Survival Analysis -implementation(Updated).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survival Analysis : Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting lifelines\n",
      "  Downloading lifelines-0.24.4-py3-none-any.whl (325 kB)\n",
      "\u001b[K     |████████████████████████████████| 325 kB 212 kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.2.0 in /home/akshat/anaconda3/lib/python3.7/site-packages (from lifelines) (1.4.1)\n",
      "Requirement already satisfied: numpy>=1.14.0 in /home/akshat/anaconda3/lib/python3.7/site-packages (from lifelines) (1.18.1)\n",
      "Requirement already satisfied: matplotlib>=3.0 in /home/akshat/anaconda3/lib/python3.7/site-packages (from lifelines) (3.1.3)\n",
      "Requirement already satisfied: pandas>=0.23.0 in /home/akshat/anaconda3/lib/python3.7/site-packages (from lifelines) (1.0.3)\n",
      "Collecting autograd-gamma>=0.3\n",
      "  Downloading autograd_gamma-0.4.2-py2.py3-none-any.whl (3.8 kB)\n",
      "Collecting autograd>=1.3\n",
      "  Downloading autograd-1.3.tar.gz (38 kB)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/akshat/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.0->lifelines) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/akshat/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.0->lifelines) (2.4.6)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/akshat/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.0->lifelines) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/akshat/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.0->lifelines) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/akshat/anaconda3/lib/python3.7/site-packages (from pandas>=0.23.0->lifelines) (2019.3)\n",
      "Requirement already satisfied: future>=0.15.2 in /home/akshat/anaconda3/lib/python3.7/site-packages (from autograd>=1.3->lifelines) (0.18.2)\n",
      "Requirement already satisfied: setuptools in /home/akshat/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=3.0->lifelines) (46.1.3.post20200330)\n",
      "Requirement already satisfied: six in /home/akshat/anaconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0->lifelines) (1.14.0)\n",
      "Building wheels for collected packages: autograd\n",
      "  Building wheel for autograd (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for autograd: filename=autograd-1.3-py3-none-any.whl size=47990 sha256=632d974806dd3ec3c95016f591aa48b679be0931be29a6ee3ca1d56961c5da3a\n",
      "  Stored in directory: /home/akshat/.cache/pip/wheels/ef/32/31/0e87227cd0ca1d99ad51fbe4b54c6fa02afccf7e483d045e04\n",
      "Successfully built autograd\n",
      "Installing collected packages: autograd, autograd-gamma, lifelines\n",
      "Successfully installed autograd-1.3 autograd-gamma-0.4.2 lifelines-0.24.4\n"
     ]
    }
   ],
   "source": [
    "!pip install lifelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ppscore\n",
      "  Downloading ppscore-0.0.2.tar.gz (38 kB)\n",
      "Building wheels for collected packages: ppscore\n",
      "  Building wheel for ppscore (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for ppscore: filename=ppscore-0.0.2-py2.py3-none-any.whl size=9629 sha256=5d08c8727cd2e0d1991487fc65c197052cbbf2b5ff41aa2567a3fe69ca848e2a\n",
      "  Stored in directory: /home/akshat/.cache/pip/wheels/bb/35/a9/b66dcda859b25d96509b26b47878164067f49ac1feb2a555ac\n",
      "Successfully built ppscore\n",
      "Installing collected packages: ppscore\n",
      "Successfully installed ppscore-0.0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install ppscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from lifelines.plotting import plot_lifetimes      # Lifeline package for the Survival Analysis\n",
    "%pylab inline\n",
    "figsize(12,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example with a fictitious data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"/home/akshat/Desktop/data.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " in case of user 4 and user 5, we don’t know at what time the event will occur, but still we are using that data to estimate the probability of survival. If we choose not to include the censored data, then it is highly likely that our estimates would be highly biased and under-estimated. The inclusion of censored data to calculate the estimates, makes the Survival Analysis very powerful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ni is deﬁned as the population at risk at time just prior to time ti; and di is defined as number of events occurred at time ti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.KaplanMeierFitter:\"Kaplan Meier Estimate\", fitted with 6 total observations, 2 right-censored observations>\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAzHICFcU+xrk7aPT/Lh1trCJK9K8r+q6iHP3Vo7r7W2orW2Yv78+Y98WgAAGJJBQngsyQETthfmoac+vDHJxUnSWtuYZF6SfYYxIAAATIdBQviqJIur6qCqelJ2fBjusklrvpXk5UlSVT+ZHSHs3AcAAGas3YZwa217kjVJrkhyXXZ8O8S1VXVWVR09vuxXk7ypqr6c5GNJTm6tTT59AgAAZow5gyxqra3Ljg/BTdx35oTbm5OsGu5oAAAwfVxZDgCALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOiSEAYAoEtCGACALglhAAC6JIQBAOjSQCFcVUdU1fVVdWNVnTHF/e+rqmvGf91QVd8f/qgAADA8c3a3oKpmJzk3ySuSjCW5qqoua61tfmBNa+1tE9aflmT5NMwKAABDM8gR4ZVJbmyt3dRauzfJhUlWP8z645N8bBjDAQDAdBkkhBckuXnC9tj4voeoqmckOSjJ3z720QAAYPoMEsI1xb62i7XHJbmktXbflE9UdUpVbaqqTVu3bh10RgAAGLpBQngsyQETthcmuWUXa4/Lw5wW0Vo7r7W2orW2Yv78+YNPCQAAQzZICF+VZHFVHVRVT8qO2L1s8qKqelaSf59k43BHBACA4dttCLfWtidZk+SKJNclubi1dm1VnVVVR09YenySC1truzptAgAAZozdfn1akrTW1iVZN2nfmZO21w5vLAAAmF6uLAcAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANClgUK4qo6oquur6saqOmMXa/5TVW2uqmur6qPDHRMAAIZrzu4WVNXsJOcmeUWSsSRXVdVlrbXNE9YsTvLOJKtaa9+rqn2na2AAABiGQY4Ir0xyY2vtptbavUkuTLJ60po3JTm3tfa9JGmtfXu4YwIAwHANEsILktw8YXtsfN9E/zHJf6yqz1fVlVV1xFRPVFWnVNWmqtq0devWRzcxAAAMwSAhXFPsa5O25yRZnOQlSY5P8sGq2vshD2rtvNbaitbaivnz5z/SWQEAYGgGCeGxJAdM2F6Y5JYp1lzaWtvWWvtmkuuzI4wBAGBGGiSEr0qyuKoOqqonJTkuyWWT1vxlkpcmSVXtkx2nStw0zEEBAGCYdhvCrbXtSdYkuSLJdUkubq1dW1VnVdXR48uuSHJbVW1Osj7JO1prt03X0AAA8Fjt9uvTkqS1ti7Jukn7zpxwuyU5ffwXAADMeK4sBwBAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRpoBCuqiOq6vqqurGqzpji/pOramtVXTP+678Mf1QAABieObtbUFWzk5yb5BVJxpJcVVWXtdY2T1p6UWttzTTMCAAAQzfIEeGVSW5srd3UWrs3yYVJVk/vWAAAML12e0Q4yYIkN0/YHkty2BTrjqmqFyW5IcnbWms3T7EGAJ5QNt96R173gY2jHoNJVi9bkBMOO3DUYzDDDXJEuKbY1yZtfyrJotbaTyX5myQXTPlEVadU1aaq2rR169ZHNikAzDCrly3Ikv2eOuoxmGTzrXfk0mv+edRj8DgwyBHhsSQHTNhemOSWiQtaa7dN2PzTJP9tqidqrZ2X5LwkWbFixeSYBoDHlRMOO9BRxxnIEXoGNcgR4auSLK6qg6rqSUmOS3LZxAVVtd+EzaOTXDe8EQEAYPh2e0S4tba9qtYkuSLJ7CTnt9auraqzkmxqrV2W5C1VdXSS7Um+m+TkaZwZAAAes0FOjUhrbV2SdZP2nTnh9juTvHO4owEAwPRxZTkAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuCWEAALokhAEA6JIQBgCgS0IYAIAuDRTCVXVEVV1fVTdW1RkPs+7YqmpVtWJ4IwIAwPDtNoSranaSc5McmWRJkuOraskU6/ZK8pYkXxj2kAAAMGyDHBFemeTG1tpNrbV7k1yYZPUU634nydlJ7hnifAAAMC0GCeEFSW6esD02vm+nqlqe5IDW2uUP90RVdUpVbaqqTVu3bn3EwwIAwLAMEsI1xb62886qWUnel+RXd/dErbXzWmsrWmsr5s+fP/iUAAAwZIOE8FiSAyZsL0xyy4TtvZIckmRDVW1J8twkl/nAHAAAM9kgIXxVksVVdVBVPSnJcUkue+DO1trtrbV9WmuLWmuLklyZ5OjW2qZpmRgAAIZgtyHcWtueZE2SK5Jcl+Ti1tq1VXVWVR093QMCAMB0mDPIotbauiTrJu07cxdrX/LYxwIAgOnlynIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl4QwAABdEsIAAHRJCAMA0CUhDABAl+aMegAAgGHbfOsded0HNo56DCZYsv9T81uvOXjUYzyIEAYAnlBWL1sw6hF4nBDCAMATygmHHZgTDjtw1GPwOOAcYQAAuiSEAQDokhAGAKBLQhgAgC4JYQAAuiSEAQDokhAGAKBLQhgAgC4JYQAAujRQCFfVEVV1fVXdWFVnTHH/L1XVV6vqmqr6P1W1ZPijAgDA8Ow2hKtqdpJzkxyZZEmS46cI3Y+21pa21pYlOTvJHwx9UgAAGKJBjgivTHJja+2m1tq9SS5MsnrigtbaHRM2n5KkDW9EAAAYvjkDrFmQ5OYJ22NJDpu8qKp+OcnpSZ6U5GVDmQ4AAKbJICFcU+x7yBHf1tq5Sc6tqhOS/NckJz3kiapOSXLK+OZdVXX9I5h1mPZJ8p0RvTZT857MPN6Tmcn7MvN4T2Ym78vMM8r35BlT7RwkhMeSHDBhe2GSWx5m/YVJ/mSqO1pr5yU5b4DXnFZVtam1tmLUc/Aj3pOZx3syM3lfZh7vyczkfZl5ZuJ7Msg5wlclWVxVB1XVk5Icl+SyiQuqavGEzaOS/NPwRgQAgOHb7RHh1tr2qlqT5Ioks5Oc31q7tqrOSrKptXZZkjVVdXiSbUm+lylOiwAAgJlkkFMj0lpbl2TdpH1nTrj91iHPNd1GfnoGD+E9mXm8JzOT92Xm8Z7MTN6XmWfGvSfVmm86AwCgPy6xDABAl7oK4d1dKpp/e1V1flV9u6q+NupZ2KGqDqiq9VV1XVVdW1WPt1OfnnCqal5VfbGqvjz+nvz2qGfiR6pqdlV9qaouH/UsJFW1paq+WlXXVNWmUc/DDlW1d1VdUlVfH//vy/NGPVPS0akR45eKviHJK7LjK+GuSnJ8a23zSAfrXFW9KMldSf6stXbIqOchqar9kuzXWvvHqtorydVJfs4/K6NTVZXkKa21u6pqbpL/k+StrbUrRzwaSarq9CQrkjy1tfbqUc/Tu6rakmRFa813CM8gVXVBks+11j44/i1kT26tfX/Uc/V0RHi3l4rm315r7e+TfHfUc/AjrbVbW2v/OH77ziTXZccVJhmRtsNd45tzx3/1cRRjhquqhdnxtaEfHPUsMFNV1VOTvCjJh5KktXbvTIjgpK8QnupS0f7jDg+jqhYlWZ7kC6OdhPEfv1+T5NtJ/rq15j2ZGf4wya8luX/Ug7BTS/K/q+rq8SvaMnr/IcnWJP9z/DSiD1bVU0Y9VNJXCA90qWhgh6raM8knkvxKa+2OUc/Tu9bafa21Zdlxdc+VVeVUohGrqlcn+XZr7epRz8KDrGqtHZrkyCS/PH4KHqM1J8mhSf6ktbY8yd1JZsRntXoK4Ud6qWjo1vh5qJ9I8pHW2idHPQ8/Mv7jxA1JjhjxKCSrkhw9fk7qhUleVlV/PtqRaK3dMv7Xbyf5i+w4NZLRGksyNuEnWZdkRxiPXE8hvNtLRQM7P5j1oSTXtdb+YNTzkFTV/Krae/z2HkkOT/L10U5Fa+2drbWFrbVF2fHflL9trb1+xGN1raqeMv4h34z/6P2VSXwr0Yi11v5fkpur6lnju16eZEZ8AHugK8s9EezqUtEjHqt7VfWxJC9Jsk9VjSX5rdbah0Y7VfdWJXlDkq+On5OaJL8+foVJRmO/JBeMf/vNrCQXt9Z8VRc81NOT/MWO/5/PnCQfba19ZrQjMe60JB8ZPxh5U5L/POJ5knT09WkAADBRT6dGAADATkIYAIAuCWEAALokhAEA6JIQBgCgS0IYYBpV1d5Vder47f2r6pIhPe/aqnr7+O2zqurwYTwvQE98fRrANKqqRUkub60N9ZLIVbU2yV2ttXOG+bwAPXFEGGB6vSfJM6vqmqr6eFV9LUmq6uSq+suq+lRVfbOq1lTV6VX1paq6sqp+fHzdM6vqM1V1dVV9rqqePfkFqurDVXXs+O0tVfXbVfWPVfXVB9aPX3Hr/Kq6avw1Vv8b/hkAzEhCGGB6nZHkG621ZUneMem+Q5KckGRlkncn+UFrbXmSjUl+cXzNeUlOa639TJK3J/nvA7zmd1prhyb5k/HHJMlvZMclgJ+T5KVJ3jt+CVqAbnVziWWAGWh9a+3OJHdW1e1JPjW+/6tJfqqq9kzy/CQfH79kbJL82ADP+8nxv16d5LXjt1+Z5OgHzitOMi/JgUmue2y/BYDHLyEMMDr/OuH2/RO278+Ofz/PSvL98aPJj+Z578uP/j1fSY5prV3/KGcFeMJxagTA9LozyV6P5oGttTuSfLOqfiFJaoeffpRzXJHktBo/tFxVyx/l8wA8YQhhgGnUWrstyefHPyT33kfxFCcmeWNVfTnJtUke7YfcfifJ3CRfGZ/ldx7l8wA8Yfj6NAAAuuSIMAAAXRLCAAB0SQgDANAlIQwAQJeEMAAAXRLCAAB0SQgDANAlIQwAQJf+P/WCadXLyvAnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "## Example Data \n",
    "durations = [5,6,6,2.5,4,4]\n",
    "event_observed = [1, 0, 0, 1, 1, 1]\n",
    "\n",
    "## create an kmf object\n",
    "kmf = KaplanMeierFitter() \n",
    "\n",
    "\n",
    "## Fit the data into the model\n",
    "kmf.fit(durations, event_observed,label='Kaplan Meier Estimate')\n",
    "\n",
    "## Create an estimate\n",
    "kmf.plot(ci_show=False) ## ci_show is meant for Confidence interval, since our data set is too tiny, thus i am not showing it.\n",
    "print(kmf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Right censoring</b> – a data point is above a certain value but it is unknown by how much. ... The observed value is the minimum of the censoring and failure times; subjects whose <b>failure time is greater than their censoring time</b> are right-censored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real World Example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will be using Telco Customer Churn data from Kaggle\n",
    "https://www.kaggle.com/blastchar/telco-customer-churn/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  create a dataframe\n",
    "df = pd.read_csv(\"/home/akshat/Downloads/WA_Fn-UseC_-Telco-Customer-Churn.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanation of Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>customerID:</b> Customer ID\n",
    "<br>\n",
    "<b>gender:</b> Whether the customer is a male or a female\n",
    "<br>\n",
    "<b>SeniorCitizen:</b> Whether the customer is a senior citizen or not (1, 0)\n",
    "<br>\n",
    "<b>Partner:</b> Whether the customer has a partner or not (Yes, No)\n",
    "<br>\n",
    "<b>Dependents:</b> Whether the customer has dependents or not (Yes, No)\n",
    "<br>\n",
    "<b>tenure:</b> Number of months the customer has stayed with the company\n",
    "<br>\n",
    "<b>PhoneService:</b> Whether the customer has a phone service or not (Yes, No)\n",
    "<br>\n",
    "<b>MultipleLines:</b> Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "<br>\n",
    "<b>InternetService:</b> Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "<br>\n",
    "<b>OnlineSecurity:</b> Whether the customer has online security or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>OnlineBackup:</b> Whether the customer has online backup or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>DeviceProtection:</b> Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>TechSupport:</b> Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>StreamingTV:</b> Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>StreamingMovies:</b> Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "<br>\n",
    "<b>Contract:</b> The contract term of the customer (Month-to-month, One year, Two year)\n",
    "<br>\n",
    "<b>PaperlessBilling:</b> Whether the customer has paperless billing or not (Yes, No)\n",
    "<br>\n",
    "<b>PaymentMethod:</b> The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "<br>\n",
    "<b>MonthlyCharges:</b> The amount charged to the customer monthly\n",
    "<br>\n",
    "<b>TotalCharges:</b> The total amount charged to the customer\n",
    "<br>\n",
    "<b>Churn:</b> Whether the customer churned or not (Yes or No)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Have a first look at the data\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "## Data Types and Missing Values in Columns\n",
    "df.info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Convert TotalCharges to numeric\n",
    "df['TotalCharges']=pd.to_numeric(df['TotalCharges'],errors='coerce')\n",
    "\n",
    "## Replace yes and No in the Churn column to 1 and 0. 1 for the event and 0 for the censured data.\n",
    "df['Churn']=df['Churn'].apply(lambda x: 1 if x == 'Yes' else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7032 non-null   float64\n",
      " 20  Churn             7043 non-null   int64  \n",
      "dtypes: float64(2), int64(3), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "## after converting the column TotalCharges to numeric\n",
    "df.info()  ## Column TotalCharges is having missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Impute the null value with the median value\n",
    "\n",
    "df.TotalCharges.fillna(value=df['TotalCharges'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ways of filling missing values - https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a list of Categorical Columns\n",
    "cat_cols= [i  for i in df.columns if df[i].dtype==object]\n",
    "cat_cols.remove('customerID')  ## customerID has been removed because it is unique for all the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name:  gender\n",
      "Male      3555\n",
      "Female    3488\n",
      "Name: gender, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  Partner\n",
      "No     3641\n",
      "Yes    3402\n",
      "Name: Partner, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  Dependents\n",
      "No     4933\n",
      "Yes    2110\n",
      "Name: Dependents, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  PhoneService\n",
      "Yes    6361\n",
      "No      682\n",
      "Name: PhoneService, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  MultipleLines\n",
      "No                  3390\n",
      "Yes                 2971\n",
      "No phone service     682\n",
      "Name: MultipleLines, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  InternetService\n",
      "Fiber optic    3096\n",
      "DSL            2421\n",
      "No             1526\n",
      "Name: InternetService, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  OnlineSecurity\n",
      "No                     3498\n",
      "Yes                    2019\n",
      "No internet service    1526\n",
      "Name: OnlineSecurity, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  OnlineBackup\n",
      "No                     3088\n",
      "Yes                    2429\n",
      "No internet service    1526\n",
      "Name: OnlineBackup, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  DeviceProtection\n",
      "No                     3095\n",
      "Yes                    2422\n",
      "No internet service    1526\n",
      "Name: DeviceProtection, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  TechSupport\n",
      "No                     3473\n",
      "Yes                    2044\n",
      "No internet service    1526\n",
      "Name: TechSupport, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  StreamingTV\n",
      "No                     2810\n",
      "Yes                    2707\n",
      "No internet service    1526\n",
      "Name: StreamingTV, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  StreamingMovies\n",
      "No                     2785\n",
      "Yes                    2732\n",
      "No internet service    1526\n",
      "Name: StreamingMovies, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  Contract\n",
      "Month-to-month    3875\n",
      "Two year          1695\n",
      "One year          1473\n",
      "Name: Contract, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  PaperlessBilling\n",
      "Yes    4171\n",
      "No     2872\n",
      "Name: PaperlessBilling, dtype: int64\n",
      "-----------------------------\n",
      "Column Name:  PaymentMethod\n",
      "Electronic check             2365\n",
      "Mailed check                 1612\n",
      "Bank transfer (automatic)    1544\n",
      "Credit card (automatic)      1522\n",
      "Name: PaymentMethod, dtype: int64\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "## lets have a look at the categories and their distribution in all the categorical columns.\n",
    "\n",
    "for i in cat_cols:\n",
    "    print('Column Name: ',i)\n",
    "    print(df[i].value_counts())\n",
    "    print('-----------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Power Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b8ab2390>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sRNeuHfls6kf07PUmlStXoGvXTnh4NsfdvTib/lpB5SqNSbLwnPHObVvRs0tHxk2eaXb+7v2HuXo9mA0rF3LyzDkmz/yKX77/kphbscz/aTkrF84FoNug4TRtVA8nx4JP7f4wGAyMnzaK17sOJzQ4nJWbfmL7pt38fSEwuUxIUBjj351M/zdTf1m5d/ce44ZN4mrANYoVd2aV7yL2bj9A7K3bFuWYMH00A18bRlhwGL9uXsy2Tbu4fCEgRY5Qxg7/HwPf6p1qWadCjrw96nVebdUXrTW/bVnKto27uBUTm+UcymCgz6TXmdl7ElGhN5iwdjp+vocJvnQ9ucxV/wAmdRhN3L04mvVuQ9exfZg/zPjFMO5eHBPbjsrydtMyGAzMmPUJ/9epP8FBoWzd+Rsb12/jfIofPXr3fZWbN29Ry7Ml/9elHZ9M+oBB/d8DIDDgKt4NO+ZIjpFThjOix2giQiL4fsM37N28n8CLV5LLhAWFM3XEDLoPTd/pGj9nDEvmLufI7qPkzZeHpCRtcY73p7zLuz0+IDwkgh83fMvuzftS5QgNCmPyiOn0Gpq+s3P/Xhz9Wr9u0bbT5nj707cZ13MckSGRzFk3h4O+B7l68WpymfCgcGaNnEWXIam/zNZuXptyVcvxdpu3sXewZ8bqGRzZfoQ7t+9kOYcyGOg3+XWm9fofUaE3mLR2Bke3HCb44sN6GngmgI/bf0DcvTha9G5Dj7F9+WrYrOT5r77fg3MHz1iwFx4yGAwM/3QYo3t+SERIJN+sn8f+zfu5kmZ/zBg5k9eGpP5S7bfvBEPavAlAwUIFWbLnJ47sPGpxDluppx9PH82g14YRFhzOqs2L2b5pd6p2LDgolLHDJ6VrxwB+/Ppn8uTNTbe+/2fR9lPmGD31PYZ1f5/wkAgWb/iO3Zv2EpDq/RLOpPc+o/fQ9J1ygCGjB3H8wIls5VAGA70mDWZ270lEh0Yxfu00/HyPEJKmPf20wxji7sXRtHdrXhvbh++GfQHAoNnvsP6r3/Dfc5Lc+fKgLfzMNxgMDJ48hEm9JhAVeoNpa2dxZMshrl+8llwm4MzfjGk/krh7cbTu/TJ9xvbni2Gfc//ufeaN+ILQwBAKuxRhxvrZ+O06zp1b/1iUY8inbzKx13huhNxgps8XHPI9yLVUOS4zst0I4u7d56XeL9N/3AA+f3sGBZwK0P29nrzf7j00mtnr53DI9yD/xGQ9h620Hw9yTE+R41iaHFfOBDAhRY7uY/vy9RNox2zh80XYHhnRtAKllEUd/Dq1vbh8OZCAgKvEx8ezatUaOnZok6pMxw6tWbr0VwB++209zZs1Mk1vw6pVa4iLiyMw8BqXLwdSp7aXxa+hlme1R3YOt+85QMeXWqCUonrVysTG3iYiMoq9B49Sv7YXTo4FcXIsSP3aXuw9aNkXI1vZH9VqvMjVgOtcvxJMQnwCf/3pS/OXUv9aF3wthAv+l9J9+bry9zWuBhg/ICPCIomKjKZw0cIW5fCoUYWrAde4fiWI+PgENvzhS4uXUv/KHmTKodPkaNSsHvt2HiTm5i1uxcSyb+dBGjevb1GO5z3LE34llIhrYSTGJ3DIZw9erWunKnNu/2ni7sUBcPn4BQq7FrVoW49Ss5YHAX9f4UrgNeLj4/n9t/W83L5FqjJt27VkxfLfAVjz50aaNLXsNT9KZa9KBAUGEXI1hIT4BLau2U6jNKNOodfDuHz273THpUyF0tjlsuPIbuN75O6de9y/d9+iHC96VeJ6YDDBphxb1myjSZuGZnNY+gNUZlT0rEhwYDChV0NJiE9g59qd1GtdL1WZ8OvhBJ4LROvU++O5Cs9x6uApkhKTuH/3PgH+AdRsWtOiHOU8yxMWGJJcTw/47KFmqzqpypxNUU8vHb9AEbeH9bRM1edxdC7EqV3Z60hU8nyBoMBgQkz7Y/uanTRonbp+hF0P4++zAenqR0pN2jXm0PYjFtcPW6mnxnbM2J4a27HNj2hP09fTA7sP848FPzykVcWrMtcDg5LfL5vXbKNJm9QjcSHXQ7mUwfulUrWKFClWmAM7D2crR1lTexp5LdzUnu7FM017en7/mRTt6cXk9tStfEkMdgb895wE4P6de8nlsqq8ZwVCA0MIvxZGQnwCe312U7tV3VRlzuw/lbz+i8fPU9TNOLIaEhBMaGAIANHhUcRExuBYxNGiHBU8KxIaGELYVWOO3T67qJOm/Ti1/xRxpvp3PkUOL+8a+O0+zu2Y2/wT8w9+u49Tw/vpbj9yIoeTcyFOZzOHrXy+2LSkJOs+rOSZ62gqpT5WSp1TSvkqpX5RSo1SSpVTSm1USh1VSu1WSlUylV2klJqrlNqnlPpbKfWqabpSSn2llPJXSq0HXFKsv6ZSaqdpXZuUUm6m6TuUUlOVUjsBi86jcS/hyrXrwcnPrweF4O7ummGZxMREYmJuUbRoYdzdzSxbIvWyOSks4gauLg9P3ynu4kxYRCRhEZG4ujw8baN4MeN0S9jK/nBxLUZocHjy87DgcFxcs356UlWvF7G3t+da4PXHFzajuGsxQoLCkp+HhoRR3C1zOYq7uaReNjic4m4uj1giY4WLFyEq+OExjQqJonDxjDuSTbq24NSOY8nP7XM7MGHtdMb/8RleretkuNzjuLm5EhQUkvw8OCgUN7fUp/O5uRcn6HooYKwft2JuU8TU0X+udEl27FmDz1/LqNeglsU5irk6Ex4ckfw8IiQCZ1fnRyzxUKnnS3L71j98+v0nLNz0LW+NfwODwbJm25jjYT0ND4mgWCZzADjkduDHDd/yvc/X6b5AZIWzqzMRKfZHZEgkRTP5Q0PA2QBqNa1F7jy5cSzsiEd9D4q5W3YqYGHXokSF3Eh+HhVyg8KuRTIs792tBSdM9VQpRa/x/fll6mKLtp2Ss5szESEp6kdoBM5uWf/hpVnHpmz/c7vFOWylnrq4FiM0RVsUFhKe6XYsJxVzdSYs7fvFLXP7QynFuxPfYu7k+dnOUbh4EaJTtKfRITcoXDzjetq4a3NO7TgOQPHn3bhz6w5vffsBE9Z/zqtj+6AsPC5FXIsSGfIwx42QSIo84n3bvFsrju9I/yNy+eoVyOWQi7AroRblKOpalMgU9fRGSCRFH/H50qpba45uP5pi2ZSv4Uam2560bKX9sCTHyRQ5euZQDlv5fBG255k6dVYpVQvoAnhhfO3HgKPAAmCo1vqiUqou8A3w4OR9N6ARUAlYC6wGXgFeAKoBxQF/4EellD0wD+iktY5QSnUDpgADTesqpLW2+GJApVS6aWl/8TdfJnPL5iRz61ZKYW6T5rJlhq3sD7PryuI6nF2K8tlXE/lo+CTLj0t2XpOZQ/Bv5KjfuQllPMoxrdvHydNGNRjCzfBoipUqzuhfPuH6uStEXA0zu3wWY2SyfmjCQiPweNGb6KibVPesws+/zKdBnbbExmb9lGZz+9bsG8EMu1x2eNSpysA2QwkPCuOT+R/zctc2rF/xV9ZjZLPOv1KnG5FhN3B/zo2vVs3m8rkAgq4EP37BdEHMTMtkjGO7jlGxekVm/TmLmBsxnDt2jsSExKxnyCBGRjkavtKE56uV59Nu4wFo2fcl/LYfS/UFLydl9b1XxKUIZSuV4fDOI5Zv1KbraZZXk21mP5cymePV/p3Zt+1gqo57NoKkj5HBDqnXuTGlPcrxebcJANjZ2VGhdiUmtfuAqOBIhnw1koavNmXPqm1Zj2GmgmSUo/ErTSlXrTwTuo1NNb2QS2He+WIEX70/JxufL+knZbQu71eaUt6jPOO6Gq/1z8m6ZSvtR1betg1eaULZauWZYsrRou9LnMipHLby+WLDtLbss+pp90x1NDF2GNdore8CKKV8gDxAA+DXFG+U3CmW+VNrnQT4K6UeDIc0AX7RxloTrJR60Gq/AFQFfE3rsgNCUqxrZUbBlFJvAG8AKDsnDIb86coEXQ+hVEn35OclS7gREhJmtkxQUAh2dnY4OTkSFRVNUJCZZYOz/uU9s1xdnAkNf/jLYVh4JC7ORXF1cebw8ZMPp0dEUtvLw6Jt2Mr+CAsJx9X94ehfcXcXIkIz/wUjf4F8fLNsNvOmfcfJo5ZfJxEWEo5biYcjdq5uxQkPzdxocVhwOHUaPjyFyNXdhUN7LTulOTr0BkXcU9yMwq0IN8Oj0pV7saEH7Yd1YVq3j0mIS0iefjM8GoCIa2GcO3CG0lXKWtTRDA4OpUQJt+Tn7iVcCQ0NT10mKJQSJV0JDg7Fzs4OR6cCREfdBCAuyniq0Qm/MwQEXKVc+TL4HT+d5RwRIZG4pBh1K+ZWjMiwzH2wh4dEcPH0JUKuGpuRPZv28mKNFy36Ah8eEoFLinrqkoUcQHLZ4KshHNvvR8Wq5S36IhAZEplqFNLZzZkbWcixYt4KVsxbAcDoeaMJDrDsy0hU6I1Up5AVcStKdFj6elqloQcdh73KlK4P62n5Gi/wQu3KtOzzEnny5yGXfS7u/3OPldN/znKOyJBIiqUYsSvmWowboelzPErTDk3Ys3GfxZ1usJ16GhYSjmuKdqy4mwvhWWhPc0p4SATF07xfIjLZnlarWQXPuh506deJfPnzksvenrv/3OXrqQuynCM69AaFU7Snhd2KJreRKVVuWI12w7owo9uE5HoaHXqDa/6BRF4ztnvHNx/iea+KYEFH80ZoJM4pRnSLujmbfb9Ua1idLsNeY0LXcana9bwF8jLupwmsmLmMi8fPZ3n7yTlCbuCcop4WdXMmysznS/VG1XltWDc+6vphco7IkEiq1q+WYtminN5/yqIcttJ+mMtx8xE5pqbIUaHGC1SsXZkWKXLc++ceqyzIYSufL8L2PGunzpr78ccA3NRae6Z4VE4xP+WFJimXN/dTjQLOpFhPNa116xTzM7ziXGu9QGtdS2tdy1wnE+DwET/Kly9LmTKlsLe3p2vXTvis25yqjM+6zfTpY7xRQ5cu7di+Y2/y9K5dO+Hg4ECZMqUoX74shw4fzyhOtjVtVI+1G7eitebE6bMUKJCfYs5FaFi3JvsOHSPmViwxt2LZd+gYDetado2EreyP08fP8tzzpSjxnBu57HPxcudWbN+0O1PL5rLPxZxF01n76wY2+2T9wz+lU8f9Kf38c5R4zh17+1y0faUV2zbtytSye7YfoKF3XRydCuLoVJCG3nXZs/2ARTkCTlzCpYwbziVdsLPPRZ0OjTjum3qk5bkqZek3dQhzB08j9sat5On5HPOTy8H4+1eBwgWpULNSqpsaZMWxo6d4vlwZnitdEnt7e/6vSzs2rt+aqsxfG7bSvafxpiGdOr/E7p3G11zUuUjyqX+ly5Ti+XKlCQy8hiXO+Z2jZNkSuJVyJZd9Llp0asaezfsyuex5ChYqSKEiTgDUaOhF4IUrj1nKvLN+5yiVIkfLTs3ZnckcBZ0KYO9gD4BTYUc8alclwMIcF05cwL2MO8VLFSeXfS68O3pzwDdzdc1gMFCwkPHa8DKVylC2clmO7rLsB5G/T1zCtawbxUoZ62m9Do045pv6errSVcoy8LOhzB70GbduxCRPn//ul7zXYAgjGg1l+ZTF7P59h0VfEgHOnThPibIlcDUdl2advNnnuz9L62jWqRnb11h+2izYTj01tmOlUrRjrTPdnuYkf79zlCpbEnfT/mjdqTm7N+/N1LIThn1Kx9pd6Vy3O3MmzWfD6k0WdTIBAk9coniq9rQhJ9LU01JVytJn6hDmpWlPA05cJp9TfgqYroes3KAqIRa2p5dOXMStrDsupvdtww6NOex7MFWZslWeZ8hnbzFt0Kep3i+57HMxesE4dv62nf0bMrcPM3LxxIVUORp3aMIhMzne/GwYUwZNJiZFjuM7j+HV2Iv8TvnJ75Qfr8ZeHN95LO0mMsVW2o/M5hjw2VC+MJNjRIMhjGw0lF+mLGbP7zss6mSC7Xy+CNvzrI1o7gG+U0p9hvG1twO+BwKUUq9prX9VxqFID631o66M3gUMUUotwXh9ZjNgOXAeKKaUqq+13m86lbai1jp7t/MySUxM5N33xrNh/XLsDAYWLV6Jv/8FPpk4iiNHT7BunS8//rSCxYvmcs5/D9HRN+nZ+y0A/P0vsHq1D6dObCchMZHh736Urck40F8AACAASURBVAuyP5g4jcPHT3Lz5i1adO7NW4P6kJBg/JWs2yvtaFK/Nrv3H+blrgPJmycPk8eNAMDJsSBD+veg+2DjZapDB/S06I6ztrQ/EhMTmTp2Jt+tmIOdnYE/flnH5fMBvD36dc6cOMeOTbup6lmZL3+ajmOhgjRt3Yi3P3idzt49ealjS2rW86JQYafkPyny0fDJnD9z8TFbNZ9j8oczWLhyLgY7O35bvpZL5//mnTFDOO13lu2bdlHV80W+WjQDRydHmrVuxLDRQ+jQpBsxN2/xzeyF/LrZeK3GN7MWEnPz1mO2aF5SYhLLJvzA+0s+xmBnYPeqbQRfvEbnEd0JPHUJvy1H6Dq2L7nz5eGtb94H4EZQJHNfn4Z7+ZL0mzqEJK0xKMX6+X+kulttVvfH6FH/Y/WfP2JnsGPZ0tWcO3eJsR+9y/Hjp9i4YRs/L/mVb7+fyRG/LURH32TwAGM9bdCgNmPHv0tCQgKJiUm8/95EbkbHPGaLGeVI4ovx85i1fDoGg4H1K/8i8MIVBo3qz7kT59nru59K1V9gysL/UdCpAA1a1Wfg+/3o23wQSUlJfD3pO75cORMUXDh1EZ/l6y3OMWv8XL5cPgODwcC6lX8RcCGQ10cN4OyJ8+zx3Ufl6i8wbeFkCjoVoFGr+gx+fwC9mg+gTIXSjJk2Mvm4LP3ql1R3E8yKpMQk5n88n09//hQ7Ozs2r9zM1QtX6fN+Hy6cvMBB34NUrF6Rj7//mAJOBajbsi69R/ZmaMuh2NnbMfM3452u79y+w+fDPycp0bL3bVJiEosn/MDoJRMw2BnYuWorQRev0WVkdwJOXubYlsP0GNeXPPnyMPwb412QbwRHMnvwZxZt71E55n38FdOXTcVgMPDXyk1cuXCF/qP6cv7EBfb7HuCF6hX53w8TKeBUkPqt6tFvZB8GtTD+uZ7iJYvj4l6ME/tPPmZLj2Y79TSRTz/8nB9WzsVgZ+D35T6mduwNUztmbE/nJbdjjXln9Bt0aGK88+vStQt4vnxp8uXPy3Y/H8aPmMJeC340S0xM5POPvmTu8pkY7Az4rNjA3xcCeeODgZw9cY7dm/dRuXolZiycjGOhgjRu1YA3Rg2ge7P+Fr3ujCQlJrF8wg+8t2Q8BjsDe1dtI/jidTqN6Ebgqcuc2HKE18b2IU++PAw1tadRQZF89fp0dFISv05ZwqhlE0HBldN/s2vFFotz/DDhO8Yv+QSDnYFtq7Zw/eI1uo3syeWTlziy5RB9xvUnT768vP/NGAAigyOYPngK9ds3onKdKhQoVJCmpj8p8vWoOQT6BzxqkxnmWPDxt3yydBIGOwNbV/py7cJVeo7sxaVTFznke4gBHw0kb748jJ7/YXKOKYMmczvmNivnrmSWj/GOvCvnrOB2jAWXQ2Bb7ceSCT/wgSnHLlOO/zPlOL7lMN1NOd5JkeOLHM5hK58vNu0Z/Tua6klep2eLlFKfAD2AK0AEsAPYAszHeD2mPbBCaz0p7Z8tUUrd1loXMHVG52G8jvOCadU/a61XK6U8gbmAE8bO7Jda6++VUjuAUVrrx15Ek8uhhNUPyt3gf/8XZHPyujd+fKF/QaXCpawdAYAEGznHv36+56wdAYA1N7L35TqnVHG0jf2RgG18kBUy5LF2BACKGvJaOwIAIYmWfZnNaXE20n5Exmf9zyY9CQVz2Ub98HD49292ZM5NbdkdaXNavI18IS+oHKwdAcjgelAruJSQ/pRta9kftN1Wdkum3fNbZ9Xv9nk821tlnz1rI5oAM7XWnyil8mEcmZyltQ4AXkpbUGvdP83zAqZ/NTDM3Mq11n4Yr+FMO71ptpMLIYQQQgghni7P6J91eRY7mguUUi9ivAnQYq21ZSfoCyGEEEIIIYQw65nraGqte1o7gxBCCCGEEEL8lz1zHU0hhBBCCCGE+NfYyLXH/7Zn7c+bCCGEEEIIIYR4wqSjKYQQQgghhBAiR8mps0IIIYQQQgjxpCTZxp+X+rfJiKYQQgghhBBCiBwlI5pCCCGEEEII8aTIzYCEEEIIIYQQQojsk46mEEIIIYQQQogcJafOCrPyuje2dgQA7gbvtnYEwHb2h624dDPY2hFsyv6Ic9aOIIR4Sh3jkrUjCCGetCQ5dVYIIYQQQgghhMg2GdEUQgghhBBCiCdFbgYkhBBCCCGEEEJkn3Q0hRBCCCGEEELkKDl1VgghhBBCCCGeFLkZkBBCCCGEEEIIkX0yoimEEEIIIYQQT4qMaAohhBBCCCGEENknHU0hhBBCCCGEEDlKTp0VQgghhBBCiCdE60RrR7AKGdF8yrRp3ZQzp3dxzn8Poz94O918BwcHli+bzzn/Pezb40Pp0iWT540ZPYxz/ns4c3oXrVt5/ydyjJ86mybtutO591Cz87XWTP1iPi93Hcgrfd/E//yl5HlrNvjSttsg2nYbxJoNvtnKYSv7Q3JIDskhOSTHfyuHLWWRHJLjacghbIjW+l9/AB8BZ4CTgB9Q14J11ALmWrBcHWAXcB44B/wA5AM6Ah+aynQGXkyxzCSg5b+1f+zs3bW5h33ukvrSpQBdvmI9nSdfae134oyu6uGdqszbw8bqb79bou3s3XWPXkP1ylVrtJ29u67q4a39TpzRefOX0eUq1NWXLgVo+9wlzW7ncY9/M0dcxOVHPvb5/qn99mzSbdu0Mjt/y5pf9MC+PfX98Ev68PZ1ukvnDjou4rKOuOynmzdtoiMu+6X6f0bbsZX9ITkkh+SQHJLj2clhS1kkh+SwlRzW6Ltk93Fn+0JtzYe1Xve/PqKplKoPtAdqaK09gJbAtayuR2t9RGs9PAvbzaWUKg78CozRWr8AVAY2AgW11mu11tNMxTsDL6bY1gSt9ZasZsxpdWp7cflyIAEBV4mPj2fVqjV07NAmVZmOHVqzdOmvAPz223qaN2tkmt6GVavWEBcXR2DgNS5fDqROba+nOgdALc9qODkWzHD+9j0H6PhSC5RSVK9amdjY20RERrH34FHq1/bCybEgTo4FqV/bi70Hj1qUwVb2h+SQHJJDckiO/1YOW8oiOSTH05BD2BZrnDrrBkRqre8DaK0jtdbBSqmaSqmdSqmjSqlNSik3AKXUDqXUdKXUIaXUBaVUY9P0pkqpdab/F1FK/amUOqmUOqCU8jBN/0QptUAptRlYArwNLNZa7zdtW2utV2utw5RS/ZVSXymlGmAc3fxcKeWnlCqnlFqklHpVKVXLNM1PKXVKKaVN2ymnlNpoyr5bKVXJNH2RUmquUmqfUupvpdSr2dlx7iVcuXY9OPn59aAQ3N1dMyyTmJhITMwtihYtjLu7mWVLpF72acuRGWERN3B1cU5+XtzFmbCISMIiInF1KfZwejHjdEvYyv6QHJJDckgOyfHfymFLWSSH5HgacgjbYo2O5maglKnT+I1SylspZQ/MA17VWtcEfgSmpFgml9a6DvAeMNHMOv8HHDeNkI7D2Kl8oCbQSWvdE6gKPHLYSmu9D1gLfKC19tRaX04x74hpmifGkdCZplkLgHdM2UcB36RYpRvQCOMo7jSyQSllLm8mymRu2actR2aYW7dSCnObNJctM2xlf0gOySE5JIfk+G/lsKUskkNyPA05bJZOsu7DSv71jqbW+jbGzt8bQASwEhiCsRPoq5TyA8YDJVMs9rvp36NAGTOrbQQsNa1/G1BUKeVkmrdWa303J1+DUqorUAP4UClVAGgA/GrK/h3GzuUDf2qtk7TW/kDxR6zzDaXUEaXUkaSkf8yWCboeQqmS7snPS5ZwIyQkLMMydnZ2ODk5EhUVTVCQmWWDUy+bWbaSIzNcXZwJDX84UhkWHomLc1HT9IiH0yOM0y1hK/tDckgOySE5JMd/K4ctZZEckuNpyCFsi1XuOqu1TtRa79BaTwSGAV2AMw9GC7XW1bTWrVMsct/0byLm/ySLuaGoBz+FpOy1ncHYybWYUqoKxhHU7tp4r2IDcDNFdk+tdWUz2TPKaQyr9QKtdS2tdS2DIb/ZMoeP+FG+fFnKlCmFvb09Xbt2wmfd5lRlfNZtpk+f1wDo0qUd23fsTZ7etWsnHBwcKFOmFOXLl+XQ4eNZ3wE2lCMzmjaqx9qNW9Fac+L0WQoUyE8x5yI0rFuTfYeOEXMrlphbsew7dIyGdS2rGrayPySH5JAckkNy/Ldy2FIWySE5noYcNispyboPK/nX/46mUuoFIElrfdE0yRM4C7RWStXXWu83nUpbUWt9JpOr3QX0AiYrpZpivAb0lpmh+K+AQ0qp9Vrrg6Y8vYG0N/qJBdLdYcY0SroC6Ku1jgAwbSdAKfWa1vpXZdyoh9b6RCazZ1piYiLvvjeeDeuXY2cwsGjxSvz9L/DJxFEcOXqCdet8+fGnFSxeNJdz/nuIjr5Jz95vAeDvf4HVq304dWI7CYmJDH/3I5IsrHi2kgPgg4nTOHz8JDdv3qJF5968NagPCQkJAHR7pR1N6tdm9/7DvNx1IHnz5GHyuBEAODkWZEj/HnQf/C4AQwf0fORNhZ6G/SE5JIfkkByS47+Vw5aySA7J8TTkELZF/dvnQCulamK8HrMQkABcwngabUlgLuCEsQP8pdb6e6XUDmCU1vqIUsoZOKK1LmPqUI7SWrdXShUBfgLKAneAN7TWJ5VSnwC3tdYzU2y/PjADcAGSMHZSRwBdgVpa62FKqYbA9xhHI18FPgbWAflN2f9+sD6ttadSqiwwH+Mps/bACq31JKXUImCd1nq1adu3tdYFHrePcjmU+I+dmG65u8G7rR0BgLzuja0dQQghhBDimZcQF2TZTTWs6O7WBVb9bp+3xRtW2Wf/ekdTPJ50NB+SjqYQQgghhHjgqexobvnWuh3NlkOtss+sco2mEEIIIYQQQoj/LuloCiGEEEIIIYTIUf/6zYCEEEIIIYQQ4pnxjN7cSEY0hRBCCCGEEELkKBnRFEIIIYQQQognRcuIphBCCCGEEEIIkW3S0RRCCCGEEEIIkaPk1FkhhBBCCCGEeFLkZkBCCCGEEEIIIUT2yYimEEIIIYQQQjwpz+iIpnQ0hU1zLtPK2hEAuBu829oRALjcYJi1IwAQGONo7QgAtI+2jePyqVsza0cAYHzIdmtHAOBDd29rRwAgl1bWjgCAvbUDmMQo2/ii44BtHBeDjeRodDfR2hEAaGsj7eksV9toT08Z7lk7AgCldW5rRwAgWtlGPRVPFzl1VgghhBBCCCFEjpIRTSGEEEIIIYR4UuTvaAohhBBCCCGEENknI5pCCCGEEEII8aQ8ozcDkhFNIYQQQgghhBA5SjqaQgghhBBCCCFylJw6K4QQQgghhBBPitwMSAghhBBCCCGEyD4Z0RRCCCGEEEKIJ0VuBiSEEEIIIYQQQmSfjGg+Zdq0bsrs2ZOwMxj48adfmPH516nmOzg4sOinOdTwqkZUVDQ9er3JlSvXARgzehgD+ncnMSmJESM+ZrPvzqc+R4uWTZg+42Ps7OxYsnglX8z+Ll2O776fiadnVaKiohnQbzhXrwYlzy9Z0o2DRzYxbepc5s39weIc46fOZtfeQxQpXIg/f/423XytNZ99+S279x8mT57cTPnofV58oTwAazb48t3iFQAM6dedTm1bWZwjf+OauHw0BGVn4Oavm4ha8Guq+U6vtKTYmEEkhEUCEP3zOmJ+3ZQ835A/L2U3fsdt3/2ETZpvcQ7nZtWp/Gk/sDNwfdk2AuatNVuuePu6eC0cwb7W47h14m/ylipGo92z+OdyMAA3j17Ef/RCi3PYSj0t6+1By4l9MNgZOLFiBwfm+6Sa79mrOTX6tkInJhF35x4bxy7kxsXg5PmO7kUZvGU6e778nUMLNlicw1b2RwVvD9pO6IvBzsDRldvZlWZ/1O7Vgrp9WqGTkoj75z5/jv2BiEtB5C1UgB7z36WERzmOr97FuomLLM4AUN7bg5dMx+XYih3sSZOjVq8W1E5xXHzGLiTiYhDPN6pKyw+7Y2efi8T4BHynLidgn7/FOZ739qD1xD4oOwN+K3awP02OGr1aUDNFjg1jFxJ5MQj36s/T9rPBxkIKdn/5O+c3HbE4R0Xv6nSa0BdlZ+DQyu3smJ/6fVuvV0vqm47L/X/u8dvYHwi/ZGxPm73Vidpdm6ITk1jzv8Vc2HXS4hyW1o9yjarSekwP7OztSIxPZNPUZfy93/LjUt7bg7YTjMfl2Mod7DZTP+r2aUVSUhJx/9xj7diFyTlajXlYPzZNXU5ANnIUbVadSp/2Q5na08BHtKfVF47ggKk9BSjw4nO8+PlgchXIi9aag20+Iul+vMVZbKENKe3tgfcnxuNyZsUOjnyT+rhU690cD9P7Jf7OPbZ+uJAoU3vqXKkUzT8biEPBvOgkzYoOE0i0cH9U8fakx4QBGOwM7F65lb/m/5lqfqtB7WncvQVJCUnERt3ip9FfExVk/OxdcHkl189fBSAqKJKvXp9uUYYHynl70MbUhhxfsYN9ZtqQ2n1bkWRqQ9anaEPamdoQpWBnNtqQSt7V6TyhHwY7AwdWbmNbmvbDe1Bb6nZvTlJCIrejYlk5+luiTfuj/Yc9qdzMCwDfeb/jt26/RRmE7XlmOppKqUTgFMbXfBbop7W+k4Xl3wMWZGWZnGYwGJg7Zwovte3B9eshHNi/AZ91mzl79mJymYEDehAdHUOlFxvRtWtHPpv6ET17vUnlyhXo2rUTHp7NcXcvzqa/VlC5SmOSLBjKt6Ucs2Z/QueO/QgKCmX7rj/YsGEr589dSi7Tt99r3LwZg1f15nR5tT3/mzyGAf2GJ8//bPp4tmTjC/MDndu2omeXjoybPNPs/N37D3P1ejAbVi7k5JlzTJ75Fb98/yUxt2KZ/9NyVi6cC0C3QcNp2qgeTo4Fsx7CYKD4xLe4NuAj4kMjKfPbl9zeeoC4y9dSFYvdsCvDTqTze325c+h01redKofixWkDOdx1CveCb1B/01TCNx3lnwtBqYrZ5c9D6cEvcfPoxVTT71wJY1+LD7OXAdupp8qgaD25Hyt6TSM2NIr+aydxccvRVB1J/zX78Vu2DYDyLWvQYnxvVvWbkTy/xYRe/L3jRDb2hm3tjw6TBvBT78+4FXqDoWs/5azvMSIuPawfJ9fs4/CyrQBUalmDlz/uzZJ+00m4H8/WWatxeaEkxSuWytb+UAZF28n9WdrrM26FRvH62smc33KMiIsPc5xas48jphwvtKxBm/G9+LnfDO5Ex/LLwJnEht/EpWJJei8dw+y671ic46XJ/VluyjFw7WQubjlGZIocp9fs45gpR4WWNWg5vhcr+s0g/Px1FnYYj05MooBLIQb/NZULW46hEy07Lq9MGsD3vacSE3qDd9ZOwd/3aHJHEuD4mr0cWLYFgBdb1qTDx31Y2G8aLuVLUL1DfWa1/gBHl8K8sewjZjQbgU7SFuWwtH7ciY7l50GfJx+X/ks+ZEa9YVnO8CBH+0n9WdzbeFyGrJ3MuTQ50taPlz7uxdJ+M/gnOpZlgx7Wj75LxjCznmX1A4Oi8rSBHDW1p/U2TSUig/b0uTTtqbIzUO3rtzn19tfc9r+KfeECJMUnWJYD22hDlEHR9NN+/NFrGrdDoujuM4m/fY8mdyQBzv+5n1M/G9vTsq1q0Pjj3qzpOwNlZ6DNnDfZ9N63RJ69Sp5Clu8PZTDQa9JgZveeRHRoFOPXTsPP9wghl64nl7nqH8CnHcYQdy+Opr1b89rYPnw37AsA4u7FMantBxZtO30WYxuyzNSGDF47mQuPaEMqtqxBq/G9+MXUhvyQog15w8I2RBkU/zdpIN/2nkJM6A1GrJ3KGd+jhKV4vwT5B/JFh3HE34ujQe9WtB/bi6XD5lC5mRclqpRhVtsx5HKw5+2VEzi7w4/7t+/myP6xGXIzoP+8u1prT611VSAOGJrZBZVSdsB7QL6cCKKUsqiDX6e2F5cvBxIQcJX4+HhWrVpDxw5tUpXp2KE1S5caR7F++209zZs1Mk1vw6pVa4iLiyMw8BqXLwdSp7aXRfltJUfNWtX5++8rBAZeIz4+nt9Xr6Ndu5apyrRt15Lly34H4M8//sK7af3kee3atyIw4FqqD0lL1fKs9sjO4fY9B+j4UguUUlSvWpnY2NtEREax9+BR6tf2wsmxIE6OBalf24u9B49alCGPR0XirgQTfy0U4hO4tX4XBVrWf/yCJrmrlCeXcyHu7Dlm0fYfKFSjPHcCQrl7JRwdn0jon/so/lKtdOUqfNiVgK99SLpn+a/rj2Ir9dTNsxzRgWHEXIsgKT4Rf58DVGhVM1WZuBQfqPb5cqN5+AW9Quua3LwaQWSaL5ZZZSv7o6RneW5cCSP6WjiJ8Ymc8tlP5dap90fKLxgO+XKDNu6P+Lv3uXLkPAnZGJF5oIRnOaICw4i+FkFifCKnfQ7wQquMcxiPi1HomSvEht8EIPzCdXLltsfOwbLfbd1NOW6mqB8VH1M/Hki4F5f8hdAut/2D3WSRUp7libwSSpTpuJzw2U+V1qnft2mPizZtsErrWpzw2U9iXALR1yOIvBJKKc/yFuXITv0IycHjUtKzHFFXHtaPUz4HqPTYHMb/52T9cDLTnrqYaU/Lm2lPizb1INb/Krf9jSNn8dG3wYLO/wO20IYU9yxHTGAYt64a3y8XfA7wfOtHvF/yPqwfpZtUI/LsNSLPGvfHvZu3LfoxBKCsZ3nCr4QSeS2cxPgEDvnsxbN17VRlzu8/Q9y9OAAuH79IYdeiFm3rcdxNnzEP2pAzZtqyzLQhubLRhjyXpv047rOPqmnaj0v7/Yk37Y8rxy9SyLUIAK4VSnD54FnjaOvd+wSfvUol7+qWBRE255kZ0UxjN+ABoJT6EygF5AHmaK0XmKbfBmYDbYD1gDuwXSkVqbVuZpo/B2gP3AU6aa3DlFLFgG+B50zbek9rvVcp9YlpHWWASKBnVkO7l3Dl2vWHv9pdDwpJ11CnLJOYmEhMzC2KFi2Mu7srBw8dS7WsewnXrEawrRzuxQm6HpL8PCgolFq1UzdObu6uyWUSExO5FRNLkaKFuXf3Hu+NeIPOHfvxzruDLdp+VoRF3MDVxTn5eXEXZ8IiIgmLiMTVpdjD6cWM0y1hX7woCaEPl00IjSRv9RfSlSvYuiF5a1UlLjCI8KkLjMsoRfEPBxP8wUzy1/e0aPsP5HYtwt3gG8nP7wVH4VQj9ZfOglXLkMe9KBG+xyj7ZvtU8/I+V4wGWz4jIfYuF6etIvrgOYty2Eo9LehamNiQqOTnsSFRuHuVS1euRt+W1B78Mnb2ufilx1TA+CWp3pvtWdFrGnXfaGfR9h+wlf3hWLwwMSnqx62QKEqa6ZTU7dOKhoPbYmefix97TrFoW4/M4VqEWyFpcpg5LrX7tqK+6bgs7pE+x4tt6xB65gqJcZaNjBR0LUJsmhwlzOSo2bcVdU05fk6Rw92zHO0/fwOnEs6sHTHfotFMAKc0xyUm5IbZzmL9Pq1oMrgddva5WNDzU8B4TK8ev5Ri2Sicihe2KEdO1Y8qL9chJDvHpXgRMznSH5c6fVrRwHRcfjKT48Vs5sjjWoR7mWxPI32PUSZFe5qvnBtoqLFiLA5FHQn9cx+BX6c+pTIrbKENKeBamNjgh+3p7ZAoXM0cF4++LfF63Xhcfu9ubE8LPe+KRtN56WjyFnHkgs9+jn67PssZAAoXL0J08MPP2+iQGzzvWSHD8o27NufUjuPJz+1zOzB+7XQSExP5a/4f+G0+bFEOMN+WmWtDaj2iDeloakP+tLANcSpehJsp6unNkChKP+LHprpdm3F2hx8AQWev0ubdLuz8YT0OeXNTvv6LhF68nuGyTy25GdCzwTSa+DLG02gBBmqtawK1gOFKqQc/OeUHTmut62qtJwHBQDOtdbMU8w9orasDu4DXTdPnAF9orWsDXYCUF/7VxNghzXIn05Q93TSd5ucn82Uyt+x/I0faMumX01oz7qP3+Obrn/jnn3/nTGhzr1EpZfbXQ3OvK1PMv9hUT2O3H+Rys/4EdnybO/v8cJv+PgCFerXj9s4jqTqqFjMbP0UOpag8qS/nP/k5Xal7YdHsrDGMfS3Hcm7iUjzmv4NdgbyWxbCRemp2h5hZ1bElW/iuyfvsmLaCBu90BqDRyP/j8A8bib9z38Jtp0hhK/sjk+s6uNSX2d4j2DTtF5qa9seTZi7H4SW+zG0yki3TVtAkTY5iFUrQ8sPu+Iy1/DrizOY4usSXb5qMZNu0FTRKkSPY7zILWo3hx44f0+Ctjtjltrdso2bbj/ST9i/1Zbr3e2yYtpzm77xiWvTxbXF2cmS1frhUKEGbD3uwZpzl191n9NmR1qGlvnzpPZLN01bgbaZ+tP6wO2vHZaN+ZKI9fSGD9lTZ2VG47guceusrDnWciEvb2hRpXNXyKLbQhmSyrp1csoXFjd9n72crqD3ceFwMdna416rIxuHf8GuXSZRrU4tSDatkPUOGOcy/nnqdG1PaoxybFqxJnja6wVA+7TiG74d/SfcJAyj2XHHLcmTAXJYjS3z5OoM25NtWY1jY8WMaWtiGZPb9AlCzcyNKeTzP9gXGHz0u7D7J2e3HGf77JHrPfYfAYxdJsvAHM2F7nqWOZl6llB9wBLgKPGj5hyulTgAHMI5sPvhJKhH47RHriwPWmf5/FONIJUBL4CvTttYCjkqpB+dUrtVamz3pXCn1hlLqiFLqSFLSP2Y3GHQ9hFIl3ZOflyzhRkhIWIZl7OzscHJyJCoqmqAgM8sGp142s2wmR1AoJUq6JT8vUcKV0DQ5glOUsbOzw9GpINFRN6lZuzr/mzyGk2d28uZbA3h/1Ju8PqSPRTkyw9XFmdDwh524sPBIXJyLmqZHPJweYZxuifjQSHK5Phw1zeXqTHx4VKoySTdj0aZrUm6u2kieqsZfHPN6VqZw7/aU2/YTxT4chGPnFhQb1d+iHPdDosjr/vA15HEv/IcSeQAAIABJREFUwv3Q6Ie5CuShQKWS1Pl9At6H5+FUszw1lozCsfrz6LgE4+ldwK2TAdwNDCN/Obd028gMW6mnsaFRFHQrkvy8oFsRYsOiMyzvv/YAFUyngrl7lqfZ2O68uecLag1sQ/23O1Kjn2U3i7KV/XErNAqnFPXD0a0IseEZ749TPvup3Cr9qYLZdSs0Cke3NDnCbmZY/vTa/VRKcSqYo2sRui8YwR8jvyX6arjFOYz1I3WO24/IcWbtfiq2Tr8/blwKJu7ufVwqlrQoR0ya4+LkVpRbjzguJ3z2U8V0XGJCoyiUatkij1z2UbJbPxxdi9Dzu5GsHjmfqGwcF/M5HlE/zOTo8d0Ifs9m/bgXEkWeTLSntX+fQGNTe+ppak/vh9wgat9Z4qNiSbobR+QWPxyrlbE4iy20IbdDoijo/rA9LeBWhH8eUT/O/z979x1f4/n/cfx1nSMxSmwyjCha1KxdalbMGLU3pa1qKapKKVqjvtX2pzroUKvULkKM2GLGihGbIEOMTKuSk+v3xzkiJ4l1Es5Rn+fjkYec+77u+7xd133uk+tc932dlbspbjmf3giPJHTPCe5E3SDhzl2CNweSv6znE2cAiLp8ndzu999vc7vlJTqNHKVrlaP5R235qe8kEpKNasdYyl67dIWTu49R5LViNuWAtM9lDzuHHF25i1fTOIdcOxNGvI3nkOgU54BcDzgHlKxVlrc+asOMvpOtRvk3/Lyc75oN59fuE1FKce18eKptxfPpRepo3rtHs6LWeoDW+q5Sqh7mjmFNy8jkQcyX0ALc0VqbHrK/eH3/4xoT9y9DNlj2d++5PLTWcZZ1afcgAa31b1rrKlrrKgbDS2mWCdh3iBIliuHpWRgnJyc6dGiFz6r1VmV8Vq2ne/f2ALRt25zNW3YkLe/QoRXOzs54ehamRIli7A04mOo5Hoej5Diw/zDFi3tStGghnJyceLtdC3x9N1qV8fXdSJeubwPQuk1Ttm01z2TW1KsT5V+rS/nX6jLtl5l89+00fv91rk05Hke92jVYuXYjWmsCjx4ne/aXyJ8vD7WqV2bn3gPExMYRExvHzr0HqFW98qN3mIY7R07h7OmOU6GC4JQJl+Z1uLFxt1UZY/77l7Nlb1g9aaKg8KGTOVuvF2cb9ObqpBnELt/I1W9n2ZQj5uBZsr3sStYi+VFORlxbv8GVdffvO02Iu82mMu+xteoAtlYdQMz+Mxzo8S2xgedwypsDDOaPRrMWLUC2l125fcG2Do2jHKfhgefIU8yVnIXzY3AyUsa7Bmf8rO+Dze15/9PsEg0qEhV8GYB57ccxrfZgptUezL4/17Hr55UcmO1nUw5HqY/QwLPk9XQld6H8GJ2MlPOuyQk/6/uS83rev6TulQaVuG6pj4wUFniOvMVcyVXYnKOsdw1OpsiRJ1m7lGxQkUhLjiwu2egycygbvlnIpX2n0p0j5fFxKkWO3Cly3Ds+chbOjzKa38ZdPPKR92U3okOuYouQwLPkS9YuFbxrEpQiR75k7VIqWbsE+e2ngndNjM6ZyF0oP/k8Xbl06Ay2SM/xkcUlG91nfsr6bxZwcX/62iU08Bx5PF3JlZSjRqocyY+PVxpUtMrRzXJ8pDdH7GOcT7eUeY/tVQew3XI+PWQ5n17bfJgcZYpgyOqMMhrI/UZpbqTjXm9HOIdEBJ4jVzFXXCyvl1e8a3Auxfk0V7J2KdawItGWdrmw7TD5ShUhUxZzfXjUKEXkadvqIzjwDAU93chXqABGp0xU865FoJ/15a+FXytG94nv82PfScRdj01ans3lJTJZ7tnNnjsHJSqXIiwdl4reO4fkstTJa2mcQx50LsuV7BySMx3nkEuBZ8nv6Uoey+ulkvcbHE2RweM1T9pPfJcZfSdzI1l9KIMiW67sALiVKoJbqSKc3G77rNUOKzHRvj928qLeo3lPTiBKa31LKVUKqPGQsnFADsz3Vz7MeuAjYDKAUqqi1vpQRoQ1mUx8PGgUvqvnYzQYmDV7IUFBpxg7Zij79geyapUff85cwOxZUzkR5E9UVDRduvUHICjoFEuW+HAkcDMJJhMDPx5p04yRjpZj6Cdfsmz5LIxGA3/NXcKJ46f5fNQgDh44whrfjcydvYjf/viOg4GbiIqK5p1eH9v0XI/y6ZhJBBw8THR0LA1bd6N/n+4kJJg/revYpjl1alZl+64AmnZ4h6xZsjDu88EA5HTJwfu9OtOprzlXv95dbJtxFsCUSMRX0yg8YzwYDcQsWc/dMxfJN7Abd46e5samPeTp0YrsDaqjTSZM0XGED/8+Q/7/yWlTIkEjZlJlwefm6fj/3syNkyGUGNaemMBzXF334MmO8tQoTYlh7dGmRLQpkWPD/iA++oGfzzyUoxyn2pTI+tGz6ThnGMpo4PCirVw7HcqbQ9oSfvg8ZzYcoHJPL4rWfo3EeBN3Ym+yesivj97xE3KU+kg0JbJq9Cx6zhlu/vqKRVu4cjqUhoPbEXrkHCc2HKB6Ty+K1ypLYkICt2NusvST+7Mkf+L/A5mzZ8XolInSXpWZ1X2S1UygT5LDd/Qsus/5zPyVAIu2cvV0KPWHtCXs8HlObjhAtZ5evFy7LInxJm7H3uSfIeavLqrW04s8ngWpO6ANdS2Xj87tPombyf54elzalMi60bPoPOcz89ffWI6POpbj4/SGA1Tp6UWxZDlWWnIUrvIqb/T3JjHehNaJrB01k9uWKwJsqY8Vo2fRd84IDEYDAYu2EHE6BK/B7Qg5cp6gDft5o6cXJWqVS2qXhZZ2iTgdwuFVuxnq9y2JCSaWj55p8yQr6Tk+avTwIm/RgtQf2Ib6A83tMsvGdkk0JbJ69Cx6WNrlgOX4aDC4LaFHzMfHvRymBBN3Ym6y7BNzu1Tv4UWeogWpO7ANdS055qTj+DgxYiavW86noX9v5ubJEIoPa0/sI86nCTE3uTB9NTXWmu/Hu7rhINc22PYBETjGOUSbEtnyxWxazzWfT4MWbiXyVCg1hrQl4sh5zvsdoHwvL4rcO5/G3GS95Xz6b8wtDvyxhk6rvkJrTfDmQII32fbnWaIpkfmj/2DQnFEYjAZ2LNpE2OkQWg3uSPCRswRu2Ef7Ed3Jki0L/X4x36Jy72tM3EoUovvE99Bao5RizbR/rGartaVO1o6eRRfLuSzQcqzWtZxDTlnOIS/XLovJ8h6T/BzSqb83Jss5ZI2N55BEUyLLRs/kvTmfYzAa2LtoMxGnQ2gyuD2Xjpzj2Ib9eI/oSuZsmen5yyAAokKv8ee732J0ysRHi8cC5gm25g3+SS6d/Q9Rtt939HxRSt3QWmdPsSwzsBzwAE4C+YGxWustKcsrpQYAHwLh9yYDurdeKdUOaKG17qWUygf8DJTG3JHfprXuZ5kM6IbWOu3vv0gmk7PHi9Eoj+El5yyPLvQMXAu2bRQpo519w7ap+jNacIyLvSMA0CJqu70jADDerf6jCz0Do8I32zsCAMPd69o7AgCZtI33O2cwG++azHAxyjH+eHNO+8bDZ87gIDlq337YxVPPTjMHOZ9+5+oY59Mjhjv2jgBAUZ350YWegSjlGMcpwPfBCxzjxfsEbi/6yq5/22ftMNoudfbCjGim7GRalv2LeWKgR5bXWv8I/JjWeq31EmCJ5fdrQMc09jfWxuhCCCGEEEKI59ULMrCX0ot0j6YQQgghhBBCiGfghRnRFEIIIYQQQohnTr5HUwghhBBCCCGESD/paAohhBBCCCGEyFBy6awQQgghhBBCPC1y6awQQgghhBBCCJF+MqIphBBCCCGEEE+LlhFNIYQQQgghhBAi3aSjKYQQQgghhBAiQ8mls0IIIYQQQgjxtLygkwEprbW9M4gUMjl7SKM4mIlu9e0dAYDB+7+ydwQAsrm/ae8IAMgLRQghxNOQzSmzvSMAkDdLDntHAOBS3DV7R0iScDdU2TvDk7o9Z4Rd/2TJ2uNru9SZjGgKIYQQQgghxNPygg7syT2aQgghhBBCCCEylHQ0hRBCCCGEEOIFppRqopQ6qZQ6o5Qansb6IkqpzUqpg0qpw0qpZo/ap1w6K4QQQgghhBBPi4NPBqSUMgI/A42AECBAKbVSax2UrNgoYJHWeppSqgzgC3g+bL8yoimEEEIIIYQQL65qwBmt9Tmt9V1gAdAqRRkNuFh+zwmEPWqnMqIphBBCCCGEEE+LnUc0lVLvAe8lW/Sb1vq3ZI89gEvJHocA1VPsZiywXik1AHgJeOtRzysdTSGEEEIIIYT4j7J0Kn97SJG0vv4k5VS5nYFZWuvvlFI1gblKqbJa6wf2ouXSWSGEEEIIIYR4cYUAhZM9LkTqS2P7AIsAtNa7gCxAvoftVDqaQgghhBBCCPG06ET7/jxaAFBSKVVMKeUMdAJWpihzEWgIoJQqjbmjefVhO5WOphBCCCGEEEK8oLTWCcBHwDrgOObZZY8ppb5SSrW0FPsEeFcpFQj8DfTSWqe8vNaKdDSfM4296nHs6DZOBPkz7NMPU613dnZm/rxpnAjyZ6e/D0WLFkpa99mwjzgR5M+xo9vwalRXcmRgjmJ1y9N302Te3fod1T/wTrW+YtcG9F73NT19J9BlyRfkLekOgEuhfAw++Sc9fSfQ03cCXhN6pyvHqInfU6d5J1p365fmeq01E/9vGk07vEObHh8QdPJM0roVvn4069iHZh37sMLXL105vLzqcfToNo4H+fPpA9pl3rxpHA/yZ0eydsmTJzd+6xcTFXmKH6aMT1cGcJzjQ3JIDskhOf4LWSSHtbca1WH/wQ0cOryJwZ+kft91dnZm5uypHDq8iU1bllGkiAcAlSuXx3/XKvx3rWLH7tW08PZKV446Dd5g454VbA7wod/H76SRw4kf//iGzQE+/LP+LzwKm/8GyZQpE9/+PI4125fgt+sfPhiUetsn4SjtImyjtfbVWr+itS6utZ5gWTZaa73S8nuQ1rqW1rqC1rqi1nr9o/bpMB1NpZRJKXVIKXVMKRWolBqilLJbPqVUsFLqodcdP2Tb1pbvl8lQBoOBqT9MoIV3N8pVqE/Hjq0pXbqkVZl3encmKiqGUmVqM2Xq73w9cSQApUuXpEOHVpSv2IDmLbry49SJGAy2Va/ksKYMirfG9WRxz2+Y8dYwSreskdSRvCdoxS5mNh7B7GYj2Tt9NfVHdUtaF30hgtnNRjK72UjWj5xpU4Z7WjdrxPTvH9xB274rgIshYfgunMHYYQMZ9+1PAMTExjFt5nz+/n0Kf/8+hWkz5xMTG2dThnvt4u3djfIV6tPpAe0SHRVD6TK1+WHq70y0tMudO3cYO/YbPvtsnE3PnVYOex8fkkNySA7JYWsOR8oiOVLn+O77L2nbpjdVKzemXXtvXi1VwqpMj54diI6OpWL5Bvz80598Oe4zAIKCTlG3ditq12zB26178cOP4zEajTbn+Oqbz+nVoT9eb7Sh5dtNKPHqy1ZlOnRrQ0x0LPWrejNj2l8MHzMIgGatGuHs7EzTN9vh3aAzXXq2S+qE2pLDEdrFUelEbdcfe3GkVrxt6R2/hvnLQpsBY+ycyVatgQzvaFarWomzZ4M5f/4i8fHxLFq0gpbeja3KtPT2Yu7cxQAsXbqaBvVrW5Y3ZtGiFdy9e5fg4EucPRtMtaqVJEcG5HCrWJzo4AhiLl0lMd7EcZ/dlGhU2arM3Ru3k353ypaZ1BN5ZYwqFcuR0yXHA9dv9t9NyyYNUUpRoWxp4uJucPVaJDv27Kdm1UrkdMlBTpcc1KxaiR179tuUIWW7LFy0Au8U7eL9gHa5des2O3YGcOfOvzY998NyvOjHqeSQHJLj+cvhSFkkh7UqVSpw7twFgoMvER8fz9Ilq2jeopFVmeYt3uLveUsBWP7PGurVewOA27fvYDKZAMiSOTMPv/jw4Sq8XpYL5y9x6UIo8fEJ+PyzlkZN61mVadS0PksXmG+3W7PSjzfqVAPMVzlly5YVo9FIliyZib+bwI24GzblcJR2EY7FkTqaSbTWVzB/18tHysyolJqslApQSh1WSr0PoJSqp5TappT6RykVpJSafm8UVCnlpZTapZQ6oJRarJTKblkerJT60rL8iFKqlGV5XqXUeqXUQaXUrySb5lcp1U0ptdcy4vqrUspoWX5DKTXBMgK7WylVUCn1BtASmGwpX1wpNdCS77BSaoGt9eLu4cqlkPsTQIWEhuPu7vrAMiaTiZiYWPLmzY27exrbelhvKzlsy5HdNTdx4ZFJj+PCI8nhmjtVuUo93uLdbd9Rd0QnNo6Zk7Q8Z+H89PQdT+eFIylU9VWbMjyuiKvXcS1wf6C+YIF8RFy9RsTVa7gWyH9/eX7zclu4e7gSkqxuQ0PD8XjMdslIjnJ8SA7JITkkh605HCmL5LDm5u5KSEh40uOw0HDc3QqmKFMwqYzJZCI2No48lve6KlUqsCdgLbv2rmHQwFFJHc8n5epWgPDQy0mPL4ddwTVFjoJuBQgPu5yUIy72Brnz5GLNyg3cunWbPUEb2BG4jt9/nk1MdKxNORylXRxWYqJ9f+zEITuaAFrrc5jzFcA8nW6M1roqUBXzjajFLEWrYb45tRxQHHjbcsnrKOAtrfXrwD5gSLLdX7MsnwYMtSwbA/hrrSthnmWpCCTNqtQRqKW1rgiYgK6WbV4CdmutKwDbgHe11jst239qGaE9CwwHKmmtywNp3zz3GJRK/RU3Ke/BTbvM420rOWzMkcZXD6W1q4NzNvB7nU/YOmkBNQe0BuDmlWim1xzE7Gaj2DRuHi2m9sc5e1abcjyOtP6PSqk086ZVR48jPe2SkRzm+JAckkNySI50nOAcJYvkSJkj9bJUOdL6akJLmX37AqletQn16rTmk6EfkDmzs405bK0PTYXXy2IymajxWiPqvN6Mvh/2oHBRj2ecI+NfM8JxOGxH0+LekecF9FBKHQL2AHmBexd+79Van9NamzDPgFQbqIH50tUdlm16AkWT7XeZ5d/9gKfl9zrAXwBa69VAlGV5Q6AyEGDZV0Pg3sXvd4FVaewrpcPAPKVUNyAhzf+oUu8ppfYppfYlJt5McyehIeEULnT/2vlCHm6Eh0c8sIzRaCRnThciI6MIDU1j2zDrbR+X5LAWdzmSHG55kh7ncMvDjYioB5Y/vnI3Jb3Ml9aa7iZwJ9p8mUrE0WCiL1whT7Gn9ymea4F8XL5yf6Qy4so1CuTLa1l+f4bqiKvm5bYIDQmnULK69fBwI+wx2yUjOcrxITkkh+SQHLbmcKQsksNaWOhlChVyS3rs7uFG+OUr1mXC7pcxGo24uOQgMjLaqsypk2e5efMWZcrYdkVTeFgEbslG/1zdCxCRIsflsAjcLKOLRqORHC7ZiY6KoVW7pmzbtJOEhASuX4tk355DlK/4mk05HKVdhGNx2I6mUuplzKOHVzB3OAdYRggraq2LJZvpKOVHHtpS3i9Z+TJa6z7Jyty7AcwEZEqxbaoowOxk+3pVaz3Wsi4+2bS+KfeVXHPgZ8wd1v1KqVTltNa/aa2raK2rGAwvpbmTgH2HKFGiGJ6ehXFycqJDh1b4rLKe8Mln1Xq6d28PQNu2zdm8ZUfS8g4dWuHs7IynZ2FKlCjG3oCDD4j7cJLDWnjgOXIXcyVn4fwYnIyU9q7BGb8DVmVye96/jKV4g4pEBZsvYcmaJwfKYP48JWfh/OQuVpDoi9ZvEBmpXu0arFy7Ea01gUePkz37S+TPl4da1Suzc+8BYmLjiImNY+feA9SqXvnRO0xDynbp2KEVq1K0y6oHtEtGcpTjQ3JIDskhOWzN4UhZJIe1/fsP83JxT4oWLYSTkxNt27XAd/UGqzK+qzfSuWtbAFq3acrWrbsAKFq0UNLkP4ULu1PylZe5cDHEphyHDx7D8+UiFCrigZNTJrzbNGHDmq1WZTas3ULbTuZvqGjashG7tu8FIDTkMjXfNN+vmTVbVipVKcfZ0+dtyuEo7eKwHP97NJ+KB3WM7EoplR+YDvyktdZKqXXAB0qpTVrreKXUK0CopXg1y2W0FzBf4vobsBv4WSlVQmt9RimVDSiktT71kKfdhvmS2PFKqabAvRvGNgIrlFL/p7W+opTKA+TQWl94yL7igByW/4sBKKy13qyU8ge6ANmB6IdsnyaTycTHg0bhu3o+RoOBWbMXEhR0irFjhrJvfyCrVvnx58wFzJ41lRNB/kRFRdOlW3/APMPZkiU+HAncTILJxMCPR5Jo4zXbksOaNiWyYfRs2s8ZhjIaOLJoK9dPh1J7SFsuHz7PmQ0HqNTTC8/ar2GKN/Fv7E1WD/kVgMLVS1F7SFsSE0zoRM36z2dyJybtEe3H8emYSQQcPEx0dCwNW3ejf5/uJCSYB9E7tmlOnZpV2b4rgKYd3iFrliyM+3wwADldcvB+r8506vsxAP16d3nopEIPc69dVqdolzFjhrI/WbvMmjWV45Z26WppF4DTp3bj4pIdZ2dnWrZsQrPmnTl+/LTNOex9fEgOySE5JIetORwpi+RInePTT8byz4rZGI0G5s5ZzInjpxk5ahAHDhxhje9G5sxeyG9/fM+hw5uIioqhd8+BANR8owqDh/QjPiGBxMREhgwaTeR1267qMZlMjPnsa+YsnobBaGDx/OWcPnmWwcP7c+TQMTas3crCv/7h/6ZNYHOADzHRsQzoOwyAuTMWMPnHr1i3YxlKwZL5KzgR9OTvt/dyOEK7CMeiHOUaaKWUCTgCOGG+vHQu8L3WOtHSWRsPeGMeYbyKeWbXSsBoy+NymDuL/S3bNAD+B2S2PMUorfVKpVQwUEVrfU0pVQX4VmtdTymVF/Olt/mArcDbQGVLuY7ACMwjwPHAh1rr3UqpG1rre5MMtQNaaK17KaVqAb9jHjntBMwAclqy/6W1nvSwusjk7OEYjSKSTHSrb+8IAAze/5W9IwCQzf1Ne0cAntbcvUIIIV502ZwyP7rQM5A3i20f+ma0S3G2TRD4NCTcDbVtEgk7uvXzR3b9kyXbhz/Zpc4cZkRTa/3ALxDSWicCn1t+klhuHr6lte6YxjabME8clHK5Z7Lf9wH1LL9fx3wv6D2Dk5VbCCxMY1/Zk/2+BFhi+X0H1l9vUvtB/zchhBBCCCGE+K9x2Hs0hRBCCCGEEEI8nxxmRNMWWustwBY7xxBCCCGEEEKItL2g95zKiKYQQgghhBBCiAz1XI9oCiGEEEIIIYRDkxFNIYQQQgghhBAi/aSjKYQQQgghhBAiQ8mls0IIIYQQQgjxtOgX85u/ZURTCCGEEEIIIUSGkhFNIYQQQgghhHhaZDIgIYQQQgghhBAi/WREU4jH8OvNIHtHAGCURx17RwDgVth2e0cAIKv7m/aOAIB79jz2jgBA2I1Ie0cAwDV7bntHACCzwdneEQAwKGXvCADcSrhj7wgAJCSa7B3Bobyfu7K9IwAwMWyLvSMAUDKXh70jABAcF2HvCAD8k72ovSMA0N4gXQbx5OSoEUIIIYQQQoinJVEmAxJCCCGEEEIIIdJNRjSFEEIIIYQQ4mnRMhmQEEIIIYQQQgiRbtLRFEIIIYQQQgiRoeTSWSGEEEIIIYR4WmQyICGEEEIIIYQQIv2koymEEEIIIYQQIkPJpbNCCCGEEEII8ZToRJl1VgghhBBCCCGESDfpaD5nGnvV49jRbZwI8mfYpx+mWu/s7Mz8edM4EeTPTn8fihYtlLTus2EfcSLIn2NHt+HVqK7kyMAcdRq8gd/uZWzau4L3B/ZKI4cTU/+YxKa9K1i6bjYehd0AcHLKxP+mjsV320JWbVlA9VqV05XDy6seR49sJSjIn0+Hpl0f8/76haAgf/y336+PPHlysX7dIiKvn2TKlPHpygAwauL31Gneidbd+qW5XmvNxP+bRtMO79CmxwcEnTyTtG6Frx/NOvahWcc+rPD1S1cORzk+6jasxeY9K9m2bzX9P+6TRg4nfp4xmW37VrPCbx6FCrsD0Lpdc9ZsXZz0E3wtkDJlX7U5h6PUR72Gtdi6xwf/fb58+ID6+GXGt/jv88XHb35SfbRp15x1W5ck/Vy8djhd9eEor9s3G9Rk3a6lbNi7nPcekGPK71+zYe9ylqy1zjFp6hhWbV3Iys1/U+2N9OWo17A22/auwn//Gj4c1DfNHNNmfIv//jX4+P19v13aN2f9tqVJP5euH+G1sqVszlG/YW127FvD7oPrGDD43TRz/Dbze3YfXMeajQspXMQjaV2Z115htd8Ctu72YcvOlWTO7Pzc5yhRtzwDN07m4y3f8eYH3qnWV+nakA/XTuID34n0WTya/CXMOYrXLks/n/F8uHYS/XzGU6xmGZsz3OMI55Da9Wvgu3Mxa/cspe+AHqnWV6lRiaUb5nAkbCdeLRpYrfttwQ/sOb2RaX99b/Pz39OoUV0CAzdx9OhWhg79INV6Z2dn5s79iaNHt7Jt23KKFDHXRYMGtdmxYxUBAevYsWMVdeu+ka4cOeq+TqlNv1B6668U+KBtqvV52jWg7IG5vOo7hVd9p5CnU6OkdS/PHku5w/Mp9ucX6coAjnMec1iJ2r4/dvJCdjSVUrmUUv3tneNJGQwGpv4wgRbe3ShXoT4dO7amdOmSVmXe6d2ZqKgYSpWpzZSpv/P1xJEAlC5dkg4dWlG+YgOat+jKj1MnYjDY1vySI3WOsf/7jHc6DqBxrbZ4v92EEq8UsyrTvmtrYqJjaVCtFTOnz+OzMR8D0LH72wA0q9ORnu0+4POvhqCUsjnHDz+Mx7tldypUqE/Hjq0oXcq6Pnr37kRUdAxlytRm6tTfmTjhcwDu3PmXsV9O5rPh42x67pRaN2vE9O8f3GHdviuAiyFh+C6cwdhhAxn37U8AxMTGMW3mfP7+fQp//z6FaTPnExMbZ1MGRzo+xn8zkp4d+tOwZitatm1KyVdftirTsdvbxETHUqdKc/6YNpcRYwcDsHzJaprWbU/Tuu0Z1O9zQi7JoWGMAAAgAElEQVSGEXT0pM05HKc+RtG9wwfUr9mSVm2bpaqPTpb6qF2lGb9Pm8vnY4cA8M+S1TSu247Gddvxcb8RXLoYmq76cJTX7dhJw+nbaSBNa7WjRZvGqXK069qa2OhY3qrWmpnT5/Hp6IEAdOjeBoAWdTvSq31/Rnw1OF05JkweSbf2/ahfoyWt2zaj5KvFrcp07t6WmJhYalduyu/T5jDyXrssXo1XnbZ41WnLwH7DuXQxlGNHT9icY9J3o+nS7l3erNaCNm2b80qKHF16tCM6OpYalRrz6y+z+eLLTwAwGo38/NtkPh08hro1vGnTvAfx8QnPdQ5lULT4qhdze33DT42GUa5lzaSO5D1HVuzk5ybDmdbsc/x/XUWTL7oCcDMqjnl9vuXnJsNZ9sl02v5f6s7Qk3CEc4jBYOCL/w3jvc4f4127I83fbkzxFK+XsNDLjBj4FauXrU+1/Z8//8VnH4554udNK8eUKeNo1aonlSq9Rfv2LSmV4v22V6+OREXFULZsXX78cQYTJgwH4Pr1KNq1e4eqVRvz7rtD+PPP/0tPEAqNe59zPb/kxFsfkrtlHTKXLJyqWNQqf042G8TJZoOIXHD/A9wrvy3jwuB0PH9SDMc4jwnH80J2NIFcwFPtaCqlMvz+12pVK3H2bDDnz18kPj6eRYtW0NK7sVWZlt5ezJ27GIClS1fToH5ty/LGLFq0grt37xIcfImzZ4OpVrWS5MiAHBVeL8uF8yFcuhBKfHwCq/5Zx1tN61mVeatpPZYtWAXAmpUbqflmVQBKvPoyO7fvBeD6tShiY+IoV9G2T52rVq2Yqj68vb2syngnr49lq6lvqY9bt26zc2cAd+78a9Nzp1SlYjlyuuR44PrN/rtp2aQhSikqlC1NXNwNrl6LZMee/dSsWomcLjnI6ZKDmlUrsWPPfpsyOMrxUbFyOYLPX+TihRDi4xPwWbYGr6b1rcp4NavPkgUrAfBd4UetOtVT7adV26asWOprUwZw3PpYsWwNXk2tRx28mjVg8YIVAKxesZ7aadZHM1YsXWNTBnCc123511/jQvClpByrl6+nYaocdVm20Jxjrc9Gar5Z7X6ObeYckenMUalyOYLPXbK0SzwrlvnSuFmK47RpAxb/naxd6tZItZ/WbZul6zh9vXJ5zp+7yIVgc47ly3xp0ryhVZkmzRqyaP5yAHyWr6N23ZoA1GtQi6BjJ5M+fIiKiibRxvuiHCVHoYrFibwQQdSlq5jiTRzx2U0pL+sRn39v3E763TlbZrAMXlw+doG4K9EAXDkVQqbMThidbf/TxBHOIeVff42L50MIuRBGfHwCvv+sp0GTOlZlwi6FcyroTJp1vnt7ADdv3Hri503p3vttcPAl4uPjWbzYhxYtGlmVadGiEfPmLQVg2TJf6tWrBUBg4DHCw68AEBR0isyZM+PsbNuId7aKJfk3OJy7lyLQ8QlE+WwnZ6PU58sHubHjMIk3bz+64CM4ynlMOJ4XtaM5CSiulDqklJqslPpUKRWglDqslPoSQCnlqZQ6rpT6XSl1TCm1XimV1bJui1KqiuX3fEqpYMvvvZRSi5VSPsB6y7JU+7aVu4crl0LCkh6HhIbj7u76wDImk4mYmFjy5s2Nu3sa23pYbys5bMtR0C0/4WGXkx5fDrtCQbcCVmVc3fITHno5KUdc7A1y58nFiWOneKtJXYxGI4WKuFO2QmncPAralMPD3Y2QS+FJj0NDL+Pu4ZaijCshIeFJOWJizfXxrEVcvY5rgXxJjwsWyEfE1WtEXL2Ga4H895fnNy+3haMcH65uBQgLvX98hIdFUNCt4APLJD8+kvNu04QVy2zvWDlKfbi5FUh6LQBcDovALdXrpYDV6yX2gfVhe4fGUV635v9rRLIcERR0y29VpqBrfi5byphMJm7cy3H0FG81rZdBOQoSFnr//BEeFoFryuPU3fo4jY2NS7Ndlqejo+nqbp0jLPRyqhxubgUIDb1/HouLjSNPnlwUL+GJ1poFy/7Ab9vSNC/Lft5y5CiYh5iw60mPY8MjcSmY+pxdrXsjBm39Hq/hnVk9dnaq9WWaViP82AVMd20bWQXHOIcUSPZaAIgIv5Lq9fIsuCd7LwUIDQ3HI8X/x1zmfl3Exsaler9t06YZgYHHuHv3rk05nFzzEh9+/z0yPvwaTq55U5XL1bQmr66diue0z3Byy5dqfXo5ynnMoelE+/7YyYs66+xwoKzWuqJSygtoB1QDFLBSKVUHuAiUBDprrd9VSi0C2gJ/PWLfNYHyWutIy75Lpty31nqbLaHTupRAa/0YZR5vW8mRcTlIua8HPN/ieSso/koxlm/4i9CQcA7sDcRkMtmYI60Yj1Mfz/7a/bSeUymVqtruLbeFIx8fT9ouFSuX4/btO5w6fiZVuaefI4OPmwyoj0qVy3Hn9m1OZnB92ON1m576WDJ/JcVfKcY/G+YSeimcAwGBJCQ8xfMHD6+zSpbjNH3tksbCx2oXMGbKRPWalWlcrx23b99hycpZHD50jO1bd/+ncqT12ts714+9c/0o1/IN6g5ozT+f/Jq0Ln9JD7yGd2J290lP/PzWWex/DnnQ/p+1jHi/LV26JOPHD6dFi27pSZJWEKuHMRsCiFq5DX03gbxdm1Dk+0Gc7TwqHc+ZVgzHOI8Jx/Oijmgm52X5OQgcAEph7hwCnNdaH7L8vh/wfIz9+WmtIx9j31aUUu8ppfYppfYlJt5Mc8ehIeEULuSe9LiQhxvh4REPLGM0GsmZ04XIyChCQ9PYNsx628clOaxdDruCW7JPdV3dCxBx+WrqMpZPO41GIzlcshMdFYPJZGLCqO/wrt+Zft2H4JIzB8FnL9qUIyQ0nEKF749geni4Wo3YJJUp5JaUI6eLC5GR0TY9X3q4FsjH5Sv3P4WNuHKNAvnyWpbfr7uIq+bltnCU4yM8LMLqk3s394JcuXzlgWWSHx/3tHw7fZfNgmPVh5tH8tdLQS6neL0kL2M0GnFJoz6Wp+OyWXCc1+3lsAirT+9d3Qty5bL1KP7l8Cu4WsoYjUayJ8sx8YvvaVm/Cx/0+AQXlxxcOGdbDvMxeP/84eZekIhHHKcuLjmIStYurd5O32WzAOGh1jncPVy5nEYOD4/757EcLjmIioomPOwyO/0DiIyM5vbtO2xYv5VyFWy7BM9RcsRejiSn+/1zoItbnqTLYdNy1GcXpRtVuV/eNQ+dfx3MsiHTibp45YHbPQ5HOIdEJHstABR0K8CVFK/bZyE09HLSeymAh4cbYSn+P6Gh4RRKVhcuLjmS3m89PFxZuPA3+vYdwvnztr1mAeIvX7MaoXRyy0d8RKRVGVN0HNoykn397/VkK2t9r3FGcJTzmEOTyYBeWAr4Wmtd0fJTQms9w7Iu+Q1rJu6PACdwv+6ypNhf8l7iw/ZtRWv9m9a6ita6isHwUppBA/YdokSJYnh6FsbJyYkOHVrhs8r6ZnefVevp3r09AG3bNmfzlh1Jyzt0aIWzszOenoUpUaIYewMOPrhWHkJyWDt88BieLxemUBF3nJwy0aJNYzau3WpVZuParbzdqQUATVs2ZNf2AACyZM1C1mzmQ6hW3eokmEycOXXephz79gWmqo9Vq6xnbV21yu9+fbzdnC2W+njW6tWuwcq1G9FaE3j0ONmzv0T+fHmoVb0yO/ceICY2jpjYOHbuPUCt6rbNQOcox0fggaMUe7kohYt44OSUCe+3m+K3dotVGb81W2jXqSUAzVo1Srr/D8yfAjdv5YXPsrU2Pf89jlUfRZLqo9XbTfFbu9mqjN+azbTv1AqA5q282LF9T9I6pRQtWnmxMh2XEYPjvG6PHAzCs9j9HM1be6Wdo6M5RxPvhuz2TzuHKR05Dh04SrHi99rFiVZvN2P9Gut2Wb92M+07J2uXbanbJT33zQIcPHCEl4sXpUhRc47Wbzdjne8mqzLrfDfRoUtrALxbN8Z/m3mkcPNGf8qUfYWsWbNgNBp5o3ZVTp04+1znCA08Rx5PV3IVyo/RyUg57xqc8LO+bz2P5/0/8F9pUJHrweYPGLO4ZKPbzKFs+GYhF/efsun5k3OEc8iRg0EUfbkwHpbXS7M2Xmxetz3d/7cnde/9tmhRc120b+/N6tXW77erV2+ga1fzLLBvv92MrVt3ApAzpwvLls1k9Ohv2LVrX7py3Ao8TeZi7jgXLohyykRu7zeJ9dtjVSZTgfuX6+ZsVI07Z0LS9ZxpcZTzmHA8L+qls3HAvZlK1gHjlFLztNY3lFIeQPwjtg8GKgN7MV92+yBp7ltrbdPHiiaTiY8HjcJ39XyMBgOzZi8kKOgUY8cMZd/+QFat8uPPmQuYPWsqJ4L8iYqKpks385xHQUGnWLLEhyOBm0kwmRj48UibJyeQHKlzfDn8f8xa/DMGg4El81dy+uQ5Bg3vx5FDQWxcu41F85bz3S/j2LR3BdHRMXz87ggA8ubLzazFP5OYqIkIv8InH9g+xbjJZGLQoC9YvWoeBqOB2bMWEnT8FGNGD2X/AXN9zJy5gFkzfyAoyJ+oyGi6db8/J9apk7twccmBs7MTLb0b07x5F46fOG1Tlk/HTCLg4GGio2Np2Lob/ft0JyHB/IlqxzbNqVOzKtt3BdC0wztkzZKFcZ+bZ1nN6ZKD93t1plNf8+ye/Xp3eeikQo+qD0c5Pr4YNpG5S6ZjNBpZOO8fTp04y5ARH3Lk4DH81m5h4V/LmDL9a7btW010VAwf9R2WtH31NyoTHnaZixfS98eBo9XHvCW/YkhWH0NHfEigpT4W/LWMH6Z/jf8+X6KjYujf99Ok7Wu8UYXwsIgMqQ9Hed1+OeIb/lz0E0aDkSV/r+DMyXN8/Jk5x6Z121g8bwXf/jKODXuXEx0Vw+D3Pk/K8eein9CJmsvhVxjaP305Rg2bwPylv2EwGpK1y0cEHjqG35rNLJi7lKnTJ+G/f425XfoMTdo+I9tlxNBxLFg2A6PRwN9/LeXkiTMM+3wAgQePsm7NZubPXcJPv33D7oPriI6K4f13zLPfxkTHMv2nWazdvBi0ZoPfNjas3/qIZ3TsHImmRFaPnkWPOZ9hMBo4sGgrV0+H0mBwW0KPnOfkhgNU7+lF8VplMSWYuBNzk2WfTAegeg8v8hQtSN2Bbag70Dyz55zuk7h5PdbmOrH3OcRkMjF++GT+WDgVg9HAsvk+nDl5jgGfvcfRQ8fZvG47ZSuW5sdZ3+CS04X6Xm8yYNh7eNfpBMDclb/xcomiZHspK5sP+TBq8AR2bH7yS5pNJhODB4/Gx2cORqOR2bMXcfz4ab74YggHDhxm9eoNzJq1kD///D+OHt1KVFQ03bt/BEC/fj0pXtyT4cMHMHz4AAC8vbtz9er1hz3lA4IkEjL6V16eMxZlNBC5aAN3Tl/CdUgXbh0+Q+yGveTv5Y1Lo2qQYCIhJo6LQ6ckbV5i8ddkKV4Iw0tZKLP7Ty4N+5G4bU/+AYCjnMeE41H2uD/LESil5gPlgTVACHDvS8NuAN0wj2Cu0lqXtZQfCmTXWo9VSpUCFlnKbgK6aa09lVK9gCpa64+SPc/HKfettX7oR5uZnD1ezEZxYEVdHOPG9Etx6bv0KaPcDLXpNuMMl9X9TXtHAMA9ex57RwAg7Ebkows9A67Zn/0EU2nJbLD9uwszksFBpuq/lXDH3hEASEiU+6+Sez+3Y3xv4MSwLfaOAEDJXB6PLvQMBMfZdntARttVoIK9IwDQ/laovSMkOX11v2OcVJ/AzbGd7fq3/Utj/7ZLnb2oI5porbukWPRDGsXKJiv/bbLfT2DupN4zyrJ8FjArxfP88IB9CyGEEEIIIcR/0gvb0RRCCCGEEEKIp86OE/LYk0wGJIQQQgghhBAiQ0lHUwghhBBCCCFEhpJLZ4UQQgghhBDiadG2zdT+vJMRTSGEEEIIIYQQGUpGNIUQQgghhBDiaZHJgIQQQgghhBBCiPSTjqYQQgghhBBCiAwll84KIYQQQgghxFOiE2UyICGEEEIIIYQQIt1kRFOIx3AhNsLeERxKVvc37R0BgNth2+0dwaE4SrtcvhFl7whCPDcm3t5i7wgO5XR0qL0jAPBn/vr2jgBAlfDN9o4gMoJMBiSEEEIIIYQQQqSfdDSFEEIIIYQQQmQouXRWCCGEEEIIIZ4WuXRWCCGEEEIIIYRIP+loCiGEEEIIIYTIUHLprBBCCCGEEEI8LVq+R1MIIYQQQgghhEg3GdEUQgghhBBCiKdFJgMSQgghhBBCCCHSTzqaQgghhBBCCCEylHQ0nzONvepx7Og2TgT5M+zTD1Otd3Z2Zv68aZwI8menvw9FixZKWvfZsI84EeTPsaPb8GpUV3JIjv98jlETv6dO80607tYvzfVaayb+3zSadniHNj0+IOjkmaR1K3z9aNaxD8069mGFr99/IoejtIvkkByS4/nMIjmsudcrT6ttk2nt/x1lP/R+YLkizavSI/Qv8pYvlrSs7EfetPb/jlbbJuNet1y6cjhKfThKDkekE7Vdf+zlmXY0lVImpdQhpdRRpdRipVQ2pZSnUuroU35eg1JqquV5jyilApRSxR695WPt+w+lVJmM2NejGAwGpv4wgRbe3ShXoT4dO7amdOmSVmXe6d2ZqKgYSpWpzZSpv/P1xJEAlC5dkg4dWlG+YgOat+jKj1MnYjDY1vySQ3I8DzkAWjdrxPTvxz9w/fZdAVwMCcN34QzGDhvIuG9/AiAmNo5pM+fz9+9T+Pv3KUybOZ+Y2LjnOoejtIvkkByS4/nMIjmsKYOi+oSebOz2DSvrD8OzdQ1ylnRPVS7TS1ko/U5jrh64/wFizpLueLaqwcoGn7Gx6zdUn9gLZVA25XCU+nCUHMKxPOtWvK21rqi1LgvcBdL+eD/jdQTcgfJa63JAGyD6cTdWShkftE5r3VdrHZT+iI9WrWolzp4N5vz5i8THx7No0Qpaeje2KtPS24u5cxcDsHTpahrUr21Z3phFi1Zw9+5dgoMvcfZsMNWqVpIckuM/mwOgSsVy5HTJ8cD1m/1307JJQ5RSVChbmri4G1y9FsmOPfupWbUSOV1ykNMlBzWrVmLHnv3PdQ5HaRfJITkkx/OZRXJYy1upOHHBEdy4eJXEeBPBK3ZTuHHlVOUqDmvH0WmrMN2JT1pWuHFlglfsJvFuAjcuXSUuOIK8lYrblMNR6sNRcjisRG3fHzux58cF24ESlt+NSqnflVLHlFLrlVJZAZRSFZVSu5VSh5VS/yilcluWb1FK/U8ptVcpdUop9aZluVEpNdkyYnlYKfW+Zf9uQLjW5i+x0VqHaK2jLNt4KaV2KaUOWEZZs1uWByulRiul/IFhSqm994JbRmEPJ8tSxfJ7E8t+ApVSGy3LXlJK/WnJdFAp1crWCnP3cOVSSFjS45DQcNzdXR9YxmQyERMTS968uXF3T2NbD+ttJYfk+C/leBwRV6/jWiBf0uOCBfIRcfUaEVev4Vog//3l+c3Ln+ccjtIukkNySI7nM4vksJbNNTc3wyKTHt8KjySba26rMnleK8pLbnkI3XDoodveTGPbx+Uo9eEoOYRjsUtHUymVCWgKHLEsKgn8rLV+DfNIY1vL8jnAZ1rr8payY5LtJpPWuhowKNnyPkCM1roqUBV413KJ7CLA23LZ7ndKqUqWHPmAUcBbWuvXgX3AkGTPcUdrXVtr/TXgrJR62bK8o2Wfyf9P+YHfgbZa6wpAe8uqkcAmS6b6wGSl1EtPWmeW50i1TGv9GGUeb1vJITn+SzkeR1r7VkqR1lOmle15yuEo7SI5JIfkeD6zSI5H50BbFaDK2G7s+2p+Whs/fNt05nCU+nDU937x7DzrjmZWpdQhzB26i8AMy/LzWut7H/fsBzyVUjmBXFrrrZbls4E6yfa1LHl5y+9eQA/Lc+wB8gIltdYhwKvACCAR2KiUagjUAMoAOyzb9ASKJnuOhcl+XwR0sPzeMcU6LPvaprU+D6C1vvdRlRcw3LL/LUAWoEjKilFKvaeU2qeU2peYeDPlagBCQ8IpXOj+9f+FPNwID494YBmj0UjOnC5ERkYRGprGtmHW2z4uySE5noccj8O1QD4uX7k/Qhhx5RoF8uW1LL96f/lV8/LnOYejtIvkkByS4/nMIjms3QyP5CX3PEmPs7nl4VZEVNJjp+xZyFWqEI2XjOTt3f9H/teLU3/mEPKWL8atFNu+lGLbJ+Eo9eEoORxWYqJ9f+zEXvdoVtRaD9Ba37Us/zdZGROQ6TH2dW+b5OUVMCDZcxTTWq8H0Fr/q7Veo7X+FJgItLaU90tWvozWuk+y50je41sIdFBKvWLenT6dIo8i7c+jFOZRznvPUURrfTxlIa31b1rrKlrrKgZD2gOeAfsOUaJEMTw9C+Pk5ESHDq3wWbXeqozPqvV0724eTG3btjmbt+xIWt6hQyucnZ3x9CxMiRLF2BtwMM3neRTJITmehxyPo17tGqxcuxGtNYFHj5M9+0vkz5eHWtUrs3PvAWJi44iJjWPn3gPUqp763pvnKYejtIvkkByS4/nMIjmsXT90jhzFXMleOD8GJyOerWpwaf2BpPXxcbdZVO4DltUYzLIag7l64Cybe3/P9cPnubT+AJ6tamBwzkT2wvnJUcyV6wfPPtf14Sg5hGN5nA6dXWitY5RSUUqpN7XW24HuwNZHbLYO+EAptUlrHW/pFIZiHs28rLUOU0oZgPLAYWA38LNSqoTW+oxSKhtQSGt9Ko08Z5VSJuALUo9mAuyy7KuY1vq8UiqPZVRzHTBAKTVAa62VUpW01ja9ekwmEx8PGoXv6vkYDQZmzV5IUNApxo4Zyr79gaxa5cefMxcwe9ZUTgT5ExUVTZdu/QEICjrFkiU+HAncTILJxMCPR5Jo4ycckkNyPA85AD4dM4mAg4eJjo6lYetu9O/TnYSEBAA6tmlOnZpV2b4rgKYd3iFrliyM+3wwADldcvB+r8506vsxAP16d3noZD7PQw5HaRfJITkkx/OZRXJY06ZE9o6azVvzh6EMBs4s3ErMqVAqDG3L9cDzhPgdeOC2MadCueCzh1ab/0eiKZE9I2fZ/BUUjlIfjpLDYdlxQh57Us/yGmil1A2tdfYUyzyBVZaZaFFKDQWya63HKqUqAtOBbMA5oLfWOkoptQUYqrXeZ7nPcp/W2tPSiRwPeGMeSbyKeeSyJjAByGx52r1Af631HaVUA+B/ydaN0lqvVEoFA1W01knXs1myTQaKaa2DLcuSZ2mKebTUAFzRWjdS5omNpgBvWDIFa61bPKyeMjl7vJhHoxBP6HbYdntHcChZ3d+0dwQhhPhP+DN/fXtHAOCdq5vtHcHhJNwNfXqTJjwlcf2b2vVv+xy/rLFLnT3TjqZ4PNLRFOLxSEfTmnQ0hRAiY0hH03FJR/PJ2auj6bCXzgohhBBCCCHEc+8FvXTWnt+jKYQQQgghhBDiP0hGNIUQQgghhBDiKXlRb1WUEU0hhBBCCCGEEBlKOppCCCGEEEIIITKUXDorhBBCCCGEEE+LTAYkhBBCCCGEEEKkn3Q0hRBCCCGEEEJkKLl0VgghhBBCCCGeFrl0VgghhBBCCCGESD8Z0RQOzcu1gr0jAOBpyG7vCAD8FrbD3hEAGO9W394RAPCq+L69IwCw/tCv9o4AwGRXx2iXIMO/9o4AQAGc7B0BAGXvABYRxNs7AgAldWZ7R3AoAcTZOwIAuZSzvSMAcFub7B0BgEqGGHtHAGCig7zffh6+2d4RnmtaRjSFEEIIIYQQQoj0k46mEEIIIYQQQogMJZfOCiGEEEIIIcTTIpfOCiGEEEIIIYQQ6ScjmkIIIYQQQgjxtCTaO4B9yIimEEIIIYQQQogMJR1NIYQQQgghhBAZSi6dFUIIIYQQQoinRL5HUwghhBBCCCGEyAAyoimEEEIIIYQQT8sLOqIpHc3nTGOvenz//VcYDQb+nPk330z+2Wq9s7Mzs2b+wOuVyhEZGUXnrh9w4UIIAJ8N+4jevTphSkxk8OAvWO+39bnPUbluZd4f+z4Go4F1C9ax+JfFVuvLVivLe2Peo1jpYkz6aBI7fHckres9ojdVG1QFYMHUBWzz2WZzjjJ1K9B+dG+U0cDOhRtZP22F1foGfZpTq1NDEhNMxEXG8tewaUSGXuOVmq/R9oueSeVci7vz54AfCFwfYFMOR2mXYnXL89aY7hiMBgIXbGH3NB+r9RW7NuD1Ho3QpkTu3rrD2hEzuH46LGm9i3te+m74H/5TlrH3N1+bc1StV4WPvuyP0Whg9d9r+PvnhVbry1cvx4djP6B46Zf56sMJbFu93ZzvjQp8OOaDpHJFihfmqw8nsGPdTptyjJr4Pdt27CVP7lws/2t6qvVaa76eMp3tuwLIkiUzE0Z+QplXSwCwwtePX2cvAOD9np1o1ayRTRkAitYtT72x5nY5umALAb9Yt0v5bg2o0KMRiaZE4m/dYcPwGURa2iVfqcI0/PodMufIik7UzPcejenfeJtyvFa3Ip1G98ZgNLB94UbWTltutb5RnxbUTvZ6mTXsFyJDrwGQxz0fPSb1I497XrSGqb0ncj3kqk05StYtT/PRPTAYDexbuJltKY7Tal0bUr17I3RiIv/e/JflI/7g6plQsubKTpdpH+NRvjgHl2zDZ8wsm54/eY5mlhz708hRNVmOuylydE6WY1U6czhKuxSrW56GY7qjjAYOL9jCnjTOH5WSHafrLOcPl0L56LPxGyLPhgMQfvAM60fOtCmDI+WoWLcSvce8i8FoYOMCP5ZPW2q1vkXfljTs5IUpwURsZAy/fPoj10Kv4lmmGO9O6EfW7NlINCWy7KfF7Fzlb3MORzk+ytetRPcx72AwGtiyYAM+0/6xWt+0rzf1Or2FyZLjt09/5nro/efKmj0r/9s4lX3r9jBn9B82ZQDIXud13Me8CwYDUQv9uDp9idX6XG0b4jaiN/ER1wG4PuRvS2IAACAASURBVGc1UQvXk6V0MTzG98eQPRs60cTVnxYRs9r2dgHbj9V7crjnpc+G/7FjyjIC0vGe6yh/gwjHYZeOplJKA39prbtbHmcCwoE9WusWj9j2htY6u1LKE3hDaz3fsrwK0ENrPfAh23oCq7TWZZ+0jFLqK2Cb1nrDo/5/T4vBYGDqDxNo0qwzISHh7N7li8+q9Rw/fjqpzDu9OxMVFUOpMrXp0KElX08cSZeuH1C6dEk6dGhF+YoNcHcvyLo1Cyj92pskJj75fMuOlKP/+P6M7DqSa+HXmOIzhd1+u7l0+lJSmSthV/j+k+9p+35bq22rNqhKibIl+KjJRzg5O/HN4m8I2BzA7Ru3nziHMig6ftWHqd3GE335Op+t/JrDfvu4fCY0qUxIUDCTvIcTf+cub3ZrRJsR3Zjx0RRO7TrG182GAZAt50t8ufVHgrYFPnGGe/XhCO2iDAqvcT1Z0HUScZcj6bXyK05v2G/1pha0YheH5m0CoMRbr9NwVDcW9fwmaX3D0V05t8W2erjHYDDw8fgBfNrlM66GX2P66p/YuX4XF05fTCoTEXqF/w2ZTMf321tte2hnIO827gdAjlw5+Mt/Fvu27rc5S+tmjejStiWfj/s2zfXbdwVwMSQM34UzOHzsBOO+/Ym/f59CTGwc02bOZ+GMqQB07DOQerVrkNMlxxNnUAZFg/E9Wdb1/9k777CojrYP37NLs6FUARsqtthQsRvFhok90dfYSzSJRlM0zRKNscckGo0tXU1i1DfNXrDGhmJDI3ZBpfdioe3O98euwAIqLPqyyTf3dXGxZ+aZmd/OzJlzpu4CUiMTGLxlFtf9T2V3JAEu/XmMcz8ZyqVG16Z0mD6UP4YvRGg1PLdkHDvfXkXcxVvYVSiLPjPLjJwAodEweNZoFg+dTWJUAtM2zyfI/ySR18KybW4FhzC31wdkpGXQYagf/acM4+sJiwF4edEEti37nYuHz2Fb2g5pRh19kB+9Zo3ih6HzSYmKZ9zmOVz0P01srvs2aNNRTvy8F4C6XZrSffpQ1oz4hKz0TPZ8/isV61SmYu0qZqX/MB1jC9BxbtNRAnPpeH76UNYadez9/Fdcn4gOyymXLrNHsNHYfgzfPItrj2k/On44lF+N7UfSzWjWdJ9mbjZYnA6NRsPo2a8xe8hHJETFM3/zZ5zcc4KwXM+5kAshfNBzEhlpGfgNfY5hU0ayeMKnpN9P58uJXxAVGomDqyOfbPucs3+d4V7K3SLrsJz6oWHE7FdYMORjEqLimbV5Iaf2BBJxNUdH6IUQpvd8j4y0DDoP7cagKcNZNuHzbP/+7wzi0vELZqWfjUaDx6yxhAybTlZUPDU3LSJlz3HSr902MUvedoiIj74ycdOnpXP7nUVkhEZi5eqI15bFpP51Bn1q0csFil9XATo9oWeuJbyDKCyLktqjeRdoIIQoZbzuCoQ/wr4gPIHBDy6klCcf1cksLlLKGSXZyQRo0bwJ16+HEhJyi8zMTDZu3ETvXt1MbHr38uPHHw2zer/9to1OHdsZ3buxceMmMjIyCA29zfXrobRo3uQfraO2d20iQiOIuhVFVmYWf235i9Z+rU1sYsJiCL0Umq+xqlqrKucDzqPX6Um/n86N4Bv4+PqYpcPT24vYm1HE345Bl6nj1JajNPZrbmJz5dgFMtMyAAg5c5UKbo754mnSvRUXDpzJtisqllIu7t41SQyNJvl2LPpMHcFbAqjVtZmJTUauDr11aVskOUtKavk1I+lWLHFXitokmFLXuw4RoRFEGuvHvk0HaOvXxsQmOiyaGxdD0D9iSUuHHs9yYn8g6WnpZmvx8W74yM7h/sMB9H6uM0IIGjeoR2rqHWLjEjhy/BStmzehvH05ytuXo3XzJhw5bl6H1827Jkmh0STfMpTL5S0B1PR7RLmUskVKQ75Ua9+QuIu3ibto6KSnJd0x+2CD6sb7Je52DLrMLAK3HMHbz/Teu3zsAhnG++DGmSs4GO8Xd6/KaLRaLh4+B0D6vbRsu6JS2duLhJvRJBrv23NbjlEvT36k58oPm9K2YMyPzPvp3Dx5mUwzZ3Tz6ojPpeO8GTqynoAOSykX9wf11Nh+XNwSgNdj2g948kvSLEWHl3ctokKjiLkdTVZmFke2HMKnawsTmwvHzmfn95Uzl3F0dwIgMiSCqFDDrGpiTALJccnYO9qbpcNS6kdNby+iQyOJvR2NLjOLgC2HaZYnPy4e+zs7/mtnrmTnB4BngxrYO1fgvJkDug8o3bgWGTcjybwdjczMInnLX9h3bVmosBkhEWQYyyUrJoGs+GSsnMwrFyh+XfXya0byrVjii/nMtZR3EItFX8J/JURJHga0A+hh/DwI+OWBhxBiphDi3VzXfxtnGnOzAHhWCHFWCDFRCOErhNiaK/yPQoh9QoirQohX8iYuhNAKIT4VQgQKIc4JIV57lFghxGohRH/j51AhxMdCiNNCiPNCiLpG9zJCiO+NcZ4RQvQxutcXQpwwaj0nhKhVxLwCwKOSG7fDckaowsIj8fBwe6iNTqcjOTkFJycHPDwKCFvJNOw/TYeTmxNxEXHZ13GRcThVdHpEiBxuBN/Ap6MPtna22DvY06hNI5zdnc3SUaGiI4kR8dnXiZHxlK+YvyP5gDYDOnHhwNl87j692nJy85ECQhQOSymXcm4OpEYmZF+nRiZQzs0hn13T4V147a/P6ThlIHs+WgsYOjetxvXk8Be/m5V2bpzdnYmJzFkuFRsVZ1YZd+zty94/9xdbz6OIjo3HzTVHW0VXZ6Jj44iOjcPN1SXH3cXgbg5l3RxIjcgplzuRCZStmL9cGg/vwqhDn/Ps1IEcMJaLQw03QPLCj+8zeNscfMb2yBeusFSo6EiCyf2SQIVH3LftBnTm7wNnAKhYw537KXcZt+pdpm9bSP8pwxAa8x5j9hUdSM6lIyUyocD7tuWwrkw6uJhukwezdeZas9Iqqg77x+jY9hR0WEq5lC1k+9FkeBde+etzOkwZyN6PcvKjfBUXRmyfw6AN06jcvI5ZGixJh6ObE/GROfd8QmQ8Tm4PL5fOL3XlzIH8g1FejWthZWNF9M0os3RYSv1wcHMiITJHR0JkfHaHtiA6vNSZoAOnARBCMOTDkfwyb41ZaefGys2JzFzlkhkVj3UB5WL/XBu8diyl6orJWBfw/CnVuBbC2ooMM8sFildXrUvZ0nJcT448gWeupbyDKCyLkuxorgcGCiHsgEbA8SKGnwwcklJ6SykXF+DfCENHtjUwQwjhkcd/NJAspWwONAdeEUJUL0L6cVLKpsBK4EGneBqwzxhnR+BTIUQZYCywRErpDfgAYQVF+DiEEPncHsw4PNqmcGH/jToexplDZwjcF8hnf3zGB8s+4NKpS+h1Zg75FKCDh+ho0fdZqjWqwZ6vN5u427tUwKNOVbOXzRpkWEa5QEH5kd/p9No9fNX+HQ4sWE+bN/oC0G7SiwR+u5PMe+bPHuaoKP53cnR1pEbd6gQePFlsPY+iIF1CiAKrUUFlVSgeUvZ5CVq7hx+efYdD89fT8k1DuWi0Wjx8arPjzRVs7DeLmt18qNK2/pOS8dD7pWXfZ/FsVINdxvtFo9Xi1bwe/527lrm9J+Nc1ZW2/X3N1FG4+nH8R38WdZjIrgW/4Gusp08UC9FhMeVS4H2b3+7M2j180/4dDi5YT2tjftyNSWJV67dZ0/1D9s3+mZ5LX8embKn8gf9BOgriYe3Ysy90oEZDLzZ/ZbpnsYKrA28snsiKd5cW43lboJACbZ9u/ShIR8G2bV9oT42GXmz7yrCXtMvw5zi7/7RJR9VsCnHfpu49weVnR3Pt+Te5c/gslT9728TfysWBKosmEfbekofmZaGkFKOutp30Iief1DPXYt5BLBOplyX6V1KUWEdTSnkOw/LXQYD5O48fziYp5X0pZRywH2iRx98PGC6EOIuhk+sEFGWm8cHwzykM3+NBnJONcR4A7ICqwDFgqhDiA6CalDLfRkAhxKtCiJNCiJN6fcHr9MPDIqlSOae/XLmSO5GR0Q+10Wq1lC9vT0JCIuHhBYSNMA1bWCxFR1xkHM4eOSOEzu7OJMQkPCKEKRuWbeCN599g2pBpCCEIDzFv2UhSVDwOHjkjmQ7uTiTHJOazq9O2Ic9NeIGVYxaSlWG6v61Zz9YE7TqBPktnlgawnHJJjUqgnHvOCHM5d0dSo/PnxwOCNwdQy7hU0MPbi45TBjLu8GJ8Xu5G6/G9aTrCvMNvYiNjcXXPmQ10cXMmPqpoLxgde3Xg8M4j6IpRLoXBzdWZqJic0fHomDhcnZ2M7jmzstGxBndzuBOZQDmPnHIp6+7I3QLq6QMub85ZWpsamUDY8UukJd4hKy2D0P1BuDbwNEtHYlQCjib3iyNJBdy39do2pMeEF1k25pPs+yUpKp7bwSHE3Y5Br9NzdncgVRsUZXwwh+SoBMrn0mHv7kjKI/Lj/JZjPNPVvOX1jyKlAB2pj9FR7ynosJRyKaj9uPOI9uNirvZDl5FFWtIdAKL/DiXpZgyO1c2bFbEUHQlR8TjlmglzdHciITp/uTRs25gXJ/yHT8bMNXm+lCpbiik/TOeXz37i6pkrZmkAy6kfCVHxJkthHd2dSCwgP+q3bUTvCf1ZNGZ+tg6vpnXoOuJ5Fh9exeBpI3j2RV9e+mCoWTqyIuNMZiit3ZzIyqNDl5SKNKadsH43pRp4ZftpypbC8/uPiPr8J+6fvWyWhgcUp666e3vhO2Ugrx1eTLOXu9FqfG+amPnMtZR3EIVlUdK/o7kZ+Ixcy2aNZGGqzc6MuPN23/NeC+AN44yot5SyupRydxHifzD8oyPnUCUB9MsVZ1Up5UXjgUW9gfvALiFEp3xipfxaSukjpfTRaMoUmGDgybN4eVXH07MK1tbWDBjQhy1bTSVv2bqbYcMMB5v069eD/QeOZLsPGNAHGxsbPD2r4OVVnROBZ4rwdS1Px5WgK3hU96BilYpYWVvRvld7AvwDChVWo9FQroJhz5xnXU8863ly+q/TZum4GXQdV093nCq7oLXW0qxXG875m86AVa7vyeB5r7ByzELuxKfki8Ond1tObjF/2SxYTrlEBt3Asbob5au4oLHW8kyvVlzzN81bB8+K2Z+9OnmTGGpYNvTzf2azst1EVrabyMnvd3Fs+WZOr/E3S8eloMtUql4JtypuWFlb0amPL0f9jxUpjk59OrJ309NdNgvg264Vm3fuRUpJ0N8XKVu2DC7OjrRt2YyjJ06TnJJKckoqR0+cpm3LZo+PsACigm7gUN0Ne2O51OnViht5yqVCrnKp0dmbJGO53PzrHM51q2JlZ4PQaqjcqi4JV80bmAkNuoarpzvOlV3RWlvRvFdbgvLcL1XqezJ03qssG/MJqbnul5Cg65QuX4ayxn1mdds0MDkEpCiEB13HydMNB+N926hXay75my45dPLM6RzU6dSE+FDzl7cVVkfDx+io/ZR0WEq5RBrr6YP2o95j2o+audqPUo7lEBrDzEj5Ki44VK9I0q2Yf7SOa0FXca/ujmsVV6ysrWjb61lO+p8wsfGsX51X54/jk9FzSYlPzna3srbiva+ncPC3/QRsN+/E7AdYSv24EXQNt+ruuFQx6GjVqx2n/U1Paa9Wvzovzx/LotHzTfJj5Vtf8Hab15jYbizr5q7h0O8H2PDJT2bpuHfuKraeHlhXroiwtqJ8r/ak7DEtFyuXnOWr9l1akH7dcFCQsLai2qppJP6+j5TtxXvuQ/Hq6i//mc1X7SbyVbuJnPp+FwHLN3PGzGeupbyDKCyLkv55k+8xLF89L4TwzeUeCvQEEEI0BQoa+koFHnX0Yh8hxHygDOCLYamtTS7/XcA4IcQ+KWWmEKI2RT+QKC+7gDeEEG9IKaUQoomU8owQogZwQ0q51Pi5EbCvqJHrdDreevtDtm9bh1ajYfWaDQQHX2HmR+9y8lQQW7f68/0P61mzeimXgg+TmJjE4KGvAxAcfIVff93C+aD9ZOl0vPnWNLNP87IUHXqdnpXTVzLnxzlotBp2b9jNrSu3GDppKFfPX+W4/3FqNarF9G+mU7Z8WVp2acnQSUMZ12UcWmstn/72KQD3Uu/x2Vufmb10Vq/Ts2HG90xYOw2NVsOxjfuJvBpGz4kDuHn+Ouf3nOLFKUOxLW3HmBWTAEgMj2PVK4YT3xwru+Dg7szVgGCz0n+ApZSL1OnZPWMNL61933DU+saDxF0N59lJ/Yg8F8K1PadpNsKPau3qo8/UkZZyl22Tvnp8xEVEr9OzdPoyFv48H41Gw44Nuwi9cpNR747gctAVjvofo07j2sz+diZly5eldddWjJo0nFGdDVu6K1auiIuHC0HHzhVby3sfLSDwzDmSklLo3Hcor48eRlaWYaT7pRd60L51cw4dC+T5AS9Tys6O2VMnAlDevhyvjRzEwDFvATB21GCzTpwFQ7nsm76GF380lMuFDQeJvxJO60n9iD4fwg3/03iP9KNqu/roMnWkJ99ll7Fc0pPvcfrbHQzeOgspJaH7gwjZl3+fcWHQ6/Ssm/Edb6+dhtBqOLJxPxFXw+g98SVunr9O0J6T9J8yDLvSdoxd8Q4A8eFxLH/lE6Rez3/n/sg7P88AIbj19w0Ord9rto4tM1Yzcu1khFbD6Y0HiLkaTueJ/Qk/f4NLe07TaoQfNds2QJ+Vxf3ku/z6zsrs8O8eXoJt2VJora2o59eMH4YtMDkptig6ts5YzYi1kw0/b1KAjpZ5dPyWS8c7eXSsLoYOSygXqdOzZ8Ya/mNsP85vPEj81XDaTepHlLH9aDLCD88H9TRX+1GlZV3aTeqHPkuH1Et2T/2BtGTzTvK0FB16nZ7vZnzNtLUz0Wg17N+4l7Crt3lp0mCun7vGyT0nGDZ1FHalS/HOCsMJ5nERcXwyZi6te7alXov6lKtQjo79DePby99dSmhwiFk6LKF+6HV61sz4lvfXzkCj1XBw417Cr96m36SBhJy7zuk9gQyaOhy70na8ucKwqyk+Io5FY+abld5D0emJ+GgV1dd+bPh5k//uIf3qLVwnDuH++auk7jmB08he2HdpidTp0CWlEvbuEgDK92hHmRb10TqUw6F/ZwDC3v2CtItFLxcoXl19kljKO4jF8i/7OoVFlMQa6Ac/UZLHzRd4V0rZ03ga7SbAFQgE2gHPSylDc/28iTWwE3AGVgNncoWfCXgANTEsXV0opfwm90+XCCE0wBygF4aZyFigL+AAXAVyz9lPxLDfc6uU8lchRCjgI6WMM/6symdSSl+j7i+ANsY4Q416pgBDgUwgChgspXzoGk8rm0r/roXpxcDPrXFJSwDAU1P28Ub/A76OKP7o55NgjnvHkpYAwC6dZSyt2X32yT+0zWFZ0xklLQGAYE3x9/s8CVyxLmkJwEP2lZUA0RT/dNonQS1pW9ISLIpAUktaAgAVhM3jjf4H3JdPd6tCYflAYxn3yw59+ZKWAMDUyKe/uqewZGWEW0qzWmgS+/mW6Lu9w28HSiTPSmRGM28n0+h2AMO+Rox7GP0eFVZKmQl0zuN9INfnK1LKV/OEDQUaGD/rganGv9wkQ4FvJ//NFY9nrs8nMcyYPtCd7/RaKeV84AkPpykUCoVCoVAoFApLpyQP5ClJSnqPpkKhUCgUCoVCoVAo/mWU9B7Np4KUcmZJa1AoFAqFQqFQKBSK/6/8KzuaCoVCoVAoFAqFQmER/D89DEgtnVUoFAqFQqFQKBQKxRNFdTQVCoVCoVAoFAqFQvFEUUtnFQqFQqFQKBQKheIpIdXSWYVCoVAoFAqFQqFQKIqPmtFUKBQKhUKhUCgUiqeFmtFUKBQKhUKhUCgUCoWi+KiOpkKhUCgUCoVCoVAonihq6azCotkdFVTSEiyKdq71SloCACtSLaNc2pXzKmkJACxoNr2kJQAw+fTskpYAQEL/l0taAgAbr1cuaQkWxfhqcSUtAYC7CTYlLQEAvRQlLQGAkPv2JS0BgOu6lJKWAICrpnRJSwDAa4ZlPG9jPr5d0hIA6FSxYUlL+EejDgNSKBQKhUKhUCgUCoXiCaBmNBUKhUKhUCgUCoXiaaFmNBUKhUKhUCgUCoVCoSg+qqOpUCgUCoVCoVAoFIonilo6q1AoFAqFQqFQKBRPCXUYkEKhUCgUCoVCoVAoFE8ANaOpUCgUCoVCoVAoFE8JNaOpUCgUCoVCoVAoFArFE0B1NBUKhUKhUCgUCoXi/zFCiOeEEJeFENeEEJMfYjNACBEshLgghFj3uDjV0lmFQqFQKBQKhUKheEpY+tJZIYQWWA50BcKAQCHEZillcC6bWsAUoK2UMlEI4fq4eFVH8x9GNz9fFi2ahVaj4fsffmHhp8tN/G1sbFj9wxKaNmlIQkIig4aM4+bNMAA+eH8Co0YORKfXM3HidHb7H1Q6/mU6Wvg2Z8LHr6PVatj2yw7WLV9v4t+oZUMmzHydmvVqMGv8HA5uOwSAd5vGTPhoXLZd1ZpVmTV+Dod3HTVLR4fObZk57wO0Wi3rf/ydFUu+M/G3sbFm8cp5NGz8DImJSYx/+T3CbkdgZWXFwiUzadD4GbRWWn5fv5nlX3z3kFQeT6MOTRj+0Wg0Wg371+9hy8rfTfy7j+mN78Au6LN0pCSk8PV7y4gLj8W5kgsTv/oAodFgZa1l1+rt7P15l9k6anZoRLePhiG0Gs6sP8DRlVtM/JsO6Uzz4V3R6/Rk3Etj25TviLsajkfjGvSYPwYAIeDgF79zeddJs3V8OG8Rfx05gaNDBf78aVU+fykl879YxaFjgdjZ2TJ32js8U8cLgE3b/flqjaE+vTZiIH26dzVbh02LFpSbMAG0Wu5v28a9dfkHRW19fSk7ciRISeb166TMmQOAXbdulBk2DIC7P/5I2i7zy6WqbyPazzSUS/AvBzi1wrRcGgztRMMRXZE6PZl309g3+TsSr0YA4FS3Ch0XvIxN2VJIKdnYcwa69Mx/tA7bls2xf2sCaLTc27qNuz/9YuJf6vlulHt9LPq4OADu/vYH97duB6DcuFexbd0KgDurfyRt336zNACUbueD85SxoNWS8usOkr7daOJfrm9XnN8dQ1ZMPADJP28m5bedANQ8v52Mq6EAZEXEEDlhZjF0NMN16jjQaEj+dSeJeXTY9+2K83ujyYo26Ehat4WUX3dm+2vKlMZz29fc2XOUmDkrzNbxTIfGDJgxCqHVcGTDXnav3GTi33l0D9oO7IwuS8edhBR+fH8lCeFx1G5dn/7TR2TbudX04Ls3lhC0O9AsHT6+zRg3cxwarYadv+xkwwrT/GjYsgFjPxpLjXrVmTd+Poe2H872GzN1NC06tUCjEZw+dIYVH600SwMY2vVhH72MRqvhwPo9bFn5h4n/82N64TuwC7osHakJKXz93nLiw2Oz/UuVLcUne5dyctdx1s741mwdAEduRLNwz3n0enihcVVebl3bxD8y+R7Tt50hNS0TvZS86fsMz9asCMB3x67wZ9AtNBr4oEsj2tR47Dt7gdTp0Jg+M4aj0Wo4vmE/+1duNvFvP7o7LQd2RJel525CChvf/4rEcMM93GPyIOp1bAKA/5e/E7Q1wCwNYDn1Q2E2LYBrUsobAEKI9UAfIDiXzSvAcillIoCUMuZxkT6xjqYQ4o6UsuxjbN4GvpZS3ntS6T4kHU+gjZRynfG6NPAN0AgQQBLwnJTyzhNIazswWEqZVNy4HodGo2Hpkrk8130QYWGRBBzbzpatu7l48Wq2zcujBpGYmEzdZ9oxYEBv5s+bxuAh46hXrxYDBvShkXcnPDwqsmvHeurVfxa9vuhDLEqH5ep4a84bvDv4A2IjY1m1bTlHdh/l5tVb2TYx4TEsmLSQl14bYBL27NEgxnQbC0C5CuX4+fAaAg+eKrKGBzrmLJzGkBdfJTIiii171+O/cz9XL9/Itnlp6IskJ6XQ3qcHvV58jikzJzJ+9Hv06OOHja0Nfu1exK6UHXuP/cmm33YQdjuiyDqERsOo2a8yf8hM4qPimbN5Iaf3nCD8ali2TeiFG3zY810y0jLoMrQbg6YM58sJn5MYk8hHL04mKyML29J2LNy9hFP+J0iKSTRDh+C52SP5ech8UqISGLN5Nlf2nCbuani2zd+bjnL6570A1O7SlK4fDuGXEQuJuRzGt70+ROr0lHWtwKs75nFlz2mkzryh0b7duzK4X2+mzv6sQP9DxwK5FRbB9g3fce7CJWZ/toxfvvmC5JRUVv6wjg3fLQXgpdFv4tuuFeXtyxVdhEZDubfeIundd9HFxuK4ahXpR46gu3kz20RbqRJlhgwhYcIE5J07iAoVABDlylFmxAgSXnsNpMTx669JP3IEeafoTbnQCHznjODPwQu4E5nAS1tnccP/VHYHDuDyn8f4+6d9AFTv2pRnZwxl87CFCK0Gv6Xj8H9rFXEXb2FXoSz6zKyi54UF6UCjwX7SWyRMfA9dTCzO364i/fBRskJvmpil7dtPyuKlJm62rVthXbsWcaPGIKxtcFz2BekBx5H3zHjUazS4fDie8DFTyIqOo8qGL7m7P4DM67dMzFJ3/EXc3OX5gsv0DG6/+HrR0y1Ah+v08YSPnkpmdBzVNi7l7v4AMvLouLPjr4d2Ip3eHM69wPPFkiE0goGzRrN06BwSo+KZvHk+5/xPEnUtp/24HRzK/F6TyUzLoP3QrrwwZSjfTfiCK8cuMK/7+wCULl+GWQe/JPivILN0aDQaJswZz+TBU4mLjOPLrUs55h/ALZPnSyyfTfqc/q/1Mwn7TLN61Pd5hrF+hsHMRb9/TqNWjTgXcK7IOoRGw4jZr7BgyMckRMUza/NCTu0JJMKkXQ9hes/3yEjLoLOxXV824fNs//7vDOLS8QtFTjsvOr1k/u5zrBrYhorlSjFk9UE61HKjprN9ts03R6/gV9eDAU2rcz0uhQkbA9jxDf/JJAAAIABJREFUuh/X41LYFRzOb2M6EnsnjdfWH2XTq13QakQR80PwwqxRfD10HslR8by1eS7B/qeIzlU/woND+aLXNDLTMmg9tAs9pgzmpwlLqdexCZXqV2dR98lY2VgzbsMMLh0IIv3O/SLnhaXUD4tGFq1sS4BKwO1c12FAyzw2tQGEEEcALTBTSrmTR/C/3qP5NlC6KAGMU7lFxRMYnOv6LSBaStlQStkAGA0Uesj3URqklN3/F51MgBbNm3D9eighIbfIzMxk48ZN9O7VzcSmdy8/fvzxvwD89ts2OnVsZ3TvxsaNm8jIyCA09DbXr4fSonkTpeNfpKOudx3CQyOIvBVJVmYW+zYdoK1fWxObqLBoblwMQT6iI9uhR3uO7w8kPS3dLB3ezRoSGnKLWzfDyMzMYsvvO/B7vqOJjV/3jvy63jDqun2TP23bG9oyKSWlS5dCq9ViZ2dLZkYmqanmjQd5edciOjSSmNvR6DKzOLblMM26tjCxCT72NxlpGQBcPXMFR3cnAHSZWWRlGF7YrW2sEUV8+OfGw7smiaHRJN2ORZ+p48KWAOp0bWZik5HrwW5d2jb7c1ZaRnan0srWGinNlgGAj3fDR3YO9x8OoPdznRFC0LhBPVJT7xAbl8CR46do3bwJ5e3LUd6+HK2bN+HIcfMGIqzr1kUXHo4uMhKyskjbtw/btqb1tFTPntz/88/sDqRMMjSxts2bk3HyJDI1FXnnDhknT2LbokW+NApDRe+aJIVGk3LLUC5XNgdQw8+0XDJzlYtVaVseFEDV9g2Ju3ibuIuGl6i0pDtIvXmFYyk6rOvVRRcWgS7CUC739+zDtl3bxwcErDyrkXE2CHR6ZFoaWdeuY9vKvHKxa1iHzFsRZIVFQWYWd3YcoGyn1mbFVRzsGtUh81YkmUYdKdsPUqYIOmyf8ULrXIF7R04XS4entxexN6OIux2DLlPHyS1HaezX3MTmyrELZBrbsRtnruLg5pgvnqbdW3HhwJlsu6JSx7sOEaGRRN2KIiszi4ObD9LGzzQ/osOiCbkUgszTUEkJNrY2WNlYYW1jjZW1lsS4og/aAdT09iI6NJJYY7seUEC7fjFXu34tV7sO4NmgBvbOFThvZoc7N39HJlLFoQyVK5TBWquh2zOVOHA1ysRGCLhrfJbcSc/CpZwdAAeuRtHtmUrYWGmpVKEMVRzK8Hdk0fOkqrcX8TejSDDWj7NbjlHfz8fE5vqx4Oxyv3nmGuWN9aNirUpcP37RsJLmfjoRF29St0PjImsAy6kfiocjhHhVCHEy19+reU0KCJb3gWIF1AJ8gUHAt0KICo9K94l3NIUQvkKIA0KIX4UQl4QQPwsDbwIewH4hxH6jrZ8Q4pgQ4rQQ4r9CiLJG91AhxAwhxGHgP8b4PhFCnBBCXBFCPGu00wohPhVCBAohzgkhXjPKWAA8K4Q4K4SYCLgD2cM7UsrLUsp0YxxDjfGeFUJ89aBTKYS4I4SYJYQ4DkwVQmSvATB+xy25tDobPw836ggSQvxodHMRQvxm1BgohCjck7sAPCq5cTssZ5Q7LDwSDw+3h9rodDqSk1NwcnLAw6OAsJVMwyod/2wdLu7OxEbmrGKIjYrFJdcDtrB06u3Lvj/3maUBwM3dlYjwnIdtZEQ0Fd0rPtRGp9ORmnIHB8cKbN/sz7179zl5cR8B53bz9fI1JCelmKXDwc2R+Mi47OuEyHgc3R6eHx1f6kLQgZyXQkd3JxbsXMyXAd+wZdUfZs1mAti7OZISGZ99nRKZQDk3h3x2PsO7Mv6vRXSeMohdH63JdvfwrslY/094bdcCtk/73uzZzMIQHRuPm6tz9nVFV2eiY+OIjo3DzdUlx93F4G4OGhcX9LE5y9j0sbFoXVxMbLRVqqCtXBmHL7/EYcUKbIydyYLCavKELSxl3By4E5GQfX0nMoGyBZRLwxFdGH74c9pOHcjBGWsBqFDDDaSk90/v89L2OTQd28MsDZakQ+vijC4mp/0wlItzPju7Du1xXv0tFWbPRGOsE5nXrmPbsiXY2iLK22PT1Butq3nloq3oRGZUThlnRcWhdc2vo6xfW6r8sRK3xR9i5ZaTlrCxofLGL6n8yxeU6Wx+B9XK1Yms3Dqi47CumL/9KOvXjmp/rsT9i2lYuRl1CoHLB68S92nxlmUCVKjoSGJETvuRGBlPhYr5O5IPaDugExcOnM3n7tOrLYGbj5itw9nNidiInPyIjYzD6RHtaW4unr7I2WNBrD+5jvWn1nHy4CluX7v9+IAF4ODmREKu9jQhMr7AjvUDOrzUObtdF0Iw5MOR/DJvzUPti0JMahpu5UplX1csV4qY1DQTm7Ht6rLtwm38lu9iwsYAJndtVOiwhaF8RQeSctWPpMh4ylfM3348oOUAXy4dMHSyIy7epK5vY6ztbCjtUA6v1s9QwYx3BrCc+qF4OFLKr6WUPrn+vs5jEgZUyXVdGci7nCwM2CSlzJRShgCXMXQ8H8rTmtFsgmH28hmgBoZNo0sxCO4opexo7Jx9CHSRUjYFTgKTcsWRJqVsJ6V8sMnMSkrZwhjvR0a30UCylLI50Bx4RQhRHZgMHJJSekspFwPfAx8YO7VzjJtZEULUA14y6vMGdMAQY9xlgL+llC2B+UArIUQZo99LwIbcX1gIUR+YBnSSUjbGMIsKsARYbNTYDzD7ySNE/sGGvCNDBdsULqzS8c/WUdBgVFGjcnR1pEbd6pw4aP4+QPPzQ+LdrAE6nZ7mz3SmbZPneeX14VStVtk8HQXmR8EZ0vaFDlRvWJOtX/2Z7ZYQGc/k5yYysf042vfriL1zebN0FERBOk6u9Wd5+0nsW7Cedm/0zXaPOHudVV0/4Lve02n7em+0ttZPTEdhdAkhCqxHBZVhMRI2jVurRVu5Molvv03yrFnYv/ceouxDdmY80fs2v935NXtY2+4djs5fT/M3DeWisdLi3rw2u99YwW8vzqLGcz5Ublv/H62Dgsozj5C0I8eI+c8g4kaOIePkKSpMMxxKmBF4kvSAAJxXLcNh5nQy/w42f0CkwHplquPu/gBCu4zg9gvjuBdwBtd572b7hXYeStiAN4h6bwHOk8diVcX9ienIe3/cORBASOcR3Ow7jnvHzuA236CjwqCe3P3rBFlR5g3GmMoofDvWou+zVGtUA/+vTffo2btUwKNOVbOXzRqFFFpHXjw83anqVZXBLYYyqPkQvNt407BlA/NkFOT4EBltX2hPjYZebDO2612GP8fZ/adNOqrFQRaQcF59O4PD6N2gKrvHd2PZgFZ8uOUUeikLFbZQFLL9AGjatx2VG9XgwNeGPeBXDp3n0v6zTPj9Y4YufYObp6+i0+nMUWEx9cOSkfqS/SsEgUAtIUR1IYQNMBDYnMfmT6AjgLEfVxu4wSN4Wh3NE1LKMCmlHjiLYSlrXlph6IgeEUKcBUYA1XL5b8hj/+AUj1O54vMDhhvDHwecKKBnLaU8i6HD+yngiOEkpXpAZ6CZ8fqs8bqGMZgO+M0YPgvYCfQSQlgBPQDT3fjQCfhVShlnDPNgiLoLsMwY/2bAXgiRb+1a7iltvf5uXm8AwsMiqVLZI/u6ciV3IiOjH2qj1WopX96ehIREwsMLCBthGrawKB2WqSM2MhYX95zDBFzcXIiLKtoDtWOvDhzaeQRdlpkPGwwzmLlnZd09KhITFfNQG61WSzn7siQlJtOnXw8O7j1MVlYW8XEJnDxxlkZNzHtxToiKx8k9ZybE0d2JxOiEfHYN2jai74T+fD5mfvZy2dwkxSQSduUWdVs8Y5aOlKgE7HONEtu7O3In+uGr7f/efIw6eZY+AcRdiyDzfjqutc3reBcGN1dnomJyXo6jY+JwdXYyuueMVkfHGtzNIe8spMbFBV2c6Qu5LjaW9CNHQKdDHxVF1q1baCtVKjCsPs68l/k7kQmU9ciZCSnr7sjd6IfPWl/ZFECNbs2yw0Ycv0Ra4h2y0jK4uT8Ilwae/2gduphYtK457YehXEzbD5mSApmGXSf3tmzDuk7OwSd31v5M3KhXSJj4HgiB7nYY5qCLisM61wyllZszuhhTHfrk1GwdKf/dgW39nMe+LtZwj2eFRXH/xDls69U0S0dWdJzJTKlVRWeyYkzbD31SKtKoI/m/O7N12HnXo8Lg3lTfswaX98dQrk9nnCeNMktHYlQ8Dh4595qDuxPJBayuqNu2Ic9NeIGVYxbma8ea9WzN2V0n0BejXY+LjMPFIyc/XNydSSigPS2Itt3acunMJdLupZF2L43A/YHUbVLXLB0JUfEmS2Ef1q7Xb9uI3hP6syhXu+7VtA5dRzzP4sOrGDxtBM++6MtLHww1SwcYZiGjUnOWtUen3s9eGvuAP87dwq9eJQAaV3IkPUtP0r2MQoUtDMlRCVTIVT8quDuRUkD9qNW2AZ0n9OWHMZ+hy1U/9i7/k8Xdp/D1sHkgBHEhUfnCFgZLqR8K8zH2dSYAu4CLwEYp5QXj6s7eRrNdQLwQIhjYD7wnpXzki+bT6mjm3tylo+BDhwTgb5x19JZSPiOlHJ3LP29v60GcueMTwBu54qgupdxdkCAp5R0p5e9SyteBn4DuxvBrcoWvI6WcaQySJqXM3SpvAAZg6FAGSilTC/g+BQ3faIDWudKoVEBYkyltjaZMAdFA4MmzeHlVx9OzCtbW1gwY0IctW02/7patuxk27D8A9OvXg/0HjmS7DxjQBxsbGzw9q+DlVZ0TgWcKTOdxKB2WqeNy0GUqV6+EWxU3rKyt6NTHl6P+RTs1tnOfTuzdZP6yWYCg039TvUY1qlSthLW1Fb1efB7/nQdMbPx3HKD/QEO71b1PV44eOgFARFgkbYz7NUuVLkVTn0ZcuxJilo7rQVdxq+6OSxVXtNZWtO7VjlP+pqctVqtfndHzx/H56HmkxCdnuzu6OWFtawNAGfsy1PapR+T1cMwhIugGjtXdqFDFBY21lvq9WnHF33R/o6NnztLiWp28SQg1POwrVHFBaA3NdPlKzjjVcCcpLJanhW+7VmzeuRcpJUF/X6Rs2TK4ODvStmUzjp44TXJKKskpqRw9cZq2LZs9PsICyLx8GW3lymjc3MDKCrtOnUg/alpP0w8fxsbbGwBRvjxWVaqgi4wkPTAQ2+bNEWXLIsqWxbZ5c9IDzTtBMzroBhU83bA3lkvt3q0I8TfdT1c+V7l4dvYmyVgutw6ew6luVazsbBBaDZVa1iXxqnn1w1J0ZF66hLZKJbTuhnIp1aUT6UdMy0XjlNMhtm3XhqybxoM+NBqEveEAFKuaNbCqWcPsckn7+zLW1SphVakiWFtR9nlf7u43PQlT65yjo0zHVmTeMOjQ2JcFa8OMv6aCPXZN6+c7vKfQOs5fxrqaR7YO++4d8utwyTVA0KkVGUYdUe8vJKTzcEK6jCB24bekbtpL3KIfzNJxM+g6rp7uOFV2QWutxadXG875m644qVzfk8HzXmHlmIWkxuffatC8d1tObjF/2SwYni+VPD1wq1IRK2srOvTuwDH/wp1QGhMRQ8OWDdFoNWittDRq1dDspZE3gq6ZtOuterXjdAHt+svzx7Jo9HyTdn3lW1/wdpvXmNhuLOvmruHQ7wfY8MlPZukAqO9egVsJdwlPukumTs+u4HA6eJlufXG3L8XxUEObfSMulQydDofSNnTwcmNXcDgZWTrCk+5yK+EuDdwfvuT1YdwOuo6zpxuOxvrh3as1F/I8Xzzqe9Jv3hh+GPMZd3LVD6ERlK5gWCniXrcqHnWrcuWQeQfwWEr9sGSkXpToX6E0SrldSllbSllTSjnX6DZDSrnZ+FlKKScZ+2wNc606fSj/6583SQXKAXFAALBcCOElpbxmPBm2spTyShHi2wWME0Lsk1JmCiFqY9iL+SAdAIz7IoONv/lig2Em9QCGI3s3CSEWSyljhBCOQDkp5c18KRnsv8NwtG/e2VaAvcAfxrjihRCOxlnN3RhGCD41avE2zrAWGZ1Ox1tvf8j2bevQajSsXrOB4OArzPzoXU6eCmLrVn++/2E9a1Yv5VLwYRITkxg81HD6XnDwFX79dQvng/aTpdPx5lvTzDrZVOmwZB16lkz/kk9/XoBGo2HHhp2EXrnJqHdHcDnoCkf9j1GncR3mfDuTsuXL0rpra0ZOGsGozoafz3CrXBEXDxeCjhXvpDedTsf09+fx46+r0Gq1bPj5D65cus6kKeM5f+YC/jsPsOGn3/li1Xz+OrmNpMRkJowxnIy45rtf+HzZHPYc/QMhBBvX/cml4KI0CTnodXpWz/iGyWs/MhyDv3Ev4Vdv03/SIG6cu8bpPYEMmToCu9J2vLniPQDiI2L5fMx8PLwqM/TDkUgpEUKw7es/uX3ZvBdWqdOzc8ZqBq/9AKHVELTxILFXw+kwqR+R50K4suc0PiP8qNGuAbpMHWkpd9k8yfDTI1V86jDw9V7oMnVIqWfHhz9wP9H8w7Lf+2gBgWfOkZSUQue+Q3l99DCysgyj2y+90IP2rZtz6Fggzw94mVJ2dsyeOhGA8vbleG3kIAaOMewIGDtqsHknzgLodKQuWYLDp5+CRkPajh3oQkMpM2oUWZcvk370KBknTmDj44PT6tVIvZ7UVasMs2nAnbVrcfzqK8PnNWuQqfnG7QqF1Ok5OH0NvX96H41WQ/CGgyRcCaflO/2IORdCiP9pGo30o0q7+uizdKQn32XPREO66cn3OPvNDgZsnQVIQvcFEbrPrGbdYnSg05OyaCmOixaCRsP9bTvICgml7OhRZF66TPqRo5Tp/6LhgCCdDn1KCklzFxjCWmlxWr7E8H3u3SNp1lwwd+msTk/s3OV4fDMPodGQ8sduMq7dxHHCcNIuXOHe/gAqDOtD6Y6tIUuHLjmV6KmGE0VtalTFZeaboJegESR+syHfabVF0jFnBZW/nQsaDSm/G3Q4vTGMtL+vcnd/AA5D+1CmU6tsHVFTPn98vEVEr9Ozfsb3vLF2GhqthqMb9xN5NYyeEwdw6/x1zu05Rb8pQ7EtbccrKwy7jxLD41j5ykIAHCu74ODuzNWA4EclUygdy6avYN5Pc9FoNezasJubV24y/J1hXDl3lQD/AGo3rs1H30ynXPlytOrSkmGThvFql9c4tO0w3m28+dp/FVJKTh48RcCe42brWDPjW95fOwONVsNBY7veb9JAQs5d5/SeQAZNHW5s1w1LmeMj4lg0Zn6xvn9BWGk0TPZrxLgNx9BLSZ9GVfFysWfFXxd5xr0CvrXcmdSpPrN2BPFz4HUQ8HGPpggh8HKxp2s9D178dh9ajWCKX6MinzgLhvz4Y8ZqXlk7BaHVELjxANFXw+g2sT+3z4cQvOcUPacMxra0HcNWGNrvpPB4fnjlM7TWVoz/r2EnWtqd+6ybuBy9mfetpdQPheUhzN8Plici48+bCCF8gXellD2N7suAk1LK1UKIN4DxQKRxn2Yn4BPgwTGLH0opNwshQgGfB8tQhRAHjHGeNK4JPiml9BRCaIA5QC8MM4qxQF/gHoalrs7AaiAeeNdoowG2AR9IKaUQ4iUMPz6qwXAS7XgpZYAo4OdajN9lJOD64CdacmsVQowA3sMw63pGSjnSqHc5UA9Dx/4vKeXYR+WllU2lJ1Moin8d7VzrlbQEAG7cM29575OmXTmvkpYAQL2iHab91Jh8anZJSwAgof/LJS0BgI3Xn94y438i/auZN9v5pLmbYFPSEgDQW8jPDSy+b/94o/8B13XmHbz2pHHVWEZ7+s1My3i+TP/YMmb3zuks5yTY3bd3WsbNWwQi23Us0Xd798P7SyTPntiM5oNOmZTyAIbZvwfuE3J9/hL4Mtf1PgyH+OSNyzPPtW+uz3EY92ga94BONf7lpXOe67UP0b2BAmYo83YyjW4TMMxOFqhVSrkGWJPHPw7D4UEKhUKhUCgUCoXi/xmFPJDnX8f/+nc0FQqFQqFQKBQKhULxL0d1NBUKhUKhUCgUCoVC8UT5Xx8GpFAoFAqFQqFQKBT/b5AWsif8f42a0VQoFAqFQqFQKBQKxRNFzWgqFAqFQqFQKBQKxVNCHQakUCgUCoVCoVAoFArFE0B1NBUKhUKhUCgUCoVC8URRS2cVCoVCoVAoFAqF4ikh9eowIIVCoVAoFAqFQqFQKIqNmtFUKBQKhUKhUCgUiqeElCWtoGRQHU2F4h/EyYTrJS0BgLZOdUtaAgAbI0+UtASLomn9aSUtAQC/C9+XtAQAnBtNL2kJFoXW1jKOPQxMci5pCRZFtHVqSUsAIE2fWdISAEjX6EpaAgC3Pjlf0hIAiJB2JS0BgH3RlpEfin8WaumsQqFQKBQKhUKhUCieKGpGU6FQKBQKhUKhUCieEuowIIVCoVAoFAqFQqFQKJ4AakZToVAoFAqFQqFQKJ4SakZToVAoFAqFQqFQKBSKJ4DqaCoUCoVCoVAoFAqF4omils4qFAqFQqFQKBQKxVPi/+vvaKoZTYVCoVAoFAqFQqFQPFHUjKZCoVAoFAqFQqFQPCXUYUAKhUKhUCgUCoVCoVA8AVRH8x9GNz9fLvz9F5eCD/P+e+Pz+dvY2LDu55VcCj7M0cNbqFatcrbfB+9P4FLwYS78/Rd+XTsoHf9CHV26tuf02b0End/PpHfGFqhjzdovCTq/n/0H/6Bq1UoANPNpzNGAbRwN2MaxgO306u1XLB0+vs349sA3/HDoOwa8/p98/g1aNmDZ9i/ZHrKVdt3bmfiNnvIyX+1ZyVd7VtKhV/ti6bCUcrEUHS4dG9PhyOf4Biym5hu9H2rn1rMFPaJ/oXzjGibudpWc6HbjB2qM61EsHR/OW0T7HgPpOzR/HQWQUjJv8UqeH/AyLwwfR/Dla9l+m7b70/2l0XR/aTSbtvsXS4e7byN6HPqUnkc+p96EXg+1q9KjBYMifsaxUXUAbBzK0um/0+h/9TuazR1RLA2WpMOmeQscV/+I49qfKT1wcIE2th064vj9Ghy/W4391OnZ7uXnL8R501bKz51fbB2Wkh+WoqNJh6Z8uW8Fyw9+xQvj+uXz7zWmD0v2LGPRzqXMXDcbl0ou2X7T18zkx3PrmPr99HzhikoL3+b89Ndq1h1ey5DxA/P5N27ZkG93rmLfzd106GHadrt6uPL5uk/48cD3rN3/PW6VK5qtw7tDE5bsW8GXB1fRt4D86DmmN4v3LOOznUuYsW4Wzsb88HymOnP/+IRF/l/y2c4ltOnZLl/YolC6XTOq7/iG6ru+w/GV/M85+xe6UPPoeqr9sYxqfyyjfP9uJv6aMqWpcfBHXKePK5aORh2a8Nm+ZSw6uIJe417M5999TG8W7lnKgp2Lmbru4+z8cK7kwtytnzFv+yIW+i+h85Bu+cIWFUt51iksh6fW0RRCVBZCbBJCXBVCXBdCLBFC2DwmTKgQwtn4+Wgx0m4lhDguhDgrhLgohJhpblyFTM9HCLHU+NlXCNHmaaSj0WhYumQuPXsNpWHjjrz0Ul/q1atlYvPyqEEkJiZT95l2fLH0G+bPmwZAvXq1GDCgD428O9Gj5xC+XDoPjca84lc6LFfHosWzeLHvSHya+vGf//Smbl0vE5sRIweQlJRM44YdWf7ld8yeMxmA4AuXebZtb9q06kHfviNYunQuWq3WbB3j54znw+HTeaXTa3Ts40vVWlVNbGLDY/h80ufs/3O/iXuLTs3xalCTcd3G82avt+k/th+ly5Y2W4ellIsl6EAjqL9gFCcGf8LBZ9/F44U2lK1dKZ+ZtowdnmOeI/HU1Xx+z8waRuzes+aln4u+3buyatGch/ofOhbIrbAItm/4jpnvv8nsz5YBkJySysof1vHLN1/wyzdfsPKHdSSnpJqlQWgEzeaN5MCQhWz3fZ9qfVpjXyt/fliVsaP26G7Encrp7OrSMjn36X85O2udWWlbog40Gsq9+TZJU94n4eUR2HbqjLZaNRMTbaVKlB40hMQ3x5MweiSpK77M9ru3cT0pC+YVW4al5Iel6NBoNLwy+zXmjPiYt7qM59ne7alcq4qJTciFG7zXcxKTnnuTY9uPMnzKyGy/P7/+nSUTFz8RHRPnvsl7Q6cwvOPLdO7biWq1TOtHdHgM8yYuZM+fe/OFn7bkA35ZuZFhvi/zWo/XSYxLMlvH6NmvMXfEx0zsMoG2vZ8tID9C+KDnJN597i0Cth9lmDE/0u+n8+XEL5jU9Q3mDv+YkR+NprR9GbN0oNFQccZ4wl6ZTkjP1yjXwxebmlXzmaXuOMjNFyZw84UJJP+6y8TP+a1h3A88b176RoRGw6jZr7JwxGze6/ImbXq3o1KtyiY2oRdu8GHPd5n83ERObD/KoCnDAUiMSeSjFycztfskpvf5gN7jXqSCq4PZWizmWWehSClK9K+keCqlKIQQwO/An1LKWkBtoCwwt7BxSCmL01lbA7wqpfQGGgAbixHXIxFCWEkpT0op3zQ6+QJPpaPZonkTrl8PJSTkFpmZmWzcuInevUxHoHr38uPHH/8LwG+/baNTx3ZG925s3LiJjIwMQkNvc/16KC2aN1E6/kU6fHwac+P6TUJDb5OZmcmvv26hR8+uJjY9enTl559+A+CPP3bg62uoqvfvp6HT6QCws7Ut1ulodbxrExEaQdStKLIysziw+SCt/VqZ2ESHxRByKRR9noSq1qrKuePn0ev0pN9P50ZwCD6+zczSYSnlYik6KjT14l5IFPdvxiAzdUT8eYyKz/nks6szeQA3lm9Bn5Zp4l7xeR/u3Ywh9XKYWennxse7IeXtyz3Uf//hAHo/1xkhBI0b1CM19Q6xcQkcOX6K1s2bUN6+HOXty9G6eROOHD9llgbHJjW5ExrN3Vux6DN13NoUQOVu+etao/f7c3HFVnTpGdluuvvpxJ24gi49M5/9P1WHVd16ZIWHo4+MhKws0vfvw7aN6YyPXY9e3N/8B/LOHQBkUk5nIfPMaeS9e8XWYSkPJUpTAAAgAElEQVT5YSk6vLxrERkaSfTtaLIyszi85RAturY0sfn72Hky0gzpXzlzGSd352y/80fOcf/u/WLrqNekLuGh4UTeiiQrM4u9m/bTrpvpq05UWDQ3Lt5A6k3b9Wq1qqG10nLykOFevX8vjfS0dLN0eHnXIio0ihhjfhzZcgifri1MbC7kyQ9HdycAIkMiiAqNBCAxJoHkuGTsHe3N0mHXqDaZtyLIDIuCzCxStx+kbOdWjw9oxLa+F1onB+4eOW1W+g/w8q5FdGgkMbej0WVmcWzLYZrlyY/gY39n58fVM1ey80OXmUVWRhYA1jbWCE3xOiOW8qxTWBZPa7igE5AmpfwBQEqpAyYCLwshXhdC/C6E2Gmc7VxYUARCiDvG/75CiANCiF+FEJeEED8bO7IIIZoJIQ4KIU4JIXYJIdyNwV2ByAdpSymDjfZlhBDfCyEChRBnhBB9jO5aIcRnQojzQohzQog3jO65Z1h9hBAHjJ9nCiG+FkLsBtYaNW4VQngCY4GJxtnUZ4UQIUIIa2M4e2Oc1uZkqkclN26HRWRfh4VH4uHh9lAbnU5HcnIKTk4OeHgUELaSaVil4x+uw8ONsPDI7Ovw8Kj8OjwqZtvodDqSU1JxcjKMYPo09ybw5C6OB+7krbemZXc8i4qTmzOxEbHZ13GRcTi7ORUq7I2LITT39cHWzhZ7B3sat26Ei4fL4wMWgMWUi4XosHNz4H5EfPZ1WkQ8dm6mo9f2DTyx83Akxv+Mibu2tC01J/Ti6me/mZV2UYmOjcfNNedluaKrM9GxcUTHxuHmmlMfKroY3M2htJsj93Llx73IBEq5m+aHQ4NqlPZwImLPmbzBnxiWokPr7Iw+Nib7Wh8bi8bZ2cTGqnJltJWrUGHJMhy+XIFN8xZ5oyk2lpIflqLDyc2J+MicOh7/f+ydd3wURf/H33N3CQFSID0hVCNFOtJrAAEFKf7koTfFAggqRRAVUERAREVAihVQaYrSWygJLfQqvQVIcmkEUoCQ3N38/rgjySUBwhJMfJ55v173SnbmO7uf/e7s7E5dYzzuDyhPW3dvw+EQbY0vD8LT15PYLOV6nDEOL1/PB6TIpHSFAFKSbjHp+4/5YdM8Bn/0hubeKvds/kgwXsfjIf44kos/Ams+jcHRQMyVaE06DD6epBsz/WGKjsfgk1OHS5umlFs1B/9vPsRwz19C4D3mdeK++EHTsbNS0tc9hz8elD9adn+OYyGZlVt3Pw+mbvyaWXu/Z828v7gZe0OzlsLyrFMULp5URbMqYHdnSymTgKtYV7qtBXQHqgPdhRClc+zBntrAu8AzQAWgia2yNgvoKqV8FviJzB7Tr4GzQoi/hBBvCiGcbOEfAtuklPWAlsAXQojiwBtAeaC2lLIG8FsezvFZoLOUMmMii5QyHJgHfC2lrCWl3AmEAPcmNPUAVkgpNTVz2urXdshsPUK52+QtrdLxv6rDanPwwFHq1W1Hi2adGTlqCEWKPHCk+wN05AzL6ykd3nGYA9sP8vXKLxk7ewynD5/BbNJW4f33X5f81ZHrhckW/8zEvpz++NccURXf68rl+Rsw39bWC/Go5HaOQohc81FuPsoTuSXLegAhqP1xH458kpfHwWNQWHTkJiS7v/V6DKUCuDniHRI/m4jLyPcQxZ2fuAx1XR6gIwvNXwoisHogK+f/mf8qcpWRt7JIb9BTo341vv10Pm+2H4J/GT9e6Pb48wEfpqPZSy2oUD2Q1fP/sgsv4V2SYV8PZ86omdrL01yF2G+mbN/HpdYDCO88hFt7juA7daT1+L1e5FboAUzR2hrJsiJyyR/3O6cmL7WgfPWnWDt/ZUZYgvE67z8/nOHNB9P85Za4erpp11JYnnWFFGkp2F9B8aQqmoKcj6is4VullIlSylTgFFA2F9us7JdSRkgpLcBRoBxQCeuw2GAhxFHgIyAAQEo5EagLbAZ6ARtt+2kLvG+zDwGcgDLAc8A8KaXJlj4hD+e4WkqZl/EoPwCv2P5/Bfg5NyMhxBtCiINCiIMWy61cdxQZYaR0gH/GdkApP4zGmPva6PV63NxcSUi4QWRkLmmj7NPmFaWjkOqINBJQyi9ju1Qp35w6IqMzbPR6PW6uLiQk2M+VOXv2Irdv3eaZqpU06Yg3xtv1Qnr6eXI95voDUtizZNZShjw/lLG9P0QIiLwc9fBEuVBorksh0ZFqTKCof2ZLt5O/B6nRma3XBmcnXCqXpuGf42l5YCYlng2k7qJRuNWsQIk6gVQe14uWB2ZS/o0XeOqdLpR99fEWjHoQvt6eRMdmvoTFxMbj7elhC8/sRYiJs4Zr4bYxgWJZ/FHMz5070Zn3goOzEyUql6bVio/ouG8GnnUCabZgZMaCL/lFYdFhjo9D5+Wdsa3z8sJy3f5F2BIXx909u8BsxhIdjfnaNfQBAdl39VgUFn8UFh3Xo+PthsJ6+HmSEJPzFaVGk5p0Hfofprw2KWM4ZH4SZ4zHO0u57uXnRXwey/U4Yxzn/76A8aoRs9nCzk27qVj96YcnzIWE6Ot2/nD388jVH9Wb1OT/hv6Hz1/7zM4fRZ2LMvbncSyZ/ivnj5zTpAHAFBOPg1+mPwy+nphi7f1huZmMTLf2KyT+vhGnqtZzLlqrCiV6d6TC1gV4jX4N187P4TniFbSQmz9u5OKPak1q0GVoV758bUqu+eNm7A0izl2lcv1nNOmAwvOsUxQunlRF8yTWil4GQghXoDRgBrI2i5t5+Pc8c7MXwElbz2EtKWV1KWXGm4+U8qKUci7QGqgphPCwpXk5S5oyUsrT3L9ibCLTR07Z4nKvDWZDSrkbKCeEaAHopZR/38fuOyllXSllXZ0u98npBw4eJTCwPOXKlcbBwYFu3TqzZu1mO5s1azfTt6919bOXX+7A9pDdGeHdunXG0dGRcuVKExhYnv0HtA33UToKp45Dh47zVGA5ypYNwMHBga5dO7J+3RY7m/Xrt9C7j3WVvpdeeoHQ0DAAypYNyFj8p3TpUjxdsQJXr2ibi3f22DlKlfPHp7QPBgcDQZ1asDd4b57S6nQ6XEpY5+6Vr1yO8lXKc2iHtmFgheW6FBYdiUcuUryCL0XLeCEc9Ph3aUTMpkzfmpLvEPzMG2yv9zbb673NzUMXONhvOonHLhHW+ZOM8MvfbeDiNyu58tPmBxzt8Qhq2pDVG7cipeTY36dxdi6Ol6c7TRo8y579h0lMSiYxKZk9+w/TpIG2ObwJRy/hUt6X4qW90DnoKdO5IRGbM/2RnnyHP6sNYk2Dd1nT4F3iD19g54AvSTh+Ob9Os1DpMJ05g6FUADpfXzAYKNKyFXf37Lazubt7Fw61rPOmhKsb+oDSmI3aGoLuR2HxR2HRceHYefzK++NtK0+bdmzGgeB9djblq1Zg0JQhTBk4icTrifl6/HucOXqGgPKl8Cvti8HBQOvOLdm9OW9rNp45ehaXEi64uVt7y+o0qU34uSuadFj94Yd3aW8MDgaadGzGweD9djblqpbnjSmD+XzgZyRl8YfBwcB7340ldMV29q7XvN4kAKknzuFQ1h+HUj7gYMClfQtSttk/5/RemUOtnVs1JO3iNQCM703jUqv+XGo9gLhpP5C0agvxX+XaB/FQLh47j295P7xKe6N3MNCoY1MOBR+wsylbtTwDpwzmy4GT7fzh7uuBg23kUnHX4lSsWwXjxUhNOqDwPOsKKxYpCvRXUDysgqeVrcBUIUQ/KeUiIYQe+BJYADz+agFWzgJeQohGUsow21DailLKk0KIDsB6ae13fxpr5fQmsAkYJoQYJqWUQojaUsojWHs+BwkhQqSUJiGEu61XMxzrENkNQM41tHMnGcg+u3wRsAT49HFO2Gw28867H7F+3WL0Oh0LFi7j1KlzfDxhFAcPHWPt2mB++nkpCxfM5MypXdy4cZNefYYAcOrUOf74Yw0njm3HZDbz9jsfYrFo60tXOgqvjpEjJrBy9SL0eh2/LPqd06fP89G44Rw+fIL167awcMEyfvjxa46d2M6NG4kM6DcMgEaN6zFy5CDSTSYsFgvD3x3H9eva5mpYzBa+HTeXyb9OQqfXs3nZZq6cu0q/kX05d/wce4P3UbFmRcZ/Pw4XN2caPteAfiP68MZzg9A76PlyxXQAbqfc5vO3v8Bi/vdfl8KgQ5ot/D12AfWXjkXodUQsCSHlbAQVR3fl5rHLxG7K/3ld9+O9CVM5cOQ4N28m0bpLH4YM7IvJZG1l7/5SB5o3qsfOsAO80O1Vijo58ekHwwFwc3XhzQE96fHaOwAMeqXXAxcVehDSbOHghwsIWjwGoddxaWkoSeciqf7eyyQcu0zk5gcv0tFx3wwcnIuiczQQ0K