harisonmg/ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb

## ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb
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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 400, align = \"center\"></a>\n",
    "\n",
    "# <center>K-Means Clustering</center>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered the one of the simplest model among them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you learn how to use k-Means for customer segmentation.\n",
    "\n",
    "Some real-world applications of k-means:\n",
    "- Customer segmentation\n",
    "- Understand what the visitors of a website are trying to accomplish\n",
    "- Pattern recognition\n",
    "- Machine learning\n",
    "- Data compression\n",
    "\n",
    "\n",
    "In this notebook we practice k-means clustering with 2 examples:\n",
    "- k-means on a random generated dataset\n",
    "- Using k-means for customer segmentation"
   ]
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   "source": [
    "### Import libraries\n",
    "Lets first import the required libraries.\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section."
   ]
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   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
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   "source": [
    "# k-Means on a randomly generated dataset\n",
    "Lets create our own dataset for this lab!\n"
   ]
  },
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   "source": [
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>"
   ]
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "np.random.seed(0)"
   ]
  },
  {
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   "source": [
    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n"
   ]
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   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
   ]
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   "source": [
    "Display the scatter plot of the randomly generated data."
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Setting up K-Means\n",
    "Now that we have our random data, let's set up our K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
    "    </ul>\n",
    "    <li> <b>n\\_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n\\_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n\\_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, ..., 1, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "collapsed": true,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 3.97334234,  3.98758687],\n",
       "       [ 0.96900523,  0.98370298],\n",
       "       [ 1.99741008, -3.01666822]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Creating the Visual Plot\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Please read through the code and comments to understand how to plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "Try to cluster the above dataset into 3 clusters.  \n",
    "Notice: do not generate data again, use the same dataset as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Customer Segmentation with K-Means\n",
    "Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
    "Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
    "\n",
    "Lets download the dataset. To download the data, we will use **`!wget`**. To download the data, we will use `!wget` to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-04-04 06:02:15--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 34276 (33K) [text/csv]\n",
      "Saving to: ‘Cust_Segmentation.csv’\n",
      "\n",
      "Cust_Segmentation.c 100%[=====================>]  33.47K  --.-KB/s   in 0.02s  \n",
      "\n",
      "2019-04-04 06:02:15 (1.54 MB/s) - ‘Cust_Segmentation.csv’ saved [34276/34276]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File  \n",
    "Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>Address</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA001</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA021</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NBA013</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA009</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA008</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted Address  DebtIncomeRatio  \n",
       "0        0.0  NBA001              6.3  \n",
       "1        0.0  NBA021             12.8  \n",
       "2        1.0  NBA013             20.9  \n",
       "3        0.0  NBA009              6.3  \n",
       "4        0.0  NBA008              7.2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "As you can see, __Address__ in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
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       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
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       "      <th>1</th>\n",
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       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
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       "      <td>6.111</td>\n",
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       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  \n",
       "0        0.0              6.3  \n",
       "1        0.0             12.8  \n",
       "2        1.0             20.9  \n",
       "3        0.0              6.3  \n",
       "4        0.0              7.2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Normalizing over the standard deviation\n",
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use __tandardScaler()__ to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.74291541,  0.31212243, -0.37878978, ..., -0.59048916,\n",
       "        -0.52379654, -0.57652509],\n",
       "       [ 1.48949049, -0.76634938,  2.5737211 , ...,  1.51296181,\n",
       "        -0.52379654,  0.39138677],\n",
       "       [-0.25251804,  0.31212243,  0.2117124 , ...,  0.80170393,\n",
       "         1.90913822,  1.59755385],\n",
       "       ...,\n",
       "       [-1.24795149,  2.46906604, -1.26454304, ...,  0.03863257,\n",
       "         1.90913822,  3.45892281],\n",
       "       [-0.37694723, -0.76634938,  0.50696349, ..., -0.70147601,\n",
       "        -0.52379654, -1.08281745],\n",
       "       [ 2.1116364 , -0.76634938,  1.09746566, ...,  0.16463355,\n",
       "        -0.52379654, -0.2340332 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = df.values[:,1:]\n",
    "X = np.nan_to_num(X)\n",
    "Clus_dataSet = StandardScaler().fit_transform(X)\n",
    "Clus_dataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
    "\n",
    "Lets apply k-means on our dataset, and take look at cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 0 0 1 2 0 2 0 2 2 0 0 0 0 0 0 0 2 0 0 0 0 2 2 2 0 0 2 0 2 0 0 0 0 0 0\n",
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      " 0 0 0 0 2 0 2 2 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 2 0\n",
      " 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 0 2 0\n",
      " 0 0 0 0 0 0 2 0 2 2 0 2 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 2 0\n",
      " 0 0 0 0 2 0 0 2 0 2 0 0 2 1 0 2 0 0 0 0 0 0 1 2 0 0 0 0 2 0 0 2 2 0 2 0 2\n",
      " 0 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 2 0 0 0 0\n",
      " 0 0 2 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 2 0 2 0 2 2 0 0 0 0 0 0\n",
      " 0 0 0 2 2 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 2 0 2 2 0\n",
      " 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 0 0 2 0 0 0 0 0 2 0 0 0 1 0 0 0 2 0 2 2 2 0\n",
      " 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0\n",
      " 0 2 0 0 2 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 1\n",
      " 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 2 0 1 0 0 0 0 2 0 2 2 2 0 0 2 2 0 0 0 0 0 0\n",
      " 0 2 0 0 0 0 2 0 0 0 2 0 2 0 0 0 2 0 0 0 0 2 2 0 0 0 0 2 0 0 0 0 2 0 0 0 0\n",
      " 0 2 2 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 1 0 1 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 2 0 2 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 2\n",
      " 0 0 0 0 0 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2\n",
      " 2 0 0 2 0 2 0 0 2 0 2 0 0 1 0 2 0 2 0 0 0 0 0 2 2 0 0 0 0 2 0 0 0 2 2 0 0\n",
      " 2 0 0 0 2 0 1 0 0 2 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0\n",
      " 0 0 2 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 0 0 0 0 2 0 0 0 0 2 2 0 0 2 2 0 0 0 0\n",
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      " 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 2 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\n"
     ]
    }
   ],
   "source": [
    "clusterNum = 3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
    "k_means.fit(X)\n",
    "labels = k_means.labels_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Insights\n",
    "We assign the labels to each row in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  Clus_km  \n",
       "0        0.0              6.3        0  \n",
       "1        0.0             12.8        2  \n",
       "2        1.0             20.9        0  \n",
       "3        0.0              6.3        0  \n",
       "4        0.0              7.2        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"] = labels\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily check the centroid values by averaging the features in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
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       "      <td>1.032711</td>\n",
       "      <td>2.108345</td>\n",
       "      <td>0.284658</td>\n",
       "      <td>10.095385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410.166667</td>\n",
       "      <td>45.388889</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>227.166667</td>\n",
       "      <td>5.678444</td>\n",
       "      <td>10.907167</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>7.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>403.780220</td>\n",
       "      <td>41.368132</td>\n",
       "      <td>1.961538</td>\n",
       "      <td>15.252747</td>\n",
       "      <td>84.076923</td>\n",
       "      <td>3.114412</td>\n",
       "      <td>5.770352</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>10.725824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         432.006154  32.967692  1.613846        6.389231   31.204615   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         403.780220  41.368132  1.961538       15.252747   84.076923   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         1.032711    2.108345   0.284658        10.095385  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         3.114412    5.770352   0.172414        10.725824  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets look at the distribution of customers based on their age and income:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = np.pi * ( X[:, 1])**2  \n",
    "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Income', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7fdd3d2d5438>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3dwO4Gq1Jp9O4ePEicrkcHA5HXSXUZn4n1q/fCpEYq4gxYO3LUDvrFAoF0zW03AoSMdDngc9axLAUFtQn5lpkeW2mj1LqnE6nYbPZEIlEEIvFsGfPHjgcDuTzeZw7d47JQ8RKURKzirLNNs/aaM3Q0BCANdErCAIEQcClS5fUaI0ibHw+34Zmzk68frOtYbVITDvryLJsqV5RJGKuoPVNZPVIzE5PJylN5ZLJJBKJBHK5HNxut9q6/9ChQ5ua7whiI5xO5zXRmlwuB0EQMDs7i2w2C7vdrkZqFHOx2SMxVimxNouIsdoziETMFbS+yDvlibHaWkbpE6M0lVPSQ8ViEX6/H5FIBHv27IHf72/o+jFLZGmnYOR0DM/zCAQCCAQC6s9KpRLS6TQEQcDc3BxyuRzOnj2rChu/36/pRsqy4lFPdqKxdzOs9lwgEaMTnahOsqqI6UQTunK5XNdUrlqtqkMSDx061JYxzgxN9Qht0Pqzdjqd6OrqQldXFwDg5MmTGB0dVUWNEq2p9da4XK6W1yMRY6w1WK5jFkjE6ASlk7SB1XkVi0WUy2WcPXsWgiCA4zh1MvLY2FhDJstGMEI/Gq2gaE9j6LlBcxwHv98Pv9+PwcFBAGsCvDZaUy6X4fV61UqoZqI1Zu50Wwulk65ihc+zFhIxsIaxlyIxjSPLMnK5nBplyWQycDqdkCQJvb29GB8fb8n4NpmI4+mzbyNTEvFTgyP4lX37YVv3sKGN33gYNZ3UKg6HA7FYTG1mplzv6XQa8/PzyGazqrFYETabRWusImIoErOGFZ89JGJ0wuoixkwdeyVJUnt0KE3lfD6f2rpfaSr3yiuvtNzFciGTwRde/iE4AC6bHU+f/QnKUhW/dvC6a86HBSSWGmMnvEe10ZqBgQEAV6M16XQaCwsLKJVKarQmGAyq94RVRAxFYtYolUptpReNCIkYnbDZbJY19hq9nLt2SGIqlUK5XEYgEEAkEsH4+LguQ/t+snwZYrWCft+aCdPGcXhxZvoaEQPsjI2TWMOoImCjaE0+n4cgCFhcXMTExAR4nkelUoHT6VRnSJkZK1RAAe2JGKv1iAFIxOgGy9b8yno7NRIjiqLanyWVSgGAOiRxZGSEyTcPh80G1LzMiizBa7+2tbyVqpOMuDm3glXOox04joPP54PP51OjNZVKBW+//TaKxSLOnTsHURTh8XjqvDVW6jfSLmaIxFitWy9AIqYOLZW0ldNJnezYqzSVqx2SaLfbEYlE0N3d3bEhie/tH8D/M+HDYjYD2xWR95s3HLnm96y2YbIQZGYfoGjWz9xut8PpdGJoaAh+v1+N1qTTaSwuLiKTyYDjuLpKKLfbbdrzbReWIqbVZ1yhUCARY0WsYOy1YsUQcHXWx8WLF5FMJpHNZtUhiUNDQwgGg4YoNwy4XPhfH7wdL12cQbZUwpG+fhzq6r7m9zpVMm52rHY+ZqFWhNVGa/r7+wGsRWsUb83S0hKKxSK8Xq8qbAKBwI6J1rAcO9DqeyqKIqWTrIoeHXut6onRM52kDEmsNeECQCwWw969ezUfkqglIZcbH9l/YNvfs0qJNdEYRr1eG2G7SJLdbkc0GkU0GlV/v1AoQBAEXL58GVNTUwCgjlgIhUKWjda0EyFpdp12PDEUiSEaohPN7liJJi3TSeVyuW5IYrVaRTgcRjgcxsDAAEqlEmZnZzE8PKzJep2GqoaMhZnTPSxo9v3hOA5erxder1eN1lSrVbVvzfLysrqRKsLGKvcDeWI6A4kYneiEJ4aVkbidc6v1swiCAJvNpppwd+/eDYej3gxbqVSYvo96QyXWhJnQQuQpg1AjkYh6zGKxCEEQsLKygkKhgNdeew2BQECN1ng8HtOJS7OIGEonEQ1hdWNvI9QOSVT8LC6XC5FIBP39/Thw4MC2uV3ajAlic8xoTOY4Dh6PBx6PB11dXchms7jxxhuRyWQgCALOnz+PfD4Pt9utGoaDwWBHDPvNYIYSa4rEWBitN0sri5jNqG0ql0gkUCgU1CGJu3btQiAQaPomJxHTGqxKrM3+2VA6aWtYDMjkeV6NyIbDYfXnxWIR6XQaq6uruHDhAmRZRiAQUIVNo/2eWF2jZojEkCeGaBgrD4BUqG0ql0wmUalUEAwGEYlEcPDgQU1Cwqw3StrUCDNh9gZumx2/NlrT29sLYM1bk81mIQgCLly4oEZraku8N4rWmEFcsFqH0kkWRo9IjNWqk0RRVAVLLpfDa6+9pvpZRkdHNRuSWIsR02RmwApREgW9+8TovUmb8di1axhltpTNZlMb7Sko0Zp4PI7p6WlIkgS/36/+ntfrNUWah9U6lE4iGsbs6SSlsVVtUzmn04lIJIKenh4kEgnccsstmq23GSw3Y2UtK4kZq0CfSWfQ+35od+NXxiH09PSox8tkMkin05ienkY+n4fdbocoiojH4wgGg9cUD2iFGURMoVBQy+GtAomYGrS8Yc2WTlJu/tpIi8/nQzgcxsjIiDoQTsGKVTZWil7Q2AFjYJQohlHX0Pr4PM+rURilLUM6nca5c+eQTCYxMzNTF60JBoOa9Z4yQ8SHmt1ZGD0c+EYWMdVqtc7PUiqV1CGJ+/btM0xTOdbpJKuIGFaY/f0yc+TNCiJGkiTdz8HhcMDtdmPv3r3qmoq35uLFi8jlcnA4HKr4aTVaY4ZIDKWTiIZh/WDcrotuqVSqG5IoyzJCoZDavt+o6px1JKbTFV5aYdaNmWgcVgKMRXWSnqzf9HmeV8u2FURRVCsrL168iEqlUuetaeRLHSsRA7T+mZCIIQxLbcRCaf2tiJZ0Oq02nIrFYtizZ48meWEWD1HWIsZKmD1KYgXMbuzVG6NEk1wuF7q7u9HdvTbvTInWpNPpumhNbSXU+kIGliKmVag6iTAkigk3l8vhzTffRDabhdvtRiQSweDgIA4dOqT5zcXKBEuRGEJPzJxOAswvvFm8/62Ii9pozdDQEIC1aLYgCBAEAZcuXVKjNYqwqVarphAxFImxKGZ6GEiSVDckUZkcK8sydu/eDb/fr/v56DkEshaWn4uVPDFWOhczYyZTbCdgEb3Q6n1yOp3XRGtyuRwEQcDs7Cyy2SzefPNNNVITCoV0aTvRDtTszsIY+WFQLpfrTLjValX1sxw6dAhutxuSJOHUqVMIBAJMXpMStWh1JLwRoY2/OYx8z+wErCBijBqJaQSe5xEIBNRnbi6Xw/XXX68Ou5ybm0O5XIbP51OFjd/v72i0hqqTiKZp5SYtFot1QxI5jkM4HEY0GsXY2NiG6r4TfWmstuFbScSwOhezv19mFgKs/CR6H98InhitcDqd6OrqQldXl7q2Eq2Zm5tDNpuF3W6v89a4XK6Gj9/u50GRGKIpGvGNKBe5IloymYzaVK63txfj4+MNRTtYP4it6B+xkogxIopZ0ufzNWUsN/NnQsberWFRYt1Jwy3HcfD7/fD7/RgcHASwFllfH63xer1qJdRW0Zp2BRl5YoimUKIjtRdk7ZDEZDKJfD4Pn8+ntu5f31TOqBhh4KTWWEnEGOFclGs9kUggkUiooWxRFAFA/TYaDoe3/TZq1miJ3rAw1usJixJro0XbHA4HYrEYYrEYgKtfZNPpNObn55HNZlVjsSJslPujXUFG1UlEU/A8rzralf4spVJJHZI4Pj7e8CRWo0GRGGI9StfnRCKBeDwOURQRDAYRi8Vw3XXXwePxIJ/Pq92s0+k0UqkUFhcXUSqV1L4c4XCY6X2h5zpk7N0aM3titKI2WjMwMADgarQmnU5jYWEBpVIJXq8XPp8PkiS1fE7lctlwZuN2IRFzBa1upFKphGQyiUQiAUEQ8MYbbyAajSISiWBkZKSp/KeRIU+MsWFxLrIsI51OY3l5GYlEAsViEcFgENFoVBUtm6H0LYpEIgCuVnqkUilMT08jl8vB4/EgFAqp1Xd6nYNZhYCZX7uC1dNJrbJRtCafz2N1dRWiKOL111+vKwNXojXbvZdWuGbWQyKmDZSmcrVDEu12OyKRCLq7u1EoFDA+Pg6fz9fpl6o5lE7aeSiiRUkPJZNJyLKM3t5eHDp0qK1ce22lx/DwMGRZRrFYRCqVwvLyMlKpFObn59VIjZ6D/LTEzBO4WbAT00mtwHGc2jU4k8ng+uuvR6VSUb01S0tLEEVRFf7BYBCBQMBS1aObQSKmCWRZrhuSmM1m4fV61db9wWCw7oZcWFiw3EavYMUNn+U56f1g1WqgXa1oKRaLCAQCiEajOHjwICYnJ7Fnzx74/X4NXnE9HMfB4/HA4/GgWCzC7/cjHA6r/ZGUQX61vhqr5fq3wwr3n1XSSaw+i9pzsdvtiEaj6lRq5Uu1ImomJyfBcRyCwSDOnDmDQ4cOWeKaWQ+JmC2oVqt1TeVEUYTf70ckElEf3lvdgFaMVihY8dysJsyaPRdFpCuipVAo1IkWvVI6jeJwOOrKV6vVqvpN9Ny5cxBFUZ283ui8Gz03UVYdrfWCVYm+FSIxLIc/bhZd4TgOXq8XXq8X/f39AIBKpYJMJoMzZ87gG9/4Bi5evIiPfvSjuPXWW3HLLbfg5ptv7vh93S4kYq7AcRzK5bIqWlKpVF1TuYGBgabD5YqBkRWsRgEA7D0xVhpxwOJzauTYW4mWAwcOwOPxGDoMv95Xo1R5pFIpdd6Ny+VSIzVWC6/rfQ2x2vztdn23IRYCg5WIaXa0gWJvePjhhwEAH/jAB/D444/j1VdfxTPPPIPvf//7+OIXv7jtmjfffDMGBwfxwgsvYHp6GsePH0cikcBNN92Eb33rW3A6nRBFEXfffTdef/11xGIxPPPMMxgbG2vndBs7R91XMBGKCzwSiWD37t1t59x5nke1WtXo1W0Pyy66LKuTrDanqVON6GRZRjabRTweN6Vo2Y7aKg9l3o3iq7l8+TImJyfB87wqakKhkK6vx+wiwyqN6Filk1hFYlpdp1KpwG63Y8+ePdizZw9+4zd+o6G/+9rXvoaDBw8inU4DAB588EE88MADOH78OD75yU/iiSeewH333YcnnngCkUgEU1NTePrpp/Hggw/imWeeaem1NgOJmBpGR0c1vRA70UWXlYhheW4b9dvRA5YihgVKpCUejyOZTCKXy6miRYvyfjMIHrfbjb6+PvT19QGAGm0VBAEXL15UK584jkMoFILb7TbFeQHWEDEsqpOsch7KOq0+B1tpdDc3N4fvfOc7+OM//mN89atfhSzLePHFF/HUU08BAO655x488sgjuO+++/D888/jkUceAQDceeed+PSnP83kvScRoyOdEjEsYD1d2koREkAfv4ESaUkkElhcXESlUkEkEkE0GsX+/ft16b1iNg/Rel+NYhAWRRETExMQRRFer1eN1LQzTNXsIoNVJMYKqR6WnhiWIub+++/Hl7/8ZWQyGQBAPB5HOBxWU4BDQ0OYn58HAMzPz2N4eBjAWhorFAohHo+r95pekIhZh5Y3LmtPTCeiIyywmojRap1a0ZJIJJDL5eD3+xGNRtHd3Y1AIKA2zzIzen4mPM+r0ZrR0dG6WTeXLl1CLpeD0+lURU0wGDSMr4bmGhlnDbOImGYq+F544QX09PTg6NGjeOmllwBsfM0p7+1W/6YnJGKuoMebzdoTY1URw2oto4sYZYNVOuLWipZ9+/bVVeNMT09r/bI7CqsKoo1m3RSLRQiCgOXlZUxNTam+GuW/zTqgWqE6iQSGcdZod51mhz++/PLL+Pa3v43vfve7KBaLSKfTuP/++5FKpVR/zdzcnPpFaWhoCLOzsxgaGkKlUoEgCGr5t56QiKlB65vJyiKG0kntrdMItaIlkUggm81uKloI/XC73XC73ejt7QUA9QGdSqUwOzuLarWKQCBQ16+GVcdks4sYq3hiWBp7W63majad9Oijj+LRRx8FALz00kt4/PHH8fd///f4tV/7NTz33HM4fvw4nnzySXzkIx8BAHz4wx/Gk08+iVtvvRXPPfccbr/9dorEmB2e51Eul5muxzI6wkqgWU3EABuHXrcSLXv37m1KtFit541etLLB2e32upbwysyoVCqFyclJFAoF+Hw++P1+VKtV3TZRK4gYq3hiqtWqKYy9WjSEfOyxx3D8+HE8/PDDOHLkCO69914AwL1UeClxAAAgAElEQVT33otPfOIT2Lt3L6LRKJ5++um212oEEjE6YmVjL8/zqFQqzNayYjppI9Hi8/kQjUYbaqbYaYz82lhSm14Crs65UYZgnjp1Ck6ns25kgha+GquIGKusYfR0UivGXoVjx47h2LFjAIDdu3fj5MmT1/yO2+3Gs88+29Lx24FEjI5Y2djbiT4xZl9H2dyKxSLOnj2LQqEAr9eri2jpVC8a4uqcG4fDgUQigRtvvBGiKEIQBKysrOD8+fMAUNevppXJwmZPVwHWGQBpRU+MWSARU4PWD34re2JYduw1q4hRRIsSaclkMuow0OHhYXR1dVE0o8OwGjvgcrnQ09ODnp4eAFCH9ym+mkqlUuerabTxoBUiMVZoRGcGESOKoiXni5GI0RHW6STWZlurCaZ23z9lAJvSETeTyaiRlt27d6uRljfeeEN3Uy6JI2OzfnifJEnIZrNIpVI4f/488vn8Nf1q1m9eeosMq5hurRLtaXcdisQQTWN1T4zVUlfNrqOIltpIi8fjQTQaxa5duxAIBDZ8eFKqZ2fQzAbN8zyCwSCCwaD6t4VCAalUCvPz88hkMnA4HHW+GhaeGBrO2PgaLL44VKvVlv1U7XhijAyJmBq03lzIE6PdWkaIxGwlWsbGxjYVLc2uYxascB5GnWLNcVcnEit9OBRfTTwex4ULF1AqldTy71AoBJfLpeXLt4zAsEpaDGjf2KsMS7USJGLWoeUFT54Yc6210aa83tPidrubFi0braM3VhAYClY5j3ZZ76uZm5tDPp9HLpfD/Pw8yuUy/H6/moJqd8yEVSqHWEVi9J7GrazTieokI0MipgY9mt1ZNRJjxagPz/MolUqYn59HIpFAOp1WRcvo6CiCwaBm1whtzM1hRo+P3hu0Eq1RJnYrvhpBEHDhwgXk83l4PB5V1AQCgaY2QKv4VQD9rx8zeGJIxBBN0wkRw6q5nlWqk2rTQ/F4HDzPw+l0YmRkRFPRUguLKAnLdF8zGE2MsNio9aT2tdf6aoaHh9X0pyAIWFhYQCaTgd1uV0VNKBTaMnpgpTSM3phBxBQKBapOIprDypEYs1Yn1YqW2kjLyMgIenp6kMlksGvXLk3W2gwrpXqIzem08bbWV9Pf3w8AKJVKEAQBiUQCMzMzkGUZwWBQNQzX+mqskk5igRlEjCiKFIkhmsPKIoZ1JKbV8yoWi6poEQQBLpcL0WgUw8PDCAaDdQ8EURQtIy5IKFmfVgSA0+lEd3c3uru7AaxVuyj9ahYXF1EqleD3+xEKhZg8S6wiYlidRzvrUIn1DkAPTwzLjcSqkZhmNuTNRMvQ0BAOHTq07TdXI1RBmQUrbD4AuwnZehy/XWw2GyKRiFq1IkkScrkcUqkULl++jGKxiGw2q0ZqmvXVNIIVriNWkRig9fdLq9lJRoNEjIWwsrF3swf2etHidDoRi8UaEi3rMUtTPaOsAZjfoKz3mAm90eOLVyAQQCAQgNPpRKFQQG9vrxqpmZiYgM1mq+tX43A4NH0NZoSliGkVSiftAMz+jcAowkJras+rVrSk02k4HA5Eo1EMDg42LVo2wowbD2FczNxRV/HceDweeDwe1VdTLpchCAKSySRmZmYgSVKdr8aK3/a3g5WIaef5ROkkwvBYMRIjiiLS6TRyuRxmZ2c1Fy21sEz/sYjEEJ3F7IJ4s3SYw+FAV1cXurq6AFz11QiCgHPnzkEURfh8PrUKSu8RG0bADJEYSicRhod1Azo9RIwoinXpIYfDAZvNhmAwiPHxcV0fFFbzxJg91cMCFr1c9IKF56aR46/31ciyrPpqLl68iFwuB5fLVeerabV1frOwugfMImIoEkMYGqOabbdCFEUkk0nE43EIgqAOxuvv78fBgwfB8zwWFhYgiqLuDwkriRgrdQU2qxgzishgfXyO4+D3++H3+9VGfMViUTULT05Ogud5hEIhVCoVlMtl3Xw1rKqGzDAsk/rE7ADMWqWgwNoT0wqlUkmNtKRSqTrRcuDAgQ2/oVlJXLBcx6ybP9EYen++Wjaic7vd6OvrQ19fH4CrvpqlpSW89dZbdb6aUCgEt9utyTOTpVeFxWiDdtaQZZlZBIwlJGJ0RhEWLC4e1n1pGmEz0dLX14fx8fGG3heW4sJo7x+xhhm/YLD48mLESEwjKL4al8uFo0ePolqtIpPJQBAETExMQBRFeL1e1Vfj9/tbei0sIzFGFzFWhUSMziiTrHeKiNlItEQiEfT29jYsWtbD6rxYmQ+tVGJNbI5R0z1GOX4tNpsN4XAY4XAYo6Ojqq9GEARcunQJuVwOTqdTFTXBYLChZwnLSIxV5jOZDRIxOqNMsmbRS6ETIqZUKiGZTCKRSCCZTMJmsyEajbYlWtZjtUiMFQbeWQmzvld6b5x6b5pb3dO1vprBwUEAa74aQRCwvLyMqakp1VejGIY3esaaoQldo5CI2RgSMTpjtbLnWtGSzWbxxhtvIBqNoqenB/v379cl4sRSxLCAPDHGQe9md2aOlBjt+G63G263G729vQCASqUCQRCQSqUwOzuLarWKQCBQ16/GKmMNABIxm0EipgY9LnazN6Arl8t16SGe5xGJRNDT04NEIoH3v//9mq63ESwjJFbpE8MCSll1FqOJDNbHt9vtiMViiMViANY2ecVXMzk5iWKxCKfTiXK5jEwm07Kvxii0I2KsJObWQyJGZxRPDAu0uEjL5bJa8lwrWrq7u7Fv3z7Y7VcvGVY3BctxAFYRSyQwGsfMxl49MbqIWU9temlkZASyLGNlZQWzs7OYnZ1FNpuF0+msG5lgpmqddidY104otxIkYnRG8cQYFUW0KJ4WjuMQjUY3FC2dgtJJhF6Y+XMwm8hYj969VTiOg8vlgt/vx/j4OIC1zVwQBKysrOD8+fMAoIqaUCgEp9Op2+tpl3ZEjFW79QIkYnTHCBVDtWwkWiKRCGKxGPbu3du0aGFVRmq1SAyLNcy8QVsBs4sMFsdnUZZcew4ulws9PT3o6ekBsOarSafTqq+mUqkgEAioosbj8RgmmtauiLFit16AREwdysWq5c3baRFTqVTU9JAWoqUWZaPU+yZnWWJNnpidhd59YvTECiKGRZfbrTZ+pW9VNBoFsCYUstksUqkUzp8/j3w+f02/mvXHM8NoA6sOfwRIxFyDHqPtWYoYJQ+sRFoAaCZa1qOcG40DMN46rL49UpXV1phZBJg9naSs0czzied5BINBBINBAGvvQaFQQCqVwvz8PDKZDBwOR52vhud5JvdbtVptyxND6SSiJfQ29iqRFkW05PN5JBIJRKNR7NmzR1dPC0vDLa3TPFYRSqzX0goWkRi9j2/2dFK758BxHLxeL7xeLwYGBgCstZlIpVKIx+O4cOECZFlGqVTC8vIyQqGQbgZaisRsDIkYndHa2LtetMiyjEgkgmg0it27d+PUqVPYv38/M98FiyiTFdNJO41isYh4PI7V1VV4PB5Eo9ENQ/OsMfMUa72Pb/ZID6BPbxWn01nnqykUCnj77beRy+UwPz+PcrkMv9+vpqC8Xm/H50CRJ4ZomXY34EqlglQqpfZqWS9a1nepZOVTAawnLqy0TqcFmWIgX11dRSqVgsPhQCwWQ29vL4rFohqaV1rJRyIRBAKBjosaLTG7CDD78VmtoVRB7dq1C8BVX40gCLhw4QLy+Tw8Ho8qalq9ziVJarl6yqoTrAESMbrD8zzK5XLDv18rWpLJJCRJQjgcRiwWw65du7YdX8DKpwJYa9NX1mEBiwgW6/SLJElqiD0ejwPAhuMnCoUCIpGIGpovFotIJpNYWFiwnKixQrrHzGMNOrVGra9meHhY9dUIgqBe53a7XRU1oVCoobR/u31iKBKzQ9B6w7TZbBBFcdN/r1ardekhRbREo9GGRMt6rDbmgOU6rLCCJ0bxASwtLWF6ehqiKKpie3R0tOFvjG63G/39/ejv7wewJmpSqZT6sHc4HJAkCW63G6FQSPMNyczpJLOLGKtEYrYzKNf6apTrvFQqQRAEJBIJzMzMQJZlBINB1TC8ka+GPDEbQyJGZ9Z7YqrVal16qFa0jI2Ntd1sibWIsVIkhhVmNKkCaw9CxdeSzWYhyzK6u7tx3XXXafaAdLvd6OvrQ19fH4C1b5DvvPMOEokEFhcX4XA41GnHeogaLTG7CLBKdZLeDTtbMSg7nU50d3eju7sbwNq+oPSrWVxcRKlUgt/vV0WN1+ttW8RQOmmHoMeGmcvlMDk5eY1oaeYba6OwntVkJU8MS8zgiSmVSkgkEuoICpfLhVgshj179sDv92NiYgKxWEzXb3gulws+nw9dXV2IRCIQRRGpVEqdZGyz2RCJREzZRr5dWKSr9J5ibfTqpEbQImWlXMeRSATA2utW+tVMT08jl8uhWq2iWq3CZrM1nWqldBLRMOsjLaVSCTzPo6+vTxfRsh5KJxkfo4oy5dpdXV1FIpEAz/OIRqPo7+/HwYMHDRH1cLlc6O3tVScZK+WutaKmNlLTiKgx8+wkM0dizCIwOrEGx3EIBAIIBAKqr+bMmTNwOp1YXFzExMQEbDZbXb+arawHhUJBbehnNUjErKPZ6p5qtQpBENSOuJVKpS7Sks/nMT8/r4bH9YZVigewnuGWFUapTpJlGel0WjXjlstltTGi3j2GtGJ9uasialZXV3H+/PltRY0RxWSjmD3dQyKmcTiOA8/z6O7uht/vB7BWASgIApLJJGZmZiBJUp2vpjZ9RLOTCBWlCkOJtNSKlpGRkWsMWaIoMo0iWDESQ2iDLMvI5/OqaMnlcggGg4jFYjh8+LBhH3LNCI2NRI0gCKqo4Xm+TtToidkjGWYXSYAxjL1arlMrlhwOB7q6utDV1QVg7Qt1JpNBKpXCuXPnIIoipqamsLCwgNXVVZpivVPZSLSEQiHEYjEMDw9ve2GwnmJNIsb4sBw7IIqi6msRBAEejwexWAz79u2Dz+dr++HLMurXCusNlOVyWb2fp6enUSgUcOnSJXR3dzdc6moUzC5iWA2AZOG7YZFq3e5caqOOyusKBoOYn5/HiRMn8P3vfx/f+MY3cNttt+G2227DLbfcYqrrfTPMfwYaI8syksmk+qArl8sIhUKIRqMNiZb1sN7oWZltlbXMHI7vFHq+b0pH58XFRaysrCCfzyMWi2FwcBCHDh3S9GFrxjSfw+GoEzVvvvkmAoGAKmoAqBtBOBxu6yHPwhirJ2YXSazWYNWXq9nZSRzH4cCBAzhw4ACWl5fxiU98AsPDw/jv//5vPPXUU7jppps2vb6LxSI+8IEPQBRFVCoV3Hnnnfj85z+P6elpHD9+HIlEAjfddBO+9a1vwel0QhRF3H333Xj99dcRi8XwzDPPYGxsTKMz3xoSMeuQZRlLS0uIRqMYGhpqOwTHWsRQJMb4aPlQlSSpztdSrVbVah1ZlnHDDTdotpYVUczLg4ODAK42m0ylUpiZmQHQuqgxu1/MCukkq3hi2l1HqU4aGRnBxz72MXzsYx/b8vddLhdefPFF+P1+lMtl3HbbbfilX/olfPWrX8UDDzyA48eP45Of/CSeeOIJ3HfffXjiiScQiUQwNTWFp59+Gg8++CCeeeaZll5rs5CIWYfNZtO0EkPvAZDrsWKfGCvS6vsmyzJyuZwqWgqFgprerBXdSm6caA673V7nM6hUKqp58uLFi5BlWTVOhsPhbZtRmr06SU9YpZOsIJTaXafZZnccx9UZiMvlMjiOw4svvoinnnoKAHDPPffgkUcewX333Yfnn38ejzzyCADgzjvvxKc//Wlm429IxKxD6zfdyp4YlqkrK9FsOkkZnhiPx5FOp+Hz+RCLxTA+Pq7ZcLmdynYPWrvdjlgshlgsBuCqqEmlUrh06dKWosYKAt/MkR5lDRZCiYW3pJ1zaaU6qVqt4ujRo5iamsKnPvUp7Nmzpy4aOTQ0hPn5eQDA/Pw8hoeHAazdM6FQCPF4XP0yoCckYnSGtW+E0knGZ7trolKpqGbcZDKpDk8cHR1FMBhs6MFPfiV9WC9qlBYLyWQSly5dgiRJCIVCiEQiqFaruveF0hPyqxhnDaC9z6OVKdY2mw2nT59GKpXCRz/6UbzzzjubvqaNnjWsvlyRiNEZ1t+SKRJjDmpv+trhiUpX52g0ip6eHuzfv9+wXWitIpTauUdtNhui0ajaSEwRNalUCktLSwCAdDqtRmrMLGq0hlXlkBXEWLu0ImIUwuEwjh07hh//+MdIpVKoVCqw2+2Ym5tTB7kODQ1hdnYWQ0NDarSSVXM9EjEWg2X6qtkJ3WaBxUOpVCphZmYG8XgcxWKxpeGJW2EVgaE3Wr9HtaKG53m4XC643W4kk0nMzc2hWq0iGAyq5uudLGooEsOOZmcnraysqHPKCoUC/uM//gMPPvggfuZnfgbPPfccjh8/jieffBIf+chHAAAf/vCH8eSTT+LWW2/Fc889h9tvv50iMZ3C6Ip6O1gKCysae5vt2NwoyvBEZQ4Rx3GIxWI4ePAgvF6vpmtZEbPel+tn4tQO+qsVNUqkxqoNyTbCKlESM4iYcrnclGBeXFzEPffcg2q1CkmScNddd+GXf/mXcejQIRw/fhwPP/wwjhw5gnvvvRcAcO+99+ITn/gE9u7di2g0iqefflqvU7kGEjEWw8rpJFazaLQQZuVyWU0PpVIpOJ1OxGIx7Nq1C9VqFYuLixgaGtLgFW8MRWIah+XspFpRs2vXLrVEPplMYmFhAeVyWRU1kUjE0qLGKlESM4gYoLnr/D3veQ/eeOONa36+e/dunDx58pqfu91uPPvss229vlYhEWMxrGrsVaI+RhUxyvBERbgAQCwWQ19fHw4cOFD3kBMEgQRGE+j5XnX6c6gdgwBc7fuTSqVw9uxZlMtlBAIBNf1k1NEQrUCRmMbp9HVqZEjEMIKV+cuqHXuV82IxaK2Rc9poeGI4HEZXVxd27dq1bf8QVmMHiM7Ryj1fK2rGxsYgSRIymQySySTeffddlEolBAIBhMNh05vqrSRirCCUzAqJGAbo5bPYCKtGYlhOzN5sndrhidlstuXhiazOxQiTso2O0StLeJ5HKBRSh1UqoiaVSqFYLOLkyZPw+/1qpKbVCpSN0PuzNUNpciNYZQaUWSERsw49Lnilay+Li5Cl2daKa9WuUyqVVNEiCALcbjdisRj27t3b1vBElgMgic6hh0CqFTXLy8s4evQostkskskkJiYmIIoi/H6/6qlxu92GvRaMLiAbhXw3nYVEDAOUsmcWXR2tauxlsVa1WkWpVMLU1BTS6TR4ntdteCILzB4lIbaH53kEg0EEg0GMjo5ClmU1UjM5OYlisQifz1cXqWlUOFgh1cMCFgKj2eGPtVQqFcP2mtICEjEMsGqKx+zppPXDEyuVCiqVCkKhEMbHx3UTnVZIw1gFPTfSTmzSHMepomZkZASyLKuRmqmpKRQKhbpIzWaihsX1ycJLwgKjR2LaaXRnBkjEMICERftokU7aaHii4mu54YYb4HK5cPr0aUSjUV2jZqzSSSSUOosR3n+O4xAIBBAIBOpETSqVwvnz55HP51VREw6H1VlcVplrxAKjG3vNImIuX74MWZbR29vb1DVIImYTtLyJWU6ytrJgamUtURTrfC2NDE+0gl+F1RpG2KiNjNEiDbWiZnh4WBX2yWQSFy5cQD6fh8/nQygUgiRJlotU6QErY2+rKaFmu/V2gtdffx2PP/44/vVf/xUTExOw2Wz4+te/jgceeGDb8QUkYtahx01lVWFhxKjP+uGJysC+kZGRhoYnsjIQW2UNs6P3Jm10OI6D3++H3++vEzWJRAKiKOLkyZPw+XxqpKYdQ/t6rCJiAP3FqlUjMco18KlPfQp/8zd/g7m5OUiShIGBAbzwwgu47777tj0GiZgN0KOigOU8I6MJCy3YTFxIkgRBELC6ulo3PLG7u7ul4YmU6jEmZt3szCaQFFHjcrkQj8dx4403Ip/PI5lMYmZmBrlcDl6vV/XUtCNq9PaSWOkes6qIUZAkCYcOHYIsywgGgwDWMhiNvG4SMQywanSkE+kkJaevpIi0Hp5oFRFjJaFk1vMwcypGOT7HcfD5fPD5fBgaGoIsy8jn80ilUrh48SKy2awqasLhMPx+f8Ovi9U5WIF2RIyR00nK53PjjTfixRdfxOrqKtLpNP7t3/4N3d3dJGKMglWFBavUS6FQQC6Xw+TkpNoHQ6/hidTDZedhxs+jUwKgVtQMDg5ClmUUCgUkk0lcunQJ2WwWHo9HjdRsJWr0Pgcr9VZp51xEUTR8JObxxx/H7/3e76FSqeCnf/qnMTIygr/7u79rSHyRiGEAS2MvyweyXr1byuWy6mtRhidKkoTBwUEMDQ3peo7UTXdnofdcJrOlk2qP38hr5zgOXq8XXq+3TtSkUinMzs4im83C7XarkZpAIKAel0U6yYwCdSPajcQYXcQEg0H8wz/8AwqFAgDA4/E0vLeQiNkArTcAlp4Ylmj1PkmShGQyWTc8MRqNore3Vx2eeO7cuaYadbUKpXoaxyrnoRdmjui1KgBqRc3AwABkWUaxWEQymcTc3Byy2SxcLhfC4TCq1aquQoMiMWsUi0XDppMUXn/9dfzTP/0TEomEet+43W587Wtf2/ZvScQwgGWKhyWtPnyUrqLxeByrq6vq8MRYLLbp8ESWs5P0/qwoZbVzMLsnpl04joPH44HH48HAwAAAqJGaUqmE1157TRU1SqRGK+HBqtcNCyRJarl3ldGNvdlsFnfeeSf+4A/+AO9973tRrVZRLpcbLsogEcMAq0ZimmGj4YnRaBTXX399QzcYSxFjhTUA8xpiWWJW861ZRMxGKKJmbm4O733ve9VIzcLCAjKZDJxOp+qpaUfUsIjEsEpZWbk6qVQqYXx8HA888EBLf08iZgO03jBtNhvK5bJmxzMDpVIJiUQCq6urdcMT9+zZ01QFgwKraBareVAUiSHawcwiZj1utxv9/f3o7+8HsLbpplIpLC4u4ty5c3A4HOrsp2Aw2PBmzuIcWKWs2pmdVCgUtm0Y10m8Xi9+4Rd+Af/zf/5P3HHHHXC73XC5XPD5fOju7t7270nEbIKWN4BV00m1VKvVOl+L1sMTrRaJ0WKEwsmFebwTX0GP14efHdsN17pwMwmlxjBrtMQqImY9brcbfX196OvrA7BWXZNMJrG0tISJiQk4HA41UrOVqGE108jokRijVyeJooiJiQl85zvfwT//8z+jUqkgm81i3759+MEPfrDt35OI2QAzN7tjhSzLEARB7epZqVQQiUTUaIvWs4dYihhZliHJMnKlEpw22zXiQKs12uHZd87gqTM/WYscyTJ+NHsJ/9cHfgaOK3lkK6WsKC12LXp/KTJSZY/L5bpG1KRSKVy+fBmTk5Ow2Wx1kRrFS8FiHACr+U/tjB0wejppenoar732Gi5dutTS35OIYUAnIjFaP4SUJleKryWXyyEUCoHneXV4op6wTCdlRRH/ceYnWMhmwIHDB0fHcHP/gKZrtEO5WsUz77yNiNsD+5VePefiq3gnvor39PSqv2elzd8oG2ozUCRGH1wuF3p7e9Hbu3atl0olpFIpLC8vY2pqCjabDeFweMMCAa1hlU6yarM7YO3z3LdvH5aWluB0OuF0OuFwOOB0OmkApFFgLWKU9VpV7gq1wxPT6TS8Xi9isRj27duntht/5ZVXdBcwwNoDu1Ap45/ffQdTyTjCbjd+ee84BgIBzdf578UFxCFhIBBERZLw4swF9Pv9GAwENVunHYFRlWVUJRm2Kzc4x3HgOQ4li0X7zI5Ze9AA7NIkWuB0OtHT04Oenh4AV0XN0tIS0uk0crmcWv0UCoXafi7WYgYRY/R0ksPhwPnz5/HzP//zuO2229ReQzfeeCPuv//+bf+eRAwDWDa7A1oXMZVKBclkEqurq3XDE4eHh5sy1OkBx3H4j9lZXEYVPV4fBFHE3/3kND519H0IaCiiOI7DfC6LgVgMAGDnefAch2SxoJmIaXdzcNvtuLl/EK8tziPgdKFQKcPndGJ/NKbZGoQ2mNVvY+RIzHYoosZms8Hn82F4eFidr3bhwgVwHKd6atoVNWYQMUZvdheJRPDII4/A6/WqfcJyuRwGBwcb+nsSMRtgdk9Mo5EfZXiiEm1pdXgik34MAKbSAsYH+tceQjYbljIZLOWymouYqMsFQRTR5fVCuuKP8Tv1jzY1w2fffyv+9q038PbyMnZHIvjdI0cRZBARIxpH70iMnphZxCgoG7/T6UR3d7da6VIul5FKpRCPx+tEjRKpacbPZwYRY/Rmd7FYDMeOHcO7776LSqWC7u5uHDp0qOHPgUTMBughYlhHYjZ6yMmyjFwuh9XVVXV4YigUQiwWw8jISEvDE7VKXW2HjefhtPEQq1W47XbIsoyqLMOp8bocx+G2vn68LCSxmM1AlmXc3D+I0WBI03Xaxetw4FNH39fpl0Fsg5k9MWafML2ZEHM4HNeIGkEQkEgkMD09DY7jEAqF1EjNVpspVSe1z9LSEj7/+c/j3//933HDDTfg3Llz2LVrF77xjW+oZfdbQSKGAZ3yxABrKrzW16L18ERWVUM8z+ODvf34cSYNQIYE4HB3D4Y1FhccxyHocOL/eM8RJAoFOG02RA38AOgkzXz2pVIJq6urWFlZgcfjQSwWa6lf0FaUJQn5cglBp8sQUQRqdte54wONb/wOhwNdXV3o6uoCsJZWT6VSSCaTmJ6eBgA1UhMOh+tEDcvqJKulk5Rz+pd/+RcUi0VMTU2p//boo4/iy1/+Mv70T/902+OQiGEAS09MuVyGKIqYnJxELpdTb9CxsbG64WtawUqg8TyPfcEQ3rN3Hy7nsvA5HNgbjYHX+HyUjdlps6HP79f02DsJJVWpRP04jkNXVxd6e3tRLBYxOzuLTCYDr9eLSCSCSCQCr9fb8vX5/OQ5fOGVH0EG0OXx4q9+4X/HaFSFydQAACAASURBVMhY0TMtMbvIMHI3Xbvdfo2oEQQByWQSFy9ehCzLaqSmnSZ0zWDldBIA9Qu1IrhcLhelk4yEnhu9JElqfjcejwNYu+kGBgZw/fXX657m0ePcytUqZKAuVaR00h0IBDSvSKqFVcdeK1IoFLC6uorV1VXk83mEQiFVQCvlroVCAeFwWB0OmM/nkUwmceHCBeTzeQQCAUQiEUSj0Yar3iYTCXzx1ZdRuvK5LeWy+D///Xv4zp3HdTvXRqBITOeOD2gnlJQCh9gVs78ialKpFFZWVlCtVlGtVtVIjV6l3a2+X0aNxCjnc/ToUfzgBz/Aww8/jNtvvx2nTp3Cj370I/zO7/xOQ8chEcMALY29tcMT4/E4SqWSOjxR2SzOnDmDQCCgu4ABtE0nSbKMfzjzE3xnagIygA8Mj+K3b7wJDpuNebM7YnskSUImk8G7776LRCIBp9OJrq6uuhL8reA4Dj6fDz6fD0NDQ+q1nUwm8c4776BcLiMYDKJQKKBSqWx6nDPxFdQuJQNYyGZQrFTg1rhRYTOYucTa7McH2huauBW1osbn86FYLCIYDCKZTOLSpUuQJEmN1LDqV7MVRo3EKF8Y3/e+9+Gxxx7DX/3VX+Ev//IvMTw8jEcffRSHDx9u6DgkYjZA65ur3eOtH54YCAQQi8Vw3XXXbaiwWXpwtFzrvy7O4NuT59BzJa3wX5dm0OPz4Y4DhzY1K2sNiZjNqTWGr66uIpvNwufzYWxsDPv27YPNZsNSNovTyTjcmTTe09PblPGa4zgEg0EEg0GMjo6qKamJiQlMTk6C53m1NDYcDqsivc/nu+ZYbpsNLgYifjv0jMToyU5OJzWDIpSi0ag6n6haraqRmtnZWVSrVYRCITVS00oBRTvIsszkC20r8DyP6elpeL1efOlLX1J/nslkUCqVGnqvSMQYEGV4YjweRyqVanp4ImsRo9UD9czqCtw2G2xXHm4+pwNvryzjjgOHmKV