Anamaly_detation_1DCNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "import sweetviz as sv\n",
    "from datetime import datetime\n",
    "# from pyts.decomposition import SingularSpectrumAnalysis\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.python.keras.layers import Dense, LSTM, RepeatVector, Flatten,Dropout,Input,GRU,concatenate\n",
    "from tensorflow.keras import layers\n",
    "from keras.layers import TimeDistributed\n",
    "import keras.backend as K\n",
    "from argparse import ArgumentParser\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'archive\\artificialWithAnomaly\\artificialWithAnomaly\\art_daily_jumpsup.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-04-01 00:00:00</td>\n",
       "      <td>19.761252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-01 00:05:00</td>\n",
       "      <td>20.500833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 00:10:00</td>\n",
       "      <td>19.961641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-04-01 00:15:00</td>\n",
       "      <td>21.490266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-04-01 00:20:00</td>\n",
       "      <td>20.187739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4027</th>\n",
       "      <td>2014-04-14 23:35:00</td>\n",
       "      <td>18.074649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4028</th>\n",
       "      <td>2014-04-14 23:40:00</td>\n",
       "      <td>21.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4029</th>\n",
       "      <td>2014-04-14 23:45:00</td>\n",
       "      <td>20.566713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4030</th>\n",
       "      <td>2014-04-14 23:50:00</td>\n",
       "      <td>18.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4031</th>\n",
       "      <td>2014-04-14 23:55:00</td>\n",
       "      <td>21.863147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4032 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp      value\n",
       "0     2014-04-01 00:00:00  19.761252\n",
       "1     2014-04-01 00:05:00  20.500833\n",
       "2     2014-04-01 00:10:00  19.961641\n",
       "3     2014-04-01 00:15:00  21.490266\n",
       "4     2014-04-01 00:20:00  20.187739\n",
       "...                   ...        ...\n",
       "4027  2014-04-14 23:35:00  18.074649\n",
       "4028  2014-04-14 23:40:00  21.002014\n",
       "4029  2014-04-14 23:45:00  20.566713\n",
       "4030  2014-04-14 23:50:00  18.254193\n",
       "4031  2014-04-14 23:55:00  21.863147\n",
       "\n",
       "[4032 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = df[\"timestamp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"timestamp\", axis =1 ,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns:\n",
    "    if len(df[col].value_counts()) <50:\n",
    "        df.drop(col, axis = 1 , inplace = True)\n",
    "    elif not np.issubdtype(df[col].dtype, np.number):\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors=5)\n",
    "knn = imputer.fit(df)\n",
    "df=pd.DataFrame(knn.transform(df),columns=df.columns)\n",
    "# df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value    0\n",
       "Date     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"save.csv\")\n",
    "df= pd.read_csv(\"save.csv\",parse_dates=['Date'], index_col=\"Date\")\n",
    "df.drop('Unnamed: 0',axis =1 ,inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2014-04-01 00:00:00</th>\n",
       "      <td>19.761252</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-01 00:05:00</th>\n",
       "      <td>20.500833</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-01 00:10:00</th>\n",
       "      <td>19.961641</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-01 00:15:00</th>\n",
       "      <td>21.490266</td>\n",
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       "      <th>2014-04-01 00:20:00</th>\n",
       "      <td>20.187739</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-14 23:35:00</th>\n",
       "      <td>18.074649</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-14 23:40:00</th>\n",
       "      <td>21.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:45:00</th>\n",
       "      <td>20.566713</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-14 23:50:00</th>\n",
       "      <td>18.254193</td>\n",
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       "      <th>2014-04-14 23:55:00</th>\n",
       "      <td>21.863147</td>\n",
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      ],
      "text/plain": [
       "                         value\n",
       "Date                          \n",
       "2014-04-01 00:00:00  19.761252\n",
       "2014-04-01 00:05:00  20.500833\n",
       "2014-04-01 00:10:00  19.961641\n",
       "2014-04-01 00:15:00  21.490266\n",
       "2014-04-01 00:20:00  20.187739\n",
       "...                        ...\n",
       "2014-04-14 23:35:00  18.074649\n",
       "2014-04-14 23:40:00  21.002014\n",
       "2014-04-14 23:45:00  20.566713\n",
       "2014-04-14 23:50:00  18.254193\n",
       "2014-04-14 23:55:00  21.863147\n",
       "\n",
       "[4032 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x222bb431730>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
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Hb/sr1g2HPv/tfz3SQqk3PfcuWaUjvnqVzr1uYeW14CBQlo7748vWJb5XCU7SVxsTwQHYKx58Tl+48H7t/4XLdMoPbwh9bmikunoguGrgontLufCDDz/Do0Vd/fCyyncbEz/Qe8NjL7Tv/0gPe8OPb9IvblikbT/2Tz0V2Msg7fllY4OBLL+9PWbXWU2VEckuve9ZLVsbns0eHCCPTsCIijahwUHa2M/zwNSQL/zjAa0bLlRWYQdXeGRNLZgU1Ch6Xt1AVqHo6e46A5CI50+kuDiyJ4sfyFr4wnr9JjBokRaQDA7M+gOyXuDSPPLsq3TJfdUVQgxMNM6fUR4daEgKBGcZoytEftfzSn352VNLKYMI7DdvpFDU2qHRysBdkmj7F53l7td3XDaNKx96Trt9+hLdtmiFTvnBDbFBs5wJphakPhsRPF7RQdl8WhqGGJcGVkiO78/XXFv5hLT/qOIIbTrq9e39FVlJ/cfooPrkcaXdXp5dtbHyGoGsxgX3zIwee/+Y+02bP0nDfxa4PjBuUQxN4A5MhIqpEu5brTn7kvort31Z+3zBavL7oMFxZKqsNxDIQlcEO9zBFVl/TtnYMjp4WyiGAynry2kV7gykNfnzHdXv8zwvcxonlCx8oRSEun3RisprwcG/LDeU9/329sT3qiuy6KyNhWgn7rFla2MH6Px6TOqMBSek/fTfT+it596sv91ZHZCIm3F92o9r9zpDreA1FGy70h5g6j1ARV1WHqDwNzz1fe+qR3XRPc9U3kd2v7xhoV769av1rl/dpg+df0foveB1NzSSXlcTB8JbldZLdxA8X6IpvFBr5uRBSdLS1aXBgXAgK9uKrKSBiNUbRrVhJBzI8oPR/uDuaNGrqWM0Jlo9wb7gJ/5yb+BzGVMLxqzIiiJA0jh/RvnQaG1fPY5fJ2kp0GpWZHmeikVPfeXfZQCpeW/72c3a4zOX1P1ctH8XDZBE9wMM8lcJ/eamRbp54XJ98Lw7aj6TM4Z6bFLaYWtlldvkcX01beDK9Y2lKgR6QVKfvHZydfh9v+1L6ksEAy6StM3mEySVJuj4WAXZuGBqwWiwcCQptWBly4z47wwGsuJW6NFfbM13rozf2zZqeLSYOVtCsJ78eg72J6mz3kAgC10RXpFV/Xn5utqN/XzrInlRi0VP/+8Pd1bfH05fpVAoSltvNrHBkm7aKlUTuG8H98hqNf3OSGQ2DBust1d0EChptaMfTE46/MF+m583+unyrLGRgqe9/zc5bz/qCBzzUB7utD2yGlyR9e5f3yZJmjgQXu3zlYsf0nt+c7tOz5DiCWGf/ut9euS50sQIf7N7n/+wZBQe1H1+3ZC+d9WjoUG7aAqfemlIgm+f+N3rmin6JsUPZN1WnowRSi1YPvT1BlGT7nMfOv/OmkHew75ypeafeaEWlQcjFr2wTj+59vGmyr7JS2gCNyT09dKqMTjInk/YIyvozMi+nqjPb8uiwfukdC5Do0X96JrHtOdnL9VDz67R82tr92kqRK6vb1/xiAqeV1kVRKaY5l33aLZV89EVddF+uv/PuHR0vrRJisaY6oqsTCWCL60Ni+4vl9WkwT7tsuVkrq0eRRrPxiQ9ikUnFEbvc/VSCy5esT6UNShuQm+jqyoRfkaK9t1HAxPMSv+uPqtFBX+3XjyRoavO2OmTF+nH19Q+T8Ud/vgtMViR1WtoIdEVwZkshZTZE0HRTdULnqelq6sPvusSNl0P/h0/V7uflgTp/AG+4D3cv6H/8JrHW755j4yGv/8dv4jfL8YYZt42asEXLq/ZLDNpRqV/aJMDk9UzwE/rxDyx9kg64mkba0f3ZMpqwiArQ8ZC9CHn3OueqPwcrKsfXv24vnLxQ6G9LvacMzX0u/UmBxDsb8zWm5Vmuf72pidVLHqhwb1cZUVW+nf4D72ffeVute/VGSz80j8f1Jo6fROkix7hpH0302oiOMi+caSg1RtHUmdk3l7e/4B+R3Z+rDA6wJd2jXzpn6U+yv/7w51a8IXLa97/2mUPh/799KqN8jypvzzAx6za1tXrywWD9RtHCvrQ+XfGfi4utWAWLDpoXtr53+x+IGe9dk/ljKlJ64l0TgSIuNYalpQxJroiK7qXoD/hJqnP/vPrFmrfz19WyRQUdy3fvXhl6opl1Aoex2jfw38289vGGx9/Qf96cGkl6BF87H70uWoGp+CKrLg+Id2Qxp377yd0x5Mr6n8w4ooHn6t5rV4/0K/v4DVL37E3EMhCR60fHpXnedr385dVXgvOVkl74I0GsqKdA38/nyRFz6t8B2OBjTGh/MDVn1tdkRXdS+Gqh2pz9ffljDyvuQ0eNzU3P7G8kkLr+bVDdfc+8DWSWjDraiAGJ7IJdqaCD0xpKeY2jja2Iss3YSB9/yU0py9h1qQxpibNlhTe9yU6+FGvTaXv3ZjNJw1Iku5Zskqf+uu9ocE9/xKr90Dj9zW2mzmp5r2k/X/QOr9+ovexpEB+2rUTHGT/w22LtddnL810LXG9Zecf/nuWhPeEy7LZ/b1LVjf0t/wVWQxGjL3gAO21jzyf+LlmA1nBvZmozsakHa9mn5lyxihnTGzKcNTBc0/PSXoWi06oiV4vI5HVP5I0bUJ/5Wd/QN5/Zo+7l926aIXe+5vk7RlQK3gcRyL988sfKB1zv65uXbRC7/j5rZUAR7AKbni8umI5OJ4RzQ5V+j3aykZ97h/36zXfu749XxZz+I1K2zV86oJ7K5OrHnx2TWVBA+PAvYFAFjrG3/R324/9M/R6MEIevekERVdcpaVUiFP0PK0v34Aa/d1NzRUPLNX+n7+sMqMoeBMP551tbRDPr/u0G8rE8iqSZlehbCo8z9MpP7xBr/z2vxv+PX8AMDG1YODn9XVSeFa+t6FSbLqCTdFF9z5T+TktqN9s85UWyArOPkNj0rJ/xO1n9sTz6/VCOY1Wo5MBGLhtTPBw/eamJ0ODCv5P9erA/52JMSsam03fhPqS0qsmTabIGsjyZbmWPvKHu+p+BiVJM8+z9BNnTRls6G9V98iKH2jc5VMX6b6nV9W+gYYFr6uV65PTvw/0MYrfaWltnp/xolE5Y5TPmZoVJ8CmKKlVO+/mJ0P/rknB6qfsD1yjcZk2pozrL38+/u/cvZj7WCOC/bqk9jHatvn7kiWtqswFBsHmTB+f+jfRmAefXV13b+h64o7/SMHTq797nX5146LQ63+6bXHi78A9BLLQMdc+HD+TLzhDupEVWY0OIBWKXiVnMbMn0n35ogf1wrphLSpvXP/oc2t1w2Ol2SnBoFarM/b8uvc8LzENhr+vT6P7Am1q/Nlhz62p3WcijedVB3uTbuzBTkZSWqe470V9wY7zP+6uBrKGxmAF4uYTBxNXyh3z9av1qQvupW1sQtKKLEmxK7I+8oe7dOTZV0lqvA2l892YpBz5wffqHVL/c5NiAlmNpG/66LE7Z/4ski1esT729fRAVm3DlyWI/Oc7kvf2QVjSBLEsffVGm7X+nL9HlhfKEuDbOFLUryMDGGjOn+9YojufWikp/ZoZ19+GFd/c3hoyFqkF8zkjY1ht3KvoQjYmbb/ioGhf3r/8gpOn0r6LZ6/2CN6i4p6vhkYLiatVkybjDI0WKykg4/qSrO5pTPBc/96Vj7W8kDXu8Cf1VdaVJ2NzvfUGAlnomKQOdyiQldJx/tPti0P/jhsgrPf3SS2YzWB5Pwk/eHTf06t12o9vlBROLRjNCR01vs6DrV/3Rc/T2o3x+4hUV2QRyEqzfF3yTNk0Rc+rXJtJN/5gyruN5U5AqzNoUJLUFm3MuPKtEbOnjUsNuvzqxkWxK4iQLp+SR3MoYSXpmnJ7V68NjeLe1ZhovyP4YFuZMRv5zKIX1un+p6upzvx2cVx/rqbdW9fAdTplHHvUNSLpqvLTw0SlrbTvixl84Dm2vZK671lSC2adIOMLphZMGpBIu9ehMV+79CFJ6XXZbGpBNC/t0mo+kCVSCzbBhfsJj23ZLVszpHVDo5mPWfR684MiwefquCay2g+N/14G3BsTDEbF7fO3duNoYtuYVAfL1gzp6K9drcUr1seOVTHBsDHBQGI7MmTFjV1F+yofPGoHSdKvb1wkz/N4lu4RPFWjY5KCVKHUgikd58eXrQv9e8Nw4wOAfqq8Vvd26nXRQFZQcIn1SJ3jOK4/lzpAEUwtGF1x5/MDWY0OdGxqVqzLtiFsX86EbvCPLVtX2V9u5fr472hmRRYySriEkq6HVsyeNr4UdEmpQjrkjetLCGQtWblB593yVOrv1mtDo3iobUz0fA72Q/x+QPRB6oivXiVJWnjWCaHP9eVzGteXD7WB6xu4TscP0OVuRKOTJb78zwcS34tL6XN1xj0kkU3SvSNLsL7RiUp+0KRQTB6QiAteomRZgyv3fWnPTo08VyXdx5JSO6GqWPQqz2FJKwik5jNmlPbIkkaY1NQwWpzeccAXL9e8zcZnDtBHVx4XYiZKxa0erqb2p+1rh+B96JGltSnzV20YSQw61wuqHPp/V8a+TtU1JjjuWyh4pZWKLRzEuGsn+trkcf06bMcZuvaR5zVS8FLvnXAH06fQMUnPskOh1ILZO873Npj/vlD0KrMA6DCkG+wrraTy0wkmSauvy/778Mr3JPEftIqeV1mhUFuWXPlvUWdplqfsXRC01bRxoX9/7M93V35+trzpbFTcHln1Bi32mTctU3k2dUkDN6s3ZgtMNmJcfz4x6OJjFm7jXkhZDXlXOSVTElZkja3o/JngTEz/WAa7A/5GwEF+4D9vjMb1h7vN62I+n2Riyh51aN0tC1ckvpeLafc+ecG9Y1mcTU7SIFCWe0qj9x1/Faw/IBK3xxYrhJL95NrHm/q9tBVZWZ+rCkWPgb8WFDxPC58vTexMO+ZJ6bPqyeeMcjmjNWMwmQpwyVPLN2QOTibtkRVsM+MCWfVWZCGbM/54l35/y1Oh4/iH2xbXfO6alAlMzY4NMqbYmFAgqw3HbjhmP8joqru+vNEh28+Q5GciavnPwgL08tExSdHv4KyvRS/E730Q52N/vqfhv++neeKek26wPFi3MFIfnheexZAUXHrtfnO146zJNYN+UcOFan0krUAZ6KvOvEWyNRkDH9vOmBT696oN9X8vmNfbTz2XthH0AfOn100riZKk07rdK7I+/NKdJNVf5dBoYAWtaTRAzwNTY1JTC/ozYQMX4W6fvqTmO/wHrXzO1OwDs3Yo+0qS8QSyGmLaOL89bkVWPczYbEzS8RqLVdx+kGpjOcV4XNAqLeXrpm72tNoN69P4zWha/yDr9TJSKCbex7i91feTa5/QkWdfpW9e/nBl9XCcRlZUvefI7Ss/l1Zkce30Ki6xxmTdIys6mB7Xv4z7Kr9JTV6linrWDo3q97cu1hl/urvuM9Jn/35/4nvN9vl4LmtM8DmsUPRa7unHpYqM1mVfPleZyDtaTE5JDbe0FMgyxvy3MeY+Y8y9xpjzjDHjjDHbGmNuMsY8aow53xgz0K7Cwm1ZZms+/vy62M+0Q9GrBk4IiqTzV0FFDReKoWM3kpAu0h9TyLr584X3PKO/JGyqXlmRRZ2lCgaWnl65IfFz8zefEPr3qg31AyY/v35hzWtJeykdMH+6Rouebnj8Ba1KSFWIqrEe0JlQHjz3B/XqfS3XWWc1mlower5wL0sX7XcEA4eFmBmzsd9R7jf0xQSyGkktOIHUgl2TbyLNXCOr7ZA8KaPZ/TvT+Buu+5PT4gJZ/QSyEk2b0F/5uZE7SPqKrGzfMVIotmUW9qbq5idKmTK+efkjqROeGtkjKxjo91MLovcQn2xcoyuyJpW3Q/D75sE2M25yBakFW/fQs9U9bVs5js1mJIne+7jO0oVWZBW9lo9X3MTqaF+lL2cq19+tC5ezIqtHNB3IMsbMkfRBSQs8z9tDUl7S6yX9n6RveJ63g6QVkt7ZjoLCfUkDbsOFzuy5U/C8wJ5MtGBpklICbhguhI5d0moCfzZfUkAsznk3Pxn7ur8iizpLF3xoPeSsfyV+btqE8NyC59c2t1dCUiDrJ285QHc8uVKS9L2rH23quzclY31a+4Es/5qs9/ea3SAczYmb4Z6Wgi5af9RXuujxunvxysrPfn/g4aVrEn9/eLSodeV0qrmcqbmnNRLsmMCKrK5pZkUWaVazu/Kh53T6L2/t2N/ry5WuQ78f0h8TqOwjtWAmaamWfJ48fevyR/S3O59O/EzW4NRIITm1IFdcfVkH4BpJLZjLVYNX+RwrspqVdfUO3JG1Sv3xkC+ftKckye8+nB/YJzc1tSBd+aY9v7Y6WaZeloudZ02WFP+clTSuUQ+rexoTHPctFD3tv830lr4vbiuGtEDW2352i+54MjkVOdzRai+/T9J4Y0yfpAmSnpF0lKQ/lt//haRXt/g30COSAlkjMblNx0KxWA1kcc9JlxSAWj9cCNVjUpoRfz+KdgwkDOTZIyuLtFR/QY0EF9MMjcYHoHOBrx9kIKnr/I6bXxX1OtxcZ50Vd7wHUq7RV3z736F/E8hKF+13/PCa6t4wfpt50b3Pxv7uw0vXaK//vURfveQhSfErsm58fHnmsmRdoYySuIHxzSc2l+ShmfHF6J4XSHbpffHX0FjxU8T4/ZC+XG2b2dfEKrxNRXBi2HNrsk1m+sblD+uhlKB/1sG80ZTUgqgv65HL+kwgla4nf5A9n4vfUxDYFGVNcXzhPc9Iqk6q8NvDYEaTuMsqbkVWaI9pmsq6NgbSFyeNTfj8Vax7x+zj3ezzVPQ5g9tbumDAcLRY1PTyBOu4CUn1vOXgbXTf06trXo/WZX8+F1oR+bXLHm74b8E+TY8yep63RNLZkp5UKYC1StJtklZ6nudPUV0saU6rhURvSF6R1ZnBgqLnVW5wPESl608YSN0wUggNLiWlxfJnPzczCzqKPbKyyTqTqF2BrKSH5GBHYcIgqbRacdJ+rd8+/QG+yoqsOp9n8Laz4tKzNrK3yxcvfIC9fFKk3evr9T1e9o1rtHGk+pl8zjS091/09teutndTEVd3nRxgJaifXadXIvipBP1+zw5bTKr9TExwCyWN3uazPDIVil4lwJhmuFBMXFXEzPb6sh6jRlYX5HOmcr/KGcNeqU1w5tx1pJi2aPTW5j9zxY1ZxK3IKnienlq+Xjc9UZ0U9bZD5jf2RzdxQ4F+er12zw9kxfXlswayBiKTdGkuGxPeq7h6rTSTBWG7GRMT/ka4UvI5k6l/Are0klpwuqQTJW0rabakiZKOa+D332WMudUYc+uyZfXTGsB9iSuyOnQHGAk8PJGfvTkbhguhQdPvX/VY7Of8e0U7xhH8AQvqLF3WgHC7NkBP6iwGO+ppKdJQ3wl7blXz2mkHbt3Qd/gznLKmFmSPrPo+eN4dbfme7/zrkdiAcCPX6O9ueUp3BtLlISztfG9kxrpUqpdx/dlvatGHprSVdqgqFD0d981rdPE9tat8mp0ck3VWdbQcyKZek9XMTNs0fZE9sl662ywds+us0Gfa1dfpRWNxZhe9bJMERwte4rXFFdc+jQSycsZUgtH5nGn43ogS2zMLNnMfRGP8e1PcmEXcPalY9PTlix6I/Q5kE1yFde0jzyd+btaUQa3zA1kx4xNZ273ocwATQBsTPM4bRwu69P6lTX9XUj8vGNyUSn1Q+oS9p5Wn6mMkPeF53jLP80Yk/VnSiyVNK6calKS5kpbE/bLneT/yPG+B53kLZs6c2UIxYLtisZRbPWnD57HqMEcfajeUlx6P68/J8xyaPdUFSU19KbVg/d/3Z023I896dUUWHYU0Wa+jds1IybIia/wAK7LS1BsozeeM3vHibUOvpaXW+tyJu8d+R/C/Xp2hIlYh1Pe3u5L3CWnE2Zc+HJsSYeX62nzfaVh5kCztGmt0NXje1KYWTP18pK1lNmA264ZH9eCza3T+rdW9JfwAfjMPoh86Zse67V6c28mhn1m9vl5c6r+oGZOyp430ryX/Gu7P57TfNtNCn2l38KyXjEVWimLRy7R/00ihyPNXC7Ieugeeqe1bSNJJ+9au9O/LVUMcOWMy78MF9LoHn01OpxrHX60TlykhbuVy0StNEg4K9hW5FOvbOFK/L3/oDjO055yplcmarazIigbBmADamOD40d2LV7X0XfmEvmV0IkdfLkcgqwe1MvrxpKSDjDETTKllPlrS/ZKulPS68mfeKumvrRURrrv+sRf0jcsf1s0Lw3tJvGrv2ZKqN452z1aePW1c6N//ff5dkqo3L56jGrd+eDTTyqicCQ+et8I/LxhgT5c1kNWu1EyJe2QFOup0GdL96fbFqe/35XL69Ct3C70Wt6npqQvm6d1HbK8tp4yrec9f0ejXe73Llz2Xuq/RAAsrfZKlphZsaMZ66RryA1lvPXibur8THbxPStmLsLj7xoC/srSJQ9jsKq7/+t2dTf3epih4hK/+6JE172cJ4m43szY9oCRNHd+vv7//0NBr/n3N3xtjoM/UpPtpxx6tvWosAknXP/ZCps+NFJIDXjyX1ddMUD4o7rksF9gjK2cMGTCAJvn3nbg2Lu42+K0rHq6Z6J00OI94wT2ykgwXinr0ubWVf8etyMq6ijUaBCvEjE8xWSPZcKF+fWWV1LeMPt/lWZHVk1rZI+smSX+UdLuke8rf9SNJ/yPpw8aYRyVtLumnbSgnHJY0K3L78kPrcPkGEH0IbVXSDFH/BkRHPVnSQ+bvbn5K51zxSN3f928Wze6b8P6X7FBZUeffpNjXLF3WAMRYr8jKmWp+b1IzpVuzcTT1/bhO16oNtYGsvedN05kv30WTYvYkq6zIyrxHFnXWbY02dQSykqXdNxrdQ0SqphTJMkgevX6njOtnlUgGcYuv/ePdTFAqlzMMkI+xYF8vZ4zOOmnP0PtZUiXNnlo7EUOStp0xUXvOnRr5vvAeWX25XM3AFCsgkzV6PWTpfz+7emOm7xopFPXuX93WWAFQ0Wpyirh7V95UB9nzORM7MIt0HDH3tSP44Pf7su6Rde+S1borsiqFe1djNiZMrA06ZtctQmNbcYGsrOMo/oS2iQN5bTtjIqkFG7RszVDbvispOBWdENqfy6VmBmCvaTe1NPrhed5nPM/bxfO8PTzPe7PneUOe5z3ued6Bnuft4HneyZ7nte9shZMmxgyuSlJ/Xzk1yKifGiT5xj15XOMpypI6Av4NiMBIsqQ0fhffV7tnRZzKiqwW+mJblQc1lq8rDdwzwJ4u6+qCds30Slo1YozRu4/YXhLB4nrqPTRlDWT5g+sTYtpavx30xy7q/U1WZLmHh95kabeN59dm7576D0CDffnyv41O3n9u6u/EBRhPWTAvNT0o4u8b/jnezIridszCtH3vk24LDtDlc0YzJg2G3s8S+I3+TuV3Y+rPf22oPBO7P5+rXJu+flZkJWq0O33j48vrfyijDSOFmgwdvlZXG6G+uOtp5y2nBDJpZO+70/UIc+FwcI0la8cwQ1/OyJjSGFNwBZCU/XoJ9llY2VNfvdSCt3ziGJ1+2Hahc39Cf+3zctZxFD8I1t+XkzHx41NUW7IbMq7e9r0u5VkraZJUtC776qzIGiEY6SR6+RhzSSuj/BVYWVILzp46XtMn9Df0d/MJjds4UgvW1epYtn+vaHaPLE+eZk8bL0latWG4XCYqLE3WdGStjO0ENzi9d0l8/v3S3yjVO8HH1vjH8Zun7lN5zU/JGuQP4MWtavUHEP0Z8/XaPVJ42uVbr9+n7ioe7mXJ0iasNLIXmX8r8/sPuZzR/712r9TfibseCYjUF3ev99vCZvoUzaYWDGrHfp+9LHh44o5V1mD7nHK/L+jWRbV7lfmDF8OV5wdT8wzRrjTKvaibE/nqrURHulYDEXEDfwvmT69EYXLGZB48bzbrBrqD6krXjnGGnDHKG6NC0dMxX786/F7GexKT0xpTL7Xg1PH9MsaEVrOOH6jtn49kfP4dV37m7s/nlDNGVz20LHth0XAf4OyT9058Lxic+uixO1d+jm5/0ZdLD2R99m/3NVQm2IFAFsZcUqfbf+gcCWzWnGS0WGy4w5w08OCn3yIwkqzVh9x8C7OnfW89ZBudumCeTj9sO0nUVz1Z02RlXZH1tkPm62dvOyD02vQJ2VYSVFIrsLon1RcufCD1ff9hZt+tp1VeO2m/uXriy8eHHnQGy23pQF/t9dZ4akHqzCY7zZpc97pjhm2ypPtGo6u8/VuiH8w3qn9/S5qcQ22li+t/9EXasUbkcqblY86wUrorH3qu8nNcFyNLasGRQlHffeN+mf5edY+samrBQVKsZtbNNmjtUMoEAhrHup5avqGl309a4Rjc25jnLWyK2hHgz+VKfY64VY1ZJ8Swv2NjFr6wLvV9v8kLtmvRfa6kxldkldrN+M/QgiZr50Sa4DPB3OnViVC1K7JyqQHi825+qm1lQufQUmLMJbVX/oPoUPlBNO0htOg1PpCQ9Pk55YaO1ILJWl1JU900uPnvmDDQp/973V6VdDOs7kmXtQPWyEyvl+yyRejfWffiYUVWeyStQggG9SeP69OLtttMUvxkgGpqwWx7zWWdkYaxs9tWUyo/54xhNUgLkk73pDRmid9Tfiz1Vz/6bds/PnCoXhmzSrL0t2v/uCEkUlfs3hIxk2P8fTTriRuwaBT9xWTL1gzp8WXVgaS4YGN/hgk0R+86K3OgspJacDSYWjD8N0jJlKyTxyZapWtZkdWSJStbDGTFrhQ2oUwaWbuBLByporlxX9YA7t/ff2jie7nytbQqZsW/f3/bc85UffDoHRO/I5RaMFOJNl1DowVd92h6qrq4MYlxMf3CrKn1NyunBx8peInPZ/Q/krVrmGGgLxe6VoJ1EZ3cXW9FFtxEIAtjLmkAoBLIKj+IRvPbS9IRO82U5K/IauzvJrWTcyuBrMa+b1PS6qaHwZl9cfbbelpqqsjgKePP5GUjxnRZO2CtDIpnnk2WMWiCdP65n7by45IPHa7J40rXUmwgq/ya/x1JVbL1ZhMksfLRBn9+7yGVn3Om/h4/XGbJks7nJ55Pn8EZ5R9jP82jP3FgjzlTtffcqbG/kzS5gAfcdHF1Ft3rT5JmTq4fjDxsxxl63f5zW75GqLFk0VW8cf2ELAMIh+80M/ZzJ+y1Vc1r/n3Nv8biUgsiWZb+9CtijrvvX//viMx/K3o+rBmqBrIO23FG6D2us7GXNJmtMgExZzI/bzExI8yFVIt0P5Jl2RtuxqRB7bTlpMT387lSasFla2r3YPVPj48eu7NOPWBe4neQWjA7P2CR1h+sXpeBPbIGarMyZM1s4/+toZGCE9e8bbI+A733yO31ozfvn/j+tPH9odX+wb5G3B5ZXFe9h14/xlxSf9ifJesPKA32156Ox+xWmnE7bfxA7M1i73nTEv+u50nnnLavfveug0Kv+2maGExK1mp6MX+wKSnwMX4gn5riLlgz/gwmVveky7qSpt6N/MBtN0t8L/qr7zp8u9jPsSKrPbKk0woO/KWtyKrX3o2WA6FZA6IYO8GZgsYY9jVoQVwwPWcaHyioBrJK11i9e+RvT3+RhmPaZOqyvtg6i2kLs9ThGcfu0pYAB93FZNH7U9zEi7ccvE2m75o+sXaC0xsO3LrmNT+g7A88kVqwMVlO57TPpKWCj4qeH8H9MbKuqkT7JAWV/Wdsf38fqRRE3ithokbpd9pfPowd6itdlgDu3z/wYvWljF/kTOkeuGxtbSArOCaS9bkO6QrlfvYes6fU+aT0i3ccWPl5wkBMasGMz78zyxkdNowUSC3YhKwTZj90zE562e5bJr7/0WN3Do0lBuuidkVWjn1TexC9foy5pFUZEwZLN5G7Fq+SFJ9acPbUcfrSa/bUT966IHbe13ffsG/i3/Xk6VV7z9b2M6szZ7592r7V/XsYZE/U6lh2ZdAp5aaxcv1wzWtxgUn/Oz55wb2tFarHZQ0a5evsVbHrlpNjX7/tk8fUBCZP3n9u/N+o7JHFNZbEf2BKG8jxO2hxz0z+kQ1WyUDM4FLWB6IX1pWux1HqzCo5Iy1ekZ5KiBpLFtf9GOzLZ9qzJ/Q95aPsD+COjKYf9UO2n6Hh0fgNqKmvdGkrsoIPolnatkYG7uLaT9QXnWQWVy+v3X+uFp51Qt3vCqb89Pezjd/Tp1RX1z7yvCSpvy+ngXzpmWJieYCK4GOyTN3FlM80MtAavQa/f9VjlZ+j7TB11h6//8+DE99LmmDov5zLVVemvPFFtUHkuN8BekG9caF95k3TVlPHp7Z/OVNKYbZ6Q21qweCzXFq23WC7SJuYzh/7yLIyavfZ1aB8XGrBoZH4PnvU7GnjK3+b1O+Ny5qtpz/lOW3hWSfo5AXzQv1DY0wlQBkNSvazIqsn8dSGMZfUXk2MLOuNu6nkc0ZveNHWmjVlXOzNYqup42tei/sO3yHbb14ZCCGOlazZlHCVB6HKHlml/752v3DAw8jEBl6mjCudE6HUghn2VkD21Iv1buR+yp7oCp6Jg3011+CEwdql+VJ1plmWNA2bKv8aS+uo5QMzZOt9RpL6+2L2JsmnpxT0+bOXWl2Nual69xHbt/X7om1pGlYXJ4trgwb7c5n27Anyv8YfYBiJuU7mbz4h9O+42Z08RtUX1//wg/rB9i7LYHojgwzMgm5OtP2JO4xZj21wpY8/qBi3p080ANKfN5oxuZRtIS1TA0qy3DPSngMaGRBKq/tGVnYhu+Cm91FJ1VF53as+T/Tlcql9R1ILVnlMUXFevUDWN07dp+53+Pvabhyp7f8F+yNpfZPguAfnVTr/mbXRlmh8zIqsuDqLs8ec0uqvyeP6kldkUW2Jsg4zZAlOhvfIkm4482hJqkntmWePrJ5EDxJjLumBaVwklWDcbNjgzTyuPUttlLzav7PZxIHKTYfBv2TNpoSLDrz7HbWDtgunq0u6N8WdA8Sxssm6wrDeZupJKxXG9edr6m1CTPBZKs2azxlWPaYpVgbGk09wf/Vc2p5zwYeh+NSC5cBknYehDx2zY6hcaMzbDpnf1u+LtqFRnzxh17b+vV4VNxg72Jdr+KnX/xb/HhW3cjGawi55j6zG/vamJm5FeLMrshrpP/CQ25zoPSOuzWo0cDyQz1WC0HH9wugEkIF8TltNHa+LP3SYPvuq3SUxAJgmSxuU9pl2BaCidUudtUdaOtWkwUH/ui161Qkg+Zz0opR04yxGCHPhcHCFJUubfHnc7ltq2xkTQ6/lTO29KJ8rB7ISVuRLpTpIexanK5Kd3xffdatScOnY3bOlq43rVwQnn6XVweyp43X2yXvrgve9mD2ymtDOSc7hQJbR1An9NePLUqnPwuq53sMQMcZc1oHRwYQVWb5o85OWtzsouKGjKc+UkVgtkia4umeLDBuq+/z68u8VW04t/e7kceGVO0k3/riHL1ZkZZP1fK43WFfZUynmvU+esJvmTKvO9JwwWA1uRR9287n4VXcoqazISqmP6OBt8KN+ID44sBs3S7oauE8uy0n7ztHrD9i67ueQLJoat5lVAa/Zd47OO720p2Mu0pZGBWdcU2XJ4laqDvblGz7Pq6tDyiuyYqIt0ftXXPPHQ299cRMg4trALA+l/meyDJAzeNScaLA4dsJFAwf3po8frRs/fnTlHhc3ucZPI+jzJ4TssuUUUkRmkCXrQtxn+vNGR+2yhfoz7Ee24xaltO5JAf3S91FXYyFtv7ikdtMfpO/Lm8q9K2eMznz5LompChkYdAsr6NIlTb7cbuZE/eDN+4de+8U7DtS1/3OUHvni8aHXjSkFgDfWSVOXdk8M9hN5Jkvn19n2W0zUQ184Tj9884JMvzcQk8EkKGms5FV7z1YuZ/S6/edq+5mTUvbIouKSNJP1Kak+QoGs8m0vLkhMWsHeRA8SYy6pwYr2F+I63sEH2OgA0J/ec0jo32n96W+ftq8+f2JplmY+MOsM8YLpxRp5UPEfSv0by38dvZPOPnlvHRvZrDHpG/3fD3YAiGNlk3X1U729YdJmiR264wxdd+ZRlX8P5HP66/terHcfsb1+e/pBevgLL69+T86wIiuF3yymBRb99yoB4pgrJxTsj6k7/7W0fuNo0au0n3S+szvtwOr+EdMm9Ifemza+P/rxul6192wdvP3mkqqD6kkPu9vOmJR55uGmLK4JGuzLNb0iu7JHVmBF1lG7bCFJOmXBvEzfwWrwdLED6LH7JGUJZJX+O2Vc/euRjaCbE62vVge3Z00Zp80mDlT6D3Hpd8cPhDuGcZ/hMkuW5dB89Nida147920H6Ny3HZApWLjf1tMlpWd4iPZ/qLP2SAsQJjVz33vjfvrxWxZoxqTBygSQnDHqy+e0++wpsb9Di1nFueu+pJRncc+yR+w0MzSx05fPGeWNqaRrT5L27Md1lZ1/f8nnchrsi88SE6fepLKcMXr5HlvWvH7OafvWfC4O7UGyrFthBO0wc1Ls69E9spL05XKMbvQghogx5pIa8+jDb1wgq5Xc6sFvf+Xes/Xmg+dLqga8mmlINxXBzlxcHXzvjfvF/p7/Sf/GMtCX0+v2n5t5FnplRjt7ZDUsa9AoZ4xO2m9O5d+H7jAj9H6WwEfws3vNnaYzX76L8jkTWpHQl8sRyEpRqMw2Tz6/+yoBrJK4y6je2Gs1QJVSlqLHg1NTqkc12sYFz/2jdtlCC886IcO3VX+nmlow/rM5Ix2/51al3+MySxSbWrA/p++8If4eVs9AJZBVvUluN3OSFp51ghZsk5yCCdmlrciq91rNZ8rX0emHbadPvWK3ykr+D790p5rB2XppdxEveomlVcvRu2yhD790p0zf658GcX398f3VVf45E04TVLnn0S4mqjcj+rOv3E07zppc87pfF2l7e/q2mFI/mwOx47GRZYJU1LQJA3rpbqXJMYXK4HDps+MDGVOCk0hpMsM4Hm5LymwSl0o6iZGRMSb2/vOOF28rSdp99hT6G21SyZbQ4M2k3qfzOaPvv2l/HbPrFunfQzU2rJmhoV//x4v0gzftpxP22ir0ejS1oCStGy6thnzzQdtU3uvLGyYR9iBGiDHmkhqO6MtxD6vBz9S7WUTfTvq71TzgNGhJgp256HHfc85UzZoyLvb31gyNSpLmpGw0HPedvrhgJg+62WQ9n/M5o6+fsk9lj53NJg6E3m/X8mtWZKWrpBZMGRCKpuqMu+7qzX73V3GldeCCKzBpFrOLzt78w7ur6XeCudabuaKqqdTif9sYEwieUWlBjy9bqwvuWCIpPiiyz7xpesku6Q+nSfyVd1vGXItp+5IEUVvp4gaTNi/fpyYNVgMYaWlZff71M9CX0zsP3bbSpr54h81r+pykfWxO9BpLO44/fdsB+uDROzb0/XGTPYJ7IAz05UJ/k/RZ9dW7zyfV4bhyQCPLtbJ5pG8ZJ2eMvvX6farlqvsbm6akvnTSPSetX5il7vz+qf89wUkD+28zvfJcRpvpHgZzkyWlAxxNWqqV8NmkYPFLypPaZkwaTM02E7x+qa10/qSyRvc4nVIna0bcPsWH7zSz5nO0gfU9v3Yo9O9mxoZmTh7UcXtspe++Yb/QxNDgZPfoKfCi7aqTC9kDtzcRyMKYS2qvsnSmVq4frvyc1DH/+PG76OPH71JzM0n6er8xY4w9WTAVSLTxH+jL1b0h7LhF7UzOoPqpBQOfpZOQSdaOgT/IU6ysCKoe3/977Z6aGkmR9pXX7aUfvCmcGzyL0h5Z2Tv/mxqvfGjSVhz6700Y6NNXXruXznvXQTWfqRvIyrgiSxk+tyl6dtVGPbx0Tex70eDxAfM305UfOVI/eNP+oRU70TYsOEssKPh10SBmlDGkH0nysm9cow+df6ek+H7Ap1+xe9Pfve/W0/XdN+ynz7xqt5r3ohMx9t16Ws1nuJ3VF10t/8Gjd9RRu2yhM47bWf/32r0qr+czrAqJto+V0K8Xt0l7c+Xd1MVNoommWo167X5zM39/XMBy/EB1hUhSOiHuZcmaHcweH7OXcZLNJ9VfkZXPGW0xuTopgEH2eN/516Oxr0+J7D/sS3tEy9Bs1qzISsL9zC3UV7p15cm4UY0MvI8WPD27emPdzyWtyJo82JdpNStKml2RNWPSoF62W2169m1nTNQOW0zSV15X6mv6/ZuPvGwn/fIdB9Z8Pvpnd9kyffxrU3PT4y9owRcu10X3PFN5rZ0LCeJWZPkmBie+5Ukt2It4bEPbPbV8vR4JDPwlNVj1HnQlaadAaouke9S7Dt9e7zp8+8zlq6QW5IEp5KbHX9CDz66WFB5Iit4Y+vMmsS7+91W76+un7K15m02oee/mTxytT7+iNPiXFJxK26AY6bJ2tP30ZZW0PYFAyqkHbF3zuVMWzNNxMXmi6ymtyGr41zYZWVZkBWNcpxwwLzYfe9K1+NFjd9bWm02oBjvq7ZFFWCTWQV++Qi/7xjWVf//rwaWVn+MuuW1nTKy5XqLN3Zkv36Xu3/Ufco2M/v0/L6l5PzRjk1tZSHAiRtzqnqwrp5KcsNdWmjBQO4AY/d5fv/NFOj8m+MzTVNi5/35Cdz21svLv6L3sPUdsL2OM3nvkDqHB8SxtVvTaq6TOVe0kgrwxWrDN9Mq/G105tKmKawcXzN9MP35L8qbrXztlb33gqB0yfX98asFqQCV63TFYW1+zE/mCK+Hq2XxS/RVZxjBTOot7lqyKfX1ywt5/aRMA/dVVaX1Pf7/H4Mrj77xhX/3jA4eG/07iN2x6uK27b21CICttnz9J+vv7D9XbXzxf7zp8O22z+QQN19kfS0pu9+7532MrK19Rn183wZXbwVW+abbZvHas6oNH76DLP3yEXl5O3e7vdZZUJ9ExsrnTS9/Jc1nJfU+XxhVvemJ55bWi5+mA+dP1uRObn1ToC+0THnkvmMEhnzPabEKpT0KXo3cwaoy2O+wrV+qlgYG/uLb8T+85WDukrNr52dsP0MKzTggFROqtzKlJLZjwuUpqQZZkhZz6oxt13Dev1R9vWxzqtD3x/LrQ5wb68omrQGZNGaeTEmbabjF5XKU+k2ry5AVzNXvqOL3xRVsnfAJJGl2q7QdSorPa23V/78sZFViRVWN4tKjvX/WYHioH+/six3+rqdWBg7TVWj968wIdsv3miQ9D73vJDrrmjJcEVmQlnx+hc4fed6p3/PzWys+DKYN6wYHWaBVleUjNVVYOe5o7fYK+H9mX0PM8BmzrKBS9liasxAWO0wxEBtwnDvZV7nmVVEwM/dX43D/u14nfvU6PLF2jjSOFmuBj2qpESXrX4dslfnd0H63gv049YF7NZ//4nkP04OeP0xdevYf++5gddeqC8GdQK2kVzUtjZjsHBe9d0QHyoOg9Ugq3r0kToFjdk6xeu+hfW587cXfNCASPk1ZkxQUtpwSCLG89eBsduXNtWqacMaH6pcriDfTFN4JJK7Ik6eyT9678HByw9Z/fklYyStJ/HLatHvvS8aEMDa/Ya7b2mDM19DkyZkRxPFy2bightWCdPbL2nDtVn3nl7vr48btmviaCn3v9Acn9jOHRIuNVKeJWZO0Us79jnJGYeo3e44ZGSuMYwVXgQdFzI8v+kZsSv1p+fv1CPb5sraTSRJqZkwf1loPn6/EvHd/S9wfrPXrtTYxMOJw/Y6L++cHD9P2YLENbTGYVpIsIZGHMPLV8vTwvfiBp/5hN0YMPnS/ZuXb/inq3hqz9aVILpvveVY+mdprypv7AUhK/jpM+N3f6BF3/saO1zeYTsxQVAUmb1CbxP560z0ja133i+F31q3fWLrEPyhlTdxbbpujmJ5br/y5+UK//0Y2SamdznXHczpWf02YNHbPbLP329IPqPjT53+/X565bTan5zNTx/ZlSECJs15QUEsGHofccWVp5MHmwT7OmDGaage5/pLJXReR3gvdV6izeSKGoNRvjZ9hmMaPBB5u4lV5+XQeD0tRXVTCI/tJvXKOP/+Wemr3nkq6XappALzQBICjpUvM86dX7zgmtjvTbynH9eb3poG0YpM0oeJs/eLvNM//e4hUbJEkTB/I1A+RBcSuyxg0kr8jycZ3Fm3/mhfrm5Y9k+uxbDp6vWz95TOXfgzGBrC+8eo/YoOWEQB3974l76Odvr+0zrh8uhGZNI150koTviJg9W3yv2786qfBX73iRXrxD6dqsBrKSh4CMMQ31U+AO2sVkSakFR8Y4vchZgZTJcW5dtGJM/77L/C0MmlnZG1ev0YmGQ6Ol4GbSJI7VG0dC//bLkTZ5dFMSfHb9r9/dKam0kCBu/8VmhFMLht+L61vsNnuKjt29mjXldfvP1V5zp2q32bVjI7AfgSyMmcO+cqV+feOi1FmR155RTZlUd8y7TR3m6AAhpC9f9EDl5/5cru7eRv6h2332FE0MPKzWq6IXbbu5tpg8qA8cFZ+yJ27AQpL+84jtSDtYR1rw8Z8fPKySIsSvO/+6rNlIPZB6Kcnph2+nw3ZMfoAufa9pakPPXvfMqg2hf282cSC0Kfpr9q0OPjQzkPr39x+qKz9yZOXfs8r1PrW8se3vTj+oMsAxYSCvz796D33xNXsyj7QJr9pnTuJ7/sPQOaftq33mTZMk3fHpl+r6M4/O9N17zS39jj+AFQ14ForV1T3cyqpeCGwqfPXDy7RszVDKp0t22GJSzWvvPmJ7nbj37Mq//UHANHH3KL/a/DiWMawUCVo/HB44uuPJlTWTMpJWgFeC717pHnfpfx9e85no7/qzauO6GnEDIcSy6gv2paP7OO4fSNUY5QegvnbKPqnfH7f3RXBgf1xkZQl11rwX77C5XrPvnFAQJCguteCbEvZ8jEu/GvT+l+ygw3eaQSArg+Rno+11VaC/l2TrmBRa7Xmm4mKDu5au3qh3/PyWSjAiKbVgN55lg/cx0q8m81dEBfsJwVVSZ520Z+LvxgWyalZklVMLJgWyopPlktrqTVVwHCOYaaRd53Sw3qNBsYmDpTqbGLOazq/Pz75qd+5iDuNqw5i6ZeGKmtm1QcEHmHrBE7+hOXn/ufrNf7yo5v0vvHqP0L8P3XFG/PeYakOKkh9e/Xjl5768SQ0qeqp26nLGhI7z7imzaiVp6oR+3fyJY7R3eWA36KaPH516Y6O20qWtftpt9hTNjKwu2LG89H6POeFZKO26oZf2yKLWfJ7n6Tv/ekQPPLMm9Pr4/rxu+ni24EYWe86dqm1nVFc0vv+oHfTNU/ep7Ns0dUK/9ppbuk6NpDcftE0lyFUqZ9uK0lOiwYfX7jc3dNyi/EDWxuFqqpK+fC5z5/2bp+6jP7774MqeQCvWDYfeLxRJLRjn8geqe5jdunB5yidLefS/fNKe+l4kbaMkveXgbSp7vBy244zY1QRRNZMCVN3rzF+RRZWFrR8Op/IZ35+vmZSRdMlUArmSpk8ciE0nEw1kfe3kvfXBo3bQvvOml78j8H0xf4drrL60+/zv//NgPfrFl8e+97GX76IL3vfiuntw+m3mvM3G17wmlSaDxOJe1rDpEwb0jVP3SQxCJa0MkqTPR/a7SErF5PvIsTtrwkCfJqWkx9vULV83rLece3PNrH/fhIG85s9oLIPFcHmFQTv24WF8vYq+s3vOueIR/evB5/TXO5ZICq/I+s9AyuKxyi5y2oHz9LO3HRD7XlLgBGGFmD2yRsrjifvMm6bXH5i8VUVwz1XftAnh/oSf+jZp5VB0MlZlRRbtgaTwPcKfPFHwvFDf/A/vPrgmlfuFH0xONx2UtiJr4mCfrvzIkbr2f46q+b2+XHgsmPpyE4EsjClP6QGj/sCMsOHRejnbS43O6YdvpxfvUBukOvWA6s3qwc8fF5ueUArukZX65zZZfRkCEIVAuqtvvX5fXfWRI7XwrBMa3lNEki7+0GH6y3sPqawciWNkGJSoo7RUO/n9L5+0pw7dYYZ22ao02Hfs7lvqsv8+XK/aO35VSas39ZwxBIsDblu0Qmdf+rDOve6J0Ou5jClcmtWfz+nV+84JzYryZx0evH11lYmppCCkzuIMR2bu+ekmkrxm39J1te/W05r6exMH+7RgfjUF78Hbb65dtpxcmVkWTi1InfmmBx5Cg3u0xDlxnzk67cCtY4NLfTlTCYwsWzPU0CzLYCC5kj4j8EeorapoKp+Jg/ma/kfSytTgiqwk0bZ1iynj9OGX7Rw7KJGP+TvvPLQ0mHXgtrXpsDd1P7rmMX390ofqHv+4AK8kTR7XX1mtmsav/2vPqA5GBAdBooEsUkKOnbRjG3xv2xkTNWVcn9580Db6y3sPSf3O4D4WdD/Cfn3jIl3z8DJdct/S2Pej9ZFlTz+/LxOXJrJRXGphHA+3+P06f6+k9SPVfn2wj3DULvHjSVm8ep/Zie99+aS99JLydx+7+yx96/X7VN4LB7JoGJOMxuyR5a/Iituv6uaPH63rzyz1JT50zI76WmAvwRP23Eo7zQpnaHjrwfMlJU+Y2Xqz8GpX/29SYyXBfYH9QFaxGO7DHTB/s5rJ1nGZMuIE+/j+/dDfQmGwL6dtZ0yMrbsfvmV/HbPrLE0a6KPhdhjToDDm0hrz4E2m3oqsfIaVVHd9+mUqel7qTDP/mbrRPYU2FX35XGogy/OqaezypjSrr9EZgUHbzpiYuumwxD0mC3+GS9L1scecqfp1ZCXjjrMmj1ngwohBiaC4TWWl0rkdHYz4+/sP1e1Pjl1O9EeXljZcfWUgdRqXWK2vXvJg5efTf3mbfvmO6qqc4dH0+9WhO87QwrNOqPs3zn/XQfrB1Y/pyoeWpX5u3mYTdPGHDtc9i1fprIsf0I6zJump5evrfv+mJnjrynrfiAtSDfTltNOsyTp5/7l6Y0LqrDj/+MChmh2Y0OG3x/7DFveysJoVWQN9je/3GNPLvORDh+v2J1ckr9aJEZfCcIctJmmHLSZpxqTs37MpuHvxSn3pn6X28Zx/PTomf+NHb95fF97zTOx7wThkdCDJR4C/ca0EAf027tQF8/R/ryvt+fL5SKaMtN9DrbRD84Gjdqh57azX7qmzXhufSsuf5LHrVlM0f/MJ+tQrdtVVDy1LvH6yMPQcncNzWZUf/PDHn4JjH/61N2+z8bGr9tOctO8c/bm8yuubr99XF9z5dN3f+eGbF4T+HRzHos6SFWL2yBotB+uDe9P6tghMmh7sy+u1+8/VlPH9mjyuTwfF7PF58oJ5Omi7zTUvoZ389mn76Uv/fEC3Llqu59cOx/7NTVnciqyiVzvxOjrumPU4Tgxm9iqPs5x3+ov01PINqf2ZQ7afoUO2ry6K4BJzE4EsjCnP81IHyvsDDdVowkCv7/tv2k8/v36hdtqiNn2Mb+qE9BnYEh3veoIrsnbYYpL2mjO10iGTpJmTByvvt+MBNC1VSRCDEukKlRkujR2n6I3+0PJqR39FSbMYsM0mbvB0z7lTtefc9DSdrfjA0TtqsD8fm9KJq6zqu1c+Vvn5moeXhTra0RVazXrRdpvrx9eWUrtmeVjdc+5U/eY/wvvQ8JBbFQzkrx1KXzXn22bzCfrE8bvqi/8s7RX5r/93RCW9yFcDszWz2COSXte/vudOrz4EU19V0RVZEyKpBafH9Ol++tYFmjVlnG56opQ6Mu547rzlZO28ZXJf0Rf81f6+hJVfdb9l03PtI8+P+d942e5b6mW7h+9R4/vz2jBSCPVb3h8Z0Pff4Tprn0mDfYn7x/i2KM+o3mpacnaFiQN5rRtObpfp54fFDcSdvP9cvf+oHbTN5rUTCKOf/9v7X1y5B33xNXvqRdtupkN3mKGrPlranzo4kNdc+Vr6dXQYq1XD+gIrsr5/1WP60TXVbRb8ydOzp45vOA3n10/dJzRu0oy9503VkpWlPZVpFeM9+twaPb+2lHY9uCJr362n68D5m+nTr9wt0/e8dLdZqe8nBbGk0v6DP3jz/vrDrU/po3+8u3IfJLtJSXCMw5+0HrdH1lG7bKF7lqyq/Dvr+GJ/Pqfjdt9SF9/3rJau3iiplB4ymiIyDa2iuwhkYcylZakLLt0+aPvNdeE9z+gP7z449rPbzZykz51Yf3ZfVtxk4vXncxotejrtwK315ZP21EPPrtGf71ii7WdO1NtfvK1O3Ge2BvvyeuXes/Xfx+zY9N/56VsX6PxbnsrUsWZ1T7yXfeNqPb92WLd/6qUqFIt68Q6b16zsOHB+/ZRI73jxtjp291JHbv6MiZlWkmRBnVUlnebdmLy17YyJ+nJkA9wsabo2dcF0gvVWZNVzzmn7Vu5BX3j1npo5+REdsfPMhr6DOqsVDDauHSrtK7LDFpP06HNrE3/HGKPTD99OA3057bv1NG03M1tKiyymTxzQt16/T2XAkIGksGAqH0ka15/T3+6qzl5+9xHb1/zO0buW7lVzpo3XRfc8o3cF9rI4++S9tWzNUOa/v9/Wpb2ytpsxUd97w/6Jn+MaC4vbJL0TrjnjJVpT3i/ojON21pE7bVEzyMgl1n5XffTImk3to47aZQv96M37p6bhuvCDh+l//nS3Ts6QAg/xGplcsdfcaZWfp47v15vLabJaddZJe+mMP90dOxFr08VNwjV+VqBC0dNXL3so9F6rfbWfve0APbWilDXh7JP31r2BQfoszj55b2292UT94OrH6n94E/Oh392h2dPG63tXVY9NMPAxfiCv3yeMJY6VkxfM08kL5ukn1z5e/8ObkOBlNNiX097/e6lWbRipub7+6+gd9a0rHpEUv9o4zRnH7axnV2/U4Ts19gwdxJiwmwhkYUzV2yNLkv783kP05AvrdeI+s3Xs7rO0xeTk2Xxt4Q/+je1fcVZf3pRnS5T+7S8FHil4elMgxdK3T9u3pb9z9K6zKgNS9fCsFO/hpdXB2ULR0zabT9ScaWu1ZOUGvemgrfW/r9oj06yWrLOWGmFkmF0bkFQLtgwEsFK1vo0j1cFb/7q68WNHNzWo+6pAWsctp46rCSxmQ51FBfsbTy4vzWYd7MsWLX7rIfPHokg6cZ/w6lbaxaqhSCBr4mCfLrjzycq/09rH6RMH9Mf3hPffed3+cxv6+wduu5nu+szLNHV88mp+S5poq8S1ecHUq2Nl5uTByl4K7z0yfbCDq6xxSaf6jEmDmjEpvIfFXZ9+WSgNqDGmZgVd1PwZE3X+f9YOMH7ldXvpjD/eTcA4Imvb84VX76FpGTKStMspB8zTTU8s142Pv9Cxv+kCbhVu8dOXjcbcz1rNOPOSQED/dfvPbbhvMmGgT4fuMEM/uPox2sWIuFSNjexj2wlUWUmwDz/Ql9OqDaWJSNHqyuWMrvzIkVq+blj7bzO9ob+x3cxJuuB9L266jPTx3UUgC2Pq8WXrdPuilamf2W/r6ZVZsWMexBIdzXr6cqU9svwO3kAlkNWdGbg+OgXpil64w/Cfh2/f1b0H6BiEJc3usyWQ5eM6S/bh398pqZRC6auvK82K3nLq2N+z6iEwUhUMZF3zcGl16kDGQFYn2HW1d190gGZKJKD0kl2an2GZVVoQy8dAUljcno+27HXEpIx4f759sYZaXEkclCWVe1bB/ZIhLVszpAO+eHll03rfqQkr2d7UwD6O7WIMs9hdRI1V9ZXbnZGY1EE23M6qWReotXps6X8gLJh1JpiFK27sY9sZE7XtjNqUuWONM8dd9jzdoyc98MxqnXvdE5KkD790py6XJox+QTx/jyz/JuPPcmk1lVYrjAwduRRfvugBFYqlVXT+ZqXBDTC7hSqrL9iZm9TNOuOBqa6rymk7P378rlYEsCyLgVqhGLlNDfTl7AsWc4klCgUiP/oS7ZCyJ2qnEBipFdcf5Dqz24d/f5c+9ud7al7P54w+9Yr2r8pvBlVW4u+N88Azq0Ovf+E17Uuv3yq7rvbuc6G9oc7C/AB63IosG9JAd78E7uizLJDlQnvQCUn9QvqLaIfuj3Rik/HKvWdrr7lTQ5ued0O1c0KrFSefLwWy/JlKk8eVmomX75meMmQsWXa/s84Pr35cA3055XM5femkPfTuI7bTZhOzb3Q5VrjCqhL3yCq//vO3H6AdZ3Vv0JZrLLtx/XbNAaIDXlWIHIzBvpyO3X2Wblu0oksliuA6C4meusHq68b+gUlY9RgWt0LflnGkyix26iyTk/ado63Lm9l3q99IsDhsyrj44Zm8ZR01rrAwy6oHdfiZZ6IrjE87cJ72mjtVkh11ynVWny0rsmwIgNokeDyCE2VtCmRRZ+4ikIWOyRnpyJ2TNwHuFJqrOjyFVmSN68/rjk+9tBLQ6mKxkMJfkTXYl+9qQMRHxyAs6Wj4x8mGthHZDPbnu10ESdzL4hQjKWLWDxd0+mHb6dQFW+v2J1dYsZKOe1myQtG+B91SCq1ul8IusYEsWwaSul0Ax+SM0TG7bqEvvHoPvXa/xvZxaTdWhJckzmK35BqT7BjgB1pRWZEVWcr/5ZP20vWPPd+NIoVVMmV0txgu6LNsjyw6+iXBW9ZoIGBsX3VRYS4ikIWOsW3GHR2DeBMG8ip4XugmM73Lq3uMqK96CkXPvtma1FlF0qGwZhZZ+b/UWX3j+iwJZFl2vdsgutVBoejJGKOpE/pDm293i239oG6LtjfB1IIbRwodLg2yitsjy5JbWQX3svpOWTBXZxy3s4wxXdlnycetLCzu1LWlrxjENeYgKq3C70OPxtzPxpUnrG0+cbCjZQry+4sMstdnS2rBas4n6kwKT8qwcaKaxBijywhkoWNsabOqaUcQZ1x/vry6x6LpEracPJazaramJK6yqkLMZsKSPYN//gMdne/6SC1or2hqQStvHdRXop9dt7Dy85zp47tXkAiqrGpotKC/3LGk5nVrAuuWFMMFX3nd3t0uQgjXWTJb+oo+JmWEudAPs6WJtoU/cWY05vls33nT9PlX76FX7T2708WqoL6ysyXQT52FBatlJBjIsqS+JOrMZQSy0DG2NFp0vtP5S+xtWt1jT0nsZsuMJImOQVQ05ZnPlllJdpTCDeNILWit6HX2yRN261JJ4llyuVsjKXB+8v5zNcjKRyudf8tTsa/bci/zOTCujAgXggGdEJdi0cZjw8SnMMYX3OL3F+NWfxtj9OYurlIN4TKry6bxD8nO9robgv3n4dHqdWbTGKNEfbnKrmnF6GmW3WNotBIMjZYCWX15yypM5M+vx5ZgsY/qqoquFPFZ1pejzjIY7LOr68RgUlUxcgL323gfo74qktob2/oftItVz68djn3dloEJw8Yi6EG2nc2WXO5A0/yMghtHavd8tEE1TR18SdlNrFmR1e0CWCY4wWn9cCHwejdKE48JCO6yazQGPWHSYPxCP1saikpqQR5yY/l7H9g0u9YwLpGJLQNJUqnOqK6q5NSCdtQZKVezs2ZFFu1ijeh1ZsvDrc+u0tjLlnZRos6i1g2Nxr5uS5XZUg5kV521zc1Mij8K0UkaNrCwSKiDKqvyx4FGCpYGsriZ1Yirq76cse5YcZ2VBB/BNgQDWZY9mzHB0E0EstB2+249LfZ1W9osS4phLX/pr03LtG0JgtokmAph162mSLJr0JY6C0sahLClzqiv7GwLZKHKv8x22XKyJKnfpr0eyxj8q0o6FDb1P0qoNF9wVm2QTcFHiRpziV1njp1su29Ydrl3nQsDoVRZmD/xydZAls+2a7+b4urKludoieBjVPKKLIuOk+Eac5V9T/hwXlJjYFvjTpsVr7Iiy6KOgY86q/rAeXdUfn7gmdWS4vN8dxOrHquSnpMsaxbpzGUwrt+urhNVVuWn8PT7G7alqLPtereVTf0P6iws6XjYEjOmutxF/6PElePgSDE7hnuFW/z+4l1PrexuQRJUM2VwpflGC7XHwt8SwyaMf5QE28TgGJVVwcduFwBNs+SxA5sCa9os0jGlenL5ekmSTeN/pIOsdf2jz9e8tt3MSV0oSTxSC4a5k1qQWqtnXJ8lK7Loftfwr7N8uXdr08OSjyusKumeblOaXIn+YlDSsbDlXuajztxh2aljARdOXsM1Bqf55++6hFXG3VZJuMp1VnHZA0u7XYRUpOkPS1qRZds9n/pyE4EstF3SQKgtD7kM/qV79Lm1kqR83p7mgRrL5mW7zep2ESqos7Ck1IK2tIvIzqbVIhIB/iD/WPjXVb9F9zGJ/kdWeYtm0tBEZ2NLs+ivxqRddA815g7aRTfRLFYlTTC0BddYrTP+eHe3i5CKKgsLnsPrh6v7q9o09mFRUdAgu57w0XN+8Kb9Kj/b1lCw8iCdbTOiJR5y67Ft9QEPTFXJK7I6XJA6qDOHMPOvxvJ1I5Kqg9m2tYkSA+xZ2Nb/oMaCSkfj7+8/NPSqLQMTdpTCLra3OQT4wyyvrgBnCjrmXKgz27Z46LakCYa2caOUCHLk1OqoDYHUgtYFkS0rDrIhkIW2Czbeu241pfKzLR0oS4phPZs2W6+mFuxuOWwSPRT9eWPNNSaJCy0i6YHJljqzpBhoAFUWtmG4oHOve0JS9dj0W7SyR+I6y8qmZ1wG2cM8T9pi8qD2nDs19LotgSyfRadQ143adEGloI/vDruudjtwTNxStL5dZHWxcyzrB3Vb8NQdCexvZtO1Z2RY3OAoAlkYUwN91VPMorhICW1WqpGiPZtn2jLYbwvP80K5hiWpz5ad1gO4xKqSAlm2rBhhsNZdPOOWDI3W7nOQp120WtK5a9vAjW3l6ba4LpktgSwmPtUaKdjTn49jyaljDVdOXa4xuMyisfRYtIvuIjCSbFx/ToftNLPbxajgOnOXfU/4cJ7nSXvPm6Y7P/1SDfblK69b85Bb/i+3mHRrN47W/1CH0TEo+csdS2peCy7ZtoERg39BwXGkgby9AX7qLN1XX7dXt4tQQYA/LBgU9s/ifssuMLtK031J93Sb0o5wmYUl3SI4TvYKzoS2GX18d3C9h7ly5nKNVRUced5xo5SQ6ONHxZ27P3/7gdpn3rROFyWVI00BIghkYUwM5nOaNmEgtCLLlk4vg3/ZrB2yMJDFjUaStGL9SOXnyYN9XSxJMi6zsOAy+uCxsSbAzyz2TPrzNnabqLQa5RPZlhWPQVxj9dk2wGRXabrPX8F7wl5bVV7LWXKt+WWjzqqCK7Is6XKEWFikrrKs+UvkSDE7xvbxBbtL13k2pTeLU6kvu4vZFYN9Nj6LBVBniWx7jra82UYKu84k9ITgbJ/wygO7WgpXHhS6ZY1FK7IsO3W6biCw78v4gXzKJ2GLpIFZW85tS4phPZsCI/aUxD7+1dZn2QOTNRe8JZL6YTYNMFFjYcE+/rdfv2+lTbSmabSlHBYJBrJsfvaxuWwIIx01XJeU8t0WfmCUVXS1JjGJ1wlxWV5sDEJyhbnJvjMJvaHckAc3Wrelca+sPKDZSjVryrhuF6GCB6aw4EpH22a2+EqpBbtdCnskpcqyLsDf7QJYrs+a0doqrrOSuMNgU+AR2Vm3Isuu4nSdf9vK5UxlUCJv272MSqtYGVjFbyPLTp2uc+X5lGsMLrN860BGPlJMGLR7Ei8tYzLbxq2MDPcyR9l1JqEnBNuC4DJ7WwZs7SiF3V622yydfti23S5GDe4zJcFOQDBYbBPbU2x0WtLMP1sG2isz/7jGUtm0wqc6KQNR/nlsx9VVZVt5ui1xRZZNJzX3spBonfnBfVvu+ZYUwyor1g13uwiZ0P9wB9dZmCvnrivl7ATbV2T5HClmR00csHRFlniWDoo7DLaNW3Evc5c9IzLoKXFtgi2BLB83mWQv2WULSwdsqTTJjRVZEvUV5K/IOuO4nUOvWxLHYoA9I5suN1aqhsXd0y3rdlQw+y+dTakFJYLFQZ7C9wvb+iCWXvJd9YL1gSxqLciV24MjxUSZrf2hbrE9kMXexckmWLqtAtdYfbb1GSXuZa6y70yC85IaA1vadmax12db+iy7StN9A3n7A1mkFgzzA1lvfNE2oddtmcXuI/iYzrb6krjOKrzgj3YeFAtPn65KqiWbBpioslrBdrCvPLt2tGhXniaLTqGuW7He9kBWia3tNmrRx4frklK+26KyuqfL5bDRREv3yPJxLyuLOQwDFu6RBTdxJmFMxA3W2DOAY01BrNVn2bJfHw9NJeEVWXbWlT3Xux38gdloKsFx/XbMKmPmXzY2ndZcY8mqqQXtPEhcZyX+yrTPvnK30Ou27V3BCrqq6KE4fMeZkqTx1tzL7Lzmu4k9stxCcwOMPcvjWEix9WYTul2EWNzK6huwcAI291w32XcmwX0JjYFtD5cMTCTry9nVNLCKLiy4CsumFJBRXGJV/sBsPtIOMvjnFhuPE/eykuAMyEogy7LqYoZtvKN3nRX6t1Ursiw7h2zzxdfsqSs/cqSmTRjodlFCmBFdtX640O0iZGLRZd9VLpy7xhj6HgEu1JnENRZkWwrjqOoEQ7vL2Q0fO37XbhchFVVWEtcu9lu2IsvG53pkY/e6TDjL1lnQEkGRLGxb5WPz+dQNLtxzjYwzD3ad4A/MRmPEtgSyfNRY1aTBPq0dGg29ZtOl55eFOqv1ou020/3PrNZmE+0aXHeh7e4kF1ILIix6Xx/oy2nbGRO7VJpalXaRU6hi40g4kHXB+16sDRYFt2gW0Qvsv79bX8COiutn2HQv83ErqzUxsEfWwdtt3sWShNnfBnSfbWOMEteYqwhkoe28mq2g7WJvyeyRt2xFlo9ZSWWBw2DtgB8XWoifiz26ImvQsplJjP5VbTZxoDaQZdN5bVNZLBA8dT9+/K5600HbaPa08d0rUIrSvYwK9EWvK5v2rqCWIjzL2sEIm8vWLeuHR5Uz1VRa+8yb1tXyRDEjOiyuGxYcuLWFPa000LhC5EL7xTsO1P7bTO9SaWrRLCYL3jN+9vYDuliSeLSNJXH3sn7LxhiNxNiHo+w6k9AznLj50mYlsm2PLFbRJbP53mtz2TotaY+sXM6ea82JdruD+vJGJ+y5lSaPq875sXF1KNdZSfAw9Odz2n7mpK6VJYl9Z0+XJZy7c6fbtf8B15h7qLKqDSNFTR7X3+1i1MV1luy1+8/tdhFC6C/CddH2Zs60cZo0aN8cf9rFdH02PUfTyw+JO3dtGveQuJe5zL7WGs6LNlpnnbSnfnfLU90pTAx/Fgdpz5LZNlsCYcEz19ZVakYMJAX5M6Ftn3lMnYUZE86jb1P/mwcmd3GdhQXbxX3mTdOHX7pTF0sTZnub3Wme7H7wp12stWF4VJPH9WnVhpFuFyUWNZbukg8drh22sG9iBjeyAEeOhSPF7Ijoym/bHqcNCcRr5Iz03iN3CL0WnSBqA1vHZhCP2nITgSyMieBD7usP3FqvP3Dr7hUmwr7bnX0s7BNIsq+T2S3B42BRBqYQY6ivEAcOhpETxeyc8rEIpR+xsG1kUkaJCw+ONgcAuiHu3L3gfS/uQknScY2FuRAscqA56JgNI4XyiqwN3S5KKq6zsPceub12mjVZO285udtFqeFCG9Bptt/fbS9fp0VTC9rW+lSy0dhWsC6LnsdWTTaizkL8wzCQz2m4UOxqWZJYdPagQSy7QNu50nZzk0k2ZNnNxpBbMNGpB8zTpME+HbrDjG4XpRb1VWH7LHapdJ0xkFRVqjOjol3NYYVhsqaz6H+U+MfB5qbR5rJ1g+0B42p30e5ydtKG4UIoRa5tbO8bdZp/ie279XS9et853S1MCq4wuMzzPM2dPl6Ty+kEbbu10S7WsqyKalBl8eZOL+1X/MYX2bOwIci2ax/Z2NurhdNsnqnFDJf6hkbsGrmtjtdSaVL1OPzh3QfrgPmb6U0HbdPlEtUyMvJk13nUbfa2ikhiJI0GIlm7bDmle4WJ4HwK8+8OMyYNdrUcaayaOWoR2w8L/cUw2+sLYeuHC5ozbaDbxaiL6yzM5suMNiCMU9c9xaI0YSCvCQN5rVm61toxBjtL1R2eV20X//zeQ3TzE8u7Wh6ki058OnDbzbpUkmRM4nUXgSy0nfWzNa1+NLDDqGVLEHhgimfzYaHOwuKaxc0m2jWwRGrBMP9e5qfvfPDzx2lcf76LJYpHlZX45+5/Hb1D+gctwENTSfAo/OqdB+r5tUNdK0sS7mVhrpy53MuqNo4UNGHQ3kd+rrEwV+4Ptj/vd5oL4wvUWVXR85QzRl8/ZR9998pHtcNMu/ah888nqiyifMPYb+vp2m/r6V0uTBiT1eL5p7CNx8e+EiEre3u1cJqF7VQN+gW1/vGBQ/XH2xbrZbtt2e2ixKIzV+LKcXClnJ3gyavpwF1/5lFdKk08Y2gXo4JV1p+3KxuzjQ8EVuC4OMfI6LAdZ3a7GIm4l1UFZ0TbiMu/VtGT+sqb3x6xk8XXWbcLYBmbz2WLi4YE1Fm83WZP0XffuF+3i1HD5usf6egzlviHwQ+g23pKU19usmtUBj3B9ragmlrQ9pJ2zkBfTv95xHbaY85UffZVu2ugz66mga1gwqozW7pajFQERWr51fXqfUp7HuRzdlWgC7NJO8k/fz934u6aPK7PuvrycSsrcWUWu0Sd+Vw4DrSLtQiiu8VvG2/6+NH64Zv373JpanGNhbnQLkr08eE2V85fl/q2Y8n2YIhkd9m6ad5mEyTZl4lGsnssDelaWpFljJkm6SeS9lDpfvAOSQ9JOl/SfEkLJZ3ied6KVv4OgLFn84MkgyZJ7D0uNp9P3RAcmPjia/bQx4/f1boVPpI7AyidYiS95eD5esvB87tdlBqVSRk85JaUD4PNLQ+3sni2HxeusSrbj0Q1HZPtJe0cfxXdrCnjul2UVNRZmM3tojH0F4M4d93jeXaPL1Qm8XJqhVhcZRX0GUv8c/cjL9tZbzpoG714hxndLVACrjE3tTqK9i1JF3uet4ukvSU9IOlMSVd4nrejpCvK/8YmxJXGwJFidoYjB4MHhRJXjoMr5ewET9XOd18+p6kT+rtanliGzneQ7aevA89yXeHCQy5KnGhvGLCtYfMlxvVfy/NkeaV1uwB2caG5sTkA0C0cEvfYXGXVyWqQ3OiH0QbE68/ndOzudm5bIhmuMUc1HcgyxkyVdLikn0qS53nDnuetlHSipF+UP/YLSa9urYhwkc0d3Gpqwe6Wwyal/Xu6XYpkdObi2V5n1FeY7avU7C5dd9h8L/NxLytx4TDY3gZ0mn/u2nxUbC5bN7gyQcWRYnaMC20PVVZSTaFld505MREBFQ50ZzvM9vOXCotje7so0f+osv9A0C66q5UVWdtKWibpZ8aYO4wxPzHGTJQ0y/O8Z8qfeVbSrFYLCbfY3mS5cAPsBpuPis1l6wbbrzHUcqZT60o5O8D2QRo63/FcuMc70x50iuVVRnVFWFxfFhcNCaizBBYfGIuLBmRSSi3Y7VLU58rkkbHmwlFg4nU8268zrjE3tRLI6pO0n6Tve563r6R1iqQR9EpnReyZYYx5lzHmVmPMrcuWLWuhGLCR5e1VGY2Wz5X225VyjjkHZrFL1FeQJ8/6CmMVXZhnf5VJos58LrQ3tj/MdZoDVUadRbhQZ5I75ewEz7M764LPhTa8E1w5DNRXFYfCTTa3izaXrRsqK1UtPi4uTKTrJBfuEdSYu1oJZC2WtNjzvJvK//6jSoGtpcaYrSSp/N/n4n7Z87wfeZ63wPO8BTNnzmyhGLCO5a0WqQVrBffvsVK5cLavkOg0m9OeGUPO4Sh7a6uEDngMqw9JuV3kZiapen+wuFms4F4WZn3bQ3VVWR7g9/tFNIthLtQZwqw+KlYXrjtcOCS0i1W2HwoXzqducOG48FwWxi0eY6HpQJbnec9KesoYs3P5paMl3S/pb5LeWn7trZL+2lIJ4SSbGyyby9ZNNg8k2Vuy7nBhEJQ6i7C/yiTR+Q6y/VBwL4tn82GxuWxdYftFJrv7Rt1ic+DB3pJ1j/1Xmc+dko4lB5pFSdSWa7iXhXme58QxcaU9GGsuHAaLu0Zd4UqdcY25qa/F3/+ApN8YYwYkPS7p7SoFx35vjHmnpEWSTmnxb8AxrrQFrpSzE5wZvHakmJ1ifX/JlfOqA6xf9Sg6c3GceMjtdgEs4dK561JZO8H2ttGFySOd4sqxcKWcnWD7XjB+0WgXw+wOGNtbNiAriy+x6upi7mWSqvcHm+vMR42F2Xy/sLlsSNdSIMvzvDslLYh56+hWvhfus7lJ8BssHpiqbB9kZ/PMMBfOXZvPp26xvbNkd+kQRX3Fs7ntsbls3eDArYw6i2HzIaG+4tnc/6DOolxoGeVMMTvBhecyhNleZQT44TpXzl2CxW5qZY8sIJbtjRYPTPFsPiwEH8NcmZVEdVW5surRjVJ2ju3XmCQqrcylw+BSWcdS5V7W3WLU5Ujz3RGuHAtXytkJrgzSuFHKzrG5XXSib9RhNq+g87nSFnSCZ/1+j90ugV2q++Daf2Dof4TZXGU2lw3pCGRhTDhxk6EzV2H7DdeB06krrJ5hK/vPq06yPbWPVGq3qbMq24OPpB0J8+vL7nbR3rJ1k819RouL1jU2H5Nquwif7f0P2sUwy7seFfQ93GJzG9A1DhwUV9oDBPuyVJrkzj2Ca8xNBLLQdrY3WizVTuBCZ87yc6tTXDgKNg9MdovtR8T28nWDzcfE5rJ1lQMHxvYgaae4chzcKGVnOFJlCLA9fbiPcyvM5jpjsloYh8I9ttdZJRtNl8thCxfaG4ub7K6y+bgYwzXmKgJZGBO2N1ioZfNhIfgYz/ZzmcBjlStHgjqrcuVI0C6WuHAcbG+zu8Xmw8JqkTBPnhvHxIUGoaPsrTPaxTAXzlzqrBaHxC2e51ldZ1xj8Vw4LnQ/Slw4Dk70ZxGLQBbazoVGS3LjQaETXJgR7UKnpZOcqDO50xZ0Qim1j+UnsqHOgqxPx2Rx2brJhcPCZVbiynFw4Z7bSba3PbaXr9NcOX25zsJsH2CjtuA6F+4VtIthNreLLpxP3WD7ceEacxOBLIwJuxus8lJtGi1Jgc3Wra6zEmqsxIXj4ML51Gm2HxLby9cNNj8w+biVwXU23y9sLls3uNLeOFLMDvGsPo8tLlpXuHCNudA3Qi0Xzi2EUWUlLp27DhV1TDlxHLiVOYtAFtrO9huNzQ9z3WTzQ4kh+BjL9nOZ6qoiZZ97bK8z8ueHVSdlWN4wirbR58pxcKSYKGNFeC37W0Wusyjbb2U8k1W5cCxsP586zfPsbhcr9WX/qdVRNp/HNo+ldUO1XbT7uHCJuYlAFsaI3Q0WqpxovDmdQhx4XpJk3Di3OsTzZP15bIxx4mG8k6x+YLK4bN1k82FxIcjWDTz8u8OFOwTXWZj1t3WqK8SFfhiXWAyOiXNsvlfYXLZusH1yYZADTXhH2XwqG8mNji1qEMhC29neFljclnaF/8Bk803GR8cgzObBPxfOp06z/ZBQZ2GutDcuDHp1gksPuS4VdSy5chi4xKqc2O9RjrUHY8yTG/d3rrMSN+awu9N+A3FcuUe4Us6xVsm60N1ipHLhPoswF/qziEcgC2PChTaBB6Ywm6vM5rJ1hxsnLwPs7qHGwly4lyHM5jqzuGhd4TkwMsFDrnuosVpWT3yyuGxdZfFhsbhoSEEfv8r61ILl//IoHeZCl4zgY5jtVUZtuYlAFtrO9sFrf1CCm0yJC0eBgaR4Nh8Wi4vWFZ7nWX8es69ImCuHwpVyjjWXzl36H2GWN43UVojnxP3dpfZgrNn+XOajXSxxpLqcKWcnuHAoCBjXsrnvYXPZusGNa6yEtrHEhePAZeYuAlkYEzY3CjaXrRsqE6IdODAu3BA7wYXj4ML51EkupPaxPdDWaaXrzN5j4leXC+1BJ1TSMdlbZdSZgyw+nbrG5mtMKpWPS6zK9v6HzWXrJqsDD1RaDY6IW1zphzlSzDFX2QrD4iuNZjGe7eMLrkz2QRiBLIwJy9srSe50YDrF5ptMZYYL3TlJbgzYSlxjUZZXlySusSibrzGbH+a6yebjYm/Jusv648LNrMKFQ2FzG9ANtqfQqnDg3OoE+mHA2PPkWX2v8Mvmwj23k2x+LvNRZyUu3MtcOJ8Qj0AW2s72xpsZ0WEu3WSoszDbO+AunFud4sK5S2rBKFcOhivlHFsuzahzp6Rjy4Uq4yG3lgvHxIVzq5NcmKyGMIurLJBCiwvNJdRXhM3XmD/2QY9Rkiv9ZotPqC6y/ai4cW4hikAWxoTtA+yocqFPa/PDXDdQZ+7x5MAeWaRjqmFzjVl+OnWNzcfF9jagW2w/LrSLVU4cC7tPp45zZfDajVJ2gAMHwvImu/McqDPaxTDbm0Wqy10EH0tsv8YkJvG6jEAW2s6VxtuNUnaOCw8l1FmY7XVGxyDM8uqSCyXsJFfOX1fKOdZcOgyuDCyPNRf6i7SKYZ5ndzomnwvnVqfYfiT8QDbNYpj9Vxl1FmT7hAyEeeIac4kLx4EmIJ7Nx4V2210EsjAmbG4TqmnqHLgjQlIwTzR1JrkxQMPqnjBXTl1XytkpTtzLulsMa7hw7tp8PnWDX2e2HxYXzq1Osv08NhINY4TNdWZz2brBhVPXhWA2UI/VbQ99/DC/v2h1pZXQZyxx5Ti4MK6GWgSy0HauNFooqQ4k2dsxcKDP0lEuDP7ZfD51gyf7z2PD6F+I7UeCayyeEw+53S6AZWyuMhfOp05y4dylyiJcqDQxmBTlQttDjZW4cu66UcoOsfxg0MePZ/NRsbls3WTzuWxvyVAPgSyMCQf63rb3XzrG73xTZ+6xvc5YQRdld4XZXbrusLnz7eMy89l/IOw/mzrL/horcWWQslNcOI+psapSCi17a83eknWHC/d0258/usH2Q2J7+TrNkxtpcp1oEDqAfph7XKkxLjE3EchC29neFlQ637YXtMMc6Mpxoylz4jCQWjDElXPXlXJ2gu2BWAaS4rlwWCw/tTrO5sEke0vWHS6cuzafT93iwv3ChXOrk9yoMyoN7rL5GiN9eDy764z9HuPYXGcy1JerCGRhTNj8EOlCqoZOcqHxps6S2Htc7C1Zt3h2d+RU3tfMgfagU1xIBykxS9HnxLnrwgnVQU7UmdwpZ8c4cB4zwF5l+7Fw4HTqKBfu6VQZXGd5s1i5xmwvZ6e4cBxoF8Ns73tIdo9ZIx2BLLSdC42W5MaDQif4R8GNB0nqTHLnGqO6wmy/xOjM1bL5iPCQG+bSvYz+R5jNdWZz2brBhTOXSRlhpdSC9qPKSlzYB9dHnZU40964Us4OsH2yGpN4wyp9/K6WIhv6+MDYI5CFsWHxXYbBv3g2D2JTZ/Fs7uMaY+jGBbhy7tL5rrK9zmy+/rvJhXsZSlxpb2xvCzrJ8zzrz2Pby9dpnmf7/cLqwnWNzXVmc9m6xfZjQmCkls39RZ8zk1c7xObz2OKidYULZy515i4CWWg72xstGqwwFzpI1Fk8mw+LzWXrBvsHkpjFHsfmByYfVVbi1LnrUlnHkBt1Zn8b0GkONItcYhFO3MvcaBDGnEtHgSqDq2xvb9jSPcz2+gpyqKgdYXv3w6VzC1UEsjAmLG+vJNEx8LmVjgmSOx0kOgZhts/8s7t0nWf/+UuNBfmre2y+l9lctm6y/bjY3hIgzIWgTSfZvvKR6kpi74HxrzHbzy0gif2pBbtdAru4MF5lc9m6woHbg5ETxUQMAlloP8tbA38w2foxSlRQZ/FsHqyxuGhd4crDvhulRJD9AbfOcqHpocbcUVqpSo0FOXGNUWUVnudInXW7AJagvXGPK1XmSDER4Mq51Sncy9zDeBXGAoEsjAkaLHe40EHy68yVYMBYc+U4uFHKznAjtaBxoj3oFNsPhe3nU6e5cO7aviqzW2w+LvaWrDvcuM7c6Sd1jMUnssVF6yoX7vEutAedYvN9TOI6i7L93K1M4u1yOWxhe31JwYnXDhS2A1zph1FdbiKQhbZzpS1wpXEdc+XDYHXwsdsFsIx/w7X5uBjRMYiyub4Qz+JmEQlcqDPaxhIe9t1kc39REjfbCGeuMmcKOrZcOAy2NwFAPaXUghafyP4kXvpJYTbXGWLZXGO2T0BAMgJZGBM2NwmVzTPpF4TYXGc+6izM5r6c1Q8HXeDKqUuAP8DyQ8G9LMyF40CzGM/m42Jz2brBlXuEC+1Bx3h2D9bQX4zHUQHGls3XGM1imBN9j0oGIUju9MOcOLdQg0AW2s76mSN0DEJcaLwrqQXtL2pHuHIcXDi3OqWUWtDuxsew42mIJwb/XFJtb+w/LrSNJc7cyxwpZye4sN+S7eXrNE+eE4OitItlDhwGm/tG3eDKuWv9GE0ncSzc4kg2GtSyuf9R2ge326VAMwhkYUzY3GD5aLNKKmnqrK4zqwvXNTY/SJJa0D12twHd4cIxcWUApVNsrjOLi9YVLoQebb7PdovN1xjcw+kUz4XJKvTzq2yvLtvL12ml1ILdLkUysi7Es7nOfNRZiQuHwYXzCfEIZKHtbG+0KoMS3GVCXGjHGbAtceIouHBCdZAr564bpewM22eucomFWV5dIS6VtRNsH7B1pf3uBBfOXWOM9e13J7mwik5y49zqBBfaG8ubbCATm09j2/tFnWZ/q0idRVX3dLf7uLhwbqEWgSyMCZubK+4xYS403qQWDPMHaGw/l6mvAM/++jJi8C/K8iqTxHUWZXOd2d4GdJoL5y51Vsv2QQnqLMz6lQf08WNZXGUVLgTdOsHFc/en/35C88+8UIWig4VvA1fqjGusxJWgSAl1FmRz/8ONOy3iEMhC27nTMYAUDIrY25DbWzIkcaOj2VkWX2KS7C9fp9l+j2Dwz11UWZjtTQ/XWJUrg2pulLJzbO6T2Vy2bnChvaHG3OSfWrcsXK7P/+N+SdJIodi9AnVRae9Ae89kUgu6x96zqTuc6S+6UUxEEMjCmKBj4B6LqwwRLpy6nE9hLtSZ5E45O8Xm85jBv7Dqfo/2HhfqLMyFh1yLT6eu8DxZP1pjefE6zpWV1m6Ucuy5sXdxiSOnFhQc//B08g9u6GpZbGHzJVaZrNbdYljD7y/SLrrH5iornU9UmIsIZKHtbB+YsHmQqxvsrq0Sv87oGITZfiq7MnjSCZ7nWT+IbcQ1FuTKsXCkmB1j91VWQtsYZv29rNsFQMO4xKpcSS2IMJv7jNSZe9YNF7TohfVavm6420Wxgu33CJuv/26y+agQfAyz/RqT7D6fkI5AFsaEC40CA0kl1ZzD9rK5bF3hwKlr5EQxO8b2gSSJIH8cm4+JxUXrCtsn0UjiZhbhQjeMwaQwBxZkyRjjRnvQQbbXmcRzmc+lo+BSWceSC8fhsvuXSpK+efkjodeLm+h15zmwd7HkRj+pE1w4DvQXE1h+WFw4t1CLQBbazvbGoLK0vqulsJADvTkGJsIYZHeLC4eEK6zKlfaGwb8wF9oeqqzEPww238skrrEoy6vLiXttJ3H6usnm68wfsKVtrLL9PuZ74vl1oX9v2lVob51VV/ds0hVUUe0vdrUYmWza11SVC4fBhfMJ8QhkYWxY3CjQYIW50EGqdObsL2pHuFBnEvUV5MKxKKUWdKCgHeJ5Vt/KKqixEhdOXRfOJ0RQaWEOXGeSG+1BRznw8EOVlbjQD3PgdEKEX2dPrVgfet3+s21sbKr/v11n86on2sV4NteZRFvgKgJZaDsH+t+S3CnnmHMhtSA5h0PcSAdJap+gUmpBm2tMkuEaq2Fxldl+OnWaS7M1UeZIR8yNUnaO7YMStAFVflDE5kNCfbmLttEd/u127cbR0OubbmpBz4m2ZxOtnhouBPh9LpV1TDlwHIwM9eUoAlkYEzY/5FbSIXS5HLaxuTNn8/nUTVbXmcVl6xbbD4nt5es0Z+4RzhS0M2y+X1gfzO4C2w+J5cXrOFcmqLhRys6x/TqTnBjz6ggOg3tcOndHi+HCulT2drO5WXShze4Ki4+LxUXrKpvPZZvLhnQEsrDpocEKcan/yoyJEleOAtVV5cy560gxO4WgiDucucZE2+hz5jA4U9Cx53kuPPgbrrEyF46DzffZbrL/OnPj/OoUB6pLklSIBLK4v9mJfejCXDoMDhV1TLlyHFwpJ8IIZGFMuNH5ptmSgmnqLK40UgvGsrnODGnqQjzJ+qdcY0gHGeLIoaDOIiy+zvyiUWdVFleXJALGcWw/JLaXr5MqKVetv9IkZ266Y8yFx1PaRXcNjxZD/950Uwvafa+wuWzdZPVhsbpw3WPzYbG5bEhHIAttZ3uAiI5BPJuPi8VF6wrLL7Eyai3K9iNie/m6wYV20Y32YOy5cBhsPp+6wZVz15FidoQ7x8KdknaCzW2PzWXrJieCj1xmzhkuhANZm3IVunCNudJP6hQXgujUWYkrx8GVciKMQBbGhP23GPhcmhnOjabErzPb+3LUV4Ajx4I6q7K9bbT9+u+06upi+3Gdlfz/9s46vI1j68O/kWSZMXbQThxmhgYaatI2UEyZ8ZZuGVPmNl+5t3DLdMtMSdMmDTTMzGDHDhliZkva74/VrnallWwntjSjPe/z5Ikt8mjPzsyZgxIk7o0SfI8uNPBu/ON7dMGF9+BCLQINtYXh/0LQHPOGf5n5Q6Q1ojnhXsd3/8/3KIOHCLepWg6SpKaDZz2f57ERgSFHFtHs8L50UxS7HhGMf55NhoSmhW+ZASQvD0IYbBmti97wLTEZEpkenucZx0MjAmBWQ58RolwLQYbZ4nhKC/ILrYvGiHBdyGDrgXd5JUZHGD5uVgnyX1qQ48GFADWIN8TjCASJTI84+qIY4yT0kCOLaBF4XshJMTCG58vC8dBCggj7LcnMF96vCe9R9sGG93lG8vKGc4FpEGekLYskCbAu8j7AEMD7NaGgDF94lxlA66KCCPeuCPcToWfhvRPQNS3W53Gz9sgCxLiPTSweQ0SQGW1mengXGYlLTMiRRTQ7omy4FEUmI9JVEOXeChqcawYkLw+iXAtaFz1IEOPAJMq9FSx4Fhk5H30RYo6FegAcIcK1oHnmQYT9geRljBBrowD3VzAQ4TqkxNrRNS3O9wkBxt4S8P61PbVoeB9pcBBhjgmwZAcVAUQmxD5LGEOOLKJF4PlQQqUF9SjptFzLzD00EpmMCNeBMTHGGSx4L2EBUBS7EWKsiyQ0QKx7l8pYyIhwFfhdAUKDKLcurYsynp6q/N/JotxbLY0Il4EKvvsiwBRDhNXX9GdWGUqSJIaOb1YB+UGEeUYi08O9zEhgQkKOLKLZ4f3wyP1iGmRUBYnj68KzohkSRHA+cjy2UEHXRCx4dzbQ3aRH3co4vjA8jy1UiLAucr4UBB3enSKcDy+oiHDvkrz0eHoX83theF8DCGMirL5yM2tpQQngWpGmOaZHhLuUZKZHhKWF532WCAw5sogWQYR1XIC1NagIIDIhNsRgwvs8490REEx4d/AriDHK4MH7HANoXfRGhEMJiUxGhHuXDBN6BBAZADHurWAiwm0sip4ULISQGU00oTDMyCIRcg2JR0aECkIKNKf08C4zEpeYkCOLaHZ4X7yVxZT3cRIePOn1JDRAjA2XSgvqEaO0IKN1UQP3l4Lz+ynY0L0rKALcx2Rg18O7yBgEWL8JFSr5rkeE9YZ3fTbY8C8xmQgblRZUkfjfywDQwugFz2sPz2MLBSLcuXJbBRFGSnhDjiyiReB5Ied5bKFALWHB8YWhWuzG8CsxQkTofvJFhGtC66IejrcyFTozyQhhsA31AHhDgJuXZ3022IhRpi7UI+ATES4L/6tB8OB5jilEWDxjTI6JAGBeI64E/vcKzocXVES6S0XQbYMKx/cxx0MjGoAcWUSzI8rSTZuMHq4Xcq4HF3xEOHMwiDHOYCHOpRBnpC0N7/evajThfaBBQgRjDO9Gk6AjSES0ALdWUBHhNiaZ6RFCZqEeACeIcO8KcDsRBiilBTNSovHQtN4AxLjfWgoR7mMTi0c4KLtYjwjnMoDmmKiQI4toIfhXDQRZW1sckRx6JDMZtU40x5YJnscWCuTSgnxfEzm9PtSj4AyOZcbx0EKCcuuKcVlooilwfx/zPr4gQ3euWIih49MkM4L7tRGkM4pGndMFAMgtqlbPJGaVoQhGdgoK9SDCdRBhzQ4FPF8XnsdGBIYcWUSzw/tGQwuWHk9pwdCOIxBqXzMhDuTBg2ORARDjkBBMeJcXz2tAqBDhktAs84JjoXE8tJAgyr1LW5kHSZQsOmHurpbFU1qQf0hnlBHiKpDCqEOUeze/rFb9WakyaNa1Ui4tGOpRBIYxZlr5+MJ/EK8CSUwP7xITZPkmvCBHFtEiCLDHEF7wLDOexxYKRNlvRRlncBDjaogxypZHBKMELYt6BBCZikhjbWl47yvC+/hCAe+GJMZAm5kXPIuM57GFFv4vDBnZPYhwHz92Zh/1Z2W8LhOLkHeR8T6+UMD3NeF7dMFGhLMOOYvFxRbqARDhCN+LgZrdI8LqGgSEugpCDbbl4fnQxPPYQoFcWjDUowgMA6N10QveZQaIcVAIBspBhGfHgwj3UzARYb0hmekR4cBPMvPAv7QIb4RYF0M9AOK4aJ8UjV/+PQYSgOzCSgBi3G8tgShfW5RxtjQiXQezzil/8Bz8xO/IiIY44YwsxpiVMbaBMfa7+/fOjLFVjLG9jLFvGGP2Ex8mIRo8Lwocr6UhQe23xLHU1OaZIR0FPwijH4kyziDB+9rD+/iCiQhzzNPfQIDBBhER7mOSmAch5EVzTIcAIqM55kYkHZ/QI8LaSBNNPAZmJGFQRpJ6f5lVhBIkrg3sgLt3cagHwQlqH1yORcbz2EKBCIFPgBhnfsKX5igteAeAHZrf/w/Aq5IkdQNQDOC6ZvgbhECIshiIMs5gwfPmy7uiGWxUZY7j4z8DE0R9CQ6iXAtRxhks+J5jhA4Bbl6e76dQIIIeRhLTI4bMSGoKIhj/FES4twgZsztBvBHxOlAwFP/7u1wpI9Sj4AsR9ncSmR6uJcb14IhAnJAjizGWDmA6gA/cvzMApwD43v2STwGccyJ/gxAP3ptnUnaPHpGuAylzXvA8zzgeWyiQJIl75ZuB5piCSJdBpLEGA75nmQzNMxkJgsgr1APgDBH2dzMbZ0WDgtWM4fmq8K7PhgLRrohqDzHpUmnW7y0qIshLXQMEGGswEEFmAIlLVE40I+s1APcDcLl/bwWgRJIkh/v3gwA6nODfIASEZwWXDkx6RNhkPJF/Agw2CIhioBFlnMGAdwc/AAEGGHx4viQ8jy0UiLDakMx84V0n43x4QUeEbZ3KMXkQQV4KpOPLCCUzgcZK6DF7Vp0kQiQNo3XRG551Mt712VDB82VhIIVRVI7bkcUYOwNAviRJ647z/TcwxtYyxtYWFBQc7zAIDhHFeC3IMIMGz5svvyMLLRyLTM7uCfUgOINjcamQzGRE2ccA2ssUlOvA816mQIYJGVHuXVHGGQyUjks8w/fogowA6yK/IwsNyv7Atcz4HVpIEHGPsKilBUM8kBDCc+A14F4bTSwfLSLpzSKNtSUR4SrQXiYuJ5KRNQbAWYyxbABfQy4p+DqAJMaYzf2adACHjN4sSdJ7kiQNkyRpWFpa2gkMg+ARnhcFT2lBEZbXYCDOdTCzsi0aPK8BoUCEe1cuLSjAQIOApw8dvygHcJKYHp7XHo6HFjJ4vya8G7pCAc9zTIG2Mj0CiIxk5oUQMiMNxIMIC6MGZbQumnjcItgt1aKowWqhHUZAeB5bKOFdj6Z9TEyO25ElSdKDkiSlS5KUCeBiAAskSboMwEIA57tfdhWAX054lIRQ8L4UkFKgRwjFwOTlD7wRQWYAGSW0SAD3iw/nwwsJXF8TnscWAkQ6iNDaKCOKzEQZZzAQIdiB50yWYCPCvUvi0iPAFCP1IwxQz9YC3G8tgSRJQqw9JhWPX4SQGQkNgBjXQYDbifDDifbIMuIBAHczxvZC7pn1YQv8DYJzRFgURFhcgwnfioFS/oCEpoVnYw1jTAgDSjDhV1qENyItNbQu6uF5nnG8ZIcOzq8JycwXES4JrYoynpKroR1HY6CtTA/JjGhJlDOkmc9qvE8xBkY6vhsRLoMIa3Yo4P26iHBvEb7YGn5Jw0iStAjAIvfP+wGMaI7PJcSE98XAo7gRgBjXgfcNMNiIcOggkekR4SAilxYM9Sj4gm9ncahHwBci3bsijbUlEeU6iDJOQobK5PrC83bBe9mhYCPCrUv6hx4BROaD2mpBxME3AyJ8bcbMKx9vJCGKvsuQzGSEsFcxMdYCwpeWyMgiCK6Nf4QeT5k6/mVGG40e3iVGipwe3pdFyqLzQNdBPNQjLtfzjOvBhQTerwjf91PwkSQBrgnv4wsiIu1kIo01GNC5TCz4l5YeNbDXpEIUYi8jhEKENZvQQzITF3JkEc2OMFGQoowzSPCszKlDI5EBEOTWpQgXHzieYgD4Hx+hx+zRtP7h/04mR6kHEQKfSFp6RDj4k8xklDMZz/PM06uHpAaIce+KsAYQgVF1SCHuuJaB9/uYQYz1IBgIVSY31APgBFG2dNI9xIQcWYQpoTRSDyIosDwfwEMJz5eF98NBsBFFRxJlnC2NCNeB1kU9IhxESGR6RJCZCI7RYCKEzhjqAXCEGJmqhBEiyEyMNZwwwuK2AppVhELsZYyZVj7+4HlZpKAMY3jey3geGxEYcmQRzY4ISzetWR48pQX5RwSlMxgIcxWEGWjLI0Hi3vFAtdh94VxkAGhd9EYImZHIVEhe4iGCzGhZ1EMiEwchjKCqwTa0w+AFIWTmhRJw6BJw7M2BCKUFOR8eQYQF5lwBxYccWUSLwLtiAJDy7Q3PMqMSWnpE6GsmZz2SwLTwKy0Znu8nwheSlh4RVhuSmR4hZEZC0yGCHkb9Hj2IIa9Qj4BoKiQyX4S7jxVnZGhHEVJEkBntZTKe0oL8C40kpodn+wKDGHoS4Qs5sojmR4DFgA65HkRYvAXQWUICz9eF46GFBBHmGUAHJgURnMUKotxbLY5A2cWEBzHkRZNMC8+6ByDKPRUc1D2dd6EBNM3c0GUggoHFvSaYVYcU4mtTpQwfeN7JRNhmg4mImaqEOJAji2gReDf+8T264OIxXPN/VWg/lBHF2UDy8iBCCQs6MPnCs8wYRdMawnO0JjO54cgbEa4Dv3dTaBBAZADEuLeCCc/3Mc9rdijh+bKQzMTHU+3EnIul/LX5vo/5Hl1wEcX2AZD+4Q3X2wXXgyMCQY4sotkRZe2mTUYPz+u44hglkYmDXFqQ0EIOfnEQ4cDE+/0UbMSQGaFFhN6BAOmLWiRJ4n7tEeCWCh4C3bsirOFBQaDLQGujuFAwFP97hQj6UbDh+ZLwrhsFGxH2B5KYuJAji2gReN5kADKyaxFik+H8fgo2QsiMVAMdohhoxBhl8BDhLhZhPQgmQsiMZpoK7/Ii/cMAAa4JrYsyas0FjmXG8dBCgrI/8GzE5ndkoUO0a2L20oKinHjMmjHnjViXQajBtjgirI00z8SDHFlEsyPCQkBGdl9EuCIi3FvBhOMzLgCSlxZJAveTjDGQ7u1GhFuX9/kfbEhm4iGCzABaFrWIcC0YqA+uNyKce0RZD4IF/xKjoAyRUe4vl0knniTxP8co8NqDEEEZHI8tFIhw75LMxIUcWUSLIMKaYFK9zS88R/4pkMj08GyYIOXbF36lJcPz/RQqBFgWyZDkRtnThZAZiUyFd3nRuugL71eE93sqmIiw1pC89JDMxEMEmfmglBYUcezNBO/3MefDCwki6GRmnlNaPOcykhnR/JAji2h2hFgHGBn/FERYuBkp2zpEyHTiX2UJLvxLTIbWRRmRroIAy0FQ4fmQK8BZLqiIcuuKsOcGDUEuhUuQcbY0njJ1IR5IIyCR6eFZZkpZuveX7MeOI2Woc7hCPKLQI4KxVoun/7Q5Z54o35rUDxkR9DDqO2cMzyujZx0kRIMcWUSLwLsupx3eu4v3YdnewpCNJdSoh9wQjyMQNou8VDnJMqGD93kmgM4ZPCT+5cUYycwbnp0ihB6Rbt2nft+OB3/cjCd+3YaKWkeohxNi+J5jvK/boYB3g63NyrDlYClq6p2hHkrIUSOiQzuMgCj77IM/bsGCnXk47dXFqHea1zEiwl4WaZPPZZ+vzMHU15fg6d+3h3hERFOxKIuCCDdcCyBJEvc6PmMMP64/iN82HQ71UEKOeptyLLIIq7wumnn/0iKCk5xzdZYIADmyiGZHGEOoBByrqMXzf+zE5R+uCvVoQoYI5Zjs7gNTndNjlNh2uBRVdeY0AAoxx3i+oUIE/wemUI+AH0SK/CP08HxdlDVg3YFifLU6F58sz8aarKIQjyp0CDDNAJjWzmeICNeiosaBo2U1uO/7zaEeCjfwvC5que+7zdidV4GSqvpQDyXk8KwzRkVYdb+vzykO0UiI40UJSDBzjCjv6yIDUFnnxG1fbQj1ULiBZ5FFu9dFCqLRw/s8Azzn/tLqeszdekQIO4DZIUcW0SLwHq2pDG/zwVIAsjHliV+3hXBERCBUR5a7dEVlrQPT/7MUt5tUsVMbnoZ0FIFxuKOR1ucUkzIAMaKSAPneqq5zInPmbHy9OifUwwkZQjQVdq8Aa7KLaI5BDOcj4Y3E9RwDPPusJEnYeqgUe/PLsb+gIqRjCiVyFDvf1Lp1xXXZ5nUSK4iwKmrXAEXP531daElE2MqUjCwFM8sLEEfH1+Ipgybe2JsDEb612eeVFhHWRcXBX11HjixADJkpU2xfQSWOltZg4JN/4abP1+PPbXkhHRfRMLZQD4AIP0RQiGrqXXj3n/3o3iZefeyT5dl44qy+IRxVaBDBYGu36h1ZF7yzAgCwMbckVEPiAp4dxgeKqgAAM95ejhlDOqBNQhTOH5qOrmlxIR5ZaJBEKC0IhnUHinH3txsBADN/3IKLR3QM7aCIBlm0qwA/rj+EpJgIzN+RhyfO6otIm7XhN4YZ/GseMIw+qDV5bxHOl0UA8vr988ZDuOubTepj2bOmh3BEoYX3vczqrplV43DhcEk18strcayiFpN6twnxyIKP4uDnObtHi7IeOpxCrOgtCs/zLNIrI8vC82CDhGhXQCktKIKxuSWQJPFkRvBt+4iKkO1VNSbX673hWWYKp7/2D+IiPa6Rosq6EI6GaAzkyCJaBP6XK5n88ppQDyHkiHDIVTKyPl6ejfJaB7YfKQPg6Z1lNkQ4dGjvph/XHwIALNiRjz/vGheaAXEA73qcMs/+2Ho0xCMJPULMMc39tLegAv/sLsC2w2WY0q8dxvdIC93AQgzv88ybOhPX0hdjnsk3VM6x6hCPhA8EEJnap6Kosg6jZy1QHzez85FjFV83NGU9LK6qQ9vEqNAMKMSIEBCqGGwVOL69CL8opQX5v99aCv4N7LyPL5jwf59G2TwZWUdKq9EuMTrEIwot/EtMf2bU9iy2WWju8Y45rcBEiyKSPnSgsCrUQ+AHjtdrpYTF/oJKvDB3l/p4hI3jQQcB0b69w2Vig22oB9AIvEvFAIDTzMXzIcIhV6be4UJBeS0AufSqGRFB9zC6mwrKa01dFlGEKVZR6zC9vqGFroQ4iLq0TH19SaiHEHJ4nmc+Wd8iLOSEDk9pQXMigt5lJUutitrTPbTDCIjFwmC3WfD9uoMY9fwCLN9XiJ1Hy0I9LCIA/s75VnJkcQ8tj0TLIMjczyqsDPUQQg7/apwnuraxj4c7IkRrGo3Quzm0mZD7ivC9MBo5su5xlxk0HfxPMd3d9MHSLFS7mwtXmbw2O+/zzJunf9+OD5dmhXoYIUEAOxIA2aGvDaIxM6LIjNDD9arI9eCCjwhzzDsjy+w2PxFk5o3F5J4sEb622YMJjeDdZx5ls+BQiZzBf+n7qzDltSU4VlEb4lGFCBEXRjfkyOIfc1qBiRZFpCVrNTWCFiLCxd9mEmHy0oI8K3MOg3JZRo4SsyCBb3kBBhG2AH7eeDgEI+EHnkXmHUVWXiNnYlXXmTMjSwTtw1/k35I9hUEeCT+I5nhUqKk3r8OY90xVESLtg4VHX+RXZvXUD8sYfkXmE5hGPbL41/G9UYZr7tKCoR5BYGht9CDKlYi2+56lj5m43xLvc8wf5MjiH/NaFYkWRVTDhJnh+ZDrD7OX+uFZZkbKt5FyR/CD3cSORm9EyHr0h1kzskRw8PsbmrbBsJkQYZ75u59Kq+uDOxBOEEFm/I+Q0GIU+GRmRLh/vQPT1h0oRp2D5CgSakKWCDdcSyDA96aMLF94tzEaVZ8przGrvigu5MjiH7JaEc2PyKuWKRFXYOYtLcg/9QaGiWV7j5k2vV6Eg6KZM+a8EdkpYlZHlgLPMovx48w3qyML4FtegXCY1MAkSVwnihBeKI5HnmXWqVUs0pOjfR43e2YdzwZbI2Ptgp35IRgJcbwo95dZZ5kEvucYYO7e0t6Ish1EGVQ3Kasxa6UMvnUPwP8ZhLKM+YesVkSLINLcT46JAAD0bpcQ4pGEBhFKC/rDrI4sEXD4KYeg1I02G3JpQb5nWWQEzSdv+JaYMWYteSbCGTfGbuywijWpI0sEw4Q/Q5fLpI4sAGIujCZFhKAMALj+5M4+j9XUm9SIK8DCaHT+MnOJOhG/uicjS8DBNxO8r4vajCyXS8K+ggrT6h7Kfcq7zGIjjTKyzOnIEmFp8afj2ygji3vIakU0OyKUHdEytnsaTu/bxtSKHMC/YmCEaTcZAe5V74ysIR2TAJg3ih3g3/Zn1CPLrIhwl/pbs82akeUJyuB3pvnLvIozOPiaATkiWkzMarQV4WuLMMZgw7uObzfQPypN2u9RuX15l9krFw7U/W72ecez7mGEcn+Z9Vgmgt1HW6Z/X0EFJr28GC/P2xXCEYUe3mdZYnSEz2NlJi1FDfAfxOsPC3lJuIdERLQIIi1ZsZFWWBgzbR1ikb+1aQ1J4P+AW+81n5Jj7ABMHMUuwL1KpQV94VkB9zc2szqyFDgWGWL8OKxiTJqRBfA9xwCgzmk8n8y6lQHiGWzNjCi3qZH+UVVr8r0s1ANogMzUWN3vogWymh3POm5OuYkWSFPgLs3/++YjIR5JaBDlLjVyZJk2I0sAqfk7glBVT/4hqxXR7Ahgr9URY7fBYmHmdYoIEMXuDzNvMrxLy7t5d5LbkWVmhzHn9lpyZGkQIVLTHzUOcxr/RDgwxfopLWjlfXFoIUSYZv7Km5l1LyPEQi3HxLnWGBclr41tE6LUx8przRnFLsK6CPjuZ7VmLQUJMfQPb5SMA1Hut5ZAJNWrpEpeD4+U1oR4JKFBvU85l5mRI8uob7hZ4FxcfnGaeWEUBLJaES2CSIpBrF3OyDK7TUIkmSl0bR3b8IvCEBH2Vm+lLcndi87Mxj/ep1ikQfPuPibtHajA+7rYISla93uXtFjzZj264VlkUX760NGBiV+q/fScE9nZfSJIksT9uiiiUbml4V1mp/RqjVcuHIhnz+2nPpZfVhvCEYUe3rNVY+x6nfGe7zaFaCScwLe4fFCc22ZVGUXbwvfmVwAwr+6hwHtQhpEjy6xtFUS4Vf3dTWafZyJAjiyi2RFt2sdG2mBlJi5TJ9j37t46Tv3Z1BlZnB9wHU79fZWsOLIEu9+aCxG+tndG1rBOyag1bXaPGPx51zi8f+UwAMDA9ETYrRbTOotFmGPe67anR4UAg28BRHA4VNYal4Qx614G8G+vNbFofBDlUkRYLZgxJB0dU2LUxw6VVIdwRKFDlHOZUc9Hs+ofIqLoHyLswy2BBIn7s/Rvt56MC4amAwA25BQDAKJM2s9YlPtUqUCjxWligxXnU8zv+EycRCcM5MgiWgTeoyW0tIqLpB5Z4N8wEWGVRzipdxv1MTL+8Uu9i0oLahHhwNQlLU73e3yUDbUOc2tyfEtMNiR1aiUb/rq2joPNSnsZ59NMx+l92gIwce9A8C8vf+WyzGqXEOFONalqaIho18Jm9ZgmzOrIUuB8aTTs+VhcVReCkRDHg9ohS7A1ojnhfY71T0/ErPMGwG6zYENuCQDj6hlmgnedcVyPVJ/HTJuRFeoBNII8P5nfZrUxigQ5sohmR5RIMoWEKLlHlmDDbn44Vwxi3LXYtaUsKCKaX+od3hlZ5nZkAfzLrF97fRnBuKgIv/1hwh2RlpYebeLxwZXD8Mw5/WBlzLQHJg98z7QdT03Bu1cMBQAUupt3m1ZkAnzv52f0xxuXDPZ53MyHXN4NSYQWd48sQYRms3jGWeUnGzLcEWVlsVt9zUjKnmY2RNwOlDVBwKE3C6LIzGphsDKm9siKtpvUfCuIvLq1jscPN49Wf7daGJxOQQbfAvCe3LDlUKn689YnT1d/NrOOLwomXQmJlkaQ8xIAucGwhZnXwC7KOt2zTTwAffkzs0axiyAz7xrRySbvkSWCzGxWC/Y+OxWPTO8Nq4UhKToCdaYtLegWmCCb2eQ+bRBjt8kHJtPOMTG+d7Tdin4dEgEA+eWy0c+0MgP/UywzNRZnDmyPdy4fgktGdMSpfeSscLMeck36tYkQYPagDN7XRiPnaGG5eTOyOBeXD2ppQZMu6hIgjND6u3VGAIg0bWlBGRFENrRTsvpzdITVtHuZCEvLxcMz1J+15XLNquOLBDmyiGZHtGnfNiEKVgsz7YKlGGx5j5h494qheP3iQWibGKU+ZlK9QAjj38fXDNf9nuB2bJl2nkn8ywyQnVnXj+2Cfc9Ng91mMe0cUxBAZDpsFvP2yFIQYZ61T4zC9Sd3xhuXDAZj5jUkAfzrHgpT+rXD8zP649IRHQGY1/kIiCMzLUbZI2ZAWVpEkZhWRzTrHBN5O6iuN2fwk4hQaUFx9jLtmVqMEbccomQXK0SYuOQ7AO5v2BlD0nW/v3j+AADmLR8uEubU6okWh/M1C19cfxLm3TUO3944Cl3S4sCYeR1ZCrzrBcmxdpw9qAOGdPREuZi7tCDfAmufFI0tT5ym/m53Z9KZNSpJhm+ZeWOzMDjMqskJeptaLOY1/okEYwyPnNEHAzOS5B6dJt3LRHTgKbqSWaeZCD067zu9p/rzB1cOw43juphWxxetd2CbBE+wmtn3Mt71fAAY1aWV7vd6p0l1RgGxqKUFTTrPBPrasZE2bHrsNJzet41p10XRtvAXzhuAdy4fCqvFYl4dX5BJtuT+iZh31zgAwEj3nmZWmYmEreGXEETTEGHej+mmb8RoZcy0RglB9hiVjJQYZM+ajtNeXUylBTknPioCS+6fiKzCSvXAZFoFPNQDOA7MXKZOQRTjn4LNYkGVw6R9RQTLPFAwtf4B8eaY1d3DR0QnXHMgQnbxOYM7YFBGEirrHOjbPhGbDpaY1pGlIIJTBACiIqzInjUdY19YYFr9Q6Rv/dUNI1FR68De/Aqc89Yy0zqyJIine6hBGeYUGSRI3O9lWhJjIsxdpk7t9xjigTSSC90l6574dZvJe2TxT0ZKjPqz2XV8kSBHFtEyiLLLuDF1jyz3/2JJTI4kM6vMAAgjsIyUGGSkxCCrsBKAmUsLinVgApSMLJPKK9QDOE6sFgYTn5cAiFd2hDET93sM9QCOA7MHZQBiqPiZqbHqz8ztLJb3YQEG34yIqnLZLBbT6h8qgtyqcZE2pMTYAQD1JlZARFtaFOe2eSUmzBRTsVL5cOGwmvgsLeLiouj4ZhWZSFBpQYIAYDFxjywF0Q73FhNHsYuSqq3F5o5wcZj5kBvqATQRq8UCSTKvkR0QJ4pdwWZhcJo0vFbEdRGAqXt0AuKti2Y/5Ir4ta1KCS0RB3+CiBbFrmDmjHARI8EjbIqOb079Q0SUNUHE+605EPFrm7nku6hVF2xWM5/LxNM93OYq0+ofIkGOLKJFEGzNkp0iJl2wRFTkAJMb/yQB55hFMf6ZVGYCYrO6DRMmXBtFvU0tFmZqZzEg4NrIGMxq+xNxnimHXHPvZWLNMpKZaBKTnY9mNdgqiGQAtFlkk1K9CfVFUfE4skI7jlAhopHdajWvvqggnMyYiTOyIF5AqIVKCwoDObKIZkXUSS87RUI9itAgarSmxcTRmoB48rKq5ZhCPJAQIUJfEW+UOtE0z8TBZmIHv6hf28LMa2CXDUliTTKryYMyRPzankCaEA8kBIgoL8DcGVkKIq2MEe7Ap3qHWZX8UA+g6Sh7r6jZ7M2BaEZ2c1ddEBMz72Ui2oWpfLg4kCOLaBEEs0uAMcAp4GLbnAgmMtMb/0TD4xQxpwIOiHlgAmDKqGhRD/ZmrsWulh0Ra5qZvrSxYOJSjX9mPuSKNseU8Zpxnom6LtqstJeJRIRVNimZUV9UEE3HV0brksy5n4loZDe3ji9mV3czywwQT/ewmrx8uEiQI4toVgTUCQDIi5aICk1zIOrXtjKzG//E0gzMnt0jomPEzDITtRa7mSP/FIRbG028l4mod1nVsiMhHkjIEO+LewwT4o39RPHoHoKti7SXCZWtqpSirjd5aWORUPayB3/cgq4PzQnxaIKPiKUFbbQuiiczK7UvEQnm9o6YUV8UDXJkEc2K57gk1i4j96gw54KlykwskZm6tKCQxj8lil28oTcLIpYW9GRkmVRoEE9mZjb+ifqtmYl7ZAEQzb5u+kbQkog9OinCVri9zMwGWxEDnyKUHlkm3cxElFnr+Ej0ahuve0zE8+WJINiyKPfBNe26KCZWi8W0MgMEnGMmDnwSDXJkEQSU0j6hHkWoEWursTDArBUsRHSKWK1UWlA4mbkNE2Y0Jon6jc1s/FMQb56Zz3ikIOK3pkOueHOMmdj5KOptavZyTIBYpzKLhckyM2u0GkRcFxkGpCeqv1/wznJc/N5K06yTIq6NpOOLtS4C5paZiN+aSguKAzmyiGZFMcaIpswpEbZmTP0V1YBmpb4iQqFmZJnUjyXinUoZWeJlF5vZ+CfqXmbmjHBAvL3M7Nk9In5tTzlIEUffPIg2z2wWi2nXRVFvU5uFmTYjS1TS4iPVn9dkF2NVVhF2HCkL4YiCjGAGK6t7XTTlXiboV5bPZeZdF0UqkQuYO/BJNMiRRbQIYi1ZZq+fLyPYPiMb/0woL0BMXc5i8prDkiQJ6RQBAKcJI2xFPSRSaUHxsDATZ4QL+L3NvpcB4jn4ze58BMQzJpm5hJaCYCJDhNVCPbIEIzUu0uexgvLaEIwkNAg2xdQAQzMujUr5TtH2MquJg9VEVJMp8EkcyJFFNCuiTnmLYrA18aIlllrgzsgyqWIAiKfI2dzWP7OWHZEA4SaZ0rzbzJFkwsnMxFHsCoItjbBYzOsUkSAJt5eZOfAJEPNwb+a+ZgKKC4BSjsmcuoegIkOE1byZB6LOM7M6skTcxwCPkd2s8wwQ7lgGm9XcQRmiycti8gpCIkGOLKJFEMwuoS5aguo1J4ag39nUGVkCfm3VkCTi4JsJwZZFT0aWCRVwUW9TM2dkibqXWRmVyRUJxsy7LiqIpuMzZt4IWzWKPcTjaCryXhbqUYQW0TIfbVaLqUsLirYuAn4cWRVmcGTJ/4smMxudy4TDzOcyScCDmdpuRtQbzkSQI4toVkSd86aO1hQ0VdvCGLQBSXvzy/HxsqzQDSiISJAEO97K95eFmbMPHQAhjezUI0tM41+d02VKg62CiHuZGXUPQEyd0VN2JMQDCREifm2ricsxKQi2LOoyslwuCR8vyzJFpggg7toSYWEoq3bg0+XZ5tX1BSMt3u7zWFFlXQhGElzUtgqCaflWE5/LRHY+Ol0S9hVUmFPXF0xejDEwJgc+1dQ78d4/++AwcYAGz5Aji2gRRDMkKYrBv79cj/U5xSEeTXBRFYPQDqPJWL3KMZ371nI8+dt282w2ogkMctmzLYdKsa+gItRDCToSRFwXZRXBlIq3oJRV16PO4cLnq3IAADX1Tny5KscUji0RI/8A+VBeU+9Uf3938T5sOVgawhEFF8GWRV3gU2lVPT5bkW2K+aUgSeKpH94RtoUVtaiqc4RwRMFD1FvTqumRta+gAk/+th1nv7k0xKMKLqKtjRE2C2ZvOYLHf92GBTvzQz0cohGkxUX5PGaaczTEm2Nm7l0sKlYLw7bDZZj08mJ8tNQcAdcKouofStWndxbvw3NzduKbtbmhHhJhADmyiGZFXEOSrBgs2lWAGW8vx+LdBSEeUfARUZlzuiQcKqnGDZ+tRXmtbJSoM4ECLqxiYAEW7y7ApJcXh3ooIUGwKWbqjCxP5J9YUjtUUg0AeG72DmTOnI1z3lqGh37agr93hL9RSdSgjH0FlZi/Ix8Hi6sAAM//sRNnmsRgK+JeppSi3pNfgYFP/YXHftmGtQfMFQAl2rroXQ5y2DPzMfX1JaEcUtBQMw/EEpkaxQ4AtQ5Zrz9cWhPKIQUNUc/Sxyo8mTxZhZV4dd5u0zj5Rf2WCdE2n8fqTOAkEfW+VPSPNxfuRUlVHe78egMyZ87G6qyiEI+s5RE1i07pEQ4Af2w9gsyZs7HjSFkIRxRcxJKWjNMlYe7Woyirlm2L1XXOBt5BhAJyZBEEAKvXKnvVR6uROXM2Vu4/FpoBBRFBdTlYmBytef/3m/DX9jz18TpH+DuyADEVA6vGknK0tAaZM2fj7x15Ad4RPoh4aPL0yHIbkUqqUWiC2vlaRJtnZTVupdud4bPzaDkAjyHQDIhmsFU4cKwKeWXmMNRqEc0oYXGvi+8s3qc+VlFjjuweQNC9zKAP7oFjVUJ+l6aifEfR5pnVYoHDqXdkmQVRb0tt9v6zc3bg9b/3YMRzf4dwRMFFtDkGyE7+R6b3Vn+PirCYIiPL4xQRC0VH/HBpFp78bTt+3ngYADB78+FQDiuoiKbjK2dpANh6WHZgTX19iSn0D5HZV1CJj9wtS0QL3jIL5MgimhVR12SLxXiBWrjLBFHs7v9FU8A7JEfjYHGVT5SE2Q68ImHRKAJbD8mlsz5bcSBUwwkqcmnBUI+iaagZWW5j0uhZCzDsmfnYmx/+pSHFjYg2djTabeGv7okpMQ9WC8NJJjL6AWLOM6vBQk56B98oAdEuSdIZj8zQC0ZFMP3DavE4RswSoOaNaDqj1eAsbZa+ZiJz5sD26s+tYiNNVYVBtDl2oKhK/fmnDYfUn80gM1EdP0kxEerPkZqzmFn0xnBwAn20NAufLs8O9TAIL8LfskGEBNHWLIu/AYu5Zx4XosmsV9t41Dsl5BZX6x6vrQ9/xUCSJOEVg7KaegD6PmfhjmgS82Rk6WV0w2drQzGckCDaNDtvSLrh45FmcGSppQUFE5obv3pIGCNJ4s0xo7insur64A8kRIi4Yytz6/k/duDtRZ5Muv2FlaEaUtAQUV6AnJHldC/q9SbIEAkH/MSEEpzTJiEK2bOmI3vWdERGWKhEP8dcOybT8HEznKVF/YZpcZHqz9rztBnK1YnqfPTmUEk1Hv91W6iHQXgR/pYNgmgEZjQgKYi6yXRIigHgm4FQ5wx/xQAQz/gH6Md897ebAHiyfcIdEaeZzWrcI0vAr9JkRJQXANw5uTtGdWnl87iI68XxIup3JWOtGBgFkdz/w2Zc/6lJHPwCOh8Vmf25LQ8v/rlLfTznWJW/t4QNovYO1PbI0mZkiXpmOR5EC8pIibWHegghI2zuSwmYvfkIit3ZqjuOlGF/QfhVYVCywUULCh3aKQUvnD/A53GznKUB8fSPtHiPI0srp8o6c5SkFk1ehDiQI4toEURTvv2WXRLraxwXoqo+ye5Ube9s+hozZGSFegDHSZlBHxEl4ja3qAoP/7QlbGuzSxAvi87qrsfkcLlQU+9xENssDCVVdfh5wyH8VxPdHo4IJjIwxhAZ4bufmeGQK2KZOi21DuMgDJdLwuO/bMXOo+HXHFpEiRmVzwKA+Tvy8L+V5iiVK5qOb1QOEvD0EjQD4ukfDEWVdSirqdc5+RfvLgjhqIKDqE6Rj64ejrsm9/B5vM7hwlUfrcbyfYUhGFXwEGyKGaJkqf7f3J0A5F4+p7y8OJRDIrxorXGMKChO/0PuXsauMCw1KOiyiFRNRpY229EUGVmhHkAz4++cRoQGcmQRzYqom8ypfdpgev92hs8dOFaJRbvycdP/1mHLwdIgjyx4iKaAJ/uJ/Kt1uOB0SXjm9+3ILQrfaFvBxAUA+PfErj6PSe6eFWNfWIgvVuVg7YHiEIzMmOY2Jogmswh3RtYdX21Er0fnqo/vya/AoKfm4c5vNqqH3UC4XJJwhyqxRqvHKMP4mk/WYMvBUlTVOfDvL9fjUEm1wTvDA9HmmYK/IIzl+47h0xUHcN0n4mT8NGXtFM3AHqh81qM/b1X3tAPHZIPgjiNlukAA0RFxbfQns0ByyS2qQqGfnoO843Rpe4GJKDEgPTkaADBvWx6qNAa/B37YjCd+3YafNf1hRMHlknxKNQdCsKURXdLicMfk7pjSt63u8exjlVi8uwB3fL0xNAMjmkx5rT7wMK+sJkQjaRlEtVcBQHKMr/3D4ZKwJrsIY9y9jL9Zm6t7vjSMyh+LFkij7ZGlpVKzrzmc+oDRg8VVyC8PjzknlrQCU1jh21d188ESlFTVCRuAIjLkyBIY7wmzIacY87bn+bxux5EyZM6cjZ1Hy7DlYCk+WLLfx6iYOXM2nvh1G3KLqnDGG0sa1Zx1yZ4CvLlgDzJnzsaKfcd0z4mmfCdGR+Cty4agY0qMz3OnvLwYV3+8BnO3HcUd32xQH/e3YB0qqcb7/+zHk79t02WX7Mkrxx1fb8COIycWVV1d58Sgp/7CoKf+avR73l28D4t25au/u1wSDhbLTh7t4yKRFG2sGNQ6nNhyqBQfLM3Cvd/J5esqax34eFmWet/vPFqGb9fkGr4fkKMH3128D28t3IvdeeXH1Wg651gVxr+4EMv3FWLn0TL8vcMzN99ZvA+ZM2frIjuW7yvESc/Nx6+bDus+55W/dmHe9jzdPBZ1r7zv9F544sw+useq651Yp3FeNabMpyRJuPT9lcicORtZ7uhBp0vCK/N2+5SaBIDX5u/GA99vxvn/XR7QuVlT78QdX2/A3zvykDlzNjo/OAdL9sjRv/llNbp184oPV+GXjYdwsLgK+zRlN4oq6zD06XnYlFviNeYGvxZ3xEXaAPgeaBvLkj0F+HPbUXR5aA7Of2e57rkHf9yMyz9Y5fe9VXUOPPHrNlT4+dtzthwJmP1QWlXvd40+XFKNXo/+ETDDRXmvaAcmwP+h4ZPl2fhjy1HM3nwEL87dCadLwv/N3WlorJ29+Qi+W2u8RlbVOfD16hyszS464bHWOpzILapC5wf1esTOo2X4xyvyfsHOPFR63Q+l1fXqYyLOMS3+Iv0u/1CeJy1VhiSrsNLQoJ9zrAqrs2QZP//HDqzcL8vH5ZLwv5UHDN9TWl2PC99Zgc4PzjH8W3/vyMPmgyXq7yLKzNJAI5jcomp8vCwb419chN83H8bU15fg1fm7G/zcJXsKsCev3OdxSZLw/j/7caRU73ye8to/uPKj1Th8HE5pvaNDprrOaSjT1VlF+GvbUdz65Xp1fxVNx/fnLH1m9g5sdO/VDqdL7d1ZVefA2BcW4pqP1+DVebuxYt8xvLVwL56bswP1ThfGvrAAc7cexQdL9qsGwq2HSrH1kHGw28bcEvxv5QFsO1yK0qp69HzkD8x4exmcLgmfLs/G16tzDN+3r6ACmTNn6z53T155wKhgp0tC14fm4IU/d2Hu1iM4778r5GsQ+BJxx1kD2wMA7vluE+5x6/IAkFdWi0+WZ+PObzaq5xhAnifvLt6HfLfBvaLWYagDKHqcJEm44sNVWLBTf27eV1CBqz5ajS9X5eDsN5fqHE+vz9+DVfv1591th0sbnSX2r8/WYtrrSwAAL8zdiRs+W4tL31+pK3H59O/b8dJfDa8XPPPOFUOx+qFJuH9KTwBQA2fiI22Yvz1Pd/++98++RpVlral3InPmbHy4NKvB12rlXudwqUEF3q9pTEbEM79v18n8gyX7kTlzNu7+ZiMW7MzDFR+uQq3DKai72D+1XnvBSc/9rTurAbJtwV/P3KLKOszdejTg35AkCfd8uwlrTkCXPHCsEme/uRRfrc7B5FcWY1NuCdZmF+HBH7eoc73e6cK4FxbigyX7fd4v2l4GAPFRNp/HnJKkO3fuyfOcS7MLKzHwyb/wlXufWZNdhE+XZyNz5mysO9D4a19Z68CyvcZZld+uydXNs4+XZRnaJBtiT165oZxkxJxl8VHG9qqqOgcOFlfhq9U5uOaTNbqA0ZP/byFGPPu37vV78ytQVKl3pEiShIU78+FySahzuNR7XtEdjjeDecmeAlTVOXDH1xvwyl+7DF9TUF6LKz5cpdqtdhwpQ35ZDeqdLlTWOrDlYCk+W3EA5QbVeHjntYsGGT7ufQaVJAlnvbkMl7y/Che+u0Ldy977Zx+GPzsfu710+s9WZOPJ36jXVnPhuxIS3ONwuvDl6hw89ss2fHjVMEzq3QYAcO7bsqEwe9Z03et/cy8w87blYVVWEZbuLUSf9gkY3TUVa7OL8MjPWwHIRq5PlmcDAL5bl4tbJnQLOI4rPlyt/rxodz5GdW0lfGmfqjrvBUrfmFHRjX/ecAgzf9yMv+4cj/PfWY7Hz+yL6QPkjK7xLyxUe8qM75GGo6U1qKl34onftgMAftl4GDeO64JOrWJx6UkdA44nq7ASD/ywGS+ePwCdWsVi++EyTPvPEvV5l0vSGVWW7S3Ewp35mDm1F2xWj5/6+T/krAnl3vhpwyHc890mfH3DSHy1WjZWimaw1X4/LXUOF2wW2fFU63ZAvTZ/N95fkoU/tx3Fyv1FYEyW5RkD2yHGbkO904Xej87F6f3aAhIwID1RvWYv/rkLV4/OxBNn9YUkSZi/Ix8vzN2J728ajYd+2oJHzuiNdonRPuPYk1+OA8eq8MjPW7G/QFbulOv/3j+yklZSVY82CVbsySvHpe/Lxsrbv9qgHuBr6p34z4K9AIBbJsjZTPO25QlZpk5h+oD26lwAgK2HytTvCDTcLHp/QYWuzMWq/cfQOTUWy/YW4j9/78H+ggq8eekQ9fm5W4/itfl71N/fWLAHL5w/UP39UEk1xsxagJsndFXL5P2y0eNM/Gd3AbqkxWHMrAWwWhh+vHk0BqQnYsmeQizZ41HoFdmu2n8Mxyrr8PaivXj3imFYsDMPtfUuHCyuRv8OYkXlx0Y2TkXYX1CBtxftw7Pn9kOkzao+rt0j1ueUAADGzFqgywaqd7oQYTCXP14m70cJUTZkpsZi5f5jeOH8gVidVYTOqbG45Yv1AIBOKTFIT45Gh+Ro2K0WPP7rNgzKSMLd327CC+cNwIXDM3w+e972PNTUu/DFyhw8fU6/gN9NxGnmb8wWBtUxGBtpw4hn5+NYZR325FXgg6uGIb+sRq3l/u8v5et7zuAOOvlsyClWdQ3AV98wot7pQn55LRxOFzq1ilUf33qoFGe+uVTdV+/9bhOm9GuLcwZ1wJlvLgUA/H7byejXIRGbcktw7SdrcfXoTJzety2SYyPQs008Bj75F1rF2rHqoUn4z9/ueS6gzADgSKk++jKvrAavaAyagQIq8spq0Do+ssn7Qq3DiYkvLcK0/m3x9mVDdc+Ne3EhAGDjY6fi3cX78e7i/ZjYMw3nD83Aoz9vxcHiKswYnI7W8ZFIjrWjpKoOg56aF/DvXec+5Cn3jfehUAS0wRbPndsfeWU1eP1vzx6zOrsIT/0u73Hfrj0IACgo0zuLXS4JV360GimxdvznksEAPOvlvLvGoXubeADA2uwifL/uIL5ek4ulewvx8dXDsSqrCFV1Duw8Wo6dR8sxetYCfH7dSSirqcewTslonRDV4Hc4682lyCurwdpHTlUf6/2YbES57/SeGJiehFu+WIdlM0/Bhe+uUF/z++YjiI6w+nwe7/grBwkA57y1DK9fPAj3f78ZqXGReOPSwZjhXuO2HCrFlkOlOvmePzQduUXVuOnzdQCA3XnleOH8gTjjDXnNun9KT4zrnobPVmSjT7sEvLVonxocGGO3YsaQDqh1uLA+pwSHij0NxC8eoT8PFFbUYpJb1/lpwyH065CIwyXVOPXVf3DNmEw8fmZf9bWnvLQIqfGR+PbGUShzO9be+2c/RmSmqK8RbS9LMAhWaxVrxzGNIa+kqh7pyfLPmw+W4vk/dmLe9jy8etEgjH1hIWLsVrxz+VCM65EGQHbW9n5sLu47vSeuHdNZ1eO0+9hbC/di8e4C1QBYUetAYnQEHvppC75clYOEKBtWPDgJfR//Uzc2772wuLIOD/20Bc+c0w+t4iJR53Dh751y4GBuURXe1pRlfu3v3XjmnH7YX1Cpc9QIJjIdrROi0CFJPhspjrr88lpc73Z8XH9yZ4ztkYbn5hhn9S/clY+Plmbh7EEdkNkqBj+6M/DeXrgX153c2fA9e/MrcME7y1FcVY/bJ3XH3af2wAM/bMZPGw5h+1OnI8ZuwzdrcjCxZ2v8vPEQnpuzE+sfPdWnt5fTJeHvHXnokByND5Zm4YOlWcieNR3ZhZWY5T4b/rjhkDqmb9bkYkNOid/MCxEpMQgGe/HPnfj6hlEAZAeuck5W2Ha4FGlxkWidEIUnft2mGrgvH9kRz5zT3+dvlFU78MP6g5i95TB2Pj1Vffym/61DXJQNL10w0Oc9Wr5YdQAP/yTbsDYd3AIAeOmvXSiqrMO2w2U4Z1B7jOicghHPzkdxVT2emb0D14/tovsM0WwfgLFjpLbeqQvUjLF79unD7iCYb9fm4pIRHXHBO549/Y6vN+KXf4/Bvd9twgvnD0RafCSq6hxYuf8YureOx7bDpUiIjsD2w2X4eFk2DpVUY9aM/liVVYRnzumH2EgbKmsduP+HzejUKgaL75uIRbvy8aT7nO/vjPDxsiz075CIYZkpKKyoxavzdiM6worPVhxAndOF1+bvwS+3jsGklxfjv5cNwdT+7Tz9HgUTmZHjEQCqap248qPVqo0IkHW+YZp9G5ADnW0WCy58dwW6pMZiwb0T1Od+23wEt3+1AbdO7IY3F+7FJSM64vkZ/dUKUt+uycWwTsm46fN1uGFcF8zfnocHpvbCu4v3Y/uRMrx56WAwMF2LlQPHKnHFh6txzqD2qj3kaFkNnjq7H6I0+t+GnGIs2VOIoso6pMbZcen7q2C3WjClX1v8uumwuu9699kWgW6t4wwf9w6uLauWf/dOUli8uwAF5bX4anWOTldbnVWE7YfLgDObecAmhRxZAvLWwn1qdOeDP25Bu8Q9+PiaEerzNfVOREVY8eumw+jcKlY1fthtFvUw9dumIyivcWBjbgl2HvWNAH1h7i5cPrITBjwhZ/3sfXYqft54GOcO7gCrhfkoN1E2/cFWsD1GpcorOmtVlj5SJauwEgt25uHObzYCkB14+eW1eOr3baojS7tgF1XWYeaPW3z+zrtuR8YFw9Lx6rzduHFcVyRqFOB/fbYWgzsmIbuwEquzinDnNxtx47iu6sFZ4dav1uPNS4Ygv7wWc7ceUR0Ef+/Mx8J7J2DRrnzdAb6osg73fLsRGe7MM63BXjTFAACeOrsv6p0S8str8O5i+Zre//1m5Lvvc+WrK30QVu6X5ancvmP/byHWPXoqcoqq4HBJmL35CADgQJE+em/2liOYtz0PAzMSsXzfMZRU1WOgOyPOYmF445LBqKx1YH1OMU7ulorPVx5QHYxaBSVz5mzseXaqWrLtkvdW4v4pPX16RylzeLtmY1TuK8WBJ6C4AOibnipoMy8Yk51PkiRhav922HqoFFV1TozonIINOcX41O1sV7DbLKiuc+K5OTsAeJyXALBwZ77PnCmtrofTJanzYrM7gs1fr6fYSJtaUsPpknDVx6uxfOYpfr+f8rnKWnKtphzYHw1EJ/JGXCMdWY//ug1L9hRiSt+2mNynDf7adtQwMt3biQXIxh4jw6sSGa91ct52Sndc+O4KpMZ5jA5XfiQbf68c1Qm3T+qOz1YcwGcr5EytVVlFPo6sv3fkqYZDl9c+tjuvHJsPluL8oemG+6I4GK8OVgtTlfCvVueo/QVXZx3DTxsO4q5vNmHG4A6qcQaQozr7tE9Qf9c6sQA5c+rS91fho6uHY1BGEgB3hOCufEzs2Rrbj5Rh+n+Wqq/PnjUdc7YcQVl1vc/eeKikGh8uzdIZ8s54YykuGZGB/h3kzy6rrscl76/Uve9YZR0ueHeFuu6LZph445LBuO2rDXhhrj7q8eNl2bryMPVOFworavHH1qO4YmQn9fFv1+Ti/h8247+XDUG/Donq/u6PdQeK8eaCPXj/ymGoqZPXyzlbPGtTUWWdzrCndU4t3FWAhbvk9bqoog6nv/YP0pOjsfSBU3ycWKXV9YixWxFhtaDO4dLpGA6nC28u3IsVXtkNIqDtt1TncPoYKX7f7NGrFKdCe7dBV9Gdy2scWOqObH5gai/V4AsAD/20Bd/dNBoAcL7G4OSSJLw6fzfe0KyJCgt25uOjZVlomxCFlQ9N0j1XWlWPe77biMfP7IuMlBhU1zmx7bD+0P3LRs+c/2DJfnRMiUFZjQO7NVHdCtX1TsFmWMMBMkrJs0Ml1bhI47gz4ilNIA7ge254Ye4un7msfe3nKz3ZV06DjKG9+eVonxSNRbs8epFLkvDd2lzc9/1mAL5Gk/2FldhfWImHf9qCGUPS5c92SULOL4UoA4dpu6QonSNLWy5LiVRfe6AYY1+QnfBVdbKh8Mt/nYTRXVNx1K3Lfb0mB5ef1AneTHp5EfYV6PX/2nonEB2BxW55pMVH4rUAGZabcksQGWHB83N2YvHuAgzumIQbxnVV9Q5AnuNaYuxWXP3xGjUDNlxQdMgct4Fdq3MpDiIFRS+vrHXAamG45uM1AKALGAPk/V6S5GA+5ayk8MSv21Bc5dYd/96DWyZ0VZ0p1XVOFJTX4oEftuDkbqnqfXS4pNrHkXX1x6t9/i4ATHhpkeH3fOwXWbYlVeFTvm3tgWK8s1ifGbNyfxHqHC7YbRadMwSQ7SPT/7MUk3q1xodXD9c99/nKHKzNLsYDU3phYq/W6uOFlbLOppVhrcOJudtkfaQhR5bixNJiYUx14uzOr0CnVrHqPaFFxGxwBSPHyPwd+uo6ucVVuPPrDVi0uwDnu/eE/DLf6gsHi6vx7j/7sXBXAb5enYMbx3dFn8f+9Hmdllfn70ZeWS0m9mqNswa2V7PFlQzHq91z15viyjrERFrx7dqDqqPrvSuG4ob/rfN5bUWtA6+7g1C/XZuLqZr2H6LZq/w6suqdPjI5/50VeMMd3ATIa9onGrvH/sJK7DxahhlvL0dVnRPXjpGd+m8ulPXCr1bn4PkZ/fHOYtmmMX9Hnhp0oaxpn67wVDMZ8tQ8VNY58fC03vjXuC74anWOamfRtnr4du1BjOmWirMHdQAgB4Qqe+62w2VqMHad06Wuud5VNUSiS1osTu3TxierUAm++2LVAczbnodHz+jj894tB0uxbK+sexV7ZdDVutdPonk4bkcWYywDwGcA2kDO9XxPkqTXGWMpAL4BkAkgG8CFkiTx0/QkDNirKWOVX16L/PJabDvsMRoeLK5G17RY3P7VBt377DaLuvh/tTpHTTH2xyxNpM136w7iwR+3oKSqDkM7JfsYsxQlRGTFAADGdk/Fn9s8i5Z3eTBAb5hWlFclc8G7LIt3CrA3v206jLcX7UNVnRNPnCV77CVJwrzteZi3PU/t27Uhp8THIA/IxqfCs2px9cerdYZXpdyatzIxf3ueaoQCoCt9KCJXjsoEAMzdekR9LF9TFlNxKiT4SetWDjJ78/XGmq2H9EYCxQFs1Fem2p3F9+jPW/HjhkP44MphePQX/2nDP64/qGY47C+sxE2fr8cDU3rpXlNcVQenS8LHy7IByI1dlWbXJdV1hspouLBoV4FqqPvrrnFqlPPuZ6b6rDuAbKh4Z/E+9f6ftz0P13+6FvN35Pm8FgD+3JaHXo/+gQX3TEBGSgyi7IGjy0uq6nUlBWPtNr/l7gDA5nZSKlFKIhNpoGwlRNl8HK+KcryvoAKT0cbwUAIYz59jlXVIirFjd145+rRLgMXCcOP/1urWYQXFOGVUo3p9TjFKqvSPt0/ydZBpjcCrs4qwcFc+hmemYMvBUtVB0rNNvJrxJeJc83fIYwxqiQdtgFxZjQN3fSOXbvrRq+/I0bJq9GmfgMKKWpz95jKfz3xr4T4UVdZhbXYR0uIjsTm3BBW1Dtz3/WZM6tVa3YsUprz2T5OdhF+tzkXaKbIDfKGfUrgb3Bl/IjKpd2vDx73L/tY7JQx7Zj4AYHTXVoiOsCItPlJ1dt3svmfvPrUHOiRFY0TnFEOn1m1frsfh0hoMfPIv1egNyKUboyNsuOT9lfj4muE+7/Pmu3VyttHB4mos3Okrl4FP/oW+7RMw+/axmPDiQsRoHONrsot1mbIioZ1fp/Vti5RYO56ZvUN9THvPK46sOqcLj/+yFZ+uOIB2iVH49sZR6mvmb8/D5RrHpOKIVZz5CqlxkfjaT0nkHHfwzdGyGhwtrcGhkmp0ahWD1LhIbMgtxvwd+Zi/Ix/nD033MdoC0PWuKa6qR3GVfKZoyAEkCt5BGfee1sNv+bZ6Z+CDzFKv0koxDegQgbhJs1c6XRKem7MDHy7NwvgeaZjcp436nCRBdWIBQBs/WXdfrMrBn9uMA2ZEc/Ab0SY+Clvh0c/LNI4s7/mi5dL3V2HNw5NVXT4xOsKwPKO3EwvwOCrTk6NxqKQaEnyjsrWc/ZZ+n0yLj0RlrUN35vZ2ksTabYZOLFErLygoZ2MlOMUa4Pt0fWgOLh6e4XeN03LXNxvRs20C/m/uTsy/exy6tZYzWL2rwtTUO9WqKvd8t0l1DpfXOtSAwmqvc/sdX28wdGI1pbdZuPDThoM+j/2w/iAYfB34h4pl/V7JOkyN0wct7jxajgd+2Ixfbh0Dq4WhdXwUjrl1eW2Wr5HOPeW1fzClX1vcObkHAOBoqZyBboTVwlQ9t7rOgSs/Mi5hrtwrIk4xIye/N9pAZcVh7K8n5D63DeTnjYcQGdGwkT3PLaPymnr8vOGQGojd0AwZ/LRvxv6tXrZKLYpDRGn7UVTle7+IQKzd2Nx+oLDScFd+ZrYnWOYTr+BdAJjymqcy00fL9KVWLUy2IyrnrNoGWmMofbqenbMD43qk4UFNgOHBYv2Z/cU/d+FgcTVe/NM4UEchKsLit+evKMTYbXj/ymE4/7/LdQ693zcdwcndUlUn+oQevs46paIIoA+2+XJVjjsoPqnlBm4yTiQjywHgHkmS1jPG4gGsY4zNA3A1gL8lSZrFGJsJYCaAB058qISCzeBkqWSSALLx4+cNvgp9pM0asLyGN1+u8ijdigf6jQV7cXK3VIPP1m98IioGAPD6xYNx6quLkVvUtJ4DyiHZWwFrKDrL6Hmt8Xv2liNIT4722Uy01Na7sCffN2rWCG8DhlaBF1VmAGCzGCtea7KL8faivX5LMrVyX49DAa5vQyjK/H638Wp3fmAj7QM/bEHn1FjdYz+u1x8WKmsdmPzKP+rvLglwuI0rSuaZNislnFBKXQLAaa96rsHffhxTxZV1urI/APw6sRTqnXLZkKvHdDZcT7WUVtfrekR1SIpGZa3/EoHaslPezZFFU76NjChtE6NQVmO83rz7z36M7NKqSX+jqLIOv2zcjXcW78Pk3m3QvU2coROrIZJj7D4Ormi3gXFTbgke+XkrHprWW+2fAQB78itwzcdrfIwn36/z/NyQo5NH+ndINKxNr51bjUUpd7dkT4GhI7LCbTx0uiSM/b8FcEnAjePk0i1/Gzg3jjfTTcnMM4qs9Ua0vSzSZnyPBbpWl7y3EvnltRjcMcnHiffKPI+BXinrsnxfIRKjI9C9dTwOu2VaWefU9Zn7acNhpLgNEq/N859xYMQ1nxhH4G47XIbqOqf6NxVyNFnPp/dt4/02rtHq0Uqm1Z2Tu6uOuQOafjeKXqCUEwbkOaU93D7+6zacPai9+vvq7CJ8tDTLp6/W0r2FfvvXbtQEXf2x9Yga7XzDuC66v/39uoPo3yFR/T1z5mxseNRTXtAbfz1kjNYCnhmemYIfbh6Nj5ZlYfbmI40qv9hYYvwYqRrDLk3/hFX7j6kGf21pO0DOZImPtKm6SEqsHUdLa/DNmlxcN1ZfZs0o0AMQb100IilGr/caZWT5Y+uhUtUomBRt9zHw+Tsn7M2vQGZqrPr6/QWVSDQoe+hwunwCN+THJVym6QeqlaNCtIB6RmPwdiB7O428aYwTCwB+3ngYgGzkziqsUh1ZLi8Rav+eNsMxKToC5W7dxTtiXusA0NJSPSp5xigj90GDKjOFFbXI1fSru+WLdbosbwXGgFHPLwAgO7qO+cnIUijVVD/ZebQcd07ugXcX78Pzf+zE25cNgRHHKmpV3amy1mn4HXRjCvisGAztlOzTv8wIp+TbGxPw6Cn7Cip1pT7tVgvqAgQ8f7AkS7fmFZTXYuHOfPRqG6/KYOX+Y9iQU4Kb3e0RvGlMD/KCilrU1DuxOqsIreMjDfvZ84xRX9VBGUn4Z0+B4Q1YXHn8mZ3REdaAgRaBOP21fwI+3xgnFuC7DovMtzeOQpeHPD1/v1mbi8Edk9S54V3dwJuyGgeq6hz4aGmWGjwV6ac1CtF0jlv7liTpCIAj7p/LGWM7AHQAcDaACe6XfQpgEciR1WyU1dTjJ6+IaUCv/GkjQ71piiNLixLdUlpdj9lbjvg873C5UFxZh2yDhqoiERVhxWsXDca/v1iPuCibT6aOP2LsVkiShCe8GvhpIwQn927tk/qtlFuIsVuRXViJLYdKfQyQNfVOpMZForDC2IBRWecwjBT7bq3vgeB6r6as2nrKItOtdRySYiIMHYMvzN2FC4amG7xLzgYZ98JCjDFwzgbiy+tPwqXug6kSzRIbKSviRpHpWqXOCG9HpLejpKiy1if6LVyJjbSi0GDarcn2VdJj7Fbd4akpKJmttQ1EDRVX1eEJTUkYJbrWm583HFJLjip4K+jz7xl/XGPliUAp8UWVdT7RyA1RVFmn9nGcvyOvQSekP6rrnGp0p8KbC/bqSjxd8v5KQwOUdzPWvzRr8CUGPbZ4598Tu2Hp3sJmKVP08E9b0TYhym/Gk2JI/GKVp1RhoMALwhdvvawxUelK5nFDmWj/7C5Adb0TN7ozP965fKjf1yrzEAA2HfQtDXq83GiQTf7ADx5j2G2ndG+2vxUMbBaGDknRuH2Sp4+sd/R5Q+R46V7evys9trT4c2IBeueF1mCrLdOp4L0vjfm/BX4/99IPjKPZ8wOMhUcsFoahnZLVPnqtNEFd953es1HGGX8YZS4fD79t9j1bKfy04ZDO+cHA8MP6g3h1/m4cbqRTUcRyNt/cMBJREVZVr1D27y5psdhfUKk7Yx31cpZ7o3W2L91bqHOCAbKuZ4T23DQwPRGbDpYarruvzNut63ulUF3v1Dmazx+WrlZbULD48TKKbmT3zlZUSqR79zo7EWodTmQVVqJtQpRPuWh/WQGLdxeo/eOe+n07bvjfOvxz30R0bBWj9lD2pipAAJvZUTLFFYycWIA+K1Rr04iKsKo9v7UyO1ii3xfrHC7McZdoVyrmeAfzaHUX777nAHDef5dj3YFitRe1qGjnUI828Y1yZLlckqEzOb/MeO20WRkCmR4OGNj8VmcXqY5JxoCL35MrXVw2MnBP+EDM2XIUc7bMRadWMejdLkHITNWMlGg1SH7eXePw6vzd2OXHLhTIedgQlXVO/LbJvy4RDBxh5MkyckJqy+Ir/ef8se5AMW74bJ0ukz9Q9jjRNJpFq2WMZQIYDGAVgDZuJxcAHIVcepBoJvY0EFkSiOxjlcdd+q+hA8Jzc3Zi8NPz1NJfIpewGNopGSsfmtRg9sTMqZ5ycFYLw2+bj2CBlxNDGz3eTxMFq6BEB769aB8mvLQIt321QU2lVpAk4Lfbxvh1xvhrlv7sHP8OTYVsTeRwQwZ9nslMjcXGx07D02f3NXxeKYFkRE5RVYNlNr3R9jPblFuCzJmz1Xq4Rg6X968chvE90jCsUzJS4yINozZHdknB1zeMBOArU5ck903QI+4cC0RZtfEGX15Tj3aJ+ijqpOgIHD3O0m9bDpVBkiTUGJSX0VJaXa+LsK9zuvDBkv0+r3vkZ99a7SVe30XbB0U0HpneG2O6tcLOI/r78EQj4277akOzRPcXVdap0Z0KRs5fowy89V5GqSOa/c4mYOSU1cLw7Y2jsP+5abho2Ik74q77dK3ad8wbpcSI1hBvFOyiEGO34oebR/l9vjkQcWW8dkxnDO6YhIem9cK/J3ocJHe5S+m0Pc4Mkis/Wq06sQDg7UW+/ZVamobq5CcblLrjGcYYls08BRcN9xhmFPnENjKzQildqqDVxZpKkkYfYQw6B7ZRkJO34+N4gmT87dO807ud3O+vS5qnkXcXrwx5f7RJMHZW7smv0JVsSo2z4/fbTsYpml4wWib2TDPsqxBIDy33Kue7LqdYdb7N2do4o9WJlEAMFSd1aYWBGXL0M+AJGEuJscNqYTpnlPf5qyG0JZumvPZPQEexQkZKjE9FBQV/gSM5XnO7q+beU3iliRmwouCvz2rbxObLiFy8qwATX1qE3o/NNehr7d9msulgCQBP4M24FxfiSGk14v2M+XizHETF+7zVWPq0S/D7nD//Q1l1Pbo8NAffrsnVraWPeZXp/3H9QTUAV+k1fvP4rrhkhLGeW2mwtykOH8XWcqQB+xavrHv0VKx7ZDLWPDwZcZGNW9vLahw+TnTA+DoBDesGRtU2XS5JtWFobY7rG+Foa4gDx6qQkSLmOXrJ/afgncuH4J3Lh6J7m3gkRttRWm0ciH6iePdgDDbhVoX111vH+O3X15j1w7sc9Yk4Kgk9J2yhYYzFAfgBwJ2SJOny6yQ5f9XwdmaM3cAYW8sYW1tQIG4zuGBjZAA3ijA34r1/9humxjcmSu+on2iNcEa5Vv4UpGvGZKo/b8gp8elJBgAlVXWq4fTUPsfn03VJEtolRuscYVonm7aXmf5vN2xo0B4CwyHjZ3DH5Ea/NpCy3RBNKSWz9IGJyEiJwafXjsCgjCS/mXVf/WukeujTRlZ1ay0fend4ORD8fY4I/HHHWPV6eOMdJatQUevw6V2RFGNvdDSyN5tyS9D5wTkNzhPvyNtdR8vdZU18x+fNVR+tPq6x8cTKBydhw6On4vqxXfDF9SN9HPL/3D8R2bOm46bxxmUjgkV+ea1PRpYR3iV9whmLham9F1uKpjoh/zW2Czq1apzh2B/+DIkKIkZrPnZmH/x0yxjcMK6rrq/VpN6tsfrhSbhgmHEgS1PZ3IyZVs1FK8EcWUaM6JKCDknReP68Acf1/iyD3jyNRRsgMSA9qcHyXc2xBvrbp3nn7lN7YO6dY3VrSKwfw7XRe41YsDMfd3zt0f/PHNge/TokGgZ5/H7byfjgquGGuk9T0Pbv9XZyAcZBMydSAjHULLh3PN6/cpj6HRiT+3WWVss9WjJnzm50iXWF7zUBbjuPlqv9WAH/TsuoCKvf5/yVB/zAKyuyQ3LjDbECbmU6jOZWjN2qc743lfE90nS/BwpU1Pa09saod0x+Wa3f9dEouyec0TohmtLTJdD97a9Hk6JH3v/DZl1bC+8so5k/bvEpIWqzMvRsE2/4uRUGa6M3O44ELg3GM63iIpEWH9mkbNvGZh97z7PGcqS0BpW1DjX4QGGtQZBvY/AOwOiQJFZZQS1T+rXDlH5tAci2W6OAoIbaHTSFzFbiXiueGJCehPOGdDB8rqFEDyMcDfRhJRrPCTmyGGMRkJ1YX0iS9KP74TzGWDv38+0AGIZISZL0niRJwyRJGpaWdnyLpRkpr6kHY8Bfd41TN+4zB7YLGDnz9Dn91J/3GxyWdz8zFTMMJmjf9h5D/8r9x5o0TtGVb8BTRuCykzoZPq/ta+HPCVRUWYdImwXXndy5QeObP5RojVaankhxUfqm6YF4TdGQzwAAKKJJREFUYEqvgM8rVNeLr6Sn+Wn+6k2bhEjYrMd/k0ZHWA3njBHpyR5FIj7K/+GNMaYe+l7WRGemxNphYeEVwdG7XQLG90gzzHr0F8nzx9ajPs67pJgIHDnBbJ6mlLIb2SWlUVG74UTbxChdxsRHVw/HKxf6Ribdf3pPvH7xoEZ/bvIJGDKMqKh1YNGuhiOyG1OPPZzQrnP3T+nZ6PfNmtG/JYaD+Chbox0XD0/rjRvHdcG+56apj/3vuhG4VhNEYoSIJbT8kRJrR+v4KLVUS5e0E3MC+mNK37bqz43NUDlR3r1iKLJnTW9U43LeSYiKwLKZp2Bav7Y+z/kLmhmY7gkK+GxFdsDPD9RHrF2ix3CY3gQj+YkgamkUu82CXm1lebxy4UC8etFA9GrrMYJ6n12uHePpPzUgPcmvPq0tG64EJBllQPVsGw+rhTVbOUJ/GGW8NDZbkEfSk2Nwap82akaW0yUhMToCy/Ye8ynp3BxER1gxuqtvv89ImwUJfvT4+kbq6CJn5jcVIx0/NS4SsQGcqto5Z4RyJn78TN+sxhNFqVxjFCDcGKdIfJS4zmJvlLl2yYgMfHvjSMPXaPUGhUD9mxsTAPGjQfsMAIb92QFgROdWfnUI7