Lab1-XGBoost-For-Timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/header.png\" alt=\"Header\" style=\"width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Predictive Maintenance using NVIDIA RAPIDS and Deep Learning Models</h1>\n",
    "<h4 align=\"center\">Part 1: Training GPU XGBoost models with RAPIDS for Time Series</h4>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with time series algorithms can be difficult to configure and apply to arbitrary sequence prediction problems, even with well-defined and “easy to use” interfaces like those provided in the tf.keras deep learning library in Python.  One reason for this difficulty is the need for Deep Learning layers to input and return sequences rather than single values.\n",
    "\n",
    "In this course, we will take a publicly available time series dataset of hard drive operational metrics provided by <a href=\"https://www.backblaze.com/b2/hard-drive-test-data.html\">Backblaze</a>, to build out and compare machine learning vs deep learning approaches to predictive maintenance problems. \n",
    "\n",
    "The course materials are categorized in three sections. In this lab, we take a machine learning approach called “XGBoost” for classification of faulty/normal hard drives and discuss performance optimizations by utilizing accelerated computing. In lab 2, we move on to deep learning models and start with recurrent networks to predict the time series. We further explore variations of recurrent networks such as CNN-LSTM models. Lab 3, addresses applications of autoencoders to classify hard drives into faulty and normal bins. Throughout these labs, you will have the opportunity to get familiarized with details of implementations for each model, and learn how to curate the data and evaluate results. To begin, we start with \"Training GPU XGBoost models with RAPIDS for Time Series\". This lab covers the following topics:\n",
    "\n",
    "* [Background](#1)\n",
    "* [Predictive Maintenance: Predicting Anomalies Accurately and Early](#2)\n",
    "* [Lab Overview](#3)\n",
    "* [Dataset Loading and Pre-Processing](#4)\n",
    "* [Training GPU XGBoost models with RAPIDS for Time Series](#5)\n",
    "* [XGBoost](#6)\n",
    "* [More on Data Preparation](#7)\n",
    "    * [Exercise 1: Remove \"zero-valued\" columns](#e1)\n",
    "* [Define XGBoost Model Parameters](#8)\n",
    "* [Train XGBoost on CPUs](#9)\n",
    "* [Training with GPU without cuDF DataFrame](#10)\n",
    "* [Training with GPU and cuDF](#11)\n",
    "* [XGBoost Model Accuracy](#12)\n",
    "* [Measuring Model Accuracy](#13)\n",
    "* [Interpreting the results](#14)\n",
    "* [Dealing with Imbalanced Data](#15)\n",
    "    * [Exercise 2: Implications of balancing data samples](#e2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1\"></a>\n",
    "## Background\n",
    "\n",
    "One of the important recent topics of interest for industrial organizations  is to move away from reactive and schedule-based maintenance to <b>predictive maintenance</b> that helps optimize the operations and improve productivity.\n",
    "\n",
    "<a name=\"2\"></a>\n",
    "### Predictive Maintenance: Predicting Anomalies Accurately and Early\n",
    "\n",
    "Whereas schedule-based maintenance provides a regularly scheduled time for part replacements and repairs, predictive maintenance represents a condition based approach driven by data and analytics. Predictive maintenance strives to identify anomalies that can lead to costly breakdowns, such as improper machinery lubrication, contamination, misalignment, or suboptimal humidity and temperature conditions.\n",
    "\n",
    "By identifying such anomalies of many part failures, estimating remaining useful life of these parts and then mapping anomalies to failure conditions by interpreting service logs, predictive maintenance helps to manage failures and avoid costly unplanned downtime. Other benefits include fewer unnecessary repairs, optimized spare parts inventory, and longer lifespans of equipment and parts—ultimately increasing equipment availability while improving operational efficiency. \n",
    "\n",
    "In this regard, our goal in this lab is to show you how you can take your company's time series data and leverage it to predict outcomes.\n",
    "\n",
    "<a name=\"3\"></a>\n",
    "## Lab Overview\n",
    "\n",
    "One of the common problems in a datacenter is failing data storage disks. Current business applications transfer large amounts of data and put very big pressure on the disks.  Using hard drive S.M.A.R.T. (Self-Monitoring Analysis and Reporting Technology) data provided from Backblaze, we will build conventional Machine Learning models in addition to Deep Learning methods to predict RUL (Remaining Useful Life) of a hard drive.\n",
    "\n",
    "- Dataset - https://www.backblaze.com/b2/hard-drive-test-data.html\n",
    "- Research paper from IBM - https://researcher.watson.ibm.com/researcher/files/us-mqiao/BigDataCongress_2018.pdf\n",
    "\n",
    "After completing this Lab, you will know how to take time series data and:\n",
    "\n",
    "- Predict Failure using a machine learning classification model with XGBoost\n",
    "- Predict Failure using a deep learning classification model using LSTM\n",
    "- Detect Anomalies using an Autoencoder (AE) or Seq2Seq model\n",
    "\n",
    "\n",
    "Let's start by importing some libraries used throughout this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries that will be needed for the Lab\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import sys\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import datetime\n",
    "import time\n",
    "import gc\n",
    "import math\n",
    "import random\n",
    "from random import shuffle, randrange\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import *\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"4\"></a>\n",
    "## Dataset Loading and Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set consists of a daily snapshot of each operational hard drive in the Backblaze datacenter at the time of measurement.  Each snapshot includes basic drive information along with S.M.A.R.T. statistics reported by that drive.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define location of our input and prepared data files\n",
    "data_dir = './data/'\n",
    "\n",
    "csv_train_file = 'Lab1-2017-Full_data.csv.gz'\n",
    "csv_test_file = 'Lab1-2016-Q4_data.csv.gz'\n",
    "\n",
    "pkl_train_file = 'Lab1-2017-Full-ST4000DM000.pkl'\n",
    "pkl_test_file = 'Lab1-2016-Q4-ST4000DM000.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading training data set...\n",
      "Finished reading training data set\n"
     ]
    },
    {
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       "      <th>smart_2_raw</th>\n",
       "      <th>smart_3_normalized</th>\n",
       "      <th>...</th>\n",
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       "      <th>smart_252_raw</th>\n",
       "      <th>smart_254_normalized</th>\n",
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       "      <th>smart_255_normalized</th>\n",
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       "      <td>2017-02-01</td>\n",
       "      <td>MJ0351YNG9Z0XA</td>\n",
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       "      <td>127.0</td>\n",
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       "      <td>3000592982016</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>126.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>PL1321LAG34XWH</td>\n",
       "      <td>Hitachi HDS5C4040ALE630</td>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>MJ0351YNGABYAA</td>\n",
       "      <td>Hitachi HDS5C3030ALA630</td>\n",
       "      <td>3000592982016</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2017-02-01</td>\n",
       "      <td>Z305B2QN</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>124084032.0</td>\n",
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       "         date   serial_number                    model  capacity_bytes  \\\n",
       "0  2017-02-01  MJ0351YNG9Z0XA  Hitachi HDS5C3030ALA630   3000592982016   \n",
       "1  2017-02-01  MJ0351YNG9WJSA  Hitachi HDS5C3030ALA630   3000592982016   \n",
       "2  2017-02-01  PL1321LAG34XWH  Hitachi HDS5C4040ALE630   4000787030016   \n",
       "3  2017-02-01  MJ0351YNGABYAA  Hitachi HDS5C3030ALA630   3000592982016   \n",
       "4  2017-02-01        Z305B2QN              ST4000DM000   4000787030016   \n",
       "\n",
       "   failure  smart_1_normalized  smart_1_raw  smart_2_normalized  smart_2_raw  \\\n",
       "0        0               100.0          0.0               135.0        108.0   \n",
       "1        0               100.0          0.0               136.0        104.0   \n",
       "2        0               100.0          0.0               134.0        101.0   \n",
       "3        0               100.0          0.0               136.0        104.0   \n",
       "4        0               117.0  124084032.0                 NaN          NaN   \n",
       "\n",
       "   smart_3_normalized  ...  smart_250_normalized  smart_250_raw  \\\n",
       "0               127.0  ...                   NaN            NaN   \n",
       "1               126.0  ...                   NaN            NaN   \n",
       "2               130.0  ...                   NaN            NaN   \n",
       "3               137.0  ...                   NaN            NaN   \n",
       "4                91.0  ...                   NaN            NaN   \n",
       "\n",
       "   smart_251_normalized  smart_251_raw  smart_252_normalized  smart_252_raw  \\\n",
       "0                   NaN            NaN                   NaN            NaN   \n",
       "1                   NaN            NaN                   NaN            NaN   \n",
       "2                   NaN            NaN                   NaN            NaN   \n",
       "3                   NaN            NaN                   NaN            NaN   \n",
       "4                   NaN            NaN                   NaN            NaN   \n",
       "\n",
       "   smart_254_normalized  smart_254_raw  smart_255_normalized  smart_255_raw  \n",
       "0                   NaN            NaN                   NaN            NaN  \n",
       "1                   NaN            NaN                   NaN            NaN  \n",
       "2                   NaN            NaN                   NaN            NaN  \n",
       "3                   NaN            NaN                   NaN            NaN  \n",
       "4                   NaN            NaN                   NaN            NaN  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read entire SMART training data set\n",
    "# - Full 2017 dataset takes about 4 minutes to read in\n",
    "\n",
    "print('Reading training data set...')\n",
    "df = pd.read_csv(data_dir + csv_train_file)\n",
    "print('Finished reading training data set')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30471787, 95)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set Overview<br>\n",
    "\n",
    "The dataset in this DLI course is provided by Backblaze. More documentation can be found [here](https://www.backblaze.com/b2/hard-drive-test-data.html) about the type of the data and the different sensors which indicate the health of the hard drives. Following is a list of key parameters reported in the dataset. \n",
    "\n",
    "- <b>Date:</b> The date of the entrie recording in yyyy-mm-dd format.\n",
    "\n",
    "- <b>Serial Number:</b> The manufacturer-assigned serial number of the drive.\n",
    "\n",
    "- <b>Model:</b> The manufacturer-assigned model number of the drive.\n",
    "\n",
    "- <b>Capacity:</b> The drive capacity in bytes.\n",
    "\n",
    "- <b>Failure:</b> Contains a “0” if the drive is OK. Contains a “1” if this is the last day the drive was operational before failing.\n",
    "\n",
    "- <b>SMART Stats:</b> – 90 columns of data, which are the Raw and Normalized values for 45 different SMART stats as reported by the given drive. Each value is the number reported by the drive.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the snapshot above, one important thing to notice is that we have several different models for which data is recorded. First, let's take a look at number of samples for each model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ST4000DM000             12237899\n",
      "HGST HMS5C4040BLE640     5154696\n",
      "ST8000DM002              3523493\n",
      "HGST HMS5C4040ALE640     2728104\n",
      "ST8000NM0055             2664020\n",
      "                          ...   \n",
      "TOSHIBA HDWE160               64\n",
      "WDC WD10EADX                  59\n",
      "WDC WD10EARS                  32\n",
      " 00MD00                       14\n",
      "WDC WD1000FYPS                11\n",
      "Name: model, Length: 68, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# let's review the available hard drive models and select one with lot of samples for this exercise\n",
    "print(df.model.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because SMART stats usage can vary in meaning from manufacturer to manufacturer, we will limit our training data to a specific hard drive model, to reduce  potential of other manufactures' usage from influencing our model. We will pick the `ST4000DM000` model, which has the largest number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading test data set...\n",
      "Finished reading test data set\n"
     ]
    }
   ],
   "source": [
    "# ST4000DM000 is the most popular drive so let's use this to train\n",
    "harddrive_model = 'ST4000DM000'\n",
    "\n",
    "# keep only the selected hard drive model we want in our training data set\n",
    "df = df[df.model == harddrive_model]\n",
    "\n",
    "# Load test set from Q2 data and pre-selected model\n",
    "print('Reading test data set...')\n",
    "df_t = pd.read_csv(data_dir + csv_test_file)\n",
    "print('Finished reading test data set')\n",
    "\n",
    "# keep only the selected hard drive model we want in our test data set\n",
    "df_t = df_t[df_t.model == harddrive_model]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting string to date time format for sorting and plotting time series data \n",
    "\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df_t['date'] = pd.to_datetime(df_t['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cmap(n, name='hsv'):\n",
    "    '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct \n",
    "    RGB color; the keyword argument name must be a standard mpl colormap name.'''\n",
    "    return plt.cm.get_cmap(name, n)\n",
    "\n",
    "def plot_df(df,loc_col_list,axs):   \n",
    "    df_normal = df[df['failure']==0]\n",
    "    df_failed = df[df['failure']==1]\n",
    "    \n",
    "    n_rows = len(loc_col_list)\n",
    "    cmap = get_cmap(n_rows) \n",
    "    color_list = random.sample(range(n_rows),n_rows)\n",
    "    \n",
    "    for i, cur_col in enumerate(loc_col_list):\n",
    "        cur_min = df[cur_col].min() \n",
    "        cur_max = df[cur_col].max()\n",
    "        \n",
    "        axs[i].plot(df_normal['date'], df_normal[cur_col],'.', color=cmap(color_list[i]), markersize=5,alpha=0.2)\n",
    "        axs[i].plot(df_failed['date'], df_failed[cur_col],'.', color=cmap(color_list[i]), markersize=20,alpha=0.6)\n",
    "        axs[i].set_ylabel(cur_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_constant_values(df,check_str): \n",
    "    col_list = df.columns.tolist()\n",
    "    loc_col_list = [] \n",
    "    \n",
    "    print(\"Range for    : {:>25s} {:>15s}\".format(\"min\",\"max\"))\n",
    "    \n",
    "    for cur_col in col_list:\n",
    "        if check_str in cur_col:\n",
    "            cur_min = df[cur_col].min() \n",
    "            cur_max = df[cur_col].max()\n",
    "\n",
    "            if cur_min != cur_max : \n",
    "                print(\"  {:20s}   {:15d} {:15d} \".format(cur_col,int(cur_min),int(cur_max)))\n",
    "                loc_col_list.append(cur_col)\n",
    "                \n",
    "    print(\"\")\n",
    "    return loc_col_list "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning and understanding input data \n",
    "While building any machine learning/deep learning model, it is important to understand the quality and structure of the input data. For example, there can be missing data for several days for some of the sensors. There can also be sensors which do not have any data. The input data needs to be curated to remove such sensors and adjust the data for the sensors with missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range for    :                       min             max\n",
      "  smart_1_raw                      20608       243737992 \n",
      "  smart_4_raw                          1             297 \n",
      "  smart_5_raw                          0           43248 \n",
      "  smart_7_raw                          7 218859378419151 \n",
      "  smart_9_raw                          0           39525 \n",
      "  smart_12_raw                         0              76 \n",
      "  smart_183_raw                        0             684 \n",
      "  smart_184_raw                        0              62 \n",
      "  smart_187_raw                        0             690 \n",
      "  smart_188_raw                        0               7 \n",
      "  smart_189_raw                        0             264 \n",
      "  smart_190_raw                       14              42 \n",
      "  smart_192_raw                        0             285 \n",
      "  smart_193_raw                        5          574352 \n",
      "  smart_194_raw                       14              42 \n",
      "  smart_197_raw                        0           21608 \n",
      "  smart_198_raw                        0           21608 \n",
      "  smart_199_raw                        0            2683 \n",
      "  smart_240_raw                        0           39339 \n",
      "  smart_241_raw                        0     51579811864 \n",
      "  smart_242_raw                     3912    417629711029 \n",
      "\n",
      "Range for    :                       min             max\n",
      "  smart_1_normalized                  79             120 \n",
      "  smart_3_normalized                  91             100 \n",
      "  smart_5_normalized                  67             100 \n",
      "  smart_7_normalized                  28             100 \n",
      "  smart_9_normalized                  55             100 \n",
      "  smart_183_normalized                 1             100 \n",
      "  smart_184_normalized                38             100 \n",
      "  smart_187_normalized                 1             100 \n",
      "  smart_189_normalized                 1             100 \n",
      "  smart_190_normalized                58              86 \n",
      "  smart_193_normalized                 1             100 \n",
      "  smart_194_normalized                14              42 \n",
      "  smart_197_normalized                 1             100 \n",
      "  smart_198_normalized                 1             100 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# First, let's remove columns that have no valid data\n",
    "df = df.dropna(axis='columns', how='all') \n",
    "# Next, let's remove a few rows that had invalid data (most likely error reading SMART statistics)\n",
    "df = df.dropna(axis='rows', how='any')\n",
    "\n",
    "df_failed = df[df['failure']==1]\n",
    "df_normal = df[df['failure']==0]\n",
    "\n",
    "col_list_raw = remove_constant_values(df_failed,\"_raw\")\n",
    "col_list_normalized = remove_constant_values(df_failed,\"_normalized\")\n",
    "col_list = ['date', 'serial_number', 'model', 'capacity_bytes', 'failure'] \n",
    "col_list = col_list + col_list_raw + col_list_normalized\n",
    "\n",
    "df = df[col_list]\n",
    "\n",
    "#keep the same columns as the training dataframe \n",
    "df_t = df_t[col_list]\n",
    "\n",
    "#fill in the small number of null values in remaining columns \n",
    "df = df.fillna(0)\n",
    "df_t = df_t.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collecting the number of failed hard disks to compare the distributions of the sensor data with normal disks and also to plot time series data \n",
    "\n",
    "serial_num_list_failed = df_failed.serial_number.value_counts().index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of sensor data \n",
    "Once the data is curated, a typical histogram plot of the data can reveal key operating points, outliers and provide further suggestions for preparing the data. The raw SMART sensor data is plotted below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5 \n",
    "m = len(col_list_raw)//n \n",
    "if len(col_list_raw)%n > 0 : m += 1 \n",
    "    \n",
    "fig, axs = plt.subplots(figsize=(20,20), ncols=n, nrows=m)\n",
    "axs = axs.flatten()\n",
    "\n",
    "for i,cur_col in enumerate(col_list_raw): \n",
    "    axs[i].hist(df_normal[cur_col],alpha=0.2,color=\"tab:blue\")\n",
    "    axs[i].hist(df_failed[cur_col],alpha=0.6,color=\"tab:orange\")\n",
    "    axs[i].set_yscale('log')\n",
    "    axs[i].set_title(cur_col)\n",
    "\n",
    "for i in range(len(col_list_raw),axs.shape[0]): \n",
    "    axs[i].axis('off')\n",
    "\n",
    "i = len(col_list_raw)\n",
    "axs[i].plot([],[],alpha=0.2,color=\"tab:blue\",label=\"Normal disks\",linewidth=15)\n",
    "axs[i].plot([],[],alpha=0.6,color=\"tab:orange\",label=\"Failed disks\",linewidth=15)\n",
    "axs[i].legend(fontsize=16)\n",
    "    \n",
    "len(col_list_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series visualization \n",
    "\n",
    "Of all the disks with available data, several disks fail. Since the SMART sensor data is time series, it is useful to visualize the sensor behavior for the failed disks. In the plots below, the raw sensor data are displayed for 10 failed disks out of the 1061 failed disks. This can give us insights to the behavior of the disk until the time of failure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE : \n",
      "    The large circles represent time of failure.\n",
      "    The lines show the data leading up to the point of faiilure. \n",
      "    Only serial numbers of failed disks are being used.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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BO3WQYe5wjav6hzwY7+O+N81Z5zinUynKIliAfMibe3bbChHY1siumh1hczMRhOb6QvPPs7lzVMpQLGryfR/xIK2IRyLce3MYwxDcugHnGjuKPlPU+v98FkZfJvAqPSYBPjXa/DvwvOK5Ik+GYfwq8BpwGsgC/5BAzfvcQKPX3a/1cV8an49o4zZj3GGZi9hEaEdgkOI8CrWeDBDni+RPXmDFh3Zp8snEH6O8GaQzwKnRX6RSDq2vTtYHdUdz077FW6KAU7IxBk4z+KvHmMr63PUl1/+wiAGkxiKMnRMNO2yjiX2YTrQymPE+4YNkCMd9wGG1H2l/j5jZzmD7NIdzL5E4fIDcskVXAmLyOILjoG2UEqzIRXpSP2K/2kdR/oiyGCZKaiOOTUNKmSjZgRTBM2mvt0u3EdE93IpE6Z6c5MaBJGv5KYpqCmkfAB8y8wGPMsNgx2E1L3jbjdA343L8ZJRIcRn6kmg3w021SMbO42AyismK9lgp9dFmzaLFMjadCO2vT+hCCFJ0o1hD6xL/5t48OpdksjPN4exbiIV5Ks4K8dQl0izgKYVjSkSiJ5jwEz2bFDNpBQqe6wVfpRUQ1AVPE15dwxMxkmtZ3FA3yrA2tYhlamKREl7aIpYQm1apD7Wf1lhaEesy8d68TczwsG60w1gKbVuPHjSeSKC//vfQX/u7tE0vYjg9Aakrl4MYRNOEr389KJ+iNdqJseI+oN3pRTRQDmHneKskQ6SZZI4pEgwFKmW9Ym6GYeIawvUwnX6QO8Sw7RBOsePnzPCOCt36pNxuUYn0YKWzQeLEDveryNPHAdJM0s/BbV2NOyk+W1WHetIAgfLzoxsw/6BCzJjHGuqmOJ3F9xOEDrhU3G6KStMlV8lEb3LlSoh9B9pYWjrMlcsCVawpwBpRLm3czx7d2I8Tw1Z//12O5q4cJ49HWCVY8kbpSYpNZOVR1a/GCpfApoMjnGZWZVn1jhNKZsm4ZSpKImzBMgtNuW0bKURC7O6irScasLv6t5fn2egcjci4JcF2SnziKgYTUSLHpyjlDrN/SD4Vl+ijqGn1/T9swvWJ5tTK+mvV951MOlAv19+BJ+Qq/6zxXJE84H8B7gD/GPie7/v3nq05e4dCrbtf08yRxaMHB4XBAnCJLqaIMlIleBBMznZd/NR71w3eXrXxYz7nenNEvBk6kwfIu5OUywvY9kbtsVqn9mWJknjACXophB+Ac4T0QpSIo1l7+31Ov3cF8FHlFOrYeezobrFCPu+SZY5FOmUb7Y5Br+vjOgY9EhQ+vWKN+VQ/R9VrnDUTqDJoOY4ig7Hcwe0br7C4IOh0onSmesnbU8ToJUIUA4GhZVAq4sZ1xMIC9nqCwkZiQ23AS3uCu12vEnqtzHTRpTPRTV6CqSuEllaolCSWJcgrWIr69Kc9rr3ic32ml4ELxzA/uULYdSk5MTLSIEngei3qDq5e34ebK7IcixE/NUc/JcbGHyC8hfUJPRjsQyyXIqzmHMKOi56O4qeXkL1J4nNT3Mykyd89wvUFGSShjJ5FlEtoGaYsNgZNIQIXbW2e1Br+6Jua3Du3GLA8Dp1q427vS8T6I8jI5uW9mBjnzFKGUnsn1rmXEWKbV1hrePddxMwsZ7pilPaXsfYnEFkXrQp8bH/8yEHjWq/yyaEcy//ol4j/u0mO/MdrtHleEKv3K78CX/7yen08LeDG6Arz82t0d68wIhqH5W3nbg7i77IcUa9wQg5ji7rMbyE2VLIm1Mn1Y+o/C0Ef3Hrc1s/toNDV+uhCBrJzAidxmu7OMmdGTcQ2tphIYgTPqI+DnObVbdtgO8VnO7db/WQb7YCzY1AqhLFutCOyWSL93ZzmJHNumYLzKbfkm0REhP5X84xxhEx+ATNaopiP0NMHXlpTunyNSDEL8Vhw4oXcntzYjxPDVn//vlR8KILzeNLFdkp4bmQTWdlKYqA55apYCOK5+vcFalD9xG9KmwHZh+tkWXEdEo5ZvV7Qbz3SRIjSTuNY4b24SLeS1Ep5s73bqX/NYkcFcTsyLjTDqTWE8inLe3TisDpospB9dLK5HR4nm7fW/4uFndu8XimtJ4OnRjf6TiQKxTzBO/CEXOXPAs8VyfN9v8cwjBHgC8BvG4ZxFLjp+/6vPWPTmoZEEtfdTKosCzLLe2KCBD2Mco44Pvcp0QPIbR69KsHsEoh9PngG830d2Il95N1JpDOAlPFNn691ao0khsMSc3QLh+MjknIJzJUSt9+aYwaBYUD/ahrpl4CdM/YKrPAh7wIF7guDk6lR7qp5YrKfmEiS5GVyzJAUA1h2IlCRQgXyOkP7+CL5BzfJ3XPouTSMl23jgvppsPNYRKhQwtQmYvwaPHgA9+7BpZfQWRelski75yF340ASJt0Q7Zd+ihtiFktqumnn0PhVotl5oqFufrjvAiXTIFLUHFubg4Ukq/1T3Iy+xVqqSJfq4Jg8hyOWcCnioKHURiF3koSzzFvltzlRmmS2CCOZGB19Aw9N6LYlOBVKMTel6I20I3P/Cq5do90YpCS+SP6ejTMmgkmiLLBsa9tBU0QUIFnwBOnJMic6PO4VkrzafpcXUgbS2aK4VpM5xHyOyORVkG3oVy+iKiLgKNSVbCkU4K1xaGtDTE4ROXcasi7aiZOTBXJkcOh7eMLdreyL1lTG38HPvMX83Fkm+o6Q+63fJJWKNPy40ooPJtbAO8H9qmvQFg/PsFvdzWhBScFRM8UHEyXynsWdavZ2Qy5UT/h2Q+2zu8XP1Z9zBwWw1kejXXDtChw8JsgtS0pliGwzwu4a99jA5K3EpFm3mxAQ6RAwFpBWIS3OIlhQZT6R0zgiiUeajlCU0Gt3OawSnDYtPpqoEqVoGSufhb4euD8NvgEHBnd0R29FIxK/F/dt/ThXO09MOBxPWVQaqGNbFc3d1C+tg4zMB/eDeK7RlwMCsEE2BKdSKQ6nFL6SRGTt3RQcZ5QPq7GVN5louGjai4t0N6WtUV/YK7Y7RyMybiKDMUx4RO04w1wKxouUeCqq1qO4k7diN/W71q4yAoU8JPo2CPVWNfBJusqfBZ4rkmcYRiewHzgAHAS64HNW4VILzPHztHlLKOcjOlIWGZFFUUTzCWnGWWQFhzFO8nDdMGlBfydMegZ+zGfQCjE6+lcpqxxSxrdVbh6aNIQIwnq0xbGXejlQmcb3fayX+xD2Ni6qTahwH3iAoJ0HvCi62S9fxVYhhDQYFmMPDdAGkjbVwVrmOkbPATru5Pn0zhJDBzuxZRvQsU54upTNCa+MGBqEyUn01H0+GsozJd8nTjenOYHExpIiiLNwDcYcwWE7zHGR4H8EKoUSYc9jqTdB4kEGQxbpitv8tR9ZnDxhkD/8H4hZefIiRD+XyNlZVnSJ88thZo2bRCM5DCuNE0sxs9BGtC/H9YyHWnnAwdIRvpwOEdriahUCxk4JVMlGrhVhtpfcgcN44wWO9rTx7rRgZhr6qgG/Ow6aVeJ3NJ6i74DJvXccBqVL/FCMkCMfJjNSQjQK770PA33o3DIT4yUylQhOpyZVGkcsVEu2HD8BhgGsgdEGoxfQIYOP5Q1y4goLzAEQI7HhJqwv+xKPw8iph4M4VYmwV8TvOkjmyhKDx5IUPGvbAc9XEu05tCddVlwHX0m2E3Bq7uZVrZkYL1LwLGREoGqK0pMeWJuMnwuM214BrPXRhQz0D8HyInQndlc3dox7bALNBO4/dA/V+xNA3A4Whgu4xEgECS2ijGkH7/Q6UTJNxESV4Pb3Befazd3d4F43kXgerb5dw3FuO2K7B/WrpIKSGxdeDeIYj44EE/4mRU0JbNt+qP+uUKZMsfGiqYq9tNVOSluzeNTkgUZkfOsYJqr/eAJksxH23K8bYKdnWE8is9WM4fpr1fedesJXUtAVfzy7ngWeK5IHvFn3/x/6vj+92wGGYewD/gXQR0AIf8/3/f/1qVq5A4IOZJBMRnnb7WJOTXHI7kDjk2GWDtZYpY0MMw0Hg4BEGIyUBFhgCxC0EbIbbw9Vq7EXZIoaaEw0oFjFxGBFlDAvnkWePh4c0CjzoiEkSfpZ421MjjGhs9waf4OoV+aos4+R1EvILYqMgcAyX+GjXIjC9QITOsNM1CDjr3Bav8iaWFkfLDw5w5IToTObRVy8iBoZZMq+So9IcJU3KHKbXg4xLMY4U10xhqXPhPiAHPP81yTIyBN84jgIN0Op1+F1v4NjH4U5bBucixwjJyaRjPIxbzHPFDE9QOjdm1x/L82Mv0zk0hnOn7vPhVOKk6UIPfTyJ+mPOG6e4PaRbsYGz9HjOMHz0po1VaAoV4mITuxIG1qbfJQIU8jMkN6XQJdXOfuS5swRsV6uoplBcy2k+MVftln48jHiliLUIdECyhQ2qxxCwMsvw8e3YGaOsjbJdIRJ9oI3rSgXPeT+ap0/YcDFizA7C/390BGlLBRLLOAQTNTDvEiMDdV0vVZgTw+8+Q7MZmFwcLPCJS2E08OJDBSHkhQWR4hviQ2sR0QKzjjHyLhlEo5JRDbue/UlUq6qD7ji+QwkJTp9kmhUPJ2Btdn4vBq2UQBF6uxGHzU/u0DtxyUDjdTEUN2UsDHZNSC4j1BaRiDW+79GP7r79hHI8W7qV41YLGSDjMzaZ5shG/XveMc2mba7tdVWVfNx1LrHSR5o1Cd2Lan0hPEkSG7tPLslKMUTOz8fIYLf15cmSl3aXI7oecdzRfJ83z/zCIetAn/b9/0JwzA6gCuGYfwH3/c/esLmNYWw9Jl1NHddzZrTy+syRgFAW3QV+pnnRwg7REIMbFZQqoOmJpgsbGns2olWteZy6Sp5K4sj4mh9gNnSCrlwkeSKQcGcZp8oEBM9DHfsLe7KRpBihAkMDHIYqoSdKbPc5bCQyaKUwrajDx1XqoSYHrxI+MACb12Z40vLf0r+0wfcKH/E6S/8Bp0iiF+ZEfMsjIaIzce42H0WGRI49DDDFO1M0gG4pDnECBHRQcSGQl28o0uGX+AI9vC5qsE2GoHqDeYeQ9jYDLGGxzAXaWOEsILrD/6MP5ndT9fSNPhlho8l6IpKOiKCl/QlprRBdiFPv50gVk/wxie4nfkzlqNF/FdfYjT0OiXhM5V6gaQ6zJx5lX2r36cz3EtEbDzrZgdNEYJErwDsnYPUV1Zg3wCcOom5kCMhFd50G7GEiVmpq/Nn2zB2HtTw+mRsIunUcRbVFDHZR0xUCV7NRWuawbFT04EKODQI2fnNCldV0QqpEinTClzSOwzEQsDZlKCk5EPxZOvuNLFxvyYRijLPgHOYGTfPhUSJ0dFIw0H4sUsdbFHnGhLrRmigAAo7sj6hNFN6RKNReqvr79Fu4XHUlKYJ01Z3+B4KNNdQ36+jxOkgvv4OtGtJ8RkGtG9HLJohG7V3XFHg0x0ybbdrqydRWHn9XDqIJ5zPBC7IbBreu6xZLgaLrLOp3fva1j5Rf3+Pg/o4uN1I55NwSW+HRm290zu71X0sxOeH4MFzRvIMw0gAfwcYAdaX1b7vf2m7Y3zfnwVmq98vG4bxMTAIPBOSVxbQlfL5ghK8JwULwqBfm9x512D1LcnhlRSjl9qwL6aCoOw6VUDHu/mQs+QWRMPAzvoaqwCXr5f4fs7nQGw/hZP3uPdREiMXIbN6lSPtZe4n5jh27BxLwmt6payre2BI2rhImNOcARRXzQI/nLvNypVVooMHMFf9wKAtvT0QRwRz83EOJe+wfHcKKxzFvTVJ+cICwx0p5smyoN8nP3GQT7wCIafCWCpCSpxnmUE+4Q55DGwM6qsxSCRxurlHhm5tYo5fA2+xbkeJapybNjEKZSJyFF+UMZD4QFYukum26cxVyMf66Wo7ju1v7FIRFiF+8fBr5EqKTstkRSgEEqFKlDLTrGQn6b+ywiw/pPjay9gihiMk0/YCYWbpDqW5zZ8hMLFx6OQrdPA6IRINB83t4pF2DFKXMkhq8DxEwmF09RPK83OYHf2IsdEgw7U+ns6OVFWCAqY2GR5vZ8UL0+60I1JBiz+0U8rwMNy4FRC8eCxo5/q2rk74gu1jzuqxdcDeqjQcSRXIiAckGQrqjokoInWPIZXgrLQINRiEn9hWS9V72dNEuxcFsEFhZI3mhh7ng/EVtOdwxj8dhuQAACAASURBVDnW1OS70zmfW9TZGijJVSVfuxxTlzgkg8VHUAqlQSbvZ3B/6yqakES2lN5plmzUXJj5JjNt6/EkCivD5gQgL4jGQEY1N/O3CPd5zLoOR9UxotuVF9oF93icZK365CRweoOQhmazXp90N9gLidzOfexl4I3vwPe+HYRodHTBX/oKvPY6OI0db88EzxXJA/4A+FfAzwF/C/gNINPswYZhHATOA+80+NvfAP4GwP79+x/f0m0ggYQw8GyfV+hkhA5Qgjce3EClP2JaHaDz7SJnT1egI7RJFSjdnyfnV3AOWBvxRzJQWbQpGf9IrBfQPX7YJ7sUYp9jMekVSC320j4fRYo59FQRL5VkyHMpqxmicpCVgmSNnWVmjc8483iUcao7O3TQBkQ5V7ZZ6o0Rf2EJdWWeyp+PExqMQeosWhjrxFAIo5o5Knil/AKX/9DBMtfw10w0JgJBNz10qiSfeAWGkhLl1mK6BJ0kOckrKGaR9GMQoYBf1TwNNKe4w8c8UFn6vA843vMSYuoBevg4H3fcZElnSI7PccjrhXiC0kiKsA0T4n0ywiP9msBigPb5Tl4ZjGDbm7coCwlBdySImcuRIUKU0/JlrKhD15USCwcsOpbasdwyJdtn1O4mJyZ5nz9ghiJdJLAZxKBMjm+yyLfo4+tYnNp0nZ0UlHqlL6od1pRE1wY6IQJCqxSsriH+r99HCh/mJuH0cejYrK7Wk5daHKSZrAuax1/fzk2nXUrzZayeDsTY+Wrm883AddsoMeER9+bdFBPjam6qj0nb90gzyQgXGSa1KTZs41obE/+jBmdvF+y/l4lWIygNn8WiFMS37jRLNUjsKKPIqhx4h2hPumTcMiUlt7V/k82ax9obe1s7nwZp3HL/Zup0oORrl7nxfVQ8m7gjeGG4ri3TmuLlq7QXZ4OSO6nzO9rUTPLGTmRhL+R+N9Kx6b3dQ4Hk3dyhzSao1N6JnmrY5OkXwexW3J7wWHOTCMfFkIptg2J3wOMS0YeTk4Kft3tvn6f9chspfzevw+98DVbKgX19Q1Auwb/9Jvz7b8FvfR2On9r93J8FnjeS5/i+/08Mw/hN3/e/D3zfMIzvN3OgYRhR4N8A/63v+0tb/+77/u8BvweQSqX8J2l0PYK6d4IgbzL4eVUWMJ08twsHGLLvsyz3b9Q/q1MFrP5uumjngevTG9dYqwrevQ4LC5QiDp6fIpkQpD2fgpjn/kAZP5fgtViEl22TxJ3rKHeOvkqF/oRC9oxSNo/zybs2//qtILv23MXtC1fWdrhIYuHqIkotI2UEJcqY0qQ78SPmp5foMWYwh16ArIdWinG7tIkYCmFgR0BaXRy68DMsp6fp6BvCtjuqz0jwijxL2Cmh3GDP29rqyEAQZQwbhY/FFcBjDQeDYQzSrGBRBDlEOv4xB9/8AYYRQt+4ytLYMt2qi4p3hVLPMW5dXiE3U8Ya8vFS8/SJJH4ozakvhuhWHUS3cYmXUczrDFmVY15ewRdw7tWXGWCV7qU5wnMlbvzhJDkjT8fLceJjv80Lop81IoQJEZTGkQiGWGOJNF9jiN8lRKLpCfUgw2gNt8dtxr0tyq4QgTt2eTmodEsb+GvAw9160+As01ScWFAEuF6Bchx02uXDuX3k3pPEEnAmJQKleSEXuCXTbqDs9XSvu7AfdW/e2so4mwYRLaFMj7NcYo4pDjNCiBACsSOxqSU77CVWb6cJvdm4o/rdF2JOJGiT7S64TWKHiaRHxph23C3lOLZcSAXldz4WExs2q5OIZpNFmoHW6PH3WfHmmiJVe8KW+xeqwrCdYkEpKp5cr28HdXXwoiUy+bdZ7NNE3VscVEcJ2RsFt7dz829H0HYjC82Sl2ZIR02hL7DMLSb4gB8QI7Gr6rWTsr8XEro11qy7BxCSc6l2suouPTKG3G4nmF3wOHF5NUdAZxwWveaSk55Ehu2TRC02r6SCbNzf+Vo1uqVOsbNkQPbyS8Hff/t3nw9F73kjeSvVr7OGYXwFmCHYd35HGIbRTkDw/sD3/W89RfuagsDYNEy0CUnqUoiKuUIpdwyz/zRmxKDAKlK0IdbjgkzKQLFQIXf3FvrP7yHuTcKll7DcLAlH4Xo20fgaeavMpYMWU5USoyGTTy6vkJ/ysQcPcTgpCKXOQ083FSXIzkBbW2BLZnb7F8bUbURKJulwkQMTtzG9FT5yMkylHBxhcyy1iB7uI3wjjZGNgNOPku14LK7XnVOsYWrBglLEpWRk7GWUUkgpEaJt/VohIRhLRdYTKpRYW9/hwkDgI/EokiFMH224+IDBACaTxDHEPHLkDJOz7+MNWXRnbxAtDDNDnqH4EPr+ErnKEM5gO1lP0FHoJismUXKeB2KNgh0MvkGWymY1ql1LZsf7+cDLMeSMUkjlKYdWkK99ETvrUXzjQ3LFHhw8JmfuIJVPp921qYDzetvTSYVplvkuXfo/pzL+DmGviHB6Gqow9YP6RrHXbWo92ZLKyyOEZ+YRA4MNt63bNDiLBOHUKKgKGWXxnT8VfPsHsLSUojukOORI/vJBsbHCri1A0i56LsPKe2/TnugPiEAtSSNZTfTYZtu8hu+HCIKdxy9DIW+xOLEPkZoiIYaQjWISGxAbYUf2FJyt0eTIbls6ptmSJnuafLZx6woEI6JROY6asRsKWMWxyI3msMsD5GSasvSRe0kW2QVaFZny3mUxCV3uJPvUcUSDWNtHQoP7Fwji0ibmaGZdFSTj2BuZvL65xu2JNSruA244SyzJK5zji9S2MKsnWkdTiiWxM0Hbrb2aJS/NtrvWcE19yMfyDfpFsJNJM6rXdsr+nhQ0oTnaoMTLiEhRth+x8HmdfXsp+VNDfZt1xeHFLwTv624xeU8iw/ZJov4+rr4T2N/dA6sr0BaqFjKoItoJ+Wl487vwn3z12dlcw/NG8v4HwzC6gL8N/G9AJ/BbOx1gGIYB/BPgY9/3/+enb+LeYSDoDKX4yy8pSiWJZRlMiHmyKDrQvCwGCdkRFD4eqxRCHvfLWez9XaQmQUxNI4YGGR2VqAqYVhvjIswUBfosk0qhxELeoueQxJssUDmWJFRVXCwJyYGg9pNhBNsdbVcnauK6QT7XTWd4mRPeKit9nSj3Kn3qMGl7kSMiSrjDpW0shaFOgYwgRbD1WZoSUUzaNPzR+E1mPMWAI/nF1PH1BI0g3m9D4RQCLDtwEbsowmh+igFCCMZ5nwzzzNEPHCNBGzYGY4QY4RigwF7jw8GPSWbbSMfXaLtxgZWFEPmuEFbsPWIhhffDe8RfOsipGyvkF4pMOguYqVPBJva6gDV+A9+bwXAGMFKBxFlRAsc7w/mkT8ZdJaLaMG0ZuOmkQ7i/m9iUh+d3Exr4f4L9fLeBxmeVTub5t8yqfRjeW4ST+zjkgmigwtQP6tsVew3OqwOFZ6xIl+rhhKzGeNb9vTYgby05cX0mxNf+AZRXAtf/YJ9AlW3euA4//AD+q9+oKSVBYoLOZpl6749Z7Fuky50KiICMBApeLdFD7m0UrpShVIRkn0CkT3Ai20u8J44QAkVh88S2DbFpNq6mRhpzZBqXjqmimSSEPU0+W8uu1LWPQAT1Ahtdrk4BC6Vd3Mv9pIur9Dr7aE/ZzRd+bgJl6bPorBFzQ+ScVZLS34NGswu2u3+hCaXGaVc5QjIG1USloA6ejU6N8Imao0emUKK4Tmy2Ei1fSTrtnQlaM+11kGEAJPa25KWZ82gNE+MlPvDC+PFRJkeuMWYf3H4f5SZQI6GedrFUgnbZoLQSdYtD4dFp17YF3Ej+2j70YA+1CptN0qlDo6SFUGj35KQnlWH7pFB/H29/D5KDQXJLpRwkknQnNhO9mBPE6rVIXh0Mw2gDjvq+/6fAIvCXmjz0VeDXgGuGYXxQ/d1/5/v+v3sKZu4d1bglQ0pCwiYagQKrZFEoPuYOaSIc4TwXkRhE0bxnwhHZztrUA0oXXyQychJsG0OADBXw13NSNFO8z5pcYz6xH8Vx+g+uYL26MaAKAafHfPpHVgHosUMIYTxko/IU3rykLyFwM1EqUQfLzSKdAabkIg49xDiLQRlDSAwJKIWQklER5zIr5BH8QJWY9hSHkjaTboF5VcS2LUwNE6USnhXGEW1Vp0MQy+eiuM597rHMLB7/qT5MTj2gTw6BmOVFDtKDTW2HEBuBwsIUBu2pC9xVc1i6n/KbNv1JgTdZomKUOfVaN4vTk3QejiLen6EjeZiYmyatpum0BwkrTcl7i7VkG23uJJYawbA7sCQ4TgjS50lES5w2LdCCq+OaOa9Mb/w0p/96hYqwmLGv0iYGGzc9Pp/iskSJEAt0yUUGnX0od4qKcwCrgQpTryxsV+wV6sig6GPBdilTXi+w3cjFUxucM/PwtX8Q7NKV6N44X8SCo6chl4Nv/AV84UvVvwtBuUeymNCbiUB9bOCWmLxmJo5ge7Yy3kw78cxt4u89CLYBS53FFFvUFfF4xKasC+TVDI4cBNGgdMwesOfJZ2tWahPQMkzFsQi7LpVoktjSMPviBZYzHVRUtS7co2S3Nogpa8dmZXiMB8zQafcFz/pJosH9l1HkhUe/vVmdqvWbEfEqvi1Q5DeR8UZEazeCtlN7bbdf8na30UytvYJn0d9jM3E5wsDMAfyhE6iUQoqH3bDNkCuB4LhOMTFeYsmzuF4tCL71+o8SM/cks3q3w+Mock8zw3Yrdou3rL+PlZWA2C3nwLSCWLy1VQjVuXLCJsx5n43tu+G5IXm+768ZhvHzwO/s8bg3AWPXDz4LbBO3JGnD1ppPVJZ9coCSyK2/lC/TxkfiFndOzuIf6uJFOQqiHR9NkXFWyFDAYp6j9CG4gssL4hjzqQU6VZluKaltC6bxKbDKNZHjnY55DHwu0sMYzjphqtkoMx5DuW5ui/P0d7djnQy25DpphjikckjZTVCIObTpvrQTJ5c6SV5Y9CGYkWF6nAiTboE+x+K2VCzqHM7Ep2R8SZ+0cU++gBICG5C0EUZzj2UO00lGF1gbn2C/dw/PmSSRukiPiKzbW58cEqedEXEc7BNIbXPdqdZTGwgTpovb2bt4gxahnisccT5Burfoj79GVJ+hS9sYssCaYxBKa1ajK/imxidwHZ8YNfjBeJliMcyH44pjwybvZu4gulwmvSRHQ0eJ2oI23UUhv4btg2G3Ub8lW4VVlinToUPMVTroDXcymxJ0qSHC8qWGo8mOxV7rEh0eIkJ1KkZtwI/pJOmKy8Hwxi4T33kzUPDqCV4NhhHUQJ6eg+/+EL76c8E1TQVto6PMll2iso4I1GID121rEEPWaOLQGjFxlTP5eUrtNlZiGdGX3OSKfcg1JHi02DOtMcdv0efNsuhM0zmaIlzuASl2CKbbGU9r8glqXhb4VNwgn8oFO7S0DxP/xqfk3lfEhySWeYxHMbxRTBnA9XHBkpfCdkocT1l7zph83IK7tbpyq6xyk41+c4ZXWaG8iQTVE62w1NwUzRO0Znd32IkY7dbuloS4IyhPn2TIrzC2L8SH2dssq/sk7dj6u7CuLGsPSyU4K0cJ7fAAK0pQ9CLbhm1sfabNxsztdv9PIrv1eVPkGqGpeMu6+xjYH5C6sBkQvLAZuGzrUSlDZ9dndw874bkheVX80DCMf0iQYbtelMf3/YlnZ9JjoEHckrYlBa2IjPcR8Y6z4OQ5loquS/orlNlPhdPiGIu2R5kKIdrxUayQ4VOyeNyjQAFfn2FQdbEgPdbEAQ7bFtl1l2hAhh5Q5EcsY9KGgcEsCsUadq3pazb2JQhxGzHiBDO96EZjUbo8gcxnEIm64PrqMTrZw5T7JjNqliX7MCUOMygsXk8dZVGVCMt2fiiy9BWgkpmj4+BJ3HwBp1RBRtqDyV4pfkr2MSs8MigOqBCdXo5Y8mXK7hSmOoGoS/mvJYf0EOZ93mEe2EeCEZGqmwAMsvoEN+e76OsW3Az9PoupRboLOco3vkT5TUnEKXEhJTFGx1i9/AZt+RD+xEdcSb1ARpS5X7nNXMnmrHub1fsdVIo2Mu0RumKzOuTimwNo3cGdD3+Fwjv/N72rqxy4MM/aWGKd6IUJYWuLK/c04dwvsD/0KmdPlZG2BC0oFjZWtvWDoNAgq35tTVXACgf71tYWDCKVYlg0jpExkXRoh3fuuazmHGRIMnYqOPe3fxC4aHeCE4Nv/wV89WcDMi88j2EnTjn105iigWKyJYZsKZULFMbtJs6qO1L09RBJu9AZ3eyK1RqhSsg9Zu02hCohvBz7ki+TTM/wyeUzjBcblyh6lqhN/BmmSXOfM+JVFu0sK4VFzvQ+oHQsibX4AKGGAqP3qGg2iimDajZmUuC5ESqKhrtHNLR3L9mPW5KNGtWVM4mgyG/ES+oyhrJrsR3rqBEt9QTKjjzpIr81IvDCsKD3hkUmqxCOR690WKyzsYwipz2y4weZ8fKsOaVttwWE5tSwR4mZ2+n+99K+u5HBz1KRexTsaXtAG770c0EWbe9gQPa2xuRBcL6f/5XPxv7d8LyRvFeqX79e9zsf+NIzsOWx4UuTNUdScR9gxROg1xjXV5hRi8xmNHRpZjLdeIV9aJHBkA5SSBI4eMyTwKnuZBFsGbZKFI8r2BzB1HBm/A5RT1BxIkRTL5AV4GAgAaVXyKkFhmQXd0WeMmuEEfQjkWwkQCAlOA5ld5as00FPOIZHmYJe487lCgvf19gHDnCOT1lTWUy7ByEl2ulmyZ0k50BMDqD4PoIpoJ+QOE/CttH4QbyeVOxzEly4N0s50YO0JEL762pg2HH4auo8OVEmJk1CzvvgZpHOEMjNb5ukDQeTaRaYQuBzgBk8DlPEFlEs2+ddPc+ViTUeeJKEU6EzZdAtiniil+JCmXLyGtOuT0UZvMoIRjGN0bePkpsmpxJ02RZpK41vDfCj+1Os7T/L6ILH2dhdMse7GVhcQZYha6xSnjtPr/9PyYQHSc5mmVI57tkVKmjCCJKlEHLmJKPxXyLnhaAUAmtzBXUDWFyoqxFWJXM67jBupfCWBImwYtTzqopXsGAQtv3QyjuYSwUHSiluzxTo6Q6Ko9bC/xaXYah3535rhsFbYNMiRbguUolgC5atUCV0Zp5SV5JwJk2X6mDB3qbIbS3VLh4LiF2iB0ZPQ7mykUTwJEuEVIP/hevhRwdZzFs4T2OLtMdETVXpZR9pJnGZJsEgpowjEt1EPDdg3zduBhnPe3w29UShy9H4MiADsTh4kxViA2GsRruRbJMN3nQCyjZlZAQCoaGo0sTlIJ5wsXSUWeXRbTrcmpAs1quOYrN780kQtEZbralmimHvdE4B0Q44OwZFZXJXtrMoNttoIrFUghkvz0BSUnC33xawds5mizI3S3Rr7uLjjD6kmMLeEk2edqmTp1kvD/buUn7t9aBMSmE5SLLYivwStJtw6ctP3tZHwXNF8nzf3zEOzzCM3/B9/59/VvY8Dnw0BXGFT1IzLBXaSd4ocOjN77PqfEr/ubPcnHuPxSv9JAZd9LXLLC4uIZ19yNSvkxLnKVAEwoCBX01Z6OQiWSJ8yAr7lSDqZQkl+wm5LmNqBWWHg4Wv9pHj77Pfu4vndPBK6kWGRaya9Vsr8VGFEOjUKJWCx+SNEDNv+vQ4EdaGBQt5k8UDFjcnl5jbv0yPfpNOPcBx8SITqcMsqDhL0iYu7rCGZpA489VoQ7saQ5eiO8icTfUjVJlQbbIoFDZUznSaUDZHT091h4ltYr2A9XPuJ0qaMmE8ynSj1wfpNWZVCeGF2ZcU9Ll9xNXrLNrfJy7bMR3JuKsZcqIsy/uUeAHbGQI3S9jpISYFWfL0izjp1DRHyx305dNcd1YpIkkuFDiauMiELJFhmbleSeTer2FX/jl3+u8zLfsJaYu4KtEm83hWO/+x868jPJMLsWCurB9AZ+4DPgwcqA6m84pI9bmU77vkIorkkI2bkSjbQdx3MfsdxJZEB61h/DrrdRTPnYSV3o95X3n09TuYVhCI3dUBpQpEwpr2FcVKu8Q3Nj/jcgW6OlhfAOyWXKFNiw/nBsldUcSG9nHKfIEVyrTrBkVuJ6oTfjwGl17a2GavFoldKDa/n2wzqAv+t0yL2MRT2iLtMVEjLTmynOJlDjESxJnVJy9oHdQsbObZbFXPqkShqDR35TgfCo9OHecU7VSMJSxiCM6ioamafE1PjNvtD9zAjV6ZSLHirVCMmKi8WN80vqg0k/bDsWOPku25FTVi9KTj04SAqC0YaWCjQHBWjrLmlChsKSG10/me1IKkmXtttn2fdqmT7cIMniTp26tL2UkEdfB+52tBFm3M2djFI+cFBO+3vv58lE+B54zkNYHfBD4nJE9RxCUnunGES2lBUUlCj/spn+YKHOn1KB8LszY3RyytkQdG8N27+Goe7AQfU8ZjiW7aOcmn+MxToocOznCRFXIyRMW5Sqg6AQsZwa6RN1VEePMcS75A2Z3FVBGEHW5op0YzLq4yI3IsL+wj0bOPzAOfieMrGIk1bnGC/v1LTCamOfqWRyU2Qy51HI8yfVgYVDiCYAGP9/kefezDxFw/f41YPhRTVSMQ6TTMzcN7H6CdHkojZwnbUH7YU1NntEFUhTkrj+GKMn2Y2NVPmhhU5A2mnRUst48xJ8FZmaLEMJYAPyUR6hrL8j4J0R0M8KkLFNUSvvyIA+JPGETzChe5GjpB/jWDVaWYl3fpI0lazdAnj+IJRR8WnCnR+8LP8D/5qxyyrzHM2yTG7yG8EPecF7mS+gXMU538f6UP+avWKEJYmwbQRH+g5K0Ppt0bxMrsd4hZEteDeExwzU+xVFbELPnQsKxKAcFLOuB6sFRRDBzxeKGcpGi6rAhFCJuvfAH+5bc1J1bHiSqPvHS4P5jaRPS8HPzKz7Er4a6hVP7/2Xuz4Eay9c7vlwdL4iC5AEgA3FlLV7GatbC6WOjqru7SXN2rbeQry+GJUWgkexThRePwEhPhFzvkJcaeF4cfHH5xxIQnZqzR4pEmQpZkj2TZWq/u7equ7mKxa1+6urqKOwkgsZAAEgmQJ/2QIAiCIAlWV9+u1vX3UkUSyDzIPDjnn//v+/5/QWFgAnPCwSrq1BxB2O+n0r4B5KqEWzd8IfYe86h+su1To9NTf6P4X/DDrw86SoH9vqBlu3lBqe6uzX7smQCfYVNqpDmL9gJb+SDhcU8cW9kVHhkPu9Lk63pjbLmfKhqjqkKElNdZvp1GH0wvU8+dZ8byM5T07zGN12Tn1OzLdHvuF6/KdaI99hujXwhSDQmpH3atWjeftdv7+1VLnbSDyEoZPn/06pnDo4LoM+c9HbwP/tTrol2zvBq8n/9lj8F7XQAefPNA3uvZYNEhNCRhkkR4jiWjDJqCQPoeo+bb9MUCrCXOYVsW/pGTnGEVN/0czRwFGaHMOlmqDCBZpUBJZei3IwRlmriokydEQmiEUilcu4IrQRMtF6cBoEQ6gzQHQO7PhNgNP9gRmeBFNE9+1scJUeL50iojbymiG6P0Bo/Rd3eLarRGdGGLyBkYe/I5VWuBYXOZQOpt+sVj3uAdbGrUcXYZnXeMbQCRtXBvzlKP93Hre5tUFqusjc/Rn1okIWKkuISmFK6dQ5MxXPyNJzuNftPPT6T8GIImO1mnyqjY5FQqTtHO8qas4kfSUxZNJuoap7A55QE8pXG9mqcs8yTFU8YRBPEBa7zPRRwRQjc0ZimRxsI0hojRh4kiTZWE0JnpybLGAAGOky7/JKetWTaSMXrTObbsMP1GgI2wzZ+zxi+pY9jV3abYSkE+3yiF9O8AKyE9MNckcWaFx+pZe0kcGfIYvLTl/RsJSSLCZF2mibSkin7mGvzx/20j8hYbZpLecppA3aYW9Fa49RLoAfjp91ru0yH6dyEJkYTAsjwh5e2Ffs8GEDsAwLUyTy/ZSas2FXevOxRKOpFE5y7EgxbzJkDUd2y1tr2kX2YjPio7dChoOUCSZVfsw55tOzA0PWPlIAEz0LwfjnQ9ZxTipFlkXJ4m0OjyFWZ8D6jsamPcluEpV7n7IEThg0Y95HQI0Uij6+YQgVh4X9N4DmgyelXRlCphFUkPgZYH1a8qvq5atfZU936+wd2M76turGhfQ+D1EUk2E55Eyusgk3JQfNNA3lfmVPGqQ0Ng8DZvcY6qCCKnXao5P5uxGr3+AczURXJli5CWIBzS0JxcA+DdplDJoYUkK+INEsqgbyaNa80SNEd5O6VTFT4koAmXivGILSx8mIRJeT6sXTIw4PnBmsTIigzvngtTKWXIjgUJZ5aoPDhNzXlGWI7z49pJ+CCNX9MRsw84W6lRGZxkIb3KE/sZ60aMMA5Rks1FuF0Xb08IgRuPUjbTzPx1llvPzjKUdFnK2ByzB7CMNSpqAzHzf+Bai2jmKO7kr1DICMx+Byujo5UFVeF12znCRkcnQpx1YTFgmEil73Q4R6PeafN5DNNETaf4wcM6f7Ve4mRfDP95k0GRJYgPP8P4CDcZwstcpEKOMDF8+Lw0dMPK7X/mCSZBbAo8lTdZMv8UI71O2ewlLwuMchWTfv5ILXP6/rFmSjXVsL2Zfciu34kWYLVNgCq1G8S1kzhC0LCT297/OzNDiRj81/+55Df+GxO5lKYWN3H8kqrjjUEPwD/+h527b/eL/Rb6Pb8HmDzt/XE7TQudmaejpmiVonr9HoW/VpjHJFlOkbcdokZ3qbxmWiijiKx9xtTAEpgHe0kfFl8JO9SNJEsHNrQVcPYQZYprSGF4/sWNSaML6CHKXa7jssX3RI5YKknU7udNeYFdOoxHqZMSgqoIU8i3bM6OINwCWIUQe+bQjpbaq0nNHjhEBGeY5h7XqVDiCbNfiaTIUaJbFrjbe9FJO7O9pOJ8SlEXO+fs5thC7LhBvGqg176GwOslkvxNiG8ayPvGMHkALhoOIcJKQ8zeImTVqZphgqmL3OI+N55v4hbKXI28QeqsSbmc5eNnVW6riKDZsQAAIABJREFUQeqRDO8e3+Ky3Y/PGsBNTuCmC1TtCtLoQ6ChqFBXGep2jIDM4Aq76bWhhEZZ6lDVOMheUyBIcQkbG90I8LzvX9E7t0ZRL5G9X+Zi3Q/1z6n//BmMtTSMDqPSWao9PrS0Rda8gC4nCaHxJv3E6G3IBLjMqE0KdoWIDJMS/o5AzxUO5XMJ7KUxBpI10vOCxMkwBblAghgh28axFhHJk6j0F+gqS2Qti3XLpm9Y8vTeWQpFWDGfNNm/6dZi4tYO5/l5zwbs2DFIpynnyzzdfEhgbJ67Vj/HqldIhL5FzdaQ0kBrXDQXRZVZXCyqDTAtEM0O5QIOOovc5ndRYotQKo5hJ3Fkhk3xr1lUf8FZ+x9iu4O7Uqp21bsG7b/btYc3ZFOElKTOiwNJnPb9fz9m6PwZwX/xT1L8xfds/vVHkkLaq9X75Z/zGLyjALzWc28/UbdvDGGDzkBuO/ar2zpK2FVCpSyRY2Nk50oUjt/lsVwj0mV9VTMt1O9g3bKpTiRRS1nWnBqDp0Pks0dnDV5192bX0YHxc9oEphutD7vKKARwknNYLBMhzizfY1ycoWhs7NZhfIli+05pPYWgSrih+uk9pIXaOs+bDwyvMDW7X9RxqFJpdvjalJtNHj9ssNctC9ztvdhPO7O1pCKbVtyxZ6kZGfowOaNSu2tq9zv2V9x80c4ovgxzqJSX6oWD/dv/JsY3DeRd/7oH0G206rkl7E2mrDT3kjqb6WVq9gvSWgFROAZmlsXMEMEP71HJZ5hfr6GuVQnm4xRqfhypY5gJ3HSWR2aQBbmBySYpYrgqyJ2HCst+gSl7uHY2CMI79ycqx0cvHLSCzhV/hAvnK57yQgfRUA+wGJRFiYVUnCH7JLXNFc481Cj73yC+aSHDAkaGUdksDxMZFqajRJ1ePpMTLAmXUQJEGgAPwFaK+sxNTlg50maMcupthKgh2xZMDUnYiNE3VsDNmJw6oTP9/gR1MYZEokmFZo6i0l+gmaP4hGRqYIHqxCBqbZmZ1TfoOQbL6R32z8HB2N4QWpsHhoa836XTEI0igmWCfVmGrBMMRFaYDho8munZs1i5DeEWoZKsVy30kI2/oRXnaZvd5xN+G5cam9RQYhO/IYgRILSZxH99jDulT5kwf45o3142bl+Grk1nUaRSGOFXszol4oK/93cN/t7fUahyhSohQob40ovfvgv+QUDuS9bhbR9DJGJMsUDheJhH7+cwD5JxaYsmCMnoREYlwfwS9wojLK4HWUp7ns9HZQ26ag7o0sv4yNGG+LsFnBKDBKMUyJBUw1TsNH1yeJdrw55i+7IiLA7+DJ0Ymabdlek5YJSERY8y2ZxJUWz3a/4KY/uhJCglfcK7Rj1Eec4DNsh/ZULBB0W3LHC3jQ/7Ha8VfBtmlarMYDZeU7RtCpZx6LF/mD6zL9NpqxTc+QRmP+JQ//a/ifHagzxN0/4913V/HcB13f/s6x5PV6EUtr2BJW2SIsyqLDNjajxPf06/OYgrQ8ToZSFi4VomCamoZ7NEhweJWasUVk/iG5CMBENIEUClpsnZaeblJgMi7HnEqjpk1ymXBxnrC5Ip1ajaNQwjgM0WyzUHUdBRZpFb+Ud8Xr9JUNc4x7uc48qeBUvhcg+dzxhlnhUuG8NMXtKpr1joQzFEbxhSF7FtiwU5S1IMMO+3iBDmDEGKnhcGopGi1e0KppUjnUwQS6/xyP4Yy7Ax8ersts+vIegRKd5J2ThlHUkVIUIEtxc0IZCpX8G1c7gySgUXaUYIL79gc2AIvR5kbXWLwUSIglwiQawpO7P9flIplF3Gkd5mJ8o26sF9+PATrkWzLExBIjSAzw53XKw0JJoy+eR+lUJhnJGI5O2G7pynOniTdVbw4yOAgZ8QLpuUVJ7T19eRf/0G+WML9GNx/ppCq9nIqJe/dLCZPi9xqmLvHmnbkMlAf8T79yB/2Bah5KOsfps37jDzgy0q/gixq8eZuuAgDLlz/g7HO2ihrbYxA3nb9lKmBwG5bmvNDorGMYRdJSKDRMTskRi0HRAiCOkTVHPjFG+GeHtKsLIAE+declgHMVD7NEh8FSEUTNpncaTbWeuwZbyTpHBUmcDMY7asDAEz4KV1G2/ZxcpFFaEHdyB/+GdoZWQqZW+eROMwv+gQniww0ptkxbaoWw5DSdnsrPV9Sd/Vg2L3Q4ngfCpFXdgoFHf54JU3Yhw0jtbvVLegvNvGh/2O1wq+gzLEE7Hzmn4puzp2+xiCOnuY2FcRL8MYKuVJSK0udeff/jcxXnuQB/z3wK9/3YPoOpTCnfkE3VpizOxhLjWJTyiyqfNgH2dWbvEtIXmfy6TOV6EqkUGXx7NxKtk1Tp9N8BPnjyPCYAgf4PKAWTLk2UADzhJXOnLmPmQs4pubZIkTNwxkY+OU+BgKBvg88hm1fJlI/z2CAc9GzGKl44JlA3nl8v7MBpa1znGzB3/qIv5t/bLGt0kaJiZx0mQYIoZSOmlbY1Bq6MJlBhcLF1OGmDbjHEtbKLOP78sycTXAgm0xLiskhQG4TZbDj8T/qPOGpwk/rpHgJllWVJ5TlJl0/dx70sed5AZO3yaX3zK5aMcxZGxX7RCAEvDIeNRMVZwRb/J5fg4rGSGWDnPFTSFFAqTouKhpCLRqCrvgMG7qZCxBuarwh23W2eCx+pAeW1CXQXzC13iPn4Ddj1FaZ+HYCsm5EzjjNwk/GkDkPaeQRylYF3n6hMlkuANToOte5/GtOzA65v28z5zr5KxyyDSlnKly//tb3Hw+zIhhwfcfUl1bJTzUEH/K5/cc77CFdnvBz6YVefMhj+WClzIVKcRBQG6fWrOj1n5td9FOksJWnll7u7vFfsfcASGCUDxMJOFtEAOjr3BD2HYG0UNUc1VCGU8U+rA09VE8Rjuec+YOwsp5HsCpiwdeD4FA2j6w1vEnR/aOTShOp7xrG1ZV+H6OSn+SUCbd0Y+5U4SkpxF56zq4rk7k3hDhC8+Iy0E2Tb2p5/dczlD6Cm232lmomi0IG56kyqtKtR82h7e/U/mM11V8+X3w+7urQ+y28eEgVrl13re77XR17FagqMP92a8mdXtUxnD7uuYysF6Ara2D/dv/psZrAfI0Tbu735+AQ6RbX69w7XLTD/XEao18dovVuI+i6CEux7lo+3lHmviFj15hQGM9PJu6gm3bSCkRYkesuKwqfPpJnfrqSfyDa/x4qpdw3g+ZPGIwSWo1jX3iBDIebb5PoHFZ6AwcXyZUHWMu5PJCOCiCmAx1XLAkYNo2T60FqskIA+l5zjhTCKNn1+t21fApyb0ZjaDloqKK8jkbywiSFD7SApzU2xh2FSV1otzhxkyZ59YQS2aAy9M1+pyP2ZAb9IgEx+2zGFYOsU9dVpk6N5glWLbwrTxnMPG3yTyo4juzgVHspfbRHfwV5XUBtrEJ7amKgtzCMntJpoukzV7GZbRZn7TfohYOCcyIzlLeJhHRmQvNsoHFh+oWl2a2GLIMXphbzKYUQnhLaFkKrESAIW7Rf7xE+a0o4sOrkExST89RsjWixvj+TIFTg4FBmDjtqRg7tc7O3h2cVQ7qiN3W1FteCpHPRhgO51gq9DGSWCI0asLCnLcajo/vOd6+TF1j09he8PO2zWO5gClamBBhdF9rp9ROR+bLND4owdMZY89m0y0bcNSuwa5AWANsqUzO0xVMnCKSGWGKhm/vPmnqo3TpdhzHAanyfa/HPsxrcyzCos8wObM5zf2mRuIIU3oI0UUKWgiPHc0sw9AIWB+GeHNN0j8SgGmoOuBKm7vi1cuatMZ+TNir0uHrZr5VbQ/g5bJw75bXXXjlx7zmqW4+b7ddut3UNba/putjN163zdB+Fanbo8q1bK9ViUHv529/1xvL/1+T9/XEAPAzQL7t9xrw4Q9/OC8froS66eKu2qi1ObI3a2wlTpOfDlOb9bNh+bHMIv9uKkIQWrrLfBhGDwpFmXKzdq1e1vn8fi9bPasE7sb5SCj8lSDHCzHOkkUk4hjx2K5Zq1Dc5gnrYplIeIlzvMsFzqE1vsAdjbzRmJQhymYPw+kCWbMXWwYxOi3YSkDVoLoFBctlPbbJw5klahtrRIf6SJ89hSl8nom94fWnvlm+wB9af8liMk91NY95t5fx/KcM9ib4XqqXZ9JleGyI1MIKoi2d53nwVnDVOrEHBXpfrCCff0Ri5Ds8L/ZCzwaDpQ30wRMdAWKAEEH6sVgjQpyI6COQusJzu0hE9qMT2JVe6LgoCUXP+RnsmoUeDFMUngXTx/ZfM2IFySd9nEoLlmw/GWOLOuAT8CzVT8TupSItrnKyWR8YiA7Su2lT3HhBnzGyq+apGTLkOUJkstBjeFYUnaJL0eLtKFdgeQWGhgUrb5xkoL/GOwN+Lss1yK5SHY4SREe0HW/brKIvupup6ye6I96LV9cXNSSRl2VCGmCoulSg8GIM89pJrKw40oax31N/N2xAEygJSbiTw0f7cNtA2BmVomaLveCw4QySCw6Qn7eJT9SxxATVt8cJx/cHRK0PKRarFMgSId6h5GIfMChDKDNCPb1EwBwAPUS1vNMR2fF67JNCb39gKjrOLo3EalkRvv0JlMre3D0gfRs2PJY0t1gjquXpHx1EZLPgVAkbYRSSHmWyYlvEpdn5O/IS0c6stYP5VqC8HyjqlmHuZr6FpMfg3bsFI8egUvrmphO/St28/R689rsXrWOJJSCe3Huvvmo3jdchXheQ90dAj+u6t9v/oGna9374w3n5cIXkaaqPUvYzIh/rFPrP0J95gZs7iWX58Sdr3EjXGS3X+clHSwgr30xPKgEzfIpFjigRJpngpvaUVaUTLsYp9JaYj/yAiaEIcJVj52oIM4AUGoKdCbslbSxRIMn7ZFjkTS7QQ++hYzfwY5ydYs6tEjEinv1ZW82QQjSdFaJ9oPcrFmcWGV16SPmU5PJiGXHiGNII7OqmdWSejPkFtfRZnveucsZV1OIneVa2qDt9jEhJ9uwp7BPHMFrqwLYbWDLYxOwwo/kN+q+9R3ihn4s/Mc5pv4GrhzFmxxEd6r0ULrMUyDJOL6OcYQQ/PlIijm1E0ZWP+zPa3ifttho3B5sNYTEU8jbaMD3eJie3iJpD9KZXWDfDBGWQISVwqzpayEETLluGS4kc/wY/g5pOUc2WCX1+jzd//Q6b7ib+q0NwRWGLNtZACJi+gLr+AfXSCoHZGURqd8Wwd88FoekUwrG9lO4BtXlKwef3Ye1zWHkBF98RTJ0ONTpg3+JJ7q8oxEr0CcmkfQ0hvcfeVkaiPwrnr9l8ZiwQE3Hu8AFZVkgw0gQWX4oJaTBPodE4kbkc1sIofcOhJsjcr3uw9Vwh6dWLtVt2HbYJvYzzwS4QptLMzlSpWOE9zM22M0hu3sbajEJeJzrgpYb3Jf9atO0sVsmzxgN1k7A9sMvYXimwyjZrFBg0khTFDvOlBDxK1SnZNcJ6nc1ZmpZh56cPuB4dUujttV39Uu5oJJqK0OxN+MFHcGxs517uw94K4bmgVMc3CQ36vIeK1ocZJdicSVG3HDZNHVJi3+vUbezHrDU7w7u4/0epDevKd1Z4KVoXD+DFEt/cdOJRGfCXOX4r+D3oXhw2lh+GJdvrEK8FyHNd9z844G9Nm19N06Ku67azfa9V1HBwxBDx2AQPMwvw6QCF0eO8GznH52aFG+k6x8wAjihjF3IYyQSks7h2hbJRwSJLnAQfcINnvOBGOEfxZyd5WrSJxYuknBFe1C3eipd5YG6RF0VMdKY3I9y/XqNQ0ulPSKKpGGlhYTJC+BCKXuFiK4U+c4dzVhaViGFMn0TYTtOTdLvextbCuyQ/3j1dpZxPU30jTGJhEeP06Ybl1m65lJDoZyBVo27/KXYgSuIPi1yaz7M6PgBXJsiCx/4ZgV3vtTcdNnLLDMYGQF7grNlLJOsgRuPQG6ZHaEAQUm91TBF5fbEOA3gNKw4ufjzm0sBPyXZZzdQY7t8il9Gp2oKw3FvjprcIskZIND0fo+L3KKS2uF/OIdjCVRr2w0lUoR8RKRI++xDNsQnJAD/Jt7k7Kygs+ol87jAV9BP0+VHLKzy2r1M0Kns2FuXYLFQ+pTjooz89z0j5HDXRu6dDMWIKpqZl0/d2v9q8qg1FS3H1gsNyRmfqtKCnF09nbvYmWLcZMsdYSQkcQyAb42hnJE4LT2x5jQVAI8koRbK7UmoCgVSyeV8OFRbeZo31oOc3m80y9W6MymSQp49g5gMPuE2dqyJadIE6b8wwxR2qWoEQEVAXqTTYtYMW/qNo220DywB6c26E7ATrVoh4EjKrXgouFvfOU3UE+YFT9E1s4OZ7ufCOIBI7YBNq07Y7w2UeqVvkZk5yv8XYHuD2DcXtHwhWgqcZu7rMxZTJliNREhzKlOxV+uUIK3bea2yI61gLNWqTQaZShws+d9JY21O3paqI75fg2CjMLcDx0YM7pZVncxe2ctDfB+GwxwDO3oPURaq2oGiJZhNGNzVYh32Ow5i1bu7/UWrDugU9fr+Xoj0KOPpSdZpfYXSb4n0Vcdi9OGgsP8yu4K8zXguQd4T4C2D66x7EQaEj6SdB2ilQSVzi0tgAq9U+put+3k0FGbXrOHKTAXqREa/mxTUjVOR9trCIk2UBBw2NEY5RIk9PfJV4Ikog2IvC4motwjtByQ1hkSREWtnkr9+m8GebmKf6sDhDyr6EZtiNTlONDRQAhtdG0Byvx5QpMo5NuZ5nKOkjkV5kwp5E6bLFk9Srt5FiR/Ij2g9PViShRZ2YW2bizAl4P4USGjabHhOoNKo2GFLnJ8Qv8OfGM86U+wkZd1m5mmK4UOSUs4Xj9+0VTd7cRP/N3+Xk4kOKo8MkfuWX6Eu9h21XGjp24FL2ul+Fl5baLmq3a163qhQ+THTSVBtJQ69uUSmo2C53A1nSa8/I3FKcH40R0ieaNW5uMo6bXkCzJxFG7x5myo+fn+On+V1+n3/z0Rir1iMy4U1uu2ECiRz1TC8nr9v0VUr8bfPv0jsZ45EF5piONTdI1XlAOFinNhyjKEtE2Sv54UgomhqRNOSiGmsPBJW89+R5arLdNmzH93ZXLV0LKxnSIbL2kNwtm8FRSVie9fxKbYuAVSGYHMNOL9Bvj6IbO3RCOyMRloJJNc1x+xhfyEGKIrs3LdvSPaqihwgLt3eaTl8Ap4aQIYQtKObBjCusD76gurJAeCTSTAV23JhtDZHPER6Po1aznhNGRTbPvd9ivm9XY1vZwp4UbQP0B6TkvinIrIK1Bvdueg4OUylPsDufeMxjy2ZgQNIXO8v92f11yHZ9LrWKtE8QVgnutxnboxSZ7z9HPK8zbITpH3uLiiO5WQEjWuYtHjOY9zxi49MpNqMBrA++IKLlCD0IIK5cPDAtvZ/G2nY0N1LllRcoBdWB44SuXgZEMzW8B7i01grOL4KrwbGdRo+QDHed+uuWlTmMWeumq/XIKUmh0IwdWfh9X3YEcNQV4/iagsBXFdvMfn/05dLDX7Ul2+sS3zSQ99qLIW+nqY7rZSKZRQqL895mqk8ghOAnjWTTKYFpk0quij+2xbr4kF4GOQ1McJkHzLOssnznYZgVzY/f0EiNTjHuwLBM4MdHorxJWlYwKy79Nz/CKCbJXpdEfnmMkNTQNnUqOZu7sQAf+1004F18XMHXBFM2YOHSr/t5ZPZyZn4NywxjywCavduTtOoIwobnrFCuQNmGWw8Fyfem+HA5zfzbiiF/EYA8NaJKR5+JUbQ0IqbGtdQAflGjJAXjiQFOWiVkYgAhDfxoXsdnCxmnchbW4k1qJ3tJfPGYRE4xmyxiGQ4mDmf5AkXOc/tQ02gz91CZHA8LMRZGLhKNwbnzNtMigoOLbHzu7Q1h1dpiLlzlbyXWWJnoY6K4gHDGQUpcM0o1/QFbpoYmHxBuyM5sb25qU2Hnq/xU9Nv8sfOHRKxNgslpkquLfGZmKeYN+kMWE1YYNTjMv5W+SogqETOMlRVErp4hNDkKAoKGpG8fyQ9dGPhS77Jir+BXI5Q/kMQboA7abcM61Oa1a+1NTjZ1BkPFBXCO8cj/iHWZIWnmGFsdpNRzgj797T0deLvdKxTM3MOwcpwzIzipa3ukOVS5SnWpQGg0TnUxR96t0ntMI59uMKatG1qjXq3JGju1ZpqvuRgv1DxgMhqD7E7tZceNWdJsHKj2xCmUdMzBLpiXTmnmDlIntiiTYanJYNZxvLnRuE65rAfw4i3n1AybgdQCJ+1BynKBDfvEgTpkzc+lVknO5JDWXS5GY9SjExTSYmdjKldJBC2WjCHcUpmkkaBUUhQH7/B4zqZHe8Cx8SmS6Ty6cx7O1aiuLBAa9djSw8Snu2Y3hUBNX+TudYf8uk7ouiCow0ZxH+DV2twx1KiObym5OErqb5uVicZhdREqk3gMdUtsM32tlml7mry7KDPYHte2uO5B8TLpf0970zv4fjXUh92TlznvNylaQX1fFFLXjt5Q8VWnll+X+KaBvI62Zpqm/W/AzwFp13XP/3CHtDcEAsPxcXFgmepEklBxyQMP/nAzTbipFLOzVTYsnWVT4UsNkRQFrhAH1Y+wkwSVzptzNj8VO44+l2Fx7imZ0jolo4ezQZ3pYpGqGSF4+iyLaxAkS6RHcfytT1hXFR7+Jqwvj/DkTJTavx1F8wuWCGLjay4HEjDRyAgfsYmTrIyZxGQfUgRwpaI/UcWyQkQSYteTzqMvIJPzFD6quGwN+RkN+1ikjKsUJ2zBnKoSsrYYSfqx0op1+zZTxiplIRlIfYuAvdmsHdvu+Gzae51VOLpifUQS/aJEcbSH9VgQC4ckITLkKLNGgBiKDK6dR7NyOJE49oMsyYkK3y+ssFKdZyQcaWjzecB2e0MYTvp4sdrDXJ+fYxt5ZOJ4E2G6qXPU7RX8cpRNkcWlxU1kU/HwX93BXsohR2L82i/8Gr9l/pfI9BoyMcSVaZfSZpaqv4x/Ns5/mv47RMzjYIRaFhWBEL2N+UJnYGFXETLEpLiCYzQsiExBNq0wzCohI9SWahN77ezK5d2dt4BImIStVUiY2BJvsxCDrE6DdT1FpdRPZHav9+supqG8w8KIdNaT3Ghhg5TC64x9MUZkLsfZK33kxTMep6sMmJKgPEsrq7Fdr7arS7PlvFMpqE4GCT3wvFYrPXFCjdd03JgFzcaBkB4iMiu6fmLf04XY1p2q7ArPjQes8oJV5jjHu7uAuRBeijaaaGcJGn7CxiqRLnTImp/LttCt24jGPey59im2qOKXERAphBHirfcCTCx9DrEYwfcD/MHtu9xJP2ZkuIc5nrGZTmOYxzkhQwiEx4RmDxefVgq2bEmPNMmLw5toqo4gX5LkcvDiYzD64L1ve6B3j2hye3PH9rVuKbkQAkLG4YzUbkkWePoALraUrx6l/qpbd43PHx1+vKNa2ykUD/iEB9wAXM5zlbMddE0P89o96Lx/Exi+9lTr9nQ6avwwU8tfV3zTQN5+8S+A/wX4za95HDvRUOAPW2nPJ2pXM4BnH3PLcokmJTPpMcY24iy6vWhhHXH7exSsNKf6x7iX62fsgUUo2cdWLEOf7VB58BgHH/Lbf4twNkv5mI/5k9OUtcfM9/azFCzTn/PzZHEV4w2Twtwct4vPwNSIkkDnLLAjt5JCYCPQRQyn0XCh4WKLGU6kLOp2gpicZlt/rlxVrKzbDCe89MPlsxrzIZssgiGlY8zcpmqtcTI6wFZ0sKF5VcUn1/icAuvcYVnopIz3mouMXW2x98ooqtfvEKpkEZPnmPuuTjh+nIg/gkmBNFWi9PKCHCXu0keSS7IXEY2gLy0gh4b4rKyhxXKMhkyyZLCxMTC8TcuF3qhiMW1jJJYoTQs2nCTIqeZKoQkDnzHCJll8mLhKNlnGcr5CYWmF6PFhKi8sJvLv8KuT/4Sb/DV/ZPwlWfEFff443+IKfz/1q4zZA82NS9B5Udm1sbQxRyJ10ZMfEZ635B17lqrM8BiTY9UUhhQglCdrIySiVT5FSs+zd24ehoe89G0LENQFTRYs6CSwKkGGByF3WI3KIS4VVRsKeeF1xi6MsnGhzoDxQZPFqosT+Fs2u6rTgTVuWZ2EgHCvQKU8pqhQ0ncB0Y4bc6NxQHCEJ/ZO3eRtn9WRLhvkucg11ljgJOf2bJadWYL9dciC+m43iFbgLqUJpgcwa2aYDaPAUKubhzAQVy7S0xi3LWyGUysEbViTM1iYnLDfIy3XGRYNxrEL8ekdYCToN1NMpWykOBgYtHaKjr8B63lYWYCB4X1Ek9ubO9oYxW4ZqV2SLGMNUNkyf49Sf/UytX2VcgOQtr3nqNZ2DjYWK4jGI2mW5Y7AsNVrt8w697jOBd7H39jS288bUJKK7ZUMPBHffIbvRyXV+irimwbyOqZrXdf9vqZpx3+4Qzkk9pEgAO+LXJUZhs3jPE8X8feX2LiZQS1nWalXuZyssTwcJTufIRu/xsSbEVY3ggyp65S+uI0cHyewvIYz/xRffIjNqI/77/Vwyx5hNBqlV66zqS9ROD3KnbUAxkied/qX8KERo4zDyV0brBdbCHxIBDYVdBRbWARFEmGk0bABTyR0LjRDftgiUzA5H5km+WyWZD5L1YwQmjwP1iZOcgI9bcG1KlVhoMsQBdHDupolZg+xKjcoiwqCEBIfMuiSCNqkM5JEqIpuZWGwj+PpUYZ730L3m3gafTFstlBU+YABkmxSYIWcuo60BWHX4ewbdY5N6TwI+8mKDGbDBWObLUwXNlnsv0PvuVWyxjzXxDtk/RY2TtOPVkMQJoWLjaskt+4Lr6M4olBvPmJhxCL9IsuJoUn+/Nlz0sUqA+ZlfiP17/CUWa9YXplE7WMoeUS7sAN0zerCpmZkiKkkH79I82zZZqBP0nN+hg1h0aeiTNrnETLcMucCoAW8f7fnprHdHNEQDqbMM/mAjHmbtbTJlDnR7Eg96vz2+IDFAAAgAElEQVSGlkU4K4iMhug3grtYLB25i1EISbHTpXlAd2HVERQqck/q9bCNuasn9v0cKNo+6zYwzqksvfY4ujQ6llp1OmcnHbKQ3N257OKBox2GaOf8QRnsnNpvAUs6kphIIAwY4zvo6OSNIn0kvNd3aaW2G8gIXFviHOI+0d4p+sakB77CVBEf5FBxL3Ufmqx6LjqHxFGYsKYkS3bvxt8tKHiZ2r7+KHz2oP2eNa7HEbvMdSQmQ6wyB7jEGd4XGNZxqFBinRyf8Sku8BY/Rnt3e0DJpgdt2KxST1m79SsPYRZbx54nwyf8v3zIH1OiSA/9vMd3ucLPECVx4Gfbc+wvIV/yo5JqfRXxWoE8TdN+y3Xdv3/A737iSxz7HwD/AGB8fPzlB3mUEAIlw17DYMjFEV56VG+kbUi9YMSOUyxt8uJfzhEfMdlaKZMeCTKdrjM6dAIZGiS97iMRh6k338fWg7BeZO7dKhVnmUJpgc3b65Sna1ypj7KiZ0iKc2yKUUq/0MNUQaMYkWz6F9HZZITBXbZfrR67UQLAHHnymER4kwhF1ujBRGtsyjmyFEWGt48nma8tcK4+jvjAguQA4XQa0MCMI9OWxz4YknCDxYioqyQ+eUJxxWJ4yM+9VJm12gZDQT9XZr9g2spR7THRp9/Cub2Km57FjQ7jqn6UsgkJHbDZ9vUw6aXAGj3qGPevz1P5ASSOJUjlLXo1hyvCE23e1hwsVyFbUGyMfMiD8h3e9UdwxRYLLJDAJKD0XTWBWiO5Xq56qemIAYs5B9/mOqd+8Rwr+RXM4Dg3PpxnNNnDYrrEhu0waXhuC5/NSGZaPTjZx3qs3ZLsAJZs+wl9tZZms2AyHJUs523KVQtzK0n9wUfU8zl0c2gHmOQLMD62r6vC9qZQFnkup5Ks2fNMyDHA2G1P1BinkjqOcBqs4QHyGKndjFTrZgfsZmhEqqsuz45G919CCmHXJnaQv24LiBLAGZVidqbKuhXivrk3tb1zX48GppbnAA2Gx9sZIoFocfM4sGaMvde6+XpF11Zqu4BMm/vEdqNJpzF07BRVIa/55rpDwR0j8iDEVBf+oUdhwg7a+LsFBd0yfq3HU8rr/N63w7MDy7zjl6uot0gnCQTnuMJJzgGda/Jau7rD9PCEWwxyDJvS3u52DCq7BMxD9NkJ8sbh17OdRdUJ8c/576jj0I9JklFqVPkzfofv8fv8Kv+Y4+p8V8Cr1Y0i3AOp9zvrvB8UPwqp1lcRrxXIg8bMboSmaT7g8vbPruvmXvbAruv+U+CfAqRSqY61fa86tpmjTMFlbUgx8IZLQnjp0UlSOMJGNyRKd4km09RfFAmPjnH8O2cx6lsIKbmC2ClZEX4evX+atL1KQa2S+r7ks/4KlzMVpGNgGkUmiGOyQpE8J/xDxOJnuUCSScYRgNG2aNhskVEOETvEoizgEznGSbKoVll0TuLXkwyIPi4plxv2XdZlmg2R5p74DBHy8SA4xxUzuiOc25YObP2ma+U6WzfOYosIgecWt5SNuxljKZjjQiaNiA0TKqRRToHi2QTG1knuPNrk2Qd3EWaFN1NPGRcFBBqSS1ziKgXqFO1Vbq9rRAY3KT5fY/L4afqkt2hup2jLDWWO3qjN0/IGY3KIrP6Ead7HwMVRRf7PZ7MYKyniEUHq/M7Q9SAUl+DREgyO6JwMRkj7M/Qn+kmofpKmjyfpHGmzzn8iP2WDLXpsnTet4/xUMkoh7YnEhh7MUF0pEBqKIK6kPEmRsiJ07xPE6oqXTr1yZV+WbHtxP8M0x4MO0i/JWoJEn2Tl7hhPl9c5Pj/I+feHIWvtvL8NMHbCHtubaVGkSRomupK7gdO0QszOoKwMz8010qkB+kTiwHRP+yLcutnZlPcyNMI4dNHutFm/rMr+nlSgnPaEuPdJQbdGzRZUrHCzCaZ5zlYZmNl7XYOpflOxknaID+toePWDfVGvtqzYxhC9jHNB8/92ZX8g236Mlmvd6j5hscpdruNQ2Rfw7dmAhaB67iKF5RrmWLBrceujMmEHbfzdgIJWYNsX3engPIgdVupoqcOmjZnlCYoPpBaICHOXxqSxj65p+5w9x1VcwKZEZJupPeAzRU3BeTlNvYvr2cqiLvCUP+NfIjF2MXY6kiSjlFnnn6p/xHdn/hlbVvTQh62q7QG8fBbu3/JSdG//2Jdj5H4UhI1fJl4LkKdp2q8B/xUgNU1b3/41UKMBzL6JYTcYoFAPLJRdJhwNS7qNxGfLIuxXnPnFN1F5DaM/hKg7TYAk2FmDy2xhiToDxhBLmyZ/sqaRudnH/NAg3w68yWV6Ebjc5gf0YHKSFSY5TQwDsc+ioSsfxZkwj6xNBs0oJ1Mxllnj+gPFkrvKSRnm/IkApdkSty2HYXOcuVSGoujhDKewRBE7dRXDFrtBXQdbrSqSghsnVrNIbyWxi/2Exh2ctX4eZZK4j9KEhk1KT2OslI4TCuaoWBtsDT5mKz3Gmj3PoGHgY5UtciilSNsaywGXemaV+nIU32AAru72Wm1t6HjnrCRcT2DrGaS4xgRvcZ8PMZxBVu00l+M2VtagXHURYa8LuuZoDEXg9HEoFQTn7Qvc4a8os8Zd439nOCX4f+wKS3KSuPDTTxBbKn5gLvNhOst/bI4TVD7u3qiTF8fomVvm4mSZh096KSzYRH4wz9RxCzG3BOfOQW/vnlqlPYBEpLhy3nsAcLcEzvff5Fi8yMacSW1xGf9IbAfFtQDGVjFrM0ITzLZvplVbdJRnqSX7qaVvEbMnyBlWx64+R5XRba0tZdw2745Yq9Q8vvIK+ENUEYQAsYdxcqWN6gIQ7EkFCgfZRa0aShFSVSLREFZrl2trujcchlIJBpOHgimEwp+aIWAXCMoIk3iOGYcxRC8Vh9RSdhqbZtgEW7QAw/RQwXN8aQd8B4H+kOGl7q0O6dQDh9BlI8SriNbO2acPGtqMhzVqHDF1uM0W9iYdHqdtTtqDrBurXdm2tc/ZLeq8xY911Q3cyqrvLdfZG63f0Xkes8UWYdVP3Q7gl3U0scOVGPRRtDN8Yn3KTye/c+h83a7dvP+KXD6Owub/qIHB1wLkua77P2ia9j8C/8x13X//6x7Pq4ptBujBEmgnYeVsjWPlGlL2QMNntrl5+y36zCiTM4C11xgeQOIjSoBFCow5KW4lNlk3NnmW9RF9pHH5AgREiGX8rPIZSZJMofAqZDqrz9RsjSGrn9PJLUppH1P2NBnN4pb1nEmzxnWVg3WduFVgJPmcubREs2O8aWwxxyoneIOQkCjDQWs7i+vJLHs6dgj8uuB53wT5JYfkuM6liEYxs0XE0HgydJ6Jc4qnBQM3IxiITZDZWKe/5z6FtI5rPichBwiyTlWVEZWL1O/fhnyF0+FhthJZxMRxRotb9NQVNETzdzV0WFCvCS6Fd6ey+jAp6GkGpUlhRRKLuDwI5cjjeELTMkY04bliRE2FuHed2Cd/wLgbZObqC37/yigxKThrj1CSEnCJ2HXC0xp5p8Zvy7u8XZ4m78bYZIb7bpia/YSaNU08oZGt6KzXN+nzKUS5oW3Xtvp0rE0SBkbY6/aNrn1O4ZZNdCRE6DtvQ28LwGqUDVRtr+mk9Xrsyki2bKZ70qINeZZgOkPQHCUtW2q8GqFQPFKfsDUzQ7/lY8y8gkhd6riSvowjhlJw9xNF4SNP423q3QAidQHh1JiaDlFx4Lmc4a7orqi8I9BsbwToNIiZOwgrx1Q0RvXaRUKG8FLx2ZxnQTeYhNWGe8N+YKqFTnWETUlYDBneva1jEzIktrLpN+VuINnNRToIpB5SS7nrUPtpAaLzhFnyapUe209JrmOK4UNrvL4pdVTbZZjFfPcAuxuWcFsaRUnoNw0KaZ0BU1KWC8061cOi05ztCgR3qdW36y0t39E/4TfoV3HSM+M4Vg+6WSKZmt8F9GKyly/Mv8RKf+fQ+SqEl6LV8ABe9Eu6fHSbZv9RcblojdcC5AG4rqs0Tbv4Mu/VNO13gB8H4pqmLQL/yHXdf/4qx/cyUXNgKAJvHIfZXInejz4kVMmgzAlE6h1cATZZimSIMUjRnqduuejJY/sYzbvAHC4WfTKG2XeCG3NlzsS2eKY/4Ps1m0hIElQTTFbPcCf0kL8QHzFKvCEhsnc2hyRETY1C2k/UhLDUGFNRLi39JbmbK/z4qR7e/+4ZbpovkOm3SJlLhGWJChucpJ93uEiVWerKolZNEA1OU6sJQiFFVcywhYUPk5BK8dFtwUJFMHhakrYhvA7RfsFT3xNm0xqfpHV+fgqW7xvcuCMYGunjx38hyHlnHR/HiPEtlHL44P5TiitlTn5eJ3xlEDs9z+m+Ewxu6A3dvZ1NWoZ2xJvNSGNfa1sUt1Pn77whcUYEKrTJB8KTaklTxRFbTKX8DVV/G/48hyF6KVKgtFwmVt7k9KNFYtafUIoOk+cERr5MyexFS51mQSj+2shw5uo4s8s5hoYjqPg8hlklmwlSecfHcl+ejaLGyK07iA6enx0BybbMilJMDSxROZVkc20Zy44T7Q0jtut+dLg/u1PY39+nWMzbDEQkMrR/qrX96Z9UCmHbnNADDNsFdBlrdlyDB0RL9ipDlp9C0mUwvYZ+UDrwiAxN1cZL9/lyWJhUl14Qdj6Bio0wY/hSpygdwdD+pazXWur2RDrrSYIQ8hi8TBbWLO91iXhT0HkPmGpr8NBTF+gTLZ2Q6B64EhY9KZOUnSLcTfPOfo0jtLMXhwDZRrQ/WDS1AIFJNU1t5mP8VoU5s0A6JegTndOFrfFNqaN61d2bCsVDPuE+H4HQmEy9y9v2FULyLHVxouv5d5Q521r390TMUFAWITuxyw7voPc1u7sxKLNO1D6JY/UgkxvY6V427QABo9Z8ny6C6KnbXOkSxPv9Xor2y4B+1bLGdXO/flRcLlrjtQF5jbihadrbruvePMqbXNf9pa9qQF8mQtJ7Qlm1tqCnSMD6lAcjdcJLS5yYfJNq71PqZNBZIwf0yyECJpBOo8wojlToqOYX2cbGIkuSXorC4hevThBdeIKTX8R+alE6NcBTNc/qvQDu6htUB0Jcm4pgiWxTQqQ9WjdzXSoQNj5b8bMRg7XjU7wovOCP6wt8nCqg2TmiUvBTwuXbvEGSAkLlqZeXuPNsgnShwnrOYdCUmNEqb573unM3SVOp2pRsg+Nj8PSFR3KMD8Pnqw6LOLz/VoC7+b+kOBxDLU/yYycnqBQEQWea/kcfo1kVNPMR5bMXqRamGR222XjRw6nFHL6R4wSn30Y49T11gEJ4KclDM3DskDiKvS4Zzc1JSRgaYXxuCMc1+a3hGCX8hK08xeQ4iflFNDdM9tgovekCAbuGaQT5E7HML10Zw2frVOU8EWFyJhWiaNs81jX8ufcp3bxJbbCPUDq3J723Z3FvLaCPRiAa4cmNCn8x30NpYZUL765xUpxlIy+QYSiXIDEI2bSi99osfYEMPUETRIr9nuw71VUhJWJmBn0tQzXQR+g7VxHBHdmGHjlIwVykP+0jYA4cng48QoQkRIaDWHMxIppFKBZGrZeoRgcJZdLotkafsTcF3EkXbGcjE548TbchQygzQj29RMAcQDRcVrByHoMH8PYliMe869WpmrytwUPYNSaNnXu7C1yJND7DRnQDhvdpHHlZ9mI/GY6QBGHXCFlVSCY5kYZh+y10w+wOKL/i6Cb9dlRtuFfFOm6PzZU2WbGMDx8ukBcr+Awbv+cYfqRjdvNw1HrPw2YVZ9oiN3ucZatEPVrl/Llw87tdsRWubqM5kpAumg+ErXOlh36U3EA3S9jpXnSzhF/Wd52zhkOv6DsSaOoE+rtNp7bP64NErrfjR1F65XUDed8G/iNN0+aAMh6b67quO/X1DuvlYnuhOFn28fxzjU8Wa6T+ryzE6zgPbrB1RREUg7wBBHgbKeKIFCi7zAP9AVn7I+IywjnhpZ2CBEmT5g6PGMKkr+7yC9FNCifOkil8jz91vsDkNE9vmJzYirDy+A3mTz7lWF9sV0dtp3FKQ1HBY96EjPAs4eOGdZ2/SgjmZIw3RBJpQBWNPyDDH7HCf6sSpGa+gKV5ttIFItMpHj7QOXMMcoUQ1apJLTyPZIhwSJLoV/jKFY5fgEAkTDYvOJbUKW8FsD++ySX/Iy4E3+KGmSWfHyeSCBLAxrGK6MlhtLSFdKvEI2GyeYPo25dQI1WkGUL4BfiD+36+/YgLhWKGT7HIESXCOd7EwOAyESqUCNPDLqs1IeDKFcS5c0jggfEJI0DdDBBLr5IbGiTPAL3pAiWzl7oMoqNhYeMXgkvG9C6dtKghiZDAiq2RCCfZfFFCjcRAD2FX2jViWwBJWwF99dI7LM1tUvYtYGh9zL/IEZIOx8cl2VWv/sVKg2FWcYwMQy8poYBto9Yy3L0pKHyxROTFA6b+wwsIf0O2QVzBSZ07tCbvZUIImLoiqJ47