4u23tOJen8o7+kFRYdWMwkz5pBic+nI3Q67mxYj/lKOMUHvEr62TOkhe0h7cB+HOvWw/2nhWC2kPLdXGRSEgAlZszCULoMomhRPJb+TvL0aaQdPPCQgxZefxQWHRazhR/Gz2f8oo/R6XVsXb6Fa+ev0WNELy4ev8CBLfvp98EAnIoVZdScMQDER8Ux5TXrTKJJv0+h1FMBOBV34vu9P/Ht6Fkc3fHoL/Bms4UZH81i+uLP0el0rF+2gfBzV3h11ADOHjvL7uAwKtesxKQfP8HFzZnGbRrx6sj+9G81EIvFwpyJ85mxbDpCwNkT51mzeN0ja7jnjx/Hf8eHNn9sX76ViPPX6G7zx8Et++n7wSs4FSvKyDmjbf6I5/PXPqPRi02oUr8qLiVcaNm1FQDfjppJ+CkNjQNmC7GfziXgx0mg05O4YjNpF67iMawvqX+f49b2fZTs2xnnlg2RZjOWxGSix36p6ZwfhMVsYcH473l/0QR0eh0hy7cSef4aXUf05NLxCxzecoDeH/THqZgTb895D4DrUXF8+doU/AMD6PPRAKSUCCFY991Krp29qllLYXnWKQoX4kmNgbbNu5wDVMbaK7geGAX0BOpKKYfa7NYC06WUIUKIcFtcvBAiRUrpLIQIAkZJKV+02c8GDkopFwghagEzATesleYZUsrvhRBLgTpYK7Um4EMp5SYhRFFgBtZVYQUQLqV8UQhhAKYBzwPpwPdSytlCiGbAj0AMsM+mLcj2uZQUKeV0m6YMjUKIisAfgAUYJqXcKYTwBS4DflLKh67pbXAs9d81MF2RbzgZtM2bzG+aeFQuaAkAbI05XtASChWrSj7ed0fzi7Yn87zA+BPljxqP/+3A/yZaV3n8FYPzg62n83e47b+d3x20faInv4k35Wmg1hPHx5DP8341Mqlo2sON/gE+uZN9QF3BsNy4/+FG/xCmtMiC66LTyLkqzxfou33F0xsLxGdPqkcTKeU1ILcvHC+w/e7ZvZjl/3JZ/ne2/Q3BOp/yXvjQLP8fBXK8WUkpc35J2Bp+B3gzl3ATMML2yxq+E+unWbLbf5xtO0OjlPIcUCNbkqbAH3mpZCoUCoVCoVAoFIr/HgryW5YFyROraCqsCCFmAS8A7Qtai0KhUCgUCoVCoVD8E6iK5hNGSjmsoDUoFAqFQqFQKBSKgkFa/jd7NJ/UqrMKhUKhUCgUCoVCofgfRVU0FQqFQqFQKBQKhUKRr6ihswqFQqFQKBQKhULxhHhCH/ko9KgeTYVCoVAoFAqFQqFQ5CuqR1OhUCgUCoVCoVAonhBqMSCFQqFQKBQKhUKhUCjyAVXRVCgUCoVCoVAoFApFvqKGzioUCoVCoVAoFArFE8Ii/zeHzqqKpkKRB1r71ChoCQBcuRtf0BIAqKB3KWgJAGwtaAE2WnhXLWgJAFwS+oKWAEBU2zcKWgIAXY9/V9ASAIh+4fWClgDA7euOBS0BgGfdrhe0BABkIXnx25pavKAlAHDZXDiuSzMHn4KWAEDaXUtBSwCgvCha0BIAaOxVuaAlKP6FqIqmQqFQKBQKhUKhUDwhCkvD1j+NmqOpUCgUCoVCoVAoFIp8RVU0FQqFQqFQKBQKhUKRr6ihswqFQqFQKBQKhULxhJCyoBUUDKpHU6FQKBQKhUKhUCgU+Yrq0VQoFAqFQqFQKBSKJ8T/6udNVI+mQqFQKBQKhUKhUCjyFVXRVCgUCoVCoVAoFApFvqKGzioUCoVCoVAoFArFE0J9R1OhUCgUCoVCoVAoFIp8QFU0/2W0axvEyb93cObULka/91aOeEdHRxb/Npczp3axZ9caypYNyIgbM3ooZ07t4uTfO2jbpoXSkY866gY9yw8h3/Pzzh/pNuQ/OeKrNajG7PWzWH95LU3bN7WLGzj2VeZvmcv8LXNp0bH5Y+lo2rIRG/b8waZ9f/L6sP45dTaszYotv/B3VBjtXmxlF9elewc27l3Bxr0r6NK9w2PpeKZFTT7eOoNPQmbSdnDnHPGtB3ZgfPBXfLjhC975bRzupTwBqNioKh+sn5bxm3n2V2q2radZR2HJH/WC6rIg9EcW7fqZHm91zxFfvUF15m34ls3hG2jeoVlGeK3GNZm/aW7Gb8OFtTRp11izjjJBNegT8gV9d37Js0M65oiv1qcVPYOn0GPjZ7y8Yhwln/bPiPOoXJquKyfQa8tUegZPQV/EQbMOp8b18P/zZ/xXLcR1QI8c8cU7tiVg6x/4LZmH35J5OHd5AYAidWtmhPktmUeZsPUUDdLuj48mf0XzDj3o0mdQrvFSSiZ/PZcXur3KS/0Gc+rshYy4VeuDad99IO27D2TV+mDNGgCcGtXD948F+P65CJf+Of1R7MV2+G9egc9v8/H5bT7FO7fPiHMb9ga+y37Ed/lPlBiZM48/CkUb16XUyp8otXoBbq/kzKfOndpSetvv+C+bh/+yeTi/ZL0uTnVrZoT5L5tH2X3rKNZS+3Up1vRZyq3/gXIbf6Lka91yxLt2aUOF3Usp8+e3lPnzW1y7Pm8XrytejAohv+L90RDNGu7pKL/he8pv+hH313OW664vPcdTe5ZS9q/ZlP1rNm5d2+XUEfoL3uMGP5aOqi1qMWnrN0wOmcULg7vkiG8z8EUmBn/Nxxu+ZORvEzLKUwB3f0+GLxrHp1tmMDH4azwCvDTraNyyAX/tWsKqsGW8MrRPjvg6DWuyePNPHIgI5bkXgzLC/QJ8+G3TjyzdsoA/Qn+la7+c5/AoPN2iBu9unc6IkK9oPjhnOVa/d2uGbZzK0PWTef33CXgFlgKgaAlnBi75kPEnf6LjJwMeSwOAc/M6VNw6l4rb5+M1qGuO+BIvt6bKwV8JXPcNgeu+oWT3tgA4VSnPUyu+4OlN3xK4YSZuHZrmSPsoFBZ/1A+qx687FrB41yJ6v5WzHKvZoDo/bJzHtiubadHB/l3H29+bLxd/zi8hP7Fo+0/4Bvg8th5F4eAfGzorhAgAvgWewVrBXQu8J6VMe0CacKCulDJeCLFHSqnpySWEWAC0ABIBJ2CJlPITDfsJAkZJKV/UouNx0el0zPzmM55v35OICCN7w9azZu1mTp8+n2Hz6is9uXEjkcrPNKVbt05MmfwhvXoPpkqVp+nWrTM1arXC39+HTRuWUqVqMywWi9KRDzremvQWY3t9QLwxnllrv2Fv8D6unr+aYRMXGcuXI76k65sv26Wt36oegdWeYnC7t3BwdGD6H9M4sP0gt1Nua9Ix/vPRvPqfocRExfD75oVs27SDi+cuZ9gYI6MZ+/YnvDrE/iXBrYQrb416na5t+iGlZMWWX9i2cQdJicmPrEPoBD0mDmRmn0nciL7O+6uncDz4INEXIjNsrp0KZ0rH90lPTaN5nza8NLYPPw6dwbmwk0xuPxqAYm7FmRg6i1M7jj2yhnv+KCz54+1JQxnd633ijPHMWTeLsM1hXMmSP2IjY5k2Yjr/edP+ZeXonmO82c76kupSwoVFu37mYOghTf4QOkHQpP6s7DWVFGMC3ddO5FLwIW6cj8qwObsyjL9/3QZA+TZ1aDa+D6v7TkPodbSdOZjgd+YRf/oqTiWcsaSbNOlAp8N9zDBih4zBFBOH36/fcid0D+mXr9qZ3docwo3PZ9uF3T14DGNPa6VQ5+qC/6qFpO7V5g+ALu3b0OvlTnzw6fRc43eGHeBqRBTrl/3I8ZNn+HT6bJZ8P4PEpGTm/ryYZT/OBKD7wLcJatoQN1eXRxeh01Fy9NvEDh2NOSYOn4VzuLMjDNPlK3Zmt4NDuPnFLLswxxrPUKRmVaJ7vg6A9/ffUKROTe4e1nDP6HS4jx1GzKAxmGLi8f9tNrdDw0i/lP26hJIw1f66pB48RlT3zOtSas0C7oRpvC46Hd7j3iJy4Aekx8RTdvlMbm3fS9pFex0pG3YQO2lOrrvweLsftw+c0Hb8LDp8xr9FxKs2Hb9/Q8q2fTl0JG8IJfbTubnuwvOdvtx5TB1Cp6P3xNf4qs9EbkQn8NHqqRwNPojxQkSGzdVTl5nUcQxpqWkE9WnLf8b2Zf7QrwEY+NUw1s1ewaldxylSzAmpoQwDazn2/pSRDO72LjHGWH7b+AOhm3dx6Vx4ho0xMoYJ73xGvyE97dLGxVxnQMdBpKelU7RYUf4I/YXQTbuIi4nX4A9Bx4mv8HOfKSRFX2fw6kmcDj5MXJbny7FVe9j/21YAKj9Xh/bj+rCw/+eY7qaz5cs/8KkUgE/F0pr8kIFOh//EQVzuOw5T9HWeWvUVSVv2cffCNTuzxHU7iZow3y7MknqXayO/Ii3ciMHbncA1X5O84wiW5FuPLKOw+EOn0zH8s7cZ0XM0ccY4vls/h12bw7hyPrMci4mMZfLwafQYlLPR5sNvxvDLzMUc3HmIosWcsFj++z46qb6j+QQRQgjgT2CllPJpoCLgDHyW131orWRm4T0pZS2gFtBfCFH+Mff3j1O/Xm0uXgzn8uWrpKens3z5Kjp1tG9B7dSxLb/88jsAK1aso1XLprbwdixfvoq0tDTCw69x8WI49evVVjryQUelWhWJCo8i+mo0pnQTIatDadS2oZ1NTEQsl8+EY8lW0pR5ugzH953AYrZw985dLp26TN2gZzXpqFGnKlcvXyPiSiTp6SbW/xVM6+fte+Iirxk5d+oCMlsh3rRlQ/aE7iPxZhJJicnsCd1Hs1aNNOkoVyuQuCvRxF+LxZxu5uCaPTl6Jc+FnSQ91drGdOnIeUr6uufYT532DTkZciTD7lEpLPmjcq1KRIZHYbTlj+2rQmnc1r44i4mI4dLpyzmuS1aad2jG/u0HuZt6V5MOn1pPcTM8hqSrcVjSzZxbvZcKbe3zWnrKnYz/DcWKZDwZyzSvTvzpa8Sftr5kp95MeaDWB+FYrRKmiChMkUYwmbi1KYSiQU0eeT/FnmtO6u4DSI3+AKhbq/oDK4fbd+2l0/OtEUJQs1oVkpNTiItPYPe+QzSqVxs3VxfcXF1oVK82u/dpq1g5Vq1M+rVIzDZ/3A7eTtEWeXzcSRCOjggHA8LBAWHQY064oUlHkWqVMF2LwhQZnXFdimnoLS7Wphl3HuO6ONWoRPpVI+kR0ZBuIml9KMUfoSwq8kwges8S3N59WNPxM3VUJP1qVIaO5PWhOLdu+PCE93RUDUTvUZJbj6mjfK1AYjPKUxP71+ymVrby9GzYSdJs5eTFI+cp6esBgF9gADq9jlO7jgNw93Zqht2jUq12Fa5djiDyahSmdBObVm4lqF0zOxvjtWjOn76Yo5JgSjeRnpYOgGMRB6yvhNoIqBVIwpUYbtieL8fXhFElWzl2N0s55pilHEu/c5crB8+Sfjdd8/HvUazm06RdMZJ+LQaZbiJxzQ5c2zTIU9q0y1GkhRsBMMUmYLqeiMHDVZOOwuKPKrUrExkeifGqEVO6ia2rttM02+ib6IgYLp2+lOPZUfbpsugNeg7utJahd26nan7OKQof/9TQ2VZAqpTyZwAppRkYDrwqhBgihPhTCLFRCHFeCDEttx0IIVJsf4OEECFCiD+EEGeEEL/ZKrIIIZ4VQoQKIQ4JITYJIfxy2ZWT7e8tW5rxQogDQoi/hRDfZdlXoBBiixDimBDisBDiqWx66gkhjgghKgghPhZCjMoS97cQopztd0YIsVAIcdymuZhWJ/qX8uVaRGbvQ0SkEX9/3/vamM1mEhOT8PAoib9/LmlL2adVOrTp8PD1JC4qLmM73hiPp+1B/zAunb5MvaC6FHEqgmtJV2o2qoGXv7ahTT6+XhgjYzK2o40x+PjlbV8+ft72aaNi8fHz1qSjhI87N6KuZ2zfMF6nhE/OiuQ9mnRrxcmQoznC63ZswoHVuzVpgMKTPzz9PIkzZuaPuOg4PP3ylj+y0rJTENtXbtekAaC4b0lSohIytlOMCTj7lsxhV73/c/Tb9SVNPuhB6PhFAJSo4AtS0unX0XRfP4k6g7QPrTZ4eWKKjs3YNsfGoffO6Y9irZrht+w7PKeNR++TMx8XbxfErU3bNOvICzFx1/H1zhyG6OPtSUxcPDFx8fh6Z2ry8bKGa0Hv5Yk5JjN/mGPi0Ht55rAr1qoZPou/x2PqhAx/pJ04Reqho/hv+B3/jctJ3XsQU/jVHGnzpMPbE1N0pg5TTDx671x0tG6K//L5eH0x7v7XZYP2fGrw9sihw8EnZ/5wbtuUsivn4jfjQwy+Np1C4DXmDeK/+EHz8TN0+HiSnuW+NUXHY8hFh0ubppRbNQf/b+x1eI95nbh80FHSx50bUZl564bxOiUfUJ4269aKEyFHAPCp4MftpNsMmfce49d9QdexfRE6ba993n5exERl3rcxxli88vh8AfDx92bZtoVsOPQXC779TVNvJoCrT0kSszxfkowJuOXijwZ92zAi9Gvavd+LtR8v0nSsB2Hw9SDdmHkO6dHXccjlue/6fGMCN8ykzJz3cfDLeT8Vrfk0wsFA2pVoTToKiz88fT2JzfIeFGeMw8s35/nmRukKAaQk3WLS9x/zw6Z5DP7oDXQa82lhxiJFgf4Kin/qSlYF7Jp7pZRJwFWsw3drAd2B6kB3IcTD+vBrA+9iHYZbAWgihHP98HYAACAASURBVHAAZgFdpZTPAj9h32P6hRDiKBABLJVS3isxZ0sp60kpqwFFgXvDYn8DvpVS1gQaA8Z7OxJCNAbmAZ2llJceorUS8J2UsgaQBGieNJJbK6DM1kOWu03e0iodWnXkDMvrrg7vOMyB7Qf5euWXjJ09htOHz2A2mTXpyE1Ins8p13N4ctflHvW7NKNsjQoEf7faLtzVqwT+lcpoHjabVx3/RP7IjUfdl7u3O+Url+NA6EHNx7zfuWbnxMItLGo6kj1TllLvbetcKp1Bj1+9imweNocV/zeRCs/XJaBJVa1CcoZl03Fnx14iX+yDsfsbpO47jOfE0Xbxek93HALLcydMuz/yQm7XSQiRq98099LklizbAVJ3hhHVqTcxvV4ndf8h3CeMAcAQ4I9DuTJEdehOVPvuFKlbmyK1q2vU8fCC7HZoGBHt+xLV7U1S9x3B89P37OL1nu44Pu51ycO9lxKyl8ut+3Oly2Buhx3Bd4q1nbdEzxe5tWM/pmhtlZiHku26p2zfx6XWAwjvPIRbe47gO3WkVUevF7kVeiB/dDxCWdSwSzPK1niKTd+tAkCv1/N0vcos/2whkzqNwauMD026BuWbjkcZCxgTFUv3Vv3p3Kg7Hbu9gLtnzkauvMnImz/2/RLMVy2Gs2nqEoKGPd6c0PsIeaiO5K37OdtsIBdeeJuUXUcJmP6uXbzBqySlvxpBxHvfaB5XWVj8kXv2yNs56Q16atSvxrefzufN9kPwL+PHC93aPTyh4l/BP1XRFOQoou3Ct0opE6WUqcApoOxD9rdfShkhpbQAR4FyWCt01YBgW4XyIyAgS5p7Q2d9gda2yiJASyHEPiHECaw9r1WFEC5AKSnlXwBSylQp5b1Jc1WA74COUsq8NB1fk1Le65b5Fch11rcQ4g0hxEEhxEGLJfdx+pERRkoHZC7OEVDKD6Mx5r42er0eNzdXEhJuEBmZS9oo+7R5RemwJ94Yb9cL6ennyfWY6w9IYc+SWUsZ8vxQxvb+ECEg8nLUwxPlQowxFr9SmRPoff18iM3ji05MVLa0/t7EZulVeBRuRF+npH9my25JPw8SY3MO56vcpDrPD32Jua9Nw5RmP9/v2RcbcXTTfixaK90UsvyRpeXfy9eL69EJD0iRk6COzdm1cY/2RghsPZj+mS3dzn7u3Iq5/zDLc6v2UqHdsxlpo/adIfVGCqbUNK5sP4ZXtXKadJhi4zD4ZvaW6729MMfZ3y+WxCRItw7nSvlrPY6VK9rFF2vTgtvbd8Nj+CMv+Hp7Eh2beQ/FxMbj7elhC8+8P2LirOFaMMfG2/UM6n28MMff3x+3Vq7HscrTABQNakra36eRd1KRd1JJDduPY7VntOmIicPgm6nD4OOZy3VJztCR/Od6ilTJdl3aPv51McXE59BhirW/Xyw3k5E2HYm/b6RIVas/nGpVoUSvTpTfshCv0a/h0rk1niNe0azDIct9a/D1xBSbzR/ZdDjZdBStVYUSvTtSYesCvEa/hmvn5zTrsJanmT1DJf08uJlLeVqlSXU6DH2Z2a9NzShPb0Rf59qpcOKvxWIxWziyeT9lqlXQpCM2KhYf/8z71sfPmzgNFem4mHgunr1MnYY1NelIjE7ALcvzxdXPnaRc/HGPE2vCeKZNXU3HehAmY7xdD6WDrwemGPt8ar6ZjLRdi4SlmylaLTAjTudclHI/TSD6y1+5c/SsZh2FxR9xxni8s7wHefl5EZ/H96A4Yxzn/76A8aoRs9nCzk27qVj96XzXqCgY/qmK5knALmcLIVyB0oAZyDoY28zDFynKzV4AJ6WUtWy/6lLKttkTSilTgBCgqRDCCZiDtRe0OvA91qG1D2qaNgKpWHtV72HC3pdOWf7PXsHOtYlHSvmdlLKulLKuTlc81wMfOHiUwMDylCtXGgcHB7p168yatZvtbNas3UzfvtaJ1i+/3IHtIbszwrt164yjoyPlypUmMLA8+w8cecBp3h+lw56zx85Rqpw/PqV9MDgYCOrUgr3Be/OUVqfT4VLCOkesfOVylK9SnkM7tM31OnHkFGUrlKFUGX8cHAy0f6kN2zbtyFPaXdv30qRFA1zdXHB1c6FJiwbs2p63c8jOlWMX8S7nh0eAF3oHPXU7NuZ4sH0PR0DVcvSa/DpzX5tG8vWkHPuo16kJB9doHzYLhSd/nDl2llLlS+Fb2heDg4GWnVuwJzjskfbRsnNLtq/SPhwRIObYJUqU88W1tBc6Bz0VOzXkcrD9/DG3cpmNDeVa1+JmuHU419XQ43hULoPByRGh11GqQWVunI9EC2knz2IoXQqDvy8YDBRvF8Sd0D12NnrPzApx0RaNSM82HLT48624tfHJDpsFCGrakNUbtyKl5Njfp3F2Lo6XpztNGjzLnv2HSUxKJjEpmT37D9Okgba51WmnzuBQphR6mz+KtWnJnR32/tB5ZPFH80YZCyeZYmIpUqcG6HWg11OkTo0cvsord0+exVDG/rrcDrXPp1mvS7EWjXIs4OT8fMvHGjYLkHriLA5l/TGU8gEHA67tW3ArW1mk98rSYNKqIWm2BYuiR0/jcut+XH6uP3HTfiB51Vbiv/pZo45zOJT1x8Gmw6V9C1K2ZdeR2Svn3KohaRetC8EY35vGpVb9udR6AHHTfiBp1RbNOsKPXcCnnB+eAd7oHQzU79iEY8EH7GxKVy1P38lvMuu1qXbl6eVjFynmVhxnd+v8vyqNq2E8H4EWTh49Q5kKAfiX8cPgYKBdl9aEbN6Vp7Tefl4UcXIEwMXNhVr1qhN+QVs+jTx2EY9yvpS0PV9qdGzEmWD7Z6ZHucxpDpVa1eZ6uLZhqQ/i9vHzFCnnj0OAD8LBgFvH5iRt2W9nY8iSP1yfq89dW/4QDgbKzvuQG39uI2n94z3nCos/zhw9Q0D5UvjZnnOtO7dk9+Y9D08InDl6FpcSLri5uwFQp0ltws9deUiqfx9SigL9FRT/1KqzW4GpQoh+UspFQgg98CWwAHj05TVz5yzgJYRoJKUMsw2lrSilPJnVSAhhABpgHWZ7r0IYL4RwBroCf0gpk4QQEUKILlLKlUKIIoDeZnsTGAhsFkLcklKGAOHYhtwKIeoAWRcaKnNPE9ATyFvJnAtms5l33v2I9esWo9fpWLBwGadOnePjCaM4eOgYa9cG89PPS1m4YCZnTu3ixo2b9OpjHal76tQ5/vhjDSeObcdkNvP2Ox9qWkFT6ciJxWzh23FzmfzrJHR6PZuXbebKuav0G9mXc8fPsTd4HxVrVmT89+NwcXOm4XMN6DeiD288Nwi9g54vV1hXvLydcpvP3/4Ci1m7Pz59fxo/LpuJTq9nxeLVXDh7iWFj3uTvo6fZvmkH1Wo9w+wF03B1c6Vl26YMHf0mHZt3J/FmEnO++pHfNy8EYM6XP5J4M2cFMK/+WDr+J4Yt+hCdXsee5dsxno/gxeHduHriIse3HOLlsX0oUsyJ1+eMAOBGZDxzX7dOz3YP8KKknyfn957SdPys/igs+WPWuNl8/ttkdDodG5Zt4sq5KwwY1Y+zx84RFryXSjUr8skPE3B2c6FRm4b0H9GXga3fAMAnwAdvfy+OhR1/LH9Is4XQcQvp9Oto6wIhy0JJOBdJg5EvE3v8MpeDD1NjQFtKN62KxWTmbuIttgy3rpZ4N/E2R7/fQLe1EwFJ+LZjhG/LOa82T5gtJHw+C+9vp4JOR8rqjaRfuoLboP6knTrHnR1huPR4iaItGoHZjCUxmfgJmVP39X4+6H28uHvo8fwB8N6EqRw4cpybN5No3aUPQwb2xWSy9kB0f6kDzRvVY2fYAV7o9ipFnZz49IPhALi5uvDmgJ70eO0dAAa90kvbirMAZgs3ps3Ca+bnCL2OlNUbMF26guubA0g7fZbUe/5o3hhpMmNJSibhE6s/7mzdgVPd2vgu+QEkpIYdIHXnozViZNWRMHU2PnOnWK/Lqk2kX7xCicH9uXvqHHdCw3Dp2YViQY3AZMaclEz8+C8ykhv8fdD7epH6uNfFbCFu0hwCfvgMdDqS/txM2oUreAzrS+rf57m1fS8l+3SmeKuGVh2JyUSP/fLxjnkfHbGfziXgx0mg05O4YjNpF67adJzj1vZ9lOzbGeeWDZG2fPokdFjMFhaP/4F3F32ETq9j9/JtRJ2PoPPw7oSfuMixLQf5z9i+OBVzYtAc69DdhMh4Zr/+OdJi4ffPFjHqtwkg4Mrfl9ixdIsmHWazmc8/+Jo5S75Cp9ezaslaLp29zODRr3Hq6BlCN+/imVqV+eqnKbiWcKF5myYMeu81urboQ/mnyzHi46H35iWwaO4SLpx52Kyj+/tjzfgFDFj0PkKv4/DyEGLPR9J6eFciT1zizJbDNOzflqeaVMNiMnEn8RZ/jMxcFXjUrm8o4lwUvYOBKm2f5ee+U+1WaM27QyxETZhH+UWfgE7Hjd+3cPf8VbyH9+bOifMkb9mPx4COuD7XAGk2Y76ZTMSobwBw69CU4vWroi/pQsmurQGIGDWD1NOXH3TEQu0Ps9nCjI9mMX3x5+h0OtYv20D4uSu8OmoAZ4+dZXdwGJVrVmLSj5/g4uZM4zaNeHVkf/q3GojFYmHOxPnMWDYdIeDsifOsWbzukTUoCiciP+cdPfBA1nmXc4DKWHv/1gOjsFa+6koph9rs1gLTpZQh2T5vkiKldM7+iREhxGzgoJRygRCiFjATcMNaiZ4hpfw+2+dNHLFWfN+WUkohxCSgB9bK4jXgipTyYyHE08B8wBNIB/4DlLl3bCFEGWAD8CpwHFgFeAMHsA6PfcF26uuBHVjneZ4H+mYZhpsrBsdS/6OLIBdeWvvUKGgJAFy5+4TmHj0iLYsVjkWbv496vNbg/KKFt8b5ivlMF6FtAaf8ppNX/reYa8F/83cFLQGA6BdeL2gJAJjTC8cCG2l39Q83+gcoyFb+rHyRmvsopn+aQ6nGhxv9A7R3etjsqX+GHuLRP+/1JFgqNTZi5TM707VNH3kS7IjcWjhu3kdgn///Fei7fYOoPwvEZ//YdzSllNeAnF+StfZqLshi92KW/8tl+d/Z9jcE69DXe+FDs/x/FMjxxXsp5YAH6PoI63zO7OHnsc7ZzMqle8e2zc/M+naZY5iuEKIcYJFS5v41cIVCoVAoFAqFQqH4L6RwNG8qFAqFQqFQKBQKheK/hn+sR/N/ESllONaVcBUKhUKhUCgUCsX/IP+rc+JUj6ZCoVAoFAqFQqFQKPIV1aOpUCgUCoVCoVAoFE8ISyFZfOyfRvVoKhQKhUKhUCgUCoUiX1EVTYVCoVAoFAqFQqFQ5Ctq6KxCoVAoFAqFQqFQPCEKy3d7/2lUj6ZCoVAoFAqFQqFQKPIV1aOpUCgUCoVCoVAoFE8IS0ELKCBUj6ZCoVAoFAqFQqFQKPIV1aOpUOQBR6EvaAkAuBqKFbQEABxVG5UdQhSOuReWwiGj0BDT4bWClgCA74YfCloCANf/79WClgBA8SJpBS0BAFFI3oBKnC5R0BIA0IvCUa47FZK5bM5uqQUtAQB9omtBSwBAV0iec4p/F4WkmFUoFAqFQqFQKBSK/z4k/5sV9cLRfKVQKBQKhUKhUCgUiv8aVI+mQqFQKBQKhUKhUDwhLLKgFRQMqkdToVAoFAqFQqFQKBT5iqpoKhQKhUKhUCgUCoUiX1FDZxUKhUKhUCgUCoXiCWFRiwEpFAqFQqFQKBQKhULx+KiKpkKhUCgUCoVCoVAo8hU1dFahUCgUCoVCoVAonhDqO5oKhUKhUCgUCoVCoVDkA6pH819Gu7ZBfPXVRPQ6HT/9vIRpX3xrF+/o6MiCn7+hTu3qJCTcoGfvwVy5EgHAmNFDeWVAD8wWC8OHj2NzcKjSkU866rSow+sfv4FOryN46Wb+mPOHXXzn17rQtmdbzCYzSQlJfDNqBnGRcQC06tqK7sN6ALBs1lK2/bFNs46GQfUZ+ekwdDodq5asY9HsxXbxtRvUYPjEYQRWqcBHgyeybZ39ORd3Lsay0EWEbNzJ9A+/0ayjcoua/N/4/uj0OvYu28aWuavt4oMGtqdRj1ZYTGZSEpJZPHoeNyLjAej0fi+eaVUbodNxdudx/vxkoWYdhSV/1Auqy5CPB6HT69mwZANL5yy3i6/eoBpDJgyiQpUKTHprMjvX78qIe/2DgTRo1QChExzeeZhvJ8zVrKNsixq0+LgvQq/j5NIQDs5ZY6+jTytq9GuDNFtIv53K1vd/JOF8FACelUvTasqrOLoURVokSzuOx3w3XZMOp8b1cB81BPQ6Uv7aQNKCpXbxxTu2peS7b2COteaJ5GWrSFm5gSJ1a+I+cnCGnUO5MsSNncSdkD2adBRpWI8SI4YidDpurV5P8qIldvHFOrTDbdibmOOsOlJ+X8nt1esBcBv6Bk5NGoIQpO4/ROJXszVpAPho8lfs2L0f95IlWPnrvBzxUkqmzJjHzrADODkV4bMPR/JMpUAAVq0PZv5Cq//e7N+Dzu3baNbhWL8+rm8PBZ2eO+vWceu3xTlsnFoG4fzKAKSUmC5cJPHTSdbw59vh3K8vACmLfiF14ybNOhzq1sd5yDCETsedDeu4syynjiLNW1Ks3wCQEtOliyRP+RQAt8nTMFR5hvS/T5A0bqxmDQAOz9an+CCrjtSN67jze04djs1aUqxPpo6UaZ+irxCI89ARiGLFwGLh9tJfSNuxXbOOSi1q0nl8P3R6HfuWbWd7tvK0+cD2NOjRErPJwq2EJJaPnp9RnnZ4vydVWtYGIHjWnxxbu1ezjkYt6zNq4jvo9DpWLl7Lwtm/2cXXbliTkRPfJrBKBT4c9Alb14UA4Bvgwxc/foZOp8PgYGD5TytYsWiVZh0AT7WoQbsJ1rLsyNIQ9sy1L8vq9G5NvX5tsJgtpN1OZd3YH4k/H4l/zQp0mPIaAEJA6Iw/ObvpoCYNRRvXxX3MEIROR/JfG0j8aZldvHOntrgPfx1T7HUAkpauIuWvDQDofb3w+ngkeh8vkJKYoR9iiorRpCOwRQ3aj7f64vCyEHZm80Xd3q1p0LcNFouFtFuprB77I3EXInmqaTXajOmB3sGAOd3EpsmLuRx2SpMGgPpB9Rj6yRD0eh3rlmxg8bf25XqNBtUZ+vEQnqpSgYlvTSJ03U4AajWuydAJmeV6mafKMPGtSezapK1cL6xYClpAAZHniqYQwgycABwAE7AQmCGlfGTfCSHqAv2klG9rSBsC+AGpQArwqpTy7COkHwBsllJGaTh2OaCxlHKxbVvzeWhBp9Mx85vPeL59TyIijOwNW8+atZs5ffp8hs2rr/Tkxo1EKj/TlG7dOjFl8of06j2YKlWeplu3ztSo1Qp/fx82bVhKlarNsFgePesrHTl1DJo0mHG9P+K68TpfrfmafcH7uHb+WobNpZMXGdFhOHdT7/JCnxd45YNXmPbWNJzdnOn5bi+Gd3gXiWTGum/YF7yPW4m3NOkYPfldhvYYSawxjoXr57Nz024un7+SYRMdGcvEd6fQZ1CPXPfx5uiBHNl77JGPnRWhE/xn4qvM6fMZN6OvM3L1ZE4EHyLmQmSGTcSpcKZ3/ID01DSa9GlDp7G9WTj0G8rVqUj5upX4/PnRALzzxycENnyGC3sf/eFXmPLHsElvMabXWOKM8Xy7dhZ7gvdy9fzVDJvYyDimjfiSbm92tUv7zLPPULVuVd5oOwiAGX9+Sc2GNTi29/gj6xA6QdCk/vzVeyopxgR6rJnIpeBDGRVJgLMrwzjxq7Who3ybOjQb14dV/aYh9DrafTOYTe/OI/70VZxKOGNJNz2yBgB0OtzHDCN2yBhMMXH4/fotd0L3kH75qp3Zrc0h3PjcvvJ29+AxjD2tvtC5uuC/aiGpew9p1lHyvXeIG/Ye5tg4vBfM5c7OPZguX7Ezu7MlhJvTZ9qFOVavimONasT0tr6wen33DUXq1OTuYW33Tpf2bej1cic++HR6rvE7ww5wNSKK9ct+5PjJM3w6fTZLvp9BYlIyc39ezLIfrfq6D3yboKYNcXN1eXQROh2uw9/hxohRmOPi8PhuHqm7dmO+kukPfUApivfuzfUhQ5EpKehKlABAuLjgPKA/119/E6TE44fvuLtrNzIlRZMOl2HvcnPMSCzxcZScPZ+0sN2Yr2bRUaoURXv25ua7byFTUhA2HQC3f1+KKOKEU4eOj37sbDqc33qXxA+sOkp8M5+0ffY6dP6lKNa9N4kjbTrcrDrk3VSSp3+GJSoSnbsHJWZ9z41DB5C3Ht0fQid4aeIrfNdnMonR13ln9WecylaeRp4KZ0bHD0lPTaNRn+foMLYXvw6dSZWWtSlVtTxftX8fg6MDg5eN50zIMe6m3NHgDh1jJo/gre7DiTHGsWjD9+zYvJvL58IzbKIjYvj4ncn0HWz/fImPuc6rHQeTnpZO0WJFWRaykNBNu4iPuf7IOu755PlPB/Bb7ykkRSfw2upPObflMPHnM33y96o9HP5tKwAVn6tDm496s6T/NGLPRvBDx4+QZgvO3iV4Y8Nkzm05jDQ/Ytmu0+HxwTCi3xyDKSYe/8WzuR0SRvql7OVYKNen5GyE8po0hps/LCZ172FEUSeQ8pH9AFZfvDhxAAv7WH3x5upPORN8mLgs+ePEqj0ctPmi0nN1eH5cb37pP41bN5L5beB0kmNv4l0xgH6LxjC94TBNOnQ6He9MGsaoXmOIM8Yxb9237N68hyt2z7lYpo6YRvc3u9mlPbrnGK+1s5brLiVc+G3XQg6EaizXFYWORxk6e0dKWUtKWRVoA7QHJmg5qJTy4GNWznpLKWtirex+kT1SCKF/QNoBgL/G45YDet3byIfzeCTq16vNxYvhXL58lfT0dJYvX0Wnju3sbDp1bMsvv/wOwIoV62jVsqktvB3Ll68iLS2N8PBrXLwYTv16tZWOfNDxdK2KGMONxFyNwZRuYseaHTRo29DO5kTYCe6m3gXg7JGzePh5Atae0KM7j5CSmMKtxFsc3XmEZ1s8q0lH1dpViAiPJOqqEVO6ic2rttG8XVM7G2NENBdOX8q1wlS5ekXcvUqyN/SApuPfo2ytQOKuRHP9WizmdDOH1+yhetu6djYXwk6RnpoGQPiR85TwdbfFSByKOGBwMGBwdEBv0JMcd1OTjsKSPyrVqkRUeBTGq9GY0k2ErA6hSdtGdjYxETFcPnMZS7Z2OykljkUcMTgacHB0QO9g4Eb8DU06fGo9RWJ4DElX47Ckmzm3Zi8V2trntbQsL6AORYtkvPyUbV6d+NPXiD9tfWlIvZmCtGh7MXKsVglTRBSmSCOYTNzaFELRoCaPvJ9izzUndfcBpO2+emQdz1TGFBGJOcqq407wNoo2b5y3xFIiijiCgwHh4IAwGDAnaLsuAHVrVX9g5XD7rr10er41QghqVqtCcnIKcfEJ7N53iEb1auPm6oKbqwuN6tVm9z5tL2gOVSpjjozEbLT6I3XrNpya2l+Xoi++yO2/VmZUIC03rfdmkfr1SDt4EJmcjExJIe3gQYo0qK9Jh6FSFcxRkViibTpCtuHY2L4cc3qhI6mr/8rQIW9mlhHpRw4jb9/WdGw7HRXtddwN3YZjw2w6nu/InTVZdCRadVgiI7BEWV/0LQnXsdy8gXBz06SjTK1Arl+JJsFWnh5dE0bVbOXpxSzl6ZUjF3Czlac+T5fi4r7T1l69O3eJOn2Fyi1qatJRtXYVroVHEpnxfNlKi1yfLxexZCsbTOkm0tOsox8cizig0z3erC3/Wk9xIzyGm9esZdnJNXup1OYBZVmxIplaUtMyKpWGIg5a63cUqVaJ9GtRmCKjreXYxhCKBeWt/HCoUAZh0JO69zAA8k6q5nIsoNZTJFyJ4ca1OMzpZk6s2UvlbOV61oYFx2JFwHbO0SevkBxrzbOx5yIwFHFA76htoGPlWpWIDI/CaMsf21aF0KStffkRHRHDpdOXkQ9osG3RoTn7th/IeF9S/PvRdLdLKWOBN4ChwopeCPGFEOKAEOK4EOJNACHEMiFE+3vphBALhBAvCyGChBBrbWHOQoifhRAnbGlftoW3FUKECSEOCyF+F0I45yJlBxBosw8XQowXQuwC/iOEqCWE2Gvb519CiJJCiK5AXeA3IcRRIURRIcSzQohQIcQhIcQmIYSfbX+BQogtQohjNg1PAVOBZra0w7Odh7sQYqXteHuFEDVs4R8LIX4SQoQIIS4JITRXTP1L+XItIrP3ISLSiL+/731tzGYziYlJeHiUxN8/l7Sl7NMqHdp0ePh6EB8Vl7F93RiPh4/Hfe3bdG/Loe2HMtLGRcVnxMUbr+Phe/+0D8LL15OYqNiM7VhjHF62Cu3DEELwzoQhzPxU+7DMe7j5uHMzKrOl+qYxATcf9/vaN+zWktMhRwEIP3ye82GnmHhgHp/un8eZHceJufjIgw+AwpM/PH09iM2SP+KM8Xj45u26nD58mqNhx1h+cAnLDy3hYOghrl649vCEueDsW5LkqISM7RRjAs4+JXPY1ej3HP13fknTD3oQOmERACUq+CKRdPllND3XTeLZQR00aQAweHliis7Mp+bYOPTeOfN8sVbN8Fv2HZ7TxluHl2WjeLsgbm3SPsxc7+2JOSarjnj0XjmPU7RlM7x//R73KRPQe1vj0/4+xd1DR/Ff9wd+638nde8BTOFXc6TNL2LiruPrnZlnfLw9iYmLJyYuHl/vTM0+XtZwLeg8vTDHZuZTc1wcumz+MJQujb50AO7fzsJ97hwc61srkzqvbGljc6bNuw5PzHGZ18USH4fe0/5+0QcEoC9VmhIzZlNi5hwc6mqr1D5MhyWbDp1HNh2lrDrcps/G7es5ODybU4ehYmUwOGAxaivH3HxKZitPr+OWy317jwbdgjgTYu1Zjzp9hcpBNXFwcqRYSRcCGz1DCT9tzxdvXy9iIu2fL955LMcAfPy9WbJ1AesOrWDh7N80E5Mb1gAAIABJREFU92YCuPq6k2TMTJ9kTMDFN6dP6vZrw1s7vqL12J5smpA5BcO/1lMMCv6cNzdNZf2HPz16bya28iM6a56Px+CT0x/FWjel1O/z8Z4+LqMccygbgCU5Be+vJuC/bC4lh78OGivfLj7uJEbZ+8I1l/xRv28b3g39irbv92TdxzmnozzzQn2MJ69gTtM2UsXLz5M4Y2b+iIuOw0tDXmvVKYhtK7WX64UZiSjQX0GhuVlJSnnJlt4bGAgkSinrAfWA14UQ5YGlQHcAIYQj0BpYn21X42xpq0spawDbhBCewEfAc1LKOsBBYEQuMjpiHc57j1QpZVMp5VJgETDGts8TwAQp5R+2ffWWUtbCOgR4FtBVSvks8BPwmW1fvwHf2npOGwNG4H1gp61n9+tsWj4BjtiO94Ht+PeoDLQD6gMThBAO93HrAxEiZ0aR2ZrjcrfJW1qlQ6uOnGH321fQS0EE1gjkz/kr7ptYawtrbudEHvfVdUAX9mzbZ1ch0kpuMu53UnW7NKVMjQps/c46p8SzrA8+gf5MaDiE8Q0H83TjqjxVv7JGHYUlf+SaQfKU1r+cP2UDS9Ojfm+61+tF7cY1qd6gmiYdec1rxxdtYWGzkeyespR6b3cBQKfX41+3IhvfnsPvL0/kqXZ1Kd2kar7pyJ5P7+zYS+SLfTB2f4PUfYfxnDjaLl7v6Y5DYHnuhGmbW2UTkosOeyGpO8MwdulFbJ/Xubv/MCUnvG89foA/hnJlMHbshvHFbhSpWxvHWjUeQ8uDyS3vCSFyvX655re8kJf7Vq/HEBBAwtvvkjhxIm6j30M459YGnEvaPOvIQzmm16MvFcDNke+QNHkiLiPeQxS/jw7NPNyPwqYjccw7JE+diPO79jpESXec3/uQlK+n5qs/7rerOl2aElCjAiG28vTczhOc2X6UoX9+Qp+Zw/6fvfMOj6po//c9u0mAUEJ6R0CQDkEIRapUBSm+augighUsoKJ0X0BAREUQEBABCwJWWiihd0INCoRQEiDJJtkUEgKk7O78/thN2SRosuA36/ub+7pyXTnTzmefM2fmzJln5nDt1CWMRqONOooHleUnJcYnMajrC/RvO5CnQp7AzePeg2VbKOkeOfFtGIs6jmP3nLW0f6N/fnj8mSt81f19VvSdQrvX+6KtYMMjWSn6iDv7jnDjyWHEPfcKd4+dxnPme+YIrZaKzZuQ+ulS4gePxjHAlyr9epRdQ8kySrRF+HdhzO80jh1z1tKpkC0APOv60+ODgWycuMImDRYlJegoWwluXm7Url+L8H33064r7I373XU2r2b1AJ4XQpwBjgHuQF1gK9BFCFEBeBLYL6UsujigG5C/Q4eUMg1oAzQEDlnKHA48VCjPD5bwdsC7hcLXAQghXIDqUsq83TtWAx1L0F8PaAyEWcqbDAQIIaoC/lLK3yyasqSUf+eL0x74zpJ+N+Bu0QGwRUqZLaVMBpIA76KZhRAvCyFOCCFOmEwlr8+Li9URGFDg9Rvg74tOl3jPNFqtFheXaqSmphEXV0JeGxeeKx3WJOtS8PAreHvv7utBalJqsXTN2jcjZMwAZo6cgcHy1jBFl4ynX8FbUA9fd1JtfNObpNPj7eeVf+zl64k+oXSzG01aNOK5EU/z+7G1vDX1NXo925PRE1+2ScfNhFSq+xW8yazu60Z6UnG3wkfaNab7mKdZPuqT/LeoTXsGE3P6Mjl3ssm5k82FvWd4qHldm3TYS/3Q65LxKlQ/PH09SCnlNW7f8zHOn44k604WWXeyCN9zggbNG9ikI1OXSlW/gpnlKr5u3C7huuRxceNRHra4YGXqUok7FklWWiaGrBxi9kTg2bimTToMSXocfArqqdbLE6Pe2h6m9AzINbvaZf4WilP9R6zinbt34s6eQ2Cw8aEZy0yqd2EdHhiTre8XU0aBjtsbtuBU31wXK3XuQM6f580ub3ezyDoSjlNj265LafDx8iAhqUBbYlIyXh7ulvCCl0OJenO4LZj0+vwZWwCtpyemovbQ68k+eAiMRoy6BAw3rqMN8C+e16t43jLp8Cy4LhoPT4wpRXQk68k+chCMRkwJCRhjb6D1D7DpfPfUkaxHU0SHqYgOY7KenDwdidY6hLMzLtM/5s7qFRgibd9gJb1Ye+pORgn3bd12jek6pj8rR82zmpXateh3Pu81gWXDZoEQJEcn2KQjSafH279I/5JY9mucnJjClYsxNG9tmwsvQEZCKtUKzZZV83UjM/HeSyz+3HiEekXcjQGSL8eTezcbr0fKXneMiXq0PoXrvAfGpKLt2K389uPWL6FUaPCIJW8y2ZGXzW63RhN39hymQn3b+rmMhFRc/KxtkecOWxJ/bjpCg+4Ftqjm48agpWP5ddxXpF1Pume+v0Ov0+PpW1A/PH08SU4o27PM4306cWDbIYz30a7bM6Zy/isvbB5oCiFqA0bMAycBvGGZ6QuSUtaSUu6QUmYBezHP5g3APMNZrCiKv68UQFih8hpKKUcWih9iCe8vpSzsR1bWHVQEcK7QeZpIKXtQmleZJZdVlLzfVdjZ3EgJmzBJKZdJKVtKKVtqNJVLPMHxE2eoU6cWNWsG4ujoSEhIPzZt3mGVZtPmHQwb9hwAzzzTmz17D+WHh4T0w8nJiZo1A6lTpxbhx0+X9TcqHSVwKSIKv1p+eAd64+DoQMc+HQkPO2aVpnaj2oyePYYZI2eQnpKeH35q3ymad2hOZZfKVHapTPMOzTm175RNOs6fiSSwVgB+gT44ODrQo18XDuw4VKq8U8fMpG9wCP1bD+SL6UsI/Xk7i2Yts0nH9YgreNb0wS3AE62jlkf7PMafYdbrxvwb1WTArJf4etQnZKZk5IenxadQp3UDNFoNGgctdVo3tNr0oizYS/24GHER/5r++FjqR+e+nTkcVrqdH5Pi9TRr3RSNVoPWQUvTNk24ftk2F83EiKtUr+VDtUBPNI5aHunThqth1nWtes2Cd2C1ugZxM8b8UHpt/1k86tfAoaITQqvBv019Ui/Zdl1yzl3EIdAfBz8fcHCgcs/O3N1nvbug1qNgQFypU1tyi7ilVn6iC7e33Z97Vc6FSBwC/dH6mnVU6t6Fu/uPWKXRuBfoqNjhsXwdxoREKjRvBloNaLVUaN7sH3Wd7dy+DRu37UJKScSfF6hSpTKeHm60a92Cw+GnSM+4RXrGLQ6Hn6Jda9vWeOdGXjS7pFrsUbFrF7IPWV+XrAMHcXo0CADh4oJDYCDGeB3Z4cdxCg5GVKmCqFIFp+BgssNtW+ttuBiJ1j8AjY9FR+cu5ByxbseyDx3EqZl5zbSo5oLWPxCjja6p99QRFYnWLwCNt1lHhU5dyDlqrSPnyEEcS9Lh4EDVKTPJ2rWdnIN770vHjYgreBRqT4P6tOVckfbUr1FNnpk1ipWj5lm1p0IjcK5unmH1rV8Dv/o1iDpQ9o3EoHD/4mvpX7qyf/vBv8+IeVBaoaITAFVdqtAsuAkxV2y/X+IjruJWy4fqlrasUZ82RBWxiVuhtqxulyBSLW1Z9UBPhNb86Ovi74F7bV9uxpbdkyf73EUca/jj4G9px57ozJ191u1H4XbMuXNbciwbnmWfu4imWhU0rua5iIqtgsi5ar0JWWmJi7iKW00fqlvqR5M+bYj8C1s80iWIFIstKlZzZujKd9k5dx3XT0bZdP48LkZcJKCWPz6W548u/TpzOKxsu8Z27deFXRv+N91m/3/GplW/QghP4CvgSymlFEJsB14TQuyWUuYKIR4B4qSUtzEPLkdhXhv5QgnF7QDGAG9bynYFjgKLhBB1pJSXhRDOQICUslR3gpQyXQiRJoToIKU8AAwD8mY3bwF5uy5cBDyFEG2llEcsLq2PSCnPCSFihRD9pZS/W2ZktUXyFmU/MASYIYToDCRLKTNsdmMqAaPRyFtvTyZ0yxq0Gg2rVq/j/PkoPpz2LidORrB5cxjfrFzL6lULiDx/kLS0mwwe+joA589H8fPPm/gjYg8Go5E335pk0w6aSkdxTEYTX035iv9+Nx2NVsPOdWFcj7rOkHFDuPTHJcLDwhkx6UUqOlfkgyVm1zt9vJ6ZI2eQmZ7J2gXr+GyT2RP7xy/Wkpluw06NFnt8Mmk+C9bMQ6PVsGltKFejYnj5vRe5EBHJgR2HadCsPnNXzKBa9ap06P4YL787goGPv2DT+e6FyWjil6kree3biebPm6zfQ8KlWJ4c+xw3/rjKnztP0m/CECo4V+CFxW8DkBaXzNcvzeNM6FHqPtaI97d/AlJyYV8E53bZNvC2p/qxcMoi5nw/C41Ww7Z1O7gWdY3h7zxP1NkojoQdpV6zR/hw+VSquFSlbbc2DB/3PKO6vcz+LQcIeqwZy8OWgpQc33eCozuP/f1JS0AaTeydspr+341HaDWcX7eP1Kg42ox7hsQ/ookOO0XTF3pQo30jTLlGstJvs2PcUgCy0+9w6uutDNw8HSklMXsiiNl9xiYdGE2kfrwQr0VzQKMhc+M2cq9ew+XV4eScj+Lu/iNUHfg0lTq1Nc8Upd8iedrc/OxaX2+03p5kn7TtgbmwjpvzFuKx4GOERsvtTVsxRMdQ7eUXyLkQRdaBw1QZ8B8qdXgMaTRiysggbfrHANzdvZ8KLZvj/cMKQJJ15DhZB4/89fn+gvemzeH46bPcvJlB1/5DeX3kMAwG86zUgKd707FtMAeOHOfJkBepVLEiMyaOBcClWlVeeWEQA0e9BcCrIwbbtuMsgNFIxvwvcJ33CWg03A3diiEmhiovjiD34kWyDx0mJzycCsEt8fh2FdJk4tbir5AZ5oHN7dXf4r7MXF9ur1qNvHXLNh0mI5lfzsdl9jzzZ0W2h2K8FoPz8BcxREWSc+QwuSfCcWoRjOvXq8Fk4vbyJchbZh3VP1uINrAGolIl3Nb8xK3P5pJ7woZBr8lI5pL5uMycB1oNWTtCMV6PwXmYRcexw+SeDMfp0WCqL10NRhO3V5h1VHi8O46Nm6GpWo2K3Z4A4NZnczBevWyDDBO/TV3FS99OQGg1HF+/l8RLsfQc+yw3/ojm/M6TPDVhMBWcKzJssbke3IxLYeVL89A6OjD6J/OejVmZd1kzdhEmG9YjgqV/mfg5C3/8FK1Ww8a1W7gaFcMr743kQkQk+3ccomGz+nzyzUcF/ct7LzKg8/PUqvsQb08bg5QSIQTff/UjVyKv2qQDzG3ZtqmrGPzt+withoj1+9BfiqPTuGfQnY0maucpWg7vQe32jTHmGsnKuM3GcebPBgW2rMfA1/tgzDUipYmtk1dyN82GPtdoImX2l/gsmQ0aDbd+307ulWtUf304OeeiuLPvCNUG98e5c1ukwYgp4xbJUyx7V5pMpH62DN9lc0EIss9f4tYvRVeVlQ6T0cSWqat4/tv30Wg1nLLYosvYZ4j7I5qLO0/RengPHm7XGKPB3K7/+o7ZFq2f74HbQ950evNpOr35NADfDpvD7UIvK0ptDqOJL6Ys5JMf5qDRaNi6bhsxUdcY8e5wLkZEcTjsCPWa1WPm1x9SxaUKbbu35YVxwxnR1bxzt0+AN55+nkQcuc92XWF3iNKuOyrh8ybfAZ9JKU1CCA0wE/OaSQHogf6WAZ8jkABslFKOsJTVGXhXSvmUZZOfRUALzLN9/5VS/iqE6AJ8DORtFzZZSrnR8nmTd6WUVk7cQogYoKXFPRUhRBDmwbAzcBUYIaVMs2w2NAu4C7TF7D67AHDBPPCeL6VcLoSoCywFPIBc4DngBrDNErYKOF3od7gBK4FawB3gZSnlWSHEh0CmlHKeRdefwFNSyph72drByd/GxRyKf4onfWzbcfRBk2iw8eHtAdPWybe8JQCwKP5AeUsAoLO3jesmHzB9hG0bsDxo+nvY5p73oNE62seXy7y3fF3eEgBI+c+L5S0BAG0F++jihJ18SXzOBVs3wn+w7M2OLW8JAPRzqlHeEgAYWt12V9IHycp0r79P9H/AfoNty0f+CfbG7iy/3W1sJNR7YLk2fL0S15aLzUrdzEop7/nJEMu3NCda/orG5WJes1k4bC9ml1qklJmY12AWzbcb88ZCRcM730NDzSLHZzCv9Sya7hfgl0JBZyhh/aaU8hLQpYRTdS1yvNeSPhXoV0I5HxY5to8nUoVCoVAoFAqFQqH4h7CT93kKhUKhUCgUCoVC8b9HeX5ipDy5311nFQqFQqFQKBQKhUKhsEINNBUKhUKhUCgUCoVC8UBRrrMKhUKhUCgUCoVC8Q9h+v/Tc1bNaCoUCoVCoVAoFAqF4sGiZjQVCoVCoVAoFAqF4h/CpDYDUigUCoVCoVAoFAqF4v5RA02FQqFQKBQKhUKhUDxQlOusQqFQKBQKhUKhUPxDyPIWUE6oGU2FQqFQKBQKhUKhUDxQ1IymQlEKtiacLm8JdoWrt3N5S7Ar9ib+Wd4SABjh8Xh5SwCgkmtueUsAIOxCYHlLMNN0ankrACDk7DflLQGA9XZiD3shxelueUsAwFFjH4+Eh02p5S0BgCeSq5S3BAAuOtwubwkAHEy6UN4SFP9C7KNVUSgUCoVCoVAoFIr/QUzlLaCcUK6zCoVCoVAoFAqFQqF4oKgZTYVCoVAoFAqFQqH4hzAJ9R1NhUKhUCgUCoVCoVAo7hs10FQoFAqFQqFQKBQKxQNFuc4qFAqFQqFQKBQKxT+E+o6mQqFQKBQKhUKhUCgUDwA1o6lQKBQKhUKhUCgU/xDq8yYKhUKhUCgUCoVCoVA8ANRAU6FQKBQKhUKhUCgUDxQ10PyX0bNHZ879uZ/I8wcZ/97oYvFOTk6s+WEJkecPcvjgJh56KCA/7v3xY4g8f5Bzf+6nR/dOSofS8Y/paNm5BV/vXc7KAysIef25YvGNWzfmy9CFhEZvpn2v9lZxIye8yNKdS1i6cwmd+nS8Lx32Yg970eHbuSl9DnxC30Of0nBMn3umC+wdzJD473FrWsusz7UKXX+aSMilr2n50fP3pQHAKbgVbqu+w+3bH3AeOLjENBU6PY7bN6txW7GKahOn5Ie7zJ6Lx4bNuHw0+7512Is97EXH5Fmf0bH3QPoPfbXEeCklsz5fwpMhL/L0869x/uLl/LgNoWH0GjCSXgNGsiE07L502Is97EVH405BzNq1gDl7v6TXa08Xi+8xsg8zw+YzfetnvPfDNNz9PfPjVlxZz39D5/Hf0Hm8ufyD+9LRpnMwa/ev5qeD3zNs9KBi8UGtm7Jq21IOXNvJ472t2+6D13eyesdyVu9YztyVM+9LR4vOLVi+dzkrDqzguXv0LwtDF7K5hP7lxQkvsmTnEpbsXELH++xfqnVuTuP9X9Lk4GJ8Rv+nWLx7yOMEnV1Fox2f0WjHZ3gM6lYQ99zjNDm4iCYHF+H+3OP3paNZp+Z8vnsRX+xbQr/XiuvoPaovn+5cyNxt85m8ZjoelvrxUMNazPhtDvPCFjB323zaPtXuvnSA/fR19ohJlO9feVFuazSFEO7ALsuhD2AE9JbjVlLKnFKWMxNIllLOLyFuKjDAUrYReFlKefx+tduCEGIcsFhKmWVrGRqNhgVffMQTvQYRG6vj6JFQNm3ewYULl/LTvDhiEGlp6dRv2J6QkL7MnjWJwUNeo0GDuoSE9KNpUBf8/LzZvnUtDRp1wGQqu9e40qF0/J2O0TNHM2HwRJJ1ySzc/AVHw45x/dL1/DT6uCQ+Hfcpz77yjFXeVl2CqdP4YV7rORpHJ0fm/TyX43tOcCfzzr/aHvagQ2gEwbOGs3vgHO7oUnkidDqx20+ScSneKp1D5YrUH9mT5JMFgwhjVi5nP/mZ6vUCcKkfULTosqHRUPXNt0kb/w4mvR7XxUvJPnII47Vr+Um0/v44DxpC2pujkZmZiOrV8+PurF+LqFiRSk/d+8G/NNiLPexFB0D/Xt0Z/ExfJs6YV2L8gSPHuR4bT+i6FZw9F8mMeV/y4/L5pGfcYsnKNaxbsQCAASPfpHP7NrhUq1pmDfZiD/vRoWHY9JeYN3Q6qQkpTN34MWfCjhN/OTY/zfXz0UzvM56crBweH9qTkAnDWDLmMwBysnKY1uvd+9IA5nbsnY/e4q1B75Gk0/NN6Fcc2HGYmEsF921CXCIzxn7MkFcHFMufnZXD8B4vPRAdo2eOZqKlf/li8xccK9K/JFn6l2eK9C/BXYJ5uPHDjLb0L3N/nssJG/sXNBoe+uhlogZ9SI4uhYahc7m5I5ysS7FWyVI3HuL65OVWYdrqVfAbG8L5Xu+BlDTcOo+bO8Ixpt8uswyh0fDijFf4aMg0UhJSmL3xE07sDCeukI6Yc1eZ8NQ75GTl0H3oEwyZMJwvxswj5242i8Z+QUKMDlcvV2Zv+ZSI/We4k1F2HWaT2Edfp7Avym1GU0qZIqUMklIGAV8Bn+cdl3aQ+VcIIToAPYDmUsqmlv9j/zrXP4MQQguMAyreTzmtgptz5UoM0dHXyc3NZf36DfTt09MqTd8+Pfjuu58A+OWXLXR5vL0lvCfr128gJyeHmJgbXLkSQ6vg5kqH0vHAddQLeoT4mHgSridgyDWwd+M+2vZoY5UmMTaJ6MgYTNJ6w+8adWtw9tgfmIwmsu9mc/V8NC07t7BJh73Yw150uDd/mFsxiWRe12PKNXJtw1ECexa3bbPxz3J+8WaM2bn5Yca72ejDo6zCbMWhfgMMcXGYdDowGMjes5sKj1nPOlTs3Ye7G39DZmYCIG/ezI/LPX0KeceGB8Mi2Is97EUHQMugJn85ONxz8Ch9n+iKEIJmjRtw61Ym+uRUDh07Sdvg5rhUq4pLtaq0DW7OoWMnbdJgL/awFx21g+qQdC0B/Y1EjLkGwjcdpHmPYKs0kUf+JCfL/Nh05XQUrj7u933eojRsXp/YmHjir+sw5BrYuWE3HXtaz4AlxCZy5cLVf3Rw8EiR/mXfxn20KdK/JMUmERMZgyyhf/mjUP8SfT6aFjb2L5Wb1yU7Rkf29URkroHUDQdx7dmqVHldOgWRcSAC481MjOm3yTgQgUtn29r1OkF1SYzRkWSpH4c3HSS4e2urNOcK1Y9Lpy/i7muuH7roeBJidACkJaWRkZxONbdqNukA++nr7BUTolz/ygu7dJ0VQgwXQoQLIc4IIRYLITSW8N5CiFNCiAghxI5CWZoIIfYJIa4KIfLm6n0Bfd6gVUqpl1LqLOXECiGqW/5vI4TYafl/phBitRBijxDikhDiRUt4N0vY70KI80KIRUIIYYkbKoT4QwjxpxBiliXMQQhx01JeODAe8AIO5J3LFvz8fbgRW/A2NTZOh5+fzz3TGI1G0tMzcHd3xc+vhLz+1nmVDqXjQehw9/FAH6/PP07WJeNRygefqxeiCe7ckgoVK1DNtRrN2jbF08/z7zOWgL3Yw150VPJx5U58av7xHV0qlXxdrdK4Nn4IZz834naesekcpUHr4YFJn5R/bNLr0Xh4WKVxCAhAGxBI9S++xHXhYpyCS/cAVxbsxR72oqM0JOpT8PEquFbeXh4k6pNJ1Cfj41Vwn3p7msNtwV7sYS86XL3dSI0vsGWqLhVX73u3px1DuvLH3lP5x44VnJi68WMm/zab5j1sv488fTxIii+4b5N0ejx9PP4ihzVOFZz4JvQrlm9aVGyAWhY8Suhf3EvZv0RfiKZlof6l6X30L04+buQUui45uhQcS9Dh2qsNjcI+5+Fl7+Hk527J614sr5ONLwfcfNxI0RWUlaJLwdXH7Z7pHx/QjTOF6kceDzeri4OTA4nXEmzSAfbT1ynsC7v7vIkQojHwNPCYlNIghFgGDBRC7AaWAB2klNeEEIXvpEeArkB14IIQ4itgGzBZCHER2AmslVIeKIWEJsBjQDXglBBiiyW8NdAQuAGEAf2EECeAmUBLIB3YKYR4ynJuF+CUlHKy5XeNtmi/iY1YxrZWFH1jV3Ka0uVVOpSOB6OjeFhpizq1/xT1mj3C579/SnpKOhdORWI0GG3UYS/2sF8dVl+QFoIWHw7lyNtLbSq/DEr+WgeAVouDfwA3x72FxtMT1/kLSR05Ank788GpsBN72IuO0lBS3RNClHh/l/i7SoG92MNedJTUoN6rDWjbvyM1mz7MnAEFa5rffewVbial4RnozfgfPyQ28hr