Clustering Analysis of Smart Contract Risk tags.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1jfe99wQDyoV9eddzARZ7e6jFF82WWM-E","timestamp":1729642070499}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Clustering Analysis of Smart Contracts\n","Welcome to this interactive tutorial on clustering smart contracts based on risk tags! This Colab notebook will guide you through the process of applying Hierarchical clustering to a dataset of smart contracts. You'll learn how to prepare your data, select features, implement clustering, visualize the results, and interpret these findings to gain insights into smart contract vulnerabilities.\n","\n","**Goals:**\n","- Understand and implement Hierarchical clustering.\n","- Visualize clustering results to interpret data.\n","- Apply these techniques to your dataset.\n","\n","Before you start, make sure you have uploaded your dataset to this notebook's environment or access it directly from a public URL.\n","\n","Ensure that you have a basic understanding of Python programming and familiarity with libraries such as pandas, matplotlib, and seaborn. If you're ready, let's start by setting up our environment and loading the data!\n"],"metadata":{"id":"pxQuVWv7YkwQ"}},{"cell_type":"markdown","source":["### Step 1: Import libraries"],"metadata":{"id":"aIYqn6nCYvST"}},{"cell_type":"code","source":["# Import necessary Python libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import scipy.cluster.hierarchy as sch\n","from scipy.spatial.distance import pdist, squareform\n","from scipy.cluster.hierarchy import fcluster\n","\n","print(\"Libraries imported successfully!\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HkiRjYs6Yv1C","executionInfo":{"status":"ok","timestamp":1729641405671,"user_tz":300,"elapsed":8034,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"dd8e719c-5afe-4f90-8077-8db162834ed2"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Libraries imported successfully!\n"]}]},{"cell_type":"markdown","source":["### Step 2: Download the dataset"],"metadata":{"id":"Hc13c8B8Yz4B"}},{"cell_type":"markdown","source":["This step would download the dummy dataset. If you have your own dataset, then please add it to colab's environment."],"metadata":{"id":"SlJGqzQY8S6c"}},{"cell_type":"code","source":["!gdown 1NYR65tYzMe4Rwc0kFyTEj2nerJJFRf6n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d3D84FPQY3ku","executionInfo":{"status":"ok","timestamp":1729641437676,"user_tz":300,"elapsed":4511,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"81034ce6-a7a5-418e-e147-112ac19c57ed"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1NYR65tYzMe4Rwc0kFyTEj2nerJJFRf6n\n","To: /content/webacy_risk_dataset.xlsx\n","\r  0% 0.00/26.4k [00:00<?, ?B/s]\r100% 26.4k/26.4k [00:00<00:00, 43.9MB/s]\n"]}]},{"cell_type":"code","source":["print(\"Setup complete. Imported pandas, seaborn, and matplotlib. Downloaded Compile Risk Data dataset.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rQo-Sp2lY6zV","executionInfo":{"status":"ok","timestamp":1729641465516,"user_tz":300,"elapsed":163,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"5a46587a-f543-4354-f3b6-b4387ef4195a"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Setup complete. Imported pandas, seaborn, and matplotlib. Downloaded Compile Risk Data dataset.\n"]}]},{"cell_type":"markdown","source":["### Step 3: Load the Data Section\n","\n","Now even though we have downloaded the dataset, we still need to load it into our Python environment. For this we will utilize the Pandas library."],"metadata":{"id":"icYIrcZNY_aR"}},{"cell_type":"code","source":["# Loading the dataset\n","\n","data = pd.read_excel('/content/compiled_risk_data.xlsx')\n","\n","# Display the first five rows of the dataframe\n","data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"SD1rsI8AZACs","executionInfo":{"status":"ok","timestamp":1729641487775,"user_tz":300,"elapsed":1354,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"058f7196-3835-4de0-c880-4e3b8ce786ac"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     project_name                                     Blog post link  \\\n","0  Data Analytics  https://chainsecurity.com/security-audit/circl...   \n","1  Data Analytics  https://stackoverflow.com/questions/75030483/w...   \n","2  Data Analytics  https://stackoverflow.com/questions/71115106/s...   \n","3  Data Analytics  https://stackoverflow.com/questions/75030483/w...   \n","4  Data Analytics  https://stackoverflow.com/questions/69466137/h...   \n","\n","                                       Audit website     Chain  \\\n","0                          https://chainsecurity.com  Ethereum   \n","1  https://studygroup.moralis.io/t/compilation-er...  Ethereum   \n","2  https://ethereum.stackexchange.com/questions/1...  Ethereum   \n","3  https://studygroup.moralis.io/t/compilation-er...  Ethereum   \n","4  https://ethereum.stackexchange.com/questions/1...  Ethereum   \n","\n","   Is_closed_source  hidden_owner  anti_whale_modifiable  Is_anti_whale  \\\n","0             False         False                  False          False   \n","1             False         False                   True           True   \n","2              True         False                  False           True   \n","3              True         False                  False          False   \n","4              True         False                   True          False   \n","\n","   Is_honeypot  buy_tax  ...  centralized_risk_high  centralized_risk_low  \\\n","0        False    False  ...                    0.0                   0.0   \n","1        False     True  ...                    0.0                   1.0   \n","2        False    False  ...                    0.0                   1.0   \n","3         True    False  ...                    0.0                   1.0   \n","4         True    False  ...                    0.0                   0.0   \n","\n","   event_setter  external_dependencies  immutable_states  \\\n","0          True                   True              True   \n","1         False                   True             False   \n","2         False                   True             False   \n","3         False                  False              True   \n","4          True                   True             False   \n","\n","   reentrancy_without_eth_transfer  incorrect_inheritance_order  \\\n","0                             True                        False   \n","1                            False                         True   \n","2                            False                        False   \n","3                             True                        False   \n","4                             True                        False   \n","\n","   shadowing_local  events_maths  \\\n","0            False         False   \n","1            False          True   \n","2             True          True   \n","3            False          True   \n","4            False          True   \n","\n","          Summary/rationale of risk tags marked true  \n","0  Bad Contract: Assigned for flaws that indicate...  \n","1  Summary/rationale of risk tags marked true: Th...  \n","2                                                NaN  \n","3                                                NaN  \n","4  The smart contract is marked with risks such a...  \n","\n","[5 rows x 37 columns]"],"text/html":["\n","  <div id=\"df-df6f904d-f10c-448e-8393-9c17bce5c8fc\" class=\"colab-df-container\">\n","    <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>project_name</th>\n","      <th>Blog post link</th>\n","      <th>Audit website</th>\n","      <th>Chain</th>\n","      <th>Is_closed_source</th>\n","      <th>hidden_owner</th>\n","      <th>anti_whale_modifiable</th>\n","      <th>Is_anti_whale</th>\n","      <th>Is_honeypot</th>\n","      <th>buy_tax</th>\n","      <th>...</th>\n","      <th>centralized_risk_high</th>\n","      <th>centralized_risk_low</th>\n","      <th>event_setter</th>\n","      <th>external_dependencies</th>\n","      <th>immutable_states</th>\n","      <th>reentrancy_without_eth_transfer</th>\n","      <th>incorrect_inheritance_order</th>\n","      <th>shadowing_local</th>\n","      <th>events_maths</th>\n","      <th>Summary/rationale of risk tags marked true</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Data Analytics</td>\n","      <td>https://chainsecurity.com/security-audit/circl...</td>\n","      <td>https://chainsecurity.com</td>\n","      <td>Ethereum</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>Bad Contract: Assigned for flaws that indicate...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Data Analytics</td>\n","      <td>https://stackoverflow.com/questions/75030483/w...</td>\n","      <td>https://studygroup.moralis.io/t/compilation-er...</td>\n","      <td>Ethereum</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>False</td>\n","      <td>True</td>\n","      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  <td>https://studygroup.moralis.io/t/compilation-er...</td>\n","      <td>Ethereum</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Data Analytics</td>\n","      <td>https://stackoverflow.com/questions/69466137/h...