RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "continued-scene",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import datetime as dt\n",
    "import math\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import attention\n",
    "import transformers\n",
    "import torch\n",
    "\n",
    "import helper\n",
    "from helper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "conservative-departure",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_and_preprocess()\n",
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "potential-farmer",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = math.floor(0.75*len(df)) #train/test cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "silent-action",
   "metadata": {},
   "source": [
    "### Simple LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "leading-college",
   "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>datetime</th>\n",
       "      <th>Description</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Direction</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>clouds</th>\n",
       "      <th>rain</th>\n",
       "      <th>mist</th>\n",
       "      <th>snow</th>\n",
       "      <th>shower</th>\n",
       "      <th>thunderstorm</th>\n",
       "      <th>fog</th>\n",
       "      <th>other</th>\n",
       "      <th>Intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-10-01 13:00:00</td>\n",
       "      <td>heavy clouds</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>12.680000</td>\n",
       "      <td>230.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-10-01 14:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>91.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>12.684650</td>\n",
       "      <td>230.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-10-01 15:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>87.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>12.697790</td>\n",
       "      <td>231.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-10-01 16:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>84.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>12.710929</td>\n",
       "      <td>233.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-10-01 17:00:00</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>80.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>12.724069</td>\n",
       "      <td>234.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime   Description  Humidity  Wind Direction  Temperature  \\\n",
       "0  2012-10-01 13:00:00  heavy clouds      93.0          1001.0    12.680000   \n",
       "1  2012-10-01 14:00:00  sky is clear      91.0           986.0    12.684650   \n",
       "2  2012-10-01 15:00:00  sky is clear      87.0           945.0    12.697790   \n",
       "3  2012-10-01 16:00:00  sky is clear      84.0           904.0    12.710929   \n",
       "4  2012-10-01 17:00:00  sky is clear      80.0           863.0    12.724069   \n",
       "\n",
       "   Pressure  Wind Speed  clouds  rain  mist  snow  shower  thunderstorm  fog  \\\n",
       "0     230.0         4.0       1     0     0     0       0             0    0   \n",
       "1     230.0         4.0       0     0     0     0       0             0    0   \n",
       "2     231.0         4.0       0     0     0     0       0             0    0   \n",
       "3     233.0         4.0       0     0     0     0       0             0    0   \n",
       "4     234.0         3.0       0     0     0     0       0             0    0   \n",
       "\n",
       "   other  Intensity  \n",
       "0      0        3.0  \n",
       "1      0        0.0  \n",
       "2      0        0.0  \n",
       "3      0        0.0  \n",
       "4      0        0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Temperature'] = df['Temperature'] -273.15\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13637318",
   "metadata": {},
   "source": [
    "Splitting the dataframe into 75% training data (first 75% of observations), and 25% testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "certain-encoding",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:cutoff].reset_index(drop = True)\n",
    "train = train.iloc[:, 4:5].values\n",
    "test = df[cutoff:]\n",
    "test = test.iloc[:, 4:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "optional-contract",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "test_std = sc.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4018346a",
   "metadata": {},
   "source": [
    "Splits the sequence (`Temperature` column here) to pass into an LSTM model.<br>\n",
    "Takes the column as input as well as the number of observations that should be in each X vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "russian-police",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(sequence, n_timestamp):\n",
    "    X, y = [], []\n",
    "    \n",
    "    for i in range(len(sequence)):\n",
    "        end = i + n_timestamp\n",
    "        if end > len(sequence)-1:\n",
    "            break\n",
    "        sequence_x, sequence_y = sequence[i:end], sequence[end]\n",
    "        X.append(sequence_x)\n",
    "        y.append(sequence_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "needed-gibson",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split(sequence = train_std, n_timestamp = 10)\n",
    "X_test, y_test = split(sequence = test_std, n_timestamp = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "streaming-victor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33929, 10, 1)\n",
      "(11303, 10, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "missing-spell",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33929, 1)\n",
      "(11303, 1)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2985162f",
   "metadata": {},
   "source": [
    "Simple LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "musical-london",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "\n",
    "model.add(keras.layers.LSTM(units = 50, return_sequences = True, input_shape = X_train.shape[1:]))\n",
    "model.add(keras.layers.LSTM(units = 50))\n",
    "model.add(keras.layers.Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "physical-homework",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'mse', optimizer = 'adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "interpreted-covering",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "849/849 [==============================] - 8s 7ms/step - loss: 0.0722 - val_loss: 0.0386\n",
      "Epoch 2/20\n",
      "849/849 [==============================] - 5s 5ms/step - loss: 0.0164 - val_loss: 0.0089\n",
      "Epoch 3/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0113 - val_loss: 0.0085\n",
      "Epoch 4/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0109 - val_loss: 0.0083\n",
      "Epoch 5/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0108 - val_loss: 0.0079\n",
      "Epoch 6/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0108 - val_loss: 0.0077\n",
      "Epoch 7/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0108 - val_loss: 0.0075\n",
      "Epoch 8/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0107 - val_loss: 0.0071\n",
      "Epoch 9/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0107 - val_loss: 0.0068\n",
      "Epoch 10/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0106 - val_loss: 0.0066\n",
      "Epoch 11/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0105 - val_loss: 0.0064\n",
      "Epoch 12/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0104 - val_loss: 0.0063\n",
      "Epoch 13/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0103 - val_loss: 0.0062\n",
      "Epoch 14/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0102 - val_loss: 0.0061\n",
      "Epoch 15/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0101 - val_loss: 0.0061\n",
      "Epoch 16/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0100 - val_loss: 0.0061\n",
      "Epoch 17/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0100 - val_loss: 0.0061\n",
      "Epoch 18/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0100 - val_loss: 0.0063\n",
      "Epoch 19/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0099 - val_loss: 0.0064\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "hybrid-walker",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "about-convert",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_inv = sc.inverse_transform(y_test)\n",
    "y_pred_inv = sc.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf74945",
   "metadata": {},
   "source": [
    "Plot of actual temperatures and predicted temperatures in a small sample of days (first year of test data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sonic-tongue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "chicken-insider",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.9756000736883195\n",
      "R-Squared: 0.9918121200291249\n",
      "RMSE: 0.9877246952913142\n",
      "MAE: 0.6783960246893853\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fbe4a5",
   "metadata": {},
   "source": [
    "MSE and RMSE are about 1. Will compare to more advanced models below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artistic-confidence",
   "metadata": {},
   "source": [
    "### Multivariate Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f195f1",
   "metadata": {},
   "source": [
    "Splits data into sequences of input variables and corresponding target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "manual-merit",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_multiple(sequence, n_timestamp, target):\n",
    "    X, y = [], []\n",
    "    \n",
    "    for i in range(len(sequence)):\n",
    "        end = i + n_timestamp\n",
    "        if end > len(sequence)-1:\n",
    "            break\n",
    "        sequence_x, sequence_y = sequence[i:end], target[end] #takes up to n-1 observations in all sequence columns, and the nth observation of the target\n",
    "        X.append(sequence_x)\n",
    "        y.append(sequence_y)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3fba9d",
   "metadata": {},
   "source": [
    "Using all numerical variables as inputs and temperature as target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "worst-sphere",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:cutoff].reset_index(drop = True)\n",
    "target_train = train[['Temperature']].values\n",
    "train = train.drop(['datetime', 'Description'], axis = 1).values\n",
    "\n",
    "test = df[:cutoff].reset_index(drop = True)\n",
    "target_test = test[['Temperature']].values\n",
    "test = test.drop(['datetime', 'Description'], axis = 1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "professional-expansion",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc2 = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "target_train_std = sc2.fit_transform(target_train)\n",
    "test_std = sc.transform(test)\n",
    "target_test_std = sc2.transform(target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "empty-behalf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split_multiple(train_std, 10, target_train_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "connected-classification",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33929, 10, 14)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "addressed-psychology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33929, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "classified-document",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "\n",
    "model.add(keras.layers.LSTM(units = 50, return_sequences = True, input_shape = X_train.shape[1:]))\n",
    "model.add(keras.layers.LSTM(units = 50))\n",
    "model.add(keras.layers.Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "presidential-experience",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'mse', optimizer = 'adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "limited-occasions",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "849/849 [==============================] - 7s 6ms/step - loss: 0.1035 - val_loss: 0.0883\n",
      "Epoch 2/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0324 - val_loss: 0.0333\n",
      "Epoch 3/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0178 - val_loss: 0.0137\n",
      "Epoch 4/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0133 - val_loss: 0.0097\n",
      "Epoch 5/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0118 - val_loss: 0.0088\n",
      "Epoch 6/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0115 - val_loss: 0.0083\n",
      "Epoch 7/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0114 - val_loss: 0.0086\n",
      "Epoch 8/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0115 - val_loss: 0.0092\n",
      "Epoch 9/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0115 - val_loss: 0.0093\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fatty-mustang",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = split_multiple(test_std, 10, target_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "italic-volunteer",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "y_test_inv = sc2.inverse_transform(y_test)\n",
    "y_pred_inv = sc2.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "inclusive-newspaper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "urban-attraction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.9822338469177403\n",
      "R-Squared: 0.9867868824010761\n",
      "RMSE: 1.4079182671297863\n",
      "MAE: 0.9219468909950354\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fd441f",
   "metadata": {},
   "source": [
    "Interestingly, this performs slightly worse than the simpler model. Previous temperatures are the most important indicator of future temperatures, so this makes sense."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "white-scope",
   "metadata": {},
   "source": [
    "### Incorporating Engineered Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442b0f8b",
   "metadata": {},
   "source": [
    "Using engineered features and the 20 most important features as per our RFE analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "adjusted-smell",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakehogan/Desktop/MMA Courses/INSY 695/Assignments/Group Project/Cloud-Computations/helper.py:139: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  df['week'] = df['datetime'].dt.weekofyear\n"
     ]
    }
   ],
   "source": [
    "df = feature_engineer_important(load_and_preprocess())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "blessed-premises",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['datetime'] = pd.to_datetime(df.index)\n",
    "df = df.dropna()\n",
    "df['Temperature'] = df['Temperature']-273.15 #convert from K to C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35db422e",
   "metadata": {},
   "source": [
    "This starts a week after the original dataframe because there are some lagging variables which do not exist for the first observations. This dataset contains data from over 5 years, so one week is not a big deal to drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "known-fabric",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Direction</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>hour</th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag12</th>\n",
       "      <th>lag30</th>\n",
       "      <th>max daily temp</th>\n",
       "      <th>...</th>\n",
       "      <th>min weekly temp</th>\n",
       "      <th>mean weekly hum</th>\n",
       "      <th>mean weekly wind direction</th>\n",
       "      <th>rolling_mean_temp</th>\n",
       "      <th>rolling_mean_pressure</th>\n",
       "      <th>rolling_mean_wind_dir</th>\n",
       "      <th>rolling_mean_wind_speed</th>\n",
       "      <th>rolling_mean_humidity</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
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       "      <th>datetime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-09 19:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19</td>\n",
       "      <td>284.82</td>\n",
       "      <td>283.56</td>\n",
       "      <td>278.42</td>\n",
       "      <td>278.63</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599704</td>\n",
       "      <td>198.071006</td>\n",
       "      <td>1015.863284</td>\n",
       "      <td>1.656805</td>\n",
       "      <td>77.294310</td>\n",
       "      <td>11.82</td>\n",
       "      <td>2012-10-09 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 20:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>284.97</td>\n",
       "      <td>284.82</td>\n",
       "      <td>277.22</td>\n",
       "      <td>279.57</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599588</td>\n",
       "      <td>198.329412</td>\n",
       "      <td>1015.866588</td>\n",
       "      <td>1.658824</td>\n",
       "      <td>77.262353</td>\n",
       "      <td>12.43</td>\n",
       "      <td>2012-10-09 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 21:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>285.58</td>\n",
       "      <td>284.97</td>\n",
       "      <td>276.02</td>\n",
       "      <td>280.78</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.600468</td>\n",
       "      <td>198.368421</td>\n",
       "      <td>1015.869853</td>\n",
       "      <td>1.649123</td>\n",
       "      <td>77.230769</td>\n",
       "      <td>12.60</td>\n",
       "      <td>2012-10-09 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 22:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>285.75</td>\n",
       "      <td>285.58</td>\n",
       "      <td>274.95</td>\n",
       "      <td>282.35</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.597209</td>\n",
       "      <td>198.476744</td>\n",
       "      <td>1015.873080</td>\n",
       "      <td>1.639535</td>\n",
       "      <td>77.199552</td>\n",
       "      <td>11.89</td>\n",
       "      <td>2012-10-09 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 23:00:00</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>285.04</td>\n",
       "      <td>285.75</td>\n",
       "      <td>274.61</td>\n",
       "      <td>282.37</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.574855</td>\n",
       "      <td>198.427746</td>\n",
       "      <td>1015.920057</td>\n",
       "      <td>1.641618</td>\n",
       "      <td>77.001867</td>\n",
       "      <td>8.58</td>\n",
       "      <td>2012-10-09 23:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Humidity  Wind Direction  Pressure  Wind Speed  hour  \\\n",
       "datetime                                                                     \n",
       "2012-10-09 19:00:00  71.861538     1016.424953     203.0         4.0    19   \n",
       "2012-10-09 20:00:00  71.861538     1016.424953     242.0         2.0    20   \n",
       "2012-10-09 21:00:00  71.861538     1016.424953     205.0         0.0    21   \n",
       "2012-10-09 22:00:00  71.861538     1016.424953     217.0         0.0    22   \n",
       "2012-10-09 23:00:00  43.000000     1024.000000     190.0         2.0    23   \n",
       "\n",
       "                       lag1    lag2   lag12   lag30  max daily temp  ...  \\\n",
       "datetime                                                             ...   \n",
       "2012-10-09 19:00:00  284.82  283.56  278.42  278.63          283.52  ...   \n",
       "2012-10-09 20:00:00  284.97  284.82  277.22  279.57          283.52  ...   \n",
       "2012-10-09 21:00:00  285.58  284.97  276.02  280.78          283.52  ...   \n",
       "2012-10-09 22:00:00  285.75  285.58  274.95  282.35          283.52  ...   \n",
       "2012-10-09 23:00:00  285.04  285.75  274.61  282.37          283.52  ...   \n",
       "\n",
       "                     min weekly temp  mean weekly hum  \\\n",
       "datetime                                                \n",
       "2012-10-09 19:00:00           282.16        78.779569   \n",
       "2012-10-09 20:00:00           282.16        78.779569   \n",
       "2012-10-09 21:00:00           282.16        78.779569   \n",
       "2012-10-09 22:00:00           282.16        78.779569   \n",
       "2012-10-09 23:00:00           282.16        78.779569   \n",
       "\n",
       "                     mean weekly wind direction  rolling_mean_temp  \\\n",
       "datetime                                                             \n",
       "2012-10-09 19:00:00                 1015.584173         285.599704   \n",
       "2012-10-09 20:00:00                 1015.584173         285.599588   \n",
       "2012-10-09 21:00:00                 1015.584173         285.600468   \n",
       "2012-10-09 22:00:00                 1015.584173         285.597209   \n",
       "2012-10-09 23:00:00                 1015.584173         285.574855   \n",
       "\n",
       "                     rolling_mean_pressure  rolling_mean_wind_dir  \\\n",
       "datetime                                                            \n",
       "2012-10-09 19:00:00             198.071006            1015.863284   \n",
       "2012-10-09 20:00:00             198.329412            1015.866588   \n",
       "2012-10-09 21:00:00             198.368421            1015.869853   \n",
       "2012-10-09 22:00:00             198.476744            1015.873080   \n",
       "2012-10-09 23:00:00             198.427746            1015.920057   \n",
       "\n",
       "                     rolling_mean_wind_speed  rolling_mean_humidity  \\\n",
       "datetime                                                              \n",
       "2012-10-09 19:00:00                 1.656805              77.294310   \n",
       "2012-10-09 20:00:00                 1.658824              77.262353   \n",
       "2012-10-09 21:00:00                 1.649123              77.230769   \n",
       "2012-10-09 22:00:00                 1.639535              77.199552   \n",
       "2012-10-09 23:00:00                 1.641618              77.001867   \n",
       "\n",
       "                     Temperature            datetime  \n",
       "datetime                                              \n",
       "2012-10-09 19:00:00        11.82 2012-10-09 19:00:00  \n",
       "2012-10-09 20:00:00        12.43 2012-10-09 20:00:00  \n",
       "2012-10-09 21:00:00        12.60 2012-10-09 21:00:00  \n",
       "2012-10-09 22:00:00        11.89 2012-10-09 22:00:00  \n",
       "2012-10-09 23:00:00         8.58 2012-10-09 23:00:00  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-salad",
   "metadata": {},
   "source": [
    "#### LSTM with New Features <br>\n",
    "Using all the numerical features from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "rocky-david",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.drop('datetime', axis=1)[:cutoff].reset_index(drop = True)\n",
    "target_train = train[['Temperature']].values\n",
    "train = train.values\n",
    "\n",
    "test = df.drop('datetime', axis=1)[:cutoff].reset_index(drop = True)\n",
    "target_test = test[['Temperature']].values\n",
    "test = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "mobile-software",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc2 = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "target_train_std = sc2.fit_transform(target_train)\n",
    "test_std = sc.transform(test)\n",
    "target_test_std = sc2.transform(target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "recovered-poker",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split_multiple(train_std, 10, target_train_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "private-briefing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33929, 10, 21)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "advised-potential",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "\n",
    "model.add(keras.layers.LSTM(units = 50, return_sequences = True, input_shape = X_train.shape[1:]))\n",
    "model.add(keras.layers.LSTM(units = 50))\n",
    "model.add(keras.layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "figured-invasion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "849/849 [==============================] - 8s 7ms/step - loss: 0.0952 - val_loss: 0.0630\n",
      "Epoch 2/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0396 - val_loss: 0.0319\n",
      "Epoch 3/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0248 - val_loss: 0.0229\n",
      "Epoch 4/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0174 - val_loss: 0.0235\n",
      "Epoch 5/20\n",
      "849/849 [==============================] - 5s 5ms/step - loss: 0.0165 - val_loss: 0.0201\n",
      "Epoch 6/20\n",
      "849/849 [==============================] - 5s 5ms/step - loss: 0.0147 - val_loss: 0.0151\n",
      "Epoch 7/20\n",
      "849/849 [==============================] - 5s 5ms/step - loss: 0.0138 - val_loss: 0.0130\n",
      "Epoch 8/20\n",
      "849/849 [==============================] - 5s 5ms/step - loss: 0.0145 - val_loss: 0.0110\n",
      "Epoch 9/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0101 - val_loss: 0.0108\n",
      "Epoch 10/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0095 - val_loss: 0.0124\n",
      "Epoch 11/20\n",
      "849/849 [==============================] - 5s 6ms/step - loss: 0.0086 - val_loss: 0.0089\n",
      "Epoch 12/20\n",
      "849/849 [==============================] - 6s 8ms/step - loss: 0.0091 - val_loss: 0.0123\n",
      "Epoch 13/20\n",
      "849/849 [==============================] - 6s 7ms/step - loss: 0.0106 - val_loss: 0.0163\n",
      "Epoch 14/20\n",
      "849/849 [==============================] - 6s 7ms/step - loss: 0.0097 - val_loss: 0.0131\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "permanent-applicant",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = split_multiple(test_std, 10, target_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "incorporated-apache",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "y_pred_inv = sc2.inverse_transform(y_pred)\n",
    "y_test_inv = sc2.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "single-bones",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cardiovascular-google",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.8759305328087237\n",
      "R-Squared: 0.9809070303552292\n",
      "RMSE: 1.6958568727368248\n",
      "MAE: 1.2188123635028592\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21c5e9a",
   "metadata": {},
   "source": [
    "Not quite as performant as the original model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "packed-toddler",
   "metadata": {},
   "source": [
    "### Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5539e5a8",
   "metadata": {},
   "source": [
    "Full disclosure, transformers are a completely novel concept, and the examples that we could find online were nearly exclusively for text-based problems."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-trial",
   "metadata": {},
   "source": [
    "#### Attention Model<br>\n",
    "LSTM with an attention layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "substantial-devon",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakehogan/Desktop/MMA Courses/INSY 695/Assignments/Group Project/Cloud-Computations/helper.py:139: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  df['week'] = df['datetime'].dt.weekofyear\n"
     ]
    }
   ],
   "source": [
    "df = feature_engineer_important(load_and_preprocess())\n",
    "df = df.dropna()\n",
    "df['Temperature'] = df['Temperature']-273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "minus-banana",
   "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>Humidity</th>\n",
       "      <th>Wind Direction</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>hour</th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag12</th>\n",
       "      <th>lag30</th>\n",
       "      <th>max daily temp</th>\n",
       "      <th>...</th>\n",
       "      <th>min daily pressure</th>\n",
       "      <th>min weekly temp</th>\n",
       "      <th>mean weekly hum</th>\n",
       "      <th>mean weekly wind direction</th>\n",
       "      <th>rolling_mean_temp</th>\n",
       "      <th>rolling_mean_pressure</th>\n",
       "      <th>rolling_mean_wind_dir</th>\n",
       "      <th>rolling_mean_wind_speed</th>\n",
       "      <th>rolling_mean_humidity</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-09 19:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19</td>\n",
       "      <td>284.82</td>\n",
       "      <td>283.56</td>\n",
       "      <td>278.42</td>\n",
       "      <td>278.63</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599704</td>\n",
       "      <td>198.071006</td>\n",
       "      <td>1015.863284</td>\n",
       "      <td>1.656805</td>\n",
       "      <td>77.294310</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 20:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>284.97</td>\n",
       "      <td>284.82</td>\n",
       "      <td>277.22</td>\n",
       "      <td>279.57</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599588</td>\n",
       "      <td>198.329412</td>\n",
       "      <td>1015.866588</td>\n",
       "      <td>1.658824</td>\n",
       "      <td>77.262353</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 21:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>285.58</td>\n",
       "      <td>284.97</td>\n",
       "      <td>276.02</td>\n",
       "      <td>280.78</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.600468</td>\n",
       "      <td>198.368421</td>\n",
       "      <td>1015.869853</td>\n",
       "      <td>1.649123</td>\n",
       "      <td>77.230769</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 22:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>285.75</td>\n",
       "      <td>285.58</td>\n",
       "      <td>274.95</td>\n",
       "      <td>282.35</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.597209</td>\n",
       "      <td>198.476744</td>\n",
       "      <td>1015.873080</td>\n",
       "      <td>1.639535</td>\n",
       "      <td>77.199552</td>\n",
       "      <td>11.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 23:00:00</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>285.04</td>\n",
       "      <td>285.75</td>\n",
       "      <td>274.61</td>\n",
       "      <td>282.37</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.574855</td>\n",
       "      <td>198.427746</td>\n",
       "      <td>1015.920057</td>\n",
       "      <td>1.641618</td>\n",
       "      <td>77.001867</td>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Humidity  Wind Direction  Pressure  Wind Speed  hour  \\\n",
       "datetime                                                                     \n",
       "2012-10-09 19:00:00  71.861538     1016.424953     203.0         4.0    19   \n",
       "2012-10-09 20:00:00  71.861538     1016.424953     242.0         2.0    20   \n",
       "2012-10-09 21:00:00  71.861538     1016.424953     205.0         0.0    21   \n",
       "2012-10-09 22:00:00  71.861538     1016.424953     217.0         0.0    22   \n",
       "2012-10-09 23:00:00  43.000000     1024.000000     190.0         2.0    23   \n",
       "\n",
       "                       lag1    lag2   lag12   lag30  max daily temp  ...  \\\n",
       "datetime                                                             ...   \n",
       "2012-10-09 19:00:00  284.82  283.56  278.42  278.63          283.52  ...   \n",
       "2012-10-09 20:00:00  284.97  284.82  277.22  279.57          283.52  ...   \n",
       "2012-10-09 21:00:00  285.58  284.97  276.02  280.78          283.52  ...   \n",
       "2012-10-09 22:00:00  285.75  285.58  274.95  282.35          283.52  ...   \n",
       "2012-10-09 23:00:00  285.04  285.75  274.61  282.37          283.52  ...   \n",
       "\n",
       "                     min daily pressure  min weekly temp  mean weekly hum  \\\n",
       "datetime                                                                    \n",
       "2012-10-09 19:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 20:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 21:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 22:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 23:00:00              282.16           282.16        78.779569   \n",
       "\n",
       "                     mean weekly wind direction  rolling_mean_temp  \\\n",
       "datetime                                                             \n",
       "2012-10-09 19:00:00                 1015.584173         285.599704   \n",
       "2012-10-09 20:00:00                 1015.584173         285.599588   \n",
       "2012-10-09 21:00:00                 1015.584173         285.600468   \n",
       "2012-10-09 22:00:00                 1015.584173         285.597209   \n",
       "2012-10-09 23:00:00                 1015.584173         285.574855   \n",
       "\n",
       "                     rolling_mean_pressure  rolling_mean_wind_dir  \\\n",
       "datetime                                                            \n",
       "2012-10-09 19:00:00             198.071006            1015.863284   \n",
       "2012-10-09 20:00:00             198.329412            1015.866588   \n",
       "2012-10-09 21:00:00             198.368421            1015.869853   \n",
       "2012-10-09 22:00:00             198.476744            1015.873080   \n",
       "2012-10-09 23:00:00             198.427746            1015.920057   \n",
       "\n",
       "                     rolling_mean_wind_speed  rolling_mean_humidity  \\\n",
       "datetime                                                              \n",
       "2012-10-09 19:00:00                 1.656805              77.294310   \n",
       "2012-10-09 20:00:00                 1.658824              77.262353   \n",
       "2012-10-09 21:00:00                 1.649123              77.230769   \n",
       "2012-10-09 22:00:00                 1.639535              77.199552   \n",
       "2012-10-09 23:00:00                 1.641618              77.001867   \n",
       "\n",
       "                     Temperature  \n",
       "datetime                          \n",
       "2012-10-09 19:00:00        11.82  \n",
       "2012-10-09 20:00:00        12.43  \n",
       "2012-10-09 21:00:00        12.60  \n",
       "2012-10-09 22:00:00        11.89  \n",
       "2012-10-09 23:00:00         8.58  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoff = math.floor(0.75*len(df)) #train/test cutoff\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "warming-housing",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:cutoff].reset_index(drop = True)\n",
    "target_train = train[['Temperature']].values\n",
    "train = train.values\n",
    "\n",
    "test = df[:cutoff].reset_index(drop = True)\n",
    "target_test = test[['Temperature']].values\n",
    "test = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "specialized-genealogy",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc2 = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "target_train_std = sc2.fit_transform(target_train)\n",
    "test_std = sc.transform(test)\n",
    "target_test_std = sc2.transform(target_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "interior-suite",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33766, 24, 21)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train = split_multiple(train_std, 24, target_train_std)\n",
    "X_test, y_test = split_multiple(test_std, 24, target_test_std)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "atmospheric-resistance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 24, 21)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_6 (LSTM)                   (None, 24, 50)       14400       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_7 (LSTM)                   (None, 24, 50)       20200       lstm_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "last_hidden_state (Lambda)      (None, 50)           0           lstm_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "attention_score_vec (Dense)     (None, 24, 50)       2500        lstm_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "attention_score (Dot)           (None, 24)           0           last_hidden_state[0][0]          \n",
      "                                                                 attention_score_vec[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "attention_weight (Activation)   (None, 24)           0           attention_score[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "context_vector (Dot)            (None, 50)           0           lstm_7[0][0]                     \n",
      "                                                                 attention_weight[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "attention_output (Concatenate)  (None, 100)          0           context_vector[0][0]             \n",
      "                                                                 last_hidden_state[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "attention_vector (Dense)        (None, 128)          12800       attention_output[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1)            129         attention_vector[0][0]           \n",
      "==================================================================================================\n",
      "Total params: 50,029\n",
      "Trainable params: 50,029\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_input = keras.Input(shape = X_train.shape[1:])\n",
    "x = keras.layers.LSTM(50, return_sequences = True)(model_input)\n",
    "x = keras.layers.LSTM(50, return_sequences = True)(x)\n",
    "x = attention.Attention()(x) #incorportating an attention layer in the model\n",
    "model_output = keras.layers.Dense(1)(x)\n",
    "model = keras.models.Model(model_input, model_output)\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fossil-gnome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "845/845 [==============================] - 14s 14ms/step - loss: 0.1509 - val_loss: 0.1671\n",
      "Epoch 2/20\n",
      "845/845 [==============================] - 11s 13ms/step - loss: 0.0526 - val_loss: 0.0734\n",
      "Epoch 3/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0293 - val_loss: 0.0597\n",
      "Epoch 4/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0263 - val_loss: 0.0487\n",
      "Epoch 5/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0209 - val_loss: 0.0385\n",
      "Epoch 6/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0196 - val_loss: 0.0319\n",
      "Epoch 7/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0171 - val_loss: 0.0373\n",
      "Epoch 8/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0152 - val_loss: 0.0253\n",
      "Epoch 9/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0140 - val_loss: 0.0169\n",
      "Epoch 10/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0132 - val_loss: 0.0196\n",
      "Epoch 11/20\n",
      "845/845 [==============================] - 11s 13ms/step - loss: 0.0130 - val_loss: 0.0170\n",
      "Epoch 12/20\n",
      "845/845 [==============================] - 11s 13ms/step - loss: 0.0145 - val_loss: 0.0090\n",
      "Epoch 13/20\n",
      "845/845 [==============================] - 11s 13ms/step - loss: 0.0111 - val_loss: 0.0121\n",
      "Epoch 14/20\n",
      "845/845 [==============================] - 10s 12ms/step - loss: 0.0146 - val_loss: 0.0133\n",
      "Epoch 15/20\n",
      "845/845 [==============================] - 11s 13ms/step - loss: 0.0138 - val_loss: 0.0120\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "suffering-return",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "y_pred_inv = sc2.inverse_transform(y_pred)\n",
    "y_test_inv = sc2.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "encouraging-aquarium",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "whole-concern",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.084145883789443\n",
      "R-Squared: 0.9861606933270769\n",
      "RMSE: 1.4436571212685658\n",
      "MAE: 1.0437642782307863\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "married-launch",
   "metadata": {},
   "source": [
    "#### Multi-Head Attention Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f9a3e0a",
   "metadata": {},
   "source": [