5SMTUU6lUEI/Hsbq6ilQqBa/Xi66uLlx33XW4fPky7Ha7WuXxbnwVj//4ZVQkCTKA/dEY/uiW/63lCjKe59VJxsPDw/B4PEilUkgkErhw4YLabn48HMYHh0fwX7OXrlwnMh794O0dN/dSx97OHR/onFCy2WzXiJp0Oo1kMom5uTlUq1UEg0FVjDeyUVvxmVStVmGz2fDYY4+pU6xFUYTL5cJDDz2Eo0eP4rd+67e2PQ6JGAOw0fDEaDSKgYEBHDx4sOkbkaWI0VJc9Pp8EGvSboVyBb1XvmVTOokNsiwjnU6r0RZZlje9Ftc/vL/1kzfhtNnQ5fWqoxBeX1zArUPDbb8ujuOuMVyWSiUkk0lcvnwZv+oJ4OjufSg7nTg6NILxK3N2rIrZRQaT3lIGSVkpYjsSiQC4KmpSqVSdqFEiNSw6oBuJarWqdltWzr1cLjccsSIR0wGUjUJJEZXLZU2HJ5o1EvOhPfvw2uIC5jJpAECX14u7Dl6v+TpbsRNFjDJeYnV1Fel0GsFgEF1dXThy5EhToW9BLMJz5drlOA4cxyFXLun1suF0Outm6LynUEAymUQykcDJS5fg8/nUzcPj8TCPzJjd2KtnFINFfxVWU6ybXaNW1OzatQuSJKmRmoWFBdULpqROWaefWKF8/rt378Zbb72F97///ejp6cHKygqWl5dVI/V2kIjZAK1vLlmWIUkSLl26pA5PVDaKw4cPa266Mmskxud04n998Ha8G1+FJMvYH43Be8UUR+kk7VAq2pTyZ5vNpkkZ/k19A3jp4gx6fV6I1Sp4jsPeaGMPIi3weDzweDxq5VMul0MymcTU1BSKxaJa+RSJREz/bdfskRJWE6bNIJQUr1c4HFaPqURqzp49q7bNWFpassS1q6C8b3/0R3+ET37yk7jrrrvQ29uLs2fP4nOf+xx+8Rd/saHjkIjRifXDE4vFImRZbrhyox3MauwF1sqqaycxK1A6qT3y+bwqWvL5PMLhMLq6urB7927NKjg+dt1hSJKEEwvz8Dkd+P2j78NYKKzJsZuF4zj4/X74/X4MDw9DkiRks1kkEgmcPXsWlUoFoVAI5XIZ5XJZlwoSvSMxemJ2kQQYNxKzHbWiZmxsDKVSCadPn0axWMQ777yDUqmEQCCgRmq2+hJshvERdrsdf/3Xf43V1VUsLy9j7969TUWfSMRohDI8UfG1rB+eeOLECQwPDzNpjGTWSMx265hZxJyLr+L05SW47XbcNjyi+fHXU61WUS6X8c477yCZTMLlciEWi2H//v1tNZWrZf0x3HY7fvvIUfz2kaNtH1treJ5XK5/GxsbUCpLl5WW89dZbkGVZjdJoNelY7+vVzCKDlSeGhYhhIRKcTifGxsbUNTOZDFKpFM6dOwdRFFVREw6H6ypWFaOskREEAT/4wQ8wPz8PAPjhD38IURTx8Y9/XPW/bQWJmBbZaHii4mvZu3fvNd9uFWHBSsSUy2Xd11HWYjV2wKxi6fTlJfzN6f8PLrsdZamKUwvz+ACv7Td/JX2ysrKC1dVVlMtlVKvVpod5trKuGVEqSFwuF44ePaq2ml9dXcX58+dV30I0GkUgEGj5vjWzJ8bsJdYsBAYroVS7Bs/zCIVCCIVCaiuCbDaLZDKJiYkJiKIIv9+PV155BTfccINhRYxyXp/97Gdx/vx5HDlyBG63G5IkIZFINLyHkYjZgtobWdkkFNFSKBSaGp7IOjrCanMxs7hgtc7/e2EKIbcLgSuTsOcyGVyQ2je8lstl1ZArCAJ8Pl+dz+qVV15p6JsMcW2reaXNwcLCAjKZDFwuF6LRKCKRSMPpYGp217njA+ZNJzW7Rm2UcXR0FLIsI5VKYXFxEc8++yzOnTuHu+++Gx/84Adx7Ngx7N6921AppomJCXz7299Wq7eahUTMBigfcLFYVPu1pNNp+Hw+xGIxHDhwoOlqBzP7VLZby6zigtU6kiyDx9VrhQOw1kmlOWRZrptHJMsyYrEYBgcHcejQISZRvp2C0+lEX18f+q6UaheuVD7NzMwgl8uplU/RaHTTlu5KhZYemF1kWKnEutMiZj0cxyESieBzn/scpqen8fnPfx4PPPAAXnrpJfzhH/4hfuVXfgX33nvvhn87OzuLu+++G0tLS+B5Hr/7u7+Lz3zmM0gkEvj1X/91zMzMYGxsDP/4j/+ISCQCWZbxmc98Bt/97nfh9XrxzW9+EzfddFPDrxNYq076/ve/j2PHjsHlcsHpdKr/NQKJmE1YWFjA4uIiYrEYRkdHEQwG27ohrCpiWHlizBzx+eDIGJ468xNUJAklSYLLbsOYrbEQryiKas+WTCajziMaGRnBbC6LMyvL8KQS+Cm/Dz7emqWYRmCzyiclfF8780l5+FIkZnOMuPlbcY1isQiPx4MjR47gyJEjeOCBB7b8fbvdjq985Su46aabkMlkcPToUfzcz/0cvvnNb+Jnf/Zn8dBDD+FLX/oSvvSlL+Gxxx7D9773PUxOTmJychInTpzAfffdhxMnTjR8XjabDXa7Hb//+7+P66+/Hj6fDzzPI5vN4vnnn0cwGNz2OCRiNmFwcBCDg4Oa3cgsJ1lTJMZY69wyOAQ7z+O1xQV4HHb83K49uPiTtzf8XUmSkEwmsbq6WmcQ3717d1235tcXF/DYqy+jKkmQIeM7UxP44rH/oZaks4LFZ2I0381GlU/KzKczZ86olU+VSgWVSkWX+T16vyd6+0msFIkxcrRHETGN0t/fj/7+fgBAIBDAwYMHMT8/j+effx4vvfQSAOCee+7BsWPH8Nhjj+H555/H3XffDY7jcMstt6hpLOUYW6H49P7kT/4EX/jCF5DP51EqlVAqlVAoFOD3+xt6zSRiGKHlEMhG1rJaJMbMIobjOLx3YBDvHRhUf3YRVx+ySvnz6uoqisWiWv68VePDv33rNFw2G/xXHlCXBAGvzs/iZ8d2a/rajYKRcvjrqTVa1lY+Xb58GadPnwbHcWo5rFaVTwBVJ20Hq7EDZojEtMLMzAzeeOMNvP/978fly5dVYdLf34/l5WUAwPz8PIaHr3bkHhoawvz8fEMi5nvf+x7cbjeCwSA8Hg98Ph/8fj/cbjdcLlfD50wihhFWTSexqoQyczppPco39LNnz6qzsbq6ujA+Pg7fBsMMNyJfLtXNJOI4DsXy5pOe9cCqPXXapbby6eabb0a5XK6rfLLb7Wrqye/3t7RBmb0ZnZUGQOp9HtVqteU1CoVCS81Us9ks7rjjDvzZn/3Zlimdje7/Rt/z733ve0in08hms8hkMshms8jlcsjlcshkMlhaWmroOCRiGGFVEWPmCAmrdWRZRjabVaMtiojp7e1taTYWANw2NIIXpiYQdrtRrkqwcxyuuzJ/hDAGysPc4XCgu7tbHZQpiiKSySTm5+eRyWTgdrvVHjXNVD7puXlSJMYaa4ii2HQkplwu44477sDHP/5x/Oqv/ioAoLe3V00TLS4uqrOOhoaGMDs7q/7t3NwcBgYGGlrnz//8z5t6XZtBImYTtN7MrOyJYRUhYYFWn3upVKorf/b7/XXlzz/+8Y8RiURafjj9xuEbwHEcXp67hKjHg9+64UjHuuMSG7PZdeRyudTKJ1mWr6l88vv9qqh5K5nAd85PwmW34a4D12FfJKoe28wiw0qeGBYiptUUZKFQaErEyLKMe++9FwcPHsRnP/tZ9ecf/vCH8eSTT+Khhx7Ck08+iY985CPqz7/+9a/j+PHjOHHiBEKhUEOpJC0hEcMIq3piWK7FglZFjFL+vLKygng8Do7j1I7N119//TUP03bFktNmw2/ecAS/ecORlo/RDLIs483ly0gUCtgfjWGogaoBYns4joPX64XX68Xg4KAatUsmk/jHkyfwN5em164dDvjR7CX8xc9/CHvCEdOPHWhnY24GFl9+jCyUmk0nvfzyy/jWt76Fw4cP48YbbwQAfPGLX8RDDz2Eu+66C0888QRGRkbw7LPPAgA+9KEP4bvf/S727t0Lr9eLv/3bv23pdbYDiZhN0DoSY1VhYTVfRDPVVsViUU0R5XI5tfx5bGxs21k8RjaqrkeWZfzJyz/Ef85Mg+cAgMMjP30Mu3h20cWdAsdxCAQCCAQCePWdt+F0OuDk1u7nrCji//7RD3HvgUOoVCoNlZ+2itk9N1ZCkqSWK9yaTSfddtttmz7//vM///Oan3Ech7/4i79o6bVpBYkYRlhVxFgtEgNsngZQ2mEr5c8Oh0OtIqotf24EM4m/05eX8J8z03DZbeA5DqVqFV94+Yf469uOdfqlGZp2RYAky+DAgbfZwNtssMsyenp7EI1GcfHiRaRSKXWysVL5pJUwsEI6ySq0G4mJRqMavyJjQSJmE5RNRqsbzarCglWfGFbUno8sy3Xlz6IoIhKJoKura8P5WM1ilvctXiiA5zjwilGV55Erl1GWJOifENi53Dl+AF8++SoKlTJkGXDaeHxozz7EYjFkMhl4vV5EIhEkk0ksLy9jcnISDodDHY8QCARafn6xSCeRiGmMdkusW6lOMhMkYhhhs9lQKrU/L6cRrNgnhhXVahWiKOLMmTNIpVLweDzo6urCwYMH4fV6NVvHTJGYfVe+yZWq1SsCpoKxUBhOm42Zz8uMtPv5/vyuPbDzNrwwNQGHzYbfuO4wDsS61GNzHAeHw4Genh61WqRYLCKZTGJubq5O6EQikaaml1M6yTh0qk+MWSARwwiWxl7WAyDNshlvhCzLyGQyarSlWq2iXC6jv7+/5fLnRjCTiBkNhfG52z6AP3n5R8iVyxgJhvCln/k5SILAZH2zvE9aU6pW8frSIt5NxOG22/H/s/feUXJd15nv79xQOYfOEY0cCeYgBomUKIuWKCclm5THHq95XvZ79nt+9nCN38w8r2WvRS3N2GM/yx6PLcuSg5ItK5kSRVIEKQaRBAkSAJFTAw10qNSVq246749CFbsRG0B3sdGqby0SZKH7nntu3XvOd/f+9v4mS0W2JBtk5WIkw+PxtLquNiufstksx44do1KpEAgEWpGaS72hd9JJywftLrG+3tAhMW1Cu6Mj7cL1GIkxDKNFWgqFAsFgkEQiwbZt29A0jZ07d674PPKV4r7hUe4ZGqFmWfh0Hctx+Or+fRzN51hXLvLQ6rUEFmjYdjVY7hteplrl/33xOfamUsQ8Hn7/zruv+Zj/c9frfP/4UTyqSsU0+eyrL5P0+dne3bOg359b+TQwMNCqfMpmsxw4cADDMAiHw0SjUSKRyDzDvQ6JWT5oZ3XS9YgOiWkTVqIAFq6PeTmOM8/9WQhBIpG4oLGn4zjXbVO9pYYiRMub6Uu73+Spk8fxKConjh1hXzrF7991z7wuwj9J+I87nuFgNo2uqqSqFX732af53cHRazrmC6dP4VIVVEVBBYqGzc6pM2zv7rkqEjC38ml4eLj1XDTTT47jEA6HicViS65ZWer+Ku14ttq17nXSSZdGh8RcBIv9ALez2V07sVw342q12oq2VCqVBZc/X8+dgS+EpXjjrZgmL0ycJOn1goRgIMBEocCJ2Rxrz2o2fpJQsywOZtO4VBUhBC5VxZGS45XyNR035HKTq9XQz+5fioCwu/FWvRjfq6IoLb0MNOww8vk82WyWYrHI7t27W/YIoVBoUUnHSoj0tEvX0yExl0aHxFwEi/0AtFMT0060OxJzscXJtm1yuRypVIpcLofL5SKRSLBmzZoFt3KfO8ZSYyU04Wo7ZBXFfg1FpnFEAke9BcS7v0DrioKmKDhSojbJqQTfNUal/o+bbuH3djxD2TRQhKDXH+BDq1YDS7NJNx3T4/E4hUKBTZs2USgUmJ6e5tChQ7hcrnmeT9cy/kogMe3o1nut43SqkzpYNFwPaZerwSXnJQ2wjyKwkMogKNfWFn9u2buUknK53Iq2GIZBNBolmUyydu3aq+4G2s6N/3qNxPh0nbsHh/nB4YO4FZViqchQKMxIJLqo48yDtNGsHSAzSBFCcY6hyDyW9iEQ724KS1UUfvvm2/gfO1/BsGxURbCtq5t1/sA1HXdrVzd/9eBDvD49iVfTuGdwCL/e0K20o2Ovy+W6YOXTyZMnKZVKrcqnWCyG1+u9ovtsqQnAcvc0uhLYtt0224HrER0S0yasZBJzwQVV1lBqXwLnNCAQwoPj+WVQFiZKvBimp6fJZDLk83l8Ph+JRIJNmzZddw/qoqSTnALCOYVEBXUVCBc4eYS1H5DoamFRzvVCeGTLNtyGwZHZWdYP9PPQ2Nol1cMIyiDTSKUbACk8CGcaKAHhJRt3oXh4zTpGI1H2p1PEvV7uGxrhjZ07r/m4w+Eww+ELz6/dkYxzK58qlQq5XI4jR45QrVYJBoMtUuN2u6/4+Et9/ouN6yES06lO6mDRsJI1MReal7D2gjMBymDjAyeLMJ5Ceh5Z8LGllBQKhVa0pVQqUSgU6O/vZ+PGjdd1n4lrJjFOCqX2tyArCBxQ+nFcD6PU/wFkGRAMJTLgbAVl8Q3ZNEXhvv5B7kh0MTp6bQLWhUEFJEgHhNL4E3n28+WBrckutibfcRK/3jfpSx1fCIHf78fv97cqn4rFIrlcjn379mGaZqvyKRqNnqdDa0czvXZEYtoRue1UJ10aHRJzESyFJqadJKZJLpb6Qb54JKYCzFm4hAchi1xu267X6y3SUiwWCYVCJBIJtm/fzltvvcXo6Oi8UtCfVAjjaZAWKGdt750JlPo3QNZAGQBAyizCfBG0n2/beZ0uFjiUyeDSVLZ397aqma4VUvhxlI2ozm6kdCEwsJWtIK4+ZdOOlMxSHns5aZ6EEIRCIUKh0HmVT6dOnUJKSSQSaZVzLzUBWGnC3qudi2maK3697JCYS2AxH4J2C3ubpGmpH7KLRRSkOoown3mHzMg0UnvgvJ9zHIfZ2dlW+bOqqiQSCUZHR89rm75cK6GuBtc8F1k4R9Sqgywx75GWWoPUtAPS4HT2KX504mVyho+9s2M8eTTJf7zzPS0dx7XCUW9DKv0IJ4dUokgxsCjHvR7RjnTMteBClU+zs7OtxnvVapWJiQmSyeSiVz7BytLEwLXtRcuJ7C4FOiTmErhevZOa472rG746iOP6GIr5JFBF6ncj9bsA5vkRVatVIpEIiUSCVatWXdKPqENi5kDdgDB+gFTcgAWYOPp7UcxnGgQHBU2t4Chb25JwEcYPOJX5IX5Np9+fZjRY4xvjN7DzzBnuHR5ZpEEEUgw2ROKLgOs5UrLcIjGXg6ZpJBIJEolGCf4bb7yBz+djamqKQ4cO4Xa7W6TnWiufYOWRmA4ujg6JaRPavQEvCyGxvglH34Rt22fdnw+Ty+Vwu90kEgnWrVt3RX4uy2JOi4RrvR+kfhcONYT5KggN6fpZ0LfjKF0I8wXAZrpwD+HBjYt30hc9mQrCPsJMLYQD5E0/EVeOiKtM1TKv/fBL+NxcT0RgLlYCme/q6qK/vx9oaDfmVj75/f4WqbnSyidYWcLe6/UebRc6JKZNaPeN+G7aATTbmzejLaZpEovFFqX8eSUs3rAI94NQka4PIF0fmP+5tg6prQOgVH+1TddLASEYCYfZnUoT0DUs2wIE6xPJRRnhelvI323h7XI+NpyvJ/F6vXi9Xvr6+lrtE5qVT7VarVX5FI1GL1v5BJ1IzE8SOiTmIrjeFs1z0e6ohWmaZDIZ0uk0+Xwev99PIpFgy5Yti6aOX0kkBpb+bbodm6iUEoQHqd7M1sRLuBSd6VKGtDHAL219HyPha+sNdL2iHaLh63mNutT5CyEIBAIEAgEGBwdxHGde5ZNlWfM8ny7UgbtdkZjr+TtYKeiQmBWKpSYxUsqWH1G5XOb1118nHo8zMDDApk2bluThXkkkpp22A+2A1O8EpYsNPdOsJwraOhDLe3lZ6o2uo4m5OK7k/BVFIRwOEw6HGRkZwbbtVuXTyZMnkVK2ojThcLjVzmIlRGKu5fm1LOuqo97XE5b3KtPBVWMpSEytViOTyZBKpSiVSi0/Iq/Xy+23376oY10IHU3MMoYQoK1BsmZxjidNFHsPQs4Q8uRBDgLXT9OuTiTm8rja81dVlVgs1nKatyyLXC5HOp3m6NGjqKqK2+1e8jYT7Sixvpbv+SfBNwk6JGbFYjE2fMdxWotDNpttVRiMjY3NqyA4fvz4YpzyZbHiNv4lxnV7vaREtZ9DOCeQIkTAfRSX3AHOQwg5gZCzSBFBKiMN8rRM0YnEtAeappFMJkkmG/qrer3O+Pg4+XyenTt34na7icViRKPRK/ZRuxSWe7SnQ2I6ABZ/sWjX4nO1JKZSqZBKpUin09Tr9Vb589jY2CXLn9slZLwuN+ULoB1zWW6bXFOw6fF4LtOAq4xwTiJFLwiBYcdRmUKxf4giJwA3gjq23Iyj3XZN57NU16hDMt49uN1uQqEQHo+HoaEhqtUq2WyWEydOUC6X8fv9LVJzLZu84ziXXBMXA47jXJNv0krv1gsdEnNRLMUC1OzdspxIjGVZZ8uf0+RyOTweD8lkkg0bNuDz+RY01lxjxqVEO9NJ7dBLXO+amIXMwbKsVgpydnYWj8dDvV6flxI4t6khKDQsBSQgzv5poDjHkMowCAUpHRRnP47cAmJh9+lKwlITsOv9ZWHu9fF6vfT399Pf398i0tlslkOHDlGv1+d5Pl1Jd9t2CHtt2+5EYi6DDom5BJbCeuBabsorHetCG36z/DmVSpHJZLAsi1gsRnd3N+vXr7+qc2vXhtzucVYCiXk3UKlUmJmZIZVKYVkW8Xic/v5+Nm3aRK1WQwjRIs8TExMUi8XW23EsFsPj8eEo61GdfUi8uLU0NhvRyDd8k+CsfxLA8tRIXe/N7q73KNLF0jBzK5+GhoZalU/ZbJa9e/di2zbhcJhYLEYkErlkpKWTTloe6JCYNqKdkYS5HXsNw2iVPxcKBfx+P8lkctHKn5vzWmol/EojS0uNJd2IZImg8o+E/QdQSoNk6x9hKuUhm83idrtJJpNs3rz5oouoy+Wip6eHnp6eeW/H+/fvx7IsIpEE3fFthAImuUoEb+w9uNSXEM40UgQRsoBU+gD/1U/hOk75dFJhl8ZCRbdzK59GR0dblU/N9JMQouX51Kx8utIxrgUd88fLo0Ni2oh2OVk7jkO9Xmd2drb1IMbjcQYHBwmHw0sSYWrHpt+ucdqlV7me00my9FcIexflio9a/Qgu/c9IxP8Ta9bcdh6ZFeYrCOP7CCSqvAtHfc/8vz/n7di2bWZnZ5nOKBw8OkutFkaqabq7byXkOYJCBkfZhKNuXbbC3us5ErMSSMzV6lXOrXwyTbPl7Xb06FE0TWulntoRVe9EYi6PDolpI5bSBLJWq7U65JZKJVRVJRQKsWXLlgs2g1pMtKs7cDvHWQkkZjHHkFJSKBTOpiEn2dT/GpZM4vKoRCJ9CGeGgLeKPI/A7EGpfRGpRJAouOx/xhQepHpxQa6qqsTjceLxOAC7du3C5XJx8uQ0pZIgEBghHo8TiwmWq0HvcjdovNyxr3cSs1hz0HX9vMqnXC7HxMQEmUyGQqFAuVxe9MqnJjok5vLokJhLYLE3msVMJ9m2Pa/8Wdf1eeXPExMTSCmXnMBA+9JkKymd1PZNQkqgCrhBLCztZ1kW6XSaVCpFPp8nFAqRTCYZHroVj/FVKjUNyxQIKQEHKS7QDt7ehRQeEM20TwDV2YXFwquKVFWlq6uLoaGhlqYrk8nw9ttvY1lW68343HD/5XC9b9adSMzFsVR6Fbfb3UqD7t+/n3g8jmmaHD9+nEqlQiAQmOf5dK24VhLTSSd1sKi4ls1eStkqf85kMtTrdaLRKIlEgjVr1py3eCuKgmleu/neQtAucrGS0knQRtsBJ41S/VOEPQHCheP5NaR+8wV/p1wuk0qlWqLcRCLB4OAgmzdvnrexOXwKrf7XoFggy0htOyirL3ASAWDufWgiufpqIiEEwWCQYDDIyMgIlmXNC/fru1jzcAAAIABJREFUut5KByzFmzHSAco0yODFw0DXezppKdMk7Xq22uH/5PV6W0aWTYKdy+ValU+hUKhFaq6k8qmJa9XEdCIxP+F4tyMxzfLUdDrN7OwsXq+XRCKxoPLndouIO+mk5TcGnN2Qqn+OcM6A2gWyhlL7S2z1j0DpwXEcstksqVSKbDaL1+tdkOhbuu5k1nJh1I7iia9DqlveqRya+3P6PSjWa0j79NnzcWMo70Ms0ptyswFjIpEAGm+fc3uChEKhVk8QTdOQjkTV3iH8V7TRyTya+c8ocgaJgq1+EEfbdpGftVFFBaQJYmmioddrJGalOEyfS/bmEuxm5VOhUCCXy3H69Gls226JhC9X+dTEtcyjXq93SEwHi4vLCXullBSLxVa0xbZt4vE4vb29bNiw4Ypu5nerEmopsdLSSW2pgJImQh4H0X12YA+OlSc99QanpnupVCoth/F169Zd0T1mMUjZjCC1C0RgaCzAUkYxtP8TYe4FaWO7NiCJ4dh2Sx+mKApCiEXZdDweD319fS035GKxSCaTYdcLuxl/6zRer5exLaPc8v4bkFzZ9dfM76LIbKMJnzTQ7CcwlR6k0j3/B2UGr/wqA7FT6MaPsbQPI9VV1zy3dmGp+5+sFBJzuTEURSESiRCJRFqVT7Ozs+RyuVbBxVzPpwsd61ojMU2B8kpGh8RcBov5wF2IWBiG0RLkFgoFgsEgyWSSbdu2Lchy/krGWiq0K0Ky0rQ37SFKKpIgllmgVlOo16u49SJ1y8eaNWsWN+XiOFD9nyjmM0jc2Ooj2Pp7ESKMcN8NgH52QXYcZ94/0NB5NcnMlS7cF3pOhRCEQiHMko2dEmzctoFqrcKhXYeZyUzRv7aHzJkcQS1C32gPHt8lnjcpUeQpJF1nD+5CShAyi6R73s9p5jcxqWNYcaTwo1nfxFR+DUTwiub0bmElRGKWI1E6V7Bumia5XI6ZmRkOHz7cSoVGo9FWA8iOsPfy6JCYS2ApSpGbZmXNaIuiKCQSCYaHhwmFQktKmJYKnXTS1Y2xlDBNk1qtxqHDh1GcO1nX/x3cukbApyLdH8XnvndRypOllNhnoypq9S9wOf+CxItgFo/1GUxXDKmdr785l6g4joNt2/OOJ6VEVdVLfhezM3lef/ItCtkyfau7ueG9m3D73JRyZUzDIhDxkU8X0Fwabo8Lt8eFd4MPs2aS3pdn/NgEk2+fBk3npg9uo3egh2g0er4gXgikSIAsAmGQNgIHKQLnnFENIXNI4iCyILwg82f9nhaJxMgifZHn0Os/xlFWY2sPLGrKaqkJwHKIkiyHMXRdp6uri66uBjGu1WqtyqdisdiSDASDwav6TjrppA4WDdVqlXQ6zZkzZ1piyWQyyejo6JJVD7U7ErNS0jztGmexx2g2jGuKch3HwbIsBgcH6e3djpAfQDincUQQlNFrIjCNNJHEcRzS6TRer5doNIpH7kDiQyhnF04nh2I8g30BEnPuuZ85PEVuapZAzM/Auj6EIigWi62WAY7jYJpmS8CuKAq1cp0nv7CDM4enUFWV8b2nqOQrdI92c+ytRrje43MzuLEfy7AwDQvpOFSLVWqVOi5Xmu1bnsPrhcy0HyffQykS4NSpUwCtqqdQKNR4AdE+jGZ+BeQUAgdbvROpDJ4zGzcIL0KWz07OOkt2rr4p3/yLZeAyPkcssB8he9Hsgwg5jaV/etF65nQiMQvDYhMlj8dDb28vvb29rUKOI0eOkE6nmZ6eJhAItCI1C6k66jS76+CqYdv2PD8il8tFIpGgu7sbt9vN0NDQkp/DSozErCTtzWKM0bzPUqkUuVwOn89HMplk69ateDwedu/e/Y4vkUgglcRVj9UkRU3y0sznq6pKNpvl1KlTbB82cbsMBG5URaFhCXB5kr7n+f28tWMfQkrKlSqxkTD9NyQJBAIkEgluvfVWNE2bF6ERQjB1Yobju8eJ98XRXBrVYpVX/u1NNtw2RnI4gaIoFHNlpk+ksEyLl775GooQRHsjrL9tADv3DCCRRNDdZXzON1g1+meIEReOeYJ8sc7UVJ1Dhw7h9XobVU/RR/B5qmfLxuPnT0YoWNpHEMZXcKs5FOlgqfeDWBxtgpCnEM4UdSsGIoSUQVTnDSw+BtdQ9TUXK4HEtCsSs1TzEELg9/sJBoOEQiHi8TjFYpFcLseBAwcwDINwONzS1FzoZbiTTupgwWi+BTe1LYZhEI1GSSaTrF27tvX2ePr06baVPa9UYe9KSiddzRi1Wq0VbanVakSj0Yv6Xl3LAtuMtti23frvpl5F07TWWHNz/HbpV1GsP8GxMpjSQeIlVb6HsGZctLy0mC+x419eZOr4DLZjo2satVKNB37+XqJdkXk/q2navCiQWTexLYlQwbEtNJdKvVxDqO+kqvwhL6cPT+Hxu9j+wGYEgmqpCmaGSsnGjY4iJNWSl56BEqr5NLr9NUDi9UsSofdiao9SrdXIZafITH6XkjKO6kqA+6cIRdacV2UilUFK8lFmSgcI9m4DMX8e1wYFxNz7pmmS2fiu61UDVVPQ9Ktf2ldCOqmdRrtLiea1auq7QqEQw8PDOI5DPp8nl8tx6tQpHMchEom0+iVpmtYhMR1cehMwTbNV/pzP5/H5fCQSCTZt2nTRG2clim3bOdZKSifBJYS99hGEfRgI4Gg3Mps3WhoqTdNalUR+/+VTFFcyj7lC26bIVggxL4VzKaiBh3HMBIr5NBIveeshinkv47t34zhOq3+LqqpkMplGv6OSwelDU3QNxPH6fTi2TepUlmqxTrTr/LlkzuSoler4Iz76x3qJ90aYPDyD49ioqsLw1kEcx8GoGugenXy6iMfvwe3RCcUamhSXxwWKwra7Hd7eqWBZsP0eh+5BAfbXkQRAuEE6aPaz2Nq9+HyjBPXdKE4GKUeo17PUal9hz+4HkPiJRqPE4/FW5Eviw7C7LklgSrkyu57ew+xMgb7V3Wy9byO6+9KRKykGcMQIHn0XQmoga9jaPdRrKj/+zstMHU+hKILt929m9Y2jl//SLzRGJxKzbHCxeSiK0orCAK1+Sdlslj/5kz/hueeeIxKJsG/fPm644Yar6lFzvaBDYi6BuQ9as+16M9oipWy5827cuHHBZmMrLTrSHKtDYhZnDGHuQta+TrUqqdWLlKvfYdb8OInEAKtWrboiP5jLzeNi0RYhxLxoy5XA0e/C0e8CIAAEQjA8PEwqleLMmTMtvYnX66W3t5fgSIgf9+3GqlmYqoFRM/EGPOju8+f59osHOfTaUYSiIB2HzXevp2eki9TJNKqioigKW+/eQHwgzt7n9+PYNpHuML2ru9n34mFsx0ZVVMqFCv1r+ula8zBdg1/B55OAxNR+Cd36xwaBgbNO2SpCFpHSRnUOIGnodTzeXryeSW5M9lG3Blu9QJoGq16v95LPhFEz+cEXn6NWrOENetn7wgHK+Sr3fOz2S19goWO6fp2p/N8SiIeQYgRbvZM3n3yT6RMpkgMxLNNi5/ffIpLU6e7ZiyCHI9bgqDctSDfTITHLBwudx9x+SX/4h3/I5OQkv/Ebv8GTTz7Jn/7pn9LT08ODDz7Ib//2b1/0GL/yK7/Cd7/7Xbq6uti7dy8A2WyWj3/845w4cYKRkRG+9rWvEY1GkVLyW7/1WzzxxBP4fD7+7u/+jhtvvHHR5n0l6JCYy2BycpJ0Ok2xWCQUCpFIJNi+fftVMduVqFOB9nbsXSlkae4YzU6fqVQKv/wHHEfF44viD/YQi2UYcHuQWvdljrgwXIi4NCuFFtKr5XIbkHBOI6x9GKZgJjfATLpGrVYjFosxMjJCJBJBCEGlUiGTyXDsxFHcSZX8RAWzYBKKBOld1UMoFsC2Hcyaie7RqRaqHH79OImBWENka9q8/uRbuH0e7vnYHRh1E5fHxdSxFDd9YBvD6/sx6ia6u6GlKWXLnDpwBgSEE0HGtg9TZYgzM2HWrY0jRS9S9KDaO1DkJJIojc684IgkoCClipBHEBSReAEPoONyueju7qa7u7uVWj5z5gyzs7O89tprhMPhliCzGdXKTc1SypZJDjZScW5fghN7T3HHwzddNhqD8JAq3MKIfkvro5mTacLJEACarqEoNuWpv0VN5gE3qnwVS2aw9QcvfewFfMfXiqXuQwMrwzoBrp6M9fb24na7+exnP8vAwAAnT57kzTffvOTv/PIv/zK/+Zu/yaOPPtr67PHHH+f+++/nscce4/HHH+fxxx/nM5/5DN/73vc4fPgwhw8f5pVXXuHXf/3XeeWVV674PBcDHRJzGdi2zejo6DsCyWtAu1ysYWUSppUUiXEch1KpxL59+8jlcvj9fpLJJF3BKKoWfqelvdP61xWjeb8200PNVNGVpokADmcz/NFLP+JMqchoOMr/c9c9DIZCrb+XUlIp7kGrfQ7TrKIIiLm7iKz+HXyB3vOO5/f78fv9DA0N4TH8fP2/fYdyoUxmJkd8fZiD+w5x8PnjGBWLYNjH1vdumkeyNF3FcSSFbJGp4zNYhok/6ieSDCElqJqK92xnXk3TuPH+ray9aQzLsvAEPFiWSWomRdXsxpAbUYSCIgSm67fRzc+h2IcQlLGVdejml7DVn0KKMKrzY5BehDBwRG+jlPqcax4IBFqEZvXq1eTzebLZLMePH2+5IFNXkPKd6JdjOyiqQFGvLnoQjAfJTeYIJ0NIR