diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/README.md b/Reinforcement Learning/Waste Management through advanced RL techniques/README.md new file mode 100644 index 00000000..cda7191f --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/README.md @@ -0,0 +1,71 @@ +# Project Overview +Epsilon Explorer is a reinforcement learning project designed to explore and analyze the performance of an agent using an epsilon-greedy strategy. The primary objective of this project is to investigate how the agent learns over multiple episodes by balancing exploration (trying new actions) and exploitation (choosing the best-known actions). This project provides valuable insights into the agent's learning dynamics, highlighting the effects of epsilon decay on performance and score improvement. + +# Key Concepts: +Reinforcement Learning +Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal strategies for maximizing cumulative rewards. + +# Epsilon-Greedy Strategy: +The epsilon-greedy strategy is a popular approach used in reinforcement learning to balance exploration and exploitation. The strategy employs a parameter, epsilon (ε), which determines the probability of choosing a random action (exploration) versus the best-known action (exploitation). As training progresses, epsilon typically decays, allowing the agent to rely more on learned knowledge and less on exploration. + +# Score Tracking: +Tracking the scores achieved by the agent during training provides valuable insights into its performance. By analyzing these scores, we can evaluate the effectiveness of different learning strategies and make informed decisions about tuning hyperparameters. + + +![ep_RL2](https://github.com/user-attachments/assets/4b369c79-64ff-4600-b329-30df9552ad18) +![epsilon_RL1](https://github.com/user-attachments/assets/8b577058-7e29-4776-9cb3-e9f8eaad2016) + +Further an real life application of this concept is implemented through the use of a Daily AI Waste Management technique +-------------------------------------------------------------------------------------------------------------------------- + +## Project Documentation: Waste Management Reinforcement Learning + +Project Overview: +------------------ + +The project aims to develop a reinforcement learning (RL) agent to optimize waste collection in a simulated environment, minimizing overflow events and improving efficiency. +1) Environment and State Representation: + +The state is represented by four features: +Waste Level: Current waste level (0 to 1) +Time of Day: A random value representing the time (0 to 24 hours) +Weather Condition: A random value (0 to 1) indicating the weather +Distance to Collection Point: A random value (0 to 10) representing the distance to the waste collection point. + +2) Action Space: + +The agent can choose between two actions: +Wait (0): Do not collect waste. +Collect Waste (1): Proceed with waste collection. + +3) Reward Structure: + +The reward system is designed to encourage efficient waste collection: ++10 for timely collection when the waste level exceeds the threshold. +-5 for premature collection when the waste level is below the threshold. +-1 for each time step to penalize waiting. + +4) Training Process: + +The agent is trained over 100 episodes, where each episode simulates a series of time steps (up to 20) where the agent makes decisions based on the current state. +The agent learns from experience using a replay memory and updates its policy through Q-learning. + +5) Evaluation Metrics: + +Performance is evaluated using: +Average Reward per Episode: Measures the effectiveness of the agent's actions. +Epsilon Decay: Tracks the exploration rate, indicating how the agent balances exploration vs. exploitation. +Overflow Events: Counts occurrences when the waste level exceeds the maximum capacity as per previous updation. + +6) Visualization: + +The results are visualized using Matplotlib to plot: +Average rewards per episode, showing the agent's learning progression and rewards gained on successfull execution and implementation of a specified condition +Epsilon decay over episodes, illustrating the shift from exploration to exploitation. +Overflow events per episode, highlighting improvements in waste management techniques + +Further the results have been visualized with the help of graphs: +![use_case_Waste_management_RL1](https://github.com/user-attachments/assets/4d8724d1-c9d3-4d96-adf0-12b977398edd) +![use_case_waste_managent_2](https://github.com/user-attachments/assets/7153c560-52fb-46c3-b3c0-62fcacb35247) +![use_case_waste_management3](https://github.com/user-attachments/assets/1a8a7109-1c40-4286-bd05-c6cde101f355) + diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb b/Reinforcement Learning/Waste Management through advanced RL techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb new file mode 100644 index 00000000..2f93c32b --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb @@ -0,0 +1,333 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5zt---XLb3Oj", + "outputId": "580db4bb-e9f7-47aa-cc1d-e5c9fd647d66" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/util.py:55: DeprecationWarning: jax.xla_computation is deprecated. Please use the AOT APIs; see https://jax.readthedocs.io/en/latest/aot.html. For example, replace xla_computation(f)(*xs) with jit(f).lower(*xs).compiler_ir('hlo'). See CHANGELOG.md for 0.4.30 for more examples.\n", + " from jax import xla_computation as _xla_computation\n", + "/usr/local/lib/python3.10/dist-packages/gym/core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n", + " deprecation(\n", + "/usr/local/lib/python3.10/dist-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n", + " deprecation(\n", + "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n", + "/usr/local/lib/python3.10/dist-packages/gym/utils/passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`. (Deprecated NumPy 1.24)\n", + " if not isinstance(terminated, (bool, np.bool8)):\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Episode: 1/100, Score: 41, Epsilon: 1.00\n", + "Episode: 2/100, Score: 13, Epsilon: 0.99\n", + "Episode: 3/100, Score: 10, Epsilon: 0.99\n", + "Episode: 4/100, Score: 38, Epsilon: 0.99\n", + "Episode: 5/100, Score: 24, Epsilon: 0.98\n", + "Episode: 6/100, Score: 20, Epsilon: 0.98\n", + "Episode: 7/100, Score: 9, Epsilon: 0.97\n", + "Episode: 8/100, Score: 10, Epsilon: 0.97\n", + "Episode: 9/100, Score: 9, Epsilon: 0.96\n", + "Episode: 10/100, Score: 10, Epsilon: 0.96\n", + "Episode: 11/100, Score: 21, Epsilon: 0.95\n", + "Episode: 12/100, Score: 17, Epsilon: 0.95\n", + "Episode: 13/100, Score: 9, Epsilon: 0.94\n", + "Episode: 14/100, Score: 35, Epsilon: 0.94\n", + "Episode: 15/100, Score: 30, Epsilon: 0.93\n", + "Episode: 16/100, Score: 11, Epsilon: 0.93\n", + "Episode: 17/100, Score: 15, Epsilon: 0.92\n", + "Episode: 18/100, Score: 13, Epsilon: 0.92\n", + "Episode: 19/100, Score: 17, Epsilon: 0.91\n", + "Episode: 20/100, Score: 14, Epsilon: 0.91\n", + "Episode: 21/100, Score: 19, Epsilon: 0.90\n", + "Episode: 22/100, Score: 31, Epsilon: 0.90\n", + "Episode: 23/100, Score: 13, Epsilon: 0.90\n", + "Episode: 24/100, Score: 22, Epsilon: 0.89\n", + "Episode: 25/100, Score: 13, Epsilon: 0.89\n", + "Episode: 26/100, Score: 13, Epsilon: 0.88\n", + "Episode: 27/100, Score: 11, Epsilon: 0.88\n", + "Episode: 28/100, Score: 24, Epsilon: 0.87\n", + "Episode: 29/100, Score: 33, Epsilon: 0.87\n", + "Episode: 30/100, Score: 8, Epsilon: 0.86\n", + "Episode: 31/100, Score: 12, Epsilon: 0.86\n", + "Episode: 32/100, Score: 68, Epsilon: 0.86\n", + "Episode: 33/100, Score: 23, Epsilon: 0.85\n", + "Episode: 34/100, Score: 9, Epsilon: 0.85\n", + "Episode: 35/100, Score: 11, Epsilon: 0.84\n", + "Episode: 36/100, Score: 12, Epsilon: 0.84\n", + "Episode: 37/100, Score: 17, Epsilon: 0.83\n", + "Episode: 38/100, Score: 12, Epsilon: 0.83\n", + "Episode: 39/100, Score: 22, Epsilon: 0.83\n", + "Episode: 40/100, Score: 26, Epsilon: 0.82\n", + "Episode: 41/100, Score: 27, Epsilon: 0.82\n", + "Episode: 42/100, Score: 33, Epsilon: 0.81\n", + "Episode: 43/100, Score: 39, Epsilon: 0.81\n", + "Episode: 44/100, Score: 10, Epsilon: 0.81\n", + "Episode: 45/100, Score: 52, Epsilon: 0.80\n", + "Episode: 46/100, Score: 13, Epsilon: 0.80\n", + "Episode: 47/100, Score: 23, Epsilon: 0.79\n", + "Episode: 48/100, Score: 18, Epsilon: 0.79\n", + "Episode: 49/100, Score: 20, Epsilon: 0.79\n", + "Episode: 50/100, Score: 23, Epsilon: 0.78\n", + "Episode: 51/100, Score: 18, Epsilon: 0.78\n", + "Episode: 52/100, Score: 40, Epsilon: 0.77\n", + "Episode: 53/100, Score: 18, Epsilon: 0.77\n", + "Episode: 54/100, Score: 22, Epsilon: 0.77\n", + "Episode: 55/100, Score: 11, Epsilon: 0.76\n", + "Episode: 56/100, Score: 11, Epsilon: 0.76\n", + "Episode: 57/100, Score: 27, Epsilon: 0.76\n", + "Episode: 58/100, Score: 18, Epsilon: 0.75\n", + "Episode: 59/100, Score: 11, Epsilon: 0.75\n", + "Episode: 60/100, Score: 30, Epsilon: 0.74\n", + "Episode: 61/100, Score: 21, Epsilon: 0.74\n", + "Episode: 62/100, Score: 19, Epsilon: 0.74\n", + "Episode: 63/100, Score: 35, Epsilon: 0.73\n", + "Episode: 64/100, Score: 34, Epsilon: 0.73\n", + "Episode: 65/100, Score: 16, Epsilon: 0.73\n", + "Episode: 66/100, Score: 116, Epsilon: 0.72\n", + "Episode: 67/100, Score: 14, Epsilon: 0.72\n", + "Episode: 68/100, Score: 19, Epsilon: 0.71\n", + "Episode: 69/100, Score: 29, Epsilon: 0.71\n", + "Episode: 70/100, Score: 24, Epsilon: 0.71\n", + "Episode: 71/100, Score: 31, Epsilon: 0.70\n", + "Episode: 72/100, Score: 14, Epsilon: 0.70\n", + "Episode: 73/100, Score: 27, Epsilon: 0.70\n", + "Episode: 74/100, Score: 16, Epsilon: 0.69\n", + "Episode: 75/100, Score: 46, Epsilon: 0.69\n", + "Episode: 76/100, Score: 12, Epsilon: 0.69\n", + "Episode: 77/100, Score: 10, Epsilon: 0.68\n", + "Episode: 78/100, Score: 15, Epsilon: 0.68\n", + "Episode: 79/100, Score: 11, Epsilon: 0.68\n", + "Episode: 80/100, Score: 10, Epsilon: 0.67\n", + "Episode: 81/100, Score: 15, Epsilon: 0.67\n", + "Episode: 82/100, Score: 21, Epsilon: 0.67\n", + "Episode: 83/100, Score: 16, Epsilon: 0.66\n", + "Episode: 84/100, Score: 10, Epsilon: 0.66\n", + "Episode: 85/100, Score: 46, Epsilon: 0.66\n", + "Episode: 86/100, Score: 59, Epsilon: 0.65\n", + "Episode: 87/100, Score: 31, Epsilon: 0.65\n", + "Episode: 88/100, Score: 50, Epsilon: 0.65\n", + "Episode: 89/100, Score: 57, Epsilon: 0.64\n", + "Episode: 90/100, Score: 45, Epsilon: 0.64\n", + "Episode: 91/100, Score: 31, Epsilon: 0.64\n", + "Episode: 92/100, Score: 14, Epsilon: 0.63\n", + "Episode: 93/100, Score: 38, Epsilon: 0.63\n", + "Episode: 94/100, Score: 25, Epsilon: 0.63\n", + "Episode: 95/100, Score: 16, Epsilon: 0.62\n", + "Episode: 96/100, Score: 45, Epsilon: 0.62\n", + "Episode: 97/100, Score: 20, Epsilon: 0.62\n", + "Episode: 98/100, Score: 28, Epsilon: 0.61\n", + "Episode: 99/100, Score: 14, Epsilon: 0.61\n", + "Episode: 100/100, Score: 17, Epsilon: 0.61\n" + ] + } + ], + "source": [ + "import gym\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.keras import models, layers, optimizers\n", + "import random\n", + "from collections import deque\n", + "\n", + "# CartPole environment\n", + "env = gym.make('CartPole-v1')\n", + "\n", + "# Hyperparameters\n", + "state_size = env.observation_space.shape[0]\n", + "action_size = env.action_space.n\n", + "learning_rate = 0.001\n", + "gamma = 0.95 # Discount factor for future rewards\n", + "epsilon = 1.0 # Exploration rate\n", + "epsilon_min = 0.01\n", + "epsilon_decay = 0.995\n", + "batch_size = 8\n", + "episodes = 100\n", + "\n", + "# Memory to store experiences (state, action, reward, next_state, done)\n", + "memory = deque(maxlen=200) # Significantly reduced memory size\n", + "\n", + "# build model\n", + "def build_model():\n", + " model = models.Sequential()\n", + " model.add(layers.Dense(8, input_dim=state_size, activation='relu')) # Fewer neurons\n", + " model.add(layers.Dense(8, activation='relu')) # Fewer neurons\n", + " model.add(layers.Dense(action_size, activation='linear'))\n", + " model.compile(optimizer=optimizers.Adam(learning_rate=learning_rate), loss='mse')\n", + " return model\n", + "\n", + "\n", + "model = build_model()\n", + "\n", + "# select action using epsilon-greedy strategy\n", + "def choose_action(state, epsilon):\n", + " if np.random.rand() <= epsilon:\n", + " return random.randrange(action_size) # Exploration\n", + " q_values = model.predict(state, verbose=0)\n", + " return np.argmax(q_values[0]) # Exploitation\n", + "\n", + "# replay training\n", + "def replay():\n", + " if len(memory) < batch_size:\n", + " return # Not enough samples to train\n", + "\n", + " minibatch = random.sample(memory, batch_size)\n", + " states, targets = [], []\n", + " for state, action, reward, next_state, done in minibatch:\n", + " target = reward\n", + " if not done:\n", + " target += gamma * np.max(model.predict(next_state, verbose=0)[0])\n", + " target_f = model.predict(state, verbose=0)\n", + " target_f[0][action] = target\n", + " states.append(state)\n", + " targets.append(target_f)\n", + "\n", + " # Train on the batch\n", + " model.fit(np.vstack(states), np.vstack(targets), epochs=1, verbose=0)\n", + "\n", + "# Main loop\n", + "for e in range(episodes):\n", + " state = env.reset()\n", + " state = np.reshape(state, [1, state_size])\n", + "\n", + " for time in range(200):\n", + " action = choose_action(state, epsilon)\n", + " next_state, reward, done, _ = env.step(action)\n", + " next_state = np.reshape(next_state, [1, state_size])\n", + "\n", + " # Modify the reward if the episode ends\n", + " reward = reward if not done else -10\n", + "\n", + " # Store experience in memory\n", + " memory.append((state, action, reward, next_state, done))\n", + "\n", + " # Move to the next state\n", + " state = next_state\n", + "\n", + " # End episode if done\n", + " if done:\n", + " print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {epsilon:.2f}\")\n", + " break\n", + "\n", + " # Training the model using replay memory\n", + " replay()\n", + "\n", + " # Decay epsilon\n", + " if epsilon > epsilon_min:\n", + " epsilon *= epsilon_decay\n", + "\n", + "env.close()\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "episodes = list(range(1, 101))\n", + "\n", + "\n", + "scores = [\n", + " 41, 13, 10, 38, 24, 20, 9, 10, 9, 10,\n", + " 21, 17, 9, 35, 30, 11, 15, 13, 17, 14,\n", + " 19, 31, 13, 22, 13, 13, 11, 24, 33, 8,\n", + " 12, 68, 23, 9, 11, 12, 17, 12, 22, 26,\n", + " 27, 33, 39, 10, 52, 13, 23, 18, 20, 23,\n", + " 18, 40, 18, 22, 11, 11, 27, 18, 11, 30,\n", + " 21, 19, 35, 34, 16, 116, 14, 19, 29, 24,\n", + " 31, 14, 27, 16, 46, 12, 10, 15, 11, 10,\n", + " 15, 21, 16, 10, 46, 59, 31, 50, 57, 45,\n", + " 31, 14, 38, 25, 16, 45, 20, 28, 14, 17\n", + "]\n", + "\n", + "epsilon_values = [\n", + " 1.00, 0.99, 0.99, 0.99, 0.98, 0.98, 0.97, 0.97, 0.96, 0.96,\n", + " 0.95, 0.95, 0.94, 0.94, 0.93, 0.93, 0.92, 0.92, 0.91, 0.91,\n", + " 0.90, 0.90, 0.90, 0.89, 0.89, 0.88, 0.88, 0.87, 0.87, 0.86,\n", + " 0.86, 0.86, 0.85, 0.85, 0.84, 0.84, 0.83, 0.83, 0.83, 0.82,\n", + " 0.82, 0.81, 0.81, 0.81, 0.80, 0.80, 0.79, 0.79, 0.79, 0.78,\n", + " 0.78, 0.77, 0.77, 0.77, 0.76, 0.76, 0.76, 0.75, 0.75, 0.74,\n", + " 0.74, 0.74, 0.73, 0.73, 0.73, 0.72, 0.72, 0.71, 0.71, 0.71,\n", + " 0.70, 0.70, 0.70, 0.69, 0.69, 0.69, 0.68, 0.68, 0.68, 0.67,\n", + " 0.67, 0.67, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65, 0.64, 0.64,\n", + " 0.64, 0.63, 0.63, 0.63, 0.62, 0.62, 0.62, 0.61, 0.61, 0.61\n", + "]\n", + "\n", + "\n", + "plt.figure(figsize=(14, 6))\n", + "\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(episodes, scores, marker='o', color='blue', label='Scores', linestyle='-', markersize=4)\n", + "plt.title('Scores per Episode')\n", + "plt.xlabel('Episodes')\n", + "plt.ylabel('Score')\n", + "plt.xticks(range(0, 101, 10))\n", + "plt.grid()\n", + "plt.legend()\n", + "\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(episodes, epsilon_values, marker='x', color='orange', label='Epsilon', linestyle='--', markersize=4)\n", + "plt.title('Epsilon Decay over Episodes')\n", + "plt.xlabel('Episodes')\n", + "plt.ylabel('Epsilon')\n", + "plt.xticks(range(0, 101, 10))\n", + "plt.grid()\n", + "plt.legend()\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "B5-ndEmzpuk1", + "outputId": "db3c4ac1-9ce2-436d-ab2c-c393e886ada8" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + } + ] +} diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/Waste_Management_through_RL (2).ipynb b/Reinforcement Learning/Waste Management through advanced RL techniques/Waste_Management_through_RL (2).ipynb new file mode 100644 index 00000000..07143a83 --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/Waste_Management_through_RL (2).ipynb @@ -0,0 +1,5596 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2-mm9PVGHDGK", + "outputId": "e5b79c87-3808-4be3-b43c-28154ce7302e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Episode: 1/100, Score: 21, Epsilon: 1.00\n", + "Episode: 