SYhh0IPc/e3PKXy6LaAc3sNydNJZQ4mX78YV8ChVp2TX6JF1JgUe0Nuuc0vEdwDeftGhLq51437ZesX96u1elr3YT4bDa8TxKPJtsCulSbdpwFcZ3QDYl3V/+LKsY+vY+k1JLDXMqvDap02G9tzXVylS3HrPs+kQwdwAy1aJwbjk4fUQxWVIDHslNfesz1hZcxga0DnTM0G1JHY5tYQNeI/v8mfid0imJJsdavIAilj8FL+8z4i6i6M8kLTP65rTRSPPN6Rk4FXG6wbyfvbrHsCrDiFA87nYGxWqU3EW0xli569xzNrEtfvYNNIESBAmjoYAAbYU3J7ZAusEC+YaA6kN4qKPiqoQtGNckaPkxQZV6dKbiBOzsiwlLuJIH0tlRW+9h/Vlia8winkrzqVvBXFFmS1ANNxr28PF9gCeSrJWXeP3psf4PUeBDDIqSgS9qjrCwBhD5KnxP9ll/lfLpnf0PYaWF/giO8DYEBRLYEYhE1Ks4xIBJlGkSp9Sm71BMOSH965gT19ChgVXysdYn3uONXqJzewib10bwxJlMjLHb/OYCXOOeHqeCfNthBFq2qo9eCb44EkYMwLT53dLEXQbHjuaI47JB9xghVVGGOQsW7jkqRDFxzlCrV3JQnjNEUA/EgfBndTPIO0NbPn/sffmsZFk+Z3f5708IiIjSWZmMHkVi6yqrmLdR7Oz2N0z3TOjcwYaS5YgG5BmVoIhrCQY8AEfkCF4NSvPYm2vFgtjgb1G8i7sNaQRvGvZXmlmNWNd01M9Pd3Nqu7pquqjqrq7WGQVyWQGM3lkRuT1wn9EJplMJs+6u+NbICoz40W833uRGe/7fmcXIIk4FapGFE9KytTpIdqwrkoM3dyYR0uN406+zgdv9TJTS9AzcIzyNUl+GZLdgID80sZAifYFXe+NceAwXJ3roihWOXsowpDU/Ii69PouN2rofCB3JhB+bd9G2oxWHzfDwI10U/joLtaxbuy5Ou6iS6wvtjYeQ3bxsPzkpfSTIkPMj6rtkEC5lbR28mX0HHPfJpsyDssyT9I8uME3crd+bmuD2Kb9vszI21x3r9qLTua6UtuCWirDVDvZ3Z2UDxS7IbB7rTrxcGSTZJwJnjE7p0bZLxHdSuvVHlF7KnWBiOWyOKOz6EkGD8LsDJS9Ml5fHufyCIzdYWmpTDJubPquTPBF/oY/oSSXMM3uTXIUWSaCxgQ/va1ce5uznYM39qOVexAuA09T8MYTRfI8z5sCEEL0AQ/OzvMY4aFQeg6ZyJPM/RRzxUEGXx3g2kgPz2gniFEmRGwD8fIcA2VbhPrmuZ01eNVZZdCoEZ7sYdEeIWvluZBJEpPdkMngOCvcM1b4MWkwZS6RPD3E23OS8xeBkk7emQTzVUAQ4wVMJjb0p1C4KDyV5I1rLrPLR3hjLIybjqIRJlyNEQqHQfhegQvolImxYkR41XL4Um6OgeeydJ+ZJK73EqpkELrD23KRGAcoYFMu2hivXkKfvg1mHGaHMc6fwJExDNMgdeAA3TmNVesgnpnhBgW6HcGUZnPw1AS3vVkOmmMY+KkXwCc+fRbM24oflSepGC3aGtjVImlgYJFimnsIBMMcwGaGEjV0dZArxdeo8yF95hFOyefXrtUMKvkyA3yTGQxpoEw4xnc5qF5DUkERZZoXeZ1n+Vl1fnNUK+s+dcxWWAqPYIVtpqdqrJhhjhyBmTl/zkeH2gIl2hZ0KSXPTsCx0yOUcegxDMquRHjrNR19y+HOBKK5m87aZeatKs9l+liS64RG//EXSdy+jj1XJzESQ0/t/qe6lem0WQd0L/Uno4ZCTzvctaF/C8ftrerati4OUa2GU8yjGUmk3P6RuKWWbZd+bmvYof1u/RU3zWeH6+5Fe7GVJqV9QRVGB7L7IIjTQg6+8zfwrb+EpWXo6YYv/wR88Qu+lrQNu1nodyoBthfsRdvWLlvMkFtmOdgPEd1O69XJpzaTiVE66UcNL+YgPQgeGot2EmM4B0tJ+tMaZzuYPZOk+XW+zh/wNYrM0INFFI0KZZawiaDx63ydJOn7Cm7YC3F7XFq5py1444kieUKInwP+ETAEZIFR4D3a8uc96VANvZiOwOUynlzgxTM5PrYPUiieZSB5gNeXND4ue1hh+CwesnGOQYiYITlnjXEnexDLWmXYiHDXKaPb8Lm+09zNumQcnZAZAgmG2Y1FjRxlRmQPF14Oo8s62dUy3WmPsDFDvVHGrMbsxjqsjYjIVWeOEH2UChc4Zulkw+8wVOtHFBWeUyUX9auz1YSgjMRAUZYx/vdML4ecFeaMOZLSwMQjFnZQrEf5DtDPRaXhEiJqxpGrS6hUkkkjik0dSwrGM2d525ll3hDM4nBz0qS0UKVaOMOlAy6jqWeInDL50RXFgl3GSmr0dAum7DpW0sXVbFL0Msc0IxznDh/salfsV9F4lpOMcZ33yWFjMYiu6rz5wzyTl+r0hCusfOYVDk+cxpTdeKybtn+MOP83EOUDvsC/RKgaH04+x4qdostaZDTzXYblX/Kc+4+5WRhdj2otKcx3133q9P5uElM2OS/FajFK3obJOTh/0Q/Ebg0cWf+eSVxijUQi/oOmu0uilLmJULaPebsFpLmbHuiNMDfdxfzJe/R1rRMaGQ1z7m+fxV100VMNU/maUFtHdyoU19UkOaew5oaAkvzoDbjyGggBF16AC7tIkqtQvCff4KPMD6k5IYaMiyAnaNcnbVXXdr2cWI2pK9+kYt8jag1xOPPL2xK9PTm+79X8tsvkyU3UqHFVfR/XydNtDHFSbq5xuib3LrUXrgOFBYXVU8Ze0HAduUa8WxdUZJu/HhoOxfszNV57H772e36wipWE4UFwy/DN/wf+5Nvw9d+CMyc2jWunhb61BFiJVT7gCseVn6Ymqu3sx9XEXrVteyEh+zHR76T1ar/nUkK8y6/pu15CT3LcGVvzyWsG+HRyJT3CGX6Lb/AG3+UHfIsCNnF6+Cm+wgQ/vZY/736CG/ZK3B5HIM/TFrzxRJE84O8BLwB/4Xnes0KIHwOeyKCKrbChgoSqcdDN06MPcFzCCesMH/X3MmcLaukay8YNruGgkBiMkKeKpSJknBhnLxgcWIpxK+mSky4DhoZmhcjPeQzFBaYmqFOnhEMMgwwpSlTRqeCFa0y9/B7TjoNj6ByVA9TwfUHCDCJaHiBlVaQ+OcmgHSafnKGn5yy2HUI/uMxhYtgOGJr/rK3VIBzx0FA4SLqosyTLLJl1UhxmkSlqHEJgUEZhcornOMmiErz2cYxw9wTpyF2OnrcofvZ57jmCA4YgJz0WpceCGaIHnR8WqwwvdLEcN1DXE3xmrIpb0Fi04fK9G+gHbGZzKQ6+1IsI1YjoYbpkgtd5jRoCyY8Is4LFwKZdcXtaF5RCOi5dhsmEfG6tQkaxWOPjS9+i56Nb5GMH6b8rUI4Cc920HaaPLrL8XQ7wHn+HFSJUnSFW7BRG3xIL2QRGcZjxUAiif5+exB+QtZM+WfPanO9fep5zZyWLRZ13LktO9MG9GTh3zH+AtK/92+0m29PGdApw3c7coBuQSCoWL93mrHA4fr0LfWK9djGADMs1E+2Gi25TTcFRDm9PVsE+zIyV5UjGQTgm2XsQ8vcgLMxuXQe0Fc0an2EpiZgei1vU+ITOpLa5ODjFPBX7HnrfIdzsbcpOHsPcvjzT2vWUYs1xsgOh3ZP5bYe529QcxTvq+0xP/lsG7W5q1gHKmdO+qXy/UAq9ViIxN4192SUxbKBrYzSJ88YFtcVfr5FSZaux7sq0tZDzCZ4WhbS1/rmhow4M4eZd9N/5h8jf/webNHpbLfSt/VZlGZeSn9tPZbky6VJciJGbB6sfUumdNTL70bbtloTsx0TfsfrLLjYW7TLFTQmYu2IDSdJ8ka/yRb66J7n2gic9AvtpC9540khe1fM8WwghhRDS87y/buTPe2rgL/9lepXOq9eq3C4cpC+R5+KZNHHZy/mMpOR4aEaZ70uHUUwWmUGQZFT1kL3xLsXbZaZsi+kD50mmUrx0po4pQ3ChjvtXr6EvL+NdSXEpY5CTDr2YfJaTCN6mhE2eOLMyxFGzmylWqHIWi7PAZp88zRF0L4SYT3qk7RCnPycoh2DEiKCooUXDuGX/2duw2NKHSw1BlRoxdHqIswT0c4geXsTFQUOnV2kUnGW6vG5WlyMMvDTB7XsOgxcNbr0tmLcVs5bHsxlBSkosNBZwGY1Eublaw70ZIeWFWF0O023Cu/eKfFy0MX7Ux+CZWZbMCCMyRRaXI5yghwUsBlnGZpD4hl2xwqNEFXgLxSIhLGJqHDG5XpVAZs5jYoDjomrLRFSekjFB7+r7HExdxDSaC6iGJEaVOYSyiDuXOWvEmZcJpgwHrCVWst0cTdbo+fAsNwtx5vWbfGb8z4jVfqWRxqXNSd6MIaUkZfoLzmJWMZByiTVMse0kbbvdZKe0Ma3YydwgJZw77eLOTqMPp5C5HDgVMHd4XDguapc5T8wAACAASURBVGERt6cPfSHrlwlrEbzphhDpy1LNWniOgWFA3xDMTPmaPKu/c5WHduylxud20IwkUWsIN3ubqDVEREtuLOW2FXYgZbsiBK2au+1KqnVAGYeyk2fQ7ma2b5VT2QrKKaHMzWWwdoVm/r+79zhXuIv7hRfQV+4iyyMQ3j4FTsfqJU1LwW5NW9/5G1+D10rwAOUJ3skOUyjHSCzf4dyffw/5K7+4q+GsBz0ojmYUcZkkTxbdSbNs68R74OplODTmt9tJI9OqbesiiWr8e1ARxXtNKbRZu7o/v74HjU96cMPTNr4njeQVhBBx4BXgD4UQWaD2mGXaE4xG+o0Zt4xXiHHEOs6CXUG4GiImERLipuBlDCQhVrmLRQroIltexrJXweyn8PYsfUePki10Id0wUlfw2hvE3vg+jI5SlC55J8mA2cccKxTJI9a0S3MM0ccUKwxhkiRGqGGu3QQtxkphgtq786wM+TnmjLDHSbr592qGQ1oIK95NKCpBQBWPMIIIHvNU+AqjHOcABRbpJsEN3vYfMirJ+GSYsm2jknGux3uw71VJ9BsIIVmy4cU+yb0snHEgbAoypHBUFfXaNS7fydPdbTJ9MoMWDbO4BFNzBudfjDCz+DFnzvbgyZ61NCcpusgT5yofMaQsxp3zdBkhTGkCgjfUIvNOgUHjY07JI1S5i3JGCbUsrGqlhPv2h0SXc9wpdBPqitMXmeXohUNcePnLSBnCQ+FwhTqreCrGtyZNlhbex4yf5uXPrnAs3IXKFCk7Feqe4M3JIxy+9z6VjxxW3T8m/bmvNp4KnZ3kpfRLiLmvXkFfXUBesWB8fFO+te12k9uljVHK98dZXPBTqmxlbpCmTuxAAnK7qIzQICsqEuWd+QMULjejXfUNS0zTDWEhWyZtaWumofMTcKzFIWM3VR72UuNze61GmMOZX6bs5IloSa5dCe/O12YHUraj+a2dJI6f3VMlCg2DbmOImnWAU9kKnmXxjjFJN9uXmtsSzfEMDyGnZojlZnxz6S5S4Gw31l2btr71l76Jtg1uLUyhHMMyVrHLA7h/+gqxBsnbTkPY7DfVp3gre4OCc4e02c05XkIzTK5ZkvwCDA7DypK/sdpJI9PUtjkU+YjrvMOlPZOpvTjs76Ztq9bLuY8AkwcZ2dsu1ycRT9P4njSS9x8CLvBfAV8FeoCvP1aJ9giJT1ZO6nWuJ0LkbI/upEJvPCvXN+8hXpYX1syDCljUTBKhHB9cv0LBU0y/f5XD55/H0EP+SatFGB2BqSnCI59BKIsZtcKANDFJUsaiRhaNNP8RF1iiTAKD8FYED3AqkjsHLpAcK3KnaHKkIiFc44SKMTf5Id32El3WADOZ02RFlTJ1NEJoSDQkP0kfV/gRtsrR7UgwXCw5xJIzTdUO83FfEWfuR8Rj73K2NIThpsHIkLAki1nBgAWx1oerU8VYtjlYnqf2+h1GvDL3xl5mZEgyPScJTWW4OODwnGEgEWt+jA51+hnjiDrKtyenmbc/ZDQZ42fPHAe9zltXFBHbImsNMZB5BV2GcIx+jGSC8p1Fov0prl2RFL5XJ9bbh7c8x8mf+BLTBZdjz2uEwhFg3VQbYYAFZ4a7CwZqJc7710fQtFlenLiNlB6GWUEpgaXlifwgR49ZJ/7WHXiu6EfmbuN/JcsOsdJCo3LCHLzqJ/xt1RjttJts979Xyo9IvnXNX/zsef/zTYtbq1y7LPHVJCturJdC+jTWWHVDtGurTOczEtcxNsjc9BVqXm63JqiONT6bhNOI8p7c2oS4ca7CGGZ6bzVwdygP1p5+xHXkxvvUThLLlU3l59xtNIqt6WqUU+IdY5Kk3OyesGs0x5NbhBcycPq4/wXahZqio6mxcR90TSdhyZ1NW0vLPqlsgx6ukdBK2E6cRNcq+qpfvnwnDWFzEzSXLSMtm37DYoksEklYyhafzN375DXHKpGskt8zmdpr6a29Ovfvu1TgPiN7HyR2IrRPUzTrk4YniuR5nlcEEEJ0A3/6mMXZNySCLhkmc8aP+nQ1mw+kxXGV4co1iV2AZA+cfEYgYzGU9LhCHluWiZ9OE51Lcvj8MPm5LKefcZAy7j/40ynKlAmNHOL/0ia4cwkSVpGfy5iEZYQQmQ3+Zr1EdpRV0z3mUytcLtQYTik0vQdJiGcciWlL/rQviZbNUXaKrJphIsBdSgyg8z9yjjgKW+U4NJllxZ7CSFrcPi0YMvpRVh0n+z5d8T6WVmcoj5zAyOaQZYdzGXPDj9b3ZVTYRoR0f5ILtTepvjBKaGCF1dgK2Xw3L5wXnD4qMWPrKUjMxlfYIEQag4+cAmXb40gqxuQPqoTzDgPJGMKOUh+oQHaMsLNI3DxAhRzv8DmWvBBGOUpxWZFWs+ReWyF9yONOUSc5eAcvNk0JixgZRENXWyNL0ujHivfwo+tDDI3kcF2D1dUwquwQT4YIh+H0ibt4A0VisSWkiAFiZ/8rwy8hRjYL8bhP7jvUQN3tbrJZv/feLMzfghcn/M/PXoRUb0vXneRqZ4rtpK+FrOhzORLdVewVg8QWmpGdZL4vE1SL/BVLZzlTWCc+qojhhLYlrHvytdkhDQr4hEBTZufFuhNJbDDzDQt8UnHutIs0O6daMWQXyjTpJr33vHp7HM+2p7eaGlvug7RSnBs/j1uW2y/QPd2+4287WRYe5/pmcGth9Ooqsh5f00bnF9iUz611OOcyUHI0PjYiLMlW1w1FWfql4ySyY5DBdtgvmdqLw/5+nPv3m3rncaWYaWInQvu4olk/KcTyiSJ5QojfxNfcOfhFCAR+Bokjj1Ou/aIqHSqGjdX48RRcB7tg0puEVybrfL/gED4Q5cIRj5IsM4DOnKkYHOqnZGdJDPYSMzUURTypcTmjKDiAijF9CbS+Kd7L1ph0SnzW9MuWiZYfp2qJ2N2Q0LcFZVmn/0yJMVdnSS/hEEcWJWPaLIctl0xWccU6xhtGghnqGNT4OSz+U56hv/HATDsmq/YdtN5Rbr2a5sa9MXLDSU6MxxFlwYfaMrUrGmTzmFaaU4aOknWK5jJVQnQRx0Fg49EnQ2THz1BxiuirK7ybjrI8vkK8UiOjpwjLzuNoalCPG13Ukg4L37vF8LTidH+JO9oE5+LdLGfr9FmCLmOIGjmqTpqlvIE1Kv2EwZqLLXtJfn6YE/0zjJ4tULem16p2ePiVB2JNMi0N/tZnIal9yKJ7h57uEO99Z5ZytoTWb/Dsz6X58IPDFM3DdFU/ZPylIz5p2qYyQTPpscxk/LJ2mgZXru6+oHwHNAMxDgzB3Y/g4w9hZESRMlzfN5DNhI1szlf9SQGG4T/wXr2KvppDpluIaQtZkekU58Y13D1oRjrey/2aoFrkj2az9Dhd5M0s3SqJNnmjEW68dVBDR+3odhGvu0ibsuVivQ2pWjunV2Ff+gh3dto3nW8l9z4X944TsJc0MFuhvaJH2SW203W//BN+FG0HbZ4UHrFIFeYXUb/0C7wz6bsbNLXRW9U9ldIPKjjNxlrV96u12u9872UTse8ccHv064ONpDWuLOqOgWpssB6kCXcr7ERoH0c069OWJmU7PFEkD/hvgdOe5+UetyAPAu07voRukEx43JxXJO5epdZbhZzOwtAxumNh5nBIS4NzmRcoOy66oeHKt6hjUyWGLV36zGHuqQUS1irXsg6jVgyMzYtfa5SvhUaGVEeiZxAiLTXsmIulNG5OCvL2MrpV5/z4S0TLM3zO+AmkjNELTFPnp4jQ1Yy4QzJuvIBr1SnOrPKql+TQwSQf5uoUyjBuvkiOEm9mwkSdCtOGzkHp8ef8JVd4kwgRfoYf4yLPkqTMPFF6wxH0l1/CcVaZNlYYkDGyYZcydcLbfGUlgh4Z5ZdOj5K/e4uZoRSVj+7Sf7jIhYtdVMphdAOEzOCpIqYSJJKsJww+F6VCBb2aQ/aniScTlByTmjFHCAvhKDAUnhSNR58gGhZ8eeJ5brl/SGXF493XSsSG45RmVlm8J1nKd9Hz0iz29Cihs7++iRg1iVtT27ae9kQiG3WL1fj59XQl+3jSGDokE369Wi1tUI9B6MMfQX7Rz4vTrLGqRdflSibg+jXI51FJi3ecMxS+r0iMHuQc08gWAtiery/2AJ8qe9KatBJOq5cTxlnKlNEcD2m/vqughg2axjbNptqNRqoNuuE7/s9m/bQ/SkmUarpldiZVawv8dIWEWEQfTvm+kY6LMmIdtQv7Wdw7Yo9pXDadjqJs1NGsBHIvG5MvfsFPk7K8At0dIoSXV0CL4r78eQrv+f6k0EEb3S5L45fanJvtgkT2gv3M914c9rdq+6B956DF11A53Jg0mLQlPZYinGlLhP6QiN5OhPZRRrM2tXdKPV1pUrbDk0byPgRKj1uI+0VTg6YhGeE5JA4GMX9RPKPQlhxYdLhR16hGF7mjpjlfNEkbYcZlkrCUhM04iiJVbKr0E2IWix6yLNAvE3w28z6HnXcQhkNKvkgEnSI1DEKAR44VFnAZwCCLi0N9zbTZiqYGzKGOcgSv2jfp6VtgIVtitbxK3BxEQ5DCI4tHL1U0FV6vYSl9n6ZY5mWiJ0t0X6/yWs5DsyLcMgS9CHoxSUvFnBnGQlBmlRlmMNBxKXOHaUZZZIw6NfqwGEfK0Fr+v9YaspvmusOaFDINzAM2Y699m3o0jukNEpIvEDbXnpSIyVtgL3IumcJ96bw/litXCddd6E7ChQuIK9eJ2WW8ZDcChchfQlkpJjNHsGV1jTxHZZojsf+emejvEOmvUZopoPXHSQwViWU/YjFncXj4K8TN/sakb9biOCXIFRSJXodczsBx/YhapeCdK5KCHaM7CWOnd5kwWClfE2gYICF+ZhJ9xUZ9aDFaP0X52iLlI71oC3NUXn2FaKmOtHrXCZ+qw6VL0NeHe8emoDysUQN7qog7kiLWIIBYFmQyKFN/KLv+ZkWQstNMVrzDythGOA3CYKiN5FUpPx9QWzDLpil0SlTtWSJ9QzBn886rZQolY287e+kvlqFigdvXh1i+dIqkJbc9f22BPxlFvx7xo5utFErTtzdr3S8B2MaNYLf1Yd9jkmVp051JctJ5fvel7dK9fh68r/0ezMz6QRha1L9Hdt5//fXfQh/pJZH1F95UenuC10ljt++ScQ8Ie3HYb2/bKc/kgyR6wjFZahCb2WyZiFNg0Hz4Jtzd+Bc/imjWDRHZSX8ZeFrSpGyHJ43k/TbwAyHE60C5+aHnef/F4xNpb2hq0BYoM49OP12kMcggcIC89DjUozN1tpvP3LxBtD/Fv70GC/kYtlUlk1F0Nb7FHjpvMcQ9lkgxzI9zkhpVNBSr8hXGzVPUWCDFKa6wyCJLJOgCprFZZB4TOEQavSNBasI38oapGUW6LJvl7ACWNU/SOEqV2zhc4gRJbiBw1SJ/9sYwyXtHSA4ZnJuQa1qJcFecn51QhJwKB4waealwCGEiGAcKuCQwgDjDDHOF2UZQSJZ3mcfiMEcAgYsihgOMk6SMWjM5t5I6aNd++Q8AD4faIYPo3WfwRjREbs4nPGbTBrhuTpLZHDHp+t+2ZpH5bA4Ky2Av4vX24d66RzRSona4n3p2nsWiRbdMsWC4ONInz1FO0xf+Z3z+57/LXP7fYySXiYQNns9cJOL8NHEjvfHh1KbF0aI13J5XuVxZZWAwTSSaoVSUazvKZC9cfhUW7ir6UmWOPqdhxmXnB55SMDkJtg2WRTlzkhVpc6irj/lElpkZj2cOpIgUstyOz1BeXSA6MMLhLMhyhSa7VFaSanaK6OAQCQxse4zEoTL6hRr8wCeAZLMop8h75nsPx3FbKeTkVYxd5o/rqB1rkr9iCa5/gHrlNarzd4n0DyLTfR2vqVC8Z1yjZn2Emb1BX/xlCqsa1jYRyc3zHPzyHQYmjnLIOQWSpPnAXmWgZ478Qh+uE97RNzHWJWFinbS6jtyoXSgq/7tr6H5N3ft1nt/GjWATuWTz7mqDb5fMUjYFxl5kOHMCvvF78N3vwbf+wid3PV3wlV+An/48pHuR7G7B38rP7IGZth8DOuWZNGWL9eY+fchaNWZpSyNsJB4ZGd6Nr+7D1qS1m4UzL/n9Bj55DxbfAP4KuIrvk/fUoZknrwedy1Q5qurMlB1OajFMGcJCkJUeQ8ePIUe6mVGCrlcktWQFbUFDOKG1up8l4D1C3CNCARMd+DwxPOrcJMcyb9PNMAZhlvk+aVaZI0yZGKMMAlkuEidFF8VGJhqT8Jb+eSFpcC4ToeR8jK4lqDgmdSNPVPaxyh0qeHQVR7n12i0OhRYoTA3hnh5r1BKlcf06QlxjkgpDxIlykhouH3CdZfJrC9Av8pP8OBMoXG4yiUlsLZmyh86b1JijzAAaEw2Z202aJ490SPqrK8TkDSILeWpLC9SyvXjpAZRmrOet3SoysvWzVAKVTPHOq2Xy3hFK3e9zaO6viFrD5K4nuZZXDFkxtEwIJZumcQ8r/B+QSf/q+hxLdq7jqhS1K69yfOF7nOoaZeEUvH3FpdTQ3vUkYW4GPE/Rv3SDq3/pkL8qiX/uLOPn2Fyz13F8gtcgYZoD3aZFQWbJHLIYHTAxP3OectlmXnMZvGLgZKepWKPojblQEt7LwKojiBtwBqg4Et0w/AW+GRhiWZQN9l6Sabdapz3mj9tiev3oViTkF7ndc5fQ5R9SH3ueQ7aEoosrY+tBQAryjsOiYbOSMck5H3FKW6bnSuedfXNxjRp+FY7r/BDwOKVepDqZ4Y49Aj1Z9LkPuHV5it7hOFHti+zq8dtCWjeYrZIK/XrD5G6lKGeOsizv0wy5xe9ig4/gdAX3eJjYB1c3afz2pCXbyiyc7oWv/qL/t82U7LTgbyfLAzNtP2J0yjPZGutyvz5kGzVmEuTTSYb3i01l6PZQYvFJxpNG8mqe5/3Xj1uI+4FBiKTSmKm4DIYjXJ65gSgucdWTnD91lnFpUiaEIUNgDnO8VqdYkEy9W6NrqEZU8wCBhwJsPPIUGCaBTZ4BHCJAmRwD9DFGlmXGWCHBCnkSpCmgiJNlnnQjR96kyvFmcRqBR8YcYUKmOxI9oSDunMTQ4OoVX3WvWwc5kZnGkIMkgGXuMCgcljhFUuTQcWh90lQmX+e4/decTR8lPx5nSeZRLJLlNgO8RIHc2gKUbiQUzdHPEpJ+NULSuUDJULwtb6CxyBwpTnOCLkJrAQS9lsdMHo4DVkJsTPrruAi7QHTgRaYpk704QCwlWX0X8q3+bp2c3ts+c0+fp3CvQtfBOu9nCwxfTJM1lum9ZHC8L8pqNkTFEXhmDZsyfegbTOO7dm9yHKL2KvrAKJXsFGbhCEu2Tm/LjvLYabg5WWbuLxwqpsmgnObO7CpXh9/CTa3SRZpRN4OpS2RrdK5lIQ1zXXshDWTMF0YLW3SrNPMnK3TxPFHz+Y1aGZknaY6QJ0tVFdHxFZ6aNNcDQwyDCBAtprGNLAm5865/TykbtCjEYjCX9QnAHgNPNkaq6owlY5TtOXqGRyktzeNaY9y4rlNoJF8+Mw7XrkDeNliwBlnJXGbQPEqZEhcaFTo2+Egp+NGkX2qv21KUMrNI6f+6Zp15hF3l2b4xpqYSDCTexDyexFu6RrU8QTi8fWWNdmxYhJWLvLROfjVH0G3epxlyi2CQZgUU+9JHvo/glZpflqQt4nvXWrI9VvfYLw5xEuicQ/FpRKc8k008qOCEjQT6wZPhJzli9VGZhR81njSS99dCiN/AT5/Saq5dfHwi7RFKwLUUoeU6R6IrLIg7jMZTfLB6nYqTJW0MctI54/uqAOF8mcSghjv2EdPFEq9VBC+Fz+JyhToLZJjHQyBJtuT0N7DoJcsiFr3EsTjBYUpkiXGYKBeY4w3iLLGsXsd5Y4Gzr70BApwXnseZ+BlMGW2T2zfxCdumEkuztDqONSCxs6cIO4cxTYOTQNksEnnhOpXZKfTBBNI01siMVi8xZV/ldp+Dmf0hQ86XCJurhBnGZIpFpkkwvGlX7Tv9rsDkFYR9iZBlEs4IKrKPMDkkDhBHi4IR9bjkKBjyeC8myJyRlF2xviYZOp6VoJL9mKV0lO7eY8xWciwvOwxbpq/xW61hunlIJdu81yXK1NdyF+qmpGc4ymzOoS8dY7V3hl4sapbOUlaSXNPo+Amws7hYKoLhlFGaZPJducmU3BGGgbTSHM5CxTpEOPUi11pyizV3lGdfjFB/t0zxZo635lOcSFwm+vr3SVijfK8XPpxz6I+bfpqZ8Qyy7JMwhWxoMc2Nw1VwchKqtiBi6cjM+sENmhCVJDJ5lWn7dZYsQSjzgl8n1TRRCq5NwrI9jmm5HM/sHByyZkpTvSw5dykbxc7luJTyI4tXVyFu+v6Ce3zyblz8JOql5/FknSltiZ5yD0o9T+GS9I/PKQrTLoUFnd4BicqeY9gp4pnLdKv0WoWOprbPdaCmFO8sfEC0Z47ZhX5OOINkTb8Kx6DRT93SWMxKhod6idNHLf8hWnoIzWhL/LvLHcHaIqw2at2kEXtoEbZStlVAyS75qX06BFZ00pJt0to+AO3sdui0iXiYaCcuuyUye/GhbKZ9OZsxqLTlmYSno9TWblKlPG6C9SjMwo8aTxrJ+0rj/9/GT53SxJHHIMu+4LiQLwiGLclbzvtEQzO8u3oHzZD0aYPUJiep2otoyX6gimHnOVCIcQWToVg3y9EpVslR5i4xhjkOnGIMsBr7UQEIMjy7Rkb8pLAXieH4pk5VZM6pkjQOcta5xeC9Be6F/DMHZwsYTgXMNpLXYuLT57Ik4i52NkbCksQMs9Er/mI8MYEsOrgYRJXkyru+hi3WDaEej9N3BpgdrDNivECIm9TJcZIXCHG6QfAcvGbtWPwf91u560QWfoA5MMqJrOIFp5useYc0SQx8v6BrV6BkA90enxkRLEqPMhvXB09CKVOl6ghqhk5RZklH0xjdhq/xi9cw/s03UdPTlAcOov3aLyOj/s9AoZjkLWwWsUgxLs9TzvyIilNg0OjmvHzJX8Aysu1hJBhXKfLFKsnr15D5PKVYCts7T19abjAld1zIpYRMBuk46A0W0SmVR+X1q6iVJY6dD/Nx4gjH1Q+YGxhleWYK6ocY6NJ48/US9+Z1htJwZkigR+DK+5v9Fpv3XNp5tL5RX+vX4re4QSvjKKr2/8dSX4hEFmadWcqmr41tkqjePomdjVFxILwbU5pKUpl8nQE7hGbdgMyzm5/sTTLQ1BiVK50rp2+DzSaYMCfkS/64wgYo6R+fUyTmb5BQd0ksHMBmDCsd5ozxWcotUYet2r6CDXqPQ2z2Klw2iQ5f45T2Rc40q3DI1u9KGPjllgCSlnHsR7PVQesmYSPBus9I2Q3dtVRAUVYv7smz6LLSMX8frBOYTXVt1ThSKT8A5j7SAnVC0x/SoUiBhY61q9vl2y0h3oqAtBOX1u/GdmbT7bTZm0hja1tpcdL027a3e9K1UNtpGz9JKUueNDxpJO+/A/7c87xlIcTvAOPA39vpJCHEl4B/DISA/9XzvP/54Yq5NZp1Q+/lHaJDBS6OjDNfnqbHSLLizDFgh/xovTsz4FWRoyOcUXO8Xe1hzl4kemWAa+O3WCrPkDKmOC1fIE7vhnqzACiJcg2KuoMpjbUceUVVpzZ5laP2e2Qtjdr4aY4NDTA6tYonFPrg8TUt4kbBDZSVpJydQksPcW5c3zLfmULyznumv8jFwPZgIA1zCzqDmKx4H2MxSpwuPJWh5DrEdAMpocQkdWy/diwZPCWZnHT54YLG0fkLDPI2lfRLnDI+yzOU1x7CpcYDYngAprOC2bLHUFhsMkp5ONRlnog5wgFCmGQwZRrOSH+9W87Dv5vmw+gham/cpHjcZvzz/Ujp52SzWaSPNFkWWCRPXi5y0PTf+9RHQnvUm4Jrk4LCjCJxp8a5z/aiZ3OkLZesHfNNydEdFnIp1wND6LCjdFyiqzmWjtvM3pumfiGEVrY4nAW3/xBO9EVKr1xl6ONFjukJ3vqrGMsRl55hg9zYGANpsO+5OEd0zLhcu+etZl2MjbPZTObrKkU0OURPdoqCJYgbg2va2P1oECSSk84ZqvYikb4h5BoLbvtedko1s9vFuUFwpKFzbhMpb9E2NRfHnIv+5l0YsDguP6J+cZhYbxwpJZWiScFWxPuKzGVhaNGgYPvav9yU4HRyidUTS6SXapjlEDIcbx0sMWOdbBlmBxPtPjVbColLbD3T4aaopAdoEm2QSlV0eee6TuEHkoQVbgRhtMu1Tkoi6KywyAAjFFSWyuTr6Lbrk7yXnt91VY2doFBcx/eH9PAw6QYgQXqT6XqvFR62IyDtxKWwuDuz6VaBIZ36KsvNbbdKsv0ka6G2e1Y8jlx4nxY8aSTv73ie938KIV4Cfgr4R8A/B57f6gQhRAj4p432M8CbQoh/53neu49C4HZI6WtLiq7BlG6xLG36wwc5zjhVw0GzbviL2uAgUIVslkp3mtBqlIEBmxtzEd5+1SNZ+gIj1gJnMqcR7ZF/Ct64pnit8CEiscgLZ8JMSD8ZsuE49Np5cn3PMZi9i1U+TWgijnH6rH+y2dmbVEmYzIQoOGESRoiMZMt8Z60/yNwcdDUCH/r1MufsXqqjz6Bll6BY5vLHJnbBxErA+JkidenX120mF3YdE8fWGR6IcYsBei/+GHrvBFKG/fQXDTQfEItZwYQlOdrwOW71LfRQeCgECd7jFgW66EaSQaxboaJJSgMHWHr7VdyTBu+bH3LcTdMVkxgYWKTIsoBFihTJDe+NLfycSo7HnF1n6GCExakU7sw0sQMpxsd1nGaWjg4LuTJiO1cNa5IaQ6OWjjG0ME3o7EHss0Wq8jkMRxIzDCaKLs7Hi3zU28vye3PIxQQDLyexPyrSc6yE+4ObjNQWMboScHZs/XvQ4lu3KcJ0bcGRJJITnHnpNH1mwyevmSdxnxoEacTQrEH/i7OVNqdNSUMlPQAAIABJREFUW7VTBOmaZkPzCbU7W/BdCibOEzO3FkxKiPXqqHSC6exrLKXrhHpTnJQTgCRqKBat67ySvUXMchhIHaHHmsDOSpJDOmOMU8/PE0n3b95A7UZLt0OJtE7YRAbGFfJKSz8njz14k6iUuDJGIb/9YuxQZIG79DLEVV6lRpV57nDWeZaoXWoEBOX8eXhA6poyDjaza7aOLhKc5CIJejcRuL1WeNiOgLQTl0Rqd5uerQJDOvWlm5vb7kSKHkY+vfvFds+Kp8Hc/LTiSSN59cb/Xwb+hed5/68Q4nd3OGcCuOV53kcAQog/xq+B+1hIHvhf3q6Y5FSbj0xYdvlmqdbdtuMgNIW88g7V7DCleJauVZPSwCKV7CA45qboTMeFe4UyIcsGu5dZdwon5mBiIo0YY1aa0ayNZg0jjXhDoA4+T63XxMGWBfrMEbIs4OBfrxNaf5DJtG+iKFfAiBrIK2nCWRusNEVhbIh+dV2DUMzPfOfHGfu+JUlLQvY4h9Iumd7OPl0bHxBi09rgoda0hBW6yTFOmhRZKhtzBIbDeL/289x8W8PhMOHeRaTuB49I5CYzePv7dig8rhmLfGwppnJRJl48jH56CEx/HGbzF9a2kCtN75j+ZeO1N5ppjmcuIpw6eWOFhEyjYUKDvEhTxzyY4vRCjtIXeol9GGPxdpHkiMGZcaguL6INpZCv/QDm7sDwAZ/gtWkQW9Huz1aRXcQ6rBf70iBs4eTfsV2DnJS3SWS7IceVXsZ7q8pyeITElO2XBevageBISTkzxpwzTY9xgLxcDxCqSodk5hbPOLNEjBoFqXNxLQhDInmW8Fbj2I2Wbrdz0YJNi/yiS6y1H9gzcdwNdlqMFYqPuc4ct5nmBiGijPMFFrjHIeNZpPXRA5cJfNJkMcgcvj9kmgMdCV6z7V5y5W035k7EZTebnq2CVDr11antdjI9CbVot8JWz4qnwdz8tOJJI3l3hRDfAH4S+AdCCI3N1oB2HACmW97P0EHz1wjo+A2AkZGRByPtDugYqt/u2GyaGCguZCIsOLfRNYPClSG8bIjnrNSGCKomDB2GEhpTtoVI2AzqiXUtk5TIzEWMLbQzW6Fdi7WV1qo5hPYfpO8qtVEzZCCxEqxFv8Z0P/Vya31dsXYtiW7EthV3OzLhNZLXhOlDMU+Kg2SpdEyibES7OJs5wkLFJh21MOTGQJBWctv+vh0OdfKyTCajc9cpcdQwkbIDoWhbyB1Xbk7/0nZau8ahKqucMF/qvENvXF86LnFD5/znaKmSAeEDKbh7FzwPDg43Kig4WxI8eAS76w5O/tthu8W5lfTcu61BxWIobGN7KVx0dtOLJk3i5hB5chuur2GQln5AhYdHL0MY0mA9Rdk249itlm6Pc7Hp3qTa+jFj91WLdivstBiXcVghz3leYpYperBYZpE0B3x/3ocgE/i/09NMcKTpD7lNRO1ec+XtNOb259JuNz2d1oet+mpvu51Mj7sW7X7xpJubn1YIz/N2bvWIIISIAV8Crnqed1MIMQic9Tzvu9uc8x8DX/Q872833v8KMOF53n++1TmZTMabnJx8wNLfH9YdlXUc5SGcELEOGqu19gqKroIWn7wHIcN2Wqt9XfPB+X5vi1ZNXggLnedw8diqbu+DMmfstnzcpvM2lTHbQZN3vztypRrJgDdWqtjppjwJEW8b5NnivrVnq/eUYnmuQmIoup6w+36u35bkeE/34SH9CDbdm0f1Y9tOprbv7HHGqbb41gZ4+HiSNXkB9g8hxGXP8/YcKv5Ekbz9QAjxIvC7nud9sfH+twE8z/uftjrnSSR5Ae4fXqOgXFNL+KjQLGO3FaHc8rxdrMkP3LdmQ7mzT9aDv5X0wJNFTj9NeBL9wT5tCO7BJw+fZpIXBm4APwHcBd4EvuJ53vVtzlkAph6yaL1A7iH38WlCMJ8PD8HcPjwEc/twEczvw0Ewrw8P+53bUc/z9pZBnSfPJ2/P8DyvJoT4z4Dv4KdQ+VfbEbzGOXueqL1CCDG5H9YdoDOC+Xx4COb24SGY24eLYH4fDoJ5fXh41HP71JM8AM/zvg18+3HLESBAgAABAgQI8KQgMNYHCBAgQIAAAQJ8AhGQvIeH33/cAnzCEMznw0Mwtw8Pwdw+XATz+3AQzOvDwyOd26c+8CJAgAABAgQIECDAZgSavAABAgQIECBAgE8gApIXIECAAAECBAjwCURA8gIECBAgQIAAAT6BCEhegAABAgQIECDAJxAByQsQIECAAAECBPgEIiB5AQIECBAgQIAAn0AEJC9AgAABAgQIEOATiIDkBQgQIECAAAECfALxiahdu1f09vZ6hw4detxiBAjw6FFTMJUDISDUYY9XV+B5MNoL4WAPGCBAgABPAi5fvpzzPC+91/M+lSTv0KFDTE5OPrwOlAKnClrI/x/AiPivlQIp19+3HnvQ7TodMzX/s6dF3qCvB9vXn1yGP3oThnp8oheW/v+tmMnDV56Hr75AgAABAgR4/BBCTO3nvE8lyXuoUAomp2BhBWaXYLkEnoBuDZZdfwEdTvnvV8rg4b9ebbzu0mC5DDOLMJSEhN5yTIcVx1+U4zosOf71DiagOwYrLgggrkHBhXstfS2XISTghWdg4tA60dtW3oYcw0no1v33oiFjq0zLJZgu+O16DFh1N8rbvN5SCWaW2tp5EDdguTGW4R7oie2vr62u1236x5pzs9S4Dwdbx9U4r3m95v2azjf6arkPcR1WW8a17GyUqXXMa/c8Cd1Gyz3S/dfNvpYcmCk07qWxtUwd2zWut1zyjw33QI+58fvQbPfHb/jaunsF//P+bujr2kj0LBO+9U5A8gIECBDgKUdgj3nQcKpgr/qk4PYC1D2fSN3MQrkKpQq4FbiRhXp9/VhN+e9vZP3jpQpUqv6xuvL/bs7716s1Xper4FTArTWONdtl/XObfd3MNjQ8AmYL61qdneRtylGurl+jef16Q96b837/TgXKFf99rU1epfxxlev+NTe08+DWnP+ZW/Hb3NhnX1td79acf3695T44FXBbx+Wtt1sbf6NdubpRjltzbfNUa2k333aseR+ax+rr96+uWuam1nIvt5Bpy3YNmcq19TG3fx+a85lb9Qldve4T4lLFl6cVWsQnkwECBAgQ4KlGQPIeNIwIWHFfW3Qo7WvPpIRjff7iGYuCHoWxPgiF1o+Fpf9+rM8/HotCNOIfC0n/71i/f71w47UWASMKerhxrNmuzz+32dexPr8f5cFgwpdxN/I25dAi69doXj/UkPdYv9+/EQUt6r8Pt8krpT8uLeRfc0M7AUcH/M/0qN9mbJ99bXW9owP++aGW+2BEQW8dl1hvtzb+RjstslGOowNt8xRuadffdqx5H5rHQuv3LyRb5ibcci+3kGnLdg2ZtPD6mNu/D8357I375C4U8sleLLrZ965c9TWSAQIECBDgqcZjMdcKIX4c+KHneaXH0f9DhZSQGX1yfdzaffKedHmDvh5sX1ZsZ588u+j75O2ABVy+wxzf4h5LVOkhwpcZ4osMkEbf8fwAAQIECPBwITzPe/SdCvGvgRcAG/h+4++S53n5R9F/JpPxHmrgRYAATyoWVuA3/7Wv9evuoK1bdnyz7zd+FdJdW17mGgW+xjXKKCyi6EhcFDYVNCRf5wxnSDzEgQQIECDApwdCiMue52X2et5jMdd6nvernueNAb8IzAD/FFh4HLIECPCpQroLvv7zPpGbyfv+fcrz/5/J+59//ee3JXgLuHyNa2hIhjEwCCEQGIQYxkBD8jWusYD7CAcWIECAAAHa8bjMtX8LeBk4C+SAf4KvzQvwKYHCo0gNAIMQDnUUHrJBFhzqG4496Haf6r7O9KG+8UvIv3wf48+u4ywuQY+O8ZWLOD99AtJdGKgtr/dtZilSp4cIVRRhJK0G324izODwXeb5KqP3+1UJECBAgAD7xOMy1+aAD4F/Afy153m3H2X/gbn28ULh8QY2r2HjoYgTYZkKM7gMY9BDhGVqCDy6iLBKDa/xepkq0zgMo9NDdMOx5uv263UTYZUqAkGcMEuNYwcbx5p9xRvtPATdhLfsa6d2K23HZigxTIxuIqy0yNHabrcybdeufQ5b+2qdzyWqzGwzruY5Barc6yD7N7lDHY8Kijhh+tHpQ99A9Bzq1PD4I158bN+zAAECBPik4Gkz1/YCvwbowN8XQrwhhPg/tjtHCPGvhBBZIcS1ls9+VwhxVwjxduPvZx6y6AEeABzq3MMhBNSBW6xSxsOlThnFDVZQKOp43GSVOh514CYruA0NUxmPm6xQw1trV2u0a7/eLVapw1q7cuMaLoqbrDb6Yq2dany+3pfaIMf27VY2HSuhGn35x2ot41KN8ZbxdpRpp3atY/b7Wp+b9flcoYxqtGudw/W+mvJVqLfI7stbwyNHGdFo5wGlBqFrhYZkicoj+T4FCBAgQIDOeCwkTwjRDYwAo8AhoAdQ250D/G/Alzp8/r94nneh8fftBylngIcDgxBDGNSBEHCUOBoCnRAakjG6kEhCCI4RJ4QgBByjCx2JQQgNwTG6CCPW2oUb7dqvd5Q4IVhrpzWuoSM5RrzRF2vtZOPz9b7kBjm2b9e16VgM2ejLPxZuGZdsjFdrmES3k2mndq1j9vtan5v1+exCQzbatc7hel9N+aKEWmT35Q0j6EXDa7QTQIwQYTZG6JZR9BB9JN+nAAECBAjQGY+r4sWllr9/4nnezE4neJ73ihDi0EOW64FANbQtGvKJ888yN3lQPR55M6Q4Tc8TNTdBX7trl0Ljj7jDUMNEu/kbBTYVvhL44wXYI7wFD/WdOnyrjrcEogf4cgj5xRAi3f4tCxAgwE54LD55+0WD5P2Z53lnGu9/F/hPgGVgEvhvdpOG5WH65Ck8JllkAZdZXJapbOuf1TzW7nc2Q4khYiTafNJWtvEtW9nGn2qZGiHgBXqZwFpblvci73785Db6nfm+YCMYfIb0BjkCPD1YwOU3mURD0k1k0/FlqpRRfINMkC8vwK7hXVPUv1aFMmDhO/O4+Im2NAh9PYI4E+TvD/DpxFPlkyeESAsh/qEQ4ttCiL9q/u3jUv8ceAa4AMwC/2ibPn9DCDEphJhcWHh42Voc6tiU6SHKbYob/LPKLf5ZN9p8t5p+UTcafmclFBXqLb5g3iafrnbfslYfrHZ/KoVCIpjFWdPI7CRvU44yqs2va7d+cutjvNHiC1aHTXIEeHqQRufrnKGMYqZxH5va4Bkcyii+zpmA4AXYNbwFzyd4GohhgTAEQjT+HxagQf1rVbyFp0cpESDAk4DHtS36Q+B94DDwPwC3gTf3ehHP8+Y9z6t7nqeAPwAmtmn7+57nZTzPy6TT6f1JvQsYhLDQWKLCIcwN/llai3/WWJvvVtMvaqzhdxZDEiXU4gsmNvl0tfuWtfpgtftTSSQKj8FGXrPdyNuUQ0O2+XXt1k9ufYxjLb5gIdgkR4CnC2dI8A0yfIVRanjcw6GGx1cY5RtkgkTIn2B4ykOtKOpLddSKQtWU/3/L6+2OdWpX/3YVioDhX58qtMbyiG4BZfC+G2wMAwTYCx6XT57led6/FEL8l57nfQ/4nhDie3u9iBBi0PO82cbbXwCubdf+UaDpb/a0+OQ9Lnk7+QYGeLqQRuerjAa58D5F8JSHeqOO96qCGYU3LKAbWBYgPIjjO8/MgDfsQY+AFQECiHvr7bqa7Ty8IQH/Rvmhd8tAVODFAB3fD6/5mLDA+1Ydvvq4lq0AAZ4+PK5fS6OQJrNCiC8D94Dh7U4QQnwT+ALQK4SYAf4u8AUhxAX8Pd9t4DcflsB7QZPEwP/P3ptH2XFehb6/XXXGniV1a2xJLUctO7EhA7p2yATOYCcWJI8hLOLcXAJZ2LxHHu8yPEIeYLjisoB7YXFzb3jBTsjNQOIQeDcQYhOLmDghZJSTmCjBlmRJtlqD1WqpT09nrNrvj69Onzqnz2l1t/r0IO3fWr1OVX279re/XdWnd3+1a3/QHZssjW/P17bccs3214K9hmGsM/K4b+sQmBEoCDynMADgwfEAej0oKBQ9OBbCQPR7f6wqJ+54rwczQAlXEn8AUIFJhT43c0cFZtM+06BjKzlYw1j/rFaQ959FpBf4VeB/4P4X/OX5TlDVtzQ5/BdtsM0wDMNoRhbYDpwEOtS9HLEZNwMnIewVmIiOp0MYjoI2AYarcuqOT6jTkRLoxxXNTCp0iwv8MtT/hSpGb9sahrFgVjzIExEfGFbVzwA54PaVtsFYGhoq5EHT7hNwX/p51yaezO7H2xYqtxw6rC/ry/pqb1+y30Nu9pa1r3BzBT6usAPwQQJxf53iGR1jIHdbHq9hLIYVD/JUNRCRNwJ/utJ9G0tHQ0UPh+hoiJ5X9x+5xnJrTsdycKak1jYp7mF6j7qQfqRRjihXR2FE0EFcaex47k5c33x9tdLXA0wBIq4tF7XtJJZPFJ03hXtk1KOt++qK9dVMbrKhbURruUuTMTsmY31VfXMlm5rKNeZCRT6M9xX3Z9PrEBtXtd9x4GwL2+dri4/riterIT8rri+uI97Wyt6m17XZvREbf48019HsPoznlrW6l7ubjL+ad9ZH83sj3lbVH/dno00txx+7/tV7fjHXIX4PVX0Y99sUIB7yg4J3q4/nxdIvumm+PV9bbFvuShL8XQny0UsWDZkdOqHuzds7LMgzjMWwWklSXxaR94rIK0XkJdWfVbLFWAh5YEyRXg9O4B6tBJ7LsymIay9G+xWBQNx2IC5/5yhQFFf3qlHueJS/U4hkjuLOCQSOLaKvVvqOqzvf1Xxxx/I4XcdifVXlwkhutq8GO47HxjVHrklbNXfpWKS/0sI3V7KppVxk06x/Y/qrY571p7a4DrFxVSLdJSLbIx1N2xr6Oso8Poz1Fd8+isu/auyr0YdFml+HK13XOfdGbPx1Y57vPmyUa3Evt7r+papv4m3Mbas08Wfcpmbjn2PTIu7lxuvf6MOg/j4UH5eTV52RWyZkQPAPJt0btCOK5tX9Y5lXdEShGNXJs4LIhrEoVqUYsoh8vslhVdVXr0T/7SyGfK2ykJk8FjiTxzwzeSxwJq9pX630tZjJY4EzeXP6ajGTxzwzeSxwJu+KNjWVq5+FYYEzeS3HVe03mq1ravt8bU1m8lpfr/qZPOaZyWOBM3lzfNNiJo95ZvKa3odNZvLm3MstZvKYZyaPBc7kscCZPOaZyZv3OsTvoQXM5Im3/AGXjip6KEBjK17IAR+5w1a8MK5vlloMeVVevFDV2+drF5GfUdUPr5Q9y027c9cWKtesTTplzpfzgu19viDP95E1njNkfVlf1lf7+mr2HbJcyIAgb01YmRTDWCbW5LJmIvJNVW3b49t2zuTVzXidq8540XpWp/qfdrNZjep/+I0zY61mLuabaZkQl9D8Uq/uv/AF29snyFZBBjyXeN2mL3nDMAzDMOpZV8uaLYD1G0HEc9dONeTnxPOOGnOX4vlJ8VydeF5Ms7yg2fyZhvykxnyqEBeYnYvN1l3J3qodReAETmas4XzDMAzDMNYkazXIW3vTiwslC2wSNBfCkICvzsvDQJpabal9gB/W2hLq9vfh2jsUUlGbj9MzHOlLRNtpdf1lYm1+dE4q1tcw4EWPZbdFj2EWYm/VjjRwA05mU8P5hmEYhmGsSdZq4sO6nckTT2C/h+S9dZGTtxh7pRh92qPaOeioEvx1BT5SgWejg7sFeZuP9+aEJY0bhrHu0VElfCSA2IsxHPDx7rQXY9YqazXI+5fVNuBqEE+gEwS56vpRyyLXbD/Ggu1dq3fLKqNHQoL/WIIj0UxqZ9TwrKIHKwT/X4D/nhRyy1qdODcMw5gfPRIS3Fd26TubgEHQAvBgQPC/Alfixr7j1hyrfkVE5CONx1T1nU3kPigiF0TkSOzYRhH5RxE5Fn1uaLe9hhFHR5XgXSV4Ul1w1yOQiH56xJWleEoJ3lVGR9dvFoJhGNcvOqouwEuDDAqSFUSiz0GBNAT32XfcWmRF52ZE5NONh4DbRaQPQFXfOM/pHwLeC8SDwt8AHlXVPxSR34j237V8FhutaCy7cjWPlzWt6EWFSggJDzYCl6Lzmm0vh9xy9fUPFTip7oUXAUKtZZQK7t+oEHg2JHi4jPfjiUX55nooyWF9WV/W19ruK3y4AtNAL2hZkYTUJVVJj7gi1ocCVwLHWDOs9NUYBL4HfAD3p1CA/cCfXOlEVf2iiAw1HH4T8MPR9oeBx7Agb3nJj8LpR+CZh6CUg1QvuvMudOx16PgmVxh5XJe+rFmgcDGE77o2dgSwAVcCRogK0uJ09EbHTwODkVwusrMvagNXMmYcOBPp64t0SNR2OeprZ1A7r9pW7auvSV+z5WVi2/8KPIcL8nK4l1QSUZsfHS8DM8B7A8IxtWXNbFmz9busWatr0vQ+1Fpx8KqNcX82jv9K90YzuSv5sJW9jfdrK1/H9TW75+tKYEXXv/E+XKivq7/z3Q33V/Uerd43C72X498hc5Z5bLgPWy3LWB3/g4H7LjsLdAm6BWRzfaDHJtCHAqtxuMZY6ce1+4HHgd8Ecqr6GJBX1S+o6heWoG+Lqp4DiD43txIUkXtE5LCIHB4dHV1CV9chl47AF+6F4w+Cl4TOQfd59BPI8V9A0t9zs1hXs6xZUeBJXCBUwOV7PElUNibarsS2qzKFJnLV7aei9qrsU9HxSiSXj+l4quG8Rh3FSD5uR1xunNq/K0TjUNxvVik6JjgfXMSWNbNlzdb1smZzy0HNdx96DX5r8GfcN41lpGbLRjX46dg8bc182OjrVssLzt5D3tzfofl+H8JGm+bx4ZV83ep3tHEZxfg1aSbX+DvfdJlHaf07Gv9eqo7/YvQ9FuC+32bUfR/GSYPmMNYYKxrkqWqoqn8K/CzwmyLyXlZoNlFVH1DV/aq6f2BgYCW6XN/kR+Hr94GfdsFdIuv+201koWcHZDIw8jswdNGVcMkC6dCVcklofckXD1caJq2uLEtcLq1wE5AkasPt+9HPTbg7pLpdlck0katu3xi1V2VvjI4nIrlsTMeNDec16khH8nE74nJ9uC8/jfyWpBbUpaJj1aCvH1cmJ9HCN7PlcCJ5X2Gv1ErttJQLndysf3VuSZ2qvmFpfh2qOmZL9DC3DE/Ttoa+GksDzd4bWt9XfHsf9eWFqn3FdVRL+cyWFIrZWx1jKx/G78m9DeOvG7O4Y1Uf7mvQ0Vi+aI4PG65r3PZU1Tc6t0RRvC3RxJ9xm5qNf45NDffNnOsQ09F4/Rt9WC3f1Khv9nrNdx+GDX5r8GfcN41lpGbLRjX4aXietmY+bPT17P0azvX1cKOvF/D74DXaNI8Pr+TrVr+je6V5X63u5cbf+b0N93Wjvsbf0fj3UnX8/dSeTgjQIXP/chejt22NNcVqLWs2ArxZRA7gJovrEJENqnp5AaqeE5FtqnpORLYBF5bb1qUQliswmifsS8JolNQwkHXb5QokE7X9eNtyyzVp87Z34yXrL3sze2Xi75HpaUh1QakI2YT7Tw/cF3xPNyQnkBv/Cd3wlih3zV9gjlujnL+8eXIrnZP3J+oeAXfivgSrAV81+JvCvY32Tt9y8qwv68v6Wnd9hRuBjytsB4Q5OXkAjIHc7WOsLdbVsmZRTt5nVPWWaP+/AmOxFy82quqvX0l/O5c1C8sVwg9/HZ57Di6dh2KUvJXqgWIOgrOQ2A6pXihN1tpKU9F2t8t9q5wFbxtk+qA86YKFVFckJ5DqdLqDs5DYEZ035X7xkt1QGIfwXKyvCRAfbr4N7x0vmw30Wtnr7fobCAIIyiCdwBaY6nezeSncf3qpAvRVCKc/0nrx8yY5eXVyMneptfWEjirB24vwRDTTlGoYQ1ndY5IXevgfSlktKcMw1h06qgT3ltzbtT1zv8N0QqEI/v32HdcurvllzUTkQeArwI0iMiIi7wD+EHidiBwDXhftry6jebh4ETb0wcwpXBJDCPljoEWQGQiLMHMMl9QQwnR1u+KOh5GclKLzAvczc5zZ5LKZ45G+PGgh2o/k8sfcudW+8lGChufBuXO1Gb/57GUMQgGpAAr5GdemuGDNFyilYSp3VTl5TZdaW0fIgOD/UQpuEvf22YRCJfqZUDeLd6Pg/1HSvvwMw1iXyIDgH0xCEfcWbV7duud5RUeiAO+gfcetRdbqazBzphdV9S0tZF/TZlsWx0AW+vvdzFjHUG0mLzvsZvLCDpfnlhmuzeR1Dtdm8jqGo5m8DtCUO6886XR37K3N5HXsdbrDLEgGOgZqM3nZYTeTp1FfqWE3kxeGsG2bs/FK9rIJvACChJu9y3bAlF974zVQSBWhq9e9LRfPyZtSN5M3LDCpTt0+3FtnmXo5DUG2ee78dYrc4uE/mCb8mwr60QqcihqGohUvftJWvDAMY30jt3j496fQQwH6UICOuRw8udtH7rAVL9Yq6+px7XLRzse1cK3k5H0KOfpXkNriZgAbc/JKAsURuPFu2HH3VeWFNC61ZhiGYRhrmdFL8MiX4KEvQm4ServhwKvgzlfAwMbl72+pj2vXapD3LVV9cbv0tzvIuybIj7ryKX7a5Qw2UpqAoAg/dD9k7W1lwzAM4/rgyDG4779DsQyb+iCTgkIJxsYhnYSDvwS3DC9vn+sqJ09EPnqFY2vrEez1SHYAbj3oArnpEajkQUP3OT3ijt960AI8wzAM47ph9JIL8NIpGNwC2XSU0ZR2++mUax+9dGVdK8Fq5eTdHN8RER/4geq+qq4R91znbLzFzdSdPgTPPgTTY5Duhb13w847LMC7zghDyBfcl1i+4PY9D7IZtw/Nt5dDzvqyvqyv9ddXZ4c7fi19b/zt59ysXV+Py4hKJOrfFO3pgpHn4NCX4a0/wqqz0mvXvhv4f4CsiFTr4wmulvgDK2mLsUCyA7Dvre7HuG4JQzh8BEYvw/ld8spvAAAgAElEQVSLcGkCzp6Hwa3Q0wmTM9GqVl0wOR1VzemEiSk4Hcn1dsLUTLT6U0dsuxMmJt0X4+AWV4KxUcfI+Vrb1JRbTaor0lHtNxfJ7dzqvmgnpqNVomJ9dXW447P6uiIdUVtuKrJjK/Q26JiMy8V09HbVj2tyZq7t27dCX6NvojHH26r64/7MTdbbNGf8jTZF12RqpsV1iOlo5ZuqDxv7arR9sMHXdf6Nrvms3zrr/VmVq45/arp2P8za18RP87U1+rCpvZ3113KyyT3UzPbxqeieb3KPxv2Za+HDK/k67ptck3u0sa+W93I0lrprMlU//pb3Rme93+L+rPvdmOd3ueqnXVvhthe54GlsHM6PwuXJ1r978X5b/i63uDfi16TZ70OjvU3vjUhH3G/N7vnHvuEC1stRBLOlHzZvbFjhrQ8eemxtBHkrveLFH+DezfyIqvZEP92quklV372SthiGsXDyBfdF3dsFJ067+tgzRSgU4dgzUUlFhaOnIAghDODYKdeeL0IxkquErj2+ffQUFEuRvpI7LwjqdcwUncyxU66fIITjz7jPQN3xYtRXoej2w8Dpifd1/BknVyg4fVUdlcimYqnWdrRBR51cTEfjuJrZXio28U1pblslnOvPWZuKzcc/x6Zira3pdYjpaOabuA8b+6rqm71ecV9XdcTtjdvY4M+4b46dqr8fAo3kGvx0bJ62Zj5s9HWh0aaYr6v3UCvbZ+/5Jvdo3J9NfdjiPpxzvza5vxp9H78mzeSqY2l1Tea9N2Jtjf5svK6VcO64jsb8FCicOgNnL7jvjZMj8//uVf15tNXvcpN7I/69McfeYhN757k3qt8vcb81u+en8zCTdwUkPHHblYYl3tIp98/ZWmDFc/JUNQReuNL9GoaxdLIZ999pbgpu2AmpNHSkIZOG4d3g+6504r4h8D3wfBgecu3ZNKQjuYTn2uPb+4bcl2JH2iUwDw85fXEdHWknMzzk+vE92LvbffrijqejvjJpt+/5Tk+8r727nVwm4/RVdSQim9KpWtu+Bh11cjEdjeNqZnsq3cQ3qbltCW+uP2dtSjcf/xyb0rW2ptchpqOZb+I+bOyrqm/2esV9XdURtzduY4M/474ZHqq/H3yJ5Br8NDxPWzMfNvo602hTzNfVe6iV7bP3fJN7NO7Ppj5scR/OuV+b3F+Nvo9fk2Zy1bG0uibz3huxtkZ/Nl7XhDd3XPtifvIFhnbA9s3ue2PP4Py/e1V/7mv1u9zk3oh/b8yxN93E3nnujer3S9xvze75zix0ZF0eXqhuO9HwTLRYcm/brgVWKyfvqyLy71T1G6vUv3Ed0JhDBm3M1ZgZxTv1KbInP0Q+51bXy/ZtIT/0M4RDP4bXMbBuc1Cqcs+/AZ6/Qn1di/lJ1pf1db31dS3m5H3ys/CpR2Frv2trzMkD99Tj7jXwqBZWqYSKiHwPVx73Gdw6AQKoqn7/SvRvJVSufeI5ZOdGXd7JlXK35uRgtMhjmiN34Synv/0vDCb+ld7MFJPBJlShJznGRKGLkfItDL7wlfRs3j4nF+hKOSiNfc0n1zQXqiE/pzHfJ55PVpfj1ZCD1NcLWze5+k/7b3FfiIZhGNcbo5fg3t9xQWtP19z2iSk3k3f/f1reennrqoQK8AbgecCrgR8FfiT6XBIickpEviMi3xYRi96MuhyyUyMLyN1qkoPRKo+pTu7pKQpPP0J+eoYiPRybuImAJCFJjuWeT0F7mJkuUHj6EY49PbXoHJR4X1eSa8wTi+caxXOh4nlnjXlCrXKQTpx2jx/Gxmv/3RqGYVxvDGx0dfCKJfciRr7oHtvmi26/WHLt7SiIvBRW5XGtqj4DICKbcQtdLQe3q+rFZdJ1dVTryflp9wmQyNZqzYlX24+3Lbdcs7Zkpzu2XuxdglzoZank82Q8uHzJY2hHmonxEiLC8O4kuVyJbAIyvrB5d5KJyTKiyt5dKaYmSqjn5CZyZbIJJe3D8O4UUxNltCo3VUIRhju+xURwgazXRzpRZLjrGSZLXSjK8KZTTBS66EgUyIQTbO5+gknv5QguL6Q6k7dvyM3QVXNQNm+qzbzt3V2babuS3MR0fZ5YdSZveKgmN7zbzeTF5ar6q30J7pzxqVq+y9YBl0g8sNE9ujAMw7heuWXYzdQd+rJ7i3bssvsn+O4fgTtetnYCPFilIE9E3gj8CbAduADsBv6Nhvp56xIN4cJhyI/CzDkoT7jXcJI9bpWI6RHoHHSrSJSjtWuTPVCO1q5NdsfktkOqr76tMumeGSa73Bq30yPQuTOmr9o2DtNnY31NgPiw5aWw5dZaoLcoeyci/d1N7D0dyfU2sbc6xhxMjdTLiUKiq9ZX1yAke5fWV3mKUJWvn9zDV5/sQYs5XrSvyJ0vmKSYLwBCtjNDfmqGcNc5vK5tZLs6yE/PgAjZjgz56QKgZDs7mshV2zLkZwqgSvbZB8mnzhCGiuenyGYhrxsAyCYL5MsZwiDE0xLZHU+Qf8XLXds6ykGpyhVL7tMe1RqGcb0zsNGVSFkLZVLmY7VevPg94KXA51T1xSJyO/CWq9CnwCERUeB+VZ1Tc09E7gHuAdi1a9dVdHUFKnkoREWDz3/FfQLkjrmArTIDQQFy5yG7udbWMeBGkTsakyu5tuxmN8TxSE6q+npdf0EBcs/hihNLrG2mvs1LwvQ5d06ycwH2VnUUIzsGam1xm9JVO4puvzMaS9VeonGl+6BSgDAmFwLjxyHT69qCIswcrffNQvqKfJgvJDl3ZgKPLGiJy5MJypefoXtDn/PN9Gm6071Qvgzpbpg+Q3d1XFMjdFftnT7TRC6yIy5XuUB3MgpI/TSQpDs1NRtEd6dnnFxpCoLLdMdyOFptz9e2ULnl0NEo1/gGmWEYhrG2Wa3/ycuqOgZ4IuKp6ueBF12Fvper6ktwuX6/KCKvahRQ1QdUdb+q7h8YaONKDYksZDZBMQc9Q+79a/Ggd9gFAYkO8DPQu8/NrFXbSLj93n2uPdEBfsq1ie9++oadPhIxfdlIX0yud9idO9vXsOtHQ+jc5s5ZkL1VO9I1HVX9cZv8TGRH2u1Xx1K1V7xoXGlIZMCLyXk+9O11xxKZqK99i+8r8mE2E7JtRw8hSQIvxfaNBbIDuxt80+g3r2aHNI4/Gxt/1Y69tXFl+t2xKl5i7uPwMHDHqwG0YRiGYawAq/W/+biIdAFfBD4mIheAyhXOaYmqno0+L4jIp4BbI90rj3iE/fvJT+VJb0+Tn3Z5YtnOLPnpvHt053uz+/G25ZZr1tbZ04kXD0LWuL1Lkdt/c5abb3e5e50dHl6qffl/ZPrhiT+G4jgkO+YGeABhAdIbYdeBBdxAhmEYhrE8rFYJlU7AJUnBW3GrYHwsmt1bii5PVSej7X8EDqrqZ1ud084SKrOlOy7BuYvudepWy6fULYsTW9Kn5TI+LZZW2rl17tJSjUvwTEy7IpIvfSHc+v21vKrF2Nt0aZmodEerpaualQlpVrojvuxO3XJSi+hryqXWzRljW8mPwj+9HcaecDN+fqrhhii7x9D9L4TbP4St92sYhmEslnVVQkVVp1U1ADqAvwf+Eve3eilsAb4kIk8AXwcemi/AazezpTu6a6U75iwFVJq7xFF8WZiWy/g0WcZotpxGQ8mMxiV4wsAFPedG60tgzGdv0+Vemizj03Tpqpi9s+U/5ind0XI5qUX0VQmbj7GtZAfgB/8I+m6CyrR7MSSsQFBxL4+UpmDDjfDSP7IAzzAMw1hRVuvt2nuBg0Ael3ovuCDvhsXqUtUTrKFl0rIZt/zT6CUYGqzNjA0PuZmx+BIs1Zm84aHaTN6+mFwqDcObopksL1YWg1opjNlyGhvrS2bEy19sHnIzYGEI2wbqS2AsxN7qci+zZTiGarNrw1HpjtllcTbWZtealQlpVbpjIta2b4l9Tc00H2Pb2XgLvO5BePpv4OhHYeqUO941BDe+DW74SQvwDMMwjBVntXLyfg24ec3UtVtGPM+tCBBfxgXWzlI1nR31jzHXur1LkWsc44owJfDFDHxkIzw77Y7t3ghvy8Bmgez8pxuGYRjGcrNaOXmfBX5cVWdWvHNsWTNjmTnyJPzH34YjT7mVrzuygMJM3j1nvvlGeM/vwS03rbalhmEYxjpkqTl5qzWT927gyyLyNaBYPaiqv7RK9hjG0hi9CO/6PXjyaTeFmErW2nq6oVyGp552Mh96Dwz0r56thmEYxnXFagV59wP/BHwHl5N3bRGGa/f5Z7NnmWvZ3rXe18OPwrNnXAJhIlYvr0oyCaUynHkODn0B3voTc2UMwzAMow2sVpBXUdVfWaW+20sYwuEn3AzPuVGYmIhqiHRFNUnOwuD2qIbKVK1tKtrujslt3w593VGbQnd0juDqpOQmohoq2yJ9k66GSFcXjE/C2VhfE1NRDZX9cOuLG2qoLNDeiWgZsu7uufaePgeD29xruo32VvXlpiJ9W6G3p6ajq8v1O3Le9dXbdeW+ntcFe07ChsOQnAHtgdM3wfkXQGpbvb6qf1v5bSLWNjVVP/7T55rbW93+q7+FM+fdCtWlsgsAG9/6yKRdnw99zoI8wzAMY8VYrRUvPi8i94jINhHZWP1ZJVuWl3wBxi65oOBUtYZICMdOQrEQ1TUpwtGT9W2zNVROuvbZGionY7VRTsZqqJx09UbyhahOyslYrZWTsRoqkY4wdKs0nDvfpIZKC3urdhQLNR2zdgRRXZOTrv98IaprUh1LzN4wGlexAIV8VEMlNubjJ6IaKnknc/QKfXWdhhf/JfQ8ChUPpvsgl4fBb8BLPgaXvlKv7/gJd37VN41+C2N21I2/Oq5SvR1xuYuX3Xa1AlCl4gK+OL7vjucmV+w2NAzDMIzVCvLuJsrLAw7HftY/2Qxs2uhmboZ2g++5WbPhPZDORHVN0rBvT31bwnP7+/a49tkaKntckOBLtO052eE97vFqNuPqpAzvcTK+77ZT6Vpfw3tcP2EA27Y2qaHSwt6qHelMTcesHb6THd7j+s9morom1bHE7PWicaUzkMlGNVRiY957gzuWyTqZffP01VuEV38FSENiEEi5VSa6NkJxA4RJeM3XoKdY07f3Bnd+1TeNfvNidtSNvzquVL0dcbn+DW4bcf5MJMCT+nsiCNzx3u4VugkNwzAMY/Ue174L+KyqTojIbwMvAX5vlWxZXjwPfuD7IH8ZUt3uEyC7wW0HFfATtf1423LLNWvrGphbQ2Ut29u4nXs/lLaDvwMSaQhKgO+CqEoQDeo83NAPif+9/Tl5/Rvgj98H4xPurdpmtVsKRdi4AQ68ttkdYxiGYRhtYbWCvN9S1U+KyCuA1wF/ArwPuG2V7Fk+NITKN8EbhcI5YAJQyPeAToCOAINun8laG1PRdrc7R0dAt0O+z7VptW0SEMh3QZgDRoCdkf4oJy/fBToOejbW1wTgQ/ml4N1aW2N1Pntn7YjpUInsaLCX05FcbxN7q/pysfH3xnR01fqabWvRV+WDIAVgBoI0SAdIBmTAveQAoP3Ao9D7jtp16e5qvj1f20Lk7noNfPLTcHnCBZmphiCvXHaPb3dsgTt+CMMwDMNYKVbrcW11yuUA8Oeq+ndAah759YPmIRwDvxfCU0AAEkJwDKQIzAAFCI+CRG3hMZCK2w+PunZmQErReUHUdizSV4nOKbr+tBDtR3LBMXduta/gmOtHPAjPuXMWYu+sHcWYjqC+r/CY61/zTq5uLMdq+sKjQDEK0GJyBBAeb2g7Ok9f07j8tyAKalPuHK3ELkI6CoBXgIF++KPfhpueB9Mz7iWOSuBy8CYm3TIcNz7PyVj5FMMwDGMFWa0g74yI3A/8FPCwiKRX0ZblRbLgbYIgB94Q4Ls1yfxh0DRuud4MePtAozZvGDTh9r19rp0O0FR0nh+1DUf6EtE5adefZKL9SM4fdudW+/KHXT8agrfNnbMQe2ftSMd0+PV9ecPRTFrWydWNZbimz9sHpEEz7rMqhw/e3oa2ffP01YnLf/NBukFL7hyJT0oXwett40Vu4Jab4MH3we/8Cuze4YK96RnYPQi/86vwifdZIWTDMAxjxVmtFS86gNcD31HVYyKyDfg+VT20Ev23fcULDaPZsnRt1kyy0axbNKNW3Y+3Lbdcszavs/aodj3Y27g9/T9h5m8gsQPwcTOFCWZffAAIRqDjbuh866Ium2EYhmGsRZa64sWqBHmrjYiMAs+0uZt+4Jpbm3cV6QcuJhMkhnayW5UwCOYW0vZ9PBG8U6d5plyh0kSPMRe7V9uH+ba9mH/bg/m1fSzVt7tVdWCxJ12XQd5KICKHlxJ1G80xf7YP8237MN+2F/NvezC/to+V9u21kQdnGIZhGIZh1GFBnmEYhmEYxjWIBXnt44HVNuAaw/zZPsy37cN8217Mv+3B/No+VtS3lpNnGIZhGIZxDWIzeYZhGIZhGNcgFuQZhmEYhmFcg1iQZxiGYRiGcQ1iQZ5hGIZhGMY1iAV5hmEYhmEY1yAW5BmGYRiGYVyDWJBnGIZhGIZxDWJBnmEYhmEYxjVIYrUNABCR1wPvAXzgA6r6hw3tErXfBcwAb1fVb0Ztp4BJIAAqC1n4t7+/X4eGhpZzCIZhGIZhGG3h8ccfv6iqA4s9b9WDPBHxgT8DXgeMAN8QkU+r6vdiYm8AhqOf24D3RZ9VblfViwvtc2hoiMOHD1+17YZhGIZhGO1GRJ5Zynlr4XHtrcBxVT2hqiXgE8CbGmTeBHxEHV8F+kRk20obahiGYRiGsV5YC0HeDuB0bH8kOrZQGQUOicjjInJPq05E5B4ROSwih0dHR5fBbMMwDMMwjLXLWgjypMkxXYTMy1X1JbhHur8oIq9q1omqPqCq+1V1/8DAoh9rG4ZhGIZhrCsWFeSJyKtFpGOZbRgBdsb2B4GzC5VR1ernBeBTuMe/hmEYhmEY1zWLncl7O/BtEfmKiPwXEflREdlwlTZ8AxgWkT0ikgJ+Gvh0g8yngf8gjpcCOVU9JyKdItINICKdwB3Akau0xzAMwzAMY92zqLdrVfU/AIjIduAncW/Fbl+sngadFRF5J/AIroTKB1X1uyLyC1H7nwMP48qnHMeVUPnZ6PQtwKdchRUSwMdV9bNLtcUwDMMwDONaQVQb09/mERb598Arge8DLgJfAv5ZVb/SHvPaw/79+9VKqBiGYRiGsR4QkccXUge4kcXOwP034Gngz4HPq+qpxXZoGIZhGIZhtJ9F5eSpaj/wc0AG+H0R+bqIfLQtlhmGYRiGYRhLZrFv1/YAu4DdwBDQC4TLb5ZhGIZhGIZxNSz2ce2XYj/vVdWR5TfJMAzDMAzDuFoW+3bt97fLEMMwDMMwDGP5WFSQJyIDwK8DN+Py8gBQ1Vcvs12GYRiGYRjGVbDYYsgfA54E9gD/CTiFK2ZsGIZhGIZhrCEWG+RtUtW/AMqq+gVV/TngpW2wyzAMwzAMw7gKFvviRTn6PCciB3Drxw4ur0mGYRiGYRjG1bLYIO8/i0gv8KvA/wB6gF9edqsMwzAMwzCMq2LBQZ6I+MCwqn4GyAG3t80qwzAMwzAM46pYcE6eqgbAG9toi2EYhmEYhrFMLPZx7ZdF5L3AXwHT1YOq+s1ltcowDMMwDMO4KhYb5L0s+jwYO6aA1ckzDMMwDMNYQyx2xYt58/BE5GdU9cNXZ5JhGIZhGIZxtSy2Tt6V+L+WWZ9hGIZhGIaxBJY7yJNl1mcYhmEYhmEsgeUO8nSZ9RmGYRiGYRhLwGbyDMMwDMMwrkGWO8j7l2XWZxiGYRiGYSyBxZZQAUBEXgHcChxR1UPV46r6zuUyzFgb6KgSPhLAQwGaA+kFDvh4d/rIwCpO3I6OwiOPwEMPQS4Hvb1w4ADceScMDKy4OUePnuWBL3yOz31fjvO7siiw7XSe1/1rLz//Q69l377tK27TvORH4fQj8MxDUMpBqhd2H4Cdd0J2Yf4bG4V/fgQ+/xBM5qC7F24/AK+8Ezat/CUwrgGmGeUEj/A0D1EgR4ZenscBbuBOOrGbarWZGoV/ewS+9xDkc5DthRccgOffCV3X2+UZLcIjF+Ch85ArQ28SDmyFOzfDQHq1rZtFVK+cRiciX1fVW6Ptnwd+EfgUcAfw96r6h221cpnZv3+/Hj58eLXNWPPokZDgvjIUgU1ABigAY0Aa/INJ5JblngxeAEeOwH33QbEImzZBJgOFAoyNQToNBw/CLbesmDmf/dy3+J3EP3Pyxg78ipLJV1CEQtYnTAg3HJ3hd8uv5PWvffGK2TQvl47A1++DsAjpTeBnIChAcQy8NNx6EDbO77+njsCf3gflIvRtgnQGigUYH4NkGn75INy4cpfAuAa4wBG+yH0EFMmyCZ8MAQXyjOGT5lUcZDN2U60WZ4/Aw/dBUITOTZDIQKUA02Pgp+Gug7D9erk8Rybgvn+DYgibUpDxoBDCWAnSHhx8PtzSs6xdisjjqrp/0ectMMj7lqq+ONr+BnCXqo6KSCfwVVX9vkVbXK//9cB7AB/4QGPQKCIStd8FzABvr66ycaVzm2FB3pXRUSW4twRpkJ65M3Y6oVAE//7Uys7ojY7Cvfe6YK6nyS/RxIQL/u6/f0Vm9I4ePcvPPfPXnLipg1QxIFGu/32qJIVS2ud5T87wF7vfvPozevlR+MK97ls51cR/pQn3Lf5D97ec0Rsbhd+MLkFXExVT0SX4/fttRs9YGNOM8g/ci0+aNHNvqiITBBR5A/fbjN4qMDUKn7jX/QOXafI7X5hw//D99P3XwYzeaBHu/bYL5nqSc9snyi74u/9Fyzqjt9Qgb6GPaz0R2YDL4RNVHQVQ1WkRqSy20zgi4gN/BrwOGAG+ISKfVtXvxcTeAAxHP7cB7wNuW+C5K04+N83pL5+k/+atnP3aSQTYdtsezn3tJMWpAumuDNtu2zOnTYHtTeTibfFzzn7tJKV59DXT8bw7XkC2t/OK9g4+cwP5E+MUe4okLqTo2bKRibFLgNLTv4mJS5fwLwrZj/VxevhpijMFUh0Zdrz4Bs5+6wQK7HjxDZz51gkE2B5tl2eKJDvSTeVatcW3c3/8ATJPT8O2Tnr9MrmLZUgk6N3gkbscAhn68uOce8/HufzyV5HKptjxgh2c+d4ZgNntYr5IOptu2rYYub/51mFkT8D2I1MkQxjrh/4LgMDFAdg+CmVRvCI8+PCj3FP6YQB6d20i9+xY3Xa5VCaZSjZtW6jcFXUUDpF7NkHZ7yeZgt6NZXLjWdfWVyA3vhXy5+n9t4fJdb2RcikgmfLp3d5J7qxbyfCLj3VRvlSmdyAgGPfwOtNIqehupnSaLr9IPic89pkkr71ziqAS4Cd8shs7yV9yOpptzye3HDqsr7Xb14lND1OeLpLWTYTdIUyCkIDuEJn0SQUbmEqc4enOQwyP/RQAyY0+5UsBYUXxEjK7H29bbrnrta/v/oNPOB6Q2qrotEDGJxG4tiDh05kJyI3Bkw/77P/xAEIFT6AzCjHygQuK8rG2rO/2ofn2csi1o6+/PQuFAPqSUA4hISCxiY6eJIzk4dAFeOtOVpuFzuSdAkLc27MKvExVz4tIF/AlVX3Rkg0Q+UHgd1X1zmj/3QCq+gcxmfuBx1T1wWj/KeCHgaErnduMds7k5XPTfO7Ag3BO8S4nSZSSiEI5WcYvJckW0+TTRYJkmUQlieDaEpUkKFQSZfxyJJcqEqTr2xKB+8+hkqjgFz2y5Q7y6QJBskKinACJdBSTZEtRX6kyyXISfMjfPMMdn717NtBrZW8630mo7r+ACoqmKighghD4AaIeEniAMrXxEh1TXcx0TFLKVEiVU6BKKVUmVXa2l1MlksUUndPdTHdOUkqXnRxKMVEiXaq1ldMlkqUUAOV02W1H+jadr5AINlD2x1F/BrxOQk8op0r42kGIgk7jBRlmNgwy1VMgnymTKSZBlHKmgj+ToG8iw0RvgUq2gl9I4CHkU05ORQkyFRL5BD25DOM9BYKOCslCAlQopMtkS0lClOmuMplCkr6xDJc2FCh0lumYSaJAoaNCdipB33iG8Q0FypkyG8qdiEDQEeJNeyCKdoJMCJlLSYqbSoRdiswIIIQLkVMh7AzxJr2orUzQ5c4TlLBTkBl1cskc/kyKdC5LoTdP2BXgFzKoKmFHGS+fdOdkK1DaSHZMKPQL2uMh04oATx/fQbZSor9S5nI6SdCt9KTzCFBIJMhUKgQVIe+luPWHv0NyLE95Uxa6Uki+BAramUJmattMlubIqQIdKZgukbwYtXWnkOkSEOmobnekYKpeBzMlRECzDf1OlkhcnKGyqQPtiXSI0yETJRJjM1QGOtAu17dU26py2aivuI6Z+nHN2j5rUwa608h0ydWZqhtzrY1m9kY2lfs7nG9mGsZfta86rv4ONPLT3L6yc3U0uw79kVyDr1vpY6bk/jh0ppCpEonRyN7uBr/FrkncN7l9XyU5mUJCn3JXQCbXBZokzKRJXU6SPpMhv2uK8sYC2772c6iC1+sTjlfgdAF2ZPA2JghzAQJInwtKREH6EoTjZSc3mMHb0EROQHp8wssV5EwB3dEg1+sTXqrAmQLszOD1RW2Rfp2o1Gy6XEFGCuhgBm9DstbW56PjAQp4Pc52GSmgO7O1NonpqNrRlyCcaNJXVUejnID0JNDxitPXSi7mGzldtTc+Ln92/Me/7ZOuVOgqFsh3ZpC+BN0drq2U9EmWA8IyFHyfV765AmcLsCsLt21wwdNYCc4X4HIZRtx1oCcBU4GLKrp8mAxcdNHjQ67i5HZm3f5EJNftu3M02p6o1OubjOnLxdp6E/XnzfaVcDNvszqSTmdVx1QTuccuuoC1GthtScPmdH2glw+gEsLH/93VBRQxljqTt6CEKlUdUtUbVHVP9Hk+agqBH4sZsWGxBgA7gNOx/ZHo2EJkFnJu1bZ7ROSwiBweHR1dgpkL4/SXT+JdUArzHz4AACAASURBVOiDzFSHc7B4dMx0kMDDJ4GP2xev1uaJh3jQMdOBH8klqm14eEK0DZ54dExnnS71Sajb9wS8SHdC6vtCPBDwzwinv3zyivb66u5ZHw8RIVFOgnioB4lyCg9BPCVRSeEHSRJhEk+T9Ix3O3vxom0fT326x3vxQ2evFyRicj69uR68wLX5YYLu8V489fHw6B7vwVMPT3x6xrvxNYVHEo8UicoW8BL4oUeq2A8qeHgky5tJBkmSgYcfeGwYy+Kj+OrRO5olFSZIVnySFY+e0Sy+enhKnVzPaJZkxSNZ8UmFCXojOR9lw1gWT8FXj4HzHaRKPqnAJ11OsPlcB76CH3psPpclFXikor42PdeBpyAhZEZSEIAEQmokgZQhGYCUhfSZFBIIXgjpM0l3znxyGslVqm1unwBEhfSZBBIIBJB+rgcJfJKVJF7okz7XiSh46pM+34kX+oj6pM9l8coeybLglSF9Opy1d/NUkZSEpIOQpIb0T+XxQsEDBqan8FASvrJpooBXqpAoKl4pIDGSQwLwQkiert/2SsGsXHIkhwSKFyjJkRxesdaWOJ1z9oaQeHYCLwRRnFxVR7ni9gNFAq3rN/HsBF65gl8Cr1Kp6QgiOypRW6ni9iMdydO5WbnESA6vVNPROJY622fHX9PhBUqibsxz9UugblyNNo00GX/V9uq4yrVx1fqq1PtX516HxLMTNblic1/Ptb3m67iOOh9WbWy4JnHfdJ/swAsSeKFH98lOJEghYUDX8SxeySdRSODNJMgeT0FFkQDCJ6eRfIhfDKAQuv1AIdBoGzRQwqem5sh5AXVyVNw2BcUrNNdHwemQfDhHv7Mp0pdXpFhxOmbbnL0EihdA+JTTJ8UKUggIn5xGg5oOCiFeIUAKIeFTzo45fT01jTSRo+LkZvW1kJv1TdWOvM6OWQOtG39XbpokIX4Y4IchXeNuLKLQnZvGU8X3lc7RaSiFMBNAoHBqxgV8vUk4OeMeYxYC93nc6aCicGwaAtxM2dFp156vONlj0+54gGsLou1jkdyMu16ujZq+2b6i/YobS11fx6bcuTORTcemor40si8mV+1rOnCfiougZgKnO07acy9jrAGuKmteVWdU9WTs0KNLUNMsoatxerGVzELOdQdVH1DV/aq6f6CNuVo7X7aHcLPAOBS6ZggBNGSmY4YKIQEVAty+hrW2UEM0hJmOGYJIrlJtIyRUom0INWSmM+90SUBF3H6oEEa6K1rfFxqCQrBD2fmyPVe0NxBQhYAQVaWSLIOGSAiVZIkQRUOhkigR+GUqXplQykz0TTp7CaPtgFACJvtyBJ6zN/QrMbmAXO8Eoe/aAq/CZF+OUAJCQib7JgglJNSAib5JAikRUiakRCXxHIQVAi+klL4IooSElJMXKPtlyn5I4Idc3pQnQAgkJDeQp+RVKCcCyomQiYE8gYSEQp3cxECeciKknAgoeRVykVyAcHlTnlAgkJDRrTOUUgElP6CYrHBh2wyBQOCFXNiWp+SHlKK+xrbMEAqoB4XBEvigvlIarKBJKPugSaW4o4T6SuhBcUfZnTOfnERyiWqb28cHFaW4o4L6Cj4Ut0ygfkA5USb0AorbplGBUAKKW6cJvQCVgOK2PGEypJxUwiQUd3qz9l7oSlNSj6LvURaPi11ZQk8JgdHOLkKESiCM9WQIUwkqaSFM+VQGe1EfQg/KO+u3w5Q/K1ce7EV9IfSF8mAvYbrWVtnZ6+z1oLKrh9ADFZxcVUcy4fZ9QX2p67eyq4cwmSBIQZhI1HT4kR2JqC2VcPuRjvLO3lm5ymAvYaqmo3EsdbbPjr+mI/SFSt2Y5+pXX9y4Gm0abDL+qu3VcSVr46r1laj3r8y9DpVdPTW5dHNfz7W95uu4jjofVm1suCZx30zumSH0K4ReyOSeadQvoZ7P1N48YSqgkqkQdlTI7y1BQlAfvJs60axHkPYh47l9X8CXaBvEF7wbu+bIhT51ciTcNhkhzDTXR8bp0Kw3R7+zKdKXFTSdcDpm25y9+ELog3ej06fpBJrx8W7qRPyaDjIeYcZHMx7ejc6OOX3d2Ik2kSPh5Gb1tZCb9U3VjqzMjll8qRv/VG8nZTwCzyfwPKb63FhUYLK3k1CEIBCmBzoh5UGHD77AUAdsz7iAZ0+HC34yvvvc63SQEBjudFn1nsC+TteeTTjZ4U533Me1+dH2cCTX4a6Xa6Omb7avaD/hxlLX13CXO7cjsmm4K+pLIvtictW+On33Kbhprg7f6Y5TDF1guwZYUgmVeVhKBv4IEH9wPQicXaBMagHnrijZ3k5e+9Bb5s3J619gTl7/AnPyWulrpqMxJ6+VvYPP3ED+L6OcvGSTnLyLUU7e22o5eVsXmJO3ZZ6cvGZt9Tl57yfz6IOwbTO9/ckmOXnQlz/PuR98PZdfvmdZcvL2XiEn75/2PEc+LU1z8vqjnLyOIrz62QHe9NqXAquck/fNz1D2t7TIycu4nLyXvJ5c1yua5uTpY10c+niZ/oEA8VxO3mgsJ49ikdFzwp3/PslNd954zeeTWV/LkZOX5snpT9Kl25FuKE7m6nLyNICpxBme3/lT7BobAtZm7tq12tf4P/g88bGA7q2KeC4n72IsJ8+vBOTOwAvf6sO1npP3yTPwqXOwNePaGnPywD2evnuQtcCCcvIWrEzkm6r6kkWekwCOAq8BzgDfAO5W1e/GZA4A78S9XXsb8N9V9daFnNsMe7v2ytjbtQvD3q6d225v1xqLxd6uXdvY27Ux1tnbtatQ5KweVa3gArhHgH8DPqmq3xWRXxCRX4jEHgZOAMeB9wP/x3znrvAQrklkQPAPJqEIOqJoXtEw+hyJAryDyZUviDww4OrgFYswMgL5PISh+xwZcccPHlyxgsj79m3nt+RV7Hw6z0xngunuBIEPFV+Y6kow05Vg14k8vymvWv0AD1zgdutBF8hNj0Al7x7lV/JuPyi69nkKIm8acHXwikU4PwKF6BIU8m6/WHTtFuAZC6WTAV7FQQKKTDJChTxKSIU8k4wQUORVHLQAb5XoGnB18MpFGB+BcvS1Uc5H+0XXfs0HeOACt4PPd4HcSL42w5cP3H4xdO1rpCDycs/kzdbTW8vYTN7C0VFFDwVobMULOeAjd6yBFS8OHZq74sUdd6zaihcf+MLnOHSLW/EC3IoXd3ynj3f80GvWRoAXJz8Kpw/Bsw9BMQfpXth1AHbesagVL750yK14MZGDnmjFi1fcYQGesTSmGeUkhzjOQxTJkaaXvRxgD3dYgLcGmBqFJw/Bd2MrXtx8AG664zoJ8OKMFl2ZlMYVL+5oz4oXbS2GHOvko6r6tlbHRGSjql5arBErjQV5hmEYhmGsF1bqce3NDZ36wA9U99dDgGcYhmEYhnE9sKAgT0TeLSKTwPeLyET0MwlcAP6urRYahmEYhmEYi2ahxZD/AOgFPqKqPdFPt6puUtV3t9dEwzAMwzAMY7Es+HGtqobAC9toi2EYhmEYhrFMLDYn76sisnyLsRmGYRiGYRhtYbErXtwO3CsizwDTuBUuVFW/f9ktMwzDMAzDMJbMYoO8N7TFCsMwDMMwDGNZWVSQp6rPAIjIZiDTFosMwzAMwzCMq2ZROXki8kYROQacBL4AnAL+oQ12GYZhGIZhGFfBYl+8+D3gpcBRVd0DvAb4l2W3yjAMwzAMw7gqFhvklVV1DPBExFPVzwMvaoNdhmEYhmEYxlWw2BcvxkWkC/gi8DERuQBUlt8swzAMwzAM42pY7Ezem4A88MvAZ4GngR9dbqMMwzAMwzCMq2Oxb9dOA4hID/D3bbHIMAzDMAzDuGoWFeSJyL3AQdxsXkhUDBm4YflNMwzDMAzDMJbKYnPyfg24WVUvtsMYwzAMwzAMY3lYbE7e08BMOwwxDMMwDMMwlo/FzuS9G/iyiHwNKFYPquovLatVhmEYhmEYxlWx2CDvfuCfgO/gcvIMwzAMwzCMNchig7yKqv5KWywxDMMwDMMwlo3F5uR9XkTuEZFtIrKx+tMWywzDMAzDMIwls9gg726ivDzgcOxnSURB4j+KyLHoc0MLudeLyFMiclxEfiN2/HdF5IyIfDv6uWupthiGYRiGYVxLLDbIexfwQlXdA/xP4AngJ6+i/98AHlXVYeDRaL8OEfGBPwPeALwAeIuIvCAm8qeq+qLo5+GrsMUwDMMwDOOaYbFB3m+p6oSIvAJ4HfAh4H1X0f+bgA9H2x8G/rcmMrcCx1X1hKqWgE9E5xmGYRiGYRgtWGyQF0SfB4A/V9W/A1JX0f8WVT0HEH1ubiKzAzgd2x+JjlV5p4j8q4h8sNXjXoAol/CwiBweHR29CpMNwzAMwzDWPosN8s6IyP3ATwEPi0j6SjpE5HMicqTJz0Jn46TJMY0+3wc8D3gRcA74k1ZKVPUBVd2vqvsHBgYW2LVhGIZhGMb6ZLElVH4KeD3wx6o6LiLbgP97vhNU9bWt2kTkORHZpqrnIl0XmoiNADtj+4PA2Uj3czFd7wc+s+CRGIZhGIZhXMOIql5Zql2di/xXYExV/zB6a3ajqv56g0wCOAq8BjgDfAO4W1W/Ww0QI7lfBm5T1Z9eQL+jwDPLPJxG+gFb43f5MH+2D/Nt+zDfthfzb3swv7aPpfp2t6ou+jHkagd5m4BPAruAZ4E3q+olEdkOfEBV74rk7gL+G+ADH1TV34+OfxT3qFaBU8C91aBvtRGRw6q6f7XtuFYwf7YP8237MN+2F/NvezC/to+V9u1iH9cuK6o6hpuhazx+Frgrtv8wMKc8iqq+ra0GGoZhGIZhrFMW++KFYRiGYRiGsQ64boO8qOTKBRE5sgDZ3SLyaFSq5TERGVxAFw8sg5lGDfNn+zDftg/zbXsx/7YH82v7WFHfrmpO3moiIq8CpoCPqOotV5D9a+AzqvphEXk18LP2qNgwDMMwjLXMdTuTp6pfBC7Fj4nI80TksyLyuIj8s4jcFDW9ALfsGsDnsRU3DMMwDMNY41y3QV4LHgD+T1X9AeDXgP83Ov4E8BPR9o8B3dGbwYZhGIZhGGuSVX27di0hIl3Ay4C/FpldZCMdff4a8F4ReTvwRVy9vspK22gYhmEYhrFQLMir4QHjqvqixoaopMuPw2ww+BOqmlth+wzDMAzDMBaMPa6NUNUJ4KSIvBlAHC+MtvtFpOqrdwMfXCUzDcMwDMMwFsR1G+SJyIPAV4AbRWRERN4BvBV4h4g8AXyX2gsWPww8JSJHgS3A76+CyYZhGIZhGAvmui2hYhiGYRiGcS1z3c7kGYZhGIZhXMus6RcvROSDwI8AF5oVLBb3Gux7cOvczgBvV9VvXklvf3+/Dg0NLbO1hmEYhmEYy8/jjz9+UVUHFnvemg7ygA8B7wU+0qL9DcBw9HMb8L7oc16GhoY4fPjwMploGMb1TEjINNMAZMmSJ1+3HRLi4c1pK1IkSxbPHqgYhnEFROSZpZy3poM8Vf2iiAzNI/Im3LJkCnxVRPpEZJuqnlsRAw3DWFGWGlAtRm4xOgC+y1N8jcMoSjddTDKFovTQzQSTjHCW7Wylj97Ztj562cpmBuhnPy+2QC/iMqN8nUf4Mg8xRY4uenkZB7iVO9nAoicxDOO6Z00HeQtgB3A6tj8SHZsT5InIPcA9ALt27VoR4wxjPbMSAdVC5ZoFVF10McUUAnTRRY4JRjjLTgbpoYsJJhGE7khOI7kJJhjhHINso4eeJjrOMcgOeht0TDaR20o/IGTJUKLCMU4wQD8AxzhJLz3MUKBEmaOcZDMbAeEEz3IjexnjEnnydNLZ9uu51jnBEd7PfZQp0ssmNjNIiQL/yIM8xv/i5znIDcy7zLhhGA2s9yBPmhxr+rqwqj6AW7aM/fv32yvFxqoTEpInT5r0qsxCzSeXJs2/8FW+xXdmA5t4kDNOjrOcZ5Dt9NBd11abyephgglOc4ZBttMbBVQKdYFXd13gVdNXmw1zbVsYwAVUaUpUOM4J/n/23j3Kjey+7/zcqgIKhXc/AJLN5nOIHo04smZG9Dwy8kN2NJKl7Hr3RI4jzWrt9dlYyVlvYsdZO96zO5uMkuPoHHuzPrETje3k7MZryU85lj1ja7yx4odMPUbUaziy2ByS0wSb7HfjWVUAqu7+cQtAAY1uNslukDNT33P69AXuRVUBjW58+vt73AJTCLpAlcHGxcFhiRUKTAEyBF6Si1wiRwYHBxe3dwwQzA/M2VxgheLAMbau09Dx8HFxMYhR4mTv2kucoEqNJAnixJjjRG/uJEepUKXANBbWvr/X7nVtsMKv8AxxzAHHzsSiyCwNqvwKz/BTPBc5epEi3YLGBnlCiO8BPi+lbO7hYcvAkdDtWWBxD48f6Q2i/QCq3a4bNWdicpYvUqXGEqtssjnShUr3YEgBz3Zu1aBDpVyoPngpUOo6UhXqlLnGLIcGwCvsjJmYlLmOg8tBCsxzOYCcPlA1cXBosRRAlEAOuFXzvEqOHDYuLq0B2AqPL3CZCdLB8VyWWB15Lg2Bj+wB1SlO9q63xAkqVLEwSZCgyHTvtSkF6yRwipNUqZIggYnJKQqhY5yk0puzmKMwcIzaiHUGBu/kDKd5yy2D95shJ0/93ik32MTC7b3/1bj72pzlBRwapMnj4+PjoWMggv/jU2RpUOaLvMh7ePquPZ9IkV5vGqeT98PAx4UQa8BfBF9/KaXcuINjfhr4MSHEb6AKLipRPt69pVuBq/0Crz5QNVhmmY0eUB3uuU1bgWp7F2oYvKrUuUq5t26UCzXseJmYLLPGDAe4wgIpkiNcKH/AabrAZfKkt10XdqHmWe6B1zyvDsBV2Mkadrm651plkaPMco6v4TNNKXChukC1SYXCSpqHP5Nj7vlZ9IqHm5Ncf/80f/2eCs2CR4n7qAbgZRIfgK3weI4TVKmSDPCqyPSQG6bmYsR47DaBaj/d0BSpAVDLkBk5Hr5t7OLPbxeS+u/lraAUHu+0bi+OcSvnArjMeV7hi0gkSdI0qQKCJBkaVFmmTJEZ/jO/jcSnQZUYcUySxEkwQaEHejmm+CuejyAvUqRb0NggT0r53wMIIWaADwC/BMzsdA3BrhTfDUwLIcrA/wHEguN9HHgB1T7lIqqFyv+wf8/g9adhwAK2fCBtt24vPjS3ulXVbR2kYRdqVD6VCusNAtWo8N/w8SxMVlhlhoO8xgJJUgEotVjqAZA/FLobdqGUQyVhaF0/TNh3qwbnBh2vPkQd4yiLLDHLIZo4I1woredWSSRz27pV2pBDZVGiEIKy+3ogq1yoes/JKgVOVtfl6rpaJzlBkWnu5xSP8HZSpAZdqJdXiD/zKrgSfSpBZ9YHx2fukz7v/BS0nj2J+WBhz8HrdoFqu7E6bl/aNt+3G2vs3q26nblLnOcVPs8yCxSYJUWOBnUEkhRpGtRR0JSmQYUVyhSYJU02mJOkyFKnyjJXKQ4dI0mGZm9dOlhXpsgR0r18xfC51PFqVFmhHBwv24M3tU4dY4IDyCBfsU2HMvNMUEACZeZJkcOlSZsWFVbJU0AiaVAjTZ42Lh4dDPUnnzgmm6wRKVKk3Wuc4dr/DvgO4G3AKqo1yl/s9Bgp5QdvMi+B/2mvrnE/dLfChA0anOPr1GmyzDKbVNDReZwzPMo7eh+UPj4v8RVWWOU6y1SpDjhZZRaDBPXcQMhvN+CVwGQ5gKuuW7Wdg7R9LlQfrpQLlQ2Ayh1Kct/+eBusc4wjLHKDw8zQxA5AKU6xB0CDQLXVhTqxBYb6TlN9wK0a5UKpc/VDfmGIeoJHcXHvqZy8UeHEHhytuBjPLIBpQqH7AQxYwCxo1TaxZxbguUkyhZsDVXi8E1zdKVANr4OtTlMXlPow1HeemtQYBVQpcj1Q6q+TJMkOuFVp8jSCuUFQCs+JwPGq0aGDxENDD/4lcdlgnjxFJJIFLvby064yT5pcb113TgRzKXK0sLcc42oAXgALXCTdW+dwlaUt5+ofL49LkxYOGyz14K17DBcHDS0UXo8zS4lm8DdklhINqpgkiREnxzQ+HgYxUmRo0yJOAj30EdXCJU2OSJEi7V7jDNf+X8CrwMeBz0opr4zx3GNVF+xixHpO1nIv72p0cvkgNIXbLuSpU+vNba3w6+dnhXOwVljlCgu8g7ezQJk8eWJoXOcG4Wo+G5s11smR5SxfIkeWvpOlKgMVUIVDfrsDr2tcD+Cq61a1tnWQwi7UYC5UH65KnAwB1eAxts+7sjjAAQ7sA1Dtdt1ucrLiCpN62o0LNXxbuW2N3u07ASolnwa1PjT5TbB9Ei9sIBsufi6OaPvoRgxPdADQMfCyElFuIV68jvP0VA+ornCe83whAKqwg5TpAU/XhRoFSgVmyYxwl2TPQar0XKiwuzTKrVqhzAQHAZ8ESdp0BkDJxWWNC0xSRMIADPVdKAVU66G57jrBVrdqgQtMUBwAr+G57vHzFIhj0greP+pfEpMJSj0X7iinek7eEUo0qPTW9eckRyhRp0oca8sxjlDqvTZHORVal2CSYu+1Hj5eLQA0FVIt9uBNratikkAnxtt4nJOcvqmTmafIn/AJpjmMjr4lJw+gwhrv5kNEihRp9xpnuHZaCHEa+E7gXwohSsC3pJQfHtc1jENhZ2yBayyxHIQJr5LC6iWX32A1lKDeTWqXQd5Vptd2YZ5XKTKFDKr6Biv8svTzswZzsKaYJE2aBa5ymBlsHHx8DnGQcDWfhcUUk6ywynGO9Zy8rpOV7EFZMRTy2x14neT4LblV+wleewlUtxPiGzVH4ErtRS5UzI9zvvGXzPvfAE1iWmlcWwGQZaVwbOUSqfurrPjXmNYOk7CyuHYDkCSsFI7dCNalcOwKK/51prXDWGaG5NkWRlXj7b80he95yMUOpA3aBzSaRRcpIEaMDm20KYg/f40/f/oay1xlMgjdJbDwaA05SBdIMxEAj0OZJfIjQcnl6kgnS/TWdV2osLvUhSZ1ru2dpjAomZhMMkcjeM8rGFKv4ewQUIVBqb9ObnGrjjLXc/LCoDQ4JwLHq4bE5yG+k+OcDt47r6+cPPW7NxheNwZyEvvjJ3gff8Hv08ImRXZLSonK1TN5lKeIFCnS7jXOcG0WOAocA44DOQj+xX8DKeyMLfFVZjjIIksc5hA2di+5fG4ouXwwCb3fdqHEfT0nb1SFXzg/azgHa5ZDPMy38SSP4eICW3PyNDTO8PBASBn2DrxuB662mwvD1a3kTBE8ei+AyscPhfi8kNPUz3caHa6bJeVncRvKeTGtFK6twCBhZbAD8CposySsDG4AZX3w2mGdL5g+nyL++Sb3LWj4syabuQ1mankQUElvMFPNIwVsZjY4UMlwrHwYOZuklttgpp4HCZvpTQ7V+485uJnm+OIMcjaBbVWYWM5hz3QQax3kBNABKX3aTQc6GsSgTgUTC8+EzpoK57Wwe0DVxkXHGHKQ5kLAk2CW4jagZAau1KC7JEPg1XWhwu7S7Ai3ygxCgW/jnVucprsBQ9sdYxiSYHtQMoZ+h25nbq/X7VYTFPh7PMuv8AwNyuSYCpxMlwprxDD5ezwbtU+JdM9oBYfPcIPnWaRCmxwx3s8M7+EgBRJ3+/J6EiqtbQwnEuLrwF8GX38upSyP5cQjdObMGblf25qFnTzVCmKSLJm7EiaE0YUWt/p8XGximHf1A8/H5wrneZkvsMRrIaAK50JtDcl1E8O3C+ulAxDrJY37dXQbEmYG266x4pcpBI6XYzfwadO22qTsFB3arFrL5Ox8sG6WTWuTaVuF5Lpzy766jvj5Nie+eAgkVNKb5Gp5hISN7CaZSgZRbiKPWNSzdfJdKEtvkq/vvE54ArdjkzZytC+tw5EkqUqKZsFGCkFmOUGtYCOA5IqFnbPRrrr4R0ysikWz4KjXbcWiUbDVa7GSoJlroi208Y6aWI0EleMNrEWDI3+YwZcesrWDk2dDvBPnzz+hnLwDHOWtuwzdjdOFGgVRke6e1I4XL47Y8eKpCPAi3TN6mU2e4WVcfKaIk0DDwWeNFiYaz/IgD5Lf03MKIb4spTxzy48bF+TdS9pPyIPBYovb7YW1F3A1am7Uh9p257JpcIFz2NTZYJk6m/TbH/Sr87Y6WVuT0BVoVXq5VeF1W6FsMOG9QR2PNjJ4Pov+q8zYx9g0FVABrFnLTAXjVWuZvJ0PHK/DPfACwZq1NABllWBOAqvmMnNnZ4lVdbzlBunNNOKqjZxNUs/VyNXz6FKjmXaJ13V0qSOycfxqC67aMGuh5eL4tRZC0p8r24gDFnFp4louWltBVKNgI4HUsoWdt9EWWvhH4yQrFvUAyrrgtdM6GYOEbWKRxVqKkZ89QjyXpFOzQYCRTtKpNtU4Y9GpNKHswqyJkUvSqdsgwUgn6NSd/mM2G8hFBzFrYeSTtIsgswbmZQ/5yTL+TBwhxNacPDqIsov40JFeTl4EVJEiRXojaAWHj/ASJhrZoPI7rCptXHye48yeOnq3C3njDNcWgJ8CTkP/mUspv2dc1zBO3UmYsAtXTepsskyNaqjdwWCF32CieQ3Z6/e2yTKLvWR1lVukc5rHOc2jvQ9bH59v8hKbrLDOdepU6ULZBmtc5xL38whLXCVLPpTj1K/OS4cq99b9eaYbCrauWvM9V2vBukDensD32/haqzcHsGBdJG/n8f02ntZiwbpAwT6ARFK25pmyDxDHpGk10RqS4+cOUqjneevyYWS1hZQwlykg68E4XcCvtnignELOJtGyh/HrLRAwl57Gr7R4SzkNRyy0bHAMAW8xC4iVNs6MR/a1GZykjWZLfNdgYn6GetEBKTh18T5yhRkEAmPep5MTCNsGN4E+D15BQ0J/rmkjtSSGr9NpdSAmMEqWgjAJxlwAXpYLiQRGMQRlpwLw2mkdAuO7CrRPm5gk0DQdLB3D9tRbcNTYl6CJW1vn+mDp8NYWfHoFHA2yseD93g/Fa1UBZhyeOkQKcy9/i6enBQAAIABJREFUrSJFihRpbPKR2HiYaNh4+Ej+E9dw8MgTo42PgRYqD4IsMcrYvMgST3Psrl17V+Osrv114DeBvwX8feCHgJUxnn8s2gmawhWECrz6uVvhSsNhuMowsaXdwahE83yQhN6vElSJ7FeDJHSdGGtcx8XGCqprXWyq/ipZO8vL5llydl5V/1nzTNoFsn6WVa1MwTqMZzuAYNYq4dgNVZ1nlXDsGkk/hUWCE+dnSH+h0wOvrqtVyhbwKyHwyh3eHspyffCayxTw6y2EFExnHqC4dgBxyYZHshhXO3TyGgKJPu/TKehqfNHHy4F0bISbRL8oFXgJBV5eDrAdpGNhLEGnoGr49GuSzvEYYtFFP5yiYztgOWCaGKUUnXoTJMRPJdHqvtpAr6RjVD3VPsRMQMnAqHmhuQ4kgVgCHpvEOJ3ZHq5uB7y6cykDQxva5S+j3Xx8K+uM4HbBhGcfgGe+CWUbpuJgauD6sNZS42cfUOsiRYoU6S7IR9JARRgsdGy8bcc+Eg0xMGeicZZVqrRZxmWTFmWa/BdWMNHZoAUIDmBSJDEAelPEeZ7FNx3kTUkp/70Q4h9JKf8M+DMhxJ+N8fxjkYtNlTXS5HiZs6TJbekt1cZhgRu9dgqD1X8XyFMg6adZs8sUzMN07FYPqFSyfhe0qgquNIsJq9irpjxilbDtCik/TUKzmLSKOHYNgWQqdRBT61fXmn6C4ksZnJUNvv36HH7V7UNZtUWyPE1qtshM9n68ahD+yyR7wGNkLDpVG646cDCOQYyO5SNaCrx6rtYFHy8vkE4T4Vro82wPZRcknaL670if99VYQmxeok0nIC1gQcJsEsOVfaCqB3B1KoCrhADTUrfrntrpuKRjVDpgCUgkoBhTawVw0sA4YML9MXhiUjlX24AXw+PQum3nUob63tVegNfw7XHpwSw89xC8uAzP34A1F3Ix+NAsPFWMAC9SpEh3pGEXDW4N0D7HKl9lA4kkQ4w6nd64RhuJIItBlTZlmsySJEuMKh0EEhOdFVxmsHiNBikMmvg08PCQJDHQgSYeHSSxEOaZaKwF0bm7rXFCXjv4fl0I8X7UHrOzYzz/WGRikfUnqdorzJjHse2gZYJVomnXhsBLuXezVqnXxuKoNYfdaPDAufs4Vr+PE8unFFx1gaqXZxXKrTqSwMhagzlY4XyqbBDW0wXxxw+iPSp68WTNlpxYO0Ur52Gc3VCwBRjzkk5Ooq3ZGGWJ9o1LxKSAhAazFsa35yEVg3kbIxcDuw3CAl9iuLpyfUp639Wa0zEqngrUdx2v7aBsLnDHulBW7ztj1D2YteDhHDw5pdwjuH3wGp7rhiQ1AXF98Id7p87YG00FE54+or4iRYoUaYRGhTyHoWx43KDDOTao02EZh03aiBGwNgxoNdoIBCYaZZq4+BRJME+dQpA6Mk8tGEvmqZMjRhMfFz9YFwcEi9gcI8UiNoexsPFIopFCx0RHoNqDJIOOjmG5+OSGukncLY0T8v6FECIH/CTwb4As8BNjPP9YpPmCB146RXuliH79LcrlkgSg1UCWu+AVysnKDjlja020SzbGI1NoV11iEyZIoYCq65DM2xg5E+wOODFYsjEKCRAS5psYuTg0vWCuqR4X0+G6q6AmFfzoLR1tyiSx4sLxtIItiYKwb9XgcxW1kVw+pq6h7cNCAxaa8N4izKWh6oFlQEzA45NwOnvrcHWzueFx1xULg9jtglf4tvEGhrKQWiuw+hlYeR46FTByUHg/TL8H4lERY6RIb0rdLMS5G0AbXneODVZwuIHDBi5lHGZJkCVOPYCyNMaAu7aKy2WavIMJXqPBBDHU/t01DpBABoCWDwDNwecGNYokAMkqNkdJcY5NCkhKpHtwWCLTO1eJNFXaJNEw0YLbysk7QZoDmNxPlieYCnpjSH6Lq/weZQ4GpQXDOXkAa7T40D0QqoUxQZ4QQgdKUso/BCrAu8Zx3rsi20Nba2PmsnD2hgIxUOCVD4HXDRujqN6QzAcQhoALAcilgQVHuVZuN/8rBbWuw5WCSkfBVUKHYjyYE1BKw2Ybkrpy3oppqHYURB1KKFjqShNwZkLBk6n1IaregU9fh4eykI2DIaATVGIbAjZa8Nd1+MclSBt7G5Lcae6N7IyNSbWX4eIzIF2ITUFiFnwHbnwSlj4Fp56FzIN3+yojRYp0q9opxLkToPmov+3nqfBF1vCAbABeXQir0OoBWo74gKMWHm/SZpEmR0nxEBM06ZAjzllWsTBw8HCRXOy5azLkoEkuUGMakzQ6CzSYxcLGRyCZIxOciwFAS6AxF8CbQHCSNMUA0B5hghTGLYd8LXRcfCx0NARx1Ofmf8Nh/ojr2HjbVteaaDzFgf37Qd+Cxtkn77NSynsC7va1hYov4aUNWHHhuqPgSgJZA6ptKDswm1C3u6HMbBCGlEAmNN6PkOQwhHWveRjyPrUInyjDTEKBoyHU97DKtsrBisJ1rxu1VuD8R0A3wchune9UwXPh9HORoxcp0rg0ykG7VdesXygwGOJME6MaArSugxbOSbuKzQHiCAQJDNp4LOMGzphqG5IjzlWaHCFJhVbQHkSyQqsXCl3GIU+MBWxKpDlOigImdTq35OR1x48wwZNM4wb7JtwuoO21hvvkmWi492ifvHGGa/9KCPGLqArb3gabUspzY7yG/dd2zthehCT9fQDy7aD0N6+B78Oio5y6AyYUzUHQm4qrpPsI8l43Wv0M+C7E8uC3QRiDP1IjC50yrL4IM0/fveuMFOle1l6GNUE5aF9gHYlPhtiAazYMZV3XbBjeLIygUCDJa0EoEwQXg7yzrQ6a38tJs/FQfQrAxcMYcsZKZKjQIoGOGdwOO2rda5ojw2bgrukIZklyhklc/FvKyeuOU0EwtOuiAWRCDcrC4+Hbxj725HyQPM9xhhdZ4nkWWcMmR5wPcYynOHBP7XgxTsj7G8H3Z0P3SeCN1yevG7a8EyjzJTQ6g3B4vgpf3AQpIa2rcG3ZgSOWcgO7ody0ocK1i13XMKbgTRfw+AQ8Otl382xPtb3IxeHsuqqQBFh1YTIOHV+dr+mpcG0sRASmpqoqIyFXJN5vd+A/dmAhuPOYQHxYR/sBA1EQSB86DfUj0izw7MExPghN3fZVuib6La7DB7Stj+uuW/kDkG2of1PBXbyovsKgF5uC1ecjyIv0xtSdANp2Yc1wuLILaEewyA6FMkcB2sEgx8xCp4MqDMgR3xbKiphI2AJvG71CgWZQKKBCnKdIUw0B2inSATRqvZCnhU4MjceZ5jS5HZ//rbiLXUjrAtdOULYTvN2LKpDgaY7dE21SdtLYIO9moVohxA9JKf+fcV3PvqoLaN+oKCjz5K2Fa6sBvM0kVMFDvQMtqWAroSvYmm8oMLM74Hiw5ECv8KKuYK3pgePDUl1VQsY05dYNFV4wFVdO3vFk38mbNpWT1w3TJnU1Dsv1+1B4j0v6Cno0c/dA5dvqcV1o2ha8XvLp/HQL8U2JpoMfdKjRrkj8f9ZB/oaH/rE4dalR/SIgQUuDpwqvMbLQroBbhsSRwE1TG36gp8Gr725dpxrMzYKeC44vwEj319VeBhED4QfHboLsqPu60kxw18b2o4kUaVt1gexm4HWrrtkXWUNCL0y4Xd5ZeK6ba3YAC5Ak0PGHwEsBmnLGHHyWenNyoMLz4pCD5gMOPvERrtkwlHVBcRjeDowoFNjt6zQMZV3t1jV7vQHam0njdPJupn8EvP4hrxv+vGbDtxqq8MEPwCsfAq8bDegVXjSCvmICLjT661q+elzRVK6Z0wHXg7i2/4UX0/Gb5+SttVRO3i7UdbHYAZq2m9sy3sW68PE0EzbPglcFdxnaG7sDqk4VnKtgzkIsB51gLrxOF5LWL3dwr+rE45IY0KkJkBJDh04H3C8LzB/qwN+JEZsWeB1w5sEMct4aF8DIq+v3HWgsqTkJNC/efB3BOiOn7vddcIPjSwGN0LkA9ISqqNU80JMqZDvwFnbVsSJFul3dSY+z8JwKY66xQIMZkuS3aaHRnattk+PVXacqM8HCoBNqmSGgF7rsOmOq1Yb6G63mVJ80DfWnRYU19S2VmxVaWOgk0Cj25hgJaMpBEzw2wkG7nUrWUYUCsHtAi/TG070EeXufHXk31A1/zibhclNBmaEp8KqGwGsuqJSVQgFb18mbSykgS+oBzKWVkwfwndNb25PsQeGFRARZGAT/V4L21EH4nRvImo/IxhBSOT4S0HSQm23QNPieIn5tZ8cLH+rnYfPz4CwoaDJy4NeVOTnsQhm5PngZmRBcZRSoOVehXqryubd+gz899FWqZpOsl+Rd8w/xHVfeRlHPDhxPs6C9AvEZsF8DI7kLoJpXYOfbqgq1MR+ENaVaFy+oN2zzv3jom+pDTWrQ7ICpS6QmsFsSQxfKQagJuODRyRroMUiW+k5eak45dJoFWgJSRfX8hYDkqT5QbreOYF2nqu7XzP7xhYBUqb8u9wTUv6bWx6ZBi21l9/YaHPzQXv5SRHq96E53CRjscdZmCZcqrR54ddtTjAK04bBmC0/9vUHQxKeFxzzugDPW7XHWwuMCTq+FRrhaM7xOuWaSFh6xwBnr552lt+Sdhee6uWYxNB5jal/Cml3drmu2n3lokV6fupcgbzxlvvutbvhz1YW/MQkPZAZAS3YknivQ8jreuqeAalLHX1d/0PRg7HckmiEG56YNPFdtwdUFMmDgdnfso3WNrN5tifqBhz/TpQ+Vl1TVpXs9gAEJRtaE+x8g95++iR6zkbm4+hPZ8TFaLXypsfHeB/D/0OyB1xZ4yyjQkK3gZJoCK+lCc75fvdm8GABV4EI1LoCptrUdhKsAvL41cZV/+72/S8vvMNFJc8idpNlu8+nSWf7ovi/xY3/wtzntHukdr7UByWPgLkLisApR3hSoAjDSLPW2tO6TeGvqTWqdAC9IjUzckHRs9UEkWpKkIemg3FsrDh1P/RyFLcltSLIfUWx8t3LyvDq88hHl4OkjenV2qiBMmH7qDn4HIo1dexHWVLsErPAVNkYm+Q9XZI7aJSBNjHVaXKLOI0ywQJOJEGwVggaxw4A2j9sLZXZ7oZnoOHhBw1mNODolEiNbaMTRmSMxAGWjeqHpaLzzDgAtCmtGer1pbC1UbiYhxFeklA+P41z72kIFFMitewrkulBmgdeAyjkFQe5SGKjUh6+UyrlqBy6UOaOqIAecrMCd0dIq5NYNO+rZwRys9qaCGnMWYkHoUeiQexzyjypIABVC3fhzdd6VP1aQJgB3RQFV+1sueWcZ6xs3iMk2vhWjcvgg7TNFZMrsrXMWIHFUOU1dl8tdUWMtBh1bXXfrxp05ecvtKv/0yf+AFY+RxUIG+6iIWFBQELdpmW0++sKPkJ7PquPlIX5Avc75JxT4dUFJmBJvVf0OaJPgr9Mbe6sS/5yPsCXaJvhrgJRoE+BvqD/w2ic8/AUFUCIJmgG+pXYU0YQyU6WUiAboD2nEXrj7230N98nTTPWatNcU4EV98sanvQxrfp5VFrB3bJOxU48zE51r2Dj4HMRklVYv12ylB2GqZUaeGK/R5ChJKrR7uwSs4DJNnBu4WAgOk8TBuy0nTwCPBkAG7JtrFinS60GvhxYqN9Pn7vYF7IWkD5VzgtaKMeSMQWsN7EuQfQc4r0FsQj2mMd8Ho8aFoL1FUzlgw05WPAgtOqFwou8oaAznYMVywTFCIUkRU26dZ4ORUufWLfVB31oB63j/elMlBZtyyqQ5e4T2u48MgJcMwCtZUiFUzVKAkCz14S0ckpz6LkifDl6kO8jJ+9P219CMFlMyiWH4eB2JjoZmCGQHUlhc021e+eDX+cH6E4EbCnIzuEYNdEAiwQf5ko/2BYKKZYlWR72IWYm+BvolCY8IWABtUoDU4IqHVtDUE/MV2AHgAQkFtfSfqrrfBLG3bZNuW5kHVR+81RdVFa27psD64IeUgxf1x9tZu3HNwuOdw5rr1OlsCWvupglt113rIPGQ6Iibtsnobt0ULhTotntYxOYoSc6xgU98Sw7ZcEVmtwltMbRLQHfdESweuoMeZ8MtNMKKXLNIkXavfXfyhBD/EPg9KeXVfT3RLWg/nbwdnbFpaN9QW7yahxWA3W0nD7ZWnoZDfL7fDy+OAi9h+dRsF8/30TRB2orTsNvqQ8iKU7eDD65UDEdrhf6ox2n6LbAhYRrYdhvP99A1HcuKYdvKoguPTUvnffLnqWATwyCu6yS9OAniFPQMoiNAQnPTpbPu8f8+96Nqd+QsUEe9cGkJFQllAQeDFyAhoC1gxYOCQkBWJEwLuCEhIWFWgCsCGATq6ly84sEfAXZwniRsSS9tSJgUaP+Lgfb0vfR/1ZtTd9pCY9g1G3bGRiX8hwsDunNrI8OaXddMNaZdwiE/0IS2vcVdi6NhB8/nBu5tO3lZ4hzAJE3sjnYJ2AnQIkWKdHu6l528jwL/VAjxKvBJ4LellCtjOO9d0U7OWKeuoCvzMEw8qUJkwzlT+k3gaqf8rO0AjQDEJJJsKoHQBv+rlZqPm2phYmCnAvDCwKaNh4eOjkUMmwC8grGPzytc4/PGqyzINQ4zSY4kdcNGSshgUdNtxVZ+gqpjUxbrzMopsrEE9ddsfBvS6ya1TpOy2OSIzJHVk1R9FykgJ0yqUvXiS+gxXntwFcuOoyOoxVvkK0kcvUWn1cFwDfDBlAZr2To4KDC76Ct4ExLmgZxQUCYCaHOAuIRTAup+UAyDAsNZAQ9riCc1hBt8YFmoxwOyriOvtuA8wW/TcBWDcvs4LBBP6US6c93u1k3QzTtb5atsDLhhW10zi1wo16wLbx0kPhIRcs3Cbth2m58PFgYoN22aOGliLNBklgQO/kA+mUAwt6UJ7VZ3zQe+k+KehDWHdwnYrTMWuWaRIt2bGgfkXQLeAfxN4AeBfy6E+DIK+D4lpazt9GAhxHuBX0BF2X5VSvmvhua/G/h94HJw16eklM9ylyQ0yJ3Z3hnz8GmnWkjNwIm3AKGgKdMGJBZx7EwbHw+tC1fhuVuAMC/IhTnPNb5kXEJKeIISj3EfWvCH2MfnS/4lVtwai8YGlStNsCXZDZOK53JVrDErJ8lriS3g1dY8fFNi2ALbcHF9h/nsJoVKRoWCcosUN7P4SObz18nVLJrCxZY2S8kNCqsZRE5w0b9BTiSxtRaO77PkLTNNBoCLcplpkQEE19x1JuwUm7qN1YmTqSZoiQ6Jdgx9U4MEoINLm5xjqdtmCN5EAG8VCZaAOPCYhjitITQxAG/hsUgJNR8uVMj0v8v/08T78Ra8LMGRgZsHNFGh2tMC/WMxRCFyNcIahrXdJLyrrZvWqNLaEuIcbkIbBrThvDMXjwMkhvbPDDeh9blAjWJQDNAFOTN0PVu7/2+/+Xm4MCDc72yWBA8zec+ENaPqzEiR3lgaB+RJKaUPvAi8KISIAd8HfBD4OWDbDCAhhA78EvBuoAx8SQjxaSnlK0NL/0JK+bf25epvQz4eDu0gjNIHrwYuX+YKTdtlyaxSaSmXKxNLUGs7yv2KJ6i2Ha6yxhEmyccsqt25WILqFRthQ2bDZKPTZFHbZFbmyWlJKtJBAFlhstlxKOvrHCSHsCRmI46mCa4dWqM5d4S0ppyHpt9i9UKVzJrJ5fgSE2sp/Kxk3l8mKy1c0aYl28z7VablIHgl2jHsmouXgEQ7TlzGOXUjS31KhWVPXShSm2qhIyldKFLN21giTkLGKS5lqc+0kBuSU/GD1EQTy4+TkBpFvdgDylOi2HPyTsSKfMe1Nn9VeJW31A8SMww8T6L7ApEVBC8164cbfNB8AvHPjW3hTfoSoYk+wHWVYfR4B4kHNfRPmvi/00H+WgeuBBPHgx0vPmC8IQDvVqAsPB61Lgxry7hs0hqo1hwV8pwliYXOMi4zWLxGgzxxBP0+ZuEmtOFKzm7l5iI2x0jyZTYphNywrVs3qW2duqDYhTflmhVu6prd7Pnv1dZNkWsWKVKknTQOyBv4dJNStoFPA58WQlg3eeyjwEUp5SUAIcRvAN8PDEPePSPP9/jSK6+y3Khy3dmkGsRas5rJKg0uGcs8Uj9OubBGvppEIpjPX2e6kgEpmc9vkK1ZOJqL3XZZyoecsfR1iqtZ/BzM+0tksXBo4dBh2V9imgxCoABNWLi00Toa/qqHm+xgihiHruewjsQgKLyw7BhTaylWcnWOzxeoFhz0TUnJLFIRLiYx4iJGSctsAS9fl3xH5y2cPn8QroN2SGAl49jLHZBS5dMtqeZ6VtLAvtHGv+6rdSkTO9VBZATWY3Ect3PTnDzLinHN2eSy+eu40sc0dLSghSAG0IEqNmYsznvF29FDH5q3A2+3IkEdXX4J/LNAsF2EnAb5OPDt+3PS29Tt9EIb3vx8nRaLQ1C2XRPaUblgiRCsXaFOOghrDgLaYMjTxWedFsdIs0iTGSzcwK3u9jgLN6HtA1o/xHki2BVgjuy2eWd76ZpFgBYpUqS7qXFA3g9uNyGl7PorCCEmpJQbQ0sOA+GCjTLw2IhDPSGE+BqwCPwTKeX54QVCiB8FfhTg6NGju7/6W5Rtt1mza+QTKc7W55nQUvhSOWNTXpqMa3E1uc7hS5M4Ex0IOV4SKF0oUsm7mCKOKWOULmSoTrtICaeuFKkdbKFtSkrxA2zQJEGcBAYHtAM9J6+kFdnsOJjEMHSNxzv38dZvHEbTNdJnLDSr/+GiWRpnYidpll3iGzq22b4l8EpZJpqt9ZwxLIiHXLPBsTWwznTVd6EJzPhga5F4Jj5yfCJd5F/wAZ7hd7nGBlPxNCYxXNqsxeuYGHyUv02B7B7/ZHfQy9fgxz+pvus6pIKdTBbW4dk/hN89B7/wQXjw8B2f6k43Rh/OSRveTzOcn9bdxmnYQbtCgxT6FigTEIQ4+3lnw1WdXUetjM1xUixiM0sSG2+gWnNUyNNE4wBpiiS4nzRPjAhx7mXe2c3mIkWKFOle173UJ++clPKRoft+AHiPlPJ/DG5/GHhUSvk/h9ZkAV9KWRdCvA/4BSllaadz7Wd17U5OXtV3kZuSh91jPFk/iWurHS+spIHthNyvZhv/hod2QMdKxfpzqTj2od27X925LogBW8OTqNAlNkhTquKCALzuZa1Q5UW+wfN8jQpNciR5P2/nKd42XsBbqcEP/3v4WhlMA+JD/ze1PXDa8PYj8H//CH4hfYdFA6pZ7XDfsfCG592igbC71l2X6PVCUzlpq7i9ooGVUEPaFVxyGCwEbTUadHpQVsSkgXdHTp6q5EyQxejB2m4b+Q5DWqRIkSK90XUvV9fuVqP+YpeBI6Hbsyi3ricpZTU0fkEI8W+FENNSytX9ucydpWs63/7W+7DtNnFT2wJe0pektQSapZHYpeMVnrsd9wvYMVooNAEpEIh76x2xgwpkeZoneZon9+0cA66ZL7AbTTW2Eti2o8YvfBV7dRM/raFZBlYL7IR6K1uOxM4Z+DGJtr6J+eLXOfv0qV7Is0KrB0PbFQ2Edxqw0Cj3mtUmQjsIiN6G507PNVNVnt09Obv5addocpRULyctXDQwmJ/W38YpgcZB0hSHNj+/0z5x2+2zCdHWTZEiRYq0F7qXPtJHWYpfAkpCiBPANeDvAgO7agohDgJLUkophHgU1X92bb8vdifpmk46pT60dg1eO+WMhW/fSz+xMWtLE9rtwCsY+76Hpum3PAdgWiafc5c4x4Yqjik3qF9bReL3wUsKsguXqX5vnKuHUswueeRcQW0ipuYaHappg/IBjdlrLSz7WyyTDRUNxADJBWrkiY8oGhADTW3LOBwlFTSrNYNQZr8JbTVUNDC872Z33UkyFG+SkxYe72bzc7i9nLQI1iJFihRpf3VPI4OUsiOE+DHgM6gWKv9BSnleCPH3g/mPAx8A/oEQooOqofy78l6JQb/OtWP+l98Gu41lxncFTXcKXj6S85bNF9orvCYbKvxXblC/tobEJyPi1GW7l1tW9V3KosWsjJPVzH6umTCoeC5lrcURaZLV4lT9DkLIAXizdINyzMWxWxwgxrwFRT2G9CQX/ToFzVSFMlOS3LLGhmVxTSvwtekDOKt5QGIWazzkLpE015mOtVlPehzDYhGbw1g4eMhQL7RRRQPhnQbuCwDt/jsoGriTXmgRlEWKFCnS60v3EuSNTLCRUr4AvDB038dD418EfnF/L23/dVvtKfw2fsNR63YJTalUCk0bdGF838O2HUwztsXJ+orYQAiNdMyk2nYREjKxGPXXlpF2m8y6S9VzuCpazPpxcnqiB1sZEacmW8rV0gK4CsArvG4YynK62QOvjIhRlx3amsQ3DTS7g204uJ0Y83Eo6AZ0YF7WKAgVvr7o18kJA0fr4PqxAMpUYcC8XyeHjk0HhxhLfp2CNEHQW4eEa606x2oGX063KXgxSutQT3SQusYpLQAvISitCS46ab5QfQv12iSxawaxvAtS0PnrPGdzGVKZdQ44F7m/qgdFA/2QZ/jnOq6igQjWIkWKFOnNobFBnhDi16SUH97hvu8d17Xst0ZB007gZVomZ91lqrRZNjtstp3+VkixBPW2G/TQi1NrtdQOEkaMyuUblCvrHKlrZAOg6rpVm50Wi7rLrEyQ1WJU/Q66EDw+fYxHTz/YAz3f93jplVdYaVS57lSo+q2ek3Ut5mI7LQ4Kk/m8TmFTNRSezwiKKy1kLsG8bAT9yTq4GMx7tcDlgnm/1nO8Lnh18iHw6q1jK5Rd8Or9JrSyTlGYxNsSu9bATxhYbdUOplSHuuUhdY2SyPSg8ZSWpuq7JHwDU4oelAkEJS1NxXOxMJRrpqUVUAaP68LbyViGYrXD3OUYj8gkqTOnsE8XQNMGfnbN8wV+8hdzZJwCBc3HdMC3FLxptoabEDj+Ya4nD/GTz9RJc3BkyDMCtEiRIkWKtNcap5N3OnwjaHT8ju5tKeX6GK9l37QdNGVJwsF9AAAgAElEQVQ1g6rfoiz64FWTQWjQMFjWWsw0dK5MQ6rh09QcWm0FV8WKDHaQEBQ2JQiYzwhyqzZ2ChwRZ8kPEu2FVG6V0GnSxpExlnyXgjSJCY3r9Q1s2yGVUo3ybNthza6RS1icrZfJBVB2rVXnaM3gy+kOvmdQutCiOqUaz5Yut6gfSiE3bEqmRVV0sDBULpieVLAlJCUto5w8IZgL4KoLXiV9eyib09M9J68k0tRlB3TBd3UmOH2+DdfbaDMprDP3jQSvvcjJs6wEru1i+UIBccokhYbT3QUjKGT4Sx5mrVEjbscwtTZCA6PRBzGrIZFejHU5zec5wfffU+Z5pEiRIkV6I2vfP3GEED8D/K+AJYToVsIKoAX88n6ff9zaDprm/Tp5qdMUCrxueC7FIDRYduoc9xMspjrMXm5jZwySokNcKriqT8eREkoXWtSmVNixdLlF5aCF1aiS0AyKsUzPyStpaTY7LZLESAidopag6quChUPpCSwr0btey0owZWVYaVQ5bmQUlPacLI+5y20e8ZOkkhb2DbXZrpWaxE6mIGNhPXYfttva95w8gJRlodkd/I6P42owaSLcPlCJ0L5jIujbJm5jritJnDqqkifegXNnoV6FtSVoVMGX8PxvWtzQ40jRou3pJPw2pu4hkKqUSIJpSmqZCT77FzG+/x/c9lsrUqRIkSJFuiXtO+RJKX9WCPEx1L6zP7Lf57vb2g6aSlqaqt8HrzktoZw8IbgvnqG47nH/psETtTzutRb+jTbagRRWKoG97KpdI6wJ7CUbhMBKTmInM/ipONpDs1ip5G3l5Gmazpm3vnVkeHnAybJipBpt5WTlY4hND6wYaBqCxK6g6U7BywfqfoyXvwrNOqwtK+hCQioDjToICVZa3X+jDIdmIZVV66WAdBrqFVgsw8wRSGfVWk2oxzXrIGX//sWrMDMLZhLWl6E4A9deg2xeXdv6KvjoiKSJlBpeSyK9DkIAhg55Cy2dwHN0qpXbfltFihQpUqRIt6yxxI6klL4Q4u3jONfd1k7QtJNb5dquytWy4sTtdt+tyscQ68GmrJND400PYcYQWtDoeJfQtFU6ItjnbFsny4dzXzWoVxXsVKsGQkIyo1ytLgylcgqUCObsmnK80lkFV9fLcHAW0sG6UVCWzqnbQih4qwfrkhnYXIOrl+D0I3D9KuTyyjC7Mg9TBTV+7aI6huOA48LqRZgsqONdmVdzrg2uA6tLas4PHjcZ7KR8ZR4ywTrHhcoGzByD5UU4MANtVz2vyWmoboL0NYRpok+ZCCs38Or6HugGZAfvjhQpUqRIkfZV40wQ+rwQ4tullF8a4znvkkZD007gJYnTBBJA0zc4H3KrwkBl1wykgFQa6hVjwJFq1PpuVW0TbiwqoMp03SodHnocHnoUAi7E9+HrL8H6Cqxc74ch01moBeB1cBYSVsjJugK5SbXutRA0OS6shmFrPgRNFyCdV+DlurC2E5RdgMli8Lh5mCr2zzUxDck0XF+AA7PQcgEJx0t9J+/YKfV8EwlImDB1qu/kHS8p2DQtMBPq2F0n79ipvpN3vKTuNy11jMJBmC7Cyfvh4SeC86Ku51d/ToGelQShbS0Tdx31er3r/Xv1/ooUKVKkSJFurnFC3ruAjwghXgMaqM9CKaX8tjFew75rt9CU6UJZMNcI3K9UBjaG3aqJPuRMFtQLF3aknJAjFXarnCa0HLgSzBkxdU2ODUnFoDi2cscyOfjKWfUd+k6W01RAU1mHw10na1aBmibhWEk9xy4MTZX6Tt6xUt/JOz6n4CqRADO0bhSUTc/1nbzjJeXkdc/VrCvH8IGH4R1P9mErYdEriuiOfV/B7K3Obbeu5arvmgbxgM+/+33w/G8pp8/rQGzIMG231TEOHoZ3PrUPb7hIkSJFihRpG40T8r5vjOe6a9oRmvIh8Lox5FYFrtaVCzBRGHSrwkBl17Y6UomEcpkaobnaJiSSEE/A8cCt8n0oHFKg0lXCgvyUgtLZ430oPV5SUJpIQtyEwgHleg07WXcCTbcLXqAgNQxbAOnM6PHtzg2vM0b8tkwV4Kc/Bh/9cZh/GXRHvWYAdhM8D+ZOw099TK2NFClSpEiRxiUx7s0hhBBFoFfeKaVcGOsFAGfOnJEvvfTSvhx7L5y8bg7aAw/DmT12q7pwNHzNjq1gbrdOVqRBra3AH/8O/N6vQfmKclsPH4f/9sPw3g9EgBcpUqRIkW5fQogvSynP3PLjxgV5Qoj/Gvh5YAZYBo4B35RSnt7xgfug/YQ82D007datihQpUqRIkSK9eXW7kDfOcO1HgceB/09K+bAQ4l3AB8d4/rFJ0/o5b3sRJowUKVKkSJEiRbpVjdMnaksp1wBNCKFJKT8LPDTG80eKFClSpEiRIr1pNE4nb1MIkQb+HPh1IcQy0Bnj+SNFihQpUqRIkd40GqeT9/2ADfwE8MfAq8B/NcbzR4oUKVKkSJEivWk0NidPStkAEEJkgT8Y13kjRYoUKVKkSJHejBob5AkhPgI8i3LzfIJmyMDJcV1DpEiRIkWKFCnSm0XjzMn7J8BpKeXqGM8ZKVKkSJEiRdoDrbDKZ/jPPM+fUKFKjizv5928h++lwPTdvrxIIzTOnLxXgeYYzxcpUqRIkSJF2gO9zCt8hJ/gk/weMWLMMkOMGJ/k9/gIP8HLvHK3LzHSCI3TyfsZ4K+EEF8A3O6dUsp/OMZriBQpUqRIkSLdglZY5Rl+FhNzwLGzSDDLIarUeIaf5Tn+deTo3WMaJ+Q9B/wp8A1UTl6kSJEiRYoU6S7Kx6dBAwALCxt7y/gF/oQGTXJkadPGwEAgesfIkqFMjRf5LE/zA+N/EpG21TghryOl/MdjPF+kSJEiRYp0T2gUTPn4aGgj4Wqnub1aZ2LyOb7AOb6GQJAhTY06EkmWDDXqCOCTfAoPj0VukCbFAYoUmR4AvSkmeZ4XI8i7xzROyPusEOJHUe1TwuHa9TFeQ6RIkSJFegPJx8fGxsTcNxi602P4+JznW3yBl5BI0qSpUqXMIkeYJUuaKrUeaFWoUuY6sxwiS5Z6D7yy1KkhIXSMwXUCSJOhShWB2HadRGKRoMx1HFwOUGCeSxSYAgQXuEyRSUCwyjqT5PHwkEiaNOngEQshhEmcNaKP83tN44S8DwXffwbVOqWrHVuoCCHeC/wCoAO/KqX8V0PzIph/H6qw44ellOf26qIjRYoU6c2kvYCmcbhQFhYNGpzja9RpsMRqADYKgGohMOoCVRdyaj0YSg+Mw3CVG1q3SYVFbjDLTM/lGgavPqAtcoQZsuSoU6NFJ4AqkxYdLnKJHBlsXBwcllgJ4MpnPphzcHBxuRgCr3lepcgUEnrHGF4ngHleDXLj/G3XgeAa1znGLF/mqxSYosTJ3vOa40Tv+U8ziYeHjo5AkCSJgT7wvnFpkSO7D+/ISHeicULeTwN/LKWsCiH+d+AR4KM7PUAIoQO/BLwbKANfEkJ8WkoZLuP5PqAUfD0G/Lvge6RIkSLds9pNLtS4XSgFTV+nTp3lAJq6rlF9AJoqAQwp4Om6UMo1qlPm2khQCrtVVWqUWWSGg+TJBcdXoBQeD67LDwDVGhtc4jKP8HYWKDNBHgVDl3oFAPO8So4sTRwcWiwNQJNaJ5DMc5kc6R4MzQ+su0yOTHAMlyVWgzkGwEsBWjaAtxZLwZxJHAcbFxeDGKc4SZUqFiYJEhSZDl4bjRInqVAlQQITk1MUeq99ift6z797jOF1AihxXwC82rbrJJKTHKdIkTlO8QjfRorUyPfGJJN8gt9hhoMIxJacPIA11vkQH9irX49Ie6RxQt7/JqX8LSHEO1HQ9vPcHMgeBS5KKS8BCCF+A7U9Whjyvh/4j1JKCXxeCJEXQhySUl7fl2cRKVKkSHcoH58v8mU+z0t4eAP5Tzdzl7oA1AWvUeuGQ3xVqlzlGrPMDBwjDFRp0qyxzmWu8A4eCqAp13ONwm5Svgc8LZa43HOhhl2jYVBSYxnAUI4mDi3aA3MXuMwBJpFDcKXWdYFKzU0zQZoMCwFUOrQCyOk7Ul3gSZIgQZxiMBdep8YnBuCqRGFgbpNKcAyTItMDx++CVxfQFLzFKQZzAN/Jk5zmLVvCuHfbDXVxsbDQgo5qGTK992l3/D7eze/zAg4u2dB8V1VqmMR5infd6a9GpD3WOCHPC76/H/i4lPL3hRD/7CaPOQxcDd0usxUKR605DAxAXpAP+KMAR48evaULjxQpUqS9lI3NdW6godGmM5D/FHaXLnCZ/IC7pNZtBa+dQ3w5coGP1Bo4Rt/xklzkElMBNL0WQJOL23ON6gPQVAlB04meC6Vco/q2oNR18kqcpEqNJAnixChxogebc6Fx9zH9dWGgUutmOcTDvI0neRw3SPe+13LyAFKkeiAFgzA1fHu3c3uxztgFBhSY5ll+hmf4WcrUmGISkzguLdZYxyTOs/xM1D7lHtQ4Ie+aEOI54G8CHxNCmNy8GbMYcZ+8jTVIKX8Z+GWAM2fObJmPFClSpHHJwuIQB3mNMhpiIP8p7C7NjXCXugDUBa/uY3YK8XVDgybxAaAq9eCtf7zDHOQR3s6TPHbb0DQuF2oYoOLEe6/xfkLT7R7j9awHeSvP8a95kc/yPC+yxjo5snyID/AU74oA7x7VOCHv7wDvBX5OSrkphDj0/7P35lGSZfV95+e+F/uaWZFLVVbW0t2V1aK7Ra80jVpCIM00ktoGdAYJBJKlsc4Ie6yRZFsegT2DfdBoJM6RZevAHAuObS1YIGkQGsANogUCAVIB6m62rgZVlbors7KyKjNyi3199zd/3BcRLyKXyszKqPV+zumTL957EXHjVfTLb35/3/u7wL+6wnPmgSOBx9PAwh7OsVgslhsGB4dHeZh7+S7gxsnk7bdo2u7YMISXZbiMM8bb+DHbJuUm4pqJPBGpAh8NPL7EQEl1E/4WmFFK3QFcBN5Cb5Zuh48DP+/n9V4JFGwez2Kx3Og4ODsSNtfThbJYLDc319LJ2zUi0lZK/TzwaUwLlf8qIqeVUv/EP/47wCcx7VPOYVqo/M9Xet1nn312WSk1O7yRAzAGLA/5PW4n7PUcHvbaDg97bYeLvb7DwV7X4bHXa3tsL2+mzKRUy36jlHpGRB653uO4VbDXc3jYazs87LUdLvb6Dgd7XYfHtb62V5r4YLFYLBaLxWK5CbEiz2KxWCwWi+UWxIq84fGB6z2AWwx7PYeHvbbDw17b4WKv73Cw13V4XNNrazN5FovFYrFYLLcg1smzWCwWi8ViuQWxIs9isVgsFovlFsSKPIvFYrFYLJZbECvyLBaLxWKxWG5BrMizWCwWi8ViuQWxIs9isVgsFovlFsSKPIvFYrFYLJZbkND1HsD1YGxsTI4fP369h2Gx3JQIbVrMAgpwNznDA4Qwx1C35y3GYrFY9pVnn312WUTGd/u82/IOfPz4cZ555pnrPQyL5aZkjf/GOh8mxCSAL+RU3zlN5hnlrYzytuswQovFYrm1UErN7uV5t6XIs1huNwSNUAOiCDUEjcJBEff3s+n2ZucV+ASaFnW+DUCIScJMEBR6IXIUecqKPIvFYrmOWJFnsdyADIoy2F54bXcMolQ5hUeRNot4rNNmnhDTOGQRyoCgSPnbCkXKP2+BMNM4ZNCUUCjqPI8iDGgcUghVhLa/D388UTxWruUls1gsFssAVuRZLPvAXkXZZucNijJNEUF8oVWgxTwRjqDIoCmifFGmKfaJt44oU0Rps0SYKZqcxyWJpo7QoMlZwpiYR4tzhPztJmdxyfoCrk6Txe4xAIcYHgXAQ5HYkL0TGjhkh3vRLRaLxbItOxZ5SqkfAL4sItUhjsdiGTqCRlMBNhdbwe2dCDRNhSrPoSlvIsqKtJjvumFCCbqirASbirc4bZaI+KIsxCgATc7gMuKPq04rILxanMMli6YOvngzxxQtLhLhGC0WCDGNUMUhhiJKhJmukxfmRNfJizCDx7ov4GJEmPDHq0jwKup8gygncBnDIcxgJq/NCqO8dXj/iBaL5Zqzkocvfho+9xSUCpDOwmufhO97HeR2PSXAci3YjZP3M8DvKKVWgC/6/31JRNaGMTCLpcN+5smMS/bX1PgaoHBI4QXcMKGMILhk8CjS4gIhpn1Xq7+s2RNyKzR4kTgP0WIuIMo2umHhgFPWc836xVubVSIco8kCIQ4DDUCIcBJNAUU8ILyKgCLMCTRFHGLgi7eOKItwJyEmiHE3cV4FNK7qGnqUucjbcUjgENnw7+VRxCFKmif29XtgsViuH3/3PPyHd0GrASM5ODgNjTp84sPw5x+Ff/5uuPu+6z1KyyBKRHb3BKWmgDcBvwxMichNV/J95JFHxM6uHR7Dy5MtoVmnyQXfGcsGxFa6T4T1ly4zXYcKorSZR6gT4iAey7iMo4A2eUKMI4BHHocsLeYIcxSPgj+5QPrOa5MnzBgtLqOIEWIaqF+Vk+cwQogJQmS6ouxqrqEiDjR8cbg/rTHrPM9l3oWmQYgciihCgzYrOEQ5yLuJYe/4FsutwEoe/s3bIRqFVGbj8XIRGg34tfdbR29YKKWeFZFHdvu83ZRrfxL4PuC7gWXgfRg3z3KTcy3zZFsF/jfmyYxD5RDr5skazBIi4b9ff55sMFvmBEqXvayZos08YY5S4zlCaL8s2XPDOmPq7DcCaauypnT3h5kmzoMkeJzdiLKtzusXZUG3LD3wr5fe4bH9/Vssxn1M835KPE2Rp/BYwSHLKG8lzRN9+T2LxXL90RrqNYhEzU+AWNxsaw2O03scPAbw+U9CvQKZLLRaEAqBCiQ0Uhkoz8OXnoY32An1NxQ7dvKUUsvA3wO/A3xORM4PcVxD5WZ28nZTutzsmEOyz80RNFWeoU2eFpe6oqxXrjQuVOexEV5B1yyNUOy6a4oEXiDkH2LUd7yWCDHin3fEd8aMEGh3XbMLRDhCm3VCTADgsd7Nk7lMALWrcvIcMoSYwCFFgodwSF51Jm+ra2uxWCx7ZTtRFty+kkCLxaFagdPPQaUMK4tQKYIWX5wVYeECTE1DMgu1Uu9YtQSiTElWPGg2IZmC3CTkJvqFXr0GXhv+44eu3TW6nRi6kyciY0qpe4FXA7+mlJoB/k5Efmq3b3o7sd95shqnaPulyzZrtPtmWprZlA4p2oH2Fx2BpnBJ8BhJHu2KEaGGxwouWaqcwvVnRDY564uoatc1c/tcM1O6NNmybNdd81glynE/T2ZKl8E82VaB//482cmuk7ffebLNS5dbuWE7dc0Gz7NYLHuikIdnPw3PPAWVglEdjzwJD78Osje+O6y1EVQ7EV7bCbRqBZ5/DqqbibIyOALxlNl/ad7k41IZqJVBK0iljHhzlDlvfRXmXoTvfhgW5iAzYt7r/FkzeaJRg3oDls/CAf8ynz8DByZMyGVtGUYOgOeZcdSrRtCFel2TiERh0XZNuuHYTbk2AxwFjgHHgSyghzOsm5uOsBPCfaLMY31DPqtTygw6Y50cV4gpQoygfbdKEcPrzrqcxemWLoMzLdWAQDOBf5M7C9PmEkINRRIwoscl52fLjvviKliuTHRFmdcVXj2BZraLvmiKEmESl0nSAVHWeZ/9yZMNBv33ItBuuhipxXLrc/55+G/vgnYDMjkYm4ZWHb7wYfibj8JPvhuO70/Oc6+lyysdO3MavvFluDgHU0d6okwJJNPGSUMgkYJqES7OB87riLI0FJZh7iW492G4NAuZUf8SnTUOWltg9hykslCvmzzcyjkj0JQy5x0YN7+gZ8/ByBik0rAwC5OHzeQJLXB8xrxvNA6xKORmek7e8ZM9J290zDh5rmvGGEuAO3AbbTZMOddyY7Gb33ZfCvz3PhGZH86Qbm6C5c8mc7RZ9EuXs35psCO8LvtuGAPZss5MyyrQ9J0y45q1uEgsMOtSUx2YadmZTTlDe6D9hUcRQRPikC+cDAqHBI/sWybvZsiTWSyWG4xC3gi8SBRGAo5dJA5j00iliPf774Kffz/1iDm+m3Jl8LxIFL52yoib5SUoF4yQSaXM9qV5U7pM+KVLUaZEWVqHywvGNUtnoFICCbhrXtM8Vso4Y406LC8aUabFF2jjIPQEWqNmRNryYkCUnYXRnBGCl+Y2EWVlcAWOnTBOXixmJkTkTvScvI54c5Q5r1qGyWm49wF4+HEjyHZzDUcOwCc+BBNTgNqYyQNYX4HX265JNxy7Kde+fJgDuVUIlj/bLPqu2wJhDqOpBYTXScSfXdnvjJ30G9wmgIjfCsMci3InLhOkuJvEDkqXO8nkmfOdrrO3P+VKK8osltsZ3dbUCzUiySj1grn3xLLxDdu6rXFCDvGvfxJdrUB0BNfTeE0P3BBuSNFuw1ohg7s+z9n3Ps3p7Nu6zlipCJfn4eAUpEd6zlMy1S/COucdmjau1eqSESwXZ03psuN+dZyxWgPyvhumCByrQrMO5xcDZU3fXYtEoVYHpc17RGNmf6e8enym5+QdO2GcvGjciLSxiYAomzGf49ARuGcLURbc3qnIBUgkfaEb+Ns7NXD7Dj7ubL/mR+AvPmaE61aza8NR+F7bNemGYzcTL8aB/x24F4h19ovIDwxnaMNjmBMvgk5e2y+TumR2JMqC29eqFYbFYrnN0RqaNbQTpbrWE16bibLg9lbnVVcqzH7mOVrFMs3Li0ipCEqhkimkXERQOMk0Uikiy/NwYIqTa/8v2tNoFUaFIzRJ4Dkx2slxGg1FpQhjozXqlTZ//cCHEIHVvMmTLczBoaNQXu9lyFbyPREWPG/qKNSqcPgY5BdgdMKItv1w8hwF9z8KM/ea972aTN6gKLsRGOyTF4ka8bm+YgSe7ZM3XIY+8QL4Q+CPgX8A/BPgp4H8bt/wVmew/GnzZBaLZT/ZziXTnsZxNxdom4qydJTmt04h1SJLLyxy6TvrsDKPyh1BpTNIpWyUTDKFVMooBJXKoEtFc97YNCqV6Yo3lUyhC6u4+ReROx9CL87hZEZBg54/25084c2fRSWzOK0qXruJU1nGi42jEHSlhJcawfEaVAptwrEwoTCUqlFy4RWU23PGSkWTD4tGYCyQITs+0xNhfefFYOygcdjuuBseetXuSpd7EWWbOWOD21c6diNw932mD96XnjYrXiyumAze699qHDzbH+/GZDdO3rMi8rBS6pud0q1S6q9E5PuHOsIhcDO3ULFYLDcX24qynbpmvniLJKOc+f9O0Vwr9rlkJFJIuYBamUdyvvAKiDIpF5HlC3BgGieTRSplRAQ3EcMtL1F2p0hUz6PDSViewztwFFUtoEZMHliv53GypgE4hTwkRnBWZtG5o1AtQNY0FNeFPCozBuuXkVAcNXEYVa9flZPXaikUMDpSY+xAm9o/Mz069prJi8WNsIvFbxyXzGK5EtfCyWv5Py8ppZ4EFoDp3b6hxWKx3MjsOE+2jWvW2a6uVpj9i+doF8s0OqKs44aVi+CLHCczgpRLRgwlUuhKCeULI69UwOmIt1gCWVvCHZ/CuzyLkx4xLtnFs5DMotp1pN1ALp7rijKZPwuJLE67htdqoOfPokYmUCI0F+bRB46T9hYoRg4T1jWcUBwnHENNT3SdPOfwiZ5onJ5Bl4rocAIViaEOTPTE2+ETJniWO0L6oQe4+396nGalsfU17GbyJtB/9SEYPYwbcfsyeZ5n/l1C6yuox966Jzds8LyQLYhYbhN281X/v5RSWeBfAu8FMsA/H8qoLBaLZSfsNU+2hUDr5MnaxTLNxSV0cd0vQ6ZN/a8jwnzXTOemcVJZM31RzGwAqZS6Dpq3vtItXcriHCoziogRXiqRRfnlSj1/xrhm2i9ljph2SHr+DCRHoF1HWg0oreFOHcNbWkCNTZneGY7COTyDlAsQiqFCUdSAKJNyER2Ko8JRnNyMcfIQQtN3UpBJyuG7mfwfX8XJEw3QV5fJ62wnckmckEMk1YuppGLpTbd57Edw//ZjIDVwMzjxnsXmOJhZCuEoPGiT/RbLbthRuVYp5QK/ICL/YfhDGj62XGuxXCcGRBnsTDRsmSf7psmTLX5nicvfXvNzYkf6cmJOMo1UA9mycjEg0DLGefJLnrpgRBl3PYwONCiTQh5nxPS/0Ot5SGZxVi+gc0egUsDJmmmYej2P6jTtLeQhnUOtL6LDMZxxf0X3q3DynOwo0ckJIiMZZn70VRtcsl1n8rJx6qUGROIk0s71LV8O9skLR03Kv7gCoei+9smzWG429lqu3U0m73Mi8tpdj+wGxIo8i2WH7FGUbXpeMOT/7SUu/10REcFJpbtBfsaM8NLlnhsmpSKyMg+5aRx/MoCI4MajhMp5iuEp0rVZ2m4ClufQB3o5MQBZ9wWaCgi0lYBA64i3Qh7SYziFRSQUg4nDqHrtqpy8zljTDz+4aelyL85js9Iglo3jhG7BQFkhD197Gr4aWPHi0SeNg3cTrHhhsQyLayHyfg2zysUfA5XOfhF5brdver2xIs9yq7KfebL6WgVeeg5VL7P4nSUWz5iusSqZQhfXYXXBzK5MZvqcpz6BFgj8h1IJHD/kn6zPIvERPA+ksASJUZyVWSR3FAkINAp5JJHFXZlD545Bdd2UNUVoF9bQueOk2guUmMBtV3ELV+fkif85UjvJk+3kGg6ULi0Wi2UvXIuJF9/j/3x3YJ8AN12fPIvlRkK3NdWVyr71J2sXyzQWl9DFQld49VpcpJFKAbXc70KJCCqZ6goelUxBIU9o9Tyl8YdJN+Zw4yN4fisMSWRxW1V0s45eW+zLkKnsxKaB/8bCGvrAMRPyDx8m7DVwlOBMn+wG+YnEcA5M9ITi9AzSORaO4kz358mKepJS6m4OPfEqZnaTJ7uCQNtVnmybY4PnWSwWy7VkNytebFuqVUr9tIj8/tUPyWK5QdnPPFng2MUvnWbtr78My3N9LhSoTfqTFTbNnalkCims4vh5Mu/yHBnT55oAACAASURBVCo9ghFevf5k+qIRXqpdh3YDffEsTqf9xcVzpnTpCzkvmaPppUi35ihHDhNq13FdhTM9gy6ud2dXOqMTaL+U6Uyf7BdogcB/OHeQdZnoC/k7zlVew748WX8fyp0KLyvKLBbLrcp+TiT/RcCKPMuNw7DyZC8scvlsEfTGkqRKZ012ayBPpsamIZnplgM74XppN0FAuQqnXUM368j8ouk7pnzh1W2FcQZJjOAOnoc5j8wYEk0jl+dQE4fN+kzKiLJei4sZpGJmYRKK4hye6Tp53fYXjnkOpSLF2DTF3IMcet3jzOxClG15rbcQZbsRXv3HbC8Mi8Vi2Yr9vEOqK59isWzDNqIsuL0jcdHJkzXKLL6wyOLZYiBPVoDV+Su6ZkHx5qYTuKVO09hZnOQInmwsScrFszgj4902GcGypqwvmlmY0muT4URjSL0GnhjHK9B3DL/vWK8Vxkl0qbDhvP7+ZNM760+2wzyZ1uAkknsWZRvPs6LMYrFYrhX7ecfdMINDKXUE+APgIKCBD4jIbyulDmAmcBwHzgM/LiJr/nPeCfws4GHatnza3/8w8HtAHPgk8IsiIkqpqP8eDwMrwJtF5Pw+fi7LNuw6T7aVuAiIMjPzch0RhZNKd2comhmOvvDKTaMCebLBcL0qrRJaeZHS+MOk6rM48VG0mNKlxLMb3bANrlm/eGsurCGjfp4sMkW43cB1NpYk1QGznpJS/XkyFYmhRie6Tp47PWNKnCKMvub7Ofy9ZsHLa96fbJtjtnRpsVgsNzfDdvLawL8UkeeUUmngWaXUXwA/A3xWRH5DKfUO4B3Aryil7gHeAtwLTAGfUUqdFBEP+E/AzwFfxoi8HwI+hRGEayJyQin1FuA9wJv38XPd8ux12aVOnmz9b76C5Gc3CK9u24luX7CNgf/NRFm6PouTGO2G/Dvd+fVF00C2zzULrIXZbZNx8Rw6OUZTp0k35yiFDxPWDVynkyfb6IZtdM02z5OV9jNPts3MS5sns1gsFsvVsp8i768Hd4jIJeCSv11SSn0bOAy8AXiNf9rvA58HfsXf/0ci0gBeUkqdAx5VSp0HMiJyCkAp9QfAGzEi7w3Av/Nf6yPA+5RSSnbaG+ZGZR9Kl5sdGxQUuq05+/FnqF3KU1+4tPmyS53+ZJ3SYMdd81qIp1Gus6nw0n2lS5MnGwz8byrKItOEWrVuyL8zm9I5PAOVgGvm58kQwZ2e6Tp5ndJlKTZNaewBDt6weTIryiwWi8UyPHYk8pRSdwE/ChzBuHNngQ+LSKFzjoj8/BVe4zjwIPAVYNIXgIjIJaXUhH/aYYxT12He39fytwf3d55zwX+ttlKqAOSA5YH3/zmME8jRo0d38Kn3zlUvSL6hdFnsd8Y6wf1SIFs22KvM72PWaS4r5SK4LqOPP8bJNz7aFXr1Qo3G0gqRA1mqz55CpbIbll3S/nqX3RmaHXctHEHaZuXvzYSXEWWd0uVJdHlj4H+nomzX19DmySwWi8Vym3PF31pKqV8A/iHwV8ArgK9jxN4ppdT/KiKf38FrpIA/BX5JRIpKbTlHY7MDss3+7Z7Tv0PkA8AHwDRDvtKY98q2zth2Xf393JmTSkNxhdDKixTHHiLdvIAbH6WtpeuMdda3JNHLlvX3KusF/qVZR/u5M8cJU5u7RL1QI5FLAkYQRSdy1C7lcQ4dR0rFbp5Ml4tIsD9Zx8nz3TWA0dd+P9Pfdy/IDvNk2y27tANRZvNkFovFYrHsjJ1YE/8L8ICIeEqp3wI+KSKvUUq9H/gYxp3bEqVUGCPw/lBEPurvXlRKHfJdvEPAkr9/HiMgO0wDC/7+6U32B58zr5QKYVblWN3B5xoK2zljJEZwusLrcl/TWNVphTF/Bp0cp+WlSDfnqESnCXWaxvrOGI4f3A/MtOzvVdbrY9ZpLivlIqI18aOHiGXj3fE6IYeZ1z+yZ+fR5sluUrw81D4N9adACqCyEHsS4q8D1y4fZbFYLNvRJk+JT1PkKTwKuGTJ8CRpXkeIG+ceutP6Uwgz2zUKpAFEZM4XcFuijGX3X4Bvi8hvBQ59HPhp4Df8nx8L7P+QLyangBngq77ALCmlHsOUe/8R8N6B1zoFvAn4y+uZx9vWGevr6j/QNLbj5E2fhHKZYmyaQu5BDv3w3kuXO8nkgRF6HWfPirLbgObzUHgX0AAnB2oaqEPtw1D7KGTfDRG7ELzFYrFsRp3nucy7EBq45AgzjVBnnQ9T4KMc5N3EuDHuoVdcu1Yp9YuYGaxfBl4NvEdEflcpNQ78qYi8epvnfi/wReBbmBYqAP8aI9T+BDgKzAE/JiKr/nP+DfCPMdm/XxKRT/n7H6HXQuVTwP/mt1CJAR/EOIqrwFtE5MXtPtOw16696kzehtLl0IZqud3w8rD6dlBRcDIbj+siSAMOvN86ehaLxTJAmzzzvB1FFJeN91CPIkKDad6/r47eXteuvaLI81/8XuBlwPMi8p0tzhnt9Lq70Rm2yLNYbjhEg9Sg+hGofgjcKUCBCrEh1urNQ+KtkHzb9RipxWKxDBVBI9SAKEINQaNwUMT9/Wy6LWgK/AkF/owwB/1jG++hTeYZ5a2Msn/30L2KvB2Va0XkNHD6Cqd9FnhotwOwWCyb0BFlRP2fgIqbbdGgnN7jKx0jCq1T4BWh9P8AHngLoFLgToI7Qd9NysmZrJ4VeRaL5QZgUJRBv/DaSqBtdgyi1DhFmyJtlvBYp8U8YaZxyeBRRKFQpBBM+67O/hbzVPg8DlE8jKcVYpIw/ffQEDmKPLWvIm+v2GXNLJb9YK+ibLPzgqLMWwQxM7QhYyZJ6Hlwj5jH+EuykTLb7XkITQNZEDMRx4xpybh3sgJqFPBACUgVpA198doo6JWhXzKLxXJr0hFlirj/eG+umcLcD6ucwvNFmaZo+qaSRvvCK8w0DhmEEvgCTVOgzTwhpnHIIpQx99EYHkuEmaLJLA4JhCpCgyZncf0Sa4tz3XJrk7M4jPjnVfDwcEmgcP19bRS9e6giiseNcQ8d6rJmFssNiWjQFbO9U+G13Xm6Aq3nQJc3EWVFU/4MTZvH/k0IUr4I20y8xY0oc6bAOw/uqHkv7wyoEX9cdZDFXm7OOwdOFlQdaIA+C864EXneRQgd8927UX9sLogCJ+GXbIM0zGtZLJbbgu2csuD2TgSapkKN59BUcckB4LFCi8t4rAWEV8YXXh1RZlZHcsigKdBinghHUMRp+6KswXnCHADEF2Qd4VWnyWXfUcM/lkVT74q3sC/Y2swT4TgtFghzGE0NRQJFlAgzeP4fzmFOdIVhZ785L4lDFFD+9Uj4Jdvg9WzgcGPcQ213V8uNz6BLtltnbLB02fhraH2NPgesu935a893ybwLZvapkw0cGzhPVsB7EcIPgTcHzqh5Oe+s/7wqUAcdFGUdEcYm4m21J8qcw2YiBALOSb/dSRxUDNSELygVOCfMeCVmPqMzY0QkCtw7wZmA0N3gHjezaLfL5OkVk8mzWCw3LIJGU9m1M7aZKKvyHJoybRYDTtmVnbGOo9YRQ4o0HnmanCfJ96Cpo1CEmaDCKRziXeE16JT1ts/44q2GUKfNKhGO+aJsGjD3wwgz6K7wihHhZHe85lgBh1hXvPUE252EmCTK3SR4FdDY90xemxVGuTHuobZca9l/hpUn85ZA1sG7QHl1hm//5T288PQhaoUo8azHPU+c5WU/eJ7UmEt/6TID/l+MphQ5b8SUe9CIrdC40Wv6XE+Etc+CmzXjcHxnzJ0AxHfNxgNCbszk27w5cPx2JCK+0CoCCSBmhFZHeHVF2CbizTnYE2URcxO6qmuo4uY1VBz0PVD/uPn8W82uJQqxJ/bjm2Cx3PbsZ54seKzGaWp8mRZzG5wxh1RftkxT3OCg9bJmKzR4kTgP+a9lKgdNzmzqjDHgjPVvn8FlDJcUDc6R5gd9J26FCHfgsdYVXj2nrCPKzP0wwkk0BRRxX7wdJMQEMe4mTu9+eDXXEBr+6ztAr/G+Ib3tdpY3UuJTaGpbzq51iJLmxriH7mh2LYBS6oMi8lNb7VNKHei0QbnRsbNrB7hWeTIK/ZmxPmdsIE9G0ZQTVayXJ2vPgkqw8HybT77nzXhNTfKAJhT1aNdbVNZGcCOaH3nHJ5i6r25cOPcI6IIv3hR4q+AeNeVV905QOa7ayaNM0RM+W3mQ38w/zmLD3IQmo3F+erzGj45pxiO7dB47okwNoX/OYJ88omZbr5ht2yfPchuzV1G22Xmb5cmM45Xq5slMSTLju1IKRXrANesXZZoyQhNB4+DS4EXCHEFT8N0wRZslXEyD/TZ5HLK0uECEI3j+eQJ45HEZo81lFDFCmD9Sr8bJ0/79NM6DpPg+FM5VZfI2irLrT6dPnqZBiByKKEKDNis4RIfSJ2+oLVT8N3hORB4KPHaBb4nIPbt90+vNTS3ydlO63OyYk+wXDqKh+Yzpn9a+NCDKfOHlToPyH4sClaYnctJ0xZAzDSoREGXnA6XLJXBGNgovAXTeiKjusXXjmglmu5snm6C8DH/0i48TjkaIZQBa5jwVBlrUSxFajShv+Y+fJDVydqOTpzLmtZ2UKa86yavO5J2u1HjH38Mz1SSOckg45tJUBTyBe5Pw2yfgvuS+fhOuDi8P9afNLFpdMNc/9qRx8Gx/PMtNxH7nyQZLlx2R47FO2y8ZmtyYcZ46wqtXrjRiKMwRnECerMksIUaAoPCaI8JR2r7wUkCLPK4vyoLirSPKQkygCNGmisLB4/JVOXmdscd5kASPM+iU7fYadrYdkjeMKBsGZsWLpynylF8a7qx48cRQVrwYmshTSr0T08A4jgkXgfm13QQ+ICLv3O2bXm+uqcjb7zxZ85QRYu0l8NZAAjMtu7MpU35ZcyEgcoqAC9HHIPpoT+jpCjS+AE4aan8OKmv+ddt5U65sz/kiaz1Q1swHSpd58xxvDkJHzeuFjndFGdTZi5OndZFWNYwbT9JqHQKVJpx9JV/5PY9v/GmI7JTghEKI18YJgVnRrg3A+nyIh3+iwSM/0dy+dLkPLlm+CT/zHfhGBaIORAZesqWhruH+JPzed8H4YGXAYrkN2c88WY3n8DYRZUER5m07C7PUFUMeKzR5kQQP0QyULnvCa44wR7tuGPSEF8iG8zTVbp7MZYJOnuxqnTzz6R8lzr3+tdj7NbxdRNnNztD65InIryul3gP8ZxH5x3sa3e1GR9hJ2Igy7efJ9PomMy17Jb8+Z6w9b9wwNWKOifSXLj1TukQHZlp2ZlN28mSdwL+3aAQaYdCXzNiUbyupuCnZeXlwjhsBKQLujBkHCZCoeSy+k+fMgPjjdcx5WhK0yinc+CSt6hSolxHOvpJWzfxVGE7EaVVraDSOON3Hmx1zY3Fm/6ZBvQjlfJRasY0QIZ5x+NLvQLsOtSKEohBJRAjHIDUOShkFlRyD05+K88hP9dbo7c9WbP211wI1DVFlfmoBR0HcMY+hf/uTKzDXMLo4pPx/wkA6NexAU+BiE55eg7dN7vRLZLHcOHREGexPnqzOaap8xRdSU37QvueGBUuBHTEUYooQI3650hzriLKNebJgkP+sX17szMJc7MuTdUqcJneWwyVNgzk/5F+nkxnzWKcX8p8IZMhmum6YOW+4ebLtRdn2ebKdHbPcSuy0GbJWSt0/7MHcEvSVP+f8GZW+KHOS9GZaXvbdMEx4vzvr8owpa1IF1fQnBviumb4IbmDWpa7SP9OyM5tyxjh5ncC/25mFqcE55LtZPspBhx6h1azhxqIbhNcyy3xW/Q2fTnyOYrVKRqX44fhr+YHqE+Q4QDgRp1GucfFr0Kg4G0RZrRRBCcQyUCukWZ+HkWmIZ6FeTiMCsRTUi71j4QSUl0KMTMHKeYgfCKEEls5CqwI4IB40SuZ1GjWItUFc85HcCBTyUGhtLdA2E29RBaeKUPRgqQnrbbjQgOkIZENQ1kb/ptze9h/n4WLDrNnXFPN6cYc+oRdTUGjDUytW5FmGy37myTaG/L+K4G3qjOktS4PZAdesiNDGiDkHoQo0N4T3w0wC0g38B8/ruGZm24T8W74ok4AoC4qwXvsLI9D6Z2H2b4eZJs4Dm5Yurz5PFrTydyO8rCiz7I3dzK79slLqFSLyt0Mbza2A1EyA3c1Cc9HvddZphVGjN9PyZGB2ZdAZO0nPQYv4gs04eVrdSaN6CNyXEc4Yl0zrAWdMtnfNomGTG+ugNcw961DOJylegloxjRKIZuClYonPzL9AY7pFPPNqRoopWqrFf0+v8hflL/JD8oPckU5TWUmz/CIceQhWL0BiJITCiLL0uBFAS2cgPgLNGlRrUFyE6BigoHgG3AxUKxDxL1/qKCzOw8hhqPshgdxd4CaMkycOROKQr5rLtNw0IgvAbUArAv/2PExHIeNC2TMmZ8oxgmu+CUf8Y0X/WFRBvgVTUZitQ8I1ArAhcLYOE2Fj1J2rwnjMvNdyy+TuOpquLebzBv++dn1XsODt/9fNcnMz7DxZf7f+C4SY9kVTv2s2WEIMc2STkH8c0AONYY0zFgo0kN2qP1nHNXOI+rM0TY8xiAy0uDhJsD9Zp01G57ygayaUCTNNjAdJDiVPthdRNnie7VRmuX7s5tv3WuDtSqlZoIL5vSYi8vKhjOxmRcXR5GgVV3FDR2lVD/aLsm3KlcFtjcbBIRz3H2tYfCHO+a+2gQixtEO1EKEwDyNHIJ6BWiltxEoK6utp1hZgdNq4aI1iGly44zE49ig4vgpp1aCyYhyxl05BImtEyuzZMk9nv0ikOkGklsNdjOGNVXAkSu7MCSrjqzzFX/LkmTcwkkuhErB4HnLTUDP3b3J3Qblo3ufACSgWYUlBXkN1HGpl46jFx035tdiCTAOSB6AZAfc+yL0Cyn5HkkwSGmuw9t8hMQWhENTbkIgYERf1P1NpGVJvNFm4hoZzDRiPgvLFWtYXb3WBxTqM+43KL9bheBwW6nA4DDXfmYsqmIn13LsTCX8bGAsbx883AwkpNhRQPDH7s+5QvnGWa8y1ypP1nLHNO/d3ll3qTQxYpslLJHjYL4OakH+vXFmj1/6i54aFAwKtc15PvE3gEKVNFaEJuJs6Y52xhzmBpjjQnyzolJkbQpJXDylPtpUo26tAs1hubnYj8n54aKO4hdDiMPfNRygvNSheDlMrtFEqQjTjUC9GWJs3wqsryvxSZqd0GU8bN21tHkanIDYCjXKadhMQiMRCtFvGJYtnjUhr12FpEVJjxs0qnDHzKKoViFSgeBkiOQg5sL4AUzWI+pG8cBwSB2B1EUaPQWnd7F+YeZ7GWh0v4tF06zSPlaDg21fH81CMUXOq/NWh0xxceyWFOOReDhOP9YuyYtVsZ5OwXILZGjyQhoKCrAcoWAaybWjUfXcuhFkJMAYvejDhj/Xvm5B+ArzPms/cTEI6AhUPkg54mEUnknFIvRpKjhF+JxI9J28mZpy8uGPKqBMx38kD7kzAZBjuTsCrMsbBu1ImbywMvzlvhF7C9QXeQMfIusCBEDyZG853zrI9+50n29ifrN8Z6+/c38uTmdxZqeuuta+YJ1PdxrD9zlgn5N/fTDbsly47Obdg01gjyuKwoTFsb7sj0Hp5sgnfyYMUryHOvRvKuDZPZrHc2OxY5InILIBSagKIDW1ENzmtGlRWHeKjcV76CiSyIbRA/qwRbF4VWnUoXTaTBZoC62cgNmaeXzwDri/QwjUoLkFkDNwQ0IByDdww5E5AadXMifDCcOAu+E5eU5Ym9YkQ68UWZd8ZS0+EKZVauHXhlekY3x0L3FwVXL5bc+FgjWUVZcUv+X7s8H+mXK1R0BVSEY0KjZCrHkKhqERbJBthWqrOhdiXeevCK6nXTbeUFz0YD4iy8ZRx0M41YTwDqRhcdOBIxIgoAU46UGxDLAbxKBx0e8JrJtxz0GbiUIyA+0vgvNcI2OgYZKKQEyivmDzek++BkXu2F2hbHWv47p2jNmmR6Wzc/pEc/Eke1tqmVLvZ7FotcDgCT4xe/ffrVmZYebJOyD9Yktx6aaV+16zjUJleYCWgjUajUNuWJDvb/XmyweayV8qTqW5j2M069w82kxVKhDmyr3myrUWZzZNZLDcDOxZ5SqnXA/8emAKWgGPAt8H33C3A1s5YbsaIMh0BHTbly+/kNWVtRFmp3AItpMYilIotii1ItSA7EaZcaqGUMPpghNrJGo4Do/E4K5Ual+qaqbhDMhblG81nSIWrnI9eQjdLLOg8U+4kOpQg0q4SclxSyQd4LQ+T9m/YFa35Cl+jkVvmC+UlVLoIWjgfPU04GqKFUNVJMlTwQlVcCZF1oBVt4IoQCq2TSEM8AjEXJqP9oqzklzVP+ttHovBAEh7PGkEFVxZeGwTaIag9BLOfheefgtYyMAKvfBt81xNGPA+ymUDb7PFu0zPjEXjPXfBLZ+H5qikRb9Yn7z133XrtU4ax3qUpXS7hUWDQDQszPdCTbLBcWfDXpey1pOiE/E2erLkhMzbohvV39T/jh/yDXf2j4H+mzZdM6g/89+fJOssu9WZk3vh5MivKLJabmd38TvtV4DHgMyLyoFLqtcBPDGdYNzFbOGMHEnFWqzUu1TSH4g7JeEeUlTkfWcRtVREFDSeG69VY8BaZcsbR4RTRdg1R0AzFCCfKKA3NVgqVLDLPAtNqGu1FiYyusNKapB2+gMQSaLeA6BS4lxBvzGR8opfBqQG+3ebUkPAqqpmB2N+iJIMoRVgJER1DO0UyIY8ZN04OF4WZSOAJ1GmiVYafm9q5QANIuhudsu2E12bb2UNw8CfhlT+5T/9uV8F9SfjwPfCRPHxwEc7Xzf7jMfipSXjT+I0j8IaTJ1vy20YoX3j1SpI7C/wHW2E8TIs53ECezO22v2j0za7sCTQVOM/kyVr+ZIBeyL8BhDZkxjYurWRcM1PWPNl18nrZMk2KVxOzeTKLxXITsBuR1xKRFaWUo5RyRORzfv88S4CtnLFWK4OTLDDPJQ6rKXQ7RnRkmZXGQdrRORwZNfaXcwnRabRTwPNSqNAionOIVoi7gJKcUUixFxEvg3IbKKkTdlc4po5STVzmwdghilJlQWIcVlHGnLtY0UUcNK92D5IMtFBJqjivShxgPrZMVh1jVZsZeZPOo3yN55nkTibVASIqjAqEzRwFi6zyVt5EeuBbdCWBdisyHoF/etj8t59c2zzZoDO2scN/r2msWYS8E/I3okxtKEluDPxvvt5liPFA6fKwL8wGFyGP9s2uHBRo/f3JOpMBeiF/myezWCy3G7sReetKqRTwBeAPlVJLdJYYsPTYwhkj/veIlwanDrpBOLbOUaapphZ5MDZFWUypJufcwbIucVFiTKkw485drOgSAAecO1nTJgidc+4kr4tclCjTKsa4M8GIjDHunOBx9SgNGt0ZunHi1BzzCypJEifwC8rB4VH1IN/t1ogS7Z5XpsLb+RckiBPdkE6DIiWiRHiC1w75gt5c7FaUbXfeYH8yI7QKW3TJ39ifrCPKOksc9UqXzqZ5ss1Kl5u1yeg1je2E/A8H8mT9JcmdBP47LTNCTBPfonRp82QWi8Wye3azdm2STvtveBtmXao/FJGV4Q1vOAxzWTON5qvyNeb1MitqqeuM5ZwMeV1gQYyTN+FkycoY4066K8oAI8io9Qs0/5fVZtvB8xo0iBPvE3FXw/O8wLv4dRo0yXGAKBEaNFlhlSgR3s07uY+bbuliYH/zZEINqS3Ci39GbfF3qSXnEQTHHUNPPUBrfJJweGbT5ZR6ubON4g3adPqTCS1/0fGRTde7bPvLKTX9Rch7x4KLlYfR1P3zL1+VkxdchHw4eTKLxWKxdBja2rWbvFGGgAMoIqu7fdPrzbDXrtVoavjO2DUUZcMgzzJP8zme4mkKFP0lmJ/gCV7LOGPXdCzDy5OtB/JkvTUit86T9c5zyCCVv6Mw+2ec/rspnll6OcvFQ4TCLaYnXuJlqZe457sukJ76h3ipeMA1y3edsa3Em0OYNlUcQoB71U6ewsWud2mxWCw3H0MXeUqptwPvBmqY3q+dZsh3bvOc/wr8A2BJRO7z9x0A/hg4DpwHflxE1vxj7wR+FtPy7BdE5NP+/oeB3wPiwCeBXxQRUUpFgT8AHgZWgDeLyPkrfZZhizyLYXjrXc767lKW4BqXg537t2qT0WkaGwz5d9ywEOMIZtFxZ2BB8k5/suB57eYsK9/4Kl+6fJDT5+7HCXlE8Wh5IQqlUWLxMi87fprXHp7n0EM/hkRhp05eUJQNN09msVgslhuZvYq83WTyfhm4V0SWd/Gc3wPehxFiHd4BfFZEfkMp9Q7/8a8ope4B3oJpyTIFfEYpdVJEPOA/AT8HfBkj8n4I+BRGEK6JyAml1FuA9wBv3sX4LFyb9S47Qmarzv0bXahgf7IyQssvXTr+628d5O907u/Mqtw4CzPnh/yNWNRSg3aLsHsH4q0BEHHvwvOWfUEkRNy7EM8kE8x564DQnKvzia8/QjPexEVz/vT9zJ8/iYhDsxFDhdp81nszn5+Y5Xd+eZ2xVzy542sINk9msVgslr2zG5H390B1Ny8uIl9QSh0f2P0G4DX+9u8Dnwd+xd//RyLSAF5SSp0DHlVKnQcyInIKQCn1B8AbMSLvDcC/81/rI8D7lFJKdluDvgnZSelyJ6JB0FR5hjZ5Wlxi43qXvf5k3oDw6rXCKNLkQp+7pjFLdLjEELQ/g3Kwc//momxjf7IJFJHeZ5I4qg0R907EM2mBnghThN070N6qXwZXG8Sb9lYIyQHi6gGSzmtg8QvQKqEqa0jbfHWUq5E2SAVUElRII565YsoVRJsGgB/+yEE+/pUnWW9miLlNWs0EEvzfqhmi0Yzx0uI0n/nsOd72CtufzGKxWCzXht2IvHcCf6OU+gqdlDUgIr+wy/ecFJFL/nMv+StoABzGOHUd5v19LX97cH/nciv4rQAAIABJREFUORf812orpQpADrNSVh9KqZ/DuIEcPXp0l0O+evYrTxZ0yap+K4xeia+XwWoHQvMdgaZwSfAYSR7tCj2hhscKLlmqnMIlCwTbX/T6k7l9rllv7ctQtz9ZT7x1+pNpvz9ZRE6g2ys4OCiCAm2jKIu4J9C+a9YTb22S7uPEW8dh+euoVg1VXUfa5lMo1xdhSqMcQdqCVASV0kaUeQIoX7xpdOkSDTeJxOeoFdYhcZB4/SI1x6w9FvdmqakD6GIEx4sQl1lq7iFAiLfnqIUOAvCJ0y/n8vphPO1QVRByNLFwq29ps1ioSaGe5qlv3sfbhvDdslgsFotlM3Yj8t4P/CXwLXrrse8napN9ss3+7Z6zcafIB4APgMnk7WWAO2Ww/NnJk1U41XXGOjMtu+H9vrUve+tdGvcrmDvrLa0EisHmr52SpDPQ/qIzu7LNJYQaym+GrIjjkqNNnjDH/TKpEJEZvPYSCheF9suXg8KrI9ZWfEEqAfHmkXS/h3j7TpSAWn0RWf4iUhJUUlAh2UaUaV+UGUfNa0GtqYhFv02t/LfotVmciZcTb8xTc3MgvigLHTTb7TlqzgF0MWoEmu6IMkXcm6UiB3ju23dQdg6ytB5mXd+PahZIZY5SrrgIQib1AMWi5sLSK5ie1GTTLuWyIEAqdT/lCogILywfYb2RRYkmEamDKDRtnMDX0FWamkQoNLPD/NpZLBaLxdLHbkReW0T+xT6856JS6pDv4h3CLJEGxqE7EjhvGljw909vsj/4nHmlVAjT1uW6zvbdrPypaZpZKjgB4XW5r2lssDGsWci8s95lr8P/4NJKdEuXqluGBCPC2l7eF2h0BZrgEQpNogLNkBUOCXkIaa+CmzE/RRtRls8jZVBJ5ZcrA6XMYOmyr6wp/aKs+nXQLeKhFrV2Ar3eEV6z1MJbiDI577tmEG3NceriyynWQyzlNeutu7iw9BDTB0pkR++gXDFyKp18gHJFEAWp5P0US5r5y48wfVDIpB3KZfMXQSp1P8vrwksXwzw8U2auMMHIaASik5xrHmV83AOBs8Uw2VyLWtOhMaI5Ww0zMd5CgHOFMONjLRBoh+s4joAomjpK0q3hqP6/ITytCLlCNmcXr7VYLBbLtWM3Iu9zfsnzE/SXa3crqj4O/DTwG/7PjwX2f0gp9VuYiRczwFdFxFNKlZRSjwFfAf4R8N6B1zoFvAn4y+udx9us/OkQwzSL1SiJ+aLsRMANCzpjJ9DeMgqXTuBf97lma4BHyn2cWOsYrPily8q6nyfrlCRlIE8moARnHNQkPQ9UNHL5OWqFVaLNeWqVqhFl4Ra1Vhy9Hg6UK02JMu4FS5ezvkAzZc1oe45Tl76bYi3MYl5T9HKIcsmEyxRqUeaX7mf6oJBNuVuKsmzapVg2i8DHog5L6y5TIxVmi2MkwlVqkqaROMLZ5lHGJzzQRpSNTxjhda4QJjvaot5QNEaFc9Uw4+MtFHB2PUxuskUqrJh1hOl7wtTqbZQb4UTCoVw1VvDMcSiWQ8QTEE3CzCSUyyFEwYljUK6GEOB7HhE+9bkqtWaIZLhKItz9X8O/vkK9HeNAqs6Tr7u2LWcsFovFcnuzG5H3Vv/nO+kviW7XQuXDmEkWY0qpeeDfYsTdnyilfhaYA34MQEROK6X+BHgBs5LGP/Nn1gL8U3otVD7l/wfwX4AP+pM0VjGzc68rijiujNJuzxN2j6D90mXCfZx4nygbCPl3nbFg4F82BP69tqbWCBONvkCt+BV0cQ5n4n5TulQ5OiVJkycL43jhnkBzIyRji6hcDcKmXKubNZ75Zot8ZZpLZ8sU28eNKItUKFajzC89wPSkkMk4XeGVTj3QLV2mkw9QLPXKmol4mKU1h6mRCudLY4zGi6AVZ5r3MzKeo95yaYwKZ6tbi7IzlTATYy0ALlbDHLunxcISHL43TK3aIh5RRCcizCQDoiwgvE4cMwItloBoAk6Mm2NKYOYOKFVCHJ6Ch14Gjz8Ijab53yAeg5q/7mxnW2twnM2PAZSrCS5c8jj9nToh1QI0SFdB0/JCaBXm8LE0T7wmM8RvnsVisVgs/eymT96PA38uIkWl1P8JPAT8qog8N8wBDoOh9skTjSx9FalfhEoer7VCreESj45SL5XRhVmc8fuJN1+ipoyzE/cu9/Jk3iVqKocuXsTJHCYuq13XLNpc5NSCX7pc1qw1DzC/FOJIrkomG6NUdfySpGK92GZh0e0TaK5SPPbKMR59zb04rpl4UalovvDZM6SdFf78y3GysQKIIu/dSXZsnLlFOHYQ1qthxjNGeOULYSZGTOkyXwiTTbaYu+Rw9KCm0gxzfLLFQh4mxsLU600ERSYdpVBxmL8M05OQTdEVaKkEFMswvwjTB82xYsWYjdkUTOQgk4RXPQCN5pWF104FWjJhjl8tz5+FX/rVJs+fbeHqCgnX5CurXgrPSXHvyTC//X9EuG/m6t/LYrFYLLcf16IZ8jdF5OVKqe8F/m/g3wP/WkReuds3vd4MVeS1Kuj5L1CTLOH5T3Pqwnf18mTNHPN5l+lcmcxIglLF8UP+Ts8ZSymKRc38omJq0mMkE+oei0VdlgoOU9kqs2s5Em6FuZU0R4+lKHCM8ayHUrBUCJONt5hbhKOTUKgZgRaNwJHpKE887pBMmOFqDc98S5PPN7i0FqZY7ImyYtkxwmsSMume8EonoVw2hlU6YfZfuGwE2kgaJgdEGexMeG11rNE0P/dDkA2L/Cp85Gn44Mfg/EVAwfHD8FOvhzc9AeMHrvcILRaLxXKzci1E3tdE5EGl1K8D3xKRD3X27fZNrzdDXbvW0zzzxRfIL1aYW2iyuKpMnmx1jKRbYXYlw9FjKdY5ysSICfnnC2HGR03pcmk9zEiiX6BNZFuIGDft2GSThWXFRC5MtdpkfsnhyKEImZRDqQpKIJWC9TIsXO4XaK6Cx+6HR1/eL5i0NqIqGtm9G7bZeTeDKLNYLBaL5WbhWqx4cVEp9X7gfwDe4y8pZn+ND1BrOKw495A93mAxH2bqZU0W8orD95g8WSICsbEoJ9MOpYqfJ7sDyhWTJzt5BxRLIRIJiAwE/u/MwUQuxN0nOi5ZaNfO2GYlSseh6+ylU739we3tjg2eF9rNt8pisVgsFstQ2M2v4x/HLCf2myKy7rc/+VfDGdbNSzwGuRGH/Gqco1MwPrpzURbc3knpMhLpf++dCjSLxWKxWCy3PjsWeSJSBT4aeHwJuDSMQd3MOA48cl+v/LkXUbadQLMumcVisVgslp2w40zerYRSKg/MDvltxthkeTXLnrHXc3jYazs87LUdLvb6Dgd7XYfHXq/tMREZ3+2TbkuRdy1QSj2zl5CkZXPs9Rwe9toOD3tth4u9vsPBXtfhca2vrZ04YbFYLBaLxXILYkWexWKxWCwWyy2IFXnD4wPXewC3GPZ6Dg97bYeHvbbDxV7f4WCv6/C4ptfWZvIsFovFYrFYbkGsk2exWCwWi8VyC2JFnsVisVgsFsstiBV5FovFYrFYLLcgVuRZLBaLxWKx3IJYkWexWCwWi8VyC2JFnsVisVgsFsstiBV5FovFYrFYLLcgoes9gOvB2NiYHD9+/HoPw2K5adE1qC8AGlQI8+eiBmmb7dgUOPHrO0aLxWK5VXj22WeXRWR8t8+7LUXe8ePHeeaZZ673MCyWm5JmHk6/HdyXQyiz8Xi7CF4D7n0/RHZ9S7JYLBbLIEqp2b0877qIPKXUB4EvAF8Uke9cjzFYri+iwauAAG4cvBqgAcc81rXesQ3bOzhv8PW8GiiMu9Q5phzzePB5nfO2e6/tzhs8pjU4Tu+9g+PY7ZiudN5Wn3m787b7XJuNPf9J0BUIZUG3jJOnVO/fNpSB9jwsPw1TbxvSF8hisVgsV+R6OXm/C3wv8F6l1J3A14EviMhvX6fxWK4hoqHwVVg/BSLgpoz705iH2LQRD+0ioCCUhnYZEHDT4BWhfgGi/nm67L9GOrA98HpuBryy/3opaBX8Y0d8QeK/l5vyz5Pe/s57hbOBcWxzXigLXmngWHAcJSOInFT/eTsd03bn9X3mzjj8z9w9Lw1ewYxpwzX030v5r9dah8aC//kz0PbHfvnDoD1TrnVTEJ2EyES/0AvnYPn/Z+/do+M4rwPP363q7upGAw3wAVKkQJGSCVoOOZGSMBo579gTK7bWiTeOz4yl4908zljjPdlMMrMzjs/ZkWNms4nzmDnJziRRXnacxEycOLFjy47ksZ2R7cSRKY8fjC0RtESRICkRBAk0Hv2suvvHV92objSeAtAN8v7OwUFV3Vv33u/RjQ9f3fq+R22QZxiG0U268uKFqn4K+EXgPwG/DxwH3rbcPSLyhyJyRUROJ679vIhcFJEvxT+v29TAjQ0hjPO5xAcNYf4saAWiMkQVmDuDm2kKYW7M6RDC/FisU3L682MQ1ROyemd782edjjbsVeJZqrI7J1q4j9Cdz7X5aonjbHzcQW9+rFUWliGadzrzY+7+qL5wTOTKG60mpiX0GjFFyTKPtZa5qTe2oLeoDuNyRfW4vFUXuzZ8xbLqVTeg0zqgEM7Hxwm8AOrTm9qNDMMwjBXoyiBPRD4JfA74l8AzwLer6p0r3PZe4Ac7XP8vqnp3/POxjY3U2Az8nEvM19AN9PoOgwTgZd3gIH8E1zN9yI86HXzoG411ck6/bxS8VEKW6myv77DTkYa9wNnwsu4cb+E+fHeeb/PVEsfh+LiDXt9oq8zPgtfndPpG3f1eauEYz5XXW01MS+g1YvKSZR5tLXNTb3RBb1EdxuXyUnF5My52afiKZZndbuZPUrjZwb74OEFUcbOEhmEYRvfo1uParwDfBhwDpoEpEfkHVS0tdYOqPiEih7YmvJeGRm62ygtWn5/VnluW1FttftZyvho2Unmnv55415Mn1ylePCgch/6jlpO3HXPy0jvhhfdDZr+b0WvPyQOoTcItD6zzA2QYhmFsCF0Z5KnqzwKISD/w47gcvVuAYB3mfkpE/jfgFPDvVfX6hgW6DjSC6VPuDcTK5TgXqpFPFedMJXOcGrIwzotKDST09kN6qC0nLZHTVU/kZzXyvZp5Z+35VEU3SzN4LwzdszDQW1O868iTa8lPi3PBsrfB0He0xuENtNZj8nyp443QM19r1xt+HVz5sHuM6y/xdq0EsPs1i2WGsSwTE/DYY/DoozA9DYODcP/9cN99MGyvahvGWunW49qfEpE/x71w8QbgD4HXrsPUbwMvA+4GLgO/vozPt4rIKRE5NTExsQ5XqyMsuVmM9CCUzrXlblUSOU5naM3/ivOi5s44eTTvcqLac9I0kdOVzC1rz8FalE8Vz/5ULsczOauItxFHVGnN61pLnpy255OVnaw9DmP7kBmGwyfcMinlcdeOjdng8ri7fviELZ9irJHTp+Ghh+DkSUinYWTE/T550l0/fXplG4ZhtCCquvVORf4DbgmVp1TbU7aXve8Q8FFVPbYWWTvHjx/XzVonz2by1jeTZ2w/qhNumZSrj7q2TQ3C7vvdDJ4N8Iw1MTHhBnJBAIUO08PFIlQq8MgjNqNn3JSIyFOqenyt93Xrce2vAojIHhHJJq6fX4sdEdmnqpfj0/8V6Pq/euLB4PHtk5O3lng3MievU26gsb3IDLslUmyZlJuMKIK5uYUvh1wOSvG0fON4OVknvY99zNkcGnLXwhBSiWTPQgHGx+Hxx+FB63CGsVq6NZP3euA/A/uBK8BB4OuqenSZe04C3wfsBl4E3hmf340bU5wDHkoM+pZkM2fyDMMwbliiCJ58Ej73OTfoGhlxA7Bi0Q3I+vvdcUM2OAgzM60yERgYWNDbvx/+4i+c7XQaMhno64Ns1s3aNQZ6pRLU6/D+93e3DgyjC2yrmTzg/wHuBf67qn6LiHw/8OblblDVTvI/2IzgDMMwjA6USnDpkhuQzc9DuQwvvrjwCPXsWTewK5fd49WxsQVZ+/HgoLNRrcLVq06m6gaFQ0Pu/nrdDfzAPcqdnNz6MhvGNqZbD8xqqjoJeCLiqeqncTNyhmEYRq+Sy7mZN89bmG0bHXXnvg+HD7vBWDbrfo+OuuupVKve6KjT6etzM3e7d7tHtI1ZvmrV3Z9KzENUKm5gaBjGqunWTN5UvHzKE8CfisgVYNUvYBhdIorcf/JB8NJzcDYij8d8mS/ztfW+jh+Ho0c31teePe4x7K23ukFge04euFm8B2zxRcNYC90a5P0wUAJ+FngQGAROdCkWYzVEEZw65d6Ce+EFl0+jupBbc+HCQg7O7OyCbGbGHRcKbt2rZK5OQ69THk8ydydpbzlfS9krFJyskRfUiOPAgcX5RA0bjeudfK2klyxzexzJ/KROdbNSTMvprTYXarl2SNbT1JR7NNcp9uVk7eVfqb2S+VlJe0vV4VLxLlU36+kbnfphsoxL1WF7n0/mnQ0NrSxL1uFKvpYqf3s9rbYdknXTqMNOvl75SrjnHjc4azAw0Pl4OVny+HWvgw9/2A38CoVW2+DiCgJ4jS2+aBhrYcsf14qID3xYVSNVravqH6nqb8aPb41epVRy/0kPDsKzz7r/tMPQ5daUy07eyMGp1xdkYegGiGfOOHkyV6ehd/Zsq+zMmYU37Bo2VuNrKXtnz7baqFScjXLZnTd8NfSiqLOvho3V6CVljdylhqxe71w3K8W0kl57/SbLnKzP5dohGV+1unTsnWRh6GJcrm46tVejXO322utwqXZYqV1X0zca5VqpH3bSW6rPJ2OvVjvXTbss2TeSddOpXZeLfbV9uVPdJOuw3Zfvu8F9Y0ZuoxgehhMnnP/x8YVZvlLJnVcqTm7LpxjGmtjyQZ6qhsC8iFhyxXYil4Ndu9xswR13uC/7ZG5NLreQg5NKLch83/1XfuTI4lydhl57Hs+RI625O6v1tZS9w4dbbQSBs9Epn6gRbydfDRur0UvKkrlLyfyk9rpZKaaV9DrlQjXKnKzP5dohGV/yLcf22DvJfN/FuFzddGqvRrna7bXX4VLtsFK7rqZvNMq1Uj/spLdUn2/PO+tUN+2yZN9I1k2ndl0u9tX25U51k6zDdl9h6GYec7mN/445dsytg/fAA27geemS+/3AA+76sRWXQDUMo41uLaHyAdzbtZ8A5hrXVfWnt8L/pi+hstm5a6vV6yTL5xc/CunleM2X+TJfveWr03eIYRibynqXUOnWIO9/73RdVf9oK/xv6iAvmbt2+fJC7tpS+VkNWaf8pEauTnu+z1I5SMvlTBWL7j/xe+9tzadZbbxDQ3DLLe5xyfHj9iVvGIZhGFvEegd5XflLHefhLfppyEXkg92Ia0NI5q6dO9eaF5PMO2rPz0nmJyVzddrzZ9pzcDrlz3TKmWr8d375cms+zXLxJnOhnn3W6UxObnw+jmEYhmEYG06vTsfc0e0A1k0yd+3Qoda8mGTeUXt+TjI/KZmr054/056D0yl/plPOlOe5wdu+fa35NMvFm8yFuuMOp7Nr1+bk4xiGYRiGsaF0awmVldj6Z8gbhee5x5m9muPWnk+zlngrFffbHtUahmEYRs/Tq4O87Y3nucEUvPT1ozZCr9P5euJNWXdZijkm+Dp/wWnexzTnARjkIMd4C6/gTeSxpR8Mw9jmlCbgwmPw/KNQnYbMIBy8Hw7cBzn7jutFenVKRlZWMYze4Aqn+RBv5rOcoMgF0uRJk6fIeT7LCT7Ev+IKp7sdpmEYxvq5dhr+x0Nw9iR4aciPuN9nT7rr1+w7rhfp1UHe29sviMgfisgVETmduLZTRD4hImPx7x1bG6ZxszPHBJ/i7UzyNBnyBBTwSeGTIqBAhn6u8Qyf4u3MMdHtcA3DMNZOaQKefBj8wA3uUjm3kkMq5879wMlL9h3Xa2zp8zcRKQDvAEaAj6vq+xOy31LV/wNAVR/vcPt7gf8KvC9x7eeAT6rqL4vIz8XniwaIxsajRNQokSKgRgklQvBIk6OGy+vrdNxRTwNqtauo1hFJkU7tpFa/5mQdjjdCb6N8fSP1cebrz5HWkLQIYSoiVXcppfWUENQ9ahpRlvM8l/oYx+o/4iowlYN6qfVYIxCvs2y1ehthw3yZL/NlvpLHz38M6nPu8WxUAy9FywO3TAHmxuHC43DkQYzeYauTrN4DjAEfBH5CRN4IPKCqFdziyEuiqk+IyKG2yz8MfF98/EfA32GDvE1HibjEKeaZYI4XKDHFDBcYYIQsg1SYBZSAAarMoCgBBSpMU2S8RU80ZPDFqwQv/BPMjkP/rUh6B1qbAgRJD6K1IqBIepCoOoXMXYD8CJLZgdamAZD0kLtHQFIFtDoFcxdje0OxDUHSBbR6HebG0fwBvEzjPnH3NX0NdfDVkBXQultP8Hr6KxyqvsjAXMhsfpp6JiBXT4EqtbRPuhqSn6sxk5+nkvmvUJsEBNL9UJsBFNIFqE278ucPuC/MWrxnaqofarPL6zXtFeM6HIH0YGw/IUMgPZCwMeK+tJv2+6E+CxrfU52CuUuxXqHV3nKyZLmqRYjrsNXXQOtxtej+SLTYS9pIypaKdzrWa6+b2JdoXJ/FRPkLnW006zBhoz6zoNepDkUhNbBE7PshMxTbWE6WrMOlYlqi/I2Y+kfivjK7xnZI1s2Bhbpu1lu7vWSbdOiHDV8t5UrUZ3v5V+wbHfRWrMOl4m3vr0vUddJexz6vC3XdaP9F/XC1dd34zLd/Rht9tN3XCn05+R3SKH+jbyTttfeNZB9tlP/sSSB05U/3Q99eyO2hZaAX7ILzj9ogr8fY6se1L1PVn1PVD6nqDwFfBD4lIrvWaW+vql4GiH/vWUpRRN4qIqdE5NTEhE0pr5Y6E1znT3ieN/Msr+N53sxV/pB5niVgkCmeI6RMnRIhFa4xhlJHCbnGGBEhSsQ1zlCnQp1yix71CuW5p4m0BvUyhBWi6acRDUFDdPpp0DpoSDT1NBKWkXoFDctEU08DCT1CiEKiqWcgKi/Ym3oGNASto9NPo2EJqVeQsEw0/Yzz1bhPQ0RDd73pq9QWxzMQOb389BSpUPHrQiqEwakaEimiHoPTVVIRpOqCH0YEU1ddHNRheiyOKYLpM1CvuP+aw3IsiyAKYersynoa64WVZpmZatgP2/TGFvSiWI/6go0oEV9Yhfp8wtcKMg1djM14x5y8XnI+p5O+EsfTZ+KY2u3FNqJyLKu0+mrE2yhjuETdNPxGbfUUVVrLPDXmrjXqcPpMa70l66beoQ6jMFHvyfLPu/qaPhPHu4yspW/EddOIqb5E+Ruxt7T/2eXboWmjrf0b/StKtMOifjjWob2W6Yct5Uq0SUv52/pDsu+26y0p61SHbXUdlRfaaFFdJ+qw3iH2jn0+aqunRD+M2j8bS9T1os98e/8629pvkm3SqS8nP/MNvaitbyzqh6XW9mp83zTKX74KSFxvCrV5iOqtfyz8ACrTm/sHyVgzWz3IC0Sk6VNVfxH4XeAJYL0DvVWhqr+rqsdV9fiwbXK9KsqcZpyHmOIkQpo0IwhpZvlr6vwOJb7MELfjkyVFDp+AnYwipBB8djKKh4/gsZMjpAhIkW3RIxWQzd+JJ2lIZcEP8AbvRMUH8ZHBO0FSID7e0J2on0VTAeJn8YbuBBJ6+OD5eEMvBy+7YG/o5SA+SAoZvBPxc2gqQP0s3uDLna/GfeKj4rvrTV+5tjheDp7Tmxscou4LYUqp+zA9lEY9QSViejBD3YN6Sgl9j8rQbhcHKRgcjWPyYPAIpAL3eMTPxjIPPB+GDq+sJ7GeHzTLzFDDvt+mN7qg58V6pBZseIn4/Ayk+hK+VpCJ72Jsxjvq5Kmc8zmY9JU4HjwSx9RuL7bhZWNZ0OqrEW+jjP4SddPw67XVkxe0lnlo1F1r1OHgkdZ6S9ZNqkMden6i3pPl73P1NXgkjncZWUvfiOumEVNqifI3Ym9p/8PLt0PTRlv7N/qXl2iHRf1wtEN7LdMPW8qVaJOW8rf1h2TfbddbUtapDtvq2ssutNGiuk7UYapD7B37vNdWT4l+6LV/Npao60Wf+fb+dbi13yTbpFNfTn7mG3peW99Y1A9zre3V+L5plD+7G9C43gTSffEj2wRhBQLbkr7X2OrHtR8BXgX898YFVf0jEXkR+P/WYe9FEdmnqpdFZB9wZYPivOmpM8ELPIwQkEos/yHk8BjBY5qQx7idR1D615+TJ5DeG1DbuT1z8mZSH+dU/dep6HXSkidM+S05eam6UtNZsrKL46mfAsvJM1/my3xtN1/ZnXD2/dC33z0Cbs/JA6hMwuEHMHqLruxdu17inLyPquqx+PxXgcnEixc7VfU/rmRnU/euBSKtUq1fJOUPU61fAiCT2k+1folIq3iSaZ4nZRut10mWTR/Ck8yK8c6nnmBK/wSfYQQf38sTRRUAPC+LRhXqvMgOeQs76m94aV806by7tg2ZY4KP8GNc4cukCPBprduQGiFlhrmL1/NeWy/PMIztR2nCLZPiBy73r51q0c3kfe8jtl7eJrHevWt7ZnVbEflxVX3PMvKTuJcsdovIOPBO4JeBD4jITwLngTdtRazLEWmVS9dOUK19g2r9eVSnUBSPApFOg1wFHUakADqDiiIUgBlQQAZQLYJMoLoLX4ZQ4iRZGXB6ABRi21cRhhEZBHWJtkI/oU4hMgk6jCcFIop4+PTlXs/Irnc1B3pLxRvuvOJmsaiA5vCifqTmuf/dvIBU5CECRf832fHclaWTupNJvB2T1QX23gt779mWA708w7yKd/MJfoYJTuNRJkUfAHXmiQgZ5iiv4t02wDMMY3uSG4Z7TrhlUubG3UsWfuAGdpVJ9zj4nhM2wOtBeumv6ruWE6rqm1V1n6qmVXVEVf9AVSdV9dWqOhr/vrZVwS5FtX6Rev0cKX8fUfQ8SIQnEZE+D1IFqYNUiPRcU6Z6DpEIJIqvV0DqiFSJ9HmEMCGLkPgelQri1xGpoQ0ZobvHirXBAAAgAElEQVSnxdfzeBIhnke19nWq9YsrxqveLBABihChURGROOm2Oo2Hj2pEPZpYnLibTK5PJvF2SvgXD+YuL8zqbUP2cIw3cJLv5p0UOEiNOWrMUeAg3807eQN/xh6OdTtMwzCM9bPzmJupO/wAaN29aat1d/69jzi50XNs9Tp5X1lKBOzdylg2i0zqVlKpQ1Rr38DzDqI6RYTiyUE3k0cKNMCTYdAZIhSRQ6i6mTxPDrmZPFKoZvBlT3Mmz5NDbvYPEDkEOoWGKSAdnxdRETw56GbyEr4iLeJpRCZ4BZnUrSvGK1G/e/sVQfHwvH40jGfyMoNEUYiIR8obXpy423jFf2h0YSZv8IibyUsmq9dm3QAwv8/dv43JM8y38Ta+jbd1OxTDMIzNITfslkixZVK2DVv9uHYvcB9wve26AH+/xbFsCp5k2L/z4W2Tk7dUvKvNySvIW+DozZuTZxiGYRi9ypa+eCEifwC8R1U/20H2flXdkldzNvvFixuBOhOM8xBCgM/iRNuQIkqFER5pefvWMAzDMG505pjgWR7jGzxKmWmyDPIy7ucO7tuU/Ov1vnixpdMnqvqTnQZ4saw5wLM9aLtPimFu4QRKhSrjRPHyJxElqoyjVLiFEzbAMwzDMG4qrnCaj/MQX+MkHmkGGMEjzdc4ycd5iCuc7naITXr1Gdknux2AAVmOMcIj7OABlDp1LqHU2cEDjPAIWXuZwDAMw7iJmGOCJ3gYn4ABRkiRQxBS5BhgBJ+AJ3iYOXpjZ62eWUKlDVlZxdgKUgyzgwfZgSXa3uwoETVKpAg6L2zNKhfAXoee+TJf5mv7+cqQB7ihvjee4UOElMkyREQNj9aFoQMKzDDOczzOsR74u9mrg7zts0KzYWxDrjPBkzzG3/Mos0zTzyDfwf3cw33s6PAIXom4xClKTDDLC5S4xgyXGGCELAUquE3eA/rjYyWgQJkiM1yI9QapMBvLBlqOnd54m72kjaTM3ZehnxqzaOy3zHSsdyDWK8YxOV+CkqafKkWKsb2AQkcbBUYIGGyxUWWmqVdhummjUS5ByTCwROz7yTIU21hOlqzDpWLqXP5GTAVGyFCg2qzf1bVDpqVuDjTreqHe2u0l28TVU6atfrMMtpVroT7by79y31ist3IdLhVve3/tXNdJe2WmOvR5Zz/Z/ov74WrruoggcR+a7tBHW32t1Jcb9tLx8UyibyTttfeNZB9tLX/nfpj8fLnyu3oqcBu38s8RPMpMMssLlLm+TLzJ+pxeVDfJz/Ly3xvr/YyOtLTDUn3+PH9HioBS/P5onr3k2UNyoJdjF2d5tCcGeb36uNYwjE3iWU7zKzzEJzhJijR7GCFFmk9wkl/hIZ7tkE9So0SJSTIMMsWzhFSpM09ImWuMoYQoIdc4Ex9HXGOMkDJ1SoRUYr16rJc8PkNIpYO9pI35hI2Gr7NEzeOx2EYpYSNq8RXF99SpUKcc22vYqCdslKlTicuyYCOp12ojaX+p2Ktc40xsYzlZfVF9tsbUqfytMdXjci3fDmGHdmjUTbIOw2a9tdprb6+ozcZC3bSWK0q0V7L8rf0hGd9ivaVkneqwva6T/au9rtvbf6ytb3Tq81FbPXXqh6ut66hpo3Mfbfe1VF8O2+ydbSvX2Q79cKFvLMjOLPPZWPxZjpr91dWTEjLNOWa5FH9vPLfsZ08TfjvVTee+keyHZ1rade2f0cqieuvU5906qPO4NWQ9aswTUW/5vvQJqDC9Rd/oy9Orgzx7XGsYm8B1Jvg9HiZDwB5GCOJ8koAcexghQ8Dv8TDX2/JJ0uTIsYsq0wxxBz4ZUvThk2Unowg+gs9OjsTHHjsZxSdLihw+QayXivWSx0fwCTrYS9roS9ho+DqM1zwejW3kEja8Fl9efE+KgBTZ2F7DRiphI0uKIC7Lgo2kXquNpP2lYs+wkyOxjeVkqUX12RpTp/K3xpSKy7V8O/gd2qFRN8k69Jv11mqvvb28NhsLddNaLi/RXsnyt/aHZHyL9ZaSdarD9rpO9q/2um5v/9G2vtGpz3tt9dSpH662rr2mjc59tN3XUn3Zb7N3uK1chzv0w4W+sSA7ssxnY/Fn2Wv2V1dPgs8gh+hnf/y9cfuynz1J+O1UN537RrIfHmlp17V/RoNF9dapz6fJk6YPt4ZsRJq++JHtAiEVAga38Jt9abryuFZE/lhV37LMtVd3IawNIyKiQok0AZX4mX5AjgolIiI8vOZ5UrbRep1kOfJ4bWP7Xo73peiF1Imo0McQc7jNUPLsZI5rhNTxSTXPk7KN1uslX5/j45SZIs8t1JjDI4sQxj0hRY4sc0zyeT7Gq/gRkrkqu3kF8IobKmfIfJkv82U5eWvR+xofYIy/Js8tAIty8gBKTHKULVkRbkW2dJ28plORL6rqtybOfeCrqvpNW+F/M9fJi4j4OqeYYoJrXGaWIqDkKTBDkQnGGWaEAQrMxXkBfRSYj5/95xlo6u1hP/0MMRvnEvQxwHycc9BHP3NMc4Vx9nCAPAXm4/yJfvqZZYorXEr4KiL4HOVejnJPc6C3tniLse8B5hIxzVHkChfYwwh5BptlWYjX2WvEO9yml6ef2WaZnWx9vhbszTDFi3yRAn341CgxjSLkKDDPdWbjfI8cQ5SZAoRsnHcBSpYhSkwxwwX6GaGPHZQoIuiyejl2xNP0Ddn1RH7OUHMKP9v06/RWG9NyevNMMcdF+rmVbBxHo8wNvaf5Ej4hUEbI08cQffFeu+5/vjohNSJ8XsObmjlIOYbIcwt9DLOf40jPPgQwDMPYPOaY4OM8hE9A0GEN2QpFQiq8lkc2dL28bbFOnoi8Q0RmgG8WkWL8MwNcAT68lbFsFhVKFJmkn0Euca6Z33CBMWpUqDBPjTLnE/k544wRxfkC5zlDjXKsV+VCIgfjQpwvEVFnnDGqVKhSoko5tuFyFZyvatPXhTjPwMNjksvNGa+V423EUWnaiAgZb4upSjmOoxLHWG+JV4k4zxkqVKhQXqR3nrNUY1mFCufjXKi1+Bpvs1dhjipFQnwmYhtCnUmeJqJERIWQMpM8A3G9TfAMSgiETPIMIWUiKkSUmORpJM5BadV7epFeUlaPZc5Xq6xxfJWnidr0OsW0kl5EOc4vqTTtuzIvlFHjAagQxQ8bpoB4T+JY5uNTZaIlB2mKZwkYpMRk879bwzCMm408w3wPJwipMMM49XiGr06JGcYJqfA9nNiUBZHXw1YvhvxLwCDwPlUtxD8DqrpLVd+xXrsick5EvioiXxKRrm5lEZCjwC5mmWY/h5r5DQcYJU1AQB9pstyWyM8ZYRQvzhe4jSOkycZ6GQ4kcjAOxPkSHilGGCVDQIYcGbKxDZer4Hxlmr4OxHkGERG72EdAbpXxNuIImjY8fEbaYsqQjeMI4hhTLfEKHrdxhICAgOwivds4TCaWBQTcFudCrcXXSJu9gDwZCviEDMc2lBS7uBOPHB4BPll28XKI622YlyP4gM8uXo5PFo8Ajxy7uBONc1Ba9e5cpJeUpWKZ89Uqaxzv5k68Nr1OMa2k55GN80uCpn1X5oUyCoM09iN2acNDgI973OBkISEZhltykIa4gwrT5NhFmu29z7BhGMZLYQ/HeC2PcJQHiKgzyyUi6hzlAV7LI+zpoTVku/W49ilV/bYNtHcOOK6qV1ejv9nbmvVyjtvNkJOXIWCWq9smT26rc/I+yZ+yi1vigXRrTh7UucpFXs2Di3Ly6lRIk7NHtYZhGFvMeh/XdmuQ99+A96rqFzbI3jl6aJBndA8l4iJPcpHPA8qtvJJbuccGJjHXmeBXeIgMAfkO+SRzFKlS4T/ySMf18gzDMIytZ1vk5CX4fuAfROQbIvKV+FHrV16CPQUeF5GnROStnRRE5K0ickpETk1M9MZ2I8bGU6PELJebr9zPcslyyBLsYJh/zQmqVLjCOJU4n6RCiSuMU6XCv+aEDfAMwzBuALo1k3ew03VVfX6d9var6iUR2QN8Avg/VfWJpfRtJu/GxWbyVofb8eLxDjtevMYGeIZhGD3Gtnpc23TuBmXZxrmqnt8Amz8PzKrqry2ls9mDvPY9PqF31j7KkF804OnleF+K3lLlNQzDMIztxHoHed1aDPmHgF8H9uOWTzkIfB04ug5becBT1Zn4+DXAiQ0Md0009vicZ4JZLlOlyOK9KjvvfbiavRo77SW4sL9fcg/G9n0W3Tp5t3Jvy8zW2uJdbv/AzvtiJvdBXGovwfa9Fdv3DF2tL2dPFpVxS5iegM/8BXz6fXAl/l9lz0F41Vvgu94EgzY7ZhiGYWwt3Zri+AXgXuCMqt6O2+Hic+u0tRf4rIh8GXgSeFRV/3Zjwlw7NdwenwGDTCXWnVt6n73GfoTJfTyX3qtx8V6CnfdIXLzPopv9muVyS47a8vEu3tNvYa/C9v0jO+9PmtwHcem9BNv3VjyzLl9KvWMZN51zp+Hdb4Y/OwFXL0A2734mzsPJE/Ar/8rpGIZhGMYW0q1BXk1VJwFPRDxV/TRw93oMqeqzqnpX/HNUVX9xY0NdG409PitMM5RYd27pffYa+xEm9/Fceq/GxXsJdt4jcfE+ix5KRD/7WtY5Wz7exXv6LexV2L5/ZOf9SZP7IC69l2D73opH1uVLSHUs46YyPQHveTtcfNoN7HIF8FPup68A2X4Yfwbe+3anaxiGYRhbRFce1wJTItIPPAH8qYhcAepdimVDETz2R99KrXqNVKpArerWJ0tndlKrXkOjOuKlmudJ2UbrdZJlsnsQz9s28a5HL5PZjVTnIIrA8yCTg2o8s9c4Xk62Fr0vfAyungfEDezaSaWhXoXJi/A/H4fve3AVvcgwDMMwXjrdGuT9MFAGfhZ4ELfUftfy6DaUKELGvkhmegKuXyaYK4Io5Aru+Oo47B6BvgJBaWZBNu9yy8gNEMzHesP7oW+oVVZyeXf09RPMTcPkOOw6APkCwXxCNj8FE5ecr3yBoFQEfLjzXnj5PW7gspZ4GzZUoG9gcbyTF2DXCPQNEpTa4hWFbIGgNL1gL5fQ6+tfKHNDVl6jr6ReXz/Mt9Y187PNuqGt3miWq9/pxeVnLuErPxjb0Fa9J/4crl4EIjeYC3JuEJgkk3U+n3zUBnmGYRjGltGVx7WqOqeqIdAHfAT4E9xad9ufagmKk25Q8OI50BDCCC6NQbUCtXmoleHymVaZ1t355TNOXpuHStXJojCWjbnfWoeLY1CrQKXk9C/Gsih091SqC74ujTk/vgfXLi/MQq0UbyOOamXBhoaLY6qV4zgqcYz11njDKC5XBarlxXqXzrbJzqzd18Vl7F0621o3yXprKdfZxeVv+ErGkdQrXoUoJIqc6szlOhNjEVfGYPIczF2HMPIhrLuBnmEYhmFsEV0Z5InIQyLyIvAV4BTwVPx7+5PJQWGX+4O+9xCI7wZX+0chE0C6D9JZ2HekVSYpd77viJOn+yDIOJnnx7JR91tScOsopAM3c5TOunPxne7+UXdvw9f+UecnjGDnvtaZpuXibcSRCRZsiL84pnQ2jiOIY0y1xut7cbkCN6vVrrf/cJvsyNp93bqMvf2HW+smWW8t5Tq8uPwNX8k4knqF3YTqU5mD6izU6ynwPMSDsAZzV6F4KaQWptxA2jAMwzC2iG49rv2/gKOr3YZsW+F5MHrczZClgs3LBVuNXidZNr/wqHY7xNvjvsq6m9k/+DVStSnq9AEeEleteG56Wmplpq/vJH/n/Vv1OohhGIZhdG2Q9w1gvku+N50Ij1qYx09BLRwAII07jiLwdOE8KdtovU6ygMXTt70cb6/7+trV1zE4/wGG09cRrRORaQ7yAHypIRoxVb6V8YnXrO8VcsMwDMNYB90a5L0D+HsR+Ueg0rioqj/dpXg2jCiC86dgdgKKl11OvygEBSgX4fo47Bhxef2JdxIoz4K6dwsoNfT2Q3YIKknZDIi4lTnmp2F6HIYOJOwJBP1QnoLrl5yvbAEq8XsXt98LB1vfu1hVvA0bKi6O9ninLsCgexeiVZYoY2kapsZhyL1b0dTL9jtfLbIVfKVGJzj/TY/xT/sepRxM0xcOcnjsfg6fu49d/nCLvUb9dqq3ZLmy/bFeovydypXU++KfD+Nfejf/y8Gf4Zb8aSLK1LQPEUjLPB4hV2pHefzyu8l/cpi7f7Kr3dMwDMO4iejWOnmPAJ8CPo/Lx2v8bHtqJZibdAOVyXNA6AZSE3G+fxi/C3HlzILsyhhEdXd+JX7vIpyHWtXdF4WxbCy+p+6Ow4rzVy8nZGHsq7rga2LM+fE9mL7s7llVvI04Kgs2GnEkY6qXnZ2w0laWsUQZz0C94mzW2/QmzrbJzizv64Udp/nwqx/iyb0nkXqaQnGE2lSar4ye5G9e/RBfmzndYm/i7ELdJOstWTeNONrL31KucLHe3FW4PHOM9z9zkk9efCdT1YNkvDkyMsd0/SCfuf5OPnzlz3hh7hgle+/CMAzD2EK6NZNXV9V/1yXfm0o6B/ldbmZs1yE38+QpDI+6mSE/fheicMTNcnkKe0YXZon2HHH3+H2QzsDA6MJM3p7R+B5xx/PTzl8qC3v2LMiGR91MXsPXwB43WxVGMLjP3bOmeIM4jnjGqyXeUXdPOgd+0EHWKOMRN5OXzrrUv6Te8GHnqyk7sjCT127vam2Cf/zOh8lpwJAME0aAwEA+Rzg/QiVT5As/8jB7P/YI6YlhUkFsf3ZxvSXrRiWhlyh/p3Il9fK7oTQF8/VhvnD1bXxp7m2kc7Q8so1Ct4Rezt67MAzDMLaQbg3yPi0ib8Utn5J8XHutS/FsGJ4HI8cjZkslskHAbMlNm/XncsyWStSjiJTnNc+TsvXohVGEv4ReJxtD+Txe4sWLjYp3uThWq7caGx+pfYjB1BzDOkQqFVGvh/ik8FPiZgYpcDU1jvcTj/OD1QfxPDdQa8xeNo4b71N0kq1FL78bPvVrbqCX6XMvWyQHeOBmBPt2wtH719CRDMMwDOMlIqpbvzydiDyXOG0GoKp3bIX/48eP66lTm7NiS0TE1znFFBNc4zKzFAElT4EZikwwzjAjDFBgjhlA6aPAPLOx3kBTbw/76WeIWWYRlD4GmMcteNxHP3NMc4Vx9nCAPAXmmUER+ulnlimucCnhq4jgc5R7Oco9ePGT+rXFW4x9DzCXiGmOIle4wB5GyDPYLMtCvM5eI97hNr08/cw2y+xkS/n6a36LKmXyDJIhIKCPDFl2MIzEw6sKJerUeRfv35Q2TjI7AX/yY3Dpy24WMpVplYc1N8i79S548L3QP7zpIRmGYRg3GCLylKoeX+t93crJeztwl6reDrwH+DLwo12KZUOpUKLIJP0McolzRIQoERcYo0aFCvPUKHOeM03ZOGNE1IkIOc8ZapRjvSoXGEMJiQi5wBgRIRF1xhmjSoUqJaqUYxshGuvVqDZ9ORsRHh6TXKZCaZXxNuKoNG1EhIy3xVSlHMdRiWOst8SrRJznDBUqVCgv0jvPWaqxrEKF85xZ0leJORQlImSOGdJkqFEhTOyKlyFglq1JgOsfhh96N+y5Eypz7hFvWHc/paJ7rLvn5fD6d9sAzzAMw9haujXI+79VtSgi3wX8APBe4Le7FMuGEpCjwC5mmWY/h/DwETwOMEo6nnlKk+U2jjRlI4zikcLD5zaOkCYb62U4wCiCj4fPAUbx8PFIMcIoGQIy5MiQjW34SKyXJtP05Wx4RETsYh9BYrW25eNtxBE0bXj4jLTFlCEbxxHEMaZa4hU8buMIAQEB2UV6t3E4npXLEhBwG0eW9JUjjyB4+OQZoEaVNAF+IvOgSoV+ti4Bbv8x+LGT8Np3wo6DUJ1zPzsPums/9mdOxzAMwzC2km7l5IXx7/uB31HVD4vIz3cplg3Fw+MVHKdCiTRBc9YsIEeFElE8o9Y4T8o2Wq+TLEe++ah2O8Tbfgzwaf6SYW7FxyfC5eRJIhNumkl+gAc2ojlXTf8wfNfb3I9hGIZh9ALdysn7KHAR+BfAtwEl4ElVvWuL/E8Az2+ym93AjbejR/fYDVyVNKngEAdRIg2J2pXEx0PwKud4XmuJZ7jGclhf3TysbjcXq9/Nwep181hv3R5U1TUn/XRrkNcH/CDwVVUdE5F9wD9T1ce3PJhNQkROrSdJ0uiM1efmYXW7eVjdbi5Wv5uD1evmsdV125XHtao6D/xV4vwycLkbsRiGYRiGYdyIdOvFC8MwDMMwDGMTsUHe5vG73Q7gBsPqc/Owut08rG43F6vfzcHqdfPY0rrtSk6eYRiGYRiGsbnYTJ5hGIZhGMYNiA3yDMMwDMMwbkBskGcYhmEYhnEDYoM8wzAMwzCMGxAb5BmGYRiGYdyA2CDPMAzDMAzjBqSnBnkiku12DIZhGIZhGDcCPbVOnoicBV4EPgM8AXxOVae7G5VhGIZhGMb2o6cGeQAichvw3cB3Aq8DplT17o30sXv3bj106NBGmjRuUpQ6VZ5DKQHC4slxBSKEHBluR7qzXbRhGIaxjXnqqaeuqurwWu/rqb84IjKCG9x9N3AX8E/AZzfaz6FDhzh16tRGmzVuMJSIiDkAhFw8kGs9nuKvuMqvETKFRx/SIQMiZI4UOxnmP7CDB7euAIZhGMYNgYg8v577emqQB5wHvgD8v6r6b7odjHHzokTM8STz/AOK4jGAMgsoQj/KLIoyzZ9T4yJKhFLFI4dHrsWWR5aQaYo8aoM8wzAMY8voqRcvgG8B3gc8ICL/ICLvE5Gf7HZQxs2HUqLOJQQfCKkyBoRASI2z8XFEyNX4uHFfHSVqs+aj1Imw9FLDMAxj6+ipQZ6qfhn4I+A9wKeA7wX+U1eDMm5KhBwp9qOEgE+GUcAHfNIcjo89fHbHx437Uh0e2YYIKTwGtyp8wzAMw+itx7UicgoIgL/H5eJ9j6qu6zm0YbwUBI8895DjaHzeOScvxe4Vc/IiyqTYSYH7t64AhmEYxk1PTw3ygNeq6kS3gzAMcAM9n4HElcXHg7yOaT5AyHWgDmRabCg1ICLFrQzwmk2O2DAMwzAW6KlBnqpOiMj9wFEgm7h+ontRGcbSpBhmP+/mIj9DmdMIZYQ+ACLmEUICjrKfd5NizW+/G4ZhGMa66alBnoj8DtAHfD/w+8CPAk92NSjDWIEsxzjISab4S67zx9Q4B0DAIYZ4C0P8qA3wDMMwjC2npwZ5wHeo6jeLyFdU9V0i8uvAX3U7KMNYiRTD7OZt7OZt3Q7FMAzDMIAee7sWKMe/50VkP1ADbu9iPIZhGIZhGNuSXpvJ+4iIDAG/CnwRtyfU73U3JMMwDMMwjO1HzwzyRMQDPqmqU8AHReSjQFZVbQVZwzAMwzCMNdIzj2tVNQJ+PXFesQGeYRiGYRjG+uiZQV7M4yLyRhGRbgdiGIZhGIaxnemZx7Ux/w7IA3URKQMCqKoWuhuWYRiGYRjG9qKnBnmqOrCcXESOquo/bVU8hmEYhmEY25Vee1y7En/c7QAMwzAMwzC2A9ttkGe5eoZhGIZhGKtguw3ytNsBGIZhGIZhbAe22yBvESJyQEQ+LSJfF5F/EpF/2+2YDMMwDMMwuk1PvXixCqodrtWBf6+qXxSRAeApEfmEqn5ti2MzbiCUCKWEkEO2//9ChmEYxk1Iz/z1EhEv3vUCEcmIyLeKyM6kjqre236fql5W1S/GxzPA14FbtyJm48ZEiZjnFLM8wTynUKJuh2QYhmEYa6YnBnki8gbgMnBRRH4Y+Azwa8BXROT1a7BzCPgW4B87yN4qIqdE5NTExMSGxG3cmCglQiZJsYeQSZRSt0MyDMMwjDXTK49r3wncBeSALwPfrqrPiMhB4IPAR1YyICL9se7PqGqxXa6qvwv8LsDx48ftBQ5jSYQcPruocwWfXQi5bodkGIZhGGumVwZ5qOoLACJyXlWfia8933iEuxwiksYN8P5UVf9qcyM1bnQEjz6OW06eYRiGsa3pmb9eicHcTySu+UBmhfsE+APg66r6nzcvQuNmQvDwyNsAzzAMw9i29MpfsLcSD+ZU9cnE9QPAL69w73cCbwFeJSJfin9etzlhGoZhGIZhbA964nGtqn5hievngHMr3PtZbCcMwzAMwzCMFnpiJk9EjscLGv9JvLjxJ0RkWkS+ICJ3dzs+wzAMwzCM7UZPDPKA3wJ+BXgU+HvgEVUdBH4O+O1uBmYYhmEYhrEd6ZVBXlpVP66qJwFV1b/EHXwSyHY3NMMwDMMwjO1HrwzyyiLyGhF5E6Dx4siIyPcCYXdDMwzDMAzD2H70xIsXwL/BPa6NgPuAt4nIe4GLuDdvDcMwDMMwjDXQE4M8Vf0ybnDX4N/GP4ZhGIZhGMY66JXHtUsiIj/e7RgMwzAMwzC2Gz0/yAPe1e0ADMMwDMMwths98bhWRL6ylAjYu5WxGIZhGIZh3Aj0xCAPN5C7D7jedl1w6+YZhmEYhmEYa6BXBnkfBfpV9UvtAhH5u60PxzCMlZigyGN8lUf5EtPMM0gf93M39/HPGKbQ7fAMwzBuekRVux3DqhGRHaraPtu3Zo4fP66nTp3aiJAM46bkNBd4mA9Soc4u+smSpkyNSWYJSHGCN3KMA90O0zAM44ZARJ5S1eNrvW87vHiR5JPdDsAwbnYmKPIwHyQgzQg7yZFBEHJkGGEnAWke5oNMUOx2qIZhGDc1vfK4drVItwNYDRERJaoEpChRBYQcaUrUiAjx8OPzVhkoOTId9JKy5D1VIhQPr6O9TjbyZPHaxvarj9fZyJJijikA+igwT5GQOj6p5nlStlq9jbCxUb7yDFEmJCLCiwcwS7dDrU1vtW2+XFuuts1b9ZyscxxL6y1XrsWxf4wvMUeVQXLUqJPCRxIfzQI5xinxOF/lQb5z3Z8jwzAM46Wx3QZ5Pf9sOSLiFM8xwQyXmaLIPIpQIKBIiXGuM8IOCuSYoYICBbLMUkIRBhJ6+9nJEFlmKTdlMzrHXvkAACAASURBVJQRoJ8c05QY5xoH2EGBvlim9JNlijKXuNb0VaSCj3AvL+MeXtYc6K0l3iIVICLgBSY5AygD9FFkjguxr0H6maVEhMYxlVAiCuQpMsc417iVnQySj8sM/WQpMs9FrjHCzqYuSNPGanwtZW8gloEwQI4pZrnIdQ6wkyHyTDGH4NEftwMIuzhCmb2MM8UIOxkk12yj/kR7FWJfF7ge62WZidurIWtvc9dGuVgPCgRxW15vtmWx2Za5uP2X01soc8PXYIuvLEUqCDCQsNGIt9G/GuVq9K8pSok+tNC/TvJ5QpRLXKefgL0MsodCy0BvF/08ypdtkGcYhtFFttvj2p6nRJVJZhkkxzkmCFEiIsZ4kQp15qlSps4ZXozniSLGeIE6SkjEGV6kHOtVqTHGi4SxrHFcRxnjBSrUKMX2xniBkIgQZYwXqVJr+hrjxeZM02Wm4hmZleNtxFFJ2KhTZYwLKB4RylkuUaHe/DnLJepxuc5yiYgIjfXK1CgTUqHWovcNLlOJZeVYpnEcDRsr+7q8pL1vcJkoYa9KGNuocYbLTV8NPUUY4wJl5ilRpUItrl/XDmfj40bblakn9F5cJEu2Q0jUbL9G+5+J+0ayLaO4Lc+uSs/F5MqcjGOhPzT0XD9s6FUZ48Vm32uUqxFfax96oRn7VWYRoE6EAvNUqRO1fA4C0kwzv7UfPsMwDKOF7TbI6/nHtTky7KKfaUocYhgfwcNjlL0EpOgjQ5YUR9iLjxfLbiGF4ONxhL1kY70MaUbZix/LGscphFFuISBNLrY3yi34ePgIo+wlQ7rpa5S9ePGgbB9D5MisKt5GHEHCRooMoxxA4kHjYfYTkGr+HGY/qbhch9mPh4fEelnSZPEJSLfovYx9BLEsG8skjqNhY2Vf+5a09zL24SXsZfBjG2mOsK/pq6EnKKMcIEsfOTIEpOP6de1wOD5utF2WVEJv7yJZsh18vGb7Ndr/SNw3km3pxW15eFV6LiZX5mQcC/2hoef6YUMvwyh7m32vUa5GfK196JZm7LvpR4EUHgL0kSHV9lVSocYgfVv74TMMwzBa6KnHtSLyx6r6lmWuvboLYa0JD4/j3L5tcvLWFq/l5FlOnrCTft7P59nPIIIsyskDmGSWB3jluj9HhmEYxkunp5ZQEZEvquq3Js594Kuq+k0b6ceWUDGM9TNBkYf4QwLSFMgtkhcpUaHGI/yErZdnGIaxAWzrJVRE5B0iMgN8s4gU458Z4Arw4S6HZxhGgmEKnOCNVKgxzrXmzGCJKuNco0KNE7zRBniGYRhdpmdm8kTEA35fVX9is33ZTJ5hvHQmKPI4X+VRvpzY8eIuXmM7XhiGYWwo653J65mcPFWNROSubsdhGMbqGKbAg3ynLZNiGIbRo/TE49oEnxeRb+92EIZhGIZhGNudnpnJi/l+4CEReR7i1XBBVfWbuxuWYRiGYRjG9qLXBnmv7XYAhmEYhmEYNwI9NchT1ecBRGQPkO1yOIZhGIZhGNuWnsrJE5EfEpEx4DngfwDngI93NSjDMAzDMIxtSE8N8oBfAO4Fzqjq7bgdLj7X3ZAMwzAMwzC2H702yKup6iTgiYinqp8G7u52UIZhGIZhGNuNnsrJA6ZEpB94AvhTEbkC1Lsck2EYhmEYxraj12byfhgoAT8L/C3wDeD1XY3IMAzDMAxjG9JTM3mqOgcgIgXgI10OxwAmrsFjn4VHn4DpGRgcgPu/B+77LhjeaXE1uDpxldN/8SQ733eNPefdx2riYJ3Jt+zg2Jv+ObuHd299UMswwVUe45M8yieYpsggBe7nB7iPVzNMb8Vq3Dz02ufaaGOiAo9dgUdfgOkaDKbh/lvgvj0wHHQ7ui1logqPXYNHr8F0HQZTcP9OuG8nDGe6Hd0CPbN3LYCIPAScwM3mRSwshnzHRvqxvWtXx+kxePg3oVKDXUOQzUC5CpNTEKThxE/DsVGLa+z0GYo/8yQHTqep+0q5L0QQgnkhFXpcOFqj8Bv3MHrsyNYFtQyn+RoP80tUqLGLHWQJKFNhkusEpDnBOzjGN3U7TOMmo9c+10Ybp4vw8NehEsGuDGQ9KEcwWYXAgxOvgGM3x57Vp+fg4eegorArlaiKOgQCJ26HY/mN9bnevWt7bZA3BrxSVa+u8b4fBH4D8IHfV9VfXk7fBnkrM3ENHnonBBko9C+WF2ehUoVH3rW1/2H3WlxXJ67y7I/9LSNf9qkGStj2H5xfE9Jl4eJdde547w92fUZvgqs8xM8SEFBgYJG8yAwVKjzCf7EZPWPL6LXPtdHGRAUe+pIbzBXSi+XFmhv8PXL3DT+jN1GFh87EVdHhWWixHlfFkY2d0VvvIK+nHtficvDm13KDiPjAfwN+ABgHviAif6OqX9uE+FbFzFyZL469yOj+Qb40dgUQ7h4d5ktjE8xVq+Qzmfi8VQbK3aN7OuglZcl7rjBXrS1pr5ON7777Ngby2RXjPfP8fs5PKoOFCtcrPrv7c0yXXNPs6MszV69yZcrnA4/DXS+/xny5Rl82xZ0Hh3j6+WlA4+MpQLjz4CBPPz9FqVwnt4Te0rKF4984WeHC9Sz7divpasB0qYLvK4VslplKBUSZLWV55K+n+Bf3zBKkU9y+fwfPXboOCLfvH+K5S9cp1+pk0+n4fArQDnpTlGs1sk0bi/Uuf+Z/4k/CzFCIZoThSshEzkeA3aWQq0M+VJTadbj20S9wx2vuBWD/jjyXrs+1HFdrIZm031G2Wr2VbHx6xyeYuZ4jV9vDfFoJdlSIrru/qv6OOXLXR5jiRf5mx6d57fU3UK8pqbSwc4fPteshQPN4OdlG65mvG9vXxz/vM11W9vVDPfSoRBEpD9KeR10jghxMzsHHPiP8yPc4G7m8T2kuJAoVz5fmeVK2Lr1I8bzOehvuaxm95eLYaL0V4/3YC5SqEdGuDJ5CDqWU8p0sjCjtCOBSidzjVyj9ywNECp5A3qlQitwMVymiKct57hw6H2+E3mb4+tBVN2s3lIJaBCkBEZoUUjBegcevw4N76Tq9NpP3LcB7gH8EKo3rqvrTy9zzSuDnVfW++Pwd8T2/tNQ9mzmTNzNX5o2/MMaFmRxXNU81nSVCyNRKVLwc5cGAbLFCpl6ins6i4mS1dA5USdXKVFM5yoWA7HSFICpRTecQVVLVMmGQRRHS1RLlVJZKISA7WyWolqgl7CV9BaGz4YnyLekLfOTnjzQHekvFW5rNEAUeusNHrodk6lUk44FEeFVFMylCwCtHDA3OMtvXR//sPLmoQiUVoArZMHEcVSh7ATP5PP2zc009gEzdyWb78wzMzTV1AXJRhbK/YO9iNExYSJGeruNXQ7ycABFBrYYGHoog5ZBaJsWOXXPsrBTpo8K8BAiQ0wqzGjAV9LOjOku/VJjD+epTpweQx+ldz/QzVHF6JQnQNr2B6RIlP8vVwQF2Ts/QXykzn8uiCv3VMjOZLNcKBXYWi2QpE+7dgQB5r8ZclHZ6qRrFWpqrXpZhLTOQqjFbTyPi9GajNCyjh0C/V6NYT3PVyzGsJQb82D4w4NeYie3NeBephQPMyw7y0XXyqTJRVACEwCtRiXIoSsafZ3f9MBfF44BGDKWU6dC9pzXoR1yrS1O2w1emov+fvXuPs+wqC7z/e9be51L36q6q7vS9G+gESASBloACokhC6M8MXsBXg46iY4KOOoPzvgOMmoH2dUAdnWEcBhK5KAJxUETQBBKNYLgESYcIJCSdDvQlfUl3dVVXnbqdy97rmT/WPlWnqquq637r5/v51Ofsc9az13r22nVZtffaezsEaI88g6lDFTpjZSCBpyRip0/ZHIc4gA7nGfQT9V1MhFNT2gp1TJTtVM+mGEqpoEC785Qa2r1YE065KKvDM5A4RKDT+fF2251nIBFOu5gdPqEzVkoNOV2sl2nKpoY6GvPtdNn2Z3VsinViW7K4eu4DifCUOHaoZ/Ok7ar3zeSyev0DU7ZrvK2cUkrdpO2/JPeG7ZpuPzTW0bhd9b455SJ2ajptX9fru5gwaX/V+6ndeQZSx2mJLu2baPI+uZhO9M0j54qkOcEB+cQTFyEWzyYH1UgZLUJuBOLU8dPPGQrf1zmlVA1/ULfnobMAQzVBgNacMlQTVKE9D6WqcqoCOwvh/dS4+vJgBU5VhZ15pWNKfQMVOFOdXEe9/uEa4znV69hVUNrzMqlsuBb6szXOcq8KuwrQnldKVUFkch078xrqTy5tq7GOxriQL5SqYfAxU1xj3zxVmbzNoayhb75ygcFCxKm2AjtHa3TkheGuPCrQVvUM5yO0qrRVagy86ZmcqcLuIlzfFgZPfQk8XSF831RgZz4MhoZ9mJPV6mAoDdvVHsFgGuJ2FcL7Uhri2qKwjmpYLmVxO7O4oTTbZjdRx858mC837LP9MKWtqXWUGuoYnibuC4NhwFof2G3Nw5bc5IHeWAqJwseXcNbLQo/krbWra28H/hH4KvBQw9dsdgBPNbw/lX02iYjcIiKHReRwb2/vEqV7qa8fPcfZkWa6CxWG2trwTkBgqKMDLTgoRPh8xFBnO0T1snbUCUTCUGc7Ph9lcY6hjg7EAS4rc4I6odTRjo8dmo8gdpQ62sEJ4kJbPt/QVkcHCDgVnhpp5+tHz10233RLDHmBApCDWlch5Osc1c15NILIQaUrj3cxPh/jczF9rR2oOLxz48s4oa+lkzQKMWlDnIqjv7WDNJeVRTF9LZ3gBO8cF1o6UefARfS1duDjCM0JmofK5lzoG3GUO/MggohQ2ZzDxxFpnCOJcjwdb0IlQp3jbLyJJIpIcwUSF3M2twkVQZ2biBPhbG4TiYtDXBRxNt6EOodKFOKcQ0U409lFLZcjiQtUczFnurpInUMjx6nNXVRzOZLs6+nWLjxCiuNk2kqqQirCyVobFSJqLqZKzFNJK6nU49rCOrPEeYSTaRtV56hFEVVxnEzaSDX8QTmZZvWpMJBsw0ueJMrhJU9fshUlRom4kHajREBMb9JNWYWKc4whPJ5EpApptlwvKyM8noayBOHxWkSKkAocSUN5xQljEOKQ8TrGl2tRFjelLeGSto6kjiRb70gakZK1m0SMARUnlAnrpQKpNrYLR9KIsoNyJJSF8TrGt0tCWUU0bEu9jqQhLo0oM1HHxLZwae7ZdpW13jeM983YdGXT9Gc9p/p2Td3+8dzr2+Xq2zXDfmioo3E/HMm2q+KEsjb29Uz1Zfu/3tdZHRXRSfk25ti4Txr7ZqA1DHBwUGp1OKfUJGKgqPgIkggkht5iWD9VODoC5RRGVah6eGIofJ4oHB0Oy16VoyM6Hlf2cHQIUpVJcfX6Kp6Q13hcVt8IVH1WRxreN9Y/XsdQqGOs3lZj2XCoK1XhyUlxytGRUFdjHWWEisKTQ9O39eRQmBM2NS5si47XN1PceN9keTRus1ed3J8tBSq5iHIcUYmFo6358L0hjqNteRIR0rzjiUKOqsKoD+sezwbGHTEcK2d5+LB9T44Sfn4VjpbDsgeeGAtxYx7KWZknlD8xlm0j4fOKD22VNStrqG+8rex9Ul9vbKKto+Ww7miWU2Nb9fz8lLZG0vCq4c8yo+ml93krZIPMtWCtDfISVf0NVf2wqv5Z/esy68g0n11yeFJV71DVA6p6oKenZ2myncYL929lW8soFyoF2oaGcF5BoW1wEKl4qKS4akrbQCl8tyq0DZYQH3462wZKuGqaxXnaBgdRD/iszCvilfbBEi7xSDWFxNM+WAKvqA9tuWpDW4ODoOBF2dVS4oX7t1423+h8AlUNx1NrkOurZD/9nnx/FUkh9VDoq+J8gqsmuFpC1/Agoh7n/fgyXukaGSBKQ0zUECfq2Tw8SFTLytKErpEB8Irznu6RAcR78Cldw4O4JEVqilSh0F8LfaOe4kAVVFFVCv01XJISJTXitMZVyUVEU8R7tiUXidOUqFYh9gnbahcRVcT7iThVttUuEvskxKUp25KLiPeIpiHOe0SV7QN95Go14qRCvpawva+PyHsk9ezs7yNfqxFnX1cN9+FQIjy7o2EiUSJVdueGKJCS8wl5EnbFw0RajxsK68wS51B2R0PkvSeXpuTVszseIhJFILSlSiRKZ3wWp1XitIbTKl3xOST7U94dXcj+pCf0xBcoilLwniaUZ8cpkUCULdfLiijPjkJZjPLsXEqEEilcE4XygleaIMSh43WML+fSLG5KW8olbV0T+WxIqlwTpdmQNKzTBBS8UiSsF2n4v2SiXbgmSil6KKZKURmvY3y7NJQVVMK21OuIG+KilCITdUxsC5fmnm1XUep9w3jfNE1XNk1/1nOqb9fU7R/Pvb5dvr5dM+yHhjoa98M12XYVvFKUxr6eqb5s/9f7OqujoDIp38YcG/dJY990DiuqgIf2YY/3Qk5TOsuCSyFOQRPoKYf1I4H9LVCMoFmUvIOr28LnscD+1ux/UhH2t8h4XNHB/jaIRCfF1esrOEJe43FZfS2Qd1kdUXjfWP94HW2hjqZ6W41lraGuSJRnTYoT9reEuhrrKKIUBJ7VNn1bz2oLR5SmxoVtkfH6Zoob75ssj8ZtdiKT+3OkQqGWUkxSComyf7gavjfUs3+oSqxKVPVcXamRF2h2Yd29hXCUdTCBfcUsDxe271nNYRJ9LLC/GJYdcHVTiGtyUMzKHKH86qZsGwmfF1xoqyhZWUN9421l7+P6ek0Tbe0vhnWbs5wa26rn56a01RKFVwl/lmmOLp33VvHQES3RoGKR1tqcvM+LyC2E26c0nq7tn2WdU8Cuhvc7gTPLk97ltbUU+eRv78/muDWtwTl5V0+akzdTvk+c2M5H/y6hw4+QiyO6NzfMydvcwsBolfP9ET970PH8azyj5YvZnLzuhjl03Q1z8rqzeXf92by7S+NmLptY/h93lrnngTAnr3NzUzYnz9He0hHm5KEMVVt5xYsG+JEXVynkWtm3fVfDXLtdDXPy2rL39bl2U+Pqc/JaZ4w7+8UniT44SDzM9HPymrI5eW3AL3XxjBv2AbPNtSvOcU7e9HGhrHOWOXlf5a8ufp4ttS24aebkpRdbOMs5Xr/ph7np4vPW7RyvucXl51228Lil2K7F5bTy+2vuffPZr0Z8/HPKtm4o5HNhTp5AThxJ6tFhOHMB3niT8OOvCH+2bE7eCs7J82OM/eUZ/LYiTiTMyRtsmJMXuTAn7w3bGdvBhp6T94le+NQFuCq7qGLqnDwIp6dv3sKasNbm5B1reDue2Gy3UBGRGHiC8Jzb08CDwM2q+ugs6/QCJxad8Oy6gXldJby25GLye/eg6sMxu6kih4ijevwE1FbiqSRZf66tvHJxrrgnf9U1TdU4VnfpT5MoiArlfJIerz59pJbUysud06xyxOx1e1A8KaH/lCLh7BpEOATHcX+Cmj1tZgms898DK2XBP9fWv8tjUr/miOK9bNmjqE/D+ZNJIsQJ4o5z/kSNdGP/3ohyMduy71U/zfeqy75Xzx4/QTrt36CFfs/uUdV5n4Zca0fy3gp8TlVLIvLbwAuB35ltBVVNRORXgXsIR1c/NNsAL1tn+c7XZkTk8EImSZrpWX8uH+vb5WN9u7ysf5eH9evyWem+XWtz8n4rG+C9jHBLlD8F3ne5lVT1blW9WlWfqaq/u9xJGmOMMcasdWttkFe/HuUg8H5V/TSwhh4QYowxxhizPqy1Qd5pEbkd+EngbhEpsPZynKs7VjuBDcb6c/lY3y4f69vlZf27PKxfl8+K9u1au/CiGXgN8C1VPSoi24DvUdV7Vzk1Y4wxxph1ZU0N8owxxhhjzNJYr6dCjTHGGGPMLGyQZ4wxxhizAdkgzxhjjDFmA7JBnjHGGGPMBmSDPGOMMcaYDWhegzwRKV4+yhhjjDHGrLZ53UJFRJ4EzgFfBO4Hvqyqg8uUmzHGGGOMWaB53ydPRHYDLwd+AHgtMKCq37uoJEReA7wHiIAPqOq7p5RLVv5aYBT4eVX9elZ2HBgiPBItmcuDf7u7u3Xv3r2LSdkYY4wxZkU89NBDF1S1Z77rxfMJFpGdhMHdy4HnA48CX5pvo1PqjID3Aq8GTgEPishnVPXbDWE3Afuzr+uB92WvdT+kqhfm2ubevXs5fPjwYtI2xhhjjFkRInJiIevNa5AHnAQeBP6rqr55IQ1O48XAk6r6XQAR+QvgdUDjIO91wEc0HHb8qoh0isg2VT27RDkYY4wxxmwo87269gXAR4CbReQBEfmIiPziInPYATzV8P5U9tlcYxS4V0QeEpFbZmpERG4RkcMicri3t3eRKRtjjDHGrG3zOpKnqt8Qke8A3yGcsv0Z4BXABxeRg0zX1DxifkBVz4jIFuDvReRxVb3/kmDVO4A7AA4cOGAP7DXGGGPMhjbfW6gcBh4Afgx4HHiFqu5dZA6ngF0N73cCZ+Yao6r11/PApwinf40xxhhjrmjznZN3k6ou9bnOB4H9IrIPOA38FHDzlJjPAL+azde7HhhU1bMi0gI4VR3Klm8ADi1xfsYYY4wx6858T9f2ishB4Fqg2PD5ggdWqpqIyK8C9xBuofIhVX1URN6clb8fuJtw+5QnCbdQeVO2+lbgU+EOK8TAx1X1cwvNxRhjjDFmo5jvLVTeDzQDPwR8AHg98LXFJqGqdxMGco2fvb9hWYF/N8163yXcysUYY4wxxjSY79W136+q/wa4qKrvBF7K5LlyxhhjjDFmDZjvIK+cvY6KyHagBuxb2pSMMcYYY8xizffCi78VkU7gD4CvE25j8idLnpUxxhhjjFmUOQ/yRMQB96nqAPBJEfk7oKiqg8uWnTHGGGOMWZA5n65VVQ/8YcP7ig3wjDHGGGPWpvnOybtXRH5CsnuWGGOMMcaYtWm+c/J+A2gBEhEpEx43pqravuSZGWOMMcaYBZvvzZDbZisXkWtV9dHFpWSMMcYYYxZrvqdrL+fPl7g+Y4wxxhizAEs9yLO5esYYY4wxa8BSD/J0ieszxhhjjDELsNSDPGOMMcYYswYs9SCvusT1GWOMMcaYBVjwIE9EfmXqZ6r6ksWlY4wxxhhjlsKcbqEiIr8x9SPg7SJSBFDVP1rqxIwxxhhjzMLN9UjeO4HrgVagLXuNsuVZ751njDHGGGNW3lxvhnwt8EeEp128U1VHReTnVPWdy5eaMcYYY4xZqDkdyVPVk6r6euArwN+LyOuXNy1jjDHGGLMY87rwQlU/DbyacOr21LJkZIwxxhhjFm1ez64FUNVR4P9bhlyMMcYYY8wSmevVtQeAPwBOA28HPgR8H3AUuEVVH162DM2qqvbChXug9y5IBiHugJ6D0H0j5HtWMbHBXnjoHjh8F4wMQksHHDgIL7oROlY+sW/2eX7zQpl/2OGpFAGBYhleddrxu11Fnte1tu473kuZe3iauzjDIDU6yHGQ7dzIVfRQnFsla2wfmPWvlxL38C3u4l8YZJQOmjnI93Ij30MP7audnrGf+QlpL4zdA+W7QAdBOqB4EJpuhGjt9IWoXv5JZCLyNeC/AJ3A7wNvUdW/EpFXAf+/qr50edNcWgcOHNDDhw+vdhpr3tAj8ORtoBXIdYErgi9DrQ+kAM86BG3XrUJixx+Bj94GSQXauyBXhFoZSn0QF+BnDsHelUvs4ydqvLmzwlAriIL48Lk6QKB1BN5/scDNe3IrltNsHmGA23iECp4u8hRxlPH0UaWA4xDXcR2ds1eyxvaBWf8e4Slu45NUSOiilSI5ytToY5gCMYf4Ca5j12qneeWyn/kJ1Udg8DagAq4LKAJl8H1AAToOQX5p+0JEHlLVA/Neb46DvIdV9QXZ8klV3T1d2UKJyGuA9xBuy/IBVX33lHLJyl8LjAI/r6pfn8u607FB3uVVe+HRWyEqQDzNP9BJCdIKXHv7Ch/RG+yFP74V8gVoniax0RJUK/Brt6/If5bf7PP8oBtlsA2cD4O8RurAC3QOwRd886of0eulzK0cpoCjnUsHnSVqVPDczoGZj+itsX1g1r9eStzKhyiQo52mS8pLjFGhxu38gh3RWw32Mz8h7YX+W8ORDjdNX/hSODKy+fYlPaK30EHeXOfklUXkBqADUBH5UVX9GxH5QSCdb6ONRCQC3ku4oOMU8KCIfEZVv90QdhOwP/u6HngfcP0c111xoyNDHDv2GFu37OTkySMIwq7dV/PUyScYqw7TlG9l1+6rLylTlN27r7kkrrGscZ2TJ49QqY5QnKG+6ep49nMO0NzSdtl8mw8/l5HRp/GFYeKRJoq5PZT1OABFt5dy4Ti+5Djx6e2M/sCjVCrD5Ast7N76bJ46dyTku/XZnDz3eMhp6zWcPPc4teoIufz0cTOVNS6XPvfHtA8eJ928nbZKxFBtEHUR7bk2SrVhQOkcG+LUP7yX3ue/klzcxN7N+zjefwxB2LN5L8f7j1GtjVHINbNn815O9B9H0UviTvQfp1IbJZ8LdUwX998GtrGp6zibhssgTZxu2c2O4ZMgyumWPewYOQFaQSjw3/ueybvSswB0tu5jYPjYpOWkVibOFactm2tcUqsQ5woz1nFXa0xx+AhX1UBzBSqt22gdPgfASOtWdg2f42nK3N3azo8Pg6+VcbkiTa27GRs+AQjND38FX+3FN3UjtRqxayNxY4AQaxNJkyJj/bjDn2b0+h/Gp1VclKMpv5Wx6nlAp10OcXma8lsYq54DJFueWta4XmPcOXxamyFu8johLjdLHdPlu5C4+WzX5PoXn9N0bV1uP8zWh3PfJ7PlMd0+v7vwHVLfT6e242OHS0eJ1ZHETeTSMlt8yik3xL3uId5YuSb84spvhmo/pAlE8cT7xrKljrtS23rws5AOQPEq0BGgCHH2pz+Noa0IfX3w8N3w8h8H9SAOXEuI0TGgEF7rZdKUfc70y0sRtxxtjf0NaBlcJ2gNJCY8HyLj2iE9BeV7oeWNrLa5Hsl7PuE0rQfeAvwy8HOEOXq3qOqXneA1JQAAIABJREFUF5yAyEuBd6jqjdn7twOo6rsaYm4HvqCqd2bvjwCvBPZebt3pLOeRvNGRIf75w/+WtrGn6JBzNOfLoMpYrYmmeITWthGGS62MpU00xWUQQlm+DF4ZS4o0RWO0tg8zPNTMWNJKU24MFEZrRZoLldBOpUBTPEpb+wjDw62MVptozjXUFw/T2jaatdUc6hDHEb2el/zbPx0f6M2Ur5zfQq6lRNPmi4xd3ES50krcOgBAtdJKoTCCKFRG2qjuPUlnYYyBchOjWqA5qgLKaFqgOa6CZstSpaNpjNJYkZF6nMBILUerq9LeVGZwrIlRzdMcZdvpizS7CgKMpHn2jvQTt3uqQ0JSjpAWwQOjaYFcIQWEtKzkcwn9u5q5kLYxrEXaXKhvxDfRJCP0xEP0pe2M+Gaa3SjgGPZ52lwFRRn1zbS4UbqiEr1JG2PaQosLP/BDvkCrqwKefprpcKNslRJP+zZKNNNBBRVPSZtpZ4Stbojzvp2SNrEzy6OiTRRkFAVq2kyeEdpciZJ2UNVm8jKKIFS0SEHKKHr5OEZpj4Yope1UaKIgYyhCTZvJyQgC9GpMC2W6ohH602bKtNAq4ZHTo5qneXy5wFVSo1kGKesmvLYSyTAgbDl2AQpVpDVBRwpUpZlqq0NEoBqjuQTnU6QS8dj11yCuF592EdGBuhHwitA6adlLCZELqPbgtA1lCHXgfFtDWTdOO1A3FH4T0QKMgAPxraQM4qI+1Ic6vBtCPAht421BC54hxF1AfTeOtqwOQXwLKaWsrIeIKXUwPE1cNxHtk7drUu5DiPSS0kXsO1CGUac4355t1+Syev3KULbcOk1bw1O2/9Kc6ts1qa1J+yHU0bgf6n3joqyOafp6or7BSfsr9JNkdQwjrhf1WVsN29XYn41980AHNFEl8jAmRTanVWKEiBaaamNsTi5yrtDOiGvhzSeaQRWijjDwSJ+CeAe4zZAOhr+3rjOUiU4sp09BtBOiTdPESfbH+SKkpyHaMSWuA3w/JKch2gVRZ1aW1e9LDTldDH/k622Nl3WCHwAFovYsp1MT9fkBUGmoo55HJ6Sladqq1zE1LtuW8fpmiputb+pl2fZ/+18gn0BrBUZbQh2dzeGPXpKDuAZpDcoR3PAGSM9AvBvy14fBk++D5GnwWd/EO4F2IPxOgVbQoexUSHuY4+azvqEdKGVxbWEdVZA20NKU+oYa6huc2A/SkbWloY7GtihBUo9rbKu1YZ32ibaqXwAKoZ8FcFsh2sKkgZ6OAQl0ffyyY4a5WuiRvLneJ+8bqnqjqt6kqo+r6r9X1U5VvXYxA7zMDuCphvenss/mEjOXdQEQkVtE5LCIHO7t7V1kyjM7duwx2qtnGM13s6ntIiKKE+juuECc88QFTy6f0NXRj0TgBLo6+nCiSARdHf3k8glxwRPHSnfHBZwDF3m6O/qy+pTujj5yOU+c98RxKHORx7msrVjH2+ruuIATQB1dteMcO/bYZfNt23aaKFcjKqTEcY2OLWeIHLhIs2XFxUr7lrMUnCeOIR+nbM2XiCRs19Z8iQiIVNhaKJGPE+JIiSM/HhcBVxWHiCNPHCn5OGFroUSkghPYUhjI6pOwTuRxOZBYKHYlaBThnNDWUUacQxwUO6vkXcin6BJ25vpDW6Jsi3spuBq5SMmTsD3uJRIlFj8pbnvcS56EXKQUXI1tWVwksDPXTyyeSJR9rpeiJORipSg1nuH6iMQTo+yTXppIyMeeoiTsi/oQFBGlU87jsvo6XS+RqxHHSk5qbJLzYZtROtwFhNnjJIuLJSGKUmKp0ekuhPpROty5rA+V7a6foniKkaconq0ufO/FKFvcRWKUSJSt0kskKS72OGoU5QyIIuKJ20fRnBDlQHMg7aM4UVTAd4wgEfhYiFuHwmkLqYJL8HICNAXnSd3xhuUTqNbA1VCt4N0J1CmqPixTL6vh3XHwHlwow3nw9eUktKXVsJ561Omkdr07gUgV52qIq07UoSmpO464elklyyvUkbrj43FeTkBDHZO35fik3NEKuBriE9KsrVB2HJ2mrF6/OgUf+qaeE5LlO3X767nXc5KJ7aq3NWk/NNTRmLt3YbuQKsr0fT2Re9gn1PeXTsSJq2T51ib32yX7ZGL7u6SPGI84z1btJY+ipPRUnyZPlRxVmtKUTTwNPgFJofp4+GMqFUjL4b2kQDqxrClUj0zEMUOcT7L6yuDK4XVqXFoOdejYpfU35uTHgHpbU8pIw3LtSGiDykRbmjZsV5YH5RA7XVu1I6F8apxPsm1OZ48b75ssDz91uxq2v3kQcoR9GXtoHci2RaElGxRGERR7w/cQo1kux8GfyQbJx0I7Um/vyayOBPzREC8e/BNZv4yF3PzR8LmkoUyy7fJHs306GrZrvCyrr96WVLLYJLTX2FZ6NKzLaPZ9lLVFGvJrjKu3pSOgo4TBnwvLmkwZCRTADy5+QLEEFj1BSETetNgqpvls6uHFmWLmsm74UPUOVT2gqgd6epZvzsC+fc+hlN9Oc/UCF4c2oSp4hQuD3SQ1R1Jx1KoxfYOb0TT8A9032IVXQVPoG9xMrRqTVBxJIlwY7MZ78KnjwmBXVp9wYbCLWs2RVB1JEsp86vA+ayuR8bYuDHaHf9TF05fby759z7lsvkNnd5DWcqSViCTJMXh+O6kHn0q2LPhEKJ3fRsU7kgSqScS5ajuphu06V20nBVJRzlXaqSYxSSokqRuPS4Gny20kqSNJhWoSc67STiqKVzhf6czq07BO6vA10EQp98VImuK9MjRYRL1HPZQH8lR9yKfsY07VNoe2VDib9FDxOWqpUCXmTNJDqkKiblLcmaSHKjG1VKj4HGezuFThVG0ziTpSFY75HsoaU0uEsub4ru8iVUeCcEx7GCOmmjjKGnMs7UIRVIUB3YLP6hvwPaQ+R5IINc1xUbeEbUYY9N0os8dpFpdoTJpGJJpjwHeH+hEG/dasD4UzfjNldZRTR1kd53z43ksQzvtNJAipCue0h1QjfOLw5CjrdlBB1ZGUmpGakoa/42ipGa8S/vkfbAljhkRJhttACqB58DFO9xBGgI7I721Y3oNIDnwOkQLO70G8IOLCMvWyHM7vBefAhzK8A1dfjkNbkg/riUO8TGrX+T2o5vE+h/r8RB0SEfm9qK+XFbK8Qh2R3zse53QPNNQxeVv2TsodKYDPoS4mytoKZXuRacrq9YsXcKFv6jmhWb5Tt7+eez0nndiueluT9kNDHY25Ox+2C80jTN/XE7mHfUJ9f8lEnPpClm9ucr9dsk8mtr9Pu0hwqHeckx6qCEJEb/4qquSpkWcsirjIVeBi0AjyzyacRitAVAzvNQKiiWWJIH/NRBwzxLk4q68Ivhhep8ZFxVCHNF1af2NOrgmotzWljCgs564JbVCYaEuihu3K8qAYYqdrK3dNKJ8a5+Jsm6PZ48b7JsvDTd2uhu0f7YAaYV8mDoY7s20RGOkIr2kK5Z7wPURzlstecNvDgMftC+1ovb1nZXXE4PaHeHXgrs76pSnk5vaHzzUKZZptl9uf7dPmsF3jZVl99ba0kMXGob3GtqL9YV2as++jrC2ikF9jXL0taQFpJgw/fFiWqTPfKmFguwbM+z5503gn8OFFrH8KJl0ytRM4M8eY/BzWXVHNLW1c/6YPcOzYY2yaZk7eiTnOyTsxxzl5T80yJ2+6Ol4yZU7eTPk2H34uvX/zNH7TMHE0zZw8fxzf69j6Y9sZvf5R+uc4J+/iLHPy+ucwJ6//c39M+8P3hDl5+U6GBsOcvK25Nkqj4dB69+AQp174ckrXvJKWuIlrG+bQPTebkzdYG6Ml18xzG+bkNcZdm30+WBulNdfEdQ1z8p7bEPf+gW080HUcuPycvFf0PZN3bZ99Tl7bHOfkzRSX1Cq0zjIn7+HWmPuGD884J68lm5P3qtYDHJhpTl78FfyXP4bv6EZcRLtrIxltmJMno0j/07iX/Qytu2xOns3Ju/ycvNOF7/DX/ots13Z83MJTU+bkSTYn78fdD8Imm5O34m2VPwtf/hhsugrEAUUoNczJixLoOw0veyO0bfQ5eZ+AsU9BdFVWNmVOHoTT0803sxbMdU7eN2cqAq5W1cKCExCJgSeAVxHm+D0I3KyqjzbEHAR+lXB17fXA/1TVF89l3enY1bWXZ1fXzo1dXbv6+8Csf3Z17RpnP/MT1tnVtXP9i7MV+DfAv5rmq2++jTZS1YQwgLsHeAz4hKo+KiJvFpE3Z2F3A98FngT+BPiV2dZdTD4myPeE++ClFSifgjT7JyYdy95XQvmK3xC5oyfcj6lagQunoJolVh3L3ldC+Qr9onlel+O9pQKtI5BG4ctL+ErDWTBaR+F/lQqrPsAD6KHIIa6jgucUY4yR4lHGSDnFGBU8h7hu9hsir7F9YNa/Hto5xE9QocYp+hmjmn1fVjlFPxVqHOInbIC3WuxnfkLUE+6Dp5VwIUb9CJ+OZe8roXyN3BB5rkfyPgh8WFW/NE3Zx1V1bRyXnCM7kjd31V64cC9caHjiRfdB6L5hDTzx4uF74WsNd15/8UF4wQ2r9sSL38qeeFFueOLFq085fqd7bT7x4l7OZU+8qNJBnoNs5wa2zu+JF2toH5j1r5cS9/It7uIbDU+8eD432BMv1gb7mZ+Q9obbpJTvyuYcZk+8KN6wLAO8Zb0Z8jyS2KSqF5eswmVigzxjjDHGrBfLfbp2ru5b4vqMMcYYY8wCLPUgb7pbmhhjjDHGmBW21IO8pTv3a4wxxhhjFmxtzQY3xhhjjDFLwk7XGmOMMcZsQPMa5InIn1/ms1ctOiNjjDHGGLNo8z2Sd23jGxGJgBfV36tq/1IkZYwxxhhjFmdOgzwRebuIDAHPE5FS9jUEnAc+vawZGmOMMcaYeZvTIE9V3wV0AB9R1fbsq01Vu1T17cubojHGGGOMma85n65VVQ88fxlzMcYYY4wxS2S+c/K+KiLftyyZGGOMMcaYJRPPM/6HgFtF5AQwQrhliqrq85Y8M2OMMcYYs2DzHeTdtCxZGGOMMcaYJTWvQZ6qngAQkS1AcVkyMsYYY4wxizbfmyH/axE5ChwD/gk4Dnx2GfIyxhhjjDGLMN8LL34HeAnwhKruIzzh4stLnpUxxhhjjFmU+Q7yaqraBzgRcar6eeB7lyEvY4wxxhizCPO98GJARFqB+4GPich5IFn6tIwxxhhjzGLM90je64Ax4C3A54DvAP9qqZMyxhhjjDGLM9+ra0cARKQd+NtlycgYY4wxxizavAZ5InIrcIhwNM+T3QwZeMbSp2aMMcYYYxZqvqdr/1/gWlXdq6rPUNV9qrrgAZ6IbBaRvxeRo9nrphniXiMiR0TkSRF5W8Pn7xCR0yLyL9nXaxeaizHGGGPMRjLfQd53gNElbP9twH2quh+4L3s/iYhEwHsJT9t4LvDTIvLchpD/rqrfm33dvYS5GWOMMcasW/O9uvbtwFdE5J+BSv1DVf31Bbb/OuCV2fKfAV8A3jol5sXAk6r6XQAR+YtsvW8vsE1jjDHGmA1vvkfybgf+Efgq8FDD10JtVdWzANnrlmlidgBPNbw/lX1W96si8k0R+dBMp3sBROQWETksIod7e3sXkbIxxhhjzNo33yN5iar+xnxWEJF/AK6apug351rFNJ9p9vo+wlM4NHv9Q+AXpqtEVe8A7gA4cOCAThdjjDHGGLNRzHeQ93kRuYVw+5TG07X9M62gqj8yU5mInBORbap6VkS2AeenCTsF7Gp4vxM4k9V9rqGuPwH+bq4bYowxxhizkYnq3A9qicixhrfjKy70ClsR+QOgT1XfnV01u1lV/9OUmBh4gvCc3NPAg8DNqvpofYCYxb0FuF5Vf2oO7fYCJxaS8zx0AxeWuY0rifXn8rG+XT7Wt8vL+nd5WL8un4X27R5V7ZnvSvMd5P0k8DlVLYnIbwMvBH5HVb8+34az+rqATwC7gZPAG1S1X0S2Ax9Q1ddmca8F/gcQAR9S1d/NPv9zwrNzFTgO3Fof9K02ETmsqgdWO4+Nwvpz+VjfLh/r2+Vl/bs8rF+Xz0r37XxP1/6Wqn5CRF4GvJowB+59wPULaVxV+whH6KZ+fgZ4bcP7u4FLbo+iqj+7kHaNMcYYYza6+V5dm2avB4H3q+qngfzSpmSMMcYYYxZrvoO80yJyO/CTwN0iUlhAHVeKO1Y7gQ3G+nP5WN8uH+vb5WX9uzysX5fPivbtfOfkNQOvAb6lqkezK2K/R1XvXa4EjTHGGGPM/M1rkGeMMcYYY9YHO9VqjDHGGLMB2SDPGGOMMWYDskGeMcYYY8wGZIM8Y4wxxpgNyAZ5xhhjjDEb0KoM8kSkuBrtGmOMMcZcKVblFioi8iRwDvgicD/wZVUdXPFEjDHGGGM2qFW7T56I7AZeDvwA4Tm1A6r6vSvRdnd3t+7du3clmjLGGGPWNU/CICcQBCG6pFxJUZQO9uCIVyHDje+hhx66oKo9811vVfaGiOwkDO5eDjwfeBT40kq1v3fvXg4fPrxSzRljjDHr1rf4KN/mTtrYOWPMEKe4lpu5jjeuYGZXDhE5sZD1VmvIfRJ4EPivqvrmVcrBGGOMMZfxHe6iia5ZY5ro4knuskHeGrNaV9e+APgIcLOIPCAiHxGRX1ylXIwxxhgzgzKDRMx+vWREgQo2tX6tWZUjear6DRH5DvAdwinbnwFeAXxwNfIxxhhjzPSKdJBSJqZpxpiUCgU6VjArMxerdQuVw8ADwI8BjwOvUNW9q5GLMcYYY2b2TA4yRt+sMWP08SwOrlBGZq5Wa07eTarau0ptG7P0Bnvhi38Jn/8InD8ZPtuyB374Z+Flb4COeV8UZYwxa8IzuJEjfJIx+inQll1BK+PlFUpEFNjHDauXpJnWap2u7RWRg8C1MHGiX1UPrUY+xizK8Ufgjv8AJx+BKIJiS/i89yTceQi+8kn4pffA3utWN09jjFmAZrq4htfzVd6NJ6WdPbSzg5QqY/QRUeAVHKIF+2d2rVmt07XvB/4f4NcI/w68AdizGrkYsyiDvfDht8Lpx8Pgrqkdojh8NbdDsRVOHYE/fWuINcaYdabGGDmaeA4/zVUcoMYwJZ7Ck3AtN3MTt7MF+yd2LVqt07Xfr6rPE5Fvquo7ReQPgb9epVyMmR/voTwSlh+8Gy6cBCQM7KaKc5BUoe80PHwvvNJuL2CMWV9yNNHKdgY5wVW8kBfxK+zgxciq3aDDzNVqDfLK2euoiGwH+oB9s60gIrsIt125CvDAHar6HhHZDPwfYC9wHPhJVb24THmbK533cORr8PhXgRTu/z9w4TTgw2Cu0AT5KVeg5YswMghfu8sGecaYdUdw7ODF9HAtAHlabIC3TqzWXvpbEekE/gD4OmFwdudl1kmA/6iqzwFeAvw7EXku8DbgPlXdD9yXvTdmeVTHoP8sRA5SD6UL4FOoPx0wTUD95HVcFD4fsXtIGWPWH8VTY4w8LRRoswHeOrLiR/JExBEGZQPAJ0Xk74Ciqs76F1BVzwJns+UhEXkM2AG8DnhlFvZnwBeAty5P9uaKl2+Czdvg/Ikw0GvvhuEBwsFlwilbmfIL0Kfh8xa7h5QxZn1RPGc4zBh9NNHFdg7YIG8dWfFBnqr6bA7eS7P3FaAynzpEZC/hqRn/DGzNBoCo6lkR2TLDOrcAtwDs3r17oembK51zcM2LYU84bUFbN3zqv4WBXrH50gEeQLUMbZvhxXYPKWPM+lJjjDH6aGELI5wfP6Jn1ofVGo7fKyI/ISJy+dDJRKQV+CTwH1S1NNf1VPUOVT2gqgd6euwyb7MIzkFzW/j6vtdC925AwynZqZJaOH3btQNeYPeQMsasLzmaaKKLEc7TRBe5WZ56Ydae1brw4jeAFiARkTLhNiqqqu2zrSQiOcIA72OqWr8a95yIbMuO4m0Dzi9n4sZM0tEDb/q9hvvklSHfHMoqo+FU7Z5r4ed/z26IbIxZdwTHdg6M30bFTtWuL6t1M+S22cpF5FpVfXTKZ0J4tu1jqvpHDUWfAX4OeHf2+uklTteY2e29Dt56J3z5r+Af/xzOHQ//tmzZG5548QOvtwGeMWbdEpydol2nRFUvH7XCROTrqvrCKZ+9DPgi8C3GZ7nznwnz8j4B7AZOAm9Q1f7Z6j9w4IAePnx4yfM2xhhjjFlqIvKQqh6Y73qrdbr2ci6Zq6eqX5ru88yrljcdY4wxxpj1Za2eXF97hxeNMcaYK4jiqTKC4i8fbNaktXokzxhjjDGrxO6PtzGs1T1WXe0EjDHGmCtV4/3xxuijxthqp2QWYEUHeSLyvLnEqepLljsXYy7LexgdCl9JMrHs7dSFMWZjs/vjbQwrfbr2YRE5RnhO7Z2q+u0Vbt+YufEejnwNjjwAXsONj0eHQQSe/ZLw1Au3Vg+EG2PM4tj98TaGld5r3wR+NGv3MyLyDRF5W/aYMmPWjuoYXDwDEoGmcOZo9gxaB/1nQ7kxxmxg9fvj2QBv/VrpPaeq+oiq/qaqPgv4JWAL8EUR+coK52LMzPJNsGl7GOBJBNv3g4sg9bB5Wyg3xhhj1rCVPl076T53qvo14Gsi8h+BV6xwLsbMzLlwSnbPteF9vmni6F2xxU7VGmOMWfNWepD3B9N9qOGxG/+0wrkYMzvnwly8unjWp/EZY4wxa8qKHo5Q1Y+vZHvGGGOMMVeqlb6FygER+byIfFREdonI34vIoIg8KCIvWMlcjDHGGGM2spWeWPS/gd8H7gK+Atyuqh3A27IyY4wxxqwye6TZxrDSg7ycqn5WVe8kTMX7K8LCfUBxhXMxxhhjzBT1R5qd5H7OcNgGeuvYSg/yyiJyg4i8AVAR+VEAEflBIF3hXIwxxhgzhT3SbONY6atr30w4XeuBG4FfFpE/BU4Dt6xwLsYYY4yZwh5ptnGs6CBPVb9BGNzV/fvsyxhjjDFrgD3SbONYM3tORN602jkYY4wxV7L6BReAPdJsA1hLe++dsxWKyIdE5LyIPNLw2ebsNixHs9dNy5+mMcYYs/HYBRcbz4qerhWRb85UBGy9zOp/Cvwv4CMNn70NuE9V3y0ib8vev3WxeZoJvSj3kHAXKYMoHQgHibiRmJ7JT6kzxhizjjVecDHCeWqMkadltdMyi7DSF15sJczJuzjlcyHcN29Gqnq/iOyd8vHrgFdmy38GfAEb5C2ZR0i5jSoVoAvYCZRR7iThr0k4RJ7riFY5S2OMMUvBLrjYeFZ6kPd3QKuq/svUAhH5wgLq26qqZwFU9ayIbJkpUERuIbuCd/fu3Qto6srSi3IbVQow6YhdE2GwV8rKb6doR/SMMWYDsAsuNp6VfnbtL6rql2You7m+vBxz61T1DlU9oKoHenp6lrr6DeceEipA+wwDuHaECnAvyYrmZYwxZvkIzi642EDW6l68b45x50RkG0D2en75Urqy3EVK12ViurI4Y4wxxqw9a3WQN9fzf58Bfi5b/jng08uTzpVnEL3sc+YKWZwxxhhj1p61Osi7ZOQgIncCDwDXiMgpEflF4N3Aq0XkKPDq7L1ZAh0I5cvEVLI4Y4wx65PiqTBEhSE8CVVG7NYpG8hKX3ixYKr60zMUvWpFE7lCHCTiThJ2zhLTB9xsV9caY8y6pHhO8zVO8wCKUqSDVq6iiR62c8Dm5W0Aa3UP2uGhVXYjMQXCVbTTKaEUgBvWz/8J8+M9lEfCqzHGbEA1xhjmDEKEkjLAMfJ0MEYfNcZWOz2zBFZlkCcif36Zz+zo3CrrQThEngpwCmUMxWevp1AqwCHyG/P2Kd7D0cPwrfvDqw30jDEbUI4mWtmOkiJEdLKPKoN2j7wNZLUOw1zb+EZEIuBF9feq2r/iGZlLXEfE7RS5N3viRV/2xIubibhhIz/xojoGpT7YtAUung/vi3bXd2PMxiI4dvBierI/yTmaSKjYPfI2kJV+rNnbgf8MNIlIqf4xUAXuWMlczNz0ILyRHG8kt9qprJx8E7R3hQFee1d4b4wx65TiqTIChIFc/VRsfVDXeF+8/EadgnOFWtG9qarvEpHfAz6gqr+wkm0bM2fOwf4D4Qhevim8N8aYdWjqxRV5WqkxnF1o0WkXWmxwK75HVdUDz1/pdo2ZF+fCKVob4Blj1rGpF1f08ySeFMUzwHftQosNbrX+gn1VRL5vldo2xhhjrghTL67YzLNwRAiOTp5hF1pscKt18v2HgFtF5AQwQpiXp6r6vFXKxxhjjNlwpru4YuqcPLvQYuNarUHeTavUrjHGGLOhTb3QYurFFQXaxmPtQouNbVX2rqqeABCRLXDZR6QaY4wxZg4aL7QAJU87rWyj2S6uuCKt1s2Q/3X2vNljwD8Bx4HPrkYuxhhjzEbReKGFJ2WA4xTs4oor1moN6X8HeAnwhKruIzzh4surlIsxxhizITReaOGI6GQvFbu44oq1Wifja6raJyJORJyqfj67f54xxhhjFsieYmEardYgb0BEWoH7gY+JyHkgWaVcjDHGmGWneGqMEVOglj0RXHCXXPF6ubLLxeVpsYsrlttgLzx0Dxy+C0YGoaUDDhyEF90IHT2rnd04UdWVb1SkBSgTbp3yRqAD+Jiq9q1E+wcOHNDDhw+vRFPGGGMMiucMhxmll2HOUmGAEqdoYxdF2qlQQhDytFFhMCvbSYH28SdUFGinwjCCkqOVKqVL4kDYwUvZwYvtyN1yOf4IfPQ2SCrh0Ze5ItTK4ZnncQF+5hDsvW5JmxSRh1T1wHzXW62ra0cARKQd+NvVyMGsb73DcM8RuOsxGCxDRxEOPgduvAZ6Wlc+n8fo5R18krv4S0Y5jQBN7OQgb+Ad/DjPYe38Zwcw3AuP3QPfvgvGBqGpA557EJ5zI7TOMdXefrjnS3DX/TA4BB1tcPAVcOPLoGfz8uZvNqZeytzD09zFGQap0UGOg2znRq6iZ53fiKHGGGP0UaCDUzyQnUYdI6X+Cf0/AAAgAElEQVRMP+dopgcF+jlKgQ4SyqRU6OdJWrLfH/0cpZktKEo/T1KcJi6iwDBnqDFGnpbV3eiNaLA3DPDyBehs+GWZb4LunTBaCuW/dvuaOKK3WkfybgUOAWOAZ+JmyM9YifbtSN769sjTcNvnoJJCVzMUYygn0DcKhQgOvQauu2rl8vkUj/CLvJ0BjiDEOIooiqeMkNLBNXyQd/FjLO1/dgt15hG4+zZIK9DSBXERkjKM9EFUgNcegu2XSfWRo3Db/4RKDbo6oZiHchX6BqCQg0O/DtftX5ntMRvDIwxwG49QwdNFniKOMp4+qhRwHOI6rqNztdNcMDuSt0H840fh/jvDgG4mF07BK2+GV75xyZpd6JG81RrkHQVeqqoXlqi+1wDvASLgA6r67tnibZC3fvUOw61/BYUY2qf5x75UhkoCt79+ZY7oPUYvP8gv08e3cRRw5CaVe2p4KnTxXP6J9636Eb3hXviLWyFXgGL7peXlEtQq8FO3z3xEr7cfbv0vUMhD+zR9XBqGShVuf6cd0TNz00uZWzlMAUf7lJ8hgBI1Knhu58C6PqK3knPybIC3TH7/pyHOhSN3M6mOQZLAf/r4kjW7rk7XAt8BRpeiIhGJgPcCrwZOAQ+KyGdU9dtLUf9CDI6O8OCJ41zds5WHnzoBDl6wYw8Pnz7BSLVMS74Y3k8pw8MLdk0T11jWuM5TJxhJKrTkC9PWN10dr7zm2XQ0t8w73xft2MNjp4+Dh2t37eXRKcujlSrNufylZU8dBwfX7gjLY7UqTTPEzVjWsPy/v9DP+bNFetog19HFcKmPyEFLexejpTClc4ge3v8PJ/n+fb0U4wL7d+zj6OljAOPL5WqFYn76MoD9u57J0b6zlKtlivki+3t2cLT3dChrWP5gz5eR3hId1U7ifJGLPcNs7g0Tnvt7htjcu5mkWiHKj/Cenrt4S+8PAbCtp4ezvb2TlqvVGvl8btqyucZVqwn5fDxjHWf+sYcL9FLoSMgR0+x7qBRCWbHaQ7m7l+Gn4et397D7pl5qSY1cnGNLew/nSyHuvi9upTIyQkdTjVolIs61gy+FbybXTluxxEjJcfeXmjn4I+dJfY3I5WjP91CqhjqmW54tbinqmByXELl43mULi1uK7Vp8Tkvfh7Nt1/z65q78GEm1n24KaL4VqY4QIfh8C1F1hC6fctrVuCd/kjdUw38WufxmatV+1CeIi8ffN5Ytddxi60iqA+Eq13yBQpVwHssBecJ7GpZnK7tsnIfqSLbcFAYd3oNzE+/nWraYuGL2t6Y6FuaqLWdbK7VdpT7o2Q1JLZRFMYgwSa4AgytyicFlrdaRvBcAHwb+GajUP1fVX19AXS8F3qGqN2bv357V9a6Z1lnOI3mDoyO85f0fJ+kdpfniEC3lMuAYzudpKZfZMfJ/2Xv36EjSq8DzdyMiMzJTqUeVpHqoVVUydJUb3IANRds8Buxl8YNm8CwYD7SH3WHguL0LM8ywywH2LD2HZhhYWJY34/aAwQbcMGs8MNCNbdbYGJuHXe31m+6qNl3VpVY9VKrSM58RcfePL1IZmUo9SlVKqVT3d45ORsa9ce/97vdF6Cri+0JzzJRHWSkUKNdrKB7LYZ7BehOIWQ4LDNRqTCzPMTMwxnIhZLDRAJSlME+5XkfwWQoDypU6R1euMTM0xkoYMlhvAMJSPk+5Vmdi5Roz5VGWwwLlRgPxYPZLJviVH/ie1UJvo3jL9VYco9TyAUP1ZRShmg8YaDRJgEoux0CjwfjKPFeLw1QKIQPNJopQyQUMNN2JUMnnKdYaHK5c50pxZFUPYCWXp1Srcbg6z5XSQaqFPKVGo6cv/8YA9zQvMZMbZylXZliXQZRKLk85qYAIS1qiHFWYCp/h+tAA1VyeYjMGlGo+R1hrMLayxFx5kFo+JGzW8RCquYBiMyYhoV4oEtYajC4vMlceploKKdbrKFAL8xTSXM+HMeVqxOGlFa6USyyXAsp1RYCl0GOwGnNoaYUrgyWqxQJH6jkQaOQL5Oo1FIgKRYKVKsPLcywOHiQqFvHrNQShERYI61WSDfQ8oB4WyVcqDC3PsVgeo1kqkqtXEaBZKOLX3PZSvUgYVRmrX2OueJBmboBC+td/3S9SSGpoojS8AoODFYbmbrA0dhAdLOIt1VCB859/EeVkkcN6havBYaLBAoPFeTyUmj9IGC+TxMKKDvGyB58iN3eVaHQcBgaRygoKSHEAqq3tMkllify1WRpj43gDg1BZcpM4ioPoyhK5VVkZrbhfXlIagJa9UhldWSSYmyUaHUcGhqC6BApaKiPpMZQG0MoywbWrRGOHkFIZWrLiAFp1snhsHIpDUHM2KJah6vS0NAArywRzqY2MjOIAVJdXY09W0naNj+EVh1KZQrFMsrK8Vpbal4o716RYRqtLLt7RQ8jAIFpZ7mh/d+ytdmllBcn4avWDDAy1bWT7oTSAtto1eghJcy2reeu019Ff1SVUJY1pCf/aLM3RQ3ilTG5K5Y4+0Uz7P1k6QKlScWO5WGS4WsEHpFSitLLCyLVLzI4foVIa4H+oXAcUKQ2TrMwj1y6i4/fgFQ+i1YU0NyNoZR5EkeJIW29sEq90YK0egpSG0JUbMPcCjN6DdOgNk1SuI7MvoGPH8AZG0MrCqn2tLLqYisNo5QbMTaOjk3gDB9qy0gFKlYAcA4yUXoKsLMHcNIweg4EhqC6CChQHobLgZGOTUBqCSmvcpNuoy2dl0T0azOohaa5Te+vpteytLMLcRRidhNKw6yNRd6u/uuTsFctu8m7LRnE4Ha8KpcF2TMVBqM7D7IwrhF78clc8Lc3B9cuwcmNtu1rxVjJ+W+3vlZus35WudrXiLZXdite5adeugeGuGJc629+ykfVVKq/N07Vp+OyH3Hw8xJ3nw4dh5FBnobeH7uTt1v3cx4C/BP4OeCrzsx3uAS5mvk+n+zoQkTeLyBkROTOb3tXYCT5+4TzJ9ToyXGRifo4ACDRh6sZlikmTQlKnEEecuH4ZDyHQhBddv4JPgodw4vplCnHk9JKIqRuX8VXxFU7MXSEAfBJOXL9KSERJGxSjiBPXr+Ir+KpM3bhMIYlWfU3duEygCYnn4c1W+PiF81uKtx1Hk+MLs4iCT8zkwnU8wE+UyYXrhFFMSRuE6mS+6qpeK6bJ+TkKSURRmxRI2noKkwvXKJA4WRIxOT/XYSPrq6gNSlQpaJMXVS/ie4lrc+WF1F7Ci2rThElMqBG5SDm8sIigeCiHb8yTi2MKcUwujjm8cINA3YnQ0vOBQ9dvkEtiCnFELoo4cm0ujQOOzl13/ZAkTF1bphgnFOOYMEqYmqsQAJIoU9eWCeOYYhwRxgnH5haRRPESZfjaZUQVP1GGZy+TixuEcQM/bjJ87Qp+ongkjMxdQpKN9SRRRuYuEWiTMI4IkgbDc5cQVQRlePaS01NlvHqJkAZhEpFPIsZWLuGp4qOMLl/C0wQP5cDKFfxmRBA18OsRAxevIKp4qkxWnycvdUKq5GlwpHYeP47xUA7V/xGfiJzf5MjyebxmHb/ehGYD/4XnEU3wNEEuXcxsP4806njNBtJs4M1cAE0gTvBmLiDNVNao4808jyTuOGacPUmcHs0Gfr3ZthEnoMmqX2kd02zgN5p4zXrbRhqT16zjN1y8cun5VRuteCW1R6rnNesdMrl0sSN2rxV73dmTVZmLo1u2aj9tl1x6PhNT3eWju/2t+NJ2SbMBM887ndRXth+yNrL9wEzarnrT5TzNdStvWXut/vLSmIizNlr5rXXkprtPsrk5PHOJID1/D1+aIZckqCrjM8+Ta9bINxsU63UOzkxDEoHG6MzT0KwizTrUaySXngaNnewFty1xTDLzzKqeNHvrkUTozNNoswaNGtqtN/M01GvOV7PqvmftpzEll55Gm1WknvrKyHjhGXIaEukyzZnPu3kR9apblTlzLs1NDJfOOVmj5j5nnnX748TpJS4mZp7trZfEnfbW02vZa9bSOOrOd0t26Wza/mhtTJfOuf0awwuZ7Utn3VyNZsXFMXsebsy4Iuvqc73j0Ci1kfG7bm7OZtp/Dhp156tZ64z3ha54O2I8t7b9zYqz1Z23rF7L19F73eILVcCDesX1cZbFOXjgwR2rM26G3SryIlX9YVX9bVV9R+tnm7akx741tydV9W2qelpVT4+P79y8qK8+MYV3MEQXqsyMjBIBkXicP3CEqpej5oXU/IALB4+QoETi8dzBw8R4JCgXDh6h5gdOzws4f+AIsQixwIXRw0RAjMeFg4eoE1CRPNUg4MLBQ8QCsQjnDxyh5gWrvs4fOEIkHl6SkIyX+OoTU1uKtx1HjueHx1GBGJ/p4YMkQOwJ08MHqQc+FclTFyeLRVb1WjFNj4xS8wKqkqOG19YTmB4eo4bnZF7A9Mhoh42sr6rkqVCkJjmeKx4jTjzX5tI9qT2P5wqT1D2fugQ0A+HK8BCKkCBcOTBC0/ep+T5N3+fK8AEicU88WnoxcPXgAZqeT80PaAYBl8dG0zjg0uhB1w+ex/mxMlXfo+r71AOP86MlIkA94fxYmbrvU/UD6r7HxdEh1BMST1gYO4KKEHvCwvgRmn6eup8n9nMsjB0m9oQEj/nRo6i3sZ56wvzoUSLJUfcDIi/PwuhRVARFWBg/6vREmC0epU6euhfQ8AKuDRwlESFGmCsfJRGPBOHGwGHiXEAU5InDgJVjh1EREhGmi8dpaEidIg3yXC5MEfs+CcLV8IuICWjGOS6Xp0hyIXGYg1ye+J7jqHgk4qFHj2W2j6P5kCSXR3N5kokTIB74HsnECTSXyvIhycRx1HPHMeHsqef0yOWJw1zbhu+BeKt+tXVMLk+cz5HkwraNNKYkFxLnXbx69PiqjVa8mtoj1UtyYYdMjx7riD1pxR46e7oqc3F0y1btp+3So8czMYUuH93tb8WXtktzeZg47nRSX9l+yNrI9gMTabvCnMt5mutW3rL2Wv2VpDHhZ2208lvoyE13n2Rzc2XiKFF6/l45OkHT8xARZieO08wVaOTyVMOQ6xOT4AUgPjJxH+SKaC6EsIB39D4Q38nucdvq+3gTL17V01xvPbwAmbgPyRUgX0C69Sbug7DgfOWK7nvWfhqTd/Q+JFdEw9RXRsY9L6YpdQIpk5v4UvdILyy6129MnExz48PRk06WL7jPiXvdft9zep6LiYl7e+t5fqe99fRa9nKFNI7Q+W7Jjp5K2x+sjenoSbdffLgns330lJusmyu5OMan4MCEu6t26EW945AgtZHxu25uTmXaf9LdUcuVnF423nu64u2I8eTa9udKzlZ33rJ6LV8n7nd3DZt1IIGw5Pq4RWXRPZp+2at3rM64GXZrTt4HReTNuNenZB/XXt+GrWngWOb7JDBza+Ftn+HSAL/4loc2nON27xbn5N27xTl5JzeYk9dto3tO3lbj3cqcvC/b4py8+zeYk9dT1jUn74/PuTl5g8Oj/GNmTt61dE7eDcb5hqPPc1+f5uQ9Pvtumo3ldebkDRI16uTzg7x+/J/zbXtgTt7fPzFL9VDEMAGluGtOXn6W5Vl45bduPCfvPU+scGisiXhuTt5cZk4eySKXr3p8xz8t8Sqbk2dz8rZgYyVf5T2NsxwhhHyZS11z8iSdk/cd+RczsS/m5A3s3jw5m5N363qveD383k9A1HCPjVF31zD7nrw98PoU2L05ec9lvq4GsJ1XqIhIAJzF/f/bF4CPAw+p6uc2OGYWuHCzvm6SMeC2rB42gFY+vVzA8NQJVBM0TtZoie8h4rFw/gJJc+f/i0oxKPDiwovxmwEquvbGsrqfJBfzTO0ZqlFtx2PaAI9cMMTUCUUTxeVP0YIgNQDB9wTxFjl/IWG9/OUC8mkf0KMPSPugcf4C69q4a7DrwFbI+QFT425Mxbp2TPnixtT52Qs04+yYsvzuDJbXTcgJwXCOoeGAYV/wYyVeiFhYaLLY1A3/g9d2c3tCVW+6ctytO3k/CrxXVRdF5CeArwR+ajuGVDUSkR8E3od7hcrbNyrw0mN2vMQWkTPbmSRp9MbyuXNYbncOy+3OYvndGSyvO0e/c7tbc/L+j7TA+3rcq09+B/hP2zWmqk+q6ilV/WJV/enbFaRhGIZhGMadym4VeXH6+SDwVlX9EyC/S7EYhmEYhmHsO3aryHtBRB4D3gg8KSLhLsayU7xttwPYZ1g+dw7L7c5hud1ZLL87g+V15+hrbndr4UUJeC3wGVU9JyJHgS9T1ff3PRjDMAzDMIx9yK4UeYZhGIZhGMbOst8ekRqGYRiGYRhYkWcYhmEYhrEvsSLPMAzDMAxjH2JFnmEYhmEYxj7EijzDMAzDMIx9SN+KPBEp9MuXYRiGYRjG3U7fXqEiIs8CV4C/Bj4MfFRVF/ri3DAMwzAM4y6jr+/JE5HjwD8Bvg74FmBeVV+6gf7bgW8Frqrq/T3kAvxyaqsC/EtV/cRmcYyNjenU1NS22mAYdzSRwoUKCODLWnmsoMCJEgQ95IZhGEbfeeqpp66p6vjNHhfsRDC9EJFJXHH3T4CvAD4HfGSTw34H+DXgnevIXwecTH9eDvyn9HNDpqamOHPmzJbi3haJQjWG0HOfAEXfbScKnrS/Z2W3W6+XbCBw++6UeM3X7fX1nhl41zRMFEDEFXLSNR6mq/DQJLzpGIZhGMbuIyIXtnNc34o84Hng48B/VNW3bOUAVf2wiExtoPJ64J3qbkf+nYiMiMhRVb10y9Ful0ThzA2YrcOlGixG7s7IUACLTZiuwWTBfV+KU5kPy+n2oO+Oma65X8QjOViO2rKl2N2FKfuwkOodKzobq7IA5psw0/KVczZ9gVccgAcOtgu9Lceb2pA0jo54Y7hYdXrDQacs28aF2BUQ3XrlTJsnizCc2rxZX+vZa+V3Td662lXO2Gi1/2Ktd7zLmXZ1x5Rt82ocmT5v+erITVdfLsY9YtpErzuH2fHQauMfTEOMGxvlAA6HcCjsLPRG8/DEZSvyDMMw7nD6ubr2Zbg7cg+JyN+KyDtF5Ptu0eY9wMXM9+l03xpE5M0ickZEzszOzt6i2w2oxjDXgOE8nK+4x1+JwrllqCdQiaGWwNkV98s2UTi34h6jxbj9tVSvkbjj4lR2bsVtR+kxdYVqBLU4leHk55bdsS1f55bbd3gu1dp3dTaLtxVHPWMjG0druxa7OOpdbWnpJeraVY+dbj3J6Ck8u+L21WKnc3Zle77Ws/dsNjfZvGXbpW29bPuzvlpxPJtpV6+YOmTZfkjtt/qv5etsGm+2L3vFtK4ea9vcPR5aetcarthrPZatxC6eLKEHC80dO0UMwzCM/tC3Ik9VPwW8A/ht4C+BbwR+4hbN9po01HOSoaq+TVVPq+rp8fGbfqy9dYq+uxOy0ICpkrt75gmcLLtfniUfCh6cGgCfVDbgHpv5uP2FVC/vueP8VHZywG0H6TGhQDGAgp/KcPKTZXdsy9fJsvOTKBwtuBi3Em8rjjBjIxtHa7vguzjCrra09Dxx7Qp9pxt6GT2BewfcvoLvdE4NbM/XevbuzeYmm7dsu6Stl21/1lcrjnsz7eoVU4cs2w+p/Vb/tXydSuPN9mWvmNbVY22bu8dDS28s784QX9zZU/LXzr2rJzCc27FTxDAMw+gP/ZyTdwYIgb/BzcX7BlXd1jPmDNNA9pnSJDBzizZvDU/g9IG9O8ete07eXo/XfN1eX6P5zefkzTXcnDzDMAzjjqafr1AZV9Wbfk6azsn7s3VW1z4I/CBude3LgV9R1Qc2s3n69Gnd0YUXhrFXma3Dw590Bf1Qj7t1i013J++xl8J42P/4DMMwjDWIyFOqevpmj+vbnTxVnU2LspcAhcz+R9c7RkQeB14JjInINPDvgVx63FuBJ3EF3rO4V6h8707Fbxj7gvEQHv0SeOQf3CKY0bwr+OqJu4MXek5uBZ5hGMYdTz8f174VKAGvAn4TeAPwsY2OUdXv3kSuwA/crhgN467g/iF3p+79V90q2rm6m4P30CS8+pAVeIZhGPuEfr5C5WtV9ctF5NOq+pMi8gvAe/ro3zCMFuOhe0WKvSbFMAxj39LPV6jU0s+KiEwATeBFffRvGIZhGIZx19DPO3l/KiIjwM8Dn8C9yOE/99G/YRiGYRjGXUNfijwR8YAPqOo88Eci8mdAQVUX+uHfMAzDMAzjbqMvj2tVNQF+IfO9bgWeYRiGYRjGztHPOXnvF5HvEOl+86phGIZhGIZxu+nnnLwfBgaASERquH+qpKo61McYDMMwDMMw7gr6+TLkwY3kIvISVf1cv+IxDMMwDMPYz/Tzce1m/O5uB2AYhmEYhrFf2EtFns3VMwzDMAzDuE3spSJPdzsAwzAMwzCM/cJeKvIMwzAMwzCM28ReKvIaux2AYRiGYRjGfqEvRZ6IHE//pRkiMiUibxCR+7M6qvqKfsRiGIZhGIZxN7DjRZ6I/BjwV8Dficj3A+8FXgf8oYj88E77NwzDMAzDuBvpx3vyvgf4UqAEnAe+SFVnRWQA+Hvg/+5DDIZhGIZhGHcV/SjyYlWtikgDqAJzAKq6Yv/hzDAMwzAMY2foR5H3CRF5F+5fmn0AeIeIvBf474DP98G/YRiGYRjGXUc/irzvB74T9x68dwMvB74beAb49T74NwzDMAzDuOvY8SJPVSPg8cyuj6Y/hmEYhmEYxg7Rj9W1Xy0iHxSR3xORYyLyFyIyLyIfF5GX7bR/wzAMwzCMu5F+vCfv14GfA54A/gZ4TFVHgB8DfqMP/g3DMAzDMO46+lHk5VT1z1X1cUBV9d24jQ8Ahc0OFpHXisgzIvJs+s69bvkrRWRBRD6Z/jxy+5tgGIZhGIZxZ9GPhRc1EXk1MAyoiPwzVf1jEflGIN7oQBHxcXcCvxmYBj4uIv9NVbtX5f61qn7rTgRvGIZhGIZxJ9KPIu8tuMe1CfAa4H8Wkd8BXgDevMmxDwDPquo/AojIHwCvx169YhiGYRiGsSH9WF37KVxx1+KH0p+tcA9wMfN9GvcKlm6+RkQ+BcwA/5uqfq5bQUTeTFpUHj9+fIvuDcMwDMMw7kz6MSdvXUTkezdT6bFPu75/Ajihql8B/Crwx70MqerbVPW0qp4eHx+/+WANwzAMwzDuIHa1yAN+chP5NHAs830Sd7duFVVdVNXldPtJICciY7c1SsMwDMMwjDuMHX9cKyKfXk8EHN7k8I8DJ0XkRbg5fN8FPNRl/whwRVVVRB7AFa5ztxa1YRiGYRjGnU0/Fl4cxs3Ju9G1X3DvzVsXVY1E5AeB9wE+8HZV/ZyIvCWVvxV4A24xRwRUge9S1e5HuoZhGIZhGHcV/Sjy/gwoq+onuwUi8qHNDk4fwT7Zte+tme1fA37t1sM0DMMwDMPYP/Rjde33bSBbffQqIgdUtftun2EYhmEYhrENdnvhRZYP7HYAhmEYhmEY+4W9VOT1el2KYRiGYRiGsQ32UpFniyUMwzAMwzBuE3upyDMMwzAMwzBuE3upyLPHtYZhGIZhGLeJvhV5IvK7m+z7pn7FYhiGYRiGsd/p5528l2S/iIgPfFXru6pe72MshmEYhmEY+5odL/JE5MdFZAn4chFZTH+WgKvAn+y0f8MwDMMwjLuRHS/yVPVngGHgnao6lP4Mquqoqv74Tvs3DMMwDMO4G+nL41pVTYCv6IcvwzAMwzAMo79z8v5ORL66j/4MwzAMwzDuWnb8f9dmeBXwsIhcAFZwr0xRVf3yPsZgGIZhGIZxV9DPIu91ffRlGIZhGIZxV9O3Ik9VLwCIyCGg0C+/hmEYhmEYdyP9fBnyt4nIOeA54K+A88Cf98u/YRiGYRjG3UQ/F178FPAK4Kyqvgj3Hy4+2kf/hmEYhmEYdw39LPKaqjoHeCLiqeoHgZf20b9hGIZhGMZdQz8XXsyLSBn4MPD7InIViPro3zAMwzAM466hn3fyXg9UgX8HvBf4AvBP++jfMAzDMAzjrqGfq2tXAERkCPjTfvk1DMMwDMO4G+lbkSciDwOP4u7mJaQvQwa+qF8xGNsgnoXq+6D2BOgCyDAUHoTia8Af37WwZrnG+/gAT/AXLLDIMEM8yDfzGr6Jccb6Hs/K5Y9S/f1/zdDjnyS4qABEx4TFh15K8aFfZeDI1/U9pp1mr/WBsQ+ozsLF98GFJ6CxAPlhOPEgHHsNFHfvemOkzM7C+94HTzwBCwswPAwPPgiveQ2M3139M8si7+MzPMEnWaDCMCUe5KW8hi9jnKHdDm8VUdX+OHKvT/kaVb12k8e9FvhlwAd+U1V/tksuqfxbgArwL1X1ExvZPH36tJ45c+Zmwrg7aXwWFh4B6uCN4l5vWINkDghh+FHI39/3sD7L53mEn6FOk1EOUCCkRp05bhCS41F+nPv50r7FM////RKFf/Mj5J+O0AAo4v58qYLE0LwvoPorP8/Iy/5t32LaafZaHxj7gOufhY89AkkdwlHwCxDXoD4HXggPPAoH+3+9MVI++1l45BGo12F0FAoFqNVgbg7CEB59FO6/O/rns1zkEf6IOhGjlCmQo0aTOZYJCXiU7+B+jt1WnyLylKqevunj+ljkvRf4dlWt3MQxPnAW+GZgGvg48N2q+vmMzrcA/xpX5L0c+GVVfflGdq3I2wLxLFx/GCQEr8dfJckiaB0OPtbXO3qzXONh/h0hIUMMrpEvskSdOo/xi325m7Ry+aN4b3oV+c83IQRyXQpNoA6Nl+RIfu+D++KO3l7rA2MfUJ2Fv3oY/BDyPa43jUWI6/CNj9kdvd1gdhYeftgVc0M9+mdx0RV/jz227+/ozbLIw7ydkBxDFNfIF6lSp8lj/Kvbekdvu0VeP1fX/jjwNyLy90C9tVNV/80GxzwAPKuq/wggIn+AW8Dx+YzO64F3qqtW/05ERkTkqKpeuu0t2CL1+nUuzH2II+Uv58KNjwBw4sDXc+HGR6g3FgnzQ6vfs9b338QAACAASURBVLLt6DWai+RzvfV62Th15NsIw4ObxztwjRdqz1FPyoRensn8YaYb7ibsZH6c6cYccJ175n+XC8uTNJpL5HODnBh9gAtzH3M2emw3msvkc+WblrW231b5VS6XnmMiGadeVFaq1/HxKBUPUq3eQIHlYpPfu/6LfOvilxHkyhweeylXrn0SYHU7ai4R5AZ7ym5GL/fEfyCuJyRHfYIQDl6PuT7qA3BwLub6hE9UAy9K8P/4x1l44687WfkY15cvdmxHcYPAz/eUbVXvdtjYTO/Ph55iIbnB4fgAKxJRyA0QNxsABLmQQlOY8xZ5Ut7Lty99LUncxPNzFMuHqC5fBei5ncQRnh/ctKxTb6u+Gnh+/qZl29O7He269Zi2npvb0V83mZvLf0m1uQS5EsWkSTVpgngUvYBqEpN4ObzGdYoXnqR6j/sPmcXiENXqIkkS4XnB6ves7Hbr3bW+nnyCarxCMlLGo05RA6p5cbImVMfL8MIixfe/l+qb3kBCjIfPACEAVRqEBFRpkKB4CEXyVGkAQpHcmm2n56Xfm4Cmx3Rut3ytb0N6HJfV67axsd4f8xQ1moxQoklEgI/gcgEwRJFpqryfz/Amdv+P+n7eyfsY8BHgM7g5eQCo6js2OOYNwGtV9fvT798DvFxVfzCj82fAz6rqR9LvHwB+VFXXvVW3k3fy6vXrPPLMq3musMKFkrAYul/45VrEYiHgWrHEoWqFwVrEUsHV2G47B+jq/qvFEmPVCkO1iOVCDk1ly4UAEMq1JguFgLmBEuMrbXuSyrK+yulxArx2xudn7vvL1UJvvXiHcg0W/TxXgxLjUYXhepPFIIcqDEcRi7k03ihisZJndrDA6FKV4WrMcjFw8VZjlop+Gm/EcsHn2mCRgz30Foo+1weLjC1VKdfitJ3KYLrd0vv8PQENv0ixWSNIIjx8vEQo1yOavg8iEDVp5AKOLOa4Z6HB8ErM4oCPKAxWY+aLHi8cKDB5o85wNWax6IPAUKqHwlAa0/SBkHtu1BipJiwVfbRL7+CVKjfKeaaPlLjnUoUDiw3mR/IAHFhocGMkz8XDJY5drjBSb7B4/DgAI4088/kGqjDSzHMjV2O6mHCsGjDSzDOfayACQ40ci/nmlvTmcw1eGIi5Z8XnQDNkId9AgKFGW2+4kedGrp7qeRxoFljIuwKt5Quc3vVcjRdKMccqOUaa+VW9T40v0sSj5ucoxzEjdY9i5CMCuURoCjQlwRf450+PczGsMFkvMawDLHsrKMJgUurYXpQVpsMVJusDDOkAS14FVWVIB9bIlr0KCpSTIsteFQHKSYkFWWE6rHCsXlo9TqTTVzkpsigVpgsVJmslhrSU2lDKyYCzkcqGu2wseZV19bJt6RX7RH2AkXXalZW17LvtTLs6fHW3f21MrXZlfXX2g7PRMzeZHHb7Wq9PWnnqzu9wV7uy+exs/0WWcx6qwmAkLOdyaCIMxiUW/YTpMGaipowksCyn3fVASiwmK0zLDSbiYUaCQZY0HQ9SZEnTXHsDq3qTeoAhb4AlrbhrZarX2l6Il1f1hv1yh735aIkZf6HDRsv+slZdPls2vBsc04MZmYu3tV2m6GLK6C0mK4h4bRtyg0k9yJBXYpkaqkmHrzIFFpMK03K9Q68Vb8veenote4vJChflOpNJu80t2Wr7//oMCyVhelSYnPcZ9gdYPjTo2lyF5ZKgjYjBlZj5t/wLZrjOcQ7ycu7FQ5hjmcvMc4MK09xgkoMMUWSZ1D5FlqihwBAhC1SZ5gbHOMAQJRbTdg0SutgRBimwuGrvAEMUWaLuxhpFFjK+hlNfCgxSYIn6qq/F1FfLxmLGxnImppbeh/gHQnKrhd1hhjjEUEehV6VBRMK7+F9uQ1Xh2O6dvH6+QiVS1R9W1d9W1Xe0fjY5Rnrs665Kt6KDiLxZRM6IyJnZ2dmtxnzTXJj7EBcKFQ41QqYHC6jnkYjH8yMDNAOfyPep+z4XRsqo72QXRgZIAg/1PS6MlKn7Tq/h+zw/MkDsCeo5G+p5JIE7pun7RJ5HI/C5kMpiT3h+ZICG3/b1/MgAiXgECM+Ua1yY+9Cm8Z4PB6mTxoHP+VKZBA8Vj/PFAWIExeNCcYB6ziMKPJo5j+dHSyS+kHjtbfWEiwdLNAKPKAhoBBk9X7hwsNghu3iwhHrOxoXRErEIiSfpMQF4PokI1VwRUSHxYKFQQEVQoBI6e41cQC3wODseEosQeR5nDxep5X0ny3ucO1wk9l3eWnqx7/bX8qmNvM/Zw0UizyOWVM9zek9PDVEr+NTzAbVSwNNfPEgSCHHg8fQXD1Et+NRzAbWCz9P3DBILxMDT5RViIPaUpwcr1PyEeg5qfuxknhILPFOuuGO2oFfzE2q+UPfh6UFnPwKeaenh9td9qPlCLdBVvQ5faXw1T6nnhKoXt+MF5nMBqpB4HokqCzkh9kAVFnxQUXyF2ZxS82KquYS61+RcfpYIJUY7ts/mZ6l7TSo5pSZOL0ZJJOFcfpaaF1HJqbMRzhKTEKM8m3N6MU6v7jWp5hJqXsy5/CyJJGt8PZubpe43qQUJdb+5aiNK9dqyiLNdNjr0vLaN7nZ1xh5TySkNGpzNysJZatJcI4syNmISl5tWTF4rN53tXxt7u12rvrr6oWVjTW5Wcxj1zHXL3mp/deXa5TfqircdYzaf2dycKwsR4s6vASHGIxblXLFJzVMqgUfD9zlXarqYNOFcMkNNm1SIaEjC2XiGmISIhHPJJWJNSFQ5l1xa1atpk3PxTBpTWy/G2asTUfNi6kSpXsveDA1J2jaSmQ77Lk8J5+IZ6sRUV/UuZeK9RNTKZ3KJOpHTI+JcMkOimrGRxiFNnk1tdPt6NrlEXZpr9OK0XS176+mt5oYmVSLqxKttTlQ78zkWUQ89agWPeh7OjceuLZ5w7rASeRDnfc4eqNGgSYUGMcp5rjHDPMOUeI5rrl00qNPgWS63xwaXnV8SznIlzU3D5YbLJGm7znKlfc5zmToRlVTPyZJVe21fTc5xuX2OpnoJCee4Qi21USfiHFdSX5rG19Zr+VqhToU6iuIBlbSgyxKSY4Etz0zbUfpZ5H0wLbSOisjB1s8mx0xDx+zFSWBmGzqo6ttU9bSqnh7fwTkDJ0ZfyYlaiav5OpNLNSRJ8DTh+PwKuSgmiGPCOObE/DISO9mJ+RW8KEHihBPzy4Sx08vHMcfnV/ATRRJnQ5IEL3LH5OKYIEnIRzEnUpmfKMfnV8jHbV/H51fw1A3+Fy8XODH6yk3jnaovEZLGQcxUZRmPBNGEqeoKPoqQcKK6QthMCKKEXDPh+FwFL1a8pL0tiXLseoV8lBBEEfkooxcrJ65XO2THrleQxNk4MVfBV8VLND0mgiTGU6XYrKKieAkM12qIKgKU6s5evhlRiBJOzdbxVQmShFNXqhQasZM1Ek5eqeLHLm8tPT92+wuN1EYj5tSVKkGS4Guqlzi9+84vUqjFhI2IQiXivi8s4UWKHyXc94VFirWYsBlRqMXc98ISvroVRPctD+ADfiLct1SiEHuETSjEvpMlgq/w4uWSO2YLeoXYoxArYQz3LTn7AfDilh5ufxhDIVYKkazqdfhK4yskQthUionfjhcYaUaIgJckeCIMNxU/cTdRh2MQFWKB8aZQSHyKTY8wyXGyMU6A4CMd26ca44RJjlJTKKjT8xE89TjZGKeQBJSa4mzUx/Hx8BHubTo9H6cXJjmKTY9C4nOyMY6n3hpf9zbHCeMchcgjjHOrNoJUry0LONVlo0Mvadvobldn7D6lppAnz6msrD5OQXNrZEHGho/nctOKKWnlprP9a2Nvt2vVV1c/tGysyc1qDoOeuW7ZW+2vrly7/AZd8bZjzOYzm5uTy0qAuvNrRfFJ8FU4Wc1RSIRSlJCPY05Wci4m8TjpTVCQHCUC8upxyp/AxyPA46R3FF88PBFOekdX9QqS46Q/kcbU1vNx9kICColPSJDqtexNkFevbcOb6LDv8uRx0p8gxKe4qnc0E+9RglY+vaOEBE6PgJPeBJ5IxkYah+a4N7XR7ete7yih5tbo+Wm7WvbW01vNDTmKBIT4q232RDrzeS0grCcUaglhA07O+q4tiXLyihAk4DdiTt0okCdHiTw+whRjTDDCAhVexJhrF3lC8tzLkfbY4Ijzi8cpDqe5ybvccAQvbdcpDrfPeY4QElBK9ZzMW7XX9pXjJEfa52iq5+FxksMUUhshASc5nPqSNL62XsvXACElQgQhAUrkCbpKqTpNhintWJ1xM/Tzce1zma+rTlV13VeoiEiAW3jxTcALuIUXD6nq5zI6DwI/SHvhxa+o6gMbxbLTCy/2zZy86u9vMCdvFrjOPaWH+jon71crv8oTpb9nIhmnXDzYc07e9WKT112/v39z8h4/Q1Kh95y80XRO3hD43/W1ePtkTt67kndzOD6AJ/6aOXlRs84L3lXeJN9pc/JsTt7W5+R94Q+hdJiil187J08TvNoVil/8kM3J2w1f73mC6nv+gGTCFUFr5uTlgBdmKH77d+37OXn/hY/zXznDEYYB1szJA5jmOg/xNbd1Tt6dsLr2jcB7VXVRRH4C+ErgpzZ73Um6evaXcDcR3q6qPy0ibwFQ1bemr1D5NeC1uFeofO9G8/FSm7PAhVtu1MaMATf1upi9RC4gmDrGCVWSOO66Fw34Pp4I3vmLXGhGffn3dC6fOQKmvBNoev9+TWB4CB7nkws0dz6ugQIDJwNe7NcQ9Vg7eUBBEogL6LmIZ1ZqrOx0TNvg5sbqHuuDPc4dfR3oFzmfYGo0vd4kPa43Xnq9meNCM+4YU5bfnaEjrzkIpuCEQhLTo3/AE/DOw4Xmfv93pTkJmCqn1z/tcf2T9Pq3fIGm9srFdsfsCVW96ceQ/SzyPq2qXy4iXw/8R+AXgP99s9ed3KmIyJntVN1GbyyfO4flduew3O4slt+dwfK6c/Q7t/2ckxennw8Cb1XVPwHyffRvGIZhGIZx19DPIu8FEXkMeCPwpIiEffZvGIZhGIZx19DPIuuNwPtw772bBw4CP9JH//3mbbsdwD7D8rlzWG53DsvtzmL53RksrztHX3Pbtzl5hmEYhmEYRv+wx6WGYRiGYRj7ECvyDMMwDMMw9iFW5BmGYRiGYexDrMgzDMMwDMPYh1iRZxiGYRiGsQ+xIs8wDMMwDGMfsitFnogUdsOvYRiGYRjG3cKuvCdPRJ4FrgB/DXwY+KiqLvTL/9jYmE5NTfXLnWHsK5IIrl8AEfD8HvIYVOHgCfCC/sdnGIax33jqqaeuqer4zR63K5dgVb1XRI4D/wT4VuA3RGReVV/aD/9TU1OcOXOmH672JUkC9RW3nStCs+r2eV77e1a2Vb3bYcN87byvT78HnnoXDE24Qs8P3GeW+Wn4qofg9Jt6jyHDMAxj64jIhe0ctytFnohMAl+HK/K+Avgc8JHdiMW4OZIELnwMzv8tJArFQaguwvxFGJ6E0jDUlt2dnOIgVJdAFApDUF1wv/xHJqGY0SuUobbYJVsEFWejw94GvtazVxhyMhEnqyzAwjSMHHOyeuqrUE71FMKh1EYPX1m9wtDamDravAg3puHAJBSHUlnqK6uXjak41G5/t68N9bpy2PIVltttLA5u0g+ZPFXn4cbM2tg/8ThoDAszzvbgYRg81FnoDYzC556wIs8wDGM32a2FF88D/xb4c1X9GlV9UFV/ZpdiMW6CZtX9chcfiOHqOYhqbn9cd9+TqC0jdoXh1bMQ1aFZc59Zvdlnu2Rn3TEtG0m8NV/r2Zt9No0j1YvrzkazBrPn2r5W9RK3v9ntK16r1yumrKxZg7jiPldl0drctGKKUr1eMW2m153fVpuzbbx6buN+yMbXbPSOfeUaIKCRKw4bldRGhiB0xaRhGIaxe+xWkfcy4J3AQyLytyLyThH5vl2KxbgJckUYnnB3cvDh0EkICm6/H7rvXtCW4bvHfYdOuV/8uYL7zOqN39slO+WOadnw/K35Ws/e+L1pHKmeHzobuQKMn2z7WtXz3P5cty9/rV6vmLKyXAH8kvtclQVrc9OKKUj1esW0mV53flttzrbx0MmN+yEbXy7fO/aBMUBB0se0+dLauXdR3d0lNAzDMHaP3ZqT9ykR+QLwBdwj238BfAPwW7sRj7F1PA9OPABHXuK+32nzyczXresNHNx8Tt7KnJuTZxiGYeweu7W69gwQAn+Dm4v3YVXdcFKhiLwdt0jjqqren+47CPwhMAWcB96oqjc283/69Gm1hReGsT2WZ+EPHoZc6OYJdlNbhGYdvusxKN/0WjDDMAyjGxF5SlVP3/Rxu1Tkjavq7E0e8w3AMvDOTJH3c8B1Vf1ZEfkx4ICq/uhmtna6yEsSd9fDD/feXZ1wIH10dwvx9rJh3F3MfBaefMTNERwYdY9+o7q7g+eH8C2PwsT9ux2lYRjG/mC7Rd5uPa6dFZEHgZcAhcz+Rzc45sMiMtW1+/XAK9PtdwAfAjYt8naSJIHnz7i7HYuX3OrK7GrNNSsttb36M7uC9MY0HJiAwgjUu1er9lglml39GJahllkZ2VpBig8veoV73Noq0rYa70Y2jLuPifvdnbqn3+9W0S7PuTl4X/UQ3Pdqu4NnGIaxF9itV6i8FSgBrwJ+E3gD8LFtmDqsqpcAVPWSiBzawOebgTcDHD9+fBuutkaz6u5mFIfhub91r9RIcCscCyPt1YpLl91rJxLcysXyOAhuBeWqXgOWzkH5kCu8rp6DwfH2McXhzErLK6lMUl/DGV9XnP1cDhYuuWPCgS3E27JRT+NYx4Zxd1Ied69IsdekGIZh7E12617M16rq/wjcUNWfBL4GOLaTDlX1bap6WlVPj4/v3G2GXNE9vqouwOgUnas1w8xqxVN0rMJcXSV6KrMiM5+ujMysLs2ufuxeaZldTZldGdlaXRknMHzUHbOleFtxhBvbMAzDMAxj77Fb/3Soln5WRGQCmANetA07V0TkaHoX7yhw9bZFuE08D46fvnPm5G0nXpuTZxiGYRh7n90q8v5UREaAnwc+ASjwn7dh578B/xPws+nnn9y2CG8Bz2s/ygwG2/uz2xvJbrder++3Gq9hGIZhGHubvhd5IuIBH1DVeeCPROTPgIKqbvh+fBF5HLfIYkxEpoF/jyvu/kv6IuXnge/c0eANwzAMwzDuEPpe5KlqIiK/gJuHh6rWgfoWjvvudUTfdBvDMwzDMAzD2Bfs1uPa94vIdwDv0d14UZ9hGIZhGDdFxCxLvI9FniBmAZ9hhniQQV5DgL03aS+yW0XeDwMDQCQiNdzbQ1RVe7w/3zAMwzCM3aTGZ7nMIyh1fEbJMYlSY57HWeA9HOFRCtgb0Pcau7JGUlUHVdVT1byqDqXfVws8EXnJbsRlGIZhGEYnEbNc5hGEkByTeBQRBI8iOSYRQi7zCBE39Y+sjD6wW3fyNuN3ga/c7SC2i5KgVIEw/QShiFJFSRC81e9Z2e3W6yXzGEC6avu9HK/5Ml/my3yZr931tcCTJKwQMIzSRAhwD+AcPkM0mGaJ93MAezv6XmKvFnmyucreREmocIaIWZpcImERRfEZImaRJtPpX0JDKEsomm4vA4owSJLqBUwQMELCcqo3iLIECEKZmAUipslzDGGIhCXcX1dlIuaJmCHH5KpvwafEKxjgAVqF3lbjbX0HSeNox6ss0uBi2q5htCveVhuTNN4go+dslElY7JAl2/K1nr1WfjvzluNYJjdO1rLXbv9FAibxu+Jt+x1ajamtl23z2j5vxZGwBJncNDv6cm1Mm+mtzWF7PGT7bqN+6MxTewx5mfG1mazV/o1z2Nmv3XnqzE3nOEzWjbf7fOg1hjYeG9256bSxtK5eZ1vWxh4wgc9Ij9x0yrJjQzb1Vd4g9qEtjuW1uWlmzo3uXK93LdtoHHa2KzsOu9vfGVPrvN7oetgrv93n13rX3q3lemnD86FlP8nE3n19STa4RvU+l7vP0c6+TLYwllvnfO8+2WxsZH8vufbP8zgQ02QGjzIBh8lxiOyv6oBRFnnCirw9xl59pe0duxhDqRIzh88wTc4DMZDQ4BxKHaWCUqPB2Q4ZREBMg7MoNZQK0EhlcSprbUfpdh33V1ith14j4+scpH+pRVyi9dfZ5vHWUhv1VRudccQZvWpGL+rSS9L21kmordFr8iyayqCe6m7HV297TZ7tyk133pLV49a2v5rG1I5ja3rdOaz16L92bnRLMW2ml81h93hIOtq/Xj90xtc9hjaTxWvG9drcrO2vdru67a03Drv7YeN+XdvGdvu727z5OOzU23jMt2N353Kv3HTLojX5XOuru/3t68HaMb/xWO513vS6pqy1191fa8dhZ7zRmj5Ze51rj4e153zv6+HmOdzqeRjTu183G/NJj3xudF3eKNdJzzi6c9/ZJ7304jV90vt6uNn1q53PmGsoghIDmuYhIosQkrDhm9CMXWCvFnl3LEIRn1FiFsgxBfiAR56TCCFCCaFAnlMdMndT1SfPKYQCQgnIpzI/lbW2g3Q7RCim9rr18hlfJwEPJSHgKEJxi/EWUhvhqo3OOPyMXjGjF3TpeWl7QzwKa/Ry3IukMghT3e346m0vx71duenOm7d63Nr2F9OY2nFsTa87h4Ue/dfOjWwpps30sjnsHg9eR/vX64fO+LrH0GYyf824Xpubtf3Vble3vfXGYXc/bNyva9vYbn93mzcfh516G4/5duzuXO6Vm25ZsCafa311t799PVg75jcey73Om17XlLX2uvtr7TjsjDdY0ydrr3Pt8bD2nO99Pdw8h1s9D3169+tmY97rkc+Nrssb5drrGUd37jv7pJeev6ZPel8PN7t+tfPpM4agCD7uzmYJ6XoQqNTxGMbYW+zVx7WN3Q5guwgeJU6nfxntvTlu3XPy9nq85st8mS/zZb5215fPQRZ4FwETuEfAnXPyACLmOMBDGHsL2Y3X1InIaeAY7t71OVV9up/+T58+rWfOnOmnS8MwDMO4I4mYZZqHEUJ81r7pLGYRpc4kj9n78nYIEXlKVU/f7HF9vZMnIt8I/AIwD3wV8FHggIg0ge9R1Yv9jMcwDMMwjI0JGOcIj3KZR2gwTcAoQohSJ2IOj5AjPGoF3h6k33Pyfgl4nar+97hXpDRV9euAnwZ+q8+xGIZhGIaxBQrczySPcYCHUCIiZlAiDvAQkzxmL0Leo/R7Tp6vqq23JT4PnABQ1b8QkV/qcyyGYRiGYWyRgHEO8CZ7TcodRL+LvDMi8lvAB4DXAx8CEJESbhmPYRiGYRiGcRvo9+Pah4GngK8F/l/gR9L9Crymz7EYhmEYhmHsW/p6J09Vm8Bv9NhfBS70MxbDMAzDMIz9TF/v5InIaRH5oIj8nogcE5G/EJEFEfm4iLysn7EYhmEYhmHsZ/r9uPY3gJ8DngD+BnhMVYeBH6PHHT7DMAzDMAxje/R74UVOVf8cQET+T1V9N4CqfkBE/q8+x2IYhmHsIWaX4X3PwBP/AAs1GC7Ag18Cr3kxjJd3OzrDuPPo9528moi8WkS+E1AR+Wew+pLkuM+xGIZhGHuEz16Gh98Nj38Scj5MDrvPxz/p9n/28m5HaBh3Hv0u8t4C/K/Av8Ktpn2ViMzjHtX+0HaNish5EfmMiHxSROz/lRmGYdxBzC7DI++FMHDFXTEHIu5zctjtf+S9Ts8wjK3T79W1n6LzVSk/xC0Ud128SlWv3SZbt0SSQDWC0HOfAMXAbScJeF77e1Z2u/V6yQbybt+dEq/5Ml/ma//7evIfYKUJI0VIFGKFQFyhBzBUgOkFeP9ZeNNXYhjGFun3nLx1EZHvVdXf3u04bpUkgTMXYXYFLi3CYh1U3UVqseYuVJPD7vtSRrZcdy8LHAzbehND7qK3KsvDUgMEKIduzsr0PBwbadtryearMLPY9rVYA9+DVxyHB463C72biXex5i66g2FnTIt1uDgPkyMwHMJyozPelr1WvN165dTGqmw7vjJ63faGUr01eTvgZC1f5Xxqr9X+Oly80Y4pa79Dr9YZ01KjRw5HYKir/7L9392X68W0kV5HDsNOX6v5zMNCff1+yOapewz1HF9pfrOybLvWy+FgvrPvNjo3umXd/bDe+dBrDG04NrpzU+jMW69zb7Mx3/LVOpd7tSsry+awp6/12t8Ve6tdN9MP2dy0crjRmF8d1+E2xmHYmZv/59OuuFusQd6HUg4KORgfaBd6oyU3V8+KPMPYOv1+XLsRP3kLxyrwfhF5SkTe3EtBRN4sImdE5Mzs7GwvldtCNYK5CgwX4fwN9xdponDuGtSbUGlCrQlnr3XKovSv17PXnLzShEbkZHHifs6lx0QK52advWoEtdh9z+o1oravc9ecH09cIdf6a3qzeFtx1DM2VuNInO65a85/NYJ6lGlLJt4kbVc9glq3nsKzc52ys9vxNbu+vWfn3P6WvdW8Zdulbb1s+6tRu/2tONboxV16WVlXP2T7L046c7Pal+vEtJled36z4yGbz436YTW+HmMoTtaRZcZe97juyGGmj7rHfPbcaPnqzuF6/bCmX+OuNm9hbLTateE4zJ576+RwTeyZc7lXbrpl2bHRymfPfu3RX73atdlY7nUeVlM7G4357v7acBxmYsz2Sbb911bcH6GKK3DzgTs+0va1KgxcwWsYxtbp93vyPr3Oz2eAw7dg+utU9SuB1wE/ICLf0K2gqm9T1dOqenp8fPwWXG1MMXB/cS5UYeoA+OKKq5NjEObaf6GeGuuUBeK+nxpz8lLOXehOjrmLn++l2+J0T447e8UACr77ntXLB21fJ8ecn0Th6JA7ZivxtuIIMzZW4/Cc7skx578YuIvwalsy8Xppu8IACt16AveOdspObcfX+Pr27h11+1v2VvOWbZe09bLtLwbt9rfiWKPnd+llZV39kO0/3+vMzWpfrhPTZnrd+c2Oh2w+gP03jwAAIABJREFUN+qH1fh6jCHfW0eWGXvd47ojh5k+6h7z2XOj5as7h+v1w5p+9bvavIWx0WrXhuMwe+6tk8M1sWfO5V656ZZlx0Yrnz37tUd/9WrXZmO513lYTO1sNOa7+2vDcZiJMdsn2faPDbjiT3B3+RqROz6Q9rWqHrm7kIZhbJ1+P649jJuTd6Nrv+Dem7ctVHUm/bwqIv8VeAD48Hbt3QqeB6eP7d05bt1z8vZ6vObLfJmv/e/rUBne9Um4Z8gVgd1z8sA9cXjIXplvGDeFqOrmWrfLmchvAb+tqh/pIXuXqj60DZsDgKeqS+n2XwCPqup71zvm9OnTeuaMLcI1DMPYC8wuu9ekhIGb89fNYs3dyXvsDfa+POPuRESeUtXTN3tcXx/Xqur39SrwUtlqgSciB27C7GHgIyLyKeBjwBMbFXiGYRjG3mK8DI++1hVy0wtQbbrpJdWm+16PnNwKPMO4Ofr9uPb/Z+/NgxvJzgPP33uZABIgCIA3i8U61dWlvlutch+WPJblsVp2Sz4kjdeWdjwh7461sTu7c8Sux45Ya+yemLE9jnWM5whbCnvskceSD9mSLctSS+ORL10ldqvvq7qryCoWi0USIO4EEpnv7R8PIEHwKFZ1kaiufr8IRB7v+vLLl8DD+7783m75S2BX71Bprc8C9+ytOBaLxWLZS+6cNDN1X3rZvEWbLxsfvA++Bd51qx3gWSzXwo06yBNXzmKxWCyWm4mxtAmRYsOkWCzXhxsphEo3++coaLFYLBaLxXITcqMO8iwWi8VisVgsr4EbdZBnzbUWi8VisVgsr4G+DPKEEL97hXPfu4/iWCwWi8Visdx09Gsm747uAyGEA7y1c6y1Luy7RBaLxWKxWCw3Efu9rNnPCiEqwN1CiHL7UwGWgD/dT1ksFovFYrFYbmb2OxjyLwJZ4BNa60z7M6i1HtFa/+x+ymKxWCwWi8VyM7Pv5lqttcIGL7ZYLBaLxWLZU/rlk/cNIcR39Klti8VisVgslpuefq148T3AR4QQc0ANEzJFa63v7pM8FovFYrFYLDcV/RrkfX+f2rVYLBaLxWJ5Q9CXQZ7Weg5ACDEOeP2QwWKxWCwWi+Vmpl/BkH9QCHEGOAf8NTALfKEfslgsFovFYrHcjPTrxYt/DTwIvKy1PoZZ4eKrfZLFYrFYLBaL5aajX4O8ltY6D0ghhNRafwW4t0+yWCwWi8Visdx09OvFi6IQIg38DfB7QoglIOyTLBaLxWKxWCw3Hf2ayfshwAf+OfBF4FXgvX2SxWKxWCwWi+Wmo19v19YAhBAZ4HP9kMGyO5ar8NhL8PkXoNSArAeP3AYPn4SxdB/lCuCxAny+AKUQsi48MgwPD8NYfP/lOXvxJWY++Uu8+Y/+kgMXWgAsHorzwj94J6c++DMcP3hy/4XaAb2sUY9F8PkIXQKRBR5xkA87iDHRb/EsFovlxma5Ao89C59/Gko+ZJPwyN3w8J0wNthv6dYQWuv9b1SIjwCPYmbzFOvBkI/vR/unTp3SMzMz+9HU65pnF+GjX4RmBCMp8FxohJCvQ8KBR98Nd072Qa4afPQcNDWMuOBJaCjIh5AQ8OgxuHNg/+T56rc+R/z//mcce7FG5Aoa7aBAng9OpJm9bYDmr/x73vYdN8ZktX5WEX20BU1gBBPEqAHkgQQ4j8YQd/Zrkt9isVhucJ69CB/9LDRDGBkALwaNFuRrkHDh0R+GOw9e1yaFEI9rrU9ddbk+DfLOAA9prVeuU33vBn4NcIDf1Fr/0k757SDvyixX4SOfNv01s0Ukw3LD9O+PfWB/Z/SWA/jIy5CQkNliHrocQlPBx27dnxm9sxdf4vKH38ux5yq0EhDGNw6O3EATa2rO3THIxG9/ru8zenpZE30kgASIzOYZO13W0ATnY3E7o2exWCy9LFfgI59o/zgmN6eX/faP409c1xm9ax3k9evFi1eB+vWoSAjhAP8Z+D5gHviWEOLPtNbPX4/6r4VGvcns7AqTExnOz64i0Bw6OsyF2QLNRkjCczl0dHhTmkZw+OjQpnzdad1lzs+uEjQDEonYlvVtVcett03gpRJXlPer5TGqy028eEAt5pIYHEDVqmjATafxqlWKVYc/fTzOO4eXaTVauF6ciUPDLF3IoxFMHBrm8oUCAs34oREuXygQNgPcxNb5tkvr3v/kizUaZZeMJ4iyA0SlGi4akUujS1XSQCk5yGe+XeC9iSoyHiM7PUJpPg9ostOjlObzqGYLmXDbxyuA2CLfCqoZIhOdOjbne/yL/4n7zoFoDqJjQMtH6hQAStZRpCDQjJ53eOJPPs7R9z9qlD4cg0Jrw74OI4TrbJm223xXqkN9QUAxgEmFrkmEF4eonc+NIbwWOq/Rf6ER79OgFEgJyRj47Xyd/Z3Srnc+25Zty7b1+mtroP1b47eM+edmuK7PPmFm7XIpaEXgShBdf4gzSZhfhS89Bx96kH7Tr5m8twC/DXwTYzQCQGv9f11DXQ8BP6+1frh9/LPtun5xuzJ7OZPXqDf57Uf/B/XLTSgExIIIhUbFHGTQYrDeopqKE8ZdYq0IBaiYgxNGCA2tmIMbhKTrAZVUDBWP4YQRaGjFJPGWRgtNGHNwGiEZP6CaitNKuLhBBAIid2NbUdxFtiKkFIjbsvzjR793baC3nbznUkOkg4DJaoml9CBNN8ZQq4EGGrEYqbBFpAW1RIxTq5dIVAP8AZco5RBrgEYTeoJYU4OG0JPEfEWi2qKedoiSJh9C00qA4ytS1YhmOkYrKXEbCk27XFOhNYSe4JWhEcbzZVaG0jS9ONlaAzfS6IRgoBoAsDqYIF0L+MBfv0RtxKU5KIjXNEIImilJvNwivaqpDbsEg5J4NQIhaA5g8mkI0g7ximKgEFIdEgSZGIm6QmtNMCBI1ACtGbh8iXQ5hiRJKOqEsQAcD7RGiQCp4zhhCi3qrI5LDtz3sOkoOQ+KDdBALgGrPswX4VAOckkoNo0TQ8Yz06a7yVesw8USHMzA0IBxohQCMjEzsBPAk3GI6tAowUAOcgOIVMPI5HoQNtAtjXA8nH8QwsIqTA9DJgGVdlvphJF9YRWmh8yXWqWxnlZpGnkzCSP7hXa+bBKq7WsZ9Lr22/nme9rqrmO+05YH1XZa2oOqb64xnTDXO79qdJNJQrkt71pbGtJJKNeNDjuyr9XhGd+a+VWYzkI2tbGOSne+rjrWrkvDYFsXvbJPDZl7XmmAFu20JswXNqZ16u/WZ+e6pnPttra6/m6ZcpBJret3033o0mH3fUh767o51K3rtt566+u+Jx09pT0o12C+tK6bStc96tZn2d+omzWZEhv3e/W0U1qvDtfkbefb1F930nVqo+xrfX6LPtrR02ByvQ8d6umv2+q6u7926tiij3b6Tfc92bYvJ9brSyfXdb3WN7rq631GK11p3fos+Rt1s+lZ1ma/o6fDw/DAcZAC8lVYLMNqbftnr/t+lfwtdLNV39jie6NzX3f9jHb3DW+j3rbq83/1kpnF6wzsJjIwPrhxoOcHECr45E+9luHEBq51Jq9fjjcfA/4H8A3g8a7PtXAQuNB1PN8+twEhxE8JIWaEEDPLy8vX2NSVmZ1dobEcEM/GSFcaCDSOhkzZJxYJElGIozSZko/Q4GgYLPk42vSrTMnHUZpEFBKLTDmpQbbT1uor1nGVJh62TH3F+nq+sk8sYr2tsqkfIWhdajA7u3JFeafLq3hhi2QYkggjpktFHKWJacXBYhEZKeJacWC1hIw0sUjjKkEqHyAxA6VUPkAqc13JfICING6kcCPW8kkFA/kWbgRupBCRNnnb+ki19532vhtpks0QrxVx4HKReKjQQnPwgpFPKs3RuQIojRMJ3ECTnW8hI4GMIHchwG0J3FDjhJrshQCpBUKxnk8LshcCnFDjhhq3JchdCJARyEiQnW8hFEgtSNYHMZPJEi1ixMIcAomQDm6YRRBDIhA6RrqUgkibz4uX2/sKXlyCRgTNlnF6fHHZnI80vLS4+3yNyPzDbEbr9YcRvLSynq+0BDoC1TLb4mWTpjWULputo9HLyxC0oB5AI4AzSyZfqMz+WloLzrTriBS8vARRZP7xnlky6X5gZD5z2ZSPoo37Ly+Z9N62uuuod+pop0URvNKlmzPtOvzA6ObMkikfqa62tCnTDE07zWC9jrCdr9mWodmWq7uODfm66uiuv6OLXtmDVls3uist2JzWqb9bn2syhVtf/yaZwnbadvehq47u+9DRjd+5r0vr/aa3vmb3/e/oqVNHtFE3azL26LNbN2eWNvaHzvX36unMDmlb6bBX12v9Ndqs6zO9uu6RfavnoVN/1HX/1/phr7zb6bq7v2o29a/Ofe5ta7u+HKmN9b2yuPG6euvrfUY7ab367NVNuMXz9XKXniIFs3lYKJlB4bmVnZ+9jj5fXtpGN1v0je7vjc53z1U/o8HG75duvW3V52tNs9XaDGDr7QFdN4mYGajeAPRrkBdqrf+F1vq3tdb/tfO5xrq2chzaND2ptf641vqU1vrU2NjYNTZ1ZY4eHcUbixOUWlQHPTSCSEA5k6TlaJqOSyQF5WwSLSASUMkmiYT581TOJomkoOm4tBxTTglQ7bS1+nIpQikI3JipL5daz5dJ0nJYbytj6kdrYgc8jh4dvaK885khGm4M33Vpug7z2RyRFLSE5GIuh3IkgZBcGsqiHEHLEYRSUx+JoxBogdmX5rr8kTjaEYSOJHRYy6ck1EZihA6EjkQ7wuRt66Pe3o/a+6Ej8BMujZjDpYkcgSsRWnDxkJFPScHskWGQgsjRhHFBaTqGcjTKgeKhOGFME7qCyBWUDsVRQqMl6/mEpnQoTuQKQlcQxjTFQ3GUA8rRlKZjaAlKaPxUBa0jQCF0i5ZbRKPQKiJ0S2haKDRatKhm6+AI83nzRHtfwpvHwXPMF4PnwpvHzHlHwMnJ3efzHOMAnHDW63cdODm6ni87DsIBGTPb3IRJEwKyE2YbCcTYGMRjkIqDF4cT4yafK83+WloMTrTrcCTcOg6OY0wfJ8ZNejJuZD4xYco7zsb9W8dNem9b3XWkOnW00xwHbunSzYl2Hcm40c2JcVPekV1tCVMm4Zp2EvH1Otx2vkRbhkRbru46NuTrqqO7/o4uemWPx9q6EV1p8c1pnfq79bkmk7v19W+SyW2nbXcfuurovg8d3SQ793V8vd/01pfovv8dPXXqcDbqZk3GHn126+bE+Mb+0Ln+Xj2d2CFtKx326nqtvzqbdX2iV9c9sm/1PHTqd7ru/1o/7JV3O11391fBpv7Vuc+9bW3Xlx25sb5bJjdeV299vc9oJ61Xn726cbd4vm7t0pMj4egITGXNrNqx0Z2fvY4+bx3fRjdb9I3u743Od89VP6Pxjd8v3Xrbqs8PJMxWCFDa7Ls9Q6lmy8we3gD0yyfvK0KIn8KET+k21xauoa554FDX8TSw8NrEu3a8VIIPf/SdrxufvO3k/Wp5jD+ZaTIaD5Btn7zlLp+8qFplserw/u+Ic+8++uT91os1PlN2GfcEbnaAuR6fPICLyUF+OCrQ+lcP4MVjTHT50E20ffJazRZewm0fG1+7zflWaDVDkokYk10+eZNd+b7yxV/lvl9/jPQqNNMQORt98qRKkahoKiMOT/0fP8D73v/dRvH99Mn7PeOTh9zsk0fYgosa8SEPrE+ebcu2Zdt6LfluRp+8PzwAn/k2TGZNWq9PHpi3bD/4ADcC/fLJO9d1uCbAtYRQEUK4wMuY9W8vAt8CPqi1fm6HMsvA3NW2dZWMAtfl7eG+IGMu2aNH0FqhI7UpXTgSISSl2TlUaz9WKzH6dGIuB9pyqS3kkm25Ls3OEe29XLGEO3AsJk96TSXM7OPGh11oY7puJKQ+11IvtZphba9l2okYMfcoR44olFIoBaDQnkQ0ACRSSqScZW6uxb7c15ud1/f3wI2P1e/eYPW6DTGke5T0EQ0qQm/6DXIQUoCcpTrXQm31HXqtuj2itb5qM2S/ZvL+JfBFrXVZCPFzwH3Av76WirTWoRDinwCPYUKo/JedBnjtMntnr20jhJi5FidJy9ZYfe4dVrd7h9Xt3mL1uzdYve4d+63bfvnk/b/tAd7bMaFPfgf49WutTGv9F1rrW7XWb9Ja/5vrJaTFYrFYLBbL65V+DfKi9vYR4De01n8K9GExKovFYrFYLJabk34N8i4KIT4G/CjwF0KIRB9l2Ss+3m8BbjKsPvcOq9u9w+p2b7H63RusXveOfdVtv168SAHvBp7RWp8RQhwA7tJaf2nfhbFYLBaLxWK5CenLIM9isVgsFovFsrfcbCZSi8VisVgsFgt2kGexWCwWi8VyU2IHeRaLxWKxWCw3IXaQZ7FYLBaLxXITYgd5FovFYrFYLDchfRnkCSG8frRrsVgsFovF8kahX3HyXgEuA38L/A3wVa11ad8FsVgsFovFYrlJ6VucPCHEYeC7gLcBPwAUtdb37kfbo6Oj+ujRo/vRlMVisVgsFstr4vHHH1/RWo9dbTl3L4S5EkKIaczg7ruAe4DngL/br/aPHj3KzMzMfjX3+kIp8FuQjIG0LpsWi8VisfQbIcTctZTryyAPOA98C/i3Wuv/rU8yWHpRCmbmIF+FkTScOmIHehaLxWKxvE7p1y/4W4BPAB8UQnxdCPEJIcT/0idZLB38lhngjQ+ard/qt0QWi8VisViukb7M5GmtnxJCvAq8ijHZ/s/A3wN+qx/yWNokY2YGb6litsnYeprSUAvN/oALUvRHxhsFpcGPIOlYXViuDaXAb0DSszPmFotlT+iXT94MkAC+hvHF+3ta62uyN1uuI1IaE22vT57ScLoAX18FATw4BPcPv3EHN0rDzCrkAxiJw6mhN64uLNeGUjDzFOQLMDIMp+6xAz2LxXLd6ZdP3vdrrZf71LZlJ6SEgcTGc34ECw1wAARcaphzA/3qPn3Gj8wAb9yDpTe4LizXht8wA7zxUVhaMccDqX5LZbFYbjL6Za5dFkI8AtwBeF3nH+2HPJYrkHRgyoM5H4SGA54590Yl6ZgZvKWG2b6RdWG5NpKemcFbWjHbpI0Pb7FYrj/9Mtf+BpACvgf4TeADwOl+yGLZiFLQ8MFLdlmPpDDm2Tsy5viN4JO3k8+dFMZE25u+3ITHluDzi1BqQTYGj0zCw+MwltjchuWNi5TGRGt98iwWyx7Sr2+W79Ra/wSwqrX+BeAh4FCfZLG0UQqenoHTf2O2SnUlSgGDMfN5IwzwZlbhb1bMVm0RMFyKjYPdZ8vwkSfhU/MQkzCdNNtPzZvzz5b39xosNz5SGhOtHeBZLJY9ol/fLo32ti6EmAJawLE+yWJp0/ChmIeRcbNt+P2WqE90+9zlA3O8E8tN+OgLkGgP7pIOCGG200lz/qMvmHwWi8VisewT/RrkfU4IkQN+BXgCmAU+1SdZLG28JORGIL9ktl5yY3qoFMu1BuGGKb6bkK187pSGSst8QmXCyShtPp9dMMeD23g/ZGLQVPClpf29DovFYrG8odl3nzwhhAT+UmtdBP5YCPHngKe1Lu23LJaNSAl3n9rCJw8zwPvDmYss5AOmRuL86KmDuDermanX5w7WQ8hoDRnXvHwyEgcF/Po5s10JoNiCloK4hMOp9Zm9kbjx1fuQ9UqwWCwWy/6w77/SWmsF/H9dx007wLtxkBJSA5vdhFb9gIV8wJFxj4V8wKof9EfA/aLb5647hEwEzNbNSxWXGjBXg1II5+tw3jdxBDvlXqnB3+ahEBiTbcmuIGKxWCyW/aNfUzFfEkK8Xwhxk3vw3zwMJeNMjcSZW2owNRJnKBnvt0j7RyeETIQZ6B1NmQHbAc+YYlfaA94Bx7xsAeAIc+wAM8X1t20tFovFYtkn+hXB9V8AA0AohGhg5j+01jrTJ3muKwqFT0ASMxDq3q9hLjehYpT8kKFkHFfKDWVkz9i7E9bETShKzWCtzJ7IriJ8v0Ey6SHlevw3ieQ9Jw/g+zWyw0lW/b2V44ageym3U0NrIWRUwqFeUJB08P74AsH0ALWC4rH6FJ9ZPMDF0MQ8Oxhr8MOZS/yAe4mRl6vw/5zo15VYLBaL5Q1Iv4IhD+6ULoS4Q2v93H7Jcz1RKGY4R54qQ5gI9qvUGSKFQnOasyilqM6M4+VzTI8k+MCpAzwp58hTZYQ0pzi2NtDrhDXJLysev7wEEzWmx/bGJ06piJnnnyLvlxhJZjl1+z1I6RgZTiuib1wgo8s8nqnz/OQYU2OJm9c3b5ul3BSCJ0/Dk9+QqAgyf9AiiB3hj1aGuRR5uEKTckLQgoVWgv+YP85jsXF+LnqBk+8a7/dVWSwWi+UNxI366/y7/RbgWvEJyFNlnAyXKLJAkXEyLFBkjhUkgqYvOZf3OTDuspAPuOTX1srkqeKz7u/WCWsSz7a4MK+Yyib3zCfO9xvk/RLj6RHyfgnfb6zJUL7UYlhWuRgNUDlb5XjWubl987r98OT6Um4NH5YvGZ9FpeDlpTifuTjORVIkRURahLiAKzWDMmKAkLNBin8X3EYeGxDZYrFYLPvHjTrIe9366iWJM6TSXKhVGQmzeJU05yplxlWWUZWhXhGIUHB0KMmlpZCpkTgHkgNrZQbDFKVKxHypymLFh1iISjVYXY04OC1YKPlMjcTJJl1qNFBcv3AmsUQcR6RZKK8wksySbC+15CVh8IDDJZXggFNh8Hias6Xo5vbNSzqoSY9qVVCtQTjmUQ0dwhBGJ8wAT0qoyTgrvouQEEsIdMcnr31bYgmQCcmi9vi7L/Xvciw3GEpBrd4TcXybfJWq+Si1+3IWi8VC/3zyrsQWSwy8TlCSxMwx3OWAr15aZbncwhNZTjwQUEVRPn0X03qIUw+4HHl7yMiA8cHrlPnrxQIfK57j0rxgfEozPCyQ4zXiGc17Hh7i9tYhcsnYtubdayVUik8/scj88hCj6SEefuuRdZ88qWjeP8fSHQ1cPP5+8g6+o6luap88heAphnlCZkDBwPMu1W8JpIC774cPfNgM8n72D9M0Lgq0gLJ28VxNwlMb/qUkAkXFjfOVz8MPfahvl2S5UVAKZp6CfMGsW3vqnq1XvVAKTn8bvjEDWsADbzH5Vos7l7NYLJY2r5tvCCGEJ4Q4LYR4SgjxnBDiF9rnh4UQXxZCnGlvh/opZ8OHUl6SzQrOz0I6StFwAs7Ohai5IUIZ4ToOq4sug9LDlXJDmdmzmqDh0KxLggDOno2IDYVEdZfFVoWBAUFLhtuad6+VToiUY5NJCnVBqbm+yoNPwKqscmhwkPxgQMtVjA14N+0AD8x9XFoUOKkYUSzG7CsCHZnf1PxlcF1ID0I55RFqidQgEEQKtO4a4imQUhPFHMo2UJAFzHq1+QKMj5qt39g+36VFkA44EmbnYeHylctZLBZLmxv1V3qrUUsTeKfW+h7gXuDdQogHgZ/BBFc+Afxl+7hvdFaNCEoxjh4V1ByfdJTiTYfi6PEyyTCBEzmMT5m3ZZdrDYiFkGpSX3U4flwS9yISKUU8DsePO4Qll8RIk8PJDDHlUqtpBsMUF2pVhlR67c3d18J2IVKUAl2LMxQOkK8sM1aRJJW78+oXnbdSe9d87T6vNKoSUq/oHS1PSmHyVML1lSa6tqrUor7YQoV6c7natVu1vCSMT0EUgePA0RMgHFPf2IH11UCyEw6xrIPSoJXGESDQxlzbal/ncAInIclkr00Wy01G0jMzcUsrZtt2i1ijY6INQ5gYBxVBFMLEKEyOweISpJIQczeacvtFv03InfbD8MbQh8VyA9GPFS8EcD9wEGOWXQBOa63XfqW11g/2lmunV9uHsfZHAz8EvKN9/r8CfwX8y72R/sqsrxoh+a7EOKt+QELFefUFuFwKyb7F5a77JN6A4tNPXOTCcpOLl0OmxlwmMh7/9OFJys0coYpwpUMuGaPcbJFIapLE+fTMJeaXA9TlNHdOHCIxFodT8jUP110p+dFTBzeERlEK/vbL8LUvQ/Nv4Hj5LOVkRPk9DV79zklKbLH6hdIws2rWfB2Jm9AjUmw8PxRrv7GrKeo4uYcGuft+scnytJbn6xVyOuDuTBU5kTBrwI7FUYtNnn7OoVh3yT2Q4u5/lEW6Yu2N5GLeDLjvPnX1Vi0p4Z774cQd5thLrq/l2x0s+p3vgVdelJRiLnFa1JVPPtFCORopHdKOh+PDUBa+55Frvz+WmwgpjanVb5gBXnfn3MpE+4/+J3jyOajXTf5UCsoV+MSnoVwGIeGht8L9b9l/8+1uTc973f7yClxa7r8+LJYbjH19AoQQ7wLOAD8P/ADwCPALwJl22pXKO0KIJ4El4Mta628CE1rrSwDt7ZZxKoQQPyWEmBFCzCwvL1+X69mOzqoRcVcyMegRdyWVVcmhyTithsR1odQ05tHRbIz5+YjxoRj5epNaK2RyMMl0Ns3kYBLPdRkfSJKVKUp+aJYVyya5OK/JZgWlvFwbfLxWXCk3mGGfmYH/+Avw/FdbZP0CmYxGuh5Pf26ZP/35BIlyevMbtn5kBnLjntn60ebzCw0ac02K0mPECSguRFteQ8OH4kJk8kQxGrMN8CTM++C5NM74FMuSkWxE8VyLRiFaL5eHkXGzvVb9SGlMsunBdfNsenDj78Z3PQyTB6ElFYvJOuWhCJFycRMxRExSigLyqsbAQZ+3X7GHW94wSAkDqc2DkF4T7eIytEJoNGBy3BwXizCUg9k5iJTJt3C5P+bb3Zqe97r9bObG0IfFcoOx339zfg34+1rr79da/6/tz7uB72un7YjWOtJa3wtMA/cLIe7cbcNa649rrU9prU+NjY1d8wVcCx0Tbn7JbL3kunl0pdRietphudS64tuqnTILJZ9D05KgFFur73qTX4Zf/0VIpSGei9EYGkFKl5jTJH5knFgmyZ/+2gBZ6W2UubNO61LDbDtrv3afn/LwjiTIqQb5KE7NknJgAAAgAElEQVRuytnyGrwk5KYck8dp4R31oKHMerCNEO9EklxGkS855I7F8Iad9XI9+t4rRsbgp365SuPNC6haHMoehAIVSqJyDFFNkDxZovLLn0aNlfdOEMvNQdKDA5NtE62CqQkYzq2bd6cmTHqpDEePmAFNJ1+v2Xe/5N3J9Lxf7d8o+rBYbjBEl5V07xsT4gxwm9Y67DkfB57XWt9yFXX9K6AG/GPgHVrrS0KIA8Bfaa1P7lT21KlTemZm5uovYJd0VqiIJ9ZnkTrmvk7oDS8Jdd+scpFNulSa4QYzab1myvWuIxsqxaofMBiLUy1KMsOKlhuQwKVJSJI4SsFKrblm8h0dSOz4kkRntY2EitOomXxf+gx8/g9g4qBxB3IdRTxsAhGtWAqF4MJ5xfs+BD/yPm0GcbL9woHSZuau91zHH08KE6LEVzRwcJOagt9AKUFaJkgPSFCaRiEinnMImuARQULSKER4CW3MsgmH6koLnxYj40mkdGj464O6zr6UXdfYpaed3kheu4dJRVOacj4tQDOA+fHorFDySb7Oby3PUP70LRR/93b07BACQfxomel/eJbpD1xkaWyBD/IQH+JtV9udLK9XlNraJLubcrW62e/M9nXXBWY/EV+frdpqVnAv6ZWnI2/S2yhTb1oz2KiPa9XRVrLshT6uh3yWdbbq253zu9Vzb96tyr6W+nZ7HfvcL4QQj2utT111uX0e5P0s8KPA7wMX2qcPAT8G/KHW+hd3KDsGtLTWRSFEEvgS8MvAdwN5rfUvCSF+BhjWWv/0TnLs5SCv4w9WWDZBc2tl41pz7wPGgfDp02ack8nA6CQMj230GVOK9ooKoDXc95DxDet123l6BlbziksjFxk8tcKyLDNBliE1wJnTMb781RqL83DgsOZdD2X48funtxzodVboWFZVVk8PE3zjIFpL/vovjHzJ1PbX2qhroqWAf//PVjf6321qpO2Pt9yEy02YSMBYAk4NEaL55Ol5/vvXqpTPJ7j78CDvvX8E58Uq5YWI3LTD3T+RASnW/fNEwJ33S56RWU6vXkaNVLnnPofkE8co5eUmP7y1a6TMZYyexhjcNvTMZv3mWZKrlPERSO7nOBLBKnVGSPNR/phvM4dG4xFjgAQRigYtHCSnOEaSOCGKT/K/X0Vvsrxu6bev2l7Se2333QVPPLPZL+6Bt5j8p58wX2aZjHlxZGzU6ANubB3dzPewH3T8Tb/+OAgND54yfpOwez1v1/e6y76W+nZzj/vUL651kLevPbY9iPsQJtjxQ8B3tvc/tNMAr80B4CtCiKeBb2F88v4c+CXg+9qzhN/XPu4bHX+wwSzMz0LYfjtzfg4W5kxf0BFcOGvy9PqMda+o4DiwtLDZp6zTRnq8xUI+IOUPME+BLEnO+2XOLPgopWnWJSoSnF1obrsyRWeFjpyf5cKlFqEMcRxYzRs/tJ2IO5pyQW/2v9vUSNsfLxsz/nTZ+Fr+VT/g3KUGRALdiFGMfM6dCViejRg5HqM4H9EoRBv986RHca7F5YUAxqvE8oNcKPhczodb+uF1rjFLak1PO4We2azfFGdZIUIjEcyxsraSyVmWeJxZJDDQXtFCoXFwGMDDQTLDORSaEvWdFWq5eei3r9pe0nttheLWfnGz8+YjHfNG/LnzJk9HHze6jm50+V5v+A3jK+lI0ycuLV59P9iu73WXfS317eYev876xb6/Xau1fh54/hrKPQ28ZYvzeeB7r4No1wUvCbkhTWE+4uAhSWFZEWmHN08Jqj4szWtEFHHoqKSyqBiedognBNWSMXHGcw6DGZivhiBhfNLFS66bPFUtQimHzJBgecEh7rgsBWWmEsMs+A3SKsmx8SSvvuwTiyt0KDk8kSAWxqlWNpt/k8TJhmnmKlWmxrJEcy6RhqERCFusrT0ScxSxqEHoemhhKggiQWZYbPa/a6NCY3L1chKG4tQvBDCSIrUawFiCRijJIjk24fHqqxWEG5ATgxy9JU5YD1h+pcXQIUEr1kCESbKTDitzcbxmE3c0xthAnLmlNK2RCgczHo6G5bMthiYF6nKTcMojiBxiiThJf5BFKkwlhlgs1MipFJUBRWxAEdQE+EbORiFCKchmXQpLDsMjkmqyzHFGKeOj0BxihJZqsewv8mJyHkcKHBxCFC4S2RUKOYZLQINZljnM6NadRinwW5CM2ZmCm4VuX7WhnLnHYbh35tXtzGDbpcO6ibPXhHolOte2uATpNOQy5nh5xfjFlcvrIV8ScVhaAlfCscMmkHNm0IR/8RvgJUw9I8PrK3psFxh6u7eRtzu/kz6uVL77Ovvlb3ijsVudbkfSM76Sc/NmJm9i3DwTYJ6RnfTcuU8x17xd3ukzMRc8b/24t77Os9fbr7aqb6zdX2v1jW4R3WbhTqigK8l7A7Hf5to08NPA+zEvTwTAq8BvaK1/Z7/k2FOfPKVRp1epzwc8eUHxNZ0iTMZouSm8FoyVq7zvaI10uUEw4hE/EOcZneOJP/KhGpKeEIxmFanZCndMBaTfmUM+OGyqPr3K099QFHWc1KkBHntqhReflAyk4e23Z3mxmqcpWgynY9ySyVFagqFxSascw69KhIB7H4R7u8y/YQh/8gnFhfmQg1Mu736/efv3sT+B3/+4sbKgFG+NP8VkqoDvDbMwfg9aSBbn4Qd/XPNDP9Ljf4cZ4D39iTLF+YjMlIM+OchTX1NoV3LvvQqRkJS/VSUnAm4/pSm0AtRqSCo7wNnkCKU8eG6LaPxZnnlaIRs57rvrGEFD8sJMhIw73P2g4Na7FDoRcPZ3fYpf9/HcFtn6IkEYspLIIn7oNlZWHCplRUuHZJfrFF70eb4RkLy7xeibBhh7fhBZDUm2AupFQMDdPxCj9mMFKoNlMtLjId5EkwhFxAvqIuHMtxnM+3x85Em+cEqwKn0yJEkRp3dVvoiIKk1+hR/b7JOnFMzMQb4KI2k4dcQO9G4WOj8Kz71k/vHvVYiP3rArvXX3msnuv68d1bsAl/MwMbJuQt2tPGEIXz0N1Zope99dZrDY+ZF84hkT8mVkGG47YepNxI0M5TIs5c1WA/fcYX4oS+WtzV/bmcd2Or+TPq5Ub28e65O3van1anXSPVB67qW2Kb8dKuiu27b/g9IJk3M5D2MjkE5CPA6nnzSmsXvuNP1r5sn1+u44CS+c2bxCzFb1ZQbgoVMmVFG+YAZxsF72vrtMme7QRgfG4W+/CV/4ium72Qw88r3w8DvMM3GdeV2Ya4HfA84CD2NCp/wH4B8C3yOE+Lf7LMve4EfI1QAxGmO5EOKNONAMmb0QMTUWUVjRtLIu7qJPaiJGcLnF0kshTiMkGnCZf1GRqTVoRg5uAuRiw5g7/YjGpdZa2JG5MwHnz2tGh2LUKvDi8xGNSJEOU1yc04wMSxqrSSaHE1yak6jQ9O/lSxtNmcUCLM5Ljh+Ps7QgCVsmTMgD320CAZuXHhrEKgUq8VFSzQJu2KBaNuuyvv1hAQPuJl+8RiGiOG9MrsuzERdfUcikixOTzC+5LJ9Xa6bX8EKLyVWYOpIlXo4oL0aMTglKfsTFlRqxYBgGVlk4H7CyLHBTLtIVZuUJKYkVXUpnQ0aHImolhf9qldjhLNXZGl7QYH4WVCRxG5LzL0ArklQ1eMsxzj2vadQCwoTD3MuaKBI4jmDhXJNiq8yUzFEnoIVikCQuLmW/yEReURj3IF/goO8hETRpobZYkS9CoVC8i7u26C8tM8AbHzRbv3WdO6Slb0i5vgzZXob46A270lt3r5lsdt7kz2Zg/uJGE+puaQZQ901Yl3zBHA+kjI+H666HfFktmuPBtAkFU6+bH9Bz540J13VhadmEhtnO/LWdeWyn8zvp40r1drNdqJs3GtuZWq8WKU1fcF24vLQxVFDnedmq7Y47wPxFGMpAsQTnL5qybsz0ofMLG+trBqb/bddvuuur+1Asr/eHhcvmGrvNwt196vS34Z/+HPzBn5nZwOkDZvupz8JHfhqeffG16fs6st8996jW+ne01vNa618FflBrfQb4MPC+fZZlb2iHCklWAw6Mu6h6RDzncuyIw6W8w9S0YKjVMmFASgHegRjjJ10iz8WphUy/WVJNe+RSIZ7UMOWZOpMO3oHYWtiRYyfjvOkWKJRaZAcdbr89RtqJU3MbHD4m0Y0YB6YhaMD0MZDu5tUaAHLDcGDa+AgemDbHAFOH4Sf+T2i1YLno4XvDpJsrFMUw84sezSb880dNCJGt8IYdctMO+bMtxo46HDzhoJQZOE4fgbGj7dAoqoF3JGGuc6lhrnHKIb8EE1NxDk0M0IoXoDbE9NE400dMHd3X4g075I7HyJccRkYkA7ekaZ0vkT46QCPuMX3U+DcKz+HobZJBBBkhaIy1OHa7wBuI4zYjjtwqcBxNFGmm35RgMjfIEmVGWF9VJEmcXHKYpRGHyaUWjAzTSMY4xDASgU9ASIRGExJRo0GLiPs4xhiZLfpLzMzgLVXMNhm7vv3R0l/2I8THVmFXuuvumMkiZfIcnV4PwzJ9cH0G7Wrk2cmUuV1aty6OHTYmXBXBkUNGvu3MX1eqb6vzO+ljN9dg2UhvHzow+dr0tdt71Mnb6Ted/npgEo5Mr8tz5JDp19uFHtquH3b3/61CFXXKDufW5S1V4LNfMKvOTB8w9QlhttMHzIziR/+dmSm8Adhvc+3XgJ/WWv+dEOK9wD/RWj/cTnvpSqFPrhd7HkIliPCXGiRyMfx8AMkYiXSM4uUWsbCB6woGJjwot2gkPeJJSWMpgEYLbzppZs8qNTOznPOoL7ZgyDNhWBYaMOSRGpSEtRbzFUVSxhnKCVbnAxq0OHA4Sdhy1twQ4jHN6vkQDYwedXHjG2fdwtDM6OUyEW65AcMeIWagtbIMz5yGb/ylorHaIDns8Y73SN7+rq0HeN3hX7yE8ckj6eANiE0hZAJf4xEhkxJ8M9/lD2gSxGn4oJMBSRz8WoDAIzXgoFCs1JqAZnTAM06lfgsVc2kUlBnAJgT1hTrRoMaJPLxcbC1Qsjfs0ChEhGFE4Ibkhj2Cgt6QBpAad8HV+KpB0hfIRAyaESRjKEn7vOK/JR/n9+Q3OUiOFhEXWeU8eQJC4rgcZgSJ5MN81/bhU6xP3o3F1fp/Xamu7cKLXIsP2ZXa6TxgveEputO65ejd75hcO356sDk8yVb5dmvm3C7cSaed7UKy7HRdV9Jnbx07XX9vvu7r2+3932sT71bhdHYj43ahRnr6hqo18ENIDnrIVrDhniyXFf/9Vcmfn/XwKw2yCfjeezwePtxgLM3W96sjY29/2clPdKf71Qw295PecD1bhe/ZTZ/v7edb9Y0//DP47Bfh4KR5s9J1zCCvm/lL8MEfgQ+9f1e3dDe8XkKo3A38JnAr8Czwk1rrl9vhUX5ca/0f9kOOPQ2hEiqe//wc/kKF4WqJY45AkkClhnnuqUVWZpcg53Lw7gwyPkRJpHHSWe6unkWKAB6YAAScPotqKc4/q3nx8iDNTJbkcY8Jz8c7NMCJ2zKwGvL05SSro4MsfaNK9dkmCM29j8QRd2YplwSZrEY/W+LJzwcIBPe+J8a9HzbLf20gjOATz8B8ifBAlj9Wd/HU4w5eCh75UXjLg7t7s/yp0/DE102fv/t+M3FRWjVBie+8D559ome5MUx4FZVvMHNokfztkiE5ALAWoqQT6kShOM2rfJ1XEcCD6jj3zzjIfH2DP5tSETPPPUX+zGVGfI9TtUPIqjZCPTRkQr08UdwypMsGs3PHX265ApfLMDEIY5kNfnPLlPkI/4UEMTJsjrpcxqdJi4/xk1vP5FluLK7W/+ta6toq3258yK5Fdtg55ER3+JPLeRgbguVV46c30p7W7/Yn1JgXLbpDoVzvF0g6vl86gsFBqFbNs9vtB3a1uv36t4x5b3rKvPhRqa6Heen4Jy4uw2rJ/EBPHzBrEXauc6tQHdu1t5fhNbrr38pvbDsZt+sbHd3MLcD0JGoww/kLVWot8EYyHLttDDlm+sHsq0X+84vDvDh9F29dfIqTZx+nFUXknUEyzSrf/2aYOpiGYgUWFuDwQXjgrbvz/+xdnq7Yvg+HDkAue/X9rVdPSsE3vw1amVA+B7YJ5dOt085+r+y/+jFjmq3WodmAhAdjwxsHen7DzJ588tev8UZv5nXhk6e1flprfb/WOqe1frvW+uX2+eX9GuDtNX6lhb9YZXDCIzybp9mKQRTgv7BKtVgHIXAaLZovLLMSeAxHJaqv1AiiEBwP5gowtwJS0KqGhK8UUEMZvJUC9RcKuIcGaZ4t0ZjzaWQ9ivMRaVrMv6yIBDiOYP75yIQgGYfl2Yj55yMcRyBdWD6zvvzXBgoNmC/B8RGqZ8osvtBgMAtNHy7O7m5psIZvQr44jnkOF+bMcSesSbGwxXJj7fAq/qRD3i8x3kyzQJFL7RAl3aFOfAIWKOIgkAiW/BWa+eImfzbfb5AvrTLeHCAfr+PPlSACHGChAYVg25AuG29m218u68F8AbKpTX5zY2R4lPfTpMU8BXwCFBqfgHkKNGnxKO+3A7zXC1fr/3UtdW2Vbzc+ZNfS3pVCTnSHP5m/aN5U7PjpdfySuv0JlYKz56/Nj2+319Dx/Yo0nDln/DN6/cCuRrcLl00d9SY0mu06276Rc13+iefOQ7NpvpiazY0hX7YK1bHbe3C99dOp/9KiubadwonsJFdHN2F7hq8Z0Hr5HH5TkYkp1KvnaaZNPyi+ush/OjPKSKPArU6R8eplkBIPzcniOeJC8dgLiupz54wjd71pdD47b9q4kv9nt5/c7JzRv9+ARrA59M616Gluft1VYnZu+1A+3b54Hf32yt7xM202zPPSbJhJkm4ScWPWvQG4YWxDQogP91uG60FyMIY3mqJ2pohzMItTKRIWfLxjg6STMUQzRGtF/E1DjNUKrFYd0kfixEMNLR8OD6FHc7QqIVJFOMdyOJdLtNIDpG7JEL24RPzwIImDCWLnymQzIfVCwPRxRSJoIBo+0yc048k6hW+tMp6sM31SEEUaFcLYifXlv9ZQChICDmbQZ5ZJTcaZvCVGZaVFQrYYHqsTT2wTAw8Iw4ilxRpBEDE6oYkaISrUTB2B0cmIi3M+2aGI3DBkRxQXlwKyI23TaseHcTFixMuypEpMhoPk6gkWVXGTP9wUOSI0Cs1ocgQ1kkItlTb4syWTHiPZIZYSNUaCFIkjGWqJABUZH0c17FIb06hSEzWdoFItUxmLCJOaCnUq+ISE1BItVMqFVR+mh2G1Cqk4xARUfPNRijs5xMf4ST7IQ4QoLqkCTq3FB9WDfIyf5E4Oba04paDWNP/4KtW1+tbT6iatY07pnO+0HYamfCdEQGd/J3ab71rzXw3dpqLrVV3QpapQUVutEwaKSgMqjXVV9R7XApOfMFwPyTCUW783Mdf44HRCNSTi7XtWhTBElarU8lVUEK6dV7E49cwwatH49aiEt0G+NaGVQo2N02hGqNfir9cbtiUMzac73EOvn1LnuOOfVK/D+Bisls3sSS5nftQ6/oRSwvHD1+bHt9tr6Ph+OQJOHDP/Gjt+YJ23dxPx3fnTdepzHEglTMiWE8fWf/CPdPknHjsMiYTxJUkkzHHnOnOZjaE7eu7/2n5vP0l6W/fzK/X97nSleupvyzExbmYbF5ZMm5n0ettDOVQQUstXCRshlbqiksxRm18hHMxRqYZUqiHh2Bi+ltTjHnUZR95yDDcuKQUScfwwaqVILZXl7+rjJAorBNlhSskcxdwEQiuUFFyePEY8Lgm05Kn0MbPUUyphdH502uj/Sv6fvb6riYQ558U33ofd9rfuZ6Hbf8+Rpv7u+rrzdvviHZjcWvbOs5XwzAtGCc+YbLtpBpAd3J2se8y+mmt3QghxXmt9eD/a2ktzrQ4V5d/+NsGZy4iLy7jn5qDVIvbmI4TH7qT27XmidIKkUySWr4JMkL5rEue+cRgZQCdGKH5N0fjm84iBBnpsGLEscZVPXC7SHB8kfmIMdA79jWWc88u4UwliBzUtTyO0IOZHNL5aIVwo405n8T50K/4jt4PrkBp3N5pqu0J46AGPyuOXUas+tBI0YpM8Hz+PfzJg7M2TnLrjHqTc2JnDMOL3/+AMT81IUjGP97xpkJPDAXI0hve2DDMvPs1iscJkbpBTd97FDOdZ9OtMJlPcL9srTrSXPFPPFakVqzzXnCc/GZAeTPO2N9+HK9dfRlAoajRNKBMusapqjPkJ7kueQEq367Ii/JpPAocnkvPkm2VGGOC+5Jt4Qp4nryoMNTxUHL4ZvoqOQ1qmqNIwFimV4ORMk9Flya3pw8gHjsE356DcMKbbcr0dCuM43H+sxyyyi5Aoa6bgMiyeh3IJRAIevM+U6TahdcwE990FM+fhG6+2VxBIwmQWRgYAAau13bW523AtV8q/XIHHnoXPPw0lH7JJeORuePhOGLvCF9x1NmspBTMXIF+HkRTcd0Dx4heeorZYYNEd5uzxexCO5P72ePub54115f5D7Rdgq4pDL3+b28/NIIWGu+8wP+TfesqYDbNZGB8xpp6H3mruz9cfB61QA+l1E9dQmmNuBRyH54++lQu33MOYG3DvmzyeuCTX5Dt1CCRGB2q5wPNBjoWpEwynJfedTCHda9RFZ3DwzAvbh6fo9c3qjhn29RkoV0z8u0TcDPDS7fASzXbw8K2WJ7uebOfHmPQ2m5p3I8dOPolX8gXr+Gl1nseOLp54xpjXI20GV9WqMWVnMuZ5zWTgbd9h6rmSCX2nkDEdU+PpJ0xbuYwZhKdTRq58AeYXYWoc8iVjbk+nUa7L+S8/RaWpKLgZltNjnJfDOLef4PDCS+SefQKtBOW738LcwZNcKsKBIUk663Eg1mAgDk4yzsJjM7h+jc8tDnNm4iRlJ8VAUjKZUky7dYSAVtwjFjTwW+C7Hv/tfbv0ydvqPu10H65lecDt/Dx3Wl6vI2+vT2qnzCf/xLxF+zrxydvXmTwhxNPbfJ4BJvZTlr0iKjTQl4okjo6gX70AQQuRSqLPzBPUJMmwhDM4hH7lIq5wka5Cv3IZhtJQahBdaBK0JLJeIUpPol4pkKJi/Ehml0geG0G9mid6cQV3wEWUqsSSLu4rSyTj4CUU+oUllK9I0EA1BfqlAmkvIj0V2+yL1xXCI5pfReWruEdG4OwKqXiZZqbMpD9AvrSKv8VU+WqhwYWLAZlkCj+oszDbxD2QIN0IaBZrrDZLHBobYrVZouBXWJVVDg2kWZVdK05IAVIgVyNkJs5qtchkfIR6rU6zZ6UOiVwLZbJKnXGZZXkgwJfhxnzSYWAwTXNQkHfrjA8Mkx9oUpB18lQZl1kupSrMuQUczyWS8AqLa+FOLviXyeUhPylp1utQDqAewFASZpfNF64jYKG0MezJbkOirJmC43BuAcIYyBAu5aFQ2WhCWzMZVeBS0egrVHBuxZiSF0rm/G7b3G24lp3yP3sRPvIJ+NRpiDkwPWS2nzptzj978Qp1X1+zlh+aAd74oNkWVhv4iwXiB0YpzhcQjQZStL0hiu1JqfbxQhkmYw2aFxZpCQcc1/yTP7+wbjY8d9784NbrJtTCmklRrZm4Bl2FfvkcrVATaEnzwmUmEwHLpCgEcoN8friug+bIKP5SkbEhl2WZxlev4Wu5E4Zip/AUvS8wdI5boQklMTUJxaIpNzluzrVCE/qiE/5iL8OKdMJsdNrq7DeDjX2mE7blSnJ06stmNtfZ0Usn/EtvvoHUersdXRTL6+Z1pdZNyh1TYKefNIPdmdB3ChmzcBnmLqy3dfa8CfmxWjL3eChnBp+pVDscSA6KRZqzC9RCSUoqwlfnqHkZYqUiqhFQnlsi0g6RlBTnlmlql1UvQzWe5lzZJTOepuCkuZgPiTd9qkPtUDhSEgqJBmqRpJZM00yZAWUzlYZMmtWwR4e9+t3pfl3pPlxtf+vu5719qre+3ry9+91lHn7H+kxuzN08wCtXTPq7vvvq5N0j9ttcOwH8BPDeLT75fZZlT3CGPeT0MOpyGXHiKDrpoZtNxK2HSYw6hLkJROgjbz1E5ICDxDk5YaZ9p7I4R5LEExBlR5FBAfe2ccKhURJBFfmmCcKlMs6JMZzbxgiDCEbSOGELTo5DwgEvhnPnBDIlCWUC6Wmc24dheDtzxv/P3psHSXJfd36fPKoq6766+pqe7p6buDgAOAQBkdJSGyaFFWVuhCwpLGoVcljhlb0OH2tvrKSwl7vBjbX3L0esj/ByvbIsbohaWbLCOsiVSF0URYMAByAGIDDAnN0zPX1UVdddlVlVefiPlzlV3dM90zOY7h4A+Y3oyKr6Zb73fr/8ddXL997v+xtReGiLRdRjJeybNdRTEyRjKYp2knKySzGbJ75DqDxfMDh6JErL7BGPJpg7HsNoWpKCLSQpxrOUO5sU41kK8TRFUnfQkogdftq2DsVUnvKgRjGe3VEnSOp2V1l3Oa9A8vb7GXIsUMTBQ0PhJNNoqKioHI1P0ShCsewRK+agkJBxalqwWBIHz/FgNruV9mSvlCjBec0BHJsFfQiuDjNFKKR3WeKfhpmcRD51FY5NiD2zWfl8rzr3Stey2/mVNnzx/4WYLs5dPOpTCETlfUyX9spdalIeMn1FXJcIWbktx0LeID5dYLBWJTdXwDMMXA8WCrCQ88vL/PezGVgfGsSOThPxnFEab3FulDYcTxkVcmMpRZXI6WPEYyptW0U5fYyIrhBVXGJHp1gfGmKPsdW+uD4ag9hmlfh04fa58fe6D9H90FNsv248xXU3WpPDwGFRnmzXO06noaqjlPK9UoF3+2w3fbNTQg8S6ApS5UFaMfiOsKwt9CKx43MkdZeeq6KfWCBptRjmCzj5HJnFaTTFQXNdcsem0FIGcR1iEThWkKD8TAaOzhiY2QKpZhWvWMCKGGiKUL0nIrA9XtC35ZnzA4/SBHzpH4oTv7mOUXgAACAASURBVLI2ijqalrzvD6R9HwiRHwQHvbr214Bf9zzvr3do+6rneV84CDv2m0LFs12cmoWaieJUeyjWEG02CY0uw4GGZesY0wbWqokB6LNR3PUOnhFBy8VwrzVwU1FUI4JaMHCrJp4FyrSBWR6QyEdQ4wr2jSZYoLg26vEsTrmHYhioOR330iauNUQ1IigfydNzTMAjmcxuSWsCeAMH55aFOhXFafRRAK0Qw6t16Qx6uLZGaq4ATgQtDgpbKT/swZDajRaGkSA1EUFtmOK0pQ1ZhGBaxOJRTPq4XQsVhXgyTV91iROVlC3Ir67p4MbA7PeJx42t6eGAaiSmQd/BjSmYfZN4PCkyun05Lxnb8pTmujam2SUeTwI6XdsFfUBSFcew7fYxbY+CHqWvDgCFuKvR77aIE0dNxsfSWr7+IKoVH1Gr3D6nbco/fCENQ++2vVsoUmwbqk35Qk/GJJXQ7cvTYjqOa7uY5TbxQhwaXcyIQSwbp+9C3DRR+xbkkrg1aYtnY6j94ViKI7LVxt3s3Q3jtXiqKn2oNqXtG+/C77wqDt2O/wAuLFfgCy/ATz2797TI3egw/PNcVKF30HdgiLBdzLYl1A8quO0epg2xdOJ25CyejGK2LelWOkE8qmIO3J3bdDCbYlM8bWB2JV0TfO7aLqquSlvbGp0XvM4mMF11dEt0P4LH6LVru6h9sTmI4O143l1kACT9Z5xukFFV3ZHtvh3j5+11DB+ECsR1d5a/2+dbT7iHvvdKNfOgGE8l3otq434pOXajddmB1iSW3DYP2xZuJIo6HMj83TZHYTQnd5zXO8zRvjt60Oha0u/fu2Lw1TdUZtPyPKerO2Qom/CFZ+Dnnt2vm/CIoVKFb3wLvvanssgim4bP/XsSwXuEdrx4ZGryxqEoSt7zvPp+yd9vJ+8O+Mv43ZdeZWXZozx3juuZZ8h3VFKOy8Lm94lcfBVcG90ZErFrKAWD+N/7m6DrdF9uMPAKXEl/lG6rS16xeCr1FtabKwwvrGPnpiCnM4yWcJMF9LiLUX4Vbt6CozOsT8dZLTZBU5h4/ixPPPep246eZ3s0v9JisDzEu/EOsXkLdT5P8kyKGy+fx/1eAy81SWrqadTnTxAtQZZllLpfp/XsUakTe2kNvAikdehUfcqDE/DcIq4Kr7hXuP7KK0y/tMmCUqT7/Bwrz+UoqunbFCn3HMOghm2jLTVflbbQmhTT8qT78pI8Zvp6R1QLUlfm5lOcn1pg01Jv10XB1lqu8VqpvdEljNWsPXtU3n/3NfAGUpczeRQqna30K64LX/ljWNmAuSn4O5/1a7zeBSWCe+5p3q5omBtt8s0WWtukO1S4euo4+nMLLFx/k8fVKqxXuHHDpTuM4X38GR6f9lBfWRInKx2XlQUKkIpBqy8RqfGxuet8vQ4vXZPrzy3CW2/B178t7asKnDkFyTspY/BcKN+CZgN6LfjiT+1MVbDXbajG6pPcfIHzU2e33L/b3dgDtYS7UeX62xWsWotkTGXuxz7Ga9NnGZ5/k4JZY5At8Ge5s9xoqszlJCrRsmQqp6PQHoDiuhy7dgG7WuOGWsD92FlyCZWZjJRGui68clNKJj+xIGa8cmP0XlVgswvrbWiY8sM4l4UXFsfaWtDsj25d04KVBrdt6vSl/CsVhc5AzvvEAuBJreF4m+dBxr9mSw3i+HwfLyfd/r9wnz7UbjLuKft+KFH2k6Lknp27DxvHa2qDvXlf/v5oaznw6yY9qYFsdeRmBnV9PpWNW5zg/NRZKqbKelvmZHCPW/3R3MjERvMhY8B0GkpJOHfERX3tvY1ZpQO/9LsSpM/sEK1rWRLJ+/JPIXx5IR463hc1efeBPztsAx4q/KXqQ1vFMjUy/XWqVyziDpgDi8HFDTxPRRnacPUqajaK1xrivL2Ms7zBUJtAdWr0LnUpuANaDlgXN7CroDht6Lt4l27iRTOo9XWciyuomotX7+NpFt6lZTTLA1Wjs7aBaXZvm+bUHIYrDpFJB2epjjJZwL1ewbq8hmUNiDdVBtE+g6s1IsaQ4doQZ3WsTqvWk5owzQDHgssVsD35xVprgDnEZEDZrJJa7YOmUVNNOmsbTJvxLRQpdx/DoIYtIXQmhjaiNVlrSHGVpmzRu+W6yTT9codGbbilLmp7Ldd4rdS96RK21azVelJTp9rg6HBtFQzlTvqVWlscvONH5HirAqsV0GKg2vSvVejfbJCeTDC4UqHdczBiCtbNJsV+G3O9Rj+VYXjtJj1TJRW3GV4r079WG6WRL29Iqs5x4XLZjwpsG5u7jfVqczSel1fh4pI8vmsqVKuww/ZtgEQo+10pUK/Vdqcq2Os2VGPX9Ms1GjVr6726bfMuNAhj1BL9VAbn2jJp3aVrq3SWN2ivNZgc1FiLT1C7WUO1LCxbfrAuVSW47LhwuSrDqlgWmzdqNNMTROo13K7FUl3WnKy2ZBqqinRjqSZ/4+9XW3LutRpYQ+gNRcd42/Xa6NZdqootgU2Xq36bB1c2/dJQX3ZQazje5nqja8ZrEHcawx3/F+4Tu8m4p+z7oUTZT4qSu+F+bRyvqR2n8VA1qbNbWpHXtl/X57qjur4xKptg3gfzZvz+j8+N4J47rpyXjftj3X7vY1ZKwZdeFD0rfhmy68lxpSmff+nF0MF7FPGoOnnKvU95dOG6suuDbUOnDR3bwJmaQhm6RHSHRmSaiZMGpuKS9FyiZ0o4tstA0fGOnWC4OcCJR+gvLuDNTeF2ypi9OPHjcWro5OwB0dMlvLTL0I5hKyruqaNYjQYDPYdyZpa+peKlYzh9A47P41kOWs0iWSr5aUuBVtCIzGkMyxrqYp7+Sg17poR+fBpslTYDok2X6PEcQytCZCaCNp2E5Trkk9gpnTpDBmaTHh7uiTyuDpY7wJ5K0LWbxNpDJiMFzLwOjkPBjZOammTDbZBwI8RcdQslyZZUoevitrt07Q7DbIzaRp1uLoXbsWGugFvv0ZvIYc/n6Tp9WqbFcCpFN+7gun6eK5/EXW3hGlEyWY31FhjY2Kt1IrZNIiLRk7zhYm92aW90cVNp3Ou36BIVqgFL6AjcZmdEaxLTIJ/AvbJGzzRxe13cfBqrY+N6A9z5acxyFzeXgLWqhIPqDamfmZmAKzehlIOZIu50CbNn0ms76DNZIgmF5q02+skSRkzBbFsYUwmazQGaEcWttVAXjxKN2HSaNvrRAnYhjtkZ4g4HuAtFrF4ft9+XQpvhUFaCTWXA7kO9DeubYsuq72Ralnw2GMBkSsg+2x2p+TszLz9GjivRANOStm4XnKHsnzewQAVPizJstXHzBcxKCzti0O3ZuNqI/sHN5uhaLq7tCtVIUigfcFyh7thCXZKA1XVi6QS5jE5ltUNq0CFi27QrHdqVDrai04vEsW+u00tlsYuTmLequPkR/UWs1UA7vkDbVkmqNqnJLOlSik03zky3TOFoAdcwMHSJWJyeGDlopybE5/UMg+J8gWy7yjBfQE0aLOaljmk2IzV+gWO4WJC/8fezGTn3eAGMiNQ2qcrWtmMF0ampYkNM57ZNpyb8NgVOFn2f3oX5HEwmRyWEQZuqjK6xXZjMwHRqVBsYU0d0MhGEIWi1KXZFGLXZ9p2vm70727bIiI5k2LasWdqu97aMiEE3VRCKj2yBtmLcIdt1wY0Zt2lp3HyBrmbcyUyyjaYmoMzZyd7dXm9hMhr4bYrYGOjeycbgvHaiQLfSwp45QrfSoluYxp6bw7Rcen0H9+hR3Pk5en0H01OxTxzDdFR6nop9dAGzY2HPHMGsttCLBWIZg3pP5s34/R+fG8E911Q5r2n69Z/ph1PL+OS0ROq+8IzMpdWWHL/wjHz+5PQDiQ2xz3hU07WveZ63b5n9fd3xwoU3zkOtApU16LYkGv9EykXd7NHxIP58go99EuyXLqBv1rhazfG9lVOgqkSLUez1NiuNCMXFFOmMS+qN7xE1Oxx5ocCx41mizTZXKkmufH+D3lKT+JE8F059jNw7r5Gxu0zOFlg3TrFeFV/iGettJt9+GRSP+OdfIPOLH0cZo2jwbA+76nDhHYWL3+4ziEaJp9aJfO0NJlbeIjMd46lfeQH14x9Diyso5zdhrYdd1HnlG9/EubYO2pDI506RnJ/CfKJIjS79t97kxMs1Ul6aucxj9KcSkIoRf/oY7rsbvF2/TCuvknPjPP5KH9VT4ROL8nRb74pz5trcePk8ba/Pt4+W6KykyVrwQ8cmeezzJ3h92WPD1VhN3GL5+nUGdZ3kEYNnz2kce63N45tZSCd5Z2VItzEgOpWhevoIg698g3RlA2d2itZPv0gqrpJ//Rr27/wpEbNNbi6Lm4qweX2N9lQeNZOm6HZIRWF+IYM6WxLus/6Q9f/jG0QuXkHJFemdXqBy8iSxqSxeNIL+0jtk62WmTxZRO224eANQ4MUfgvk8uA5uaYJXCk9w4+vfI1avc3TpGvGkgTI1Se2nPgsvXyDd3uQjt66yvlRn3Yly7ZM/TOfZj3Pm2qtk2nViZou3nSLJygZHTxaItOoMyw0SUTh6IodayEMhL57Fd9+B712CZFq4GRt+rV02C9G4fP74NFRXYG1Nir2f/xgs+Knmf/JV+MNvgWNCKiNcanFAUfGiMZqkcBp93vz830Y/HkG78CYpwyNRzHLsIyVIJni7EcWstjAmc+CBVa6RzMb5yGefRH338lbKiImiREJmprHLmyxdb2HaUNNT4nQDai5DbCrP8FaFyNEZNowCsY+eoVA0OFd5E3VT6C/cT5yTmrM33kTtdXDLdfqFPLGcUF50Xf2e9W9xVerW3JghNXnb65j8wPT2OrnxeriYurXWbqe2cb13retz4WJZojaJKDw7K/LGz+sO4LVbEjksJuCJafn8/C14acln/4hJJGatCTNZqHa3pgbH078ty081+2nCIK0dyFhtyrNBpSMpRQX4xDw8NbNV742GOLe5OHRMF61vEc8YdIayijMd2yHV3HEpKhZu3KB+j9ILkPR5oGsuKza2B6N0eLt/5+sXFuHcERmzylh6XcPl+ZLofmVFxXFH6XAPeY5r9uFWzWUxbpFIR7E6A4Yxg3QMrIbQjjx7OgEKvPZuD88DI2PQa1ooCiQyBrOxAeuDKNPRAeu2QSmjkonBC/My1/YyN4I5qarsrd4xxCOND1q69n0Ly5TdHNJZWFnyaXRcqFxVqUdTpNMprJaK3bJIWDWc6Ql6NxuoWR0rnWJpJUozVaTtZujbKrfeHaANevRLk/SvVlArNYbTeczrDSJWl0rqKAPHYmOlQ0ox2chOsnm9gaXptL0Mnq5jX66h6kkiRgrnahWntjVcr+gKw7TOZl3DySZwVY/qWzZ6SxaPtPoxulcr6KqF0negbsN8hvpSDefqOtqJKbSVKtFEilZzkzW9TUY3sNZvYWgZTKeNs7ROMl8gaWmoQ4VhvU17Msr0KgyXNxiqfghiuTaiA1lr0F/eoKsN0ZUYw6sNCl2bailOddOk1vaoECObdbjSHNDoZWHRY7lukqpFMDfr9Cfj9FdbmGWT1HyW9lqH9tUa2coGzekj9Jc2KA3atOpD2pfLRAZdbCONe3mFVsskMrRRrSHu5evENQez7zK8tnw7BdO/dAOnZRHDZjCw8a5cJz2To11u0bu+QSoZZWh2GFjAO1cBP7/27rIU685O0i/X2LzVIjqwsNI5uLGCenQKu7xJa7lOKdKjl8wxvLqEFdGJDm3itRobqx1yWNRyJazra3hxg1SrwqqWZnj9FsmkRs9TGS7dkm18Wg24sQaWInvNqSpcuubTPzhw+SrE4tBpwjvX5dfOdqTObqMK+bSkkRLIN4fdl1rIZkVCSJ6D3awxGKhoOry1sEiy16Qx0EmonjDopzP0N5v0b5VJHZ2gs7RBZ2md1Pwk3VYf07RH9B8BZUTCkChEwsC5doPh0CGhubiXrvnRRQfn6g30VILhWgW1kKGz3iCTVGk0BvTLI/oL1bFJJnTUgQX5HOqtW8RLOVSrhzockDYks542fCYHY4fXUZV0MUE2pd5uS0bHmBr883Z7n4yO5AW6dmob13s3m3Sfr3s6IylgXb/zPF2XtN50Rs5VVXECVlujFO9SXZzEcleO21OD4+nfvp9qtoby3vW2yqj4Mpbqo5TyemerXscVGQM/DW0rKn0jwaWaOqo02CnVnFW5ZSdY7aj3LL0w7a26LHuUerc9qS7ZrktT5ZqaJXKCNKnrgaqpXLcSLDVUVGVrOtwZHxtXpRNJcLmh048lcFC5vKliJVK4qRTLDZWlhoqbSmElUlxu6AwSQkmy1NaJZBOsdP1jWyWfEPuH7H1uBHMSuJM2J8SHBo/qHX/fpmuNuOzL2m7C3KLwJNoqlE5BVoO2C7kZMAo+fUKzSvJ4gb5qCEH4aYgaYPhcl0ceMxikC0SbVRKnSkQXi8SabeKniljZEjmnipotMHUyRy1RoGRVKZ4uoCUMjATYSYPIY1MoiotnO2inptF2oFMx4lCaCZ4KNSaeimIVcjg9hUzOI/URn4bBpzqhbJE/PoF2Yhrn2gbDxRIDzyJTnGAqPkE97mLMzmE5LeJamsjijORLiikoJIgVcxTLHhuzCpGFKSKu/0uzUBjRgczkiC1MkXQi2F6fyKk8G4U4xdqAwpEshXyEYgKa7Qgn8zESJZN+OcZCMUGnMCRezBMrm8TmcsSOZuncbJM9kmLyTIH25BTZ9VvEFqfYjKWZmoyQOT3JMJpEt9qop+dJT+QYRiO4RgT11DFMRyMeU4kcXxhRFXxkEa2YoE+EaFRHOX2MZsMktThN4uQsraFLJJkmmonAYycBP7/2+HFYnIVyldhkgeLRHGa2QGRgwuJR3JUyxuIUuZMlytECWbpETx8j5djYsQidmVmm5nM0EgWmnBbxE0dQ+hat0hFmIgMiJ+dp2wrxmEYkoF2YmYaFWTA8yOblZp8+7tM/aHDqhOxjl8rCY8f9fKLPXB/QcBTScPYkfOQIKH64IFWQHw/bQ3cNjEGPaz/7IvHH52gXpsjFXHqugnpinlinRWxuitjRaTo3q6QWp0gtyuv4dIH45DZ6iuPzI3oIyyJyfJ54TKPnqqinj/t1ghraiXnsnoU2fwS30SI1W6DuGeQKBrHJXSgs7sXC/z7BdvqYnShYdjonrksULUjxLubFaZvLynF7anA8/RvzU81GRN6rys4yFvOjlPJsZqteTRUZUT8NrfsyTk+M0tVBqnmc7qbcFoqP4PV4f3brY6DL0Eepd12BU6U7dQW2BrQ3QXo9cOoWxtLw4+lwbWxs4vqYrkB+SXRul6Erdx/DuexY6vW90uuE+NDhUNK1iqL8G8/zfn63zxRFKXieV9sv/fu9uta2oVMDIwPdmh/GL0DPfzKOpyGWBMd0iSAh9E5XxTYhkZNrXRtUXX4/O1UXxbJIzxnYXZfhWpvofJpqTSWhWCgRAyOnUl13MRyL1IxBuaIyHEIkAqWiS2NZlsxPnkoQMbb69gFtQiRu0Kqr2Dqk4zZr1xtoQx0jp1OcTTAYqngRiKse/eaAuD3EzWpsrjeIFtLoVpt4IYvZ7wIesUiKRrlOIZ1BTcbpth2IaSS9AWpMx601MeMq0Vgac32I4faw1ASJyRg0hniAXtDwanVMBkQmStQGNrGyiZ6MkcwLUakZMYjoUB706VseMwmddreDrhnYrSb5iTz99gDMIcnJJNTadE0bNJ34ZJp+tS1fnoU03XXhdotPZzG7Dq49vE2T0e9at/nNzO5Aal1AavVaJqqmynWmPaIxqHdw233UdEyoDNYlNRqfzQuthWmRzEooJ6AriMd1+rUu8ck06DrmwCU+tGTMqm26WgTSKeJRlb4tbcSidJvWrrQege1q0sBt9+jWTFBUktNp1IbPZ1dIy6IQgImsUED4uSA3nqBr+xQf2PTX68Q3m6gvXYFvX8OrdbAzBupPPIv5Q6fwjpZu05O47Z4EErLG7fsVU/2C8HEqiCD9OU7/4VOXBDQRybRQUtymkxjvozkgFo/KMWnQR1KpKjukqrZTYrzP01j3pCfZdg7snBruu/JZcNwxXb2HNOFOMu6Wrt41Nb7HdPV7lfGo69qSeg3xocT7ikJle82doiga8KbneY8fhP79rsm7cR46FWitgdmSepJhSqJ71grMHBVnLj0NqRLMPQsrr8k17XXoNaC+AvlZMHLQ70gtjBF3cb91AbVeo5orUPvUWWKWyhNnoFeBXhPwYG0I7yxD+xZk5mA4BdddCby8+Az8nRclnA/i4L39tQv01mr02wUqxbP0DOheuUCmUWPZLmA/fRalq5IpgJeE5KTDme+8zUS1ycnTKV779BMMz/+AQrdMxLlIv1Mj4cRwnCepZxYxZjK0P7rAKzdg8p0LPBOtcsapoHZcHCfGD4ZP0642iL9xmWgmwnDqaSY1Dd1qkyzdIFW8haIp8NyzuB7c+JPv0+27GIUMxx4vQWmCV6bO8tINFc91OXb1AnalTHn1JsPpBHO9Jmdsj7SuMG9bqBdvyk35W58SOoOvfwdw4clFeP4juKXJXak6xqkg8nHAp73YCNhcEvJZvQf5mEv8jQtcu1TDzBToPnVWCHO5s/7nuQegq9g+8dxXLnDpnRqb8QLac2dBkV0WNpoup5cvMGHVOHkyx8UKXL7coJ8tcOwzZ3luUR3V7exAEeG6QgPyXZ8KJBOTtF8pubUWqjI2DqUkPOvXNN3eamzsfTB29R7kE1upR4Kx2V7/NZ97SGP1IcfDoEo5SL1b/ucSgCcp5/F547hS1zedvpPK5n7mTKBr+1zelW7pAcbtXuNwWPcnxKON90VNnqIov6ooShv4qKIoLf+vDZSB3z9IW/YLQxO6mxDPwuYS4IBjw8Zl0PpSszewoHpNzuluglkbXVO9BkMLnB4MB1C5LJkrHNj8gYVaq2HlJnCXa0xELKrL4OlQvS7nDIaw9i4wgIEJ/T6sXENKwRS4tiYLKwOYbdn+KT45Qfd6DVOz0KsW3Zs11OkJOhs1SrrF1TL0dKAHK8sWuUqLzekineUW7WWhodiIphheu0HGjtK2B/Sv3SA9FaV1q8PNtSHRgUWiVWNDE/oPHJW+beNeXiM/XMdqxIjFbZR3Kni1BiSieJdWcCxu0w70r63QtVXSuotzbZl+SigGNsqWsBNYFrWVGk0jRaq8hqKn4eotIqqN1RliX7wijp2mwpuX4c0r8hrg3etgJO9K1TFe97PWGtFerDTlGHw2mYZy2aJ2s4ZVmCDSrLG0at1RCxTU/zwIXcUWmJbU9qUm5F6ULdZ82zYqFrmetHWW1+ksbdAvTBBv1iiXrZHuXSgiTFv6pSpi+/VxeoaxWqjxcdjsjWqabm81Zt05dpPprdQj47VRQb2TZUs9kuM+pLH6kONhUKUcpN7x61ZbMne2zxvXG9GGbKeyuZ85s9tc3pVuaR/G4bDuT4gPJg7UyfM8738EssBXPM/L+H9pz/OKnuf96kHasl+IxCFZBLMJxUXA3wpz6hQ4Mal9ixowcRx6ddATEMuNrpk4DhEDtAREolLLp2oip/ikgZMrEKtWUY8WKFsGiRKYXSguCI8sKkydAaKyUDIWg7njSCmYB8dnpH4+QDxtYEwXaN6qEl8soFgGZtIgMSdUAampAhXb4NgkaL7jOXvEoDaRIb1WI340gzadY3VYIFnv4M3Ns9GyibhRIsfnaa5ZxLMas3kFr2lj2QbJegNl4Sjdgc2gY+POl9ho51BjXQZNG/dMkWE6g7XRxF2cpT+AnuXgHjlCbGaCpGbTtlXU4wu4tRaOHmcio+PUOyi2TWE2R7bTwkllod+AE0cYujpGKoL+2EkZKMeFp07BUyflNcCZY2B1iU0WyBUMym2py1FHbC7CyJIQypVcXOgomuaobmYqAwVd2icnDQpHCxi1KsNsgcVZ445aIMeFUhxq68Jcci+4rk/L05aygF7Xp3uIS+1ZsVOlHC0wNWkw41NyTJUMaokC6VaV+Nw0kbkpYrUqZrbARMm4TQHhxgyhPfGpR9yYQXcgqbWZzKh+6FhBInBGRGwIaDPqva31Q9u38spFuU1XM15TNU49Ml4bFdQ7Gbpcp6mjuq4QD4691PA9SnrHr5vNyNzZPm9UZUQbsp3K5n7mTKBr/H/6bjV/+zEOh3V/QnwwcVjp2lc9z/vYgSv2sd81ea4rET0tJkcQ588yQfHrLLQYXHgJOh3Il+DJZ8Hpj64J6jEi8ZEMLQbXv+XSvWGRWDS4sKry7k3QkvAjLwJDSWNMFOBECnClri+dhrowTVDKjVK1ga2vv+xSXrFYjxgodZWYDR877ZI9ZpGZMGiaKu9uwg9+F/Q6PH4aBp90sOoW6xhMLGlcet0loliUOzqxYZfIhMYP/bxB+vvfZ1hrkH1nk+xSi1sVuHr6Kd759McoXn6VaKVOYbnFoJInM1wncbZE+iem+O4bENvYJGEVcafO4BThY09e5sloDRIJGtNP8eYfGri/+SrRdou4t0l0oUX8tML8z36U4Zu38MpVhtM5Ev/x5xn2XalpiUdH23NN+ttylf3NVSayshG7v31WdwDV18UZT+ShPCXRqKwhO4P1BlvpKEwbLl+Q1dXxHHz8OakH6zb92rOoekfdzWAAv/1rULkFk3Pwi38Pojtvwyv36hV4/bt+WjMDpWkolOCj50SX27Vkm7OoPL+ZvhP20pLYsW4blFKQ8Sw+umhwcVPl5RtjFBW4tGoWmYIBikrdHKVaA9tjKnxnGb6/6rPrxyTqEFA8DBnVDwV1YDF1REeRisEnF6R9vEbs9pZcd6lP2rJiMMQDYy81fI+S3p3qCXeaN7tR2TyIrqCucNzWhzVu95JzWPcnxKOL90W6dgzfVRTl44eke9+hqrKwQtdlkUVc6udJpSGZlff2UPh0J6bFKRj0t14TnDcuwxuCbauUnk7Q2lRpViA+KZGm5U2ounBkRva8LX5wYwAAIABJREFUT+VhagZKJWG9mJmQP33bU6FlQquhkp9PsFZVSThAHtpNlWwxgZFQSaVhcxOSLdBmYfMmNJsamdkkq5saah1WVZUMCTY3ogxn8wzbGW4u2fSaHTLFHOqlZWzHZeDpzFpNNpY62E2LfrGEemuNQSxByquxYZWoXawQa5Yx52bor7XoFHWiTZ32coN+aRLzksXSP9Sx/sAm4vWwijn03g1iSy6DP9XofusmiWqF5OOL5Bpdor0+yWIKNZsSD2q2JH8Bt0TwPhq9TTOgqhB1xMHLTEK9DOWaODMb7RFlRUBHoeugOdCqQWkK+k25p6oudBvphLoj/YXVEgdv7hiUV6BS3n1eWaZwL6qqLMxZuS5UPY1NaUNVUdMJkoa6hZJjCPQclcyET8eQUulFEgxVlfXOKGq2XIPVjkpxSugpgrTYZk9+7ALbhwhnmK6O0rfjFA/jP6qBDX1X5ExnxDnub3PaxqlG7kYhEv7gPRwclsP8oHq3z5Xd5s1uVDYPousOGpL3YP/d+vMg7SFC7BWHNYV+FHhJUZSriqK8oSjKm4qivHFIthwKAqqVzbIcjR22Ad2OIBXcKkNxDmYWYVgDMnBsapTGuJ8Qf2BHd1McxEEKYh3IT4o+EFnT0zAogrYOUwvyvmmKb+QVYB6oRSE/A1oVmICFUwbZIwU6jRacWcDQVQzNZSU9RelkDm+qgNpu4c0fIepaNKNHmCq0KJ2dwpyeRqtXiR0pYLQNelMG6WMFtGtVbv52AS1lEF0wMJMFosMWdm4eO6GiRx3W/3yB4cQcXLsle8MW0ncbgj2Nd34SJgu70zeMj+X93NPSpETwVq7LsTR593t1m+ZGF8ew3by3rt1SUAVjRKGxnaJiPC22fT7F9TvTt/eieAhTUCFChAhx8DisdO3CTp97nrd8EPr3O127E4IUbiQ+SmM1e0KXUpwA0wQ9Pop67JYq6A5AH0rUz3Wh2ZTrfDaPHUP8rivbiYJct/3pMKB8SeREZyB//Dzbhtom0IbcrIvS6NGPQCSZoLEOaseipxoU51XqVenr7DFQ+0LPEi9E6a9aDCzoqAnycyrVmy5RLIoLUdprA9q9KOnEgNRMlMqyFP1PLCRo1lRicchmXdb/tcX6HxgYcyqoMBy4aK6FGovi9S00HaxygqmfcZn4G20iR9Ko0fv3KIKt6VwXIn5kdnx87zbWlilO172ewoNxj6agVoVcGhLZ0fywTNAjco4el0gwiF0gOgb93XWNz7nA3u3zKphTsDvdw04Rhe3X7YXiIUxBhQgRIsSD4UHTtYfyPB04c4qiTALvTwbS+0BAq9LdlMjQ3LPwvRvwrW/CoAaxrlCsaDmY+ChM+9sB3XX5vp/aLRa36kpG79S9/AosvSTrDY49DwvPbXUcV14b2TZ/DlTjThk3z4sM13E5Xvs+xcqrqHGPW4lnqbyt4txsoB4t0H7xLKsrKq1luBKB40+oaHoCMwW36ik6r0EyDu0yKEOVVCHB0/85tBSd7/4u9Foq6Y0LZOwa5Au4/8FZNAfSPVhwVFZ+LYFrgd0CNQZaQgUjgZYANSm7Y+tFuPFbKo2pPMmK36f7rMm58Aq89pLUqj39PDz93CiFsttYg5yTSN75+XbYNvzFV2BzBQqzcOJxWG6O5scPXoPaBlz7royVloRP/YzYkhoLTG5Pv4/3YXzOzZ8b2Tt+SZDW2o2mYnx7qO39TI/Nk718kWwfvxAhQoQIsb84lOdpRVE+ryjKZeA68C1gCfh397jmqKIof6EoykVFUd5SFOW/8j8vKIryTUVRLvvH/L534D4R0KpkJuXYbsOtMnhNUJOwfh28BAybsLQhqxYf1vL9oQnNVVA0qb1qro0Wcuxk23jbTjIifYvBpQ2UhMqwoTF88ybRwQZ9a4KoU6P+ukXjEuSPQ/caDDpSP9a6DHTBacHQg/412Vih24LGD2DzsmxskNAsBtdquJkJvEaN6lsW6TZ0otC+Bk5XFh3ggN0GJQpuH7zx8dDEeb5bn+4Gy4Tyqmz0oKpSB2fdp4x7oVMTB2/qOFSWYHN5ZG+n5i/eMGDtKqgJ8ExYW9q7HXu5r+PYjaYipHAIESJEiPcvDqsy5p8CzwN/6nneM4qi/Cjws/e4xgb+W8/zXlMUJQ28qijKN4H/CPgzz/P+uaIovwL8CvDL+2j7fWO8titZlNWuRybhShbcGkwfg2EP9BzMTIHZh8mUUFKUkhL1gRF1Ryoqabe96s7OQn0ZXAWyM6P03U62BW3jK4RdFzLTImMYM4icnGKwtoIa8VAeP4r5A5VIpMpALZA7a9C+DrWrkD0uqUhVh/RJaGyIMxtRIHZctkiNZyD7uNDEGBeg1zJIHy+gtqqQL1B8zKDWgXgVYkfBi4JrgeJAJCUOnhIFVFnMAuAOQctCc13IppUIbK76m7BPyHlBitO2oVGWVaLZSflMcWXV6sqyUOplcmJfbV3SpomkjI3DKGo3nkJ1+qNxHE/Rj8PIQCIDa5ehtAj5eaguQ25W0uYxA5plmD4uJNlaUmr2zCY4A4ngBSuvAzvGdWgxoeIpL0HhiIxBozzahaW+CRdehb/6E6nrS2Xh6Kfh6FlYnAE8qZ/Lx+X+2/aoXzulbsfTwuOlAXcbg91wN3n3G5HdaZV7sIp9e3/uVdZwvzbej7wPGsbHfrd5sxM6Fbj4J/D212Sux7Pw+OfgsR+T/+UQIULcHw6rJu+853nnFEW5ADzjeZ6rKMornuc9dx8yfh/4X/2/T3uet6Yoygzwl57nnbnbtY9KTV7bAtuEZBK6XXEg4jq8dANafXHmoiq8siJpw3NzMHRldWKQxt0ri/vdfmx2si3YtaO9AekpcQAnH5PzV7/vcvM3e3h9GE4nuBEF3bP4yMcNJidUepuAB6c/KytOPRfeeBNe+7ZE3M4+A0/+CFz9U1kxnF2Ao+fExr4JiYxLb90ikjVYf1vlr/9vGDTB9CB+AdLXwcsJN2A2J2TTQxNsv0bM64L6Gch+Hp76BPzlv4HX/0icvNITcOwFKE7B40/DX/wGvPZH4uSd/XE4/RRYTaFMyZ+Bt1+XaOPy98QR1uNw6nGwezBUJPUN8PrL0ue5LByZlvECWZ17Ow3uj3uQqi0vy7Z1n/lP4N03YHMNClNi59J3QbHhqRegcAKUGHzv9yT17rbhxDlZgV1tga1JGv7sc6P7t/wKvPkd6NvS383LUL0hUcPKMlx9F9QJmDoL8QT0LXH8FAP+wT+BM09Izd1bG1DrAFdgXpcf2vG+jKeFE3m510uviHM8/wk5b6cxuNtcHZfnunDj5Z1LDfYiJ9hFxmxJBDiekfncqcgx6A/ImF3/rti++MLuunbq8/I2GwN5u5VJfJCx0/fH9nmzE1Z/AF//ojiFySLoBtiWjLMWgx//Esw+eXD9CBHiUcL7jUKloShKCvgr4DcVRfkXSKRuT1AUZRF4BngZmPI8bw3AP+64PlFRlL+rKMp5RVHOVyqV92j+/SOgVRmnlsgmpKbOMOSYTfh0F0OYzQpNxY3WiOLiZn1E3XE/aTRVHdGw7PQlu9228V07Gity7NVHjCOdVRXUFE4+Re2qih5XUacS1E2Vdhnyc/KlrHgQSYOtw2YFokmIZYXixbbEYSqeFNlOX5yWwjQYCZXC8QRRQ6W+As4QomnZ/aOal8iV4sg2bn0T9BhYbfkR9/xdPnKfFoqa2hqsX5ZoIsDqu5AwJB1avSWRNE2T9tWLo7Rprw4MZXeSVELO0xPgtGHlooy9ocL6skT8NA1woHzd7+OapLh3SpcGqdrZUxKtaGxAsw5T81BZF5lxXRyuTkWibKojkb5EAnot6LdkF5WBK3ZsjqWUhyY0VsGJSP3jrStiV3YK1t6Gqxelv3FVoqqKIhHImTl5sPgX/1gcPlWVaHIhInZFi3f2ZTwt3FyTXV40VVL79WX57H7S5tvl1Zd3LzXYi5x4VnaDcW25P5tLEuEM5nVg19AU+YHtzdXddW2xcVVkbrfxXmUSH2Ts9P1xr/vfqYiDF4lBbk4eOBVFjrk5+fzrX5TzQoQIsXcclpP3twET+PvAHwNXgX9/Lxf6zuH/A/zXnue19qrQ87x/5XneOc/zzpVKj27cf5xqYiYDC7mdKS72k4YiSOH26pCaHEViInH5S8/DQAe3CYVT4Ebl6X16QdKNQeo3SJN26lAsyTmOA5OzEsHaKU283Y7igjjBAwtKZ0DJwNVZ0DyIe6B6sn1bLAY0AQeiPwubljikqgaTx/wfeWDmFNSrwFB+RKaOi02uDTOnJb28cV1+mPSIOITdnlw37IjjNXMG1CF0ujAxA9mkRP/sAUwugOlHHpIlkTW0xYmzLEn5uhrkj8DGNaHCmTgiNCjlVdE3MQNdE7ShjKcWkx+5iQXo9STNG8vILipRFboWZIvi9NY2Jf2aLIFniTM6cxzSRaivwTDmU5+o0lclCu7A/3NF/7ADf/V1wIS0DRt1mJiEfgWiCbmvZhu6TYlKJvKSGk/koDAv89VzIOfv0dxYHV3XD3bo2AbXFZnb5WXnRZZty/txGcE1wXXjsrWY6OzVYeKY7+RrsgvNoCeRpYDk2rblLzPl/6858nnQz0B+8FqJiOzGqh/x9W10XJERyEtP32n7dnnd5p3yd3r9MM57WLp2u3/97qjvEUPGNzcnx/F5s5Out74OAz+b4bryYDeeZDIy8iD4zjfeyzdbiBAfPhxKuva2ckXJMFYX6Hle7R7nR4A/Av7E87z/yf/sXd4H6dr7wXZ293HmdjgYGgrbhuvfEbJeIwPHPilOk+vKjgvn/1yiaWd/FD5yduuq0qEpX+h//hujNOkzn4Pnf1pkBPVj29PEu45F20/j5iQK5tjgbMKb/xsMXoaIB1NPQOGzkP00VNfg9b+Ca29IJPGxj8OzLwIq1K/Cd34bVn4AkRw8/Tl49m+Jrtf+HbxzHojAY2ch2pMfmiPPSOrxyl9CvwObdWhuSu2e2xFHddCCE8/AiU/ClbclOtcZQmcTyhchm4V4XhxiPQUv/CScOC01grouO1988/+UiI/uwLGPQrYEi5+E1ddHkZHsomxXp+viyCx9R2r3rl2AlXWo3oSJo+JszD0N6QJ0lyRymMrDN/9anuzUDmguYAEDuUfogO3XNmrw+Z+G+gqkjsCJFyTV3u9Apwy9GtRXxak7+hx4A3GextP65YsSeWncklrQ7ubOqbsgvRykSue2yZs4A2uv+7oro/KBIDVMkIadFtlzz8qK8U4FYilYeEEcBJAxW35J5nUsBVoEls+P0suTj43pKoPZkHRrPO07eIroSk5IpC43JzqDPm+8NUpXL/jyVn157Q1JGyueOOlWS8Y3Pys7pATyjdS2102JiGXnhGLH6si8NFLyevt5uTn5n7U6W3U1bt4pI57eKs9sQXNFHHQjI3aP61KVO1PZ4+nZ1prfR0Xm3/wLcPNl0X/HeI71//u/I/dTi4AWFacwYvg1tcroe8Wx4Re++h6/3EKEeB/ifZWuVRTllxRF2QDeAM4Dr/rHu12jAL8GXAwcPB9/APyC//oXgN9/+BYfLHZjdN/O9r6fcPryI5ublWPwI2mZfuouA5G8pPWC3TwC+2JJ6DVGaVJNg/UrwHB0XtDPexWkq+pYGteAiVlJayaOQH0eUr8MjZ+BY/8LLPwixKeFcmTogdOUFGvlhuwXnM1D85b8WGFLHd7GVVnkEDPEQYrlAAtuXZQfFF0HqwFOBzRFxmPzJsQj4KiSMo0ZMkYA1Wsj52f9Elh1P7oxhI1LEjnzTNndwkiPKFB6DbG7NC/pv2QehpZsvRukBk1/gUQ6P9oBZWhBsgDla4AGw7pECtevQ64Azaosvpg5JWneVh0SaYgqoBngtQFPnAGvKUddA7Mi+xQ7lkT9mitiYyIvfRwOwOlJ1Kt5Q9qy01vT+kGkrFP2I1+7pO7uSJVuk4ffz0R+JKO5Kql1TRUbqtdGss2aHLPTcl+84dZdY4J53atD7eZIb3t9q67qNT9q5UD5shxdW9K/0YQ4NYn81j63NkbyWuuje5TIy33FEZmVy/K54+8HXb40kn9blyOf233/3L60uY60V65svSY4z+6PtY3pGpp3yihfHqWyK1ekfWjK+ZUddO2Uyh5PzwZ9VDUZl2FLxnun8Rzvf7cqsvGg3wY9CsP+KAIPUpZhNvf4BRYiRAjg8NK1/wB4wvO8Rc/zjnued8zzvOP3uOaTwM8Df1NRlNf9vx8H/jnwGZ+S5TP++xDvEbutujXikm51/B+Q0szOuy2kCjB9Sr6kHUecjFTh4dmXKkiqM0h5BrIjcfkBTxqywtbuSbo2VZC2iUWJaKFL9CqwK1WQ8wZNUNMw9zhouqTgsjMQ99PL/aashh1qkIjB9BmpAUxkJKU6dRpKx8R5mDkDiUmI6JL6nTotESolLruVjI9b0J/qTUkpOpboC/TulNYO7pFjweRJwBHHW3Vg5gT0LEmhTx0fjVN2QhZjKDHfljSgSHRFycrRdiBegkgU4R/UIL8g42A2YeL4zm3jNga2mU2JLA0tOZrNnfuRnRmlebfLC8YgkGU2ZcV4cUGuUTWxKZC9lzFrlbfK8Bx5P65r4rj/AKLB5Ck5qrqkf3fqz/Z+bJdXXPRlqFA6JZEqLSFjOXl6JP+2Lk0+1/2V0lpM2lRN2ksnt14TnKfHxtrGdEXid8qYPDVKZZdO+qUBcTm/tIOuoF87jel4H/cynuP9T07INSgQS0vpQyQ2qqUFcV7j2Qf6uggR4kOLw1pd+8fAT3qe1ztw5Tz66dpHBbulU4PdIOBO6o5x2LakEj0gN7k7ce+DItgxIlXYKjuoD+rbQrESpESDNrMNvTYo6la7bBta1a00KTCKNo7TQlgmaMjrTk1+kHR9tBNJsIOIZUpkQlHFzm5t9/EY33nEG25d7bxbWjtoUyLQrkr9X0SH9BhVTGBPqgB/9G/hD3/Ld9QtIZRWHF+Yn64t34Kf+Dl48Sf9LdTUrXQoASXJTm3bKUnGaTS2U5lsf303ebvRoewmI6BKuV/Kl73quh8alrvJG7d3r/160PMOUtf99v+N34NXvwrZI+JYeo44eEGqFiSK+7EvwLmfI0SIDx0eNF17WE7eM8CvI6tj+8Hnnuf9lwehP3TyQhwGdtqF4mGl3fcqe7MC/93fFSdd1yEag8JY3VOnJZHJf/ZlWSzzMHE3WpPMzN5oNnbr5/a6vrtRoIR4cNwe5+9AbUVW0idyo3rIB53TnQr821+ShyUjc2e71ZL07X/45ZAvL8SHE++rmjzgy8CfA99F6vGCvxAhPrC4310o9kN2sQT/2a9CryMOXaclqTHLhPUVcfD+/pcevoM3buNOtCZ7odkYl7G9n/dDgRLiwRFQw7iuX09nyb3c6/3bDamS8OAN+xKxG5qyEGNo+u/70h46eCFC3B8Oa8cL2/O8/+aQdIcIcSjYrc7xoGU/dQ7+i38ML/0ZXLwA1bLs6vH5L8CnPrs/Dt64jZ2K1LUFkbzior+opHTvMdmtn0E9XM3fpWR73ViIh4NgB53adb+ezpAFLnu9f3fD7JMSqXvnG/DW12RlejwrKdqPfDZ08EKEeBAcVrr2nwHLwB+yNV17VwqVh4UwXRvisLAX2piDkO26Er0z9sGOe+ndbauxvY7J3WpFP6zbiB0kgnHeS/1jiBAhHg7ebzV518fe3jZgDytsH5b+CuJk7icmgOo+6/gwIRzP/UM4tvuHcGz3F+H47g/Ccd0/POjYLnied9/x7MNK1/4y8Mee57UURflHwLPAPz0o5Q8yUPeLYH/e/dbzYUE4nvuHcGz3D+HY7i/C8d0fhOO6fzjosT2s4Pp/7zt4n0K47f4v4H8/JFtChAgRIkSIECE+cDgsJy9g5voc8C89z/t9IHpItoQIESJEiBAhQnzgcFhO3i1FUb4M/AzwdUVRYodoy37hXx22AR8whOO5fwjHdv8Qju3+Ihzf/UE4rvuHAx3bw1p4kQBeBN70PO+yoigzwFOe533jwI0JESJEiBAhQoT4AOJQnLwQIUKECBEiRIgQ+4sPWoo0RIgQIUKECBEiBKGTFyJEiBAhQoQI8YFE6OSFCBEiRIgQIUJ8ABE6eSFChAgRIkSIEB9AhE5eiBAhQoQIESLEBxAH5uQpimIclK4QIUKECBEiRIgPOw6MQkVRlCvABvBt4K+A73ie1zwQ5SFChAgRIkSIEB8yHChPnqIo88APA58EfhxoeJ739IEZ4GNiYsJbXFw8aLUhQnxAYIO9DCiAtkO7A3igLwD6gVoWIkSIEB9EvPrqq1XP80r3e92BfQMrijKHOHc/DJwF3gL++qD0j2NxcZHz58/vnwLPBc8EYv4RUOLy2nNBUUfvx9se9nk7talJ+ez9Ym+o69HTZf4e9L4K2iyggKLLcRzOCiS+AMmfI0SIECFCvDcoirL8INcd5GP2DeB7wP/ged5/eoB6DxaeC4Pz4FTAXgOvBXhARl47K6DPyXvaozY6/us00AJ7RX5ElZy0eR4oaf8aBUiB2wRvBbSjvvw2KH6b1wBndUxXC9Ag9jzEnhs5enez97Ydc6D47z3Ft2Obvc5NUOdAye5gbyCvKfL0OSA7JiM10nW77UF07SYvGN9dxo3WqO22jMxIlzIH6nZ773beWJ93vOfBPWqDEpzXBHcPNt31vG1jOD4ftozn3e7D2Dhtn0OBvN5vAY60KSnQpkCbZIujpxbB+lro5IUIESLEIeIgV9c+A3wF+IKiKC8pivIVRVF+8QD1Hww8E9xN0LLgLgEOKC44l0HpAz3AAvcSKH6bexkUW967l6SdHigD/zrHb7vsy7P9a/p+tMXy3/vnOZfl2kCXc1n0KCq4a6OIzL3svW1Hf0yGs1WXe1n0e6act6Uvl0fy3EtAHxRLjsF5OOBe2dZ26cF07SrvylYZwbiNj01wnbKt/1t0BTKujPVrB5vG2xRr7J4HMuyx12Njs+Ve7mDTbucpO/V5+3wYG8+d7sOWfgX2DXa23asiDp0tTqrXA8/e9o8QE2c6RIgQIUIcGg7MyfM87wLwG8CvA38O/A3gHx2U/gODEpcohtMEdRHQwFNBOwVeDEgABqinwfPb1FPg6fJePS3tJMCL+tdpftspX57uXxMTfYrhv/fP007JtYEu7ZTo8VxQZ+Savdh7247YmAxtqy71lOhX4nLelr6cGslTTyPpYEOOwXlooJ7c1nb6wXTtKu/kVhnBuI2PTXCdt63/W3QFMk6O9WsHm8bbPGPsngcy9LHXY2Oz5V7uYNNu53k79Xn7fBgbz53uw5Z+BfZFd7ZdmUAif7ofHUz4Kdtx9P2oZogQIUKEOCwcZE3eeSAG/H9ILd6PeJ73QDnmRxqKCtFzPLI1bttr8h51e0Ndj54urXDvmjx3U2ryQoQIESLEoeEgKVRKnudVDkTZPXDu3DlvXxdehAjxQYZTgdovgRIDNXNnu9sCrw+FL4N234vBQoQIESLENiiK8qrneefu97oDi+R5nldRFOVzwBNIHjD4/EsHZUOIECEeArQSZL8EzS/KohK1iATp+xLBIybtoYMXIkSIEIeKg0zX/kukwOdHgX8N/BTwykHpDxEixENE9EmJ1FnfkFW07qbU4CW+AMZnQwcvRIgPIAYVqP4JVL4GdhP0LJQ+BxM/BtHwX/6RxEGma9/wPO+jY8cU8Hue5332QAwYQ5iuDREiRIgQIfaO9g/gyhelEiNSBNUA14LhplRunPwSpJ88bCs/uHjQdO1BUqhY/rGnKMosMASO3esiRVGWFEV5U1GU1/3FG9vbFUVR/mdFUa4oivKGoijPPmS7Q4T4/9l78yg5surA+3cj91qyqrS1lpJQ0yoZ6AYbkJsGPGMYPruBto3PGDwMGHtszqHxhz3eFzye9hiPt8+7xwvN2GMbDG1zAAOmG7pt3BhjVjWDoRdbpV5VkrpVkqoys6pyjbjfHy8iMzIqs7auSpWk+9Opo8i4N+69774XkS9fvHhhGIZx1dKYdR28VA7yk5AqgIj7Pz/p9p+8zekZ24tBLob8tyIyDvwG8GXcGgz/e43HvlxVz/eRvQqYCv9eBPxx+P8lQwPwq+Dl3P8CeAUIqhAE4Hnuc1KmuJMmqReXxY/xq0D48GMve71spPu88GIt8SZj7NoOAG+5rFe8/fT6ydbqayV7K+WtX7xrLVdSb611vpGYVtPrV+a15no97XUt5Vprfa3H10Zys9628XTb8mbmcKMxbaQe1nNurFQna2mH67m+rKS31na4kVxv5Pq93na4mefyhvVWOB8EmL0L/EXIjEHQdA/US+yB+nTRra9+/h7Yb+ufbysG0skTEQ/4pKrOAx8UkY8BeVXdjNVSXwO8W91958+LyLiI7FPVs5tge91oAKXj7hdN/Sy0whdIpIvQLEN9BnKTkClCq9KR+eGLG9KjMb39kBmHVvhCgtQo+OFLB7wRNyeiPgP5g5AqOhkC6RFozkP9TMxXGSQFYzfBeOKFF2uON3y5Qno0EVMZaqecXnoMgrAsUbyRvVYJajM99Eac7fqM+1WYHtuYr372UmF+27mJ5S0dK1dqJNTTzv7IV2YsFscKeumxRJnLrsztOGL1F9dba0wr6XWVeay7PbT1Rt2SiP3qwV8If6H3akOV5bJ4udq+kuVfpb662nyPcyPKYVuWrIfV6rVP28hNds69ZedUoh32O/dWbfNR7NG5XOlxfsVkXW0jUa5+7bBnTMU1tuV+502Uwz5tvhk/X5PttdzdvvxYu+nKZyI38Wtg5Hel62FPWb/zK3k9WEOuk7H3Oh/a+Y3ls5Vsh2vNdSw3K9ZlrE56teVl53w/vbVcv8Lyn70D1HflT41A7hrI7unu6GV2wvk7rZO33RjI7VpVDYDfin2ur6ODp8A9InKfiLylh/wAcCr2eSbc14WIvEVEjovI8dnZrRtT9qtujkJmDKqPuRODABanIah6xQIyAAAgAElEQVRDsARag8UTQFwWvqxh8YSTB0ugDSdT38mWwu2gFW7Xw1+XNfcZ38kXp92xbV/TtH8x1s+Gv+rWEG8UR1Dv2MBfHlNQC38N1sPPre54CVy5grrTjeupD0snw7LUQl8nNuarn72lk4ncxPIWlSs6rqtOYr664jiZqNdETHGZH+UwVkdR/ZHMzWox9dGLYgriZU60h7beND3rIV6uoNWnDfWQLWt7yXa9Sn1Fbb7r3JjutuEn2mGyHlaq115to6v8sTK3z6ke7TB57vVty9M9yh+dyyd6n19xWbxtRPnsiqlH+ZMxxdv8qm05Uf9R+/JrHb2e50a9U//92mH8fIjaTVedJK5zXddAP3b+97keriWHyVz7fc7DrjYUO4dWOx/a9v1O/WuPHK6U62XnfLJ9Ja438TrppRc/5/tdD9d0/Yrls3nedejUBxT8Hi+58XKuc2psLwY5J+8eEfkukXjff028VFVfgLst+zYR+fcJeS97y54mUdV3qeoxVT22e/fWPQaUKrhfNM0SFA670TM8GJ5yJ4E3BJKH4aNAXBa+rGH4qJN7QyBZJ5OUkw2F21463M65IXUv7z6TcvLhKXds29eU86MB5Pa5GNcSbxSHl+vYILU8Ji/v4pBc+DndHS+eK5eXc7pxPUnB0JGwLPnQ19GN+epnb+hIIjexvEXlio7rqpOYr644jiTqNRFTXJaKchiro6j+SOZmtZj66EUxefEyJ9pDW2+KnvUQL5eX7tOGesiWtb1ku16lvqI233VuTHXbSCXaYbIeVqrXXm2jq/yxMrfPqR7tMHnu9W3LUz3KH53LR3ufX3FZvG1E+eyKqUf5kzHF2/yqbTlR/1H7SuU7ej3PjVyn/vu1w/j5ELWbrjpJXOe6roGp2Pnf53q4lhwmc53qcx52taHYObTa+dC2n+rUv/TI4Uq5XnbOJ9tX4noTr5NeevFzvt/1cE3Xr1g+M7vcCKSkcKODPV5yE9TdSKCxvRjknLwfB4aBlojUcJ0zVdUeq6l2UNUz4f/nRORvgBuBT8dUZoCDsc+TwJnNDHw9iAdjx7hs5uStJ16bk7c2PZuTt7Yc2py89bUNm5O3ug2bk7dBvVXm5GV2wNn3QT72kpvkcE3zAuy1l9xsOwa2hMpqiMj1qvpAYt8w4KlqJdz+O+AdqvqJmM4twA8Br8Y9cPH7qnrjSr5sCRXDMAzDWBuNWXjgVkjl3Fy9JK0y+HW4/nZbL2+ruByWUFmN9/TYdw3wGRH5F9zCyXeq6idE5K0i8tZQ5y7gEeAk7mnd/3cg0RqGYRjGVUB2t1sHz6+7h2X8cPTPr4af605uHbztxyBv167Gsrl1qvoI8PU99r8ztq3A27Y2NMMwDMO4ehm9wY3Unb/HPUVbv+Dm4O19A+z6VuvgbVe2Uydve9w3NgzDMAxjGdndbokUWybl8mE73a41DMMwDMMwNont1MlrXOoADMMwDMMwrhQG1skTkUyPfbuibVW9aVCxGIZhGIZhXOlseSdPRF4uIjPAGRG5R0QOx8T3bLV/wzAMwzCMq5FBjOT9f8DNqrobeBfwdyISjdqt9+0XhmEYhmEYxhoYxNO12WiRY1X9gIg8BHxIRH4We6LWMAzDMAxjSxhEJ68pIntV9UkAVX1ARF4BfAy4bgD+DcMwDMMwrjoGcbv2Z3FvrmijqjPAy4BfG4B/wzAMwzCMq44tH8lT1b/vs38e+OWt9m8YhmEYhnE1Moina0dE5B0i8oCIlERkVkQ+LyL/Zat9G4ZhGIZhXK0M4nbte4FHgJuBXwR+H3gT8HIR+ZUB+DcMwzAMw7jqGEQn77Cq/rmqzqjqbwPfoarTwPcD/3EA/g3DMAzDMK46BtHJWxSRbwIQkW8HLgKoaoCtk2cYhmEYhrElDGIJlbcCfyIiR4H7gR8AEJHdwB8OwL9hGIZhGMZVxyCerv0qcGOP/bO4+XmGYRiGYRjGJjOI27V9EZHvv5T+DcMwDMMwrlQuaScP97RtX0TkoIjcKyIPhUuw/EgPnZeFS7N8Jfy7bevCNQzDMAzDuDzY8tu1IvLVfiISb8LoQQv4CVX9soiMAveJyN+p6oMJvX9S1W97urEahmEYhmFcKQziwYtrcGvkzSX2C/DZlQ5U1bPA2XC7IiIPAQeAZCfPMAzDMAzDiDGITt7HgBFV/UpSICKfWqsRETkMPB/4Qg/xi0XkX4AzwE+q6gM9jn8L8BaAQ4cOrdWtYRiGYRjGZYmo6qWOAQARmVDV5GhfJBsB/hH4ZVX9UEJWBAJVXRCRVwO/p6pTK/k6duyYHj9+fLNCNwzDMAzD2DJE5D5VPbbe4y71gxdxPtlrp4hkgA8C70128ABUtayqC+H2XUBGRHZtaaSGYRiGYRjbnO3UyVv29gsREeBPgYfCV6ItP0hkb6iHiNyIK9OFrQzUMAzDMAxjuzOIOXlrpdd945cCbwK+JiLRnL6fAw4BqOo7gdcCPygiLaAKvF63yz1owzAMwzCMS8R26uQtQ1U/wyrvt1XVPwD+YDARGYZhGIZhXB5s69u1hmEYhmEYxsYYWCdPRN6zyr5XDCoWwzAMwzCMK51BjuRdH/8gIinghdFnVb04wFgMwzAMwzCuaLa8kycibxeRCvA8ESmHfxXgHPCRrfZvGIZhGIZxNbLlnTxV/VVgDHi3qhbDv1FV3amqb99q/4ZhGIZhGFcjA7ldq6oB8PWD8GUYhmEYhmEMdk7e50XkGwfozzAMwzAM46plkOvkvRy4VUQeBxZxS6aoqj5vgDEYhmEYhmFcFQyyk/eqAfoyDMMwDMO4qhlYJ09VHwcQkT1AflB+DcMwDMMwrkYGuRjyd4jINPAo8I/AY8DHB+XfMAzDMAzjamKQD178EnATcEJVr8W94eKfB+jfMAzDMAzjqmGQnbymql4APBHxVPVe4BsG6N8wDMMwDOOqYZAPXsyLyAjwaeC9InIOaA3Qv2EYhmEYxlXDIEfyXgNUgR8DPgE8DHz7AP0bhmEYhmFcNQzy6dpFABEpAn87KL/G02ORWR7hbh7mTmqUyDPGddzCM7mZYXZbXCH//Aj88F/CV/4ZtAQoyAR8w0vgf30PvPSZAw9pRbZb/gwDAH8WqndD7U53IskY5G+Bws2QsnZpbB9mOc/dfJI7+TtKlBmjyC18CzfzCnaz61KH10ZUdTCORG4F3oEbzQvoLIY88K+/Y8eO6fHjxwft9rLjHPfzaW7Dp06BnaTI41OjygVS5Pj3vIM93HDVx/W7/wg/9ZvQegpIARlAgab7P30N/MZPwI9+88BCWpHtlj/DAKBxP5RuA+rg7cSttFWD4AKQg7F3QNbapXHpuZ8HuY1fpU6TnUyQJ0eNOheYI0eGd/B2buA5m+pTRO5T1WPrPm6Anbxp4MWqen6dx70S+D3c1+efqOqvJeQSyl8NLAH/RVW/vJJN6+StziKzfJxbSZEjR3GZvE4Znzqv4vaBjvxst7j++RF4+U9A80ncuHgqoeADLcjsg3t/89KP6G23/BkG4EbwLt4KkgNvebskKIPWYcftNqJnXFJmOc+t/Fh4BR1dJi9ToU6d2/mdTR3R22gnb5APXjyM64StGRFJAX8IfAswA3xJRD6qqg/G1F4FTIV/LwL+OPz/krHYWOKByoMczh3mocWHAHj28LN5aPEhqn6VQqrQ/hyXbbZeL9mxiRcynB1aNV5v7F+Y4Sxea4Kst8SOzDjzjQUAJrKjzDUa1FLn+Ip+ECm/iGqrRiGd5+jwtZxYfBSg5/ZKemuxcW/rdk7lnmRE97EjU2euuQCaYkd2mLnGIgCZ7AL3LLybXYuvJJfOcGRkPycXzgC0t+utZl/ZevTe/oH9DOXPwIEmpDJU5vYzusPJKhfDbb8J2Qy/8NH9/NUPO1lR9lPW7m1fm6Qk01O2Vr3VbDwh97Kg5xnSPQQyT0aG8bUBQEpyoCmWmGdaPsp1+moCbeFJmoJMUNU5gPb2SrLN1jNfV7iv6l1UtQTefgraokoDJE2BNFV8Ai+P51+kULuL6vB/dDYoUKVKQICH1/4cl222nvlan94wwwBUqZIjd0WU68PcSY0644zRpEmaNIIQUWSUGSrcw728kddxqRnkSN7zgT8DvgDUo/2q+l9XOObFwP9Q1ZvDz28Pj/nVmM7twKdU9Y7w878BL1PVs/3sbuVI3mJjiZ9/8j8zl32MUm6WZqoGAqlWhma6STldZ6yVI9PK0Ew1EYF0K0Mr3UQVMr7TK6XrjLZyZFsZgnSTQCHjp2mlWhAeU081qWTqFEO9ZqqJJ+C1MjTSTSqhr3Qrg59uIupxeP4mfm3v+9sdvb7xphs0UgFlCSgGHlk/Q0sBUTJBmpYEqCgZ9fCXhlnILTFSL5BtDtPKVFEg0yzQylZRVTLNIZqZJRYKS4xUC2RCPQTSjXwoqzJSHWrrgpBtFmhmaoCSbhao7niSsvgU/Qx536PpAYGQ8/PUpYUAGfVoeA1YGia3tIN0Y5Qgu4CI4NVHaGbmqQ/Nka/tJF0v4mfLEMqC7AKgpBpFWrkytfwFcksTZJrjBLkFVBWvMUKQWwBVqrNFKJQJxi/gzU/AwjgyWgEUXSwiIyX88QukLu6CVpHnPqsCCiOMsiAVUGWEIgtS4gwX2ac7GaXIAmVEhGEdYUEWVtRDhBEdYUEqnNWL7JMdjGgxtC+MMkIl1GvoaWoSMKcBE5JmVAs0yYEoBU1RFR9VnzwpxnguZ3iSSfZTZJyKlBGFEYrMczEmm6AipbasImVUlSJFypQ4JWeY1H2MsYMFSqjAqLrYo+0y88xwpsuXszGWkI2xQAUVGFGXw8hviTlm5CwH1dkoU0JEGNWxtt8RHaUsJWb0DJOyj6KOd9uQOWb0LJOynzGN2yjGyj8W6jkbYzrRVZZese9nL+PsWJabpCyyH89nMqbI17Lyy3wY036KOsaCVPrUQ3e8cXtlcTG5HC7PdTL2qL6iPCXzO6YTXe0mXidl5jrlb36NBQJUPEY1y4KXRkkzKuOUvRYzssT+IM14kGKh+J9RYJQRylRiORyjwgICjDBChQWUKN5yGO8+ihSX6UXbJcrMcJZJ9jGW0JunFGvzo132F6i0YyqFvg7G22soW2ABDe2V23qTFMOyCBKzcbYd70IPXx0b3Xou3lHKlBGkr148N6c4zST722V2stEVcxOVJV6u0VieDnGAF3EMD48LXORJzjHHfJ94R7r8llbITdxXp1z71xVv/7Yx1pW37jyVmOEsn+Iz5Mi3u3XXsIc97Orq6FWp0aLF+/jfm9a32OhI3iCfrr0d+Afg88B9sb+VOACcin2eCfetVwcReYuIHBeR47Ozs+sMfe08UHmQUnaGkdY1lHJl1HMTEEv5RVoorZTQQpnLL0Iom8svogCe2470fAJK+UV8QAXm8kuo56Z7zeUW8b2AVgoCT5nLLaLi7g66Y4K2r1J+kQDwxGMuf5IHKg+uGu/FVJMWShOhKcpcukngQaDCXLpB4IEqXMzU0HRAKxUQpHzmh8+jAiLK/PB5EAWB0sh5glRA0wvw0x09RJkfuoCf9ml6AUEqoDRyHmI2VBQ8t+17SkvAl4AL2Qaq4sqfdh1GBebTdVopxU+1INVkqXgKPEUJ3Ha6iZ/2Uc/J1FMQ7eh5Gu53eqRDPQIIZYjT072nkEwDP+MjmSbB/ieQtCIpRfedgkyTIBNArom/6wkClMBTHuUUgQbh9hNUtUFdferS4DFOEXiKr6Fe+5jeegFOr6YNql6LmjZ5lCdC+wGPRHoa8CR1Wgp1T/EVzrJE4AUocIZFl0AvxWnKNLwGS9Kg5tWYZhpffVpei2mmY7I605zAVx/f8znBCXxaBF7ANNPUvLqLmQbTnKDltfBpdW2f4AR1r77MV9zGkjSoe3Un83x8WpwM9XzPZ5pp6jSoaoOaOL3AC/DV7/hSn5NMU9c6Na9OXRttG65cJ6hrI5TVOcGJLhvdeh0bcfuRXjL2htdwuemS1ZbJIvvxfMZj6lX+5THVnaxvPXRsxOshyk1Vo3oN9cK8xe116iuW65iNeG7i7Saez3hupqVMy/PwgROpBXxJ4UuTaa9MTQOWRGloimnvIj4+PgHTPEqNOkvUaNDkBI/iShUwzSP4BAQo0zzc1qvRCGXdej4+0zxKnTo1atSpJ/QepUEztFFnmke77Pvhv2kece2Qeujr4Vi8j9AKfZ3kEerUQ70a0zxKgMZsdOI4GcaR9HWyj56z8XDbXj+9Tm5cvPVYbgJ0WT6TuXFl6S7XiViefHweY4YzPMUYRR7l8Z5xJOvrRFgPvXITxRTVf71dr/U1xuv3bRtOrztv8TxFvhapskQVRfHwWKJKC7+rH5AjS4nylvUz1sMgO3ktVf1xVf0zVf2L6G+VY6THvuTQ41p0UNV3qeoxVT22e/fWzem4fvQ5jDUmWUg/xVi9iAQuyWO1YdIIaV9JI0zUhiGUTdSGXSECtx3ppfAYqw2TwvWVJmpDSOAKPFEfJhV4pH3wAmGiPoyomxLmjvHavsZqw3hAoAETtSNcP/qcVePd4WdII2RQMipMtDJ4AXiiTLSyeAGIwI5mHml5pH0Pz08xvrgLUVAVxhd3ud6pwtjCLjzfIxN4pFodPVQYX9pJqpUiE3h4vsfYwi6I2RAVCNx2KhDSCin12NnIIqKu/K2Ce6IVGG/lSPtCyk+Dn2GofBACQfDcditDqpVCAieTQECloxdIuN/p0Qr18CCUoU5PnjyINrOkmim0mcE7cwhtCeoLcvYgNDN4TQ/qGVLnD+EheIFwLQfxxAu3D1GQLDlJkdMshzmIFwgpCfXax/TW83B6eclSCNLkJcO1HArtezwz0hOPveRIC+QCISWwjyG8wEOA/Qy7BAY+ByiSDbIMaZZ8kGeKKVKSIh2kmWIqJssxxVFSkiIVpDjKUVKk8QKPKabIBzkXM1mmOEo6SJMi3bV9lKPkgtwyX3EbQ5olF+ScLEiRIs2RUC8VpJhiihxZCpIlr07PCzxSkur4khRHmCInOfJBjpxk2zZcuY6Sk2woy3GUo102uvU6NuL2I71k7Nkg63LTJcsvk0X24/mMx9Sr/MtjyjlZ33ro2IjXQ5SbgkT1GuqFeYvb69RXLNcxG/HcxNtNPJ/x3ExpkXQQkAKO+iOk1CelGaaCInnxGFIhKz5TwQ5SpEjhMcW15MkxRJ4sGY5yLa5UHlM8kxQeHsIU17X18mRDWbdeihRTXEuOHHny5Mgl9K4lSya0kWOKa7vsp8J/UzzTtUNyoa/rYvE+k3To6wjPJEcu1MszxbV4SMxGJ44jYRxJX0f66Dkb17Xt9dPr5MbFm4vlxkOW5TOZG1eW7nIdjeUpRYrDTLKfayhR5lqe0TOOZH0dDeuhV26imKL6z7XrNbfGeFN924bT685bPE+Rr2EKDFFAEAIChiiQTkzGrtNgrMec50vBIOfk3Ssib8EtnxK/XXtxhWNmgIOxz5PAmQ3oDIzh7BD/c+8dbo5bZgBz8hbWOSdvb/ecvH7xemP/wn3Be8M5eZnOnDyFidwoc40KNe8pXqjfgwQvoloJ59MVY3PoemxXS/31VpJF2/fW/oBTubsYYR878qNuTp6XYkc+PidvjuLCzexqvZLcUIYje2Lz6XbH5toVMu3PcRnQPqbeapIbTszJi9n7yX/cz1f/7xlorj4n79g37ufHvv7Sz8n7qr6XId1DSlLL5uT5WqfCGZ4nb7Q5eeZrcL70LqpL74PUfgpkqfqJOXkonn+GwtAbqGJz8i4XX1finLz383X8DXeylz0Ay+bkAVzgIm/gtWwHBjkn79HYx7bTlZZQEZE0cAL3ntvTwJeAN6jqAzGdW4Afwj1d+yLg91X1xlVimQUe30Ax1sMuYF1PEm8nvAzpicM8Q5VAfYKkXFJ4Inhzj/F40BzIm0t2Aee3XVzZ0WEKU19HS9xZnhxXjlp6WpXq9L/RqCxueUwr0Ct/quRFqMElqdcrncv6OjAoMmnShw+6dun3OK9TYbt87BSPN1td7dLyuzVYXvuRIc1h7xloeL86iRv883gseJze19CN5vYZqrru25CDHMn7GeATqloWkf8OvAD4pZUOUNWWiPwQcDfuTuT/UdUHROStofydwF24Dt5J3NO7379aIBtJ1HoRkeMbmSRp9MbyuXVYbrcOy+3WYvndGiyvW8egczvITt7Pq+r7ReSbcEui/BZrWO5EVe/CdeTi+94Z21bgbZsfrmEYhmEYxuXLIB+8iB4/uQV4p6p+BMgO0L9hGIZhGMZVwyA7eafDNe2+G7hLRHID9j9o3nWpA7jCsHxuHZbbrcNyu7VYfrcGy+vWMdDcDvLBiyHglcDXVHVaRPYBz1XVewYSgGEYhmEYxlXEwDp5hmEYhmEYxuC4km+XGoZhGIZhXLVYJ88wDMMwDOMKxDp5hmEYhmEYVyDWyTMMwzAMw7gCsU6eYRiGYRjGFciaO3kikt/KQAzDMAzDMIzNY81LqIjISeAp4J+ATwP/rKqlLYzNMAzDMAzD2CDrWidPRA4B/w54KfBqYF5Vv2GLYtsydu3apYcPH77UYRhXAq0WPPooVKsgAl5icFwVggAKBbj2WkgP8nXRhmEYxpXAfffdd15Vd6/3uDV/44jIJK5z9++ArwceAD6zXofbgcOHD3P8+PGtcxAE7ks/l3P/g/uSr1adzPM6n+OyzdbrJRseXt4R2c7xbndfH/oQ/OZvwvw8DA0tzy3A4iLs2AE/9VPwxjculxuGYRjGCojI4xs5bj3DCk8AXwJ+RVXfuhFnVwVBAMePw+wsnD0L5bIbzSkW3fbMDExOus+VSke2sOC2R0c7evv3w/h4t6xScSNGIyNQKjm9gwc79iLZ/DycOdPxVS5DKgU33QQ33tjpjKwn3nLZ2R8dXR7vqVNOb2xsebyRvSjepN7ISLevsbGN+epnL8pvv7xFvkZGOjai/b18xfX++q/h9GnmJlrc+5o5PvHdDZ48GIDA3tMZbv5Ikf/w8WEmzpXgzjutk2cYhmEMjPU8Xft84N3AG0TkcyLybhF58xbFdflSrcKFC65T8Nhj4PuuIzU9DfU6LC1BrQYnTnTLWi33+cQJJ19agkbDyXzf/UXbrVbHXrXq9JN6jUbH1/R0Z0Tq7NnOKNRq8UZx1OsdG/E4ou1azdmJ9KKyRHpB4MpVrzvdpN7Jk92yEyc25qufvZMnu20k8xb5ivTi5Y/7imzE9c6f55GjDX7hT5f4Pz+5xFP7fQqLQn5ReGpfkz972wV+4Xef4pHrqq5zaRiGYRgDYs2dPFX9F+AvgD8D/gH4ZuC/b1Fcly+FAuzc6b7QDx92o2eeB1NT7nbo0BDk83D0aLcsnXafjx518qEhyGadLJVyf9F2Ot2xVyg4/aReNtvxNTXl/AQB7NvnjllLvFEcuVzHRjyOaDufd3YivagskZ7nuXLlck43qXfkSLfs6NGN+epn78iRbhvJvEW+Ir14+eO+IhsxvbkjY/zhL9Z54khAYVEYXvRIqUc68BheTDG05PH4M+r84c+VmJvMXapWaRiGYVyFrGdO3nEgB3wWNxfv36vqhu4RX9F4HsGxF1CvXiSTK1KvXgQgV9hBvXqRIGjheen257hss/V6yQrDe/Di88a2ebzb3dfnvvEhzl3zIEhASlPgSVdzSLeEZhrO74cvvmkvN6+rMRmGYRjGxlnPnLxXqerslkVyhRAQ8JD3ZeaHZ7nIWRZGy4AyTJHKaJlZZtjNJKMUWRytAMoQRZZGF0K90bbeHvYzwjgLowsIyhCjLI1WAGGIERZHS5xjhj0cZJgiS6MVFGGEERZG5znHmZivMkKK67mJ67kRLxzEXV+85dD3KIuxmBZHy5zjFHuYZJixdlk68Tp7Uby7E3rDjLDQLrOTbcxXP3tFFkcXUIThRN5GYuVysoV2vAsJX1Eccb2/v+k+ZmsBQRDQykKu7pGrxzrRqmRrsDAufPbwk9bJMwzDMAbGem7XzorILSLy0yJyW/S3lcFdjtSpUuYCI4xxhscI8FECTjFNkzp1lmhS4wlOtGUzTBPQIsDnCU7QpBbqNTjFNIpPgM8ppgnwCWgxwzQN6jSo0qAW2vDRUK9Jo+3L2Qjw8LjAWepU1xhvFEe9bSPAZyYRU4NaGEc9jLHVFa8S8AQnqFOnTm2Z3hOcpBHK6tR5ghPr9jWzor2T7dwk8xYvV0cvWOYrHkdcr5Qt4Q/lIJVCUXwvQNHO0ilBQCqdw99RZCFfv4Qt0zAMw7jaWM8bL94J/CfghwEBXgc8Y4viumzJUaDIThYosZ/DeKQQPA4yRYYcOYbIkOcQR9uySabwSOOR4hBHyZAP9bIcZAohhUeKg0zhkcIjzSRTZMmRpUCWfGgjhYR6GbJtX86GR0DATvaRo7DGeKM4cm0bHikmEzFlyYdx5MIY013xCh6HOEqOHDnyy/QOcYRsKMuR4xBH1+1rckV7R9q5SeYtXq6OnrfMVzyOuN4Yu0ilszA0jAwNkyKNBAEE6uYI7tqNf2AvqWyBEcYuYcs0DMMwrjbWc7v2Jar6PBH5qqr+ooj8FvChrQrscsXD49kco06VDLn2qFmOAnWqBOGIWvQ5LttsvV6yAsPtW7WXQ7zb3dcYu7iD36SSmic/PIQ3vPx3U4NFiuzgJdyy9oZkGIZhGE+T9XTyauH/SyKyH7gAXLv5IV3+eHgUGAYgzWh7f3x7Jdlm6/X6fDnFu519vZhX80neT4U5fFp4ZLuOadEkIGAXB7iRb8UwDMMwBsV61sn7WxEZB34D+DLwGHDHVgRlGJcLE+zmbfw6h3gWVRZZpIxPixYtFimzxALP4Ot4G7/OBOt+I41hGIZhbJg1jeSJiAd8UlXngQ+KyMeAvKra6q7GpaE6C6fuhsfvhEYJsmPwjFvg4M1QGGxn6pncwC9yB/fyAT7Be3iSxwDYy2FexZt4Ga+1Dp5hGIYxcERV16Yo8jlVfWNHKv0AACAASURBVPGGnIikgOPAaVX9NhHZAfw1cBg3IvjdqjoX6r4deDPgA/9VVe8O978Q+HOgANwF/IiqqojkcG/ieCHuFvJ/UtXHVorn2LFjuqXvrjW2lov3wxdvg6AOuZ2QyoNfg/oF8HJw4ztgxw2XOkrDMAzD2BRE5D5VPbbe49Zzu/YeEfkuEZHVVZfxI8BDsc8/ixsZnAI+GX5GRJ4DvB64Hngl8EdhBxHgj4G3AFPh3yvD/W8G5lT1CPA7wK9vID7jcqE66zp4qRwMT0K64N47my64z6mck1dtSUfDMAzj6mY9D178ODAMtESkhltGRVW1uNJBIjIJ3AL8cmgD4DXAy8LtvwA+BfxMuP+vVLUOPCoiJ4EbReQxoKiqnwttvhv4TuDj4TH/I7T1AeAPRER0rUOUW4EG0Kq6DkcrXJMuXXDbGoB4nc9x2Wbr9ZJlht2+yyXe5PajH4bWIuTGnb764KVxzRHIFmFxBk7dA0ffuKHqMwzDMIwrgTV38lS1/+OZgIhcr6oP9BD9LvDT0PWo4jWqeja0e1ZE9oT7DwCfj+nNhPua4XZyf3TMqdBWS0RKwE7g/FrKteloAOeOu5GkpbPQLLuFcTNFaJRdB2R40nVGmu5tEGSK0FwIt0djevshO94ta1VABTIjbi7a4gwMH4zZi2TzsHgm5qsMkoJrboJrbux09NYVbzm0P9oj3lOh3liPeKMylmBhpltPFNIjHV8jk5AZ6+/rgT9yt2YbJXdrNjPkbtcWdtPu6OV2whN3WifPMAzDuKpZz+3a1XhPcoeIfBtwTlXvW6ONXreCdYX9Kx2TjOUtInJcRI7Pzm7hrbxWFWoXIDcG5ccg8F1HqjQNfh1aS66TUjrhRqEiGS33uXTCyVtL4DecTH33Nz/t7NGK2auG9mJ6pWl3bNvXdGeUbPFsZ2Rs1XijOOodG5H9eEx+LYyj7j5HZYni1SAsVx1aNTeXLtILfJg/6fa1aqGvE/19tRZd9Qa+69R6WXdM0OqUKZWDuj0TZBiGYVzdrOd27Wr06my9FPgOEXk1kAeKIvKXwFMisi8cxdsHnAv1Z4CDseMngTPh/ske++PHzIhIGhgDLiYDUdV3Ae8C9+DFxoq4BtIFyO90I2PFw52RsbEpN1qVDkeexo52RvLGpjqjWmNHY3rZjgyF8anOSN7YlBvNShdCe3s6I3ljU24kr+1rTxhHAMP73DHrijcXxlHu2I/H1CiHceTc56gs7XjDcjVLkM67EbhIz1MYPxLayIe+jvb3dXrYdSq9FHhDEDRcGb1YU/brrtNqGMblxWwF7r4f7vwqlKowVoBbngc33wC7V7yZZBhGDzazk7es46SqbwfeDiAiLwN+UlW/R0R+A/g+4NfC/z8SHvJR4H0i8tvAftwDFl9UVV9EKiJyE/AF4HuB/xU75vuAzwGvBf7hks7HEw/2HNu+c9ySc/K2e7zJbQEe+QAMH3C3n5Nz8sA9ZXvkDRupPcMwLhX3n4bbPgz1FuwchskJqDXhji/Ch74M7/hOuOHA6nYMYwDorBLc7cOdPloCGQNuSeHdnEJ2b+T51K1hzUuorGpI5Muq+oIV5C/DdfK+TUR2Au8HDgFPAK9T1Yuh3n8DfgBoAT+qqh8P9x+js4TKx4EfDpdQyeNuFT8fN4L3elV9ZKVYbQmVy5jqLPzjra5Dmu3xzE+j7Ebyvvn2ga+XZxjGBpmtwK3vhlwaioXl8nLVdf5u/14b0TMuOXp/gH9bE+q4JwDyuHeCXQBykHpHBrlhM2fDbXwJlc3s5H1eVW/aFGNbjHXyLnOWrZOXcx07WyfPMC4NQQDVJuRS7v8gAM+DQsZ9ht7bkd6H7oP3fQkOjEPKAz+AtOeWR4qYmYM3vAjeeFl8zRhXKDqr+Lc2IAdSXD5ip2WFOqRuz27qiN5GO3nreeMFqhqISBa4AXgsGn0LZXbmhWigUAXNuf8BN/5YdTLxpP05LttsvV4yGRa37zKJt6de6tnIC26H8/fAyb+H+kUYLcDeN8HkK6C4EyrB5vgaZLnMl/m6HH1lWvAPj0GlBnMlOL+Eni4jB8ZhZw4u1l1nbSIDF8NO3o4sXKyjp+eRfePwkS9AKw0XZ92oSK4A+SzszYOfRhWkWIS//Sp8x41XXg6vUF8yHH7XxL5fLvdy6Yd9N2o3DtpUJC1ds4WkKOiMovf4yBs3c0bcxlg1AhH5TuB2IBCRtwI/BywCR0XkB1X1b7c4xssKDRQ9HqCzAXpWoYybrVgMt2cUnRQoAhXcQxRFhQVxDyiM0tHbLzBOt6wSNqgRhZLCjKAH6dgTcbJ54EzMV1kgBXKTh3djqt3RW1e8ZXEPVIyyPN5ToJMKY7I83sheCZhJ6oVlKYdlmcQ9OrOqr1EYey0svK4jeyiy1+y2VwQWYrlJ5q0cy+lCrE76lWuETuy99Cqycp1HccTrP8rNajH11AtjKkf5DXMY9xXPZ896iJUr8ptsQyu1r37lWqlt9Grzvc6NpKxfvD3rNe4ramux8helt41e7bDr3OvTlkd7lD9+LvdqG3FZZD+ez2RMfcsfq/+oza+nHuJtKMphPG9JezOK7geemoWvLcBoHhbToHvg9G70gIBWITPuytKoQGaHu8A0F0BG4fQu9ADwyHUwUgQEdAmKO6Dlw1waWllYGkILS1AtwZ+0+l8rkzns14aS7bVfruP2erX5rut3WP/JdrjWXEfn/GiifUVtNGo3a23L8WvIsuvhGq9fvb5vku0wfn6NxvJ0yIMXhYMKF0CfDGBO+8e76vVwjdeNdl2u8xzt+p7r3+b5lEIOcO/oQq8B2SPdj57uBL3Th23QyVvLTeNfAL4eeAlu7tv3qup/wD05+wtbGNvlSRW4oMiYB48p+AIBMI27f78k7lfACcD3OrKWuM8ncPIlgUYo83F2pkN7rXC7Ls5fLSbzw2MaMV/TQIA72c5q59fJavFGcdQ7NlwcUUye81sL46iHn1vSHW8QlrcexpPUO6luXy3UObFBX/3snVR3fJSbrrzFfEV67fJHvhJxnEzmKa7XQ7YksTryOvWXzM1qMfXVC2Nq57dHe2jn0+lVy/CVRzw++mHlTz8rvPdLHvf/k7K4FLWvHm3I7yVL+Eq262RuonYe3z6RODciX8kcRu0wWQ+r1euythFrhyeT51S/dtjj3OvVlvvVfyPKTY/zKy5r9chnPKZe5V8W0zracrweotzEcxhde5L26rif+hcb8IjC+CiU6tDYiRs7yAMZ1+ETwX0DRsuheuF2BshCkIHsXghCPd0DTYVMBipFCNJulK8u0NoZux6uIYfJXPc7D9ttyFt+Dq10PkT249fofjlcLdeJc3RZG0366teWk+d8z+vhGq5fyXwmc9Pr2nsilicf99LSMyBjAo+ucu6tej0MrxXx+u/1nbruc5Te33O92vySuHa/hOv8eeF2bAUvAHKg22QVrzV1M1X1SQAReUJV/y3c93h0G9eIUQB2CjobwGFxv6AU95xwGRhSyAscBSqBayhTwIK6XydHY3rZSIaTTQlU1F0vp8T9sioI5BX2RLLwmPmYrz3OpgaK7PNcjOuJNyedz6KxmAIXR1mdnVz4OSpLFK/iylXS8FZMXA84IlAOXKy5WA7W66ufvSMCC0EnN115i/mK9KI6afuK7IdxHIn56qVXSciGwnLvEVfnIjE96eSmwMox9dULYypH+Y23lWTdCWdPK186BU9NBHj7hNGG0gyUT3pC8X7lVc+Cvb3aULvtxWV0t71ku16Ww2R9Jdp83FfcRrwdJushiqlfvS5rG7F22GVDoNSvHfY493q15Xi9xmPPRrnpcX7FZUJ33R2lO6a+5Y/HtI62HK+HKDfx9hVde5L25gMoV9w6mqNLkCrArj1www43wjETwH4PduE+K26C+oXwuhMtV3861NvvwedOu4cuci1o5Fz5sxVoZCDdgtoSfNMkpKLr4Rpy2C5/sr0mzsP2tSGea+nT5nu00fg1OtkO47KVch1va/H2lbzexOukb1tOXEOWXQ/XcP1K5jOZm17X3qOxPKXEvZ3eA72gcK24kbx+596q18PAydr5TFw3omtPVK9rPkfp/T237BwK9YZxI3mi7ofJEMt7UvXwadttwJrn5KlqgHvqNdqXArJbFdjlingCxzyk6m3LOW7JOXnbPV7ztbm+LvjwM3dA7rXKaEFoZiHTcLJmFmpluNeH33sd7ExdPuUyXwP2da6OXnMaOTQBpSY8dxJ2DsGwtzFfCwfhLf/knq6dyEArfGV52odWCi3XkEYL/ueLYCR9ZeTwKvDVPSdvg21jm5VL3+/D3yjsdaLknDwALoC8IcV2YNWna0XkG4GvqWotsf8w8E2q+pdbFt0WcSmerg0CWGzEHjpLQzUc4u21vRl6vWTDWbcvGVu1BTlva+J4OvGar8319aGvwfu+AvtH3Q/e5AOMADMleMPz4Y19F0QyrmqCAL74GHz+YTcy8uJnwo3XLr+wrJfkOnm5DNSbcGHRdf5snTxjG3C5PV27aUuoXE4MspMXde6+dhY+97j7Ap0cg2IeKnU36lzMw0I9nBOag3LN6e0vwnghJstCpRHOCc1BqQYz83BwvGMvks1X4Uy546tccysT3HQIbjzUuR4HARw/BbOLcLYM5VhMURxxGyIuxnhM5TqcmofJcRjLwUKjO97IXhRvUm8ktNGWbcRXTC9prxjqLcvbhJNFvkayob2o/HU4NdeJKW6/S6/WHVOl0SOH41BM1F+8/pN12S+mlfS6cpjr9hXp/dVXwFdotNz+a0Zgz0h3R6/ahFYA77NX/xq9qFTh7x+C/eNwZh7+n2e7p9s3g9kK3PPA8jdefOv1tj6esW1Ytk5eDre9DdfJW8vTtSPATwPfhXudWAN4GHinqv75eh1eFYRrRgWZFF+ebnKmDP9ayZCpNwnmAnw8Ts5n2JdpgsLD8xn2ZpoIcHIuw05xeorHw6UM+9JNFHi4mWFv1i1B8PDFDDukic4H+J7Hw3MdGw/POVnk6+H5DHvTTbJpODufo7rfYzi80V5twYWFgAmafGE2xQ6vE1NkI4jZILS/L91EBR5uZNiRcnEE4vFw4GIUjcWrcHLelYukHvBI0/mKZCfnnf11+brY394jGuZGw9wQy1sQlkvccXtjdbLDC33h8TCdOFbU006ZI1kwF+CLx8N+d/3tzbjtKDftmOZ7x7Sa3o5kfmPtIaq7iyXX7rxGQD7wqKUypKRJxoNmNkOm0SSj8HgtA5VV1jtb71poT0fPfG0PX0EAD5yBxy7A4xfgputgOMemsXvUrYNna+EZ2xi5wSN1exa9x0fv9NELbg6evCGFfOtl9sYLEfkI8DfA3wPfjZt2+FfAzwOnVfXntjrIzWZLR/KCAI4/DrMVlp4oceLhJcYKwr/WcqTLNdJn59D9O/Amcmi5jip4xRxaqQMgxRzBXB05cxH/wATp8TxadkN0MpJHK1VAkNE8frmKd2oOnRzHGxtCF2qgzkZrvkbqdMdXUKojKWHXy6/j+u84jJd2vzKCVsCDdz7O4pkKtZkSwfwSqoI31olDD0zgjeedDQ9kJBbvaJ6gtITMzBNMTpAaK6CVWle8kT1/bgk5XUIPTJAaD/VQKBYI5qrI6Tn0wBipiaEN+uptz5sYdkNZgIzm8Mu1MG+xcok7jnLN1clYjmC+hszMERycIFXMt+NwQ2hhuYo5glLVxXRwglSx0ClzMUdQqoVxTOBF8YZl0YUaGjhf/nwVb2Y+rMsCQbl3TL31XEzB3BJyet7lcHy4qz1E+fyn83mGlhbZebFEaecE3niBPeFMjOpQnqGlKs1AWMjleOuzanBmDiZ3uCHPrqHiSDbhYgzroTNESWeY9FSoN1aAMCZG87HtaPg64StuYybyFR9SzcNCNRzKjIZo58JhzoIb2pS4L4WRApSXwiHPMPa2jbwbPZqZc8PXY0PdNipxvZiNdrnUjWhVastj3z8B4/lQJqGsDjMXu2WR/Xg+o3JNjoe+epU/HtM4FIc6+V1WD/FbB7F6GMl3cnMwnuswbws1aPhuweOXXAenS/AtmziKZxhGX7ZyMeTDsRG73xaRL6nqL4nI9wMP4tbNMyKqTYLZBar5PBceOEnJH2J+Ubk+OMfuvQVoNWBfg0ylRPPACCBk5s7RPDAKKJmL52juK0CzAdc0ySyUaU6OgkLm4lMxvado7ing1RsEe1tkKk/R3D8KApkL52heU4BGzNfkKOTS5HQer96EtPv17dWbPCe9QP1ZeTJPnKR5YGh5HHubZCphHODinRwFDePYW8BrNAiuabh4D4R6XfGeo7m3gDQb6N6E3oUnYzKfzMK5p+Grh73Kk04m4uKI8hbPb3TcgVFXJxddDr1Gg2BPk8xiuRPHhSdpHii6cs2d68S0pxmLKZRF9RD5iur8Ylhf0O1rb6u7/P1i6tILY9o7FMthoj2E9nbMP8Vxr8Azcg0u5BsMz5VZHHM2rjn7JIvFItW68pLUOWgUYKkBtQY8VercLps+5zobSw33btGnyk4mwIlz7v4v0tGrNtzcqulQD4XppzrbJ87BxBp8RTb2uHrg5JOwu+iecov7qrXgqXMxG091tk8+6TpvtQbUG3Cy5GxEMY3lQ5kfliVpI9IrdGy0y5XwFY+90YQTYezoKrKnuvM5HsXUcscly78splYoG+1fD5GNeLmi3FRj9brHXXva9vJpZ/90yXUYN3MUzzCMTWctI3mfBX5aVT8jIt8O/JCq3hzK/k1Vv24AcW4qWzmSF42MlZ6oUDlV4uuyS5RrwtR1OYaWVhit6JqUl/iFH//VXekx6nAw/OUe//U/nxiFKdfdI+03XQc3Hk5MynMjj5wtuV/yyZGGaASlPaqR646pvBROSusxWlOpduyVlsIJaonRj5GCezdlzxGUdfjqZ6847GTJkZGD8XLFRitWG/2IRlDaeap2xxQvc9coVHzEK9894lOqxibbFfrH1FOP7lGYyTEYG+5uD6FeKZPnA59ZZE+pxOKeCaqFAkM1N1JYHcqTKruRvNd+U46xqo3k2UheYiRPBF50LTz3gOvgPd2HLQzDWBNbOZL3VuBPROQocD/hMioishv4w/U6vNKpBh6njjyDPc9q8qXTKeqFJgeKkJ8KnxS71HNwkhdmz4Njz6DrvZObHcfTidd8baqvsUKGm17V5FfuDqipx+hYhpGgSb0FT7YyjAZN/tu3wNjhy6tc5muAvqxzZxiXDfZ07SYTPa16Ycndgbp+b+9lSwzjUjK7APecgDsfcgNFY3m45dnwrUdh98iljs4wDMOIs5UjeSs5/X5V/bOnY+NKw/Pg2EGottz6ZNa5M7Yju0fcOni2Fp5hGMaVy9PtgvzipkRxheF5NnpnGIZhGMalZS3r5H21nwi4ZnPDMQzDMAzDMDaDtdyuvQa4Gfea6TgCfHbTIzIMwzAMwzCeNmu5ofgxYERVH0/8PQZ8aqUDReSgiNwrIg+JyAMi8iPh/h0i8nciMh3+PxE75u0iclJE/k1Ebo7tf6GIfC2U/b6IexGTiORE5K/D/V8I36lrGIZhGIZxVbNqJ09V36yqn+kje0O0He+oxWgBP6GqzwZuAt4mIs8Bfhb4pKpOAZ8MPxPKXg9cD7wS+CMRSYW2/hh4CzAV/r0y3P9mYE5VjwC/A/z6amUyDMMwDMO40tnMRwM+mdyhqmdV9cvhdgV4CDgAvAb4i1DtL4DvDLdfA/yVqtZV9VHgJHCjiOwDiqr6OXVrvrw7cUxk6wPAK6JRPsMwDMMwjKuVzezkrdixCm+jPh/4AnCNqp4F1xEE9oRqB4BTscNmwn0Hwu3k/q5jVLUFlICdGy+GYRiGYRjG5c/TWicvQd9VlUVkBPgg8KOqWl5hoK2XQFfYv9IxyRjegrvdy6FDh/r5NwxjjSwyyyPczcPcSY0Seca4jlt4JjczzO5LHZ5hGMZVz5av5CYiGVwH772q+qFw91PhLVjC/8+F+2eAg7HDJ4Ez4f7JHvu7jhGRNDAGXEzGoarvUtVjqnps9277AjKMp8M57ufj3MqD3IFHhlEm8cjwIHfwcW7lHPdf6hANwzCuerb0dm04N+5PgYdU9bdjoo8C3xdufx/wkdj+14dPzF6Le8Dii+Et3YqI3BTa/N7EMZGt1wL/oFfju9oMY0AsMsunuY0UOUaZJE0BQUhTYJRJUuT4NLexyOylDtUwDOOqZs23a0XkPar6phX2vaLHYS8F3gR8TUS+Eu77OeDXgPeLyJuBJ4DXAajqAyLyfuBB3JO5b1NVPzzuB4E/BwrAx8M/cJ3I94jISdwI3uvXWiZjdWYpczdf406+QoklxhjiFr6Bm3kuuyluig8loMEiSoDgkaFAkypAz+3N0DNfG9d7mLtoskiOMQKaeKSJ/8bLUaTCDI9yDzfwxqfXOAzDMIwNI2sd9BKRL6vqC2KfU8DXVPU5WxXcVnHs2DE9fvz4pQ5j23M/p7iND1KnxU5GyJOhRpMLLJAjzTv4Lm7ouru+fpSA03yRGT5LmScoMkmOMeqUASHHKHUWACXHKDXKVDjFKJPkGeuS1VlAUDKMUKdMhRmKTJKlSIOF0N4INUpUmGGUg+QpUqeMIGQYCfWUHMW+vrKM0GQB7aPXoJKQzYSyInUq7TjcttOrU6K8hph66YGQZYQGZcphmV0O477i+Yxs9CtXlKd5KpxZFvtD3EGAj0+DLCMMcw3D7CHe0WtRJaDFd/K+p9U+DMPYRsyeh7s/BXd+EkplGCvCLa+Am18Gu3dd6uiuaETkPlU9tt7jVr1dGy5OXAGeJyLl8K+Cm0f3kVUONy5TZilzGx8kR4ZJdlAgiyAUyDLJDnJkuI0PMkt5wz6UgEXOU+E0ik+LGi3qXOQESoDic5FpFL+97VOjRRWfeihrxfRaBPhc5CQ+9Zi9kwkb9dBGLTwuaB/n9II+viIbJwlW0FsuW4r58lFase2Ai5ygtaaYeutFMbW6ytydt3g+I73lOYzK1Qpjb/SMfYnzgKC0AKXJEgGtrrpNkaNO6ek1QsMwtg/3/yvc+tNwx4chk4bJfe7/Oz7s9t//r5c6QqMHq96uVdVfFZFfB/5EVX9gADFd9vi0qFBiiGEqlABhlCIVyvg0SZEJP3fLQBllrIdeXBY/poSPT4p0T3u9bIyzk1Si2nvF+zHup0qZcUZo0SJFHi/8IlcyjAGnWeAT/F++mxes+/Zfmhyn+BxNytSYA4Q0edLkGOZoe+RpB1PtkaYdTFGjTJoCKXLLZNFI3g6OUKfctreDI+2RPGejFNrIs4M97VGzjt5qvo60R/J66UUjeR3ZUMxXJVauSmjvKPU1xdRbj7ZeOZbDqYSveD5LpMmvUK4oT/P/P3vvHiTJcR52/rK6uqvfMz3vmZ3dGSx2ASwAiSC0WgASBZGySEqGKMp6XYjUSSE7JJ0vwg6f4mzZFzpekBdx4ZAsO+S4O+txtiWaIq0HJetBUqbeokgAyyVEggAW+8A+5z3TM9Pd011d3VWZ90dWbT+mZ3ZmsfPY3fxNTFR1VVbmV1nZ3V9/35df9pQ9zRCSABG6aeOkQ5dt+5jycOi7e28qg8FwcCyvwEd+HpwEDLdlKEsltbJXrujzv/LzxqJ3yNhRTJ5SSgoh3rHXwtwPBPj8BX/AMrMsM4PLKgpBihw11tlgjiwTpCjgUUIhSJKnQQkFOPThshaWmyQduuTULVdbGQEkyONSYoNZckyQpB+PCiJ069Uos8EMWSZI049LhRgW38jf4718/y1Fbyt5zzFPnjoN1vEZIEMfWSwEoEgiqDNNwJc4zwme3tKF2u66bHcT2qRxWaKfh8jKYU5U301cOgjLJp4q0HT1Usm99pX033a5u1HHg9pWOt3H6/KTZOUoQlhYtgN+aMmzY+AHuGKJJ6wPQ20DpATL0l8Ibl2X67W/Xbm7UYdpy7Rl2rqzOj7751Ctafdsswm2De2p0PI5mJmHz/81fPgHMBwedpMn7yUhxDcrpb68Z9LcB1QoUWSePINc5K9Da4ZihUukyRPgovBY4QJZRgBFkYu38oqtcIE0fWE5lxUWyTKEQlDkAhlGUUCRi6TC+iReWMfIrbZSHW1dIsMQgiSLXKVCif4wX/RW8sZYwCKHoEkMnxhLKIbDWZSLBOSIA01mCHi8w12ZDu+lc/8iDv233IR1VuljmnV5lfwbE2Q/P4u4sQCTE9CXxSlvgFCQy+FsbOjMh7ksTrkCN+f1r8e+6FxXuWwWp1yGmbC+fFafE+G5Unju6Djkc2Fb4bmojnx7W2PQl++sf7tyla5zM3OhHDl9LmqrUgEldLlSRX9I3k6m7cq133NfWL8Q4bm2/ixthDJtcV9RfesVmGuXXdf38JDk4jMr+JV5HHsAEnH9wQ8Qj+OJCrFYk4deqsDSf+noG9qeZcd+Vz/R1jebzrXJS5u8dD1X2u6545pb/TQG+XzvOsI+7Kij0l4ufA697quX7BMT0B/WET7Lnuei+tueXUsmPeZve//t99Xe1u2eQzaSqTW+NrXVLXv0TNrGIeVofIXyVrrORf3Zff+bZNqu3BbnuvtwqzG0ZV9Hz7WH7F3vh47622XvHoft8m7X11Ef5rrGV/ez3OlY7ngmuxgb7WM0uv9P/TcIJMwtQjYNoyMwMtip6A0W4DN/ZpS8Q8ZuUqi8B3hRCPGWEOJVIcTXhRCv7pVg9yo5+hiUY5S9BQr+QwhPQgOG/BPQiBNrJhANmyH/JMJT0IBB/wRWQyE8pY837LZyJxANEJ5k0H8E4UmshmLQP4nwbGKNBFbDDl9LRNhWdx00QHkeo3KaXJsbbSt5AzmG9AXKjxH4FoEcQTQVNCW+HIGmotkMEHKCmAt2LUasphjwjyNcH1FrMuAfx6oFCNdnwD+BXRPYrkWsBv3+FLlqgam1d3DswggiAOoueHW4eFX/sgwUXLoKQaA/YC5dhXpD/7r0PP3abyvnM//KWwAAIABJREFUS13u8hXwGq36Ll/Rx4NAl/PCOuoN/Vq2XRdI/bqjrUanHLct137Og5rXaiuQWs5LV7XcUur79bzby3S7cu33HNUf3XN7f3r1sFxjc7+1y9eIZPda9fmSzNeLPP9X7yewmlTSRfzqKkpJfKtBhRkCq8nzf/s9ZL5e3Nw3UVvt+xdDmbrb6u7DW/fV9Ry2fa7bjY3G5ntu78NN47C9nNe7DzfJ3tCyNzxdX6+x0X6ufWwEQdg3kUxe7/vfJHtj52N5U99E42uLtqL6bj2vXuOw3iVvm4zt/dneN93jIQjaynmd5bY616sPN70Pe8jU/j659Vx7lOv1fojqb+/PW+Owh7w9+7qrD7vHV3fftz+TXs+84z2/TbnbfX619+fKmlboAl8rgLUa+EHnl5+T0Mqp4VCxm9m1U72OK6Wu31WJ9oE9nV0rJcG5V6iUbpC+XqbSWAYFucQglcYKwfI8seFxcokhKs1i65wf7sfbyg2Nk3OGwnOKXDy8RkDOHqTSWCZYWiQ2MhbWtwJC6HPeCsFKW1uNIsQE/U89T+zMaW2a30beP3zc47cfusH4chORyhCLZbB8HV+n7BTCd5nNNvjhq8f44a8nUfMLiPEx4ukBmvU1UIp4akC7+BTE0wWatTXU/DxifJR4ehC/XiLejCOy+bZfmj0sKN0WnzZL3ta/kjstebe3+NDbWtHL+nG7cr2sNVtZECJLQGmLX/XdbW1XrrsP2y0U7f3ZZcnb0jLUy3IR1bdeoVq6xNXn1rj86AU8q4zTSHFi7jQPXThOppbbuQXJWPKMJc9Y8u4NS16jubUlz63rsI1P/oe78U1q6OJOZ9fu2F0bKXNCiBEguduGHhjcOrFiif7cEbh2gcG+0Gp2fZbB/izMxyGegPVZBkcGW+eiYNalWQb7c61ypVkGRwb0h8lyW7nlWQb7crBgQ8LR5YYH9QfoUniuvY7hQf1La35Zvxkz6W3l/Z6/2eCPxh1cpcg347BRRaUcfU2pSqnPJt5M8F1f8HDsDKwBOQtmb+JEMs507ffnYF1BLgazN0lEMg32w/e+T8eAHJYYFNPWbctlpORJy+LJ9nMnkvDN9/Z9mbZMW6atrjoGCvDJ34eJMa30dcfkARTX4EP/AMPhYjeWvO8FfhGYQKdPmUKvZPHE3om3N+y1JY9zX9OzkeaX9S+o7aw63b/O2q0avX65Vnr8co9+CXZZWjp+dZY3ICbg2dNw5p0dlryt5H0tvsBHnr6Il3EYDFI4boBnK4pZhdOQfOxLx3iyMbq9da39HntZkEQPmQwGg8FweFhe0WlSnIT+PummXNHuXjO7ds+4U0vebpS8rwHfAfyZUuqdQoj3AD+ilPqp3TZ60Ox5MmQp9S8gJ3H4fp1l0puVqW3kXVYlPh+/wGecNygFG/SR4gXrad7nnmBY5e6OvL1kMhgMBsPh4bU3dZoUr6EnWTgJvV9c0/sf+xfw5GMHLeV9y34oeeeUUqdDZe+dYVqVs0qpM7tt9KAxK14YDAaDwbBLlld0mpTP/JmOG+zLwQvfCe/7dmPB22P2PCYPWBdCZIG/AX5TCLEEXWnuDQaDwWAw3J8MD+kUKSZNyj3DbnxkHwRc4H8B/gR4C/jAXghlMBgMBoPBYHh77GZ2bRVACJEH/mjPJDIY7kVKy/CF34G//Dgs3dDHRqbgO/5HeNcPQd/wwcpnMBgMhgeOHSt5QoifBj6GtuZJCFe4guN7I5rBcI9w7TX41X8GN16DWAySGX18+QZ86mPwpU/DT/4STD95sHIaDAaD4YFiN+7a/xV4Qik1rZQ6rpR6SCllFDzDg01pGf7zz8Lsm1q5S+UhZuv/dB6SWZi5AL/+s7qswWAwGAz7xG4mXrwF1PZKkPsKKaHhgu3oLUAipfejFCLR6/Zzd7tcr3PJTO8UKodV3sPe1pc/Cys3AKEVu27sOPgNKM7C330e3v3hzWUMBoPBYNgDdqPk/SvgS0KIlwEvOqiU+qd3Xap7GSnh0jlttVmbh2pZr0KRyuv9lRkYmtRWHrfSOlfbABSkclALyw1PQLq/85xbAQSks1AtQXEGBo9CJg+1tnO1dVie021l8uCWgRg89iw8eqYzGfJO5I3qUALSuc3yFm/C4CSk+8DtklcoSObBLbXqS7WVS2db9xydq++yrfZy3fWlo/7dot9u3VdWl2u//6itTF9X/WG5v/ktWJkFpFbmnJRWAttJJHWbZz9jlDyDwWAw7Bu7cdf+CvAXwEvAV9r+De00XCgXtVKweA1UuJj03CW9wHWzBs06zF/sPKd8/Xr+oj7frOlEk3OXQAbhuUt6q3yYvQRNDzxXl58Nz8lAX+M1Wm3NXdLtxCxYnW9ZoW4nbyRHw2vVoYLNMjXroRxeKKPfKW8gw/vyoFHfXG7ucte5i7tva3ab+uYud/ZNe7913NflzfcftdUuR3u58kp4POzPwAclO8eEFdPHq6V9GoQGg8FgMOxOyfOVUj+jlPrPSqnfiP73TLJ7lUQK8oP6C310GkRMK1cTJ/Uas/E0xJMw/kjnOWHr1+OP6PPxtM4iPnFSKwkiBuMn9VbYcOQkxB1tOYon9WsR02UnTupro7YmTup2AgkD452Wpu3kjeRIOK06RGyzTPFkKIcTymh3yhuzwvtytFWru9zEia5zj+y+rSPb1DdxorNv2vut475ObL7/qK12OdrL5YfC42F/xmwQ3e7wQB/P9O3TIDQYDAaDYXfu2r8UQvwUOn1Ku7t29a5LdS9jWXDy9OGNceuOyTvs8h72tnJD8Pv/BjbWIZnerOCBtijmBuDMC5vPGQwGg8GwR+xmWbOrbS9vXXQvzrAVQiwD1/e4mSFgZY/beJA4lP0ZF9gPpZlOWaQlSNX23gAQWu2zXEntao1rTXUoV4k5lH17n2D6dm8x/bs3mH7dO+60b6eUUrtOuLobS97PAn+ilCoLIf534Gng/9xtg4eBO+mo3RKt9bvX7TwomP7cO0zf7h2mb/cW0797g+nXvWO/+3Y3MXk/Fyp47wLeC/w68B/2RCqDwWAwGAwGw9tiN0peEG5fAH5ZKfUHQOLui2QwGAwGg8FgeLvsRsmbFUL8CvDDwGeFEM4ur3/Q+NWDFuA+w/Tn3mH6du8wfbu3mP7dG0y/7h372re7mXiRBr4L+LpS6pIQYhz4BqXU5/dSQIPBYDAYDAbD7tmxkmcwGAwGg8FguHcw7laDwWAwGAyG+xCj5BkMBoPBYDDchxglz2AwGAwGg+E+xCh5BoPBYDAYDPchRskzGAwGg8FguA85ECVPCJE8iHYNBoPBYDAYHhQOJIWKEOIysAh8Afgb4ItKqdK+C2IwGAwGg8Fwn3JgefKEEMeAbwO+Ffj7wLpS6qn9aHtoaEhNT0/vR1MGg8FgMBgMb4uvfOUrK0qp4d1eZ++FMLdDCDGJVu6+DXgH8Drwt/vV/vT0NOfOnduv5u4pJBIPF4cUlgnZNBgMBoPhwBFCXL+T6w5EyQNuAF8G/i+l1P90QDIYupBIznOOMkXyDHKK00bRMxgMBoPhHuWgvsHfCXwc+JAQ4kUhxMeFEP/ogGQxhHi4lClSYIQyRTzcgxbJYDAYDAbDHXIgljyl1NeEEG8Bb6Fdtj8KPA/8x4OQx6BxSJFnkDWWyDOIQ+qgRTIYDAaDwXCHHFRM3jnAAb6EjsV7Xil1R/5mw93DwuIUp3vH5CkJshoWzIB4wN24SoJyQaRMXxjuCCnB9SFlg2WGkMFg2AMOKibvu5VSywfUtmEbLCxSZDoPKgneWWi8CEqA8yw4Zx5c5UZJaJwDWQRrEBKnH9y+MNwRUsK5m1CswWAaTh81ip7BYLj7HJS7dlkI8QLwBJBsO/6xg5DHcBuUC3IOREy/lvOhFSuz/XX3K8rVCl5sBIKlB7svDHeE62sFbyQHSxX9OpM4aKkMBsP9xkGtePHLwP8A/BNAAD8ETB2ELIYuIreskq1jIgXWBKhAH7fG9bH7mV79ECFS2oIXLOnt/d4XhrtOytYWvKWK3qYOyqdiMBjuaw7qo+VblFLfKIR4VSn1USHELwK/d0CyGCK2ckMKS7tn40/ocvd7TN7t3LHC0se6Y/KCZXD/O9Q/A6oEog+SL0Dq/RDbdQ5Lw32MZWkXrYnJMxgMe8lBfbTUw21NCDEBNIGHDkgWQ0S7G1IW9esIYUEsp//vZwUPtu+HCGF1KruN12D1p8H9FIg4WJN6635KH2+8tr/3YDj0WJZ20RoFz2Aw7BUH9fHyR0KIfuAXgFeAa8CnDkgWQ4RxQ2p22w/BMpQ+AsKB2GRo3RN6G5vUx0sf0eUMBoPBYNgn9t1dK4SwgD9XSq0DnxZC/DGQVEqV9lsWQxdbuSEjpA/NVYgPgHUfBxH16of2FDIiBXgt5a/230CuAwoaXwYaQAJix8CeBCsPwQzUPw+ZDx/MPRkMBoPhgWPfv6mVUjKMwXsufO0B3n7LYdgCYfWeKSp9WPw4NGYgMQmjP3b/K3pRP3SkkFEg8mCPayufkrDxb8FfAMsBKwdkgACCyxBc1QqjNahj9YySZzAYDIZ94qDctZ8XQvyAEEIcUPuG3dJc1Qpe8rjeNlcPWqL9oz2FjAhAXgOrT6eSaX4d/JkwvYxN6y0VC5XEmJ7EoSRIY6w2GAwGw/5xUErezwC/A3hCiLIQoiKEKB+QLHcfJaEZpt/o3m9U9H/QAG9JW8i6r+lVn6xuvmYvkH7vNux+ZHwQ13sDaY+C8vdWjsOAkhBU9H2KsTCFTAzEMfDngTw0ZwABCiDGpreUiKOtete0YmgwGAwGwz5xUMmQc9udF0I8oZR6fb/kuasoCUvnoF4Ep6CPeWt6X0lYOquVheQaJG1wjsHIj8LKV/U1yUEYaUvZEaXz8Bdh/WXwbUgc2xt3qfRh7uPgzkBqEibCNpRE+q9wvuBSlgnyG9c4tfirWHslx2Gge5WPxBlI/0N9rvF1aH4R/Bvg/YnOGyjndf/JeSBS1OPatWtlwL8C2X98UHdjMBgMhgeQwzp5/78ctAB3jO9qZS09AtV5qM3p/docVK6HQfweuJcgcVS7PuuzrWvqRV1HRJTOgyT418A5unfu0uaqVvAyx/U2akO5eHKecixBQcUpcxUvOXl/u207XLQWqEWtzFo2sBzOwJWgVsEqADWgDATo/N4CaIIqQjAPqgHJ9x3gDRkMBoPhQeOwKnn3bqyendLWuNoSZMYhPaH30xOQm9IWIuFA6iQ0bupJDMkjrWuSg7qOiCidB3Wwp8ELr4kP3H3Z4wPagle9ordRGyKFY42Tl5K1eJK8dQKnPrN3chwGtlrlQ6T0vpKgLCAP/iX0W0m0/Vu03l4N7uUhbTAYDIZ7k8PqZ1MHLcAdIywYflpbuGJ58FeBYxAf0ha6/mO6jD0AQbmVjqT9Gm9Bu/4sW5dTkzrf2uh7W9cIS8fw2T1Snbwdhr5L974z0nLDCgsrfppTzWm8eApnMIWVX7+/U6lEaVSsY/p1bKiVQiVxurX6R/OCtsoSRy/DHKBze0u0YufoYyJtUqgYbiHlzla7kBKqDb0frW1rVskwGAw75T79hj5AlITmKzqGbvklCBYhlgUe1wqaPQPZKeA5vVRYlIMtumbpRdh4Tcd72Ue0Fa+ZAHIw+EMw9qxuJ4r7647hu1N6pUhpv6flV7DqRVJRe87I22vvsBPFQjZeBKlA5IANQIDzbNuzWwZ8tGYsAbsrBY3UMX0ok0LFAGjF7dxNKNb0urWnj/ZW2KSEszfgpRs6c88zU2AJWLvNdQaDwRBxWD8iGt0HhBBJIcRZIcTXhBCvCyE+Gh4fEEL8qRDiUrgt7L+4bbTH0AVvQTwbzqh9A+KetuZJX8d7RctltV/TvKhj9vBA1qFxEaQDVg1q1/X17XF/3TF8d8p2KVL2or3Dzqa0KZf0Vlh6csWtZ1cDkQwvkmhLXqTwhRa92Ig+ZlKoGNCWuGINRnJ6624xSd31Yb6sFbuYBddWYa58++sMBoMh4sAteUKI/1kp9f+2H1NKPdujqAd8h1JqQwgRB/5WCPE54PvRK2j8ayHEvwT+JfCzey74VkQxdHIRYg9DcxESOVCPQbMIMScM4J8AFdfpSqwsBALYgPgj4L0GOGAltTWv6YHMQXoKrLh2AcdzULsBznhnDN+dEh/QFrz6lc5Yu/VFOPeHyC/+Fq63hnQmyLxrGevp90I61ttlq2TvVTPaj0PL/dm+Bmw3vVaawGltZVXXGRvqlGMrGXZKFJPnX9dpU6yToDaAtvg80JMuRAqIgWpC4EFzQ1vvhA3xPoiH+yaFigHtah1Mw1JFb1Ndb5/IRSsljObg+rq25I3mdNmFMmQdHSBQCVcBP8g1cHfqet7r9h2rpfiaNYENBs2+KnlCiJ/pPgT8KyG0KUQp9W+3ulYppdD+MtCfb3G0yeSDwLvD478B/BUHquSFsVxxF5LvD+PsHPDPQ3MeYs+Cc1orBkufAO86NG7oWbPxCRj7OQjWO2PygnUgpZW5pU/o8jEf+t+h9Z27gWVrF237smXXXoWP/1NkUOX143FeFs/ibxR46q+/wpm//CWsF56F6W/qTKMSuTllUSu7idMtl3R0XITpZBpnQShItLmu2+m50sSoXgPWGtarTPiva2ta4hnI/vitlC89ZdgNwtIyRbF3ItWy3rUrpcnvgeabOm+e19CKu0iG56VW+IKyTpWTfOGOH4/h/sGytKu1l2K0yUV7DH78afjqAtQakIpDOg7lOnz8FSh7+kP0uWk4cwDu2526nve6/eWqtnoedH8YDIeN/X4LfBR4BsgCuXAbC/e3zZ0HIISICSG+CiwBf6qUehkYVUrNA4TbnsFiQoifEkKcE0KcW17e44XihaUVgVgCkmN6SwmS02A1tCLir2u3aHwUmte08tJcArkByQlIH9NbOwnOGDh9rWucozq5rt0Paq2lfLxdLLs14aK0DJ/4ObDBGxlnOWMR1IZx+kos5MZxrRh89mUoXuh07Uau59iI3na7pGMj2g0qw3QyItbpum6n10oTIglyBmJJ7UKlDLE+kFdBrm4vw24RFsRy+t+yW/vtCmPq/SCGoBHm8rbi4X2F11sWoKC8Djx9Z3IY7jssq7e1qdtFu7Chnf71JozlYaEC6y4U0nBtDQKly82VD8Z9u1PX816335c6HP1hMBw29lvJewKt1GWAX1BKfRRYU0p9NNzfFqVUoJR6CpgEzgghntxpw0qpX1VKnVZKnR4eHr5T+XeGlFCvgu9DrRJ+2hSguQD1pH4t8qCG9Uza+DT4Sy03qZT6ulpF70dELlXvJlhTUF8H1Qe+1Ja/aMWMwIf1OSje0NvgNp920Wobgd9q99znwA8gU8BpNhlu2MQyq3jVIcaETyqT0YrNpdBSGa3UEbmrgyW9jdyaOEAa/DndF+Jo6FINgFGdMNqdAb8Uumh9vdoEo50rTQRrwBD4NRAP63hFbxnEFIj+0LXrbJYhusf2frpdn8i28tJvrVbSvpKJNQiVvwdeRltXRQMdlxfofdEEfwDmH4HZV3Y5kAz3Mu1u152SsmE8r+f6BBIm8jCQbLl3J/L6fMmF6QLERKtct9t3L4nuzbG0bAtlSCdaLuRKXZeRsvXa9zf3x530UTuR63uv+uPtymfopH083Ok46C7b69q3U99O7+NeGRdCe0H3uVEhPgj8C+DfAT+vlDp+B3X8H0AV+Eng3UqpeSHEOPBXSqlHt7v29OnT6ty5c3cg+Q6QEi6d05awtXmohj/LHzkDQR0uf03Ha6XzUBiCjAOPPK/jveIDgAUXzsKbL2lX5qPPwaNnWj/5pQ/eClx9CzYWtCt3bATqy5AahUQBrrwGX/0jWL4Jw1Pwzu+BZ38CYj0+9aIVOmrLMD8Pi2UQAr78WRgYg0QK8JHEcG2JbKbICB+LQCuZ/jr8s5/Z7Jptj4drX7UjmNHpYGLDYJ/S5xZ/GyqfBbEGmadg4IcguABqDsQEpH5A19N4Ta80gQX2O0EkoPgF8JuQeA8MJrRl0xqE+NOA11Iyl87pPqot6n5KDW89KzmSN1iG0iJ4wzqHYSPsm5HQteyt6dnNr/4iNOcgfQUGlyEV6HqaWag+Bu6jeiqR8uE7P3nXh5zh8PF23JjdaVMsqzPuDQ42Bq373p4ahxdvaFfpYqXlMn1mSpc/G7qe8462Rg5ndH/A3XH17lVM3kG7ou83pISzN+HFa/pj9NljcCbMULXTfu5+Jk8fgVdmO699O/Xt5Bkf1LgQQnxFKXV6t9cdyJBVSv0B8D6063ZmJ9cIIYaFEP3hfgr4TuBN4A+BHw+L/TjwB3dd4N3QcKFchEwfLF7TVigRg6UbsLKkFS0ZwNIVyA6Cq/TP9shN2nBhdV77HEQM1ub0sQjL1uk8qmXIj8LarHZhbsxol+76dVg4r9toeGFqlEtQ22JlimjmbLwPlq+hLVAxqBS1rEKAiGMJi0xgk7OaWEKFMWsZqK1vdotG7upbS7NF7tOCTjliF0JlzAbZBO+tMH+wD811aL6mXbP28VDRa4Yxf4t6MopIAEvgz4DMQqoA/qxWtCJZ8FoyRPeY6Gv103azhG/Ndu4LU8okoXIlfJaWXrkkWsmkXoTydVi8ANcD+Lth+PIJOPswfKUP3lwC19XWTs/Mrn1QeDtuTMuCXFL/R18e7e7daN+2N5fbD7rvbb0BteZmF/K1Vf1vCfAVXF3VbtWoP+6Wq3ev+uOgXdH3G66v3egxS4+J+fLux0F32dX65mvfTn07ecb32rg4sN8lSqmqUuqfK6We3+El48BfCiFeBb6Mjsn7Y+BfA+8VQlwC3hu+PjgSKcgPQrUEo9NaYVIBjEzp/0CCFYOR47pMfjC0lrVdPzCuy6kAChOd59vbqJSgMAmqDtlJrUT0T8HYKd1GIpzJO3oS0lusTBGt0NEswfA0epZoALnB27t5mwFkBnq4Zru4NeO4BNYkBKVW+fgAJE+GWUdsiPdD/EmwpvV6r9YkWAM9VqCYgvgUJALwJCSm9MSVXrJE99gotfqpe2WRXvJS0u7xRh1yx8NnKfXKJdFKJsKC6ozubyuh/4WlX9sZvV06p9t2zOzaB4XbzaC9l+m+t8id3O0ynR7Q/1KBLeChAV0m6o/D3keHXb57jZSt3eiB1GNiPL/7cbDV2Gu/9u3Ut5NnfK+Ni3111wohsmg37Q+g4+oawFvALyulfn2/5NhTdy1o5chbRdo5qo11EElSTgavUScV+FiiAYl+8MvghKtXuBVtQXIGtOXOr+ifo+mRlptVSQiqOhI7noJ6RcejWWnIDLWsdal+bWnyLbAlZCa1SxMgmdn8M9dvQHUWUuOtci/+Pnzht2BgFCRIK45LgpQILXkAKzPw7T8C3/4Peqcqkb6eDGGFCmawgo6Xsztnq+JAYwlkDRLDenJDlBpF9GuXsJ3SljlZ1f9WRv8rV4fAxZywfpBWPx6LOGIcSwTcSrUSuGDltcXPsoGEXolEua22oskbsSHA0/GOwtLtR5Y/O4VsuNp9dvP3sF79N+CtQzzd2/3rV/Vzfcc/h0c2J0OWSDxcHFJYhzZ1pWG3tLsRPbm37tVeLt7tzkOnbLtNf7Jdfa6vz1uWrje651777eW2k2OrNC3bHd+uP253/U7PP0jstE93Wkf3eNjJ829/D/UaQ931bTW+t3Lxw+awiOjabtlnKvA3b8Hn3oRSHfqS8MIpeP+jMJzdfd/cjjt11+63DvqbwO8D7wd+GD0B478CPyeEeEQp9b/tszx3HyXBfwXJMmebDV6KTxOoFCcuLzNWWSOTrvD4VB7LW4bYKDQG4WoAb/wOBBuQHYW+fkjNwcOT0PxWsM7ouutn4dJLsKGg/zSsfg4uvaLzrw29B4rnIajByRF4xxmwiyCG4c2/hrfKgIDHnu2M8Qt8ePMTUJqBvkk49WNaqfym98MX/xPMfxVpJTkX/xaKqaMM2lVOJ25guSWwHXj6/VrZ6kb6UP24ngkrJsB+DJrndJxh/Bldpvmyjk9MhDFu1hoEq2E+4TWgD1ZeA+8cOGkY/EFdLrru1soTEjZ+A5ovI1WC83aFcqxJXjqcsj+IFayAKgMKmsvQfB3cm6COQuIJSLnABgRNUOval+C8ALUntQUuWuUjkQMlkYvnOHe9STEYYPDqWU7bMSy/CkhI5HsMiph26x593+ZuQnKec5QpkmeQU5w2it59QvTFs9cpPrrTrnTX3R0LdeaYduEUazqGbjTXipPbjTznFzvjkqIvyYzVO2ZJSn3NclVP1Ch7oTzh+a1W8tgqBmq749v1x+3qbSdyBT/obBVPt9vxG4Ui7PQZRW1H76FovA6mtTXw5RutMYRoxX8+Nw2ne8TrtY+b9vqGM53xfYU0oGDNbcX+nZtpyTuSg999FRqBPj/ZB3UfPvVV+L2vw8e+C54ce7u9fnfY72+TaaXUryulZsKceN+rlLoE/AQ6qfG9TxjP5cYKzFPDEklijTXWyksk+gq4pUVcPx2mAemD+rxOQ0IdRAYW34TkBmzUoClb6UWUq8tWLcjHYOl1WLgI8WForsHMK3rmZyINxbegIXQbfhJWr4LwdTDE6nxnjJ+3qhW8geN664WWrHwWvu/7wPNx6w2KrsWIWKXoxXFXlnS8349+DPq2mKksV3X79nEdXxdcaqVMCa6H6VBi+pi8ru8zNgJyvrXfvA71y5DsA68OzUv62lvXhStPyFWQV8Dqw6NImcsUrGOU1TU8GmFbge5jeR5UQ68qkmjoPHtyDYQDwUWwwpjE5hvgXdu8yofv4m6sU1QjjFgLFKvgWgOQO6bjCxsVreAq1ZqZi4LsQ3qyRxceLmWKFBihTBGPB2A1kQeI/Ujx0Z12pbvu7lioa6u6fF8KZkqdcXK7va9ecUlbnWvvi6urOk7PEnD9Nit53K6+Xse364+d3IOhk63i6d5OfTt5RlHZaNxE43W+rJOER/Jcb4v/jOrPOKv3AAAgAElEQVTrFa+3VX3d8X1zZd1Ge+xfJK/bhH//t2BbWrlLxbWimYrr144NH/kTWN7ofT/7zX4reVUhxLsAhBAfAFYBlFIS/QP33kek8K0CtWCRQRI05Cp+Ik0hO4S7Nk89l6Rsr9CwxnCb80jyUHhYz1611pCjjyLraap5m2q8ga8GcKvzyFodaQ/iOmVkqQnDp2DkIb2ihp2DI9+oR2B9CQrTOlaNEbBrUDgG/gY0VqF/uDPGzxnQFrzVK8j8OG7cRyofZBwmTsAHPkDqkVMMWjWWSpJBWSL17h+Af/IrMN0jg42SOvWJioM4As3LIMbBOhGmJQnAGtHpUKL4OnEUnWJmDoJ+EGM60bEYgeTDUC+Bk4T4SbCOQbMWplAZRQoH13KQ1jTIEg6D5DnBmn+dvBrFCXzgqFbAJGA9piduiAQ0YnqGr8iC9CD2CMgwJjH+ODjTyNoCbjKNb8dxqSJth1S2n0GxxJIcYzADKSoQS8PwN0H/SUBp+VDQdwKGz0BusudwcUiRZ5A1lsgziMNdWL3E8LbYKj3CnaRNcCydWmSttn2Kj61SS+yE7rQrhZRut12GQkqH0EoFxwrQn9IyTfbpbZT+pNroTHUS3bPvd6ZCkVJbO3rFJW0Vs9Se7uShAR2nJxVMDej+iMo7Vmdbvg/JuLb+tdfnWDoxdPfx7v4Yy+k6SrVWndG2kArTv8Rb6V/ay90uVUcv9jq9Rnv9O0knsp1c0bhrv+dSXbFQVzR8dat8NIaieLrRXGc/tj+v9vq6x1BUX69UQY7VOdaiOuLo5xON15Krr53qb8kz1Rb/GdXXn2iNj0KqVW+c1nsyqq+Qbt1jVH401xobeVu/Z3wJry9qBa8/pe0w3RFv+SR4AXz+4t48/92y3zF53wj8f8AjwGvAP1RKXRRCDAM/opT69/shx17G5PkE/L56mXk1i1KrnPJLFAKbJxopPq1m+GJyCewCjzLN6bkSA5U4j8osFyaLlGM+ufS7CYIG52PnUPhk5y4xcGOJfD2PyDxEpRAjHzvCqSPvwAqWofIm2MeAr0Pta4CEvu+F5GN6JQiRhvoFqPyxntzQ90Ho+0d0LAEW+Mj6Euetz1C25snLcU5dlFgXzoFKwCPfh3ziPbjY28emdKxQIcB+GqhqK6I1DLFHwf+qlssa1AoW6NVAgmVYnYXgCDiDgAeNmk4J0z8N8TAGb/EsLP0VIJAj38b5MZuyWCMv+zgVTGOJDFLF8Bb+b5yNS1gJBwqPgyjpn1vxd0FsGhqv6AkgwtWzfq0cJN6t3bUA9ghSwHn/i6zbZdbEMgVG6WeYU+ppaHi4pEhd/QTW5d+EzJGwT3v8VqnOwIkP9YzHAxOTd5jYrWtwJ3UtVyGbgOeO6fgg6Ixp2o3raru2KnXtbqo1N6cpWa7qL7anxuBCEYpVvTTaM0fg5RntOl3e0LFEyxuhSywDKH3PkatZKf3lNha6zJ4Y2zoGcLsYuq1ioRwLzs1qt6ACcg5seHr/nRPwbQ/pWbQdfevAt07p4+3tRArF64varXdjXX+p5x2oNPQ79cwkNJRuY6kCa3WYWYfJfigkWylfeqXq2M61uFfpNdrrL6QA0XJzbydjL7mg5YK9vqbvOecozjckVaUYTQmeyVkMZ8StcdCIKxbGfT69LJm9IXAQfJMDU55NFkHO0cm658pwrF+n0bGEHm/doQHQGeP2ymwrrGE9fA5H+6E/qS1q+SQ8d1SHpEcKfXdcaK/60glICDg7o8dR3tFy5J3We/L1Be2a7UuC57dmi3u+HhvReyPrwP/zRf2jo9rQ5x1b35No++h3m1oh/GTvj/w74p5IoaKUelUpdUYp1a+UepdS6mJ4fHm/FLy9Zh2XBVFk1BphQdwgr2L4dsAqbzGX9IhZcQQuN9VFnGqMcr+i7F6h7CQoOAVWYnMsJlew4hmU9JkL3iIbFCgmVlipv0kh8TBlOYfXuAnOMGSbkM2BdVmP9GwGuARqUc82VYvARUgnIZMC3mpNLoiI2Xgpm7I1T8E6Trl5CW/9vF5RI+ZDdRlLercPtu1YocICZrUSZU/oGDvRBOpgj7VSqFh2GH9X0FbIVAHcBZ2aJT2mY+LsPv0fePqcyILI4Lk3Kftz2tVplfDiupwla6TcClZqQM+MDS7rVUdiWRArehWRuAOph0EsQeJIaOdXkJjQ/5aNJzzK8RpZUWCJGbL0aZeq8LCcDBnHwpr6bj2L1nfpqeA1ymA5PePxIiwsUmSMgncI2K1rcCd1jeX1l0aT3ik+duO62grL0kpO3dftdaeTGMvrVTOaaKVgLK+XSSu3faHNlPSXV+TCilxW7a5mqeBKmAplzW2ldOklT6/Pi63SnUTHPdlyCwYSLq3orW1p5SFSkjv6ttE63t5OLqnbWdjQddSaun8urbTc5jfKsB7GYF1d1V/adV9v21O+bOX62+qZ75ULuL3++XKnm3s7GXvJFblgfaX3vSacX4GKUqSV4PKqItk2DjbyAb/gN/hdfJob0G+BJRV/saz4bdVkRgVcXIFGOKYCqV2oc1uEBrSnCvJkZ1iDF8pXD/RzKKT1c27SmUqoewx11zeW18/3RjSmlK6/kG69Jy1Lj+WRnB4r666+brGsj0fvjUJav4cqDYjHtIzJuN76XbYyx9aTMQ4Dh+ZbRQjxEwctw92gnxRjDLHIOhNiGt+yyAcxRqyTTMkMEokiw9HYY3i5GPlSjHzmBHkRY80KGBJTjDKFRCLsLBP2CTZiaww2hhhKnmKteZN88jhOcipMRRK6Aa1H9CewCiD2uE5BEizpbexxfVz6YJ1szXZtwxED5MUka/IK+fhJnP5Tun6VgoHpzWlcetErzYnVltbEGmDTShRRuhJRgtSkds2mJyAzrlOUtKc6sVP6XFi/k54ib09sdnXGByB1HNySniwRf0yvmKECLU8kR6+ULu19ErpSNygxwiQblDa7VFPDcOZjWgGtzmhlT0m9rc7o42c+1jMez3D4uJ2rcS9SLfRyXd1JWoZe7d0u5UR7+pPJPv0lFrmweq2uYQk43pUK5W7SnmYjZsHJIb1tT7mx1b1uV18sdO0m7bDO0G0+1d+6x4cG9Jdz0tbb9pQvvVJ17PQZ3O3+ieofz3e6ubeTcauxMZHXbvOUDU4cTg1BTghqQnFiQFAPx0Eyr/ilik82rTieFPTn9cdcwhJMDkNcwJ+KgPEhRcLWfR2ztAt1It8aX1uNm+6VS5xQvmRsc+qdO+mnyLUbE7r+rVL5RGO+vX+7ZS+k9GJQjq3fL46t+7Adz9dWwcPAgax40QshxA2l1LH9aGuvU6jUpc81f4MJK8VSs4JQdY4kBvHEKvOyQUNmmI71UWyWGQjKZJLjeFYZRIoUGaSqsawauIHFmEhSrV8niUU8McmiV2Y8PYQVg3W5SF4mCNggLkbw5BwATmwSVxWpyjIZK09GDGMFofXOHul01dJyF8ZVHDdYoxr0MSQEtnsTlA2JBCQ3X3eLoKGTBtsF7VKVq1phiuXCyQezED+irWnS12vdxgda9UUrZKi4TpcSy+utSGklrT0tSbScGOhUJoGLZ4Mjuixh0ofGilYMRUq3aYcyga4jBuDocyKlXcLtaVICF9eWICwcUrcmRbTv37LAuctw8/Nw4zNIbx0vlcWZ/ADW0e/aUsG71e8ypX/Z2i4pS7tsu9Nv3JrGj8SlekuOJt4tpXMnLt/duoa3K7/GMmf573yJz7BBiSx9fAsvcIb3U+D2Su3dTk8hUbhACrAQ+FKx6kO/rfDC+jOhtbWK6njtAo4EtyFum/6jl2uo6uu2B2zwfF1nKqGP4UPGFmB1ytfeB3FLy5oCcglxx/2xVaqJjjHU1e/d1+wk/cmdpF3ZzT30SrNxuxQXt6uv133dLv1LdJ+wvWtwq3HSq78jmXaauuV2bW31vKIx2W8r1qUeY00p6LdgNSw3YOt9XypsS9x63X4O4HN2k0/KgKO2wLIEMakzb4G2lvo+zKL4sG3z/X78lky7Sdez3Ti80zQ/W/Vhd329VpTpXl0muuaTf6dn0R7JawueLTpdtaAtfx96J3z4Li5Xfqfu2v2OyXt1q1PAI0opZz/k2NOYPKn4+M0S16tNrpZ95rx5Amp8w/RNHj+iODs7jKg+QlCJk01/hf70LN88OctQ4gz9DPNoQ3E2WOZ3Fkao1McZzSiOjn2WuFpiYe4b8OvPM5GJkZ08y3LwFYR/nqdUgrX4GNX4AApBprnM+eA8iywwxhH+vvXdPJH4CSyxWUlrT+GRkQN8+eYjzFZ9JhM3+bHBv8RePgdBBrLPwZEf36zoBQ2Y+yg0vqJz/+XeoxMIx4bBfgrmPwHujLbSjf8orHxVz1aN0pLcWhVDtpY+W3oZmjakj8HEj/VWLqPl2HrVtV254adh+RX92imE518GFNhZaG4ACpnIc75QoZwU5Puf41Fxmgu8wjrLrDLPBmUEgid5jsc5c0sB2mlKlKjcuixy/eYkpZpNPL3KM0fjnOI0r9y0Nk/xPyo5b53ldV5CociQZ5Ax+hhEARus7ajNnaZr2a78FV7j1/gITTz6GCRBkgZ1ShSJ4/CTfIzjbL209N2OXZIoziEpohhE8JQUfOKm4kZN4qcVhaOKmAVnQvlfRuqYLCwsBEUJ8zcE5RsWQgmeOQYIePl6GMrqdMZntceNZRI6jmkDxUhCMNCwiAH5acnYUcWwJXgawSuoW/KdxgIpdFxZTbGYloyGZU+HMt1xX9xhjN928V6HYUmvg1pOqtdSWlE6jUDqWLEobjDvaAvQdku39Tq2VcqYQppb6UsC2RYP2RYvuVjpiqVMQ6AUv3NDUkHiOxDLK2oZxVNHFOszgvM3LJRSPD6tGDyqmLUUk0ABizEsBsMfSZ++qajW4G/TPsmjkqYlyAKjWIwgOkapi8JH8EkOiQlrj1jegJ/+XW3By/e41XJdW/J+5Qfvbr68eyImDxgFfgz4QI//4j7Lsies+gEzNZ/RlM3ltSYNVSdhOVwp11ioZ6jUmsRTLpfXPTLOIuvuBNeac2RVkrKaY13Oc132U6tKMlmPi9UKQXOVSnCUK9UaR7Mub9WqXG0uMCoVc7ESiiTzXEAqD6XqXOU8ZeFj41IVkjl1EU/1XtasPYXHjF/kes3leFYxU6uy6ilorEMqA+4VbfHqpjkLjbd0EuNGEZrndWoYWYTGrFbwMsf11p3VylV3WhJoLSUmk1C/Bpmj+ppebUJrqbJedW1Xrr7ael2d10uUiZie9bt++ZYr2KtepJwIKNRjlP05