</td>\n","      <td>https://ethereum.stackexchange.com/questions/1...</td>\n","      <td>Ethereum</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>True</td>\n","      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For clustering smart contracts, we select features that represent different risk tags effectively. This might include features like transaction frequency, error rates, or specific security vulnerabilities.\n","\n","Below, we select a hypothetical set of features. For simplicity and effective visualization, we will select just three binary risk tags. Adjust the features according to your dataset and analysis needs.\n","\n"],"metadata":{"id":"noBnsUbpZC7s"}},{"cell_type":"code","source":["data_new = data.copy()\n","# Assuming the dataset has multiple features, we select a few relevant ones\n","feature_1 = 'hidden_owner'\n","feature_2 = 'Is_honeypot'\n","feature_3 = 'exploitation'\n","selected_features = data_new[[feature_1, feature_2,feature_3]].replace({True:1, False:0})\n","print(\"Features selected for clustering:\")\n","\n","print(selected_features.head())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HqLV59ooZeUc","executionInfo":{"status":"ok","timestamp":1729641493753,"user_tz":300,"elapsed":162,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"deafa233-5685-474b-bcff-8cf553682101"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Features selected for clustering:\n","   hidden_owner  Is_honeypot  exploitation\n","0             0            0             1\n","1             0            0             0\n","2             0            0             1\n","3             0            1             1\n","4             0            1             1\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-5-2b98f632d507>:6: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n","  selected_features = data_new[[feature_1, feature_2,feature_3]].replace({True:1, False:0})\n"]}]},{"cell_type":"markdown","source":["Feel free to use as many features as you would like. We are using only three features here because that is easy to visualize. Otherwise, feel free to utilize features that make sense."],"metadata":{"id":"gXLEdT2g9HH8"}},{"cell_type":"markdown","source":["# Applying Clustering\n","## Hierarchical Clustering with Jaccard Distance\n","\n","In this section of our Colab notebook, we will apply hierarchical clustering to our dataset using Jaccard distance. This method is particularly effective for datasets with binary attributes, like ours, as it measures the dissimilarity between sample sets.\n","\n","\n","### Compute Jaccard Distance\n","\n","Compute the pairwise Jaccard distance between observations. Jaccard distance is a measure of how dissimilar two sets are, ranging from 0 (identical) to 1 (no common elements).\n"],"metadata":{"id":"EOHytTW-9Xe7"}},{"cell_type":"code","source":["from scipy.spatial.distance import pdist, squareform\n","\n","# Assuming 'selected_features' is your DataFrame with binary data\n","distance_matrix = pdist(selected_features, 'jaccard')\n","distance_square_matrix = squareform(distance_matrix)  # Convert to square matrix\n"],"metadata":{"id":"iyeeEuTpHN0M","executionInfo":{"status":"ok","timestamp":1729641499852,"user_tz":300,"elapsed":211,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}}},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":["### Perform Hierarchical Clustering\n","\n","Using the complete linkage method, we perform hierarchical clustering. Complete linkage considers the maximum distance between observations in two clusters."],"metadata":{"id":"7wSZyyeUdwTk"}},{"cell_type":"code","source":["import scipy.cluster.hierarchy as sch\n","\n","# Create linkage matrix\n","linkage_matrix = sch.linkage(distance_matrix, method='ward')\n","\n"],"metadata":{"id":"AuY2e0y4HObd","executionInfo":{"status":"ok","timestamp":1729641504265,"user_tz":300,"elapsed":137,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["linkage_matrix"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Us82wzweJtae","executionInfo":{"status":"ok","timestamp":1720009729086,"user_tz":240,"elapsed":20,"user":{"displayName":"Chaitanya Baweja","userId":"09943888125622581847"}},"outputId":"151bbdec-03bd-4927-d46e-15ff6d732a73"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  0.        ,   4.        ,   0.        , 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   , 172.        ,   0.        ,  40.        ],\n","       [ 74.        , 173.        ,   0.        ,  41.        ],\n","       [ 75.        , 174.        ,   0.        ,  42.        ],\n","       [ 77.        , 175.        ,   0.        ,  43.        ],\n","       [ 78.        , 176.        ,   0.        ,  44.        ],\n","       [ 79.        , 177.        ,   0.        ,  45.        ],\n","       [ 80.        , 178.        ,   0.        ,  46.        ],\n","       [ 82.        , 179.        ,   0.        ,  47.        ],\n","       [ 83.        , 180.        ,   0.        ,  48.        ],\n","       [ 84.        , 181.        ,   0.        ,  49.        ],\n","       [ 86.        , 182.        ,   0.        ,  50.        ],\n","       [ 87.        , 183.        ,   0.        ,  51.        ],\n","       [ 88.        , 184.        ,   0.        ,  52.        ],\n","       [ 91.        , 185.        ,   0.        ,  53.        ],\n","       [ 92.        , 186.        ,   0.        ,  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structure."],"metadata":{"id":"xoMH0EWEd5W5"}},{"cell_type":"code","source":["# Plot the dendrogram\n","import matplotlib.pyplot as plt\n","\n","plt.figure(figsize=(10, 7))\n","dendrogram = sch.dendrogram(linkage_matrix)\n","plt.title('Hierarchical Clustering Dendrogram')\n","plt.xlabel('Data points')\n","plt.ylabel('Jaccard distance')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"x0BCSaXhd2Bp","executionInfo":{"status":"ok","timestamp":1729641521321,"user_tz":300,"elapsed":12092,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"adbb93b2-205f-4277-d458-6eea2f40b508"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x700 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1kAAAJwCAYAAAB71at5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACbA0lEQVR4nOzdd5wU9eH/8ff2272y1xscB9zRwaMjCoKCAlECqKBEg6BCYtRI7CQqGpNgidFYiTEC/uwFlVhIFEUUsCBdBen9jitc77fz+8PMfG+lyOngcfJ6Ph77gJ2d8pk+7/l8Zs5hGIYhAAAAAIAtnM1dAAAAAAD4KSFkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBOOG1bdtWkydPbu5ifC+33367HA6HCgoKvrPfYz2fDodDt99+u63jnDx5stq2bWvrOH8s27dvl8Ph0Ny5c5u7KMedoUOHaujQoc1dDAA4ZghZAH5S5s6dK4fDoRUrVhzy96FDh6p79+4/cqnwbaWlpbrjjjuUk5OjqKgo+f1+de/eXTfddJP27t37o5Xj0Ucf/UmGoMWLF8vhcFgfn8+nlJQUDR06VH/5y1+Un5/f3EUEgJ80d3MXAACa28aNG+V0/vTvOR0v87l161YNHz5cO3fu1Pjx4zVt2jR5vV6tXbtW//rXv/Tqq6/q66+//lHK8uijjyoxMfGY1PBlZmaqqqpKHo/H9nEfrd/+9rfq16+fGhoalJ+fr2XLlmnmzJn629/+phdffFFnnHFGs5UNAH7KCFkATng+n8+2cdXX1ysUCsnr9TbrOA7Fzvn8vurr63XuuecqLy9Pixcv1qBBg8J+//Of/6y77767mUpnj8brLyIiolnLMnjwYJ1//vlh3dasWaOzzjpL5513nr788kulpaU1U+mOrLq6Wl6v90e5MXCs9jkAJ67mv6UJAM3sUM8qFRcXa/r06crIyJDP51N2drbuvvtuhUIhqx/zmZu//vWveuCBB5SVlSWfz6cvv/xStbW1uu2229SnTx8Fg0FFRkZq8ODBev/998Omc6RxSNKGDRs0YcIEJSUlye/3q1OnTvrDH/5w0DwUFxdr8uTJio2NVTAY1JQpU1RZWXlU8/m73/1Obdu2lc/nU+vWrTVp0iTrGa+jnY+j9corr2jNmjX6wx/+cFDAkqSYmBj9+c9/PuzwZjO4xYsXh3U/1PNPubm5mjJlilq3bi2fz6e0tDSNGTNG27dvt5bHF198oQ8++MBqVtf4OaEfug0cqkyTJ09WVFSU9uzZo7FjxyoqKkpJSUm6/vrr1dDQEDZPhYWF+uUvf6mYmBjFxsbqkksu0Zo1a37wc145OTl64IEHVFxcrIcffjjstz179ujSSy9VSkqKfD6funXrpieffDKsH3MdvPjii/rzn/+s1q1bKyIiQsOGDdPmzZsPmt7jjz+urKws+f1+9e/fXx9++OFB/ZjjfP7553XLLbeoVatWCgQCKi0tlSS99NJL6tOnj/x+vxITE3XxxRdrz549B43npZdeUteuXRUREaHu3bvr1VdfPei5Prv320ceeUTt27dXIBDQWWedpV27dskwDN15551q3bq1/H6/xowZo6KioqNeRwBaPmqyAPwklZSUHPJlEHV1dd85bGVlpYYMGaI9e/boV7/6ldq0aaNly5ZpxowZ2rdvnx544IGw/ufMmaPq6mpNmzZNPp9P8fHxKi0t1RNPPKGJEydq6tSpKisr07/+9S+NGDFCn376qXr27Pmd41i7dq0GDx4sj8ejadOmqW3bttqyZYv+/e9/HxREJkyYoHbt2mnWrFlauXKlnnjiCSUnJx+xVqi8vFyDBw/WV199pUsvvVS9e/dWQUGBFixYoN27dysxMbHJ8/FdFixYIEn65S9/2aThvo/zzjtPX3zxha6++mq1bdtW+/fv1zvvvKOdO3eqbdu2euCBB3T11VcrKirKCq4pKSmS7NkGGoexxhoaGjRixAgNGDBAf/3rX/Xuu+/qvvvuU1ZWlq644gpJUigU0ujRo/Xpp5/qiiuuUOfOnfX666/rkksusWXZnH/++brsssv03//+19qW8vLydPLJJ8vhcOiqq65SUlKS3n77bV122WUqLS3V9OnTw8Zx1113yel06vrrr1dJSYnuueceXXTRRfrkk0+sfv71r3/pV7/6lU455RRNnz5dW7du1c9//nPFx8crIyPjoHLdeeed8nq9uv7661VTUyOv16u5c+dqypQp6tevn2bNmqW8vDz9/e9/19KlS7Vq1SrFxsZKkt58801dcMEF6tGjh2bNmqUDBw7osssuU6tWrQ65DOzYb5955hnV1tbq6quvVlFRke655x5NmDBBZ5xxhhYvXqybbrpJmzdv1kMPPaTrr7/+oMAK4CfMAICfkDlz5hiSjvjp1q1b2DCZmZnGJZdcYn2/8847jcjISOPrr78O6+/mm282XC6XsXPnTsMwDGPbtm2GJCMmJsbYv39/WL/19fVGTU1NWLcDBw4YKSkpxqWXXmp1O9I4TjvtNCM6OtrYsWNHWPdQKGT9f+bMmYaksHEahmGMGzfOSEhIOOJ83nbbbYYkY/78+ca3mdM42vkwDMOQZMycOfOgcTXWq1cvIxgMHrGfxi655BIjMzPT+v7+++8bkoz3338/rD9zOc6ZM8cqoyTj3nvvPeL4u3XrZgwZMuSg7nZsA98ukzk/kow//vGPYf326tXL6NOnj/X9lVdeMSQZDzzwgNWtoaHBOOOMMw4a56GYy+mll146bD85OTlGXFyc9f2yyy4z0tLSjIKCgrD+LrzwQiMYDBqVlZVh4+7SpUvYtvH3v//dkGSsW7fOMAzDqK2tNZKTk42ePXuG9ff4448bksKWuznO9u3bW9NpPI7u3bsbVVVVVvc33njDkGTcdtttVrcePXoYrVu3NsrKyqxuixcvNiSFbUN27rdJSUlGcXGx1X3GjBmGJCMnJ8eoq6uzuk+cONHwer1GdXW1AeDEQHNBAD9JjzzyiN55552DPieddNJ3DvvSSy9p8ODBiouLU0FBgfUZPny4GhoatGTJkrD+zzvvPCUlJYV1c7lc1vMdoVBIRUVFqq+vV9++fbVy5cqDpvntceTn52vJkiW69NJL1aZNm7B+HQ7HQcP/+te/Dvs+ePBgFRYWWs2tDuWVV15RTk6Oxo0bd9Bv5jSaOh/fpbS0VNHR