2/100, Score: 21, Epsilon: 0.99\n", + "Episode: 3/100, Score: 21, Epsilon: 0.99\n", + "Episode: 4/100, Score: 21, Epsilon: 0.99\n", + "Episode: 5/100, Score: 21, Epsilon: 0.98\n", + "Episode: 6/100, Score: 21, Epsilon: 0.98\n", + "Episode: 7/100, Score: 21, Epsilon: 0.97\n", + "Episode: 8/100, Score: 21, Epsilon: 0.97\n", + "Episode: 9/100, Score: 21, Epsilon: 0.96\n", + "Episode: 10/100, Score: 21, Epsilon: 0.96\n", + "Episode: 11/100, Score: 21, Epsilon: 0.95\n", + "Episode: 12/100, Score: 21, Epsilon: 0.95\n", + "Episode: 13/100, Score: 21, Epsilon: 0.94\n", + "Episode: 14/100, Score: 21, Epsilon: 0.94\n", + "Episode: 15/100, Score: 21, Epsilon: 0.93\n", + "Episode: 16/100, Score: 21, Epsilon: 0.93\n", + "Episode: 17/100, Score: 21, Epsilon: 0.92\n", + "Episode: 18/100, Score: 21, Epsilon: 0.92\n", + "Episode: 19/100, Score: 21, Epsilon: 0.91\n", + "Episode: 20/100, Score: 21, Epsilon: 0.91\n", + "Episode: 21/100, Score: 21, Epsilon: 0.90\n", + "Episode: 22/100, Score: 21, Epsilon: 0.90\n", + "Episode: 23/100, Score: 21, Epsilon: 0.90\n", + "Episode: 24/100, Score: 21, Epsilon: 0.89\n", + "Episode: 25/100, Score: 21, Epsilon: 0.89\n", + "Episode: 26/100, Score: 21, Epsilon: 0.88\n", + "Episode: 27/100, Score: 21, Epsilon: 0.88\n", + "Episode: 28/100, Score: 21, Epsilon: 0.87\n", + "Episode: 29/100, Score: 21, Epsilon: 0.87\n", + "Episode: 30/100, Score: 21, Epsilon: 0.86\n", + "Episode: 31/100, Score: 21, Epsilon: 0.86\n", + "Episode: 32/100, Score: 21, Epsilon: 0.86\n", + "Episode: 33/100, Score: 21, Epsilon: 0.85\n", + "Episode: 34/100, Score: 21, Epsilon: 0.85\n", + "Episode: 35/100, Score: 21, Epsilon: 0.84\n", + "Episode: 36/100, Score: 21, Epsilon: 0.84\n", + "Episode: 37/100, Score: 21, Epsilon: 0.83\n", + "Episode: 38/100, Score: 21, Epsilon: 0.83\n", + "Episode: 39/100, Score: 21, Epsilon: 0.83\n", + "Episode: 40/100, Score: 21, Epsilon: 0.82\n", + "Episode: 41/100, Score: 21, Epsilon: 0.82\n", + "Episode: 42/100, Score: 21, Epsilon: 0.81\n", + "Episode: 43/100, Score: 21, Epsilon: 0.81\n", + "Episode: 44/100, Score: 21, Epsilon: 0.81\n", + "Episode: 45/100, Score: 21, Epsilon: 0.80\n", + "Episode: 46/100, Score: 21, Epsilon: 0.80\n", + "Episode: 47/100, Score: 21, Epsilon: 0.79\n", + "Episode: 48/100, Score: 21, Epsilon: 0.79\n", + "Episode: 49/100, Score: 21, Epsilon: 0.79\n", + "Episode: 50/100, Score: 21, Epsilon: 0.78\n", + "Episode: 51/100, Score: 21, Epsilon: 0.78\n", + "Episode: 52/100, Score: 21, Epsilon: 0.77\n", + "Episode: 53/100, Score: 21, Epsilon: 0.77\n", + "Episode: 54/100, Score: 21, Epsilon: 0.77\n", + "Episode: 55/100, Score: 21, Epsilon: 0.76\n", + "Episode: 56/100, Score: 21, Epsilon: 0.76\n", + "Episode: 57/100, Score: 21, Epsilon: 0.76\n", + "Episode: 58/100, Score: 21, Epsilon: 0.75\n", + "Episode: 59/100, Score: 21, Epsilon: 0.75\n", + "Episode: 60/100, Score: 21, Epsilon: 0.74\n", + "Episode: 61/100, Score: 21, Epsilon: 0.74\n", + "Episode: 62/100, Score: 21, Epsilon: 0.74\n", + "Episode: 63/100, Score: 21, Epsilon: 0.73\n", + "Episode: 64/100, Score: 21, Epsilon: 0.73\n", + "Episode: 65/100, Score: 21, Epsilon: 0.73\n", + "Episode: 66/100, Score: 21, Epsilon: 0.72\n", + "Episode: 67/100, Score: 21, Epsilon: 0.72\n", + "Episode: 68/100, Score: 21, Epsilon: 0.71\n", + "Episode: 69/100, Score: 21, Epsilon: 0.71\n", + "Episode: 70/100, Score: 21, Epsilon: 0.71\n", + "Episode: 71/100, Score: 21, Epsilon: 0.70\n", + "Episode: 72/100, Score: 21, Epsilon: 0.70\n", + "Episode: 73/100, Score: 21, Epsilon: 0.70\n", + "Episode: 74/100, Score: 21, Epsilon: 0.69\n", + "Episode: 75/100, Score: 21, Epsilon: 0.69\n", + "Episode: 76/100, Score: 21, Epsilon: 0.69\n", + "Episode: 77/100, Score: 21, Epsilon: 0.68\n", + "Episode: 78/100, Score: 21, Epsilon: 0.68\n", + "Episode: 79/100, Score: 21, Epsilon: 0.68\n", + "Episode: 80/100, Score: 21, Epsilon: 0.67\n", + "Episode: 81/100, Score: 21, Epsilon: 0.67\n", + "Episode: 82/100, Score: 21, Epsilon: 0.67\n", + "Episode: 83/100, Score: 21, Epsilon: 0.66\n", + "Episode: 84/100, Score: 21, Epsilon: 0.66\n", + "Episode: 85/100, Score: 21, Epsilon: 0.66\n", + "Episode: 86/100, Score: 21, Epsilon: 0.65\n", + "Episode: 87/100, Score: 21, Epsilon: 0.65\n", + "Episode: 88/100, Score: 21, Epsilon: 0.65\n", + "Episode: 89/100, Score: 21, Epsilon: 0.64\n", + "Episode: 90/100, Score: 21, Epsilon: 0.64\n", + "Episode: 91/100, Score: 21, Epsilon: 0.64\n", + "Episode: 92/100, Score: 21, Epsilon: 0.63\n", + "Episode: 93/100, Score: 21, Epsilon: 0.63\n", + "Episode: 94/100, Score: 21, Epsilon: 0.63\n", + "Episode: 95/100, Score: 21, Epsilon: 0.62\n", + "Episode: 96/100, Score: 21, Epsilon: 0.62\n", + "Episode: 97/100, Score: 21, Epsilon: 0.62\n", + "Episode: 98/100, Score: 21, Epsilon: 0.61\n", + "Episode: 99/100, Score: 21, Epsilon: 0.61\n", + "Episode: 100/100, Score: 21, Epsilon: 0.61\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import random\n", + "from collections import deque\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "class EnergyManagementAgent:\n", + " def __init__(self, state_size, action_size):\n", + " self.state_size = state_size\n", + " self.action_size = action_size\n", + " self.memory = deque(maxlen=2000)\n", + " self.gamma = 0.95 # discount rate\n", + " self.epsilon = 1.0 # exploration rate\n", + " self.epsilon_min = 0.01\n", + " self.epsilon_decay = 0.995\n", + " self.learning_rate = 0.001\n", + " self.model = self._build_model()\n", + "\n", + " def _build_model(self):\n", + " model = Sequential()\n", + " model.add(tf.keras.Input(shape=(self.state_size,)))\n", + " model.add(Dense(24, activation='relu'))\n", + " model.add(Dense(24, activation='relu'))\n", + " model.add(Dense(self.action_size, activation='linear'))\n", + " model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))\n", + " return model\n", + "\n", + " def remember(self, state, action, reward, next_state, done):\n", + " self.memory.append((state, action, reward, next_state, done))\n", + "\n", + " def act(self, state):\n", + " if np.random.rand() <= self.epsilon:\n", + " return random.randrange(self.action_size)\n", + " act_values = self.model.predict(state, verbose=0)\n", + " return np.argmax(act_values[0])\n", + "\n", + " def replay(self, batch_size):\n", + " minibatch = random.sample(self.memory, batch_size)\n", + " for state, action, reward, next_state, done in minibatch:\n", + " target = reward\n", + " if not done:\n", + " target = reward + self.gamma * np.amax(self.model.predict(next_state, verbose=0)[0])\n", + " target_f = self.model.predict(state, verbose=0)\n", + " target_f[0][action] = target\n", + " self.model.fit(state, target_f, epochs=1, verbose=0)\n", + " if self.epsilon > self.epsilon_min:\n", + " self.epsilon *= self.epsilon_decay\n", + "\n", + " def load(self, name):\n", + " self.model.load_weights(name)\n", + "\n", + " def save(self, name):\n", + " self.model.save_weights(name)\n", + "\n", + "if __name__ == \"__main__\":\n", + " # Environment parameters\n", + " state_size = 4 # Example state: [current energy usage, time of day, temperature, price]\n", + " action_size = 2 # Example actions: [0: reduce usage, 1: maintain usage]\n", + " agent = EnergyManagementAgent(state_size, action_size)\n", + " episodes = 100\n", + "\n", + " # Train the agent in the environment\n", + " for e in range(episodes):\n", + " # Reset environment for each episode\n", + " state = np.reshape(np.random.rand(state_size), [1, state_size])\n", + " done = False\n", + " time = 0\n", + "\n", + " while not done:\n", + " # Take action\n", + " action = agent.act(state)\n", + " # Simulate next state and reward (replace with environment logic)\n", + " next_state = np.reshape(np.random.rand(state_size), [1, state_size])\n", + " reward = random.uniform(-1, 1) # Example reward\n", + " done = time >= 20 # Example end condition\n", + "\n", + " # Remember the experience\n", + " agent.remember(state, action, reward, next_state, done)\n", + "\n", + " # Move to the next state\n", + " state = next_state\n", + " time += 1\n", + "\n", + " # Replay experience to train the model\n", + " if len(agent.memory) > 32:\n", + " agent.replay(32)\n", + "\n", + " # Print progress\n", + " print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ky6lNNgvbFSW" + }, + "outputs": [], + "source": [ + "state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point]\n", + "action_size = 2 # Example actions: [0: wait, 1: collect waste]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YYrGz8uTbGBq", + "outputId": "573c1a02-0ad1-4d60-a503-b80bc49bb885" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Episode: 1/100, Score: 21, Epsilon: 1.00, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 2/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.76\n", + "Episode: 3/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.05\n", + "Episode: 4/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 5/100, Score: 21, Epsilon: 0.98, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 6/100, Score: 21, Epsilon: 0.98, Overflow Events: 0, Average Reward: -1.05\n", + "Episode: 7/100, Score: 21, Epsilon: 0.97, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 8/100, Score: 21, Epsilon: 0.97, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 9/100, Score: 21, Epsilon: 0.96, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 10/100, Score: 21, Epsilon: 0.96, Overflow Events: 0, Average Reward: -2.76\n", + "Episode: 11/100, Score: 21, Epsilon: 0.95, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 12/100, Score: 21, Epsilon: 0.95, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 13/100, Score: 21, Epsilon: 0.94, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 14/100, Score: 21, Epsilon: 0.94, Overflow Events: 0, Average Reward: -1.10\n", + "Episode: 15/100, Score: 21, Epsilon: 0.93, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 16/100, Score: 21, Epsilon: 0.93, Overflow Events: 0, Average Reward: -2.57\n", + "Episode: 17/100, Score: 21, Epsilon: 0.92, Overflow Events: 4, Average Reward: -1.24\n", + "Episode: 18/100, Score: 21, Epsilon: 0.92, Overflow Events: 0, Average Reward: -2.95\n", + "Episode: 19/100, Score: 21, Epsilon: 0.91, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 20/100, Score: 21, Epsilon: 0.91, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 21/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 22/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -1.43\n", + "Episode: 23/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 24/100, Score: 21, Epsilon: 0.89, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 25/100, Score: 21, Epsilon: 0.89, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 26/100, Score: 21, Epsilon: 0.88, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 27/100, Score: 21, Epsilon: 0.88, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 28/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.76\n", + "Episode: 29/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 30/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.57\n", + "Episode: 31/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 32/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.57\n", + "Episode: 33/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 34/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 35/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 36/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 37/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.05\n", + "Episode: 38/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 39/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 40/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 41/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 42/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 43/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 44/100, Score: 21, Epsilon: 0.81, Overflow Events: 1, Average Reward: -1.81\n", + "Episode: 45/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.57\n", + "Episode: 46/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 47/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 48/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 49/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 50/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -1.67\n", + "Episode: 51/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 52/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 53/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 54/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 55/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.76\n", + "Episode: 56/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.76\n", + "Episode: 57/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.57\n", + "Episode: 58/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 59/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 60/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 61/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 62/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 63/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 64/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 65/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 66/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 67/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -0.71\n", + "Episode: 68/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 69/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -0.33\n", + "Episode: 70/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 71/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.43\n", + "Episode: 72/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 73/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 74/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 75/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 76/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 77/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 78/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 79/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 80/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -1.43\n", + "Episode: 81/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -0.86\n", + "Episode: 82/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 83/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.43\n", + "Episode: 84/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 85/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 86/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.62\n", + "Episode: 87/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.81\n", + "Episode: 88/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 89/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.05\n", + "Episode: 90/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.05\n", + "Episode: 91/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -0.71\n", + "Episode: 92/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -2.19\n", + "Episode: 93/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -1.24\n", + "Episode: 94/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -0.86\n", + "Episode: 95/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -2.38\n", + "Episode: 96/100, Score: 21, Epsilon: 0.62, Overflow Events: 1, Average Reward: -1.24\n", + "Episode: 97/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -2.00\n", + "Episode: 98/100, Score: 21, Epsilon: 0.61, Overflow Events: 6, Average Reward: -0.86\n", + "Episode: 99/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -0.86\n", + "Episode: 100/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -0.71\n" + ] + } + ], + "source": [ + "# Environment parameters\n", + "state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point]\n", + "action_size = 2 # Example actions: [0: wait, 1: collect waste]\n", + "agent = EnergyManagementAgent(state_size, action_size)\n", + "episodes = 100\n", + "\n", + "# Waste management specific parameters\n", + "threshold = 0.7 # Waste level threshold (between 0 and 1, where 1 means full capacity)\n", + "\n", + "# Train the agent in the environment\n", + "for e in range(episodes):\n", + " # Reset environment for each episode\n", + " waste_level = random.uniform(0, 0.5) # Start with a random waste level below the threshold\n", + " state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n", + " done = False\n", + " time = 0\n", + " overflow_count = 0 # Track overflow events\n", + " rewards = []\n", + "\n", + " while not done:\n", + " # Take action\n", + " action = agent.act(state)\n", + " # Simulate next state and reward (replace with environment logic)\n", + " waste_level += random.uniform(0, 0.1) # Waste increases over time\n", + " if waste_level > 1.0: # Overflow occurred\n", + " overflow_count += 1\n", + " waste_level = 1.0\n", + "\n", + " next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n", + "\n", + " # Reward structure\n", + " reward = -1 # Default reward for time passing\n", + " if waste_level > threshold and action == 1:\n", + " reward = 10 # Positive reward for timely collection\n", + " waste_level = 0 # Waste collected\n", + " elif waste_level < threshold and action == 1:\n", + " reward = -5 # Penalty for collecting too early\n", + "\n", + " rewards.append(reward)\n", + "\n", + " done = time >= 20 # Example end condition\n", + "\n", + " # Remember the experience\n", + " agent.remember(state, action, reward, next_state, done)\n", + "\n", + " # Move to the next state\n", + " state = next_state\n", + " time += 1\n", + "\n", + " # Replay experience to train the model\n", + " if len(agent.memory) > 32:\n", + " agent.replay(32)\n", + "\n", + " # Print progress\n", + " print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}\")\n", + "plt.figure(figsize=(15, 5))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/" + }, + "id": "Q6xdPil6fQ7M", + "outputId": "6cf5f96c-a5d3-4c3f-9f9a-a5eceaeec02c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n", + "\u001b[1m1/1\u001b[0m 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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n", + "\u001b[1m1/1\u001b[0m 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YlpYmX3bw4EFRq9WK3rxFDRgwQAQgvv766/JlJpNJ7N69u5icnCyazWZRFP33XKhoDe6+pk2bJm83Z84cEYA4fPhw1fUfeOABEYC4d+9e+bKUlBRxwoQJ8vfdunUThw0bVuE6pPucnZ0tX7Z3715Ro9GI48ePly/z9jE/deqUqNVqxRdeeEH1c/bv3y/qdDqXy4mI6oKlS5d6fM83GAzydtK+LzIyUjxz5ox8+bZt20QA4qOPPiqKon2/CUB89dVXK/y5AwYMEAcMGOBy+0uXLpUv83Y/IO2PlPtqURTFkSNHivXr16/0MZgwYYIYHR3t8d/37Nmjuo8FBQViQkKCOHXqVNV2mZmZYnx8vOry/v37i7Gxsap9lCiK8t8eouj6d5koiuIff/whAhA/+ugj+bJFixaJAMRDhw7Jl5nNZrFBgwaqfSwRUV2Qm5srAhBvu+22CrcbPny4CEDMz88XRVEUx4wZIyYnJ4sWi0XeJiMjQ9RoNOK///1v+bIbb7xR7NKli1haWipfZrPZxD59+ojt2rWTL5P2o3379lXdpiiK4ubNm0UA4meffaa6XNpvSVJTU0UA4pQpU1TbPf744yIA8aeffhJFseqfcz1JSUnx+DfAvHnz5O2uueYasUePHqrrbt++XbWfstlsYrt27cTBgwe77ONatWol3nTTTS7335v9dnR0tNt9XHx8vKpW4C1vP8P68tlR+vy8cOFCn9bg7qtDhw7ydtLzp2nTpvLzVxRFcc2aNSIA8c0335QvmzBhgpiSkiJ///DDD4txcXEuz0mlRx55RASgqikUFBSIrVq1Elu2bClarVZRFEVxwYIFIgBxzZo18nZFRUVi27ZtRQDi5s2bRVH07TlAoYFxLhRyVq5ciYYNG+L6668HYO/quuuuu7Bq1SpYrVYAwA033IAGDRqosjYvXbqEjRs34q677pIv++yzz3DZZZehY8eOuHjxovx1ww03AIBL7MiAAQPQqVMnlzUpj1ZeunQJeXl56Nevn+rUJynu44EHHlBd98EHH1R9L4oiPv/8c9x6660QRVG1rsGDByMvL6/SU6oA+4CxpKQkJCUloUuXLlixYgUmTZqEV199VXX/4+PjcdNNN6l+To8ePRATEyPffykK5pdffgEA/Pbbb9BqtfjnP/+JrKwsHD16FID9CH3fvn0hCAIAQKvVymcC2Gw25OTkwGKxoGfPnm7vwx133KE6bdBqtWLDhg0YMWIEWrRoIV9+2WWXYfDgwZU+BhKdTodp06bJ30dERGDatGk4f/48du3aJT8W/ngueNKyZUts3LjR5cvdWQUzZsxQfS89R7777juPt5+QkIC//vpL/l04y8jIQGpqKiZOnIjExET58q5du+Kmm26Sb9uXx3zdunWw2WwYPXq06jFr1KgR2rVr5/KYERHVJe+8847Le/7333/vst2IESPQtGlT+ftevXqhd+/e8vtyZGQkIiIisGXLFtXp3b7ydj+gdP/996u+79evH7Kzs5Gfn1/ldQCQh34VFBQAsHd35ebmYsyYMar9iVarRe/eveX9yYULF/DLL7/g3nvvVe2jAMh/ewDqv8vKysqQnZ2Ntm3bIiEhQfX3x+jRo2E0GlWxgBs2bMDFixeZZ0tEdY70nhwbG1vhdtK/S/uCu+66C+fPn8eWLVvkbdauXQubzSZ/9s7JycFPP/2E0aNHo6CgQH6fz87OxuDBg3H06FGcPXtW9XOmTp0KrVZbpfsi7dNmzZqlulwa+Cl10lf1c25Fevfu7fZz35gxY+Rt7rrrLuzatUsVtbp69WoYDAb5LKjU1FQcPXoU99xzD7Kzs+XHrKioCDfeeCN++eUXl8GX1dlvJyQkYNu2bTh37lyl27pT2WdYXz87GgwGtxG9Ffn8889dHvelS5e6bDd+/HjV83zUqFFo3LhxpZ+3i4qKXOJSlb777jv06tULffv2lS+LiYnBfffdh1OnTslxtd999x0aN26MUaNGydtFRUXJZwdIqvIcoOBinAuFFKvVilWrVuH666/HyZMn5ct79+6N119/HZs2bcKgQYOg0+lwxx134JNPPoHJZILBYMC6detQVlamKqIfPXoUhw4dcsl7k0iDSyStWrVyu90333yD559/HqmpqaocK+VONi0tDRqNxuU22rZtq/r+woULyM3Nxfvvv4/333/fq3W507t3bzz//POwWq04cOAAnn/+eVy6dEkVb3P06FHk5eUhOTm50p/Tr18/eaeydetW9OzZEz179kRiYiK2bt2Khg0bYu/evbjnnntUt7F8+XK8/vrrOHz4MMrKyuTL3T2WzpdduHABJSUlaNeuncu2HTp0qHAnp9SkSROXyBTp9PFTp07h6quv9ttzwZPo6GgMHDjQq22d72+bNm2g0WhUWa7O/v3vf+O2225D+/btcfnll+Pmm2/GuHHj0LVrVwCQM/c6dOjgct3LLrsMGzZsQFFREQoKCrx+zI8ePQpRFN1uC3AYCxHVbb169fJqsKi799D27dtjzZo1AOwfIl9++WU89thjaNiwIa6++mrccsstGD9+PBo1auT1erzdDyj3l86Faun08kuXLiEuLs7rn+2ssLAQQHkhRipSSAeunUk/68SJEwAgn3LvSUlJCebNm4elS5fi7NmzqqzbvLw8+f8TEhJw66234pNPPsF//vMfAPZmjaZNm3pcCxFRbSW9J0vFdE+ci+1SVvPq1atx4403ArAXhLt37y5/5jp27BhEUcQzzzyDZ555xu3tnj9/XnVQ2dfPW0rSZ2/nz9qNGjVCQkKCKo+8qp9zPWnQoEGln/vuvPNOzJo1S87mFkURn332GYYMGSLv86R944QJEzzeTl5eniqutTr77VdeeQUTJkxA8+bN0aNHDwwdOhTjx4/3eoBsZZ9hff3s2LRpU5+Hpvfv39+rwaLOaxAEAW3btq3w8/YDDzyANWvWYMiQIWjatCkGDRqE0aNH4+abb5a3SUtLQ+/evV2ue9lll8n/fvnllyMtLQ1t27Z1OSjj/DdaVZ4DFFwsolNI+emnn5CRkYFVq1Zh1apVLv++cuVKDBo0CABw9913Y9GiRfj+++8xYsQIrFmzBh07dkS3bt3k7W02G7p06YL58+e7/XnNmzdXfe8uH2vr1q0YPnw4+vfvj3fffReNGzeGXq/H0qVL8cknn/h8H6Ujif/4xz88vllKhdGKKHfegwcPRseOHXHLLbfgzTfflI/I22w2JCcnqzqwlJQF5b59++KDDz7AiRMnsHXrVvTr1w+CIKBv377YunUrmjRpApvNpsrP+/jjjzFx4kSMGDEC//znP5GcnAytVot58+a5DDgF3D++NcUfz4VA8abjoX///jh+/Dj+97//4YcffsDixYvxxhtvYOHChZgyZUpA1mWz2SAIAr7//nu3XSJSpyEREVXPI488gltvvRVffvklNmzYgGeeeQbz5s3DTz/9hCuuuCJgP9dTB6CyKF0V0pA5qbgh/e2zYsUKtwcGdDrfPpI8+OCDWLp0KR555BFcc801iI+PhyAIuPvuu106tsaPH4/PPvsMv//+O7p06YKvvvoKDzzwADQanpBLRHVLfHw8GjdujH379lW43b59+9C0aVO5KGswGDBixAh88cUXePfdd5GVlYXffvsNL774onwd6b338ccf93hGsXPB2x+ft7z5HFWVz7nV1aRJE/Tr1w9r1qzBU089hT///BPp6emqmSPSY/bqq6+6zBKTOH/eqs5+e/To0ejXrx+++OIL/PDDD3j11Vfx8ssvY926dRgyZIiX96yc82Pv62fHYNYG3ElOTkZqaio2bNiA77//Ht9//z2WLl2K8ePHY/ny5QH5mVV5DlBwsYhOIWXlypVITk7GO++84/Jv69atwxdffIGFCxciMjIS/fv3R+PGjbF69Wr07dsXP/30E/71r3+prtOmTRvs3bsXN954o1c7WHc+//xzGI1GbNiwAQaDQb7c+bShlJQU2Gw2nDx5UnXk89ixY6rtkpKSEBsbC6vV6nXnsjeGDRuGAQMG4MUXX8S0adMQHR2NNm3a4Mcff8S1115b6U5K+qNh48aN2LFjB5588kkA9uLte++9J3d79+jRQ77O2rVr0bp1a6xbt071+M6ZM8erNSclJSEyMtJtRMmRI0e8ug0AOHfunEt33d9//w0A8qAyfzwX/OXo0aOqzotjx47BZrNVOjwuMTERkyZNwqRJk1BYWIj+/ftj7ty5mDJlClJSUgC4f9wOHz6MBg0aIDo6Gkaj0evHvE2bNhBFEa1atZK7TIiIyDfu3m///vtvl/f8Nm3a4LHHHsNjjz2Go0ePonv37nj99dfx8ccfe/VzvN0P1IQVK1ZAEAR58LU01Cw5ObnCv32kbjipCO/J2rVrMWHCBLz++uvyZaWlpcjNzXXZ9uabb0ZSUhJWrlyJ3r17o7i4GOPGjfP1LhER1Qq33HILPvjgA/z666+qSArJ1q1bcerUKVVUJmCPJ1m+fDk2bdqEQ4cOQRRF1Rng0vu3Xq/362dcT6TP3kePHpW7gAH74Mfc3Fx5nwhU7XOuP9x111144IEHcOTIEaxevRpRUVG49dZb5X+X9o1xcXF+fcwq+qzbuHFjPPDAA3jggQdw/vx5XHnllXjhhRe8KqJX9hk2lD47Ov/tJYoijh07VmmzYkREBG699VbceuutsNlseOCBB7Bo0SI888wzaNu2LVJSUjz+nQWU/y2WkpKCAwcOQBRF1e/D3edtwP/PAQoctmBQyCgpKcG6detwyy23YNSoUS5fM2fOREFBAb766isAgEajwahRo/D1119jxYoVsFgsqh05YD/aevbsWXzwwQduf15RUVGl69JqtRAEQc5jB+wRIV9++aVqO+mI+7vvvqu6/O2333a5vTvuuAOff/652w+JFy5cqHRNnvzf//0fsrOz5fs7evRoWK1W+RRmJYvFovqw2apVKzRt2hRvvPEGysrKcO211wKw/9Fx/PhxrF27FldffbWqW0w6wqw88r1t2zb88ccfXq1Xq9Vi8ODB+PLLL5Geni5ffujQIWzYsMHr+22xWLBo0SL5e7PZjEWLFiEpKUn+Y8gfzwV/cT5IJD1HKvrjJTs7W/V9TEwM2rZtK8cLNW7cGN27d8fy5ctVv9cDBw7ghx9+wNChQwH49pjffvvt0Gq1eO6551y6G0RRdFkTERG5+vLLL1U5sNu3b8e2bdvk9/zi4mKUlpaqrtOmTRvExsaqIuQq4+1+INBeeukl/PDDD7jrrrvkpoLBgwcjLi4OL774oir6TSL97ZOUlIT+/ftjyZIlqn0UoP5bQ6vVuuyX3n77bdXfahKdTocxY8ZgzZo1WLZsGbp06eLVGX9ERLXRP//5T0RGRmLatGkuf8vn5OTg/vvvR1RUFP75z3+q/m3gwIFITEzE6tWrsXr1avTq1UtVUE1OTsZ1112HRYsWISMjw+XnVuczrjvSPm3BggWqy6WzjocNGyZfVpXPuf5wxx13QKvV4tNPP8Vnn32GW265RXUwu0ePHmjTpg1ee+01OQZNqaqPWXR0tMtBZavVqoo7A+y/syZNmnj9t0Zln2FD6bPjRx99pIotWrt2LTIyMnz6vK3RaOS/F6THaOjQodi+fbuq3lFUVIT3338fLVu2lGeqDR06FOfOncPatWvl7YqLi13ifAP1HKDAYSc6hYyvvvoKBQUFGD58uNt/v/rqq+VOIqlYftddd+Htt9/GnDlz0KVLF9VRaAAYN24c1qxZg/vvvx+bN2/GtddeC6vVisOHD2PNmjXYsGFDpXmmw4YNw/z583HzzTfjnnvuwfnz5/HOO++gbdu2qlPhevTogTvuuAMLFixAdnY2rr76avz8889yR7TyCORLL72EzZs3o3fv3pg6dSo6deqEnJwc7N69Gz/++CNycnKq9BgOGTIEl19+OebPn48ZM2ZgwIABmDZtGubNm4fU1FQMGjQIer0eR48exWeffYY333xTNeyiX79+WLVqFbp06SLnbl155ZWIjo7G33//7ZITd8stt2DdunUYOXIkhg0bhpMnT2LhwoXo1KmT252AO8899xzWr1+Pfv364YEHHoDFYsHbb7+Nzp07V3qqoaRJkyZ4+eWXcerUKbRv3x6rV69Gamoq3n//fTl7zR/PhYrk5eV57BZ0Hl528uRJDB8+HDfffDP++OMPfPzxx7jnnntUUUTOOnXqhOuuuw49evRAYmIidu7cibVr12LmzJnyNq+++iqGDBmCa665BpMnT0ZJSQnefvttxMfHY+7cufJ23j7mbdq0wfPPP4/Zs2fj1KlTGDFiBGJjY3Hy5El88cUXuO+++/D4449X8REjIgpv33//vdx5pNSnTx9Vvmjbtm3Rt29fTJ8+HSaTCQsWLED9+vXxxBNPALB3pd94440YPXo0OnXqBJ1Ohy+++AJZWVm4++67fVqTt/sBf7BYLPJ+r7S0FGlpafjqq6+wb98+XH/99aoPinFxcXjvvfcwbtw4XHnllbj77ruRlJSE9PR0fPvtt7j22mvx3//+FwDw1ltvoW/fvrjyyitx3333oVWrVjh16hS+/fZbpKamArD//bFixQrEx8ejU6dO+OOPP/Djjz+ifv36btc6fvx4vPXWW9i8ebPqVHoiorqmXbt2WL58OcaOHYsuXbpg8uTJ8vvshx9+iIsXL+LTTz+VO2Qler0et99+O1atWoWioiK89tprLrf9zjvvoG/fvujSpQumTp2K1q1bIysrC3/88QfOnDmDvXv3+u1+dOvWDRMmTMD777+P3NxcDBgwANu3b8fy5csxYsQIXH/99artff2cW5GzZ8+6/dwXExODESNGyN8nJyfj+uuvx/z581FQUODS8KfRaLB48WIMGTIEnTt3xqRJk9C0aVOcPXsWmzdvRlxcHL7++msfHhW7Hj164Mcff8T8+fPRpEkTtGrVCh06dECzZs0watQodOvWDTExMfjxxx+xY8cO1VldFansM2xNfHZcu3at23iTm266CQ0bNpS/T0xMRN++fTFp0iRkZWVhwYIFaNu2LaZOnerxtqdMmYKcnBzccMMNaNasGdLS0vD222+je/fucp3pySefxKeffoohQ4bgoYceQmJiIpYvX46TJ0/i888/l6Pipk6div/+978YP348du3ahcaNG2PFihWIiopS/cxAPQcogESiEHHrrbeKRqNRLCoq8rjNxIkTRb1eL168eFEURVG02Wxi8+bNRQDi888/7/Y6ZrNZfPnll8XOnTuLBoNBrFevntijRw/xueeeE/Py8uTtAIgzZsxwexsffvih2K5dO9FgMIgdO3YUly5dKs6ZM0d0fgkVFRWJM2bMEBMTE8WYmBhxxIgR4pEjR0QA4ksvvaTaNisrS5wxY4bYvHlzUa/Xi40aNRJvvPFG8f3336/0sUpJSRGHDRvm9t+WLVsmAhCXLl0qX/b++++LPXr0ECMjI8XY2FixS5cu4hNPPCGeO3dOdd133nlHBCBOnz5ddfnAgQNFAOKmTZtUl9tsNvHFF18UU1JSRIPBIF5xxRXiN998I06YMEFMSUmRtzt58qQIQHz11Vfdrvnnn38We/ToIUZERIitW7cWFy5c6PbxdWfAgAFi586dxZ07d4rXXHONaDQaxZSUFPG///2vy7b+eC54WgMAj18S6T4dPHhQHDVqlBgbGyvWq1dPnDlzplhSUqK6zZSUFHHChAny988//7zYq1cvMSEhQYyMjBQ7duwovvDCC6LZbFZd78cffxSvvfZaMTIyUoyLixNvvfVW8eDBgy5r9uUx//zzz8W+ffuK0dHRYnR0tNixY0dxxowZ4pEjR7x+jIiIaoulS5dW+J4v7X+V+77XX39dbN68uWgwGMR+/fqJe/fulW/v4sWL4owZM8SOHTuK0dHRYnx8vNi7d29xzZo1qp87YMAAccCAAfL30u0r9/ei6N1+QHq/v3Dhgtv7dvLkyQofgwkTJqjuc1RUlNiyZUvxjjvuENeuXStarVa319u8ebM4ePBgMT4+XjQajWKbNm3EiRMnijt37lRtd+DAAXHkyJFiQkKCaDQaxQ4dOojPPPOM/O+XLl0SJ02aJDZo0ECMiYkRBw8eLB4+fNhl36nUuXNnUaPRiGfOnKnwvhER1QX79u0Tx4wZIzZu3Fj+LDpmzBhx//79Hq+zceNGEYAoCIJ4+vRpt9scP35cHD9+vNioUSNRr9eLTZs2FW+55RZx7dq18jbSvmbHjh0u19+8ebMIQPzss89Ul7v7nFJWViY+99xzYqtWrUS9Xi82b95cnD17tlhaWupyu75+zvUkJSXF4/5f+flX8sEHH4gAxNjYWJfPe5I9e/aIt99+u1i/fn3RYDCIKSkp4ujRo1Vr8mW/ffjwYbF///5iZGSkCECcMGGCaDKZxH/+859it27dxNjYWDE6Olrs1q2b+O6771Z6n335DCuK3n12lD7De0tag6evzZs3i6JY/vz59NNPxdmzZ4vJycliZGSkOGzYMDEtLU11m841i7Vr14qDBg0Sk5OTxYiICLFFixbitGnTxIyMDNX1jh8/Lo4aNUr+G6VXr17iN99847LmtLQ0cfjw4WJUVJTYoEED8eGHHxbXr1+vWq/Em+cAhQZBFKs5OYiIKpSamoorrrgCH3/8McaOHRvs5dQ61113HS5evFhpfmoomDt3Lp577jlcuHDBq6niREQUvk6dOoVWrVrh1Vdf5Vk7IeCKK65AYmIiNm3aFOylEBERhY1w+gy7ZcsWXH/99fjss89UZ9wT+Qsz0Yn8qKSkxOWyBQsWQKPRoH///kFYEREREVHdtnPnTqSmpmL8+PHBXgoRERERhSlmohP50SuvvIJdu3bh+uuvh06nw/fff4/vv/8e9913H5o3bx7s5RERERHVGQcOHMCuXbvw+uuvo3Hjxi55tERERERE3mInOpEf9enTBzk5OfjPf/6Dxx57DH///Tfmzp3rMsmaiIiIiAJr7dq1mDRpEsrKyvDpp5/CaDQGe0lEREREFKaYiU5ERERERERERERE5AE70YmIiIiIiIiIiIiIPGARnYiIiIiIiIiIiIjIAw4WrYTNZsO5c+cQGxsLQRCCvRwiIqpjRFFEQUEBmjRpAo2Gx74rw/02EREFE/fbvuF+m4iIgsmX/TaL6JU4d+4cmjdvHuxlEBFRHXf69Gk0a9Ys2MsIedxvExFRKOB+2zvcbxMRUSjwZr/NInolYmNjAdgfzLi4uCCvhoiI6pr8/Hw0b95c3h9RxbjfJiKiYOJ+2zfcbxMRUTD5st9mEb0S0illcXFx3KkTEVHQ8BRn73C/TUREoYD7be9wv01ERKHAm/02Q9qIiIiIiIiIiIiIiDxgEZ2IiIiIiIiIiIiIyAMW0YmIiIiIiIiIiIiIPGARnYiIiIiIiIiIiIjIAxbRiYiIiIiIiIiIiIg8YBGdiIiIiIiIiIiIiMgDFtGJiIiIiIiIiIiIiDxgEZ2IiIiIiIiIiIiIyAMW0YmIiIiIiIiIiIiIPGARnYiIiIiIiIiIiIjIAxbRiYiIiIiIiIiIiIg8YBGdiIiIfPbLL7/g1ltvRZMmTSAIAr788stKr7NlyxZceeWVMBgMaNu2LZYtW+ayzTvvvIOWLVvCaDSid+/e2L59u/8XT0RERH4xd+5cCIKg+urYsWOwl0VEROR3LKITERGRz4qKitCtWze88847Xm1/8uRJDBs2DNdffz1SU1PxyCOPYMqUKdiwYYO8zerVqzFr1izMmTMHu3fvRrdu3TB48GCcP38+UHeDiIiIqqlz587IyMiQv3799ddgL4mIiMjvdMFeABEREYWfIUOGYMiQIV5vv3DhQrRq1Qqvv/46AOCyyy7Dr7/+ijfeeAODBw8GAMyfPx9Tp07FpEmT5Ot8++23WLJkCZ588kn/3wkiIiKqNp1Oh0aNGgV7GURERAHFTnQiIgoYURSxO/0SikyWYC+FguyPP/7AwIEDVZcNHjwYf/zxBwDAbDZj165dqm00Gg0GDhwob1PTfv77An48mBWUn01ERBQujh49iiZNmqB169YYO3Ys0tPTg70kIiIKcYcy8nGx0BTsZfiERXQiIgqYrUcv4vZ3f8fcr/4K9lIoyDIzM9GwYUPVZQ0bNkR+fj5KSkpw8eJFWK1Wt9tkZmZ6vF2TyYT8/HzVlz/8fvwiJi3djodX7cHhTP/cJhERUW3Tu3dvLFu2DOvXr8d7772HkydPol+/figoKHC7faD220REFD4y8kow9K2tmPrRzmAvxScsohMRUcAcv1AIADhzqSTIK6Haat68eYiPj5e/mjdv7pfbvaplIq5uXR9FZiumfrQTOUVmv9wuERFRbTJkyBDceeed6Nq1KwYPHozvvvsOubm5WLNmjdvtA7XfJiKi8JGVb4IoAhm5pcFeik9YRCciooDJKykDAJRarEFeCQVbo0aNkJWljkbJyspCXFwcIiMj0aBBA2i1WrfbVJSzOnv2bOTl5clfp0+f9st69VoN3rnnSrRIjMLpnBLMWLkbZVabX26biIiotkpISED79u1x7Ngxt/8eqP02ERGFD6tNtP9XFIO8Et+wiE5ERAEjFdFNZSw+1nXXXHMNNm3apLps48aNuOaaawAAERER6NGjh2obm82GTZs2ydu4YzAYEBcXp/ryl3rREVg8oSeiI7T440Q2nv/moN9um4iIqDYqLCzE8ePH0bhxY7f/Hsj9NhERhQfRUTwXWUQnIiKyyytmJ3ptVVhYiNTUVKSmpgIATp48idTUVHmY2OzZszF+/Hh5+/vvvx8nTpzAE088gcOHD+Pdd9/FmjVr8Oijj8rbzJo1Cx988AGWL1+OQ4cOYfr06SgqKsKkSZNq9L4ptW8Yizfu6g4AWP5HGj7ZxmFpREREkscffxw///wzTp06hd9//x0jR46EVqvFmDFjgr00IiIKUXInui28iui6YC+AiIhqL3ai1147d+7E9ddfL38/a9YsAMCECROwbNkyZGRkyAV1AGjVqhW+/fZbPProo3jzzTfRrFkzLF68GIMHD5a3ueuuu3DhwgU8++yzyMzMRPfu3bF+/XqXYaM1bVDnRnjspvZ4fePfmPPVAbRNjkGvVolBXRMREVEoOHPmDMaMGYPs7GwkJSWhb9+++PPPP5GUlBTspRERUYiSYlxYRCciInLIlYro7ESvda677roKT79btmyZ2+vs2bOnwtudOXMmZs6cWd3l+d3MG9ricFYBvt2Xgfs/3oWvZl6LZvWigr0sIiKioFq1alWwl0BERGFG+hgZZmkujHMhIqLAkQeLshOdwpwgCHhtVDd0bhKHnCIzpizfiSKTJdjLIiIiIiIiCiscLEpEROSkvIjOTnQKf5ERWnwwvicaxETgcGYBZq1JhS3MTkEkIiIiIiIKpnCNc2ERnYiIAkIURXmwqMUmwmJlNzqFvyYJkVg0rgcitBps+CsLCzYdDfaSiIiIiIiIwoYUCxpmjegsohMRUWCUltlgVhTOTRYW0al26JGSiBdGXg4AeGvTUXy7LyPIKyIiIiIiIgoPUpmAcS5EREQoj3KRMNKFapM7ezbH5L6tAACPfZaKA2fzgrwiIiIiIiKi0CdnojPOhYiICMgtMau+L2UnOtUys4d0xID2SSgts2HqRztxvqA02EsiIiIiIiIKaaKiA10Mo250FtGJiCggpDx0iYmd6FTL6LQavDXmCrROikZGXinuX7ELJguf50RERERERJ4oY1zCqRudRXQiIgqIXJc4F3aiU+0TH6nHhxOuQpxRh93puZi9bn9YdVMQERERERHVJGXhPJxy0VlEJyKigHDJRGeHLtVSrRpE452xV0KrEbBu91m8/8uJYC+JiIiIiIgoJCnr5mFUQ2cRnYiIAiO/xDnOhZ3oVHv1a5eEZ2/pBAB4af1h/HgwK8grIiIiIiIiCj2qTnTGuRARUV2XW8xOdKpbxl+Tgnt6t4AoAg+v2oMjmQXBXhIREREREVFIUWWih1ErOovoREQUEM5xLhwsSrWdIAh4bnhnXN06EUVmKyYv34HsQlOwl0VERERERBQylDOkxDA6YZ1FdCIiCgiXIroljPaORFWk12rw3tgeSKkfhTOXSjD9490w87lPREREREQEALAqPh6xE52IiOq8XOfBouxEpzqiXnQEPpzQE7EGHbafysG/vtiv6rYgIiIiIiKqq1RxLsxEJyKiuk7qRI/Q2Xc1pRwsSnVI2+RYvH3PFdAIwGe7zmDx1pPBXhIREREREVHQqeJcwqjZiEV0IiIKiLxiMwAgOdYAADBxsCjVMdd1SMbTwzoBAF78/hA2HcoK8oqIiIiIiIiCS9l9zjgXIiKq86RO9EZxRgDsRKe6adK1LTGmVwuIIvDQp3twJLMg2EsiIiIiIiIKGlURnXEuRERUl9lsolxEbygX0dmJTnWPIAj4922dcXXrRBSZrbh32Q5cLDQFe1lERERERERBYVN0n9vCqNeORXQiIvK7QrMF0gHlJEecCzvRqa7SazV4b2wPtKwfhbO5JZi2YhfjjYiIiIiIqE5SNp/bGOdCRER1WV6xvQvdoNMgPlIPgJnoVLfVi47A4glXIdaow660S5j9+f6wGqJDRERERETkD8xEJyIicpCiXBKi9DDqtQDYiU7UNjkG7469ElqNgHV7zuLdLceDvSQiIiIiIqIaZbOJbv8/1LGITkREficV0eMj9TDq7buaUnaiE6FfuyTMvbUTAODVDUew/kBGkFdERERERERUc9RxLsFbh69YRCciIr9TFtENOnsnuomd6EQAgHHXtMSEa1IAAI+sTsX+M3lBXhEREREREVHNUEa4WMOois4iOhER+V15ET1C7kRnJjpRuWdu6YT+7ZNQWmbDlI92IDOvNNhLIiIiIiIiCjhVnAsz0YmIqC7LLVbGuUiZ6CyiE0l0Wg3+e88VaJccg6x8E6Z8tAPFZkuwl0VERERERBRQysI5i+hERFSnqeNcpE50xrkQKcUZ9Vgy8SokRkfgwNl8zFq9N6wG6xAREREREfmKcS5EREQOeSVmAEBCFDvRiSrSPDEKi8b1QIRWg/V/ZeK1H44Ee0lEREREREQBwzgXIiIiB2UnupSJXsrBokRuXdUyES+P6gIAeHfLcXy283SQV0RERERERBQYyubzMGpEZxG9Lvg7qwB9X/4Ja/ihnIhqiDrOxd6JzsGiRJ6NvKIZHryhLQDgqS/2488T2UFeERERERERkf8pI1wY50IhZduJbJy5VIIf/soM9lKIqI6QB4tGsROdyFuPDmyPYV0ao8wq4v6Pd+HkxaJgL4mIiIiIiMivVINFWUSnUGJxPCFZwCKimuKuE52Z6EQV02gEvD66G7o1T0BucRkmL9uB3GJzsJdFRERERETkN6oievjU0FlErwukUyMYpUBENUUqoidElg8WNVlsEMNoaAhRMBj1WnwwvgeaJkTixMUi3P/xLpgtPAhORERERES1g1Xx8cYaRjUCFtHrACs70YmoBlltIgpKLQAcnej68l2NicVAokolxxrx4cSeiDHo8OeJHPzri/08AEVERERERLWCMsKFcS4UUsrjXNiJTkSBl+/oQgeAuEg9jI44FwAw8WAekVc6NorD2/dcAY0AfLbrDBb+fCLYSyIiIiIiIqo2dZwLi+gUQmxynAuLV0QUeLmOInqMQQe9VgO9VoBGsP9bKWOliLx2fYdkzB3eGQDw8vrD+G5/RpBXREREREREVD3KCBcrO9EplLATnYhqknKoKAAIgiAPF2UnOpFvxl/TEhP7tAQAPLo6FXvSLwV3QURERERERNWginNhJzqFEukJySI6EdUEqYge5yiiA4DRkYvOTvTa5Z133kHLli1hNBrRu3dvbN++3eO2ZWVl+Pe//402bdrAaDSiW7duWL9+vWqbuXPnQhAE1VfHjh0DfTdC3jO3dMINHZNhstgw9aOdOJ1THOwlERERERERVYmy+TyMGtFZRK8LLIxzIaIalFtsBgAkqIro9k50HsyrPVavXo1Zs2Zhzpw52L17N7p164bBgwfj/Pnzbrd/+umnsWjRIrz99ts4ePAg7r//fowcORJ79uxRbde5c2dkZGTIX7/++mtN3J2QptUIeGvMFbiscRwuFppx77IdyC8tq/yKREREREREIYZxLhSylJnoYhidJkFE4SnfKc4FKC+i82Be7TF//nxMnToVkyZNQqdOnbBw4UJERUVhyZIlbrdfsWIFnnrqKQwdOhStW7fG9OnTMXToULz++uuq7XQ6HRo1aiR/NWjQoCbuTsiLMeiwZGJPJMcacPR8IWas3I0yK19PREREREQUXhjnEmAvvPAC+vTpg6ioKCQkJHh1HVEU8eyzz6Jx48aIjIzEwIEDcfTo0cAuNARZFE9OFrCIKNCcM9EBwKBzxLmwE71WMJvN2LVrFwYOHChfptFoMHDgQPzxxx9ur2MymWA0GlWXRUZGunSaHz16FE2aNEHr1q0xduxYpKen+/8OhKnG8ZH4cMJViNRrsfXoRTz7vwM8OE5ERERERGFFWThnET0AzGYz7rzzTkyfPt3r67zyyit46623sHDhQmzbtg3R0dEYPHgwSktLA7jS0KM8NYIFLCIKtNxiexE9IUpRRJfjXHggrza4ePEirFYrGjZsqLq8YcOGyMzMdHudwYMHY/78+Th69ChsNhs2btyIdevWISMjQ96md+/eWLZsGdavX4/33nsPJ0+eRL9+/VBQUOBxLSaTCfn5+aqv2qxLs3i8NeYKCALw6fbTeP+XE8FeEhERERERkdeUJ9SG08m1YVNEf+655/Doo4+iS5cuXm0viiIWLFiAp59+Grfddhu6du2Kjz76COfOncOXX34Z2MWGGCs70YmoBrkdLOroRDdxsGid9eabb6Jdu3bo2LEjIiIiMHPmTEyaNAkaTfmfIkOGDMGdd96Jrl27YvDgwfjuu++Qm5uLNWvWeLzdefPmIT4+Xv5q3rx5TdydoLqpU0M8M6wTAGDe94fx/f6MSq5BREREREQUGlSd6MxED76TJ08iMzNTdap5fHw8evfu7fFUc6B2drRZ2IlORDUo112cCzvRa5UGDRpAq9UiKytLdXlWVhYaNWrk9jpJSUn48ssvUVRUhLS0NBw+fBgxMTFo3bq1x5+TkJCA9u3b49ixYx63mT17NvLy8uSv06dPV+1OhZlJ17bEhGtSAACPrE7FnvRLQV4RERERERFR5RjnEmKk08l9OdUcqJ0dbTZVEZ0FLCIKLKkTXRnnYmQmeq0SERGBHj16YNOmTfJlNpsNmzZtwjXXXFPhdY1GI5o2bQqLxYLPP/8ct912m8dtCwsLcfz4cTRu3NjjNgaDAXFxcaqvukAQBDxzSyfc0DEZJosNUz/aidM5xcFeFhERERERUYWUiRlWFtG98+STT0IQhAq/Dh8+XKNrqo0dberBoixgEVFg5bvpRDc6OtEZKVV7zJo1Cx988AGWL1+OQ4cOYfr06SgqKsKkSZMAAOPHj8fs2bPl7bdt24Z169bhxIkT2Lp1K26++WbYbDY88cQT8jaPP/44fv75Z5w6dQq///47Ro4cCa1WizFjxtT4/QsHOq0Gb4+5Ap0ax+FioRmTlu2QD2IRERERERGFonCNc9EF84c/9thjmDhxYoXbVHSad0Wk08mzsrJUHWxZWVno3r27x+sZDAYYDIYq/cxQpXxyshOdiAJNHiwaGSFfZmAneq1z11134cKFC3j22WeRmZmJ7t27Y/369fIZYOnp6aq889LSUjz99NM4ceIEYmJiMHToUKxYsQIJCQnyNmfOnMGYMWOQnZ2NpKQk9O3bF3/++