664k2yLi/tujpVgNITkzBr4YvX67/jCuR0cRdi//7jMWElBBWhv7lkWaP8Kmlf4m8j/6lNB3dzbATpP5+AJljwHNYT2rNf4uLIVNLvqal/RFFZZSmPbXQ/ulOPNykDh8OmGQVXt3LlTGfv83id76wuX8B++nrFPaFPc5odgOCgRNCiDNAJ+BhoC2wR0p5DUBKmVooz2YpZY6UMglIBTyllBnAo8CrQArwsxBiWCnO/7uUMstS1n6LFoCjUsoYKaURWAu0xzz43C2lTJZS5gJrgLzV5TnAb6X90UKIl4UQJ4QQJ0ymkv3j42J1BAb45R8H+Pui0yXeM41Wq8XFpRqpqWnExZWQN77snY3SoXT8Hcm6ZKu3xB6+HqQkppQ6/48L1/L6E2OYMGQSQkBctA0PI9iPPexFxx1dKs5+Be/nnH3duJuQln/sWKUiLvUD6PbLJPod+xyPRx+m06px+RucPCiMyXo0nl75xxpPT0wp1rNfJr2e7MMHwWjElJCA8cYNtAH3vwaxMPZiD3vRURp8vDxISCq4VolJyXh5uFvCC2aZEvXmcFuwF3vYi460hBTc/ApmDt183biZlFosXcN2TXlqzDN8MWo2hhxDfvjNJLNm/Y1EIo+e46FGtulL0unx8iu4b718PUkuQ7uelzb+uo5TR87wSOM6Num43/5l7cK1jHliDJOGTAIB8Tb2Lzm6FJwKXRcnX3dyE62vizHtFtJyLfQ/hOHcpLYlb3LxvAnFr2lpSElIwd23oCx3X3fSEouX1aRdU/4z5lnmjpplVT8qVanEBysns27eD1w6HWWThjzspa+zV2Q5/5UGIcQTQogTEK4DAAAgAElEQVSLQojLQoh77h4mhHhWCCGFEC3/rkx7HGgK4JtC6zXrSSlnWMLvZavsQv8bsczUSikNUso9UsqpwFtA3nZcBgp+e9F1k0XPIf8i/K9e2d6VZXgdI6VcJqVsKaVsqdFULjHN8RNnqFOnFjVrBuLo6EhISD82bd5hlWbT5h0MG2behe2ZZ3qzZ++h/PCQkH44OTlRs2YgderUIvz46dLKUzqUjlJzMSIK/5p+eAd64+DoQOe+nTgadrRUeTUaDVWrm9eI1apfk1oNanFyv21rvezFHvaiI+XMVarW8qFyoCcaRy0P9WtD7I4CF6rcW3f5pfFrbGg9lg2tx5J86gr7XviM1LPRNp3vXhgiI3HwD0Dj4wMODlR4vAvZhw9Zpck+dBDHIPP6HFHNBW1AIEadbQ+E98Je7GEvOkpD5/Zt2LhtF1JKIv68QJUqlfH0cKNd6xYcDj9FesYt0jNucTj8FO1a27b2zV7sYS86oiMu41XTF48AL7SODrTq057TYSes0tRoVIvhs15hwag53ErJyA93rlYZByez41oV16rUbVGf+Eu2bVVx4UwkgbX88Q30wcHRgW79unBgx+FS5a3qUgVHJ0cAXFyr0TS4MdFR1/4mV8lERUThV6h/6WRj/1LzPvuX22cuUaGWL06BXghHB9z6tSdth/Vek45eBa7W1XsEk2XZwCl93xlcOgahdamM1qUyLh2DSN9nm/v1lYhL+NTyxTPQXD8e69OeE2HhVmlqNqrFqNmvM3fkLDJS0vPDtY4OvLNsAvt/2cvR0NJdy7/CXvo6hW1Y9pNZBDyJ2YNzkBCiYQnpqgJvAsdKU67duc5idnP9WQjxhZQy2bI7bWXgEDBfCPFQnutskVlNK4QQDYBcKWXeVnDNgLyWLQZogdkF9pkiWfsLIeYCVYEOwFjM7rRthBA1gDggBFgInAQ+sWhMBwYCJW/XB7csZdrsOms0Gnnr7cmEblmDVqNh1ep1nD8fxYfT3uXEyQg2bw7jm5VrWb1qAZHnD5KWdpPBQ18H4Pz5KH7+eRN/ROzBYDTy5luTbF6wr3QoHX+FyWhi0ZQlzPp+Jhqtlh3rdnAt6jrPvzOMqLNRHA07xiPNHmHq8ilUdalCm26teX7cUF7u9ipaRy2f/mK+he5k3uHjNz/BZPx328NedEijiROTVtNlzXiEVsOVtftIj4qj6XvPkBIRTdyO4ut2CtPv2Oc4VqmExsmBwJ4t2TVoTrEdOEuFycithfOp/vE8hEbD3a2hGK/FUPmFF8m9GEnOkcPkHA/HqWUwbt+sBqOJzGVLkBnmB+jq8xfiEFgDUakS7mt/4ta8ueScKPtm4vZiD3vRAfDetDkcP32Wmzcz6Np/KK+PHIbBYJ79GPB0bzq2DebAkeM8GfIilSpWZMbEsQC4VKvKKy8MYuCotwB4dcRgm3acBfuxh73oMBlN/DD1a975dgoarYYD63cTf+kG/ccOJOaPy5zZeYKQCc9Twbkiry9+B4CUuGQWvDQHvzoBDJ/1CiYp0QjBliW/We1WWxaMRhOfTl7A/DVz0Wg0bF63leioGF56dwQXIi5yMOwwDZrVY86KGVR1qUL77m0Z9c4IhnQZQc26D/H+nHH5Or778ker3WrLao8lU5Yw8/uZaC39y/Wo6wyz9C/HLP3LlOVTqOJShdbdWjN03FBetfQv8wr1L5/cR/+C0cT1ycupt2YaaDQkr9tFVtQN/N4dxJ2Iy9wMO473i72p3iMYaTRiuJlJ9NsLzVlvZhI//ycabvkEgPjP12O8aduyAJPRxDdTlzPx22lotFr2rt9J7KUbPDduEFfPXubkzuMMnfgCFZ0rMnbxeACS4/V8MmoWbZ9qR4NWDalavSqdnu0CwOJ3F3DtvG0vS+ylr7NXyvMTI6WkFXBZSnkVQAixFugHnC+SbgYwFyjVdtbCHnyghRAfAplSynmW48GYN9DRALnAq1LK40KI3sBHmGcS46WUTxb9vIkQIhKz+60vsADzWkkjcBHz501ShRCdgeVAAhAONJNSdrOU5YF5zWcgMFtK+Y0QohvwPuZBYiNgLzBaSikt7rjjLZo2SSknCCEcLJry9+QXQozF7MZ7Q0pZ8DGlEnBw8i//i6JQ/AVdvZuWtwQAdiWeLW8JdsVqj/v7HtuDonuDG+UtAYCwC4HlLcGuCDk7vbwlALC+6dTylmBX7HS6W94SAIjMtc1980FTXXNfG/Q/MKab7EPHPAf7eCT8RVcuXwcsEUNOnP0P24rwrf/Qcr2Qw+N/eAV4uVDQMinlsrwDIcSzwBNSylGW42FAaynlmEJpmgOTpZTPCCH2Au9KKa1dLIpgFzOaUsoPixyvwbzesWi6LcCWImGTixzXt/wbi3ldZ0nn2wvUvYecSCllSV+rvi2lLPZlYCnld8B3RcIMmDcmKhz2OfD5Pc6pUCgUCoVCoVAoFA8cy6By2V8k+cuttixfAPkceKEs57WLgaZCoVAoFAqFQqFQ/C/yL3AEjsXszZlHAFDY378q0BjYa9kl2AfYKITo+1ezmmqgWYiis6OFwndiXjuqUCgUCoVCoVAoFP9LHAfqCiFqYd6PZiAwOC9SSpmOeXkhAP8q11mFQqFQKBQKhUKh+F/EPlba3hsppUEIMQbYDmgxfwHknBBiOnBCSrnRlnLVQFOhUCgUCoVCoVAo/j9GShkKhBYJK3H3Nill59KUaY/f0VQoFAqFQqFQKBQKxb8YNaOpUCgUCoVCoVAoFP8Q/4LvaP4jqBlNhUKhUCgUCoVCoVA8UNRAU6FQKBQKhUKhUCgUDxTlOqtQKBQKhUKhUCgU/xD/gu9o/iOoGU2FQqFQKBQKhUKhUDxQ1IymokQqO1UsbwkA1K7iU94SAKhTwePvE/0fcC33ZnlLAKC2tmp5SwDgqot91I/2lWuWtwQAOnjoylsCAHGXqpe3BADa+ySUtwQAqvjnlrcEAOK6vVLeEgAIObu0vCUAkP3xO+UtAYCDPzmWtwQADNJY3hIAyLWTuZ9HetwqbwkABO+yj34u0atBeUv4V2Mftfr/HjWjqVAoFAqFQqFQKBSKB4oaaCoUCoVCoVAoFAqF4oGiXGcVCoVCoVAoFAqF4h9Cqu9oKhQKhUKhUCgUCoVCcf+oGU2FQqFQKBQKhUKh+IdQmwEpFAqFQqFQKBQKhULxAFADTYVCoVAoFAqFQqFQPFCU66xCoVAoFAqFQqFQ/EMo11mFQqFQKBQKhUKhUCgeAGpGU6FQKBQKhUKhUCj+IWR5Cygn1IymQqFQKBQKhUKhUCgeKGpG819Gzx6d+eyz6Wg1Gr5Z+SNzP1lkFe/k5MSqlV/waPMmpKamMWjIa1y7FgvA++PHMOKFgRhNJsaOncKOsH026+jarSMfz52CVqvl29Xr+PyzpcV0LF0+j6CgxqSmpjFi+Jtcvx6XHx8Q4MuxE9uZM2sBCxd8bbOOxx5vzXsz3kaj1fD7D5tY+eX3VvGPtmnGu9Pfom7Dh5nw6jR2bt4LgG+AN/NWzEKr1eLg6MDaFT/z87e/26wjqFNzRkx7CY1Ww661Yfy+5Ber+KdG9aXrwB4YDUYyUtNZ/N5CkuP01GxYi5c+epVKVZwxGU38+uVPHN580GYdbTq34p0Zb6DRaNjw4xa+/XKNVXzz1k0ZO/0N6jSozeTXprN7i3UdqFzFmXX7vmXvtgPMm/SFzToadmpGyNQRCK2GQ+t2sWPJBqv4riN7025gV4wGI5mpGXw3fgmpcck80rYRz04Znp/O52E/VrzxBRE7jtuko0OXtkz+6F20Wi3rv/+dZQtWWcU7OTkyd9F0GjdrwM3UdN566QPibuhwdHRgxqeTaNysISaTiZmT5hF++KRNGgAadwpi8NQX0Wg17F+3i9Alv1nF9xjZh44Du2IymLiVms434xeTEqcHYMWV9cRevA5ASlwyC16aY7OOim2DcX13NGg03P49lIzVa63iKz/Vk+pvvYwxKRmAW+s3cHtDKBVaBOE67rX8dI41a5A8cSZ39x2ySUfVTs0J+PAlhFZDytowEhdb3y9uz3bBb9IL5CakAJC8OpSUtWEAPPztNJybP8LtExe4OmKmTefPo+Jjwbi9+zpoNWT+tpWMVUXs0acHrm8Xsse6DWT+vpUKLZvh9o61PfQTZnJ372GbdDg+2orKL70BGg1ZYVvI+nlNsTRO7R+n0qAXAIkx+gqZ82ag8fSm6sQZoNGAgwNZm34le9tGmzQAVHqsJW7jXweN2R7pK9dZxVfp2wPXt1/CqDdfl4y1G8j8bSsVWzbD7b3C9ghE/8FH3Nljmz0mz/qM/YfCcXOtzu/ff1UsXkrJ7PlfceDIcSpWrMBHk96hYb06AGwIDWOppV6/Mnwg/Xp1t0kDgLZecyr0HQkaDbnhO8nd86tVvFOfEWjrNAFAOFZAVHHh9tShAFQcNQVtjXoYoy+QtfIjmzWA/bSnbTu34p0Zb+b3L6u//MEqvnnrZoyz9C+TXvtvif3L+n3fsXfbAT6ZNN8mDQDBnVvy+oevotFq2frjVtYuXm8V36R1Y16f9iq1G9Rm5uhZHAgt6FNfmjiS1l1aIzSCUwdOsWjaEpt1ODQOpuJg8/2Su38r2aFri6VxDO5EhX7PAxLjjavcXToLbf1mVBpUcL9ofGtwZ8lMDKdtu19qdmpKlw+HIbQa/li7l/DFm6zimw3tQtDz3ZFGEzl3sgj7YAUpl+KpFuDBiN1zSbuiAyD+9GV2TlxpkwaAVp2DGfPf19FqNWz5cStrFlnbo2nrJoz58HUeblCb6aNnsm/Lgfw4Lz8v3vtkHF5+nkgJHzw/kYTYRJu1KOyHch1oCiEmAYMBI+Z1sq8AbYFlUso7/8daQoHBUsqbZczXE/jYclgHiAPuApeALkBtKWV6ofS/A2uklOuLlvV3aDQaFnzxEU/0GkRsrI6jR0LZtHkHFy5cyk/z4ohBpKWlU79he0JC+jJ71iQGD3mNBg3qEhLSj6ZBXfDz82b71rU0aNQBk6nsy5M1Gg2ffvYh/fsOJy4ugT37fyM0dBcXIy/np3l++HPcvJlO82ZdeObZp/jvjPcZMfzN/PjZH09m530MdPN0fDD7HV4LeZtEXRI/bPuafTsOcjUqJj+NLi6RaW99xPOvD7LKq09M4YU+r5Kbk0sl50r8vO879m0/iD4x2SYdI2e8wowh00hNSGH2xnmc2BlO7KUb+Wmiz0Xz/lPjyMnKocfQJxg24QU+H/MJ2XezWTh2PgkxOly93Ph4y6ec2X+aOxm3bdIxftbbjBn4Dkk6PatDl3Jg+yGiL13LT5MQl8T0t2cz9NWBJZbxyviRnD4aUeZzF0ZoBAOnj2TB0JmkJaTwwcbZnA07QcLlghcNN87HMLvPB+Rm5dBxaHeenjCUFWPmE3XkHLN6jQfA2aUy0/ct5Px+2/RoNBo+nPMBLzz3Ognxifyy4zt2b9vH5ajo/DTPDulPxs0MurXqT+/+PXhv6pu8/dIEQoY9DcBTnQbg5uHKirUL+U/3YUhZducXodEwbPpLzBs6ndSEFKZu/JgzYceJvxybn+b6+Wim9xlPTlYOjw/tSciEYSwZ8xkAOVk5TOv1rk02sEKjwfX9N0kaPR5joh6fbxdzZ/8RDNHXrJLdCdtL2tyFVmHZJ8+QMOQVczHVquL727dkHT1hs47Ama9wecg0cnUp1Ns0j/SwcLIK3S8ANzcdJHbqsmLZE5f+hqZSBTyG9LTt/IV0uL3/Bkmvv48hUY/v94u4u+8wudHXrZLd3rGXtI+/tArLPhGBbtCr5mKqVcVvw2qyjtr4IkKjofKrb5Mx5R1MKXpcPltK7rFDGG8UXBeNrz+Vnh1CxvjRyNuZCJfqAJjSUkh/bzQYcqFiJap/uZKc8EPI1BSbdLhNeIPEV9/HkJiM3w9fcmffEXKvFrXHPlLnWNsj60QE8QMK7OG/aRV3j9j+YqZ/r+4MfqYvE2fMKzH+wJHjXI+NJ3TdCs6ei2TGvC/5cfl80jNusWTlGtatWADAgJFv0rl9G1yqVS27CKGhwtMvc3fZh8j0FCq9ORfDuXBkUsF9m7Op4KHcsV0vNH61849z9/5OrmMFHNvcXz21p/Z0/KyxjBk4jkSdntWhy9i//WCR/iWR/7496579y6vjR3Hq6Bmbzl9YxxszR/P+4Anodcks2ryQw2FHuX6poJ4mxemZO+5TQl551ipvwxYNadSyES/3MNfV+b9+SrM2TYk4erbsQoSGisPe4Pa895GpeqpMXUTumcOY4gt0aLz9qdB7EJmz3oI7mYiq5vvWGBlB5jSzBlG5KlXmrMZwzrb7RWgE3WYO56chc7ilS2XopulcCTtJyqX4/DQXfj9CxPe7AXi4+6N0njKUX56fC0D6tUS+fXKSTecujEaj4a2Zb/Du4PfR6/R8tWURh3Yc5prVdUlizri5DHglpFj+iV+8z3cLfuDkgVNUcq6IyfS/52hqEuWtoHwoN9dZIURb4CngUSllU6AbcAN4G3C+Rx7tP6VHStmrrINMS77tUsogKWUQcAIYYjl+DtgB9M9LK4RwAdoDm23R2Cq4OVeuxBAdfZ3c3FzWr99A3z7WnVjfPj347rufAPjlly10eby9Jbwn69dvICcnh5iYG1y5EkOr4Oa2yKBFy2ZcvXqNmJgb5Obm8uvPm+ndu5tVml69u7HmB/Pb399/20qnzm3z43o/1Z2Y6BtWA2RbaNy8ATeiY4m7Ho8h18D233fRuWcHqzS6GwlcunClWKNlyDWQm5MLgFMFR4SwvQWoE1SXhJgEkm4kYsg1cGjTAVp2b2WV5tyRP8jJygEg6vRF3Hzdzfqi40mIMb9NTEtKJT05nWpu1WzS0ah5A2Jj4oi/rsOQa2DHht107NneKo0uNoHLF66W+IKhfpNHcPN05eg+295251EzqA76awkk30jCmGvkxKbDNOsRbJUm6sg5ci32uHr6Eq4+bsXKebRXG87tPZ2frqw0fbQR12JucONaHLm5Brb8voOuT3a2StPtyU78us58O27btIu2HczXrU692hzeHw5AanIaGem3aBLU0CYdtYPqkHQtAf2NRIy5BsI3HaR5EXtEHvkzv35cOR2Fq4+7Tef6K5wa1cdwIw5jnA4MBu7s2INzp8fKXE6lrh3JOhyOzM62SYdzUF2yYxLIuZ6IzDWQtukALj1a/X1GC5mHzmLKvGvTuQvj1Lgehth4DBZ73N6+l0qd25W5HOduHck6dByZZZs9HOo2wKiLw5Ro1pG9fzeOra3v24o9+5AV+hvydiYAMt3SVRkM5kEmIBwdzTObNlKhcT0MN+IxxCXk28O5c9nrh3P3Dty9D3sAtAxq8peDwz0Hj9L3ia4IIWjWuAG3bmWiT07l0LGTtA1ujku1qrhUq0rb4OYcOmbbA7ymRl1MyTpkaiIYDRjOHMSh0b3rqUNQBwxnCmZojJf/gOz7r6f20p42at6AGzFxxFn6l7ANu+h0j/5FljBIyOtfjt1n/1IvqB7xMfHoridgyDWwd+Ne2vVoa5UmMTaR6MhoTNK6n5NS4lTBCQcnBxydHNE6OpCWnGaTDm3tepiS4pF6HRgN5IbvxbG5dfvh1LEX2bs3wB3LfXur+COmQ8uOGP44Djm23S8+QQ+TFpNI+nU9plwjkZuO8nCPFlZpcgq1l46VKoANL0z/jvpB9YiLiUdnqR+7N+ylXQ9reyTEJnL1QjSyyPPHQ3VroNVqOXngFAB372SRfR/th8K+KM81mr5AspQyG0BKmQw8C/gBe4QQewCEEJlCiOlCiGNAWyFECyHEPiHESSHEdiGEryXdS0KI40KICCHEL0IIZ0v4KiHEEiHEHiHEVSFEJyHEN0KIC0KIVXlihBAxQggPIURNS9xyIcQ5IcQOIUQlS5pgIcRZIcQRIcQnQog//+Y3/ggUfrX3NLDN1tlaP38fbsQWvKWKjdPh5+dzzzRGo5H09Azc3V3x8yshr7913lLr8PMmLlaXfxwXl4Cvn7dVGl8/n/w0RqORjPRbuLm74uxcibfHvsyc2QtsOndhvHw9SYxPyj9O1CXh6etZ6vzefl6s272arSd/Y9WiH2yazQRw83EnRVeQN1WXgvtfDBS6DujO6b3FH37qNKuLg5MDidcSbNLh6eNhZY8knR5PX49S5RVC8Na011kww3Y3ojyqe7uRFl8wo5KmS6G6d/EHnzzahXTh3N7ib7lb9mnH8Y22uWYC+Ph6oYsrcL1JiE/Eu0j98PbxJMGSxmg0kpmRiatbdSL/jKLbk53RarUE1PCjcbMG+Ppb1/HS4urtRmp84fqRiqv3vetHx5Cu/LH3VP6xYwUnpm78mMm/zaZ5GQZkRdF6eWBM1OcfG5L0aL2K1w/nLh3w+XE5Hh9PQ+td/H6q3ONxbm/fY7MOJx93cgrZI0eXgmMJ9qjeqy31t39Bza/ex7GU9bgsOHh6YEgouF+MSXq0XsV1OHfpgO+6ZXjMnVqyPXp25vb23Tbr0Lh7YEou0GFK0aN1t/69Wv8AtH6BVPv4S6p9shjHRwvqgcbDE5cF3+C68ifu/rzGttlMzPXDkFCofiQml1w/urbHb/1SPD+Zcm97bLW9fpSGRH0KPoW0eXt5kKhPJlGfjI9XgSZvT3O4LYhqbsibBXllegrCpeT7VlT3RLh5mQeXDxh7aU+L9i+JOn2p+1shBG9PG/1A+hcPH3eS4gvqqV6XjLtP6dqHC6cucOZIBOtP/Mj6kz9yYt9Jrl++8fcZS0C4eiBTC923qXqEq3X90PgEoPUOoPLE+VSevBCHxsFFi8GpdWdyj9neflT1ceVWfGr+caYularersXSBT3fjVEHPqXjxIHsmvZtfrhLoCfDQmcyYP0k/FvVs1mHp68Hel2BPfQJejx9S/fCNLB2AJkZmUxfPo3l277i1ckvo7mPl2b2iqmc/8qL8rySO4BAIUSUEGKxEKKTlHIBEA88LqV83JKuMvCnlLI1cAxYCDwrpWwBfAPkLX74VUoZLKVsBlwARhY6lytmN9axwCbgc6AR0EQIEVSCtrrAIillI+Am8IwlfCXwqpSyLWZ3379jG9BCCJF3tw3EPPgshhDiZSHECSHECZOpZLfJkmbdirrylZymdHlLy73OYZ2meD4pJRMnvc3iRSu5ffsBeEaXfJJSZ0+MT2JAl+H0azuAPiFP4uZRvHG2lXvZtsPTnajdpA4bl1qv0avu5cobn49l8bsLHuh1Ke02Z8++0J/Du49ZdeC2Upa61qp/Bx5qWpuwZdbryqp5VsevXg2b3bwsQv5Wx720/rxmIwnxify28zsmzXyHU8cjMBhKc8vbpiOPtv07UrPpw2xdVrAG693HXmF63/dZ+uZ8Bk8dgWcN2wa8JVJEx90DR4jrM4SEQS+RFX4S9w/ft4rXuLvhWKcWWUfuY1aiJOeBIjrSdx7n3GMvEdnzLW4djOChz96y/Xz31PH398vd/UeJe2oougEvk3XsFB7Tx1vFaz3M9rh7xEY34nvoKFY9tFq0fgFkTHyLzHnTqfzGe4jKVQAwJetJf/NF0l4eTMWuTyCq29iOlaI9vbPvCLG9hhEf8gpZx07jMeM9a5kebjjdrz1KQUn3jxCixObfZm+VMvQvDkHtMZw9AvLBP87ZS3t6P88Qz77wNId2H7UaqD5IHaXt9/1q+vFQnUAGthrCgODBNH+sGU1aN7ZVSQk6ihxrtGi8/bn98Tvc+eojKo0YB5UqF5Tg4oYmoBaGP//h9gM48+1Ovu7wDvtnr6Xtm2ZHu9tJN1na5m2+6zWZvTN+oPeC13GqUslWIaXSURJaBy1NWjVhyYxlvNr7dXxr+PJESA8bdSjsjXJboymlzBRCtAA6AI8D64QQH5SQ1Ajk7RRRD2gMhFkaGy2QN7XWWAgxE6gOVAG2Fypjk5RSCiH+ABKllH8ACCHOATWBoq//oqWUeWEngZpCiOpAVSll3mrtNZhdf//qN+YIITYCzwohfgGCMA+wS0q7DFgG4ODkX+LtGRerIzDAL/84wN8XnS6xxDRxcTq0Wi0uLtVITU0jLq6EvPG2LbSOi0vAP8A3/9jf34eEIjriLWni4xPQarVUc6lKWupNWgQ3o2//J/jvjPdxcamGNJnIys5m+dLvyqwjKT4Jbz+v/GNvXy/0CWV/e61PTObKxWgebdMsf7OgspCakIJ7oRkXN193UhNTi6Vr0q4Z/xnzHNNCJmHIMeSHV6pSiQkrp/DjvO+5dDqqzOfPI0mnt7KHl69nqe3RpEUjglo35Znh/XCuXAkHR0fu3r7LolnF18j9HWkJKbj6FbzJdPV1Jz2puHtS/XZNeGLM03w+4EMrewC0eKotZ7aHY7J1cId5BrPwLKSPnzdJReyRoEvCx9+bBF0SWq2WKtWqcDPNvKR61pTP8tOt2/IN14qsVSstaQkpuPkVrh9u3EwqXj8atmvKU2OeYc6AKVb2uGmxnf5GIpFHz/FQo1ror5f93jUmJVvNQDl4eeZv6pKHKT0j///M30Kp/sZLVvGVu3fm7p6DYLT9uuToUnAqZA8nX3dyi9jDePNW/v8pa3bg/8HzNp/vXhiS9Dj4FNwv2lLYw/VNa3s4d+/EnT2H4D7qqSlZj8ajQIfG3RNTanKxNIaL58FoxJSYgCnuBhq/AIyXIvPTyNQUDNdjcGzYlJzDZV//bkzU4+BTqH54e5Rgj4LrcuvXUFzfGmUV79zj/u1RGny8PEhIKrBRYlIyXh7u+Hh5cPx0wXq7RH0ywc2b2nQOmZ6CqF5QT4WLOzKj+H0L5oFm9m9lbytLg720p0X7F29fT5JL2b80tfQvzw7vb9W/fDlr6d9nLoJel4yXX0E99fT1ICWxdLP47Xs+xvnTkWTdyQIgfM8JGjRvwB/H/s45rTgyTY9wK3Tfunkibxa5X9L0GK9cAKMRmZyAKeEGWp8AjNEXAXBs1QnDyUP31Z7e0qVS1d6khGoAACAASURBVK9ghruKrxuZJdSPPCI3HqX7RyMAMOYYMOaY3XoT/4gh/VoSrrV9SDwbfc/890Kv0+PpW2APTx9PkhNKd130umQun7uM7rr5cf7g9kM0bN4A81yN4t9Ouc5NSymNUsq9UsppwBgKZg4LkyWlzLsLBXAub02klLKJlDLvtccqYIyUsgnwX6BioTLynL1Nhf7POy5psF04jdGSxtZFfHnus88CG6SUuTaWw/ETZ6hTpxY1awbi6OhISEg/Nm22Hrdu2ryDYcOeA+CZZ3qzZ++h/PCQkH44OTlRs2YgderUIvz4aZt0nDp5locfrslDDwXg6OjIf559itDQXVZpQkN3MXjIfwDo//ST7N93BIAnewykaaNONG3UiSWLV/LpvCU2DTIBzp2JpEbtAPxq+OLg6EDP/l3Zu6N0O7Z6+XpSoaITAFVdqhIU3ISYy7YNJC5HXMK3li9egV44ODrQrk8HToSFW6Wp2agWL89+jY9HfkRGSv7eUDg4OvDesgns+2UPR0Nt23Euj/NnIgmsFYBfoA8Ojg706NeFAztK5yo1dcxM+gaH0L/1QL6YvoTQn7fbNMgEuBZxBa+avrgHeKJ11NKyz2OcDbN+YxvQqCaDZ73EklFzuZWSUayM4L7tOLHJdjcvgD9On6dmrUACavjh6OhA7/492LXN+gF817Z9/GeA+X3RE326cvSgeaauYqWKVHI2NyHtOrXGaDRabSJUFqIjLuNV0xePAC+0jg606tOe00XsUaNRLYbPeoUFo+ZY2cO5WmUcnMxNVBXXqtRtUZ/4S7HYQs75SBwD/dH6+YCDA849Hufufus6p3EveGCp1LFtsY1xnHven9sswJ2IS1So5YtToBfC0QHXPh1IL3K/OHgVzMq5dG9F1mXbfvNfkXPuIg6B/jhY7FG5Z2fu7rO2h9ajkD06tSU3xtoelZ/owu1ttru9ARguRaL1C0DjbdZRoWMXcsOt637O0YM4NDGvqRfVXND4BWJKiEfj7glO5nZMVK6CY4PGGONscwXMPvf/2LvvsCiOPoDj37kDBQQRBAREo0Zjj5jYezfFlje+ttg1thhjjbEbu8YUe0tRE2uaxi72hrGjsYANVDg6YkXhbt4/7ignGHEx4ZJ3Ps/jI7c7e/u72dm5m53Z2WDsilrnxwNLvZ0qY3441c9cPpzfaPiXD5sFaFCnBr9t342UkqA/LuLsnA9PD3dqV3+dI8dOkXjnLol37nLk2ClqV3/92W+YBdPNy+g8fBBuXqC3w86/DsYLmXvyhacvwtEZU1hwTj9WlmylPr1w5hJFi/vhW8T8fdu0dWMOZPP7ZdzAybSs+l9aV2/PnEkL2frTDk2NTIDgoGAKFyuMd5FC2Nnb0aBVA44EHM3WttERMVSq/io6vQ69nZ5Xa1TkhsbvfeP1YPRehREe3qC3w75aA5KfmDU25dQR7MqaB80J5/zovP0wRaffdmRfvRGPczBsFiAy6Bpuxb1xLeKJzl5PmZY1uBpwyipNgWLpF1xLNPYnIdR8e46juwtCZ/5p61rUkwLFC5EYpq3XOTgoGL/ihfG2/P5o1LoBRwKy95vm0plgnF2dcXV3BeC1Wv6EZZhk6t/i/3XobK71aAohSgMmKWXqjDD+QBjmHkYXIKtLZcGApxCippQyUAhhD7wipTxv2cZgWfYe5tlfXxgpZYIQ4q4QooaU8ijW917+mb3ACuAD4MOcxGA0Gvlo8Fi2blmNXqdj+Yp1XLgQwsQJwzlxMojNmwP49ru1rFg+l0sXDpGQcJtOnQcAcOFCCD/9tIlzQXtJMRoZ9NEYTTPOpsYxfNin/LJhOXq9jh++/4lLFy8zeuxgTp86x7atu/l+xXqWfv05p4P2kJBwm57dX/zQN6PRyMzRX7JwzRfo9Ho2rtnMteDr9P+4NxfOXGL/zkOU8y/DF99OJ38BF+o1rU2/Eb1pW78zxUsVY+jEganjilm5aA1XLl3TFIfJaOKb8UsZs3IiOr2Ovet3c+vyTdoP7cTVs1c4sesYXUb3wMHJkWELzUPvYiNimdl7KjVb1KZstfK4FHChYdtGACwYPpfQC8/fqDEajXw25ivmrp6NTq9j09qtXAsJpc+InlwMusTBnUcoW6kMs76ZTP4CLtRtWos+w3vQoWF3TZ/7aUxGE2vHf8uHK8eg0+s4sn4vhsu3aDGkHTfOXeXsrpO8O6ozeZ0ceH/hUAASwmNZ9L55Fjx3P0/cfDy4fPRCjuIwGo18OmoW366fj16n56c1G7kSfI2PRvbj3JkL7NlxgB9XbWT2wsnsOraB2wmJDOkzGoCCHm58u34+0iSJNEQzfMC4HOXHqvFfM2zlOHR6HQfX7yHi8k3aDOlA6LkrnNl1gnajupLXyYEBC4cB6Y8x8S3pR7dpfTFJiU4Itiz61Wq22ufLEBPxn83Da95M0Ou4/9s2kq+F4dq3O48vBvPwQCAuHd7BsV4tc8/ZnbvETZyVtrnepxD6Ql48OpWzWYkxmrg1bikvfz/R/HiTdbtJCrmJ99BOPDh3hTsBx/Ds0QLXptUgxUjK7XuEDUt/1E6pn6aR92U/9PkcKP/7N9wYMZ+7BzRcNDOaiJ85D68FM8yP8/htuzk/+nXj8YWQ9PyoX9OcH4l3iZ3wZH548uikhhkrMzIZub/4K/J/Oht0Oh7t2orxRiiO7/Uk5fIlko8dIfnUMewrV8V1wQowmXjw3SLk3TvY+VfBpecAzGP2BA9/XYcxTFs9htFE/Iz5FFo03ZwfG3eQfDWMAv278ehCCA/3B+LSsQ1ODWpCihHjnbvEjv8sbXM730LovT1Jyml+ACMmzOD46bPcvn2Hxm06M6BXF1JSzL107d95m3o1q3Iw8DhvtuuJo4MDk0cPAcA1vwt9u3ekQ2/z902/Hp20zTgLYDLxaMMyHN+fYHm8yW5MUTfJ06wjxltX0hqd9v51STmT+QKnY/+p6LwKQ14HnMYs49GPCzCGPP+Mq7ZUn86yfL/o9Tp+s3y/9B3Rk4tBwRzYeZhylcow65sp5C/gQp2mteg7vCftG3Z79ps/B5PRxLxxC5jxwzR0eh3b1+0kLCSMbsO6EnI2hMCAo5Su9AoTl43H2dWFmk1q0G1oV3o36cOBLQfxr1WJZQFLQEqO7z/B0V2/awzExMNV88g3zFx/JB/cjikijLxtumEMDSHlTCApfxzHrsLrOE/5BqSJpHVLkffNFwJEwULo3D0xBufsfJFGE7vHreDd7z9Gp9dxbt1+4kLCqT30XSLPXedqwCkqd2/GS3XKY0o2kpR4n21DzY18v+plqD3sXUwpRqRREjD6O5ISn3/GewCj0cSccfP4bNUMdDod29ZtJzQkjB7DuxEcFMKRgEBKVyrNlK8n4uzqTM2mNek+tBs9GvfGZDKxaPISvlj3GUIIQs6GsHn11hzli2I7hNb7wXK8Y/Ow2XmYh7qmAFeAPkBHzI0yg5SyoRDinpTSOcN2/sBcwBVzQ/krKeUyIUR/4GPMjdVzmIe5drdM+LNZSvmTEKKY5e8KlvfKuC4UqIJ52G3GNMMBZynlRCFEdWAZcB/YB9STUqZNqyWE2AcMl1JaXW4UQswB/gv4SfnsmzieNnT275Qvj8OzE/0NSjhrm7DoRSuZ98VPRqJFWPJzT4z8l3g9zwu8TzAHdt2/mtshAFAnX7HcDgGATz1so3zERTs/O9HfwN1D24+mF825sOaBLC/U3Zt5cjsEAArv0tab9aI9mjkst0MAYMSP9rkdAgAnHmubiO5Fc9VrvU/wxfq5yV87BDy7lu22jd9Bm022UT4A9t3a9Y97WMjsop1z9bf98Bs/5Eqe5eY9mieBrOZOn2f5l5rO6heL5d7Jelm83yIg05RmUsruGf4OxXyPZ1briln+jH0iTcYHeZ23PIoFy/2kVg1KKWWDLD4PUsqPgL9gNgtFURRFURRFURTbk2sNzX+ot4UQozDnWxjQPXfDURRFURRFURRFsT2qofkcpJTrgHW5HYeiKIqiKIqiKP8Mpn/cYN8X49/3RFRFURRFURRFURQlV6mGpqIoiqIoiqIoivJCqaGziqIoiqIoiqIof5HcfJZlblI9moqiKIqiKIqiKMoLpXo0FUVRFEVRFEVR/iK5+hDNXKR6NBVFURRFURRFUZQXSjU0FUVRFEVRFEVRlBdKDZ1VsiTl/2snf9Z02MYDkKSNDL5IsZE4TKqcWpH/rw/qegpbyQ9dHtuIw1Y8mjkst0MAIO/Iz3M7BABMP47M7RBsiq1832JS3y8ZGdX3bY6YbOR3099N9WgqiqIoiqIoiqIoL5Tq0VQURVEURVEURfmLqMebKIqiKIqiKIqiKMoLoBqaiqIoiqIoiqIoygulhs4qiqIoiqIoiqL8Rf4/pwJSPZqKoiiKoiiKoijKC6Z6NBVFURRFURRFUf4iajIgRVEURVEURVEURXkBVENTURRFURRFURRFeaHU0FlFURRFURRFUZS/iEnkdgS5Q/VoKoqiKIqiKIqiKC+U6tH8h2nerAFffDEJvU7Ht9+tYdZnC6zW58mTh+XfzeG1yhWJj0+g43v9CQu7BcDIjwfSo3sHjCYTQ4aMY2fAfs1xNGlaj5mzxqPX61ixYj1ffr44UxxLls2mcuUKxMffpnvXD7lxI5zXX3+VOfOnASCEYPrUOWzetFNzHLUaVmfE5MHo9Do2rNrEd/N/sFr/Wo1KDJ/0EaXKvcyofhPYtXkfAD5+hZj9zTT0ej129nas/eYnflq5QXMclepXpvuE3uj0OvasDWDjol+s1r/duxWNOjTFmGLkTvwdFo+YR2x4DC+VK07vqX1xdHbCZDTx6/wfCdx8WHMcNRtUY9jkQeh0Ojau2cKK+aus1leuXomhkz6kZNkSjOn/KXu2WJeBfM5OrN//Pfu2H+SzMV9pjqN8fX86jO+BTq/j4LrdbF9knbdNe7WgTofGmFKM3I2/w/KPFxIfHguAu68HXWf0w923IFLC3B7TiLsVoymOeo1qMW7acPQ6Pet++JUlc5dbrc+Tx57ZCydT4dWyJCTcZlDvTwi/acDe3o4pn4+lon9ZTCbJ5DGf8fvhk5piAKhQ359O43ui0+s4sG43Wxf9arW+Wa+W1OvQGFOKibvxiXz78ULiws2f+Zur67kVfAOAuPBY5r4/Q3McDrWq4j58AOh13Pt1G3eWr7Van69lM9wG98EYbT4Wd9dt5N6GbeStUgn3Yf3T0tkXK0rMqCk83HdEUxwu9SvjN/F9hF5H3NoAohb+bLXevW0jfMd0JzkyDoDYFVuJWxsAwMsrJ+BU+RXun7jItR5TNO0/lWOtKrh/PAB05vxI/G6d1XrnVs1wG/w+xhhzHHfWbuTer9sA0Ht74jFhGPpCniAl0R+OISUiSlMcdv7VcOo5EHR6Hu3ewqNfV2dKY1+rAY7tugMSY+hV7n+V4bM7OuE6ZwWPjx3i4ddzNMUA2vPDoUol3EdkLB9FiPlkKg/2aisf+tKVyduqF+h0JB/bRfJe6/o0T8se6EtWBEDY50U4u3J/fGcAHHqPQ1+0NMbrF0n6bqqm/acaO+0LDhw+hrtbATb8sDjTeikl079azMHA4zg45GXqmGGUK10SgI1bA1iywnx+9e3WgdZvNdUcR/n6/rSz1KeH1u1mxxP1aZNeLahtqU/vxd9hhaU+faVmedqN656WzvtlX5Z9+BVBO49risNWvl+qNHid/hP7o9Pr2L5mO+sWrrdaX7F6BfpN6EeJssWZ9sF0Dm49lLau9+heVGtUDZ1OcOrgaRZOWKQ5DruKVXF47wNzOd2/lUdb1mZKY1+tPnnbdAMkxhtXebjY8vvH3QvHXsPQuXuChPtfjELGaqs/itV/lUYTuyD0Os6t3cexhZus1lfq3Aj/rk2RRhOPHyQR8Mk3xF2OIL+fBz32zCLhqgGAiNNX2DX6O00xAFRrUJWPJn2ATqdj85qtrFpgnR+Vqldk0KcfUKJsCT4dMIV9Ww6krfPy9WLk7GF4+ZrzY0SXUUTe0pYftsr0f/qAk2w1NIUQY4BOgBHzxEl9gZrAUinlg78uvCxj2Qp0klLe1rBtA2Av0FtK+Y1lWWXgFDBCSjlbw3t+DXwhpbzwvNs+L51Ox9w5U3njrY7cumXgaOBWNm3eycWLl9PS9OzRkYSERMqUq0O7dq2YPm0Mnd7rT9mypWjXrjWv+jfC17cQO7atpWz5uphMzz8Plk6n4/MvPqV1y66Eh0ey7+AGtm7ZRfClK2lpunZrx+3bd/B/tRHvtm3Bp5NH0qPbIC5cCKF+ndYYjUYKeXty5OgWtm3djdFo1BTHJ9OH0b/dYKIM0aza/jX7dx7iWkhoWhpDeBQTPppK1wEdrbaNiYqje8t+JD9OxtHJkZ/2f8/+HYeIiYp97jiETkfPyX2Z+t4E4iLjmP7bZ5zYdYzwy7fS0oSev8aoFsN4nPSYpp3f4L1R3ZgzcDaPHz5iwZA5RIYacPNyY/qWzwk6cIYHd+5ryo+Ppw1hYIehRBliWLF1KQd2HOL65bC0NJHhUXw6eBqd+3XI8j36fdybU0fPPPe+MxI6HZ0m9eLLzpNJiIxnzG/TCQo4geFKen7cuHCdqS1H8jjpMfU7N6PtqC4sHfglAD2/GMiW+b9w8dBZ8jo5IDWUUTDnx8SZI+nWdgCREVH8GvADu7fv50rI9bQ0/32vDYm379CoWmtavNOMkRM+YlDvT2jf5T8AvFWvPQU93Ph23XzaNOmMlM//RSF0OrpMep/ZnScRHxnH+N9mcibgOBFP5Meklh/zOOkxDTs3p92oLiwa+AUAj5MeM+Gt4ZrywIpOh/vID4keMJKUqBh8fljAw/1HSL5+wyrZ/Z37SJg532rZoxNBGDr2M79Nfhd8N64g6ajGhrdOR5Epfbny3gSSDXGU3jSbxIBjJF2+aZXs9qZD3Bq/NNPmUUt+ReeYF4/3mmvbf4Y43Ed9SFS/kaRExeK7aj4P9geSfO3J/NhP/Iz5mTb3nDKS21+vJunoKYSjA2goG6lxOL3/EfcmDccUF4PLzMUkHz+M6Vb6eavzKYzDO+9xd8xA5P17iPwFrN7CsWNPUi4Eadt/hji05kfSiSAi2qeXj8KblvMwUGP5EDryvtOHh0snIhPjcBw0i5Tzx5DR6efL403pP4bta7+FzrdE2uvkfRtIts+LfY0clg+gzVtN6fRuK0ZPzvqnwcHA49y4FcHWdd9w9vwlJs+ez5plX5F45y6LvlvNum/mAtC+1yAa1KmBa36X545B6HR0nNSLryz16ajfpnM2i/p0f8uRJCc9pl7nZrw7qgvLBn5JSOB5prw1AgAnV2em7J/HhQPayomtfL/odDoGTvmATzqNJtYQy7zNcwkMOMqNy+nlNDo8htlDP6dt33etti33elnKVylHv2bmiyJf/PI5r9Z4lbNHzz5/IEKHQ9dB3J/1MTI+BueJC0k+HYgpIsN5W6gweVt05N6UQfDgHsIl/bx16jOSR5tWk3L+JOTVXn8InaDJlG78+N4M7hri6bxpElcDThJ3OSItzcUNgQT9sAeAl5u+RoNxnfm56ywAEsOiWPnmGE37zkin0zF06iCGdPyYGEMMy7Yu5PDOQEIzlI+o8GimDZlFh37/zbT92DkjWTl3NScOnsTRyQGT6f+zUfZv9Myhs0KImkAL4DUp5atAE+AmMBhweso2+hcZZEZSyre0NDIzOAe0z/C6A6D5G1pK2fvvaGQCVKtamatXQ7l+/QbJycmsX7+RVi2tv0xbtWzG99//CMDPP2+hUcM6luXNWb9+I48fPyY09CZXr4ZSrWplTXFUqVKJa9fCCA29SXJyMj//tJm3W1hfrX27RRPWrDL3Umz4dRsNGtQC4OHDpLRGpUPevJp/mwFUqFyWm9dvEX4jgpTkFHZs2E2D5nWt0hhuRnL54tVMlVZKcgrJj5MByJPXHiG0D54v6V+KqFAD0TejMCancGTTIao2rW6V5nzgHzxOegzA5dPBFPQpaI7vegSRoeariQnRCdyJTSS/e35NcZSvXJaboeGE3zCQkpxCwMbd1G9exyqN4VYkVy5eQ2ZRiZep+Arunm78vl/b1e5Uxf1LEhMWSezNaIzJKRzfdBj/ZlWs0gQHnk/Lj2unQ3DzdgfAp6QfOr2ei4fMX/yPHiSlpXtelV6rQNj1W9wMCyc5OYXNv+6gyZsNrNI0ebMBv6zdDMC233ZTs25VAEqWLsGRg8cAiItN4E7iXSr6l9MURwn/kkSHRRJjKR/HNh2icrOqVmkuZSgfV0+H4OZdUNO+/kyeCqVJuRVBSrgBUlK4v2Mfjg1qP/f7ODWpR9Lh48ikR5ricPIvxaPQSB7fiEImp5Cw6SCuzaple/t7h89iuvdQ074zyluhNCk3I0gJj0zLDydLPfUs9iWKgl5P0tFTAMiHSZrzQ1+yDKbIcExR5uOSfGgPeapaH5e8TVrwaPsG5P175v3dSf8K1Jd4BZ2rO8lBJzTtP20fOciPjJya1uVhDsqHrmgpTLEGZHwUGFNIOXMIu/JPLx92/nVJOXMw7bXxyjl4lPPyAVDFv+KfNg73HjpKqzcaI4SgUoWy3L17j5jYeA7/fpKaVSvjmt8F1/wu1KxamcO/a2t4F7fUH6n16YlNh6n0RH0aEnieZEv9cf10CAUs9WlGr79Vgz/2nU5L97xs5fultH9pIkINRN6IJCU5hf2/7adWs5pWaaJuRXH90vVMFwalhDx582CXxw77PPbY2etJiE3QFIe+RBlMUeHIGAMYU0j+fS/2r1mfL3nqv82j3b/BA8t5e9d83up8XwK93tzIBHiUBI+1nS/e/i+TEBpF4o0YTMlGLm06ysvNXrdK8zhDfWnvmFf7RbE/UbZyGcJDwzFYysfujXup09w6PyJvRXE1i/JRrNRL6O30nDhozo+HD5J4pLH+UGxPdu7R9AFipZSPAKSUsUBbwBfYK4TYCyCEuCeEmCSE+B2oKYR4XQixXwhxUgixQwjhY0n3vhDiuBAiSAjxsxDCybJ8uRBikRBirxDimhCivhDiWyHERSHE8tRghBChQggPIUQxy7plQojzQoidQghHS5qqQoizQohAIcRnQog/MnyeG4CDEKKQMLcu3gC2ZXh/fyHEUcv2vwoh3IQQZYUQxzKkKSaEOGv5e58Qoorl72aWfZ4SQvwohHC2LJ8hhLhgec/n7jVN5VvYm5u30q9S3Qo34Ovr/dQ0RqORxMQ7FCzohq9vFtsWtt42u3x8vbl1y5D2OiLcgK9PoSfSFEpLYzQauXPnLu4F3QBzQ/X349sJPLaNwYPGaurNBPDy8SQqIjrtdZQhGk8fz2xvX8jXi3V7VrDt5K8sX7BKU28mgLu3O3GG9G3jDHFpDaesNGzfhDP7TmVa/nKlUtjlsSMqLFJTHJ7eHk/kR0y280MIweAJHzB3svZhRKkKFHInPiIu7XWCIZ4ChZ7ecKrTrjF/7DsNQKESPjy8c5/+i4czbsss2o7qgtBpu5W8kI8nhoj0vIyMiKaQj5dVGm8fTwzh5jRGo5G7d+7h5l6AS+dDaPJGffR6PX5FfalQqSw+ha3LeHa5FXInPiK9fMQb4nH7k/yo164x5zKUD/u8eRj/20zG/jqdys/RIHuSnacHKZHp5cMYHYPeK3McTo3q4rNuKR6zxpuHhT4hX/MG3N+xR3McebwL8jhDfjw2xGGfRX4UeKsmZXbModjikdj7eGje39PovTxIiUwfkp0SFYveK/N+nBrXwXf9Ejw/G5eWH/Yv+WG6ew/Pzyfgs3YRbkPeB43lVOfuiSk2PQ5TfAyioHW+63yLoPf1w2XqPFymL8TO31IOhMCx2wAerMz5eZuT/MgoX/MG3N+2V3McIr878nZ6+ZCJcQjXrM8XUcAT4e5lblzmgqiYOLwz5FEhLw+iYmKJionF2ys9bwp5mpdrUaCQOwnPUZ/WbteY85b6NKMqLWtz/LdDWWyRPbby/eLhXZCYiPRyGmOIpWA2L8xdPHWRM4FBrD2xmrUnV3Ni/0luXrn57A2zINw8kPFPnLdu1ueLztsPfSE/8o2dQ75x87CrWDVtuXxwH6cPJ+I8aTEO7fuA0FZ/uHi7cTciPu31PUM8LoXcMqXz79qE3gc/p97oDuyesDJtuWsRT7psnUL79WMoXK20phjAXD6irY5LDB7e2au3i5Tw496d+0xZNpFvdixmwNg+6DTWp7ZM5vK/3JKdI7kTKCKECBFCLBRC1JdSzgUigIZSyoaWdPmAP6SU1YHfgXlAWynl68C3QOrNEr9IKatKKSsBF4FeGfblBjQChgCbgC+B8kBFIYR/FrGVAhZIKcsDt4HUcRLfAf2klDUxD/d90k/Af4FamIfNZrx0shIYaem9PQdMkFJeBPIIIVLH57QHrG4KEEJ4AGOBJlLK14ATwFAhhDvwDlDe8p5Z3lQkhOgjhDghhDhhMmU9bDKrXrcnr9hlnSZ722ZXVp1/meIgy0QAnDgRRPWqb9CgXhuGDe9P3rx5NMXxlECyvXlURDTtG3Wjdc32tGz3Ju4emSvnbIWR5WfNOm2dd+rzcsWS/LbE+h69Al5uDPxyMIuGz8vBcdF+jNt2f4fDe45a/ZDQKsvO4afEUb1NXYq9WoIdS38DQKfXU7JqWX6cupKprT7Bo6gXtds20BhHNsrHU/Lsx1UbiTREs2HXD4ydOpxTx4I0XxB52j6yUrNNPYq9+jLblm5MWza8Vl8mtRrJkkFf0Wl8DzyLamvwZn2+WL98eOAo4S06Y2jfh6TfT+Ex6WOr9XoPd+xLFudhYA56z7JRPhJ3Hed8rfe51Pwj7h4K4qUvPtK+v6fG8ezy8WB/ILfe6kJEu74k/X4aj8nmYYjo9ThUrkjCF0swvPcBdoV9cG7VTGMcWSx7snzo9Oh8/Lg7fjD3v5xEvgEjEE7O5H2jDcmnjiLjtN3DbB1HDvLDQu/hmqRLgAAAIABJREFUTp4cl4/s1+t2/nVIORsIMncehZ7VeSyEyDJczaNmnrM+fenVEuy01Kep8nsWoHDpopzXOGwWbOf75Xnq0yf5FvOhaMmidKrWmY5V38O/lj8Vq1fQGEcWy56MQ69H512Y+9OH8mDRVBx7DgOnfKDTY/dKBR6uXcK9iQPQefpgX1fjUO+n/OZ70pmVu/i67jAOTF9LzUFtALgffZslNQbz/Vtj2Td5FW/PHUAeZ0eNcWSxLJvHRW+n59VqFVgweQl93hqAT1Ef3myX86Hvim14ZkNTSnkPeB3oA8QA64QQ3bNIagRSZ3QoDVQAAoQQZzA3wPws6yoIIQ4KIc4B72FuSKbaJM01xjkgSkp5TkppAs4DxbLY53UpZeqA/5NAMSFEAcBFSpk6C0HmWRXMjcT/Ah2BNakLhRCuQAEpZeod7CuAehm2aWf5uz1gPUsC1ADKAYctn7kb8BJwB0gCvhZC/AfI8p5WKeVSKWUVKWUVnS5fVkkIv2WgiJ9v2mu/wj4YDFFPTaPX63F1zU98fALh4Vlsq3HiiojwSPz8fNJe+xb2wRBp/QUSEZGeRq/Xkz+/C/Hx1iOeQ4Kvcv/+A8qV03YVLToimkK+6T1UhXy8iIl8/qvGMVGxXA2+zms1KmmKIy4yjoIZelwK+hQkISo+U7qKtV/lPwPbMqv3NFIep6Qtd3R25JPvxrJu9iounw7RFANAtCHmifzwJDab+fHq6+Vp1+M/bPx9HR+NH8BbbZszcHRfTXEkRMbj7pt+hdnNx53b0Znzo2ztirw98D/M7z0zLT9uR8Zx88J1Ym9GYzKaOLPzOEUrFNcUR2REND4Zevy9fb2IiozJnMbSs6/X63HJ78zthESMRiNTx35Oy4Yd6ddlKPldXQi9an2vWnYlRMbh7ptePtyfkh/lar9Ki4HvMqf3dKvycTvaPLQr5mYUl46e56Xy2vIjJToGO+/08qH38kyb1CWVKfEOJJuHlN/7dSt5yrxitd6paX0e7D0MKRob3Zh7MPNkyI88PgVJfiI/jLfvIi15ELd6J04VX9a8v6cxRsVg553eI2NXyCOL/Liblh93f9lK3rKvWLaN5XHwFfMwU6OJB3uPkKdsKU1xmOJi0Hmkx6Fz90TGW5+3Mi6G5OOHwWjEFB2JMfwGOp/C6F8ph8Ob75B/0Vocu/Ynb/1mOHbuoymOnORHKqdmOS8fMjEOUSC9fAjXgsg7mc8XsDQ0Mwyb/bt5e3kQGZ1+rKKiY/HyKGhZnl7XRMWYl2txOzIet2zUp2VqV+TNgf9hYYb6NFWVFrU4s+MYphwcF1v5fok1xOLpm15OPX08iM/i+zYrtZvX5tLpSyQ9SCLpQRLH9x6nTOUymuKQ8bEI9yfO29tPnC/xMSSfOgJGIzI2EpPhJvpCfsiEGIxhV8zDbk0mkk8dRv+StvrjriEeF9/0EVTOPu7ci376cOBLvx2lpGVorfFxCkm3zcN6o86FkhgWjVsJbSPdYgyx5ol8LDx9PImNivuTLdJFG2K4/McVDDcMGI0mDu04zCsVteWHLTPl8r/ckq2+aSmlUUq5T0o5ARhIes9hRklSytRaTADnpZT+ln8VpZSpl3uXAwOllBWBTwGHDO+R2rNowrqX0UTWExdlTGO0pHnmZUMpZSSQDDQFdj8rvcU6oJ0Q4hXzW8jLT6wXQECGz1xOStlLSpkCVMPcCG8DbM/m/jI5fuIMJUsWp1ixItjb29OuXWs2bbaesXXT5p106WK+0frdd99m777DacvbtWtNnjx5KFasCCVLFufY8czDa7Lj5MmzlHi5GC+95Ie9vT3vtm3B1i27rNJs3bKbju+Zi0mbd95k//5AAF56yQ+93nwLb5EivpR6pQRhN26hxfkzlyhawg/foj7Y2dvRvE1j9u3M3tAgLx9P8jqYe1JdXF3wr1qR0CvaGhJXgy7jXdwHzyJe6O3tqNWyDicCjlmlKVa+OL2nD2BWr2nciUtMW663t2PY0lEc+HkfR7dqm6Ex1YUzlyha3A/fIub8aNq6MQd2Zm8G23EDJ9Oy6n9pXb09cyYtZOtPO5g/bYmmOEKDruBVzAcPP3N+VG1Zm6AA6x6OIuWL0XlaH+b3nsnduDtpy68HXcXJNR/OlvtUy9SqQMRlbeXj7OnzFCtRBL+ivtjb29Hinebs3m49C+Lu7fv5T4cWALzZqjGBB833Dzk4OuDoZK6aatevTorRaDWJ0PO4/kR+VGtZh9NP5EfR8sXpNq0vc3vPsMoPp/z5sMtjrvqc3Vwo9XoZzfnx+HwwdkUKY+frDXZ25GvegIf7rcuc3iP9B4tj/Zokh1qfE/neaMT97dqHzQI8CLpM3uI+5CnihbC3w61lXRKfOF/svNJHF7g2rUbSFW2f+c88Oh+MXVHr/HhgqadSZcwPp/o10yZOenQ+GJ2LMzo3VwAcqvmTfC0MLYxXgtH5+KHzMsdhX6cRj09YH5fHxw5hV948qEe4uKL3LYIpysCDOVNJ7NeeO/078HDlIh7t38nDHzJPoJQdOcmPVM5vNMzRsFkA083L6Dx8EG5eoLfDzr8OxguZ7+sTnr4IR2dMYcE52l9ONKhTg9+270ZKSdAfF3F2zoenhzu1q7/OkWOnSLxzl8Q7dzly7BS1q7/+7DfMQmp9WtBSf1T5k/p04RP1aaqqrWpzbJP2YbNgO98vwUHBFC7mi3eRQtjZ21G/VX0CA45ma9voiGgqVq+ITq8z96LVqKh56Kzx+iX0hQojPLxBb4d99YYkn7Y+b1NOHcaurOW8dc6PztsPU7QB47VgRD4XhIu5/rArV9lqEqHnERl0Dbfi3rgW8URnr6dMyxpcDbC+NadAsfRRMCUa+5MQar5dxNHdBaEz/2R2LepJgeKFSAzT1ut86cwl/IoXxqeIN3b2djRu3ZBDO7P3m+bSmWBcCrhQwN2cH6/VrkxoiLb8UGzPM2edFUKUBkwZGlb+QBjmHkYXIKtLWsGApxCippQyUAhhD7wipTxv2cZgWfYeEJ7zj5FOSpkghLgrhKghpTyKebKfrIwHvKSUxtQhIVLKRCFEghCirpTyINAF2G9Zd1UIYQTGkbk3E+AosEAIUVJKecVy76kf5iHGTlLKrUKIo8CVLLbNFqPRyEeDx7J1y2r0Oh3LV6zjwoUQJk4YzomTQWzeHMC3361lxfK5XLpwiISE23TqPACACxdC+OmnTZwL2kuK0cigj8ZomnE2NY4Rwyby68YV6PU6vl/5I5cuXmbM2MGcOnWObVt3s3LFOpZ+/QVnzu4hISGRHt0GAVCzVhWGDO1HckoKJpOJoYPHEx+n7WZ8o9HIzNFfsnDNF+j0ejau2cy14Ov0/7g3F85cYv/OQ5TzL8MX304nfwEX6jWtTb8RvWlbvzPFSxVj6MSBqeOKWbloDVcuXdMUh8lo4tvxyxi9cgI6vZ5963dx6/JN/ju0I9fOXuHkruN0Ht0dBycHhiw0D0WMjYjhs97TqNmiNmWrlcOlgAv12zYCYOHwuYRdeP5GjdFoZNaYr5i7ejZ6vY7f1m7lWkgofUf05GJQMAd2HqZcpTLM+mYK+Qu4UKdpLfoO70n7ht00fe4/y4/V479h8MoxCL2Ow+v3EnH5Fq2GtCfs3FWCdp2g7aguODg50G/hMMD82I4F789Emkz8OPV7hq0aD0Jw449rHFyb3WtB1oxGI59+MpPlPy5Ap9Px0+rfuBx8jcGf9OPcmQvs3n6A9as28PnCyew5tpHbtxP56P1RABT0cGP5jwswmSRRhmiG9R+Xo/xYNf5rhq0cZ37cy/o9RFy+SZshHQg9d4Uzu07QblRX8jo5MCBDfsx9fwa+Jf3oNq0vJinRCcGWRb9azVb7fBliIn7mPLwWzDA/vuK37SRfC8O1XzceXwjh4YFAXDq8g2P9muaes8S7xE6Ylba53qcQ+kKePDqpYYbGJ+K4NW4pL38/0fx4k3W7SQq5iffQTjw4d4U7Acfw7NEC16bVIMVIyu17hA1Lf2RHqZ+mkfdlP/T5HCj/+zfcGDGfuwc0XDQzmoifMZ9Ci6ab82PjDpKvhlGgfzceXQjh4f5AXDq2walBTUgxYrxzl9jxn5m3NZmI/3Ip3ktmgRA8vniZuz9v1ZYfJiMPvp6D87jPQKfj8Z5tmG6G4tChB8YrwSSfOELKmWPY+1ch/1fLwWTiwcrFyHuZGxQ5kpP8AOx8C6H39iQpp+XDZOLRhmU4vj/B8niT3ZiibpKnWUeMt66kNTrt/euSciZz48mx/1R0XoUhrwNOY5bx6McFGEO0zXQ6YsIMjp8+y+3bd2jcpjMDenUhJcXcW9j+nbepV7MqBwOP82a7njg6ODB59BAAXPO70Ld7Rzr0Ng/57tejk6YZZ8Fcf6wd/w0frRyDzlKfGi7foqWlPj276wTvjupCXicH+ljqj/jwWBa+PxOAgn6euPl4cPlozuYstKXvl/njFjLth6no9Dp2rNtJWEgYXYd1IeTsZY4GHOWVSq8wYdk4XFxdqNGkOl2GdqFPk74c3HII/1r+LA1YjJSSE/tPcnTX7xoDMfHw+3nkGzHTXE4PbMMUHkbed7pjDA0m5XQgKeeOY1ehCs7TvgWTkaR1S5H3zedt0tol5BtpnrLDGHqZx/u2aApDGk3sHreCd7//GJ1ex7l1+4kLCaf20HeJPHedqwGnqNy9GS/VKY8p2UhS4n22DTU38v2ql6H2sHcxpRiRRknA6O9ISnz+Ge8BjEYTX46dx+erZ6LT6diybhuhIWH0Gt6dS0HBHA4IpEyl0kz95lNcXJ2p1bQmPYd1o2ujXphMJhZMWsJX62aDgJBzl9m0Wlt+KLZHPGtsuxDidcz3WxYAUjA3lPpgHnb6AWCQUjYUQtyTUjpn2M4fmAu4Ym7QfiWlXCaE6A98