0U0erqn8fr+8Xq8WL16sAwcONHl4O7aBIznU+tq6dav1feHChfJ4PJo6darVzel06sorr2zyvBxOVFSUysrKJEmGYeiVV17R6NGjZRhG2DyPGDFCJSUlB63vKVOmhD3DNHjwYEmy5mPFihXav3+/fv3rX4f1N3nyZAWDwUOW6ZJLLpHf77e+m+P4zW9+E/Z829lnn63OnTvrzTfflCTt3btX69at06RJkxQVFWX1N2TIEPXo0eOQ07Jjvx0/fnzYvAwYMECSdPHFF8vtdod1r62tPWQTRwA/TTQXBPCT1L9/f/Xt2/eg7uZF85Fs2rRJa9euPexF8/79+8O+t2vX7pD9zZs3T/fdd582bNgQ1kzxUP1/u5t5oXq0r5v/dhCLi4uTJB04cEAxMTGHHGbLli0677zzvnPcTZmP7xITExMWJo4Vn8+nu+++W9ddd51SUlJ08skn65xzztGkSZOUmpr6ncPbtQ0cSkRExEHjjYuLCwuDO3bsUFpamgKBQFh/2dnZRz2d71JeXm4F3vz8fBUXF+vxxx/X448/fsj+vz3PR9rmpG/mQZI6dOgQ1p/H41H79u0POY1vL0dzHJ06dTqo386dO+ujjz4K6+9Qyyc7O/uQAcmO/fbby8AMXN9uCml2/z6BH0DLRMgCgG8JhUI688wzdeONNx7y944dO4Z9b3zn3fT0009r8uTJGjt2rG644QYlJyfL5XJp1qxZ2rJly0H9H2ocTeFyuQ7Z3TCMHzTeps7Hd+ncubNWrVqlXbt2HfKZnO9yqFo8SQe9NEKSpk+frtGjR+u1117Tf/7zH916662aNWuW3nvvPfXq1euI07FjGzicw62rH1NdXZ2+/vprK8Sbz49dfPHFh33u69u1wMdim/uh+8EPnVZTt/fDLYNjtT8CaDkIWQDwLVlZWSovL9fw4cO/9zhefvlltW/fXvPnzw8LBjNnzjyq4c07/evXr//eZfguWVlZ3zn+Hzof3zZ69Gg999xzevrppzVjxowmD2/WlhQXF4d1N2syvi0rK0vXXXedrrvuOm3atEk9e/bUfffdp6efflrS4UObHdvAD5GZman3339flZWVYbVZh3p73/fx8ssvq6qqSiNGjJAkJSUlKTo6Wg0NDbbNc2ZmpqRvagUb/z2uuro6bdu2TTk5OUc9jo0bNx70N702btxo/W7+e6jl05RlZvf2DuDExTNZAPAtEyZM0PLly/Wf//znoN+Ki4tVX1//neMw72Q3vnP9ySefaPny5UdVhqSkJJ122ml68skntXPnzrDf7Lobft5552nNmjV69dVXD/rNnMYPnY9vO//889WjRw/9+c9/PuQ4ysrKDvmKelNmZqZcLtdBz0Q9+uijYd8rKytVXV0d1i0rK0vR0dGqqamxukVGRh4U2CR7toEfYsSIEaqrq9M///lPq1soFNIjjzzyg8e9Zs0aTZ8+XXFxcdYzXi6XS+edd55eeeWVQwbv/Pz8Jk+nb9++SkpK0uzZs1VbW2t1nzt37iGX+eHGkZycrNmzZ4ett7fffltfffWVzj77bElSenq6unfvrqeeekrl5eVWfx988IHWrVt31GW2e3sHcOKiJgsAvuWGG27QggULdM4552jy5Mnq06ePKioqtG7dOr388svavn27EhMTjziOc845R/Pnz9e4ceN09tlna9u2bZo9e7a6du0adhF4JA8++KAGDRqk3r17a9q0aWrXrp22b9+uN998U6tXr7ZlPl9++WWNHz9el156qfr06aOioiItWLBAs2fPVk5Oji3z0ZjH49H8+fM1fPhwnXbaaZowYYJOPfVUeTweffHFF3r22WcVFxd32L+VFQwGNX78eD300ENyOBzKysrSG2+8cdDzQl9//bWGDRumCRMmqGvXrnK73Xr11VeVl5enCy+80OqvT58+euyxx/SnP/1J2dnZSk5O1hlnnGHLNvBDjB07Vv3799d1112nzZs3q3PnzlqwYIH1t5YOVwP3bR9++KGqq6vV0NCgwsJCLV26VAsWLFAwGNSrr74a9nzaXXfdpffff18DBgzQ1KlT1bVrVxUVFWnlypV69913m/x3njwej/70pz/pV7/6lc444wxdcMEF2rZtm+bMmXPYZ7IONY67775bU6ZM0ZAhQzRx4kTrFe5t27bV7373O6vfv/zlLxozZoxOPfVUTZkyRQcOHNDDDz+s7t27H/W2avf2DuDERcgCgG8JBAL64IMP9Je//EUvvfSSnnrqKcXExKhjx4664447DvtmtMYmT56s3Nxc/eMf/9B//vMfde3aVU8//bReeumlg/6Q7uHk5OTo448/1q233qrHHntM1dXVyszM1IQJE37gHH4jKipKH374oWbOnKlXX31V8+bNU3JysoYNG6bWrVvbNh/flp2drdWrV+v+++/Xq6++qtdee02hUEjZ2dm6/PLL9dvf/vaIwz/00EOqq6vT7Nmz5fP5NGHCBN17771hLwnJyMjQxIkTtWjRIv2///f/5Ha71blzZ7344othL/u47bbbtGPHDt1zzz0qKyvTkCFDdMYZZ9iyDfwQLpdLb775pq655hrNmzdPTqdT48aN08yZM3XqqaeGvWnvSB588EFJ34SV2NhYdenSRXfccYemTp160Ms3UlJS9Omnn+qPf/yj5s+fr0cffVQJCQnq1q3bEf/e2pFMmzZNDQ0Nuvfee3XDDTeoR48eWrBggW699dajHsfkyZMVCAR011136aabblJkZKTGjRunu+++2/obWdL/NUW9/fbbdfPNN6tDhw6aO3eu5s2bpy+++OKop2X39g7gxOQweAoTAIAW4bXXXtO4ceP00Ucf6dRTT23u4rQIPXv2VFJSkt55553mLgqAEwjPZAEAcByqqqoK+97Q0KCHHnpIMTEx6t27dzOV6vhVV1d30LNyixcv1po1azR06NDmKRSAExbNBQEAOA5dffXVqqqq0sCBA1VTU6P58+dr2bJl+stf/vKjvuq8pdizZ4+GDx+uiy++WOnp6dqwYYNmz56t1NTUg/74MwAcazQXBADgOPTss8/qvvvu0+bNm1VdXa3s7GxdccUVuuqqq5q7aMelkpISTZs2TUuXLlV+fr4iIyM1bNgw3XXXXcrKymru4gE4wRCyAAAAAMBGPJMFAAAAADYiZAEAAACAjXjxxSGEQiHt3btX0dHRR/0HHwEAAAD89BiGobKyMqWnp8vpPLo6KkLWIezdu1cZGRnNXQwAAAAAx4ldu3apdevWR9UvIesQoqOjJX2zIGNiYpq5NAAAAACaS2lpqTIyMqyMcDQIWYdgNhGMiYkhZAEAAABo0mNEvPgCAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARs0asmbNmqV+/fopOjpaycnJGjt2rDZu3BjWT3V1ta688kolJCQoKipK5513nvLy8o44XsMwdNtttyktLU1+v1/Dhw/Xpk2bjuWsAAAAAICkZg5ZH3zwga688kp9/PHHeuedd1RXV6ezzjpLFRUVVj+/+93v9O9//1svvfSSPvjgA+3du1fnnnvuEcd7zz336MEHH9Ts2bP1ySefKDIyUiNGjFB1dfWxniUAAAAAJziHYRhGcxfClJ+fr+TkZH3wwQc67bTTVFJSoqSkJD377LM6//zzJUkbNmxQly5dtHz5cp188skHjcMwDKWnp+u6667T9ddfL0kqKSlRSkqK5s6dqwsvvPA7y1FaWqpgMKh9+YWKiYmxdyYBAECL5ve45HA4mrsYAH4kZjYoKSk56mzgPsZlapKSkhJJUnx8vCTp888/V11dnYYPH27107lzZ7Vp0+awIWvbtm3Kzc0NGyYYDGrAgAFavnz5IUNWTU2NampqrO+lpaWSpP5/XiSnL2DPzAEAgJ+EvplxeunXAwlaAA7ruHnxRSgU0vTp03Xqqaeqe/fukqTc3Fx5vV7FxsaG9ZuSkqLc3NxDjsfsnpKSctTDzJo1S8Fg0PpkZGT8wLkBAAA/VSt2HFBVXUNzFwPAcey4qcm68sortX79en300Uc/+rRnzJiha6+91vpeWlpqBa0VtwxXwOv60csEAACOL5W1Der7p3ebuxgAWoDjImRdddVVeuONN7RkyRK1bt3a6p6amqra2loVFxeH1Wbl5eUpNTX1kOMyu+fl5SktLS1smJ49ex5yGJ/PJ5/Pd8jfAl6XAt7jYjEBAAAAaAGatbmgYRi66qqr9Oqrr+q9995Tu3btwn7v06ePPB6PFi1aZHXbuHGjdu7cqYEDBx5ynO3atVNqamrYMKWlpfrkk08OOwwAAAAA2KVZQ9aVV16pp59+Ws8++6yio6OVm5ur3NxcVVVVSfrmhRWXXXaZrr32Wr3//vv6/PPPNWXKFA0cODDspRedO3fWq6++KklyOByaPn26/vSnP2nBggVat26dJk2apPT0dI0dO7Y5ZhMAAADACaRZ28E99thjkqShQ4eGdZ8zZ44mT54sSbr//vvldDp13nnnqaamRiNGjNCjjz4a1v/GjRutNxNK0o033qiKigpNmzZNxcXFGjRokBYuXKiIiIhjOj8AAAAAcFz9nazjhfku/IzpL2rD3eN4JgsAAKiytl5db/uPJOnLP47g+gA4QXyfv5N13LzCHQAAAAB+CghZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI2aNWQtWbJEo0ePVnp6uhwOh1577bWw3x0OxyE/995772HHefvttx/Uf+fOnY/xnAAAAADAN5o1ZFVUVCgnJ0ePPPLIIX/ft29f2OfJJ5+Uw+HQeeedd8TxduvWLWy4jz766FgUHwAAAAAO4m7OiY8aNUqjRo067O+pqalh319//XWdfvrpat++/RHH63a7DxoWAAAAAH4MLeaZrLy8PL355pu67LLLvrPfTZs2KT09Xe3bt9dFF12knTt3/gglBAAAAIBmrslqinnz5ik6OlrnnnvuEfsbMGCA5s6dq06dOmnfvn264447NHjwYK1fv17R0dGHHKampkY1NTXW99LSUlvLDgAAAODE0WJC1pNPPqmLLrpIERERR+yvcfPDk046SQMGDFBmZqZefPHFw9aCzZo1S3fccYet5QUAAABwYmoRzQU//PBDbdy4UZdffnmTh42NjVXHjh21efPmw/YzY8YMlZSUWJ9du3b9kOICAAAAOIG1iJD1r3/9S3369FFOTk6Thy0vL9eWLVuUlpZ22H58Pp9iYmLCPgAAAADwfTRryCovL9fq1au1evVqSdK2bdu0evXqsBdVlJaW6qWXXjpsLdawYcP08MMPW9+vv/56ffDBB9q+fbuWLVumcePGyeVyaeLEicd0XgAAAABAauZnslasWKHTTz/d+n7ttddKki655BLNnTtXkvT888/LMIzDhqQtW7aooKDA+r57925NnDhRhYWFSkpK0qBBg/Txxx8rKSnp2M0IAAAAAPxPs4asoUOHyjCMI/Yzbdo0TZs27bC/b9++Pez7888/b0fRAAAAAOB7aRHPZAEAAABAS0HIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGzUrCFryZIlGj16tNLT0+VwOPTaa6+F/T558mQ5HI6wz8iRI79zvI888ojatm2riIgIDRgwQJ9++ukxmgMAAAAACNesIauiokI5OTl65JFHDtvPyJEjtW/fPuvz3HPPHXGcL7zwgq699lrNnDlTK1euVE5OjkaMGKH9+/fbXXwAAAAAOIi7OSc+atQojRo16oj9+Hw+paamHvU4//a3v2nq1KmaMmWKJGn27Nl688039eSTT+rmm2/+QeUFAAAAgO9y3D+TtXjxYiUnJ6tTp0664oorVFhYeNh+a2tr9fnnn2v48OFWN6fTqeHDh2v58uU/RnEBAAAAnOCatSbru4wcOVLnnnuu2rVrpy1btuj3v/+9Ro0apeXLl8vlch3Uf0FBgRoaGpSSkhLWPSUlRRs2bDjsdGpqalRTU2N9Ly0ttW8mAAAAAJxQjuuQdeGFF1r/79Gjh0466SRlZWVp8eLFGjZsmG3TmTVrlu644w7bxgcAAADgxHXcNxdsrH379kpMTNTmzZsP+XtiYqJcLpfy8vLCuufl5R3xua4ZM2aopKTE+uzatcvWcgMAAAA4cbSokLV7924VFhYqLS3tkL97vV716dNHixYtsrqFQiEtWrRIAwcOPOx4fT6fYmJiwj4AAAAA8H00a8gqLy/X6tWrtXr1aknStm3btHr1au3cuVPl5eW64YYb9PHHH2v79u1atGiRxowZo+zsbI0YMcIax7Bhw/Twww9b36+99lr985//1Lx58/TVV1/piiuuUEVFhfW2QQAAAAA4lpr1mawVK1bo9NNPt75fe+21kqRLLrlEjz32mNauXat58+apuLhY6enpOuuss3TnnXfK5/NZw2zZskUFBQXW9wsuuED5+fm67bbblJubq549e2rhwoUHvQwDAAAAAI6FZg1ZQ4cOlWEYh/39P//5z3eOY/v27Qd1u+qqq3TVVVf9kKIBAAAAwPfSop7JAgAAAIDjHSELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsFGTQ1Z9fb3effdd/eMf/1BZWZkkae/evSovL2/yxJcsWaLRo0crPT1dDodDr732mvVbXV2dbrrpJvXo0UORkZFKT0/XpEmTtHfv3iOO8/bbb5fD4Qj7dO7cucllAwAAAIDvo0kha8eOHerRo4fGjBmjK6+8Uvn5+ZKku+++W9dff32TJ15RUaGcnBw98sgjB/1WWVmplStX6tZbb9XKlSs1f/58bdy4UT//+c+/c7zdunXTvn37rM9HH33U5LIBAAAAwPfhbkrP11xzjfr27as1a9YoISHB6j5u3DhNnTq1yRMfNWqURo0adcjfgsGg3nnnnbBuDz/8sPr376+dO3eqTZs2hx2v2+1Wampqk8sDAAAAAD9Uk0LWhx9+qGXLlsnr9YZ1b9u2rfbs2WNrwQ6lpKREDodDsbGxR+xv06ZNSk9PV0REhAYOHKhZs2YdMZQBAAAAgF2aFLJCoZAaGhoO6r57925FR0fbVqhDqa6u1k033aSJEycqJibmsP0NGDBAc+fOVadOnbRv3z7dcccdGjx4sNavX3/YMtbU1Kimpsb6Xlpaanv5AQAAAJwYmvRM1llnnaUHHnjA+u5wOFReXq6ZM2fqZz/7md1ls9TV1WnChAkyDEOPPfbYEfsdNWqUxo8fr5NOOkkjRozQW2+9peLiYr344ouHHWbWrFkKBoPWJyMjw+5ZAAAAAHCCaFLIuu+++7R06VJ17dpV1dXV+sUvfmE1Fbz77ruPSQHNgLVjxw698847R6zFOpTY2Fh17NhRmzdvPmw/M2bMUElJifXZtWvXDy02AAAAgBNUk5oLtm7dWmvWrNELL7ygNWvWqLy8XJdddpkuuugi+f1+2wtnBqxNmzbp/fffD3vZxtEqLy/Xli1b9Mtf/vKw/fh8Pvl8vh9SVAAAAACQ1MSQJX3z5r6LLrpIF1100Q+eeHl5eVgN07Zt27R69WrFx8crLS1N559/vlauXKk33nhDDQ0Nys3NlSTFx8dbL98YNmyYxo0bp6uuukqSdP3112v06NHKzMzU3r17NXPmTLlcLk2cOPEHlxcAAAAAvkuTQtasWbOUkpKiSy+9NKz7k08+qfz8fN10001NmviKFSt0+umnW9+vvfZaSdIll1yi22+/XQsWLJAk9ezZM2y4999/X0OHDpUkbdmyRQUFBdZvu3fv1sSJE1VYWKikpCQNGjRIH3/8sZKSkppUNgAAAAD4PpoUsv7xj3/o2WefPah7t27ddOGFFzY5ZA0dOlSGYRz29yP9Ztq+fXvY9+eff75JZQAAAAAAOzXpxRe5ublKS0s7qHtSUpL27dtnW6EAAAAAoKVqUsjKyMjQ0qVLD+q+dOlSpaen21YoAAAAAGipmtRccOrUqZo+fbrq6up0xhlnSJIWLVqkG2+8Udddd90xKSAAAAAAtCRNClk33HCDCgsL9Zvf/Ea1tbWSpIiICN10002aMWPGMSkgAAAAALQkTQpZDodDd999t2699VZ99dVX8vv96tChA39jCgAAAAD+p8l/J0uSoqKi1K9fP7vLAgAAAAAtXpNCVkVFhe666y4tWrRI+/fvVygUCvt969atthYOAAAAAFqaJoWsyy+/XB988IF++ctfKi0tTQ6H41iVCwAAAABapCaFrLfffltvvvmmTj311GNVHgAAAABo0Zr0d7Li4uIUHx9/rMoCAAAAAC1ek0LWnXfeqdtuu02VlZXHqjwAAAAA0KI1qbngfffdpy1btiglJUVt27aVx+MJ+33lypW2Fg4AAAAAWpomhayxY8ceo2IAAAAAwE9Dk0LWzJkzj1U5AAAAAOAnoUnPZAEAAAAAjqxJNVkNDQ26//779eKLL2rnzp2qra0N+72oqMjWwgEAAABAS9Okmqw77rhDf/vb33TBBReopKRE1157rc4991w5nU7dfvvtx6iIAAAAANByNClkPfPMM/rnP/+p6667Tm63WxMnTtQTTzyh2267TR9//PGxKiMAAAAAtBhNClm5ubnq0aOHJCkqKkolJSWSpHPOOUdvvvmm/aUDAAAAgBamSSGrdevW2rdvnyQpKytL//3vfyVJn332mXw+n/2lAwAAAIAWpkkha9y4cVq0aJEk6eqrr9att96qDh06aNKkSbr00kuPSQEBAAAAoCVp0tsF77rrLuv/F1xwgTIzM7Vs2TJ16NBBo0ePtr1wAAAAANDSNClkLVmyRKeccorc7m8GO/nkk3XyyServr5eS5Ys0WmnnXZMCgkAAAAALUWTmguefvrph/xbWCUlJTr99NNtKxQAAAAAtFRNClmGYcjhcBzUvbCwUJGRkbYVCgAAAABaqqNqLnjuuedKkhwOhyZPnhz2JsGGhgatXbtWp5xyyrEpIQAAAAC0IEcVsoLBoKRvarKio6Pl9/ut37xer04++WRNnTr12JQQAAAAAFqQowpZc+bMkSS1bdtW119/PU0DAQAAAOAwmvRM1o033hj2TNaOHTv0wAMPWH+UGAAAAABOdE0KWWPGjNFTTz0lSSouLlb//v113333acyYMXrssceOSQEBAAAAoCVpUshauXKlBg8eLEl6+eWXlZqaqh07duipp57Sgw8+eEwKCAAAAAAtSZNCVmVlpaKjoyVJ//3vf3XuuefK6XTq5JNP1o4dO45JAQEAAACgJWlSyMrOztZrr72mXbt26T//+Y/OOussSdL+/fsVExNzTAoIAAAAAC1Jk0LWbbfdpuuvv15t27bVgAEDNHDgQEnf1Gr16tXrmBQQAAAAAFqSo3qFu+n888/XoEGDtG/fPuXk5Fjdhw0bpnHjxtleOAAAAABoaZoUsiQpNTVVqampYd369+9vW4EAAAAAoCX7zpB17rnnau7cuYqJidG55557xH7nz59vW8EAAAAAoCX6zpAVDAatP0AcDAaPeYEAAAAAoCX7zpA1Z86cQ/4fAAAAAHCwJr1dEAAAAABwZN9Zk9WrVy+rueB3Wbly5Q8uEAAAAAC0ZN8ZssaOHWv9v7q6Wo8++qi6du1q/Y2sjz/+WF988YV+85vfHLNCAgAAAEBL8Z0ha+bMmdb/L7/8cv32t7/VnXfeeVA/u3btsr90AAAAANDCNOmZrJdeekmTJk06qPvFF1+sV155xbZCAQAAAEBL1aSQ5ff7tXTp0oO6L126VBEREbYVCgAAAABaqu9sLtjY9OnTdcUVV2jlypXq37+/JOmTTz7Rk08+qVtvvfWYFBAAAAAAWpImhaybb75Z7du319///nc9/fTTkqQuXbpozpw5mjBhwjEpIAAAAAC0JE0KWZI0YcIEAhUAAAAAHAZ/jBgAAAAAbNTkmiwAAJqDYRiqqq9q7mLgBFZZ19Do/1WSw9WMpcGJzu/2y+FwNHcxcBiELADAcc8wDE16e5JW569u7qLgBGaEPJK++VuhQ18cIoezrnkLhBNar+RemjdyHkHrOEXIAgAc96rqqwhYaHYOZ52iu9zc3MUAJEmr9q9SVX2VAp5AcxcFh0DIAgC0KIsnLJbf7W/uYgBAs6iqr9LQF4c2dzHwHb4zZF177bVHPbK//e1vTZr4kiVLdO+99+rzzz/Xvn379Oqrr2rs2LHW74ZhaObMmfrnP/+p4uJinXrqqXrsscfUoUOHI473kUce0b333qvc3Fzl5OTooYcesv6uFwCgZfO7/dy5BQAc174zZK1atSrs+8qVK1VfX69OnTpJkr7++mu5XC716dOnyROvqKhQTk6OLr30Up177rkH/X7PPffowQcf1Lx589SuXTvdeuutGjFihL788ktFREQccpwvvPCCrr32Ws2ePVsDBgzQAw88oBEjRmjjxo1KTk5uchkBAAAAoCm+M2S9//771v//9re/KTo6WvPmzVNcXJwk6cCBA5oyZYoGDx7c5ImPGjVKo0aNOuRvhmHogQce0C233KIxY8ZIkp566imlpKTotdde04UXXnjI4f72t79p6tSpmjJliiRp9uzZevPNN/Xkk0/q5ptpRw0AAADg2GrS38m67777NGvWLCtgSVJcXJz+9Kc/6b777rO1YNu2bVNubq6GDx9udQsGgxowYICWL19+yGFqa2v1+eefhw3jdDo1fPjwww4DAAAAAHZq0osvSktLlZ+ff1D3/Px8lZWV2VYoScrNzZUkpaSkhHVPSUmxfvu2goICNTQ0HHKYDRs2HHZaNTU1qqmpsb6XlpZ+32IDAAAAOME1qSZr3LhxmjJliubPn6/du3dr9+7deuWVV3TZZZcd8pmqlmLWrFkKBoPWJyMjo7mLBAAAAKCFalLImj17tkaNGqVf/OIXyszMVGZmpn7xi19o5MiRevTRR20tWGpqqiQpLy8vrHteXp7127clJibK5XI1aRhJmjFjhkpKSqzPrl27fmDpAQAAAJyojjpkNTQ0aMWKFfrzn/+swsJCrVq1SqtWrVJRUZEeffRRRUZG2lqwdu3aKTU1VYsWLbK6lZaW6pNPPtHAgQMPOYzX61WfPn3ChgmFQlq0aNFhh5Ekn8+nmJiYsA8AAAAAfB9H/UyWy+XSWWedpa+++krt2rXTSSed9IMnXl5ers2bN1vft23bptWrVys+Pl5t2rTR9OnT9ac//UkdOnSwXuGenp4e9re0hg0bpnHjxumqq66S9M3f9brkkkvUt29f9e/fXw888IAqKiqstw0CAAAAwLHUpBdfdO/eXVu3blW7du1smfiKFSt0+umnW9/NP3x8ySWXaO7cubrxxhtVUVGhadOmqbi4WIMGDdLChQvD/kbWli1bVFBQYH2/4IILlJ+fr9tuu025ubnq2bOnFi5ceNDLMAAAAADgWHAYhmEcbc8LFy7UjBkzdOedd6pPnz4HNRH8qTSzKy0t/eYFGNNf1Ia7xyngbVIWBQDYrLKuUgOeHSBJ+uQXnyjgCTRziQCgeXA8/PGZ2aCkpOSo806T0sPPfvYzSdLPf/5zORwOq7thGHI4HGpoaGjK6AAAAADgJ6dJIev9998/VuUAAAAAgJ+EJoWsIUOGHKtyAAAAAMBPwvd62KiyslI7d+5UbW1tWHc73jgIAAAAAC1Zk0JWfn6+pkyZorfffvuQv/NMFgAAAIAT3VH/MWJJmj59uoqLi/XJJ5/I7/dr4cKFmjdvnjp06KAFCxYcqzICAAAAQIvRpJqs9957T6+//rr69u0rp9OpzMxMnXnmmYqJidGsWbN09tlnH6tyAgAAAECL0KSarIqKCiUnJ0uS4uLilJ+fL0nq0aOHVq5caX/pAAAAAKCFaVLI6tSpkzZu3ChJysnJ0T/+8Q/t2bNHs2fPVlpa2jEpIAAAAAC0JE1qLnjNNddo3759kqSZM2dq5MiReuaZZ+T1ejV37txjUT4AAAAAaFGaFLIuvvhi6/99+vTRjh07tGHDBrVp00aJiYm2Fw4AAAAAWprv9XeyTIFAQL1797arLAAAAADQ4jXpmazzzjtPd99990Hd77nnHo0fP962QgEAAABAS9WkkLVkyRL97Gc/O6j7qFGjtGTJEtsKBQAAAAAtVZNCVnl5ubxe70HdPR6PSktLbSsUAAAAALRUTQpZPXr00AsvvHBQ9+eff15du3a1rVAAAAAA0FI16cUXt956q84991xt2bJFZ5xxhiRp0aJFeu655/TSSy8dkwICAAAAQEvSpJA1evRovfbaa/rLX/6il19+WX6/XyeddJLeffddDRky5FiVEQAAAABajCa/wv3ss8/W2WeffSzKAgAAAAAtXpOeyfrss8/0ySefHNT9k08+0YoVK2wrFAAAAAC0VE0KWVdeeaV27dp1UPc9e/boyiuvtK1QAAAAANBSNSlkffnll+rdu/dB3Xv16qUvv/zStkIBAAAAQEvVpJDl8/mUl5d3UPd9+/bJ7W7y410AAAAA8JPTpJB11llnacaMGSopKbG6FRcX6/e//73OPPNM2wsHAAAAAC1Nk6qf/vrXv+q0005TZmamevXqJUlavXq1UlJS9P/+3/87JgUEAAAAgJakSSGrVatWWrt2rZ555hmtWbNGfr9fU6ZM0cSJE+XxeI5VGQEAAACgxWjyg1SRkZGaNm3asSgLAAAAALR43+ttFV9++aV27typ2trasO4///nPbSkUAAAAALRUTQpZW7du1bhx47Ru3To5HA4ZhiFJcjgckqSGhgb7SwgAAAAALUiT3i54zTXXqF27dtq/f78CgYC++OILLVmyRH379tXixYuPUREBAAAAoOVoUk3W8uXL9d577ykxMVFOp1NOp1ODBg3SrFmz9Nvf/larVq06VuUEAAAAgBahSTVZDQ0Nio6OliQlJiZq7969kqTMzExt3LjR/tIBAAAAQAvTpJqs7t27a82aNWrXrp0GDBige+65R16vV48//rjat29/rMoIAAAAAC1Gk0LWLbfcooqKCknSHXfcodGjR2vw4MFKSEjQ888/f0wKCAAAAAAtSZNC1ogRI6z/d+jQQRs2bFBRUZHi4uKsNwwCAAAAwInsqELWueee+90jcruVmpqqM888U6NHj/7BBQMAAACAluioXnwRDAa/8+P3+7Vp0yZdcMEFuu222451uQEAAADguHRUNVlz5sw56hG+8cYb+s1vfqM//vGP37tQAAAAANBSNekV7kdj0KBB6tu3r92jBQAAAIAWwfaQFRsbq/nz59s9WgAAAABoEWwPWQAAAABwIiNkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNjvuQ1bZtWzkcjoM+V1555SH7nzt37kH9RkRE/MilBgAAAHCicjd3Ab7LZ599poaGBuv7+vXrdeaZZ2r8+PGHHSYmJkYbN260vjscjmNaRgAAAAAwHfchKykpKez7XXfdpaysLA0ZMuSwwzgcDqWmph7rogEAAADAQY775oKN1dbW6umnn9all156xNqp8vJyZWZmKiMjQ2PGjNEXX3xxxPHW1NSotLQ07AMAAAAA30eLClmvvfaaiouLNXny5MP206lTJz355JN6/fXX9fTTTysUCumUU07R7t27DzvMrFmzFAwGrU9GRsYxKD0AAACAE0GLCln/+te/NGrUKKWnpx+2n4EDB2rSpEnq2bOnhgwZovnz5yspKUn/+Mc/DjvMjBkzVFJSYn127dp1LIoPAAAA4ARw3D+TZdqxY4feffddzZ8/v0nDeTwe9erVS5s3bz5sPz6fTz6f74cWEQAAAABaTk3WnDlzlJycrLPPPrtJwzU0NGjdunVKS0s7RiUDAAAAgP/TIkJWKBTSnDlzdMkll8jtDq98mzRpkmbMmGF9/+Mf/6j//ve/2rp1q1auXKmLL75YO3bs0OWXX/5jFxsAAADACahFNBd89913tXPnTl166aUH/bZz5045nf+XFQ8cOKCpU6cqNzdXcXFx6tOnj5YtW6auXbv+mEUGAAAAcIJqESHrrLPOkmEYh/xt8eLFYd/vv/9+3X///T9CqQAAAADgYC2iuSAAAAAAtBSELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEbu5i4AAAAA0NIYhqGq+qoffbqNp9kc0zf53X45HI5mm/7xjpAFAAAANIFhGJr09iStzl/drOUY+uLQZpt2r+RemjdyHkHrMGguCAAAADRBVX1Vswes5rZq/6pmrUk73lGTBQAAAHxPiycslt/tb+5i/Giq6quatQatpSBkAQAAAN+T3+1XwBNo7mLgOENzQQAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbEbIAAAAAwEaELAAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbETIAgAAAAAbHdch6/bbb5fD4Qj7dO7c+YjDvPTSS+rcubMiIiLUo0cPvfXWWz9SaQEAAADgOA9ZktStWzft27fP+nz00UeH7XfZsmWaOHGiLrvsMq1atUpjx47V2LFjtX79+h+xxAAAAABOZMd9yHK73UpNTbU+iYmJh+3373//u0aOHKkbbrhBXbp00Z133qnevXvr4Ycf/hFLDAAAAOBEdtyHrE2bNik9PV3t27fXRRddpJ07dx623+XLl2v48OFh3UaMGKHly5cfcRo1NTUqLS0N+wAAAADA93Fch6wBAwZo7ty5WrhwoR577DFt27ZNgwcPVllZ2SH7z83NVUpKSli3lJQU5ebmHnE6s2bNUjAYtD4ZGRm2zQMAAACAE8txHbJGjRql8ePH66STTtKIESP01ltvqbi4WC+++KKt05kxY4ZKSkqsz65du2wdPwAAAIATh7u5C9AUsbGx6tixozZv3nzI31NTU5WXlxfWLS8vT6mpqUccr8/nk8/ns62cAAAAAE5cx3VN1reVl5dry5YtSktLO+TvAwcO1KJFi8K6vfPOOxo4cOCPUTwAAAAAOL5D1vXXX68PPvhA27dv17JlyzRu3Di5XC5NnDhRkjRp0iTNmDHD6v+aa67RwoULdd9992nDhg26/fbbtWLFCl111VXNNQsAAAAATjDHdXPB3bt3a+LEiSosLFRSUpIGDRqkjz/+WElJSZKknTt3yun8v5x4yimn6Nlnn9Utt9yi3//+9+rQoYNee+01de/evblmAQAAAMAJ5rgOWc8///wRf1+8ePFB3caPH6/x48cfoxIBAAAAwJEd180FAQAAAKClIWQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI3dzFwAAAADAsWEYhqrqq2wbX+Nx2Tlev9svh8Nh2/iaGyELAAAA+AkyDEOT3p6k1fmrj8n4h7441LZx9UrupXkj5/1kghbNBQEAAICfoKr6qmMWsOy2av8qW2vGmhs1WQAAAMBP3OIJi+V3+5u7GAepqq+ytUbseEHIAgAAAH7i/G6/Ap5AcxfjhEFzQQAAAACwESELAAAAAGxEyAIAAAAAGxGyAAAAAMBGhCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAAADARoQsAAAAALARIQsAAAAAbORu7gIAAAAAPybDMFRVX/W9h2887A8Zj8nv9svhcPzg8eD4QcgCAADACcMwDE16e5JW56+2ZXxDXxz6g8fRK7mX5o2cR9D6CaG5IAAAAE4YVfVVtgUsu6zav8qWGjEcP6jJAgAAwAlp8YTF8rv9zTb9qvoqW2rCcPwhZAEAAOCE5Hf7FfAEmrsY+AmiuSAAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANjquQ9asWbPUr18/RUdHKzk5WWPHjtXGjRuPOMzcuXPlcDjCPhERET9SiQEAAACc6I7rkPXBBx/oyiuv1Mcff6x33nlHdXV1Ouuss1RRUXHE4WJiYrRv3z7rs2PHjh+pxAAAAABOdMf1HyNeuHBh2Pe5c+cqOTlZn3/+uU477bTDDudwOJSamnqsiwcAAAAABzmua7K+raSkRJIUHx9/xP7Ky8uVmZmpjIwMjRkzRl988cUR+6+pqVFpaWnYBwAAAAC+jxYTskKhkKZPn65TTz1V3bt3P2x/nTp10pNPPqnXX39dTz/9tEKhkE455RTt3r37sMPMmjVLwWDQ+mRkZByLWQAAAABwAmgxIevKK6/U+vXr9fzzzx+xv4EDB2rSpEnq2bOnhgwZovnz5yspKUn/+Mc/DjvMjBkzVFJSYn127dpld/EBAAAAnCCO62eyTFdddZXeeOMNLVmyRK1bt27SsB6PR7169dLmzZsP24/P55PP5/uhxQQAAACA47smyzAMXXXVVXr11Vf13nvvqV27dk0eR0NDg9atW6e0tLRjUEIAAAAACHdc12RdeeWVevbZZ/X6668rOjpaubm5kqRgMCi/3y9JmjRpklq1aqVZs2ZJkv74xz/q5JNPVnZ2toqLi3Xvvfdqx44duvzyy5ttPgAAAHBiMQxDVfVVR+yn8e/f1a/f7ZfD4bClbDj2juuQ9dhjj0mShg4dGtZ9zpw5mjx5siRp586dcjr/r0LuwIEDmjp1qnJzcxUXF6c+ffpo2bJl6tq1649VbAAAAJzADMPQpLcnaXX+6qMeZuiLQ4/4e6/kXpo3ch5Bq4U4rkOWYRjf2c/ixYvDvt9///26//77j1GJAAAAgCOrqq9qUsA6Gqv2r1JVfZUCnoCt48WxcVyHLAAAAKAlWzxhsfxu//cevqq+6jtruXD8IWQBAAAAx4jf7af26QR0XL9dEAAAAABaGkIWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANnI3dwEAAACA78swDFXVVx11/437bcpwkuR3++VwOJo0DE5MhCwAAAC0SIZhaNLbk7Q6f/X3Gn7oi0Ob1H+v5F6aN3IeQQvfieaCAAAAaJGq6qu+d8D6PlbtX9Xk2i+cmKjJAgAAQIu3eMJi+d3+YzLuqvqqJtd64cRGyAIAAECL53f7FfAEmrsYgCSaCwIAAACArQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANiIkAUAAAAANiJkAQAAAICNCFkAAAAAYCNCFgAAAADYiJAFAAAAADYiZAEAAACAjQhZAAAAAGAjQhYAAAAA2IiQBQAAAAA2ImQBAAAAgI0IWQAAAABgI0IWAAAAANioRYSsRx55RG3btlVERIQGDBigTz/99Ij9v/TSS+rcubMiIiLUo0cPvfXWWz9SSQEAAACc6I77kPXCCy/o2muv1cyZM7Vy5Url5ORoxIgR2r9//yH7X7ZsmSZOnKjLLrtMq1at0tixYzV27FitX7/+Ry45AAAAgBORu7kL8F3+9re/aerUqZoyZYokafbs2XrzzTf15JNP6uabbz6o/7///e8aOXKkbrjhBknSnXfeqXfeeUcPP/ywZs+e3fQClBdIXtcPmgcAwA/ki7T+W1RdpKr6qmYsDIDj0bE6Nvjd/iZNp6n9N0VTx9W4LC3h2Hm8lrGurq7JwxzXIau2tlaff/65ZsyYYXVzOp0aPny4li9ffshhli9frmuvvTas24gRI/Taa68ddjo1NTWqqamxvpeUlEiSQjWVKr2nu+odtT9gLgAAP1Tl9V+roapBknTWM2c1c2kAHI+O5bHhvfHvNekY1NT+j1ZTx3WsynGsHK9lNJehYRhHPcxxHbIKCgrU0NCglJSUsO4pKSnasGHDIYfJzc09ZP+5ubmHnc6sWbN0xx13HNR9z2OTlfY9yg0AsNldHI0BNJ+0K5p2DGpq/8fK8VKOn4qysjIFg8Gj6ve4Dlk/lhkzZoTVfoVCIRUVFSkhIUEOh6MZSwYAAACgORmGobKyMqWnpx/1MMd1yEpMTJTL5VJeXl5Y97y8PKWmph5ymNTU1Cb1L0k+n08+ny+sW2xs7PcrNAAAAICflKOtwTId128X9Hq96tOnjxYtWmR1C4VCWrRokQYOHHjIYQYOHBjWvyS98847h+0fAAAAAOx0XNdkSdK1116rSy65RH379lX//v31wAMPqKKiwnrb4KRJk9SqVSvNmjVLknTNNddoyJAhuu+++3T22Wfr+eef14oVK/T4448352wAAAAAOEEc9yHrggsuUH5+vm677Tbl5uaqZ8+eWrhwofVyi507d8rp/L8KuVNOOUXPPvusbrnlFv3+979Xhw4d9Nprr6l79+7NNQsAAAAATiAOoynvIgQAAAAAHNFx/UwWAAAAALQ0hCwAAAAAsBEhCwAAAABsRMgCAAAAABsRsgAAx6UlS5Zo69atuvjiizVhwgQtWbKkuYsE4DjwzjvvaOrUqVq9erUk/ST/TM9bb72lt956S2+++abGjRunt956q7mLhCbi7YLf8uGHH+rKK6/Url271KZNG23btk2VlZVKT09Xt27dNGzYMAUCAUVGRurGG29UWVmZIiIi5HK51LVrV5mL85JLLtGDDz6ozZs3a+rUqTr77LP1xz/+UQ899JBKS0v1xBNPaNeuXcrPz1dBQYH+8Ic/6Oqrr9bjjz+uzp07q3Xr1rrtttu0Y8cOderUSbt27VJtba1uuOEGvffee/rNb36jqVOn6quvvlJZWZlSUlKUm5srp9OpiooKpaamqrS0VE6nU1FRUerdu7euu+467dy5UzfccINGjBihhQsXqr6+XsFgUGVlZZKkiooK+f1+3XHHHVZ5WrdurVWrVmnJkiVWGXbv3m2Vc+rUqdq2bZv+8Ic/6LLLLtPjjz+uadOmSfrmImnTpk16+umnVV9fL0maMWOGPB6PXnzxRbVp00ZPPvmkgsGgPB6PvvrqK4VCIUVERCgUCqmhoUGVlZWKiopSamqq8vPzJUkJCQlKS0uTJNXW1qq+vl6hUEg+n0/btm1TdXW1nE6n9du0adP04IMP6uqrr9aoUaO0cuVKPfHEE7ruuuuUk5NjrY/GZYyKigqbv9GjR2vEiBF6++23tXv3bg0cOFAJCQl6+umnlZ+fr71798owDCUlJam8vFxut1vV1dUqLi5WRkaGKioq1Lt3b/385z/XggULNGTIEN1+++2Kj49XXFycCgoKVF5errq6OmVmZqqsrEx33nmnLr/8cp177rk6++yz9fTTT+vrr79W+/bt5XQ6tW/fPu3Zs0der1fl5eUKhULyeDxKSUlRXV2diouLVVdXZy2b6upqGYah2NhYlZSUyOl0Ki4uTj169NDevXuVl5enmpoaxcXFyeVyqb6+XiUlJXI4HIqNjVVSUpI2btyompoadevWTWVlZdqzZ4+ioqJUU1MjwzDkcrlUXV0tScrMzNS+ffvkcrnU0NCgTp066dprr9Utt9yimpoalZeXq1u3blq9erWcTqfatWunDRs2yOFwyOfzKRQKWdMdP3680tPT9eCDDyovL0+RkZGaNGmSCgoKtGzZMm3ZskVt27ZVaWmp6urqVFNTo8jISJWVlalXr17auHGjsrKyVFZWJq/Xq127dqm6ulpdu3bVY489poceekiPPfaYtd2+9tprev7555WSkqI9e/YoNTVV1113nW6++WYVFRXJMAw1NDQoLS1NeXl5crlc8vl8MgxDoVDIWu5Op1MOh0ORkZGqrKyU2+2Ww+GQw+GQy+VSenq69u3bJ7/fb5W7oqJC8fHxqqiokNfr1amnnqqNGzfq8ccfl9PptPYp8/hx3nnnqba2VqmpqTrzzDP1xBNPaN26ddq8ebMqKytlGIYiIiLkcDjk9Xqtfcjv96ugoEDV1dXKyspSbGys9u3bp7vvvlv333+/1q5dq7vuukv9+/fX7Nmz1a1bN6WmpmrNmjV6+eWX1alTJ+Xn5+uSSy7R559/rmAwqPXr1+v0009XUlKSTj/9dM2fP1+ff/65Vq9erZtuukmFhYUqLi5WQ0ODxowZo127dun111/X9ddfr+eee04fffSR1q9fr0GDBumtt96S2+229tldu3bJ7/drxIgROnDggJYuXSqv16v6+no1NDRo9OjR2rZtm9auXWuVtV27durTp4+efvpp7d+/X+Xl5UpISJBhGDrttNO0cOFCGYahgoIC9enTRx999JESExNVU1OjqKgo+f1+XXTRRXrsscdUXFysUCika665Rm+//baGDx+u0aNHW+tj//79ys/PVygUUkJCgkaOHKmnnnpKDQ0NMgxDiYmJqq2t1RlnnKHdu3crEAho6dKlatWqlf70pz8pFArpk08+Ue/evfXcc89p9erVio2NVUZGhkaMGKGsrKyw48/555+va665RkVFRfJ4PFq/fr0aGhpUV1cnt9utqqoqZWdn66abbtLWrVt14MABffnll7rwwgutY4nf71dqaqpat26tzMxMvf766xo4cKBWrFihPXv2qKqqSoFAQL/4xS/06quvyuv1Kjc3V3V1dYqOjlarVq1UUVGhlJQUrVu3zjoW9+/fXwcOHJDb7damTZvkdrvV0NCg5ORk5eXlKS4uTsXFxYqIiFBVVZUkyTAMq7+qqio5nU55PB55PB5VVlYqFAopNTVV1dXVcrvdSk5OVn5+voqKihQZGak2bdrIMAylpaVp586dqqurs7aPYDCor776Sm3atNH+/fsVCoWUk5Ojxx57TF999ZU++eQTderUKWy/Ms/L119/vX7zm9/otttu0549e9S/f399+umnKiws1Pbt2xUMBlVaWqrq6mpFRESovr5edXV1iouLU11dncaPH6/Kykr9+9//VlVVlZKSkhQKhXTaaadpyZIlGjJkiHJzc5WXl2edR2JiYqxyJycn6+uvv1ZFRYUcDofKy8uVlpamAwcOSJISExNVUlKipKQklZSUyDAM/exnP9P27dt14MABbdq0STExMWrdurX27NmjyMhIuVwuFRQUWH8C56abbtKNN96oJUuWWNcftbW1OnDggIYMGaLo6Gg9++yz+vLLL1VbW6suXbqooKBAHTt21Omnn66//OUvcjqdioiIUDAYVFFRkXJyclRTU6O77rpLQ4cO1VtvvaVVq1ZZ502Px6Nt27apa9euGjVqlLZt26YtW7Zo7dq1uvHGG7VlyxalpKTI7/erffv2eu+995SRkaGXX35Zn3/+uVwul2pra61zjsPhUHR0tNq0aaOtW7fK4XAoOTlZdXV1mjBhgp577jllZWWpoqJCubm58nq9at26tbKysrRv3z45HA7dfvvteuONN1RbW6tRo0bpv//9r9auXatPP/1UCQkJOnDggMrKyuRyudS2bVvl5uYqGAyqtrbWOt94PB5JUvfu3bVy5Uq53W55PB7t37/fujbx+Xyqra1VmzZt1KFDB61fv16lpaXq1q2bJMnr9WrFihVKS0tTfn6+de7Ozs7WqFGj9Mwzz6ihoUFlZWUaPHiwPv30U2VlZemrr77S2WefrdGjR+uvf/2r7rjjDv385z/XE088ocsvv1zvvPNO2LVXVlaWqqurtXbtWnm9Xnk8HpWWlkqSGhoaVF9fr9atWys3N1cNDQ0KhUKKi4tTZWWlhg4dqhkzZujqq6+Wz+fT9u3b5ff7tW/fPrVu3Vrt2rXTqaeeqs2bN2v69On6wx/+oLKyMt14443atm2bPv74Y5WWlqq8vFx79uzRSSedpMmTJ6tjx456/vnn9dBDD8nhcFjLLykpybq+i4uLk9Pp1Lhx4/TKK69Y59Q2bdpoy5YtioiIUExMjPbu3aukpCTt3r1bDodDQ4YM0eLFi9W+ffuw62bzfL137161atVKDodDBw4cUMeOHdW/f38NHTpUXq9XI0eObFqoMGB55plnjKysLMPv9xsxMTGG0+k0IiMjDYfDYUgyJBkul8vwer2GJCMQCBhJSUmGJCMyMtJwOp1GIBAwJBlut9vwer1Gamqq4XQ6DZfLZbjdbms8koy4uDjD7/cbfr/f6l+S4XA4DLfbbXg8HmvaERER1nAOh8MIBAKG2+02EhMTw4aJiIgwnE6nVYbY2FjD4XAYERERhsvlMpxOp1UOp9MZ1n9sbGxYP2Z5zHG1bt3amhdJhsfjMdxut+F2u422bdta3cxlFAgEDKfTaU3HLIvH4zG8Xq/Rtm1bw+12G9HR0dY8eDwea1wRERFGdHR02HLxer2G2+22+jXXiTkNczin02k4HA7D7/cbwWDQ6secl0AgYLRq1coqvyTD7/cbvXv3NhwOh/VxuVyGz+czEhMTDYfDYbRr185ISUmxpmEuK5fLZTgcjrDl6/P5jIiICKuc5r+N16NZTnN+YmJijOTkZGt9muup8fqKjY21vjscDiMrK8uQZCQmJhperzes/7Zt21rLzeVyGVlZWdY2k5KSYm17Ho/HiIyMNAKBgOHxeMLGkZGRYW2HbrfbiIyMtObF4XAYsbGxht/vNxwOhxEVFRW23Zrj8Hg8B81/MBgMm3+/329IMlq3bm0tm1atWlnTMccVGRlpREZGGpKMqKgoa58zx2Wua3P7dLlc1jbrdDqNqKgoIyEhwXA4HEZSUpK1fCQZ0dHRhtPpNHw+nyHJ6Nevn9GjRw+rPOY0u3XrZkgycnJywtatOe9mOSIjI61lbG6rkozk5GTruGF2N/uJi4uztuVAIGDtRw6Hw4iOjrb6M/dpc9nExMRYy8pcFwkJCdZ0zP1GktG/f/+wcqelpVnTMLdrl8tlrROv12v16/f7reXbePtsvA7MeXK5XEYwGLTKZo6r8ToxhzO7uVwu67j67X4ab1dm2YLBoOH1eo1gMGgtI/M41Pi4HR8fH7ZtmMspLi7OaNeunTW+jh07WuNpvDydTmfY8cf8mP3FxMQYgUDAcDgcxqmnnho2bEREhLVNmPuXeTxMTEwM29/Mfd+cTuPjizmPZr/mcObvZjnN/XnChAlWuRtvw26324iPj7e6p6WlWcfibx//Gq/jxtteZGSk9bvf7w87bnx7HZnLrfE6a7xfm/uKOb/mccU8LrpcLiMjIyNsfs1xxcTEhB1zzGN24+Ns49/MssTHx1vn0cb9mccXn89nuN1ua9/weDxGIBCw5jkmJsY6Z5r7qjlf5nHCLK/P57PG0bhb4208JiYmbH9uvI95vV7r2B4VFWVNo/H1iXnMML+b688cp7mMzGsOp9NpREdHh207Pp/POlYnJSWFncPN9d+mTZuwY0Xjc4F5vG68nN1ud9h20Xh6ERERRlxcXNi233j+Gu8X3+4WGRlpxMbGWsslEAiErXePx2N07979oOHNdeX3+40uXbpYx9jG52Rz+ZjLzVwuwWDQiIqKMtxutxEVFWU4HA4jOzs77PhrdjfPKY3PDeb2EggErH0oIiLCOn9ef/31Ycdp83jVeNtsPH9er9c6l5jTufLKK63tpPF1UuNtwufzGS6Xy2jdurXh8Xisfs35bHz8Tk9Pt85LiYmJ1jI2r228Xu9Bxw3zmGiO1+/3h11PNt7eIyIijEAgYJx88slGRESEtT6dTqfRunVrw+v1GgkJCdaycLlcxqhRow7a9szxm8d6czk2vs4w17O5z2VkZFjXXea+a5bBPCaZ26vf7zeioqKM8ePHNylX0FywkQ8++ED9+/dX69atlZSUpLi4OBmGIcMw5PP5FBsbK4fDodraWnk8HsXGxsrj8cjhcGjQoEGKjIxUTU2NpG/uAPTo0UMFBQVyuVxyuVwKhUKKjo6WJOtulMPhUHV1tTIzMyVJTqfTmmYgENCECRMkSXFxcYqLi1NSUpIMw1BVVZUiIyMlfXPHw+v1WonfMAzFx8fL4/GooaEh7O61z+dTQ0ODNU5zWhEREYqPj1dsbKxCoVBYeczy7t27V+3bt1cwGLRqOQzDUGRkpHbt2qXOnTtb3erq6tTQ0CCv1yvDMFRdXW3VbiQkJKi2tlZ79uxRXFycdRczEAiE3ZE6+eSTrfGZ8xYTEyOPx6PIyEh5vV75/X45nU6rxiA1NVU+n08+n09Op1P9+vVTeXm5nE6n3G63VaNSVVWl3NxcZWZmWrWPPp9Pq1evlsfjsZajuXwPHDhg3UXJy8tTdXW14uPjrbI5/lcrcdFFF8nt/uZvfJvbzcCBA+V0OhUMBhUdHa2OHTtay8HpdOqkk06Sw+GQ0+mUy+VSIBCw1ll2dra17pxOp2JiYlRSUiK3223VmhQXF0uS2rVrZ931dblckr6pmTS7Gf+7W2/WWhqGYd15NO9YRUZGKj4+3lrnklRXVyefz6fo6GhrPoPBoCTJ5XJZ25kkxcTEhA3vdDoVCARUX1+vpKQkRUZGKj093VqugUBAHo/HmjdJqqystLbtmpoa+f1+ZWdny+VyyeFwqK6uTp07d5bb7VZ9fb2qqqqsO08RERGKjY1VfHy86uvrFRUVpYSEBCUnJ1vr2PG/O1SGYai8vFxt2rSxajDLy8uVkpKi2tpaSdKaNWusO2CSrLvsBQUF1j7k+N/dU3NZREVFKTY2VoFAQE6n0zpOSFKnTp2su22S1LFjR/n9fmtdmtu1WZ74+HgFg0FFRkZaxwrDMDRgwADV19crNTVVhmFo6tSpqqqqUmJiorXd1tTUWOvC5XLJ7XYrNTVVkrRr1y5rGzJrv81lZG4XwWBQNTU11j4TCoWsY6Xxv9pQs/bO7XZb+5i5/YVCIas22rx7a+4b5vHT6XTK6XSqVatWCoVCVi2gWdsXFRUlh8Oh9PR0BYNBeb1eRUZG6qSTTrLKUl9fr7i4OJWXl8vlcqmqqspafua+HAqFVFZWpvj4eIVCIQ0bNkwul0tOp1MlJSXasWOHdRzavXu3gsGgtU1HRUVZ697cTlu1amUdq8vLyxUIBKzjgWEY2rRpk7U9GIahLl26yDAMDRkyRHV1dUpLS1NFRYUMw1BhYaGSkpKsc4lhGKqvr7dquBsaGhQIBNSxY0eVlJRYtWvmftOpUydJUnp6uvx+vzV8dXW1/v3vf8vj8ai+vt5aJ06n06qBraurk9PpVH5+vlXbahiGPB6P/H6/4uPj5XA4tHDhQmt9SNLw4cOtGgRz/2+8LSQnJ1vfJenee++Vw+Gwjm0RERHy+/3WPuLxeNS5c2cFAgHrWBcREWH9Pzo6WgUFBVYrh4iICGsbMcdhHt/NMpjLyeVyKS0tTR6Px2ohYZ4zpG/OO+Z26fP5VFdXp/r6eqtmeN++fQqFQtYyNI/51dXVVm2bub81Ps9KUjAYVEZGhrUfOZ1OtWnTRi6XS36/X9HR0QqFQoqKilJ1dbXS09PlcrkUHx8vp9OpxMRE6/gcCAQUGxtrbdsOh0NxcXHy+XzWcdRchy6XSxkZGdY5RZJOO+00ORwOa5+Qvjk/BINBa1ma24hhGCoqKrJq4c3la157REREWDWzZisJ43+1by6XS5mZmVatZUNDg6KjoxUIBNS2bVtr+ZvLoH379taxzuv1Wsdoj8ejLl26SJJ69+5tHb8SEhKsbbqsrEy1tbXy+XxKTEy01oW5DMxaivHjx8vn8ykyMlKxsbHWtdrXX39tza+5XZrnGPN7VFSU1SKmoqJClZWVamhoUGJiotxut7Zu3Rp2XKuvr7dqqczymMvLvAaKjIxUKBSyaj7N4WfPni23220dR6VvzpNJSUmSpDPPPFMJCQnyer1qaGiQ3++3jkvm8eCJJ56Q3+9XeXm5dTzKzs6Ww+FQ69atJX1zfep0OrVnzx7V1dVZ57va2lprfOay8Pv91vWXea1jbg9Op9M6BxuGoYyMDGvdmfutuS7M85JZ1pqaGuu8Wl9fr7Vr16qmpsa6Zg2FQioqKrJa5JitAdxut9avX28dk81pDR48WG63W16vV8XFxdYxyLyeNPdLt9utxMREeTweq5VOZWWlSktLrema1xqVlZVyuVxKSUnR2LFjFQgE9OKLL6opCFmN1NTUKCcnR263WxdccIH69++vU045RREREerUqZNqamqUkJBgXUBJUnV1tTp16qSdO3daB68uXbooKirKOhjFxcWpffv2iomJsTbMVq1aKT093TpZmxcRZmgIBAIyDEOLFy+W2+1WeXm51ezHvDgKBoNKTU1VVFSUevTooZ49eyomJkbR0dE66aSTlJ6ebp38ampq1K5dO2VkZCgyMlJJSUmqqqrSiBEjNHnyZMXGxioiIkIdOnSQz+cLK4/b7VbPnj0VGRmp/fv3WycLM+yYF3JmE5WYmBhrR8vMzFR0dLTVPRgMavTo0WEXK61bt1ZiYqJcLpd69+6t9PR0derUSVlZWdYwv/nNb5Sdna2+ffvqZz/7mdq3b69AIKC6ujrrZOX4X9OAQYMG6de//rUCgYAqKioUERGh1NRUxcfHKycnR61bt7YuhtPT09WzZ0+53W4rWEdHR1sncrOJhdPp1OzZs9WjRw/rgDVs2DDFxsZaF9rp6enavn27BgwYoOjoaGVlZenXv/61Bg4caDW/CwQC1knWvDi4/PLLrTJOmzZNERER8ng81ryZJ79AIGA1gZs4caISExOVkJCgqqoq9ezZU9XV1YqOjtaECROUmpqq9u3bq1+/fmrXrp0SEhIUExOjiooKxcbGavDgwSosLFRcXJyCwaBiYmLUoUMH5eTkqLa2VoMGDVJERITat2+v9PR0DR8+XNOnT1cwGLSaZl1yySXyeDzq27evOnfurNjYWHXt2lUDBw5Uly5d1KNHD6WmpioiIkInnXSSRo0apYyMDP35z3/WkCFDlJKSourqaiUkJCg1NVU1NTU66aSTrJOUGZzM9Wpu75mZmdq7d698Pp/GjBljLZdAIKBu3bopLi5Ojz32mE466SSlpaVZJ6OIiAhlZmaqb9++atu2rSIjIxUREaE2bdpYFwHmOomJiVFMTIySk5MVFxenjh07avjw4UpKSlJiYqJ1wVNTU6PMzEwrTHfq1EkNDQ2qra21ThbG/5ovdejQQTt37lQwGNSmTZuUnp6uhIQEnXPOObrmmms0YcIE66QUExOjvn37qlevXvL5fNYFYqtWraxmJ+ZxwuFwKD8/3zrxud1ua/85cOCAAoGAMjMzlZCQYN1AMS/+OnfuHBZy/X6/dQIyp5WQkKB27dopMzPTCpmGYai0tFR+v9+6ADTntba21mpC3bNnT6Wnp1vhybxgMW8iORwO65iVlZVlXfi2atVKF110kdVcdMKECda4cnJy9Mgjj6hv375KTk5WYmKikpOT1apVK/Xo0UPJycnq1KmT4uPj1dDQoKysLPXs2VMpKSmqqalRTEyM1bwuJiZGWVlZGjBggNq0aaP09HSlpqZq5syZ6ty5syIiIvTLX/5SHTt2tLb3QCCgdu3aKSoqyjqp+/1+VVZWKj4+Xp07d1aXLl2Ulpams88+W6mpqSoqKlJiYqKGDBkit9utG264Qd27d7fCtvRNky9zm/T7/dYFncvlsi7CzYsF80afJBUWFsrr9So5OdkKv9nZ2YqJiVFqaqrOO+889e3bVwkJCXK73erevbuioqJUWFioqKgojRkzRkOGDFGfPn10zjnnaPDgwTr99NM1evRonXbaacrIyNC7776riy66SLW1tcrMzNSKFSvk9Xqtc2F6erq1TwWDQfXp00dXXnmlvF6vsrKy9PjjjyspKUkJCQnq2bOn1TQxLS1NZ511ltLS0pSZmakuXbpowIABat++vbV+5s+fr7i4OMXGxioqKso6tmdlZenkk0+2bhSYx6DRo0dr/Pjxio+PV1JSkjp16qQ2bdqoT58+Ov300xUbGytJGjNmjIYPH67k5GRrO2zVqpViYmKsZtSdOnWyjgvmeklLS5PP51NWVpbatm2r5ORkJSUlKTY2VsFg0Dq/9OzZU126dFFWVpbS0tLUvn17ud1utWnTRqmpqVazNvPGkxkSW7VqpeTkZPXv31+BQEBDhgzRKaecorS0NKWlpSklJUXBYFCTJ0+W2+1W//79ddppp8nlcikrK0s+n09DhgzR+PHjrZsXgUBA69atU05OjsaOHasBAwYoGAxaN/Pi4+Pl8/nk9/utGxnS/13gt23bVl6vVzExMerSpYuCwaASExPl8/mUnZ2tcePGWWGpf//+GjJkiHUNY67H6upqlZaWKhAIWDd4y8vLtX79ejmdTqv5v3kjqWvXrtq3b59iYmLUpk0beTwelZWVqaKiQgkJCWrfvr28Xq+6d++uAQMGKBQKKSYmRhMmTFBCQoJ69+6tHj16qGPHjnr66afVqlUr64ZyfX29FfQGDRpk3WAyj9nmdVOvXr2sJnSSrBBr3lA55ZRT5HA4lJiYqOrqavXq1cu6BnS73crMzJTH49Hs2bOtm4ySVFJSIr/fr+7du+u5555TcnKydTzs16+fdYPFPCb3799fCQkJamhoUOvWrZWamiqn06kzzjhDsbGx1nmzZ8+eamho0Kmnniqfz6e4uDhFRERYj46YTRnNc0Xbtm111llnKT4+XgkJCRo+fLi1DUdFRSk+Pl6tW7dWz549VVVVpdLSUiUlJWnKlCkKBAK68MILNXHiRF1zzTU6++yzrfNGVVWVkpOT9bOf/cw6ZpshLTIyUn6/X/X19Vaz2traWlVVVcntdqtbt25q1aqVIiIi5PP5dOaZZ6pfv37q0aOHdX4+cOCABg0apNTUVJ1yyiny+Xy65ZZb1LNnT6Wmpsrtdlvn/549eyoxMVGdOnVSZGSk7rnnHiUlJemmm25Sly5dlJKSoilTpuiCCy5Qdna2oqOjNWrUKGvfcLvdGj58uFauXKlWrVo1OVfwTFYjlZWVmj9/vvbs2WPVxoRCIXXq1EkLFiyQw+HQmDFj9MILL1gXVSNHjtSmTZtUUFCgr7/+WhdeeKFmzJihhIQEnXnmmerdu7fefvttvfHGG6qoqFBJSYnS09N1zjnn6NFHH1WXLl0UHx+v/Px8bdy4UTk5Obrssss0Y8YM+Xw+VVVVafz48frggw9UUVGhVq1a6ZZbbtHNN9+s2tpaZWRkqGfPnlq1apX1nE52drays7NVXFyst956S/v27VMwGFRmZqY2btyo2tpatW/fXpdddplKSkqUn5+v5ORkVVRUKDs7W//5z3+0bt06LV68WAkJCZo2bZqeeuopVVdXa//+/SopKbEumv/6178qOztbgUBAGzdu1IgRI7R9+3Y5nU5FRkZq7969CgaD8vl8uvbaa/X6668rEAjorbfe0mmnnably5crEAioc+fO6tatm3Jzc1VTU6MOHTpI+uaOe2JionUHRpJWrFih5ORkLV68OGydeL1enXLKKRo5cqT1LMrAgQO1c+dOrVmzRk888YRycnJUVFSk6OhoeTwe7dy5U1u2bFFWVpZ1wRQMBvXZZ5+pe/fuqqysVM+ePbV06VKNGzdOn376qS666CIlJCRozpw52r9/vzZs2KCEhATrIq6goEAXXnih4uPjtXv3btXU1FjjX7BggSorKzVy5EgtWLBA5eXlGjlypDIyMqwal+3bt+uDDz6Q2+3WZ599ZgWdJ598UlFRURo6dKj69++vp59+WtXV1Wrbtq3i4+NVWFiolJQU66K8qKhIycnJWrt2rVq1aqW+ffvqrLPO0tdff63ly5dbd9ZatWqlIUOGWDUotbW1evfdd9WqVSsdOHBA0dHR+vrrr5WVlSW/36/Y2Fi53W7169dPPp9PxcXF+uSTT7Rw4UJt3rxZDQ0N8ng8io+P16hRo7Ro0SIFAgEVFxerW7du2rRpk84991yNGTNGBw4cUEVFhdatW6eYmBiFQiH169dPH3zwgebNm6ctW7YoFAqpqqpKv/jFL7Rr1y5t3bpVubm5ysjIkGEYqqysVEpKikaMGKEePXro8ccfV6tWrZSdnW3dlJg7d65at26tffv2qX///vrqq680bdo05eXl6YsvvtCAAQP00ksvKT09XWvXrlV2drY2b96sbdu2yeVyKSkpSe3atdPGjRvl9XqVmpqq3r1768CBA4qKipLT6dTEiROVmZmpf//739q9e7cGDRqkuLg4rV69Wl999ZUyMzOt0GAuP/NuvSRt3LhRycnJ+uSTT/TRRx9p8+bNqq+v1/79+1VWVqZ77rlHFRUVevjhh7V//3517dpVxcXFqqmpUUREhPLy8lReXq57771XpaWlevnll63n0Xr06KHIyEjl5+dbtSrLli3T4sWLrYuxzZs365xzztFjjz2mnTt3at++fSooKLC2abNWSvrmDqL5TJdhGNq7d691gWLe6TRvxIRCISssNL6AMQOhYRiaOHGiunfvrnfffVennnqqtUwMw9Dnn3+uDh06aNOmTaqrq7Nqp7ds2aIhQ4ZoxYoVGjBggF599VX94Q9/0J133mld5Gzfvl2nnXaaysrKNHfuXGVkZMjtdis+Pl6fffaZgsGghg0bpo4dO6q2tlZZWVnatWuXDhw4YNVMlJaWyuv1avfu3ZJkLY/u3btr1KhR+v3vf2/dcTWne/rpp2vBggVq166dIiMjtW3bNknShRdeqFdeeUWDBw/Wyy+/rNGjRysrK0vvvPOOYmJidOmll+qmm26SJO3du1f19fXKzs7Whg0b5PV61aVLF61du1ahUEgpKSmqrKxUcnKyxo0bpy+++EKFhYXq16+f0tLStHnzZh04cEDt2rXTmjVrlJycrF/+8pf63e9+p4SEBOXk5Oikk05SYWGh3nzzTW3btk0RERHq1auXMjMzVVpaqsGDB6tfv37yer164403tHPnTp122mlWDd22bduUkJAg6Zu70Z9++qn69etn7ccOh0M1NTVWzY4k5ebmavXq1froo4+sc6158ez3+xUTE6OTTz5ZX331lRwOh375y1+qX79+kqR3331XK1as0ObNm1VbW6sOHTrI6/WqsrIy7DhWWlqqmpoaJSUlKRgMaufOndZxMBgMauXKlcrLy9OKFStUWVmp9u3ba9CgQbr33nslSR06dFBqaqoKCwuVnp6usrIyax+pqqrSZZddpsWLF+vDDz9UcnKyunXrpq5du2r//v1h56v169frwIEDqqurU3Z2tl5//XWdeuqpysjIUGVlpfbs2aOEhARt3rxZe/fuVa9evbRjxw5r28/KytLKlStlGIb69OmjxMREnXXWWfriiy/ClkOXLl3kdDqVnJysjh07Wq0FIiMj9cILL6hTp07Wvjpo0CDrovbqq6/Wxx9/rBEjRigyMlJr1qxRZGSkVcO+a9cutW3bVl9++aXOP/98rVu3TtHR0aqpqVHPnj21YMECDR48WC+99JLOO+88bdq0SYFAQD//+c/1zDPPKCIiQtHR0Xr77be1adMmjRgxQoZh6Nlnn7We4ZX+r8WMeZPFrO2Pjo7W1q1brZs3hmFo6NChVoBo3769iouLdccdd+j//b//p6KiInXu3FlFRUWKiorS5s2b1aFDB+sZ5Y0bN2rx4sUaPHiw7r77bnXp0kXbtm1TRUWFqqqqrGe8zHKYNXmhUEgDBgxQQUGBxo4dq379+mnt2rXauHGjRo8erRdeeEHBYFBt27bVv//9b2VlZSkhIUHr169Xr169dMEFF+jPf/6zKisr1bdvX6smzbxBtHz5co0dO1alpaX64IMPVFdXpwULFqiqqkodOnRQfHy8WrVqpaioKOXl5am+vl5XX321evTooXfffVfz589X69atrdq9Dz/80LrxWFZWprS0NCUnJysmJkZ5eXnKysqS1+vV9OnTZRiG5s+fr7y8PP3qV7/SokWLrOWWlJSk/fv3q6qqSl26dNE555yjUCikt99+W4mJicrOztayZcvkdDpVWVmptm3bavXq1Vq7dq08Ho/i4uJUUlKid999V5WVldqxY0dYDVlVVZXq6uo0YMAANTQ0aMuWLerYsaM6duyou+++W5999pmWLl0ql8ul1NRUvf766xo6dKhV4fDZZ59p0qRJys3N1YcffmhVEvziF7/Qo48+qnXr1ikQCOjSSy9VQkKC9uzZo44dO+qjjz6Sx+PR+++/r9raWt18880aNGiQli5dqttvv12rV69Wdna2Kisr1aZNG7ndbp133nlau3atEhISrGP00SBkNZKXl6d77rlHc+bMsVa+eVFhalyLYz4YXVJSYlXZn3/++Vq4cKFuvPFG7du3T5L05JNPWg8KduzYUStXrlRZWZkSExOtOzvl5eVhLxsIBAI6cOCAQqGQSkpKlJmZqTPOOEOpqal6/vnnJX1T1W/e7YyIiFBlZaUk6VCr1OPxqE2bNqqqqlJRUZF1J0f6pgYqMjJScXFx1gOj5h2g1157TSUlJZK+aY4WCAS0efNmq0lKXFycCgsLlZmZqR07dujss8/WO++8o8zMTJWXl6uoqMi6mz5s2DD94Q9/UGpqqiZOnGhdPJp3SMzaG+mbg615Z/y7HGqdlJaWWrURXbt21RdffKGxY8dqzpw56tChg/Wwal5enhU2kpOTVVNTY9VAejwe9enTR2+99ZZ69+6tFStWqF27dioqKtIVV1yh8vJyvfHGG1YTr6+++kr19fXWSb9x089vz4d5hyQ6Olr5+flWkyxJVlOl4cOHW826Pv74Y3Xo0EGtWrXS3r17rQfWzWp+c32azR4OtfzM2sX09HTl5+db2/fhymgOY47T/N2sWfL7/erbt69Wrlyp0tJSFRUVWRfSZrkkhTXNacy8m9X44j0iIkIZGRnaunWr1QzWfImEx+ORz+dTaWmp9VC/eUc1PT1dK1assJq1fbt5m8mszYuOjlZxcbHi4uIkSeeff76WLVumefPm6a233tLs2bOVn5+vk08+WZGRkXrvvfesZilmmb+9bJ1Op3XhW15ebjXrOdw263A41KVLF3Xr1k11dXX67LPP5Ha7rRd3mPNp3hGuqalRbGys1XTFrI2IiYlRYWGhunTpov379ys1NVV79+5VbW2t1dT1u/YjczkMGzZMCxcu1Omnn66PPvpI5eXlVmivra21xmlu32aT4NTUVO3Zs8dq6te4ma9Z+2LOt9vttl5IYO5rZi1N44ss5/8eDjcDe2RkpAoLC619tnH/5jwUFxcrPj7eai7i9/tVWFio6upqdenSRfn5+dbwZhOWqKgoK3jv3r3bmtdvr19zW5Zk1aKuXr1afr9fRUVF6tSpk5YuXWq9nMC8Yy59s0+bF3Lp6enKzc1VYmKi8vPzrRqx1NRUDR48WFu3btWePXuUlJSk6upqrVy50moK1q9fPy1btky/+c1vNHv2bPXq1ct6OcGuXbsOud2bFzSGYWjjxo069dRTdeONN0r6pkbsxhtvVGxsrPXiHPMYbM6v3+9XMBhUZWWlqqqqrCbx36XxujZrbbp166Y333zTapZXW1trvcSi8XHg26KiopScnKyCggI1NDRYTY3q6+ut5opmbWnjJm+H43a7rQvd+Ph4VVdXq7y83Go2bzZPqq+v14ABA7R06VIlJCSodevWatWqlVatWqXo6GhVV1dr586dYTUgR1oe5jo3z1nmeXH58uUaMmSI3n33XRUVFVnNucx12TiMmE0gv70cvF6v6urqDnssl2TdnTebBGdmZmrXrl3WOXPkyJFatmyZUlJS1KFDB91www2aOXOmVq9ereLiYuvYfrjxm8vebLGTlZWlwYMHW+vlhRdeUE1NjXWjY9++fVZNS0REhGpqaqz9r/H4oqOjrea8tbW1SkpKso5r5rlOCg9pjc9b5v7Yu3dvffrpp2GPUZjlMefJ3G/NstTV1Sk2NtZqPvnt/cusZfZ4PKqurlZMTIwqKysVGxurAwcOWPPl8/msJsJmiwCzrDExMSorK7PCsfniib59+1o3xs2XnJj7uHl87NChg7Zs2aKEhATrpnZVVZV1AzQ5OVn79+9XRkaGevfurf/85z/Wy9vKysqsc5W5TbhcLusYYDZzNL+bIf+ll16ylrm5LM11ZtaQ7dmzx1omZi2t2aTbbHprNqs0X6ZRWFho3YQ8cOCAdY4wl3vja8TG27TZjDYUCsnv91s1hWVlZdbyNtdb4/UXHx9vPX7h8XjUvXt3FRQUqKqqKuyFXebxora2Vmlpadq0adMht/9DIWQ1Mn36dH344YfauHGjdQCMjIzUzp07Jck6qYdCIet5krKyMqt9e1VVlSorK60L/crKSuvC32x3a1bZmhePzv+91cS8GDFPFubBwVzJKSkpKi4uDmuP3fh5J7PpXkREhA4cOGA14zGbLTU+6YRCISUlJamiosK6YDOfGzPLYp4EpG8OauazUJKs4cwyG4YRdlL49kW+ecfC/G62u5Zk7dzmAa1xdbrZhM4wDO3cuVOxsbGKjY1VYWGhdTIxaxu/vU7q6+sVHR2tiooKa6c228tHRERYz2c0NDRYb+gx785Ksp5V+HZokWRdXJoHx8TEROsC01xuZtV343Vijqeurs5ar+bzPCUlJVY7bbPde+M7/eZdL7MWoKioSElJSSosLLSGj42NtbatyspKBQIBVVdXW02PzIBmHkzNC3hznZrNXVNTU7Vp0yarhsYcV2lpqYLBoMrLyw8bYsw3oX399ddW23bzBoD5dr20tDTt3bvXWlZmf+b+Yd7hNNenz+dTYWGhdbAzA6V5gS8p7MI/MjLSes4oKirKatpmPjNgNnMz73Sbb/Pzer1hJ1HzjWmm/fv3WyfgyMhI6+ZBfX299YyGue/ExcVZFw7mMjT/b/ZvnkgPxSxHTk6O9dzdvn375HQ6raBjtp9vvM/6fD5rOZjzU11drUAgoKSkJBUUFFj7bjAYVGFhofX8R01NjXUiN2vYzH7N7bC2ttYKfSkpKWF33SVZzZ3Mty+63e6wiyrzOCF903KgsLDQmpa5T3fo0EFDhw6VJC1dutS6IeJ2u7Vt2zY5HA7Fx8fL7XZr165dateunXbs2KGkpCTrjWHSN8/amCfQ2NhY1dXVWfu9+UyduazNY6m5T5q1dNu2bbNO0MFgUMXFxdZbGNetW6eGhgZFRERYy9q80WAeZxtfAMfGxlo1j2Y/0dHRVpnMY6F50868kdD4DZWNg7t5fDGXpdfrtW7amBdg5jnAfFZ1z5491vlJ+qZWrq6uznobrdfrVUlJibXfmW9TNLevqKgolZWVhW1Pjd9GaoaVESNGaNGiRVaQOtQNM/MYVF9fr27dumnv3r0qLy+X9M15wbw4NS9IzYsrv99vlb+oqCjsOGaub/MZpZKSEuvZQnMcDodDqampys3NlWEY1nNXjbdh8zlUM8SYF8RmMG/8zKL5zGFERITS0tLCzlehUEg1NTVhF+zm8TotLU3V1dVWWDCfyzGPh2YzaPP4aTZZLCwsPORycLlc1vZQVlZmrRNznzZrfczzp7lOzf83volm1kCb5z9z2238nJh5XWTeMDKPi7Gxsdq7d2/YYw8Oh0PFxcXWc9fmmyLNbcB8ZtMMaGZz4pqaGut4b143BAIBXXzxxZozZ471BtaysjKNGzdOTz31lHX8b3zeMpuqmdcLZjBqvC+Zx2+Px2MdX5OTk63mi0VFRVYz8b1791pvdDSvGxISEpSXlyePx6Po6Gjl5uaGbeeXXXaZnnrqKevazbx5bt6MN8tjXq9IsprCmt3atGmjgoICFRUVWdeQ5jWjub/Hx8erqKjIei4rNjbWumYzy2KGRvN5usTERO3atUsJCQmqqKiwbvw3vgHm8XiskPjt/TgYDFrLwty3zBYt5vHGfEbMPDaZzQXNG0CRkZFh17jm8TEyMlJFRUVhz94VFBRY14JJSUnat2+fdZwwz7GN30lgzo/ZJHjDhg3q0qWLdZOk8fWxeWw2yyp980zg8uXLdcUVV0iS9Rbio+E+6j5PAA0NDdYrrx0Oh5KSktSzZ0/rYCJ9U/1uXrSkpqYqMjJSu3fvVl5eXtiKMgNW44vzmpoaqx9zp2nVqpW2b9+urKws65W+ZWVl1o7l8XhUVVWlqqoq6zmi6upq1dXVhdVEmBc/Xq9XKSkpCoVC1onDvPA0N56SkhIlJiYqOjraagLT+IRm1hqYbbnNE05RUZEkWc+HBAIBFRYWqk2bNtq+fbvatm2rbdu2hZ2Iq6urlZGRoc2bNyszM1OZmZlaunSp9Ypz84LTXP7m3SrzQtdsCmneidq/f7+1I5nDNV4naWlp1joxmyiZFy9muDTvZJt3vU3mMo+KilJBQYEiIyNVWlqqzMxMbd++Xe3atdP27dut15+bd1a3bNliDW8eIMyavqqqKuvZHvOOuvlcR2pqqrZu3Wq93t0c1ryYMIOM2eTCfMGFJOsumfR/B2fzIU7zpND4db9mmcwa1aqqKutOqHkgMS+2t2/fLpfLpezsbOshcLfbra+//lqJiYlq06aNqqur9fXXX1t3YleuXKl9+/apuLjYeoWw2WSsoqJCkqy7WOZFYL9+/XTddddZr3PNysrSueeeq2eeeUbbt29XQ0ODNU/mSbbxc07myaqurs5aBo2blXg8HvXr10/vvfeedYcrPT1dKSkp1nZkvmLfDLLmjQPzBGaeKL1er3URaQYXc9mbz0g2vigOhULKysqyXqgQFRVlPR9mHgvKysqsmw1mQBo0aJBWrlyp4uJiVVRUaO3atda25XA4rNoa8zhj7q/mtmkGfbNmyVxOWVlZKikpsUK6YXzzwgUzeBYUFFh3qkOhkPVclVlD7PF4NGDAAG3YsEFjx45Vr169tGnTJiUlJWnu3LnKz89XZmamfv/732vFihV67733JEkZGRnasmWLCgsLNWzYMPXo0SNsO3711Vd1zjnnWN8/+eQT/eUvf7FevHPTTTeppKRE+/fv17Jly6xjU11dnVXDXlBQoK5du1rH78YvwzHn37wwDYVCqqiosO6smjc8zAsJc7v1er3q1auXtm3bZh0XzLv6paWl2rt3r3X8DYVCYRcQ5gWPeVFjvpa/oaFB559/vp577jl16dJFX3/9tTIyMvTFF1+ooKDgoIsX82Fyc32ZrQyysrK0bds2K6CZx8Phw4crKirKejC7vr5eaWlpKioqsu7Cm8cYs9WDy+WS1+tVXl6etV2a24D5JxfKysqslz+Y82meH8wbH5KUn5///9s786Cq7vP/v9n3XRC47CK5gCwigooJanRAxaW1Iwaj0sS0RDvRqtO6zeiI2sQQtcTYZJyxqDU2OmqwiSRFVKwLLmyiglqBC7KDUEVA1t8f/J7Hc9G4Ys33+31eM5l4uOeee5bPeT7P/uFrpxqPyspKAA8bQZB8c3JyQlNTE9ra2tDd3c2F7AQ9k56eHnh4eGDJkiXYunUrbt68yfMhoZRj5MSiuh1q1d/S0sJyRK1WQ09PD1VVVWhvb+d27s7OzqioqOCxQREUki8mJiYYOHAgampq0NraymOKrl9XV/ex8xX9bmxsLPbt28fynmSQ0vlIWRFVVVVacpyep5GREezs7HD37t3H3gcy6PX19TnVkDJjAgMDkZ+fj66uLq7ppPFvZmbG9csUYerq6mLHHynkpJMo9SIdnd6GKPRcyegoKyt7JOpJ44YMxzFjxmD06NGoqKjApUuXWIa6uLggOjoaR48eRV1dHaKiotDU1ASNRoOpU6ciISEBra2tsLOzw+9+9zvExcXhu+++e+y8RQp1e3s7O6vWrl2LtLQ03LhxA11dXbCzs+N0t4kTJ6KsrAwVFRUYPHgwnJ2dsWHDBtjb2+O3v/0tzp8/j4qKCjQ1NWHIkCHQ1dVFdnY22tvb4enpyWnGFB0hJ+tf/vIXzigqLS3le0RyippwGRgYoK6ujuvMAXBNZ2hoKPbt2wcACAgIgI2NDc6ePctjxcjIiCOlDx48gKurK+7evQtnZ2doNBrY2Niwk6mrqwtWVlaYMGECUlNTWQ948OAB13ySU48MbbVajfz8fHR0dHDDHVqSo7u7m9+tiooKrWV1pk2bhmPHjsHU1BQtLS0YPnw4enp6oNFoUFVVxeM+KCgIZ86cga2tLRobG7V0X6qVrK2tRXt7O+vYpNfS+0FGJdDrbPDy8kJFRQXPZWVlZfyOU/SfHHfkILewsEBwcDBu3ryJlpYWvP322wCADRs24HmRSJaCM2fOYNmyZbh06ZKWNU2KF3n5TU1NUVJSwgJEqfSZmJhg5MiROHPmDHckMjMzY2OIlDMaHCQMaKBQ1IGELgBW2CoqKlhhVqJMyaFUAWWUiQaQWq2Grq4uzp49y8ftuz9NsIGBgVxfQwoIHU+5v5WVFU+UxsbGaG9vZ2MSeNgt0dDQEL/5zW8QGxuL1atXIzMz85F0HOoEp6PT28nQwsKCoym3b9+GmZkZTE1NucZj+PDh8PPzg5GREbZu3cpCjQwNPz8/Dl3r6uri0qVLKC8vBwBOV1J2rKHnRZ40OncyuskLBICLhUlxvX///iOpTkqlBniY+qAsMKZoRF1dHRuddAwHBweuLaCoDQk84GEnLDp/fX19NjAoMqncps/puSi9mMrCXookkXJNY5auX3kM5W/SNZNyaWRkxN3e6PwAsKFJ2xTlVEZeSCC2t7ezIkBRGhqj1J2K8tSV56BM2yEDhSJldnZ2nBZGzUWoW9SUKVOQnp6OqqoqjgiRJ5s8v05OTjA0NOT1kCwsLNgJ0dPTw41T6urq0NXVxfeB7qu5uTkMDQ21jBwDg961Sah428DAAA0NDZwioaOjg9bWVu4iSg1aSJExNDSEn58f8vPzOf3C0NAQgYGB8PHxQUBAABtW58+f59SzxsZGTi00NjZGREQE9u3bh5aWFjQ1NfGzCQwMREtLC1asWIFZs2Y9VZb2FySTc3NzfzJNU+lxJKcQjWMAWpMwfYe8r42NjewNV8o54KEspWYdRkZGuHfvnlYWgbm5OUxNTVFdXc1eWwAYO3Yszp07xxFxAFzzaW5uDpVKhcLCQtja2qKsrAxTp05FWFgYDA0NsWnTJl4/ihwTnp6evK4bAO785+3tjYsXL/KcoZyr6BqouxuNP6pLKioq4iiEqakpR/eU7zWNeaWnnz6n49E2ySIyUpVyQZmCSYYdGQh95xZSUElukGxqa2vj6DfJZZJz9Lz7yhkyCiiiR5+TMkWGGXnd6bfJ4UAp2WTIKiP5APj/dE5908lIfnR3d2PEiBHIzs7mTol0fGWEniIt1N3RzMyMz0cpW6hGsq/ssbKygrGxMTuHKMWJoqcke5qamuDs7AxLS0toNBo8ePCAHRQqlQoDBgyASqVCbW0tzp8/DwMDA7zxxhtYsWIFOxV37twJR0dHDBs2DDo6OsjOzsaIESM49fizzz7jWjpzc3OkpaXx+2FhYYG4uDioVKrnqm/pS0REBEedq6urce7cuUfGAI1HqjemsaZSqZCUlIQbN25AX1//ieexYcMGJCUlsQMReNhZt7OzkzMkBg8ejJUrV+L06dNISUmBtbU1XFxcUFZWppU6SHoeRcIpDZRkeXR0NEaPHo2DBw+yMzwvL48NZppXqeEOvd+DBw/GxIkTkZ6ejmvXrrHTRJkdRdlFZJjROH3c/A5A652leYjGK/3N3t4eTU1NaG9vh42NDUdvyaDz9PTEqlWroNFokJaWhpMnT/K9PXjwIDZu3IgrV66wg5miUpTxQ8GKwMBA3L9/nzv+kh5VXV3N0Uoympubm7ljJxmfNBZpXJCuEB0djStXrmDy5MnIycnB2LFj+fycnJwwd+7cFx6jYmQpOHjwIJYtW4ba2lpWbCithyZQul0k+N3d3VFcXAwnJydUV1dz2JwGsaurK8rKyrQGOr2YJHxpkVh6AWlQ962roUGvHHwUzjUzM+P6Bnr52traONeavKF0TJoUlPuTAKQJVHk+SmWTftvKyopfrsbGRlasqUaEOoaZmZnxS0UFqxTpUkbJyMiha1am1D1um54PtWx3cXHBjRs3WBnr+z0jIyN4eXmhs7OT265aW1uzJ5dSTBobGzlFgdIXgIdhfFI8SJml2qp79+7x8yRjTZnOQW2Yi4qKOJWNvNXkWSGljbycdO9JWFIHsdraWgDgjk8NDQ1siADgKF7fbRKKfT/v2+b0Sd950jaNb1Ko+qahAHiubXp29Cxo3CprPWh80+d0X+idIqWb3kvyWJaWlsLR0RE1NTV8HHo3aJyRsKfjKeVCe3s7K2E0Hl7FNqWFGRoaYtKkSTh9+jR7kamdMEWrKSWKzp+iuE+D7j3df6rBIWP2jTfeQGtrK+bPn/9SStHzQjKZjIv6+npWBLy8vFj2VlVVsawj49nW1hZ3797VaudOMpCuk/Ylw6mvLCVFhJZO0NHR4XedupfSsUnpBx5GGEnWK8cvySJawJQieyTLgN4IYGdnJzcUoXRaABwlI2cGpbCSJ5ccXqQMkVylZj8UXaGMh5+SJT8lK54mS55XblBKN0VW6F68jNx41m2lYkbbNF5IJtCz7fssgYfOqvb2dk5PV87nZHiSs8re3p4j+TSmKC2Rjkf3gWp7XoVsoeulsUv7JCYmYu/evQB6I5O0bImenh5HjpX0lRt90dPT4zRKb29vVFRU8Ge1tbWwsLB4rvqWvnzwwQfYs2cPO1+UjpS+z1ypP1BqHbWa19XVfeJ5REdH4/r16xwlomVh6L+2tjbExcVh//798PT05GhnZ2cnL75NTl0yqkkOUMdTSunX09ODv78/XFxcUFJSgqamJoSEhCAlJQX6+vpardmV9590UErRJP2k73tpYGCAgQMHYsCAASgqKuJIkTItkzIy6O/K3yKdhPQhqicDwI526npM2UAkuygNWJmuvXDhQowbNw7x8fGcYUNzLDmCKQJGddk2NjZa2VrkeGxoaNDSscnB8lPvgEql4qgeOThIZ6Rx+7JznhhZChYuXIj6+npuZtDY2Mjec/Jmmpubcz4seb8olaCrq4s/p4nP2NiYFWoSvCS4aRCQF5mKIyl/mxR1qsEgQa/cpoGunMSVBgZt0/40AfRVXvsaJH3PR+m5UEZTaEDTb9FkDzyM3JAnc+DAgSgvL+cJigxV+s1x48bxug0ajQbe3t4A8Njt0tJSODg4oK6ujoWJiYkJHBwc4O7uzquf37t3Dw0NDVwTQ95LioKQkkT3CAB7Ucl4o5ot8kQD4M9NTU05pE2KgzJ9hYQFPSO6F3RP6fjKqAkAFsQUBSFFhIwAug66h2SE0fk97zYpAwBe+Bg0Xnx9fdHQ0IDbt29z8T4Vk5Mi+6zbdF+Bh8K9q6sL1tbWqKur4/unr6+PlpYWuLi4sHCvrKyEs7Mz10ZVV1fz2jxtbW28JAN53UixJk8jdS1sa2vjNVLUajV32CSjje7fq9ru+f+pjH1FtXLS66vs0Da9W5TaAQDFxcVwcXFhz15zczOvj0Q1EPR9kg9mZmZwcHB4qjLS35BMzsrKgr6+Pi+VQbKQZC/JWqojIJlKXt6WlhYuPCfj6XGGUF+DXllsrawHJFmprB2iZ9XR0QErKyu4ublxl9PAwEDulEnGOsldS0tLjnySLKMoMikENP/cu3cPenp6XDNF0U8ArFCQ7CHFhqJpyui2UpYoI1Q6OjpaRifw8rLlads0r1KHVKrxehm58azbVJ9TV1cHZ2dnVFZWsiFEsoXuEa1XZWBgwF0dqQ6ltbWVa6xMTU21ZI+FhQXKy8vh6uqKiooKuLi4aDU2ofRGBwcHNDU18XOncfsqZAtdl9Ih9iyqoKWlJTdnuHXrFjeOAMAKN31ODbIeF32mufD9999/rvqWvnz44YdITEzE9OnTce/ePRQVFcHZ2fmJc4mubu/ahTExMSxXQkNDn3ge3t7erFdYWVlxGqyzszNqamowcOBANDY2cpSUMltIf6P2/7q6uhylpvls5MiRyM7O5milMiMG6I1GhoWFISsrC4MGDcLVq1dZJg0YMACNjY28TIQygt+3tomMHYpuk4NGmfasjNJ3d3c/Ul9L9xToHUfKdVuV9bUU/SVI36ISC6XBHhsbi46ODpw8eRIuLi4oLCzkzA8KDigjxQC03l9dXV24urqisbGRS2EAsO6up6fHtV593wHSb2nOJF1DubTGy855UpOloL6+HlOnTkVBQQGHOcPDw3H8+HEevLq6urC1tUVrays8PDygp9db6J2XlweVSoXq6mr4+fnh+vXrCAkJQX5+PnsQaWIHwBY+hfOprqivR4YGEQ2uvsXUXV1dcHZ2RmtrKwICAlBZWQlzc3N0d/e2is7JyeF0M+X3HRwcON2A9icviEajeeR8lJOwrm7vor+UW+7m5oYTJ07gl7/8JQoLC+Hm5oYffvgB3t7eUKvVaGlpwfHjx2FnZ4fo6GjU1NTwC6VWq/neOzk5YdWqVQCAy5cvay042nc7KSmJ19Vobm5GRUUFv1zm5uaYOXMmCgoKMHv2bOzduxcZGRmwtLTEihUrsHr1atTU1MDLywujRo1Cenq6Vl4xvdh9BQUpY2Q8kpeF9rGzs+MJm1IfyRizsbHBrVu32OCmIlIdnd4FAqmmrbW1ldNRSPCRAqdMd7Czs0NnZyevPUPF67TYbF5e3nNtazQa9iy/6DHy8vIwdOhQTJ48Gfn5+di/fz+cnJxQU1ODoKAgAL3tm59nOzMzEy0tLbCxseG1ZIDePHB9fX2e0CllKzQ0FEDvoo1nzpxBREQEDh48CABcf+Xj44Pbt2/Dx8cH586dg6enJ6qrq3Hjxg2O4Pj7++Pq1au8CGRQUBAKCgrg4uICLy8vODk54V//+hfefPNNHh+vapvqVaqrq+Ho6Ija2lqMGDECOTk5CAkJwXfffYeTJ08iLi4OoaGhSE1NRWZmJpKSkgD0RlE3btzIxy0qKsLRo0exZMkS3l63bh1CQ0OxZMkSrF69GsXFxQgJCWFl3djYGBs2bOD3878FyeSbN2+ivb2dZdyYMWNQUFDAsvf999/HV199BW9vb9TU1MDNzY2L0D08PFBeXs6paKTs0rp/VNRPtY9KWarRaODp6Ylbt25h3LhxOHnyJC9eS81s6P0n4+2Pf/wjNBoNDAwMkJycjMTERKhUKoSHh2P8+PFISEjAl19+ie+//x7h4eFYtWoVJk2aBKBXlp09exbDhg2DRqNBQUEB3wtlejq1O1emsTo6OqK+vp6jmtRggWoCaeFWNzc3rkei950a31BbbEqNB15MDjyv7BkzZgw6Ozu5ls/AwOCl5MazbtM9vX//PlasWIHt27dzShQ5JmhpBMpK8PT0REFBARwcHODr64vg4GBcuHABXl5emDBhAgBoyZ4ZM2Zg0aJFSExMxKeffsq1fTSfeXl54ciRIzAzM+O16y5evAhvb2+e4/pbtly9ehXDhw/HhQsX+G81NTUwMjJCSEgIcnJyUFxcDC8vL3z99ddISkria1q2bBkAYPXq1bC1tWU5snLlStja2vLnM2bM0FqOYf/+/fjVr37Fi86fOnXqhWpclMyePRsDBgzA6dOnsWnTJuzevfupc4mvry/i4+Nx5coVvPXWW/D29sbs2bOf+DvURt3T0xORkZH4/vvv4ebmhsmTJ2Pbtm2Ii4vD3r17sWnTJpw7dw7m5uZISkrCe++9h/T0dEydOhVpaWkIDQ3FyZMnOZJtZGSEBQsWYMeOHQDAzuG4uDgA4LU53d3dUV9fj+XLl8PPzw9r1qzB1atX4ebmhtzcXHh6ekJfv3epkg8//BAJCQnYsmULjI2NOUuIZHl4eDiuXbvGRpm9vT07A6ZPn87vRGtrK3x8fLTqa+meenh4IDs7G2FhYfDx8YGpqSm++uorREVFAehduqC6uhrz5s3DkSNHEB4eDqA37XLp0qVa93bt2rV45513oKOjw+UcZAwrm5nNmzcPBQUFcHJyQkZGBlQqFTf7sbKywrZt2zBz5ky4urpi4sSJ6OnpwYkTJxATE4PS0tLHvgPkWJw6dSoA4ObNm7h48SKysrJ4v5ed8ySSpaCwsBBpaWmYM2cO7O3todFo4O7ujsLCQqSkpHCxsIGBAd59911eH6TvPvb29vD390deXh6Cg4N5jYg1a9YgNzcXlZWVXHhK6yT97W9/Q2hoKH788UeYm5tzlyATExPY2dmhtrYWw4YNw7Vr19gLYGJiAg8PD5iYmCA+Ph7+/v6PXFN5eTkOHDiAf//73ygrK0NxcTGCg4OhUqke+U55eTkKCgpgYGCA9evXa50PLXxbWlqKWbNmIT09HYmJibwo3Pbt2x9rFI0ePRoAHvmcoM+fl9OnTz/1OKdPn8bo0aNx+vRprfNRfjciIgKpqakoKipCTU0NqqurUV5ejs7OTjQ1NUFfXx8qlQphYWHIz89nb5iFhQWuXr0KPT09jBw5EqNHj8axY8fQ0dGBqKgoxMbGsteE6OnpwbfffovDhw/jwYMHaG5uRkdHBwYPHozExERuwkH7HTp0CCUlJaxIOTs7w8nJCfX19bh9+zYcHR0xefLkx/7W/xaKioq0tqlAn7rahYWFaf2diIyMfOQYarWanzPts3v3bj5GcXExpzG4u7sjLy+P6wsoJzszM/OR479qaI0loqmpiZdMcHd3x9mzZxETE4Pc3FxYW1uz0kqNagBoNXihY3p6evI2fdfT05NTVNzd3bk7Gx3jzp07jxzrVaKUyfX19fj666/x0UcfPSKf09LSEB4ejiNHjqCjowMrVqzA5cuXsWPHDjQ1NWHLli2orKzEnj17EBUVhSFDhmDDhg2IjY3FN998g9DQUF5KQikXaf23d999F4sXL8b8+fMREhKC48ePo7m5Gf/4xz9w584dBAYGIjs7G2vXrsW0adNY9gDacohk5YIFC/ga+8oyoFee9fT04PDhw0hOTmZvdVhYGPLy8lBWVsZ1py4uLlzfFxUVhZkzZ+Kf//wnrl+/DhcXF+7KSPLt9u3bGDhw4P85WfI4SDYUFxdj0qRJyMzMhEajQVFREb/zSkMEAL979O/IyMiflAtFRUVQq9U4evQoHz8yMhJ//vOfWSEluXThwoWnHq+/UcqW8vJyWFhY8PXRuQ8dOpSXYqHGCcrvkhwheUOfnzp1Cq6urnx8Wo+LaGxsxKBBg17p9SlRzgPA88nyx+mGra2t/OzUajX+/ve/Y9asWfyMd+3aBXt7e4wYMQJ//etfER0dzXKFvkPQd+ie0T29desW33OlTOl7XWq1mmU6nUdubi6MjY2xc+dOuLu7IysrC52dnXB1dcW8efNw8+ZNHD58+Ik6y9Pu5+OuAdAeGyUlJU+dQwoLC5GcnIzq6mqMGDECWVlZHOEPDg5mOU/3sKenB6mpqbh+/TpcXV3xi1/8AiYmJk88p8fxuDGs0WgwdOhQ3udl5zwxsgRBEARBEARBEPoR3dd9AoIgCIIgCIIgCP+bECNLEARBEARBEAShHxEjSxAEQRAEQRAEoR8RI0sQBEEQXhFr165FcHDw6z4NQRAE4b+MGFmCIAjCayU+Pp7XlKM19SZMmICdO3fyujHPSkpKCqytrV/Nib4Ay5YtQ0ZGxnN9x8PDA1u3bn01JyQIgiD8VxAjSxAEQXjtREdHo6qqCqWlpUhLS8PYsWOxaNEixMTEaC2s+T8NWjNPEARB+L+FGFmCIAjCa8fIyAiOjo5QqVQICQnBypUrkZqairS0NKSkpPB+mzdvRkBAAC/eumDBAjQ3NwMATp48iV//+tf4z3/+w5GxtWvXAgD27NmD0NBQWFhYwNHREXFxcaitrX3iOXl4eCAxMRHvvPMOzMzMoFKp8MUXX2jtU1ZWhmnTpsHc3ByWlpaYOXOm1rptfdMF4+PjMX36dF5Q3c7ODgsXLkRHRwcAYMyYMdBoNPj973/P1wD0rg03ZcoU2NjYwMzMDP7+/jh69OiL3m5BEAThFSNGliAIgvCzZNy4cQgKCsKhQ4f4b7q6ukhOTsbVq1exa9cuHD9+HH/4wx8AAKNGjcLWrVthaWmJqqoqVFVVYdmyZQCAjo4OJCYmIj8/H99++y1KS0sRHx//1HP49NNPERQUhNzcXCxfvhyLFi1Ceno6AKC7uxvTpk3DnTt3kJmZifT0dBQXFyM2NvaJxzxx4gRu3bqFEydOYNeuXUhJSWFD8tChQ3BxccG6dev4GgBg4cKFePDgAU6dOoWCggJ88sknMDc3f95bKgiCIPyX0H/dJyAIgiAIP4Varcbly5d5e/HixfxvDw8PrF+/HgkJCdi+fTsMDQ1hZWUFHR0dODo6ah3nvffe4397eXkhOTkZw4cPR3Nz8xONlYiICCxfvhwA4OPjgzNnzmDLli2YMGECMjIyUFBQgJKSEri6ugIAdu/eDX9/f1y8eBHDhw9/7DFtbGywbds26OnpQa1WY/LkycjIyMAHH3wAW1tb6OnpccSNKCsrw4wZMxAQEMDXIAiCIPx8kUiWIAiC8LOlp6eHU+YA4NixY3j77behUqlgYWGBOXPmoKGhAS0tLU88TnZ2NqZMmQI3NzdYWFggMjISQK/x8iRGjhz5yHZhYSEAoLCwEK6urmxgAYCfnx+sra15n8fh7+8PPT093nZycnpq6uJHH32E9evXIyIiAmvWrNEyPAVBEISfH2JkCYIgCD9bCgsL4enpCQAoLS1FTEwMAgMDcfDgQWRnZ3ONVHt7+08e4/79+4iKioKlpSX27t2Lixcv4vDhw0/93qvCwMBAa1tHR+epXRTnz5+P4uJizJkzBwUFBQgNDcXnn3/+Kk9TEARBeAnEyBIEQRB+lhw/fhwFBQWYMWMGgN5oVHd3Nz777DOMGDECPj4+qKys1PqOoaEhurq6tP5WVFSEhoYGfPzxx3jzzTehVqufGjkisrKyHtn29fUFAPj6+qK8vBzl5eX8+bVr19DU1AQ/P7/nvt4nXQMAuLq6IiEhAYcOHcLSpUuxY8eOF/4NQRAE4dUiRpYgCILw2nnw4AGqq6tRUVGBnJwcbNy4EdOmTUNMTAzmzp0LAPD29kZHRwc+//xzFBcXY8+ePfjyyy+1juPh4YHm5mZkZGSgvr4eLS0tcHNzg6GhIX/vyJEjSExMfKbzOnPmDDZt2oQbN27giy++wIEDB7Bo0SIAwPjx4xEQEIDZs2cjJycHFy5cwNy5cxEZGYnQ0NAXvhceHh44deoUKioqUF9fD6C3Fu3HH39ESUkJcnJycOLECTb2BEEQhJ8fYmQJgiAIr50ffvgBTk5O8PDwQHR0NE6cOIHk5GSkpqZy/VJQUBA2b96MTz75BEOGDMHevXvxpz/9Ses4o0aNQkJCAmJjY2Fvb49NmzbB3t4eKSkpOHDgAPz8/PDxxx8jKSnpmc5r6dKluHTpEoYOHYr169dj8+bNiIqKAtCb5peamgobGxu89dZbGD9+PLy8vPDNN9+81L1Yt24dSktLMWjQINjb2wMAurq6sHDhQvj6+iI6Oho+Pj7Yvn37S/2OIAiC8OrQ6enp6XndJyEIgiAIPzc8PDywePFirY6GgiAIgvAsSCRLEARBEARBEAShHxEjSxAEQRAEQRAEoR+RdEFBEARBEARBEIR+RCJZgiAIgiAIgiAI/YgYWYIgCIIgCIIgCP2IGFmCIAiCIAiCIAj9iBhZgiAIgiAIgiAI/YgYWYIgCIIgCIIgCP2IGFmCIAiCIAiCIAj9iBhZgiAIgiAIgiAI/YgYWYIgCIIgCIIgCP2IGFmCIAiCIAiCIAj9yP8DMBphY2BivD0AAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Analyzing