SeSkpJq+u6FjWiDDksmXoUR7/yGY+cLMf3jXVg2qRcidLX2ZEMiIiIiIgpjNkVpMoxq6MEtoiclJQXsg3GrVq3QqFEjbNq0SS6a5+fnY9u2bZg+fXpAfmaoYiY6EdWkvIo60fkeVKvMnDkTM2fOdPtvW7ZsUX0/YMAAHDx4sMLbW7Vqlb+WVqc0ijdiycSrcOfC3/H78WzMXrcfr93ZFYIgBHtpREREREREKsoIF2sYVdHDpk0pPT0dqampSE9Ph9VqRWpqKlJTU1FYWChv07FjR3zxxRcA7FmhjzzyCJ5//nl89dVX2L9/P8aPH48mTZpgxIgRQboXwWFTxbmwE52IAsdksaLEUShXF9Edneh8DyIKiE5N4vDfsVdCqxHw+e4zePsnz8NYiYiIiIiIgkVZpwynwaJB7UT3xbPPPovly5fL319xxRUAgM2bN+O6664DABw5cgR5eXnyNk888QSKiopw3333ITc3F3379sX69ethNBprdO3BZlGcJ8FOdCIKJKkLXRCAWGP5LsagYyc6UaBd3yEZzw3vjKe/PID5G/9G88RIjLyiWbCXRUREREREJFNloodRET1sOtGXLVsGURRdvqQCOgCIoqjKWBcEAf/+97+RmZmJ0tJS/Pjjj2jfvn3NLz7IrIxzIap19qRfwvSPd+F0TnGwl6IiDRWNM+qh0ZRHScid6H6cy5BfWoYZn+zGxoNZfrtNT87mlmD6x7uw81ROwH8WUXX84+oUTOtvnyfzxNp9+ON4dpBXREREREREVM6qqJtbw+hk9bApolPVWRnnQlTrfLItHd8fyMRXe88FeykqBaUWAECMQX2ik5SJXmrx34G8nw6dx7f7MrDo5+N+u01Pvtl7Dt8fyMQHW08E/GcRVdf/3dwRQ7s0QplVxLQVO3HsfEGwl0RERERERAQgfONcWESvAzhYlKj2KTTZi9VFjv+GCun9JkKn3r0Y5MGi/juQdy6vBABwqdjst9v05FKxvcM+LTu0Ov+J3NFoBMwf3R1XtkhAfqkFE5fuwIUCU7CXRUREREREpCqcc7AohRTlk5Od6ES1Q5HZfkCs2BxaB8bKHOdi6RRRLgBg0EmDRf233sy8UgDlOeyBlFdiL9Sn5xRDDKMj5VR3GfVaLJ5wFVrWj8KZSyWYsnwHis2hddCNiIiIiIjqHis70SlUWRRhQxzqR1Q7FIdqJ7rj/UanVe9e5DgXP74HKYvogS5sS4X6YrMVFwsD3/lO5A+J0RFYOqkXEqL02HsmDw99mhpWnR5ERERERFT7qAaLhtHnExbR6wDlk7OUnehEtYLUgV4cYgfGLDb7e4xeq+5ENzo60f15Nkxmvr2IXmYVURLgx0HZ7Z6eUxTQn0XkT60aRGPx+J6I0Gnw46Es/OebgzybgoiIiIiIgkbZ2GMNo88mLKLXAcxEJ6p9pFiG4hDrRC+TOtGd41zkTnT/FdEzHJ3oAJBbHNhIF+Xtp+cwF53CS8+WiXhjdHcAwLLfT+HDX08Gd0FERERERFRnKevmYdSIziJ6XWBlEZ2o1ikO0Ux0j3EuUie6n96DzBYbLhaWD0oMdC668vY5XJTC0bCujfHU0I4AgBe+O4Tv92cEeUVERERERFQXWRnnQqFKWUTnYFGi2iFki+ie4lwcnej+eg86X1CqOnod6E70PGUnOovoFKam9muNcVenQBSBR1anYldaTrCXREREREREdYwqzoVFdAol7EQnql1EUUSRI85F+m+oKI9zUe9eDHr79/56D8pURLkAge1Et9pEFChic9IY50JhShAEzB3eGQMvS4bJYsOU5Ttx4kJhsJdFRERERER1CONcKGSpi+jsRCcKdyaLTd7plIRaJ7rV02BRKRPdT0X0fHURPT+ARXTn22acC4UzrUbAW2OuQLdm8bhUXIaJS3eoopGIiIiIiIgCSVmntHGwKIUSdZxLaBXciMh3RYqu6KJQGyxqc9+JLsW5lPopzsW5Ez23xOyX23Un11FEl4alXiw0yYNdicJRVIQOiydcheaJkUjPKcbk5TtD7oAcERERERHVTspMdMa5UEhRPjnZiU4U/pQ56CUhFtEkdaLrXDLR7bsbq02Ut6mOjBqMc5Fuu2GcEfGRegBAOiNdKMwlxRqwbFIvJETpsfd0Lh5atSes/oAlIiIiIqLwJIrsRKcQZbEyE52oNlEW0cusIswhNDBYer/Ra50y0R1xLoB/utGlTvQ4ow5AYAeL5hbbu9zjIvVIqR8FgJEuVDu0SYrB4vE9EaHTYOPBLMz96i/VH7RERERERET+xjgXClnKJ2coFduIqGqch4mGUgxDmc3+HqPVqDvRDbry3Y0/DuZl5JUAADo2igNQM53o8ZE6NE+0F9HTWUSnWqJny0S8eVd3CAKw4s80LPrlRLCXREREYeqll16CIAh45JFHgr0UIiIKYco6ZTidDcsieh2gjnMJnWIbEVWNc9HcuageTOWd6OoiukYjIMJRSDf54WBeVr59EGLHxrEAAltElwaLJkRGIEUqojPOhWqRIV0a4+lhnQAAL31/GP9LPRvkFRERUbjZsWMHFi1ahK5duwZ7KUREFOKUzedhVENnEb0uUB7V8ddQPyIKHudhoqE05FLORNe47l6kbvTqHsyz2kRk5dvjXDo0CnwRXYqKiVfGubCITrXM5L6tMLlvKwDA45/txe/HLwZ5RUREFC4KCwsxduxYfPDBB6hXr16wl0NERCFO2exrC6MqOovodYCyiG5iJzpR2HMeJlocUnEu9vcb58GiAGDU23PRq1tEzy40wWIToRGAtkkxAGomziUhSo8WidEAgPTsooD9PKJg+dfQyzCsS2OUWUVMW7ELhzPzg70kIiIKAzNmzMCwYcMwcODASrc1mUzIz89XfRERUdUUmSyY/vGusDuTVBXnwkx0CiXsRCeqXYpM1gq/DyapE915sCgAGPX+iXPJcAwVTY41on5MBICaKaLHRerRwtGJfuZSiXxfiWoLjUbA66O74aqW9VBQasHEJTvk+QNERETurFq1Crt378a8efO82n7evHmIj4+Xv5o3bx7gFRIR1V47TuXg+wOZWPLryWAvxSeMc6GQ5RzYX8bCD1FYc45vKSkLnTiXMkcmuk7j2olu0PmnE10qojeKNyIuUg/AXugO1GlguSXlcS6N4oyI0GpgsYnyOohqE6Neiw/G90Tb5Bhk5pdi4pIdAT1IRURE4ev06dN4+OGHsXLlShiNRq+uM3v2bOTl5clfp0+fDvAqiYhqL+nzt9kaRpVoMM6FQpjzpFt/DPUjouBxjm8JqU50myMTvaJO9LLqvQdJeeiN442IdxTRRREoMAXmYIIyzkWrEdAsMRIAh4tS7ZUQFYFlk65CcqwBR7IKMG3FTpgsofM+Q0REoWHXrl04f/48rrzySuh0Ouh0Ovz888946623oNPpYLW67jsMBgPi4uJUX0REVDVSvS+cCtGAa7NvuGARvQ5wzheqbhcoEQVXkTmUB4va32/0bjrRjY5O9OoW46QO8IZxRhh0WkQ6stbzigPTLZunGCwKACmJjuGi2SyiU+3VrF4Ulk66CjEGHf48kYPHP9sXdn+cExFRYN14443Yv38/UlNT5a+ePXti7NixSE1NhVarDfYSiYhqNakALTWzhQPRqUZpC6NMdF2wF0CBJYqiy1EdFtGJwluJOYQHi0pxLm470aU4l+rt4DMdGc2N4+2nDcdH6lFSZg1Y5EReibqI3kIqoudwuCjVbp2bxOO9f1yJSUt34Ou959A43oinhl4W7GUREVGIiI2NxeWXX666LDo6GvXr13e5nIiI/E9qmg2nXhfnGmU4FdHZiV7LuTstoroFLCIKLuf4llAqoktHwPVad5no9l2OPzPRAXvMChC44aJynEukfYhpi/rRAIDTjHOhOqBfuyS8MqorAOD9X07gwzAbWkREREREVFtJZ4qGUySKc1pGOK2dnei1nPLJGanXoqTMylxTojAnxbfoNAIsNjEk41zcDRaVOtGrO5chU85Et2eTS8NFc0vM1bpdd0wWK0ocRX/GuVBddfuVzZCVb8LL6w/j+W8PolGcEcO6Ng72soiIKARt2bIl2EsgIqozLGFYRHduPA+jpbMTvbZTvpCiDf6JUiCi4JI6zxvEGACE1mDRMqvnwaIGffU70UVRlDvRlXEuQGA60aXbFAQg1mg/7pxS315ET88udslzI6qt7h/QGuOvSYEoAo+uTsWfJ7KDvSQiIiIiojotLDvRGedCoUr55IyKsBeATMxEJwprUud5g9gI1fehQDoS7j7OpfoH8nKLy2B2dLInx9kPIiQEsIie77jNOKMeGkd3fXNHJ3qByYJLARpmShRqBEHAnFs74+bOjWC22jD1o504nJkf7GUREREREdVZUvqEc0RKKAvnOBcW0Ws5dSe6o4hezSgFIgou5070UMpElzvRNe4Gizo60asRKSV1odePjpCL8nInegAK2rnF6qGigD2WpqGjgJ/OXHSqQ7QaAQvu7o6rWtZDQakFE5Zsx9nckmAvi4iIiIioTgrLOBenkiQ70SlkqIroEVIXaOgU3IjId6FcRJcz0d10osuZ6NXoRM/MtxfspKGiQM3EuUjDSyUpifbhomnZRX7/mUShzKjXYvH4q9AuOQZZ+SZMWLIducX+n0dAREREREQVC8s4F6eiuS2M+nxZRK/lpBeSIACRUhGdg0WJwpoc5yIX0UMpzsW+B9S7y0TX+a8TvbGiiC4VuHNrqBMdKI90SedwUaqD4qP0WH5vLzSKM+LY+UJMWb6TB+iJiIiIiGpYOHaiO681nKJoWESv5aQXlE4jyAWs6nSBElHwFZukTnQpEz10ilfK9xxnUid6dYptmY4iurITPa4GOtGdi+jScNE0xrlQHdUkIRLL7+2FWKMOO9Mu4cFP98Bi5d8XREREREQ1JRw70UXnTnQW0SlUSC8kjSDA4IcCFhEFlyiKKHJ0nifFhm6ci7tOdKN0IK8acxky5U70SPmymohz8VREZyc61WUdGsVi8fieiNBpsPFgFp75318ufxQTEREREVFg1IbBorYwOgDAInotZ1V0hRp1UpwLO8WIwpXJYoO0j0mS4lxMoRPnIg8WrTATvRqd6Pn2InrDOGWci70jvyaL6C2kOBd2olMd17t1fbx1d3cIAvDp9nS8uelosJdERERERFQnWMOwE51xLhSypCejViPAoGecC1G4U3ad13cU0YtCqRNdPnDnJhPd8R5UWo33IHeZ6EEZLFrfPlg0M7+UZ/dQnXfz5Y3x79suBwAs+PEoPtmWHuQVERERERHVfuFYRHeumXOwKIUM6YWkVXWis+BDFK6kIaIGnQYxRh0AoCSUiuhWabCom050x3uQqRrvQe4y0RMcRfRCk0XuhPeX3GIzANdO9HpResQY7I//6Trcjf7OO++gZcuWMBqN6N27N7Zv3+5x27KyMvz73/9GmzZtYDQa0a1bN6xfv75at0mhY9zVKXjwhrYAgKe/3I8Nf2UGeUVERERERLWbsngeLrEozgV/ZqJTteWXlmH9gYxqdziWF9E1MMpdoKFTcPNFaZkV6w9kIL/U/92mROFC6kSPNugQHWEvSputNr8Xj6uqzJGJrnOXiS7PZajaWgtKy1DoiK5pFOc6WBQA8v3cjV4e5xKhulwQBDnSJa2O5qKvXr0as2bNwpw5c7B7925069YNgwcPxvnz591u//TTT2PRokV4++23cfDgQdx///0YOXIk9uzZU+XbpNAy66b2uKtnc9hE4KFP92DHqZxgL4mIiIiIqNZSFqQt4VJEdyqah1MXPYvoIeqdzcdw/8e7sW732WrdTnkRHTDIXaChUWzz1dpdZ3D/x7vx7ubjwV4KUdAUOYrIkXotoiJ08uWhMlzU4jgXS6dx7UQ36Kp3IC8r3wQAiDXoEG0ov+9ajYBYx/f+jnTxlIkOKIaL1tFO9Pnz52Pq1KmYNGkSOnXqhIULFyIqKgpLlixxu/2KFSvw1FNPYejQoWjdujWmT5+OoUOH4vXXX6/ybVJoEQQBL4y8HAMvS4bJYsPkZTtwJLMg2MsiIiIiIqqVlAXpcOnoFp0Hi4bJugEW0UPWeUex6GKhqVq3Y1XkE4d7J3qWY6DghYLqPSZE4axE7kTXIkKnkYvVUsxLsFkcneh6N53oBn31DuQVOM5CiXNT0I53ZJbn1mARvUUdLqKbzWbs2rULAwcOlC/TaDQYOHAg/vjjD7fXMZlMMBqNqssiIyPx66+/Vvk2KfTotBq8PeZK9Eiph/xSCyYs2Y6zuSXBXhYRERERUa2jjHAJl45u55Pow2TZAFhED1lmR5GpuqdjSNfXaMqjFMJ1sKhUPAyV2AqiYJCGiEpd6FGOSJciU2gcHJNenzp3mejVPJAn3ccYRRe6JBDDRUVR9DhYFIAizqXIbz8zXFy8eBFWqxUNGzZUXd6wYUNkZrrPwh48eDDmz5+Po0ePwmazYePGjVi3bh0yMjKqfJuAvTifn5+v+qLgiozQ4sMJPdEuOQaZ+aUY/+E2XCoyB3tZRERERES1iiUc41xsjHMhP5M6Na3VHFOr7ESXohSqM9QvmKSBqCyiU10mdZxLxXOpmB4qw0WlHbde46YTXRpuXNUiuuO+Rxu0Lv8mFdH9mYleUmaVM97dxrkkRgMA0upgJ3pVvPnmm2jXrh06duyIiIgIzJw5E5MmTYLGzXPFF/PmzUN8fLz81bx5cz+tmKojISoCy+/thcbxRhy/UIRJy3aEzBkzRERERES1QTgOFnWOb2ERnarNbPVPJ3p5JrpQ7aF+wSatm0V0qsuKnTvRHQXlohApTlnkwaIVdKJXMc5FyoOPdtOJLnWK5xb7r4gu3ZZeK8gHLZSkTPQzOSVh8weLvzRo0ABarRZZWVmqy7OystCoUSO310lKSsKXX36JoqIipKWl4fDhw4iJiUHr1q2rfJsAMHv2bOTl5clfp0+frua9I39pkhCJj+7thYQoPVJP5+KBlbu5DyciIiIi8hNlQdp5YGeoci6iO2ekhzIW0UOUydGpabX6qYguCGGfiS6t21zNx4QonEmFZKmoGx1inehltoriXOxrNltsVdpRykX0iJqJc1HmoQuC6/1pHG+ETiPAbLUh0zGzoa6IiIhAjx49sGnTJvkym82GTZs24ZprrqnwukajEU2bNoXFYsHnn3+O2267rVq3aTAYEBcXp/qi0NGuYSw+nHAVjHoNthy5gP9bu6/OHXQiIiIiIgoES1hmojt1orOITtXlt050sbwTXY5SCNc4F0cRvayKXaxEtYFysChgzx4GQqMT3WoTIe3/3Me5lF9WleGich682ziXCACB6UR3N8gUsA9QbFovEgCQll33Il1mzZqFDz74AMuXL8ehQ4cwffp0FBUVYdKkSQCA8ePHY/bs2fL227Ztw7p163DixAls3boVN998M2w2G5544gmvb5PCU4+UenhvbA9oNQLW7TmLed8fCvaSiIiIiIjCXjgOFnWNcwnSQqrAtZ2PQoJZzkSvbie6/Xa0GgEGRyd6uA4WZZwLUXkhOVJvf/uOdhTRi0NgsKjytVlRJzpgPyim/N4bUid6TQ0WlYeKeiiiA/bhomnZxUjPKcI1ber77WeHg7vuugsXLlzAs88+i8zMTHTv3h3r16+XB4Omp6er8s5LS0vx9NNP48SJE4iJicHQoUOxYsUKJCQkeH2bFL6u75iMV+7oisc+24sPtp5EgxgDpg1oE+xlERERERGFLWtYFtHV34dTnAuL6CFK6tKsfia6/b+qTPRw70RnEZ3qsBKn4ZpSNnooDOxTvl/p3HSi67UaaDUCrDaxap3oJqkLv2aK6PmKOBdPUupHYevRutmJDgAzZ87EzJkz3f7bli1bVN8PGDAABw8erNZtUni7o0cz5BSZ8cJ3hzDv+8OoFx2B0T05CJaIiIiIqCrCsYjOOBfyu/JO9OoVjJWd6EZdeA8WLWEmOlF5pIk0WFSOcwn+wTFLJZ3oAGDUVX02Q3kmumsHuzRYNK/E7PPtepLruK0Ki+iJ0QCA9Jy6WUQn8tXU/q0xrb99mOzsdfvx48GsSq5BRERERETuWMNxsKijiK531AzCpfgPsIgessx+6kSXrq+Ocwl+sa0q2IlOVN5xLg8WNYTOYNEyq7IT3X0R3aCv+sG8QrkLv4bjXKIiPG7TPDEKAIvoRL54ckhH3HFlM1htImZ8shvbT+YEe0lERERERGFHWYC2hUkxWlqmdPZ6mNT+AbCIHrJMjsiV6mei26+vU8W5hGcRmpnoRECx3IkeeoNFLY4zX3QaAYJQcSe6qQqxUsWm4BTRPQ0WBexxLkDdjXMhqgpBEPDSHV1wY8dkmCw2TF6+A4cy8oO9LCIiIiKisKKsGVa3CbemSB3zOnaik7/4qxPdquhEl4pXZostbI5QKUlFt7IwPQhA5A/SAFEpzkWKNgmFTnSLVb0zdMdYjU50ORM9wnMRPbfYf0V06bYqGywK2AvueX782US1nV6rwX/vuRJXtayHglILxi/ZjnQejCIiIiIi8lo4ZqJL9cgIrb1GGS4xNACL6CHL7Oi2tlYz/9uqinMpzxE2h2E3t1QkZCY61WXFZY44F4PUiW4vKIdCJrp0lojezVBRSUQ1MtELTeqhqkrxjkx0k8VWpdt2J8+LwaLRBh0axBgAMNKFyFeREVosnnAVOjaKxYUCE8Yt2YbzBaXBXhYRERERUViwKQrQtjApRtucOtHFMFk3wCJ6SLLZRDlb2G+d6EJ5JzpQtQJWsEkxNOYqxEAQ1RbFTt3YUie6FHUSTNL7lTed6KYqnFEi5cHHuIlziTXooHXksPsr0sWbIjoAtEiMBACk5RT55ecS1SXxkXp8dG8vNKsXibTsYkxcsgP5pTyrg4iIiIioMpZwjHORY6c1qu/DAYvoIUjZJW61Va9jXDotQqsRoNNq5GF/VYlSCKYyq01+YZWxE53qsCKnwaJRjoJycQh1ouu0nnctRn11OtHVUTZKgiAgzmi/3N9F9ISoiovoKfWjATAXnaiqkuOM+HhybzSIicDBjHxMWb4zLA/2ExERERHVpPAcLOqIc3E0+trE8OlGZxE9BCk7NP2ZiQ4AhmoM9Qsm5YdpDhalusx5sGiUo7O7OBQGizoOcOk1njvRDTopE93396Aik+dOdMD/w0W970S356Izz5mo6lo2iMaySb0Qa9Bh+8kczPxkNyzc3xMREREReaSMcAmXjm5pmTpF3SBMaugsoociZYG7ui8CqaglFdGrM9QvmEoUBTeLTQybI2xE/iSKoqKIbi8kS9noodCJbrH50InuY5yL1SbK7wPuMtEBID4qAoB/hovabGJ5Eb3STnR7EZ1xLkTVc3nTeCye0BMGnQY/HjqP//t8P/f3REREREQeWKzhV0SX41wUdYNwGS7KInoIMvuxE10O7Hcpoge/4OYLk1PRv6yaMTdE4cisiDWSiudSMT0UiuhS1JJXmeg+vgcpO+2ja6ATvcBkkY+GV9aJLhXRT+eUVPvnEtV1vVvXxzv3XAmtRsDnu8/ghe8Ohc3pnURERERENUnViR4mfzPLcS6KukG4HABgET0EKYvo1e5Ed1xf4xLnEl5FaOeiP3PRqS6ShooC5TEu0mDRopCKc6mgE11XtcGi0kECrUaQ38ecScXu3GKzT7ftTp6jm92o18gRNJ40d8S5nMsrCbuoLKJQNLBTQ7xyR1cAwIe/nsS7W44HeUVERERERKFHWTMMl0K03Oyr6EQPk/o/3LfzUVAFIhNd6kQ3hFgnepHJAotVrDQuocS5iG6xAYZArqx6LhSYEB+plwcl1EaiKCIzvxSN4yP9cnvnckvQKM4oH/Dxp9M5xcguKi+sRkdo0TY5BoLg/5/liyKTBWaLDfWiI9z+e1Z+KRKjI6B37FyKHa+DCJ1G3uHUxGBRq03EhQITGsUbK9yuTI5zqSATvYqDRQsdeejREVqPv7cERxE930MnepnVhkMZ+VC+raYkRrl9/OWhopHufzdKSTEGREVoUWy2YvPh82ikeE10bBQrd98Tkffu6NEMuSVl+M83B/HqhiOIj9TjH1enBHtZREREREQhIxyL6NLYI2Umerh00bOIHoLUnejV6xh3HixqrGIBKxBEUcQtb/+KgtIy/PbkDRV2ezpnuIfycNFzuSXo/8pm9G+fhCUTrwr2cgLmnc3H8NoPf2PRuB4Y3LlRtW5r85HzmLR0Bx64rg2euLmjn1Zot/1kDkYv+sPl8v+MuBzjglyQGf7fX3Gx0IxtT93oUmg9mlWAwQt+wbCuTfD2mCsAAMWKQrJE6kg3W2ywWG0V5pFX1bP/O4CV29Kx7oE+uLJFPY/bWayuR5SdVTVSShoq6inKBVB0onsooj+yKhXf7s9QXRZn1OGP2Te63K63Q0UBQBAEtEiMwuHMAtz/8W7Vv218tD/aNYyt9DaIyNXkvq2QW2zG2z8dwzP/O4C4SD2Gd2sS7GUREREREYUEazgOFnWsU9l0Gi5rr71tsmHMrCgQW6oZW+JSRHcUqn0d6hcIVpuIkxeLcLHQXOkgQOeCmzmEi+inLhbBYhNxJLMg2EsJqEOO+3c0q/r38+C5fADAzlOXqn1bzo5k2m87Uq9Fs3qRSHCc9bDzVI7ff5YvCkrLcPxCEfJKynChwOTy78cvFMImqtdZ5DRUFCjPRgfKO9X97S/H7+dQRn6F21kcr0t9BWcTGKsYKVVkkoaKei6iS79bT5nof53LAwAkxxrQrF4kNAKQX2rB2VzXLPPcEvuZC5WdJSO599pWSKkfhWb1IlVfgTioQVSXzLqpPcZdnQJRBGatTsXmI+eDvSQiIiIiopCgLI3ZwqSb23l2I4CwmYHETvQQpOxEr+6LQC6iC1Kci6OAFQKd6MqoGnMlBbVwykQ3Od7FQiGjOpCkrmjnswSqQip6puUUVfu2nEmF56FdGuP10d3w3f4MPLByN9Kyi/3+s3yRmVcq/7+7rmzpcc3ML0VpmRVGvVYerhml6ESP0Gqg1Qiw2kQUm6yIM3pX9PWFFI9S2cDOMnnKdkVxLtXsRI/wfMZKXCWDRaXLP57SG+0bxuK6VzfjVHax2+196UQHgNFXNcfoq5p7tS0ReU8QBDw3vDNyS8rw9d5zmP7xLnw8uTd6tkwM9tKIiIiIiIJKmV5R3TjomiJ1z2s17EQnP1AOpqt2JrrTkzOUOtGVkSyVdaW6ZKKHcCe6dEAgkBnVoUC6f/4YpCgNcczKN/k9aqhY7t62P/dbOIZApucEt4ieoSiiu3v+S4+rKAJnLtnXKg0WVRbRBUGQvy8O0IGbPC+L6HInegXd11UdbiwdlPImzsXdOm020aUwLm/v5kwYX4voRBQ4Go2A1+/shus6JKG0zIZJy3bIZzAREREREdVV4ZiJblPMbpTGnYVLJjqL6CFInYnurzgX+/fGUOpEt3rfiW5y6naubPtgktYmZVTXVlJx2h+d6FJ0BmAfAupPUse8FHvSor69iJ5TZEZBacVF4UDythMdKC/4S3EtyjgX+/dSEd3/r2tRFOWMcXfFZiU5E72iOJcqd6J7EedSQVG80GyRB4rKRfQo+9BQdxnq0m0ksIhOFBIidBq8N7YHrmpZDwWlFoxfsg0nL/r/7CUiIiIionARlkV0xzK1GkFOzQiTGjqL6KFI2aFZ3Ux06fpSJ7o0vNPXLtBAKFOcdlJZxnmpJfw60YHAZVSHAqnr2R+d48rOYX/HrMiFZ729+Bpn1KOeI+c6mN3oGaoiuuvzWfm4So+JfEDAKdIk2lFUlyJP/KnIbJV3xpXHudjvR0U54FInuq8HX7yJc4mvIBNdKoobdBq5kF9R5zo70YlCT2SEFosnXIVOjeNwsdCMfyzehow815kGRERERER1QTgOFpXWqdEI0DiK6OGydhbRQ5DJj53ozoH9Uie6vyMzqsKXTvRwykRXHhCQ4jdqo/I4Fz90ois6h9MC1IkerRjA2aJ+NAAgPYi56Jn5yjgX1+eJ8nGViujyYFGnbmypyz4QB21yi82K//euE11fQSa6VMD2NQao0FR5nEtCZHlnufNgEqkonqAYFFreuW6GM+m+ejtYlIhqRnykHsvv7YVWDaJxNrcE4z7cjpwi19cwEREREVFtp+hNDbvBohoBkGLRWUT3sxdeeAF9+vRBVFQUEhISvLrOxIkTIQiC6uvmm28O7EL9QFlQrm4musXxitLIRfSqRSkEgi9F9BKz+t/DpRO9Ng8XLTL5rxM9X9EJnJ7t39Pz5cKzIgIlxZGL7u+CvS8yFd2TlXWiSx3zJWb33dhSl30gDtoou7Qr7UR3vC51Gs+7lvL3IN9ew9KZDzFeZKJbbaL8e5e46yxnJzpReEqKNWDF5F5oHG/EsfOFmLBke1DjuYiIiIiIgsEShoNFpSK6VmCcS8CYzWbceeedmD59uk/Xu/nmm5GRkSF/ffrppwFaof+oM9GrVyyWas1SJ3pVh/oFgjrOpeLin3OcS2XxL8Gk/P2V1OLhotKwV38MqVXFufi5sF1idh3GmVI/+MNFM6qQiS4VhiOdi+iGwA0W9aWILh091lXQiV4e5+JrJ7r7PHglo16DCEeUjPNaWUQnql2a1YvCism9kRgdgf1n8zB5+c6QaBAgIiIiIqopytKYLUyK6NKaVXEuYVJFD5si+nPPPYdHH30UXbp08el6BoMBjRo1kr/q1asXoBX6j8mPnehSEV4rFdFDthO94vvpEucSAgcBPFEW+AORUR0KzBabHKlT3SG1ZVabqmvY34Vt6WwAZRG9uaMTPVTiXJwPEjlflp5TDJtNlA8IRNfgYFHlkM7KiujS+5Xei050Xw/kSQcIlLE8zgRBkONXcp0iWuR4FkfkC1Ae1eJ2sKgc/xLh8m9EFBraJsfgo3t7Idagw/aTOXhg5e6QPlONiIiIiMifbGGYia6Oc2EmekjZsmULkpOT0aFDB0yfPh3Z2dkVbm8ymZCfn6/6qmlmP2aiS58ltS5xLsH/kKn8oFvpYNFwykSvA4NFlR321e1Edy7Mnskp8esbaEmFcS7+jY7xVmmZVZUvbnLzelReZrbYkFVQKh+UcelElwaLBrgTvdBkgaWC16oc51JhJrrjbBgfXxvSfa8ozgXw3F3OTnSi2unypvH4cOJVMOg0+Onwecxaszds/ggnIiIiIqoO5efzsIlzcaxTqxHkWqXzTLNQVauL6DfffDM++ugjbNq0CS+//DJ+/vlnDBkyBNYKokPmzZuH+Ph4+at58+Y1uGI7ZbSJ3zrRBec4l+AXd5X3rfLBomGUiV4HBosqi7XV7USXipXREVrotQLMVpuqS7u65E50gzLOxT5Y9FxuaVCeS5l56vtXWSc6YB8uWix3oquL6NL3gYgPcu7Szi/1XKgvHyzqRSa6jwdfpMGizkNVnZUPC3VfRHc7WNTpPpZZbfLPYxGdKPT1apWIheN6QK8V8PXec3j6ywNh84c4EREREVFVKUuG4TJY1Cp3ogtw1NAZ5+KNJ5980mXwp/PX4cOHq3z7d999N4YPH44uXbpgxIgR+Oabb7Bjxw5s2bLF43Vmz56NvLw8+ev06dNV/vlVpexArW43lUVxhAcIrU505RGzyovo4ZmJHoiM6lCgvF/VjQaSOrLrRUegWT1Hh7gfh4tKBzKUESjJsQYYdBpYbSLO5ZZ4umrAZDgX0d12oqsf1/ScYvlxd84Fj5Q60QM8WBRwjUlRkuYcSDMY3KlqJrp0ACGmgjgXoKJOdLPq34HyOBfngrty0G2cseKiPRGFhus7JGPBXVdAIwCfbk/HS98fZiGdiIiIiGo1Zc0whMtkKlInur2IHl5xLkGtDjz22GOYOHFihdu0bt3abz+vdevWaNCgAY4dO4Ybb7zR7TYGgwEGg8FvP7MqlAViq02EKIoQBM9FqYpIR6KkeAUpSiEUMtHLVJnolQwWdYlzCd13B3URPfiPcyAo71d1h9TmKzqE60cbcPJiEdKzi9GnTbVuVlbsZrCoRiOgeWIUjp0vRFp2sdyZXlMy89WFe3dnhkiPa6xBhwKTBenZxXJ2fJTBQyd6WWDjXNx9ryR1ouu86ET39XkjdYY758E7k4rkzh30lcW5KN9npW1jDboK7wsRhZZhXRuj0NQF//f5fiz65QRijTrMvKFdsJdFRERERBQQ6iJ66NbJlKQlq+NcgrggHwS1iJ6UlISkpKQa+3lnzpxBdnY2GjduXGM/syqcu7KtNrHCjOGKSEUtjRznUrUCViBYFC/wyjrLSxxFdEGwv7hCerCoYm2ByKgOBcqO52p3ois6hFPqOwZ++mm4qM0mys+dKKcIlBSpiO7nQabecO5Ed5eJLj2u7RrGYHd6LtJyij0OFpUy0gPSie7Upe1uCKdEOrtEX1EmuuM9yGoTUWa1VRj9oiRlokdXloke5b4TXTrjQR3nYh8aarGJKDJb5bx16T7GRzHKhSjc3HVVCxSUWvD8t4fw2g9/I9aox4Q+LYO9LCIiIiIiv7OK4deJro5zCa9O9LBpsUtPT0dqairS09NhtVqRmpqK1NRUFBYWytt07NgRX3zxBQCgsLAQ//znP/Hnn3/i1KlT2LRpE2677Ta0bdsWgwcPDtbd8Ipzgbs6uehyJ7om9DrRLVbfM9GlIldIDxZVvHMFIqM6FCg7nqsbDSQVaeMj9WghD/z0T2G7RPE8d45AaSEV7P0YHeMtKRNdOurq7vUoPa7tG8YCsK9TOijjPFhUKiwHIj7IuRidX0ERvcwmvd943rUY9OX/5sv7kBzLU83BonGKTnSjXoMIRxFfuT2HihKFtyn9WuPhG+0d6HO++gtrd50J8oqIiIiIiPxLFEV1J3qYtHOXx7kAUukgXNbuVSf6vn37vL7Brl27VnkxFXn22WexfPly+fsrrrgCALB582Zcd911AIAjR44gLy8PAKDVarFv3z4sX74cubm5aNKkCQYNGoT//Oc/QY9rqYy7TvSqkgrwGqdM9FDoRC+rQiZ6nFGPglJL2GSiB6IzOBQo75fJYq1W5FBeiTTAMUIuoqdn+6eILhWdBaH8AJIkJdG/Xe++kDrRm9eLxKnsYrevRyniRS6i5xTLZ5I4d6JLXfaBiA+SzhQw6jUoLbNVEufiyESvoBNdykQH7O9DsV6sQRRF+XfpPFTVWWWDRZWFcUEQEBepx8VCE/KKy9A0IRJA+YECFtGJwtcjA9uh0GTBh7+exBNr9yI6QoshXUL7TEQiopqye/du6PV6dOnSBQDwv//9D0uXLkWnTp0wd+5cREREBHmFRERUGedSYfjEuZTPbtQKUpxLLSqid+/eHYIgeFUos1oDUzRctmwZli1bVuE2ygc9MjISGzZsCMhaAs2fnehWm1Mnuk4aLBr84q7yfpkqKYrL+dCOIX+VFd2DSfn7C0RGdShQdtjbRPuZARG6qhXR1XEu9mxyfw0WldYZpde6vHeV/6yaL6Jn5ZfKaziVXexVJ/ql4jLote470aUu+6IAFNGl4nNKYjSOZBXIsSjuyJnoFQwWFQQBBp0GJovN6/ehkjKr/AdCVeNcpKJ6glNhPCHKXkSXnoeA++gXIgovgiDg6WGXochkwaodp/HQqj34IEKL6zokB3tpRERBN23aNDz55JPo0qULTpw4gbvvvhsjR47EZ599huLiYixYsCDYSyQioko4N9yGcK+pirROjUaQG37DZe1exbmcPHkSJ06cwMmTJ/H555+jVatWePfdd7Fnzx7s2bMH7777Ltq0aYPPP/880OutE5yHDFanE126rhQbIUUphFsnulQMjTPqXa4bapRd8rW2E90pNqS0ksGwFclTDBaVOtHzSy0uncRVIT3+UW4Kr80Vneg1fdRT6kRv6YiUcV9Et19WL1qPBjH2s2ekGKNoT4NFAxHn4vg9SPE3FXWiy3EuleScS93o3kYBSb9HQQAi9RV3opcPFi0vilusNhSYLKp/d94+n3EuRLWOIAh4YWQX3NK1McqsIqat2IVtJ7KDvSwioqD7+++/0b17dwDAZ599hv79++OTTz7BsmXL+JmeiChM2JzqGM7fhyqbqIhzCbNMdK860VNSUuT/v/POO/HWW29h6NCh8mVdu3ZF8+bN8cwzz2DEiBF+X2Rd41xQtlTjlAznInpIdaL7konuKNJKneghXURXFJQDkVEdCpxjQ0xlNsBYtdtSRmdERmiRFGvAhQIT0nKK0DUqoZrr9BwB0jwxEoJgvy8XC81Iiq2ZmCezxYaLhSYAQMsG9m5493Eu9ssMOi1aJEbK1wGAKH3NDBa12kTkl9ofQ+kAhzdxLhUNFgXssVL5pRaXA4aeSENFo/Ra+Ui1J/GOYaHKdUr3wf7vTp3oUtFdcdBG+v84FtGJwp5WI+CNu7qjxGzFpsPnMXn5Tnw8pTe6N08I9tKIiIJGFEXYHJ8xf/zxR9xyyy0AgObNm+PixYvBXBoREXnJObXCEsKzA5XkOqVQHucSLgcAfB4sun//frRq1crl8latWuHgwYN+WVRd55z37Y9OdHeDRYOdOaQ8OOBtJnp5ET10X2DK+xKIjOpQ4HxwoDoHZXKL1V2/Ula5P2JWpMc/MsL1eKFBp0XjOHvlvyZz0c8XlEIUgQitBo3j7RncFXWiG/UaOXpG4mmwaImfD44VlJYXlqUiekVxLmVynEvFuxZpNoO3neiFjiJ6ZVEugGKwaLFrZ3mMQefSJe9uEKl8dkQk80CJagO9VoN3xl6JPm3qo9BkwYQl23EoIz/YyyIiCpqePXvi+eefx4oVK/Dzzz9j2LBhAOxnoDds2DDIqyMiIm841wrDpRAtd6Ir4lzCZe0+F9Evu+wyzJs3D2Zz+anyZrMZ8+bNw2WXXebXxdVVJqfCUnWOJsmDRR1Hd6TBhDaxelnr/qAshFc0KFQURbnYFuuIcwnpwaLKOJdaWkR37nj2tqPYnfKCpf13K8WG+KOwXVEnuvpn+SeD3RuZjiiXhvEGxUEt9fNZFEW5E92o18oFbMBefI/Qqd+6o+ROdP+