wo3Rpxm8B1E46yBHZrUa9SfM15LU5yXWn7ddBgl1fVI98qcWp3d9FLnIzqn+KwhbRObVsaZVxKjI1DndKGqzonxPdLwhHs963iczqedT0/BiM4puscyAlRM8ddjrrnwHovoGAVIMibbf/1lHvujJbLwzMpxa9JMluiHAHZIkvSK5qlfAVzl/vkqAL8c//AIb64Z0xn7n5uGHm3i1UUi1m7De1cM071Oa9BPa0TtfqPaz3abRd3Iah0uTB/QTn3uwam9MLKL/0XKKNJJVNomRvntLfbBlcMwtrteuTp3sMfBcayiDrUOF6IiLIix27Bs5ilN/vtKVFSKxvjXlEXzvKEd8O2NDZdy6t8hqalD446URhpIGRiuO1l/WBrTzffQ6o9ouxUvXzBQVTwai/age9lJvvWijQwNTpckXGPTxvLyhQMxpGOS3+cbKouWHGP3WxJBq4QH6guj9BNpSJZZz09DjN3WYLR7uJMSazd0ylssDGcMaHzN+S+uNz4YnwiB+vrce5pxNH0glj4w8USGwwXa6D5/fXx6tPEtdXTR8Ay8fdkQdG/t+5w3FzaQLTROE9kZFWEFY3Lpwwk9AwcxjemWigen9dbtv2O7pyEhQBb63/eMb/KhgkeUQCUlgEUx1GgDI5qT1hrDRneD+yEQjXVge0eanh4GBiRvjEqRRkZ4HvvpltGqs+SBKb1w4/guANBgv5hWAfR4bSBbcxnJVz44Sf158X0TfJ7313tGJGYMSce5g9PROiEK5w2R17AMr/n1yPTe6s/xUTZcP9bYyK41TCifYeTIinDfH1ERLevIirAynyzpmDBYFxWjphLQbFSlpKkYGeOjIqx40aCUkN1m8VsFZeX+xhlqk2MCn1O0zmgRs4u1GPUXaRVnV4MxjYJOvPWR7FnTseohz3o0qbdcstP77H0iKLrRPnewr5GM7/1uU4Ofox2nqPR3V114/8phmNAzDTOn9vbJrgGAOyZ1x9T+vnMnUAuLE7HfXnZSR3x74yh8eNUwteTytzeOQlykrdHBMN7nzetO7uxjCxCRnm3j8fKFg9TfzxnkexYblJGkBtXcMM6zN3x41TCf1wL6PaqpDtqqOqdPllxjM9+0Z7VvbxyFdkn6oIym9iPlFW1A+nUnd1Z1x8zjrFgRabNA8pp8gwM4IxubiQ7guMo5Pji1cUH0IqMEZNhtlibtR40NeiEa5kQ06TEArgBwCmNso/vfNACzAJzKGNsDYLL7d6IZURRbJaIvxm5TI2cUJvZMQ2u3M8vIuP/rrWN0vxuVvbNbLXhKU5ZogsYYNaprKyRF+1fGjyfVkjc+vHoYbhzXBa1i7T7Rk4ryPblPG7x7xVD06yArB1P7tcWrFw1SX3e0rAYOl6Rmb8UcR+Sx0rx2ZOdWqtEjwiub6FIDp4hCq9jIgNE3j57RB9mzpjc6m4lnIqwWTPLTl0ALY8DZgzpg77NTVaWoh5/SBMM6+ZYrjLHLxti2iU3b3LXNpB+e3hs/3DwaV4zshPtOlzMlvA208VE2vHj+AAwxGEM40D4pGj/eMga3n9LN8PmxPQIrBoF6Pnzndt7arRY1lT8QF4/wP4em9G0LxpjfshhG/OeSwY1+rWj4OzT6c/gbEXmChjxtxjDQsHP/nMEd0KmJkW4t5TQIJlpDmL/M7Zsn+JaFZIxhWv92+OOOsbh6dKbOCaE91I7ITMFVozMDjqFWM2+UvWtE5xSdTmFEKz+RvYHKKRv1HxGR/103AmsenqzqDsrlzzDIuLlipHHWeFNI0hhXMwNkZF1jkA3XJiFKNSwFYvH9E7Hw3gkAwqOcoD9umdAVz57rqYSg6I9nDWyPwR2TVUNfq7hIPDi1t9FH+HDtmEzMv3scfrh5NACgfWIUBruNclrdrX0j+5r0bqC0sjajR2vwuGRER7RJiAy7/e3Js/vi2XP74TSvs5DWEJ8QHaE6ogIxyp3JMy7A+ma3Nv0s0CUtFmsenqz+PmNIB3Xf8z7DdU2LwwNTeiLreU82a0N9f0VAuRddLsmnr5zSY7ap/HtiN+x9dqrusagIq+EaVVPvDBhI0Rga0lXCQU5abp/UHYM7JqnBZQlREWjvPjud1DlF12+6X4cEnD3It9pFG02PyKtGZWLvs1PRrbXvmc0oKKcxvHiBXBL2P3/vASCvc94c9mPXUHRRu80idPlOha9vGIkVD56CLmlx+OSaEUiMjvBxqKbGReL8oemGmaX+KtScbeBceWR6b6xupPMvym7FiM4pmNS7De6Y3B3Zs6arAd3egQETe6bhhnFdfD7jxvFddfNL5CxVb7Tf67WLffdnCZ4zs3ZtG2pgX/j02hGI0lQd+uDKYfj02hFNGo82kGJ4ZrJhCVwjvB1Vz53bX7WxAbJNKxwYp3F8dGsdpzqwtL01vZ2M2qDB1vGRuuB4xhjq3BEe14zJxOzbT/Z7ho2xWw3PEv44rY+vDWX27ScHfE97TVBVU6q1iITSUqKosg7vXzkMc24f26j3UUZW83HcliRJkpZKksQkSRogSdIg9785kiQdkyRpkiRJ3SVJmixJUtNzSYlGoWQGRNsthrW5q93KhHcJp6gIi0/U5nlDfSOq7TaLzjCp3VwSoiJw/djOfkv45IVBT62+7RPx4LTeYIypGQhfXH8S7pjUXRetGmO34ZYJsiHe3+KklHfyV0M9EE631cNiYTh7oKzgWzRK5eqHJuGxM/rg4uEZPobk587tD6uFoVOrWL+GplpHeGWYXOMuSxFo41Suks1qwbUnZwLQZxH+eMto9edPDJQ3xZjhr2HxpF6tkRgd4ZO6ffuk7urPMXYbhnZKxtPn9MO/J8r3T1SEFW9oDES3n9IdXdLicM3ozmoz+XDk7tN6YufTU3SPZT0/zScyacaQDmqpVLvNojMUfXn9Sfj8upNwxoB2OG9IOuw2C2ZO7YWf/6132gOevmNajA5kSk8MJXJq+b7Gl1j1jvJtipOHd9o04l70znBTnLUKERb/6od3JPmuZ6b4vMZ7DFufPB0fXzPc57orTo+0+Eg8dXY/eHOblxM1nMv+9NSU0NIabAMdDG1WC544qy82PX6aegieock6rnU40To+8P2gLSdj08j9ylGZ2PjYqQA8xj0lYnRUl1Y6Gf966xgsuV/OkGtKJKGotE6I0jkolGuYEmtXS/8NcJem065dTe39qERaa5uFB8oYMIq2jrXbcMfk7oYGKe38irPb1LJDjS15LCL3T+mllqS2WRhcbtXC+75VHLU3agxuZ2gqH2hLD3ZrHY9urePVADUJnjKpWiNeYw2pk91ZDUaR1sqZ4bEz+uCSERm6MmDPz+iPVQ9NxlkDG599KwJxkTZcdlInXWaiz2vc1+HPO8f5DQ67cVwXtZzPgPQk7NE4SL64/iT1Z+9AjmkGmQ3ejOmaqlsTnp/RH+cMluWgGCZj7Fa8fvEgPDK9DxhjOgN0OOggylo3PNO3Ioh279aWi/SHcp/bbRafTMo2CZGIirDi42uG470rhuK/bl2+oLwOlU3oMecdWLfpsdPUc2B0hBWbHj/N5z3hkDmn5e5Te+CnW8aofd3aJ0WrRs6y6no1WBMAOqXEItpuVfUCIywWZpj5CgBz7xgHAIg3uIbeAaCAJ9NgXPc0Xd+uU/u0Qfas6Y0qr/bVDSPxn0sG4xeDs4aIxEbadOVqvYmwMqx9ZDIyUmJ0LRYUzvSzN3TzCjJ65cKBuO7kzmgd4DyxyB34AgR28EZ6PffaRYPx0LTePmcJSQJO05Tgqw2jzAhlfVd06YX3TsDymaeoNoVDxVXq/az1S3qXSl028xSM75Gmm5cDM5Iwvkca3r5siOHaaiSbAemJeHhab6x6aJLheu2Pdl5nsAHpSfj9trFqUL6/ADfR0PYKbpsQhZO7p2L27SfrqvWM1axLL10wEC+cPxBf/uskjO2eis+uG+FzXlV0wkEZSejbPhEXDEvX2bN6tY3HQ9N6YdF9E/xW+kmLj8Rn147QBQo7XZLub80Y3EE9W3tnaz59dl+sfmgS2iREYd5d4/DHHWMNgxPCgcm95bWkW+s4REVY0ad9gq4sf4ekaLx4vm/fXAc5spqN8NKWTIayuI3umqoekMf3SEOPNnH417gu+HG9XGc42Suq7IvrTzI0BP19z3hMenmx+nukzaKL2tQqeenJ0chMjcXuZ6aizuFCXlkNxr6wUH0+P8z6yHx8zXCs2HcMY7qlYoxBnWbFseRvbTpQKEcO+quLnxwTgeIq49rRLo2epSgW2sOpogTOOm8ARnVthTu+3qg+p83UumNyd7w639N7KT05GqO7tsIlw/1noojIyd1TsfLBSWibGIUPlmRhyyHfcmPa63fhsAzM3XoUV47qhC9X5cBqYRjS0ROhFCh6UjHKdWoVg+fP7Y+S6np8uDQLz8/ob6ict0mIwovnDwjY9FTbBLykWs7g6p+eiJUPTULmzNkA5L53OwVuUGuE99xgjKkZNFeO6oT05Gi1HML0/u1gtTAkRkfgqd+3o6C8Fq0TotCtdRxO1kQ5aQ8xP9w8Cj9vOIz/rTxg+PcZY/jh5lF4Ye4urMqS4y8U5Vw5AL920SC1H8Pn152Eyz9c5ff7aA/NHVNi8NC0xkXei0BKrB3TB7QLmP1yslea/b8ndoMkSXjpL88aFGO3ok1ClE95oIRoz5z7+OrhusPy2O6pWLKnEKO7tsKCnXJfEiUzYWLP1pjQIw0bc0uQVViJH9YfxMsXDEJucRUibVZEGay/o7q2whsL9qq/f33DSN1eFi7MGNxBLTM8Y3AHvHLRIHU96aPJbjt/aDr+2nbU5/2xkTa0irOjqqhal71TXuvwiVz/5oaRyC+vxUfLsvD2ZUNw3tvL1ee0PbssFoakGDt+uHk0OiRFo21iFOZsOYJbvljvY2AfkJ6k/uxvH714eOBSpCJT4tYPEqMj0L1NHPYXVqqPMQY1ErB7mzgcLK7GxJcWAZD7SizdW2j4mYzJwRg5RVWI1jgrLh/ZCbP+2Gn4ntP6tsEny7MBAFePzkSXtFg1Ir11QhQ2P3EaBjzxFwA5q2xs9zR8vvIAiqvqERNpRYTVguxZ00/sYgjC25cNQZ92Cfh54yHkFFWpzqO4SBsqah2qw/Cm8V3x7j/7AQD/uXgwOrWKwVsL9yE9OQbT+rfD6K6etVTrjBiQnohth8t02eRK2S0AuHF8F7y7eL/6+x93jEVOURV2Hy1Xs+6SY+wor3FgdNdWmNa/Hc4d3EE9H1zrLrmklKtpTJlR0Tm1T1s8N2cnLhyWrl73UV1aYcX+Y2p2Vs+28TipcyvD/ivea5M2g0t7dtC+7l9jO+PG8V0xZ4vvuqslzmtNjLRZ1TPa4I5J+HpNLiyMha3RCJCz3N66dAhO79sG43qk4lP3WhRttyIjJQZjurXCsr3H8Mk1I/DD+oN48c9d6ntfPH8A7vt+MwB3f7T5u1FcVa/uSctmnoL/zN+Db9bmqme5iT3l+bTugKwTFlbUYlLv1vh6TS6eOrsv/tldgPk78vHO5UNx0+frcMmIDHy1Olf9mzdN6Ip/jeuCi99bCQBIdJ+jVz80SS1T+PttJ+OGz9aqGT/HU7lDBO6f0hP/+XsPHpjSE1sPyeeXWodLFxyh9N4zcshHWJlPhsEHVw7DD+sP4o+t8tyxWBj+uGMsoiOsmODeAxUykmOw361rPnlWX/Rpn4CebeNRU++E1cKQHGNX99T27nJmXdNiVQeclnevGIrUODvSk2OQEBURdo59I548qy8yUqJ1TgllHVO2pVN6tcapfdog6/lp6PzgHACyo7jO4dJlaGx54rSAPaMB2embGBOBSJsFtQ5XwN6nWqO8Vr8wMtYnaTIqq/1kj4nKB1cOUwPWlABsxXbXu10C7jmtB1Ji7Zjarx3m9juKhbvyYbEwrHtkMoY+Mx+Ax8FfWOGpIKNU4ZjWvx12HCnDzqPl6nOPn9kHZw5sjxlvL0dOkSdLljGGf7mDdBrTI75Laiz2F1Yixm7FFSM76WyOAPDJNcPx5aqcRrVLEYVPrhmObYfLVN2ib/tE3fN2m0WthqGciUZ3TdXphFqUPuKK048xvT1r7p3jdK9/YEov/N9cva6vZH3365CI1W5byISeaThzYDuc998VePuyIZjWvx2Ouf+WzcqgnUZtEqJU+1d3P5WOROXsQe2xZE8hPr1mBFyShIEZSahzuHSOYa2ed9nIjrhgWAYuGJahnrcBwBFGDvRQQ44sgbl1YndM7dcO/dz1jH+8ZTR6tIlXDe+dWsVgX0GlbtNWDPzedVQBuRTFkvsnYkNuCW7/agMm9W4Dq4Xh1ond8ObCvUiLj0S31nEoqqzTRUPZbRa1prdiNBnfQMkg0WgdHxXwcDihZxqmD2iHmV5RxvPvHo/JryxGr3byYs4Yw8guKZgxJB33uw9Ut0/qjhvHdcF1n67R1Vjf8+xUdH/4D5yk6UWmGDFSYo0VQEWp7JoWi3tP62n4GkCOEnjhvAEY7ad5qugomVKfX3cSBj4lG9Xm3jkW2w+X4e5vN2FYpmdjT42LxK+3yinSP94yWs0CeO2iQWq0nmI8f/3iQdh22ONAap8YjX+N7YwLhmWoxqRp/T0R1UYom5o/tOW/CsuN+2Y0Rykp3mCMYfF9EzD+xUVIcCts8VERhkZPbbnUlBg7CsprIceo+2dopxT0bpeAb9bm4t7TeqB3uwRERVix5WCpaugd2ikFHVNiVEeW4owa1kmeg+cM7oCuaXFIjI5Ax1Yx+PqGkUiMjsCN/1uH84em44tVB5BXVqt+n9Q4Owor6rDgnvF+I0hF5a1LAzeK7dY6Hj/cPAp/78jHJHfU0tmDOuClv3ajX4cEpCdHY9Pjp4EB6PbwHwCAJfdPxAdL9uPq0Zk4b0g6kmIiVCdWr7bx6Nk2Hq9dNAibDpZiYHoinpm9AwDwmSZrkjGGwR2TMbhjMma4+54o64F3SaAdT01BtN2KhfdOQFp8JOocrkb32RMJpbwUYwy7npmiZsN9du0I/L75sJptPbVfW7x0wUDAoDcIADx2Rl88/stWXD06E79vPiz3k5Bk41FGSjRyi6px4/guOKmLXFpLiczt0TZeNdIZNXnXljZRSrleHmCNU4zwioH5+Rn9DUsBhRMXDEvH5ysPYEq/tpjavx3qHC6cPagD7vxmI84dnK5zRnZOjcV3N41Cl9RYJMfY0eUh2aA0a0Z/5BZXYVNuKWYM6YAx3VJRXuPA+//sx8XDM3CsohZXjOyEuEgb7pzcHa/N34NbJnTFvoIK/LktD/PvHo9ureNwxchO+N/KA3hCU3paQTHC3nZKN4ztLuuBf98zAVsOlTaqLFs4oegCd07ugTs1GfE//3s0NuSUqPpcUkwExnZPxVWjMmGxMIztnoa3Fu7DqX3a4JzBer1TWcPOGdwBd0zqjktHdEL/9EQsuGc8OqfGgjGm2zMfnNobpdX1KKuuR0ZKDHq3S8DpfduqBorkWDueOKsPhnRM1jmotTDG8PttJze5NKuIdE6Vy/elxNpV+Xx8zXC1hIzCxcMz8Oumw5g1oz9m/rgF957WA33aJ/g1MHmjOAvvnNxdvTcyUqIxOCMZN4zrgjPeWAoAePbcfth2uAxfrspRK118d9MolLsN/peO6IiB6UlIT47GAz9s8TH+AcA9p/bAiv2NzybnmQirRe3XPKFna0zoqc94+vjqESisqEXbxCjcNL4rTuqcgvPfWQGrheGCYRkYnpmCCJtcleTvHfnILaqG1W2F6pAUjctHdsI3a3N9DOBdUmUn7ql92uDi4Rk4c2B7xEXacOGwDOw4UobBHZOx8+kpiIqwYkq/drjqo9UAZL1FMdhfMsKj92sD3fp1SMSZg9qrTufxPdNQVecI2PNTRG6Z0E2tXjKySwquHp2JkV1aoU+7BNXhqBhdjZwW25/yzcyf3KcNJvdpgys+XIUle2Q9XnHuaoMqbpnQFRcNz8D4FxcBgK4cspKRMjwzGVmFlRjfI011pD19dj+c1DkFma1icZHbGbnh0VN9AoTNgFEJaaW6hQRg25Onq8F/2mDRuXeMxe68cl3JYu8A0S+vPwnfrz+oBmADQJRdvgceO7MPHv5pa8BKEEqWurdD8eLhGbDbLFifU4wvV+UgLtKG+6b0wqqsIuw8Wm7Y+0tkJhu0CYmx27D4vglIirEjxm5TK8D8V9M7ulVcJP68cxz+2HpEvfcVEY7sos+muml8V0gS8ObCvbBbLWoVnNm3n4w9+RWY8fZyTPeyg2gzG//vvP74ecNhJETb8Oe2PJwzqD12Hi3HaX3b4j9/70HbhCi18oqWAelJuoC2cMBoDwPkoKOt7iBsxYloVAISAB6a1kvV5247pRv+t+IARnbx7flulJF984SuOGNAO7y9aC++Wp2rq7ylZGS9d8VQdYxrH5msBi4qmcVDOyXD4ZQQHyXfWwPSExGuvG5QstN7r8pIiUH2rOlwuSSdg0sJYAOAmWEU2BxqmJFDI9gMGzZMWrt2baiHEXbkldVg++EyTOzVGi6Xpzydwq1frscZA9ob9o85VlGLlFg7GGNwuiQcLK5Cp1axcDhdcEnGSmZJVR1i7DZU1zsRH2kzbPJqFkrcvZCSYuzIL6tBalykz/VYuDMfJdV1OHewp6zj/O15yEyNQUWtE4MykrDzaBkykmPUQ68kSXh/yX6cM7gDyqodiI206koAOF0S3liwB9eM6WzYR2ThrnzszatQo2TMQJ3DBauFqZv4tsOl6JoW1+jmsABQWetAVmGl6jRuaZ74dRu+XZuLP+8cp5apAYDn/9iBrmlxuDCAI0x09uZXICHa1mC5MoWswkp8vCwLj5/Zt1lK5xRX1uHtRXsxqXcbDExPwtoDRTi5W2qjm27vK6jA5oMlOHdwOnKLqrAxt8RvqY1w5GhpDVySpIu+bIjfNx9Gl9Q4nTG+MXyzJgf9OiT6RLEF4ru1uRjXIw0RVotfp9XmgyU4VlGHiY3otxcu1DlcsFlYo/dtSZIw64+dmDEkXY1qrnO6fMqUAHKU9boDxdh1tBw3jO3SbLqBJEmYtz0PE3u1Np2TRMHhdDXoJP9pw0H075Bo2FPEHy6XhJyiKmSmxkKSJDXrlQgOOceq0NGP46i8ph6x9hPTsSVJwhsL9mJ011YY1oSyP4QHSZIapRccq6iFwyX5GGJdLslHhspnLt5dgLYJUera+tBPW3DjuK668rDe73tn8X5M6de2UdHvZiK/rAYRVouP86GqzoFle4/59BdbuqcQwzKTfc4IlbUOREdYG5x3kiThi1U5OHNge/UcVu90wcr876819U58tToH43ukoXNqLMprHcgtqmqSbiM6uUVVaJ8Urerxny7PxtBOyY06dzldEiRJ8tkLX523G2O7p6pr3MbcEsTarYaZAjX1Tny37iAuHp5hqE+U1dTD5ZL8OvzNymcrstE1Lc6nWs2iXflIjrHrnBiVtQ4UVtTqyqpp+XPbUSzYkY8x3VN1TqmaemeDZ/aaeicirBbDc2B1nRO/bT6MC4amgzEGh9OFdxbvw9VjOjfYs86sVNY68NXqHFwzprPhNa2odYDBt2Sy0fopSRK+WZOLU3q3Vs/2NfVOlNXUq787XRIOl1TrbB4EsCevHBtySnDhCVScWLa3EJmpsQFL5x8qqUac3aZmDecWVWHWHzvx8oUD/c69dQeK0bNtPM2hRuByyfbbi0d0DNjnmfCFMbZOkqRhhs+RI4sgCIIgCIIgCIIgCIIgCIIgCIIIFYEcWeYMYSUIgiAIgiAIgiAIgiAIgiAIgiC4hxxZBEEQBEEQBEEQBEEQBEEQBEEQBJeQI4sgCIIgCIIgCIIgCIIgCIIgCILgEnJkEQRBEARBEARBEARBEARBEARBEFxCjiyCIAiCIAiCIAiCIAiCIAiCIAiCS8iRRRAEQRAEQRAEQRAEQRAEQRAEQXAJObIIgiAIgiAIgiAIgiAIgiAIgiAILiFHFkEQBEEQBEEQBEEQBEEQBEEQBMEl5MgiCIIgCIIgCIIgCIIgCIIgCIIguIQcWQRBEARBEARBEARBEARBEARBEASXkCOLIAiCIAiCIAiCIAiCIAiCIAiC4BJyZBEEQRAEQRAEQRAEQRAEQRAEQRBcQo4sgiAIgiAIgiAIgiAIgiAIgiAIgkvIkUUQBEEQBEEQBEEQBEEQBEEQBEFwCTmyCIIgCIIgCIIgCIIgCIIgCIIgCC4hRxZBEARBEARBEARBEARBEARBEATBJeTIIgiCIAiCIAiCIAiCIAiCIAiCILiEHFkEQRAEQRAEQRAEQRAEQRAEQRAEl5AjiyAIgiAIgiAIgiAIgiAIgiAIguAScmQRBEEQBEEQBEEQBEEQBEEQBEEQXEKOLIIgCIIgCIIgCIIgCIIgCIIgCIJLyJFFEARBEARBEARBEARBEARBEARBcAk5sgiCIAiCIAiCIAiCIAiCIAiCIAguYZIkhXoMYIwVADgQ6nEIRCqAwlAPgiDCHJpnBNGy0BwjiJaH5hlBtCw0xwii5aF5RhAtC80xgmhZaI41jU6SJKUZPcGFI4toGoyxtZIkDQv1OAginKF5RhAtC80xgmh5aJ4RRMtCc4wgWh6aZwTRstAcI4iWheZY80GlBQmCIAiCIAiCIAiCIAiCIAiCIAguIUcWQRAEQRAEQRAEQRAEQRAEQRAEwSXkyBKT90I9AIIwATTPCKJloTlGEC0PzTOCaFlojhFEy0PzjCBaFppjBNGy0BxrJqhHFkEQBEEQBEEQBEEQBEEQBEEQBMEllJFFEARBEARBEARBEARBEARBEARBcAk5spoBxlgGY2whY2w7Y2wbY+wO9+MpjLF5jLE97v+T3Y/3YoytYIzVMsbuNfg8K2NsA2Ps9wB/8yr35+5hjF1l8PyvjLGtAd4/hTG2izG2lzE2U/P4re7HJMZYalOvBUG0FGE2zz5kjG1ijG1mjH3PGItr6vUgiOYmzObYJ4yxLMbYRve/QU28HATRIoTZPFuimWOHGWM/N/FyEESzE2Zz7BTG2HrG2FbG2KeMMVtTrwdBNDeCzrGPGGP53q9hjF3g/g4uxtiwplwHgmhJwmyePc1ku8dGxthfjLH2TbkWBNEShNkce4IxdkhzLpvWlGshGuTIah4cAO6RJKkPgJEA/s0Y6wNgJoC/JUnqDuBv9+8AUATgdgAv+fm8OwDs8PfHGGMpAB4HcBKAEQAeVyaX+/kZACoCvN8K4C0AUwH0AXCJe7wAsAzAZAAHAn1hgggB4TTP7pIkaaAkSQMA5AC4NdAXJ4ggEU5zDADukyRpkPvfRr/fmiCCS9jMM0mSxipzDMAKAD8G/uoEERTCYo4xxiwAPgVwsSRJ/SCfzXyMHgQRAoSaY24+ATDF4PGtAGYA+KeB9xNEsAmnefaiJEkD3Pri7wAea+BzCCIYhNMcA4BXNbaPOQ18jtCQI6sZkCTpiCRJ690/l0O+eTsAOBvyAQTu/89xvyZfkqQ1AOq9P4sxlg5gOoAPAvzJ0wHMkySpSJKkYgDz4L6ZmZzZcTeAZwK8fwSAvZIk7ZckqQ7A1+6xQpKkDZIkZTfiaxNEUAmzeVbm/hwGIBoANSskQk44zTGC4JVwnGeMsQQApwD4OcDnEERQCKM51gpAnSRJu92vmwfgvMDfniBaHgHnGCRJ+geyEdL78R2SJO0K9F6CCAVhNs/KNL/GgmwfBAeE0xwzG+TIamYYY5kABgNYBaCNJElH3E8dBdCmER/xGoD7AbgCvKYDgFzN7wfdjwHA0wBeBlB1nO8nCO4Jh3nGGPvYPd5eAN5oxJgJImiEwxwD8Ky7jMWrjLHIRoyZIIJKmMwzQD7g/e1lqCCIkCP4HCsEYGOecmfnA8hoxJgJImgIMscIQmjCYZ4xxp5ljOUCuAyUkUVwRjjMMQC3um0fH2kzvcIRcmQ1I24v6g8A7vQ+zEuSJKGByAPG2BkA8iVJWnecf38QgK6SJP10PO8nCBEIl3kmSdI1ANpDjvy46EQ+iyCakzCZYw9CdhIPB5AC4IET+CyCaHbCZJ4pXALgq2b4HIJoNkSfY+4xXgzgVcbYagDlAJzH81kE0RKIPscIQgTCZZ5JkvSwJEkZAL4AtVUgOCJM5th/AXQFMAjAEchOsbCFHFnNBGMsAvLN/4UkSUqPgDzGWDv38+0A5DfwMWMAnMUYy4ZcVuIUxtjnjLGTNE3bzgJwCPqIvHT3Y6MADHO/fymAHoyxRUxuYqe8/6YA7ycIrgm3eSZJktM9BioVQ3BBuMwxd6kASZKkWgAfQy7dRBBcEC7zzD3WVMjza3bTrwRBtAzhMsckSVohyb3oRkDu4bMbBMEBgs0xghCSMJ1nX4BsHwQnhMsckyQpT5IkpyRJLgDvI9xtH5Ik0b8T/AeAAfgMwGtej78IYKb755kAXvB6/gkA9/r5zAkAfvfzXAqALADJ7n9ZAFK8XpMJYKuf99sA7AfQGYAdwCYAfb1ekw0gNdTXlv7RP+VfuMwz9/fopvlOLwF4KdTXl/7Rv3CZY+7n2mm+02sAZoX6+tI/+idJ4TXP3M/fBODTUF9X+kf/lH/hNMcAtHb/Hwm54fgpob6+9I/+iTbHGvMaAIsADAv1taV/9E/5F07zDEB3zc+3Afg+1NeX/tG/MJtj7TQ/3wXg61Bf35b8ZwPRHIwBcAWALYyxje7HHgIwC8C3jLHrABwAcCEAMMbaAlgLIAGAizF2J4A+UiN7C0iSVMQYexrAGvdDT0mS1OiGb5IkORhjtwL4E4AVwEeSJG1zj+12yLU92wLYzBibI0nS9Y39bIJoQcJinjHGLAA+ZYwlQN48NwG4ubGfSxAtSFjMMffTXzDG0iDPsY2Qje0EwQPhNM8AufTZrMZ+HkEEgXCaY/cxuWSNBcB/JUla0NjPJYgWRKg55h7DV5ANjKmMsYMAHpck6UPG2LmQexWnAZjNGNsoSdLpTflsgmghwmaeAZjFGOsJuX/QAdC5jOCDcJpjLzC5RKEEOSnlxqZ8rmgwt8eOIAiCIAiCIAiCIAiCIAiCIAiCILiCemQRBEEQBEEQBEEQBEEQBEEQBEEQXEKOLIIgCIIgCIIgCIIgCIIgCIIgCIJLyJFFEARBEARBEARBEARBEARBEARBcAk5sgiCIAiCIAiCIAiCIAiCIAiCIAguIUcWQRAEQRAEQRAEQRAEQRAEQRAEwSXkyCIIgiAIgiAIgiAIgiAIgiAIgiC4hBxZBEEQBEEQBEEQBEEQBEEQBEEQBJeQI4sgCIIgCIIgCIIgCIIgCIIgCILgkv8HTlBmKh7KQfwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,10))\n",