the Dendrogram\n","\n","The dendrogram shows how each cluster is linked, with the y-axis representing the distance (dissimilarity) at which clusters are merged. The height of the merge point indicates the similarity between clusters: lower heights suggest greater similarity."],"metadata":{"id":"J3qEGPrjd_Dt"}},{"cell_type":"markdown","source":["## Visualizing Clusters\n","Let's visualize the clusters formed based on the selected binary risk tags.\n"],"metadata":{"id":"Vh8NU_kNZ4F1"}},{"cell_type":"code","source":["# Example: Set maximum distance at 1.5 for cluster formation\n","cluster_labels = fcluster(linkage_matrix, t=5, criterion='distance')\n","\n","# Add cluster labels back to your original DataFrame\n","data_new['cluster'] = cluster_labels\n","\n","# Summary statistics for each cluster\n","cluster_summary = data_new[[feature_1, feature_2, feature_3,'cluster']].groupby('cluster').agg(['mean', 'std', 'median', 'count'])\n","cluster_summary\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"rQ09IsDTHQU9","executionInfo":{"status":"ok","timestamp":1729641527551,"user_tz":300,"elapsed":142,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"a957a4cb-6f2d-4c3a-be54-d311dd9978d1"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        hidden_owner                        Is_honeypot                    \\\n","                mean       std median count        mean  std median count   \n","cluster                                                                     \n","1            0.00000  0.000000    0.0   446         0.0  0.0    0.0   446   \n","2            0.00000  0.000000    0.0   329         0.0  0.0    0.0   329   \n","3            0.52809  0.502039    1.0    89         1.0  0.0    1.0    89   \n","4            1.00000  0.000000    1.0   103         0.0  0.0    0.0   103   \n","\n","        exploitation                         \n","                mean       std median count  \n","cluster                                      \n","1           0.000000  0.000000    0.0   446  \n","2           1.000000  0.000000    1.0   329  \n","3           0.629213  0.485752    1.0    89  \n","4           0.504854  0.502421    1.0   103  "],"text/html":["\n","  <div id=\"df-998a0892-ff80-49fb-8098-ac132ac4a518\" class=\"colab-df-container\">\n","    <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 tr th {\n","        text-align: left;\n","    }\n","\n","    .dataframe thead tr:last-of-type th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr>\n","      <th></th>\n","      <th colspan=\"4\" halign=\"left\">hidden_owner</th>\n","      <th colspan=\"4\" halign=\"left\">Is_honeypot</th>\n","      <th colspan=\"4\" halign=\"left\">exploitation</th>\n","    </tr>\n","    <tr>\n","      <th></th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>median</th>\n","      <th>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>median</th>\n","      <th>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>median</th>\n","      <th>count</th>\n","    </tr>\n","    <tr>\n","      <th>cluster</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>446</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>446</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>446</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>329</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>329</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>329</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.52809</td>\n","      <td>0.502039</td>\n","      <td>1.0</td>\n","      <td>89</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>89</td>\n","      <td>0.629213</td>\n","      <td>0.485752</td>\n","      <td>1.0</td>\n","      <td>89</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.00000</td>\n","      <td>0.000000</td>\n","      <td>1.0</td>\n","      <td>103</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>103</td>\n","      <td>0.504854</td>\n","      <td>0.502421</td>\n","      <td>1.0</td>\n","      <td>103</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-998a0892-ff80-49fb-8098-ac132ac4a518')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n"," 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\"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"exploitation\",\n        \"count\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175,\n        \"min\": 89,\n        \"max\": 446,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["Plot histogram of cluster labels to see distribution of cluster sizes"],"metadata":{"id":"dClOj4ZCfDmJ"}},{"cell_type":"code","source":["# Plot histogram of cluster labels to see distribution of cluster sizes\n","plt.figure(figsize=(8, 6))\n","plt.hist(cluster_labels, bins=np.arange(1, np.max(cluster_labels)+2)-0.5, rwidth=0.8, color='blue', alpha=0.7)\n","plt.title('Histogram of Cluster Sizes')\n","plt.xlabel('Cluster')\n","plt.ylabel('Number of Points')\n","plt.xticks(np.arange(1, np.max(cluster_labels)+1))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"yVyx_1s8IAvM","executionInfo":{"status":"ok","timestamp":1729641534382,"user_tz":300,"elapsed":302,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"a4c56932-6206-4fb0-a947-467fd24f2887"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Calculate the mean for each cluster and feature\n","cluster_centers = data_new[[feature_1, feature_2, feature_3,'cluster']].groupby('cluster').mean()\n","\n","plt.figure(figsize=(12, 8))\n","sns.heatmap(cluster_centers, annot=True, cmap='coolwarm')\n","plt.title('Heatmap of Cluster Centroids')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":699},"id":"0ABFr4VaemaN","executionInfo":{"status":"ok","timestamp":1729641538163,"user_tz":300,"elapsed":531,"user":{"displayName":"Chekwube Ononuju","userId":"17624332106070577396"}},"outputId":"a70a499c-ff07-4556-fc21-a4aeec60d3d4"},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Summary of Insights\n","\n","The hierarchical clustering analysis of smart contracts based on binary risk tags, such as hidden_owner, Is_honeypot, and exploitation, successfully grouped contracts with similar vulnerabilities. Using Jaccard distance to measure the dissimilarity between contracts and complete linkage clustering, we identified clusters that reflected different combinations of these risks. The dendrogram provided a visual overview of how contracts were merged into clusters, with contracts sharing similar risk patterns clustered at lower dissimilarity levels. This clustering helps simplify the analysis of large sets of smart contracts by grouping those with shared vulnerabilities.\n","\n","The cluster composition revealed distinct groups of contracts with varying levels of risk. Larger clusters contained contracts with minimal risks, while smaller clusters represented contracts with unique or higher levels of risk, such as exploitation or hidden ownership. The histogram of cluster sizes showed that the majority of contracts shared common risk profiles, with only a few clusters representing outliers with rare risk tag combinations. This suggests that most smart contracts fall into generalizable patterns of risk, while a smaller portion contains more specialized vulnerabilities.\n","\n","The heatmap of cluster centroids provided further insight into the dominant risk characteristics of each cluster, allowing auditors to focus on clusters that exhibited higher occurrences of specific risks. This approach offers a practical application for smart contract developers and auditors, enabling them to prioritize contracts based on shared vulnerabilities. By identifying clusters with heightened risks, companies can deploy more focused and effective risk management strategies, improving overall smart contract security and mitigating potential vulnerabilities."],"metadata":{"id":"vuTlNwWXfy9N"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"YtNoQbWBsrZZ"},"execution_count":null,"outputs":[]}]}