e+SD9bqIitHKkTH5Fnei2ygeLAuVxLiYvi/7SwZAYL4roUo55fqlF/qOioniWuAqK6IxzIao9jHotPhjfE1e2SEBeSRnGfbgNJy4UBntZRERB8cYbb2D37t2YOXMm/vWvf6Ft27YAgLVr16JPnz5BXh0REXnD5pKJHh6F6PI4FwHSiebhsnav4lyUFi5ciFtvvRXNmjVD165dAQD79u2DIAj4+uuv/b7AusifnejSk1MqakmZ6IC901VfSXZxIFm8zERXRl3ERTo60UMg090T5X0JREZ1KChxOjjgbUexO7kl6uiMlET/DRct70T3UERPjMKfJ3JqdLhopmOoaOO4yPKObIvzQYnyx9Og0yClfnkR3d19kQaLmiw2WG2iHN9UXbmKYZxScVqZNe5MOuDnTZwL4H2WvhznYqg4Dx1QF74LSsuQEBWB3GKzy79Jyu+XsohuVv0bEdUO0QYdlk7qhTHv/4mDGfkYu3gb1ky7Rp6RQURUV3Tr1g379+93ufzVV1+FTudziYCIiILAJc4lTArRythpbW3vRO/VqxdOnDiB559/Hl27dkXXrl3xwgsv4MSJE+jVq1cg1ljnuGaiV6MT3ercia4soge3EK28XxUV0ZVRF7HhMFhUcV/qymBRv3SiR0md6PaIE390oktnAkS7iXMBIMekpNdkEd3Rid4o3igXk53PPlF+79yJ7q6rPkpxmT9z+PMUBzjcxZ44k16Xlce5SJ3oXg4Wlc8oqPxDnV6rkR8P6SBARZ3lFcW5sBOdqPaJj9Tjo8m90CYpGhl5pRi7eBuyHAc3iYjqitatWyM723XQcmlpKdq3bx+EFRERka9cBouGSRFdWqa9E91RRA/dEp+KT0X0srIytGnTBunp6bjvvvswf/58zJ8/H1OnTkV0dHTlN0BecS4o+6UT3VHUEgShPEqhGoVPf/A2zkUq9us0AiIdRcfwyUSvnZ3ozlnvVT0gU1pmlR8vqWDZItF/he3iSjqYpeJ0Wg1momc4iuiN440eX4tSh7ZWI0Cv1cixM4D7TnSDTiOfBuXPHP5cRTHZmyK6dGAsUJ3o3sS5AOXRQNJa870ooueziF4l77zzDlq2bAmj0YjevXtj+/btFW6/YMECdOjQAZGRkWjevDkeffRRlJaWFzDnzp0LQRBUXx07dgz03aA6pkGMASunXI3miZFIzynG2MXbkK0Y3kxEVNudOnUKVqvr32EmkwlnzpwJwoqIiMhXzrVCa5h0c9vk2GmUx7mEydp9OldLr9erPuxSYDgX1CzVOCQjZ6IrDpcY9VqYLLbgd6IrCuemCjrRpWGJkXqtHD8TLpnodWWwaFUPyEidwlqNIBdIpeiSjPxSmCxWOce/auu0ryvKQ5xLih/z173lrhPd+bUonX0hFdmTYgyIitCi2Gx1O1xTEARER+hQYLL49TmnPEsg3nGmQGmZDaVlVnntShYvO9Gl++X9YFHH79HLInpcpB7n8krl9cuxNG7iWeQ4F8UgUudht+Te6tWrMWvWLCxcuBC9e/fGggULMHjwYBw5cgTJycku23/yySd48sknsWTJEvTp0wd///03Jk6cCEEQMH/+fHm7zp0748cff5S/52nlFAiN4o34ZMrVGL3oDxw7X4hxH27Hp1Ovlt/riIhqo6+++kr+/w0bNiA+Pl7+3mq1YtOmTWjVqlUwlkZERD5yKaKHSSe6VDBXxbmEydp9/mQ6Y8YMvPzyy1i8eDE/2AaIXzvRbepOdMAepZBXoo5JCYYyH+NcDHqt3OEaynEuygMC/s6oDhX+6kRXdvwKjtN46kdHIDpCiyKzFadzStA2OaYa66w4BkTqRL9QYEKx2SJniwdSRl4JAKBRnLE81sRDJrpUqBYEAS0So3A4s8DjAYEogxYFJotfh4sqO7hjInTQCPZTr/JLytwW0aUzRCodLCrH2Hg5WFTuRPfugIpzzrkvcS6lZVb58WcxrWLSmWiTJk0CYJ+Z8u2332LJkiV48sknXbb//fffce211+Kee+4BALRs2RJjxozBtm3bVNvpdDo0atQo8HeA6rzmiVH4eEpv3LXoDxzMyMeEpdvx8ZTeXp/1QkQUbkaMGAHA/rflhAkTVP+m1+vRsmVLvP7660FYGRER+Spci+hu41zCY+m+Z6Lv2LED69atQ4sWLTB48GDcfvvtqi+qmMWL4q/UyRzh6LquVia6HNhffpnU2VtR93dN8HawqFREN+o1iHB0sIZyEd35vtTGSBepiC49R6t6QMZdcVMQBLSQsspzqjdctLLBoglREYgz6hw/q2a60bPy7ZEBjeKNMOrK44mUOzz5Oa+YYSANvvNU6Jcu92ucS7E0YDMCGo1QaaSLdNZMZQOLpftd6uV7UKEPmeiAa2FcjqVxUxSPj4ywb+O4r9J1NAIQUwMHVcKV2WzGrl27MHDgQPkyjUaDgQMH4o8//nB7nT59+mDXrl1y5MuJEyfw3XffYejQoartjh49iiZNmqB169YYO3Ys0tPTA3dHqM5rkxSDj6f0RkKUHqmnczF52Q6X4dlERLWFzWaDzWZDixYtcP78efl7m80Gk8mEI0eO4JZbbgn2MomIyAvOESjhEomijnOxF9HDZe0+VwgSEhJwxx13BGIttdrfWQWYsGQ7tBoBv/7fDR63s9lEuZszMkILc4mtWkeTyqfeqjvRAe+7QANFeb+8yUQ3KuJcyiyuj8kHv5zAF3vOYuWU3qgXHeHn1XpHFEWX+1JstsoDUf0tM68UE5duxz29W2D8NS19vv4Ta/fiQoEJH064ChofuuWlzuB60Xpk5Zuq3IkuFS6dO4RbJEbiUEZ+tXPRiysZLArYh4vuP5uHtOxidGwUp/o3URTx4Kd7UFpmw/vjelT6GO1Jv4SZn+xBQWl5kTnWqMe7Y69Et+YJsNpEeYBd4/hIGPTKQb/lUS3K57y8TrmI7qET3XF5IAaLSr+f+Eg9LhWXyUVpZ9IgY10lj5N0v90dfFnw49/46fB5rJzSW37dFMnZ9j4W0Z0K4xV1oheYLLDZRNW2vrwm6pqLFy/CarWiYcOGqssbNmyIw4cPu73OPffcg4sXL6Jv374QRREWiwX3338/nnrqKXmb3r17Y9myZejQoQMyMjLw3HPPoV+/fjhw4ABiY2Pd3q7JZILJVJ5nnZ+f74d7SHVJx0Zx+OjeXhj7wTZsO5mDaR/vwgfje1QrToyIKJSdPHky2EsgIqJqculED+HZgUrldco6EOeydOnSQKyj1kuMjkBGXikEARXmPCsLsFERWuSVlMmFqaqw2lyLWr4O9QsU1WBRLzrRK8tEX7fnLA5l5GNX2iUM7NTQ5d9rgsUmwvkAWiBz0TcezMThzAI8/+0h3HhZQzRNiPT6ulabiDU77YODMvNL0cSH6xY57lNitAFZ+aYqZ6J7Km5Ka8nMr96gt6JKBosCQIv6Udh/Ng+n3XSi5xSZ8c2+DADA2dwSuRvck82Hz+NsbonqsvxSC+Z+/RfWTe+D7EITLI54n6RYA5QlWpPFhmiD9P+OTn9FJ3qftvWx+NeT6NosHu5IQ3f92UEpZYPHKYroAJBX7L6IXuboRNd52Ynu7myYz3efwemcEmw/mYMbL7O/josdmejex7nYD6Ll+RDnIopAQamFQ0UDaMuWLXjxxRfx7rvvonfv3jh27Bgefvhh/Oc//8EzzzwDABgyZIi8fdeuXdG7d2+kpKRgzZo1mDx5stvbnTdvHp577rkauQ9Ue3VtloClk67CuA+345e/L2DmJ3vw7tgrKz2zhogoXG3atAmbNm2SO9KVlixZEqRVERGRt8J2sKgozW4U5MY1W5isnZ8Makj96AhERWghisCZSyUet1MWlaTO0up0osuDRYXycp001M8U7MGiNv/GuUid9cGMqVGuK9YRE+LPjGpnaY5ObbPFhtd/OOLTdZUdy74W+kvkInr5sMmq8FSwTIhUF0GrShpK66l7Gyjv8E5z0/WepiisexP3Ij337uzRDJseG4D/zbgWkXot9qTnYv2BTGQ4hoomxRigdeww3EXiuOtEv6FjQ+ybOwj39W/j9mdL2/rz+S8PFpWK6I7idGWd6PpKMtGNFXSiSwVz5e+j0PEacjdU1R3nOBep6C89r5QidBr5+ZFbYi4fKhoVnLNZwkWDBg2g1WqRlZWlujwrK8tjnvkzzzyDcePGYcqUKejSpQtGjhyJF198EfPmzXP58C5JSEhA+/btcezYMY9rmT17NvLy8uSv06dPV/2OUZ3Ws2UiPpzQExE6DTYezMKjq1PDJluSiMgXzz33HAYNGoRNmzbh4sWLuHTpkurLF++99x66du2KuLg4xMXF4ZprrsH3338foJUTEZHE+e/UcOnmlovoggCp3zdc/uauUuDr2rVrsWbNGqSnp8NsNqv+bffu3X5ZWG2jHAyYnl2MNknuhyVKHaiCUJ5dbvFQXPCG9ORUDvoLxU70ijLOpXUaKxksKhXkgjkwVXkwoF5UBApKLQHtRFcWeb/YcxZT+rZGpyZxFVyjnHJdvkSAlFlt8pkA9RyFxupmoic4ZVXHR9rfmvKrWUSXO9EriHORhoumuSmSK+Nk0rKLcW3bin+eVMBOijXIr/Gp/VrhrZ+O4ZUNR/DYoPYA7HnoEoNOA7PV5lRELz9wpBRXQSxQRYXpqnIX56K83Jk8WFRT8fFZ6b3N3cGXIsdzUXnQoqiKmehSQbyy7vL4SD2KzVbklZSxE91LERER6NGjBzZt2iQPKbPZbNi0aRNmzpzp9jrFxcXQOD03tFr7c0H00HlQWFiI48ePY9y4cR7XYjAYYDAYqnAviFz1adsAi/7RA/et2Ilv9mXAoNPi1VFdGe9ERLXKwoULsWzZsgr3r95q1qwZXnrpJbRr1w6iKGL58uW47bbbsGfPHnTu3NkPqyUiInecC8/VmadYk+Q4F0GAVqjlnehvvfUWJk2ahIYNG2LPnj3o1asX6tevjxMnTqhOwyZXKfXtxbqKOlqlImyEViMXjKvVie4odqo70T0XsGqS94NF7f9m0Gnlrt0yNxE30pDCYB4ckO6HIJR3ogdysKhU5G2aEAlRBF5a7z6L2B1lh3yRyfvHTFl8T3Rkz1e1+9lTwVIaAJlbYna5ji+ktVbUid5Cel1muw4xVXZDp3kx5FQeCqyIYblvQBvUj47AyYtF+O9P9m7axsoiut719SgVwn3J4y1/Xfu/iC4d5JAOblQ+WNS7TnTnuQxWmyg/DmmK34f0/KxyJ7oXRXRpOxbRvTdr1ix88MEHWL58OQ4dOoTp06ejqKgIkyZNAgCMHz8es2fPlre/9dZb8d5772HVqlU4efIkNm7ciGeeeQa33nqrXEx//PHH8fPPP+PUqVP4/fffMXLkSGi1WowZMyYo95Hqpus7JuPtMVdCqxHw+e4zeOZ/Bzwe6CEiCkdmsxl9+vTxy23deuutGDp0KNq1a4f27dvjhRdeQExMDP7880+/3D4REbnnHN8SLoVoqcSpjnMJ4oJ84HMR/d1338X777+Pt99+GxEREXjiiSewceNGPPTQQ8jLywvEGmuNlPrRANzHRkikIqxBp5ED9qtzNEm6qjoT3f8dq1WhvF+mCjrRpeiQyAhFJrqboq0c5xLEgwMmxUEQqWs2UJ3ooijKB2ReGHk59FoBv/x9Ab8du+jV9ZXrKinzvtAvHRTQawXEyIMwq3Yf5eiMAMW5lBfRKx4sCthjlpwPWCkPeLnLTHcmHwRTFNFjDDo8dGM7AMDhzAIA6k50uaCsOPgjPY+cO9ErYpBvx/9xLtLvR/69FLs/uCEPFq0sE91D9EyJ4nmk6kSX4lwqOBiipCyKmyxW+XbjoyououcWl8n3LYFF9ErdddddeO211/Dss8+ie/fuSE1Nxfr16+Vho+np6cjIyJC3f/rpp/HYY4/h6aefRqdOnTB58mQMHjwYixYtkrc5c+YMxowZgw4dOmD06NGoX78+/vzzTyQlJdX4/aO67ebLG2H+6G4QBGDltnT855tDLKQTUa0xZcoUfPLJJ36/XavVilWrVqGoqAjXXHON32+fiIjKuXSih8lg0fI4F9T+OJf09HT5qHVkZCQKCuxFoXHjxuHqq6/Gf//7X/+usBaRhhKmV9DRKhdhdVo5EqF6mej229NqXDvRg5kdDqgjWcwWG0RRhCC4drDKcS46jVxEdxvnEgqd6IpOZGmYZaCK6BcKTSgps0IjAH3aNMDY3ilY9vspzPv+EL6a0bfSU8+V6/KlE13aNlKvVRRD/TtYNK6S2BBvSQX/6AoGUjaKMyJCa49UOec0PFT5Wq3o4JdEeSaJ0pheLbD0t5M45biNxk5xLoD7TnRlJnpljG462qvDbLHJzxHv41wcg0Uree6V32f186ZYcXbE6UslsNlEaDSCvA5vO9GlznllZ7kgALEers9O9KqbOXOmx/iWLVu2qL7X6XSYM2cO5syZ4/H2Vq1a5c/lEVXLbd2bwmSx4Ym1+7Dkt5Mw6jX45+AObv9WISIKJ6WlpXj//ffx448/omvXrtDr1X/3zJ8/36fb279/P6655hqUlpYiJiYGX3zxBTp16uR2W5PJBJPJJH+fn5/v+x0gIqKwHSwqx7loBLlWGS5d9D53ojdq1Ag5OTkAgBYtWsinaZ08eZIdOpWoaIChxN+d6MonpyRkOtGdjpK5i2gB1EMWI3TuM9HLrDb5vgYzpkb5+5MiRAIV55IuF2QjEaHT4MEb2iLGoMOBs/n47kBGJddWr6vEh0J/iaKgWR7LUbXHPNdTnItTpnVVyZ3oes/FV61GQLN6kQBco5aUr9X07OJK3+OUv3+lCJ0G/xzcUf6+YZyyE911RoHJw+1URC5M++kgkqr4bJQGi1ZcRJfeq/RedqI7r1V5YMdssSEz3z6ItTqDRfMUZzt4OrCkLLrnesjpJ6K6aXTP5vjPbfZM33e3HMfbP3kecktEFC727duH7t27Q6PR4MCBA9izZ4/8lZqa6vPtdejQAampqdi2bRumT5+OCRMm4ODBg263nTdvHuLj4+Wv5s2bV/PeEBHVTc6DRMNzsKijiB4ma/e5E/2GG27AV199hSuuuAKTJk3Co48+irVr12Lnzp24/fbbA7HGWkOZiS51WDqTOpkNOo08DNRajcGiUmFZ2RkqFYey8k1ur1NTnAemmq02VQyGRCr2K+NcnAvuyq76qnZF+4OyE1mKEPGly9sXUoFXel7VjzHgnt4t8P4vJ/DbsYu4pWuTCq+v6kT3odAvbRsVoS3P4a7iY54vFywjVJdLz9GCUgusNlF1EMhbNptYXkSvoBMdsOein7hYpBoeWmK24nxB+WukwGTBpeIyOQfeHXeZ6JKhXRrh6taJ2HnqEro1S5Avl7v5FQciTNXoRPdXnJFUKI816OTHXz644aaILopi+fuN15no6rU6Pw/TsovRON5YHudSye9RIsXOFJutuFBoUq3dHXed6HHsRCcih3HXtITJYsPz3x7C/I1/I0Knwf0D2gR7WUREVbZ582a/3l5ERATatrX/Ed2jRw/s2LEDb775piqyTTJ79mzMmjVL/j4/P5+FdCKiKnBuuA2XwaJSKVBZRA+TJBrfi+jvv/8+bI57PGPGDNSvXx+///47hg8fjmnTpvl9gbVJk4RIaDUCTBYbLhSaVN2oEqmoFKHsRK/is0kURVVgv6RDozgAwJHM4J4651wIN1tsgMF1OznaQhHnYnbqRFd21QczE11ZRJXym0sC1Ime5uiabqGIH5GiSC4VVd7BrSyi+xI5U6LIGS8/q8HPg0UV3xeUlrkU2b2hLOxXNFgUUJwloohvkbrS44w6REXokJlfivSc4gqL6GUVFNEFQcCySb2QX1qG5FjXOBflwZ9SS/nZF94yVvOAhrM8x1BX5WNfUZyL8vWs11TciW7w0InufEZEek4RrkxJkP8Y8LYTPdaogyAAolieZe9VEb2YcS5E5N6Ufq1hstjw6oYjeOn7wzDoNJh0batgL4uIqFqOHTuG48ePo3///oiMjPQYr+krm82mimxRMhgMMBjcfOgjIiKfhOtgUWndWk15akat7UTXaDTQKAokd999N+6++26/Lqq20ms1aJJgxOmcEqRlF7stoput9iJShE4jd49XNRNdeT1lJ/pljWIB2IcceuqIrwkunegeMtqlArlBr1Vloiv/yFMW0YMZU6McLBkpdaIHKBNdKg62qF9eRE90FDxzPAx+VFLGufgSOaPsRK9OJrooih4Llnqt/SBEkdmK3OKqFdGlMwAEobzA7EkLx3BR5fDQdMXjG6W3F9HTsovQvXmCx9spHyzr/ucZFTnyyssA989hoy9xLn6OaXL3u5FjT9zE7Chfz5V1orvLgQdcXyvpOcWqMzmiKxgQq6TRCIg16JBfapHP2KiwiO54fuWWmOX7xsGiRORsxvVtYbLY8Namo3ju64OI0GkwtndKsJdFROSz7OxsjB49Gps3b4YgCDh69Chat26NyZMno169enj99de9vq3Zs2djyJAhaNGiBQoKCvDJJ59gy5Yt2LBhQwDvAREROReew2U4p9s4lzA5AOBzJnr//v3x7LPPYtOmTSgtLQ3Emmq1lER7sS4t2/1wUX9moiuvpyyUt2oQjQidBsVmK05fqnxYYqC47UR3Q5WJ7iiii6L6/qnjXIKfia7sRA/UYFHpOSQ9pwCgnqPIeamo8iK6sjjpS+RMsUnqRFfEuVShE73QZJHf5N3lT1c2xLLSdUrFfr220gNFLdzMK1A+vtKBivRKhosqf//ekqNNlM9hx+Np8KkTvXr59M5yi12L6MrfiXM+vPL1XHmci/uDL8rBooD99yFFuRj1Gp9ifaQDL2m+dKIrB4syE52I3Hh0YDs5yuVfXxzAmh2ng7wiIiLfPfroo9Dr9UhPT0dUVHlDzl133YX169f7dFvnz5/H+PHj0aFDB9x4443YsWMHNmzYgJtuusnfyyYiIgXnWmHYFNEVsxulj/jhMhTV5070QYMG4ZdffsH8+fNhsVjQs2dPXHfddRgwYACuvfZa1U6YXLWoHwUccx1gKDEpinA6R8d/VY/IKK+n7ETXaTVo3zAGB87m41BGAVLqR7u7esBZnCJZpC58ZyVSJrpeC72u/H6UWW1yZ3qodKKbFJnokYEeLJqjzkQHgHqOqJFLXnSil1RxsKhcnDboqtX9LBUrI3Qat7ElcZF6nMsrdZu/7d06pSz9yt/mUhRFcukMB3Unun19aR5et5IqFdF1bjrRHcVlXwaLVuesAHfcFZOlrHGLI29eGa+ifD1XFudi9HDwRfqdSVEs6TnF8pkPMV5GuUikwrh04KOiQaEJikG2nobdEhEB9miu/7u5A0wWK5b+dgr/t24f9DoBI69oFuylERF57YcffsCGDRvQrJn6vatdu3ZIS0vz6bY+/PBDfy6NiIi8FK6d6FZFJ3q4xbn43In+9NNP44cffkBubi42b96MW265BTt37sSwYcOQmJgYiDXWKlL2cqVFdG31M9FVnehO2XYdHbnohzKCl4vufNTMUwe5HG2hL89EB4Ayi6jYxuayfTCoMtEdRb9AdKIXmiy4WGgvlKviXOQielmlb0JFVR4s6uhE12vdFoC9lVtJbIYcHVLNTnRvhlFKnejS8FBAMbg1Maq8E72yIrq1/PXrLYObXPnSKgwWdXc71eEuzsWo18j3zfnghvR61giotPPf08EX6XfWsr50xk6xYqiob0V06fmT7kMnekZeafnZEZG+RwgRUd0gCAKevaUT/nF1C4gi8Niavfh677lgL4uIyGtFRUVum99ycnKYV05EFCbKi9Hq70OdPFhUI8i1gzCpofteRJecOHEC+/fvx969e7Fv3z7ExsZiyJAh/lxbreQuNkLJrOpEr14mus1DJjoAXNbYXkQ/HMThot7GuZgUcS7K+6EcLmpSdaKHQpyLVh5mGYhOdCm7OyFKjzija2a11SaioLTin6ss7vvSiS5tG23QuY0i8VZ+JR2/1Y9zKT+DoTJGvRYN4+wfGKSiqzJzXnrdBiLOxeCuE12Kc6lmR3t1uItzEQQBcZHuc9GlIrrOiwMI5V3z7jvROzS0z23IKynDuVx7bFiUl3nokjin54+3cS6A/SCI9NwmInJHEAT8e/jluKtnc9hE4JHVqVh/ICPYyyIi8kq/fv3w0Ucfyd8LggCbzYZXXnkF119/fRBXRkRE3pJqhVL9IVw60aXUDK2giHMJk7X7HOdyzz334Oeff4bJZEL//v0xYMAAPPnkk+jatatfJnnXdpV1tJrkTHStXzPRnbOElcNFg8UlzsVTJ7qlvBgqCAIitBqYrTaUKYvoqkz0EBgsqtXIRT9f8sa9peySVjLotIgx6FBosiCn2FxhrrOyuO9bJ3r5YNHqZKJXFpshFza9iKZxR3rcve1gbpEYhax8E9Kyi9Clabw8L6BFYpT8u8zML0VpmdVjh7hypoG33BWUpeewL53ongrTVSUd5HA+UyAhSo+LhSbklqh/L9LrWe9FbrmU32622FTDjaWzHBrERqBBjAEXC03y2TIxXpxRoOT8vKqos9w56iU+Ss/9GRFVSqMRMO/2Liiz2rBuz1nM/GQP3vuHBjd1ahjspRERVeiVV17BjTfeiJ07d8JsNuOJJ57AX3/9hZycHPz222/BXh4REXlBKjzrtRqUltnCphBtVZzFrq3tg0VXrVqFsrIyTJkyBffffz+mTp2Kbt26seDgJSl/PKfIjIJS1w5bVSe6VupEr1pRTPnEdP79dHR0oqdlF6PQFJjM7so4HxwwW93fT6nzWYqA0DseF2UR3V0XbzCYFVnW0mBRX7q8vZWeYx962cJNnn29aHtBMKeS4aLKTnRfImeUg0XlTvRqZKJ7yqqWBkNWe7BohHfF1xaOAa3p2cXIyCtBmVWEXiugcXwk6kXpEesoxp+uINJFGefjLang7u45XLU4Fz91ons4yCF9n+/0e5HOLPGmE105MFVZ9Jdy+qMjdHJOvVRE9znOxWndcRV0osca3d9HIqLKaDQCXr2zG27t1gQWm4gHVu7C5sPng70sIqIKXX755fj777/Rt29f3HbbbSgqKsLtt9+OPXv2oE2bNsFeHhEReUGq+Uk1hXDJFZcK5uo4l/BYu89F9OzsbCxevBhmsxmzZ89GgwYN0KdPHzz11FP44YcfArHGWiXGoEN9R261u250ZRG9up3o0gtK52bIX2J0hBxfcSRI3ehlPnaiSwVFveMNQlVEV3SflwazE11RRJUGi/rS5e0tT53oAJDoKD7nVtLBrexE96mIXiYV0XXV6n6WiuOeipv+inPxtoguFW3Tcorl2Jbm9aKg1QgQBMGrXHTlmQjekh5Dd5novnS0y2cF+HmwqPNBjgQPvxeL42CfdJCrIkbF/VIW/YvkYbBa+bl9UCqi+xjn4qn4745WIyDWqPNqWyIiZ1qNgDdGd8PQLo1QZhUx7eNd+OXvC8FeFhFRheLj4/Gvf/0La9aswXfffYfnn38ejRs3DvayiIjIS3Kci6P+UNXaYU2TlqnVCPL8Rg89tSHH5yJ6vXr1MHz4cMyfPx+7du3Cvn370L59e7z66qvMRPdS8wrylc3W8uKZVPyu6ikZcie6h9+yNFw0WLno0sBUKbPaYxFd6sp1FAml4aJmxWBRk6IAaQqFTHRt+WDRwHSil+d1O5M6uCvrRFfGzBT7cDaCtK09zsXxu7D6fupQ+WBR9zEbUnE9t7iag0W9LL6mKIrk7h5fucheQS56VTLRy3Ply38f0u34FucinRXg38Gizgc54j38XqTXs7uDds502vKZD8oDMNJzKzpCJz/2Wfkm+2U+xrm4FP8riDZy/ndPw26JiDzRaTV48+4rcFOnhjBbbJj60U78fuxisJdFRORW27ZtMXfuXBw9ejTYSyEioiqSBolKjabh0s2tinOpC53o69atw0MPPYSuXbuiY8eO+Oabb3Drrbdi/vz5gVhjraPseHUmFcAC3YkOlA8XleISaprUuSp1CnuKcylVdKcC5UfZPMW5hEImul4nyPcrEJ3ocpHXXSe640yHS5V0oiuL+8U+RIDIHd4GnarI6+vjXtnAR08dz94qdnreVEY5PDTNzeMrH/zy0IkuimIV41w8d6L7MtyyvKM9cINFAdeBnRLp9eg8f8ETdzE2yt9ZitMBIl8Hi/rSie787+xEJ6Kq0Gs1eOeeK3Fjx2SYLDZMXr4Tf57IDvayiIhczJgxA99++y06dOiAq666Cm+++SYyMzODvSwiIvKBzakTPVwy0eU4F6G8Ez1comh8LqInJydj+vTpOHfuHKZOnYo9e/bg4sWLWLduHR5++OFArLHWkWIK3HW0SkU4g6JTs6ovBIvi6I47lzV2DBfNCFaci319UYaKI0HK41wqyES3KAuQwetEN0lFVK1WLvr5e8CDxWrD2UslAOBSaASAenInesXFZ2Vxv9iH4ady1rheq4ob8bUDOl8uorsvjvorzsWXwaKAfXjo0awC1WUAkOLITE/LLnJ7feVBoOp2opdWoRNdLkr7YbCoKIrlg0Wj1GcKSB3brnEu0lAT74roctHf4lpEjzZoXQ4QxfiYie7cQV9pJ7rijIiKBvISEVUkQqfBu/+4EgPaJ6GkzIp7l+3AjlM5wV4WEZHKo48+ih07duDw4cMYOnQo3nnnHTRv3hyDBg3CRx99FOzlERGRF6TP4FL9IWziXBzrtMe52C+z1tZO9H379iErKwtr167Fgw8+iC5dugRiXbWa1NHqbkCh1Mls0GvlgH0pJsFX0tEdT4P+yuNcCoJy1MfiKDpKcRvu4lysNlEutrvEuVjdR7iEQid6hE6jyuIu8VN3MACcyy2FxSYiQqdBw1ijy78nOgaLXqokzkXZiW622lwy6j0p70TXqmI5fM3izi2xr8+5SCvxVKz1llTsj/SyEJ0YHSEXav88YS94pCgGt1Z0Bgmgfv5WJRNdHUnkeya6URGLVN3Xc2mZTX59eerozvXQie7NYFHlepX3u3wYrE4e9CrxfbBo+fNKrxUqfR6wE52I/MWg02LRuB7o164Bis1WTFyyHbvSLgV7WURELtq3b4/nnnsOf//9N7Zu3YoLFy5g0qRJwV4WERF5wepURA+Xbm6rohNdjnMJk7X7VpUA0LlzZ1gsFmzZsgXHjx/HPffcg9jYWJw7dw5xcXGIiYkJxDprFakwl5bj2tFqUmRql3eiV62zVCq+S6dHOGudFI0IrQaFJgvO5pbIxf2aUuZ4kchxLm6K6MqoB3mwqBznUv4iUxZwy6wirDbR61gJf1IW0Q06DTSCfWhCscnicyetJ9LzpkVilHygRameI84lp5I4F+eYmWKzFfGRlRdA5W5hx8EPo16LQpPF5zMAKotzqXYnuqm8q9kbgiCgRWIUDmbko9CRza3s9Jc6o8/klMBmE10e+6oW0cs7yKvXia7c1my1wajxLUNcSTrAodMIiHaKw5F+L/nOnehyJnr141yiIrRoEBOBqAitqjvdF8pu8vhIPQQP74OSuDpWRN+0aRM2bdqE8+fPw+a0j1myZEmQVkVUexj1Wrw/ricmL9+B349nY+KS7fhoci9c0aJesJdGRKSyfft2fPLJJ1i9ejXy8/Nx5513BntJRETkBalxVo5zCZNubqleropzCY+l+96JnpaWhi5duuC2227DjBkzcOHCBQDAyy+/jMcff9zvC6yNpMLcudxSl+5fZRHWf5no7otHeq0GbZPtBz2CkYsud6I7isvuMtGVHdxS0U06ylaminBRd0H7KxfaV9Lv06DTQBAEudBc7MfhohXloQNAoqOzu6JOdKtNdCl6F3uZ3V5kLh8sCriPI/GGnLntITpDKmQWm60eh856t07vD144x+M0r1f+feN4I3QaAWarDZn5pS7XlZ6/Oo3g9uCGJ85Z5mWKIa3S2RfeUHatV/f5rzzA4Vx8ls4QcBks6ijE6r08gGCQ41yUnehSEV0nH9SQeDsgVl6nj0Vx1WDRWh7n8txzz2HQoEHYtGkTLl68iEuXLqm+iMg/IiO0WDyhJ3q3SkSByYLxH27H3tO5wV4WERH+/vtvzJkzB+3bt8e1116LQ4cO4eWXX0ZWVhZWrVoV7OUREZEXnONcwiYTXRXn4mgeDpMDAD63xj788MPo2bMn9u7di/r168uXjxw5ElOnTvXr4mqr5FgDDDoNTBZ7tnXLBuWxBVInukFX/Ux06UlYUUd2x8axOJiRj0MZBRjUuVGVfk5V2GyifKTJm050g04jFybdDxa1uVzP1/gHfzArziQA7B+gC0wWvw4XTc+uuIjuTSe68uBEhE4Ds8XmdaFfOVgUcD8Y0xuVdaLHGvUQBEAU7dsmxRp8un1lV7O3lI9pcqxBNZRUp9WgWb1InMouRlp2MZokRKquqzwA5ovygxA21X8BwODDYFG91n7gzd0BEl95GiqqvMx1sKgUH+VtJrrjfqs60dUHaFokRuFwpj2f3tfXc1SEFjqNAItN9KqIXpfiXBYuXIhly5Zh3LhxwV4KUa0XFaHD0klXYeKSHdh+KgfjPtyGlVOuRpdm8cFeGhHVYR07dsRVV12FGTNm4O6770bDhg2DvSQiIvJRuA4WLY9zAaQevHCJc/G5E33r1q14+umnERGhzjFu2bIlzp4967eF1WbKDst0p3xlqZvX3oleveEAUgxMRUX0To2lXPSa7UQvU8QHVJSJLhUDlVEVep39/qgy0Z26oD0NKQ00aU1SIVUq/PmzE10aSOtuqChQPljUuVNYqdgRVyII5Z3r3gwXtVht8u8pyvE7kQq9vnQ/W20iCkrta/BUsNRqBMQ6Hr+8koqjadxRdjV7q4XiMXX3+LZwRDGlu4liqmoRvfwghFX1X/u/+ViQ11XtrABn8gEONx3Z8Y6scZfBoo4iul7jZSa6zrUTvcikjgpS/g58jXMRBEF+brGIrmY2m9GnT59gL4OozpAK6T1T6iG/1IJ/fLgNB87mBXtZRFSHHTlyBNu2bcPDDz/MAjoRUZiSSmJyJroIiGHQ0S0V+zWKs/jD5QCAz0V0m80Gq9W1QHPmzBnExsb6ZVF1gachhcpCXLU70R0vqIoyipXDRWuSclhqlKM45m6wpVRQVA4FdJeJbnLTiR4MzoVUqaPWr0X0nEqK6NFS3IbZ43NHmWsuFSe96ZYvVjyu0u9NKob6cuBCmaddUcEyvhrDRavSiZ6iGGbpPNjS/u/uD34B6nkGvjDKByFsjv+Wn31RWY63MzkipZqd6BWdJSBnopeWqY4WS3Eu3naiOx98sdlE+QwJ6bnVQjHYtSpnlkjPH0/Da5XU8S+Vbx/OpkyZgk8++STYyyCqU6INOiy7txeubJGAvJIy/OPDbfjrHAvpRFSztm/fDqvVinbt2rn9d5PJhDVr1tTwqoiIqCqsbiJVw6EYLdX5tXUhE33QoEFYsGCB/L0gCCgsLMScOXMwdOhQf66tVpMKdOnZ6o5Ws5ypra12JrpU1Koon/myxvYDH6eyi7zOxPYHZRFd6jp1V4SVCmxGRayF3m2ci3MmenA60Z0LqXIR3eSfx1YURZyWM9Fdi7xAeSe6TXQd/iiRCuaREVq5U7vEi0K/1K2u0wjyfTRWoRNdKtJGR2grzNBO8ND17A3p+exLB3NKZZ3ojiK6dDaAkvNZCN5y7UQvj3TyldHNsM6qyHOcxZBQQRFdFCGfTQAo41x860SXXjPKiCHpdZOiiNepymBedqK7V1paivnz52PAgAF48MEHMWvWLNUXEQVGjEGH5ff2whUtEpBbXIaxi7fh4Lman0lDRHXXNddcg+zsbPn7uLg4nDhxQv4+NzcXY8aMCcbSiIjIR1IsirIGEQ7Z4nInuiBAKxfRQ3/dQBUy0V9//XUMHjwYnTp1QmlpKe655x4cPXoUDRo0wKeffhqINdZKcie6UzFO6qiO0Gnkjk6rrfKC8LLfTiLGqMeoHs3ky2xedKLXjzEgKdaACwUmHMkswBUt6vl0P6rKorhPUu60u8Gi7uJc3GWiOxfgK4uzKLPa8MbGv9G3bQP0adug0vXuPJWDjQez8MjA9qqcbGeunejVj3P5dHs6fj9u/2PXarOh0GSBIADN6kW63V6v1SDWqENBqQU5xWY5I12pRO5E18oFS2860ZXFd6lL2rkI7MnirSew94y9604qilfWISwVM52jaTb8lYkjmQV48Ia2Hru1pWgQX+JcpOGhFpvoNnNeintx14le9Uz08mKyKIryc1f5nK/KbTlb+ttJ7E7Plb/XCMAdVzZD//ZJLttW1IkeodMgKkKLYrMVeSVlcre3NChY7+VQVedMdOk1IgjlBXbl78CXMwokCb4U0aPqThF937596N69OwDgwIEDqn/z9ewHIvJNrFGP5ff2wvgPtyP1dC7GLv4TK6dcjU5N4oK9NCKqA5xP83d32n84RAEQEZFrnAtQXgcMZXImugZhF+ficxG9WbNm2Lt3L1avXo29e/eisLAQkydPxtixYxEZ6b6oR//P3n3HR1HnbwB/ZmuygSTUhF5EuqCiVD315AT1xF5RlENOEfQU9ZTzTs+KP3tDUQS7Yvc49fA8rChFQRTuqIKEFhBCCimbLfP7Y/c7Ozs7W2ZLdjZ53q9XXprNZvPdyS4Lz3z2+UYSAejuqvqwy5Vp1uAmgUD41LaeitpG/P2f/4PDasG5R3dRQhBlEj1OKHJ4x1b4tcaNXw7UNmGIHtr0VISwMTcWDatzkSKub3QS/bttFXj6i5/xzZb9+MeM4+Kud/a/NmDV9oMY1qNNzA1YtdPIoTqX5CbRaxo8uO39tRFvbenVviBmyNq2wIGaBi8O1jYCkRkpaoOBZb7DZqhypl5VAyOEwtDox3znwTrc89H6iMu7RDkRIESrc5n13lpU1DZi3KBS9CvVr5Gq12xSmQib1YLDS1pj/Z5q9O8UebtdgpuJ7q5siPiadlPZRKk3D3V7/bonjhK/Lf0TGgcOuXHnP/8Xcf21u6rw2Y0nRlx+oNYNIPpJjsI8uxKiCx6/sY1FtSdfxHMk325VXki7tMlH6zwb3F6/8g4LI7q2cQX/G/+1qVNRPqwWCe1bOQyfCMk1n3/+ebaXQNSiFebZ8fKU4bhs/kr8GAzSX586EgM6MUgnouzjCXUiotwgBm7VGUQgBzSeJTQlcbLWapEgZvCa7SQ6ANhsNkycOBETJ05ULtuzZw9uvvlmPPXUU2lbXHMmpoO1E7bJdKKLyo5Gnx+NPr8STokHYbxQS3QN1zc23SkrMUVus0hKYKUXotcrneh6dS6h49KgmTzXfq5V3RA4ZonWhGzbH6jdiTetne5J9O0H6uCXgcI8G274XV8AgATguMN1knGVNi4Hth+oQ0Wt/oacol6mwGGFS2x+mkDlTK07MpgOTT9Hv4+/7A9MbpcUOnH1CYcBCJzc+W3/jjF/npgIVv+equo8yv06cMgNIDLslmVZ6W83MokOAE9PPBq/7K9V9gtQE88Vval7j1LFZHQz0NCxdHv8ymS2usIoUc4odS7VwdoVp82CW0/tj1q3Fw/9exPKDtTB55cjNh8Wk/bddKbxgVBFjvrkkJhET7jORdMFr7cRrN1qwetXjoTb60uqE33m7/pi1GHtcPKA2I8zIHDi6bUrRzT7KXStnTt3AgicICeiplOYZ8fLfxiOSfNX4MedVZj4/Aq8PnWE7msPEREREZFWzk6iq+tcLM24zuW///0vPv/8czgcDlxwwQUoLi7G/v37ce+992Lu3Lno3bt3ptbZ7OiFg0AoiHTaLLBaAk+EeJ3o6mC30RsK0cUEuzYg08qLMr2aSWJtdqslZoge6kRXTaLbdOpcNFPQsaaiA7cb+HptAuF2TUMotNVbo5r4ujMYJIY27Uzu2Iow87COrTB5TK+Ev69t8CTNwbooIboILJ02FDgSX2OdZuNHQB3cRj822ysCJyEGdS4ydD/06lzEbQFAZZSTIA0ev7JZhdEakF7tC9CrvX7ffKz+92TrXOzWwNlXvxx4/osqFqctmTqX4LsCNI9TcfKj2GXH5DG94PPLeGLJFjT6/NhdWR8RlouaqWib1+qdHFKe0wnXuYSffInWYX9E16KEbk9PmwIHTjuiU8LXH9m7XdI/K5f4/X7cc889ePjhh3Ho0CEAQOvWrXHjjTfitttug8XSvCfxicyiKN+Ol6eMwGXzV+CnnVW4ZB6DdCLKvP/9738oLy8HEBg82bBhg/L3gf3792dzaUREZIAInu2qwVmzd6LLsqw0LVhUG4s2uzqXRYsW4bzzzoPXGwg6HnjgAcybNw8XXHABhg0bhvfffx/jx4/P2EKbG9HVW9PgDZsEVUJYA5Po6g0h1SGv+D5rnLfkiRBUr0c5U0TVjM0qKYGzbid6cE3qaV29TnQxeS66muN1oouvJ7KZprq3Pm6IrqlzyU9xY1ElzIwyERxNcbAGpaJWP2QWgaXLntzGoi67us4l/kmYsgNiM1SD9yP4PFFvkKr+fUR7J4H6xFJ+ErUo0SgnqPwyvD5/2NR1shuLSlKg0qje40ODx6+7mW6iov0u6jVT+VaLhK5t87H111qUVdSFheiN3kCwDkR/3OlVAHn8xibRtSdfRId9On9fpO+2227D/Pnzcf/992PMmDEAgKVLl+Lvf/87GhoacO+992Z5hUQtR1G+Ha/8YQQuW8AgnYiaxsknnxzWe/773/8eQODvpLIss86FiChHiFzNarFAkgBZDt9/0IzUGX+gzkVMomdpQQYlHKLfc889mD59Ou6++248//zzmDlzJq677jp8/PHHOPbYYzO5xmapUFUZUF3vUepd1NOsSid6nCeBeoJYHUT75EQn0aNP12aKqGKxWWJPoutVWyid6OoQPRjEFecHuprj3ZfQJLo37l8W1ZtIxjvRoJ1GFt3hdUke27Lg1HX3dvqT0dG0dYm6oHiT6MltLKqeRI+1maUQb7I5GmUSXRWWq38f0UJ0Jex3hPq10yFP01+uDozdSXaii9ut9/jQ4PUpJ4SS6kQXwXSUSXT1VH6Pti5s/bUW2w/UYUyf0HV3VdbDLwfW1KG1U/fn6D1mQu8uSbATXRP4i8dkMrUtZMxLL72E559/HhMmTFAuGzJkCLp06YJrrrmGITpREytyMUgnoqaxbdu2bC+BiIjSRERiNosEm0WCxyebvs5FPSlvkQARn/hzJEVPOO3ZuHEjpk+fjlatWuHaa6+FxWLBo48+ygA9SXarRanRUAeE6iqHxCfRw+tcBJ8/wRBdbPAXZ3o7ndSBW8xOdGUDTPXGopHXF2G7ODkRL+wWwZ0sx9+EVB3a6k3Lq0V2omdnEl2clInWiV7bGAqZxRoTmUTX21jUmcBJmO0VqYXo6rC8TDWJrt1TQKjzGN9UNBHqd0Ro72+ydS6A6kSEx69UERntVg+/nfC11al+30KP4IkZ9eNb/Xn3tq6oJ5f0evSVTvQEq0C0J1/qktgIlpJTUVGB/v37R1zev39/VFRUZGFFRCSC9CFdi1BR24hL5q3A+j3V2V4WETUzPXr0SOiDiIjMz6/K/JRaFJPXuajzTUsOrVtIOKWpqalBYWFgIsZqtSI/P58d6CkqDk4Li4DQ75eV/vPwSfQ4nejuOHUucUJ0EYLG6xFPJ4+qzkVM7rp161xER3xkiB5e5xL4fxG6xptEV4fsdXEmsA3VuWimkfW6o/dWN+Dx/2zGvpqGmLcFqAJNg+FzvE50ceKlwGFT1phIJ7qYPFaf1HDGOQkjyzJ2VCRX51LkigzR1Z3oUetc3OH1Jelisager5rHQihET2WCPPQuCmcSk+gi5NeuTW/TTlHhUqY6ngBQdiD47oe20d/94AquTf0OC48/sY2MBe0mqHpBP2XG0KFDdTcBf+qppzB06NAsrIiIgMggfeLzDNKJiIiISJ9XZ4NOn8/cYXRYnYvUjOtcAOCTTz5BUVFgkze/348lS5Zg3bp1YddRvz2cYivMt2NXZb0SBKqnnB02ixJGxZtEV4fA6vDMa3ASPV6PeDqFNiGMt7FosBNdFSiK63u8gdvw+2Xle0UXeLzpcnXIXtfoQ6ztBNUhY7wQ3e2LMomuCqjnfvkzXvjmF/hkGTN/1zfqbSXSTR1NG1eik+g2ZSPHRKblQ5Po6jqX2CdhKmobccjthSQBXdskWedSpz+JXlUf7SRB5gJZp82CRp8/chLdF34CxQh1l7nePgAJry3KuwL0Nu0Ujyn1SSL157HeNVCgTKKrNxYNrNue4P1X7rNmEr0gzSc+KNIDDzyA008/Hf/5z38watQoAMCyZcuwY8cOfPzxx1leHVHLVuSy45WwzUaX4/WpIzGgE6tdiIiIiChEbCxqs6hCdJNPdIfXuYTWnSt1LobSissvvzzs86uuuirsc0mS4PM1XRCb64ryA4df9FarQ0inzQJrsBbBG+dMUl2UTnTxILTF7UQXAV4TbizqC02ih+pZIh87IgxUbzYoOpfFJLr6xEGik+jq+1oXZwI70Ul0WZaj1rmou6PX7aoCAFTUumP+XNFNnW+3Ru2mjiY0iR6tMzxUnZGvMy0fjbL5oyroDNUB6R8bUeVSWphnuOdbvFujut4TOL4+P/ZUhyb4420smpEQ3W5Fjdsb8XxJpc7FqXoOhibR07exqPjd5qs2hBUhedmBurB9AbYn8K4B3Y1FfYn9eRNaqzj5EriN0GOLk+iZdsIJJ2DTpk2YM2cONmzYAAA455xzcM0116Bz585ZXh0RFeUHgvRJ81fgx2CQ/tqVIzGwM4N0IiIiIgpQJtHVIbrJS9HD61yg7GEXb3jYLBIO0f0m/0XkouL8UEAIAO7gCQhJCm0MAMR/MIVtLJrEJLpS59KEk+hK9YN6El2nzqVed2PR8Our112UYCe6+ntibajp8YWmwaOtMXTd0O/JaQ0EgWJiV0xGy7KMDXtqAIRP8erZrtRqRO+mjqZtQeA4RJtEV28sWqAEovEn0UPTwqo6lzid6GJy3GiVCxD6fQYmv/3YXVUf9vafqBuLKpPX6Z9qzovyfGlU9jNIIkS3hW7TncIkep5Nv2pGnDRRT6KLOpcatxcH6zzKiRfl9xVjEt2l85jxKhVNCU6ia06+iOc6NxZtGp07d+YGokQmVpRvx8tTRmDSgpX4cUclLnl+OV67cgQGdS7K9tKIiIiIyASUTnQpUI0ChDYbNStZFegE6lwC/+83+QS9wLQii7RVFeo+bUn1tgZvnBMYadtYNAuT6HarpASIelPebp06F20nuli31SIpAZyRSfRYG2ruOlgf1s0UaxJdW8cDhKZqRVC/q7IeNcFA81Cc+pQdSfahA+F9+16fPyLYVPdP5+tMFUdTp7PRa7QebiGRepBoChxWWC0SfH4ZlfWNSsArSYEuragbizZGvoMhXUJd3ppJdF/yk+h5OpPoeUlMojvjTaKH1fBYUVqYh/LqBpRV1KFtgQOyLCs9/LEqhPR69EMVTQl2okdMogf79jPwOyPgp59+Svi6Q4YMyeBKiChRgYn04Zg0fyXW7KjEJfNW4LUrR2BwFwbpRJSaBQsW4KSTTkKvXr2yvRQiIkqSkvlZLapJdHOH0WGT6JKkhP8M0SmuYs2midpJ1mQm0dWbbYZC9NhhXLxJ4kxQqh+s8TrRIwNFhxKiy+HXsVkSrqZRb4JZGyPMFtUWQqxJdPX6xX0S/c4iqF8fnEIHYk/AA6HwOZkJ7uLgCRoAqKz3oH2r8DqYOqXuxKasMbEQPXLCO1qFiFCW5KaiQKAiqjjfjgO1jaiq9yjT+X06tMLmfYeiT6K7MzfVHOry1p9ET6oTXbXJpt4+AImKFvDXKl324ceje1sXyqsbsP1ALY7sVoxfD7lR7/HF7a8Xk+jqE3jq53QitI8bpW/fyRA9E4488khIkhR25l8Pa9mIzKUwz46XpwzH5QtW4oeySqXa5YiuDNKJKHmzZ8/G1KlT0aVLF5xwwgk44YQTcOKJJ6JPnz7ZXhoRESVIaZ9Qbyxq9hBd3YlukXKuzsV42kNpU5gfHqK7lU7lQIgUmkSP04nujjOJHmcw1BlnkjgTlOoHi6SEjrohuleE6KpJdJt+J7rTbg2rxYjFnWAnetmB2rDPY06ie0MT8eJ3p3SiB0PdDXuqlevXxqtzqUh+gttmtaje6RBZ6aKeRNer5oimTmfDTuWYRw3Rg7U07QoSXX4Y9Ts2yioC1ToiPKhp8Or+Yas3eZ0u4rGo3UjVnUInunKbXr/y2E2mFiZ0O/obi2o74ruretHV/+1clB/zfriCJyfUj2HxnLbH+wNHrFXz5444qZTPjUUzYtu2bdi6dSu2bdsW82Pr1q3ZXioRaRTm2fHyH4ZjWI82qG7w4pLnl+PHHZXZXhYR5bDNmzejrKwMs2fPhsvlwkMPPYR+/fqha9euuPTSS7O9PCIiSoCY3rZaAlPdgPk3FhXLE5mZRZlEz9aKjGGInkVKOBh1Ej3w33hnZKJtLOqTE5tEz8vCJLpS/WCN04neqBOia0L3ZCbR1SFjzBA9GGSL8DGREF09iaxM7Hp88PtlrC9Xh+ixQ+tUusSB0OaiFbWR09q1qkl0JRBNZGNRJUSPnESPW+eS5P1Qn2wSgfwRqreyV+tMo+t1t6dLtBM1KW0sqppE16swMn47mk50nd8bEPqdiBM2ib77QenRV/2Z4TW4saj2HTB1yrQ8J9EzoUePHgl/EJH5tM6z46U/DMexPdugpsGLS59fgdVlB7O9LCLKYV26dMHEiRPx6KOP4vHHH8dll12GvXv3YuHChdleGhERJUDdPmGz5sZEt1ifiA1EfJYrdS4M0bMoos7FFx6iJzyJrgo/3bqd6LHXEa+OIxPEFLnNKimho8cnKxsjCHrVFo6ITvRQ0B5t00ct9X2NNYEtQsU+HVsBiFPnEqxAUIeo6jqReo9P2VQ08HOjrzGsmzrJCe42ruibi6qrMwpUJwi8cXahUDaoDOvWjn4Spr7Rh301bgDJTdQD4c8T8fs4rEMrZQ16lS7qkwTpFu350qj0/KcwQe7xpdSJHm1toRqeKJPowcdaWYLvflB69N3qOpckNxYNPsejBf2UHosWLYLH41H+P9aHEXPmzEHPnj2Rl5eHESNGYOXKlTGv/9hjj6Ffv37Iz89Ht27dcMMNN6ChoSGl2yRqKVo5bXhx8nAM79UWNW4vJs1fiVXbK7K9LCLKQf/+97/xl7/8BaNHj0a7du0wa9YstGnTBu+88w5+/fXXbC+PiIgSoM78QhuLmjuMDoXo4ZPoZl+3kFRaUVlZiXfeeQc///wzbr75ZrRt2xarV69GSUkJunTpku41NltiEr0quDmimEAVIWyiZ5Lq4m4sGqcTXZmsbco6FzG1agkLnRt9fuRZQkGfUueiuo5dFboD2joX/aoNLfV9TWQSvU/HVvhpZ1XMSXRxm+oQ1WmzKJtgHjjUiG2qephYG4uKbmqLBHQpzo95X6IRk+gHdepcRA2Hy24Lqzyp8/hQGCME1atJccbYmFYcv9Z5NuXxbpS6lkYd8hbl21Hb6FPeyaG3Tm19STqEThpoNhb1Rp5EMXqbgToXcTLN+NqjvSsg2karYuJcqXNJcDNbvR598ZxOuM5FdcJLlmXlnRmZ+J0RcNZZZ6G8vBwdO3bEWWedFfV6RjrR33zzTcycORNz587FiBEj8Nhjj2HcuHHYuHEjOnbsGHH9119/HbfeeisWLFiA0aNHY9OmTbjiiisgSRIeeeSRpG6TqKUpcNrw4uRjMeXF77Fs6wFMmr8SLwSDdSKiRI0fPx4dOnTAjTfeiI8//hjFxcXZXhIRERmkDqRzpVtcW+ci/mvyZSsMpz0//fQT+vbti//7v//DQw89hMrKSgDAe++9h1mzZqV7fc1acX4g5AxNooeHcMokerzpYHWdiyo8CwXVsUOtRCtQ0smrTO1KYfUn2knvBp3QNtokutNmCQWcBibRo23wqZ4GVybRY4ToItRXd1lLkqQEjj/sOAhZDv0+at3eqJv8iVCzU5xu6ljauESdS3iI7vfLqA/ef5fTCofVoqypLk5Pu/7GotGPuTr0lqTEwlUtsUnq5r2H4Pb6YbVI6FycH7GnQNg6xUmCDGwsGtpDQL/OxZnEJHroRESqk+j67wqIttGqeJdDeXUDGjw+ZePWeHUuej36yiR6nJN2grjPfjnw3BGPSW4smhl+v18Jof1+f9QPI5uKPvLII5g6dSomT56MgQMHYu7cuXC5XFiwYIHu9b/99luMGTMGl1xyCXr27IlTTjkFF198cdikudHbJGqJXA4bFlxxLI7r0x61jT5cvmAllv18INvLIqIc8sgjj2DMmDF44IEHMGjQIFxyySV47rnnsGnTpmwvjYiIEiQqnG2WUKZj9loUsWbtJLq2lcKsDKc0M2fOxBVXXIHNmzcjLy9Pufy0007DV199ldbFNXdFmhBQ26ltS/BMkjoEDutE94c2uoxFXSXRVETgbLNawkN0TUjd4I2sc1E60UWIrlzHoqqIiBeiqybRowTH+w81oq7RB0kCerdPoM4lSie2OAGwenugu3RQ50IAgZMc0W5P6RFPsgIFANqISXRNiF6vOjYuhxWSJIXqOeJsLqo30Rxto00ASijbo21ylTRA6HmydlcVAKBzcR7sVktEHVLYOsVJgiR6xeOJOonuS2Vj0dBtipMRzqQ60fUn0WujbCzaxmVH62CwvqOiLnTSI87vS69HX+lET3AS3ak6SdDg9Snvjsi3s84lG8QJ8UQ1NjZi1apVGDt2rHKZxWLB2LFjsWzZMt3vGT16NFatWqWE5lu3bsXHH3+M0047LenbJGqp8h1WPH/5MTj+8Pao9/gw+cWV+GbL/mwvi4hyxPXXX4/33nsP+/fvx+LFizF69GgsXrwYgwcPRteuXbO9PCIiSoC6zkWE0fHqoLNN24meKxuiCobTnu+++w5XXXVVxOVdunRBeXl5WhbVUohwsN7jg9vrU9WSGOtEr48yiS7y2fghemLT2+nkDQb8dkvgbSeiAiIiRFc2DVWH6IHrislXt6oT3amqxYglvBNd/36LTSw7F+WjVTA0NLqxKBDqD18V3ADs6B5tlK/VRgnwtyfYTR2LMomuqXMR91eSQsdVr55Dy+sLVY2oJ5qdqsePdrI+0XqQWMTE+aa9gT55EfCG6pAi62qU7vYMTDU7o5yoSWVj0VANS2hjUWdKYXz42uqj9I1LkqT8bv63pxr7DwWOZfw6l8gefeU5neAkvvr+uT1+1Efpbaf0+7//+z+8+eabyufnn38+2rZtiy5duuDHH39M6Db2798Pn8+HkpKSsMtLSkqi/l3gkksuwV133YXjjjsOdrsdhx12GE488UT85S9/Sfo2AcDtdqO6ujrsg6glyLNbMW/SMTipXwc0ePz4w4vf4ctN7DImosTIsozVq1fj008/xSeffILPP/8cfr8fHTp0yPbSiIgoAeoKZ6UWxeQhusiMIutczL1uwXBK43Q6df+BumnTJr7gGtQ6zwbRcFFV71ECytAkeuC/cSfR3dFC9MQm0UUo6PHJTdaf5NFMrWorWoDAk0sJ0R2qTnRxXW/gNhq8odAxWsCpFd6Jrj99LabBu7d1KcFozBBdZ2NRIBRcrg9uKjq4c5ESdtZG6UUvU2o1kp/gblsQCJm1k+ji/rrsVqU3yxUMLqOtBwhNdwPhE83imMty5KS++hgmqzh4MkA8NEXAq61DUqtVaoAyt7Go9kRNtJMoiRCBsnoSPS+JSfRoa4s2iQ6EfjdigrHYZY/bX6/t0QdCJ/vi/XkjSJKkut++0LsHuLFoxs2dOxfdunUDAHz66af4z3/+g8WLF+PUU0/FzTffnLGf+8UXX+C+++7D008/jdWrV+O9997DRx99hLvvvjul2509ezaKioqUD3HfiFqCPLsVcy8bhrEDSuD2+jH1pe/x+YZ92V4WEZncGWecgXbt2mH48OF47bXX0LdvX7z00kvYv38/fvjhh2wvj4iIEuCXQ5Po1hzpRPdpQnQRH5g9/BcMpz0TJkzAXXfdBY8nEFxJkoSysjLccsstOPfcc9O+wObMYpFQmBfaXFQ7yao8CWKckVH3WwPaOheE3U406u5lbc9zpnhVdS4AdEPqRp9fCU7VgaLDFh64qyfRE+13T2QSXV2p4khg89Vok8giuBR/mPXv1FqZbI/Wx65McKcQPotJ9IN14SGzUpuhCiuVjusYJx/ENLNFCp8iDn/8hB+fUD1I8vdDG+iKY1IUo85FmWrOwCaV6uBXzZ3CJLqobmnw+pTHbl4SG4vqrc3nl5Xb1A3Rgycllm4OhOiJPObUPfricSGe04luLAqEnteVdR5lgxFuLJp55eXlStD84Ycf4oILLsApp5yCP//5z/juu+8Suo327dvDarVi7969YZfv3bsXpaWlut/zt7/9DZdddhmuvPJKHHHEETj77LNx3333Yfbs2fD7/UndJgDMmjULVVVVyseOHTsSug9EzYXTZsXTE4/GuEElaPT58cdXvse//8t3hxJRdP3798fLL7+MAwcOYNWqVXj44YcxYcIEbjBKRJRDxL/BLZKUcJNFtolcTOyZZ0kg9zQTw2nPww8/jEOHDqFjx46or6/HCSecgD59+qB169a49957M7HGZk3d66zUuQTDMxFSyXL0szL1USolANUkepwNHZ2qsK6pNhcV1Q/iPuqF1Oq1hNe5aDrRVZUv0QJONVmWE5pE3xEMgLu1dYXqZmJ0omvfSSCoN7e0WST06dhKmbaNOomehjqXtqITvU7biR5ZmyHWE2tjUbHWAoctbJNQh9WivKNCG97uPJh6nYs2RBeBvLi8si76JHomppqjnahJrRM9XRuLhm5HvE1K/WeEdmNRIFSPs7uqAUBiIbq6R188LoxuLBpYb+C66sqh/Az02FO4Nm3aKEHz4sWLlQ5yWZYT3ljU4XBg2LBhWLJkiXKZ3+/HkiVLMGrUKN3vqaurg0Xz+LBaxTtZ5KRuEwi8Q66wsDDsg6ilcdgseOqSo3H6EZ3g8cm45rXV+NfaPdleFhGZ1IMPPojf//73KCoqyvZSiIgoSX7VxqIi9zN7LYpYnlivsu6miSJTZjhhKioqwqeffoqlS5fip59+wqFDh3D00UeHbQRGiVMHgRGT6KqJTq9fhkNnolw7Ra0Oh7Vvk4jGGuwk9/jkJptEV+pcLJpJdFVILSbMLVL4dGtEJ7qqS15dZyHLcljYq9yutuoigV5yZyJ1LtEm0VWh4GEdWsFpsyphpt7PPuT2JtxNHYvYWLRCU+cS2sBRHaLH31hU2VRUMyksajkaPP6wzUX3VNXD45Nht0roVJSf9P0QJ5oEcUy0G/OqhTrAM7exqPa54kkhRHeqTiKFHs9J1LkETzb55cCfGXarpPTDa99BIGhP1CR64qbAYUNNg1d5XIgz3oluLAqEQn9ROZSvqhiizDnnnHNwySWX4PDDD8eBAwdw6qmnAgB++OEH9OnTJ+HbmTlzJi6//HIcc8wxGD58OB577DHU1tZi8uTJAIBJkyahS5cumD17NoDAW8cfeeQRHHXUURgxYgS2bNmCv/3tbzjjjDOUMD3ebRJRdHarBY9fdCRsVgn/WLMbM974AY/6ZUwY2jnbSyMiE/ryyy/x0EMPYf369QCAgQMH4uabb8bxxx+f5ZUREVEilE06c6nOxa+tc8mN8F9IekzzuOOOw3HHHZfOtbRI6iBQG8LaVGFStCeCNvQMn0RPvKM4z2aFx+dtukl0n9iEMLwTvVFnEj3fbo2YfAZCQXxDWJ1LeLWIXq+0W3MftdP8glLn0rYADmtoI8Vook0iu1QT3/07tQYAtIrRQV4W/LltXHal7icZbYN1LjUNXnh8fmWCX4Se6qnkRDYW1fs+Ic9uDYToqmBZ3I9ubVwJ92TriVrnIk5AaUJ0WZZDHeAZ3Vg0fZ3o4nFa3+hTbicvqVqY0Pc0eHywWy3K782leQeBoJ0875FgD3/oxIuoczG2sSgQCvUPBEN0biraNB599FH07NkTO3bswAMPPIBWrVoBAPbs2YNrrrkm4du58MIL8euvv+L2229HeXk5jjzySCxevFjZGLSsrCxs8vyvf/0rJEnCX//6V+zatQsdOnTAGWecEfYutni3SUSx2awWPHLBkbBZLHh39U5cv/AHeLx+nDusa7aXRkQm8uqrr2Ly5Mk455xzcN111wEAvvnmG5x88sl48cUXcckll2R5hUREFI+S+anqXEwfosuiziXwufjnotnXLRgO0Z944gndyyVJQl5eHvr06YPf/OY3ylRZOvzyyy+4++678dlnn6G8vBydO3fGpZdeittuuw0OhyPq9zU0NODGG2/EwoUL4Xa7MW7cODz99NOm+se4OkQXAaRT04kOiPqTyGOqnWQO70RPPER32q2ocXvjbsiZLtqpVYctMqSuV4XjaiKk8/nlsL7nPJsl7Lpuj36I3qCZINYLsmvdXuw/5AYQmHwWk82x6lzidaIDwIBOhcHLAk+9Q3oheoXYVDT5KXQAKMy3Q5ICb5c5WNeIjq3zAKg2FnVETqJH62hXf59e3UZgAtoTFixvV9XhpEIdorctcKB18MSCmFCv1oTogXchBP4/M3Uu+pPojUodUxIhevB71PclmY1F1T+7weNH67zYm4oCQKeiPNgskvKcTPT3pWxG2yjqXMS7S5KfROemok3Dbrfjpptuirj8hhtuMHxbM2bMwIwZM3S/9sUXX4R9brPZcMcdd+COO+5I+jaJKD6rRcKD5w2BwybhjZU7cNM7P8Lj8+Oi4d2zvTQiMol7770XDzzwQNhr/3XXXYdHHnkEd999N0N0IqIcIAJpm1UKdYubPIyWNY0Z4r8mX7bCcGLx6KOP4tdff0VdXR3atGkDADh48CBcLhdatWqFffv2oXfv3vj888+VjctStWHDBvj9fjz77LPo06cP1q1bh6lTp6K2thYPPfRQ1O+74YYb8NFHH+Htt99GUVERZsyYgXPOOQfffPNNWtaVDupp2shJ9FAgFu2JIPqthUZVsKcE1YmE6AlsnJlO2v5kvY1FG6KF6Kqg0OMLTT877VbYLBIsUuAJGLg8cpI7YhJdZ/p6R7DLuyjfjqJ8u3L8RXCvd2JCCVE1k7gFqmCwf6mYRI8++a1sKtousYngaKwWCcX5dhys8+BgrUcVokdWnYj/1zsWQmgSPTKMFRPQ6pMw6o1ZU5FnD3Tdu73+sBML0epc1CdFMtGvrVQGRZtET6ETXX1fkgnjRbVOoBYm8LuIV21js1rQtU0+fjH4+xKBt7KxqN/4JLqonxGd6NxUtOls3LgRTz75pPIW7gEDBuDaa69Fv379srwyIkoHi0XCvWcdAbvVgpeXbcet761Fo8+PSaN6ZntpRGQCW7duxRlnnBFx+YQJE/CXv/wlCysiIiKjlDoXSVJyP7Nv0CnmUkUXeq7VuRhOae677z4ce+yx2Lx5Mw4cOIADBw5g06ZNGDFiBB5//HGUlZWhtLQ0qYm2aMaPH48XXngBp5xyCnr37o0JEybgpptuwnvvvRf1e6qqqjB//nw88sgj+O1vf4thw4bhhRdewLfffovly5enbW2pEkFgdb1HmXIWIaw6p422w27EJHqydS46IWgmiV2ERZ2LU7NZqHot2g0W1f3oHp9fmX522iyBd0RE2fhRuV3tJHqjVzkbJmgDYHUwGq3SJfokeihEF5PoIojWm0QP1cikFj4D+r3oYqJcHe67YnS0CyKc1psWFmGo+iSM2Jg11Yl6IPQ8UQe80TYWVbrb7daUamSiUTav1U6ip9KJHnyMiw1RbRYJtiRqYcLWF3z8J7LJqjhh47BZUFqYl9DPcWk2FhXPaSOd6OJ+hybRGaI3hXfffReDBw/GqlWrMHToUAwdOhSrV6/G4MGD8e6772Z7eUSUJhaLhDsnDMKVx/UCANz+j//i+a+3ZnlVRGQG3bp1C9vIW/jPf/6TtkE4IiLKLHXmJ8Jos0+ii/UpdS45sm7B8CT6X//6V7z77rs47LDDlMv69OmDhx56COeeey62bt2KBx54AOeee25aF6pVVVWFtm3bRv36qlWr4PF4wjY87d+/P7p3745ly5Zh5MiRGV1fokQlRWVdo9K1LDYUlIJnk7zB6Wc92knmZOtcQsFzekP0qnoPftpZidGHtQ9bhyc4tSqCQr1J9Kh1Lhb1JLocMbGeZ7eirtEXEXIK4vqt8wIbIwam1sOrX0SftwiA1T3XjV5/xOaaQIxO9OB12xY40LG1M3iZCK316lzEJHrq4XNblwNbUYuDdaEQXdlYVD2JLjq5Ve9skGUZX2/ej73VDQCAZT8fCLs/anonYbYHa2l6pDhRDwSeJ/tq3GEnForzHcE1B3rExXHXm7RPJ73nit8vK3UmSXWi28LXmkyVi/p7qxtC1UxiY9FYfePiuHZrk5/wxp7iJIx4nmrfXZII8WfeAda5NKk///nPmDVrFu66666wy++44w78+c9/zvjrNxE1HUmScNvpA+C0WzDn859xz0fr4fb6Mf2kxDcRJqLm58Ybb8R1112HNWvWYPTo0QACnegvvvgiHn/88SyvjoiIEqHO/EQMYfYwOnqdi7nXLRhOLPbs2QOvNzL483q9KC8vBwB07twZNTU1qa8uii1btuDJJ5+MWeVSXl4Oh8OB4uLisMtLSkqUdepxu91wu93K59XV1SmvNxZ1JUWxK3yTTSDwgPL65aiT6OnaWDRTdS73fvQ/vPX9Tsy99GiMH9wpYm3iLSdiulxvY1FtoGixhE4uBOpcwq+n3Jcok+ji+m1cDtQ0BI5fXaMv7OeIOhcRoqun390+/ZqYaBtLFuYHnmYDOrVWNnaMVeeSzgluMYl+QDWJLkJP9QahepPoK7dVYNKClRG32Tov8o8Np2b6X5ZlZaI+HfejbfB+qAP51nk2pfO9qt6DDsETFIfcnuB9ylSIHvlcUZ+8SqXORUimykV7W2J9ymR+jIBaTPj3NHDCI1+ZRBd1LuHvLklsrZxEz4Y9e/Zg0qRJEZdfeumlePDBB7OwIiLKJEmScNMp/eCwWvHofzbhwU82wu3x4Ybf9dXdcJqImr9p06ahtLQUDz/8MN566y0AgWq3N998E2eeeWaWV0dERInwqwJpMcxm9jBa1M1YlDqXwOV+k4f/guGk5qSTTsJVV12FH374Qbnshx9+wLRp0/Db3/4WALB27Vr06tUr7m3deuutkCQp5seGDRvCvmfXrl0YP348zj//fEydOtXo8uOaPXs2ioqKlI9Mv52tKDhNW6XTiQ6EQmafL0qdSzAg0wuhzTCJvm5X4CTEnqqGsMtDdS7hk+huVRhZ3RAIQwt1QlvxfY1ev7JmETwq9yXOJLrLYVVCPO1EuKg/ad8qEMxKkqQ7La/mjlLncsrAUlw8vBtm/q6vcpkrRp2L9menoktxPgBgZzCYB9S1LKHAsiD4/+qTMv/dHfjdlRbm4aR+HXBSvw44dXAprhgd+dwOVYgEjm1lnUc5QZGOEP263x6Oi4d3w7jBpcplFouEwjxxEip0kmDnwXoAQKfC/JR/rh4xPa1+rqQaomtD89Qm0cVJpOAkulLfE/02zz6qCy48phum/zbxyUTtYyZU52J8El28U0J9Yocy58QTT8TXX38dcfnSpUtx/PHHZ2FFRJRpkiThT2MPx62n9gcAPPHZFtz/rw0RdXZE1HKcffbZWLp0qVLRunTpUgboREQ5RAyyWaXQxqLeKNmhWah73NX/NXuXu2A4sZg/fz4uu+wyDBs2DHZ7IMDyer04+eSTMX/+fABAq1at8PDDD8e9rRtvvBFXXHFFzOv07t1b+f/du3fjpJNOwujRo/Hcc8/F/L7S0lI0NjaisrIybBp97969KC0tjfp9s2bNwsyZM5XPq6urMxqkqzcWFdO26kBNBOBi0z6t+mCAVZTvwP5D7rDpWJ/qCRVPtM0SUyHLsjJVre0nV6ofguG/IximqQPqqmDXtThGanarhHpPILxsiDKJHu2EgNKhbrfC5bChwdOoTGcrP7s+8mc7rRY0ev3RO9Gj1Lm0KXBg9jlDwi4LTaKHh+g+v4zqBvE7jbzfRokAu0wVouttNJmvBKKh4yC+58yjOmPWqQNi/hzt9LP43o6tnbrVN0aN7tMeo/u0j7i8KN+OqnpP2IacShVPGupw9ISqa1ST6KrHRFJ1LtpJdHvyk+hKyO8VIXpkfY9Wu1ZO/N95Q6J+XY9L824KUdFkN9BDr0yiB5/r6XisUHwTJkzALbfcglWrVinVZsuXL8fbb7+NO++8E4sWLQq7LhE1H1efcBicNgvu/Of/8OxXW+H2+nH77wcmXOVFREREROYQVuciJrpNHkaL5UXWuWRrRcYYDtFLS0vx6aefYsOGDdi0aRMAoF+/fujXr59ynZNOOimh2+rQoQM6dOiQ0HV37dqFk046Sdkg1BKnd1eE/EuWLFH6XTdu3IiysjKMGjUq6vc5nU44nalPACdKvbGo3iSzmOqM1mskqhTauOzYf8gd3okuJ1Pnkr5J9IN1HtQEp561gbZS/RD8PTqskVPeekG2II6Rx+dXJm5FIKetFtES9zHPZoHLYUVFbeQkuvjZorNe+Znu8KljtWgbi+oRfdKHNBt51jSEwuB0hOiipkNUqwCBjVSB8P5psZ5anRA9kUly7TsZtleEb8yaKeo6JEH52WmYgNejO4muqvJJ5q3x2kl0py31SXTtxqIFae4bFz36dY1e+Pyy8mJoZBJdPG7En2+xpuUpfa655hoAwNNPP42nn35a92tAYHLV52uazaaJqOlMHtMLTpsVt32wFi9++wsaPD7ce/YRGdmMm4jMo02bNgn/PbWioiLDqyEiolT5wzrRY2eHZqFMolvCJ9Fzpc4l6VSlf//+6N+/fzrXEtWuXbtw4oknokePHnjooYfw66+/Kl8TU+W7du3CySefjJdffhnDhw9HUVERpkyZgpkzZ6Jt27YoLCzEtddei1GjRplmU1FAvbGofp1LaBJd/wElJqjF7ejVudgS6CjOixM8J2P7gVrl/7Vd65GT6DFCdJcj4rZFnYvHKyu3LYLHvDgnBNRd62Iau74x/iR6vDqXaJ3oesQmj9HCe5fDmlQtiJYIscsq6iDLMiRJ0t14M3QcQusRv78ebeP3ZCvT/8FjXhb83m4ZCrIF9fNHSOfGrHrUU/fimBo5gaLHYpHgsFqUEzR5KUyih9YXvrFouvvG1ZPoHtWJpUT+vBG0Jw9i9bZT+vijvLOJiFqOS0Z0h9Nmwc3v/IiF3+2A2+vHg+cNMXQilIhyy2OPPZbtJRARURp5VdUo4q9w0bJDswh1ogc+F/PRzbbOBQB27tyJRYsWoaysDI2NjWFfe+SRR9KyMLVPP/0UW7ZswZYtW9C1a9ewr4kuR4/Hg40bN6KuLjRx++ijj8JiseDcc8+F2+3GuHHjIqbusk2EtF6/rASBYRuLin6gqJPogYCsOBg0qwNe0YVkSajOJXYFSjLUFSIRk+ia/mQRpjWqph4rY0yiK53ovlAnurgP8U4IqDvUXToT2EAolNUL0T1xJtET2RSyQNnIMzxE1/u5qejaJhAkH3J7UVHbiHatnKoQPfrGon6/jB3BbvFEpslDPdyBYyAm3xMJ4FNRqDOJXpbGDU31qKtW3F4/8uzWqFU+Rm9XCdFTmEQP1RkFNxb1RP6+08Gl2lhU/UJtj/MuITVtjQ0n0TPrtNNOwxtvvIGioiIAwP3334+rr75aqTw7cOAAjj/+ePzvf//L4iqJqKmcO6wrnHYLrl+4Bu//sAsNHh8ev+iotJzEJyLz+fHHH3H33XejoKAAX331FUaPHg2bjQMMRES5SlS32KxSqBbF5CG6X1M7Lf4ry1CGFM3M8N+SlyxZgn79+uGZZ57Bww8/jM8//xwvvPACFixYgDVr1mRgicAVV1wBWZZ1P4SePXtClmWceOKJymV5eXmYM2cOKioqUFtbi/feey9mH3o2uBxWZVPQfTWBzTedqmAp3iS6CESLg2GiR6fOxZZAqKXtUU6HMlWFiHYq3