+JYWfz+LIiBxveDhWo/g629/4KdleeiHZGY5V7+vFywWN5JQ0NDly0oueeeezhx4sS8z771rW+xY8cOAD796U9z33338ZnPfIZvfetbPProowghuP3225mdnWVycpLe3vOf9aVGh8RcBgMDA4v2MKxEYtEca6ncuefieicx1Wq1JcotlUp4PB5Wr149T5QrjLsQ5jNIIiBrIPxI5cqq2eaKX2dmZlAUBV3XrzpNVDIMHtvxDFXTJORyM17I89iOp/n8B3+awlnfonw+z/qBJ3D7PHj9Q43IjjyDre/D4eILm2Va7H3+ANFkBK/Pi8urUZ00+dE/vcrpI5M4SFQUTp+cYnB1H/l08WzvlyLxvij7Xj6MkBJvyMv0iRT+kA/1AkRACIHiEuTzsxwdb6SDY7EYY2NjAHMqn8JY2n/GJf8GRaQBd6OBnfVtwMJW7kFQOduXxUSQQnK+NklKiW3aqKra0gLBOy7IqWIa01Vn/xsH8Af9aIrGHR+5ZZ4lwpXgpvdvYcdXXiZzOottO6y5oYfekQNzrwACBxbQpXglpJN+0iMxsDjeSdPT0y1i0tvby8zMDNAoUhkcfKe9wMDAAKdPn+6QmJWOdm32zbE66aSrw6XmU8qVyU7NomoK3cMJdHYirJdBaDja+0Hf0PrZZgXBzMxMy2k8mUyyYcMGCoUC5XK5VdnThNTvxRFesN4GJYh0vQ+U8GXP+UKi3MHBQaamptizZ0+rW2gikSAQCFzRAj9eyFO3LfwuF47j4Aam8nmefuXHrOvppa+vjw0bNqDXnm+4ZZ990zfrCrtfHGdqYgfBWICt923EH55fBlyvGhzfcwqP3020O4imTDJ75iBeb4Hb7nZRqQwyNZng5L4Jhm7tIX0iQzotGFzTz5qtY1SKNcyaQa1UZ/1ta1BVBduyUTW1df2bvlAej6flC3VuN+x5aTbbQHGOojDd/FsggFSiIIJI0RBTCucMcP4Gk52a5YnPP83MRIq3Vx3lfb/4HuJ9jd+Z64K8Yd0YB3YeYPJUDjxQcRU4fPgwsViMSCRyRY7cwViAB3/lPgrpIppLJZzQEcZuhDONFF6ELGJp9yzIwXwlpJPgXXCKXwIsVxfrC+r53qXr3SExl8BSdOxdacQClge5aMc4mTM5nv/6yzi2g+NIEj057v3QW2ieODg2Sv0L1O1/TyobIJVKUSwWiUQidHV1MTY2Nk+UWywWLzwXoSD120G/tMDTcRyQNRzjdaRTxxarQemZF23RdZ1gsFGRU6/XyWQyHD9+nHK5jI6Lk69PUi+Y9K7q4taHbsQX9GLUDCqFGt6gB7fX1djQ6gaVWp2sVcIGXKqGV9e4+5ZbCc9ZJB3tNlTjn5EIwOa1pw2OHlIIJS1OH5kie+Y4H3y0F83Tg1QbfkuNayCQjkM8sQfhTBONpAHweXXC4SmktZVSOsJtd95K+KPBVmXTwWMHmJ6Zon9VLz1jXdiGQ71aJ5VOtXRs4XCYZDLJ2NjYJUlBc6NQVRWkjlKeAUcgFX/ju2UGQ96BxklkM52kDDV8lObAMi2e/uJzGFWDaG8Y27R56u+e4+d+56F3tC5SolpP4ba/xU3bHZybNmK6fhXbcbcqTI4dO3bFjtzFbIns9Cwen5tAtBvD/Rto1lMImcVW1mOr77nk7zexEtJJS412ra3XQmKaHa+vBd3d3a000eTkZKu78MDAQEukDzAxMUFfX981jXW16JCYNqLdJAYApwByBvCC0rdoXT3nYiU1obvUOHue34fL6yIQaaQPUkd/zORJL90jOuWKgW3MMlv+HtLz05fVUV3NJjE/WlDBbTyOLidACIRwYXl+r0UOzoXb7W4ZIxp1k6//92+TnsqA5jB+dJwTR05y10du5dVvv4lRMzHMOuvuHcUd1QkEg3h0jYxRB6DmGPSHQ4TmRDOMmsGbPwwxdXgd4fApbrgnzLFDIwg9ytTxFNHYBNXUToonbboGbGzXL2C7P4XH52Zs2xD5qUNUC2kCIYf7PlKiVNDZ+ayKZXpRxCFGNv8c4UQQXddbkYyNGzcS9exm13O7OXFgHMuxWXvXKKlUioGBgZaI+LKQFRTj31CcI0gRxdE/CMoQgslG6khxkAwi9dup2zeDfRKUCLbYguIoGLUatVIdb8BDvVKkkD5JrVxi6pRNV38PvqCHYq5MMBagXq7j951Ad76BIxKAhuLsQzP/GVyPzuu7c64jdzAYnJeamouT+yf4/ud3UClWUVXBhjvW8d5P3gn6L1zxfbYS0klLjXZpbt5tbc9HPvIRvvjFL/LYY4/xxS9+kYcffrj1+Z//+Z/ziU98gldeeYVwOPyupJKgQ2LainYKewE8egql+scIaSKxkfqdSNeHF53IrDQDyIuRslrVwOXRW6LcSqXG5OkCroiD3+cjHAgT79uEdI0taJyFELK5KaK5olyXfBWVCVDPLhwyj2p8Gcv7ny97zGK2hFE2GdvQ6CNi2xZH3z7BP//Zt1HcCh6fC03oHHruBI/8548xXiliS+jzB7BpmDjO1mqkcv9An2c/iAg/+vYGTh2xiCQ3ks6M8vS/wuT4JMXsUbxBlb7uH5FKuxGaH4SDan4dR78XTe/nrp+7jcMvT7F2g4PLbZLsh+SASqI3T2pSwePTUbvub0Uymu0OUqkUlr/GuntHCfnD9A33giobUZqDB1udsuPxOD6f76Ibp2J8E8U5hhRdQBnV+CpSGQY8SBGioX2xqJTC/PDLu5k8miYQrXLfp0axrTzPfOlHmIaF12fx0C/uxqlME/bV8Yy6ObLXQPf6mTmZ5vt//UMs0yIQmODBjwmivWfnQxTFOXjeeV3IkTubzbJ3716mT6Z48ugzRGJRNt+2nqf+7nlSJ9N4Ah6MssWr332DdbeO0b+mB6Nq4vLqC94Ml3rjXCkkph1zaGfU6pOf/CQ7duwgnU4zMDDAH/zBH/DYY4/xsY99jM9//vMMDQ3x9a9/HYAPfehDPPHEE6xevRqfz8cXvvCFtpzjhdAhMW1EOzUxAH3RHUACqcZBOgjzJaS2GdRVizrOSksnwfkEo1Kp4IoovLnjbXwRDx63B929gbWbjhBJ2EAeRBhHu+XCBzwHF5vLBXu3YOGyv4JmvwTCg+36OIgK8/UYbpCFBY2t6xqO7VAuV6hUyhQLRcrFCsFwgJENQziOTalUZvL4FE/+69PsOzxDqdfAjUIw4kfXNMpGBmEdAsKY9TRn9j9BcuTDCEXH5dGZHk9hGgZCEQhpkJ7U6Oq3CUVtEBpIFWQW6Kd/dQ/R5MO4jT1oSgFVNQCDcDxIMK5i6Y9g6y6qmf+FVTtMthinZD1IMhlnTd9ONLkLRBjL/e+R6gaSySTQEDZmMhkOHz5MrVqiN5kmEvESCG9H1c920pMm0jzKGy96mTg0RTihc8v9Ju7Y7Sj2WwhnHEQAS/s5fvClXcxO5+kaTlApVHnyb3ZgWzapiSyz1Qr+deOcKJyia7Ob17+pg2NhVCeQ6ip2/NOLVIpVrLpNKFLh6S+X+IXffBNVtZH4sZVbL3u/NDu2evHzb//jWVRbwzQMfvTtlzjz9gxdg3FcHg014GHqeIoTe06xe8c+auU6Xr+HOz56M9Huy+urVkLH3qXGSkiJnYsvf/nLF/z8mWeeOe8zIQSf+9znlvqUFoQOibkErmdNDFKiq0UQI43/FwoIBSHLC6hPuDK822mexYaigCb3Ucq+QTqrcmYmgNvtYdUNw4TDUaaOzODy6Gy7dxOhQRtpHQBUpLZlQSJcmD+Xy/Vu0c1voNjPgkgAFmr9b7Bdv9iIqMkK4AKZQ+o/dckx53aZVuNwYv84/qAfr+7nzkdu49DOY9iGgzfgRTgK9CtUxuuEqwqx01WyHqjZFp6wl5u6Zun2SoQzjip0FMXBqqfQvYGzlVGSRF+cUCJIpVDipttfZXBsFkXxgawCClK8U93gC3nRqm6ErePQjZAlHLpIVT7KiclBRmL/N5HAadSAj1j4OFK1kQRQ7ReQhEHOoFX/CNP3WVAa0Smv18vAwAAD/d2o1ceR5ttYlo2RFhyYeQRfaDPxeIzd36nw1o9m8YfdnDxQZuJgjZ/9vSC67xGQFgiNWrlOeuJHdA01/Kf8YR/5mQLH95xEeDTeChvckK6y45+8eDQLxe8QcIGiSU4fyyBtiT/kRfe4yGdMfJ4qRrWE1y8QVJDKwkPxz331ZTLjOeKJOIpUyZ8oEwwFqBTqGIZJvWZgVE12v/g2I+sHSQzEKBcqvPTN13jwV+67rCVBJ510eawEIrZS0CExbURbSYwQlOt9JGUK6ALqIEEqyUUf6t1O8ywWDMMglZqBylcRcj9ID/1RF8MDDyM8jS60I6PA++b/nnRdeSM6KSWWZVGv18/r3XKeB5L9eqN9vdAA7WzEpYTt+t9Qza+ALOPoD2DrP3feOJVKhVSqIXS1bZt4PM7g4CAbf2cjpw9NUcyViCRD9K3uYWBtHz/8xxcoz5ZRNIU7PnITT/7Ns6TP5LhBSMadKvk+g20PDvB/bXoCx0ghhQtVhVvf6+LFH5YQWgbbtFm1bQiX28WRXceJdEc5cfKnGFj9BJpeBAKYnsdAeaclv3AmQKrU5S1UKlWq1TIe1ywF6xbWrzEIk0XSe5a4SRT7LRrmjbGzFTcuhMyg2AdwziEEivUyirMPtG5UXeD25NkSfJnTxZs5fuwEO55QGRot4dbrBMIqM6cjTJ9yM7COs9ccdLeG7tKoVw3cXheO3Ujx1at1KkEwVah4fFTTJXIFHW9cEhA2+awGAoxqnWhPBFVT8Psq2LYL4dmEoypIqaA4hxZ875x4+xTFTAWzZDcck4Rg+/s3U85VqFcMvG4f8c0RTNskPZsiPZvC7/djFC0qxWrLfuFS92aHxFwa7SIxV7vetStivRzQITFtRLsf3KnZuxkZPgLOKUBHuj8BSs+ij3O9GkA2dQbNTR6gtwu6oidAHSMYDIG0ENZT2PI9IK7eJfjcaIuu65RKJXbu3EksFiOZTBIOhy+8MIowyFkaHWKhUfIbwtFvxdHnpyEcu0q98K8Y1UNk8yHy9feQSPSxadMmPG4XIFqaqMH186sJeka7uPfjdzBzMk2sO0LP6m4mT6TwBdyEgl6Chpf0oRz/4XduIRn5FsIpYDuNOQ2ur7PWDlKv9dLT38PopiGkIwnE/EyPpwhGb0br/iRmQDbmcbYM23Eccrkcxdxpou40Diper5/uriiKcBP0jiKck1A9/9mReAATUBul3QDiAiWlMo9AIM82lctN16iVD/Dai7u566O3EIuvBtcqKuYslaLFTFpy8tRJfEkPkUgERVFQNZX7PnkXP/jCc9TrVcrlCj3r4yRyMSYKZQRwKBdHsep4ykVKaZWpWpDCrJ9gzE//ml7K+UqjekwIHvjZOooaxpYCIfNI4V3wxljJV6nOVhFBcByJVTPpHk6y8RNrOXN0Gn/Yx+iWQZ79pxcJxgMoqkI+l6dcybN33x4CoUBLIOz1es9bl5YqVdKc30qIYiz3OdTr9fPaCKxUdEjMCobl+LHd/wEh6oBrQT0irgbXkwGkZVlkMhlSqRSzs7MEg0GSyeQ7VhL2OLWMji2bC7vaSL9Js2kUvGBcylDR7/dz8803Y1kW2WyWyclJDhw4gN/vb/VzaS5CtutTaLXHQaZASqQy3PIngkaaqOHpNUOX7x+IBU/h8YaIh04iNUmNX6N46r/j6HvRXAHciV+lYtzA6z/YTXoiQ3Iwzk0f2MbRt8Z57YldIEA6krU3jzG4rpdjb40zdXwKzSUY3jiAy6U3etiIbShqBUXoqNJk801rmMr0kMlkeO21aWKxGN1r46y7dWzegm8YBul0mlQqRaVSIRaLkUjcQNR3EkXuR2AjcbD1T4IQSKUfqaxGOAeR0o2gjqNuw1HvQzP+HGQRAKmc9Qc6B1Ida5R9S4PUqVkEGVJT65g8Ms13/uIpNt+9jree3Yc30A11g403xRhZP8T09DQHDx7E7Xaj6zqGabDmAwPo0k3vYA+rN69i19N7ePHbO8lMjFM2HN4eGMN7ZJJwXSGke/AFIdIVZvv9WxqpJyFwuWMMrjfQ1JlGdFRo1MSHcc423Gt2Jr7YJqloCm6/jqqraLLRvdjldrFq2zCrtg23fu6mD97Azu81Ws0LBB/69PvpX91LpVIhm81y5MgRarUaoVCoZYug6/qiR0rSE1me+tJz5KbydA8n2PjA6pZFwlKgHS9Uy53ELEaju+sFHRKzAFyv4c+Gmy4IsbRiVtr0AAAgAElEQVQ383JPJ1UqFWZmZkin05im2TLu3LRp0/nfq9KNI0OoIgPS1RCfKkMgLh2CB1rXwLKsBRsqnjpwhtOHJglE/Gy//UYsp0FI9uzZ03J+TiQShIN/iOocBOHCVrZSqTikUidIp9M4jkMikWDVcJCwyFIqdFGvOHj8ATzeA5x4479yYvcJZs4E6OovcvuDf8obr/404/uyhEInOPqqSfbUa2RTCeJ90bMt/R0Ov36UarEGlOnuS1GrQik1g897A7Z2H6r5A6QII2QNRBhPYDMjwSAjIyNYlkUul2sRAZfLhaZp1Go1FEUhkUgwNjY2r/+JIz+NtN9GyBJSGUSqZzdkoWF6fx/V+BeEcxxHWY3t+lkQHky1G2EfABGkXLuR0kQRf9iHL/TOPS/Vjdjuf4dS+3sEaTLp1Rx8+x6iPW5Sp9Ks2jpM13CSM4emCCUDbLlnI5ZjYlgGxWIRxy7QG38av+sY9WSAWePn8YdDOI7Dje/fStdwkttOp3khN0lOsZBE6NE9VGar+GN+3F4Xt334Rsa2j1DKlUn0xwiOfALH2tUQFmvrcSn9LbLbjNg1iwDO9bXqGU1SKs6QSAaolFwYNZN4//lu2UPr+0j0+aiVSniDMbxBH0gLv2cGf5/NQP9aHOmiUCiQzWZbfT9UVSUUCi3KRl2v1PnuXz6FlJLkYJzsVJ7n/+kVfvp/f+CajnsptINgtGOMa9l3arVah8R0cP13fGxX6Ha5pZOaKYpmp1a3200ymWTTpk2Xf7CFh1nzUwTU7+OjitS2Id0PX9Rv5mJO0AsxVNz93D6e+fsfIWXjOId2HuVnfutDjIyMtIhAJpPh9OnT7C8U0HUviqJQq+3C7/eTSCTmdZ91jEmy47Nkp8oIVUE6kq5Bye7nc0yOh/B4Bft2KtRKJQKJN+jqzgMuPF7B6aOvIdVNaHpDuKooCkhweRUGRjLkszrRbo1IAozc13DifwAigGK/jSOi2PqH5hE9RVFQVbXVbKt5XVRVxbIsDKOKXd+HcHtBHWzoY4SO1G64sPBc+LDdj5z3sVTXItW1nNx/mu/+5b9iWzYIwYP/7j7W3vxOFZ6jP4DJfXz1f32RUDKKpmtIKZGORHfrrLtljO41cTLpcTITf0TQN0OXf4y+bZ/GK/8e1Z5A0oekQsT8CsfSEY4dO4ZAMDtewi453D/ax9pbxvjWfoMzR6bQPRpmzaRvrJtgLEgkOV/07ej3zPt/RVEoz1Y49tY4pmExtLGfaE+4RWZs2wYp+dAjFYzZt1GEQmYmxN4372P1jaPnXBiJaj1J1PUsxCSOshbT+QV062sI5wgNYXUM0/VrRCIRIpEGCTJNk0OHDjE7O8vOnTvxer3zUk9XinyqSL1SxxvyUsgU8Ye8nD4xSa1Uv+JjLRTtcslezk7cHRLTQQvXM5FpZ+nzu51OqtfrLV+iSqVCNBolmUyyZs2aK+5aKUWMnPExQv6zkQBpIGpfQ9iHQR3G0j+KIz3n9W65EkNFKSU//McXOH1kCunIRu+TVIHbHrqJ4U0N077mm3nz7dzlcrXK9Ov1OrVawyHa5XIhhGByXHJmV5TsVJ58RtA/ZpCZCTB+UKdv1MKxXXj8kvFDCsNaCVUTKKoHx5Goukm0K0v6TI5IMkQpV8IX9iGUKj3rVBS9Ef6fOWWiqA6ChpjY0d95o25WPzW9oSKRSCviMvc7sK0aFH8XN69BycGwQ0zX/ivh2E34fFemOxL2EazqAb73l3vx+AfwBEKYVZMffOFZ+lZ3txoTAqi6xu0P387zX3sZVVMxDZPetV3MFKc59uMjhMNB1vZ+Fa92BpQgQu7Fsf8Y4UwiRTcIgWUEqRZqDPc6jI7dxve/8EMO7DwCisNrz+5i5JVBsqcL7Hl+P47toOoqo1sGz/dzcmZQrFcAG6ndhFQGKWVnefJvvoFZNxBanIOvHuGBR++heziJ4zhYlkUx9zJdva+QUQdw6gqrt5bYdI+JJzE/PaM4+9Dsp5D0AArCOYhu/BUKk0ipI4SNcKbRrCex9I+3fk/X9ZYZaTKZbKWeDh06RL1eJxKJtGwR5nagvhjcfhfT42mKmSKK2liPAkk/bt/S6TXaFSVZztGepbQcWG7okJgVjOWe5rlSzCWUcxuepdNpFEVpkZaFtGi/3Dit+UiJqPw3FOs1pHSB+RqivhvL9V8Qin5VhopNnNx3HE0r4A1IHBkkNVFj/NApHJ/ZEhonEglWrVp13pyahGFiYoJCoUAwGKR0pso3/rKfeI+fYLjG4b1u6mY3QpbpMY+garWGczJJogPrOPzKXnSPhWlINt+hsvWeXl56qpfp8RSxvhh3fORmju8+wu4nX8XlqWDUdQbXSmLdGjZBFONbOMYuyhWd45M3UanHiMfjDA8PX7JbsW5/C019FYEbUPGoRXqVP2bXwf/S2igTiQTRaPTSVgHmS2j1/49KxqGc1cgdPEax0IU35CXWE6U8W5lHYgA23LkaS6lzfP9JVI/CptvX0d3TTTgcRsgZ9MokiGRDi4MH4UzREEPVmT6p8b2/r1Av2yi+17n94QRnDk4ztnGkQeSlw8Gdhzm2+yTJ1bFGJE4o7Pjqy9z/yN0EIoHGSTjT6LXPnC01F2A+g+X+DU69+VWs0im6+lUQXmaLd7L7+f1sfsAilUpRLpcZ6T1BLOxnaM1AQx/l1IEpqqbZajMvhEB1JgDtHR2cjKDIwwgn1fheZKPKS9gJ0Odf17lp0Kbb+ODgYMuLqtlFWFGUVpSm+X3XK3Ve/bddTI+nSQ7GWXM2GiZl4x/bbvxHOVfm6R3PUy3VWHPjKOtuW71oL4zLnWC0Y4yOJqaDJUMzatEOUdhKi8RYloVpmuzZs4d8Pk8oFCKZTDI8PIyu65c/wAIhhGhFQBxzGrf5Og6xRnt/GUCTx5DqFFIdvfzBLgYnQyA4STHnoCoC08jj4Cebz7DaPcy2bdsaQuOLYG77/WaV1RvH3qJQKFO3A3hnk1h1i2DYzQ33DfP2c6fRXQZCKLznZ2Lc+jO/wOjqE1SLGUIxQd9oCNv3Ed77qZF542y9bxuxrkfJnvgKoYjBqi0+8tbHqZ/5BzziZSwngs8r2LrqaRz/fwLl/Jb450Ix95zVSDe+M4Ebl5Zm+/btrVRgJpPhyJEjuFyultD53K67qvF5pPDhCrg4fayKaVQJxG0qxRonsxO4vA2RarlcbpFdIQSJ4QQbbl6H33+O+7TUGhXcDSVZ80Ns/eeR1X/h+3/voGqQGOqnWu9jx1deRMxRewsEwlHweD3E43GkdKgbBvmZIi8+/zI9w13E43F6oy+gU0M2KwWdLGr9b5FGDqEEcFCxjVnK+ZfIFG9g1Z39jI6OEggEUGwPmrGzkXJTVBBlHLEGXddbkUEAYYdRHQOpOAihACWk1KmXq5w+5seRgv6RKp5I6bzv52LpGEVRCAVDLTsHwzCYOj3FntfexnQMYt0x3vzOfmYnCoSTIWZeSHPszRMkB2MMbxrAqJp4/G7Gj5ziyb9+jkAwiMut8+yXX8SybDa/Z/1l752FYLmnetoxRr1e75CYDpYG7SwxbGck5mrHEeZbCPNJQCL1+5H6zfP+vrkBpVIpLMtqlPMODrJ58+arW6hkjUaZ8fnh7LnCyqmpKVRVJRGTuEVjg3rHrkHANbQMrNfrVGZ3sPmOKm+/4sGxBd6gRv+YxXvefyeJvssTgblodnNdvX41m27fQHm2TDFfwpP0orrhPR9+g43bLHTdJhBW6B6aRHKMoY29KPZk47IoZ12aL3DsvvW34k4OkcuM8/I+i2DQy/q+/eiudSjKWSLiTCKd48gFkBip9SINicChcS3NRh8cGvfSXP+gZtfdI0eOUKlUiEQixONxYtEoLllBigj1iqBrSCE9YVEt1BBqkMRQlEP7DyOP23i9XpLJ5GWJISKGrd6FYj8Pjg6YONptOK6HKM16qVafJDkYQ6pb8OgqWq5MrC/KxKFJNJeKZdqsv2MN+UyR2Zk8waifYrrM0NoBHnjofa25TE+NE/IUEKoHj8eDS1NxnFniQw7lH1YwToHboyEsyYOPPMDw2MA7107dhqPehmK/Bo6CVKI47o+hKu/ojxzHwbZuxLb2odj7kCg4IsLkqWHe+H6aDTcWEAJe+J6f1TdF6Nt2zvdzARJTK9d56ovPcXz3SVxenff90t2EYgGe/IsfYdYMpCPZeLfC1NEZPBEXuXwWj89DejKLoihkTudwpETT1UZFvCNb3YM1l8qe5w8sKolZzgSjHWNUq9VOOqmDBha7S2w7G94t+3SSuQ+l9jnA3+AVtb/CclQyxcGWKLe5AW3ZsgWPx8NLL73UEiFeEaSJqH8VxdoJCBz9Xmztp5GI80S5TRFjJpNh774UY119xILHUDUfqmIjlTVIZWjhQ0tJqVSal/oa6bV56FEFpIfMpIOiOrzv53US/VdGYOaiZzTJ8MYBZsZT9K7qplauc8dHbyYR/yxd0QlsWyCEBBvM6rN41DNIZVMjwuRMoxrfxHZ/Gmjk1Jtl0PV6nXg8Tnfv/8/ee4fHdZ13/p9z63RgBjPoJAAS7E2iqmU1S5atalm2o7jFdlwSZ2Mn+SWbrLPxs4l/STabOHHW2Y1L7CRuSmRbsmX1SltdpEixiL2ABEG0wVRMv+3sHxcYEiwSKVGKkvB9Hj0PH2o459w7957znvf9luUsXtris62qUfCdlPzBBcArJAjHhGt8HMVeB/LITB3DwDH/K9VSjc2PbaOYKdO/ah7LLl10VHW3txfP8ygUCk037sHOXhLR/Wh6gnDUIbJCks60IlUDoQhSXSn6Bue9MiZKukAdu6FTLlYJhj9CyFyC8IaRSi+edgWK8zwR4w503aMxPYkZrdFw1iKEYMGafvZuHKJerhOKBln+9sWsuWoF//oXPyU/nmf+0h4+/MX3oaoqkUiESCSCcN+PWjtEtV6lXCqgUGZqeiXdyX3c8sku9m6SuE6BBWsvoHdZ79z5CgVPu8oXPJQ1PO0dJ1S/FEVBMYKMH7mZXc9G8FybBWsvYffzG9m7NUE23YVQID/ZIFeM8t7TSGJ+ceezHNx2mNS8Nqy6zcPfWoemqwQiAVp623Bshy2P70ITOh0dnQgktVqdjJWj0WiQOZhD1TR0Xafv/B5wj36/63oYgbO3FZ0D9p4D9p6LNzDeTBPIt3o7SXFeAAwcGaZRq2NbFsXqDym5HyeVSrFkyZKzdtoR9hMozot4dIL0oP44jprA0y4+KShX13VisRgDAwPY1nLKxTvwarspVuKUnZtItGVpa2s7JbjRdV1yuRyZTIZCodAES55//vl+68tbiF65hw//9svUSgpGwEDEPs/r+bU0XeP6T72DPRsOUClW6VrYQd/yXrSy41+jFkFKF+mVqJePUJN1EAXMQADTCOE0hjl45ADZbNavQiWTLFmyxAfbSheo4CcuGq5+G5r9A/AUwEWq/Uh1yelNVIlgh7+LYj+CkBVc7SIa9kJ++Bc/ITuWxwjqbH96F9OZEhfdcB57Ng6RG8vRPdjJwKr5TRfn0nQf+eLfE1B2EO8O8fidURS1gKooXPGBt7FgSf8rT8Nej2p9C7teZXyv4MmHLqdei3LNRy9n4ZoZ5pB0Ua3vohotvPsjCo98v0qpMIzU27n01ney4cHNLFq7ACOg06hZPHv3Bj74h+/lv33/cyeMN/tMTE1ZeI0r6U1tIxQ20cPXoyVWUSvdS6TlYc672qHmrsY134Ft23Nbpd44WuOrvu4NKpr9HRyhILXz5ow1eWiKO/70HhQBQlXY9PijzFva5eN83FGkK/DkfKToxbKspi7N7IHk+A364LbDNOoWO5/bhxnU0QMGdt1i8IIFWDULzdAwAjodAynG909iBA2smsXgygUU0kVWXryMcrlCrV5n4mCaRHsLh3YPE45E8ByXd/3q1QDUynUUVcEMnl5CfLL4jwTsPVNSwmycS2LOxRsW/xErMWd6IpFSUiwWcUsFdLI4rkcgECASDhBrW4gMLjtrc2tiBKw9eG4YKSQCBSECaGIEeYxo3KlCNyLoqV8HIDoz90wmw/DwcHOzTyaTqKo6IzqXoV6v+2JvHR0nTcaELCIECDVFuEWCUHG9A6/7eo2Awaor594/qfQiZAMoIIQKWi+x+PkIdyvlqkK5NE1dmWKysJYaVRYtWtTEPQAI7zBq4x8QsoQkiGt8CqlfjKMkEO4BEFE8ba2vYeN6FNJFVFWlJXVqcC9KBM88apMwumOY7Hie9j6/jeTEQzz/sxfZ8+J+nvvpi3ge6KbKzb/5Tuat7ebhb60jd6RI//JBbvrMb5BNP8j510nK0xUc12bfjv1s37yDvsH5fvXjuHkIbxTN+jqejDB2sES0pco173mRpx+9hSe+9zQdX0wRiYcBC18V2KB3UPDh349SLtiYyYuo1lMgJUbATzLMoEE5n8euDGEYrSA6sB2HTCZDOp2mVquRSCTo6uqitXUZQnyoOZ+gCcHQLSjOAjzPwq7Oo5BtMDy8GSFEs8XWGtwEuEjRgZSgCBXV/gUuDop7EKm042mX8/JTu0BK4l1+i3A6W6Kr+yUm96bJTugIBVxngkveHUbTtGYr1bZt6vX6jP/V0WRgOlNmZPcY4dYQ1VKVRsWiZ0kX6+/fhPT8Db19IMmHPvJe0oezZEZyJLpbCbeEePCbj6NpOq2trURsh0q2wnt/5wb2bDhALpOnra+Vhlbje1/6IQe3jKBqKlfcfilXfuDS11TteLMqMafDzno94bruuXbSacS5JOZV4o1oJ71ZTtZvqlfTq8QsmyadTjM9PU1LSwudqWtoCQ2hCBv/lK/hGu963WOdTLtFJ4kmDiKUGAIQnoVUU2eMbBFCNHU1pJRks1lGRkYYGhpCSkk0GqW7u5uurq5XXICEdwiEhlQG/b+QLoq3kzfiyfC0axAygxTzkF4Dy2qwd2wJIb1MV3wn8RYdxbwaI/VRMtkyIyMj7N69m5aWFpLJFrrCXwchfZqxrKBZ/4Ct/jGeshCrMQ/N1FGFQq1c564v38eRfT7OZsXbl3LTZ995Ir34JOG/Y0d/DSEEpXyVnS/swwjqeNKjXm1w91ceIDW/jcMvjyE9yfDmMdIHssTaYnR0J2nv9nVuJoanwBMcOnSIcrlMLBZrJgK6riO8IzNsGQ3X9kDo9Pbt5ObbJ9m3fT7lwrUzSUxgRi34AMgEwXCVYFjHDg6i1MNohka1VCMUDeLUp7j25seJ6k9j5xtMFQY5nL2OZLL9BGG/E29AEa3+NwgviwokzQitC34XBgexLKv5nE2xn9GNeZ57qIZA5aJrNa67fYoj25/kuQc0PFew9rrHgOtOGGLlBbuIhqd59IctCAnv/nCB/sGdNMRVTI6myeWzVBtVYrEY0oGD2w9jmDrt/UlUQ0UIKKSLCCGIxCM0qg1yY3nAZ2aFWoJouoYQAjNsEooG6R7spL0vxaFth1E0v9Kz7KpFpLqSdN/W7UsHBHV+/JV7ee6nG1ENFZD8+Mv3Emwxuei68884IXmr41XejDHOVWLOxZw4m5n9m12J+bcyAjuWFTI1NYXruiSTSebPn+/TWWfup/S+iGc/D0ikdgmoPa9pvGPl/U+m3SL0GxH1YYScBCSeuhBPe/UqzPHhum7TCbpYLBKNRunq6mLlypWoqko+nyeTybBhwwYCgUCzSnP8qUiK2MyeLWcAw1UQJwJrzzhkFdW6C+HtQ4ouHP12phuXUi1mUd31uF4UW/kg8/rPIxy+FkENcPGIYApBT08LPT09TTptIbePojWG7SUIBccJmg6KYlPJHuHuv9vGkT3jqLrKDb92LeP7Jziyb5xkbwIkvPzUTuYv6+a8a1aeYq4Sxb4X1X6Mvm6DlngPUyM5zJBBbjJHsC1AuVhBFEBRFWb4PxzaPIIZNtF0Daths/PZvVzxgUvITRRobY9RypaJJaIsXjGIETCadPxMJsPIyAjFyRLxUJrzVlYxg62YAZdU+zBCkYTCedZcnIPQ94Av+Nowgd9EbXwbxd2DFG245qdASWCG4LqPX8Xj33+a0fQYb7/uSeLd0+SnOwkG4vS0D9Hdp+JpC05y7WWEuxcQSHUxiv1zhJdHKr6flfDSqPYj1BqLaOR+QhSPRO/1HNhxCY/f+TK2XcdzJY/90KM2XWT7hhjBsIKiwN3/5wBXf+QShCLIjRdQVIFVd8iMW6y7K4oZ8JASnrgrSqBtK4/c+w8UR8sEggEuv/US2ud18pO/fIB6pYHjuAxe0E+90pgB5mo+FNuyGD9QpntRF4riH/QKk0V+8pUHOLj9CK7tYIZM3vXxqwjFQuQmClgNm1RvG229ray/bzNDm4ZncEV97HxqP6VMBbvhIAQYAZ0XHt6IbLGJRqNNKvcrArObj9WbU4l5qycxs+D4/+