    "plt.plot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_engineering(df,col):\n",
    "    # train_size =int(len(df)*0.5)\n",
    "    train, test = df,df#df.iloc[0:train_size],df.iloc[train_size:len(df)]\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    scaler = scaler.fit(train[[col]])\n",
    "    train[col]= scaler.transform(train[[col]])\n",
    "    test[col]= scaler.transform(test[[col]])\n",
    "    # print(train.head())\n",
    "    return train,test,scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(X,y,time_steps=1):\n",
    "    Xs, ys = [], []\n",
    "    for i in range(len(X) - time_steps):\n",
    "        v = X.iloc[i:(i + time_steps)].values\n",
    "        Xs.append(v)\n",
    "        ys.append(y.iloc[i + time_steps])\n",
    "    return np.array(Xs), np.array(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(train,test,TIME_STEPS,col):\n",
    "    x_train,y_train = create_dataset(train[[col]],train[col],TIME_STEPS)\n",
    "    x_test,y_test = create_dataset(test[[col]],test[col],TIME_STEPS)\n",
    "    return x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(x_train):\n",
    "    model = keras.Sequential(\n",
    "    [\n",
    "        layers.Input(shape=(x_train.shape[1], x_train.shape[2])),\n",
    "        layers.Conv1D(\n",
    "            filters=32, kernel_size=7, padding=\"same\", strides=2, activation=\"relu\"\n",
    "        ),\n",
    "        layers.Dropout(rate=0.2),\n",
    "        layers.Conv1D(\n",
    "            filters=16, kernel_size=7, padding=\"same\", strides=2, activation=\"relu\"\n",
    "        ),\n",
    "        layers.Conv1DTranspose(\n",
    "            filters=16, kernel_size=7, padding=\"same\", strides=2, activation=\"relu\"\n",
    "        ),\n",
    "        layers.Dropout(rate=0.2),\n",
    "        layers.Conv1DTranspose(\n",
    "            filters=32, kernel_size=7, padding=\"same\", strides=2, activation=\"relu\"\n",
    "        ),\n",
    "        layers.Conv1DTranspose(filters=1, kernel_size=7, padding=\"same\"),\n",
    "    ]\n",
    "    )\n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mae\")\n",
    "    # model.summary()\n",
    "    return model\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualization2(train_mse_loss,axs):\n",
    "\n",
    "    sns.distplot(train_mse_loss,bins=50,kde=True,ax = axs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def visualization3(test_score_df,axs):\n",
    "    sns.lineplot(x= test_score_df.index, y= test_score_df.loss,label='loss',ax = axs)\n",
    "    sns.lineplot(x = test_score_df.index, y =test_score_df.threshold,label='threshold',ax = axs)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualization4(test,threshold,TIME_STEPS,ax,test_mae_loss):\n",
    "    test_mae_loss = test_mae_loss.reshape((-1))\n",
    "    anomalies = test_mae_loss > threshold\n",
    "    print(\"Number of anomaly samples: \", np.sum(anomalies))\n",
    "    print(\"Indices of anomaly samples: \", np.where(anomalies))\n",
    "    anomalous_data_indices = []\n",
    "    for data_idx in range(TIME_STEPS - 1, len(test) - TIME_STEPS + 1):\n",
    "        if np.all(anomalies[data_idx - int(TIME_STEPS/2) + 1 ]):\n",
    "            anomalous_data_indices.append(data_idx)\n",
    "    df_subset = test.iloc[anomalous_data_indices]\n",
    "    test.plot(legend=False, ax=ax)\n",
    "    df_subset.plot(legend=False, ax=ax, color=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-04-01 00:00:00</th>\n",
       "      <td>19.761252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01 00:05:00</th>\n",
       "      <td>20.500833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01 00:10:00</th>\n",
       "      <td>19.961641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01 00:15:00</th>\n",
       "      <td>21.490266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01 00:20:00</th>\n",
       "      <td>20.187739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:35:00</th>\n",
       "      <td>18.074649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:40:00</th>\n",
       "      <td>21.002014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:45:00</th>\n",
       "      <td>20.566713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:50:00</th>\n",
       "      <td>18.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-14 23:55:00</th>\n",
       "      <td>21.863147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4032 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         value\n",
       "Date                          \n",
       "2014-04-01 00:00:00  19.761252\n",
       "2014-04-01 00:05:00  20.500833\n",
       "2014-04-01 00:10:00  19.961641\n",
       "2014-04-01 00:15:00  21.490266\n",
       "2014-04-01 00:20:00  20.187739\n",
       "...                        ...\n",
       "2014-04-14 23:35:00  18.074649\n",
       "2014-04-14 23:40:00  21.002014\n",
       "2014-04-14 23:45:00  20.566713\n",
       "2014-04-14 23:50:00  18.254193\n",
       "2014-04-14 23:55:00  21.863147\n",
       "\n",
       "[4032 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get data values from the training timeseries data file and normalize the value data. We have a value for every 5 mins for 14 days.\n",
    "\n",
    "24 * 60 / 5 = 288 timesteps per day\n",
    "288 * 14 = 4032 data points in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START value\n",
      "1 value\n",
      "(3744, 288, 1) (3744, 288, 1) (3744,) (3744,)\n",
      "2 value\n",
      "Epoch 1/50\n",
      "30/30 [==============================] - 2s 14ms/step - loss: 0.7534\n",
      "Epoch 2/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.1667\n",
      "Epoch 3/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.1284\n",
      "Epoch 4/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.1072\n",
      "Epoch 5/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0932\n",
      "Epoch 6/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0827\n",
      "Epoch 7/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0745\n",
      "Epoch 8/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0695\n",
      "Epoch 9/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0642\n",
      "Epoch 10/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0608\n",
      "Epoch 11/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0574\n",
      "Epoch 12/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0539\n",
      "Epoch 13/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0510\n",
      "Epoch 14/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0484\n",
      "Epoch 15/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0461\n",
      "Epoch 16/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0439\n",
      "Epoch 17/50\n",
      "30/30 [==============================] - 0s 14ms/step - loss: 0.0418\n",
      "Epoch 18/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0404\n",
      "Epoch 19/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0390\n",
      "Epoch 20/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0369\n",
      "Epoch 21/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0351\n",
      "Epoch 22/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0338\n",
      "Epoch 23/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0328\n",
      "Epoch 24/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0317\n",
      "Epoch 25/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0310\n",
      "Epoch 26/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0302\n",
      "Epoch 27/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0297\n",
      "Epoch 28/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0292\n",
      "Epoch 29/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0287\n",
      "Epoch 30/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0285\n",
      "Epoch 31/50\n",
      "30/30 [==============================] - 0s 13ms/step - loss: 0.0281\n",
      "Epoch 32/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0278\n",
      "Epoch 33/50\n",
      "30/30 [==============================] - 0s 12ms/step - loss: 0.0286\n",
      "3 value\n",
      "4 value\n",
      "Number of anomaly samples:  38\n",
      "Indices of anomaly samples:  (array([2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835,\n",
      "       2836, 2893, 2897, 2912, 2913, 2916, 2917, 2918, 2920, 2921, 2924,\n",
      "       2925, 2926, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2936, 2937,\n",
      "       2938, 2940, 2941, 2944, 2945], dtype=int64),)\n",
      "Finish value\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TIME_STEPS = 288#needs to be configed to the right jumps \n",
    "anomaly= []\n",
    "ig,axs=plt.subplots(df.shape[1],3,figsize=(50,15))\n",
    "axs = axs.ravel()\n",
    "i=0\n",
    "for col in df.columns:\n",
    "    print(\"START\",col)\n",
    "    final = df[[col]]\n",
    "    train,test,scaler = data_engineering(final,col)\n",
    "    print(1,col)\n",
    "\n",
    "    x_train,x_test,y_train,y_test = train_test_split(train,test,TIME_STEPS,col)\n",
    "    print(x_train.shape, x_test.shape,y_train.shape,y_test.shape)\n",
    "    print(2,col)\n",
    "\n",
    "    model = get_model(x_train)\n",
    "    history = model.fit(x_train,y_train, epochs =50, batch_size=128,shuffle = False,\n",
    "    callbacks = [keras.callbacks.EarlyStopping(monitor = 'loss', patience = 1, restore_best_weights = True)])\n",
    "    print(3,col)\n",
    "\n",
    "    x_train_pred = model.predict(x_train)\n",
    "    train_mse_loss=np.mean(np.abs(x_train_pred,x_train),axis=1)\n",
    "    x_test_pred = model.predict(x_test)\n",
    "    visualization2(train_mse_loss,axs[i])\n",
    "    i+=1\n",
    "    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)\n",
    "    THRESHOLD = np.percentile(test_mae_loss, 99)\n",
    "    test_score_df = pd.DataFrame(index=test[TIME_STEPS:].index)\n",
    "    test_score_df['loss'] = test_mae_loss\n",
    "    test_score_df['threshold'] = THRESHOLD\n",
    "    test_score_df['anomaly'] = test_score_df.loss > test_score_df.threshold\n",
    "    test_score_df[col] = test[TIME_STEPS:][col]\n",
    "    anomalies = test_score_df[test_score_df.anomaly == True]\n",
    "    anomaly.append(test_score_df[test_score_df.anomaly == True])\n",
    "    anomalies.head()\n",
    "    visualization3(test_score_df,axs[i])\n",
    "    i+=1\n",
    "    print(4,col)\n",
    "    visualization4(test,THRESHOLD,TIME_STEPS,axs[i],test_mae_loss)\n",
    "    # visualization4(test,anomalies,TIME_STEPS,scaler,axs[i],col)\n",
    "    i+=1\n",
    "    print('Finish',col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value    20.897166\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[anomaly[0].index].mean() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value    44.494254\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('TensorFlow')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7842d1971238c40bc91f0e3c4b1f67715db27be3d2f267cdf8f729dc15e9bd8b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}