Chapter 5 - Sup. Learning - Regression and Time Series models/Case Study 3 - Investor Risk Tolerance and Robo-advisors/InvestorRiskToleranceAndRoboAdvisor.ipynb

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   "cell_type": "markdown",
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    "_cell_guid": "83708667-4fdc-1563-7b3a-06b6575d2865"
   },
   "source": [
    "\n",
    "\n",
    "# Investor Risk Tolerance and Robo advisors\n",
    "\n",
    "The goal of this case study is to build a machine learning model to predict the risk tolerance or risk aversion of an investor, and use the model in a robo-advisor dashboard.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1. Problem Definition](#0)\n",
    "* [2. Getting Started - Load Libraries and Dataset](#1)\n",
    "    * [2.1. Load Libraries](#1.1)    \n",
    "    * [2.2. Load Dataset](#1.2)\n",
    "* [3. Data Preparation and Feature Selection](#2)\n",
    "    * [3.1. Preparing the predicted variable](#2.1)    \n",
    "    * [3.2. Feature Selection-Limit the Feature Space](#2.2)\n",
    "* [4.Evaluate Algorithms and Models](#4)        \n",
    "    * [4.1. Train/Test Split](#4.1)\n",
    "    * [4.2. Test Options and Evaluation Metrics](#4.2)\n",
    "    * [4.3. Compare Models and Algorithms](#4.3)\n",
    "* [5. Model Tuning and Grid Search](#5)  \n",
    "* [6. Finalize the Model](#6)  \n",
    "    * [6.1. Results on test dataset](#6.1)\n",
    "    * [6.2. Feature Importance](#6.1)\n",
    "    * [6.2. Feature Intuition](#6.3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='0'></a>\n",
    "# 1. Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the supervised regression framework used for this case study, the predicted variable\n",
    "is the “true” risk tolerance of an individual10 and the predictor variables are demo‐\n",
    "graphic, financial and behavioral attributes of an individual\n",
    "\n",
    "For this case study the data used is from survey of Consumer Finances which is conducted by the Federal Reserve\n",
    "Board. The data source is : \n",
    "https://www.federalreserve.gov/econres/scf_2009p.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# 2. Getting Started- Loading the data and python packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.1'></a>\n",
    "## 2.1. Loading the python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "5d8fee34-f454-2642-8b06-ed719f0317e1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "import copy \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "#Libraries for Deep Learning Models\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import LSTM\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "#Libraries for Statistical Models\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#Libraries for Saving the Model\n",
    "from pickle import dump\n",
    "from pickle import load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.2'></a>\n",
    "## 2.2. Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "787e35f7-bf9e-0969-8d13-a54fa87f3519",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataset = pd.read_excel('SCFP2009panel.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Diable the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19285, 515)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a>\n",
    "## 3. Data Preparation and Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.1'></a>\n",
    "## 3.1. Preparing the predicted variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset from \"Survey of Consumer Finances\" contains the Household's demographics, net worth, financial and non-financial assets for the same demographics in 2007 (pre-crisis) and 2009(post-crisis). \n",
    "\n",
    "We prepare the predicted variable, which is the \"true\" risk tolerance in the following steps. There are different ways of getting the \"true\" risk tolerance. The idea and the purpose of this case study is to come up with an approach to solve the behavioral finance problem using machine learning. \n",
    "\n",
    "The steps to compute the predicted variables are as follows: \n",
    "\n",
    "1) Compute the Risky asset and the riskless assets for all the individuals in the survey data. Risky and riskless assets are defined as follows: \n",
    "* **Risky assets** is investments in mutual funds, stocks, bonds, commodities, and\n",
    "real estate, and an estimate of human capital. \n",
    "* **Risk Free Assets**: checking and savings balances,certificates of deposit, and other cash balances and equivalents.\n",
    "\n",
    "2) We take the ratio of risky assets to total assets of an investor and consider that as a measure of risk tolerance of an investor. From the data of SCF, we have the data of risky and riskless assets for the individuals for 2007 and 2009. We use this data and normalise the risky assets with the stock price of 2007 vs. 2009 to get risk tolerance. \n",
    "\n",
    "* **Risk Tolerance**  just defined as the ratio of Risky Asset to Riskless Assets normalised with the average S&P500 of 2007 vs 2009. \n",
    "Average S&P500 in 2007: 1478\n",
    "Average S&P500 in 2009: 948\n",
    "\n",
    "3) In a lot of literature, an intelligent investor is the one who doesn't change its risk tolerance during the change in the market. So, we consider the investors who change their risk tolerance by less than 10% between 2007 and 2009 as the intelligent investors. Ofcourse this is a qualitative judgement and is subject to change. However, as mentioned before more than being accurate and precise the purpose of theis case study is to demonstrate the usage of the machine learning and provide a machine learning based framework in behavioral finance and portfolio management which can be further leveraged for more detailed analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Average SP500 during 2007 and 2009\n",
    "Average_SP500_2007=1478\n",
    "Average_SP500_2009=948\n",
    "\n",
    "#Risk Tolerance 2007\n",
    "dataset['RiskFree07']= dataset['LIQ07'] + dataset['CDS07'] + dataset['SAVBND07'] + dataset['CASHLI07']\n",
    "dataset['Risky07'] = dataset['NMMF07'] + dataset['STOCKS07'] + dataset['BOND07'] \n",
    "dataset['RT07'] = dataset['Risky07']/(dataset['Risky07']+dataset['RiskFree07'])\n",
    "\n",
    "#Risk Tolerance 2009\n",
    "dataset['RiskFree09']= dataset['LIQ09'] + dataset['CDS09'] + dataset['SAVBND09'] + dataset['CASHLI09']\n",
    "dataset['Risky09'] = dataset['NMMF09'] + dataset['STOCKS09'] + dataset['BOND09'] \n",
    "dataset['RT09'] = dataset['Risky09']/(dataset['Risky09']+dataset['RiskFree09'])*\\\n",
    "                (Average_SP500_2009/Average_SP500_2007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "   YY1  Y1         WGT09  AGE07  AGECL07  EDUC07  EDCL07  MARRIED07  KIDS07  \\\n",
       "0    1  11  11668.134198     47        3      12       2          1       0   \n",
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       "4    1  15  11917.722907     47        3      12       2          1       0   \n",
       "\n",
       "   LIFECL07  ...  TRANSFOTHINCPCT  PSAVINGPCT  LEVERAGEPCT   I   RiskFree07  \\\n",
       "0         2  ...              0.0   93.125197   270.403054  57  7994.813847   \n",
       "1         2  ...              0.0   93.125197   249.593620  57  7994.813847   \n",
       "2         2  ...              0.0   93.125197   209.233358  57  7984.457871   \n",
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       "4         2  ...              0.0   93.125197   232.690767  57  7994.813847   \n",
       "\n",
       "   Risky07  RT07  RiskFree09  Risky09      RT09  \n",
       "0      0.0   0.0       16000    17000  0.330422  \n",
       "1      0.0   0.0       19000    18000  0.312036  \n",
       "2      0.0   0.0       13000    12000  0.307876  \n",
       "3      0.0   0.0       25000    13000  0.219429  \n",
       "4      0.0   0.0       17000    12000  0.265410  \n",
       "\n",
       "[5 rows x 521 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2 = copy.deepcopy(dataset)  \n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us compute the percentage change in risk tolerance between 2007 and 2009. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset2['PercentageChange'] = np.abs(dataset2['RT09']/dataset2['RT07']-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for the rows with null or nan values and removing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values = True\n"
     ]
    }
   ],
   "source": [
    "#Checking for any null values and removing the null values'''\n",
    "print('Null Values =',dataset2.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values = False\n"
     ]
    }
   ],
   "source": [
    "# Drop the rows containing NA\n",
    "dataset2=dataset2.dropna(axis=0)\n",
    "\n",
    "dataset2=dataset2[~dataset2.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
    "\n",
    "#Checking for any null values and removing the null values'''\n",
    "print('Null Values =',dataset2.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the risk tolerance of 2007 and 2009. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218832b3128>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset2['RT07'], hist=True, kde=False, \n",
    "             bins=int(180/5), color = 'blue',\n",
    "             hist_kws={'edgecolor':'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the risk tolerance of 2007, we see that a significant number of individuals had risk tolerance close to one.Meaning the investment ws more skewed towards the risky assets as compared to the riskless assets. Now let us look at the risk tolerance of 2009."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x218832f8be0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset2['RT09'], hist=True, kde=False, \n",
    "             bins=int(180/5), color = 'blue',\n",
    "             hist_kws={'edgecolor':'black'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset3 = copy.deepcopy(dataset2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, the behavior of the individuals reversed in 2009 after crisis and majority of the investment was in risk free assets. Overall risk tolerance decreased, which is shown by majority of risk tolerance being close to 0 in 2009. \n",
    "In the next step we pick the intelligent investors whose risk tolerance change between 2007 and 2009 was less than 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset3 = dataset3[dataset3['PercentageChange']<=.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign the true risk tolerance as the average risk tolerance of these intelligent investors between 2007 and 2009. This is the predicted variable for this case study. The purpose would be to predict the true risk tolerance of an individuals given the demographic, financial and willingness to take risk related features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset3['TrueRiskTolerance'] = (dataset3['RT07'] + dataset3['RT09'])/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us drop other labels which might not be needed for the prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset3.drop(labels=['RT07', 'RT09'], axis=1, inplace=True)\n",
    "dataset3.drop(labels=['PercentageChange'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.2'></a>\n",
    "## 3.2. Feature Selection-Limit the Feature Space "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.2.2'></a>\n",
    "### 3.2.2.  Features elimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to filter the features further we do the following:\n",
    "1. Check the description in the Data Dictionary (https://www.federalreserve.gov/econres/files/codebk2009p.txt, https://www.federalreserve.gov/econresdata/scf/files/fedstables.macro.txt)and only keep the features that are intuitive\n",
    "The description is as follows: \n",
    "\n",
    "\n",
    "* AGE: There are 6 age categories, where 1 represents age less than 35 and 6 represents age more than 75.\n",
    "* EDUC: There are 4 education categories, where 1 represents no high school and 4 represents college degree.\n",
