74hV971Ix1PMHPBReQYKkk7brTY2CU2+wSD9241xwXDcAC7tA6CG3R4QpALiI4pWXPF3Vlzxd3wkHhyrTLu6HPSxWmbaZSmyYB+ZBKAo/D2Ks7pywnMoxQb1jdscM9OmwbGuk+mMhZbRo8cfA639vg4d/bQPRknToyn7tqenBSb4iF1swVYKryDr7qMgzFDj0MAE1oPtfX0OgC8sEjs01NiSyy2rXkeyaeb/zWUvGm++/FJEnQ+92v+PmI46ZmQ1KqkRGhXtUzUOgmVu5bgpTEJaOv2pgHXMyJY3O6og62tGe3mxrwUiyWtgEhQtWCK85lmFcyMMT28yeW3iehDACCihtNefxnAZxqF12c8wRy40dB+MjfHDWqyIOrvKb1nzl07Pyy/oHr9bNbHxwB7k2zD1MVfyWvemSfyKlJOXURSqixvX2y1u6+y2sbHEM3QOiRuUBgBnmDmQo8/6cepRs/k7Hnizux54k54SDy+Rhl/7Fnp4vRtVKD5o81hbhiAFgA7ABT35QShQ55hqGwqd09L6TGZ2ZdWGYKg5KemHt3c2JjfZaWrE54xHkjteujOGLLNGNNFijGNDtme2vXikXJP8aU3Vsb19HpLDLYcS2eTezoGAJIlectPWtt8ek9wipIqS11PTFQjuazE6te6pyYXQSdoUtwmknTibPz72JV4d7IRLj64z0dEaHI6Yw63tgxxN5pz02zWlBEA45Xf4WVYWVmZrqysrJvbXcHBvP3TXwWwn4ieOO3jGV/3ZwaASwHsCWVc/uAJ5sDxi2aOCxPPFV1WciBh6OafbXtvJAN6vJjsq4s30qQ14+hYTQrLVWM9bhAjIpOi7LzcZrfeZLGNS1AUflHJhRrvw8xpgX/dcUGhZ3LOWeL2nLPE7XCT7li5UnT8WenijB00vEDr2MJUIoBYABUA+vTUn9DmLtHvbi33FCb0mFDsS6uMYflb9Y2N+V2/yGA4Jph3jlAyurw2itEnNHWXYGYsSuk2MCJXYsv+Hivd31gg9NrughG1vVNb79PQrVpKrCx1/aU40OQyAU3SmPgjcna0JtXCkdSx90q8bVuA/0wVQEO1iKE/ImJ1DkdsdXNztmxuzM1sb0/MB5CsdVxcj/Tw3ow7GOJ9ZwG4HsBuxtiOrz92P4BrGGPF8LbIOAbg9hDH5TOeYA4cf+yP48LIqqGTpxyPSz/8ZPnTVh0FPr2YAdF/eFM+dvN9oqQIjP/M5HwWpSj7zre3m5e2WQsyZLlY63i4QY0n+jgtDNM6AG7gMzAp92xxa+7Z4la4SFf9lVJ84jnp4sxdlD9C69jCjB7e5HL51//tNbkq1naUKFHiWjk/rvsEJ2OxZfTowedwS7dJWL3eVWwy1e+zWNK7fP9cpavtGOHO6PLcOH2Szew82fXWQlS3n4NO6qgSFU+3fWxdOlRtHC1M7O71b9zX2rY3Q5b73MKsnhI2l7r+UuSBLqAeyRQhVrqmp+TBKE4LZB3fNyYlGebNS/BSVDG2F4Z0735KUdiJjo74E81N2TCbc4c6HKahALr+gubC2UiEOMFMRGvRdVeEz0MZhxp4siRwvIKZ48LMkfis4def+0DTSyv/tCvW4wh4OEKUG2Pv/Zey+onLxFIVwuMGAYNCR87q6Dh5e5slN98j8RuRXLjgCWYutMpMEeBvsLkQMzIpb6G4JW+huAUu0h9ZqUyoeUa6JGsf5XZTPjsolQCohLdYKqa3g3WHbdMpUrdZyYzq9ukrC4uf9D4trrgK73RbHT2iYKNly+ZLunytkVm6rY6N1Sc6u3uNsZhuE8wJbQdbu3sNAD6cKZjRS9/VbI9nwxKLrc/J5QaK3zLX9eR4N/Q9DkjsCQF2eVjsdmlEnGrzZfq2MTmG4+CWpXguOws1oU1q9zOKIhyx2xNPNZlzdE1NOXkuV/RQALzCu//jvycCwBPMgeNVGRwXhtoiYpOvXfhQ7NOrn1yXa6sPeGjatCqaU1it7NmdJ4xTIz5u4BGJamY6nIfvaLNkjHe5R4JfoHDhhyeYuVDLA59VwmnIyDz554mV+eeJlXCS/vB/lcmnnpMuzj5AQ/l7OGAqvJV60QB6bP/AAJ1+d+tYj1HcqyQZx3Z33Ce4rLgUK2vS0NDlU4QREbapkZGWE19Xd3aiMMqzMsepOIo8I5ZYfWL3wQnR3VYJpzVs7rZlnsxQ/8l01mMSlRGZ/1bb0OeWK43e5HJhQMllHdvtnpYSRzH6kCWXGSlNU7Fh7014ZVwcrKFNavcDRFAURXfIZkuqN5tzIpqbhuZ7PJH54Nf6AxH/Nw0ATzAHzqdeTBzHhY5H1BnvPOtns3669d3VZ53cWsICeJPLAPFXHyixS+5j7S4DO2MACTc4MaKmCS7X3tvarIkzHc5xDAi4LQvHBdEQlJkiUWZxaB0IN2jwJB4XNiKYZ/hF4obhF4kb4CDDof8ok2uflS7JOURZuVrHpqECAGZ4h0f1WETBgCj9lqZ098zUaorV53V9EIsto0cOPY9burweYgziiIINx3btXNhlpedBsfboZCn/jPfX0TpTt32MmRAd2eULRO6kln3d9l9eO5ZVySLrsbf0b5taqhMVZWpPx3zDTKatc1xPjnPB4FdymQCPkhG53lOYMBuMqTKwvDc68lQvwqc1l+GDKQZ4+OC+rxHBI8v6Kqs1pdncmBvV3JxdIMuGkeil2p0bEHiCOQA8wRyA3GUrIgEkaB0Hx3E9e3zSNaUHEnM2/nDnh+MZEOXvOjoFOWVvyxW/WqLjd/YHMyLLKLdn9y0Wa+TZ7R3FovcxU47rDxi8F85hP4WaGzB4gpkLS5HMPeIScf2IS8T16CDDwS+UqXXPShfnHqEhOVrHpoEUeIf/rYN32FS3GJBk2NDocM1Nb0CEmNbVMVYWP7GnVhlxceYper2z2eOJSPr+a0eFBt3kLvI7EWJUt8UdjEV3eW2vkzoOdNd/mYD25QuEou7WBIDRLnfFpfb2Pl3zN1HctjmuJ8e4YIjoy/FnxCPgiHtyspsSjCG5powm265r8JazFCunMO+TJoMaEZySZKyytKW1mc25cS0tmQWKoudPrQ5OPMEcAJ5gDgyvXua4fmJF3szpR+MyD/x57bMmkcjvfpD59Zgzf4eyaVWxwPuSDSZEHTmStPNGi1W42NZebAA0meTNcSoYDp5g5kKHJ5i5sBfF3AWXiWsLLhPXop2MBz6XpzU8J1+UV02Zg6mfagS8yeXV8N447/apP0bIMq5tqHKVpEdCL3TZgqKnVhmMITJ/eOWmA/vnln7/NStzjFZAsoDOFbx6wdhtURcTIrvsIR3fdqjb/ssHsrDFHtl99bJIVPNaXUNxd6+frplit892PTXaCWPXldQ9IIAowVDhnpQ8BSLz+XzfNiM5DfWbb8ELsWOxJ+AZNf0ZEeweT0RVW2uGvbExL6GtLX0UkdjjDQdu0MgtKysTy8rKZK0D6Y94gjkwPMHMcf3I/qTcUTee8+uGl1b+eV+U5PJ78Npt/1bytw1n5rYYlqJmfFyYIfKkyfL2a602z5VWe1E00QytQ+I4FfA+zFwo8QQz169EM9eoK3RrRl2hWwM7RexfIU9vfE6+KP84pQ+WFlilADYAKAbQbcKTyTTSWNGw3VWaPhYCO7MHci+tMpKTjxcKguRQFF3nPRji64TWPUOUxHGdPyykdhsxizR19eH0hs2xXX2cAOXF88Tcbtcjoicam5piiHr9N2+h2B2zXU+P9DO5XOcpSqhV0qPm+nqubxtR+yjs23IrXsjLQO30oO4VpojQ5nJFH2xtzXSYG3NTLJbUkYAwSeu4uLBkAJAN4JjGcfRLPMEcmMFyocFxA0ZzZHzaNYseintu1RMbhrQ3+ZUwFIDkR9+QN9/5Ix1PMA80REqCouz8gc1uv8FiGxffx757HNeP8AQzF0o8wRymZIUw+eV2DIkV8Nnizh0GWh2Emz9x4EiLgggd8NrFkRiXKsLcruDS9x1ocxL+MN+IS0Z52+Je/F4Hnj8/ApmxghafStDEMOfoq3SrR1+lWw07Rez7RJ5pfl6+aPhJSh3oRUYzAOwDkAggvbuDmEeZYFjXsME9O206GDuj4tnK4ie+R9euuRpvn5FAZQxJOTk711RXTzrjtQPiqeYhSuL3jmcRBiGy1a04vl/J7GBMd2Zyt4f+y43xqKxNYt0mWqc4XRXzOxy9Jn1bKWbnbNdTIxww+tx+T4nSrXdPSxkDQ/CSnIyUxllYs/96vF4UA/ugaudGBLPTEXu4pSXL02jOzbDbkoYDjF/Tc301FDzB7BeeYA7MQL+44LgByS0aIm9d8Mvpv6lcvnpW3Z5Sf9ZIsmHKtV/Ja96eJwa36oALiWhF2Xuhvb3pljbryHRZnqB1PBwXRDzBzIXSoO/tGa6e2uTG6GQBVteZrz1S4UJxmoiProrCgSYZP/zciZU3ROPdPR7cWKTH1eP0WPh2By4ZpcenVR5MTBcHXHL5+2KYc8xi3SpcI64iGyL3fCzPan5BurDgFFL8brsW5sYAqAOwH8Do7g4SOuQZhs1N5e6pKV0mMD/FpRPm4X9dtsrIHHIgp7p6ogKwTl88p4SWxO8fCwAx+nhzi+v7CWZmQReV1jrJUSUq7sKu1nn5XKHbfs4GoqPPNzT2mohso+ids1xPD+9AhE+DvwmwSAVxe+W82Jm+nOcLPbmPXIiP6i7Ch1P0kAZFYpmIneroiDvW3JytmBtzszo6EvLg7S3Ocf4YqD/Xg44nmAPDE8wc118xxv4w7abSyw6tXn/r3s8mMm/vOZ9ctJEml4+j6poUxt9A90NGRTm8oMNRc3ubJS/PI43VOh6OCxGeYOZCo8yUBMCn5AsXGjVWBSsOSfj1HCOe2OA+4/V9TQp+Ndvb9WBUsohjbQoa7Ar0AoNDIrhkgsAASSE8ucmNT6/xe35yv8MYWBwc467X/Q/Xif8jK6J2fyTPbn1RurCgDkndVvv2UxkATAA2Aui24ldodZfo9rSWS+MSzkxmMhZbRo8eeg43D2Hf6+ssCEpORmbVxrraUZ3WdkMa44LHYoS+U+uLOH2ipcVV970NRBu6qLKObzvU0lWs7Ubs2TVM6DLxDCLphfpGp5F6fk9goahds1xP5/ucXDYI21zTUzIQqQtKcjmWLDuuxRvSbKyZxAb4oDJFEY61t8fXNDUNZU3m3FynM3YIeG6GUw9PMPuJJ5gDw3+IcVw/9+GI0plHTZl7Hl7/cpoA8ulONwOiHn5Tdiy5T5QUgfGfp/2AjujkLIfjyB2t1iHj3O4R4Mk2bvDJRplJjzKLR+tAuAGPv0ELU/d+4cSfFkTA5qYuXy9KE/Dhfgmzh+pQeUrG8TZCjZWwuFCPxR868OZOD/64IALPbXbjhvF6ROm7nQc3oDEGZkJH4U26/+JG8b9kQfSuD+U5bS9JF4ysR2Ka1vGpJArANHiH/5V2d5B4qmMuRerWyvmxZwxBtjLTxPfoujXX4G9nPPWXm7sjtq72e50sGMQjYsP+MXJWp8RzrD7JecbGTNfRVTzpjV33X35/rmDr7nM4q8OxdorTVdrd6wBgoajdM11/zWtH14MFu0KAUx4avUkaZZrbVSuRgBBJmThVeSueTxiJA8Wqrh0miECKIh622xPrmsw5+qamnHy3OyoXQK7GoXEDF79+8RNPiASGJ5g5bgDYkVow7pazl516ftVjByNkT4Ev50a6Mea+j5TVj18ulgYpPC5AAlHjRKdr/+1tlqTpTtc4eAc3cNxgJQBIAlCvdSDcgDfQqjkHhM8OepAazTApU8TqY1KXxyybbcQ9XzhR/IIdhWkiJmQI0AmAKYJhxdf9mlsdhD+uc+HDq6Kw9BMHWp2En84wYEb24Hx7yRhYPNrH36z7AkvEL5Q2xOz8hzzX8rJ0/uhGJPT3R/UZvMnldQAmAzB2cQDTHbZOp0hxi5IZNfn7r3+GSybOx5dntMrQ6TxjExNrdra0ZBWd/vFDYp1njNy5q0asPlE5MzL9mU1eiDxJzXvP6L8sCaj578Su+/BGKsr+Pzc2zerqtW9YKXLPLNfTOe2I7DJ53RUS2QH31GQ9xRnUbVVBZBuLXdtuxQvDU9EYtHYbWiCCLMu6Kpst2Ww250Y2N2WPkKSIEQBGaB0bN2jwBLOfBucVgHp4gpnjBoj66KQhixeV2V9Y+efKVEebT0Mgph6kOYXVyu7ded08cseFHpFltNuza2mbJXp+h6NIBAZFDzqO66Nk8AQzF3w8wRyG1p2Q8UmVhM8P2eCUAKuLcN2HDvztsu/a2MYZGV6/2PtnIkLeU3bkJXTusfy7chd+PceId3d7MClTxOJCPS5+rwNf3cjfXjIGIQH2oqW6z3Gr+LnSitgdf5dLbC9L541qQnx/TjbPArALQCa8v0c6YYBOv7t1tDtC3EeJxjGdX2QxZfTowa5aZQwfvslVWdk5mdzEbGe0n4vRxYtn7MkizujxopMcB7rqv/zlBHZEEdgZvaBB5HqjrkGvB/RnvPY1G0Xunen661A7ouK6O6bTkoCspESs9RQnzoTAul3XVwLJdXPx1cHFWF4cjY4BcW1LBLckGaqs1tQWc2NuTEtLVoEs68f0fmboSJKE119/HbIsQ1EUjB49GvPmzet0DBHhiy++wKFDh6DX63HJJZcgIyMD7e3teP/99+F0OjF//nyMGuW99/Hee+/h/PPPR2xsn+9XcKHDE8x+4lcAfspdtkIA/8LjuAHFoTPG3HTO/ZN/t+HV1ZMbq0r7eh4DxF99oMTdfB+zOw2sz4/McSojas/zSDtvslh1F9jbiw3AHK1D4rgwlaR1ANygwBPMYejRBRF4dIG3xezqYxIeW+/ulFwGgDYnIUoPGESGV7Z5MDdHhzjjdznBQ80yau0KSnJ12FHvQqSegQFwdl0QPagxBiERtuLbdZ/hNvEzuRlx296X57W/Ki0c0wJTf/xZPB5ADYBD6KKilAHRhs1Nqe5ZqccoRp97+mvdtcowGDumREe3HGlvT/y2bzAxympl9mMJFPPtGhFi9BnNvhmLkL//sXjL4TP6LxNgebdUmNTVJ3S5rX3jGLen22StnSL2zXQ9ndXn5DLDCc/EpDYlOUK1BLCBnFWX4h9N5+GTqTrI/ToHQYQOj8dYZbGkW82NuabW1syRiqIL6yIdURRx4403wmAwQJZlvP766xgxYgSysr67X3H48GG0tLTg7rvvxqlTp7BixQrceuut2LNnD4qKijBu3Di8/fbbGDVqFKqqqpCens6Ty+GrX3+PaYknmP2XBv73x3EDDjFBeGDm0tLr9v+nYnHVl9NZD9UMp9MpyCl7W65YtkTHk5qhROTOkOUd11ls0g9s9qIoogH1mCDHBckZlWccFwQ8wdyPvLDFWwh6x2QD9ptl3PAvJ0QGjEkR8OpFnRPQv17lwsPzvV0SrinU45L3HHhqkxu/Kz2jcwJ3GsYgJsM68Ye6j3GX+LHUBNO2d+V5Ha9Ji8a2ITZB6/h8kAXABmAzgCnff5EByYb1jQ5XSboZRrFTxfZnuGTiPPyvJh3132bmGAMrKNhQu337+Z0G01WJtcenSwW53/zZIER0GvrnPfnMAZNpDZvPKPbYMYxtdxpY6fc/HifLOx9sbun22r2dIvbPdD09xIboM/fughKnX+ueklwMnTC0L8f3xkStW2/Aa2w61k8EMFKNNUONCFa3O/Jga2um3dyYm2yxpI8kEiZoHZcvGGMwGLyDTxVFgSyfcV8DBw4cwPjx48EYQ1ZWFpxOJ2w2GwRBgCRJkGUZjDEoioJNmzbhmmuuCfWnwfUdv37xEyPqergD17PcZSsmw/tLleO4AWpq/b6dD218LVsAEvt6zguLhE2rioVpwYxr0CNSEhVlxxVWe/v1VluhSVHitQ6J4/qZ21FmeUnrILgBrsz0NwDXah0Gx4U7IkiNiN/5rjzf8bq0cJwFMfFax9RHMoC16KYNGYnsgKs0fQh0QqcyzViybH8eNxef3iqDCO7KTZe3ut1R3w5HjCZj5TWu2VO/O4asHxz7U6cqYtE4cY0+qvS7imgiT0nFT9yi4o7+9kOA5667xKZmE+tclUhk/7SmriVXkrpMBreTcf9M118z+vLvQYBZGhtfLWdF+9Rmr+vFyJON45VL8VxqPo70u77DRGh2OmMOt7YMcZnNualWa0oBwITezwxviqLgpZdeQktLC6ZMmYKzzz670+vvvPMOZs+ejaFDvV9Ob775JhYsWIDExER8+OGHsNvtWLBgAcxmM4xGI4qLizX4LDgfGMvKys5owcP1jFfg+o/3X+a4Aa4yfUzR7Wf94vizXz3RalCk/N7PAG77t5K/bTgzt8Ww/txjLyzFKMqei2ztzbdYrKNTZXmi1vFwXD/GK5i5UOAVQBzXB4xBl4a2SffqPsQ94oeeBiRseVs6y7VcPmecFTF9qpzViAhvcrkCwHR876k/JtMoY0XDNldJ+jgIzPDNx23MNOFdun7NYrz1bWKYMRhGjNi4f+/e+d8mmNvhGitDcYsQDN5jWJyOGWwSub9NWDMhulNfZp3sqBIV97jTP1aTjMpmEztjgN+tFuv2XEnqsnq5g4wHZrme7ltyOUKsdE1PyYNRDCy5TGQpwvYdN+PFgmT0PHAwnBCxeocj9mhLc5bcaM7NbLcnDgPYgCu2EQQBd9xxB5xOJ95//300NjYiNTW11/MiIiKwePFiAIDD4cC6detw1VVX4ZNPPoHT6cSMGTOQnc3nj4ehdAAntA6iv+EJZv/xBDPHDQI1sak5ixc9ZHnpf3/amuiyddm77XQCkPzo6/LmO+/W8QSzCoyKcuic9o5Tt7dZ83MkaVzvZ3Ac1wf9se8n1//wBDPH+Ygx6NPROvmn+n/gJ7p/uOuRuPlv0gL3cvmcwr72ANbAHADbAeQBiD/9BeZWJhrWNa53z06dAca+rVhegYsnzseXnVplJCSemiCKHpss670JZIboGqF5R46SUvzNMTH6eHObu/G7BDOL6dSXxdR2uPn7wb24SDzjd16KJG25p9XSTXLZUDXT9XRaG2Lju3r9GwTY5WGxO6QRcbN7Oq43Ask18/HfI1fj7YmRcIT94D5FYSc7OuJPNDdnk7kxN9vhMOVgEP28j4iIQE5ODg4fPtwpwRwbGwuLxfLtn61W6xk9lsvLyzFnzhzs3r0bmZmZKCwsxHvvvYcbb7wxZPFzfZYBnmD2Wb9/VEFDPMHMcYNEuz7SdP3CB4r3Juau6cvxSXZMuW6V3KdjuTPpiE7Ma+9Y/f6pusNbjteMeKSppTRHkvitfY5TD69g5kJh0CQcOC4YGIMhg7VM+bn+g1m7jbca1xnvrrxD/GR9NBw2rWPrwgQAbQCqv/+C0CHN1G9p6nxdzFhMGR4xE0DffQimvLyt204/7IB4ynL6n2N0CW2dF4/u1CA8vWFLp/7LLHt2AAABAABJREFU1khsP5jFRnXamqj1ndqGLHTBQYaDs1xPp/TWE5t0bLd7dmpLIMllIzn2X0uvb1iOqzOW4JWSSDjCcuKboghHrdakiqNHJ66r3HRp3bq112Vv33bBrBPHi2Z/nVwe8Nrb2+F0OgEAHo8H1dXVSE7ufCk1cuRI7Nq1C0SEmpoaGI3GTgnm5uZm2O125ObmwuPxfHu/RZL4dNQwxQf9+YFXMPuPJ5g5bhBRmCD+bO6P5i7d/cmaS4+smcl6+fl54SaavLqQqmtSWF6oYuzPBKKGyU7XgdvbLMlTna6xAFQZjsJxXJd4BTMXXGUmPXyYX8BxXM8Yg3EImqcu07+HX+rec9ZQyqbl8jnyu/L88e2IPGOonUZyAVgAbAPQqZWZ2OIuoT2t5dK4hG8rdG3MNOEdumHNtXjz21YZ6RmHRxw+PFUCBB0A1AltnXoQxBmSHOj47s9MiPq21zKIpOTm3Z2SyW+dJZyRvftZS9v+dFk+Yyi0gwyHZrmeSmpFXLc/uwjwKBmR6z2FCbPBmNjdcd0iokQ0b7kJrxgmYXORz+cHGREURREP22zJdU3mHGNT09B8jydyGIBhWsemJbvdjn/9619QFAVEhLFjx6KgoABbtmwBAEyePBkjRozAoUOH8Ne//hV6vR4XX3xxpzVWrVqF+fPnA8C3lcubNm1CaWlpqD8drm94gtkPfMifn3KXrfgCwLlax8FxXOjNObVj6682/204A3rsi+cwYN+S+8QCRWD8Zl4XGFHrGLd7z9I2a8y8DkeRwJ+q4bhQ2YQyy3Stg+AGsDJTGoB6rcPguIGOCI4TlLpzuXwuvSfPG9+BiOjezwo6CcB6AHNP/yABJI2IWy8Pi/2uvzCR/XH8qO30VhnV1RPW1ZwcN+vrk+ha15zmSBiSAeC4fW/5RvNn3yapjaY7mpkQlQQAouTYU7L2Z9+2U/OIOHrtz8W801tz5Hg8Gz6rqZvx/YCdpD88y/V0fDNM3T7hQwKOuCcneyjBOKq7Y7pF5MrD0c1L8VxGDo71aa5LKBBBkmV9ldWa0mQ250a1NGcVSJIxnPt+c1yolJWVlf1W6yD6G5708F9YPsLCcVzwVQwpnlQTk3r0qdVPtelJ7vbRsEg3xvzkI2X1Y5eLpSEML7wRtQ/zSDuWWKz68+3tE/Tevn0cx4UWr2Dmgi1ce8Vy3IDCGCJzWOP0B4W38IDurY7jlLbxdXkhPpBLxztgjNIoLB28yeVyALPhHQYIBjDdIetUihS3KhlRk77+BGLK6JFDz+PmIQxgADB06K6UmpNf54kZ2CGxrmq8nJMMADG6hM7FCCzi22Ro/Pf6L382lZ0CY99W3gpEjW/VNoz8frBO0h+Z7XrK1F1ymQBSEg0VnonJUyGyCJ/+JohaJ6Fy1xK8NCYBbQH1alYDEZySZKiyWNLazI25sS0tQ0Yqin6s1nFxXBjiN1r8wBPM/tPqFzbHcWGg2pQ57NqFD7a+svKPO+LcHcXdHTflIM0prFZ2784TCkMYXnghcmVK8o7rrDblBzZ7USRRv5mMzXEDFO/BzAUbTzBzXIgxhqhc1jD9t8JylOmWt1dT+obX5UXs7/LcIieMkb2voLoSAFsAFODrnwkM0Ot3tY50G8V9lGgcA5zZKkMU5YKUlOqtZnPeJAA4JNZj/Nf1HJG6mNMTvDbGhG+LvtIbN39bva0ATf+YLUw5PZjfNbUcT1CUTh9zkf7IXNeTcU2I73I4NwF1nqLEOiU9cm5Xr3dHJOn4Ofj8+BV4b7IRLs0G9xHB7vFEHGxrS7c1NubFt7VmjCISw641B8eFIZ5g9gNPMPuPJ5g5bpCzGaMTFi98KOYv5U9XjLCc6rISlwHirz5QTDffx+xOAwuXHnnBRyQnycqOq2w2x2KrrdCk0DStQ+I47lsmlJlElFlkrQPhBiyeYOY4DTGG6GGsfsbvhdfxO93r9iOUsf41eZH4T3lukQsG36pwAzMZwBF4ezNnAwADYgybm1Lcs1KPU4w+BwA+x0WT5uPLkxmoywaAYflbmNnsHWPSxuzDv1nMIESc9rOFWfDNU8VEUlLznm9bV2waxfZ4dKz0mz+PcbkqLra3d7pWd5Gueq7rL7GNSOgyuaxE69a7p6WMhV6Y2NXrXYmk9j1X4W37WfjvVAEU8gF4RLC4XFFVra2ZDnNjXorFkloA9D1+juO+xRPMfuAJZv/xBDPHcZAFUf/jeffNuXvHP8oXHds4h3XRR1inYGjZ3+SKZTfrBnY7CCKKVWjPxXZ7y80W65gUWZmkdUgcx3WJwdsmo1HrQLgBi78x47gwwRhihrO6mY8Ir+Fh3Wu2QzRk2yvyebp/ybOL3NAbQxBCPoBmALsAjAcABqQY1jeecJWkm2EUU8BYdBk9cugFLMliADMYnBNj4xoP2Kypo4ghrZFZDqWSaYTI9Kc9gSO2f/t/srNKJ7vGAgABztfOEb7txSwS1bxa11h8ekBu0lWXuJ6MakBipyGCX5/fJo2M2yfnxp4xCLBLREoKGjcvwUtRRdgR0icWiWB2OmOPtDQPcZvNuek2W/JwgE0NZQwcN0Dx6xg/8ASz/8JhgALHcWHir8U/KDmQMLTyvu0fjGHAGZXKwxowZ8E2ZeP/JgoDbrBWhKJULWzvqLutzTo8W5IGbysQjutfeIKZCyZewcxxYYgxxBawUzP/JLyMP+petlRR9pZX5PMMH8uzijzQGYK4dRK81cZr4e3LDEYYaqxo2O8qTY+EToixs7jit+nGNddh+VwAKCjY0LJ1y8UAgCqxtjZVMo1gjCUJEF0KZCOY3vHN4vGWI03f/P+RDGy2RDNvUQcRPdnY1BRD9O0QQTfpjpW4/hJZj8S07wdJBmGba3pKBiJ1vSeXiRzDcXDLUjyXnYWakDypR8RqOzrijjU3Z8vmxtysjo6EPABdVmBzHBcQfh3jB55g9h+vYOY4rpMvc6ZOPR6XfvCJNc9Ei6QM+f7rt/5HGbF1BGtsjWVnVEv0Nzqi4yUdjuo72izZo9yekQDOGJrCcVxY432YuWDib8w4LswxBtModnLWY8KL+LPuRct+Grr5Zel846fKjCIJOn0QtjTAm1xeDW9/ZsZkGm2saNjqKkkfD4Hp/40LJ52FL09koHZoZKR1akSErcbpjM06ITbFQvIuEq03Ndg8LUMZMzi/WTitwdt/mQB64Tzx22vwaU7XmtIOx7c9kN0kHi91PWGsQ1L66YER4JCHRm+WRpnmgDHW0yfBSGmaig17b8Ir4+JgDerTiYoiHO9ojz/Z1JzNzI25OU5nXBaAzGDuyXGDFoEA2BnQIUJ09no8dwZGRFrH0O/kLlshAOB9CzmO61KC02p+6X9/qo+RnGdU87bEYMsdd+smaxFXoASiuqlO18Hb2ywpk52uMVrHw3FcQC5FmeVfWgfBDVBlpvsBPKx1GBzH+U4htO2nnD0vShdErlCmF8kQg1GUtgnAOHz9VLASrVvnnpU6E4yxGLLueAFLihjA2trSynfvOqcEBOdNrlLSQYxcU/+PnXWOI0VMTKswxl07B0TS3LU/dehkV2xzLDbf+SPdFAAwKHRk/YmTQ4yECADweJPLhlNIyTg9EBLZfvfUZAPFGfJ7ClhHnupF+LTmMnwwxQCP6n2siUCKIh5ptyfWmptydE3mnHy3O+qMKmuO477mTQi3M6CdgXUIEJwiBJcOgkdHoscAnWwgnWKAjoykZ0boBCPpxQjodUbSG4ykNxqhizSQLtIAXbQOYjTDtzeYTmX935ysnrbnzsQrmP3Dq5c5jutWa0RcyuJFZXHPfPXEuqH2xlmnv5Zox+TrV8pr3jpL9GkatVYYUcs4l3vPbRZr3NwOx3gByOj9LI7j+oEkrQPgBjRewcxx/ZTAED+WHZ/9tOFZPEnPteyl3H0vShdE/VuZOl7FZPM0AFXw/qzIENqlWfqtzeWeycklp7fKMJkaJut0zlZJikg4Jpi3DlfSJ8UZktrrHEfAWKQCdO6//PrZgjc+Iuml+kb3acnlE/PcT+hPTy4TICupEWs9RYkzIbBuK7ajybbzGrzlKsXKKQzIU+nzBxFkRdEdtFqTG5vMOZFNTUOHS1LEcADDez2Z4/qjwBPCEQbojEbSRZ2WEI5BF+0pVcBb4vqBJ5j9w7/YOI7rkUfUGW9f8ItZv9jy9urSmu0lzDtUCwBwQSVNWV1I1SdTmWoXqaoisud7PDtvtlgNi+wdxXqgXyTDOY7zSTB7bXIcTzBz3AAgMEosZNWznzH8FQqx5t2Ut+956cLo/ypTihQIYoDLj4R3FsBeAGPFZlcJ7W0tl8YmlHzbKoPVDs3P37K5qmp26UGx1j5cSUecPtHjDS6KAMBkOdoEAE49qipHChMA4OwOx9pJLlcpAHhIrJnvflysoZRvW0sQw3HPxCSrkhxRgq4QyWmoq7wVL8SNwd6iAD/Pr5eEW5YMVRZrSovZnBvd0pxVIMuG0QBGq7E+x6mu64SwWwfBpSNROi0hDCPpYIT+9ISw0Uh6gwG6iBAlhNXGc35+4Alm//AKZo7j+uRPk68t3Z+Ys+HOXf8qYl//7GBA5B/elJ033yd6ZLH7iomQInINkeTt11utdLmtvTiCaFbvJ3Ec148JWgfADWixWgfAcZy6BEZJRezonBcMT0EhZt5B+QdekC6K/VKZOJ4g+Ps7JRXeG1LrAcwUazrmUpRunZwXO6uMHj70ApZkp6RWjz14cIarQbBkAUCMLoEBAGPRIgCkN3r7L/9zltAEYGSUouz/U2PTbACQSKhZ4P4zTlLqt32ZlTh9hXtK8gTohJwzoiFqH4V9W2/F83kZqJvh5+f09VJweDzGKosl3WJuzI1rbc0cpSg6PgybC54zE8IuEYJTB8H9TUJYT6JihJ6MpGMDLCGsNn3Nsgp91v/N8WgdSH/CE8z+4QlmjuP67NNhs2ccjcvc/8e1zyeIoHQAiPRg9E8+Ulb/+QdiqWaBEcnJsrLjKpvNsdhqK4xTaLpmsXAcF2qBVp5xXE/4tTLHDWACo5SJ7HDKS4YnIBMzb6cR+5+XLjStUiYU+pFsjgAwE8BqBpTqDlqnUqS41Z4eN+lvdFP59eyNkqE5uyqOH5swxwZHXZQuzgAATIjR4f/Zu+/4OMprb+C/Z8rOVu2qd0tusuUqV7lJMr0mQIAAoYVATHEgJKRAbuBCKm8KaTcJgdAhCZBAIJCEFIPk3rvVbKtYva92pW0z87x/yAYX2Wq7Oyrnm4/vtbUzz5yVhbz67ZnzcK4ltO2boTE0vreULQXn/pcam00SIKlcqL8w+GNew1MyAYADreocV5WWbjtjYz7G9ZaVKCm9FS/Mt8M7rDv3OEd3MGip6OpM9ba0Tk5wdyXncC7mDWctMkGcGgj7BAi+4yMjzhkIK5BlM5dlhcvKiUBYhmSTJ3YgHAk2AF1GFzGWUMA8PNQuTwgZkoMJU3LvuPhbjb/7749LLVowFwAWV/KCeUf1/fumCNHrZuCcx+j6/mu8PV13dHXPitf1RVG7NiFkNKGAmUQSjWAhZIIQGU9czCoSnzP9FBpnzTt5Tvlv1U+7PtTz5uKTDbMGYzWATQxYKO/tzAkqYuk/XVcuvhD/qs3IOJRRU53HK6TGI/P09FgAYIJNEbVAuaQFZq2bx3ZqIku9vtuzaWYwVKRyoeGi4I+0ap46CQC4WdwaWJY4FYq49OQLyjx45FN4u/EqvLVUgtr/uIyz4BwdgYCtsqMj3d/akp3c3Z04HRDG5EbeZJAGCIRliJqJS/0FwpL5+KZyJkhmU1+HsFWGaKdAeFSzggLmIaGAeXioK4MQMmSt1tjUmy573PmbdT/dktbbvowB4sNv6s47H2Qen8IiejuxRdfLLuvpbfpil3t6hqrNi+S1CCFjAgXMJJIoYCZkAhIZT17KypOXmn4MjQuN2/mMyt+on44t0efNGWTYvALAQQYkmra1JQRXJbU/bv1+x9PCHfNTUg5vO9pgExaw7HgAYMxqdbqPNHPA+8r5wgKnpu39dntngcaFxouDPwpV8bQsDni0qY696rSYVSdfxMHde27Gi+oqlCxiwNTBPDfOWbPP5zja0Z4Ram3NTvN646YCLH/onyUSNZ8Ewr3HR0YMJhAWjncI9xcIU4fwxEK53xBRwDw89IVGCBmWgGSy3nnRw/mPbX2xeHnTwSJJx6THX9M2fPML0qqBzx4amfOq1b2+mrs73ZNmhEIzAcwM9zUIIWMWBcwkkkbH/gKEEMOITE9dxkpTl5lKoXKhcZs+s+I32lXxG/Q5swcIm2cDaGCAx7SxxewtSkl/xXRHyU2TX4vd0jg9E4CdgakQbLbklv9aD2ViZ48ZC9+ra47jnLVcHPx/waM8LYtLbF9wWVIstx1/jc25moa6rXfh6bgZKMsbqH5dZ3W+XmdNW3smb2vNzuztdWUBSA7H54acRV8g3HvSDOETgXBI4mJoCIGwxUQjI8jI0X4lQ0QB8/BQwEwIGT7G2HeW3VF0fcW6jXcc+vviyc1YdeEufct/FgojnoEscN64zOevuLvLnbQwEMwFMDkMFRNCxh8KmEkkUQczIeRjEtNTV4iHUleIh6ByoX6zPvvwr7VPJ27RZ886yylpAHoYxwFlfbPjg6LLci+UP/C54hrqmprdokVyxGow2xLa9k374V2id01X956MkDb9suCTvsM8PUNPsxaH5rgKwJgAzj1zsG/XXfjt9ES0nnUTa10Xqnp6YuvaWicJra3ZkwMBewaAjIh8QsaLIQTCpr5AmJk/mSFsMnFJUSCfHgjbQCNJyegwlBE/BBQwDxd9wyOEjNibOeevPOJM3//dzb9PuesDffrO6ayl08GShroO47x9XiB4cE2X21ng889jQGok6iWEjCsUMJOIKZyU3hpk7JDAoQvgmgDoIocugusiBz/5/0scXALnIgdkznUJgMQ5ZM65DLDjv4fMARmcycf/bOIQJM6ZDM5MHEzmXJA5ZybOBRkQpL4/CybOBYlDlMHFvmMgSuCixCHKnIsSuCRxSCLnogxIIueyTD8jERIxEtPTC8T96QXifoS4WLdRn33kN+pVSdt4bu5ph9oALGEaL1E2tPQ+Ufhd9vNpXw6Wty0IOKRY1aNDbHf4u/UYSPfVdudcHvxhbznL1IJLE49yl6lI4FpjIf+w4nN4Kc+G3lPmK3MOXdfFw15PfGNrW5bS1po1NRSyTMZEaMzgH88QPh4Is6AEMXhyIGyCBIXLODkQVrhsUrhkUiBbKBAmhPSHXjwND3UwE0LCYlfyjLl3XvTNuqf/+xPfD19U3ffcLw0uYObcMz0U2ntnV7f5kp7ePAkY1o7XhJAJi277IxHTKYrpAM7WmTg2cK4BUI//0hgQYn3/X2OAygCNcWgMXBcAVQD0E4G6CHDhk0BdF/sCdF0C5ycF6lzmnEvgkPjxUB3gMv8kRJc5mAyOvo8BJs4FCZyZ+v4snBaqi9KpgbogcS4eD+IlCVyUOcS+358I1LkkcsgSveFEDCIzLWO1uC9jtbgPIS7WrtfnVv1avSp5J59xYqybAGA1C+obglsD+hvLrteXOQ7zTFNKh+iuYi9dJJhfqm9xXB78gedQ7LT60MKEfJMQqLmGv7rxcry7VIKWCgCcQ9U0udzTndDW2pptbW/PnK6qSg6AHMOe/GCdGgj7RbBAP4EwV7jMKBAmJKyog3mIKGAeHovRBRBCxo8mW0LGTZc97vndf3/cddt/uktevlDsPyzm3J+hqntud3twjdebp3CEfW4zIWTCoECJRNLY/6GMMRF9/50oAMCP/zr1mI//z9jXF6iH0H+g3heqf9KRrvUF6lwXAF0ANJFzXQS4yKEdD9T58UBdFzmHBPC+EJ3zE4G6iXMuAScH6ie61E+E6EzmnEngwsmh+vHfixL4iZBdkDmk48eJUt/vP+5SF8Flqe/4vm51+v43KslMm3S+uGfS+eIeBLlYU6LPr/61elXqbj49B8AqwaPuLdm7tDFv2ktcrsnWEdjHlsX61Hs6/lfYP3+B6Ejy22/DU4eWYdNCzpGtqqbSNndaZ2trtqOzIz1H0+TZUXkinwTCPgHs+MiIwQTCkqxwWaFAmJBRY5z8Ax89FDAPD32hEULCyiebHbdf8j+LHtv2+48mtRw5WpvEpgAAOFcTNW3PTd3ewI3dnrkOzkc8p5kQQkABC4ks6pAfaz4J1AGM80Cdcw7g40Cd9f1e/bgz/fgvgXONHQ/Qhb4/68fHvWgiOD/+ey59MvKFSxy6xDmOh+vo61IHZM7P1qUunOhSP3nUi8w5M5026uX0LvW+rvRTOtNFqW/siyhxyCe61YUx+N+jiWlZF4q7si4UdyHIpaoP9fm1v1GvTtvbMnXWn2xX7HggplLrTt0rrwt+Ge2rEj2PyU8kZAWPqV1dKd0HW87b19mZOoNzcd6gLjbEQFjhsmSGLPUTCFtliHYKhAkhExUFzMMTNLoAQsj4w5kgPJG/5vwF1evWOeP/6f6Mx+v5vLt7dpyuLza6NkLIuEMBM4mkcZBCknGLMYa+n4Ml4JMg/cxAfZx8GfcF6ifGvYSOB+onutT1kwJ1VTj+Z+GkEP34uBetr0Od6yL6QvSTu9SPj3r5uEtd5h/PT+fyJ13qTO4L3YW+2enHA/a+QP34xz4O0U/uUhcz+P60H/N9kgfmxn825Sv7zTPqAnPOt5/vLGXmskqpo6ugp0yXIUMUzVzqzIK0+XggLChcFsyndAhLFhOXzcc7hCkQJoSczTj5RyB6KGAenoDRBRBCxq/9jsKCyprfgzHIRtdCCBm3KGAmkTTmOiYJGbf6AnX5+C/LWAzULX7evbSSly+pMHkTbOkhdZ7JnL2jXIzBbGaVNCFbtoZcpiTY5VirIlriBYgZjDF6HU0IGYnR+01xlKKAeXgoYCaEREwOq6tlDFONroMQMq5RwEwiiX4oI4QMmxLkPYsreWnBQe6deYwnKiFxStXkT/tqkidbcyy+UM20OsHDfVAP/sUkd39OPBgb51S7N3fpam0KoGcwMM0muepilaSWOFNqj0tJ1h1yrMUs2OIFJqYzxsxGP0dCCBlvKGAeHhqRQQiJmMVCeQtAATMhJKIoYCaRRAEzIWTQTCHuW3iYlxYc5N2zanmCNYAZDFjMAV6beeGmo5M/1aKGKiwOdbvZF7symGjbIL8o3Mq+M//LyocNL4i3fhjr2D/vXm+P65o0Ta09qPm3tXnVuhletXPhsZ7y0y/HrWJMk0tJao4zpXpilWTNIccpFtEeJzIpjTFmN+JzQAgZdei1zBBRwDw81MFMCImYpUI5vYlFCIk0CphJJNGIDELIWUkqD+Qd5aWFB3jX7Boea/cjlwELTz6mMTl/e3nODTG6qKwM+Tas5/6dC8/L+nLDa9LGhCxTI2tLSXYLR2YHzNMr1R8muANPvPb9aV3OnNL9c9ZwzXF9Eedc10NVe1X/NjfXGnIBJB5fmvVq3Sm9vd0pDb2Hz6jNLNpaXaakpjhTSneskqLGyPGyRbK7JGZKZ4w5o/DpIaOQXw3guj/cj6AagqZruHzGajxU8IVTjuGc43//+0usO7IFFlnBU5c/grkpM9De24UvvvU/cAe8+HrBXbg0pwAA8IW/PIIfXPwQUhwJRjwlMjAKmIeIAubhofCHEBIxs1i1yegaCCHjHgWAJJLOGO9KCJm4JI0H51bx0sIDvHNuNXc5fJjJgLz+jm2Pm7XvYO7nocq2JQAQ9L77kR46vHp1yg3FbWJPEhRPkkPwyFqqJeWRsruEvS138csmKZX33yM6n3q2Qi7a8LWcmswLNh2ZclW2aJoyXzRNAee6pocO71L923q51jIHgOtstfq1nsQmX1Vik6/qjMdMgrnTZUpqjFVSuuJMKaEYU4JkFR1OWVBSGGOUEo5jimjC6zf+HDaTFSFNxWdeW4vzpuRjYfrsj4/58OgWVHXUYf2aP2B3wyF8619P4W+3/Q7vHPoPrptzKT6dewFuffNruDSnAP8+vBFzk3MoXCbjCgXMw0MdzISQiEll7fRKgxASadTBTCKp1+gCCCHGETUeml3DywsP8LZ5VTzG2YuZDJh/rnPcjuzyA3O+2B1QXEsAgHNdDXpe28K11tWZtpk7k8xZhe/I2zfExLT6LeidC5MoeURb6Xv68pY/12/JWz0po3PNA2Lqz57RdmQd+++KjPoSX+nMWz9qSVy4lDHBKppyFoqmHHCuhfRg+XbVvyPI9ba5AGIG+7yCuj+2xV8b2+KvPeMxmZm6Y0yJDXFKclesKSXgNCWKNsnpkAUlmYElMzaKd1EkA2KMwWayAgBUXYWqqzj9r/RflRtw7ZxLwBjDwvTZ6A540extgyRK8KsBBLUgGBOg6iqe2/EmXrj2SSOeChk8+m92iChgHh4KmAkhESFCU80IZRldByFk3PMbXQAZ1zxGF0AIiR5B51ruMV5WuJ+35h3ldlcPchkwZzDn9liSa/bPWVPfa01efiKx4zzgCbhfqADvXWUSLB3LEj+VHmRqdxvzLJrqbN4hQpfAuV+PNTX/T9udC/cJm/VXGppDN6YlC/etFRc88apWMqM+VDjn0POr/crbjXvn3be7x5q6AowxxkRZVGYtEZVZ4FwNaMFDWzX/Tp3rnfMBWIf7OQjxYEx7oD6mPVB/xmMik3pj5IT6WCW5I86U4neZkgSb7LSbBHMSg5DKGKO7isYATddw+UtfRHVnPW5feDUWpM065fEmbxvSYpI+/nOqIxFNnjZcPetC3P/ud/Dngx/gW0X34OVdf8W1sy+BRaa9Jkc5mlwwRBQwDw99oRFCImI6qz/GGCYbXQchZNzrNroAMq5RwEzIOMY412fUobzwgN6y4Ai3xnkwgwGzBz7zE36Tq/nA7LvKu2Oyl4Oxj5sruO5pDrhf7ARCiwDgorTbKgQmLNslVZaAodAR08YAgIF7tXSb09sWiHlHX/nRNcGNq7/e0bXpx/GxKx69TSq88wOt+OJdvMAc6EzN3/791E7X9IP756zRVck69+PnwSRFUublS8o8cB7q1QL7N2uBXYzr3XkAwpb+aVy1dgabpncGm3AUe095TIAQdMjx9bFKclucktLrMiUxu+SymkRrogAhnTFGmc0oIQoiPrjjebj9Hnzx7W+jrPUoZiZO+fhxzs+cDsUYQ4xix0vX/wgA0OX34DdbX8Oz13wP3/jHj+D2e7Bm6Q1YlD6o92NIdNHdWENE36yGhzqYCSERsUioaAEoYCaERBwFzCSSvEYXQAgJI875tAZUFB7QmxYd5paEbsxgQC76fg1JSLJ2Hcz9/N6OuFlLwVjhyY/pauvRoOdVE8BnAsBs18r1dtlVAABlYkM6AFgs3bEAIEDv1RLNuRzwfzv0hUVXCRs7b+v2rPjIainZbjEXPneJWFSVrG+9+x/6HAbYYrsqZxdu+DpqM87fdGTK1VlcENNPvjZjslUyL1wumReC84BH8+/dqQb2mMC9eQDkYX3eBkGHbnKHWie7Q62Tq70HTnmMgal2ObY21pTcGquk9MSakrhdjrWYBVu8wMQMxpgSqbrI2TnNDizPzMNHR7eeEjCnOhLR0N3y8Z8bPa1Itsefcu7PN76I+5ffhncO/RdzU2bg6lkX4s63voU3bvpF1Oong+YzuoCxhgLm4aEOZkJIRCwRyui2dUJINFCHKYkk+voiZCzjnE9pwuGCA3rj4sPclNiFHAGYgb5fw6IJpt7ynBu3NyUvzQNjRWc8HqraF/K+PQnHN+BzyPE1s10rFwBAjdC6R2N6HgCIYmgSAIhQfZoomWESdvYELYv+qq/66DPihtXPNLWsKJqUvrdbFOevyxPy6+NZ6ROvaXECRzIATKpbtyK9Yb2vbMbNxc1JixeDMdvptTCmOCTL0pWSZSm47utSA7sPaIF9FvDePERxDwMOLnlCHZM8oY5JtT2lpz+s2yRng8uU1BKnpHpiTcm6Q45TzKItTmRSOuvneZHha+/tgiSIcJod8IUCWF+zE/flf+6UYy6avgov7nwLV+VegN0Nh+BQbEi2f7K1TlXHMTR727B8Uh4OtVTCLClgYAioFC+NUlEPmBljmQBeBpACQAfwDOf8F4yx6wE8jr439ZZyzndEu7bBoIB5eKiDmRASEXNYdcQ6JAgh5CTUwUwiiQJmQsaYSS38aOEBvW5JBZeTOzFdAKaj79eI6EwIHZly9eZjGefNBBPOCJYBQPXv3qz6PlwIQAH6OncvSrvVy46PztgqVfoAQDF7GhhDGgDIUANBAFqi2SvV9+LR0B2LrhY2dkiMx71d35R6UWZas85Ycnkmy73/HrHhqWe1CkVFDgCIesgyu/TFoqlH/9q0d+59e3psaSvO2LHtOCZYXLJlxSrZsgJc72lV/TtLteCBGHD/PABGzk4WelR3Wo/qTqvvrTzjQYtob3GZkprilJTuWFOKFmOKN5lFu0tichpjzGlAvWNai7cdX3n/B9C4Bp1zfGrmebhw2gq8svsdAMCtC67C+VOWYd2RzVj1zE2wSAp+evkjp6zxo5Lf4xuFdwEArsq9EHe99S08v+PPeKjgC1F/PmRQjBiRoQJ4iHO+izHmALCTMfZvAAcAfAbA7wyoadBYf3NiyLllP/x+JoAzt44lhJARKlU+X2FhwRyj6yCEjHufxuPuvxldBBmf5r4091EA3zG6DkLI2WW08uqCg/qxpeVcTO3E1BMdvuHCwfTazAs3H518ZSYXpElnOy7U+99iLbC3ACeFtSuTrv4owzZjNQD0INDyR2VDLBjkpKSjO2bM3LgYAL6EZ3d0srjFrDNQpmxrmwkAP5V/W3ytuL4IADZYzPvuTU7MBWMyAFgC3POzZ7TyOC8Wn15Dp3P6of1z1miq/Ml85gGfn+5pUv3by7XgoXjw4JgaoKsI1g6nKbExTklxxykpoRg5QbZIDqfMTCmMsfiBVyBk3AtmPFlg+Agaxtg7AP6Pc/7v43/+CMDXqIN5fKF7GAghYSdA18wInvUFOCGEhBF1MJNIog5mQkaZ1HZ+bNUhvXpZGRfS2jFF5MgGkB2JazWkLNtWMf2GWF00rTzbMZxzHvL+uURXj53S1Zximbwv3ZpTcOLP2+TKQ2BYDQBOV1PPiY8r8IcAgLtMMzjQzoD4x0KfX3SNsKFdYDx+lc8/7+ZuT/FrzpgiAPApzHHfWnHBE69qJTPqccrs51h35azCjV9Hbcb5m49MuXrS6fOZ+8MER4psPT9Ftp4PXeuq0/zbDmvB8hQgNHOQnybDBPTeuBZ/TVyLv+aMx2RBcTvlxMY4JaUzVkkJOuUE0SrFxJgEJZkxIaxvQhAyihm+wR9jLBvAAgBbDS5l0ChgHh4akUEICbuprOEYY5F5oU8IIaehgJlEEgXMhBgsqYvXrzrIq5aX6choQ7aoIxNAZiSv2RY/Z++hmbcLqmxdeq7jOFcDwe6Xd3G965RwWWam7oLka2MZYyIA6ODaUaH54zv7HI72j/MLM3wqAIAxxq1SOetVV/TAYv+zVrDjs1LJagB4uKOraIvFsvGISV4JALrAxEdvkwrv/EArvngXL2CnjbiYVLdu+UDzmfsjiK4MwXZxhmy7GLrWXqP6t1bpwcPpgDriESPRFtIDzrZAnbMtUHfGYyKTe5xyQkOsktwRp6T4naYkwSY5HSbBnMTAUtlZxowQMgYZ+jqGMWYH8BcAD3LOx8xrdgqYh4c6mAkhYbdYKG9BhDpJCCHkNBQAkkjyGl0AIRNNfDdvWnmIH1lRquuTWpAl6ZgEYMBO3HDoiplcdmD2Xd6g4jpj/MTpuO7rCnS/UAPuX376Y+en3bxPYOKqE3+uEBt2cIb8E382m70fj2+wolc/8Xs9xaIJR/v+WXtc/fyS68T17QLj8QDwx4amBYWT0iv8gvBxUP3cJWJRVbK+9e5/6HMYcEqIPJT5zP0RxPgsk+3yLNgAXW05ovq31Omho5MAffJg1xitNB6ydQQbp3cEG3HktFcRAsRAjCm+PtaU3B6rpPS6TEnMLrlsimhJZBDST7xpQMgYYdjrZNY31ucvAF7jnL9lVB3DQQHz8FAHMyEk7JYI5VHfqZYQMmGNmW4IMibRGxijGNc5jjx+BHKsjKyvZJ3yWNemLrT+vRUAIJgFpN2WBsskC9RuFbW/qoXWqyH5M8mIWRQDAKj5RQ3SbkuDHEt7FEeby8tbVx7ilSsP6VpWMzJlHdkAUqJZQ481pXrfnDUNPkvS8sGEsLrWVRfsfikAaPNPf2x6zKLNLlPSqpM/tlM6elIoyXVBULNP/MmGno83k9LSrVOk4wFzL8y2N7XC7TdIxasBwMK59Y2GJuXT6alunLS53bo8Ib8+npU+8ZoW19/8aXOgKyV/xw9SOp3TDu2fc7eqytZ5Az2/0wlS0lST/dNTAUBXG8pV39YmXa2ZCugZQ11rtNOhKV3BlildwZYpVd79pzzGIITscmxdrCmpLU5J7XGZkuCQYy2KaE0QIKYzxkwGlU3I2RjyOvn4XQDPASjlnD9lRA0jQQHzMFQ/eYWW/fD7QQD0jZAQEjZzWBX9dEYIiRYKAEkk0dfXKNb+r3YoaQp0n37GY6ZEE6Y8MgWiTYRnnwcNLzZg6mNT4d7qhmulC858J2p+WoOYRTHo3t0NS5aFwuUoienh7ctLecWqQ3pochPSTRqmAkg0oha/Etu4f/Zdhz2OrOXomxM6IF1tKA96XncB/Ixw1SrFNC6IuyD35I91sZ4aH4KLPj7G6q49eZSc7aQbJbhVSucM1axvrjSeUG9fcr1Y0iYwngAAk0Nq1nfbOrY9mhC35OQgvDyT5d5/j9jw1LNahaKi3022Y92HZxVu/DqOZZy3+fCUazK5IA4rHBaktBkmxzUzAEAL1R7U/FvbdLUuB+Cpw1lvLOHQZU+oPcsTas+q7Sk9/WHdLrnqXaak5uPhs+6Q48xm0RYnMimdMWY1omYy4Rn1OmYlgFsB7GeM7Tn+sW8BUAD8Cn3f899njO3hnF9iTIlnRwHz8LUAGHfvPBJCjJPB2uKMroEQMiGE8Lib7pggkUQjMkapUEcInr0eJH4qEe0ftJ/xuHX6J1mOdaoVoY5Q3x9EgIc4uMoBAeAaR/u/2pH1YNYZa5DwsPfyzmVlvHzVIT0wtRFpJhXTGHDGWIloCkm2zoOzPr+vIzY3H4wVDHxGHy1YtjPU8/ccAI5+HtYvSru9iTG24OQPbpYqqsHw8RdYjLOlGSeNkrPDc8r8ZO401bKuYDbQ18X8hrZ6x43Shx/PeL7a27P0I6vlo//arKtPPq/VxdLWPCB6fvaMtiPOi7OO+Mis+3B5WsN6f9mMmz9qTlqyZLDzmfsjypNmi/IkcM65rlbtVX3b3FxrzAW4IW8YGEzwql3pXrUrva634vTHuFV0NDlNSc1xSoonVknWYuR4k0W0x4pMTmOMxRhRMJkQDOlg5pxvAHC2u0HejmYtw0EB8/A1gQJmQkiYMOi6BQH6KY0QEg3UXUoijb7GRqnGPzQi5YYUaD5twGM7SzrhmNeXB7qWuXDs6WPo3NiJlM+moGNdB1wrXRAUYYBVyGBZ/dydX87LCw5y37QGnqKEkMOAZUbXBQCaYOopm/G57c1JixeAsaKBz/iE6tuyQfVvygfQb6v7koRLS8ziqaGvCs1fL3TMPfljTmez/+Q/2+E9ZaavlmY1CV2fbJX0hHrbks+KH7UK7JPQ9qmWtsILM9N2tErSKUGyT2GO+9aKC554VSuZUY/Csz0XUVfNs0tfWj31yDvN++bdu8drSx/SfObTMcaYKE+ZL8pTwLmu6aHDu1T/9h6uNc8BEDvcdccR1qt5Unp9npRG35EzHjSLtjanKbEpzpTijlWS1Rg5QbZKDqfETGmMMfr8kZGgUXLDQAHz8DUbXQAhZPyYzJrqGMMko+sghEwIFP6RSKOvsVGoe083pBgJlmwLvKXnbjL3lnrRWdKJyf/Tty+ZaBWR/dVsAIDWo6H1/VZMun8S6p+vh9arIeHSBFin0Z3sQ2EJcM+SCl5WcJD35tTxJHMIMxiw1Oi6TqYzMXh46jWb69KLZoEJq4d6ftD7frEeKj9rIB2vpJdNts9bcfrHD4jHdoJh5ckfs9vbTxlPaYfnlMBaS7HkSoe6NAaIAOCDYv2Tdt72z0nrPr6+AAhv1TdNPW9S+jGVscyTz9cFJj56m1R45wda8cW7eAEDzvruiTnYlbx0xw+Tu5xTS/fNuTukyrYhz2c+HWOCKJpyFoqmHHCuhfRgxQ41sD3Atba5AKhTtx9+rSfB7+tJaPZVn/GYSTB3OU2JDXGmlK5YJSXolBNkqxQTIwumFMaEidgpToam0+gCxiIKmIevyegCCCHjxyKhohmggJkQEhXUlUEijQLmUai3shfdu7vh2esBD3Fofg3HfncMmXefkrPBf8yP+ufrkf1QNiT7mT8utrzTgqRPJcG9xQ1LtgXO5U7U/qIWkx+eHK2nMiYpQd6zuJKXFhzk3pnHeKIliBkMWGJ0Xf3hYHrNpIs3VWVfkc0FcUgdywDAua4FPX/axLWms54rMsl3XsqNcn8bvO2Vapynf0xRepNO/rPjtIAZsuCExA5C5bNPfOi76q1LbhA/bBVP6mJ26XrsC43NzbemJvvAmOX06zx3iVhUlaxvvfsf+hwGnHMMhst9JLdw4zdwLH315sNTPzPs+cynY0yURSV3sajkgnM1oAVLt2r+HRrXO+djgJpIn6Dud7X6j7la/cfOeExistdpSmyINaV0xCkpAacpQbBJTocsmJMZWAobQVc6GTco7xsGCpiHjzqYCSFhs4SV9RhdAyFkwqDwj0TU/tv39859aa6Oc3QAkuhLuT4FKdenAOjrUG7/Z/sZ4XKwPYjaX9Uic00mlBTljDUCTQGEukKwzbTBV+uDYOr7K9ZDZ24YONGZQty34AgvLTzAu2fV8nhrADMZzj7jd7RoSF2xtWLa9fG6aFo1nPM5D/YE3C8eAveec0ZzUcoN20VBOmMcRTNzl4eYOufkjzGmBQVBO6URwwbvGV+gepzSKrZ8MknDB8X6R+387bdI/z0l6M4LBGd+qcu94f9iXf0+x3V5Qn59PCt94jUtTuBIPtfzAIDM+o+WpzVu8JflfO6j5uSli8GYfaBzBosxSZGUufmSMhech3q1wP7NWmA347o7D4A5XNeZSFQesrcHGnLaAw1nvCISmOiPkRPq40zJ7bFKqs9lSmQ2yWlXREsig5DGGBP7X5WMM5T3DQMFzMNH72gQQsJmnlBFW7ATQqKFOphJNHhBt3WPCR3rOgAAcefHofWdVqheFQ0vN/Q9KALTHp/28bHNf2lG8rV9eZtrmQs1v6xB+7/akXRN0hnrTjSSygN5R3lp4QHeNbuGx9r9yGXAQqPrGqzW+Hl7DuXeJmmSJX+4a3C9pzXgfr4VCJ2zMzvLPnt7ojmj31nHW+SKFgAzTv6YzdZVwximn/wxO7xndB9r6dbYkwNmAPieesvSm8R1p3QxA8DdXd2rSiyWkn1mpd86yjNZ7v33iA1PPatVKCpyzvV8gOPzmcteXj316LvN++beu8drT185kvnM/WFMtkrmhcsl80JwHvBogb27VP8eCdybB+CMTnAydDrXzF3B5qldweap8O475TEGIeSQ447FKsltcaaUXpcpCXY51qKIlgQBYgZjjH6eGz8o7xsGxjk3uoYxKfvh9z8L4HWj6yCEjA8HlC+U2pk/1+g6CCETwht43H2D0UWQ8W3uS3OrAGQbXQchkSJpPDi3ipcVHOQd86q40+FDLhuDHaVdMVNKD8y+qzeoOBeNZB1da68Odr8iAPo5R74pgrXtqklreX9zcINQu19WikWwU8dApKcf2jxl6s7lJ3+sHfFND7BnUk4tggeVfzeETh9t8V3p+eJbpf+cMa4jCAQLszIqewRh9umPnWAJcM/PntHK47xD6z4/Pp85qMq2+UM5bzi47nergV37tcA+C3jvfFAjYdQxMM0muRpjlaSWOFNqj0tJ1h1yrMUsWOMEJqWzfsaxkFEtL+PJgr1GFzHW0Dee4aN3NAghYcK5DX6av0wIiZYWowsgE0I9KGAm44igc3V2DS8t2s/b51XxGGcvZjJgxJu7GcVrTa3aP2dNk8+SuGyknbZaqOZAyPuXVADxAx17cfrtRxkT+t3McJd0dDcYzgiCY5wtwdM/ZkXvmbOIBWaCIuxFQD+lg/r76s1LPyf+t0Vk/JRWexNgequ+Me7SjLRWzli/G7/5FOa4b6244IlXtZIZ9ei327k/n8xnLtpyeOq1GeGaz9wfJpidsmXFKtmyAlzvaVP9Ow9pwQMx4P55oFFFUcHBRa/ameFVOzOO9ZSf8bBVjGlyKUnNcaZUT6ySrDnkOMUi2uNEJqWxMI5UIWFDed8wUMA8fDSThRASFtmsqZ4xROxFJyGEnKbG6ALIhFBvdAGEjISgcy33GC8vOMBb8o5ye6wXMxkw1+i6RsqnxDUemHPXYY990nIwNuKdGdXA/q1q77/nARiwQ3NebFGJVYo5a0hbKtb3+3rYbu84Y20zfHZwzk8Px7UkS6907NStTfxQLK9qF26/Xfr3GbNc0lQt9WctbbsfTEqIw1nm6+oCEx+9TSq88wOt+OJdvIANIbTNrC9elta40V+e87mPmsI8n7k/TLAlyNbCQtlaCK57mlX/9jItWBoPHpgNgDavMwbr1bpTenu7Uxp6D5/xoFm0tblMSY1xppTuWCVFjZHjZYtkd0nMlMYYc0W/3AlPA9BqdBFjEQXMw0fvaBBCwmIhq2wEKGAmhERNrdEFkAmhwegCCBkKxrmeU4fywgN6y8Ij3BrnwQwGzELfrzEvKNs6Dubesb8zdmY+GDvnBnyDFeotLtECO1dhEIGrU06omunMP+uYiRqhda/G9H7HSZhMvpTTP8b6wlIvgFMCWy3dmnZ6wAwAP1BvXnqL+J9mkfEzNu27oNe34DPenuK3HPYzuqdP9twlYlFVsr717n/oc04fw3Euoq6aZ5W9vHrq0Xda9s69d4/XnrECjEW8s5gJjmTZen6ybD0fuuau1/zbDmvBsiQgRGP5RhG/1pPQ5KtKaPJVnfGYSTB3uUxJDbFKSlecKSUUY0qQrKLDKQtKCmMswYByJ4K2jCcLaOfaYaCAeZiqn7zCnf3w+36MwTlbhJDRZalQ3mt0DYSQCYUCZhIN1MFMRjfO+bQGVBQe0JsXHeZKQjdmMCAXfb/GDVVUvGU5n9vZkrRoARg7Z4A6WJxzHvL+tURXqwa1HoMQuiDtVj9jzHq2Y7ZJh89MhQEIgtrLmN5vIwYD7+E4tSOYO03TOdDCgFO6lQMwmV/WLt56h/TBGQEzADzR1lG03axsPibLy/t7/IR1eUJ+fTwrfeI1LU7g6Hets1GC7qSlO59M6oqZUrpv7j1Rmc98giA60wXbRemy7SLoWkeN6t9apQcr0wF1+sBnE6MEdb+rxV/ravGf+dJNZqbuGFNiQ5yS3BVrSgk4TYmiTXI6ZEFJZmDJLMybTE4g1Ew6TBQwj0wzgCyjiyCEjG3zhCP93o5HCCERQiMySDRQwExGnclN/HDhAb1+cSVXEruQIwAz0Pdr3NGZGDw89TNb6tILc8GEsATLAMC5Fgx2v7Kd6x2DXnNV8mc2yoJp9dke70Wg1c16l/T3mN3RXsNY/6G/CK1X7ad5mtulSuZVzxiH8aR6U/5t4r+aRMbP6IgGgDfrm+YWTso4EhTY1LPVCgDlmSz3/nvEhqee1SoUFTnnOrY/ru6juYUbv4G6tMItldOuS+eCmDnUNUZCEOOyTLbLsmC7DLrackT1b63TQ0cnAdqIR6aQ6AnxYEx7oD6mPXDmP7cik3pj5IT6WCW5I86U4neZkgSb7LSbBHMSg5DKotBBP4bRONxhooB5ZChgJoSM2CTWEmd0DYSQCSMI6swg0UEBMzHcpBZ+tPCAXrekgsvJXZgmcEwDMM3ouiKJg2nVWZdurs66LJsL4qA3pRvU2rrfHeh+4Si4b+Vgz0mzTtuTaplyzjq2yocP9be5HwA4nc3tZztPhOpXIZ/xcS3FyoXD3Wd8PACT+SXtkvIvSP/sN2C2cW7/Q0NTy3XpKR4w5jhXza0ulrbmAdHzs2e0HXFenHX0x7lkNJQsS2vcFCibcVNxU3L+woGuGQmClDTVZP/UVADQ1YYK1b+1QQ/VTAX0qIbeJLw0rlo7g03TO4NNOIq9pzwmQAg65Pj6WCW5LU5J6XWZkphdcllNojVRgJDOGJvoOSG9Th6mif6FM1L0zgYhZMRs8E8yugZCyIRRh8fd3OgiyIRAATOJuow2XrPqoF6bX87F1A5MFTimAJhidF3RUp+6akvltOsSdVFeFe61udbdGOh+0QuoCwZ7jiwo7pVJ1ySdq1uSg+tHheazhv7OmBbtrOsjFAj0s7eglm6ZJvcTMAPAk+pNy24X/9UoMj21v8dnhEJT/qe9c8v3E+KWne26J/gU5rhvrbjgiVe1khn1GFaYL3BVmVX2StHUo++27J17z16vPTMq85n7rUVKyzHZr8kBAC107JDm39qqq8dyAN7v54qMTTp0kzvUOtkdap1c7T1wymMMTLXLsbWxpuTWWCWlJ9aUxO1yrMUs2OIFJmYwxhSDyo4mCpiHiQLmkaEvPELIiGSylgbGkGZ0HYSQCYPGY0wgjDERwA4A9ZzzK6N8eQqYScSldPBjBQf16mVlXEhrxxSRIwsT8A7TloT5u0tn3mrSJMuAoehw6GpzZdDzBzvAhzSv98LUWw8KTFhxrmMqxMYdnPGlZ3vcaus662Z6CgJBb38PmKUULuAI03HGqIsgZOUF7ZLKu6R/nDU0vdHjXfaR1VK80WoZcAyILjDx0dukwjs/0Iov3sUL2CA2POxP33zm/5fUFTO5bN/cewPRnM/cH1HOnCXKmeCcc12t3qf6t3VytWEWwBONrItEFgeXPKGOSZ5Qx6TantLTH9ZtkrPBZUpqiVNSPbGmJM0hx5nNoj1OZFI6Y2zQG1+OctRIOkwUMI8MfeERQkZkIatsAChgJoREDW3wN7F8GUApgJhoX3j/7fv9c1+a2w4gPtrXJuNXYhdvWHWQH11epiOzDdmijkwAE/ZW/k7ntEMHZt/pD5liFkbqGlrw8O5Qz7tTADiHct5MZ/7GGFP8gKM0dkpHzhnImkz+9LM9psAfOttj3GmqZ53Bfmcp/0i9Mf/z4geN0lm6mAHg182tq86blL67UxQH1bH93CViUVWyvvXuf+hzGDDsoM3VXTWzbz5zwZbKaddHfT7z6RhjTJQnzxPlyeBc1/TQkd2qf7uXa02zAdCYv4lF6FHdaT2qO62+t/KMBy2ivcVlSmqKU1K6Y00pmsMUL1tEe6zE5DTG2JC+fxiswegCxioKmEeGOpgJISOyVCjtd8dsQgiJEAqYJwjGWAaAKwB8H8BXDSqjGhQwkxGI7+ZNKw/xIytKdX1SC7IkHZNAb8zDa0s7un/OmhafJTEiHcsnqP4dm1RfyWIApqGcZ5dcdfNii+YOdFwX66ntRfCs4bgoBt2MnX08gwW+s47P0NKtZqEz2O9jfV3Ml1Z+Ufr7WdcWAfHtusbMCyalN2iMDeprbl2ekF8fz0qfeE2LEziSB3PO2WQ0rF+W1rg5UJ5z40eNKcsWGTGf+XSMCaJomr5ANE0H57qqB8t3qIHtAa61zcEQ34Ag449P8yb5fN6kRt/RMx5TBGuH05TYGKekuOOUlFCMnCBbJHuMzJRUxthoe51QZXQBYxUFzCNDATMhZETmC0dFo2sghEwoNCJj4vg5gG8AMDKUqAawyMDrkzHG5eWtKw/xwytK9VB2MzJlDZMB9Lsh20TkM8c17J+95ojXnrECjEV0tnSo54NiLXiwEAAbynkMTLso7baO429yndMWqfIoGM66F0lMTGstgLMG1Vb0nnVPAS3ZMks60BVi6GcXQAA/Vm/Iv0P85zm7mON1PeGZppaDd6YkJYCxQYXs5Zks9/57xIanntUqFBU5gznnbASuKrnlr66eUvVu67659+71GDif+XSMCZKo5C4WlVxwrga0YOk2zb9T5XrHPAB2o+sbqZCm4Tcfboaq6dA5x7yMVFwy59S/Ts453tl9CKVNLTCJIm5YOh8ZsU54/QG8uGknfMEQLps7A3PS+76FvbBhBz6zaA6cFrMRT8lQAb03rsVfE9fiP/NlqCwobqec2BinpHTGKilBp5wgWqUYh0lQUhgTRvRGzTCdmZCTQaGAeWSOGF0AIWRsm8SaXUbXQAiZUKiDeQJgjF0JoIVzvpMxttrAUqgLiJxTTA9vX17KK1Yd0kOTm5Bu0jAVAM14PU1QtrcfnPWFA52unGUYZDftcHHO9aDn9Q1caxhw/nB/liZesd4kWlYPdJwGPVAntJ+zy9npbOk61+M2eM++aa0k2CGxfVD5vP4eDkJWntMuq7xbev+cG9gt9Qdmf8HdXfK8yznoTfxaXSxtzQOi52fPaDvivFg82PPORgl2Jy7Z+f8S3TGTy/fNvccXku15I10znBiTFEmZu1RS5oLzkE8LHNiiBXaB6+75QD+7MI4BkiDgnqJlUGQJmq7j/9ZtxszURGTFx358TFlTK1q9PXj4stWo7ejCX3YewJcvXIndtQ1YnJWBvElp+H3JNsxJT8HBhmakx8ZMyHB5ICE94GwL1DnbAnVnPCYyuccpJzTEKskdcUqK32lKEmyS02ESzIkMLPVcG4gOkzfjyYLWMK85YVDAPDLlAHQMc5A/IYQ44Dtr1wYhhEQABcwTw0oAn2aMXQ7ADCCGMfYq5/yWKNdBATM5hb2Xdy4r4+WrDunBqY1INamYxoDlRtc1Wqmi4imbccuulsQFC8HYsALfoeA85At2v7SP692DDlNPlmjOPJRlm7VqMMceEGt3guGcGwDGxLScPUAGYIf3nN3VeoK5Q2zynfXxn6g3LLtT/EeDxPRzhvZf6XQXbrJYNpQppkE9NwDwKcxx31pxwROvaiUz6jGsz+fpnN1VMwo2fvP4fObr0rggjbqfIxiTLZJ5wTLJvACcB71aYO8m1b9HAvfkYYijVozEGIMi98Vlms6h6/oZxxysb8bi7HQwxpAVHwt/KIRunx+iICCkadB0HYwBmq5jfUUVvrBqSbSfxpin8ZCtI9g4vSPYiCOeUx8TIAZiTPH1sabk9lglpddlSmJ2yWVTREsig5DGGBtO3kmvW0aAAuYRqH7yCl/2w+/XAsg2uhZCyNiTjtYmxui2T0JIVFHAPAFwzh8B8AgAHO9g/poB4TLQNyKDTGBWP3cvLeflBQe5b3oDT1FCmM6AiM4NHg90JgUqp127pT5t1RwwIeLBMgBwvbcj0P18PXgwfzjni0zuKUq5wTrYUGevVDPg+B6L1X3ODUrt8Jyz0UtLtyacK2AOQTL9Xrv88D3SewN2hb/a2LS4YFJGqU8Qcgc69gRdYOKjt0mFd36gFV+8ixewMDWmnTSfubgxZdnC0TCfuT+MmeySeckKybwEXPe7tcDubWpgnxm8Jw9jIIvSdY6f/2cD2rw9WDE165TuZQBw+/xwWT5p0HZazHD7/FgwKQ2vbd2NnTX1uGLeTGw6XINF2RkwSTQZMZx0aEpXsGVKV7BlSpV3/ymPMTDVLsfVxpqSWuOU1B6XKQkOOdaiiNYEAWI6O/vIGxqPMQKj/j/qMaAUFDATQoZhgXC4ATRXkBASPa143H32n7QJCT/qBJpgLAHuWVLBywoO8t6cOp5kDiGHAUuNrmus4GBaVfZlm2omXTqFC2JUgmUA0LWO2mD3yxqgD7gx39mcl3rTLpGJBYM5toW5K4JMHfBashw45xxnO7znTOz0OGUmB7oZcNag+qfqZ5fdJf69XmJ6+rnWUjjMf65vclyZkdrJGYs917Gne+4SsagqWd969z/0OQywDeXcszk+n7loStW7rfvm3LPH45i0crTMZ+4PE8xOybJ8lWRZDq73tqv+nQe14AEHuG8+Rukd4YLA8NWLC+ALhvDixh1odHuQ6vwky+c4s8GegcFiknFXQd+3vd5gCB+WHcHtKxbhze370BsKoShnCrIThvQlRIaIg0ueUPskT6h9Um1P6ekP6zbJVXc8fPa6TEm6Q44zm0VbHGOszIh6xwsKmEeuDMBlRhdBCBl7lgplnoGPIoSQsKHu5QmIc/4RgI8Muny1QdclUaIEec+iw7ys4AD35B7jiZYgZjCA7gMfhr6xB9cmcUEeVEgbLnqo7lDQ+0YiRjD7eopj/tZ4JXXQdW+WK5qBc2+AJ8u+NsaQcK5j7PD0u4HfxwQmwSyWwq+dtSs7BMn0jHbl0fukd88ZMAPAJFXNeLK1fcc3E+MXDjXMXZcn5NfHs9InXtPiBI6wbVymBLsTl+z6UaLbkV2+b+69vpBpdM1n7g8TrPGytaBQthaA655m1b+jTAseigUPzMUQN5WMBotJxtSkeJQ3tpwSMLssFnT5Pnnf3u3zI8ainHLuvw9W4oLcadhd24D0WCcWZqXhhQ07cO95NBnIQEKP2pXRo3Zl1PVWnP5Y7UM4z4iaxgUKmEfujLdDCCFkMPKEw6Py3XpCyLhFATOJqv237/fNfWluMxC+MIUYyxTivgVHeGnhAd49q5bHWwOYyYBFRtc1lrUkLthdOuMWRZPMUR8dogUObQ/1/nMWRtBVaxHtzYvjL5k22OODUD2trHvBQMfFxLTWAQMGzAPO9NWSzQGppuecxzylXrdsjfhencT0c3ZMA8DlPb2Li62Wj/5ut60e6NjTlWey3PvvERueelarUNRzB+xD5fRUzyjY9E3UpRVsrZx2XeponM/cHyY4kmXrecmy9TzomrtB82+r1IJliUBolpF1ef0BiIIAi0lGSNVQ2dyG82ZOPeWYWWlJ2Hi4BnmZaajt6IJZlhBz0iZ+rZ4edPv9mJoUj4aubsiiCIBB7WeeMxk1zkicyeBRwDxy1EJPCBmWLNbsMroGQsiEUmN0AWRCOgoKmMcsSeWB+VW8tPAA75pTzWPtfsxkwEKj6xoPOp3TDx2Yfac/ZHIY8vkM+Tas1/zbVgAYyWBYflHa7ccYY4sHe8JuqWo32MCb3jldzd0DHWOH1zzQMVq6LXOggFmFJD+tfarqS9I7AwbMAPBka3vRLrOyrUmShjz+pdXF0tY8IHp+9oy2I86LQX/eBiujYX1+WuOmYN985uULwNg551iPJoLoTBNsF6XJtougax01qn9rtR6sTAXUsIbxg9HtD+BP2/aCcw6dc8zPTMOstGRsOtz3UmrFtCzkpiahrLEVT/79I8iSiBuWzDtljX/sL8dlc2cAAPImpeHFjTuwvrIKl8yJ+tMhg0cB8wgwzs+5MSsZQPbD7ycAaDW6DkLI2HNU+VyXwOAyug5CyITxJTzu/rXRRZCJZe5Lc58B8EWj6yCDI2o8NLealxYe4B3zqrjT4UMuAwYM8cjgeWzpR/bPWdPmtyQMazO9cAh63/1IDx1ePdJ1FsRdUJzjXDykWdEvKB9WakyfPtBxeXl/X++IaT/n2I0GpNV8nf0qa6C1lH/VNzCOc27kJ0ENlSp3NMtMG1TI3C0wd9GkjC6VsQGv3x9B59oTr2obZ9QPHLYPV0B2tO6be0+Zx5E1quczD0RXW4+q/q3H9NCRTECbYnQ9ZNzyAbA99Pp7FJIOE3Uwj1D1k1e0ZT/8fjuAeKNrIYSMHSnoaBYYdXQRQqJq/8CHEBJ2e40ugJydoHN1dg0vKzzA2+ZXcYezB7kMmDfwmWSofOb4+v2z11R57enLwdjUgc8IP851Neh5bQvXWlePdK1YU3Ll9JhFQwrJa4W2vRrT5w/mWIvF4xroGCt6rYNZi9vlo8wTOmfAfKKL+X7pr4MKmGN07nylobnlprTkXjA2qDpOpgtMfPQ2qfDOD7Tii3fxAhaBje6UkCdxya4fH5/PfE9vyOQYcDTJaCRIiVNM9iunAICuNlao/q0Neqh6CqCPiTEgZMw4TOHyyFDAHB6lAFYZXQQhZOxYIFQ2gG4ZJoRE1wGjCyATEgXMo4igc23mMZQXHtBb8o5ye6wXMxkwx+i6xrOg7Gg7MOsLB7tc05eBsQE3kosUzgOegPuFCvDeEf/cKkAMnJ96M2eMDam7fatUee5ZFScRpeCA4aEFvfbBrKWlWgTBExrwuJ+r1y6/R/zbMZlpmYNZd04wOP2rnV0bn4qLXTmY4/vz3CViUVWyvvXuf+hz2AhmYZ9L33zmh1GfumprxfTrU7ggDavrejQQpNQck/3qHADQQ8cOqf6trbp6bDrAz/kGAiGDQOMxRogC5vAoAwXMhJAhWCqUeYyugRAyoTTgcXeH0UWQCWkfAA6AGV3IRMQ413Pq+gLlhUe4Nc6DGQyYhb5fJIJU0dxdOvOWXa0JeYvB2JDGSIQb1z3NAfeLnUAoLBsyFqZct0US5CE9p14EWt2sd1AzhxXF28gYUgc8DkELONfA2DnnSGtp1ulSRTdnA3wf0iBKv9E+XfNl6e1BBcwAcIfbs7LEYineYTEP++94XZ6QXx/PSp94TYsTeOQaUNIbN+SnNm0Olk+/obgxdcWYms/cH0HOnGWSM8E557pavU/1b+vkakMuwJOMro2MSeVGFzDWUcAcHqVGF0AIGVvyhCP0gzYhJJqoe5kYYv/t+7vnvjS3GsBko2uZEDjnUxtRWXhAb1xcyc0J3ZjBgFz0/SJRoAmSv3LqdVsb0lbOARNWG12PrrYeDXpeNQF8ZjjWy7TN3Jlkzhry3OBt8uGDYFg9mGNjnC31wMAB83FeAM5zHqGIiRBYOXQ+Y6DFfql+Ztl94ru1MtMGPX7hmaaWFasnpe/rFsVhj5cpz2S5998jNjz1rFahqIjYLnAC10y5FX8omlL1t9b9c+9Z3z3G5zMDAGOMifLkeaI8GZxzXQ8d2a36t3m41jQbNMqUDB7dcTVCFDCHR5nRBRBCxpbJrHFMdwwQQsYcmr9MjLQXFDBHzOQmfrjwgF6/uJIriV2YLgA5QOQCKtI/DqZVZV+xuTrr4qlgoqEdyydooap9Ie/bk4DwbCptEiwdyxI/lc4YG1KjBAfXjwjN0wZ7vMvZPOhRGgL0Hh3iuQNmAHqsqUlsDwwYMGsQpV9rV9U+KL016IBZBuS36ptSLs5Ma9EZG3b3bKuLpa15QPT87BltR5wXg+r2Hi4l5ElcvOvHid2OrIq9c+/tGavzmU/HGBNE07QFomkaONdVPVSxQ/Vv93OtdS4GeiOCTHR7jC5grKOAOTyog5kQMiQx6KFNKQgh0UQBMzHSHgBXG1zDuJHZwqsKD+rHlpRzOaUL0wSOaQAGHd6R8OIAr0sv2np46jXJXJBHzdhE1b97s+r7cCEAJVxrXpR2W4XAhGVDPa9SaNrJGV8y2OMdjrZB5xQiNJ+Oc07IAABo6Vab2B4Y1Jq/Uq9ZtlZ8p0Zm2qBnFSdrWtL/Nbfuuy85MRaMyYM973Q+hTnuWysueOJVrWRGPYbcKT5UMZ6anIJND6MhdcXW8uk3jOn5zKdjTJBE08zFomkmONeCWrB0m+bfoXK9Yx6AQc3vJhOGF8Bho4sY6yhgDo8aAD4AFqMLIYSMfonobBMYEoyugxAyodCIDGIkuu10BNLbeE3BQb02v5yLqR2YKnBMBnWEjwrNiYt2ls34nFWTzEMOXSMp1PvfYi2wtwBA2EYfzHatXG+XXQXDOXeHfGRIx5st3rjBHitB9YdgGvA4Pckym6MzwAYRuGsQpV+p1xz7qvznIYWtBT7/vJu6vSV/dDpGFAzrAhMfvU0qvPMDrfjiXbyAhfHv8WzSGjflpzRtDVZM/2xxQ+rKPDA2rrp9GRNNkjJnqaTMAechnxY4uEUL7ORcd+eBchwC7Hvo9fd0o4sY6yhgDoPqJ6/Qsx9+vwLAfKNrIYSMfguEw8cACpgJIVGjAzhkdBFkQqOAeQiSO3hdwUFevaxcZ+ltmCJyZAEYN12F40GHK+fgwVlfCIZMjrBsmhcunHMe8v65RFePhXVEh0OOr5ntWjmsEQpu1lvXi8AQPk9cFwR10F/vMoIBH6wDHygyC2RhD0J63mDW/bV21fIvSW/XmoYwixkAvtXRWbjFYt5UZZJXDOW8/jx3iVhUlaxvvfsf+hwG2Ea63kAErplmVvyxaErVe2375t69vtuRvWKgDRTHIsZki2TOWyaZ88B50KsF9m1SA7sl6J48YBDvVpDxaI/RBYwHFDCHTxkoYCaEDMJSocxjdA2EkAmlEo+7fUYXQSa0agDdAGj/gX4kdvGGVQf50eVlOjLbMFnUkQEgw+i6yJk89szD++d8scNvjl9qdC2n41wNBLtf3sX1rrCGywxMvSjtVi9jbFhvcmyRKg6DDf7r2WLprmMMgw51FQSCgz1WT1S6xIbB/XOoQRR/qX6m9mvym0Mea/d6Q1NewaT0yoAgTB/quadblyfk18ez0ide0+IEjuSRrjcYppAnYfGunxR0OyZV7p17n3e8zGfuD2Mmu2RevEIyLwbX/W4tsGe7GtirgPfkgfKyiWSP0QWMB/QfTPjQHGZCyKAsEGi8EyEkqnYZXQCZ2Pbfvp/PfWnuPgCjZj6tkeK7edPKQ/zo8lJdy2rBJElHFoA0o+siZ9drSajbP3tNdY8tbQUYG3Xzrrnu6wp0v1AD7l8e7rWXJ121QRaU1cM5V4MePCa0zxnKOU5ncxMw+IDZDL862GPVdFvSYANmAPiNdtXyB6S3q01MzR70SQAsnFvfaGgyXZWe6g7HqInyTJZ7/z1iw1PPahWKGr0NPGM8tdOPz2feVj79huTxNJ+5P0wwOyXLspWSZRm43tuhBnYd1AL77eC++YjCmBJiqD1GFzAeUMAcPruNLoAQMjZMZo3jaqYZIWTU22l0AYSgb0zGhAyYXV7euuIQr1xZqqvZzciUNUwGkGJ0XWRgAdnRenD2nYe6nNOWg7FR2VWua111we6XAoAW9rtpky3Z+zOsOcOauwwAB8Vj28GwcijnOF3NQ7rjxoLeQc9N5bGmmRzoZEDsYI7XIYi/UD9T/3X5jeyh1AQAU0Jq1nfaOrY9lhC3BIyxoZ5/ulYXS1vzgOj52TPajjgvFo90vaFIa9y0tG8+8/XFDamrxt185v4wwRonW1YVyJZV4Lq3RfXvKNWCB2PBA3MBjPjvk4wqKmgz7LCggDl8NhtdADk7rgbR9IdvgqshQNdhnbESroKb0VXyCnoPbwUYg2h1If7yByE54s84v+63X4BgsgCCACaISL395wCAzo9egO/oTpiSJiPhyocAAN4D66D7PYhZfFU0nyIZQ1zoSTe6BkLIhEIdzGQ0mDBzmGN6ePvyUl6x8pAemtKEdJOGqQASja6LDF5ItHSXzrxlV1vC/MVgLKwjJ8JJVxvKg57XXQAPe/gtMZOnMPk6FxvBDN49UrVjqOfY7R1DmoFrRc/gN+ZiTOAWsYz5tEF3ev9W+/SyL0tvVZmYOuSNNa/x9iz90Gop/tBmDcvXkE9hjvvWigueeFUrmVGPEW0kOFR985n/VDSl6v22fXPWrO+OmTwu5zP3hwn2JNm6Okm2rgbXuhtV/7YKLViWCARnGV0bCYvyh15/z290EeMBBcxhUv3kFa3ZD79/GMCou2WKABBlJN/4AwgmC7imoum1b8AyZRFi8q+Fq/BWAED3jnfh3vRHxF/ypX6XSL7pBxCtn7xZqwd6EKgvRdoX/g+tf/sxgq3VkFyp6DnwHyRd/52oPC0y9sTD3S4wThv8EUKihYMCZjI6jNuA2e7jXfllvHzVQd0/rRFpJhXTGBD2UQUk8jRB8ldM++zWxtQV88DYaqPrORctWL4z1PN+DoAhh7iDcUHqzXsFJg77roNW1l0ZZOq8oZ6nKD1Dep1sQ8+Q1tdTLKpQ5R388RDEp9TrGh6W/zTkgBkAft7SVnBBZvqONkkMS9exLjDx0dukwjs/0Iov3sULWJRHN5hCnoTFu39a0G3PrNw77z5PyBSzMJrXNxoTY1Jl24Wpsu1C6FpHrerfVqUHK1IAdYbRtZFh22N0AeMFBczhtQkUMI9KjDEwkwUAwHUV0DWAMQjKJzsO85AfQ7vbhYFrKjjn4GoQTBDRve0tOBZ9Gkyk/7RI//KEw3UAzmyTJ4SQyKjC42630UUQAuAAAB3jYI6l1c/dS8t5ecFB7pvewJOVEHIYkG90XeES0HXcdqwWQc6hco6LHQ7cn3BqA7ZH0/DNxgY0qipUznFHXBw+43ShQ1XxQEM9ujUNDyQk4kJHX/a5tr4O/5ucjCRJNuIpDUhnglqVfeXmmkkXTQMTRm3H8gmqb8sG1b8pH0BEPqHTYxZudilJIxpps1muaAQwpE3uGNNDgqBlD+UcOzxDGlegpVuzpCEEzADwjHblsq9If65ShtHFLADC2/WNU1dPSq/Twjhm5blLxKKqZH3r3f/Q5zDAFq51ByvGe2x6waZH0JCyfFt5zo1JXJCyo12D0QQxbpLJdukk2C6FrrVWqb6ttXroSAagTTW6NjIkNO42TCgFC69NAG4zugjSP65raHzpQaidjXAsvAJKWt+bjJ0lL6PnwDoIihXJN/2w/5MZQ8sbjwEA7HmXwZF3KQTFCuuMFWh88QGYs+aDKTYEGyvgWnlTtJ4SGYOWCuXdRtdACJlQqHuZjAr7b9/fO/eluZUAxlyXlznIvUsqeFnBAe6dUc+TzEHMYMBSo+uKFBNjeD5zEmyCgBDnuKW2BoU2O+ZbLB8f84euTkxVFPwmIxMdqorLq47iyhgn3vd046oYJy6PcWDNsTpc6HDgQ68HsxTzqAyXOcDrMs7bcnjK1alckIY9aziagt6/f6SHylZHan2rFNO4IO7C3JGsEYLqbWHuBUO+tq2zljEMKZyzwzukMQ3cJk/iDMcYR+Zgz+nrYr6+4RH5j8PqYnbpeuzzjc1Nt6cm+8GYeThr9GddnpBfH89Kn3hNixM4ksO17lCkNW1emtK8NVQx7bPFDWkTYz5zfwQxcbLJfuVkANDVxkrVv7VeD1VPBvRxvTHiOLHH6ALGCwqYw2uT0QWQs2OCiLQ7fgXd70XL299HsLUapsRsxBbehtjC2+De/AY8O9+Dq+DmM85NuflHkBzx0Hq60Pz6tyHHZ8CcOQfO/OvgzL8OAND+j1/CVXALPHs/gL9qN+SkbLhW3Bjtp0lGuQVC5eDnxBFCyMhRwExGk70YAwGzKcR7F1fy0oID3JN7jCda+gLlqG6qZSTGGGzH9yRTj3cxn3EMGHp0HZxz9Oo6nKIICYAMBj/XEdQ5GOs7/+XOTvwmffTtj9eUtHhn+YzPWTVRGRPjTDjXtaDnT5u41rQ6kpe5KO32JsbYkMPhk+2SqnaDYciBvdPZ0gIMLWB2wDPkTIPHyNXMHRp0wAwAz2hXLP+q9OZRhalThno9AFgYCObe1+Xe8JtYV1g3Oy3PZLn33yM2PPWsVqGoyAnn2oMlcF2eWfmnoinV77Xvm3N3SXfM5JUTZT5zfwQpdbrJfvV0ANBDdaWqf2uLrtZOAzjtwzM67TG6gPFizN+iNsocBEC3oY5ygtkOc+Zc+I6e+jO3bdZq9FZs7PecExv/iTYXrDnLEWioOOXxYPORvuNi09FzYB0Sr34YodYahDrqI/AMyFg2lTVMyHf1CSGGoYCZjCZ7jC6gP7LK/UvL9d1f+4v20fNPqfte+YkmP/iOvmjREb7aGsRsNgGbcjTOcU11FVYdrsQKm+2U7mUAuDnWhaOBIIqOHMZV1VX4VlIyBMZwRUwMNvb0YE1dHdbGJ+CPXZ24KsYJizB6fuzsiJ25f/3K/7fn0Kw7FmmiMqJO3WjhPNgTcP9+F9eaItplvTj+khKzaB1RuAwAh8S6lOGc53Q2B4d6jh2eIbfGa2nWoYfSEISfqp9tGup5J7u3q3vVHH9g/UjW6E+ri6WteUBM7bBjR7jXHgpTyBu/ePdPCxfv/H9HTcHunUbWMloIckauyXFtkeJ6ME22f2Y/kzKKAdZsdF3kYxUPvf5eu9FFjBcT7sVSJFU/eYWe/fD7WwFcbHQt5FRarxtMECGY7dBDAfhr9iAm/zqEOuohx/W9kdh7eCvkuDO7K/SgH+A6BMUKPeiHv2o3nKeNweha/yriLvkSoKsAP96gygRwNRDx50bGFhe89M41ISRadABbjS6CkJNsM7oAAJBUHphfxUsLD/CuOdU81u7HTAaMOFQbT0TG8Hb25L5ZyvX1qAwEMF1RPn58Q08PZpoVvJCZidpQCHfVHcMiiwUOUcTTGX2NoW5Nw3Md7fhFegYea2pEt6bj83FxyDstrI6Wbntm5f45azoD5rgxNd6E6z2tAffzrUBoSSSvE6+klU9xzB9xN/cxoX2fxvQhb+4HAHZ7x5DHR9jhMQ31HC3FMlMqdetD3SDvWe3yZQ9Jbx5RWGjYM3ZfamzOL8zKONgjCLOHu0Z/fApz3LdWXPDEq1rJjHoUhnPtoYrxHpu+atMjaEhZtr0856YELkjDGi0ynjDGmChnzxXlbHDOdT10ZI/q397NtcbZoP15jERTCMKIAubw2wQKmEcdzduBtvd/1hf+ch3WmQWwTluK1rd/gFBHHcAESDGJiLtkLQBA9bSj/Z+/RPL1T0Dr7ULrW9/rW0jXYZtVBMuURR+v3VuxGaaU6R93OStpM9Hw3FrISdkwJQ3rDioyTrng6RQZTxz4SEIICYs9eNzdZXQRhJxkM4AQIrQx2dmIGg/NrealhQd459xqHhPTi1wG5EWzhrEqRhSxxGrF+h7vKQHz22437oqLB2MMWSYTMmQZR4NBzDspPP5texvujk/A37u7MctsxpWOGHypvh4vTpoU1efQa0k8tn/Omtoea+pyMDZ6WqkHQdfaq4PdrwiAPiuS1xGZ5Dsv5SaJMTbksPZ0W6WKoe2gdxKTyTfkzmcbvEOfaWwSYyGyQ9D4kD6vHILwY/Wzzd+WXxt2wGwCTG/VN8ZdmpHWxhlLGO46/dEFJj56m1R45wda8cW7eMFQA/RwS2vasiSleVuoctr1xfVpBRN2PvPpGGOCaJqWJ5qmgXNd1UMVO1X/dh/XWucCoM9RdPV/CzsZFgqYw4/eARmFTEmTkXbHL8/4eOI13+r3eMkRj+TrnwAAyK4UpH3h/866tjVnOaw5n7zZH3v+nYjFnSOsmIxHC4TDxwDEGl0HIWTC+NDoAgg52fGN/nYCWBbJ6wg6V2fX8LLCA7xtfhV3OHuQy4BhdVRORB2qCokxxIgi/LqOzb09uCvu1Aa7VEnGlt4eLLZa0aaqqAoGkSl/8r5BdTCIFlXFEqsVZQE/FCaAMSDAo7cVRcAU03pg1p2lbufUZWBsSPN2RwMtVHMg5P1LKqLQ3ViU8tntoiCNuOvVh2BbF+sd1rxyQVB9jOlD/nuyoWdYLfF6nNIqtvqHfN5z2mXLvia9ccQ8gi7mNFVLfaqlbfdXkhJiIzGr+LlLxKKqZH3r3f/Q5zDAFu71h0Lgujyj8vWiydXvt++fs6bEHTNlQs9nPh1jgiSaZi4STTPBuRbUgmXbNP8OlevtcwE4jK5vAqCAOYwoYA6/Lei7JXVMvTtOCIm8JUI5zWgnhEQTBcxkNFqPMAfMgs61mcdQXnhAb807ym2xXsxgwJxwXmMiaVVVPNLUCJ0DOjgudcRgtd2OP3V1AgBudMXi3oR4fKuxEVdVVYGD46sJiYiVPvnR8hdtrfhyQt9NW5c7YnB/fT1e6ezA/QlhbdjsV0i0uEtzb9vdFj93CRgzdFTAcKmB/VvV3n/PAxDxeSJZ9tnbE82ZYfk8bZMOHwDD6uGca7d31DCGmUM9z4reYQWoWro1ZjgBM4cg/Ei9sfkx+ZVhB8wAcGGvb8E13p6P3nbYV49knbNZlyfk18ez0ide0+IEjuRIXGMoTCFv/KLdTxV67JmH9867zx00xSwa+KyJhTHRJCmzl0rKbHCu+rXgwS2afyfnetd8AFaj6xuHOgCUGV3EeMJ4P7sCk5HJfvj9vaAuCULIaf5k+m7JMqF0TP6gQwgZczQAcXjc3W10IYScbO5Lc68E8LeRrME416fXo6LwgN688DC3xHswg9FtxROeJsi+iumf3dqYsnw+GBuzd4yFeotLtMDOVYhCw5IiWNuumrSWMyaMeIQbB9efVz6s54wPq1s8M3P/xuzJe1YO9TwVYuh29sbQx+5oPKD8p0FnwwjxGXS9VLnjqJmFpg35uqe5LCNtS50sReyujsQu3vDUs5pXUZETqWsMR2NK/vaynJsSuCBP+PnMA+E82KMF9u1VA3tE6N15AJSBziGD8t5Dr7/3KaOLGE+ogzkyNoECZkLIaaaxervRNRBCJoxdFC6TUWoDhnq3H+d8aiMqCw/ojYsOc0uiGzkMmAkMvduRjD86E9Sjkz+1qTbzwhwwYbXR9QwX55yHvH8t0dWqomhd8+L0248yJoRl08PDQtMuzviwxmMAQIyzRR3OeRI0GZwHwNjQQjeRKTAJOxHUh9xJyyEI/0+9sfV/5VdGHDC/Wd84uygr42iQsYhs3tPqYmlrHhA9P3tG2xHnxbD/fsIttWnrkuTm7aHKadeV1KcVzgNjrmhcN6QG8fN3H4SqhaBxDQsmF+KKJZ8/5RjOOf686dc4WLsVJknBrau/gczEHHh8XXj2X/8LX8CLK5fcgfmTVwEAfvfPR3FDwZfhskXmDg3GTDbJvHiFZF4MrvvdWmDPDjWwVwHvmY8o72kwzmwwuoDxhgLmyNgE4B6jiyCEjC6x8KQbXQMhZMKg8RhkVNp/+/6uuS/N3Q9g/rmOy27iRwoP6PWLK7kpqQvTBSAHGF0deMRYHODHMs7ffGTKVek8DPODjcS5Fgx2v7Kd6x1RC5fnxRaVWKWYsH3edshHRjRg22brHPasYAbu5RhiwAxASzJ7pbreYV3zBe3SZd+U/lRpZqHpw1rgODvnjtcamlquT0vxgrGINKP4FOa4b6244IlXtZIZ9Rg1/630zWd+o3By9fsd++fcHZX5zJIo44FP/RSKbIGmqXjq3S9j1qSlmJz8yX6Ph45tQ6u7Dv9748uobinFnzb8Al+/5tfYeXgd8nMuxqKp5+E3f38Y8yevwv7qTchMmB6xcPl0TDA7JcuylZJlGbje26EGdh3UAvtt4L75AGi29dAUG13AeEMBc2TQRn+EkFPEwOsWGTd8/hkhZMKggJmMZiU4LWDObOFVhQf1Y0vKuZzShWkCx1QAI5pxSsavxuSlO8pzbnToorLC6FpGiut+d6D7haPgviGPhxgup5xQNdOZH7Zu1m7WW9eDwIjWM5n8acM9V4Deq0EY8maIWrotdbgBM8DYD9XPtT8hvzSigBkAZgZDUx9p79z8w4S45QMfPTy6wMRHb5MK7/xAK754Fy9go2jPKFOoJ+7j+cxz73UHFWfE5jMzxqDIfVNRNF2FpqtgYKccs696I5bmXAzGGCYnz4Iv4IW7px2iICGkBqBqITDGoOkaPtz/Fu659HuRKvecmGCNky2rCmTLKnDd26r6d5RqwYMu8MBc4LQnRU7XA2BHtC/K+jacfRlACvru5nqGc/4LxlgegKcBmAGoAO7jnG+Ldn0jRQFzBFQ/ecWR7IffbwaMH6ZPCBkd5gtHj4HmQxJCokMF3fZHRreS9DZ+1aqDek1+ORdSOzBV5JgMgGZxknNqj83dd3DWHVyVbaPmVv+R4Fp3Y6D7RS+gLojWNRmE0AVptwYYY2HbNGyLVHkEDBnDPV8Ug92M8WEHzCI0nzaMaIM75ekcaGXAsGZQv6RdnP+w9MdKCwuOOGT+nMe7vNhqKd5ktUS0i/25S8SiqmR9693/0OcwYNhd45Hg8B6btmrzt9CYnL+9bEbk5jPruob/99a9aHXXo3D2VchOzj3l8a6eNsTaPvmScNkS0dXbhsXTzseL636ArRX/xtX5X8T6g+9gac5FMMnmSJQ5JEywJ8rW1YmydTW41t2o+rdVaMGyBCA42+jaRqmND73+3rDG8oyQCuAhzvkuxpgDwE7G2L8B/AjAE5zzfzDGLj/+59UG1DciFDBHzmYAVxtdBCFkdFgqlHUaXQMhZMLYgcfdXqOLIORs3vih+hGATACTDC6FjBHdjqyK/bO/6A6YY5cYXUu46GpzZdDzBzvARxxODsWq5Gs2yoJpdbjW06AHa4W2WQMfeXYOR1stgDnDPV9CyB8czr5njDFukypZjzrMTQ4Z+4H6ufbvyi+G5e/wN82tq86blL67UxQj+obDujwhvz6elT7xmhYn8NHXFJfavHVJcsv20OGp15bUpReFfT6zIIh45Lpn0Bvw4tl/PYaGjiqkxZ07y2ZgsCh23HvZDwAAvQEP/r33T/jixU/gD8U/RW/Ag/PnXY8pKcbnuUyMSZVtF6bKtguha53HNP/Wo1qwIgVQZxhd2yhiyHgMznkjgMbjv/cwxkoBpAPgAGKOH+YE0GBEfSM1am6LGIc2G10AIWT0WMQqRjSXjhBChoDGY5BRLbestA3APqPrIKNfryWpdsuSb2/asfDr08ZTuKwFD+8Oel5LAnhqNK+bZp22J9UyNawzeA+JdTvAhtcBfILT1TyiRgwTgsHhnqunWEb0Gv0V7aJ8HzdVjGSNE0RAfKu+MUPoC6EiqjyT5d5/j6gFJISl9nATuC7nHH6zsGDjN7mz63AxOA97t6lVsWN6ah4OHdt+ysddtgR09rR+/OeunlY4radOYPnHzldwyYKbsePwOmQm5uDm1V/H37Y/F+4SR0wQYzNl26VF5tgHZphibqsS5BnFgHjE6LpGAcPnLzPGsgEsALAVwIMAfswYOwbgJwAeMa6y4aOAOXL+a3QBhJDRY5pQH5FNOwghpB8UMJOxgF4rk7MKmJwtOxY8VLJl6WOpvbbUFWBs3Pzcqvp3bAr1vDsbUR6dJguKe2XSNYkszJ/LPVLViF/jxsS08pGcryAQGu65Wrp1ykiuDTD2ffXmjpGt8YkETU98pqmlA5wPOzQfrFYXS1vzgJjaYY/+LNrBktWe2EV7fla0ZOeTNaaAe8R1enxd6A303eQVVAMor9+JZFfmKcfMzVqBbRX/AuccVc2HYDHZ4LR9EjC3uOvg7mnH9LT5CKp+sOP/C6kR/ysbEUFMmGyyX1Fkjv3yVJPjc5WCPPUjQKgxui4D9ALYPuBREcT6NvT8C4AHOefdAO4F8BXOeSaArwAYfe9WDALjfETfy8lZZD/8PgNQDyCq70oTQkanw8otjRLT6fsBISTSggBi8bh7uLsWERIVpTNzLwPwd6PrIKNLSLK4D+Xevrs9bs5ShHFG8GgR6vmgWAseLIQBG3Bdln7XphhTfFg3RWxl3ZXvKNtHPB4if9kbu02mwLDHQvwPfry+mk0pGO75yr/qqxgfyQx4zg8pd1RYWTBsIwh+Guta/6IrZtjPaSgEnWtPvKptnFGPsHa3R0Jj8tIdZTM+F8cFeVhvDNS3H8ErH/4IOtfAOcfCqUW4bNFtWH/obwCAglmfAuccb2z4JUrrtkOWzLhl9deRlfjJX+1z//4OPrX0C0hyZsDj68QzHzwGX7AHVyz+PBZMGfWfwjPooboy1b+tSVdrpgF82LPUx5D3H3r9vSuNujhjTAbwHoAPOOdPHf+YG4CLc84ZYwyAm3Mec651RiMKmCMo++H3fw/gTqPrIIQYy47e7gPmu8bcPxCEkDFpIx53rzK6CEIGUjoz1wagE4BsdC3EeJpg6i3PuWFbU3J+XrjnrY4GnHM96Hl9A9caDEmfZjrzN86PW70y3Ov+zbSjpFlwj/g5rSp4pY0xJAz3/O/hieJSNmfYm+OZtraWCF3BET2Pz4n/2foD+fn8kaxxumvTUjZUKKao/Zt+5wda8cW7eAEb5Xe660xQD0+9dmNdetH88fj9wiicc66rtQc1/9Z2Xa2fAfAUo2uKkLUPvf7eb4y48PHw+CUAHZzzB0/6eCmAeznnHzHGLgDwI875IiNqHIlR/Y1jHHjP6AIIIcabLxw9ZnQNhJAJg8ZjkDEht6y0B8AWo+sgxtKZEDo85eqS4oKfeppSlq0ej2ER5yFfsPu57UaFyzbJVTcvtmjYG+idTQiqt5m580a6jiz7RhQuA4ANPSPqmtPSrcPYIfBUf9AuWNrLlfKRrnPKmo1Niy26XhbONc/luUvEot9dJmznQE+0rjkcAtelnMNvFh2fz1wSifnMExFjjIly1hyT47NFiuvBJNl21R4mppYAaDO6tjAz8u6plQBuBXA+Y2zP8V+XA/gigJ8yxvYC+AGANQbWOGyS0QWMc/8GEACGs6UtIWS8WCKUjWjjEkIIGQIKmMlY8h8AUbkFnIwuHOC1mRduPjr5U+lckMbePeWDxPXejkD38/XgwbB2tg6BflHabe2MsbDf9r5bqt4FNvKRCo6YtjpgpAGzd0Q1aMmWXOlgl8YAcfirMPYd9dauJ+Xfj6iWkykc5jfrm2yfykjt5IzFhm3hc1iXJ+TXx7PSJ17T4gSO5Ghcc7iOz2cu9NjSj+ydd19nUHEtNrqm8YIxJoimqXmiaSo41zU9VLlT9W/3ca1lDgCX0fWNQOlDr79XbdTFOecbcPYRSWOuY/l01MEcQdVPXtED4COj6yCEGGsRq9SMroEQMiEEAGwyughChuA/RhdAoq8xJX97ccHPKo9MvWYFF6Qso+uJFF3rqA24n3GDB+caVUN+4hUlimiZH4m1D4nHwhI+upzN3SNdww7PyHINWYiBxA6NtI4/aefn93IlrB3HWaqa+cPW9iPgXA/nuudSnsly779H1AISKqJ1zZFw9NRPXbX5fxbPOvTiDkEPHTG6nvGGMUEUTTMWKTG3rFJcX7bJ1ku2MyF+IwCP0bUNA+39EEEUMEcejckgZILLEY7ZjK6BEDIhbMHjbr/RRRAyBNsAdBhdBImO9rhZ+0pW/mhf6czbluiiKcfoeiJJD9UdCna/ZAH0EWwcNzKJ5oxDWbbZYZ+7DAB1Qvt+lelh2dDOEdM64g0P7fCOoPO4jx6vtI90DQB4XL1txIH56a7o6V18aU/v+nCvey6tLpa25gExtcOOHdG87kiktGxfXLj+q1kZdR+WgHO6gzQCGBNlUZm9RHHevlJxPSBL1gu2MMG1GcBY2VyaAuYIooA58ihgJmSCi0d3qtE1EEImhHeNLoCQocgtK1VBr5XHPbcju3zj8u9v3ztv7TxVts0zup5I0wKHtge9b2QBPNGoGkQm9xSl3GBhjEVkE82tUmXYQlSLpds10jUc8Ix49KeWbosb6RoA8IZ23tIerpSGY62T/ai1vTBZVbeFe91z8SnMcd9acUF5Okqied2R6JvP/OfCgo3fhKurkuYzRxBjkllS5i9TnF9Yrri+xCVL0WYmxGxF3x11o5EHQFTfqJloKGCOsOonr6gGcNDoOgghxrDB5xWhpxldByFkQnjL6AIIGQb6uh2neizJNVuWPLpp58Kv5QQU1xKj64mGkG/j+lDvPxcCMPTutfNSb9olMiki3dM+BNs7WU/YZt1KUnDSSNeww2Ma6Rp6vJLLMcJhzsc9rt4e9tEBDGB/qW+cIXFeE+61z0UXmPjobVLhBwtZMQeiNqZjpGS1J3bhnp8XLt3xg1pToGvMdGGPVYyZbJJ50XLFeVe+4lobkMwrN4LZtwMIGV3bSf7z0OvvjaZ6xh0KmKODOjMImaDmsKpaxs46yJ8QQsJlFx53VxtdBCHD8C8APUYXQcLHb3I171jwtZKtSx9N67WlrABjE+J1UND7brHm31qAEW0UN3JTHPO3xiupEds8c4d0ZD9YeDaxVxRvE2NwjnQdG7wjr0dgMhQxLJ3Hb2qrl3q5ecQznU/n1Lnz5YbmADiP+jiC5y4Ri353mbCdj7Hv1/aehil985lfoPnMUcKYEiNZ8leaXWuWKM57vaI5fz2YdRcAo/clovEYEUYBc3RQwEzIBLVUKKP5X4SQaPiL0QUQMhy5ZaU+AP80ug4yciHJ2rVn7n3Fm5Z/L6bbObkQERrPMNpwrquB7lc26KHDRUbXYhHtzYvjL5kWqfU5OK8UG6eEa72YmNb6cKxjh9ccjnW0ZHPYgtvH1dsjEsTODQZzvtLZtTsSaw9kXZ6Q/9gtYq3O0GzE9UcipWXH4sL1X83KPLauBJzT7P8oYYIlVrasLDC77lmoOO/uEJVFJWDmfQC4AeVQwBxhFDBHx2YAYdk0gBAytiwSKmjuFyEkGihgJmPZ20YXQIZPE0y9h2beVrx+5Y9YR/zsIjBmMbqmaOE86A24n9nLtdZVRtcCgF+Udvsxxlh8pC5wRGjeqTM+4pEWJzhdzWEZSWFFT1i+5rR0a0Y41gGAP2tFSyLRxQwAX3B7Vi70+w2Zi1yeyXLvv0fUAhIqjLj+SAhcl6Yf+UthwcZvCK6uCprPHGVMsCXK1qJCs+u+eYrzriZRmV8MmKI1TnbPQ6+/1xCla01YFDBHQfWTV2igzgxCJqQZQp3V6BoIIePeITzuLje6CEJG4D2MrjmNZBB0JoQqp36mpLjgp96mlPwiMDbiUQdjCde9LYGu39WB9y4yuhYAWBB3QYlFsodtNnJ/tstHwjqD1+FoC8s4ESt6wzLzmseYpnKgKRxrAcCjoTsiNk7i940tyx2avi9S659Lq4ulrXlATO2wY0zONpbVXtfCPb8oXLrjB7VKoHO70fVMREyISZWtFxSZY7802xRzR51oml0MyGURvCR1L0cBBczRQ2MyCJmAEuBOMboGQsi4R93LZEzLLSt1A1hndB1kcDiYXpN50cbigp81Hsu8oBBMSDK6pmjT1dajAfezQSA00+haACDWlFw5PWZRfiSv0c189T3whzVMN5s9cWFZBz57ONYBAG6XDodrrbf1giUebolIh6YMyG/XNyYLnLdEYv2B+BTmuG+tuKA8HYZ0UoeDvadhysrN314y+9DzOwSN5jMbRRBjM2TbJUXm2PtnmmJurxbkGcWAGLb/Do97P8zrkX5QwBw9/wRAt2AQMoFYEOiVoIXtVjtCCDkLCpjJeEBjMsaAhpRl24oLnjpyZOrVK7kghW1Uwliihar2BT2vxAF8VLzGEyAGzk+9mTPGwjKH+Gy2ShWVYOHcwJBzUVSzw7GSAC6A87B0C2up1rBuSvlo6A5fONc7WbKmJf+qubXRqFEPusDER2+TCj9YyIo5ENbu9mhKbtm5uHDDV7Myj/2X5jMbTBDjs032K4rMsV+eZnLcfFiQp30ECNUjXLYefWNrSYRRwBwl1U9e0QVgo9F1EEKiZw6rqmUME2LndEKIYY7gcfdeo4sgJAzewRgOKMa7tvg5e0tW/nh/2cxbl+qiabrR9RhF9e/eHPK+PQOAy+haTihIuW6LJMg5kbyGDj1UI7TNDueaFkv3McYQtlFyDGEKmNOsYd0k8a/6qsXd3HIgnGuerNDnn3+jx2tozvDcJWLR7y4TtnMgYiNBIq1vPvNbffOZOytKwDmNbTKYICVPM9k/vdoc+2C2yXFDmSBlfwSwumEs9ZeHXn/PiE0FJxwKmKOLxmQQMoEsEcppc09CSKS9ZXQBhIRDbllpE6jDaNTpiplctmH593fsm3vvfFW2zjW6HiOFev9brPo+zAegGF3LCRnWGbuSzVmFkb7OIbFuBxgSw7lmjLMlbLOOAUCE1huWhcxiMhdQGZa1jvt26AuBcK53uv9p7yzKDoY2RfIaA1mXJ+Q/dotYqzM0G1nHSMlqr2vh3l8ULt3xgzrFT/OZRwtBSp9pcnxmtTn2Kxmy/dqDgpRZDLDBfg95M6LFkY9RwBxdfzO6AEJI9CwWymksDiEk0ihgJuMJjckYJXqsKdWblz62adeCh2YEFVdEN44b7TjnPOh5s1gL7C3CKPr52SSYO5cnfTqVMRbxu+V2S9WWcK/pdDaHJxA+ToQatlEUusvUEK61AOBdfeWiSHYxA8DrDU3zFV0PazA+VOWZLPf+e0QtIKHCyDrCwd7TMHnllm8vmX3w+Z00n3l0EeWs2SbH9UWK68Ek2Xb1XiamlQBoPcvhDaBJAlEzav6BnAiqn7yiHMB+o+sghETHDOFY2F+ME0LISeoBbDW6CELCiAJmg/mV2MbtC7++fuuSb2f4rMkrEIXwcjTjXA0Eu1/YoqvHioyu5XQXpd1WLjAhNdLXaWOeIwEWygv3ug5Huymc68kIha1LWEu3hW10xwnfCt0V0S5mK+e21xuaZHDujuR1BtLqYmlrHhBTO+zYYWQd4ZLcunNR0fqvZNN85tGHMSaIpinzlZgbCxXXg3Gy7cpdTEzaAKDrpMNoPEYUUcAcfa8aXQAhJDoS0ZVidA2EkHHtLTzuphfNZNzILSs9CoBmihsgJNk698xbW7xp2XdjPTHZBWBMMromo3Hd1xVwP1PG9a7lRtdyutmulRvscuyyaFxrs1xRH4l1FaUnrCM3TAgGw7WWnmSexYGwzuB9T1++yM2tEW02mxpSs59o6ygH54a+NvApzHHfWnFBeTpKjKwjXBi42Def+etibGd5Mc1nHn0YE0TRlLNQibllleL6sk22XrqdCQkbAdPrRtc2kUz4Fw4G+AOAJwHa+IuQ8cyMgE+GNip2FyeEjFs0HoOMR28DmG90EROFJph6ymZ8bntz0uIFYCzsXbohNYifv/sgVC0EjWtYMLkQVyz5/CnH+AJevLTuh+j0tkDjGi6Y91ksn3kpPL4uPPuv/4Uv4MWVS+7A/MmrAAC/++ejuKHgy3DZEsJd7sd0rasu2P1SANBG3deiQ46rme1amReNa4Wg9jSzrghcS1cFQcsK54pm+MM3mk4SbJDZXoR4WP/+vxW6K/hr0y/DueQZPuPtWfqh1fLRRzbr6oheaAC6wMRHb5MK7/xAK754Fy9g46C5UVZ9zgV7f1nktaZW7Z13X1vAHLfE6JrImRgTZVGZtURUZtUBMHQ2+UQz5v8jH2uqn7yiDkCx0XUQQiJrFqupZYy+xxJCIqYVwHqjiyAkAmhMRhToTAxWTLuuuLjgp73NyUtWgzFnJK4jiTIe+NRP8cj1z+KRa5/BobrtqGo+dMoxJQffQUpsFh65/ll8+VNP4e0tT0PVQth5eB3ycy7GQ1f/Cv/d+wYAYH/1JmQmTI9suKw2lAe7X5ABbWrELjJMDEy9MO1WD2PMHo3r7ZVqdoEhJtzr2mxdNYwhrCMyzPBp4VxPTzB3hnM9AHhfX7bIza37wr3u6X7R0laYoGo7I32dwXjuErHod5cJ2znQE471ArqOG2qqcU11FT5VdRS/ajtz9C7nHN9vbsYlR4/g6qoqHPL7AQAdqopbamvw6aqj+I/H8/Hxa+vr0KIOvinZ3ts4eeWWR5fMPvjcTkELGjr3mpzTn9Y+fT7d6RdFFH4Y4zWjCyCERNYSobzd6BoIIePaX/G4O6w/TBMyGuSWle4DUGZ0HeMVB9OrJ12yobjgZy11GecVgQlhHVNwOsYYFLlvSwpNV6HpKtjpN3IyhkDIB845AiEfrIoDgiBCFCSE1ABULQTGGDRdw4f738KF8z8bsXq1YPnOoOdPaQBPjthFRmB50qc3mATznGhd74BYG5GvD6ezuSXca1rRq4dzPS3dGpHn/kjoixHfBFwAhLfqGyeLnNdF+lqDsS5PyH/sFrFWZ2ge6VomxvB85iS8nT0Zb2VPxoaeHuz1nbq/Y0lPD2pCQfxz8hQ8kZKCJ5qbAADve7pxVYwTf8zKwgsdfeOUP/R6MEsxI0mSh1xLcuuuRUXrvzols/Y/JeA6/ew3+vzR6AImGgqYjfEmgIgO+SeEGGuxUEGzuQghkUTjMch49pLRBYxHDakrthYXPHX06JRPr+KCGLUxXrqu4Yd/XoOHX74WM9MXITs595THi2ZfjaauGvzPq5/FD968C9etWAuBCVg87XyU1u3Ar//+MC5fdDvWH3wHS3Mugkk2R6RO1bdlQ6jn/XkAHBG5wAglm7P3Z1hnFETrevVCx0GV6TMjsbbT2RK2eckn2OANa6eiHqfM5EDYN8z7u56/sIvbIt7FHKvrcc81tnjAuT/S1xqM8kyWe/89ohaQUDGSdRhjsAl9MZbKOdR+xk2v83pxVYwTjDHMt1jg0XS0qipkMPi5jqDOwVjf+S93duILcXHDrwdcnH707cKCjd+QYjvLaD7z6FG29unzdxldxERDAbMBqp+8wg3gfaPrIIREzkxWE5mffgghBGgC8B+jiyAkgl4BENZuxImsNX7enuJVPzlQNuPmfF00TYv29QVBxCPXPYPv3fI6alrL0NBRdcrjpXXbkRE/Dd+/5Q08ct0zeHPjr+AL9sCi2HHvZT/AN6/9LTITp+NA7RYsmFKIPxT/FL//1+M42nQwbDUGvX//SPVvWgVg6G2MUSAxk6cw5TonY0yM1jW3SBVhHxFxgs3eoYR7TTu84d3jiDGRW8SI3E3xcOiLUbkDaVEgkHtPV/f2aFxrMFpdLG3NA2Jqhx07RrKOxjmuqa7CqsOVWGGzYb7FcsrjLWoIKdIn240lyxKa1RCuiInBxp4erKmrw9r4BPyxqxNXxThhEUYei/XNZ/5VUf7279Ur/o5tI16QjBR1LxuAAmbjvGp0AYSQyElmXaPy1kpCyLjwIh53R/wWW0KMkltWWg96E2XEumKmlG5Y/oOd++fenadJlqiNVTgbq2LH9NQ8HDp2at61pfwDzJ+8CowxJDrTEe9IQXPXsVOO+cfOV3DJgpux4/A6ZCbm4ObVX8fftj834po417VA9x/W66Gy1SNeLILOT/3cXoGJk6J1PT+CnZ2sZ3Gk1leU3rC/TrbDE/ZsQ0+2ROSu43/qSxd0cdveSKx9urVd7oLZgcCo2bPBpzDHfWvFBeXpKBnuGiJjeDt7Mj6cOg37fX5UBk79a+qvlZ2BwSGKeDojE29mZ2OW2YxirxcXORx4rKkRD9bXY89pozaGw9bblL1yy6NL5xz8/S6az2woCpgNQAGzcd4HELF3hQkhxjEhFJChZhpdByFkXOIAfm90EYREwYtGFzBWea2pVZuX/u/mXQu+OjOoOBcZWYvH14XegBcAEFQDKK/fiWTXqS+RYu1JKK/fDQDo7u1Ac9cxJDhSP368xV0Hd087pqfNR1D1gx3/X0gd2ZQFzoM9Affvd3GtKWpjJ4ZjeszCzbFK8qpoXnO7dGQfGCJyNx4TVD9jetjDcju8Ye/u1tKtEQv1vxFaE7W7NF5uaF5q0/VDAx8ZHbrAxEdvkwo/WMiK+QjuVokRRSyxWrG+x3vKx5MlGU3qJ+/DN4dUJJ3U0QwAv21vw93xCfh7dzdmmc34XkoKft565oaBw5XUunth0fqvTplU++/1NJ856rasffp8CvcNQAGzQaqfvCIIeleFkHEpl9XUMIao3cJICJlQ1uFx9xGjiyAkCv6KCMw/Hc98Slzj9kXfWL9tyf9k+qxJy8FYeEcGDEN3bzt++beH8IM378KP37oPMzMWYW7Wcqw/9DesP/Q3AMClC29BVfNBfP/Nu/Cr976Oq/K/CLvF+fEaf9v2PK5cegcAYPG087G14gP85K9fwgUj2OyP6z2tga7f1YB7l4zsGUaWVYppXBB3Ye7AR4YPB+eVYmN2pNa32zsi8jrZDk/Yx5twu5zNGSKyUd6/9CULOrl9TyTWPp0JUP5S3+hinI+qoPO5S8Si310mbOdAz2DP6VBVdGt9E0b8uo7NvT2YYjp14sr5djve6XaDc469Ph8cooDEkwLm6mAQLaqKJVYr/FyHAAbGgAAPb+bPwMVpR/9aULDhG1JsB81njqJnjS5gomK8n6HoJDqyH35/ETCy+UOEkNHnLvH9Td+WX1thdB2EkHHpRjzuft3oIgiJhtKZuc8A+KLRdYx2QdnWcTD3jv2dsTPzwRjtATEAXWuvDna/wgA9y+haBsCvmnT/HrNoXRDNix4Rmnd+aDoQsc73jMwDGydP3r0y3OvuxqJ9P2HfmhfudU2bW9YL3aGIdLlfJOzY86zpqbxIrN2ff1stu76alDAfUZzlPRgzjvHSJ17T4gSOAUenlPv9eKSpEToHdHBc6ojBfQkJ+FNX383hN7piwTnH91qasaGnB2Ym4PupKZhj/mRO81ca6vHlhERkm0xoV1XcX18Pj67h/oQEXOyIidjz7LGmVO+Zd19LwBy/NGIXId0A0tY+ff6g37Qg4UMBs8GyH35/D4D5RtdBCAmfp+Wnii8VdxQZXQchZNxpA5COx90juy+ckDGidGbuCgAbja5jtFJFxVuW87mdLUmLFoCxyKUi44gWqjkQ8v4lFUC80bUMZHH8JcVTY/Ki/nryT8rGbV7mj1gANnvOf4vj4hrC/rwqkVP+OPvhjHCvK1Z7N8vl7uXhXveEXcrde+KYJy9S65/u2wlxxe847KPu55TELt7w1LOaV1GRY3QtkdaSuGDXoZm3OXTRNN3oWsahp9c+ff69RhcxUdGIDOO9YHQBhJDwymW1Yd8ZmxBCALxE4TKZSHLLSjcBKDO6jtFGZ2KwYtr1JSWrfuJrSV5cROHy4KiB/VtD3r9MxRgIl+OVtPIpjvkRCzXPxsN8DV74Izq322brskZkXXgj0r2vpVqm8/73jQuLr4fWRGrpfn2vraMoPaRuiepFB6HVxdLWPCCmdtjH/x3en8xn/td6cL3N6HrGGRqPYSAKmI33KgD6YZGQcSSFdSQZXQMhZFyizf3IREQ/LB7HwbSqrMs2FBf8rKUuY3UhmJBodE1jRai3uETt/fdiAJYBDzaYyCTfeSk3SYwxU7SvvVWqrECE9xGRZV9aJNa1ojciwTUUMQEiK4/I2gD+qy/Ka+eO3ZFavz9/rm+cLXNeFc1rDoZPYY771ooLytNRYnQtkdY3n/mdgoIN35DjOkqLwTllQiO3a+3T5+8yuoiJjAJmAIwxkTG2mzH2XrSvXf3kFe0A3o32dQkhkSFDDZqgRmzHaULIhLUej7upk5NMRC8DCBhdhNHqU1dtKS74WXXV5CtXcUHMMLqesYJzzoOet4u1wM5CYGxswFyU8tltoiBNjfZ1deihGqE1ohsKimLIwxiPSMBsQa89EusCgB5rao7U2gDwtdA9Uc1l7Jw7Xmto0sG5N5rXHQxdYOKjt0mFHyxkxRwI7657o5Cs+Zx5+/6vKH/7dxvN/vZtRtczxtEb0gajgLnPlwGUGnj95w28NiEkjGaw2lrGIA18JJkI/CrH0me9mP+0F7N/48X/fugHAHz9X37M/D8v5v3Wi2te70WXv/87L3+xJYA5v+k79+dbPslXvvlvP+b91ovb3vZ9/LFX9gbxiy0TPoMZz54xugBCjJBbVtoG4G2j6zBKS8L83cWrfnKwfMZNy3RRjnroOJZxrgWD3S9t0tWqUTdv9myybLN2JJozDam3VKzfwdnAm6yNhMPRVssYWCTWVhC0gHMtEmtr6daIhdcA8KG+YH4bj4lqF3NuMDT14Y7O/dG85lA8d4lY9LvLhO0cmBCbtdl6m7NWbHls6ZwDz+wWtGCl0fWMQT0A/mB0ERPdhA+YGWMZAK6AsbedfgCgzsDrE0LCZLFQ0Wp0DWT0UERg3e027L3Hjj132/DPIyq21Km4aKqEA/fZsO9eO3LiBPxw/ZnB8IEWDc/uCmHbF23Ye48N71WoqGzX4PZzbKrTsO9eOzTOsb9Zgy/E8eLeEO5bEvW7aUl0dAL4s9FFEGKgCdeV1Omcdmj9ih/uOjBnzQJNssw2up6xhut+d8D9zEGud6w0upbBUgRrW37iFVlGXX+3VBWRGcYnczqbOyN8iYh05OqJltkc8Edi7RO+Fron6h32N3d7ly/z+Yqjfd3BWpcn5D92i1irM0S0g3w0SWrbu6Bo/VenZNV8QPOZh+b1tU+f3210ERPdhA+YAfwcwDdg4O0X1U9eoQN4zqjrE0LCZ4lQTi2k5GOMMdhNfY06IR0IaQADcPFUCZLQ9/FlGSLqPGf+E1TaqmNZhgirzCAJDEVZEt4uUyEwIKhxcM7hCwGyCPx4UxAPLDVBFiPSFESM9yoed0f