KvpT441iV4cpRMdCNzfUIieaCd66PpiIlzdTe73y8qmpkXqOhermHA31omuRwnRG/Un0dMVoufZrSgtzAMQOqkhgntx34HITSLLqxvQ6PXDZpHQqSgv/s/RPH7KmqjORTw2xMmHBo8P5dUNwZ+dmQBfHXCLkwZG3oUQ9XZVz/tUJtGdmmqdOp3fdzq4VI8Zb5KT6Nr76eLGohn1ySefwO12K5/fd999YW/X9nq92LhxYzaWRkRZ8vshnfH0xKPhsFrwr3XlmPbqqrRvMk9E5vDkk0/i0KFDAAL1q6xsISLKbep9EEV2aPaJbhFtautc1F8zM8NJzaxZs3DTTTdh7dq1yMvLw7vvvosdO3bghBNOwPnnn5+JNTZrkiQpgelBnUl0EUj5orz9XoSebQoiJ9FDmwzEX4dTM0mcDtvDJtG1dS7hk+i6neh1gXc56HeiW4O341fVuYRPokcLu93eUGVGvj18c0QAqHF7lX7npOpcDHSi17rj18ikqnu78M1F6z2RdS7ajUXFdbu2yU/ord3ax0+mK1UEbSe62Mi2tdOGNq70HUM1u1VS3nokTg7p7WdglPodDCl1otvCH/+JbCyaDPEYDtS5hF7tbAY6dbX302XnJHomyZq/UGk/J6KW6ZRBpZh3+TFw2ixYsmEfprz0XcTG50SU+3r27IknnngCX375JWRZxrJly/DVV1/pfhARkbnJshwWSMdrsTCLyDqXUH5g9rUDSdS5rF+/Hm+88Ubgm2021NfXo1WrVrjrrrtw5plnYtq0aWlfZHNXmG/H/kOhWhzdTnRfnEl00Ynu0wvR4wd72knidEhkEj2iE90XOYlepBOGOoLf1+iNnESPV03jVupcrChwBjdfVNWqVAVPZuTbrWEhX+Kd6PGDwGid6LEqbJLVo60LK7dVKBUrIrjX21jU7fXD55eV312ineah4NYXNg2eqUoVQb0xLxCqkenW1pWxtwFJkgSnzYp6j085OSR+93YDU9ha6ZtED3/812VqY1H1JLp4PlskQ8ddez/TPS1PRESJOaFvB7w4eTimvPQdvtlyAJPmr8SCyceiMC8zJ6SJqOk9+OCDuPrqqzF79mxIkoSzzz5b93rcWJyIyPzUgbPNIilhtNmDaPVmqOr/AqF6GjMznNQUFBQoPeidOnXCzz//rHxt//796VtZC6KtK1FPpNriPBFCdS6BSXSPT1YelEYm0TOysWhFaGNR7e2KkwKiP9lhi6xKiV3nErh+XaNPOfsmglwRfLuj1bmoJtH1NhaNNg3usEYG/WpG6lxaOUOhtboKQ4TBxWmcohZBtgiYxeahepPoQCAUFb+7ROtY1I+fnQfrIMuBiph2BZGbwqaTsrFo8Lg1VY2McqImeHIo9LtPYYJcFShru8IN3Y4t/LksKoPSPYmu3lg0tMeBsZMI2vvJjUUzS5IiT3KYvXOOiJrOqMPa4dUrR6B1ng3fbz+IS59fgYO1jfG/kYhywllnnYXy8nJUV1dDlmVs2rQJBw8ejPhgzQsRkfmpa1ssFimUHZo8iPYrk+iiziXya2ZmOLEYOXIkli5digEDBuC0007DjTfeiLVr1+K9997DyJEjM7HGZk8b1jrDJtED/x+tE11MMqurKxp9fuRZrIYm0Z06IXYqGjw+7K12h32uJrrblUl0TZ2L3y/HrDYRIfoh1SS3U9lYNPZ9casm18VhVW8sWlmvXyOTzjoX9RR4baMPRfmB76mMUWGTrFCdSy38fll3o0mnzQKrRYLPL6Ou0Wd4Y1D19HOoyqUg4wGdts6lqWpkAuGvJ7ITPYU6F3XXeiKb00Zfm3j8B37P9RmfRPcpz2cjm4oCkXUu3Fg0s2RZxhVXXAGn0wkAaGhowNVXX42CgsDzXN2XTkQt09Hd2+CNqSMxacFK/LSzChc9txyvXDkcHVvH3x+FiHJDXl4eXnjhBTidTmWzcSIiyi3qQVurpJpEj9JiYRZi3aEQPbfqXAwnNY888ghGjBgBALjzzjtx8skn480330TPnj0xf/78tC+wJSh2hU/rOgxMoot+6yJNiK7+nkQ6ivM0mxGmaoeqDx2IDLTFSQG7ts4leL1DjV4l4C7UC9GD168JbgAKRHaiR99YNNShrt1QE4heI+OMF6L7Eq/0cNgsyomDsCoZZRI9fRPcYoPNsoo6NHh9St+7ujpDkiQlFK11e5XfX6JhtLqHOxTAZzbIBoCi4DswqpQ6l+AEfYLhf7KUzWvFJLro5U9hY1FnmibRnVEm0V2Z3FjUn9wkulO7sSgn0TPq8ssvR8eOHVFUVISioiJceuml6Ny5s/J5x44dMWnSpGwvk4iybHCXIrz5x5Ho2NqJjXtrcOGzy7G7sj7byyKiNLHZbJg2bRr8UfbcIiIi8wsL0XNwEl23ziUHXpYMJRY+nw87d+7EkCFDAASqXebOnZuRhbUkkZPoobBL6USPOokeXucChAI9n+ZtErHE6xE3avuB8BBde7tK/YOmzkUE0aKXPM9u0Q0URSd6TUMgIHTaLMrUsxJwRqmmEcFnnt2qTOnXJVLnotPbrtao2eA0HpfTisY6v26IrnfiIFkizN5b7UaF6m3ZedpNHR1W1DR4A5PoIkRPMAhXT6IrIXqGp8GB0O+oMvh42d5kdS7hJ2oag72RaZtET6XORdnkNXwS3ZXuOpdg4N3g8SsT+YlsQqum9xikzHnhhReyvQQiyhGHl7TGW1eNwsTnV2Db/lqcP3cZXrtyBHq2z+xJaiJqGsOHD8eaNWvQo0ePbC+FiIiSoA3Rc2VjUWUzVJ1J9FyoczGUeFitVpxyyik4ePBgptbTImkDU/1JdP3gVkxQFzitEZUoPgPToU7VJHE6iDCzbbATO16diwiexeWxqlyAUJ2LCNHDN2UMbXKpRwR+TrtVmcZWT6KLQFbbxa49vlpGNhYFQvUaYVUydenfWLTYZUfrYH/1xvIaAIGw0qJ5h4JYT3lVg7KOREN0dagsKlUS3ZQ0FeLdAtX1Hvj9MnZWBCblMr2hqQi501rnYk9XnUv4JsHiRFum6lyA0DtC7Am860VNu7FouqfliYgoeT3bF+Dtq0ehV/sC7KqsxwXPLsPmvTXZXhYRpcE111yDmTNn4qmnnsKyZcvw008/hX0QEZG5Ra1zMXmIHqpzQdh/AfNP0QNJdKIPHjwYW7duRa9evTKxnhYpIqwN60SPPonu98tKnYvLYYPDZkGjz68Eet5g8G5kEj3aZpxGiTqQviWtsHxrRYw6l+AkuiagDm0qql9rog3R1aGjM84mqSJcdNosEEdGPYleHWcSPdqJBiMbiwKhzUV161zSGKJLkoTu7Vz47+5qrN9TDUB/4ldsPLmhPHCd9q2cKHAm9keEulO/qTb3BELHqdHnx7YDtWj0+WGzSOhUlNnu1jxtnUvwnRWOVOpcVI+blDYWVZ7L/rA/I9K9sai6R188bo1Ooms70VnnQkRkLp2L8/HmVSNx2fMrA9Uuzy3Hy38YjsFd2KNMlMsuuugiAMB1112nXCZJEmRZhiRJ8PnS829CIiLKDBE4S1L4xqJmn+bW1rlIkgSLFJhQ95v8BACQRCf6Pffcg5tuugkffvgh9uzZg+rq6rAPMi7WxqJiUlvvbJK639rlsEbUjYjhdWOd6GmaRA92U/craa17u8okukW/Ez3eRHYoRBe1L6pJdE3AqdWg2lhUhMR17vjT4PZEJ9EN1LkA4ZujxpvAT5YItNcrk+iRYaWYVBbXMRKCi+Nfr5pEz3QvORB43IvH0NqdVQCArm3yDYe5RmlP1KRjEt0Z9m6KVML40CR6veqkWLon0SVJgiu45uoGEaKnNomen8LJAyIiyoyOrfOw8I8jMaRrESpqG3Hxc8vx/S8V2V4WEaVg27ZtER9bt25V/ktEROYmMkJR4yIm0b0m31hUBOXqZgQx+JsDGbrxSfTTTjsNADBhwgSlgxoAz1qnoFi1gaUkhYfeorNb74mgnp7Ot0fWuYhJdGsCIXpokjhNnejBIPXwYIiunXAX90eZRNdMecfrBnfYwjvR1WGcM87GouJn5Nksys+t8+ht7plcJ7rRSXTdTU3THKJ3DwbaGxKZRA9ex0gligjRdx6sR6PXD6tFQufizE6DA4Egt9hlx/5DjfhxZyUAoHu7zIf3yoka0YmeljoX1SS6LfVJ9AaPX9lUVJJSC+ajcTmtqHF7lcet3WKwE11z4iCRP6uIiKjptSlw4LUrR2DKi99j5S8VuGz+Sjw3aRiOP7xDtpdGRElgFzoRUW5TQnSxQafoRDf5JLpP04kOBAN1v2z6tQNJhOiff/55JtbRoqkDU4fVEnZywhaj10hMT+fbA/3W2iBaZL2JBFPpnET3qbqp+5W2DluTIAJ+MbmqnAAILrqyPrABpjbIFkT4Lqa41bUQeXFqV8R9zLNblfutnkSPFuBrp+XVZFkO1bkkOAUtJoMPuUNBrDgxEu1+J0sE4tv2B94hoBeii354cR0jIbpTc2y6FGd+GlwozA+E6GISvXvb/Iz/zFDvfhon0cM2Fk19ot3t9YU2FbVbw/5cSZfAOxrcqjoXYz9D/a6bdE/KExFRerXOs+OlPwzH1a+uwpebfsWUF7/Hk5cchXGDSrO9NCJKwiuvvIK5c+di27ZtWLZsGXr06IHHHnsMvXr1wplnnpnt5RERUQx+Td4n/mv2ShS/MkEfukycADD72oEkQvQTTjghE+to0dQhunZDwVid6LWqTUUBwB58FIY2Fk18El2ZXk3DJPre6galm7pncCq40eeHzy/Dagl07XmCp59slvBJdG0neuJ1LpF90tFOCIipeKfNooTJteqNRaP8bGeMOhf1dLrROhfRiS7uMxD4h2o6iWoW8TDS6zoXFS/iOsnUuWh/XlMQv6f/7g5M0DdFjYxTO4kefAdOKp3o6ZpED63Nr2wq6kqw294o8fyprg88ho2eOFE/bripKBGR+eU7rHhu0jBcv3AN/rWuHNe8thoPnT8EZx/VNdtLIyIDnnnmGdx+++24/vrrce+99yrvJi8uLsZjjz3GEJ2IyOSU5gkpPETXyw7NRHSih9e5hH/NzJJKfL7++mtceumlGD16NHbt2gUgcCZ76dKlaV1cS1Gkmjp2aMKz0CR6ZHArppZF+Cm+VwS62rd3xCKmYN1pmETffiBQ5dK1Tb4S8AOhqhj1VL0I/rUherTNPUPfF76xqDqMU6ppotS5qDvRRQiot7FosSt8U9NYk+jqy7QnQqJR6lw0IXphni3ttRbaqXK97mntdLqxED38PhuZYk+V2FxU9H93b4IAX5lE19S5JPq7j3Wb2v9P9nYaPD7UB2uK9N55kA5iejxU55L8JLrLzkl0IqJc4LRZ8eTFR+Hco7vC55dxw5s/4pXl27O9LCIy4Mknn8S8efNw2223wWoN/T3xmGOOwdq1aw3d1uzZs3HssceidevW6NixI8466yxs3Lgx3UsmIiIVZYNOq2YS3eRBtKhsiahzgX4Dh9kYTnzeffddjBs3Dvn5+Vi9ejXcbjcAoKqqCvfdd1/aF9gSJDuJLrq0RUCmDXnFAzCRjUWdqkl0OcUnXVlFsA6kXUFYRYWYDFffFzG56tT0jYvNPaPVmjiCf1DUKHUukZPo0epclE509caijaH7XVkXqJLRBvixOtHVIXrCdS7O8DqXqmCFTVGaq1wAoHNxftjjINYkutDdwES305b9SfSm/NnipEF661zUvf4phPG20ONfmUTPUFWK6NFPdmNRm9WiPC45iU5ElDtsVgsePG8ILh8V6FX+2wfr8PQXW7K8KiJK1LZt23DUUUdFXO50OlFbW2votr788ktMnz4dy5cvx6effgqPx4NTTjnF8O0QEVHilPpmzSS62YNov2ZDVCB3TgAASYTo99xzD+bOnYt58+bBbg+FV2PGjMHq1avTuriWwmmzKqGcNoSL2YmuTKIHwidt3YgIqy0JdCGL4FmWo2+cmSgxid6jrQtWi6RMm4tJdI/q9sX9s2vWnmidizIBrDPFG21j0QadOhefP9Bp7vH5USt6ybUhegJ1LjaLFPa2lFgKlCn48En0dG8qCgT+UOraJtQVnq8zmayeVnY5rGjfyhFxnWiyOYmuPV7d2mT+Z4uTBqE6F2N9+HrCJtFTqXNRNhb1KY+tgkxNogeDb6UT3eDGokDofmdqWp6IiDLDYpHw9wmDMOOkPgCABxZvxP8t3pDyMAYRZV6vXr2wZs2aiMsXL16MAQMGGLqtxYsX44orrsCgQYMwdOhQvPjiiygrK8OqVavStFoiItISdS4if1I2FjV7iC42Fg2rc5HCvmZmhscTN27ciN/85jcRlxcVFaGysjIda2qRivMdKPc06EyiBz6PPYku6lzEpHQg2BNncRKZDs3TTIxrJ4uNKKsIhujBieA8mxUenzc0ie5T17kk2YmuOU7q9Wv7qrUaVJPo6gndOrcv7MxX5Mai0Sfck5lEDk2ih4foxfmJh9dGdG9XgF+CJzj0QlV1iNm9rcvQRpTa0NfIFHuqilS1O+1bOXWn7NNN2UMg+Jh2p2ESPbzXP/Uw3u0JbVSrd9IkHfKDFSzVSW4sCgTu6yF35qbliYgocyRJwk3j+qFVng33/2sDnvniZ9Q0eHDXhMEJDxUQUdObOXMmpk+fjoaGBsiyjJUrV+KNN97A7Nmz8fzzz6d021VVVQCAtm3b6n7d7XYr72YHgOrq6pR+HhFRSyQan8Vgaq5UoviUYd/QZZYcOQEAJBGil5aWYsuWLejZs2fY5UuXLkXv3r3Tta4WpyjfjvLqhpQm0bVBtFfnbRLR2K0SJCkwiR6YGE9+GlqE6GIa2Wm3oMYdCrU9qn538cQRE7xevwy/X1bqXOJNogt6G4vqhd3e4Aan4nusFglOmyVQfdHoVb6ntTOylzyROhdDIXowNBQbi8a7z6nqoZoO1wss1eGz0Ulyi0WCw2pRjk1T9JIL6uPVVDUyocdYeCd6SiF62ImgFDrRVY9TcYKmIEMBdWgSPbixaBKT6OK+ZmpanoiIMu/qEw5D6zwb/vrBOry6vAyHGrx48PyhEX9fIyJzuPLKK5Gfn4+//vWvqKurwyWXXILOnTvj8ccfx0UXXZT07fr9flx//fUYM2YMBg8erHud2bNn484770z6ZxARUWS3uJIdmjyHVrrcLeo6l8B/cyFEN/w326lTp+JPf/oTVqxYAUmSsHv3brz22mu46aabMG3atEyssUUQPdjaOgixSYBX55lQF+w7FuGnum7E75chhqoT2aRSkqRQl3KKm4uKOhcRpDo1E9zivgSC+/CNRYFA+BdvY1GHZuJVHTqKQN3rl+HVBN4NYRuAhtdI1Df6QhPwOr3koZMUkRPuyiSygX8sit+bqI9RNhbNVIjeTh2ix55ETyaMFu8AaN/KoWya2hTUtTs9mqhGJvRuhzR2oqdpEl1dbXSwNvCYylRVijgZI56v9iQm0cX9zuckOhFRTps4ogceu/BI2CwSPlizG9NeXRX1XYFElH0TJ07E5s2bcejQIZSXl2Pnzp2YMmVKSrc5ffp0rFu3DgsXLox6nVmzZqGqqkr52LFjR0o/k4ioJfIFh1NF3hfqRE8tz8s0v97GosH/z4VGQMOpxa233gq/34+TTz4ZdXV1+M1vfgOn04mbbroJ1157bSbW2CKIsFi7oaAtxhOhNljnkq+ZRHd7/cpZKSCxEB0IBHf1Hl9K/+CpqvMoYbCYZs5TdTQDoRBdPbWqDh/rGn3KhqHFLv1qk1iT6GGbmXr9aKW6rvq+iSDU5bDhYJ0HtY0+VMWYBk+kE91YnUtgndpJ9Gibqaaqm3oSPc7Gosl0mjvtVtS4vWE/pymof1dN9bNFUN3gTWMnunoS3Z76JDoAVNQG3iqbqU07RTiv7AmQxP3P4yQ6EVGzceaRXdDKacM1r63Gf9bvw+QXvsO8y49p0pPrRBTfPffcg4kTJ6JXr15wuVxwuVL/O/SMGTPw4Ycf4quvvkLXrl2jXs/pdMLpdKb884iIWjKfts4lRypRlA1RdTrRfTmQohtOPCRJwm233YaKigqsW7cOy5cvx6+//oq77747E+trMUQQGDGJHnxg6XWi1wcnmAu0dS6qyhL1bcSjnRhPhqhy6dDaqYSy2k0YRZ2Luj9Zfb8PHAp15BXm6f+jKzJEj+xEBwC35oSAurtadEaJMLuu0RvqJdcJsp3prnNxhte5xJu+T1XYJLpOSBvWid7OeKe5OJHRVNPggvpdA01e55LWSfTId1Mkw2a1KC+kFcETM5mqc9FOuNuT6L8V95UbixIRNQ8nDyjBS38YjlZOG5ZtPYCJ85bjYG1jtpdFRCpvv/02+vTpg9GjR+Ppp5/G/v37k74tWZYxY8YMvP/++/jss8/Qq1evNK6UiIj0KN3ilvA6F5MPoqsm0UOXWXOkzx1IIkR/9dVXUVdXB4fDgYEDB2L48OFo1apVJtbWoohKCiOd6KFJdM3Got7wED3RnmLtxHgytlfUAggPUsXtRta5hNYlSZISpO+rCYTorZy2qJOt2uOkDs4tFkn5eoPmhIC4b+ppXXH86ty+mBuaitv0eCN/F43J1Lk49OtcijMUoqunywt0JpPD6lySmUQPHp9kAvhUFGejE115fIV3oms3BjZCfK8kpTbRDoRCfhFaZGpjUW23fjIbiyq1SpxSJCJqNkb2bofXp45AG5cdP+6swgXPLkN5VUO2l0VEQT/++CN++uknnHjiiXjooYfQuXNnnH766Xj99ddRV1dn6LamT5+OV199Fa+//jpat26N8vJylJeXo76+PkOrJyIin2YPRIsygGvuFN2vCf+BUKAuN8dJ9BtuuAEdO3bEJZdcgo8//hg+H7sO00GZRNdsKGi1hPq9teq0k+iquhH19RPd608Ebw0pdKJv3nsIQPjGkqHbDU6ii+qHKBt3/hoM0WNNZMeaRAfUndXhj0/xuXrqVxy/2kZvzM09E9lY1EiI2koziV6Z4Ul0l8OGDq0Db53U658Wk/FWi4QubfIN3774HTT5JLrqeHVv2zQBvlPzXEmmzkdLHD+nzaLsFZD0+oLrqAiG6JneWFRIqs6Fk+hERM3SkK7FePvqUSgtzMPmfYdw3txv8cv+2mwvi4iCBg0ahPvuuw9bt27F559/jp49e+L6669HaWmpodt55plnUFVVhRNPPBGdOnVSPt58880MrZyIiHyaDTqtSiVK1paUEGXd6k705jyJvmfPHixcuBCSJOGCCy5Ap06dMH36dHz77beZWF+LcUK/DuhUlIexAzqGXR5rEl1sLComOEVw5vH5lbM7gdtI7NfsVDrVkzsxUtPgwSvLtwMARvRqq1yurb4Q90UbhIsAcl9NYFIpVpjssIWHjNr6C21wL4hpePX1dTcWzY/sYk93J7pLqZHxwe+XY07Bp8t5w7qiZzsXhnQpivhaj3YuDOpciLOO7BLxu0nE+EGl6NomH8cd3j4dS01Y+1ZOjOrdDif164D2rfQ79NNNTKKLuiDxmEjmuAk92rkwpGsRTj+ic+rrCz7+KzI8iZ5vT73O5ZRBpehclIdRvdula1lERGQSfTq2xttXj0LPdi7sPFiP8+Yuw/o91dleFhFpFBQUID8/Hw6HAx6Px9D3yrKs+3HFFVdkZrFERKRkfkqIbpXCLjcrZRJdFaJbc6gT3fB4os1mw+9//3v8/ve/R11dHd5//328/vrrOOmkk9C1a1f8/PPPmVhnszekazGWzTo54vJY3UCizkX0W6vrXMIm0RPMtbTTtUY999VWVNQ2onf7ApxzdGgzGW0479XpRAdCIbWYRI+1waY2rHRqJvi1FTJCqM4ldH2Xqlalsj4QOsaaRNfrjPckEaKrN9mq86gC/AxtLAoAt4zvj1vG99f9mtNmxUfXHZ/0bV978uG49uTDk/7+ZFksEt7448gm/ZnKiSGvphM9hRDdbrVg0YzjUl8cQhsUV9QFJ9EztLFogVNb52L8/l88vDsuHt49XUsiIiKT6dbWhbevHo1JC1Zi/Z5qXPDsMrxwxbE4pmfb+N9MRBmzbds2vP7663j99dexceNGnHDCCbjzzjtx3nnnZXtpREQUh1dTiyKCaL0WCzMRy7PobCyaAxm68RBdzeVyYdy4cTh48CC2b9+O9evXp2tdFGRLZGNRp6bOxedXyvqtFinhaoho09uJ2FfdgOe/3gYA+PP4fmEht7YmxhN8f0l661z0Q/WISfTgGpy6k+heZXNPvQA/dJIi8vi4kwhRnTYLLFLgD5FatxdVMapkyFycmv0D3GnYWDSdxONfhPva7vJ00VawJNOJTkREzV+H1k4s/ONITHnxO3y//SAunb8Ccy8dhhP7dYz/zUSUdiNHjsR3332HIUOGYPLkybj44ovRpUuXbC+LiIgSJAZtRa5mteTGJHqLq3MBgLq6Orz22ms47bTT0KVLFzz22GM4++yz8d///jfd62vxxFsyfDqbA4gNKbUbi7pVk+hWA93KzhiT1vE8+p/NqPf4cHT3YowbFN6jp92wVG9jUfX69yURojvtUSbRNVP1Yho+2iR6zI1FVScptBqTCFElSVImefcfciu3yxDd/PI0J2nS0YmeTtqTSpnqG9eG8/ZEN2AgIqIWpyjfjlemjMCJ/TqgwePH1Je/xz9/3J3tZRG1SCeffDLWrl2LH374ATfddBMDdCKiHOPXhNFKi4XJx7lDdS6hy0S8Z/a1A0mE6BdddBE6duyIG264Ab1798YXX3yBLVu24O6770b//vo1EZS82JPogToXZWNRVZ2Lth8pEclOom/Zdwhvfb8DADDrtAERk+/KVHgwwPYkWOcSq9ZEO/Gdp61zsYm6De3GoqITXbWxqDPUiR5rY1Gn6vhqhUJ0Y2GlqHTZXRnogbdapLCaFzKnaHUuRjaWzSTt84GT6JQpc+bMQc+ePZGXl4cRI0Zg5cqVUa974oknQpKkiI/TTz9duc4VV1wR8fXx48c3xV0hoiaQ77DiucuOwRlDO8Pjk3Hdwh+U/XSIqOnce++9GDhwIPbv34/9+/dnezlERGRQqM4l8HmsKmgzEeG/fp2LudcOJBGiW61WvPXWW9izZw+eeuopjBo1SvnaunXr0ro4AqzBZ4R+J7qYRI/eia6tTIlFbJbYYHBj0QcWb4DPL+N3A0twrE6/pVMzFe5V6lxSmETXbCzqjLqxqH4nujrsFMev1u2NPYke/B6/DHg10+jKJLLBTmgRQu6urFd+bqL1O5Q94vHj9cvw+vyqTvTMTHwbpX0+ZG4SXbOxaAqd8JR73nzzTcycORN33HEHVq9ejaFDh2LcuHHYt2+f7vXfe+897NmzR/lYt24drFYrzj///LDrjR8/Pux6b7zxRlPcHSJqIg6bBY9deCQuG9kDsgz87YN1eHLJ5pz4hxNRc1BZWYnp06ejffv2KCkpQUlJCdq3b48ZM2agsrIy28sjIqIE+P3huZoIos0eoosozaoTouuUPpiO4fHE1157LezzmpoavPHGG3j++eexatUq+HzG+7QpuliT6HVuMYkerHNR1Y34/JFnd+LRht2JWLuzCv/+315YJOCW8f10r5OnmUQXAbRdO4keDCZjBdlCRCe6ZvJWhJzaqXplY1H1JHrw+NU1+lCZQIgOBI6xehPF0CS6sQA8NIkeCNGLWeWSE9SPnwavP6mNZTNJ+3woyNAkesTGogb+vKHc98gjj2Dq1KmYPHkyAGDu3Ln46KOPsGDBAtx6660R12/bNvwk68KFC+FyuSJCdKfTidLS8FowImperBYJd505CG0KHHhiyWY8/OkmHKzz4K+nDzD0d1ciMqaiogKjRo3Crl27MHHiRAwYMAAA8L///Q8vvvgilixZgm+//RZt2rTJ8kqJiCgWbeZny7VJ9LA6l9xYO5BkJzoAfPXVV7j88svRqVMnPPTQQ/jtb3+L5cuXp3NtBNWDyRf+YJJlGXXBQNjljJxE124ykAht2J2I9XuqAQCjD2uPPh1b614ntAljcGNRv/4kurYKozjfEfXnRtS5RJlE1/a7i8/Vk7piEr2itlEJw/U2FrXrhObaz41OoosQclcwRC9kiJ4T1I/V+kafcpLLNCG65vmQn6FJdLE5rmDjJHqL0djYiFWrVmHs2LHKZRaLBWPHjsWyZcsSuo358+fjoosuQkFBQdjlX3zxBTp27Ih+/fph2rRpOHDgQFrXTkTmIEkSZv6uL+44YyAAYME323Dj2z8qJ6aJKP3uuusuOBwO/Pzzz3j22Wdx/fXX4/rrr8dzzz2HLVu2wG6346677sr2MomIKA6fsg9i4PNcCaJDIbp6Ej3w31x4V6KhxKO8vBz3338/Dj/8cJx//vkoLCyE2+3GBx98gPvvvx/HHntsptbZYkWbRG/w+CEeX6Lv2BlW5xL4B4iRaR4leDYwie4O/kNH9Irr3q4tPNAWk+jROtGFVDYWddqjTaKL7urISfQ9VYEgO1ovuc0iQTzPI0L0JCeRxe9OXedC5mexSMrjtabBo1xulhDdqZ1Ej/H8TIUkSWF969p3l1DztX//fvh8PpSUlIRdXlJSgvLy8rjfv3LlSqxbtw5XXnll2OXjx4/Hyy+/jCVLluD//u//8OWXX+LUU0+N+S43t9uN6urqsA8iyh2Tx/TCoxcOhdUi4f0fduGqV1ahvpHvbCXKhA8++AAPPfRQxOs3AJSWluKBBx7A+++/n4WVERGREWITTqu2zsXkQbRPZ//GXFk7YCBEP+OMM9CvXz/89NNPeOyxx7B79248+eSTmVwbQX02KTy0rQtuKgoA+XbNJLrPD3F1Q5PoUYLnWBLZUFO7YanoRNcG4doAUm8aXNCGdXk27VR7lE50r6hzCV1fTPLvqQps7lmYZ9PtJZekUHCqnXAPHQdjIWorp+hED/zsWPeZzEWcqKluCD0Xjb4TIVPUj29Jiqx3SSd1L7r23SVE0cyfPx9HHHEEhg8fHnb5RRddhAkTJuCII47AWWedhQ8//BDfffcdvvjii6i3NXv2bBQVFSkf3bp1y/DqiSjdzj6qK+ZNGganzYLPNuzDpAUrlHo/IkqfPXv2YNCgQVG/Pnjw4IROhhMRUXaFwujA52JI1Z+Dk+i5MkUPGAjR//Wvf2HKlCm48847cfrpp8Nqkg30mjvxRNBOotc1hsJg8YAToXTYJLqBTSqdNv0KlFjc3siNOiNvNzyc9wTXpg34tQF07I1F9etbQp/rb5IqpuzV13dpql+KXTFqZFQnKsJuN8mNJUWdy96aQIjOSfTcIR5D1ap/5JtlElv9+M63WzPaLxsWopvk/lPmtW/fHlarFXv37g27fO/evXH7zGtra7Fw4UJMmTIl7s/p3bs32rdvjy1btkS9zqxZs1BVVaV87NixI7E7QUSm8tv+JXj1yhEozLPhu18O4sJnl2FfdUO2l0XUrLRv3x6//PJL1K9v27YtYg8TIiIyH+1Et8j+9PZTNBMx8GvRmUTPgUH0xEP0pUuXoqamBsOGDcOIESPw1FNPYf/+/ZlcGyH01gztGRkRoqs3DHSoQnRxdsdIqJXaJHr0h5K2nzzqJLrm81j94NrrakP8aNU0yiS6us5FU90S6+eqK3PUkp1EFz9b/GHBED13iOdLTXAS3WGz6L6DIRvUzwdXhjYV1bt9s5xEoMxzOBwYNmwYlixZolzm9/uxZMkSjBo1Kub3vv3223C73bj00kvj/pydO3fiwIED6NSpU9TrOJ1OFBYWhn0QUW46tmdbvHnVKHRo7cSG8hqc88y3+GV/bbaXRdRsjBs3DrfddhsaGxsjvuZ2u/G3v/0N48ePz8LKiIjIiFCIbgn+Nzcm0ZUaGnUnenOcRB85ciTmzZuHPXv24KqrrsLChQvRuXNn+P1+fPrpp6ipqcnkOlusaJ3otcE6F5eq61gEuG6vXwmqrUYm0TW1K4lIZENNETa6xSR6tE50VfBnkYDWOr3kgjqAt1mkiA0Nlel3r7YTPTg5r7OxqBAryFafqFBLthO9QBNwMkTPHeKdG9XBTnSnSapcgPA9AlwZ2lRUUPets86lZZk5cybmzZuHl156CevXr8e0adNQW1uLyZMnAwAmTZqEWbNmRXzf/PnzcdZZZ6Fdu3Zhlx86dAg333wzli9fjl9++QVLlizBmWeeiT59+mDcuHFNcp+IKPsGdCrEe9NGo0c7F3YerMd5c7/Ful1V2V4WUbNw1113YePGjTj88MPxwAMPYNGiRfjHP/6h7Hu2fv163HnnndleJhERxeGXwzcWFdmh2XvFRcivfrO8uA9mXztgcGNRACgoKMAf/vAHLF26FGvXrsWNN96I+++/Hx07dsSECRMyscYWLVo3UJ07EAa77KpJdFXVSGiTASN1Lvp937GIMFkdSkfebng/uTghoA3c1MF4Yb49ZgWF1SIpTzptlYv6Mu0JAXHf1B3q2iC7OFaIHqXOpTEY1hufRE88wCdzESeHRJ2LWTYVBTR1RRkO0fM5id5iXXjhhXjooYdw++2348gjj8SaNWuwePFiZbOysrIy7NmzJ+x7Nm7ciKVLl+pWuVitVvz000+YMGEC+vbtiylTpmDYsGH4+uuv4XQ6m+Q+EZE5dGvrwjtXj8bAToXYf6gRFz23HN/+zHfAEqWqa9euWLZsGQYOHIhZs2bhrLPOwtlnn43bbrsNAwcOxDfffMO9RYiIcoDI1URuZokygGs2IqvUq3Mx+xQ9AKT0Pv9+/frhgQcewOzZs/HPf/4TCxYsSNe6KCjaJHqdziS6umpEb8fbeKIFz7GIUDrWFK4yie4VG4sGvkcbuKkrKBIJk+1WC9xev24fe16UEwJiGj4sZDQQZDvi1LkYnUbWVskwRM8deZpJdHOF6KqTRDHe0ZEOBdxYtEWbMWMGZsyYofs1vc1A+/XrBznKhEF+fj4++eSTdC6PiHJYh9ZOLLxqJKa+9D1WbKvAFQu+w+MXHYlTj4he70RE8fXq1Qv/+te/cPDgQWzevBkA0KdPH3ahExHlEJ8ynBrI1aw5EkSL5enVuZh86QCSmETXY7VacdZZZ2HRokXpuDlSCU2ih4e2ohNdPWUqNrUMbCyaSohufBI9kU50cbsen35fu/o2Yk2DK9cPBtZ6k+hOpRNdW+cSOTnv0nx/sSuJED3ZOhdNwBlrU1MyF6dOJ7pZOG1NOYnOjUWJiCgzCvPseOkPwzF+UCkafX5c8/pqvLZie7aXRdQstGnTBsOHD8fw4cMZoBMR5Rjt4Kw1RybRlToXVXwiAvVmWedCTUtMdoqOcyEUouvXufg1Z6USEapzMdCJHgyP1aGdlrJhqZhED54Q0E6tOjR1LvHYg+vVq5IJbZKqmUTX2VjUZrWEBaCJdKJrJ9yT3ljUQB87mYsyiS7qXEzUia6eRM94J3pYnYt5jgERETUPeXYr5kw8GhcP7w5ZBm57fx0e/8/mqO9qISIiImruooXogLmn0X1KJ3povbmyKSrAEN30RM4c0Yku6lzUk+jBANfnl5VQN1avuFYyk+juBLrAQ53oos4lcF+0dS5hk+gJTGSL78/TCfDFZdoTAuK+aafX1ccxVoAfvRM9/gareiIn0Rmi5wrxGKo24SR6XtgkembrXFxhdS6cRCciovSzWiTcd/ZgXPfbPgCAR/+zCbf/478Rfz8mIiIiaglCG4uG17kA5p7o9uvs3yiW7jfxugXzpD6kS0xra/+RUOuOPokOAPXBwNpIqKXtLk9EIhPYTntoeluWZVWdi2YSPWwaPH7wJ6Ze9SbRnVEm0UWQr+1RV0/Txt5YNFSZo+ZOchK9FTvRc5Z4DNWYsBPd2YST6Oo/g7TPaSIionSRJAkzT+mHu84cBEkCXlm+Hde+sdrQXj5EREREjJv5dwAAUglJREFUzYF2Y1GrakjVzEMGIijXm0Q387oFJh4mF63XqM4TmH4tCOtEjwzR1Q/MeMTEuNvQJLqoc4nfiS7LgQluUedit0SfRE8kTBbXjzWJrv2HlbLeGJPoidS5pKsTXftOAr1+dzIn8Riqrg9OopsoQFY/jjK+sahqY17tu0uIiIjSbdKonnjq4qPhsFrw8dpyXPHCSmWTbyIiIqKWQFvhHDaJbuIwWrfORWyKykl0SpUtyhmZOnfkxqLqAKs+2JluZKM/bXd5IhKZwFYH7A0ef/RJdNXnxfnx61xCG4vqTaKLOhf9SXTt96iPY6wqGXFfPNHqXFKYRE9kM1UyD/EYqjbhJLr6xFJ+hk/MhG0sajHPMSAioubr9CGd8OLkY9HKacPyrRW48Nnl2FfdkO1lERERETUJn2YSXf1PcXPXuQT+G17nIoV9zcyYeJhcaBI9PLRVNhZVhbCSJClBngjRrQZCLRG8eXxywmeuEukCd1gtSseR2+uDNxhAawN+o5PoSp2LziS6CLu1k+ihOhftJHroOMacRLdFmURPQyc6q1xyi5j2rmkw3yS6us5FPSmeCeEbi3ISnYiImsboPu2x8I8j0b6VE+v3VOOcZ77F1l8PZXtZRERERBkn2irEFLd6oM3nM28aHapzCV0mohQzT9AL5kl9SJcImhPZWBQAnMFHn6hzMZJpqYO3RHvRG6PUo6hJkhTa6NPjV57sdk3Ar55Yj7W5p6BsLKoziR7aJFUTonv9ut9juM5FO4nui19ro0c9JcwQPbeI3/Uht7k3Fs3P8Mai6kl0KzcWJSKiJjS4SxHemzYaPdu5sPNgPc6buww/7qjM9rKIiIiIMkqE0aK9Qv1PcTNPomsn6IFQjuBniE6pskXrRG+M3FgUCAV5dSlMogORG3JGI8L2eFO4oY0+fUoVSqxJ9GJX4pPoej3ioWqa0P2QZVkJ/bXfIyb6A73kMabqbaFNUtU8Sda5WCyS0mufyH0m89A+hkwVoqsn0TO8sWj4JLp5jgEREbUM3du58M600TiiSxEqahtx0XPL8cXGfdleFhEREVHGaMNoSZKUIN3MYbRS5yKxzoUyQITg2rdjiEl0bUDm0NSYGMm0LBZJCcO1E9zRJLqhpjKJ7vXD6ws/