    "* MARRIED: It represents marital status. There are two categories where 1 represents married and 2 represents unmarried. \n",
    "* OCCU: It represents occupation category. 1 represents managerial category and 4 represents unemployed.\n",
    "* KIDS: It represents number of kids. \n",
    "* NWCAT: It represents net worth category. There are 5 categories, where 1 net worth less than 25 percentile and 5 represents net worth more than 90th percentile. \n",
    "* INCCL: It represents income category. There are 5 categories, where 1 income less than 10,000 and 5 represents net worth more than 100,000\n",
    "* RISK: It represents the willingness to take risk on a scale of 1 to 4, where 1 represents highest level of willingness to take risk. \n",
    "\n",
    "2. Keep only the intuitive factors as of 2007 only and remove all the intermediate features and features related to 2009, as the variables of 2007 are the only ones required for predicting the risk tolerance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_list2 = ['AGE07','EDCL07','MARRIED07','KIDS07','OCCAT107','INCOME07','RISK07','NETWORTH07','TrueRiskTolerance'\n",
    "]\n",
    "\n",
    "drop_list2 = [col for col in dataset3.columns if col not in keep_list2]\n",
    "\n",
    "dataset3.drop(labels=drop_list2, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the correlation among the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21881dc5390>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fvPHKYE6fSqZp87507nofX3zyMHXqtSY0rB6vv3QLxXyLM+jOV9m142f+ST7hvQ2S/4z/VPnZlVdeSUBAgLfDKJS6dg/n84+/AWD92t8I8PcjKLhihjZJSSf5ecUaAE6dOsXm37YSGhbs8Vg96dJql7E/dh+RRw5wOuU0P25cSLvL2mdoExV3kN0Hd+H6j1/116tXL959910AfvnlF8qVK0dISEimdmcSmmLFilGiRAls6n7bvn07O3bs8FzAXlK6ckWS4xI4dfQY1uUi/ve9+NWrmqldUIfGxP68BXs6JW1eyUrlOLbX/ctwyomTpCT/Q+mwipnWLQou5L1XI6gmvj6+/LrTfb5K+ieJ5FPJHovdk6pWuYzYw/s4EneAlJTTbNq8gMvqZ9xPu/eu41Tq9v+9bwsB/pnP2w0u68QfO39Oa1cU6ZjKm8pVLiXuyH6OxkXiSjnN778tpF79dhna/Ll3PadTt//Avi34BwQBULFSTf7auwHrSuHUqZNEH9zJxXVaeXwb5L/pP5XUSPZCQoOIPBCVNh0ZGU1IaPYJi7+/HxFdO7Dip6L7qx5AJf9KxBw9W94TEx9NpYBKeV6/RLESvD18Nm/c93amD8+ipnLlyuzbty9tev/+/VSuXDnLtvPnzycmJobExEQ+//xzT4VYKBT3K8Op+ONp06cTTlDcr2yGNqVCKlA8oCyJO/dnmH8y+gj+9aqCMRQvdxGlQwMp7l/GI3F72oW896pVqsaxpESevGUas0e8z33XDMfHFM2PuwD/IOLjz+6n+IQY/P2Dsm1/ZdNebN/5c6b5DRt0YeNvPxRIjIWFjqm88fOvRHx8TNp0QkIMfjnsp0bNerJrxyoAoqN2cnHdVhQrXpLSZQKoUasp/gFF+8fPf8vawv1womzfkcaY54wxbTwZjHiPMZnvbGuzOap9fX155c1neGvm+/z91/4s2xQV57NfstLniZ7cNmMQEz98hBHXjqJyYNZf8ouC89lX3bp1IzQ0lJIlSxIeHl7QoTlAxv0U2uVKohb8mqlV3IZdnEo4Qe07exDa9UpO7IvBuhz66ZOLC3nv+fr40rBmY16c+wK3zRhEWGBlrmnWI79DLLyy2U+NG15Nlcr1+Wn5uxnm+10USEjwxfyxc5UnovMaHVN5lMV+IpvddEXDboSF1efn5e8DsGfXGnbu+Jnb7nqD626Ywv6/t+BynS7AYEXOyulnhv8BLxhj/jLGTDPGNM7rkxpjhhhj1hpj1s6cOfPCo5QCMfj2m1j405cs/OlLoqNiCKt8tlQoLCyY6KiYLNd7evqj7N39F2++9l6Wy4uSmPgYgsqd/ZUpKCCY2ITYPK9/pm3kkQOs37OeumH18j1Gb7r33nvZsGEDGzZsIDIykqpVz5ZRValShcjIyGzXTU5OZs6cOfTq1csToRYapxJPUDzgbM9MMf8ynEo8W2/uU7I4JYPKUXNQN+oOv47SVSpR/cZwSoUGgrVELfiV3TO/5e9PluBbqgT/HEnwxmYUuAt578XEx7Aj8g8ijxwgxZXCsi1LqVflkoIK1aviE2IISPdLeIB/EAmJhzK1u7h2c8Lb38as90eRknIqw7IGV0Tw+9YluFwpmdYrSnRM5U1ifAwBAWd7+/z9g0hMyHxM1ax9JW07DObj90dnOKZWLJ3FzJdu4f13hoOBI4f3ZVpXpCDklNTst9Y2AzoDicD7xpjtxpiJxpi6OT2ptXamtbaZtbbZkCFD8jNeyUez3vqIiPZ9iWjfl/nzFtHvRveXyybNGpCQkEhMdOaT/ZiHhuPnfxETHnrS0+F6xbZ9W6lasRqh5cMo5luMzo0iWL51WZ7W9SvtR3Hf4gAElAmgQfUG7I3em8tazvLKK6/QuHFjGjduzNdff80tt9wCQIsWLYiPjycqKipD+7Jly6ZdZ+Pr60v37t3Zvn27x+P2pqQDsZSs4E/xchdhfHwIuKwmiTvO9ni6kk+x/ZlP2DHjC3bM+IKk/Yf46+PF7tHPivliirvHdylbKxTrspkGGCgqLuS9t23fVvxK+1GubDkAml58ZZF7752x/8BWKgZWpXz5MHx9i9Hwii5s3Z5xP4WF1uO6Xg8x+4NRHD8el+k5GjXoWuRLz0DHVF4dOLCNCoFVKVc+FB/fYlzWIIId2zMOCBQSWpdreo3lk/dHcyLdMWWMD6VL+wMQFHwxwSEXs3vXGo/GL/9dOY1+ZgGstTuBKcAUY0wD4CbgO+Digg8vf40aNYo1a9YQFxdHu3btGDZsGP379/d2WIXCooXL6BTRjp/XzScp6SQj7xuftmzhT18S0b4voWHBjHjwbnbu2M2CpV8A8M6bH/Dhe194K+wCl+JK4dmvpzH9zhn4+Pgyd80c9kbv4c4ud7Ft/zZWbF1G/SqXMnXQNPzK+NO2flvu6HIXA569gRpBNRl73Thc1oWP8eG9JbMzjLJT1Hz33Xd0796dXbt2ceLECW699da0ZRs2bKBx48aULVuWOXPmULJkSXx9fVm8eDGvvfYaAL179+bFF1+kUqVKzJs3j40bN9KtWzdvbU7BsZbI73+hxoDOGOND3MadJB86SlCHRiRFHiZxR/a/ahYrW4oaAyKw1nI68QT7vy66Iw9eyHvPZV28OPcFXrzrFQyG7Qe2880vX3l7kwqEy5XCN3Of5o5BL+Lj48uv6+YQHbOHLp3uYv+BbWzdvoxrug2nRInSDLxxKgBHj0Yz6wP3UM7ly4VSLiCYPX+u9+ZmeISOqbyxrhS+//YZBgx+AWN82Lh+Lodi9tKh051EHtjOju3L6dxtGCVKlqHfTY8DEH80mk/eH42PbzEGD3GPYJl88jhffTYJW8R7AP8ta7Mo85MLYrKrJzXGbLDW5rnkLAdFs+A7n4VVuNTbIThG9dvL5t5IWP3MWm+H4AibH53l7RAc487El7wdgiNcVVIfe3m1PFlf7PLi6hK+3g7BMSY8vtoRB9Xq/QmF+kTRsoq/I/ZjejmVn13lsShERERERET+pWzLz6y1x4wxAUA3oDLuHpdI4Adr7VEPxSciIiIiUqQU0Xu3elVOQzrfAqwHOgBlgLJAR2Bd6jIRERERERGvy2mggPFA03N7ZYwx5YFfgHezXEtERERERMSDckpqDFlf5O9KXSYiIiIiIuerUA8T4Ew5JTWPA+uNMQuAM2OMVgMicA/xLCIiIiIi4nXZXlNjrZ0NNAN+ApKBf4ClQDNr7SxPBCciIiIiIpKbbHtqjDGXWGu3Ax8bY0paa5PTLWtprV3tkQhFRERERIoQ3Xwz/+V0n5oP0/296pxlrxRALCIiIiIiIuctp6TGZPN3VtMiIiIiIiJekdNAATabv7OaFhERERGRPNDNN/NfTklNFWPMDNy9Mmf+JnW6coFHJiIiIiIikgc5JTWj0/299pxl506LiIiIiIh4RbZJTeqQziIiIiIiIoVaTkM6twVqWWvfTZ3+HKiQuvgxa+1iD8QnIiIiIlK06Or0fJdT+dmjwLB00/WAwUBZ4CFASY2IiIiIiHhdTkM6+1trt6ab3mmtXWetXQb4FXBcIiIiIiIieZJTT0259BPW2r7pJoMLJhwRERERkaJNQzrnv5x6arYbY645d6YxpgfwR8GFJCIiIiIiknc59dSMBOYZY/oB61PnNQVaAz0KOjAREREREZG8yLanxlq7C2gALAdqANWBn4DbgPs9EZyIiIiISJFjTeF+OFBOPTVYa5OBt40xjYGbgInAXuALD8QmIiIiIiKSq5zuU1MXuBF3MnMY+AQw1tqOHopNREREREQkVzn11GzHXXrWM7UUDWPMSI9EJSIiIiJSRGn0s/yX0+hn1wFRwBJjzBvGmE6AM4vsRERERESkyMppoICvrLU3AJcAS3GPhhZsjHnVGNPFQ/GJiIiIiIjkKKeeGgCstcettR9Ya3sAVYCNwP8VeGQiIiIiIkWRLeQPB8o1qUnPWnvEWvu6tTa8oAISERERERE5H+eV1IiIiIiIiBQ2SmpERERERMTRcrz5poiIiIiI5DMN6Zzv1FMjIiIiIiKOpqRGREREREQczVhbsOO2hVW41KEDw3lW5JGt3g7BMUKb9vB2CI5QNTzG2yE4woEvy3o7BOc4cdzbETiCKVHC2yE4RkrFct4OwRHKtTnq7RAcY9uMFY64UfzyzccK9ffjq664yBH7MT311IiIiIiIiKMpqREREREREUfT6GciIiIiIh5UwFd//Cepp0ZERERERM6LMaabMeYPY8wuY8z/ZbH8eWPMxtTHDmPM0XTLUtItm5Mf8ainRkRERERE8swY4wu8DEQA+4FfjTFzrLVpI19Za0emaz8MaJzuKZKstY3yMyYlNSIiIiIinuT8m282B3ZZa/cAGGM+BnoB2Q3nexMwsSADUvmZiIiIiIicj8rAvnTT+1PnZWKMqQ7UBBanm13KGLPWGLPaGNM7PwJST42IiIiIiKQxxgwBhqSbNdNaOzN9kyxWy274gxuBz621KenmVbPWRhpjagGLjTGbrbW7LyRmJTUiIiIiIp5UyMvPUhOYmTk02Q9UTTddBYjMpu2NwNBznj8y9d89xpiluK+3uaCkRuVnIiIiIiJyPn4F6hhjahpjSuBOXDKNYmaMqQeUB1alm1feGFMy9e+KQBuyvxYnz9RTIyIiIiIieWatPW2MuQ/4AfAF3rbW/m6MmQystdaeSXBuAj62NsOdeeoDrxtjXLg7WKamHzXt31JSIyIiIiLiSUXg5pvW2u+A786ZN+Gc6UlZrPczcEV+x6PyMxERERERcTQlNSIiIiIi4mhKakRERERExNF0TY2IiIiIiCcV8iGdnUg9NSIiIiIi4mhKakRERERExNFUfiYiIiIi4kFG5Wf5Tj01IiIiIiLiaEpqRERERETE0VR+JiIiIiLiSSo/y3fqqREREREREUdTUiMiIiIiIo6m8jMREREREU+y3g6g6FFPjYiIiIiIOJqSGhERERERcTSVn4mIiIiIeJJL9Wf5TT01IiIiIiLiaEpqRERERETE0ZTUiIiIiIiIo+maGhERERERDzIub0dQ9BT5pGbKkw8RHtGOpKQkRg59iM2/bcuwvHTpUrz+zvPUqFGVFJeLhfOX8MTk570UbeExbtw4li5dSmBgIHPnzvV2OF4xZfQQOrVpRtLJZEZMms7m7bsztWlwSW2mPzqSUiVLsGjlWh55eiYArz05htrVqwAQ4FeW+MTjRNw8nOLFijFt/FAaXloHl8vyyDMzWbVus0e3q6C0rNeSB659AB8fH75Z8w3vLnk3w/LGNRsz8tqRXBx6MQ9/8DCLNy9OW7bqqVXsjnLv36i4KB6c9aBHY/eGyROGEd6hBUlJJxk55im2/L4zU5uxD9xOvz5dCPD3o26D7mnz/3dTTwb9rzeuFBfHTyQxZvyz7Nz1lyfD95jJUx4gvFNr934aMZktm//I1Gbs/91Dv37dCSjnR92LO6TNn/ToSFq3bgq4z/WBFctz6SWdPBW6R02edD/hHVu599ODT7Bly45MbcaMHkK/vl0JCPCj3qVd0ub373c1Dz90L1FRsQDMevcLPvq46J73L+Tcflndmjz10FBKlihBSkoK/zf1VTb+nnlfO13b+i14qO/9+Pj48Pmqubz54/sZljer3ZBxfYdTN6w2D8yexIKNS9OWzbznWRpWv5T1e37jnpljPRy5/JcV6fKz8M7tqFm7Om2adWPMyIk8+ezELNu99tI7tGvZgy7tr+PKFk3o2PkqD0da+PTt25c333zT22F4TXibZtSqGkbr3kMY/dhLTB13b5btpo4byujHXqJ17yHUqhpGeOoXqLvHTSPi5uFE3DyceYt/5rslPwMwoE9X9/PfcB833Pswk0bejjHGMxtVgHyMD2P6jOH+t+7nhmduoGujrtQMqpmhTdTRKCZ/OpkFGxdkWj/5VDIDnx/IwOcH/icSmvAOLahZozJtwwcydvyzPDl5ZJbtFi76mWv63JNp/lffLqJz99vp0vNOXpn5MRPHZ318Ol14eGtq1qpK29bXMXb0kzw5NesvSAsXLOea7oMzzZ808Xm6RAykS8RA3n77U77/bkkBR+wd4R1bUrNmVdq2v5Gx457myceyfg/9+ONKevQakuWyb+cupmv3W+kgJDRYAAAgAElEQVTa/dYindBc6Ln9kftv5bmZHxFx83CmvfYBjwy/1ZPhe4SP8eGR/qMY8tqD9HxiINc07UztkBoZ2kTGRTPugyeYt+7HTOu/vehDxr7/mIeiFTmrSCc1XbuH8/nH3wCwfu1vBPj7ERRcMUObpKST/LxiDQCnTp1i829bCQ0L9nishc2VV15JQECAt8Pwmm7tW/DZPHdPwvotf+B/UVmCKpbP0CaoYnn8LirNus3bAfhs3mK6dWiZ6bl6dm7L1/OXAVC3VlVWrNkEwOG4eOITj9Pw0joFuSkecVm1y9gfu5/II5GcTjnNgo0LaHdZuwxtDsYdZNfBXbis+ty7dm7D51+5k7v1G7cR4F+WoEoVMrVbv3EbMYeOZJp/7NiJtL/LlCmFtUVzaNCu3drx+WffAbB+/Rb3OTwoMFO79eu3EBNzOMfn6t27C19/nTmhLgq6RFzF51/MB2D9ht/x978o6/204fdc91NRd6HndmvhorJlAPC/qAxRsUVvfzaoXp+/D+1n/+FITqWc5rv1PxJ+RdsMbSKPRLEjcneW5/PVO9Zx/OSJTPPlHK5C/nCgHJMaY0w5TwVSEEJCg4g8EJU2HRkZTUho9gmLv78fEV07sOKn1Z4ITwqxkKBAIqNj06YPxhwmtFLGLwmhlQKJjD77gXYw+jAh53yRaNn4MmKPHGXvvkgAtu7YS9cOLfH19aFqWDAN6tem8jmJthNV8q9E9NHotOmY+BgqBVTK8/olipVg9vDZvHXfW7S/rH1BhFiohARXJDIyJm36YFQsISHndxwMGtiblYvf5+GxdzFh8ov5HWKhEBISRGTk2ePq4MEYQkKDzvt5KlcJoWq1MFauWJuf4RUaISHnHk8xhJzneeXqq9uzcP4sXn91CqH/Yh87xYWe2yc8M5MJI25l7bx3mDDidp58cbZnAvegoHKViDp69niKPnqI4PM4n4t4S249NbHGmB+NMbefT4JjjBlijFlrjFl7IjnuAkP897Iq68nuF01fX19eefMZ3pr5Pn//tb+gQ5NCLk/HTpZtMk737taer35Yljb90ZyFHIyOZf5705n8wJ2s3bSd0ykp+RKzN2VZQncenQfXPnEtg2YM4pEPH2HktSOpHFg5/4IrhLI+vs7vOWa//zVtwgfy+FMzuX/o//IpssIly8PqX/RK9erVhXlzF+NyOfTnx1xc6PG08MeVtGrTn4hug1m+Yi3Tnxufj9EVLhd6br+lf3cmPvsmza65lYnPvcGzE+4viDC9ypD3704ihUluAwVsA6YDNwHTjDErgI+Ab6y1SdmtZK2dCcwECKtwqUffCYNvv4kBt/QHYOOGzYRVDklbFhYWTHRUTJbrPT39Ufbu/os3X3vPI3FK4TO4/zVp17xs2rqTsHS/dIYGBRIVm7EM6GBMLGHBZ3/hCw0OJPrQ2V/3fH196N6xFV0Hjkibl5LiYuJzZ69VmvP20+z9OzLft8XTYuJjCC53thc0KCCIQwmH8rx+bIL7l9PII5Gs37OeemH1OHD4QL7H6U2DBvZmwA3XALBx83bCwoJgnXtZaEhFotP9enw+vpm7mCenjMi9oUMMGtyPAQN6A7Bx01bC0pUDh4YGER2V9+PqjF69Ihj/0LR8i7EwGHRLX26+sScAm37b5j6eUoWGBBEdk/fj6ejRhLS/P/zoWx76v8zXcTlZfp7br+/RKW3QgG8XruDZh4cXdPgeF300hpByZ4+n4HKViEn4d+cnyZ5xKVHMb7n11Jyy1s611g4AqgAfANcD+40xHxZ4dP/CrLc+IqJ9XyLa92X+vEX0u7EXAE2aNSAhIZGYLL44jHloOH7+FzHhoSc9Ha4UIrM+m5d2cf/3S1fR/5pwAJpcXo/EYyeIic3Y6xgTG8ex40k0ubweAP2vCWf+T7+kLW/XvBG7/tzPwXQ17KVLlaR0qZLu5S0akZKSwo69+wp60wrc1n1bqVqxKmHlwyjmW4wujbqwfOvyPK3rV9qP4r7FAQgoE0CD6g3YG723IMP1itnvf02XnnfSpeed/LBgJf36uEefatKoPgmJx7O8diY7NWuc7cnq3LEle/8sOgng7Fmfp13c/8P3P9Gvv3vUtyZNLich8dh5XxNSu3Y1Asr5sXZt0Rhl8IzZ736ZdmH//AXL6XddNwCaNL6MxPPcT+mvv+kS0ZZdRWwkvfw8t0cfOkKrplcA0PbKhmmlxUXJ5r+3U71SVSpXCKW4bzG6N+nMks0rvR2WSK5y66lJ64NM7Zn5FPjUGBMA9C7IwPLDooXL6BTRjp/XzXcPc3nf2S71hT99SUT7voSGBTPiwbvZuWM3C5Z+AcA7b37Ah+994a2wC4VRo0axZs0a4uLiaNeuHcOGDaN///7eDstjFq1YS6c2zVj1zRsknUxm5KTpacsWfjiDiJvdv87935OvMH3SSEqVKsHiletYvPJszX6vru34Ol3pGUBg+QA+emky1loOxhxm2CPPemaDCliKK4Wnv36aGXfOwMfHh2/XfMue6D0M6TKEbfu3sXzrcupXqc+0QdPwL+PPVfWvYkiXIdz47I3UCKrBuOvGYa3FGMO7S95lb0zRS2rSW7R0NeEdWrBy8fsknUxm1Nin0pYt+PYNuvS8E4DxY++iT89OlC5dkrUrPuXDT+fx3IzZDP5fH65q3ZTTp08Tn5DIiNFTvbUpBWrRopWEd2rNylVfkpR0klEjp6QtW7DwfbpEDARg/MPD6NOnC6VLl2Ltum/58MM5PPfsGwD06t2Vb75e6JX4PWXx4lWEd2zFimWfcDLpJKMefCJt2Q/fvUPX7u4RusaPu4fevSIoXboUv67+ko8+nstz09/mtsH9iIhoS8rpFI7GJzDywce9tSkF7kLP7Q8+9iJTHhyCr68vyf/8w+jHit71bCmuFB77/DnevPc5fHx8+HL1PHZF7WVY99vZ8vd2lmxZyeXVLuHFO57Av7QfHS9vw7Crb6fnk+4y2Pfuf5lawdUoU6IMSyZ/ycMfTmXl9jVe3ir5LzA51UkaYx601j5zIS/g6fIzp4o8stXbIThGaNMe3g7BEaqGZ11qKRkd+LKst0NwjhPHvR2BI5gSJbwdgmOkVHT0eEQeU67NUW+H4BjbZqxwxH0SVi5KKNTfj9t08nfEfkwvx/KzC01oREREREREClpuQzoHGGOmGmO2G2MOpz62pc7TzysiIiIiIuJ1uV1T8ymwGOhgrY0CMMaEAIOAz4CIgg1PRERERKRo0ehn+S+30c9qWGufOpPQAFhro6y1TwHVCjY0ERERERGR3OWW1PxljBljjEm7UYAxJtgYMxZw/ji0IiIiIiLieLmVn90A/B/wkzHmzJ2YooE5uO9XIyIiIiIi50PlZ/kux6TGWhsHjE19ZGCMuQ74b9/MRUREREREvC638rOcPJ9vUYiIiIiIiPxLF5LUOO6mPCIiIiIiUvTkdk1NTlQMKCIiIiJynjSkc/7LMakxxmwm6+TFAMFZzBcREREREfGo3HpqengkChERERERkX8pt6SmOBBsrV2ZfqYx5iogssCiEhEREREpqlzeDqDoyW2ggOlAYhbzk1KXiYiIiIiIeFVuSU0Na+1v58601q4FahRIRCIiIiIiIucht/KzUjksK52fgYiIiIiI/Bdo9LP8l1tPza/GmDvPnWmMuR1YVzAhiYiIiIiI5F1uPTUjgK+MMQM4m8Q0A0oAfQoyMBERERERkbzIMamx1kYDrY0xHYHLU2fPs9YuLvDIRERERESKIGNVfpbfcuupOSMWiEr9O6aAYhERERERETlvOSY1xpgA4BugKvAbYIArjDF/A72stQkFH6KIiIiIiEj2cuupmQKsBcKttS4AY4wPMBV4HBhWsOGJiIiIiBQxLt19M7/lltR0BhqcSWgArLUuY8xDwOYCjUxERERERCQPchvS+R9r7elzZ6bOSy6YkERERERERPIu15tvGmMa476WJj0DlCyYkERERERERPIut6QmCnguh2UiIiIiInIejEtDOue33O5T08FDcYiIiIiIiPwrOV5TY4wZk+7v/ucse6KgghIREREREcmr3AYKuDHd3+POWdYtn2MRERERESnyjMsW6ocT5ZbUmGz+zmpaRERERETE43JLamw2f2c1LSIiIiIi4nG5jX7W0BiTgLtXpnTq36ROlyrQyEREREREiiKHlngVZrmNfuZ7oS9Q/fayF/oU/wmhTXt4OwTHOLhurrdDcITQ8pd4OwRHsN0qeTsEx3DF+Xs7BEdo3+Qvb4fgGCu+0zGVFwkLSns7BJFCL7fyMxERERERkUItt/IzERERERHJR8a6vB1CkaOeGhERERERcTQlNSIiIiIi4mgqPxMRERER8SCn3uCyMFNPjYiIiIiIOJqSGhERERERcTQlNSIiIiIi4mi6pkZERERExJNcGtI5v6mnRkREREREHE1JjYiIiIiIOJrKz0REREREPMio/CzfqadGREREREQcTUmNiIiIiIg4msrPREREREQ8yFjr7RCKHPXUiIiIiIiIoympERERERERR1P5mYiIiIiIB2n0s/ynnhoREREREXE0JTUiIiIiIuJoKj8TEREREfEklZ/lO/XUiIiIiIiIoympERERERERR1P5mYiIiIiIBxmr8rP8pp4aERERERFxNCU1IiIiIiLiaEpqRERERETE0XRNjYiIiIiIJ2lI53ynnhoREREREXE0JTUiIiIiIuJoKj8TEREREfEg40rxdghFjnpqRERERETE0YpcT03Leq0Yce0D+Pr4MGfNN7y3ZHaG5Y1qNmbEtaOoHXoxEz4Yz5LNi9OWrXhqNbujdgMQHRfFmFkPeDR2T5gyegid2jQj6WQyIyZNZ/P23ZnaNLikNtMfHUmpkiVYtHItjzw9E4DXnhxD7epVAAjwK0t84nEibh5O8WLFmDZ+KA0vrYPLZXnkmZmsWrfZo9vlLePGjWPp0qUEBgYyd+5cb4dTqEyZOp5OEe1ISjrJiHvHsfm3rRmWly5dipmzplOjRjVSUlJY8MMSnnj0OS9FW7A6NmzKlFuG4OvjwwdLFvDSnM8yLC9RrBgv3vsADWpeTNyxRO56YSr7YmNoXLsuT98xDABj4JnPP+T7tasAeP6u+4lo3JzYhKN0GDPU49tUkB6/5046X9mUpORkhj37Apt37cnUpsHFtZnxwHBKlyzJj7+uY/yrbwDQ86rWjB54E3WrVqHr/aPZtHMXAMWLFeOZ4ffSsE5trLWMf+1Nfv5ti0e3q6A0qtOaW3uMwcfHh0W/fsXXy97JsLxHm4F0urIPKSkpJJyI45UvJhF79CAA4we/TJ2qDdj+1wamvjvcG+F7xJQxd9Gp7ZXuz74Jz2X92Vf/YqZPHuX+7FvxK49Mex2Ay+rV4qnx91GyZHFSTrv4vydfZuOWHQT4XcTzj46gepVQkv/5h5ETp/PH7r88vWkFZvL4ewlv15ykk8mMHPc0W7buytRm7Ihb6derMwH+ftRtem3a/CGDr+OmfldzOiWFI0fiGTX+GQ5ExngyfPkPKlI9NT7Ghwf6jGHUW/dz0zPXE9GoCzWCamZoE3U0iimfPsrCjT9kWj/5VDKDnh/AoOcHFMmEJrxNM2pVDaN17yGMfuwlpo67N8t2U8cNZfRjL9G69xBqVQ0jvHVTAO4eN42Im4cTcfNw5i3+me+W/AzAgD5d3c9/w33ccO/DTBp5O8YYz2yUl/Xt25c333zT22EUOuER7ahVuzqtm3Zl9IgJTH12YpbtXn3xHa5q0Z2I9n1p3qIJ4Z2v8nCkBc/H+PDkrfdw81MTaffgPfRp3Y66latmaHNzx64cPX6MViPv5PXvvubhm28FYPu+v+g6/n46jxvGTVMn8PQd9+Hr4z5tf/LTj9w0dYLHt6egdbqyKbXCQmlx29088MLLTLvvnizbTRt2Nw/OeIUWt91NrbBQwps1AWD7n39z65SprNrye4b2/7u6CwAd7rmf/uMm8uidtxaJ85SP8eH2a8fx+KyhjJzelzYNu1ElqFaGNnsPbmfsywN48MXrWb3lR/7XbUTasm+Wz+bFz8Z7OmyPCm/bjFrVKtP62jsYPWUGU8ffl2W7qeOHMnrKDFpfewe1qlUmvE0zAB4ZcRvPvf4hETcMY9qr7/HIiNsAGH7H9Wz5Yw+drh/K8IefZcqYuzy2TQUtvF1zalavTNuugxk7YTpPTsw64V24ZDXXXD8s0/wt23Zxdb+hRPS6i3k/LOPhB+8s6JAdx1hXoX44UZFKai6tdhn7Y/cReeQAp1NO8+PGhbS7rH2GNlFxB9l9cBcua70Upfd0a9+Cz+a5e6bWb/kD/4vKElSxfIY2QRXL43dRadZt3g7AZ/MW061Dy0zP1bNzW76evwyAurWqsmLNJgAOx8UTn3ichpfWKchNKTSuvPJKAgICvB1GodOteyc++/gbANav3YR/gD9BwZUytElKOsnPK34B4NSpU2zetJXQsBCPx1rQGl9cl71RkfwdE8WplNN8vWoZXZtlfE91bdqCT5ctAmDuLytoe3lDAJL+SSYlddjPUsVLYDl73lq9/XeOHkv00FZ4ztWtmvPpoiUArNu+g4CLyhJU4ZzzVIXy+JUpw9ptfwDw6aIldG/dAoCd+/aze/+BTM9bt1pVlm90n6di4+OJP3acRnUuLshN8YiLq1xO1OF9xMS5P/dW/vYDzep3yNDm9z1r+efUSQB2/P0bFQKC05Zt2b2GpOQTngzZ47p1aMlnc93vr/Wb/8DfL5vPvrJlWPdb6mff3EV06+h+n1pruahsGQD8LypL1KEjANStVY0Vv2wEYNef+6kaFkzFCuU8sk0FrWunVnz+zY8ArN+0jQD/iwiqVCFTu/WbthGTuj/S+/mXTZw8mQzAuk3bCA2plKmNSH4rUklNJf9KxByNTpuOiY+mUkDe30glipXg7eGzeeO+tzMlQ0VBSFAgkdGxadMHYw4TWikwQ5vQSoFERh8+2yb6MCFBGdu0bHwZsUeOsndfJABbd+yla4eW+Pr6UDUsmAb1a1M5uGIBbokUdiGhwUQeOJg2fTAyitDQ4Gzb+/v7EdGtI8t/WuWJ8DwqtHwgkYfTve8OxxJa/pz3XYVAIg8fAiDF5SLxxAkq+PkD0Lh2PX56+hWWTHuZMW++nJbkFFUhgYFEHjq7vyIPxRIaeM7+CgzkYOzhdG0OE3JOm3P9vmcv3Vq1wNfHh2rBQTSsU5vKlZx/nqoQEMTh+Ki06SPx0QT6B2XbvlOzPmzYscIToRUaIUEViYw6lDZ9MDqW0KCM//ehQRUzfj5GxxKS2mbC0zOZMPI21s6fzYRRt/PkjFmA+7Ove6c2ADS6vC5VQoMIKyKffSHBFYk8eLZc7GBULCH/cttu6nc1S5atya/QRLKV4zU1xpj1wJfAR9bazAWohUxWpQT2PHpk+jzRk9iEWMIqVOalu15hd9QuDhzO/IufU+Vp/2TZJuN0727t+eqHZWnTH81ZSJ2aVZn/3nT2H4xh7abtnE7RqB7/ZVlV9WT3XvT19eXVt57lrdff4++/9hdwZJ6X5fsuL21S99eG3X/QfvS91Amryox7RrJ401qST50qiFALhbycp87n+Drjwx9+pE7Vqix88Vn2xRzi161F5TyV1fGV9b64qlF3alW+lIlv3F7QQRUqeTpecmhzS//uTHzmDeYtWknPLlfx7MT7ueHu8bz49qdMGXM3Cz95ke07/2LLH7uLyDEFJqvj6l9UuPTt2YmGl9Xluv8VvZL+C1bEf6DyhtwGCigPlAOWGGOigI+AT6y1kTmtZIwZAgwBqBlRneCGnul2jImPIajc2V+DgwKCiU2IzWGNjM60jTxygPV71lM3rJ7jk5rB/a9Ju+Zl09adGX5FCg0KJCo2Y7fxwZhYwoLP/uIZGhxI9KGzv4j6+vrQvWMrug48W5OdkuJi4nNnryuZ8/bT7P07x0NEiqDBd9zMgFv6A7Bp/WbCKoemLQsNCyEqKuuLRJ+ePpk9u//ijdfe9UicnhZ5JJawwHTvu8CKRMUdztjmcCxhgZU4eOQwvj4++JUpQ9w5pWU7I/dxIjmZS6pWZ9OezBfsOtltPbszsFsEABt27CIsXQ9KWKWKRB3JeJ6KjD1MaMXAdG0CiT6SuQQmvRSXiwkz30qbnvfcU+yJPJjDGs5wJD6awICzZZsVAoI5knAoU7sraregb4c7mPjG7ZxOKbpJ8RmDb+jBgL6pn32/7yQsXflTaHBFog5lfA8ejI7N+PkYXDHts+/6np3TBg34dsFynp1wPwDHjicxcuLzaeus+e4d/j4QhVMNuvlaBvTvDsDGzX8QFhoEuK9NCw2pSHTM4RzWzuyqVo0ZfvfNXPe/B/inCP8QI4VHbuVncdbaB6211YAHgDrAemPMktTEJUvW2pnW2mbW2maeSmgAtu3bStWK1QgtH0Yx32J0bhTB8q3Lcl8R8CvtR3Hf4gAElAmgQfUG7I3eW5DhesSsz+alXdz//dJV9L8mHIAml9cj8dgJYmLjMrSPiY3j2PEkmlxeD4D+14Qz/6df0pa3a96IXX/u52C6k1vpUiUpXaqke3mLRqSkpLBj776C3jQpZGa9+SER7foQ0a4P33+3iP439gKgSbOGJCYkEhOd+YvW2PH34+/vx4RxT3g6XI/ZuHsHtUIqU61SMMV9i9G7VTsWrPslQ5sF637h+nadAOjRoi0rf/8NgGqVgtMGBqhSsRK1wyqz71DRG0Ho7W+/I3zoSMKHjuT7Vau5vlNHAJpeUpeE48eJOXLOeepIHMeSkmh6SV0Aru/Uke9X5VzeUrpkCcqUdJ+n2jduyOmUFHb87fzz1K4DvxNasRpBqZ97bRp0Ze22nzK0qRFajyG9H+ap90aQcDwum2cqWmZ9MpeIG4YRccMwvl+yiv493O+vJlfUI/HY8aw/+04k0eSK1M++Hp2Yv3Q1ANGHDtOq2RUAtG3ekL1/u3/s9PcrS/Fi7t+GB/Ttyup1Wzh2PMkj21cQZn84hy597qZLn7v5YdFK+vXqDECThvVJSDye5bUz2bmsfm2mPjqCW++dwOEjRwsqZJEMTE7dicaY9dbaJufM8wUigBustbfm9gKtRl/p0SvyW13SmhHXjsLHx5e5a+Ywe/E73NnlLrbt38aKrcuoX+VSpg6ahl8Zf/45lczhxCMMePYGrqjegLHXjcNlXfgYHz5Z/hHf/jrHY3H/uTj76w3y0xNj76Zj66buIRonTWfTNvcvvgs/nEHEze7RTRrWv5jpk0ZSqlQJFq9cx/hpr6WtP33SCNZv/oN3v/g+bV6V0CA+emky1loOxhzmgckvsD8q8xfY/HJwXeEZOnnUqFGsWbOGuLg4AgMDGTZsGP379/d2WACElr/Eq6//xNOP0LHTVSQlnWTk0IfYtNE9fO7CZV8R0a4PoWHBrP/9J3b+sZvkf/4B4J03PuDD9z73aJy2W+0Cf41OjZoxOXVI54+WLuSFrz9hTL+BbNy7kwXrfqFk8eK8dO+DXF6jFkePJXLXi9P4OyaKfm07MqxXf06dTsFlXTz35UfMX+v+ovXqsDG0rn8FFfz8ORR/lKc//4CPli4o0O1wxXnmLgBTh95FeNPGnEhO5v7nXkwblnnxy88TPnQkAA3rXOwe0rlECRatXc+4V9xDz3dv3ZIn7rmTwIAAEo4fZ8uevdwwfhJVg4P45PFJuFwuog4fYcTzL7I/pmDOU+2beHZY38Z12zK4x2h8jA9L1n3Dl0vf5IbO97B7/1bWbv+JR257jWohdTiaWo0QG3+Qp95z97ZPHvI2lSvVoFSJMiSeiOfVLyexaafnrm1b8V2YR17niXH3nv3sm/g8m7buBGDhJy8ScYN79K6Gl9Zh+uSRlCpZksUr1zJ+6qsANG90KVPG3IWvry/J/5xi3BMv89u2XTRtcAkzHnsAV4qLHXv+ZtSkF4hPPFYg8fuc9HxPx+OPDKPDVe5bQIx66Bl+27IDgAVfvUaXPncDMP7BO+jTI5zgoECiYw7z4eff89xL7/Hx209xSd2aaYnQgYMx3HqvZ0ZrPLB9oSOGNdz88oZCPWLVFUMbO2I/ppdbUvOxtfbGC3kBTyc1TuWppKYoKExJTWHm7aTGKTyR1BQVnkpqnM7TSY2TeSqpcTpvJDVOpaQmfzgxqcmx/OxCExoREREREZGCluvPbsaYrkBvoDLuQXsigW+stfMLODYREREREZFc5Tak83SgLvAucGas1SrAcGPM1dba+ws4PhERERGRIsW4isbw34VJbj013a21dc+daYz5BNgBKKkRERERERGvym1I55PGmOZZzL8SOFkA8YiIiIiIiJyX3HpqBgOvGmP8OFt+VhVISF0mIiIiIiLnw7q8HUGRk2NSY61dD7QwxoTgHijAAPuttc69Za6IiIiIiBQpeRn9rBjucrMzN73YZoyZb609XaCRiYiIiIiI5EFuo5+Fwf+zd+dxNpb/H8df15lBthmMGcaWJUpl30pSdlFZQipFC62WEUna+0rEjygqVAppVdbQ2EpZxlLWEAmzGTMYS4OZ6/fHaMyYMWfkLHPG+/l4zKNz39d1n/M+dzPH+Zzruq/DMiAK2EjqSM2dwBhjTDNrbaT7I4qIiIiI5B3GavUzV3M2UvMmMMlaOy79TmNMP2AE0NNdwURERERERHLCWVFzk7W214U7rbXjjTF/uCeSiIiIiIhIzjkrak5l03bSlUFERERERK4I+vJNl3NW1AQaYzpnsd8AAW7IIyIiIiIickmcFTUrgbuyaRMREREREfEqZ99T08tDOURERERErgz68k2Xc2TXaIwZl+52/wvaPnFTJhERERERkRzLtqgBmqa7feHyzTVdnEVEREREROSSObumxlzktoiIiIiI/Bf68k2Xc1bUOIwxxUkd0fn39r/FjZ9bk4mIiIiIiOSA0yWdgfWcL2Q2pGuzbkkkIiIiIiJyCZytfqubl3cAACAASURBVFbRQzlERERERET+k2yLGmNM3ezarbUbsmsXEREREZELpOiaGldzNv1sTDZtFmjuwiwiIiIiIiKXzNn0s2aeCiIiIiIiIvJfOBupyZIxphXwnLW2lYvziIiIiIjkaUZLOrtctl++aYxpbozZaYw5boyZboy53hgTAbwFTPJMRBERERERkYvLtqgh9ZqaPkAQ8DWwGvjMWlvPWvutu8OJiIiIiIg442z6mbXWLj93+ztjzCFr7TtuziQiIiIikndp+pnLOStqihljOqfbNum3NVojIiIiIiLe5qyoWQHcdZFtC6ioERERERERr3K2pPPDngoiIiIiInIlsJp+5nLOFgrAGONnjCmZbju/MaaPMWa7e6OJiIiIiIg4l+1IjTGmO/ABcMIYswt4FfgMWAc8kJMHWD064jIjXhkaDGrg7Qg+I7T4dd6O4BOiEnZ4O4JPKNWsg7cj+I4SKd5O4BNWzQ7xdgSfEdAy0dsRfMKRLSW8HUEk13N2Tc2LQD1r7W5jTF3gV6C7tXa2+6OJiIiIiORBmn7mcs6mn5221u4GsNZuAPaqoBERERERkdzE2UhNiDFmYLrtIum3rbX/555YIiIiIiIiOeOsqJkMFL3ItnVLIhERERERkUvgbEnn1y7WZozRle0iIiIiIpdK19S4nLORmgyMMdcD3YH7gKNAfXeEEhERERERySmnRY0x5mpSi5j7gLPA1UB9a+1f7o0mIiIiIiLinLPvqfkFCARmAV2stbuMMXtV0IiIiIiI/EeafuZyzpZ0PkTqwgClgOBz+7RAgIiIiIiI5BrZFjXW2g5ADWAD8JoxZi9Q3BjT0BPhREREREREnHE2UoO19qi19iNrbSvgJuAVYJwxZr/b04mIiIiI5DHWJufqn5wwxrQ1xvxhjNltjHk+i/ZexphDxphN534eS9fW0xiz69xPT1ec00ta/cxaGwOMB8afW0BARERERESuIMYYP+A9oBVwAFhnjJljrd12QdcvrLXPXHBsCVIHSeqTelnL+nPHJlxOJmcLBcxxcvzdl/PgIiIiIiLicxoCu621ewCMMbOADsCFRU1W2gBLrLXx545dArQFPr+cQM5Gam4G9p97kDWAuZwHExERERG54vn+6mdlSa0R/nUAaJRFv3uMMU2BnUCYtXb/RY4te7mBnF1TUxp4AbgReIfUIaY4a+0Ka+2Ky31wERERERHJXYwxfYwxEel++lzYJYvDLlwheS5Q0VpbE/gRmHYJx14yZ6ufJVtrf7DW9iR1kYDdwHJjTN/LfWAREREREcl9rLUfWmvrp/v58IIuB4Dy6bbLAZEX3Mdha23Suc3JQL2cHvtfOF0owBhTAGgP3AdUJHWhgG8v94FFRERERK5Ivj/9bB1Q1RhTCTgIdAfuT9/BGBNqrY06t3k3sP3c7UXAm8aY4ue2WwNDLzeQs4UCppE69Wwh8Jq1dsvlPqCIiIiIiPgua+1ZY8wzpBYofsBH1tqtxpjXgQhr7RygnzHmbuAsEA/0OndsvDHmDVILI4DX/1004HI4G6l5EDgBVDsX7N/9JjWTDbjcACIiIiIi4lustQuABRfseznd7aFcZATGWvsR8JEr82Rb1FhrnX45p4iIiIiIiDdd0pdvioiIiIjI5bH2rLcj5DkaiREREREREZ+mokZERERERHyapp+JiIiIiHiQxeeXdM51NFIjIiIiIiI+TUWNiIiIiIj4NE0/ExERERHxJKvpZ66mkRoREREREfFpKmpERERERMSnafqZiIiIiIgHWU0/czmN1IiIiIiIiE9TUSMiIiIiIj5N089ERERERDzIctbbEfKcPDlS884777Br1y5+++036tSpk2WfhQsXsmnTJrZs2cKkSZNwOFJPRZcuXdiyZQvJycnUq1fPk7E96qZrb+KrwV/xzZBveKjZQ5na61Sqw6f9P+WXt36heY3mGdp+Hfkr08OmMz1sOqN7jfZU5FzjjbeG8cv6RYT//D01al6fqb1gwav47Iv3+WnNApb/MpcXXhnohZS5z9ChQ7n55pu58847vR3Fa4b37c3q6e+zbMo71KhaOcs+NatVYfnUd1g9/X2G9+2dtn/Iw/ezbMo7hE8eyxejXqVUUAkAihYuxGfDh7F0yjhWfDyB7m1beOS5uEuz2nX5Zfwk1rz7AX07dcnUnt/fnw8HPseadz9g4YjRlA8OAaB4kaJ8+9pw9k7/khGPPZ7Wv2D+Asx44WVWjZ/EynHv8WKPnh57Lp70xgtPseqHj/nxu/epcf01WfYZ0r8XEUtnsCvi+wz7G9WvwaJv3uPvzQtp3/pWT8T1mlurN+SHF2ew+OXP6d3qgUzt9avU4tvnprJ13DLa1L49Q9uUJ0ezbuQC3n98pIfSep47XqMCixTm49eHsmzKO/ww8W2uq1jBI89Frjx5rqi54447qFq1KlWrVqVPnz5MmjQpy37dunWjdu3a3HjjjQQHB9O1a1cAtmzZQufOnVm5cqUnY3uUwzh4rtNz9J/an3tH30ub2m2oFFIpQ5/oI9G8/uXrLN60ONPxSWeS6DG2Bz3G9mDQJ4M8FTtXaN6qKZWrXE3jem0YPOBl3hrzSpb9Jk34mFsbtaPVbZ1p2KguzVvm7TcKOdG5c2emTJni7Rhe06JRPSqVDeWmHk8waMx7jAp7Mst+owY8waAxE7mpxxNUKhtK84Z1AXjvi9k0e6w/LXqHsWR1BM8+dC8Aj3Rsxx/79tP8sQF0HjCMV598mHz+vjkI73A4GNn7Ce4b/ipNBjxN5yZNqVaufIY+D7RozdHjx2n0zON8MO97XnqwFwBJZ04z8vMZvPrpR5nud+Kc2dzS70laDOpPw2ur07xO3vrAqnnTBlS6uiy3tH2Y514Zx4iX+2XZb8ny1bS7t2+m/QcjYxkwdDSz5y91d1SvchgHL3cdyGOTBtF++IPcWa8lVUpXzNAnKiGGodPfZN76HzMdPyX8c5777H8eSut57nqN6v9AV7bs3kOzx/rzzIhx/K/vYx57TnJlybaoMcYU81QQV+nQoQOffvopAGvWrKFYsWKULl06U7/ExEQA/P39yZ8/P9ZaAHbs2MHOnTs9F9gLbqhwAwfiDhAZH8nZ5LMs3rSYpjc0zdAnKiGK3VG7SbEpXkqZO7Vt14KvZqV+yrkh4jcCAgMIKRWcoc+pU//wy89rADhz5gybf9tGaJnMv4NXmgYNGhAYGOjtGF7T9paGfLV4GQDrt+8koHBhQkoUz9AnpERxihQuRMS2PwD4avEy7mjSCIDjJ0+l9St0VYG01yxrLUUKFQSgcMGrOJJ4nLPJvrmqTt1rqrI3Oop9MTGcOXuW2T+vpG2DRhn6tG3YiC+WhwMw99dV3FqjFgAnk5JYs2Mb/5w5k6H/qdNJrNqyGYAzZ8/y+94/KRMU5IFn4zltmjfm6++XALDhtx0EBhQmJLhEpn4bfttB7KH4TPsPRMawfedeUlKs27N6U82rq7Mv7iAHDkdxJvks89eH06JGkwx9DsZH80fkn6TYzOdi9c71nPjnpKfiepy7XqOqVSzPTxt+B2D3/oOULxVCcPEr99+Cf1mbnKt/fJGzkZo4Y8yPxphHfaXAKVu2LPv370/bPnDgAGXLls2y7w8//EBsbCyJiYl8/fXXnorodcEBwcQciUnbjj0aS3BgcDZHZJTfPz/T+k1j6jNTue2G29wRMdcqHVqKyINRadtRkdGEhpa6aP+AgKK0atuMn1b86ol4kouFlgziYGxc2nZUXByhJYMy9Yk6dDhtO/LQ4Qx9hj7agw1fTOWelrcx6uOZAEydvYBqFcrz+9cfs/yj8bz47uS0NxO+pnSJIA7GpTtH8YcJvaAASd8nOSWFxJMnKFE0IEf3H1CoMG3qN+Snzb+5LnQuULpUEJHRh9K2I6PjKB2Stwo3VyhVLJjohNi07ZgjhyhVrKQXE+Uu7nqN2vbnXto3vRmAOtdVpVzpEEKDdd7F9ZwVNduBcUBz4E9jzPfGmO7GmILZHWSM6WOMiTDGRLgqaE4ZYzLtu9g/8G3btiU0NJQCBQrQvHnzLPvkRVmdIy7hPdDdb95Nz/E9eWnmS4TdHUbZoKyLxrwoy1N3kd8vPz8/Jk0dw9QPPuPvfQfcnExyvaxemy74w3P2+zVi6nTq3vso3/y4gkc6tQegWYM6bNm9l5pdHqb5YwMY0e/xtJEbX5OT1+9LeY1Pz8/h4IOwwUyeP5d9MTFO+/uSrM+JF4Lkcln8eek8peem16jxM78hsEgRwieP5dFO7dm8a4/PjiZL7uasqDljrZ1nrX0AKAfMALoBB4wxMy92kLX2Q2ttfWttfRdmvainnnqKjRs3snHjRiIjIylf/vwc7HLlyhEZGXnRY5OSkpgzZw4dOnTwRNRcIfZoLKWKnR9dCAkM4dCxQ9kckVHcsdRPciLjI9mwZwPXlrnW5Rlzk16P3c+SlbNZsnI2MVGxlCkbmtYWWqY00dGxWR739rjX2fPnPia//6mnokou83DHdoRPHkv45LHExMVTNuT8p5OhJUsSHZdxKlDkocOEBp//1LNMcBDRhzNPF/o2fCV3nvvks/sdLZj/U+pI4F+R0fwdFUPVCuXc8XTcLupwHGVLpjtHJYKIjo+/aB8/h4OihQqTcDzR6X2PeeIZ9kRF8uH8Oa4N7SW97r+LJd9OYsm3k4iJPUyZ0udH28uULklMuk/TJVX0kUOULh6Stl2qWDCxR+OyOSLv88Rr1PGTpxgwajwteofxzIhxBBUL4O+ovPXBguQOzoqatJrcWnvKWvultbYzUBlY5NZkl2DixInUqVOHOnXq8N133/HQQ6mreTVq1IijR48SHR2doX/hwoXTrrPx8/OjXbt27Nixw+O5vWXb/m2UL1meMsXL4O/nT+varflp2085OrZowaLk88sHQGChQGpeXZO9MXvdGdfrPpkyk1ZNO9GqaScWLgina/fUArhu/VokHkskNiZzQThkWH8CAory8tA3PR1XcpGPv1tAi95htOgdxsJVq+nauhkA9apXI/HECWLjEzL0j41P4PjJU9SrXg2Arq2b8cOqtQBUSldMt2nckF1/HwTgYMwhbq1bE4Dg4oFUKV+WfZEZX/N8xcbdu6gcWoYKIaXI5+9PpyZNWRSxNkOfRevWcO/tqSu83XXzLfy85Xen9/v8fT0IKFyYFz+e7Jbc3vDJzLm06vwkrTo/yQ/hv9ClQysA6ta6jmOJJ7K8duZKt/nvHVQMLke5oFDy+fnTvl4Llm7+2duxvMoTr1EBhQunLV7So30rVv++LcP1N1cqS3Ku/vFFJrthe2PMIGvtZa3Za4zx+ODuu+++S9u2bTl58iQPP/ww69evB2Djxo3UqVOHkJAQ5s2bR4ECBfDz82Pp0qWEhYWRnJxMx44dmTBhAsHBwRw5coRNmzbRtm1bt2duMKiB2x8jvcbXNWbg3QNxOBzMXTuXj5d+TJ/Wfdh+YDs/bfuJ6uWqM6rnKAIKBXD6zGkOJx6m+5ju1Li6BkPvGYq1FmMMs36axZx1nv3kc/+UYx59vAu9+fZLNGtxK6dO/UPY0y/w26YtACxZOZtWTTsRWqYUG7auYNcff5J0+jQAH0+ewczPPHvdVlRC7irUBw4cyNq1a0lISCAoKIi+ffumrTroTaWaeW6UdkT/x2neoA6nkpLoP3ICv+3cDUD45LG06B0GQK1q1zD++X5clT8/4Ws38ML4DwGY+toQrilflpQUy4GYWAaPnUR0XDylgkowfkg/SgUVxxjD+Jnf8M2PK9yS35Zw/8IhLerW438P98bP4WDm0h8Z982XDOn+AJt272JRxFoK5MvHe/0GUqNSZRKOH+fxsaPSppNFTJpC0YKFyO/vz9GTJ+j2+ssknjzJb5M/YeeB/Zw+t4jA