yqxSpkiWPua4hiQgRowV5jrSnuw0JUpULuWPcbNWxsrNE9RGWPbXWffdnlP8132XIvPa0ofPPFfJ0scKcxSZ33GbO03XslX5NZb5NT5CAocRJnFIIRA4pBhhkgQOv8ZHWGN5y7rvduySCxRRjCAoopj1YaamOJoTXK7pOCsLuI7iOooY4tbrORR9vuBaWRGEcXHX13U8URQb1h2fNdcW4xPFMWWV4OKKwpM6D/nVsq63iGK1Sz63rQ/6clrWqOzbTaJzpzF+28V7HYb0IgeV8qTXUlpR/0rVihuM4r1ut3Tb7e6j/fxcWacHidqK4iHb4yW7Yynny3BhHeqWwlGCy2uKWEpRrglKdXizjM5XagnOlwUbPrgIPOAqij4E8ygu+opaDVI5WK3ppbcDQCGooejufgcocTg8g3vJcFbnwfN83eduUz8bt6lfe74+vxcJke+E/Vby/hjIKqWud/1fA/5qn2XZEwbsGJNpm0XX50QhTkIkaUiPh3Jp+qiTiDtUa3GO9ydY84bIpOaYjE1w3avjeWMk5DCJxhIiblGpOJxM52ioYWxxk+lMirc2bI6nY0xZo1z1Y2QaWVYbkiH/UarNFJ6KMylPIdws5fog0k0zJI/jN+tUG3M6PUobDilyqp95eZVR0U8hkeBSRXAkncJJuJTsOMv1dRqpaarxPBJ1y2UokRA/gkwcx23OIeN5ZPwRqsE8VSuJTIxrC171CiQnkLEsVTugWruCdPpCN20YMR3F5ll1SE5D9aa+Nt6KH4za9fFxbYmf7MN1byCTBaTtUKVClYqWK8JOIZOFVrlkgWpyCFlb0nF/uSmkCqjaDrL/xK1JI/H0I9hVh6ITkLcnyDNwKz5vgmksYgQEDDCGDP8A4jgkZJqZxjJpWUAitbxRf4XEpINqZKlYcxxN5/HL4zTjy2Qth7zt0JcOuOnWGO9rMu9WsdIbpO04GcZoyoBGI8mw/xALjRIx2U+BEYosECeJH/5F/RHt+/jkKOwoXUskd4IkRRbIyAJrdZ+FeoUvyc/RxCNDr8TPkJJ91BrwJf/zPVM2VBva/dEeI9SeNmO71CUSRRWF7PoySQEFKbjRUBSkYNyC4bjgRllxIg0JC9wGjPuQqcNGXeFLOAr0SZiXkqM58JVkQwYM90uODkhqUtK0JMcGJAuuJJmWZBMSJyWpyICmkDw6JEkKqAjJySHAkjSlZCoPa7YkiSKOpAAsIEkDcSS+LUmkAxYqkiNpXTY6Vwn//XBbIuh43b0vwz6phPUO5yU1peUfzetj7eV69qENhbTiekUxmlcd8V67iS3bqv6tjvd6zr2IYqcWynrGpLNP317tMVtReprRnP5it0QrbrA93quQ0vcRpf9oT+Ph+9pCt1DW8X/RsWj8Ryk+5kr6mqMD4CtFIBTT4Tjsy+tnvOBKBvsCVpuS8T79Op+XPNwvsSV4QnGiIPBrgnRckUvAY3l09gOpOJVXZG1IoXCAhxCUUIwjeMQWpNPgVmAgDcLWES4CRRqxaSUFD+i7TzKh3Y4nx7Sl7kPv1LNo58p6+6F36uOHJREyHKKYvHaEEAWl1Npe1b/XMXk6Figgb1ms+E3q0mV2McOLN5pco8TIxDpDU/OsqQS2qFOeO84b120gRiwxi2oEpFWOH544yUDc5kV3DTJr9I2vk29cZwqXYOFJfs/b4M1KjVxakXKfIJarkU0HZGSK+Rsxrq0LpvskJ4Zf4uG+TxFD8UTqe3hisOW6lSrgbPMLzAaL/N38FJb7MAVH8Pyx81yo/SavihvExSiZzHfzZOw5hkiS5QoVVskzyKM8zQX/Rcren5AjiYwN80Z8HawET4hv4Qn5NFZjFbl6kTc2Ps1rqWuQneCJ4CmeqE5iJYdbrtbu2Ly2uL1b7k2WWWORPoYpqSUKfj95exwEvM7LgOAJnuWJ0BEnkZxXZyn7c+TscTbEadaUYtBvcDqc0HHOr1FUMGgnOO03QMK5xRTLFY9cDr7laArbsloxdDh4uEgk1zlPJXSTPsrTnJev8NLNBrVajuE0HD06Q8labKVf4TRSwu/efIOZWpWRdJwfHP9GXrwJr3tvkMqUOXM0xnkkN/11hDVDTbo07YCMdYZT8jTzN5sMVWMUKwku514nyJT45qMWD1seb/F1QJEmR40KCkGWLFXKCGKc4gwn+IZtZ/NKJG9wltd4EYXiIfkOLtwY5M9urCEU1Kc/zeNHy6StzUqilILZm8dYq8YpV7L8dO4fb+m6evpIa+mh9mzy7e7FTteVgqOSNavl8myPbTt7UzFXg7E0WEqwXFPEHXj+qOLleViuKr5WVix4iiTwwWlB+gicnVU0QtfY0nDAvAXTCegDVsI4ngEb1iXYlmBtFhaqUErAOya0i7bgCzIIsrbiRV+hgHclBE0LvoZEIPhmBE0EGyiWUKyjuCkVI77gXbZFYLXOlVAIBFkUJWAGmAytLBsIFIossAEIBM+0uaEVirSEYij7UAJqluhwT6/B5j5EcVZK5n3FuK3Peb7YVSxUt9s8qn+r461xszNXrO/DF6/BRuPOVum4UyIF9PVF7cIupOHU8Naxi68vttKGRKk3ojQeQsDpSZ1L7WtzOlYp62g3nxDa5Tuc1UreZD8MZhTuiGTVkqzakjUJtq2f/6oPCxaMST3btSQhYQvyQL4hyGHxvCX4wk1BxVMMZwTPHWnF2g0lwLP0c7MQpAAv3AJUpML1dUzef7UCJhCIUMHrVudmUHwImw8T3/sH8gByr7hrd8qfH7QAbwfbEowkbJK2xWTSYdzup7gRJ2bFUVaA4+aYoULacYAcb1Ykworjq4ArRUFGDtCIVVmq+MxtKNJ5Raw2yKysMmV7LJPlWrVCkFzDKx+BZJO3SpJ0KkG5nOXNksBRfdT8FCmpXcVukMKybIrNS3h+ywXqqg3WVJm8nORa1Wcq57PWbDDTnMGTDTYSNo5U3JA3SBOwSIlllm658MqsUraWKcRGWYknWRBXiIkYlrApMo9nNSGWw6vPshKv6HONKsXmW3jpvk5Xq7D0xI1YApzOiR6R6zBLH0vM4JBkScySjY9SFIvMc50YMSws3W7o9PJwKYs1CvEplsUai9QYETGK8SSusHCFRTGeYiSRpmjFcBNZXCtHsWYz1p+hWsvg+fptEqU6sbHJkMPGpsJaR18s++vI2ijJ3Co3axXiflf6FVzWfZe5mstDuTwrtYDlhkel0SSXryBro1zzF5m35phKZJmzr+EmPDKWxQ3msP0GS7U0fak0V0oBjdQG6doo1/1lFlnCJoYk4CaXkASocF8hsbBYYxEr/NsKD5cV5ogRI4bNkl/krXIJO3T/LZfjKL+3FdD341RrWbKpgJVSZlvXlSdbaTO2ci92uK5qMO93ujxvjWMf1mqCqZxgoSyYL8NEXqAago2GRb1m8f+z9+ZBkmT3fd/nvcyqysy6uqrvu+foWczuAntg9iJAUhANgBCogySIcACyw5JtihEO25JMOwiHBYVXEYIiFAqGwjYtUA6SgGRJlAQ4KIAHSIMHRHgPzC52gV3Mzt0zfV91V2Udmfn8R2ZVV/d09xyYnt5ZvE9HRx358vf75cvsqtfv98vvy9mSq0VIqXBAtVSRLDbBbEiMNFxtKKQpCCzoSMEVqYhbgrgF102FGRe0A7jUEFgZcDsC34QlUzFpSdoWrJqQtCBlCZYkrAMmAgPBTSAUC5JcQ9EEXAmxeNi2u+06Cp8wNXYJRQtoEn75Xo7e9xFcQeFHthf60tA+gmu92AVXZbhPf3p63z4EilIxExcUZXizyt2uUbo3be7e5v3+83cnqdhWJIXSLxPzIOhKdBSjGIuNHRmYfjkY09xp102l5pzdMh5SwGIxHACaMizev7wV3V0dpXydeKjxlnNgzYWKqRi3BDdNhRkHJcNzrOLQNkM504XoGlUSFmR4TQoLGkhoS05lDJoNiR9IJqzwNy4laSRZDNJITCRJBDL6zUrJWFzyMzJOEkGTcKZx7wCvEs0EfuyBr5SquR3v1kHee2rOt3eremBgqwSJTI3TZhYDj7hpcDYTRwSCmIhxcggaskRKZTibt5jNGPjVOIbjMRcboCxNxmWN08ksyeYEdnYTo5PldDZO25XkMgFnBwx8EZCLGXTMGKcyaWzDJQg8BmPzJMydFKgtUgyKAZpyi7lkjMWayaxjM2fNkjDSpDrQijnMmLM0MBglyzAjvZRfhjwZMRHKvwQGY2IeX5gEBAwyHqYETZuEM8tQJ42PT5AYCONolHdLpBzCXjmTFs2erMkg44wzG9ru99u3X5ENhhlkFIeNaDbBJkzzDSJ2v3eHMgh7V6vIkGfYHMBwNvCrY8w4WTpm8Rb5lQHTZsKxuVGtMeHYjFs2Y06CVnUQw9lgzhxlkgnWKDPDHFkkDQJmmMA3LaacUNrgdDaG4w7iRvvMMIePj8RgmnkkBiJ6LqJZzf6+Oayvh5jo9ee0OcV8ZgAvkvgYznQQ5v6VY6bZIenUqLkGQ9n6gVIFh61Q0C8hskvewIFxc/e56v8b208GoV9aounCqZygLhRWIHgkA7OWIHAUZlUw7wgSZjjLl0BxBoFEYQDzCAwUlglnHEWrChlHkTRhLkpxTSCYRRAAPoo5BHN7Xk9EbU8isAAHkHu2nUBgEKbGziBIQC+m+eh9A8XpKKau7VkEPmrXNtm3TwDMRn727cN9/hbuloNs3M723UiiHKVEyWHcbYz90huHrdBgRilfKXZSvrukbKLrvnvd9J//nWuD3jk3gJPRtTRIeMfsD9tnwwheJE6LcMbOjdLuLoql6B+RF4kz/N766n5P8G5N176ulLqPNx/v5qjTtWHtWJsEJi5tQJAI4my1fQI6pOOSpIxRDzq4nkFGSpaiZbDGzAQ3vTIpLHLxODYxNtptmnhMxW06oo1UVWIqz1qnxVZQxZEOE6bNNc/FQjBl2qx5LWSgCKRgzIix7m6gBMw448SN3dPpgfJxVQ1D2az4TXKmSVIYrLdWuNnpMGRmmU0M4kuwCfXmWqpAQuQJ/ICCu0wi4WCqGonYJC3RAs8lIfK4rRbSgoRM0OpsERAgY+lQ/qNTCAccsSSu2JEkgR05EABXVcFziRlZin6JwIyTEjFsHFo0CbCxVYDbWcelQ94cx/R9AjNBS7SIkcClQRPI4tBC0g58ql6TMdOmE01VJAjYDuq4HoxJh4rXoh1YmFKG6Tov/HJKmqJ3O31AwEa7Rj0ISErJgGmz7rUYwMY2YS2ok5YBfmCQkTalaNZywLSpeKH8STouCVCstX3auIyYCTYCN6yRkjbFwMX1YETabHgtZGARlwZ5E7a8gDYuY6ZNKQAl6xiBICNtVj0XG8ibDpXInk0ylCeQdVoBt8YUtUtgU2i7BAEkZRLbhIIXSre8an6Vb3hfJRdMICRYpsL3Qq0E0+zQ8UzWWeYT5s/xjPdZYoGiE8kzdOVFEpGsQz76wtlqhxImphSHykQkTMVWpDy6V+6h5ImerwFTUQlCnxLRk09ImKFUSddX97VNOGvlSrUrddUdyvY/TwSh34QZyk7sTXHtlWfZV64lms3q+tpvW7/f28V0J+0OSsl14zsqX/3b9vrtb1ePZCzyJj3Jm/79e/0ZAJHERUvukaRhH5mavlrAOzmurq9umnnX+QpARilsV6oDbXiBouWFciWl6LPCZmsHRWwAACAASURBVCf9PxIPY9to738tVwJBRobXcL8E0L30tUTcN7miTRR/iMfv4lOOygc+icHHMPUA74i513Stnlu9zwQEnOc6m1RZpUSFBgpBRo5SswIEHZ5nmHPBSS4u2mw2AtacJUrTP0BIRZoEFdNliSIT5MkEFhfW4zQaFs84Qzw3vUZVbnMzcHlpfYwbjTYzTpzkZIwL6zY04KzTIje9yZosMBHk2F48xYWFLAj4mZk6f2Mmiyl3/iClMLBFmle5ysvGVXzl45SafGUtx9LyMFMxn78/Bx+aNpEigPbr2ME2XpDm31/5PkuNCiK+xrMTGfKxGc56Z8Et8uqixxumj5FpMHq2xWB8LJSJ4WkuqvNU6i+Rbir8eIoL+RoIwWM8B0hqFEmRA+XzduXfoTo1aJ3kreQUXqzN085ZflY8x9vYbCuf3PYPoPB1ikaHQZHlaXmai+ktKvlRkiLPBRQbbDPCIKPBU3x58SKVRpNRx+Lj02cZkoJrwat8dfEabsNi3onhqDyv3ZjEYppcQhJrQRLJ+xOSiYxg0IGOUvz6zU0uFQsMDlSYsqaYyJxg0lEooXirvkSt2uLplMVm1eBaaxkBfHRmiFPiMcquJOcovEmfr6wHVOpxytXr5NNFZpM2Pz55lteXk/h1RbVyg3daLtvYvG9uiscnBfV1iarbbFcVGylFrebwdApKVcFmy8FB8LglmUinGXQgEAHfrV/k+9UbpFINmtU8pVb4RTOQADtTJJ1sckK9n42Fx1gqS2YGunVyaQIC1hdP89L156mWR8gNVMknDIzWEBJB3HJppdcgmeDa5Ed4Z93jtQVIAmMJyfMZyWASfqACVlzFuAMoWHVhyhH89UnJ+SXRk6cYsGEktbOs0FoN3moqagR04lBqh4XgA5ZgJqVYqgmm04ogKXhuGoaRsCgpNkRU6yUYie8sibVeFbuWWkrv+aJK7/dcwlh899+9uavdXhu7X4f/xohdtg/btrfd3m231ruFA5T+dkkU51FsE/TaALyKz0v4KBQZBKMINoFhYANFBXbV/ykgA1QIZ3SmEL3XAm6xsY6iEtUXvoDBs6E4Zc/vTQImgAEENSlQcUUaenWH3ee76gkl5KL+768tBG6p+9vrqxtvlbCWMYXa9/kLGJxD8jqKTQLWCCihMKTg2XjXblgQkdkTbxnFkoTpuLhlW83aORaAV63wNpQUioq5fx9uAqMIhpGcQ+66ng67NvqvyX7Nuh+GYQSfJabr7h4idLr2PuPSZpsaWWwW2MRHESC4TBGfJhKLVaoUvHYkn9DhWsMl8Ex8Ai6xThOPBm3adLjgbVFtxMimO1xtVFjytnDIcd1bo9IAI11lu2HyVtOFBpBWXGi4NLyABm3qnuRCpY2SYArB5UqHgufvG/cqpfC/Ps/jjU6djUaaFD7buLxdaYWzK12pE2OEUuM6K/UikymHxWaTWDBIpX2dlruAa+RYK5dIZGu0KgmWm4u9urQKhXA5sqbBlu2z7l1BeqEkySo3enIg26yy6l3E8Jr4Zoqrcom2WcLuOCx5a6zhhnU+XpuVToHVmGSkI9luLVGyTSreEjkvyyqrLLPCFKMsssUPvALVRpNM2uF6w8XwmizS4KK3TruRwknXuVKpcrOQR0kXL+hwaUuRCAQ1pbhU3JEq+EGpRUlVEV6cRifG5UKHUbvN5YrinUqLtF1ku5ynGtvkUrGFUgaGhIulBjcrrV6t2eVmKFdg2k0WygETdoprDZd3mg1kQ9Cym7xT9GirDEq6FCttLjQVfkPQtBWXyop4DMplRTEGlwoKJxDUAsXlwk68NystfHudYjmDGetwudDBj2qBLhU9LLtJpZLmYqFBS7V7yxN16+RKnstqRTKtHqXTieN2AtY2c3gBtIOAxUKcwK7wgcYvsdwcolYRlCVYgeBqtEzSjQosVBQn04LLFcXl6PlSQ7G8R57iWmFHHsKKweUC1AKFpQSXthW+UnhBuASTExMslBUDtmShobA8waoX3oyxn4TFfkstPYzcrt7toDYusNJXx7eAwkKwFD1e69UG7tT/hZ9l4Z2UDcJawctAsMuG7NlY6KsbXNnj149stCObXuTrUl9NYrcGcW894eo+tYWHHaO/K94wJq/Pfr+vbqxd2Zts1BcBO7I7CyhkdNz9dZKXo9Sli+j1Tb99r+9Yuja6cQQHnIelKIb7Ia+j+dHjWAZ5Qoh/cZv3fuoBhnNfsYmTw2GdAlPkaOPSos4pMrSjGaoMkowpcZwGxarBSccGs41Hm5MMAh0MOgR4zJsDxJ06m1WfE06SjLS42l5nXA5jOh06VZuM0+BRK4bnNPGqHU47cTpmG48Az2xwOmPQDAJqyuNkRpI3jV0xe4Gi2o4xGGSo49IyTc6aDqlYnbIyyBDjTMbAM12qIo4nB6l6a8SsLOPJNEu1BiNWkpJfxjFnaatxvEaJwUyG7bJNLNNiwppikyIyyCHaORwmWJKCZMNn0DxJSUKpHaCCQdIMscQyBkkG5WmqfpZWvcMJf4pYZ4Ci3ybljZP3LJy2YE3GGYvlyXiShbhBOj5FsypQcoqrrS3ynRGSzUkuNjcZ9QYZ82KYMUmpUmPGSbDldXCaPicZRsZb1CtJTmVSjOcLENiYMsaZIUFLKlJCcDqnWHdDmYn3DcRJiTSB2cKKtTmVj7HmxpnJwCOZOKVGDidVxGgNcSYXxxc+zQDmBywGMzEWqgFjjmLeCuUKPNdiLitZcWucdGzeZzkEjiLhWrwvZxIXFURgk8vEOWsJDEdhuYIzWUGrDcmkwG4rTuehJgMsCafyAasNRcZRTKcS0BglkazTaBvM502MqBboTM7EbVhYdoP5nENCxHvLE41lFJ4ZYJgJTmQcbJFmKjbCWGyEweE1mrKELzs8n5/lGfeXyTuznLYEqUxAMlC4UnEqL3BdxUhGMZuBK9VQZuRU9HzIUWSsHfmPjgiYywcUO6FMRLETMJ9XpKSgKRRnBgWGEJgyXIKp0VHMZQUlN2DOETTN8C7RiQOW+rrdUksPC3dSS7e3TSJKX45Br45vDkEzmp1r7lP/1V/jlyCsJ7SAeYhqF7s2gp6NOXbqBif2+DUiG/HIphn5OsNOTWK3ntBDMQKMEcrQDPQ9t1B4BMQieZq1SK7Gi2Rm+n2F8YYxmX32+335KHJAJrJXJOAkYX1mN44ZwhrH/ppHA8U8YZ2cjer1Tb99M9pvmlA01uuLo78PGwQMA42oL8uHnFuN5jCOpSZvb82dEMIAvq+UevRB+D/KmryAgFd5jSXW2GabAiUUkGeSLVzWuMosQ+QZYCQYIeON8Jz5GC/J82yxRYkiGxRYZpkJJhkmx2ZQwvNMhmSa/7ico9BIEHdiPDZZoRWUeM7MUZVFloMSeAZN0+Z7ssIKK0wyQT6YZ609gcTg5+JZ/ms5gxmlTcIVOgJuNnzazkWc6QVggMuLJ6mWXbZsj3OTAZZVJy+TCNJkgxyB//8QD1Z5ws+y6n2MH2yP4tWa5Fe3qNfb+EacxuN1EsPXGXcyzBgv8GrQYnnRYaZukqkqGqkfYNmbbE63ubQyz/XGDYadOuPTLinpEgRN0otPYC48gRF4vDBn43ptvroi8IgzlpA8lxGkkxCb6vCdoIkbBFTWEmxUFDeLVSbiRXDTxMQwhvAYSSyx0FrBU4pnJtNkDZM/XiwhgVOJSU6kx8kkFPm5ONuySaxt8zQmQ6ag5IVfUhdMxXqgGI0GBTfbHgtBjQkpmDFTNAKJb4b6a68uBLzVauIkEjw6E9b8CQQjcYcaEs8T/IRp8KyUvZqkjAzYDJqMm6F0S7cGKSEVBa+BhxXVdYIbCPDCO96+uRjwRjPAsOD5aagFYd1TUULxpiDeljxjC970fc636tgWfHra4n1BG0lYx/etxRb1FownbR4bjv4HlIq34z6vSh8FJIMAp+2SCeCcNMmbSV7yYIuAoqnIBTBkSl6Q8FIAG+2ANJK/YAq+E0DNVGRRVDzwTcgBJU/xjhlgSIETqF3SJcUA1qViPJB8WBo85klahLVKh9UxdaJ6OQJx4LJSt1tq6WFhv1q0g9okUL3VN3LA2Si9263jSqB6j/dSk3eYjb1+ieI9rE6ujuJ1AhqE10p3FjFHuMrCm5Hy3gCCEQQpBLEoPa2A55A8hrwrXzVUL12aQfICAhd6cQwidvXb3dYrXoj6wQGeju5o7dV8ongJRYWg57uDPPTcat77PBQSKkKIzwkhqsAHhBCV6LcKbAC/8yBjOSpcXIqUGGOIRZYwkcQwucZNBHU6BLTpcI2b5GSGRnyLiizTpM44wyywBAT4BAgCrnIdS8pw6aRgmWIjyUC6xY1Gm3yQYzu+RkqmWWSJtDRIxBUX5Q0COnRo00FxSW6QsDwyFlyXLgXavXgLvZUB2lxrtBDeJB2vxvWGx2zepuSZDJsxrskmLWwC6lyhiFRrBPIka8YW1bhDvJnAsCyuFCReLEtbNLlRdplJzLBpNLhBG+ElaTYkTVtxpeyTcBrUW5Ncb1VJNGLU09skGhNc9zaoUyXmjXO10gIpsc0sy4U4y7UkQiZIKcnVosK2BcWG4KYvMWI2SjhcaRgYlqBUNjCsLFeLMSxf0Q4kF7cCCCS2IdiqNrlaroeiqsrkatFjyjGhk6YYmMzJNDHLIGdJLFMyFklSlM1QZmJdKtakYsoyaTtpTlpZiqakGYcJKVkOYKMjGM7aNDuSKwgcK4NtpbkmJUqCExesyfALsStX4Jgms/EUcWmEtVVSkI4L4qZkzEoxZZlkZSh30N3mI2i1JemswGlLVoCOBZNWmDozO4JYRnG1CosujGSTqLbDOgZZK82YlUZhotpJ5jJJyg2JaULWAdMKB1nd1NJNKZizMsScDFkrjW9KmlYo8bBmKsbjkqaECuHjactAWFAzBY24YkwKNiW9ftqQUI2Hi537wFUJcQviluB6JA/RNMGMw4YZSoOMWQLL3JGCsEzJSFz0HuOyTwpiHymQ7numefcyIe9GujdvHDYI6LZpRam/EUK9PBO5Sz6j/zHdt23v837ZjfQd2NjP70E2+p+bSJoIxpCsASXoPd8glKgJopRqDkkRWCT8cjMQrN2Dr1yUcs4haQCdPXHs7bc77ZvuYzE6hmbUR/3tOpHPCYye79udW43mIB7oR5tS6guEGqNfVkplot+0UmpQKfW5BxnLUWFjM0ieMhXmmI2UxgTznCBBGps4FnFOMkOZCoPkyZPr7XOSGSwSOFjEiTHPqUivTPK4OUnecSlX08w6CepmlTkmgTYnmMVAYmHwKKeIEydBAhuDs4xhEMNDMI9Dnp0K3LwZFrwvVuOcdiwsc5OkmeOkE2etGjDjQMP0OYVFAhdJktMiTyCmkeoaU2Kc6dgAgaOQ7RjzgwKzUyEu05zIJdkwV5liiFM4GCbYDliu4HQ2RuDmSDnbnLCy+I5JqjpB21njhDlGhmE8c53TGZtEEO/JDjwyENZ31YXiVC6UEplwYM4M5SMSpmLeAb8tyWUVNBuczvk0DYOMNDk7ZIIM8AN4Xz7DowNp/ACE8Didi+O6icOlOthJe42zI3vRTal039tAMWuGshzlKmQcOGvemibqprF+2DSMbYb94FdDSZA5UzAexTRnhmldvyqYz8B8RlCuKhwHZs0+mYuDJE6AcUIpEJMdVfy9MjT9/TCIIM/uFGH/6/5+6pce6U+bdVNhXakRA+5LX/2ocz+kUh6k3/79Jgiv673XjYzSy/tJ2dzNNXPQtXyQ3NK9cFs5mfvkR6OB40vXvqaU+uADdxzxYCRUXBIkcOlKg9i4tAnoEIrq2tE2g2S0oHP/PkEkXrvTLrRRDmqsei3mzCwV2cLDx8RgCAc3ajmARQG3ty2PTYEOoBjB7qVqu7QDn2WvzahpUJKRnEZgs+75jJoGNemTwWCLSMqFJC2laKsiWZHDFQbloE3bCxgRkoVmjQEzw0jCoIyL79kMSMGS59EOfOJSMWUm2PICKpSZMB0ueBUIDJJScjqeYYsOm16FeTnAludhYTNiGrS8cOHsZdnkFXONr3lrVGgzaJo8643wMSZ4xLTZ8gJcv4YdSIYsh5JvhB/UpupJgQzFw3sZt9rh67yZpBPIcHAju6mtgBZNbGyIUip7U1C701NBJJsTJ4kkCGDLU9gmYXq1b7/u8xgeq1SZJEP8NnetBQTUaQICmxgtPGziyMhX3VMQ+YIdSY5+eQqXsF3SFLtiShJKLRSidjLadrdSG7ukG/rSiP02+iVDDrK/ny89o3F/uJP07rvJ797r6KDr5iApm3vx1f2bvp1Ey71wOzvHdX40717uNV17XIO8/wP4LaXUdx64c45+kHcndKVWtqkxSIpznOCwVQh29vku2xTIMsDb+HyHAg55PsVTmBj7Lld0J3FsUmOVDBXWEZR5gUGe5elw8IDiZTr8W67QoMozWDzGWYoIVgkoUuA1NnHwWKONR5w8Nr8UnODqosnNumKhUme75bKMy9Rcm0cnk1xfjlOs+9Sq1yimirRrOZ5OT/AXk+P8mapSdD1izjqj0y3SZHh8cZ6JhknFqfLb099jA5/CooOqSzoVQbtlkhAx/pfpcRJyibWGy4Rj86npRzHvIR/X3985csATFBHkou17+/pezmmbDi/yDRbYYo4hPs/HDxzohfWeV3mZqygCMtiMkWOY9KG+uhIbmwSso3pyDE8jOE/AS/jsXfLqoGPs2nsVn5cJa/QyhKmnrsTDQV9ahy1rpdFoNJqDeShq8vr4CPCSEOKqEOJ7QojvCyG+d0yxHAtdqZURMmxTi2Z/brePyzYFRhjmBmtcZo0sQ7gUuUyDVQ6XUTgsjiwZFqjgs4XBECsUejOILrBAE5cqWbJcps4CTbIIFvCo4VLGQBGwQJssNmWavOE1udZQjNqKK8WAllI0ZUCnEufNZputhkfK9rhRlgQxi6BssmaX+U6lTbESkE0LrjUUhpem6tW53GgTpFv8b42bCM/E9ixEw8C0oV5IkFEJhPD5p4UbXKjUmU2nWGm4PbHfuz9HO/29QoFVGgfKN9zrOV2mwgJbPMI4C2yxTOXQc9WVufFQXGeLLPZtfXWlJbLslmMosCOhsVeiYgV14PXkAqvs1Ohd5/YSD3ci86HRaDSa+8txiQZ84pj8HhvdFK6NjUSSIEYch3fYYJoByrTwMElGBcr7pQoSWDjkWGODWcao4fMdtnDIM4+DeUAdR5jiC1OSyT0L03clX5ZZZ4Zhagzhs8UEg1GKMkwZzBBHYrLJOs8GGVJtk2UCJuKCVXxMr0bD7DArTYrUcID3mZKY47NYh5M5yWbLwwwC/EyNx60Ul5wmxbrJTDag1HFR2Rg5N8P7U7DuNdmsBJxIQtOskiTFKcfkD6vb+E6LrBkW6UsnwKsLMvkWQUtgK5NEvklJNrhc7TDrZBm4g2XT9jtfHmDhsMY6EwxCtCTaeHQ+bl26Kc4gKTaoMEgKm8PVRz08YnjMkOcdlpkkyzipvuulTQxJgQo2NkkSjDPADbYxEZxgiDIuw1Gt537HEJYAWAwi2OyTYxhGkiescbtBgCCUdegQSlCMEd7FuEbAMPKWusRxBDfYXaO3tx179tF1RhqNRvNgOdZlzYQQIxAVpAFKqZsPwu+DTtf2p/0GyfM0T/IqPr/Na5TZIKBAnGGSDPIYTzKOcYvKORCl3HxSNPlQpI2/RQMbm3RUZ3frkj6hpMtLfAeB4HnO8Swf7A30uttX2GCMER7jSSQtkji72rzMa/wpr9IKfJqLH2Zr4QRxIUlPX+X7coGlhs+4I/jEdIaLssg2dZKkORs8SsuT5OU6q95F3qSAE09TlYJmYON4Gf5b+SyF4CK/L7+N7ykKa2O0GjZOIuA/mT1N2ZzBBIaCDr/mLdA22wzIOHEk8cAg5hmMyzhtT2Bi4MU7rLPJf+VJps0Rnpcf5HZp073n61Wu8lKUFn2KKX6cR5EY+9YF9acdu4Ozbp3cQXh4fJnfZYl1hskxwgAxfMYY5mme4HVusE6Jl3mdDbZIkubTfJRnOUV3qby9NXm3u+ZayFv+eehf8qlf1iFBmKpNIfhQdPffbvtq31qow1Kwus5Io9Fo7o2HKl0rhPgrQojLwHXgz4AF4Pdvs8+0EOJPhBAXhBBvCyH+++j9vBDij4QQl6PH3GF2joP+tN82BQq43KBBkyIOOa6ySpwMVYpcwe0pxvenwLrprjEMGtH6qyYGY6TJYiKj4uK9RcYuLiusR/fnSlZZ66Vhu9uLlJhlkjIlTNqkSe0aNLi4rLFOCpO4l+RqpU1KQl20eLvQQVVsvHSDWMPigldkixLvY4hr1GnIJnbc46ZZwLRaBFYLR3osUGdIplDxCtfNAq14laQZkJQGN9wyQxmXTtvkcrBNEo8EFtdkg2a8DTK8a66GR1IaiHgApiBpCRJWgCkDSrLN6fgkZVnadbx3dr7arFDCIBw0lmjRwtvVvwfdBCCRJLFuO6gsUGGJdU4yxTLrtKkxyVh0fVTYpoaFwVUWcUjjUmOBDVp4pHFIY2NiHuhr7zXXorlL2qIbd7j81W5ZhyKwHj1vEA7g9tLdr18243YDN33jhEaj0TxYjqsm7x8AzwOXlFInCFe4+PZt9vGA/0EpdTba978RQjwK/ArwTaXUPPDN6PW7iq6sygabkWSKzSwODnlalDnNBG0qpMlxGrunGH8/bt+3sZlgFB+fgIBxxnpp2P1is/ex3G8jYbY5k0nQCAQDyuL9+RjxjItVtTGcNk+aI5xhkGtscZIkqWgwMs8QSQwyGAgsTpKiTI0MGR5nlHkmsTDxzSYnnSyVqk3e6fB+cxSDFJIO8yRJYiAI9a/SmLQJSEQyNV18JHmsQ4/p8D6LM8FAtCSdYpyB26Ze75Y8GaYY5RpLzDHJLJN910eGQVI08TnFNA2q2KSYY+SO47iT87q7/f4yFTq1qtFoNA8vx3V37Xml1DkhxJvAU0qpQAjxqlLq2buw8TvA/x79/gWl1KoQYhz4U6XUI4ftexx31+6tyQtQVPFxcRkgQYlWVHclb6uYDncnC3BYTd5+sfW/lyBBi9YuOZhYYHGzXQVgIu6wQgnPU2TMOCMyQ506RVpMkKPTqyeELYp4BJjEGCDJCiUsTMbIIRFUqeLSIROkWPNq5MwYSemwQY0mLcbI8H9yia+xzgQWKSyCKFoDA59w+YM12vw8E3yacZIkCQjYoAAIhsjSIeilOD08NigSysvkkcg+KZtOJGUjSGBSoIpNgiTWLfI3dVqAwiZOKzqXwC392iX0G8Y0Et3LWqBCngwSSTUSxBkgRYkaNjY2MQpUokGteYsMT7+PgKDXn0MM9OxncHozha+xyDd4izINsjh8gif5Cd7PTLTMeb+MRL+UzGHXT9gXO9faYX1w2PV6kL27TbvvJ2XUvZ6756n/mr9TX3cS493Ye6+x9/PjTs//Jlt8g2/yu/wRZSpkyfBJPsrH+SmGGXoAkWs0704eNgmV/xf4a8AXgCFC4fJnlFI/dof7zwHfAh4HbiqlBvq2FZVSh6Zs3w0SKgfRLzWRi16/EhXG90tcHKUMRbeea5Mt1tlilCGGGeIcTwHwMuf5t3yXOjBMnhwOMQye5QSSCkVKDJLnHE9FA9p+6Q94gVOc4wSv82avZqzbdm8coa8/oEoTD1ilzitMkyLBKUbIk6ZFiwYubdo0EQhsfpkTnCbPk0zzJb7G1/kzQPEYj/ECzzJKNtr2db7OnwLwl/hx3s8sZcpRHVu3Nq7MK5xngy1sUjzKJA2qCCyejfrkFa6jCMjSYgyHQfIAt/QF7K7Hm2KUv84neINFtqmRw4n667so2rzAWZ7lgwQE/BZf4xVeo0qBc5wlS4YKCgOL5/kgz3Kqr79f4yUuIIhzjie4yDvcZI0FVrjBMhdZY4hZnuBxHCyadNimRgKTF/l5Hme6T3rFZ503GaXIMIO7jmW3zMxA5Pt1FIrnOBddr7f2we2uvR17ilc4v2896Z1ew2tsUKESyb1kGGWITQq3XNev8hovR4thvcAzB/ra75hf4bVdMXbtHVQL+17msM+Pw47/LX7A5/kCLToMksMiQZMW2xRJEONFPsfjPJCVLzWadx0PVU0e8FcJJwr+DvAHwFXgL9/JjkKIFPAV4G8rpQ7Wm7h1v18UQpwXQpzf3Ny8h5AfDP1SE6sobhwgcXGUMhTdeq4sGZZYJkuG7UhSxcVlgXVcTBwUF9mgTQeJ4AYbrLDRqwPbkWDZkf4wEKxQimaTCre03RvHDZZp4mFhcJGrKGoM8ho+gkUabFMjRowCdUpIOgjOsc0MSbapscw2l7kZ3TgguMgNLOSubUZUV3aBa9xgua92slsbJ7nMEg4ZqpS4wDU84kg8brDBDbYxEPh0uM4KWXKsssYK6/seX3893hLrLLPdk15ZocQNNpB4GFissIGLS4EKCyzjYFKhRoUGl7lOgIHEY5XtnoxKWIe5gYGFxOMSS1xnhVEG+QGXuMAKJiYyqrYLh8VxpsiTIMbn+QqbVPqkV5osUSC7z7H01/6tssYCS0gkBgY3uMkqa4ee44Ouva69G9w8sJ70Tq/h69zAI8DHZ4EbWFj7XterrPViX2H9QF+7pXXWWWDplhhvVwv7Xuawz4+D2GSLz/MFEiSYYhwbK7ouLaYYJ0GCz/MFNtl6gEei0Tz8HMsgTylVV0r5hKsVfQ34l8BtpxSFEDHCAd7/rZT6avT2epSmJXrcOMDnryulzimlzg0PD9+PwzgS9i6bNYuIasNglp2loI6yVqpbz1WkwgiDvZkYO/qZYQSTJmUC5hkmTowAxSwjTDDSqwOLEWOFDYqUGSZNgIqWGRqI6s5uXws4G9XqNfF5JFpcLUOTJ7jKT+IgibFME4ckH6TFp6nxDCOsUsQkXArrBFN4BIBinhm2qNChiSDgJJP4BHgEnGGOFEmus0SWDDEEFoIGHvNMUaOMRZJHmKVD8fElkAAAIABJREFUnTodxsmRRFKmQpsOs4yzzTYJHIYZ5DpLeAQUqdGkyRpbGBhMMsw1lphilEkGGSTFCkUsBOPkcPHp0GCCERIkSGAwyzgNPDKkyOAwzwkkHZr4DJKmTJVtypSpM0yOJjU8JCcZY5ABVtkgQQYV/dOQIkUcSZsmbVwCPCygRp3f47t4VIlTYRWPCQYoso6DTYwYVaqUKePhkWOANdYZYIAZJgiiAdU0UwwwwAqrvf3q1AmiJHs/3fTybntZZpjCx8fDY4CBXTa6+3T367edIIGDTZESJ5jFjAZvc8zSoMEwQxQpkWMAL/oZZSSK3SNHtnecXfvd5zFiONissE6OLDNM9mpeRxnp2RtjpC/2/e2VKd9if7/n96Pd/fJ10PmrU+8du4VFkRJTTFKktOu62c/X7/FH1GlgYxEQ0MFD9X0lZEjTos0f8if350NOo/kR4bjStX8LeJHuikUgAKWUOnnIPgL4ElBQSv3tvvf/MbCtlPpHQohfAfJKqf/pMP/v5nQt3LqEz0FLQR3lXYoeHt/mZSpUyZDmQzyPiRmlUC/zx7yFD3yER3mCmejOyZ3l2WLE+tKk8El+kl/gY9EdoYleWvF29Vr9tWUDpChEXxCg+AYvs8wGY+T5L/grdKIauu9zgW/xPb7HFdIM8AyP8NO8gMTgKpf5bb7BW1xhgCE+yU/wCT4MwO/z/3Gei8RI8AQzNNhAAU/xJM/xFH/Ky9SoUqTNNlUCOtSosME2FYo8xfv4EM/wA5ZZp0AHxTZVLvAOWRxyZImTIkWGn+MjnGGUIQYwMWnT5jf4D6yygY/LBzjNMHk+xHO8wfd7MyNznMAmholJggTf5hU22OZNrrJGk0VWmGacUYZ5ksfIk2aBayyxSo4B/px1wKPGFgEdmtRoR8ukmcTxaNHBxyDGL/A0S6wyzhQf4oN4tKnhssEGBYqssMYMkzzLB2nTpoHLIHnOcgaAC1xikwLLLDHOGNuU9k3dddPL3VTpszy9y94jnOYN3qJGrZdmHSTflxoOyJBhjBGGGYrS7G+yyRYpkrzAs7RoAeHg7yVepUKdFA4xTM7zRi+9fJb5nq8NtihRRiBIk6JKDUGY8h0izwrrTDHOMEO9Y36bd/rS1R/kLGd4g+9To846W1HaWJEhQ4UKS6wwwRgDZHv2U32+UqQoU2GJVaYYJ0uGGjVUtK12QLtMr92Or0WWmWJil410ZKNrrxvTNBNkyFKjuseXuCWV3Z+eXY1S4wJ4kg/wAs/wCuepUGeDjV39WaHaO/5/x+8QEBAjRpw4DjYWFsMMInqfe008PP4V//z+fMhpNA8RD1u69peBx5RSc0qpk0qpE4cN8CI+BPxnwF8UQrwR/f4l4B8BH40kWT4avX6o2SvV0ZWqOEy6437TrXObYJwGbu9L0qXNGhUypMiRYpsaJgbpSFcvlBBJUqLWS5MaSK6wSIcO6b4BXbftYXU6EkmWLGMMYWExwTAzjGNhscoWp5lhgxIVGqRJY2KywTaKGGWKOCS5yRoWFjlSLLNOjRYeHSDgKstYJLBIsMgaA+RoUucC1/FQmJiUKFCjjsBngnEWWSeGQhLnMtexMGhEqahr3GSFDWaY4BKLFCniAR08LnGdOBYuNZYokCaFGemRl6ixQYEZxlhgmRw5mjQpUe6lBsNC9CQ5cqRJ06FDkyZ58lyL0ppFtjGIc51l8qTYosICK8wzR4UaRWqkSSEIsEhSpYCKfspsoVAYSDZZp02HJi1MfJZYokSZHBmucZM2HRo08fG5GW0bY5QiJczoJ5wpy7DBNg7Ogam7vanSvfY6eDRpkmOgZ2OFdW6wiETi43ONmz3bBYpsU2CMURq40XWX7vVZeF2PUqTMIss9v2ts7PJ1jZvR7HPAZa5Fs3IB17mBg8MmW+QY2HXM62z02duM7LXIMcACN6IZP8VlrtKkRYMmbTpc4nrPfugrnA29xHVatGjSpEWLy1E7H58rfTFd5tqudt1tO77a0V9xe5eNy1zrpbKvRDZcWjRpc5mrt/jaL5Xdn57tHqOBSZEyFao0cHvXTX9/Nmn3jn+LAgYGCkWVKnFitGji4ff8JIhTPmRFGI1GcyvHNci7CjTuZgel1J8rpYRS6gNKqSej399TSm0rpX5KKTUfPRaOKOYfKQ6S4LhTeZE8GeaZib5AAuaZIU/mvsXXL0EyxWjPdij3MoKFIssgDeqcYII8GWxs5pgih4NJHJC9uPJkOMEEZUqkGeBRTmISrkc7zhh5cgySp0yROSYxsEmgeISTtFBkSJHA5AwnOMEUi6zxCLOMMIqJIBZta9PcVw6lezyLbDDHFE3qkZxK7sC0dvccNalzmhl8fHIM4tPmFNM0CZhlhJNM9PppiCweigQObVzS5BHRT5YhBAKfgGFGiRPDwcLAYJYZxhmjTIWTzOy7rT/GbmxlKkwxSZMmU0xSprLvcYwz1kvz7rW30/eVno0JRpllmoDwzuqTzPRs30mfbbC5y4aPzwSju3ydjGaoDSTznMTAwERygtl9j2fvcey1N8dsVKMnmI9KDxws4sQ4w4me/dBXOFA8wwkSJLCwSJBgPmpnYHC6L6Z5Tu5q19224yuOTYIE8V025jnZS2WfjmzYJLCIM8+pW3x1j2u/Pu0/xjvpT4t47/iHyOPjR7N8adp0SGBhRiLvAC3aZO/jZ4hG86PAcaVrnwJ+E3gFoikiQCn13z0I/+/2dO27hYPSqWH9TSgZcpjw716ZkO6s1f3Cw+vJjvTb3qkPCmckuynR7rZqlACWGLvi8vDYokR3CbHubEV3tnG3JEcb8EmQ2CVr0pVsKVBhgBQuHapUkRjkSVOgcmB/dI9ngBQdOn1yOwentbvbYsTYooSHj4nBEAM9qZhuPHky/Bte4V/zEhNkadImgYmPDyhMYnh0WKbEZ/kwP8fThBIycpccSleSZL9tdyrDsyP/svP8MHsHyaEcZKMrlXK3ki936utuZFgOs9cf750e1722e5C+7vb4v8rX+Vf8eyYZjwaJASZGL1ULsMQKn+FTfJZfQKP5UeNhk1B5Ffhz4PuwU8WrlPrSg/CvB3ma42DvUmN3Iilyv21vUuEX+Q3qtDAJ11AeJt37Mq3g0qLDF/mbDN/nWZPDZE3Go3q62/XJQce5t67vMAkUzb3T7edv8wpLrDDFBANke/WQ93pNb7LF3+LvkCBBJtJp7KdClRYtvsivar08zY8kD1tNnqeU+rtKqd9USn2p+3tMsWg0D4S9S43dT0mNO7U9TIbP8Zep0aSCSwWXNj4ubZYo0KLDi/z8fR/g9ce4n6zJnchsHHacdyOBorl3utIwAYoGTZq0uX4X5+8ghhniRT5HixZLrODSjGYAmyyxQosWL/I5PcDTaO6S4xrk/UmkWzcerT2bF0LkjykWjeaBcLdLjR2V7XOc4O/zs/wY85hINijjEfAZXuCL/E0eZ/q+xbVfjGUqt8ia7Ferd5iNW2tFb62Hu5/9qwnpLnEoEThYWMQ5cRfn7zAe51G+yK/yGT6Fh8cKa3h4fIZP8UV+VQshazT3wHGla6/3vewFcAd32N4XdLpWc1zciWzMg7Adtm3TXd7tQXHYUmN32ieH14r+aC4j9iDp9vOd1D9qNJr7w8NWk/dp4A+UUhUhxN8Dngb+gVLq9QfkfxO4ccRuhkDLs99HdH8eHbpvjw7dt0eL7t+jQffr0XGvfTurlLrrlRyOa5D3PaXUB4QQHwb+IfBPgP9ZKfXcAw/miBBCnL+XUbdmf3R/Hh26b48O3bdHi+7fo0H369HxoPv2uObWuwqXnwT+mVLqd2AfsTWNRqPRaDQazT1xXIO8ZSHEF4FPA78nhEgcYywajUaj0Wg07zmOa2D1aeAbwE8rpUpAHvgfjymWo+LXjzuA9xi6P48O3bdHh+7bo0X379Gg+/XoeKB9eyw1eRqNRqPRaDSao0WnSDUajUaj0Wjeg+hBnkaj0Wg0Gs17ED3I02g0Go1Go3kPogd5Go1Go9FoNO9B9CBPo9FoNBqN5j2IHuRpNBqNRqPRvAc5skGeEMI6KtsajUaj0Wg0msM5Mp08IcQVYB34j8C3gG8rpcpH4uwuGRoaUnNzc8cdhkaj0WgAD7hBgAAMxC3bfRQKmEViPujgNJp3Aa+99tqWUmr4bvc7sr8XpdRpIcQM8OPAzwC/JoQoKaWePCqfd8rc3Bznz58/7jA0Go1GA/xLOvxrPKb2GeB1WULxGUw+S+wBRqbRvDsQQty4l/2OMl07BXyIcJD3FPA28NtH5U+j0Wg0Dye/i8/gbdoMRu00Gs2dc5Qz3zeB7wD/UCn1S0foR6PRaDQPMWUUU7dpkwC20ctwajR3w1HeXfsU8GXgM0KIl4QQXxZC/JdH6E+j0Wg0DyFZBM3btGlF7TQazZ1zZIM8pdSbwJeA3wT+GPhJ4O8dlT+NRqPRPJx8EoPt27TZjtppNJo75yhr8s4DLwE/C7wD/IRSau6o/Gk0Go3m4eTjmCSAygHp2AqKBPAxfW+tRnNXHOVfzCeUUptHaF+j0Wg07wGGEbxInM/TZgnFIGENXotwBi8BvEicYZ2u1WjuiqOUUNkUQnwSeAyw+t5/8ah8ajQajebh5HEMvojFN+jwH/DZQjGA5DMYfAxTD/A0mnvgyAZ5Qoh/BjjAR4D/C/gU8OpR+dNoNBrNw80gcAaDv4FkEME5JFIP7jSae+Yo7679MaXUfw4UlVL/K/ACMH2E/jQajUbzEOMSyqSMINhG4R53QBrNQ85RDvK6d8Q3hBATQAc4cYT+NBqNRvMQYwODCDZQDCKwjzsgjeYh5yhvvPiaEGIA+MfA64AC/vkR+tNoNBrNQ4yMUrQu4YBPp2o1mh+OIxnkCSEk8E2lVAn4ihDi64CllCofhT+NRqPRvDeQCJLHHYRG8x7hSNK1SqkA+Cd9r1t6gKfRaDQajUbz4DjKmrw/FEL8vBBCz7drNBqN5lACFHUUgV6fVqO5bxxlTd7fBZKAJ4RoAgJQSqnMEfrUaDQazUNGgOI8AdvRDRdaOkWjuT8cpRhy+rDtQojHlFJvH5V/jUaj0Twc9EunbETSKbouT6P54TnKdO3t+BfH6Fuj0Wg07xK0dIpGczQc5yBv37l4IcRvCCE2hBBv9b2XF0L8kRDicvSY69v2OSHEFSHERSHExx9E4BqNRqO5f3SlU34CQ6dqNZr7yHEO8g6qrv0t4Kf3vPcrhJIs88A3o9cIIR4F/lPC9XF/Gvg1IYRxJNFqNBqN5sgIpVOEHuBpNPeR4xzk7YtS6ltAYc/bfxX4UvT8S8Bf63v/30QSLdeBK8CzDyRQjUaj0Wg0mncxxznIa99F21Gl1CpA9DgSvT8JLPa1W4reuwUhxC8KIc4LIc5vbm7eS7wajUajOQK0fIpGczTc90GeEOIDd9JOKfX8/XC3n+kD/P26Uurc/8/evUfHld0Fvv/+zjlVp0qPKlkPP9Vt2W13d9KdbhJMp5OBLDJc2gGv8AgZbujAyoQwhLXmDrOGO4vH4o7n0qzLMGvWMJe5C4YQGAJcMJO5twcSzKQbuAMJeTXOg44T0n7bLcu2ZFkqSfU+5/zuH/uUVJLlV0dHsuzfx0urTp39q723TpWlrbNfqnpgZGRkDYo2xhjzjeosn/JJYo6RWEPPmDWUxZ28L6UTIX4xHTO3Fq6IyA6A9HEyPT8OPNAVNwpMrFGZxhhjMta9fMp0unyKMWZtZNHIexk3Zs4DPiYifyciPysiY99Anh8D3pcevw/4k67z7xGRUET2APuBl76BcowxxqwjWz7FmOxksRiyqupx4OeBnxeRp3AzYD8lIq+q6ltv9mIROQJ8OzAsIuPAvwZ+GfioiHwAuAD8o7Sgr4rIR4GvARHwT1U1zuB7MsYYk4HO8il1XIPPZtcas3ZEdW3HP4jIl1T1jaucF+BtqvrXa1rga3DgwAE9duzYRlfDGGOMMeaWROQLqnrgTl+XxZ28f7faSXWtyQ1v4BljjDHG3A/WfEyeqv7hWudpjDHGGGPuzJrfyRORPuCngR/AzXZtAaeB31DVj6x1ecaYOxcxxTwvMMdRYir4lClxiH4OEmBLDBljzL0gi9m1fwCcAQ4CvwD8R+BHgLeLyC9lUJ4x5g40OM44H2SWIwg5cowi5JjlCON8kAbHb52JMcaYu14WjbwxVf2Iqo6r6q8A36OqJ4H3A+/KoDxjzG2KmOIyhxFCcoziUUQQPIppYy/kMoeJsF1hjDFms8uikVcVkW8FEJF3ku5Dq6oJq+9QYYxZJ/O8gNLEp7Rquk+JhCbzvLjONTP3I9vOzJhsZdHI+wngV0RkFvgZ4J8BiMgI8GsZlGeMuU1zHMVn6KYxAUPMcXSdamTuV7admTHZW/OJF6r6MvDUKuencOPzjDEbJKZCjtGbxgghMdPrVCNzv+rezmwy3c6sd6MrZcw9Jos7eTckIu9fz/KMMcv5lFEaN41RmniU16lG5n5l25kZk711beThZtsaYzZIiUO3vEsXMU2JQ+tUI3O/6mxn9jZ8DuDZdmbGZCCLdfJevlESsG2tyzPG3L5+DlLheWLmVp18ETOHR0g/z2xA7cz9xkOsi9aYDGWxrdk23Bp5MyvOC/CZDMozxtymgBG28xyXOUyLcQKGEEKUJhHTeIRs5zlbENkYY+4BWTTy/hToU9Uvr0wQkb/KoDxjzB0o8DijfIh5Xkx3vJjGo8wWnqWfZ6yBZ4wx9whR3Zhp6yKyRVVX3u1bFwcOHNBjx45tRNHGGGOMMXdERL6gqgfu9HXrPfGi219uYNnGGGOMMfe0LLprb5dNpTLGmPtQZ6cLgF7EZtYak5GNbOTZ8ubGGHOfSVBeIuazxAjwND5P4VtDz5gMbGR3rTHGmPtMHZhA8RE84FK624UxZu1Zd60xm5jb2r0OhCh1lCTt/Cou/upc7bgT56WdZcaslyKwE+E8CQLssN0ujMlMZo08Efl9Vf2Rm5z7jqzKNuZ+oCTUOEbEFG0uETNLxDgBo3iUSZhHEDz6iJkDBI9+EipEjJPjQXp4K708ZQ09s248hKfweSz9zNmYvM1jigYvcJmjTFChTZkch9jJQbYzQmGjq2dWkeVP9se6n4iID3xz57mqXsuwbGPueUqdmGl8yrQ5BzRJaABNWpwEYiBOj5Ou405cTGSdZWYDeAj9ePTbdmabxnFm+SDHOMIFcniMUiSHxxEu8EGOcZzZja6iWcWaN/JE5OdEZB54QkTm0q95YBL4k7Uuz5j7lVDEZ4iYCjnGgBCPAhCSZz/gA3567HUdd+J8AussM8bcwhQNDnOcMG3cFfGR9CfQKEVCPA5znCkaG11Vs8Kad9eq6r8RkX8L/Jaq/uha52/W2dQ8vHAcjr4MlTqUi3DoCTj4OIz0b3TtNi0lIaEKXD9O7nbH0wlFCrwOeN1rzsPG5Jn1YsumbF4vcJkmCSOEq6aXyDFOnRe5wnvZvc61MzeTyZg8VU1E5Mks8jbr6PhFOPzH0IxgqBdGt0CjDUdegue/CM99Hzy+a6NruekoCVVeosbnUGI8SiRUaDNOngcQSiTMIQhCH8oCil4X5zFAju0EjNDDATxWNrr7b+PYmOzZsimb21EmGCJ/05gh8hxlwhp5d5ks/4T/nIh8S4b5myxNzbsGXhi4xl0xDyLucXSLO3/4j12cuSNKnYhL6R20hBYnUJrpHbbGsjF0bU7hxtatFncWnzIx0zauztzVbNmUza1Cm8ItmgshHhVa61Qjc7uybOS9HfisiJwWkZdF5Csi8nKG5Zm19MJxdwevvwDtGFbucVwquvQXv7ox9fsGdbpLEyJi5omZX/U4onLDtM5xQhUlWbWM1fJIiPDZlr7GI8/DCCFCEaGwbAxdjn24sXWrxe0hpoLPkI2rM3e1zrIpcfqpt5Ggm0uZHI1VfsZ1a5JQvsXdPrP+slwn77syzNtk7ejLMNgDUwvQbEOYg5E+dzevY6jXxb336Y2r52uwfOmRy8TMXtc16lMiZo42rxIwik8ZZQHQFXHLu0w749s6XbJVPk2bcXKM4lFCmQeEHt7MIO9fNn7utYzJg2ba6LNxdebuZcumbG6H2MkRLjB6k6b5NC2eta7au05mvxlU9byqnsfdqdeuL7MZVOoQ+K6BV8i5x2jFX3JhzsVtMp2lRwLKtDjD6l2jJ1EaaQNr+ZIky+POEKzSZeq6ZCdwo5FqXd2wMYJPxGU8Anz6Fx8Dysue3+i4O84mTpjNwpZN2bwOsp0Qjznaq6bP0SbE4xm2rXPNzK1k9ttBRL5HRE4CZ4G/Bs4B/z2r8swaKxchil1DrpHeyQtWfFyabRe3yXSWHomokGcvq3eN7kcopHfLli9JsjxuL9EqXaZCkYCduF9tPV3dsD5KTMBO67AyxmwKIxR4jsdpkjBOnTpxutdOzDh1miQ8x+O2IPJdKMvu2l8Engb+QlXfKCJvB34ow/LuGastr3Gj45t18a2Wttqdn5VbYwHIocfgyOdhVwmiHBLkUYnS/AOUCKYr8OxbUOZfU1fjnaStdVzIIxS6lh65Lo8kRKuzaBIhXoAUS2h9zqV1H/cOoF6TmakzvPLffp6+j5xhYNJd18o2Yf59e3jw+/8xW0b23vJ9MOZeY8umbG6dhlwRn8cZ4EMc4EWucJQJpqlTJs+z7OYZtlkD7y4lunJA/VplLHJMVQ+IyN8Bb0yXVXlJVZ/KpMA7cODAAT127NhGV2NVK5fX8CmRLC6h0b84pkvoI063p1padmNpG6uIWSImyDG6OL5M8Onh6WXbWK3cGithDkUJpor0fvAEcdhASn0IeTSdOSXkkbkGNBMWPvQwyUidFq+m487KXUt+9KcNwKXlP7q33eoe45Ywt2JLrqVtuJbi+lHmblLWjfIrpWmSprnr1MNbVt/SK0ngpbPw2dNw/pqbTVwuwELTDTjoK8BCHcRD3zLG2aEXqb34W/ReAqo5oqEWIBQ/O0LxiyNcfT30/Oo/4YHHv319PkTG3AU6y6Z8jhgF3mLLpmwqCcoxrjFNkyFCDjBo790GEpEvqOqBO31dlrcSZkWkD/gk8Aci8qtAlGF594Trl9dYuT1VDESL21MtX3ajO661YiyYu6u1chur67fGcmPNmiPnWHiujDQjvPEmSX3WNX7qMYxfg2ZC9blhmiPnFseu6eLYtWhFfd3yH53ttFbGtTmFLtuS6wTLt+Fa+r5uXtbq+S2NoYtXjIubWH0hh3obJipuDGK95bqlT05CnEAcw6nLECv4wvzJ49T+4I/ovewR9SitR+ch55rarT11msNthl6B+Z/5LWanzq7dB8WYu1wdt1SKGwwhN/rfZu5S7jdDk60UmKZJnXijq2Regyy7a78XaAD/AngvUAaey7C8u0Y7iZiJrtHnlZiN3Ba9A8Egs9E12klEzgsWn3enAZSDAWbjHC3mERH6vIeYS6ZBoEf2MqczoEqP9xDzOkWsHg2g13uIeZ0GdcdzyRQJPj7QJ3uZa17BSxoM97wJCZaPHaNVYvbaWfzSDuZnrqAe9JZ3Mzt0mdlfgN7Ph5T/Qkiu1NGSR/SDo8y8rUUyFNPTGmOuNUmsiq8tegtjzLWuQqL0FMaYa0yhHvTl9zDfmCTyEvykvSJuN/ONKWIvoZm06S3sYa41hSSkcVOQQG9xjFY0SaQJSoswP0arfRVVJZ8eo0qQ302rNUUsCWibXH43zdZVRCCXG6PVmgKtIewnDoV63fWvhsUSzfocJAnh9h6i8y0oKoQRwf4RooU5UAj2jRAtzEPcZuL0lym8UoCHq2giyPk+6ItQSfAmiySxj3rQczHm/IvPM/De/3VdP4vGrLdOF22Csg04DyjKTjwbhboBurtcb3Unrjs2xKMHn8vUGSIkIqFCCw+hl8Du6m0SmXXXLhYgUqKrMamq1zIt8DZk2V3bTiKOXvmvXGtNMN++QqxusWBfemnrPB7TJAyRk35iraZpPcTUQRVf+pmslbhUKRInD9Ef9rHQbiBAT1CgFrm9AYu5ArVWjYkK7CgLvfki9XbD9SbmCsw3a1yah+0l2DYucK6KeMK3PL6V73/HtxEE7i2JWi0++esfoz5+lca5OkGthWpCXCwg9QYLrRzX9vUyvWeERtIGhDDI0YgiQCn47nhyPsdIb5NiPk8ziUCV0M/RiCOEhDDI02i1mVzIM9zbpCeNU4WiH1BttbhaDdna16KQz9GMWsvK8kR5fU+F1/MK3qVrBP1lymfylL48T1BX4r48s4/A3OsCkuESSb2CXKqgO8p4xTJJYx5E8Ap9JLWKu1O3dSteqQ+dq6Li4fUVSBbqiMDCm4e5/FjEFOOMeKMUigM06vMISqHYT6M+D0nC69/9BR74Yp6pcoGP7Xsjf/TA25nc0geqDFfqvOvVT/POiy+xtTrPlbfkeeIPP5LJ524j6JSSvBDD0RitgJSBQz7eQR8ZsV8A96OVO1s8hcdj6WxaG5O3/u6ky7U7dku63t00TXoIyCG8xDQXqPMgRd7KCE8xdF+/n1MoLxBxlJgKShnhED4HCRjJ4Lq81u7azO7kicgHcXfu6ri+N8GNaNqbQVnvAH4VN+Xxt1T1l9e6jNs1E11jtnWFvtwgM80v4Hn9oEqkryLag+81UW0RJa/ieQOgSqzjwAAi0IzGWWg8BTRoRgEX5z1GensB5cKssrW/F4BXZ5VyoZdK06OvrVyswEhfDyCMV5Ry2Mtsw6Psx5y5lLAr8QgTZfxSndn5OYa3DAIwO3mN+vgC+cEBor+ZQ/tzKJB7tUWzv0CrHRJO+1wqFOjdUgCFKzMBA70RonC5FtAfxlRrPfT5Va7MBQz2thEVLtUChnsiVODyTEB/GFGr9tLwq0ymcZ4K52oB/fmQWrWXWlDlSiVgsDdcVlZIzJVrs+wtFxg+HbDjq02kWSMayNPqB1loMHgsYMuXYi5+yxWa+3rxZn3ifp/k4hQyUkIFklcI3v1oAAAgAElEQVSnoNyDN+uTlGKSK5eQkRJCQnLqMjJSIgmFhcsXaD85TK23RZ2Iq5xmoH8rAJOcYUv/CEEVitPw9dID/PSb38vfD47ia0yx2kKB8bCX//DwD/BnO97MLx37fYYrFzfgE5kNPZ4QH25DExgCRkEbwJGY+PkY/7kc8rhNLLnfdO9sAcoV4Ml0mpFZf91drpM0qBPTe4Nf+92xF9LpMrvp5TxVmsTEQCN9vJTOsL1RXve648QcptX9448GyhEinifiOfI8jr/BtXSy/Cn8L4HHVHVMVfeq6h5VzaKB5wO/hlt8+fXAD4nI69e6nNu1JRhkIL+NhfY1Am8UVQ/FJ5AHUMkTa4iSJ/AeWEzzZRTER9UjDEbpK1RpJSU8L8+jwx6eB74nPDrs4wv4Ag+PeBRybnexgu+e+57gefDosEcYKIUcBEVh744YP46JVRnd0ctAf2mxvgNbBymOlmhM1YhGQxIRVJT2AwXET8hJRK2UY8eOCI8Yz0t4cKSJJ4p4CWPDTUI/Ie+1yfkxY8NNfA+8NA1P8YjZPdIk7yfk/DZBVxyeMjbcJPBjcn6bvJ+we6R5XVmxJ2wbzdEXxez4IiR5j/auXjQv4Ak61Et7wCfJwa4v5/DbHkkhgDDA27cd9QV8D2//Dgh9kqIPhcA990B9bzHOi6Fv5268YpE8RfKEjLIfwcfD50H24eHyuLi1n3/11P/MyYEd9EYN+qMGviYEmtAfN+mNG5zu3c5zT76HyS1bNupjuaZ0Sl0DLwQZFaQoiKSPowIhxIfb6JQti3m/sZ0t7i5usaiQSRoMEVK8ScOjO3YHRXZSZJIGOymym158oJAOAdpB8aZ53cumUA7TIgRG06XoJX0cRQiBw7SY4u74+ZdlM/w0UMsw/46ngFOqegZARP4INx7wa+tQ9nVyXsB3lt/JK6cuMLpzmNOnLyIi7N27kzNnJqjVW/QU8+zdu/O6NFXloYd2cebaJRoIhd6QfXuHOHVmGlTY99Agp05fA1F3/vQ1ml6bsBAsPl9MOzPt0oo59j0zxKl9Lo8n3rBrsasWIMjnefrHD3Li1BSjO/uuz6MZ8chAgX0PDHFqfAZF2D86cN1xvZ1QyPnXpZ0cn0VQ9o1u4eT4LI12fMO4G6V1H099+DNU9r4C2/sZycFU2zXQtgbKZOz+Ztl+6Rq5oSeY/uFHKQY+Dw+XOHHVLXnSOa5H8Q3TAB7bWiI33WJbHFP0fR7uDzkx33RxXcdH/6dPcWGyhAQt/KhNuybketyi0e2aR74nIgqUS70l/nLHM+ycd130O4s+E/V42XE7UXKerJp2u3Frkcet4nb9mTAfK60BJUAYUGE2736gDbQ9ZkcSvIvQ/2LCxfdCi5g8PjvxmUgHb692fLO4tcjDylqfsgaJ+c40bRSPy+ms/EF8rqVxneOIhABv1bTbjVuLPO7lsp6gvBh3mdZN8+jEdtJmSSjiUwTGKC3GDROk9/qUIixOqOkcJ+hiFz3puc4CXZ201V63Wh7fSFwWZf0xEQ2UATzauEZU933qEsI4yotEvJccGy3LJVTeCPwO8Hlcpw4AqvqTa1zOu4F3qOqPpc9/BHizqv4vN3pNlmPyWo0aR371y8xWE2bVp5EPIYEwatLIhUxv6WVotkrYbNIqhBC7tFY+RAXyjSbNMGR6oJehawsUohatvOu6zLWatMIQAcJWk0aQZ3qwl6FKjUKzSTMXgkB+Ma2PodkqhSiimc/jifBQqc0P//jj5PNuTaNW1OK3j17g6lxMpa40xSdRJSShKR6VME+p0aJAQhMPQcgT0fJ8UCGvEU3xqYQ5So2IAhEtSdOIaEvg8tOYpgRUCgGlZkRBXZwg5OOIuh8wFwaUGxFhmmd3WZoIEkVsmVhgcls/w1cXKNaaLJR6UXFzZethAUXpqzaoFkK+9KZdhFFCTpVYBMQjSGKaCK2cTyFOyCcJLc9HNMFP41Q88klMy/No+B75dkyIEnk+rIir1SJiX5CCB/UYbSX4eQ9USdoJXs5Diz7UE0IfBkoe4FGUmJoKqEfRi6nHwpznU9KEHi+hlviIJIQoTQS9jbhGIsyJR0kTir5SVzfLtyAx9cQHSSiKujzEo18TenyloT6wVFanftXIY94XyqoUvYRamt+TfxMwV1CujMDWWcj5ytSwgkB/A+YK4LdhoCp8/CeaTCEMkzAAzKU/Dt3iOkvHFYSrKCNIuvmboECpK20YSRfNcWl96bGkxxXgKh4jKCWUuTStc0waNwdMIwyhlICFZfXopEG5K4/u+pZQZrvyKMOy76W77nMIUyhDCAO3kUZXWvf31V3Wyu//+rovfV/Ly3LvQ3ce3d9X59pcxWMYXfVar3xPOu9X93VKSKiQsJ+EQTwq6R2NMsIsigADCDMkvErCLrzr4txCTp3XLI+7WVonj4G0LF08dnGjeGxZJc59TlydLqLsQpbFlRGukXCRhAfwGEjTOvl312mGhHGU0TSPuWX10PQ9dnUaR3kAYQBvsR7lFfUYWJFH57iTx8o4WUxzM5tfRw8LCC/ToEy87PouXRvlATxeTw91POq02UqOLRTxkcXP4Tiua3LpM6T0rzieRZkAHsTjzel4zGmUyyTptRFGIf2Mdha2couDaXq+gjIO6eJgpAtqcV1Zc7B4rUvQtcCYpnkIo7jxct2v6y5rZR6dsvrS+q2M+6v0Z6X7xAvbELamzzrqKBHCH67h2oF34xIqHwL+P+BzwBe6vtbaaoM9rmu5isiPi8gxETk2NTWVQTWci2cqzNWgNy9cK5dQz0MDjytDZdp5j1YxIAo8pobLKC5taqiMeoLizkeBi2vnA66kaYkn7jWeh3rClaESrVxAsxgQ+QFXhkoknqRpZdr5YLGsK1v6UB8EZWYu4eJEdam+11rMLCSUez2uBTm3BbV4XM3liPBoBz5J+hzxUA+mcyGqrlt3OhfSFqEdBMSeuDRkMa6T33Q+T+RDKxeQCItxCXA1zJOIS4t8mM7nrysLUWYLIXEuoFHM0crnmNi1hTjwSTyY2Fom8TzwhVd3bgFPiAN3PaqB77p0RVkI3GzXOHAfkvnAB1HoikOU+cBHSeM83OtWiaMY4Pniuns9Qfp9RMATD68vAN8jCTwk59EOA3dtUKYS1xBWVa7GPhGSLo4DU4mPqqIIVxPfveY24iIRYk+IRLgap/mjLg9VUHe+ExenccqKstL6RaLEQFtZqi/K+JDQLEAzLzRzcGEoXfDGhzNblcSHdh5e3eIW8olQmijn0vc7wW1/033cIiHCo0mSxrnZmecQ2igRHi0SztNZlMfN2kzS5+cQmmlZ7fR5dx5J12vaCG08WshiHkv1cGntxTp28uiOE1qwmMfK76W73FZX3ZfilPO4BYWuT1uqe7yiTq00beX33ym3vVj37u/LlbXyfYi5/n1wcaTXUFe91kv5dequXde6E6fU8agBXydKFy+CrxMTo8QkfH1xgSOhQZKmdeKidDEkd94tmOTiXrlJ2vKyEmISYpRXiLriVtbJxUVp/RqQful1dW+Q0ESoo2l9l/KPuupeJ6GZ5rO8vslifq+kd4NcnHRdG5dHA03rIbyyWI/lZbk8uC4uStPc6oTCcdpM0iRCaC+mLeXn3gf3nh6nzSwtesjxClH6fwpOLtbVxZ5Mz8fQtXiVcAKlhVBb/H+pTKSNrLNd70ETOIUSI2n+nc+ay8NdZxfbWQCsk3/cVW4T113YgDRtKb+lsmRZHU+meSTp+UaaR3NFWZ36deI6ZVVRarjfHx5QS69FtxAW/0DYaFk28iJV/SlV/R1V/d3OVwbluAb/klFgYmWQqv6mqh5Q1QMjIyMZVMPZtbdMqQeqLWWwMockCRIlbJuukGsl5OsRQZQwcrXiPj5Rwsh0BUlck2fkaoUgcnG5VsS2NM1L1L0mSZBE2TY9R74dEdYjgjhi2/QcXqJpWoVcK1osa9vMAhK7ZYK3lDx27exdqu9gni19HpVqwmDUdh8ITRhutwlIyEUxXvocTZAEhtpNRBRRYajdJKdKLorwE3Vp6GJcJ7+hVosghnw7wlMW4zxguNnCU5cWxDDUal1XFioMNJr47YhCvU2+1WbnxRn8KMZLYOdkBS9JIFYemJiBRPEjdz16oxgSV9++KEYS8KP0L8Moxs3IWIpDhf7IzQ70I5AE97pV4mjEJIkgsZIkis7HqEKiCcmCm2XsJaCJkGvG7togjHgxiCIiDPsxAYqPu/U/4sVujBvKsJe+5jbiAlX8RAlUGfbT/BGXh7hG6bC/FOencW5ESVdZaf0CFXwgJyzVF2F0WgkbELaUsA0PTqcbw8Wwd9KNacy14IEZyCMECCHCWPp+e8BY+tg5zuMRkBDipXFuJuYYSg4hICGPx246G8rBbjob0sEYSpiWlUufd+fhdb0mh5IjIY8u5rFUD5eWW6xjJ4/uOCUPi3ms/F66y8131X0pTtgNhOn3vDxtqe7+ijrl07SV33+n3Nxi3bu/L1fWyvfB5/r3wcWRXkNZ9Vov5depu3Rd606cUCShB3iUAD/N51F8fNzI1kfxKOC68Ap4aVonLiCANM6niCzGPXKTtOVleekIWuERgq64lXVycUFavwKkX3Jd3Qt4hChFJK3vUv5BV92LeIRpPsvr6y3m9wgBBSSN065r4/IoIGk9lEcW67G8LJcH18UFaVpn8svj5NhKSICSW0xbys+9D+49fZwcA+Sp0eYRgvT/FOxfrKuL3Z+e94H9aZk+ysO4ZfN7Fv9fCjsRKih7ut6DENiH4KNp/p3PmsvDXWcXuz8938nf7yo3BHrSej28Ir+lsnRZHfeneXjp+UKaR7iirE79OnGdsnoR3BRH94dPT3otujVxd2TvBlmOyfsfIvLjwMdZ3l271kuo/C2wX0T2ABeB9wDPrnEZty1f6OGH/vk3cfFMhZGdRS6fmwOU7WNlLp+r0G7E5ApD6fPlaSBsHyt1xQ2vktb9mjnajfYN81ssa+8WLl9xf3s8uHvLYlctQD7I84FDD3LxWouRknBpxm1AvWNLjkszbdpRRC4IFp93p6113K3y2FbOMfnhY4R/9jlkW5mhIGb6qo8GPsNEXE0/zsMXpxj//jcz/USe0Bf29PucnXfjTDrHjVgp3CDt+rj8DeM+c8rnN16Kma0rvfmQuOGT63Fp7bpPrhgz31SGCsIPv8Xn7a/LZpzcuo7JuyDMP6+0dt5gTF4uHZP3Lo8fI9xU48msrG887jJJ2gD0N9XYtXu1rGEC6sA8PXjITfPoxNaJGcRPh2+89jFu9+KYvI8S8d+I2Z7eI1s5Jg9gGniWu2NiSpZj8rqX918sJKMZtt8N/J+4Bv5/VtX/4xbxU7iehSwNA1czLuN+MgxczeEFY/TtVkhiNFkZ5COegHeOhfNtkux3WPFyAeU9YwTFHjRJYOV/KBHE84jqNSpnz5G078ZdX+7os5ojF4yxe3dCkiQk170HHp7n4XnnOH++zV35/a4n+zmQLbu+2bDreiO5IGBs9263oOz1P//wPQ8Rj3Pnz9OOVvv591qv7W5VveNuyCwbeT8IfEJV50TkXwFvAn5RVb+YSYF3mc7evRtdj3uFXc/s2LXNjl3bbNn1zYZd1+ys97XNckze/5Y28L4V+E7gI8B/yrA8Y4wxxhiTyrKRF6ePh4DfUNU/gXSvFGOMMcYYk6ksG3kXReRDwA8CfyYiYcbl3W1+c6MrcI+x65kdu7bZsWubLbu+2bDrmp11vbZZjsnrAd4BfEVVT4rIDuANqvpiJgUaY4wxxphFmTXyjDHGGGPMxrmfuk+NMcYYY+4b1sgzxhhjjLkHWSPPGGOMMeYeZI08Y4wxxph7kDXyjDHGGGPuQZk18kSkkFXexhhjjDHm5rJcJ+8UcAX4FPBJ4NOqWsmkMGOMMcYYs0ym6+SJyIPAtwH/APhuYFZVvymzAm/T8PCwjo2NbXQ1jDHGAEQK52sggC/Xp8cKCuzugWCVdGPucV/4wheuqurInb4uyKIyACIyimvcfRvwJPBV4G+yKu9OjI2NcezYsY2uhjHGGID/+1U4Mg6jxRvHjNfh2VF47wPrVy9j7hIicv61vC6zRh5wAfhb4JdU9ScyLMcYY8xmdvQyDOVvHjOUd3HWyDPmtmU5u/aNwO8Bz4rIZ0Xk90TkAxmWZ4wxZjOqtKFwi19HoefijDG3LbM7ear6dyJyGjiN67L9YeBtwG9nVaYxxphNqJyDRgJF/8YxzcTFGWNuW5ZLqBwDPgt8P/B14G2qOpZVecYYYzapQ9thunXzmOmWizPG3LYsx+R9l6pOZZi/McaYe8HBrfD8BMy1obTK3bq5tuuufWbr+tfNmE0sy+7aKRE5BDwGFLrOP5dVmcYYYzahkRCeex0c/ns3i3Yo7xp1zcTdwQs9lz4SbnRNjdlUslxC5TeAHuDtwG8B7wZeyqo8Y4wxm9jjJfjQN8ELV+Bjl+FqEwZybtmUZ7ZaA8+Y1yDL7tq3quoTIvKyqv6CiPx74PkMyzPGGLOZDeXh4T54/4Pu+MAW8GzxY2NeqyyXUGmkjzUR2Qm0gT0ZlmeMMWYzq8eue3ZrwT3W442ukTGbWpZ38j4uIgPAvwO+iNuU5sMZlmeMMWYzK/ruDt5kwz3ebEkVY8wtZdLIExEP+EtVnQX+XxH5U6CgqpUsyjPGGHMP8MR10dZj18CzrlpjviGZdNeqagL8+67nTWvgGWOMuSVPoDewBp4xayDLMXkvisgPiIj9TzXGGHNziUI1co/GmDWR5Zi8nwJ6gUhEGoAAqqqlDMs0xhiz2SQKx2bcZAubVWvMmsnsTp6q9quqp6p5VS2lzxcbeCLyWFZlG2OM2URsVq0xmciyu/ZWfn8DyzbGGHO3sFm1xmRiIxt5q96LF5H/LCKTInK869ygiPy5iJxMH7d0pf2ciJwSkVdE5OB6VNwYY8wa6syqfduwddUas4Y2spF3o9G1HwHeseLcz+KWZNkP/GX6HBF5PfAe3P647wB+XUTsT0BjjNlsbFatMWtuIxt5q1LVTwLXVpz+XuB30+PfBb6v6/wfpUu0nAVOAU+tS0WNMcYYY+5iG9nIa91B7DZVvQSQPm5Nz+8CXu2KG0/PGWOM2Sxs+RRjMpFJI09EHky3NENExkTk3SLyeHeMqj69FkWtcm7VnxIi8uMickxEjk1NTa1B0cYYY75hneVTPnnVPVpDz5g1s+aNPBH5WeCvgc+JyI8BnwC+C/gvIvJTrzHbKyKyI81/BzCZnh8HHuiKGwUmVstAVX9TVQ+o6oGRkZHXWA1jjDFrypZPMSYzWdzJ+xHg9cA/AP4D8G2q+gHcWLkffY15fgx4X3r8PuBPus6/R0RCEdkD7Adeeq0VN8YYs85s+RRjMpPFjhexqtZFpAXUgWkAVa3ezg5nInIE+HZgWETGgX8N/DLwURH5AHAB+Edpnl8VkY8CXwMi4J+qqv0ZaIwxm0Vn+ZR67Bp4NrvWmDUjqms7/kFEPgLkcVua1XCNr08A/xDoV9UfXNMCX4MDBw7osWPHNroaxhhjjDG3JCJfUNUDd/q6LO7k/RjuTpsC/w/wZuCHgFeAX8ugPGOMMcYYs8KaN/JUNQKOdJ36dPpljDHGGGPWyZo38kSkD/hp4Adws11bwGngP6nq797stcaYdTI1BS+8AEePQqUC5TIcOgQHD4LNPjfGmHtCFrNr/wA4AxwEfgH4j7gZt/9QRH4pg/KMMXfi+HH44AfhyBHI5WB01D0eOeLOHz9+6zyMMcbc9bKYePF3qvpk1/O/VdVvEREP+JqqPrqmBb4GNvHC3LemplxDLgyhVLo+fW4Omk340Ifsjp4xxtwlXuvEiyzu5FVF5FsBROSdpPvQqmrC6jtUGGPWywsvuEbcag08cOebTXjxxfWtl7k/2XZmxmQqi0beTwC/IiKzwM8A/wxAREaw2bXGbKyjR2Fo6OYxQ0Muzpgs2XZmxmQui9m1L+N2t1h5fgo3Ps8Ys1EqFTcG72bCEKan16c+5v7VvZ3ZZMM9781iVS9j7l9Z3Mm7IRF5/3qWZ4xZoVyGRuPmMc2mizMmS7admTGZW9dGHm62rTFmoxw6dOu7dNPTLs6YLHW2M3vbsHu07cyMWXNZrJP38o2SgG1rXZ4x5g4cPAjPP+9m0d5odm0YwjPPrH/dzP3HE+uiNSZDWfzv2oZbI29mxXkBPpNBecaY2zUyAs89B4cPw/i4m2QRhq6LdnraHT/3nC2fYowx94AsGnl/CvSp6pdXJojIX2VQnjHmTjz+uFsH78UX3Sza6Wk3Bu/ZZ90dPGvgGWPMPWHNF0O+7YJFtqjqyrt968IWQzbGGGPMZnE3LYZ8u/5yA8s2xhhjjLmnbeSIV5tKZYwx96POThfgJl7YzFpjMrGRjTxb3twYY+43icJL1+CzM+5P/ae3wFOD1tAzJgMb2V1rjDHmflOPYaIBPq5hdynd7cIYs+Y2spFnf7YZY8z9pujDzgLEuLt6Owq224UxGcmsu1ZEfl9Vf+Qm574jq7KNuW8kCdTrbn27et099zwoFt1zWP24E9fb6x6NWS+euO7Zx9LFuG1M3uYRT0H9BWgcBa2AlKFwCIoHwbell+5GWY7Je6z7iYj4wDd3nqvqtQzLNubelyRw7BhMTcGlSzA76xY4Hh11697Nz4MI9PW5nSxEoL8fKhUX9+CD8Na3wlNPWUPPrC9PoD+30bUwd6J1HCqHgSZ4QyCjQAPqR6D+PJSfg/zjG11Ls8Ka/2QXkZ8TkXngCRGZS7/mgUngT9a6PGPuW/X60kLG5865XSsaDfd48iTEsfs6edI1CDvHnbg4do3Dzl0+Y4xZTTzlGngSgj8KUnR/NEoxfR669Hhqo2tqVljzRp6q/hugDPyeqpbSr35VHVLVn1vr8ozZlJLE3Wmbn4coco+VyvLnNzruxOVy0NMDMzMwNua6bAsF97h/P/i++9q/392p6xx34nwfduxwXbjGZC1RmG+7r8QWV9hU6i/g7uCtst81pOeb0HhxPWtlbkMm3bWqmojIk1nkbdbXVAteuAZHr0ElgnIAhwbh4CCM5De6dptUksBLL8HnPufuppVKS12oDzzgnne6V/v6YGEBVK+PGxiArVvd+YMH3R06G5Nn7ka2bMrm1jjqumhvxhtycb3vXZ86mduS5U/3z4nIt2SYv8nY8Sp88AQcmYKcwGjoHo9MufPHqxtdw02qXnfdpJ7nGlsnTrgGWr3uulG7u1dPnXKPq8WdPQtbtkCtBu22G29XLrvHIHCPNzruxFkDz6wHWzZlc9MKULhFUAhJZT1qY+5Alj/h3w58VkROi8jLIvIVEXk5w/LMGppqweGzEHqucVf03Y2lou+eh55Ln2ptdE03oWLRdZN27qY9/LDrQi0WXTdqd/fqvn3ucbW4PXvcnb2hIetyNXc3WzZlc5My0LhFUBO88nrUxtyBLGfXfleGeZuMvXANmgrDPrTVfVCkq2elFMB4E16cgfdu27BqvnYrlx6Bm3dr3qz7s9l0jyvviiUJVKur53HgADz22J2XtTLuRmUbczexZVM2t8IhN4vWH71xTDINPc+uX53Mbcmskaeq5wFEZCu3vs9r7jJHr8FgAFNtaCbuzt1IbnlDbyiAo9ObsJHXvfTI5ctu6ZHVxr/NzcGrry4tSdJJ644bGIDt22FkxDXcOo2tzri7T396aVmTUmlpWZO3vGX50iX9/cvr2P38Rsfgul+N2Qxs2ZTNq3jQLZOSzK0++SKZA0IoPLPuVTM3l9mf/yLyPSJyEjgL/DVwDvjvWZVn1lYlgkBcA6/gu8doRUzoQWUzDqvpXnrkzJnVx7+dPOnGvdXr1y9J0h135ozLZ3p6+VIk9TpMTLiYWm1prF0cu+7XiQlbusQYszn4I24dPG1CPA5aB03cYzzuzpefswWR70JZ3gb4ReBp4C9U9Y0i8nbghzIs797R6eaDO+u6u5201WZTrtJ1WfZ7iVoRYQyNWAgDn6AduZlxfgBxRDOBshfA/A26JNeivlnERZEb0zYzA3v3Lt3J27dv8Q6d7t9PPDmHekXQEP+h/STTCyiKv2cf8ewCooo/theZmeFqO+bPf/2TVP/rTgbO94JCbqTGjkcv8OiIUCoU3EzY+XnX0Nu508bRmXtfolBN/zy0LtrNp9OQk6Jb6HjwQ26ZlMZR1z3rlV0XbeEZa+DdpbJs5LVVdVpEPBHxVPV/iMi/zbC8e8Nqy2t0ugb7+5fvYrBy2Y3utNlZd7eo0004N+fuID399PJuwpW7JszNgSqHdr2JI8UH2TUzSdTXR5ALkHYLELc+W7vNdK6PZ+vn4BNfWr1bs1Pflct/rNb9OTe3PK17h4bu/G7UhdodtzK/zjXsXJtOnd70pqWlR2CxMagJVL5SpDJVp05CiEeQFImpg4KnRZKkjgiUHwm5cuKjXP3lL/HIpRItvxcvN4uKx+zlb+HVyX/O8ddd5pv/cT9PHnjs5o1tY+4lnWVTPjcDCrzFlk3ZVDSB1rG0MTcE+QOuIdf7XlsmZRPJ8rfMrIj0AZ8E/kBEfpXre/zMSiuX11i5c0EcuztRnZ0Lupfd6I5rtZZ3E3buaq3c4WDlrglpN+TB458kjNrM45OLI6RScT+oVaFSYU5yhEnEM8c/dX23ZhQtr2/38h/dOzJ04k6dWp524sTyHRq6v6+blXWj/Drdq9355XLuTl5n6ZGu5UXioJ/GZECc6ydOyiTST/VsQJLvR/P91M65Y/r6ufy1q5z9vyYJrw6QFJr0BCcQLwYf8oWLtPpjimce4nO/GDMxU1kqyxp45l5Xj91SKZ64pVMmbNmUTUXrroHnb3WPasNLNqMsf9N8L1AH/gXwCeA08M4My7trJFGL6uSrRI0F5idOMz9xJj0+Q+XC17ueL0+bnzhNJDHVnFKbn6berhE9tId6q0qtXSPaO0YtqlFv14keGqOmLWpeQp22e/soNV0AACAASURBVN6uUW9V3Wu0Rd2PqdOmvXcP1YUa1WsV4pGR5d2ExSLtUpnJCxPUd+3icr3N5Vqbvl1b+ckzn6IS5DkX9FItD9BsR8zH8OrgdqoJ/OyJv2Bg5wjVVptalFBttGiPjVFdqFNdqLnjas0d79lDtdmm2kqo1ttLcdUa7d27qdbTtGbbxS7UlvKYr1KdT/MAap5HVZX23r1U222q7Tbthx5yx1G0GFf1PKoi7nm7TbXVWoxbWFigUi7TyuWYmZ9nZn6eVhQxMz/PXDSPvz2iFdSJijWaYY3C/oiWX6fp1wj3RbT8GvW4zvGJSdrzo4gmJAhzwRgJPiQJtWCEdj4AT8lf7OPTL57cqI+kMeuns7NFlMC20D2PcUuo2LIp608TSKru8Y5iQ6AHossgWyCJIKpAPH97eZm7Qpaza6sAIlICPp5VOXebJGpx9vd+jdb588i5V/Fm5lFNSAb68GbmKIxfpfHACMlAP36liqqSDPThV9wYvHigj6afJ5lvMPuGh/ASD6EKCqqCJDUQdUMlqOKhKDVIriJaBQFNrpLoAj6CJlX8sw3Cl/sREYIdVZ54OsFP7yS144T/csZncnKYqeoI9aaiQPGKUG8rY5eu0h4IObtzO3XZQl+zydNfO81bv3oCanX+drCXYLpEOC40dvUQD3sE1/qBhGjQI7jWBypEQz7+1X4KE6M0di6Pa2/xCab7XNrFXuJhn2C6H0SJBn2CGTej9NobBrnaN0adJ+npmWdg31fo2/EZ/LBKoiXmvvYm5s98M4luI9Y6DX0jhUTwtYBqA0TwkpAoqdOcgFzOJ3/mJVpzieuF7hPaCwoC8uZ5pn/0VS5ymZ3eNkrFHqr1Bgr0FgvuOFHCdx9g+8x3MBk/wbb2l9nT+iq9yTQKVOe/zJlywoXex8hXSkwfnYF7qJdjCuUFIo4SU0EpIxzC5yABI1iX3H1p5c4WTw3A+x90d/NsTN76W63LVW5wb6c7Vrak5xZAelwDr/rbEF2A4EEI3wrhUzfO6z4wwxQv8QKf4SgLVOijzFs5xFMcZAt3z/jEzBp5IvJB4Dnc3bwE919egb1ZlXk3qF+7QjT+KsG2rXh/8RmSgT5UhfCVC8SlXrxmG6/eInf5AvHWAVSFwtcvEG3dAij5E+MsvOFh8tU2kgjJ2SlkawlVRc9O4424Bo+enUbLRUgCFA/OTsNIHyKCnp1Cy70ksQ/tgODrcyS5YcgFJKdnqF6bo7R1EICr03NcnY0oD/bwlUpCb5AgChNJQJk2pXqdgcYs73vhS+TKvmsoTjaIthZQr0h4okYykEfiIhKHhCdqxFvdijnhiRrR1iKoEp6oEg+EeFEMXXEKFE5WicohXpQgSUh4oko0EiIiFE5UibYWSAIffaVJ/GSe3PaL7PruD0MSESclomo/SbtF6bG/of91n+f8x99PUx4iUo+WJETnlHCkFwSaZyFX7qUd+3ithLmTCfkREIT5UwnhiJCECdOXK8RPtljonaNBP1e4wtb+QRQ4xQQj/UN41Rz56R7K0RRvaPwxpeQyiQhtvw9QeqJ5npj+FA8ufJ0v97+Thcq9s3zEcWIO06IJDAGjQAPlCBHPE/EceR7H7trcd7p3tkDgShOe9FwDz6y/7i7XeDKdRNF769joAqAQ7IbovJs9KzFIA4ghuXTzvO5xZzjOhzlMmyZlhtjKKC0a/DlH+Cue55/wHHt5fKOrCWTbXfsvgcdUdUxV96rqHlXNpIEnIu8QkVdE5JSI/GwWZdyu4uA2gtEHiK5M0t73AOoH4AvNRx4kKeRIwhxJMU/z0QdR3wdfaDz6IOp7qO/TeniUXBzRHugh7snj7d8BviC+h79/G/geBIK3fxuEvuv+KOTw9m9DfA98wdu/A80L9ARov0/0+kHAQyIlv3+I3sGldY6Gh0oMb8kzOxexPWjjkeCh7AyaiA9RzmOo2YC9fajvkfjQeLRMEgjqQ/PREnHBJw6FuODRfLREksY1Hy2hPqjv0Xy0TBx6RAWPpCtOfWg8UiYpuLQ4dLEry4IEeV0v/kiV3d/3YVTzICNoEoIIud6Qdm2EJMmx512/QzgwAwXFC6F/n4f4IL5Q2i9ICFKM8QtQ2u/heSC+Lsb5sce2nWXyRY8iISF59rMHP/23j734+FCM8fqneazxcfqTKdqSJ/JC1PNQzyfyQyIvT6l5jScrLxCGcxv2uVxLUyiHaRECowhFBEkfRxFC4DAtprBN6O87trPF3UWK7g5ePOke5SYz+pfF7gBvZ3q8E7zduB/mBcB36TfL6x42wxQf5jB5QrYySpj+BAwpspVR8oR8mMPMMLXRVQWynV17GqhlmD8AIuIDvwZ8JzAO/K2IfExVv5Z12avxgjw73vMBzp4/zraRMc5OuGrs2fl6zk58jcv1BXqKfYvPu9MWjy/9PTWJKef62LNtH2evnAKUPdv2c/bKSUDS8yepRXV6gp7F50tpp6hFVXqC3mV5PLL7SfyuBXRzuYB3vft1nL10gm1bRrvyeISzV07RiJTB3E52bd/PxJWTKMqubQ9fd9yMapSC4nVpF6+cQBB2btvPxSsnaEUNSkFh1bgbpXWOH932MKdrv85cj1BIBhns6eVarYaox2BvkWvVBtBLf981+t719wQz304uCNm5fTcTl88Dws7tDzJx+TytqEk+KKTP3V+t3XG7dj7Olcu7aLUb5HMh2wd2c3nWxbljFzf+6OeZuTjBvEaAR+lyQGVHDAjlSx6VHQlCRORNMrbzFFdm3d855Z5BKrVry46juE3g51ZNu924KI4I/OA28rjduOvL+rO+MvM6T28c0xCfXFCCyDVgJSjRG80x68EnKPOu6gxJ3Mbzc4Q9wzRrVwFWPb5Z3FrksTwuwvODO057bXFr8X1943Va+2t4g7hHqyT7PVePvh6aegliCGWQprrPUOc40QhPglXTbjduLfK4p8vKPbEUl1y+eR5p7GIasyBFQoo082NLcd4wTXFDjEKKNKkvO05I8PAo4u70NamTI1yWttrrVsvjG4nLoqxP8se0aNDHAG3aBARI1/CUXkpUGeclXuTgXTA+R1Sz+WtbRN4I/A7weaDZOa+qP7nG5bwF+N9V9WD6/OfScv7NjV5z4MAB/f/Ze/MgSfKrzvPz3D3C48zMqjzq6MyqanVXS0INQqiQBDpApgupB7QMLEcDszPMmITtancNZpeBXWiDxmCwwRhmtMNILSSYgV0JZDZoBOoeqUEghIQYVN1CUrdoVVWr68iqrsqrMjPuCHd/+8fPIzPyqsrIzKir38csLT3i9/z9nv880+Mb7/d+7idPntzLMFZotpv85tf/AxeDWWby89TDJqDkWznqYZ1qYZlSfYh8q0AzbAFKrpWjFbbc1GUrpLFiN0yhlacRNgAh1wpphm0ECFsh9bBBvbhEsTZCvpWjudJXnnrYoFpYolQfotAp0Mo38cTjNdX7+ZeT/zth4KZUO3GTP7rwb5lmhoveHJVME1QoRVmWgxbzhWVG62WGohzVoI1KQrkTUs10AKXYCakGLWbyy4w3y5Q7OeqZFglKqZOjlmmBCsUoQzXTYi5fYbSxaqdAoRNSzTSZz1cYa5QpdUJqQWdjX/WAqwdmuOpF7IsCSolHAw9NhFIzQyNI3BjGCfUMVBeKTCwNUW4VqOaaiCqlVo6lsMHccIWJ5TJDzQLLYQMRodjMUc01QZWhVp7lXJ2ZoQpjS2WGW3mqYRMVodTMUcs1UVVKMwnL+Q4zE1VGrxQZqmSoDyckqpQrAcvlDnMHaozPFNwxT+UQhFInpBK4f4tynGfZrzGbn2eiMUY5zlPxm4goxU6BaqYOKlvaIUqpU6ASVJnJX2WisY+hqEglaLq70EQ5ln333MlylGM5qK2xq2aaKM5HLVMHhFKUY8mrM1eYZ6IxSjkupDHBk4emUK0Qe88TxgcptnKUO0sIilAmoULkeyRS5se+8lVmvFkm4nGKXpk6VRAoaGllu0iRSlJj1rvCeHKAklekntQAoeAVqCVVZrwZxpMDlL0SVa25sdESNamg6lGUArW4yow/y0QyTtErUU/qgFKQbl9KUctUtcKsN8t4Mk5JytSkAioUKFFL27o+ar0+1tgtM+vNMZ6MU5Tyiv+ClqlrdUPsE/EEJb9EjSokHkWvSCWpMutdWduW+q9r1f2tSYFqUk3jHaPolalpfc3xr4999biqkAhFr0g1qayeB7+04mPNedASVV1mxptjIhmn5JXTsU4o6lp/K7EnE6tj7SlFStTGrjKz7xLj4xMUZR91cSvzCwxTx92TsqAj1LjKDBeY4C6K7KfOUhrHMFWW0/iGqbLIbGpXYv8122orfY1Ql0VQpcgIlRW7SYr0xuTsVMV9OMtVZvUi43IXRV0be40FZrjIOFOUGEnbnP/emGpcZUammdBJSuzraRtJjx+KOkRVFplhmgmdoshIz9gMr4mjpCPUZHlDX10f6+1UhRJD1FgE8Sj6L6UqVWaTrzCeDFPS4XX+FpmRC0wwRdH/JupeA7RBUSaoBftAfPf3xRKzTDPOJEWG3d8NSoHyynaRsvPHJSY4wv28Gg+PJeaZ5zJVrq74cDGm/yuUqFNJfQxRZcmNDVMUGaLOMiCp/yqS9ltlecVfmSFqVFb8XTve1b4qG3x0+0r/J9fZfYnPEBJCKuz2c4B9TKwRei0aRET8Mh/eM20hIk+o6ol+9xvkdO0jwF8Afws80fOz19wFXOh5PZ2+twYReZeInBSRk7Ozg0ujTl+9yOVggf3xMFeHF8BX8JT5kTmSICEJIpJMwvzIHASubWFkDg0SCFK7TGoXRKkd4KX7+IoGidvHj0j8mCTosDAyB14CAWlf0Wpfw3MkoogK57zLPF+9uBLvYv0il2WWUUa4WLiKiqKecqG4QJSJ6EhEx4+5UFxAPUVFOF90doko08V5OpmIxI9oezHTxXkSARVhuji/6q+wQNuPiPyYjh/12CmXCnN00ra2H3GhsEVfQwt0/ITIS+gIPJ/poCqop1zKtVARVOBKJqbjx3QyHVqZmHMjM6gkJB6c3T9HJxPRzrRp+jHPjc6ReEos2mOnPDc6R9OPaWfadDIRZ/fPkXigknBuZIZYlMRTzk1VaOcTOrmEKKecv7tK7Cvqw/ljFTphQhTGtPPKhUMVFCWWhLPFy6gXk3gJZwsXaXsd2n6HZtDhbOF5Ei9xMRUvoug17RRn1w4iOkGblh9xtnjJ+ZeE57p2XszZ4iVavrNrp3axrPqIJY2v8DydoNtXxNnC86gXE0tCIziP0AS/hUqLdnCWxItRUTp8AyEh0Iiaf462tGj7Hdp+m2k5RyJKgnKhZ/u8nKfjtVxc0mKa8yS+i3ea87TFHVPHa3GBc+4cpfsluHMyzXnaftv15Tkf6sUkftLTL5yX87S9Ni2/Q9trr/hIxMXUbWt5Lc7Lqo8Lcm7FblrO0ZbOio8LPf4vyLl1safH1dOXejEXOEdnk7au/8RPUEnWxisdd1zrjn997KvHpSt9rTkPPT7Wn4e2uHPelnbPWLPBX/d8taVnrFHOe+doFVq0og7tqMkFniHRmMSLmeYZZ6duu+01aUmLjtdkmmdIvJiEmPM8g3rRil3Ha6zYXeDrW7adX9dXonEa79dX7NpeGhPr7SIXkzZpeU3aujH2zkq8jQ3+uzFd4BnaNGhrt6+18a4e49dpa9PZSXPN2FxYF8d5vr5pX10fG+2i1C52546naCcztIho09ngr+2lcWibCzxFkiyiUuC8fp1EWyREXOA0bVq0aNKmxQVOkxCREDPds32eU3Ro06KOEnOZs8xxiRLDPM9za3yc54w7D0Spjxgl4TynaNNy40iTaU6jJCv+lXil3w4tWtTp0OQ8p9b46+2rN8YLPX1d4DQdmqkPd1yrfZ1ZZ+f6alKjSR1FETya1InX3R0uS0iVpYHpjH4YpMiLVPVnVPX3VPU/d38G0M9my7U2pCdV9QOqekJVT4yPD27ly+S+uzgY7WfBX2Lf0n6IBRJhdHEML/LwogCv4zG6OAaRa9u/OIZEHkSpXSe1i4LUDkjSfWJBIs/tEwd4sY8XZdi/OAaJBxFpX8FqX0tjeCqoKEeTgxwqrWrgkcJdHNRx5lnkrvo+RAVJhKnafoJOQEYDMrHPVG0/kgiiypGas/NUmKyNkukEeHFANvGZrI3iKYgqk7XRVX/1/WTjgCD2ycRBj51wuD5GJm3LxgFT9S36Wt5PJvYIEo+MwqFOBhFFEuFwM0RUEYUDHZ9M7JPpZAg7PkcXJxD18BI4tjBGphOQ7WTJxT53z4/hJYKv0mMn3D0/Ri72yXayZDoBxxbG8BIQ9Ti6OIGvgpcIx6bL5Joe2ZZH0BaOPFfCjwWJYepsmUzbI2j75Fsex54fRhB89ThWO4gkPl7icax+F9kkQzbOkIsyHKsfwks8F1PtLgS5pp3g7LJRQCbKEsYBx2qHnX/1uLtrl/gcqx0mjJ1dNrXzddWHr2l89UNkom5fAcfqh5DEx1ePfHQEJQdxiGhINjqGl/iIChlehOIRSUAxPkpWQ7JxhmycZVKP4qngIUz1bB/RI2SS0MWlIZMcwYtdvJMcIavumDJJyBRH3TlK9/Nw52SSI2TjrOsrcT4k8fFir6dfOKJHyCZZwjhDNsmu+PDUxdRtC5OQI7rqY0qPrthN6lGymlnxMdXjf0qPros9Pa6eviTxmeIomU3auv692EPUWxuvZtxxrTv+9bGvHpes9LXmPPT4WH8esurOeVazPWPNBn/d85XVnrFGOJIcJayHhEGGbJBjipfgiY+X+EzyEmcnbjub5Ag1JJPkmOQleImPh88RXoIkwYpdJsmv2E3x4i3bjqzryxM/jffFK3bZJI2J9XaBi0lyhEmOrGyMPbMSb36D/25MU7yELHmy0u1rbbyrx/hispJzdppbMzZT6+I4wos37avrY6NdkNr57txxP1lvgpCALJkN/rJJGodkmeJ+PG8E0TpH5MV4EuIRMMVxsoSE5MgSMsVxPAI8fCZ7to9wHxmyhBQQfA5yjDEOU2WJQ9y9xscR7nXngSD14SN4HOE+soRuHMkxyXEEb8W/4K/0myEkpECGHEe4b42/3r56Y5zq6WuK42TIpT7cca32de86O9dXjiI5CgiCkpCjgL+u8q1NixLDA9MZ/TDImry/FJF34W6f0jtdu7DH/UwDUz2vJ4FLe9zHtsllc/zLF7+H6asXGSuOMr14CQ84PHaYS4uXaM63yGVDDo8d3tCWAJOb2PW29e4zvXiJzlyLcAt/XR93jR3mcuMSKBybfNHKVC1Axs/xw1M/w2L9IsVwlMW6G7qRwmEW65foxC0yhXDlNcDI6MbtTtwikw83bVvrr00mn93Cx9ZtACP7DnO2/h+5mvtz8v4ByvkclXYT0YByOUul1QYgzM3iLb2NcvhmgrtChkcmWFqcAVjZjjotgsObt22w28IHwPBXzrL04b8gqtcJslmGFwKWxmJQGJ73WRqNidpNglKJ4R97C0uvutftt1X9275t1uRtYRfFEcG+bdbkbctuY1+PlYb5sFY4GMd44pMZXluTp9EyFz14kGHe+RqryXvB1eQ150lyiWu7nWrX7tS+vDFXT5ZUXN3ZtXyktmjDtYn76N5pjdudWJP3F3yUz/Ix9nMQYENNHsAS87yFB7kVGGRN3nM9L1c62esVtiISAKeANwEXgS8CD6rq09fYZxY4t5dxbMIYMDfgPl5IjAFzQYbg8DGOqpIkbjZiDZ6PJ4J36Sznos7gn7CSwQvupnwsj19I0ETXZZEFxEO8BnH9OSpnOyS34lNf+vtbzQQBx44eRTUhTjbeFdX3PEQ8zp47Rye6FY/3RmLXgcFi4zsYbFy3QDIE4TGOom42f0O7S2p7rbOc080/g3Y6tkdVte9pyEFm8v4V8ElVXRaRXwS+DfiVve5EVSMReQ/wKdzdmX73WgIv3WfgdyoUkZM7KZI0NsfGc3DY2A4OG9vBYuM7GGxcB8eNHttB1uT9QirwXoe7vcl/At43iI5U9TFVvU9V71HVXx1EH4ZhGIZhGLcTgxR53SdRPwC8X1U/DmQH2J9hGIZhGIaRMkiRd1FEHgF+CHhMRMIB93er8YGbHcAdho3n4LCxHRw2toPFxncw2LgOjhs6toNceFEAvgf4qqqeFpFDwDer6uMD6dAwDMMwDMNYYWAizzAMwzAMw7h5vJCmTw3DMAzDMF4wmMgzDMMwDMO4AzGRZxiGYRiGcQdiIs8wDMMwDOMOxESeYRiGYRjGHUhfIk9Ecte3MgzDMAzDMG42fd1CRUTOAFeAvwY+C3xeVZcGFJthGIZhGIaxQ/q+T56IHAFeD7wWeAewqKrfuqsgRL4H+PeAD3xQVX99Xbuk7e8A6sA/VdUn07azQAX3GLVoOw/+HRsb02PHju0mZMMwDMMwjBvCE088Maeq4/3uF/RjLCKTOHH3euDlwNPA5/rtdJ1PH/ht4C3ANPBFEfkTVf1aj9nbgePpz6uB96W/u7xRVee22+exY8c4efLkbsI2DMMwDMO4IYjIuZ3s15fIA84DXwR+TVV/aicdbsKrgDOq+g0AEflD4J1Ar8h7J/D76tKOfysiIyJySFWf36MYDMMwDMMw7ij6XV37CuD3gQdF5Asi8vsi8s93GcNdwIWe19Ppe9u1UeBxEXlCRN61VSci8i4ROSkiJ2dnZ3cZsmEYhmEYxq1NX5k8Vf2yiDwLPIubsv1x4A3Ah3YRg2zWVR82r1XVSyIyAfyZiDyjqp/dYKz6AeADACdOnLAH9hqGYRiGcUfT7y1UTgJfAL4feAZ4g6oe22UM08BUz+tJ4NJ2bVS1+3sG+Bhu+tcwDMMwDOMFTb81eW9X1b2e6/wicFxE7gYuAj8CPLjO5k+A96T1eq8GllT1eREpAp6qVtLttwIP73F8hmEYhmEYtx39TtfOisgDwMuAXM/7OxZWqhqJyHuAT+FuofK7qvq0iPxU2v5+4DHc7VPO4G6h8s/S3Q8AH3N3WCEAPqyqn9xpLIZhGIZhGHcK/d5C5f1AAXgj8EHgB4G/220QqvoYTsj1vvf+nm0F/pdN9vsG7lYuhmEYhmEYRg/9rq79TlX9J8BVVf1l4DtYWytnGIZhGIZh3AL0K/Ka6e+6iBwGOsDdexuSYRiGYRiGsVv6XXjxpyIyAvwG8CTuNia/s+dRGYZhGIZhGLti2yJPRDzg06q6CPwXEfkEkFPVpYFFZxiGYRiGYeyIbU/XqmoC/GbP65YJPMMwDMMwjFuTfmvyHheRH5D0niWGYRiGYRjGrUm/NXk/AxSBSESauMeNqaoO7XlkhmEYhmEYxo7p92bI5Wu1i8jLVPXp3YVkGIZhGIZh7JZ+p2uvxx/ssT/DMAzDMAxjB+y1yLNaPcMwDMMwjFuAvRZ5usf+DMMwDMMwjB2w1yLPMAzDMAzDuAXYa5HX3mN/hmEYhmEYxg7oa3WtiJwApoAIOK2qz/S2q+pr9jA2wzAMwzAMY4dsS+SJyHfhnnaxCLwS+DywT0Q6wE+o6oXBhWgYhmEYhmH0y3ana/8d8HZVfTPwbUBHVV8L/CrwoUEFZxiGYRiGYeyM7Yo8X1Vn0+3zwFEAVf0z4K5BBGYYhmEYhmHsnO3W5J0UkQ8BnwbeCXwGQEQKgD+Y0AzDMAzDMIydst1M3ruBJ4DvBP4c+D/T9xV42wDiMgzDMAzDMHbBtjJ5qtoB/uMm7zeAc3sdlGEYhmEYhrE7tru6tgT8LPCPcbdQaQPPAu9X1f80sOiMm051Fv7hU/C1R6GxBPlh+KYH4KVvg9L4zYtLZ5XkUzE8GqNLIMPAAz7e23xk/MY/Xe8fLpzll55+jEfvPUt9rIoI5OdKPHDqGL90/zt46dSxGx7TNVmahSc+BScfhdoSFIfhxAPwyrfB8DZP7OwcfOoz8OinYWkZhofggTfB274bxscGGb1xhxIxS4VPscyjxCzhM8wQD1DmbQTcxAuOAcAsy3yKr/Iof88SdYYp8ADfytv4ZsYZutnh3VBu1c/G9Yjq9Z9EJiIfBz6Gm6r9IaAI/CHwC8BFVf2/BhnkXnPixAk9efLkzQ7jlufSU/DYQxC3oDgKQQ6iJtTmwQ/hHQ/D4ftvfFz6VEL8UAdawCiQA5rAPBCC/3AGuf/GPczlY098nn9e+ASLhxeR2MPrBChKkokQXxm+tI8P1R/g+1/52hsW0zU5+xT8vw9B1IKhUcjkoNOE5XkIQvjxh+HYdU7sU8/AQ/8GWm0Y3Qe5EJotmL8KYRYe/lm4/yU35niMO4ImT3GZh1Ba+Iwi5FCaxMwjhBzkYXLchAuOAcBTXOAh/gstIkYpkSNDkw7zVAkJeJgf4H6mbnaYN4Sb8dkoIk+o6om+99umyPuyqr685/UXVfXbRcQDvqaqu7qai8j3AP8et4jjg6r66+vaJW1/B1AH/qmqPrmdfTfDRN71qc7CH74bMiHkNvmC1lyGTgt+5JEb+61FZ5X43W0IQYY2Zux0WaEF/iPZG5LR+4cLZ/muxQ8yf9dVvMjHi9euQ0r8hCSIGL24j78a+Rc3P6O3NAv/z7shG0JhkxNbX4Z2C/7XR7bO6M3Owbt/1om5ofLG9uWKE3+P/BvL6BnbImKWad6NEOJvkhGKWUZpMckjltG7CcyyzLv5XUIyDJHf0L5MgxYdHuEn7/iM3s36bNypyNvu6tqaiLxOVT8nIt8LLACoapIKsB0jIj7w28BbgGngiyLyJ6r6tR6ztwPH059XA+8DXr3NfW84teUq5750iol7DnPhq88CMPXN93Dhq8/SqjYJS7mV171tO7KrNQmLm9tt5uO+176c4lDpuvEufO2lVM83CIcatK9myIzuJ16eB5RgeIyoOk99JuCJDxcYe8XTNOstcoWQQ/cd5/lTpwE23b6W3XZ8LL/3CtnzBeQQlPxRqsvzqK+Uy2NUqnMADC2PM/u+FYjv9wAAIABJREFUf2D+TTPkcjkOH72PS+dOAaxsN5vNLdv6sXvv2ScZOTjDcL2JEHJxf5a75t3T/S6OdrfbeFHEb5/5U36x9d2oCEMHJqlcvrBmO+q08bMhQwcmWb4yjahSPjjVl13UaRNkshv2W7F75nNUqnWiQoEgalDOBCyrIChlT6iQRxsVhr7wX1n+lrcQRy38IGRo/xiVhVkUYeS/fY7OUpPOgSJ+rU0uF9CM3d9SLhCauSLefI3gsU+z9I/fSJzEBJ5HOT/McmPZ9bXJdpxE+F7AUH6ISmMJRRjKD21o692v167SWCJKEnzP32C3fp8oSVZi2szHZvHuxK6f41rvf7cxbezr+ufhWmPYzzm5VhybnXPJf4Jqo0PAKLm80mwA+OTyCa2GT5LsI/EusZD/JNL4QQSllM9SaXRIkhjP8ynnM1QbbRTZsL1du2qjTSdRMp5Qyme37aPS6KzEVG20iRLF97wNbdu1u14ce20XJwnBJm3d7U/k/556I2E4ydPxIMgr0si4f/p8xFCjzEWu8sn8V/nBxmtJEvA8CIvOpNNwGa5Og5W2TN69hs2398JuEH19+b+6rF1+BKIO+AH0qqDcEDSn4ZnH4cSPcdPZbibvW4APAvcBTwE/qaqnRGQc+FFVfe+OAxD5DuCXVPVt6eufB1DVf91j8wjwGVX9SPr668B3A8eut+9mDDKTV1uu8icPfoRk2kdnckgzh6oiuTZJPUNcLxMUq5BvIq0cqkCuhbRygKJhExo5oloJv1DBK3TQVhZR0LCJtN23KA2baD1L3CgRFOuQayLNEBWQcG1fkm+jzSyeJyTfPsP3f/THV4TeVvEm1RISQ6RZAq+F5GIg/SQPFCIBFdRT/Ik5Olf3kRlZQIp1aBTcuutCHWkUUU2QYgOtlehcHSHosRMEzdfQWoFocT+ZfYtIsYrW8oAgxSZazyGA5uuUv/Eiqp0MxUyDIFA6HiRejFdoEsUeqPvmEkU+VyYWYGIWLdXwakVASQoNqOaQuQkYn0OLdbx6DvBIil27hKTQRGoFmB1Dx2ag1MSru5h67ZrlKkG1iDdzgGhihrhcJVMrggpRqUqwXMSbPUAyMUNUrLG/sR/xwC91iCo+IASlDp3lgNaVMcIDV8kMtYmqAagQlFtElRBEr2vXWc7SujJKeHDBtVUCwCModZwdSuAt0GkUac0dIhy9TKbcJG6WUZSgUCeqFxDAz9focIj2xTLZqSqZESVaEgQ4+OXLNDp5Ks0JyqU5/JEO9XwZBMJMg1Ynj3Ricn6Lr//wi2heyJKdbJMbhk4VVIVsWXu2obWstKaz5CbbZIeEdgVQJTskK23hZJtwSGhXFVTIlqBdBUTJloTmktKezpKbSn0sK8i6vkrQXobGdIbcij8QUTIlaC1BczpLfrJDdpg1PtoV2WCXm2wTrjuudkVWYm8vK83pLOHhNuHI2uParK3rv12RleNqLWlPXxuPf2WfJaExnXGxD6Vj09vXynno8bFubFbGemqLsV53TlbOV3ec1oxvy8W7Mm5rz0l7mZXjHx/5Kp1qEU3/ltvVMon6ZMoezeU8lekRyodnyY/UOFd9AFWhUBbqywnL00LpsFIaEeoVxRPIlTzqFQV1Cev6srI8LQxNKoUhoV4BT3TFTkTJlzxqSwmVaShPKsVhb42/6mJC9VLXx1r/jaqiCoWyUF1SqtMwNAWFIUnbXLzNakKiQr4k1JdjKtOS2nnUlxVEKZQljUMYmoT8kEejmoDKmr7yJY/Gcpwe16qdCKl/97/h7BKWp2FoUskP+Wv81ZeV5QtQmoTSsKTHlbalx//F0tfxlkKC6SF0skpp2GOoOuw+l8ptqGbpaExUbvETi2/i6iXYfwSOvRrEc9OYlctQuwpL0zAymYqhqhNIuRI0KiDq3q8vpXZTkB9y2TEVyJfdPqpuu7EMV6dh36Sza1RW/XV9DE9CYXjdfmlfYeq76yM3BK20r1wpjW+d3bOfcVk8BAQoHYDyxFqh12lAHMH/9OG90xY7zeRtq3BJVb+iqq9S1RFVfZ2qnkrfn92NwEu5C+h9LNo0G2+wvJXNdvYFQETeJSInReTk7OzsZiZ7wrkvnYLnA/zRGF0cQdRD8IgWRxDNkJBBk4D46n4kcW3x1X146iGJR3x1P5oEJGQQzbj98FA8osX9iHp46vYhyZBoFqKA+Oo+lM376vrAU7wLORfjdeLVKAviofggPnErC+KDeMSNnNv2hbiZh04GTbLQyRJdPogkPmhAdPkgqh6iAZ3nD6KdgCTOQrxqp+oRPX8A4ixJnEU7zlY0AA3oPD8B6kPiO3+JR0RAjM9yK0fiK6I+1WoRxUPFo9IOoZOFTg6vnUXOHcZLfEgC5MJhvHaItvNIO4tcOIzEGXcuUjuJM+79dhZt5/HaIXLhMCSBaz932NnHGcLzU3idEFp5Mu0M2bNTeEmApx7huSm8ThZarq/w3CSa+GjsU3t2PyQZSHxqz42RtHNoK0TbAbXnRiHx0cSn9o0xNPG3ZZe0Q7SVQ1sZas+OQuKOq3bW2ZFkqF06gnZyaCdHEoXUpu9CE8/5uHDYxaduO2n6xK0QbfjUnilCLGjscXXxMJGGREmOKAm5unQAjQVVYX7pAKoeiR+wODtO0hSShk/SEiqnA4g8iIXKaX9le/mUh7aEpO6TNF2bxEDitpOma9NWt03cfmd8iAVJ/SWttK+m5/wngsSs6Xf5jPOjTR9aqz6I0n3SNm25uFZ99NoFJD0+eo9lq9iTduovlmu3pf4lBknHphtTssXxd2PX3ti7bb19NTb6WHMezvSO4RZ9pf7Wny8S2TC+SctbN25rz0nv8VdPH0AjH2KfyqkDJHEWL4arp8eImwFRPSRu51g6Peb8xcqV0wlRU2jXPZI2XD6VQOyRRB6XT8cQKyTKldO6Yhc14fLpBGLW2sXi/LWUTtMnaklq5/xdOZ2QtEl9ONte/y4mj8unE+IWdBo+UZOeNuXy6Zgk8iCGK2cSopbQafjEXX+JrviIWpLGoVw54+JY39eKj/V2sTi71J+zWz2u9f6iZhpHi5VjJtE145mcHsZrZfGaAdLKkJweIo5AYw9ODyORRxAHdE4V6bQhrkMSw/xZWLrkFiXMPedq2DpNVw48e4b0PMDM6XQ7gZlTqV3DZcxmTrv3iV1bkuYbZk67adG47nzOnFrrr9tX3Ep9RKv7dfuaPZ3a1J2v2Z6+VuLr2qV9tWvQruMSGZ7bTqK1OiAI3WKMW4FdV6eLyD/brYtN3lufXtzKZjv7ujdVP6CqJ1T1xPj44Go6jr7iPjgUEc/7yMgiKglKQjCyiEoHjw7iRfj7FlDPtfn7rpJIgnoJ/r4FxIvw6KDScfuRICQEIwuoJCTi9sHr4Ekbggh/31WEzfvq+iARkqmmi/E68UrQBk0QYtAYP2yDxqAJfr7ptmPFzzUg00G8NmTaBAcvo14MEhEcvIxIgkpE5tBlJBPh+W3wV+1EEoJDV8Bv4/ltJONsVSKQiMyhGZAYvNj58xICInxihsImXiyoxJRKNYQE0YRytgWZNmSaJNk2evQSiReDF6FTl0iyLSTbQLNtdOoS6nfcuUjt1O+497NtJNsgybbQqUvgRa796CVn73doHblAkmlB2KCT7dA+doHEi0gkoXX0AkmmDaHrq3V0GvFixI8p3rMAXge8mOLdc3jZJhK2kGxE8e558GLEiym+aA7x4m3ZedkWEjaRsEPxnnnw3HEVjzk7vA7Fw+eRTBPJNPGCFsXJi4iXOB9Tl1x84ra9XIwftpB8TPElNfAV8RP2jVwikBaB1yTwWuwbvoL4LhsyOnwFkQQvjhgZn8XLKV4+xguV8vEIggR8pXw8Xtkeui9BQsUrxHg516Y+4LltL+faJOy2qdvv3hh8ddP1x10fXj7GyyXOv6eoz5p+h+51fiQXQ7jqgyDdJ22T0MW16qPXLsLr8dF7LFvF7mVTf75euy31rz5oOjbdmLwtjr8bu/TG3m3r7Su/0cea83Bv7xhu0Vfqb/35wtMN4+uFybpxW3tOeo+/dPwKEsTgx5Tvu4Lnt0l82Hd8Dj8XERRa+Nkmw8fnnD9fOHDcI8gp2UKCl4WD93ngJ3hBwsHj7ksonnDguKzYBTk4eNwDn7V2vjp/oZDJxQShpnbO34HjHl6W1Iez7fXvYko4eNzDDyGTjwly9LQJB4/7eEECPhy41yMIlUw+xu/682TFRxBqGodw4F4Xx/q+Vnyst/PV2aX+nN3qca33F+TSOEJWjhlP1oynd3yJJGyT5CI07OAdX3bTlH4Cx5fQICHyIzL31chkwS+A58PoMRg+7ATP2N1uyjaTcyJo/F7S8wATx9NtDybuS+3ybkHDxHH3Pr5r8/x0+7jLqPkF53PivrX+un35YeojWN2v29f48dSm4HyN9/S1El/XLu0rW4RsAac+ErftrSt8i1pO2N4KbLcm71r8MvB7u9h/GtYsyZkELm3TJruNfW8oxaES3/fhH71OTd7BbdbkHdpmTd6Ba/g4uK4m761ravK2infhay/lqx9xNXlBdpOavKV5ajMBL3+wtyZvbJs1eZvbbcfH8nuvkH08rckbvkZN3hvOpTV533SNWruXbbMmb2u79559ks8c/BoafKOnJm8R6NbkLQLP4UU53jx7P7/4zbdATd7nPk5UGCMIvLQmb3a1Ji9RdGmGode/89o1eR9+jM6BK/iel9bkLQNpTV60gHdxhuDH/hEnrCbPavK2VZPXZLbxMQIOkssLzcZl1tbkQeJdYjz/A0jjHqvJu+E1eQv8UeMk48kIktbkJY0Z9yGSj6ARcJmr/HD+BN/ZU+N2J9bk/f1H4asfg/JB17a+Jg/c9PQrH+SWYLs1eV/Zqgm4T1XDHQcgEgCngDcBF4EvAg+q6tM9Ng8A78Gtrn018F5VfdV29t0MW117fWx17faw1bW2utbYPba69tbGVteucrutrt3udO0B4J8A37vJz3y/nfaiqhFOwH0K+Afgo6r6tIj8lIj8VGr2GPAN4AzwO8D/fK19dxOP4SiNu3v9dFqwOO2+xWjifi9Ou/ff8fCNv+mjjAv+wxlogU4r2lA0SX9PpwLv4cwNuyHyS6eO8Uj77QxfGSIOO0Rhm8RLiL2YTtgiCdsMXxnmkfbbb77AAyfcfvxhJ+TmpqGdnth2I33dcu3XuiHy+Ji7D16rDdPPQ6PpvuI2mu51q+3aTeAZ2yRgnIM8jNKizTQJDZSEhAZtplFaHORhE3g3iXGGeJgfoEWHaRZo0CZB07OzQIsOD/MDd7zAg1v3s3ErtpvJ+xDwe6r6uU3aPqyqt0hicntYJm/7VGfdUvCne+7q/bIH4CVvvflPvNDHY7TniRfygI+89eY98eLhpx7jE/eepTZeRYDCXIl/dOZuHnrZLSLwelmahS89Dn/X88SLVz0Ar3hrf0+8ePyv4NE/h6UKDJfhgTfDW7/LBJ6xI9wTLx5nmUdJWMJbeeLFW03g3QLMsszjfJVH+XLPEy9ezltfoE+8uJGfjQO9GXIfQexT1at75nBAmMgzDMMwDON2YdDTtdvl03vszzAMwzAMw9gBey3ybvw8mWEYhmEYhrGBvRZ5ezf3axiGYRiGYeyYvRZ5hmEYhmEYxi2ATdcahmEYhmHcgfQl8kTkD67z3pt2HZFhGIZhGIaxa/rN5L2s94WI+MAru69VdWEvgjIMwzAMwzB2x7ZEnoj8vIhUgG8RkeX0pwLMAB8faISGYRiGYRhG32xL5KnqvwaGgd9X1aH0p6yqo6r684MN0TAMwzAMw+iXbU/XqmoCvHyAsRiGYRiGYRh7RL81eX8rIt8+kEgMwzAMwzCMPSPo0/6NwLtF5BxQw90yRVX1W/Y8MsMwDMMwDGPH9Cvy3j6QKAzDMAzDMIw9pS+Rp6rnAERkAsgNJCLDMAzDMAxj1/R7M+TvE5HTwHPAXwFngf82gLgMwzAMwzCMXdDvwotfAV4DnFLVu3FPuPj8nkdlGIZhGIZh7Ip+RV5HVecBT0Q8Vf1L4FsHEJdhGIZhGIaxC/pdeLEoIiXgs8D/JyIzQLT3YRmGYRiGYRi7od9M3juBBvDTwCeBZ4Hv3eugDMMwDMMwjN3R7+raGoCIDAF/OpCIDMMwDMMwjF3Tl8gTkXcDD+OyeQnpzZCBF+19aIZhGIZhGMZO6Xe69v8AXqaqx1T1Rap6t6ruWOCJyH4R+TMROZ3+3reF3feIyNdF5IyI/FzP+78kIhdF5O/Tn3fsNBbDMAzDMIw7iX5F3rNAfQ/7/zng06p6HPh0+noNIuIDv4172sY3AT8qIt/UY/Jbqvqt6c9jexibYRiGYRjGbUu/q2t/HvgbEfnvQKv7pqr+bzvs/53Ad6fb/xn4DPCv1tm8Cjijqt8AEJE/TPf72g77NAzDMAzDuOPpN5P3CPAXwN8CT/T87JQDqvo8QPp7YhObu4ALPa+n0/e6vEdEviIiv7vVdC+AiLxLRE6KyMnZ2dldhGwYhmEYhnHr028mL1LVn+lnBxH5c+DgJk3/93ZdbPKepr/fh3sKh6a/fxP4yc2cqOoHgA8AnDhxQjezMQzDMAzDuFPoV+T9pYi8C3f7lN7p2oWtdlDVN2/VJiJXROSQqj4vIoeAmU3MpoGpnteTwKXU95UeX78DfGK7B2IYhmEYhnEnI6rbT2qJyHM9L1d23OkKWxH5DWBeVX89XTW7X1V/dp1NAJzCPSf3IvBF4EFVfborEFO7nwZerao/so1+Z4FzO4m5D8aAuQH38ULCxnNw2NgODhvbwWLjOxhsXAfHTsf2qKqO97tTvyLvh4BPquqyiPwi8G3Ar6jqk/12nPobBT4KHAHOA/+jqi6IyGHgg6r6jtTuHcC/A3zgd1X1V9P3/wD37FwFzgLv7oq+m42InFTVEzc7jjsFG8/BYWM7OGxsB4uN72CwcR0cN3ps+52u/QVV/aiIvA54C64G7n3Aq3fSuarO4zJ069+/BLyj5/VjwIbbo6jqT+ykX8MwDMMwjDudflfXxunvB4D3q+rHgezehmQYhmEYhmHsln5F3kUReQT4IeAxEQl34OOFwgdudgB3GDaeg8PGdnDY2A4WG9/BYOM6OG7o2PZbk1cAvgf4qqqeTlfEfrOqPj6oAA3DMAzDMIz+6UvkGYZhGIZhGLcHNtVqGIZhGIZxB2IizzAMwzAM4w7ERJ5hGIZhGMYdiIk8wzAMwzCMOxATeYZhGIZhGHcg2xZ5IpIbZCCGYRiGYRjG3rHtW6iIyBngCvDXwGeBz6vq0gBjMwzDMAzDMHZIvzdDPgK8Hngt7tmyi6r6rQOKbWCMjY3psWPHbnYYhnFbEimca4II+Ju0x4AqHM1BIDc6OsMwjDuPJ554Yk5Vx/vdL9iuoYhM4sTd64GXA08Dn+u3w1uBY8eOcfLkyZsdhmHc8iQKjQRCcb8B/ngWPjwLh7MgOCEn68TcdAsenIAfO3DDQzYMw7jjEJFzO9lv2yIPOA98Efg1Vf2pnXRmGMbgSBRqsdvOe6uibLPtRMGTa9vVYniyAtUEZtqwFDkx95EZiBUutaDkw4EsTGTWCr3RAB6dN5FnGIZxM+lH5L0CeB3woIj8HHAa+CtV/dBAIjOMO5z1WbLtCK+t7EKBzy/B39dAgbIHldgJr5IHy7HLupV9WIpdpm0yC8OBE3GqTrBVE2dX8mC2A2db8MoSnG/BSAAozHVgf+CmbRWoxxBlINNzbKEH8+0bNZKGYRjGZmxb5Knql0XkWeBZ3JTtjwNvAEzkGS8INpu63E5mbCtR9oVlJ76utGExcsJrKoQhPxVlqUCrxk5MDfUItDV2OFF1qe38H8zC6QaMZ0EUTjdhPFVgpxtO2DUTaCWrdgBn6jCeA9J9RgMn/M634K4MNNXFNJZxmbxAXN8Ff+OFpJXA8GYFe4Zh3LbMtuFTC/DogsvsDwfwwH542/7V64hxa9FPTd5JIAT+BleL9wZV3dEcsWEMmutNXfabNVsRZRFc6ThxpboqtC70ZMYqydq26RZMpqKsm10LgZkOHM7B2QYUA9dfU+FKjyg7U4fx0G2fasBIZnO7S004kocnqpAkcDy/2tfx3KoYPJ53QjHnOWF4PLuaybu3kGbyevaZzMIrSvDaYWila7T2B9evyZuPXE2eYRh3Bk/V4KHn3HVgNHDXtGYCH5mFP56Dh++G+4s3O0pjPf1M175dVWcHFonxgmYv68nWT112s2HdzNhSBNPt62fNesVbwYOZyAmbcw0YSYXX6aYTdo3EXfxON1ZF2emmy2bVu6KsmzVTmIvc6tNLTee/ru4YcgITPaLs3sJqTPelAm0zu7sLrjbuvjx8WxmK/u5r8rrbRd/Zdr+ov2MUPj7vLvBDm1xBliN3Dt66b4d/DIZh3FLMtp3ACz0Y7/mfz/sw6bv/+Yeeg0fus4zerUY/07WzIvIA8DIg1/P+w4MIzLh12UyQ7XU9WW8NWTfTVN4ka9bbVk3c/qGsnbrsZsO6U5fD/vWzZuvF29UOHA2d38M5956ymvHKe67f4/k0k9fTVuiKsp7s2otybrHCi/PwHUPO327GMO85H3nPtZGOY5ettvux6zKedd/aH3rOZSlHA3fxbyUugxeKa7eLvWHc3nRLVB6bh1riromdZGP2fihw14LHr9piq1uNfqZr3w8UgDcCHwR+EPi7AcVl7AGb1ZB1szLXs9tKUCQKT9fgv1e2J7x6py43Lfhnoyg71YCJVCCcbjoxpKT1ZJl1WbMeu/EMIOnUZQ6eqLmpy242rDsNuRRdP2u2XrwdzLmYXlxYFWXXGqfttPWKsvV6aCcCrZ+0/G65v+i+tT9+1a2inW878fzghMvgmcAzjBvLbkpUrlc3/NuXbEX97Uo/nwvfqarfIiJfUdVfFpHfBP54UIG9UOn+o+42q7P+9heLEfgCrxmCVw2tCr1E4WTFpeOfb2+sNeutJ1uO0xWV6tL0ba4jvBrr6sk2K/jfRJTdl++pIcutisbj+Y1Zs5W23KpovLsABzJwX2HrqcudjO9Womy7mbH1r2+kKBsE41l3QbeLumH0z17WDYP78v13ldUSld4ylOW0RKV7Le9eN69VU5z30rrhLMx3YF/ghJ6tqL+96Odzppn+rovIYWAeuHvvQ7r92c0qzG6W7HzT/XONbJIZ697iYn1tWe8tMza7/UVGnJBrJE74gNue77h/7i8swXBPZmw4cPVkrQROd1ymLPRWhVog1xFePVOX921V8M/2RNl2x7C7vd2py2u1rbe73UWZYdzqzM/CX38K/vJRqCxBeRje+AC8/m0w2ve9/veWQdQNf6m2ej3virKyt3bWYyhYK8q6183uPtMtOJiBBLfSPV63ov5Mfe2K+jOtdBU9164pnm7BsbzL3u0LXJ++rai/7ejnc+tPRWQE+A3gSdw5/52BRHUb009mrNJT5F9NXK1D9+JQT6CtG6crx0NWbnGxWW1Zt+5s/e0vGmnN16Gsu9h0yXswmnHxHksF2/p6stCD4xn3GuANw/Cy4sZp3FupnswEmWHcXnz9Kfith6DTgpFRODgJrSb86Ufgk38MP/0wvPj+7fkaVN3wl6qrX6TXX7+79cTrv3xvqBtWd0292HLX7oMZON1ed5ujTI8oq68VZRMZUEnFW2rnifPbSiDrrV1Rf2/BZfK6X7C7q+ivV1N8Tz4tUcnDsRA+Mmcr6m9HtvXsWhHxgNeo6t+kr0Mgp6pLA45vIJw4cUIH9VizWgyfXXT/0J+cTzNj6jJrIz6ca7mpycXOat3ZbJoly6SZP1F3m47dZPK6/+Drb3+x25q8rXwYhmHslNkZ+Pl3QTaEkWFXFgJOTEQK1WVot+DXHoH8qGu7Xt3w31VcVutatzLqirX1K+8nQ/clunu9DXGirIETZXPRqiib7awKr9mWy4ydb8GR0GXkul/Su9d5gKttd8ujJytuEdZoZneZvCOhK8VZ/+X7WuO0nbbul+/5Drz7lBOJW62obyW2unaQiMgTqnqi7/22I/LSDr6gqt/Rd2S3IIMUebvJ5AnwqrL7R4XdZcZMkBmGMQi2UzccxdBpwlAeFmuubaS4cTuKwfPg9/8QPvlHUDwAYQDtGMR3Wal22p/OwMu/H47+D2sXfU23Vr8QVxKI0utizodO+gV7OHAlMEdCJ+TG133BFlz92bDv6tOmcrDUWZ05WYzcl+leUbabTN5Q4BYvlLy9qxse9LV+/X3yNltRb/fJGxw3QuT9MvAV4I91uzvdogxS5MHOa/LARJlhGHvLtURZCCxU3Hav8Ar8zUVZonAmgc/PwHQDJvOwvwgLaW3ZSBHmK/C1J2GkDVyFyrLLcg2VYaniZirKQ7C8CJcvwuhdcPYT4CkQQBxAoQB+DhhxYqIaQ64DrQi+9z+sZsZ6xdtiBBMhZAUa6XOWA9mbTN6Q72ZeriXKerd3Ujd8OzDbXl1RvxS7MXpg1FbU3wh2KvL6KV36GaAIRCLSxP3tq6oO9dvpnU73WxXsrJ7MMIwXLpvVk/WTGesVaF1R9oVZOFeHyRyMlmCxDkkMzWfhuSeBGEplWFqGy9NwaAqGh2F5OS0PKUG1Ah2BbBHaFXj+AsxPQVSEsAkk0AjBr8Lzz4G8HOanYd9+J+zOnE4XTyg8e9otqmg1QFvQvAr51M5vgD8CSQuGgTgVa5ks6KIr/u+uqO/WDWfFLe6qpALrDSNWNzwIbEX97Uc/N0MuX6tdRF6mqk/vPiTDMIxbn2tl7GE1S3atzNhWouxvZ122aqQAc8vwtS/BSAdkwQkxUSfKqlWXJSsXXZbs8rRbsDBc3kKUTUJcglwD4giqHdd3rPDsGSe82k1oNmFuxokyxbWNTkCgsPQcZEogbaANmasg6dNNss+DNwK5IjQuwZFJaDRdjPceX83k3XPcZfLCPGTyMD7m7lsZZCAsu+xddhjGyxDjYmg3QA/Avzi0dqy3Uze80xW8zbnoAAAgAElEQVT1t7soM4y9/Lv9A+Db9tCfYRjGnrKdqctribLu9kIVnlxyj6NbCtx0pQL7CnA1vd9j61n4xkknriYmYV8ZKlVIxImwpXS6srctUgiKQCMVkSF4S3D53GpmbP/+tcJLFL7xLJSGoNNwK1KfvbJWlIVDIB0nzDLTwH6XGRtTaLfd8d5zrxOQ2RzkcnDgwGom7557nWhUD068DIYb7v6bB8Zh/3FYXHK38Bg54vZJ9sFLXwHf8TpYbmw+hr0i93P3wCc+AhN3uddxDH7gplG7OmvhKnzfg1Du+dS6ligzDGNvRd5tVFlgGMatzPosWa8oq0Vumm+zqcvt1JP9zSycT6cu96dTlyRQP52Ksmk4lGbDlmppDVmpJ2tWhoVFZ3fPFNRyELTcBbARQq4FSeTizAcu1nbDCa/96S0meqcre9sCgeWzbgWp70H2EsgwhCVoXoKjU1BrpNmwe1djetE9LkuWyUOYg4nxdaKsCTMtODDmROLVJSegXvkWmHqpW/ywnTEEN2btphOynge5PDRTIde7XSi69sLKQzDdE202237jA/Dnf+LGIzMEwTqxVl2GTAive+su/7AM4wXGXoq823oxhmEYO+daU5f91pMNFeCzV1y91ZIPc9XVIv99eTj9RTf9mCykRf3A0BAsLqb1ZJMwPLK2yL9SgThxWbKoBpcuwPxh6BSg0F6dusz7LhvWbMJcV5SlmbL9487fs6dheAj8NszXIVwAb5+LI3sJZMRlyUZx9zAL85DNwz0Tq5m8e467TN76NhX49pfCSAvwYN8ULCwD+/vLjG1XlHWFWJeD64rntxJl2cxau1J58+3tMjru7oP3Ww9BddrdJy8butumLM47gffTD9/8GyIbxu2GlRkYxguQrUTZ+izZdurJekXZou+mMhW36nK+Ak/3rLRcroAkTghUam67XO6pJ7sLSsMwt5Q+3zgEqnBlGuYmoRNCsAiFA24Kc/9+l0E7c9r5aTeh2YbZdUX+Y2OuYH/pLGSH3LSlRK6GjH1OUIzibgmRyafTleOrmbwX3bOaNbvnOFQW3Ri9ZBjuf7E7LhSGj7ipS8+HV74aJl/qxnq7NXmwKspg55mx9a+vJcpuFV58P/zqI/C5x90TL67Mw9Cwm6J93VtN4BnGTthLkWdPrTOMAdDP1OWO68kUhtMsWVSFZB4WlmAmFV7DI67WivRWGFd72kpD/z977x5m2V3W+X7etfa99qXuVd1dfe/qAB0khCYkBgHFCYxhBs6IygFRn/ERZ47PUfSoyHiOzomDyFz08dEZhSMeB2aIMgyKnIBE5KLEEEg6AdJA0p2+VnVVV3Xd9q6qfV3rPX/81qq9ateuW3fv7urk93mePL32Wr+11m+vXdn1re/7/t7XLA7ojkExAfEy4EA1BTIPk+FKy0vQ222E0pnnoK/fbD932lyjWoZqDWZPw64hWKiCexkkSPKXOqRLkNoDS5dhf5jUj0nqn58P8skSMNSS5F8Ktu96MeTrJnQ5OAA9R4K8M8eELkdetLEoi277PmTjptzHRuHKKFcjynaiIOsUfQPw5neY/ywWy7WzJZEnIgLcBezBfKdeAr4WrZenqnd3ZIYWyy3G9cwna3XJwtDl3iB0+ezXoR6ELucXmuHKfJDIDyaUuRK6zMJcyYQro/lkKCy5kCgal+zyeejKNIXX6VNGlEVds2rZuGazzxlRNl8LRFkf4EH8YrDSMgvlCbPScrli3LtDh5tO3uEjZn7JNCQDgRYvm7pkxw4FCxSCfLL+XsgOQtcr4VX3rg1dXks+2XZEWatAu9ZwpcVisXSCTUWeiNwH/BfgFDAe7B4BjojI/6aqD3dwfhZLx9kodLm0aATaZq7OVvPJng2cMm/GOEgqUOiC+eLafDJHTSmK2ZIJXZYSIMtw+SLM7oVqwoQuu4LQZbYrkk82Bb39Jrn+uVMmnyzMLSsU2ueTZeYgtRuWL5sE/+JyU3gdGW06eUdGjZOXTBvXbDgUZUk4dsC8FwfoPgBz86C98OI74J7v23o+2cKy+QxClywqympV8+9GocvW17da6NJisViuB1tx8n4f+EFVPRfdKSIHgc8AL+7AvCyWtrQrFBtuX898sp40PPMYnPqG6Tm8Uhg2Up8sWgpjIqhPls0bUZZ3V+eTzeyFahxiC0aUXb4APd1AGK4srM0n84HxMzA0GAld5pqlMDIlSO4KRNkeWKw088kGB5pO3uFI6PLQYRO6bM0n8xV6D0B+ADKvhLsDl2yrNd5aRRlcWz5ZKvK6VYTFbCaxxWKxbImtfF3GgLE2+8eBeJv9K4jIXuAjwDDmd9aHVPX3RaQX+AvgAHAO+FFVnQvOeS/w05gamD+vqp8L9r8C+DMgjRGXv6CqKiLJ4B6vAGaAH2sVpJabx3qhy+22UwIjyL40CU+VjGDo6TLlL1ShO1x1GeSTzQc1yMLQ5WIpaK3UsgozW4CZecgnYDEGsQrgQNkFfwoWlyAzbEKU/S2FYSEIXQYCrVqBuWkYbpNPRijKAqds3wgsl4NE/iPG/WrNJ3MUDhyBRAWGknDsYCR0OQB9PZAbgK67mqHLreTkbZRPtp5LBlcnyqxLZrFYLDePrYi8PwW+LiJ/DlwM9u0Dfgz48CbnNoD/Q1VPiEgOeEJE/hb4KeDvVPV3ROTXgF8D3iMiLwHeBhwDdgOfF5GjquoBfwS8C/gqRuS9EfgsRhDOqeoREXkb8IFgbpZt0s4l20iURbe3k0+2JwG1M0E7Jd+0TFooNkVZtD5ZPt900NI5mF6A4iUY2AO1tKlJBrDsQnyhueoym2mGLqcvN4vGtq7CnD0NuwKnzJkKKvd7kJ6DxB6Y/wa4fjNcGa1P5odV/IOE/2TKCLx41TQfX5VPFoiyrn7IRpyyzZ7h1YYu4eryyaxLZrFYLM8fNv1KV9X3i8hfAW8G7sHkXo8B71DVb29y7gQwEWyXROQ7mMUbbwZeFwz7r8CXgPcE+/9cVavAWRE5DdwlIueAvKo+CiAiHwHeghF5bwb+bXCtTwB/KCISXRTyfORqK/f3582+KA3PhCm/WzftlDxtNh0fK8OeJFRPw5knAX+18MrmzHYYrizkTUI9CplsM3RZSoBThsmLMD0IFQ3aKflBO6V8+/pkUQftwnMw2A1TZWhUITEX5JMpZOYjqy73wGJ5degydPJaV2EOj0KiDEMOvGQ/zJdMPlnvAZPgn3olvORO6M1tvWfo9c4ns6FLi8VisVwNW/oVoarfAb4TvhaRQVWd2s6NROQA8HLgMWAoEICo6oSIBMEv9mCcupCxYF+d1SHjcH94zsXgWg0RWcCUvLrScv93YZxA9u3bt52pXxe2Ispgm5X7p+BCsNKyt8u0U0KhfApOP2ES9MPyFwslUyfs9a+Gt/9AU+g1PPj4l+HsJDx7EXTRNASvpiC2ZETZ1LCpH5bvMk3No8LrzHPNfLJqNVIaA7j43OrQJbmgrVIMsmE7pViznVK7+mRRB+3QEYhX4FgP3D5gynEslEB96D0IuZZVl1vNJysuQ6pNPllUkMHWnTEryiwWi8WyE9jK6treNru/JiIvB0RVZ7dwjSzwP4F3q2rRVGRpP7TNPt1g/0bnrN6h+iHgQwDHjx/vqMvX8IzgyqVWi7JHpporLUNRFva4PHsC8ILCsMVIT8sg4R+arZXC/pb1RdMXc3avqdyfqoAGlftTsWb5i3B1ZTwBZ8fNfQfy5ppzS3BpCvb1wlOPQixnxGD8ImiQT5ZwIeuaa7mxFuF12GwnUpBMwuDhppN36EgkdHnQ5J1drsJQL7zynu23UwoFWQIzrrWd0vXOJ7OCzGKxWCy3Mlv5NXYFON+ybw9wAiOmDm10sojEMQLvv6vqJ4Pdl0VkV+Di7QJCV3AM2Bs5fQRTk28s2G7dHz1nTERiQAHYVHh2itAZuzgJFy9BccHYkIku04pp4qJZaVlPG1Hme2ZVaCFjwqTPnYZcd6Sn5ZRZaamR1kphf8t4trnSMjFn2inFgsr9Zb9Z/mIwSOT3fTi4xyxYCOnpgt2DMD4Jdx6FXKVZ/mJmDqYqsKsXjn+vKRQbFWVw9flkV9tOKWXzySwWi8Vyk6lNw5XPwfRD0FiAWAEG7of+N0BiB3Vn2cqvxV8FfhD4FVX9FoCInFXVg5udGBRR/jDwHVX93cihvwZ+Evid4N9PRfZ/TER+F7PwYhRTdNkTkZKI3I0J9/4E8Act13oUeCvwhZuZjxc6Y/098JVHjGCLAQtnA1EWrLRMzJkk/7DHZa1mRFIYulzpaTncdPKirZVe+WIolOFy3YQ3e4JcM8fduHJ/a05ezIUffa2ZdyG9tp3SVkTZ1eaTWW4i01fgc1+Ch/7O/MAV8nD/6+ENr4OB/ps9O4vFYtmxlJ6G078BWoV4H6RGwK/A5INw+ZNw5AHI3X6zZ2mQreghERkBfg+T+/abwDdUdUMHLzjv1cA/AN/ClFAB+DcYofZxzCrdC8CPhGFfEfl14F9iVua+W1U/G+w/TrOEymeB/z0ooZICPorJ95sF3qaqZzaa1/Hjx/Xxxx/f9H1fDe2cPBW4rd+IsqkxGNwLfQXTeNx1mj0utxO63G7lfotlhae/C7/x7038va8HUkmoVI11m0zAA78Kt7/oZs/SYrFYdhy1aTj5s+AmIZZfe7xRBK8Kxz54fR09EXlCVY9v+7ztmF4i8s+AXwcOqOrwdm+2U+ikyIO1OXlgRZllhzB9BX72V42Yy7exVoslI/4++O+to2exWCwtXPpvxrFL7jE58BIznYWiVMZg+O2w+zr2YL5akbetLCZV/bSIfB44vN0bvZCIuc2FDbadkqWj+D6UK0a0lSN/RaRT5jWs3v7M38HSsgnP1usmkTH6DZXPwdgEPPxleMcP3/j3Y7FYLB1AffDK4CTNvwI4afAjaUnhawXcdPtxU58Ctxuqk6B1cFLGsYt+jcb74MpD11fkXS1bEnki8iLMYovHVHUReDrY/0ZV/ZsOzs9ief7g+0ZgQXsRtplAax2XTMCjT5ikzctBm4+xSRjZDYWs6V8mYqpNFxfNcug//5QpTnjpsqkYPTQIg32rv6H6euChz1uRZ7FYbgrqQ2NptcAKhVfrdlSgRUWZV8YkiTlG2M0/akKptSmzUAI14dZ6EapjkByBeN6MQcDNgre4dtzi0xDrBa00w7XaAImYOE4SqjM39JGty1ZKqPw88HOYOnkfFpFfUNVwocRvA1bkWZ6fbCbKotsbCTQ/SEc9+Qx87YRZKp0NRJgK5LNm+fPYBOzdZdy04qL5tsoGdXOUtePSKZiagd1DcG4MuoIcgGoFTk3DQB+ID6fOmm2AK3OmwKDXMNdcXoZGtyleGJJMmPw8i8Vi2QJRlywqvNygkkPUQQuFV6tTFm5LEuYegdKT5m9PN9sUXrEcNELhlWsj0IK/a52sEXLhMTcD9SlI7IbyeUh0m3ktnYJ4Afxl8KvmdTKoAbt82myH42LdZpzEwStCvMfcL5kzIdsoftWstt0JbMXJ+xngFaq6GBQ0/oSIHFDV36d9jTqL5cbTTpBtJrw2cs2SCXjk63DiW8YBy+WaYqtVeBVLcHECRoaDlh8tx8YumSXQGtyj4UWEl8KzZ6E7Z+5dqcHliCg7faaZG9c6bnYe9o8YV27PMJTLJtkzmYLR/sDJc2D0YNPJ6+8xTp4bM//3ZjJrW6BUa6a3nMVieV7SKsp0E+HV6oxFHbWoS1afgtosVC9Bah90v8p8BdVmTHizPtcUXrG8ccpCUeaVADXXroyZ1aqJYaicioitZyExZL7KosJLK7A0CclBM8XKKSOy/IpZAVuZg/R+qF0ynZH8irlX16gRik7GvI+uUfM+RCBzpOnkRcd13Q7LT5vtWDckh9bm5NVnTE7eTmArIs8NQrSo6jkReR1G6O3HijzL1bJdUbbRON83LtljT5pvq2zWhDDD0GU+EGWhM7YQHNvINUunjGNWCRrPnjoLg73GeYsKr1NnjbArV4w4irpmp84asbRcNbVtfN/UyonHjPAKnbyjB41Dlw56rQ0ebM7pyKHmnFrHDQ+YUOtth+Ge4+b+mz3Dvl742F/C7mFz/dacPDAu3tv/l0586haLZZtsFLrcyBlbb1yrKKvPQ+ViILwKTbEV28AZixXAXzRBCScVccnOmUL9/jKoZ147KUgMmns66abwWj5tctmU1UKuPGYEYukExP2m8EKg62jTyYsKL0kFx0qmo1Rm1MzXSRlnMDNsBGDXbdB9j3HarjYnrzELz/yicfTiPWu/PhtFc8/++zr+o7EltiLyJkXkDlV9CiBw9N4E/Cnw0o7OzrJzuJ75ZLCxKCtEcsiiDlqu6ZrNdB/kHy6O8sUnhygtxcmlCnz/S4/wfYfP0Td5xgivMHR5esqIMtGm8NrMNRufNE1wn/gmDPQYURbOIyq8Rg9CsUQ1rUwmH+f86BP4pXlUwR3tYX/xToYzu0jGXXjVXXDstqt3F9uNq9bMv44DiZaChbns2u0fej186nOmSfB6q2uTCbjvtdv8AbFYLLD1fLJQeMk64gLfvF46CQtfM6Iplm+KnDCM2eqMIRDLrs4ti45z01CfhmQQunQzwb2rsHwqWERA4JoNNkVYPHDG/JZxlfGmS5YeMe/dyYC4kD7QdPLSB42TtyK8jjSdvMxoU1x2HYZ4IMgKd5qQ77Xm5LlpM283bebjRr4qnZavwejrdtuJAhz5d6ZOXnXcLLJwkub69Rnz3o48sHMKIm9aQiWokddQ1ck2x+5V1UeC7R5VvSUSeTpdQuWmEwqydqKhK7O2XkvrCk3YQJR56wovRnYZAbUY/ImXy60OXS4smtDlcD8gkE5CrWHKehTycHEc9u6B+ZJxqACmZ5oO2vQMFPI881SD3zv5ZupVn+5un2RCqVaV+eU08bjyi//iJLflLl6bk9edh8EBszjhzpea57aO8Joqn+AL/q9yxfkuflpIlTMAVNLLOGWl37+NH3D+A4Ndx3dGrZzWOnnJhNm2dfIsL0BaV11u5IytJyiiocswn2zFDQtFWa7pfkWFV2pvMC4UZbmmaxYfNmm1sQz4dahOG+GFmnptsQJUL5pr1BcCYSFmcUGYW1abNgKtchGSe8FbMuKreslcyy9fm5MXKxiXzM0bl0yr15aT57QIsp1KbRquPGxW0YYdL/rvNw5eJwTeDamTt8kETqjqndflYh1mx4m8ToQuH30CxsYDkRMIGVfg7uNw18ubYsP34fFvGKE1MW0ctTX5ZIOAmBBhvdFWeHFhHPbtNuHEgYhAC7enZkw+2cVxOLQ/yAsTiMW35eTNXKrz6x95FclcjGxeTBkQAQb7oaebxUWhWnd53+8t09d7DTl5rS7ZOiwxzaf5Kab4BjGSuKx20zzqeFQY4GX8M/6MLnbIn3fTV0yZlIc+bz6zQg7u/0Hj4Nn6eJYdivpGpGzVydlMoEkQuvSKUJ1qyRkLBE+Y/F+fN8IomuTfKuQkZdwdLZt8stoVI6REA4EWuF/VUKBdMKHJ+sJqURaKt/Qh83c1Djix6+PkxbtNXlssIsquJSev1SWzdIYbUidvszlcx2vd2mzmjG2UT5aLiKvdu40wanXG1nOkSiWT1O9jxixXTU7Z5StGbMUTMDFp7ttl3CbKFZiZNULt0cehECwJWpVPJqZHWqUGCXd16DIIV5JOQTJpEv7D+Y4ebDp5Rw8aJy+VNnlgr37F2tBl5Nn4SxUqZUh0p6jMmmeY6k3xtx9qUN7vkt8NmnLxqp5ZQxA3uWXZXlgcg6/8Y5Y3R2sUtQtdbnZsC81wz/A5SowjODht/ndyieNTY5FxzvIwt7MDCieBEXLv+GFbJsXSUTYLXa6XC9VunACLJ2HhscBBiq6u3G3ES9R5ijpS4bjUiHGcQvEmycD1CkKXsczakOSqcGWY5H959crL0F2rj0FmHyycgFiYTxaIssxo0/3KHDHCy0mb8GXXYCThP5pPFoPuV0Pu2MZh3e3k5K0RZS1ZHpuFK9ses33DdyzX86O5af1idxRbccZCd620aMKVaBC69JsrKJerUKvCqStN16xdUn9rblkyAeUqeEAmaVpWDfYbZ8z3YNewEVUh6ZRJxp++Agf2N+cbijd8eOqkceUQM7c3/RN4yz+F/r4VQbZcMt/Wqd7Voqx122/4ODGHVH+GStX82ZcCKmTD7yASPpx4KstiEWanoFjMIgqZHHzs/41RqcDiMsSTkM44JFPQO9D8K6O7D774EKtFXguKT50yMZLUMa1H4qTXbCs+gtP2GMBpPk2VueB6yzjEiJFadS+XFBUWOM1DO0fkWSwthIJsozyxrSart+aTRZ2nMCTpFc0qylCgeYtN8dauTEZigBWRQm11+Qutrc0hi4qwcJxXgWog0HygfgW69kMlWHXpLRtx5SSbeWIiQZL/fCTJf7Dp5EWFXPqQccmGt5BPtpWcPByzkCHqkG1ZeG00zoqyFwz2o77ebOSMdWeb7trkFZN3lgpEWbVqQpdHA3GVSUJiHWdMMNsLxdUrMksl88XwmruNSwab5+Q5Dv6dL6MyWyGRT6wWZf/4NP7DT+I0aqSGh6nEs1Cpkfrzh6h84kvw6+8mcedtPHEiy8kTwV+hOVhezJq/XHNQLmXxFbJ5WFzIMjEGwyOQLcByEJFNZ2GxCJNjsGsEkhkj7gZ3w/g5KPQaI/H8KZ+lJcVxoOEptZJDttujUoVGw8WJNwCIJ2PMzHhUWG4r0GosMckJ6iyyxBQV5hGEOFlqmG/rJHkqFClxkRwjpChQDY4lyFJnEUWZ5ttUmAeEBBl8WBGGIYKLUqbKQmd+5iwvSFpdsk1dnfVCbYFgWzoJ81+FyoVInlgkNNiuMGxqpCWfLNsUb4khsy+WMmUZo8JruVWgtSb8R8pkhOPENV+DXtWIlFWrKxPN1ZVrEvkj49xgpacXrsI8ZJL805FVl9eSk9cudHlVzpitYGS5Tthw7fVmM2csdNeO9jdDma+9+6pCl6ucsahrFrhkvm+csdApA8hARH6Y23zzhMPsdIbpCVgqBqLMKVL68LeZrL2V4Z5lcherLFaTiAhd8YpZx/AzjxP/8V1MFfNUy9A/BOdPQe+Aj0ed6VMuhYE6Cpx9Nk5Xd52lirBcVWZOxSkM1AGYOh0nXWgem5uNM3ygzqVLMDgSZ7laR1QpjI6hXQWWKj6O65PMCDO1KvFUg4VYAx9zvUY1RaPQ4O/5EHlGSJAPxJuQJMsyV5jnLMO8giIXSNGNArOcXsmZm+UUSQo0KONRZZZTZDAxmeg4n8aKoGtQI0HXKoEHoHgIMZLskOqYlhvKVtspbScXKhRloUu2JjE+v1qUNYrN5Pp4Ye1iAK8ONDCVfspNxysMQ65XGNZvCV0un26uwhTH/HHWqJrVjNFSGJlRIwZXBNpo08lbt0xGDHrugeyxtc/ianPyWpP83S2GLjc6Zl0yy05iyz+OIvJRVX3nBvtef11ndquykTO2iSiDjUOXM5eNCFOBrqxxxi6Nwe69xilbKplj2SyU5mHyknHNcnnjlDku3HE33HFX08yrlGF+xiddqDL2aJxMwYiyc1+4SFdVWHbTVOrLzJT76EmXUFXOLfTTky6i5Sqzj1xg8P7DnHwC8gPKvtE444vn8bSMMzrLRMlHVckddZha8LiSEmJJJT/qMrGooJA9AtNFf+VY95BLaWiGntvq3HZPiaVqBZ8GsXSVH+RlPPKJvQzuaaBuFcfL4MZ8qlImRhqA2RmPe9/+DA0qNKiyuCLKhFlOkaaPBFmKnCfHCA3KCEIvR1acvF5GqVAkRhqXJL2Mrjh5vRxZcfJG+F6e47M0KJOgiziZNT8SHhVS9HKE+zv5k2e5TqwpFEv7hPT1ktBXjVsy+VneItQuByKnxQ1bN58sSKCvR8RbKND8QJTFMk2XLBYtcXG6TbgyKJMR5pCJNvPOYgmTzuv5Zt5uyqyYXMknW6cwbDSfjLCAbDHIJ4tD793r55NtNycvXK25Xuiy9bXNJ7NYtufkHYu+EBEXeEX4WlVnr9ekbmXWdcbyUCpmmRxrCq+lUtZ0uMrDUvgFmoNSELoc3g2prtWhy3yPj0eN6VMx0oU65bLDUsXnyuU4+YE6InDuWZd0epnlBYflrM/MRIZ89zKJBEyPZ6mUY2SCpe3JtE+95yRjYwvo3hqTs6bd1f7Jp5jvSiPzdVRnGEzXKJIDgf7CBUr1JJJp0Df5GUq7DzNy9BIvutMn2xWnrzxPF8OU02dwyv0IUE9P4JT7mPcv0e3sxk/PEi8Po0A9PYlb7o0cmyFR3UUiXWfRmSaTGMQlRh3lzrc8yxOf3U29nCSXT+I5dUBIUcCjTrmYIJNM8rL7LiGkiJGMiDcJxFqJHHsY4k72cS8NqsDV5eTVWaTERaY52XbhhUcdxSfLHg6yQ6pjPg/ZLJ9sq8nqjSVThLWx2CwUu5JDtoVyEuG4UMjVZqB8BvJ3mjBorCcISW4hn0zF7I93N8VbKNBCURa6ZNFkfSe5VpSFSf6SXGcxANDzWiPI4Prk5HUqn8xisWydrfSufS/wb4C0iBTD3UAN+FAH53ZLYpwxyBXgyUchV/BpUOPcqRjp7jrLy7BcgSuTcboHqmjd4+xkiu5CGVCmLmXIhAIt5zM/m2F4eIlL5xwGdiWZXnyOhlZxDi9wZbrOjCPEUHKHXSZnPFDocUtUThSpj6UpT1fJditLzyhLjsuBnl6SyTcSfvQeS4zyZY6KsKBfxvGNG7an8nUqyTwar6ADWdyuKssDwxB3cGMLeI0C+EpmapKTP/BWyukzpJ29VFhiIHeAEhfoZjd+roKiJDlCNbeAAlkcUhymmltEUOIcopormiopOGZsrAi4Kw6aAvt4LQMDxxh5wOEPfiNFbcyl0NcgkYR6NcbcjEcmCb/0gHBw4Cc2FWhhaDVa9iRJru32eseS5Hg9v8vf8m6meRqHCrHAzWuwjDcytZ4AACAASURBVI/HAMf4AT6wc8qn3ETWc8k2y3faTFCUTsJCaz5ZJMm/XX2ydq2VajNQOQP5VwQ9LnvMvFtXWkZbJoXHwnGrVl32GTeufMHcT9u0U1ovn0yCCv/1iHgLBVpUlEnkmV1tTp6yVpCBzSezWG51NhV5qvp+EfkA8Ceq+i9vwJxuaVJpKPQ0mBpbZPfeFOOzRpS5gSi74jq4+OQPucw+PY+UG+QbZUrPmJTGfMGnNO9Qv5ikcrlMrg/cy1McSpV58Z4Jqn6RhOSo+/NADGUKYQjfLxPTHHg+TnYW75V56J9E9g6SKM9QP7obEkmyvbvxqq/CjZnfRPGy0DXnUhmqMPCkUCtUUCDpO8Qoo+kqsqx4vd1IpmZ+aZLDS1SRcp1YTzeZLhePFC5JuhgiyxB93MYI96xxyTYSXlsVZS+9Hd7/QfjKw/DFhxJMz0C+AG95u8Or74O+AWALAu16McjtvIUH+S6f4Ft8lAXOAVDgAC/lnbyIt96yAu9a88mi4iLaTqk2ZcJ6a0phtKtBFq7CDI+1LAbwG+YejgTzas0Ti4qw0y0CraW1UqLfzKdywVTv98o0c8giImxVy6RoaYyWhH9v0cw793LouZcN2ylttz7ZRi5Z62sryiyWFyZbLoYcFOJ7xeYjdz6dLIasfoPLX3uQxfHL+NOnKM8USZCj3jsPs3H88cs4e4bQ3CLxiQyN4S6SkxepZ4YASMxPUMv0ohenVgRao3cXSbeGOzOLO7CLKkXS00K94CIXJtB9u0gsQG3ARRMu8bJL3S/D9BQ6shsnn0eLRXAdMne/gcG73oGEiSi+jz7+JPXpCWITs3jFOfOL99vn0Ee/bgoT9/fgDO/Cd0z3C4cYPg0Ym0Te/sPU3/HGVQKtQZU46TULECydpbVQ7JZrZm2ST+YvmkKxjYW1hWGjdcdWirC2CrQg4d/NmBBocjcsnzdhSMFcO9YD1fOrC8PC6qKxyf3QmF9bXNZJBO9JTd7btTh54fvI3wHd9xoheC05eRu5ZBaLxbJVbkQx5K+KyCtV9evbvckLiXp5Dn/uEtmhXspPXiBdGKDKFIWzQq3bR7wySpXEZJLywThdl2Zh714S1WVQRYYPEisWkUUPzSvOvkPEF4NG9qOHSJbipGWIntHb8RfmIT2OpnYTG+zFK82jPsRecxf1Y3uN8HJixNM91Mum41yia6Ap8AAcBzn+chLlF0MygROWWllcgnf9iinP0tMNIri4K6e5xYqpdXLf969xxhK2Ms+WaNdOabNcqPXGhe2UFp9qyQ1rrX6fA2+h6YytyifLNgWPmzWV98vngtDlBUh0G10TLQzrR+qOhTlkyUHz/loT/itzpsdlNahJptXmaspVjcZba5CFSf6t+WRh3pkPva+BrmPmOV1LTl64vZLgv06PS5tPZrFYbgW24+R9GzgKnAeWMN+Fqqrf07npdYZOO3nTjz9IfXqM+sQFMkUXR9P05G/HK84hY5fQkV3EuvtpDOYgnyV2z/dSr5oaaqEgU7+xRqDF0z00ygvGJUtn1m/P1a4/7dVge5yuYsPQ5WXF/1QD5y8a+OfNeOeAoD/qIm+O4QxJ23ZKjaJxn+rz6/SxbK1PttB+nJM0BV2j7ZRC4RX2sQTjfoV9LKP9LsNWS2FYszZt8slql0HSRpT5lWtz8uI9a3tcXo+cvFWizGKxWJ6HdLx3rYjsb7dfVc9v96Y3m073rlW/Qb08RyyZ21iUbaE36k3nFu1xulHocquu2br5ZK2lMJ7xKf+FR2JGiceURtzkV8YaSqMuVHuF9NtcYrc5a9oppXYbt8zpWr+P5YpAmzL1yVrHAdTmzb7SE6bqfrzv2p288JxrySdbk/Bve1xaLBbLtum4yIvcaBCafZtU9cIGY/8UeBMwpaq3B/t6gb8ADgDngB9V1bng2HuBn8Y05fp5Vf1csP8VwJ8BaeAzwC+oqopIEvgIppTLDPBjqnpus/fQaZFn2T7tGo+vu73JKsE1ocvQDWupO7aZa7alfLLzivu5BpUxnxTzNCplkhgVWZMMsWSGsuRJ7YnReEOM1H4xZWPmTTul8iVTcd9fvjYnL5Y3XQLcbLOd0rXm5Nl8MovFYtkZdDwnT0T+OfCfgN3AFLAf+A4t9fNa+DPgDzFCLOTXgL9T1d8RkV8LXr9HRF4CvC243m7g8yJyVFU94I+AdwFfxYi8NwKfxQjCOVU9IiJvAz4A/NhW35Pl6mgryLaSyL+OuGgVZbFcRLxE+liu9Lu82L4+WbjdGroMC8OqrM4TC1dhJvoV6rA8rsTDOmYTEEtDY0GJ54XaDGSGoXIOksPgL5l7pRY9GrNlYpV5HIp00cAjCThktEijskSMKjLXQ2Y5je/GNmyndLU5eZ1op2TzySwWi+XWZjsZ8r8F3A18XlVfLiLfD/yvG52gqn8vIgdadr8ZeF2w/V+BLwHvCfb/uapWgbMichq4S0TOAXlVfRRARD4CvAUj8t4M/NvgWp8A/lBERLdrTz7PiYqydq7MVstkrGqn9FgkwX8D4RUtDNtYJ0zYKspak/ej1fnjBfDKil+CpUkl2W3GLV8yCwMAKlOQGobiU0osL6QPgmfSGkkfhsY0iKOAkD6kNL6laEVJ1sE7FYi3bmjMK85FQach3Qvxy0o6BfndivomJOs8WYb5k8AigoPjKH4Qfnfw8VVQbxxKOWT2dviZ/DW1U7KV+y0Wi8WyVbbza6GuqjMi4oiIo6pfDOrnbZchVZ0AUNWJIPwLsAfj1IWMBfvqwXbr/vCci8G1GiKyAPQBV1pvKiLvwriB7Nu37yqmfWMIBdlmiebtXJ12Ak2AxUCUIVC4G7rvago99WHhcZP7VZ1Yp+1SWJ+sCNoIBFwaqEUajZdXF4YVgu3AJVucUOIZ8BYUPy8sTbIi0NaIsiPgzZhj6cNmWzG5Zo0risz4aB3SHnjPmmOp7uZ2smAE2WAJ8o7iKvgKiOD4io+iiHlWNfDzwFEHZ9LDH3UBxVlQ/AOC9vnIPnCWQXsFxxXkvAd5BRQZn0J0CcEDN7hHUGoGwEFBFPWX0XNXcHP55jEryiwWi8XSQbbza2ZeRLLA3wP/XUSmgMZ1nIu02acb7N/onLU7VT9E0KHj+PHjHXX6rqaAbNQlm3/MFGNd6WkZDVe2aa2U2rt2hWO4+jFhyu/hpoywq06Y+8aCtmZeGeozRqjNPwqxvBFlSxPaDFfmhKUJ45RJzMzTmweJB8JrJnDGVEgfAm82EF4HFe9pRctKug6NOZBxYBrSPRuIMh98FUBxfPARUMXRoPBtlyIvdXCmfPwjoSjzA4EGzoKHdjk4k4q4Amc9nLwRWzzn4GQ8KCtUHFh0cIZ9mASGHZzFqnmIhx2cWQ/Saspo7IrBnA81H2QRHl8AX5HyBKJV8wH5GiyiaZaaMTiINmB+GjjUwZ88i8VisViabEfkvRmoAL8IvAMoAA9cxT0vi8iuwMXbhcnvA+PQ7Y2MGwEuBftH2uyPnjMmIrFgTje1h+62nLFIKNNbBK8eVO53Ij0t2zQTD6vzr4QuF6A6oSvhyqVx07i8saAkegR1g6bhCUgOCW66qY3dNMR71Mxrr0/jm0aUpRrQmDPhSqaUVI/QeMYIxcKLlWwOJBRehGpccVSaoqwOfl7gKDgTin9Q0AFF9grOso9/tEWUTfiI58NpwekO3LDnHLOtAs9hxFrWN5/8/hjOYs1Yi4ddnLmamcghB2ar4GCW8DiL8HjQhDTvwlwNxqowk4aeOFxuIGmBfS4iwX0dF2iglBHSge3mGYWbB+51oabw1ZoRjIr5kERp+7eJKNJ9Pf8mslgsFotlY7Ys8lR1CUBE8sCnr+Gefw38JPA7wb+fiuz/mIj8LmbhxSjwNVX1RKQkIncDjwE/AfxBy7UeBd4KfOFm5+OtccYKrDQkj+XVhCuzsBg6Y8DSJCQK4MTB84J6ZG7gjLWEK705QE0Ysz7VDF2mPGg8a1y+VB7qs4ozLjCtFF4C2azRHzFCpykUIUoOn4z4UNMgdAnOpOLvB+3TIFyp+EfNOW5RkaSAOnCmgZPyoexDzYHnBKdgrsuE4AwDkz7sdnGKHqSaztgqUXalAjMlqDeM2n22biZciMMpzwi5QgyeacBCGV6SRuJxhCAm7biAZ4SduCANVMqIn4ScwPfGoO6bcw8m0AGQEaC8jPSkIA5ytgwDKTP355bNvcOY93PLMJiETAzKnlHjaQcdScJ8AxqhsBNWCbxQ8yUcONSSeGexWCwWSwfZzuran8U4d+EaSRNP2yD+JCIPYhZZ9IvIGPCbGHH3cRH5aeAC8CMAqnpSRD4OfBsTBv65YGUtwL+mWULls8F/AB8GPhos0pjFrM69qUSdsdRepTFr9En6kFJ/ykdOA1cgXTCiLEzy954xZlXhxUq2K8j3UlkbrvTFFL/1Fb+haJcg3yM4l338I445Nq/4BwhcM3ArICkxwuyymk8wCNdSBpnxieV9/G/4OMNqRNkuF6fkQcaHpGNezwae3SHXhC4937hkT83DxTJcSUJPAp71AN+4ZFMekgL2xRDHM8JL0ibuGxVlfg3NLiMv64bLZTiaNPNbKMNtSUBgvgwH4zAGMiKBKAuOPbccbANnQ4GmkHGNABMfuhwYysJCwwjGbhf2paDYMD/NR7pg0TMfxGgWinVIxyApMBocA3htHxzLgyNIfwL9d6fhQg28QDyHf2aEAi/mw74E8sPDHf3Zs1gsFoslynbCtb8MHFPVNYsa1kNV11t9+/p1xr8PeF+b/Y8Dt7fZXyEQiTuHiDOmih9G/2qK36XoSwUZAmcB/KMOK0n+R81KCHdBzaKIBtBoRMKVRFwyTOgy50NWYcyEGp1SzVh5hx2cK4FASzmw24VZH22ADCWMVA5JKlxeQM9X4EIFxhtGlO2PI2G4UoNwpQQiJ3TNfB9yCq8UtB9krwPLoSiLwXy56ZKdKUN3ILxQONMiyvqT0J+A6QrsTZsQqGBE2aJn3v9QFooeFGPmfQ11NY+NRraPdBnhlnIhJvDq3hVRRto1Tpyvq19D++1247piZh/ADw0jHx9Hl+pmidAizUzVGObziYMcTsF94Roji8VisVg6z3ZE3nPAcqcm8rwh6ow94eOkA1Ez4eCM+PCUBwMOHHFxZuusJPnPNVZyy7jSgJkFqHng1+FUw4QHC3F4tmHO6XbhmRrMV+FYBknEkagIk5Z8MvFABKEHoW9lulL1YaiO7E2htQUkGw9EWeCGeQqORpwyWRu6jGGcsbwLe5OrRVkp2D7aBQueEV5JZ60oW/RgJAV3dMO9vVAN1PFWhddG46KiDCDXUkMm+nq97XavwTyTD9yOvPub8HQJesW4hygsB27ni/PwgdubotZisVgslhvAdkTee4F/FJHHgGq4U1V//rrP6lYm6oyNV2CuboRMTxwm67BUQWJpxImvdcbC3DKvjmaXkDsKQegyQeiMGZcMs30gEYQuA1E2kApWkEYEGhGBlnTgctUIoK7go0+7yEASpqvI4YxxwJRmuDITirJsM6zZLnQJV++MtXPJEpEVqlsVXhuN6yS35+HBV8InxuGjF+Fc8LfQgQy8cy+8dY8VeBaLxWK54WxH5H0Q+ALwLUxOnqUNrc4YozFMPtky0hWHCUUEOL0Mg5Ek/9AlO70MAwnoi8NUBfZmjKsVdcbCMOZCA/IxI5KGEsGxIJ9sPhBoKQcGA4HmK+xKmfEhjsDxHiOyks7Vi7IoV+OM3UhR1gkGkvCvD5n/LBaLxWLZAWxH5DVU9Zc6NpPnC+s5Y0M544yF+WRHu4JQpqwOXR4NQ5dpeHkB7u27+tDlZvlkIWFIE144osxisVgsluc52xF5Xwy6Rnya1eHam1qXbsdxrc5YJ0KX7V5bLBaLxWJ5XiNbLSsnImcjL1dOUtVbLj4lItPA+Q7fpp827dUsV419np3DPtvOYZ9tZ7HPtzPY59o5rvbZ7lfVge2etB0n7z3A36hqUUT+L+BO4Le2e8OdwNU8qO0iIo+r6vFO3+eFgn2encM+285hn21nsc+3M9jn2jlu9LPdTgzv/wwE3quBf4IpTvxHHZmVxWKxWCwWi+Wa2I7IC7tP3A/8sap+CtOgymKxWCwWi8Wyw9iOyBsXkQ8CPwp8RkSS2zx/RyEifyoiUyLy9BbGvkZETohIQ0Te2nLsJ0XkVPDfT0YOfei6T/qFjX2encM+285hn21nsc+3M9jn2jlu6LPdzsKLDPBG4FuqekpEdgEvVdWHOznBTiEir8E0ofqIqq5pmdYy9gCQx7R2+2tV/USwvxd4HDiOWYzyBPAKVZ3r3MwtFovFYrFYNmfLCy9UdRn4ZOT1BDDRiUndCFT17wPxtoKIHAb+MzCAaeH2M6r6XVU9FxxvLQL9BuBvwzIyIvK3GCH8YEcnb7FYLBaLxbIJ21ld+0LgQ8C/CpzKVwH/BfiBDcbvAS5GXo8F+ywWi8VisVhuKlbkBYhIFvhe4H+IrHSE2KzhqLTZt7X4t8VisVgsFksHsSKviQPMq+od2zhnDHhd5PUI8KXrOCeLxWKxWCyWq+KWXR17vVHVInBWRH4EQAwv2+S0zwH3iUiPiPQA9wX7LBaLxWKxWG4qL1iRJyIPAo8Ct4nImIj8NPAO4KdF5BvASeDNwdhXisgY8CPAB0XkJKz07f0t4OvBfw/YXr4Wi8VisVh2AlsuoXIjEJGUqlZu9jwsFovFYrFYbnV2msg7DVwG/gH4e+ARVV24ubOyWCwWi8ViufXYUSIPQET2Ad8H3Av8ENtfDLEp/f39euDAget5ScuNoqFwftmsa3bbLG721Kxv3p+BWLvFz9cXvwEzZ6FeBhHWJkAoqA/xNPQdBMcudbJYLBbLNnniiSeuqOrAds/bUb9yRGQEI+6+D3gZJi/uK9f7PgcOHODxxx+/3pe13Aj+20V4cAxG0qBqRF9MAoUVMFaGt4/AO/Ze0618H6pLZjueNkKudfubn4Qv/Ecoz0MiA+KsratTXYJML7z+V+D4O65pShaLxWJ5ASIi56/mvB0l8oALmAUMv62q/+pmT8bSYXyFsgdJx/zrKzgCade8hrXbn7oEeRdqHszV0aqPH3eQwSS+b+SV05PA/6tJqm/ai+O0F2i+z7rHANwknHsExk6ACqRzUFk0ujKVNduicOIvYGHcuHVezVwjnl4t9OIpqCzAyYesyLNYLBbLjWOnibyXA68G3i4ivwacAr6sqh++udOyrIuvsNTYmkCLCrmkA4/OQrEOl6v4c3Ua5yqwJ43T5+LPmPNWbfe6yDdK4AKZGMSE5WQKXfIozyoNz0grN+YQu1Lly78J3SOQygeiTIxAW16AhTHo3muOVYtGyEXFWyxtxtQrkB+CqVOQHTTHpk9DbgB8YOkK+B4rqs5vYA5EwrbigleGss0utVgsFssNZEeJPFX9hog8BzyHCdn+OPAaYFORJyIu8Dgwrqpv6uhEXwhsRbz5CidL8NgsnC/DSAoKMfyih1cD6XHx5zzQpljTsTKyJ4WTd/An6uhQEplYZmEqRuU7HpUzHkmvQiWRRBHS9SWqwXaqukR3SaDs4+XBcZSa28DJu1SWBDdptFZ11sdPxKlXoFGNiDIxYi1dMI5dvQKly5AdMOdFxdvCOPTsh4tPgDcAg6NNJ2/giNl2FLr6TahWg67GTow1eXnqgRsz97VYLBaL5Uaxo0SeiDyOaSX2j5hcvNeo6lbj0L8AfAfId2h6z1/ahU0D8eafK9MYSuH0xfFnGkDEXav7OK7i+w4sNCCvMLHEwnKSekVoXFqinEgiCqnaElUnjox7sMcnIXWWshnij1fwelLUpn2SFRffcZn2EmTwjPCiy2wrTEsX7kCe/NlZUGEpnsQdSFCuCom0GEcNyDk1Lh0bIZ6CWDIiysSIteWFIKyagtxg08mLirf+Q8a5GzwKe+6EZFf7sG5X/+Y5efWKyck7dv8N+TQtFovF0mmmp+Fzn4OHHoKFBSgU4P774Q1vgIFtr4/oGDtK5AH/VFWnt3tSsGDjfuB9wC9d91ntJLaTx9buWFfM7Itcz//aHI2xKs5MBX+2AXXFcZVGA8pnPCozPo0ri5Rjq901z3FJOh71qqKXYui0QKGLxrRHJg/zThcZ9fBpijUTa3VY0CTSlWKpL8ul7l767/H59pNK/0EhPeRSmvVQINnrUprzUDXbZy8OcujBb+HkXJzuOLGMkEoYQaY+UKrj1B3S7xvkYO/6eXeb5eTF0+BVzb9O4MzFcs3HFm4f+yF48uNQnjOh2lhi9cfl1c28uvfAi+67Lj8BFovFYrmZPP00/MZvQLUKfX0wMgKVCjz4IHzyk/DAA3D77Td7lsDOLKFyP3AMSIX7VPWBTc75BPB+IAf8crtwrYi8C3gXwL59+15x/vxVLVS5NsIQKGw9dy16bCWPrQFTFZirw1gF9qbxcy6NK2EeWwx/uo5eqpjQaH/gwjlC7DU9OHf3rgg9v9Rg6k+usFSPE//yJGUnDo6QEJ9aRfHGaiRuS1Esx0k7DQRYqrtkEh6CMJnvpnEgz9w49OwTlmsuA7s8ipOQ3uXilYwjl+h1qc54LIwrhT1Css+ltuDjxVzS3UJuCJLZjV2zcFufLpL47e/geD7ancDJOEjNh5maeUYPvBhuv3GG7qWn4ZPvhomnwXUhnjH7a8smX2/XMfgXvw+7d8b/8xaLxWK5Wqan4Wd/FpJJyLf5PVMsGvH3wQ9eV0dPRJ5Q1ePbPW9HOXki8sdABvh+4E+AtwJf2+ScNwFTqvqEiLxuvXGq+iHgQwDHjx/vrLJtddvAbD8yA08umHhe1oWSyVcj78Ki2fa7XBpXGuh4BdmdwhmIhElzYvLYhlO4l5bwE3FYaODnPJZPVLhSSiGqJGqLVDWOM+Ghe3wSukg1nkQTDgUqDL7Uw8mZj76Oy5IkSGuVSTJktIH6wvl8H94deZamwU0Juf0uxVIbd60/hrcoOEtAHvq7ITvk0H8H7L8HvKqxwYxAc1octOaxrbhmK9v35OFP74CHp+ChSZioQiFuyqbcNwgDyY59tO3YfTv81IPw1Cfg6x+F2XNmf+8BeOU74Y63mrw/i8Visdwi+D4sBTW00mkoByGgv/or49p1d0O9DrHY6hJe+TyMjcHDD8M7bn45hR3l5InIN1X1eyL/ZoFPquq6gS4ReT/wTqCBcf/ywTk/vt45x48f147VyWsX/lTMQoNzFah4MJjEKdbwu1OA4ixU8QtJVIXSdyrMzcWRsTL+rjRJqVNLJPFVSHlVFrsyJEtV6EvQKHrIWAV/d5pGyiXV8GgoLNdipKWOM17B35OirHEy8QaxmODf3cOBn+slmQucPB8ufE1ZGvdYmHaozhjnLd4fo7osqMLel8OBe40Qg83Dn62CzWKxWCyWq8b3jchKJs2/sFp4OU7z9VaPbTTO9+HkSfja18DzjHArFo14+9KXzDxCYTc0BIODq4VeuQyNBnzsY9ftETwvnDwg7Fu7LCK7gRng4EYnqOp7gfcCBE7eL28k8DqNv+Qx9WiNpXqC+JdnKTtxRCHpVVjKpEk8M4+/LwF9XfjPeogK0t2FPuOhPtTjXcTcBoKL7ziUNEsGEya9ol042STL/TnGu3rJHvGZ61d6Dwm+4+J4Hm4c4j0uxSse8xNK9x4h0Wty3HBg/2tixLuaP4yOA/vuEurlGG5ytbsWCrlkVyDYIvlm6zltYP6wsVgsFsvzhNDV2q5o6uoy+6MCbbvXSCbh0UeNyJqaMv+qQi7XFF4jI0aIlUpGbGWzZjFEeKxQgMXF5nmlktmOirfwGsWiEWi+b+bi+3DqlHHulpfNc/A8yGRMfs7yshkfjzefVzIJMzM35rPZhJ326/jTItIN/AfgBCaY+f/c3Cltj3bhTx+4ohk0nWLiQBb/Zd0sVWIUjprwZ3HBJX/UIxaDcsMl5nkU9yndu4RE/+qFB9V5H024DOaF5QUXpxskB4deBcPHnFWh0Hah0RXBFsFxzH7YWLxZLBaLZQfRLqS43vbVOl6hq/XVr8KFC+1FU3R7fh4uXYJ9++BVrzLXnZmByUmYm1stqBYXm6IsKrxCgbZ3r5nH1BTs3g3nzkFvrxkXFV6Virn+4KCZ+6lTZo6VismPO3WqmR/Xul0omGtExyWTZl+tZoTc6KgRf5mMEa6hk+f7Zl+rs1GtmuvuAHaMyBMRB/g7VZ0H/qeI/H9ASlW3XEJWVb8EfKkzM9wa8S7BvbuHuXGPSsFhIQx/9rrU5nzmplx6eoXBPJRLDqIwOAyVRYeGwOhdMHTMqLB2uWutTtvKHzst4m2Nu2YFm8VisWyP1jDh1Yb/rmbcZtdIJuGRR+Cpp4zoyWZXi6ZicfV2q7iKOlkXLzbFW1RslUrGpfI8I3Y2E03PPtsUXp5nRFkqZcTXo4+a+YfXOH1642uUy2bs7Czs32+E48iIOVd1tfBKpeDo0aaTNzpqhGIqZZ7T6GjzPY+ONt9j9BrhuGLRzON1r4Njx9aGcT/+cfjLv4ThYTOuNScPjKh9+9uv9afvurBjRJ6q+iLyn4B7gtdVoHpzZ7V9Ng5/trprRI6Z7XZO24aLECwWi+X5xHoJ79c772qzca1hwtnZptAIBVAohqJOVtSFaj0WbkeFVzguFGXRa7QTZeF2Mgnj40b0DA01RZPIatF0+vRqV6tVXBUK5n23irdnnzXiLCpw1xNN4fbRo8bJC0OZBw40nbyDB42TF17jyJGmKI0Kr6NHzftPp83Y4WEzj9tug3vuMfO8ET8bXS2/jHPBL9y3vAU++1kzbr3Vtckk3LczambttIUX/zfwTczCiY5NrKMLLywWi2UnslHyenT7RrhQmyW8P/bY2ryr3buNwxMVTa3HojlZG+VdtY5rl8eVyawOE3Z1mXDlvn1mXCiGpqeNCFE124XC6nHRY9FzWse1Hrt40QjA9e41P2/OfeIJOHTIhDE74eSJmLDrYMg4IgAAIABJREFUsWNmDjc6Jy+dNsIuvYNW87XWyUsmzfbMjNnuQJ28q114sdNEXgnowqyUrWCKjaiqXteiZ1bkWSyWjrBTXKjWY0tLcOKE+eXemry+kbsUPdYqDFqT1dsJilA0tAqP9cRbvd5MeK/VNhdN0WPz882crO0IqtD9mppaLa6Wl5thwsFB8wx3kpOXzxsHL5uFO+80gqpTorzV1bKYn52HH17b8eK++zrS8eJ5IfI2Q0SOqerJa72OFXkWywuIGyW8Niq7EHWaWoXSei7UZqsEW3OwNhIGMzNw5owRAxcuNJPXNxND67lQ3d1w/vz2XajNhFc8bp6l65pcp5vp5PX0mPefzzfDhDspJ28nOlyWjvFCEXknVPXOa72OFXkWyw5iK+UZrlf4b73Q1XqCKuqubCa8ajVzj1TKOFKbOU2buVAicPmyES9R0bRR+G89cdXfb1YfplKrk9d3mpMnAnfd1T7h/Wa4oVZEWXYIz5c6eZvR2vvdYtkRTM/C//gb+Min4cI4ILB/N7zzn8OPvAEGem/2DLdJJ4VX67GTJ02Ce6vTsp6g2MzJioa/Gg0zNgz/bSUJPTwWJp6Hx9YbFy27EO6LxVav3Eskmqv/Wlf1RY9Fk9BFmkno7RLeWxPXo+PCY9HE+JERePnL4d571yavd+Jzvd4J7+1eb/XY1Y6zRT8ttzjWybNYrpGnT8G7fxuePm2iTJmg6/JyxUTtjo3C778Xbh+9DjfbrC5Wp4VXKKjCWljrhb/WS+puzU8Kc7AcB557bv3w34ULJj8qdLyiTlY0n6pd+K9SMdePxTrr5O00F8rmVlkszxteKE6exbKjmJ6F9/xHeOaMT3+ijCaTpDzzCzWRSeNWy4w94/Ob73f4499JM5C+hl/QYV2sJ5/cfAXdRmGyzXK8wrpYjtMsNHr5cvsCotFjImvdr/XKM4TbiUTzvYf1rgYHN65jtZGTFZZdGBxs1rt6zWtunPDaSS7URscsFssLgltN5NVu9gQstyjXoaipv1SmXIFkIU15zoz7zFfSzFxY4lVzJ+iNLdJXv0yuUURRlmJ5svUifaUxigsjPPtAnoFD6zhe7UKN4bhQ1KTTZkylYmpHbVQLKzzWWjNro0rwYegyfEZh656o8AoFVVgLq/VYu5BkOr1+bS2A17528/IMt5Lwslgslh3Cjhd5IvIiVf0ugKrefbPnY7mJXGX/RD+epPzFR2GxSHJuitrUHFwaQ/bsJd6fpz5bREVIFLLU5hYRUeK9eepXiuj4Rdg9Qry/wPnvLFKuKrNk8ecXUZQLz+Z5w9QM+ypnOJm/k2HvApV0DwgcWDrFYrxA1llmoVzhylcvw/4tVHiPumbRnLGxMROKPHHCvLdQRIkYV6tYXJ27FRVl4biNKsGHzpjvN90v6HyosV0I0Qovi8ViuWZ2vMgDHgb23exJWDbhOhRa9Rs+5ZqzyilL9wTbvk/6zEnqX3kMvXgedo+Q6C9Qn1tEVUn05ajPLqKiRqzNFpHxMXTPCOOlNFPfnWYpv5u+0nkWJYMzVkYnK+S8y/z/7L17kCTXdZ/53cysysp693NmunsGA2B6ABkQJUAwwIdE6hUgRdJebyylWAPLDXkdIhWxDq3WkmNtRSy0hiO0ofXKDjlsrUCZloIKgdJKopchgSYhrbSi+BBBgE9AAKaHBAbTPa9+1bOrsiozz/5xs7qyq6t7pmemp6tn7hcx0Vl1Tp57Mqum7i/PvXmz4k6BglLrLFVvCgTK/gJ1u4RabBHN+eQ7Cyw705TyQrSkhZcITKwusMQkTTvPjP8WS5k5irRRCG/m5sl3a/hWlg0yfMebBntgcv2wFd57Q4098dYTYffe21/5fad1sQ6T8DIizGAwGPaVkRB5Sql/t5MJKN/KXO5IdhNoQ8RA5Ho0VxMibFUvtGptNHCrV+iu1rTwGi/QqdRRoitjndUqalELr/RELNASomz524vUynOsUSKs1FAoVKmAVBpYYRc3HdHqWjjntPAqBgtUMz2BprclFmsNu4RaahMs+Tj+Oq2jd+GtX+ANNUtBbaCUh6gMF51pJlQNUCykTjFBA5RwxpmnTE374bKcnedYpsF6XUjfewq70UAhrE7ME600WErP8XrpIb499S7KaX3nYtv2yIRavHZCi4ce9uBnbnBy/eCSDkZ4GQwGg2EHRkLkAf8I+AWGP6v2H97iXA4n17rg62CFJoqIXngRf2mZ1MpFOuu1TVHWXanBhcV4uDIe1hTF4kaBpe/o6pIqFZDVVVLnvkvz/oeZbJynnipvCq9KrzLWPkPdKaMW28hFn0K4VZRVVQnOt/HmfaLlM8iEnrwvry2gpqfpptLU11pk3Iiu8hDlcik1z4RqAMKZdH97IXUqFmgZrJRLvXgEP3+E9en7qH3fO1iq+ly8FHHsqEV2zONSpYUCMiWPy9UWAmTLHpfWW5t++XGPbL5FIQePPebhV/X5PP8Fj9/47RaVOqh8jrRtsUF68/RuWAWaXb327E/8KDcuvMySDgaDwWC4Rkalx/gq8LKIfGnQoJT63259OreYaxRo2ypo67pKZFngfecVOl96IRZoebor8RIXSYGmbDLvfjvW2x/dFHpRs8WZl1ZZDUt4f/Vlqk6pP1xplbCWNghnfYrRAhV3Sj9xqLaAPT5NGArhawtE45N07DyZ5bd4IzNHWfmAcCY1zwR1XRlLnaZMFaUyiOVy0doqyiasGraXYWXDxbvnNKoRV/Lun0cqDWwgeOd7eGX8AS5eQQu0sseVWJR5pf52ptQXaDMzFo884vHAnI+V8/CyFq12un96M9BqaxE1bDvp53cKeBn9Op3Vfu//Mfi//7zA0uvgRpC2t360vRVCZqfh8Xfu43fIYDAYDIYBRkXkfQj9rNptiMjdtziXGyYKIlrrLdyCu21uWRREWI61fa7Zl76ChPHcss0K2gypybKeaybCYqvA0tk6iEIV84TVKtbiIhw7ipeCDZXBCkJK/gIVu0RqcYNotk0hiuedpVMcS13k9Pe2sAr6wdEt5bHqTFDqLvNa6iRlqQ0MV2bBcjljzTOhatgpxdqReTJ+A9sW7PvnUdUG9fE51h58iMbb3sXFur8pvLZVxi72K2hJUXa52kKFEW/7Hot3vKNfKds8T4A3kaPVsfYs0HJZsKz+V72Q3/p5JV/vtA3Di2hT4/Crv9hfJ6+9wzp5v/oLh3BBZIPBYDAcakZC5InI2uB7SqlpEblyEPncCFEQ8bd/8CLNt5Zpv3WRaF0vp2GVikTr8c0Ac3NY5SJSraPCDpkUbIiHijqUW2eo22WspQ26cx3K4QJVd5owEjLVBayJKaIQotd7S2G0CEKLlWZENucT2mkt0KRCV2URK8NFS887cyTionOM48ojF+frZS1Sjz3C4sUWlaMuF9YTomytxaXLEUePWGTH9bCmreDhhz0emG1podUTrxFYhdxmpQyuXhkbJtC0IOtXygAKxxLb/ZFQ/foaBdp+8uA8fPLX4I+eh9/9NLwZP/Hi5Kx+4sWHHjcCz2AwGAy3npF44oVSarALVMBLwEPoHLeJwBthP5940Vxu8re//XnS4wVWfv+zWOMlIgG1vIwUy1hL54hmT0C1gjU9TWCnaFdb5HM2oe2wGhR0BW1xEZmdoaLKTDoNLEtoOgXcVh3H2VrJk+N3Ufs7j/HWxAMoZZEZ82iut7h8qS/QNmKB9sijOR79fmtwWp5e/y2t/0JPeDEgyrStJ8QMBoPBYDDsP4f9iRcrwLmB92aBrwEC3HPLM7pOvDEPb3aC5lvLOPMndfUOwbpvnmi9BpksystgHTuNVOvYSgjf+R5eHn8AsSy8RAXtyFGL3JjHlfUWytqhgtYb/o2HMmH3qtkwgdYb0oTdq2G3sjp22FimzR9ynk/wBm+hh5fvIsuHOclPcpwpMgecocFgMBjuNEalkveLwI8D/0xEvh2/98Z+zcfb72fX7mlOHgwRaKaCdph4mQo/z9d5mSo2iiz67osNIkIiHqDEr/MQD95mqwEtN+Bzr8Nzr0K1DaUMfOB74L33wZS5IDAYDIabxvVW8kZC5AEopeaAfwucB34Z+KaI7EsFb79FnuHOYZk2P81X+CYVXGzSbFXhXSLahHwfY/wOj942Fb2XL8FTnwU/hIksZBxoB7C6Aa4NT78PHjx60FkaDAbD7cH1iryRqQuJyKKI/CTwl8CfAdkDTslguCqf4xJL6HX2HNQ2ewoLC4slWjzP5Vuf4D6w3NACz3VgrgReSj+cw0vp166j7cuNg87UYDAY7mxGZU7eJiLyJ0qpPwfuPehcDLcHEUKTAAAPmxYhEYKF2nydtO3F709Y4hJtQoQOXTxsPLYulpfBokqX57jAk9y19/wjaAXgWvrv5lC+o19Df3s3283y+8yr0Ozq4dluCI6lRV6PYgYWq/D8GXjy4T0frsFgMBhuEiMj8pRS96NvtviKiDSAl+P33ycinz3Q5Aw3TBRBs7N3geI6wkqgX4w7NmtBSBAJjqU2Xydtg36WBd91qnwxWEOAMcdmNeiyFPnMWRnGHSfeTxhzHNaDAOjFDliM2sxaLhNOivUgRAb8vuVUaQRgR4q0ZdNxBCewsQDlhEhgIxH4Vpd1p0N9j4LKteDL56HWhst1qLRhsQLHy1pM1dpaYOXTUPO1ba4MJRfqHX2bet7t+xXSUB3wa3T03U35tN7u7VNpwYWars4VM1D3te2T34Aw0rZ8Go4UYDq3VehNZPVcPSPyDAaD4eAYCZGnlPo54H8EXgU+rpT6n0Tk07H5V4DbQuT1hA7sb6VlN1suvf3mjcFK0Y3kMUyURRG8ccXmC+dCzq/DbBkmXJu1TqgfTpy2WfNDlmLhMZGxWW2HiIKau8FrfgtBKKWh4sP52G/MhWpHAULZhYqvt4ux31JFcawsuG5A1CkRIXRcH9XOslhRzI0J4nZItbOgFN20T6qTAxG6mQ7Kz7K4XmKuLEimQ9rPIbDFbz0zQ9hW+JUMnXKbjBvS7aQRATsTELYdutUMqtxiJZ3htxIiqu6DiBZQ1R3Em2vDchNmivDmuv78WgG0Q7i8AlPxgodnV3VlrR2AH8BCXd/8oICFnp/o7ZI34Ffox+jdMLGwogXgRhfaXbjciGMoWGnCeBaCSOe/0YEgC6mEyHMdWK3t8T+IwWAwGG4qIyHygJ8BfkBEGkqpk8AfKaVOisivw5CJTiPOMNHkWvDFc/D1C8M7+YavqymFuOqyWNUde9lL2NJbqzODwqBXaRlWham1wbbg7Sfg0RN9oRdF8MJ5YbEZslqzWPNDRLTQWmmHLFVhpgRTsfBCaYG26sdVs7TeXqxo8VZ3N3i104JYlFU7EAQWLgpfhZwPHGaDkKiewi10AaG9msLOBCwFtratOKRzAaEIzRWbck4RKnhtRShnwQ8U7UB4rQ7TBQGB15ZlU6y8vqooZYR2AFEAK400hXyELQpWCojbRQW2Hmus5yEXoBCi1TwqHyCiYCUPmQAV2Eg3hEYBcl2UIvbrImIxtTKF79VoBTbZroPUs1AIsCSis5LHzgQEXYt8N810bZIwznFhuS+ozqxAOTNcvF2owV3jcKGqP8uNrhbWGRumJ/ti8NSEruRlHC2w5if735X5hN/8pK7kJf16lbxTE/1K3vyk/g5lU5BJwXSh//2azOlKXm+YNpsGZ+B/qR9o0WkwGAyGg2NURJ4dD9EiIm8qpX4YLfTu4pCJvCiCF8/r6svFmu54RfSk9MWqrqAczeuOfTrPZnVlS4cfV1A6gbZN53QnvLDSr7osLOtOtBUI1TDk4rJFOa+F19KKTdYNqXYh34WLDZtyLiStYKnm0AoUufjJEc1A+PJGja7n81fnLCyvi4iiu+Kg3IClrmK2K0jDIZULQKC7kiKd64ICfyWFne2yGNgcCwLadZtyQRGK8NqKFgd2KmIlCPBwwOmCo1CTdei4CGBNNMB3N21MNqCdJqUUE5M+gW9jo7h/UlfoXEfIOHBsMq7kKeH+KbVZybtvQqjEQiblwLsmMhQ7WS1QJ22W2yFLDsylYDKvxatSUJ6wWe/oczgxabPihyw6MJuCqUJf2I5N2FQ6IQK8ZzLit9pNlhwhSkWk8k2k4xAB6ckG3bYNKZuxlM2P5XLQE1FTfZF/elIL9mHi7e5xPRx63yS84wT40cHPyRvPwrPfgJmCznFwTh7ou2yfeOjG/z8ZDAaD4foZFZF3SSn1/SLyDYC4ovdB4D8B33uwqe2NVryMRMmDL5/TfxFYqsKJMry4JJRyIacmrU3RcO9kX0CcmtQVNDsF4sC9CXFxz6QWIQLcO2VzpR2y6rTp2iHhVJszvqBQFCeFtRYspWA5BeUCvN5WWCKMFT1cp8SmdnZCVLaDNNMwVkP8FBBtCi1SoFIghfi1UjBZB18LNDVZR/w04giOAxOTPt2Oja20KKt2AFG88x4hN13lIsk5bv7mHLe1oMVS5DNrZZhwHNYCHwt4lzPOPUEJC3Vdc/IcSzHpOPiBPl4tVqwB8WJtse3V74eCaf6X6Ju8atWwnQgv0Aq65XRxAsWDUZH/w/o+HnHS1yW8/Ej/tSwYeKobBWf49m62G/V7//fAp1/RFyzFIdW6WlsPMz9+ervNYDAcXpbX4HNfgOc+D9U6lArwgXfDe3/QPLpxVBmJdfLiNfICEbk0xPYuEflivD0mIus32t5+rpO30/DnlGdTyAcsuXVmZnyq6RarQZfeJP/epH59I0DAUtTimOUx6TjbbArFmGNzKfB5M2rzkFXmstMgG6RRKCpOGy9IcT5qc9zyaDkdSoFLGptTaY/3WcfIxfo+QnghWmMp8Fm2tubUy2PW8rbcoDA+JKfFqM2cleGHnIltogxgMm3jW7vfrTrMlsPBOgTF3GXa/BGL/C5v8iZNAE6S48PcxYduwydeDK6T5zp6iNask2e4ETrLsPI5WH4Ogio4JZj6AEy+F9JTB53dnc3LC/DUvwO/CxNlyKSh3YHVCrgpePrn9HO8DfvDoV8M+VpQSn1NRG74fr19FXm7iKblqMN5p8Ej1jjnaDJGGhCW6TCFC8AV2pRJ8xYbnMCjSsA0LoKwjL8pFpbxmSDFZXw8bGbxaBOiUORxqNDlAhvMkaVIihoBNvB2JnmUiS3CKUJoEeJi7XkJkUG/wyLKDDfOckMvkzL4xIvHT5snXhj2Tv1lOPsUiA+pCbAyELWhuwrKhVNPQ+HBg87yzmR5DT76y/r55sUh/7drDfA78My/NBW9/eJOEXlfF5EbnumznyKvScDnuUKBFJ/lIiVS9ITcJGku0Y5FWYY2EYKiiEODAEEokKJGl0U2mCFLmdQWW53uppCr00VQPMwY72ISnwi4PSpjBoPhzqGzDK98FGwXnOJ2e1CD0IcHnjEVvWslivQjMd30zo/K3O0xmsntT/0ZPPsZmJmK5+E62yfLL16GJz4IT37wlh3iHcX1irxRmZN3rYy8IvWwmcBlmTYnyVGjg2AxT54GAXN4PMT4UFGW3L7WChr0hVs6sQhvYeBhJoOvDYY7CYkgbIHl6r9EgAW2p18rwPK226KW/tG5Xr+o1e9ArT3YrrWtoKk7Yq8EzXUIA7AdyI3p19Df3s12s/32GqP+X6BTg/QxUCEEHVC2FhNBCGSguw5XPgul/1rHuFaBcjP9Dktbbhq+/A2oNeHyKlRqWoTNHYViDhob+jtUzOkq3PlL2lbKQT1hqzfjtTE/E6+NeQXyWTgyAdMTW4XeRBme+/+MyBs1DpvIG3ksFI8wvuPwJ1xdlO0m0HbzM9w4STEQtfTra+nIZQfRsNeOXFmJmEPauppfUgwMCgA42I78oNrKlmD5ryBoAGvQWoXOIqRnITMOfhUQyIzFtiVw5yAzAa01bfPGoV2J/ca3xvAmoL0OKMiU431UvM8K+BcgNQPZSe0ncbzWCnQGbMn4SsXbKzqn9FzcVpxTegy++7qeF1ULdX7tRcjMgVuGblUff7oEnQq0FuPjKuvXqNgW+6VK4FfAXwRvDtIJv1RpIN46tJYSfskY69Be0nlkErZkW+mi3m4tgjsLk1/Tnx1v6P97gQ1RCqxxvcpAcwPyDiw9A28GIAoKOajVtXiZOQrlfF+U5LNbxUrPrydy6htb/ZRAPg/VBiwOCB4F5HNQacCFSzB3BIoF3VYvfqMR55SFalPHOH5UD20mbT1xlc9qAZb0q8W5J2PMHYljbGxvKxkj6adEi7wr6zAzDW8uQc6Ddht8H86uwNSE/g4tvKlvnmj52raQsJ15Uws5gJV1GC9DGMZrY7YhCPTqBT3cNKze8Ix5w83msIm8QzHO2JubBodDlA1WOXYSKElBsW37JoiLgxYNSTGgKtC6Av5S3AlP9TtXd0wLg15n3V5J+E3qznawI3dntGhoxwKg15H3xEBPNLizkJnUQkEpcONOU3bzi3Nqr8e2OS0AvvOanidTk7izRne07V4nPKuFTbKDTnbk7XV9XIMd+TZbSVdhkO1+W8RFz4+BDn8X0ZCM4c3q49omPOJ224MCpRcjB+EKdMqQq0FbaT/3ImRCaHv6HGba0LL1cbmXIBvARiYWWy1oJf2cfgwvhHYm/oxaOp4IZH0dr7MIzhzkAtiI77bP+tCytHhL2nrx27Gf14aNZE4htFzdVqoJDSfu3C+AVQDaeh5b/TVwp/X/0drrkCqCtEFaULukbQDV1yATb9df0zc6SFv/DtQugzulz/VO8cI4Xnpa/27UXwOnHOfRguolyBzRx1J7re9Xe11/fhLnG64BJe3XqQLTQBdqVUhldDUvSOvPMRT9e9MTKBs+dPx4WapYlCyc688NS/q1/XgdygE/pfp+PTG0kPRLxujA5Te3xp+e0J/5wrm+aOq1NT0R53uuH+/sEL+pcX2uezHabf3/9+y5raKs19bZHfwUsHQF7pqFC8swexRaLchkwHXh1HhfNM6f1JU8z9W2+fG+OD59si+aJ8d0Jc+247UxM7rKmsTv6HwMo8VIiTyl1O+KyId3ee/HDiCtPRMGWjhkCqMnZIpT+r0eEkHlBagvQbQyvNKQngFvMlHVSFQTMuPQXtWdUHoG3AldXfB9qEbbhcFO4sLfwdbr5DerDoMViWRloDY8njt2bWIgFYuBoAz5GrR6YmBOd+S9zjXT6nfCvY6855cJtBhgoJNPJ2zJjlwEsm3YcGKBdkm3teHqY97S1tX8esLjErg+VFNQykNzCexJ/WNdfQ1ScScctrd38m6iQ+51+EEL2glhUBsUA0NEw17ERfU1/dkOtiVxWz0xELZ1THdqeL6p0nDhUfkuuCfArsAFBXkLukpXQ1ctKKM7rsUUFIhtClZTMKZ0HouphF8aCrI1xjC/8ykoihaVGfTrTZsDRYbbFtN624pjDOY0rnTBtpqFKRcqTcjeDWEDyOgbFgr36++8Aor36f83KgPKg+Kx/v+p0v3973/hfl3JUxl9seYl/LbFWx/wS8ZYj/PwoJSwFRNtFe/T2yrO1x6PK3mW/i4Ega7kFUu6kufY4ETgToKtQKxYoNQh60LahdMTfVEyf1dfrCT9Mi5Mj/crdD0/Jdqv2uiLofmk30ldycu6+q7S6ZP9St78yX51bf4uXYXzem1NxDZL23ri6tRdugqX9Kslcq824zzS2jcpynpt9WIM+imBe07o47zvJLzj+7UAu5Gh4fHS1efkrVb0nDzDaDFSIg94IPlCKWUDP9B7LSJrtzyjPRIG8JU/gPW3oPqW/jGEa6hWDKt+9ATPkKGVdBHa1YRYGaimDKvCKAX3vA/e+WRf6AVNeOUlqIbQ/CstIgYrDelZ8CItKJTS1YWe4PGS4mIO0j7U0noopXEBUpNayOwoLuIqgV3StsjXNrd3xf/aVmHQEwObVYKe8Hg99hsSr/7aVoGyRQxc1hO5lYLadyF1HJx1WLK0GPDjX7KVROe6NNiRR1v9hnXyqK22Xkfe8yugY6g4Rk80DLa1m19SDFRzCQFwD4he1YXS/fGwYAbsDOQGOuhuokNuxx2540F2oLNuJzv5o/3vXk80DBUXR/tCPCkuSvf3RUOyLRW31YthZ4YLDyTOaVCgxPHL90HThc4xyD8I9RpcuKiH+Apjulqk0IKitq6H5GaOQXEMKpWELelX6ccoxjEk4SdAsQz1ON6xnl8lYVuDC5e32ra0JfH7a1tzqsaV3cIYFPJwLAfveAxa1dGulF9tTt7FZ/WcPMcdMidPoHMRZp6EHzdz8q7Jz+/ov5YF6YEFNgv54ds72d7/Hvj0X+iq405317opePyd222Gg2Uk7q5VSv0L4JcAD9jovQ10gI+JyL+4me3t5921tWX44m9Ddhxe+31Ij2uR01nWVQ0/Fk3dar/i0bnSFyHtK1qE9MRVUOtXLtpX+kMffuznL2kx2K32Y/jL+g61ZIz0lL5zrXwv/Mg/0RU90FeFf/17uhL07b+BnOgTvx7ElYZ4jk8NGHN0vpUQxm0teCpBLGTieUwtG6Yy+srZG++Li0NRyStrMRBmwHkb1KuwdKnfkTfjzjVXgmbckRfijnzTrwyNAduF2FaIbQodI+nXExezsfCoV/p+zWv0q/eExzEojcORnJ5n1BMAMDrz5G51WxtVXVnKZm/fjvwwY+6uHX0G18lz0/r7Z9bJuzUc+iVUlFIW8B9F5H/Y437HgU8AR9G642PxM293ZD9F3mGr5EURfPVbsHIRLla1KNkUKHGlYeYIFMb7giJf7guefCwueiKnNA5H8nqi8l7FxSiIhr2IgeT2KIqG20UAGO4Mtq2T5+pKvFknb3RYXoPnv6Tvot184sUP6wqeWR9vfzn0Ig82D+IHru65ZZ9jwDER+ZpSqgC8BPwDEfnbnfbZT5EHh2tOHmxfTwluTHgYcWEwGK6HzjKsPA8riSdeTH4AJh83FTzDnc3tIvL+A/A7IvLVG4jxaeDfi8if7eSz3yLPYDAYDAaD4WZxuyyG/CMxvHRhAAAgAElEQVTAR5VS54AmenRQRORt17KzUuok8BDwlSG2jwAfAThx4sRNStdgMBgMBoNhNBk1kfcT17ujUioP/DHw8yJSG7SLyMeAj4Gu5F13hgaDwWAwGAyHgJESeSJyDkApNY1ePuqaUEql0ALv90TkU/uUnsFgMBgMBsOhYaSmxiul/r5SagF4A/gr4E3gv1xlHwV8HHhVRP7NvidpMBgMBoPBcAgYqUoe8K+AtwN/LiIPKaV+BPiHV9nnXcCHgW8rpb4Rv/dLIvKZfczTYDjULHfgc2vw3BpUAyg58IFxeO84TKWvvr/BYDAYRp9RE3ldEVlVSllKKUtE/lIp9au77SAiX+CQPNPWYBgFXm7CU2+ALzDhwJwL7Qg+uQyfWoGn74YHcwedpcFgMBhulJEargUq8Q0Unwd+Tyn160BwwDkZDLcNyx0t8FxLizsvfuC4Z+vXrqXty52DztRgMBgMN8qoVfL+K6AN/M/Ak0AJePpAM7rDiQRaEbhK/wXwrO3bkegHqg+zXavfzYhh2to9xmdWoRnp4dluBE78rNseRQcWfXh+HZ48cvXvh8FgMBhGl5ESeSL6SadKqSLwJwecznWzmzA66A4/Z+v3riXfZgjffmOZ7J9/jtLzz6GqVTqFEhd//AO8/u73sjE+RcGGWgjnfZhLa/HQiPTzegs21OPtog3VUAuILX5A3oJaAIsdXU0qxTEVOkYy3m5t7RSvaEMj1GImb+k5aIsdOB7baglbI4wfIL9LW/lETsP86gO2Rb+fRz3RVj3RVu/cXC2noX7onJJtlQba6vn9/jKEAhd8vc+RNEyntgq9CQeeWzUiz2AwGA47IyXylFIfRVfuWujn0Cp0/3bPQea1FyKBF+t6uOtiR3euu3X4vc57UMgs+jCThvIQ0aQYLlaSnXolgAsJkVMLwVbw9iI8WuwLvd3yVS+/zMP/51NMRD6rxQnsY3PYfpsj//mTzH7mU3zxF57m1bsfpJTS4tAXWGj1J+4vtGDK1dtnWvpY2hH4UcJPwdkNLY56tjMdmO7FaGsRInG8Hdtqw1RqeLyzvs5DifYr2TpGW+Bybz/0fr18F9o6xmZbiTzObsBUZge/1kAMGzZ6bfXylUS+vXOTunpOV/MrpRLnt6PbUgNtrXRh3NFCT4CNEIIUpBLfYdeCVTNcazAYDIeeUZuT94vAAyJyUkTuEZG7ReTQCDzQHfBqV3f8b7YgRKvVhbbufHsd/pkBWyD69ZmWtm9E0Ik76DC2LcT7BPH7vvQ7/IW2toXxdkf6bS20dTuW0kKuV63bLd8LS8u8+98+Ba7L5ek53KwHShFlPNTsHJHr8oO/9hTfu7FMJq4WugrmPT0EaKO3bfSX7LSnxUPG0n+TfqeyW22nPb2PDcxntDjtxduxrczO8U5l9fu20n5uHCMTv+611fOz4nhb2krksaufN2CzINtrK7Y5vXjJc3MNOV3Nb/D8Jo+55zcZC1Vb6YuFrL39Ss+PtDg1GAwGw+FmpCp5wHeAjYNO4kbwLJiwI9aqLU6mXDYaLURgPuex0Wwx3orIhxbTOY9Wo4Wgbe2m9jud8MsFFtP5vm0+3kcB81mPjUaLsfbWeEppW7PRYjxh22i0sBQcy+XwLOuq+T7ypc+QajVJTZc4qrocyTqE8U3MjoLAK6KWFrnnW8+z8d8+edvNXbtd25pIwbPLukqs2D4nD2A1gCemd/yKGwwGg+GQoERG5wlfSqmHgN9GP3vW770vIj93M9t55JFH5MUXX7yZIftEEdFXX8S/vEzq8kU6lRoKIVUu0l2rweIiHJ8jVSrSqdW1rVSkW2sgIqSLBbrVGpxfhLkZUuNlupXYVirofZQiVczTWavChUXU3HFS5SKdSmwr5+muVWDxgm5rrEi3UkOUTeZdb8d67FHoCb0d8nX/8A+Iggg6PnYhjzpyBKantyqCVguCAJ59dn/OpeGms9yBj57R1b7ikEu8WqArec+cNuvlGQwGw6iglHpJRB7Z636jNlz7DPAXwN8ALyX+HR5aLay1VbzxEs65N8kS4hHhLCzghT5eZwOv28Y5e6ZvO7uAJwFZQpyzZ/C6be0XdPR+KiSrQpzvLOh9JMA5u0A29Mn6rTjeAlkV4qlQ7xN0+m0tLOARkU1bWJcuanF2lXytlRUcC5wwQInAxoYWdElcF6rVW3t+DTfEVFqvg+dHet5nK4xvvInngfqRthuBZzAYDIefURuuDUTknx50EjeE58HEBCwvw8mTUKvpOxnm5/V2NguZDJw+DfV639Zo6O3Tp/t+6fRW2/y83kcpvV2t6vYyGV1lS9oqlX5b09M6ZhTBsWN6n6vlOzmp/R1Hx8xm9XYS34dS6VaeXcNN4MGcrtQ9v67vol3t6Dl4T0zD42NG4BkMBsPtwqiJvL9USn0EvXxKcrh27eBS2iOWBY88oqtlrtuvmnme3o4i7dN7nbTdbL9htlyuP1S7W76Tk3oYdmZGi7ye2EuyugpPPHFzz5/hljCV1kukmGVSDAaD4fZl1ObkvZF4uZnYzb7Ddl/n5N0uLC/DRz+qhV+xuN1eq+lK3jPPwNTUrc/PYDAYDIY7hOudkzdqIu+ngM+KSE0p9b8CDwP/SkS+dpPbWQbO3cyYQ5gEVva5jX0lC5lZmFWgQggiEAuUDY6ALMHShn5Cya3g0J/PEcac2/3DnNv9xZzf/cGc1/3jes/tXSKy54rKqIm8b4nI25RSPwj8CvBrwC+JyGMHnNqeUUq9eD2q2zAccz73D3Nu9w9zbvcXc373B3Ne949bfW5H7e7aMP77AeA3ReTTgJkGbjAYDAaDwbBHRk3kLSmlngF+CviMUspln3JUSv0npdQVpdTL1+D7bqXU15RSgVLqQwO2zyqlKkqpP92PPA0Gg8FgMBiuh1ETeT8FfA54n4hUgHHgn+1TW78DvO8afd8CfhoYturvvwY+POT9j11XVoadMOdz/zDndv8w53Z/Med3fzDndf+4ped2pObk3WqUUieBPxWRB+PX9wL/AZhCP17tZ0TktYT/78T+fzQQ54eBXxSRD96SxA0Gg8FgMBiuwqitk3fQfAz4WRFZUEo9BvwG8KMHnJPBYDAYDAbDnjEiL0YplQfeCfyh6i/66x5cRgaDwWAwGAzXjxF5fSygIiLff9CJGAwGg8FgMNwoo3bjxYEhIjXgDaXUTwIozfcdcFoGg8FgMBgM18Ude+OFUuqTwA+jV5++DPwy8BfA/wUcA1LA74vI00qpvwv8Z2AM/YSHSyLyQBznr4H7gTywCvxjEfncrT0ag8FgMBgMhq3csSLPYDAYDAaD4XZmpIZrlVKZg87BYDAYDAaD4XZgpCp5Sqmz6KHTvwY+D3xRRKo3u53JyUk5efLkzQ5rMNwZRAE0zgEKlL3dLiEgkL8LLHNvl8FgMNwoL7300oqITO11v5H6BRaRU0qpE8APAR8EfkMpddPveD158iQvvvjizQxpMNxeSATdpv6rLHA8CFra9t1PwdlnITsDSsVCTm3dv7kIp56A00/e8tQNBoPhdkMpde569hspkaeUmgPehRZ53we8AnzhQJMy7A89EQF9ATFMUAzbvhl+pq3dY6y9Ape/DI1FyM1BugjdOqDg7CeBEJoXIJWH7BHwptki9NwJeOs5I/IMBoPhABkpkYd+RuxXgV8RkZ896GQM+4REcPkFLSIQcPLQqenqT34OUiXo1gAFqQJ0G9ovVYj9zsfCo7TV1m2AGhavGPspLUo6VW3LHY/FS01XpJx8Il5xl7byEDRAdvAL6gO2IUIplY+3Y79uNRZUV8lpmN9mvJq2bZ7D+lbb5vmsJsTbkOMKAyAELAg2IGxD9TJ4U1rHtVfAHQcJ9H7Bhh7CtVL9z9h2obl6q75RBoPBYBjCSN14ATwEfAJ4Qin1ZaXUJ5RS//igkzIMIBF06vpfFOi/fnXr6522/SpsXIHmkp7PFYVQOQuRD0EbQh+qZ3QbEkJ1Qf+VECoLWnAELe1XWQCCvh/B8HiVs/0Y1QX9XtCKxcuCbqu3n4T6dXWgrS15nNX+w/wqC1ttUbsvlCpxDIJEvEgfb3AtOe3g18spTB7zwtZj3vRb6PtFA+ewd1yOC6L0e04W7AyU5uP5dw5kJrWYVvEwrZPdPvcu9MEt3epvpsFgMBgSjFQlT0S+qZT6DvAd9JDtfwe8G/j4gSa2VyTSHbHtjt7QXSqn37vefHtDeVe+ElerrlJdS1a1BitIUQiWDeVTOoaT0TmUTvcrT6X5frzyfOznab/yfL961/OzZHu88ql+Ja80ryt5jheLl+l+1WzTL46XbGtLHqf6lbxBv/J8/5g3bbFQKk/3q2ul+X4lr3Ran5ur5rSDHz2/5DHPD7TV85uPY2TAGjiHm8cVwcy7YfwB/V0Y/A5lxq8+J89f1XPyDAaDwXBgjJTIU0q9iH5e7JfQc/HeLSLXNdnwwJAIrrwIrWXYuKg7112H7iQxnJgUTYuQm4F0eYho2mnYMTE816noOVObbdV0JebI2+HIo32ht6d8a7oiJ6I7fenoSpBb2lrJyk2BoLez8c1A1TPglnUFCdHDfSf/nhadoyKATVu7XwykC/rvXe+HNz+tq4np4vb/A52aFpDHH99uMxgMBsMtY6REHvATIrJ80EncEEEL2qta+Fz6cn/IqrqgBdvmHKdL8WT12JaNhVH1TMKvo23eNCB90aR68XriKjFnCpWwDcynslLQvKj3SeWuId9ejHi40pvSVaIwHhK0nN2ra1uqWqf7FSRlQ34WstN9IdETED2Sr3favhl+pq3dbcPwpuDRp+GFp/RFgDsRfy98XcGzXG339ny3v8FgMBhuIiMl8kRkWSn1AeABIJN4/+mDy2qPOB5kJnRlrHiyXxkbHLornU4M1yWHHU8n/NLbhyt7lbyhw46J4blOJdFWPKwnEeSO6X32lK+bGPIDjsVDeYPDuHupIA2rFBkOD+MPwnuegfPP67tom/GFwqkndAXPCDyDwWA4cEZK5CmlfhPIAj8C/EfgQ8ALB5rUXlEWTD9yeObkXU++gzGut4JkONx4U3qJFLNMisFgMIwkIyXygHeKyNuUUt8SkX+plPo14FO77aCUOo6+I/coEAEfE5FfvwW57pKU1R8OHcWhu5uRr8FgMBgMhpFm1EReO/67oZSaAVaBu6+yTwD8goh8TSlVAF5SSv2ZiPztfiZqMBgMBoPBMMqMmsj7E6VUGfjXwNfQtyL81m47iMhF4GK8XVdKvQrMAkbkGQwGg8FguGMZGZGnlLKA/1dEKsAfK6X+FMiISHUPMU6iF1T+yhDbR4CPAJw4ceJmpGwwGAwGg8EwsozM7Y0iEgG/lnjt71Hg5YE/Bn5eRGpD4n9MRB4RkUempsydfwaDwWAwGG5vRkbkxTyvlPpvlFLq6q59lFIptMD7PRHZ9UYNg8FgMBgMhjuBkRmujfmnQA4IlFJt9LK/IiJDltXXxILw48CrIvJvbk2aBoPBYDDcYSzX4XMvw3PfgmoLSh584G3w3gdhyqy+MIqMVCVPRAoiYolIWkSK8etNgaeUemDIbu8CPgz8qFLqG/G/99+ypA0Gg8FguN15eQk++gn45AuQsmFuTP/95Av6/ZeXDjpDwxBGrZJ3NX4XeDj5hoh8gW1PRzcYDAbDYWO5A59bg+fWoBpAyYEPjMN7x2EqfdDZ3cEs1+Gp/wdcZ2vFzkvDXBpqLW1/5r83Fb0R47CJPCPmDAaD4Tbk5SY89Qb4AhMOzLnQjuCTy/CpFXj6bngwd9BZXgNRBE1fb3spaHV33o4isKy9226239VifOab0Ozo4dluCI4FyanzRQ8W1+H5V+DJt9/c82m4IQ6byJODTsBgMIwwUaQ7Jte+PTrXO6StZSfFUwvgWjCVtiCMIALPtpizImqB8NSC4pn7YCoY4eNybfjid+Hr5/QzxosuNHzdcxUy0Gjr7XwGahuwWIHjZS2Sar5+fnnBg2psmxvTtkZrIJ5A3uvHSPopBXm3Hy/v6Urb4jrMlaGY3Rqv1obz6zpGyYN6wlZv63iffAFCgQsVHftIEaYLW4XeRE7P1TMib6Q4bCLPcJi52hXuKP1Y3+ZtLUchf25F/Kln0Wp1KKH4MS/De1vCFOpwHlfTh6+9pTvSK3WotHXtf7NzvVrHmNGTyRfXYa4EpWzcUcYx6km/RIyS149f8HTHKPQ70MV1mBmDcia29TpXHxbXttp68etx7nkXqu1+B13y+qIhnxnIvZeT7sijRpuWsnHzDq1Gl2hxHWt2DK+QorURgICXS9Ha6OrtbIpWwydaqmDNjeHle36Cl033/XrxluJ4+RStRgCqF6NNtFSN20rTanZAKbys0/fLpWg1OjrGsTE+MztLMztFuesTOYowEByJUCkbuiHFhs9iucjzr63w5MXFWDRdwznc/Bxiv95nvnl+dzvX2cRn7urv04V1mBuPBZDfj98TXpk0LK1BK4CjRVi4osWQAAuX46FMgbOXdPxWB9oBXL7SH+ZcuKw/53YH/A6crcJUUe+XjNeLMeinYr9evLOXEvEC/ToZr+TpPPwuLNT6OZ6J20JgpQHjWQhDfZwbHQgiPSevh5uC1QqG0eKwibzOQSdwTexWTTjojjHn6vdudb6uDV/8Dnzj/MBV7eCV5mDnOnAlXNuA84Od61XiFXPaNvhDfnwMipmtV7yNgQ568wo3s71z3eysW1tzqg9cJS+u79y5CERFl2a9E3euZbx8Ou4Mh3SuQ/0GOujZEl4hQ6vZjW1bO9fv1tu8sLTK6myJdxXSZDa6dAUuZFP8wUaHx5XDPVmXVq1LdHEda3Zct90M+vE2bWM6j2RbzWCrGNgUF4ljSYqLXFI0JNraSVD0BEo2RWsjISDW2kRvrmA9dBzvzRVaE7oz9BYu0zpSBhTe2Uu0xgpE3QirG+rXR8b6fmO52AbemSu0jpa3xtj0y/djJONvboO3cEX7+RFWEPXj7Wbbkm+cw3gvJ9H7HS1r4bUt9/ymn3v2El+++wQ1y+HKRkilOM75U0eYc6FU36AxVkBEKFQaNMqTCEK+0qBWHmPRO8rxNBQbPT/IV+o0xiYQFMVKnVppnMXMEeZiv9p4AZWMkZ1hLi2U6hvUxyb1V75Sj/2gUKlTK46xmDnCTBr+MDNFZDvULIu03yXrWGSikKlqE5V2IIiY6Pg8F+V4Moxi0RQLlI0OdLpwptYXJZuCasCv3YXLNZjKA6rvp3p+mb4YSoqyLTE6cLm6NX5PeC2uw4lxfbERFWB+uv+7MX+k//ty6qj+LfPSkHFgerr/OzR/RAv2TBrcNJwq9X9vNuNJP8agn4r9evFOHdW/UZm0nlN36ujWeLW2zsNNwXyx//t1erovmifzupJn2/p1Nq2HbJP4XX2ODCOFEhmdEVClVEpEugPvTYrIys1s55FHHpEXX3zxZobsE0Xw4jk9UfViVf8n3Nbh73QlyPCr06TIqQ+pOhyPS/DJK9LBq86aD7aCt98Lj57sC71d801e/WY2hVdUcGkmKwENn2ixMrwjb3ZAFJ4LrUt1onaANV3Eu1Ltd2SX13UHtRhXENYa/c7wcqXfqV2uar/z61jHy3jrzUSMys7x1uvaphTepUoixhjeemPrfr3O+vIQv808Krpz7bW7Y7xhtlI//pEyEfBKS/E3M0d4y2ezc62VC5sdY6Oc153rep1qMcviNr+4cy1kWeyofudaLuivynp9M16qUudLhQztjqIQ+1XLeQDKlTrL5QKC8IOVFq18lgsdNjvyeiKnyjCbgvxanfpYvi8G8lnOd+iLizi+Fhfx9nqDWnxcyXgCmzEW4xjFhEDJxzF6x1gt5VkMLI4HPsV8ilpkoRQUUopGV7Q+dxS1ThSfJyimFY1A9HGlFFVfdFvpiJJrU+vIZox6N+nXj1Fy+/F7fiI6dq0jLPowk4aye3VbL/7Wtno5CSXX2nIsW3OPNj9/z7VZjixmOj7n8jmy7Q5v+YoTrlD1XKZbbUSEZc9jqq0vjpY9j1KrzVsdixOZiGrGY7rV6vu1WoBiOetRbrU517E4kY6oei5TG22gH+N81+Z4KqTqZfR+SrHsZWI/WM5mKLV83ZYT8fGjx5nq+iiBjmNz3G/TVYqjBKQ6ATR8opzLhWyW5868NLqVvGJWD2XmXXj4uL6ovh1GAD71Ejz7VZgp6fMxOCcP9Hl74jEzXLtPKKVeEpFH9rzfKIg8pdSPoO+cdYGvAx8RkTdj29dE5OFddt8z+yrymj58/oz+Afrsy/qHArSIKntwbk1f6VVaMB1fTS7X+1eMVxra763Yr9rqXyVu+sXbpQG/3hXplR1srgPHx+HxB/SPz9Xy7cW4S+cbTRVoWjbfbiu+cnxmsxIwvCPXV+ubnXVtg+pUicW1NnNZRSnjDHS8wzvXfEqx3hUEoZxSVDoRS77iWFoYdy1qXSECxlIW1W5EhFByLNY70aYIKaUtqoGgUJRTsOpHXOwoZl1hLG2x3gFURMnRfohiLA3ribYmXItKFwSh5CiqQQRibfMbdy0q3UEbzLpQTltUuoIFlFIW692IAAvLdYh8nzc6Dsddoem5jG34CMKa5zHeaqFQrHkuuZbPYkcN9cu3Wix1LWZTQtPLMB53rmtehvG4E/62l6HRahF2LXKOUM+6TMSd/KrnMdlqsaEg42V5oNnhrcDiRFqoemndQStY9jKUmn5s63fySsHlTIbphBgotdpbxMVUqy8oeuLiiucx1vZj0ZBoi1gMtNu85dvclY6oeJltwkMpxRUvFg3pDCemXKqz40zFfdCyWEwpXf1ZxqIkEee7WihXsZgi0sclFiUiznfgeAoqymJaRf0YSb8o4nyQiJGIP5XYpyQRb3XgRDqOl4yxi21KRSjgsliUezk5ULV0TjIsp95xpaBpWZy0Qy50FNNZi1Y74nxHmEsrSp5Fox3FOsmi4evtfNqi5kcsdoTjaUUx4ZdPWzQ6eruYsai1tN9c7Fdr63zzaSsW0cJcSlHKWNT92Jbp+xVcvb3YEWbSij+sOUSRkFKQthVZhIwlTGUsVBiBCC1RBJbFsycGpnyMkhjyUuCH+u/giMlhZrmul0lxHS2SB6m1dPXT3F27bxx2kfdV4KdF5BWl1IeA/x34sIj8jVLq6yLy0M1sb78redGLb+Iv10ldrNCpbaDEIlV08WttmhcrMDtBMZ+i2uyiRFHMO9Q2uohAKZei1ugSLa1iHZugWExR2wgQEUq5FNVmB6UUxWyaat0nWlrHnhujmHcTNodarUt0cRUrbqvWCBAbJv7u3TiP3b2lkhd89U0qK03yF9eoNnyUWDqnXh6zE+TzKf469FhJpThXLJLyu3QE1j2PfLvNeV9XEJpehvF2CwTWPI+xnkDJZshlU5zPuMzN5NhQNmUVgSjWUeQQzneE4ynFhlLaBrwqFg0VAEJLbFISstSF2RR0lY2nIkBoi01G6c5gA5s0IUsdi5lURFfZZFUIKNpiYUvIhS7MpCBUNq4KUQIbaD8BfLG3+WXitjbEJkuIKO3nxDnNpCBQNl7CZkvIxQ4cS8e2RB4ZFWIpwLLodkMudi1m00LJs6j3OuG0Rb3T7xirbX1cQ/38SOeRhpKrO2wL3bn2/L7pgvghfscmmwopZhxyvq4GWWmLqBPRBchYfKhlcyHRkdfb/c660or6tmRH7urtpBjYFBeZvqBIiotkh59saydBMSg8ejlVWxGLXcXxnE0xZVEL4xF/GxrxCF/eglpIv2roaJuKbdWAzaphyWZLjHrSLxGj5PTjFyyoh3GBx+63NZOGsqNjiOxs68Wvh3EBaUhOjThG3h7IvZeTq+MdSenje0dR37EaCVgKPAta+r/X0O2b4bfXGJ9ahmeXYTatBxxC9FyiZLFo0YcnpuHJI9f1y2y4UV5e0suk+IG+ycJN6SHa1aYWf0//A3hw9qCzvG25XpE3KnPy0iLyCoCI/JFS6lXgU0qpf84hu6M2shQv/MBxLjV9LmUU1WZH//hnU7y6EvK3TUVR2bpjizvGQkZ3XBFQjDu8pfgKt3fVvWmLO+6Ca1FJdPjljEU10alX4054NtFpWkrxExMp/pFSmx98oBSfuO8452e7fPd7T1FJ5FRr6Txm04psxmJ9I2IiLfjKptONUErndKYdsdRRLKcjShmb7/j9fBf8hEDpCOcjhyuuYswRzkQWoChaQiWwWFLCsqsYs4XXQ4sIRdoWVOQQiELZEWGYIrAUYRqUExFFDiIKbCGKLASFZUWEQZpQCZHrYNkRYZhGKQELosAisBSRK2BDFOqhB8vSftB/P7AUUVrAQccXpeNFad3T2hAm/JQjW2ybMVxB2bIljzC0iZRQKEQ8kHOYBI4raFlwOtKfUNWKuC/erlgRd0cO4zLc757Ijm2KDSvidGSjUFQsif2Eb1oBY5HNhkBROZQtOBrZpNAjMEFkEyFcseBno/RN7chvpWgwbWlh51n69eAScwXr6ts3w+9aY7x/Aj69qo+h6Gxfpb8WgKvg8TEMB8WDs7pS9/wr+i7a1Yoe7XniMT06ZCp4I8moVPJeBD4oIpcS780BfwrcKyI39duzn5W8uggfr4f4Hfh8J0KFCkRoK+h2YE3gWDwEOZ0GQbHcFaZTChCudKCUUiy1hRlXUQ+FqYRt2gVE71Ow4YKvmMtANRCmUgA9m+KCL8xmFLVAmEwpPEu411X84rjDtK0vka+Ewm9WQiZt+L3ViHIaRBQrXaHo6DxmXUUjimP5wkRJ+Dvx3fNVK8KLFOcFTijYsIRSZAFCdXNbC5RcZHFehONb/HSMbBzjuFI0rYhyZOEghBZEkcJGGLdgLYJFEWYVTFiK9Ughsa23PRb7LQnMKmIbKBRjlrAaCUsCc0ptxgQYi/2AXdu6mt/agG1JhNlEW708en6TNtSVsAgcR1EEasQVGqBBXBkCquzsVwMWgTmgBNTRbeWRTb/fJyRE6KDIA8e3IO0AAB65SURBVEewmEZtWYCyhRCgeJbMDf9/MBiulcF18lwL/AhWY4F3aNbJMxj2gcNeyfvnwBFgU+SJyKJS6j3APzmwrK6HCKSrUE4EVVBpQVCkukLJhdWGwnGE+z2ohlo03J+BaqTngt3vCZVQcC1FRgkzGahFgsS2aqRA6X3WAv1DmFHCsQxUQoVSwn0ZWAt0DG/TJgSimE9rsdFj3IK5lOJcJ+KUp1gP9byz++J9XEuRsYSZNNhp4YgnfG9JaFs696MoahbUEYpo8dGwtCg5iqJuyeZ21RJKMOAnHIljaJtwHEXNEmwUj2LxgGVhofDQ1asI2fIaGLq9m9/NiHFT22LAj622a/W7WowJujxLyAwKha7oDkyfZhV4AhuD4VbyYA6eOQ3Pr8Nzq7Da0cPTT0zrCp554oXBsHdGopI3DKXUtIhc2Y/Y+1nJiwReqOubCFY7wlq8rNCUA6U0eJbwUEExabOt4pPcDkRwBipNe/UbZpu2Fc7AXVGB6KpSUcmuMSrx8E9O7V1cXK9AyaGwtskQw/WyjPBR2rhAcch5rSH4wDNk9Hp5BoPBYDhwDvuNF+ODbwEvAQ+hc1y7me3t640XaKHXivQQQ3LOTHKOjMFwULxMyFP/f3tnHiXZVd/3z+/Vq726q/fRTM9oNJJmkEdsWpDYAsQYRKIcIDZwAhzAJ8FgOwHn5DiKiW2BhR1sTuIlwSRg4LAcB4wBB3MkjFgdzDZIAqMZRKZHy2hWTe/dtS/vlz/uq+7qnuqZ7lZ31+vW73NOnXrv3vvu/b1f1Ux9+3d/9z5qVIFB3JL2Ki6ClwTuIsHTLZJnGIYRGbb7dO0EcHJZ2SjwAG7m7+ott+hJ4Alkw9/I9uTiqDjbeGrzdGJ8iBT30uBumkyi5BHeQIyX41sEzzAMY4cQFd1xB/ALwH9U1QcBRORRVT3QXbMMY2cyjPBG4ryReLdNMQzDMDaJ5SvVu4Kq/lfgrcCdIvLHIhLuDGwYhmEYhmGsh0iIPHCraVX1tcA3ga8CmS6bZBiGYRiGsW2JynTtAqr6JRH5GnBNt20xDMMwDMPYrkQmkici14nIS0Ukp6plVT0alr+i27YZhmEYhmFsNyIh8kTkncAXgXcAR0XkVW3V/2UV139MRC6IyNHNstEwDMMwDGM7EQmRB/wKcJOqvhp4CfC7IvIbYd1q9nP4OGARP8MwDMMwjJCo5OTFVLUAoKqPichLgM+JyH5WIfJU9f+KyFWbaqFhGIZhGMY2IiqRvPMi8uzWSSj4/gUwBDxjIwYQkbeJyH0ict/4+PhGdGkYhmEYhhFZoiLy3gycby9Q1Yaqvhl4UatMRPrXO4CqflhVb1bVm4eHh9dvqWEYhmEYxjYgEiIv3CPv/Ap132k7/foWmWQYhmEYhrGtiYTIWwP2UE3DMAzDMIxVEJWFF6ul46POROTTuFW5QyJyGni3qn50Kw0zDMMwDOMpQnkcTn0FTt4NtVlI5GH/7bDvNkhHJyVsu4m8jqjq67ttg2EYhmEYTwGmjsKROyGoQnIQsnuhWYETn4ZHvgC33AUDT++2lYBN1xqGYRiGYayO8rgTeLGkE3d+GkTce3avKz9yp2sXASIl8kTkU5cpe+kWmmMYhmEYxlMRDaA2715Bw71XZ+HR/+Oidn4agjoXZZElel2E79S9XTF7OVGbrr2+/UREYsBNrXNVndpyiwzDMAzD2Dw0gHrRHftpaJRdmXiL5+11q2233j40gKljcOEIaBPivVCbg+JpOPst8JNQnXZ9ZHZBeoQlE43JQXj8bjj0xk102uqIhMgTkXcB/xlIi8hcqxioAR/ummGGYRiGsdPRwImbWHL9gqpTu3jWlbf3vRpBVZ+FwmnI7nORsfocIBDvgXoBUHfcEl7ZvWG7+bBdrq2PvW5RRL0AouD3hO10qXhb6GPORe40gHgaggBmxyDRB40SNIqhnRmQmCsLGuDFF/0ZS0JxcvM/t1UQCZGnqu8TkT8CPqKq/7rb9hiGYRhGpGlFv55sxKtehPEHnAgqP+FEjoYCqDEPGoqm2qwTQ7m9EM8vFVvtAqo2A8WzkLsSdt3qxqxOQvE81KbbhFcvNArQDAWVn3Lvs8dDQVV206KzTyyuVp09Duldbtx24dWswOx5F1ETwro8NCrQrLrzzDAEwEx4THu70mK79LCL1NVL0KiBF4P8QScG/Qz4WVePhHZnwFsmpZpVSOY39eNfLZEQeQCqGojIs7pth2EYhmFsCZeKoF1uGnLqGFz4Psw/vlQ0tURZfS5cEJBbjFbl9obirbAo5CqTMPcIDN/o+kqFD5aaPb4ommbGINUmmopjkB126WjtAmpBoJVctGv+MSfe0iNw7nuh/RWXszZzwl3nJ6Fedv16PuQPuSicn4ZYCvIji5G8/KFFcdkuvGKpsC6M5OUPhn2knG/zB911nkLfwcVI3pI+Wu3m3H3teQkMXH9x1PHhz8KjfwOZK5yfPJ+L1oRWJ+HaN2ziF2f1REbkhXxfRJ6jqj/stiGGY5pxjvAVvsvdFJglR57nczu3cBv9RGcvIMMwjFXRKf+r/Xgzcrw6tasXYSKMoJUuuAiYSDidOHtxxKslyuoFCGpuDC92sWhCFiNSiitfItBOLI1kpYcgkXMCL7cXgoobK39oMZLXd9BF8lqiqe/gYvSuXUDlD7n78MOpzJ6rFiN5vQdcJM9PgZeEvmvD+wpgz4s6C6pu5eSBm2qWtrWpiR73fuDV8PiXXbtE78Xfr9qcu799L1/Dl3LzENWO+wt3BRH5KXAIOAkUcfJYVfWZGznOzTffrPfdd99GdrkjeYSj/AV3UqdKnkESpKhRYZZJ4iT5Fe7iaqKxF5BhGBEnCsn10Jb/pWHEqy2Pa/mU5JLpynAackn+V5vwWpiuDPtYyCdbNq1JOG51EuYeheGbYP4kJMMIWmncTfUVTkHPPqjMLoqy8riLisV8N50oHpTOP7lIXsv2wRtg9wucEFzP59Cp3Wpy8uBiQRV1lu+TF0s6v1UnncDbhH3yROR+Vb15zddFTOTt71Suqic3chwTeZdnmnHez9tJkCTLxX+tFJmjRpU7+JBF9AwjamyWoFptuxWT638Qiov2pPk9boqvoxi6TLvlde2irFNyfSufKx5uf1EaD/O9BMoXXH5W4RTk9rntMjLDLtRQGg/rHne5ZrXZxTyxlvBC2wTa49BzpeujVVced/1p2F960OXAxdKQHYVmefWRPBEYucVFv2BjPtftJrS6TXncbZPy+N3uc07m4crbXQRvE554sV6RF6np2paYE5ERINVlc7aMgIAy7j/kJGmqlAkI8PAWztvrVtuuU12aLN6y7REDAqqUiZNcaPc97qFCkSx56tTx8ZG2vIMsvRQ5zRHu5Ta6v0zcMCLJk5kaXG87WGH7h1NLVxp2ii7V55atamxFqHJt7VZYkYgs6yMcqzHvkutpOkGjjWUJ77XFvC6lQ3L9snYt0XRR3fGliffpDsn1nudWSzZqbjVk/iBL8rhaU5J+EtIHF0Vj/uDiNORCnlhbXlfLTwvTmmnwWu0KHdqFU5XZURi6sXMEbT2irDWleKnjy9UZqyM97LZIicA2KZciUiJPRF4J/DdgD3AB2A88xLL986JOJ9G0kvAKCHiMYxzjByhKlh7mmWOc04ywhxx9FCggKBl6KOH+Q8qQo8gsFzjNCPvI0kuJeRQhR44CM1zgLMPspYdeiswhxLie53I9tywIvYCAh7iPGcaZ4hwF5gDl6/wVTQImOEuaHAPsop+RJUIvzyDf5W4TeUY0uJygaj/eiryrWBLOfQcmfsSCOGrML0ZkLhuFutyqxtyy6bkw+pPZ5cTSku0f8uFqxepSoTTTlkA/c8JFIxpll5c1+4Rrp9qW70WHVY1tqx+X53+1VjIuJNdXwEssS3hPLBVAKybXt7XrWHdoqSjrlFwvMdjzwovzv7b6u9FJrMUSi99lE2XGBhEpkQe8F3gu8DVVvUFE/imwrZ5Lu5JoytK7IN4Whdc8DWooTgQ2qfE4x8nRR5USdWqcYow+RlCUU4wtTI2eZowseWqUqVFhmifow007nGKMHPmwjwqneIJ+hokRZ5JzVCmTxuVKVCkzxyQ58hzle+TIu8VUTNDLAE0aKEqFEk0a+CzuBZQgyQzR2Auo29TG4fxfw9lPQuVxV5baD3veBFe8FhJPlRntbuVdtQTV5I+W5iS17621EJGa6zAVtpKgautjiQhbxR5csTQUz7hpuPSuxUgTrDIKJa482edEU6MKpXaBdmJxOrF99aPEXLTsou0f0ktXGqJLE+j7rnX3uzwKtVC30qrGttWPC320Jeg35qOVXL9SQv3y4/XWrbedYWwCURN5dVWdFBFPRDxV/Wa4f962YSXRdIoxsgvircLjnKefEZKkqFCmTpUYPldyiHnmSJIhToJ9HFyI5O3j4EIkby8HKTJLgjQJUvQzshDJc9fMhH2k2McIReYICBhkN0nSC/YmSdPLIDOMs4erFkRpH0M0CYiF07QpMsSWfV1qVMnRpb2AxifgK9+Cu78Os3OQ74XbXwq3vQSGh7bUlPmjcOzXYe7HUKtAvel+d/0JmDsGZz8D138QeqK4RmUjVxquuEv85aYJV9rUdKWpwQ6RrFjSia6g4rY2WD5dt7BHVlukqX1FYqdtIkptoqwVkVq+B1dQXYyGLd+Dq3gacvth4n5IDbVFmlYbhQq3jFg+hbgg0K5dvP++ti0jxO8crepmTh5cWlwtP99s4WUYTxGitvDia8CrgfcBQ7gp2+eo6vM3cpzNXHix1kieIBzmVg5w/UXTuN3Myft7vsBX+d8MsgdBLsrJA7jAaV7GG7Z+uvboz+DO90O1BoP9kEpCpQqT05BMwF13wNOv2xJTauPww1+EmSMB9YaiXgAS5kWphwQevi8M3Opx8+fbInqRWmnYSoZfNv3XcTXhJeqCMO/KT0Oz7hKTlyertyeet6YJyxcg0Q+Fk52T2pP5cHuH/W7c9iT31pYR1Sl37fgDkL/arXjrdiQv0QeZEdd+6EYncrYiJ69TtMowjG3NTlldmwUquL+X3wjkgb9U1Q2dE9zs1bVrycmDzsKr20R2de34BLz9DifmensABa8OgQ9eA+bmoRzAB94NgwObllujtTLNCpz8VJqfvXseqkpTlKCZIhZ37Zr1NJ5fwQsESQuH39vDgX+zQsSr02N8WtsftCe8r/i4nzbxsiBQ5pfWLQiesI90uJlnPO2mDFui6VKrCS9V5yWcj8Rz04RbGclL9DrxF8+5TV07CarVfv4bKbyaVfdugsswjCfBTlldWwQQkV7gS102Z914CukGEAO/ERb64bESzuW11wXQ2KKoTqe/8Dvsut7v9/O2xh18Qn+fGZkk7Y+QbcSoUWXKnyXd8Hkbv02/37+Yc7MVUagv3+N+2HddAUETMmchVgW/BF4V+oGJEtzzbrhuYHFfqHqBixLZs/vQRC/NwhyKEMvkaJbc1LiX7SUozKHFU5DZSyyXJygVUBQvnaM0ViCoKKV7c+y/YZZU/gzl2d3Uy33EsvMIAfVKL/G0q6vM7KP8nR54XnnxuYiXe4xPKxl+IeH9Eo/7uWjD07GLN0aFpVOSnud82Ah3mm+f/ltxNeGl6rqcd7VcUG3l9N9Kdcsfd2QYhrGFROp/IBF5O3AXUMZluAjup+7qbtq1JjSA80dg9gw0JqA4DShkB6AwAaWzkNkD2SEoTQICmQEoTbl2mUEotrXLDUFxyk2nZQegNBO2G4DCuGuXHXX9FSdd9Gf5WK0+ENj3Itj93MUfwkvYe6AwwR2lYU5mivy/3M+IFeeIa4o92Ru5pjRKlm9A5sdu3OIZt0owOwSl6c72liaduMqMQnbQtUMgk3f2lc64aE1m0PlDNWw3tdBHcP/HaD6tDMFZPC9JU4pIvQcvPUdQTbnA3mCN4NjPaFxxI9SrxPwTNAMneGLeGM1mHimW0WqF5tQTlE45QRXLnKBZGkYVkrkxavN5pFImSFVJ9IxRL4yAKF78BPXCMH4PpFLHadT6iCXKiF8jO3KCankIQckOj9Go5IklqsRiVbzgLOgoq36Mj8iyhPdLPO6nU8L7kkf8tNqtIncLtmfelQkqwzCMJUTtf8XfBK5X1YluG7JuakWCB4/QnCwjs99Bi4CC5JrofACVM5AaRXpAiwlQdXWFGIgg2Ro6T1s7b2ldMR7211jWjrBOO4zV6iNO7OcEb/AZkOxZlb3xyhmuTY1ysGcXWtgT2jGOFqaoL9grbWMpWoh3sLeOFsUJufQokmu1A8nU0KIH5TOQGUWy7X00Fu5fkzU4dQ7NNmjMVSAFzdk80vsE9foQSBNB0foQPhNcqCSoez7NxAFi9QJIQDN+DbHqFH4zQcOL09Br8P1ZCIRG5QB+ygmqRvUa/MRs2M6jUb8GPzULKjRqB/Cz83BKKZy/hlS8RLOWRutJivPXEsu6qFZx/CDx9CzNWpJmPUlher/L29rpKw0NwzCMSBA1kfcwUFrrRSLyCuDPgBjwEVX9w402bLUEVWXupxeoV4rEH5lFfdwUX1XRWJOYVml4c3gNQRMxVJRYNaCZjCEKUmsS+IofVGnILF4zRpD0QBWv1kSTPgrEqq5drFml6c/i1YVmwgMRvGpAEGviL4zlESQFER8ak+RfqHjJy9sb+AF+UKUps0gzRpAUF/GqtdvbQOMQa1RpxuaQhhIk/WX2Kl41QOMQb1ape4vtBJBKA40L8aBKPTaH1AnveXEsFJpTQqbk4fkBWmui0z6NIIFOJJk/fTVe1s1/61wMPzXE0cd+DX+0Rn1ykOTucQQonxsk0T9N41wCf1+V2nwfqfw0xJRSpZdMag6AUqWXlF+gdsYnPlqn2ughm55HVSlVe0nvuYCm6jx24mpGZ7LEgUCEZjOJn3Rf4WY9TSxexlOoA3PPOMuthw84x9tKQ8MwDGOTiZrIexfwXRH5AVBtFarqO1e6QERiwJ8DLwNOAz8Ukb9V1Z9utrGdqKtw7rFnIfMzVI+MEJMiaECzmcOjQHJ6mmp/P03N4vtuVrrRzBH3i6gKjUaamBRdu75+Asnhx4oors6PlxGUej2LT4nEzCTVgX6aQQ7fLyEojWYWTwskZ9xYATlisQL4PujPkVEhuUZ7F/rAo9HMEG+zKeYVSU5NU+vvp6FZ4vHSwr34cRc1ajSy+FIkMTNNra+tHUKjlsH3CkvqfL+4dCwVavUcVwRH6Dk/Ri01gPoeTS9NEGSQnhpac3eVTpzj1NBtBKTAh+TTTqNzfQQCqevO0pzO04iliMUg/fzj1HI+4kGmf4zG9DAKZAfGqE4NMn9ayI8qmcHj1KfdxrCZ/jEas8PgC/Gff4Dy37wUr5JG/QYi0Kwuiqig0os2ParZEiNvOgKJDX0Ms2EYhmGsSNRE3oeAbwAP4nLyVsMtwAlVfQRARD4DvAroisgLvCyzV95ErDjFqbN9pIJ5AEpBDyktUtM4id11KpIlE1usy8YKLkrUbGu3p0rFa9VBKciR8eYREYrNHMmgQF3jJK5oUPGyZLwCIlBs5kgF89RIurG8LBlvHs+H7N48gZddnb1By47QXs/leJWC3KK9rXZNn/juOlUvd5G9AKVmD0ktLNz/YjvXRzJoq5Nc6JvFscQLqCfTlEYO8bRvf5DUVQ28KzIEpQD1S/SmqgSVAJmvEqPJ0O+XyeU/TsIfITUQpzrVAITkgE95qkatcYGkP0JmKEG1WkIQEukUtfITKEoqnaFSPk8tOEfc200qnaFWvgDoQjsQritU+Mqj+5D7D5Nr+OApTQlABU8FUaHoN5h49hgveE3f1n0RDcMwjKc8URN5DVX9D2u8ZhQ41XZ+Grh140xaG8msx67nX8Xcmd2MDMcpTdYAYXggTnWqTuUMJEahdyBOdaqGIowMxKlM1xFVRgYSVFrtdkPPUJzadB1VZddAgvJUDUTY1R+nPFmjegYSe4X8QJzyVA0RYbg/TnWiTuWcG6snHFs8ZfTFKZJZb832tvoAZ2N1ug4LNtWpnFaSo9A3mKC8pM71NzIQpzJZo3JGSI7q0nb9rl2rLj/ofLBkLFGueUGCkUN7SZ/fh3/X/0CeaKKDPZBMINUAnZ5w+6PddQd7n74fJUDwENLoHpeT1jpeUpdoq+tZerykXc/F7YKeErd++Pf49q+/mfiPDnNFJUk6iAFQ9pqcS1VoPOsh/skHP8kVw7+3FV9BwzAMwwCit0/eHwAncduntE/XTl3imtcCt6nqW8PzNwG3qOo7lrV7G/A2gCuvvPKmkydPbvwNhASBWzwZCxdRgtuKrF52dZ63eN5et9HtOtUls65su9i7ou3jE3Dv38PdX4PZecj3wO2/AC9/8ZY/8aLCUcbG381Dn7uasU+9jsJjVwKQu+pxDr3ps1z3moc5OHwXKaL4yAvDMAwj6uyUzZAfbTtdMExVV9xCRUSeB7xHVW8Lz98VXvO+S1wzjhOTm8kQsH1XCUePSPvTj+PnB+jLDzDoJ1zKY6NGbXaKidkpZhp1Gpfro4tE2rfbHPPt5mL+3RzMr5vHen27X1XX/OSBqIm81wF/p6pzIvK7wI3Ae1X1gUtc4wPHgZcCZ4AfAm9Q1WNbYfMl7LpvParb6Iz5c/Mw324e5tvNxfy7OZhfN4+t9m3UnrXzO6HAeyFutezHgf95qQtUtQH8O+ArwEPAZ7st8AzDMAzDMLpN1BZeNMP324H/papfFJH3XO4iVb0HuGczDTMMwzAMw9hORC2Sd0ZEPgS8DrhHRJJEz8bV8uFuG7DDMH9uHubbzcN8u7mYfzcH8+vmsaW+jVpOXgZ4BfCgqo6JyG7gGap6b5dNMwzDMAzD2FZESuQZhmEYhmEYG8N2nQo1DMMwDMMwLoGJvBAR2Sci3xSRh0TkmIj8Rlg+ICJfFZGx8L0/LB8M2xdE5ANt/fSIyI/bXhMi8qcrjHmTiDwoIidE5L+LiITlLxKRB0SkISKv2Yr732gi5s9fDct/LCL/ICKHt8IHm0XEfPvLIjLe1sdbt8IHm0HE/PonbdcfF5GZrfDBZhIx/+4Xka+LyE9E5FsisncrfLBZdMm3fyAip0SksKx82/9+tbNRvg3rXh9+H38iIn8nIh137r/E93btvlVVe7kp693AjeFxD27vvcPA+4HfCst/C/ij8DgLvBD4VeADl+j3fuBFK9QdAZ4HCPBl4J+F5VcBzwQ+Cbym277ZAf7sbWvzStxejF330Q7x7S9fqs/t9IqSX5e1eQfwsW77Zyf5F/hr4C3h8c8Dn+q2f7ahb58bjltYVn4V2/z3azN8i9vN5AIwFJ6/H/cgh7V8b9fsW4vkhajqOQ03XVbVedyee6PAq4BPhM0+Abw6bFNU1X8AKiv1KSIHgRHg2x3qduPEx/fUfXqfbOv7MVX9CRBs0O1tORHz51xb0yxtT1PZjkTJtzuJCPv19cCn13tfUSFi/j0MfD08/mZow7Zlq30b9vF9VT3XoXzb/361s4G+lfCVDSNzvcDZ5eNttDYwkdcBEbkKuAH4AbCr9UUO30fW0NXrgb8KP6jljAKn285Ph2U7jij4U0T+rYg8jPvr6Z1rsT/KRMG3wC+F0w+fE5F9axgzskTEr4jIfuAA8I01jBl5IuDffwR+KTz+l0CPiAyuYdzIskW+fUryZHyrqnXg14AHceLuMPDRDk03VBuYyFuGiOSAzwP/flkEaD38K1b+C1w6lO24f0xR8aeq/rmqXgP8J+B3nqQdkSAivv0ScJWqPhP4Got/2W5bIuLX9us/p6rNDm23JRHx728CLxaRHwEvxj0SM8rPl14VW+jbpxxP1rciEseJvBuAPcBPgHd1atqhbN3awEReG+GH8HngL1X1C2HxE2H4tBVGvbDKvp4F+Kp6f3gea0tmvQunztuTfffSIXS7nYmoPz/DDphqjIpvVXVSVath+V8ANz3JW+sqUfFrGzvqhzYq/lXVs6r6i6p6A/DbYdnsk7/D7rHFvn1KsUG+fTaAqj4cRkc/Czx/s7WBibyQcI78o8BDqvrHbVV/C7wlPH4L8MVVdrkkj0ZVm6r67PB1ZxjenReR54Zjv3kNfUeeKPkzzC1pcTswtq6biggR8+3utn5eictX2ZZEya+hPU8D+oHvrfumIkSU/CsiQyLS+v17F/Cxdd9YBNhq326EzduFDfTtGeCwiAyH5y8L+9xcbaARWL0ShRduNYziQqg/Dl//HBjEJeiOhe8Dbdc8BkwBBZz6PtxW9whw3WXGvBk4CjwMfIDFzamfE/ZXBCaBY932zzb3558Bx0Ibvglc323/7CDfvi/07T+Gvr1kP1F+RcmvYd17gD/stl92on+B14TjHQc+AiS77Z9t6Nv3h9cF4ft7wvJt//u1Wb7Frbh9KOzrS8DgGr+3a/atPfHCMAzDMAxjB2LTtYZhGIZhGDsQE3mGYRiGYRg7EBN5hmEYhmEYOxATeYZhGIZhGDsQE3mGYRiGYRg7EBN5hmEYhmEYOxATeYZhGIZhGDuQ/w906Ckr5iTdcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x2268 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "\n",