hxLol5lTjbL8ObmcS8WdTn2Tja858in88TCoVIpVKsXr361KVNpRNp3nbGY51KKffUEv8xnMDv+G7JKEhlXtOh+NWiXq83QbmzINeuri6WLVt2wvNxLLOmUqmQyWTYsWMHjuM0qcItLS2grsHVLkR1NiGlAKHjmL92xvdhTkiJ2vgGwtmF7YSwrWGq9S0cLnyGZPJKWtvfd5JN4OTicoqiEI/HibeuQq8l8ZwpFO8A0vXAdfnR33yLQ3s66OrvwLE87vvao7SmYoRbZmjQMxtR+nDmlNNV7PvQrO8hCREIOHzwcwe5584ryUx6rL15JR3d7Wy672U0Q0PVVay6hefKo5upEBimjl2zueTmCziw9RBj+yZo70vx7k9ejRHwr1UIQUtLCy0tLeT3lXj4r5+mXChx/Yfgyvfspq3bBeFRqwaRnooZ0tHVJ2jwBX+iogU38HtzqmRSSgqFAkUrz8A1HSzs3ML81M4ZAlsBT7sQpI7rjZ944V4Orf7nCHcUP4npQyoLkMcAo6UI4NR2k959F1NjHtKDVPceRndewejBELruIIRESsHGdRahFkkwYiMEuI5k6OXDfPR/3M6mR7fhOi5rrl7Oo99cj6JN09LmgISpMZ2ffcOkXCpSK9epaDXW/cuzbH1yJ9ViFcdy0QIae9YfoFaq+RVMVeB5Es/xQEhq5Tqe66HpPjvrxYe24Ngu0pUomsKP/+Y+yvkq+YkCABNDaQqFAt19nXQv6EQIwf6XDnLo5cPUqw0MU0dKqJbqONOSiy++mFKpRC6XY/v27XieRzweJ5FInNIw9c1KMN7KidI5sbtz8YbFfzRg7+wGX61WWb9+PfF4nI6ODpYuXXr6L6C08XEHwWNozCfGbLJSr9ebf3e6Ev8AB7dPsuGBl3Edj7Xvcll68eDJp3OMkF42m0XX9bkg19MIq27h1jx6unvo6+trGj2Ojo6ya9cuIpEIyeQHaE9cg6Y2kMoAKK/95GTbNvnsIVrERqr1KIYhCQQ6SYamiffEkWr3q3/JyUIEcfT3Yjh/AIoCaEiSTA2NEm7tIJ2eQlUVatUGSTPOdLZEMBoACVbdpn1+8qRfK6VE1h+jbik0bH/zjba6fPjzYdSon8yN7B6jf8U8xobSOJZLIBSgYyDF6N5xprMlZrVcIvEQia5Wtj1ZRdVU6uU6TsM5Ycx6tc6df3EPCEFHXwcvPNbCro0ZfuOvOzCMB1ENPzFCkbiedsKJfrYKNzU11WyJtre3s3ggQ6CxAWQAaABVhLvN939Sek+Yh2r9CMXZ0hSiE84UrhZDUEPKOqAgZImR3R6ZwyVyaX+ZthsewtmI63QRTfjPSilXRtM1FDwMQ0VKG9fVyJcEBycOMP+yDoQQTJTHGNoTpZxxsBsOUgpG9utYlmQ6dwDX9RCAbhq0z2sjP1HA9TyQEGoJkuiKU6s0qJfrqJpK75IuSrkKY/vGfcNQCa3JKJWihRkyUFUVq2Ezumcc23LQdP8a6pU6QxsPM2+wh9xMYmOETFzH9RV9Xc9X6VFE81ASi8WIxWJzPLnS6TT79u3DNM1mlWZWR+jNAN2+1QX1zrWTzsUbFv/eMTGzJ9DZDV7TNFKpFKZpctlll53x9wnrcZTGTwAPqS3DC3wGxNFE4dgWked5tLe3Nz1d2tvbSSaTp1ViHtk9xo/+8l4CYRNFEfz0fz/I+3/3JhZfuBA46m6dyWSYnp4mFouRTCbp7+8/YwDf0LZhHvnHn+PYLqFYkFv+y7ton5+kvb2d9vb2pjhdNnOEkYM/x9CmUc3VhFquJvIKSrfHR7VabdKgXdcl2RamIxUkHGlDKOpRmdTjJVnPNNQ+pEiBMPCXDI1Eu8N0BTo7O3Esm2p2nM7VbeQKOQ7tGsYwTNZcvYJVVy1vfs0sRXq2UrdyvkM0KIkGIgihIDwL12hpVjxa22N0D3bSs6TL3xTrFpF4mEDIYOfz+/E8F0VRWXLRQp74wdMIBO3zk5QLFe792iN87Eu3z2G5FCdLNKoWbTM09kAkyOhwmPGx62lv24SmZpB4SKkwkr+BgztfIBgMYhgG9Xody7Ka5qHLly9v/k5q41EkDigtCC8PWAhZxNV+Fame7w8uZzRxhEBxXpr584zYnpxGyClc45dR7IcBF1e/lY0//zELFrmomq9ppGk2hbSDETAoTE2DBN3U6Fp6ORP7tvHSU2UEYWId87jxVy8EPCqVCrqu+2aiC5YysnsLU6M+Wy0QCVEu2lg125+HAMeqMX4wDUhUTQUhKKSLVIpVqtO15r08sOUQkUSESEsYT/qJh2W5OLaLlSv7XVJV4DounuNhuRaKEHhS4noe+148OFMpk7iOS7QtQnW6ilAUFCGwLYfexZ0nPIqz600qlQJ8AGsul2NoaIharUY0GkVV1TccdPtWbyedE7s7F29Y/HtkJ9m23dwsjz2BLliwoLlYjI2NnfkXO7tQGj8E0Q5oCGcn1O7ENT9+yjbRggULGBgYaLZqtm3b1nRxTqVSJ2WiAGx/Zje6oRJr86XfPU+y8dGtBNr1E9ytj92gzvndC7UAACAASURBVDTK+QoPfWsdkdYQZsiknK9w/9cf5ZN/HEZ3HwOh4Oq3EYteRkL7FxRvGE+qOM52RqeOsH3H+bS0tJBKpUgkEnMolrOsrtkE8lhjy9nSsbBuRNgPgqcBLp563kmrAWcSUrT75o6yAiIAssqtnzH47lcCZEdzeJ7HpTdeyE0ffSc3fujdDO8dplAoYCsWW7duwa66DL00TL1WZ+nFgyxaNeh7WskIeu1PfadwCSituPq1zXGjiQi3fO5dPPjNx2lUG7S0x7j8/Zfw0Lee4NqPXE692iAQNpnOFJjOlugZ7AIgljCxawep5vdgBlY0zTtbOmKYIYNyoUK4JUS90kBVFdp65mOFvo1q/Rghc1huHMMME48MU6j0+e+QlyUaLKArEimTc6o0vvKuh/B8vBUoM07btwMOauNOX05AqLj6bUjR6gv9ydl30wMRx9OvwtOval7/9hefpqNziFS3X3mslgUvPd2F3bCpl33RRimha9FiDu2aBn0ay7Gp1sA0TFZdsHwOPTu9ucizd7yEN6PRUq83kN4xmLmZP87Sul3LRSLxpEe1WEPRfFNQBNgNB6fhMH95L95MFWXiYJpaqYbnuP6HHImYAXULRRy9VATBaLD5Od0wePv7VvDMj9dTr9RxkfQs6uKGz7y603UwGKSnp6eJ4yqVSgwPD1Mul8nn883WUywWO6tJx5uVxLzWZOwcO+lcvGHxZicxr6Xqc6w429TUFFJKkskkfX19xGKxs9YLFu4woCDRkEikbEHau7AV+xUNFYUQTeGw/v7+ZpI1y0Q5WRJgmDqu41FvNKhVa2Qms6gt/nctW7bsrJ1aprMlpCebBneReJjW2HrUxiHQEiBt1MY/gjuBkCNIpcMv4ys2/Z0b6er/NYrTJTKZDENDQ2iaRiAQwHEcqtVq02phwYIFJ9WQ8PT3IZWBGfZTO552yVH/nNcaIoAd/BJa/c8QXgZEK+0rvshn/3Ye6cMZzJBJ92BHs6XXu7AHc8rwBQsnCjz9nRepV3zX4kMbRmn5Q99EUKorsYP/E8XZ4NOztaspTGnUKpPEO1oJhE0GVs7ns3/7cRo1i0DYpDg1DR6EW8MkuxXWXvo4La0jNGoBtm66llq1k4svvxczkCcZeQlRX4sT+G0QOoGQyS//4Xv50V/dS3YsR3e/w2/9lU138itUy8sZmbqSoPxXktGNxA2dVL+ONG9Dii70+t+BrOB6GmOF97B+91o6EiOk4lOEgxq6rOG3RGco/d4UauPbSJFCcZ5Bik7AQbXuxFWvQsgDCG+mJSaSuPqtJ9z2xRddwHe/PM7CldMoiuDgzgjRVBeONe63XSRY9QaP/WAdsWSUxRctIBAMMD1V4si2CdZeMVfF7tF//AWaoc+ApCWu7WKfZH3wNYUEyswGatUsXOG3lnyywEzrydCo5CsEowEqxRqaoaNpCpbjIfGTPF1XUTWl2d5TNYVgS4AF583DDKhoSgar7tK9sIU/uut32PXCPsyAyYXXryHSemZmoIqizDDqkiQSCdrb23n+gRd55IUnEYZkzXUrmLegh7a2tld930d2j/LAN5+gnCuz8Px+rv/0NQQjR5OCt3ol5lw76Vw042yDt95sTIxt26f12eNBubPibOeddx6meXqOs6fLCpi9fs+LonsOHo5fZpcVUPvP2AdJ13W6urro6upqsmqmpqY4cOAAuq770u5dgsJ0geL0NKZp0NoS532/dgvd804sWb+eiLSGkVJiN2x0U6daqrH24jGEGjlqdSDrKN5O34SvebtUwEVRBMFgkEAggK7rNBr+5u+6brMapWnaK9znMorzIoq3GynafRE68foqMQBSHcQO/TM+5sMEIYjE/STteEViVVVJpVIsW7aMDfu3EA5E6F/Uh/Qk6dEpnrr7edLFSab25TC1ACsvfRsDK/p4/mcbeeG+TSiKghkyeN/v3kRHXwpVUwlF/QW5tb2F5Zcv5sUHNvOejz1DNJpHC3QRSUguNB9h4kgHgUCOUHweihoG9yUU+xd4hk85vuj68+hfMY/pzDDzOr+KolkUcgqmsYeelhEigWGEWOBXb6QLjZ8hGAfZAGGiKhbzEnfT2RlDWOuwLBCNUTzNQUoDRfEQQkVQ9a0BCCJF60w1yACpgzBxzc+j2E8ACp7+bqS2+oR7fuvnb2B4xxH2vXQQgPnLuinmytTKdVRdRRECp+FQy1v0Lmht4rWkJ9F1B7X+DRR3G1LpxDE/TSlfxrEdxMyScCrioh7wWV9e1UIIQTQRpjZdx7ZspPSrP4qqcMtvvouND2+lOl0jGA2w6qolPPTNdZhB0DQX1wGheMTbU1gNe8Z7DJa9bRFWrU5LZBtCTlMqCgYH99A7uJzexVe+7uRgdvPf+MBWfv6d5wlGTKy6zbOjL3HrH7SxL7ePer1OS0sLiUSCeDw+p+JRSBe583/egxE0iLZF2PXCflzX4wO/d/MJY7yRcc4A8vTiXBLzKvHvmZ30amPVarVmtaXRaDTF2c4IlDsTs4C6U92vk7KJWIOqrEGR2xFCRYoQrvmhk/770w1FUTBNE9M00XUdy/IXYiOq847PvI3c0DSRSIS171hNZ3/76xrrZBFLRrn2o5ez7o5nAIGqq/QuXYxgX/MzAhtP6UPIPHhZEEE8N0+ufB57dmyakwQcuxDNJprpdJo9e/YQCoWaFG7TNEFKtMY3Ee4+pEiAN4ZW/wp28E9AxF7/xQkB+KfRY/EtuVzuREXimbDqFqp2tKUQjoQJm1H2P3qE9EgGx3N44d5NLLlyAUMvHKFroINQOEQ5V+HBf3iCX/3zDx43BUE0EQFsOnuylIohOuMGoZYIZsAl3uGh6r1o5qxjtES4L4F3AZ6bo1jIki7qqOpmFK2GonXSGjYR0kXIl5EEm+0nUAAbISs+JR5ABEFOo9v3I9VugkEb4cURMo+UHp5UwJupktgWmh5BVWvH4LxsUBJ4+tV4+tVzrq1SrHJ41yiKIuhfOY9gJMDnvvEpdr64i0wmS7QjxI/++0OgCHRDRwCu4xKMBlHVGtIuUJ4O4nka1976LKqzEwiDO4lR+yOiiQiFybnrgVAF0p2bzWiKiu3aiJnfuVqq0bOsi+zhHPVyA83QWXheH+e9YyWBcIDJg1PMX9GDa3vopiAYbqBqEtsSgMdNn2lj/YMe4UiZhauT3Pz5D3PopXvY8lgJlDBXf8Bg8eoSjv1j6tpvNhP2WQXhM43ZdeipH79AdbpK+nAGI6ATrjRoTDmsvmL1HEfu4eHhJhsvkUgwPpTGdTzCLf5vlupNsH/T0Jyk4q2exDQajXPtpHPxxsSZVEfOxljHJjGzL246nSaXy6HrenOzPF3WzauNdexL92raLYpi4Om/gfSGAHuG9hw547FnN9RMJkMulzspVgTmgncPjh8gW5kimUy+on3Aa4nlly1h/rJeKsUqsWSUUGgKan86g5kAKVqxtPcwXb4M0fhXpDNFw7sEgu/n/PM7T1mJmk1uUqlUU4cnk8nw8ssv43keqbYQC5K7ULVuP+EQcfCmfGE3dflJv/NMwplRn52amqJcLtPa2koqlWLRokWnXGyXXbKIzY+9TDlfQaiCSqHCglXz2Pn83iaGpTpdY2pHATNgUq/XKBTyaJpGbihHtVqd82xOZ0psuH8zfSv6Uc0YERWyYznCLUE0XUEJLPVND2UE4U0g5H5ojKA07qZmxYkqrayY140I3oxmGUAW4dlIQkgRASIziWUUKCKV+Uh3FKTlA5vlLOvJQbjb/UpaE7QLqnABFZtBsD32j55HKvIkqigSDKnogUV42qUn3Kf8ZIHvfvGHlAtVXNcl2Brg8k+sJT2cY3zbFNFYlBUrlrPq7cvJHS7guS4SCARNVl/ewkc+/xSHdtoIRaG1/9eJhB8HaQBlhNDBK9HT5zCy68TWrDxO8rFRs2cuR6AqAs+VdMxrI9ljEo7kaFQlrV1RnrrrORqVPH0LR8kPb2BsrJtIXNK/pEpPv0WpqILUuO0TBd7zKyZCHkFVJ0D5c1Zduog1F2qgRP1BpYumFDEMo2nCOuskD5w2AxGObv7jQ5OUcxXMkEmtVKeYnqZcqDS/Lx6PE4/72kyWZZHP5xkbG2P/0EGKhSKBVoNgIIjdcAhEgnMOaG8Wxfq12g6cq8Scizcs3uxKjG3bjI2NMTU1RalUam48CxcuPKsb9yz+RogTDRVPrd0CCAWpnpzq/Eoxi4PJZDJzcDALFy485YuvaRodHR10dHTMoVEfOnSoyXpIJpOvO6EDv83iK76CZB528M/w6puYLpUZTXdTqgwRj8dJpf4/WltbX1Pl63hcUC6Xpl63qDUm0LQAgYBJQHeB02sHnixmKfRTU1PYtt3ERkVPk0XVu6SbD/z+LTx3z0Y8x+Wq2y/DsWyeunsDO57bh+s4xLvi9C3rxTRMYpEW2traSI9mSfSE2LVrF47jkEgkSCaTNEo2QhGomsaW9Vdz4WWP+4rPXgbXuALX+ARU/gppbcdUD+F6GkJIFMUlHMjjqcsQMofrbEPILEIW8Y0oJI7+S7jmh9Gs7yK8UTxlDa7xMVTrTjTrX0E2kICnXo20d+LUKtRqAk2ThCIBMN+NkHsBF1UEIfh+OtRL+fLHx9j/0kGEAufdHOLaT+9uagaFQiGklDz6vSeZmsiimgKhKxQmiww9Ncbe9QeoTtcQQjC0ZZgbPn0NB7cNU0xPA9CaCnHVDev5zl+Y5NMmUkL/sh/wK783jeeUyUzo6Lok0aFRzned8Cx4zolrkWM5voCdAngS6XksvzhGX/8WRvfbxNs9Vr5tjDu/NZ9bPriBSMwCCcWcQU/3AEd2u2QnDMIxj9s/n0ZxPYQSQkgd8JDeOEKGENhIaePbWk/jqe/wqy/CxlcBNpr2B8ceiOCVk5rZ9dVnV4E3sx4pmtLUDjo+DMNorg2LFi2mNFxn1wt7cZwpPM/jnZ++gmw2S2trK5qmnZJibVsOT9+1nuHtI3QOpLj6Q2+fg6U5k3g9tgPnMDHn4g2LNzqJmcUnpNNpJiYmsCwL0zQZGBg47Y3nTGI2WQGfoZRKpdB1/ZSg3NcaUkqq1WoTdzHLSDqTDfXYOFYAbXBwsOnavGfPnmZrLZlMvqYE49g5z6oWZzIZIEkqtYz+BammpsXZCl3X6ejoQbE/ScT6Fxyngm3nOZJewJF8lmTS93h6Rel75j4/s0aQJ2ttvVI49qzhpP8MLFjdx4LVfc3//+JDmzm4bRhVU1E1lbF9EyS749z22zfyxA+eRnqSREcrt/32jbS2x5oaO+Pj42TSWcq1EvZhG9vpZWzobXT3TXFZz2pGjrydbHYHhnErfZ1r6Az/vT8HWcVvDQGyhhQhFLkbKdtmaM4WHi0o3n5cpQMn8Adzrsc1f52xI4MUJ3YSS/XTueg6pvbfjlMPEU24lKuCzIRGy8CVhJN/BFQBA4TO33/+/3Jg8wixtlZcx2PLfXtZe/kFJC9PsmPHDqrVKlJKDuwaInskj9NwEQhCsSAvP7mbg1sP4zr+xq2bGsvetpgbP30tT9zxDIqq8J7PLmbP5k1MZwTt3TZSwtC2ANtfgPWPpchO6HgerLy4zu7NJ9/AFW0mGZjB2AhFzLDBZ1rEqsLAot1ceJWD/Y4YVsPCtdLccNsmYi0WtWoAz/MIxyze+8nDVAo25YJKW5dHLC6QAhTvyMz9F34bTO3BMT6Iav8EcP32mnYjauOfUJxnAd8uwzU+iKr6lcljk5nZNdRxnDnmlbPPsKIopOYlcV2XUqaCEdJpn98204p85dA0lV/+g/cytGWYerlOqq8NI6ax+cmX2fL4doSi0H9hNwsXLpxDdJBScseX7ua5n72Ipmk4tsOe9Qf43Nc/2dTJOZM4J3Z3enEuiXmV+PcA7D1WhOtYx+RFixaRyWRYuHDhWR3veO0WIQSDg4NMTk6ybdu2OVoOr+c0cLyuSCAQIJVKsWrVqtMGG59uBAIBent76e3tbWJPJicn2bNnT/N+trW1vaomzfFYkVnV4jVr1pyWns2ZRK1c5/l7N5IZzTGwaj4XvGs16O9AKj0I9wh6OE5n2xrill+1OnDgANVqlXg8TjKZJB6PN5/HfD5/Aqh7/vz5ZwSydmyHx777JNt+sQuhCN5264Vc/r6LjxpJursQ3mGGXx4iFAvOiJtJEtEAUyM5Vl25jMUXLaRRtQi3hpqeS5qmHdXYWSaZ39nHfd94lEM71jMweJgVl9ZQ7EPMi4/Q3/eHaLoJ3nxE5e9n3LdVwG8DKd5OJEE89VIUsQEh60gEgnIT6FouVCjnK8Q7WjBDJi8+vIWf/Z+Nvqibt4NrPpqg1TDp6peMj4TRdAdVlClMjFLM/pzD+0yCkVaWX7aYA1sOYYYDOLY7o6Dt8uLjm2iYFQojJRLJOEsuGeQFbyvF9DSh1iBCUciO55HjEqtuzbCJoF5p8Mzd6+noSxFri+K5Hk/dtZl4zMJxDA7uMlE1iWG6fO/LKVrbXGKJBp6jsuXZOI59krVM+EBgT3pNwG4kEcau2f7hRIARNEBtUCrVQNExDZNwsBWvXqJWUrAtcF2FWFwjGFIxAgaJjgISqFtRhKJgaDYCP4GQVIAqnnEjnn4DPu9aRbHuR3GeAjoAieI8jlS68PRr/N/uOIzMsevQrHllrVTHaliImKC1Pcq2X+xAepJqSWAEDHqXnAjk9zyPkV1jVEs1OvpTJDpbUVWFRRcMND9zYPMhXrxrG+FYCNd2eeHOLbTGWzFaNWQd2rvaicfjvHDvRqLxCJqh4TouO1/Yy6HtIwyeP3DCuK8WryeJsW37rK83b9U4l8ScRpzNROZsid3NViWmpqaaIlyzUvizD36xWDwrCdPpSPzruk4s5oMfa7UamUyGXbt2NfVXZuX2X+1eWpbVbBNVKpXmhjs4OPia+8NnGsdjT2aZN1u3bkUI0QTTzlY1jtXROXbOr4QVeb1hWw7f+eIPGd07jhHU2frEdtLDGW769Xci1cVIdXHzs6ZpztHSKBQKpNNpdu3a1ayizborL168+DXPef0Dm9mybgft89uQnuSZu9bT1t3K8rctQbHuRbO+B0iiQR1VjdDWPR8EVIo1zJC/4JpBY45A3bExi2kqO9Nc+MvLuGDBo2hGBGQcy7ZQ7M0cHnqYYOxi2traUMzfR298eUb/ROC3UgS+C7SGkGUkmk9Bl76H1MZHtvCzv3sEkBhBg9u/cCt3f+VBcmM56jULI2Dw8LfX0da5ips/uptkRwnHURB4lMZ+wA//LoaUKq5YzPwVCzCDBmP7J2ZssiRCKASDYTb9604818PzDrHryQMsW13k2uvTFDIuT/y0k+lgnKlD2ePwKpL8ZJFQNMCRveMoqkKiXeAlNfa8FCIY8e0J6nWFVKfL0E4FKf3WqKp6M/idufc03BqmUWnMAO0loWiIS25ey87n9tBoWHiuS9dgO51L1hCN3jEDerYR0iXafiGB0LNUy2AGIdpi47EUTdnCLNVcU/OU6314XglNqfptKqKgzShIC4GfZILi7gbCR4HV0kS4e2AmiTk+jk1qMmNZvvvHP2JsaAKJx3t+491se3IXZshEKMJPZIpVRvaMs/iCo15Wnudx/9ceY/vTu1FUgVAUfun3b2HBmr45Y217cieBkEkg7H+fMaUztbtAbjRPKV+mXm+QWpSgXq9jODqu66KbOgJBrVTntcTrSWLeDP+ot0qcS2JOI94KBpDHnvCz2SyGYdDe3s7y5ctPieF4Pa2r4/vQJ4JyT/1yBYNB5s2bx7x585obz6zc/qzOSVtbG6qqnqTlAslkkoGBgVMK172ZIYQgGo0SjUZZsGBBM8nau3cv5XIZ8JOezs7ON3XOR/aMMb5/gmRvwschxT1euG8T1338SoQQFNLTBKMBIq3h5r+ZZaNlMhkcx6G7u5toNEqtViObzbJ///5mG+1UvjRz5rB3nOHtIwSjAZZftoRDLx8mGg/7/04BM2Qwsnuc5ZfOR7O+729eQufK9zqsu6vO1NgUiqIhFIWP/ckHTjpGo9E4IVmfN28esegizKrmg3GFwAwEwLPpDsUZz9bZunUrUrbRnvwK89ruJaAcADU+wyu2UOSYDySnCtJGihSZiSD3fPUhIvGIT4+frnLHl+5idN84qqZiBg0cy2HiYJrrPvEBfvSNALrhcP5lQ9z80R1kxuG3/jLD3q0RXnjCZOcGGwfbr8Lgt2ZUQ1DOVVA1BTNoIFRBYXKI1ou3ceQAbF8fIhrLo5txpNVGenjKZxBJiZAKQpFMHEwTa4shpWTiUB7F1Whtd6hOKwgBLXGHek3QqKnEEi5SQjGjEgy51MpzDwJmyGDV5UtJj2RQNIEe0Uguj3Fey3Jyw0Xae5Pc8Olr6ehvx3ViKPb9gIJr3IqnXkxA/T1CkQ34pqoXgFdFSgOBCUgkFqFgCSE68WQYy3GQToGhI22U7c1zsUFKB8LbBcwy6SykciKD0KpbPPgPT7Dj2T2ohsL5ty5n/U9fYmx3Bk1R0U2dh76xjnK+Qix5tNVcypXJjGQZPL+/+Wwf3jnKtl/sRCJp1FzMkMH9X3+M3/rGp+cOKgSHd43SqDYAAbpk9/P7SXS20jE/hedK0oczROMxJg9mUIQvqBlrj5KeSvPN33sRp+Gw9JJFXHn7pejmq1c43wwG1H+EOJfEvMlxJomFZVnNBbxSqTRBuadblTiTsY7tMZ82KPc04ngw7Sw7at++fc1xIpEInZ2db0jL5WzFydRyBwYG0DSN6elp0uk05XJ5LuX5jZyP57tfN1s1QoCEyUMZ7vyLe6hOV5ESrvrIpfSs7mDdHc+w//lDBIIBrv3IFVx9+9vnJFt9fX3NtuT4+Di7d+8mHA43r+f432X3hv385G8fQFEEruPx0mMvk5qfZPzAJOEWH6xq1Wxa22MzVQ5A+At3LKHxpR9UeeL+C6mU4qy+ajlLLzkK7j7W/RwglUqxZPFCIsZWhNyKp/QjxVo8dSWKux1JHN8F3CAQWclALMHAwAC2bZPNZpnMJmkLvoQrDEzDxDSqoC4Bdy+IDn9eMkMu0wdCaW4woViIiaFJkD641bacppXDwjUDrLlqBeMH0yxZ9HXMoEUw7ONKzr+8gG2NMjU5SHGsRLyjBen575NtOUwenKJarGI1fCPGcKTIjhejTByCeMpBKA75TJ53fvw93P3XD1AtVQFBS3uU3mVdjOwao1iYRhEKwaCHJwW9Aw1CURfXEezcGKY16avtlgo6Qkji7Q6FrDwhidF0BYIenSuSBIMBarkGl155Cf0r5p1wePO0K/HU1T5La8YywQ5/FbyZ31cJopc/hpCCWXNVIW2kCOEYH0V17sVUdNzwe1mUvInpYom92/az/+UhAq0GybalLGjfjConUTVQjPl4+vVz5uu6Lj/8m3vY9Mg2jIiOpmg89c8vMjWcwao7aIZKpVhDeh7BSIBGpeG3dmwXM2DQ3p/kwJZDlPMVUvPbKKQLHNk73sTReK5LvCs+szbhm6pqKkjIHMnhzRzoPDwikTCFdJHi1DRm0KB9fhJV820TPFciFEG8vYUX7t6MHlLx8HjqZ89TKpe48VPvPK3W9Gtdd/+tD39vZpxLYt7keCVMzKynzrFViVnGzWs54b9aEnOqNtGZ0BlPNxqNRrNNVK1WSSaTRKNRLMsim81y5MgRarUaqVTqlEBd23I4sPkQVs2iZ3Enbd2Jsza/k8VsFWmW2TWrBnq8Wm5XV9cc4PEs5bmtre0Vr+f1RM/iLhJdrWRGcwTCJtXpGmvfuYq7//Z+ysUyWkilVqlx/9cf5YIb1jD60iTzF/biuZKf/+A52uelWHHZkjnfqarqHH+ncrlMJpNh69atAM1TczQaZd0dzxCNR3yzR2Dy0BRr3rGCiaEJpo7kkJ6kZ1En512zEoQ2g9MZndFbqRCLG9z02dtAxOYkiJlMBtM0aW9vP5rUSolW/wpKYwMS4csC6rfhBD6P2vgWirsDRMp3AVcSCHszivssKkG62q9Hil9Fq03i2Qew7RKFYoD9k5fQnRqgs+URVBWkOkCs92Mg7zwqVDhdJRIPEy6GmRya8hMRRdA5kCLR1YIR0WmRIVxKSCnRdEGtoiCEZOGKGuYDJqFYkEqxSqgliJQSJ1fGsRxyE0VUVcGTHo7lkR5RiXcoeK6GIm0SnTEO7xr1WxcBY0btX2H+Ep/FZdVtpPSIteW55IqD/OBvEigqOJYgFPHoGtCZfFqiqjae51dlytMmQvHp4LNtxN4VXWhSR/M07GmPZRcvYt7SHuC4jVCW/d/A3Q2Aq9+Aa3zUbwcpR3Fvnn4jSuN/gyzM/I2Gp9+EZ7wTzzhqI1DKlfmnL9zJ1OEMElhy0ULmf/oKvv7/L2bv+l1omsLbf3k17/qYSzDot2vT6TS1Wo2Xn9xFOVvFGrFRVEFLMkalWCUYDaKbOpohKecqrLlmFduf3El1uoZh6lx624Xsef4Am5942V/rFMGC8/upleszbTb/YFAv13n42+t48s7nkVJyyS1r2bPhgO+i7nhIAaqqMDE0Rbng+0RJAfmJIqFYkJ7FXQgBnuuRPZIn0dFKb18PEkk1UWX/S4fYfonvyN3a6qtXn4xA8HrbSf9Z4lwS8yrxRovdHbtRFgoFotFoUzTs9VYlTpbEnAyUe7ptojOJ41VcFUUhmUyycOHCExgysxThbDbb9D053jrAthzu/POfMrzziE+xVRU++N9vo3/lvLMy39k4nlLcbF+8it2CEIJwOEw4HD7l9cxq0pwNbI8ZNPjU//ow6+54hqkjGVIL2ug+L8WTP3uOZE+CQCBAS0sLufECo9snicYjM2wgMIM6Q1uGT0hijr+e2TbawMBAM9mcvZ70eJqWeAzp+RgBRREYAZ1P/NkHGR8apiW8nnhHHaHvRorV2IEvotW/iuLtQyqdNIzPkcvZpNO75jz3JzPcFN5BhLtpxoRSIKWLat+DrCWsngAAIABJREFUa9yCG/hdjkWYKfbzqI2vzVRXXBR3A1bgSzz4oyvYv76MwKNn+ZVc96nLKBQLbBteSrWaJxxpJ5kMcvN/eSf3f/1xX1Y/YHD7f7uVP/3AV5rvkfQkufECmzZuYmJ3hvJEjWvf00Vi1V6irZa/eXkCV8b52JduZ8NDW3jkn9ZRr/o+RW3dCWqVui/FbzmAQNNVNK3BjvUGriMAg0DUIxDYztLzi4SiCrlMG6MHPOy6QyBoUJ2uoygQa1vOle8rkOrZzfMPRzFMj47FPQxtqWEEJK7tv8+Oo9DeF+KGX9rP4KoqmXGDn3y7j3d/5B0sXDtAejhDMBKgb+W8JqD62FAb30dxdyNFG+Ch2g8g1UE87W1zPueJAVwZBbfoM5zUVjz1Ar8dnp5G0zVibREe+vY6JoenaOuKI6Vk53P7yIzm2faLnVgNC4AH/u8vsDWX9kW+Q3VnZydLly7ln8fvpjBeRKgK0vOoTtdIdMWx6zZW1fLbOMkoipCE4yGMoIGqq1g1i2d+up78eBHXcQlEAuQni0jPV9iW+OtfvVLne//jx01jzMO7RtENHXtGdRjpWzY0jTPBd21v2Bi2OcPMU2aA4mKmeOcfEKULnb0drF27FsdxmvpW+/fvP8GR+z8TruX1xLkk5k0ORVGwLItDhw6d1HjwbFY/ZkHEs/+dzTbRyWKW1ZPJZOawpI5XcT1Z6LpOZ2cnnZ2dc8TrDhw4QCAQoDhcYWj7MJ39vkdPpVDhkX9ax69/5eOva86zejGzVaJZZtXr9VM6/nqKxaIvsnfwIIZhNNs0r3WMWq1Gvpyj97IUXW6iWfUZWNJHreTTKx3bQXqSVG+Ckd1jTQVS23KJJsKvMsLcMAyjae0gpST3rjLP/nQ9+XABz/HQVZ22vjhmULJowdcQ3jC4Aur34BifxTOuo2788TFieVPE4w4dHR0sWbLkVZ5Dy1dyaS7oCj6tpsGxjucAin2f3+oQYX8T9SYZ2fpjHv9entb2+SiKYOgnLxNpS3HNhy9vXk+xWCSbzaK0e7z7v15OQA3Sv6SPsd1p3yhyRnVY4lcEt9+7nz0bDmA3HIY22PzRN4OkugNE4hJEmOD8P0WqvXQOpKgUK2z7xU4CYYMP/P6tfO1z/4jrephhEySUCha5bIrpbBkJSDTMYJ6VF2fJjOmEIpJwtEx+oh3XcXElvjqwJ9CNGsLLcN4V7ay+zBepU7RpvrpJZfklKvWqRFXBrjtc9b48F16eQ1U9Fq2usuIShcjgILrZSqq37RV/f8Xb28QfgYqUKsI9gCsuIT9RQDc0WlIx7On7GNmnUSomEUCy2yOYWsf3/pfG4V2jSCm5+MbzmRiabFpJCCHQTZXNT7xMKVtujlkr1SkN1bngwgFe+vlWDhuT9Jw3RiFXxJUewvHXMtdxWXLRAqaO5GZ0qjxSPQmGth2mkq8iFIFVt9jxzG4K6Wm/LQiU8mWMgIHneDi239pTTI3sWB7XPnr4s2rHOH0fj4wWoCgCz5MgIRQ1qZVrqKqKY7ssvmiAgVXzGd07gVB9faObPnsZ4LfaZ9cBmHmn8/mmI3e9XmdycpJEInFmLEHHecNdvN9K8Z/nSv8N41gKazabpV6voygKK1aseEMEiY5tE1WrVQ4ePNhk08DZq7YATX2VTCZDvV5vWhe8+sZ06lAUpXkiAR8j8fSO5ymXykylBYGg7ytULtRe0/cf6xNVLBabVYC+vr435OU/Xh10FmB7Juyt41uNs2rLx6sSf/AL7+X7f/Jj8uMFXM/jXZ+4imWXLeafvvAvZEZzSA9S8xJcfOPa13w9Qgiu/8Q11EsNtjy+g2A0xDWfvIzMdJrq9odY1H0ARU2g6TrSreOWv8XmQ22vSSwP8FVzRQy8PIgwQk7jKYMgWk9yn1wyh7NMjY6iqIL5SzTSh6fQzSCa7lfAQrEAezYc4JoPX968ntbWVlpbW1m4cCG1Wo3Dhw+ze+8uDm457LsJ66oPRPcktuWw+bHtIAS6qTM1pvA/fqWbP/nOERIdHpl0CiWRIN4J40NpxvZNEE1EEAJ2PbeHvmUapmlRyLo0qiq6qZFLq4RaW3AdD6EI3v/ZffQvtfjh37UxOaIghEfPQAnHspk4MEmj5ld9dr+QZ/sGnbVXh5C42JaDsLP0Lgqz56UIHfMUHBuyY3UuePtBzJDAc3U0VdIZLWEp25Fc/qq/gSc6UZ11+N5ZKpLQ/2PvzAOrKM81/pvl7Cf7TsK+CCiCUFTcUaliQW2rgrXaaqtVa2uvtlVb29vaRb2t99YuWrva1mqrsomKiBsiCojIKmsiIQnZTk6Ssy8z890/5sxwEgKEkABtef6Ck3Nmvplz5vve732f93kIdxbxu+/+gb3VTQgB0y6fwvhT6nlnsYuN7/pwugQXfradhj1bqf2ogILyfIQhWLV4HUPGDSIWiuPwOEjE43QEOwkHI13OKQzB2pfX89bT75JKpJEkGDFpGIqkIGHykwwEkgRlJxUzYfo4aj7cQ/mwEkZPHcmDcx7FnetGaAJZlQk2dZhGpFlxiJbWuwj+6VqqV79JCxJSpgplKh+ffdXpBOqCJKJJcgr9XPXNWRRXFVK7uZ5UMk3FiFLyS/N6PJbH48Hj8TBo0CCEEKxatYpYLEZDQwOGYdi2CIci3f8nqfXCiSBmwJDdVWFpc1j8lrVr1zJkyJB+PZ9hGGiaZitcWtmWT3ziE7S1tbFz5057wSwpKemzG3X2YmqJoVntxFaQ1N/w+Xycds5EPnzxI9xuJ4ZhUF/byLDJlWzbto2SkhIKCgoO+mBbnJzW1lY72KqoqOiTT9SRwuPxMGTIEIYMGWILuVndW1ZAZZXRsvVb/H7/IfVbKkaWcecTN2dacT3kFpuy7rf+4gvs+cjkWIw4dYjtst1XbF29ix1rasgryUFLa2xYspUbHrgGn6sNojLxeBw9EgFJ4HamGTFiOPn5BX07meQl7fkeavKPSMZedPUMdNdNmaxAV6x+o5Kq8tV4c1zIskHjx4KG3UPpaN1Fy54AQgjcPhdjzxgNmDYGyUQKf6GXjs4Om3dRWFjIhAkTGDdyPG/8eg2drSF0zcDt1fiv/23klKmdJOIqz/32JDa+56GtEVa9Wsrm1SrJeBxDPMj5132Oda9tpn5HI8lYEkmWKCys4RsPbqKp1qC+2sWq13JZv7IMp9NBe3Mnsmq1FxsMHgU33q+xc30aj09j2KnjePTuduKROL48H4YwCDS009kWRqR3o0igqA6EMpJLvngp6fhiardLyApc9qXT8Od9DDgRqtk5YxgpdtesojB3Gbne3ShqGbr7lp4VtKU8IJLxKzUX+vm/qaN+Z6NNXF654H3ef9FD/c60qUkjJJ5/vBBfvqCgzEFnaygjqgfeYjeeDie1H+3B4XRwzpVn8Mzmhfudtrk2YIsiCmGw8/0anG7VFgBEgOpUiXTEWPHsSyRjSTw+N2mhoWkazTWt9rGcbnW/REpPisW9hazKZhZGF8gSePN9nH/1NCpGlhELx/HleW2Ru5GnDTusY1sl/uHDhzN8+HA0TaO9vd1uinC73fZGz+PpaomQTCZPBDEnsD8OVZ/MlrHP5oBYi3v2Z/uDdHUwUm52mUhVVbxeb5d257q6ui4WBIWFhQddyK2ulUAgYGcuiouLD1sM7UhQNrSEq789m6V/epNYKMEFnzmLi284j0jM5N3s2LHDFpYrLi42MzUZYmogELD1XQYy2OoLugi5CUEwGKSuro6tW7diGAY5OTlUVlYeln6Ly+uifHjX1lR/vo/xZ405wCcOH+88t4rc4hw8fjdCCOp3NbL8xXcoG61x6hAJj1tDUTxAhM74GVRX12DoUcpKJPIKhpGbV3V4QbRcgea5H0QcNfk4jtjXEfJgNNfXQS6x3/baP6Bq2CcYP7mJdEph+eIyioa4CbWG0TTTLT0eTlB1UgWLH3+Vt/65krSWJqfYx9Xfmc3IcSO6kujz4aq7Z7P0T2+QjKW4+f5NTDkvSGebgtOZ5oa7tvDw10aQiqmse1vF4YSiMtD0KG88vZLG6ma0tI7b5wKhE2gI8P4bbt5amIuugyyDqsYRcg5yRiYfIXhzYSnnX9nC8HEaw8fJILlJu29Ccb6MpunE43G0tEZheYIp58cxaVYSoGHIubiKP8dn7zuNVGQPirsSxTUaEX4ViXYkQJEF4GNEZRNCqyaR8CDEDuTovbTpD1BYUIBLbQIpByGPRNY3oonxdLbpuL0SPn+Mum0f48szW/wlRUKSJWo+0klGVMz0BMRjEg63zPa1NQjDwBAGDpeDSTPH45Rc5BWaAZDL7bLoJl1hMprRMyrQhmGQTKS7BCO6rrPi72uItEdBCNqlTp5/aDGxUGKf8rAhSCU0+hPDTx2CJASxSALVqTJ5xgRKhxcjKaZJ6ZFujrKfj2wBUTB1woLBILt27SKRSJCbm0thYSGqqhKPx/9j1HrhRBBzSFgZjZ6QbYjX2dlpa6AMHTp0QBb3I9Fuga7tztm6Mzt37rQDAMs2wCK4BgIBksmkLaZ3LDIXFkadNpxRv+qqfOnyuCgqKrIzRHV1dezcuRNN0/B6vVRUVHDqqacOeMvzkSDbTVzXdZsAraoqbW1ttLS0sGfPHjub1xcrhHQyTWNNC5IkUTGytE8y6F2Ol9bQdI1YMILb047TncTr9jDxtGmojEZN/gFEO4Y6HV/ODXyiuBo19iiaFkPTNHZtvoQE59icgF49L0LgiN+HrK0DBOi7kPXNpHxPm+7SgC/Px9Z1RdR/bIqVBRraQG2nauwgVNXkiCWTKV5+chnhQJTiQYV4fF5CrWE+XPgRJ40fw7Inl9NS18a4aaOZfPEEZt48nUh7hLptDUw+bzkev4SuS0Q6JJyqwfipMQaPVqmrdpFXJBAYyI48ZEVCGIa9Mquqyat4Y14eLrdBXqGBrAiqNwuMtKDqpAo6W0MosozBIGIJHzliCwDR9Llsr4W8oV7aA260pIHH4+G089K4vbkIZZitTiyJehBpUMfhzB+XuXcpwA+E991OXCjGLoRagc8hAfkYegCl/U3+cu8KPlqlkFsEn//eyZSVuHjoliiNu83MzmdulagYXsCW1XFcHifCEAhDoKd0m9BqBRqdbSGS8RRkZAEwYOfK3cTDcVSXA0mC1S+uw+Fxkop1K+fIoKe6ioMKo2ukI3RBKBDu8lqsM2Hef4tUmxG8OxLIqozQDbOM6HZw1bdmkZOfQ2N1EwXl+Uw4bxyKQz4i88rewuv14vV6qaqqwjAMQqEQzc3N3HTTTRiGgcvl4t133+X000//t+fHSIfICvzn9GkdBImEqbgoSVIXDQtN0+zyTH5+fq93lu+++y5nnXXWId93MKXcvtrU9wSrk6i+vt6+LpfLRVlZGYMGDeoXQ8SBQk9quSUlpjeRxXtJJBJHFAD0N6yAy/InsvgtJSUlB9xB6bpul5aySdO9sUKIheL87QfP0VwbAASDRpXz+e9fZWYHDjjIEIgoSCW25kf2vV63dBMfr9rF57+6ibJBbSCBr2Q6SsF9th7MvmNpOGNfRgjNJNyKFBJxgsaPaQnItLW1AewLaBQnW9/bQTqpMXLSULuVvr2pllLXdUgZgTwLac/DGOpUAHZvruN33/wb6YTZSVIyuIiKMaWsnLeacHsUCYmcIj9VoyoIB2OUVBWarc9pnWQ8iaEZ7PygBimz8Hzmvy5j9+Y61r6yASSJX7zwAcXlGh6/TDImEELwcfV5jDhZ5fH7dhNslvEXOGhpH0UqIpFb4Kd5dxuJcALFKVFR2UQ6kSC3UMPlBiTBipfyGT1lDL48L/4CP+mUxunnrWf6lbuQJB3DEEiyk7j8FWKJi/nHg4uIBKMYhsE5s51c+KmlgAukjJs2CinfU0AaRNgsBYk2nLE7EUI3lYold0aQLpH5TNJ0vZYdPPVzmWd/5bYVfnPydIqH+Ni9JYbTBYYuARJfe+xmVsz7kGBjB4ZhcOoFJzPvkcWmqXcWJFlCkrC/M8MwKCjJI5VMo2vmZszlc+L2ummsabazL6pDRksbR7QKWec1jCM7TlFlPgVl+UTao8iqzMhJw7j90Rvx5vZctrHma2vTmb3O9iaoMQyDdevW8YlPfOKwx7pixQoeeeQRRowYwZo1axg1ahT3338/p5122mEf65VXXuHOO+9E13W+/OUvc++99x72MfoJB1xcTwQxvYBVgsn27znYgnMoHCyI6SlwsX70/R3NW+WlQCBAKBSy+Rh+v99eMC2V1CPh0fQ3shV+hRD2+A6kpdO9a8rilhQVFR21cphhGHZQld1SXFRUdNg7pWwNl2yV45KSkh4NHpf++S1WL/6A4iozYxWob+O8a6Yx/dqzezo4SuoplNTzgIROKXWdX6WpRbeD9tLSUrxeL60f/YQ8/3JSKT95Jbk4HWE0140YzivMAEhEQSoEEcIZuw2RRcSVRIi0+z6Eeiqwz25ib10jz/3oJUJNERyqitvn4Y5f3cjHm+t4/S8v8oM/LMXh0FEdZOTpfaS8v0Co+4jKLXsCbHxnC9FElPxhfvZ8sJf5P15qLySSJDHr9hm8t3At7c0dCAFun4sxU0awY23G10mWSSVSpFOm+KM/z4esykybsYtrbqsjr0ggKwJQSXn/ilDGENizgT9/fxnBxhSKQ+WiG8/Bkavwwv8txdAEDtXB5OkVGOEX2LJaJbcozebVucTCHm7/9Y04PQ7ef/VDNJHm5m++QXFhLUhyZvYW6OpFaN6HSMaStNa14XA5KKnKw5W4EdnYgTnPS6Rd94BciZr8BaAhpDw01zdxxO5Aog0rShDkYygTUPTV1pePQTGfHVNIIqbY5R1JEnh8MrJq6swIIYhHDM6ZO5Vrv/sZOho7CUVC6M40P7vsd2ZnT5bFgRnESHaHl5Y2Jfkto1DrvXklOQQbO+zPOVxqRmAw6/fZg3XCgSApEkLPerOM5YZwUHjz3CTjaTsD5PI6eODFe9A1g13rPsaf52PqZaeRW3RoM0kLPZlXWnN7TxtSTdPYuHEjkycfPgF/zZo1PP/88zz++OMIIdixYwd5eXmUl+/vG3Uw6LrOmDFjWLZsGVVVVUydOpVnnnmG8ePHH/aY+gEHXHj+vfNM/Yji4uIj6rg5GI6Wdgt0lZ7v3t6dvfj5fD6qqqr249F01285GrBalC1TxcM1guzuhWQRk/fs2WMTk63sTX+ie5aoPzq3oGcNl0AgQE1NTRfvJsvgsa0+aLbzZj7r8rho2xvs+djaB0jJZ0mknWiaQFVqKXb/iYJxD+1HFhw8MoJklO5rcxYOJGMXcuol1NRfzJekEtLu+wA3iJj5XpEGjC6S8k6nk0GDBlH9Xh2JthTFg4pIpVKEgiEe+9afiQXj5JfmYugKisfiNuhAjLpdTrz57RiqTktLC52dnQyaVEJJyXiKior44S8ewelxoKoqAoGuGax9ZQPtLZ12Z0kinKBlTyDTMisjEKgulVg4jupUbcLt6tdG0FSr8o1H2ugM+tmycTZFQw0mnAfRSAnphBOHyyybqJKDcz55BhM/MYHa7fXEk1GUXInazcW0vfI6Nds1FEVh4ozheIepIAkuv3sGpaWl5BuvIwmDfSu2QBLmd+byuqg6aVDm+/oQScQR0jAzEyMMFO0lJBFF4AIpB0QYNfaTrAAmczw6SIb3MO/xMVRvNigbDJ++TUcYEpIkkGVzPtA1MAwZdAmH6s4svglUj8rbS9+hetUeiiryufiG85l00Sl88MrGfQGMIpFfkktHc4h0MmPCqZrkXkPflx1RVJmO5pDd5WO2TvcQcRzGttoOYKzA5wABjL/ARywcRwiBw6Vy7g1T8RV5qH63Dq/Pw+xbL2Xs6SYR/GDaSgfDocwrNc28N4qi2NyfI3Gwtp5VSZI46aS+jdnK4owYYXpNzZ07l0WLFh2rIOaAOBHE9AKWiFl/ZSGya6YDrd3SXS7f6XQelg5Kdx6NZRtQXV2Nx+OxibT9bRfQk1qu1d11JMGTJEnk5uaSm5vLyJEj7Rbx7du329yfkpKSXplV9oSe+C3DB9hTyQoABg0a1IXrtGvXLtxuN75yN9E1UVsnJhFN2KqssO830tLSgtNYzrCSBLLsw+d1IEluoJFUD78VQx6Gou8CMn8TacCNmnoSgT8jONeKI/koac+3ccQfBtEJgOa8GeT9d4aR9ohtKOpwOHDIDpLxJJIs43B3AgappIKiCGTFQTQsWPTLX7JlbT7nXXcml9wwnTFjTiLcFsbpdCLLMlo6jazIdiCXiCaItEdRHQpur9vkrcgSoYxGSVtTu2msLJvlqGQ8SSwUx+11EY3Eqdk+lAV/v5ZX/vAmurYeSd7AeVefSbCpA4fLQV5JLlpa542nVjBmygjKhpVwyrSxmXsUYtLo1znztBDbNvppiU5l0NjhFJcUU1FRse+ZjHjoXpcR0v4K1ZIIZGomlt+QgWSYhFykTIAv5SDxMftHAIJXn0nz3isKvjyVLWugvlojp1Am0AC6lbmSZU4+5xQ+WLYR0WnKGuQU+8gryOPpbywGw7SpXP3sRqbfcgZbVm4jFUsjyRKjJw8nGorR3tyZfVr0tG4GkLLJnekesAghuplf9g1W8CkMo+vtzAQ2qkvlpp/O5dUnl6NrBmdePoVP3zkTJAh+IUhbWxsdHe18+KHp81RUVITX6z3iZzk7qOmefQeIRCJ9Dmayg5gjQUNDA4MH7xMTraqqYvXq1Qf5xLHBiSCmF5AkiUOU3Q6J7FSiqqps2bKlS0ajPwOXdDptly7C4bBNOB6e8frpK7L1TrKNG7Pdna1SQ1/QU5aoN2q5RwK3201VVRVVVVV2F1b3dueDlXyyu9Isfktpael++i1HCz1p7OT6c9mzo56adR/jdDiZPONUJl10sh1sWaT00tJSSnLPwJlabvJgJAlEDCEP7/FcuuvzyMYuJGM3AIZ6KkIeBbyVxYvJQzJqEPJ4Ur7HkIwWhFQA8r52a103kDMlh1GTR/Dqk8tJxVMoDoVIe5Spl01iw5tbkJGRFYlUUjZ13jFJlqozl9KqEtYt2szpF0zhb4/PY291Ewg4f840Lr7hPJ64628Z8z5zwRx7xmg2vLmZcCqCnOleKSjPw+Vx0VIXyKjNyoyZOoJLb5rOb772ZzpaQhRVFnLTg5/jf2/6LapLxZvnJJ1M8/az71E5Kp/BY4sBN6pDQZJlQm1hyoaVmCrHLY2UuX6E172XQVUehgxtAUWmkysJtHWwdetWUqkUhYWFjC1N0X1KED1M10LOuC2LdCZoDCGUkUhGIwjN/B5FAvCAAMNK7khmZ9SuzbkUlGpIkoLLrdMZdDJ66ilEwztIJ9Kmhk5FLqFwJ6pDQZcMZEnCl+Nl/v+83KVcE9gT5M3frsbpciFLCoYQNO1pJdoR6zpoCYwM6dUm2x5GmajXkDCdqSUJhKWgu88nDmDU5OFcdP15XPj5c+3yjoXsbqB4PE4gEGDnzp3E43Hy8/MpKiqyO4KOBNY50+k0zc3NNDc3I8uyPScdLkE4kUj0y9zT05p3PNAJuuNEEDNAyDZU7K7dMmnSJDs7Ul1djd/vp7S0tE/8CAuWb08gEEDX9QEPACRJwu/34/f7GT58uK2LczgZjZ4CgP5Qy+0runsHWWPbvXu3PTbL3LEnfktPkvn9CcMw0DUDh7P35/D5fIwYNYKv/ewWgi1B6hvq6Qh38M7Kd/B6vZSVlTFmzJh94xZF6OJClPRbmCQCP5r7rgMc3YOgAknsMD8qDULI5YAwswiSDEQQUnlG08WPUPbxCNLJNP94cBHrlm1EUWU+desMLph7FnO/82le+PUrxDpjnDF7Cp+6/UKKR+bzwqOv8upzVVz8mXrcPplEVKd2ZzkNu4tIJGNEozGe+ulzdDZHKKksNH2inl7JF38ylxt/MpeXnngNwxB88ovnUz6ilI3Lt2CkdXQhgSQorirC4XIw5OQqtJSO0+0gUB9kzNSR/Hrtg/a4azbtQdd1PC5zoXA4FZKRCG5XkHBzNXnFfuLpSWhamlCyk1WrVuF2u6ksS1HgbwdKsAknxm583na8Q4YyZMgQm78l9A6ElEUEkEAWnXTt0wGhjEFzfgk19ScQAqFUknbfz4p/PsPv7llLMqZROljw/X/OIdfxC/x5aeTMqUMdKp3t5RjIKFIUAxcGfqZ99kz27GihdXcbnlw3c++/kj/e/Yxd/jEQBJs6uvJNMgjUdy1TpuIpM1DJequREfQbaMqlv8BHOp6xCkAwcfrJlA4p4Y2nVyAMQcWIMu596g7g4F2oYGo7DR48mMGDB3dR4N69ezeyLNtZmsP1SUskErS0tNiBS1lZGZMmTbIz290Jwlb5CQ4c1PSX2F1VVRV1dXX2/+vr6xk0aNARH7e/cYLY2wukUik7CDkY+mKo2L1TxSr3lJSUHJTvkS3NHwwGcblc9iJ7rDUCrIxGa2sroVCoC48G6FEt90gCuKOBcDjM7t27CQaDaJqG3++3SzhHgxv0wbKNvPTbZaSTGidNHcFn756Nx3/o79maJLPLW1a2zArSgsFglyDN43YjGXuAmLnTl3rOrCnJZ1BSz4JUgDlVdKA5voxEI0p6KWYQ5CLt+X6PAmoLf7mEN59ZaXJdNINQMMotP7uO8WefRDQapaWlpYsVRJ4/j3Q8TUHBZlR2sODXG1j7VgXe3BxSyTTRjiiSKoFsmvQ5HCrR9gSfuuViZlx/fpdzv/rkcv783X+QiCRsLsTQk6vMlv1BBUiyRCKaRE/rfPfZb3R59mPhOHeecT/pZBqn10k63oFEgu/8PslLf1HpDOqktAJOn3MFZ1w6heLiYpPrYNTjiN4G+PcFMURI+x5HyFVdxucMfRKJFvM/mVk4EJ5AU/z7trpzl/lEJEDEQcrj4y313HPhjwCBokqkkwY5hW6+9tONDBmdxNBBVqB2m5v1629ixbx1JGMpFFVm+OlVOFSrqrhjAAAgAElEQVQnDR8143CqZoaqLJ/NK7b1q6lgf7Q8HwyyIvPAi/fw7MOL6AyEGXv6SG566FrcXjexSIJULEVu8ZFruYApLhcMmqWncDhMTk6OHdT0VGZPJpN2xkWSJLtc35uSfE8EYaBLx+pf/vIXJEniq1/96hFdl6ZpjBkzhtdff53KykqmTp3K008/zcknn3xEx+0jThB7BwrZ0XFfSLnZHI1Ro0Z1cUIWQtgBjc/ns4migUCgi0nikfJE+hvdMxoWiXbTpk1IkkReXh5VVVWMGzfuuExPWrC+i9bWVgzDsEty2ZmY+vr6fjd37I49H9Wz8NEl5JXkoDpVtq+p5sXfLuPqb87e773djTet76Kn8pYltQ/70uVWScPMpFWQl+c54Owh6RtBcmd2uhIIFdn4iFD6q6xfVkAi3EbF6CmMO7sHBVhg63s78WU6gWSnjBAG776yhrDagcfjobS0dH/frQKA8zE4nymX72X9u0/T2RoCCa655wo+fG0TNRtqyS/NJZVME9KiBCMB1q5da7dw+3w+arfUkbB0SjD9kIKNnXzyi+ez6e2tpqmlJHHtdz+932/Um+Phyz//PH/+zjMkI0kcLo1r7wwx7GSd2x9SSEY1vAUjUPJORkn9HinZga6ei6F+CkM9DVlbC0IBDAx1CkLqaXebZalh6e7lOog782hqamLbtm14vV77mlwut/ldAOuWbUIYAqfbXBQdboOOliiPfmsw197ZTNXIJHU73Tzzy1L85VsJtYbNLiRZgpjMnpq9RDqiGdIzhILhfndF7u8AZsj4Shp2NGEYOrKq8Omvz2TCuWOZcO7Y/d7r9bvx9mID0Fu4XK4uvmLhcJi2tjY2btyIYRgUFhaSk5NDMpmkpcUMTMvKyvqkX3UggnA2z/Ljjz+moqLiiK9LVVV+/etfc8kll6DrOjfddNOxCmAOihOZmF4gnU6jaZrtCn00tFvAjNgbGhpoamoikUjYhmGVlZXHTbtzT+jJwdra5QP2DhvoEqQda3Qvb1lZsYNlt4QQdkasra1tQDJiqxZ/wEu/fY3iwWYmK53S0BJp7n36613G0NLSQjAYtDVkei0i1w2WFUJ3hebuLelK4pdm2UkuyGQV2kmKK/jplztpqmk2d9tC8Jn/+pTdzp1OplGdKpIk8djXn2Tr6u0ICXRdQ08bXHbrRcy++ZL9gsG9u5p49n9eoG1vOyNOHcrV35ptlguSaTpaQ/jyvHhzPLTtDfLE3X8j0hZBNwSTpp/MNfdegabt2wDEYjFe/817fLhkiz3DyYqEP9/PH7f/L9Uf7iYaijNoZBmlQ4r3uz9WyWfXR9U01bYwYdxGxo55G1nOxYw4wuiO2abXkEgACqChO69Fd85FSb+ApO9CKKPQHZfv4w/ZJThwhc7E1HDZB0M6mXTO3+3vPBqNEggE+OD1Dax8+gPQYerMSRSWFPD7bz2Fw+1AlmQ0XScZS5haeN2gOBSTZJup7iiOfd0xEvuE4rINEYGB4bD0EapT4YktP+Ojd3YSaAgycuJQJk4/9ottKpWisbGRvXv3kkql7BK8nfHsx5J5dXU1zz//PC+99BJOp5NvfOMbXHPNNf12/OMAJ3RijgSRiNm1YNUmYV8Ntb+1W7K7S7J1aQoKCuzW4HA4bIu3Hcoz6Ggh2+Syvb29Vwtpdz+jI+0M6gsOZAbZ1/JW9+yNpatyJN1Jm97eyj8fXkRxlSnxHmqLUFSRz1X3f6pLya60tNRuq+4vdPfKsuw0SkpK8HmSOOLfQRJm666QK1m35jp+f+8i8orNbpl0SiOdSHPPU3fwx3ueZm91M26/k4tuO5tQeyfz/nspRkZXJL80j0fe/uF++huRjij/c/2vSSU0vDluQm1hhp48mDt+c1OP9zQZT9H8cQtOt0JF1WZk2jDksQh1EmD+Vv/584U89+CLZmdS5hgjTh3CI2//sMf7YKlzt7S0EI1G7e81Ly8PiRRq/IfI+nrz+OoUDOU01OTvTaE5yBBvZVL+5/Y/uBHAkXgISd8OUj5p9104Ynch0c6+uVugKxeg+X7R5aO1W+r5+RcfIxlPoQsDXdOZOPMkNr+6nba6DgTm7r3y5HLqNu499BeOGdCZ5N9MpJLReEn3s2x/XzF+2hj21jSRiKVweR1MuWgidzz+JRTl2M+DqVTK5rgYhkFpaSllZWW43WZreiwWo62tjba2NpLJZBeC8OE+t7W1tcyfP5/FixdTWFjInDlzuPLKK8nL69lg8l8cJ8pJR4Jnn32WRx99lAsvvJDZs2czderUfl0oLJ2PQCDQRedj1KhRXc5jETGtQKelpYUdO3b0CzH4SMbd3eSytz4/LpeLyspKKisr9+sMsjqqBqJEkz3ueDxOQUFBv1kqeL1ehg4dytChQ+3y38cff0w0GrW9qg430Bh/1hhOmjqSbWt2oesaBgZT5oyjs7OzR42f/sSBWtJ37txpBp6Ft1FR3IE/JxfU8SQTW01n3wwURSaaTPHobb+jrbEdl99JMpni1V+9zYgJQ6kYVoriVJGAVCLN+jc2c97VZ3YZQ8POJpKxFP4CH1paJ68kl9qP6omHEz0qpro8ToaMq8ARvxcpuQ7QUFHRXLejOz+LLMuMmjAct89FMprMLPQSOYN8rF69msLCQoqLi/F6vbS1tdHc3EwqlTpIu7wLzfMTEK2YbU3FyOmX6TrvCkAGI4ia/BOS2IMhj0d3fQFH4sdIeg2mK3cCR+LHGMokFH1l5nMCcGKoF/PRuzt4/akVSLLERdedw44PamwlZqvr6ONVDSRjaXsHamCQjPbendnhdpg2AhanT5XR0t3SOAOQifHkuomH9mWf3LkuJl10CqsXrTPLTxJM/tQEzpw7kff+8SHx9iTDTh7MnG9fcUwDmHQ6bQcumqZRWlrK+PHj98u0SJJky3VYJO6Ojg7a2tqoqalBVVXb0f5Akh4NDQ3Mnz+fRYsW4fP5uOaaa3jppZcoKio6Wpd73OFEJqaXiEQiLF26lAULFrB+/XrOOussZs+ezbnnnnvYGinZ7cnZiqvFxcWHvWPvKzG4r+iulnswpdi+oru2TbZKcl/1aLIzJAM17oMhO8MWDAa7mFUe7JqstvOmxiaaawL43D7Gf2Is5YPLBnzMh4JlhRAIBOzsmxMXf/j6P0gn06BAtCPK8E8MpmFzM4Xl+4K3SDCCy2ua/lmO2h0tnZx39TSuvHNml/PUbqnnoet+aeu4yLJEYUUBP1/+3zhcPWf5JG0dzvi3QVit4jpIOkn/UpBUlv1lOW88/Q4OlwNd03G6nfjyvNz+my+we/duu83f8t8aNGiQ/T3J6ZVI+iqEVI7hmANyDxsHI4gzdntGF8fkv+jOLyJry8z2Z5xAEqGcgqRtMUty1u/QCKO5b0FJPYtkNJsvqWeyacPV/OaOv6KoSqZd2qBiVBkfLN2A0+XAEAItpSErsnn/M+GkAJBBaL2bzgvK8mwhQCTILcohGophaAbCyJTPFTB6ebyev6CsfwuzilYyvJi84hz0pIHH70FRZO7601doqWtj5wc1jJ40nJPOMPlV1kYkGAz2ikzb30in07S2ttLc3Ew6nbYzLkdSIkokEnaWJhqN8vLLLzN06FCmTZvGO++8w4IFC1AUhauvvpqrrrqK0tLSQx/03wcnykn9iVQqxVtvvcX8+fN55513mDBhArNnz2bGjBkH5HZ0L7f0dhE7XHRfrI+Uc3IgtVyr1fhoINuvCnrHo+keCFlclYEK7g4HBwpiLdXg7M6c3vgqHQ+wCNz19fV8vKWWD+ZvIR3VmDT9FK68fSbfn/0zXF4nDpcDQzcIByNMOHcca5eux9DNllvVofKlhz/HaRdN6HLs1vo2vj39ARKRpMnP0A0qRpTyv+/88ICZMzn9Nmrix0go1gBBSpL0LwbJz+YV23jqgXkUVxZgCIPmPa1UnlLGeV843dTLydzv7vYOJ1UtpcS3AGtqFNJwUr7n2E/UBcBoNgMR0YGhnoOQSnHEvwuSb9+YCAMySB6QnJnXQqQ9P0bIYzIaPC6EPITf3vUUH727HX+++flQMIziVKjdVNdFyM2X7yXaEbM1UoyMOWNv4XQ7SCXS9v8dLkcmKOqGI8jG5JXnEglG7aCoaFA+n71rFu8uXGt6RBqCGV84f7+sXE+wuGxWAADY2bT+5A5qmmZuKJqaSKfTlJSUUFZWNiDecq2trTzxxBOsWLHCNui98sorufrqq5k6depx3ck5QDgRxAwUdF1nzZo1zJ8/n2XLljF48GBmzZrFZZddRiKRYN68eUydaprTHW0eSyqVorW1lZaWFpLJpL1QHurBTqfTtqfSsbIaOBCsa7J4NIWFhbaxY7Y/Ubar+EB1DfUXEokEdXV1NDc3k0wmcbvdlJWVMXjw4GMecB0M1uJhZQHdbjelpaU2gdtqs49Go+zdFOCtP75nSv8LwflzpjFkfCW/uPn3JKJJQFBYns+PXr6X0sFdybRbV+3kz9/9B7IkkU6lcfvcpBJp/nvB3faCvh+MVlzR6zHdmx0IKYWQR5H2/R4wW+bnP/oSa5dsMIXFThrEl376OQpLC3o+HpBKxMhJnpvp1DHLGxKQdN4LnjmHvF+Svg1H/B4Qvkx2yAAiaK7bUFO/s24qhnoOmvtum+Rr4fffeooNb27B6XeQTqWJhxO4PS7qPmrMps6QV5JLqM3sKDLJuRw04JAy9gJC7CtJ2e+1rI16+Kwv30s8nLC7Mj0+N7FwfL/3+Qv8JGKJjNkj+Aq8VI2tAF3C6XYgdIHT6+TuP95GpCNK29528ktyqRjZt2yjNX+1tbURCoXw+/12luZwnycrcLGeTStwGYhGhPb2dhYvXsyCBQsIh8