1IXzmRGeeWVHV/HfluS2+76YN196htub1OfUP0mEvTCa37fuAmDJt5No1Tl1FasXBz1Gx/bNKB0SRHTsYT7/+gfGvPcZtW6sxtQJr1AsoCj/nD7Nobh4mt3VxyO5i7b07JvbptffxAv39MPPOPhm9XzeX/wZ/do9ypa/d7B0yypqVLiOdx8bTkChoiSdPU3csXjufDP1g9IZA96lcsjVFCpQkCMnjjJs5kh+3rHWySO6xpEtmRd+cAd3vEbVv/5aJgwdQHJKCjv/2k/Y2xM4evyE255DzLLvs5ppmOtseHlUrp78WPf153ziPKaXbVHjkgfwQlHjizxd1Pgybxc1viK3FTW5lSeLGl/niaImL/BGUeOrPF3U+CpPFTV5gYoa1/DFoibbLzMwxgQCQ4GOwL8TdmOB74G3rLVH3BtPRERERCRv8dVlk3MzZ9fUfAkkALdba4OstUFAs3P7vnJ3OBEREREREWecFTUVrbUjrbVpV51aa6OttSOBCu6NJiIiIiIi4ly208+AfcaY54Bp1toYAGNMKaAXsD+7A0VEREREJLMUH11hLDdzNlJzLxAErDDGxBtjEoDl5/Z1c3M2ERERERERp7IdqbHWJgBDzv2IiIiIiIjkOs5WP7sL+N1au+/c9svAPcA+oL+1Nm9/66KIiIiIiItZe9bbEfIcZ9PPhgOHAIwxdwI9gEeAOcD77o0mIiIiIiLinLOixlprT5673RmYaq1db62dwvnvrREREREREfEaZ6ufGWNMEeAk0AKYmK7tKrelEhERERHJo6xWP3M5Z0XNOGATcAzYbq2NADDG1AGi3JxNRERERETEKWern31kjFkEhAC/pWuKBh52ZzAREREREZGccDZSAxAD1AVaGmMAtgM/WC3bICIiIiIiuYCzJZ3LAMtInWq2ETDAncAYY0wza22k+yOKiIiIiOQdKVbX1Lias5GaN4FJ1tpx6XcaY/oBI4Ce7gomIiIiIiKSE86Kmpustb0u3GmtHW+M+cM9kURERERERHLOWVFzKpu2k9m0iYiIiIhIFrSks+s5K2oCjTGds9hvgAA35BEREREREbkkzoqaFcBdF2lb6eIsIiIiIiIil8zZ99Tou2hERERERFwoRdPPXM7Zks4Ds2u31v6fa+OIiIiIiIhcGmfTz4pm02ZdGUREREREROS/cFbUTLXWHsiqwRhzsWttRERERETkIjT9zPUcTtrDjTEVL9xpjHkYGJept4iIiIiIiIc5K2rCgCXGmKr/7jDGDAUGAre5M5iIiIiIiEhOOFv9bIExJglYaIzpCDwGNACaWmsTPBFQRERERCQvSbGafuZqzkZqsNaGA72A5UBloIUKGhERERERyS2cLemcSOoqZwYoALQAYo0xBrDW2gD3RxQREREREbk4Z9PPslvSWURERERELpHV6mcu53T6mYiIiIiISG6mokZERERERHyaihoREREREfFp2V5T4wqbX/vE3Q+RJ7SZ9om3I/gM2zbY2xF8QqlmHbwdwSfELPve2xF8RrVnmno7gk8oUjHJ2xF8hsNYb0fwCbc3/tvbEcTFUnRNjctppEZERERERHyaihoREREREfFpbp9+JiIiIiIi5yVr+pnLaaRGRERERER8mooaERERERHxaZp+JiIiIiLiQVr9zPU0UiMiIiIiIj5NRY2IiIiIiPg0TT8TEREREfEgTT9zPY3UiIiIiIiIT1NRIyIiIiIiPk3Tz0REREREPEjTz1xPIzUiIiIiIuLTVNSIiIiIiIhPU1EjIiIiIiI+TdfUiIiIiIh4UAop3o6Q52ikRkREREREfJqKGhERERER8WmafiYiIiIi4kHJWtLZ5TRSIyIiIiIiPk1FjYiIiIiI+DRNPxMRERER8SCtfuZ6GqkRERERERGfpqJGRERERER8mqafiYiIiIh4kFY/cz2N1IiIiIiIiE9TUSMiIiIiIj5N089ERERERDxIq5+5nkZqRERERETEp6moERERERERn6aiRkREREREfJquqRERERER8SAt6ex6ea6oKVKlDKFtGoLDkLBxF3GrtmTZL6D61VToeju7J8/jn6jDGIeDMnfeTMHQIKy1RC9ay4l9MR5O71mvv9yX5rc34tSpfwh7biRbtu7K1GfIs4/SpVNrAgOKUq1mu7T9D953Fz0f7EhKcgonTp7iuWFj2LV7nyfju1WzWvV446E++DkczFi2mHfnfJWhPb+/PxOeepaala4h4Xgij7/zFvvjYqlTpRpvP9YXAGNg9NczWRjxKwBjH+9PqzoNiTt2hNufe9rjz8ldhvftTYtG9Tj1TxL9Rr7D5l17MvWpWa0K44f046oCBQhfs55hEyYDMOTh+2l7SyNSbApxCUfpN3I8MYfjKVq4EBNfCKNsqWD8/PyY9MV3zPoh3NNPzSuGDh3K8uXLCQoKYt68ed6Ok2vcWr0hw7r0w8/h4Ktf5vPhkhkZ2utXqcWwLn25tkxlwj5+jUWbVngpqec1vvYmBnUciJ/Dwew1c/hk6acZ2utWrs2zHcKoGnoNQ6e/RPjvSzO0Fy5QmG+GzGLZ5hWMnD3ak9E97uZrb2JQh2dxOBx8t+Z7pi3LeK7qVK7Ds3eHcU3oNQyb8WKGc7Vm1K/sjvoTgJgj0Qz8eJBHs3vSDVUb0639YBwOBz9HfMeilR9naG95Sw9uqd+JlJSzHD+RwLRvXyP+SFRa+1UFCvPqgG/ZtG0ps+aO9HR8uULlrelnxlDmjpv4a+aP7J74PYE3VKJAycBM3Rz5/QlqWJ2TBw6l7StetyoAuz+Yw1/Tl1C6VQOPxfaG5rc3olLFsjRp3oMhw8Yw4vWwLPstCf+F9p2ezLR/9txwWrZ7lNZ39Wbih7N4ZdhT7o7sMQ7jYMTDT3L/yFdoOuhJOjVuSrWy5TP0ub9ZG46cOM7NYb35YMF3vHj/wwDs2L+PNsP603JoX+5762XefuwZ/Bypf2ZfrPiR+9562ePPx51aNKpHpbKh3NTjCQaNeY9RYZl/VwBGDXiCQWMmclOPJ6hUNpTmDesC8N4Xs2n2WH9a9A5jyeoInn3oXgAe6diOP/btp/ljA+g8YBivPvkw+fzz3GcwWercuTNTpkzxdoxcxWEcvNItjN4TB9Pufw9xZ70WVCl9dYY+UQkxPP/Zm8yL+NFLKb3DYRwM6TyYvpMHcM+o7rSt05pKpSpl6BOVEMOrs97gh42Ls7yPJ9s+zvo/N3oirlc5jIMhnZ6j35T+dH37XtrUaZPpXEUnRPPqF6+zKItzlXQmiQfG9uCBsT3ydEFjjIP77nqeCdOe4dV37qFBzbaEBlfO0OfvyB28OfEB3phwL+u3hHNPm/4Z2u9u+RS79q73ZGyRvFXUFCxbkqSEY5w5chybksLRrXspem35TP1Cbq9D3C9bsGfPD/0VCC7G8b2pnzIkn/yH5KTTFCxT0mPZPa1Ny1v4enbqi/aGTdsJDChMSHCJTP02bNpO7KH4TPuPHz+ZdrtQoauw1rovrIfVuaYae6Mj+Ts2mjPJZ/nu15W0qX9Thj5t6jXiy5WpIwfz1vxMkxtrAXDqdBLJKanLNF6VLz+W8+dl9Y6tHDme6KFn4Rltb2nIV4uXAbB++04CChcmpETxDH1CShSnSOFCRGz7A4CvFi/jjiaNADh+8lRav0JXFUj7PbLWUqRQQQAKF7yKI4nHOZt8ZQzVN2jQgMDAzB/GXMlqVqzOvriD7D8cxZnks8zfEE7Lmk0y9DkYH80fkXtIyUOvRTlxY4XrOXD4AAfjIzmbfJZFG5dw+w1NM/SJSohiV9RuUmzmJWSrl7uOoKIlWL1zjacie80NFW5gf7pztXjTYm7L4lztvsi5ulJUKncjsfH7iUs4SHLyWSJ+X0St6rdn6LNzbwRnzvwDwN79v1MssFRaW4Uy1QkoEsS2Xb96MrbPSSYlV//4omyLGmNMP2NM5qogl8pXtBBnjp5I2z577CT5ihbO0Oeq0iXIF1iYxF0HMuz/JyaegGvLgzHkK1aEgqFB5Aso5JHc3lC6VEkiI2PTtqOi4yhd+tKKuJ49OrJq6XReHPI4L78+wdURvSa0eBCRh+PStqMOxxFaPChjnxJBRB5OHelLTkkh8eRJShQNAKBOlWtZ8fZElo16j+emvJdW5ORFoSWDOBib7lzFxRFaMihTn6hDh9O2Iw8dztBn6KM92PDFVO5peRujPp4JwNTZC6hWoTy/f/0xyz8az4vvTs5ThbNcmlKBJYlOOP96FZ1wiFKBwV5MlHsEB4YQfeT8VOnYo7GE5PDcGGMIu6sf4+aNd1e8XCUkMJiY9OfqSM7PFUB+//x82n8aH/edym033OaOiLlCsYAQEo6eP08Jx2Iols15uqV+R7buXAWk/k51uWMg3/ww1u05RS7kbKTmDWCNMeYnY8xTxpgc/fUbY/oYYyKMMRFfRSy/7JCXJ+MbodDWDYhevC5Tr4SNuzlz7CRVet9JaJsGnNwfi03Ju2+ijDGZ9l3qe8Zp07/jluY9GD7yQ/o//aCLknlflucmJ33OncCNf/7BbYOfou2wMPp16EqBfPncETN3yPJcWWddMhQoI6ZOp+69j/LNjyt4pFN7AJo1qMOW3Xup2eVhmj82gBH9Hk8buZErT9Z/k3n39flSZPHnleNz063xPaza8QsxR2Kdd84TLu/fvTuH381D7/TkxRkv8WyHMMoGlXVhtlwk61+qLDWq1Y6ry1zP4p+mAXBbo25s2flzhqJIxFOcTVLfA9QDWgL3Aq8ZY9YDnwPfWmuznEtjrf0Q+BBgy+vTPPYvz5nEk+QLPD8y4x9QiDOJ56dJOQrko0BIMSr1bJvaXqQgV3dvzr5ZS/kn6nCGYqfyw3dwOv6Yp6J7RM8eHXng3tQ3jZs276BMmRA4N+U1tHRJYmLisjn64r6ft5QRbwxwVUyvi4yPo0zQ+VGr0KCSRCccztjncBxlgoKJij+Mn8NB0UKFSLhgatmuyP2cTEriuvJX89ue3R7J7gkPd2xHj/atANi0YzdlQ9Kdq5IliY7LOF0x8tBhQoPPj8yUCQ4i+nDmkOVc1gAAIABJREFUKY3fhq9kxoiXePuTz+l+RwsmzPwGgL8io/k7KoaqFcqxcUfmxSwk74s+cojSxUPStksXDyb26H97vcprYo/GUrrY+ak/IYEhHMrhualRsQZ1KtWma+N7KFigEPn88nHy9EkmzJ/orrheFXs0llLpz1WxEA4dO5TNERnFHUs9rwfjI1n/5wauK3stBw8fdHlObztyNJbi6aaTFQ8oxZEsztN1VRpxx+2PMmbKY5xNPgNA5fI1qVqxDrc16sZV+Qvi55ePpKRTzF58ZYwGXooUH53ilZs5G6mx1toUa+1ia+2jQBlgItCW1IInVzl1MI4CJQLIV6wIxuEg8IZKJO48P80sJekMO0Z/wc7x37Bz/DecOnAoraAx/n6YfKk1XuHKodgUS1LcUW89FbeYNv07Wt/Vm9Z39WbR4lV06dQagLq1q3Ms8USW185cTKWK5z+hatnsJvb+lXde2Df9uZPKpctSIbgU+fz86XhzUxavzzjffPH6NXRr2gKAOxs1YdXW3wGoEFwqbWGAciWDqVKmLPsP5a1PQT/+bgEteofRoncYC1etpmvrZgDUq16NxBMniI1PyNA/Nj6B4ydPUa96NQC6tm7GD6vWAlCpbGhavzaNG7Lr79Tfo4Mxh7i1bk0AgosHUqV8WfZFRrv9uUnutHnfDioGl6NcUCj5/PxpX7cF4b+v8nasXGHr/u2UL1meMiVC8ffzp02dVqzYujJHx7444xXa/68Ddw7vxLi545kfsSDPFjQA2/ZvO3euyuDv50/r2q1ZufWnHB1btGBR8vmljroHFgqkVsWa7InZ6864XvPXwa2EBFUgqHgZ/Pz8qV+zDb/tWJ6hT/nQa+nRYRgTp4eReOL8a/5HXw1j6NvtGDa6PV8vHMvqTfNU0IjHOBupyTAIaa09A8wB5hhjct9cEGuJXLiGig+0xBgHCZt2kXToCCG31+ZU5GESd+6/6KH+ha+i4gOtsNZyNvEkB77L2Qudrwpfvprmtzdi1dLpnPoniYFDzi+5uHjuZFrf1RuAYUMep9NdLShYsAARP3/JzC/n83/jp9HrwU7c2rgeZ8+e5eixRAYMfstbT8XlklNSeOGTSXw+9A38HA4+X76EPw78zXNderBp7y4Wr1/DzOWLefepQfw6djJHjify+IRRADS89nr6dujKmbPJpNgUnv9oIvGJqSN+k/o+R+PqNShRNIAN707j7a9n8PnyrFcj8hU/rl5Pi0b1WTP9fU4lJdF/5Plrq8Inj6VF79RV9YaMfZ/xz/fjqvz5CV+7gfA1qUOEL/Z5iGvKlyUlxXIgJpbBYycB8H+ffcn4If1YPvUdjDG88eE04o/lrUUWLmbgwIGsXbuWhIQEmjZtSt++fenatau3Y3lVckoyr385jqlPj8bPOPh69QJ2R/9Fv/aPsOXvP1i6eRU1KlzHe73/R0ChojSr0Zh+7R+h/fCe3o7udskpyYz8djTv9RmPwziYs3Yue2L28kSbPmw7sJ2VW3/i+vLVGdNrFAEFi9L0+lt5ok1vur59n7eje1xySjJvz36bCb3H42cczFk3lz0xe3i8TR+279/Oym2p5+rtnqMIKBTArdffSp/Wfbh3dHcqhVTkhS5DSbEWhzFMW/Ype/NoUZOSksysuSPp32siDuNg1YbviYrdw10tnmTfwW38vmMF97QNo0CBQvS5L/Xfvvgj0UycnndmbIhvMtldfGuMqWat3Xk5D+DJ6We+rM20T7wdwWckN8y7Czi4ko2+MpZAvlwxy773dgSfUe2Zps47CUUKJHk7gs9wGL1FyIl6Bc54O4LP+GD4xqyuCsp1pr7QJlf/8j/65iKfOI/pZfuux1q706ReodkQKEvqpWKRwFqrpYhERERERCQXyLaoMca0JvUaml3AvxdNlAOuMcY8Za317bkzIiIiIiLi85zNT3kHaGmt/Sv9TmNMJWABUN1NuURERERE8iRf/YLL3MzZ6mf+wIEs9h8E8vCXb4iIiIiIiK9wNlLzEbDOGDML+HfpsPJAd2CqO4OJiIiIiIjkhLOFAkYYY74DOgA3k7rE8wHgAWvtNg/kExERERHJU5KNpp+5mtM1X62124HtHsgiIiIiIiJyyZxdU3NRxpiFrgwiIiIiIiLyXzhb0rnuxZqA2q6PIyIiIiIicmmcTT9bB6wgtYi5UDHXxxERERERydu0pLPrOStqtgOPW2t3XdhgjNmfRX8RERERERGPcnZNzavZ9Onr2igiIiIiIiKXztlITYq19o+sGqy137khj4iIiIhInqbpZ67nbKTmRY+kEBERERER+Y/+85LOIiIiIiIiuYGz6WfXGWN+z2K/Aay1tqYbMomIiIiI5FnJWG9HyHOcFTV7gbs8EUREREREROS/cFbUnLbW7vNIEhERERERkf/AWVGz6mINxphS1toYF+cREREREcnTtPqZ62W7UIC19pn028aYQGPMI8aYH4ENbk0mIiIiIiKSA85GajDGFATuBu4H6gJFgY7ASvdGExERERERcS7bosYYMwNoCiwG3gWWAruttcvdH01EREREJO/R6meu5+x7am4EEoDtwA5rbTLo/4KIiIiIiOQezq6pqQV0AwKAH40xPwFFjTGlPRFORERERETEGWcjNVhrd1hrX7bWXguEAZ8Ca40xv7g9nYiIiIiIiBNOFwpIz1obAUQYYwaReq2NiIiIiIhcAi3p7HrZjtQYY0YZY57IomkA0M49kURERERERHLO2fSzO4EPs9j/DtDe9XFEREREREQujbPpZ9Zam2l8zFqbYowxbsokIiIiIpJnaUln13M2UnPSGFP1wp3n9p1yTyQREREREZGcczZS8zKw0BjzP2D9uX31gaGkXlcjIiIiIiLiVdkWNdbahcaYjsBgoO+53VuAe6y1m90dTkREREQkr9H0M9fLtqgxxlwFxFhre16wP8QYc5W19h9nD9A78d3LjHiFOKlLlHIqJSHA2xF8QwktF5kT1Z7R6vQ5tfPdld6O4BPKVdTvVE7N+mS+tyP4hH5zW3o7gkiu5+yamvHArVnsbwWMdX0cERERERGRS+Psmpom1to+F+601s4wxrzgpkwiIiIiInmWpp+5nrORmuzmRDk7VkRERERExO2cFSaxxpiGF+40xjQADrknkoiIiIiISM45m342GPjSGPMJGZd0fgjo7sZcIiIiIiJ5UrLR9DNXy3akxlq7FmgIFAR6Ar2Aq4BG1to1bk8nIiIiIiLihLMlnf2BQcAjwN+kXmNzW2qTGWatPeP+iCIiIiIiIhfnbPrZ20BRoJK1NhHAGBMAjD7309+98URERERE8hatfuZ6zhYKuBPo/W9BA2CtPQY8CbRzZzAREREREZGccFbUWGttplLSWpsMKjFFRERERMT7nBU124wxD1240xjTA9jhnkgiIiIiIiI55+yamqeBb40xj5C6pLMFGpC6GlonN2cTEREREclzdE2N62Vb1FhrDwKNjDHNgRtIXf1sobU23BPhREREREREnHE2UgOAtXYpsNTNWURERERERC5ZjooaERERERFxDU0/cz1nCwWIiIiIiIjkaipqRERERETkkhhj2hpj/jDG7DbGPJ9F+0BjzDZjzO/GmHBjzNXp2pKNMZvO/cxxRR5NPxMRERER8aBkbwe4TMYYP+A9oBVwAFhnjJljrd2WrttGoL619qQx5klgFHDvubZT1trarsykkRoREREREbkUDYHd1to91trTwCygQ/oO1tpl1tqT5zZXA+XcGUhFjYiIiIiIXIqywP502wfO7buYR4GF6bavMsZEGGNWG2M6uiKQpp+JiIiIiHhQbl/9zBjTB+iTbteH1toP03fJ4rAsn5QxpgdQH7gt3e4K1tpIY0xlYKkxZrO19s/LyayiRkRERERE0pwrYD7MpssBoHy67XJA5IWdjDEtgWHAbdbapHT3H3nuv3uMMcuBOsBlFTWafiYiIiIiIpdiHVDVGFPJGJMf6A5kWMXMGFMH+AC421obm25/cWNMgXO3SwK3AOkXGPhPNFIjIiIiIuJBybl79plT1tqzxphngEWAH/CRtXarMeZ1IMJaOwd4GygCfGWMAfjbWns3UB34wBiTQuoAy1sXrJr2n6ioERERERGRS2KtXQAsuGDfy+lut7zIcb8ANVydR9PPRERERETEp6moERERERERn6bpZyIiIiIiHpTbl3T2RRqpERERERERn6aiRkREREREfJqmn4mIiIiIeFCytwPkQRqpERERERERn5bnRmpuuvZmBtz9LH4OB3PWfs9ny6ZlaK9dqQ4D7h5IldBreHnGMJZtXprWVqpYKYZ2fZFSgaWwWAZOHUB0QpSnn4LHvP7GszRv0ZhTp/4hbMDrbNn8R6Y+Q55/ki5d2hFYrCjVrrk9bf+rr4XRuHE9AAoWvIqgksW5/roWnoruEcOf7E3LBvU4lZRE3zHvsHn3nkx9al5ThfHP9qNggQL8uG49wyZNBuCuWxszuMd9VCtfjjb9B/Pbrt0A5PP3Z3S/p6hVtQrWWoa9P4Vfft/i0eflSs1q12X4I73xcziYHr6ECbO/ztCe39+fd/sNpFblKsQnJtLn/0ax/1AsxYsUZerg56lTpSqzloczdMoHABTMX4Apg4ZQsXQoySkpLI5Yy/+mT8vqofOMW6s3ZFiXfvg5HHz1y3w+XDIjQ3v9KrUY1qUv15apTNjHr7Fo0wovJc1dhg4dyvLlywkKCmLevHnejuN1r7/Sj+bNbuLUqSTCBo1gy9admfo8N+gxunRuS2BgEa69oW2m9vZ33MYHk96g3V29+T2Lfw/yms1bfuHzL0djU1K4tUlH2rXtlaF91pdj2PHHegBOn/6HY4nxvDtuueeDekHja29iUMeB+DkczF4zh0+WfpqhvW7l2jzbIYyqodcwdPpLhP++NEN74QKF+WbILJZtXsHI2aM9GV2uYHlqpMZhHDzb6TkGTu3PfaO70ap2ayqGVMrQJ/pING98+RpLNi3KdPzL3V9jxvLPuG90Nx4d34uE4/Geiu5xzZs3plLl8jRpfA9DBo9gxFtDsuy3ZPFPtG/XK9P+V18ZS+tWPWjdqgcfffQlCxcsc3Niz2rRoB6Vy4TS6JEnePad9xj1zJNZ9hvV9wkGjZ9Io0eeoHKZUJrXrwvAjr/+5uE33uLXLVsz9H/wjtYA3P5kf7oOfYXXej/MuW/Z9TkOh4ORvZ/gvuGv0mTA03Ru0pRq5cpn6PNAi9YcPX6cRs88zgfzvuelB3sBkHTmNCM/n8Grn36U6X4nzpnNLf2epMWg/jS8tjrN69TzxNPxCodx8Eq3MHpPHEy7/z3EnfVaUKX01Rn6RCXE8PxnbzIv4kcvpcydOnfuzJQpU7wdI1dofvtNVKpUjia338+QF95mxPCBWfb7MfwX7uzweJZthQsX5OFeXdiwcWuW7XlNSkoyMz4fSVjf8bzx6lesWbeIyMiMH1x17/Ysr740k1dfmkmLZt2oW6eZl9J6lsM4GNJ5MH0nD+CeUd1pW6c1lUplfC8VlRDDq7Pe4IeNi7O8jyfbPs76Pzd6Iq7PSs7lP74oTxU111e4gQNx+4mMP8jZ5LP8uGkJTW+4LUOf6IQo/ozaTYrNuJRexZBK+Dn8WLdrLQCnTp8i6UySx7J7Wpu2Tfn6q9Qvgd2wYQuBAUUJCQnK1G/Dhi3Exh7O9r46dmzNd99l/cLmq+64uSFfhqcWaut37CSwSGFCShTP0CekRHGKFipExPbUTzS/DF9Gu8aNANi1/wB/HjiY6X6rVSjPT5t+AyDu6FGOHj9B7arXuPOpuE3da6qyNzqKfTExnDl7ltk/r6Rtg0YZ+rRt2IgvlocDMPfXVdxaoxYAJ5OSWLNjG/+cOZOh/6nTSazashmAM2fP8vvePykTlPn3Mq+oWbE6++IOsv9wFGeSzzJ/QzgtazbJ0OdgfDR/RO7J9Jp1pWvQoAGBgYHejpErtG7dhK+/Tf2gbsPGbQQULUJIcBav5xu3EXso69fzwc8+xqQPZpKUdNqtWXOLPXu3EhJSnuDgcvj756Nh/dZs/O3io6Br1i2mUYM2HkzoPTdWuJ4Dhw9wMD6Ss8lnWbRxCbff0DRDn6iEKHZF7SbFpmQ6vnq56wgqWoLVO9d4KrIIkMeKmuCAYGKPxKRtxx6NITgwOEfHVgiuwPFTiYx4aBTTBkznmfb9cJg8dXoyKF06hMjI8+cqKiqW0qEhl3w/ZcuVpnyFMqz6OcKV8byudFAQkYfi0rYjD8UResGb69CgIKLiDqfrc5jSTt6Ab92zl7Y3N8LP4aBCqRBqVa1C2eCSrg3vIaVLBHEw7vw5ioo/nOkcpe+TnJJC4skTlCgakKP7DyhUmDb1G/LT5t9cFzqXKRVYkuiE2LTt6IRDlMrha5bIv0qXKklk5Pnfo6joQ5QunfPXlRtuqEqZ0BDCl/7qjni50pEjsZQoXiptu3jxEI4cic2yb9zhKOLiDlL9ugaeiudVwYEhRGd4LxVLSA5fl4wxhN3Vj3HzxrsrnshFZXtNjTHm/4BvrLWrPJTnsmQ1jcfm8NNNP4cftSrVoee4HsQcieaNHm/Svv6dzF03x9Uxc4WsZjzl9Fyl16FDa+bPW0pKSuZPa3xZTn6X/ss5nLnoR6qWL8+SCWPYH3uIddt2cDbZNwd6c3aO/tvfpJ/DwQdhg5k8fy77YmKc9vdVWZ4ffSGbXKLL+bfPGMOrLz1D2KARro6Vq2V9drKeCrx23SLq1W2Bw+Hnzki5RlZnIaevS90a38OqHb8Qc5ECUc7zzX/5czdnCwU8CDQ1xgQDXwCfW2udTpI0xvQB+gBUanU1pWp55pPH2KOxhBQ7/8lLSGAp4o7FZXNExmN3Rv5BZHzqlKGVW5Zz49U18lRR07NXFx54oCMAm37bRpky589VaGgIMdGHLvk+O3RoxbAXRrksozc9clc7erRtBcDGnbspk24EpUxwSaLjM15jFRl3mNCSQen6BBETn/11WMkpKbz84dS07fn/N5I9kb65GEXU4TjKljx/jkJLBGU6R//2iYo/jJ/DQdFChUk4nuj0vsc88Qx7oiL5cH7e+fvLSvSRQ5Qufn6EtHTxYGKP5uw1S65sPR/sxP333QnAb7/toEyZ879HoaWDiYnJftrwv4oUKcS11Srx1ax3AAgOLsFHU0bwyGND8/RiAcWLhRCfcP4Dk4SEWIoVy/q9ytqIxfS4L+vrTvOi2KOxlM7wXiqEQzl8XapRsQZ1KtWma+N7KFigEPn88nHy9EkmzJ/orrgiaZzNrzpgra0PtAQSgenGmB3GmFeMMdUudpC19kNrbX1rbX1PFTQA2/dvo3zJCoQWL4O/nz8ta7fip20rc3xs0YJFKVa4GAD1rmnA3pi97ozrcdM++Trt4v5FC1fQpWs7AOrWvZFjicedXjtzoSpVKhBYrCgREZvdEdfjPpq7gOZPh9H86TAW/rqabi1SLwqtd101jp04QWx8Qob+sfEJHD91inrXpf4pdGvRjIW/rs32MQoWyE+hAgUAuK1OLc4mJ7Pz7/1ueDbut3H3LiqHlqFCSCny+fvTqUlTFkVkfP6L1q3h3ttTV8W76+Zb+HnL707v9/n7ehBQuDAvfjzZLblzk837dlAxuBzlgkLJ5+dP+7otCP/dJwbGxcumfTabNu0epU27R/lh8U906Zx6vUfdOteTmHjiotfOXCgx8QQ1697NzU3u5eYm97Jx47Y8X9AAVKp4PTGx+zkUd5CzZ8+wNmIxtWs1zdQvOvovTp5MpErlml5I6R1b92+nfMnylCkRir+fP23qtGLF1py9l3pxxiu0/18H7hzeiXFzxzM/YoEKGvEYZyM1FsBauwt4A3jDGFMTuA9YAOSqK5yTU5IZ890oxvUej8Phx7y1c9gbs4ferR9n+4Ht/LxtJdXLXc9bPUdRtFAATao34bHWj/PAmHtJsSlMmPcOEx6fiMGw4+AOvl8z29tPyW3Cw1fRvEVjVv36LadO/cPAsDfS2hYvmU7rVj0AGPZiXzp1ak3BglcRsX4uM2fO4f/GpL7Z7NCxDd9/t8Qr+d3tx7XradmgPms/ep+TSUn0/78JaW1L3xtL86fDAHhuwvupSzrnz094xAbC16Uu/9mu8U28+WRvggIDmfn6S2zZs5d7h71KyWLF+GL4q6SkpBB9OJ6n3x7rlefnCskpKTw/5X2+eOk1/BwOZi79kT/2/82Q7g+wafcuFkWsZUb4Et7rN5A1735AwvHjPD72/KhexKQpFC1YiPz+/tzR8Ca6vf4yiSdPMrDLvew8sJ/wt8cBMHXhfGaE562FKP6VnJLM61+OY+rTo/EzDr5evYDd0X/Rr/0jbPn7D5ZuXkWNCtfxXu//EVCoKM1qNKZf+0doP7ynt6N73cCBA1m7di0JCQk0bdqUvn370rVrV2/H8oqly1bTvNnN/Lzic/45lcTAweenki1aMJU27R4FYNjzT9CxQ0sKFryKdb9+zedfzOf/xn3srdhe5efnzwPdBzP2nb6kpCTT5Ja7KVumCt/NeZ+KV1endq3URYbWrFtEw/qtfXaVyv8iOSWZkd+O5r0+43EYB3PWzmVPzF6eaNOHbQe2s3LrT1xfvjpjeo0ioGBRml5/K0+06U3Xt+/zdnSfoulnrmeym3drjNlora1zOQ9w8+AGmiCeA39Pv3JeMC/XmVqh3o7gGwrnreuc3KVY6FFvR/AZO9/N2ae1V7pyFTN/4i9Zm/XJfG9H8An95rb0dgSfsWHMGp94Q9V9WO1c/f541vBNPnEe03M2/exWj6QQERERERH5j7KdfmatPW6MCQTaAmVJnY4WCSyy1h7xQD4REREREZFsZTtSY4x5CNgA3A4UAgoDzYD159pEREREROQSJNvc/eOLnC0UMAyod+GojDGmOLAG+NRdwURERERERHLC2TU1hqy/oyqFi31LlYiIiIiIiAc5G6kZDmwwxiwG/v0yjQpAK1KXeBYRERERkUugJZ1dL9uRGmvtNKA+sAJIAk4Dy4H61tpP3B1ORERERETEmWxHaowx11lrdwCzjDEFrLVJ6dpustaudntCERERERGRbDi7pmZmutu/XtA20cVZRERERETyvORc/uOLcrJQQFa3s9oWERERERHxOGdFjb3I7ay2RUREREREPM7Z6mfljDHjSR2V+fc257bLujWZiIiIiEgelOLtAHmQs6JmcLrbERe0XbgtIiIiIiLicdkWNeeWdBYREREREcm1nC3pPCe7dmvt3a6NIyIiIiKStyVbrbflas6mn90M7Ac+B9agFc9ERERERCSXcVbUlAZaAfcB9wPzgc+ttVvdHUxERERERCQnnF1Tkwz8APxgjClAanGz3BjzurV2gicCioiIiIjkJb76BZe5mbORGs4VM+1JLWgqAuOBb90bS0REREREJGecLRQwDbgRWAi8Zq3d4pFUIiIiIiIiOeRspOZB4ARQDehvjLHn9hvAWmsD3BlORERERETEGWfX1Dg8FURERERE5EqQoiWdXc7pNTUAxphmwA2ABbZaa5e7M5SIiIiIiEhOObumpiypiwL8A6wnddpZN2NMQaCTtfag+yOKiIiIiIhcnLORmneBSdbaT9LvNMY8BEwEOrgpl4iIiIhInqQlnV3P2TUz119Y0ABYaz8FrnNLIhERERERkUvgrKjxy2qnMcZxsTYRERERERFPcjb9bK4xZjIwwFp7AsAYUxgYCyxwdzgRERERkbxGq5+5nrORmueAo8A+Y8x6Y0wE8BdwDBjk5mwiIiIiIiJOOfuemv9n787jbCz/P46/rkNkm8GMYcaSXci+hoixRUVKi3yLQnxlV0KLSEUp0SqKlPRtsaR+oUFK2cu+U2E2Y8bOYOb6/TFjGLOco+acM2e8n4/HeTj3fV/3mc99Pc65nc+5rvtzXwCGG2OeAyqSVP1sr7X2jCeCExERERERccZZSefugLHWzga2XLG+N3DaWjvHzfGJiIiIiOQoqn6W9ZxNPxsGzE9n/RfJ20RERERERLzKafUza+3Jq1daa08AN7gnJBEREREREdc5q352gzGmwKXKZ5cYYwoBeVz5A7fltf80tuvKnDx5vR2Cz2hR9y9vh+ATVs0L8nYIPqFg2Xhvh+AzSpVt7u0QfMKhP1d6OwSfUbyl7uHtkgCdz3MaVT/Les5GamYAXxljyl5akfx8bvI2ERERERERr3JW/ex1Y8wp4CdjTMHk1aeAV62177k9OhERERERESecTT/DWvs+8H5yUmPSu8ZGRERERETEW5yVdH4knXUpz621n7ghJhERERGRHEvX1GQ9ZyM1DdJZZ4C7gJKAkhoREREREfEqZ9fUDLj03CQN0TwMjABWA+PdG5qIiIiIiIhzTq+pMcbkBnqQdLPNNcB91tpdbo5LRERERCRHSkDTz7Kas2tq+gODgDCgvbVWNwgREREREZFsxdlIzVQgGmgGfHtFkQADWGttTTfGJiIiIiIi4pSzpKacR6IQEREREblOJFpvR5DzOCsUoOlmIiIiIiKSrTm7puYkkF4ueWn6mZ9bohIREREREXGRs5GaQp4KRERERETkeqCbb2Y9h7cDEBERERER+TeU1IiIiIiIiE9zevNNERERERHJOpp+lvU0UiMiIiIiIj5NSY2IiIiIiPg0TT8TEREREfEgTT/LehqpERERERERn6akRkREREREfJqSGhERERER8Wm6pkZERERExIN0TU3W00iNiIiIiIj4NCU1IiIiIiLi0zT9TERERETEg6ymn2U5jdSIiIiIiIhPU1IjIiIiIiI+TdPPREREREQ8SNXPsp5GakRERERExKcpqREREREREZ+m6WciIiIiIh6k6WdZL8clNZUr3UqnDsMxDgdrN8xnxcpZqbbf1uRhGtbvRGJiAqdOx/HlvLEcOxZJhXL1uKvD0JR2xQLLMud/o9i24ydPH4LHjB0ziFaUIjzNAAAgAElEQVQtb+Xs2XMMGf4yW7fuTtPm6af6cF+Xdvj7F6JKtbYp67vedwfPjvovkZExAMz85Gs+n7vIY7F7Uu1KTeh559M4HA7C1s1j/sqPU22/s2l3QhvcQ0JCAifOxPHu12OIORYBwOge71CpdE12/vU7r34y0Bvhe9S4Uf+lVfMGnD0Xz5BRr7Nl+940bUYM6kHXTm3w9ytIpfqdUtY3ql+DsSP7UrVyefoNe5nvlvzsydA9qkmVxgzvPJRcDgfz1ixk5rJPUm2vW742wzoNoVJwRUZ++hxhm5el2l4gbwG+HjGX5Vt+YsK81z0ZuseNfWEgrVo25uzZeIYMf4Wt29I5Tw3vxX1d2uPvX5Aq1dun2d7xjhZ88N44OtzVm81bdnki7Gxl5MiRrFixgoCAABYtypnnaWfGD+hNaKN6nD0Xz8AJb7Flz/40bWpWrsCUEQO5MW9ewtZsYPTUD1Nt73d/Z8b060nVTt2JPXGSe1u34MkHuwBw+uw5np78Htv3/emJw3GLlrXr8lLP3uRyOPgsbClT53+Vanue3Ll5e8BQapavQNypk/R5YyIHj0RTpGAhZgx/htoVKjF3RRijZnwAQIEb87Fw3Ksp+wcHBPL1yuU8N3O6R49Lrh85avqZMQ7uuWsEMz4ZyKQpXaldox1BxcqlahMesZMp7/2HN99+iC3bwujYLumL5r4DG5j8zsNMfudhPvioHxcunGP33tXeOAyPaNWyMeXKlaZZiwcZMfI1XnlpeLrtfvxxFXd26pPutm8XLaNdh56069AzxyY0DuPg8btHMn5mf4ZM7kLTWu0pFVQ+VZsDETsZ8c7DDJ96P6u3/sh/2g9O2bbg51lM/XK0p8P2ilbNG1DuppI0bd+Tp1+YzCvPp5/ELV2xmg4PDEiz/nB4NINHvs6875als1fO4TAORnR5igEfDubeiQ/Svk5byhVPfZ6KiItizNxx/PD7knRfo1/7J9iw73dPhOtVrW5vTLlypWh2ezdGjHqNV8YPTbfdj2G/cmenJ9LdVqBAPnr2uI+Nv29zZ6jZWpcuXZg+/fr9IhnaqB7lSgbTuHtfhk96h4lD+qXbbuLgvgyf9C6Nu/elXMlgWjWsm7ItpFggLerX5mBkdMq6vyKi6Dx4FC17DeKN2V8waVh/tx+LuzgcDl7t1Zdu48dw25D+3NOsOZVLlU7VpltoW46dPkXjAU/wwaIFPNe9BwDxF87z6tzPGDP7o1TtT587S+hTg1Ieh45E892a3zx1SHIdylFJTelS1Yk5epDYuMMkJFxk05YlVK/aIlWbfQc2cOFCPAB/H9yKv1/xNK9Ts3oou/b8mtIuJ2rb5ja++voHADb+vg0/v4IEBQWkabfx921ERx/1dHjZRsVStxB59CDRcYe5mHCRVZsXU7/q7anabNu/nvMXzgGw++/NFPW//J7aum8tZ+PPeDJkr2nXqglfLVgKwMZNO/H3K0BQsaJp2m3ctJPoI7Fp1h8Kj2LH7gMkJlq3x+pNt5SpxqGjhzgcG87FhIss/n0pt1dvnqpNRFwEeyL2kmgT0+xftdTNBBQqyurdazwVste0bduMr75ZDMDG37fjV6ggQcXSO09tJ/pI+uepp4b14r0P5hAff96tsWZnDRo0wN/f39theE37pg35cslyADbs2I1fgQIEFS2Sqk1Q0SIULJCf9duTRvK+XLKcO5o1Stk+tv/jjP1gJpbL56f123Zy/NTppNfdvovgwLTvTV9Rt2IlDkRG8Fd0FBcuXmT+qpW0b9AoVZv2DRrxvxVhAHz72yqa1agFwJn4eNbu3E78+QsZvn65EsEE+vuzesf1++PC1aw12frhizJNaowxNT0VSFbw9wvi+PGolOXjJ6Lx8wvKsH2Dep3YuefXNOtr1WzLH5sXuyXG7KJEiUDCwy//4hQRGU2J4oHX9Bp33NGCpT/M5IP3xhEcnHE/+7Ki/kEcPR6Zshx7PIqATN5TofXv4ffdv3gitGynRPEAwiOPpCyHR8ZQIp1E+XpXzD+IyGOXz1PRx6MJ8i/m0r7GGIbcNZDJi6a4K7xspUTxq89TRyhRwvXzVPXqlQgJDiJsmX4dvp4FBwZwODomZTkiJiZNAhIcGEDEFYlx+JGjKW3aNWlIZMzRTKeWdevQhmVrN2Zt4B5UomgA4TGX+yj86FFKFL2qj4oGcDi5TUJiIifPnKZoIT+XXv+eZi1Y8Ov1+X+jeI6zkZrfjTF7jTHjjDHVXH1RY0wfY8x6Y8z6TRuPON/BnWz6v/rWqXUHpUpW5aefU89lL1QwgBLFK7JrT87+T9CYtFl4Bl2VrqU/ruLWpl1p074HP/+ynslv5NQpVun0E+l31G21O1C+ZDUWXnUd1/Xi376nrhfp/f6V0Xvqavc3uZdVO38l6li088Y5QPrvKdf6yhjDmOeeZOz4d7I6LPE16b2PrvrMpdMEay358uZhcPeuTPh4ToYv37R2Dbp1aM24ab577k/vs5bmBP4vPo+dm97GvF9y7jXKkj04KxSwGfgP8BCw0BhzGvgcmGut/TOjnay104BpAE8/W99jX2uOn4jG/4qpP/5+QZw4mTapqlihIa1aPMb7M/qQkJB6uLRmjTZs276cxMQEt8fraY8+0oVuD94FwKbNOwgJuTziEFwiiKgrfsly5tixEynP53z+LaOeSX+Osq+LPR5FgH+JlOWi/sWJPZH2PVWjQiO63N6LFz58nIsJGQ/B5zQ9ut3Fw/d1AOCPrbsIKXF5xCGkRCBRGUwJup5FH4+mROHL56kg/yCOHHfts1ejbA3qlKtN1yb3ki9vfm7IdQNnzp9h6nfvuitcj3v0P/fQ7aE7Adi0aedV56liREW59p4qWDA/VSqX48u5bwFQrFhRPpr+Co/1GnldFgu43vTs3IHuHdsA8MfOvZQMujzCFxwYSGRM6imw4UeOEnzF1MaQYgFEHo2lbEgwZUoEsWz65OT1gSyd9ibt+w3nSNwxqpW/iTeG9+ehZ8YSd+KkB47MPSKOxhASeLmPQgICiIyLTdOmZGAgEbFHyeVwUCh/AeJOOT/majeVJXeuXGzevy/L4xa5krOkxlprtwKjgdHGmIbAg8DPxpiD1tombo/wGhw6vJ3AgNIUKRLCiRPR1KrRls+/fDZVm5DgKtzbaRQzZg3g9Om4NK9Ru2Y7fljytqdC9qhZn3zDrE++AaBVq1vp+ei9LFj4I3XrVOfkyVPXdO1MUFBASvu2bZqxd+9fbonZ2/Ye3kZwYBmCioQQeyKapjXb8dYXo1K1KRtchT6dn2X8zP6cSOc9lZPNnPMtM+d8C0Boi4b07NaJ+d+voG6tmzlx8nS6185c77Yd3EHpwNKEFA0m+vgR2tVpw6hPn3Np32c/eyHl+V0NOlKtVNUcldAAzJo9j1mz5wFJBU16PtqFBQvDqFunGidPns7w2pmrnTx5mpp1705Z/nLuW4wb/64SmuvEx/O/5+P53wPQunE9HuvckXnLfqZe1cqcPH2a6NjU5+ro2DhOnTlLvaqV2bBjN13btmTGvO/YceAvqnd5NKXdus+n0e6JYcSeOEnJoEA+GjuS/q9MZv+hcI8eX1b7fe8eygeHUCaoOBGxR+nctDn9JqeurLh4/Rruvz2U9bt3cdetTfll62aXXrtLsxbM+2WlO8L2aSrpnPWcJTWpetxauxZYa4wZBjRPfxfvSUxMYMGi1+j16FQcjlys27CQqOj9tA19gkOHd7B950o6th9Injz56P5gUpnBY8eimPlZUkWdIoWDKexfnP1/+u68WFctW/YbrVreyi8rv+Dc2XMMHf5yyrbF339Muw49ARg9sh+dO7UhX74bWbf6Gz6fu4g3Jn/EYz3uo02bZiRcTODY8RMMGT7eW4fiVomJCcxY+Cqje76HwzhYvmEBh6L38UDrfuw7tJ31O3/iP3cM4ca8+Rn20GsAxByPYMLspApoY/t8RMliZbkxT37eH7GY974Zw6YcOrUx7Ke1hDZvyK+LZ6aUdL5k6Tfv0aZL0mjes8N70bljS/Lly8v65Z/x+Vc/MOmd2dS6pTIzpr5AYb9CtGnZmOED/kPLu9KvvOfLEhITmPDN67zTZwoO42Dh2m/ZH3WAvu36sP3QDlZu+5lqpasyqcdE/PIVonm12+jbrjddX3vI26F73LLlq5POUz99zrmz8Qx96pWUbYu/n0G7Do8DMPqZvnTu1DrpPPXbV3z+xXe8MfnjjF72ujN06FDWrl1LXFwczZs3Z8CAAXTt2tXbYXnMj6s3ENqoPms+fZ+z8fEMmjA1ZVvYh28S2nsIACPefJ8pzwzkxjx5CFu7kbA1GzJ93WGPPEgRv0JMGJxUee9iQiLt+g5z34G4UUJiIiOnv8/cZ18kl8PB58t+ZNehv3n6gYfZtG8Pi9evZU7YUt4eOJTVUz/g2KlTPPHmxJT91707nUL58pMnd27uaNiYB8Y9z+5DBwG4u0kzuo1/0VuHJtcRk9l8SGNMN2ttxhNJXeDJ6We+bM7sG70dgs+49eFT3g7BJ6yalzOLN2S1Eh2OezsEnxH9dV5vh+ATDv2pX6VdVbxlJ+eNBALSVkKU9EV99a1PDIFU7N8iW38/3vvOTz7Rj1fKdKTmyoTGGFM0aZW9vubXiIiIiIhkIV8tm5ydOSvpXMYYM9cYcwRYA6wzxkQnryvriQBFREREREQy46yk8xfAPKCEtbaStbYiEAzMB+a6OzgRERERERFnnBUKCLTWfnHlCmttAjDXGDPOfWGJiIiIiORMqn6W9ZwlNRuMMe8Cs4CDyetKA48Cv7szMBEREREREVc4S2oeAR4HXgRKklTi+SDwLTDDvaGJiIiIiIg45yypCbLWvge8d/UGY8xdJCU3IiIiIiLiIlU/y3rOCgWEpVflzBjzGDDZHQGJiIiIiIhcC2dJzRBgqTGm0qUVxpiRyetbuDMwERERERERVzi7+eb3xph44P+MMZ2BXkADoLluwikiIiIicu1soqafZTVnIzVYa8OAHsAKoDwQqoRGRERERESyi0xHaowxJwFLUtWzvEAoEG2MMYC11vq5P0QREREREZGMOZt+VshTgYiIiIiIiPwTzkZq8gMXrLUXkperAB2AP6218zwQn4iIiIhIjqKSzlnP2TU1PwBlAYwxFYHfSLqu5kljzKvuDU1ERERERMQ5Z0lNEWvtnuTnjwKfW2sHAHcAHd0amYiIiIiIiAsynX5GUpGAS1oBrwFYa88bYxLdFpWIiIiISA6l6WdZz1lSs9kY8zpwGKgILAEwxhR2d2AiIiIiIiKucDb9rDcQQ9J1NW2ttWeS11cDXndjXCIiIiIiIi5xVtL5LJCmIIC19lfgV3cFJSIiIiKSU9lETT/Las5KOm8h9XU1qVhra2Z5RCIiIiIiItfA2TU1d3okChERERERkX/I2fSzv9Jbb4zJBTwIpLtdRERERETSp+pnWS/TQgHGGD9jzEhjzNvGmLYmyQBgP3C/Z0IUERERERHJmLPpZ7OBOOA3oBfwFJAH6GSt/cPNsYmIiIiIiDjlLKkpb62tAWCMmU5Seecy1tqTbo9MRERERCQHUvWzrOfsPjUXLj2x1iYAB5TQiIiIiIhIduJspKaWMeZE8nMD5EteNoC11vq5NToREREREREnnFU/y+WpQERERERErguqfpblnE0/ExERERERydaU1IiIiIiIiE9TUiMiIiIiIj7NWaGAf+3neM0ZdEVCYGFvh+Azfvle9Slc4ddahQpd4TDW2yH4jLkzv/N2CD6heMtO3g7BZ0QtX+DtEHxCp1G1vR2CZDGb6O0Ich6N1IiIiIiIiE9TUiMiIiIiIj7N7dPPRERERETkMquSzllOIzUiIiIiIuLTlNSIiIiIiIhP0/QzERERERFPStT0s6ymkRoREREREfFpSmpERERERMSnafqZiIiIiIgHqfpZ1tNIjYiIiIiI+DQlNSIiIiIi4tM0/UxERERExJMSvR1AzqORGhERERER8WlKakRERERExKcpqREREREREZ+ma2pERERERDwpUSWds5pGakRERERExKcpqREREREREZ+m6WciIiIiIh5krbcjyHk0UiMiIiIiIj5NSY2IiIiIiPg0TT8TEREREfEkVT/LchqpERERERGRa2KMaW+M2WWM2WuMeSad7XmNMV8kb19jjCl7xbaRyet3GWPaZUU8SmpERERERMRlxphcwDvAHUA14CFjTLWrmj0OxFlrKwJvAhOS960GPAhUB9oD7ya/3r+ipEZERERExJMSs/nDuYbAXmvtfmvteWAu0OmqNp2AWcnPvwJCjTEmef1ca228tfYAsDf59f4VJTUiIiIiInItSgIHr1g+lLwu3TbW2ovAcSDAxX2vmZIaERERERFJYYzpY4xZf8Wjz9VN0tnt6rvvZNTGlX2vmaqfiYiIiIh4ks3e1c+stdOAaZk0OQSUvmK5FBCeQZtDxpjcgD8Q6+K+10wjNSIiIiIici3WAZWMMeWMMXlIuvB/4VVtFgKPJj+/D1hmrbXJ6x9Mro5WDqgErP23AWmkRkREREREXGatvWiMeRJYDOQCPrLWbjPGjAXWW2sXAjOA2caYvSSN0DyYvO82Y8z/gO3ARaC/tTbh38akpEZERERExJNcqzCWrVlrvwe+v2rd81c8Pwd0zWDf8cD4rIxH089ERERERMSn5biRmsZVbmXw3cPI5XCwcO0CZi+flWp77XJ1GHz3UCoEV+T5z0azfMuylG3FCxdnZNdnKe5fHItl6IzBRMZFePoQPGbcU30IbVqfs+fiGTxmMlt27kvTpubNFZj84hBuzJuHsFXree61pGvGqlcux4RR/cmbJw8JCQk88+p7/LFtt6cPwa3GPf0Eoc0aJPXP82+k3z9VKzJ57NCk/vllHc9N/ACA6lXKM2H0k+TNewMJFxN55pV3+GPrbvwLFeTNFwdzU6lg4s+fZ8gLk9m17y9PH5pb3Fa1IaPvHYTD4eDL3xbx4dLPUm2vX6EWo+4dSJWQ8gyd+SKL/1iRsm16v9epVbYaG/Zvoe8HIzwcuWfdWqUxwzsNw+FwMH/NAmYt/yTV9jrl6zDs7iFUDK7I6M+eJWzz5XPUmom/sTci6X0YdSySoR8P92js3rRl6698/r/XsYmJ3NasMx3a90i1fe7/JrFz1wYAzp8/x4mTsbw9eYXnA/Wg8QN6E9qoHmfPxTNwwlts2bM/TZualSswZcRAbsybl7A1Gxg99cNU2/vd35kx/XpStVN3Yk+c5N7WLXjywS4AnD57jqcnv8f2fX964nC8buTIkaxYsYKAgAAWLVrk7XCyjTqVmtD7zqdxOBwsXTePr1d+nGr73U2707bBPSQkJHD8TBxTvx7DkWM597uTZF85aqTGYRwMu+dphs4YxEOv30+b2m0pG1QuVZvIY5GM+9+LLP1jcZr9n3/wRT5bMZuHXr+fx6f0IO5UrKdC97hWTetTvnQITTr34amX3ubVkf9Nt92rI/vz1Etv06RzH8qXDqFVk3oAPDeoJ29M+5w23QYy8f3PeG5gT0+G73atmtWnfJmSNLm7F0+Nm8Kro59Mt92ro/vz1LgpNLm7F+XLlKRV0/oAPDf4Md74YA5tHhjAxPdm89zgxwAY2Ot+tu7aT+j9/Rn47CTGPf2Ex47JnRzGwfNdh9LrveF0HP8f7qzXmgolyqZqExEXxchPX2bRhh/T7D897HOenv2Sh6L1HodxMOKepxk4fRBdX3uAdnXaUa74VeeouEjGfDGWxb8vSbN//IV4Hn6zOw+/2f26SmgSExP47PMJDBkwhXFjvmTNusWEh6f+Av/g/cMY89wcxjw3h9CW91O3TksvResZoY3qUa5kMI2792X4pHeYOKRfuu0mDu7L8Env0rh7X8qVDKZVw7op20KKBdKifm0ORkanrPsrIorOg0fRstcg3pj9BZOG9Xf7sWQXXbp0Yfr06d4OI1txGAdP3D2SF2f258nJXbitVntKB5VP1eZAxE6GvvMwg6bez69bf6RH+8FeilaudzkqqalWpjqHYg4SHnuYiwkX+fGPpTSv3iJVm8i4CPZF7CXRpi6HXTaoHLkcuVi3J6n4wtnzZ4m/EO+x2D2tfYtGfPld0i/AG7fuwq9gAYICi6RqExRYhEIF87Fhy04AvvxuGe1vbwyAtVCwQH4A/ArmJzLmqAejd7/2tzfmy0VhAGzcsgu/Qhn0T4H8bNic3D+Lwmjf8lL/2Cv6pwCRR5IS5Mrly/DLmj8A2PvnIUqHFCewaGGPHJM71bypKn/FHObQ0QguJFzkuw1hhNZolqrN4dhIdoXvS/PZA1i9ewOnz53xVLheU71MdQ4ePcTh2HAuJlxkyR9LaFG9eao2EXER7I3YS6LNAROus8j+A9sICipNsWKlyJ37BhrWb8vvm37KsP2adUto1KCdByP0vPZNG/LlkuUAbNixG78CBQgqetU5qmgRChbIz/rtuwD4csly7mjWKGX72P6PM/aDmdgrbg+xfttOjp86nfS623cRHBjg7kPJNho0aIC/v7+3w8hWKpW6hcijB4mKS/pe9fPmxTSsenuqNlv2r+f8hXMA7Pp7MwH+xb0QqQ9KzOYPH5RpUmOMGWiMKZ1Zm+ykmF8xoo9FpSxHH4+imH8xl/YtU6wMp86e5JVHJjJr8Kc82XEgDpOjcr5USgQFEB4Vk7IcEX2U4GKp//MKLhZAeNTlZCUi6iglgpLaPP/6NJ4f3JP1333M84Mf55Wpqaf5+boSQYGERx5JWY6IiiE4KDBVm+CgwNR9GBVDieQ2z782jeeHPMb6H2bx/NDHeWXKTAC27z5Ah9CmANS+pTKlgoMIKZ76dX1R8cLFiIy7/Gtv1LEjFC/s+8eV1YL8ixF15TnqWDRBLp6jAPLkzsMng2bx8YAZtLjqB5uc7NixaIoWufxFqUiRII4di063bczRCGJiDlP15gaeCs8rggMDOBx9xfknJiZNAhIcGEDEkcvn8PAjR1PatGvSkMiYo5lOLevWoQ3L1m7M2sDFpwT4BxFzPDJl+ejxKAL8gjJs36b+PWzY/YsnQhNJw9m39nHAGmPMz8aY/xpjXP/f1wuMSXsjI5vOr8LpyeXIRa1ydZi66C0em/IoIQEl6Vj/zqwOMdtwqa/SbZP07yNdO/DCpOnU79iTF974kEnPD3JHmF6TzqGn0z8Zt3mkawdeeP1D6rd/lBde/5BJLyT1z9SP/oe/X0GWfjGVxx+8m6279nEx4V9XMfS6dG8N/K/vDZwTZfyZcsWd4+/mkbce5dnPnmNYpyGUDCiZhbFlX+l3Ufo3rlu7bjH16obicORyZ0jel975+aqeyug8li9vHgZ378qEj+dk+PJNa9egW4fWjJuWs36wkmvl/H12SYvaHahYshrzVuo9I97hLKnZT9JdPscB9YDtxpgfjDGPGmMKZbSTMaaPMWa9MWZ91KYjGTXLctHHowkqfPnXvCD/4sSciMlkj9T77g7fRXjsYRISE1i5dQVVSt3srlC9okfXjiydM4Wlc6YQdSQ21QhBcFAAkTGpryGKiI4hpPjlX/6CiwcQlfyr3/13hvLdsl8B+HbpL9SpXtkDR+BePR64k6VfTGXpF1OT+qfE5Rw+uHggkUdST7GLiIpJ3YfFAy/3z12t+S5sFQDfLvmZOrdUAeDU6bMMeeFN2jwwgAHPvk5AEX/+PhyJr4s8doQSRS7/ele8cDGij7v22bueRB+PpviV56jCQRw54fo58tL57HBsOBv2beTmklWyPMbsqEjhIGLjLo9wxcVFU7hw+r+xrV2/hEYNc+bUs56dOxD24ZuEffgmUTGxlLxi9Dg4MDDNOTz8SOoR+JBiAUQejaVsSDBlSgSxbPpk1n0+jZBigSyd9ibFiiRNha1W/ibeGN6fR599mbgTJz1zcJItHT0eRaB/iZTlAP/ixKZzzqpVoRFdb+/F+NmDuJhwwZMh+i5vTy+73qafAdZam2itXWKtfRwIAd4F2pOU8GS00zRrbX1rbf3itTw3uLPj4HZKB5YhuEgIuXPlpnXtNvy8faXL+xbKV4jCBZJO6vUqNuBA1AF3hutxM7/8jjbdBtKm20D+b8VvdO3YCoC6t1Th5KkzRMfEpWofHRPHqdNnqZv8hbxrx1b88NMaAKKOxHJrvRoANGtQiwMHwz14JO4x84tFtHlgAG0eGMD/Lf+NrneGAlC3RhVOnjqdfv+cOUvdGsn9c2coP6xYDUDUkaPcWj+5fxrW4sDfhwHwK1SAG3InFR18uEs7Vm/YyqnTZz1yfO605e+dlC1WilIBwdyQKzcd64WybIumIFxt+8HtlA4sTUjRpHNU29ptWbntZ5f2LZSvEDfkugEA//z+1Cpbk/057ByVkXJlqxEVfZAjMYe5ePECa9cvoXat5mnaRUb+yZkzJ6lQvqYXonS/j+d/T2jvIYT2HsL/rVpN17ZJxRDqVa3MydOniY696hwVm3SOqlc16Uenrm1b8sOqtew48BfVuzxKg4f60OChPoQfiaFNnyEciTtGyaBAPho7kv6vTGb/Id8/r8u/s+fwNoIDyxCU/L3qtprtWLsj9fVs5YKr0K/zs4yfPZjjp+MyeCUR93NW0jnVuKO19gKwEFhojMnntqj+oYTEBCbNn8jk3lNwOHKxaO1CDkTtp3fbJ9hxaAe/bF9J1VLVePXRiRTK70ezqs3o1fYJHp70AIk2kamL3mLqE+9iMOw8vJMFa+Z5+5DcJuyX9YQ2rc9vCz7k7Ll4hoyZnLJt6ZwptOk2EIBnXnmXyWOGcOONeVi2agPLVq0HYPhLUxk3vA+5cuUi/vx5nnppqleOw13Cfl5HaLMG/PbtjKT+eeHNlG1Lv5hKmwcGAPDM+HeYPHYIN+bNy7JV61n2S3L/jJ3CuKefSO6fCzw1Lql/KpUrzZSXhpGYkMju/X8zdMxbnj84N0hITGDsl28y/b+TyNtLh7sAACAASURBVGUcfL36O/ZG/snADo+z9e+dLNu6ihplbubtXuPxy1+Ilrc0YUCHx7jz5UcA+Gzw25QPuon8efPx09ivGT1nAr/sXOvlo8p6CYkJvDbvNab2nkIu42Dhum/ZH7WfJ9r1YcfBHazc/jPVSlfltUcn4pffj9uq3Uaftn144PUHKRdUllH3jSTRWhzGMGv5Jznuh5eM5MqVm4cffIo33xpAYmICzZreTcmQCsxf+D5lb6pK7VpJ1xetWbeYhvXbpju9Nqf5cfUGQhvVZ82n73M2Pp5BEy6fg8M+fJPQ3kMAGPHm+0x5ZiA35slD2NqNhK3ZkOnrDnvkQYr4FWLC4KTKjBcTEmnXd5j7DiQbGTp0KGvXriUuLo7mzZszYMAAunZN976B143ExASmLXyVMT3fw2EchG1YwMHofXRr3Y+9h7azdudP9LxjCPny5ufph14DIOZ4BONnqwKaeJ7J7JoTY0xla+2/uvnIrU810Mx6F/y5TNVCXJbgo+OiHuZ3m6aNuKJQ3pxb5TCrTb4zzNsh+IR7X+zu7RB8RtTyBd4OwSd0GlXb2yH4jAUv/+ETv2oEteuUrb8fRy9e4BP9eKVMR2qstbtN0k9eDYGSJF2vGQ6sta5egS8iIiIiIuJGmSY1xpi2JF1Dswc4nLy6FFDRGPNfa23au8OJiIiIiIh4kLNrat4CWltr/7xypTGmHPA9UNVNcYmIiIiI5EyJPje7K9tzVv0sN3AonfWHgRuyPhwREREREZFr42yk5iNgnTFmLnAweV1p4EFghjsDExERERERcYWzQgGvGGMWAHcDt5JU4vkQ8LC1drsH4hMRERERyVkSVW8rqzkbqSE5eVECIyIiIiIi2ZKz6mdbSCrjnGYTYK21OfO2zSIiIiIi4jOcjdTcmfyvAb4DOrg3HBERERERkWvj7Jqavy49N8bEX7ksIiIiIiLXziR6O4Kcx1lJZxERERERkWzN2TU1da9YzGeMqUPSVDQArLUb3RWYiIiIiIiIK5xdUzPpiueRwBtXLFugVZZHJCIiIiKSk6mic5ZzltSMstb+5pFIRERERERE/gFn19S845EoRERERERE/iFnIzXGyXYREREREbkWqn6W5ZwlNeWMMQsz2mitvTuL4xEREREREbkmzpKaI6QuFiAiIiIiIpKtOEtqTlprf/JIJCIiIiIi1wNNP8tyzgoF/OmJIERERERERP6pTJMaa22X9NYbY9oYY5a6JyQRERERERHXZTr9zBjTEvgACAHmAy8Dn5BUFW2826MTEREREclpEnX3zazmbPrZG0AfIAD4ClgNzLbW1rPWfuPu4ERERERERJxxVijAWmtXJD+fb4w5Yq19y80xiYiIiIiIuMxZUlPYGHPldTXmymWN1oiIiIiIiLc5S2p+Au7KYNkCSmpERERERK6BUUnnLJdpUmOt7empQERERERERP4JZ4UCMMbcYoyZZYxZb4xZl/y8hieCExERERERcSbTpMYY0wmYR9K0s8eAXsnPv0neJiIiIiIi18La7P3wQc6uqRkLtLHW/nnFuk3GmGXAguSHiIiIiIiI1zibfnbDVQkNAMnrbnBHQCIiIiIiItfC2UjNBWNMGWvt31euNMbcBFx0X1giIiIiIjmUqp9lOWdJzQvAj8aYl4ENJJVxbgA8A4xw5Q/ckSfXvwrwevF502PeDsFnnFiSz9sh+IRjW4t6OwSfcHuTv503EgAGftva2yH4hoAgb0fgMzqNqu3tEHzCgpf/8HYIItmes5LO840xB4BhwADAAFuB+621mzwQn4iIiIiISKYyTWqMMbWBTdbaRzwUj4iIiIhIjmYSfbPCWHbmbPrZdKCcMWYjsAr4FVhtrT3h9shERERERERckGn1M2ttfaA0MB44DwwE9hhjNhlj3vVAfCIiIiIiIplyNlKDtfYMsMIYsw5YAzQFHgHauzk2EREREZGcx0dvcJmdObumphvQBKgNxAOXEptm1tpI94cnIiIiIiKSOWcjNdOAncD7wEpr7W73hyQiIiIiIuI6Z0mNP1CLpNGaMcaYKkAE8Bvwm7V2mZvjExERERHJWVT9LMs5u09NArAx+fG2MaY4cB8wBBgL6M6aIiIiIiLiVc6uqalJ0ijNpUcekkZpppJU4llERERERMSrnE0/m0lS8vJ/wHPW2r/cHpGIiIiIiMg1cJbUbLHWDvBIJCIiIiIi1wGja2qyXKY33wRqeCQKERERERGRf8jZSE1+Y0wdwKS30Vq7MetDEhERERERcZ2zpKYkMIn0kxoLtMryiEREREREcjKr6WdZzVlSs9daq8RFRERERESyLWfX1IiIiIiIiGRrzkZqXslogzGmjLX27yyOR0REREQkZ1P1syznbKTmmUtPjDFhV22bn/XhiIiIiIiIXBtnSc2VBQKKZrJNRERERETEK5xNP7MZPE9vWUREREREnNH0syznLKkJMsYMJWlU5tJzkpeLuTUyERERERERFzhLaj4ECqXzHGC6WyISERERERG5BpkmNdbaFz0ViIiIiIjI9cDo5ptZLtOkxhjzfCabrbV2XBbHIyIiIiIick2cTT87nc66AsDjQACgpEZERERERLzK2fSzSZeeG2MKAYOAnsBcYFJG+4mIiIiIiHiKs5EajDFFgaHAw8AsoK61Ns7dgYmIiIiI5Egq6ZzlnF1T8xrQBZgG1LDWnvJIVCIiIiIiIi5yONk+DAgBngXCjTEnkh8njTEn3B+eiIiIiIhI5pxdU+Ms6RERERERkWthE70dQY6jpEVERERERHyakhoREREREfFpTquf+ZoKlRrTruMQHA4Hv69fyKqVs1Ntb9z0IerUv5vExATOnI5j4TfjOX4sEoDQdv2pVKUJACuXf8z2LT96PH5PaVa1EaO6DMLhcPDVb4uY/uOnqbbXr1CLkV0GUjmkAsNmjWHJHytStk3rN4laN1Vj4/7N9Js2wsORe8fY0f+lVfOGnD0Xz5CRr7F1+940bUYM7sl9nVrj71eIyvXuTlnfp8e9PHTfHVxMSCA29jhDR7/O4fBoT4bvVuMH9Ca0UT3Onotn4IS32LJnf5o2NStXYMqIgdyYNy9hazYweuqHAIzo2Y32TRuRaBOJiTvOwAlTiDoai3/BAkx+eiBlQ0oQf/48gydOZeeff3v60NymeqUm3N/xKRwOB7+sn8/ilR+n2t66aXea1r+HxMSLnDodx6xvXiT2WETK9hvzFmDM4G/4Y/sy5n47wdPhe0yTKo0Z3nkouRwO5q1ZyMxln6TaXrd8bYZ1GkKl4IqM/PQ5wjYvS7W9QN4CfD1iLsu3/MSEea97MnS3a1m7Li/17E0uh4PPwpYydf5XqbbnyZ2btwcMpWb5CsSdOkmfNyZy8Eg0RQoWYsbwZ6hdoRJzV4QxasYHABS4MR8Lx72asn9wQCBfr1zOczOne/S4PKlOpSb0vvNpHA4HS9fN4+urPod3N+1O2wb3kJCQwPEzcUz9egxHrvgcXs9GjhzJihUrCAgIYNGiRd4Oxzep+lmWc3mkxhiT7RMgYxzccddw5swawrtvPUT1mm0JLFY2VZvI8F18+G4PPpjane1bl9O63ZMAVKrShOCQKnzw9iPMeO9xmtz2MHny5vfCUbifwzh4rutQ+rw/nLte7k7Heq2pUKJsqjbhcVGM/OxlvtuQNrH7KGwOIz59yUPRel+r5g0pd1NJmrXrwYjnJ/PKCwPTbbd0+Wo63j8gzfqtO/Zyx339adPpCb5bvJJnh/d2d8geE9qoHuVKBtO4e1+GT3qHiUP6pdtu4uC+DJ/0Lo2796VcyWBaNawLwDtfzKNlr0GE9h7C0tXrGfbIAwAMergrW/fup2WvQTz5ymReGtDLY8fkbsY4eOiuZ5g660nGvHUvDWq2J7hY+VRt/g7fycvvPsy4qQ+wYWsY97YblGr73a3/y54DGzwZtsc5jIMRXZ5iwIeDuXfig7Sv05ZyxculahMRF8WYueP44fcl6b5Gv/ZPsGHf754I16McDgev9upLt/FjuG1If+5p1pzKpUqnatMttC3HTp+i8YAn+GDRAp7r3gOA+AvneXXuZ4yZ/VGq9qfPnSX0qUEpj0NHovluzW+eOiSPcxgHT9w9khdn9ufJyV24rVZ7Sgel/hweiNjJ0HceZtDU+/l164/0aD/YS9FmP126dGH69Jyb8IpvcprUGGPqG2M2AfuTl2sZYya7PbJ/oGSpasTFHuJYXDiJCRfZtnkpVao2T9XmzwMbuXghHoDDB7fi5x8EQGCxcvx14HdsYgIXLpwjKmIPFSvd6vFj8ISaN1Xl7yOHOHQ0nAsJF/l+44+0qtEsVZvw2Eh2h+8jMZ0L2Vbv3sDpc2c8Fa7XtQu9la8WJCV3GzftwN+vIEHFiqZpt3HTDqKPxKZZ/+uaTZw7l/Se27BpB8Elirk3YA9q37QhXy5ZDsCGHbvxK1CAoKJFUrUJKlqEggXys377LgC+XLKcO5o1AuDUmbMp7fLfmBdrk365qly2ND9v3AzA3oOHKV08iGJF/N1+PJ5QrtQtRMceJCbuMAkJF1m/eTG1qt6eqs3uA+u5cOEcAAcObqawf/GUbWVCquJXMIDte3LuF06AW8pU49DRQxyODediwkUW/76U26unPp9HxEWwJ2JvuuepqqVuJqBQUVbvXuOpkD2mbsVKHIiM4K/oKC5cvMj8VStp36BRqjbtGzTifyvCAPj2t1U0q1ELgDPx8azduZ348xcyfP1yJYIJ9Pdn9Y5t7jsIL6tU6hYijx4kKu4wFxMu8vPmxTS86nO4Zf96zid/Dnf9vZmAKz6H17sGDRrg758zzsmSc7gyUjMV6AwcBbDWbgJauzOof6qQXzGOH788refEiWgK+Wf8BbJ2/bvYuzvpi0FU5B4qVr6V3DfkJV9+f8qWr4dfDj2BBRUuRuSxy/0UdewIxTPpp+tdieKBhEdc7q+IyBhKFA/8R6/10H13sHzl2qwKzeuCAwM4HB2TshwRE0NwYECaNhFHjqYshx85mqrNyMe7s/GLGdzbugUTP54DwPZ9B+jYPOlHhTo3V6JUiSCCi/2zPs9uCvsFEXc8KmU57kQUhTP5/DWt35ltu1cBYIzhvjuG8vUPb7o9Tm8r5h9E5LHL/RR9PJogF89TxhiG3DWQyYumuCs8rypRNIDwmMufu/CjRylR9KrPXdEADie3SUhM5OSZ0xQt5OfS69/TrAULfv0l6wLOhgL8g4g5HpmyfPR4FAF+QRm2b1P/Hjbsztl9Ih5mE7P3wwe5ktQ4rLUHrlqXkNkOxpg+xpj1xpj163/34LUDxqRdl8GUxRq12hMSUpVff066lmT/3rXs2f0rjz3xIfc+MI5Df28lMfGiG4P1HkPafrr0C7mklVX91eWuUGpVr8x7M77MirCyh3Q+c/aqD126H8sr+u+VGZ9S94HH+frHn3jsno4ATJnzNf4FCxL24Zs8fk9HtuzZz8WETE87viOd/sjoPNWoVgduCqnGkp9nAdCi0f1s3f1LqqQop0q/m1z73N3f5F5W7fyVqGM559q1K5n0P1RXN0qniWv917npbcz75ad/EpoPcX7uuqRF7Q5ULFmNeStnuTsoEfkXXLlOJtwYUw+wJulM2h9Ie5X0Fay104BpAGNHN/bYt+WTx6Px97/8S4ufXxAnTxxJ065chQY0u70Hs6b3IyHh8hD8Lytm8suKmQDcc/+LxB496PaYvSHqWDQlCl/up+KFixF9IiaTPa4/j3a7m4e7dgDgjy27CAkOApKmYgSXCCQq+mgme6d12611GNi3G/f+ZxjnL2Q87cMX9Ozcge4d2wDwx869lAy6PIISHBhIZEzqKXjhR44SXOzyr8ghxQKIPJp2mt43YSv57JXneG3m55w6c5bBEy//yr7u82n8HZEzvsgfOx5NkStGgYv4FedYOuepmys04o7bH2fS9F5cTD5PlS9dk0pl69Ci0f3cmCcfuXLdQHz8WeYtyXkjEtHHoylR+HI/BfkHceS4a+epGmVrUKdcbbo2uZd8efNzQ64bOHP+DFO/e9dd4XpUxNEYQgIvf+5CAgKIjItN06ZkYCARsUfJ5XBQKH8B4k6ddPra1W4qS+5cudi8f1+Wx52dHD0eRaB/iZTlAP/ixKbzOaxVoRFdb+/F6A8fT/kcikj25EpS0xd4ByhH0hS0ZUD6VwN72eHDOygaUJrCRYI5ceII1Wu2Yd7/nk/VpkRwZTp2GsGcWUM4czouZb0xDm68sSBnz54gqHhFipeoyPy9OWea0JW2/L2Tm4qVpmTRYKKPH6FD3dY8NetFb4eVrcyas5BZcxYCENqiIT0e7sSC75ZTt1ZVTpw8ne61MxmpXrUCr744mO69R3E09pi7QvaYj+d/z8fzvwegdeN6PNa5I/OW/Uy9qpU5efo00bFxqdpHx8Zx6sxZ6lWtzIYdu+natiUz5n0HQLmSwRw4nFRNqF2Thuz5+zAAfgUKcDY+ngsXL9K9YxtWb96e6vobX/bn4W0EBZQhoEgIx05EU79mO2b8b2SqNqWDq9C902imzHqSk1ecpz76cnTK81vr3MVNparlyIQGYNvBHZQOLE1I8nmqXZ02jPr0OZf2ffazF1Ke39WgI9VKVc0xCQ3A73v3UD44hDJBxYmIPUrnps3pNzl1dbfF69dw/+2hrN+9i7tubcovWze79NpdmrVg3i8r3RF2trLn8DaCA8sQVCSE2BPR3FazHZO+GJWqTbngKvTr/CwvzuzP8dNxGbySyD9jVP0syzlNaqy1UcB9HojlX7OJCfzft6/zcI+3MMbBHxsXcST6ALeH9ib88E527/yZ1u0HkCdvfu57aDwAx49F8cWnT+HIlZsefZJKW8afO828L8dgE3PIdJerJCQm8NJXbzD9v2/gcDj4ZvV37I08wIAOj7P1750s37qKW8rczNReL+OXrxAtb2nKgDse565X/gPA7EHvUL54GfLnyc/ysd/w7JxXWbUzZyaAAGE/raVV80asWjKLs+fiGTrq8peHJfPep+09fQEYPbwX99zZinz58rJ+xRzmfPV/vPH2bJ57qg8F8ufjg8lJX8gOR0TT87/Pp/u3fM2PqzcQ2qg+az59n7Px8QyaMDVlW9iHbxLaewgAI958nynPDOTGPHkIW7uRsDVJlbue7fMIFUuXJDHRcigqmqfefA+AyjeVYurIwSQkJrL7z4MMeW1q2j/uoxITE5j77QQG9XgXh3GwauMCIqL3c1doP/46vJ3NO3/i3vZDyJs3P30emghA7LFI3v30+qq8lJCYwIRvXuedPlNwGAcL137L/qgD9G3Xh+2HdrBy289UK12VST0m4pevEM2r3Ubfdr3p+tpD3g7d7RISExk5/X3mPvsiuRwOPl/2I7sO/c3TDzzMpn17WLx+LXPClvL2wKGsnvoBx06d4ok3J6bsv+7d6RTKl588uXNzR8PGPDDueXYfSpqZcHeTZnQbn/N/5EpMTGDawlcZ0/M9HMZB2IYFHIzeR7fW/dh7aDtrd/5EzzuGkC9vfp5+6DUAYo5HMH729fU5zMjQoUNZu3YtcXFxNG/enAEDBtC1a1dvhyXXOeNsjq0xZhrwtLX2WPJyEeAVa21fV/6AJ6ef+bLPT2b7itnZxokl+bwdgk+4GJwzS5Jntc5Ncs79b9xt3bk83g7BJxz+K+MLziW1xpVz5jTvrLbg5T+8HYIvSe+SvGynVNnm2fr78aE/V/pEP17JlW/SDS4lNADW2jhjTKPMdhARERERkQz4aIWx7MyV6me5jDEpdSCNMYVxLRkSERERERFxO1eSk7eAX40xn5NUeLQbkPNvkiAiIiIiIj7BlUIBM4wxvwOtSJqn+B9r7e9uj0xERERERMQFLk0js9ZuNMb8eam9MSbIWpsz72omIiIiIuJOKumc5ZwmNcaYJ4CXgBNAAkmjNRao7N7QREREREREnHNlpOYp4Jbk+9WIiIiIiIhkK64kNYeBI+4ORERERETkuqCSzlnOlaRmF/CjMeZbIP7SSmvtu26LSkRERERExEWuJDVHkx+6RbKIiIiIiGQ7rpR0HumJQERERERErguafpblXKl+VhQYAlQHbry03lrbwY1xiYiIiIiIuMThQptPgUiSkpq3gGPANncGJSIiIiIi4ipXrqkpYa19xxjzhLV2sTFmCbDc3YGJiIiIiOREVjffzHKuJDXnk/+NMsaEAuFAGfeFJCIiIiIi4jpXkpoJxpjCJN2E8z3AD3jarVGJiIiIiIi4KNOkxhiTCwix1h4D/gBu9UhUIiIiIiI5laqfZblMCwVYaxOArh6KRURERERE5Jq5Mv3sZ2PMJGAucPrSSmvtdrdFJSIiIiIi4iJXkprWyf82uWKdvWpZRERERETEK5wmNdZaXUcjIiIiIpJVdE1NlnN6801jTKAx5h1jzPzk5arGmP+4PzQRERERERHnnCY1wExgDVAueXkvSeWdRUREREREvM6Va2qKW2s/McYMAbDWXjDGaMxMREREROSfSNRX6azmykjNmeSbb1oAY0xd4JRboxIREREREXGRKyM1TwPfAeWMMWFAWXTvGhERERERySZcqX62xhjTCrgFMMBWa+05t0cmIiIiIpIDWVU/y3IZJjXGmA4ZbGpljMFa+72bYhIREREREXFZZiM1mZVttoCSGhERERER8boMkxpr7UOeDERERERE5Lqg6WdZzuk1NcaYgsAooHnyqp+AV6y1qoAmIiIiIiJe50pJ5xlALqBP8sMBfOTOoERERERERFxlrLWZNzDmD2ttbWfrfIkxpo+1dpq34/AF6ivXqJ9cp75yjfrJNeon16mvXKN+cp366p8LKVot8y/gXhYeu914O4Zr5cpITbwxpsGlBWNMfSDefSF5RB9vB+BD1FeuUT+5Tn3lGvWTa9RPrlNfuUb95Dr1lWQbrtx8sz/wqTEGku5TkwB0d2dQIiIiIiIirsrsPjWdrLULrLXrgZuNMUEkTVeL8lx4IiIiIiIimctspOYFYMGlBWtttPvD8RjN/3Sd+so16ifXqa9co35yjfrJdeor16ifXKe++oesSjpnuQwLBRhjNlpr63o4HhERERGRHC24yM3ZulBARNxOnysUkNlIzc3GmI3prDeAVcIjIiIiIiLZQWZJzQGgq6cCERERERG5Lmj6WZbLLKk5b63d57FIRERERCTbM8bkttZe9HYcIlfK7D41qwGMMXmu3mCMKey2iEREREQk2zHG1DfGbAL2Jy/XMsZM9nJYIkAmSY21tl/y06+NMSkjOsaY4sBSdweWFYwxbxhjmno7Dl+hZNU1xpiNxphnjTEVvB1Ldqf3lHPGmIHGmNLejsMX6JzuOmNMTW/H4Av0+btmU4HOwFEAa+0moLVXI/JR1iZm64cvymyk5pLvgS+MMQ5jTBlgCfC8e8PKMv8B3jLG/GWMmWiMqePtgLK5GGPMj8aYx/VlNFNFgMLAcmPMWmPMEGNMiLeDyqb0nnJuHLDGGPP/7d15mCRVlf7x79sNDQ00m2zKrgiCIMqqjKMCoqOioIgCKqAo48IiiAg6gyuCgqA4I/5QZgQX0J8jmwoiCOijDKtsArLvArLJvjXv/HGjurKzs7KqmuqMiK7341MPGRFVXedJI6PixL33nD9I+rikZesOqMFyTR+7P0u6QdKXJa1TdzANls/f+EyxfXPXvpm1RBLRZdSkxvbRwO+BXwC/BPawffq8DmyC3GF7I8pThEeAH0m6VtLnJa1Zc2xNdA3wTWAL4EZJp0jaQdL0muNqmgdt72d7FeBTwEuBSyWdI2n3mmNrmpxTo7sJWIlyc7UhcLWkMyTtImlGvaE1Tq7pY3cF5Yn6FOBUSZdLOkDSarVG1Tz5/I3PXZI2BKxiD+CGuoOKgP59avbq3AQ+SLlIXgxg+6h5Ht3z1KvXTjUkvyOwve016omsmTrfr+qm8+3ADsDrgd/Y3qnO+JpihPNqKrAV8F7bH6wnsubJOTW67vNJ0oLAWyjXqTfazpPjSq7pY9fjvNqE8tnbHrjd9ma1Bdcg+fyNT7UE4T+BLQEDvwM+Pp81aB+I5Rd/caP71Nzz8E2t61PTL6n5cr8ftP3v8ySiCSTpz7YzPWGMRnq/JC0BbGv7uBrCahxJJ9reoe442iDn1Oj6XackTbf9xKBjaqpc08euz2dPwOtsn1dDWI2Tz1/UJUnNxBsxqen5zeViuIjtx+ZdSBNH0mK2H607jraQtJ/tw+uOI+YfOadGJ2lN29fVHUcb5Jo+dpJ2sv2TuuNounz+xkfSMcD+th+qtpcCDrH90Xoja58kNRNv1KRG0vHAHsCzlKlnywCH2j5i3of3/FVPhP8FWJEyVHoXZdrLQ7UGFq0m6c2U+eqd59Upts+oNbBopeqB0SbMfj5d6PE8dZokck0fP0lLA7b9YN2xNFE+f2PXa2QrI6hzZ/kZqzX6/LrnkVtal9SMpfrZerYfptzAnUlZULfrvAxqokjaGbgUeAOwCLAosDlwSXUsOkhaQtKh1cLb+6uva6p9qVxVUanJvzdwHvB14LDq9V6SvlVnbE2Tc2p0kt4EXA98AXgr8Dbgi8D11bGo5Jo+dpJWkXSipL8DFwAXSbq32rdavdE1Rz5/4zZV0uJDG9V1vF8j94iBGctIzV+A9YEfA0fbPlfSZbZfOYgAnw9JfwU27X6CVw2XXmA71XI6SPoNZdHfcbbvrvatAOxCWTC5VZ3xNYWk63qdO9XTvutsv7SGsBop59ToJF0DvMX2LV37Vwd+bXvtWgJroFzTx07S+ZTKgz+3PbPaN5VSKOCTtl9dZ3xNkc/f+EjaDdgHOIEyqrUTcKTtY2sNrIUyUjPxxjJS833gNkpvjvNUetW0ZU6zKB+6bs9Vx2J2q9n+2tDNJ4Dtu21/DVilxria5smqklC3jYEnBx1Mw+WcGt0CwB099t8JLDjgWJou1/SxW8b2joEw5QAAGMNJREFUT4cSGgDbM22fCLygxriaJp+/caiSl52Bp4BngA8koZk7dTfXnB+bb446ZGj7SODIoW1Jt1N6TrTBwZT+IWcCt1f7VqGU3u1b3W2SulXS/pSn6vfArPKNuzL8/kV5P46uehgM/TFcGXiYlkzNHKCcU6P7L8rUoBMZfk9WppTfzc3C7HJNH7tLJH0HOI7Zz6tdgD/XFlXz5PM3TrYvlXQL1T2kpOVS0jmaoF9J5x1tn9DVr2aWNvSpgVnTEt5MWQAoyk3ob7Jgck7Ve3UAsA2wXLX7HuBU4Gu2H6grtiaqplHNOq86RyOi6HFOCbgbOI1ScCTnFCBpbcp71HmdOtX21bUG1kC5po+NpGnAbsx+Xt1O+ewda/upGsNrFEnrAO8gn79RSfpX4CuUh3gzqUZPM/Vz/JZbbJVGTz+799HbWjf63S+p+bjt74zUr6YlfWpeZvva6vVCnRdxSa+2/b/1Rdcukraz/T91x9EUkhagNGh7WbXrGuAM28/WF1XE/C3X9LGTtJLtXtOqkPR226cNOqZoP0k3AP80NPIecy9JzcQbV5+aWT/UkoZUmr2beXfX4Dk6U8fIJN1mO2sgAEkvAs4B/kaZxiHgVcAKwOa276oxvEaR9HbgCtu3VtsHAdsBtwJ72765zviaTtLptt9SdxxNkWv62FVFFd7cYwH8h4DP2X5JLYE1jKQr6b1Oa2gE4hUDDqnRJJ1H+TvXzkUXDbLsois1Oqn5+2N3tC6p6bumppr7/kLgKtvPSloG2IsypL3iAOJ7vjTC617b0V/er2FfpVQC/Gbnzmqq5iGUOetRHAy8GkDS1sD7gR0pSeB3KdOIJjVJI92IC2h8lckByzV97PYBfivprbavB5B0IKVa1etrjaxZtq7+K+BXlLLOMbK/AmdJOo1SLAAA29+pL6SIYsSkRtKelLrtNwFTJB0BHAX8BNh0INE9fx7hda/t6C/v17BX2961e6fto6qnozHMth+vXr+LMpf/Esoi5o/XGFeTXETpc9Trpjy9fGaXa/oY2f61pKeA0yVtC3yYUqHxdVl/NGxoFBlA0lOd29HT/dXXcqN9Y8Sg9Rup+Riwlu37qkZd11GGHP84iMAmyEqSjqLcLAy9ptpuw0jTQI0yDL/8gMNpsn5TLx/vc2wykqTFKO/LlkDn07yF6wmpca4B/nXoaXqnqtpkDMs1fRxsny1pV+Bc4E/AlrZTdj7mmu0D645hfpEZfBOvX1LzpO37AGzfUjUcbFNCA/DpjtcXdx3r3o7hYfjobwlJ7+qxX8DiPfZPZt8ELqNUyrnG9sUAkl5FWZMUZUR8pJ5hew4wjjbINX2MJD1CeUglYCHKQ4V7qybBtp1rFXNM/5xeXZtmjZravnTwUTWXpKUpUxtfTseDKduZthe161f97F7gRx273t+5bXvfeRtaDJqkNYDlu5NXSf8M3GX7xnoiaxZJP6DPVBfbHxxcNM0naUXKVIXLhxaXSnohsKDt22oNrgEkvcv2L+qOI2IyknROn8O23Za+fAMh6deUtUd7VV+7AHfa/nTfH4w5LLPIixo9Zfa+x+9q3TrFfknNbv1+sA0dZCW9Fnix7eOr7Z8DS1eHv2L7d7UF10CSfgl81vYVXfs3Aj5v++31RBZtlvLX/aVq19hJOrXfcdvvGFQsTSdpEeAZ289U22tRFsHfYvukWoNrEEmvsX1+3XG0xdD1StIVtl9RjfydY/sNdcfWNi+YvkKjk5r7n7i7dUnNiNPPupOW7p4ALfFFZp++sRalk/miwGeBJDWzW607oQGwfXG1rioASd+0/cnq9d62v9Vx7Ae9ighMViOUv94a+IaklL+O8XoNpYHkCcAFpOJZP2dQKpVeX43Cnw/8GNha0qa2D6g1uub4TyAPFcbu6eq/90jaErgLSLuHmEM1VfGnwGrALcB7uouUSHolcDRl6v5M4GDbP62O/YBSqfEf1bfvavuyfr+zb0nn6h/dBDgWWAJYRdL6wIdtt2Gu9+JdHYGvryovIemQmmJqsn4Lt6cPLIrme13H612Ab3Vsp6fB7FL+enQvkzTHwwTSJ6OXFYCtKGXBd6JMgznB9l9qjaqZluooPrEL5X3aU9I04BIgSU2RxHh8viZpScr6tqGb0f3rDSka6gDgbNuHSjqg2v5M1/c8Duxs+/rqIeglkn5j+6Hq+Kdt/3ysv3DUpIZSxnlr4GQA25dL2nysv6Bms5VDtd25uDvVvOZ0kaSP2P5e585qKuIlNcXURP16ZcTsUv56dDcDmdo5BrZnUkYgzpC0ECW5OVfSl2x/u97oGqdzassWwGEAtp+WlLJLw1bvN60xUxqHSZoKvKi64byMMnIac8nM9x/DbYA3VK+Po1RhnC2psX1dx+u7qvX8ywIPMRfGktRMsX1rmTY5y8y5+WU1uFbS22z/qnNn1QQwN1Rz+iRwkqT3MZzEbARMA95ZW1TNM0XSUpSKVUOvhz4gU+sLq5FS/np0T6c3xthVyczbKAnNapQHbym0MKcrJB0O3AmsAZwJUD1lj2F/B75RdxBtYHumpO0pU/ZiPidpd2D3jl3H2D5mHP/E8rb/BmD7b5L69jaqZoZNAzqLUh0s6SDgbOCA0ZbBjCWpub36Ra6y9D0pPWvaYB/gV5LeDQyVZdwQ2IyUL56D7XuAzaqRuHWr3b9KQYU5LEFJ+oYSmc6Sn41e+FeDlL8e3Yil8iUtX30uA5B0HOXadDrwRdtX1RxSk30E2JuS+L2pownuOsDhdQXVQI/YPq/uIFrkD5K+AZwIPDa0s2uqf8wHqgSmbxIj6SzKtOBunxvP76oqov4Q2MXDDXwOBO6mJDrHUEZ5vtT33xmp+lnHL1qO8iTsjdWus4A9hnrYNF31VO99lJrqBv4CXA/saPsTdcbWVJLWo6NSVW4cYm5J+u9+x1P+ek6SlgC2o6wZWdt2mkpWqmlTj3XsGvoDlt4rMVck/aJranr0IalXpTjb3mzgwbTcUgsv0+iHoA8+ed/zml5fTTF/QzVK80LgXNtr9fi+xSlT0w6x/f9H+LfeAOxnu++AxKhJzfyiaqi1I/Aeyhz2/7H9H/VG1SzVzdQpwMrAFZQbhfWA24BtbD9cY3iN0dWsbQ5p1hbjJWk68A5KIrMBMAPYFvi903Y65oKkK+nfTysFKPqQtBWwv+2t6o4l5k+TIKk5DLi/o1DA0rb37/qeaZSR99N6FBR6YZUQCTgSeHK0qo1jGan5Hj0ujLZ37/HtjSJpTWAHSjJzP6W03H62V601sIaSdBSlXOP+HU0SpwCHAtNbUvFunkuztrGT1LdJr+0jBhVLU0n6MaWi3pmUKR2/A26wvXqtgTVYNUV21ui77XPrjah5JPX9O5d1XEV1Lv0/4EWUgkhfBY6nPNQ7OI1xZydpGUq7jBVtbytpbWAj2z+sObTWmQRJzQuAn1FKft8GbG/7gar34Udtf1jS+4H/psyiGrKr7csk/Y5SNECUwhQftf1o3985hqTmvR2bC1MWjN/ehhvcaqrCH4DdbN9Q7bvJ9ovrjayZJF0NvKK7KWLVPPFK22vXE1m0laTP9zls233nx04Gki6nXLSPB35q+/Zcp3qTtCKlKMCTDK9r24BScv6dtu+sMbxWqNbG7mD7x3XH0gSS/kxZf3s+pUnw8cC/d/Yfi2FVk+6fAZ+yvb6kBYFLMvI3fvN7UlOHUQsFDDXBGSLph8Bv51lEE2s7ykjNOZLOoDwFbd3/SQP0dK8u77afldS2xqsDl+kKPR1r+45eBySljDFQ3Ri8jDL17KyqpOUMSSvYvrvm8JrmPyh9j37QuVPSzsB3KCVEg1nz1D8BrAicSvm7vQewH+WpZ5Kawh0jfSdL+nsSmr6Wt328pH0AbD+TEuFzJzOLJ95Yqp91Wx1oxfQt2ydRShQvSpmfvg+wvKSjgZNsn1lrgM2zcLX2qDvxE7BQDfE0kqQtgO8ywnSFGkNrorMlvdn2LZ07JX0Q+DfgtFqiahjb1wIHAQdVQ/M7AhdKuiMLcGezju05ystXN1njqrYzCfwQeJAyAvFhSrPEaZT1kX27ck8yS3ZVaFTndqafzeHxqiy4YdYa075TgiIGZSzTzx5keE3NFOABSq3on83j2OYJSUsD2wPvzdqH2Uk6l/4LS9vSdHWeynSFsZP0VuBbwFuHuptLOpAyKvGWkUZxotxZAa9Ludlhkm6wvUaP/VOA63odm6wkXWl7ver1VOA+YBXbj9QbWbOMUqHRtj80sGBaQNKmwBGU0uCXUkqGb58COeO35EJLN3r62UNPPdC6mU19k5rqj+rKlOZdAM95spRLixiBpEttb9CxfaPtl9QZU5NJ2pKyEHdbyhPjjYGtbT9Ya2ANIenrwE22v9u1fx9gBduf6f2Tk4+kI4HFgE/afqzatyjDlXH2qjO+JulxnZptO2JuVa0y1qXMTrjK9pM1h9RKS0xbstH30/94+qH5K6kBkHSJ7Q0HFE/USNL+tr9evd6+s164pK/a/mx90TWHpJso89KHHN65nekKc5L0WspUvT8B78kfwWFVgY51u0s3V6MPV9het/dPTj7VouRDgF2BWykjy6sCxwGftf10fdE1i6SZDPf0EaWYwuOkp88cJK1LmZ43VFHvauBw21fWGliDVKPuI7L960HFMr9IUjPxxpLUHA18L0OL87/OJ3l5yjeyTFcYO0mPUG4ShtZlPQPMJDdWs0j6i+2Xj/fYZFb19VmDch7dYPvxmkOKlpK0DeXB1CHAxZRzakNKN/P9bJ9SY3iNIemEPodte6eBBTOfSFIz8UYsFCBpgaoS1muBj0i6kfLUZ+hmJDe48x+N8LrX9qRl+4N1x9AWtmfUHUMLPC7ppUNrjoZIeinwRE0xNVLV00BVT4wrO/Z/BHjM9k9qCy7a6kvAVl3FTC6vemScUn1NerZ3rDuG+c1zIy9hjrnUr/rZhZT6/9sOKJaon0d43Wt7UqsW3i5l+75qexplSsw+6ecT43QQcLqkr1B6rwBsRHlS/MnaomqmT1EalXb7KXAOkKQmxmvB7uqMALZvqaY7RgdJiwGfZfhzeB5wyGhNESMGoV9SIwDbNw4olqjf+pIeppp/Xb2m2l64vrCaRdIOlIXvj0m6HvgCpXzqRcD7agwtWsj26ZK2pczpH2pqfBWwXeb0z2Fqr+pdth/ODWjMpWckrWL7ts6dklYF5ujbFhwL3ALsXm1/APgv4D11BRQxpF9Ss6ykfUc6aPuIeRBP1Mj21LpjaIl/Aza0fUNVo/98Sofuk2qOK1pI0sLAPbZ36dq/nKSFU1RhNgtKWnSo8tkQSTMoPVgixuvzlKa3X6WMlJpSofEAIJUH57SW7fd2bB8oKX2P5kKab068KX2OTaWUzpwxwlfEZPW07RsAqgIaNyehiefhKOCfe+zfilKqOIYdC/xc0mpDO6rXJ1bHIsbF9smU3nVbAD+g9B3bnFKl8eQaQ2uqpyRtPLRRNQt+qsZ4ImYZsfpZql1F9CbpDkrzsSH7dm5nFDPGQ9LVttcZ4Viqn3WR9FHKeqPFql2PAofaPrq+qKKtJL0SuDw9+MamSmJ+NLRJqWb5/lTIHb/FFpzR6HPu0WceaV2BqFHX1ETEHL7H7KOVnduNvkhFI/W71vYbTZ+Uqial360WLKvXGpuIcfg+sLqkS4E/Unpp/a/th/v/2OQiaRvbp9i+GHiZpOUon7976o6trTL9bOL1G6lZ2vYDA44notUkbWz7orrjiPaQdB7wadsXdu3fGPiG7V7VviYlSTv3O277+EHFEvMPSYsAmwCbVV8bA3cDf7T98Tpja4rM3pl4iy6waKMfgj727GOtG9wYtflmRPQnaR1gB2BH4B+2N6o5pGgRSZsAP6PM5+8s6bwzpQDFBTWF1jiSvt1rN/B2YEXb/WYfRPQlaVHg1cA/UT5/U2y/uN6omiFJzcRLUjPxktREzIWq3OeO1dezwKrARr36HUSMpprK8SngJZSb9BsoozT31hpYg0kSpYT6Z4CrgYNtX1FvVNE2knaijM68krLg/SLgAuB823fXGVuTSHocuLbXIdKQfa5MX2B6o2/An3j2idYlNXmqFTFOkv4ELEGpuPRu29dLujkJTcwNSQsA+wEfAm6j3CS8vhzS52w/U2d8TVO9X7tSksALKJ/Bv9YaVLTZMZSb9e8Cv7d9Xc3xNNXNlCpxEY2VpCZi/P4OrAQsDywLXE8KBMTcO4xSaGL1oUXvkhYHDq++9q4xtkaR9AnK+3E28C+2b605pGi/JYD1KaM1X5C0FvA3Sv+x823/rs7gGuTpNGOPpsv0s4i5IGkJYDvK9LM1gCWBN3cv9o4YjaTrgTW7S8pKmgpca/ul9UTWPJKeA+6lPFjofL+GpsC8opbAYr4haXng3cA+lAcNaUoNSDra9sckTbP9dNexJW0/VFdsbZXpZxMvSU3E81T9EXwvpVjAyrZXrjmkaBFJ19lec7zHJqNqLduIMnIT4yXpFQxXPdsMmEYZpfkTpfrZxTWG1ziSTgPeafvZant54Je2N+7/k9Ft4akLNfoG/MmZTyWpiZjMJK2aG6sYD0knA7/oLkcs6f2UrubvqCeyiPlfV3+aP+X63Z+kjwFvpKyvWQk4DTjA9um1BtZCSWomXpKaiHGSdGq/47kJjfGQtCLwC+AJSklnU/pkTKc8Eb2zxvAaRdIj9F6/NjT9bPEBhxQtJ+k427vUHUebSNob2Bx4MfAJ23+oOaRWSlIz8VIoIGL8XgPcDpxAqb7Uug9+NEeVtGwqaQvg5ZTz6XTbZ9cbWfPYnlF3DDHfWa/uANpA0l5du1YDLgNeJelVto8afFTt9lzqC024JDUR47cCsBWlSMBOwK+AE2z/pdaootWqKkuptBQxWItIehUjPJyyfemA42mqZbu2Txthf0RtMv0s4nmQtBAluTkM+JLtXh3PIyKigaopjRfRO6mx7S0GHFJrVA1wF7H9WN2xtNG0qdMafQP+9MynWzcLJSM1EXOhSmbeRkloVgOOoqyLiIiI9rghicvYSToe2AN4FrgYWEbSobaPqDey9nnOz9UdwnxnSt0BRLSNpOMolXI2AL5oe2PbX86C7oiImM+tZ/thYFvgTEoFtF1rjSiikpGaiPH7APAYsCawVxmBB1KBKSKibQ4Z6YCkVWzfNshgWmCapAWAbYCjbT9dNcWNqF1GaiLGyfYU2zOqr8U7vmYkoYmIaJUDhl5I6q44ePKAY2mD7wO3AUsB50laBXi03pDayQ3/XxslqYmIiIjJqnMx9NJ9jgVg+0jbL7L9JpdKU7cDWZMUjZDpZxERETFZeYTXvbYnLUk72j6hR7+aIelTE7VLUhMRERGT1XKS9qWMygy9ptpOD5ZhS1X/zXsyQVL9bOKlT01ERERMSpI+3++47S8OKpa2kjTd9hN1x9E2khp9A267ddMvk9RERERERF+SlgdeCFxl+1lJywB7AbvZXrHe6NonSc3Ey/SziIiImJQkHdTnsG1/eWDBNJikPYEvADcBUyQdQVlH8xNg0xpDa602Jg1Nl5GaiIiImJQkfarH7kWB3YAX2F5swCE1kqSrgdfZvk/SasB1wOa2/1hrYBEdktRERETEpCdpBrA3JaH5GfAN2/fWG1UzSLrU9gYd21fZXrfOmCK6ZfpZRERETFqSlgb2Bd4HHAdsYPvBeqNqnJWqKWdDluvctr1vj5+JGKgkNRERETEpSToMeBdwDLCe7UdrDqmpDhxlO6J2mX4WERERk5Kk54CngGeZvdmmKIUCFq8lsIaTtJDtp+qOI6LTlLoDiIiIiKiD7Sm2p9ueYXvxjq8ZSWjmJGkTSVcC11fb60v6ds1hRQBJaiIiIiJibI4CtgbuB7B9ObB5rRFFVJLURERERMRYTLF9a9e+mbVEEtElhQIiIiIiYixul7QJYElTgT0pPWsiapdCARERERExKknLUaagvbHadRawh+376osqokhSExERERERrZbpZxERERExKknfY/bS1wDY3r2GcCJmk6QmIiIiIsbirI7XCwPvBG6vKZaI2WT6WURERESMm6QpwG9tb1l3LBEp6RwRERERc2N1YNW6g4iATD+LiIiIiDGQ9CDDa2qmAA8AB9QXUcSwTD+LiIiIiL4kCVgZuLPa9ZxzExkNkulnEREREdFXlcCcZHtm9ZWEJholSU1EREREjMWFkjaoO4iIXjL9LCIiIiJGJGkB289KuhJYG7gReAwQZRAniU7ULoUCIiIiIqKfC4ENgG3rDiRiJElqIiIiIqIfAdi+se5AIkaSpCYiIiIi+llW0r4jHbR9xCCDieglSU1ERERE9DMVWIxqxCaiiVIoICIiIiJGJOnSFAOIpktJ54iIiIjoJyM00XgZqYmIiIiIEUla2vYDdccR0U+SmoiIiIiIaLVMP4uIiIiIiFZLUhMREREREa2WpCYiIiIiIlotSU1ERERERLRakpqIiIiIiGi1/wNdHRRh6nyUvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation\n",
    "correlation = dataset3.corr()\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 81 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot Matrix\n",
    "from pandas.plotting import scatter_matrix\n",
    "plt.figure(figsize=(15,15))\n",
    "scatter_matrix(dataset3,figsize=(12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the correlation chart above, networth and income are positively correlated with the risk tolerance.\n",
    "With more number of kids and marriage the risk tolerance decreases. As the willingness to take risk decreases the risk tolerance decreases. With age there is a positive relationship of the risk tolerance. \n",
    "\n",
    "As per the paper \"Does Risk Tolerance Decrease With Age?(Hui Wang1,Sherman Hanna)\", Relative risk aversion decreases as people age (i.e., the proportion of net wealth invested in risky assets increases as people age) when other variables are held constant.Therefore, risk tolerance increases with age. \n",
    "\n",
    "So, in summary all the variables and their relationship with risk tolerance seems intuitive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4'></a>\n",
    "# 4. Evaluate Algorithms and Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us evaluate the algorithms and the models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.1'></a>\n",
    "## 4.1. Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing a train and test split in this step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split out validation dataset for the end\n",
    "Y= dataset3[\"TrueRiskTolerance\"]\n",
    "X = dataset3.loc[:, dataset3.columns != 'TrueRiskTolerance']\n",
    "# scaler = StandardScaler().fit(X)\n",
    "# rescaledX = scaler.transform(X)\n",
    "validation_size = 0.2\n",
    "seed = 3\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.2'></a>\n",
    "## 4.2. Test Options and Evaluation Metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_cell_guid": "5702bc31-06bf-8b6a-42de-366a6b3311a8"
   },
   "outputs": [],
   "source": [
    "# test options for regression\n",
    "num_folds = 10\n",
    "#scoring = 'neg_mean_squared_error'\n",
    "#scoring ='neg_mean_absolute_error'\n",
    "scoring = 'r2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.3'></a>\n",
    "## 4.3. Compare Models and Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_cell_guid": "772802f7-f4e4-84ee-6377-6464ab2e5da4"
   },