    "n_rows = len(col_list_raw)\n",
    "fig, axs = plt.subplots(ncols=1, nrows=n_rows, \n",
    "                            figsize=(10,1.5*n_rows),sharex=True)\n",
    "for cur_num in serial_num_list_failed[:10]: # [:1]: \n",
    "    plot_df(df[df['serial_number']==cur_num],col_list_raw,axs)\n",
    "\n",
    "print(\"NOTE : \\n    The large circles represent time of failure.\\n    The lines show the data leading up to the point of faiilure. \\n    Only serial numbers of failed disks are being used.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the train and test data sets...\n",
      "Completed saving data sets\n"
     ]
    }
   ],
   "source": [
    "# Save datasets for future use\n",
    "print('Saving the train and test data sets...')\n",
    "df.to_pickle(data_dir+pkl_train_file)\n",
    "df_t.to_pickle(data_dir+pkl_test_file)\n",
    "print('Completed saving data sets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"5\"></a>\n",
    "## Training GPU XGBoost models with RAPIDS for Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will leverage the Backblaze Hard Drive SMART data to train an XGBoost model that will predict potential future failures.\n",
    "\n",
    "XGBoost will use the historical collection of SMART data to learn common feature patterns present in disk failures.  The model we create will be able to predict a failure based on the characteristics learned. To implement our XGBoost model we will be using  NVIDIA's [RAPIDS](https://rapids.ai/) which is an open source, GPU accelerated, data science platform.  RAPIDS includes cuML which is a \"scikit-learn-like\" library that contains GPU-accelerated versions of some of the most popular machine learning algorithms.\n",
    "\n",
    "In this lab, we will be using RAPIDS to train XGBoost models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"6\"></a>\n",
    "### XGBoost\n",
    "\n",
    "XGBoost is widely known today as a \"go to\" model when working with structured data. In fact, there is a disproportionately large number of XGBoost-based winning entries of Kaggle competitions. In this lab, we introduce the model, but we do not intend to go through a rigorous mathematical formulation of XGBoost. For curious readers, we recommend reviewing of the original paper on [XGBoost](https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf)\n",
    "\n",
    "Traditionally, in tree-based ensemble methods such as Random Forests, we train each tree independently and the predictions of multiple trees are summed to obtain the final score. The figure below classifies whether someone would like to play computer games or not. The left and right trees are classifying gamers based on different features, namely age and whether samples use computers on a daily basis. A weighted combination of such trees would result in a more accurate decision.\n",
    "\n",
    "<br>\n",
    "\n",
    "<img src=\"img/twocart.png\" style=\"margin-top:10px;\"/>\n",
    "<p style=\"text-align: center;color:gray\"> Figure 1. Tree classifier to detect computer gamers<br/>\n",
    "<i>Image credit: XGBoost: A Scalable Tree Boosting System, T. Chen and C. Guestrin</i></p>\n",
    "\n",
    "\n",
    "\n",
    "In the case of XGBoost, an implementation of Gradient Boosted Decision Trees, we repeatedly build new models and combine them into an ensemble. Unlike Random Forests, we build trees one at a time, where each new tree helps to correct errors made by previously trained tree. The incremental training procedure is shown in figure 2.\n",
    "\n",
    "<br/><br/>\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"img/Ensemble Tree.jpg\" style=\"margin-top:10px;\"/>\n",
    "<p style=\"text-align: center;color:gray\"> Figure 2. XGBoost training pipeline</p>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "The loss function controls the predictive power of the model, and the XGBoost regularization term ensures simplicity and manages overfitting. Since \"Boosting\" focuses iteratively building learners for the most difficult parts of your data, it provides an efficient algorithm to deal with unbalanced datasets by strengthening the impact of the positive class. With this brief introduction to the method, we are going to further curate the data and implement the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"7\"></a>\n",
    "## More on Data Preparation\n",
    "\n",
    "Now that we have filtered the dataset based on the `model` column, this column is not required anymore and can be removed. Also, the `date` and `serial_number` do not possess any useful information for our training. Later in the course, where we treat the hard drive date as time sequences, we would need to further sort the data using the `date` column. Let's start by removing these unnecessary columns at this stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RAPIDS\n",
    "import cudf \n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping unneeded columns...\n"
     ]
    }
   ],
   "source": [
    "# drop unneccessary columns\n",
    "print(\"Dropping unneeded columns...\")\n",
    "df = df.drop(columns=['model', 'date', 'serial_number'])\n",
    "df_t = df_t.drop(columns=['model', 'date', 'serial_number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training/testing our model, we require labels. The `failure` column provides these class labels. We need to extract these labels as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Separating Training Data Features and Labels\n",
      "Separating Test Data Features and Labels\n",
      "Completed\n"
     ]
    }
   ],
   "source": [
    "print(\"Separating Training Data Features and Labels\")\n",
    "# separate training set features from labels\n",
    "df_train_target = pd.DataFrame(df['failure'])\n",
    "\n",
    "print(\"Separating Test Data Features and Labels\")\n",
    "# separate test set features from labels\n",
    "df_test_target = pd.DataFrame(df_t['failure'])\n",
    "\n",
    "print(\"Completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each SMART measure on the list of columns has a companion \"normalized\" twin column. The values of the normalized column are just the scaled version of the \"raw\" columns. We can choose to opt either of these columns for this lab, as XGBoost is not sensitive to the scale of the values of each column. Here, we choose to keep the \"raw\" version.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['capacity_bytes', 'failure', 'smart_1_raw', 'smart_4_raw',\n",
       "       'smart_5_raw', 'smart_7_raw', 'smart_9_raw', 'smart_12_raw',\n",
       "       'smart_183_raw', 'smart_184_raw', 'smart_187_raw', 'smart_188_raw',\n",
       "       'smart_189_raw', 'smart_190_raw', 'smart_192_raw', 'smart_193_raw',\n",
       "       'smart_194_raw', 'smart_197_raw', 'smart_198_raw', 'smart_199_raw',\n",
       "       'smart_240_raw', 'smart_241_raw', 'smart_242_raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [c for c in df.columns if c.lower().find(\"normalized\")==-1]\n",
    "df=df[cols]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e1\"></a>\n",
    "## Remove \"zero-valued\" rows\n",
    "\n",
    "Another important drawback with the data is \"zero-valued\" rows. These rows have zero values for every entry of the dataset and do not possess any useful information for training the model. We can remove these rows from the training sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['capacity_bytes', 'failure', 'smart_1_raw', 'smart_4_raw',\n",
       "       'smart_5_raw', 'smart_7_raw', 'smart_9_raw', 'smart_12_raw',\n",
       "       'smart_183_raw', 'smart_184_raw', 'smart_187_raw', 'smart_188_raw',\n",
       "       'smart_189_raw', 'smart_190_raw', 'smart_192_raw', 'smart_193_raw',\n",
       "       'smart_194_raw', 'smart_197_raw', 'smart_198_raw', 'smart_199_raw',\n",
       "       'smart_240_raw', 'smart_241_raw', 'smart_242_raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove zero valued rows\n",
    "df = df.loc[:, (df != 0).any(axis=0)]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that these columns are removed, we should apply the same to our test set. Note that for our test set, even if they contain non-zero values, since they are removed from the train set, they will not be useful. Also, we need to remove the `failure` column - which is our class labels! - from the training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed\n"
     ]
    }
   ],
   "source": [
    "cols = [c for c in df_t.columns if c.lower() in df.columns]\n",
    "df_t=df_t[cols]\n",
    "\n",
    "df_train = df.drop(columns=['failure'])\n",
    "df_test = df_t.drop(columns=['failure'])\n",
    "print(\"Completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a few records from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>capacity_bytes</th>\n",
       "      <th>failure</th>\n",
       "      <th>smart_1_raw</th>\n",
       "      <th>smart_4_raw</th>\n",
       "      <th>smart_5_raw</th>\n",
       "      <th>smart_7_raw</th>\n",
       "      <th>smart_9_raw</th>\n",
       "      <th>smart_12_raw</th>\n",
       "      <th>smart_183_raw</th>\n",
       "      <th>smart_184_raw</th>\n",
       "      <th>...</th>\n",
       "      <th>smart_190_raw</th>\n",
       "      <th>smart_192_raw</th>\n",
       "      <th>smart_193_raw</th>\n",
       "      <th>smart_194_raw</th>\n",
       "      <th>smart_197_raw</th>\n",
       "      <th>smart_198_raw</th>\n",
       "      <th>smart_199_raw</th>\n",
       "      <th>smart_240_raw</th>\n",
       "      <th>smart_241_raw</th>\n",
       "      <th>smart_242_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>124084032.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>407077929.0</td>\n",
       "      <td>9938.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33920.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9690.0</td>\n",
       "      <td>3.179327e+10</td>\n",
       "      <td>8.596073e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>22057080.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>789891767.0</td>\n",
       "      <td>17785.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70545.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17310.0</td>\n",
       "      <td>2.169678e+10</td>\n",
       "      <td>1.860347e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>119535272.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>338259228.0</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9086.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8514.0</td>\n",
       "      <td>2.755353e+10</td>\n",
       "      <td>1.631456e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>90592144.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64202331.0</td>\n",
       "      <td>18105.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14150.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>18051.0</td>\n",
       "      <td>2.150714e+10</td>\n",
       "      <td>2.376942e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4000787030016</td>\n",
       "      <td>0</td>\n",
       "      <td>240109536.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>664498121.0</td>\n",
       "      <td>14657.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46665.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14303.0</td>\n",
       "      <td>1.645696e+10</td>\n",
       "      <td>7.311286e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    capacity_bytes  failure  smart_1_raw  smart_4_raw  smart_5_raw  \\\n",
       "4    4000787030016        0  124084032.0          8.0          0.0   \n",
       "7    4000787030016        0   22057080.0         21.0          0.0   \n",
       "8    4000787030016        0  119535272.0          7.0          0.0   \n",
       "12   4000787030016        0   90592144.0         12.0          0.0   \n",
       "13   4000787030016        0  240109536.0         20.0          0.0   \n",
       "\n",
       "    smart_7_raw  smart_9_raw  smart_12_raw  smart_183_raw  smart_184_raw  ...  \\\n",
       "4   407077929.0       9938.0           8.0            0.0            0.0  ...   \n",
       "7   789891767.0      17785.0          21.0            0.0            0.0  ...   \n",
       "8   338259228.0       8600.0           7.0            0.0            0.0  ...   \n",
       "12   64202331.0      18105.0          12.0         4466.0            0.0  ...   \n",
       "13  664498121.0      14657.0          20.0            0.0            0.0  ...   \n",
       "\n",
       "    smart_190_raw  smart_192_raw  smart_193_raw  smart_194_raw  smart_197_raw  \\\n",
       "4            24.0            0.0        33920.0           24.0            0.0   \n",
       "7            18.0            0.0        70545.0           18.0            0.0   \n",
       "8            29.0            0.0         9086.0           29.0            0.0   \n",
       "12           21.0            0.0        14150.0           21.0            0.0   \n",
       "13           25.0            0.0        46665.0           25.0            0.0   \n",
       "\n",
       "    smart_198_raw  smart_199_raw  smart_240_raw  smart_241_raw  smart_242_raw  \n",
       "4             0.0            0.0         9690.0   3.179327e+10   8.596073e+09  \n",
       "7             0.0            0.0        17310.0   2.169678e+10   1.860347e+11  \n",
       "8             0.0            0.0         8514.0   2.755353e+10   1.631456e+10  \n",
       "12            0.0          564.0        18051.0   2.150714e+10   2.376942e+11  \n",
       "13            0.0            0.0        14303.0   1.645696e+10   7.311286e+10  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"8\"></a>\n",
    "## Define XGBoost Model Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to other machine learning models, there are hyperparameters to be set for the XGBoost. Below is the list of parameters of the model:\n",
    "\n",
    "- <b>max_tree_depth</b>:  Defines the maximum depth of the tree.  Deeper trees are more complex and able to learn more relations amongst the data, however, also tend to over-fit as the relations may be specific to the training set.\n",
    "\n",
    "- <b>tree_method</b>:  Used to control GPU vs CPU operation.  'hist' = CPU histogram method.  'gpu_hist' = GPU histogram method.\n",
    "\n",
    "- <b>subsample</b>:  Denotes the fraction of observations to be randomly sampled for each tree.  Lower values make the algorithm more conservative and prevent overfitting however, too small of a value might lead to underfitting.\n",
    "\n",
    "- <b>regularization</b>:  Applies an L1-regularization term to the weights.  Increasing this value makes the model more conservative and can speed up performance on very high-dimensional data sets.\n",
    "\n",
    "- <b>gamma</b>:  Gamma specifies the minimum loss reduction required to split a node.\n",
    "\n",
    "- <b>pos_weight</b>:  Controls the balance of positive and negative weights to deal with unbalanced classes.\n",
    "\n",
    "- <b>early_stop</b>:  Used to control over-fitting.  Attempts to find the inflection point when the performance on the test set starts to decrease while the performance on the training set continues to increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_TREE_DEPTH = 8\n",
    "TREE_METHOD = 'hist'\n",
    "ITERATIONS = 85\n",
    "SUBSAMPLE = 0.6\n",
    "REGULARIZATION = 1.3\n",
    "GAMMA = 0.3\n",
    "POS_WEIGHT = 1\n",
    "EARLY_STOP = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"9\"></a>\n",
    "## Train XGBoost on CPUs\n",
    "\n",
    "To demonstrate the benefits of GPU computing and the RAPIDS platform, we will first train a XGBoost model on the CPUs in our training enviroment. Let's take a look at the specs of the CPUs running on your cloud instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model name:            Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz\n",
      "CPU(s):                48\n",
      "On-line CPU(s) list:   0-47\n",
      "NUMA node0 CPU(s):     0-47\n"
     ]
    }
   ],
   "source": [
    "# Show information on the CPUs\n",
    "!lscpu | grep 'Model name:'\n",
    "!lscpu | grep 'CPU(s)'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to convert our data into XGBoost DMatrix format. DMatrix is optimized for memory efficiency and training speed in XGBoost. Next, we need to create a dictionary of hyperparameters discussed above and finally call the `train` method to start the training process. We also measure the total time to compare it with the GPU version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting data sets into XGBoost DMatrix format...\n",
      "Completed converting data sets\n",
      "Starting CPU XGBoost Training...\n",
      "[09:30:19] WARNING: /conda/conda-bld/xgboost_1591204597597/work/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttrain-rmse:0.47570\teval-rmse:0.47497\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 10 rounds.\n",
      "[1]\ttrain-rmse:0.45096\teval-rmse:0.45146\n",
      "[2]\ttrain-rmse:0.42852\teval-rmse:0.42858\n",
      "[3]\ttrain-rmse:0.40691\teval-rmse:0.40738\n",
      "[4]\ttrain-rmse:0.38703\teval-rmse:0.38696\n",
      "[5]\ttrain-rmse:0.36739\teval-rmse:0.36762\n",
      "[6]\ttrain-rmse:0.34873\teval-rmse:0.34928\n",
      "[7]\ttrain-rmse:0.33146\teval-rmse:0.33169\n",
      "[8]\ttrain-rmse:0.31547\teval-rmse:0.31510\n",
      "[9]\ttrain-rmse:0.29973\teval-rmse:0.29952\n",
      "[10]\ttrain-rmse:0.28434\teval-rmse:0.28449\n",
      "[11]\ttrain-rmse:0.27039\teval-rmse:0.27032\n",
      "[12]\ttrain-rmse:0.25638\teval-rmse:0.25681\n",
      "[13]\ttrain-rmse:0.24413\teval-rmse:0.24402\n",
      "[14]\ttrain-rmse:0.23185\teval-rmse:0.23181\n",
      "[15]\ttrain-rmse:0.22039\teval-rmse:0.22012\n",
      "[16]\ttrain-rmse:0.20954\teval-rmse:0.20916\n",
      "[17]\ttrain-rmse:0.19872\teval-rmse:0.19879\n",
      "[18]\ttrain-rmse:0.18880\teval-rmse:0.18886\n",
      "[19]\ttrain-rmse:0.17960\teval-rmse:0.17938\n",
      "[20]\ttrain-rmse:0.17028\teval-rmse:0.17051\n",
      "[21]\ttrain-rmse:0.16212\teval-rmse:0.16189\n",
      "[22]\ttrain-rmse:0.15379\teval-rmse:0.15393\n",
      "[23]\ttrain-rmse:0.14628\teval-rmse:0.14622\n",
      "[24]\ttrain-rmse:0.13891\teval-rmse:0.13892\n",
      "[25]\ttrain-rmse:0.13201\teval-rmse:0.13202\n",
      "[26]\ttrain-rmse:0.12531\teval-rmse:0.12545\n",
      "[27]\ttrain-rmse:0.11926\teval-rmse:0.11921\n",
      "[28]\ttrain-rmse:0.11323\teval-rmse:0.11325\n",
      "[29]\ttrain-rmse:0.10752\teval-rmse:0.10765\n",
      "[30]\ttrain-rmse:0.10235\teval-rmse:0.10225\n",
      "[31]\ttrain-rmse:0.09719\teval-rmse:0.09722\n",
      "[32]\ttrain-rmse:0.09233\teval-rmse:0.09240\n",
      "[33]\ttrain-rmse:0.08794\teval-rmse:0.08782\n",
      "[34]\ttrain-rmse:0.08361\teval-rmse:0.08342\n",
      "[35]\ttrain-rmse:0.07934\teval-rmse:0.07929\n",
      "[36]\ttrain-rmse:0.07541\teval-rmse:0.07539\n",
      "[37]\ttrain-rmse:0.07177\teval-rmse:0.07166\n",
      "[38]\ttrain-rmse:0.06824\teval-rmse:0.06811\n",
      "[39]\ttrain-rmse:0.06488\teval-rmse:0.06477\n",
      "[40]\ttrain-rmse:0.06162\teval-rmse:0.06162\n",
      "[41]\ttrain-rmse:0.05858\teval-rmse:0.05858\n",
      "[42]\ttrain-rmse:0.05584\teval-rmse:0.05568\n",
      "[43]\ttrain-rmse:0.05308\teval-rmse:0.05299\n",
      "[44]\ttrain-rmse:0.05051\teval-rmse:0.05039\n",
      "[45]\ttrain-rmse:0.04801\teval-rmse:0.04793\n",
      "[46]\ttrain-rmse:0.04568\teval-rmse:0.04560\n",
      "[47]\ttrain-rmse:0.04355\teval-rmse:0.04343\n",
      "[48]\ttrain-rmse:0.04146\teval-rmse:0.04131\n",
      "[49]\ttrain-rmse:0.03948\teval-rmse:0.03933\n",
      "[50]\ttrain-rmse:0.03761\teval-rmse:0.03748\n",
      "[51]\ttrain-rmse:0.03581\teval-rmse:0.03568\n",
      "[52]\ttrain-rmse:0.03418\teval-rmse:0.03400\n",
      "[53]\ttrain-rmse:0.03257\teval-rmse:0.03240\n",
      "[54]\ttrain-rmse:0.03101\teval-rmse:0.03089\n",
      "[55]\ttrain-rmse:0.02963\teval-rmse:0.02945\n",
      "[56]\ttrain-rmse:0.02820\teval-rmse:0.02811\n",
      "[57]\ttrain-rmse:0.02699\teval-rmse:0.02682\n",
      "[58]\ttrain-rmse:0.02577\teval-rmse:0.02561\n",
      "[59]\ttrain-rmse:0.02464\teval-rmse:0.02447\n",
      "[60]\ttrain-rmse:0.02356\teval-rmse:0.02338\n",
      "[61]\ttrain-rmse:0.02254\teval-rmse:0.02237\n",
      "[62]\ttrain-rmse:0.02155\teval-rmse:0.02139\n",
      "[63]\ttrain-rmse:0.02065\teval-rmse:0.02048\n",
      "[64]\ttrain-rmse:0.01984\teval-rmse:0.01963\n",
      "[65]\ttrain-rmse:0.01905\teval-rmse:0.01882\n",
      "[66]\ttrain-rmse:0.01826\teval-rmse:0.01807\n",
      "[67]\ttrain-rmse:0.01759\teval-rmse:0.01735\n",
      "[68]\ttrain-rmse:0.01692\teval-rmse:0.01668\n",
      "[69]\ttrain-rmse:0.01626\teval-rmse:0.01606\n",
      "[70]\ttrain-rmse:0.01569\teval-rmse:0.01547\n",
      "[71]\ttrain-rmse:0.01516\teval-rmse:0.01493\n",
      "[72]\ttrain-rmse:0.01465\teval-rmse:0.01441\n",
      "[73]\ttrain-rmse:0.01416\teval-rmse:0.01392\n",
      "[74]\ttrain-rmse:0.01372\teval-rmse:0.01347\n",
      "[75]\ttrain-rmse:0.01330\teval-rmse:0.01306\n",
      "[76]\ttrain-rmse:0.01294\teval-rmse:0.01266\n",
      "[77]\ttrain-rmse:0.01256\teval-rmse:0.01230\n",
      "[78]\ttrain-rmse:0.01224\teval-rmse:0.01197\n",
      "[79]\ttrain-rmse:0.01193\teval-rmse:0.01165\n",
      "[80]\ttrain-rmse:0.01166\teval-rmse:0.01136\n",
      "[81]\ttrain-rmse:0.01138\teval-rmse:0.01110\n",
      "[82]\ttrain-rmse:0.01115\teval-rmse:0.01084\n",
      "[83]\ttrain-rmse:0.01091\teval-rmse:0.01062\n",
      "[84]\ttrain-rmse:0.01072\teval-rmse:0.01041\n",
      "CPU XGBoost training compeleted - elapsed time: 58.672789573669434 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "print('Converting data sets into XGBoost DMatrix format...')\n",
    "xgtrain = xgb.DMatrix(df_train, df_train_target)\n",
    "xgeval = xgb.DMatrix(df_test, df_test_target)\n",
    "print('Completed converting data sets')\n",
    "\n",
    "params = {'tree_method': TREE_METHOD, 'max_depth': MAX_TREE_DEPTH, 'alpha': REGULARIZATION,\n",
    "          'gamma': GAMMA, 'subsample': SUBSAMPLE, 'scale_pos_weight': POS_WEIGHT, 'learning_rate': 0.05, 'silent': 1}\n",
    "\n",
    "print('Starting CPU XGBoost Training...')\n",
    "bst = xgb.train(params, xgtrain, ITERATIONS, evals=[(xgtrain, \"train\"), (xgeval, \"eval\")],\n",
    "                early_stopping_rounds=EARLY_STOP)\n",
    "\n",
    "timetaken_cpu = time.time() - start_time\n",
    "print('CPU XGBoost training compeleted - elapsed time:', timetaken_cpu,'seconds')\n",
    "\n",
    "# free up memory\n",
    "del xgtrain\n",
    "del xgeval\n",
    "del bst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"10\"></a>\n",
    "## Training with GPU without cuDF DataFrame\n",
    "\n",
    "We will now leverage GPUs to accelerate the XGBoost Training and compare the results to the CPU version.\n",
    "\n",
    "First, let's see which GPU we have in our lab system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jan 10 09:31:11 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            On   | 00000000:00:1B.0 Off |                    0 |\n",
      "| N/A   28C    P8     9W /  70W |     11MiB / 15109MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla T4            On   | 00000000:00:1C.0 Off |                    0 |\n",
      "| N/A   29C    P8     9W /  70W |     11MiB / 15109MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  Tesla T4            On   | 00000000:00:1D.0 Off |                    0 |\n",
      "| N/A   29C    P8     9W /  70W |     11MiB / 15109MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  Tesla T4            On   | 00000000:00:1E.0 Off |                    0 |\n",
      "| N/A   29C    P8     8W /  70W |     11MiB / 15109MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To configure the XGBoost training algorithm to use GPUs, we set params[tree_method] = 'gpu_hist'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting data sets into XGBoost DMatrix format...\n",
      "Completed converting data sets\n",
      "Starting GPU XGBoost Training...\n",
      "[09:32:49] WARNING: /conda/conda-bld/xgboost_1591204597597/work/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttrain-rmse:0.47501\teval-rmse:0.47501\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 10 rounds.\n",
      "[1]\ttrain-rmse:0.45127\teval-rmse:0.45126\n",
      "[2]\ttrain-rmse:0.42871\teval-rmse:0.42871\n",
      "[3]\ttrain-rmse:0.40729\teval-rmse:0.40728\n",
      "[4]\ttrain-rmse:0.38693\teval-rmse:0.38693\n",
      "[5]\ttrain-rmse:0.36760\teval-rmse:0.36759\n",
      "[6]\ttrain-rmse:0.34923\teval-rmse:0.34922\n",
      "[7]\ttrain-rmse:0.33178\teval-rmse:0.33177\n",
      "[8]\ttrain-rmse:0.31520\teval-rmse:0.31519\n",
      "[9]\ttrain-rmse:0.29945\teval-rmse:0.29944\n",
      "[10]\ttrain-rmse:0.28449\teval-rmse:0.28448\n",
      "[11]\ttrain-rmse:0.27028\teval-rmse:0.27027\n",
      "[12]\ttrain-rmse:0.25678\teval-rmse:0.25677\n",
      "[13]\ttrain-rmse:0.24396\teval-rmse:0.24395\n",
      "[14]\ttrain-rmse:0.23178\teval-rmse:0.23176\n",
      "[15]\ttrain-rmse:0.22021\teval-rmse:0.22019\n",
      "[16]\ttrain-rmse:0.20921\teval-rmse:0.20920\n",
      "[17]\ttrain-rmse:0.19877\teval-rmse:0.19875\n",
      "[18]\ttrain-rmse:0.18885\teval-rmse:0.18884\n",
      "[19]\ttrain-rmse:0.17943\teval-rmse:0.17941\n",
      "[20]\ttrain-rmse:0.17048\teval-rmse:0.17046\n",
      "[21]\ttrain-rmse:0.16198\teval-rmse:0.16196\n",
      "[22]\ttrain-rmse:0.15390\teval-rmse:0.15388\n",
      "[23]\ttrain-rmse:0.14623\teval-rmse:0.14621\n",
      "[24]\ttrain-rmse:0.13895\teval-rmse:0.13893\n",
      "[25]\ttrain-rmse:0.13203\teval-rmse:0.13201\n",
      "[26]\ttrain-rmse:0.12546\teval-rmse:0.12543\n",
      "[27]\ttrain-rmse:0.11921\teval-rmse:0.11919\n",
      "[28]\ttrain-rmse:0.11329\teval-rmse:0.11326\n",
      "[29]\ttrain-rmse:0.10766\teval-rmse:0.10763\n",
      "[30]\ttrain-rmse:0.10231\teval-rmse:0.10228\n",
      "[31]\ttrain-rmse:0.09723\teval-rmse:0.09720\n",
      "[32]\ttrain-rmse:0.09241\teval-rmse:0.09237\n",
      "[33]\ttrain-rmse:0.08783\teval-rmse:0.08779\n",
      "[34]\ttrain-rmse:0.08348\teval-rmse:0.08344\n",
      "[35]\ttrain-rmse:0.07935\teval-rmse:0.07931\n",
      "[36]\ttrain-rmse:0.07543\teval-rmse:0.07539\n",
      "[37]\ttrain-rmse:0.07171\teval-rmse:0.07167\n",
      "[38]\ttrain-rmse:0.06818\teval-rmse:0.06814\n",
      "[39]\ttrain-rmse:0.06483\teval-rmse:0.06478\n",
      "[40]\ttrain-rmse:0.06164\teval-rmse:0.06159\n",
      "[41]\ttrain-rmse:0.05862\teval-rmse:0.05857\n",
      "[42]\ttrain-rmse:0.05576\teval-rmse:0.05570\n",
      "[43]\ttrain-rmse:0.05304\teval-rmse:0.05298\n",
      "[44]\ttrain-rmse:0.05046\teval-rmse:0.05040\n",
      "[45]\ttrain-rmse:0.04801\teval-rmse:0.04795\n",
      "[46]\ttrain-rmse:0.04569\teval-rmse:0.04562\n",
      "[47]\ttrain-rmse:0.04349\teval-rmse:0.04342\n",
      "[48]\ttrain-rmse:0.04140\teval-rmse:0.04133\n",
      "[49]\ttrain-rmse:0.03943\teval-rmse:0.03935\n",
      "[50]\ttrain-rmse:0.03755\teval-rmse:0.03747\n",
      "[51]\ttrain-rmse:0.03578\teval-rmse:0.03569\n",
      "[52]\ttrain-rmse:0.03410\teval-rmse:0.03401\n",
      "[53]\ttrain-rmse:0.03250\teval-rmse:0.03241\n",
      "[54]\ttrain-rmse:0.03100\teval-rmse:0.03090\n",
      "[55]\ttrain-rmse:0.02957\teval-rmse:0.02947\n",
      "[56]\ttrain-rmse:0.02822\teval-rmse:0.02811\n",
      "[57]\ttrain-rmse:0.02694\teval-rmse:0.02683\n",
      "[58]\ttrain-rmse:0.02574\teval-rmse:0.02562\n",
      "[59]\ttrain-rmse:0.02460\teval-rmse:0.02448\n",
      "[60]\ttrain-rmse:0.02353\teval-rmse:0.02339\n",
      "[61]\ttrain-rmse:0.02251\teval-rmse:0.02237\n",
      "[62]\ttrain-rmse:0.02156\teval-rmse:0.02141\n",
      "[63]\ttrain-rmse:0.02065\teval-rmse:0.02050\n",
      "[64]\ttrain-rmse:0.01981\teval-rmse:0.01965\n",
      "[65]\ttrain-rmse:0.01901\teval-rmse:0.01885\n",
      "[66]\ttrain-rmse:0.01826\teval-rmse:0.01809\n",
      "[67]\ttrain-rmse:0.01756\teval-rmse:0.01738\n",
      "[68]\ttrain-rmse:0.01689\teval-rmse:0.01671\n",
      "[69]\ttrain-rmse:0.01627\teval-rmse:0.01608\n",
      "[70]\ttrain-rmse:0.01569\teval-rmse:0.01550\n",
      "[71]\ttrain-rmse:0.01515\teval-rmse:0.01495\n",
      "[72]\ttrain-rmse:0.01465\teval-rmse:0.01443\n",
      "[73]\ttrain-rmse:0.01417\teval-rmse:0.01395\n",
      "[74]\ttrain-rmse:0.01373\teval-rmse:0.01350\n",
      "[75]\ttrain-rmse:0.01332\teval-rmse:0.01309\n",
      "[76]\ttrain-rmse:0.01294\teval-rmse:0.01270\n",
      "[77]\ttrain-rmse:0.01259\teval-rmse:0.01234\n",
      "[78]\ttrain-rmse:0.01226\teval-rmse:0.01200\n",
      "[79]\ttrain-rmse:0.01195\teval-rmse:0.01169\n",
      "[80]\ttrain-rmse:0.01167\teval-rmse:0.01140\n",
      "[81]\ttrain-rmse:0.01141\teval-rmse:0.01114\n",
      "[82]\ttrain-rmse:0.01117\teval-rmse:0.01089\n",
      "[83]\ttrain-rmse:0.01095\teval-rmse:0.01066\n",
      "[84]\ttrain-rmse:0.01075\teval-rmse:0.01046\n",
      "GPU XGBoost training compeleted - elapsed time: 21.32212996482849 seconds\n"
     ]
    }
   ],
   "source": [
    "# GPU, without using cuDF\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "print('Converting data sets into XGBoost DMatrix format...')\n",
    "xgtrain = xgb.DMatrix(df_train, df_train_target)\n",
    "xgeval = xgb.DMatrix(df_test, df_test_target)\n",
    "print('Completed converting data sets')\n",
    "\n",
    "params = {'tree_method': \"gpu_\"+TREE_METHOD, 'max_depth': MAX_TREE_DEPTH, 'alpha': REGULARIZATION,\n",
    "          'gamma': GAMMA, 'subsample': SUBSAMPLE, 'scale_pos_weight': POS_WEIGHT, 'learning_rate': 0.05, 'silent': 1}\n",
    "\n",
    "print('Starting GPU XGBoost Training...')\n",
    "bst = xgb.train(params, xgtrain, ITERATIONS, evals=[(xgtrain, \"train\"), (xgeval, \"eval\")],\n",
    "                early_stopping_rounds=EARLY_STOP)\n",
    "\n",
    "timetaken_gpu_nocudf = time.time() - start_time\n",
    "print('GPU XGBoost training compeleted - elapsed time:', timetaken_gpu_nocudf,'seconds')\n",
    "\n",
    "# free up memory\n",
    "del xgtrain\n",
    "del xgeval\n",
    "del bst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"11\"></a>\n",
    "## Training with GPU and cuDF\n",
    "\n",
    "In this section we will be using cuDF instead of pandas DataFrame. cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data, which is based on Apache Arrow columnar memory format. cuDF provides a pandas-like API for familiarity which allows us to easily accelerate workflows without going into the details of CUDA programming. For more information on cuDF, please refer to [RAPIDS Github Repositor](https://github.com/rapidsai/cudf).\n",
    "To use cuDF with the XGBoost algorithm, we load the Pandas Dataframe into a cuDF Dataframe (Python object type cudf.dataframe.dataframe.DataFrame) first.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load into cuDF Dataframe\n",
    "\n",
    "gdf_train = cudf.DataFrame.from_pandas(df_train)\n",
    "gdf_train_target = cudf.DataFrame.from_pandas(df_train_target)\n",
    "\n",
    "gdf_eval = cudf.DataFrame.from_pandas(df_test)\n",
    "gdf_eval_target = cudf.DataFrame.from_pandas(df_test_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are going to measure the XGBoost training time using the created cuDF and on GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting cuDF data sets into XGBoost DMatrix format...\n",
      "Completed converting data sets\n",
      "Starting GPU XGBoost Training with cuDF Dataframes...\n",
      "[09:33:05] WARNING: /conda/conda-bld/xgboost_1591204597597/work/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttrain-rmse:0.47501\teval-rmse:0.47501\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 10 rounds.\n",
      "[1]\ttrain-rmse:0.45127\teval-rmse:0.45126\n",
      "[2]\ttrain-rmse:0.42871\teval-rmse:0.42871\n",
      "[3]\ttrain-rmse:0.40729\teval-rmse:0.40728\n",
      "[4]\ttrain-rmse:0.38693\teval-rmse:0.38693\n",
      "[5]\ttrain-rmse:0.36760\teval-rmse:0.36759\n",
      "[6]\ttrain-rmse:0.34923\teval-rmse:0.34922\n",
      "[7]\ttrain-rmse:0.33178\teval-rmse:0.33177\n",
      "[8]\ttrain-rmse:0.31520\teval-rmse:0.31519\n",
      "[9]\ttrain-rmse:0.29945\teval-rmse:0.29944\n",
      "[10]\ttrain-rmse:0.28449\teval-rmse:0.28448\n",
      "[11]\ttrain-rmse:0.27028\teval-rmse:0.27027\n",
      "[12]\ttrain-rmse:0.25678\teval-rmse:0.25677\n",
      "[13]\ttrain-rmse:0.24396\teval-rmse:0.24395\n",
      "[14]\ttrain-rmse:0.23178\teval-rmse:0.23176\n",
      "[15]\ttrain-rmse:0.22021\teval-rmse:0.22019\n",
      "[16]\ttrain-rmse:0.20921\teval-rmse:0.20920\n",
      "[17]\ttrain-rmse:0.19877\teval-rmse:0.19875\n",
      "[18]\ttrain-rmse:0.18885\teval-rmse:0.18884\n",
      "[19]\ttrain-rmse:0.17943\teval-rmse:0.17941\n",
      "[20]\ttrain-rmse:0.17048\teval-rmse:0.17046\n",
      "[21]\ttrain-rmse:0.16198\teval-rmse:0.16196\n",
      "[22]\ttrain-rmse:0.15390\teval-rmse:0.15388\n",
      "[23]\ttrain-rmse:0.14623\teval-rmse:0.14621\n",
      "[24]\ttrain-rmse:0.13895\teval-rmse:0.13893\n",
      "[25]\ttrain-rmse:0.13203\teval-rmse:0.13201\n",
      "[26]\ttrain-rmse:0.12546\teval-rmse:0.12543\n",
      "[27]\ttrain-rmse:0.11921\teval-rmse:0.11919\n",
      "[28]\ttrain-rmse:0.11329\teval-rmse:0.11326\n",
      "[29]\ttrain-rmse:0.10766\teval-rmse:0.10763\n",
      "[30]\ttrain-rmse:0.10231\teval-rmse:0.10228\n",
      "[31]\ttrain-rmse:0.09723\teval-rmse:0.09720\n",
      "[32]\ttrain-rmse:0.09241\teval-rmse:0.09237\n",
      "[33]\ttrain-rmse:0.08783\teval-rmse:0.08779\n",
      "[34]\ttrain-rmse:0.08348\teval-rmse:0.08344\n",
      "[35]\ttrain-rmse:0.07935\teval-rmse:0.07931\n",
      "[36]\ttrain-rmse:0.07543\teval-rmse:0.07539\n",
      "[37]\ttrain-rmse:0.07171\teval-rmse:0.07167\n",
      "[38]\ttrain-rmse:0.06818\teval-rmse:0.06814\n",
      "[39]\ttrain-rmse:0.06483\teval-rmse:0.06478\n",
      "[40]\ttrain-rmse:0.06164\teval-rmse:0.06159\n",
      "[41]\ttrain-rmse:0.05862\teval-rmse:0.05857\n",
      "[42]\ttrain-rmse:0.05576\teval-rmse:0.05570\n",
      "[43]\ttrain-rmse:0.05304\teval-rmse:0.05298\n",
      "[44]\ttrain-rmse:0.05046\teval-rmse:0.05040\n",
      "[45]\ttrain-rmse:0.04801\teval-rmse:0.04795\n",
      "[46]\ttrain-rmse:0.04569\teval-rmse:0.04562\n",
      "[47]\ttrain-rmse:0.04349\teval-rmse:0.04342\n",
      "[48]\ttrain-rmse:0.04140\teval-rmse:0.04133\n",
      "[49]\ttrain-rmse:0.03943\teval-rmse:0.03935\n",
      "[50]\ttrain-rmse:0.03755\teval-rmse:0.03747\n",
      "[51]\ttrain-rmse:0.03578\teval-rmse:0.03569\n",
      "[52]\ttrain-rmse:0.03410\teval-rmse:0.03401\n",
      "[53]\ttrain-rmse:0.03250\teval-rmse:0.03241\n",
      "[54]\ttrain-rmse:0.03100\teval-rmse:0.03090\n",
      "[55]\ttrain-rmse:0.02957\teval-rmse:0.02947\n",
      "[56]\ttrain-rmse:0.02822\teval-rmse:0.02811\n",
      "[57]\ttrain-rmse:0.02694\teval-rmse:0.02683\n",
      "[58]\ttrain-rmse:0.02574\teval-rmse:0.02562\n",
      "[59]\ttrain-rmse:0.02460\teval-rmse:0.02448\n",
      "[60]\ttrain-rmse:0.02353\teval-rmse:0.02339\n",
      "[61]\ttrain-rmse:0.02251\teval-rmse:0.02237\n",
      "[62]\ttrain-rmse:0.02156\teval-rmse:0.02141\n",
      "[63]\ttrain-rmse:0.02065\teval-rmse:0.02050\n",
      "[64]\ttrain-rmse:0.01981\teval-rmse:0.01965\n",
      "[65]\ttrain-rmse:0.01901\teval-rmse:0.01885\n",
      "[66]\ttrain-rmse:0.01826\teval-rmse:0.01809\n",
      "[67]\ttrain-rmse:0.01756\teval-rmse:0.01738\n",
      "[68]\ttrain-rmse:0.01689\teval-rmse:0.01671\n",
      "[69]\ttrain-rmse:0.01627\teval-rmse:0.01608\n",
      "[70]\ttrain-rmse:0.01569\teval-rmse:0.01550\n",
      "[71]\ttrain-rmse:0.01515\teval-rmse:0.01495\n",
      "[72]\ttrain-rmse:0.01465\teval-rmse:0.01443\n",
      "[73]\ttrain-rmse:0.01417\teval-rmse:0.01395\n",
      "[74]\ttrain-rmse:0.01373\teval-rmse:0.01350\n",
      "[75]\ttrain-rmse:0.01332\teval-rmse:0.01309\n",
      "[76]\ttrain-rmse:0.01294\teval-rmse:0.01270\n",
      "[77]\ttrain-rmse:0.01259\teval-rmse:0.01234\n",
      "[78]\ttrain-rmse:0.01226\teval-rmse:0.01200\n",
      "[79]\ttrain-rmse:0.01195\teval-rmse:0.01169\n",
      "[80]\ttrain-rmse:0.01167\teval-rmse:0.01140\n",
      "[81]\ttrain-rmse:0.01141\teval-rmse:0.01114\n",
      "[82]\ttrain-rmse:0.01117\teval-rmse:0.01089\n",
      "[83]\ttrain-rmse:0.01095\teval-rmse:0.01066\n",
      "[84]\ttrain-rmse:0.01075\teval-rmse:0.01046\n",
      "GPU XGBoost Training with cuDF Dataframes compeleted - elapsed time: 13.460415124893188 seconds\n"
     ]
    }
   ],
   "source": [
    "# GPU, with cuDF\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "print('Converting cuDF data sets into XGBoost DMatrix format...')\n",
    "xgtrain = xgb.DMatrix(gdf_train, gdf_train_target)\n",
    "xgeval = xgb.DMatrix(gdf_eval, gdf_eval_target)\n",
    "print('Completed converting data sets')\n",
    "\n",
    "params = {'tree_method': \"gpu_\"+TREE_METHOD, 'max_depth': MAX_TREE_DEPTH, 'alpha': REGULARIZATION,\n",
    "          'gamma': GAMMA, 'subsample': SUBSAMPLE, 'scale_pos_weight': POS_WEIGHT, 'learning_rate': 0.05, 'silent': 1}\n",
    "\n",
    "print('Starting GPU XGBoost Training with cuDF Dataframes...')\n",
    "bst = xgb.train(params, xgtrain, ITERATIONS, evals=[(xgtrain, \"train\"), (xgeval, \"eval\")],\n",
    "                early_stopping_rounds=EARLY_STOP)\n",
    "\n",
    "timetaken_gpu = time.time() - start_time\n",
    "print('GPU XGBoost Training with cuDF Dataframes compeleted - elapsed time:', timetaken_gpu,'seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the training time for the three configurations described above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU Time Taken:\n",
      " 58.7 seconds\n",
      "\n",
      "GPU (no cuDF) Time Taken:\n",
      " 21.3 seconds\n",
      "\n",
      "GPU (cuDF) Time Taken:\n",
      " 13.5 seconds\n",
      "\n",
      "Total speed-up with RAPIDS:\n",
      " 435.9 %\n"
     ]
    }
   ],
   "source": [
    "labels = 'CPU', 'GPU', 'GPU+cuDF'\n",
    "sizes = [timetaken_cpu, timetaken_gpu_nocudf, timetaken_gpu]\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "x_pos = [i for i, _ in enumerate(labels)]\n",
    "plt.bar(x_pos, sizes)\n",
    "plt.xlabel(\"XGBoost Version\")\n",
    "plt.ylabel(\"Time (secs)\")\n",
    "plt.title(\"Training Time\")\n",
    "plt.xticks(x_pos, labels)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"CPU Time Taken:\\n\", round(timetaken_cpu,1),\"seconds\")\n",
    "print(\"\\nGPU (no cuDF) Time Taken:\\n\", round(timetaken_gpu_nocudf,1),\"seconds\")\n",
    "print(\"\\nGPU (cuDF) Time Taken:\\n\", round(timetaken_gpu,1),\"seconds\")\n",
    "print(\"\\nTotal speed-up with RAPIDS:\\n\", round(timetaken_cpu/timetaken_gpu*100,1), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, using the GPU + cuDF we see a significant speed-up. In the next section, we are going to explore the accuracy of the XGBoost model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"12\"></a>\n",
    "### XGBoost Model Accuracy\n",
    "\n",
    "Let's look at the model's accuracy on the evalutation set that we will use to compare against our Deep Learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting XGBoost prediction on test dataset...\n",
      "XGBoost prediction completed\n"
     ]
    }
   ],
   "source": [
    "print('Starting XGBoost prediction on test dataset...')\n",
    "\n",
    "# Use trained model to predict on test data set\n",
    "preds = bst.predict(xgeval)\n",
    "\n",
    "# Convert predictions to \"normal\" (0) or \"failed\"\n",
    "y_pred = []\n",
    "\n",
    "THRESHOLD = 0.5\n",
    "for pred in preds:\n",
    "    if pred<=THRESHOLD:\n",
    "        y_pred.append(0)\n",
    "    if pred>THRESHOLD:\n",
    "        y_pred.append(1)\n",
    "\n",
    "# Output array of classifications        \n",
    "y_pred = np.asarray(y_pred)\n",
    "        \n",
    "y_true = df_test_target.values.reshape(len(preds))\n",
    "\n",
    "print('XGBoost prediction completed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Eval) 0.99993\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy (Eval)\", round(accuracy_score(y_true, y_pred),5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"13\"></a>\n",
    "## Measuring Model Accuracy\n",
    "\n",
    "In this section, we are going to plot the confusion matrix. While accuracy rate provides us with an overall outlook on how well our model is performing, we need more insights into the accuracy. Especially, since our dataset is small, and we need to compromise some aspects of accuracy rate in favor of others. The `classification_report` provides us with the accuracy stats broken down into `precision`, `recall`, `f1-score` and `support`. Before introducing these measures, let's define some abbreviations:\n",
    "\n",
    "For brevity we are using the following abbreviations: \n",
    "\n",
    "\n",
    "- __True Positive (TP)__:  test correctly classifies the input to the class (hard drive failed, and we classified it as fail)\n",
    "- __False Positive (FP)__:  test incorrectly classifies the input to the class (hard drive is normal, but we classified it as fail)\n",
    "- __False Negative (FN)__:  test incorrectly misses to classify the input to the class (hard drive failed but we classified as normal)\n",
    "- __True Negative (TN)__:  test correctly misses to classify the input to the class (hard drive is normal, and we classified as normal)\n",
    "\n",
    "The first measure is focused on identifying positive cases and is called __recall__. We define recall as the ability of the model to identify all true positive samples of the dataset. In mathematical terms, recall is the ratio of true positives over true positives plus false negatives. By other means, recall tells us, among all the test samples belonging to the output class, how many of them are identified correctly by the model:\n",
    "\n",
    "\\begin{equation*}\n",
    "recall = \\frac{TP}{TP+FN}\n",
    "\\end{equation*}\n",
    "\n",
    "The next measure, is called __precision__ and is the ability of the model to identify the relevant samples only, and is defined as the ratio of true positives over true positives plus false positives:\n",
    "\n",
    "\\begin{equation*}\n",
    "precision = \\frac{TP}{TP+FP}\n",
    "\\end{equation*}\n",
    "\n",
    "\n",
    "Selecting a proper threshold, usually stems from a good balance between the precision and recall values. A well-known measure that provides such a balance is `F1 score`, which is a harmonic mean of precision and recall, and defined as:\n",
    "\n",
    "\\begin{equation*}\n",
    "{F_1 \\: score} = 2*\\frac{precision*recall}{precision+recall}\n",
    "\\end{equation*}\n",
    "\n",
    "F1 Score is generally considered a better overall measurement than accuracy when an uneven class distribution exists (large number of True Negatives) as in the case of predicting a hard drive failure.\n",
    "\n",
    "One very important measure is the recall rate on defective samples. We are interested in models that retrieve as many defective hard drives as possible. We try to predict at least half of the defective hard drives. Let's measure how our model is performing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      normal       1.00      1.00      1.00   3196318\n",
      "        fail       0.78      0.03      0.06       234\n",
      "\n",
      "    accuracy                           1.00   3196552\n",
      "   macro avg       0.89      0.51      0.53   3196552\n",
      "weighted avg       1.00      1.00      1.00   3196552\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred, target_names=[\"normal\", \"fail\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"14\"></a>\n",
    "## Interpreting the results\n",
    "\n",
    "When trying to predict hard drive failure, we can interpret the classification report above in this way:\n",
    "\n",
    "- Precision is the percentage of times the model is correct when predicting the given class.\n",
    "- Recall is the percentage of times we predicted that class against the total number of instances of the class in the data set.\n",
    "\n",
    "Therefore, the classification report above is telling us that the model predicted a failure for ~67% of the 234 fail sequences, and was correct 3% of the time. As for normal hard drives the recall rate is 1.00 and we are accurate ~100% of the time. \n",
    "\n",
    "<b>note</b>:  your precision / recall percentages may vary slightly depending on the weights learned during training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <b>Group Discussion</b>\n",
    "\n",
    "What do the results above tell us and any ideas how to improve?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of training data:\n",
      "- Number of disks :  12237697\n",
      "- Number of failed disks:  1061\n",
      "- Percentage of failed disks: 0.0087 %\n"
     ]
    }
   ],
   "source": [
    "print('Summary of training data:')\n",
    "train_all_count = df.shape[0]\n",
    "train_fail_count = df[df['failure'] >= 1].shape[0]\n",
    "print ('- Number of disks : ', train_all_count)\n",
    "print ('- Number of failed disks: ', train_fail_count)\n",
    "print ('- Percentage of failed disks: %.4f' %(train_fail_count/train_all_count*100),'%' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test data:\n",
      "- Number of disks :  3196552\n",
      "- Number of failed disks:  234\n",
      "- Percentage of failed disks: 0.0073 %\n"
     ]
    }
   ],
   "source": [
    "print('Summary of test data:')\n",
    "test_all_count = df_t.shape[0]\n",
    "test_fail_count = df_t[df_t['failure'] >= 1].shape[0]\n",
    "print ('- Number of disks : ', test_all_count)\n",
    "print ('- Number of failed disks: ', test_fail_count)\n",
    "print ('- Percentage of failed disks: %.4f' %(test_fail_count/test_all_count*100),'%' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our training and test sets have large class imbalances between normal and failed disks.  During the training process, our model is trying to find the optimal function for the lowest error loss.  Naively, if the model classifies every hard drive as normal, then the model is right more than 99% of the time.\n",
    "\n",
    "Thus, we need to come up with a strategy to address this imbalance. Some strategies to resolve class imbalance data include:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Up / Down Sample Classes\n",
    "- Weighted Class Cost Optimization\n",
    "- Evaluate various error metrics\n",
    "\n",
    "\n",
    "In the next section, we are going to resample the \"normal\" class to match the number of samples within both \"normal\" and \"failure\" classes of the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"15\"></a>\n",
    "## Dealing with Imbalanced Data\n",
    "\n",
    "When the data samples do not equally represent their classes, it is referred to as \"imbalanced data\" problem. In our example, we have __3196318__ samples of the normal hard drives, while this number for the \"failure\" class is only __234__. This clearly results in a model that is biased towards the \"normal\" samples, and the gradients of the \"failure\" class tend to vanish during the training epochs.\n",
    "\n",
    "\n",
    "```\n",
    "              precision    recall  f1-score   support\n",
    "\n",
    "      normal       1.00      1.00      1.00   3196318\n",
    "        fail       0.67      0.03      0.05       234\n",
    "```\n",
    "\n",
    "\n",
    "To mitigate the issue, in the next section we are going to resample the \"normal\" class to be roughly equal to the number of samples in the \"failure\" class (by a factor of 1.2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled Dataset Sizes:\n",
      "- Train: Number of Normal Disks : 1273\n",
      "- Train: Number of Failed Disks : 1061\n",
      "- Test:  Number of Normal Disks : 281\n",
      "- Test:  Number of Failed Disks : 234\n"
     ]
    }
   ],
   "source": [
    "# Sampling training and test data sets for smaller, more balanced test\n",
    "num_normal_delta = round(train_fail_count * 0.2)\n",
    "num_normal_test_delta = round(test_fail_count * 0.2)\n",
    "\n",
    "# Sample training data\n",
    "df_tmp = df[df['failure'] > 0]\n",
    "sample_train_count_failed = df_tmp.shape[0]\n",
    "df_tmp = df_tmp.append(df[df['failure'] == 0].sample(n=(df_tmp.shape[0]+num_normal_delta)))\n",
    "sample_train_count_normal = df_tmp.shape[0]-sample_train_count_failed\n",
    "\n",
    "# separate training set features from labels\n",
    "df_train = df_tmp.drop(columns=['failure'])\n",
    "df_train_target = pd.DataFrame(df_tmp['failure'])\n",
    "\n",
    "# Sample test data\n",
    "df_tmp = df_t[df_t['failure'] > 0]\n",
    "sample_test_count_failed = df_tmp.shape[0]\n",
    "df_tmp = df_tmp.append(df_t[df_t['failure'] == 0].sample(n=(df_tmp.shape[0]+num_normal_test_delta)))\n",
    "sample_test_count_normal = df_tmp.shape[0]-sample_test_count_failed\n",
    "\n",
    "# separate test set features from labels\n",
    "df_test = df_tmp.drop(columns=['failure'])\n",
    "df_test_target = pd.DataFrame(df_tmp['failure'])\n",
    "\n",
    "# Output name data set sizes\n",
    "print('Sampled Dataset Sizes:')\n",
    "print('- Train: Number of Normal Disks :',sample_train_count_normal)\n",
    "print('- Train: Number of Failed Disks :',sample_train_count_failed)\n",
    "print('- Test:  Number of Normal Disks :',sample_test_count_normal)\n",
    "print('- Test:  Number of Failed Disks :',sample_test_count_failed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e2\"></a>\n",
    "### Exercise 2: Implications of balancing data samples\n",
    "\n",
    "Before proceeding with training, take some time and think about the implications of rebalancing the data. we have down-sampled the \"normal\" data points from 3M to ~320, a 10-thousand-time reduction in the number of samples. \n",
    "* How do you expect this down-sampling to affect each class? \n",
    "* What would be the effect on the precision/recall and f1-score values for each class? \n",
    "\n",
    "Write your answers below before training the model:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Downsampling the dominant class will increase the overall perfomance of the model, although we will see a decrease in the final accuracy value. This is one of the reasons why accuracy isn't an appropriate evaluation metric for problems with an imbalanced dataset.\n",
    "The precision, recall and F1 Score values for the \"fail\" class should increase dramatically, while the values for the \"normal\" class should decrease slightly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to build the cuDF data from the pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load sampled data into cuDF Dataframe\n",
    "\n",
    "gdf_train = cudf.DataFrame.from_pandas(df_train)\n",
    "gdf_train_target = cudf.DataFrame.from_pandas(df_train_target)\n",
    "\n",
    "gdf_eval = cudf.DataFrame.from_pandas(df_test)\n",
    "gdf_eval_target = cudf.DataFrame.from_pandas(df_test_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally perform training on the resampled training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting cuDF data sets into XGBoost DMatrix format...\n",
      "Completed converting data sets\n",
      "Starting GPU XGBoost Training with cuDF Dataframes...\n",
      "[09:33:34] WARNING: /conda/conda-bld/xgboost_1591204597597/work/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\ttrain-rmse:0.48700\teval-rmse:0.48652\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 10 rounds.\n",
      "[1]\ttrain-rmse:0.47508\teval-rmse:0.47411\n",
      "[2]\ttrain-rmse:0.46404\teval-rmse:0.46311\n",
      "[3]\ttrain-rmse:0.45345\teval-rmse:0.45215\n",
      "[4]\ttrain-rmse:0.44353\teval-rmse:0.44249\n",
      "[5]\ttrain-rmse:0.43378\teval-rmse:0.43313\n",
      "[6]\ttrain-rmse:0.42537\teval-rmse:0.42522\n",
      "[7]\ttrain-rmse:0.41774\teval-rmse:0.41780\n",
      "[8]\ttrain-rmse:0.40999\teval-rmse:0.41028\n",
      "[9]\ttrain-rmse:0.40275\teval-rmse:0.40352\n",
      "[10]\ttrain-rmse:0.39594\teval-rmse:0.39686\n",
      "[11]\ttrain-rmse:0.39017\teval-rmse:0.39238\n",
      "[12]\ttrain-rmse:0.38473\teval-rmse:0.38815\n",
      "[13]\ttrain-rmse:0.37935\teval-rmse:0.38493\n",
      "[14]\ttrain-rmse:0.37438\teval-rmse:0.38024\n",
      "[15]\ttrain-rmse:0.37002\teval-rmse:0.37631\n",
      "[16]\ttrain-rmse:0.36579\teval-rmse:0.37235\n",
      "[17]\ttrain-rmse:0.36152\teval-rmse:0.36926\n",
      "[18]\ttrain-rmse:0.35810\teval-rmse:0.36623\n",
      "[19]\ttrain-rmse:0.35479\teval-rmse:0.36377\n",
      "GPU XGBoost Training with cuDF Dataframes compeleted - elapsed time: 0.17805767059326172 seconds\n"
     ]
    }
   ],
   "source": [
    "# GPU, with cuDF\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "print('Converting cuDF data sets into XGBoost DMatrix format...')\n",
    "xgtrain = xgb.DMatrix(gdf_train, gdf_train_target)\n",
    "xgeval = xgb.DMatrix(gdf_eval, gdf_eval_target)\n",
    "print('Completed converting data sets')\n",
    "\n",
    "params = {'tree_method': \"gpu_\"+TREE_METHOD, 'max_depth': MAX_TREE_DEPTH, 'alpha': REGULARIZATION,\n",
    "          'gamma': GAMMA, 'subsample': SUBSAMPLE, 'scale_pos_weight': POS_WEIGHT, 'learning_rate': 0.05, 'silent': 1}\n",
    "\n",
    "print('Starting GPU XGBoost Training with cuDF Dataframes...')\n",
    "bst = xgb.train(params, xgtrain, 20, evals=[(xgtrain, \"train\"), (xgeval, \"eval\")],\n",
    "                early_stopping_rounds=EARLY_STOP)\n",
    "\n",
    "timetaken_gpu = time.time() - start_time\n",
    "print('GPU XGBoost Training with cuDF Dataframes compeleted - elapsed time:', timetaken_gpu,'seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is trained, let's run it over the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting XGBoost prediction on test dataset...\n",
      "XGBoost prediction completed\n"
     ]
    }
   ],
   "source": [
    "print('Starting XGBoost prediction on test dataset...')\n",
    "\n",
    "# Use trained model to predict on test data set\n",
    "preds = bst.predict(xgeval)\n",
    "\n",
    "# Convert predictions to \"normal\" (0) or \"failed\"\n",
    "y_pred = []\n",
    "THRESHOLD = 0.5\n",
    "\n",
    "for pred in preds:\n",
    "    if pred<=THRESHOLD:\n",
    "        y_pred.append(0)\n",
    "    if pred>THRESHOLD:\n",
    "        y_pred.append(1)\n",
    "\n",
    "# Output array of classifications                \n",
    "y_pred = np.asarray(y_pred)\n",
    "        \n",
    "y_true = df_test_target.values.reshape(len(preds))\n",
    "print('XGBoost prediction completed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally review accuracy with the new results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (Eval) 0.86214\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      normal       0.81      0.98      0.89       281\n",
      "        fail       0.97      0.72      0.83       234\n",
      "\n",
      "    accuracy                           0.86       515\n",
      "   macro avg       0.89      0.85      0.86       515\n",
      "weighted avg       0.88      0.86      0.86       515\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy (Eval)\", round(accuracy_score(y_true, y_pred),5))\n",
    "\n",
    "print(classification_report(y_true, y_pred, target_names=[\"normal\", \"fail\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the recall/precision values for the \"fail\" class are hugely improved. The cost we pay here is the reduction in the precision/recall values of the \"normal\" class. Could you explain some scenarios where each of these strategies would be applicable? \n",
    "Finally, Compare the obtained results with your answers to exercise 2. How closely were you able to predict the results?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this lab, we learned how to work with a sample dataset and curate, normalize, and filter data to prepare it for our XGBoost model. We reviewed the XGBoost model and learned how to utilize the cuDF data structure to accelerate the training/testing phases for the XGBoost model. Moreover, we learned how to modify the data to mitigate the issue of imbalanced classes.\n",
    "In the next two labs, we will use deep-learning-based approaches for classification and prediction."
   ]
  }
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