jLmxeg7zMNfulgl/NkspfxJCFLP8XcHyXhnXhQJVAOcn0gwHnKWUE4UQ1YFlwH1gH1BPSlnb8niT4VLKFk98xonAPSnlbEvcizHPqHsN6CGlTMiwj8+A4lLKUMuyfZb3PCGEaATMBPJa3noscBzYiLnnVgCzpZQr/izP7fIUzvV5nZ3s8z470d/gZRefZyf6G7ySN/uTDP2VriVnb4jQX80/j8b7BF+wPfeu5nYIANRz1jac9UWb6J747ER/g/jYrIf//93c3P/Wp289levLybkdAgCJV+1zOwQAPJo6PzvR3yDvyM9zOwQABlYZmdshAHDqsW08t9BNn+UDDf52PzayjfN22V7b+B200Wh4dqK/ycHw3dofFZBLRhbrmKu/7WeGrsmVPHtmj6aU8iTmSXOeNM/yLzWd1TeH5d7Jek9uJKVcBGSaekxK2T3D36GY7/HMal0xy5+xT6TJOJvrecvEOwghPsE8MQ9Syn2YG55P7nviE3HXeDJNhn3MfmJZgwx/7wGqkpn2qSIVRVEURVEURVH+YZ7Z0PyHelsIMQrz5wsDuuduOIqiKIqiKIqiKP8//pUNTSnlOrK+j1JRFEVRFEVRFOVvk+v3xOWSf98TURVFURRFURRFUZRc9a/s0VQURVEURVEURbEFufksy9ykejQVRVEURVEURVGUF0o1NBVFURRFURRFUZQXSg2dVRRFURRFURRF+YuY/k+nA1I9moqiKIqiKIqiKMoLpXo0FUVRFEVRFEVR/iL/n/2ZIKT8f/3otssuT2F1UJQslXErktshADBdFM/tEAB4J/5AbodgU+p4lc3tEADw0DvldggAxBof5HYINqWIPn9uhwDATeOd3A7BpgScWZrbIQDg6Fs3t0MAoK5XudwOAYAUaRvzhBbQO+Z2CABsizyd2yGkSXkcLnI7huc1pFiHXP1t/2Xo2lzJMzV0VlEURVEURVEURXmh1NBZRVEURVEURVGUv4ht9I///VSPpqIoiqIoiqIoivJCqR5NRVEURVEURVGUv4j8P50OSPVoKoqiKIqiKIqiKC+UamgqiqIoiqIoiqIoL5QaOqsoiqIoiqIoivIXUZMBKYqiKIqiKIqiKMoLoHo0FUVRFEVRFEVR/iImNRmQoiiKoiiKoiiKouScamgqiqIoiqIoiqIoL5RqaP7DNG/WgPN/HODShUN8POKDTOvz5MnD6lWLuHThEEcObeKll/zS1o38eCCXLhzi/B8HaNa0vorjXxhH7YY12HR4HVuP/kivD7tkWv96DX/WB6zgTPghmrZomLa8dPlS/LBlGRv2r+aXvT/wRusmOYrDs2ElGh76nEaBX1JyYKunpvNpUY2WkWtwrVTCarlj4YK8efU7SvR/O0dx2MpxsZU4qjWoysr937Hq0Ao6fdAh0/pXq1dk6bZF7A7dQf2366Yt969Via93LE77t/PKVuo0r6U5Dv/6rzFnz0Lm7V9Cm/7vZlrfondrvtw1n8+3z2XC6sl4FPYEoFi54kz9dRZfBpjX1WpRR3MMYDv5YStxVKxfmVl75jF7/wJa9H8n0/o3erdkxq45TN3+BZ+snkhBy3FJ5eDsyJzfl9F1Um/NMYDt5IetxDF22hfUe7sDbTr3y3K9lJJpXy7izXY9eadrfy4EX0lbt3FrAG+178Vb7XuxcWuA5hhS2UJdVrVBFVbs/5YfDi2n4wftM61/tXpFlmxbyK7Q7dR74rgs27E47d+OK1uoncPyserActYcWsl7WZSPStUr8s32xewN20mDt+tZrfPy9eLz1TP5ft+3fL/3W7z9CmmO47X6r7Fo72KWHFhK2wFtM61v3bsNC3YvZO6OeUxZMxXPDOdto7aNWLJ/KUv2L6VR20aaY0hlC+XDVslc/pdb/nUNTSGEtxBirRDiqhDighBiqxDiFQ3vM1gI4fSCYiomhOiU0/fR6XTMnTOVFi07U7FSQ9q3b0PZsqWs0vTs0ZGEhETKlKvDV3OXMX3aGADKli1Fu3atedW/EW+3eI95c6eh02k7/CoO241j7Izh9O80hFZ1O/LWO80o8UoxqzSG8CjGfjSZrb/stFqe9DCJ0QMn0aZ+J/p2GMzIyYNxye+sKQ50gorTe/B7p5nsrTcc33dq4fxK4UzJ9PkcKN7rDRJOXs60rvynXYjec0bb/lPDsKHjYitxfDTlQ0Z2GU23hr1o1LohL5UqapUmOjyaGUNnsWvDHqvlZ44E0bt5P3o378eQ9iNISkri+P6TmuPoPbkvU7t9ypAmH1CnVT38ShWxSnP9/DVGthjKsDcGEbj1CF1GdQfg0cNHzBvyJUOaDmRK14n0mNAbp/z5NMdhK/lhC3EInY5uk9/ns25TGNnkI2q2qotvKT+rNGHnrzO+xQjGvDGU41sD6TCqq9X6tsM6cun385r2n8pW8sNW4gBo81ZTFn8x5anrDwYe58atCLau+4aJHw9i8uz5ACTeucui71azZtlXrFn2FYu+W03inbua47CFuiz1uHzSZTTdG/amcRbHJSo8mplDP2N3Fsfl/eb9eL95P4ZajsuJHJSPoVMHMbzzKLo07EmTNo0oVuqlTHFMGzKLXRt2Z9p+7JyRrFm0ni4NetLn7QEkxN7WHEe/Kf2Z2G0CHzQeQL1W9SnyRH167fxVhr49hEHNP+TwlkP0GN0DAGdXZzoO7sSwVkMZ2moIHQd3Ip+rtvo0NZbcLh+K7flXHUUhhAB+BfZJKV+WUpYDRgNaLhUNBrJsaAoh9M/5XsWAHDc0q1WtzNWroVy/foPk5GTWr99Iq5bNrdK0atmM77//EYCff95Co4Z1LMubs379Rh4/fkxo6E2uXg2lWtXKKo5/URwVXyvHjeu3uBUWQUpyCts2BNDoDeurqBE3DYRcuILJZH19K+zaTW5cvwlATFQs8bEJuBV00xSHW+WS3L8eyYMb0chkIxEbAvFuXiVTujIj23Fl4SaMj5Ktlnu/UYX7N6K5G3xL0/5T2cpxsZU4yviXJjw0AsMNAynJKezZuI/azWpbpYm8FcW1i9eRpqdPxF7/7Xr8vvc4j5IeaYqjpH8pIkMNRN+MIiU5hcObDlK1aXWrNOcDz/E46TEAl08HU9DHAwDD9QgiQw0AJETHkxibSH73/JrisJX8sJU4XvYvSVSogZibURiTUzi66RCvN61mleZi4B9px+XK6RDcfQqmrStWoQSuHgX440CQpv2nspX8sJU4AKr4V8Q1v8tT1+89dJRWbzRGCEGlCmW5e/ceMbHxHP79JDWrVsY1vwuu+V2oWbUyh3/X3uC1hbqsjH9pIkIjMNyIzHBcrHsloyzH5cnvuYzqv12XYzk4LmUrlyE8NDytfOzeuDdTr3XkrSiuXryGfCKOYqVeQm+n58RB87F4+CBJcxyl/F/BEGog6oa5Pj2w6QDVm9WwSnMu8Fza+wdnqE9fq/8aZw6e5l7iPe4n3ufMwdO8Xv91TXGAbZQPxfb8qxqaQEMgWUq5OHWBlPIMcEgI8ZkQ4g8hxDkhRHsAIUQDIcQ+IcRPQohLQohVwmwQ4AvsFULstaS9J4SYJIT4HagphBgvhDhuec+llkYuQoiSQohdQoggIcQpIcTLwAygrhDijBBiiNYP51vYm5u3ItJe3wo34Ovr/dQ0RqORxMQ7FCzohq9vFtsWtt5WxfHPjsPL25PIiOi011ER0Xh5e/7JFlmrULkc9vb23AzV1tBz8HHjYURc2uskQxwOPtaN1vwViuHo6050wGmr5XqnvLw8sCUhs3/WtO+MbOW42Eocnj4exBjSy0dMZAyeGRoK2dWoVQP2PNFT8DzcvQsSa4hNex1niMXd++lxNGrflNP7Mv84LlmpFHZ57IgKi9QUh63kh63E4eZdkHhD+nkbb4jDzdv9qenrt2/M2X2nABBC0Glsd9ZMW6F5/6lsJT9sJY7siIqJw9vLI+11IS8PomJiiYqJxdsr/TugkKd5uVa2UJd5+HgQbYhJex0TGYuHj8efbJG1hq0asHvD3ufeLpWntwfRERniMMTg4Z29OIqU8OPenftMWTaRb3YsZsDYPpp77wp6FyQ2QxxxhlgKFnp6OW3avhkn955M2zYmIr08xBriKPgndfGz2EL5sGUmZK7+yy3/toZmBSCry3X/AfyBSkAT4DMhhI9lXWXMvZflgBJAbSnlXCACaCilTL2RLR/wh5SyupTyEDBfSllVSlkBcARaWNKtAhZIKSsBtQAD8AlwUErpL6X8MqvAhRB9hBAnhBAnTKb7WX44S1vWipQyG2myt212qTj+QXE853t4eBVk+vwJjB08WXMcZBGHVSBCUH5SF85/+kOmZKVHtOXa0m0YH2i/+p++Gxs+LrkQB2S9j+fh7uVOiTLFObb/hMYYQGQZR9aB1H2nAS9XLMnGJb9YLS/g5caHXw5hwfC5//j8sJU4sjhrnxpHrXfqUbxiSbYs2QBA465vELT3lFVDVTvbyA/biePZsjoHhBBZxptVnZJdtlCXPU/98TSpx+V4To7L85wwT9Db6Xm1WgUWTF5Cn7cG4FPUhzfbNX/2hlmFkdXX7VPiaPBOA0q+WpJflvz81I01V6fYRvlQbM+/raH5NHWANVJKo5QyCtgPVLWsOyalvCWlNAFnMA9zzYoRyNjN0lAI8bsQ4hzQCCgvhHABCkspfwWQUiZJKR9kJ0Ap5VIpZRUpZRWdLusx8uG3DBTx80177VfYB4Mh6qlp9Ho9rq75iY9PIDw8i20jrLfNLhWHbcYRZYjG29cr7XUhXy9iImP+ZAtr+ZydWLjqC+bNWMLZk9rvs0qKiMfRN/2qqINPQZIiE9Je2zk7kL90EWr9Mp7Gx+fi9lpJqq0YjmulEhSoXJJy4zrR+PhcSrz/JqUGtaFYz2aa4rCV42IrccQYYvD0SS8fnt6exEY+X8OgYcv6HNx+GGOKUVMMAHFP9EAU9PEgISo+U7qKtSvx7sD/MqP3FFIep6Qtd3R2ZPR341k7exWXTwdrjsNW8sNW4oiPjLMaCuvuU5DbWRyX8rVfpdXAtnzZe3racSn1WmmadHuTLw4tpuOYbtT5TwPajeysKQ5byQ9biSM7vL08iIxO75mKio7Fy6OgZXn6d0BUjHm5VrZQl8UYYvDySe+l9fT2IE7DcTmU4/IRi5dvhjh8PImNyl4c0YYYLv9xBcMNA0ajiUM7DvNKxVLP3jALsYY4PDLEUdDHg/jozOdtpTqVaDewPVN6TU47b+MMsXj6ptfFHj4Fic/mZ8iKLZQPW2bK5X+55d/W0DwPZDXA/M8u4WXsOjECdk9JlySlNAIIIRyAhUBbKWVFYBng8Iz95NjxE2coWbI4xYoVwd7ennbtWrNps/WkLps276RLl/8C8O67b7N33+G05e3atSZPnjwUK1aEkiWLc+z46Uz7UHH8c+P44/RFipYoQuGiPtjZ2/Fmm6bs3XEwW9va2dsxZ/lMfvtxKzs35WyY1+0zV8lXwhvHop4Iez2+bWoSuTN9oEHK3YfsKN+H3VUHsbvqIBJOXeFYt9kkBl3jSJtP05ZfW7aNy3M3EPrtzj/Z29PZynGxlTiCg4LxK14Y7yLe2Nnb0ah1A44EHHmu92jcuhG7N+asfFwJuoxPcV+8ihTCzt6O2i3rcjzgd6s0xcuXoO/0AczoNYU7cYlpy+3s7fh46Wj2/7yXwK2HcxSHreSHrcRxLegK3sV98Czihd7ejhot63Aq4LhVmpfKF6fH9H582Wu61XFZ9NFXDKnVl6F1+rFm6goO/bKP9TMzj1jIDlvJD1uJIzsa1KnBb9t3I6Uk6I+LODvnw9PDndrVX+fIsVMk3rlL4p27HDl2itrVtd+DZwt12aWgYApnOi6Bz/UejVo3ZPdG7cNmAS6duYRf8cL4WOJo3Lohh3Zmr3xcOhOMSwEXCri7AvBa7cqEhoRpiuNyUAi+xX0pZKlP67Wsx7En6tMS5UvwwfSBTO41mcQM5+2p/aeoXLcy+Vzzkc81H5XrVubU/lOa4gDbKB+K7Xlao+qfag8wTQjxvpRyGYAQoiqQALQXQqyop2vRAAAgAElEQVQA3IF6wAigzJ+8113ABcjqhgYHy/+xQghnoC3wk5TyjhDilhCijZRygxAiL6DP8F45YjQa+WjwWLZuWY1ep2P5inVcuBDCxAnDOXEyiM2bA/j2u7WsWD6XSxcOkZBwm06dBwBw4UIIP/20iXNBe0kxGhn00RhMfzKBgYrjnxnHtFGzWbJ2Dnq9jl/XbOZq8HU++Ph9zgddYt+Og1TwL8tX380kfwEXGjSrwwcj3qdN/U680aoJr9eoTAE3V9q0Nz9SZMygyQSfzzwj7LNIo4k/Ri+nxppRCL2Om2v2cS/4FqU/bsvtM9eJ2ql9MornYUvHxTbiMDFn3Dw+WzUDnU7HtnXbCQ0Jo8fwbgQHhXAkIJDSlUoz5euJOLs6U7NpTboP7UaPxuZHVXj7FcLT15OgwLOa9p/KZDTx9fgljF05EZ1ex571u7h1+Sbth3bi6tkrnNh1jC6ju+Pg5MiwhSMBiI2IYWbvqdRsUYey1crjXMCFBpap+BcMn0Pohev/2PywlThMRhMrx3/NiJXj0el1HFi/m/DLN/nP0A5cP3uV07uO02F0VxycHPhw4XAA4iJi+bL39Bzt90m2kh+2EgfAiAkzOH76LLdv36Fxm84M6NWFlBRzr1T7d96mXs2qHAw8zpvteuLo4MDk0eapIFzzu9C3e0c69P4IgH49Ov3ppELPYgt1mcloYu64+cxaNd1yXHZkcVxeYXLacalBj6Fd6dH4fQAKvcDy8eXYeXy+eiY6nY4t67YRGhJGr+HduRQUzOGAQMpUKs3Ubz7FxdWZWk1r0nNYN7o26oXJZGLBpCV8tW42CAg5d5lNq7doisNkNLF43GI+/X4SOr2OXesCuBFyg/eGvsflc5c5FnCMHmN64uDkwCeLPgEgJiKGKb0mcy/xHmvnruOLTeY7utbMWcu9xHs5yJPcLx+K7RH/tjHQQghf4CvMPZtJQCjmezD7AG9ivltsipRynRCiATBcStnCsu184ISUcrkQ4kPgA8AgpWwohLgnpXTOsJ8pQAfL+98EwqSUE4UQpYAlgAeQDPzXsn67Zdnyp92nmcouT+F/10FRXpgybkWenehv8D/27jwuivqP4/jry4JXCIrcqKFh3orlfaImXnmkiWVapl0eXaZ2WGmldplWlnZZmWlqed/gLZ54H3grIrBcKqCZcuz39wcrsAKKC/5Y6/PswSNm5vudeTOz7ux3vt+Z/VhVKe4IADx2cXNxR7ApLd1rFncEAFwNRfLNUIWWmFGgOxf+MyoZrHtCb1E7n5FS3BFsSsj+H4o7AgClvVvdvtD/QSv3WsUdAYB0bRsNnXKG0sUdAYBVsbbTw5ieGn1XRxDeDc/5Pl6sn+1/ivirWPbZv61HE611DBCUx6JR5p+cZTcCG3NMD8/x+1Rgao5px5vqvgu8m8f2T5J5z+bN2hckvxBCCCGEEELc6/51DU0hhBBCCCGEsBW20T/+//dvexiQEEIIIYQQQohiJg1NIYQQQgghhBBFSobOCiGEEEIIIcRdovlvPudTejSFEEIIIYQQQhQp6dEUQgghhBBCiLtEHgYkhBBCCCGEEEIUAWloCiGEEEIIIYQoUjJ0VgghhBBCCCHuEpP+bz4MSOn/6B9uy+xL+MhBETbPp2yF4o4AQPTlC8UdwaZ87962uCMIIe7QVvtrxR0BgO93f1bcEQAY1/Dd4o4AQDIZxR0BgNMZKcUdAYCQuIPFHQGA9NRoVdwZ7tSA+3sV62f7WecWFss+kx5NIcQdk0ambZJGphD3HmlkWpJGpiVpZP47/Fd7kOQeTSGEEEIIIYQQRUoamkIIIYQQQgghipQMnRVCCCGEEEKIu8T0Hx08Kz2aQgghhBBCCCGKlPRoCiGEEEIIIcRdoqVHUwghhBBCCCGEKDxpaAohhBBCCCGEKFIydFYIIYQQQggh7hJTcQcoJtKjKYQQQgghhBCiSElDUwghhBBCCCFEkZKG5j2mY2AARw5v5lh4KKNHDcu1vESJEsyZPZ1j4aFsC13G/fdXzFr25ujhHAsP5cjhzQR2aCM5JMddy9GmXQvW71zKprDlDHl1UB45HPjmp8/YFLacxcGzqVjJGwB7e3u++HY8a7YsYN32xQx9bXChctjK/rCVHD4B9ei96XP6hH5BvWHdci2v0b8dj639mJ5rJtB14XuUq5Z5XOwcDLT64oXMZcET8GxWU3JIDsnxf8pRp40/E9d9xccbp9JlSM9cywMHP8r4kCl8sOoLRs4eSwUf16xlP52ex7iVnzNu5ee8/OObhcrx7sTJtO76BD37v5Tncq01E6dMp3PQIB57egjhx09lLVuyMoQufQfTpe9glqwMKVSOam3q8dq6SYzYOJnWQ3Ifl8ZPtefl1Z8wfOVEnv9zLG5+PgCULufI4D/G8P6Rn+n2wcBCZQCo2aY+762bwtiNX9FhSI9cy9sN7sqYkC94e9VnvDz7XcrnOC493nqKMcGTeHftZB4fW7gsDwc8zI8bf2TGlhn0Gdon1/I6TeowdeVUlp9dTssuLS2WDXp7ENPXTmf62um07ta6UDnAds51tsiELtaf4lKohqZSKkMptV8pdVgp9adSqkxRBctnewOVUt8U4foilFKHzH/DIaVUjxzLtpn/76uUOmz+PUAptdz8e3el1FtFlaUg7Ozs+PqrCTzarT9167elb9+e1KxZzaLMoGef5NKlZGrUasmXX//IxxPHAFCzZjWCgnpQz78dXR99iqlfT8TOzrrDLzkkx+1yfPTZOzwTNIRHmveke6/OVKte1aJM3/69SE5KoU2jR5kxfRZvjX0NgK49AilRwoGOrXrTtd0T9Hvm8axGqDU5bGV/2EIOZadoPv4Zggd8xoK2o6nao2nWB+MbTi/ezqJH3mZxxzEcmr6CJmP7A1C9X1sAFj3yNquf/JQm7/UDpSSH5JAcdz2HHf0/fI4pAyfwbofXadK9Jd5+FS3KRIaf5cNubzK28xvsXrWdPm8PyFqWei2VcV1GMa7LKKY+/6lVGW7o2aUD300en+/yLdvDiIyKYeW8GYwb/QofTcr8uJaccpnpv8zhjx+/5I8fv2T6L3NITrlsVQZlp+j24bPMHPgZX3UYRb3uzbMakjccWLKNqZ3e4psu77Dl+2V0eS/zuKRfT2PtF3+xeuJsq7Z9c46gDwcxbeDHjO8wgoe7t8DzphznwyP4rNvbfNx5NPtW7aTn208BUOWhB6nasDoTO41iQuAb3F//Aao1rWVVDjs7O4aNH8Z7T7/Hi+1eJKBHAJWrVbYoEx8dzxcjvmDD4g0W8xu1a8QDdR5gWMdhvNbtNXq/1JsyjtZ/jLeVc52wLYU9iv9orf211nWAVCDvy1xFQCl1tx5c1FZr7Q88Dnx9Y6bWuvmtKmmtl2qtP7lLmfLUuFEDTp+O4OzZSNLS0pg/fwndu3W0KNO9WyCzZv0JwIIFK2jXtqV5fkfmz19CamoqERHnOX06gsaNGkgOyVHkOfwfqkPE2UjOn4smLS2dZYtW06FzW4syHToHsGDuUgBWLg2hResmQOYV8TJlymAwGChVqiRpqWlcvnzFqhy2sj9sJYeb/wOkRMRxOTIBU1oGZ5bsoHLgwxZl0q78k/W7fZmSaJ15FbRcNR9ith4B4NqFFFJTruJav4rkkByS4y7nqOrvR/y5WBLOx5ORls7OZVvxD2xkUebY9iOkXksF4My+k5T3rGDVtm6noX9dnJ3K5rt8Q+gOundqj1KK+nVqcvnyFRISL7J15x6aNWqAs1NZnJ3K0qxRA7bu3GNVhor+flw8F8el8/FkpGVwcNl2at50XK7nOC4lypQE83FJ++c653YfJ+16mlXbzsnX34/Ec3FcMOfYu2wb9W46Lie3HyHNfFwi9p2kXNZx0TiUdMDewR77Eg4Y7A2kJCRbleNB/weJiYghNjKW9LR0Ni3dRNPAphZl4qPiiTgWkfX6vKFytcoc2nkIU4aJ6/9c52z4WR4OsNyXd8JWznW2Shfzf8WlKC8XbAH8AJRSi5VSe5RSR5RSL9wooJS6opT6Qim1Vym1TinlZp7/gFJqtbnOFqVUDfP8X5VSk5VSGwCLS3FKKTel1AKlVJj5p4V5fhtzD+V+pdQ+pVRZpZSXUmpzjt7XVnnkdwIu5cx6qz82Z++qOefXSqltSqkzSqnHzfPtlFLTzPthuVJq5Y1l1vD28eR8VEzWdFS0EW9vz3zLZGRkkJycQoUK5fH2zqOuj2VdySE5iiKHp5cHxui4rGljTByeXu65ysTExGXluJxyhfIu5Vi5NISrV68SFr6O7QeC+eHbmSQnpViVw1b2h63kKONVnr+NF7Omr8Ze5D6v8rnK1XzmEfqEfkGjMU+w4/3fALh4NJL7Ax9CGexwrORGhbq+OHpb92FWckgOyVFw5TxcuBiTmDV9yXiB8h4u+ZZvFdSOQxv3ZU07lCzB+0s/ZcyiiTS4qSFU1OISLuDpnj081MPdlbiEROISEvF0d8ue75Y53xpOHuVJjrmQNZ1ivIhzHvujyYAOjNg0hY5v9WP5uN+s2tatOHu4cClHjkvGCzh75H593NAsqC3hG/cDcHbvSU5uP8KEsO+ZuOt7jm4+QNzpaKtyuHq6khCTkDWdaEykQgEvNJw9epaGAQ0pWaokTuWdqNesHm7ebrevmA9bOdcJ21IkvYTm3sbOwGrzrEFa64tKqdJAmFJqgdb6AnAfsFdr/YZS6n1gLDAc+AF4SWt9UinVBJgGtDOv60HgEa11hlJqYI7NfgVM0VqHKqUqA2uAmsBIYJjWeqtSyhG4BrwArNFaT1BKGYCcYwM2KKUUUBUIKsRu8AJaAjWApcBfQC/AF6gLuANHgZ+t3YDKY+jNzVeo8i5TsLqSQ3IURQ7yGCGWO0feZfwfqoMpw0Tj2o/gXM6JP1f8SuimHZw/d+cnYVvZH7aSI68Dk9eqjs5cy9GZa6nasxn+r/Rk8+vfc2LuJsr5edNj5UdciUokfs9JTOkZkkNySI67nONO3gOa9myFb70H+LTv+1nzRjV/iaT4S7hVcmfUH+OIOhZJQmRcnvULK69cSqk891tef1dBFHR/7JwVws5ZIdTr3pyAl3uy4I3vrNreneTIr9OoUc+WVK73AF/1HQeA6/0eePj58G7TIQC8/Pu7PND4IKd3HbUiSB7zCniK2Lt5Lw/Wf5AvFn9B8oVkju09RobV/15s6VwnbElhG5qllVL7zb9vAWaYf39FKfWY+fdKQDXgAplfIzPPPP93YKG5Mdgc+DPHC61kjm38qbXO65X/CFArRx0npVRZYCswWSk1G1iotY5SSoUBPyulHIDFWuv9OdbTVmudqJR6AFinlNqotbZmrN5irbUJCFdKeZjntTTnNwGx5p7ZPJl7fl8AUAZn7Ozuy1UmOspIpYrZ94hU9PHCaIzLs0x0tBGDwYCzsxMXL14iOjqPujHWnWwkh+S4ldiYOLx8PLKmvbw9iItNsChjjInD29uD2Jg4DAYDZZ0cSbqUTI/Hu7Bx/VbS09O5kHiRPTv3Uc+/tlUNTVvZH7aS46rxIvd5ZV/5L+PpwtXYS/mWP7NkBy0mPguAzjCx84Ps+5oeXfw+KWdjJYfkkBx3Ocel2Au4eGf3Epb3qkBSfO4ctVrU5dHhvfm07/ukp6Znzb9RNuF8PMd2HKFy7Sp3raHp6e5KbHx2T2VcfCLurhXwdHclbN/B7PkJiTRqUM+qbSTHXsQ5R++wk5cLKXnsjxsOLdtOj/GDWGDV1vKXFHuB8jlylPeqQHIeOaq3qEvH4b34su+4rONSv2NjIvadJPXqdQCObNxPlQbVrGpoJhoTLXohXb1cuRB34RY1LM2dOpe5U+cCMHrqaGLOxtymRv5s5Vxnq+R7NK1z4x5Nf631y1rrVKVUAJmNwGZa6/rAPqBUPvW1OUNSjvX4a61zPqLt71tkb5ajjo/W+rL5vsnngNLADqVUDa31ZqA1EA3MUko9nSuI1qeBOMC6O7Lheo7f1U3/vy2t9Q9a64Za64Z5NTIBwnbvx8+vCr6+lXBwcCAoqAfLlgdblFm2PJgBAzKfOta7d1c2bNyaNT8oqAclSpTA17cSfn5V2BW2L9c2CkJySI5bObDvCFWq3k+lyj44ONjT7bFOhKzaaFFm7eqN9H6iOwBdundg25ZdQOZJqHmrxgCULlOaBg3rcfrkWaty2Mr+sJUcCQfO4FTFE8dKbtg5GKjaoymRIXstyjhVyb5AUKm9P8nmD8eGUiWwL515/c+7VR10uomkk9Z9IJEckkNyFNzZA6fw8PXCtaI7Bgd7mnRrwf6QMIsylWtX4emJL/L1c59w+UL2rQZlnO7DvkRmf4Jj+bJUe7gGxpNRVuUoiICWTVm6eh1aaw4cPoqj4324ubrQosnDbNu1l+SUyySnXGbbrr20aGLdvYDRB05TwdeT8hXdMDgYqNetGcdCLO/3rOCbPeSyersGXIiwrpF/K+cOnMbN15MK5hwPdWvOwZDdFmUq1vbliYnP8f1zn3Elx3G5FJOIX5Na2BnssLM3UK1JTWJPWXdcThw4gbevNx6VPLB3sKdN9zbsCNlRoLp2dnaULZd5z61vDV+q1KzCns3W3TsLtnOuE7blbjxgxxm4pLW+ar7XMuddyXZkPnRnLtAPCNVapyilziql+mit/zQPY62ntT5wm+0Ekzns9nMApZS/1nq/UuoBrfUh4JBSqhlQQyn1DxCttf5RKXUf8BBgMWhfKeUOVAHOFXYH5BAKPKOUmgm4AQHAHGtXlpGRwauvvcvKFXMw2Nnx68x5hIefYNzYkezec4Dly0P4+Ze5zPz1a46Fh3LpUhL9+g8FIDz8BH/9tYxDBzaQnpHBK6+OwWSy7vqK5JAct8vx/psT+e3P6RgMBubPWczJ46cZ8dZQDu4PZ+3qjcz7fRFTpk9kU9hykpKSGf7caAB+mzGXSVM/ImTrQpRS/DlnCcfCT97z+8MWcugME9vfm0mn2aNRdnacmLeJpBPRPDSyN4kHzhIZspdaAwPxblkbU3oG15P/ZvPr3wNQ2tWJjrPfBJOJv2MvsenV6VZlkBySQ3LcGVOGid/f/4kRv72LncGO0PnriTkZRc/X+xJx6DT71+4m6O0BlCxTiqHT3gDgQnQiU5//FC+/ijwz8QW01iilWDl9ETFWNmgARo39hLB9B0lKSqF9z/4MHTyA9PTMXrq+j3WldbNGbNkeRuegQZQuVYqP3nkdAGensrw48EmeeO5VAF56tt8tHyp0u/2x7P1fGfjbWyiDHXvnbyT+ZDTtX3+c6ENnOLZ2L02fCeSBFnUwpafzT/Lf/PVG9v4fGfoVJR1LY3Cwp2bgw/wy4BMSTt35iBlThon57//MsN/eQRns2DF/I7Eno+j6eh8iD53h0No99Hy7PyXLlGLwtMz9cCk6ke+f/5x9K3fwYPM6vLNmElprjm7az+F1e2+zxfxzTH9vOuN/H4/BYCB4XjCRJyIZ8MYAThw8wc6QnTxY/0He+/E9HJ0dafJIE/qP6M9Lj7yEwcHApAWTALh65Sqfv/I5pgzr+91s5Vxnq/6rQ4FVYf5wpdQVrbXjTfNKAosBH+A4mQ2scVrrjeYH7EwBugDJQF+tdYJSqgowncz7HB2AuVrrD5VSvwLLtdZ/mdc9EGiotR6ulHIFviXzvkx7YLPW+iWl1FSgLZABhAMDgSeAUUAacAV4Wmt9VikVAVw2l3UAvtBa/5zzb1NK+Zoz1DH31o7UWj96U5abc96oa0fm/aatgRNkDgmerLW+5ZdI2Zfw+W++GsU9w6fs3Xmq4Z2KvlzwIUL/Bd+7t719ISGETdlqf624IwDw/e7PijsCAOMavlvcEQBIxvr7FYvS6QzrHohX1ELiDt6+0P9Jemq0dTf5FqPHKncr1s/2iyKXFcs+K1RD8443lkfD9N9OKeWotb6ilKoA7AJaaK1vOY5DGprC1klD0zZJQ1OIe480NC1JQ9OSNDRzk4bmnSuuhqZ8G+rdt9z8wKQtwEe3a2QKIYQQQggh/j1M6GL9KQilVCel1HGl1Cml1Ft5LB+hlApXSh1UmV9Tef/t1nk37tHM13+tNxNAax1Q3BmEEEIIIYQQIi/mr3/8FugARJH59ZRLtdbhOYrtI/O2watKqSHAZ0DfW61XejSFEEIIIYQQ4i4xFfNPATQGTmmtz2itU8l8cGuPnAW01hu01lfNkzuAirdbqTQ0hRBCCCGEEOK/ywc4n2M6yjwvP4OBVbdb6f916KwQQgghhBBCiP8fpdQLwAs5Zv2gtf4hZ5E8quV5c6dSqj/QEGhzu+1KQ1MIIYQQQggh7hJdwAfy3LXtZzYqf7hFkSigUo7pikDMzYWUUo8AY4A2Wuvrt9uuDJ0VQgghhBBCiP+uMKCaUqqKUqoE8ASwNGcBpVQD4Hugu9Y6viArlR5NIYQQQgghhLhLCvoVI8VFa52ulBoOrAEMwM9a6yNKqQ+B3VrrpcDngCPwp1IKIFJr3f1W65WGphBCCCGEEEL8h2mtVwIrb5r3fo7fH7nTdcrQWSGEEEIIIYQQRUp6NIW4h1Rx9izuCAD4lCxf3BEAiL58obgjAFDV2au4IwAwUxuLOwIA1R1cijsCAMfTLhZ3BJsix8U2ZaTZxpC6ZnWfKe4IAGw/NLO4IwDQ46HhxR0BgFSdUdwRAPB2tI33j3uV1rbx7/z/TXo0hRBCCCGEEEIUKenRFEIIIYQQQoi7xFTcAYqJ9GgKIYQQQgghhChS0tAUQgghhBBCCFGkZOisEEIIIYQQQtwl2sa/R/NukR5NIYQQQgghhBBFShqaQgghhBBCCCGKlAydFUIIIYQQQoi7xCRDZ4UQQgghhBBCiMKTHk0hhBBCCCGEuEu0lh5NcQ/oGBjAkcObORYeyuhRw3ItL1GiBHNmT+dYeCjbQpdx//0Vs5a9OXo4x8JDOXJ4M4Ed2kiOf2GOVu2asWb7AtbuWswLrwzMI4cDX/74MWt3Leav1TPxqeQFgIODPZ98PZblm+axdMMfNG7+cKFyNA5oxG+bfmF26Ez6DXsi1/J6Teryw6rprItYQ5uurbLm+zevz09rvsv6CT61kpYdm1udw5aOy+rtCwjZtYgXXnkm1/KGzRqwaN3vhBt30LFbe4tlj/XtSvDOhQTvXMhjfbsWKkfjgEb8vvlX5oT+xlN5HJf6Tery0+rvWH8umDZdW1ssc/d254s5nzJr48/8tuFnPCt6WJ2jdht/Plr3FRM2TqXTkJ65lncY/CgfhExh7KpJjJj9Pi4+rlnLXLxdee23d/lw7RQ+CJlChYpuVuewlf1hKznkuNhmjiYBjfhj80zmhc6i/7An88hRj59Xf8+mcyEE3JRjc2QIvwb/wK/BP/DpL+OtzgDQrG1jFmyZzaJtf/DM8KdyLW/QtD6/B89gx/kNtO8akGv5fY5lWLl3IaMnvFaoHO9OnEzrrk/Qs/9LeS7XWjNxynQ6Bw3isaeHEH78VNayJStD6NJ3MF36DmbJypBC5Xi4zcP8sOEHftr8E32G9sm1vE7jOny94muWnVlGiy4tLJY9+/azTAuZxrSQabTu1jpX3TvRKKAhv26awW+hv/DEsL65ltdtUpfvVn1LcMQqWt90vv1+zfSsn1WnltOiEOdbgDbtW7Bh51I2717B0FcH51peooQD3874nM27V7AkZDYVK3kDYG9vz+RvxxMcupB1O5Yw7LXcdcW9yaoeTaVUBnDIXP8o8IzW+mpRBisMpdQ7WuuJOaY18LvWeoB52h4wAju11o/eYj3+gLfWeqV5ehxwRWs9ycpchapvZ2fH119NoFOXJ4mKMrJj+0qWLQ/m6NGTWWUGPfskly4lU6NWS4KCuvPxxDH0e2oINWtWIyioB/X82+Ht7cGaVXOpWbsVJpNJcvyLcoz75C0G9hlKbEwcC4JnsX71Jk6dOJtV5vGnepKSlMIjjXvStWcgo95/hdeef5ugAY8B8Gibvri4lmfG3Kn06jDAqitwdnZ2vDr+ZUb2e5MEYwLfrfiWrcHbOHcyMqtMfHQ8n4z4jL4vBlnU3b/tAM91zPzwULZcWWaHziRs0547znAjh60cl7GfvMmzfYaZj8tvrFu9mdM5josxKpa3Xh7H4KEDLOo6l3Ni+Mjn6dXhabTWLFo7i3WrN5OSfNmqHK9PeIURT44mwZjADyunERq8nXMnz2WViYuOZ+Lrn/HES7k/NI356k1mfT2H3Vv2ULpMKUwm667OKjs7+n04mCn9P+JS7EXGLP2YAyG7MZ6KyioTGX6WCd3eJPVaKm36B/L42wP4YfgUAAZNHs6KbxZyNPQgJcuUQltxTMB29oet5JDjYrs53pjwKq89OYp4YwI/rZxOaPA2IixyxDHh9U958qWgXPWvX0tlYOALVm375hxvThzBsL6vE2dM4LdVP7I5eCtnT0RklYmNimPcqxMZMCR3oxzgpTefY+/2/YXO0rNLB/r17s47H+X9UWrL9jAio2JYOW8GB48c46NJ3/DHj1+SnHKZ6b/MYd6MrwHoO/gVAlo2xdmp7B1nsLOzY+j4oYx5agyJxkS+XPYlO0J2cP7k+awy8THxTH5jMr1f7G1Rt1G7RvjV8WN4p+E4lHDgsz8/I2xDGP9c+ceqHK+MH87ofm+RYExk2oqpbA/enut8+9mISfR58XGLuvu3HeDFjkOAzPPtb6G/sNvK8+2NLOM/G8NTvV7AGBPLsnVzCVm9gZPHz2SV6du/F8lJKbRu2JVuvTrx9rjXGTZ4FF17BFKiZAkCW/aiVOlSrNu+mCULVhF1PsbqPMI2WNuj+Y/W2l9rXQdIBfK+rFR83rlp+m+gjlKqtHm6AxBdgPX4A12KMlhhNG7UgNOnIzh7NpK0tDTmz19C924dLcp07xbIrFl/ArBgwQratW1pnt+R+fOXkJqaSkTEeU6fjqBxowaS41+Uo95DtTkXcZ7z5wXfzJ0AACAASURBVKJJS0tnxeJg2ncOsCjzSOc2LJy3HIDVy9bRrFVjAPyqV2Xb5l0AXEy8REryZer617IqRw3/6kRHxGCMNJKels76JRtpEWh5NTc2Ko4zR8/e8sNom66t2bkhjOvXrluVw5aPyyOdLXtIo88bOR5+CpO23B8t2zZj66ZdJCelkJJ8ma2bdtGqnXVXnGs2qEF0RHTWcVm3ZEOu3uLM43IGfdOH4vur3Y/B3sDuLZkfQv65es3q41LF34+Ec7Ekno8nIy2dsGVb8Q9saFHm+PYjpF5LBeDMvhOU93QBwMuvInYGA0dDDwJw/eq1rHJ3ylb2h63kkONiuzmiIqKJycqxnlZ55Dh99IzVjfuCqN2gJucjook25wheso42HVtalDFGxXLq6Ok8G9U16j1IBVcXdmwKK3SWhv51b9k43BC6g+6d2qOUon6dmly+fIWExIts3bmHZo0a4OxUFmensjRr1ICtO61rWD3o/yAxETHERsaSnpbO5mWbaRbYzKJMfFQ8Eccicl2grFytMod2HMKUYeL6P9c5E36GhgGW/9YKKvt8m5ljw5JNNA+0fH3EZZ1v87/Y0bprK3Zt2G316xTA/+G6RJyNJPJcFGlp6SxbuIrAzm0tygR2actfc5cCsHJJCC1aNwEye6HLlCmNwWCgVKmSpKWmcfnyFauz2CITulh/iktRDJ3dAvgBKKUWK6X2KKWOKKVeMM8brJSacqOwUup5pdRkpZSvUuqYUuonpdRhpdRspdQjSqmtSqmTSqnG5vL3KaV+VkqFKaX2KaV6mOcPVEotVEqtNpf/zDz/E6C0Umq/Ump2jpyrgBvjzp4E/siRKdc2lFIlgA+BvuZ13RiPUEsptVEpdUYp9UqOdYww/x2HlVKv5Zg/Ril1XCm1FqhemB3t7ePJ+ajsqztR0Ua8vT3zLZORkUFycgoVKpTH2zuPuj6WdSXHvZ3D08sdY3Rc1nRsTBweXpbD1zw83Yg1l8nIyOBKyhXKu5Tj2OETPNI5AIPBQMXK3tSpXxMvH+uGerl5uZJgjM+aTohNwM2rwh2vp133ANYvXm9VBrCd4+Lh5Z61zwFiY+Lx8HIvYF03jDG3PqYF5erpSnxMQtZ0gjEBN0/XW9TIVqlqRa6k/M34H8fx05rvGPLuC9jZWXf6KOfhwsWYC1nTl4wXKeeR/+ujZVB7Dm/cB4BHVS/+SfmbId+N5L0Vn/H42wNQVuawlf1hKznkuNhmDjdPV+Jjst9P442JuHkW/D2gRMkSzFg5nR+WfUOrji1uXyEf7p5uxEXnzJGAewH3h1KK18cO56uPplm9/TsRl3ABT/fsbB7ursQlJBKXkIine/a+83DLnG+NCp4VSIzJrptoTKTCLf695HQm/AwN2zakZKmSOJV3ol7zerh6FWxf3szVy5UEY47XaWwCrlacb9t2D2DD4g1WZbjB08udmOjYrGljTBweXh75lsnIyOCy+TPIyqUhXL36D7uPrmfHwWB++HYmyUkphcojbEOhGprmIaidyRxGCzBIa/0w0BB4RSlVAZgLdFdKOZjLPAv8Yv7dD/gKqAfUAPoBLYGRZPdKjgHWa60bAW2Bz5VS95mX+QN9gbpkNggraa3fIrvHNedNBHOBJ5RSpczb25ljWa5tAA7A+8A887rmmcvWADoCjYGxSikHpdTD5r+rCdAUeF4p1cA8/wmgAdALaFTgnZsHpVSueTcPbcy7TMHqSo57OwdW59D8NWcpsTFxLFo7izHj32Bv2AHS0zOsy0Hef+udcHF3oWqNKuzatNvKDLZzXPJYVYHXlVeOO96ZRZDDYG+gXuM6fPvR97zYZSjelb3oHNTx9hULmCO/v6lJz1b41qvKmh8yr4DbGQz4NarJnxN+Y0L3t3Ct7E6LxwOKLIet7A9bySHHxRZyFO69qHfjJxjcZQjjhk3g1Q+G4XO/t1U58nhbL/BbUZ+Bj7F13Q7icjSY76a89o9SKs+8eb7HFkBhjsu+LfsIWx/GpEWTePObNzm25ximjKLrjb7Tc5WLuwtVavgSVojzLRTmnKvxf7gOGRkmGtVqT4sGnXl+6NNUzvHMhH8DXcz/FRdrG5qllVL7gd1AJDDDPP8VpdQBYAdQCaimtf4bWA88qpSqAThorW80TM9qrQ9prU3AEWCdznxVHgJ8zWUCgbfM29sIlAIqm5et01ona62vAeHA/fkF1lofNK/zSWDlTYtvtY2brdBaX9daJwLxgAeZjeNFWuu/tdZXgIVAK/PPIq31Va11CrA0v3xKqReUUruVUrtNpr/zLBMdZaRSxeyTREUfL4zGuHzLGAwGnJ2duHjxEtHRedSNsaxbUJLDNnPExsRZ9EJ6ensQH2t5tTbWGI+nuYzBYMDRyZGkS8lkZGQw8b3JdG/bjyFPv4GTU1nOnYnEGgnGBNxy9Ni5ebqRGHvhFjVya9utDVtWbyXD6sauLR2X7H0O4OntTnxswi1qWNb18rY8pnGx1l2BTzAm4u6dfTXfzcuNxLiCHZcEYwInD5/CGGkkI8PEljVbebBuNatyXIq9iIt39hX38l4uJMVfzFWuZou6dB3ei2+e+5T01HQAkmIvcD78LInn4zFlmNgfHEblOlWsymEr+8NWcshxsc0c8cYE3L2z30/dvVxJjCv4e8CNzDGRRvZt30+1On5W5/DwyZnDjYQC5qjbsDZBg3qxdNd8Xhs7lC59OjH8nRetylEQnu6uxMZnZ4uLT8TdtYJ5fvZ7b1xC5nxrJBoTcfXO7oV09XLlYh7/XvIz75t5vNz5ZcY8NQalFNFnC3I3V9453HKMcnHzdONCbMFzAAR0a03o6m2FOt9CZg9mzpE/Xt4exMfG51vGYDBQ1vwZpEfvrmxaF0p6ejoXEi+ye9d+6jWoXag8wjYU9h5Nf631y1rrVKVUAPAI0ExrXR/YR2aDDeAnYCCWvZkAOQeDm3JMm8h+UJECeufYXmWt9dE86mdw+4cbLQUmkWPYbAG2cbO8tnmrS2IFuoygtf5Ba91Qa93Qzu6+PMuE7d6Pn18VfH0r4eDgQFBQD5YtD7Yos2x5MAMGZD6YoHfvrmzYuDVrflBQD0qUKIGvbyX8/KqwK2xfQaJJjnskx6F94fhWqUTFyt44ONjTtWcg61ZvsiizbvUmevXNfP5Vp27t2RGaeb9MqdKlKF0m859rizZNyMjIsHiI0J04fuA4Fav44FnJE3sHe9r1CGBbyLY7Wkf7Hu1Yt8T6YbNg68dlc4Hqhm7YTouAJjg5l8XJuSwtApoQumG7VTmO7T9GxSo+eJmPS/sebdkaXLDjcmz/ccqWK4uzizMAD7VoQMSJc7eplbeIA6dw9/XCtaI7Bgd7GnVrwYEQyyvplWr70n/iC3zz3KdcvpA9fOrsgdOUcb4PRxcnAGo0r0PMySisYSv7w1ZyyHG5V3K0IzS4YO8BZZ0dcSiROZjMubwTdRvVsTpH+P5jVKpSEe9KXtg72BPYoz2b14QWqO57wz7i0YaP071xEF9+MI2Vf67mm4nfW5WjIAJaNmXp6nVorTlw+CiOjvfh5upCiyYPs23XXpJTLpOccpltu/bSool1T1g/ceAE3lW88ajkgb2DPa27tWZHyI4C1bWzs6Nsucx7TH1r+OJb05e9m/dalePYgeP45Djftu3Rhm0hd3aOaNujLRuWFG7YLMCBvYepUvV+KlX2wcHBnm69OhOyeqNFmZBVG3n8ie4AdOnRgW1bMp8NERNlpLn5fs3SZUrzUMN6Vn8GEbalKL9H0xm4pLW+au65bHpjgdZ6p1KqEvAQmcNW78Qa4GWl1Mtaa62UaqC1vt0nvjSllIPWOu2m+T8DyVrrQ+aG8e22cRkoyOPINgO/mu8PVcBjwADz7zfm2wPdAKvfXTMyMnj1tXdZuWIOBjs7fp05j/DwE4wbO5Ldew6wfHkIP/8yl5m/fs2x8FAuXUqiX/+hAISHn+Cvv5Zx6MAG0jMyeOXVMVY9QVNy2HaOD97+jJ/nf4PBzsBffyzh1PEzvPrmSxzaH876NZv5c/YSJk37iLW7FpN0KZnXX8gcoV7BtTw/z/8GbdLEGuMZOfQ9qzJk5jDx1XtT+Xz2J9jZ2bFq3moiTpzj2ZHPcPzACbaFbKd6/eqM/2kcjs6ONOvQjIEjnuHZ9s8B4FnRAzdvNw5sP2h1hhv7w1aOy4dvf86M+VPNx2Upp46f4ZU3X+Tw/qOsX7OZuv61+Hbm5zg5O9E2sBWvjH6Brq36kpyUwrTJM1gQ8hsA337xk9X3rWRkmPjy3alMmvMpdnZ2rJy3iogT5xg0ciDHDxxna8h2atSvzvgZH1DW2ZHmHZox6I1neKbdYEwmE9M+/J4v501CKTh+6CTL5qywKocpw8Sc92fw2m9jUAY7ts7fQMzJKLq/3pdzh05zYO1uHn97AKXKlOKlaW8AcCE6kW+f/xRtMvHnhFm8Mft9UIrIw2fYMnfdPb0/bCWHHBfbzTHl3alMnvMpBjsDy+et4uyJCJ4bOZBjB04QGrKNGvWr8/GMDynr7EiLDs147o2B9G83iPur3c/oT17HpDV2SvH7N39YPK32znJk8Pk7U5j6xxcYDHYsnbuCMycieHHUYI4eOMbm4K3Uql+Dz3+egFO5srTq0JwXRg2ib8DTVm3vVkaN/YSwfQdJSkqhfc/+DB08gPT0zN71vo91pXWzRmzZHkbnoEGULlWKj955HQBnp7K8OPBJnnjuVQBeerafVU+chcx/L9Pfm874WeOxM9gRPC+YyBOR9B/Rn5OHTrIzZCfV6lXjvR/fw9HZkSaPNKH/iP4MeWQIBgcDny/4HICrl68y6dVJVg+dNWWYmPreN3w6e6L5fLuGcyfOMXDk0xw/cILtITuoXv9BPvhpLI7OZWnWoSnPjBjA4PaZTyL2qOiBexGcbyHzNfLe6InM+us7DAYD82Yv4sSx04x4exiH9h0hZPVG5v2+kC+/+5jNu1eQdCmZ4c+NBmDmjD/44pvxrN22CKUU8+cs5lj4iUJnsiWm/+j3aCpr7jtSSl3RWjveNK8ksBjwAY4DbsA4rfVG8/K3AH+t9RPmaV9gufnJtSilfjVP/5VzmflJsV8CzclsuEVorR9VSg0EGmqth5vrLwcmaa03KqU+BboDe7XWT+WTNwAYaV5XfttwIbMR6gB8DNQkx9eTKKUOA49qrSOUUiOAQebV/6S1/tJcZgzwNHAOiALCb/f1JvYlfP6br0ZxW1WcrXsgTVHzKVm+uCMAEBqf38CD/6+qzl7FHQEAzxLOxR0BgOoOLsUdAYDjaXc2hOzfTo6LbcooxvuncrpuuvnafPHYfmhmcUcAoMdDw4s7AgDXTenFHQGAk1eNxR0hS+TFQ9bdXFuMWvu0L9Z/6Juj1xXLPrOqoWnVhjIbglO01tZd5vwPkYamyI80NC1JQ9OSNDQtSYPGkhwX2yQNTUvS0LQkDc3c7sWGZqtibmhuKaaGZlF8vcktKaXKKaVOkHlfpzQyhRBCCCGEEOJfrijv0cyT1joJePBub0cIIYQQQgghhG246w1NIYQQQgghhPivMtnIEPn/t7s+dFYIIYQQQgghxH+L9GgKIYQQQgghxF0iPZpCCCGEEEIIIUQRkIamEEIIIYQQQogiJUNnhRBCCCGEEOIu0VqGzgohhBBCCCGEEIUmDU0hhBBCCCGEEEVKhs4Km2aws41rIRkmU3FHAOBscmxxRwAg0i6+uCMAUK7UfcUdAQCfkuWLOwIAf9VPLe4IAGzbYxunlpLarbgjAGCPbbx//J1hKO4IANxfqkxxR7ApD8XsLe4IAHg62sb7WN1afYs7AgCHwucVdwQAmtV9prgjABD796XijnBPk6fOCiGEEEIIIYQQRcA2LjsLIYQQQgghxL+Qlh5NIYQQQgghhBCi8KShKYQQQgghhBCiSMnQWSGEEEIIIYS4S+R7NIUQQgghhBBCiCIgPZpCCCGEEEIIcZfI15sIIYQQQgghhBBFQBqaQgghhBBCCCGKlAydFUIIIYQQQoi7RB4GJO4JHQMDOHJ4M8fCQxk9aliu5SVKlGDO7OkcCw9lW+gy7r+/YtayN0cP51h4KEcObyawQ5t/RY7ADgEcOriR8CNbGDlyaJ45fp81jfAjW9iyeWlWjvbtW7F92wr27A5h+7YVBAQ0L1QOW9kftpLD2uPi4lKONWvmcSHxGF9O+ahQGQDaPdKKHXtWs2t/CK+8/kIeORz46Zcv2bU/hDXr/6RSZR8AKlX24XzcQTaELmFD6BImTfmgUDkaBTRk5qaf+T30V54c1jfX8npN6vL9qmmsjVhN666tsub7N6/Pj2u+y/pZc2oFLTpa/1p1eLgx5b6fRbkfZ1OqT788y5Ro2Rbn6TNxnvYrjqPeA8BQ1Q+nSdNwnvYrzt/8TIlWba3OAODWtj5tQ7+g3fYp+A3vnm85r0cb0y32D5zrV7WYX9qnAp1P/0LVIV0LlaNC2/q02DqZlju+xPfl/HN4PNqEwLi5OOXI4VirMo1XfEjzTZ/TbONn2JV0sDqHS9v6NNn6JU13fM39L/fIt5zbo01oFzefsuYcHr1b0mjdZ1k/bY1zcax9v9U5bOW4OLZ5iOrrplN94/e4DXk81/Lyj7en1p7fqbbyK6qt/AqXvoEAlKpVhQcWfs6Dwd9SbdXXOD/a8l+RA2zjvT2gfQs27VxG6O6VDHt1cB4ZHJg2YxKhu1eyLGQOFSt5A/DY411Zs+mvrJ/IxIPUqlPd6hwt2zZj1ba/WLNzIc+//Eyu5Q2bNmDB2lkcjtlOx0fbWSzr2bcrq3csYPWOBfTsW7jX6bsTJ9O66xP07P9Snsu11kycMp3OQYN47OkhhB8/lbVsycoQuvQdTJe+g1myMqRQOZq1bcyCLbNZtO0Pnhn+VK7lDZrW5/fgGew4v4H2XQNyLb/PsQwr9y5k9ITXCpUjMDCAw4c2ER4eyqiReb9GZ/8+jfDwUEK3LLM47wevmc/FC8f58svxhcogbI9VPZpKKQ38rrUeYJ62B4zATq31o1asrxzQT2s9zTwdAIzMa11KqY3mZbtvsT5P4EugEXAdiABeA7zzW++9wM7Ojq+/mkCnLk8SFWVkx/aVLFsezNGjJ7PKDHr2SS5dSqZGrZYEBXXn44lj6PfUEGrWrEZQUA/q+bfD29uDNavmUrN2K0wm0z2d46uvxtOlaz+iooxs27qc5ctDOHYsO8ezA58gKSmJWrVb0adPdyaMf4f+A4aSmHiRXr0HYTTGUatWdZYv+52qDzS64wy2tj9sJYe1x+Xatet88MEkateqTu3a1n8QuZHj0y/G8niPZ4mJjiVk4wJWr1zHieOns8o89XQfkpKSaezfgcd6d2XsB6N47tnMk23E2Ujatsz/Q/+d5Hh1/MuM6vcmCcZEvlvxDduCt3PuZGRWmbjoeD4d8Tl9X+xjUXf/tgM83zHzQ0zZcmX5PfRXdm/aY20Q7hvyGinvvoEpMQHnKd+TtmMrGefPZRfx9qF00FOkjBqGvnIF5VwOAH3tGlcmT8AUE41yqUC5r34kbW8Y+u8rVuRQ1P34WXYETeQf4wVarZ5AbPAerpyItihmuK8UVQZ34tKek7lWUfuDAcSv33/n274pR81PBrEnaALXYi7QdM1EEtbs4e88clR+rhNJOXIogx11vx3GoWHfciU8EofyjpjS0q3OUf2TwewLGs/1mAs0XPMxCWt2czWPHJWe60zynhNZ8+IWhBK3IBSA+2pWot7M0Vw5cg6r2MxxscPnw5c42/890mIv4Ld0MikhO7l+6rxFsaTlW4gZ+73FPNM/1zk/YjKpEUbs3V2otnwKlzfvw5Ty972bA9t4b7ezs2P8Z+/Sr9fzGGNiWbFuHsGrN3Dy+JmsMk/070VyUgotG3ahe6/OvDNuBEMHj2TRXytY9NcKAGrUrMaM2V8Tfvi41fvi/U9HM6jPcOJi4vgzeCbr12zm9ImzWWWM0bG8/coHDBra36Kuczknho18nsc7PI3WmgVrZ7F+9WZSki9blaVnlw70692ddz6alOfyLdvDiIyKYeW8GRw8coyPJn3DHz9+SXLKZab/Mod5M74GoO/gVwho2RRnp7J3nMHOzo43J45gWN/XiTMm8NuqH9kcvJWzJyKyysRGxTHu1YkMGPJEnut46c3n2Lu9cP9us877XTLP+9u3rWD58mCO5jzvP/sEl5KSqVWrJUF9ujNxwjs81T/zvD/ug8+pXbs6tWvXKFQOWyYPA7ozfwN1lFKlzdMdgOhblL+dckDubg8rKKUUsAjYqLV+QGtdC3gH8CiCdRfrUOPGjRpw+nQEZ89GkpaWxvz5S+jeraNFme7dApk1608AFixYQbu2Lc3zOzJ//hJSU1OJiDjP6dMRNG7U4J7O0aiRv2WOP5fSrVugRZlu3QKZ9ftfACxcuIK2bVsAcODAEYzGOADCw49TqlRJSpQoYVUOW9kftpKjMMfl6tV/2LYtjGvXr1u17ZwealiPs2fOcS7iPGlpaSxasILOXR+xKNO5a3vm/rEIgKWLV9MqoFmht3uzGv7ViYmIwRgZS3paOuuXbKRFoGWvZFxUHGeOnsVkyv9E1KZrK3ZtCOP6Nev2jf2DNcmIicYUa4T0dK5vXo9DU8uellIdu3Ft+SL0lcwGpE5OAsAUE4UpJvMtXl+8gCnpEsrZ2aoc5Rv48ffZWK5GxqPTMohZvB3Pjg1zlavxZhCnpi0j43qaxXzPTg35OzKey8ejrNr+Dc4P+XH1bCz/nMvMEbt4G+6dcufweyuIs98uw3QtO0eFgHpcDo/kSnjmxYK0S1fgFsfuVpzMOa6Zc8Qv3oZbp9wXvaq+1Zdz3y61yJGTx2MtiVu01aoMYDvHpYx/NVLPGUk9H4dOSydp2WacApsUqG7q2RhSI4wApMdfJP1CMvYuTvd0DrCN93b/h+sScTaSyHNRpKWls2ThKgI7W/YWBnZpx59zlwCwYkkwLVvn3l89endhyYJVd7z9G+o9VJvIs+eJOhdNWlo6KxeF0L6TZS9t9HkjJ8JPoW/6N9mybVO2bdpJclIKKcmX2bZpJ63aWf+e39C/7i0bhxtCd9C9U3uUUtSvU5PLl6+QkHiRrTv30KxRA5ydyuLsVJZmjRqwdad1FxBrN6jJ+YhooiONpKelE7xkHW06Wr6vG6NiOXX0dJ7nlxr1HqSCqws7NoVZtf0bcp335y/J+7x/4zW6cAVtza/RrPO+lec2YdsKM3R2FXBj3MGTwB83FiilXJRSi5VSB5VSO5RS9czzxymlflZKbVRKnVFKvWKu8gnwgFJqv1Lqc/M8R6XUX0qpY0qp2eYGJDm2MVgpNSXH9PNKqclAWyBNa/3djWVa6/1a6y23Wq9S6n2lVJhS6rBS6occ8zcqpSYqpTYBryqlHjD/TWFKqQ+VUldyZBhlnn9QKfWBed59SqkVSqkD5nXnHjdXQN4+npyPismajoo24u3tmW+ZjIwMkpNTqFChPN7eedT1sax7z+W4aV3R0UZ8bs7h7UlUjhwpKZepUKG8RZnHHuvCgQOHSU1NtS6HrewPW8lRRMelsLy8PIiJis2ajomJxcvbI1eZ6CijRQ4Xl8wcle+vyPoti1m68neaNsv9gbugXL1ciTcmZE0nxCbi6uV6x+tp2z2AdYs3WJ3DroIrpsT4rGlTYgKGCpY5DD4VMfhUwunzb3D6YhoODzfOtR77B2uAgwMmY0yuZQVRyqs8/8RcyJq+ZrxAKS/LY+9Ux5fS3i7Eh+yzzFemJA8M78aJSQus2rZFDk8XruXMEXORkp4uFmXK1vGllHcFEkP2Wswv84AXaHho7ts0DfkY32HdrM5R0tOF6zlyXI+5kCuHYx1fSnq7cuGmHDl59GhWqIamrRwXB48KpMUkZk2nGS/g4FEhVznnzs2ptuprKk97C4c8/j2Vrl8N5WBP6rnYXMvupRxgG+/tXl7uGKOz/4bYmDi8vNwtynjmKJP5fnqF8i7lLMp0e6wTSxauvOPt3+Dh6YYxOi47hzEODy+3gtX1cresGxOPx01/Q1GKS7iAp3v2a8LD3ZW4hETiEhLxdM/O7OGWOd8a7p5uxEVnv6/HGxNw9yzY+UUpxetjh/PVR9Os2nZOPt5eRJ03Zk1HR8fi7eN1UxlPonKcb5NTUor8vC9sT2F66OYC7yullgP1gJ+BGzcXfQDs01r3VEq1A34D/M3LapDZGCwLHFdKTQfeAuporf0ha+hsA6A2EANsBVoAoTdt/6BSarTWOg14FnjRvO5bXRrKb73faK0/NG9/FvAosMxcp5zWuo152XLgK631H0qprIH5SqlAoBrQGFDAUqVUa8ANiNFadzWXs64bILNurnk331ycd5mC1f135shdL2eZmjUfZOKEd+j6aO77Goo2h63sD1vJkbteUd8ob/X+QBMXG49/7QAuXUyivn9tfpszjRZNunDl8p0PfVMUft+6uLtQtUYVwjble8dAAYLk9bfexGDA4F2RlLdexc7VDafPppI89NmsIbKqvAuOb4zhyuSPM184RZTDIohS1P5wAPtfnZ6rWPVRj3Pmh1VkXC2CK995xLAIohTVP3yaw3nkUAYD5ZtUZ0fHMWT8c52Gf71LysGzXNxy2Ioceb8Gcy6v9uEzHH01/w+DTg/5kfFPKn8fO59vGWtyFM9xyfPNwWIyZe0ukpZuQqem4/JUJyp98Rpn+r2btdzerTyVJ4/g/Mgvi/h1Wgw5sJH3dqszZJdp8HBdrv3zD8ePnspVrihz5F8396y7+YCWvNatlMrzpZDXviuQ279M89Vn4GNsXbeDuJj42xe+XYwC7Nui/JxxL9IydPbOaK0PAr5k9mbefHmqJTDLXG49UCFHA2uF1vq61joRiCf/Ia27tNZRWmsTsN+8rZzb/xtYDzyqlKoBOGitDxUgen7rbauU2qmUOgS0I7MxesO8HL83A/40/z4nsiz44gAAIABJREFUx/xA888+YC+ZDepqwCHgEaXUp0qpVlrr5LxCKaVeUErtVkrtNpny/jAbHWWkUkXvrOmKPl5Zwz/zKmMwGHB2duLixUtER+dRN8aybkHZTI6b1uXj40XMzTmiY6mYI4eTU1kuXkwyl/fkz/k/Mmjwa5w5Y+V9TdjQ/rCVHIU8LkUlJiYW74rZV+69vT2JNcbnKuNT0csix6WLSaSmpnHJnOfA/iNEnI3Ez6+KVTkSjAm457ji7ubpyoXYC7eokVvbbm0IXb2VjPQMqzJAZg+mnWv21Xs7VzdMFxJzlUndGQoZGZjiYjFFncfOO/OBDap0GZzGfcrVWTNIPx5udY5rMRcp7Z3dM1TKqwLXYi9lTds7lsKpeiWaL3yf9mFfU/4hPxrPHIlz/aqUa+BHrff60T7sa6o+35lqr/TEd1BgXpu5fQ7jRUrlzOHtwvWbcjjWqEijhe/TKmwqzg/74f/bSJzqV+W68QIXtx0l7eJlTP+kkrh2P051fa3Kcd14gZI5cpT0rkBqjhwGx1LcV6MSDRaOpVnYNzg9XI16v43OeiAQgHvPFoXqzQTbOS5psYk4eGf3yDh4VSAt/qJFmYyky+jUzHtiL/4RTOk6flnL7BxLU+WXscR+8TtX91l3H6At5QDbeG83xsThlaMn1NPbg9jYhHzLZL6fOpJ0KfsjT/denVlciGGzAHHGeLx8sj82enp5EB9bsN7AuJib6nq7E3/T31CUPN1diY3PzhYXn4i7awXz/OztxiVkzrdGvDEBD5/s93V3LzcS4gq2P+o2rE3QoF4s3TWf18YOpUufTgx/50WrckRFG6lYKbsH08fHE2NMbO4yOc63zk5ORX7eF7ansE+dXQpMIsewWbO8Ls3caMrnvOSZQf69qgUp9xMwkMzezF/M844AD+ebOI/1KqVKAdOAx7XWdYEfgVI5yhWkG0MBH2ut/c0/flrrGVrrE+Y8h4CPlVLv51VZa/2D1rqh1rqhnd19eW4gbPd+/Pyq4OtbCQcHB4KCerBsebBFmWXLgxkwIPOBIr17d2XDxq1Z84OCelCiRAl8fSvh51eFXWH7cm2jIGwlx+7dB/Dz883O0ac7y5dbPr1t+fIQBvTPfFpgr15d2WjO4ez8P/bOO76KovvDz0lBVAgdQgII2BEFFKQqRUHpYAEL9t5AfREbiq8FG3Z/1hcFFaQpIr1JC7230JWaEHoCCkJuzu+P3ST3JqFdwt4LnMfP/Zidnd35Mrs7u2fmzJkYfhval+6vvMPMmScwSkT41Ee46DiR65KfLJy/lMqVK1LhvHJER0fT/uaWjBk1MSDPmFF/cNvt7QFo0+5Gpk2ZCUCJEsWIiHCax/Mqlqfy+RVZvz640aKVi1cRXyme2PKxREVH0aRtI2aMn3lc52jStjEThwXvNguQvnolkfHliCgTC1FRnHVtEw7NDqz3g7MSiLrcmb8lMUWIiC9PxtYkiIqicPc3+fePsRxMmHxCOvYsWse5lWM5u0IpJDqSuHZ12Tou2wklfe9+xl72MBNrdWZirc7sXrCWOff0InXxn8xo99+s9D+/Hc2aT39j/XfjjlDa4UlbuI5z/HTEtqvHtrGBOiZXeZhptZ5iWq2nSJ2/lkV39yJt8Z/smLSEwlUqEHF2ASQygmL1Ls0VNOdY2btwHedULktBV0fpdvXYMTa7TfLt3U9ClQeZWetJZtZ6krT5a1hy93vsXewGYRGhdOs6pPx2Ys9QuFyXfxavoUDFOKLLlUGioyja+lrSxs8JyBNVKtvdLqbp1RxY5zybEh3FeV+/zO5f/yB11InVR7jogPBo2xcvWEalyhUoXyGe6Ogo2t7UnPFjAtuk8aMncettTgC1lm2bMX3a7Kx9IkKrts34/dcTMzSXLkzkvMoViK8QR3R0FC3aN+WPsVOP6diESbOo37A2MUUKE1OkMPUb1iZh0qwT0nMkGjWow+9jJqKqLF62gkKFzqVUyeLUr30VM+YsIDVtL6lpe5kxZwH1ax/ps/XwJC5aSflK5YgrX5ao6Ciatb2OqWMTjn4g8MoTb9Cq5i20uboDH//3C0YNHsPnPb8++oF54Lz3A+/RPN/7mffoSXrvhzMZqiH9hYoTDW7zHZCqqktdd9dMpgJ3Am+46TtUNe0IrgF7cVxpjwtVnS0i5YErcdx3wRnl7CkiD6nqtwAiUgs45winyjQqd4hIIeAWYMhh8s4CbsYZ5fQP4TUW59/bT1X3iUg8cAinjnep6k/ufM57j/ffmYnP56PL090ZNbI/kRER9Ok7kMTE1bzWoyvz5i9mxIjxfPf9APr2+ZSViQns3r2HOzo5MZYSE1czZMhwli6eRLrPR+cuLwcVUTTcdDz99CuMGP4TkZGR9Ok7kBUrVvPqq/9hwfwljBg5nu/7DOD77z4mcfk0du3aw113OyG3H3vsXs4/vyIvvdiFl17sAkDLVneyffvxjTSFW32Ei45grwvAqlUziClcmAIFomnd+gZatrozIGLt8eh44bnXGTy0NxGRkfT/cQirVq7lhZc7s2jBMsaM/oN+Pwzmi2/eZ86i8ezZncpD9z0DQN36tXjh5S6kp/vI8Pno+vSrAT3zx0OGL4NPX/mc9/q9TUREBKMHjmX96g3c1/UeVi1ezYzxM7m42kW88b/XKFSkEHWb1uG+Z+/mvuseAqBMuTKUiivF4plLgirfTwh/f/kxMW/0gogI/h0/Ct/G9Zzd6X7S16zk0OwZHJo/h+gatSjyZV/IyOCf775E96ZRoHFToqpWQ2JiOOv6GwHY99E7+P48fhc49WWw7KU+1Pn5RSQygk0/T2bfqs1c3O0W9iz6i5RxQUbVDULHyhe/58oBLyGREWz5eRJ/r9rM+d1uJW3xn2wfe3gd6al/s+GrkdQZ8xYA2ycsZMeE4Dpm1JfB6he/o/qAl5HICJJcHZW6dWDv4nXsOIIOgKJ1L+Xf5J0c2HBi7m/hcl3wZZD06ldU/uG/EBnB7kET+HfNRso8cyf7l64hbcIcSt7Xmpjra6M+H749e9nc9RMAirRsQKGrLyOqWGGK3XIdAJu6fsyBxL+OVGJ46yA82nafz8cr3XrSb8jXRERGMrDfUFavXEfXF59g8cLljB8zmQE//conX71NwrxR7NmdyuMPPpd1fJ16NUlOSmHjhhMLFuXz+XjjhffoPfBTIiIj+aX/76xd9SdPPf8IyxatYNLYqVStXoXP+7xHTJEYGjdrwJPdHqH1tR1J3ZPGFx/2ZvC4vgB88UFvUvekBa3luR7vMHfhEvbsSeO6dp14/IG7SE93Rrg7tm/JtXVrMW3mXJp3uJ+zCxbkjZec90uRmMI8cu/t3Pag8+3x6H13BBVxNrM+3n/pIz77+QMiIyP4fcBI/ly9nkeee4AVi1cyddx0qlS7hPe/e4uYooW5pmk9Hn7ufjo2ujvof/fhdDz99CuMHNGPiMgI+vYZSOKK1fR4tSvzFzj36PffD6DP95+QmJjA7l176HRXdgzQ1atmEhPjvPfbtL6Bli3vCIhYa5y6SDD+0SKyT1UL5UhrhLt0iIgUxxlhrAT8AzysqktE5DVgn6r2co9ZBrRS1fUi0h/HWBwNjMRvGRIR+RyYp6p9ci5vIiIvANVV9TY/LXE4y5tcBRwge3mT+COc900cw3E9sAnYoKqv5VHehcBPOCOYI91/W7y7rwvwoCtjH9AJuAB4H8jAMTwfO9LSLABRBeLPTEfuPIiMCI+lXn1BGl2nK+FyXQoXOPvomTzg8pjg1y7MT4ZUCy6gVX4zY37c0TN5wFlhMv8nivBoP/6WyFBLAOC8gkEsi3Mac2XS4YM8eUlsofAIzFIoKjza9aWJA4+eyQPqXp57rdBQsGRXcB0lJ4OD/24OclJr6Khapk5IX0jLUmaFpM6CMjTDCTc4z0eqOvGomfOnvHOA/aqqInIbcLuqnvhie36YoZlNuBg0ZmgGEi7XxQzNQMzQDMQMzUDM0AxPzNAMxAzNQMzQzM2paGheVqZ2SF9Iy1Nmh6TOQrou5IkgIkWBOcBir4xMl6uAz93lT/YA93tYtmEYhmEYhmEYRthzyhqaqroHuCgE5U4DqnldrmEYhmEYhmEYpx6hDMgTSsLD/80wDMMwDMMwDMM4bTBD0zAMwzAMwzAMw8hXTlnXWcMwDMMwDMMwjHBHMddZwzAMwzAMwzAMwzhhzNA0DMMwDMMwDMMw8hVznTUMwzAMwzAMwzhJWNRZwzAMwzAMwzAMw8gHbETTMAzDMAzDMAzjJHGmBgMyQ9MIa3wZGaGWAICEWoBLuDRTs0pfGWoJANTaOi/UEgDYUGBHqCUAcMWMg6GWAECxAv+GWgIAxaPODbUEACIlPJyHdqX/HWoJAMQcOjvUEsKK2ELFQi0BgK37dodaAgAFo8LjPr37qmdDLQGAmUv7hloCAGfHXRNqCcYpSHi8/QzDMAzDMAzDMIzTBhvRNAzDMAzDMAzDOElYMCDDMAzDMAzDMAzDyAdsRNMwDMMwDMMwDOMkcaYGA7IRTcMwDMMwDMMwDCNfMUPTMAzDMAzDMAzDyFfMddYwDMMwDMMwDOMkoRoey/V5jY1oGoZhGIZhGIZhGPmKjWgahmEYhmEYhmGcJDIsGJBhGIZhGIZhGIZhnDhmaJ5i3NCsEcuXTWVlYgLdnnsi1/4CBQrQv9+XrExMYEbCcM47r1zWvue7PcnKxASWL5tKs6YNTUc+6mjWrBHLlk1lRWICzx1GR79+X7IiMYHpOXR06/YkKxITWLZsKk1Pk/oo3LAGl076gipTv6LM4zfn2l/8liZUXfgDF4/+iItHf0SJ25pm7Tv/hx5cvrQflb/vfkIaIHzq49om9Zgwayh/zBnGo53vy0NHNJ/+7x3+mDOMX8f+QHz5sgBER0fx3qevMXrqIEZOHkjt+ledkI5G1zVg6pwRJMwfzRNPP5inji979yJh/miGj/+ZcuXjAGh/a0vGTf0l67dp51Iuq3pJ0DrqN67D8OkDGTVrMA88dVeu/VfVqc6g8X1ZtCWBpq0aZ6VffNmF/DTyW36b0p9fJ/3EjW2vD1oDwNWNavHT1D70T/iBO5+4Ldf+arUv539jvuKPDeNo2PLagH2l40rzQf93+XHyd/ww6Ttiy5UJWketRjXpO+U7fkrow+1PdMy1/4ral/P16C+YsH4M17a8Jiu9er1qfDv2q6zf2LUjqX9DvaB11Gtcm2EJPzN85iDufzL3dbmyTnUGjPue+Zuncn2O6/LDiG/4dcpPDP7jB25oe13QGsC5Lv2m9uHnI1yX3mO+YtKGcTQ6wnX58QSvS7joaHRdfabMHk7CvFE80eWBXPsLFIjmi969SJg3iuHj+2c/t7e0ZOyUIVm/jTuWUKXqxUHrgPBpU69vei0LFk1k8dJJPPufR/PU0feHz1i8dBKTpgylQoV4AK6qWY0Zs0YyY9ZIZs4aRes2zYLWUK1hDT744//4aMqXtHnsplz7WzzYhvcnfMa7Yz7m5f6vUzK+FAAl40vx1ogPeHvUR7w//lOuv/OGoDUAdO/5Ide2vI12nXLXA4Cq0vOjL2ne4X7a3/0YiavWZu0bNmo8LTo+QIuODzBs1PgT0gHhc38Y4YOnrrMiUgKY6G7GAj5gu7t9taoezJG/ONBBVb86ynmjgB2qWtTdvgT4CLgASAcWA52BNkBVVX06f/5F3hIREcGnn7zFjS1uZ/PmZGbNHMXwEeNYsWJNVp7777ud3btTuaRKAzp0aMPbPV/mjjsf49JLL6RDh7ZcUb0JcXFlGDt6AJdedg0ZGcc/Odl05K2juZ+OEXno2LM7lUtdHT17vsydro6OHdpSzdUxZvQAqpzi9UFEBOXffIS1d/bgUPJOLh7ei9TxcziwZlNAtj3DE9j86je5Dk/5eigRZ59FyRN8+YZLfURERPDfd1/g7lseY2tSCr+N78eEMVNYu/rPrDwd7mxH2p69NLm6La3a38DzPbrQ+cEXuO0u5+Ol+bUdKFGyGN8N/Jx213dC9fhdcCIiInjr/Ze5vf1DJCelMOqPgYwbPYk1q9Zl5bn9rptJTU2jwVXNaXNTc15+7Vkee6ArQwePZOjgkQBcUuVCvuv3GcuXrTxuDZk6ur/TlYc6dGZr0jYGjv2eSWOn8efq9Vl5krek0L3LG9z72B0Bxx7Yf4CXnnydjX9tolSZkgwa34fpk2axN21fUDqeeaszz97eje3J2/lm1BckjJvJhjUbsvKkbNlGz2fe47ZHb811/MufPM+Pn/Zn3rT5nH1OQTIygnOLioiIoMubT/HcHc+zPXkHX438nBnjZrJhzcYAHe8++z4dHwnUsWjGYh66wfm4LFy0MD8l9GHelPlB63jp7a480qELKcnb6D+mN5PHBV6XrVu28kqXN7nn8dzXpftTr7Pxr82UKlOSn8d9x4xJs4O+Ls++1Zln3Ovy7agvmD5uJuuP8bp0/+R5fsin6xIuOt58rzt33PQQyUlbGTlxIOPGTGLNquz247ZON5G6J40GNVvQ5qbmvPTaszz+QFeGDhnJ0CHuc3vphfTu9ymJy1YFpSNTS7i0qR9+9DptWt3Fli1bmTptGKNGTmDlymwD6p57O7BnTyrVLm/MLbe04o03X+Ceu58icfkqrqnfBp/PR5nYUsyaNYpRIyfi8/mOS4NERHDfG4/Q884e7Ny6k7d+f5/5E+awZc3mrDzrl//Jy63+w8EDB7m+043c8eI9fPpkL3Zv202Pm54n/WA6Z51TkPfHfcr88XPYvW33cdcFQLsWTbnj5ja89EavPPdPmzmXjZuTGDWwN0uWr+SNXp/z87cfk5q2ly+/78/A3p8C0PGBzjRqUIciMYWD0hEu90e4Esx7+3TA0xFNVd2pqtVVtTrwFfBR5nZOI9OlOJB3F81hEJGzgRHAZ6p6oapeCnwLlDhR/a5BGzKurlWDdevW89dfGzl06BCDBg2jTevAj/E2rZvx44+DAfjll5E0adzATb+BQYOGcfDgQdav38S6deu5ulYN03ESdAwcNIzWOXS0PoyO1q1vYOBpVh/nVL+Qf9dv5eDGFPRQOruHT6NIs6uP+fh905eQsW9/UGX7Ey71Ue3Kqmz4axObNmzh0KF0RgwdS9PmjQLyXN+8Eb8MGA7A6N8nUO8ap74uuLgy06fNAWDnjt3sTd3L5dWrBKWjxlWXs/7PTWzcsJlDhw4x7NdR3NCicUCeZs2bMPjnYQCMHDaOBg3r5DpPu5tbMOyXUUFpALj8yips/GszmzckkX4ondG/jafJjYGjQUmbklmduDbXx/mGPzex8S+nw2J7yg527dhNsRLFgtJxaY1L2LJ+C8kbk0k/lM7EYZNokGM0cOvmFP5c8SeaQ8d5F55HZFQk86Y5Rt3+fw7w74F/g9JxSfWLSVqfRPLGraQfSuePYZOp3yxQR8rmFP5c8dcRjZWGLa9hzqS5QeuoWqMKm/7azJaNznUZ89sEGt1wTUCepE1bWbNiXa6PP+e6OB/Y2delaFA6jvW6rMvjulTMx+sSLjqqX3U56//a6D636Qz7dTTNmjcJyNOsRRMGD/B7bq+tnes8bW9uwbBfRgelIZNwaVNr1qzGn+s2sH79Jg4dOsSQIcNp2appQJ6WLZvS76dfABg6dDSNGjnXbv/+A1lGZcGzziLYb/8Lql/I1vXJbNuUgu9QOjOHJ1CzaWC9J85cxsEDzqft2oWrKF7W+Qz1HUon/WA6ANEFopEICU6ES83qlx/ROJyUMIs2N16HiFCt6qXs3buP7Tt2MX32fOrWqkGRmMIUiSlM3Vo1mD47uI4qCJ/7wwgvwsZ1VkS6icgy9/eUm/wOcLGILBKRd0QkRkT+EJEFIrJERFrlcaq7gKmqmvUlpKoTVXWFu1lORMaKyBoReduv/G9EZJ6ILBeRV/3SN4vIKyIyHWgvInXcsmeIyPsissjNFyUiH4rIHHf/g256vIgkuP+GZSIStF9TXHwsmzYnZW1v3pJMXFzsYfP4fD5SU9MoUaIYcXF5HBsfeKzpCF7HZr9zbdmSTPwx6oiPy33sqV4fBWJLcDBpR9b2weSdRJfJ3c9TtEVdLhn7CRW/ep7osiWDKutIhEt9xJYtTXJSStZ2clIKZcqWCshTpmxpkrdszdKxN20fxYoXZcXy1TS9sRGRkZGUqxBH1WpVTkBHGZK2JAfoiC0b6M4XG1eaJD8daWl7KVY80GBo3f5GfjsBQ7N0bCm2Jm3L2k5J2kbp2FJHOCJvqtaoQnR0NJvWbz565jwoGVuSbUnbs7a3J2+nVOyx3YflK5djX9rfvPnta/xv7Fc81v1hIiKCe52WLFuSbcl+OrbuoGQQz0PjNo2Y+NukoDQAlC5biq1+9+m25O257tNjoWqNS93rsiUoHaXyuC4lg7guvcd+xeMncF3CRUdZv7YBYGtSCmXLlg7IE5uj/Uhz2w9/Wre/kWG/Bv/cQvi0qXFxsWz2a8u2bNmaW0dcmaw8Pp+P1LS9lHA7pWrWqs7ceWOZPXcMXbq8fNyjmQDFYouzMzn7PbczeSfFYosfNn+jjtezePKCrO3iZUvy7piP+XzW//j9q1+DHs08FlK27yS2dPa9W6Z0SVK27yBl+w5iS2c/42VKOenBEi73R7iSgYb0FyrCwtAUkauBO4GrgbrA4yJyBfACsMod8XwB2A+0VdUrgetx3GNzUhU4UpdMNeAW4Aqgk4jEuekvqGpNd39TEfEfNvhbVeur6mDge+BBVa0H+HdDPQxsU9WrgVrAEyJSAegEDHdHcasBS46xWnIhkrvXK+dQfN55ju1Y02E68kMHeXXO5jhX6oS5LK/3ECtv6MLehMWc92GX4Mo6kowwro+cpzpceYP7DWNrcgrDJvTjlbeeY8GcxaQH8VHklJGXjhz1cRSxNa66nP37D7Bqxdrc+Y5ZRx7/1uM8R8nSJXj78x50f/qNE3hecqcd67kioyK54uqq/N8bX/NIi8eJq1CW5h2Cc/XOq86P999UvHRxKl9SiblT5gWlAU6sPjIpWboEb332Kq8+/dZJbT8Oh/91ebjF45Q9gesSPjqCbccCn9sD+/ef0HN7LOUcPk+4vOucPPPmLqJWzRtoeE1b/tP1cc46q8Dxa8izrcw7b4P2Dal8+QUM/3poVtqu5B08f+PTPHPto1x7c2OKlCxy3BqOlbzqWUTyvJ3zqrdjJVzuDyO8CAtDE7gG+EVV/1HVvcBvQIM88gnwrogsAcYB5UXkeLt+J6jqXlXdD6wEKrjpt4vIAmABcCngb2gOBHDLKqCqc9z0/n55mgH3uSOcs4GiwIXAXOBBEemBMz80z0krIvKwO6I6LyPj7zyFb9mcTPlycVnb5eLLkpycctg8kZGRFCkSw65du9myJY9jkwKPPVZMR24d5fzOFR9flqRj1LF5S+5jT/X6OJi8kwJx2Y9lgbIlOLRtV0Ae3569qOs6tLP/OM65/PygyjoS4VIfW5O2UTYue+SwbFwZtm3dniNPCmXd3tvIyEgKxxRiz+5UfD4fb3b/gFaNb+ORu56hcJHCrF+3kWBITkohLr5sgI6UrdvyyJOtIyamMLt3p2btb3vTibnNAqQkbyM2LntEpkxcabbnqI8jcW6hc/ii34d89s7XLJm/PGgd25N3UDouuze/VNlS7EjZeYzHbmfNsrUkb0zG58tg2tjpXHT5hUHq2E5pv5HDUrEl2bn12HRk0rh1QxLGTMeXHlwnBEBK0nZi/e7T0mVLsW3rsY9unFvoHD7/qRefv/sNSxeE5rpsy3FdEk7ouoSHjmS/tgEgNq4MW3M8L8k52o8Yt/3IpM1NzfntBN1mIXza1C1bkinn15bFx8fm1rFla1aeyMhIisQUZteuPQF5Vq1axz9//0OVy44/QNKurTsp4ed5UKJsCXan7MqVr2r9K2j35C30erBnlrusP7u37Wbz6k1cfHVwUyKOhdjSJdm6LftZTtm2g9IlS7jp2fdSynYnPVjC5f4wwotwMTSPtQvlbqAIcKU7QrgDKJgjz3LgSKEZ/SdK+IAoEbkQ6AI0UdUrgDE5zptp+R1JpwCP+805reS67P4BNAKSgX4icmdeB6vqN6paU1VrRkScm2cBc+ct4oILKlGxYnmio6Pp0KEtw0eMC8gzfMQ47rrLCUxw880tmTR5elZ6hw5tKVCgABUrlueCCyoxZ+7CI/xzDo/pOLKOjh3aMiKHjhGH0TFixDg6nmb18c/iNZxVqSwFypdGoqMo1voaUsfPCcgTVTp7Xl2RpldzYG1w7o9HIlzqY8nC5VSsXIFyFeKIjo6iVfsbmDBmckCeiWOmcPNtrQFo3uZ6Zk6bC0DBswty9jlOU9SgYW18Pl9AEKHjYdGCZVQ6vwLlK8QTHR1N25taMG50oKvluDGTuPX2tgC0bNuM6VNnZ+0TEVq1bXbC87yWLVxBhcrlia9QlqjoKJq3a8qksdOO6dio6Cg+6fMuvw8exbjhf5yQjpWLVlKuUjxly8cSFR3FdW0bM33cjGM8dhWFixamSHFnFOLK+jVYv3rDUY46zLkWryK+Ujyxro4mbRsxY/zM4zpHk7aNmTgseLdZgOWLVlChcrms63Jju+uZMi7hmI6Nio7io+/fYfjg0YwffmI68rouCcd5XYrmx3UJEx2LFyyjUuXM5zaKtjc1Z/yYwDoeP3oSt97m99xOy/3c/v7riRua4dKmzp+/hPMvqMh555UjOjqaW25pzaiREwLyjBo1gTs7ORHP27dvzpQpzjN13nnliIyMBKB8+XguvKgyGzcc//tn3eI1xFYqS6nypYmMjqJu6wbMz/Geq3hZJR58+3F6PdCTtJ3Zhn/x2BJEu6Oo58acy8U1LyF5XRIni0YN6vD7mImoKouXraBQoXMpVbI49WtfxYw5C0hN20tq2l5mzFlA/drBRzYPl/sjXFHVkP5CRUiD2/gxFfhaRN4HIoG2QEdgL+CyQl5PAAAgAElEQVQ/w7kIjntquog0BeLzONePQDcRuVFVxwCISAtg/RHKj3HLShORssANOMZmAKq6XUQOiUhNVZ0H+Mc7H4vj8jvF1XcxsBEoDWxW1W9EJAaoAfQ7WoXkhc/no8vT3Rk1sj+RERH06TuQxMTVvNajK/PmL2bEiPF89/0A+vb5lJWJCezevYc7Oj0OQGLiaoYMGc7SxZNI9/no3OXloKN5mY68dYzMoaNHj67M99PRp8+nrHB13OmnY/CQ4Sw5jeoDXwabX/mG8398DYmMYOfAiRxYvYnYZ+/gn6VrSRs/h1L3taJI06sh3Uf6nn1s+M8nWYdfOKQnZ51fjshzC3LZ7N5sfO5z9k49/hdOuNSHz+fjtRfepe/gL4iIiGBw/2GsWfUnT7/wGEsXJTJxzBQG9vuND794kz/mDCN1TxqdH3oBgBIli9F38BdkZGSQkrydZx8LfskXn89H925v0f+Xb4iIjGBgv6GsXrmOri8+yeJFyxk/ehIDfvyFT796h4T5o9mzO5XHH+iadXydejVJTkoJ6qMsp46eL/bi6wGfEBkZwdCfR7Bu1V880e0hli9eyeSx06ha/VI+/v5dYooWplGzBjzx3EO0a3gHN7a5nqvq1KBosSK069gSgJc7v8Gq5WuOUmpeOjL4uPtn9Or/LhEREYwaOJr1qzdwf9d7WbV4FdPHz+SSahfzZu//UrhIIeo1rcv9/7mHe5o8QEZGBl+8/jUfD+yFCKxauobh/UcGVR8Zvgw+feVz3uv3NhEREYweOJb1qzdwX9d7WLV4NTPGz+Tiahfxxv9eo1CRQtRtWof7nr2b+657CIAy5cpQKq4Ui2cGPSvDrQ8fb7/0IV/+/BERkZH85l6Xx7s9yPJFK5kyLoHLql/KR9+9TUzRwjRs2oDHn3uAmxp24oY213FlneoUKRZDm44tAHi1y1tBX5ePun/GB+51Gelelwe63stKv+vyVo7rcrd7Xf7PvS4IrD6B6xI+Ony80q0n/YZ8TURkpN9z+wSLFy5n/JjJDPjpVz756m0S5o1yntsHn8s6Pr+e20wt4dKm/ufZHvz2+w9ERkbw4w+DWbFiDd1feYYFC5YyauQE+vYZyP96f8TipZPYvTuVe+92Qn/UrVeL//znUQ6lp5ORkcEzT7/Czp3HPz8yw5dBn1e/5cUfehARGcnkQRPYvGYTtzx7O38tWcv8CXO546V7KXhOQbp80Q2AnUnb6fVgT+IvKEen7vehqogII74ZxqZVwXVEADzX4x3mLlzCnj1pXNeuE48/cBfp6c7oacf2Lbm2bi2mzZxL8w73c3bBgrzx0jMAFIkpzCP33s5tDzpTVx69746gI85C+NwfRnghobJyReQ1YJ+q9nK3u+GMWAJ8raqfuekDcdxYRwIfAsNxjNEFOCOFTYCtBC5vUgVn/mZl4BCwCGfEsi1+y5uIyBjgTWA68APOSOifOEuiDFHVn0Rks3vMHveYesDXOIbpVKCuqjYUkUjgLSAzQNE2t7xbgWddHfuATqp6xBYlqkC8OaaHGScWEy7/CJcbY25szVBLAKDW1uDnpOUnFWJKHz2TB/zryyt4t/cUKxD8x0p+Ujwqb+8Qr4mU8HAe2pWe97QMr4mJPDvUEsKKv/aHh4vg1n0nLyDN8VAw6vjnTJ4MWpeqFmoJAPww/8NQSwDg7Lhrjp7JI9IPbgmXz7JjpmzRKiH9hEvekxiSOguZoXmqIiKFMudZisjLQHFV/U9+lmGGZvgRLi1auNwYZmgGYoZmIGZoBmKGZiBmaAZihmYgZmgGYoZmbszQPH5CZWiGi+vsqUQbd/Q1Cscd996QqjEMwzAMwzAMwwgzzNA8TlS1P4HRZg3DMAzDMAzDMPJEw8YnzVvCw5/HMAzDMAzDMAzDOG0wQ9MwDMMwDMMwDMPIV8x11jAMwzAMwzAM4yRxpgZftRFNwzAMwzAMwzAMI1+xEU3DMAzDMAzDMIyTRIYFAzIMwzAMwzAMwzCME8cMTcMwDMMwDMMwDCNfMddZwzAMwzAMwzCMk8SZGgzIDE3DOAbOzObh8PwQcXaoJYQVG9O2hVpCWJHy955QSzAM4xTlQPrBUEsAYN4/m0ItAYCLLm4fagkA7E+aFmoJximIGZqGYRiGYRiGYRgniYwzdETT5mgahmEYhmEYhmEY+YoZmoZhGIZhGIZhGEa+Yq6zhmEYhmEYhmEYJ4kzNRiQjWgahmEYhmEYhmEY+YqNaBqGYRiGYRiGYZwkMs7Q9QtsRNMwDMMwDMMwDMPIV8zQNAzDMAzDMAzDMPIVc501DMMwDMMwDMM4SVgwIMMwDMMwDMMwDMPIB8zQPMW4oVkjli+bysrEBLo990Su/QUKFKB/vy9ZmZjAjIThnHdeuax9z3d7kpWJCSxfNpVmTRuaDtNx0nRc0rAaL078kJcmf8x1j7XJtb/hAy14fnwvnhv9Lo/1606x+JJZ+1q9cAfdxr5Pt7HvU71V3RPSES71YTpMh+kwHaeDFtORzTVN6jJ25i9MmPMbD3e+N9f+WnVr8NvEfqxIns2Nra8L2Ne+YyvGzx7K+NlDad+xVdAaMrm2ST0mzh7GpLnDebTL/bn2FygQzWf/e49Jc4czdNxPxJePAyAqKope//cGo6cNYfzMoTz2dO5jj5XuPT/k2pa30a7To3nuV1V6fvQlzTvcT/u7HyNx1dqsfcNGjadFxwdo0fEBho0aH7SGcCZDNaS/UGGGJiAisSIyQETWiUiiiIwSkYdFZESotfkTERHBp5+8RavWnbi8WmM6dmzHpZdeGJDn/vtuZ/fuVC6p0oCPP/2Wt3u+DMCll15Ihw5tuaJ6E1q2upPPPu1JRERwl990mI4jIRHCza/fzzf3vsO7Tf9DjTb1KXNBfECeLYnr+bD1S7zf/HkWj55N6xfvBKBK4xqUu6wivVo8z8ftutPk4VacVejsoHSES32YDtNhOkxHsDrCSYvpCNTw2jsv8OBtnWle/xZatb+BCy6qFJAnafNWnn+qB8N/GROQXqRoDE91fYhbbriHm5vdzVNdHyKmSOHj1uCv5fX3XuLeDo/TrF572tx0IxdcXDkgT4dO7Undk0bjWq3p/eVPvNDjaQBatG1KgQIFaH7NLbRucjt33HNLlhF6vLRr0ZSvPnzzsPunzZzLxs1JjBrYm9e6deaNXp8DkJq2ly+/78/P337Mz99+zJff9yc1bW9QGozw44w3NEVEgKHAZFU9X1WrAC8BZU7wvPk+//XqWjVYt249f/21kUOHDjFo0DDatL4hIE+b1s348cfBAPzyy0iaNG7gpt/AoEHDOHjwIOvXb2LduvVcXauG6TAd+a6jQvUL2LFhKzs3bcN3yMfC4TOo2qxmQJ61MxM5dOAgABsWrqFobHEAylwYz7rZK8jwZXBw/79sWbGRSxtWC0pHuNSH6TAdpsN0BKsjnLSYjmyuuPIyNqzfxKYNWzh0KJ2Rv43juuaNAvJs2ZTMqsS1uebmXdO4LtOnzCZ1TxppqXuZPmU21zapd9waMql2ZVU2/JWtZfjQMTTNoaVp88b8MuB3AEb/Pp56114NOKOM55xzNpGRkRQseBaHDqazb+++oHTUrH45RWIObzBPSphFmxuvQ0SoVvVS9u7dx/Ydu5g+ez51a9WgSExhisQUpm6tGkyfPT8oDUb4ccYbmkBj4JCqfpWZoKqLgGlAIREZIiIrRaSfa5QiIutFpKT7d00Rmez+/ZqIfCMi44AfROReEflVRMaIyBoRee9EhMbFx7Jpc1LW9uYtycTFxR42j8/nIzU1jRIlihEXl8ex8YHHmg7TkR86ipYpzp6knVnbqcm7KFKm+GHz1+7QmBWTFwGQtGIjlzaqTnTBApxbrDAX1q1C0bIlgtIRLvVhOkyH6TAdweoIJy2mI5vYsqVJ3pKStb01KYUyZUsd07FlypYmOcn/2G2UKVv6uDUEatkacL7YsoFjJU6ZTh6fz8fetH0UK16U0b9P4J9/9jM7cQLTF4/l2//rS+qetKC1HImU7TuJLZ09TaZM6ZKkbN9ByvYdxJbOrrsypZz00w0N8X+hwqLOQlXgcF0nNYDLgCRgOlAfSDjK+a4CGqjqfhG5F6junudfYJWIfKaqm4IR6tq5AeTsKcs7z7EdazpMR37oIPepnELy4Kp2DSh/RWU+7/hfAFZNW0L5KyrT5dfX2bczjfUL1pDhywhORpjUh+kwHabDdJxIxMlw0WI6AgoI+jx5HBqi+0OpdmVVfD4fdS5rSpGiMQwa+T0JU2axacOWoPUcjrz+jSKS5+dBXnqNUxMb0Twyc1R1s6pmAIuAisdwzO+qut9ve6KqpqrqASAROC+vg9w5ofNEZF5Gxt95nnjL5mTKl8v2nS8XX5bk5JTD5omMjKRIkRh27drNli15HJsUeOyxYjpMx5HYs3UXReOyRyGLlC1O6rbdufJdVL8qTZ9sT+8H38d3MD0rfcL//UavFi/w1V09QYTtfyUHpSNc6sN0mA7TYTqC1RFOWkxHNluTUigbnz1qGBtXhm1bj20UbmvSNsrG+R9bmm1btx+3hkySk1Io6zcqGxtXmpSt23LrdUd9IyMjKRxTiD27U2l7S3Om/jGD9PR0du7YxbzZi7ii+mVBazkSsaVLsnVbdh2lbNtB6ZIl3PTsf3/KdifdOD0wQxOW44xC5sW/fn/7yB4BTie77grmOCanlXi4cwSgqt+oak1VrRkRcW6eYubOW8QFF1SiYsXyREdH06FDW4aPGBeQZ/iIcdx1160A3HxzSyZNnp6V3qFDWwoUKEDFiuW54IJKzJm7MM9yjobpMB1HYtPidZSqGEvxcqWIjI6kRut6LB8f6DQQf1lFbu35EP978H327cx205EI4ZyihQAoe0kF4i6pwKppS4LSES71YTpMh+kwHcHqCCctpiObpQsTqVipPOUqxBEdHUXLds2YOGbKMR07bdJM6jeqQ0yRwsQUKUz9RnWYNmnmcWvIZMnC5VSsXIFyFeKJjo6idfsbmTA6UMuEMZO5+TYnAnzzNk2ZOW0OAFs2b6XuNc58zbPPOZsaNS9n3Zq/gtZyJBo1qMPvYyaiqixetoJChc6lVMni1K99FTPmLCA1bS+paXuZMWcB9Wsf7rP81OVMjTprrrPwB9BTRB5S1W8BRKQWcKSY1+txjNPRwM0nXaGLz+ejy9PdGTWyP5EREfTpO5DExNW81qMr8+YvZsSI8Xz3/QD69vmUlYkJ7N69hzs6PQ5AYuJqhgwZztLFk0j3+ejc5WUyMoJzSTQdpuNIZPgy+OXV73nkh5eIiIxg9qBJbF2zmRufuZVNS/9k+YT5tHnxTs465yzu/cKJfLd7yw56P9SLyOgonhr8GgAH9u3np2c+D9p1Nlzqw3SYDtNhOoLVEU5aTEeghv+++B7fDfqcyIhIhvw8jLWr/qTL84+ydFEif4ydyuXVq/BF317EFImhcbNr6NztEVpc04HUPWl88eH/+HX8jwD83wffntC8SJ/PR4/n3+aHwV8SERnB4P6/sWbVOp554XGWLlrOhDFTGPjTUD768i0mzR1O6p40nnqwGwA/9h7A+5+9ztjpvyICQ/oPY2XimqB0PNfjHeYuXMKePWlc164Tjz9wF+npjrdSx/YtubZuLabNnEvzDvdzdsGCvPHSMwAUiSnMI/fezm0PdgHg0fvuOGJQIePUQk7EL/x0QUTigI9xjMcDOIbkb0BbVW3l5vkcmKeqfUTkGqA3kALMBmqqaiMReQ3Yp6q93GPudfc96W6PAHqp6uQj6YkqEG8XxQhrnoq7JtQSAPgsaVqoJRiGYRinIZWKBB/AKT9Jz0g/eiYPWL1qaKglZBFdsvIpN4mzYMEKIf22P3BgY0jqzAzNMMQMTSPcMUPTMAzDOJ0xQzMQMzRPjDPV0LQ5moZhGIZhGIZhGEa+YnM0DcMwDMMwDMMwThKhXMsylNiIpmEYhmEYhmEYhpGv2IimYRiGYRiGYRjGSeJMjYljI5qGYRiGYRiGYRhGvmKGpmEYhmEYhmEYhpGvmOusYRiGYRiGYRjGScJcZw3DMAzDMAzDMIwzDhG5UURWichaEXkhj/1nichAd/9sEal4tHOaoWkYhmEYhmEYhnGS0BD/joaIRAL/BzQHqgC3i0iVHNkeAHar6gXAR8C7RzuvGZqGYRiGYRiGYRhnLlcDa1X1T1U9CAwA2ubI0xbo6/49BLhORORIJ7U5mmFI+sEtR7xox4KIPKyq3+SHHtNhOsJVx0dhoiO/MB2mw3SYDtNhOkzH6Ud+fNufCCLyMPCwX9I3Oa5DPLDJb3szUDvHabLyqGq6iKQCJYAdhyvXRjRPXx4+ehZPMB2BmI5ATEcgpiMQ0xGI6QjEdARiOgIxHYGYjjMYVf1GVWv6/XIa+3kZwjm9bo8lTwBmaBqGYRiGYRiGYZy5bAbK+22XA5IOl0dEooAiwK4jndQMTcMwDMMwDMMwjDOXucCFIlJJRAoAtwG/58jzO3CP+/ctwB96lHVbbI7m6Uu4+L+bjkBMRyCmIxDTEYjpCMR0BGI6AjEdgZiOQEyHcVjcOZdPAmOBSOA7VV0uIq8D81T1d6A38KOIrMUZybztaOeVM3UBUcMwDMMwDMMwDOPkYK6zhmEYhmEYhmEYRr5ihqZhGIZhGIZhGIaRr5ihaRiGYRiGYRiGYeQrZmga+YaIRIrIM6HWEU64dfJTqHXkREQiRCQm1DpCjYicLSIXh1pHOCEi54aw7MhQlW0cHhEZdyxpJ7H8W0SkoFflnSqISJdjSTPOTESkjoic4/59u4i8JyLlj3acYeQnZmieBohIYxH5VUSWu78hItLIax2q6gPael1uXojITSKyRkRSRSRNRPaKSJrXOtw6KeWGig4pItJfRGJcQyIRWCUiz4VARwMRuc/9u5SIVPJag1t2a2ARMMbdri4iOUN5e6HjfBE5y/27kYh0FpGiIdBRT0QSgRXudjUR+cJjGWtF5H0RqeJxuQGISEERedZtV38RkWdCaei4HVZxIlIh8+dRuQXcDqkyIlLYbT9iRKQc4IkGlzuBjSLyg4g0D3WHhPvBHiMi0SIyUUR2iEinEEi5J4+0e70WAVnv3Jy/60SktEflP3uknxcacui5VUQKu393d9uSKz2W8Q2wX0SuAF4CUoCQdHyLSLz7jrk28xcKHYb3WNTZUxwRaQl8DrwOLAAEuBLoDjypqqM81vMWzgKuA4G/M9NVdYHHOtYCrVV1hZflHkbL1zjX5HcC6+RDj3UsUtXqInIncBXwPDBfVa/wUEMPoCZwsapeJCJxwGBVre+VBj8t84EmwGRVreGmLfGyPtwyF+HUSUWcsOK/49RPC491zMZZF+t3v/pYpqpVPdRQGCdc+n04HaHfAQNU1dNOIhEZBOwl+6PsdqCYqt7qpQ5Xy1NAD5yPxAw3Wb24T10PlWeB0m754u5KA75V1Y9PtgY/LTFAe5z7oxowDPhZVad6pcFPS2Zb2h5oBzwDTFLVah6VfztwB9AAmOa3qzDgU9XrvdCRQ9NIoC4wyU1qBMwCLgJeV9UfT3L5Pdw/LwZqkb3+X2tgqqo+eDLLz0PPElW9QkQaAG8DvYCXVLW2hxoWqOqVIvIKkKyq/8tM80qDq+NdoCNOB7fPTVZVbeOlDiM02Dqapz7PAe1UdbFf2iIRmQd8BnhqaAL13P+/7pemOB/0XpISDkamS5L7i8D5EAgV0SISjfNh9LmqHhKRox2T37QHauB0iqCqSZm9viEgXVVTQ1AHOclw169qD3ysqp+JyMJQCFHVTTnqw3e4vCep/L3At8C3bo/3z8BHIjIEeENV13ok5eIcRsMkEVl82Nwnly6unp1eF6yqH4nIp8BzqvqO1+Xn0JIG9AX6ikgJnE6Rz0SkuKp67Q4Y7f6/BY6xu8vjdmQGkAyUBD7wS98LLPFSiB8ZwKWqmgIgImWAL4HawFTgpBqaqvpft9xxwJVuW4KIvAYMPpllH4bMtrMl8KWqDnO1eMnfrtfSXUBDEYkg+971knY4bdi/ISjbCDFmaJ76xOYwMgFQ1SVuQ+8pqtrY6zIPwzwRGQj8BmQ1bqr6q9dC/F6A56rq30fLfxL5GlgPLAamish5QKrHGg6qqoqIQmjnAwLLROQOIFJELgQ643zAec0hd4TiHpzedwjNx8AmEakHqDiu3p1x3Wi9wnWJbIkzolkR5yO6H3ANTqfZRR5JWSgidVR1lqurNjDdo7Jzsgnvn9MsVNUnIu2AkBqamYhIMeAmnBGS4sAvIZAxXERWAvuBx0WkFHDAq8JVdQOwwfVOSVLVA+DMOQfK4bTzXlMx08h02QZc5BrhhzzUUQE46Ld9EKct8ZotrjfT9cC74kyP8Hq6WkegE/CIqia7LveeelK5/InzTjND8wzEXGdPcURkvqpedbz7TqKeMkBPIE5Vm4sz16quqvb2WMf3eSSrqt7vpQ5XS12gN1BIVSuISDWchv9xj3VUUtW//LYFuEBV13iooStwIdAUx53ofqC/qn7mlQY/LecALwPN3KSxwJuZH20e6qgCPArMVNWfxZmz2tHrESQRKQl8gvNhJMA4oIuXI2ki8ieO611vVZ2RY9+nqtrZIx0rcFzwNrpJFXCM7gw8clv109Lb1TKSwE4zzz4YReQNYJ6qDvOqzBzlF8YZFbmd7GkIA3DcVUPyEeMavGmuIX4uUFhVt3qsYR5QT1UPutsFgOmqWstLHW7ZX+A8J5mjhzcDm3G8rkZ41QktIi8DHYChblI7YKCqvu1F+X46zgFuBJaq6hoRKQtcrqqeBdFydZQDLlTVSeLMM4/0usNbRH7BcXefSGAb5kl7boQWMzRPcURkD45bSq5dQANVLeaxntHA98DLqlpNRKKAhap6uZc6wolwmPvmlplrbkaIOiOa4hh3AoxV1fFelh+OuCMRFVR1Vai1hBIRaaCqCTnS6quqp6OJ7mj/YXFHlLzS0iOv9ExPCY807MaZe/8vziieOBK0uEfl78DpCBoAjFFVL0fI8tJzDs7c1Qqq+rDrEXGxqo7wWMciVa2eI22xV3NFc5QrOMZlfZz7IwH4JRQdAeIE3bkGZ9rONFUNyVQEd37mhar6vTvqXci/s9eD8u8HngSKqOr5InIR8IXXc3hFJK+gVahqXy91GKHBXGdPfY4U5bWXZyqyKamqg0TkRQB37pmn87wA3Ab1S6CMqlYVJ+paG1V902stENq5byJyCXAZUEREbvLbFQN4FknTdYsc677kQm5cish44FZV3eNuF8MJPHODxzpa4zyrBYBKIlIdJ3iGp4ESRKQvzgimf3184LEXwKc4I1b+fJZH2skmz49jVd2YV/pJFZLtel/Y2dR9XmvAmQsYSv5Q1TtDrMGf74H5ZMck2IwzkuepoQlsF5E2qvo7gIi0BXZ4rAFwbkxgiPsLNT5c7wOyA2h5ivgFvsO5X6Jxgot5GfiuM3A1MBtAVVd7PaXKfe83VdVQRGU2wgAzNE9xVHVKqDXk4G83UEPmHLw6hGZ+0bc4LjtfQ9ac1f5AKAzNUM99uxhoBRQlew4gOIEjHvJKhOti9o+IFFHVkM0586NkplEFoKq7xaNQ/Dl4DedjYLKrY5GEZsmXK/KojxpeFOy6l9fDWQrIfymCGCAUS1mMxGnDBKczphKwCqfDxlNEpCpOIJXi7vYO4G5VXe6VBvfZbQFkLkkwWVXHeFU+cIGHZR0L56tqR3duNaq6XyQkUcUeBfqJyOc49+om4O4Q6MDtxHwXJ0KxkD3q7el6zeKsI/oQztxdAX4SkW9CMD0jHALfHVDVg5m3poRgWSC37SglIgUyXbyNMwszNE9xRGQph+l9B/ByLpHLszjzZ84XkelAKRy3Ua85R1Xn5Hj3p4dABzgfA58A8Tg93+OAJ7wq3J1XNUxE6qrqTK/KPQwHgKXuaKL/Ui+hmKuRISIVMkepXHfJUMwlyCv6bSh0RIhIMVXdDSAixfHuHVEAKOSW5/8xlkYI2o+crv6uK94jXutw+QZ4VlUnuVoa4XSk1TvSQfmJOMtW1Qf6u0ndXDfn7h5JOMft9MjTmFOPl88CDrru7pkdqucTgkAnqroOqCMihXCmQu31WoMf7xEeS4o9ANTOnIcoztIaM3E8I7wkHALfTReRbkBBEWmM893h9ag7OMGppouzTnXIlngzQoMZmqc+rUItwB9VXSAiDXFG0QRYFaL5NDvcl39mI38LTjh4z1HVHTgLjoeatSLyEk4Evqxn32PXyJHuLxx4GUgQkUyvgGuBh0OgI1yi334AzBBnKRGAW4G3vCjY9cyYIiJ9vJz/eKy47ZrnAVZczs00Ml0tk0Pw0doaqKGqPgAR+Q5npMYrQzMe5/7My9AMxfJZPYAxQHkR6YdjhN/rsQYgay3ty3CMCQBU9fUjHnRyCJclxYTAqSk+DtNBcZIZJE7U2aIi8hBO4LtvPdbQDeedthJnmaSxuF5eHhMuS7wZIcCCAZ0G5Jj7FioNNx1pv3q8rIiIVMYZCagH7Ab+Ajqp6novdbhaSuG48lQkdAYeIjIDZ3Hv+fi9iFU1FMsDhAXiRFqtg/MhMtPtFPBag3/0W8H5GHhDPY5+62qpgvPRLsBEVU30qNyPVfVpERlOHqO5IZiv6u++G4EzR7SE1/N3XS1DcYy6zHUIOwE1VbWdhxqWAA39RruLAVO88pgRkYWZgdTCBXeKSGbbMStEbcdXwDlAY+B/OKP/c1T1gRBo+QSIJcRLirnP7j0ERp3to6ofe6nD1WKB74wzHjM0TxNcl4S7QjX3TbKXEymNY9z94W43xpnPc0RD9CTqOheICKVLUbgYeHlFKPQaEfmLvA2Jyh5quERVV7rukLkIgRteSBGRGFVNc11lc6GquzzQcJWqzne9IfLS4OlcdAmM9JqO4/r1S4iM/2LAf4EGOB+sU4HXMo0+jzR0At7AWZ5AgEbAq6RzmiMAACAASURBVKraz6Pyw8LQDLe2Q0SWqOoVfv8vBPyqqs2OenD+awmnJcWuIjv67dRQRZ0NNSKykNzv21RgHvC2F227q2NSHjpQVa89EYwQYIbmaYKIDMLpXQ3p3DcRGQE8pKrJ7nZZ4P+8NjTFiXT7PvCiGw0vz+U9PNIScgPP1fEmMENVR4VQQwm/zYI47pnFVfVVDzV8o86SBJPy2K1ev/zEiZDcldwj3p7oEJERqtoqj06AzGAennQCuJ4ZfcMhOqGIVMzp/SAitVR1bogkhRwRiQdqu5uzVXWLh2U3U4/XHzyMjnBrO+ao6tUiMgu4CdgJLFPVC73UEW64bUkZAttTTyNGi8heDm/k/UdV//RAw3s47Xjm3OrbcDq79wF1vPIUcQ3/TAriLIOTrqrdvCjfCC1maJ4mSJisUyQ51ocUkQhgiXq/ZuQSnDk0NYCOqrorVL3i4WDguTr2AucCB91fSKIC5qErQVUbhFJDKBGRxcBX5B7xnh8yUSFCRMbiBBQJaXRCEZmPsxzSFne7IfB5ziBBJ1lDuLkTt8EZVVUgQVWHe1m+qyGv4HeZH+9vqupOrzWFEhF5BSfIzXXA/+HUzbced9x1U9X3ROQz8r5Pve7sfgpnDm0K2fMz1Ss3bz8d/8WZl9jf1XAbjmvxKuAxVW3kgYZc79bMNBFZ6mV7loe2KaqapweLcXphwYBOE1S1r4THou+T3Y/Fn3FeOrcBefX+nmzSVbWbiHQAponI3YQmkic4k/BfEpF/gUOEyMBT1ZBPws/hchaBs85YSHS5Bt4AYJA60RtDRbqqfhnC8gEQkWE49TFMVf8JkYz1hEd0wkeB38RZ4/RKoCfQwmMNmXMyQ7EecgCuEVEF5/4A6OyOMj7lsZTROMaD/wgNONGJ+xC4fNNJIxzaDrcTd6I6SxL94noTFQzB9JnMAEDzPC73cHQBLg6DTocbVbW23/Y3IjJLVV93g/J5QeHMaQmQ9f7N/O7wLAp/jmkZEcBVOEa3cQZghuZpgoTJou+q+qQbGOgaN+kbVR16pGNOEuLqGSQiy3EM3woh0BEWBh6AOCEJ7wQqqeobIlIeKKuqczyU8YHf35lz3zp4WL4/bYCOONEBM4CBOB+OnrpYAcNF5HGc4BX+QTQ8mT/jx4c49fGOiMzBqY8RHs9LDIvohKo6V0Q64yxFdABnwfHtHmvIHNGurqqf+O8TZ61AL+etNgGq+k1D+A5Y4mH5mdRXVf8F75eKyHRVre/OI/WKkLcdqpohIh8Add3tfwnNEivD3f976j11BDYRmrW7c5LhdnRnRvH2X6bJq07vh4EfRSQa55voIHC/G7viPY80gOOtk7kucTpOcEbPA1YZocFcZ08TXFevJjiBd2q4aSF1jQgl/r147nYM0E5VfwiBlomqet3R0jzQ8SWQATRR1UvdICPjVDVUyzaEDeIsK/IKcKeqerqotTs3MieezY3MiTu/qQlOpOQbQ+1a7SV5uKlWwVkWaTd4767qaso1t9zraQDiRL7trKqb3O1ywAeq2tErDW65i4GHVXW2u301jqtotRBOjQhl2/FfHIP/Vw3Rx9zhXLsz8XAeYGak6MtwllcbSWDHnadeEeJEvv8EpyNAgVnAM8AW4CpVTTjJ5UfifPP84sZGEA1BZGTDsBHN04ewWPTdHc18Fyf6rOCxm6iINFHVP4DzROS8HLv3eaHBT0tBnNDzJV2jLvPixABxXmpxqa2qV7qR6FDV3SJSwEsBInIWTiCAigQGagjFum+ISEWcEdWOOC55ngcnUNVKXpd5OFz3+9Y49XEl4PUc71BHJwy5m2omInI7cAeOh8rvfrsK4wR98ZIiwAo36Aw4QYFmiMivAOpdsLcHge/Eia4qOC6zD7ojNG97pAEIj7YDeBZn3n26iBwgNNMywuWZyfSA2Oj+Crg/8PhbyDXy2qrq4Vy5T6qRCaCqPhF5GidadqjdiBGReuR+73ve8W94jxmapw/hsuj7ezjBPEK1cHNDnKVV8mrgFfByTa9HgKdxjMr5ZBuaaTiBG7zmkPsCzHR/K4Uzwuklw3DcmuYTAjcvf0RkNhANDAJu9SIKYI7ym6jqH3KYNWjV+/XnBuIYEGNw7s/Jqur1/dHV7++s6IReFa7uMioiUglIznQbdg3wMl7pcJmBM5pakkCX871477b6lsfl5Yk6UX8vF5EiOCM0e/x2D/JKh1/bMZgQtB2ZhMO0DPV46aHDoar/BRCRW1X1/9s783C5qip9v18AmUJABkUFBAMEEAnzLJMg0iiCoIAos4CigLS0NlMAZwVpwQEZRRoZFAcGmec5TIEw2jYoAg5MYjqAgfD9/li7cs+t1E389ZO79+lb+32ePLnnVOrZX25VnTprr7W+9bPmY5I+mlnLdEkfBk7MuW4PrkzB5gUM7nn/e04Rks4BxgKTGDC8M1ADzT6gls6OENSSoe+dfpmcaw6hYznbT8zuXAYdcwGH2/5yznWH0LIbgzNVOwFHdn8pD7OGQa7EpUhGGv9m+xsFNRxre4JaMH8u/T4OJ2arTZ/dv89JCXdCSXcDGzq536bM/60lysxTCd4z3UGvu8avZNCxBLAOcYN4d+6e1aShFRURSvM0c645hI7ibRnq7QQ8A+d3e+1Vap59tJmkrxKVAN1BXrZZq5L+2OO0bWf1q5D0CLBKqfLuSllqoFmZo0j6LuEm9isG90fkzs70+rK5x/ZaQz1nGLXcbnuD3Ov2QtJKhBW+CMfCrJlnSacCJ9uenHPdIbTcZHuT0jp6IWlH2xdlXrP4+3QId8KTbI/LrGOm2beS7rc9PqeOtG7xoFfSXsBxhAGRiDEnR+c2gJF0BQMVEc1RQCcM+aTh0bEwMUKjc/24kTDfy2JC02jLuB7YjMFtGZfbXjmHjqSlu0VlELb/kEnHNoQz9MeI4K7DGCLIWTeHjoaeVsxabQOSfkb0eP+ptJZKfmrp7AhB0tpERmJZBu/0Zt1NJC7qLxOZ1RkyyFSymgKpdwMLd5UkjiFK8UpwlaQdKWTY0HXz/lfCgXfGY87rbroxsGcywPkHlJlxlrha0heYecc5t9trL04EsgaaFH6fJtriTvispO1sXwyQyuBKGWnM7cZcUdvTcvdWA18C1uxkMSUtDtxK5h5eYCnbH8i8Zi/OBB5kwDH7k8BZQK5e1da0ZeQKJP8JniFGrGxH/E46TCFMeLJie/Pca/Yi3ROtQuP+x/ZPh37GHF27YxS1EPCwws28mYDIbq5WyU/NaI4QJD0GHAZMptF316IvgSykG8LtiS+bpoHGFOB829n7ViVNIRk2EKMSchskPcHAzfsyhIOmgEWAJ3Oa0Qy1+13ifaqWub02kfRH20tnXrPzPp0OvEIZY5FWIGkscC5xIy9iZMLutn9XQMvVRBVAM+g9KHN55HXA1rZfS8fzAFfk1JDWbUVFxBAZ75nOZdDxOdsn51yzh4ZbbG+crh/NG8oi14/03pyb8jPFkbQtsfHdDPKylXlLOpLY9F+JaKfaGrgll3mXpE8Rve03dz20KfC07TNy6KiUpQaaI4TOxb4FOlYEfkj0EK0qaTVgO9tfyaxjA9u351yz7Ug6BbjY9m/S8TbAlrb/NbOOjYEVbJ+V+r5G5+6dbTuSnszdR9MGJB0InNsxeVG4Ne9q+weF9IwmvienlFg/aegEve8gbuSfInPQK+nHwKpES4SJzby7gEcBbJ+UScfDwPJEprtYRYSk24HDnEZUSNoIOL5E6Xl18xyMGjPFbS+nQjPF0/ftAsDmwOmEJ8JE29kqNFL/7OrAvY4RQG8DfpTrdyHpUsKj4oGu82sDEzy0K29lBFEDzRGCpPcBuwLXUrY38kYis/ojD8zzzGYAI+nfbH9L0sn0HpNwUA4d3aQb5hUYvLN5U2YNM/WoSrrb9toZNUwA1gbG2V5R0tuBn7mAgVQy0DqU2PneT+HWPM72pZnWH8pEQ8CKtufNoaOhR8BuwHK2vyxpaeBttidm1NArU1RqPmLRbEQPPcWCXkmzNDOzfVQmHa2oiEjBy9mE2YuAF4A9bd+fWUdPN89S33NJ0wJEqebvXWBuo3rPFH+gwGbEA7ZXa/w9mmhLeP9snzznNEy0vW76nWxGjHibnPF+bMh7P/XxnPd+o/Zojhz2Isoj5mGgdDb3OA+ABWxP1OB5ntnGEwAdc5u7M645SyTtCxwMLEXcEKwP3E58GebkuVRK85/Ee+MT5J/HtwOwBnAvgO1nJJWy6D+L6OXZMB0/RYwryBJoAh/MtM4/yw+Ia8cWwJeJm5LvE06juRglSZ0eUYVrc+5+xCGzEbl1JC1vBb4GvN32NpJWATbIWXaWK5AcCkljHCMZimWWm9ieBIyXNCYdZx0X0WBtCrt5StoOOIkIto8krhl/AZaV9EVnNoyi90zxEryS/n45bag+D+SemXyfpEWInuK7iR7ebK63zNoXY/5sKipFqYHmyGF8S3aHnkulXp0bxZ2IWXC5uFrSEt1fbpLeQlxkS3AwcbN+h+3NU3P+sQV07Eo4Jf4yHd+UzuVkmm1L6rw/Fsy8fpOxtneWtCuA7VeU8e6kmYVJmZoVbF+jGF9R4tq8nu01Jd2X9L1YwHTmSuDCFOgZOICY65mbDRvZiGMlnUD+TbsOPyY2RY5Ix78lDKyyBZqpT7RXhUiu7MxPiY2ZplnUDBlAlr5qSYcOcT6E2N/JoaPBg4TLe0k3zy8TfYALEy64q9l+PH3nXkt+w6i2zBS/NAV53yaCOxObVtmwvX/68fuSrgTGOON4FeAuSZ+yfVrzpKR9GGzYVBnB1EBz5HCHpFVsP1xYx4HAqcBKkp4meml2y7j+ScSNafdN4VaE4+mnM2rp8KrtVyUhaV7bj0rKOq4BZripHpx73S4ulPQjYJFkFLA3cNpsnjNcTEtBXSfoHUuj7DwX6fewH7AoUQa3FHAKMYYmJ6+lDGLn97EEDWOxTHyRcNT8NBFMXEXmm7NEG7IRHRa3faGkfwew/bqk3LNOj2z8PB8xyzLbZ8X2B9PfpV6DDscTVSmXM9AjWpLFKe/m+Ybt30IYrNl+PGn4q6Sc1UwdPkdsyvyDcFi/kgiGs+KB2dkXpV7F+Zxp/E0TSUsSJoBzp+MNnc8U8RDgl4oZ3p3Acm2iSmWHTBoqhamB5shhY2APlR8bsT3wG2JncxQxNmLL1B84KcP6G9ver/uk7XMlHZ5h/V48lXY2f0VkXF8krNizojBq+gIzG0fkLOFdAvg5kV0eBxwNbJlx/SYTiE2JpSWdC2wE7FlAx4HAusCdALb/K2UDcnMSke1+i2LY+E4MDjCGHdtvJOOZ6wo7RvbKRpTaEJkqaTEGNgDWJ2ZJZsP2nV2nbkz9+NlRGMwty+BrWK5s85rALsC2xI3zecQ84lKlq8cUWrfJqORBMAp4I/3cCcBH5RZj+2Ui0Dxidv92uFGXUZOkrEZNkr5GtMg8