0B1tCGGOZjLEPGWOljLGDjLEvH//4dxlj+xhjexhj/2KMReT29gF8COCwAdeNOq8t7ejm/Me37F7wlVkhU8xCo+sZi7jW3RhwP9MC7ltgdC1DcVH67UeZQXO125nnqJ+FIv75inG2RPRnbgF6ZLpdRWaGSTgUkbWP+0jPmxftLmYA+G1T60qnpu2J9nUHqzyT5d5/j6gFJFQYXUu0MHBxatW7x+czH6L5zIMz4d6IHo0mdMDMGLsSQAvnfKfRtQD4DWi+HCFj3mxWHfHuDzK2aDpH3tNeJP3Yg4umSMjPOHWCyvN7Qrhs2plTVeYkCSip0dDeq6M3xPH3wyqOuXU4FIZrc2Us+F0PJrsEOBWG7Q0arpopR+spkeij8RgkGlQAD3HOcwEsA7CWMTYLwI855/M453kA3gPwWLQLyy0r5Rjnm1z6zHEN2xY9vH7b4m9l+SyJy4yuZ6zS1ebKQPdzANTpRtcyFPNii0psUsxSo66/Wa44Fo3rWK1uRyTXF6H5Bj5qeLQEc8S7I78aujfqXcwSIP21vjFd4Lwx2tcerFYXS1vzgJjaYccOo2uJpr75zL8uyt/23Uazr32r0fWMYgfWPn3+FqOLIBM8YAawEsCnGWPVAP4E4HzG2KtGFFL95BUtAAy5NiEkfFJYB+3oTk4hCgx77rGj7qsObGvQcKDlk/GA3y8JQBKAm+eeGQ7nJor45koTLnqlF5e+2ov5ycLHXc/fWKlgzz12/PQSMx79MIDvrFbw+11BfPbNXnyvhN6rHGe24HH3AaOLIOMf57yRc77r+O896NufJJ1zfnKoYgPQ/9D4yHsRQMiga0dMULa3757/QPHm/O/Eex2ZBWBsTGxENxppwcO7g57XkgCeanQtQ+GUE6pmOvMXG3X9ELTeJtaVF41rybI/PZLrS1AjdrePlmFNidTaJ5To8+e1cmfUm98SND3xd00t7aO5U9anMMd9a8UF5ekoMbqWaLP5mrNWbH0s//h85gnTyT0E1L08SkzogJlz/gjnPINzng3gRgDrOOe3GFjSUwZemxAyQiI0VUFoktF1kNHJZWZYnSXhn4dVAMBLe4J4r1LFa5+xgLH+R1vcudCEXXfbUXKHDXEWhunxp/6zvbuxL6zOiRfw8t4Q3rjeigMtGirbI7LHDTEGvWgmUccYywawAMDW43/+PmPsGICbYUAHMwDklpU2o68hZFxQRcVzYNadxRtWPGnqjJ1RBMYUo2say1T/jk2hnndnA3AaXctQMAihC9Ju9TPGrEbVsE+q3gkW+c+bJPk7GENSJK8hIxixd9m5y5TDgY5IrX/CV0P3GrKhxjJ/YM7t3Z5R3QWqC0x89Dap8IOFrJgbOOLUKMfnM087Pp+Z9v3p4wPwitFFkD4TOmAebaqfvOIQgH8aXQchZHhyWF0NY6A5BeRjrT06uvx9zX6+EMd/qlTMTBDwz8Mq/t/GIN690QKrfPa5yS09fa+da9063ipVcdOcU7+8Hv0wgO+cpyCkA9rxnkKBAb3jrsdvwurGOArUyNjAGLMD+AuAB090L3PO/4dzngngNQBfMrC8nxh47bDQmRQon35DccmqnwRbkhYWgbGIjgyYCEI9HxSrvpLlAMbcTrcrk6/ZKAumXCNr2C8eS4jGdWJiWiM+hkNBIHIduIwJ3CqWR2z949br8+a2GNDFDABf6+gqnB4MbjDi2kPx3CVi0e8uE7ZzIDIzt0cxBi5MrXq3oHDD15W4jkMfjeau8yh5ee3T50d681AySBQwH8c5/4hzfqXRdYC6mAkZs5YI5fROMjlFo5fjvJd6MO+3Xix5tgcXTZFwZY6ML/3dB0+Q46JXepH3tBf3vNc3MrDBo+Py13o/Pv/aN3yY9WsvPvXHXvz6cjNiLZ+E0X8tC2FJmog0hwCXmWF5hoi5v/WCMWB+Ct1hPU78AY+7ewc+jJDwYIzJ6AuXX+Ocv9XPIX8AcG10q/pEblnpPgD/Mur6I8HBtKPZl68vLniqrT69sAhMiDe6prGOc64Huv9UogUPFqFvD90xJdUydW+aZWqhkTU0CB0HVaZFJeB2uprdkb6GGX41kuvrKZaIrn/CV0JrDbuj4Q8NzYvMuh7xIH2k1uUJ+Y/dItbqDM1G12IESfPH5O379eq++cxtE3U+Mwfwc6OLIJ9gnBs1Ro2cTfbD7+8DMNfoOgghQ/NL+VfFnxY3FxldByFkXNABzMbj7jKjCyETA+ub1fMSgA7O+YMnfXw657zy+O/vB1DEOb/OmCqB0pm5FwL4t1HXH466tIItldOuTeKCPMXoWsYLzkO+YPdL+7jenW90LcMhC4r76kkPeAUmRHQm8UDeMm3d0CF4V0XjWvPz/rE+JqatIJLXeALfK6lguREL7VmPWqdsaM6I1Pon26rctzOZdS2KxrVOVy1JtZ/OSI3hjLmMuP5QJHbxhqee1byKihyjazFSa8K83QdzP2/VRWWG0bVE0T/WPn3+5UYXQT5BHcyjE3UxEzIGzWHVNB6DEBIuf6VwmUTZSgC3om/T6z3Hf10O4EnG2AHG2D4AFwP4spFF5paV/gfAHiNrGKyWxAW7i1f99FBFzo3LKFwOH673dgTcvzs8VsNlALgg9ZYDRofLfgQ7O5g3agGmxdId8TnPVvREdC4vt0kZnKE2ktc44Suh+8zRuE5/slV10g9a2w9jDHQjtrpY2poHxNQOO3YYXYuREtv2LSha/9D0rJp/TqT5zD8zugByKgqYR6c/AGgyughCyNCksrZEo2sghIwbPzS6ADKxcM43cM4Z53we5zzv+K+/c86v5ZzPOf7xT3HO642uFcBPjS7gXDqd0w+tX/HkrgOz71qgSeZZRtcznuhaR23A/YwbPDhm7/ac6Vy6yWlKWGl0HTuko/vAYInW9SQpGPGNsG1RGMnLY+TqiF8EwCZ9zuwmHmtYaHplT+/iS3p6i426/lD4FOa4b624oDwdJUbXYqS++cx/65vP3H6wGJxHbNPLUeDA2qfPH1N3M00EFDCPQtVPXhEE8H9G10EIGTwBumZGKMvoOggh48J/8Lh7QnfiEDKAPwGI+IZhQ+WxpR/ZlP/E1t0LHpwVMjkWGl3PeKOH6g4Fu1+yAPpko2sZLpvkqpsXu3q20XVwcF4hNkTtdavJ1NPMGFyRvo4dnojP4tbSrFHbTPLB0H3/v737jpOyOvs//jkzs32XpdcFsSFroWNlQbFn037RNGMkxkSJhGhCCimaSfIkz6SXJwVNNDHGKInGxECiMSECNhBRXIWlD33ZpQ3bd8r5/TGLgrQtM3NmZr/v12tfCzP3zvkOZfeea677Oil7A+BYvl+3d9qgSOQllxk6KuYx3rtu8k19aoJZbONjxnosX7Sl17iqX067cPk3d2fxfOafuA4gR1OBOX3NA7Sxj0iGONNs32pM5u1eLiJpSd3LIidQXr0mAvzUdY5DmvP77Vg+8cvPvjTpyyNbCvpn7NiGdBZtXf1SW8OfTgGbyVeLxa4cetNeY0zSR0WczGZP7SsxY0emar1epXUpufKhmIak73IcHVw4OlUFzBdj55xTY/s4K/AaMI/t2DXKZ+0WVxk6676rvdPuudbzkiUF7exprrC5dsTFy75+wXlV97zqibam/caNnbAL+IPrEHI0FZjTVDBQuZf4RisikgEmetb1yB2MRSThluMPLXIdQiQD3AuEXAZoyynZs3LsHYtfuOAb/RtKyqZgTNKLWz1RuPm5peGmJycARa6zdMcFAyqX5HkLxrrOAbDct6Etlev1Lt1dn4p1Sqj3JX2RXE9vvGZN0tdpd0fbLKf/7ktjtvSBXbtbsLa5s18ba4ux8Rsb2XDXBtZ/ZT27Hz/65VLrzlY2fmsjb3ziDfb8c8+bt0cORtj07U2s/+p6Dr588M3bt/x0C+H94ROuu2ic54K7b/RujRn0+gwYsPe1cdOWzjlzZPCfz2bJfOafzZo3PZvHf2QsFZjT24+BtB+sLyJwvqdaP+REJBHUvSzSAeXVa+qJF5lTLuLNP1h1zieeefbi/80/0GfUNIzJc5GjJ2hreGJxtGVZBZDRxfsB+WWrTyk6x/ncZYAGWnY1mJbJqVyzpFddSv7+iqlPyYbbsb55e05+VGIss2efvcv2dTqmYkxr21mf2R9a2dmvMzmGkV8ayRnfOoMzvnkGDVUNNG048iJtb7GXIR8ZQv9r+h9xe2hZiN6X9Oa0r532ZuH54CsHKTilgJw+J/9rXjvclM+e6Y22+ljX2dzZyGA9pwUXTJn67Ofz++19PZPnM9cDv3IdQo5NBeY0FgxUrgf+7jqHiJzcuSaYkhNaEclqq4G/uQ4hkkF+Cpy4lS2Boh5fS/WZH1q8ZMr3w3UDxl+KMcWpWrunsTYWaT344LOx8IZprrN0l9fkNE4b/MECY0xanCu+mLNuHSa1Bfv8/Pq+qVinmPqUjKuLlhWmdMyJ6y5mgE+GDl4yvqWlU5voGWPw5sf/qdmoxUYtvG1Ktq+Xj8LTCo9+C8kLNmyxEQue+Nfv/dde+l/bn46q622G3voZ75B9xWhfi3a+aGvJ2Kpftc9nrnvRdZ4uuHfWvOlOr16S41OBOf39yHUAETm5oWZPx892RESO7bv4Q7pySaSDyqvX7AAeTvY6FhPdNPKdzy6u+NH+ncMqpmE8/ZK9Zk9mbVtDa+jeVTZaN8V1lkS4bMiHVnqNLy02JowRi2zx1J2V2lWt9XojKdlQsIiG/FSsE+uff7ZN4X5Jy2352TttP+eb7d23q/aikmisqjNfY2OWDXdtoPoz1RSfU0zh6YUd+rreF/amvqqe4A+DDHzvQPYt2kfvS3rjyetcCas5z5TcPss7fu0wOlUcz3bx+cz+C8+rmpdJ85nDxK/ylzRlrNXrmHQ3cu7CFcBE1zlE5Ng8xGIb825sM4aUnNSKSFbaApyBPxRxHUQkk6wZXX4e8FoyHtuC3T5s2rINp/+/QdaTkxYFwmxnYw21raHf7oPw6M58XTga5Zf/fYFINEbMWsaUDeHqc0cdccx/qzfyytadAERjMWrrG/jGu68kZi2/e/5lmtvCXHveWZw7bDAAv312Be+beC6lBV0/vTuteMyyyQOuTZuNH1d7t7/4fM7aC1O5Zn7BwW2TJ/9teCrW2s2g7Z8zvyxLxVp5z+xaYVpjk1KxFsBkU73mz3nfLE/VesdT4/XWXDV8qNca06kNN6ONUbb+31aG3DiE/LKj/0/tfnw33nzvMTuUo41Rtv5yKyNmj6DmjzVEm6L0v6Y/hWd0rFh9yC1PRRdftdJWGDVaHsFiYptHvuP54CnXnIXxpPNGqr+bNW/6za5DyPHpP1ZmUBezSBo7zezcpuKyiHTTD1RcFum88uo1VcBTiX7c3QMmvrxkyg+r15/5gQtVXE6NWKRuU2vo122dLS4D+DweZk67kDlXT+VzV1VQXVPHlr37jzjmstGn87mrKvjcVRW8Y8xoThvQj8K8XF7ZupNJp5Qx+/JLeKZ6EwBv7NzNsD69ulVczvcW107qf80ZXX6AJFjp25TyeeGlvWprUrVWIU0pGyURHZDfmKq1AF6yo8t32H7LU7nmsQyORgf/3+66nVjbqXMWb5GXotFFNFQ1dHrN2r/VMvBdAwm9GKJgZAHDbhnG7kc7v3/ffVd7p91zreclCyn9u0t38fnMC9vnM1c9k6bzmSPAt10sbIwZboz5rzFmjTHmDWPMHe23jzXGvGCMqTLG/N0Y08tFvnSiAnNm+BOw0XUIETm2iZ512qFYRLqjFrjPdQiRDPaDRD3Qvt6j3lh6ceCVN875+MSoL995t2BPEQ1vfq2t/sG+YLvUfWqMIS/HF3+smCUWi53w+Fe37mT88KEAeD0ewtEo0VgMY+LdzUvXbebSs07vSpRD7FVDb9pqjEmbcSr7TMPmFhMen+p1S3vvTtkoiQKaUjYXPVpWNDRVax0yu212WhSwpjW3jP1AfcNzJzsucjBCtDEKQKwtRsPqBnKHdG5MdmtNK+EDYYpGFxFri71ZwYqFT/x//HgWjfNccPeN3q0xg16/vU18PvO8Sy9c/o3agvSbz/zgrHnTNzhaOwLMsdaWAxcCs4wxZwO/AeZaa88DHge+4Chf2lCBOQMEA5UR4Ouuc4jIsZ3vWZuyE2cRyUo/xR9qdh1CJFOVV6/5N7CsO49RXzx8w/MXfnP5q+PuOCecW5LyIlxPFml55YVww+OjgN7deZxYzPKjfy3F/8TTnDmoP6f063PM49oiUapr6hhTFh+FMX7EUNburuPXS5Zz1TmjeH7DFiaOLCPX1/V98Mb3vXxJga8kZeMTOuIF37qtLtYtKd6bss0NfURzUtV9aXvlnGGhLhVrHbLSjhq93fbv1ve6RLlr7/5pp4TDz5/omEgowubvbmb919az8RsbKT6nmF7jerFv0T72LdoHQPhAmOrPVrP3qb3UPlFL9WeriTZH33yM3Y/tZtD7BgHxucz7n93Ppm9tov81Xd/+Zu1wUz57pjfa6mNdlx8kixU21w2/aJn/wjFVv1rlibZWu85DfPbyt1wtbq3dZa1d2f7remANMAw4C96c7f00cJ2bhOlDM5gzxMi5Cz3AK8AY11lE5Ej/yv3C86M8Oy52nUNEMtJBYAT+kHbEFumGNaPLryD+Aq9Tmgr6b68659ZgY9HQizFGzTcpFm76z+Jo66oKEtj41NwW5nfPreC9E85lSGnJUfe/unUnL2/ZwS0Vk4+6r6ktzB9eWMmMiyfyxKuraQqHmTbqNEb2P3ax+lj65A7acOXQGWXGmLQZnxYh2vy7vGdaMd0r4nfFxZf8cZ3XGx118iMT40b+vNemaCPO3Gd3P+9pjKT0NcB4s37tX3K/PsoYTCrXPZYmYxqnjhi2q9XjSatRMB1V0Grrf3xvdG3fBtLqzaB0Ep/PfO1zwVOuPQvjGegoxr2z5k2/zdHaRzDGjCReVD4XeBL4rrX2b8aYzwHfsNYe/UOnB9FJVIYIBipjwFdd5xCRow0ze/q6ziAiGetXKi6LdF97F/MzHT2+NaekbuW4Oxe/eL5/YGPxsCkqLqeWtda21f95cbR11TQS/Jq0IDeH0wf2Y+2u2mPe/+q2nYwfcezpBk+/sZ7Ly8/gla07GdanlA9OHsM/qzrewOfB0zZ9yEei6VRcBljl2/Kyi+IyxCIeT/SUVK7oIZayKwujQwq6NqehG16xZ5613Q5wPosZoNDaokd27vZi7UHXWbqiOc+U3D7LO37tsDe7UOVt4vOZ/1Ex9dnPF/TbU7XYwXzmNuB/UrzmMRljioHHgDtt/N/8x4mPy3gZKCGetUfTiVQGCQYqFwAnnXUkIqljiMUKaU3pibOIZI0W4MeuQ4hkkbtOdkDYW3DwtXM++cxzF/9vwYHeZ07DmM4NBJVuszbS2nbwty/GItumJeoxG1paaW4LAxCORFm/ew8Dex09jre5LczGun2cM2zQUffV1TdysKWF0wf2IxyN4jEGMEROMs/5cBWDr3/B58k5q8tPJEle92510gxRVHRgqzGkdGNBL9GUjZyKDi3s1qDurpodnt3bWtLiUvQzwuFT7967bw0Zeml8zGO8d93km/rUBLPYQsrfMMgUvmhrydjX5027cLm/rqCpNpXzmX89a970bSlc75iMMTnEi8sPWWv/AmCtrbbWXmWtnQg8jPZNU4E5A33ZdQARectIU7PdGApc5xCRjPRr/CFtMiOSIOXVa54FnjrWfVGPr2XNqBsWL53y/eieAeMuJd6JJClmY80HWkP3VtvYgYsS+bgHW1r51TMv8sOnlvCTfz/LmYMGcPbQQTy/YQvPb9jy5nGv76jhrEH9yfP5jnqMf1at5Zpz47XhcSOG8lJwG//3n+eYdtZpHcpQVjhq5aD8kVMT84wSZ5fZvzpsome7WLtXaW3Kf8b5CLekbLEC3xDrYVPK1mv3qj3jrG12YFrMYgZ4f33jBVObWzK6C/i+q73T7rnW85KFRtdZ0llh856yi5Z/48Ixr/1ylTfauibJy7UA30nyGidljDHEN+NeY6390WG3D2z/7AG+BsxzkzB9aAZzBho5d+E/gGtd5xAReL/3mZe+n3Pv0UP8REROLAScgT+0x3UQkWyyZnT5JOClQ7+PGU9k88h3vrBlxJVnYDxDHEbr8WLRA9vbDj7QClEnXZ/JlOvJ3/+eEbNbPGn4b+zx3GXP7vU0THGx9ujyJYsHDNiSsE71jpjFr1ccMH1TNlM3d3ndYs/+tpQ+R4CxZsO6v+befWY6zGIGiEJ0+ohhq/Z5vRNcZ+mOs7bZNd94KNrXYzn6Mgc5gsXEgqdc8/zmke84E+NJxp/XT2bNm/7ZJDxupxhjpgBLgSre6nL/CnAmMKv9938Bvmx7eIFVHcyZ6SuQHpfEiPR0k83alM15E5GsElBxWSTxyqvXrAD+asFuLbvs+cUVP96+5ZSrK1RcdisW2bm27eBvc7KxuAxwxdCbqtOxuNxC+MBe0zDR1frFxXtTOh4DII/WcCrXiw4tdHIl4yp7xqitadTF7AXv49t3neK1dofrLN2xdrgpnz3TG231sc51lnRnsJ5Tt/xzytRnP1/Uf89rz2BtIq8eaAICCXy8LrPWPmutNdbaMdbace0f/7DW/tRaO6r9Y25PLy6DCswZKRiofBWY7zqHiMB5nk1HX2MpInJiW4GfuA4hkq2aCgZ8dcmUH67ZcMb1F1uPb6TrPD1dtG3ty231jwwFm5Udgef0vvjZkpw+CR35kSgv+zatwuEot7y8ppT/nRfQHEnletFBBaMtpHTNQ2aHZ/dLl1nMAH1jsX6/qakNOdgILqHqepuht37GO2RfMStcZ8kEvmhr8ZjX77n0wuX+PQVNtS8k6GF/OWvedI2RyzAqMGeuu3D0g0xE3jLc1PVxnUFEMs5X8YdSNyNSpIeZ+MqS1VFf/ksnP1KSLdL84rPhxoVjgBLXWZKhJKfvlnN6TxnnOsexWKxd690xwtX6xkRbjYmlfP0CmlK7UVuOpxc+szqla7Z7zZ5+5hY7KG26mAEmtbSefeuBg8td5+iu5jxTcvss7/i1w8jo2dKp1D6f+aKxr/3iNW+kpTvzmRuA7yYql6SOCswZKhio3EB80LiIOGNtES2nuE4hIhnlZeAh1yFEeoC7iG8QJI60NfzjmUjL81OAHNdZksFgIlcM/Wi9SdMNI4Oeuldixp7qav3i4n1bjMGb6nWLaEx5R2+sX/7+VK95SLp1MQPMPhCqOLu1danrHN0V8xjvXTf5pj41wSy2b83elZPot2/1mKnPzhl96ua/P4eNdaUL+Wez5k3XGLkMpAJzZvsm0Ow6hEhPdYrZvdMYilznEJGM8nn8obR6ISiSjWbNm74N+LnrHD2RtbFo68E/Lo2Fqy91nSWZLhr47mdzPfnnus5xPMt9G9pcrl9aurvOxbpFNKR8zWhZobMrGqvsaWdutoNfdLX+8Ty4c/f5hbGYk87uRLvvau+0e671vGSh0XWWTGHAnLrlyUva5zMv7sR85jrUvZyxVGDOYMFA5U504izizASzfqfrDCKSURbgDz3jOoRID/IdwFlnYU9kbVtja+g3K220psJ1lmQalD+yqqzwrLR9jg201NSb5kkuM/QqrXUyzrGY+pTXOGJ98862cDDV6x4yOzx7gLXp1WGbC3mP7dhVaqzd6zpLIiwa57ng7hu9W2MGzQXuhPb5zNMuWubfU9C0uyPzmb8+a950Z/+XpHtUYM58ASDkOoRIT3SBp7rJdQYRyRgR4AuuQ4j0JLPmTd9PmuxC3xPYWGNd64F7tmAbJrvOkkw+k1s/dfD1pcaYlI9/6KhlOeurMTjdiLq4aH+hk3VpSP3fi8f4yPdWp3zddm/YU8/YbIekXRdzWSQ67Ae1e7ZgbVoVv7tq7XBTPnumN9rqY53rLJmmoGVP2UXLv3nR2FU/f80baTleZ/tq4N5U5pLEUoE5wwUDlfuA77vOIdITnefZlLYvLEQk7fwGf8jZi0+RHuxnwDbXIbJdLLo32Br6dROEz3adJdmmD7lhlcd4nW2edzIxYpGgp/Ys1zlycpuHuFi3hHonhfXowHynoys/HZ49KN26mAGuamqe8K6GxqzZKK+utxl662e8Q/YVs8J1lkzUb/+aMVOfnVN+6ua/P3uM+cxfmDVvetRJMEkIFZizw09Al2qIpNoIU+ts3pqIZJR64OuuQ4j0RLPmTW8B7nadI5tFw1tebzv4QAnEsn7j4zNKJrzQJ2/QFNc5TmStd+cKa3BS3D3E6w03GGOHuVi7mPpcF+tGhxUNd7HuIavtyNM3daCLuSViOf/XDYyd18A5v2zg6/89ejTuQ6+FGfOrBsb8qoGL72tkVU285lfXGGPK/Y2c+8sG/lodfvP49zzSxM7649e2v71n37Sh4ciyrjyvdNScZ0pun+Udv3YYWVM4T6X2+cxTpi2dU9y/btWh+cxPz5o3/R+us0n3qMCcBYKBykbiG/6JSAoV05y23Ssikla+hz9U6zqESA/2e2C56xDZKNJatSzc8NjpQD/XWZKt0Fuya0K/K0a7znEyK32bnRRYD1dcsmeLMRgXaxfRkOdiXdsr5zQLu1ysfcinw585aRdznhcWzShi1cxiXr2tiCc3Rnhx+5Hjsk/tY1j8sSJe+1Qxd03N5dYF8ebsh18PM2NsDi/cUsT3n4/vIfn3tWEmDPYytOT4pSUD5tEdu8pzrN3c3eeYLmIe473rJt/UpyaYxZb06xzPBN5YW9GYN+6ddtGyr9cWNez4rOs80n0qMGePe4CVrkOI9BTDTe1OYyhxnUNE0t4O4IeuQ4j0ZLPmTY8Bn0JFgIQKNy1eEml6ehJQ4DpLCtgrh83YZYxJ66vX9puGYDNt413nKC3d7WxzzWIa8l2tbUtyNrpaG2CNPeX0jXboCbuYjTEU58Zr/+EYhKMc9U7AxcN99CmI33phmY/tBy0AOR5Dc8TSGrV4DERilp8sa+MLl5z8PY0Sa3v9YWdNFGsbu/DU0tZ9V3un3XOt5yULWfW8UqmgZe/Cj/3ho2+4ziHdpwJzlggGKqPATHTiLJIS4816px0KIpIxvoY/5HQuo4jArHnTVwK/cp0jG1hrbVv944ujrS9PBXrEfhST+l29JN9bNMF1jpN5wbduC446hw9X2qvO2RzVQhqdveERHVLgvL7y6fBnBp+sizkas4yb18DA79dz5Wk+Lig7/tjq+15p49oz4vffcF4OT22Mcs0fmvBPy+OXL7Vx05gcCnM69k/u7LbwGV/cd2BVJ55ORlg0znPB3Td6t8aMxpZ2wX7gLtchJDGcfwOUxAkGKl8i3sksIkl2vqe6wXUGEUl7q4hfmi8i6eFraN+SbrE22tZ28IHnY5HN01xnSZW+eUPWnlYy9iLXOU4mQrR5p2f/GNc5AAqLDji7yq+QpiJXa0eHFp5pwbpaH6DajjhtvR32womO8XoMr84sZvvnSli+M8rrtcd+P+C/myPc90qY714RnzpSmm9YeEMhK24tZsIQLwvWRbju7Bw++UQz1/+piRe2RY75OIf76MH6iy9oblnchaeW1tYON+WzZ3qjrT7Wuc6SYe4ur16z13UISQwVmLPPl9GJs0jSjfVs1PdPETmZz+MP6coikTQxa970A8AXXOfIVDbWEmoN3fuGje27xHWWVPEYb8v0wTf4jDHHnQHQEmnlnb+/lavuv5nLf3MTP1x6/zGPe2HrK1z9249z+W9u4vo/zgZgb9MB3veHWVx+3wyeXLf0zWM//tiXqanf06msr3m3vowhLUZ45OS0OtngDyCf5mJXa5PnHYDHbHC2frvZ4dlDreWkXeS98w2XnuLjyQ1HF4Zf2x3lE39v5m8fKqBf4dEve765uJWvVuTxcFWYiUO93P+eAr6yqLVD+ebV1F5SGo2+2qGDM0hdbzP01s94h+wrZoXrLBnidXRlUVZRgSTLBAOVIeBzrnOIZLtTzO60OIEXkbT1N/yhf7sOISJHmjVv+oPAEtc5Mo2NHtzVGrq3FtvsfL5vKk0b9MFlXo/v9BMdk+fNZf6HfsK/Pv5bnrz5fp7ZvIyVO44cJxpqqeer//oR91/3v/znE79n3nvi+7P/bfW/uf7ca/jbjb/inuUPA/D0huc4b9AoBpf071TWKt+WtDg39fla9htjB7la34P1uJzzG+udu9PV2oestSNOXWfLjjmLua4xxoGWeJN1c9jy780RRvc/siy0NRTjffObePD/FTCq39FTcNbvjbKzIca0kT6awvF5zAZoOXkDMwA+8D2+Y9cwj7U1nXtm6a85z5TcPss7fu0w/ZzpgDvKq9c4G6cjiacCcxYKBir/CPzHdQ6RbFZC83DXGUQkbR0EZrkOISLHdTvQwVKIxCK717cevA+InOk6SyqNKDp7xcCC4ScdBWKMoSi3EIBILEIkFsGYI2fS/nX1v7lm1FSG9YrXXfsXxWvBPq+PlkgrbdE2jPEQiUW4b8WfmXnBhzuVtcYcWBM20XM69UVJ0qvXnm2uMxjcFZijwwqdjeg43KfDnxl2rC7mXQ2Wyx5oZMyvGpj860auPM3HO0flMG9FG/NWtAHx7uS9zZbbF7Ywbl4Dk+49cjLgVxe18j+XxcdmfPi8HH73apgL72vk8xedfLO/QwZEYwN+VVNXh7Xh7jzPdBTzGO9dN/mmPjXBLLbaI+t4/lxevWaR6xCSWMZapyOCJElGzl04CngNyHOdRSTbDGVPzfP5nxnsOoeIpK1P4Q/Ncx1CRI7vFzMXfR/4vOsc6S7atuGVcOMTpwGlrrOkUp6ncM97RsyKGeMZ2JHjo7Eo73jgkwT372DGhPfylUs/dcT9/n//jHAswro9QRrbmvj4pOu5/txrONjawOwnvkld036+Mm0m6/ZspiSviPefd22n8j6eu3zpXk99Rae+KElOPfXlJWXDV091mWEGjwQjJmekk8Wjsaa8f+/yGeh4tTVJnsz90nOjPdvSeqTN9/r2XvJgaS+n/16SafqrsWW3/TN2roG0eOMhTRwAysur12RdB3tPpw7mLBUMVK4Dvus6h0g2Gu/ZsMN1BhFJW0vRhrsimcAPbHcdIp1FWlY8H2584hx6WHEZ4MphN23qaHEZwOvx8tTN97P89kd5dVc11XWbjrg/YqNU1azjgeu/yx8+8AN++vwDbNq3jV55xTzw/u/xjxm/5tzBo/j3xud5x1nT+OI/v8dtj9/FyzteP+narYRDe039xM4/y+To1avOdQS8RJrdLe4pJMez2tn6h5kdnn3MLuZ08sV9B6ae3tb2nOscybJonOeCu2/0bo0Z7ZN1mC+quJydVGDObv8LON9kQCTbnO9Z4+yyOxFJa63AJ/GHdHmYSJqbNW96I/BZ1znSVbjxqWcizUsuIg26MFPtvD5Tlxb5Ss/vyteW5pdw0fBxPLNp2RG3DykZwKWnnU9hbgF9C3tzQdlYVtce+TLtJ8/9jtkX3cTfVv+H8wafxQ/eMZfvLvn1Sdd82bdpFYbCruRNhoLCkPM3JHIId2y3uSSJ9c8LuVz/kPW2bGS1HfGC6xwn88jO3RPyY7G1rnMky9rhpnz2TG+01cc611nSwBLgN65DSHKowJzFgoHKFjQDUiThxnk2mpMfJSI90Lfwh7L2BZJItpk1b/qjwFOuc6QTa22stX7+4mjbG5cS37erRynN6b+5vPTCTnUD7206QKilHoDmcCtLt7zMGf1OOeKYq86YwvLtrxGJRWgOt/DKrjVHHLN53zZ2N+zhohHjaI604DEGg6E10nbS9au9O8o6kzfZfL425/uU5NJ28j+4JIoOK+rcDo1J9Onw7BHp3sWcb23Bn3fUFGBtWhTmk6Gutxl662e8Q/YVs8J1FodagVvLq9eoESNLqcCc5YKByn8B813nEMkmI02N884MEUk7rwHfcx1CRDrt08Rf9PZ41oab2w7e95KN7DjpxnbZyOAJXz70oy3GmE51A9c27OWDD9/Jlfd/jHf+/lamjpzEFWdczIOv/I0HX/kbAGf2H8mlp17AVfffzDt/fxsfHlPJ6AGnvfkY31vyG75Y8QkA3lN+BX+u+ifveXAmt53/wROuHfTUvhIz9rQTHpRCublNtcbQ13WOfFqcbuIZ65tbbuNzZp3baIeNWJMBXcwjI5ER/7Nn3zqyeJOw5jxTcvss7/i1w1jiOosj3y6vXqNGjCymTf56gJFzFw4BqoFerrOIZINNeTcc8Bh6u84hImkjBlyIP/SS6yAi0nm/mLnoG8DdrnO4ZGNN+1oP3r8D23ae6yyuTBl03TPDCs+41HWOzvhT7vMvHvQ0X+g6xyH9+wdXlp+9dILrHHcRWLrJnOl008PcJTUvepqjafF3c5rZueU/uZ8fZgw+11lO5nMD+y9+uqgw69/kuuWp6OKrVtoK03OaPt8AxpdXrwm7DiLJ01P+MfdowUDlLuBrrnOIZINB7KtVcVlE3uanKi6LZLT/BTad9KgsFYvu29oaujfUk4vLQwpOXzW04PSprnN0RiMtuw+a5kmucxyutPfuetcZAAppirnOEBtU4HRMx+E22aGnrLanvOg6R0f8oHZPxYBIJOvHSNx3tXfaPdd6XrLQE/b2iQGfVHE5+6nA3HP8AnjZdQiRTDfes2GH6wwiklY2ozdxRTLarHnTW4DbgB53aWcsvH1128EHCiB2qussruR48kJTBr2vvzEmo14bL8vZsIY060jtVbLH6zoDQBENzv8vR4cVjjzR/TbSxq7ff5ad93+anb+5nQNLHzrqmIY3/hu///5PU/Pg52mrjb8PFm0KUfOHL7LzvttpWvfW9Ivax75FpH7vMddrn8XsdHRIR3jA8/iOmjN81m51nSXZFo3zXHD3jd6tMcNu11mS7Ffl1WvSfkyLdF9G/RCVrgsGKmPATOLvHolIF53vqT7oOoOIpJWZ+ENNrkOISPfMmjf938A81zlSKdq6+qW2hj+dAnaA6ywuXT7kxtc9xjPMdY7OiGGjmz27z3Kd4+3yC+r7uM4AUEyD8w0qbXHOCGvYftwDvDkM+tB3GPrxnzPk5p/RvPllWndUH3GIr3Qwg24IMPTjP6f04g+x98mfA9C4ejFF505n8I0/4ODyvwDQtGEZuYNOx1fS75jLbbZDR7xhR2ZEF3NpLNb7d7t2N2Nts+ssybZ2uCmfPdMbbfWxznWWJNkOfNl1CEkNFZh7kGCgcgXwU9c5RDLZeM8Gfd8UkUN+jz/0L9chRCRhvkAPGZURbn5uabjpyQlAkessLp1Vev5zpbn9L3Gdo7PWeXe+bA1DXOc4krVeb3iE6xQAxdSnxfm6LcnZfLz7jDF4cgvix8UiEIuCObIunl9Wjje/GIDcYaOJ1u+Jf63Xh420YaNhMAYbi1K/4m/0uuB9J8zz6fDsU6wlI8YUjG1tO2v2/lCPuAK7rrcZeutnvEP2FZONo0E+XV69Ji1G50jypcU3XkmpLwNVrkOIZKqRZlep6wwikhZqgc+6DiEiiTNr3vRG4Gay/Iq/toYnFkdbllUAaTHOwJUiX+/tY/tceq7rHF3xsm9T2v3d5efX7zSGEtc5AIppSIs/n+jQwhOOMLGxKDt/O5vt/3cj+SPHkTf0+E3pDav+RcFp8ZHbRWdPo2XzSmr//HVKL7mB+pULKTrncjw5+SfME7RDhlfZUzOiixng1tDBKeNaWpe4zpEKzXmm5PZZ3vFrh5FNz/fR8uo1f3MdQlLHWOt8PJGk2Mi5C88DlgMn/gkkIkfZlPeRfR5j+7rOISLO3YA/9LDrECKSeL+YuehHZOEbSNbGIm31D71oo3VTXGdJA7H3jvhMVZ63YKzrIJ213zRueSz3xREYjhoDEYlE+O1vf0s0GiUWi1FeXs5ll112xDHBYJBHHnmE3r17A1BeXs60adNobGxk/vz5tLS0MH36dEaPHg3AI488QmVlJSUlJ64dDxy08aWzznp+coKeZrcs4dKX7jGz3Wdpi+7L+29Nb3OSxr5YSwO1j3+bvlfcRu6AkUfd37LlNfb+65cMvvF7eAt6HXFftKWBPX/7LgP+31fY/59fE2tpoNf5/4+8YeXHXOsUU7P9mdzPDTKGnK4/sdQJQ3jqiLLqBq+nx2xCestT0cVXrbQVJ/t3k+ZqgHPLq9cceyi4ZKVM/gcrXRQMVFYBc13nEMk0/TlQp+KyiAALVVwWyWpfAapPelQGsbatoTV07yoVl+PO71+5JBOLywAv+tYFj1VcBvB6vcyYMYOZM2dy2223sXHjRrZvP3oM8IgRI5g5cyYzZ85k2rRpALz++uuMHTuWW265heeffx6AtWvXMnjw4JMWlwF6l9akzX4ExdTnus4AQK63L15z0tm6nvxi8oefR/OmlUfd11a7mb1P/oyB1911VHEZIPTcw5Re9AEaVy8md/AZ9HvHnexf8vvjrrXFDi57zZ6WMV3MOZDzlx27+htr61xnSZX7rvZOu+daz0sWGl1n6YZbVFzueVRg7rl+BjzpOoRIJpng2bDDdQYRcS4EfMp1CBFJnlnzprcAM4Co6yyJYGMNta0H7tmObZroOks66J9XtmZk8TkZN3cZIEK0ZYdn35jj3W+MITc3XluNxWJEox3/J+zxeIhEIkSjUYwxxGIxli1bxiWXdOyPqrhkb9p0xBbTkOc6wyGxPrk1x7o92hQi1tIQPybcSsuWV8npV3bEMZGDtdQ9/h36Vc4hp+/R+1CG9+0g2rCP/BHnYSOtYOLlHRtpO2Gm2eHZp2bKLGaAIdHokJ/V7tmBtRHXWVJl0TjPBXff6N0aM+x2naUL7i2vXvMP1yEk9VRg7qGCgUpLfMZcj3knUKS7JnuqD7rOICLOfQJ/aJvrECKSXLPmTV8OfMd1ju6KReo2tYZ+3Qbh0a6zpAOv8TVdOuSD+caYtCmGdsbr3q0rMPQ50TGxWIx58+bx/e9/n9NOO42ysrKjjtm+fTvz5s3joYceora2FoDzzjuPjRs38oc//IFp06bx0ksvMWbMGHJyOvZHlZ/f0L8LTykpimgocJ3hkOiwoqPbjoFowz5qHv4KO+//NDW//yz5I8dTeMb51L/yD+pfidfmQs89Qqz5IPue/iU7fzubXQ/cecRjHFjyIL0rbgSgqHwajVX/pubBz9Pr/BNv9rfVDipbZU/PmC5mgEubmsddX9/wnOscqbR2uCmfPdMbbfVx0i74NLIB+JzrEOKGZjD3cCPnLnwX8ITrHCKZ4NFc/5JJnnVTXecQEWfm4Q+pe1mkh/jFzEU+4DngfNdZuiIa3vxauOHxEUBv11nSxRVDPrqkX/7QjD2XeyDvmTfCJnpOR45taWlh/vz5XHvttQwcOPDN21tbW9/sdF6/fj1PPvkks2fPPuJrm5ubefTRR/ngBz/Ik08+SUtLCxdddBHDhw8/zmqx6JSKhyLGkBadw/vpXfdpc98A1zkAiNrWvH/vjBlIm6I3wHBTu2NJ7p0DjCE9xol00DvLhrywJSfnItc5Uqmg1db/+N7o2r4NTHKd5SSiQEV59ZoXXAcRN9TB3MMFA5V/B+5xnUMkE5xmdpa6ziAizlSRhZt+icjxzZo3PQLcSAbOwYy0vPJCuOHxUai4/KZTi8csz+Ti8m5zoLqjxWWA/Px8TjnlFDZs2HDE7Xl5eW+O0TjzzDOJRqM0NR05Pnnx4sVUVFRQVVXF0KFDec973sOiRYuOu1ZhYWhruhSXAQppLjrefbatlb2fupG9n/gAe26+jobf/eq4jxOufoPdV0ykZfHTAMQO7GPfZ25mz8evp+XZ/7553IGv3Ul0T+2xH8Rr8sj1rO7iU0mabXbgsFfsGRnVxQwwf0fNebkxu9F1jlRqzjMlt8/yjl87jCWus5zEd1Rc7tlUYBaIX8KQVRuZiCRDbxqPHn4mIj1BE/BB/KEW10FEJLVmzZu+HrjTdY7OCDf9Z3Gk+b8XAPmus6SLfG9R3eT+15zmOkd3vJCz7qSjDRsbG2lpif+oCofDbN68mf79j5xc0dDQwKGrmHfs2IG1loKCt5pr9+7dS0NDAyNHjiQcDmNMfD/BSOT4429LS2vTak5sHq2FWHvsAdQ5ufT50b30+82f6PfrR2hd/jxtq1876jAbjVJ/70/JnfRWs2zLoifJv+pd9P35AzTNfwCA1ucX4zuzHG//gUc9xiHRAfkN3XxKSfGZ8OwzrOXEA5vTTJG1xQ/vrPFgbY8aXRjzGO9dN/mmPjXBLLYQc53nGF4AvuE6hLjlcx1A3AsGKptGzl14A/AiZNYlMiKp0pfQXo+xaTNbTkRS6tP4Q2tchxARN2bNm/6bX8xcVAm813WWE7HW2nDDo0tikW3TXGdJN1cOnbHFGJPul5cfVyvh0B5TP+FkxzU0NPDXv/6VWCyGtZZzzjmHUaNGsWLFCgAmTZrE6tWrWbFiBR6PB5/Px/XXX/9mERlg0aJFTJ8+HYjPZX7kkUdYtmwZl1566XHXLe29u/V497W1xfjsnbsIhy3RqGXq1CJmfKzvEce8+mozd99dw5DB8XnPU6YU8dGb+nDgQJSvf72GxoYYN9/cl0umxBuT77qrhjvu6E///icsZzQAR119aIzBFBTGfxOJQCRyxPM/pOnxR8ifejnhtW+8daPXB20tEG4DjwcbjdD02B/p/e2fnCgH0WGFg3w7mk54jAvb7YChK+2ZSyaa9RnV2T8qHD71rr37X/xW/74Xus6Savdd7Z22eVBs2W3/jJ1r4Lid+il2EPhIefWarNgYV7pOM5jlTSPnLvwi8F3XOUTS0XTPylX35/5grOscIpJyD+EP3eg6hIi49YuZi/oRH5UzxHWWY7E20tp28PcrbexAj5pN2hHj+k5ffFbp5Iwuur/gW7f4DV/6vnEwafJfXywoqD9msc9aS0uLpaDAQyRiufOOndw+qx9nn/1Wg/2rrzbz5z+F+PZ3Bh/xtY//JURunuGyy4r58txd/PRnw3jh+UbWb2jjpptOuNchH+VPu2LGe8z/rzYaZd/MG4ju2EbBez9Iya13HHF/tK6W0Le/TJ8f3svBH3yDvAsryJ92JbGGekLf/gqx/XspvvUOosGNmKJiCq5+94n/gKy1ef/auddA2jWrDKNu17N5d/TLtFnMALcPGvDM0sKCS13ncOGsbXbNNx6K9vVYBrnOAny0vHrNH1yHEPc0IkMO9wPgvyc9SqQHusBT3aMuwxIRIL4Ttjb1ExFmzZu+F/gYkHbdOTbWfKA1dG+1istH6507cOOoXpMucJ2ju6q9O8pcZziRvLzG486HMMZQUBAvO0QilkjEcoyG4WPy+gxtrZZw2GKMIRq1/OUvIT7wgZNvi+IletyWYeP10u/X8+n/p6cIV79OZPORc6rrf/F9im+9A+P1HnG7p7iEPv/7f/Sb90dyziyn9YWl5E29goM/+CYH/J+n7Y1Vx1nQGFvoW9+Bp5xyOxgw5GU7KuNmMQP83+66ir7R6ErXOVxYO9yUz57pjbb6WOc4ykMqLsshKjDLm4KByhhwE7DfdRaRdDPesz7tXlCKSFK1EZ+7XO86iIikh1nzpv8L+I7rHIeLRUM7WkP37sW26Cqrt/Hgabt8yI0RY0xGz6Le4qlbFTWx013nOB5jom3GxEac6Jho1HLbrdu5/rotTJxYQHn50X8lq1e3cOsnt/PlubsIBuNjgadPL+alFU18ee4ubprRhyf+dpArrywhP//kZQwfkZPum+ApLiF37CRalz9/xO3hdasJfWsudR9+B62L/83Bn/7vEZv6ATT+/l6KbryFlv88iW9UOb2+4Kfhvp8fd63Y4ILjD7F27DNtnz7TWo475iRdecH7+PZdp3it3ek6iwt1vc3QWz/jHbKvmBWOImwEbne0tqQhFZjlCMFA5XbgVtc5RNLNaWZXiesMIpJSX8Qf6pFdMSJyQncD/3IdAiAW2bm27eD9PoimbfHRpYrB17/g8+Sc5TpHdy3zrU+/4b2HKSrev8WYE+/t5PUa7rm3jEfmj6C6upXNm4/cV+7MM/P448MjuPfXZbz3/5Xy9btrACgu9vCd7wzhl78q48wzc3nxxSYqphbxwx/W8Q3/bla/cfwacg7hYxZMYwf2EWuIv3dsW1toW7kM34iRRxwz4I8LGfDwPxjw8D/Im3YFve74MvlTLnvz/sj2LUT31pE7dhK0toDHA8ZA2/FrtNFhhWm7yeRO+g9ZYc9a5jpHV/SNxfr9uqb2ANZmXIE8EZrzTMnts7zj1w5jSaqXBq4rr16jq3zlTSowy1GCgcpHgd+5ziGSTvpQP8x1BhFJmSfwh37qOoSIpJ9Z86bHgBuALS5zRNvWvtxW/8hQsOkwfzPtlBWOWjkof2RGbVx2LI201h40zWm9OWFp6e66jh5bXOxl7LgCXnrpyJp5UZHnzTEaF1xQSCQCodCR+4U9+OB+bvhIbxYtamDUqDw+/4UB3HffvuOulUtr+Fi3R/fuYf/nPsneT3yAvZ+6kdyJF5B30VSanvgzTU/8uUPPo+G+X1D88XjjZv70a2h58gn2zbqJwg/cdNyvsYW+YdYQ7NACDtzRNisju5gBJre0nv2J0MHlrnO4EvMY7103+aY+NcEsthBL0bIzy6vXHGcmjPRUJ3ynUXq02cCFwGjXQURc6039fq+xx50tJyJZZTtws+sQIpK+Zs2bvvcXMxddBzwLpHz8QqT5xWcjLc9fAOSkeu1MkOvJ33/RwPcMMaajk37T1/Kc9asxXOo6x4mUltYes5B7yIEDUXy+eHG5tTXGypeb+dCHjpyhvG9fhD59vBhjqK5uIWYtvXq91Qu3fXuYvXujjB1bwMYNbeTmmXjDcNvxJ9jl03zMkRQ5p4+i372PHHV74bvff+zn96VvHnVb769/781fe/r0pe/PHzhujsPZ0tyt5kDbyA4dnGI76T9kuR29+AJTnbabSZ7IHftDFc8VFCxdk5db4TqLK/dd7Z22eVBs2W3/jJ1roCiJS80rr17z+yQ+vmQodTDLMQUDlQ3Ae4ADjqOIODfWs3G76wwikhJR4MP4Q8dviRIRZ4wxvY0xjxpjqo0xa4wxzja1mzVv+svArFSv29b4j8WRluenoOLycV0x9KZqj/EMcZ2ju2LY6CZP7Zmuc5xMUdG+ghPdv29vhDlzdvHJT2xn1u07mDixgAsvKuLvfz/I3/8ev7p+yZJGPnHLdm795HZ+/vO9fO1rgzj8/YH779/HzTf3BeCy6cX866l6Zn96B+8/wWZ/BTRHj3unI9GhhXmuM5zInW2zzrKWk86uTlcP7qqZXBiLrXGdw6VF4zwX3H2jd2vMsDtJSywD7kjSY0uGM9Zq3yo5vpFzF14DLERvRkgP9gXfI0tn+Z7ose+Gi/Qgd+EP/Y/rECJybMaYB4Cl1trfGGNygUJr7QGXmX4xc9G9wCeTvY61sWhb/SPP22iNzkdO4OzeFz97Xp+KKa5zJMJa787lS3PWnO86x8lcMuUP2zweO9x1jrf7Hl95ZpWZeKnrHEcIx0J5i3YVG/C6jnI8j+R+a/GFnjUZ2cUMsM3n215ZNqTQGtPXdRaXBhywO3/062hDXoRRCXzYPcCE8uo12xL4mJJFVDSUEwoGKp8E5rrOIeLSRM96vRMnkv0WAd9xHUJEjs0Y0wuYCtwHYK1tc11cbjcbeCmZC1jb1tga+s1KFZdPrMTXZ+u5vaeMdZ0jUV72bUz71+oeT7jRGFvmOsexFNPoOsLRcjyl+Ey16xgncmfbrNGZ3MU8PBIp+17d3s1Ym6pZxGmprrcZeutnvEP2FbMiQQ8ZAz7surhsjBlujPlv+1VMbxhj7mi//fvtVze9Zox53BjT22XOnirtf2iJe8FA5feBh1znEHHldLOz2HUGEUmq7cBH8Id69IsRkTR3GlAH/NYY84ox5jfGmGTOmOyQWfOmtwLXE+/sSjgba6xrPXDPFmzD5GQ8frYwmOgVQ28KGWNKXGdJhAOmcWsTbRNd5ziZkpK9W4whLWddF1GflrliffM6vCmiCzX0HfRi7OyM3jDvmsamie9sbFriOodrzXmm5PZZ3vFrh5GIP4u7yqvX/DsBj9NdEWCOtbac+J5hs4wxZwNPA+daa8cA64AvO8zYY6nALB31CUjYu18iGaUv9UNdZxCRpGkG3os/VOM6iIickA+YAPzKWjseaCRNrrKbNW/6VuBDxOe4J0wsujfYGvp1E4TPTuTjZqMLB7xraa43/zzXORLlRd+6TaRp4fZwpaW703bPgmLq03IMRXRYYR/XGU7mzvDto62l2XWO7vhO3d5pQ8KRZa5zuBbzGO9dN/mmPjXBLLbxLuSueAL430Tm6ipr7S5r7cr2X9cDa4Bh1tp/WWsPbez5IpCWV1ZkOxWYpUOCgcoW4L2AXoBLj1JCY8hrYoNd5xCRpLkZf+hl1yFE5KS2A9uttYcKBo8SLzinhVnzpv8H+FqiHi8a3vJ628EHSiB2SqIeM1sNyj/l9eFFo7NmfEiUWOt2z76MKJb3Kq1Nu430DimhIS0LzLH++Wdb0nF+x1t203fg87FzMrqL2YB5dOeu0TnWBl1nSQf3Xe2dds+1npe68G9vA3BTefWatBsZaYwZCYwnvvHg4T4O/DPlgUQFZum4YKByB3Ad0OY6i0iqjPNs1CYGItnrf/CH5rsOISInZ62tAbYZY85qv+lyYLXDSEeZNW96AHi8u48Taa1aFm547HSgX/dTZTefyWmYOvj9JcaYtCwmdkWVd+sKTGb83RcVHUjbMXLF1Oe4znBMHpNDnietvncdy2fDt5+d6V3MvWK29MGdu8NYm9YF/VRZNM5zwd03erfGDLs7+CVNwHXl1WtCyczVFcaYYuAx4E5r7cHDbv8q8TEaGvHqgArM0inBQOXzwKdc5xBJlcme6gOuM4hIUjwO3O06hIh0ymzgIWPMa8A40nNjzo8Ba7v6xeGmxUsiTU9PAgoSliiLTR/ykVc9xptVXd6v+bb0cp2ho3JyWoe5znA8xdTnuc5wPNGBBU2uM5xMLX0GPBc7N6O7mAHOaWs78wv7DqxynSNdrB1uymfP9EZbfaw7yaEW+Fh59ZrXUpGrM4wxOcSLyw9Za/9y2O0zgHcCH7HWpl3HdU+gArN0WjBQeT/wf65ziKTCRLM+bS/9E5Euew34KP6QTj5FMoi19lVr7SRr7Rhr7XuttftdZ3q7WfOmHwTeRycvQ7bW2rb6xxdHW1+eCmRNN24ynVEy/sU+eYOmuM6RSLUmtLbNRDJiPIbP13rAGJu2Y+SKaMh3neF4osMKM2J/l8+FP3W2taR9MfxkbjpYf/H5zS2LXedIF3W9zdBbP+Mdsq/4hHtsfb28es2fUxaqg4wxBrgPWGOt/dFht18DfAl4t7U24//NZioVmKWrPgcsch1CJNnO9OxwvkO9iCRUHfBu/CFdLikiSTFr3vTVwA10cEMla6NtbQcfeD4W2TwtucmyR6G3ZNeEfleedfIjM8sLOetqXWfoqJKSPVtdZziRIhrT9ioAW5p7pqXDYwqcqaXPgGdj573kOkci3FNTe0mvaFSdzO2a80zJ7bO849cOY8kx7n6ovHrNt1IeqmMuAT4KTDfGvNr+8Q7g50AJ8HT7bfOcpuyhVGCWLgkGKiPAB4DNrrOIJFNfDqbtpX8i0mmtwPvwh7a4DiIi2W3WvOlPAHee7Dgbawm1hu59w8b2XZL8VFnDXjlsxi5jTB/XQRKpjcjBOnMwbTauPJnS3rvTbi7r4QppLHSd4URssW+D6wwd8dks6WL2ge/xHTVDPNamfWE/VWIe473rJt/UpyaYxfatN0SfB25xmetErLXPWmtN+5VM49o//mGtPcNaO/yw22a6ztoTqcAsXRYMVO4F3g00uM4ikgxFNNf7TGyI6xwikhAWuBl/6FnXQUSkZ5g1b/r/AT893v02enBXa+jeWmzz+BTGyngT+121JN9blDGF2I5a6dv0KoaMuXKuV6/atB4zVUhz2m5ACBAdXJjWf36H7KH3gCWxMVnRxTwwGh34i911u7E27DpLOrnvau+0e671vBQzvAG8t7x6TavrTJKZVGCWbgkGKl8HbiL+wl0kq4zxbErrS/9EpFPuxh962HUIEelxPgf89e03xiK717cevA+InJnyRBmsb96QtaeXjLvQdY5kWOPdkRFzeQ8pLDxY6jrDifiI5GJtm+scxxMdVnCG6wwd9bnwp86xtnNz5dPVlOaWMR85WP+86xzpZtE4z1mfuMP7/vLqNXWus0jmUoFZui0YqHwc8LvOIZJo55vqtNs8SES65Hf4Q//jOoSI9Dyz5k2PAR8B3uwAjLZteKWt/qGBYHWVVCd4jLdl+uAbfMaYPNdZEm2rZ8+qqIllTMERwOdrLXOd4WQMNn2vtM33DbYeNrqO0RF7Ke3/TGxsVnQxA8zdd2Da6W3h51znSCNtwPtemPn6GtdBJLOpwCwJEQxUfhO4x3UOkUSa6FkXdZ1BRLrtv8CtrkOISM81a970JuBdQDDSsuL5cOMT5wBp3f2ZjqYN+uAyr8d3uuscybDMtz6jukNzc5vqjKGf6xwnY4il9ezgWGnudtcZOmpOeOZ52dLFDPDwzprx+bHYOtc50sQtVTOq/us6hGQ+FZglkW4H5rsOIZIoozzbM2YOnogc0xrim/pp1p6IODVr3vTdkZZXr440LzkLyHWdJ9OMKDp7xcCC4dNc50iGJlrrQqZpkuscndGrV11GFEZ9RJtdZziR2LDCtN6I8HD7KO3339i4rOliLrC28E87a/KwNq03q0yBu6pmVP3BdQjJDiowS8IEA5Ux4KPAP11nEUmE/oQGu84gIl1WC1TiDx1wHUREBOCO331uHfFO5rQueqWbPE/hngsHVI5wnSNZludsWI3JrDcdSkt3H3SdoSN8RFpcZziR6KCCcgsZ8yb4nPDMMdaSvmNHOunUcOSUb+3ZtxZre+p+UvdVzajSCDlJGBWYJaGCgcowcD2gmUaS0YpobvASG+Y6h4h0yUHixeXNroOIiBxuzvwFLwAfBDSGq4OuHHbTJmM8A13nSAaLjW307M64sR8lvfZkRB0hl7ZW1xlOyOcpxmcyZu7tfnr1XRQb/7LrHIn03obG8y9val7sOocD/wBmug4h2SUjfjBIZgkGKpuAdwKrXGcR6apzzeatxmBc5xCRTmsE3oE/tMJ1EBGRY5kzf8Hfgdtc58gE5/WZurTIV3q+6xzJst67a4U1Nu03y3u7goKDfVxn6Ig8WtK+OzjWP3+f6wyd8fnwbedZS73rHIn0o9o9UwdEIj3pvPEZ4LqqGVUR10Eku6jALEkRDFQeAK4GNjiOItIlkz1r97vOICKd1gK8B39IV9GISFqbM3/BfcDdrnOks145/TeXl1440XWOZFrh25SRr8e93vAprjN0RD4taX+lQHRYYdpvlni4/fTq++/YhKzqYvaA5/EdNWf4rN3mOksKLAfeXTWjKq3Hx0hmysgfaJIZgoHK3cCVwA7XWUQ6a6JnXdp3PIjIEeIjmvyh/7gOIiLSEXPmL/gW8CvXOdKRwRO+YuhHm40xGbMJWmeFTNO2JlonuM7RWfn59TuMocR1jo4ooCntC8yxfnln2/hor4zxhfBtY7Oti7k0Fuv92127G7E2m2fkVwHXVs2oyqq/O0kfKjBLUgUDlUHgKmCv4yginTLas63IdQYR6bAo8GH8oYWug4iIdNKngfmuQ6SbSwa+97kcT+7ZrnMk04u+dRsxmfd6vFev2p2uM3RUEY3pv3mbMV7yvRkzhxngACV9no5NzKouZoBxrW2jP30glHXPq9164MqqGVUZNZJFMkvG/UCTzBMMVK4GroXs2XFWsl9/QoNdZxCRDokBM/CHHnMdRESks+bMXxADbgT+7DpLuhhScNqqoYVnTHWdI5mixFq3efae6zpHV5T23t3oOkNHFWXIy8/ooIL03ozwGL4Qvm2ctZnVed0Rtx04OGVMS+sS1zkSbCtwRdWMqt2ug0h2U4FZUiIYqHwJeA+QcT88pecpoLXJR3SY6xwiclIWmIk/9JDrICIiXTVn/oIIcAPwqOssruV48kJTBr2vnzEmq1+nvu7dtgJDf9c5uqKkZG+O6wwdVUx9Rvw7ig4rHO46Q2eFKO79VGzSK65zJMNvd+2+sDgWe911jgSpIV5c3uo6iGS/jPiGK9khGKhcBHyI+KXMImnrHBPcYjLwkkWRHuhO/KFfuw5xOGNM0BhTZYx51RjTk3YkF5FuaC8yfxj4i+ssLl0+5MbXPcZb5jpHsq3yBTNihvGx5Oc3ZMymdMU0eF1n6AhbknOqNWTM6JFDvhS+day1hFznSLRcyP3L9l39jLV1rrN00z7gqqoZVetdB5GeQQUUSalgoPKvwCeId52JpKXJnmrNphJJf1/GH/qZ6xDHcZm1dpy1dpLrICKSOdqLzB8CHnedxYWzek1+vjS3/yWucyRbrQmtazORMa5zdI2NeTyRka5TdFQJ9T7XGTrKFudscp2hs0IU934yNvlV1zmSYUg0OuTHtXu2Y22mNsfVE9/Qr8p1EOk5VGCWlAsGKn8HfM51DpHjmeRZF3GdQURO6H/whwKuQ4iIJNqc+QvCwAeBv7nOkkpFvtIdY/tedo7rHKnwYs66GtcZuqqwMLTVGPJd5+ioYuozZpxHdEhBRtZmvhT+5Lhs7GIGuLypefx19Y3Pus7RBc3Au6pmVC13HUR6loz8JiaZLxio/AnwVdc5RI5ltGdrgesMInJcP8Qfust1iBOwwL+MMS8bY251HUZEMk97kfn9wBOus6RI7MqhM/YYY0pdB0m2NiL1tebgBNc5uqpX6e6M2iSsmPo81xk6Kjq0cJTNwKt8D1Jc+o/YBVk5ixnAv3fftOHh8Auuc3RCK3Bd1Yyqxa6DSM+jArM4EwxUfge4gwz8QSrZbQAHBrvOICLH9Cv8oc+7DnESl1hrJwDXArOMMVNdBxKRzHNYkXmB6yzJdn7/dyzJ8xaMdZ0jFV7xbX4FQ7HrHF3Vu3R3i+sMnVFEY8Z0W5Pn7Y/HZOSs3C+Hb5lgLQdd50iWP++oOS83Zje6ztEBhzqX/+k6iPRMKjCLU8FA5c+Aj6ON/yRN5NHWkkM06zeXEclADwCzXIc4GWvtzvbPtcTnqJ7vNpGIZKo58xe0AdcBC11nSZb+eWVrRhafm/Vzlw9Z7d0+xHWG7igu3pfrOkNnFNJY6DpDZ8T65O5ynaErDlLca2HsgpWucyRLkbXFf9xZY7C23nWWE2gEKqtmVD3tOoj0XCowi3PtM5k/BLQ5jiLC2WbLVmP0vVEkzfweuAV/KK2veDHGFBljSg79GrgKeN1tKhHJZIcVmbOuI81rfE2XDvlgvjEmY+bkdsc2z57XoiZ2pusc3ZGb1zTQdYbOyLQCc3RYYcZ2t385/Inx2TqLGeCscPi0r+7d/4brHMdRD1xTNaPqv66DSM+mIoqkhWCg8lHgPcQv6xBxZrJnbZ3rDCJyhB8BH8MfyoQrXQYBzxpjVgHLgYXW2icdZxKRDDdn/oJW4P8BT7nOkkiXDf7wCq/xneo6R6os861P5+7HkzIm2ubxREe4ztEZBbRkVME2NrDgbBufoZtx6ikq/Xvsoldd50imD9U3XDilqfkZ1zne5gBwZdWMqkzcjFCyjArMkjaCgcongashe+c3Sfqb7FkbcZ1BRN40F39oTrp3Lh9ird1krR3b/nGOtfbbrjOJSHZoLzK/F/iX4ygJcWrxmOX98of2mBn1zbTtOWCaJrvO0R1FRfu3GENGdZt7iHmxtsl1jg7zmgJyPKtdx+iqr4RvGR+zHHCdI5l+vruuok80mi6bGu4BLq+aUbXMdRARUIFZ0kwwULkUmE78m6VIyo02WzNnMxCR7BUFPoE/9F3XQURE0sWc+QtagHcDf3GdpTvyvUV1k/tfc5rrHKm0PGfD6xgyan7x25WW1mbkVX4G2+g6Q2fEBuRnbLNVA4W9/h67+FXXOZLJC97Ht+8a7m3fc8OhHcDUqhlVWTv7WjKPCsySdoKBypeBaYDrb9rSAw0y+zNqtpxIFmoBrscfus91EBGRdNPeyfx+4F7XWbrqyqEzthhj+rvOkSoWG9vgqTnDdY7uKi3dHXadoSs8RDOngxmIDCsc4DpDd3w1/PEJMct+1zmSqV8s1v/emtr9WOtqD6lNQEXVjKo1jtYXOSYVmCUtBQOVq4EKYLPrLNJz5BJuzSGSUbPlRLLMQeAa/KG/ug4iIpKu5sxfEJszf8FtQMaN4RnXd/riQl/JJNc5UmmDp+Zla2yZ6xzdVVS8v8B1hq7wEc2oPX5sn9zRlswt0DZQ2OtvsUtWuc6RbOe3tJ7z8dDBFx0s/Qbx4rLqJJJ2VGCWtBUMVG4CpgAZO4dKMku52brVGLyuc4j0ULuBafhDi10HERHJBHPmL/gacAeQEXPqe+cO3Diq16QLXOdItRU5G11HSIjc3OZBrjN0RQ7hzNo0zxiPLfBWu47RHV8Lf3xizJp9rnMk22f3h6aObm1L5eZ6K4BpVTOqdKW3pCUVmCWtBQOVO4mPy9BsIUm6SZ61mv0t4sYm4BL8oVddBxERySRz5i/4GXAjkNbjCzx42i4fcmPEGNOj9roImabtjbROdJ2juzyeSJMxseGuc3RFLq2uxhh0WWxwQUZvOt5IQclfY5dUuc6RCn/YVTOpIBZLxaiKJ4HpVTOq9qZgLZEuUYFZ0l4wULkHuAxY6jqLZLfJnrWZ1eEgkh1WES8uZ0eLl4hIis2Zv+CPxDf/a3Cd5XimDLr+BZ8n5yzXOVLtRd+6jZjMf81dXLJ3i8nQ55FHa1q/+XIs0WGFI11n6K67wjdP6AldzHmW/Ed31JQYa5M51uRe4F1VM6rqk7iGSLdl5A8J6XmCgcqDwNXAU66zSPYqN1t6VFeNSBpYQnwsRo3rICIimWzO/AVPEr/qL+2+nw4rHPXK4IKRU13nSLUosbZtnr3nuM6RCKWluzO2UFhAc9R1hs6yRTnDrWGr6xzd0UhByeOxKT2ii3lEJFIWqNu7EWtjCX5oC8ytmlF1W9WMqozuapeeQQVmyRjBQGUz8C7gftdZJDsNNvsGus4g0oM8AVyNPxRyHUREJBvMmb9gJXARkDbzW3M9+QcuHviewcYY4zpLqr3h3bYCQ3/XORKhtLQ2Y4tbBTQmuuiXErZXzhbXGbrr7vDHJsas6REjHd7R2DTpHY1NSxL4kC3Ah6pmVH03gY8pklQqMEtGCQYqw8FA5S3Al4CMPFmQ9OQjEs4lMsJ1DpEe4rfA+/CHWlwHERHJJnPmLwgCFwOp3HjquK4Y+tE1HuMZ4jqHC6/6gsWuMyRKYeGBjH0uRTRmxCaYbxcdWuhznaG7GikofjRa0SO6mAECdXunDYlElifgofYAl1fNqPpTAh5LJGVUYJaMFAxUfg+4Dmh0nUWyw1lm2xZjyPgTOZEM8F38oY/jD2XcJasiIplgzvwF+4ErgD+7zHF274ueLcnpe5HLDK7UmYPr20xkjOsciZKb2zLUdYauKqYhI7vno4MLRtssaKjyRz42uad0MRswj+7YdZbP2u50n68HLqqaUfV8onKJpIoKzJKxgoHKvwIVwA7HUSQLTPasrXOdQSTLNQMfwR+a6zqIiEi2mzN/QSvwQeCHLtYv8fXZem7virEu1k4HL+Ss2+U6Q6J4vW0hY2zGdqEXU5+ZNY9cbx+8Jm3G3XRVE/lFf45O7TFdzL1itvTBnbvbsLapC1/+HPHi8oZE5xJJhcz8ZivSLhiofAU4H3jZdRbJbJM9a9tcZxDJYluAS/CH/ug6iIhITzFn/gI7Z/6CzwM3E5/nmRIGE71i6E0hY0xJqtZMJ2EiDbUmNN51jkTp1asuozebK6bB6zpDV8X65tW6zpAI34jMmByzZo/rHKlyblvbmZ/bf+CVTn7ZfOJjMXpEt7dkJxWYJeMFA5U7ganAY66zSOY622zJdZ1BJEv9F5iEP9TZE20REUmAOfMX/I74VX/bUrHehQPetTTXm39eKtZKR6/4gq9gyJriemnp7gOuM3RHMfUZOwIvOqywt+sMidBEftGfope+4TpHKt0cqr9kUnPL4g4e/l3gw1UzqlqTmUkk2VRglqwQDFQ2Ae8HvgFk5EYO4tYQs3eA6wwiWeinwFX4Qz2ma0VEJB3Nmb9gBTAR6GjBo0sG5o94Y3jR6CnJXCPdveHdNsh1hkTqVVqX0a+tiqnP2CaSWP/8chsfMZbxvhG5aXLMmh41kvDemtqLe0Wjr53gkBbg5qoZVXOrZlRl9P8zEVCBWbJIMFBpg4FKP/HN/xocx5EM4iUaySN8iuscIlmkBZiBP3Qn/lDEdRgREYE58xfUEd/872fJeHyfyWmYNvgDxcaYjO0Y7a7tnr1VURMb5TpHIhUWhnq5ztAdxTTkuc7QZV6TR64nKzp/m8krfCR62WrXOVIpB3L+sqNmsMfaY4062QZUVM2o+l2KY4kkjQrMknWCgcrHgQsBDceXDhlltm81hhzXOUSyxDagAn/o966DiIjIkebMXxCZM3/BHcBNJLgzcvqQj7zqMd4e/Yb9i7719a4zJJrP1zrcdYbuKKKhwHWG7ogOzG90nSFRvhX56ORoD+tiHhSNDvz57roarA0fdvNiYGLVjKoVrnKdiDHmfmNMrTHm9cNu+74xptoY85ox5nFjTG+HESVNqcAsWSkYqHwDmAw85TqLpL9JnnVZsYGGSBpYQnzeclqeMIuISNyc+QseBKYQ34S1284oGf9in7xBPXo0RjNtew+YxomucyRSTk5znTH0c52jO4poLHSdoTuiw4oGu86QKM3kFT4cnd6jupgBKppbxtxwsOH59t/+FLiiakZVOhfafwdc87bbngbOtdaOAdYBX051KEl/KjBL1goGKg8A7wC+5ziKpLnJnuqU7awuksV+AVyBP6Q3bEREMsCc+QtWApOARd15nEJvya4J/a48KzGpMtdLvg1VGDJ3HMMx9OpVt8N1hu4qoKnIdYbusKU5oyykczGyU/4ncuP5UWt63Lnil/ftn3xRc/P1VTOq7qyaUZXW4+OstUuAfW+77V/W2kO5XwTKUh5M0p4KzJLVgoHKWDBQ+SXgw0CT6zySns4xwax6MSCSYq3ALfhDn8YfCp/0aBERSRtz5i/YA1wF/KiLD2GvHDZjlzGmTwJjZRyLjW3w1pzmOkeilfbefdB1hu7Kp7UIa2Ouc3SZMcYW+da7jpEoLeQV/DF6+RrXOVJsHXDBvTM3POY6SIJ8HPin6xCSflRglh4hGKh8hHiHxirXWST9DDV7+7vOIJKhdgDT8Ifudx1ERES6Zs78BdE58xfMAT5CJ+cyT+x31ZJ8b9GE5CTLHBs9NStjxo5wnSPRevWqM64zJEhGbwAfHVyQuQXyY/h25CM9qYv5MWAy/tDrJz0yAxhjvgpEgIdcZ5H0owKz9BjBQOUa4ALgJ4B1m0bShYdYNJ+2rHtBIJICzxOft7zMdRAREem+OfMX/BG4GNjckeP75g5Zd3rJuAuTmyozvJSzMasKgIcUFBzMis50D7GM3igvNqwwq7rjW8gr+EP0imrXOZIsAszBH7oefyjjrwQAMMbMAN4JfMRaq3qKHEUFZulRgoHK1mCg8rNAJdBT3jWVEzjTbN9qsmxenkiSxYDvAJfiD9W4DiMiIokzZ/6CV4EJwKMnOs5jvC3Th3zYY4zp8edQB03T9kZaJ7nOkQxebzgrmjA8RDvVmZ9ubIFvqDUde+MnU3wn3sW823WOJNkJXIY/1NXRQ2nHGHMN8CXg3dZajR6VY1KBWXqkYKDyn8AY4EnXWcStiZ51eqNBpOOCxEdifFXzlkVEOscYEzTGVBljXjXGrHCd53jmzF9wYM78Be8HPslx9jCZNugDy7yenDNSmyw9vehbvwGTfa+r8/IadhpDL9c5EsFHJOM39LaludtcZ0ikVnLzfx+9Khu7mB8DzsMfetZ1kK4yxjwMvACcZYzZboy5Bfg5UAI83f4zbJ7TkJKWsu4HoUhHBQOVu4F3AJ8F2hzHEUcme9Zm/AmnSIr8HhibySfMIiJp4DJr7Thrbdp3vM6Zv+A3xLuZXzn89hFF5SsG5A+f6iZVeokRC2/17DnHdY5k6FVau9N1hkTJIdzqOkN3RYcVZt3VAoHIhy+IWpMtV8M1AB9vH4mxz3WY7rDWfthaO8Ram2OtLbPW3metPcNaO7z959c4a+1M1zkl/ajALD1aMFBpg4HKnxCfzZyN76DKSZxrNvtcZxBJc/uA9+MPzciWGXIiItIxc+YvWAtcCPwYsHmewr0XDnjnCGNMtmz+1i1veLevwDDAdY5k6F26O6PnFh8uj9aMbyaKDioot/G5vlmjldz8B6JXr3WdIwFeBMbhD/3WdRARl1RgFgGCgcpXgYnAvY6jSIoNM3uy8kWBSII8TfwyvxPO4hQRkQ6xwL+MMS8bY251Haaj5sxf0DZn/oLPAZXTh9yw0hjPQNeZ0sWrvs2FrjMkS0nJnqxpwsinJfMLszmeXvhM1jVEBSIfvjBqPbtc5+iiKPANoAJ/aKPrMCKuqcAs0i4YqGwKBipvA64j3rEnWc4QixXQlhWbl4gkWAtwJ3A1/lDWXCIrIuLYJdbaCcC1wCxjTEaNmZgzf8E/e+X2uwF43HWWdLDHHNzQaiJjXedIlvyChn6uMyRKAc1R1xkSIdYvb4/rDInWRk7eb6NXr3edows2AlPwh/z4Q5n/BoZIAqjALPI2wUDlX4CxwDOOo0iSnWZ2bTeGfNc5RNLMKmAS/tBP8Yes6zAiItnCWruz/XMt8SLt+W4TdV5ZoGJPWaDifcDNQI8em/RCzrodrjMkj415PJFTXKdIlEIaY64zJEJ0WFFf1xmS4XuRD10Qyawu5t8SH4nxousgIulEBWaRYwgGKrcDlwNfI8tmXclbJnrWZcumEiKJEAN+AJyPP/SG6zAiItnEGFNkjCk59GvgKuB1t6m6rixQ8TviDRlLHEdxIkykcbcJjXedI1kKC0PbjKHAdY5EKaLBdYSEiPXLK7dkyZM5TBs5efdHr8mELuZ9wPX4Qx/HH8q6vweR7lKBWeQ4goHKWDBQ+W1gCrDBdR5JvAs81c2uM4ikiW3AFfhDX8AfyviNcERE0tAg4FljzCpgObDQWvuk40zdUhaoCAKXAV8EetTPjld9wZUYernOkSy9etVmVRNGEQ3ZsSmlx+SQ713tOkYyfD/yoQvTvIv538AY/KHHXAcRSVcqMIucRDBQuQw4D/g2PezkOdudazZnzeYlIt3wMPET5v+6DiIikq2stZustWPbP86x1n7bdaZEKAtUxMoCFd8HJgE95nLxN7zbsnqjw9Leu1tcZ0ikEuqzpu4RHZifVX83h4Tx5d4XvTYdu5hDwCeBq/CHsngsjkj3Zc03WpFkCgYqW4KByq8B44GlrvNIYpSZuqycYybSQbuBD+MP3YA/dMB1GBERyVxlgYoq4BJgFvGCTNba4dn7esTEznKdI5mKi/flus6QSMU0ZE1TSXRY4TDXGZLlB5EPXhixnnQq4j4OlOMP/Ub7koicnArMIp0QDFSuBqYBnyA+g0kylCEWK6R1pOscIg7EgF8Co/GHHnEdRkREskN7N/MvgXLgz67zJMuLvvVZXUAHyMtrHOA6QyIVU5/jOkOi2F65p1vIqhEmh4Tx5f46WrnJdQ7iTRjvxx96H/5QOo/tEEkrKjCLdFIwUGmDgcr7gNHAH1znka4ZaXbvyKbNS0Q6aAXxTfxmqWtZRESSoSxQsassUPEB4J3AFtd5EqmZtn37TeMk1zmSyZhY2OOJnuI6RyIVU59VHdm22Je1+wP9KPL+Cxx3Mf+WeNfyow4ziGQkFZhFuigYqKwLBio/ClwBpOO8KDmBiZ51WfnOv8hxHCB+2fIF+EMvO84iIiI9QFmgYiFwNvADIOI4TkKs8G2swpDnOkcyFRXt32IMWdPxC1BMQ77rDIkUHVKYHZsWHkMYX+690Xe66GKuBi7FH/o4/tB+B+uLZDwVmEW6KRio/A8wBvgftAlgxphsqhtdZxBJkQeBs/CHfok/FHMdRkREeo6yQEVTWaDiC8Q3AVzuOk93WKxd79010nWOZCst3V3nOkOiFdKYVVctRocWnuk6QzL9KHL9hRHr2Z6i5VqAu4Cx+EOLU7SmSFZSgVkkAdo3AYz/YIIlrvPIyY3xbMqqzgyRY4jPjPeHbsIfqnUdRkREeq6yQMUq4CJgNnDQcZwu2eTZ/XLM2KwaHXEsvUprW11nSLQiGgtdZ0iofO9A68neK2gj+HLmRd+1OQVL/Rs4D3/of/CH1Cgm0k0qMIskUDBQGb+0Bm5BmwCmteGmro/rDCJJ0gh8CRiHP6Q3vEREJC20bwL4c+KbAD7mOk9nvZSzsUdcBVRcvC+run0BCmgqdp0h0WK9c3e6zpBMP4lcd2HYepPVxbwNuAF/6Er8oaydZy2SaiowiyRY+yaA9xPfBPBB13nkWKwtomWE6xQiSfA48Y1Jvoc/FHYdRkRE5O3KAhU7ywIV1wPvAlLRpdht9aZ5ZwMtE13nSIXc3ObBrjMkWg6RPKzNqvOi6LCi7OrKfpskdTE3AncTHx33cIIfW6THU4FZJEnaNwG8CbgceM11HnnLCFO7wxiyrpNBerTNwDvxh96HP7TNdRgREZGTKQtULCDezfwlIOQ4zgm96Fu/DoPXdY5k83gizcbEhrvOkSQNrgMkUmxg/tk2y/f/+UnkuovC1puI81oL/A4YhT/0Lfyh5gQ8poi8jQrMIkkWDFQuAsYDNwFbHMcRYKJZV+M6g0iCtBLfYPQc/KGFrsOIiIh0RlmgorUsUPE94EzgV0DUcaSjxIiFt3rqznadIxWKi/duMSY7awQeYtm1wbfPU0SOZ43rGMkUxev7ZfTd3X39vASYhD90M/5QVo8VEXEtK394iKSbYKAyFgxUPgicBXwW2OM4Uo822bM2u04wpSeKAvcT78S4S50YIiKSycoCFXVlgYrbgTHAP13nOdxq7/YV1jDQdY5UKO29e6/rDMniJZJ150qx/nn7XWdItp9F3ndh2Hq3duFLNwHX4Q9Nwx9amehcInI0FZhFUigYqGwNBip/ApxOvOtQhU4Hxno2Zv0ljpK1LPAn4h3Lt+APdeWEW0REJC2VBSpWlwUq3gFcBaxynQfgFV8w6za9O55eveqyak7x4XKItLrOkGjRYYUDXGdItihe388j7+3M+W4I+ALxPUn+kqRYInIMKjCLOBAMVB4MBirvAs4Afglk7clcOhphavu4ziDSBf8EJuIPfRB/aK3rMCIiIslSFqh4mviIuY8CQVc59pr6ja0mPM7V+qlWVLQ/a/coyaWtxXWGRIv1zRtt03x+eSL8PPreC8PWe7JRGVHiY3bOxB/6Af5QVs+nFklHKjCLOBQMVNYEA5WzgLOB+cS7EyXJimke4TqDSCc8C0zFH3oH/tArrsOIiIikQlmgwpYFKv6AwxFzL+Ss25HqNV3KzW0Z6jpDsuTRkn0NPcZ4bYE3q+cwQ7yL+f8i/+94m/1Z4C/AWPyh2/GH6lIYTUQOowKzSBoIBio3BAOVHwImAf92nSeblZm6XcZQ4jqHSAe8ArwDf6gCf2ip6zAiIiIulAUq2soCFT8hxSPmwkQaa8yBsalYKx14vW0HjbFZW2DOpyXtNpBMhNiggh7Rqfvz6Hsvaju6i3kB8av7rsMfesNFLhF5iwrMImkkGKhcGQxUXglcCbzsOk82Gm/W96hOFMlIa4EPED9hTquNjkRERFwpC1QcLAtUHBox92OgKZnrrfJtWYmhNJlrpJOSXnuyel+HApqyssAcHVbYI67MjOHx/izyvu3tv30auBB/6F26uk8kfajALJKGgoHKfwOTgQ8BGxzHySoXeNZoY0VJV1uBW4hv4Pdn/CGNzBEREXmbskBFTVmg4nPAKcC3SdIM2te9W7N+A7XDlZbu3u86QzIV0ZiV51W2OGekNWw/+ZGZ71fRdzc129wK/KGr8IeWuc4jIkdSgVkkTQUDlTYYqJxPfD7zLUDWz9dKhbGeTV7XGUTepha4ExiFP3Q//lBWdtiIiIgkUlmgYk9ZoOJrwAjgK0DCZq/u9Ox7I2JioxP1eJmgtFdtVhZgDymiwXWEpLElOZtdZ0iyp4GKjYF3X1XwjbpnXYcRkWPzuQ4gIicWDFSGgftHzl34W6AS+DwwzW2qzHWK2d3bdQaRdtuBnwG/xB9SZ72IiEgXlAUqDgL/u33u0p8AnwS+AJR15zFf9K3P6m7eYykoDGX1HiXF1Gdtc110SKHXczApjfyu/RP4ZjBQ+aLrICJyciowi2SIYKDSEt/IYMHIuQsnEy80XweoI7cTSmga7jqD9HgrgB8Bf8YfirgOIyIikg3KAhXNwM+2z106D/goMJf4vOZOaaFt/z7TMCnR+dJdTk5rVp8jF9OQta+ZokMKzvKtDVkDxnWWBIgBTwDfDgYqV7gOIyIdpwKzSAYKBipfAj44cu7CU4HPAh8HitymSn9D2VNjDINd55Ae6dDJ8o/wh5a6DiMiIpKtygIVbcB92+cu/R3wfuLjM87r6Nev8G18DdOzrhbMyWneYwz9XedIphLqs7f2kefth9dUE7WZPNalHrgf+FkwULnJdRgR6bzs/SYr0gMEA5Wbgc+MnLvQD3wK+DSogHo84z0bdqI/H0mtRuC3wE/whza6DiMiItJTlAUqosAj2+cunQ+8i3ih+YITfY3F2nXeXSNTEC+t9OpVtx2yu8BcTH2O6wzJFOuTW+Pd05qJBeYg8H/Ab4KByoOOs4hIN6jALJIFgoHKfcC3R85d+APgRmAOUO42VfqZ7KnO3t09JN1sJ36yfC/+0AHHWURERHqsskCFJX4V0RPb5y6dBswG3sMxXgtv8tS+EjN2QoojOldaujvrC3vF1Oe5zpBM0WGFvbx7Wl3H6IzngB8Dfw0GKrNmg2tjzHDg98SbmmLAvdban7bfN5t4Q1gEWGit/aKzoCJJoAKzSBYJBipbgftGzl14P/ENAb8ATHWbKn2M82zMhrlkkt5eJj5f+U+arywiIpJeygIVi4HF2+cuLQNmArcCAw7d/5JvQ9hVNpdKetVl/TlyEY35rjMkU2xAwdmW/S0G0vl5hoE/Az9pH/mYjSLAHGvtSmNMCfCyMeZpYBDxN7bGWGtbjTEDnaYUSQIVmEWykDYEPLZTTU1v1xkkKx2ar/xj/KElrsOIiIjIiZUFKrYDX9s+d+m3gA8An66neXiDaelxm/sBFBTUl7rOkGyFNBa6zpBUXpNPrucV2mLjXUc5hn3APcAvgoHKHa7DJJO1dhewq/3X9caYNcAw4JNAwFrb2n5frbuUIslhrLWuM4hICoycu3Aw8R21Pwac7TaNG5vybtjvMfRxnUOyxkHil8BpvrKIiEiGe/hr945f69s5C/gQPWzz7CkVD4aMIauLzI0Uhm41D2b1c/RV7X/Gt7PpUtc5DvMycC/wh2Cgssl1mFQzxowElgDntn/+G3AN0AJ83lqbrV3c0kOpwCzSA42cu/B84GbiJ9C93aZJjUHsq12W/2ldiiTdZYFniO9y/Rj+ULPbOCIiIpJIfr+/F/AR4DZgrOM4SZeX11Bz/gWPZ/0m2DE80Y+aP2f11Zxmf+uavOV7XO/Dsx94iPimfascZ3HGGFMMLAa+ba39izHmdWARcAcwGZgPnGZVkJMsogKzSA82cu7CfOC9xIvNVwAep4GS6BrP8lfm5f4kHS8Zk8ywFfgd8Dv8oc2Os4iIiEgK+P3+C4jPaf4QkJUjFgYM2Pzy6PJnJ7rOkQof4dEmjMnKv0cArLV5/9q530DfVK9MvJj6G+CxYKCyJcXrpxVjTA7xcZVPWWt/1H7bk8RHZDzT/vuNwIXW2jpnQUUSTDOYRXqw9h/+jwCPjJy7sAy4ifgIjTNd5kqG8z3V9a4zSMZpAR4n3q28CH8o5jiPiIiIpJDf718GLPP7/Z8D3gd8GJhOFu1rUtq7psF1hlQx2EZLFheYjTG20LvWNEUvStGKm4mPi/t9MFC5KUVrpjVjjAHuA9YcKi63+yvx7x3PGGNGAbnAntQnFEkedTCLyFFGzl04hXih+QNAids0ifGX3LuXTvBsqHCdQ9JejHgHxkPAo/hDIcd5REREJI34/f5BxM+RPwykqpCXNOMnLHiuuHj/Ja5zpMJNPLIlanJOcZ0jmXzrDy71bapP5mueeuDPwAPA0vbN5aWdMWYKsBSoIv66AuArwL+JN62MA9qIz2Be5CKjSLKowCwixzVy7sIi