   "outputs": [],
   "source": [
    "# spot check the algorithms\n",
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('LASSO', Lasso()))\n",
    "models.append(('EN', ElasticNet()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('CART', DecisionTreeRegressor()))\n",
    "models.append(('SVR', SVR()))\n",
    "#Neural Network\n",
    "#models.append(('MLP', MLPRegressor()))\n",
    "#Ensable Models \n",
    "# Boosting methods\n",
    "models.append(('ABR', AdaBoostRegressor()))\n",
    "models.append(('GBR', GradientBoostingRegressor()))\n",
    "# Bagging methods\n",
    "models.append(('RFR', RandomForestRegressor()))\n",
    "models.append(('ETR', ExtraTreesRegressor()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-folds cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_cell_guid": "a784ab4a-eb59-98cc-76cf-b55f382d057a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -0.105127 (0.103969)\n",
      "LASSO: -0.023692 (0.118324)\n",
      "EN: -0.031108 (0.117770)\n",
      "KNN: -0.409457 (0.166871)\n",
      "CART: -0.574853 (0.176963)\n",
      "SVR: -0.106180 (0.106362)\n",
      "ABR: -0.371799 (0.085667)\n",
      "GBR: -0.650500 (0.101299)\n",
      "RFR: -0.694656 (0.096647)\n",
      "ETR: -0.717278 (0.135545)\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "    #converted mean square error to positive. The lower the beter\n",
    "    cv_results = -1* cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algorithm comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "fig.set_size_inches(15,8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non linear models perform better than the linear models, which means that a non linear relationship between the risk tolerance and the difference variables use to predict it. Given random forest regression is one of the best methods, we use it for further grid search. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5'></a>\n",
    "# 5. Model Tuning and Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "848ca488-b0fd-8e93-2e68-23d32c71d89c"
   },
   "source": [
    "Given that the Random Forest is the best model, Grid Search is performed on Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.738632 using {'n_estimators': 250}\n",
      "0.723586 (0.097313) with: {'n_estimators': 50}\n",
      "0.725998 (0.091284) with: {'n_estimators': 100}\n",
      "0.729060 (0.088277) with: {'n_estimators': 150}\n",
      "0.736697 (0.080316) with: {'n_estimators': 200}\n",
      "0.738632 (0.078908) with: {'n_estimators': 250}\n",
      "0.732768 (0.087357) with: {'n_estimators': 300}\n",
      "0.733014 (0.084252) with: {'n_estimators': 350}\n",
      "0.732850 (0.082406) with: {'n_estimators': 400}\n"
     ]
    }
   ],
   "source": [
    "# 8. Grid search : RandomForestRegressor \n",
    "'''\n",
    "n_estimators : integer, optional (default=10)\n",
    "    The number of trees in the forest.\n",
    "'''\n",
    "param_grid = {'n_estimators': [50,100,150,200,250,300,350,400]}\n",
    "model = RandomForestRegressor()\n",
    "kfold = KFold(n_splits=num_folds, random_state=seed)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, scoring=scoring, cv=kfold)\n",
    "grid_result = grid.fit(X_train, Y_train)\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest with number of estimators 250, is the best model after grid search. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='6'></a>\n",
    "# 6. Finalise the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalize Model with best parameters found during tuning step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='6.1'></a>\n",
    "## 6.1. Results on the Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=250,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare model\n",
    "model = RandomForestRegressor(n_estimators = 250)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9640632406817223\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "predictions_train = model.predict(X_train)\n",
    "print(r2_score(Y_train, predictions_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "_cell_guid": "f9725666-3c21-69d1-ddf6-45e47d982444"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007781840953471237\n",
      "0.7614494526639909\n"
     ]
    }
   ],
   "source": [
    "# estimate accuracy on validation set\n",
    "# transform the validation dataset\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "#rescaledValidationX = scaler.transform(X_validation)\n",
    "predictions = model.predict(X_validation)\n",
    "print(mean_squared_error(Y_validation, predictions))\n",
    "print(r2_score(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the mean square error and R2 shown above for the test set, the results look good. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='6.2'></a>\n",
    "## 6.2. Feature Importance and Features Intuition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the details above Random forest be worthy of further study.\n",
    "Let us look into the Feature Importance of the RF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.21642515 0.02484994 0.01507806 0.04322792 0.02467204 0.24043344\n",
      " 0.0561737  0.37913976]\n"
     ]
    },
    {
     "data": {
      "image/png": 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tgNOA+fU7IzMzq1a2b86IiPskLaU0G/pVoeovgK+nhyu2AxYB15f13SRpCnC7pKeB/wt8W9IaSjOttkLzo4ENEfFw/c7GzMyqlVXiiog3le2fVNg9OJXNBeb21T8iHgP2KVSf1kOf24B3DyxiMzOrtSyXCs3M7I0rqxlXLiaOG0OHX1ZrZlYXnnGZmVlWnLjMzCwrTlxmZpYVJy4zM8uKE5eZmWXFicvMzLLixGVmZllx4jIzs6w4cZmZWVacuMzMLCtOXGZmlhUnLjMzy4oTl5mZZcVvh6+D5Rs30zxz0VCHYVZT6/0bD2yY8IzLzMyy4sRlZmZZyTZxSTpFUkg6sFDWIunHktZK6pR0q6SjU92nJD0lqavwmZDq2iWtTp/2VLZLWdunJV01NGdrZmbdcr7HNR34NdAGXCxpR2ARcH5ELASQdDDQCtye+iyIiL8vDiJpN+CfU7sAOiUtjIg/AJMK7TqBG+p7SmZm1pcsZ1yS3gS8FziLUuICOBNY0p20ACJiRURc18dwxwGLI2JTSlaLgSllx2sB9gB+VZszMDOzgcp1xjUNuDkiHpK0SdJhwEHAvX30O0PS+wr7fwmMAx4rlG1IZUXTKc3WYpBxm5nZIGU546KUSOan7flpfxuSbpS0QlJxeW9BREwqfJ4HVGH88gTVBszrLSBJMyR1SOrYumVz9WdiZmb9kt2MS9JbgA8BB0sKYASlRPO/gaO720XEKZJagVl9DLkB+GBhfzxwW+F4hwIjI6Kzt0EiYjYwG2BUU4tnZmZmdZLjjOtUYG5E7B0RzRGxJ7AOeAh4r6STC21HVzHez4APSxoraSzw4VTWbTp9zLbMzKxxsptxUUokl5WVXQ98HDgR+Pf02PqTwB+BLxfald/jOjsi7pT0L8A9qeySiNhUaHM6cEItT8DMzAZOft6g9kY1tURTu//Jl72++JVPVk+SOiOitZq2OS4VmpnZG5gTl5mZZSXHe1zD3sRxY+jwsoqZWV14xmVmZllx4jIzs6w4cZmZWVacuMzMLCtOXGZmlhUnLjMzy4oTl5mZZcWJy8zMsuLEZWZmWXHiMjOzrDhxmZlZVpy4zMwsK37Jbh0s37iZ5pmLhjoMs2HNv9/LBsozLjMzy4oTl5mZZaVhiUvSs+lns6SQ9A+Fuq9J+lRh/3xJKyWtkLRU0idT+Q6SrpK0VtJqSTdJGl/oF5K+XdgfKekpST9O+59K+12Fz4RU157GXC2pPZXtUtb2aUlX1flSmZlZL4bqHtfvgH+U9I2IeLFYIekzwLHAkRHxjKQxwLRU/VVgF2D/iNgq6dPADZKOiogAngMOlrRTRDyfxtlYduwFEfH3ZcfcDfhnoBUIoFPSwoj4AzCp0K4TuKEmV8DMzAZkqJYKnwJuAdor1F0EnB0RzwBExOaImCNpNPBp4LyI2JrqvgW8AHyo0P+nQPdd3+nAvCriOQ5YHBGbUrJaDEwpNpDUAuwB/Kq6UzQzs3oYyntclwH/JGlEd4GkXYBdImJthfb7AY92J7SCDuCgwv58oE3SjsAhwG/K2p9Rtvy3EzAOeKzQZkMqK5pOabYWVZ6fmZnVwZA9Dh8R6yTdDXy8UCxKS3WV9FS3TXlELJPUTCnR/KRC+0pLhaoUYtl+G/DXPcSGpBnADIARu761p2ZmZjZIQ/1U4VeBL3THkWZTz0nat0LbNcDeaVZWdBjwQFnZQmAW1S0TQmmGtWdhfzzwePeOpEOBkRHR2dMAETE7IlojonXE6DFVHtbMzPprSBNXRKyklHROLBRfClwjaVcASbtKmhERzwFzgH/vXl5MTxuOBn5RNvQ3gUsiYnmVofwM+LCksZLGAh9OZd2qvVdmZmZ1NhzenPEV4L7C/teBNwH3SHoJeAm4ItVdSGkm9ZCkV4CVwCnl950iYgNwdQ/HO0PS+wr7Z0fEnZL+BbgnlV0SEZsKbU4HTuj/qZmZWa3JzxrU3qimlmhq9z/3MuuNX/lkRZI6I6K1mrZDfY/LzMysX5y4zMwsK8PhHtfrzsRxY+jwMoiZWV14xmVmZllx4jIzs6w4cZmZWVacuMzMLCtOXGZmlhUnLjMzy4oTl5mZZcWJy8zMsuLEZWZmWXHiMjOzrDhxmZlZVpy4zMwsK37Jbh0s37iZ5pmLhjoMM7OGaeTvV/OMy8zMsuLEZWZmWXHiMjOzrPSZuCSFpCsK++dLujhtXyxpo6SuwueMwvazklal7bmS7pM0KfUdKek5SZ8ojN0p6bC0PU3SMkkrJS2XNK3Q7jpJ69K4SyVNTuU3prI1kjYX4niPpNsktRbGaJa0orB/Yeq3StJxqeyAsnN7RtJnB3G9zcxskKp5OOMF4KOSLo2IpyvUXxkRs8rKFgBIug04PyI60v41wHuALuBQYFXa/46knYF9gaWSDgVmAcdGxDpJ+wCLJT0cEcvSMS6IiB9IOgaYDbRExCnpOB9Mxz2xOyBJPZ6gpAlAG3AQ8A7g55L2j4hVQHeiHQFsBG7s84qZmVndVLNU+DKlxHBeDY53B6VERfp5LSkxAEcC90bEVuB84KsRsQ4g/bwUuKDCmEuAcYOMayowPyJeSMdak+IpmgysjYhHBnksMzMbhGrvcV0DnClpTIW68wpLabf2Mc6dbJu4bgdekLRL2r8j1R0EdJb17Ujl5aYAP6ziHAC+2x0r8JNC+TjgscL+Bl6bDNuAeT0NLGmGpA5JHVu3bK4yHDMz66+qEldEPAPMBc6tUH1lRExKn2P6GGc9sIOktwMHUloqvAc4ilLiujM1FRBl3cvLLpf0MPAd4KvVnAdwZneswAllY78m3FcrpR2Ak4Hv9zRwRMyOiNaIaB0xulJ+NzOzWujPU4VXAWcBOw/ymEuAU4EnIiKAu4D3Ulqauyu1uR9oLet3GPBAYf8CYD/gS8CcQca0AdizsD8eeLywfzylZcwnB3kcMzMbpKoTV0RsAr5HKXkNxh2U7pctSftLgE8Cv42I/5fKZgEXSmqG0hOAwEXAFYVxiIhXgKuB7bqfBByghUCbpFHpQZAW4O5C/XR6WSY0M7PG6e+/47oC2L2srHiPq6s72fTiDkpPDy4BiIgngBH8eZmQiOgCvgD8SNJK4EfA51P5NtKs7cvA5/t5LsUx7qeUlB8AbgbOSQ+JIGk0cCxww0DHNzOz2lHpz32rpVFNLdHUftVQh2Fm1jCDfVehpM6IKL9FVJHfnGFmZlnx2+HrYOK4MXQ08E3JZmZvJJ5xmZlZVpy4zMwsK05cZmaWFScuMzPLihOXmZllxYnLzMyy4n+AXAeS/kjpBcLD3e5Apd+xNtw4ztpynLXlOGtj74h4azUN/e+46mNVtf8CfChJ6nCcteM4a8tx1lYucVbDS4VmZpYVJy4zM8uKE1d9zB7qAKrkOGvLcdaW46ytXOLskx/OMDOzrHjGZWZmWXHi6gdJUyStkrRG0swK9aMkLUj1vyn+Uk1JF6byVYP8bc11i1NSs6TnC78U9NohjvNoSfdKelnSqWV17ZJWp0/7MI5za+F6LhziOD8n6QFJyyTdImnvQt1wup69xdmw61llrJ+RtDzF82tJEwp1DfnODzTGRn/fayoi/KniQ+m3NK+l9NubdwCWAhPK2pwNXJu224AFaXtCaj8K2CeNM2IYxtkMrBhG17MZOASYC5xaKN8NeDj9HJu2xw63OFPds8Poeh4DjE7bf1f47z7crmfFOBt5PfsR666F7ZOBm9N2Q77zg4yxYd/3Wn8846rekcCaiHg4Il4E5gNTy9pMBeak7R8AkyUplc+PiBciYh2wJo033OJspD7jjIj1EbEMeKWs73HA4ojYFBF/ABYDU4ZhnI1UTZy3RsSWtHsXMD5tD7fr2VOcjVZNrM8UdncGuh8aaNR3fjAxZsuJq3rjgMcK+xtSWcU2EfEysBl4S5V9h0OcAPtIuk/SLyW9v04xVhtnPfr212CPtaOkDkl3SZpW29C20d84zwJ+OsC+gzGYOKFx1xOqjFXSOZLWAv8GnNufvkMcIzTu+15TfnNG9SrNSMr/5tJTm2r61spg4nwC2Csifi/pcOCHkg4q+xtbrQzmmgy369mbvSLicUn7Ar+QtDwi1tYotqKq45T0CaAV+EB/+9bAYOKExl1PqDLWiLgGuEbSx4EvAe3V9q2BwcTYyO97TXnGVb0NwJ6F/fHA4z21kTQSGANsqrLvkMeZljV+DxARnZTWzvcfwjjr0be/BnWsiHg8/XwYuA14Vy2DK6gqTkl/BXwRODkiXuhP32EQZyOvZ9WxFswHumeBjbqmA46xwd/32hrqm2y5fCjNTh+mdKO1+yboQWVtzmHbhx6+l7YPYtsbtQ9Tv4czBhPnW7vjonSzdyOw21DFWWh7Ha99OGMdpQcJxqbt4RjnWGBU2t4dWE3ZjfMG/3d/F6U/nFrKyofV9ewlzoZdz37E2lLYPgnoSNsN+c4PMsaGfd9rft5DHUBOH+AE4KH0pfpiKruE0t8KAXYEvk/pRuzdwL6Fvl9M/VYBxw/HOIGPAfen//nvBU4a4jiPoPQ3yueA3wP3F/r+TYp/DfDp4Rgn8B5gebqey4GzhjjOnwNPAl3ps3CYXs+KcTb6elYZ69XpO9MF3EohaTTqOz/QGBv9fa/lx2/OMDOzrPgel5mZZcWJy8zMsuLEZWZmWXHiMjOzrDhxmZlZVpy4zMwsK05cZmaWFScuMzPLyv8HuiyMouvawEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "model = RandomForestRegressor(n_estimators= 200,n_jobs=-1)\n",
    "model.fit(X_train,Y_train)\n",
    "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers\n",
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart above, income and networth followed by age and willingness to take risk are the key variables to decide the risk tolerance. These variables have been considered as the key variables to model the risk tolerance across several literature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='6.3'></a>\n",
    "## 6.3. Save Model for Later Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Model Using Pickle\n",
    "from pickle import dump\n",
    "from pickle import load\n",
    "\n",
    "# save the model to disk\n",
    "filename = 'finalized_model.sav'\n",
    "dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7683894847939692\n",
      "0.007555447734714956\n"
     ]
    }
   ],
   "source": [
    "# load the model from disk\n",
    "loaded_model = load(open(filename, 'rb'))\n",
    "# estimate accuracy on validation set\n",
    "predictions = loaded_model.predict(X_validation)\n",
    "result = mean_squared_error(Y_validation, predictions)\n",
    "print(r2_score(Y_validation, predictions))\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion__:\n",
    "\n",
    "We showed that machine learning models might be able to objectively\n",
    "analyze the behavior of different investors in a changing market and attribute these\n",
    "changes to variables involved in determining risk appetite. With an increase in the\n",
    "volume of investor’s data and availability of rich machine learning infrastructure,\n",
    "such models might prove to be more useful.\n",
    "\n",
    "We saw that there is a non-linear relationship between the variables and the risk tolerance. Income and net worth followed by age and willingness to take risk are the key variables to decide the risk tolerance. These variables have been considered as the key variables to model the risk tolerance across several literature.\n"
   ]
  }
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