SqOHl5g3OuzY/guwoaTNCTMxgMtsX5dj/Uo7qGZAI4QWmST8lNixupj4stmWcCdciTB9+dYwr/+IhxhWPavHciFpU6LE6Ao3ZuNlWvt+IlPWPew8WwmLpHttr9l1LrtRQ2PtxYieWRGlzSXMK+60vV7H9EbS3IRLYPbfSSrr7mRSr7P9yKz+/TCsvx0R3BV1jOzSNC+FshFp/TWBk4kbtQeJzZqP5jSe6fQiJkYBawE/tL1iLg1Jx5nAasBDNLwIbO+dU0fSsiHRerAl8EWn8TMFdLyVgT7qibb/mnn93xOvRa/srp1pXJSkWf7+c19D2mDUpBh7N972q7nWrFS6qRnNEYLtP0gaD7w3nbo5twNeYjFisPf/wAyX0Z8DmxABzrAGmsBfJa3b7ZQpaR3g2WFeexCS5iN6zJYn5pueYbtIFiDxuu0fllhY0qeBzwDvktS0Ol+IGPyeU8s7gb/Zfsn285JeJjLxK0r6Xu4NACI7dDgwv6StiN/TJbkWV7hEvmb7tVTW3dnxXpkBc61cTCCyuzdAmK5IWjangPT+mGr7uZQ93Bj4HVGRUIKHiLlz44ib+cfInyl6iIHeyNeJlohPZdYAsL7tVQqsO4hUVr4G8B7CRCxrcNfQ8TFiY+YG4rU5WdJhtn+eS4PtZXOtNRs2IObdnkdUh5Quay5u1ER8TrNnlSuVJjWjOUKQdDDxxd8pIdoBONWZhzlLeoTYQZuWjucFJtleWRnGFEhaF7iQMNBo9gTsDuzSowRsOLVcALxGDCveBviD7WI9kpKOIW6Ifsngfp4XMqy9MPBm4OvAlxoPTcmxfpeWO4EdkuPt6sA1SddqRMC1b2Y9o4B9CEMNET1Fp+e6QZF0E7BPKtldnnBXPZcYUTDR9r/n0JG0DMrupnPZMt6SjiLKpw2cT2SrbgDWA+63fUgOHV2aelUBzHSuH5B0BnBCKS8CSXsBOxN9qj8HLsydQezScz+wVUdDCoCvsT2+gJaNiO/6qZI+QZQZfzdXVVXqL9+KyDKvBlwGnGf7oRzr99DzM+Ag29mNmiSdSFzDliZ+F9cw+Du/p6lVpTIc1EBzhJCyRBvYnpqOFwRuz11+l27UdgB+nU59iCijPYEIfIfdGCj1tx3IQE/AQ8D3CpQUzZgTlcohJ5a8OUz9u91kK21qC83ARdLxhJnFv6WAb1KpMt5SdL1PvwwsavtAhePsPc7oZp0CiWuJzYgdCcfIeWwfkGn9h4kB5wsATwJL2n45fX4nOdP8uaRlSeAdxDiijzOQoRkDnGJ7pQwa1gKeSr1WJFOPjwB/IEqa/zbcGrr0bEJk+/9MAS8CSW8Q1SlPplODbqAKlGcOmkWYrmH35/zMNtZ+ABhPBDbnEK7IH7G9aQEt8xIB57eJ92m2DXdJlxDvi4WIa0l2oyaFq+uQOONopEqlls6OHESj7y79nL10xDHo/TdEuZmAA2x3Zlrmcp99nhhdsWOm9Ybitc4PDqfIklra4NjYFpovxBZAx83zjZyvkaTJ9BgZ0SFjwNvUsAVxc4btaenGOielHSNfTdUY0yT9dzIW6Xx+c5dUb01kV5cCmmMzpgC5jM1OJTLtSNqYcF09hAgoTgU+lklHhzOBTxLBXu73JsRc1TZxhWJsxXnpeGfCjK8Er9u2pA8TmcwzJO2RU0AKMLclgsxlCYOz3GOJjs+8Xi9+DSzmLkM1SStTqMy70r/UQHPkcBZwp6TOjMTtiS/l7CRzmWIzkmxPl7SEpDcV6LdrMl5SZ3aniB68vzOwCz9m6KfOeVIv3qHAMrb3U8wYG2f70pw6WsB1ki4kZs+9GbgOQNLbgJzvlw9mXGtWPJAyu88Q/cRXAShcV7PihmOkwr3yb5l7nBaR9BHiMzom/Uw6XjijDhyzgM+WtKPti3Ku3WBu28+nn3chqlIuAC5IZZu5ebKU6Q5A4R77GaQS97faPiy9Rzsbu7cTZe8lmKIYwfMJYJNUyjpPrsUlnU1UMV0OHGv7wVxrd/E08doM8h5I2finM2k4iXDJ7nbuXpa4vn4ik45KpZbOjiSSO2HnC+cm2/cVllQMxazGNYmy3amd884/ULs1pJ7Re4Ddba+qmCF5u+3VC0vLiiJtuTPwNqLH6ul0fg3gLbavLKBpScIEx8Bdtv+cce35ifmqSwJndUzEkqvmWNvnZNBwNPFaPJqyEpcTWbPpwMdtXzPcGpKOs2b1uO29cuhokgL+owlDNYAbiXLAYXfBlfQg0XM/PfXfH9AJtiQ9mLOUOK35A2ARony2WY6YNWs1RDXCS8DdwFcawflwrX8pcLjtB7rOrw1MsP2h4Vx/CE1LEiXed9m+WdIywGbONM4jVV90vuubr03Wjd02vDaSHrL97iEey/65rfQ3NaM5QpB0ju1PEjPfus/1I8+kP6OIXolKBA07S9oVwPYrKl3PW4CUITsfwmFU0pYpkHmMcBfNiqR9iUDiOgacI4+znaUiwfYrwDckHeyGU7Xt2xRuzTnYmYES2T2Iz+1bgBWBswkzi2GnRCD5T3AGMdakU6b6SaKC5SNDPmPOcSFwvaRniWz/zQCSxhKzcHMzPxFgvr9xzuQvj7yc2AT5aTrehfjsvkQY0Q13MLFsdyADYPtuZXZpbjCFKJmdrpjZvBIDJb3Dju22uKu24bWZ1b19tixzpQI10BxJDNq9SmUraxXSUhzbxwJIWigOY9xKnzMtZa86g9/H0sgK9BuSPgXsByxKzDt7BzFn9H2zet4wcBiwRicLopjteRv5S9/3AL7bdW7PHueGg2mNEtmtCbfI6cAjyYgnK4rZhF8D3m57G0mrEGZrJUw0uvvNj5U0KcfCto+TdB2R/b/Cdqcvch7CqCkrLdoI2Mj2Ro3jyZJutb1RclwdbuabxWPzZ1i/FzcB700l79cS2d2dyefNAPTeYM+86d6G1+a/JW3dXZ0j6f3EyJNKJRs10Pw/TuqJ6Mzga/YDTiPMGvoSSasSzneLpuPniJLRIlbnLWECcAWwtKRzgY2IQKJfOZAoV70TwDHe4y0FdDxFZAM6TCHmwWUhZbg/DiynwUPPFyKMtXLwj/SZ/QthuPKFxmMLZNLQ5MdE1vCIdPxb4AIiu5ibVyRtbPsWmDFG4pVcizfWVfp8zE1kM7NnNBWzifchNlZn3NDb3juzlNGS1nMal6UYqzU6PfZ6hvXvkvQp26c1Tya30VL+CEoOzfsAJ9v+Vq4NkS66N93nJu+mextem0OBSyTdyOAxb5sw/Nn2SmUQNdD8P47trwNfl/R1Z5x393+AU4FDbV8PIGkzojl+w5KiSmL7akn3AusTmxEH236usKyS/CM5qwIzbkhKNK0/TRh5/Tqt/2FgoqRDIUtf8W2EMdLixBiiDlOAmUrAhomDibmESwAn2n4CQNK/ACV6zRe3fWHayOu4zk6f3ZOGiQOAnyhm0QK8SGSfsyHp00Rp8/MMuL2amLWak3OAR4ms93FEtuyRzBoA9gXOlDSauJb+HdhXMVbs6xnWPwT4pWLcTDOQeBMxXqwEkrQB8Zp0xmvMlXHxtmy6F39tUq/7e4gy+04/5p3AZ1OrRKWSjWoGNEJQ4WHJbUPS/e4aWt3rXL/RcCg0cIvtX87mKSMWSd8C/gbsTozV+AzwsO0jZvnEOa9jwqwe75SBZ9LyTmAF29ekMuu5bU+Z3fNGGpJuIOZ4Xm17TUnrA9905pmAirmIO6WgdwyA7RKZxN8RpcPP5l67S8d9ttdQmoUraR7gSttbFNKzMHEflXWeaGP9zWnMi7Z9XQkdScumwL8Ct9r+pqR3AYfYzlpi3ZZN9za9NpVKSWqgOUJQi4YltwHFmJd7id8FhJ332ra3L6eqLMmxcXkGz1z7b9sHllNVjnQTvw9hLCJiZuPp7tOLYrNn1fZYxfibU2xn61ltS2+kwsH7ZOJG8UEi07pTL5OPDFpusr3J7P/lsGq4AXhf6pstqWOi7XUl3URsDP0ZmGj7XZl1zEtsRCxLozLM9nE5dbQRSQvanjr7fzmsGt4BvJPBr81NmTUcT7h4Z2/XkfQivatzOg68i2aWVOljaqA5QpB0b9p5Pxp42jEs+V7ba5bWVoJkSHAsjXEvwDG2XywqrCCSHgJW7QRSKdCaPJQNemV4kfQftg+RdAk9bgpsb5dZzyRSz6rtNdK5ybbfk1HD5aTeSNvjUznzfTk1NLTMDYwjrh+P2X4tt4ak4yiiJ/MCBo9qeiHD2p1s1GrACsClDB4rctJwa+jSsy9wUdJzFtEXebTtUzLruIJwmL2HcJ8FwPYJQz5phJPKZs8ARtteRtJ4YH/bn8ms4xuEC/DDDLw2LnA93RfYiwh2zyIMzoZ9JFFae5Yly6U3jCr9Re3RHDkUHZbcNlJAmd0VseU8BiwDdMqplyZfD17rSOXmxzCw893Z7c2VHelk24/PtN7saEPPapt6I9dlIGO1piScaSZgFx2jm2blgYEc79Ml0t9/Sn+yzCIcCtunpx9vJM//fyiWsv2Bguu3kf8gemcvBrB9v6QSmfgdgHG2izqqp/fq6ZLGEQHnA5JuBU7reEcM49qDrpmSFmWwG+4zw7l+pdKkBpojh50J58h9bP9ZMSz524U1ZadtWaKWsRgxLmJiOl4HuL3jNNqHv5szgM/TlZXIhe2OUcTqtgeNEJF0MHEznZMbJXXMNLYiShMvyaxhqmK8Syfrvj6ROcqKpHOIkTeTaGRFgOyBpu3lcq/ZWPuoUmv3oi2l1cBtkt5je3LmdVuN7T9q8GjmEptEjxOb7MVHd6UN/5XSn+eA+4FDJe1ve5cM628LnAgsRRh5vYNw0F5puNeuVDrU0tnKiELSWrbvScYEM2E79817axjqd9Kh3343ku60vV4LdMxU4t4xPcmso3jPalt6IyU9AqxSsl839cgeTwS8k4Ev2H66kJYrgF06pjepNeE/bW+bWUcrSqslPUz0uz9BBDSdaojVcupoE5J+DnwH+B7hbH4Q4Ysw7AFVl46LCL+Kaxlc5p3blOg7wHZJxxm2JzYee8z2uAwaJgFbAVclE62tgB1tHzDca1cqHWqgOUKQNIWBDN6biB29/7G98NDPqvQb1VV0gNTLMxfwCwbfkNybaf3O/MqNgZsbDy0ETLe9ZQ4dbaMNvZGSfgYcZPtPudduaLiZyKDeRNywbmD7I4W0TLK9ete5Epshd9lep7l2L20ZdLyz13n3qcs7gKTFge8CWxKf3auIEVq5ZvF2dPQc/WP77Mw69gbOt/1yj8cWztGvKelu22tLup+onHHHUGu4165UOtTS2RGC7YWax5K2J3qM+pIW9N+1jqarKJElWQo4BcjmKtoyOtnMtRvnDOQaldCG+ZUzaNFnpg29kYsDD6cy8+YmRM7y8oU8MPT924oZuKWYLmkp208BpNaMEhQtrZY0xjFepi8352aFYybzbi3QcXbaRF3G9mO5109VGRBl9yt1lRJj+95cpkDAS4rZrrcQs3j/ysAc3EolCzWjOYKRERZznQAAERNJREFUdIft9UvrKIGkR+nRf5d7d7VNtMFVtNJe2vCZGao3skDZW/HS+/R67EoE/ADnEhlwJS3ZAs/U6/UDoDMLcHPg07Yvz6Uh6ShaWi3pUtsflPQEEew2o4i+3MiUdDKzMA0r8Nn9EFFy/ibby0laHTgu1yaRpFkZ/dgZZ75KWgh4GRhFzIteGPhJ2hSoVLJQA80RgqRmSdUoIkuzqe0NCkkqSlv679pE53eigaHncwP39mtfkWLY+gSg44x4I3FDksuCvlnuPugh4oYkq8NnGz4zbeiNbAttumGFGUY8GxDvz1tt/zXz+qOI3r+JtGDsTCUYqlS1Q4GS1XuIqpQb+n1DVdLXbB8+u3OVynBSS2dHDh9q/Pw68Huir6dfuV7StynUf9dS2uAq2ibOJLIiH0vHnySMRrL0wXWXu7eANnxmHgSWJEqKsyPpFtsb99gEyB78294811r/JJsDY21/VdLSHeO1XIvbfkPSCWnz9KFc6w6FpNUYKPEGwPYvigkqRCpVnQv4hu3DSusBXrf9UlfJapGNK0kbMvN7JGcbwAeA7qBy2x7nKpVhowaaI4dRRON90xXwBAZmsPUbpfvv2siXCFfRycD+wG+A02f5jJHNWNs7No6PTeXFWRmq3832k5mltOEzU7Q30vbG6e/WbAJIWgA4lOg52y+50Y6zfWlGDd8jDOY2Ab4KTCX6u9fJpSFxlaQdgV8UdgQ+E1iNCHg7PW8mNmn6DtvTJa1VWkfiQUkfB+ZKn5WDiH74rAzVBkCGEUmS9gcOAFbs6u1eCLh7uNevVJrU0tkRQi8HwBKugJV2I2kJANvPltZSGkm3A4fZviUdbwQcn7vcXFJzFt98wHJESeC7M2oYRfS6XZhrzSF0FO2NVAw2HxLbL+TQ0UTSBUTf7O62V01GJ7fndFpVGsHT5fZ6v+3xuTSkNacACxJVO69Srsz8Ydur5Fyz7Ug6AVgB+BmxEQHkz/KmjZkjGDym6cu2X82so1gbQEo0LAZ8ndhg7jAld8l7pVIzmiOHUZLebPtFmHHD1Levr9oz2Ls4ihqiCcBniS9eSZoOnGz7uKLiynIA4cS3MPF7eQHYM7eI7t6hZHiyf2YNb0j6LFA00MxptjME9zCzyUsHAyXMXsba3lkxDgfbr6jbynL4eS1tRnTcXhejgHtlizLNt0taxfbDpYW0iEWB5xlcAZE9y5vGiRyR/pSkWBtAug98EfiopFWJEVoQY7RqoFnJSt8GIiOQE4DbFEOTTfSdfbWspKL8mDTYOx3/FrgA6LtAEzgE2AhYx/YTAJLeBfxQ0udtn1hUXSFs3w+MlzQmHf+9sCQgeiIl5S5JBLha0heIz0kzIzHsWby29EbaXi7HOv+fTEtZzE6QN5ZGWfFwImlu268D3wcuApaQdCzx/XJsDg1deq61/b7ZncvA2USw+Wfitei8T/vSWA3A9l6lNQBIWpvoQVyWwb2RWV4bSZcQn9WFKDwiSdKBwIHAr9KpCyV93/YPcmmoVGrp7AgiZe22IL70ru3n3Va1ZLB3G5B0H7BVt6V5KqO9ql/LqyXNC+zIzDckWbO8kg5tHI4C1gQWs711Zh1P9DjdlyMbACS9g4GZogDYvqmAjq2AI4FVgKuITaM9bd+QYe17ba+Zfn43sCXx/XKN7QeHe/2GjvmABYDrgc0YyDiPAS63vXIuLUnP74i+2ck0Mru2/5BTR5uQtCLwQ+CtqcR7NWA721/JrOMx4DAKvTZDlf83dOQckfQAsKHt/0nHo4Hb+nlDpJKfmtEcQaTAsm+Dyy6KDvZuGfN0B5kQfZqS5ikhqCX8mnhP3EOmDNEQNMsBXwcuI7JHWSmZzWtbb6SkbwI7E9fTppFH9kDT9tXJ0GN9IsA6uNfneZiYUaJr+yHKub3uT1RmvJ34vHaYQmRbc/Ok7YsLrNtmTiMCvB8B2H5A0k+BrIEm8GzJ16YTSEr6pu0vNh9L15Wc7QECmuN/XqN3W0ClMmzUQLMyUjkUuBgYK+lW0mDvspKKMe1/+dhIZynbHygtwnb2EsReFHY3bVtv5PbE/73kBgQAknYArrN9WTpeRNL2tn81m6fOCZboyrgPwvZ3MmiAcA29kDCsOlkxu3FHYozXTzNpaPJoCqIuYXBZZF+6ziYWsD2xq3349QI6Jkg6HbiWsq/NVsAXu85t0+PcHKdR8n4OcIekzsblDkTZd6WSjRpoVkYUqbftj6nPbVNiJ3xHouTsqaLiyjFeUq/+QxEup/3KbZLeY3vy7P/pnEfSLHfdc/byJM4iAr4N0/FThIPksAeaLeyNfJwY51E80AQm2P5l58D23yRNYKDvajiZCxhN+SzIj4AtU5C5CeGm+TlgdeBU8m8izk+8N97fONe3400Sz6X+4U4V0U6UmYe7F7AS8fnNPnpG0qeJGdXvSqWrHRYi35iVicCatr8l6XrgvcRn+ADbd2XSUKkAtUezMsJIJWZb2n4h3ZCcz8ANycq2+zWrWUmkcSImNtpWIIKK7IYekp4F/gicB9xJ1818bgdWSXfbXrsFIyyK90amDMB4Zs6KHJRTR9LyQPd7UtLkbrfiYVp7Ro9mSZrvQ0nfJ8ojj0nHfdl73zaSwdypxEbVi8ATwG65+1ZzfTZmsf7CwJvpPVokSwuA6mi7SouoGc3KSGOuxsV8Z+BU2xcBF0maVFBXpT18sLSAxJJEedWuwMeJ3szzUi9cCYq5m3ZoUW/kNcANREZkOvBK5vWb3C3pO0QvoomNs3tm/ZQ5RulMZoe5GuWA7wP2azyW/T4mmRPtA7ybRlWI7b1za2kLth8HtpS0IDDK9pRCUu4oOXrG9ktE7/+ukuYC3kq8R0dLGm37yQwy2lLyXqnUQLMy4mjVDUmllfyFmKG5POFMeEZ6v2TF9nTgCuCK5IC7K3CDpONsn5xbD3BM0rO0pHMJd9PcIwuK9kZKmpuYv7s38CQRaC1NlBUfXkITEVgeRYydEdEGcGCmtXOPDRmK84AbJT1HBP03A0hanjImb+cAjwJbA8cBuwGPFNDRGpL53gRiZqMl3QIcZ/v5zFI2BvZILtrFRs+kucTHEN83zRLeHDraUvJeqdTS2crIQtIRwL8AzwHLEH0KTjckZ9veqKjASnEkXUC4791MmDP8wfbBhbTMC2xLBJnLEgZWZ9p+upCexRhwN70jo7tpZ/3LgY927PhzI+lEopfq852MTJqzejzwsu1DSuiqzHAOfxsxkmlqOrciMNr2vZm13Gd7jU5Zc3LvvtL2Fjl1tAlJVxOVB/+ZTu0GbGZ7y8w63tnrfIES3t8B6xUItFtT8l6pQA00KyOQNt2QVNpHs4cnZbAmlvhSlnQ2sCpwOXB+zrmEQ+iZafB9r3PDrKFob6Sk/wJWdNcXYyqBe9T2Cjl0pDX/w/YhGhgAP4gCZlGVhKSJtteVdBNh/PJn4jrSlzNnASTdY3utrnN32147s47jic26oqPekgnPViWqZWqPZqVN1FLCyojD9h09zv22hJZKK5kxV8z26112/Dn5JDAVWBE4qKGjU+o1JoeI1G+2ALC4pDczUG41hphbmJPSvZHuDjLTyemScu/KnpP+Pj7zupXZc2r6rBxFVCGMBo4uK6k410vahRhDA+EEfFkBHY8Cp6VNxLOIvvcS5dWPE60QlzF40yxHf2RbSt4rlZrRrFQq/YWk6USABxFUzQ+8TOYAry1IOhg4hAgqn2Yg0Pw7cJrt72XQMMveSNuvzeLpc1LHr4Bf2P5J1/lPAB8rkUVMczR/04aZnpXKUEiaAizIQD/iKAaus9mvq5LGET3muwK3Etey6zOuP6HX+bbMTa5UclEDzUqlUqkg6XOFTIha0xuZRqv8gsik3kOUrK5DbEbsUKJ3VtJZwBZE/9v5RC9g9nK8ygCS3kpsjLzd9jaSVgE2sH1GYWkVZpS6f5AINJcmsqwbA1Nt75JZy0JEoF2k77xSKU0NNCuVSqUCgKQNCVOi5gzLnwz5hDm3bmt6I9O6WxCjKwQ8ZPvanOv30DMPYVy1M3HDfLXtfUtq6meSadVZwBG2x6eM/H0l5ze2gVROvAKDR77knoH7HWA7os/7DNsTG489ZntcJh2rEuXvi6ZTzwG7FxxfVakUofZoViqVSgVJ5wBjgUkMnmE57IEm7eqNxPZ1wHW51x0K26+l4MZEdvXDQA00y7G47Qsl/TvM6PWePrsnjWQk7QscDCxFXEPWB24nsvE5eRA40vbLPR5bN6OOU4FDO+W6kjYDTgM2zKihUilODTQrlUqlArA2sEqvgC8DD0vafYjeyEcL6GkNkj4A7AJsThglnQ58rKSmClPTKCDDDKfzEoYzbeJgosz8DtubS1oJyN6PaPtMSW9OGcVBmdXMpkALNntCbd8gacGM61cqraAGmpVKpVKByAQsCfypwNoHAr+QtDc9eiML6GkTexK9mftXQ6DWcCjhNjtW0q3AEoTLaj/zqu1XJSFpXtuPJkOerLQos/q4pKMYcI/+BPBEZg2VSnFqoFmpVCoVgMWJzOJEBtvxD7vTajLZWa+rN/Ly0r2RbcD2LmkI/XuBayTND8zdMU2q5EXSKCJTtikwjnivPpbLGbnFPCVpEeBXwNWSXgSeKaCjFZlVwkH7WMJcTISZ114FdFQqRalmQJVKpVJB0qa9ztu+MbeWygCSPgXsByxqe6ykFYBTbNdZeYWQdLvtDUrraCvpWrIwcIXtaZnXvsv2OpImAevZ/oekSbZXz6mjUqkENaNZqVQqlRpQtpcDCROTOwFs/5ekt5SV1PdcJWlHYuZqX+/WS5oPOABYHphMOL2WvJYUzaxKunhWj5eYxVuplKQGmpVKpdLHpEHrvW6WRYFB65WZ+IftaZIASKM0+jq4aQGHAgsCr0t6lf7+rJwNvAbcTIzgWYUoXy2C7U5P9zGSridlVjNK2AD4I3AesTmkjGtXKq2jls5WKpVKpdJSJH0L+BuwO/A54DPAw7aPKCqsUgEkTe7MD02bIBNtr1lAR6/M6usFdMwFbAXsCqwGXAacV+dnVvqVUaUFVCqVSqVSGZIvAc8SN8/7A78BjiyqqM+RNJNJVa9zfcIME6QSgV2Ds4kRTZOJzOoJJUTYnm77Ctt7EI63vwNukPS5EnoqldLUjGalUqlUKi1G0hIAtp8traWfSVmzBYDrgc0YKIscQ7gkr1xIWjEkTQemdg6JkUQvk7mcuC2Z1bT+vMC2RFZzWWIUzpnJXbtS6Stqj2alUqlUKi1D0ZQ5AfgscdOudFN/su3jiorrX/YHDgHeTsx77TAF+H4RRYWxPVdpDYlBmdVOT3NuJJ0NrApcDhxr+8EiQiqVllAzmpVKpVKptAxJnwf+BdjP9hPp3LuAHxJjI04sqa8fkbQO8BSwk+2TJe0B7Aj8HjjG9gsl9fUzLcqsvtHQ0bzB7mfDqEofUwPNSqVSqVRahqT7gK1sP9d1fgngKttrlFHWv0i6F9jS9guSNgHOJwyaVgdWtr1TUYGVSqXSMmrpbKVSqVQq7WOe7iATok9T0jwlBFWYq5G13Bk41fZFwEWSJhXUValUKq2kus5WKpVKpdI+pv0vH6sMH3MloxmA9wHXNR6rG/eVSqXSRb0wViqVSqXSPsZL+nuP8wLmyy2mAsB5wI2SngNeAW4GkLQ88FJJYZVKpdJGao9mpVKpVCqVyj+BpPWBtxF9slPTuRWB0bbvLSquUqlUWkYNNCuVSqVSqVQqlUqlMkepPZqVSqVSqVQqlUqlUpmj1ECzUqlUKpVKpVKpVCpzlBpoViqVSqVSqVQqlUpljlIDzUqlUqlUKpVKpVKpzFFqoFmpVCqVSqVSqVQqlTnK/wNY/79dpwbNTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ppscore as pps\n",
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(pps.matrix(df),annot=True,fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corelation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b6749450>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(df.corr(),annot=True,fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More details about the Kaplan-Meier graphs given below- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059453/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b63618d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets create an overall KaplanMeier curve, without breaking it into groups of covariates.\n",
    "\n",
    "## Import the library\n",
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "\n",
    "durations = df['tenure'] ## Time to event data of censored and event data\n",
    "event_observed = df['Churn']  ## It has the churned (1) and censored is (0)\n",
    "\n",
    "## create a kmf object as km\n",
    "km = KaplanMeierFitter() ## instantiate the class to create an object\n",
    "\n",
    "## Fit the data into the model\n",
    "km.fit(durations, event_observed,label='Kaplan Meier Estimate')\n",
    "\n",
    "## Create an estimate\n",
    "km.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets create Kaplan Meier Curves for Cohorts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets create three cohorts of customers based on whether a customer has subscribed for Streaming TV or not. We want to know that which cohort has the better customer retention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b62a4a10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf = KaplanMeierFitter() \n",
    "\n",
    "\n",
    "T = df['tenure']     ## time to event\n",
    "E = df['Churn']      ## event occurred or censored\n",
    "\n",
    "\n",
    "groups = df['Contract']             ## Create the cohorts from the 'Contract' column\n",
    "ix1 = (groups == 'Month-to-month')   ## Cohort 1\n",
    "ix2 = (groups == 'Two year')         ## Cohort 2\n",
    "ix3 = (groups == 'One year')         ## Cohort 3\n",
    "\n",
    "\n",
    "kmf.fit(T[ix1], E[ix1], label='Month-to-month')    ## fit the cohort 1 data\n",
    "ax = kmf.plot()\n",
    "\n",
    "\n",
    "kmf.fit(T[ix2], E[ix2], label='Two year')         ## fit the cohort 2 data\n",
    "ax1 = kmf.plot(ax=ax)\n",
    "\n",
    "\n",
    "kmf.fit(T[ix3], E[ix3], label='One year')        ## fit the cohort 3 data\n",
    "kmf.plot(ax=ax1)                                 ## Plot the KM curve for three cohort on same x and y axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that month-to-month subscribers has highest probability to churn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b61d7b50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf1 = KaplanMeierFitter() ## instantiate the class to create an object\n",
    "\n",
    "## Two Cohorts are compared. 1. Streaming TV Not Subsribed by Users, 2. Streaming TV subscribed by the users.\n",
    "groups = df['StreamingTV']   \n",
    "i1 = (groups == 'No')      ## group i1 , having the pandas series for the 1st cohort\n",
    "i2 = (groups == 'Yes')     ## group i2 , having the pandas series for the 2nd cohort\n",
    "\n",
    "\n",
    "## fit the model for 1st cohort\n",
    "kmf1.fit(T[i1], E[i1], label='Not Subscribed StreamingTV')\n",
    "a1 = kmf1.plot()\n",
    "\n",
    "## fit the model for 2nd cohort\n",
    "kmf1.fit(T[i2], E[i2], label='Subscribed StreamingTV')\n",
    "kmf1.plot(ax=a1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the curves, it is evident that the customers, who have subscribed for the Streaming TV, have better customer retention as compared to the customers, who have not subscribed for the Streaming TV. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " we can see that the survival probability of the cohort in blue is less than the cohort in red. For the cohort in blue, the survival probability is decreasing with high rate in first 10 months and it gets relatively better after that; however, for the red cohort, the rate of decrease in survival rate is fairly constant. Therefore, for the cohort , which has not subscribed for the Streaming TV, efforts should be made to retain the customers in first 10 volatile months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b605b5d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf2 = KaplanMeierFitter() ## instantiate the class to create an object\n",
    "\n",
    "\n",
    "groups = df['gender']   \n",
    "j1 = (groups == 'Male')      ## group i1 , having the pandas series for the 1st cohort\n",
    "j2 = (groups == 'Female')     ## group i2 , having the pandas series for the 2nd cohort\n",
    "\n",
    "\n",
    "## fit the model for 1st cohort\n",
    "kmf2.fit(T[j1], E[j1], label='Male')\n",
    "a1 = kmf2.plot()\n",
    "\n",
    "## fit the model for 2nd cohort\n",
    "kmf2.fit(T[j2], E[j2], label='Female')\n",
    "kmf2.plot(ax=a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1b5f9b210>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf3 = KaplanMeierFitter() ## instantiate the class to create an object\n",
    "\n",
    "\n",
    "groups = df['Partner']   \n",
    "k1 = (groups == 'No')      ## group i1 , having the pandas series for the 1st cohort\n",
    "k2 = (groups == 'Yes')     ## group i2 , having the pandas series for the 2nd cohort\n",
    "\n",
    "\n",
    "## fit the model for 1st cohort\n",
    "kmf3.fit(T[k1], E[k1], label='Do not have a partner')\n",
    "a1 = kmf3.plot()\n",
    "\n",
    "## fit the model for 2nd cohort\n",
    "kmf3.fit(T[k2], E[k2], label='Have a partner')\n",
    "kmf3.plot(ax=a1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, Kaplan-Meier curves  are useful only when the predictor variable is categorical (e.g.: treatment A vs treatment B; males vs females). They don’t work easily for quantitative predictors such as gene expression, weight, or age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative method is the Cox proportional hazards regression analysis, which works for both quantitative predictor variables and for categorical variables. Furthermore, the Cox regression model extends survival analysis methods to assess simultaneously the effect of several risk factors on survival time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cox Proportional Hazard Model (Survival Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifelines import CoxPHFitter     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>StreamingTV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>56.95</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>53.85</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>42.30</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>70.70</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  Churn  gender Partner Dependents PhoneService  MonthlyCharges  \\\n",
       "0       1      0  Female     Yes         No           No           29.85   \n",
       "1      34      0    Male      No         No          Yes           56.95   \n",
       "2       2      1    Male      No         No          Yes           53.85   \n",
       "3      45      0    Male      No         No           No           42.30   \n",
       "4       2      1  Female      No         No          Yes           70.70   \n",
       "\n",
       "   SeniorCitizen StreamingTV  \n",
       "0              0          No  \n",
       "1              0          No  \n",
       "2              0          No  \n",
       "3              0          No  \n",
       "4              0          No  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## My objective here is to introduce you to the implementation of the model.Thus taking subset of the columns to train the model.\n",
    "## Only using the subset of the columns present in the original data\n",
    "df_r= df.loc[:,['tenure','Churn','gender','Partner','Dependents','PhoneService','MonthlyCharges','SeniorCitizen','StreamingTV']]\n",
    "df_r.head() ## have a look at the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>Churn</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>34</td>\n",
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       "      <td>56.95</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>53.85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>70.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  Churn  MonthlyCharges  SeniorCitizen  gender_Male  Partner_Yes  \\\n",
       "0       1      0           29.85              0            0            1   \n",
       "1      34      0           56.95              0            1            0   \n",
       "2       2      1           53.85              0            1            0   \n",
       "3      45      0           42.30              0            1            0   \n",
       "4       2      1           70.70              0            0            0   \n",
       "\n",
       "   Dependents_Yes  PhoneService_Yes  StreamingTV_No internet service  \\\n",
       "0               0                 0                                0   \n",
       "1               0                 1                                0   \n",
       "2               0                 1                                0   \n",
       "3               0                 0                                0   \n",
       "4               0                 1                                0   \n",
       "\n",
       "   StreamingTV_Yes  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create dummy variables by using one-hot encoding\n",
    "df_dummy = pd.get_dummies(df_r, drop_first=True)\n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this seminal paper, Cox (1972) presented the proportional hazards model, which specifies that the conditional hazard function of failure time given a set of covariates is the product of an unknown baseline hazard function and an exponential regression function of covariates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of the above model -https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#cox-s-proportional-hazard-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CoxPHFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'tenure'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'Churn'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baseline estimation</th>\n",
       "      <td>breslow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>7043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partial log-likelihood</th>\n",
       "      <td>-15182.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-04-24 02:39:02 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-6.13</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>30.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0.40</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.66</td>\n",
       "      <td>7.16</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>40.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_Male</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner_Yes</th>\n",
       "      <td>-0.81</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-14.93</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>164.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>22.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <td>0.69</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.45</td>\n",
       "      <td>6.65</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>34.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <td>-2.10</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-15.79</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>184.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>9.03</td>\n",
       "    </tr>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Concordance</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Log-likelihood ratio test</th>\n",
       "      <td>941.30 on 8 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>654.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Cox Proportional Hazards model\n",
    "cph = CoxPHFitter()   ## Instantiate the class to create a cph object\n",
    "cph.fit(df_dummy, 'tenure', event_col='Churn')   ## Fit the data to train the model\n",
    "cph.print_summary()    ## HAve a look at the significance of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d11dad090>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot() #With a fitted model, an alternative way to view the coefficients and their ranges is to use the plot method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot is an another way to show the coefficient for example- PhoneService_Yes(having a phone service)-has a coefficient of about 0.69 Thus, a one unit increase in PhoneService_Yes means the the baseline hazard will increase by a factor of exp(0.69)= 2.00, about a 20% increase in the Cox proportional hazard model, a higher hazard means more at risk of the event occurring. The value exp(0.69) is called the hazard ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting point to note here is that , the β (coef ) values in case of covariates MonthlyCharges and gender_Male is approximately zero (~-0.01), but still the MonthlyCharges plays a significant role in predicting churn , while the latter is insignificant. The reason is that the MonthlyCharges is continuous value and it can vary from the order of tens, hundreds to thousands, when multiplied by the small coef (β=-0.01), it becomes significant. On the other hand, the covariate gender can only take the value 0 or 1, and in both the cases [exp(-0.01 * 0), exp(-0.01*1)] it will be insignificant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MonthlyCharges  SeniorCitizen  gender_Male  Partner_Yes  Dependents_Yes  \\\n",
       "1           56.95              0            1            0               0   \n",
       "2           53.85              0            1            0               0   \n",
       "3           42.30              0            1            0               0   \n",
       "4           70.70              0            0            0               0   \n",
       "\n",
       "   PhoneService_Yes  StreamingTV_No internet service  StreamingTV_Yes  \n",
       "1                 1                                0                0  \n",
       "2                 1                                0                0  \n",
       "3                 0                                0                0  \n",
       "4                 1                                0                0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## We want to see the Survival curve at the customer level. Therefore, we have selected 6 customers (rows 5 till 9).\n",
    "\n",
    "tr_rows = df_dummy.iloc[1:5, 2:]\n",
    "tr_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d11d46290>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets predict the survival curve for the selected customers. \n",
    "## Customers can be identified with the help of the number mentioned against each curve.\n",
    "cph.predict_survival_function(tr_rows).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from the above graph from the given graph we can see that customer 2 has the highest probability to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the survival curves at each customer level helps us in proactively creating a tailor made strategy for high-valued customers for different survival risk segments along the timeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lifelines Python Doumentation-https://lifelines.readthedocs.io/en/latest/Quickstart.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SciPy 2015 lecture by Allen Downey- https://www.youtube.com/watch?v=XHYFNraQEEo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Princeton University Lectures notes-https://data.princeton.edu/wws509/notes/c7.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for checking out the analysis<br>\n",
    "-Akshat Anand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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