4DriIzSmAcZtoq57Je/WVX1MQ9bP0ZMuW0W8qPxH/KGs3t1aREREEsPv948kPj7jw8AYt2m65qKLH17j80Vcz+1NiY/zUHWryR/tOkcymcbI9rxnd5cl+GFbgX8Rnxv8eE/csE9ETk4FZhHpkJFzF54K3AC8B5hEhhWbN+V9ZJ/H2FTPI5P0thX4I/AH/KE3XIcRERHJdMaY+4F3ArXW2nNd50klv99/NvFC84eB0x3H6SBrp1T8odmY7Jwv/Xa38btVDaYk6xtO8v61Y4uxdLdTuxH4J/AYsDAYqNS4QRE5IRWYRaTTRs5dOAR4F/Bu4HIg322iE+vPgT0r8m/v7zqHpIVXiW+6sQBYjj+kH4IiIiIJYoyZCjQAv+9pBebD+f3+84kXmj8IDHEc57gKCkJbJ01+YoTrHKkym3te2mf6T3adI9lyX6xd6gmFuzImIwT8nXhR+algoLI5sclEJJtpkz8R6bRgoHIXcC9w78i5CwuBq4gXmyuBgS6zHcs4z4YdgArMPVMz8B8OFZU1/kJERCRprLVLjDEjXedwze/3LweW+/3+OUAF8fPkdwKjnAZ7m16ltTVAjykw59Eadp0hFaJDC3M8Hd9GZA/wN+JF5f8EA5VtSQsmIllNBWYR6Zb2GVx/Bf46cu5CD3Ah8ZPodwNpMc/tfE+1NmrrWbYBC4kXlRfhD6n7QkRERFLO7/cf2jx4MTDH7/ePIl5oficwBchxGI/epbt71DlSAc1R1xlSITqksNy3JhQz4DnOITuBx4kXlZcEA5U94s9FRJJLBWYRSZhgoDIGPN/+MXfk3IVn8FaxeQrgdZFrgmeDi2UldWLAct7qUl7lOI+IiIjIUfx+/zrgR8CP/H5/KXA1cG3755SP0igu2eu0wJ1qBTTGXGdIiRxPKV7zBlF7TvstrcBS4hv1PRUMVL7mLpyIZCsVmEUkaYKByg20n0SPnLuwL/AO3prbnLIN904zu3qlai1JmYPET5IXAP/AH6pznEdERESkw/x+fwj4U/sHfr//POKF5quJj9XIS3aGvLzGAcleI50U0dhj9t6IDcp/w7uz+d/AU8Di9qtORUSSRpv8iUjKjZy70BAfnzHlsI9Tk7XepryP1HmM7VEn0FkoBLxIvDt+KfAs/lCPmKMnIiKSSdpnMC/oyZv8dZff7y8EpgFXApcA40n4OI1YZErFQzFjyE3s46av+7ht8SJz1TTXOZJkC/F9RxYBi2ouG7fLcR4R6WFUYBaRtDBy7sKhHFlwHkMCRmr0JbR3Zf6n+nX3cSTlNvDWuJXngNX4Qz3jskYREZEMZYx5GAHGuEwAAAovSURBVLiU+ObKu4GvW2vvcxoqC/j9/gJgMvFi88XtH926GrCoaN/GCRMXnp6AeBljPjcsfcJcV+E6R4JsBl4A/ku8oLzJcR4R6eFUYBaRtDRy7sIS4hsGHio4XwAUdfZxpntWvnZ/7g/GJDieJFYr8DLxQnK8qOwP1bqNJCIiIpKe/H6/Ac7irYLzJe2/77ChQ9e8cPoZKy5KQry0tZB3P/9HM+Ni1zm6YB/x/Ube/Ki5bJzGw4lIWtEMZhFJS8FAZT3wdPsHI+cu9BG/PPBQwfkSYNDJHmeyZ20oiTGla3ZzZHfyy/hDbW4jiYiIiGQGv99vger2j/vab+vHW8Xmi4l3POcf7zFKS2t73LlXMfWZUP9oBV7hrWLysprLxmnHchFJe5nwDVZEhGCgMgK81P7xY4CRcxeOAM5728doDptRN8GzXmMV3GkD1gNr2j9WA8vxh3QJn4iIiEgC+f3+vcDf2z/w+/0+4l3NY4mPnhvT/uuhAEXF+45bfM5WxdSn27zpNmAdsBJYRrygvKrmsnHaZ0REMo5GZIhIVhk5d2EO8SLzecB5i3I/1/c0T81lxDcR1JtqydFAvIPmUBH5UEF5E/5QxGUwEREREXlLe6fzmMnn/2VUfn7jOOBs4ptvZ/2G2GsZveab5tvlDpZuA9YSP09+47DPG2ouG6dzZRHJCiowi0jP4C/NAc4g3skxuv3zoV/3cZgsk+zh6CLyGmA7/pB+mIiIiIhkqP8sOr0fbxWby4HTgBHAcCArNszeTlnwS+anI5P08BbYTrwjeR3xgvKhXwdrLhsXTdK6IiJpQQVmERF/aV9gCPFLBoec4NcFriImWZj4XOQaYNfbPh/69Qb8oT3OEoqIiIiIE/9ZdHoh8ULzcN4qOr/9c6GzgB20nz61nza/GdiFLw0DO9s/drR/7Hzb5201l41rTlRWEZFMowKziEhH+UtLOXERehDxk+t84sXoAsDrIGkEaGn/2MOJC8c1wF51IIuIiIhIV7V3QB8qQg8F+h720edtn0uBYsCkMmMLeY23mD8WET9HDgEH2j8f+tjP0YXjHcCemsvG6VxZROQEVGAWEUmm+GiOwwvOBR34fR7xTolW3ioUH/5x4tv9IV2CJyIiIiJp6z+LTjdAEVBCvNhcctiHj3jx+VgfnhPcZ4AY0AQ0Hvb5zV9/xDxWX3PZuLaUPEkRkR5EBWYRERERERERERER6RKP6wAiIiIiIiIiIiIikplUYBYRERERERERERGRLlGBWUREREREREREks4Yk2+MWW6MWWWMecMY84322/saY542xqxv/9zHdVYR6TjNYBYRERERERERkaQzxhigyFrbYIzJAZ4F7gDeB+yz1gaMMXOBPtbaL7nMKiIdpw5mERERERERERFJOhvX0P7bnPYPC7wHeKD99geA96Y+nYh0lQrMIiIiIiIiIiKSEsYYrzHmVaAWeNpauwwYZK3dBdD+eaDDiCLSSSowi4iIiIiIiIhISlhro9bacUAZcL4x5lzHkUSkm1RgFhERERERERGRlLLWHgCeAa4BdhtjhgC0f651l0xEOksFZhERERERERERSTpjzABjTO/2XxcAVwDVwBPAjPbDZgB/cxJQRLrEWGtdZxARERERERERkSxnjBlDfBM/L/Gmxz9Za79pjOkH/AkYAWwF3m+t3ecuqYh0hgrMIiIiIiIiIt1kjDkLmH/YTacBd1trf+ImkYiISGqowCwiIiIiIiKSQMYYL7ADuMBau8V1HhERkWTSDGYRERERERGRxLoc2KjisoiI9AQqMIuIiIiIiIgk1oeAh12HEBERSQWNyBARERERERFJEGNMLrATOMdau9t1HhERkWRTB7OIiIiIiIhI4lwLrFRxWUREegoVmEVEREREREQS58NoPIaIiPQgGpEhIiIiIiIikgDGmEJgG3CatTbkOo+IiEgqqINZREREREREJAGstU3W2n4qLktHGGPuMMa8box5wxhzp+s8IiJdpQKziIiIiIiIiEgKGWPOBT4JnA+MBd5pjDnTbSoRka5RgVlEREREREREJLXKgRfbu94jwGLg/znOJCLSJSowi4iIiIiIiEinGGOGG2P+a4xZ0z7i4Y633f95Y4w1xvR3lTHNvQ5MNcb0a5/d/Q5guONMIiJd4nMdQEREREREREQyTgSYY61daYwpAV42xjxtrV1tjBkOXAlsdRsxfVlr1xhjvgs8DTQAq4j/mYqIZBx1MIuIiIiIiIhIp1hrd1lrV7b/uh5YAwxrv/vHwBcB6yheRrDW3metnWCtnQrsA9a7ziQi0hXqYBYRERERERGRLjPGjATGA8uMMe8GdlhrVxlj3AZLc8aYgdbaWmPMCOB9wEWuM4mIdIUKzCIiIiIiIiLSJcaYYuAx4E7iIx6+ClzlMlMGecwY0w8IA7OstftdBxIR6Qpjra5YEREREREREZHOMcbkAAuAp6y1PzLGnAf8B2hqP6QM2Amcb62tcRRTRESSTAVmEREREREREekUE59/8QCwz1p753GOCQKTrLV7UhhNRERSTJv8iYiIiIiIiEhnXQJ8FJhujHm1/eMdrkOJiEjqqYNZRERERERERERERLpEHcwiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl6jALCIiIiIiIiIiIiJdogKziIiIiIiIiIiIiHSJCswiIiIiIiIiIiIi0iUqMIuIiIiIiIiIiIhIl/x/uWOUW8xkhxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_value_counts = encodeData['month'].value_counts()\n",
    "day_value_counts = encodeData['day'].value_counts()\n",
    "fig, axs = plt.subplots(1, 2, figsize=(20, 12))\n",
    "# Plot the pie chart for 'month'\n",
    "axs[0].pie(month_value_counts, labels=month_value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "axs[0].set_title('Pie Chart of Unique Labels (Month)')\n",
    "# Plot the pie chart for 'day'\n",
    "axs[1].pie(day_value_counts, labels=day_value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "axs[1].set_title('Pie Chart of Unique Labels (Day)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "39b5f7c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From  2023-03-03 00:00:00  to  2023-06-03 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print('From ' , str(encodeData['timestamp'].min()), ' to ' , str(encodeData['timestamp'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e1455a",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec0e440",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "- Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dac4ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "featureAnalysis('Time',encodeData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "07917f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'Time' column to datetime format with millisecond precision\n",
    "encodeData['Time'] = pd.to_datetime(encodeData['Time'], unit='ms')\n",
    "encodeData['Hour'] = encodeData['Time'].dt.hour\n",
    "encodeData['Minute'] = encodeData['Time'].dt.minute\n",
    "encodeData['Second'] = encodeData['Time'].dt.second\n",
    "encodeData['Microsecond'] = encodeData['Time'].dt.microsecond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "933b07f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    185999\n",
       "13    183953\n",
       "0     182449\n",
       "2     180473\n",
       "5     179480\n",
       "7     167197\n",
       "8     160941\n",
       "16    149945\n",
       "3     147103\n",
       "4     138206\n",
       "23    135487\n",
       "11    133677\n",
       "6     130174\n",
       "15    120623\n",
       "9     119810\n",
       "1     117614\n",
       "12    111223\n",
       "14    108091\n",
       "21    102163\n",
       "18    100263\n",
       "20     90623\n",
       "17     87984\n",
       "22     51090\n",
       "19     47476\n",
       "Name: Hour, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodeData['Hour'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9cce66cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_mean_amount = encodeData.groupby('Hour')['Amount'].mean().to_dict()\n",
    "encodeData['hour_mean_amount'] = encodeData['Hour'].map(hour_mean_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c868a44a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 7172056.625544673,\n",
       " 1: 7743070.839330352,\n",
       " 2: 6946985.676793759,\n",
       " 3: 6455357.925202069,\n",
       " 4: 7549989.855476608,\n",
       " 5: 6678568.505688656,\n",
       " 6: 6756510.638145866,\n",
       " 7: 7298404.149721586,\n",
       " 8: 6275764.800181433,\n",
       " 9: 7055848.177931725,\n",
       " 10: 6964539.866386378,\n",
       " 11: 5876262.7644620985,\n",
       " 12: 6973028.758754934,\n",
       " 13: 6518307.733013324,\n",
       " 14: 5862899.363573286,\n",
       " 15: 6646526.486864031,\n",
       " 16: 6213975.608743206,\n",
       " 17: 6036591.60526914,\n",
       " 18: 6691965.274667624,\n",
       " 19: 6597425.584042463,\n",
       " 20: 6756720.920858943,\n",
       " 21: 6660163.199759209,\n",
       " 22: 7573531.1495596,\n",
       " 23: 7101789.400769077}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_mean_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "84ece0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData.drop('Time',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b806b09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_edges = [-1, 2, 5, 8, 11, 14, 17, 20 , 23]\n",
    "bin_labels = [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "encodeData['hour_binned'] = pd.cut(encodeData['Hour'], bins=bin_edges, labels=bin_labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0862dc1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    480536\n",
       "2    464789\n",
       "3    458312\n",
       "4    439486\n",
       "5    403267\n",
       "6    358552\n",
       "8    288740\n",
       "7    238362\n",
       "Name: hour_binned, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodeData['hour_binned'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b71b0d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData['hour_binned'] = encodeData['hour_binned'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "041806d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "hour_binned_mean_amount = encodeData.groupby('hour_binned')['Amount'].mean().to_dict()\n",
    "encodeData['hour_binned_mean_amount'] = encodeData['hour_binned'].map(hour_binned_mean_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4104e90b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 7227286.694480746,\n",
       " 2: 6867042.658578409,\n",
       " 3: 6785380.171448708,\n",
       " 4: 6658414.143729266,\n",
       " 5: 6468047.46025338,\n",
       " 6: 6315965.363116089,\n",
       " 7: 6697754.723374532,\n",
       " 8: 7029002.216353813}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_binned_mean_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "940051a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData['year'] = 2023\n",
    "encodeData['combined_timestamp'] = pd.to_datetime(encodeData[['year','month', 'day', 'Hour', 'Minute', 'Second', 'Microsecond']])\n",
    "encodeData.drop('timestamp',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cd84263a",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData['day_of_week'] = encodeData['combined_timestamp'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bc54571e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    505420\n",
       "2    475799\n",
       "4    475074\n",
       "5    469027\n",
       "6    435735\n",
       "3    418099\n",
       "0    352890\n",
       "Name: day_of_week, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodeData['day_of_week'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9408482f",
   "metadata": {},
   "outputs": [],
   "source": [
    "day_of_week_mean_amount = encodeData.groupby('day_of_week')['Amount'].mean().to_dict()\n",
    "encodeData['day_of_week_mean_amount'] = encodeData['day_of_week'].map(day_of_week_mean_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "04738c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 5845285.036603474,\n",
       " 1: 6935631.210242966,\n",
       " 2: 6841315.159346699,\n",
       " 3: 6923043.558107051,\n",
       " 4: 6985867.497408825,\n",
       " 5: 6923861.785713829,\n",
       " 6: 6691577.917621949}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_of_week_mean_amount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb87f28c",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "- Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a0c59140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>3132044.0</td>\n",
       "      <td>6.768677e+06</td>\n",
       "      <td>1.383031e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>150000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count          mean           std  min        25%        50%  \\\n",
       "Amount  3132044.0  6.768677e+06  1.383031e+07  1.0  1000000.0  2000000.0   \n",
       "\n",
       "              75%          max  \n",
       "Amount  6000000.0  150000000.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureAnalysis('Amount',encodeData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "47fd6c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "encodeData['log_Amount'] = np.log(encodeData['Amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5b59a5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>log_Amount</th>\n",
       "      <td>3132044.0</td>\n",
       "      <td>14.627525</td>\n",
       "      <td>1.519028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>14.508658</td>\n",
       "      <td>15.60727</td>\n",
       "      <td>18.826146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean       std  min        25%        50%  \\\n",
       "log_Amount  3132044.0  14.627525  1.519028  0.0  13.815511  14.508658   \n",
       "\n",
       "                 75%        max  \n",
       "log_Amount  15.60727  18.826146  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureAnalysis('log_Amount',encodeData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d621986",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45453ddd",
   "metadata": {},
   "source": [
    "<a name=\"2.5\"></a>\n",
    "- Terminal_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2de9994e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2249372\n",
       "7     834993\n",
       "6      32215\n",
       "5      15464\n",
       "Name: Terminal_Type, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodeData['Terminal_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "286edd44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = encodeData['Terminal_Type'].value_counts()\n",
    "# Plot a pie chart\n",
    "plt.figure(figsize=(20, 12))\n",
    "plt.pie(value_counts, labels=value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Pie Chart of Unique Terminal_Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5b412e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "terminal_Type_mean_amount = encodeData.groupby('Terminal_Type')['Amount'].mean().to_dict()\n",
    "encodeData['terminal_Type_mean_amount'] = encodeData['Terminal_Type'].map(terminal_Type_mean_amount)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba4049b",
   "metadata": {},
   "source": [
    "<a name=\"2.6\"></a> \n",
    " - Terminal_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d80d02c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "value_count_Terminal_ID = pd.DataFrame(encodeData['Terminal_ID'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b6142540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Terminal_ID</th>\n",
       "      <td>68951.0</td>\n",
       "      <td>45.4242</td>\n",
       "      <td>2299.761765</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>518407.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count     mean          std  min  25%  50%   75%       max\n",
       "Terminal_ID  68951.0  45.4242  2299.761765  1.0  2.0  5.0  13.0  518407.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureAnalysis('Terminal_ID',value_count_Terminal_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6a4633d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9858160142710041"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(value_count_Terminal_ID[value_count_Terminal_ID['Terminal_ID'] < 100]) / len(value_count_Terminal_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5947fe3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency Encoding \n",
    "terminal_id_counts = encodeData['Terminal_ID'].value_counts().to_dict()\n",
    "encodeData['Terminal_ID_Count'] = encodeData['Terminal_ID'].map(terminal_id_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1195c278",
   "metadata": {},
   "outputs": [],
   "source": [
    "terminal_id_mean_amount = encodeData.groupby('Terminal_ID')['Amount'].mean().to_dict()\n",
    "encodeData['terminal_id_mean_amount'] = encodeData['Terminal_ID'].map(terminal_id_mean_amount)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4853a774",
   "metadata": {},
   "source": [
    "- Primary ID and Second ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4d3850f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency Encoding Primary_ID\n",
    "Primary_ID_counts = encodeData['Primary_ID'].value_counts().to_dict()\n",
    "encodeData['Primary_ID_counts'] = encodeData['Primary_ID'].map(Primary_ID_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f1e18244",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency Encoding Second_ID\n",
    "Second_ID_counts = encodeData['Second_ID'].value_counts().to_dict()\n",
    "encodeData['Second_ID_counts'] = encodeData['Second_ID'].map(Second_ID_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbe576e",
   "metadata": {},
   "source": [
    "**Phase Two**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "70f3d322",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Primary_ID</th>\n",
       "      <th>Amount</th>\n",
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       "      <th>day_of_week</th>\n",
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       "      <th>Terminal_ID_Count</th>\n",
       "      <th>terminal_id_mean_amount</th>\n",
       "      <th>Primary_ID_counts</th>\n",
       "      <th>Second_ID_counts</th>\n",
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       "      <td>2023-06-02 08:38:38.290</td>\n",
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       "      <td>6.985867e+06</td>\n",
       "      <td>16.259506</td>\n",
       "      <td>1.031322e+07</td>\n",
       "      <td>141</td>\n",
       "      <td>5.451315e+06</td>\n",
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       "      <td>9</td>\n",
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       "      <td>2023-04-29 09:26:58.648</td>\n",
       "      <td>5</td>\n",
       "      <td>6.923862e+06</td>\n",
       "      <td>14.508658</td>\n",
       "      <td>5.280032e+06</td>\n",
       "      <td>67492</td>\n",
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       "      <td>8457468be95f8a197756</td>\n",
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       "      <td>22</td>\n",
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       "      <td>2023</td>\n",
       "      <td>2023-03-22 22:45:28.260</td>\n",
       "      <td>2</td>\n",
       "      <td>6.841315e+06</td>\n",
       "      <td>10.819778</td>\n",
       "      <td>1.031322e+07</td>\n",
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       "      <td>1.189212e+07</td>\n",
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       "      <td>3</td>\n",
       "      <td>15</td>\n",
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       "      <td>2023-03-15 08:55:54.592</td>\n",
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       "      <td>6.841315e+06</td>\n",
       "      <td>13.815511</td>\n",
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       "      <td>8877</td>\n",
       "      <td>4.401098e+06</td>\n",
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       "      <td>789</td>\n",
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       "      <td>281b90d9fdcc285cca2e</td>\n",