    "This model uses the full transformer architecture for LSTM predictions. Transformers allow for old information to be retained far more effectively than LSTM or RNN models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "measured-state",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakehogan/Desktop/MMA Courses/INSY 695/Assignments/Group Project/Cloud-Computations/helper.py:139: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  df['week'] = df['datetime'].dt.weekofyear\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Direction</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>hour</th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag12</th>\n",
       "      <th>lag30</th>\n",
       "      <th>max daily temp</th>\n",
       "      <th>...</th>\n",
       "      <th>min daily pressure</th>\n",
       "      <th>min weekly temp</th>\n",
       "      <th>mean weekly hum</th>\n",
       "      <th>mean weekly wind direction</th>\n",
       "      <th>rolling_mean_temp</th>\n",
       "      <th>rolling_mean_pressure</th>\n",
       "      <th>rolling_mean_wind_dir</th>\n",
       "      <th>rolling_mean_wind_speed</th>\n",
       "      <th>rolling_mean_humidity</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-09 19:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>203.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19</td>\n",
       "      <td>284.82</td>\n",
       "      <td>283.56</td>\n",
       "      <td>278.42</td>\n",
       "      <td>278.63</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599704</td>\n",
       "      <td>198.071006</td>\n",
       "      <td>1015.863284</td>\n",
       "      <td>1.656805</td>\n",
       "      <td>77.294310</td>\n",
       "      <td>11.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 20:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>284.97</td>\n",
       "      <td>284.82</td>\n",
       "      <td>277.22</td>\n",
       "      <td>279.57</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.599588</td>\n",
       "      <td>198.329412</td>\n",
       "      <td>1015.866588</td>\n",
       "      <td>1.658824</td>\n",
       "      <td>77.262353</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 21:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>285.58</td>\n",
       "      <td>284.97</td>\n",
       "      <td>276.02</td>\n",
       "      <td>280.78</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.600468</td>\n",
       "      <td>198.368421</td>\n",
       "      <td>1015.869853</td>\n",
       "      <td>1.649123</td>\n",
       "      <td>77.230769</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 22:00:00</th>\n",
       "      <td>71.861538</td>\n",
       "      <td>1016.424953</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22</td>\n",
       "      <td>285.75</td>\n",
       "      <td>285.58</td>\n",
       "      <td>274.95</td>\n",
       "      <td>282.35</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.597209</td>\n",
       "      <td>198.476744</td>\n",
       "      <td>1015.873080</td>\n",
       "      <td>1.639535</td>\n",
       "      <td>77.199552</td>\n",
       "      <td>11.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09 23:00:00</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23</td>\n",
       "      <td>285.04</td>\n",
       "      <td>285.75</td>\n",
       "      <td>274.61</td>\n",
       "      <td>282.37</td>\n",
       "      <td>283.52</td>\n",
       "      <td>...</td>\n",
       "      <td>282.16</td>\n",
       "      <td>282.16</td>\n",
       "      <td>78.779569</td>\n",
       "      <td>1015.584173</td>\n",
       "      <td>285.574855</td>\n",
       "      <td>198.427746</td>\n",
       "      <td>1015.920057</td>\n",
       "      <td>1.641618</td>\n",
       "      <td>77.001867</td>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Humidity  Wind Direction  Pressure  Wind Speed  hour  \\\n",
       "datetime                                                                     \n",
       "2012-10-09 19:00:00  71.861538     1016.424953     203.0         4.0    19   \n",
       "2012-10-09 20:00:00  71.861538     1016.424953     242.0         2.0    20   \n",
       "2012-10-09 21:00:00  71.861538     1016.424953     205.0         0.0    21   \n",
       "2012-10-09 22:00:00  71.861538     1016.424953     217.0         0.0    22   \n",
       "2012-10-09 23:00:00  43.000000     1024.000000     190.0         2.0    23   \n",
       "\n",
       "                       lag1    lag2   lag12   lag30  max daily temp  ...  \\\n",
       "datetime                                                             ...   \n",
       "2012-10-09 19:00:00  284.82  283.56  278.42  278.63          283.52  ...   \n",
       "2012-10-09 20:00:00  284.97  284.82  277.22  279.57          283.52  ...   \n",
       "2012-10-09 21:00:00  285.58  284.97  276.02  280.78          283.52  ...   \n",
       "2012-10-09 22:00:00  285.75  285.58  274.95  282.35          283.52  ...   \n",
       "2012-10-09 23:00:00  285.04  285.75  274.61  282.37          283.52  ...   \n",
       "\n",
       "                     min daily pressure  min weekly temp  mean weekly hum  \\\n",
       "datetime                                                                    \n",
       "2012-10-09 19:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 20:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 21:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 22:00:00              282.16           282.16        78.779569   \n",
       "2012-10-09 23:00:00              282.16           282.16        78.779569   \n",
       "\n",
       "                     mean weekly wind direction  rolling_mean_temp  \\\n",
       "datetime                                                             \n",
       "2012-10-09 19:00:00                 1015.584173         285.599704   \n",
       "2012-10-09 20:00:00                 1015.584173         285.599588   \n",
       "2012-10-09 21:00:00                 1015.584173         285.600468   \n",
       "2012-10-09 22:00:00                 1015.584173         285.597209   \n",
       "2012-10-09 23:00:00                 1015.584173         285.574855   \n",
       "\n",
       "                     rolling_mean_pressure  rolling_mean_wind_dir  \\\n",
       "datetime                                                            \n",
       "2012-10-09 19:00:00             198.071006            1015.863284   \n",
       "2012-10-09 20:00:00             198.329412            1015.866588   \n",
       "2012-10-09 21:00:00             198.368421            1015.869853   \n",
       "2012-10-09 22:00:00             198.476744            1015.873080   \n",
       "2012-10-09 23:00:00             198.427746            1015.920057   \n",
       "\n",
       "                     rolling_mean_wind_speed  rolling_mean_humidity  \\\n",
       "datetime                                                              \n",
       "2012-10-09 19:00:00                 1.656805              77.294310   \n",
       "2012-10-09 20:00:00                 1.658824              77.262353   \n",
       "2012-10-09 21:00:00                 1.649123              77.230769   \n",
       "2012-10-09 22:00:00                 1.639535              77.199552   \n",
       "2012-10-09 23:00:00                 1.641618              77.001867   \n",
       "\n",
       "                     Temperature  \n",
       "datetime                          \n",
       "2012-10-09 19:00:00        11.82  \n",
       "2012-10-09 20:00:00        12.43  \n",
       "2012-10-09 21:00:00        12.60  \n",
       "2012-10-09 22:00:00        11.89  \n",
       "2012-10-09 23:00:00         8.58  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = feature_engineer_important(load_and_preprocess())\n",
    "df = df.dropna()\n",
    "df['Temperature'] = df['Temperature']-273.15\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "orange-microphone",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = math.floor(0.75*len(df)) #train/test cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "loaded-robinson",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:cutoff].reset_index(drop = True)\n",
    "target_train = train[['Temperature']].values\n",
    "train = train.values\n",
    "\n",
    "test = df[:cutoff].reset_index(drop = True)\n",
    "target_test = test[['Temperature']].values\n",
    "test = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "associate-belarus",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc2 = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "target_train_std = sc2.fit_transform(target_train)\n",
    "test_std = sc.transform(test)\n",
    "target_test_std = sc2.transform(target_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a917d866",
   "metadata": {},
   "source": [
    "Constructing classes for the transformers as per this kaggle example below.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "convertible-geometry",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/shujian/transformer-with-lstm\n",
    "\n",
    "import random, os, sys\n",
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "from keras.callbacks import *\n",
    "from keras.initializers import *\n",
    "import tensorflow as tf\n",
    "from keras.engine.topology import Layer\n",
    "import keras.backend as K\n",
    "\n",
    "try:\n",
    "    from dataloader import TokenList, pad_to_longest\n",
    "    # for transformer\n",
    "except: pass\n",
    "\n",
    "\n",
    "embed_size = 60\n",
    "\n",
    "class LayerNormalization(Layer):\n",
    "    def __init__(self, eps=1e-6, **kwargs):\n",
    "        self.eps = eps\n",
    "        super(LayerNormalization, self).__init__(**kwargs)\n",
    "    def build(self, input_shape):\n",
    "        self.gamma = self.add_weight(name='gamma', shape=input_shape[-1:],\n",
    "                                     initializer=Ones(), trainable=True)\n",
    "        self.beta = self.add_weight(name='beta', shape=input_shape[-1:],\n",
    "                                    initializer=Zeros(), trainable=True)\n",
    "        super(LayerNormalization, self).build(input_shape)\n",
    "    def call(self, x):\n",
    "        mean = K.mean(x, axis=-1, keepdims=True)\n",
    "        std = K.std(x, axis=-1, keepdims=True)\n",
    "        return self.gamma * (x - mean) / (std + self.eps) + self.beta\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return input_shape\n",
    "\n",
    "class ScaledDotProductAttention():\n",
    "    def __init__(self, d_model, attn_dropout=0.1):\n",
    "        self.temper = np.sqrt(d_model)\n",
    "        self.dropout = Dropout(attn_dropout)\n",
    "    def __call__(self, q, k, v, mask):\n",
    "        attn = Lambda(lambda x:K.batch_dot(x[0],x[1],axes=[2,2])/self.temper)([q, k])\n",
    "        if mask is not None:\n",
    "            mmask = Lambda(lambda x:(-1e+10)*(1-x))(mask)\n",
    "            attn = Add()([attn, mmask])\n",
    "        attn = Activation('softmax')(attn)\n",
    "        attn = self.dropout(attn)\n",
    "        output = Lambda(lambda x:K.batch_dot(x[0], x[1]))([attn, v])\n",
    "        return output, attn\n",
    "\n",
    "class MultiHeadAttention():\n",
    "    # mode 0 - big martixes, faster; mode 1 - more clear implementation\n",
    "    def __init__(self, n_head, d_model, d_k, d_v, dropout, mode=0, use_norm=True):\n",
    "        self.mode = mode\n",
    "        self.n_head = n_head\n",
    "        self.d_k = d_k\n",
    "        self.d_v = d_v\n",
    "        self.dropout = dropout\n",
    "        if mode == 0:\n",
    "            self.qs_layer = Dense(n_head*d_k, use_bias=False)\n",
    "            self.ks_layer = Dense(n_head*d_k, use_bias=False)\n",
    "            self.vs_layer = Dense(n_head*d_v, use_bias=False)\n",
    "        elif mode == 1:\n",
    "            self.qs_layers = []\n",
    "            self.ks_layers = []\n",
    "            self.vs_layers = []\n",
    "            for _ in range(n_head):\n",
    "                self.qs_layers.append(TimeDistributed(Dense(d_k, use_bias=False)))\n",
    "                self.ks_layers.append(TimeDistributed(Dense(d_k, use_bias=False)))\n",
    "                self.vs_layers.append(TimeDistributed(Dense(d_v, use_bias=False)))\n",
    "        self.attention = ScaledDotProductAttention(d_model)\n",
    "        self.layer_norm = LayerNormalization() if use_norm else None\n",
    "        self.w_o = TimeDistributed(Dense(d_model))\n",
    "\n",
    "    def __call__(self, q, k, v, mask=None):\n",
    "        d_k, d_v = self.d_k, self.d_v\n",
    "        n_head = self.n_head\n",
    "\n",
    "        if self.mode == 0:\n",
    "            qs = self.qs_layer(q)  # [batch_size, len_q, n_head*d_k]\n",
    "            ks = self.ks_layer(k)\n",
    "            vs = self.vs_layer(v)\n",
    "\n",
    "            def reshape1(x):\n",
    "                s = tf.shape(x)   # [batch_size, len_q, n_head * d_k]\n",
    "                x = tf.reshape(x, [s[0], s[1], n_head, d_k])\n",
    "                x = tf.transpose(x, [2, 0, 1, 3])  \n",
    "                x = tf.reshape(x, [-1, s[1], d_k])  # [n_head * batch_size, len_q, d_k]\n",
    "                return x\n",
    "            qs = Lambda(reshape1)(qs)\n",
    "            ks = Lambda(reshape1)(ks)\n",
    "            vs = Lambda(reshape1)(vs)\n",
    "\n",
    "            if mask is not None:\n",
    "                mask = Lambda(lambda x:K.repeat_elements(x, n_head, 0))(mask)\n",
    "            head, attn = self.attention(qs, ks, vs, mask=mask)  \n",
    "                \n",
    "            def reshape2(x):\n",
    "                s = tf.shape(x)   # [n_head * batch_size, len_v, d_v]\n",
    "                x = tf.reshape(x, [n_head, -1, s[1], s[2]]) \n",
    "                x = tf.transpose(x, [1, 2, 0, 3])\n",
    "                x = tf.reshape(x, [-1, s[1], n_head*d_v])  # [batch_size, len_v, n_head * d_v]\n",
    "                return x\n",
    "            head = Lambda(reshape2)(head)\n",
    "        elif self.mode == 1:\n",
    "            heads = []; attns = []\n",
    "            for i in range(n_head):\n",
    "                qs = self.qs_layers[i](q)   \n",
    "                ks = self.ks_layers[i](k) \n",
    "                vs = self.vs_layers[i](v) \n",
    "                head, attn = self.attention(qs, ks, vs, mask)\n",
    "                heads.append(head); attns.append(attn)\n",
    "            head = Concatenate()(heads) if n_head > 1 else heads[0]\n",
    "            attn = Concatenate()(attns) if n_head > 1 else attns[0]\n",
    "\n",
    "        outputs = self.w_o(head)\n",
    "        outputs = Dropout(self.dropout)(outputs)\n",
    "        if not self.layer_norm: return outputs, attn\n",
    "        # outputs = Add()([outputs, q]) # sl: fix\n",
    "        return self.layer_norm(outputs), attn\n",
    "\n",
    "class PositionwiseFeedForward():\n",
    "    def __init__(self, d_hid, d_inner_hid, dropout=0.1):\n",
    "        self.w_1 = Conv1D(d_inner_hid, 1, activation='relu')\n",
    "        self.w_2 = Conv1D(d_hid, 1)\n",
    "        self.layer_norm = LayerNormalization()\n",
    "        self.dropout = Dropout(dropout)\n",
    "    def __call__(self, x):\n",
    "        output = self.w_1(x) \n",
    "        output = self.w_2(output)\n",
    "        output = self.dropout(output)\n",
    "        output = Add()([output, x])\n",
    "        return self.layer_norm(output)\n",
    "\n",
    "class EncoderLayer():\n",
    "    def __init__(self, d_model, d_inner_hid, n_head, d_k, d_v, dropout=0.1):\n",
    "        self.self_att_layer = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)\n",
    "        self.pos_ffn_layer  = PositionwiseFeedForward(d_model, d_inner_hid, dropout=dropout)\n",
    "    def __call__(self, enc_input, mask=None):\n",
    "        output, slf_attn = self.self_att_layer(enc_input, enc_input, enc_input, mask=mask)\n",
    "        output = self.pos_ffn_layer(output)\n",
    "        return output, slf_attn\n",
    "\n",
    "\n",
    "def GetPosEncodingMatrix(max_len, d_emb):\n",
    "    pos_enc = np.array([\n",
    "        [pos / np.power(10000, 2 * (j // 2) / d_emb) for j in range(d_emb)] \n",
    "        if pos != 0 else np.zeros(d_emb) \n",
    "            for pos in range(max_len)\n",
    "            ])\n",
    "    pos_enc[1:, 0::2] = np.sin(pos_enc[1:, 0::2]) # dim 2i\n",
    "    pos_enc[1:, 1::2] = np.cos(pos_enc[1:, 1::2]) # dim 2i+1\n",
    "    return pos_enc\n",
    "\n",
    "def GetPadMask(q, k):\n",
    "    ones = K.expand_dims(K.ones_like(q, 'float32'), -1)\n",
    "    mask = K.cast(K.expand_dims(K.not_equal(k, 0), 1), 'float32')\n",
    "    mask = K.batch_dot(ones, mask, axes=[2,1])\n",
    "    return mask\n",
    "\n",
    "def GetSubMask(s):\n",
    "    len_s = tf.shape(s)[1]\n",
    "    bs = tf.shape(s)[:1]\n",
    "    mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)\n",
    "    return mask\n",
    "\n",
    "class Transformer():\n",
    "    def __init__(self, len_limit, embedding_matrix, d_model=embed_size, \\\n",
    "              d_inner_hid=512, n_head=10, d_k=64, d_v=64, layers=2, dropout=0.1, \\\n",
    "              share_word_emb=False, **kwargs):\n",
    "        self.name = 'Transformer'\n",
    "        self.len_limit = len_limit\n",
    "        self.src_loc_info = False # True # sl: fix later\n",
    "        self.d_model = d_model\n",
    "        self.decode_model = None\n",
    "        d_emb = d_model\n",
    "\n",
    "        pos_emb = Embedding(len_limit, d_emb, trainable=False, \\\n",
    "                            weights=[GetPosEncodingMatrix(len_limit, d_emb)])\n",
    "\n",
    "        i_word_emb = Embedding(max_features, d_emb, weights=[embedding_matrix]) # Add Kaggle provided embedding here\n",
    "\n",
    "        self.encoder = Encoder(d_model, d_inner_hid, n_head, d_k, d_v, layers, dropout, \\\n",
    "                               word_emb=i_word_emb, pos_emb=pos_emb)\n",
    "\n",
    "        \n",
    "    def get_pos_seq(self, x):\n",
    "        mask = K.cast(K.not_equal(x, 0), 'int32')\n",
    "        pos = K.cumsum(K.ones_like(x, 'int32'), 1)\n",
    "        return pos * mask\n",
    "\n",
    "    def compile(self, active_layers=999):\n",
    "        src_seq_input = Input(shape=(None, ))\n",
    "        x = Embedding(max_features, embed_size, weights=[embedding_matrix])(src_seq_input)\n",
    "        \n",
    "        # LSTM before attention layers\n",
    "        x = Bidirectional(LSTM(128, return_sequences=True))(x)\n",
    "        x = Bidirectional(LSTM(64, return_sequences=True))(x) \n",
    "        \n",
    "        x, slf_attn = MultiHeadAttention(n_head=3, d_model=300, d_k=64, d_v=64, dropout=0.1)(x, x, x)\n",
    "        \n",
    "        avg_pool = GlobalAveragePooling1D()(x)\n",
    "        max_pool = GlobalMaxPooling1D()(x)\n",
    "        conc = concatenate([avg_pool, max_pool])\n",
    "        conc = Dense(64, activation=\"relu\")(conc)\n",
    "        x = Dense(2, activation=\"softmax\")(conc)   \n",
    "        \n",
    "        \n",
    "        self.model = Model(inputs=src_seq_input, outputs=x)\n",
    "        self.model.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "collect-clerk",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33766, 24, 21)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b249695",
   "metadata": {},
   "source": [
    "Creating a transformer architecture model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "domestic-compression",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 24, 21)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   (None, 24, 256)      153600      input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 24, 128)      164352      bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 24, 192)      24576       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 24, 192)      24576       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, None, 64)     0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, None, 64)     0           dense_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_3 (Lambda)               (None, None, None)   0           lambda[0][0]                     \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, None, None)   0           lambda_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 24, 192)      24576       bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, None, None)   0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               (None, None, 64)     0           dense_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_4 (Lambda)               (None, None, 64)     0           dropout[0][0]                    \n",
      "                                                                 lambda_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_5 (Lambda)               (None, None, 192)    0           lambda_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed (TimeDistribut (None, None, 300)    57900       lambda_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, None, 300)    0           time_distributed[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization (LayerNorma (None, None, 300)    600         dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 300)          0           layer_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d (GlobalMax (None, 300)          0           layer_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 600)          0           global_average_pooling1d[0][0]   \n",
      "                                                                 global_max_pooling1d[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 64)           38464       concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 1)            65          dense_8[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 488,709\n",
      "Trainable params: 488,709\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_input = keras.Input(shape = X_train.shape[1:])\n",
    "\n",
    "x = keras.layers.Bidirectional(LSTM(128, return_sequences=True))(model_input)\n",
    "x = keras.layers.Bidirectional(LSTM(64, return_sequences=True))(x)\n",
    "\n",
    "x, self_attn = MultiHeadAttention(n_head = 3, d_model=300, d_k=64, d_v=64, dropout=0.1)(x, x, x)\n",
    "\n",
    "avg_pool = GlobalAveragePooling1D()(x)\n",
    "max_pool = GlobalMaxPooling1D()(x)\n",
    "conc = concatenate([avg_pool, max_pool])\n",
    "conc = Dense(64, activation='sigmoid')(conc)\n",
    "model_output = Dense(1)(conc)\n",
    "\n",
    "model = keras.models.Model(model_input, model_output)\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "suitable-enclosure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "845/845 [==============================] - 43s 46ms/step - loss: 0.2839 - val_loss: 0.8753\n",
      "Epoch 2/20\n",
      "845/845 [==============================] - 37s 44ms/step - loss: 0.1892 - val_loss: 0.4852\n",
      "Epoch 3/20\n",
      "845/845 [==============================] - 37s 43ms/step - loss: 0.1801 - val_loss: 0.6820\n",
      "Epoch 4/20\n",
      "845/845 [==============================] - 39s 46ms/step - loss: 0.2947 - val_loss: 0.8336\n",
      "Epoch 5/20\n",
      "845/845 [==============================] - 37s 44ms/step - loss: 0.2149 - val_loss: 0.6368\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "objective-scotland",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "y_pred_inv = sc2.inverse_transform(y_pred)\n",
    "y_test_inv = sc2.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "homeless-boring",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vOpdFRESUNxgwIiIiIqKCc6TfBlEE1i8wIS3CoFECAByeQBpXRURElD8YMCIiIiKignNsUGpgvbY+tgwjY4kCAGBnDyMiIiIADBgRERERUQHqm3DBoFbApFPFtH2JUg65TGDTayIiojAGjIiIiIio4PRNutFQoY2p4TUACIIAo0bBkjQiIqIwBoyIiIiIqOD0TbiwqLwkrtcYNErY3cwwIiIiAhgwIiIiIqICI4oi+ifdaCjXxvU6AzOMiIiIohgwIiIiIqKCMu70we0PYlFFfBlGRo2STa+JiIjCGDAiIiIiooLSN+ECACxihhEREVHCGDAiIiIiooLSN+kGACyqiC9gZCxRMmBEREQUxoARERERERWU/kkpw6gh7qbXCja9JiIiCmPAiIiIiIgKSt+EGxU6FXRqRVyvM2iUmPIFEAqJaVoZERFR/mDAiIiIiIgKSv+kC4vizC4CAKNGAVEEHF6WpRERETFgREREREQFpW/ChYY4+xcB0pQ0AHBwUhoREREDRkRERERUOEIhEQNWd9z9iwDAWCKVsLHxNREREQNGRERERFRARhwe+IMiFpXHn2FkCGcYsfE1ERERA0ZEREREVED6JtwAgEVJlaQxw4iIiIgBIyIiIiIqGH0TLgBIqOm1QSOVpNnZw4iIiIgBIyIiIiIqHP2TUoZRfRIBI2YYERERMWBERERERAWkb9KFaqMaaoU87teyhxEREdEZDBgRERERUcHom3Al1PAaAFQKGTRKGRxeZhgRERExYEREREREBaN/0p1Qw+sIo0YJB3sYERERMWBERESUbadHHfjZi+0QRTHbSyHKa/5gCEM2d0INryMMGgXsbmYYERERMWBERESURaIo4qsPHcZPnj+FIZsn28shymtDVg9CItCQYEkaIPUx4pQ0IiIiBoyIiIiy6rnjI9jfawUAnB6dyu5iiPJc36QLANBQkXiGkbFECTunpBERETFgRERElC2BYAg//PsJ1JVqADBgRJSsvgkpYJRo02tAKkljDyMiIiIGjIiIiLLmoX396LA48c2bV6NMq8RpCwNGRMkYsnkgCEBtOAibCKnpNTOMiIiIGDAiIiLKArcviP964RQ2LS7DtaursbRSzwwjoiRZXT4YNUoo5Imf4ho1CtjdzDAiIiJiwIiIiCgL/ri7FyN2L/75+pUQBAFLq/ToYMCIKClWtx9lWmVS+zBoFPAGQvAGgilaFRERUX5iwIiIiCgLnjs2jJW1RpzfXAEAWFqlx7jThwmnL8srI8pfVpcfZSXJBYyM4dezLI2IiIodA0ZEREQZNuUNYF/PJC5fVhl9bEmVHgAbXxMlw+r2o1SrSmofBo0CAANGREREDBgRERFl2BunxxAIiTMCRq0MGBElzebyoTzJkjSjJpJhxD5GRERU3BgwIiIiyrBXT1mgU8lxXmN59LG60hKUKOUMGBElYTIFJWmGcMDI7maGERERFTcGjIiIiDJIFEW8esqCi5aYoVKc+RiWyQQsqdLhtIUBI6JEBEMi7J5UlqQxw4iIiIobA0ZEREQpMOrwYNTuWXC7rjEn+ifduHx55TnPLa3kpDSiRDk8fogiUtb02s6AERERFTkGjIiIiFLgqw8dxs0/fx3jU955t9txygIAuLx1loBRlR4DVjecXpbCEMXL6pICPGVJ9jBi02siIiIJA0ZEREQpMGr3YsTuxRf+fBChkDjndq+esqDZrMNik/ac55aGG193sCyNKG5Wd2oCRnqVAoIA2BkwIiKiIseAERERUQrYPX6YdCq81j6GX7x8etZtPP4gdnWO47JW86zPL+WkNKKEWV0+AEBpSXI9jGQyAXq1AnY3S9KIiKi4KbK9ACIiokLg8ARw64Y62Nx+/NcLp2A2qKFXK2Bz+yETBCyqKIHF4YXHH5q1fxEANJp0UMgEBoyIEmALB3jKk8wwAgCjRsmSNCIiKnoMGBERESVJFEVMeQMwaJT46nUrcGTAhq89cmTWbVVyGS5sMc36nFIuQ5NZx4ARUQImnVKGUVmSU9IAqY8Rm14TEVGxY8CIiIgoSS5fEMGQCINGAZ1agSc+uxVtQ3aUlihRWqJEICSid8KF3gkXqgxqaFVzf/wurdTj1Kgjg6snKgyRHkZGTfKnt1KGEQNGRERU3BgwIiIiSlIkE8GgkUphdGoFNjdVzNimrqxkzsyi6ZZW6fF82wh8gRBUCrYaJIqV1eWHQaOAQp78742xRIEhmycFqyIiIspfPBMlIiJKUqTXibEk+fswy2sMCIZEnBphlhFRPGxuf9IT0iIMGiVL0oiIqOgxYERERJQkx1kZRsnYsKgMAHCo35r0voiKidXlQ1mSE9IiDBoFm14TEVHRY8CIiIgoSfbwhaUhBb1TGspLUKFT4WCvNel9ERUTawozjCJT0kRRTMn+iIiI8hEDRkREREmyp7DZriAI2LCojBlGRHGyuvwpmZAGSMHfYEiEyxdMyf6IiIjyEQNGRERESYr2MEpBSRoArG8oQ/voFKc0EcVBKklLUYZReD8sSyMiomLGgBEREVGSHNGStBQFjBaVQhSBIwO2lOyPqNCFQmKKm15L2YJsfE1ERMWMASMiIqIkOTx+KGQCNMrUfKxGGl8f7LOmZH9Ehc7hDSAkAqUpyjCKBH+Z5UdERMUsbwNGgWAIwRAbERIRUfY5PAEYNAoIgpCS/ZVpVWgyaXGIASOimNhcUmAnVT2MIv3I7G6WpBERUfHK24DRPz9yBHf8Zne2l0FERAS7x5+ycrSIDYvKmGFEFCOr2wcAKethFMlUmnT5UrI/IiKifJSXASOPP4inDg/hKHs7EBFRDnB4AjCWJD8hbbr1i8owYvdi2OZJ6X6JCpE1mmGUmoBRQ7kWSrmA9tGplOyPiIgoH+VlwGjHKQvc/iAmXX64fEwVJiKi7HJ4/DCoU5thtD7ax2gypfslKkSRTKBUlaSpFDIsrTLg+KA9JfsjIiLKR3kZMHru+Ej0zwOT7iyuhIiI6EwPo1RaVWuEUi7gYB+zaYkWYnOnNsMIkH4H24YYMCIiouKVdwGjQDCEF9tG0GTSAgD6rQwYERFRdtndqe9hpFHKsbLWyMbXRDGIlKSlakoaAKyqM2LU4YXF4U3ZPomIiPJJ3gWMdndPYNLlxx0XNwEA+plhREREWZaOHkaA1Pj6cL+VU0GJFmB1+aFXK6CUp+7UdmWtAQCYZUREREUr7wJGzx0bgVohwzs3L4JSLrAkjYiIsioUEjHlC6Q8wwgA1taXwukLonvcmfJ9ExUSq9uX0uwiQCpJA4DjDBgREVGRyquAkSiKeP74CC5trYRerUBtaQkGWJJGRERZNOULQBQBY4p7GAHSpCYAnJRGtACby5/S/kWA1EC7vqyEGUZERFS08ipgdGzQjgGrG9esrgYA1JeVYGDSleVVERFRMbOHm+2muuk1ANSUagAwYES0EKs79QEjQCpL46Q0IiIqVnkVMPrb4SHIBOCqleGAUTkzjIiIKLscngAAwJiGkrQaYzhgZGfAiGg+ky4fyrSqlO93Va0RHZYpePzBlO+biIgo1+VFwMjjD+Fjv92Le17twOXLKlGhk04I6stKMOrwwhcIZXmFRERUrCIBo3T0MCpRyWHUKDDCgBHRvGwuP8pS3MMIkCalhUTg5LAj5fsmIiLKdXkRMGofdeCtznF85drl+MUHNkUfry8vgSgCQzZmGRERUXY4POkrSQOksjSWpBHNTRTFNJakSY2v2ceIiIiKUdoCRoIg/FoQhFFBEI5Oe+xbgiAMCIJwMPx1Qyz7qtSrseOrV+AzVyyFVnXmhLyhrAQAOCmNiIiyxp7mgFG1UcMMI6J5THkDCIZElJWkviRtUbkWerWCk9KIiKgopTPD6AEA183y+H+Jorgh/PV0LDuqKdWgXHfuSUBkekw/+xgREVGWpLMkDZACRuxhRDQ3q0sK2pamIcNIJhPY+JqIiIpW2gJGoijuADCRrv0DUiBJEJhhRERE2XMmYJSmkjSjBhaHF4Eg+/URzcYWnlSYjh5GgNT4um3IjlBITMv+iYiIclU2ehh9VhCEw+GStfJkdqRSyFBt0HBSGhERZY3d44dKIYNGKU/L/qtLNQiJwLjTl5b9E+W7SIZROqakAVIfI6cviL5JV1r2T0RElKsyHTD6JYAlADYAGALw47k2FAThE4Ig7BUEYa/FYplzh/XlJcwwIiKirLG7AzCmKbsIkDKMALDxNdEcJl1SMLU8DSVpgDQpDQCOsSyNiIiKTEYDRqIojoiiGBRFMQTgfwGcP8+294miuFkUxc2VlZVz7rO+rIQZRkRElDUOjz9t/YuAaQEj9jEimpXVnb4eRgCwrNoAmQCcYONrIiIqMhkNGAmCUDvtP98O4Ohc28aqvrwEQzY368qJiCgrHJ70ZhhVl6oBgJPSiOZgC2cYlaaph5FGKUezWYe2YUda9k9ERJSr0naGKwjCgwC2ATALgtAP4F8BbBMEYQMAEUA3gLuSPU59WQn8QRGjDi9qSjXJ7o6IiCgu6c4wMuvUkMsElqQRzWHS5UeJUg61Ij19xABgRa0Rh/utads/ERFRLkpbwEgUxffN8vD9qT5OfXkJAGDA6mLAiIiIMs7hCaDamL7PH5lMQJVBzZI0ojlMunyo0KWn4XXEyhoDnjo8lPYAMRERUS7JxpS0lGookwJG/Wx8TUREWWD3+GFIY0kaAFQbNSxJI5qD1eVHWZr6F0WsqJEaX58aYVkaEREVj7wPGJ3JMGLAiIiIMs/hCaQ946DGqGFJGtEcJl0+lGvTm2G0otYAAGgbYsCIiIiKR94HjLQqBcq1SmYYERFRxgWCIbh8QRjTHTAq1WDU7k3rMYjyVSYyjOrLSmDQKHBimJPSiCg1/rq3D88fH8n2Mojmld4c+gypLy/BAANGRESUYVPeAABkpCTN4Q3A6Q1Apy6Ij26ilMlEhpEgCFhZY8QJZhgRUQoc7LPiqw8fhigCX7p6GT575VIIgpDtZRGdI+8zjADprg9L0oiIKNPs7swEjGpK1QDAxtdEZwmGRNjcfpSnOcMIkMrSTgw7IIpi2o9FRIUrGBLxjceOolKvxi0b6vDj50/hy389DF8glO2lEZ2jQAJGWgxMuvkBTkREGWX3+AEg7T2Mqg3SFLYR9jEimsHm9kMUgfI0T0kDpMbXU94A2yAQUVL+uLsXRwZs+PpNq/DT92zAF65qxcP7+/Gdvx3P9tKIzlEYAaPyErj9QUy6/NleChERFRGHR8owMpakuSStVAoYMcOIaKZJlw8A0l6SBpxpfH1imGVpRJSY8Skv/vPvJ3DxEhNuXlcLQRDwhauW4coVVXirazzbyyM6R2EEjMrCk9J4x4eIiDLIEc4wSnvTayMDRkSzsYYDRulueg0Ay6vDAaMhNr4mosT88O8n4fYH8W+3rJnRs2hplR7d4y4EQ6yYodxSEAGjhvJwwMjqyvJKiIiomNg9melhpFMrYFArWJJGdJZJpxS0zUSGkU6tQKNJywwjIkqIKIp48vAg3rGpAUur9DOeazHr4AuEMMi+vJRjCipgxJpyIiLKJEeGehgBUlnaiN2b9uMQ5ZNMlqQBwIoaA9qGmWFERPGbcPrg8gWxvMZwznMtlVIAqcMylellEc2rIAJGpSVK6FRyTkojIqKMcmQowwiQytJYkkY0kzXcv7JMl/6gLSA1vu4ec8LtC2bkeERUOPrCyQ0N5dpznms26wAAXWPOjK6JaCEFETASBAH15SXsYURERBnl8PhRopRDKU//x2m1UYMRBoyIZph0+aCQCTCo0x+0BYCVtQaERKB9lGVpRBSf/kmpfcqiipJznjPrVTBoFOi0MGBEuaUgAkaA1PiaGUZERJRJdncgI9lFAFBtVGPU4WVDTKJpJl0+lGlVM5rHptOKGiMAoI2Nr4koTn0T0rVqZGDTdIIgoKVSj84xlqRRbimcgFE5A0ZERJRZDq8/YwGjmlINgiER41PsY0QUMen0ozwDE9IiFldooZQL6B7noBUiik//pAtlWuWcfQ9bzDp0McOIckzhBIzKtLC6/HB6A9leChERFQmHJ5CRhteAVJIGAEOclEYUNenyZazhNQDIZAJqSjWcZEREceubdGPRLP2LIlrMOgzaPHD5eD1LuaNwAkbhSWnMMiIiokyxezJXkra4QjrJ7J1gZgNRhNXlR1kGM4wAoK60hAEjIopb/6QrOt17NpFJaWx8TbmkcAJG4VpQNr4mIqJMGZh0oa507pO/VGo0SQGjbp5IEkVlOsMIkM45B63M9COi2IVCIvon3VhUMU+GUSUnpVHuKZiAUSRa2887PkRElAE2lx9jU77oCV66aVUKVBvV7J1CFCaKopRhpMtshlFtmQbDdg8b0BNRzMamvPAFQvNmGDWZpPMJTkqjXFIwAaNKvRoquYwZRkRElBEd4UkmS8Ip5JnQZNKhe5wnkkQA4PIF4QuGMp5hVFdWgmBIxKiDWUZEFJu+Selmz3w9jEpUctSXlaDTwklplDsKJmAkkwmoLdOwhxEREWVEx6h0QpepDCNAChj1MGBEBACYcPoAABVZCBgBYB8jIopZfzipYb4MI0A6p2BJGuWSggkYAVJNef8kU/WJiCj9OsecUMqFefsRpFqTWYexKR8cHn/GjkmUq6wu6fcg002v66MBI2YYEVFs+sIDKxrmyTACgGazDp0WJ0SRJa+UGwouYMSSNCIiyoROyxQWV2ihlGfuo7Qp3Pi6h32MiDDpkjKMynWZzTCqLdUAYIYREcWuf9INs16FEpV83u1azDo4vAFYprwZWhnR/AorYFReglGHF95AMNtLISKiAtdhcWa0fxEgZRgBYB8jIkwLGGU4w8igUcKgUTBgREQx65t0LZhdBAAt4fOKLja+phxRUAGjyC/hEFOEiYgojQLBEHrGndETu0xpDGcYdbO/AdG0krTMZhgB4ax2nm8SUYz6J90L9i8CpJI0QCp7J8oFBRUwitSUs/E1ERGlU9+kG/6giCUZbHgNAFqVAtVGNbpZkkYUzTAqK8lshhEgNb4esvF8k4gWFgyJGLS6Y+p5WF9WArVCxklplDMKKmAUidqyjxEREaVT5EQu0xlGANBo0jHDiAhShpFRo4Aig33EImpLNSxJI6KYjNg98AfFmDKMZDIBzWZOSqPcUVABo5pSDWQC0M8PcCIiSqOOcMAo0xlGANBs0jHDiAjAhNOX8YbXEXVlJZh0+eHyBaKPeQNBBEOcbEREM8U6IS0iMimNKBcUVMBIKZeh2qhhhhEREaVVp8UJk06Vld4pjWYtxqa8cHj8GT82US6ZdPmy8jsInGmDMBjuYySKIq776Wu47Icv49evd80IJBFRcesPX5suiiHDCABaKnXonXDBHwylc1lEMSmogBEQaULIO69ERJQ+HZYptGQhuwiQMowAoIdZRlTkrC5/xiekRdSFA0aRPkbto1PREpJ/+9txXPyDl/DyidGsrI2IckskYBR531hIi1mPQEiMZiYRZZMi2wtItfryErxwfAQf++2ehF6vVyvwnVvXwKDJzgkIERHlvk6LE1evqs7KsRunBYzW1JdmZQ1EuWDS5cPSqsz3EQOkHkYAon2Mdp4eAwD8+a4LMWL34FP/tx9/3N2LK1ZUZWV9RJQ7+iZdqDaqoVHKY9q+OXxDqtOS+WmsRGcruIDRDWtr0WGZwpAt/lGnHn8QHRYnbtlYjyuW8wOeiIjOZXX5MO70ZS3DqMks9UDoHmd/AypuVpcfZVnKMKop1UAQgIFwSdobHeNYXKFFQ7n0tazagLEpb1bWRkS5pX/SFXP/IgBYYpaCRJ1jUwCyc3OKKKLgAkbXrq7BtatrEnrt2JQXm7/7AnrGnMDyFC+MiIgKQke4EeWSLN3106oUqDKoOSmNipovEMKUN4DyLPUwUsplqDZoMGR1IxAM4c3Ocdy0rjb6fKVBje5u/o4SEdA34caWpvKYty/VKmHSqTgpjXJCwfUwSoZJp4JereD0GSIimlNneEJaNtPEm8w6ZhhRUbO6fQCQtSlpAFBbpsGgzY2jg3Y4PAFctMQcfc6sV2FsygtR5NQ0omI2Yvdg0OaOu3y22ayL3qAiyiYGjKYRBAGNJi1PwomIaE4dFieUciHmaSfp0GTS8uYGFbVJpzQlMFtNrwGpge2g1YM3OqT+RRe1mKLPmfVqePwhOH3BbC2PiHLAk4cGIYrA9WtrF954mpZKHToZMKIcwIDRWZpMOk6eISKiOXVaptBo0kEhz95HaJNZB4vDiykvR3dTcZp0hTOMslSSBkiTeQetbuw8PYbl1QZUGtTR5yJ/HnOwjxFRMXvy0CDW1pfGXcbeUqnH2JQXdo8/TSsjig0DRmdpNGnRN+FCIBjK9lKIiCgHdY450WLOTsPriKbwpDT2MaJiZQ0HjLLV9BoA6ko18AZCeLNzAhcvNc14zqyXAkYWNr4mKlpdY04c6rfhbevr4n5tc/g8o4tZRpRlDBidpcmkQyAkYtAa/5Q1IiIqbKIoYtDqjmvaSTqsX1QGpVzAA290Z3UdRNky6YqUpGWzh5FUlhoMibhkWv8i4EzAiBlGRMXriYODEATgpvXxlaMBwJLwJFZpUhpR9jBgdJZGE8cVExHR7OyeAFy+IGpLNVldR31ZCe7c2oKH9vVjb/dEVtdClA25UpIGADIBOL+lYsZzZoO0rrEizDDy+IP4zB/240P3v8Wm31S0RFHE44cGcEFzBWpL4+95uLhCB5nADCPKPgaMztIUTv/rYcCIiIjOMmKXsk9rshwwAoB/2L4UdaUafP2xoyyjpqJjdfmhVshQopJnbQ114YDRuoYyGDUzS+NMOjVkAmApsgwju8eP23+9G08dGcJr7WPY1TGe7SURZcWxQTs6LU7csqE+oderFDIsqtCig6XnlGUMGJ2lyqBGiVKOrjE2viYiopmGbFLAKNsZRgCgVSnwjZtW4cSwA7/b1ZPt5RBllNXly2p2ESBNaKsr1eDa1TXnPCeXCajQqWCZ8mVhZdkxNuXF++57E/t7JvGjd62HSafC/a93ZXtZRFnx+MEBKOUCrl9z7vtDrFrMnJRG2ceA0VkEQUCjScsMIyIiOsewzQ0gNzKMAOC6NTW4bFklfvL8KYw62HuPJN5AEB2WKbxychQnhu3ZXk5a2N0BlJZkr+E1IJ0zvvKVK3DXZS2zPm/Wq4umJO3YoA23/mInOixT+NWHN+Od5zXgQxc14sUTo+iwsAcLFRdRFPHkoSFcvqwKZUkEtlsq9egecyIUYmknAOzvncQX/3wQvgCzqjOJAaNZNJl07GFERETniGQYVRlyI2AkCAL+9eZVmPIG8Mj+gWwvh3LAD545gRXf+Du2//hV3PGbPXj//74FfwGWLNo9fhg0imwvAyqFDDKZMOtzZr26KErSHjswgHf88g0EgiL+/ImLsG15FQDggxc2QqWQ4dfMMqIiY3X5MWz34KIlpoU3nkezWQe3P4hhO28IWV0+fOYP+/HogQHsYe/GjGLAaBaNZi36JtwIMppLRETTDNs8MOvVUCly5+NzSaUeq+uMeP74SLaXQjngyUODWFdfip+8ez2+dv0KTDh9BdlHxu7xw5jlDKOFVBoKO8NIFEX8+zNt+MKfD2JdQxme/NxWrF9UFn3erFfj7Rvq8fD+fkw6i6c0jyjye19pUCe1n5bIpLQiL0sTRRH//PARWBxeKOUCXjk5mu0lFZXcOePNIU0mHXzBEIbCpQdEREQAMGz35ET/orNdvaoa+3snC/rilBY2ZHNjwOrGLRvqcdumBnz44ibo1Qo8dXgo20tLObs7AGMOZBjNx6xXYWzKW5CTwkRRxLefPI57X+3EBy9cjD987IJZL47vvLQZHn8If9zdm4VVEmWHJfxZbNYn12dtSaUeANA1VtxlnX/e04e/HxvGV65djguaTXjlpCXbSyoqDBjNotGkBQD0jGen8fWE04e+CTbdJiLKNcM2T870L5ru6lXVEEXgpTbedStm+3omAQCbm8oBABqlHFetrMLfjw0XXFlaPmQYmfVqePwhOH3BbC8lJoFgCLf/ejd+/lL7vEEuURTxrSeO4YE3unHn1mZ855Y1UMpnv6RYVm3AZcsq8fOXTuMnz52Ew+NP1/KJcsZYuNl9pT65DKMqgxo6lRwdRZxh1GmZwrefPI5Llprw8UtbsG15JdpHpzBgZWJHpjBgNIsmk5T+l60+Rl9/7Ahu+cVOuHyBrByfiIhmN2TLzQyjVbVG1JeV4DmWpRW1vd2TKFHKsbLWGH3sxnV1sLn92Hl6LIsrSy1RFOHwBM4ZZZ9rIhk3udDHKBAM4S97++Zdyxsd49hxyoIfPXcK337y+KyNdi0OL770l0P47a4efPzSZnz9xpUQhNl7OEX8xzvW4sqVVfjZS6dx2Q9fxv/u6ITHnx9BNKJEjDkiGUbJBYwEQUBzpQ6dY8UbMPrjW70IiiJ+8u4NkMkEbFteCQAsS8sgBoxmUWPUQK2QoTsLv5yhkIhdHeOYcPrwhzeZvktElCvcviBsbj+qjbkXMBIEAVevqsbrpy1w50k2A6Xe/t5JrF9UOiPb49JWMwxqBZ4+UjhlaS5fEMGQCGNJrpekSReLuVAq+qvXu/DVhw7j5rtfx4HeyVm3efzgIAwaBT5ySRMeeKMbX/rrIYxNeTHh9GHY5sGPnzuJy//zZTx+aBD/cOVS/MsNCweLAKC2tAS/eP8mPPnZrVhTX4rvPd2GK370Cv68pxeBAst8IwKAcacXcpmQkkmOTSZdUU/v3tc7ifUNpdFzryWVetSXlbAsLYMYMJqFTCag0aRFdxZK0k5bpjDp8qNEKce9Sd6BOdxvxcpv/J0pe0REKRCZUpKLGUYAcNXKanj8IbzWzpOoYuTyBXBs0I7NjRUzHtco5bh6VTWePTZSMKOI7eGyJkOOZxhFA0ZZzjDqm3Dhpy+cwoUtFVAqBLzn3jfxp7N6Cnn8QTx7bBjXra7BN29aha9cuxyPHhjA5u++gE3feR4X/vuLuPul09i+shov/OPl+MdrlscULJpubUMpfn/nBfjjxy9AtVGDf3r4CC774cv43IMHcN+ODrzVOV5wpZNUnMYcPph0qjknKMajoVyLQat71oy/QufxB3F0wIZNjeXRxwRByjJ64/RYwXym5brcvjWTRY1Ziubu7pLGBH77bavx1YcP4897+vDhi5sS2teBXivc/iBODTtQX1aSwlUSERWfyCCEXOxhBAAXtFTAoFHg+eMjuGZ1TbaXQxl2qM+GYEjEedNOrCNuXFeLRw4MYGfHGK4IjzzPZ3a3VLKf6yVpZoPU8NaSxQwjURTx/x47CoVMhv96zwaUKOX43IMH8M+PHEFQFPGBCxoBAC+fGMWUN4BbNtRDEAR85oqlWNdQOmM60+amcqyuK016TRcvMePRT5vw/PERPLSvH/t7JvHkoUEAgEGjwLblVbhqZRW2LatCqTa3/x8TzWZsypt0OVpEQ3kJ/EERow5vzp5/pMvhfhv8QfGcGyHbllfhD2/1Ym/3BC5eas7S6ooHA0ZzaDJpseOUBaGQmJLocKz2dE+gyqDGuzY34KF9/bjn1Q689/xFUCvkce8r0rQ7cleciIgSN2yLZBjlZgBeKZfhiuVVeOnEKIIhEfIMfnZR9u3rkW44bVp8bsBoa6sZBo00La0gAkbhDKNcL0kz6dSQCdnNMHri0CB2nLLgWzevir53PfCR8/HhX+/GD54+gatWVqPaqMHjBwdh1qtx0RJT9LWXtlbi0tbKtKxLEARcs7omGtwen/Jib88kXmwbwUsnRvHkoUHIZQLOb6rA9pVVuHpVNRrDPUaJct3YlBfmWaYGJqK+XPq9HbC6ii5gFBnksGlx2YzHL15igkouwyunLAwYZQBL0ubQaNLBGwhhxJG5YIsoitjdNYEtzRUQBAGfvXIphmwePLxvIKH99YYnrQ3ZGDAiIkpW5L20Jgd7GEVcvaoa404f9s/Ro4QK196eSbRW6WfNyFArpLK054+PFERZg90dDhjleIaRXCagQqeCJTwxKdMcHj++87fjWN9Qig9d1DRjXd97+xr4giF864ljsLn9eOnkKG5eX5u1QLNJr8a1q2vww3eux+5/uQqPfPpi3HVZCyacPnz3qTZc/p+v4KqfvIofPHMCe7snEMzxn2NRFAvidy1Ro3YPPvirt6KZucVmbMoHs16Vkn01hKtE+ieL799yX88EWsw6mM7K1tKpFdjSXM7G1xmS27dmsig6KW3MlbG7yf2TbgzZPLigWUq7u7TVjPWLyvA/r5zGuzY3zDmydC594YDRcJG+WRMRpdKwzYMyrRIlqvgzPjNl2/JKqBQyPHV4CFuaKhZ+ARWEUEjE/p5J3Liuds5tti4145H9Azgx7MCqOuOc2+UDhydckpaChrLpZtars9b0+kCvFWNTPvzk3RvOCQQ1mnT4h+2t+M9nT0IQAF8ghFs21GdlnWeTyQRsWlyOTYvL8dXrVqBvwoUX2kbwQtsIfvVaJ+55tQMVOhWuWF6Fq1dV4dLWSujUmbukCQRDGJvyYdjuwbDNg2GbG8N2L0bC/z1i92DY7oFOrcCrX9kGrar4LreeODSI10+PYccpC96zZXG2l5NRoijCMuVFZYpK0iIZRsUWMBJFEft6JrF9ZfWsz29bVoXvPd2GUbsHVTl8I68QFN87WIwaTVoAQM+4c0Z6bjrt6ZbSySMn+YIg4B+uXIo7f7sXjx8cxDvPa4h5X6IoRjOMhu3Zn85BRJTvhu2enM4uAqQmwNtXVOFvh4fw9RtXQhHnjQbKT6ctU7B7ArOWo0Vc0CKdy+zuGs/7gFG0JE2T+6exlQb1vKPs02kwPPRkSZV+1uc/cVkLnjg4iKePDKPRpMX6huT7E6XDogotPnJJMz5ySTPsHj9ePWnBi+EA0sP7+6GSy3DREhOuWlmF7SurUZdE306nN4BhuwcjNinocyYodCYQZHF4cXbykEImoNqoQbVRjRW1BrRW6/HssREcG7QXZfD+5XDmR9uQI8sryTyHNwBfIJSyHkZalQIVOlXRDTHqHHNi0uXH5ln68gFAs1lK7hiyMWCUbrn/SZsldWUlUMoFdGWw8fXurgkYNQosrzZEH7tyRRVW1RrxPy+fxts31secKmyZ8sIdnrDGDCMiouQN2zx50T/glg11eOboMHZ1jqet/wjllr3dUgni5nkuTOvLSlBfVoK3uiZwxyXNmVpaWkRK0nJ9ShogZRh1jWVnJPag1Q2ZAFTP0UtFKZfh+7etxTvveQO3hptd5zqjRomb19fh5vV1CARD2NsziReOS8Gjbzx+DN94/BhW1Rpx1apqXLWyCmvqSiGTCQiFRIw5vRixeaOBoGhQyHbmvx3ewDnHNGgUqDFqUFOqQWu1AbWlGlQbNdHHqo2acyZijdo9ePbYCA7324ouYOTw+PFWp3QT/MSwPcurybzxcAmqKUUlaYDU+LrYMoz2RT/XZg8Yleuk9/9JV3ZKfosJA0ZzkMsELKrQomfMlbFj7u6ewJamihkfOIIg4HNXLsWn/rAffzs8GHO6cKQcbXGFlj2MiIhSYMjmwZr63M/M2La8Cga1Ao8fHGTAqEjs65mESadCUzg7ei4XNFfg1VMWiKKYF8GBudg9AWiUMqgUuZ9BZ9arMDblzcq/eb/VjRqjZt5Mw/May/HsFy7D4or5f3ZykUIuw4UtJlzYYsL/u3ElOizOaObRz19qx89ebEelQQ2lTMCow4vAWWlBcpmASr0a1aUaLKnUYetSsxQIKlXPCAglUlJWFX79kX5riv62+eO19jEEQiJaq/Q4MezI+/ebeEVKUFOVYQRIAf9TI8WVrbWvZxKlJUq0mGfPkCzTSgE5q8ufyWUVJQaM5tFs0qE7QxlGY1NedFqcePfmRec8d+3qGiyr1uMXL5/GzevqYpraFilHO7+5Ag/t64fTG8hofTcRUSHxBUIYm/KixpibE9Km0yjluG5NDZ45Oozv3roGGmXu9lyi1NjTPYFNjeULXpSd31yBRw4MoMPixNI5ypTygd3tz/mG1xFmvRoefwhT3kDGM6IGre6YyrOWTctsz1eCIGBplR5Lq/S46/IlmHD68PKJUexot0AuE2ZkA0X+bNar09rke21DKQ4P2NK2/1z1YtsoSkuUeO/5i/Gdvx3HiL24xsFHpiKmMmDUUF6Cl0+OFlXwbW/PBM5rLJ/zurc8HDBihlH65f6tmSxqNOnQM+6CKKZ/ysGeLil18/zmc9NWZTIBn7liKU6NTOG548Mx7a933A1BQLTuc9jOLCMiokSNhN9Da/PkpPeWDfWY8gbw8glOECl0QzY3eidc0YEZ84n0MXqrazzdy0oru8efFw2vAamHESBNTcq0QasnqX4++axCp8I7zmvAf793I37y7g346nUrcPtFTbh2dQ3WLypDtVGT9olw6+pL0WlxwuEpngyIUEjEKydHsW15JdaEe6W1FVlZWjTDyJC6krT6shJ4/CGMO4sjODLp9KHD4sR5c/QvAoDSEiUEAZhkhlHaMWA0jyazFm5/MCPNCnd3T0CjlGFN3ewNB29aV4dmsw53v3Q6pgBWz4QTNUYNGsPT3kZYlkZElLBIwChf7pJetMQEs16Nxw8OZnsplGa7wzecLmheeEBHk0mLSoM6+pp85fAE8qLhNXAmyyDTk9JCIRFDNnd0whJl3tpwE/GjA8UTMDnUb8W404crV1RhRY0UMDpRZI2vLVM+CAJQoU1hwKhcKhkdKJI+Rvt7pf5F8wWM5DIBpSVKWJlhlHYMGM0jEmzpHk9/H6PX28ewubFiznp8uUzAp7ctwbFBO16K4Y5x34QLiyq00bvh7GNERJS4yHtovgSM5DIBN6+vxUsnR6MTpagwvdU1Ab1aEdPkM0EQcH5zBd7qnMhI9nS62N35k2EUDRhleFKaZcoLf1As2gyjXLC2XgoYHRmwZnchGfTSiVHIZQIuX1aJUq0S9WUlRdf4emzKiwqtKqVTShvCgd9iaXy9v3cScpmA9Q1l825XrlUxwygDGDCaR6R5ZLr7GPWMO9E+OoXtK6vm3e7WjfVoKC+JKcuod8KFxRXa6MUNS9KIiBI3nGcBI0AqS/MFQnjmyFC2l0JptLtrApubymMur7mwuQLDdg/6JvL3wsPuCeRPD6NwWYolwxlGkRHc9WX5855VaEx6NerLSnC4v3j6GL3YNorzGsujDYlX1BiKLsNozOFN6YQ0ANFMwQFr5oYxZVPbkANLK/UoUc3fg7FMywyjTGDAaB71ZSVQyAR0p3kc6gttUsbQVSur591OKZfhU9uW4GCfFa+fHptzO48/iBG7F4srtNAo5SjTKqMXO0REFL8hmwc6lRyGPBoesL6hFK1Vevzfm715nU1Ccxub8uL06FRM5WgR5zfnfx8ju9sPQ56UpJl0asiEzGcYDYYDRswwyq51DaU4UiSNr4dsbhwfsuPKFWdugK+oNaDDMgVvIHjO9gNWN26++3W8fLKweu2NO30pbXgNAEaNEkaNomgyjE4M2bGiduFm/FKGEQNG6caA0TwUchkaykvQk+aStBfbRrCsWo9FMYw0fed5Dagt1eDuF0/PuU1feEJaZERqjVHDkjQioiQM292oKdXk1XQSQRBw+0WNODJgw8E+a7aXQ2kw38CMubRW6VGmVeKtPO1jJIpiXjW9lssEVOhUsGS46TUDRrlhbUMpesZdRZEFcd+OTsgEabpzxIoaIwIhER2jM2++2z1+fPQ3e3BkwIZH9g9keqlpNTblTXnACJD6GBVDDyOby49BmyfaA2s+ZVolJp0sSUs3BowW0GjSpbUkzeb2Y3fXBLYvkF0UoVbIcddlLdjdPYE3O2e/O9gbCRiFS+pqSjXRhq1ERBS/YZsHtaX5d+H19k0N0KsV+P2unmwvhdLgrS5pYEakV0osZDIB5zdV5G2Gkccfgj8o5k1JGiD1MRrN8HnYwKQbBo0ir/6dCtG6+jIAKPgso/YRB363qwfvPX8xms266OMrw1kibUNn+hj5gyF85g/70WGZwspaI3aeHkMoVDhZsGOO9ASMGspLiiLDKNLzKtYMo2IIxmYbA0YLaDbr0DPuSls6/6unLAiExAXL0aZ77/mLYdar8fOXZs8y6j0rw6i2lBlGRETJGLJ5UG3Mv14gerUC79hUj78dHsJ4hnuoUPrt7prAeY3lcw7MmMt5jeXom3Dn5c9EZES5sSQ/StIA6VyywzKV0WMOWD2oZ3ZR1kWCuYXcx0gURfzb345Dp5LjS1cvm/Fck0kHlUIWDQKIoohvPHYUr7WP4fu3rcXHtjZjwunD8aHCaIzt9gXh9AWjvctSqb6sBANWd8GXmJ8YlnperYwhw6hcq4TTF5y15JFShwGjBTSatJjyBjDuTE/08sW2EZh0KmxYVBbzazRKOT5xWTNePz0WHTs4Xe+EC1qVHCad9GZVbdRgbMoLXyCUqmUTERUNjz+IYbsnGoTPNx+6qBG+YAh/2tOX7aVQCtlcfrQN23F+U+z9iyIiF7HHBvPvIi0y9S+fMmdaqw3omXDB48/cRc2g1c1ytBxQqlWi0aTFkQIOGL3QNorX2sfwhauWwXRWZo1CLsOyan00CPDLVzvwpz19+OwVS/HuzYuwtdUMAPP2Zs0nY+EgfLoyjKa8AdjdgZTvO5ecGLajTKtEtXHhf8NIc3UrJ6WlFQNGC2gySWmVPWkoS/MHQ3j5xCiuWFEV83STiA9c0IhyrRJ3v9h+znO949KEtEivjdrwVJ9RB7OMiIji1TXmhCgCS6p0C2+cg5ZWGXDxEhP++FYvAkHeOCgUe3smIIrx9S+KWF0nBYyODubfRawtfLGULz2MAGBZtR6iCJwezVyW0aDNjTpOSMsJ6xrKCrYkzRsI4rtPHUdrlR4fuqhx1m1W1BjRNuTAk4cG8cO/n8Tb1tfhS9dImUjVRg2WVevxenthBIwi0xAr0xQwAoC+ycKelNY25MCKGkNMPSPLwwEjNr5OLwaMFtAY7gPUPZb6X8693ZOwewK4amXVwhufRadW4GOXtuDlk5Zz7lr0TrhmNNCuCffd4KQ0IqL4RUpJWsz6LK8kcbdf1IgBqxsvniisaTTFbHfXBFRyGTYuLov7taVaJRZVlOBoHl7Enskwyp+StGXVUi+OTAWMnN4ArC4/M4xyxLr6UgxY3dHsk0Ly69e70TPuwjdvXgWlfPbLyhU1BoxNefGlvxzClqZy/Oe71s0IBmxdWond3RMZzcBLl8g0RJM+HSVp0rXdgLVw+xiFQiJODjtiangNSCVpANj4Os0YMFpAQ7kWMgFpaXz9YtsIVHIZLm2tTOj1H7qoEUaNAj9/+UyWkSiK6J1woXF6wCjcd4N9jIiI4tdpcUIQMKORZ765amU1VAoZ9vecW8ZM+Wl39wTWNZRCo5Qn9Po1daU4OpCHJWlu6cLAkEclaU0mHRQyAadGHBk5XmRCGnsY5Ya1DVJGX6FlGY3aPfj5S+24elX1vNcyK2uli//68hLc96HNUCtmvmdd2mqGLxDCnu78nNw4XaSFSbpK0gAUdOPr3gkX3P5gtFn6Qs6UpDHDKJ0YMFqASiFDfXkJusdTm2EkiiKebxvBRUtM0KkTu0tm1ChxxyXNePbYSLSZnMXhhTcQik5IA6QpaQA4KY2IKAGdlinUlZagRJXYhXkuUMhlMOtUGMvwaG9KD38whGOD9oSyiyLW1Jeid8IFW571frB7IiVp+ZNhpFLI0GzW4dTIzAwjpzcAm/vcf/9DfVZ89aFD+Mpfpa97Xu2I63gDDBjllNV1RggCCq6P0Q/+fgL+oIiv37hy3u3OayzHRy5pwgMf2YJy3bmZNxe0VEApFwqiLC2dGUZlWiW0KjkGCjhgFLmejQQZF1KuC2cY5dnnWL5hwCgGTSZdynsYnRxxoGfchWtWxz4dbTYfvaQJOpUcP37uFA73W/HKKQsAzChJM2oUKFHKmWFERJSADosTLZX5m10UYTaoMe4svJKIYtQ+MgVfIIQ14ebViYi89thQfl3EOvKw6TUglaW1j87MMPrKQ4fw0Qf2nLPt73b14JH9A9h5egzPHR/BD545MWtgaS6DVul8jyVpucGgUaLFrCuoSWkHeifxyP4B3HlpMxpN838+apRy/OvNq+fcTqtSYNPicrxWCAGjKS+MGsU5WVSpIAgC6stK0F/APYzahhyQCUBrVWwZRuxhlBkMGMWg0aQNNz1N3RjD546NQBCAq1clFzAq06pw+8VNeP74CN7285346kOHAQBLpvXaEAQBtaUaDDPDiIgoLqIootMyhSWV+du/KMKkU2GcGUYFIdJ7aG0SAaPVddId3GN5VpZmdwegUsgSLsXLltZqvVRu4ZP6tPgCIbxy0oLjg/Zzzi97xp04r7Ecb3xtO378rvUA4ut/NGh1Qy4TUGVIfVkMJUZqfG3N9jJSIhQS8a0nj6PKoMZnrliakn1e2mrG8SF73vd5GpvywZzG37uG8pKC7mF0YtiOJrMu5oxujVIOjVLGkrQ0Y8AoBk0mHRyeQEpH9j17bBgbF5WhypD8BIvPb2/FAx/Zgvs/vBn3f3gzHv7UxTNK0gCpLI1Nr4mI4jNi98LpC2JJAWQYmfTqvD8ZJ8mRARv0akV0kmsizHo1aks1eddXxe7x5112ESBlGInimSb6B3on4fIF4fYHYXHM/L3sHndF/9+2VkvB6tOjsfc/GrC6UWPUQDFHE2LKvLX1pRixewuiPcTfjw3jUJ8V/3TdCugTbKtxtq3hHkg7T+d3lpFlypuW/kURjSYdOi1OBEOpS2LIJSeGHVgZY8PriHKtiiVpacZPkhhEPrRT1fi6f9KFY4N2XLu6JiX70yjl2La8CttXVmP7ymqc11h+zjY1RgaMiIji1Rm+uCuIDCO9lGGUymxZyo4jAzasqjNCJlt47PB81tSX4uhgngWM3P686l8UsSwc+Ik0vn592oVx19iZ80unN4CxKW/0xl9DuRYapQztI7FnGA1Y3exflGPWRRpfF0BZ2lOHh1BpUOPtG+tTts+19aWoMWrwh7d68/ozamzKi8o0BozW1JfC7Q9Gz00KidMbQM+4CytqYitHiyjTqphhlGYMGMVgaZX0IX+wz5qS/T13bAQAcE2KAkaxqCnVYMTuQahAI9JEROkQyQZoKYCAkVmnhi8YgsMbyPZSKAmBYAhtQ/akytEi1tSVomvMiak8+pmwewJ5mWHUaNJBKReija9fax+LTrHtmTZYJfLnyM1KuUzAkko92uMsSasrSz6DnVJnVZ0RMgE4nGcZfWfzBUJ49ZQF21dUJR2wnk4uE/CpbUuwu2sCuzrGU7bfTBtzeNPS8Doi8r6fb4H+WJwMB9NXxNjwOqJcq2SGEaTs23QFWxkwikGTWYeVtUY8dmAgJft79tgwllXrMzqiuaZUg0BIxBgbnhIRxazD4oROJUe1Mf97gZgN0kks+xjlt/bRKXgDodQEjOqNEEWgbSh/+hjZ3X4YNPmXYaSUy9Bi1qN9xAGby4/D/Va847x6KGTCjAz23gnpz43TWgu0Vulj7mEUDIkYtnnY8DrHaFUKtFYZcKTfmu2lJGV31wSmvAFctTK5Hqyzec+WRagxavBfL5zKyywjXyAEuyeQ1pK0JZU6aJQyHOnPn/fsWJ0YCgeM4swwkkrSivu8xuby4/zvvYDfvtGdlv0zYBSj2zbW41C/LXq3OVETTh/2dE/gmlWZyy4CEL2LxbI0IqLYdVim0FKphyCk7k5qtph00knsOPsY5bVIz6FkJqRFRPZxNI+yHhweP4wl+ZdhBEj9iE6NOvBGxxhCIrBteRUWVWhnZBh1h/88vRdla7UBA1Z3TJlgow4PAiGRAaMctLahFIf7bXkZDIl4oW0EaoUMlyw1p3zfGqUcn7liCfZ0T2Ln6fzLMor0CKxMY9NrhVyGVbXGvHrPjtWxQRsMagUayuN77yrTKlPaZzgftY864PGH8ItXOuDxB1O+fwaMYnTLhjrIBODR/cllGb3QNoKQiJT1L4pVdThgNGrnhQIRUaw6LU60FEDDawDRNHk2vs5vxwZs0KnkaElBlnKVQQ2zXp1Xja/ztSQNkBpf90248fzxEejVCmxYVIZGk3ZGhlHPuAsVOtWMv2OkNUJHDFlGg+EJSuxhlHvWNZRi3OnDYJ7evBVFES+0jWDrUnPMU6zi9e4ti1Bbmp9ZRpGG5unOSF5bX4pjg7aCazOyq3Mcm5vK475BVx7uYVRo/x7xiPTBszi8+POevpTvnwGjGFUZNdjaWolHDwwk9QP53LER1JVqsKY+vvrMZEVGPPJCgYgoNm5fEANWd0E0vAYQTZMfY0laXjsyYMPqutKU9A8RBAFr6404NpA/5Q352vQaONP4+m+Hh3BhSwWUchmaTDr0jLuiF8c9484Z5WiAVJIGIKY+Rp0W6cLh7Gm5lH3rGsoAIG/L0k6NTKF/0o3taShHi1Ar5Pj0FUuxr2cSO9rza2LaSPimfOQmfbqsqS+F0xdE51hqhjHlgmGbB50WZ0KZa2VaJUIi4PDkTy++VOsac0IhE7BpcRnuebUD3kBqs4wYMIrDbRvrMWB1Y3f3REKvF0URu7vGcfnyyoyXN5h0vLNMRBSPyB2bQskwqtCxh1G+CwRDOD5kT0k5WsSa+lK0jzpg9+R+Sr/HH4Q3EMrbDKPWaqk3hy8YwtbwhVGTSYspbwDjTun3smfchcaKmcGexRVaqOQytI86FjxG++gUVHLZOfug7FtRY4BCJuBwnk5Ke6FNGtqzfWVVWo/z7s0NaDRp8dWHDuVVK40zGUbpDRitbci/UuKFvNEhBQcvWmKK+7XlWuncppj7GHWPO7G4QosvXLUMQzYPHt6Xmr7LEQwYxeGa1dXQquQJl6V1j7tg9wSwYVFZahcWA41SDoNGAYuDASMiolh0jkl38wslw0gpl6FMq8Q4hx/krQ6LEx5/KKVZylesqEJIPDPBNZdF7iDnaw+jxnDgBwC2tlZKj4VLC3vGnfAGghi0udFomhmkVshlaKnU4fTIwhlGp0YcaKnUQSHnKX6u0SjlWF5jyKsS0OleaBvBuobStAdE1Ao57vvQZji9Qdz52z1w+fIjc2TE7oFCJqBCm74paQCwtFIPtUKWtz9Hs9l5ehzlWiVW1sT/2Vaukz4Pijlg1Glxotmsw6WtZqxvKMX/vHIa/mAoZfvnp0kctCoFrltTg6ePDCXUUOpQnxXAmZTUTKs0qFmKQEQUo45RJwQBGZ1omW4mnYoZRnkscoGQiglpERsXlaG+rAR/OzyYsn2mSyQLypiHU9KAM4Gf2lINloQzF5vCwaGuMRf6J90QRZxTkgZIfYxiKUlrH5nCsur4pgxR5qzL08bXFocXB/us2L4ifeVo0y2vMeDu921E25Ad//jnQ3nRn2bE7kWVQZ2ScuH5KOQyrKw1FkzASBRFvNExhouWmBL6tysLB+iKtfF1KCSie1wKGAmCgM9d2Yr+STeeOjyUsmMwYBSn2zY2wOENRNMy43Gwz4oSpTxai55pZr0aFpakERHFpHNsCnWlJdAo09Pcc05BP9C9Ezj9ItD+PNDxMjA1mpJdm/g5kNeODtigVcnRksKsN0EQcPP6OrzePoYJZ24HE6MZRnlakgYAX71uOb79ttXR1gT1ZSWQywT0jDvRE25+PVvAqLXKgL5JF9y+uW9YTnkDGLC6o72SKPesrS+Dze1H34Q720uJy9+PDkEUgatWpbccbborVlThX25Yib8fG8YvX+3I2HETNerwoCrN2VcRa+tLcXzQnheBtIV0j7swZPPg4iWJTd4r9pK0EYcHHn8ITeGbm9tXVqHaqMbzCcQq5sKAUZwuWmJCtVGdUFna4X4r1taXZi1NuFKvxhhL0oiIYtJhmcKSTAf4rX3Ar68DHrgB+L/bgD+8E/j9rcCPWoEfrwQe/jjgXbiPyVzMehXGGTDKW0cGbFhVa4Q8xXewb15fi0BIxN+PDqd0v6lmd4czjPK06TUAXLmiGtdMm5SrUshQX1aC7nEXesZdAHBOSRoAtFbrIYrS+9JcToczkFqZYZSz1oX7zxwesGZ3IXEYm/Lix8+fwqbFZVhVm9mhPXdubcbVq6pxzysdsLlzO4NkxO5J+4S0iLX1pZjyBtA1nv+Nr3eelvoXJdLwGkC0BHCySDOMusKDDiKTUwVBwCVLzNjVMZ6ygCIDRnGSywTcuqEer56yxHXS7Q+GcHTQHv2gyIZKA+8sExHFQhRFdFmcKRldHrPTLwD3XgZYTgJv+znw0WeBO18APvwkcO33gcUXAkf+Ahz6U8KHMOvV0ea6lF9EUcSpYQdW1KY+GLCq1oiWSh2ePJTbZWlnStJSmGEU8AFHHwbe+Dnw4r8Bf/8aMHwkdfuPQaNJG84wckGnkkcHlUwXyU4/PU9Z2qkRKZjMkrTctazaAJVchiN51Pj6X584Bpc3iP94x7qMD+0RBAGf394KhzeA373RndFjx2vE7k17f6eIyOCDQmh8/UbHGGpLNWhKcLKjQaOATAAmi/TcJhI0bJp2vnrxUjMmnD6cGE78BuN0DBgl4LZNDQiExLhOrE4OO+ALhLA+Cw2vI8x6FRyeQEL9l4iIiondE4DTF0RDeUn6DxYKAi//O/B/7wQMtcAnXgE2fUgKEC3aAjRfBlz0GeBdvwGqVgOH/5zwoUw6Nawuf0qbIVJmjDt9cHgDaDanPutNEATcvK4Ob3aNY9Seu1OJ7O4UN70e7wB+fQ3w0EeB5/4f8PpPgT33A/dcCjzxDykrBV1Ik0mHrjEnusedaDTpZr0obzTpoJAJ805Kax9xQK2QYTEnpOUslUKGlbWGvJmU9uyxYTx1eAifu3Jp1jLX1tSXYvuKKty/swtT3txsgO3xB2Fz+zMWMGqt1kOlkOV9wCgUErGrYxwXLzEnHIyUyQSUaVVFW5LWZXFCo5ShZtrP3iVLpWlzkelzyYopYCQIQqMgCFeF/1wiCEJR37pYXmPAqlojHjkQe1nawXDD62xMSIsw66U0yTFmGRERzSty0Zz2fgTOcans7NUfAOvfC3zsBcC8dO7t170b6N8jXegmwKQPp24X6Z24fNY9NjPtPNVuXl8LUQSePpK6RpmpltIMoyMPAfdeDkx0Ae96APinHuCb48CXTwIXfho4+AfgZ5uArh3JH2sBTWYdHJ4ADvfbZu1fBEiBhiazDu3zTEo7NTKFJZX6lJcsUmqtbSjF0QFbzvefsbn8+PpjR7Gy1ohPbluS1bV8bnsrrC4//u/NnqyuYy6jdunaKlMBI2WBNL5uG7Zj0uWPBjgSVaZVFm3T6+5xJ5pMuhkNw2tLS9BSqcPrpzMUMBIE4eMAHgJwb/ihBgCPpeToeey2TfU43G+bNzV4usP9VlToVJm5Wz2HMwEjXigQEc1nJHLyZ0hjP4KR48C9l0oNrm/+b+DWXwKqBTID1r4LgAAc/ktChzSHA0YsT84/XWPnpp2n0tIqA1bUGPBkCierpJrd7YdSLkCjTDJB/tijwMN3AtWrgU++Dqx+O1BSBggCUFIOXPd94NNvAcZaKfvIkd7eTpFSjAmnb9b+RRGtVfp5zzvbRxxseJ0H1tWXweENoDvH+8987+njmHD68J/vXAdllvqvRmxYVIZLW8341Wud8zZ+z5bh8E2mTPUwAoDVdcaUlRxlyxunxwEg4YbXEeVFnGHUOeacdZrvJUvM2N01AV8g+YzyWH77PwPgEgB2ABBFsR1A5lrk56i3ra+DTAAePdAf0/aH+mxY11Ca8drf6SrDFz5sfE1ENL+R6MlfGu8WvvhtwO8G7nwWOO8O6WJ1IaX1QPOlUllaAmOZTeEbB+O8cZB3usedkMuEtN54etuGOuzrmUxZGnuqOTwBGDTK5M6lggHgxe9I5Z13PAWULZp9O/NS4N2/A3xO4OGPSa9Lk+lBorkyjACpmXX3uBMOz7l30h0ePwZtHja8zgNrw/1Mczk75LV2C/6ytx8fv7Ql2i8n2z53ZSvGpnz44+7ebC/lHBk5ZzhLrVEDq8sPbyD3Amix2t87iUaTFjWlyf27lWuVRdn0OhAMoW/CNeuNpEuWmuDyBXGo35r0cWIJGHlFUYyeWQqCoACQ2zmUGVBl1GBrayUeOzC4YEqp0xtA+6gD6xvKMrO4OZgNLEkjIorFiCNSkpamu4W2fqD9OWDzR4C6jfG9dt17gckuoH9v3IeNZJqOO/k5kG+6x1xYVF6S1jv9H76oCS2VOvzDgwcx6si9XkY2tx9GTZIT0g49CEx0AFf+P0C+wL6qVgI3/hjofk0qG02TRRUl0XjxfAGjK5ZXIiQCTx46NwusPZx5xIbXua+1Sg+NUpazfYyc3gC+9sgRtJh1+MJVrdleTtT5zRU4v6kCv369C4Ec68MXDRgZMhcwilzXTeRxiXmHZQqtVcm/Z5VpVbAWYYbRgNUNf1CcNcPowhYTBOHMFLpkxHLW8aogCP8CoEQQhKsB/BXAk0kfuQDctrEeA1Y3nj46f/r20QEbQmJ2+xcBiE7dsDDDiIhoXqN2LwwaBbSqNI3v3v87KUNo04fjf+3KmwGFBjgc/7S0SA8jZhjln64xZ9rK0SJ0agX+5wObMOX14/MPHkQwfENMFMW4JsOmy9iUN5oll5CAF3j1P4C6TcDyG2J7zYb3Axs/COz4EdD7VuLHnodaIUddqZQ5Nl9J2oZFZVhRY8CDs2RYtEcnpLEkLdcp5DKsrivN2Ulp//nsSfRPuvEf71wHjVKe7eXM8NGtTRiwuvFCW2Ya0sdq1OGFWiGDsSRN5wyziFzXjTny8/M8GBLRPe7CksrkP9ekDKP8/HdIRme4VH22gFGZVoW19aUZCxj9EwALgCMA7gLwNICvJ33kAnDN6mo0mrT47B8P4CO/2Y1jgzY4PH4cH7TjuWPD0QaVkVSwdQ3ZTenUKOUwaBTMMCIiWsCwzZO+1PJgANj/e2DpdqC8Mf7Xa4zSxe7RR6SR4HEwqBVQyWXsZZdnRFGMNrZMtxU1RvzbLWuwq3Mc//rEUXznb8dxyQ9ewubvvYDDKUhtT8aow4uqZPqK7fstYOsDrvx6bCWgEVd/B4AI9KUnYAQATWYtVAoZaud53xEEAe87fzGODNjOmY50amQKGqUMi8o5IS0frK0vxdFBWzQomyv2dk/gt7u6cftFjdjSVJHt5ZzjqpXVqC8rwW92dmV7KTOM2KVzhky2HolWjuRpxvDApBu+QGjWYEe8yrQqePyhopsE3j1PwAiQekMd6LXCmeR0wXkDRoIgyAAcEUXxf0VRfJcoiu8M/zm33t2yRKtS4NkvXIavXb8C+3utuPFnr2Ptt57DDT97DZ/4/T5s+9EreP//voknDw2hobwkubtiKVJpUPNCgYhoASMOT/qaV7Y/BzgGpb5FiVr7LsA9EfcFrCAIMOlVvHGQZ0YdXrh8QbSk4E5sLN69eRHeeV4D/u/NXvx+Vw9W1hqhksvw172x9W1Ml1F7EoFcnwt47UdA4yXAkivje21JOSDIAI81sWPHYPuKaly/pmbGpJvZ3LqxHmqF7Jwso1MjDiyt0i/4esoN6xpK4fIF0WGJbXhOJnj8QfzTw4dRV1qCr163ItvLmZVCLsPtFzXira4JHB+0Z3s5UVLAKLPXeWZdfvem7RyTfvZbKpPPiizXhifAFlmWUdeYEwa1IpptdrZLlpoQCInY3T2R1HHmDRiJohgCcEgQhMXx7lgQhF8LgjAqCMLRaY9VCILwvCAI7eHv5QmsOadolHLcdfkS7PjqFfja9SvwT9etwC/evwkPf+pifPmaZegZd+HIgA2bFufGX9WsV7MkjYhoAaN2b/oyjPb9BtDXAMuuS3wfpqXS9wSmN5n0qpwoL6LYRSekZSDDKOJ7b1+D33xkC/Z+4yrcf8cWXL2qGn87PAh/lnqHePxB2D2B6ACPuB17BJgaAa74f/FlFwHS9ppSwJO+EqKPbm3Gf7934X5mpSVK3LSuDo8fHJxx1/j06BSWpaAXCGVGpOogl/oY3f1SOzosTnz/trXQqzNXWhWv925ZjBKlHL99ozvbS4katXtRlcGG1wBgNoRLzPO0h1GnRfpcS8WNkHKtEgAw6SyuxtddY040V+rmzGzb3FgBlVyGN5IsS4ulJK0WwDFBEF4UBOGJyFcMr3sAwNlnw/8M4EVRFFsBvBj+74JQWqLEXZcvwae2LcGN62pxXmM5PntlK1776hX4y10X4es3rcz2EgEAlXo17ywTpcje7gn800OHMZmnH9Y0u1BIxKgjhSVpgwel/ienngUG9gPtzwObPgTIlYnvU2uSvrvG436pWa/O2xPMYrVQ2nk6qBVyXLG8CkaN9HP69o31mHT5seOUJWNrmG7ULp27JFyS1r0T0JqBxosTe72mDHBbE3ttir3/gkWY8gbwt8ODAAC7x48hTkjLK81mPXQqOY5kucwz4tigDfe82ol3bGrA5csqs72ceZVqlXj7pno8dnAgZxo+j9g9qMlwwEirUqBEKc/rDCOjZu7smHiU64o3w2i+84ISlRznNZZj5+n4zxWniyV8/O1EdiyK4g5BEJrOevgWANvCf/4tgFcg9UgqWDKZgPObc6cGuNKgxo72/HxjIcoVo3YPfvDMCTxyYAAAsKW5Au88ryHLq6JUmXT54A+KqE6mV8p0r/wAOPXMtAcEYNPtye2zpEzaTwIBI5NOjfaR3CmDoIV1jTuhkstQV1aStTVctqwS5VolHj0wgO0rqzN+/NHo5MIEL8p6dwGLL4w/uyiipCytJWnx2LS4HMuq9fjNzm4IgoDT0QlpbHidL+QyAavrS3F4IPsZRv5gCF996DDKtSp8I0ducC/kIxc34Y9v9eLB3b34zBVLs7oWh8cPpy+Y8ZI0QMoyytcbQJ0WJ1oq9Snp+xQJOuXrv0UivIEgBqxuvGPT/Ncflyw14UfPncJ4EkMjFswwEkXx1dm+EjoaUC2K4lB4v0MAqhLcDyXIrFfB4QkUXVMwolTpGnNi+49fxd8OD+HT25ZApZDh5HDu1NFT8kbCmQwpyzAaPS41qb7jaeDa7wO3/g9QFnel90wyOaCtAFzxpxmbwz2M2I4wf3SPObGoogTyLPanUcpluHl9HZ4/PgKHJ/Np/2d+LxM44XUMA5NdwOKLEl9ADmUYCYKAD17YiBPDDnz1ocO4b0cnjBoF1jWUZXtpFId19aU4PmjPWplnxH07OnFs0I7v3roaZdrksz0yobXagM2N5XjuWPxl2amW8nOGOJh0+Vs5IgWMUpM1WxEOGE3k6b9FIvom3BBFaWDCfC5eagYA7OpMPMtowQwjQRAcACJnlSoASgBOURSNCR81BoIgfALAJwBg8eIkT6wpyhyOLI5NedHASRpEcXv6yBAc3gCe++JlWFZtwKunLDjJbI2CMpJsJsN03inA2iON5W66RPpKFa0psQwjvQreQAhT3gAMmiTK4ihjFko7z5RbN9bjd7t68Pejw3jX5kUZPXY0w8iQwO9l7y7pe2MyAaNSwJbdpt/TffCCRlzYYkKJUo5SrRJ6lYINr/PM2oZSeAMhtI9MYVVdWi+r5nR6dAr//WI7rl9Tg+vW1GZlDYla11CGB3f3IhgSsxpMH7Un8d6UJLNejQGrO+PHTZbTG8Cw3YMlKWh4DUhT0gQBOVOimAmRn7sa4/yZx+vqS2FQK7Dz9DhuWleX0LFiyTAyiKJoDH9pALwDwM8TOhowIghCLQCEv4/Oc9z7RFHcLIri5srK3K6lzSdnAkbF8wtFlEo7T49hZa0Ry8K9IpZXG3Bq2JHlVVEqRT6EU5JePnZS+l6VhjR/rRlwJtbDCADG+TmQF0IhET3jrpwIGG1cVIZGkxaPHRzI+LFHHV4o5UK0uWlcenYBSi1Qsy7xBeRQSRogtTxYVm3AogotjBolg0V5KJIRdmTAmpXjh0Ii/vnhwyhRyvHtW1ZnZQ3JWFFrgNsfRO+EK6vriNxkykpJWp5OPY0McmhJ0eeaXCagXJu/5XmJiPXnTiGX4YIWE97oSLzxdSxNr2cQRfExAHHOI416AsCHw3/+MIDHE9wPJSgyXSRfG6SlSt+EC9Yia4xGyXP7gtjbPYmtS03Rx5bVGDBs98DmKq7JDIVs2Ca9PyY8jWm60Tbpe9Wq5Pd1Nm1FghlG4YCRs7g/B/LFkN0DbyCEphwIGAmCgFs31OONjnEM2zwZPfao3YtKvTqxfhe9u4CGzck1mo+UpLGUk1KksUILg0aR1klp33+6Df/zyulZn/vdrm7s7ZnEN29alZXsmGStrJGysk4MZbctQKQkLdNT0gDpBtCE04dQKL/elzojgxxSVJIGSGVpxZRhFM/P3SVLTegZd6EvweDqggEjQRBum/b1TkEQfoAzJWrzve5BALsALBcEoV8QhDsB/ADA1YIgtAO4OvzflEHm8AWQJQ+j0anSYZnC9p+8is3ffQG3/3o3/rynF24fezrRwvb2TMAXDEXrgQFgeY2UaXRyhFlGhWLE4UGFTgW1Qp78zkbbAIUGKG9Kfl9n05kT6mEUaQ7JTNP8EJ2QZsp+wAgAblhbC1EEdrRndlraqMOT2AWZxw6MHAUWJzgdLUJTCoT8gD//yj8oN8lkAtbWl+JImhpfTzp9+PXrXfjpC+3Rks6IvgkXfvjsSVy+rBK3bapPy/HTrbVaD5kAtGU5y3vE7oFerYBeHcssqdQy6VUIhkRY3fl107LTMgVBAJpS+LlWoSuyDKM4fu4uCV+3JJplFEuG0c3Tvq4F4IA07Wxeoii+TxTFWlEUlaIoNoiieL8oiuOiKG4XRbE1/H0ioVVTwqIXCkWaYSSKIv7fo0egUchw59ZmdI858U8PH8G/PHok20ujPPD66TEo5QLObzoz+XB5uDSNja8Lx6jdk/jo7nN21gaYl0lNqlNNawJcE0AovoapLEnLL5HU/VzIMAKA1io9SkuU2Nc9mdHjjtq9if1e9u8GxJA0IS0ZJWXS9xwqS6P8t7ahFG1DdngDqb9x+dzxYQRCInyBEH79enf0cVEU8S+PHoEA4Pu3rU3JlKps0CjlaDbrsp5hNGr3oioL5WjA9M/z/Lqu67Q4UV9WAo0ydedGFdriyjCK5+eutUqPSoMaO08n1vg6loDRr0RR/Ej46+OiKH4PQGtCR6Os0yjlMGgUeVnvmgp/3dePNzsn8LUbVuJrN6zEq1/Zhju3NuPxgwPoGXdme3mU43aeHsPGxeXQTYvm15ZqYNAomGFUQEbsXtSUpmpCWlt6ytEAqYeRGIz7AjYyTeSlE6P4/a5u/H5Xd8JpypR+3WNOqBUy1GSh3GE2MpmAzY3l2NOT2Xt+UoZRAhdlvW8Cghxo2JLcAjRl0vccmZRGhWF9Qxn8QRGnhlM/PONvh4fQaNLipnW1+L83e2ALZ6H8dV8/Xmsfwz9fvwL1ZfM3zM11K2qNOJHlDKNhuydr788mvfR5nm+VI51jU2hJUcPriAq9CpNFFDAasXtQHWMpqSAIuGSJ1McokQm5sQSM7o7xMcoTlQZ13r2xpML4lBfff7oNW5rK8Z7wdBdBEHDXZS1QyGS4d0dnlldIuWzS6cOxQTu2TitHA6SfoeXVBpxk4+uCEc+H8LzcVsAxCFStSH5fs9GGe2m54rtwVylkaKnU4YW2EXzj8WP4xuPH8F/Pn0rDAikVusedaDLpcqqp8XlN5ei0ODN2V9sbCGLS5U/s97JnF1C7DlAneXHCDCNKg7X1pQCAwylufD3h9OGNjnHcuLYWn9q2BFPeAP7vzR6M2j347t+O4/ymCnzggsaUHjMbVtYY0DvhwpQ3kLU1jNg9qM5SwKgyDzOGRVFEl8WZsobXESadCpOu/OvnlKgRhyeuRusXLzVjbMqX0A3uOYveBEG4CMDFACoFQfjHaU8ZAaQht54yxaxXY8yRP28sqfLdp9rg9Abw/bevnXHiXWXU4B3nNeChvf34wvbWrDSto9y3q3MconimDni65TUGPHloEKIo5m1qN0kCwRDGprypmXZiOSF9T1eGkS4SMBoHsDSulz7z+Uvh8Egn2J/8/b6sT5mhuXWOObGsypDtZcywuVEqy93XM4lrVtek/XgWR6S5Z5y/lwEfMLAX2PzR5BehkS7s4Ulfg2IqPg3lJSjXKnGk3wZckLr9PntsGMGQiBvX1WJ1XSkuX1aJX7/ehf09k/AGQvjBO9bmVBA6USvCja9PDjtwXmN5xo8vimJWS9JMeViSNmL3wukLYkkKG14DUvZ0SASsbn80k7pQiaKIEbs3rkBl5Ppl5+nx6O9NrObLMFIB0EMKKhmmfdkBvDOuo1BOqdSri64kzeLw4tEDA/joJc1orT73xPuTl7cgEArh/p1dWVgd5YPXT49Br1ZgfUPpOc8trzHA7glEJxZQ/hp3+hASUzTtZPS49L1qZfL7mk00wyj+JoZqhRxmvRpmvRqLTVoMWNnIN1cNWT1oKM+tspF1DaVQyWXY15OZPkajkYBRvBlGQ4eAgCf5/kUAS9IoLQRBwNqGMhyaNiltwOrG1v94CXu6Ey/7fOrwEJrNOqyqlS4MP71tCcadPrx4YhRfvHpZysuBsmVFrXRO35alPkZWlx++YCg1WckJKCtRQi4T8mqIReeYVH6Z8pK0cJBooggmwNrcfvgCobjOVevLSrC0So9njw3Hfbw5A0aiKL4qiuK3AVwoiuK3p339RBTF9riPRDmjGEvSDvZZAQBXr6qe9flGkw43rqvDH97sjdZ4E0238/QYLmypgEJ+7tvmsnAQ8gQbX+e9Ebs0SSYl6eWjbYBKD5QuSn5fs9FOzzBKXENZCUbsHvgC8TXPpvRz+4Jw+4Oo0OfW3VKNUo419cakLmjjMRoOxlfG2/R6YK/0fVEKUjdKwtkLLEmjFFtXX4pTIw54/FLj69+90Y3+STceOzCQ0P7Gp7x4o2MMN66tjWY9n99cgUtbzdjcWI6PbW1O2dqzrb6sBAaNImvnX0cHpUBfc5aGEshkQng6WP5c13VawpM/U16Sln/leYmK3ESJNxv+bevrsLtrIu6bhLH0MHIJgvCfgiA8LQjCS5GvuI6SLVMWoCM/lppJZr0KDk8g+sFUDPb3TkIhE7Cm/tzskIhPXX6mxptour4JF3rGXbOWowFnJqWdYuPrvDdsiwSMUpBePtoGVK4A0lWmqA3/PDoTG5Ma0VCuRUg883en3DHhkk58K7S5FTACgC1NFTgyYMvIuURkJHjcgdyJTkBtBPSz3yyKizqcws+SNEqxtQ2lCIZEHB+yw+UL4MHdvQCAV05aEmpQ+/djwwiJwA1ra6OPCYKAX9+xBX++66JZb3zlK0EQsLLGiBND2Tn/erFtFGqFDBe2mLJyfEBqNWLJo1YjnRYnSpTylDcKP5NhlD//FomK3NyMN+v2lg11AIAnDg7G9bpY3jH+AOAEgGYA3wbQDWBPXEfJlme+CvzhXUCQGSPTRUYwFlNZ2oHeSayqM847vnFVnRFr60ux83RyF19UePb3SmUXFzTPfkJQrlOhyqDO+qQOSt5I9K5NHB/CogiMngB2/QI4+fczj4+2pa8cDQBUWkBRknSGUX243Knfyj5GuSYy8SUX+zGc11gOf1DE4f70B1BG7V7IZQJM8f47THQB5U2pCdrKFYDKwJI0Srl14VL3I/02PHpgAHZPAG/fWI8Bqxsdlvinp/3t0BBazDqsrJ3ZgkEpl0FeAH2Lzrai1oATw46EgmvJEEURL50YxSVLzShRZa+9r1mfZxlGY1NoNqd+kENkYtx4UQSMEsswajTpsHFxGR4/GF/2YiwBI5MoivcD8IfL1D4KIAXF4GnmGAbangBCAcBpyfZqcsqZgFHh/0IBUhPbw/02bFxUtuC2q+uMaBuyZ/xDh3LboFWK5DeatHNus7zGwAyjAjBq90AmIPYL0/2/A366FvifC4Bn/wX48weBvt1ShqtrLH0NryN05uQDRuGxygOT7GOUa8ZzPGAEAHt70l+WNurwwKxXxX+BMdkFVKSw/KakjCVplHI1Rg3MejUO9VvxwM5urKk34kvXLAMgZRnFo9MyhV2d47hlQ33RDOFYUWPElDeA/gx/hnVYnOidcOHKFVUZPe7ZzHnWm7bT4kRLihteA0C5lhlGsbh1Qz1ODDvimu4cS8Aokp4zJAjCjYIgbATQEPfqMm3fA1KwCACmRrK6lFwT6QEQmTpS6E6NTMHlC2Lj4oWnJ6ysNWLS5cewnaUZdMag1Q2jRgGdes7BklhebUD7yBSCRTLOs1CN2D2oNKhjS9kXReCFb0mlKjf/N/CpN4DSeuAvHwa6d0jbpDPDCAC0FUkHjGrLpBMONr7OPZEMo/IcDBiZ9Gq0VOqwtzv9ja9H7N74T4xDQcDaK2UYpYqmjBlGlHKCIGBdQymePjKE9tEp3HFxMxrKtVhapY87YPR/b/ZCIRPwvvPT1DsvB0UaX2c6y/ulE9L1ZbYDRiadKm/69ngDQfRPutLSdF2lkMGgURRFwGjU7oFRo0gos+3GdbWQywQ8FkeWUSwBo+8KglAK4EsAvgzgVwC+GPfqMinoB/b+BjCEa3enRrO7nhwTGf0Y6QlQ6A70SSezm2IIGK2qk3oUZGvaAuWmIZsbdWXzTylaXmOANxDCgd5JjNg9eTXilM6Ia0zpZJcUrDn/Y8B5dwDVq4F3/x5wTwCPf1baJu0BI3PSPYzUCjmqDGpmGOWgyIlv3KVYGbKlsQL7eiYRSnOgfNThjb+vmH0QCPqA8hRmGGlK2cOI0mJtfSk8/hBMOhVuWiddv2xbVondXRNwegMx7cPlC+Cv+/pw/dra1Ez6zBORPpInMnzu/mLbKFbWGhc8P0w3s0ENly8Ily+2n5Ns6h13ISQCS9KQYQRI2bjFEDCK61z1LGa9Gpe2mvHEwcGYP7vnDRgJgiAH0CqKok0UxaOiKF4hiuJ5oig+kdAK00EUgZe/L93RjXyIn/gbMDUMXPZl6b8d8Y+PK2RmvRqCgKIZAX6g1wqTToVFFQu/oa+oiYznZGkRnTFo9aC2dP435pXh0bXvvGcXLvj+izjvuy8kNLqSsmvE7ok9k6E/PIGpYcuZx2rXATf9FPC7pKlKqWi2Ox+tKekMIwBoKC/JeDo/LWzC6YNMAIwaZbaXMqvzmsphc/vxvafb8NMXTuEXL5+G1ZX6k3WLw4PKeDOMJruk7yxJozwQ6WP0gQsWR/ttblteBV8whDc7Y3uPf+zAIByeAG6/qDFt68xFOrUCjSZtRjOMbC4/9vZM4soVlRk75lwiNxTyIcuoIzwhrcWc+gwjoIgCRg5PUtN8b90g9Ujb2xNbhvDc9RUARFEMCoLwNgD/lfCK0ikUBJ76R6n8DADG2oEP/BXY/SugrBHY8AHgqS8xw+gsSrkMJp0ao0VSdnWgdxIbF5fFVMtt0CixqKIEx5lhRNMM2dzYsLhs3m1W1xnxPx/YBKvLj5Ao4uuPHcWpYQeuXV2TmUVSSow6vNHeLAvq3wModUDlWVlEG94HjJ8Ggt70TUiL0JkBV/I9ZOrLtTjUZ01+PZRSEy4fyrUJ9O7JkK1LzTCoFbj/9a7oY6GQiM9tb03ZMfzBEMadPlQZ4swwmuyWvrMkjfLA1lYzvnjVMtxxcVP0sS3N5dCq5HjlpAXbV85/80EURfxuVzdW1hqxOdbPsAKyqtaIwwPWjB3v1XYLgiERV65I802hGER601qmvFhUMXevzVzQOSY1cW8yp2edJp0KA9bCv74dtXtxQUviWVpXr6pGiVKOxw4O4PzmigW3j6Uk7Q1BEH4uCMKlgiBsinwlvMJUCfqBR++SgkVbvwh86FGpVv2+bUDP68CWOwFlifThPsW7/GerNqqjDbMKmc3lR4fFGVP/ooiVNUaWpFGU2xfEpMuPugUyjARBwA1ra/H+Cxbjgxc2olyrxEiRlH0WivEpLyacvthHvfbvBeo3SdOTzrb9G8A1303tAmejrQB8DiCQXMZofVkJhmzutJcWUXwmnb6cbHgdUVdWgsPfugZd/34Duv79BqxfVIaXTqb2Jt3YlBeieKacPmYTXYBMARhT2HZTU8oMI0oLtUKOz1/VilKtcsZjFy8x4ZVTowsOY9nTPYkTww7cflFj0TS7nu68xnL0TbgzdjP85ROjqNCpsCGGgTrpFgkY5UOGUafFiSqDGoY0Zc1KGUaFXUETCokYTTLDSKdW4JrV1Xj6yBB8gdCC28cSMLoYwGoA/wbgx+GvHyW8wlQYOgz89m3Akb8C278JXPUtYMmVwEeeBgQZoNAAGz8kbWuoYdPrWVQbNUVRknaw3woAMU1Ii1hZa0T3mBNuXzA9i6K8MmSTynTirVGvNmowbCv837FCcv/rXRAE4Pq1tQtv7HcDw4eBhs3pX9h8tCbpe7KT0spL4A+KGC2SYQj5Ytzpy8mG19MJghD92r6iCgf7rCmd2DMaGR8cd0laN1C2ePaAbqJKyqRy00DuX5hRYbh8eRX6JtzoHHPOu93vdnXDoFHglg11GVpZbtncJGVJxFpik4xgSMTLJ0exbXkl5DmQ/RkZJ58Pk9I6LVNpmZAWUaFTY8LpK+hp15MuH/xBEdXxZt2e5dYN9bC6/Hj11MKN9RcMGIX7Fp39dWVSK0zU1CjwxOeAey8DLCeAW38JXPqlM8/XrgM++Trw8Zeku64AoK8CHAwYna3aqC6KptcHeichCMC6OANGIRE4yRHpBGDIJv2e1JbGHzAqht+xQmF1+fC7XT24YW0tllbFUFs/dFiaxFmf7YCRWfqeZOPrhnBAdMDqSnZFlEKTTh8qtGkIGA3sB178DnDwQWDoEOBPzXvVlSuqIIrxjwKfTySIGXeG0WRXasvRAClrHWDja8qYy1ulHjlvdMx9U8AbCOK54yN4+8Z6aFUpDJDmkdV1RmiUMuzpTr5EeyFtQ3ZYXX5cviz7/YuAMwGjfBi20jnmTMuEtAiTTgV/UIQjxkbx+SiS8JFMhhEglcFW6FQxTUtb8F1FEIRqAN8HUCeK4vWCIKwCcJEoivcntcp4DR0G/vAuwDUGXPQZ4LKvSHd6zqavlL6i/10N9O3O2DLzRZVBg7EpH/zBEJSxjI/OUwd6rVhebYB+nnHoZ1sVbl58fNCeE6mmlF2D1kiGUXxvzNVGNU4Ms7QxXzzwRjemvAF87sqlsb2gf4/0vYAyjACgf9KN84qrX2pOm3T5UKFPccDoxNPAQx8BAtOCRPpq4AtHAUVyx1pdZ0S1UY2XTozgneelphQsUj4fczP6iIkuYE2KOyhEzjs91pnnmkRpsqiiBDqVHB2jU3Nuc6TfBl8ghIuXmDO4styilMuwvqEM+zKQYRQ5L2w2py9TJh5qhRwGjQJjOV6SNuH0weryoyWN/26REu6JKV/ODotIVqTdRbKTEJVyGW5aV4s/7+mDw+Ofd9tYIgUPAHgWQCTH8RSALySzwLh57cBvrgdkcuATrwLXfm/2YNFs9NVSZlIBp6YlIhKVtBRw+YHHH8TBPis2LtCs+GwN5SUwqBXsY0QAzmQY1SzQw+hsNUYNLA4vAsGFa4MpuxweP379eheuWVWNFTXG2F7UvwcoXSyVPWeTLnyBkGzAqOxMwIhyQygkYtLlT22G0b4HgD9/AKhaBXzpFPCZPcCFn5FK9x2DSe9eEARcuaIKr50ai6kvQixGHV4IAmCOJ3DmnpSCOinPMJImWTHDiDJFEAS0VOrRYZk7YLSnWwqSbGkqvmbX021pqsCxQTucac4uGQlfO8Xc7zADKvXqnC9J6wo3vF6SxgyjyA2WiTRM68wVkT5d1fFm3c7ilg318AZCePbY/NVYsQSMzKIo/gVACABEUQwAyGxzl4lOoLwZ+NgLQM2a+F6rrwYCbinoRFGRH7JCbHwdCIbwlz19uPJHr8Dm9uPyZVVxvV4mE7Ci1sCAEQGQehiZ9SqoFfK4Xldl1CAkSj1IKLf9blcP7J4APndlHJOdBvZlP7sISFmGkU6tQLlWiQErA0a5wu7xIxgSU9fDaO9vgCc/DyzZDnz4ScBQDVQuA5Zul563LZyWHosrV1TD4Q1gb4pKQywOD0w6NRTxZENHJqRVNKdkDVGRkjROSqMMaqnUodMydw+jPd0TaKnUwaRP/gIyn53XVI5gSEz7xM8RmwdymZBT/94mvSrnA0Yd4Z/hdPYwMk3LMCpUkb5+lUn2MAKATYvLsLhCi8cXKEuL5dPXKQiCCYAIAIIgXAggs7dWVHqpobUxgUZukbu/U6md2pHvIhlGhdb42uUL4Oaf78RXHz6MSqMGf/zYBbhuTfwZACtrjTgx7ODEIMKg1RN3/yJg+u9Y4QVl813/pAsfuv8t3HT3a7jp7tfwi5dPY9vySqxtKI1tB/YhwNYHNGxJ70JjUVIOQEi6hxEglaUN5HCGkccfxM9fasfp0eLoLzcRDjabUhEw8tiBF78NNF8GvO9BQD3tDm9puHTMnpqA0SVLTVApZHjxRGrOu4ZtHlTFe2I80SV9L09xwGh6SRplxtQo8NinAe/cGTaFbkmlHgNW96zDWEIhEXu7J3B+08KjsQvdpsXlEIT0N74etntQqVfnRMPrCLNenfNT0jotTijlQjSjOR2iJWkFfLN2xOFBuVYZ943s2QiCgFs21GHn6fnPIWMJGP0jgCcALBEEYSeA3wH4XNIrjIdpCaCJsUzgbPpwdoljOHXrKQCR5pGF1pT3cL8NbUN2fPOmVXjs0xfj4qWJ1XOvrDViyhtgeQZhyOZGbZzlaMD0LL7CCsrmu1G7Bx/41Vs42GdFtUGDaoMGW5ea8bXrV8a+k4G90vdcyDCSyaWgUZIZRoBUlparGUYefxCf+P0+/Oi5U3j3vW8WRX+wyAlvSjKM3rpHKtO66tuA/Ky+DsZ66butP/njANCqFLh4iQkvpShg1DbkwLLqOEsYIhlG5SluyBXNMEp/nxQK63gJOPgHKauzSEUyMjrHzg2atY9Owe4JRKeEFbPSEiWWVxvS3vh6xO5BdQLnhemUDxlGnZYpNJp08WWLxsmkk869Czm7f8TuTbrh9XS3bKjHQvkRsUxJ2w/gcgAXA7gLwGpRFA+nYoGxSyKCq49kGHFS2nQmnRQZL7Tsh0jK7jWrqyEIif/crIw0vmZZWtEbsnpQl8DdkEht+3CB/Y7ls0mnDx+6fzcsDi9++9Hzcf8dW3D/HVtw3+2bsbzGEPuO+vcAMiVQsy59i42HzpyigJEWA5PunBtH6/EH8fHf7cVr7RZ86eplUMlleP//vlXwZcORgFHSPYxcE8AbdwMrbgLqZ2kCrdZLvXlSlGEESNPSusac6Jyn70osRu0eDNs9WNtQFt8LJ7sAXSWgjuP3OhbsYZR5kUBmCn8+802k58tsZWm7w8ERZhhJzmssx4FeK4JprBAYsXuSHmmeanVlJZh0+XO6N23nmDOtDa8BoEQlh0Ypw4Qzd/8dkjVq9yTd8Hq6pVX66HXvXBYMGAmCoAHwDwC+A+DbAD4Tfiw/RDKMGDCaQS4TUKlXF1z2Q6dlChqlDHUJlBBNt7zaAJnAgFGxs3v8cHgDCWUYmfRqyIQzzekouzz+IO74zW50jTvxq9s3Y9PiJJqD9u8DatcByhz5KNSaUhIwaigvgdsfxKRr/mkZmRQMifj47/bi9dNj+I93rMPntrfiT5+4EGqFDO//3zcLujxtMty0M+kpabt+DngdwBX/Mvc2xoaU9TACpICRTADu+v0+PHdsOOEg5JEBKTCzLtZy0YiJrtSXowHS77xCw5K0TLKHm7EXccCo2ayDIGDWxtd7uydQZVBjUUX6ynzyyZamCkx5A2nNQh22eeIehJJu21dUAwCePZabFTWBYAg94060pLHhdYRJpy78DKMUBywvaJ4/4BxLTtjvAKwGcDeAnwNYBeD3Sa8sU0rKAbmKAaNZVBvVBZdh1DXmRJNJB1mSdcUlKjmWVulx90vtuOG/X8O3njiGpw4PFVwJH81vyCr9/65NIMNILhNQaSi837FcIYoi7AuMAZ3ulZOjONRvw3++c13CpaoAgGAAGNyfG/2LIlIUMKovj0xKcyW9r1R5q2scr7WP4Zs3rcK7Ny8CADSZdfjTJy6ELxDC/a93Z3eBaTSeigwj5xjw5j3AmtuA6tVzb1daD9hTU5IGAA3lWtz7oc0IhkR84vf7cNsv30DvePw/V4f7bZAJwOq6ONsSTPakfkJahKaMTa8zKRIoSmFAM99olHLUl5XMmmG0t3sSW5orksqqLyTnNUo3g/alqY+R2xeE3RNIaUlQKiyr1qOlUodnjg5leymzGrC64Q+KaW14HVGhUxVsD6NgSIRlKrUlaQAWnCgeS8BouSiKd4qi+HL46xMAlqVicRkhCNKkNDa9PkdVeOx3Iekcc6bszegX79+Ez29vRblOiT/v6cNn/rgf53/vRWz7z5fxlb8ewl/29qF7zJlz5RuUOoM2qZ9LXYJ3kmqMGgwXWBZfrvjlqx248PsvwhZjNsxr7WPQqeS4YW1tcgcePQ74XUB9DvQvitCaUtP0OhwYzaXG16+1j0EhE/DO8xpmPN5o0uGSpWbsOGUp2PfgSacPJUo5SlRJNLbc+d/SpNhtX5t/O2N9yi/Ir15Vjee+eBn+4x1rcWLIgV++ejrufRzut6K1ygCtShH7iwI+KfiV6glpEZpSlqRNN9ElBejSJRIwKuIMIwBoqdSfk2E0YHVjwOrGlsYkMmYLTEN5CWqMGuzpTk/AaCQ60jy3AkaCIOCGNbV4s3MC4znYyygS7FzCgFFSxp1eBENitE9qqmxcNP97SCyfwAcEQbhQFMU3AUAQhAsA7EzB2jJHX8Wm17OoNqpTNvY2F/gCIfROuHBjsheEYa3VBnyhWup/4A+GcGzQjj1dE9jdPYEX2kbw133S3dhKgxrnN1VgS1M5tjRXYEWNMacmJ1DikskwAqSgbN9E7mRrFIpRuwc/f+k0XL4g3uoaxzWrF56E+PrpMVy0xARlss0W+/dI33Oh4XVEJMNIFKWbJAlqCGcY5VLj69faLdi0uBwGjfKc5y5fXonnjo+gw+LE0qr0p7ln2rjTF534khDvFLDvt8CqWwFz6/zbltYD7gnA5wJU2sSPeRaFXIb3bFmMF9pGsfN0fFlwoijiyIAN25ZXxXdQay8ghtJTkgZIk9JYkiYJBoDf3QIotcCndyX1/jOnSCAzUppWpJZU6rC3ewKiKEaziSLn8FsWKCcpJoIghPsYpSdgFOlLWZNjASMAuH5tDX7+8mk8f3wE7z1/cbaXM0Mk2NlizkRJmmrW8s1CMGyTfv5S2cMIwIIlrbEEjC4AcLsgCL3h/14MoE0QhCMARFEUc6Tr5zz0NWcmZlBUtUGDSZcf3kAwJaP5sq1v0oVgKD3pjkq5DBsWlWHDojJ8/LIWhEIiOixT2N09gT1dE9jTPYmnjkhpoAaNAuc1luO2TQ142/q6lK+FMmfI5oZMQMK1woUWlM0VP37uFPzBEFRyGd7qmlgwYNQ34ULPuAsfubgp+YP37wW05vSVuyRCZwbEoHQRW5L4nebSEiV0KnnOTIccn/Li6IAdX75m9qTmy1orAQA7TlkKMmA06fShXHduoCxmh/8MeG3AhZ9aeFtjOIPLPgiYlyZ+zDlsXWrG88dH0DvuwmJTbAGpIZsHY1O++PsXRSekNcX3ulhpygBHbpZ9ZFzb44A1nF3UtxtYfEFq9+93S4FMIGVT/PJVS6UeLl8Qw3YPasN9Ovd0T8CgVmBFTYKTpAvU6nojnjoyBJvbj9KSJN5DZ3Emwyi3ml4DwKpaIxpNWjx9dDjnAkadY06UaZWpmfq5gELOMOoakzK1mkypvdZdqKQ1loDRdalZShbpq4C+N7O9ipwTSacctXuxqCJ1dxSzJZLu2JzmDvwAIJMJaK02oLXagA9cII3t7Z90YU/3BHZ3TeL10xb8w4MHcMkSE0z63PtQodgMWj2oMmgSHgFaY5SCsh5/EBpl/gdlc0HbkB1/2deHj17SjGODNrzZuXDWwmvtUrnW1nCAISkDe6X+RbnUL0Jrkr67JpIKGAmCgEUVWvTmSFbc66el/2+XzvH/bVGFFi2VOrx6yoKPbk1TNkkWTbj8qNAl+PkhisBb9wK1G2Lrt1VaL323D6QlYHRJuG/Yzo4xLDbFdiFzuF8q+1pbH2/AqEv6nq6StJIywNKWnn3nE1EEdv5MyuRyWoD9v0t9wCiSVWReBoydAnxOQJX+c7xcFCnl6Rh1ngkYdU1iU2M5s9rPsrpOes84PmjHRUtMKd13NGCUY02vAekz/Po1tfjVa52wunwoS3bCZgp1jzkzcn0GSIMiXL5gQZ57d405IQhAY4w3XlJlwasgURR7ANgBlAIwRb5EUewJP5f7DDVSun4wdya/5IKqcHS8UBo5R8b3ZqID/2wayrV4+8YG/Ptta3H3+6TRxbtiuJil3DVkc6OuLPGTgkjKaKH1CksZ96QU5IiRKIr4/tNtMGqU+NyVS3FhiwnHh+ywued/b3/9tAW1pZrka+fdk9JFSy6VowFSxhOQkj5Gy6oNODmcG5PHdpwaQ5lWiTXzBAwuX1aJNzvH4fEHM7iyzJhwelGhTfDueOcrwNhJ4IJPxhbcNE4LGKXBkkodaowa7Dwd+8/o4X4rFDJhwXG/5+h+HdBVSv0r04E9jCTdrwFDB4FLPi81VT/2COBJ8WSqyM9jw/nh/y7esrQl4XPbzjHpXPf0qAMnRxy4OMUBkUKwKvyekY5Jx8M2L7QqOQzqOPqqZdANa2sQCIl4/nhuDXuyOLyoNmQmyGYKZzEV4qS0TosT9WUlGQ+ELRgwEgThOwAOA/gZgB+Hv36U5nWllj5c/57vja/9bqBrB9C9E+jbI9XpJyGSYTRSIE15u8acMOtVKU8/TcTa+lIYNIq4To4p9wzZPAn3LwKm/44VRlA25f5yO/Cb64FQKKbNXzlpwWvtY/j89laUaVW4oNkEUcS8ZX/BkIidp8exdak5+SkyA/uk7zkXMAr3r0jBNNCVtUYMWN0LBuHSTRRFvNZuwdal5nnvnl+2rBLeQAi7uwqv9HPS6U88fX/3fVIgcc1tsW0fCRilaRKVIAi4eKkJb3SMIxSKrUn5kQEbltcY4jsx9rmA9ueAlTenLwtQUyYFRmJ83ypYO38mBebWvw/YdIc0DODoQ6k9RuTncVE4S66Iy9KqDGroVHJ0jEoBowfe6IZKITtnIABJvUUrDWocH0x9wGjE4UG1UZOzU+nW1peivqwEzxzNrd69E04fKvSZyXiKZOZOTBVgwGhsKiuJEbHUWbwbwBJRFLeJonhF+OvKdC8spSJ3mVJwMp01Phfw27cBv70ZeOAG4P6rgJ+uA575J8Cb2N3gqnBflkK5mO20ZC7dcSFymYCLWkzRkgrKP6IoYtDqTnhCGnCmxr1QgrIpNTUKdL0GWE4Ap55ZcHO3L4hvPnEULZU6fPBCqQx04+IyqBSyecvSjg7YYHP7sbXVnPya+/cCEIC6TcnvK5UqV0hNZztfTnpXK2qlRv/ZzjI6NTKFUYc32qdoLhc2m6BSyPDqKUuGVpYZ3kAQU95A9E5pXCa6gJPPAJs/AihiLGlTaqQAkz19F+Rbl5ox4fShbXjhizhRFHG43xZ//6LTL0iBi1W3JLjKGJSUARCl/lDFauQ4cPp54Py7pJ+d+k1A9RqpyXoqRTOMwgGjIs4wEgQBS6r06Bxzwuby4+F9A7hlfR3bHsxhdZ0RxwZT/zs6YvPkZP+iCEEQcMPaGrzWbok2SM62UEjEpMuX2OdZAiqiGUaFde4tiiK6LE60ZOFaN5aA0VEAZWleR3rpww1R8zXDKBgAHr5Tms5z44+B258APvAwsOVOqUfBLy4ATjwl1ZPHQhQBrwPlJUoo5ULBXMx2jk1lpPt+rLa2mtE34UbveG70A6H4SA3hQ9FeAYmITNEYLpCgbEqdfBqAKJV3vP7TBd+//vvFdvRNuPH9t6+FSiF9dGmUcmxYVIa35skuiQRtIz1UktK/B6haCWhyrMGoSgssuxY4/oT0eZGEleHmqW1pSOWPx45wAGihQF+JSo4LmisKLmA06ZQyvBLKMNrzK0AmBzZ/NL7XldanLcMImNbHKIYbKX0TUpbb2vqy+A5y/DGpp1fj1vgXGCtNOIhVTGVpoRBw9BHgxe8AT38FePQuKUi95U7peUEANt0ulagNHUrdce0DQEkFULHkzH8XsRazDh2jU/jL3j64/UHccUlTtpeUs1bVGnF6dAreQGrLlYftnpyckDbd7Rc1QYCAHz13MttLAQDY3H6ERCQ39TMOkeNYXYXVimbU4YXTF0zLcKeFxBIw+ncABwRBeFYQhCciX+leWEpFS9JyKz0vJqIIPP1l6eLq+h8CWz4GtFwOtF4lBY/ufA5QG4E/vR+451Lg0J+AwCwpeF07gP97J3D3ecD364B/b4Ds32vxgurLeNvRzwPjHZn/u6WQze3H2JQvK79Ec4mcHDPLKD8NhkeLJ9PDqLRECZVChlEGjM7V9jdpitEVXwf6dwO9cw8maBuy439f68S7NzfgwpaZ/RoubK7A0QEb7J7ZTwxea7dgVa0R5mTvwoqilGGUa+VoEatuBVxjQM/OpHZTbVSjTKvEiRiyQNJpR7s0+awuhpLQy5dV4vToFAasuTHdLRUiE14q4m1aGgoBRx8GWq8FjHFO6TTWp/WCvNqoQWuVHq+fXri33+EBKwDEl2HkdwOnngVW3ATI09hfRFMmfXdb03eMXDJ8VCodfugjwOs/AQ7/RZrIuO2fz5TDAsC6dwNytdT8OlXsg9LPZTQDrrgDRksq9Ri0efDrnV04v7ki2tyZzrWqzohASET7SOrGq4uiiFG7N9puIFctqtDiI5c04eH9/Tg6kP3AdqSXUMYCRuHPzUKblNYR6dWbheSIWAJGvwXwHwB+gDM9jH6czkWlXL72MAr4gL9/Ddj3G2DrF4ELPnHuNovOB+7aAbztbiDkl+76/HStVKrWvVOq937oo1Ip22iblDK8+aPAVd8CtnwMA6pmrHC+Jd05ymORMYO5UpIGSHeCaks12NnBgFE+Ggqn8iaTYSQIAqqN6oIp+0wZjw3oelW6sNv4QekO8s7/nnXTYEjE1x45grISJf7lhpXnPH9hiwkhEdjXPQlASn0+OmDDvp4JvNU5jn09k7g0FeVo4x3SRVJ9jgaMWq+R7vgffyyp3QiCgJU1RrQNZa8kzeHxY3fXxILlaBGXLZO221FAWUaTrgRPsAf2SiPfEynJMqY3wwiQbqTs7ho/567/oT4rrv7Jq7jkBy/hO387jmeODEMll2FZtSH2nZ9+EfBNpbccDQiXpEF6P8hnA/uBN+8Bnvgc8JsbgJe/P3MIwUSnlE1072XAeDtwyy+Ab4wD/9wDfOGI1Ox6upJyoPVqoOOl1K3RNnBmgl+aM+DyQaR3yZDNg48yu2he0yelpcqkyw9fMJTzASMA+PQVS1FWosT3nmqDGGsFSppEAjemRKd+xsmgUUAuE6Kfo4Uieq2bheSIWG7BjImi+LO0rySdFGrpg8yRRxlGE53AQ3cCg/ulGvHt/zr3tgqVlAq84YNAx4vA3l8De38DvHWP9LxcDWz7mvThrpx58fu70X2o6/owmvp3p/Evk35dY9mdkDYbQRBw8RIzXjoxglBIhIxjT/NKpIysNsnRqTVGDUvSztb+PBD0SY1pVVrggruAV/5dCmpXzQwK/fGtHhzss+Kn79kw64jYjYvLoZQLeLNrHOc1leMf/3wQL7TNvDlw+fLYAg8ApAyN4UPSRU/vm0DLNuCC/8/eeYa3cZ1p+x5UEmDvkiiKkqguq7n37tiO7diOnThxeu/ZTd/k281mN9ndtE1PNsWpTo8TlyRuce9NvXeJFIvYC3qZ78fBgEVEnwEG4LmvS9eIIDAzgoCZc57zvM/7flGOBum1KC8EDpcQjfbcB9d+TZQkZcnKeZX87sVOIlE1r+2aVVXl/p29fPGvuwmEo1xzWktar1vWVEG928HW4yO84az0WrabnaxXZHffAxY7rLg684NWLxC5PIFxcGYg1GTA+R0N/PzZo2w+NsK5S+uJRlV+/NRhvvrgPpqryljZUsmvnjtGMBJl/cKaePlpWuy+R4z1Fl9kyLnHKQWH0fP/Bw98Wvy9vA5qFsITX4bnvifGk4OHRHi4xQqb3izGoFPdRIloWi0c8eFA+vlZyRg7MRl4XbUAhoujObNRLG0SE8UFNeVcscqgLoAlwqI6Fy6HVddOaVomUEuO48J8UF1u55+uWM7n793FI3tOcsXqwn1ehmJZQvlyGFksCrUue8k5jA73eyizW5hXAMEyHcHoFUVR/hu4F4iH3aiqutmwszKCipbiCb3e/6AQiywWeN0v018ts1jE6s6yK8WA78BDwkq86S1Qt3jWlzRXOdkc7aC96yVRcmHS1P9UHO73YLUotNW5Cn0q07hgWT13be5id89Y0tbQEvMxOKHPDa6pqow9BnTqKGr23AfupslWyWe+W+QY/eW9IlDa6oCFZzG85Aa+9tB+zu+o5zUbZi+vKXeIHKOHd/Xx8K4+jg95+fTVK1kzX2TxuBxWTl9Um/qcPAPw0h3w8h2T94rqheI6uv9BkVvkqITGFTq8AQax5ibhMDr2TE6T5lUtVfhCEY4PefPm2vQGw7z3V6/w1IEBVs2r4ttv2MgZ7WlMUBHi/LLmCg6cLGxQt54Mxwa6GWUYqSrsuReWXjqZs5MJVbFuS6MnoGll5q9Pg7OX1GG1KLz1py9S7bJjtyh0j/q5Zm0L/3PzOqpddsb8IR7f159ZsGfIL4K+17wGrAZ3Si32DKMXfijEopXXCXG5skWM/fp2w1NfF4uNrga4+FNw+tsyK21sWA5qVAhOzatzO8+gF3xDkx38qhbkXHJb7LTXu6l3O3jfxUuwWTMQU+cgFovCqnlVujqM+saFYGTm0OupvPHsNn7x3FH+6/49XLKisWCfGW0BpD5PXdIAal2OknMYHe6fYHFDRUEMCOkIRhtj23OmPKYCRdYprak4BCP/KNz9AahdBG/4LdRkuVrqrIS1rxV/ktBUVcYLoQ5u5jHhaqpfmt3xCszhfg8La8szW43MA+cvnQz5lIJRcTEwEaDWZc/5BttcWcbjY0VWDmskIb/oZHTaLULkBnDXw8WfFILN3r9BYAy23Mk3Vi9nIhDm89evSdrC9uzF9Xz3sYM0VDj49bvO5uwZOUcJiUbEBGTb72HHHyESgI4rRRbHkktEy+jNv4QHPiM6Ly2+OCfnjuFoZWm77s5NMJonxLa9PWN5E4we2tXHUwcG+JdrVvLOCxZn/L1b1lTJ3VtPoKqqadsdZ8KQJ4iiQE15BuJHzzYYOQ4XfSq7g2qlP2NdhglGVWV2vn3bRrZ3jTDqCzHuD/NPyxu59YzW+P9bVZmdG9ZnmL90+DEIjsPqmww46xmUxwTowQPGH0tvXvgh3P8pIRbd+vPp4lrzarjlDrj2q+CoEO71TGlcLrYD+3MXjMZ7xDYuGM0XY+TABDjN4ybPJ2V2Ky9+7oq8Oj+LmTXzq/jz5hO6ufz7RjXByPwOIwC71cL7Ll7Kp/60nUP9Hla0GOMcTYXW3r7GZbCYP4Vat6PkHEZHBjwFyy1LKRipqnppPk7EcCpb4PhzhT6L1DzxFfAOwpvuyl4syoDmqjLuiXaIHzpfLF7BaMBjqnI0jaaqMpY3V/D0wQHee3FxvrdzlcGJoC7taluqnXiCEcb9ISrL8nezNC2HHxc5I6uun/74hR8XfwA2/wru/RBPv/Qibz7n3JQ5JredtZBRX4gPXLo0vcypaAQe+xJsuVMsJNjdsOGNcM77T3UQnf5WaL8AHvwsnHZr+v/OQhAvS7tXTPqyFLeWNVdgUUTY+DWnzdP5JGfn5WNDVDhtvOvCJVlNhpY1VzDuD9M3FiiKcoFUDHmCVJdnKFjvuRcUK6x8dXYH1SbmBufEvHrdPF69TsfPlX8M/vEF4YoxuhwNhFix5iZ4/gdCoGo93fhj6sHRZybFolt+ltiJlU7pWSLql4ntwP7s96Ex2iW28QyjmANurHtSmJqDSLEofVbPq+KXgWN0DntZVJ/74ocWL9BUWTz3GK3z6ZGBwglGg54glU4bTlv+FtzqXA4OD+gXeF5oguEoncM+rs90MUUnUo5EFEVpVhTlDkVR7o/9vFpRlHcaf2o6U9EkQq8LHPyVlMFDYvVn45tg/oa8HLK5yskBdQFhe8VkRkeREY2qHBmYMFXg9VTO72jgpaNDjCfo4iQxJ4MTQep1qLfWVqL6xgIpnlniRKMiZPWFH4CzGtoTT+zUlrUAnO48wT9dsSzlrltrXfznjWvTDyg/8qQovWhZJ1bZP3kQrv9m4nKz+qXwxt8LV5TZWXMjePpzCr8us1tZ3OBmT2/+SrxePjrMxraarCdDy5rEQLhUytKGvMHMOqSpKuy+V4ib2U74q+YDSnF1oopG4M/vFgLFLXdk54rJhuu+AZXz4K53CMHK7ESjQvSuWgA3/9i498nhguo26NehnfdYt9hOLUkD4YCTSNJgdaw0Xa+ytL6xAPVuh+mqGZLR3iCiOrTA5EIw5AlSl8dyNNAcRqUz7zo+5CESVQvWDTydT/zPgQcBTdLaD/yTQedjHA3LIeyHLb8q9Jkk5sHPga0MLv+3vB2yuaoMFQtDNacVrWDUM+bHH4oW7EuUips3tuIPRfnlc3M7rLHYGPAEcm/FzuRK1Mm5GHztHxNBtHd/EL6+An58KRx+Qjh5kkxY7u2uIqRaecvisVmDrtNGVaFv16mPH3pEBAO/7hfCKeAwV/ZZTiy/GprWiBy8J78qJopZsHJeFXt78zMRHvWF2Nc3zhmLsnc2LGsWDlM9WygXkmFPMLP8tP69okRq9Q3ZH9Rqh4rm4upE9ch/wP4H4JovizLSfFFeK4SXkePw90/k77jpMHJc5DmFp5Rj7Pgj9GwV40ujr3eNy/VxGGnCkJahpG01IcnseAbh0GPiM/qTK+Dbm0Q5nSRvLG+uxGpR2KWbYOQvmnI0jcoyO42VTo4WWjDKU+C1Rp3bzrA3WPAOcXpxqF/rBl6YapqEJWmKothUVQ0DDaqq/kFRlH8BUFU1rChKJNHrTMv6N4rW8X/9GDSsgLazC31G0zn0KOy/H674gnBD5YnFDW4qy2zsYDmX990JQQ84zCm8JOL+HaLOfX1rTWFPJAGntVZzyYpG7nj6CG8/vx2XI53oMEmhGZwI0qDDiogWjvjQ7j4GPUEsisIFyxqoziSbpFiIhKF7Cxx5XAhDx5+HaEiExC69HJa/CjquAPfsbe5VVeX/njjMVx/cy3pXK2usx3M7n0OPwJ2vhdvvgmVXTHn8MWg7p+iudWlhL4d3PQz3fgQe/SJ0vQw3/iC568Q/CrbyaSLe6nlV/G17T15KKTcfH0ZV4cz2NMLJE1DvdlDrsnPgZGlMyIY8QRZm0sRh972AAiuvT/nUpFQvKB6H0fY/wjPfhDPeAWe9O//HX3QuXPxp0eFx8cWw8fb8n4NGYBy2/Q52/Ak6nxePLb4YXv8r0UTgkf+AeevhtNcZfy4Ny0X5WzQ6mVOXDWPdooOb1t1XE4zMKGhGI2LRdd/9Iv5i4IAI7AZRJlo5Twhg473g7Cjsuc4hyuxWOhordOuU1jvqL5rA66ksrncX1GE06AmyoCa/Qluty0EkqjLmD5fEeFv7/zOjw0jrs+5RFKUeEXSNoijnAMXXGsJqg1t+Kmqgf/8m891wHv0i1LaLlfc8YrdauGh5I/cOLQA1IiZ7RYQ/FOGHTx7mvKX1pg6V/vBlyxjyBPnNCzlOgCV5IRiOMuoL6ZJhNL+mHJfDys+fPcqHf7uFD/5mM195YK8OZ2kiBg4Kh+TXlsEdV4jrmW9EXM/e9nf45GG49Wew/raEYpEnEOaDv9nMlx/YyzWnzaN15VlYenfmdl4H/iG2238/+dh4L/TthI7Lc9u3mXG44bU/gWu+KgLGv3O6CO+ezW3Usx2+eRr88W3THl4ZyzrYl4eytFeODmO1KGxoq8l6H4qisKypkgN9JVKS5smwJG3vfbDwbKjMsXVyVZEIRsPH4K//DG3nwjVfKdx5XPgJkZt030eFSJJvgh7RYfJb64XTyT8qXETXfEUE+v/sWiEWjXXBVV/KTcBJl4blEPblXjo2emIyvwjA5hSNCMxUkjbWAw98Fr7aAT99FTz3XUARTr9X/ZfII/3MMeGAAwgVbtI+V1mzoIqXjg7p4vI+Oe4vyoy8xQ1uDhfUYRQogMNIHG+4RIKvD/dP0FDhpKpAWajJrA5akMDHgHuBpYqiPAM0AkUQ5DALrjrReewnV8Dv3gA3/8QcwXldr8CJV0R7U1v+levLVzbxH9sXQRki+Lr9gryfQ7b8/qVO+scDfPu2jamfXEBOX1TL+R31/PDJw7zpnEWU2U3caUkSb8WpRwvQMruVpz51aXyfX39oP/dt6+Zfr1td/J+DaAT+8BbY+1ew2GDFtaLEa/FFCYWhRHzij9t4cFcvn712Je++cAnKs+tg1x+Frd+dZtezmRx5Qmz3/k20aHa4hJsThOOplFEUOPs9sOg8+NvH4d4PC9HoVf8NC88Uzxk4AL+6CUI+2Pc3IS51CCfWylintD2942m3t8+Wl44OsWZ+Vc7uy2XNFfx1e0/Rd0pTVZVhb5DadAfYI53QuwOu/I/cD17dCgcfEeWcZn0Po1HRTRbg5h8lDm/OB1YbvO6X8JMr4fe3w7seyV/zkEOPwp/fIzLLOq6Aiz8z+d0GqO8Q1+e+neLavPjC/JyXlgXXvz+35i1j3dMFI4gJmiYoSRvrFk1qtv5a3AdXv0Y0clh6GZTXnPp8rQww6M3raUrgA5cs5f4dvXz4t1v49bvOzrrzbTAcZWAiWHQlaQCLG90MvBwoSPMVVVUZ9oSoc+d3fqvdP4e8Qdopfjf54X4PSwqY1ZvsW9OoKMrHgEuAvwBfAe4HfgxckeR15qZpFbz2DrEi/v2z4S/vh6EjhT2nl34sWpiue31BDn/JiiZGlUqGytpE+UKREAxH+b8nDnHGolrOWWLshEYPPnTpMvrHA/zh5c5Cn4okBf3jIqC6XqcbXH2Fk46mSjqaKnnj2W2M+cM8sufktOd0DnkZ9RZZQN+ee4VYdO6H4J93i/KHtTdnLBYd6p/ggV29fPDSDt5z0VIx2W85Tfyyb0d25zZxEk7uFsJQyCNyTkBMht1N0Lw2u/0WGy1r4e33i7K0oSPCAXbnLbD37/DL14jnvOdxqFsiVsoj4jM4v7qMqjIbe3Wy8iciGI6yrWuE0xdlX46msaypglFfiP6J4g6Y9wYjhCIqtem2INY+2yuuzf3g1a3i+zLem/u+jOL578Gxp4VrIw/dZFNSXgu3/wEUC/z6FvAOGX/MI0/Cb98gHDfveEg4WaaKRSBclG//u+iK9qovGX9OGg2xhdiBHIOvx7omg641qlsLXyHQsx1+dKkQizbcDh/ZLBy0a2+eXSwC0YkTICQFo3zT0VTJl25aywtHhvjmPw5kvZ+T48KhVIyCUXusQ9zRgfx//iYCYYKRqC5NZDJBc+iWisPoyICnoFm9yQQjK1ABVAJuhBvJCrhijxUvK66Gj26Dcz4Au/4M394AX18pBs/3fgR+dzv86BL4zhkwdNjYc/EMiGyl9bdBWZWxx0pAndvBprZatqrLRA12kQSE3bW5i55RPx++fFlRrCafs6SOM9tr+cHjhxgs8glNqTMYu8HokWE0k/OWNtBc5eTPmydt9QMTAa799lNc++2n6BwqkgGlqsIz3xZCw5X/kVMpzE+eOozDauGt57VPPqgJRr1ZCkZHnhTbSz8LFS2w8y7hTDj8GCy9ND+lGWbBYoENbxT3vcs/DydeFi7b4AS85W5oXiPKVQb2wUt3AKLEa+W8KvYYLBjt6h7FH4pypg4upmXNYmhysMiDr0d9QrRLO3dh3/1QtxQaUncUTMmyqwBFLGSZkb5dosRq5XXiM20W6pbAbb8VYsaPLpm8/hjBsefgN6+H2sXw1vuSZ3LOWw+3/VqcX75wN4jsoVyCr4Ne8A1P5hZpVM0vrMPo0KOizM9ihfc8Ibpr1ranfp3mMJKCUUG4eVMrrz9jId997CCP7zuZ+gWz0BcraWspQsFIExoK0WZ+KDaeLlRJ2lAJCEaj3hCDnqBpBaMeVVX/Q1XVL8z2J29naBQVjWLF5SNbxWRnyaWio8/ev4n29na36Dhy7Dljz2PzLyESgDMLENg4hctWNvHIxCLwnIQR83fzCkWifP/xg6xvreaiZZm5GQqFoih8+uqVDHmC3PT9ZznUX9yTmlJGE/T0yDCaidWicOPGBTy+v5+B2HG+9uA+fMEI4/4Qt/3o+eIQjY4+Dd2b4bwPi8Fzlpwc93PX5hO89vTW6V3p3A0iKDRbwejw4yJse/5GsfJ74CE4+hR4B0u/HC0Rzgq48GPwTzvg6i+LyaYmzK24RnSZevy/4w6JVS2V7OsdJxo1bhHhlWPDAJyhh8Mo1iltf5HnGI14MxCMAuPic73iGn0O3rBM5K+8+GORh2MmfMPwh7eK7/X13zJfyVzb2fDWe8X18BfXi1JQ34i+xzj4CPz6ViGcvOWejN2ceaNxhShJyxZNFKpunf541QIIjIrPfb7Zdbd472va4J0PQ/Pq9F9rlyVpheYLr1nDypZKPvHHbfhDmfduOhhrqLC4gGVB2dJW50JRKEjw9WCBBCOtJE2LgyhmDg1on73CdEiD5IKRye7EBlE1D87/KNz0A3jPY/CpQ/DB58WN2GKDwYPGHTsagZd/Cu0XQtNK446TBpetbGJLNNa5wciVMZ14ZM9JOod8fPDSjqJwF2mc0V7Hb99zDp5AmJu//yzPHx4s9ClJZmFwwjiHEcBrN7USiarcs7WbHV2j/P7lTt5+fju/efc5TATC3Paj5zk+aPKB5TPfEuUQ69+Q025+8exRQpEo775wlhXwltMgm+BrVRVd2tovFJO3tbdAJAj3f0r8fullOZ1z0eOshHPeJ9wHGooi8o0CY/C3j0EkzMp5VXiCEbqGfYadystHh2mrc9Gkw6ptY4WT6vLi75QWdxilU5J26FHx2dZLMAK44GPicxBzm5mCSEgEsw8fhVt/bl6hpO0ceN8zcM4Hxfv3lcWifOnhzwuRfbbg+XTwjcA9H4Q7bxZi0Vvvyz3g3EgaluVWkqYFr5/iMIqVqOW7LG30BNzzIbEA8Y77T81WSoUmGMnQ64JRZrfy/169moGJIA/uyrzkdk/POC6HlbZMuleahDK7lQU15RwtgGA0NFEYwcjtsOKwWhjyFFnUwyzsjzUfWd5sTsFoji7BxrDahM10pmAUCYsb/4gOOTT7H4DRTjjrPbnvK0dWtlQyVrmMTsdSYff2DEz7vZErzNnw9x091LrsXLayqdCnkjGb2mq5+4Pn01Dh4M13vMBzh6RoZDYGPAEcNgsVztxCeBOxvLmS0xZUc9crXXzhvl3Uux18+PJlrF1Qza/fdTbj/hD/ft8uQ46tC7074eDDcPZ7J1seZ4EnEObO54/zqtUts6/atZwmJh2hDLubDB+B0eOirTTAgk2ifKN/L7SsEw5Tyak0rxYdlnb9BX5/O6sbhWCxp9eYsjRVVXn52JAu7iLQOqVVlIBgJAbYaTmM9j0AZTWw8Bz9TmD+BuHCe/77IhC90KiqEHsPPy5KgMzemMPhgqv/C977hOiiZnPCc9+Dn78avrFGdJQ8mWanTFWF3ffC986Grb+F8/9J7HemkGI2GlYIN6cny/HN4cfEdma5l1Za17M12zPLHFUVHfmiYbj5x8LhlinxkjQTfJ/mMOctraetzsVvX8y8Y/GenjFWtFRisRTPIvVUFje4C+IwKlRJmqIo1LrtJZFhtLdXiJULawsnViYUjFRVzUNqn8mp7zg1w6hvBzzzTTFZyoWRTnjkP8VqiR5BlTmiKAqXrJ7Hh/zvQ/WPihaxsSyjnlEf53/5UW7+/jOmcMT4QxEe2dPHq9a0ZN3toNAsrHPx5/efz6J6N+//9SsFUf0liRmcCNLgdhjqXrt50wJ294zx8rFhPvmqFfFWmWsXVHPTxgU8e2iAQDhz23ReePY7omz3jHfmtJvfv9TJqC/Eey5OkK/RvFYM0vvTnFxpHI51R1sSE4wUBda+Vvx9rruLUnHBP8Or/xf2P8jaR95KtTJhWI7RsUEvAxNBXbuwLWuu4EDfOGqRZPHNhuYwqnGlGGBHI3DgQZE7ZNVZ3L7w46L71pY79d1vpgQ9YhHr5Z8KsWTjmwp7Ppkwbz1c9jl4xwPw6aOi4cr8DfDCD+EH5woRYsbi3DQGD8Gdr4U/vFm4Od/9CFz5hZxE+rwRD77Ooiytfx88+11Yd9upoebzN0LNIhE4nS923iW+Z5f/K9Qtzm4fsiTNFFgsCredtZDnDw9xOINYCFVV2dMzxsqWwmTN6sHiBjeHBzx5vzdqJWl6dB3OlFqXg6ESKEkzg1hZnLPtfFG3VNywp1qIT+4R20gOFrfDT8CPLobRLrj+2/oP9LLk8pXNbAsuYO/qj4rOR1t/QyAc4X13bmbMF6J7xM9tP3qet/z0RXaeKFy2wVMHBvAEI1xz2ryCnYMeVLvs3PHWM1CAd/zipfgkQVJ4BiYChuQXTeWG9fOxWRTWLqjiltMXTvvdhcsa8Yei8XwXU3HkSdjxR9j0FnBlP9FXVZU7XzjG6Ytq2dSWwGHSsk5s+zIsSzvyhAi61iYtIAJyK1pgzU3ZnfBc4sx3wq0/x9qzhb+X/RvRw08ZcpgXj4h1qTPb9XEYASxrqmQ4FhBZrKSdYdT1knBx6FmOprHoPOFaeuZbhXFFBD3i2N9cB0//r+gie/nn838eeuGsgNNugTf8Fj6+TzjLX/kFfHsj3P9peP4Hwkm062549EuiA9r3zxH/x1d/WXQynL+x0P+K9GnMUjDS3DwON1z1xVN/b7EI0fDIk/npcOwZEO62BafD2e/Lfj9WO1jssiTNBNxyeis2i8LvXkq/UqRn1M+YP8zqecXb82lxg5txfzjv98YhT4AyuwWXI/9z3Tq3o+gdRqqqsrd3vOBipRSMklG/FMI+GJ/SkaEvViYSyeIDGPLBE1+FX90IrgaRmbTsCl1OVQ/OXVrP4gY3N23eSF/dGXD/p/nmHx9mW+cIX3/deh7/5CV89tqVbO8a4brvPM0Hf7M5I4VeL+7f0UN1uZ3zltbn/dh6s6jezf+96XQ6h7x86DebCUeyzDeQ6MrgRNDw1ZD6Cic/fduZ/OD207HOWDU4Z2k9NovC0weSrD4Xgp7t8Ns3CiHmkk/ntKsdJ0Y53O/h1tNbEz+pbrFYmc0k+DoaFZOJJRdPD8WtXwqf2CdW+CWpWXMjvPU+7DYLH+v+mAjwnS1o9uWfwZ/fm9Uhnj88SL3bQUeTfnX5WvD1gSLulDbqC2G1KLgdKcLk9/1dZC12GJAgoChw6b+Iha3fvD5/zohoFLb+RggpD/8bzFsn2sbf/KPS6WzorodrvgwfeB4WnQ+v/Bwe+IxwEv3xrfDU10Qcwobb4UMvibwxkywspk11G9jKMheMtv4ajj0jmtEkKh3e8EZAEZ8To3ngM6Ihzg3fzam5AyDK0qTDqOA0VZZxxapm/vRKV9oubs1lu3Je8TqM2mNl//muaBj0BKl3G7sAm4had/E7jHrH/Iz6QqwqsFhZIndfg6iPhUAPHpp8LO4wyuADGAkJO/W3N8JjX4RVNwhrsR4tcHWkzG7lLx84jzOXNHBz95vxhcJs2v1lPnjpUq5eO48yu5X3XLSUJz91KR++rIPH9p7kym88ye9fyrwWOFsC4QgP7+njytXN2I0uR9v/EJx4xdhjAGcvqeffrlvNUwcGeOGIrAQ1A4MTgbzc4C5a3sjCWQIUK5w2NrbV8PRBEwlGw0fh17eI/IY33QXlublC/rLlBA6rJblT0GIVLd8zEYx6twvXhZZfJMmetnP405l/4Cfha1BfugN+ddN0x21gHP7x77D9d0JYyABVVXn+8CDnLKnPvfQz5Bf36V1/YdPe/+W39i/S/rfb4O4PwuNfhoEDue0/z4z6QtSU25O/L6oKe+4TeT7ZZKqkw5JL4Kb/E13YfvM64foxClWFo8/ATy6Hu98P1QvhHQ/Cm/+SvG18MdO4HN74O/hcL3zyMLz3KeEk+my3EIqu/yZUthT6LLPDYoH6ZaK8LF08g/DQvwpn28Y3J35edasoLd76a1GWaRT7HxRu2os+kVlHtETY3RCSgpEZeMPZbQx5gjy8uy+t5++NhQ6vbCleh9GSmGB0OM+C0ZAnmPf8Io06V/E7jPb2aJ896TAyL/VLxXZq8PXJ3WKbbknaaBf86BJhsa1eCG/7G7zuF6JLjQmpcTn4+dvP4jWXnMO3gq/hSutmPrake9pzqsrsfPyqFTzxyUtZ0uDmrs3561bx7MFBxv1hXm10OdqOP4kB8pNfM/Y4Ma5fLwIst3aO5OV4ksSoqsqAJ2hYh7R0uaCjkR0nRo252XW+JATssZ70nj90RIgF4QC8+c+Zd4iZQTgS5b5t3Vy+qil12U3LOujeGm/1nhT/GNz9AXBWwbIrczpHiaBjQSNfDL+Z4xd8WZTH7Prz5C9f/hn4R8TfDz2a0X6PD3npHvVzTqZO0WhEfH4f+2+44yr4ylL4UjN8ZxP88W24tt6ByxIi5PfCwX/A4/8FT3w5s2MUmBFfKPX34tizImNx3W3Gnsz62+CmHwnXx52v1V98C/lhy6/hhxfBz68V7dRv+qFoW96mY5C3mVEU4Tqat06UnRVDRlE6NK+G7i3pj5d33w2+Ibj2K6ndZJveLDqpaeHYeuMfE+P2ptWia6AeOFxSMDIJF3Y0sKCmnN+8kN6C9+6eMRbWlVNZlkYjApOyoKYcu1XJe/B1IQWjWreDEV+IiMkaN2WC1nRkRYHFSikYJaNyPtjKJx1G3iEYj02wwoHUrz+5Vwxoh4/B6++Edz5k/u4egNWi8KmrV3LzB75EtKYd60OfFd3hZtBY6eSM9joO5rEjzd939FBZZuO8DgPL0Q48DH95L6Dm7eZe43KwuMHN9q6RvBxPkpiJQJhgOEqDwRlGqbhgWQOqCs8a0UXv2DNisrk1jUDbQ48K0ds7BLf/ERpX5Hz4pw8OMDAR5MaNaQhPZ7xDlAY//j/JnxcJw5/eLgKyX/cLqCi+DopmZFXMgv9Mxaug+TR45Avi/hfyw3PfFS6Uyvlw8JGM9qs1UDh3SRo5WON9QlT449vhK0vgjivgya+AGoWVr4ZL/x/c+AN4z+Mo/9LFF5q/xadqviZKEJvXFl0ZyJgvRLUrxcRky6+EMLr6Ncaf0Lpb4bU/EZP/754Bv7lNZDGGsxSzRzpFy/nf3Q5f7YB7PiBEhVf/L3xksxCpSqX8bC6z5mbwDsC++9N7vlbyWp+G+37FtVBeZ1wo+z/+XYz3b/gu2HSa7NrLi+5aVKpYLApvPncRzx4a5MsP7E0ZBL23yAOvAWxWC211rryXpA15gtQXzGFkR1Up6ozYvT3jLKgpT69rqoEUWVF0nrFYhMtoKCYYaeVokLok7fgLwqFic8Lb/y5WjoqM5Qsa4FVfhN+/CV75GZz17lOes6ypgt++GBQlPAZPsEORKA/t7uPKVc04bTnWkifi+PPw+zeLVSWLNT1hUCfWtVbzwmFZklZoBicK19FhKutbq6kss/H0wX5evU5nR91wLCx0y51wwcdnn5wFvfDiD0WHosZVcNudky2Nc+TuLSeoLrdzyYo02tu3rIXT3w4v/QTOeDs0rTr1OVrb7YP/gOu/JTuh6UhrbTkVTht7+ybgqv8QTrOXfgJWB0z0ic5P234He+8Tol2aWSvPHRqkocLJ0sYU+UV7/gp/fBtEQ1DRLASijsthyaUJQ9eXNVXyyN5YqYHVAZH8Xcf1YMQbSn798Y+KcOT1t0226zaata+F9ovgpR+L//9f3i/e28aVIhfstFuh/cLpuWFTCfnE/+WWX4lQehA5N6fdIoLoF1+U+LWS4qTjCtEJ+JWfw+obUj8/7BdbW1nq59qcIgj95TvEYkYODRhO4egzYr/nfBBaT9dvv3a3DL02Ee++cAnHh7z84PFDjHiDfPHG007JkwTRmfnIgIdXr5tfgLPUl8UN7jnnMCr0OeTK3t4xU5RCSsEoFXVLJsvQtC1KcoutqgqRxVUn6u9r240+S+NYeZ0YBD72JTFgnHFTjgeMnpwwXDB68cgQo74QV681sKb/rx+DymZ405/FqufESeOONYP1rTXcs7WbvjE/zVVpDJgkhjAwISaXRn+eU2GzWjh3ST1PHRhAVdXcc16mMnwUFIvYHntaTNZA5NPsvht2/UWILyEvrL4RXvM90eVHBzyBMA/u6uOmTQvSF34v/Rzs/JMIIH3z3ZMTy2gE9j8AL/yfCLo+7yNw+tt0OU+JQFEUVrZUitDP11wmxLgnviLKqheeLVyznn7hVuveDAvPSrlPkV80xNULIyh/eS9c+Z/iujuTvX8TIcDzNsB134CW09ISFZY1V/D7lzvFINFWllfhXw9GfSGWNroTP2HnXcJ1tylJzosRVDTCpZ+FC/5ZuEZ6top8sd33wOZfioWWM94hJvMjnaIkf7Qz9ueEEP1qFonv85qbRE6kFIlKF6tNdDR74ivCaV+7KPnzQ14hFqXrLlv7WnjhByJjS0+n3UOfE5/Tyz6n3z5BiLuzNQ6QFASrReFLN66lzuXgu48dZNwf5jtv2HjKWGt/3zhRFVaZYNKeK4sb3Dx1YIBoVM1Li3Z/KII3GIkLN/lGE4mGizT4OhCOcKjfw5WrZxkf5Rnp+U1FfYeYVEXCQjAqqwF3Q3KH0WgXeE7CuR8qbrEIxGDu6v8RK5q/vAGOTG+vvKxJXEAP9Bl/E9wfO8bGRC249cDTL8JyKxrFoDePE431C2sA2CZzjArKgOYwMsFqxIXLGuga9nFsMAMbu6pODyaejaEjwtLvrBYTPY1nvyXKujpfhPVvgLfcA7f+XDexCOCh3b34QhFuSqccTcNdLyaZhx+HPfeK/JaHPw/f3gC/e6MoG77yP+CKL+h2npJJVs6rZG/PuLDtX/kf4n4w2gkXflzcI5ZcIgTIg/9Ia39HB730jvm5yfEibP+9+MzNXITZdz/84a0wb73IzZq3Lm1xQeu6dqBvXJSTFKFglNR+vvlX0LQG5m/K30lNxV4Oa28Wn4U3/0W0iX/N90Cxwt8/Afd+WHT6OvKEGCstOAPO+xC85V74yFa4+FOi6YcUi0ofLbx6y69SPzfkT89dpKFl6aWTb5cuQ4dF6eVZ7wZHEtE2G+yyS5rZUBSFT7xqBR++rIO/bu9hT8+pcxmtQ9qqIu6QprG4oYJAOErPmD8vxxv0FHY8XeuadBgVIwdPThCJqqYoh5SCUSrqOyAahpFj0LdbrKBZnckFI610rUmHrgpmoGWtmDR6h+EX14m22rGw3OYqJ5VOGwfykGPUNeyj3G41Now47J8MnLSVT1qk88Ca+VXYLArbZI5RQRn0iMlloTOMAC5YJkq2nsqkW9qz34FvrU/cPSYSEqJ20yqRTbL7XvANQ9cr8OgXhaPoY3vguv+NCQH6Tur+9EoXrbXlnJ6p8HvGO0T5yx/eAj+7RuTn1C6GW38BH90O539U5p4YxKp5VYwHwpwY8QmXz5nvEs7TZVeJJ7jqYMHpiXOMotFpAetaftGK4E5xnT32jMgMASF4Pv9/ojS4Za1we2bYBWx5c2wh4+RE7H5dPIJRNKoy5k8iGPXtEk6uTW82j+BiLxdOkvc9JVrFf3Qb/L+T8LHdIrvxljvgin+HJRfL7+hco2ahaECw5c5ZszCnEfIKUSVdtE6dvuHsz28mu+4WWyOywewuWZJmUt54dhsAz8wy1trTM47LYaVtlo62xUZ7g/g35CvHaCi2AFuwLmmaw6hIBSOtQ9qqeYV3t8k7dyqmdko7uUd0fbDaUwhGsdK1ppXGn1++WP0a+PDLcPm/wYGH4OlvAEKd72iu4ECf8YJR55CX1tpyfUtzZhLyTa5w5dlhVGa3sqKlkm2do3k7puRUBgt8g5tKe72LBTXlPJuJYLTrzzB6PHE3o9FOUCNCbNn4ZjGZfvlncNc7oHKeyAAyaFK3tXOEZw4O8saz2zK3Q1vtItj4zHcJkehTh+Gt98KaG9POzZFkh7a6FV99ffXX4G1/nS5YLL0cTrwy+2r/Kz8TImZMNHru0CBNFXbcvS8Kp8pZ7xUC4Is/Ftl/D3xalL69+S9QXpPx+c6rLsPtsIqGDDZn9uHMBWDcH0ZVodqV4Pqz5U6RHbTu9fk9sXRQFCFE17aL76tEArDprSJA+sBDyZ83dcEuHezlYrzm09FhtPtuIX7XtOm3Tw2HdBiZlXnV5SxtdPPModkEozFWtFTmpYTLaJY0CPft4TwJRtoCbKEyQeMOoyItSdvbO4bDZqG9Xme3YxZIwSgV9R1ie+RJCIyKwZDVkVowqlowufpRKtjLRQlCdeu0FZ1lTRV5cRh1DvtorTWw3Ww0IjIW4g6jMpETkUfWL6xhe9cI0SJuAVnsDE4EqC6347AV/vKoKArLmyuEsyMdPIOiBT0IF8JsDMUCr2vbRVhtyzrR+WrkuOiElMUEPV2+++hBqsvtvOXc9ux2sGATvPrrQiTK0HUiyR6tnevemDV/VjquANTZ21zvu18IkwcejOUXDXJjqwfFNwRt58JVXxR5SH//hLjXXvs1eOPvs76HioWMSg6cHI8JRvlziubKiE+MLRI6jPY/IN5rPUN+JRIjWf4qqGgR4dfJCPkyE4xAdErTy2E0dAR6tgmXrRHY3eLfKDElF3Q08MLhIYLhyZJ+VVXZ0zNWEuVoIKpCyu1WjvTnyWHk0RZgC+PYL3dYKbdbi9dh1DvOiuZKbNbCz0cKfwZmx1UvJiZ77hU/N62JCUZJQq9P7p69k0+pYHdNaze/rKmSgYmAoV9IVVXpGvKy0EhLqHYjL5DDCERnrDF/mKOD0rZcKAY8wYJ3SJtKrduRfv314ceAmNh4IoFgNHxUbOsWi+2mt4jtxZ+BtnOyPc2U7O4e4x97+njH+YupcEpHUDFR4bTRVudib2+SrLoFm0TG38FHpz8eDoiSM4D9D3JkwMPJ8QBXuGPdRxedJ3KGXvdL4R5792MiPyRHJ+nypgr2902kLiE3GVr735pEglFgQnSLk0iKBasdNrwRDj4M432JnxfyZiEY1YJvJKfTi7P7brE1ohwNhMMo5BFltxLTcV5HA75QhC3HJwXInlE/Y/5wSQReg1hMWdzgztscY1IwKtyYus7tYMiTZM5uYvb0jJuiQxpIwSg1igJ1S8XqO4gys2QhmpEw9O8vccGofNoqSUesU9rBfuNcRmO+MOOBMAtrDRSMtP/TaQ4jf15v7vHga5ljVDAGJwI0FGg1ZDbqXI70xdiDj4gBdNt5iR1Gw0fEZ7si1m3wjHeIzmMXfUKX803Edx87QKXTxtvObzf0OBJjOK21mpeODiV2P1qssPRSEXw9NT+r80UxEaxdDIcf55WD3QCsCu0Cd5PoRApQ2SLcY836ZP8ta66gfzxAAHtxOYy8YmBb7UogGGVatiORmIH1bwA1Cjv+mPg5mYZeQ0ww0slhtOtuESSfqptbttjLxXtQZCH8c4VzltRjUabnGL10VJQ7lorDCESntCN5K0kLYrcqVJUVbpGw1m0vyi5p/eMBBiYCrDTJZ08KRumglaVpZWbJStKGDgvrfakEXs+GfXoY9LJ4RxrjBKPOYeFoWlhn4EBZKz+zxcQCe5m4uUdTBDXqyLKmSlwOq8wxKiADE+ZzGHmCEfyhBCHWGqoKhx6FJZdC6+mi3fVs2S3DR0XLYC2nSJvoW9JscZ8FB/rGuX9nL285b1Hy7k8S03L5yiZOjgfYfiLJtWnV9TDRK7pjaRx+THTPuuLzEPIyvu9xalx2KvpeFO4igzLptA6eI0GlqDKMUjqMQr7Je5REUiw0LhfZQNt+l/g5mYZeA7hq9emSNnwUerYa5y4CUZIG0xz6EvNQXW5nXWsNT8cEo2hU5fuPHWJJo9vY7sx5ZnGDm+NDXkKRFN10dWBoIkity2Fs9mwKal2OeLe2YmJvb6w7n3QYFRGaYKS5hqz2xCVp8cDrEheMptzw5leX43JYRV6EQXQOieO1GukwCsVEMNsUhxHktebcalFYO79aOowKyOBEwFSCkWbl1ZwHCenbJSbrSy8TA/NIEPp2nvq8oaOT5Wh54juPHqTcbuWdFyzJ63El+nHZyiasFoWHd/cmftKKV4uytC2/nnzs0GPQeiYsvwbsbhq7H+OylgDKaJcQjAyiI7aQMehXiqpLmiYYzSqsajl7NukwkhQh698AfTvEYsZshP1ioS4T9HIY7b5HbNfcmPu+EuGIjV+DMnLArFzQ0cC2rlHG/SHu39nLvr5xPnr5MqwlEHitsbjBTSSqxudVRjLoCRa8gUydOwOXvonYenwEgDXzzZHXKQWjdNA6pWkiUDKH0ck9gAKNK/JyagVhRkmaxaLQ0VQhOtIYRNxhZGhJWuzfpA1YNMEo3zlGC6vZ1T02LXhPkh/CkSjD3hD1JipJi3d5SHXDOxRrab70MmGrh1PL0lRVrKTWtut6jsl4bO9J7t3WzTvOX1zwgYMke2pcDs5sr+Xh3UkySOxlcNqtsOc+MYnzDkH3FuFgs5cRXnwxmwIvcqWWX9R2rmHnu6BGLGSc9Knifh0tjuupJhhVzSYYac7eTCfVEokZWHMzWOyJXUbZOIw0wSjX6IDd98K8DcbeG7V/mwy+Ni3nddQTiao8d2iQbz2yn46mCq5bN7/Qp6Ur7Q3C6ZaPHKMhT+EXYGsziXUwEZuPD7OsqSJxeXqekYJROjSuFNuWdWKbLETz5G6RyVDKGQMzQq9BrOYaWZLWNeyjssxm7BfnFIdRTDTIc/7F+oU1BMNR9iULmJUYgtZ6s8GEDqOUNdgHH4HGVVC9QLQEdtXDiS3Tn+MdhOC4yJPJA8OeIJ+6azsrmiv58OUdeTmmxDiuXN3C/r4JjiUbaG68XTh6dt4VK01TRZkkcLz+QhYoA5w3dDc4q6B5jWHnqi1knPTGJpJFEnw96gtRZrdQZp+lRHTmPUoiKSbc9aJj2o4/irzPmWSbYRQJ5FbmFfQIYbvj8uz3kQ5xwUg6jMzKprZayuwW/vv+vezvm+AjJeYuAlgSE4wO56FT2qgvVPAYgjq3g/FAuKgW4aNRlc3HR9hkolJIKRilQ8taeMs9sOYm8bPVnlww0im007TYyycHrjGWNVXSO+ZnzG9MEn3nkNdYdxHM4jCKDcrz7DBa0SzqVQ8PGCfASWZncEITjMzjMKpzi5ttUodR0APHn5sc8CqKcBmdeGX684aOiG0eHEaqqvL/7t7JiDfI/75+PU6bcRlJkvxw1WrRnSupy2jeBmheK8rSDj0mhKEFpwPwtCK21YNbREc+A3OzQCxkdGuX0SIpSxvxBhMPsGfm7Ekkxcb622CiDw4/furvQr4sHEZ1YptLWVr3VlAj0HpW9vtIh3hJmswwMitlditnttdxZMBDR1MFrz5tXqFPSXdq3Q5qXPa8BF97gxHcjsJ2xa2NxzoUx6IRiPnfqC/E6YukYFR8LLkErLEPfaKStJBPhF6Xcn4RCCFlhqVWC742qiytc9hnbOA1mMZhpOU05aO+WDIdTTCqN5FglFZJ2tFnxDVp6WWTjy04HQb2iTbcGsNHxTYPGUb3buvmbzt6+KcrlpumBluSGwvrXKxsqUwuGCkKbLhdlEPuuhvaL4zfO5/vt7HbEnOaGViOprGsqZIB7fJdJJ2JRn0hasoTOBxndvKUSIqNZVcJV9C23576u5A38892eWxClYtg1PWi2Laemf0+0kGGXhcFF3Q0AJRcdtFUFje481KSNhEI43YWVjCq08bQRSQYbT42AsAmKRgVOVbH7KHXA/tFVy0tHLtU0UKvp9SML2uOCUYGlKWpqkrXsNfYwGtIkmGUX8Go3GGlsdLJcSkY5Z2BCTEhK3TN9VSqy+0oSgrBaP8D4vM6NUR4wSZxPerZNvnYcMxhVNNmzMkCnkCY/75/D5/44zY2ttXw3otk0HUpceXqZl46OpQ8E2Dd68Big8CoyC+KsfX4CEdqLxQ/GBh4rbG8uYIgscFqEQlGCR1G2kJNpmU7EolZsDlh1Q3injWVSEi4fLIJvYbcBKPOl6BuqSiZMxLNYSQFI1Nz+zmL+Mbr15eku0hjcb2bIwaXpKmqiicQpqLAglFtOi59k/HKsWFqXPZ4+aAZkIJRNtgcsw8+++ZAhzQQgpEamSaatda6cNoshnRK658I4A9FWVg7NxxGAG11LikYFQBNMGowUei1zWqhutyeOMMo6BGZEKuun746qwVfTy1LGz4KlfN1dSi8fHSI3714nN+9eJyfPHWYy7/+BD984jCv2bCAO956JjarvM2UEleubiaqwqN7TyZ+krsBVlwj/h7LLzo55qd71M/QmrfAq/7L+PIPhMMooMbElyLJMBrxhhJn9Wn3IikYSYqZ6oUQnJi+8KqJKJmWpLliJWneoezORVWFw2ih8dej+H1XlqSZmgqnjZs2tmIpUXcRCIdR96gfXzBi2DH8oShRlcI7jNxpNo4xEa8cH2bjwhpTfQYL+79YrCRyGJ3cLX5XtzT/55RP7FNWSWzii2i1KKyaV8WvXzhOVIV3XrCY+TX6TEq7hsWq6sK6ueEwAlhYW85LR3VoFSvJiEFPEJtFoarcXJfGOpcj8c1ux58gMAZnvHP64xWNYmA+tVPa0BFd84t2dI1yy/89N+2xtQuq+N7tm0xVey3Rj9MWVNNSVcZDu3t57emtiZ942b/CgjPiXUa3do4AsHppOyzaZPyJAgtqy8U9GQpyHc+GsWQOI9klTVIKTG0vX14j/h7KUgzN1WE0fBQ8/caXo8GUkjQZei0pLIsbJzulrZpXhaqqPHNwkHOX1utWhjcREMH2Fc7C5ldqmaQD40XiMvaGOHhyghs3mKs7n7lmRcVCotDrk3ugYcVk1lGpYp9dSPnWbRv45j8O8PNnj/KLZ4+ydkF1/MKzuMHNhy7tiLdzzAQty8dwwWimwyj+78z/RaatzsW927oJRaLYpUMjbwxOiBagimIeVR9EaN+sDiNVhZfvEK7GtnNO/f38jXBiimA0fHRaiVCufPmBvdS67Pz5A+dTZregoNBU6TTVqohEXxRF4YYN8/nJU4fZ3jXCutaa2Z/YuEL8ibGtawSbRclrnpXVolBbVQkeiqYkbSRpSZrskiYpARyxceA0wShLh1GuglHXS2KbD4eRDL2WmIT2+phgNCAEoz9vPsHH/7iNX73zLC5c1qjLMbxBIRi5Ch167XJgUWBgojgcRps7xbXMTPlFIEvSsiNR6PXJ3aWfXwTTHUZTWFTv5huv38ATn7yEt57XToXTRrndSpndwl+3d3PF/z7B5/6yg5Njma30ag6jBTo5lhKSyGE0I+A7HyyscxFVoXsk/8eeywxOBE3VIU2j1uVgyDOLq/HEZpFRdMY7RNjwTBZfBCPH4Mmvis/xeLduDqOnDvTz9MEBPnzZMhY3uJlXXU5LdZkUi+YAH7qsg4YKJ5+5awfhSHqtard2jrByXuXs7eINpL6mSvylCErSguEo3mCEmlRd0qTDSFLMOETmJcEpTpts3XP2ciGg+rIsSet8UZxPPqIk4mNnOa6TFJbFscX7wwMeIlGV7z12EJiMZdADzWFU6JI0q0WhvsKp67/NSLYcG8aiwPpEi3EFosStMAZhdYoMn2hksi1wNAJjJ/LSfajgaHXYCW56rbUu/vW66Tffk2N+vvPoQX774nE2Hx/h/o9emPbhOoe81Lsdxl90EmYY5f8io7mpjg95WVSvb+hZ17CXz9y1g4YKB19/3YaS7QKRDQOeoKk6pGnUue3sPDF66i9evkPY3Ne9fvYXnvEOsYL66Bdh6Kh4rDb3a1Q0qvI/9++ltbac288xLkBbYk6qyux84YY1vP/Xm/nZM0d5d4pg82hUZXvnKDcUwGLdWFMJJyAc9Jl+wDPqE6JwwgyjbMt2JBIzEXcYTWmSkq3DCITLKGuH0YuiQYQlD0K2xSrmD7IkTVJg3E4bzVVOjgx4+PuOHg4PiM/kqHeWhcks8QREPlKhQ69BlKX1F0lJ2ivHh1k1r6rgQttMpMMoG6yzhGjOpXa3WaySNFWV8Z83ruWDl3awt3cMfyj9oLWuYR+tRpejgVi9tTrAEvtaFDDDqG2KYKQnf9/Rw7XfeoqXjg5x99ZuvvzAXl33X+wMjAdocJunQ5pGrdvBkDeIOqUzId4h2HmX6EhVVjX7Cy1WuPEHcNqtsPXO2M7acz6f+7Z3s6t7jE9ctQKnrbD16ZLCcPXaFq5Y1cT/Prw/XjaciMMDE4wHwmxYWJOfk5tCU60ogesfHsv7sTMlLhilyjCSgpGkmHHM0l4+lw6A5bXgG8n8dUEP9O7MT36RhsMlS9IkpmBxg5vD/RN899GDLIllGo349BSMNIdR4ceIDRWOonAYRaIqW4+PmDIDVApG2aCFaE4TjObQQC7uMMr8prek0Y2qZiaEdA57je+QBkL0m5oNYStchlFzVRkOq4XOIf2sy19/aB8f+PVmFjdW8PA/X8ybz1nEj548zO9fOg7AyXE/X3twH5+/ZycHT05Me23nkJf9ffp3wDMTqqoy6BEZRmajzuWIl6vE2fY7cd05852JXwgx0ej/YO0t4trV0JHTuYz5Q3z1wX2smlfFDevNFconyR+KovCF16xFUeAL9+1K+LyTY36++Y8DAAURjFrqY4LRyCwOPZORtmA0FxamJKXL1AwjDc09l43DyFWXncOoe4uoFshDx8Y4dndWY2eJRG8WN7jZfHyEfX3jfOSyZVQ6bfF7kB5Mhl4X3inTWOEsigyjfb3jeIIRNrWZTzAq/P9iMRIXjKZ8sTTxyGq+yabuaKJKKHPnjRa0dmTAw/LmypTPj0RVukd8XLN2XsbHypiQb7IMDQrqMLJaFBbUlqdcuU+XQDjCj586zNVrWvjOGzdit1r4/PWrOTro4XN/2ckzBwd5YGcvoagI2f7l88e4clUz61qreWh3H9u7RnHYLDz96UtpqixNUdQbjOAPRU1ZklY7pS1o3Kba9aJwC7WclnoHVhu89ifgGZgMCc0CXzDCO3/+Er2jfr5263qZVzTHWVBTznsvWso3/rGfrmEvrbWTkz1vMMx3Hj3Iz545Qjii8p6LltDRVJH/c6yvAWBgxPyC96hPjCNqXAnGEXEXhvmuURJJ2sQzjGYrSctCDC2vgYGDmb+u80WxzbfDSApGEhOg5Ri117u4bt08vvrgPl0Fo3jotQkEo4ZKJ/0TAVRVNV1Tm6ns6hYLW+ta89ccJF2kwygbbNJhBGR109O6pB0bTK+Gu3fMTyiisrAuHw4j//TAxXiGUWHaMS+sc+lWkvbKsWH8oSi3nN4a77pms1r47hs3sajexQO7ernljFYe+/glPPeZy/jwpR28cGSIrz20H0VR+MhlHYQiUX7x7FFdzseMDMZWH+pNWJJWF5tATuuUFvKBM7XoGkdRoCL77heBcIT33vkKrxwb5lu3beScJfVZ70tSOty8aQEA923rmfb4Vx/cxw8eP8Sr1rTwyMcv5rPXrirIQK26UkxOh8YmUjyz8KRfkiYdRpIiZjaHUS7uuWwzjLpegrql4M7jvcxeLkvSJKZAW8D5wCUd2KwWalx2XTOMJrQMowJ3SQNRkhYMRxmPuZ7MijYGaKg036JQ4f8XixHNRTS1VEn7+1xY+UsRep2M6nI7dW4HRwbSu2FqDpuFtXnIMAr5pg/EFUUEFBZKMKotZ3vXiC77eubgAFaLwtlL6qY9Xl1u554PXUA4Ep22qv2xq1bw/ks6GA+E4o6i/X0T3Pn8cT5wSYfpwtj0YMAjvsOm7JI2xWEUZ+bn1UCiUZV/+t1Wntzfz1deu45Xr8uD409SFCysc7GprYZ7tp7g/ZcsBYQV/U8vd3Hjhvl887aNhT3B2D15bNz8DqMRbxqCkcUmHIMSSbEya0laLg6jmGCkqrN3C52NaASOPw/Lr878eLkgS9IkJuHi5U387G1ncvFysZBYXW7X1WFkpgyjxpgAMzAeoKoswf3VBIz5QmJt1wQi20ykwygbZitJm1MOIy30OrubXnu9i6MD6TmMuoaFKLUwL6HX/lNbutrKCpJhBCL4esQbYsyf+wX86YODbFxYQ+UsF8oKp23WEohyh3Va+dl7Ll7CqC/E71/qzPl8zIjmMDKjYFTnnsVhFPbnLcvkod293L+zl3+5ZiWvO3NhXo4pKR5es2EBe3vH2dcrRJm/bDnBeCDMm89tL+yJQfx+PTZu/s5E2mC9qizBYDHknxtjDElpY5+tS1oOY+jyOogEMhuTHn8OfEOw/KrMj5cLDtd0oUwiKRBWi8KlK5vi0QJGCEZOmwWbtfBSgzauN3untDF/mEqnzZRxD4X/XyxGknVJmwuDOU1UycJhBCLH6GiaJWmdQ14UBebX5OF9nc2xYS8rmMNI65SWa47RqDfEjq4Rzu9oyGk/m9pqOau9jjuePkI4Es1pX2ZkMNZBwayh1wBDnik385AvL4KRqqr84PFDLKp38c4LFht+PEnxce1p87BaFO7ddgJVVfnVc0dZM7+KTW01hT61+D3Z5/dl1J2zEIz6QlQ6bYkH2GHf3BhjSEobm0MIubM6jLJYHNRy+TIpS9tzn3CQd1yZ+fFywe7KeuwskRiJ3oLRRCBsisBrmBSMzB58PeYLzbqwbwakYJQN1pgDYVbByHyTTd3RbujhJDc9VYXo7KJCe4ObnlE/vmDqwXvnsJfmyrL8tO6e1WHkzCrcWw8W6iQYPXd4gKgKFy7LTTACeM9FSzgx4uNvO3pSP7nI0Fpu1pkww6iyzIbVojB8Skma8ZPHZw8Nsq1rlPdetNQUK0US89FY6eS8pfXcu62b5w8Psb9vgree226OcMlYSZqTUNoLFYVi1Bui2pVksBgOyA5pktLAPsNpE/IBSnaxDpkKRqoqBKOOy8GZ5yB+uwtC5r4OSeYm1S47I7qGXkdwmaAcDaYKRmZ3GIWoSlSSXmDk6D8b5rrDyGoXOQrJVknu/RD88S2z/koLvk4n0LlryJefwGuI2f1nHMtWOIeRJhjlGnz91IEBKpw21uvQ0vqylU0sbXTzoycPo6pqzvszEwMTQSqdNsrs5rjBTcViUah12Rk6pSTN+FLN7z9+kKZKJ689fYHhx5IUL6/ZsIDOIR//7+4dVJfbuX79/EKfksBiRVWsOJQQR/rNPVEb9YUS5xdB3kRiicRwHBUzQq9jjtlsRGZXLJvRO5Te87s3w9gJWHV95sfKFYdLhl5LMkdVRe6WgVSX2wmGo7o5cScCYdwmyeKpczuwKEUgGPnCiUvSC4wUjLLBmqxLmvnyTwwhla22fx8c+AdETk2kX1wvBKMjaeQYdQ178xN4DbEByywOowJlGFWX26kqs9E5lJt9+ZmDA5yzpC7eHS0XLBaF152xkF3dYwzr2E3BDAx6gqYsR9OodTlOdRjN/LzqzLbOEZ45OMi7LlycH5efpGh51ZpmHDYLh/o9vP7MhZQ7TPR5sZXhIMwRkzuMRnwhapI6jGSGkaREcLhnZBjlIIZm6jDacx8o1vwHXkNs7CwFI0kGqCr86e3ws2sTVm7ogbZYoVdZmsdEJWlWi0Kd22F+wUg6jEqMpILRHBnM2cuT3/QCE0KA6dt5yq8WNQgBKFV5QDAcpWfMT2ttIR1G5QVzGAG01btychh1Dnk5OujNOb9oKlorznQEv2JicCJAvQkDrzVq3Y68d0n7/uMHqSqz8cazFxl6HEnxU1lm54pVTSgKvMlknxfF5qDGESkNh5HBIrFEkhcc7hklaTk4ZjMRjFQVdt8Liy+cdCblE0esS5qBE39JibHzLtj1F+h8Hnb+ybDDGCEYmamjckOFk/5x82cYmbWLmxSMssE2W5c0rSTNvBNOXbGVJXcYBWItjLteOuVXVWV26t2OlJ3Sukd8qCq05qNDGpjOYQQi+LpzOHvB6JmDAwBcoKNgpJUUptvprlgYnAjSYGKHUZ3LMdklTVVn/7zqyPFBLw/t7uOt57WbZpVIYm7+5ZpV/OjNZ9BWn6drdrpYndQ7Uy9SFJoRbwrBKByYO4tSktLmFMHIm30+VyaCUf9eGDpUmHI0mPw3FnAhUlJEeAbh/k/B/E3Qcho89iUIGyN61JSL8e+ITtUDE4EwbpNkGIHIWuw3vcMoTFW5OcfbUjDKBs1hNFVIiMyhDCNIXZKWRDACITqkcqhoQkneStISZhgVrqPFwloXXUM+otHs8oKePjhAc5Uz7grS65wsChwz+eQrUwY9xeAwit3IIyFQo4YG4P5jTx+qCq87Y6Fhx5CUFgvrXFy5urnQp3EqNic1TtXUrkhVVRnzhaguTyJayy5pklLhlAyjWZqOpIu9XIzdfGlkGO25D1Bg5XXZHStX7GLBTZalSdLioc+BfxRu+A5c9m8wfBS2/NKQQ+ntMPIGI6bJMALhMBoYN69gFImqTATC0mFUUliTOIys5nUo6Iq9PLFgpKoQTCEY1bs5Npj8htk1LPafl9DrRI6NAjuMFta5CEai9I1nvhoVjao8e2iQ8zsadO1W5LBZaK11cSTF/18xEYmqDHmCNJiwQ5pGndvOsDcowsY1EdPAkrTH9p2ko6kiHr4ukRQtNifV9igDE0HG/ObMXvOHogQj0eSri6EcJtUSiZmY1WGUw72mvDY9h9Gee2HhWVDZkv2xcsER+zcGzSteS0zCwUdg22/h/H+ClrWw7EpYeA488VVDgtP1FowmTFeSJjKMUjXsUVWVf793F0/u78/TmQkm/CLzV2YYlRKzdkmbaxlGSRxGIa9wP7jqYegweAZOecriBhe9Y358wcRp/J1DXqwWhZaqPLynmmPDRF3SYLJTWjbB17t7xhjyBLlwmX7laBqL6l0lVZI27A0SVTG3w8jlIBJVGfOHJ797Bk0ePYEwLxwe4tIVjYbsXyLJK1YnlTZxrzHrdWs8IAbplckG2OFZXLASSTFyimCUo3vOVQe+kdTPO7kXFp6d/XFyRRPFpMNIkoqn/hdq2+GiT4qfFQWu+DxM9MKLP9L9cHoKRqqqmir0GoTDKBCOMhE4tRnTVPb0jPPzZ49y1+auPJ2ZQFvMkl3SSglrbFI5TTCaYxlGyUKvtXK0JZeKbdfLpzwlnoOTpKypc9jH/JoybDp090qJJgrNnIDbywrqMFpQIyYH3SOZC0ZaftH5S/UXjBY3uDk64Emp1BcLgxPiu2zmLml1MffTsCc4RTAyxv3zzMEBgpEol65oMmT/EklesTmpiAlGZi1L8wbE+SVdkc2lbEciMRMzS9JCvtwdRt4UJWnRCERDQqwqFFIwkqRDNAo9W6HjyunX/EXnQccV8Nx3xedZRyrLbCgKjHpzz0jyh6JE1RT3szzTEFsQHphI/u+7Z9sJAA70TSR9nt5oQp10GJUSibqkWexgMU/Al6HYk4ReB2JfssUXidalXS+e8pT2+tTByZ1D3vzlFyVyiKUK9zYYLYR50JP5BfzpgwOsaK6kyQCHVnu9m/FAOKvzMiODsSC8erd5Bd/amGA05A0a7mh8bF8/FU4bZ7QXoIuMRKI3NiflShhFMa9gpK16Jh1g5+rCkEjMgsMNwQkRBwC5dwAsr0ldkmaGSoB4SZoUjCRJGDosvh/z1p/6u/VvAE9/wsiPbLFYFKrK7Lo4jCbvZ+aZEzdUaoJRYhNANKpy39ZuAA71TxDJMj82GyYdRlIwKh1mLUkLzq2BnN2VOAw6MCa2FU2i7rZzimB0/AXY/oe4w+hIEodR13AeBSNNFJpNMCqgw6iqzI7VojDkyewc/KEILx4Z4nwdu6NNZXHs/69Ugq8HYsKX2bukwUyHkf7lKaqq8vi+k1zQ0YDDJm8RkhLA6sASDTK/uty0gpEnNsBOauEP++fWOENSujjcgDp5Lwv7c3QY1aUhGJmgOY0MvU6Mqor5wgs/hL9+DH55Ixz8R6HPqjD0bhPb2QSjZVcKg8Lev+l+2OpyfQQjbzAmGJko9Lox5jDqTxJ8/fKxYbpH/Zy3tJ5AOEpXDl2qM2XMp2UYmec9m4qcDWRDIoeRzbyTTd1JFnqtlaQ5K6H1LDixWVgnx3vht7fBX/+ZCoeVhgpnQoeRLxhhYCKYn8BrmFKSNjPDyFnQDCOLRaHO7YiXTKXLK8eGCYSjXLCs3pDzigt+A6Ux6NEcRg0mzjDSStKGpgpGBgx89/aO0zPq59KVMr9IUiLEsuiWNLrzbjNPF08whcNIVWOTaplhJCkBtLIwrSwt5M3tfqaFXicrkzc4+y8tZOj1qagq7H8I7rgK7rhStJHf8Sc48gTsf7DQZ1cYerYJUahx5am/K6uG9gtg3991P2x1uZ0RXR1G5hE/GirFGHqqwygUiU57zj1bT1Bmt/CBSzqA/JalSYfRLCiKclRRlB2KomxVFOXUgBuzk6hL2lxa+UsWeh2MfcGclaIbRcgDfbvg7g+ItqfBCRjrZnGDi6MJOm1pqm5rwR1G5aBGIJI8JM1I6t2OjEu/nj44gM2icPZiYwSj1tpyrBbFtAGymTI4EcRqUeKhf2ZEK0kb9gYn3X0GZBg9tu8kAJfI/CJJqWBzQDjIqnlVHDw5QXjGINEMTMQyjCoSWfjnWk6ipLSJC0ax8WIoRzG0vBYigeTOHTOUpGn/xgJGHZgK34gQiX5zK4z3wLVfg4/vg88cA3djQRdsC0rPNmhendiIsPLVMHgQ+vfretgalz4OI0/8fmYewajO5UBRYCDmMBryBDn7vx7hc3/ZQTSqEgxH+duOHq5c3cK6hdUAHDiZR8HIJwWjRFyqquoGVVXPKOA5ZIfFAhbb9FKlsH9uDeRsZalDrx0V0Br77/3rP8GhR2DNTeLngf2017sTlgd0xgSj/DuMZgpGsf/TROV3eaC+whF3wKTLMwcH2NRWa5i6b7daaK0tT1pSWEwMTASoczuwWJRCn0pC3A4rDquFIU9IDK7BkJXSx/aeZM38Kprz0Z1QIskHtjKIBFjZUkkwEjVlWZon1Yqsdg+SXdIkpcBUh5GqivFkrqHXkLwszRSCkSxJixONwt3vh+4tcN034SNb4Kx3Q2WL6Ahmc4q4j7mGqkLP9tnL0TRWXCO2+/QtS6vSqSTNY8IMI5vVQp3LQX+sYuMfu/sY8gT59QvH+exfdvDk/n5GvCFes34+VWV2WqrKOHByPG/nN+aPlaXLLmklhtUxS0naHJpg2V0QDU93WWnES9KqoHYxuBrgxCuw7Cq4+n/E7wb2s6y5gv7xgMhkmYHWRj7/GUYzS9Ji/6cFzDGqcztFGVKaDHuC7DgxygXLjMkv0mivd5eMw2hgIki929wlpYqiUOu2T88w0nnyOOoN8cqxYdkdTVJaWB0QDrCipRIQZZdmI7VgFLsHyS5pklJgqmAUDgBqbp9tV6xBQzqCUSHLOmVJ2iTPfkuUVV31RTjj7ZP5sBqxUuI5x2iXqMZoWZf4OdWtQlDaq29ZWnW5Pe50yQUzlqSBiJ3QStIe2NXLgppyPnRpB797qZN//sNWqsvtXLRcxDEsa67gYJ4dRpVOG1aTLlwXSjBSgYcURXlFUZT3FOgccsNqny6WRIJzy2GUzFY7NcNIUUQbSFcDvOZ7UNEMzmro38fKlioA9vSOnbKLrmEvTpuFxso8vacpHUaFu2llWpL23OFBVBXDAq81FjcIwUhNlhlQJAx6AqbOL9KodTliXdKMCb3+2bNHiKrwqjUtuu5XIikoNieEA3Q0VWC1KOyd5Z5TaLQBtsueYEXWwNwyiSTvOCrENjihT4m15jDyDiV+jubMLeRYXVvkmesOoyNPwiP/IaoOzn7f7M+xOqcvzM8VerTA6w3Jn7fi1aJT2sRJ3Q5dXW5nxBvKeVzvTZXJVyAaKh0MTAQY94d4+sAA16xt4eNXLecjly9j3B/m2tPmxZu9dDQJwSiap05pY/4QVSaOxSiUYHS+qqqbgGuADyqKctHMJyiK8h5FUV5WFOXl/v7+/J9hKmZeyMJ+8dhcIZVgZLFN3pRv+Da872nRNU1RoHE5DOxn1TwhGO3tOXW1t3PIx4LachQlT0prIseG9u8soMOo3u1g3B8mEI6k9fynDgxQ6bSxvrXa0PNqr3fhiYWTFzuDE0HqjeqQFvLBN0+D3ffkvKs6t7jZTZak6ScYnRjx8X9PHOLVp83jNIM/OxJJXrGVQSSI02ZlSYObfSZ0GHmDEcrsFmzWBMMyM5TTSCR64ZhSmqWHGJpWSZoJyjotFnH8uSwYeYfgT++A+g644TtiXjAbBW46UzB6t4NigeY1yZ+38lpAhX3363bomnI74aiKN5jefCMR8Uw+E3VJg0mH0WP7+glGoly9tgVFUfjYlcv55TvO4jPXTIaML2uqxBuM0D2an0iScX+YSpOWo0GBBCNVVbtj25PAX4CzZnnOj1RVPUNV1TMaG03YreeUkrTAHHMYxVaCZrvpBScm3UUgbuRV8yZ/37ACBvbTWOmkocLBnp5TV3s7h735K0cDczuMYs6XYU96NtFnDw1wztL6xBMPndA6pR0tgRyjwQkDHUZ9u2DkuCjLzJGVLVXs6h4jGIi95zpOHv/773tQVfiXa2fpyiGRFDNWR/wavnJeFXtmWaQoNBOBcPKA0JAxrkKJpCBMLUkL6eAwcsUafHgHEj/HLMHxDhcE57Bg9PwPwNMPr/2JmCskwlZW0MXagtGzDRqWT5YvJqJ5LVS36dotTWv8kmuOkRkzjAAaK5z0jwd4YGcPjZVONrXVxn930fLGaY1vljULF2S+gq/HfNJhNA1FUdyKolRqfweuAnbm+zxyxmqf4xlGsX9rIoeRI8lNoGEZTPSBb4RV86pmLUnrHPLmL/AaUmcYhQonGGnt1AfSCL72BMIcG/SyYWGNwWclMowAUwbIZoIvGMETjBjnMOrZKrbjvTnv6vJVTQTDUTr7BsUDOk0eXzwyxF+39/Dei5fmrzOhRJIvbE6RuReNsrKlkhMjvngLW7PgCYST2/fjk905NM6QlC5TS9L0aHevCUaeJIKRWURXu3vuOox8w/DC/8Gq65OHOkOsu+UcFYxSvTcgFuU7LoPjz+t2aD0FI6ctiWO2QDRUOvGHojyy5yRXrW5O2uimo1Fcow725Ukw8odN2yENCuMwagaeVhRlG/Ai8DdVVR8owHnkhnQYiW0iwSjZqkHjCrEdOMDKlkr2901vczzqCzHmD0uHUYyGmJCRTvC15vbRxBwjaa0tx2ZRij74WhPiGtwGfX97tovteE/OuzqzvY4Kp43Ok0PiGmTJffUmElX593t3Mb+6jPdfvDTn/UkkpkO7jsc6pQHsN1lZmicQxp3Mvh/WoWxHIjEL00KvtfFXDmM+qx3K64RzJRGmchgV97gpa174IQTG4OJPp37uXHQYTZwUY8V0BCOAsprZ52FZoglGI97cBKOJVAsgBUKrJAiEo1yzdl7S59a6HTRUOPPWKU04jMz3nmnkXTBSVfWwqqrrY3/WqKr6pXyfgy7YHNNDr+ecw0jL9slCMGpYLrYD+1g1r4pgeHqb465hsfKSV6dDyi5phXcYDXpS3ziPDYr3rr3B+PfOZrWwsM5V9CVpWqC4cQ6jWIChDg4jh83CRcsb6BscRtXhetM55OWtP32R3T1j/Mu1qyh3mMs+LJHognVS+Ddrp7TUJWkJFjUkkmLEVg4osZK0mNsmV+ePuzGFYGSCDCMQ/04dJ/lFg38Unv8+rLwOWk5L/fy5mGGkLTCmKxjZnBAJgE7NZ6pd+jiMvMGI6crRYHIBvrrcztlL6lI+f1lTRf5K0vwh6TAqSawzrJLhoBCR5gopHUYViV9bs0i8fwP7p3RKmxy8dw6Jfea1JC0cAJRTV57iglEBQ69jivhgGuHSmvCWD4eROI6LowPFba0ejDmM6o3IMIqE4ORu8XcdBCOAy1Y2Ewn6CFuyP19VVfnV88e4+ptPsuX4MF+6aS3XrUu+2iKRFC3avTkcZEFNOZVOm+k6pXkCKQbYZpnsSiR6YLEIl9HUDKNcP9sVTTBRBA6juVqS9sIPhWh00SfTe741JobMJbQIg3QENZjintWn+YzmMBrLUTCaSOWYLRCaw+iKVc3Y0yiXW9ZcwcG+CcO7QUejKhOBsMwwKklOKUmbYw6jeLbPLDe9VA4jq010R+jfT0dTBXarMi34Wutg01aXz5I0n/g3zezWEBeMCrcaVFVmw25V4k6YZBwd8NBU6cybFbS9wc3RQY/hF1Mj0YS4ercBgm//XnGdaD5N2LADua9UXLKiEZcSxKtmf75/fKWLf717Jxvbannwny/i9rMX5a8joUSSb7TreCSAoiisaKk0Xae09DOM5lDpu6S0sbtmZBjl6jBqSO4wMkuG0VwsSQtMwHPfg+XXwPwN6b3G5px7JWm926F2MZSl2alW5yoIPTOMkjpmC8SSRjcb22p40zltaT1/WVMF44EwfWPGfg7HA2FUVcz3zIoUjLLFap9RkhaYW4KRdsOdzWGkdUlLRsMyGNiPw2ZhaWMFe2OCkaqq3L31BGe111HjyqNjK+SffSAezzAq3E1LURTq3A6G0nAYHR305M1dBLCozoU3GElLzDIrA7FSP0O6pGnlaMtfJbYTfTnvsqHCSXO5ykgouxuLqqr84tmjrGiu5FfvPEuGXEtKH+v06/jKeZXs7R03ldAtu6RJ5hwzHUa5llu6G8FzMvHvNSe5tcDVAHbX3HMYdb0I/hE4693pv8ZWNrdK0lQVOl+C+RvTf432WdZpjlLhtGG1KIz4chvTp1wAKRAuh42/fOB8Nk7pjpaMjiYxlzU6x0hzdMmStFLE6pzFYTSHVv7iJWkJHEbJuqQBNKyA4SMQDohOabE2x68cG+bIgIdbzmjV+YRTEPbNPhA3QYYRQJ3bmVaG0ZEBb17yizRaqsX70zdWvDf1YU+QMrvFmPyenu2iG0z7+eJnHYKvAea5YSRkpX8880HCtq5RdnWP8aZz2qSrSDI3sE0fVK9sqWLcH6Z71DzXLU8gjCtp6HXsXOfSwpSktHFUxEKvNcEox7GLu0mUPIUTTHYTOcnzjd0FwTkmGPXuENtMxBCbI/H/ZSkydBjGu6H9gvRfo3NshqIoVJfbc3YYidBr82UYZcqyZhGvcsDgTmla11YZel2KWKeEXkfCoEbm1kAu7jCaMeCORtN0GC0HNQqDh1g1r5LeMT/DniB/eqULl8PKq0/Lc55KKEFJob3wGUYggtpSuXjG/SEGJgK0N+TPYdRUJd6fkwbbNY1k1BeiptzAwOvmtVC1QPysU45RY1kUPw4e25dkNTUBv37+GC6HlRs3LtDlXCQS0zNjUK11SttnkhyjaFTFG4pQkTTDSAu9lg4jSYngcE8vSct1DO1uEFvvwOy/DwfMERrvmIMOo96dYhzkSh00HEdzGJnICWooR58S2/YL03+NAVUQQjAK57QPbzCS/wyjwUPw4Od0LfesdzuoddkNL2Efi73fZnYYmVfKMjtW+2QYm7YttM01nyRyGAVjKmwqwahR65S2n5Utwn2x+fgwf93ewzVr5+Xfyhj2m9xh5Ih3QEuE9vvFeSxJa64qfofRiDcUr9vWlWhUrKptfBNUtojHdHIYuSwhotYyfvbMUQ73n3pzXFTv4rYzF57iIBr1hrhvezc3bWyl0sQ3JolEV7R7c+xevTwmGO3pGeeylc2FOqs4vlAEVSX5fS/kxxTlNBKJXjjcokwppJfDqFFsJ05C1fxTfx/ymWNhdy6WpPXuSD/IWcPqBFSxOD8XmgodeQoqmkVkR7rEQ6/1E4yqyu2MeHNzdk3kuyRtpBN+cQOMdUHrGbDmJl12qygK61pr2No5osv+EjHpMDLvuFwKRtkyNfQ6HkZpghtRvrDaQbGcmmEUiKmwybqkAdTHLogD+1m16RoAvvXIASYCYW7NdzkaJB5IaNkXM51Ueabe7WQohcNIa2+fT4dRYyz352QWpVFmYdRnkGA0dAhCHpi3DpxVYpCok8NICflprG3k0MkJDvXPsMqqEIxE2XJ8mP+66TRsUzpB3LW5C38oyu1npxf4J5GUBDMcRlVldhbUlJsm+NoTEKuLyUOvTVJOI5HohcMNYyfE+EuxiHFlLlQ0ia0nkcPIJM1pHG5xLtEIWIq/bCclIT8M7IeVr87sdVPFkFIXjFQVjj4t3EWZXOPj+Xz6zVGqy+2MxgSjQDjCF+7bzTvOb4/n+aRCVdX8hl6P98EvbxDzT1sZHHtON8EIYFNbLd98ZL+hbe/H/dJhVLrYppSkxbMF5lCGkaLEVkkSCUYpLiwOF1S3wcB+GiudNFQ42N41Sludi7PaM7Cs6kUih5HFIsTBAjuM6iscTATC+EMRyuyzDzCODgjBaFF9/jKMHDYLdW5HUTuMRn0hFurRkS8ahRd/BGtvFgNXLfB63nrxfals0U0wIuyjY34D+z9yzSm/UlWVb/zjAN9+5ACjvhDfum0jZXYrqqry6xeOsWFhDWsXpNmBQyIpBWynBoOuNFGntImYYJQ89NpvjnIaiUQvHBUiyyfkE+PJXMVQrSQtUfC1WQSjeKSDN/VYuRTo3yNiOzJ1GE0V+kv9fRo8CBO9meUXgSElaTXldo7HFqAf2NnLb144TjAc5Wu3rk/r9f5QlKgKrnxkGPlH4Vc3CdHoLXfDo/8Jx5/V9RCbFtWgqrCtc4QLlzXqum+NeOi1zDAqQaY5jOZoGKW9/NR28/GStKrUr29cDv37AFg1Tzz/tZtasVgKsIKazKpsKyt4hpHW8j2Zy+jIgJfmKmfy4FQDaKp0Gt5y0kh0cxj174EHPg2/vBF8w0IwsjqgcaX4feU8/QSjUIKQdoSF9mNXLuffrlvNg7v6uPF7z/CuX7zEm+94kUP9Ht50ziJ9zkEiKRasp9r2lzVXcnhggnAkWqCTmsQTiACpHEYmmexKJHqhZRglajqSKW7NYdQ/++/NIrrGIx1m6TJcimiB1xkLRvp2ADM1R54U28UXZfY6nUOvgWmh1797sROA+3f04A2ml2uU1gKIXuy+B07uglt/DgvPgkXni7ws/6huh9iwsAZFgc3HRnTb50y0krS8ubKyQApG2WKdkt4fL0mbQw4jEDf4UxxGsRBRR4qSNBAT6f59EPKxen4VigKvPb1AQbyJHEZgitaedTHBaHAisWB0dNBDex7zizSaq8o4OV7cDiNdBCN/7LN/chf8+nVw/HloWj1ps69s0S3DKJlgpPGOCxbzrds24LBZ6Bn1M+wNcsmKRq5bl+dAeYmk0MQH1ZPXz2VNFYQiKkdTZMPlg4l4SVqK0GspGElKCYdbBNSGfGDTQTByuMV+EglGYb8+x8kVR2ycpmM4r6np3SHmBLWLM3udSTJE88LRp8WiYt2SzF5ngKimCUaH+id47vAgl6xoxBOM8NCuvrRerwlLeQm9HjwEFjt0XC5+bjsXUOH4C7odorLMzvKmSrZ0Duu2z5mM+UQJ39QICbNh3jMzO1b73M4wgtmD+9ItSQNYeqlY8T36NO+9aCm/e/c5tNbmr5xqGikdRoUuSRNi5KAn8U3h6ICHxXnML9JoqnQWbZe0YDiKNxihRg/BSHPXnf9ROPEydL0oytE0NIeRHh0/0pw8vmbDAu790AX87SMX8rePXMjP335WwpJGiaRkiQ+qJ6/jy5vFPergycKXpXnSKknTyYUhkZgFR4UYAwbG9flsK4oIvp5IJhiZYGF3aknaXKB3JzSvEREPmWBAuZUpyTa/CCbHgTqGXleX24mq8JOnjmCzKHz5tetorS3nrs1dab1+Ip1MPr0YOgy1iyazwFrPBItN97K0jW01bDk+QjRqTMc+kY9kXncRSMEoe6zOWQSjEg9lm4mtbBaHUZpd0gAWXSBEp/0PUud2cPaSev3PMV3CSazKNmfhBaMUDqMxf4hBTzCvgdcazVVl9E8EiBh0ITUSzXZb7dJBMNLcdRtuhxu+I/7eeubk7ytbRAh2IMcJajSa3BEnkUimM0tJ2tImca080Dcx2yvyiie2Ipu0nDgcmHuLUpLSRnPaeAb0KxWraEzuMDLDfVNzOc0F54zWLTbTcjSY9bpdkgzsF7lbiy/M/LVW/UU1bTx81+YuLl/VRHNVGTdvXMDTBwfoHU39mdVKrPNSXjV8ZLory+GCeRtE8LWObGqrZdQX4vCAMa7AMV/I1B3SQApG2WN1iBC3aGQOZxjlEHoNYoCw+GI48KA+rotcSGaJNkOGUUXyDKNjA2KlqjAlaU4iUTWp+8msxAUjPS7U2mffUQEb3wQf2QLr3zD5+8pYKViuOUba9cYMA1+JpBiIr1RPXj9dDhutteXsP1l4wSitzAdZkiYpNRwxR7l3YDLXJ1fcjYlDr0MmcRjNFecMwMgxCI5D89rMXztX3ictvyjTwGsw5D3SxsPBcJQ3nCU66t60qRVVhbu3nkj5es0xa3jotarC0NFTSx0XnQcnXtE1I2zTohoANh83pixtzB+iUjqMShQtlyQSnMMlabNlGE2ZNKfD8qtg5LhQ2AtJMoeRvfAlaRVOGw6rhcEEgtGRWEeD9ob8l/Q1VYn3rRjL0vQVjGa46+qWgHXKDaCyRWxzzTGKC9RSMJJI0iI+qJ5+HV/eXMmBvsKXpHnjoddJBtghnzkCeyUSvdDGiZ5+/cbP7gbhWJqNcMAc9825lM3Tt1NsW9Zl/tq58j4dfQqqWjPPeAJD3iNtPLygpjzeFWxxg5vTF9Vy1ytdqCkW+PMWeu0ZEGJk3SyCUTQkRCOdWNJQQVWZjS1GCUa+MFVl0mFUmlhj5WeR4JQJnAlWLvLJbIJRcFxcwNItz+u4Umz3P6jvuWWCqiYPQ7SViZWpAqIoCvUVDgYnZhdljsZskovqCpNhBNA3Vnw39VGfEOB0dxjNhl4OI+07JyePEkl6xEsbpgvuy5oqONzvKXintHjmQ9KSNJME9kokeqGVpPlHdXQYNQkBarZJbdhnjnH6XHHOgChHUyzQtCrz187iDC1JurdC29mZ5xeBIaHXWgTG689ciHVK1+qbNy3gwMkJdp4YS/r6eOi10YLR8BGxnRkUvvBssdWxLM1iUdjYVmtYp7QxvyxJK120C1kkNDkInZMOo1lCr9N1FwHULISmNXDgIX3PLRO0C62JM4xAdEpLVJJ2dMDDvOoyyh35DzRu1hxG48U3+NG9JM1RkTjYsaJZbHN1GMUFowIFxEskxYbFIjqpzBhUL2uuJBiJcnyosOGznkAYl8OKxZJkwmCWwF6JRC8cUxa49FoAcTdCNAy+WZwAIZNkGGnnYIJxpeH07oD6ZZPlh5mQwBlaUqiqGBNWL8zu9QaEXnc0VfCt2zbw7gunCzHXnTYfgMf3JSj5jDGhZRgZ3SVt6LDYznRmuepEh2Kdg683tdWy/+Q44/6QrvuFWIaRLEkrUaaVpMUuZtY5FnqdqCQtnfyiqSy7Eo4/J1aZCkE49m9I2iWt8GJIfYWTgSQlaYXILwJoLGaHkVdc+GtcOnx3gyk++84KcFbpkGGU4vMqkUhOxeY8VTBqEosb+wscfO0JhlOvxpplsiuR6MU0wUinz3ZFk9jOVpZmFtF1TjmMdkJLFvlFMKXcqoTfJ9+wmEdqDvRMservMFIUhddsWHDKAnS1y05TpTPlAkveMoyGjgCK6JI2k7ZzofNFiIR1O9ymRTWoKmzr1HeuGo2qjAfC0mFUskz9ksrQ60kCE5kLRstfJVaEDj2m37llQijF/59JHEb1bgdDswRLq6rK0QFPQTqkAditFhoqHPQVYYbRSMxhpIuyn467rrJFB4eRDL2WSDLG6jhlFbYjJhgdPFnYHKOJQCR13kPYN/fGGJLSZur9UreStAaxnRl8HY2IXBMzlHUWMptnvC820c4DvmEYPZ5dhzSYEv1RfGPLtNHGg1rGZaYoiii5zpOo1lbnSkswctgs2K0GSwzDR6C6dXYReNF5EJyAvh26HW79whoURf/ga08wjKoiM4xKlviFLDQl9NoEKxf5xF4+6XbQyMZh1HoWlFXDgYf1O7dM0P4NiSbgtnJTrHDUux0MTpzqMPrJU0cY9oY4a3FtAc5K0FRZxslidBj5QlQ4bdj0uLGlI5ZWtkiHkURSCGZxirqdNhbUlBfeYRQIJw+8hlhgr/zOS0qIqQ4j3UKvRUgvnv7pj8e7i5rgO6T9WwuRjXnPB+HbG+C3bxQODCM5sVlssxWM5kLo9ZgmGGXpMIK8VkG01bvoTCEYTQTCxgdegyhJq22f/Xfz1ovtyT26Ha6qzM6SBjc7T+jrMBrzCxdUVbksSStNZg29NsGNKJ/YXeLfP9XyFxjLXDCy2mDp5SLHKEX6viGk5TDSrz1jttRVOPAGI/iCkfhjrxwb4ssP7OXqNS3cuGFBwc6tqcpJ33jx3dRHfSF98osgPbG0Qg+HkcwwkkgyxuaYdVC9vLmCAycLKxhNBMK4UuU9yC5pklJjWkmajqHXcGpJWqpxXj4pZDbPeC9Uzodjz8AdV8JfP2bcsQ4+Itwvbedm9/q5EHqdq8MIYve2/HyW2upc9Iz5CYQjCZ/jDUZSL4DowdCRUwOvNWrbQbHC4EFdD7m4wa175uFYvNJBOoxKk2mCkRZ6PcccRnH1f4qYEpzILPRao+0cYSGeuSqUD1I6jMyRYdTgFp+vwVhZ2pAnyId+s4X5NeV8+ZZ1KNl0WNCJ5soyThZhSdqoN8+CkeYwykUYlV3SJJLMsTpnLW1Y1lzJof4JItECLFbE8KRakY2EQI2Yo5xGItELuwGh1646QIGJGSVpZlrYtRYwwygwCosvgn/eBWtuhi2/gqBBof8HH4b286cLg5kwF0KvNcd5ToJR2SkdQI2irc6FqkLXcOJF9IlAOHnHTz3wj4F3AOoWz/57q12IRgMHdD3swjrhsFJ1NDfEBSOZYVSizAy9tjqya4lYzGgCy9Qco2xK0gDqloqtzmpwWhRJhlFdrNXlkCfIyXE/H/3dFgYngnz/9k36iR5Z0lzlZGAiUPD21Jmiq8MoVeg1CNtxJDB7B5d0MdPAVyIpFmYJvQYRfB0MF7ZTmliRTTLAliKxpBSxOSYXX/VyGFms4KpPXJJmhvum1QYWW2HGlf5YFYCzAjbcLuYwx/VrPx5n+CgM7IeOK7PfRyGFtXwx3iM+r7kYDqz5dRgBSe+XKRdA9GA4lsOVyGEE0LAMBg/petiFtS48wQjDXv06pcVL0qTDqESZ5jCao9kC2g0+NOXCka1gVK8JRvp+udMilcPIXi5CuXVM28+G+grxmfvi3/Zwwf88xjMHB/jCa9awdkF1Qc8LoKmqjKgKgwm6uJmVUV+IGpeODqN0Qq8htxwj7fsmS9IkkvRJJBg1i/vV/r7CBV+LzIckFv54TuIcHGdIShvNfaLnZ7uiydwZRlCYbExVFeOUsirx86JzwWKHw4/rfywtk3TZVdnvw2KZtVlBSTHem1t+EeQ3wygmGCXLMfIEwriMFoy04PbaBA4jgPoOGDoEUf0WstMRzDJl0mEkM4xKE00N1hxGc60cDaY4jGI34khIvBfZCEbVC8WNa6gAglE6DiMouMuouUqc39bOEW49o5VHP34JbzirraDnpNFUKd6jviILvh7Ry2GkDcTScRhBbjlGIZMNfCWSYiCBbX+yU1rhcow8qSz8MuheUqpoZWl6LoC4G04VjMyUYQSFca6HvKK01RkTjBxuWHi2MYLRwX+IkiBtMThb8tgBrCCMd+dWjgYJ8/mMoLHSSZndwvHB2QUTVVUZ86dYANGDocNim6gkDYRgFPbDWJduh11ohGDkL44MI3PLWWYmXpIWmsMOI00win1xArEV2mwEI6tN3FwKUZIWX3lKkmEE4v/ZmUU+k07Mrynnt+8+h46mChorzSVQamJWseUY6VaSFg4IF1o6GUaQm8MoPnmUeSYSSdpYHeAfOeXhinintMI4jKJRNY2StBT3KImkWNEcRnougLiboHvz9MfMJroWIhvTPya2msMIYMkl8NgXwTMI7np9jhPyw5EnRclbrlEdJomEMIzxXmhek9s+bGV5e48URaGtzsWxWQSTYU+QT921nSMDHl67yeAmPMNHREfEZGPu+g6xHTgANfosri+sE/fgVJ3iMmHMJ6pXKsrMLclIh1G2aCVp4YCwS85ph1HsRpyLYATiyz14OPfzypRQioGESRxGAOcurTedWASTglExdUrzhyIEw1Gq9ShJS/ezHxeMcnUYKXPzmiORZIvNmbDbTkdTBft6CyMYeYKxwWIywSg+2ZXfeUmJEReMdBRD3Y0wYeIMIyiMEBKICUbOKYLR0kvF9sjj+h3n2DNiITmXcjSNJNftoicagYk+HUrSnHkLvQZRljVTMHnxyBDXfvspHt93kn+9bjUfuKTD2JMYOpK8HA1EhhHoGnXicthoqHDqKxj5Q7gcVuxWc0sy5j47MxPPMIo5jKxzcCAXzzCKDWaDMUt/Nl3SQFhXhw7rWm+aFmk7jIpHDMk3DRUOFAX6ishhNBILrdPFYRQfiKUQjOzlUFaTe4aRvXzuhexLJLlgm71LGsDGthr29Y0z6tMvyDJdPAHRnjipwyieYSQdRpISw2FQSVpwfHpDFrOVcufRFRLHP4tgNG8DOKv1VvSonAAAWm1JREFULUs7+A8xJ2q/IPd9lbLDyNMPajT3kjRrft+jhXUujk/pFBaKRHnXL17CabPwlw+czzsvWIzFYvD4dOhI8sBrgIpmMR8d1LtTWrnuGUZmL0cDKRhlz7TQ67meYaRDSRqIL3/YJ2p680lKh9GUkjTJrNisFurdTk4WUYaRNjnURTDSxNJ0PvuV83JzGIX95lkllUiKhSSD6rPa61BV2Hwsh+6FWTIREA4jd7LMB9klTVKqaAuMeodew/Qco7jDyCSiayEdRlNL0qw2WHwhHHpcZDHqwYGHhVjk0EEELISwli+0cWDl/Nz2k2cX1qI6F95gJN7kZnvXCGP+MJ+6emV+mvCE/DB2Inl+EYhF1foO3aNO2upcdA7rJxiN+8OmD7wGKRhlj+ySNrkipF3MA9qkuWr256dCqzfNd6c0TQhK6TDyzf57CQDNVc6iCr3WBKOackfuO9PE0nTcdZUtwoacLSG/zDKRSDLF5kg4qN7YVovNovDi0aE8n5QIvAZShF6bbLIrkeiFIQ6jRrGdVTAyyeJuITKMZitJA5FjNHp8Mkg4F4aOCEeHHuVoEOuSVqIlaZrTPOfQ6/yKj2314rt6LBZ8/czBQRQFzl2iUwZWKkaOAWrqkjQQ88oBfQWjhbUuukf8hCL6VMOM+aXDqLSRDqNJISXuMNJuRjmUpEH+g6/DPlCsk0HmM4lnGEmHUTKaq8o4OV4879GIVwxC9ClJy9RhlGPotRSMJJLMsJUlLEkrd1g5rbWaF48UUDBKWpJmssmuRKIXhoRea4LRwORjqaIH8o29gCVpZbMIRqBPWdqWXwEKrLg6931BaTuMxmLVFEWYYQSTwc/PHBxg9bwqat06LL6mw9ARsU1VkgYix2i0c3p5ao601bmIRFV6RvT5XI75Q1TpMQ8xGCkYZUu8S9pcdhjpHHpdOV+8j3qscmRCKEWJj8wwSgvhMCoewSjuMMpn6DWI1aTx3lOzusa6wT+a+vUhn3QaSCSZYk3eevis9jq2d43gD0XyeFLgCYrjJQ29ll3SJKWK5so1wmE0cXLysZDJRNdCCCGJshbrO6CqNXfBKDABL90Bq64TXY/1wOYs3cXa8V5QLJOf12zJ82eptXaytbwvGGHL8RHOW5ondxHAyd1iq5kMklHfAai6zisXaoKZTmVpY74wVSbvkAZSMMoe7aYTF4zypKyaiXjodexLk0mOy2xYLFC3tAAlab7kq1va70r1pqUTjZVlDHoCutk0jUYTjHRR9tMNvQaxmhQNgW+Gm+EXN8Aj/5n69aEUn1eJRHIqKSYeZy2uIxRR2do5kr9zYqrDKEmGkdlagkskeqE5jPT8bLtik1fv4ORjZivrLIQQ4h8DFHDMGKcoCrSfD10v5bb/LXeCfwTO+2hu+5lKSQtGPeBuEjlSuWDN73tUZrfSXOXk+JCXl48NEYxEOa+jIW/H59gz0LgSXHWpnxuPOtGvcmVhnbiG6BV8LR1GpY5FcxiF5m4Irc0JKKc6jLLtkgZQvyT/JWkhf/JBRLz0TmYYJWNedRmqCl97aB9j/vx3G8qUUV8IRYHKZCv76ZJR6HWsXn1q8HU4CEOH0gvDnqvXG4kkF2xloEYgEp7112csqkNRyHtZmhZ6nZbDSH7vJaXGwrOg/cLcxo0zcbjBYpvu2A37ASVx9EC+KYjDaFyMUSyzTP3qO8T4I5TlOUXC8Pz3YOE5sPDM3M5zKiUtGPXmnl8EBXmPFtW5OT7k5ZmDg9gsCme1pyHe6EEkBMefT78DnyYYDejXKW1edTk2ixIvycsFVVVll7SSx2IRN6S4w8gkNtd8oijCIj9VMLK7wZJkpTQV9R0wfDThoN4QUjmMZIZRWtywfj7XrZvHD584zEVfeYyfPn2k0KeUlFFfiOpyuz7tPwPjwlqcjq1eq1efmmM0dkK0V9VE12SEvPra9yWSuUA8d3D263i1y86K5kpeynPwdUYZRtJZKCk1ll0Jb/vr7CJGtigKlFULt4tGKJb9pxjc7jtdbM7sxZlsCYwlbkqjlZCNHM9u33vuEa89/yPZvT4RSbLnip7xXqjKsUMaiM9SNHRqzIGBLKxzcXzQy3OHBtiwsCb5/UtPureKBdr2C9N7vrNCjLl1rFyxWhQW1Jbr4jDyBCNEVWSXtJJHy0SYyyv+MwWjbMvRNOqWigvfaGfu55Yu6TqMZIZRUtxOG9994yb++uELWD2viv/46252nkgjk6dAaIKRLgTGhc07ncHobA4jbZCWlmDklxNHiSRT0hD+z1pcxyvHhgnnsazWEwijKFBuT1aSJh1GEklGlNWAb2TyZ7NljRaiS5p/9NTAa42aRWI7fDTz/aoqPPNtseC7/JqsT29W8lxulVfGu/VzGEFehbW2Ohe9Y352nBjNbzna0afEdtH56b+mvkN07tORtjqXLg6jMS0aQzqMShyrI1aSNkcdRiCcDtokNzCefYc0jXintDzmGKV0GMkMo0xYu6Caf79hDQCHBzwFPpvEjHj1FIwm0v/sVzSL7VSHUSaCUViGXkskGZOGYHRmex3eYIRd3WN5OimYCERw2a3JnY4hnyiDz8W9K5HMJcprpjuMwj6TCUb5bYUOxBxGCRZ1NYdRNoLR8eegZyuc+yF9nWJQmPcpH4QDImMr1w5pIEQ1yOv71FYvxqBRFc7PZ+D10aegcRVUZBAUXt8hStJUVbfTaK110Tmce0yJFt9RKQWjEsfqEIpuxGQrF/mkYTns+AP86Z3iRpOrw0irNx3Ko2Aku6TpzsLa6W03zYi+DqMkA7GZ2BzgasjRYSQFI4kkI6ypV2HPWixyGPJZluYJhFPb+cMB+Z2XSDJhNoeRmZy5WqmVjpPYlPiTlKRVNImFqJFjme/3wEMiouO0W3M7v9mwlYmMx1Jjok9s9XQY5XFRu61OhNWX2S1sbKvNz0G1/KLFaZajaTQsE+KxV7/7eludiyFPkPEc81rHfKIkXZaklTpWh1j5U6OTg9G5xut+ARd+Avb+Fbo35y4YuRtFaU/eHUbJStLyr94XO+UOK42VTo4PmlcwGtO7JC2Tz37lvOwdRqEUn1eJRHIq8et44slHc1UZi+pdeQ2+ngiGkwdeg/ncERKJ2ZnpMAqZ7DtUCOd6YDxxSZqiCJdRNg6jo0/DgtNzrzCYDZujNMfe2vhPD4dRAT5LbbHW8me21+Gw5UlK6N4iMjzTDbzWiHdK068sTfv3dw7l5jKSJWlzBas91qaSuVuS5qyEy/8VPvwKnPEOWP+G3PanKPnvlJbKYWSxinKAUrxpGUhbnYtjQyYuSdNTMApOZNblpbJldodRyAPRSPLXysmjRJI5aQr/Z7XX8cKRISLR/Kz8e9NxGMncMokkM8pqZnRJM1klQCGc68lCryE7wSgwASc2Zz6JTxdbWd4DnfOCNv4rUodRQ4WDs9rruOX01rwdkyNPiu2iDD9rVQvENp0uxGmysE4s2nYO57YoPh6ICUZ6zUUMRApGuWBzTjoCzHQjKgTVrXDdN2DDG3PfV31HfkvSUjmMoDABhUXOojpXzuq7UaiqyqgvRI2rUA6jltkdRtq+EhEJQzQsHUYSSabES9KSlzdcuLyRUV+I7V0jxp8T4AlEcDtTZBNJkVgiyYzyGlGSppV8ma05TSG67/rHEjuMAGoXCcEokzK5zudBjRgoGOU/0DkvxB1GOnVJg7y+R4qi8If3nctrNizI2zE5+jQ0rQF3hplJcXFWv9LGSYdRboJRvCStTJaklTZWu1DsYe46jIygbqmYQOerbjmVwwjE6q50GGXEwjoX3aM+gmHzrQx5ghEiUVXn0OskA7GZVLaIGvZoRNRlj3dPWpODE4lfF44JcFIwkkgyI80J2oUdDSgKPLG/Pw8nBROBdErSTOaOkEjMTlm1EDK0+2nYZC69fDuMwrG81WQLW7Xt4v3yDqa/36NPi/yihWfnfIqzUoBA57ww1i0qF1x1ue/LWgDxMd+Eg9D5Qub5RWCIoFZdbqfSaeN4zoKRDL2eG1gdkzcjOZjTj8pmkQvlGzb+WOGgOI4rhWJtKxPCkiRt2upcqCqcGDGfy2jEK8RIfTOMMixJU6Pg6YexE+LvzWsm95WIUOy9lNcbiSQz0ixJq3U7WNdaw5N5Eow8wXRK0mRumUSSEWU1YqsFX6ezMJhP8p2NqY0rnNWJnxPvlJZB8LWWX+RwZ31qSSmEEysfjPeKRUIlSXfMdCnV92gq3Zuzyy8CQ94fRVFoq3dxJMdO0GP+EOV2a/5yoHLA/GdoZqyOKRlGjsKeSymh3dACeWhtPLBP1Edrk/VElGprTwNpqxeWzWOD5ssxGo2p+tXlOnxvVRWCWYReg6ip1srRmlaLbTqCkZw8SiSZYY1911OUpAFcvLyRrZ0jjHpz64CSDul1SfNLF7NEkgnlNWKrBV+brawz3w4jLc8paUlau9gOH0lvn0bnF0FhwsHzwXiPPvlFMDc6OR9/XmwXnZ/5aw0S1Na11rC1cySnvMMxX7goOqSBFIxyw+qQGUZGoE288yEY9e4U25bTkj9PZhhljF41vkagTQR1cRiFvMIhlGnoNYhVJk0wal4rtsk+99qAQApGEklmZDDxuHh5A1EVnj44YPBJiZI0tyNFhlHIJ1peSySS9JjpMAoHzHXfzLcQoo0rkpXO17SJbbrB150vGJtfBKXrnhnv1VEwii2GlNp7NJXho6ISJJsSPqsxGU9nLa5l3B9mb2/2c9Uxf6goOqSBFIxyw+qY/ADK1T/90FZA/HkQjPp2iht33dLkz7PJDKNMaaxw4rRZcq7xNYJJh5EOF+q41TsHh5FigcYV0/c3G6HYeyknjxJJZmQw8VjfWkNVmY0n9p/U7fCH+idQZ4TJhiNR/KFoGg6jgLnyVyQSsxN3GMWcNSGfucbp9nw7jGLj6WQOI4cb3E3pC0ZG5xdB/kv38sV4L1TpEHgNk+JjqQWDT2Xk+KSgmSmau1jnXNwz24V49dKRoaz3MeYPFUWHNJCCUW5Yp5SzSIeRfmgrIHlxGO2AplVgTTFgt5dPlgNJ0sJiUWirc5laMNKlS1pcMMog9NrdBCiTDqPK+ZM5WkkFI81hJK83EklGxEvSUg+qbVYLFy5r5In9/aeIPNnwzMEBLv/6Ezy4q3fa455gBCCN0GvpMJJIMkJzGMVL0gLm+g4VzGGUYmGrth1G0swwMjq/CKaIIXlqgpMPgh4IjOrnMLLOAYfRaGf2gpHFIt4jnUXH1loXC2rKeelo9nm7Y75wUXRIAykY5YZ1ymTTTCsXxU68JC3JxFkPVFUIRlopUDIcbpFTI8kIIRiZT2gbMcRhlEFJmtUGFU2TDqOatvQ+91qXNDMNfCWSYiDDCdpFyxvoGwuwvy9J18I0ufN5MQH7w8td0x7f2yMmcU1VKQTgkMwwkkgyoiyWhRkvSTOZw6hgodcpFrZq29NzGAUmRBCxkeVoMEUMKSGH0Xhs4UBzmudKqeY8aahqbg4jEGVpBoiOZ7bX8sKRoawXlsb8oaLokAZSMMqNqTcfq4luRMVOvkrSxnvAN5Q6vwhEPk0g94nDXGNhnYvjgx5dVun1ZNQXwmZRcKXKDkmHbErSQKwuaQ6jdAWjkMwwkkiyIsOch4uWNwLkXJbWPx7g4d19VJbZeGJ/P/3jk8f/9QvHqSyzccWqpuQ7CXmNXcWXSEoNZxWgCIdRJAzRsLnum/me5MdL0pJ0SQOoXQSjXRBJEfjf+bx4T40WjEpRDBnvEVvdQ69L6D2aiqdfCIbVOQhGNoch789Zi+sZmAhwdDC7SooxX0iGXs8JpjmMZImIbsRL0gx29KQbeA3CPRKUglGmtNW58AQjDHnMZSce8YaocdlR9GhpmrVgNE8MzMZOQM1CsFjB7k4vw8hMA1+JpBjIMPhyXnU5y5sreHJ/bsHXf3qli3BU5eu3ricSVbl3WzcAAxMB7t/Zw2s3teJyJBkwhoPi3lOeRdinRDJXsViEOOIbmXSnzGmHUQYlaWpUlAAl47nvQXmtsflFUJqBzro7jErQhTUVrTFMLg4jgxoXnbW4Fsgux0hVVcb8YRl6PSeYlmFkohtRsROfOBvsMOrbIbbNa1I/11kpHUZZoHVKM1uO0agvSI3LkfqJ6aAJiZl0SQOxutS/VwzOtBuhszJFSZo28JUCtUSSEVl027liVTPPHhpg54nRrA4Zjar87qXjnL24jqvWtLCutZq7XhFlaX98uYtQROX2s1MMgn2xfAQtxFcikaRHeY1wGGnfeTOVcmv38HxlY/pHwe6avtA9G7XtYjucJMfo4CNw6FG46FPGOx9LsWW8UQ6jUg291kMwmtqkSkeWNlZQ53bwQhaCkS8UIRJVZej1nECGXhtHWdVkdwuj6N0pLkCpLLoAjkpRAx8JG3tOJcaienMKRsOeEDV6XaSzCb0GsbqkRsXf44JRRQqHUWxwaXdldiyJZK6jKLHgy/QHje+9aCl1bief/csOItHMy2qfOzzIsUEvb4yJQq/d1MrunjF2dY/ymxePcfbiOpY1p1jxjwtGtRkfXyKZ05TVxBxGWvafiRZ2890uPjCWngs6Lhgdnf330Qg8/HmoWQRnvlOvs0uM9j6VUuj1eK9YFM90zJgIiw1QSsuFNZW4YLQw+33YnIa8P4qicGZ7LS8dzVwwGvOJ+aR0GM0FpMPIOFI5LfSgbyc0p1GOBpOBxjL4OiNaa2OCUZb1vUYx4gvp5zCKW72zcBhpZOowkl3SJJLMyTD4stpl59+uX832rlF+8ezRjA/3mxePU+Oy86o14rt+/fr52K0Kn7lrB51DPm4/Z1HqnWiCkUuWpEkkGVFeIxYezZj9p7mdjHLO3HkLPPbfkz8HxtMTKCrngcWeWDDa/nvhzr/i8/mZ91jzXLqXD8a6xfhPj0gEEPsxqOTKFIwcFwsmmcY+TMUgwQjgzPY6jg956R3N7DM65hc5YTLDaC4gBSPjcFYZW5IW9MLgQWhJo0MaTAkklmVpmVDusNJU6TSdw2jEG6TGpZfDaEKs8GTqMozXrytQ1Sr+mkowCskuaRJJ1ticGU88rl83j4uXN/L1h/bRPZJ++cjgRICHdvVy88ZWyuwiXL/O7eDSFU3sODFKvdvB1WvSKEnwxVYupcNIIsmMsupYSZoJS7mtdgxzhaiqaHl/+PHJx/xjkw1lkmGxigWs2QSjkA8e/SLM3wRrbtbrbJNTioHO47365RdpGBTqbApy7ZAGscUiY96fsxfXA/Bihi6jsVi3ZukwmgtMFYysOrkVJIKyKmO7pJ3cI8qBmtMUjLR8Ghl8nTFtdS4TCkYhanUTjMaF0JPpapHmMKqaPxla6KxKLRhZbGAtjhUJicRU2JwiRDoDFEXhizeuJaKqfP7eXWm/7plDg4QiKjdunD/t8Zs3CXH4dWcuxGFLYwgmS9IkkuyIl6SZUDCKu0IMcM54h0QZ3sD+yccCY+mXQNW2nyoYRaNw/6dFk46r/lM/d0wq8l26lw/Ge/TLL9KwlZV2hlGuglEW9/50WTWvErfDmnHw9aTDSApGpY8WHmcry9/Fc65gdEmaFnidscNIlqRlSludi04TCUb+UARfKKJv6LUjC6ustsI09UaYTkmazC+SSLLDlt0q48I6F++5cAkP7+5jYCK91+/rHcNqUVjRMv3acMWqJv71utW876Kl6R08LhjJkjSJJCO00Ot49p+JBCMwrkxG63DmGwLPoPi7P80MI4CmVdC7A575thCKVBX+/gnY/Au44J+h/QL9zzkRpSYYqWrMYaS3YGRcyVVBUVXxea7WQzAypqzRZrWwoa2G7V0jGb1uMsOoOBaApWCUC9qFTJaj6Y/RJWm9O4VrqKY9vedrDiMpGGVMW72LnjE/gXCk0KcCwGjMBqpfSdp4drXVrgZQrLMIRkk+9yGvuVZJJZJiwpr9oPGCZY0AbDk+ktbz9/WOs6TBjdNmnfa4zWrhnRcspjrd6493SFwncslvkEjmImU1IrPMPyJ+Ntu90yiH0diJyb8PHhDbQJolaQAXfRJWXAMP/yv85la476Pw8h1w/kfh8s/rf77JsNhAsZROhpF/VLi/9C5Jy+HeZmq8g2Lcq0tJmnHB6a01LnqyzjCSDqPSRytDM9tNqBQoqza2JK1vpyhHs6T5FdAG67IkLWPa6lyoKlm3ptabYa+4adSU6xh6nc1kzmKB8z4Mp71u8jHNYaQm6MgU8ptvlVQiKRZszqxz6Na1VmOzKGw+PpzW8/f2jp/iLsoK37AoR5MuZokkM8prxHa8V2zNNlY3zGHUNfl3rSwtMA7ONDoCg3jfXn8nvPrrcOQp4Sw6/6NwxRfyfx3SSvdKpdxqvEdsq/TOMCozrOSqoIwcE9ucS9KMzXhqqS6jfyJAKBJN+zVahlGldBjNAbSSNKt0GOmOsxJCHtHCU2/8o8Jum245Gkx2wJKh1xlz+qJayu1Wbv2/5/jgbzYXXDga8YqLtH4ZRhOZd0jTuPILsOyKyZ8dFaBGEq8UhX0y8FoiyZYFp0PnC5MlKhlQZreyen4Vm4+lFowmAmG6hn2s1EUwGpId0iSSbCirEVvTCkZl4p6uN6OdYl5idQrBKBoRi53pOoxACDVnvgve+wTc/JPCiEUa1hIKdNYEI0NCr0vQYTRyXGxzFoyM7SLXUl2GqkL/ePrHGPOHKbNbTnEhmxUpGOVC3GEkBSPd0cL5jChLe/jfhMVxw+3pv8YhM4yyZVG9myc+eQnvuWgpT+7r57rvPM3LGXYT0JORmMMo7ZKQVGRbkjYbqbKygh5wyAwjiSQrVl0vrv2HHs3q5ZvaatneNUo4xSri/j7x/V3erKPDSCKRZIbmMJqICUZmc+ca5jA6AdULoL4DBg5MjqPTDb2eStMqWHdrYR2OpdQyXhMvDQm9LkWHUSyPq2ZhbvuxOgx1qbVUi2tLJmVpY74QlUXSIQ2kYJQbsiTNOLSVEL3L0o48Ca/8HM79ICzYlP7rNAdJUApG2dBUVcZnrlnJ05++DIfNwoO7egt2LpMOoyxL0oaPwo8vg8FD4ufA+GTGVa7EhdIEnzNPP7gb9TmWRDLXaL9AuA723JfVyze21eALRdjbm/w+sC/2+5UtWUzQZuIbloHXEkk2lMVKsMb7xNZs7lx7uTGukNEuqG6FhmVCMNLG0cWag1ZKgc6aw6jCiNDrEnUYlVVPfpezxeDPUEuV0AF6MxGM/KGiCbwGKRjlhnQYGYcRXcmCXrj3w1C3BC75bGavtTnF/7csScuJaped09tqefbQYMHOYdibY+j18efhxCvwyH+In4MT2a3czUb8c59AKJ3oB3eTPseSSOYaVjusuBb2/R0ioYxfvqlNOH22pMgx2tc7jsthpbVWhwmqVzqMJJKsOKUkzWRjdSMzjKpaoWG5WODyDojHMylJMxOlJIaM9wrxQ2+neKmGXo8cz70cDQwXjObFHEa9Y+n/H4z7w0UTeA1SMMoN6TAyDiNK0h77krh5Xv/t7C7WjgoZeq0D5y2tZ3fPGMOewthnR3xBHDYL5fYs64a1lrW774auV2KCUR5K0lRVOIwqpMNIIsmaVdeLHLujT2X80tbachornWxO0Sltb+8Yy5srsVh0KOOQJWkSSXZo3xvN1WE3mcPIiC5pkZD492oOIzUCPdvE7/Ra2Mo3NmM7XOWV8R7984sgJoiUyHs0lZHjULMo9/1YnYaWpFWX2ymzW+gdTT+TbMwXokqWpM0RtNBrs61alAKpSnMyJeiF538AG94Eiy/M8pwqZIaRDpzX0YCqwnOHC+MyGvGEqCm3o2Rbkz/aJbqNlNfBA58Wj2Ubej2TZIKRbxiiIekwkkhyYemlYHdnVZamKAqb2mqSdkpTVZV9veP6BF6HA6L5g0sKRhJJxmhlLN4B0ZrdYrLyDyNcD+M9gDopGIFwREMRO4wMENYKxZiRglGJvEcaqioWaKtzzC8C8f6oUYiEc9/XLCiKQktVGb1jmYVeS4fRXEETiqRgpD96Zxh5B8RKS9s52e/DUSlL0nRgXWs1boeVZw8NFOT4I75g9vlFIEL46hbDhR+HrpfEY/lwGE2cFNsKKRhJJFljL4dlV8Kev2bVhXNTWy3HBr0MTMw+MOwfDzDsDbFClw5pMWFKOowkksyxWCcXH23lhQ1ung0jhJDRLrGtXgD1McGoKyYYOXPMgSkU1lLKMOo1TjAqFReWhm9YOPj1KkkDQ0W1luqyLBxGJhOxkyAFo1yQGUbGEXcY6dSC3Rtzs7jqs9+Hs1KGXuuA3Wrh7CX1PHuwMA6jYW8otw5pWqDkme+CqgXiMd0EoyTOOo8UjCQSXVh1vfg+aYJvBmyM5xiNzPp7LRBbCkYSiQnQcozMOE63OSFklGC0UDifqxZA/17xmAy9LizRqOjYp3eHNCgtF5bGyDGx1UMwssa+/waKai1VZWl3SVNVVYReS4fRHCFekiYzjHRH79BrXQSjitJ3GO39G7z44+TP6XoFHvgXYRfNkvOW1nN4wENPBmq8Xox6Q9RmKxipqhiQ1bSJFr2X/It4PJfP1VS00rbZsrs0h5EsSZNIcmPZVWLBJ4uytHWt1dgsSsLga907pIHskiaRZItWlma2/CIw1mGkLWbVdwCxsVpRl6SVgGDkHYRo2BiHkdVRGu/RVEaOi60uDqOYwcPITmnV5ZwcCxCNpp4b+UNRQhFVZhjNGaTDyDjs5aLeXLeStCGxzWVi7yjxDCNVhQc/C898K/nznvkGPP/9nALAz1vaIHZVAJfRsDdITXmWJWm+YZEpUt0qft74JnjLvdCeZS7WTGxl4nM/mzApS9IkEn0oq4Kll8OOP2XcLa3MbmX1/KqEOUZ7e8dprHRS586h7FVDu29Jh5FEkh3lNWJrxnG6EULIaJe4XmiLTw3LxdZiL97FbZujNNwzWvi6YQ6jUhOMYg1mavTIMIp99g38HM2rLiMYiTLkTe1iGvOLcUdVuSxJmxtoFrdivQibGUUR5Tl6dUmLO4xyWKl1lniXtK6XYi1YhxI/J+SDg4+Kvyd7XgpWtlRS53bkPcdIVVVGfCFq3Fmq+lqHNE0wUhRYcrHIStADRRHuukQlaRa7nDxKJHpw+ltFecC++zN+6aa2WrZ1jhIIn5qBtK9vTJ/Aa5h0GOVy35JI5jKaw8g2hxxGVa2TP2uCUVmV+TKc0sVWZmiHq7wx3iu2RmUYqRHDQp0LQv8e4a7VykpzQTN4GFiS1lwltIDeNMrSxnwxwUg6jOYIWkmaVYeVRMmpJJo4Z4N3UHTJKMsh9M9ZVdoladv/ILYhT+KVisNPiN/D5GQmCywWhXNjOUZqDqVtmeILRQiGo9k7jKbmAxhFos/9RD+4G4t30CeRmIllV4mJ1cs/zfill69qwheKcPeWE9Mej0RVDvRNsKJZZ8FIisQSSXZoDiO7CRd2bWX6T/LHTkwuaMFkpzRnkZajQelkGI13i22VQYIRlIawptH1CrSeoc+YNx56bdz7M686A8Eo7jCSgtHcIF6SZsIbUSlQVqVjSdqgGHTn4gRxxBxG0ag+52QmIiHY9WfhYIHE7qF9f5v8uy97hxHAeR319I75OTzgyWk/mTDiFRfprDOMNIusoYJRVQLBqE+Wo0kkemGxCpfR4cdg8FBGL72go4E186v44ROHiUzJKzg66CEQjuoTeA3iGmuxiXuPRCLJnHjotQnH6UZ0bhrtTCAYFWngNcS6pJVCSVrMYVTRrP++rcYLInnFPyrC2lvP1Gd/8ZI0IzOMxDF6xtJxGAmRuFJ2SZsjyAwjY3FW61iSNpR7MLGzAlAnHTalxKHHhKi29rXi59nEoGhElG80r409J3uHEcCFHY0A/PHlrpz2kwnDsdrimmwFo9FOceNxN+h4VjNwVs7+ufeclIKRRKInG98MihVe+XlGL1MUhQ9c0sHhAQ8P7uqNP/737SKjYtU8nVbzfcPCki9dhRJJdsQzjMwoGOk8iQ2Mi4l29YLJxyrng92dm7u+0NicEC6BlvHjPcIlbjXAVZIHB01eObEZUGHB6frsL16SZtz701DhxGpR6MvEYSRL0uYIzgpxETbSbTCXSTRxzgbvYO6CkbbKW4plaTv+IBxYp90qfp7NYdT1Mnj6RdBzoudkQFu9i5s2LuCnzxyha9ib077SZTTmMKpx5VCSVt1q7AQuaUmaFIwkEt2omgcrr4Utd2Y80L56bQuLG9x8//GDqKrKUwf6+cY/9vPqdfNYM18nwcg7JMvRJJJcmEsOo9FYiezUOYnFAgvPhLrF+hyjENhiDqM8xhcYwnivMYHXkJdQ57xy4mWx1UswyoOgZrUoNFc66UlLMBIOIxl6PVewOeGfdk5OsiX6omtJmh4Oo9gkoNSCrwMTsPdvsPpGqIxZZWdzGO39qyhZO+11seeM5HzoT75qBQrw1Qf35byvdBiOC0bZOoy6ptu9jWA2wSgalQ4jicQIzninuN7tvjejl1ktCu+9aAk7T4zx2xc7+fBvt7CsqZKvvHYdil6Csm9YCkYSSS5ogpFZM4xAR8FIy1icMUZ5w+/h1f+rzzEKgc0JqKIlfTEz3mNM4DXkpW18Xul6GRpWTDoEcyVPDqzm6jJ6x3wpnydDr+ciZVVCwZfoj+4Ooxw7zWhtSvUK4jYLe/8GIS+se92kqDbTPaSq4nmLLwJ3PTgqc84wAphfU867LlzMPVu72do5kvP+UjHiE7bm2qwdRp3GC0aOilM/Y/4RMViSgpFEoi+LL4a6JVmFX9+0aQHNVU4++5cdRKMqP3zz6bidOq4Y+kZkhzSJJBfMXJJm11swimUsVi2Y/ri9zJgyqHxRKu6ZMSMFo9h7VAqh16oquja3nqHfPq35CQWfV12WVuh135ifqjIbZXadOiznAal0SMyLFv6bqw1VVXUuSSsxwWj3PcLCvPAckZcBp4pBA/th6JAo3wCx6p1jSZrG+y/poKHCwRf/utvwjmla6HV1Np0JwgERPF3dpvNZzcBZeaqLbeKk2LobjT22RDLXsFhgw+1w/FkYOZ7RS502Kx+4pAOLAt+6bSPtDW59z80nS9IkkpwwdUmazkLI2AnRDdgoUaJQlEKgs39UuMRrDIowKYX3SGP4qJiz6SkYxR1GxmZhNVelJxh1j/iYX1Nu6LnojRSMJOalrEq4KkKp7X1xfCNwx1XQt3vyscA4REM6hV5TeiVpJ16G9gvFxMleBnbXqWLQvvvFdkVMMHLV5hx6rVHhtPGxK1fw8rFhHtt3Upd9JmLEG6Tcbs1O1R/T8gGMLkmrEo6vqa12J/rE1ojuGhLJXOe0W8R2x58yfulbz2tn879eyaUrDXD/yZI0iSQ3zOww0rtMZrRLhFxbiycXJS1KIdC5d4fYzttgzP5L4T3S6NLyi4wQjIx1qc2rLsMTjDAeC7VOxIkRPwukYCSR6ITWBjQTR0/3Zuh8AY4+NfmYd1Bs9cowKqXQ67EeIUbM3zD5WHndqWLQ4AGxalU1P/acWl1K0jRuPaMVu1XhhSP67XM2hr2h7POLRmJ273xkGAEEp3zuPf1iK0vSJBL9qW2HhWdnJRhBDiH6yQj5hXAsBSOJJHvmWoZR9YLUzys2SqEkrWeb2M5bb8z+89A2Pm90vSQWrptW67fPeEmasQ6jlmohAqVyGfWMSoeRRKIfzlgb0ExyjPr3i61Wyw2TbpnyHLMgtJK0YAmVpHVvEdupqx6uWcrNxvumu1tmE5VywG61sKShgoN9xopxI95Qbh3SwDhLscZsQqksSZNIjOW0W+HkLujbVegzEWjXVykYSSTZU14j2sqb8d6pu8MoDxmLhaAUAp17tolFV6MW/eLvURGLahonXob5m/R1yuXJgdVSJYS7ZJ3SPIEwI96QFIwkEt0oizl6MumUNqAJRl2Tj+nmMCrBDKOeraLmveW0ycfK6051D03MaAfqqtMtw0hjWXMF+08a+96OeIPUZJNfBJOfqZmBknozm2DkOSk61MnJo0RiDGtuAsUKO/5Y6DMRaIKRDL2WSLLHaof3PQWnv63QZ3Iqejpnhg6L7Jfmtbnvy2yUQqBzzzbj3EVQGu8RCGdtz3ZoPV3f/eaxJA2gdyzxcXpGRczK/BoTuh6TIAUjiXmJT5z1EoxyHHjbXUJcKaWStO6t0LgSHK7Jx1x1k++ZxikOo9pY566obqeyvLmSziEf3qBxrVNHfCFq3dkKRsfFe6DdeIwikcOoogn0atctkUim426ApZeJsjQdr2tZIx1GEok+1C8FuwlX8/V0PWz9jRifrnt97vsyG8WezxP0iLmJkYKRtQRcWAC920XmbOuZ+u5Xe38MLklrqhKf1WQlaSdGxO9khpFEohfxzKAsBKORqSVpOjmMFEW0ky+V0GtVFSVpM0P4yme4hyJhkaEz1WFUXgdqFAKjk48NHYa/vA8iycPeErG8WTi4Dp407v0d8QapLs+hJK3a4HI0mPK5nyEYmdFSL5GUEqfdKso6Ol8o9JlMujylYCSRlCaaKySTxi6zEY3A1t/CkktLM8PImh93iGH07RLj5Xw4jIpdMDIi8BrE/M3qNPz9cdqs1Lsd8ZK0nSdG2dY5Mu053SOaw0gKRhKJPmRakuYbEQHOzmpRQqVdGLyDotSgrDr3c3JWlI7DaLxHlDpNDbwG4TCa6h7y9APqqQ4jmC4s7X8Qtv0249bUGsuahbNmv0E5RqqqMuINUZtt6PVoV37yAeKlj1M+956TMvBaIjGala8GW7k5ytLiDiNZkiaRlCR6TfIPPw5jXbDxTTmfkimJv0/GukMMw+jAayh+F5ZG92bR6a9qnv77thkvGAG0VJex5fgwb/zx81z3nad5289eRFXV+O+7R3xYLQpNlQZXK+iMFIwk5iXTLmmau2jJRWI71i223kHhLtKjnMdRkZnjaSaREJzck/t56EH3VrGdv3H645p7yD8ifh7vEduZGUYgRDoNrQwwml1J2aI6Fw6rhQN9xuQYTQTChKNqdl3SVDWPglGSkjSJRGIczgpYfhXsf6DQZyJL0iSSUkevDKOtvxbd4FZcm/MpmZI85c8YRs9WMQcxMv9Se4+KPcOod+f0TFU9sTry8v7Mqy5nb+84B09OcNHyRoa9IfrGJo97YsRHS1UZNmtxSTDFdbaSuUWmJWmaYLT0MrHVBAzfUO7laPFzqsitJG3rr+EH58PoCX3OJxe6t4ia95khiXExKDZhmegT24qpJWmxSczUcOyx2L8py5I0m9XCkkY3+w0SjEa84ryy6pLmGRCDlZo2nc9qFuKCUexzFo0Kl5dbCkYSieE0rRbXskKv1HqHRNC9w13Y85BIJMaghyvENwx7/irKae3FFaKbNrb8tEQ3DC3w2sgMSmsJOIzCATGPazEouN1WlheX2qeuXsG337CRpz59KR+8ZCkAe3sn57HdI76iC7wGKRhJzIzFKtqhpluS1r9PKMiLLhA/a4KRV0/BqDK3krS+XaBGhO2y0PRsPTXwGiZLILRys/Fesa1sPvU5mqgEkyJYDjf15c2VhpWkxQWjbLqkjcYysfLhMHLM6MbnHxGuLekwkkiMp2aR2E7NwSsEvmEh3suge4mkNLFYhSici3Nm513CNbHxdv3Oy2wUs8MoHBBVBUaWo4FoQa9Yi/M90ujfK+ZHRnX6szny8v4sb67khvXzcdqsrGjRojYmF8K7R/xFl18EUjCSmJ2yqgwcRgegvmPSBRIXjAb1a03syNFhNHhQbLWa5kKhqqIkbWbgNUxxGMUEI81hNNXhMluGkVYCmGVJGojg6xMjPjwB/TuljfiEkFXrzsJhpOUy5UMwslhjpY+xG0zc4SUFI4nEcLT7x8ixwp6