N85jOf4ZprrmHIkCG0t7fz2muvsXTpUkaOHMl9993X7+c/znEiiDkaMAyDBQsW8Nhjj7F27VpKS0uZOHEid999NxMmTDimBNzuMv75+fk2EVSW5f3k8i2S7eGKNx1N6LpOc3MzDQ0NhMNhJEmioKCAqqoqioqKjttxwz4tnZaWFvv7sIIxq9uptwTpo4lsPlZ7e7tNhLa0UA72mW0f7mT31j0UlOVxypnjeOb+FwjubTfLSrJER3Mnl9x0ATO/fFGXzzfXtvLIjY/jL/ChOlTi4TiSLPPfC795UC6EpG/GEX8QSbRiKCcT1L5Bc4vJU7ICRbfqAQNyi3MPzaswOnBFLtj3/8zsuCdwKfUdV3dp4e4RQjN5OtpWdENCUQSGOh3N800kfTeSUY2QChDKJJBkYuE4e3c1Iakg+2Hjyo948X9eR5EVFFVBURR8eV62r9llHl6YpTZPjhvDELZSriRL5Jfl0t7Yud+QJBkkSbYjFUOIXpkkAhRW5FM6pJhUMo3DqZJOptm9uQ5DF7bKuSTBmHNGEGqxbCAUho0fzNzvXMlzP1tMY00zheX5zL3vStsPqj+RbZba1tZmtzxbWZqe5mRd1+3AJR6P24GL3997w8feIhQK8dJLLzF//nwCgQBXXnklc+bMsf2KTsDGiSBmoBEKhTj33HMZP348s2bN4pJLLiESibBgwQIWLVpEIpFg5syZzJ49m5NOOumYLrBWaauhocFUBxUCt9tNRUUF5eXlx3XZAnrm5RQXF9uTz/FWPrLQPYtkdbgcKDPWU6t6NpfnaKGngCs7AD5cWK3Bv73jb4QDEfz5PpwOJ6FAhBk3nMenbp2x32fefu49Xnx8GZIsozoUvvTQ5xgxcehBz2OVFJubmwkGgzYB/mAB18EPqOEKT6X7tJhy3UdcXGGXnRKJRBcTzux7tG7ZGmre+1+KyyO0NJZy1twf4sn186d7n2H3ljpKqgq58SfXkjbS/OrWPxANxRCG4LRPTuDLD17H3h3NvP3cKiRJ4ryrz2Txb5ay/Nn3zEAQkxydU2gutu0tnaZejEulfEQZtVvq97ukEROHUretIWO8CJWjytm9uW6/9/WEy267mN0b9gDmnHLx9eex7o1NrFr4AUIYgETl2HJ+vORe1iz6kN2b6ygdWsyMG87HX2D+fnXdOKqkXMuSpa2tjc7OTnw+H0VFRXb3Z3NzM7FYbEADl2g0ypIlS5g3bx719fVcccUVzJkzp89Gjf8hOBHEHA1omnbAWmVrayuLFi1i4cKFNDQ0cPHFFzN79mwmT5581DI03ZV0c3Nz7RSrlX4daGJwX9ATv8VajA40xqNBQO4N+pOjZFk7WAulVUrbT721H2B1VrW0tBCPxwek/X3V4g/4x08XIDsVkokkuq5z7Y8uZ9yUk3r0pGlv7iAUiFBcVWibWXaHFaC3tLTQ0dFhe0IVFRUd+T0ScZzhTyGRLa2fS9r7IIZ6tv2KRXhubGhi4/ItyCices548vLy+d4lDxMKRjLGh4LKMRUUlOXRXBvA43cRCUVBhrziHMKBKHlFeUgCwu0Rbn7keiZ10z/Zu6uJ+z75E6KdMdNHSlU4/VOnseL51V0E6lxeF4lIV90ZJPj1Bz/l5194nODeDoaeUkX58BKWPfn2fpcuqzJGlkmj4pC5/+U7KSosor2xE01OI/kMNE1j93t7aa1pp2pUBZfdOgPnYdhkHE1omsbevXtpaGggHo/bjvYVFRXk5+f36zMVj8d59dVXmTdvHrt27WLWrFnMnTuXU045pd/O8W+OE0HM8YRwOMySJUtYuHAhmzZt4uyzz+byyy/n7LPP7veSgdUS29ra2itPo/4mBvcVln5LS0uLHXD1ld/SvaV6IBf/o9Ut1pO+TU+CdIdC7ZZ61r22CafbweRLJqApZrZI0zTbomCgAj8hBGuXbmDVCx/g8jqZ8cXzKajKtVP/DofDDj4P1vVh3Yvm5mZbQ6m0tJTCnHocyZ8hiTYMZRKa5172uT33Dc7wLCSxl33EEQdJ33xQKru8LxFN8vD1v6Kxutkkr6oy4y8cwZt/XG2bXZpd0YK80hzcuS7bvTud0NBSOrmFGVl8CToDYa7+1mwuvO6c/ca07rVNPP2jeaSSGjNvns6m5dtYOX9NF56MrMimM3QWwdfpdlA4KJ+W2kBGFkaiYmQpDdub9juHw6WSTmn28Tw5br658GaiiSiappGTk0NlZSVlZWXHNenU2sg1NzcTiUQoLi6mvLwcv9/fpduuo6MDr9drc2n60nWUSqV4/fXXmTdvHps3b2bmzJnMnTuXSZMmHdel7uMUJ4KY4xWpVIo33niDBQsWsHLlSiZNmsTs2bO56KKL+sR6t0oQ1iJq7S6szpfDHVtfiMF9RU+kXVtQrJ/O19/idrB/59nR1u3pLkinKIodOB1s8t3+fjWPfuV3JOMpNF3Hm+Pitt9+gdGnjDwmBpzZSKc0IqEIkbh5XZYVguUbZBjGgcXnJAmMZlzRLyBEGnCAlELIE0j7Hu37oEQcZ/hyJBJAElAR5KB5f0zSOI3GXU043A7Kh5fy+lMr+OdDi8gpNCUTop0xvDluajbu6ZIhkWSJgoo8cgv8CGGKyiVjKUqHFPHRuztIpzTTqqM4h2//7Q7GnTm6y5DqdzTyf1/+LemkZotv5hT72fjmR13eJ6tmGU5LaTZ3xulxEg93y84cAE6vA6fbiaHpGAj0tM7X/3wTp188BafTaRsiBoNBW1nc0to51gu2YRh24BIOhykuLqasrOygfD9LmM4KqC13cav0dKDNTzqdtjtX161bx4wZM5g7dy5Tp0495vfhXxwnxO6OVzidTi699FIuvfRSdF3nvffeY8GCBTz44IMMGzaMWbNmMXPmTAoKDtw1kd2V09HRgd/vp7i4mCFDhhxRZsfpdNpidBYxuK6url94ERZ6ap8ePXr0gGV+shf47k7Vh5M16U6UPlyhv/5ET4J0ra2tbN26lXQ63SXzBNiZoj//4GmSqRQ5xX4cqoNwW5jqd/dw6tRjl+IWQrDsyeUs++vbCMNg3Jkjue57s3F4cmhpaaG6uppQKIQsyxQUFDBkyBDy8/P3WyBkfTNg2H5CCDeysdHsVpKOQNJAkhGU7uscEjGCzTHuv+J7tNa1gSQx6cKTGTFxKBL7nJEVVSbcGcl8Zp//oyzLnHzBGN79x1o7G3Lq9HHEOxOk4mkz4JEg1BbB6XbQXNvK1vd24HA7mDT9FFY8v4p0UiOvxMwwRdqj1Kyv3W/YhmYgux0YGUdpgwP7F5UMKSBQ32FmkBSJ/NJcoh0xDMOwhfDSQmL0+FE2f66goMCeoyy9k127dhGLxcjPz6e4uPiodjdac2JzczOhUIiioiIGDx7c6w1YtjDd0KFD7SxNa2srO3fuxOVysWHDBs444wzGjBnDihUrmD9/PqtXr+aCCy7glltuYdq0aceFmvq/O05kYo5TGIbB5s2bmT9/PkuWLCEnJ4dZs2Yxe/ZsysvLaWxsZNOmTZSXlxOLxbq0Gg/0g9O9Q+VwMg8DIWTXX+jJ8DGbR2NxUrIzA6Wlpcd1B5cVbDU0NBAKhRBC4Pf7qays5A9fe4ZAQztunxmwhdrCXDDnLObed+UxG+/mFdv403eeoaA0F0XaQ7CxibNmRrjkphK27b2JwqLBlJSUYBhGj1YI1s5f1lbhiH8PIRyZiEFDkoyM2F3fnw81/j/I2hukkgoOpw5KBd++aiQfvVuNosqZIEFw0Q3n8eHrm1AdMgaCVCzN6E+MYOfqGsLtUcDMhvgL/fhyPRh6xkjWMM38gg0dyIqCJJmBjpbWOeezp/Pha5uJdsaQJCgfXsppM05h/WtbkNV919S2N0i4Ldp14BL2b1TOeCPJioTeg2Dd0FMq8RZ4ScQTSICeFhSVFfDRyh3IslmWOv1Tp/Fff/jKIX/31lxhZWmcTqdd9uzvjYqVEW1qaiIUClFYWEhZWdmA2JhEIhF+9KMfsXz5curq6qiqquL666/n9ttvHxAy8AmcKCf9S0MIQU1NDU888QTPP/88iUSC3NxcrrjiCu66664j8uXpj7EdigPSE6H4eNdvsXg0jY2NRCIRJEnC4XBQXl4+YAJX/QkrhW5xiiytH1VV7QVly2s7effpdXhzPAgBelrnv35/C6OnHFl7pxCCxupmIh1RKkaU2d0yvcGLv13Gm0+/Q05BDJfyMYm4hNcv+N4fmzEcl6B57t/vM5YHl2XuaLbQFlDmvA9FrMfMOzhIO+/BcH/2iK6tqaaRX936ME27O/Hnu7j10a/wwFVPkIqlkFUZIUBLaQyZWMGUyyby9l9XY+iC02dOYtbtn+Sha39pT6p6WiOvNI9oZwx/vteW+Y+F4uzd1YyW1pCQEJiv+/K9JCJJXB4nQghSiRRTZ57Gh69twtAznm6yxJgpw9m0YnuXcasuNePzJiEMgaxIpBJpDH3/Kf7cL0xlz7q9qA4Hsixz5ddncvplk3j1z8up/aiekZOGcvEN56E6Dj+RH4/HbQ2qRCJhZ2n66vllBS7Nzc10dnZSUFBAWVlZj9m5I4UQgvfff5958+bx1ltvMXXqVK699lqmTZvGe++9x5IlS1i+fDk333wzt956a7+e+wROBDH/0nj++ef5yU9+wvDhw23vpnfffZeFCxfS1NTEjBkzuPzyy5k4ceIxT19a2QxLjtuaSKzA5miaO/YF2SWmQCCAy+WiuLgYVVVpb2/vNx7NQKC7SaHFKTrQhB6NRnnx96+yatE6FFXmohvP4exZZxxRZkkIwYJfvMzbz61CVmRUh8qt/3fDIduhLR7XOwtW8+7fP2DQkAhOpZWOgIMxp6W57YE2hFxEyj/voMexFrX2tm2MK70TWcqQUSUJQ55I2v9kn67LOva9F/+Y9qZOPDmm4J4kQbQzZloiZAnDnf2Z05EE7Fr/MZIsU1CWx7f+cjuLfvkKix9fBkLg9Dj51l++yp/ue5rW+iB6Wkd1KOSX5RILxelsDdvnlmQJp8dBOqlBJthBkigdXIQAUvEUCNP3qGJkGTs/qCbaGbdfKxtWQkttAKfbgSSbAYwv1024PdrF2HHQqDIe+/BhdrxfTUdLiPLhpQw9uarP9+xQ9zPbtsJ61g7l8CyEsAOXjo4O8vPzKS8vH7DAZf369cybN4/XXnuNiRMnMnfuXGbMmHHAzPHBulRPoM84EcT8K6O1tZWcnJwe9Vs6Ozt5+eWXWbBgAdu2bePcc89l9uzZnHXWWUf9QcqWy5ckyS5thUKhXumiHCt0b8s9WJCSHeRYHTTH0hbAcs1taWkhnU7bHUWHm53rL5Xm6vW7+dVtfySnyI+smIJtHp+LBxbf0+V92Rm8bPG5vJw8/vLdZ/l4/TpkmvHlCu78WSclg2IYygTSvsd6NQ4l8UvU1J8xTYNAYCABW9ueoaSksk/Zy/bmDu658Ee4c9zomo6maSTjSUZOHsbaFzfas6WsyFz1zVm8/ex7+AvMUmSkI8qoycOpWV+Ly2eKvsXDcYqrimjZE6CxptlWyy0ZXES0I0Y8krBJwKqq4Mn1EM74R1koHlaAJMDQTb6X2+uiuKqQtsYOCkrzTBHBlk5OPmcsjdVNbFm53fSK8jj4zPcuQcXFC48sI51KU1xZyD1P3UH5sGPjyWM5PAcCAZLJZBetHUmS6OjooLm5mfb2dvLz8ykrK7P/1p8QQrBlyxbmzZvH0qVLGTt2LHPmzGHmzJnHvYbWvzFOBDH/CUgkErz++ussWLCAVatWMWXKFGbNmsWFF144IN0mQogupobZsvPddyn9LZh2pLCyFq2trUQiEbst93A5RVYHg6V0bHEzBrLEF4/H7cAFzCxXaWlpv5W4jsQva+3SDfz9gXnkFucAmd9Icyf/t/IBZEXulficruns3rQLQg8ydHQdvhwQOEl7H0Mow3t1DUriF6ipv9KlzxjB7uhCWls7bVfx3hJONU2jsaGR7834GbIq4XQ5TaPFuEbRoEKaa1tIxdPIsozikBk+YQgNOxozpo4CxaHg8rhJJ1L48r32vWlraKezLYwiy0iZUo9hGAjDPOc+5VsJt9dFvJvWy6jJw6jdUm9+VggMQ6Z3CZoAACAASURBVHDpbecj6TJb3t6BrMgUDcrnins+STgRomFLM26Hh4lnn8Lg0WZbeDKeIh5OkFPkP6Zu0Nmwugj37t1Le3s7hmGQk5NDVVUVZWVlAzJnbN++nXnz5rFkyRKGDRvGnDlzmDVr1nFfOv4PwYkg5j8NmqaxcuVKFixYwBtvvMGoUaPsTierS6Uv6M5v6cuOvSdisLVIDmT2yCLmZmct+tNawRKIs/yC+otsnd02b5lCWiaFR0OQMFs40DCMgwZqDTub+PkXH8OT48HhUgkFwhRW5nPVD2cevvicSCPr74NIYSinglzY+0EbdbginwH2lUqEPIaU/5/mn7sRTh0OJ07DSVFxERXDypEkyXYqbmlpIZFIUFJSQs17dTz30Isgmd090689m/VvbCYejtuO2qG2CCNPG8bapRsyM6jZETT5konUb9tr15wE4HA62LurCdWp2AFLOmmOWejGvglYAmfm+NY919IaxVWFaEmNaMj0LHL7XZxxxWmced0kGqr3oqV0igcXMnjIYCoqKo45ef5Q6K6ynJuba9pDuN0Eg0ECgYDd7lxcXHzEz1ZNTQ3z5s3jxRdfpKKigjlz5nD55ZeTk5PTj1d1Av2AE0HMfzIMw2DDhg3Mnz+fV155hYKCAmbNmsWsWbMoKys75AKe3ZXTG8G8w0G2xslALNDdS1xW1mKgdVB6ans/HO0YazJvaWkhGAx2cT0/lh5K3QO1nkxNVy5cwz8fXkQqmcJb4Obq783ipFNHH5XOuS7QNuGM3w+iE6FMIO35Gcj7lwMS0SSP3PQ4O9fVoGs6lRPKufQb5+F0OygrK6O8vLxLJ83KBWvYtnoXVScNYsYXzuO5ny3mtb++jcefKTOldCZdOJ43n3mXZCwFmMTaabOnUL99L7UfNQBmd9Lnf3AVC3+5hGhn3G679ua4IUPwNd23JRxOlYqRpdRuaUB1KHbWZewZo4iH4/jyfRi6TntLB1UTK7jk9vMoLS3F7XbT0dFBMBhEURRba+doK1cfDFaJtrm5mba2NnJycigvL6ewsPCA3kZWQGOJ0lkdT70p9+zZs4f58+fzwgsvkJ+fz5w5c/j0pz9Nfn7+QFzegKCuro4bbriBpqYmZFnmlltu4c477yQYDDJnzhx2797NsGHDePbZZw8qz/EvhBNBzAmYEEKwa9cuFixYwOLFixFCcNlllzF79mxGjBiBlGm/bG1ttUslkiQdNc+eI1UM7qmF+0AlrqOF7h1cB+LRWIFPS0uL7cJdWlp6XLiH94TuIn/WgqPrOnn+PHK8uQwaNghVPcTYhUBOv4qsr0ZIZeiua49YWfdw8LcfPstrf12B4jLHryV0LrrpHKZceQotDa04ZRfDxw2jrLyUV/+0nIW/XGLzV86fM41r7rmCZx9exOqXPsTlcXL1t2az7K/LWfPih13OUzq0mFAgbCvfSkDZsBIu+dJ0/v7APJPY61S5/gdXUb2+lk1vb7VLTL58L3f98VYe+/qTNOxoRJIlpl97NhOmj+VP9z6DkMypWpEVvvqrGxl/5v4+PJZ+i9XFlc05Odq/L+uZaG5uJhAIkJOTQ1lZ2WHbQ2SL0gUCATRNo7CwEJ/P1+WZ37t3L/Pnz2fRokV4PB6uueYaPvvZz1JUVDRQlzigaGxspLGxkcmTJxMOh5kyZQoLFy7kySefpLCwkHvvvZeHHnqI9vZ2Hn744WM93P7AiSCmLxg2bBg5OTkoioKqqqxdu7bL34UQ3Hnnnbz88st4vV6efPJJJk+efIxGe/gQQtDU1MTChQtZsGABtbW1DBo0iLq6OmbMmMHdd999TBf/3hom9rT4H88t3NmO4Zqm4fF40DTNvsajpfdzpLDKLc3NzSSTSTszFw6bXTVW5uhQu34l8QRq+mkQGiAj5ApSvr+ANHDZsmgkSlvQ5Gj9477FBGo78PrdSJJMIprglHPHMnLSMF74zVIAPHluZnztbOb99yt4/G6cLvOZSESS/HDRt5BkiR1ra/DmuJk4/WS+PO4uOlpCXc4pKybvxeFSAQlD1xGGYNTk4XS0dCIrCnpaZ9Docr7xxM08/ZMF7Hi/mrySXK7/wVWMnDQMXTfY+3EjneEOYukoqqrSuTvKpmXbUFSVi68/l5NOH3XI6z9QZ9BAEtStsmhTUxOBQAC/328HLv31nGqaRjAYZOnSpTz88MOUlJTQ0dFBUVER1113HVdddRVlZWX9cq7jCVdccQV33HEHd9xxB2+99RYVFRU0NjZywQUXsH379kMf4PjHCcXevuLNN9+kuLi4x78tWbKEnTt3snPnTlavXs1tt93G6tWrj/II+w5Jkti5cydr1qyhqamJadOm2UTHlStXoqoqs2fP5swzzzwmLYPZisEWF8dSDM7NzcXhcBCPx21tkIqKCsaOHXvcL/6KoqAoCrIsYximyJlF3hRCcIiNxTFFMpm0ScW6rtsqxd0zZZbOTnV1NfF43C477RecCR019XdABsksH0qiFVlbheGY3utxRTtj7FhbjepQGXvmaBw9mA7G43G2rd/Bk99+ltbaIDkFPr7yfzdwyhnjebv2PUCy/YU8PheLf/MqLq8LRZGJdcRZ9dcNuNwuFFUhmUwiDIFuGKxZto6lT7yFnjFIHDyu0v53F2S4MNbfhGGgOlRi/9/eucdFVef//zkwgiD3ywwiikjmDRXBMu+3UEMY8pJSlrpmauXm5VtrrltZv92tdsvazVIrC9NNrRhAgfCG6GZe8pp38S7EZUC532aY8/uD5iwgKCgwgJ/n47GPzZkD8z7DwHmdz+f1fr3zinFW/W8rI+tGNqXFZbz48Yz/nV9hIZcvX0an02FlZYVaraab+8MVW4sBMHLi0Dq/V1ARoGeaCwT/M6ifOXNGTnk2jXhoCD+XacXF1tYWDw8PunTp0ig3GLm5ucTFxaHVavH09GT48OFYWFjw888/s2HDBtLT01m2bFmr6jK6evUqx44dY8CAAWRkZNC+fXsA2rdvL5v/WzNCxNwHMTExTJ8+HYVCwWOPPUZOTg5paWnyh6ilMHfuXB599NEqf6yKi4vZsWMH3377LYsXL+aRRx4hNDSUESNGmOUPgKWlJQ4ODpSWllJaWkpubi6Wlpby8DlHR0fs7e2brYCpvPpiMsdWv/hX3p65cOGCbHiu71DHxqjdJFwUCgUqlYqePXve0VdkZWWFp6cnnp6ecmR7RkYG58+fl8diuLm5UZGXZqTCKauv+H8sgbI616e7kc3bEz+kKK8IyQjtfVW88cNibOzaUlRUREZGBjqdDktLSzYsiSE3LR9HFwf0pQZW/XEdb0Yu5uKxK6RfyQQJfPp0wqevN79s+1Xu1mlr15bMa1k4qR0qhjpKv497cLcjccNP5OUUgFTRhXTl12t4dlVz/mDVdmjvXl6kX86UO4wUCgV9RnRDdz2rIuLfQlGR3FtupK2tNYWFhXLtJuESEBDQKJ8FW1tbOnXqRKdOneTVjLS0NM6dO0e7du3kz2FdV2VNwkWn02Fra4tarcbHx6dRhEtOTg5bt24lKiqK3NxcJk6cyFdffYW3d9Vsory8PJKSkprEDN9UFBQUMGnSJD7++GMcHJpuC7Y5IUTMHVAoFIwZMwaFQsHcuXOZM2dOledTU1Pp2LGj/G8vLy9SU1NblIgZNmxYjY/b2Nig0WjQaDQYDAZ5Nshbb71Ft27dCA0NZezYsY36iyNJUpXZSqa5R5UvoJWNwdeuXWvyzp271W4yFVtaWqJSqejVq1etIrDy3XHl87p+/TpKpVLenmmK4YyVu6GUSiUqlYo+ffrc03tqGkLq5uZWpdPq2LFjWFpa0tfbl3ZWv/5+tATYYLQIrPP3X7/8e/Ky8mjbri2SJHHjXCrf/uMH/MZ3xcrKCpVKhb+/P2VFenLS12NrX9Eya9W2DfoyPelXMvHu6cX1MylYWFjQfUBX2vuqZX+YhYUFpYWlqDq50s6pHVdP3qBcX46F0gKfPp1Iu5RBcU5xpb13id7B3UhNTqPgZhEANg5tmbokjLVLvkWhgHKDRJvfU3QfecKfg3HHMJYbUVhAwPjenL10ptGFS22Yft4qlUr+eel0Ok6cOAEgb3lW704rLCyUt4pMuT/e3t6Nsoqbn58vr7hkZmby5JNP8umnn+Lr61vr1zg4OKDRaBq8FnOh1+uZNGkS06ZNY+LEiQCo1Wr5RjotLQ2VyjyZP02JEDF3YN++fXh6epKZmUlQUBDdu3evctGvadm/uTj+GxKlUsnIkSMZOXIkRqORo0ePotVq+eSTT3BzcyM0NJTx48fj7u7eIN1KlbtybGxscHd3p1+/fjX+Ia8+/NBkDD558uQ9GYPvt/a8vDzZwHu32u9E9fMyreScOXOG8vJy+ULSUO3h1cPnbGxsUKlU91T7nVAoFNjb22Nvb18xrLK4GOvS3zAaLVEojIAFoERhvI5kWfM2bnUyrulQWFqg1+sxlpdTbignNz3/tou/pYUllpYWGPQGlG2UFZksBiNHd57iYOwR7JzbIUmQ8GUi6s7uDJ/yGHu/O4BlGwts7NsyZUkYK55fg9EoYaGs6BC6fjYVY3nFdqCFpcnYLJFzOZ/i/NLfH5MoKy4j8du9KK2UdOpZkYBbbjCSlZLNwq9n49rViZTkVNQ+7gwYF4i7u7tZV99MVP55denShbKyMrKzs7l69SoFBQXY2tpiYWFBYWGhLFwCAwMbRbgUFRXx448/EhkZyY0bN9BoNHzwwQd07969wV+ruSNJEs8//zw9evRg8eLF8uMajYZ169bx+uuvs27dOsLCwsxYZdMgjL11ZPny5djZ2fHqq6/Kj82dO5cRI0bw9NNPA9CtWzfZVPUgIEkSFy5ckDudLC0tCQ4ORqPR4O3tXeeLq6llUqfTNWhXTl2NwfdD9Q4de3t7uZW6sUzFpnTdykF91duc60L1TI7awucaFUmPdf4IwBpJoUAyGjEaDVxIm0yxcfRdk5MzMzP5/t1YTu24QNt21igUCsrLypn+16mMenrwbS/338iDRCzb9Ps3gQEhAfx2KZ3U5HSsbSq2Sorziwkc589L/5pZMQMqt4gOD3mQdjmTV4cvx2isSP+VqDDrOqscuZmRI0+Ftraxop2jLblZ+bI3R1+mx8HVDsXvgqiNVRsKc4pw6+xM+F9DUavV8myr5o5piy4jIwMLCwvatGlDaWlpowx3LCkpYfv27URGRpKcnMz48eMJDw/Hz8+vVd4w1pWffvqJoUOH0rt3b/l3/u9//zsDBgxgypQpXL9+nU6dOvH999/j4lKPfKXmi+hOqi+FhYVySmRhYSFBQUG8+eabjBs3Tj4mLi6OlStXEh8fz8GDB3nllVc4dOiQGas2H5Ik8dtvvxEdHU1MTAy3bt1i3LhxhIaG0rNnz9surqa8kczMTIqLi+U5P401W6khE4Orf697FRENQXURZfIv1JYnU33EQr3C5xoJq4IpKIy/VRh7pXJQKCi1WU1uoUeV8Q6mxGBT7ompu8XOxo6VL3/Nqf+eAyRGTRvK9HeeqvV8rp9N5fqZFJzVjvQc3I2VL63lWOIpbO0rtumK8ooZ84cRt03zvnTiKv83dLksVqBippGzhyO5mfkVk6SNEkZJwlntyK2M3EoixkB7XxWBmt78d+MhFAqwc22HZslouvT0wc3NrVlPQy8uLiYjI4PMzEyUSiUeHh63rRaZhjua8qQqt3DX57NVVlZGYmIikZGRnDx5knHjxhEeHk6/fv2a7ftTndpyXJYvX84XX3yBu7s7UCE8goODzVxti0CImPpy+fJlJkyYAFS07T3zzDMsW7aM1atXAzBv3jwkSWL+/PkkJCRga2vL119/Tf/+/ev0/cvLy+nfvz8dOnQgNja2ynMRERG89tprdOhQEQs+f/58Zs+e3YBn1/jcvHmT2NhYoqKiuHz5MiNHjuSRRx7h7NmzHDt2jNdee02e89PUwVv3khhsaifW6XQUFxc3yzlQlf0m2dnZsofIxcWFoqKiKqLrXkYsNBaK8mu0KV6AQsoFyYih7QLKrSr2+E2jLVJTU8nOzkahUMj+ourvf1FeMZZKC6xt6+fbybiq451JH5J/qwAkBe6d3Fge9X+3Td++eOwqfxr1NpJRMmXQoVAocG7vhFFvpKigBEtLC6xtregX1Ju9m/dXHPt7qMz4xSMZ/0IQbZU26IsNuLR3wigZ5YyTgoKCe55Z1RhUFy5qtRqVSlWnbS6TmdvUwn23QDqDwUBSUhJarZYjR47w+OOPEx4ezqOPPtpsfr/qQ205Lt99991tK/qCOiFETHNjxYoVHD58mLy8vBpFzOHDh1m5cqWZqmsYTIPUvv/+ezZt2kRxcTEdOnTg4YcfZuLEiQwbNszsnQJ3SgyWJEmOnTe1E5sMjc0dg8FAWloaqampFBUVyeZaLy+vZiW8ZCQDSNmgcMAoWVdZLXJ0dJSH/VlYWGAwGOQ7flO7/f3kAuluZPP/Jq8gV5cHEnTs2YFlmxdiY1f1YpuVepPXRr5NfnYB0u9hdw4udnj36kja5QzsnNpRXm6kKLeQyW+O5+KJKxyJOYXS0pLx84IInTfmju+7aWZVVlYW2dnZdZ7q3JCUlJTIwsXCwkIWLveTFWUyuZvOKzk5mQMHDhASEoJCoWDLli0cOHCA4cOHEx4ezqBBg5qFuG5ITDku+/btEyLm3hAipjmRkpLCjBkzWLZsGStWrGi1Imbnzp18/vnnPPnkkwQHB+Pk5IRer2fv3r1otVr27t1Lz5490Wg0BAUFNQtxkJ2dzfXr18nJyQHAyckJb2/vFrGvbMpmycjIoKysrMpE6/Ly8ioDL52cnOQ7/uZwwTAFFmZkZJCXlyevFtlY2WAsl2jnWPMQvuoJzdbW1vKqWl2jAP419wv2bzn8+8wiibbtKjqJNPPH3nbs+uXfs/f7AxjKDCitlAyd9BhjZ49gxazVZKfdxKA38NjkACYsCL5vb1Hl4aIGg0EeGdDQW64lJSVkZmbKHpeGEC61YTQaSUxMZMOGDZw/f56UlBQCAwP5wx/+wLhx41pLRH4Vrl69yrBhwzh16hQrVqwgIiICBwcH+vfvz4cfftgqz7kRECKmOTF58mSWLl1Kfn4+H3zwQY0iZunSpXKQ2EcffVSllbu1YDQaOXz4MFqtlu3bt9O+fXtCQ0MJDg7G1dW1SVYLqnflWFtbyysxgOzbaSxj8P1SU/icaYuuNkzbaaYp1Xfz0TQW1b1FLi4uqNVqHB0dkSSJ9ct/YEfEHgB6DOzK4rXzblsdqY6pOy0rK6vOXVxze79WkRFjQgGPhQay9NtXbjtWkiSO7TzJb5cycPSwQ93TlZs3b2JtZY2V1JYOnTxxcr/3Aau1YVp9ysrKIi8vD3t7+1pNz3XB9LnJyMgAkIVLY6yMSpLE4cOHiYyMZPfu3fTv35/w8HBGjRqFpaUlR48eJS4ujm3bthEVFdWqEnULCgoYPnw4y5YtY+LEiWRkZODm5oZCoeCNN94gLS2Nr776ytxltgSEiGkuxMbGEh8fz2effUZSUlKNIiY7Oxs7Ozusra1ZvXo13333HYmJiWaquGmQJIlz586h1WqJi4vD2tpa7nTy8vJqUNFQkyfmbsMZazIGm2slo6bwOXd393vacqicA2LKszFtmzXGFoZpRSgjI4PCwkJcXV1Rq9W3CcM9m39m7ZJvsWijRKEAQ6meYVMG8sI/n63za1Xv4jL9zKrPCnrac17FsMVKPBTow4d7lld5rPq8n3bt2qFWq5u2m4v/dWaZVp+USqU8MsDWtuYVK6hYqTNtFRmNRlm4NEZ4pSRJ8tDZ7du306dPH8LDwxkzZkyzn6TdUOj1ekJCQhg7dmyVNmgTV69eJSQkhFOnTpmhuhaHEDHNhaVLl7J+/XqUSiUlJSXk5eUxceJENmzYUOPx5eXluLi4kJub28SVmg9JkkhJSSEqKoqYmBgKCgrkTqcePXrck6AxtXFnZmaSl5eHo6Oj3MZdXxFS00qGqTW5sVpkawqfa4xAv5KSEtnArNfr5ZWM+1l9MhgMsreouLhY3ua60+rImsXfsPf7A7JJ16A34N7BhQ//+3aNx//y43F2rNuDta01U/4USsfuHTi7/wLfvPU9BTmF9BvtR/AfR5Gbn8utW7do27atLNZmdV1E/s2CKn/tegzqynvb/3LbhOXGmPdzv5SUlMjbTqWlpfLIACcnJwwGg7ziYjQaUalUqNXqRkvdPn36NJGRkWzbto2HH36YqVOnEhwc3Kpi/uuCJEnMmDEDFxcXPv74Y/nxyonuH330EQcPHmTTpk3mKrMlIURMc6S2lZjKH/SoqCjef/99Dhw4YI4SmwXZ2dls2bKF6Ohorl+/zqhRowgNDSUwMPCOF5KmaOOu3BFU3Rh8PwKjtvC5ptzyqW6grc/qU+XhkCZ/jlqtrnMnWvS/f+SHD2NpY90GhUJBWXEZvYd2Z8l//khedgHZv93E1dMFB1c7EtYmsmbRNxVzjwCllZKlG15h9eJ1GI1GLNso0ZfoGfTkI8z54Fl58rFJrH37pxiuHk3FNJpaYaFAs2AMg54JaLbCpTbKy8vR6XSkpKSQl5cnp0B7e3s3Wrr2hQsXiIyMJD4+Hm9vb6ZOnUpISEiTBEw2V2rLcdm4cSPHjx9HoVDQuXNn1qxZ88Dkit0nQsQ0RyqLmDfffJP+/fuj0WhYunQpW7ZsQalU4uLiwqpVqx7IVMqaKCgoICEhgejoaE6cOMHAgQPRaDQMGTIEKysrbty4QWpqKlZWVrIZ0mRubQofi2mrR6fTIUlSlTbyu9Eswudqofrqk62trbySYRJVZWVl8jaXwWCokz+nNkoKS3ln4of8djEdhYWCtrbWLI95jSunbvD54m/kYZlzVkxn5YtfUpBbJF8sjOVGvLq3p+BWETb2beXHpHKJz09/cNtrXTl9nb9N+ZiCW4VISDh5OjD9w4n4duuCu7u72d/7umASjenp6RgMBrkF3SRqsrOzAeRtp/uNNbhy5QqRkZHExsaiVquZOnUqYWFh2NvbN9QpNSolJSUMGzaM0tJSDAYDkydP5u233+bKlSuEh4dz8+ZNAgICWL9+/QOz/dXMESLmQeJOGTSlpaVMnz6dI0eO4OrqyubNm+ncubN5Cr1PysrKSEpKIiIigsTERNq1a4eNjQ0vv/wyU6ZMabK21DvVdzdjcHMMn7sblWdamQQLVMxI8vDwQKVS3dGbUVf0ZQbOHUjGoDfQNbALxnKJhQP/UrHa0sYSg96AAgX5OYXoS/Ty+1VeXo5HZ3fKSvS0/d0IrC/VY2PXlk8O/b3KeZhE441LKWRfysXFzZkeg7pSUFxQJTywPsMPm4rKq116vR53d3fUanWt773p86jT6SgqKqoSRlcXoXbjxg20Wi1btmzB0dGRKVOmMGHChBbZXWP6DNvZ2aHX6xkyZAj/+te/WLFiBRMnTiQ8PJx58+bRt29fXnzxRXOXKxAi5sHiThk0n332Gb/++iurV69m06ZNREVFsXnzZjNVen989tlnRERE4O7uTlhYGJ6enuzZs4edO3fSsWNHxo8fL3c6mZvKxuC8vDzatq0YVmjyMDSn8Lm7YWrJzcys6OhxcXFBoVCQk5NDaWmpfLff0F1cV05e569PfSTPKAIwGoy4eblw/pdLyLMAgBn/byoHth4h42omxnIJZRtLZv9zGgM1/ausdtnb28tbRdXf+8qmZ1PQXuWVDHNg8hdlZGRQWloqC5f61mMSz1lZWVVmlDk5OVX5XmlpaURFRREdHY21tTVTpkxh0qRJuLnVba5VS6CoqIghQ4awatUqxo8fT3p6Okqlkv3797N8+XK2bdtm7hIFdxAxzX9Qh6BepKSkEBcXJ2fQVCcmJobly5cDFa3e8+fPR5KkZtMyXB8GDRrEs88+W2WvPyQkRA7Z02q1PPXUU9ja2jJ+/Hg0Gg2enp5mOVdJkjAajRiNRgDZAFxWVib/zzQxuTliSm/V6XRYWFigUqno3bv3bb4fk4/mxo0b5OfnN2gCrVsHFySjhMFYMcDRtBLzp2//yIqZqzh38CIWFgqC5z7OhAVPMHbWCH6KPEj+zQI6+3th18GGAwcOyKtdXbt2veP7XX34YWlpKVlZWSQnJ1NSUlLFQNuYPzeDwSB3dBUXF+Pu7k7Xrl3vS0hVn5hu8ggtWbKEAwcO0LVrV1JSUrCzs+Opp54iMjKyVbU+Q8WNRWBgIBcvXuTll1/G19cXJycn+XfTy8uL1NRUM1cpuBtCxLQyFi5cyD/+8Q/y8/NrfD41NVXOnFEqlTg6OpKdnd0i76z8/f1rfFyhUODn54efnx9vvPEG165dIyoqihdeeIGSkhKeeOIJQkND6datW6MKmprC53x8fKr4cyobg69du9ZgxuCGoLCwUPb3mDqi+vbte8dtFVM8vVqtlhNoMzMzuXTpkny37+bmdk9bM/Yudsz7eAarF66j3GBEoVDw4sczcFU78bcfl1Y5VpIkSg0ldHrMo6IbyaENrq6udxUud8La2poOHTrQoUMHudstPT2d8+fPy6MrXF1dG8R4bWpFT09Pl4WLr69vowRCKhQKSktLSUpK4vr167i5ueHi4oJSqSQ5OZlff/2Vrl27Mnbs7eF/LRlLS0uOHz9OTk4OEyZM4OzZs7cd0xJv7h40hIhpRcTGxqJSqQgMDCQpKanGY2raPmzNv6imLoBFixaxcOFCsrKyiImJ4c033yQ1NZXHH3+c0NBQAgICGuRuuqbwuYcffrjWu+bKd/u+vr6yMfjkyZP1NgbfLyafgCkHxcrKCrVaTb9+/e7pwmxhYYGzszPOzs5VfDQnTpyQt2bq6595NLgfPQc+TFbqTdw6uGDn/L/3xTRnKSMjg1u3buHg4IBarebhhx9u8JWSynk6lUdXXL9+XX7Ozc2tXudWOUOnqKgINzc3unTp0mim9NzcXLZu3YpWqyU30qdhewAAIABJREFUN5cJEyawdu1avL295WMMBgMHDx7kypUrDf76zQUnJydGjBjBgQMHyMnJwWAwoFQqSUlJwdPT09zlCe6C8MS0IuqSQTN27FiWL1/OwIEDMRgMeHhUTApuzUKmNvLz84mPjycqKorTp08zePBgNBoNgwcPrtdFuyHD5ypTF2Pw/WJaCTIJF1Mrt7u7e6Nl3kCF2DO1OJeWlsp5NPVtf5ckSTZG37p167Y5S+bAlLWTlZV113MzeaUqh/95eHg0mnApKCggLi4OrVZLRkYGYWFhTJ06lYceeqjBX6s5o9PpaNOmDU5OThQXFzNmzBiWLFnCunXrmDRpkmzs7dOnDy+99JK5yxUIY++DR20ZNJ9++iknT56Ujb1arZbvvvvOTFU2H0pLS0lMTCQqKoqff/4Zf39/QkNDGT16dI130zWFzzXWvBlo2MRgU4CbqX5T8uy9RtjfL6Zz0+l0chDhnXw0JuGSkZFR44DI5kT1c3NwcJCN5llZWeTn58sZOncK/7sfioqKSEhIIDIykmvXrhEaGkp4eDg9evRo8NdqKfz666/MmDGD8vJyjEYjU6ZM4c033+Ty5ctyi3W/fv3YsGGD2bd1BYAQMQ8etWXQlJSU8Nxzz3Hs2DFcXFzYtGkTXbp0qdP3rC1boTIRERG89tprdOjQAYD58+cze/bsBj+/xqS8vJyff/6ZqKgodu3ahY+PD+PHj8fNzY34+Hg8PT0ZN25ck4fPmbiXxODqGTT29vZyK3dzykExbQmZzq1t27ZysnJRUVGLEC41YTQaycrKkkPoFAoFNjY2ckt6QyfalpSUsGPHDiIjIzl//jzjx48nPDyc3r17P5CrroIWjxAxgvuntmyFxx57TD6mtUzghgoxs2/fPr744gt27NiBg4MDnTt35vHHH2fSpEl4eHiY/YJQU2KwyathbW1dZaulJWTQVMZoNJKWlkZKSgoFBQUolUrc3d3p1KlTs5h4fjeqT+auPOBSoVBQXFwsb6mZghnvpzW9rKyMxMREtFotv/76K2PHjiU8PJyAgACzf07rQ203SzNnzmTPnj04OlYM2IyIiKjV3C9odYgWa8H9o1Ao5IuHXq9Hr9e3qD+O9eWFF17A0tKSZ555hi+//BIrKysuX75MVFQUs2bNQq/XExwcTGhoKA899JBZ3ovqxuDCwkKuX7/O5cuXMRgM2NnZ4eXl1Sjm1sbAlF+SkZFBbm4uTk5OdO3aFWdnZ9kjdPHiRdkjdC8+msakev3Ozs506NCBnj173lajjY0NnTp1olOnTvfcmm4wGNizZw9arZbDhw8zevRo5s+fz4ABA5rNe1JfrK2tSUxMrHKz9MQTTwDwz3/+k8mTJ5u5QkFzQqzECOpF9WyF999/v8rzERERLF26VO7K+eijj+SW7taEJElkZmYSExNDdHQ0GRkZPP7442g0Gvr27dukgsFoNFYJ0nN2dpY7mkyPN9eLPtR84VepVDg7O9daZ01eE1OLc1Nvj5k8Ounp6XL9arUaJyene3qfTa3ppi01a2truVOtU6dO8gqhVqvl559/Zvjw4YSHhzN48OAWIVTrQ+UgulWrVhESEiJEzIOJ2E4SNCymbIVPPvkEPz8/+XHTwDxra2tWr17Nd999R2JiohkrbRpyc3PlTqdz584xdOhQQkNDGTRoUKOYZSt3tRQUFNy2VVHb8ZXNs/c6xbshqL7Vcj8XfpPfx5SsW3lCdWMZraubi52cnGSPTkMLxMLCQhISEvjggw8oKCigoKCAwYMH89JLLzFixAizmLEbm5pulmbOnMn+/fuxtrZm9OjRvPfee8J0++AgRIyg4Xn77bdp164dr776ao3Pl5eX4+LiQm5ubhNXZl5KSkrYtWsXUVFRHDhwgMDAQEJDQxk5cuR9tV2bklszMzMpLCyUc1bq66GozRjcUEFtd3rdhhIud8KUR6PT6QBkQXO/WTvVc2ga21wsSRJHjx4lMjKSXbt2ERgYSHBwMCUlJcTHx3P27FmGDRvGRx991CqFDFS9WTK1n5eVlTFnzhx8fX158803zV2ioGkQIkZw/9SWrRASEiIfk5aWJo+Wj4qK4v333+fAgQPmKtnsGAwG9u3bR1RUFLt37+ahhx4iJCSEcePGyQbFO6HX62XhUlxcLLfjNlSOyJ2MwQ3RMVN9q+tuK0YNTVlZmSxoTKMCTDOC6vL61bu6HBwc8PDwaFTh8uuvv6LVatm+fTt+fn6Eh4czZsyY21Yd9Ho9hw8fZuDAgQ1eR3Oippul2iIkBK0WIWIE909t2QqVW7iXLl3Kli1bUCqVuLi4sGrVKrp3727u0psFRqOREydOoNVqSUhIwMXFhZCQEMaPH49arZYvqsXFxfJWRVlZGe7u7qhUqibpyDF1zGRmZmI0GuXXrs8qhkm4ZGRkkJ+f3+TCpTZMowJ0Oh25ubm1+mhMOTrp6emycFGr1Y269XbmzBkiIyNJSEiga9euTJ06leDgYLNPYjcHtd0sBQYG0r59eyRJYtGiRbRt25b33nvP3OUKmgYhYgTNm7pk0JSWljJ9+nSOHDmCq6srmzdvpnPnzuYp+D6RJImLFy8SFRXF1q1b0ev1eHt7c+nSJVxcXPjoo4+abNxAbdSUGFybMbi5CpfaqO6jsba2xs7ODoPBQE5Ozh2nWzcUycnJREZGEh8fT8eOHZk6dSqhoaFm/Zk3B2q7WRo1ahQ6nQ5JkvD392f16tUtotVe0CCIFmtB86a2tsrKGTRr167F2dmZixcvsmnTJpYsWcLmzZvNWPW9o1AocHR0xM7ODisrK3mKtbu7O1lZWfznP/9Bo9Hg5+dnto4TKysrPD098fT0lI3BqampnD17FkdHR9zc3OQQt/z8fFxdXenYsWODjkVoLEzvv+m9zczMpLS0FKPRiFKppF27dtjY2DT4eVy9epXIyEi2bt2KSqVi6tSpJCYmVpnE/qDTp08fjh07dtvjD0KDgKD+CBEjaBbUJYMmJiaG5cuXAzB58mTmz5+PJEnN/oJZGydPnsRgMLBu3Tq8vLzkx3NycoiNjeWDDz4gOTmZ4cOHExoayoABA8xm4LS0tJTNv6bk2TNnzgBgb29P586dzZJeXF+qz4qytbXFw8ODLl26yFtKphWoS5cuUVxcLPtoKoue+pCSkoJWqyUmJgYHBwemTJnCtm3bcHZ2bujTaxLKy8vp378/HTp0IDY2litXrshR/QEBAaxfv77RusIEguqI7STBHcnJySEhIYGJEyc2+h+mu2XQ+Pn5kZCQIF/wfX19OXjwIG5ubo1alzkpLi5mx44dcpjZI488QmhoKCNGjGjwqPraqN7OXbkrCmhUY3BDUVBQQHp6uixc1Go1bm5ud82Uqe6jsbe3l300dxKUaWlpREdHExUVhbW1NU899RSTJk3C3d29oU+tyVmxYgWHDx8mLy+P2NhYpkyZwsSJE+WhiX379uXFF180d5mC1oXwxAjujXfffZf3338fPz8/3NzcmDFjBqGhoY26IlBbBk2vXr3Ytm1bFRFz6NAheaBea8dgMPDf//4XrVZLUlIS3bt3R6PRMGbMGOzt7Rv0tcrLy8nKypKnK9d1SGFDGIMbCtOKi06nw8bGBrVajbu7+z2H4ZkMvyYfjZWVFRkZGfTo0QNvb290Oh0xMTFERUVhNBqZPHkyTz31FB4eHg18ZuYjJSWFGTNmsGzZMlasWMHWrVtxd3cnPT0dpVLJ/v37Wb58Odu2bTN3qYLWhfDECO6NtWvXkpOTA8C3337LhQsXyMzMlH0SjZGO6uTkxIgRI0hISKgiYry8vLhx4wZeXl4YDAZyc3NxcXFp8NdvriiVSkaOHMnIkSMxGo0cPXoUrVbLxx9/jEqlIjQ0lODgYNzd3e9pi62ycCkqKsLV1ZXOnTvXa7py5Sh907ZMcnJykyUGFxYWysLF2toatVpN//79G0R0m3w0jo6OPPTQQxQVFZGUlMSbb75JTk4OlpaWTJ06lW+++aZVplQDLFy4kH/84x/k5+cDFeGWTk5O8vvr5eVFamqqOUsUPGAIESOolVu3bpGSkkJqaiodOnTgmWeeqfK8ScBcunSJo0ePEhYWds9bTtXbKnfu3MmSJUuqHKPRaFi3bh0DBw7khx9+YNSoUS3WD3O/WFhY0L9/f/r378/f/vY3Lly4gFarZdq0aSiVSnmmk7e39x3fo+rCxc3NDR8fnwbJobmbMbihEoNN060zMzOxsrLCw8ODwMDARlstzM3NJTY2Fq1Wy61bt3juuecICgri9OnTbNmyBY1GwzPPPMNrr73WKK9vLmJjY1GpVAQGBpKUlARUrE5V50H9nRSYB7GdJKiViIgIVqxYQYcOHfjtt9+YNm0aixYtwmg0cvDgQdLT0wkNDWXHjh18+OGH7NmzB+CezLZ1yaApKSnhueee49ixY7i4uLBp0ya6dOlSp+9/48YNpk+fTnp6OhYWFsyZM4cFCxZUOSYpKYmwsDB8fHwAmDhxYotLBJUkid9++42oqChiYmLIyclh3LhxhIaG0rNnTywsLMjJyeHGjRsYjcZGCdC7GzUlBru7u9fLGGwSLibxa9oqaixjcUFBAfHx8Wi1Wn777TfCwsIIDw+na9eutx1bUlLCtWvX6NatW6PUYi6WLl3K+vXrUSqVlJSUkJeXx4QJE9i2bZvYThI0NsITI6g/vXv3Zvfu3bi5uXH69GmuXr3K+PHj+eMf/0hGRgbFxcW4urpibW2Nj48Pr7/++m3fo7G2nOpLWloaaWlpBAQEkJ+fT2BgINHR0fTs2VM+pjWmgN68eZOtW7fy/fffc/LkSZycnCgsLGT+/Pk8/fTTTSZcaqNyYnB2djZKpbJWY3BxcbG84qJUKlGr1ahUqkYTLsXFxSQkJBAZGcmVK1cIDQ0lPDy8ymfmQaXy74rJtGwy9vbp04eXXnrJ3CUKWhfCEyOoHwUFBZSVlcmdP7169aJXr16cPn2aX375he+++45OnTqxbNkyEhISWLx4Mbt27eLEiRM8++yzqFQqgNuSUCVJMkvuSfv27eVxCPb29vTo0YPU1NRWf0H66aefiI6OJi0tjWnTpqFSqTh8+DBffvklp06dIjQ0lGHDhpltkJ5CocDe3h57e3t8fX1lY/Dp06cpLy/H2dlZnlmkVCpRqVT4+/s3WqdcaWkpO3fuJDIykrNnzxIcHMxbb71Fnz59xDZJLbz//vuEh4fzl7/8hX79+vH888/X+WujoqKYOHEiZ8+eFcnegntCiBhBjezevZuUlBSOHz9Ojx495IvciRMneOihh+jUqROlpaV0796dU6dO0a1bN7Zu3SrfTQO8/PLLzJ49m379+gEVF6zmcCG4evUqx44dY8CAAbc9t3//fvr27YunpycffPABvXr1MkOFDYelpSV//etfbzsPvV7Pnj170Gq1/OUvf6Fnz55oNBqCgoLMmoJqY2MjC+C0tDR0Oh0WFhZIkoSzszN2dnYNvvKi1+tJTExEq9Vy4sQJxowZw6uvvkpgYGCz+Lw2R0aMGMGIESMA6NKlC4cOHbqn77Nx40aGDBnCpk2b5AwogaA+iO0kQY3k5OSwYcMGdu/ezYULFxgzZgwffvghixcvpry8nH/9619kZGSwatUqJEli6dKlvP766/j7+zNz5kxycnIICAhgx44d+Pr6sm/fPpKSkggKCuLRRx+t8lrl5eUoFIomWaEpKChg+PDhLFu2jIkTJ1Z5Li8vDwsLC+zs7IiPj2fBggUkJyc3ek3mxmg08ssvv6DVatmxYwft27eXO51cXV2b5EJeWloqbxUB8laRSTybjME6nY68vLz7NgYbDAb27t2LVqvll19+YdSoUYSHhzNgwACzJSTfL9VD6GbOnMmePXvkQaMRERH4+/ubucr/UVBQQLdu3di9ezcajYZz585hNBqZP38+e/bswcfHB6PRyKxZs5g8eTJHjhxh8eLFclZRRESEvLoqaPUIT4zg3ikoKOD48eMMGDCAc+fOsXDhQmbNmsXFixeJi4vjs88+w9LSkoiICCZPnszQoUP55ptv2Lp1K+vXr+c///kPa9euZfTo0fz000/06tWLlStXotPpagz/unXrFra2tg2+xaHX6wkJCWHs2LEsXrz4rsd37tyZw4cPt+owvepIksTZs2fRarXExcVhbW3N+PHj0Wg0eHl5NaigKS0tJTMzk4yMDABUKhVqtfquP3ej0Uhubi6ZmZn1MgaXl5fz888/o9Vq2bdvH0OHDiU8PJwhQ4Y0C9/W/VI9hG7mzJmEhIQwefJkc5dWI6abpLVr1zJo0CBWrlzJ5cuX+eqrr4iNjSUzM5MePXrwxRdfEBYWxvDhw4mJicHd3Z3Nmzezbds2vvrqK3OfhqBpEJ4Ywb1jZ2fHkCFDgAqz7yuvvMKePXvkfIhevXpx/Phx0tPTGThwIACRkZGEhYVx4MABTp06xZIlSwgLCwMqtgkkSSIqKoqVK1fi7+/PvHnzGDRoEAC7du3CxcWFIUOGYGVlRVZW1n0LCUmSeP755+nRo0etAiY9PV2eJn3o0CGMRuMDE6RnQqFQ0LNnT3r27MmyZcu4ceMG0dHRvPTSSxQUFMidTj169LgnQVNWViYLF6PRiFqtxs/Pr17pvhYWFjg7O8t+GZMx+NixY/JWppOTE126dEGSJA4dOsQPP/zA3r17GTBgAOHh4Xz66admG+HQGKSkpBAXFyeH0LUENm7cyMKFCwEIDw9n48aN6PV6nnrqKSwsLPDw8GDkyJEAnD9/nlOnThEUFARUCFKxCiMAsRIjuE8uXbqEr68vqampjBs3jmeffZYuXbrw7LPPcuLECS5evMju3bv585//jIuLC0ajUb7rzc/Px97ens8//5yUlBTeeecdNm7cyMqVK5k1axbPP/88kiQRERGBra0tU6dOvec6f/rpJ4YOHUrv3r3l7YK///3vXL9+HYB58+axcuVKVq1ahVKpxMbGhhUrVsjCSlARbLZlyxaio6O5fv06o0aNIjQ0lP79+99xC6a6cDGtuDTGWILi4mK2bdvG+++/T1FREfn5+QwYMIC5c+cSFBTU7Gc73SuTJ09m6dKl5Ofny11DM2fOZP/+/VhbWzN69Gjee+89sxm4q5OdnY2XlxcqlQqFQiFvKU+YMAF/f3/+8Ic/ABUxB8888wzdunVjzpw57N+/38yVC8yE2E4SNB5GoxELCwtOnDjBzp07OXPmDBcvXmTPnj3s3buXRYsWceTIkSpf8+WXX7Jlyxby8/Px9fXl1q1bfPLJJ2i1WlasWEFZWRmvv/46Tz75JB4eHlhYWFS5UJaXl2NhYdFsjJd1yaGRJIkFCxYQHx+Pra0tERERBAQEmKni+6OgoICEhASio6M5ceIEAwcORKPRyKtnOp2O8+fPY2tri8FgkIWLjY1No9QjSRInT54kMjKS7du306tXL5544gnKysqIi4vj6tWrBAUF8c477zSbC3lDERsbS3x8PJ999lmV1ue0tDQ8PDwoKytjzpw5+Pr6NpvcozVr1nD06FHWrFkjPzZ8+HAef/xxDh48yJYtW9DpdPTo0YPPP/8cjUZDz549Wb9+PQMHDkSv13PhwoUWb7wX1BkhYgRNS25uLo6Ojty8eZMFCxZgbW2NRqNBo9Hw3//+l5deeomoqChKSkpYtWoVV69eJS4ujpUrV2IwGJg1axYAX3zxBTdu3OBPf/oTnp6e3Lx5s8ZRAyYhZS7qkkMTHx/PJ598Qnx8PAcPHmTBggUcPHjQbDU3FGVlZSQlJbFx40Z27Nghz0l6/vnnmTVrFra2to322mfPniUyMpKEhAR8fX2ZOnUq48ePv00sFRUVsXfvXsaOHdtshG9DUVMI3cSJE9mwYYN8THPLQBoxYgSvv/4648aNkx/797//zdmzZ5Ekib179/Lwww9TWlrK4sWLCQoK4vjx47zyyivk5uZiMBhYuHAhL7zwghnPQtCECE+MoGkxdUS4uLiwYsUKvv32W3744Qe8vb1p27YtvXr1wsfHh1u3blFWVoa/vz/FxcWcP3+egIAAHBwc0Ov15OXl4e3tjaenJytXriQ6Opr8/HyefvppXn75ZXl7wCRgzLVCU5ccmpiYGKZPn45CoeCxxx4jJyeHtLS0Fr+3v2vXLlatWkVGRgbz5s2ja9euHD58mI0bN/LTTz8REhJCcHBwg825unjxIpGRkcTFxeHl5cXUqVP585//fMfWcFtb2yoXzNbEu+++y7vvvgv8T6xs2LBB/mxJkkR0dHSVOWTmxjS2oDKvvPIKULHKZ2dnR3Z2No8++ii9e/cGwN/fn7179zZlmYIWgBAxgkbH3d29ytZKSUkJbdu2xd/fn0GDBsmpqCdPngSQc2VOnjzJrVu3GD58OHv27OGTTz5h3759XLlyhb/+9a9MmjSJjh078u9//5uwsDC8vb2bRZdJbTk0qampVQYDmobltXQR4+DgwD//+c8qMftTp05FkiROnz6NVqtl8uTJ2NraEhISgkajoX379vUSmteuXSMyMpKtW7fi6upKeHg4u3btksWy4HamTZuGTqdDkiT8/f1ZvXq1uUuqEyEhIeTk5FBWVsYbb7zRqqaACxoeIWIETU7btm2JiIhAr9dz+fJlPD096du3L9evX+fKlSuUlZUBcO7cOZRKJb179+att95ixowZuLm54ebmRqdOndi1axeDBg3i448/Jjk5mT179jBixAg++OCDRkt0vRsFBQVMmjSJjz/+GAcHhyrPtdZheYMHD67xcYVCgZ+fH35+frzxxhtcu3aNqKgoXnjhBUpKSnjiiScIDQ3l4YcfrvF9SE1NRavVEh0djb29PVOmTOHHH398oCaX15fKIXSJiYnmLeYeqWmVRiCojZaZ6iRoFbRp04Zu3brx1ltvYW1tjYeHBz4+Pvz5z3/m0KFDHD16FBcXF9zd3dm1axdPPPGE/LWmrqjExET69evH/PnziYuLQ6fTySs6TY1er2fSpElMmzbttiA9qFh5uXHjhvzvlJQUPD09m7JEs6FQKOjcuTOLFi0iMTGRLVu24OnpyRtvvMHQoUN56623OHLkCGlpaaxevZpx48Yxd+5cHBwc2LJlC9u3b2f27NktUsB07tyZ3r174+/vT//+/YGKmVZBQUF07dqVoKAgbt26ZeYqBYKWiRAxgmaDvb09n3zyCTt37qR79+4MHz5c3loKCwvj9OnTAKxfv57CwkIGDx5MQkICs2bNwtfXl44dO5Kenk56enqT116XHBqNRsM333yDJEkcOHAAR0fHFr+VdC8oFArc3d2ZPXs2sbGxJCUl0b9/f959911Gjx6NUqlk8+bNJCYm8uKLL9YYiNjS2L17N8ePH+fw4cMAvPfee4wePZrk5GS5/VkgENQf0Z0kaBEcPHiQRYsWUVJSQkBAAHPmzMHDw4PZs2ezZs0afHx8SElJYfjw4Zw5c6bJ22jrkkMjSRLz588nISEBW1tbvv76a/nOXNB6qSn5uVu3biQlJdG+fXvS0tIYMWIE58+fN2OVAkGzRrRYC1oH6enpKBQK1Go17777LmfOnGHVqlXY2dmxZs0a4uLi2LJli7nLbBRmzZpFbGwsKpWKU6dO3fZ8UlISYWFh+Pj4ABVBYc0lF+RBxsfHB2dnZxQKBXPnzmXOnDk4OTmRk5MjH+Ps7Cy2lASC2hEt1oLWQeVOhVdeeQWdTifnknz11Vc899xz5iqt0Zk5cybz589n+vTptR4zdOjQZpMFIqhg3759eHp6kpmZSVBQEN27dzd3SQJBq0GIGEGLpV27drKAAXjnnXfkGU+tkWHDhnH16lVzlyGoJybztkqlYsKECRw6dAi1Wi3nuKSlpaFSqcxcpUDQMhHGXkGrYezYsY2aDtsS2L9/P3379uWJJ56QjdAC81FYWEh+fr7839u3b8fPzw+NRsO6desAWLdunTwcVSAQ1A+xEiNoVbSG3JV7JSAggGvXrmFnZ0d8fDxPPvkkycnJ5i7rgSYjI4MJEyYAYDAYeOaZZxg3bhyPPPIIU6ZMYe3atXTq1Invv//ezJUKBC0TYewVCFoQV69eJSQkpEZjb3Vq6ooRCASCFkitd6diO0kgaCWkp6fLqcCHDh3CaDTi6upq5qqaJzUF0C1fvpwOHTrg7++Pv78/8fHxZq5SIBDcDbGdJBC0EJ5++mmSkpLIysrCy8uLt99+G71eD1Tk0Pzwww+sWrUKpVKJjY0NmzZteqC31+7G7t27b1ulWrRoEa+++qqZKhIIBPVFiBiBoIWwcePGOz4/f/585s+ff1+vcbcsGkmSWLBgAfHx8dja2hIREUFAQMB9vaZAIBDcK2I7SSAQyMycOZOEhIRan//xxx9JTk4mOTmZzz//nBdffLEJq2s4FAoFY8aMITAwkM8//1x+fOXKlfTp04dZs2aJ8DmBoAUgRIxAIJAZNmzYHYcsxsTEMH36dBQKBY899hg5OTmkpaU1YYUNw759+zh69Cg//vgjn376KXv37uXFF1/k0qVLHD9+nPbt2/N///d/5i5TIBDcBSFiBAJBnUlNTaVjx47yv728vEhNTTVjRfdGbQF0lpaWWFhY8MILL3Do0CEzVykQCO6GEDECgaDO1BTJ0NLMw7UF0FVeUYqKisLPz89cJQoEgjoijL0CgaDOeHl5cePGDfnfKSkp8qpGS6G2ALrnnnuO48ePo1Ao6Ny5M2vWrDFzpQKB4G4IESMQCOqMRqNh5cqVhIeHc/DgQRwdHWnfvr25y6oXXbp04cSJE7c9vn79ejNUIxAI7gchYgQCgczdsmiCg4OJj4/noYcewtbWlq+//trMFQsEggcZMXZAIBAIBAJBc0aMHRAIBAKBQNC6ECJGIBAIBAJBi0SIGIFAIBAIBC0SIWIEAoFAIBC0SISIEQgEAoFA0CIRIkYgEAgEAkGLRIgYgUAgEAgELRIhYgQCgUAgELRIhIgkoEDcAAAA10lEQVQRCAQCgUDQIhEiRiAQCAQCQYtEiBiBQCAQCAQtEiFiBAKBQCAQtEiEiBEIBAKBQNAiESJGIBAIBAJBi0SIGIFAIBAIBC0SIWIEAoFAIBC0SISIEQgEAoFA0CIRIkYgEAgEAkGLRIgYgUAgEAgELRIhYgQCgUAgELRIhIgRCAQCgUDQIlHe5XlFk1QhEAgEAoFAUE/ESoxAIBAIBIIWiRAxAoFAIBAIWiRCxAgEAoFAIGiRCBEjEAgEAoGgRSJEjEAgEAgEghaJEDECgUAgEAhaJP8fxKPstoj+tZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=20, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "# plt.ylabel('Age', fontsize=18)\n",
    "# plt.xlabel('Income', fontsize=16)\n",
    "# plt.zlabel('Education', fontsize=16)\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_ylabel('Age')\n",
    "ax.set_zlabel('Income')\n",
    "\n",
    "ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
    "Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
    "For example, the 3 clusters can be:\n",
    "\n",
    "- AFFLUENT, EDUCATED AND OLD AGED\n",
    "- MIDDLE AGED AND MIDDLE INCOME\n",
    "- YOUNG AND LOW INCOME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Want to learn more?\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
    "\n",
    "### Thanks for completing this lesson!\n",
    "\n",
    "Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
    "\n",
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
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