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       "    <tr>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "      <td>2023-04-10 11:13:47.613</td>\n",
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       "      <td>14</td>\n",
       "      <td>504</td>\n",
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       "    <tr>\n",
       "      <th>3259425</th>\n",
       "      <td>6f743892923ae636c1ac</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>375d739376a7d17c7b94</td>\n",
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       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
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       "      <td>2023-04-18 21:32:15.352</td>\n",
       "      <td>1</td>\n",
       "      <td>6.935631e+06</td>\n",
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       "      <td>5.280032e+06</td>\n",
       "      <td>8877</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>14</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3132044 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Primary_ID      Amount  Terminal_Type  \\\n",
       "0        9c2594cdb4153de3f748  11518977.0              7   \n",
       "1        a8ae04d3b8f155a154af   5000000.0              1   \n",
       "2        739c869136df74852e10   2000000.0              1   \n",
       "3        60366904cbdb0d067c20   1000000.0              1   \n",
       "4        911abef1ee23db40b108     50000.0              7   \n",
       "...                       ...         ...            ...   \n",
       "3259421  6f743892923ae636c1ac   1000000.0              1   \n",
       "3259422  6f743892923ae636c1ac   2000000.0              1   \n",
       "3259423  6f743892923ae636c1ac   1000000.0              1   \n",
       "3259424  6f743892923ae636c1ac   1000000.0              1   \n",
       "3259425  6f743892923ae636c1ac   1000000.0              1   \n",
       "\n",
       "                  Terminal_ID             Second_ID labelPrimary labelSecond  \\\n",
       "0        d7b0230e4491fbd3563a  3de4bc60f38a51a82365      no_info     unknown   \n",
       "1        9a858e74cc2faefbc12c  b4980da1e29f59c92a53      no_info         0.0   \n",
       "2        7cc4db9e78fb590b06a6  69c4a29dd54b44de457e      no_info     unknown   \n",
       "3        342abaad6ca26a8aea1e  a59ad5aa2a2b5e221572      no_info     unknown   \n",
       "4        3381cb32e729571bfdfe  8457468be95f8a197756      no_info     unknown   \n",
       "...                       ...                   ...          ...         ...   \n",
       "3259421  4ff68720afddc796c9fb  42057e17833b83f303f4      no_info     unknown   \n",
       "3259422  4ff68720afddc796c9fb  281b90d9fdcc285cca2e      no_info     unknown   \n",
       "3259423  4ff68720afddc796c9fb  e32ec72f1bb0e39f0ea3      no_info     unknown   \n",
       "3259424  4ff68720afddc796c9fb  071b8737ef9df9a13712      no_info         1.0   \n",
       "3259425  4ff68720afddc796c9fb  375d739376a7d17c7b94      no_info     unknown   \n",
       "\n",
       "         month  day  Hour  ...  year      combined_timestamp  day_of_week  \\\n",
       "0            6    2     8  ...  2023 2023-06-02 08:38:38.290            4   \n",
       "1            4    3     5  ...  2023 2023-04-03 05:15:20.253            0   \n",
       "2            4   29     9  ...  2023 2023-04-29 09:26:58.648            5   \n",
       "3            6    2     7  ...  2023 2023-06-02 07:52:19.692            4   \n",
       "4            3   22    22  ...  2023 2023-03-22 22:45:28.260            2   \n",
       "...        ...  ...   ...  ...   ...                     ...          ...   \n",
       "3259421      3   15     8  ...  2023 2023-03-15 08:55:54.592            2   \n",
       "3259422      3   25     8  ...  2023 2023-03-25 08:23:36.269            5   \n",
       "3259423      3   30     9  ...  2023 2023-03-30 09:36:46.398            3   \n",
       "3259424      4   10    11  ...  2023 2023-04-10 11:13:47.613            0   \n",
       "3259425      4   18    21  ...  2023 2023-04-18 21:32:15.352            1   \n",
       "\n",
       "         day_of_week_mean_amount  log_Amount  terminal_Type_mean_amount  \\\n",
       "0                   6.985867e+06   16.259506               1.031322e+07   \n",
       "1                   5.845285e+06   15.424948               5.280032e+06   \n",
       "2                   6.923862e+06   14.508658               5.280032e+06   \n",
       "3                   6.985867e+06   13.815511               5.280032e+06   \n",
       "4                   6.841315e+06   10.819778               1.031322e+07   \n",
       "...                          ...         ...                        ...   \n",
       "3259421             6.841315e+06   13.815511               5.280032e+06   \n",
       "3259422             6.923862e+06   14.508658               5.280032e+06   \n",
       "3259423             6.923044e+06   13.815511               5.280032e+06   \n",
       "3259424             5.845285e+06   13.815511               5.280032e+06   \n",
       "3259425             6.935631e+06   13.815511               5.280032e+06   \n",
       "\n",
       "         Terminal_ID_Count terminal_id_mean_amount  Primary_ID_counts  \\\n",
       "0                      141            5.451315e+06                  1   \n",
       "1                    36688            5.460239e+06                  1   \n",
       "2                    67492            7.088591e+06                  1   \n",
       "3                   133456            6.466540e+06                  1   \n",
       "4                       89            1.189212e+07                  1   \n",
       "...                    ...                     ...                ...   \n",
       "3259421               8877            4.401098e+06                 14   \n",
       "3259422               8877            4.401098e+06                 14   \n",
       "3259423               8877            4.401098e+06                 14   \n",
       "3259424               8877            4.401098e+06                 14   \n",
       "3259425               8877            4.401098e+06                 14   \n",
       "\n",
       "         Second_ID_counts  \n",
       "0                   75392  \n",
       "1                   16529  \n",
       "2                     286  \n",
       "3                    7174  \n",
       "4                   34702  \n",
       "...                   ...  \n",
       "3259421               789  \n",
       "3259422               352  \n",
       "3259423               656  \n",
       "3259424               504  \n",
       "3259425               745  \n",
       "\n",
       "[3132044 rows x 26 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodeData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ab8394a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clustering = encodeData[['Amount', 'Terminal_Type', 'month', 'day', 'Hour',\n",
    "       'Minute', 'Second', 'Microsecond', 'hour_binned', 'log_Amount',\n",
    "       'Terminal_ID_Count', 'Primary_ID_counts', 'Second_ID_counts','terminal_id_mean_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4a0b0405",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded = pd.get_dummies(df_clustering, columns=['Terminal_Type'], prefix='Terminal_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "49a132d6",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>Microsecond</th>\n",
       "      <th>hour_binned</th>\n",
       "      <th>log_Amount</th>\n",
       "      <th>Terminal_ID_Count</th>\n",
       "      <th>Primary_ID_counts</th>\n",
       "      <th>Second_ID_counts</th>\n",
       "      <th>terminal_id_mean_amount</th>\n",
       "      <th>Terminal_Type_1</th>\n",
       "      <th>Terminal_Type_5</th>\n",
       "      <th>Terminal_Type_6</th>\n",
       "      <th>Terminal_Type_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11518977.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>290000</td>\n",
       "      <td>3</td>\n",
       "      <td>16.259506</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>75392</td>\n",
       "      <td>5.451315e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>253000</td>\n",
       "      <td>2</td>\n",
       "      <td>15.424948</td>\n",
       "      <td>36688</td>\n",
       "      <td>1</td>\n",
       "      <td>16529</td>\n",
       "      <td>5.460239e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>648000</td>\n",
       "      <td>4</td>\n",
       "      <td>14.508658</td>\n",
       "      <td>67492</td>\n",
       "      <td>1</td>\n",
       "      <td>286</td>\n",
       "      <td>7.088591e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>19</td>\n",
       "      <td>692000</td>\n",
       "      <td>3</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>133456</td>\n",
       "      <td>1</td>\n",
       "      <td>7174</td>\n",
       "      <td>6.466540e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>260000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.819778</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>34702</td>\n",
       "      <td>1.189212e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259421</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>54</td>\n",
       "      <td>592000</td>\n",
       "      <td>3</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>8877</td>\n",
       "      <td>14</td>\n",
       "      <td>789</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259422</th>\n",
       "      <td>2000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>36</td>\n",
       "      <td>269000</td>\n",
       "      <td>3</td>\n",
       "      <td>14.508658</td>\n",
       "      <td>8877</td>\n",
       "      <td>14</td>\n",
       "      <td>352</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259423</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "      <td>398000</td>\n",
       "      <td>4</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>8877</td>\n",
       "      <td>14</td>\n",
       "      <td>656</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259424</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>613000</td>\n",
       "      <td>4</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>8877</td>\n",
       "      <td>14</td>\n",
       "      <td>504</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259425</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>352000</td>\n",
       "      <td>8</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>8877</td>\n",
       "      <td>14</td>\n",
       "      <td>745</td>\n",
       "      <td>4.401098e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3132044 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Amount  month  day  Hour  Minute  Second  Microsecond  \\\n",
       "0        11518977.0      6    2     8      38      38       290000   \n",
       "1         5000000.0      4    3     5      15      20       253000   \n",
       "2         2000000.0      4   29     9      26      58       648000   \n",
       "3         1000000.0      6    2     7      52      19       692000   \n",
       "4           50000.0      3   22    22      45      28       260000   \n",
       "...             ...    ...  ...   ...     ...     ...          ...   \n",
       "3259421   1000000.0      3   15     8      55      54       592000   \n",
       "3259422   2000000.0      3   25     8      23      36       269000   \n",
       "3259423   1000000.0      3   30     9      36      46       398000   \n",
       "3259424   1000000.0      4   10    11      13      47       613000   \n",
       "3259425   1000000.0      4   18    21      32      15       352000   \n",
       "\n",
       "         hour_binned  log_Amount  Terminal_ID_Count  Primary_ID_counts  \\\n",
       "0                  3   16.259506                141                  1   \n",
       "1                  2   15.424948              36688                  1   \n",
       "2                  4   14.508658              67492                  1   \n",
       "3                  3   13.815511             133456                  1   \n",
       "4                  8   10.819778                 89                  1   \n",
       "...              ...         ...                ...                ...   \n",
       "3259421            3   13.815511               8877                 14   \n",
       "3259422            3   14.508658               8877                 14   \n",
       "3259423            4   13.815511               8877                 14   \n",
       "3259424            4   13.815511               8877                 14   \n",
       "3259425            8   13.815511               8877                 14   \n",
       "\n",
       "         Second_ID_counts  terminal_id_mean_amount  Terminal_Type_1  \\\n",
       "0                   75392             5.451315e+06                0   \n",
       "1                   16529             5.460239e+06                1   \n",
       "2                     286             7.088591e+06                1   \n",
       "3                    7174             6.466540e+06                1   \n",
       "4                   34702             1.189212e+07                0   \n",
       "...                   ...                      ...              ...   \n",
       "3259421               789             4.401098e+06                1   \n",
       "3259422               352             4.401098e+06                1   \n",
       "3259423               656             4.401098e+06                1   \n",
       "3259424               504             4.401098e+06                1   \n",
       "3259425               745             4.401098e+06                1   \n",
       "\n",
       "         Terminal_Type_5  Terminal_Type_6  Terminal_Type_7  \n",
       "0                      0                0                1  \n",
       "1                      0                0                0  \n",
       "2                      0                0                0  \n",
       "3                      0                0                0  \n",
       "4                      0                0                1  \n",
       "...                  ...              ...              ...  \n",
       "3259421                0                0                0  \n",
       "3259422                0                0                0  \n",
       "3259423                0                0                0  \n",
       "3259424                0                0                0  \n",
       "3259425                0                0                0  \n",
       "\n",
       "[3132044 rows x 17 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "60b2c63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "anomalyFeatures = df_encoded.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "17b29ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(anomalyFeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1923367e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.01, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IsolationForest</label><div class=\"sk-toggleable__content\"><pre>IsolationForest(contamination=0.01, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "IsolationForest(contamination=0.01, random_state=42)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = IsolationForest(contamination=0.01, random_state=42)\n",
    "model.fit(df_encoded[anomalyFeatures])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "013d858c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict outliers (anomalies)\n",
    "df_encoded['anomaly_score'] = model.decision_function(df_encoded[anomalyFeatures])\n",
    "df_encoded['anomaly_prediction'] = model.predict(df_encoded[anomalyFeatures])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "22b4b271",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    3100723\n",
       "-1      31321\n",
       "Name: anomaly_prediction, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded['anomaly_prediction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8a479b3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3132044, 19)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4528c275",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7722eb95",
   "metadata": {},
   "source": [
    "<a name=\"3\"></a>\n",
    "\n",
    "### Graph Based Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "04f1ff7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = encodeData.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "04dae378",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_unique_primary(encodeData_0 , baseCard):\n",
    "    Primary_ID = encodeData_0['Primary_ID'].unique().tolist()\n",
    "    Primary_ID = [x for x in Primary_ID if x != baseCard]\n",
    "    unique_values_count = len(set(Primary_ID))\n",
    "    return unique_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f1e9e653",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_unique_second(encodeData_0 , baseCard):\n",
    "    Second_ID = encodeData_0['Second_ID'].unique().tolist()\n",
    "    Second_ID = [x for x in Second_ID if x != baseCard]\n",
    "    unique_values_count = len(set(Second_ID))\n",
    "    return unique_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "16a0c8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cards = df_labels['CARD'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7ae0c4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8830"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51306ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_feature_dicts = []\n",
    "count_empty = 0\n",
    "for card in tqdm(all_cards, desc=\"Processing cards\"):\n",
    "    encodeData_0 = df[(df['Primary_ID'] == card) | (df['Second_ID'] == card)]\n",
    "    if len(encodeData_0) == 0:\n",
    "        count_empty = count_empty + 1\n",
    "    else:\n",
    "        encodeData_0['basedCard'] = card\n",
    "        encodeData_0['received'] = encodeData_0.apply(lambda row: 0 if row['basedCard'] == row['Primary_ID'] else (1 if row['basedCard'] == row['Second_ID'] else None), axis=1)\n",
    "        feature_dict = {}\n",
    "        #Card\n",
    "        feature_dict['Card'] = card\n",
    "        #Count transaction\n",
    "        feature_dict['count_total']   = len(encodeData_0)\n",
    "        feature_dict['count_received'] = encodeData_0['received'].sum()\n",
    "        feature_dict['count_given']   = len(encodeData_0) - encodeData_0['received'].sum()\n",
    "        \n",
    "        #given and received dataset\n",
    "        received = encodeData_0[encodeData_0['received'] == 0]\n",
    "        given    = encodeData_0[encodeData_0['received'] == 1]\n",
    "        \n",
    "        #Mean Amount\n",
    "        feature_dict['mean_total']    = encodeData_0['Amount'].mean()\n",
    "        feature_dict['mean_given']    = given['Amount'].mean()\n",