Hb0iWo0kkpY6tLDdXyJY7xQR7trFcqaBX6V4hOLlbjImNFowg989SoendKbaGlaQ5816yV+Ny0FzlZG+vGM9Hoyo9oz4pGEkkuuPMRDDaBw3LhC3X3STaoMNkhpEu55Ojw2jAJILRWHcs8Hrjqb+bKQaN94r3zzZFaCmvEVvNYRSNTGYdZVmSBpPB1wcM6JQ2nIvDqH8voED9Mn1PKhHOysnP/cRJsZUlaRKJ8cQFo+zC+3XDNyIFI4mk1LE5sxdChg6LaIH1t5W2EzHeMr4IS9LyEXitYXMUt2DUt1M0nahbYsz+bc6CfIZWtFTFHUYDEwFCEVUKRhKJ7jgr0wu9Dvlh+Cg0rBA/1ywUDqNoVN+StFxCr0O+ydKm7q3C5aMx3gf3fGh6iZeR9GwV25kd0mB2h9HU/CIQLpiy6snnjPcKKylANHvBaHnzqfZNvRj1ihtFVhlGfTuhfump5XtG4aycdGx5+sVWOowkEuOpmg8WmwkcRsOyQ5pEUurYyrIXjPbcJ7arbtDvfMxIMTuMeraJzs21i40/lq2suEOve3dA0yoxvzCCXMTZHFjRXMGBvgkiUZUTIz4AFsgMI4lEZ8qq0sswGjosOns1xgSj6lYhGAVGhZChV0mas1KUpE0Ve9Jl6AigQutZwt2jZQMBbP89bPkVvPAjfc4zFd1bZw+8BnFzU6zTHUZT84s0yusmBa6xKSHeOTiM2upcOG3GdErTHEbV2TiM+nZB8xqdzygJK6+DQ4/A7ntkSZpEkk8sVnH/KLTDyCtL0iSSkseeo2A0bz3ULtL3nMxGMbeM79kO89blxwGWp7bxhqCqYmHWqMBriHVJy7/DaHlzJYFwlGODHrpHxHddOowkEr1JtyRtYJ/YNsRKhqpjDiPPoPhZzy5p0XB2F+XBA2J72i1iO7Us7dAjYvvij4Rbymi6t8weeA1gsYiJSjKHEQgRThOVtLwoyEkwsloUljZWGBJ8PeINUeG04bBleNkLTAixz6ggvtm45F9g/ia498NwYrO40ZXV5O/4EslcpqatsIKRqsYyjGoKdw4SicR4ss2dGeuGrpdg1fX6n5PZUJS8tUTXlUgoJoKsy8/xrIVx0OjCeI9YgG42KL8ICpbxtLJFdPze1ztOd8xhJAUjiURv0i1JG4iJMVrGTHUrhLyTIdO6laTFWp5nE3ytncvq1wDKpGAU9MCxZ2HB6eAdgO2/0+VUExL0wNGnof2CxM/RxKBoVAhGszqMpohKWvkU5FSSBiL42giH0clxP3XZBF737wXU/DqMbA645Q7x/u/8E7gbSzujQCIxEzWLYLiAJWmDB8XAv2F54c5BIpEYT7ZlMnv+KrarXqPv+ZiVYgx07npZ/N+2nZ2f4xUoo0cX4oHXBi7MFsiB1dFUgaLAvr5xToz4qHTaqCrLotKhwEjBSGJuyqrTK0nr3wfVbZOOGa2blSbK6FaSprU8zyLHaPCQcOpUtggnlJYjdPQZYZO89HNiJeK57wmhQCPkz64ELhEHH4GwL/nKVHmdEIN8Q7GW7rM4jAwoSQMRfN096mfcn9t+ZrK7Zyze4jIj+mI3snwKRiCC/67/pvi7LEeTSPJHzSJRNhzyFeb4x58X27ZzC3N8iUSSH7IVQvbcKzI7G+eIqGzNT0t0XTnyBKBA+4X5OV6m4uPJPfDVDvjfNfDDi+DXt8Jj/wWHHs2tuU829O0Q26bVxh2jQC61coeV9np33GFUjO4iKJBgpCjK1Yqi7FMU5aCiKJ8pxDlIigRnFYQ8EEnRZn1g//QbZ1ww2iq2enZJg+wupgMHJkvm5m2YFLMOPSI6Ayw6H877sPi3HHxY/Jsf+2/4r/nw363wo0vg7g9ML//Khj33CbGn7bzEz3HVgXd4MmepcjbBqFY8B8Q5WWN15jkKRssN6JTmCYQ5MuBhzfyqzF/ct0s4y6rbdDuftDntFrjoU7Du9fk/tkQyV4l3SusszPGPPy+u0Q156sookUgKQzYOI88gHHtmbpSjabgb4cQr0xdTzc7hJ0TGlF4L1qmwlWWW0bP7HvAMiGoDdxOMnoAnvwq/ugm+skRUPuSL3p1ijG1kGbatrGAOrOXNFezrG6d71Mf8Igy8hgIIRoqiWIHvAdcAq4E3KIpioKQoKWo0gSaYpEQpGo2JMSsmH6teKLZxh5GOXdIg+5K0+qXi7/PWC1fORD8c/Ie4YNvLYM1NULUAHv8f+Omr4In/gdU3wIbbRYbN1t/AljuzP/9wEPY/CCuvBast8fM0h9FEEsHIVSdCxSNh8W/RghdzLElb1iTe4/t39BAIR3Lal8be3nFUFdbMr878xX27oHm1yHYqBJd9Ds55f2GOLZHMRbRrWaE6pR1/DtrOkWWoEkmpk02XtH1/E01eVpd4d7SpXPBPYjy/80+FPpP0CEyIjKklF+fvmJmKj4cehQWb4OYfwpv+BB94Fj59DN70ZzH3eu57xp3rTIwOvAYR9VAgl9qKliqODng4NuiVDqMMOAs4qKrqYVVVg8DvgDlShCvJmKr5Ytv5UuLnjHaKEqupq7GueuHaGTshbIia0JMr2TqMvLHyLi1jad56sd1zrxCSOi4XP1vtcPb7oHuzCMm+5adw68/h2q/AW+6G+Rvh8OPZn//RJ4XIszLFypSrVpzzuNahK0GGEYB/RGQY1baLn3N0GLXVuThvaT0/fuoIF33lMX74xCE8gRQOsxTs7h4FyNxhpHVuyHc5mkQiKRxxh1EBBKOJfhg69P/bu/P4usp63+PfZw9JmjRpmw50HiiFMEmByiBwLFwQRYQyKSAIghf1Jfegr3M9nuO5Xo8ePcfjdFU8yEucmJFBQI8gosyTTG0pHRg6pW3apm1K0zRtMz33j9/emZqdZqdrr5218nm/XnmtZO+VvZ+kT5+d/Vu/3++xgBGAeBtMX5Xlf7A1KqxmykPB0R+3zPy/fKN4pcL5qH3RLp4ePD+850zmMZf27LAeS7PP6Hl7WZW9HznmUuntP9nrUaG17rb3QYXeWCZZWrTG6YcdVKkOL+3c00bAKA9TJHXP816fuQ3Y12Hn2AvjU/+Ru4/PO3+249R5Xbc511WWVj42uCu1JYPsYZRteD32EDtOyrzQP/9jO87+H13nvv8z0pnfkD7/gnTURT0f5+D5dtViII3A+7L8D/Yz7O9FbES1BeG2r7Gv+yxJy6TZNtVb6VpAAaNEwunOz5yo2689QYdMGKn/eHSFzr3xOS1Zv2PQj7m0rlFjytOaNCrPVNDGDfbCSsAIGD5GTrQLDcXYKW1dpn/RNAJGQOylRuS3M+66V6SVT0qHnze8MhATCensb0uN68PNfBmsVU/Za0iY63g+wcfVz0q+fd+AUdaxV1r/0jd+G9z4cqlfbhlzBc8wKl5T8MMmdiUtTCFgNGB9rXD7RAKcc9c55151zr26ZUsIEU4MTakS6+FS97qVUvVl4R3SxKPto7vuAaOglA5yl7TeAaOyUdbU+L21VrfbPTuqpNzSb7Pj7+7g+baID6a2uKNdWvFHac6HrPytP9ma6/plUukoKd3HApfNMKpfJsl3BYwOsCRNkpxzOm3OeN35mZN0z3UnaU9ruy782fP6xbOr1NGRfwPwpXWNOmJylVy+f2BtXmrHQl/5ADB0JBJW1lyMndJqX7IroZPnhv/cAMLVXxnR7u09L5TWr5DuukQaNUU65YZwxjeUzDxVqjlXeu7/dWW/D1Wrn5amndi1EU8Y8gkYrXzCLh5PfX/f90+osfsW3t5zDga5AU9W58YyYQSMAt5EaIBmjq1QSdJCLmQYDdx6SdO6fT1VUl3vk7z3P/fez/Pezxs/fnxog8MQdMyl0phZ0pPf3vc/+qYl1tj62Cv3/b7OgFGADec6d0kbRMAokerqjSFZeq0kHXLGwK8UTTvRat4HU5a27m/Sri0Da5SYzR6qXy5V9lGOJlnZmmT/BlJgGUa9nXTwWD16w2k6o2aCvvXH5fr2I8vz+v7W9g69tWnnIPsXZV7IJhye//cCiK7R04uTYVT7kvWVSJWG/9wAwpVrl7SG1dL35kg/O0VadLfUsEq640IpkZaufHD47px61jftTf/dl0pvPZq7CXbLLmsOXgy7ttrfxWH2L5LsNWOgJVcrn7Dd25L9bO9+7BXSlhXWbFyS1r9mu6rd+jHrvZpv4OXZH/SdHbZpiZSusPd5hZQsleTtonvIUsmEZmf6s9L0euBekTTHOTfLOVci6VJJvy/COBAVybT0wX+UNr1hGTLdLbzD0j6PvmTf78s2vg4yw2iwTa+3vmMBle6Lc7aP0SFnDvxx0mXW22LV0/k9v2TlaMlSac5Z+z83G2RrWNV3/yKpK6jUO2BUgMV4dHmJbr7ieF1+4nT9+vnVenPDwMvT3q1vUkt7x+B3SBs93TLCAAwfxQgYtTRbY1f6FwHDQ64Mo5VPWLZ22x7poc9JPznOWhFc+TvLTh+uxs6Wzr9JatpsQaOfzrOAWvfgxZa3pZtOkm453TLrw7b6GTvOmh/u8yYH2PS6YZW0fXXucrSsIy+U0uWWZbTuZen2BVZtsPUd6Y6LpJtPlZ77kVS3cP+/57qF0l//TXry3y2Yl9XRIa14RJpxcuE3lslehMm3Z1hAaiZWKuGkg6oIGA2I975N0vWSHpO0XNK93vulYY8DEXP0x6Xq2dbLKLswte21+tqac/vOIspmGI0IMMMokbQFNN8eQttWdpWjZR15ge1+1r1/0UAcPF+qX5pfSm5jnbT4bmtmly2r60/n78z33b9I6ipJy2bhjJomyeW3rWcenHP6yodrVF1Rqq89/OaAS9OW1lm/qUEHjChHA4afMTOk5q35Z5MeiLrX7U3i9JPDe04AxZMqs6yQ3tkaa56TKidJ178qffJ++3vxk/ft23phODrmE9INi6WLfml/zz70OenOi6Ud661dwy/Pst6a762V3v1r+ONb/bRUWmWb1IRpoD16Vj5px/0FjMqqpCMWSG/cJ91+gVQxTrrmMemGN6QFP7PKiL98Xfr5fOm7B0sL7+z7cbyXHv2Kja+lyS5eZ615xvpSHXPZQH7CA1PkgNEVJ83Ql8+uUTpZpB2XD1BRRu29f8R7f6j3frb3/tvFGAMiJpmSTv+qBSfuu9qaBK74o9V4H3tF399TiB5Gkr1A5RMw6uiwXW96B4zGzJAW3JR/jXO2YXX2KsZ+n79devCztkie9c2BfU/3AFyuDKOyUZJL2pWe0ip7cUmmAy9J627UiLS+ek6NFta+p/teW7f/b5C0tG6HRqSTmjUuz53yWvfYlRQaXgPDz+hM+fCOga0zgajNNLzO1VcCQLz09SbWewsYzTzVsi7mnCVd8msyD7tLpqWjL5b+55PSR75ngaKbTpZuW2CBjc89J5WPk16/NfyxrXra/u2SqXCft7TKNqvZ+m7/56160nqnjp29/8c87kqpdZcFL69+xPpnpUqkuZfb7/gf3pIu/IU0/jDpv79kfbZ6W3K/tcT4yHetEmHRXV33Lbrb+qTWfDSvH3VQkiV2LNJOacfPGKPPzx/A73yIimaYC8PT0RdLZ/+7RadvXyC9fItUNTX3jl/ZrZErxgU7jpKR+ZWkNa63NNHeAaPBmvg+y+4ZaB+jF35iwaWP/GfP5tr96Z6VlSvDyDlpxGj7vCqz0WGypOD1wRccO0UnzKzWdx5doe279n81ZWldo2omVSqZyLPh9da3bBcJAkbA8JN9/Qiz8XXtS9L4w4Ptuwdg6EplylO6lxJtfUfaVW89ZtC/REI68TrbVXjq+6206drH7W/duZdbn6MwG2QvuX9g5V6FcOwVUtlo6aHP5y4Ra2+TVj0jzZ4/sN6p00+WLr1LuuZPUtWkfe+vnCi97xLpE3dYj9cHP9vzonHLLunx/2stOI69wjKJVj9j2WB7d0rLfy8ddUHfG+sErfP/WnECRlFHwAjRcvIXpIt/ZU3Yal+wF4REsu9zx8yUPvZj6aiLgx1D6cj8yhSyO6QNNFizP4mk/SGx6qn9N51b/5r0xLekI87vuzF4LqmSrn5NI3MEjKSuwNKoTMAokSpohpFkpWnfXHCkGve06WdPr+z33I4Or+V1jYMrR8v2ZppAwAgYdrIZRmH1Mdq42K7CTj8xnOcDUHx9ZRityWSPzzw1/PFEVfUs6+/0qYe7Au7Hfcou+i3KUSoVtLpF0sPXW5DluKvCec7uqiZJ53xPWv+y9OJP973fe2nhbdLeHQMPaDln2T/7u/A+coK939q4SHrm+3Zbc4P02L9IO+ssuyiRtE2M5KXF90jLHpZam6VjLs/npxy8VCbDiIDRoIScLwcE4KgLbXF6/ifSvE/nPs856firg3/+kkppb+P+z+vokNY+L71wo31dHWAq4sHzLTK/+hlLzW2qt0j/QUdaydy2ldJrv5Zev90CPh/78cB3YssaUW2ZVLl2SZO6+hh1ZhilC9bDqLuaiVU6dtpoLV73Xr/nrdverJ172wa3Q9q6l63sLqjMMADRMXKCXZF8r8AZRg2rbQfQJffZeppPYB9AtGUzK9p2d9225jmpcvLwbm4dhHFzpBmnSK/fJp3yxcI2VW7aIt3zSWuB8fHbuoITYTv6EgvEPPFtac7Z0oQau339a9Jj/2wXJSbNlQ4ZwOY3+Tr8Y5ZB9Mz3bJOid/9i7weOv7qrnHLMTPs3WXy3vTepni1NOyH4sfQlmQnOFqkkLeoIGCGaZp5avKsvFeOkZQ9Jt5xhzbhn/Z31IUqVSbvfs8aldQuld/4sbV9jdcUf/Eru0q7ByJbh3XbevvdVTZEaN1h/oZpzpPlf7Qrs5KO8WtpR23+GUfZKTjZglEhb09YQTB9brhdX9r9t6gE1vK59SZp2UuF3bgAw9DhnjfwLETDq6LBdkF77tZVMJEuk0/5BOuUGdmQEhpPeGUbZ/kUHn57/RT7s67irpAevk9Y+Z3+rH4jmButpVz3bKg0kW8vXvyI9/jXbJOGax+xiQ7E4J537I+mmE60ReHm1lX81b5MqJkjn3Wib7eSqzDhQH/lP6ye17m/SvGuluZdZG43ujrlM+v31Vn1x+v8Jb553lqQV/qJ2HBEwAvL10R9KU46Xltwr/ekrfZ+TrrCo+en/Yru45dvYen/GzpYuudXSOUceJFWMt53QNi+xpnPja6xeuK+a44HKBoMGkmE0qnuGUWF7GGXNqK7Q717foD2t7SpL9/3it7Ruh5IJp0MPGsDOcN3t2mY9jI75RAAjBRBJY2YEX5K29V3prktsa+PycdIHrpdO/JxUNTnY5wEw9PXuYbT1bWnXFmkW/YsCccR50qNflp76jv1u0+W2Ic6MU7qCJnubrBJg6YNWwiZn95VUWGsGl7Dvbdxg57uENOEIy2Ba+6LUtMmC/hfcLE2eW6yftMvI8dL5N1nmasUEafJx0rhDrYH1QHZJPhBlo6QvvGy/v2S673OOOF965Ms254+5tLDj6a6zJG1P/+ehTwSMgHxVjJVO+Xv72PKW9bpp22tpjqkR9oIx7tDCRfCzjlzQ8+tJ75MO+3Bwjz+i2n6e0n6yc0b0yjAKqSRNkmaMtSDcuoZmzckREFpW16hDxo/MGVDKad3f7Mj21sDwNWaWtOZ5acPr0pTjDvzxdm6W7rjAGoFe/Cu7mJDNMAAw/GRL0ja9aY2B1zxrX9O/KBjpEZbp8twPrUVEVtVU64E6coKVUDVttqyuEWMkedu8paXZ1uqOVvv3OOhIyzrdskJa/6q07hXrOVfzMdvJLrsJzFBw2IeDfT+Qj3RZ//eXVVmj8qYt0uhp4YxJ6grOUpI2KASMgAMx/jD7iKOaj1rmUn/pop0ZRlPtGGJJWjZgtHZb7oDRik07deKsQew4tO4lu2I0OYA3iQCi6QPXS28/Jt16nnT5PQf2Jm5Po3TnRZa9ePUfLEsVwPA27SRbC/7w9/ZGevWzdgFuzKxijyw+zvy6dOqXpNbdtkX8xsXSwjssUCRv/wafuFOa9v5ij3T4OOub4T9nMpthREnaYBAwAtC3oy60j/5MP0madmLXFtRhlqSNrZAkrW1o7vP+95pbtHHHHtVMGmT/osnH7v9KCYD4GjNTuvYx6bYF0h0XSRfeYllB+fY1a2uRfnuFVL9cuuy3BIsAmJJy6coHbX259yrLgjj8XPoXBa2syj4kayZ+5AXW22fHBmsfwe87/jr7hVGSNhh0cwUweLNOk679c9dCnAwvw2hMeVqVpSnVbtvV5/0rNu2UJNVMzLNmu3W3laBMY3trYNirmix9+lHrC3fvldL3Drbgz2u/Gfj2vI/+o7T6aem8n0pzzizocAFETNko6YrfSVPfbxkwlKOFY9RUKykjWDQ8ZN+nhNQ2I24IGAEITiK8HkbOOU0fW54zw2jFRtsh7fB8M4zqFlrQi/5FACTrW/fpR6QLfm6lunWLpT/cIN04T1p8j9TRnvt7X/ml7YZ26pdsxxgA6K2sSrriAdvh6n1stgEELtlrR0LkhYARgOCEWJImWR+j2m05AkabdmpMeVoTKvNsKlv7kh3JMAKQVVJhuyae/1/SF9+QrnxIKh8jPfhZ6ebTpCX377v2rX3BsosOOUs642tFGTaAiCgdKc37NI3wgUJIETA6EPQwAhCcZDrU+uDp1RV6fNlmtXd4JRM904qXb9qpmolVcvmmG9e+JI07zLIKAKA356TZp0uzPigt/Z1t2fzAtdJfvyEd9ympo8N23Vn2sPVBuugXhd81EwAA9K2zJI2A0WAQMAIQnERaag+nh5FkGUat7V517+3WtOryztvbO7ze3rRTl56Q55adHR22Q9oRC4IdKID4SSSkoy+WjrxQevtP0vM/kp74lt03olqqniUtuHlobbcMAMBwQ0la/xpW93s3ASMAwUmGHDDKBIlqG5p7BIxqG5q1u7U9//5FW1ZIe3bY7m8AMBCJhFRzjn3s2iaVVkqpkmKPCgAASPb+RCJglMv91/R7Nz2MAAQnkQptlzRJmjGuQpK0tlcfo86G1xPzDBitf9mO9C8CMBgVYwkWAQAwlDgnpcooSctle/8ZRgSMAAQnWRJqhtHEqjKVJBNa27Crx+3LN+1UwklzDhqZ3wM2bbHj6OkBjRAAAABAUSVLpbZwdnKOlNbd0u7t/Z5CwAhAcEIuSUsmnKZWj9hnp7QVGxs1a1yFytJ5NpptabKgVzZ1FQAAAEC0pUpC3ZgnMhrr9nsKASMAwQm5JE2yPkb7lKRt2qmafPsXSVLLLts+GwAAAEA8pMqkdjKM9kHACECoQi5Jk6QZYytU29As770kqWlvm2obmnX4xMr8H6y1WSrJs4wNAAAAwNCVLKHpdV8IGAEIVTItdbSF+pTTq8vVtLdN23bZVYO3Nu2UJNXk2/BaspK0dPn+zwMAAAAQDalSStL6spOAEYAwJVKhp3vOGGsBnmxZ2opNtkNazaRBZBhRkgYAAADES6qUkrS+kGEEIFQhN72WugJGtZmd0pZvbFRlaUpTRo/I/8FamgkYAQAAAHGSLKUkrS8DCBilQhgGgOEiWSL5dsl7yblQnnLqmHI5J728erteWtmg+15bp9PmjJcbzPO3NElVk4MfJAAAAIDiSNHDqE8EjACEKpFZUtpbbWEOQVk6qUlVZbr75VqVJBO66gMz9b/OmDO4B6MkDQAAAIiXVJm0d2exRzH0EDACEKpk2o7tLaEFjCTpypNnqrahWV84fbamjjmAptWtlKQBAAAAsZIskdroYdRDe6vUtFlyyX5PI2AEIDiJTMCoI9w+Rp+fPzuYB2rZJaUJGAEAAACxwS5p+2raLMlLo6dJ2p7zNJpeAwhOZ4ZRW3HHMRjeWw8jMowAAACA+EiVsUtab40b7ThmVr+nETACEJzuJWlR07ZH8h0EjAAAAIA4SdL0eh+NG+xYTcAIQFiKVJIWiJZmOxIwAgAAAOIjVUrAqLed2Qyjmf2eRsAIQHCSmUbXUSxJa2myIwEjAAAAID5SpVI7AaMeGjdIyVKpclK/pxEwAhCcZKaPfiQzjHbZkYARAAAAEB/JTIaR98UeSfGsfrarb5EkNdZJVZO7LvjnQMAIQHASEe5h1JopSWOXNAAAACA+UiWSvG0lPxwtuV+69Vzp0S933da4UaqaYtlX/SBgBCA4Ud4ljZI0AAAAIH5SZXYcjmVpa56XHvq85JLSyqektsyF/cYNUtUkAkYAQpSMctNrStIAAACA2ElmgiJtEayCOBBb3pbuuVwaPUM670apZadU+6KV5u3cmClJI2AEICxRLknr3CVtZHHHAQAAACA4qUyfnrY9xR1HmLy3YFEyLV1xv3TE+dav6J0/S83b7P1a5WQyjACEqLMkLYoZRtmStPLijgMAAABAcIZjSdp7a6Vt70jz/0kaM1MqHSnNPNUCRo11dg5NrwGEKpth1BHFHkaUpAEAAACxkw2KDKeStLqFdpxyfNdtcz4kbX1bWvuCfV1FhhGAMEU5w4hd0gAAAID4yWYYDaeStLqFFiibcETXbXM+ZMfXb7UjASMAoUpGuYdRkzV9S6aKPRIAAAAAQcn2MIrie5TBqlsoHXRkz4DQ2NlS9WypfpnkElLFBJpeAwhR1EvSKEcDAAAA4qVzl7Rh0sPIe6lusTT52H3vO/RsO46caBfKyTACEJool6S1NLNDGgAAABA3nSVpwyRg1LBK2ruj74BRtiytarIdaXoNIDTZgFFHFANGTeyQBgAAAMRNZ0naMAkYZRte9xUwmvEBu0ieDRjtJ8OIZh0AgpOIcoYRJWkAAABA7Ay3krS6hfYzj6/Z975UqXTxr6WqSfZ1ov+QEAEjAMHJNoyOYsCotZmAEQAAABA3qeEWMFokTTy6q/qjt0M/1PW5c/0+FCVpAIKTrYGNaklamoARAAAAECvZPqUtTcUdRxg6OqSNi/ouRxsEAkYAgtNZkhbBLSspSQMAAADip7TSjnt3FnccYdj2rgXGCBgBGHI6d0lrK+44BqOFkjQAAAAgdlKldmF7OASMNi6yIwEjAEOOc5JLRrQkjQwjAAAAIHacsyyj4RAwqlsopculcYcG8nAEjAAEK1kSvabX3lvqJgEjAAAAIH6GU8Bo4vu6NiM6QASMAAQrmY5ewKhtjyRPwAgAAACIo9Kq+AeMOtqljYulyXMDe0gCRgCClUhFryStZZcdszsoAAAAAIiP0kppb2OxR1FYDaul1mZp4tGBPSQBIwDBimJJWnaLzXR5cccBAAAAIHjDoSStfpkdJxwR2EMSMAIQrGRa6ojYLmmdGUaUpAEAAACxMxwyjOqXSXLS+JrAHpKAEYBgJVJSe0uxR5GflmY7UpIGAAAAxM9wyTCqniWVBFc1QcAIQLCi2PQ6W5IW4OIKAAAAYIgoGwZNrzcvC7QcTSJgBCBoyRJK0gAAAAAMHaWVtjNyW8QqIQaqdbfUsJKAEYAhLoolaa2UpAEAAACxVVplx2xlQdxsfVvyHdJBBIwADGVRLkljlzQAAAAgfkor7RjXxtebg98hTSJgBCBoCXZJAwAAADCEdAaMYtrHqH6ZtQapnh3owxIwAhCsSGYYZUvSCBgBAAAAsROngFF7q/TH/y29V9t1W/0yadxhUjIV6FMRMAIQrGQ6ej2MWpqkVJmUSBZ7JAAAAACClg0Y7YlBSdq2d6VXbpGe/UHXbfXLA+9fJBEwAhC0RFrqiFqG0S6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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "patent-devon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 110.9812306896508\n",
      "R-Squared: 0.26305384934960074\n",
      "RMSE: 10.534762963144962\n",
      "MAE: 9.045409358226797\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0bb5b6",
   "metadata": {},
   "source": [
    "This seems to perform a lot worse than some of the original models. I'm not comfortable enough with transformers to confidently explain why, but I conjecture that weather prediction is a simple problem which transformers may just be too complex a solution to effectively implement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a138fddc",
   "metadata": {},
   "source": [
    "#### Simple Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2038bf10",
   "metadata": {},
   "source": [
    "Trying transformers with just previous temperatures as inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3e804b2c",
   "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>datetime</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-10-01 13:00:00</td>\n",
       "      <td>12.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-10-01 14:00:00</td>\n",
       "      <td>12.684650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-10-01 15:00:00</td>\n",
       "      <td>12.697790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-10-01 16:00:00</td>\n",
       "      <td>12.710929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-10-01 17:00:00</td>\n",
       "      <td>12.724069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  Temperature\n",
       "1  2012-10-01 13:00:00    12.680000\n",
       "2  2012-10-01 14:00:00    12.684650\n",
       "3  2012-10-01 15:00:00    12.697790\n",
       "4  2012-10-01 16:00:00    12.710929\n",
       "5  2012-10-01 17:00:00    12.724069"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_and_preprocess()\n",
    "data = data.dropna()\n",
    "data['Temperature'] = data['Temperature']-273.15\n",
    "df = data[['datetime', 'Temperature']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1b4a9598",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = int(len(df)*0.75)\n",
    "\n",
    "train = df[:cutoff].reset_index(drop = True)\n",
    "train = train.loc[:, 'Temperature'].values.reshape(-1, 1)\n",
    "test = df[cutoff:]\n",
    "test = test.loc[:, 'Temperature'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0de94bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "test_std = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "77c3880b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split(train_std, n_timestamp=24)\n",
    "X_test, y_test = split(test_std, n_timestamp=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "41d755ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 24, 1)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 24, 128)      33792       input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_3 (Bidirectional) (None, 24, 64)       41216       bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 24, 192)      12288       bidirectional_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 24, 192)      12288       bidirectional_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_6 (Lambda)               (None, None, 64)     0           dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_7 (Lambda)               (None, None, 64)     0           dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_9 (Lambda)               (None, None, None)   0           lambda_6[0][0]                   \n",
      "                                                                 lambda_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None)   0           lambda_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 24, 192)      12288       bidirectional_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, None, None)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_8 (Lambda)               (None, None, 64)     0           dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_10 (Lambda)              (None, None, 64)     0           dropout_2[0][0]                  \n",
      "                                                                 lambda_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_11 (Lambda)              (None, None, 192)    0           lambda_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_1 (TimeDistrib (None, None, 300)    57900       lambda_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, None, 300)    0           time_distributed_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_1 (LayerNor (None, None, 300)    600         dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_1 (Glo (None, 300)          0           layer_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 300)          0           layer_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 600)          0           global_average_pooling1d_1[0][0] \n",
      "                                                                 global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 64)           38464       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 1)            65          dense_14[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 208,901\n",
      "Trainable params: 208,901\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_input = keras.Input(shape = X_train.shape[1:])\n",
    "\n",
    "x = keras.layers.Bidirectional(LSTM(64, return_sequences=True))(model_input)\n",
    "x = keras.layers.Bidirectional(LSTM(32, return_sequences=True))(x)\n",
    "\n",
    "x, self_attn = MultiHeadAttention(n_head = 3, d_model=300, d_k=64, d_v=64, dropout=0.1)(x, x, x)\n",
    "\n",
    "avg_pool = GlobalAveragePooling1D()(x)\n",
    "max_pool = GlobalMaxPooling1D()(x)\n",
    "conc = concatenate([avg_pool, max_pool])\n",
    "conc = Dense(64, activation='sigmoid')(conc)\n",
    "model_output = Dense(1)(conc)\n",
    "\n",
    "model = keras.models.Model(model_input, model_output)\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "667f34b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "848/848 [==============================] - 31s 31ms/step - loss: 0.2772 - val_loss: 0.2363\n",
      "Epoch 2/20\n",
      "848/848 [==============================] - 25s 29ms/step - loss: 0.2011 - val_loss: 0.1785\n",
      "Epoch 3/20\n",
      "848/848 [==============================] - 26s 30ms/step - loss: 0.0459 - val_loss: 0.0824\n",
      "Epoch 4/20\n",
      "848/848 [==============================] - 25s 30ms/step - loss: 0.0206 - val_loss: 0.0467\n",
      "Epoch 5/20\n",
      "848/848 [==============================] - 24s 29ms/step - loss: 0.0228 - val_loss: 0.0385\n",
      "Epoch 6/20\n",
      "848/848 [==============================] - 25s 29ms/step - loss: 0.0182 - val_loss: 0.0282\n",
      "Epoch 7/20\n",
      "848/848 [==============================] - 24s 28ms/step - loss: 0.0167 - val_loss: 0.0158\n",
      "Epoch 8/20\n",
      "848/848 [==============================] - 25s 29ms/step - loss: 0.0155 - val_loss: 0.0147\n",
      "Epoch 9/20\n",
      "848/848 [==============================] - 25s 30ms/step - loss: 0.0167 - val_loss: 0.0154\n",
      "Epoch 10/20\n",
      "848/848 [==============================] - 25s 30ms/step - loss: 0.0175 - val_loss: 0.0141\n",
      "Epoch 11/20\n",
      "848/848 [==============================] - 24s 28ms/step - loss: 0.0156 - val_loss: 0.0160\n",
      "Epoch 12/20\n",
      "848/848 [==============================] - 25s 29ms/step - loss: 0.0156 - val_loss: 0.0159\n",
      "Epoch 13/20\n",
      "848/848 [==============================] - 25s 30ms/step - loss: 0.0144 - val_loss: 0.0145\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "97823638",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "y_pred_inv = sc.inverse_transform(y_pred)\n",
    "y_test_inv = sc.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ac398ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv, label = 'Actual')\n",
    "plt.plot(y_pred_inv, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([y_test_inv[0:365].min()-3, y_test_inv[0:365].max()+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8f93fc8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.9217078438696003\n",
      "R-Squared: 0.9838849663269601\n",
      "RMSE: 1.386256774147416\n",
      "MAE: 1.0432273997034764\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immediate-youth",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radio-keyboard",
   "metadata": {},
   "source": [
    "Moving with the basic LSTM - that was the most performant model (probably because most recent temperatures are the most important indicators of future temperatures)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "authorized-fifteen",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_and_preprocess()\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dominant-converter",
   "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>datetime</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-10-01 13:00:00</td>\n",
       "      <td>285.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-10-01 14:00:00</td>\n",
       "      <td>285.834650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-10-01 15:00:00</td>\n",
       "      <td>285.847790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-10-01 16:00:00</td>\n",
       "      <td>285.860929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-10-01 17:00:00</td>\n",
       "      <td>285.874069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  Temperature\n",
       "1  2012-10-01 13:00:00   285.830000\n",
       "2  2012-10-01 14:00:00   285.834650\n",
       "3  2012-10-01 15:00:00   285.847790\n",
       "4  2012-10-01 16:00:00   285.860929\n",
       "5  2012-10-01 17:00:00   285.874069"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data[['datetime', 'Temperature']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "da886f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = int(len(df)*0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "reduced-binding",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:cutoff].reset_index(drop = True)\n",
    "train = train.loc[:, 'Temperature'].values.reshape(-1, 1)\n",
    "test = df[cutoff:]\n",
    "test = test.loc[:, 'Temperature'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "running-market",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "test_std = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dominant-criticism",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = split(train_std, n_timestamp=24)\n",
    "X_test, y_test = split(test_std, n_timestamp=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "directed-nelson",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.InputLayer(input_shape=X_train.shape[1:]))\n",
    "model.add(keras.layers.LSTM(64, return_sequences=True))\n",
    "model.add(keras.layers.LSTM(64))\n",
    "model.add(keras.layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "compatible-jesus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "848/848 [==============================] - 15s 15ms/step - loss: 0.0508 - val_loss: 0.0125\n",
      "Epoch 2/20\n",
      "848/848 [==============================] - 11s 13ms/step - loss: 0.0129 - val_loss: 0.0063\n",
      "Epoch 3/20\n",
      "848/848 [==============================] - 12s 14ms/step - loss: 0.0105 - val_loss: 0.0060\n",
      "Epoch 4/20\n",
      "848/848 [==============================] - 11s 13ms/step - loss: 0.0103 - val_loss: 0.0058\n",
      "Epoch 5/20\n",
      "848/848 [==============================] - 10s 12ms/step - loss: 0.0104 - val_loss: 0.0058\n",
      "Epoch 6/20\n",
      "848/848 [==============================] - 10s 12ms/step - loss: 0.0104 - val_loss: 0.0060\n",
      "Epoch 7/20\n",
      "848/848 [==============================] - 11s 13ms/step - loss: 0.0104 - val_loss: 0.0060\n",
      "Epoch 8/20\n",
      "848/848 [==============================] - 10s 12ms/step - loss: 0.0103 - val_loss: 0.0061\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 20, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "liquid-sweet",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_inv = sc.inverse_transform(y_pred)\n",
    "y_test_inv = sc.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dried-machine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FEEIIIYQQYmUSGAkhRBmdm03wq44fQjYGt/7WS76u9R4CwDdzrNpLE0IIIYQQQoiS2Wq9ACGE2CgMw2BubobXOr4Gu94A3fteeqeuveSw0RY9Xv0FCiGEEEIIIUSJpMJICCHKZDqa4d3Gd3AV4nDHH1z6TnYX486t9CdPVndxQgghhBBCCLEKEhgJIUSZXJiY4H3WHzDX/2ro2nvZ+037r2FbYQj0QhVXJ4QQQgghhBClk8BICCHKxP3UZ2jSkhRu/y9XvF80uA8fKTJTp6q0MiGEEEIIIYRYHQmMhBCiHDIxdgz/Gz80bqB96/VXvGuu+zoAEueeqMbKhBBCCCGEEGLVJDASQohymDuDqxDnUe+rsFi0K97V3b2LqOGhMHqkSosTQogls7EMX39mjCPn55lPZGu9HCGEEELUKdklTQghyiG1AIA32LXiXTua3BzTt7Bv+plKr0oIIV7i7x84wz8/emHx820dPr7/Wy/DbpXriEIIIYRYIu8MhBCiDPKJEAAtbZ0r3rfD7+KYsRV/5HnIpSq9NCGEWObpkTAH+gP843sP8a4b+hmaiXNuNlHrZQkhhBCizkhgJIQQZRAOzQDQ3r5yhVGr18GzxjYsRgEmn6300oQQYlE6V+DUZJSbtrRy565OfvmWQQBOT0VruzAhhBBC1B0JjIQQogziCyow6uzsXvG+FovGmGe3+mT8qUouSwghljkxESGvGxzoDwCwpc2H3apxajJW24UJIYQQou5IYCSEEGWQiYWIGm56gv6S7m9r7iZqaYa55yu8MiGEWPLMSBiA6zYFAHDYLGxt9/G8VBgJIYQQ4kUkMBJCiDIoJEKE8dHV7Crp/h1+Jws0QTJU4ZUJIcSSo6NheppddDYtvVbt7m7i9JRUGAkhhBBiOQmMhBCiHJILJCxNJe8y1O53Mav7ITlf4YUJIcSSo6NhDpjVRUW7uvxMRtKEk9naLEoIIYQQdUkCIyGEKANbNkzG3lzy/Tv8TmYKPozEXAVXJYQQS+biGcYWUovzi4p2dTcBSJWREEIIIZaRwEgIIcrAlY+gOwMl37+zycWC4UeXwEgIUQ3pCM+fVLsyHuhvWbr92Je59fuvxk+S05Myx0gIIYQQSyQwEkKIdSroBn49Bp6Wle9s6vA7CeHHkl4AXa/g6oQQAvjaBzl075sJWhLs7TWrIXUdfvIxbAtn+ZD7R1JhJIQQQohlJDASQoh1mo4kaSaB3ddW8mM6mpwsGH40Q4d0uHKLE0KIsSPwwvdxFhL8btODuB1WdfvZH8H8WfC28x6+w/mJ6dquUwghhBB1RQIjIYRYp6mZaSyagbupteTHdPhdzBt+9YnslCaEqKQH/xLD08ojxj7elvs2ZMxKosc/Db4u+Ll/xW/EODT7NQq6Udu1CiGEEKJuSGAkhBDrFJqZAsAf7Cj5MW0+BwuoQbMSGAkhKubCo3D2AUL7f42PZd+OpxCDI/8Ec2dg6H44/CswcDNT7bfwy9p3GJ2WuWpCCCGEUCQwEkKIdYrMqzaO5tbOkh9js1pwB8yASQZfCyEq5cG/BF8nDwXezFFjG8ne2+DRv4dHPgFWB1z/XgASN/4u7VqU1KOfr+16hRBCCFE3JDASQoh1iodnAXD521f1uEN7tgMQnZ8q+5qEEIJzP4HzP0W/7Xf592PzNLlsuO78fYhPw9P/Ate+HXwquO7dfyeP6nvoP/VZyGdrvHAhhBBC1AMJjIQQYp0yUbNCyF36LmkArzy4G4DT586XeUVCCAEc/QJ42vhk+BYePRfiv75uN5YtL4e+w+rrN35g8a4uu5Ufe+7GlwupQdhClOCzD53lHx46V+tlCCGEqBBbrRcghBCNLhefVx94gqt63JaedtI4GR8fq8CqhBBXvegEYc8AH//xKO+4vo+fv2GTuv0NH4fzP4Oe65bd3d6+HUaBhfPQsbvqyxWN5cx0jI9+/zS6Adf2NnPz1tI3fhBCCNEYpMJICCHWwTAMtNQ8Bhq4mlf9+JwriJ6Y4+xsvAKrE0JczXKRKZ6cs7G7u4m/eMu1S1/o2gs3/dpL7h/o3QFAZkYqjMTK/va+F/A4bPQH3fzB154llS3UeklCCCHKTAIjIYRYh4VkDq8eI2vzg8W66se7mjtoJco3nxmvwOqEEFezbHSaOaOZT//iQVz2lV+furv7iBluElNnqrA60cieHQvz/eNTvP9lm/nYPfu5EEryNz98vtbLEkIIUWYSGAkhxDpMhFO0aHHyzsCaHm/3tdHvSvONoxMYhlHexQkhrl75LN5CFGegm4FWb0kP6Q16GDE60EMyk0Zc2V/d+zwtHju/cttmbt7ayi/etInPPzzM0yMLtV6aEEKIMpLASAgh1mFsIUWAONoq5xct8rbRaU8wMp/k6Gi4rGsTQly98rFpANwt3SU/pq/FzQWjE3v0QqWWJTaAR8+G+OmZOX79jm34XXYA/uC1u+lpdvMX3zlZ49UJIYQoJxl6LYQQ6zAeTnFIi2PzDa7tAJ5WvPkFHDYL33l2kus2rW6nNSGEuJTJ8RH6gab23pIf0+p1MK514ks9DXphTW22YmN67z89wSNnQwDkCzpdTS5+6eaBxa/7nDbefeMm/ure55mKpOlqdtVqqUIIIcpIKoyEEGIdxhdStGgJ7L62tR3AE0TLJrimwyGDr4UQZTM9MQJAR3d/yY/RNI2oux+rkYfoRKWWJhpMJJnjx8/PcsNgkPfdupkP3L6VT777upfMxbprTycAPzo9XYtlCiGEqACpMBJCiHUYDycJanE09xorgzwqaNrmy/JsOF3GlQkhrmbhmTEA+voGVrjncrnmAZgBFoYhUHrYJDauZ0bVXKJfu2Mrt267/MWR7R0+NgU93H9yml+4cXU/d0IIIeqTVBgJIcQ6TC0k8JGANQdGrQBscaeYCKfKuDIhxNUsuTAJrG6GEYAluFl9MD9c7iWJBvX0SBiLBvv7A1e8n6Zp3LWnk4fPhkhk8tVZnBBCiIqSwEgIIdYhFp5VH6x16LUZGPW70sQyeWLpXJlWJoS4mhViMyQ1D9jdq3qcr2OAnGElNyc7pQnlmZEFdnT68TnNxoSZ0xAeueR9X7W7k2xe56dn5qq4QiGEEJUigZEQQqxRMpuHlLmF8ForjLyqvL/LkQBgMiJtaUKI9ckVdBzpWVLO1c9W6w36GTPaSM8MVWBlotHousHRkTAHB8zfcYYBX3wHfO4uiIwv3dEw4Cd/xQ1j/0iz2859J2WOkVidRCaPYRi1XoYQ4kUkMBJCiDWaCKcIYA6qdq+vwqjDEls8phBCrMf5uQStRDA87at+bG/AzYjRKS1pAoCh2TixTJ6DxR08Q0Oquig+BV98J2TiKiz6/v8DD/5/WH/833nrFoMHTk9T0OXkX5Tm+eee4N//f+/hLz77b6SyhVovRwhxEQmMhBBijSYjaQJaMTBaY4WRuwXQCBBbPKYQQqzHC9Nx2gljb+5c9WN7W9xcMDpxxC7dciSuLk9fUFW0BzcF1A1nH1D/f91fw8wJ+I9fge/9PjzxGdj/bsDgF2w/YiGZ4+mRhZqsWTSQC4+S+qe3sPM/7uK92nd5/8Sf8MF/uJ9IUtrzhagXEhgJIcQaTUXSSxVGnjUGRhYruFvwFSJYNKkwEkKs3wvTMdq0CN5g76of2+F3MaZ14szHIDlfgdWJRvL0yAItHjub27zqhrMPQMtmuOFX4bUfgxd+AE/+A9zyEXjL/4Ydr2XryFfxWnPcL21p4kpSYYx/eTOpkWf4JO9k4g3/Spclwjum/46f+8yjzETlApoQ9UACIyGEWKPpaJqW9VYYAXhasSRDdDa5mAjLGyQhxPoMT4Vo1pLYmlZfYWS1aMTd/eqTBWlLu9o9PRLmuk0taJoG+Syc/xlsvVN98YZfhbv+G7zqz+GuvwBNgxs/gCUV4jc7nuOB0zO1Xbyoa/rcWbRChj/KvZ/D7/nv9Bx6E5Y7/pA3Wh7hmtC9/MNPZfC+EPVAAiMhhFij6WiGTnsKNAs4m9d+IG8bJEN0N7uYjEiFkRBifeamzWHEvo41PT7XPKg+kDlGV7VIMsfQTHypHW3sScjGlwIjgFt/C277bRUWAWx+ObTv4q2573BmJkZUdv4Ul3F+6AQAr739Jm7aouY5ctvvQP+N/DfbP5GaldcfIeqBBEZCCLFGU9E03Y4kuAJgWcfLqadVBUYBt8wwEkKsSzavk1qYVJ+sMTCytw2qD6TC6Kr2zGhxfpFZQXv2AdCssPlll3+QpsENH6AjfpqD2hmeG4tUYaWiEZ0fOg7AK246vHSj1QZv+yw2rcCdU/9Uo5UJIS4mgZEQQqzRdDRNuy0FnjXukFbkCUIyRE+zi4lwSraVFUKs2fBcgqBhDhteY2DU0RpkxghQCElLyNXs6ZEwFg329QfUDWcfgL5D4FqhonbfOzGcTbzXdi9HR8OVXqZoQIZhkJgcImwN4m8KLP9iyyDP+m/ncPoR1QYphKgpCYyEEGKNpqNpglp8ffOLADxmS1qTi0xeZz4hb5CEEGujBl5H1SfetQVGvQE3F4wOctISclV7ZmSBHZ1+fE6bGoA+8czydrTLcfrQ9r6du6xP89zIbOUXKhrOsbEIbblJ8k0Dl/z6cOfd+ElgnP1RlVcmhHgxCYyEEGIN8gWd2ViGZuLgXm+FUSvoeTZ58wDSliaEWLMz0zE6tLD6xNu+pmP0tXgYMTrRwhIYXa0Mw+DoqBp4DcDwTwCjtMAIYMsduMmQG3lKqmbFS3z/uUkGLNM09Wy/5NfjvbcTNrzknv1alVcmhHgxCYyEEGIN5uJZdAO8hWgZKozUsMc+ZxKAibAMvhZCrM2ZmTib3UnVNmR3rekYfS1uLuidOJLTkJMA+2o0tpAils6zt9dsPzv7gNrcoedgaQcYuA2AXeljTMn26OIihmFw33MjdGnzONq3XvI+bQEf9xYOY3vhe/IaJESNSWAkhBBrMG2+AXblI+ufYeRtA6DTlgCkwkgIsXbnQ0n67bE1t6MBdDW7GKUDDQPCI2VcnWgUJyZUW+OeniZ1w7kfq2HXVltpB/C2kmrZxc2WExyTOUbiIicmohAewYIBLZsveZ92v5Pv6DdhycVh6P4qr1AIcTEJjIQQYg2momns5LHlE2WoMFKBU7MewWG1MBGRCiMhxOoZhsFIKEGHJQK+zjUfx261kPV0qU9iE2VanWgkJyejWDTY2emH2JQKDgduWdUx7NteziHLCzx3QeYYiSXfe26SzZYZ9UnL4CXv0+F38Yh+DRlHC5yQtjQhakkCIyGEWIPpaJpmVEVQWYZeA5b0PF3NLibCUmEkhFi9UCJLIlsgoIfBt7b5RUWW5h71QXRy/QsTDefkRJQt7T7cDiuMPalu7Dt85Qe9iG3ry3FpOZLnHq/ACkUjMgyD7z03ycvb4+qG4KUrjDqanBSwcrbtTnj+B5BNVnGVQoiLSWAkhBBrMB1N02ox3/CUaYYRyRA9AReTMsNICLEGI/PqpMqbC62rJQ3A3dqvPpAKo6vSqckoe7rNdrTRJ8DqgO79qzvIwC3oaLTNPU5Bl8HXAsbDKc6HkhxqjoLde9nB/H6nDZfdwtO+OyCXgDM/rO5ChRCLJDASQog1mIpkFnc1w9W8voM5vGB1QmKOnma3zDASQqzJSCiJkyy2XBx86wuMOoItRAwvekQCo6tNOJllPJxaml80dgS69oHNuboDuVuINO3ieuM4Z2fj5V+oaDjF9zed+QnVjqZpl7yfpml0+F08yR51UU7mGAlRMxIYCSHEGkxH07R7zJdQq2N9B9M0dXIXHac74GIqmparsUKIVRuZT9KuRdQn6wyMelvcTBktZBbGy7Ay0UhOTpoDr7uboJCDiWeg/4a1HWzzy7hOG+LZ89NlXKFoVFNmYORLjl+2Ha2ow+9kOp6D5j5IyBwsIWpFAiMhhFgDFRiZV8as9vUfcOAWOPsAPX4bBd1gNpZZ/zFr6cH/Dqe/V+tVCHFVuRBKstNrtrSuY+g1QHezi2mjhYJUGF11Tpo7pO3uboLp45BPQd+hNR2refedOLUc4RceLucSRYNSO8waOGIjlx14XdTR5GQmllFzHpOhqqxPCPFSEhgJIcQaTEXTtLvNwMhShsBo9xshtcCe7HOA6vNvWIYBD/8dPPGZWq9EiKvKyHyCnT7zteMys0FK1WUGRtb4VBlWJhrJyckoHX4n7X4njBYHXq+twsgyeAs6Frzjj5RxhaJRTUfTbLJH0PKplQMjv4vZaEbNeUzMVWeBQoiXkMBICCFWKZnNE0vnCbqLLWllCIy2vhJsbgZnHwRgMtLAgVEyBPm0amMwDP7p4WF+eEJOOoWotJH5JJvd5u6N660wanIzRRBnZg70QhlWJxrFyYnoRfOLngRfl2oLWgtXM5OenexIHS3b+kTjmopm2O8Nq09WaElr9zuJZfLkXUFIzld+cUKIS5LASAghVmk6qtrFgq4ytqQ5PLDtlTRfuBcNnclwAw++joyq/6cjFObO8tf3Ps+Xnxyt7ZqE2ODSuQLT0Qz99pi6YZ0VRk1uGyFLEItRgPhMGVYoGkEmX2BoJr60Q9rYE9B/+LLDiUsxG7yeazlLKtXAv9dEWUxH0uxymu1lLSvPMAKIW5shE4F8ttLLE0JcggRGQgixSsWhjS2uMrakAex+E5b4FDc6zjPRyBVGkbHFD6dPP0wiWyAUN2cyfesjcP+f12hhQmxco/NJADqtUXAFwLa+YfyappF1d6lPYjLH6GpxZjpOXjdUhVF8FhbOr7kdrSjVtg+nliMyerw8ixQNayqaZqttFjQLNPdf8b4dTS4AwpoZXqakykiIWpDASAghVkkNbYSAs1hhZCvPgXfcDRYbb3U9zUgoWZ5j1kIxMNKsxM4+AcBcPAvZBBz7Mpz/WQ0XJ8TGdMF8zQgY4XXvkFak+7vVB9HJshxP1L9lO6SNFecXHV7XMbWefQBkRp9e13FEYzMMg6lomj6mVYvjCqF2scIoZPjVDTL4WoiakMBICCFWqRgYNTsNdYN1fVfyF7lbYPPtvJLHOXJ+Hl03ynPcKnn7px7hXx+7AOFRsHug93qcM8cACCUyKigqZCG1UOOVCrHxXDArjLxkwOEryzHtgR71QUwCo6vFyYkoHoeVgVavCowsNug5sK5jerp3EjdcaJPPlmeRoiGFkzmyeZ2O/OSK7WiwFBjN5L3qBhl8LURNSGAkhBCrNBVN43VYcVl0dUO5WtIAdr2Btuw4nZlhTk1Fy3fcCotn8hy5sMC/HxlVM4ya+6D3IF3J57FSIJ3Tyb1wn7qzBEZClN3ofBKf04ZdK5RnrhrgCXaTM6wYEWlJu1qcnIyyq8uP1aKpwKhrL9jd6zpmq9/NSWMAd0ha0q5mU8WLbemxFXdIA2jxOLBZNCZyZmAkFUZC1IQERkIIsUrT0TSdTS4o5NUNZTo5A2DX6zHQeI3lSR492zhvjibDaubSs2MRcgsj0NxHumM/LrLc2mReFRz6kfp/agGMxqqeEqLeXQgl2BT0oOn5soXYXc0eZgiQWRhb+c6i4em6walJc4e0bBLGjkD/jes+bqvXwQl9kObIadlx7yo2HU3jJYUzM7/iDmkAFotGu9/JhbQZWEpgJERNSGAkhBCrNB3NqMBIz6kbLGWaYQTg70LrupbbXEM8dq5xBjyOh5eGdOcXVIXRKW07AG/rmqZfm8YePgfNm8AoQKZxqqeEaAQX5pNsCnpU22eZ5qp1N7uYNlrIS4XRVeHUVJRYOs91/S0wdD/kU7Dztes+rstuZci2FYeegtDZMqxUNKLpaJouzXxf09RX0mM6/E5Gkqo1TQIjIWpDAiMhhFilqUiarmaXOjGD8lYYAQQGGLAt8PhwiEKDzDGaNHeOa7YXcGdC0LyJxyIBooabGxznebnFnF2x9x71f2lLE6JsdN1gbD7FQKsHCrmyzVXrbFKBkSYzjK4KDw+patDbtrfBqW+BOwgDt5Xl2JPuneYHx8pyPNF4piIZWjEvFvnaS3pMu9/FZKwArmYJjISoEQmMhBBiFXTdYCaWpqPJeVFLWpmGXhc199FamCWWznFqsjEqcSbCKSwavGunFYC8v5ejYxHOWLfRFj3B7ZZnibt7l7ZnlsBIiLKZiqbJFnQ2tXqgrC1pLqaMIPbkdFmOJ+rbw0MhtrZ76XQDz/8Adr2+bNVqiaatZHHA5NGyHE80nqloms1ucwdYb2mBUUeTk5lYGjxtEhgJUSMSGAkhxCosJLPkCgZdiy1pGlis5X2Spl5s+SRNJBtmjtF4OEVXk4tX9qiqq9OpJo6OhlkIXIt97hS3WE5wIXATeILqAcnGabcTjeu3v/wM/+vBoVovo+JGzB3SVEtarmwn+e0+JzO04MjHIRMvyzFFfcrmdZ4YnufWbW1w7kHIxmDPW8p2/KDfw1nLoFQYXcWmo2k2OVcZGPmdLCRz6J6g7JImRI1IYCSEEKtQ3OWjq8llnpiVuR0N1A5jwOGWJI+ea4zAaCKcoifgZq9fVUR9dUhjOprB2nc9mp7Hp6U54TkM7hb1AKkwEhU2E03zjaMTfO6n58gV9Fovp6JGQuokbCDoNWcYlafq0Wa1kHR2qE+kLW1DOzoaJpUrqMDo5LdUC9Dm28t2/Fafg+P6AEw+K5seXKWmImn6HHFAU+2OJejwuwDIOIJyoUmIGpHASAghVmEuripo2vzOsrZ+LGMGRrd3ZXhyeJ58A5zsTkbS9ATcuBKT6Gh86bQaCN6x+xYA8lh5yrJXAiNRNT86PQPAQjLHQy/M1ng1lXVhPoHVotEdcJX9dang7VIfRGXw9Ub28NAcFg1uGvDD89+Fna8DW/nardt8Tp7KDUAmAgvny3Zc0Timo2k6bTFVaVxiFWSHXw28TlqbISkVRkLUggRGQgixCtm8Cm+cNot5Jb8CgVFTLwDXNSeIZfKcmKjvOUa6bjAZTquT1cgoKUcrGcOOw2ph27ad4O3gefsuJtJ2cAXUgyQwEhV2/8lpegNuWjx2vnF0Y4cdI/MpegNu7FZLWVvSAGjqUf+XCqMN7ZGzc+ztbaZ58lFIR2DPm8t6/Dafk+P6oPpE2tKuOpl8gVAiq4Zel9iOBmrwPkDU0qRmGEl1mhBVJ4GREEKsQrHax2axVK4lzd8FmpWtzjAAj9V5W9pcIkO2oNMbcENkDGvLJgD29DThtNvgHf/E/+34bVWdZXOAwy+BkaioZDbPz4bmuPuaTl63t5v7Tk6RyORrvayKmY6k6W5WJ1blbEkDcAXN7a+lwmjDSmTyPDMS5pZtbXDqm+o1essryvocbT4HLxj9GJpNAqOr0GwsA0CzEV5VYNTRpCqM5mlSr21ZmaUmRLVJYCSEEKuQM7e5t1s1NfS6Ei1pFis09eBNTbOtw1f3gdFkWM116mlWgZGrbYBbt7Xyur1mK8vgbWRadjGfUG8YcbdIYCQq6qdn5sjkde7a3clbruslndP54cmpWi+rYrIFHYfNfEtX5pa0YLCFqOEhFx4v2zFFfXlieJ68bnDrliCc/i7seDXYXWV9jjafkyx2Es3bJTC6Ck2b8x99uTB420p+XKvXgabBbMGnbpDB10JUnQRGQgixCosVRlYLFPLlbf24WFMvRMc5uCnA0dEwRh2XYU+EUwB0NzshMgbNfXzh/Tfxgdu3Lt6n1ecgFM+qP4c7IIGRqKj7T07jd9k4vDnI9Zta6A24+cYzG7dCJlfQcVjNt3RlbknranIxbbSQXdi437+r3cNDczhsFg4Hoqrtp4zDrovafKpSZM6/SwKjq9BURF0wcmZDq6owslkttPmcTGQ96gYZfC1E1UlgJIQQq5AvqODGZtFUeXQlKowAmnshMsq+vgALyRxjC6nKPE8ZjJuBUZ8jAfk0NPe/5D5Br4O8bhBN5VWFkbzpExVS0A0eOD3DK3Z2YLdasFg03nSgh58NzTEXz9R6eRWRLxjYrJr6pMyVj13NLqaMFnRpSduwHj4b4tBAC865E+qGrmvL/hytPtUmOWvrVsOL8xvz36K4tKloGjt5rJnIqgIjgB2dPo6Hzde0ZH1XXAuxEUlgJIQQq5DTVYWR3WpRJ2ZlnBWyTFMvRCc40NcEqC2P69VEOI3HYaUpO61uMHd5u9ji1eVERu2QIhVGokKOji4QSmS5a0/n4m1vOdBLQTf47rMbc3BzrqCr1yTDUC1pZXxd6mpyMU0Qa3zjtvRdzSKpHKcmo9y8pRWmjoNmgY49ZX8en9OG02ZhzvCrG67S1qJkNs9zY5FaL6Pq1A5p5vyhVbSkAVzb28yxkFV9IjulCVF1EhgJIcQqLFYYWbXKtqQ190Mhy05/BofNwrNj4co8TxlMRlL0BNxokTF1wyUqjIpXl+cTWZlhJCrqvpMz2CwaL9+5dBV7Z5efHZ0+fnB8Y4YeOV1f2iENytuSZlYYudKzoBfKdlxRH4ZmYgBc09sEU89B63awu8v+PJqm0eZzMpUzZ9FcZSf+c/EMf/vD57nlow/wxr//GScmrq7QaCqSZodPzTFabYXR3t5mposzjKTCSIiqk8BICCFWIWfOMLJbLJUbeg2qJQ2wxyfY093EsTq+IjkRTqkdmhYDo5dWGLV6VYVRKJ5ZCozqeC6TaEyGYfDDk1PctKWVJtfyf5u3bG3jmdGFxX/DG0m+YKg2Wd0MjMr4uuRx2IjY2rBQgMRs2Y4r6sML06rqY3uHH6aPQ9feij1Xm8/BeO7qG15874kpbv3oA3zywSH29jYD8PSFq+uiyVQ0zRa32Vq/hsAojpuCZpPASIgakMBICCFWIa9fXGGUBWuFAqMmFRgRHWd/XzPHxyMU9PoMWMbDaXoDaoc07F4VCL1Im1lhNBfPgjsIRgEy0WovVWxwT48scG42wRv2db/ka4cHg6RzOicmNt7PXa6gY7dZ1GsSlL1VNusx2/tkjtGGc2Y6jttupdeZhshoReYXFbX5nFxIm9VLV0lgdG42zn/+yjF2dvm5/3dfzr+87wZavY66vghUCTPRNP3OhPpklYHRpqCHJpedhLX5qvm5EaKeSGAkhBCrsLRLWrElrUIzjIpVOpEx9vcHSGYLDM3EK/Nc65DJF5iLZ+gJuNXJRnMfaNpL7tfiVd+nUDy7FChJW5oosy8+PorXYeWN+3te8rVbLM+xXxviyPmNN3A9VzCwW8zXJCh7kG34ze9nbGPOgLqanZmJsa3Dh2WmOPC6khVGTs4lzcDoKmhJS2bz/Nq/PY3dqvHpX7yere0+NE1jX19zXbeZl5thGExF0/TY1zbDSNM0ru1tJmQ0yYYZQtSABEZCCLEKOXOG0VJLWoVmGHlawabavPb1BQA4VodvMKciaibBssDoEuxWC81uO6FERgIjURGRZI7vPDvBm6/rxet80b/L0FlavvFLfNT9Lzy5IQMjfWkQP5T9dcnWbAZGUmG04ZyZjrO906fmFwF0Vi4wavU5uJC0Y1hsG75SxDAM/ujrx3lhJsYn3nWd+h1p2t8f4MxMnHgmX8MVVk80lSed0+mwRNVFNmfTqo+xt7eZqZwHXdpihag6CYyEEGIV8rqORQOLRVMDZivVkqZp5k5p42xp8+J32uryiuR4WM0k6CnOMLpMYATqZCGUuKjCSK4UijL6xtFxMnmdd9+wafkXDAO+/VuQT7NDH+b48CTGBpuflS8Y2JYNvS7v65K/tZu8YUGPSGC0kURSOaai6aX5Rd4O8Heu/MA1avM5yetguIMbfh7Wt5+d5OvPjPM7r9rBy5pD8PhnIHQWgP19AQwDjo9fHW1p0zF1YanFiKp2tEtUIa9EVRj5ycU2dtAoRD2SwEgIIVZh8cQM1PbVlRp6DWrwdWQci0Vjb18zx0br783lRFi9Eex159QJQHDzZe/b5nWqodeeoLpBKoxEmRiGwZeeGGFvbzPXmkNlFz39L3D+p7D7TVgpsCl9iuG5RG0WWgGGYZi7pGkXBUblbZXtCHiZJUB6YaysxxW1VWxz3tHpg6lnKzq/CKDNrzY/yLlaN/zw4n96eJibW5N8JPq38Kmb4fv/BT55ED7/ag4vfA8NvS4vAlXCXDwDgL+wsOp2tKK9ZmC00X9uhKhHEhgJIcQqLM4KAXPodYVa0gCa+hZ3HtvXF+D0VJRMvr62tZ40K4y6cqPqhradl71vq88hM4xERRwdDXN6Ksa7XlxdFJ2EH/6/MHAbvOkTGGgc0p7nyPmN87NX0A0Mg4q2pPU0u5k2WsiHpcJoIzkzHQNge6sTZp+v6PwigDZzll3K3rKhW9JOT0UZHPs2/5b8NbTj/wE3/Tr8+mPwqj+H1Dy+e3+Lf/Z8klMjU7VealWE4moYvzs3v+qB10UDrR4StmacucjSrDYhRFVIYCSEEKuQ1/WlCqNCrnJDr0G1d8WnoJBnf18zuYLBqclY5Z5vDSYiKdp8DpxhVWpP247L3jfoNVvSXAF1Qypc8fWJq8OXnhjB47DypgPmrJ34DDz9r/Dld0EhA2/6hAoqO3Zzk/3MhppjtHznxsq0pA22eZkyghhRGXq9kZyZieOyW+grjKoLIBWcXwRLFUYJW2BDt6Qdue/f+Sv7Z9D7boCPPAWv/kvo2A23/Tb8xhPwmv/BbfqTfOjshyEyXuvlVtx8QgVG9vTaAyNN03A3d6hP5GKTkgjBxNFar0JcBSQwEkKIVcgVDHUlH6rTkmboEJtkX38AgGOj4co93xqMh9NqmOfcC+p70TJw2fu2+pwsJLMULHZw+CC1cU7aRe3ousF3n53k9Xu78Tlt8M3fgL/eDt/6sAqO3vg/oXUrANqmmzioneHp8xunuiFr7tzoWDbDqLxBdl+LmxlacKamy3pcUVsvTFdvhzRQM4wAIlrzhm0tSl94ired/UOmXFuw/8KXIdC//A6aBjd9iHv3fpxefQL9H+7c0NVWAKFEFk0zsCTn1tySBtDc2g1ALr5xw8ZVeeAv4HOvhNEna70SscFJYCSEEKuQL5izQsCsMKpwSxpAdJyeZhdtPmfd7ZQ2GU7R02wGRsEtV6xsaPM5MAxYSGbBHZSrhKIsJqNpEtkC121qUVVrz/wb7HkzfOhn8DsnYP/PL9150824jSTO+dPMxjI1W3M55c2dG20WrWItaXarhYy7E1chDtmNM//pajc0E2dHh1/tkGZ1Quu2ij5fwG3HatEI0QSZKOQ3xr/BReER+OI7mDf8TL3hX8F1+d3AWq57I7+a+89Y4lNw/mdVXGT1heIZetw6Wj615gojgM5uVUE6NjZarqU1ttHH1YXLr75PKrZFRVUsMNI0rV/TtAc1TTuladoJTdN+y7z9zzRNG9c07aj53+sqtQYhhCi3vG6o1g9QJfyVrjACiIyhaRoH+pt5ZiRcuedbJcMwmAinzAqjM9C2/Yr3b/Wqq8tqjlFAAiNRFsOzKsDY3OaF8afUjYfep6olXrwbT/+NAFxveYGnLmyMCre8WWFUyV3SALQms91P2tI2hGg6x2QkzbZOnwqMOvdU9gIIanfRoNfBbMGvbtholTWP/m8smRh/5P0zrr929xXvem1vM0eNbRho6oLLBjafyLLFo+YdricwGuhTM+omJjZ+G9+K0lGYOQU7XguxCfjWR9SOoEJUQCUrjPLAfzYMYzdwE/AbmqbtMb/2ccMwDpj/fa+CaxBCiLLKFXTslota0io5w6hpKTACuGFzkOG5BDPRdOWecxXm4lkS2QKbAjaYP3fF+UWghl6DutqIu0UCI1EWw3Nqp6ct7V4YOwJo0HPw0ncObMLw93Cj9QWe3CCDr5e1pOmVaUkDcLWqikf9Kpi5cjVY3CGt3QyMKtyOVtTmczKZ96lPkhsrMEqf/RlHCtu49aZb0FbYOt7ntNHX3sqcrRNmT1dphbURimcZcJqViesIjHp61GtQaEZeg9TFEQNu+FV45Z/AqW/BkX+s9arEBlWxwMgwjEnDMJ42P44Bp4DeSj2fEEJUQ75wcYVRhVvSXE3gbIaoenN04+ZWAB4bro/KiOIOO/s8YXWiukJg1GYGRnMJc6e0ZH38OURjOzeXwOOw0uF3wvgRaN91+VYQTUPbdCM32c5wtM7mga3VYkvaxUOvK1D5GOhU88nCMxfKfmxRfcXX712eiJon17WvKs/b5nMwmvWqTzZShVE6imPuBE+xi3sO9pX0kP39AU4XejA2emCUyNDrUAHlemYYWbzqPVAmKjOMFi+O9B2Cmz8CW18J9/4RZOK1XpnYgKoyw0jTtEHgOuBx86YPa5r2rKZp/6hpWks11iCEEOWQ13VsixVGucq2pIFqSwurfv1reprwOW08fq4+hoWeMa9Qb9HMq30rBEbBxZa0DHhkhpEoj+G5BJvbvGgAY0+qN9BXsulm2vRZ0nMbI/jImRVG9mUtaeUPsjt6BwEIT4+U/dii+s5Mqx3SeqLH1A1mu2altfmcXEi51ScbKDAyxp7Egk6y6zCt5nDvlezva+ZkrhvmhkAvVHiFtTOfyNJtKwZGa68wwuYkZfFu2IHpq5EbeZwZ1yC/9tUhcgZwy0cgn4LzP6310mpuJprm5z/7KFOR+qjG3wgqHhhpmuYD/gP4bcMwosCngK3AAWAS+JvLPO4DmqYd0TTtyOysJMlCiPqgdknTVK+4nq/IrJBl+g7B8EOQjmCzWjg02MLjdVJh9MJ0jGa3nebEeXVD25UHpgbcdiyaucVusSVNeu7FOp2bTbCl3afaIlML0Hf4yg8wT4y3po6TzOarsMLKypkVRnbrxUOvy/+6NNjdScxwk5kfK/uxRfW9MBNna7sPy+hj4PBD5zVVed42n4NzSTMw2kAtaQunfkLB0Ni0/46SH7Onp4khoxetkIGF8xVbWy3lCzoLyRxtmqpow7P2CiOAtD2AKzuPrl+d7x0Mw+Cbz4yROPsYD8YH+P7xKf7p4WEYuAXsXjhzX62XWHM/G5rjsXPzPDy0cV5faq2igZGmaXZUWPQFwzC+BmAYxrRhGAXDMHTgH4AbLvVYwzA+axjGIcMwDrW3ryONFkKIMsrresWHyy5z/X+CXAKO/V9AtaUNzcTrYoenMzNxtnf40EJnwNcFruYr3l8NPHUyFzcDI6OgdsoRYo0y+QJjC0k18HrM3Fp4pQqjzmvJ2zwcsjzP6Hyq8oussLx+qQqj8s8wavc7mSWIIUOvN4Sh6Rg7Ov1qp6W+Q2CxVuV5m1x2pnMuDIttQ1UYJYd+xgljkFfs3VLyYwZavQzp5rSODTr4eiGpXpOCRMDZBHbXuo6X9vWxiSlm6uA9UC38/QNDfPwrPyBAjFvveC2v2t3Bx+87w1isAJtvh6H7rvoLcacm1fvKYhW8WL9K7pKmAZ8HThmG8bcX3d590d3eChyv1BqEEKLccgXjRdtXVzgw6j2oBvge+TwYBjdtCQLwRI2rjAzD4Mx0jO2dfvVGd4Ud0orafA7m4hlwqz+HtKWJ9RidT6IbsKXNHHjt8KkZRlditZHquI6DljOMzCers9AKyl1yl7Tyt6RpmkbU0YY9OV32Y4vqiqVzTETS7AkaMH0CNt1Utef2u2yAhuFuhcQG6SDIZ2mPPMd57z66mksPRFq9DiYdajbYRh18HUqoYKdZD69rflFRvm0PO7VRxkKxdR+rEd13apq3tavQvm/v7fz5m69F0+BPvnkCY9urIDwCoaEar7K2Tk2qn42hmavzZ6QSKllhdCvwS8CdmqYdNf97HfAxTdOe0zTtWeAVwO9UcA1CCFFW+YL+oiv5FQ6MAA7/inozeeFhru1txuOw8vhwbXv4Q4ksC8kc29u9KjBq31nS49p8zqVd0kACI7Eu52bVzjuLFUa9B0uqlLD3HWSHNsrYXKTSS6y4xZa0KgTZGXcXTbkNcpJfBplsltD/upvk138TsolaL6dkx0bVz/2N9nOAUbX5RQB+l/rZzLtaN8wsmtDQEzjJYt9866oep2kawWAbC9ZWmN2YFUbz8SwA3vzC+uYXmRy9e3FpORbGN+b360rSuQKnJqPc5h5WbaTtO+kNuPndu3bwwOkZfqLvV3e8ytvSTk9JhVG5VXKXtJ8ZhqEZhrHPMIwD5n/fMwzjlwzD2Gve/ibDMKS2WQjRMPK6uUuabs4+qXSFEcA1bwNXAJ78PHarhesHWnisxoOvXzB32NnTnIF0ZMWB10VtPgezEhiJMhmeUyfpg80aTB9feX6Rydl/AIdWIDNxopLLq4rFode2yrakAWhN3QT1BdLZXEWO3ygiqRz/+8dD/Ob/+BSts4/jOfbP8JmXw+SxWi+tJE9dWEDTYGf2BGiWlds4y0hVGEHW2bJhWtLOP3M/ADsP37Xqxw60ejhL3wauMFKBkTs7X5bAqHngAACFyefWfaxGc3IySq5gsC1zCvquX7w48t5bBtnT3cQfPhjFaNuh2tKuNmNH4L/3Exo5xVw8S6vXwch8knRu4w6Tr6aq7JImhBAbhWpJs0BBvQmqROvHSzg8cOAX4NS3IDbNTVtaeWE6roZH18iQeeVmh9XM/EtsSWv3O5mNZTDcAXVDsj4GeIvGNDyXoM3noHnhpApxe0s78dW61ZVYx2zjd8XnzQojm0WraEsagCvYh10rMDZ29e6UFs/kufvjP+FjP3iee1xHyGhOPlT4ffRsHP7hlXDq27Ve4oqOXJhnZ6cf5+ST0HktOP1Ve+5ihVHKEdw4LWkXHmNU62Hz4OZVP3Sg1cuJbDfG3AsbcvZMKK5a0uzpUFla0lw9eyhgwT53ct3HajTHRsO4SeOPPL/s4ojNauHNB3qYjKTJbX4lnH8Yso3fbr0qJ78BmSjzx74HwOv2dmMYcHY6Ak/989X3/SgzCYyEEGIVVEuaVvEr+S9x6H3qhPiZf7lojlHtqoxemI7hd9loSZ5XN5RYYdTud5LO6SRt5oBsqTAS63BuLrG6gddFwa2kNRctsca/qr9YYWS1VLwlrblTzVuZGT9XkeM3gu8+O8F0NMM/vuc67uYxkgN38oPcdXznlq9CoB+e+Gytl3hFBd3gmZEwhzf5Yeypqs4vgqUKo6QtsCFa0iLJDFtSzzHfej1qfOvqDLR6eEHvQcvGITpegRXW1nwii0XT0VKhslQYYXczYe0lELv6WtKOjYa5wzeOZhSgb/meUS0e9V403PtyKGTg/E9rscTaOfsgAJaRRwB44/4eAKIn7oVv/yac/m7NlrYRSGAkhBCroFrSLNVtSQO1Zf3m2+Hol9jbG8Btt/LYudpV55yZjrOj048294LaytXfU9Lj2v1OAGZy5rbKqXCFViiuBudmzcBo/AgEBsDXUdoDLRZmvdvpz5zBaPCr+oszjKowW629ZxCAhamrt8LoK0fG2Nru5RXus5CYIXDonQy0evjy8ThsfzWMPgH5+t3B6fmpGPFMnjtbZtQOnFWcXwRqlzSAmDWgdsms4+9VKZ468igtWpzmXS9b0+MHgh7OFHdK24BtaaFEls3uLJqhlycwAmbcW+lOD5flWI3k2FiEu5ouqE9edHGkxasCo5mW68HuubrmGMWmVEu6xU7H/FN0Nzk50B/AatFwDKsgifCF2q6xwUlgJIQQq5Ar6Gq4bIVbPy5p1xth/iyOyDDXD7Tw8FDt5j+cmYmzvcNn7pC2DSyl/Tpp96kdZGaThtrRSiqMxBpF0znm4hk2t/nU/IIS5xcVJVr2sIsLzEZTFVphdeT14i5pF70uVSjI9rT2AZCeH6vI8evd0EyMpy4s8M7D/WgnvwE2N9qOu3nLgV4ePRdivvNGyKfVz2OdeuqCutBwwHhe3bDp5qo+f7HCKGIxq0wbfI5R5HlVydF/4FVrevxAm5choxgYbbyqmVA8y6DbbAfytJblmPHmHfQYU+ipaFmO1wjCySzDcwmusU9CUx94gsu+3uJRr/nzGU1dXBy6b0O2OF7SuR+r/1//y/gLC7yiLYLDZmGw1UNvSFUcERmt2fI2AgmMhBBiFfKF4tDryp6YXdJ28w3p0P3cfU0nZ2binJio/i5PoXiG+USW7Z1+mDtTcjsaLFUYzcbMwdcpmWEk1ua8OfB6R0BXrRxd167uAN378Wlppi+cqsDqqiebV4GRo9iSpllLDnBXzduBjgUjOlGZ49e5rxwZw2bReOv+bjj5LdhxNzi8vO1gL4YBXw8NAlpdt4McubBAh99JIPQ0NPdDc29Vn99nBkYLmIFRsrEDI3/oOaJaE9bWLWt6fFeTi7g1oFr0NmCF0XwiS7fLnLfoCpTlmHrHNQCELzTGkPlyODam3uu121IvCYsAAmZL2kIyCwO3wsJ5SIeruMIaOvsAeNrIHvpVAF7uVGH4TS0JunJmUBSWwGg9JDASQohVyOu6aklbHHpdpRlGAMEtENwKZ+7jjft6cFgtfPWp6l/pf2FaDbzeGbRBZARaSxt4DWqXNIDZWNoMjKTCSKxNcYe07dYpdcMqfg4BfIMHAUheeKas66q2vG4OvS5WGFWoHQ0Aq42YLYg9MVW556iGhQvw5V+ATKzkh+QKOl97eow7d3XQPv8UJGbgmrcCanDx9QMtfPm5KEbXXjj/s0qtfN2OnF/g0EAAbeTxqrejgWqddNuthAxz0HYDVxgZhkFbepg5zxZYw/wiAKtFoy/oZtzWD7PPl3mFtTeXyNDlNC+wlWm4urN3LwDxkasoMBoNo2nQpCXB1fySrxcrjBYS2aVKrnT1LyhWnWGo+UVb7uBMvosZI8A1ObX76R22ZwHQ23dJhdFKVmgNlsBICCFWIVcwzJY0c4ZRNVvSALbfBed/SoujwKv2dPDNoxOLFQbVMjSjTrJ2usywJ1j6zjAtHgdWi8Zs3Kwwkl3SxBqdm02gadCdN0PTEnfqK2rfsp+cYcUy/WwFVlc9+YuHXhdyFQ+xM+5OWgohdWLSqE5+A05/B6ZK35r7R6dmmItneefhfvV4mxu237349bde18uZmTih9hvVHKNcuvzrXqepSJrxcIrbunWITUD/DSs/qAL8LhuzeuMHRjPRNFuMMbItpVfZXspgq5cX9F5VYbTB2ojmE1naHOZrRZkCo7a+7cQMN4WpE2U5XiM4NhpmW7sPWzYKxV1mL9LsNgOjZG4pULoaAqPpEyq833onp6fiPK7vomvhCBgG16aeZMxoI9z9MlVhtMH+bZXVF995xS9LYCSEEKuQL5gVRrVoSQPYdpeakXH+Yd5+fR/ziSwPPj9T1SW8MB3H77TRljPbUloGS36sxaLR5nMwFzOvgm2AXXJEbQzPJehrcWNfOAuaZVU/hwBOl4dhSz/+hQZvSSsOvbaYr0uWCofYTd10agsMhxKVfZ5KGn1C/T9e+mvnvx8ZpcPv5OXbW812tFeDw7v49Tfs68ZhtfCj9A61S9HYE+Ve9bodMecXHWoxZ8oEBmqyDr/LxlTeDA8auCXtwvlzNGlJHN2713WcTUEPx9JdqoUoMVuexdWBfEEnnMzRajOrF8oUGPW2eDht9OMMNfZrd6kMw+DYWJj9/QEVAl2iwshmtdDkshFOZi8KjDb2jKf5RJZHfvgV9cnWV3BqMsrT7MGWmIK5M3TMPc5DhX1M0gb5lLzfvBzDWNpp9jIkMBJCiFXI6cby4bKVbP+4lMFbweaCofu4fXs7bT5n1dvSzszE2N7pQwubOyWt8kS93e9UFUYSGIl1GJ5LqIHXoSF14mtzrvoYE67tdKVeaOgrj8UKo6q0pAHOYB9d2jznZhs0MLr4zXGJJ+exdI4Hn5/hbQf7sOVi6or2i6pzAh4Hhze38JXZTSrArMO2tKcuLOC2W9niNFvxmrprsg6/y8501qnCzQauMJo/r6oTg4P71nWcgVYPJ/Pm38UGakubT6rKoharWW1XpsDI67Rx3jJAS+xMQ792l2o8nGIunlWBUSp82VlQQa/DrDBqUjds8Aqjbx+bIH/mASYcA+i+bk5NRZlpNXePe/jvsObi/NTYx1DWnPlUfM8qlotNQjZ+xbtIYCSEEKuQL+jqSn6FdyO6LLsbBl8GZ+7DZrXwtoO9PHh6hrl49bYmPjMdZ3uHXw1VtHtWvVVuu8+phl5729QV1eL3UogSGYbB8FyCLW1emBuC1m1rOk64eTcBPay25W1QuYtb0vR8xVvSfG39NGtJRqcb9EQ/PALxafVxiRVGs7EMugG7uvxLJ2GXOGm7ZWsbT00VyHfug+H6G3z91IUF9vc3qyvwAP5aBUY2YpkCeNoauqImN60qXJo3rXLg/osMtnq5YHSoTxbOr3NV9WPebFttspQ3MAKY9W7HrcchsvF3bDw6GgbgQI8XcolLVhiBCq0XllUYbezAaHhqjhstp/l+cg9/+b1TnJqM4e29Rl2MPPYl0KyMNB/mRMIM0GSO0TJPDM9zdjauNq9ZgQRGQghRIl030A2W75JW7QojUHMz5s9C6Cz3HOwjrxt882h1di1aSGQJJbJs7/SpN7aBgVUP+2wrBkbFwYwy+FqsUjiZI57J09/iUv8WVjm/qCjfoSoDsmNHy7i66soVW9KsmhrGX+GWNKu5q9bC1IWKPk/FXFx6nygtMIqk1Ot9s9t+UWD00pO2m7ao17QLTdfD+BHIJte31jJKZvOcmIhyaCCoAlLNqgKbGmhy2Ymlc+qiQQNXmToXzhDXfGi+znUdZ1OrhymjFQNtQ53UhuIqMPJpKbB7wWIt27ETzbvUBzMny3bMenVsNIzDZmFnwKymukxg1OKxX1WBkXXscZxaDseOV/L5nw0zn8iyu6cZNt0Mhg79N9Dd2cWRsE89QHZKW+YjX3qaP/3mCZh7YcX7SmAkhBAlyukvGi4LNQqMXqX+P3Q/O7v87Otr5t+PjGJUoTT7nLkz1ZZ2rwqMVtmOBqolbS6eQXebgVEDtySI2piKqivWg/YI5JLQunVNx3H370c3NGLnnyrn8qoqr+vYLBqaVp2WtGIbUybUoOX9Y0+qysi2nRAvrbolXAyMPFcOjPb1NeN1WHk4v0uFd3U0x+jMdJyCbrC3r1kFRv4usNTmNMDvshFL59VFgwZ9/TcMg9bUMHPuzWveIa2or8VNXrMRd7RvqJPakFlh5DWSZa0uAqBDzY0ypo6X97h16Nxsgq3tPhw5cybRZQMjBwuJHDivjpY0nzl/8N333MNde1Roe01PMwzepu6w7ZVs7/TxbEjDcPg2VBi7XvFMnulohieG58nNvKAC3SuQwEgIIUqUN6/k2yyaav2A6rekAQS3QHArnLkPgJ871M/pqRjPmGXLlVTcynxz6/oCo7xuELeab3oa+AqzqI2piAqM+g2zsq51bRVGXR3tnDc6KUw07vbMuYI5Vw3U61KlX5PM77U3OoSuN+D8kNEnoOegCr5KrTBKqsAo4LarGSJwyZM2u9XCDZuDfGWmT1Xw1FFb2mQkBUBvwK12SPN31Wwti4GRt71hW9Jmo2m2GKNkWtb22nMxp81KT7ObWWvHhjqpnTdb5V16+QOj9vZ2xow2shOl73TYqMKpHC3LwurAJe8X8DjU0GuLVYVGGzgwmotn6MyNk7Y3Y/UG+eS7ruNTv3CQQwMtsPO10L4brnkb2zt85HXI+no3VBi7XufN9/LZgk5s7OSKVdoSGAkhRIkWA6NlFUYV3pHocna+FoZ/AqkF3nJdL16HlX97rPItIsNzcWwWjT5nUvXSrzEwApin8XfJEbVRrDDqyJpVLmucYbQp6OGUsQnnfOMOms0VdFX1CKqqpeIVRj1kbX62GSNMmCFEI7gQSvBHX3kSY+pZ6D8M3o6lWUYrCJvDewMex9JJ2CW2tgY1x+j4nEGuYy+MPl6OpZfFRFj9m+kJuM0Ko9rMLwI19DqVK1Bo4I0Pzo9eoEWL4+jeU5bjDbR6GNNbN9Rg3lAii0UDRz6xNIi5THpbPJzVeyjMDZX1uPUoksqt2A4LEPTaSWQLZPO6CowyG3eXtDPTcQa0abJNgwC47FZeu7cbi0VT70t/4zFo3armbQIRexdENs6/rfUqdgsAWObPSGAkhBDlstSSZs4KgYoPmL2sa+9Razj1bXxOG2+5rpfvPDvJglkCXinDcwn6gx7s0bXtkAZq6DXATN7sK2/QEwZRO1ORNJoG/sQF1V7U1LOm47T5HExqXXhTk2D++240ywOjKrSkaRrp4G52WkYXKw4bwTeemeD0Mz9F0/PQdxh8HaolrYRW3mJLWpPLtuJJ281bVavtqHs3TBwFvVCW9a/XRDiF02ZRlQqxyZpXGAFkHS3qpDZf2d9blRAaVjukBQbWt0Na0UCrR+3mFB2vm5+Z9QolsrR4HGjZWNkrjPpa3IwbbVijG3/odamBUcCj3o+Gi3OMNnCF0ZmZGIOWKeztVw46dnT5sFs1xow2qTC6yPBsAk2Dlw96CWSnoW3HFe8vgZEQQpRoqSXNsjT0uhYtaQA916m2tOe+CsAv3jRANq/z1acq++ZpeC7J5jbv0k4uLQOrPkaxwmgy51E3JCQwEqszFUnT5nNinR9S84vWOENE0zRSnl5sRrbk9qR6ky8Yqk0WqtOSBti6r2WHNsbw7JW34q0nxyciHLSYu8H0HVbtUPnUitsJgzph8zttqro0HQE0cFz6BHhPdxPNbjtP5gYhGytpB5pqmIyk6Q240fIZtdFATQMj9TOaspgXDTKxmq1lrbJTan5KYJ07pBUNtHo5m21R/4YbeNfGi83Hs7T6HOrvt8yBUW+LmzGjDWd2oa6Gy1fCUmAUVjdcYYYRwPxVEBgNT4bo1UK4uq4cGDltVnZ1NfF8ull9/xrwtaYShufi9DS7edugqhKec2664v0lMBJCiBIVt6+2WTUomDOMajH0GtQJ8t63w/BDEJtid3cT1w+08IXHL1RsroiuG5yfS5iB0bC6MbD6wKjNDIxmEjo4m6XCSKzaVDRNV5MLQkNrnl9UVGjuVx80aCtIrmBUt8IIcPftxa+lmB8/W/HnKpfj4yowirl7VXWRz9zGPL5yUBhJ5tTAa1AnYa6myw6Mtlg0bt7SyrfmzJav8foYqD4RSdEdcEHcDCP8a6vKK4dihVFSMy8aNGDrjHPhDEnNg7bG6sYXGwh6GDfMXes2yByjUCJD0FsMjMrbktbksjNvN3eni2zcKqN0TrWYNZVQYdRivkYtJHJmYBSu0iqrLzahdvbSSmhH39vXzNNRM7CUKiNAdQtsafdyY9M8AI/FWq94fwmMhBCiRHn9ou2rFyuMajTDCODatwMGHP8aAL940ybOh5I8crYyAcx0LE0qVzADowvg6wSHZ9XH8TttOG0WZuMZ8LbKDCOxatPRNL1+iwp5Vui9X4nWYl5Za9jASFevSVCdGUaA1nkNAPr0iYo/VznMxTNMRlIctJxhxKPWjtcMjEoYuhxO5QgsC4wufcJWdPPWVh6JBNHtvroJjCbDabqb3UvVK3XQkhanMQMjwzAIJs+VZYe0ov6gh1GjXX2yQU5qQ4ksrV6n+vst9y5pQMHXqz7YwLNpIsUdGouBkWYFx6V3tLqaWtKYNy9WBLeseNf9fc2czbSoTzZIGLsehmFwzrz425kZQUfje+NXfi8vgZEQQpRoscLIYrlohlGNKowA2ndA93547t8BeO213bR47BUbfj08a+6Q1rb2HdJAtQG1+53MxjJqW2WpMBKrNBVNc41zDgx9zQOvixxBVSWXnz9fhpVVUS4F+Sx5/aIZRnquOm2y5pbW3nBjDAt/bjxCDyE6tTAnrbvUjT7z5LyECqNwMkvAbc6rKyEwumVrKwYWZpuugYmn17P0ssgXdGZiaXqaXRA1dxas4dDrJrMlLY5L3dBgbSKz8QybjTHSgfXvkFbU6nMwYZhX+TdIABKKZ2n12ivSkgZgDRbD/o0bArwkMHI1XzakbPGaFUbJnKqCTDdWEFuqUDxDa8asKmvduuL99/YGGFsMYzfGv631CCWyxNJ5Nrd50UJDhB1d/OTclV+DJTASQogSFQMj+7KWtBoNvS669u3qhCR0Fpfdys8d7ueHJ6e4ECr/MNrh0MWB0YU1taMVtfudzMUz4GmTGUZiVdK5AuFkjq02s1JinYFRsCXAnNFEena4DKuroi/9PHz/98nmDTVbB9TrUjV2bnQ1EXF20505RzpX/wN6j49FuM6idlN6Mm+eYCxWGJXQklacIQKqzeMy21oXbevw0ep1cELbBlPHIZde48rLYzqWQTegO1BfFUYRvVhh1FiB0fmRUdq1CPau3WU7ZtDrIIWLlC2wIU5qcwWdSCpHh1tXwX4FAiNfez95w4JxNQVGl9mdEZZmGC0UK4wy0YbdzOFKzszEGdSmyDqDK4b3ADs6fURtLeQ1u1QYweJmFZvbvDD3AoWWbSSyV/49LoGREEKU6KVDrzWwWGu7qGvvUes4/h8A/Mqtm7FZLXzqx+WfLTI8m8Blt9DltUB0bM0VRqB2SpMKI7EWUxF18t1fGFc3rDMw6mhyMWa0UZivTGVexcw+D+FRs8Lo4pa06oTYqcBOdmqjjMzX/8DZ58Yj3OydoICVx+NmUOI158XEV25Ji6RePMPoyicpmqbR4nUwZN+pfldMH1/P8tdtIqwGm3Y3u9QOaVYnuFtqtp7i0OtwQc2za7RKiLnh5wBoGdxbtmM6bVb8ThsLjq4NUTGzkFRV2F1Os32/AoFRX9DPFEEyofNlP3a9iCQvUWF0GS67FbfdqnbLdTWroK6Eof6N5sx0jM2WKbXxSwlsVgt7egLMWWSnNFjqFtjS6oHQEE39e5Y2zrgMCYyEEKJEef3iodfVGS67ouZeGLgVjn0JCjk6mlz8/OF+/uPpMcbNk4RyGZ5LMNjqxRIdU29E1hMYFVvSijOMStjaWghQ7WgAHdkxNUfLtb5hqh1+J2NGO7ZoA72RNAxIzEE+Q/7iodfVakkDLF3XsEWbZHh6virPtx7HxyPsd0yw4N7EaKxAvqCr1293cMUKI8MwCCdzBNwXB0aBFZ/TZtEYsptbFdd4jlExMOotVhj5u8o2e2ctihVGC4ViS1pjBUbZqZMANPeXZ4e0olafgxlLx4aoggjFVWDU7sioG8o89BrU3Kdxo43cfON/vy5nWYVRKrxiWN3isZstaeb9NuAcozMzcTZrU9g7Sr9YtK8vwHC+dUNXo5Xq3FwCu1Wj17oAuSTOzp1c23vlnysJjIQQokS5QnHotUUFRlU6MVvRzb8O8+fg8c8A8MGXq6sun/lJeauMhkPmDmlhsxJjHYFRm8/JfDJLwRVUVREb8CqYqIxihVFT8vy6q4sAOptcjBntuJITjVO+n46ocCifJlvQl64OVqslDfAP7MOuFQiPnKzK861VKJ5hIpJmsDBMIrAL3VAtWoDaKW2FGUaJbIG8bqxq6DWAw2ZhliD4umoeGE2a/2ZUS9pkTecXgfod6rJbmC9WGDVYS5orPEQaJ1pxh8UyafU5mTDMKogGv4hSrDBqsRYDo/JXGPUHPYwZbVgbKexfpWJgFPCsXGGk7udYGnoNGzIwujA5S6e2gFbC/KKifX3NjBZaKSw0frvneg3PxRlo9WKdV23atG1nW4fvio+RwEgIIUq01JJm7pJWDxVGADtfB9tfDT/+7xAZpzfg5p6DfXz5yVFmouWZnZEv6IyEkksDr2HdFUaGAXGr+aYmITulidIUK4xckeGyBEZBj4NJ2rHq2ZLm2dSFYhtnPk2+oOOwXVRhVKWWNHfvPrWEydq2W63k+EQUH0n86Ul0c1h3seIGb/uKu6SFzRPfgNuhArlsvKTAyGbRyBtA7/W1D4zCKfwuGz6nbanCqMb8LjsLGYu68NJgFUa+5Bjzzl6wlPc0Kuh1cKHQCvlUw7dqx9NqzqNfM/+tVaIlrcXNhNGGKzWzNFdygykGRn5XaYFRi9e+NMMIGu7fVikyM2bQUWJLGqjAaNxow5achnymQitrDMPmDmnMnVE3tO1Qn1+BBEZCCFGinFl9YLdZ6qclDVRrwWv/B+h5uPe/AvBrd2yloBt89qFzZXmKsYUUed1YCoysjnVdpW73qyvLYcwy9WT9t7WI+jAVSbPFGUVLhaB917qPZ7FoxFw96pNGGTZbDDnyaXIF46IKo2z1Kh/btpPHhmehvndKOz4eYYemdtRx9qqZM4uBUQkVRsUTtib3RcFGKYGR1UI2r0PvQQgNQWphjX+C9ZuIpOlpdqtPYpPQ1FOztRT5XTaimYIKEhqowiiTL9CSnyHtKX+VVpvPwVDWnC3VKK9FlxEzAyMvlQuMPA4bEUcnFgoQmyj78etBJJXD77JhtWirqDDKLbUAbrAKo1A8Q0varChbRYXRljYfc1Zzo4PIWAVW1hgKusH5UJItbV4InQGHH3yd6vMrkMBICCFKVKwwsheHXtdLSxpAcDO87Pfg5Ddg6H4GWr28eX8PX3h8RM0KWqdluyosnIfApnVdXS0GRnOGWQbb4FdTRfVMRdLc6TGvMG66qSzHzPrN1pJGOUkrVuTl0uQK+ot2SavS65LVzqxrgLZk+Qfsl9NzYxFu8U8DEBg4AMBE2Ky89HasWGFUHDqrWkLC6sZSWtKsFvK6oSqMACaeWfXay2UykqI74FLBTDZeNxVGsXS+4QKjsYUU3VoImvrKfuxWr5MXMgH1SYPPMYql1b8bj27u2FqBwAggv/ja3djfr8uJFndozGdU5dkK89OCHgfzG7glrTi/CIDglpIfZ7FoONsG1ScN/m9rPSbCKbJ5fXGHNNq2g6axuV0CIyGEKIt84eKh19WbFVKyW39Tleh+9/cgl+LDd24jW9D5Xw8OrfvQywOjC+tqRwO1SxrAVL4YGElLmijNVDTNTZbT4PBB176yHNNoNk/+wg2yU9qyCiMdx8VDr6tY+Zho3sFW4/xi21Y9em48wmH3JDj8eDo20+y2X1Rh1K4ClOzld3oLXzxDJBVWN5ZUYaSRK+jQc526oYZtaRPhNN3N5sBrqPkMI4Aml02FCq6mxgqMpkMEtTjO1k1lP3bQ62CkYO7e1+ABSDyjKoxcRrHCqPxDrwGsQfPvYYOGAOFiYJQurbqxxWMnkspRcG7MwOjsbJxBbYqCp2PVIWRr/3YA8ht4V72VLHsvPzekAiNgsFUCIyGEKIucXhx6rVW39aNUNie84eOwMAw/+R9saffxjuv7+OLjI4wtrG/r6+G5BE0uG0GvQ1UYrTcwMiuMJrIedYNUGIkSTUfTXJM/Af03li20bQkEmKepcSqMigFrPk1eN1SIbRhVf10yOvbQo81zYbw+20EWElnGwym2cwE694Cm0RNwMxkpzjAyWxSuMLsqXKwwcjuWTr7cgRWf2261qI0S3AEV5E8+u44/ydqlcwXmE1l6Ay7VjgZ1UmFkMyuMmpZOhhvA3IRq827qGiz7sVt9DiJ40e3ehg9AYuk8TpsFW84MAx1XHqq7Vt72AQD0DTrMOLIYGJmvPStUGAU8DgwDorrZgrrBAqOJcIrNliksbaW3oxVt2ryDjGEnPHKiAitrDIuBUQCIji0GRi679YqPk8BICCFKtFhhVGxJq9Jw2VXZ8nI48Ivw8Cdg6jl+85XbQYP/ef+ZdR22OCRPS8yptox1BkYuuxW/08Z40q5OcGXotShBQTfIxubozgzDwC1lO26n38WI3oa+0CgVRhcPvTbUzo16Qd1WxQoj36b9ACwMH6vac67Gc+MRwKAjdRY69gDQG3AxXmxJ85mBUfzybWnLtrVePGlbucLIXqwwAhUaZRNr+SOs2+IOaXVWYeR32lWFkdPfUIN5EzPnAfCZQUU5tfmcgEba29vwFUbRdB6/y6bCQJsLbJV5v9TVGmTWaCI5d74ix6+1pcAorG4oYeg1wELGALt3wwVGU5EMWyzTq9ohrWhff5BzRjfZqVMVWFljGJ5L4HVYaV+cA7W9pMdJYCSEECVa3CWtXlvSiu7+C/AE4Vu/SU+Tg1+6aYD/eHqMoZm1b12/uKvCuQfVDWU4WW/3O5lNZMHTKhVGoiRz8QwHOa0+KWNg1NHkZMxopzDfIFepiy1pep5CPqeqHnUVbGCp3utScMsBAApT9blT2snJKF3MY8tGofMaQAUny4Zew5UrjFJZHDa1DfzqAiPL4kUGLDa1KUENTJp/1u56rjBqoJa03II60dIC/WU/dtCrQpW4qxsiDfJadBnxTF7t7JWJVWx+EUB/UO2Ulg819vfrclYbGAU86mdoIWm2e26wwCgSnqOV8Kp2SCvqa/Fwjl7c0fqeu1dJ5+YSbG73ooXMURVtO0p6nARGQghRosVd0qx1OPT6Yp4gvOajMPE0PP4Zfv2OrbjtVv72vrXtZpTOFZiIpNjc5oMzPwRPG3Rft+5ltvmdTEfS4G2TwEiUZCqS5gbLaQoWB/QcLNtxO5pcjBntWGOjYP47r2sXzfyyFNLqNalgBkZVrHx0BjcRw4Nr/nTVnnM1zkzHudFrVtWYFUY9ATeRVI5EJr/UknaFndIiyRwBtx1N01YVGNksZksaqN8VNQqMJswKo55ihZHDV9ET+FL5XXaS2QK6w9dQFUa26Dg6GvjLv9Ncq8882bd3NnyFUSytdveqeGDU4mHcaMMS3Zg7X720Je3Krz3BYmCUMAdfb7DAyBY+rz5YQ4WR1aIx7x6kOTMJuVR5F9Yghufi6r383BnQLCUPDpfASAghSrRYYWQxZxhVsfVj1a69B7a/Gh74C1qTw/zKbZv53nNTPD+1+iu5R84vYBiwq8sDQz+Cba9a1w5pRbu6/JycjKK7gxIYiZJMRdMctpwm3XEd2F1lO26HX1UYWQrZK1ab1I2LWjgtela1yS4GRlV8XdI0xh2bCSbWP1i/EoZmYtzkNatqOouBkfq5mYykwNuuvnaFndLCyZwaeA3q5EuzlDSPxWG7qCXNalv8+zEMg4I5D68aitVUXc0uiE7URXURqAojgKzN2zAVRvmCjiczRcLeWpEWq+LJ/oylQ1WUNNBspxeLpfP4nJUPjHoCqsLInZpQc9w2kHSuQDav07SKwKhlscJo4wVGhmHgiZtt42uoMALIBLZhwVCByVUmky8wvpBa2iEtsKnk91ESGAkhRIlyi7ukWcyWtDqcYVSkafDG/6lObL7yS/ynw+247BY+99Nzqz7U154Zw++ycYdvDFLzsP2usizx8GCQZLZARGuWGUaiJHOhea7VzsPgrWU9bmeTizGjuDtRA7Q2JOZAU0MqrYV0zVrSAML+7fTlztfdyZphGJyZiXONdQyaesHdAkBvQA2DHQ+n1Um/K3DlCqPiFX5QJ1+uZvX6ugKbxUK+GAxd1JL2n//9GB/81yNr/4Ot0mQkRZvPoYaaxqbqYn4RLAVGaYtPXYDJZ2q8opVNRtJ0GXNkvZX5HtqsFgIeO5OYQWYDD76OF2cYZWIV2yENwGGzEHN2Y9czG+59xCXnp60wcD9gzjAKJ3MbLjCKZfIM6KPolF4Z82K2zp0A5Kfrsyq2kkbnk+gGbGnzQuhMye1oIIGREEKULH/xLml6ruonZqvW1A3v+CcInaXlvt/m567v4xtHx5mJpks+RDKb5wfHp3jDvm6cww+oq+tb7yzL8m7YHARgIueRCiNREtvEk9g0HffW28p63KDHwRRme1K9B0aGoVrSzBN/m56tWUsaQLZ1N36SxMxhwPViIpImmS3Qnz+/2I4G0G0GRsvmGF1xhlGOZrf5PS0GRiWwWy3k8sUZRvbFQG90PsmPTs+o1+HEnNp1soImwmk18BrUDKO6CYzUiW3KYu6U2QDVNOdDCXq0EDT3Vew5Wr0OLhTU78ZGbktTLWmVn2EEkGsy/z4afO7Ti70kMLI61ADxK/A7bdgs2lKFUQO1e65kOpJmj3aBhH8QHJ41HaO5bw8FQyM6dvXtlHZu1twhrdUNc0MlD7wGCYyEEKJky3ZJK+TquyWtaPA2eNWfwslv8tve+8jrBv/86PmSH37viSmS2QJvva5PzS/qvV7NSCqDziYXm4IeziZcqvy+eMIrxGW0zB4hjwXLphvKelyLRSPj61WfhOt8p7R0WFWrmCetNiNrDuLPqq9X+XXJ0X0tAKFzz1T1eVdyZjqGjTzNieHFdjSATr8Ti3ZRYOTtuPIuacnsRS1p4VUERtri3Dss1sVd7LIFA8OA7x+fgnv/K3zyevjJx1TVagVMRlJ0N7tU0BibqpuWtCazwiiBeeLXACe2F+ZUYORs3VSx52j1OTmXNX/G4tMVe55KW2pJi1Y8MLK2mH8fkY01x2hZYJQKl1TdqGkaAY9dDb12bqyh15ORNLstF8i2XbvmYwx2BRk1Oq7KndKG58zAyBmBfAraJDASQoiyKw4wVRVG+fodev1it/wm7H4jwUf+P35zcIJ/e2xEDXwtwdeeHqevxc2htgJMPAPbytOOVnR4MMjJsFmplVoo67HFxtMfe4Zh27aKnIA0NQeIWprrv8Ko2HZhBkYuzAqj4lDlKlc+Bgb3A5Aee66qz7uSoZk4m7UpLHoOOq5ZvN1mtdDV5GIibFZa+tpXrDAKLGtJC5T0/HbrRUOvrfbFQLxYdfTdZyfVa55hwIN/Cf/4agiVf/eeyXCanoBbPVchU3cVRnHM6qcGmGM0MzOJW8viaRuo2HO0eh2MpMzvSbIxW6x03SCezatQsAoVRp6OQQDy83Ue9q9SJPmiCqMSw+qAx7F86HWdtQuv1fzsNH3aHNaefWs+xpZ2H0NGD/aFq2+ntOG5BG0+B00xczSFBEZCCFF+eV3HatHUbjn1PvT6YpoGb/7f0LaDj4T+gpb0CP9+ZOVS9+lomoeH5njbdb1Yzj0AGGWbX1R0w+YWRjNe9ckGmz8gyiyfYWv2NBd8Bypy+A6/k0mto+ECIyc5FWLXqCWtr6ebcaMVy+zJqj7vSs5MxznsmlCfXFRhBKotrZQKo2xeJ5ktLB96XeJJm82qUdANdN1Y1pJWnIX35IV5MpkM9FwH93xezZT44s+t8k95ZdF0jlgmryqMYubw7zqpMCrOMIoajRMYxc22S0uggi1pPgcTCcDugeR8xZ6nkhLZPIah2qOqERh1tHUSN1wk6qwtdr1e0pJW4mtP0ONYaknT85BLVnKZVVOYUhclPJsOrPkYzW47E7ZNNCcvVKyqs16dm0uogdchc5MKmWEkhBDlly8Yaoc0aJyWtCJXE7zrS9isVr7g+Tj/92fHF1vsLuebR8fRDXjrwT4Yuk/tKNR9oKzLOjwYZB7zzaTMMRJXYETGcZAj0Vz6m5zV6GxycqHQBgt1fpW6uKNXscJIM3dJKw69rvLrks9pY9gygD/yQlWfdyVnZmLc7ToOzmZo27nsaz0BNxOR4gyjdshEIPfS2W7LTthg1TOMANWWZrEttqTlCjrX9jZhGDAfS6i/r71vhzv+UL2Rj4yv5Y97SZNmFVV3wL3UrtPcX7bjr0cxMIroxcCo/lvS8gvmhZaKzjByspDMYXgad/fQWFqdiDc5dPW6VOHAqL/Vy7jRRm6jVRitMTAKeOxLQ69hw7SlOefURQlH7/51HSfu34rNyNV/+3mZDRcDo7kX1O/F4i6hJZDASAghSpQrGIsnAQ3VklYU3Azv/De69Wn+MP4/+MYzV64y+trT4xzoD7A56IKh+2HrK8FS3l8bm9u8GG5zJlKDlt+L6kjMqwoJe6AyFRKdfhcj+QBGbLK+S/iL/07ME38nOey2i4Ze1+B1ada9lfbMSN3MITMMgwsz89yUeRR2v/ElW6D3BFxMRtKq+sdrDjtPvLTKKJJSc6GaPWsZeq0uLuQLBlhtSy1pBYNdXU3s6vITjqeWWgj7b1T/H31sNX/UKxoPq8qC3oB7acetCoYdq1FsSQsXnOqGOq8w0nUDe9wM85oqW2EEkHc2bmAUN1veW6xmCFvBXdIA+oNuxox2LA28q9ylFAOjpsXAKFDS41ourjCCDRMYBaKnmdda1EYF62C0mxedZp8vw6oaQyydYzaWYXObD+bOqHa0Enb7LJLASAghSpTXdTVcFswKozrfJe1SBm9Fe/1fc7v1OUZ+8Eky+cIl73ZsNMzpqRhvO9gLI4+p+RdlbkcDNaBxcJM5sLJB3xyL6pibUq1igY7KnKx1NDmZNlrQcsn6PnldbElTQ7pdZLFbLm5Jq/7rUiKwExt59Ua0DszEMlyffQqXnoBr3/aSr/cG3GTzOqFEdunkI/7SOUZhc4ZIwG2HfFa1dpR40mYzw/VcoVhhpE6iswUdu9XCG/Z1k85kyBhW9YCuvaoNafSJVf5pL28kpAKjgVaPqjCy2MHXWbbjr4fDZsFpsxDKm7s+1fO/OdTPVLseoqDZV3VlfrVavSpAyzgCDdumHUublTGLgVFlK4w6/S7G6MSbGK3vsH+VIqkcfqcNq0VbXYWRV1UYGcWgrgF2ICxFd/oM465t6z6Op3c3AKnJ+mqjrqTzc+p3gaowOrOq+UUggZEQQpQsVzAWTwIoZBuvwsikXf/LzHfdxq/m/o1vPvTUJe/z9w8O0eSy8dbreuHE18Dmhh2vqch6dm8ZBCAamqrI8cXGEJlVV/e7eyuzQ1FHk4sZI6A+idXxz2JiTpWTmycDTi1nDr2uzQwjAK1TDZXOTh6v+nNfytBMnDdaHyXnDMLml7/k68Vt5sfDqaXA6BKDrxcDI499qWWq1AojWzEweukMI4dV43V7u7GRZyZuztGw2tUulCPlqzAamU/hcVhp9TpUYNTUU/Yq0fXwu+zM5cwKozqvgjgfStCjzZH1dlX0e1isMEraWhr2IkrUbEnzU53AyGLRiLr7cOrJS1YKNqpoKqeqi2DVM4yyBZ2U1bf02EaXz7KpMMK8f+fK911Bf1c300aAxPjVExidm4sDsLXZgNiEBEZCCFEp+YK+2GaAnq/JiVlZaBot7/gETi1Py0//hGR2+eC/U5NR7js5zX+6dTN+uwYnvgE7Xg1OX0WWc/2WTqKGh7mZiYocX2wMqfkJCoZGT09lZrB0+l3M0KI+KQ4IrkeJWfC2gk1VZjjJqcrHGrak+fp2kzOsxEeOVf25L2V4YoZXWp4ht+tNl6y46msxA6OF1FLFzSW2MH/JttZQemBkzrvLFXRzlzT1OpvLqwqjLe0+fHaYiF30+tt/I0w9B5l4Sc+xkpH5JJuCHrVRQ2SsbuYXFTW5bISzmvpdWucVRiOhJN1aCK3C38NWr3pfEbM2NezQ63hxhpFmDluucGAEkGkeVB/MD1f8uaolksqp155cSu1wWOJrT6tPhbALxflgGyAwykyexE6BVHDPyndewdYOH0N6L9pcfc3dq6ThuQSaBpsM8332KgZegwRGQghRsrxuNH5Lmklr3crsdR/hLuNRfvStLyz72v96cAif08Z/unUQzv9UzUy5RFtHuezubiKMn9h8HVd1iJrLx6YJW5pxOioT1BZb0oBLhgd1IzmnWmLsKjBykVUVRjVsSetvD3DO6KZQJxVGtqF78WgZ3Ne945Jf7zUDo7GF5NIMo9hL/87DqWJLmmPppMsdKGkNxXl3+YIBFutiS1quYCxWHwWcGnMpnbOzZkDUfyMYBZh4uqTnWMnofJL+oEd9Ehmrm/lFRX6XTQ1IdjbVfWCkKozmcQQrHBiZJ/sRmiAbg3ymos9XCcWh117MwfJVCIxaelXlSWa2Ptpiy2ExMCq+9pQcGKnfkXPFds90uAKrq67YhWfUB13XrvtY/S1uztGLL3Z2Q7UwXsnwXILegBtn+Ky6oVUqjIQQoiJyBR17sRRdzzVsS1pR7+v/gAn7Jg4e/wtOXVBhzdBMnO8+N8kv3TxAwOOA4/8BDh9sv7ti67BaNLLOIPlYY85rENVhTc6SsAUrdvygx0FIM49f1xVGIfC0LaswqnVL2mCrl9PGJtwL9TFEdOv0vYQsrWibbrnk15tcdprddtWSZnOAO3jpCqNkFk0zd/QqnnSVeNJWvLiQLeiLLWmGYSzOMALwO6CAjf94ytzBrP+w+v/I46X/YS/DMAxG5pP0t3hUdVN0og4DI7uad+P01/0uaSOhGJ3aPJZAZQOjgNuORYM5o3F3Dy3OMHIbxQqjyg69BtiyfTcFQ2PmwumKP1e1rDUwajdDx+lMMTBq/Aqj3PgxUoYDX/eudR/LZrWw4BnEWUjW9+/6MlraIe0MaFa1Cc4qSGAkhBAlyhfMCiPDMFvSGjswwuZEf93f0M0cz37uQ/y3b5/k4/e9gNNm4f23bVZDXk99G3a9Huzuii7F7m/Hk5tnIpyq6POIxqTrBp5siJy7csNmLRYNjz9ARnPX7QyjD3/xaZLhKfC2gdWBgYZTy9a8Ja3FY2fYMoAvPVn7k5NUmP2ZJzkVvPKujr0BN2ML5uuNr/OSgVHYPGGzFIfOQsknbY5ihZFuDr02dPKFgvk1FSbZjAJtTR6+9vQ4Bd0Adwu074bR9QdGc/EsqVyBTUE3xKdU5VLdBUbFCiN/3VcYZRcmsKEvDpuvFItFI+h1MFMwW8AbMDCKZ/JYNHAWEuqGKlQYHRjsZJJWUlMbucIoUNLj2szAaDaNurBQ52FsKWyzJzhtbKKrxVOW4+WCV89OaYZhMDxbDIxegJYBsDlXdQwJjIQQokR5XVdDrxdbPxo8MAL6rrub7E2/xTutDzL32Bf47nOT/MKNA6os/tyD6qr6NZVrRyvyDRxguzbOYyfPVfy5ROOZjqVpJbzu7XRX0tHkYt4arNurjg89P40zu6ACI01DtzpxklPhRA1b0jRNI+w3S9xnTlX9+S8WP/ZNHOQJbX7jFe/X1+JWLWkA/ksHRpFUTu2QBqsOjGxmYJTLG4t/J7lcFlhqV0PP0d/ezFQ0zcNDZoVl/w0w9gToeknPczkj8+rPtqnVAxFzO/g6m2G0GBi5mus+MLLFzNkfVfgetnqdTGXNE+MGDIxi6Tw+pw2t+HdajZY0r4NpWw/WyIWKP1e1RFI5mj32VbfDBs05WHOxrKruqnWIv16GQVP4FCf1Abqay3Px0t6lKpX02Y0/x2guniWWyavAKDS06vlFIIGREEKULFcw1NBrvXZX8ivBddf/C/038XHPP/GRAxZ+/Y6t6gvHv6beyG+9s+JrCO69G5umM/vc/RV/LtF4zs3EaSeMK9Bd0efp9DuZMVouOc+m1lLZApZMBCv64rbexcDIZqn961KhTW1VzExtd56JjqjWhZZtN13xfn0tHsYXUhiGcfkKo6R5hR9WHRgVN0jIFSuMgFzmRYFRIUdP0E+z285Xi21pm25SzzW7vtaa0WJgFPRAZFTdWHcVRo3RkmYYBs6UGRg1VbbCCNQJ/1gDB0bRdA6/y65CQIttsX220rL+AVozY+rfdINL5wpk8vqaWtIcNgvNbjtzcXNQdqMHRpFRnPkYZ62b8TnLc0Gkq3uAuOEiPrnxK4yG51Sl3w5vSl3Q6dq76mNIYCSEECXK67q6aryBKowAdfX7ns9htdr5z+G/pPXZf4CH/hpOfxd2v1HN+Kgwrf9GMhY3zZM/I19Y35V1sfGMTk7i1PL42yt7stbd7GIs34xRhxVGc/EMrZp5Uu1pA0C3unCRfdHrUm12b/R1DpI0nOg1HjqbnR9jymhhe9eVqxp6W9wksgXCyZyqXIvPvGQAajiVo9ljfj/TEXXyay+tJWL50Gv1uyKXV39HxaHX6HmsNgdvPtDDvSem1K5s/Teqr62zLa0YGPW1eNTAa6h4O9Vq+V02EtkCBbsP0vUbGMUyedoL5nbtVQjdWn0OLqTMSopE4wVG8XRezf3KxFQYqGlVeV5n5zYCxBidrL/X79Uq7tDY5LZDakHdWGJgBNDmcxBKbJDAaEptpjDrXX1lzOVs6fBxwegkNzNUtmPWq+E5tanCnrkfqNbkvZfeDOJKJDASQogS5QqGeSXf3AZ5g1QYARDoh7d8SvVz//CP4IG/gEIWrntPdZ7f5iDSfgM36M9ybCxcnecUDWNuWlVI+Fsre8K7qdXLRKFZzTCqs6vUM7EMrZgn1V4VGBUsDpzai1vSavO6NNjm45zRTWaqti1pmflxFqxtdDVduaqhb3GntBT4uiCffkmVSySZXd6S5mou+eS3GBjlCvri30k+p3a8Ks4wQs+Dxc7br+8jk9f57rOTENyiAsF1BkYj80k6m5y47FYVGLkCVWkNWo3tHWo9CwVnXbekzUQzdGvz5Gw+cFV+gHObz8lw0gwqG7DCKPbiwKhKOjapKsezzz9XteeslGJgtKzCaBXDw9t8TtWSthECo8mjFLCQatlZtkNubvNy3ujEFjlftmPWq3NzCexWjcCZr0LPQWhf/fdRAiMhhChRvri7TUG1FdRiVkhF7Xod/Jdh+INR+ONZ+ONp2HRj1Z7ev+dVbLFM8fSzjf9mT5RXbFa1g2gVnmE0EPQwbbSg5VN11yIzG8sQ1MyT6sXAyGlWGF3cklab16VNQS9njR60udpVGI0tJHFlZvC09qGtEOwUA6PxcFK1pMFLWhHDqRwBTzEwCpc8dBaWdknLFXSwWNXHObPC6KKWNCxW9vY2s6PTx78/NaoCqU03wcijJT/XpYzMJ1U7GqjAqM7a0QAODbYAMJY025fqLKQtmoml2apNkPFvqsrzBb0OwmkDwxVozMAok1OtQ5lYVXZIK+resgeAmfMNulPasS/Dqe8AlwiMbC6wl97a1+ZzXtSSVl+/y1Zt5DGGtAFaAi1lO2TQ62Bc68aXGle7SG5gw7MJ7gxMo02fgAPvXtMxJDASQogS5XVj+W5ENWr9qCineQXV5qhaGXmRe9erAMg8L3OMxHKZsNliUDyxr5CBVo+aYQR1t1PabDxDm2ZeKTZnGBXMGUb2OmhJ29ru5azegzMxDtlkTdbw1SOjdLFA76YtK963L6DCFFVhZAaRF80x0nWDaOpFM4xW0RLiWKwwWmpJK1xi6DVWO5qm8XOH+nlmJMypySgMvgwWzsP8cMnP92Kj80n66zww6mxysSnoYThmUd+LfLrWS7qkUDjKYcvz5PtvrsrztfrUv+GCu7UhAyPVkmZXoXsVK4ys5lbh6UZsMzIMuO9P4CcfBSCSfFFgtIrXHlAtaSowavCh14U8xtgRHs9vo6u5fLOwNE0j6u7HauSXZrxtUMNzCd5u/an6PXTtPWs6hgRGQghRItWSZtmYLWn1oGM3cUcbg5EnWEhka70aUScy+QLW5Iz6pMIVRv1BDzME1Cd1NsdoNpYhiFlh5GkFIK85cJE1h/Gbr0s1aknraHIR8Q6iYcD82ao/v64b/ODIaZxaDn/7yjtZNblt+J02FRj5u9SNFwVGsUwe3WDNgVGxwihfuGjo9cUVRroOhr74e+Tt1/fhtFn4t8cuwDYVnjO0tvA8ky8wGU1fVGE0WpeBEagqo+fD5id12pZmGX0Ct5bFvr3yG0CA2iUNIOMIQHKuKs9ZTrVqScPpI25vxRO/QDLbYFUjkVH1+jNzCnLp5RVGqfk1BEZOouk8eUeDB0YzJ9ByCY4UdpQ1MALINA2qD+Y37u68yWye8VCUW5IPwM7XgCe4puNIYCSEECVSLWlaTbev3tA0jWz/7dxiOc5Pz8zUejWiTlwIJWklgq7ZwF2+kvRLcdmtFLxmeFBvFUaxDK1ahIjhXZqJY3Xi1HIXtcpqi+1PtdDcfw1Qm62KHzkbwoiaO1n5V95NT9M0elvcl60wmomqapfAxUOvV3HSVqwiyl48wyivgnCH7eKAz7b4PG/Y18M3nhkn7huAlkE4cx/RdI6nLiyU/LyAufubuUNaJqba6eo0MDo8GGQybX6P6zQwCkw/TM6w4tl+e1Wer1hhlLIFIDlflecsp1g6j68WgRGQbx5kkzbNsdEGC0nGjqj/63mYPrE8MJp8Ftp3repwrT4VOiY1HxQykKvP6r0VjahZbk/pO+guc2BkBM0dgTdwYPST52e5yTiKN78A+9fWjgYSGAkhRMlUS9pFM4ykwqjsAnvvJqjFOfvsI7VeiqgT52YTtBMm7+moSpukO9ijPqjLwChGyGha3DY6rzlwFmcYFXI1b5Md3LkP3dAIXThe9ef+v0dG2eI0A4emnpIe09fiZmwhqWYTWZ3LAqMfHFd//zdtMa/IrjYwsly8S5oKhZa1pF1i5tQv3rSJRLbA149OwLa7MIYf4gP/+DD3fOoR/vgbz5HJF0p67hFzh7RNQQ9ExtWNzStXXdXC4cEW4pg7gtVpJUTfwhOctO5Aq8LAa4BWr/p3HLMGGq4lLZMvkC3oNLnsNQmMPF3bGNCmeXpkdSFrzY0dAc08LZ94emmXtNwchC8s7Z5YojYzdIxiVhmmw+VaaXWNPkba3ck4bXSusJHBavnb+kgZDvKh6lfEVssPTkzxLsfPMDxtsP2uNR9HAiMhhChRrqBjt1w0XHYjzjCqMcvWVwBgP//jxZNicXU7NxenXYtg9Ve2Ha2os61NncDWWWA0F1e7pM3RpKpWgJzmwEVOhRN6vmbtaEWHt/UwZrQRGz1Z1ecNJ7Pce2KK12wyXzOKLWYr6GvxML6QUkGkrxPiqrLRMAy+/sw4N24Oqm3pYc0tabmLWtLy+Yta0oqVqhddeDjQH+Cania+8NgFjG2vQsunsI89yl17Ovm3x0Z4+6ceZWgmzng4xfHxCOdm45d87tFlgdGYurFOK4y2tvvQiqFCPVYYJefpTz/PKff1VXvKDvPEeMHwq8CogX4XxtKqcq4mLWmAo307XdoCJ87X1+v3isaPQN9h1W48eZRISg0Ot02YlUerDYz8qsIogk/dkGqwAK1o5HEmm/YDGt3N7rIeuqfFzXmjk9x0A868KkE2r/OzU2PcoT2Ndu0963p/IIGREEKUKF8oDr1e3kogysjfRdi3jetyRzk7m6j1akQdGJ5N0G2NYm0qLQRYr4FWD9N6gHxkoirPVyq1S1qUeaOJdFYFRnmLEydZ7Daz8rFGO6QV9Qc9jNv6sS9U9w34t45NkM3r3Nyhtq0vpSUNVIVRLJNXV/N9HYsh4dHRMOfmErztYK+6Yy6tBjKvoSUtpxuLb9QLFwdGl5g5pWkav3jTAKenYnz0dAcZw86v9w3zD+85xD+85xBbQg8S//vb+euP/Tlv+uRD3Pk3P+G7z7501tbIfBKnzUK737k00LVOAyNN0+jvNv++6jEwOv9TLBiMBm6o2lP6nDZ8Thtzuk/9u67H78tlLAZGdgPyqarukgaAOfh6ZuQ0ut4gQVs+C5PHVGDUcx1MHF0auD/6uKp+7N63qkO2mXOwZnUzsGuwSjVAhd3RMYac1+CwWWjxlPeCSE/AzQWjC2ODtqQ9cnaOwewZ7EYWtrx8XceSwEgIIUqU13XVkqa/9MqwKB9tx10ctpzmqdMb85e4WJ1zcwnaLZGKD7wu2tTqZcZoIRuun8DIMAxm4xlaLAnChpdkTp2UZTUHLi2HzVJsSav9a1ImsJW27AiGXlr7VDkcH4/Q4XfSwQK4g2BzlvS43oC6Yj22kFxWYfT1Z8Zx2iy8dq8ZZETNti5vW8lrshcrjPJLFUbFwMhxcWD0opDvzQd68DttfObRSU4693JT4WkA7hqw8reuz3OtbZSPOz7Fs+3/L3/e9iNiX/0wqU/eAn+1DSaOAiow6g960DRNnXRpVvBVJ3Bdi+2b1Pc5Fq3DeT3nfkwcN8n2/VV92o4mJ1N5s7qtgU7242ZgFLCarftVrjAqBkYtmXGen26QoG36uAqke6+H7gMwc4pkIm4GRk+oEKnE17SiNr+qgJ/Oe9UNDfQztGjkMQCOarvoanKp17My6gm4OW904YyPQBV/X1XLvSemuMVhXrxZZYXai0lgJIQQJcoVDNWStjj0uvYnZxtR08F34NAKZI5/q9ZLEXXg/EyEZj2iTuirYCDoYZpAXbWkRdN5snkdPylieEhl1ZvbLGaFUTHIroM2WV/vNbjJMnyueoOvJ8JpegJuiE6WPL8IWGw3UzuldUJ8mmxe59vHJrhrT6eawwJw5j71/8GXlXzsYoVRXl8KjHRz6LXddvnfIx6HjffcMkBXk4stN78FLXQGFs7DD/4QazaO7YM/gZ/7V3xuN78c/zyvtzzCc/M2CpoV/u8vQXKekfnURTukjanvSR1XxO7ZrKqfxianV7hn9elnf8yjhd20Nfuq+rydfhejmeLJfh0GaZcRS6uf64DVHLJc7cCoRQVGA9o0j51rkJBk/Cn1/75DKhwyCjhDJ+n1azB5FPpXX93mcdjwOKxMZM3XgUTj7bbH6BNg9/JUpq/sO6QBdDe7OG90YtVzSxcFNoiCbvDDE9Pc5T8PrdtWdbHjUiQwEkKIEuULZoXRJWZPiPLReg8Ssnezdea+xikpFxURTmYhtYCVQvUCo1YP00YLjtRM3cwOmY1lsKDjJk3ccJM0A6OcZsdJDqvFbJWtcUsaQP92VYkxfPLpqj3nRCSlqoVikyW3o4FqSQO1qxi+TkjO8ZNT4ywkc0vtaABnfqjedLduLfnYSzOMLm5Ju/LQ66Lfu3snP/1/XkHz3tepG+79I3juK3D770HnHtjzJvjQw/A7J7jw/pO8J/+H/KHjDzDiUxhffR/j8/HlgVGdtqMV7R5UId/07GyNV/IiCxewLJzjYf1a1d5XRZ1NTi6kzJPkZOOc7EfNCiMfKXVDtQMjTxBcAa5xh3j0bIMERmNPqtef5n7oOQBAIHKSVzRNqpbENVaHtPocjKaLP0ONEzouGn0M+q5nIpqjq8wDr0HtirrgNF8bN9jg6yPn5wklMuzMnlx3dRFIYCSEECXL6cbyN/pSYVQZmsbcwOu4wXiOM+dHar0aUUOTkTTtmrlzkre9Ks8Z8DiI2lqx6Zm62VlmNpZZPAGL4SGdMyuMzJY0DEOdWNTBa1LHlr0ALIyeqMrzGYbBRDiltlyOTZY88Bog4LHjcVhVhZHZ8vijp07Q5nPwsu3mz1s2Aed/Bttfvap1FXdJu3jotZ5XJ9MOq2VpFt4lLjxomqZ+17Rug8AAnP6O2lb7tt9ZupPFAs19XNvXwt+84wD/MdXJX+jvQzv3IB8ofIn+xcBotO4DI6fTTRYH8/N1doJ/7scA/FTfS0e1A6NmF2fj5nM2UDtRPKN+rptQg9erHhgBBDdzjXOOx4fnG+Oi09gR6D2khu839ZJztXEtZzmoPa++voYKI4A2n5PppKHmSDXQzxAAmThMHcfou4GpaFq9vldAtmlQfbDB5hj94MQUO2zTOLMLEhgJIUQ15Qu6mkshLWkVF7zhndi1AjNP/HutlyJqaCGRpV0Lq0+qVGEEYBTnvcTqo0VmNn5xYOQmZQZGGcwWtHymblrSNG8bCWsT2twLVdnpMJzMkc7p9DbZ1QyiVbSkaZpGX4vbnGGk/s6fP3OWN+7vWWwp49xPoJCBHXeval0Wi4bVopmBkfpdoRcuNfT6ClVhmgY7Xg1o8KZPXnaOyev3dfPd37yNZzvezBfzr+DDtm9y9/DH1M5I0Ym6D4wAcnYv6fjCYhhaF4Z/QtrVwVmjp+xbeq+k0+9iumC2wTXQyX6xJc2rR9UN7mD1F9G6nU35YaKpDKemotV//tVIzsP8WdWOBqBpzPh3ca1lmE3J46rFbo3z+9p8TkLxrKq6aqCfIUDtGmcUiHceIpvXK9KSBuAM9qrfoxsoMDIM1Y72813mhgibblr3MSUwEkKIEui6gW6AzXLxsFIJjCqlffsNjGndBM9/r9ZLETU0n8zShllhVKWh1wD2gBk6xF66A1UtzMYy+DQVGF3ckpbFfA3Kp+qmJQ1NI9m0hd7CWFV2OpyIqO/LoCsOGKuqMAI1x2g8nEL3qp+vdm2Bd9+waekOZ+4Fhx823bLqtdmtGvmCARYrcHFgpJW+ecIdfwjvu3fFKoNdXU185YM343zj3/AD31vpO/dl+OQh9TwNEBgZDj9eUkyEU7VeypLIGPOeQUCrfoVRk4s4bgyLvaFO9ou7pLnyYXWDpwaB0dY7cWVCXKOdr/+2tIvnF5met2xjh2UM98Tj66oOafM5mYtnwNPaUG2NgKq6Asa81wBUpCUNoCvgZcTo2FA7pY3MJxkPp3iZ6yy4AtC6fd3HlMBICCFKkNPVNtY2q6ZaP6Cuh4g2PE3jTPur2Jl6hnx0ptarETUyX6MKI1+rml9TiNRPYBQ0h8jGcS9WYaQvrjCqk5Y0AFfXLrZqEzw+XPmTtYmw+r70WcPqBn/pFUaAWWGU4v88q4KK/7Tfw/ZOs43GMNTA6613gG311Vt2i4VsQV+aYZQrDr2+qCVtpb8zTxA2lXbSaLFo3HPjVl7ze/8H7f33L4VnwdJnL9WMswkfKaajmVqvZEk6QtzwYPv/s/feYZKc9bn2/Vbn3JPjzmyO0q5WOSAUQQQRDdgYMHAwHB/b2D62jw/2d3xscDYGZzhEE21MMhkEykJZK62kzXF2cuycu6vq++OtmpldbZjZ6VA9W/d1zbWanpmuVzMdqp73eZ6fImjx19e91xX2AIKSp6WpCoszxQpel4KzEJc3+Nvqv4hNrwAEvxDcxxMnrN3d8/jD96AjZNm1eVuuHwc6Ih+76DgaQHvQTSxbQve1NZXoCMj1esJMFKVQWyuHUV/Ux0mtC21u9QhGe0cSAKzJvCAFR2Xlco8tGNnY2NgsgbIqoxWnR9IaH/9YzSiXvRmn0Bh/4uuNXopNg5CCURLd5QdP/aYUtXZLh0lqdrRuxzwfM+kifT75urPYYVQ0HUblvHSSWMT1GOzbTodIcujkSM2PNWE4jDqFcYG6TIdRX9RHMl/mb34uL6iu76wsfHFqn5yes8z+IhOXUzEcRvLvoquLOozmHUaOi7rvC9J3FXzgQelOWn9rbY5RRRRfmJDIMZ0uNHopCxRSJHQ/HSEPilLdkd4XwozA5Z2RpiosThfKhLwuuWaHB1z++i8i0A791/AK516ePDmHatEeo1JFo3zqKU4oA/NdT2VV4yfxRaL3Ch1Gmg5FT0tTPYYA2R3n8jORlK8HPRFfTQ7TG/UxpHcj4ifB2BhudvaOJOhy5fAmji15s+FC2IKRjY2NzRKoqIbDSFGWHiWwWRE7dt/Ica0H5cB3Gr0UmwYRz5boc6YQdYyjAfR2dpDSfeTnLCIYZYp0e+XrThofeVMw0s1IWlE6ViziMBIdmwFIDNe++HoskcftUAiVjAlby+gwAhlJAxjsbEH3tSAyi3qrjtwj/930iotam1MRVDRtXhTSF3cY1WPapsMl+ytEfcWOi8HljxAiz7SVHEbFFDHVW/c4GjA/lS3tiDaVOyRVqBDyOKVA4W9t3GNv81305w/hLcxwYNyaPUaHJlNsEad4rjzI8ZkMAMdnMoxUIhQ8bTIK27ntou+/PSgfQ1lnpKkeQ4DcBHH5mEwWUIR0S9WC3qiXIb0bRS1Cerwmx6g3z48keGPbmPykCoXXYAtGNjY2NkvidIfREqMENiuiPeTlSd/L6U3sab7dMZuqMJct0e1I1TWOBjDY5mdab6GctMYJ5Ey6SKd7wWFkll7n5yNpBUtF0miXgpEveYyUUYJbKyYSBXqiXpTMpOxw8rcv6+evWdfC7Vs7+X/vvAoR7ILFgtHRn0HPrmW7lkxcDoVSRZ//u+hqGUWAQxGLSq8t8jdrME5/hJDIM5WyiMNIU6GUYbbkoSNU38JrkCO/o34XCUJNdbGfKVQIeZ2QjzUmjmay+VUA3Op4nidOWPP39+LQBF0iwZDWxb0H5OvO/rEUIChsfC3seMOKHIhthsiSFhEo56CUq8ay60M5B+4Ak8kCnSEvTkdtJAvpMDLOL1ZBj1FZ1dg3nuLl3uPy/bD3yqrcry0Y2djY2CyBynyH0WKHkd1hVGvy616Bgkbh0M8avRSbBhA3S6/r7DDqDnuZoQVHZrKuxz0XM+ki7W7pvMgK/7zDqKCZDqOCpSJptKyl5G3j5coLvDCSrOmhxhN5OXI5PSEnnS2zr6Ez5OXz77mG9R1B+TjLGJ1p6SkYfeqi42hglF5r2vx7haZVFqav2cMTTkN4woREgem0RRxGRelKmSq56QzX32EEclLarBZsqsLi0yJpvpbGLaRrB4T7eb33BR63qGA0euIQAFp0LfceNASj8RRel0LoF/4J3vCvK7p/02EUx+hkayLh0YykTaYKNesvAugIehgTxobAKhCMDk+mKVU0tlQOys0Od3UiobZgZGNjY7MEKobDyKksLr22T/Rrza5rb2VODzG953uNXopNA5jLlGjR43V3GCmKIOtux1No/IWaqunEskVanUVAoLv88w6jAosEIwtF0lAc6Ftfx+3Kc+wbqm1x+ESyQG/UJwWjcM/K7izYDWlDJLzvIyAcsOuXLvruXA6FsqotdBhVyrK/CBZF0mrUYdRseEIEyTKVtMiUtIIUOidLnoZE0gA6wx4mK0HIJxaczRYnXagQ9DilONFIh5EQsPkurtWf56ljE9bqxjJIjx8BYGDjdvacijOXKbJvPMm2nrB0Ia6QDkMwmtGM/r9mEoyMSNpEslCzCWkg3+sJ91LBCbGTNTtOvdg7kiBChtbEi1WLo4EtGNnY2NgsibLRYSS7J8wogV16XWuuWtfO086raJl4WEYEbC4p0tksIS0Fgfo6jACKgR6ilZmGX6jNZYtoOkSVPHhCeN3u+dLrnOkwKhsOI6sIRoBn55vxiyLa0dq5A1VNZzJVoDfig9TERUfH5jEdRqPPwN6vwA2/Dm0XP2HM6VBknNmcqKmW5YQ0WHCqWuhv1lA8YRxoJNPpRq9EUpAOo5Tup7MBkTSQxdfjJT+gQyHRkDUsl0xxcSSttbGL2fwqPFqeq/QDfOqhc7tH8iWVU3PZOi4Mkrky7rQcCrBr5240He47NM3B8RQ7esNVOUbY58TlEExVTMGo8RsgS6acBXeAqWRtHUYAXdEgc452OeCgyXl+JMGHfV9DqGW44h1Vu19bMLKxsbFZAhVjyobTIYwTfWHvDNcBIQSl9XcS0lLEjjzW6OXY1BFd11HMHdE6R9IAStGNuKigx4fqfuzFzBgRnZCQgpHPrVAwO4x0Q4gwO4ysFG8avImMI8KGmXvR9dpMKZpOF1A1fcFhFFpe4fVLCHZBJQ/f+6B0G738f63o7lwOYTiM5N9J1yqyBw/sSNqZRPoB6E2/0OCFGBiRtDT+hjmMusIehgvGdKgmcYekCxVCHgfk4+BrsGC07mZw+vjVriN85YlTTJ/Rj1VRNf79yWFu/bsHeMXHHyaWLdVtaXtHEwyIKSquIFvXDdIT8fJvjw6RLlbY0RupyjGEELQFPIboSHN1QZZylB0+0sVKzQWjvqiPMa0NktYYcrEihh7mjfr9iJt+C7ovq9rd2oKRjY2NzRIoL56SplprJ3+1s+PmN1HRFUae/K9GL8WmjmSKFSKaMSq9zpE0AE/3FgCSI/vqfuzFmIJRQDcEI5djvsMor5ml1+aUNAu5Hh1OxntewU3aHsZmanOhMp6Q8aX+gCov8FcaSTMdStMH4BUfmR91fbG4HIqMM5uikFpe6DCyhyeczta7ybnbeI/+XTJFC8SvDIdRWvc3rsMobHQYAWSt7w5RNZ1MsUK7Mw+61thIGoDLB+tv5YbKU1Q0jU8+dHz+S48dm+WVf/8wf/RfL8pyelXj6FT93G17hxMMiGlEyzqEonDnti4OTsjH3GVVEowA2kNuRppMdASgnCeny/eznhoLRr1RL6cqrejJkZoep9ak0yl+Pf3PJLxr4Jb/XdX7tgUjGxsbmyVQOW1KmoXKZS8BNgz0c9C1ndDwA41eik0diWfLdAijMLkBglHLgNydS4zUfjT8+TAFI5+WNRxGTnJlM5JmOozyRiTNWkX8rsvfSFAUmNjzw5rc/3hCOgbWuIyx2aGVdhgZTrY118HOt63svpCdd6UzHEbzHUb28ITTcXk5ueFXeLnjReLHn2n0ahY5jHwNi6R1hrzEdCOe1AQX+9mSFPraHXJEfMMjaQBbX4szNcJvbcvy1SeHmUjm+af7jvKOzz0JwGd+5Wr+4/3XA3Bitn6xtL0jcTa6ZnC0rQXgzu3yPc6pCDZ3B6t2nPagh+GcG4TSFI+hecpZssaGSC07jAB6Ij7G9FYZa27i6oPkT/6CdcoUQzf8hRRLq4gtGNnY2NgsgZdMSbN3hetKduB21qsnGB462uil2NSJWK5Eh0jITxoQSRvs62Faj1KZOlL3Yy9mJiMFI7dqCEYuhULpTMGoaL1IGtC/+5XE9RDeoz+oyf1PGAXJXRgOppUKRt07Ye3NcPffy9LcFeJ2KlRUbSG+rC12GNmC0ZlkLv8V0roP31P/3OilzJdepwnQHmyMc68r7CGmmxOurO8wShekYNQqDKdOox1GAFtfC4qT90afR9N0XvOPj/Dxnx3hDbt6+f4HX8YrtnfRF/XhcSocn85AYgQe+MuaCge6rvP8cIxefRpa1gFw/fpWAm4Hm7pCeJzVqztoC3iYyZZlPLAJXGrzlHIkK/K1sR6RtHG9HaGrC0MPmo3ZY/Tu/wxfr9zC4NWvrvrdL0kwEkIMCiHuNP7bJ4RYmUfXxsbGpskomw4jRdiRtAaw4aY3AXDk599q8Eps6kUsW6QDw2EU6Kj78bvDXk7SiydxrO7HXsxMukjI40QpZcATxu92zk9Jy86XXuetNSXNwOX2sMd3IxviD8ti7ioznigQ8jrxF2fkDSsVjPyt8J4fyJHcVcCpCPneIQQoLoRWwT1fem1H0s6krb2Tr6p30Db0o8ZPLDIiaS5/VG4UNYCusJdZImgoTXEhmy5IETSiGw6jRncYgXxOr7uF8Ikf8kvX9JMtqvzFmy7j73/xCgIeKUgoimBde4CTM2n49vvhob+Bydp1aQ3Hcnjy0zj1MrSsBcDjdPCh12zj125ZX9VjtYfczGVK6P625nEYqWXQyiQr8rWxq8YOo96oj3HdEDebtcfoob+hLFz8e/i9tASqL3Bf8BVQCPF+4JvAp4yb+oHvVH0lNjY2NhbGjKTNO4wstpO/2ulYv5sZRye+k/c1eik2dSKWLdMhEmieCLjqHwlRFMGsZ5DW/BDUqLR5Kcyki3SEPDIiY3QY5Yzox2kOI4s6H6fX3IVfz1M+em/V73s8kTcmpBnTbVbaYVRlXA5lvv8OxQlnLb22HUYmXWEPn6+8Gk044PF/aexiiklKwk1ruHrxoOXSEfKg4iDnbm2KCU4Zw2EU1k2HUUsDV7OI7W+A+En+9BqNJ/7oDt5x3SDiDAfhhs4guya+DsOPyxtma7dRsHdE9hcB0Lpu/vZ3XT/IG67oq+qxOoIeSqqG6mttntLrcg6AubKL1oAbr6u2A2Z6o17G9Hb5STP2GM0chhe/wdeVVzGwZm1NDrEUyfw3gJuAFICu60eB+nvDbWxsbBpIeT6SJoydfPskv64IwXT3LeyuPE8i3iQnPTYrIp4t0S6S6A2Io5nkIhsIaBnIzjRsDTPpIu0hDxTT4AnjdTkolOXrUVETVHDKDiOLdqtFdtxJWveReuFHVb/vfHyM39c+JyMk0YEVl1RXm9MEI4cLoVZeGkmzoMjXKIIeJ2lXOy+0vRqe+wrkEw1bi1ZIkdJ9bK/SiPOLweVQaA+6STjaZb+KxUkZDqOgajhDrRBJA9h6NwgHzkPfpfUc7ourAjHeX/wS2rpbAQFztYu/PzecYIPLeE8xHEa1os2IUxZcLc3jMCoZglHRUXN3EUDI6+KanZcD8Nizz9f8eFXnwb9Gc/n5ePZV7B6I1uQQSxGMirquz88ZFEI4gcZttdnY2Ng0gPnSa0WRXSFWmkZ0iZDZ+jb8okjmqS81eik2dWAuW6JLJFFC9S+8NhEdmwEoTx1u2BpmMkU6g04oZcATwu9ecBiVVY2K4paRNF21pPiwa7CDp7StaCcfplipYi/I/v/iM/Ff5fb092HXL8F7alOsvRJcDkFFM06ZFQfoiyNpdofRmQgh6Ap7eNJzPVQKMHf8wj9UI5LxWZKan1s21z8Ou5jOkJdp0QZp6wtGsawhGGkp+bj2NE5sO41AG6x7ORz4ztndoprG64f/ijJOhl72USk+z9ZOMNo7kuCqcAKEAyJranYckB1GADlnpCl6sIB5h9F0wVHzCWkmH3nb9eSVAEeOHuKf72uirsyp/bD/2zze/hbSSoTXXl4bl+1SBKOHhBB/BPiEEK8AvgF8vyarsbGxaSqS+TJffGyIj//0MH/8nX38+Q8OoGqrU0+uqIscRnYkrSG0br6BvdoGoi/+GxiOL5vVSzxboktJIhowIc0k2LdNrmV4X8PWMJsu0uc3Hu+eED63Y77DqKLqUjAqGp0hFhSM+qI+cr030FEc4V1//x0ePVadixbt8U8wprXx1Wu+Ba//Z3mRZzGcDoVyxYykuVC0xQ6jyvztNgt0hrycLBpCQ3q8YetIxudI4+dlG9sbtgaQMb1xNQqpxv0ulkosa0x0rCRkf1EViuOrxvY3QOwETJ3ltfzZL9I+9wx/VnknR/IhaN9UM4eRruscnkyz2TUHkf6av2abDqO0EpaRtGY4dzIEo8m8UvPCaxOnQ8HbPsjV0Swf+9kR/uOp4bocd8U8+FfonhB/OHkLr9zRRWeNHFlLEYz+NzADvAj8d+BHwP+pyWpsbGyaik88eIw/+d5+/vmBY3xzzyif/flJjkylG72smlA2hDCXHUlrGGta/fxb5S6CmSE4fn+jl2NTY2K5Em0kINC4SFr3mo3kdA/58YMNOb6q6aQKFdpd8kLM7DAqlDU0TaekalQUz/wIcCuKD0IIXveGXwRgZ+UF3vHZJ7n2L+7lVf/wMG//9BPcf2hq+XdaTCPGn+Un2jUEezZVecXVw+VQ5t87UJwIXT1Lh1Ft+zmajc6wh6M5I1rYwBhWMRMHT7gmBbLLoSvs5VQ5CoXEfFTHqsxlSrgdCq5SQpZNW4ltr5OOnv3fOf32Yhoe+AvUNTfwDfUWjs9koW2TdLfVQFyZSRfJl1W6tYnT+otqRXtQOozihKULtZis+TFXTFlOv5wuKHTXIZJmIiJr2BFMsXsgyqcfPoHewO7CJZEchYPf59DgOxjOe3nndYM1O9R5BSMhhAK8qOv6Z3Rdf6uu628x/tviv0Ebm5UzlsgzkzZO0n/0v+SHzTyqpvOd58a4bUsHx/7iNXzlV68DYDJV/Uk4VmDeYaTYpdeNwuty8GzwFlLOVnjy/zV6OdZiFb4tZzIpAuShgR1G6zpCnNB7EHNHGnJ8c+pQm3ORYOSWAkOholJRdVTF6DcCSzqMAOi+HLxRPrR1hv/z2m3ctqWTNa1+9o8n+dLjp5Z/f6ceQ2gVHtUuoyfiq/56q4TLIebfO3A4EVp5wWFkvo9YyYVhAbrCXg5n3PJ30yCHUSJXwlFK4w83XvToDHs5UTQENIvH0uayJdqCbkQubo0JaYsJtMPal8G+b0Ipu3D7z/8BsjM4XvUXdId9HJ/JQPtG6XKpweNvaE6KfpHCWM37iwBa/FLwnNONx1AzFF8bf5+c7qmbwwiASD8iOcq7rh/k5GyWJ05Y/HdlTJL82vQA6zsC3LChdp1h5xWMdF3XgOeFENbz+drY1Jj3feFpfuc/n5MXYi9+E174enNYOevE48fnmEoV+YWr+nEoYj5nPJlcrYKROSVNGB1GFr0wW+X0tUf4kec1cOxnNZ1iYnlSE/CZ2+Hj2+Ev++HPO+HkI41eVVURGaMUtIGRtIjPxYijn2C6MSO+k3kpGLU4TMEojM+YGJMvqVQ0w2FUsm4kDZAumrUvw3nq5/zqzev5m7fs5DO/cjV3bu9i/3hq+fd34iFUxc0ebTN9USsLRgpl1XQYuVD0Cu7FpddW/Xs1kM6Qh2xJRwt2Ncxh9PNjs4REjtbWxsbRwIikmSO/LR5Li2VLslQ6N2c9hxHAjR+ExDB8/VegUoLkmJzGd/lboe8q1ncEFhxGUJMeo1NzWYLkcBfj0FJ7h5HbqRD2OpmuBOQN2SboMTIiaXm8dXUYEemHfJzXbAkT9jqtH0szBORHptxnnfxXTZYSSesB9gsh7hNCfM/8qNmKbGwsQCJX4tBkmsePz5EYOwL5mLQD13BqQrPx7WdHCXmd3LlNXsx1hDwoAiZWqWBkTklzORQjkmaXXjeCwdYAny/cKnefn/p0o5fTOF78OoztkUWeV74LXH7Y82+NXlVVceaNscMNFIwAUoF1tJYnGxIHMQWjqCIt+njD8w6jXEmlrOpoix1GVnY+rr0ZEqcgvuAo2tEbYSZdZDq9vPcN/eRDjAYvp4ibroin2iutGk6HWJiSpjgRmrrIYVSxC6/PgjkVqeTvapij5qHDM4RFnta2xhZeA3SFvEzpxnj6JnAYtQbc8pzZioLRplfA3f8Ax+6Fb78f7vuw3BS+/Y8B2NAR5MRMBr1to/z+uepvSg3Hcqx11GdCmkl70MN42RCMmmFSmhFJy+OuW+k1IAUjwJub5M1X9vOTfZPEsqUL/FADSY0BkHC285Yr+2t6qKUIRh8G7gY+Anxs0YeNzarlueEEAJoOh/Y8sPCFkScbsyCLkStV+Mn+SV57eQ9eY7fb5VDoCHmYTOYbvLraMO8wUszSa/tEvxEMtvs5kvVT3v4m2PtVKK9OgfKCHPge9OyCN/0/eNVfweVvgUM/hMJFuDUsSEXV8BWNE9sGRtIAKq3GbnMNLh4uhCkYhUzByOgwAkgXZAeO5rB26fU8626W/w4tOOG298hy44OjMfjZn8C+b533Ln704gRv+9j3EVP7+PrcBvqiPjxO63YAuR3KgmDkcOHQK7icizqM7PeRl9AZlgJgxt3ZEIFE13UePTKBjyKKL1L3459JV9jLpG6IL8YFolWJZYu0+V0y9uSvXTxmRVz1bnjFR+TEtBf+E67/NWiR3S/rOwKkCxVmRCu4gzVxGA3N5dgdiMtP6tBhBLL4eqRgODGbQTBqYCQNgOQIv3TtGkqqxrefHa3f8ZdJMT5GSvdzx871RPy1fe+/oGCk6/pDZ/uo6apsbBrMs8NxFCGt0ZnjT8jde1+rLRgBJEc58JPPkCupvPkMRbs74lu9DqP5KWmKHSVoIGvb5C7ZVOfLZAwn3pioUENJjsHYM7Dt9Qu37fxFOYb64OoYYprIl+kQCflJgwUjd9cWAHINKL42BaMgC4KR33AYpYx+I9XhbQ6HUcc2eRG5KDq5vTeMkwp9938QHv0HePxfz/njqqbz4e/vZ0fpeQB2vfz1fOl919Z61SvCqShoulw7igPBIoeR/T5yVjpD8gIx6WxvSCTt8FSafNq4oLfAWPjeqJccXoqOQENLwJdCLFOi26fKTTWrdRgt5qbfhlv/EDp3wMt+d/7mDR1BAE7M5qBtY01SBcNzWbb7jG6cOjmM2gIeTuWbSDAyImkuX5CQt46vkfOC0Shbu8NcORDlP54atmz59fDJo0zorbz7xrU1P9YFBSMhRFoIkTI+CkIIVQixOrYwbWzOwbPDcbZ2h3nN5T20Jfehdu+CNdfBsC0Y8cjHufrZD/GW8AGuHmxZuD0X463cSyzRBBMYLoLKaVPS7NLrRjHY5gfglG7ElGKXoGBkikLb37BwW/81sg/hha81Zk1VJp4t0S6S6AjwN7ZHpHVgO6ouSI6cZRxzjTEFowDncxgtmpJmZQFCUWTp7NAj8yXtETd8NvBJNs7cK3tDJvfJ19ez8OixWaZSRd7bOwzuEK+889XzF3hWxXQTlVUNFOkwcp8WSbPw36tBdBkOo1nRAqX0ghhaJx4+MkNILERAG01b0MMVa6JM6a0NKwFfCoWySrak0us2ortWjKQt5tYPwa8/Br7o/E3rO+SGlCy+3lSTnsShuRwbnDNSUPPWx8HWGnQznhXg9EKueTqMdgx21/e4oR4Qipw+Brz92gGOz2R5eihe33UsgblMkcLcCGqwm8v6av84WorDKKTretj48AK/APxLzVdmY9MgVE1n73CCKwej3LW1he0MMezbBmuulbsN2SZQ52tI5eh9APyh4ysoujEWWNPgW+/jnTMf56OpP4DYiQausDa8ZEqalS/MVjGDhsPocNGwu1+KDqOD35NujfZF48SFkC6jk49IB1KTM5ct0UGSsrcVHI2N7Qx2tTKid1KZrv+ktEROiic+zZjq4w7iNR1GhpikO7yAsQNq9deldS+XsZq543D8fvjKm7lVfZx/cb9PXsCpRZg+u5Prm3tGifhc9CeehrU3NfxxsRRcijzNrmj6QiRtscNIsW6crlEEPU58LgfjmhnDqq+r5pGjs1xmpqnqdEF/IV63q5ehcpRCzLrxGLPrpctpvFZZNZJ2HnojPrwuhRNm8XVyZL5Ppxokc2WS+TK92lTd3EUA7QE3sXwZ3d/WFFPSspkUFV3hynV1dhc7XFI0MgSju3f2EvI4+eaekfquYwl84sHjdBCjf2BjXY63lA6j09B1/TvA7dVfio2NNTgylSZbUrlyoIVrfBN4RJmHswPSYQQw+nRjF9gAjk6l+dC3XuA3/+WbOJNDPKDuoi0/BE9/Vn7Dk/8Pjt/Pgd4308s0+qdukX0qqwhz0s28w8jqF2arlKDHSXvQzZGUW8YFLjWHUWYaTj1GZsNr+LUv7+Hx44sE7J1vA3R48RsNW161iGdLdIgEmr+xcTSAgVY/J+jFk6h/h1EqX8bjVHCWs7JTQ3G8JJKmOxeVPlvdsbL25fLfT98KX34TTO7jvo1/xN+l7iDbfrn82sTel/xYMl/mnv2T/Mp2BSV+AtbdUrclrwSnw3AYVTR04UBBW1R6bb+PnA0hBF1hD8MVQ6yps6tmLJFnc9QQYC0QSQO4e2cPU7RQjlt3M8AUjNodRp+alSNp50BRBOvag4bDaCOgS3G7SpyKSTGttTRat/4ikC41XQfV29oUkbTpuTh5PFyzrgGiY6QfUlIw8rkd3La1k/sPTaNpS4ulzWaKDM/VdkDGeCLPfzxxnE6RINw1WNNjmSwlkvbmRR9vEUL8NfNbWTY2qw+z8PrKgRacE88C8NWxTsrdu2RB5SXYY/SZR07wrWdH2VV6DgDl1X8NG26HB/4KTjwI9/4JbHkNR6/5M+4u/SXF8Fr42jsgPtTIZVeViqbhUIQcW2lHCRrKYFuAoVhO7tBdag6jQz8AdD4fu5yf7J/kHZ99gn994Jg8mWnbIKNpL3y90atcMbFciQ6RRIQaOyEN5FjiGc8gLflToKl1PXYyXybic0ExCZ4QwHwkLZVfFEkzsboA0b5JTkvr2w2/8Dn4vUNw1XsAOFhoA08Exp97yY/98IUJihWNt7UZ7tX1zSEYmeJQWdPQFCcuziy9tvjfq0F0hr0cLxhiTZ0dRrFsiS6X0cVogUgayOJrR6QXf2kWXa00ejlnZTZTBKAFQzBqQocRwIaOwILDCKraYzQ0l6OTOP7sKHTvrNr9Xoi2oJzqW3S3QNb6kbS5RIICnrpErV5CuG/eYQRwx7ZOZjMlnh9NLOnHf/trz/HOzxnXicUMpCervsR/uu8o7XoCBV06ourAUhxGr1v0cReQBt5w3p+wsWlinh2O0xpwy66UsT0UPe0cKUR4arQgpxJdgoLRwYk0161r4/29QxAd4JYbboC7/kqWDn/5TeBrgdf/Mz1RP6N6B4ev+COqvTPTaCqqLiekgeEwsn4cYrUy2Obn1FxO7tBdag6jA9+jHFnHP+938wtX9vPanb189J7DvO+LT5MvqTKWNr0fJl9s9EpXRCwjHUbOcOMFI4BseCMuvVz3x9uCYJSedzv4znAY4Vw0RcbqgpEQ8J4fwLu/Lyf7OT1s75X/X/snMtCzE8b3vuTHvrlnhM1dQfrjT8lOq87tdV74xeEyHUaqji6cONAWOozUiv0+cg46Qx4OZ41+qjpOSquoGolcmVaHIRhZxGEE0D+wEQcaR05Y87zKdBhFMDqnrN5hdA7WdwQZiecohNfKG6rYYzQ8l+VmxXhv3nhH1e73QrQF5KZCzhlpCodROpVEdfoW3Jj1JNIvY/2arKG4ZXMHDkVw/6HpC/7oSCzHo8fmGI7lGInl4Ccfgk/fNn9f1WAimecbe0b5lcukCEi4r2r3fT6W8pf4rK7r7zU+3q/r+l8Amy74UzY2Tcqzw3GuHIhKJ8noMzgHrsHrcnDP/kkZSxvbc85SztVIRdU4PJVmR5cPTj4snUVCQOdWuPYDoGvwxk9AoJ0eY/zlWMU4ycrONHDl1aWs6ou6J0r2znADGWwNMJEsUImshcRw3V0fDSMXg6FHeNR9IwKF379rM//0S1fwx3dv54HDM/zwxQnYerf83qFHG7vWFRLLFmknicMCDiMA3RAoyhMv1PW4pwtGpzuM0mcTjJrwdak77KU14Gb/eBJ6r4CpfVApzX/9+EyGZ4cTvHV3N+Loz2DTK+R7UBNgvmdUVNNhpJ4eSWvCv1c96Ap7GcmA7o3UVTBKGL1gLU7TYWSNDiOAbVu2AvD43v0NXsnZMQWjgJoEhKV+d8the08IXYc9EyUI91fVYXRqLscrPPsh0CkntNUJ02GUViKW7zBKFcqU8hkcnkBjFhBZI7v0jHLwqN/NVYMt3HvwwoLRt59diIw+eWIOjv5URmqnq/ecPTiRQtV0bus1nIbh3qrd9/lYimD0z0u8zcam6UnkSpyYybJ7oAXyCZg7imPN1dy4oZ1Hj83K4utKASbre9HQSIbmspQqGjd4h+Qkng2LKszu+gv44LOw8U4AOo3pJqeKxs5gZqrOq60dFU2b76NAq4DD3dgFXcKsbZeT0ubcvfKiK2ndItCqcuox0Cp8YnwTv3TtGnoiPoQQvPuGQdxOhaNTaQh1g9MnyzqbmHw6jleUIWgNwWjz5deg6oKhA/XtsDufYGRG0vTTHEbN51gRQrCjN8yBiRT07paC/MxC8fW39oziUARv6RiDQgK2vLpxi10mTjOSpmpowomTM0uvm+/vVQ86Qx5yJRUt2AOp+nUYmaJHWBgdJBZyGIU61gBw+MghS475nsuWcDkEnlJCus6btND91i2dhLxOvrVnVPYYzVZPMBqezXCDeAHW3yqnRtaJtoA8X02IsIw3W3jTe8+pOD6KePyhxiwg0i//XXQOdcfWTg5OpBhPnLsAXdN0vvnsCDesb6PF72Lo4DML10AnHqra8sbicg2duhEtbLRgJIS4QQjxe0CHEOJ3F338KdCcrwI2NhdgcX8R47K/iL6r2L0myvGZLKmOq+RtI081ZoEN4MCEtBfvyD8jx00uLhtVHLI3xcDjdNAedDOcUcDllwW9q4SyqssJaWBH0hqMOSltBGPk6qXSYxSTUYTjYg2/fuvCZAynQ2F9e4AjU2npvIj0S+dVE6OmjRMtiwhGN25dw6joIXWq8Q4jp0PB7VDmI2nCtVgwak4he3tPmCOTGcpdu+QNRiwtW6zw708Nc9uWTlpG75P/f4s3LSyOe1EkTcOBA20+poamWj9C2CAGWuWmQMrZVleH0bxgRB5cAWu9zxsXhq7cJM8a56pWIpYp0eJ3I/Kxpo2jAXhdDl63q5cf7ZugFN0Ac8egSgKde24/ES1Z99ewqN+NIiCmG5u5Fo6lPX0yhl+UCAQbLRiNSTfWU5/hNe3yfOR8sbSnh2KMxPK87Zp+rl3Xinv4YfkFfxucrJ5gNBrP43YqBIsz0l3sa6nafZ+P88mbbiAIOIHQoo8U8JbaL83Gpv48OxxHEbBrTURGzwD6ruSKgSgAzyd9EBmA4Scat8g6c2gihVMRtE09Cn1Xgy963u/vCnuZTOYh2LmqBKOKuvhE344SNJJB42LiWLld3nCJ9Bhlxg8T00O87tptdEe8p31tc1eIo9NG2Wh0TdM7jBw547Uj2PgpaQAORVBo3UJb9hjT6ULdjpvMlQmf0WEE4HUpizqMmmhK2jnY3humpGocLXWcVnz9lSdOkciV+Y1b18PhH8nCbE+DLiQuAnOToaxqqMKBiwpu5+JImoUECQtx29ZO2oMe9qUDdS29NgUjv56xTOH1PP52dMVFnyPBj16sbxH4UpjLlmgNuOVFdpMWXpu85ap+CmWNfYVO6ayvwrlsvqSyI29cV2y4bcX3txwciqA14GaqYghGFi6+fnooRqu7jMPdqEiaIRg9+Ffw8W3wo9+n/573s7VVcN/Bc6cmvrlnlKDHyV07urluXRs7Cs9Ria6H7W+U7vAqubpGE3n6oj5EelyKyHWKZ59TMNJ1/SFd1z8MXK/r+ocXfXxc1/Xq+fNsbCzEs8NxtnaH8budMLoH2jeDN8LO/igAz48koO/Ks479Xa0cnEhxRbuOMv7sknZFeiJeJpIF6QxYVZE0XUbSdN2IpDXnhdlqIOp3EfY6OZANyQvkS8RhlJk4wkm9m/fetPYlX9vUGWQ0nidbrMgMfqK5BSNXzug/s4hgBNC+4UoGmOKHz9SndFbVdNLFClH/6Q4jAL/bOR9JE07fwg816evSDqP4+sBkGnp3wcReCmWVzzxygps3tbPbPwuxE00VRwNwOU3BSEcTThxCOz2S1qR/r1rjdTl4z42D7E360TNTdeupmzMEI6+atVQcDQBFQYR6uDyU5Z79k5aLpcWyRdmVk4uBr3kdRgC710RZ3xHg25Md8obj9634PodjOV6mvEgqvFlGx+tMW8DDRNkQYXLWFIwKZZXnR5JEnBVw+xuzCF+L7JiKn4Jdb4c3/j9EaowPB77Fo8fnyJVeOqUwW6zwwxcneO3lPfjdTq5fG+Z65QDD0WvkRM9SZsGEsELG4nn6W3wyqhuqTxwNltZhlBNCfFQI8SMhxP3mR81XZmPTAA5OpNnZbxT1TR+YH3sZ8blY3xFg70gCOrbKF5LyubOsq4mDE2nuDh2R5dZLEIy6I14mUwUIdKwqh1FZNU70zV0C+0S/YQghWNse4GSsAC2Dl4zDyJMaYsrROx/XWMymLikmHJ/JSIdRbhZKuXovsWp4S4Zl3iKRNIC2dVegCJ1nn3m8LhdrKaOAN+J1vEQw8rkd86XXiruJpqSdg3XtQXwuB0+dnIOeK2BqP//5xHFmMyV+87aN0l0EzScYKWYkzXQYnVl6bTuMzsU7rhtkTmlF6GrdziXihmDkrmSsWdoc7mGDN81oPC87vyxELFuiNeCBJo+kgTzHeMtV/XxlvItydD08+6UV3+fI1CzXKIcpDNxy4W+uAa0BN6MlQzCyqMPohdEkJVUjIIqy1qIRCAG/9gj83iF43T/AFW+Haz/AtTPf4jL1EI8emyNdKDMSy3FsOs2x6TT/8dQwuZLKW66W7qSt5UMERJGfazulKxZRtR6j0bh0GJEar1t/ESxNMPoqcAhYB3wYGALq2/poY1MH0oUysWxJ9qOoRpFu67r5r1+xJsrekQR6+2bkyPjqjdpsKJp22kSaxcSzJbzpk/zC3KekANR31QXvrifiI5ErU/F3Qnb1CEYVVcelKPIkH5o2+rFaGGwLcGouBy3rLg2HUTlPS2WGSst6OcHxDDZ1Sav5kamMdBgBpMZe8n3NQL6kEtXiqMIJ3mijl7NAl5xq400cZt9Y7S/WkoZg1OYqA/ppERmvy0GqYDiMXIscRk36uuRQBK++rJuvPzPKV4ZbQC1x/0MPcu3aVq5b3wZHfgLdly/EBZoE02FUUXVUzNLrRR1GtmB0TloCbjZt3AxAbHKoLseMZUuEvE6UYsp6kTSAUA/t2iyKgHv2W8vBPZctyXLlXPMLRgBv3t2PIgSPR14Lw4/DzJEV3V/l5M/xiAqBba+s0gqXR1vQzVDBEGEs2mH09JCc4ObWGygYgXSALX7+3/HHEO7lb92f5de/9ASX/+lPuflvH+DOjz/MnR9/mD//4UHWtvm5elD2CSlDD6Gh8PXZQflc6NlZlR6jQlllNlOkL+KV3W4WE4zadF3/HFA2Ymr/Dbi+xuuysak7wzG5Gz/Y5peFsboqL0YNdq+JMpspMeUZlDfMHG7EMqvPd34NvvBaKRydwamDT/EN90fw6AV4xzeWVADZHZa73Wlnq3xTsvA0huUwPyXNdhhZgsFWP2OJPGp0LcSGqlZKaVXmRg8BEOzZdNavD7b6cTsUjk6nFwSjJi2+nssW6SBJ0dNW10kyF6RlLbrTxw7HCN/cU/vInykYtTqL8obTImkOVE0+5hVX80fSAD761l188PaNfPp4FICe/GE+eMdGuRs+8iRseU1jF3gROE2Hkaah4sCJhtuOpC2Z26+5AoAHn6lP2fy86FFMWS+SBhDuxZGZ5OrBFn66f7LRq5mnWFFJFyp0ejWo5Js+kgbSLf+yTR387eSV6IoTnv3iiu4vNPYIRVwENt9cpRUuj/agh5NZtxxek51pyBouxPBcjo6QB6Wca1wk7Wx4Qoi7/56NYpTPbnqcP3rNVv72LTv557fvnv/4t/deu7CZd+JBZsPb2RdTmEwW5LCgkaeglF3RMsaMKW3rgwU5TdRigpF5tTchhHitEGI30FxbPDY2S2B4TgpGA61+iA/JG09zGEnleE+mTb7gzq5st8EyjDwJo0/Bge+cfvvoHrb95Jeo4CDz9u/LccdLoMco440Jw85t0Tem5VJWdTkiWTPyy026k79a2D0QRdV0jqsdUEpbdsesWpw6sh+A3g2XnfXrTofC+o4AR6eMSBo0bfF1PFumQyQo+zoavZTTURyIzm3cEJjku8+PU1FfKrJXE1MwijqMku3FkTTXwrBaxdX8kTSQLqPfe+UWPvzu15IiwC8HnuFl4gV44esyEt1kcTRgPn5WrmhUhAMn6rzryB6ecGF6+tcDcPDIEUZitY/Yxs3i5kLSmg6jcC+Us9y9NcihyTSn5lZ2AVot4ln5WtXjNv5GTV56bfLm3X3sS3pIDrwCnv+Pc7rxl8Jg/AkOuC6DxQJ/HWkLuEkWNHRfq2UjadlShYhbSDHE1aDS63Ox+S7YeCe3JL7LB162lrddvYbX7eqd/1jXbqy3kITRZ2D9rQA8eXJO9hhpZelUWwFjcSkYDTqT8gaLCUZ/LoSIAL8H/D7wWeB/1nRVNjYNwHQYDbT5FyIuLWvnv761J4THqfDceB6ig6vDYVQuyD4mgPv/fME9k52D/3wHGRHkA84/p3Xt5Uu+S3N607QWlTeskh6jiqbJPgrVOGGw0rjdS5CbN3UQ9bt4cNqY+rHKe4ziowcBWLf53M/FTV0hjkylZRGicDRt8fVctki7SKIHrFN4PU/XdgZVOblrJF7bHjtTMIoI4ziLHA8+9yLByN38kbTF3La1C9/ON7CztBfxlTfDPX8IoR7ZbdRkmIJRRdNRceBY3GGk2sMTLkigHV04aNfneMXfP8S/PnCMYqV2Bdjzk74KKWt2GIV6AHhFn/wd3GMRl9FcVrogOx3GpM5VEEkDuMqIGO3teL3clDr8w4u7I7VCb2WYqfDZN3zqQVtQTtOs+Nosu5GbK6m0uI1N2QYJa+flil+G9DgM/fzc3zP0KOgqbTvvIuR18sSJGAzcIN+bV9hjZDqMehQZ3bNM6bUQwgFs0nU9qev6Pl3Xb9N1/Spd179Xp/XZ2NSNU7EcLX4XYa9LXnw6vRBcmGTgcihc1hcxiq+3rA6H0dwxQIfL3gKx47D3qzLa893fgNwcH/Z/iGjvxmXdpSkYjVWM3fBVIhhJh9HiSJq7sQu6xHE7FV5zeQ/fGzZGiq/yHiN19jhJEcYTPPeJuDkpLacid56a1GE0lynRIZI4wtYpvJ6ncwfeUox2kpyczdT0UAlDMAqJ8zuMHKug9PpMXG/+JPzBSXj39+FVfw1v+lTdxgdXE7OvqKxqVDBLr80OozIojvP8tA2KAxHq5l07XNy+tZOP3nOY1/zjI8xlijU5XDxbosMHqEXLRtIAekSMHb1hfmqRHqOYURbephiOp1UQSQPob/ER8ji5v7RDRr0vsvx6emocBR1vtKfKK1w6rQF5zlp0t1rWkZ0tVmhxGefYVoqkmWx5DbhD0vV6NnQd9n0TnD4cA9dx7dpWHj4yQ0F4Yc21cOLBFR1+NJ7DqQiiFUPws4rDSNd1FXh9ndZiY9NQhudyC9OH4kPSRXRGf8au/igvjiVR2zZJsUV96XjFpsIUvW76bei/Fh78G3j0H+DIj1Hv+DA/metiW8/yTpr8bicRn4tTBcP5kbHGCc1KqZhT0uxImmV44xV9HC0b1vdV7DAqVlRC2WEy/oHzft9mo/j62LRRfN2kDqNYpkA7SdwNPLk+J13bAdiiDHNytrYRGXNKmh/jIuxcgpFn0Yn1aipR9rfCupfD9f9DWvqbkPlImqpTwYEidNzC6FvTKvb7yFII9eAvTPOJd1zFp991FcdnsvzghYmqH0bXdWLZEt1ew0VsYYcR6Qnu2tHNnuE4M+naiGfLwRSMWlQj6tSAsfG1QAjBtt4w+yezsPudcPyBi+oGvG+PjJTv2LSh2ktcMu1BKRjlXFFLR9JaXabDyIKCkcsH298AB7770im0ahm+8+uw71tw7fvB6eHdN65lLJHn7+45DNteB5MvwL5vX/Thx+J5uiNeHOkJ6SIP1s+FvZRI2mNCiH8RQtwshLjS/Kj5ymxs6sxwLMdAm5FBjQ+d1l9kcsVAlGJFY8K9VkaTEqfqusaqM3sUENC2Ee74v9Jqee+fwuZXcWL9OylVNLZ2hy50Ly+hJ+LlWM54sV8lk9Iqmi4LTOcdRqvowqxJuXqwhbZImJijfVU7jPaNpRgQk9B2/pPNjZ3yuTrfY9SkDqNccgan0HBHLHjR0Sknpe1yjzE0W9v+kGS+jMep4K6cRTBaFElzmpE0xdWULpzVjPMMhxGASzG6r+xI2tII90BKCkSv3NHNuvYADx6u/nlFplihpGp0uQ3ByKoOI3cInvsqr9zWhq7Dzw40flNuLiN/Z+GiEZFrsmmG52N7T5iDEym0ra8DdDj12LJ+XtN0ntovKyw6utfUYIVLw4ykpZWodSNpRZWw0zjHtqJgBLDzbbI388iPF24rZeE/3g7P/zvc+kfwio8A8PLNHbzz+gE+9+hJHm97M/ReCT/8PUhf3HN2LJGnL+qD1LgUZevoUF2KYHQjsAP4CPAx4+PvarkoG5t6U1Y1xhJ5Blp90lIYO3nahDST3WuiALxYNKISzd5jNHtEXli6/bDuZtj8agj3wxs+wYHJNMCyHUYgY2kjGQ08kVUWSVMWOozsneGGoyiC11/Rx9FyO+XZ441eTs14/sQEfWKOSP+W837f2jY/LofgiDkpLTXelC5I1bg4FHXcPVsywQ4IdHCVZ5yTtRaMcmWifhcU5WvxuTqM5iNptvhgOeY7jFSNii7/Zm5hCEZaeXU5wmpFqFeOkDa4ZXMHj5+Yo1CubpeRWdzc4TIioFYsvXZ64LUfg+HH2HLokwy2+S3RYxTLlnAoAm92DIJdcp2rhO29YXIllVNKv6yqmFjexL4nT8ZQU4ZAE2jcIIc2w2GUEBEoJCw5wThbqhBxWDiSBrD2ZfI1yYylZefgi6+D4/fB3f8At/7v0zZu/ug121jbFuD3v7WfzGv/Fco5+MHvXNRk39F4nv4Wv9zcr2McDZYgGBm9RWd+3F6PxdnY1IuJRAFV0xlsDUirZjl7WuG1SX+Lj7aAm0cTRj57ttkFo8PQvnnh81/8MvzmUxBo4+BEGpdDsKEjuOy77Yl45SjJYMcqi6QJeZIPdoeRRXjj7l5OaV2UZ040eik1Y+TEAQCCPecXjJwOhfXtQY6ZDiNdlScWTUYlbfQrWHXSTud2Nonh2gtG+TIR32LBaMFh5F8USXN5DGesLWJbDpcRay+pOmXTYSQMoUMt2yLfUgj3yDH3RdkZdsuWDgpljSdPxqp6GLO4udVhlMxbMZIGsOsXYdcvIx7+KB9YM8Zjx2dJFRp78T+XLdHidyFSo3KzYhWx3dg0PTCZg64dMla0DL7+zAi9LqPvLtBe7eUtmZDHicshmNGN9xEL9hjliiohh7Epa1WHkeKAnW+FY/fC2B74/Cthaj+87ctw9Xtf8u1+t5OPvW0XE8k8H36sLNMch38kp+4tg1JFYypVoK/FcBhZTTASQnQJIT4nhPix8fl2IcT7ar80G5v6cSomT/xPm5B2lkiaEILdA1EeGy3LLPlMExdfaxrMHoP2RRehDhe45cXHY8dnuawvgtu5FCPi6XSHfcxmSmiBTshY0/q6XGQkTVlwbNiRNEuwtTtMLrAGf2lW2oJXGbqukxo3XmfO8pp0Jpu6ggsOI4DkaA1XVxv0vHEhaNXi1K7L6C2dYiaZrrrLYTGnCUauwGn289MjabbDyKq4nHKn+XSHkfGY0Sq2w2gpmL0903JS5A3r2/A4larH0uI5eaEaVcySeQs6jExe81Fo28hbT32YsJrggUONdXLPZYqyVDkxIjcrVhGbuoI4FcGBiSR075SC0RLdIcl8mR+9OMH1Xap8rnujtV3seRBC0BbwMG0OpLFYj5Gu62RLFcIOi0fSAHb+onz9/uwrZLzvXd+BbXef89uvHGjhl68b4NvPjVG+5r/D4E3wkw8tywE+mSyg6dK4ICNpFhOMgC8A9wDmyo4Av1Oj9djYNIRTc7K8bKDVL/uL4KwOI4Br17VyYjZLsWVjczuMUqNQyUP7ppd8aTpV4IXRJHduu7gpRT3GpLS8u33VOIzKqib7KEyHkb2bbxnaNl8HwNzeHzR4JdVnNJ6ntWB0EbWuv+D3b+oMMRrPkw8Yb9lNWHytFOLyP6w6mnnTnTi1Ir+mfI/hWO2KrxcEo9Rp7iIA7+LSa5cHELZgZEGcill6rVE2TrldQgNNBXT7fWQprH0Z+Nvh398GI0/jdTm4fn0bhw/ug0f/EQqpqhxmvodHmA4jCwtGniC89Qu4Skn+2PfNhk9Li2VLtPmdcoNilTmMPE4HGzuDHBhPQc9OKCSX3F/6/efHKVY0dkZL8jGsLH8Dtpq0Bd2MV4zUQM5aglGhrKHpEBCGw8jYvLYkXTug7yrZI/Tf7oHBGy74Izv7o6iazniyKAWnQhJSY0s+5GhCnmsM+itQyljPYQS067r+dUAD0HW9AtRuS83GpgGMxHK4nQrdYa8xbUnIKWln4Yb10lI65lgjHUYXkUO1BKY7anEkzeABY+fu9q0X1yGytl2+0MfE6ukwqqi6jBfMl17bJ/pWYdtNb+CU1on62CcbvZSqs3ckwVoxRcXTAr6WC37/5q4gug7Hi8b3Jpc/0aXROAsJ+R9L+P9tCBtuJ77+9fym87+YPr63ZodJ5suEfS55QXyGYOQ3HEZuh4JQFNmtYYsPlsM1X3qtUzYcRk5Ue3jCcoj0w/t+KiNiX3wdPPsl/r/Kv/LF7P+An/1feObzVTmMOekriCECW9lhBNB9GeLKX+FuHuLA4UM1dTteiFi2xFpfHtTiqhOMQPYYHZhIQfcuecMSe4y+/swIW7tDtJBsaH+RSVvQw0jBGJJgMYdRtiTdNgHFmPrn8jVwNUvgXf8Fv/kMdG5b0revNYYqDc3lIGpMvF3G4KSxuBSy17gS8gYLCkZZIUQboAMIIa4HkjVdlY1NnTk1l2NNiw9FETKSFu4Fl/es37u9N0zI6+TFYrdsyk9Xf7xrXZg1BKOOl/ai3Hdwmr6o76ImpAFc1hfGoQiGikEoJqFcWMlKLUFFMxxGecP94Lm4341N9dnQFeZ7nrvpTDwH4881ejlV5cRMlrViEqXtwu4ikPZ5gCOxitzRbDKHUaGs4tdSlBWPpU8YHa/9WzL42PTEhwy3SPU5LZJ2xuuNz3AYmVO4cHps8cGCCCFwKoKyqlEyBCMXqu1UXS5tG+B9P5MXZ9/7IBunfsxX1DuJhbbAi9+syiFiuRJup4K7nAKE9QUjgBs/iAOdX9a+z6PHGicAzGVLrHUaUeJVFkkD2WM0lSoyG9ggx5kvoccoXSjzwmiSu3f2ILKzstOzwbQH3JzImxOMrSUY5YryfdSPxTuMTLyRZRVzr22T33tqLgsthiEhsfQNvdF4HiGgo2TUDJxlMFMtWYpg9LvA94ANQohHgS8BH7zQDwkh1gghHhBCHBRC7BdC/LZxe6sQ4mdCiKPGvxbdQrRZVWgqfPnNcPD7Z/3ycCwn42ggI2nniKMBOBTBdetaeTBmPHSbdVLa7BG5g39GsWyhrPLI0Vlu39qJuMgRzX63k81dIQ6lzZ2M5ncZlSqanHgzd0zesIR4kE19EEKQ3vaLZHUP6hOfavRyqsqpuSwbHFMo7RuX9P39LfJ1bDyRlyfuyeYSjGLZEi1kKLmjjV7KeQm39fAx5X10pfbBU5+u+v1XVI1MsXJuwchwGJlTuHD57CJ+i+JyKFQ0nbIu/1ZCX+QwsjuMlk6wA97zA3jdP6L89vN8IfI/+InrTph6EaYPrfjuY5kSrX43wny+NTg+tCRaBtF2/ALvcN7HI8835ly0rGok82X6FUOAWKUOI4CDs2Xpyl+Cw2gyKTdK17T6Zc+NBRxGrQE3QzkXunDINVmITFE6jPzC2GC2ciTtIugIefC5HLICJbIGhALxZTiMEnm6Ql5cs8Zr3Vk2+2vJUqakPQvcAtwI/Hdgh67rS/HiVYDf03V9G3A98BtCiO3Ah4D7dF3fBNxnfG5jU1tS43Lk4Xd/A1KnO4J0tcJwLMugYRckdvKCyu3169v4ecIQWkynTrMxe0S+8Z0hCj1xYo58WeX2bSsbaX3FmijPxY0LmFUQS5Ol10L+3iJrVt2bWbNz4471fFN9OWLfN1dN0TrA6GycTuaWLFB6XQ5a/C4mkgX5OG0yh1EsWyIqMmieaKOXckEOddzFHs+1cN9HIJ+o6n2nCvLkeV4wOqNPxXQYuRY7jGy3iiVxOgSlikbJEIxQywuuNDvavDzcAbjqPRDu4dbNHXxi5nJ0ocC+lbuM4rmSLG4upprDXWTguPl/4qdI1+GvUFG1uh/fLAvv1oz33Uh/3ddQa+YnpZk9RktwGE2mpPDRE/FJN48FBKO2oId8GTlQwmIdRjkjkubVi1JMWWUbIEIIBtv80mHkcEG4b1mRtNF4Tk5ImzkoK1M8y59gvRKWMiXNC/wW8GfAh5HCz9mzOovQdX3CEJvQdT0NHAT6gDcAXzS+7YvAGy9q5TY2yyFljJYuJOH7v73QOzS+F+3vL+M31K/IXYBSDjKT53UYgRSMZohQcoWb22F0lv6i+w9N43M5uGH9ykZa714TZbhovKCtBsFI1XE6FJg9etaicJvGcv36Nr4mXoWilWHPFxq9nKqhzp1EQYfWDUv+me6IT+5uRgdkCWkT9azNZopERQbdqhPSFrGuI8iX1FdCOTc/walaJPPSgRL1mw6j0y9g/W7pTDFLlXH67EiaRXE7FCqaNt9hhFZZFEmz/2YXy61bOhkth0l23SBjaSt8nZvLGoJRIWntwusz6drOVM9t/KL2Q547tvQS3Wphdj+1Vabk65QvWvc11Jqo301f1Gf0GO2UVRQX2JiaMBxGvX4NylkItNdjqeelLShFmLKvzXKRtGxJiugeinIq6EUmHKzMYJtfdhiBFH2W6TDqb/HJc40l9iZVk6X4Lb8E7AD+GfgXYDvw5eUcRAixFtgNPAl06bo+AVJUAlZmY7CxWQpmE/3ud8HRe+D5/4ATD8IXXouSmeS/OX7MZu+iyQcXGF+9rSdM2OtiwjnQnA6jXEzaUc8QjHRd576D07xsU/tpU3guhisGoszoUfnJKpiUVtY0XAoyktZmC0ZWw+ty0LthF08qu9Gf/ixUSo1e0opJ5su0FIy8+jIikD0R74LDqJK33Inh+TAjaY7AygTrerCuPcCerHERUOX3AVMwOteUNJ/bmLjlXNxhtLp2ZFcLToegXNEpmg4jrWwPT6gCW3vkc+Jg+ytl9+TYsyu6v1h2kcPIG6nGEutG+M4/oFVkSDz6ubofO2ZOlytNrco4msm2nvCCwwhg8vnzfr8ZSetQjCl+FnAY9UdlTUTOGbXceUHOiKS5tcKyuoGaibVtAYZjOTRNlz1GS3QYqZrORKLAmrBLblpbVDDaouv6+3Rdf8D4+ADwUlvCORBCBIFvAb+j6/qSZ18KIT4ghHhGCPHMzMzqiRfYNAhTMHrFR2DgRvjR/4KvvAWiAzxy81cQ6Fx+4rPGhDQuGElzKILr1rfxQrkHpvY11Q4+sNDDc4ZgdGQqw1gizx0XOR1tMRs6ghQ9hkugyR1Gqqaj6xCpzMlxlrbDyJLctrWTTxXuQGQm4di9jV7Oijk1l6VHzMlPllEkKgWj/MLPNNGkNDOS5gxa32G0ti3AuN6O5vDUTjDyKGe9gDUFfZfpMPKELF0SfinjciiUNY2SZmzCqBXpMgI7RrgCOoIeFAF7/C+TYumL31jR/S0IRmlw1zfusVJ8G25kv2Mrl0+s3Gm1XGYycqqVPz++KguvTbb3hjk+k6HQtkPecIEeo4lkgbaAG0/RKAO3gGC00RiKESNiuUhaZrFgtErfywba/JQqmowrRgelU20JQ4GmUgUqms4W97TccOjcXofVns5SBKPnjMloAAghrgMeXcqdCyFcSLHoq7quf9u4eUoI0WN8vQc465Wkruuf1nX9al3Xr+7oaPyTzKbJSY1Li6OvBd7wL2i6jtp3Nbz3RzzPFr6u3krk0Nfg5MPy+y8QSQO4YX0bj+TXyqlZsRM1XX7VMWN0HacLRvcelE6g26sgGDkUwfb+NpIi3PSl12WjF6CjaOwGnCXKZ9N4btvaycPaTgquaFU6LRrNydksXSIuCyr9S7ez90S8xHNlioE+eUMT9RjNZYpEyeAONd6+fyHWtQfQUMgE1y6I8FXCFIxaHHKULt7oaV83I2nzpdev/hu46y+rugab6uByKFRUnZJmuMG0xYLRypy8lzJOh0JX2MvJjAs2vRL2f/uiJxaWKhrpQkUKRuV8UzocHo3cTXd5BE49Vtfjjhrjvj3ZsVXZX2SyvSeEpsPhpEPGvS/QYzSZzNMd8S6US1sgktYR9BDxuZiqBC1Xep0zImlOLS+v11Yha42u3KHFk9KWMJhkJCZjbOt1Y/OvY2tN1nc+liIYXQc8JoQYEkIMAY8DtwghXhRCnPPZIuR4pc8BB3Vd//iiL30PeLfx3+8GvntRK7exWQ6pMYj0gRDMefq5ofCP3DH7++yLKZyK5fhP39vkRLCnPi0z2P4L725fv76N5zTDaTL6dI3/B6rM7BG5IxcdPO3mJ07Msa0nTGf4gjVlS+KKNVGm1DBqarIq99coKprcsWsrDMkbbIeRJemL+tjQ1cIjrpvg8I+hlG30klbEqbkcXcQh2LWsiT3dEbk7NyWME9TkaC2WVxMyqTguoSKW8BrcaNa2y4vKKdea6juMjCLZsDD6Ds5wGJml106z9Lprh/ywsRwuh6CsapTmO4zsSFq16I54mUzl4fK3yOj70M8v6n4SxvNNCkY564/0PgujvXeRxl/3Dr/huRyDARVRSK7qSNrGThmBPD6TkT1GF3AYTaaK9JwmGDW+gUUIwabOIMNFv+zqslB0P2uUXjvV/Kp1GA22ydeVU3O5heuvJfQY7R+XAa1BdVgWgjdg03opZ6CvAtYhJ6XdYvz3a4C7gded5+duAt4F3C6E2Gt8vAb4a+AVQoijwCuMz21saktyDMK9AHzr2VGmyn4yZXjzJx/jwcMz+NoG4cp3g65K1XcJZWtbu0PMegfJK4EmFIyOQtvG03Y3dV3nwHiKy/uqV/Z4xZooM3qEfHziwt9sYczJIy35YWlVD/U0eEU25+K2rZ38W/JKedJ/+MeNXs6KGJrLssaVQoSX93jriUjBd6zglcJwEzn8SmkjgudraexCloDf7aQr7OEkfRAfgkqxavdtOozCnF0w8rrk6ZvTsXQh0aYxOBWFsqpTUM0Oo8Wl17ZgtBJ6Iz4mEgVY+3J5w/SBi7qfuexiwagAzupsmtWT9pYWvl25Cf3Ad2VPZZ04FcuyO5qRn6ziSNpAqx9FwNBsFnp2Qey4jC+eAys6jAA2dQU5ljUe37m5xi5mEbmiiiJAqazeDqOeiA+XQ0jByHQYJYYu+HPPjyboDnsJJo/IPktX/V+fLnimoev6KSAFRIA280PX9VPG1871cz/XdV3our5T1/UrjI8f6bo+p+v6HbqubzL+rd+rms2lS2ocwn3ous7XnhrhqsEW7vmdm7luXSuzmaKckHbz74LDs+RpRIoiuHpdOwfEhuYTjDJTLxE9plJF5rIldvRWr+zxioEoM0RQ081zwXo2yqp0GLXkTkp30Sqc3rBaeMX2Th5Xt5DzdsG+bzV6OStiaDZLnyO+bIHSFIwm0wW5q3mBaS5WopI1BSPrO4xAxtL2l7pA1xY68KpAMl/G61Jwl43qxzMmDwkh8LkcuB32a5HVMR1GRc045VYr8gNsh9EK6TYK/nVfizx/S13clLDYaYJRvikdRr1RH19Tb0eoRXjhP+t23JFYnh2+pPwkMlC349Ybt1Ohr8XHybmcdBgBTO476/cWyirxXJnusFeWS7ut0zG3sTPESNGIfFmoxyhTrBDwOBGl7KqNpDkUwZpWP6fmshDslq9ZS3AYPT+S4Io1UZg+1JDCa1iCYCSE+DPgBeCfgI8ZH39X43XZVANdh6kDTR/LWDFqBTKTEO7jyZMxTsxmefu1A7QFPXzhvdfyd2/dxW/ctkE6kN75Tbj9j5d8170RL3vUjfJNo5l+z2pZTtVZxP5x+Ya/vbd6DqPOkJe8ux1voXkuWM9GRZMOo0j2lD0hzeJcOdDCmtYgDzhvgqM/kx1jTcqpuRxtemzZglG3IRiNJwoQ7Ggqh9H8zngTRNJACkbPZIyJblWMpSXzZTkhrWBciJ1lapPP7cC5jKiiTWNwORQq2iLBSCvbHUZVoifiJV9WSRVUCHVD6uLczPOj4f0uI5JmjYv75dAb8XJQHyTdtkvG0upQfl2sqIwn86x3G6/bq7jDCGBde5CTs5mFi/ZzvOabE9K6Iz459MUi7iKATZ1B5nTjPN9Ck9JypQoBt7Npn39LZW1bgKG5nKwZiK6BxPmHksSzJYbmclzZ65Outg6LCkbA24ANuq7fquv6bcbH7bVemE0VOH4/fPIG+Ks18Klb4McfWpKSuerITMrd33AvX3tqmJDXyWsvlxdgDkXwlqv6Wd9hTMRY93Jo37jku4763TxRWi+jbON7a7D4GqEWXzKC+YCRkd3WUz3BCMAb7cajF6CYqer91pOKquOjQKAwYRdeWxwhBG/a3cenYlfKC7OD37/wD+UTlpt0mCqUyWQz+NW0vBBaBn63k4jPJU9aA51NNaVQKSTkfzRBJA3kyd9zOWMwR00Fo+hLvsfncuBy2oKR1XE6BOWKTvG00ms7klYNeoy+tvFkHsJ9curQRWAKRi0+Ic/nmvCCtdcYmX6k/xdg5hCMPFnzY47G8+g69Ctz8pwy2FXzYzaSdW1+hmZz6OE++dw9x8CbyZQUjOY7jCwwIc1kU1eQGLKPyUqCUbak4vc4mrZ0fqkMtPoZnsui67rsMUqc/7r8+dEEANeGZ+W1rFUdRsA+IFrjddjUgqn98t8bfkOO3H3m8/Av18B9H2nqi/elomk6H73nEHv3y99D2tPFj/ZN8ubdffjc1dnVa/G72KsZAlMzxdIqxbM4jFKsbfMT9DireqiWTrnjNDfdPMW7Z1JWNdYLo7jbLry2PG/a3ccL2jqS3n548QLT0l74BvztOtj71fosbomcms3RKQx31EV0ZvUYUQ3pMGoOh1+xouKtGAJJk0TSrl3XSg6vjEDOHq3a/SbzZaI+txQz4ZwOI5diR9KsjsuhUNY0Cppx3qFW7NLrKmG6KSeTBQj3yPqBiyCWLSEERJ2G86sJBSPzd/F04FbZtViHWNqwMb2pXZ2Wgt0qdzyubQ+QKVaYzalymvK5BKN5h5ERSbOQYNQd9pJ3G65YC0XSskXDYbSKI2kAa9v8ZEsqs5mS7DG6gJHj+ZEkQsAWxbiGsrBg9FfAc0KIe4QQ3zM/ar0wmyoQOyFPul/5Z/CeH8BvPUdxy+vhkY+R/dgucocfaPQKa8rQXJZ/feA4n/mBnJrxgyFBqaLxS9dWL2PdEnATI0wpPNhcgpFafsmJ6v6JZFX7i0w6euTve3z47G+szUBF01kvjBNRWzCyPGvbA1w12Mr3tRvRhx6B9Dmm9L3wDfivD8hdmwsJS3Xm5FxWTkiDZTuMYNH0INNhZMQqrUwsW6IFYzOjSRxGV6yJsqEjwDGtB+aqKRhVCJsOI6HIC8AzeMtV/bzqsuU/Nmzqi8uhUFY1Cupih5EZSbMFo5XQGzXit8m8FNbTExflFo1lS0R8LpyaUVzfhIKR1+WgLeDmVEaRF5Vzx2t+zOE5KRiFCxOrPo4GMoIMxlj01vXn7K2bMAWjsOEwClpHMBJC0NXRiYpiqc2kXFEl4HGs+kjaoPEYOjWXlQ6jfOy85enPjybY2BHEFz8i3y+W2LNbbZYiGH0R+BvkNLOPLfqwsTqxE9AmH1j7x5Pc9fnjbHn2Tbyx+BHGCx48X3sLvPD1Bi+ydkyn5Rv/jZ3yhfuvHk1xxZpoVSNXUb+MdaXbr5CCkcViLedELcqyNYNkvsxILF/V/iITX58x6nnq7OWAzUBZ1digjKMjGvZibbM83rS7j8+mr5PPyWc+T0XV+Mm+CT7/85N87KeH+a8v/j36f30ABm+Ca35VjmM24z8W4NRslu4VOYx8cpcz2CkjFk3Q5TSXKREVGcrOIDiq63SsFUII3nLVGp7LdaDNHKnae0AyV1qIpHkjZ925/7VbNvDWq1fvVKLVgsshqKg6hcUdRvMOo+Z4nFuVjqAHRZgOo16oFC7qtS6WLRmF18ZUwiYsvQYZSxtPGPG8iywAXw6n5nL4XA6cmTGIrt7CaxNTMDo5YwpGJ876mj+ZzBPyOgm4FOnisZDDCGBjV5g4YYtF0iqEXALUErhXs8PIFB1zC8+Zc7iMdF3n+ZEEu8zC6/ZN4HSf9XtrzVIEo1ld1/9J1/UHdF1/yPyo+cpsVk7sJLSu5/hMhl/53FOkCmX+4FVb+MP3v5NPbvgke7Qt8O33w8N/1zxCxzKYMjLErx3UUZ1+1vX18sHbl95PtBRa/HJ3cDqyU04eSzZJ7Eotn9ZhdHBC9hfVQjAKdw4wrUfxzbxQ9fuuFxVVZ72YIB/ob8g4S5vlc/fOHsaVPg5HbkZ98tO8/ZMP8GtfeZaP/OAADz/4U15/4sOMhXfDL/8nXPYWeRF37L5GL3uek3NZNvkMt0344iJps5kSZZ9hPW+C4uu5rBSMNG9zuItM3rS7jxN6L0opLd8HqsBCh1HirHE0m+bB6VAoLXYYqYtLr23BaCU4HQpdYa8s+DeF9YuIpcWyJdrMCWnQtA4HGUU2BKPkWM3P7YdjOda3uBDpSYisfvG6L+rDqQhOmg6jcvasHYETyYLsL8rHpYPZYoLRpq4gM1qIUtpCDqOSStRlRkKbU7BdCn1RH4qA4bmsjKTBOXuMRuN55rIlY0LaAejYWr+FnsFSBKM9Qoi/EkLcIIS40vyo+cpsVka5AMkRUv41vOuzsvjuq796Hb9+60auW9/GB+7azTuLf8DBjlfD/X/W9OOnz8Z0SjqMAoUpHJE+vvvBm7ljW3UL+VoMh9FowHDRNEssrVI8TaXebxRe76iBYBT1u3lRW0c0ub/q910vKprGBjFOPry+0UuxWSJRv5vbt3byl4k7cRTiXDn3Q/7p7bvZ+8d38J21/0XSEeUPnB+SO1lrrpXx3cM/bvSy5zk1l2O9NwVO71kLjy+E2WcRwxBfmqD4OpYt0kIavUkmpJl0R7wEemWvgDaz8uLrsqqRLalE/YscRjZNi9uhUFH1RVPSVDuSVkXm47fhPnnDRRRfx3MleT5nCkbO5hSMpMOoAJE+qORr7iwdjmXZGckCupz4tMpxOhQG2vwMzRqCEZy1x2gqVZAT0szIl4WmpAFs6gwxp4cpJauzwVENMsUKUacsn29WwXYpuJ0KfS0+w2G0Vt54DofR3pEEALu73VJU6txen0WehaUIRruB64G/ZCGO9ne1XJRNFUicAnT++TmVdLHCl9537cIkMGBrd5hbt/fz9pn3oIX7LNffUQ2m0wW8LgVndkK+edaAqOEwOuVcJy/sRp+pyXGqiq6/ZEragfEU7UEPnaHqu2ecDoWjjo205oaatmy9XFFZJyYpRuw4WjPxtmv6ebi4geOebfxB+F5ef3kX0cPfQIzv4ZmNv8Pj42Vm0kU52nrzq+DoPQtRkQYzNJtljSsl+4vE8ouNewzBaEo1p6FYZyfxXMhIWhZHkwlGALuvvBaAk4f2rvi+4jljYlPAbQhG0RXfp03jcCqCsqqRU+1IWi3ojfiYSBQWnJgX4TBKFyqEvK6mdxj1Rr1kihVyPqPbrIaud13XGY7l2OYzotyXQIcRwLq2ACdns9C6Tt5wFsFoIlmgO+xZcPZazGG0sTNIjDBaxjqRtFyxQsQsnV/FkTSQsbRTc1nwt8p+wnM4jJ4fSeB2KmxxGPHSTgs7jHRdv+0sH7fXY3EvoZy3VN7Syrz44rPy33wbX3jvNWctM/7N2zeSKKi8GHo5HL//vKVbzchUqkhnyItIjS/sPFWZoMeJUxHMFYCeK5rDYWTubC7qMNo/nqyJu8hk2LcFBQ0mX6zZMWqJIz2BXxQpRm3BqJm4fWsXD/+v21n/hj/EkTwFz30Z7v1TWHM9fbe8B12HBw4bJ3RbXi0vzoefaOiaAVKFMnPZEh3EL6q/CBbGTY9WDMGoKRxGJVpEBmewrdFLWTY3X7WTHB5Gjz6/4vuKZ6WY0OJ3ySlptsOoqXE5FcqqTkE1btAqUjQC22FUBbqNiZC6OdL9IhxGuVLFKNw1BaPmjMT0RuXr/rQwHC0XOTVuKcykixTKGpudxu+7ZW3NjmUl1rYHGJrLooXXgHC8RDAqqxozmeIZDiNrCUZ9UR9JEcFdnGv0UgA51TpXVgk7TIdRcz7/lspAq186jISQxdfncBg9P5rgst4wzsm98obuy+u3yDO4oGAkhOgSQnxOCPFj4/PtQoj31X5pZ+HrvwIf3QB/txm+/CY54cbmNFRN5+M/Pcx37peTwf76/W/gqsGz79bu7I9yy+YO/mlim3ScHLmnnkutOVOpAj0hpzx5CPfW5BhCCKJ+N4lcST6RZw7V5DhVpWJMATEiacWKyrHpTE0Fo+mAMQZyYm/NjlFLXAk5/ajcYgtGzcZAmx+x9W5pH//B/5QTKV7zUbb3RuiJeLn/oCGkbLhduu6O/KSxCwZOzcri1Whl9qImpMFCJG0455YXpc3QYZSRgpFoQoeR1+0i4RtExI6SLqzMpWY6jFr9hsPIF63CCm0ahctwGOVNh5HdYVRVeiJe8mWVVEmRF+YXUfacLan43I5FpdfN6TAyNwrGNOM1NFU7h9GpmPxdrc3tk9M4o4M1O5aVWNseoFDWmMqpsrT4DMFoOl1E1w2Xr2lysJhgpCgCAh141QxUSo1eDoWKiq5D0GG8d65ywWhzV4hkvsxoPCd7jM7iMKqoGi+OJWXh9ege8Lc39Dm2lEjaF4B7APOK+wjwOzVaz7kpF+DEQ7D2ZthwB0zugwf/su7LsDr37J/kn+4/xu0dGXRvhLX9588U/+btG3kgt568pw0Ofq9Oq6wPM+kiG/05WThXI4cRyF3geLYsrYXFlOwnsDKq8eZgRNKOTmWoaHpNCq9NlHA3s6IVxp+r2TFqSWTyCcq6g3LHZY1eis3FoDjght+QrwVX/zfo2YkQgtu3dvLI0RmKFRU8QVh3Cxz6YcOHAAzNyU4IX2H6oh1GQY+TkNfJRLIoT1Yz1o+kxTJ5QmTB11yl1yaOzi2s08d5cWxl0/biWfkaHfW77dLrVYDLoVBZLBhpFVBNp6/tMFoppkgynszL18vU8hxGFVWjVNEIuJ2LHEbNOdyiNyrXfaoYkGJksnaT0obnpGDUNvcsDFx/UdHpZmT92SalLWIyKQfudEe80mEkFNmRaDG8kU75H7nGJ3cyRfl6GDIdRu7VLRhdu04+Hp46GZMiUGL4JeedR6YyFMqaLLwe2wP9Vzf0OXZOwUgIYW57tOu6/nVAA9B1vQLU/4p4bI90wVz/P+BNn4TL3wrpyYaf2FuNw5NphIAbWlKI1vUXfHBdPdhCa9DLs76XwdGfQSlXp5XWnqlUgXXuhPykpoKRW+4Imz0TFhrNfVbOEIz2j8v1ni22WC1aA272s6FpBaP28Qd4StuK4rMv3JqW3e+CV/013PEn8zfdsa2TbEnlyRMxecOWV0P8JMyuvLh4JYwn8gTJo1RyFy0YgTkxpwDBjqZwGBUzMRR0S55cLwV/7zb6xCwnJ1Z2Ah7PyV3WVo8mx4TbglFT43TISFpRBR1xRiTNdhitlB5DJJlMFuS53jIjabmyvKTxux2yKBqa1uHQGfLiUARjyZIhntVOMDoVy9Er5nClR2Dghpodx2qsNQUjc1Ja7ORp16LzglHYEIz87aAsxZ9RX4Jt0r2cjS0/wlltckX5HAwKIwHRpA6/pbKlK0TY65SCUet6KGVe8rr1xAkZF7yqyyHPSfuubsRS5znfI/gp49+sEKIN0AGEENcD9b8iPvUoIBZelELd0jpq9YvzOjMSy9Eb8aHET0DrheMzQgiuXdfKf2avlL/PY/fWYZW1J1OskC2pDDiNCRE1Kr0GWXydyJUXYgM1nkqxYsxI2rxglCLgdjDYWrsTpNaAm+fKg+izR5uvKys+RCh1lPu13XIH0qY5cXrkhoN3wUl344Z2vC6F+w8ZYsrmV8l/j93XgAUuMJ0uMuiWkwtXIhh1R3xMpgoyLtAEHUZa1uhTaMJIGkCwfweK0KmceGRF92NG0qKKcfFql143NW6HoKRqAGjCKSNp86XXtsNopZgF/+PJvCy+XmZvj3mx6j/NYdScF6wORdAd9hol4H01dRiNxHLcGRySnwxcX7PjWI2esBePU1mYlFZMQi42//WJpHwMzUfSLBZHM4n2bQZg+mTju0WzJekw8gkzkra6S68VRV57P3UyBn1XyRtHnjztex44PM2GjgD9uYOADn2NHVB/PsHItKb8LvA9YIMQ4lHgS8AHa72wlzD0c+jasXAiaXbSpCfrvhQrMxzLsbbFKe1t5sjHC3DdujZ+mF6P6m1ZNbG06ZSh8GNcgNSowwjO5jBK1OxYVcE8UXXK0uvnRxLs6I3ITHONaA242autQ6DDxAs1O05NMLq97tWupDPsucA32zQTXpeDl21s596DU+i6Ll8nnL6a7souhalUgc1+Y6LgRXYYAfTOO4w6m2JKmp4zxPYmjaSJLa9mTmnjupHPr8j9HM+W8LsdeCuGuG4LRk2N07Fwqq0Jh116XWU6gh4UYTg7Qr2yp84UfpZAzrhYlaXXZodRczqMQMbSxhJ5uVFayw6juSw3uY/Ki/vunTU7jtVQFMHatgAnZ3ML11mLYmmTSTmhOeJzyffdQHuDVnp++jdfSV53UzjV+OnOuZIh2iKv3VZ7JA3ktfeJ2SzTwc3yvHN4QTDKFis8eSLGbVs6ZcIKFoSlBnE+wahDCPG7wK3AfwF/C/wY+AxwZ+2Xtghdh5GnYPCmhdvMk+iLmIawmhmO5dgVTMmujiUKRtevb0PFwXDHrfLi2HSgNCvJMaYMS2ibNiff+Gt4wh0NSIeRbsYG8omaHasqqAsOo1ShzItjSa5bX9sd/Ra/m32a8Xhstlja4R8z7Rkk7R/A63I0ejU2Veb2rV2MxvMcnc7ICG+go+HTOKfTRdZ7quEw8jKbKaL626XDyMIR7mJFxV02HMNNGknD6eGR7neztXwATjx40XcTy5VoMQuvwY6kNTlOx8JmjKY4DcHIaHawI2krxulQ6Ap7GU8UpMMIlnVtYF6s+lzGlDTF2dTOr56IT24UhPuk26pGr/vDsRyXqQeh/ypwXFqP47Xtfk7OZs4qGE2kCvREfAgh5PuuRR1G/W0hDol1+GZWPtlzpZgdRj7MSNrqF4zme4yG01IMGlmY0PvosVlKqsbtWw3BqG1Tw4dfnE8wcgBBIAQEAKdxm9+4rX6UczJXvHaxYGS+KdgOI5N8SWU6XWSr29hJXqJgtKkzSIvfxb1cL0ubV3Ci23ASI/D3O2h96m8BCJem5JtmDYvCWvxuSqpG3mlEXSzvMFroMHpmKIamww3razvGujXoZpYIJX9Pc01KK6Rg6Oc867lW5tFtVh23bJEnc08aeXEC7Q1348yki6xxmYJR10XfT0/Ei65DytEiHQ0WjsvGs2VaMBw1TTwVLL71l5jQWynf/1cXfaGWyJVpCbgWBKMm/n3YgHuRw0gXLqP0uizLcC3YbdKMdEe8TKbyC27yZRRfm4JRwGNE0pzNGUcz6Y36mEjm0cJ98nyvBhsg2WKFQiZBT+H4JdVfZLK2PcBILI8aGQDEaYLRVLJAl+lGt3AkTQjBhH8r3dnDCyX8DcKMhXovIcFoR28Yv9shY2kD18v0RSkLwAOHZwh6nFw92AKjzzTcXQTnF4wmdF3/iK7rHz7bR91WCLIMCs7hMFpeVnk1MxqXVtp1ypS8YYmCkZml/PeZddIefeqxWi2x9sROADpbjnyKVyjP4MtP1TSOBnJKGkBCN17grO4wMkdoOt08fnwOt0PhysHaRkBa/bIvKdGyo7kcRiceAK3M/dpV82PKbVYXPWEvTkXIrh+wRHxrOlWgR0mAOwSei9+f6TamB8VEVN5g4VjabKZIVMiTpWbtMALY0N3KJyqvxzX2JJx86KLuI5Y1HUYJeYPtMGpqnIvi3rrikGKRVrbjaFWkN+KTvT0hs65i6YLRfH+K23AYNWl/kUlv1EtZ1Um5jSlYNYilDcdy7FaOoaBdUv1FJuvbA5RUjfGMBpE1pzuMktJhRDENpbQ8p7Aohc5deCmizhxu6DrM56BbK0gh3bn66x+cDoWrBlsWBCNdhbE96LrOg4enednGdtzZcTmwpL+xhdewtA6jxlPKQMfW03Og7gB4IrbDaBHDMSkYdakT8kJjGbnZ69e3cTJRodS+tbkcIGdiPB6S7m4+7vokjtgRiPTX9JBRQwyJa0ZJWxM5jJ44EeOKgWjNo1atAfk7mgpsg7ljzVNWf/gn4I3yYG6dLRitUhRF0B70MJUydrYa7DAyC/vb9dhCvOIi6TUes1Oq4X60cPF1PFciKtLoQpHv7U3K5q4QX1dvJevphAf/+qJcRvHcmYJRtKprtKkvLucih5EZSVMrTR17shrdRl+bbm4mL6P42nQ3BMzS6yYXjHqMjYIpjGuAGhRfD8dyXK0clq/X/ddU/f6tzto2eb5/bCYDrevmBSNN05lKFeT54owxbbV9c6OWeUF8a+Xfbu7IExf4ztqSMyJpHq0g3UUNHB9fT65b18qhyTTJtisAAcNPcHAizUSyYMTRjH4pizuM7qjbKi5EMXu6u8gk1G13GC1ixBCMovkR+QK2jCfcdetkJGnMuwXG91q66+K8GI+Hf+39KzThRBSSdXAYSTEkVlTk5DGrO4yMDqNMxcH+8WTN42iwIBgNe403zmYovtZUOHoP6oY7mc6pdiRtFdMV9jCdNgWjDikYNeg10Czsj1ZmV1R4DdBlTg8qGy6lrHUFo1i2RAsZNE+kqWM6XWEPbo+fB9rfAcOPX9QGTDxbks5Vu8NoVeBSFkfSzA6jCih2J1616Il4yZdVUpofzennp088y4ujS9uYMkuv/W6j9LrJ4zC9Ufm6P6oazvEaDHF4YTTBtcphtK7LV+SCbVa29YYRAvkYa10/LxiNJ/NUNJ3eqA9mDspv7tzWwJWen4FNl5PSfeROPnXhb64hWSMW6tLyTf/8Ww7XGddfT06o8nEy/AQPHJbnabdu6ZD9RQ4PdF3WyGUC5xGMdF2PnetrdUdXT+8vMgn3LCunvNoZjuXxux24kkNLjqOZbO0OEfG5eLYyKHc1E6dqssaak54Ed4jniz38U8v/ltbGGqv7ZiQtnq/InWCrO4yMSNr+qQKaLt1ltcbvduBxKozohjW3GYTesT2QmyO+RmrntsNo9dIR8s4LNQQ65MVcg57HpnAVKM2sqPAaIORx4nIIxirGCX3GupG0eLZEi8g0b+G1gRCCTV1B7invkjeM713Wz1dUjVShQkvA2HxweMBlv/Y0My7nos07xWlH0mqA6ar5xMPHOVWOUE6M8aN9SzvPmJ/QNB9Ja+7nW6/xuxgq+OUm5nIFo/Hn4NF/PO+kuYcPTnClchzH4KXXXwQQ9rrY1Bnk2eG4vN7KxyAfZ88p2RO4e00Upg/K1++WtQ1d6/nY1BVhn74ez3Rji6+zxQpORaBUcuAJNnQt9WRnfwS3U5GxtDXXwejTPHRwgsv6wnSGvTC6B3p2gtPd6KWe12FkLQZfxjNDsfknIyBPpu1I2jzDsRxrWzyIxClo27Csn1UUwTVrW7knZlygLPMk1zKkJyDUzUy6yGTHTfC7h+Cyt9T0kGYkLZEryXJSyzuMpGC0dzyL26mweyBa80MKIWgNuBkvGTsHOevo0edkQr6BjoR2A9gOo1XMSxxG0LBJaXIdOp789IodRubzbqLoA+GwtsMoVyYqMiiB2gvYtWZTZ4jHZ/3gDsL0gWX9bCIvx623BowpaXbhddPjVM4WSSvbkbQq0mO4aj710Amyni4GXEmOT2ekU/TZL0H83JugZn9KwOOESqHpHQ5Rvwufy8GP902RcHVw6PAh9pxaxjnXw38HP/u/8Olbz+oGn0wWUKZewENRXuReolw50MJzwwm0lnXyhthJnhmKE3A72NodgpnDcsPawk5Ct1NhzLeVjuzRhk7IzpVU/G4HopSVlTOXCB6ng91rojxxco5S33VQTJEZ3cftWzplbHliL/Q1vr8ImkUwcnpJu1r51S89w//97r6F20PdkJkETWvc2izESCzHFeG0PBlZpsMI4Pr1rTyY6EBXXM3bY5SehFA3U6kCnSGPnDBU43hD1HQYZcvN4TAyBKNnRnNcNdBSt1HxrQE3YwU3ICA3V5djroiinFI1WpDlez22w2jV0hnyEsuWKFW0RYJRY9w406kCLaQRWnnFDiOQkdm5fEV2M1m4wyiWLdKmZBG+2hbw14NNXUFmcxXKbVtganmCUTwrX5+jfkMwsuNoTY9r0ZQ0HK5FkTRbMKoWGzuDbOwM8pu3bWT71i30KnGOz2Tg2L3wvQ/CU58+58/mSyqKAI9TMSJpzd1hJITguvWtPHMqzqFcmNT0EG//9JPcd3Dqwj+s6zDyFPReKTc/P3M7PPHJ077lgcPTvEwxrsUuwQlpJlcOtJDMlxkVxvv03DGeHopx5WALTocCM4egc2tjF7kEch27cFKBqf0NW0O2WJGCbSkjN1ouIa5b38a+sRS3f0O63K8Uh7ltaydMPi9fjyzQXwTNIhh5I3z2kZMkcmVOzGTRNKNbItQr33RzjdkJthK6rjMcy7HDa/wuLkowaqOEi2RoY1M7jMqBLrIlla46OUJcDoWQx0m8yRxGB2cKdYmjmbQG3MzlVPC1SPuu1SmmQXExkZGCdJctGK1azBG4M5li4wWjdJF+p9G9UQXBqDXgJpYtWWL62/mIZ8u0ikxTT0gz2dQlI4Cx4CaY2resPqx4znAYmaXXtmDU9LgcZ4mkqWVwOBu3qFVG2Ovi3t+9hd+/awtKuJeoOsf4XAr9nv8jv2Hm0Dl/NltU8budCCFWRek1wBfeey1Df/1art91OVdHc2ztCfFrX9nDzw5cQDRKnJJO1Ct+GX79cdhwO/zkQ6dNAbv/4BRvdT+KPnDDigczNDNXDkYBeDLdBq4AxVNPcXgqzdWDrfL8MTkiBzZZHO+gdLCkTzSuxyhbMgWjS8thBPCeG9fyf167jXfedTNZdzvvH5zmCg7Df7xdimdrX9boJQJNIhhVgj189pETBNwO8mWVCbNrwrTrN0MfSo2ZzZTIl1W2MiRvaN+y7PvY1hOmxe/ikFgvHUbNVnyt65CeJOuWF3zmRWA9iAZcMpLWDA4jw3Za1J3csKG+glEsW5IXhM3gMCqkwBtmIlUk4HYQ8tgn96uVTuO1YjpVWBCMGuTGmU4V2BzIyE+qJBjFsyUIdFrcYVQiTPN3GAFs7pI7pKcca+X7wTLOUWLzDiOj9NqekNb0nOYwUhaXXtvvKTUh1ItDV/lV8V3E7CEI98k+mXOQL1dkfxHIHX1n8wtG80T6UDITfPm9V7O9N8Kvf3UP955FNPrkg8d59+efQj31pLxhzbXyXO11/yC7QJ/9EgDFikrs+NOs1ccQO99Wx/8R67G+PUjY6+TZ0TT0X03pxKPoOlyztmVhQloTCEYD67Ywq4fJnny6YWvIFlUCbgcUM5ecYNQacPOrN6/n127dSGDjTQzOPoz4wt0yGvu+n1lGlG0KwWgmXSRfVvnD18im+ePTZ5xM2z1GDBsT0gazz8vM7EX0QDgUwcs3d3B/shfycUgMV3uZtSUfB7VI3CH/3ztD9XOEtPjdcmfY1wJ5i4+MNxxGwulh15r67V63+I0LV39bc3QYFdPgCTGVKtAV8crdR5tViflaMZUqyscn1L/DSNPg0A959cjH+YPKZ+RtVThRaA24ieWs7zBKZ7P49Lx8DW1yusNeQh4n+yr98oZlxNISOfn63GqWXtsOo6bHuchhJOxIWu0xJuN+0PlfxNuuhGt+VRY/F85+biYdRqZgtDocRvOE+0CrEFHjfPl917KhI8hHfnAAfdGGsK7rfOnxIR46MsOBp+8DVwA6dxg/3wubXwXPfRXUMk+djPEq7RE0xQXb39iY/yeLoCiC3QMtPHsqAQPXE4gfIqLkuWIg2hQT0ky290Z4QVuPa2pvw9aQK1Xwu02H0aU3dW+ewZuglIZ1L4cPPABd2xu9onmaQjCayxR585X93LVDOoqOzxiCkXkynRpv0Mqsw0gsh4JGy+yzK8oU37qlgycKA/KTZusxMoTDWSEvOOrqMPK7F0qvi0k5kt2ilErSobdzsAOPs35lfG0BN+liBdXb0iSCUQo8YSaSBbu/aJVjOoxm0gUZE/G11l9ceeRj8LVf5uXZe5jxDMBrPw6RNSu+2xa/m0SujOY3Oows6hxVs8Zrgr/5BSMhBBu7gjya6ZI3TC+9GyJmCEYtfrv0erXgPpvDyI6k1Q7j2sAtVO4b/C3oNC66ps8eS5u/WAUoN3/p9WlEDNE6NU7Y6+K9N61lOJbjwMisHOyRHGXfWIqJZIGOkAdGnybXuev0x+ZV75ExtcM/5oGDE7zB8Tj6xlesivjwSrlyoIUj02my3degoPHG9nH5WJo+CE6vpSekmUT8Lobcm2nJnpCCTQPIFFUCHofRYXRpOYxO46r3wC9/Hd7xDcttnjWFYATw23dsoj3oJux1LghGQeNkzHYYMRzLsUWM4CilpEJ5kbx8UweHWYMqHM3XY2TY/idU+STrrONUqxa/SzqMzPjAOXayrMAzx+Tz5b/fXt+dj5aAnCZXckebo8OoIAWjqWShbn1YNo2hLeBBEZw+Ka2egpGmwp5/g/W3ciP/xje2/D1c8z6ogqutLSifd3l3G6hFS7426bq+8JqwCiJpAJs6gzw/K6QTelkOozJel4LPpdil16sE5yLBSDhcUizSyrbDqFaEpUjyM+UmHi+sXygenjl7LC1XMi5WYVWUXp9GuE/+mxoFXed12f/iB+7/j63/thU+9XL4t1fzs/1jKAL+492Xs00M8aP4GlRt0cbCxjvl/ez5AskD99Mp4jh2/WJj/n8sxpWDUXQdnqmsR9UFdwROyi/MHIL2TZaekLaYbPtOFLT56cD1JncJdxidhtMDm++y5OOmKQSj1oCbNa1+hBBs6AxyfNpQQB0ueWJvdxgxHMtxh/+4/GTw4h1GbUEPW/s6GHYMNq3DaLgcxutS6to5IyNppYXdYIv2GE0k8+wdkhfC16zvrOux2wzBKOuINEeHUTGN7gkxlS7aDqNVjkMRdIQ8TJn9eMHO+kbSjt8PqTFKu99DvFBdsbvFL593aaexW2XBWFqmWCGoGRtBFttVu1g2d4WYzZQotW1dnsMoW5J/s1IGdNUWjFYBrpdE0srSZeSwBaOaEOqCt36Rb3b9DsdmMhAZkK6hcziMsiUVn9u5IOStRsEoMQw/+J/4H/y/uLx+vuZ8A/pN/xMSw6Re+CHXrG1lY+UoTjR+lFjDFx8bWrgPxQG734V+/H7enP06JWdQxtRsuGJNFCHg80/PcUAfZEfF2ByYOdwU/UUmroFrACibHVZ1JltUCTl1+fy7lAUjC9MUglFvdOHFe0NHcMFhBHL3zhaMGI7luNF1RO6sRAdWdF+3bOnk6eIA2vhey8YXzorxODhZCNEVrm/nTNTvIl2ooLrD8gaLTkr76E8O46SE7vBUxb2wHEyHUcYRgUoBSrm6Hn/ZFJMUHQFUTafbdhitejpD3kUOo/b6CivPfhH87Ux23WaspXpx2lbjeRcXUXmDBYuv49kyYWFsBK0SgcSclDbj3ygvHtTykn4ukSstxNHALr1eBbhOcxg5paNQtUuva8qON9LV3cuJ6Qy6EPLiffrsTr9csSILd8t5ecNqEoz8rTIa9eBfSxfry36XZ27/d/6/9C9wePsHqQS6uS31PV65oxtG5JSswPrr+dhPD5MuLHrN2v1OAG5y7Ke0+XXgss+JAEJeF5s7Qzx0ZIZntC20xl+QlQtNMiHNZN3atQxpXWSPP96Q4+dKFVqcxvmX5xLuMLIwTSEYLWZDR5DpdJGU+UJmC0YAjMxl2VHZvyJ3kcmtWzp4QVuHkjde9JqF9CR4o4xm9KpecC0Fcxc/oxgvdBZ0GO0dSfDt58a4sjeAcNb39wMLDqMEcoKQ5V1GxTQZZJdBd2QVnUDanJWusEeWXoMRSauTsJKR3RDs+iWmchpQXYeRKRjNEZU31Ov/axnEciWCGBdrnnBjF1MldvZJ4eugtkYOGpg7vqSfi2VLtARciwSj1SGgXco4lTMcRvORNFswqiUbOoKkixVm0kVZPjxzrg4jFZ/bITeyYHUJRkLIHqNSBl71zKxcAQAAdktJREFU13Dnn/DKHT0oAn50YI7nO9/ILY4XeE1vFkafhraNvP9V15AtqXxrz+j83ajhfh5XrgQgeM0vN+r/xpJcORgF4FRwF6KSgxe/Kb/QBIXXJhs7g+zRN+Gd3FN3o4Cm6eRKKmGH7O+zHUbWpAkFI/lAOjFj7EaGui/5DqNiRcWdHiZSmVtR4bXJrv4op9yb5CfN1GOUnoBQD9PpYl37i8AYgQwkdeOFzoIOo4//7AjtQQ+7enwNscKbDqOYbohqVu4x0nUopEjqhmBkO4xWPR0hryy9BikYFZJQKdX+wM//h4ynXPkrTBuCVS0cRlOaIcRkrBdJi2WLhIQhGHlXh2DUEnCzvj3AY+nlFV8ncmW5AWG+h9iCUdOz2GGkOBaXXtuRtFqysVNuTh2byciL98zUWQdu5EoVAm6n7C+C1VV6DXDHn8DbvwbX/w8AOkIerl3Xyo9fnOBTmZup4KDnyL9Lh1H/tezsj3LFmihfevwUmtFl9LMDU/xl/k2c2vDLK+pJXY3sHpAxasfA9fKGPV+Q/zaRw6gv6uM5bRPe4iwkTtX12LmyHBIUdRgbdrZgZEmaTzAy3gCOT5uT0npldKAeJ/YWZSye51rFKPMbvHHF9+dQBJ0br6KME330mRXfXz2oqJoUDsM9TKcKdIXqe4FvOozimnGiYUGH0Ugsxw0b2nBRBkf9HUZRnwshYKZivBlY2WFUzoOuklDl76nb7jBa9XSFPcxmSpRVTUbSAHI17jHSdXj2S7DmeujYwrQhWFWzZN0UsydKfkBYssMoli0TmncYrR47+hUDUX48GUEXjiUXX8fOjKTZU9KantMEI6fb6DBS7dLrGrOhw7hemMlCh+H2mH5p8XWupOL3rNJIGsD218OWV59202su7+HodIafjgiOt90Gz3xOvt+tkV02775xkBOzWR49Lt8DP//oSRLRHfS/4xOWLORtJNeta0URcPm2rRAdlJsDTTIhzcTrcnDSt0N+MvJ0XY+dK1YACCqmYBSs6/FtlkbTCUYDrX6ciljoMQp1y38zU41bVIMZjuW4Rhym4olC+5aq3OdN2/rZq60nf/ShqtxfLTk1l2Xb//0J0+NDPJ/0kS2p82Oy64UpGM2qhmBkQYdRsazidSoN29l0OhQiPhdTFeN3dJadPstQTAEwU/bicoj5OJ3N6qXTEJlnM0UIGIXwtRZXhh+HuWNw5a8AMJUq4nIIWvzVe356nA5CHidzeVWKD/l41e67WsSzJYIih6445Yn2KmH3QAsTWY1Ky4Zz9qcsRtV0kvmydGPakbRVw0tKr9WKEUmzL7xrSVfYQ8DtkBvM55iUVlE1ihUNv2uRw8i5ygSjs3DXju75Gkv3jf99IY635jpACkrtQTdffOwU+8aSPHUyxntuXItDqW/3ZTMw2Bbggd+/lTdc0buQ8miiCWkmxZYtFIQXRp+q63GzJekwCim2w8jKNJ1g5HIoDLb5FwlGPfLfSziWdmouxzXKIdT+60Gpzp/05Zs7eFzbjnfmRTle3MIcGE9RUVVa9TiPTskLrf6W+r7hm7v4c0UFHG5LOoyKFQ23U4FKUY5ubACtATdjxSYQjIzH/EzJRWfIi2KfJK16zBjYdKooI2lQe8Ho2L2yx2THG+Wx0wU6gp6qF/a3BNzEsiU5gcyCUdBYrkRYFGR/UZ3L+GvJ7jVRAKZ9G2Bq3wW/P5kvo+tIwdB8D7FLr5sep+EwUoQdSasn85OVZzJyWpgn/BKHkRmHCXgcUF6FHUbnoCvs5Zq1raxp9bF2953SgeUOzceoPE4Hb792gPsOTfGXPzpIwO3gbdesafCqrctgW0C+b5uxtCaKo5n0toY4IDbNl5/Xi6zhMApgPP9sh5ElaTrBCMxJaWaHkSkYjTduQQ1m+NRJ1ilTuNdXL1fcHvRwPLAbBVXugluY0XieVtI4UXnvXTfwlfddx107uuu6BrOfJ5EvyxN8CzqMShUNj9NhnKg2SDDyuxktGA4CC164zlNMAzBecNFjx9EuCcwY2FSqsBBJy9Y4kpabA1/r/I7aTLpIRw36sk4XjKzpMGpzFhCrKI4GsLU7hNelcFhfI8daG68r5yKek9H61sUOo1VSAn4p4zYEI5dDkTE0zSy9tgWjWrOxIygdRvOT0k4vvs4b7gbfaVPSVlmH0Tn4x1+6gi//t+sQigJv/Fd40ydPc8X88nUDKELw2PE53nr1GsJe+/F6QUyHURMKRv0tPp4ob0Cf2lfXKcYvFYxsh5EVaU7BqDPIqbms7JqwHUY4x54AQFS5iK7ccw0lnHDy4areb7UZjedY55Un4r62Pl62qf20zoB6EHA7cDkE8VxZxj4s6jDyuBRQiw3b2WwNuJnNqTJmYeUOo6K8WBvLu+iyBaNLAjPGOp1e5DCq9Qj6XEyKOAbTqWJNJjy2BdxSjPC1WlIwimVLtDiKq04ccToUdvZHeTxjbGBcoMconpWCUdTsMHKHwGFP0mp2nEYkze1QpKNQU2Uszf7b1pwNnUHGkwV5Udq5TUZDF02Bmr9YPa30evU7jAB6Ij7WthsX531XwbbXveTrd+3oQgh4941r67/AZqRjC7zhE3DVexq9kmXT3+LnGXUjQqvA+HN1O27OEG29Zo+h7TCyJM0pGHUEKas6I7Ec+NvkLk16otHLagilikZPci9lxQM9O6t63xv7OnhW24R28pGq3m+1GY3n2R48w3FWZ4QQRP1uErmSJR1GmqZTUjV5wtrgSFrMvHC1ciTNcAIMZRz02BPSLgnaAm4UAdOpgixednhqH0nLx8HfOv/pVLpAVw3611r8bmIZCzuMciUiSn7VTEhbzO6BKD+aM96XRs9fJhozBKNWc0qaXXi9KnAakWaXU5EikVqWsTTbYVRzzMnKJ2ezUjDKx057XTcvVv3uVVx6vQL+9HU7+Lf3XMO6dtv1sSSEgN3vWHApNxFrWn08p22Un4w8WbfjZktStPXptsPIyjSpYCQfTMdnsrKzJ9QNqUtTMDo6neYKcZRUy+VVd41s6w7xuLodMfmC5QSQxYzG82wwHEbzJegNoMXvIp61psOopGoAhsOocd0JrQE38WwJ3d9mbYeR0WEUq3jtCWmXCE6HQlvQIx1GQkiXUa0jafnEvMOoWFFJ5Mrz5dvVpDXgMoRaawpGsWyJoMivqglpJrvXtDCmRiiEBi8Y707kygC0BFzSYWQXXq8KhBC4HPLj9Eia7TCqNeaktGPTmYWY0KIeowXB6NJzGC2FzrCXW7d0NnoZNnWgv8VPnDDpwOAFNzeqieny86jm8+/SiIQ2G00pGK2fH5W5aFLaJeowOjQ8zQ4xhDJ4fdXve2tPmMe17Qh0OPVY1e+/Gui6zmg8x4DLKOYOdjVsLVG/EfuwoMOoWDEEI6fDiKQ1zmFU0XQ50a8JOoxSus8WjC4husIe2WEEEOyoj8PIEIxm0nJCSC0iaa0BD4WyRtkdkUKEWqn6MVZCLFsioOdWXSQNpMMIYDi4S76Pato5vzdmdBi1+I3BCbZgtGpwORSjw8gsva7Ypdd1YLAtgMOcrNy5Xd64SDAy3Q1+j2NhUpgtGNlcgvRG5bnuSOAyWXy9KLpZS7JFKdq6tbyMo1VpeJNNdWnKv0rE56Ij5JFFdnBJC0aJ40/hEiqRTTdW/b4HW/0ccm6hLNwwZM1YWiJXJltS6VYS0hHQwBOwFr9L7hBb0GFUrMgXZI/TcBg1KJLW4pfl4AVXi8UjaVKAzOCn246kXTJ0hrzSYQSGw6jWgtFCh5F53M4aRNJaA/J1MeMwBBmzUNkCqJpOIl/Gp2VXpcOoK+ylL+rjKW0L5GPEhvfxtz85xHgi/5LvjedKuJ2KjMcUkvaEtFWEUxEyEm6eo1QKtsOoDridCoOtxmTlYKd8Ts0sFF+bpdendxjZDgebSw+P00FX2MMBx1bIzUL8ZF2OmzNEW5eas+NoFqYpBSOQsbR5h1Ggs/bRAYvimngGAGXguqrft6II1ne3cti1HSzaYzQalyfdbdpcQ+NoIMWQeYdRIXXeneR6UzIcRm6n0WHUqEhaUApGOWfY2oJRIUXZ4UNDoSdq7zZeKnSGPPUTjMoFeYFi9NRMG86mWkTSTKE2IwxBxkKxNHOUvEddnYIRwBUDUb4XXwfAp7/0ZT7x4HF+49+flYM7FhHPlmjxu+R4ZjuStqpwO02HkTGFSmtcNPxSY31HkOPTWRk1bt8Ec8fmv2bGYRY6jAQ43A1aqY1NY1nT4ufpynr5ydizdTlmtqTicggcFVswsjJNKxj1t/gZTxj20UC7PAHW1MYuqs6omk5v+kXmPGtqVrC2rSfEg+WtMPWiJS/wR+NyRyhUnm1Y4bVJW1COrta8EUCfn7RlBRYiaUpDI2ltAXkilhJhKGflRbMVKabIEqAr7KHXjqRdMnSGvcxmilRUTb6mZmdqZ8s2XYg+WXo9mTQEoxo4jNoMoTaBMX3EQoJRLFvCTRmHXl6VpdcAu9dEeSoVYVqPcrVyiN9/5WaeG07w9z87AsfukxsymWni2RJrvTk49EO5CWYLRqsGp6LgcorTi65th1Fd2NAZ4ORsVr6ut50uGL2k9Nrll8KSjc0lSH+Ljz1pYxBHfKgux8wWK7JDrJS1BSML07TvVj0RL9PpAhVVw+lvA3R5EtyEzfQXy9Bshl0cJt1xK201Osa2njDffXoLv+kBhn4O219foyNdHKbDyFOYhtDuhq6lI+ihounklJC8LFtUaNtoiuXFHUaN29nsMPpZ4rp54RoDV29D1nI+9EKKuOrhps3tcrff5pKgM+RB12EuW6Ir0AFqScYTa3Hhboo2vhZ0Xecbe0YZbPPTHqjNlDSAOdWIWlioPyyeKxHCiIKswg4jgBs2tCGEYDS0i9vFUe68bSMjsTx7Hv4BPPFn89/3j8KHX8/D15DCwpprGrdom6ricgrpMFr83mtPSasLGzqClFSN0Xiete0b4fl/lz2FntAZpdd5u7/I5pKmv8XP918AvaUDkThVl2NmiyoBt0M+J93BuhzTZvk0rcOoO+JF02EmUwS/IZdYeepSDThxdD8dIoV77Q01O8bW7jAv6BtQHV4YfqJmx7lYRuM5ol6Bkp1puMOo0+i6SejGRZmFeozmp6SZkbQGdRi1GRfD06rxpmDR52w2HSeh+bhp46UjQNvIvhlAFl8HjMkwtYo7m45NXws/PTDF/vEUH7x9E4pSfYGy1XD2zcwLRtZyGAWF0eezSiNpO3ojPPVHd7L75teipMchMcyfvH47/8v3feaIEnvTv8Or/pqfOm/j2+2/Bv/tp/CHo3DZLzR66TZVwqUsKr02cTTtnm1TsWHxoJy2TfJGw2WUK1UQArwuZcFhZGNzidLf4kPVdErBNRCvj2CUKZYJek2HkS0YWZWmFYx6jJjIZLKwIBhdYj1G+eNyclnH9pfV7BhbukOUcTLnXw/T+2t2nItlNJ5nR6QEutbwDiPTPbOwi59o3GLOoFg+o/S6QZE0t1OhNeBmsmzs4lkw5giQS8VJ67ZgdKlhTiibThUX3Kq16jEyRBvNG+Uf7j3KuvYAb7yiNm67sNeFQxFMlawnGMWzJYKYgtHqdBiBfH8Qg8ZwiuHH8U/v5Wp1L5/T7ubl33byb+qr+LD6Xvb0vRMGrgOXHYVdTTgdRun1YsHIdhjVhY2nCUYb5Y1zxwHT3eCUTuJyzn7e2VzSrGmV5whpXy/UyWGUKVYIeV12JM3iNK1g1B2WF5ynCUYWdSvUCt/UHnLCh6t7R82OEfG56Iv6OCEGYepAzY5zsYzG82wNGnGGYIMFo6C82JypGGKIhRxG8x1GLrPDqHEnqh1BDyNF60VjFlPOJ9E9Ibrt/qJLCtNhNJ0uytJrqLlg9NCIysGJFL91x0acjtq8JSuKoMXvYrzoBoSlBKNYrkRYmJG01ekwmqdzO3gicOpRePij4GvhF3/tT7hysIUPf/8A8Vx53g1ms7rwuZ14XY4zHEa2YFQPIn4X7UGPLL5uXQ8ImD0KQL5cwec2isjtSJrNJU5/i3z8zzi7ITlal27gdKFC0GM7jKxO8wpGxoXcxCUqGOm6zprsPsYCOxambtSIbT0h9pZ6ITttKReXruuMxnOs9RvFyf7Whq7HdBhNFg2RwUoOI3NKmqLIXpYGRdJAlvoO54zfkQWfs6WKhrOUJhBu7OPJpv60B904FMFEMr8gGGWma3MwQ7T5x8dmWd8R4PW7+mpzHIMWv5tYTpV9TBZy9sWzJVodxmS6VVp6PY/igIHr4eD34chP4PrfYLC3ky++9xo+8Y4r2dEb5tp19uvOauTP33AZ//tVW87oMKrtuZvNAhs6AhybyUgHUXQA5qRgNN+fAobDyI6k2Vy69ER8CAGjeidoFUiN1fyY6UKFkNcJpYztMLIwTSsYtfhduJ0Kk6nFgpF1xIxaMzEzyyb9FLmuq2p+rK3dYR5LG30e09ZxGSVyZbIllX6PcbHR4ILpgMeJ3+1grGCIMRZyGJVMh5HD2C1osMPoRNbYRc9Zx+lg8vxogiA52ttqVSVvY1WcDoXusJexeL4OkbQYmnCyd7rCb9+xCUcNuosW0xpwE8uV5OukhRxGc9kS3d6S/GS1O4wABm+Qv39PGK59PwBCCF5zeQ8//K2buXlTR4MXaFMLLu+PsKkrZEfSGsSGziDHpjPoug7tm+YdRrmSis9t/E0qBdthZHNJ43bKc6BjZeP8tw49RulChZDHFoysTtMKRkIIeiJeGUlzeaWNzUK7prVmbP/PcQidwIbaFV6bbO0JcVBdIz+xUCzNnJDW5Tb6L7zRxi3GoCPkYSxrjM61lMNICkVeUZE3NKjDCKAj7GEio6G7Q5Z0GD12dIqAKNLV2dXopdg0gL4Wn3xtcbjA3w7pyZocR83FSBBgU2eIu3fWflJga8BNLFuSTkwLCUbxbIkOlyH6r+IOo3kGjc7Baz8AvmhDl2LTAOxIWkPY2BEkmS/L18C2TbLDSNfJlSqLHEZ5cNqCkc2lzZoWPwfyxgZ8HXqM0oUyUY8uHU0eO5JmVZpWMALoDhuCEciTYAtefNaK3MlnAOjdcXPNj7WtJ8wMEYruFks5jEbjsveizWEIRhY4+e4IepjJlORaLOQwmo+kmYJRAyNpHUEPJVVD87VYssNo79ERAHzBaGMXYtMQ+lt8jCWM15RwL6TGa3Kc0bFx5tQgf/iarTV3FwG0BNzEs9ZzGMVyZVpdl5DDqP9qeNuX4eW/3+iV2DSC0yJptmBULzZ0msXXWWjbAOUspCfIllT8HkPEK+dsh5HNJU9/i4/nUyEQSs0dRqWKRrGi0eo0zgHsDiPL0tyCUcTLRMo4sfe3W6pfp9bk5kbICj/+SO2nOK1tC+BxOpjwrLOYYCT/9lEyMnfeQBHEpDPsYSZTlG4nCzmM5iNpovGRtE6jWLjsbrGcyJstVjgxNiE/uRTcDjYvoT/qYyKZp6xqEO6rSYY/U6wwNTWB6olw25bOqt//2WgLuInnSuheawm1ssOoIF2PFngNrzlCwPbX2xemlyqnRdLsDqN6saFDRl2Oz2RkJA1g9ij5UgW/yy69trEx6W/xMZoqo4drPyktU5Sb2JF5wciOpFmVpheMppJFmUn2t1nu4rNWlCoaWnqagrs+HSsORbClO8RhfQ1MHwRNq8txL8RoPEfI68RTSVkijgaGwyhdtKDDSApFHiwQSTOmyeWdYcvFSJ8aiuHTLpGJTTZnpb/Fj6YbEzjDvTURjD790HGCWpqenl45zrkOtPjdaDoUXWFLOYzi2RJRpWA/32wuDexIWkPojfjwuhSOTWdkJA1g7ijZoorfs1gwskuvbS5t+lvlOVAxuKbmDqN0oQxA1GELRlanqQWjnrCXkqoZvQxtlrv4rBX7x5O06klEsD470wBbu0M8ne2WpWTJkbod93yMxvP0t/ilk6fBhdcmHSEPyXwZ1ROxlMOoWJYinwv54oyjcaObO8NSMMo4IpYTeQ+MpwhiCEarfWKTzVkxx8qOxvMQ6ZPiSilXtfufShX4zCMn6XbnibTU7zXcHNeec0agkKzLuNwLUapopIsVQiJvP99sLg3s0uuGoCiC9e1B6TAK94IrALPHyJUq+N22w8jGxsQ8B0p5au8wShfkJnbYnJRqR9IsS1MLRt0R+aCeSBbkRJtLZEraM0Nx2kUSf2tP3Y65rSfMswXjeBaJpY0l8vKFLZ+wRH8RSMEIoOgMW8phVFI1FAFO3VDxnY0TjMzfUYKQpZwOAMl8mTan0YtmR9IuSfrmBaOcjKQBpCeqdv9fenyIsqoRFRnZvVcnTMEoLYwTskKybsc+F4mcfD0KkLMdRjaXBotdRQ7nub/Ppups7DQEIyFkj9HcMXIllYDbKQV0tWgLRjaXPH1R+RyYdnbLc59yvmbHMgWjkLAFI6vT1IJRT0R2oUwmC/LEu5yr6k6wVXl6KEanksIb7a7bMbd2hzmi98tPLCAY6bpuOIx8UpixSiTNEEOyStBaDqOKhsfpQKimw6hxkbSQx4nXpRDTg1BMQaXUsLWcSSJXosttFvDagtGlSE/EhxCGwyhsTC+rYixtJJZnXdSJUs7VVeg2BaMkxgmZBRy5MUMw8us5+/lmc2mg2KXXjWJDR5DReJ5CWYX2TehzRylWNPxu58JFsS0Y2VzimNcxk4oxKThRu1SJ2WEUnBeM7EiaVWlqwajbEIwmUgUZSQNLlXnWAl3X2Ts0Q4Q0BOoXZ9jWEyKDn7S3B6YaLxgl82UyxYoRSYtbJ5IWlI/JlAjJHXy10uAVSYplFbdTAdUQQxrYnSCEoCPkYbpivDFYyGWUyJVpNwUjOyJzSeJ2KnSFvIZgZDiMktUTjOayRQZ8xslRHV+3WgzBKK5Z53kXy8rnmkfN2IKRzaXB4qJrxXYY1ZMNnQF0HU7MZKFtIySGcVOWkbR5wcjuMLK5tPG7nQQ9Toa1DnlDDWNpZoeRTxjOflswsixNLRi1Bz04FMFUsiCnpIHlOlGqzYnZLMKM3gU76nbcqN9NT8TLsHOtJRxG5oQ0q0bSYkQA3TICZknV8CwWjBo8jagz5GW8ZJyYWeg5mzgtkmZHZC5V+lt8jCVyEDJiuFV0GM1lSvR56y8YtRmC0YxqHcEonpUni65K1n6+2VwanBZJsx1G9WRDh3RXHp+RxddC1xgUU7L0umI7jGxsTDpDHo6VDSNGfKhmx/n/27vz+DjP8t7/n3t2zaZdsiTb8u7EdnZnYQskEEhpAgktW1tKTzkFTktPob/S0p4u0HLani70nBZKgZaWlr1sLYGyhAQSsu977Hi3bEuy9pmRZn9+f9zPyFawY0ue0Twz+r5fr7wSPdI8c1t5Zjzzneu67kpLWtRxu4PUkuZZDR0Y+X2G3kTYzjCqVBhlmnuO0YMHJugy7uyJZawwAjv4+onCahjbXfc2omPT9k19X8wHhYxnWtI64yGMgZGy+2l5erS+C3LlCmXCQd+J/291HHoNdqe0oZz7wswjoRrA9GyBdn8WjF+fNK5gq9tbbCgdikJLB8wcrdq5JzJ5+kLum5OW5ZthFAn6iYX8DFeCWg8ERlNz9vkoUEirok9WhgVDr1VhtJzWd8UwBrtTWtcmADaYo3aGUaXCKBCp4wpFvKE7EWbPbMyOr6hhhVGlJS3iuI+/sAIjr2rowAhsW9rwzNyJwMgDcxlq6YEDk6yPZOwXy7hLGsB5fUnuy/RCuQjje5b1vp9v0m1l6AxU3ni11W8xJwn6fXREQxwtuJ+WZzwSGBXLhPwnt6TVNzDqSYY5MOu+MPNUhdFJW3wv03bn4j0D7S0cm85SLJVtW1qVAiPHcZjI5OkNup+mLXMrbVcizOGcW13ogaB2arYAOJjcjCqMZGXwqcKoXiJBP+s7Y+waTtmWNGCDGaYl5LczUEEfFIkAPckIo+kCtK2BydoFRjPZAiG/j0BxDjAQUIWfVzVFYDS/Sxp46s1nLTx4YILLutzBxbHla0kDu1Pa0yVvDL4edwOjDp8bnnlkhhHYZP5Qzm37SB+v72JclaHXlNxWmDq3pHXHwxzMui/MMt74HYF9A6stvmV1e5RS2WEklbODr6vUkjYzV6RYdujy1+d5qzse5nAmCBhPVBhNzxVoCxYxTkmBkawMflUY1dP5/UmePmYD6nx0FZt8QwsrjNSSJkJ3PMxoKgdtg7WtMMoWSUQCkM/Y+UW+ho8lmlbD/59ZlWxheDqLE06C8cFs87akjaayHBifZVvSfdO/zBVG569KsM/pp2wCdQ+MJmfzhAM+IsWUPeCRCiOwgdH+uUoY4pUKo5KnWtJ6kmHGSeJgIDVS17VUZAslcsUycbRj00q3ut2+aRiamK1qYDSesc/d7XUKurviYUYzRYi0eiIwmprN0x9xNwbQY05WggUtaaowWm7b+pIcmphlJlsg1baVbeagW2GkodciFT3JMLP5EoVkbSuMUvOBUVoDrz2u4QOjvtYIs/kSqYJjX3w3cYXRQwfsC/x1kQwEY8v+4FrfFcP4Q4xH1tZ9p7TxdJ7OWAiTnbIHIh6qMIqHOZAO2FDGKzOMis8bel3vGUaJMCX8FCOdkPZGYGTbYyDqZPTmdYUbaHMDo8pOabPjUMie83kru4K1Omn7xnGZq2q6E2HG0jn7d6UnAqMC/RH3OUmPOVkJFrSkqcJouW3rt88zzx5LMZk8n83mCHF/4aTASDOMRHrcDXxSLQOQnbK7PtdAOlckHglAToGR1zV8YLSq1T65D1d2SmviwOixoWlCfh9dTC/rDmkVAb+Pzb1x9pi1MPrUst//ySZn83ab6Lkpe8BjFUbHM3mcWI9n2q1yxTKhk1vS6l1hlLCP27lwl3cCI3cAb6Q8q5a0Fa7fDYyOTM1B64A9mDr3OUaVVtp4OWVDm2Wek9UVDzM1W6Dc0u6JeX9TcwW6Q25gpMecrASqMKqr7X32eebpo9OMxrYSMGVaU3tUYSRyksqOz+MBd6fYGlUZpbIFEuGg25Kmgdde1vCBUZ8bGM3vlJZp3sDo6NQcq1oj+GePL/sOaRXn9yV5JNsHU4cgl6rLGsB+Ut8RC534lNxjM4zyxTKlaJdnKozy8xVG7vyrQP0rjABSwU5IDdd1LRWVCqNwMa15KitcJOinJxFmaNJtSYOqDL4eT9twpKU0XZfnrMrjLh/0RkvazFyBnqAbYusxJyvByVVFGnq97LoTYbriIZ4+NsNwdAsAscmnTxp6rRlGIpUPdY/5eu2BGs0xSmXdCqN8WoGRxzV8YNSbtBf1yHQWoh1NXWE0PJOlNxm2VSvLPL+o4rxVCR7Oum+gRp+tyxrgpMBoviWttW5reb7Km7JcuNNbM4wCPih6o8KoMxbCGJj0tXsmVKsERoFiWu0xwkB7y4mWNKhKYDThzjAKF2bqEhh1xe3jPhvwRmA0NVugU4GRrCQnVxVp6PWyM8bYDVyOzTDiW8WME6Vl7ClVGImcpNKSNlR2dyCfHqrJ/aSeP/RaPKtpAqP5ndKaODAamcnaP296dNl3SKs4vy/Js84a+0Ud29Im5yuMpuybe5+/bmt5vkpglAl0eGeXtEKZ0IIZRvXdJS3g99EZC3HcabOhWrlc1/UATM/lAQd/PqU3r8Lq9qhtSUu4JdlVeME0nskTDwfwZSehpeOcz7dY85V9vrg3AqO5PO2BSmCkkFZWAJ92Sau3bf1Jdg+nmc4WedoZxD/6BBTdwCigGUYibdEgQb/hULbFPk/V6IPdVLZAMhLU0OsG0PCBUSjgoyseZnhmzrakzY574s1ntTmOw/B0lr5EwP4Z61hhdMTpouBvgdFn6rKGXLFEKlekI+q2pHlofhGcSOan/O22GswD12O+VCYc8NvAyPg8MWyzOxHhWKkVykWYq/88lcnZAmEKmHJB81SE1e0tHJ2aoxSM2QrGqlQY5emMh2B2sk4VRva5aYa4HWJZLi37GiqyhRLZQpk2n/tGTSGtrAQ+taTV27a+JPlSmSePTLPbrMeMPGWH7oICIxFsJV53PMxoOm8LFGoQGDmOY4dehwOaYdQAGj4wAjvHaH6GkVOCXG2mudfT9FyBXLHMupY5wKlbhVFnPEx/W4z9vkGckfpUGFVahzribktapK0u6zid7rh9wTHutNrr0QOf5OcKJ7Wk1bkdraI7EeZwwX2T6IE5RlOzBTr87k5YqnZY8QbaWiiUHEZTWUiurtoMo/nZa3WcYTRRjgFOzXY+ORvTc/Z5POmrPOYUGMkK4PPZD21AQ6/rZLu7U9rDhybZG9ho5xcNPwGBFvv/R0ToTkY4nsrZAoUajNeYzZcoO7gtaWkIKzDysqZ4ZuxNRk7skgae2P2l2oZn7Ivq1UH3U5A6VRgBvPPqDTw8t4rCsfoERpXBsScqjLwz8Bog2RIg5Pcx4rihgwfmGOVOHnpd53a0ip5EmH1Z901iuv6B0fRcnv4Wdyi4AqMVb3W7u1Pa5JwdfD1z5JzPOZ7J0xsFCpm6PG9Fgn4S4QDHi27pdx3D7ErwH2fOzg1RtYWsFJWgyEOt9CvJus4Y4YCP2XyJQ6GN9uDh+zTwWuQkPYmwGxj11mQ341S2CEA87NcMowbQFIHRxp4Y+45nyIfb7IHMWF3XUwvD0zYwWhWYsQfqtEsawFuuWMNwZAOh3AROavm3RJ+cdQOjygwjj7WkGWPoToQ5WnDT8joPdXYcx21J80Ep55k3Zt2JMM9l3L8gPDD4emq2wKqwO+NJ1Q4r3up2O/x0aD4wqs7Q6/6wO7OnTs9b3YkwwwX3jVFdAyP7WIs5GT3eZGXxB21rmjH1XsmKFPD7OG+Vfc45Hl5nP0TLTikwEjlJdyLMaKXCqAav0dM5+6FRawg7mkKBkac1RWB0+WAH+VKZXTNuq00TDr4ecSuMunBbCOpYYRQO+Lnk8pcA8MB9P172+x/PnBQYebAlDewT7cGcGxhl6jv4ulBycBwIB/1QzEPAOxVGx8puJY9HWtJ6Qu6bec0wWvEG2uybh6HJWbtTWmb0xC6DS+A4DhOZPANhtwUruvxDr8HOMTqac+d01DMwclvSIuVZVfTJyuLzqx2tzra5bWnhcBh6zrcHFRiJzOtJhJnI5ClF3cCoyvNYZ9wKo7aA+5pIM4w8rSkCo53rbGn/Q8fdP04TBkbD0/aNSmvZfYFfpxlGFS998dUAPPDAXZTKzrLe92QlMIoGPdmSBjYw2jfrbs9a58AoV7SDbUN+d5c0D80wmiNCKRivSbnrYk3NFegKqiVNrJaQn/Zo0LYDJ/vtwdSxJZ9vJlukUHLoCcy6d1Cf563uRJhDWTc0rmP79rTbkhYppVVhJCuLL+iZSt+Valuf/Ts+GvJD34X2oAIjkXk9CfvBUjrY4c5jre7rhbQbGCWNW9mvCiNPa4rAqC0aYmtvgh8fdYOL2SZsSZvJ0hELEZgds7s41PkFtj/RQy7cSVdmL197+Ny3m16M8UweY6A1WLQBiMda0sC+KTuQCYLx173dKl+0nwqEg5WWNG8ERpW/jHKRLk8ERtOzeToDGsArJ/QmI4zM5KB1wB44h7a0CTfo7vLXNzDqioc4kHGfA+paYWR/H8GiWtJkhfEFFu6WJsuuUmEUCwVgVSUwitZxRSLeUtkkY9LnVkNX+XV6ZYZRwu9WbqvCyNOaIjACuHx9O/ccnsMJRJqywmhkJktvMmKrVeI9nuh9D/Xv4KLwEf79oeUNjCYzeVpbggTy7jwnL7akxcOMzRZxYt11H3qdqwRGlaHXAa8ERvYvo0ywC+owC+v5puYKtGuXNDlJTzLC6EzWtqTBOQZG9kVRu8/duKCOFUZDWQ+0pM0WCPgMvkJaLaCyslRmGEndbF2VxJhKhdFF9qAqjETmVV6jH3da7YGqB0buxhdGgVEjaJ7AaF0HmXyZQri9OXdJm86yKhm21Sp1HHh9MtOzjQ3OYR47NMFcvrRs9zuROWlravBsS5rjQLGlC9L1bkmzgVEo4LMzWDxSYdTf1oLPwLhpr/suabliidl8iaRfFUZyQk8ibCuMKi1p57BTWmV3x1YqgVH9ZhiV8VEOt9X1w5WpuQJt0SAmO6OAVlYWX0AtaXUWDwe4+eIBXrSxE3q3AwYCCoxEKnqSNjAaLlUCo+q+l0nnbIVRlDl7QC1pntY0gdEV6+2L7xlfa1NWGI2msqxqPanCyAt6txEqZ+ktj/DQweX7pHoik6cj6u6QBp5tSQPIhjvrXmE035IW8LszjLwx9DoU8NHX2sJwOVn3tr1pdwBvwuTtfAmPVGFJffUmwxxP5+ycrXASps8hMHJb0uLllG1VrVMoWXluykfq+9w0PVugtSUIuZQCWllZ1JLmCR9588W8ceca+0Z19U5oW1vvJYl4RlfcvlY4nK/sZlzdCqPK0OsWR4FRI2iawKivtYU1HS2MFmOQaa4ZRvlimbF03rakpUfrPvB6Xs82ALb5DnPPvuX7nf9EhZEXW9LcN2XpQIcHKoxs9ZdtSct7KgwZ7IxyMJ+EfBpy6bqtozKAN2ay+ktL5vUmI5TKDuOZHMR7z+kFU2WGUUt+AqKddWsrrrwInAt11vW5aWouT1tLEHKqMJIVxq+h157z9m/Ca/603qsQ8Yyg30dHLMTQbMBW31U5MEpni8TDAXwFd66jXnt7WtMERmDb0g5lW3CarMJoNGXbZPoSQTvQ2ysVRt3nAXB12xh3712+3/nErBsYZafsAQ+2pFV6f6d9bfZTfGd5d5I7mVdb0sAGRntma/PpxWJUtvhuIae/tGReZTD76EwlMFp6Rc54Ok8s5CeQGoK2NdVa4qJVwuyZQHtdK4ymZgusihQBRxVGsrL4/LaSVbwj2AJ+VX2JnKwnEeZ4Om/fd1a5EyCVLZCIBOwHxqDXAR7XVIHRFes6GCokcFLHoLx8M3VqbWTGBkYDkSw4Zc/MMCIch85NvIp7eXZobH6AWS05jsPkfIXRlD3owZa0yqf447Taqp7sdN3Wkiuc3JJW8FRgtLYjxoGcO+iunoFRZYtvZ06BkczrdXv4R1NZ9wXTuVQY5eiMh2HqMLTWLzDqjNvH/5Rpq2sr6NRsgZ6wu52uhl7LSuLT0GsR8b7uRJjR1LlXWJ9KKlt0A6OMPaDX3p7WVIHR5es7eKY8iK+YhfG99V5O1QxP2wny/X53V7C4R1rSAF71QXoyu3m/7/M8cKD2w8ZnskWKZedES5rxe7KdIRL0k4wEGC65a8vUr/UjXzq5Jc17FUajTpv9IlW/wddTs/aNa7iswEhO6E3aCqORalQYZfJ0RgMwfbiuFUbhgJ/WlqDd+SQ3A4VsXdYxPVegO+gGRvpkUVYSf1DVLCLied2JMMdnsjWpMErnbEuaHUehofNe11SB0YauGEMtm+0Xxx6t61qqaditMOo2bmDklQojgPNvpHj5O/nlwHcYe+BrNb+7yhyQ+Za0SGvdZoGcSXcizJFCpXqmfp/kVyqMQvMzjLwx9BpgbUeU45XAqI6/o8rQ62BpVlt7yrxKpeBI5QVTPnXi07BFGk/nWR/J2Mdg22A1l7loXfHQSWH28j/uCqUy6VyRrqC7na4HQ3+RmvEF1JImIp7Xk4hwPJ3DqUmFUYFEJGhfU4Vi4GuqSKLpNNX/HWMMnYMXkiMExx6r93KqZmQmSyjgI150K3i8MsPIFXjNh9kb3MJP7fsTmDxQ0/uqBEbtlZY0D7ajVXQnwhystFvVcVZIbn6XNB8U856rMJokTskEIF3PCqMCfp/BX5xVhZHMCwV8dMZCJyqMYMnB5kQmz/qg+xxex5Y0sM9NQ3m3qqcOQW0loG0PuNVNCoxkJdl4DWy8tt6rEBF5QT2JMIWSQzbcBXMTdqxFlaRyReKVGUZ63e15TRUYAVy+sZuny2vJHXqo3kupmuHpLKuSEUylrckru6RVBML8+KK/wCk75L/1OzW9q0pg1FlpSfPgwOuK7kSEfXNR+0UddyPKVwKjoN9zLWmJSJD2WIRUoANS9Rx6nae1JYjJZyAYrds6xHt6khFGZ7LnFBg5jsNEJs8av7s5QB1b0sBWTh3IVYbNL39gVJkZ1uqrBEaq6pMV5Or3wzW/W+9ViIi8oB53juO0332vVcXxGqlskWQk4O6UqrZ0r2u6wOiqDZ08UV6Pb+QJKJfrvZyqGJ6xgRGZUftmP9Ja7yX9hB0XXMRXSy/Dv+92yM/W7H4mKxVG0UpLWlvN7utcdcfD7E2HAFPnCiM7wyjk99lPBzzUkga2LW2ctroPvW5rCUJBFUayUG8yzEgqC4lKYLT46zSVK5Ivlelz3BdbHqgw2jvrBqN1eG6anrPP4ycCI71YFBER8ZLukzfwgaq+Tp9vSUuPnvhATjyr6QKjrb0J9gU3EiymYXJ/vZdTFSMzWXpbIyceVB6c2XPh6jbu8l2Gv5zD2X9Hze5nvFJhFK9UGLXV7L7OVU8yzEwenGhnXYdez7ekBX1QzNmBmx4y2BnlWKm1roHR9FyB1mill1rVDnJCbyLC6IKWtMVfpxNp+7zVVRqxIXeddwXrioc5OF9htPzPTZUKozhuYKTHnIiIiKf0uBt/PDjudiZUqSK5UCqTLZTt0OvUsAKjBtB0gZHPZwgMXGy/aILB147juC1pYftGxWPziyqCfh8DF7+KWSfMHd/+POlccf57ozPZ+cqgczU5mycc8NES9LszjDzckuYm84VIV11b0uYDI7+BcgH83qowGuyIcqiQwKnrLmkF2tRLLafQmwwzls5RDLeD8S3pBVMl6G7LH6t7OxrYCqM8Qcrh+gS1lcAoZubsAVUYiYiIeMpgR5TrtvXyyYfSAGSnjlXlvOmsfY+YiATsa5DEqqqcV2qn6QIjgNVbLiPnBEjtf7DeSzln03MFcsWy3d7Z42V7f3TTpYx0XcXGqbt43d/eyUe+v5vXffTHXPGnP+Blf3E7tz977sn0RCZPZyyEcRzvt6QlbDAzF+70xNDrEG6IF/DODCOAtZ0xu1Pa7HhVB+otxtRcnu4WwCkrMJIFupMRyg6Mz5Xs/LilVBi5gVFs7hi0rq32EhetEmbnI/V5bppyh163lGchEPFc1aOIiMhK5/MZPvELl/G26y4H4HO33s/hiXMfO1IpKmgL5O0HtR5+bytWUwZGV2xaxS5nDXMHH673Us7Z8Iwt2V/VGvF0hRHYJ5b1L7qZ1WaM7tx+/u625wj4DO9/zVYGO6O84zMP8E8/3o/jOEu+j4lM3u6Qlk/ZN/derjByA6N0oL2uW8bniiVCAR+m7IYxHhp6DbYlbdRpw+DUrXVvarZAV8j9/ag9Rk7S6z6OR2ay9vl3KRVG6RzgEEof8UyFEcBssLMu1Y/Ts3mMgVBpVo83ERERj/L5DO9+5XaKoSSR3Bh/8/3d53zOmax9vd1ZnrQHVGHkeYF6L6AWzluV4Gu+jWyavA8cx5Mzf87W8LQbGMUDkBmDuMcfVJtfDcBnr54mddkv0BGz4cR/e8k63velR/noLffS8tx/8uYrN+IPR+3w167NZ336iUzennNuyh7w8AyjypuyKV8bA3WcYZQvlgkHfFB02wI92JJ23HEH6qWGIdm/rPdfLJVJZYt0hd0KrJB2SZMTet0e/pHKHKMlVBiNZ/K0ksFXyNR94DXYGUYAqUAHHZm9y37/03MFd1fCtHZIExER8bhAchUXFfN86Ilj/MEN2+yH90uUclvSOsoT9oAqjDyvKQMjn8+Q7dpBdOxWmDoI7evqvaQlG3ErjPqCGcDxdIURAK0D0LuD4N5b6bj6ffOHo6EAH//ZLUweeQedBw/AwZNus+YquPwdsO31Z9zBayKTZ7AzatvRwNMtae3REH6fYdxptbtv5VJ1mdWRK5YJB/xQytkDHmv/6E6EmfZ32i/qUIk14/7F1RmsVBipJU1OOBEYZe2LmtFnF32OiUyeTSH3hVFb/VvSOuP2hd6kaWOwDo+5qTl3V8J8WvOLREREvC7ey4bcLPlimX9/6DDvvHrjkk81P8OoOG4PqMLI85qyJQ0gscH2W47veaDOKzk3w9P2TX6PmbYHGiGF3fxqOHTPiSogAMfB96330pk7xG0X/iU35D7MH3b+FdlrPmRnaHztV+Cvt8I3fwP2/QjKpVOeejKTpz0astVW4OmWNL/P0BkLcazUZg/UqS0tV3ArjEpuhdEZQrnlZowh2NZnv0hVZ6DeYkzN2t9Le1AtafKTuuIhjIHRVM5tSRuxlauLMJrKsTUyZb/wQEta0O+jKx5ipJyE3AwUsst6/1OzBVqjIRuihxQYiYiIeFq8h2hujCvWdfC5+w5RLi99vEgq5+6UWnADo0Z4b7vCNW1gtGXHFRQcP2O776v3Us7J8EyWzliI4KwbNjTCg2rLa8Apwb7bTxy7/1Pw5Ffh2t/n2je8k1958xv4/LEBbnrkUg7+3B3wC1+Dja+Ex/8d/vV18NGdMDux4LS5YolUrkhnLAQjT9mD3VuX8Q+2eD3JMIcLbgBRp23j86Xnt6R5a4YRQKxrDQUCMHlg2e+7MoC31e9WYKnCSE4S8PvoiocZrVQYlQswN7moc+wZTbMj5ob+Hhh6DTDQHuVw3n1uWubB1/MVRrmUWtJERES8Lt4L6VF+/qq1HByf5cd7xpZ8qkqFUUvuuB2T4eEP/8Vq2sBo6+pu9po1cOyxei/lnIzMZN0d0tywwestaQADO22r2J1/Dbd9GO78CHz392Dza+Altk3t9RcP8Olfupxj01lu+Lu7+U52G/zsP8H798DNn7DBwY/+YsFpK1sxt8dC9v9rcjXEupb5D7c43fEw+3PuJ+h1CoxyBTv0er7CyIOB0ZrOBIecHpzxPct+39PudZX0ub8fBUbyPD2J8Imh17Cox3Kp7LD3eJpNoUkIRiHaUaNVLs6a9hb2zrrzupZ58PX0bJ7WSkuaKvpERES8LdYN+RTXb0nQGQvx2XsPnvk2p1EZBRHOHrdBVAPPGl4pmjYw8vkMY4nz6E0/u+j2AS8Zns6e2CENGiMw8gfgqv8BqREbGv3gQ7YN4+Z/AN+JS+7qLd3c8usvZUN3jHd/9mH+/L+etQOHL3oLXPqL8MCn4PiJafzjafuGvjMWgmOPQv/Fy/wHW7zuRJg9lTdlqToFRpWh15UZRh5rSQO7U9q+ch/F488t+31PzdnrKl4JjIIKjGSh3mTkxNBrWFRgdGjC9vz3m+N24LVHXhit6YiyK9Niv1jmMHtqrkBbNFi3uW4iIiKyCO7rn3B2nDfuXMOtz4xwbHpuSadK54qE/D78mRFINEDnjDRvYATA6itoY4aR/Y/XeyVLdqLCaBTCrRBsqfeSzs4rPgDvfw7+YAx+6zn41ftO+cn6mo4oX373i3jzzjX8w4/2cvdet8Txmt+3n8Z/73/N/+ykO2umK5iD8T3Qd9Gy/FHORXcizN5MGMcXqF9LWmXo9XxLmreGXgOs7Yyxz1mFf3L/aedX1Uqlci1m3DkuqjCS5+lNhhlNZU8KjM6+hWv3SAqAjsKIJwZeV6xub2G4mLRfLGNLWrnsMD3fkqah1yIiIp530uufn79yLWUHvvHI0SWdKpUtEI8E7AfpjTBqRZo7MOq9yG7xfuyR79Z5JUuTK5YYz+RZVWlJa4Tqoufz+e26A6dvgwoH/Hzo9dvpa43wl9/dheM4EO+Gq98Pz30PnrsVgKHJWQBW59xtoPsurvXqz1l3PEyxbChHu+vXklYsEQ6e3JLmwQqjjij7nT585TxMDy3rfVcCo4jjflKiwEiepycRYSydp9DitsAu4rG8ZzQNQMvsUU8MvK5Y0x5ljFb7xTK2pKWyRRwHWlsCUMioJU1ERMTrKu9Bpw+zZuoBfi/2H2QOL60gI5UtkogEID2sHdIaRFMHRhs3b+cI3fgP/rjeS1mS0RnbQtSbDNtPtJs4hY0E/fzPV27mkUNT/OAZ99PuK98F7evt/KNSkYcOTtIeDbJq1t3WuiEqjOyW3PmWrjoGRmVCfu/ukga22uGwcXdKW+Y5RqlskXg4gK9gA0kFRvJ8vUn7OD6eD0MgsqjH8u6RFJtawcxN2JY0j1jTESVPkHwwuawVRpUW0K7KroSqMBIREfG2ynvQr74D/vV1vLP0Jf7b/t9a0g7Q03MFOsLYDUTiCowaQVMHRj6/j/2JyxiceQjK5XovZ9FGZmyLTG9lhlGT93n+7GWrWdcZ5a++t8tu1xgIwyv/AMZ2wf4f8eCBSS4b7MAcewwSfQ3x++hJ2nBmNthV3xlGCyqMvNeSFvD7KLZttF+M713W+07nCsTDATuAN9Biq+JETtLrPo5H03n7KdsiXiA9N5Lmio6M/cJDLWn9bRGMgXSgfVnD7EpFX2fQnammXdJERES8LdYNO98BV/0qvPVL/N81f0usPANfetuJkRdn6dhUli0x90PaBngvJ00eGAEU176MJGlG9zxY76Us2rAbGK2qzDBq4gojgKDfx2++eivPDqf45uNuX+zWn4ZQguyjX2HfWIbL17XbHdIaoLoIbEsawEygwwMzjNw3aB5sSQPo6F3DLJFlrzBK59zS2PysqovklHrcSkG7U1rvWT+WKzukXRifsQc8VGEUDvhZlYwwadqWtSVtas7d7TLgPh+pJU1ERMTbfD644SNw/Z/B1uvJ9l/JB4rvgsP3wn/99lmfxnEcjkzNsTlq2/VVYdQYmj4w6r/YzjEaeex7dV7J4g1Pu4FRpAT5VGPOMFqkGy7o47xVCT7y/d0US2UIRuC81+LfdQtBilwxEIGx3Q0xvwjs0GuAcdMBs2PLPtAZ7Awj25LmtoB4sMIIYGNPgn3lPspjdWhJiwQgn7G79Ik8z3yF0XxgdHYVRocmZskVy2wOT9oDHqowAjvHaKTcurwtae7mBUmfO2ReLWkiIiINZaC9hW8UX0R653vgoX+Gx//9rG43M1cknSsyGLIbgqjCqDE0fWC0aeNm9jFA4OCd9V7Koo3MZAkFfLSV3TcbTV5hBODzGX792s0cHJ/lrr3j9uCOnyFYmOGa4JNs9x8Ap9wwFUaxcIBoyM+o02rXnVm+T/IrTrSkuZ/oe3CGEcCmnjh7nT5Kx59b1vtN54onWtJU7SCn0BkP4zMwMpNzW9LOrsLoOXeHtNVmDPwhzz2Hr+5oYagQX9YKo5lsEYC4cYfMKzASERFpKKvb7a7du7a/FxL9dpOiszA0ZVvR+v3T9oAqjBpC0wdGPp/hYPIyBtOPnqiwaBDDMzlWJSOYyqe/K6DCCOBV23pIRgJ8/WF3t6wN15AycX4+/hCh0Sftsf6L67a+xepOhDlarOxGtPxtabYlzef5lrSN3XH2O6sIpA6fWOsySFd2a8hn1JImp+T3GVYlIxyZmrMvbmbHz+rvk+fcHdI6SqOQ7Lcl3R6ypj3KoVwcctNQyC7Lfc64LWkxx70/hbQiIiINZXWbDYyGpvPQsQGmDp3V7Y5M2g+LupgE44NYV83WKNXjrVevNVIevJooWY7vurfeS1mUkZmsO7/IDRk89ul0rYQDfm64qJ/vPjVCOldktuzjO8WdXJW/Bw7fZwevJfrqvcyz1h0Pcyjvvimqw+DrXLFMKOD9lrQN3TH2lfswODCxf9nu90SFkQIjOb3Bzhj7xzIngvuzqBZ8biRFf2uEYHoYkqtrvMLFW9MR5bjjhtnL1JaWyhYJ+X2ESu7ASw29FhERaSgDboXRkak5aFsD04fP6nZDbmDUWpqAWI82mmkQKyIwWn2JnWM0+nhjzTEamcm6O6RVKoxWRmAE8IZLBpgrlPjOk8M8eniK/yxdRbg0C099w84vMqbeSzxr3Ykwe+fcIGKZK4yKpTKlsmOHXnu8JS0WDpCODdovlnHwdTpbJB4OuoGR3rzKqa3vjnFgPHPiefgsHsu7R9Js6k3AzBFbYeQxa9pbTgRGy9SWNpMtkGxxW0ABwslluV8RERGpjmgoQHs0aCuG2tbCzNGz2i3tyNQckaCPcPa45hc1kBURGG1et5ZnWUf48I/P+jblssPde8fIF8s1XNnpOY7D8HSWVcmwfWNifBDtrMta6uGywXbWdkT5+iNDPHhgknuc7ZRbOsEpNcz8ooqeRJjdGXeYcnp4We87X7LXb3hBhVFoWdewGIGezfY/likwKpcd0nl36HUhA0ENvZZTW98ZY2q2YHc8hDMOvq7skLalO2pfSLUOLMMqF2dNR5QxZ3nbZVPZIolIEHLuznEKaUVERBrOQHuLW2G0FnBgZuiMtzkyOcdAWwsmNaz5RQ1kRQRGPp/hUOtO1mSegMLcWd3mY7fv4ec+dR9v+6f7mEznYM8P4PgucJwar9aaniuQK5bprbSkxbpXVNmeMYabLxng7r3jfPuJY2zqbcO37XX2mw00vwigJxnheNZHOZw8692VqiVXsIFRqDLDyPg9fR319/Yy5rTiLFNgNFso4TiQUEuanMG6LnttHKy0l54hYDns7pC2oz0P5QIkvRcY9SYjTPna7BfL1JI2M1cgGQlALg2+gGcrHkVEROT0BtpabIVR6xp7YOrMbWlHpuZY3R61r6FUYdQwVkRgBGDWX02YAiNP3XHGn33wwAT/9wfPccnaNh45PMUff/Tj8Nk3wMeugL/cCF/6BXj221CuXfXR8IwdCLqqNWLn3qygdrSKmy8ZwHHg2eEUO9e1w+XvgIHLYPAl9V7aorzuon6CfsMY7cvekpYrViqM3JY0j7852+julJYfXZ6d0tKVHZs09FrOYL0bGO2drxZ84cdyZeD11qjbeuXBljS/zxBurbTYLU9LWipbsBVGlV0JG6i9WERERKyBtihHpuZw2tbaA2cx+PrI1Byr20J2DqQqjBpGzQIjY8ynjTGjxpgnTzr2QWPMEWPMo+4/r63V/T/ftquup+j4OPLId1/w56ZnC/zGFx9loK2Ff/3lK/jCr1zF9bnvMU2MJy79MGx+DQw9CF98K/z9lfDQZ6Bcqvp6h6fdwKhSYbQCA6N1XTEuXdsGwOXrOmDVBfArt0G0o74LW6Q1HVHeesVa9s3FyE4eXdb7zhef15Lm0YHXFZu64+wvr8IsU4VRKmvb9OIhA4VZtcfIaa3tiOIzsG+yAJG2M1YL7h5JATAYmLQHPBgYAfR2tJE28WULs2eyRTvDKJfS/CIREZEGNdDewmy+xGSgGzBnHHw9my8ykcmzKToHTlkVRg2klhVG/wJcf4rjf+M4zsXuP9+u4f0vMLCqlz3BLUSP3H3an3Echw987XFGZrL87VsvIREJclm3w6t9D3Jn5BpuvHsD78u/i+l3PwI/808QiMA3/yc88I9VX++IW2FkW9JGV2RgBPDWK9YS8vu4cn1jz296zzWbGKON1NiRZb3fXNGGmeGg25Lm93qFUYz9Th+h7Bhkp2t+f6mcrTBqDbjznVRhJKcRCvgYaG9h31jGhj9nKL3eM5qmrzVCNOsGSx5sSQNY09HCsNMB02eePVANqWyBRDjoBkYKaEVERBrRQJu7U9pMyX1d9MIVRken7FiY9RG38loVRg2jZoGR4zh3ABO1Ov9SpPpezKbCLkbHxk75/dt3jfJfTw7zW6/ZysVr2uzBJ/4dU8rxmrf9Nu991Wb+87GjvOb/3c3+vp+Cd90BHRth7+1VX+vwtN3RqjcRsrMlKls5rzA/e9lq7vu9V9rWvAbWk4zQ2z9ILD/Os8dqH4RUVFrSQv5KhZF3B14DdMfDDAfdN9bje2t+f5WWtKRPgZGc2fquuN0prWMDTLzw9bn3eJqN3XG7Q5ovCNGuZVrl4qxuj7K31EN5GR5vADNzRRKRwImWNBEREWk4q9vdwGhq1g6+PkNgNDRpA6MBv/s+KKHAqFHUY4bRe4wxj7sta+3Lecf9l7yGgCnzxN3/dcrvf/6+w3Qnwvz3l663BxwHHv436LuI4MBFvPdVW/j6r76YsXSOrzx02M5eGHwRHL636vOMhmeydMZChPLTUC6u2AojYwztMW+HHGdrx9YtRE2Oj373sWW7z/kZRsHKDCNv/y6NMZQ7NtkvliMwciuMEn5b0afASF7I+s4oB8ZmcTo3wcT+07YjO47DvuMZNnTH7A5pyT7weXNk4JqOKAecXpg6WNO5fACFUpm5QolkS6XCKFHT+xMREZHaqARGQ5XB12eovD7iVhh1myl7YIW+t21Ey/0K9uPARuBi4Bjw16f7QWPMO40xDxpjHjx+vDrDOAcueDl5gmR3/2RF0OhMltt3jfKGSwcI+N1fy7FHYeQJuORt8z934eo2Lljdyr373OKptS+CuUkY212VNVaMzGRP7JAGK7bCqJm0dNgZJk/t2s2o23JYa/MtaYHGaEkDiPVuooQPxnbV/L7mh14bW9GnwEheyLquGOlckXRs0O58dppP046nc6RzRTsoe+aoZ9vRANa0t3DQWYWvlINUbWespdzHW6KyS5pa0kRERBpSa0uQWMhvg6C2tbaiulQ87c8fmZwj4DMki+P2gAKjhrGsgZHjOCOO45QcxykDnwKueIGf/aTjODsdx9nZ3d1dnQUEWziWvJDBmYeYzOQXfOtrjxyhVHZ40841Jw4+/G92TtEFb1zws1eu7+TxoSlm80UbGAEcOv1spKUYns7aNqz5wEgPqobnhn7dTLF/LLMsdznfktYgQ68B1q3q4EC5l8Lw0zW/r8oMoxgKjOTM1rk7pR32uQOsT9OWtu+4fXxvqLSkeXTgNZxUYQQwsa+m91UZMp+c3yVNFUYiIiKNyBjDQHsLRybnoG0NOCX7muc0hibn6GuL4EuPQEuH57se5IRlDYyMMX0nfXkz8OTpfrZWwpuvYZs5yB2PnahecByHLz9wmMvXtduZEwCFOXjiK7Dt9dDStuAcV27ooFByePjglJ1lEeuBQ/dWdZ0nKozcgakKjBqfO9yt20xzcGJ2We4yVzh5l7QcBLxfYbSxO86zzhpKyxAYVSqMWhxbJquZKvJCNriB0XNF9/n4NG2TlUB4Q2fUrTDybmDUGQsx4nf/aq5xYDQzpwojERGRZjHQ1nKiwghOv1Pa7u/ypgN/yGfzvwmP/JunXxfJT6pZYGSM+QJwD7DVGDNkjHkH8BfGmCeMMY8D1wDvq9X9n07vRdfhMw5HHv3+/LEHD06ybyyzsLpo7+2Qm4aL3voT59g52I7fZ7hv/7idY7T2Kjh0T9XWmCuWGM/kWZWMQHrYHlRLWuNzQ79VvikOjS9PYJQvVQIjf0MMvQbY1BNnd3kN4ZkDNritoXSuQEvQj79UCYxUYSSnN9DWQsBneDYVsdUx43tO+XP7jqcJBXz0h7NQzHq6Jc0Yw6UX7CDv+Ln/4YdwHKdm93WiwigAec0wEhERaWQD7ZXAaNAeOFWrvuPAN9/L9tyjZCKr4Ip3wk+fdiqNeFAtd0l7q+M4fY7jBB3HWe04zj85jvM2x3EucBznQsdxXuc4zrFa3f/pmIHLyPtaSA7fw9cfGaJcdvjSA4eJhfz89IUnFUDt+yEEozD44p84RyISZEd/kvtOnmM0dQimq7Nl+uiMbY9Z1Rq2FUbBqF5YN4OWdvAFWR/JLGOF0fNnGHk/MFrT3sKR8HoMDhyv7RyjdK5IPBKAvNsiGIzW9P6ksQX8PtZ2RNk/NgudG1+wwmh9Zwx/2p0J5PFP0v7sZy5mKjzA2KFn+f1vPEmpXJvQaKYSGAUK4JRV0SciItLABtqiTM0WyETcyutTDb4++gikjvLhws/xnYv+Fl7zv22xhTQMb27bUkv+IKU1L+LqwDO870uP8dq/vZNvPX6MGy/qJxoKnPi5fT+0YdFpWniu3NDJo4enyBZKJy76w9VpSxtxByL3JiNw5CHo2mIrmaSx+XwQ72EwNMOh8eWdYWRb0vIN0ZIW8Pu48BL7mDr63MM1va9UtkgifFJgpDewcgbru2IcGM+4gdHpKozcHdIqHyJ4uMII7GOue/B8LktM8rn7DvGX361NUDvjtoC2+tyZYWpJExERaVgD7k5pR9KOHb1xqgqjXd/GMX5+ULqE1W0ty7xCqYaVFxgBLVuuYW35MJ96fS9zhRJzhRJvuWLtiR+YOWp3aNrwitOe46oNHeRLZR4+NAmrLoRgrGpzjIbdwKg/kofD98OmV1blvOIB8V56/cs3wyhfPLklLd8QQ68BXnfNS8g5QZ597L6a3s+JCqO0PaCWNDmDdW5g5HRstL36xdyC7xdKZQ5NzLo7pFUCI29XGAGYjg30Fo7yii1dfO+p4Zrcx8ycW2Fk3BbQcLIm9yMiIiK1N+AGQHbw9VqYOgjY+cB//b1dfObuA/Dst5np2ckUifmASRrLigyMOP8GAK4r/Zjvv+/l/OD/ezkXr2k78f19P7L/foHAaOe6DnwG7t03Af4ArN5ZtTlGw9M2MBqYvN9OnN/0qqqcVzwg3kunM8nUbIFp981TLS3YJa0w2zAtV23xKJPRdfjHnuFwDcO1dLZIfL7CyEBQf5HJC1vXFSNbKDMdXWvbqiYPLPj+4YlZimXH3SHtKBh/Y2xa0LEBChleMQD7xjJMPG8n0WpIuRVGUezfcaroExERaVyr3QBoaMrdKc0dev3Zew/yd7ft4R+/eRuMPsX+zpcDJwImaSwrMzDq2ACrL4cn/p1QwHdiZ7SKfT+EaBf0bD/tKZKRINv6k9y3b9weWPsiGHkKstPnvLyRmSzhgI/ooR/ZwaqrLz/nc4pHJHqJF+zsq+UYfJ0r2hlGoYAP8o0TGAEkBy9iiznMP95Zu52b0rmi3bEpn7FvXtX6KWdQ2SntEG7V0PPa0vYdt+2NtsLoKCRWgc+/rGtcko71AFzRZv8Oe/jgZNXvIuUGtP6CW9GnljQREZGG1R0PE/L7uPXpEfKJ1TA9xCMHxvjjW57mmq3dvKPzGQC+nLoAY6CvLVLnFctSrMzACOCCN8HIkzDyvK27HccGRhtebmfOvICr1nfyyMlzjJwyDD1wzks7MD5Lf2sEs/c2u44GaSOSsxDvJZSbwE+JgxO1n2OUL5YJ+g1+n7EVRg3UchVdvYM+M8G3HniWsXTuzDdYAvsGNugGRo3zu5H6WecGRruK7s6Vzxt8vX/MPq43drstaQ3QjgbYD1KATYFRAj5j262rbCZbcHdIq7SAKjASERFpVD6f4b3XbeaO547ztw9moVzkg5//Ab3JCP/3zZfwc21PsscM8vnn/PQkwnZEhjSclRsYbb/Ztgo88eWFx4/vslvZv0A7WsWVGzrJF8s8enjKVgEZP+y/85yWlS2UuGvPGK9bnYHpQ7Dx2nM6n3hM5yYMDn8T/HuGh2u/SWCuWCbk99kgNJ9pqAojerYBsK58iC89cIpdF6pgYYVRA/1upG76khHCAR+7p/0Q7fzJCqOxNB2xEG3RkK0w8vjA63mta8D4CU0fYPtAKw/VpMKoQCIShFzKHtAMIxERkYb2q6/YxJff9SKGfbb9Pjp7lI///GW0OjOEjtxL8uLXEwn6WNOu19mNauUGRvFuG8Y88RUol08c3/dD+++zCIyuWNeB32f44a7jtrR+3Uvh2W+d07Lu3TfObL7EDTG38kkDr5vLjp+Ba3+f1/rv5433vwn23lbTu8sVS4SDfihmAaexQpGe8wG4uvU491ZaP6vIcRw79Loyw0gVRnIWfD7Dhu44u0bS0LERJha2TO49nrHtaI7TWIFRIGTnD0zs47K17Tw2NEWhVD7z7RZhZs4NaOcDI1UYiYiINLrL13Xwx2//KQA+8KIoF6xuhee+C06Znp0388V3vogPvu70o17E21ZuYARwwRvtcK7DJ+1utu+HtjS/be1pb1bRGg3y0k1dfPOxo5TLDpx/I4w/Z6uUlujWZ0aIhvxsmL4POjdB+7oln0s8yOeHq9/P73b8DSmi8Nmfhemhmt1dvlgmXJlfBHY3v0bRugZCCXZGR3j08JR9jFVRtlCmVHZO7JKm9hg5Szv6kzx1ZBqnc+NPVBjtH8vYOUfZaShkGqclDezffRP7uHSwjWyhzDPHZqp6+lSuQLIlqJY0ERGRJhPtWgfARc6z8NC/wD0fg0Q/9F/CxWva2DHQWtf1ydKt7MDovJ+2LTqPu21ppQIc+PFZVRdV3HRJP0em5njo0KQ9H8Az31zSchzH4danR7l2Yyv+g3fBRlUXNatS70X8sXm33QVv5Kma3U+uWHZ3SHPnJTVShZEx0HM+mzhEKltk31i6qqdPZe0udaowksXaMdDKeCZPOr4OUscgZ6/NVLbA8VTuxA5p0ICB0X4uG2wHqHpb2sIKI6PHnIiISLMIRSHWAw9+Gr75G/Z10DW/qw1lmsDKDozCcdj6Wnjya/ClX4BPXA351KICo1dvW0Uk6OMbj7jDTQcug2dvWdJynjwyw/BMljf1HobinNrRmtjazigPpTvsF88bmltNucLzK4waKDAC6DmfzswewOHhQ1NVPXUqZ7f4TkQCDTcQXOprx4CdvbO/vMoecNvSKgOv53dIg8ZpSQNoXw/ZKfqCc/S3RqoeGKWyBZKRoA3Ywgm9iBQREWkmb/gk3PRx+PWH4bf3waW/WO8VSRWs7MAI4PJ32Betx3fZFpgX/zpsfvVZ3zwWDnDdtlV864lj5ItlOO8GOPrI2bUZjT4Lj33Jzj3a90MO3Pl5fjPwFV68/+/AH7IzkaQpDXZGGXcSlMKtMFG7wChfKtsdCeYrjBosFOnZhj87ybpIhkeqHBilszYwmq8waqR2Pamr8/uSGAOPzXXZA25b2r7jz9shDRqvwghgYj+XDrbzcBUDI8dxmMlWhsyn1I4mIiLSbDZeAxf/HHRu1IdCTSRQ7wXU3eCL4QMHz+kUN13czzcfO8qdzx3nleffCD/4kA2BrnzX6W+0+7vw5V90hxFbNwLlgA9fcQNc/f7Ge3MvZ21tRwwwZGJrSdaywqhYsi1pDVxhBHB99wQ/rPI23+ncyYFRWo83OWvRUICN3XHumTS8DearBPcdT+MztoKQp48ABhKr6rnUxZkPjPZx2eBObnn8GEen5uhvaznnU88VSpTKjp1hNJPWwGsRERGRBqAKoyq4eks37dEg33j0KHRthq6tLzzH6Mmvwhd/DrrPg3ffBe+6g+Nv/AY35D7Mp6/+Mfz6Q/Dy316+P4Asu8FOG9wcD61enpa0ghsYNdIMI4CebQC8KHaU3SOp+ZCnGlKVCqOIZhjJ4u3oT/LwsYId6FipMBrLsLo9aqv6Zo5AvBf8wTqvdBHa1wEGhu7nutlv86ngXzH1g49U5dQzcye1gOZStiVNRERERDxNgVEVBP0+XntBH99/eti+oT3/Rjh4F2ROsRX4I5+Dr7wD1lwJb/8mrNoBfRfx7en1POls4JoLBpf/DyDLrjMWIhbyc5g+u1NfIXvmGy1Bbn6XNLclrdHaruLd0HcRLz76GVYzwuNDU1U7dSV8SgSAUl4tMrIoOwZaGZ7Jku/cCsNPUCo73Ld/gu39dr4R43uhvcGez4MRO3Pp/k+y+q7f4+W+x1n7zKegXD7nU1eGzCcjQe1KKCIiItIgFBhVyU2XDJAtlPnOk8Nw/g3glGHXtxf8TGbiGMVb/j+OtF/ON7b9P247MMc/3rmPN3/iHv74lqfZ1BNnY7deRK8ExhjWdsbYXewFHJg8UJP7yc/vktagFUYAb/wMfp/hk8GP8OT+o1U7bdp9A5vw5+0BVRjJIlS2hz0a3w7Hn+G+Zw9xPJXjxov67Y6bRx+BgZ11XuUSvObD8Mo/gl+9l3/ueC/x4iSFY0+c82lnKo+3SODE0GsRERER8TQFRlVy2dp2NvfE+fvb91DouRA6N8HdfwulAk8dnea3v/IYn/9/v4OvmOVtw2/ivV/fzS//y4N8+FvPMD1X4H+8fCP//EuX1/uPIctosCPKo7PuTmk1GnydK5Zse8z8DKMGDEU61uN74z+z2XeEyx7+PXCcqpy2UmEUNXP2gAIjWYRtbiXRk2wGp8xj999OIhzg2vN6YORJO59udQMGRttvhpf9JvScz/kvfT0Au+76j3M+7YzbAppsCaolTURERKRBaOh1lfh8hg/81Hm84zMP8sUHh3jbqz8MX3gLx2/7O372jm10mBn+JPA9Jgdv5Otv+iWmZvNMZPJ0xcOs6WjAqg85Z4OdUb66q90+Cms0xyg3X2FU2SWtQa+1jddyS++7ef3I3+Pc9wnMVe8+51OmckVCAR/hktsOqMBIFiEZCbKuM8oPM2u5Acjuv4/rd7ybSNAPhx+wP7Tmirqu8Vy99JIL2P+tQQq7bsVx/gBzDjuezMxVWtK0S5qIiIhIo1CFURVde14PV6zv4P/dupv04KsobbiW6F1/SX8wzXeveopwOUvnT/0vWluCDHbGuGRtu8KiFWxtZ5SxYpRSpKNmFUaz+RKxUKXCyEAgUpP7WQ4zF7+Lx8obyD/671U5XzpbJFHZIQ0UGMmibR9o5d5hyMQG2VbezU2XDNhvDD0AiT47D6iB+XyG4rpXsK34FD966tx2E60MmU9Ggm5LmgIjEREREa9TYFRFxhh+77XnM5bO86k79/PxyH8n5OT4/MBXiD/6T7D9Jug5r97LFI94+ZZuAj7DEX9/TSqMHMchkysSCwfsDKNQDM6hQqDeLlnbzh3lCwmOPALZ6XM+XzpXPLFDGigwkkXb0d/K0OQcD5c2cKl/L1etd1tMhx6w7WgN/HirWH/VjYRNkbt+8AI7f56F+RlGgTKUC6owEhEREWkACoyq7OI1bfz0hX18/Id7+auH4cHeN9J7+L9sCf7V76/38sRDVrdHeePONTyU6qA0tqfq58+XyhTLjg2M8hkINnY123mrEjxgLsTnlODAXed8vnS2SLwSpoECI1m0HQN2jtGtqbV0M4k/fRQyYzC5H1Y3x0y6wLqXUPKF6Dl+Nw8emFjyeVLZIgGfIVJ2A9pwskorFBEREZFaUWBUA7/9mq04OGzrS3LpL/45JPphx89A7/Z6L0085j3XbuKA04s/fezEYOoqyeRKALYlrTDbuPOLXAG/j+jGq8gSwtl3+zmfL5VzA6P5ljRVPMjibO+3O6U9Ut5kDww9AEMP2v9uksCIUBTWvohXBJ7kU3fuW/JpZuYKJFuCmMrjTS1pIiIiIp6nwKgGBjtjfOPXXsLn/vuVhOPt8J774eZP1ntZ4kEDbS30rd8BwMiBp6t67kxlF7D5CqPGr6C5Zvsa7iudR+65cw+M0tmi3eK70pLW4BVYsvw6YiEG2lrIdp6P4w/bsGjofvAFoO/iei+vavybrmUzh9i95zlK5aXtUpiaf7wpoBURERFpFAqMamR7fyvtsZD9IpwAvzakk1N75UtfBMD377qnqufN5G1gFAu5oUiDVxgBvPL8Xu5ydhCZfA5mjp3TudLzFUaaYSRL9+c/cwF/9sadmL6L4MhDtsqod0dTPN7mbbwWgEsLj/LcaGpJp5jJFtyB1+7tw4lqrU5EREREakSBkUiddQ9uA+DY3icZns5W7bzzLWlhtyWtCSpouuJhJnttwMb+H53TuU4MvVbFgyzdyzZ3c9lgux1yffRROPJw87SjVfTuoNTSxSv8j/LIoaklnWK+wihXaUlTYCQiIiLidQqMROotnKDQ0s1ahnnk0GTVTjtbqTAKB+x8pCapoNly4YuZcOJknrl1yedwHIdUtkAiEoTZSRumBUJVXKWsOAOXQXHOBpDNFhj5fPh23Mxr/fcz8fTSgtqZObfCKO9WGCmgFREREfE8BUYiHuDr2sR63zC7R9JVO2dlhlEsFIBC4++SVnHd9j7uLu+A/T8EZ2nzVHLFMoWSY1vSpg9B65rqLlJWnpNDotU767eOGjGv/EPGA7284eAfQ3Z60befrzAaehD8IUj212CVIiIiIlJNCoxEPMDftZGN/hF2jyxtPsipLGhJyzf+LmkV67pi7InvJJY7DmPPLekcaTdMS0QCMD0ErauruURZidrWQqwbop3QsaHeq6m+SJI7dvwp3eUx8t/8rUXf3M4w8sNT34BNr4JIsvprFBEREZGqUmAk4gUdG+l0JhkaHqnaKTMnt6QVZptil7SK2PmvBCDz7NLa0tJZ+7uJhwMwdRjaVGEk58gYuPjn7T/G1Hs1NdF/wdV8tHQToae+DE985axvVyyVmc2X2Jp/ClJHYfsbarhKEREREakWBUYiXtC1GYDBybvJFUtVOeV8hVHQ3zS7pFVcdellHCj3kn70G0u6faXCKBkowuyYKoykOq77ELz6T+q9ipq5aHUbHyvdzLH4drj1g2fdEppyA9odk7dBIAJbr6/hKkVERESkWhQYiXjBpuuYbNvBn/o/yZHdj1TllJlcEZ+BiK8ITqlpZhgB7BhI8p3gK+kdv29JbWmVN7AdxVF7oHVtNZcn0pRi4QCbV7XzncC1MH0YJvad1e1S2SI+yqw/fitsfrV2SBMRERFpEAqMRLwgGGHspz/NHGF6vvXfYO7cd0vL5IvEQgFMYdYeaJJd0gCMMexb/QYKBODBTy/69pUKo/a82wKoCiORs3LpYBtfmdxov9h/x1ndZiZb4ErfM7TkxmCH2tFEREREGoUCIxGPWLt+E79WfB8ts0fhK++A8rm1pmVyRaJhv51fBE1VYQSwdnAd/1W6HOeRz9qWu0VI5woAxHPH7AHNMBI5K5eubeepXDeF2KpFBUY3+O6l5G+Bza+p8QpFREREpFoUGIl4RDjgZ7zjEr7Y8auw9wew9/ZzOl8mX7IDr/PNV2EEsL2/lX8rXofJzcCTX13UbStDr6Nzx8D4INFXiyWKNJ1L17YDhqHWy21gVC6f8Tbp2SzX++8nNfiqppqlJiIiItLsFBiJeMjWVQk+N3eV/eLYuc0yms3ZljQKbvVNk1UYbR9I8oCzlYnYJnjgH896AC9Aym1Ji2SO2rDIH6zVMkWaymBnlI5YiLvL2+zA+OPPnPE2kaEf02lSFM67qfYLFBEREZGqUWAk4iFbehM8Mwnl9g1w7PFzOlcmVyIW9p9UYdRcgVFPIkJ3IsJtiRvh2GNw5OGzvm06WyTgM/hSQ9CqdjSRs2WM4bLBdv7+QD8A//tj/8Av/8sDOC8Q2Eaf/hLTToyW7dodTURERKSRKDAS8ZAtvQkcB1Jt58HwE+d0rsrQ6xMzjJqrJQ1ge3/SVmSF4vDQ2Q+/nsjkaYsGMVOHNfBaZJE+9LrtvOcN1zIdWc0N8T3c9uwoTxyZPuXP3v7w01wwcwf7B24kHosv80pFRERE5FwoMBLxkC29drvpw6FNMLkfsqd+E3Y2MrmiO8PIbUlrsgojgB39rTx+vExpw7Ww/86zvt2B8Qzr2sMwc1QDr0UWqb+thbdesZbW7a/iwuITtAQcvvrQEIw8BY99cf7npmcLPHrLJwibIttv/PU6rlhERERElkKBkYiHrOuMEvL7eJp19sDIU0s+lx163by7pIGtMCqVHUaSF8DUQUiPntXtDozNckFbDsoFVRiJLNX6qzH5FL+8IcWeR+/A+fT18PV3wTO3APDhW57ixtL3mO25jGDfjjovVkREREQWS4GRiIcE/D42dMe4J+3u2nUOc4wylaHX8xVGzdeStmOgFYAnfefZA0MPnPE2c/kSwzNZtkVn7IHWtbVankhzW/cyAH7RfIuPl/+EOX8SerbDLe/lx48/y4FHfsAmc5Toi365zgsVERERkaVQYCTiMVtXJbh/LAyx7iXPMSqXHWbzJaLhQFNXGK1ubyEZCfDjTD/4gnD4/jPe5sC4DdA2hCbtAVUYiSxNvAd6ttF78JukTZwPdvwfeMMncOamSH/tN3lX/E6ccAK231zvlYqIiIjIEigwEvGYLb0JjkxnKfbsgOGlVRjNFUoAxEIn7ZLWhIGRMYZt/UkeG85B34Uw9OAZb3NgzAZGA2bMHlBgJLJ0F74Jus/nPy7+BF/d5+NIZBOfj7yZ67mLVxZ/hLnwzU1Z3SgiIiKyEigwEvGYyuDrsfhWGH0GivlFnyOTKwLYodeFDARawNecD/cd/a08e2yG8sBOOPowlIqn/kHHgVKB/W6FUUdxFCKtEEku42pFmsxL3gu/di/XvfgKSmWHN3/iHv5o4tVMt23DOGW49O31XqGIiIiILFFzvoMUaWAXrbZzeZ4qD9qhzGO7Fn2OTN6tMAq7FUZNuENaxfaBJLlimeHkhbb9bvQUg8IdB772TvjkNRw8PkNXPEwofUTzi0TOlTEAbOqJc8naNoYm5/ill26m9e1fhNd/zFb+iYiIiEhDUmAk4jE9yQgbumLcNtlrDyxh8PV8hVHInWEUbN6WkB39NmB7nM32wKkGXz/5VXjiyzDyBB1Hfsi6zihMD6kdTaSKPnD9efy3l6zjd37qPGgfhEt+od5LEhEREZFzoMBIxIOu3NDBLUejOMHokgZfL2hJy2eausJoQ3ecSNDHbcfCOLEeOPy8wCg1At/+LRjYCckBXjH9ddZ1xWDqMLStqc+iRZrQlRs6+aMbtxP066WFiIiISDPQqzoRD7pqQyfT2TJz7ectafB1Jn/yDKPZphx4XeH3GV66qZsvP3SEO+bWM7n7bp48Ms1cvmRb0W55n23Lu+nj5C9+O1c6j3NVaB/kplVhJCIiIiIichqBei9ARH7Sles7ATgQ3Mi24e/a4MOdFXI2Mrnn7ZLW5LsUffTnLuF7T49w7LYdvHz6Pq79u28zSZJfid/F/yp+C677E+jewr7Bn2W98xdcc/hj9oYKjERERERERE5JFUYiHrSqNcJgZ5QHc6shNwOTBxZ1+1m3wiha2SWtiSuMACJBP6+7qJ+33PwzAHzyGofPn3cXv1v8e+4vn8/MJe8EYG8myi3lF9E55rataei1iIiIiIjIKSkwEvGoq9Z38p2xHvvF0YcXddu0W2EUDwWafpe0BfovBuPn8sf/kBcf+BiT62/kF/O/ze27xwE4MJ7hM8VXn/h5VRiJiIiIiIickgIjEY+6ckMH92VXUwq1wp7bFnXb2Vylwsjf9LukLRCKwaoLIDMGr/oQ7b/wGRKJJN99ahiA/WMZhuPb7ABsXxDivXVesIiIiIiIiDdphpGIR125oZMSfg61XcH6Pbcuao5ROl8kFPDZ3YqafJe0n3DTx21ItnonPuDV23r52sNHyBZKHBjL2B3SbvgIHHscfMrMRURERERETkXvlkQ8aqCthTUdLdzhXAzpYRh+4qxvO5sr2YHX0PS7pP2E3m2weuf8l9fvWMVcocQdu49zYHyW9Z0x6LsILn1bHRcpIiIiIiLibQqMRDzsqvWdfHZss/1iz/fP+naZXJFYOAClIpTyTb9L2gu5akMnyUiArz48xFg6ZyuMRERERERE5AUpMBLxsCs3dPLcXJxU2/nw3K1nfbtMvkgs5O6QBiurwuh5gn4fr9rWy/eeHgFgfdfK/V2IiIiIiIicLQVGIh728i3ddMVD/OvYZkqH7uVHjz2H4zgvfKP9dxCaGyUW9tsd0mBlzTA6heu3r6LyaxvsVIWRiIiIiIjImSgwEvGw7kSYH77/GtZe8Xr8lPnil/6Nf39oyH6zVPjJGxx7DP719bx28rO2Ja3gBkYrZZe007h6SzctQTvTaZ0CIxERERERkTNSYCTicfFwgBt/+iaccJKb4k/zr/ccgGe+Cf9nHdz2v0/8YLkM3/otcMqsze8jGvLbHdJgxVcYRYJ+XrWtl3WdUVoqw8BFRERERETktAL1XoCInAV/ALPxGl62927uH/4Czpc+j4kk4Y6/gJ7zYccb4PEvwtD9kFzN4MxBu0vafIXRyg6MAP705h2kc8V6L0NERERERKQhqMJIpFFsuo5o7jh/EPwcT7W+HH7jcVhzJfzHr8GBu+D7fwgDO+Fl7yPOLP1m/KQKI7VhJSJB+lpb6r0MERERERGRhqDASKRRbHkNJPq4rfOtvGXyXWR8cXjTv0KkFT5zA2TG4Kf/Cnp3ADBYPKAKIxEREREREVkSBUYijSLeA7/5DK2v+1PSeYdvPnYUEqvgzZ/D8YdwLv/v0H8J+Y6tAPTn95+0S5oqjEREREREROTsKTASaSTGcOnadrb0xvnC/YeYms3zZ49HuSr7d3yu49cAmPXFOOJ00pvdBwW3JU0VRiIiIiIiIrIICoxEGowxhrdesZbHhqZ56f+5nU/euY9JJ8F9B6YAyORL7CqvoSuz56QKIwVGIiIiIiIicvYUGIk0oJsvGaCvNcIV6zv49v98GS/b3MXu4RQAs7kiu501JDL7ITdjbxBUS5qIiIiIiIicvUC9FyAii9cWDXHP775y/uvNvQnueO44hVKZdK7Is+U1+MsFGH4C/CHw66EuIiIiIiIiZ08VRiJNYOuqOIWSw4GxDLP5ErucNfYbQw9ofpGIiIiIiIgsmgIjkSawuScBwK6RFOlckb1OP47xQ+a4dkgTERERERGRRVNgJNIENvXE8RnYPZJmNl8kT5BC2wb7TVUYiYiIiIiIyCIpMBJpApGgn3WdMXYPp0jnSgCUu8+339QOaSIiIiIiIrJICoxEmsTm3ji7R1PM5ooA+Hq3229ohzQRERERERFZJAVGIk1ia2+CA2MZJmbzGAOBPjcwUoWRiIiIiIiILJL22hZpEltWJSg78MTQNNGgH1/vefYbmmEkIiIiIiIii6QKI5EmsaXX7pT22OEpouEAtK+3YZF2SRMREREREZFFUoWRSJNY1xkj6Ddk8iV6khHw+eCGv4HOzfVemoiIiIiIiDQYBUYiTSIU8LG+K8bukTTRkN8evOgt9V2UiIiIiIiINCS1pIk0kUpbWiysLFhERERERESWToGRSBPZWgmMKhVGIiIiIiIiIkugwEikiWxWhZGIiIiIiIhUgQIjkSaydVWlwkiBkYiIiIiIiCydAiORJrK2I0oiHKAjHqr3UkRERERERKSBqQxBpIn4fYav/uqL6U1E6r0UERERERERaWAKjESaTGWnNBEREREREZGlUkuaiIiIiIiIiIgsoMBIREREREREREQWUGAkIiIiIiIiIiILKDASEREREREREZEFFBiJiIiIiIiIiMgCCoxERERERERERGQBBUYiIiIiIiIiIrKAAiMREREREREREVlAgZGIiIiIiIiIiCygwEhERERERERERBZQYCQiIiIiIiIiIgsoMBIRERERERERkQUUGImIiIiIiIiIyAIKjEREREREREREZAEFRiIiIiIiIiIisoACIxERERERERERWUCBkYiIiIiIiIiILKDASEREREREREREFlBgJCIiIiIiIiIiCygwEhERERERERGRBRQYiYiIiIiIiIjIAgqMRERERERERERkAQVGIiIiIiIiIiKygAIjERERERERERFZQIGRiIiIiIiIiIgsoMBIREREREREREQWqFlgZIz5tDFm1Bjz5EnHOowx3zfGPOf+u71W9y8iIiIiIiIiIktTywqjfwGuf96xDwA/cBxnM/AD92sREREREREREfGQmgVGjuPcAUw87/Drgc+4//0Z4KZa3b+IiIiIiIiIiCzNcs8w6nUc5xiA+++eZb5/ERERERERERE5A88OvTbGvNMY86Ax5sHjx4/XezkiIiIiIiIiIivGcgdGI8aYPgD336On+0HHcT7pOM5Ox3F2dnd3L9sCRURERERERERWuuUOjP4TeLv7328H/mOZ719ERERERERERM6gZoGRMeYLwD3AVmPMkDHmHcCfA9cZY54DrnO/FhERERERERERDwnU6sSO47z1NN96Za3uU0REREREREREzp1nh16LiIiIiIiIiEh9KDASEREREREREZEFFBiJiIiIiIiIiMgCCoxERERERERERGQBBUYiIiIiIiIiIrKAAiMREREREREREVlAgZGIiIiIiIiIiCygwEhERERERERERBZQYCQiIiIiIiIiIgsoMBIRERERERERkQUUGImIiIiIiIiIyAIKjEREREREREREZAEFRiIiIiIiIiIisoACIxERERERERERWUCBkYiIiIiIiIiILKDASEREREREREREFlBgJCIiIiIiIiIiCygwEhERERERERGRBRQYiYiIiIiIiIjIAgqMRERERERERERkAQVGIiIiIiIiIiKygAIjERERERERERFZQIGRiIiIiIiIiIgsoMBIREREREREREQWUGAkIiIiIiIiIiILKDASEREREREREZEFjOM49V7DGRljUsCueq9DmloXMFbvRUhT0zUmtaZrTGpJ15fUmq4xqTVdY1JrjXqNDTqO032qbwSWeyVLtMtxnJ31XoQ0L2PMg7rGpJZ0jUmt6RqTWtL1JbWma0xqTdeY1FozXmNqSRMRERERERERkQUUGImIiIiIiIiIyAKNEhh9st4LkKana0xqTdeY1JquMaklXV9Sa7rGpNZ0jUmtNd011hBDr0VEREREREREZPk0SoWRiIiIiIiIiIgsE08HRsaY640xu4wxe4wxH6j3eqQ5GGMOGGOeMMY8aox50D3WYYz5vjHmOfff7fVepzQOY8ynjTGjxpgnTzp22mvKGPO77vPaLmPMa+qzamkkp7nGPmiMOeI+lz1qjHntSd/TNSaLYoxZY4y53RjzjDHmKWPMb7jH9VwmVfEC15iey+ScGWMixpj7jTGPudfXh9zjeg6TqniBa6ypn8M825JmjPEDu4HrgCHgAeCtjuM8XdeFScMzxhwAdjqOM3bSsb8AJhzH+XM3nGx3HOd36rVGaSzGmKuBNPCvjuPscI+d8poyxmwDvgBcAfQDtwJbHMcp1Wn50gBOc419EEg7jvNXz/tZXWOyaMaYPqDPcZyHjTEJ4CHgJuCX0HOZVMELXGNvQs9lco6MMQaIOY6TNsYEgR8DvwG8AT2HSRW8wDV2PU38HOblCqMrgD2O4+xzHCcPfBF4fZ3XJM3r9cBn3P/+DPYFjMhZcRznDmDieYdPd029Hvii4zg5x3H2A3uwz3cip3Waa+x0dI3JojmOc8xxnIfd/04BzwAD6LlMquQFrrHT0TUmZ82x0u6XQfcfBz2HSZW8wDV2Ok1xjXk5MBoADp/09RAv/JeKyNlygO8ZYx4yxrzTPdbrOM4xsC9ogJ66rU6axemuKT23STW9xxjzuNuyVimz1zUm58QYsw64BLgPPZdJDTzvGgM9l0kVGGP8xphHgVHg+47j6DlMquo01xg08XOYlwMjc4pj3uyfk0bzEsdxLgV+Cvg1t9VDZLnouU2q5ePARuBi4Bjw1+5xXWOyZMaYOPBV4L2O48y80I+e4piuMzmjU1xjei6TqnAcp+Q4zsXAauAKY8yOF/hxXV+yaKe5xpr6OczLgdEQsOakr1cDR+u0FmkijuMcdf89CnwdWxo44vbWV3rsR+u3QmkSp7um9NwmVeE4zoj7wqUMfIoTZc66xmRJ3JkMXwU+5zjO19zDei6TqjnVNabnMqk2x3GmgB9iZ8voOUyq7uRrrNmfw7wcGD0AbDbGrDfGhIC3AP9Z5zVJgzPGxNxBixhjYsCrgSex19bb3R97O/Af9VmhNJHTXVP/CbzFGBM2xqwHNgP312F90uAqL4BdN2Ofy0DXmCyBO8zzn4BnHMf5yEnf0nOZVMXprjE9l0k1GGO6jTFt7n+3AK8CnkXPYVIlp7vGmv05LFDvBZyO4zhFY8x7gO8CfuDTjuM8VedlSePrBb5uX7MQAD7vOM53jDEPAF82xrwDOAS8sY5rlAZjjPkC8AqgyxgzBPwR8Oec4ppyHOcpY8yXgaeBIvBrjbZbgiy/01xjrzDGXIwtbz4AvAt0jcmSvQR4G/CEO58B4PfQc5lUz+musbfquUyqoA/4jLvTtg/4suM4txhj7kHPYVIdp7vG/q2Zn8OM4zRcG52IiIiIiIiIiNSQl1vSRERERERERESkDhQYiYiIiIiIiIjIAgqMRERERERERERkAQVGIiIiIiIiIiKygAIjERERERERERFZIFDvBYiIiIg0CmNMCXgCCGK3yf0M8H8dxynXdWEiIiIiVabASEREROTszTmOczGAMaYH+DzQCvxRPRclIiIiUm1qSRMRERFZAsdxRoF3Au8x1jpjzJ3GmIfdf14MYIz5N2PM6yu3M8Z8zhjzunqtW0RERORsGMdx6r0GERERkYZgjEk7jhN/3rFJ4DwgBZQdx8kaYzYDX3AcZ6cx5uXA+xzHuckY0wo8Cmx2HKe43OsXEREROVtqSRMRERE5N8b9dxD4qDHmYqAEbAFwHOdHxpiPuS1sbwC+qrBIREREvE6BkYiIiMgSGWM2YMOhUewcoxHgImzbf/akH/034OeBtwC/vMzLFBEREVk0BUYiIiIiS2CM6Qb+Afio4ziO22425DhO2RjzdsB/0o//C3A/MOw4zlPLv1oRERGRxVFgJCIiInL2Wowxj2Lbz4rYyqGPuN/7e+Crxpg3ArcDmcqNHMcZMcY8A3xjWVcrIiIiskQaei0iIiJSY8aYKPAEcKnjONP1Xo+IiIjImfjqvQARERGRZmaMeRXwLPB3CotERESkUajCSEREREREREREFlCFkYiIiIiIiIiILKDASEREREREREREFlBgJCIiIiIiIiIiCygwEhERERERERGRBRQYiYiIiIiIiIjIAgqMRERERERERERkgf8fDbEd90THgbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (20, 10))\n",
    "plt.plot(y_test_inv-273.15, label = 'Actual') #converting to C from K here\n",
    "plt.plot(y_pred_inv-273.15, label = 'Predicted')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Day')\n",
    "plt.xlim([0, 365])\n",
    "plt.ylim([min(y_pred_inv[:366]-273.15)-3, max(y_pred_inv[:366]-273.15)+3])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "restricted-warner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.8725874483073941\n",
      "R-Squared: 0.9926826670573241\n",
      "RMSE: 0.934123893446364\n",
      "MAE: 0.6301929959828615\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c55500",
   "metadata": {},
   "source": [
    "Custom function to build an LSTM model and test performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47bad0a",
   "metadata": {},
   "source": [
    "Adjusting the dataset to predict average daily temperature in order to speed up training times. This method will also likely be used in our final analysis as it is slightly more intutitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f15249f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['datetime'] = pd.to_datetime(df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "facd5762",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby(df['datetime'].dt.date).agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1cf35a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = int(len(df)*0.75)\n",
    "\n",
    "train = df[:cutoff].iloc[:, 0:].values\n",
    "test = df[cutoff:].iloc[:, 0:].values\n",
    "\n",
    "sc = StandardScaler()\n",
    "train_std = sc.fit_transform(train)\n",
    "test_std = sc.transform(test)\n",
    "\n",
    "X_train, y_train = split(train_std, 10)\n",
    "X_test, y_test = split(test_std, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3f2a2c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1405, 10, 1), (1405, 1))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840ecce8",
   "metadata": {},
   "source": [
    "We tested Ludwig in another notebook, and AutoKeras does not have time series functionality, so we're making our own hyperparameter optimization method. The models tend to perform much worse than ARIMA and XGBoost, so it is more of a formality than anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "furnished-reason",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_lstm(X_train, n_layers = 2, cell_range = [32, 128], dropout = 0, batchnorm = False):\n",
    "    model = keras.models.Sequential()\n",
    "    model.add(keras.layers.InputLayer(input_shape=X_train.shape[1:]))\n",
    "    \n",
    "    for i in range(n_layers-1):\n",
    "        model.add(keras.layers.LSTM(random.randint(cell_range[0], cell_range[1]), return_sequences=True))\n",
    "        model.add(keras.layers.Dropout(dropout))\n",
    "        if batchnorm:\n",
    "            model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.LSTM(random.randint(cell_range[0], cell_range[1])))\n",
    "    model.add(keras.layers.Dropout(dropout))\n",
    "    if batchnorm:\n",
    "        model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.Dense(1))\n",
    "    model.compile(optimizer='adam', loss='mse')\n",
    "    \n",
    "    return model, {'n_layers': n_layers, 'cell_min': cell_range[0], 'cell_max': cell_range[1], 'dropout': dropout, 'batchnorm': batchnorm}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "macro-latino",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_performance(model):\n",
    "    history = model.fit(X_train, y_train, epochs = 25, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.2, shuffle=False, verbose = 0)\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_inv = sc.inverse_transform(y_pred)\n",
    "    y_test_inv = sc.inverse_transform(y_test)\n",
    "    \n",
    "    return mean_squared_error(y_test_inv, y_pred_inv), r2_score(y_test_inv, y_pred_inv), math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)), np.mean(np.mean(np.abs(y_test_inv - y_pred_inv), axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2ef824",
   "metadata": {},
   "source": [
    "Training many different models to assess performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "forced-cache",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 models tested\n",
      "2 models tested\n",
      "3 models tested\n",
      "4 models tested\n",
      "5 models tested\n",
      "6 models tested\n",
      "7 models tested\n",
      "8 models tested\n",
      "9 models tested\n",
      "10 models tested\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "attributes = dict()\n",
    "for i in range(10):\n",
    "    model, atts = build_lstm(X_train, n_layers = random.randint(2, 8), cell_range = [32, 128], dropout = random.uniform(0, 0.4), batchnorm = random.choice([True, False]))\n",
    "    mse, r2, rmse, mae = model_performance(model)\n",
    "    atts['MSE'] = mse\n",
    "    atts['R2'] = r2\n",
    "    atts['RMSE'] = rmse\n",
    "    atts['MAE'] = mae\n",
    "    print(str(i+1)+' models tested')\n",
    "    attributes[i] = atts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dedicated-somalia",
   "metadata": {},
   "outputs": [],
   "source": [
    "atts = pd.DataFrame.from_dict(attributes, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "personalized-short",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_layers</th>\n",
       "      <th>cell_min</th>\n",
       "      <th>cell_max</th>\n",
       "      <th>dropout</th>\n",
       "      <th>batchnorm</th>\n",
       "      <th>MSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.003672</td>\n",
       "      <td>True</td>\n",
       "      <td>134.491236</td>\n",
       "      <td>-0.230500</td>\n",
       "      <td>11.597036</td>\n",
       "      <td>10.087220</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
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       "      <td>96.928125</td>\n",
       "      <td>0.113176</td>\n",
       "      <td>9.845208</td>\n",
       "      <td>8.421478</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.322157</td>\n",
       "      <td>True</td>\n",
       "      <td>93.424384</td>\n",
       "      <td>0.145233</td>\n",
       "      <td>9.665629</td>\n",
       "      <td>8.198400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>True</td>\n",
       "      <td>82.783245</td>\n",
       "      <td>0.242592</td>\n",
       "      <td>9.098530</td>\n",
       "      <td>7.560690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.304057</td>\n",
       "      <td>False</td>\n",
       "      <td>27.986218</td>\n",
       "      <td>0.743946</td>\n",
       "      <td>5.290200</td>\n",
       "      <td>4.085884</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.148571</td>\n",
       "      <td>False</td>\n",
       "      <td>29.657240</td>\n",
       "      <td>0.728657</td>\n",
       "      <td>5.445846</td>\n",
       "      <td>4.253765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.226688</td>\n",
       "      <td>False</td>\n",
       "      <td>23.180397</td>\n",
       "      <td>0.787916</td>\n",
       "      <td>4.814602</td>\n",
       "      <td>3.700826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.017649</td>\n",
       "      <td>False</td>\n",
       "      <td>28.420808</td>\n",
       "      <td>0.739970</td>\n",
       "      <td>5.331117</td>\n",
       "      <td>4.185121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.249504</td>\n",
       "      <td>False</td>\n",
       "      <td>31.397861</td>\n",
       "      <td>0.712732</td>\n",
       "      <td>5.603379</td>\n",
       "      <td>4.419268</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>128</td>\n",
       "      <td>0.366871</td>\n",
       "      <td>False</td>\n",
       "      <td>31.558090</td>\n",
       "      <td>0.711266</td>\n",
       "      <td>5.617659</td>\n",
       "      <td>4.390515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_layers  cell_min  cell_max   dropout  batchnorm         MSE        R2  \\\n",
       "0         7        32       128  0.003672       True  134.491236 -0.230500   \n",
       "1         7        32       128  0.351652       True   96.928125  0.113176   \n",
       "2         5        32       128  0.322157       True   93.424384  0.145233   \n",
       "3         6        32       128  0.043538       True   82.783245  0.242592   \n",
       "4         4        32       128  0.304057      False   27.986218  0.743946   \n",
       "5         5        32       128  0.148571      False   29.657240  0.728657   \n",
       "6         3        32       128  0.226688      False   23.180397  0.787916   \n",
       "7         6        32       128  0.017649      False   28.420808  0.739970   \n",
       "8         7        32       128  0.249504      False   31.397861  0.712732   \n",
       "9         6        32       128  0.366871      False   31.558090  0.711266   \n",
       "\n",
       "        RMSE        MAE  \n",
       "0  11.597036  10.087220  \n",
       "1   9.845208   8.421478  \n",
       "2   9.665629   8.198400  \n",
       "3   9.098530   7.560690  \n",
       "4   5.290200   4.085884  \n",
       "5   5.445846   4.253765  \n",
       "6   4.814602   3.700826  \n",
       "7   5.331117   4.185121  \n",
       "8   5.603379   4.419268  \n",
       "9   5.617659   4.390515  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda12585",
   "metadata": {},
   "source": [
    "Most performant models after a lot of testing contained a small number of layers (like 2-4), and no batch normalization. Dropout did not seem to impact performance much. These models were significantly less performant than the ARIMA and XGBoost ones chosen in our other analysis, however. So, we decided to move forward with an ARIMA model that showed ~0.5-0.6 RMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "worth-vanilla",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "\n",
    "model.add(keras.layers.LSTM(units = 64, return_sequences = True, input_shape = X_train.shape[1:]))\n",
    "model.add(keras.layers.Dropout(0.1))\n",
    "model.add(keras.layers.LSTM(units = 64, return_sequences = True))\n",
    "model.add(keras.layers.Dropout(0.1))\n",
    "model.add(keras.layers.LSTM(units = 64))\n",
    "model.add(keras.layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bacterial-anthropology",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "40/40 [==============================] - 5s 33ms/step - loss: 0.5007 - val_loss: 0.1900\n",
      "Epoch 2/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.2283 - val_loss: 0.1868\n",
      "Epoch 3/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.2231 - val_loss: 0.1799\n",
      "Epoch 4/100\n",
      "40/40 [==============================] - 1s 14ms/step - loss: 0.2184 - val_loss: 0.1763\n",
      "Epoch 5/100\n",
      "40/40 [==============================] - 1s 13ms/step - loss: 0.2147 - val_loss: 0.1699\n",
      "Epoch 6/100\n",
      "40/40 [==============================] - 1s 14ms/step - loss: 0.2066 - val_loss: 0.1646\n",
      "Epoch 7/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.2036 - val_loss: 0.1601\n",
      "Epoch 8/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1983 - val_loss: 0.1556\n",
      "Epoch 9/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1925 - val_loss: 0.1501\n",
      "Epoch 10/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1870 - val_loss: 0.1459\n",
      "Epoch 11/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1780 - val_loss: 0.1399\n",
      "Epoch 12/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1692 - val_loss: 0.1353\n",
      "Epoch 13/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1607 - val_loss: 0.1305\n",
      "Epoch 14/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1531 - val_loss: 0.1254\n",
      "Epoch 15/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1438 - val_loss: 0.1212\n",
      "Epoch 16/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1401 - val_loss: 0.1155\n",
      "Epoch 17/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1313 - val_loss: 0.1080\n",
      "Epoch 18/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.1279 - val_loss: 0.1004\n",
      "Epoch 19/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.1203 - val_loss: 0.1023\n",
      "Epoch 20/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1272 - val_loss: 0.0922\n",
      "Epoch 21/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.1307 - val_loss: 0.0897\n",
      "Epoch 22/100\n",
      "40/40 [==============================] - 0s 12ms/step - loss: 0.1171 - val_loss: 0.0812\n",
      "Epoch 23/100\n",
      "40/40 [==============================] - 1s 13ms/step - loss: 0.1044 - val_loss: 0.0722\n",
      "Epoch 24/100\n",
      "40/40 [==============================] - 0s 9ms/step - loss: 0.1047 - val_loss: 0.0696\n",
      "Epoch 25/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.1020 - val_loss: 0.0606\n",
      "Epoch 26/100\n",
      "40/40 [==============================] - 0s 9ms/step - loss: 0.1007 - val_loss: 0.0618\n",
      "Epoch 27/100\n",
      "40/40 [==============================] - 0s 9ms/step - loss: 0.0979 - val_loss: 0.0540\n",
      "Epoch 28/100\n",
      "40/40 [==============================] - 0s 9ms/step - loss: 0.0955 - val_loss: 0.0549\n",
      "Epoch 29/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.0953 - val_loss: 0.0523\n",
      "Epoch 30/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.0937 - val_loss: 0.0514\n",
      "Epoch 31/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.0945 - val_loss: 0.0523\n",
      "Epoch 32/100\n",
      "40/40 [==============================] - 0s 11ms/step - loss: 0.0936 - val_loss: 0.0516\n",
      "Epoch 33/100\n",
      "40/40 [==============================] - 0s 10ms/step - loss: 0.0942 - val_loss: 0.0516\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 100, callbacks=[keras.callbacks.EarlyStopping(patience=3)], validation_split=0.1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "connected-capacity",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_inv = sc.inverse_transform(y_pred)\n",
    "y_test_inv = sc.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "celtic-fields",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(462, 1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6de9e61",
   "metadata": {},
   "source": [
    "Keep in mind, this was a model with the aggregated daily temperatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "marine-welcome",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 11.490053991015658\n",
      "R-Squared: 0.8948740866699282\n",
      "RMSE: 3.38969821533063\n",
      "MAE: 2.5274030187952277\n"
     ]
    }
   ],
   "source": [
    "print('MSE:', mean_squared_error(y_test_inv, y_pred_inv))\n",
    "print('R-Squared:', r2_score(y_test_inv, y_pred_inv))\n",
    "print('RMSE:', math.sqrt(mean_squared_error(y_test_inv, y_pred_inv)))\n",
    "print('MAE:', np.mean(np.abs(y_test_inv - y_pred_inv), axis=0)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540c03d5",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9186c70a",
   "metadata": {},
   "source": [
    "In summary, the best LSTM models were the simple ones which considered only previous temperatures as inputs as opposed to more complicated models with many variables. However, the LSTM and Transformer models tested perform worse than the ARIMA and XGBoost models that we tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51dea811",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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