    "        feature_dict['mean_received'] = received['Amount'].mean()\n",
    "        \n",
    "        #STD Amount\n",
    "        feature_dict['std_total']    = encodeData_0['Amount'].std()\n",
    "        feature_dict['std_given']    = given['Amount'].std()\n",
    "        feature_dict['std_received'] = received['Amount'].std()\n",
    "        \n",
    "        #Mean Minute\n",
    "        feature_dict['mean_total_minute']    = encodeData_0['Minute'].mean()\n",
    "        feature_dict['mean_given_minute']    = given['Minute'].mean()\n",
    "        feature_dict['mean_received_minute'] = received['Minute'].mean()\n",
    "        \n",
    "        #Mean hour \n",
    "        feature_dict['mean_total_hour']    = encodeData_0['Hour'].mean()\n",
    "        feature_dict['mean_given_hour']    = given['Hour'].mean()\n",
    "        feature_dict['mean_received_hour'] = received['Hour'].mean()\n",
    "        \n",
    "        #Mean hour bin\n",
    "        feature_dict['mean_total_hourBin']    = encodeData_0['hour_binned'].mean()\n",
    "        feature_dict['mean_given_hourBin']    = given['hour_binned'].mean()\n",
    "        feature_dict['mean_received_hourBin'] = received['hour_binned'].mean()\n",
    "        #STD all time \n",
    "        df_sorted = encodeData_0.sort_values(by='combined_timestamp')\n",
    "        time_diffs = np.diff(df_sorted['combined_timestamp'].tolist()).astype('timedelta64[us]').astype(int)\n",
    "        feature_dict['std_combined'] = np.std(time_diffs)\n",
    "        \n",
    "        #STD hour\n",
    "        feature_dict['std_total_hour']   = encodeData_0['Hour'].std()\n",
    "        feature_dict['std_given_hour']   = given['Hour'].std()\n",
    "        feature_dict['std_recived_hour'] = received['Hour'].std()\n",
    "        #STD hour amoumt\n",
    "        feature_dict['std_total_hour_amt']    = encodeData_0.groupby('Hour')['Amount'].std().mean()\n",
    "        feature_dict['std_given_hour_amt']    = given.groupby('Hour')['Amount'].std().mean()\n",
    "        feature_dict['std_received_hour_amt'] = received.groupby('Hour')['Amount'].std().mean()\n",
    "\n",
    "        #Mean Day\n",
    "        feature_dict['mean_total_day']   = encodeData_0['day'].mean()\n",
    "        feature_dict['mean_given_day']   = given['day'].mean()\n",
    "        feature_dict['mean_recived_day'] = received['day'].mean()\n",
    "        \n",
    "        #STD Day\n",
    "        feature_dict['std_total_day']    = encodeData_0['day'].std()\n",
    "        feature_dict['std_given_day']    = given['day'].std()\n",
    "        feature_dict['std_received_day'] = received['day'].std()\n",
    "        #STD day amoumt\n",
    "        feature_dict['std_total_day_amt']    = encodeData_0.groupby('day')['Amount'].std().mean()\n",
    "        feature_dict['std_given_day_amt']    = given.groupby('day')['Amount'].std().mean()\n",
    "        feature_dict['std_received_day_amt'] = received.groupby('day')['Amount'].std().mean()\n",
    "        \n",
    "        #Mean Month\n",
    "        feature_dict['mean_total_month']    = encodeData_0['month'].mean()\n",
    "        feature_dict['mean_given_month']    = given['month'].mean()\n",
    "        feature_dict['mean_received_month'] = received['month'].mean()\n",
    "        #STD month amoumt\n",
    "        feature_dict['std_total_month_amt']    = encodeData_0.groupby('month')['Amount'].std().mean()\n",
    "        feature_dict['std_given_month_amt']    = given.groupby('month')['Amount'].std().mean()\n",
    "        feature_dict['std_received_month_amt'] = received.groupby('month')['Amount'].std().mean()\n",
    "        \n",
    "        #Unique Terminal type\n",
    "        feature_dict['count_total_termnialType']    = encodeData_0['Terminal_Type'].nunique()\n",
    "        feature_dict['count_given_termnialType']    = given['Terminal_Type'].nunique()\n",
    "        feature_dict['count_received_termnialType'] = received['Terminal_Type'].nunique()\n",
    "\n",
    "        \n",
    "        #Unique Terminal id\n",
    "        feature_dict['count_total_termnialID']    = encodeData_0['Terminal_ID'].nunique()\n",
    "        feature_dict['count_given_termnialID']    = given['Terminal_ID'].nunique()\n",
    "        feature_dict['count_received_termnialID'] = received['Terminal_ID'].nunique()\n",
    "        #Mean frequency terminal ID \n",
    "        feature_dict['mean_total_termnialID_count']    = encodeData_0['Terminal_ID_Count'].mean()\n",
    "        feature_dict['mean_given_termnialID_count']    = given['Terminal_ID_Count'].mean()\n",
    "        feature_dict['mean_received_termnialID_count'] = received['Terminal_ID_Count'].mean()\n",
    "        \n",
    "        #Count unique ID\n",
    "        feature_dict['count_unique_secondID']  = count_unique_second(encodeData_0 , card)\n",
    "        feature_dict['count_unique_primaryID'] = count_unique_primary(encodeData_0 , card)\n",
    "        feature_dict['count_unique_ID'] = feature_dict['count_unique_secondID'] + feature_dict['count_unique_primaryID']\n",
    "        #Mean frequency primary ID\n",
    "        feature_dict['mean_total_Primary_ID_counts']    = encodeData_0['Primary_ID_counts'].mean()\n",
    "        feature_dict['mean_given_Primary_ID_counts']    = given['Primary_ID_counts'].mean()\n",
    "        feature_dict['mean_received_Primary_ID_counts'] = received['Primary_ID_counts'].mean()\n",
    "        #Mean frequency second ID\n",
    "        feature_dict['mean_total_Second_ID_counts_counts']    = encodeData_0['Second_ID_counts'].mean()\n",
    "        feature_dict['mean_given_Second_ID_counts_counts']    = given['Second_ID_counts'].mean()\n",
    "        feature_dict['mean_received_Second_ID_counts_counts'] = received['Second_ID_counts'].mean()  \n",
    "        \n",
    "        #Time Range\n",
    "        feature_dict['min_time_day']  = encodeData_0['combined_timestamp'].min().day\n",
    "        feature_dict['min_time_hour'] = encodeData_0['combined_timestamp'].min().hour\n",
    "        feature_dict['max_time_day']  = encodeData_0['combined_timestamp'].max().day\n",
    "        feature_dict['max_time_hour'] = encodeData_0['combined_timestamp'].max().hour\n",
    "        feature_dict['std_time']      = np.std((encodeData_0['combined_timestamp'] - encodeData_0['combined_timestamp'].min()).dt.days)\n",
    "        feature_dict['range_time']    = (encodeData_0['combined_timestamp'].max() - encodeData_0['combined_timestamp'].min()).days\n",
    "        \n",
    "        #mean each terminalType\n",
    "        feature_dict['type_1'] = encodeData_0[encodeData_0['Terminal_Type'] == 1]['Amount'].mean()  \n",
    "        feature_dict['type_5'] = encodeData_0[encodeData_0['Terminal_Type'] == 5]['Amount'].mean() \n",
    "        feature_dict['type_6'] = encodeData_0[encodeData_0['Terminal_Type'] == 6]['Amount'].mean()\n",
    "        feature_dict['type_7'] = encodeData_0[encodeData_0['Terminal_Type'] == 7]['Amount'].mean()\n",
    "        \n",
    "        #mean frequency encoding\n",
    "        \n",
    "        #Time series features \n",
    "        # Autocorrelation Features\n",
    "        encodeData_0['lag_1'] = encodeData_0['Amount'].shift(1)\n",
    "        encodeData_0['lag_2'] = encodeData_0['Amount'].shift(2)\n",
    "        feature_dict['autocorrelation_lag_1'] = encodeData_0['Amount'].autocorr(1)\n",
    "        feature_dict['autocorrelation_lag_2'] = encodeData_0['Amount'].autocorr(2)\n",
    "        \n",
    "        #Number of pairs \n",
    "        encodeData_0['Pair'] = encodeData_0.apply(lambda row: tuple(sorted([row['Primary_ID'], row['Second_ID']])), axis=1)\n",
    "        pair_counts = encodeData_0['Pair'].value_counts()\n",
    "        pair_counts = pair_counts[pair_counts > 1]\n",
    "        pair_freq_dict = pair_counts.to_dict()\n",
    "        if not pair_freq_dict:\n",
    "            feature_dict['Max_Pair_Frequency'] = 0\n",
    "            feature_dict['Num_Pairs'] = len(encodeData_0)\n",
    "        else:\n",
    "            feature_dict['Max_Pair_Frequency'] = encodeData_0['Pair'].map(pair_freq_dict)\n",
    "            feature_dict['Num_Pairs'] = encodeData_0['Pair'].map(pair_counts)\n",
    "\n",
    "        #Anomaly detection total\n",
    "        feature_dict['isolation_anomaly_total'] = len(encodeData_0[encodeData_0['anomaly_prediction'] == -1])\n",
    "        feature_dict['isolation_normal_total']  = len(encodeData_0[encodeData_0['anomaly_prediction'] ==  1])\n",
    "        feature_dict['isolation_mean_total'] = encodeData_0['anomaly_score'].mean()\n",
    "        feature_dict['isolation_std_total']  = encodeData_0['anomaly_score'].std()\n",
    "        #Anomaly detection given\n",
    "        feature_dict['isolation_anomaly_given'] = len(given[given['anomaly_prediction'] == -1])\n",
    "        feature_dict['isolation_normal_given']  = len(given[given['anomaly_prediction'] ==  1])\n",
    "        feature_dict['isolation_mean_given'] = given['anomaly_score'].mean()\n",
    "        feature_dict['isolation_std_given']  = given['anomaly_score'].std() \n",
    "        #Anomaly detection received\n",
    "        feature_dict['isolation_anomaly_received'] = len(received[received['anomaly_prediction'] == -1])\n",
    "        feature_dict['isolation_normal_received']  = len(received[received['anomaly_prediction'] ==  1])\n",
    "        feature_dict['isolation_mean_received'] = received['anomaly_score'].mean()\n",
    "        feature_dict['isolation_std_received']  = received['anomaly_score'].std() \n",
    "        #Label\n",
    "        feature_dict['label'] = df_labels[df_labels['CARD'] == card]['LABEL'].values[0]\n",
    "\n",
    "    all_feature_dicts.append(feature_dict)\n",
    "print('8830 rows except ' , count_empty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67926155",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df = pd.DataFrame(all_feature_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcef2561",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.drop(['Max_Pair_Frequency', 'Num_Pairs'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5b0c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.to_csv('featuresDF_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1dc89c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_df = pd.read_csv('featuresDF_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17c93174",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "features_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677d90e0",
   "metadata": {},
   "source": [
    "<a name=\"4\"></a>\n",
    "### Train the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddabe726",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df_fillZero = features_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0b5909",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df_fillZero = features_df_fillZero.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a3bbe88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_df_fillZero.drop('Unnamed: 0',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d89aaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df_train = features_df_fillZero[features_df_fillZero['label'] != 'unknown']\n",
    "features_df_test = features_df_fillZero[features_df_fillZero['label'] == 'unknown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f66bd80",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_evaluation = features_df_train.iloc[:, 1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de3dc1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(features_df_train) , len(features_df_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed0c95c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9822d595",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = features_df_train.iloc[:, 1:-1]\n",
    "y = features_df_train['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adcf7401",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81680136",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5108a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.astype(int)\n",
    "y_test = y_test.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e953c2b",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e99f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "y_prob = rf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy : ' ,accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "301f52e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "y_prob = rf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy : ' ,accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3293c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=500, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "y_prob = rf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy : ' ,accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c9670de",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = rf.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "DF_features = feature_importance_df.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e45bfd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_features.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9627fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_features.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebb9ef06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the index of the tree with the highest accuracy on the training data\n",
    "tree_accuracies = [accuracy_score(y_train, tree.predict(X_train)) for tree in rf.estimators_]\n",
    "most_accurate_tree_index = tree_accuracies.index(max(tree_accuracies))\n",
    "\n",
    "most_accurate_tree = rf.estimators_[most_accurate_tree_index]\n",
    "\n",
    "# Plot the tree\n",
    "plt.figure(figsize=(20, 20))\n",
    "plot_tree(most_accurate_tree, filled=True, feature_names=X_train.columns, class_names=[str(i) for i in rf_weight.classes_], rounded=True)\n",
    "plt.savefig('decision_tree.png', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "304ce012",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis_features_df_train = features_df_train.copy()\n",
    "y_pred = rf.predict(X)\n",
    "analysis_features_df_train['pred'] = y_pred\n",
    "analysis_features_df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc9b3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "false = analysis_features_df_train[analysis_features_df_train['label'] != analysis_features_df_train['pred']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55b959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_0 = false[false['label'] == 0 ]\n",
    "false_1 = false[false['label'] == 1 ]\n",
    "false_2 = false[false['label'] == 2 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ff3684",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42a22471",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_1.loc[5624]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "550e17cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9229d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.value_counts() / len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2120d3f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e25995e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights = {0: 0.205329, 1: 0.537618, 2:0.257053}\n",
    "rf_weight = RandomForestClassifier(n_estimators=500, random_state=42, class_weight=class_weights)\n",
    "rf_weight.fit(X_train, y_train)\n",
    "y_pred = rf_weight.predict(X_test)\n",
    "y_prob = rf_weight.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy : ' ,accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e21589f",
   "metadata": {},
   "outputs": [],
   "source": [
    "0.9404"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "145a8100",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = XGBClassifier(n_estimators=1000, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy:', accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0ff0428",
   "metadata": {},
   "outputs": [],
   "source": [
    ".935"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8fbb5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GradientBoostingClassifier(n_estimators=500, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy:', accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbbf31e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = ExtraTreesClassifier(n_estimators=500, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy:', accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571a997a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = AdaBoostClassifier(n_estimators=500, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy:', accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34538de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(1000,), max_iter=500, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_prob = clf.predict_proba(X_test)[:, 1]\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average=None)\n",
    "print('accuracy:', accuracy)\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bda0583",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Perform PCA\n",
    "n_components = 45  \n",
    "pca = PCA(n_components=n_components)\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)\n",
    "X_test_pca = pca.transform(X_test_scaled)\n",
    "\n",
    "# Define the MLP model\n",
    "model = Sequential()\n",
    "model.add(Dense(units=128, activation='relu', input_dim=X_train.shape[1]))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=3, activation='softmax'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(lr=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "# Train the model\n",
    "model.fit(X_train_scaled, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))\n",
    "# Predictions\n",
    "y_pred_probs = model.predict(X_test_scaled)\n",
    "y_pred = tf.argmax(y_pred_probs, axis=1)\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "print('accuracy:', accuracy)\n",
    "print('f1:', f1)\n",
    "print(\"Classification Report:\\n\", classification_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2944fd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Define the MLP model\n",
    "model = Sequential()\n",
    "model.add(Dense(units=128, activation='relu', input_dim=X_train.shape[1]))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(units=64, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(units=32, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(units=3, activation='softmax'))\n",
    "\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))\n",
    "# Predictions\n",
    "y_pred_probs = model.predict(X_test)\n",
    "y_pred = tf.argmax(y_pred_probs, axis=1)\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "print('accuracy:', accuracy)\n",
    "print('f1:', f1)\n",
    "print(\"Classification Report:\\n\", classification_rep)"
   ]
  }
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