YyY4rMamssXP1Jv+FnUtjV4/5OBZOHXwrQ1AxXEsyrcrT+BYPzNdG4sCoQA/kel7Mo88ze/aVHUuqhNi2hNt6eZS1cUY2YOBiIgoXdq3cmLhH0fi+MPbo97jw5UvfY93V+3M9rKIiIiIMsKnqXMBQpUu2iFcM/HH2FjUzBP0gnlSH9IVbRK9NrixaH6UEF1Mohvd6E89MZ4Ipc4lXoiuul3R7x6xsWgGJtGBUHiuvk/a9YrjGG9zT70Q3e+Xlc1SkwlSxeaiiWymSuahrTAy0yS6uppJW/mUbi5uLEpERCZQ4LRh/uXH4qwjO8Prl3Hj2z/imS9+VoYpiIiIiJoLJURXDbKJf46buVtcBOXq6IB1LpQ24sGkfRKIznP1xpRAKMQVX7cY7Ch2qibGE+FOMEQXt9vg8SuBc8QkuuGNRQPfr9fHrg7WRXgu1mq1SBG1E6KSIt7PDXWihwJ5dT96MkGq2PiRnei5RVv7Y6oQ3RbazJcbixIRUUvhsFnwyAVH4qrf9AYA/N/iDbjzn//LiX+UERERESVKhNFWnYluv4kHCPw66xb/a+Z1C+ZJfUhXaBI9PNSuDW5mmB9lGrZemUQ3FmrlJTmJHrfOxS66xEOT6NogWwTUdqsUcb/0hCbRI3+23WpRTkBoJ9G1NRxAaBI9boiuM4meaojuUgL8zNZuUHqp9xAAzBWiS5KknNjKtzfdxqLad5cQERE1NYtFwqzTBuBvvx8IAHjx219w7Rs/JPx3WyIiIiKzUybRdTbozIk6FwvrXCgDlLc1yOFvbahXNhaNEqIrnehGQ/TQxHgiEg3RnarbVTrRNVOrnYry4LBZcHjH1jF7yYWe7QoAAL2C/9USNSkH6xqVn61ei9phHVoBAA4vaR3zZyqT6KoQXfwuLFJydS59OgZ+dt84P5vMxak5eeM0WYDcs10BnDYLuhTnZ/TnFOXb0a7AgQ6tnQmd/CIiImoKU47rhScvPgp2q4SP1u7B5QtWoqrek+1lEREREaUsVohu5nfgKXUuUm6tW+Doq8mpO4Z9sgwLJMiyjNrgxqKtotS5iE50awJhtJqYXnV740/r+PyycobLaYsdnjlVG56K79H2J7cpcODzm06MuE/RXD/2cJx1VGclANfq3taFtbuqUHagDv1LC5X7pDeJPm5QCT694Tfo2V4/kBeUSXTV9Ln4B1lhnE1Jo/n7GYPwhzG9lDCdcoOZJ9EB4M2rRuGQ24uiBPYXSIXdasHi638DSTJ+0o6IiCiTzhjaGe0KHLjqlVVYsa0CF8xdhhf/cCw6FWX2BDMRERFRJim1KGEheiCTMPNEtyjZUK9bkkLDw2ZnrtSHIqg3CRBnmtxev/LgiraxaH0wZLca7Cg2MomunsaOX+cSvF2vD16fqHOJXFuX4vyEu8FtVgv6xJha797WBQAoq6gL/OzgfdLbiFSSJBxe0jqiYkZLr85FhOjxNiWNdZsM0HOPmTcWBQIT4pmeQhc6tHaifStnk/wsIiIiI0b3aY83rxqFjq2d2Li3Buc8/S027a3J9rKIiIiIkiYaHsInusO/ZkZ6nehi3WbeEFUwV+pDEdSd5mKCW0yZA6E+bUG7sajRSXR1d3k8YSF6nPBZBNdu9caiGa6/6N4uEKJvPyBC9MB9SiXsFPdTvfFqZV0gROfGoC2LdjPdZKp8iIiIKPMGdi7Ee9eMRp+OrbCnqgHnPfMtlm89kO1lERERESVFhNE2nW5xM2/QKYJydVSZC+sWmPqYnPotDr5g+Cw2FXXaLBH1CSIgVupcDNYriFoWdwKT6G5f4GdIkv5UuZqoUGlQbyya4eqHHppJdBF8602iJyrWJHqRy5H07VLu0T6OHHEqjYiIiCh7urZx4Z2rR+GYHm1Q3eDFpPkr8eFPu7O9LCIiIiLDfHobdAZzOTNPdIul6de5mHfdAkN0k1NPkovwWUyZF+h0hztU3eNAMhuLhsLueETQ7rBa4naBi00YwzcWbZpJ9FCdS7AT3Z7CJHqMTnROorcs2seR2epciIiIKFyxy4FXrxyB8YNK0ejzY8brP+D5r7dme1lEREREhoimirBaFCkXQvToG6L64s/yZh1TH5OzWCSIx5Z4IohJ9HydiWpRKaFUphgN0W2iEz2BOpfgIzyR8FDcrtvrgyd4MsBmsK/dqB7tApuE7jxYB59fVoXoaZ5Er2sEABTlc5/eliRyEp1/nBIREZldnt2KOROPxuWjegAA7vloPe7+8H/wm/gfnERERERqSp2LKlezWMwfoisT9Oo6l+AnufB3MaY+OcAW3GFX24le4NQJ0TVBnuE6F9GJbmBjUWcCNRbqTnQxiW63ZPbhV1qYB7tVgscnY3dlvVLnou2yNsJpjbWxKOtcWhJ2ohMREeUmq0XC3ycMwq2n9gcAzF+6Dde+8UNCQyRERERE2RYKo0OZnxii9Zm4FkWE/+p1i/9lnQulhVVzNkmE6NpNRYE0hOhiEj2ROhcDobRTVTMT2lg0s5PoVouEbm0ClS47KurSO4nOOpcWT/s4SuXkDBERETUtSZJw9QmH4fGLjoTdKuGjtXswaf5KVAbfYUhERERkViIfVGd+llyoc9FZt1JDwxCd0kGcTQpNogfqXFyO9E+ii2CwwcAkekJ1LmIS3esPbSya4RAdCPWib6+oC20smsIGkHp1LpUM0VskuzV8Y1/WuRAREeWeM4/sgpf+MBytnTas/KUC581dhp0H67K9LCIiIqKo9EJ07QCuGfl0JtFZ50JpFdphNxDcxppEd2oqJaxxNvyM+P5gEOhOYBK90cAkurJhqccX2lg0w3UuANCjbTBEPxCaRHemY2NRnTqXIhdD9JZG/dhniE5ERJSbRh/WHm9PG4VORXnYsu8Qzn76W6zbVZXtZRERERHpCsZqYZmfGMA1cy2KyMktFnWdixT2NTNj6pMDbJqdasXGoglNohuc9jYyiS6C9kTCQ6dqw1KPr2k2FgWA7sHNRcsqakOT6KnUucToROckesujfiyxE52IiCh39S8txHvXjEb/0tb4tcaNC59dhi827sv2soiIiIgiiCHbsDoX0WLhM28ardS5qCfRg//LOhdKC6tS5xJ4ktQb2VjU4CS6emI8HqXOJYHwUNmw1OtXamnsTRA6dg9OopepO9FTmBgWx9et7kSvC24sykn0FiePk+hERETNRqeifLx19SiM6dMOtY0+THnpe7z5XVm2l0VEREQURkRSet3iZp5ED9W5hC5jnUua/fLLL5gyZQp69eqF/Px8HHbYYbjjjjvQ2Bh7458TTzwRkiSFfVx99dVNtOr0EbUnoteoNtbGoto6l0x2ogeftYnUo4jbrff4lPthM7i2ZPRop1fnkp5OdFmWIcsyJ9FbMPVjiSE6ERFR7ivMs+OFK4bjnKO7wOeXccu7a/HIvzdCNvE/SImIiKhl0d2gU9NiYTaBDC3w//p1Lub/u1ZkCmtCGzZsgN/vx7PPPos+ffpg3bp1mDp1Kmpra/HQQw/F/N6pU6firrvuUj53uVyZXm7aWQ1tLBp+mdEQ3UgnuttjYBI9eLuiigYAbE04iV7T4EV5tTtsLclwWkPH1+OT0egLTdYzRG95wjrRWedCRETULDhsFjx8/lB0Kc7Hk59twROfbcHOg/W4/9whPGlOREREWSeaKvQ26BRfMxv1oLlVZ91mDf/VciJEHz9+PMaPH6983rt3b2zcuBHPPPNM3BDd5XKhtLQ000vMKJtmh91YG4tq/2JvdNpbTIy7E+lEDz7CE/nHhLjdQw2hEN3eBJ3oeXYrSgqd2Fvtxua9NWFrSYb6vnp8fmUK3WG1ID+F26XclMdJdCIiomZJkiTceEo/dCnOx20frMN7P+xCeXUDnrl0GAcniIiIKKtEIK3ea9Bq8o1FfaoUPSz8z6FJ9JxNfaqqqtC2bdu413vttdfQvn17DB48GLNmzUJdXV0TrC69rJrNAWJPomvrXIz9ipVO9AQm0UUnutMWPzwWYWONehLd4NqSJabR91Q1AEhtEl19fBu9fqUPvTDfrrwFhVoOTqITERE1bxcN7475lx+DAocV3/58AOfP/Ra7KuuzvSwiIiJqwUQgrTeJbtaJbnVIro4DxV1giJ4hW7ZswZNPPomrrroq5vUuueQSvPrqq/j8888xa9YsvPLKK7j00ktjfo/b7UZ1dXXYR7ZZo06i64ToEZ3oxn6WCMQTmURXNhZNaBI9ss6lKSbRAaB72wLNWpKfGLdaJOX30ejzo7I+0MtflJ8Tb+qgNOMkOhERUfN3Yr+OeOvqUejY2olNew/h7DnfYN2uqmwvi4iIiFoovb0GxUS3z7R1LqGQXL/LnSF6TLfeemvExp/ajw0bNoR9z65duzB+/Hicf/75mDp1aszb/+Mf/4hx48bhiCOOwMSJE/Hyyy/j/fffx88//xz1e2bPno2ioiLlo1u3bmm5r6kQb88QvUZ17uh1Ltop60xOoove9ETCQxHOi+eE1SI12eS22FxUSCVEB0InKhq9flQH61yKXY6UbpNyU55qU12G6ERERM3XoM5FeH/6GPQraY19NW5c8OwyfL5hX7aXRURERC2QT2djUYvJJ9Gj1rmYvIZGLaupz4033oj169fH/Ojdu7dy/d27d+Okk07C6NGj8dxzzxn+eSNGjAAQmGSPZtasWaiqqlI+duzYYfyOpZkIwsUDrlbUuTgTqXMx9rNE2N3gMVLnkvgkumC0qz0VkSF6ag97h7L5aqgTnd2YLZM4ISNJTfuYJiIioqbXpTgfb08bheP6tEddow9Xvvw9XluxPdvLIiIiohbGq1PnEtpP0ZwpunpZ6nVLUu5Mome1g6JDhw7o0KFDQtfdtWsXTjrpJAwbNgwvvPACLEn0aa9ZswYA0KlTp6jXcTqdcDqdhm87k2zKDruBB1R9sM6lIIGNRZOdRHd701znoulNtzdhf7ToRBcS6XCPRdzfRq8flcFO9GKG6C2SOIHksFrYiU9ERNQCFObZseCKY/GX99finVU7cdv761BWUYdbxvVXJsCIiIiIMklMbas3FrWYvBYlap2LsrFoky/JsJzoH9i1axdOPPFEdO/eHQ899BB+/fVXlJeXo7y8POw6/fv3x8qVKwEAP//8M+6++26sWrUKv/zyCxYtWoRJkybhN7/5DYYMGZKtu5IUbT9QbayNRbWd6AaDPSOT6CJodyYQiDu1k+hN1IcORIboKU+iizoXX2gSvZAheoskJtFZ5UJERNRyOGwWPHjeEMz8XV8AwLNfbsW1b/yQ0N+fiYiIiFKlu7GomOg2aRjtU28sqooERaToz4EUPSd2Q/z000+xZcsWbNmyBV27dg37mhz8JXg8HmzcuBF1dXUAAIfDgf/85z947LHHUFtbi27duuHcc8/FX//61yZff6q0k+gxNxaNmEQ3FlaLULDB44csyzGna5U6lwQ6xrWT6LYk3kmQrLYFDrRy2nAouKlpyp3o6kl01rm0aOKxlEilERERETUfkiThupMPR7e2+fjzOz/ho7V7sKeqHvMmHYN2rcz1rlYiIiJqXvQ60c1f5xJYsyQhLGtU6lzYiZ4eV1xxBWRZ1v0QevbsCVmWceKJJwIAunXrhi+//BIHDhxAQ0MDNm/ejAceeACFhYVZuhfJs6qeCLIsq0L0yHMg2poUoz3N6onxxji7EYiva6ff9VgsUtj17E04iS5JUtg0eqqBp3pj0SplY1GG6C2Rus6FqCWaM2cOevbsiby8PIwYMUJ5N5ieE088UXcD8dNPP125jizLuP3229GpUyfk5+dj7Nix2Lx5c1PcFSKipJx9VFe8MmUEivLtWF1WiXOe+RY//3oo28siIiKiZkyE6LYc2lhUDJprGzNY50JppUyi+2Q0+vzKk0VvY1FtQGx4El01Md7gif3Mc3sDYX6iVRbqtTVlnQsQvrlo2ibRfT5UcxK9RWOdC7Vkb775JmbOnIk77rgDq1evxtChQzFu3Djs27dP9/rvvfce9uzZo3ysW7cOVqsV559/vnKdBx54AE888QTmzp2LFStWoKCgAOPGjUNDQ0NT3S0iIsNG9m6Hd6eNRre2+dh+oA7nPP0tVmw9kO1lERERUTMlprYtut3i5kyjlTVrQ/TgfciFOhcmPzlAbA7q88uoc4e6Fl06YXCqdS52q6R0E7nj9DoqdS6Jhuiq9dqbsM4FALqrQ/QMbCzKEL1lUibRGaJTC/TII49g6tSpmDx5MgYOHIi5c+fC5XJhwYIFutdv27YtSktLlY9PP/0ULpdLCdFlWcZjjz2Gv/71rzjzzDMxZMgQvPzyy9i9ezc++OCDJrxnRETG9enYCu9fMwZHdS9GVb0Hl85fgfd/2JntZREREVEzpNS5qDvRraEBXDMSIbk2DhR3wawboqox+ckB6k50samow2aBTadCImJjUYMhuiRJyuaiYuPQaMTXEw0Q1Rt6NvUkelidS5o2FnWzzqXFc3ISnVqoxsZGrFq1CmPHjlUus1gsGDt2LJYtW5bQbcyfPx8XXXQRCgoKAADbtm1DeXl52G0WFRVhxIgRCd8mEVE2tW/lxBtTR+K0I0rh8cm44c0f8dh/NoVVUBIRERGlSq8T3WrybnExIR9R52Ix9wS9Wk5sLNrSibNJPr+M+mAfeoHOpqJA6pPoQCDsrvf48ORnm9HG5QAAHNuzLcYOLAm7ntEQXT2xbm3iSfQebQt015EM9SR6FetcWrQ8dqJTC7V//374fD6UlIS/LpSUlGDDhg1xv3/lypVYt24d5s+fr1xWXl6u3Ib2NsXX9LjdbrjdbuXz6urqhO4DEVEm5NmteOrio/F/bTfg2S+34rH/bMb2A3W4/9wjlEEVIiIiomSpa0/CQnST16KI4D+izsXkNTRqTH5yQPgkevRNRYH0hOhtCgLB+Vvf78SzX23Fs19txTWvr0aDpt4lVOeS2D8I1F3kTbmxKAAc1jEQohe77GG7ACdDHOMGrx/VDSJEd6S2QMpJbYPPlWIXf/9ERsyfPx9HHHEEhg8fnvJtzZ49G0VFRcpHt27d0rBCIqLkWSwSZp06ALPPOQJWi4T3f9iFy55fiYO1jdleGhEREeU4rzpElyJDdK9JQ3S/To87ACWjY50LpYVV2WHXj7pgnYsryiS6zSJBnREnE6I/eN5Q/PE3vTH1+F6YenwvAIHA/JDbG3a9RsN1LqE125JYVyo6FeXjiYuPwpMXH5XybYn7W3GoEeJEGSfRW6bf9O2AO84YiL+c1j/bSyFqUu3bt4fVasXevXvDLt+7dy9KS0tjfm9tbS0WLlyIKVOmhF0uvs/obc6aNQtVVVXKx44dO4zcFSKijLl4eHe8OPlYtHbasPKXCpzzzLfYtr8228siIiKiHKae2LZadSbRTTrRLTJybU4pPjfpssMwRM8B6kl0sbGoy6k/iS5JUli1RDJh9bAebfCX0wbgttMH4rbTByI/GH6LKhnB7Q18nmiVhbpGRa/PPdMmDO2M4w/vkPLtOINr31fTAADIt1vZid1C2a0WTB7TC306ts72UoialMPhwLBhw7BkyRLlMr/fjyVLlmDUqFExv/ftt9+G2+3GpZdeGnZ5r169UFpaGnab1dXVWLFiRczbdDqdKCwsDPsgIjKL4w/vgHemjUaX4nxs21+Ls5/+Biu3VWR7WURERJSjfFEm0S0mn+gO1bmEXy7iQbN2uasx+csByiS6L7SxqMsevUJFHehqu4aSUeAM/Czxs4VGX7DOJcGNOrNZ55JO4vj+WhPo4OWmokTUEs2cORPz5s3DSy+9hPXr12PatGmora3F5MmTAQCTJk3CrFmzIr5v/vz5OOuss9CuXbuwyyVJwvXXX4977rkHixYtwtq1azFp0iR07twZZ511VlPcJSKijOhX2hrvTx+Nod2KUVnnwcTnl+O91TuzvSwiIiLKQeq6FvV2gzZLroTouVvnwo1Fc4C610jZWNQZPUR32iyoCf6/LQ1hdX6wOqbWrd+Jnugkep5dPSGfu+dvRIi+Lxiis8qFiFqiCy+8EL/++ituv/12lJeX48gjj8TixYuVjUHLyspg0fxZv3HjRixduhT//ve/dW/zz3/+M2pra/HHP/4RlZWVOO6447B48WLk5eVl/P4QEWVSx9Z5WDh1JG58ew0+XluOmW/9iF/21+KG3/VNeb8eIiIiajnUG4eqszWLyUN0OVqdi5Q7dS4M0XOAeFL4VBuL5kfZWBQID7Wt6ZhED/4sbZ1LaGPRBEN0WzOZRLeGT6IXMkQnohZqxowZmDFjhu7Xvvjii4jL+vXrBznG344kScJdd92Fu+66K11LJCIyjXyHFU9dfDQearcRT3/xM574bAu27q/FQ+cPDXvHJhEREVE06toTdR4t8j+z1qKIdWkn0a0mD//VcnccuAUJn0QPVKoURNlYFAivc0lmY1EtsYmpts7FrYToif2l39nMJtF/PRSsc2GITkREREQJsFgk/Hl8fzxw3hDYLBI+/GkPLp63XBnOICIiIopF3S2ufjebaKLw+cwZRivr1sSB4i6YNfxXy90kswUJ9Rr5VZPoTRmix55ET3RTTXXYno6amWwR91fcf9a5EBEREZERFxzTDa9MGYGifDt+KKvEWXO+wcbymvjfSGQyX331Fc444wx07twZkiThgw8+yPaSiIiaNRFGa4dTLSafRBfvSNY2ZojcMtY7ls2CIXoO0O1Ej1Xn0gST6D6/rGxmkGiIHr6xaO4+9LT3lxuLEhEREZFRow5rh/evGY2e7VzYVVmPc5/5Fp9v3JftZREZUltbi6FDh2LOnDnZXgoRUYsQbaJbxGx+k9aiRNtY1JpDG4vmbpLZgqh32K11B4JsV4yNRcM60dMYotepNhYVU9iAkUl0dZ1LDk+ia04AcBKdiIiIiJLRu0MrvH/NGAzv1RaH3F5MefE7vPjNtmwviyhhp556Ku655x6cffbZ2V4KEVGLIMLmyInuQFblNWkYrXSia/JAiSE6pZP6iVAXnER3xdh8KO2T6M7A1Htdo36InvDGonZ1nUvuPvS095chOhERERElq02BA69OGYHzh3WFXwb+/s//4fZ/rIPX54//zURERNSiiDBam/dZTd4tLpYVvc6lqVdkXO4mmS2IenOAukYxiR6rzkUVVqdhA0+xiWmdqs7F7QsE6pKU+FR5nmpjUXsz6EQXilyOLK2EiIiIiJoDh82CB84bgltP7Q9JAl5eth2TX/wO1Q2ebC+NKK3cbjeqq6vDPoiIKHGiriUiRA9+bvo6l4jw39xd7moM0XOAuhNdbCzqirWxqLrORUo9rM53RE6iuz1+5WdJCf4MZ5rD/WyJCNE5iU5EREREKZIkCVefcBjmXjoM+XYrvt68H+c8/S3KDtRle2lEaTN79mwUFRUpH926dcv2koiIcoo3aoieI3UumghRRIqsc6G0CHWi+xPaWFRdN2JNw8R3gc7Goo3Bt5cmWuUCNKNJdGv4CQyG6ERERESULuMGleLtq0ehtDAPW/YdwplzlmLltopsL4soLWbNmoWqqirlY8eOHdleEhFRTvFFDdED/zXrJLocrYaGdS6UTuGT6ME6l1iT6Lb0TqLH2lhUXR0TT3gneg6H6JoTB8UM0YmIiIgojQZ3KcI/ZozBkK5FOFjnwcTnl+Pt7xk2Uu5zOp0oLCwM+yAiosT5Zf2NRS0mr0URW71YonSim3XdagzRc0BoEl1WgmxXjEl09ZR3WjYWFXUuHlWdi9f4JLr6urlc56KdouckOhERERGlW0lhHt784yicPqQTPD4ZN7/zE2Z/vD4n3u5MLcehQ4ewZs0arFmzBgCwbds2rFmzBmVlZdldGBFRM+WN0i2u7Kdo0r8nKJ3orHOhTFL3GoU2Fk1wEj0tIbqYRFfVuSQRoqsn0XO6zkVznwsZohMRERFRBuQ7rHjyoqNw3cmHAwCe/WorrnplFQ6p/l5OlE3ff/89jjrqKBx11FEAgJkzZ+Koo47C7bffnuWVERE1T6KuxWaJMolu0jA6ap2LajJdNvk0OkP0HBA2iZ7QxqKhr6UhQ4fLGbmxaKjOJblOdJs1dx966hMHrfNsaTlRQURERESkx2KRMPN3ffH4RUfCYbPgP+v34rxnvsXOg9xwlLLvxBNPhCzLER8vvvhitpdGRNQs+aJMolst5g7RQxuL6q8bMO/ahdxNMlsQ8YCqb/Qpb9uIVecigm2bRYKUhk50sbFonWpjUbfXF/azEuFU9adrz5jlEvVJCla5EBEREVFTOPPILnjzjyPRvpUTG8prcOZT3+D7X7jhKBERUUviizKJbjN7iO7XD9HVJwPM3ovOED0HiF6jGrdHuSyRjUW1Z6WSlR/8WbU6k+jG6lxC17Xn8CS6+sRBsYshOhERERE1jaO6t8GiGWMwsFMhDtQ24pJ5K/DOqp3ZXhYRERE1kWgT3WbfWFQsS9vmYAmrc2nKFRmXu0lmCyIeYDUNgUlwh9USM4R2qibR06EgOPVerw7RfcbrXMIm0ZtJJzon0YmIiIioKXUuzsc700Zh3KASNPr8uOntHzH7X9xwlIiIqCUQr/faMFrkbH6T/n0gag2NxDoXSiMRhlfXBybRY20qCgRCdiD8gZgKlzKJ7lVK/t2iE93ARHnYxqKW3H3oMUQnIiIiomxyOWx4ZuIwXPvbPgCAZ7/ciqte+Z4bjhIRETVz0epcxES316RBdGiCPvxydTxo1il6IXeTzBbEGnxEVQcn0V32OCF6MOS1pmnaW2wsKsuh8Nyt1LnEXouaM2xj0RyeRLeqQ3RHFldCRERERC2VxSLhxlP6qTYc3Ydzn/4WOyq44SgREVFzlasbi4qhXO3Ab1idi79Jl2QYQ/QcoN0cQITa0Sghepom0fNVoX1tcLpFdKIbqXPJC6tzyd2HHifRiYiIiMgszjyyC966ahQ6tnZi494aTHhqKVZsPZDtZREREVEGKHUuUm6F6MFW6Nh1LpxEp1Rpe45ibSoKqOpc0tSJbrVIyqagdcFe9GRCdLtVUt62YU/T2rLByY1FiYiIiMhEjuxWjEUzjsOQrkU4WOfBxOdX4I2VZdleFhEREaWZCJq1mZ/V5BuLRq9zYSc6pZG25yhuiJ7mjUWB0OaiIkR3ewP/dRoI0SVJUupfcnoS3cpJdCIiIiIyl9KiPLx11SicMbQzvH4Zs95bizv+sQ5en8nfG01EREQJi7axqPjcrBuLylHCfyAUrMsmPQEg5G6S2YJoH2Ai0I5GhOjat0ikIl+1uSiQ3CQ6AGWiPZc70S0WSTlBwRCdiIiIiMwiz27FExcdiZtO6QsAeGnZdlz+wkpU1jVmeWVERESUDvFCdNNuLCq63HWqp5UqGobolCpt4JyfxUn0+hTqXIDAX+wBwG7J7YeeuN/FDNGJiIiIyEQkScKM3x6OZy8bBpfDim+2HMCZc77B5r012V4aERERpShXJ9FjhejiMta5UMqsmsA53iR6XgYqU5RJ9ODGou5giO60xQ70I25HhOg5PIkOhE4GFLETnYiIiIhMaNygUrw7bTS6tsnH9gN1OPvpb7Fk/d5sL4uIiIhS4Jf1Nxa1mLwT3R+zzkWcAGjSJRnGED0HaCfK402iH9W9GKcMLMGU43qlbQ0FzsDPrPeET6Ib6UQHgCuP741TBpZgaLfitK0tG676TW9MGNoZ/UsLs70UIiIiIiJdAzoV4h/Tx2B4r7Y45Pbiype/xzNf/Gz6zlEiIiLSJ+patBXOosXCrNPcYlmx6lz8Jv/7SeyRZjKFiE50Z+wQPc9uxXOTjknrGlzB6fdadzBED25Q5DA47X7JiO64ZET3tK4tG6464bBsL4GIiIiIKK52rZx4dcoI3PnP/+K1FWX4v8UbsKG8Gv937hDl3ZVERESUG0Rdi3bg1mrySpRQnUvk18RlZp2iFziJngO0TwxXnDqXTHAFp9/rGkWdSyBMd9r5ECIiIiIiMjOHzYJ7zz4Cd581GDaLhH+s2Y3z5y7Dnqr6bC+NiIiIDPBFmUQXn5s1RPdH6XIHQms3a5+7wAQ0B2gfYK44dS6ZIIL7Ou3GomnsXSciIiIiosy5bGQPvDJlBNq47Fi7qwpnPPkNVm2vyPayiIiIKEGizkXbiW4ze4gu6lx0QnRxX0y6dAUT0BxgM7ixaCaI4L62MXxjUYfBTnQiIiIiIsqeUYe1w6IZx6F/aWvsP+TGRc8tx5vflWV7WURERJQA0Ruuba0w+8aiYl26dS4mPwEgMAHNAVarsY1FM6Eg+DPrG7Ubi7JHkYiIiIgol3Rr68K700bj1MGl8Phk3PLuWtzxj3XwBPc9IiIiInMSL9XaiW5lc06TvpT7o0zQA6Fg3ewbizJEzwHas0vxNhbNBJczfGNRTqITEREREeWuAqcNT088GjN/1xcA8NKy7bhs/gocOOTO8sqIiIgoGl8wJY9W5+I1aYouAvLYdS4M0SlF2k70fHv2NxZtZIhORERERJTTJEnCdScfjucuG4YChxXLt1ZgwlPf4L+7q7K9NCIiItIhJtG1rRXK5pwyIJswjA7VuUTfWJR1LpQyU0yiazcW9Yk6Fz6EiIiIiIhy2SmDSvH+9DHo0c6FXZX1OPeZb/HPH3dne1lERESkIcJo7SS6+nMzZtFKnYvOJLqFk+iULtoHmCuLG4vWKRuLBsJ0TqITEREREeW+viWtsWj6cTj+8PZo8Phx7Rs/4P5/bTD9VBgREVFLotS5aDvRVZPpZqx0EX+d0JtEt6qm6M2MCWgOsFnCf02uLGwsGgrRwzcWdVj5ECIiIiIiag6KXHa8OHk4rvpNbwDA3C9/xpSXvkNVvSfLKyMiIiJAVeeiDdHVk+jmy9CVk/I6g+jKZWY/cc8ENAdonxgFWZlE19S5BEP0PDsfQkREREREzYXVImHWaQPw+EVHwmmz4IuNv+KsOd9g896abC+NiIioxROVJxEhuupznwlrUaKtG1DVuTBEp1RpO9HzsziJXusWdS5iEr3p10JERERERJl15pFd8O600ehSnI9t+2tx1pxvsHhdebaXRURE1KJ5ffobdIaF6D7zhdH+GBuLss6F0sZikSAeY3arlJUe8gJnYBK9Xlvnwk50IqIWa86cOejZsyfy8vIwYsQIrFy5Mub1KysrMX36dHTq1AlOpxN9+/bFxx9/rHz973//OyRJCvvo379/pu8GERFFMbhLERbNGIORvduittGHq19dhUf+vdH0k2JERETNlQijtQO36joXM06iixoavRBdXGbGdasxAc0R4smRjU1FAz83OIne6IXPL8Mb/IszQ3QiopbpzTffxMyZM3HHHXdg9erVGDp0KMaNG4d9+/bpXr+xsRG/+93v8Msvv+Cdd97Bxo0bMW/ePHTp0iXseoMGDcKePXuUj6VLlzbF3SEioijatXLilSkjMHlMTwDAE59twdSXv0d1A3vSiYiImprSLa4J0dWfm7FbXJyA19taUWwFafaT9NlJZMmwwFkZOSubigKhEN0vAzWqvzA7GaITEbVIjzzyCKZOnYrJkycDAObOnYuPPvoICxYswK233hpx/QULFqCiogLffvst7HY7AKBnz54R17PZbCgtLc3o2omIyBi71YI7zhiEwZ2LMOv9tViyYR/OeuobPHvZMBxe0jrbyyMiImoxxFCrdhJdXOb1y+YM0WX98B8ITdH7OYlO6RCaRM9WiB4633KwLhSicxKdiKjlaWxsxKpVqzB27FjlMovFgrFjx2LZsmW637No0SKMGjUK06dPR0lJCQYPHoz77rsPPp8v7HqbN29G586d0bt3b0ycOBFlZWUZvS9ERJS4c4d1xbtXj0bnojxsZU86ERFRkwtNdOvUoljMW4si1mTVq3MR6zZh+K/GBDRHWLNc52K1SMrU+cG6RgCAJOmf+SIiouZt//798Pl8KCkpCbu8pKQE5eX6YcrWrVvxzjvvwOfz4eOPP8bf/vY3PPzww7jnnnuU64wYMQIvvvgiFi9ejGeeeQbbtm3D8ccfj5qamqhrcbvdqK6uDvsgIqLMOaJrERZdexxG9Ar1pD/8742m/4cvERFRc+CLtUGnmOg24WuyWFOsTnROolNa2IKlQdmaRAdCm4tWBkN0p80CSefBT0REpOX3+9GxY0c899xzGDZsGC688ELcdtttmDt3rnKdU089Feeffz6GDBmCcePG4eOPP0ZlZSXeeuutqLc7e/ZsFBUVKR/dunVrirtDRNSitW/lxKtXjsAVo3sCAJ78bAuufOk7VNWzJ52IiCiTfHHqXIBQ5YuZiCXFrnNpyhUZxxA9R4hJdBFkZ0O+PRDgVwbrXBx6uwEQEVGz1759e1itVuzduzfs8r1790btM+/UqRP69u0LqzV0MnjAgAEoLy9HY2Oj7vcUFxejb9++2LJlS9S1zJo1C1VVVcrHjh07krhHRERklN1qwd8nDMKjFw6F02bB5xt/xZlPLcXG8ujvHiIiIqLURNtYVH2ZGd8dFqpzifya2FjUjOtWYwqaI8TZpPysTqIHfrboRHfYsrcWIiLKHofDgWHDhmHJkiXKZX6/H0uWLMGoUaN0v2fMmDHYsmUL/H6/ctmmTZvQqVMnOBwO3e85dOgQfv75Z3Tq1CnqWpxOJwoLC8M+iIio6Zx9VFe8O200uhTn45cDdTj76W/w4U+7s70sIiKiZilWGC0GcM1Yi+KPFf6zzoXSSZlEz2KInu+IrHMhIqKWaebMmZg3bx5eeuklrF+/HtOmTUNtbS0mT54MAJg0aRJmzZqlXH/atGmoqKjAn/70J2zatAkfffQR7rvvPkyfPl25zk033YQvv/wSv/zyC7799lucffbZsFqtuPjii5v8/hERUeIGdynCP689Dsf1aY+6Rh9mvP4D7vt4Pbw+f/xvJiIiooT5fMEQXacdQmSHXp/5wmh/rC53E4f/atnrBiFDbFneWBQIBfgHGaITEbV4F154IX799VfcfvvtKC8vx5FHHonFixcrm42WlZXBYgm9TnTr1g2ffPIJbrjhBgwZMgRdunTBn/70J9xyyy3KdXbu3ImLL74YBw4cQIcOHXDcccdh+fLl6NChQ5PfPyIiMqZtgQMvTj4WD/57I579ciue+2or1u2qwpMXH4V2rZzZXh4REVGzEJpEj9Utbr4wWpxXt8aYRDf7uXeG6DnCqoTo2ZtEdzm0dS4M0YmIWrIZM2ZgxowZul/74osvIi4bNWoUli9fHvX2Fi5cmK6lERFRFtisFsw6dQCGdCnGze/8iG9/PoAznlyKuZcNw5CuxdleHhERUc4TtSh62xRaTdyJHppEj/yauMxvwnWrMQXNEbbgNF92Q3TWuRARERERUWynD+mED6aPQa/2Bdhd1YDz5i7Dm9+VZXtZREREOc+rhOgx6lxMGEY3hzoXpqA5wmqGOhexsWgtJ9GJiIiIiCi6viWt8Y8ZY/C7gSVo9Ppxy7trMeu9tXB7fdleGhERUc4SQXOsSXQzhtE+JfyPUediwnWrMQXNEbbgtrsiyM6GfHv4JDpDdCIiIiIiiqYwz45nLx2Gm07pC0kC3lhZhgueXY7dlfXZXhoREVFOEpuG6k10i3w61zYWFZexzoXSQpypyTfDJHqwE91py16gT0RERERE5mexSJjx28Px4uThKMq348cdlfj9k0vx7Zb92V4aERFRzhHT2jadOhdxmRkn0f3BTUMtOpPooQn6plyRcQzRc8QxPdog327FEV2KsraG/GAfe70n8BZMh957R4iIiIiIiDRO6NsBH157HAZ1LkRFbSMunb8Cc7/8GbIJ/6FPRERkVrE2FrWYeGNREf5b9SbRTbxuNaagOeK20wdizR2/Q6/2BVlbQ4FmCp51LkRERERElKhubV14d9ponDesK/wycP+/NuDqV1ehpsGT7aURERHlBLFpqP4GnYH/mjGM9ivrjvyauMyME/RqTEFzSLbrU8QkuuBkiE5ERERERAbk2a148LwhuPfswXBYLfjkv3tx5lPfYNPemmwvjYiIyPRE0Cz2TlSzButcTBmii050vToXybwboqrlTAo6YcIEdO/eHXl5eejUqRMuu+wy7N69O+b3NDQ0YPr06WjXrh1atWqFc889F3v37m2iFTc/nEQnIiIiIqJUSZKEiSN64K2rR6FzUR627q/FmU99g0U/xv73HRERUUvnizWJHrzIZ8IwWux1GrvOpSlXZFzOpKAnnXQS3nrrLWzcuBHvvvsufv75Z5x33nkxv+eGG27AP//5T7z99tv48ssvsXv3bpxzzjlNtOLmx+UMn0RniE5ERERERMk6slsx/nntcTiuT3vUe3y47o0f8PdF/0Wj1+T/iiYiIsoSn9KJHn2DTlNOoovwX6/LPUfqXGzxr2ION9xwg/L/PXr0wK233oqzzjoLHo8Hdrs94vpVVVWYP38+Xn/9dfz2t78FALzwwgsYMGAAli9fjpEjRzbZ2psLl11b55LdehkiIiIiIspt7Vo58dIfhuORTzdizuc/48Vvf8HaXVWYc8nRKC3Ky/byiIiITCVnQ3Q5Vpe7edetlpOjxBUVFXjttdcwevRo3QAdAFatWgWPx4OxY8cql/Xv3x/du3fHsmXLot622+1GdXV12AcFFDhZ50JEREREROlltUi4eVx/zJt0DFrn2bBq+0H8/smv8e3P+7O9NCIiIlMRVS16tSgijDbjRHes8N/CTvT0u+WWW1BQUIB27dqhrKwM//jHP6Jet7y8HA6HA8XFxWGXl5SUoLy8POr3zZ49G0VFRcpHt27d0rX8nMeNRYmIiIiIKFN+N7AE/5xxHPqXtsb+Q4249PkVePqLLcpbwImIiFq62JPogZzO6zPf62asSXQlRDf5631WU9Bbb70VkiTF/NiwYYNy/Ztvvhk//PAD/v3vf8NqtWLSpEmQ03yWYtasWaiqqlI+duzYkdbbz2XajUUZohMRERERUTr1bF+A968Zg3OO7gK/DDyweCP++MoqVNV7sr00IiKirIsZopt5Y9FYG6KKOhcTrlstq53oN954I6644oqY1+ndu7fy/+3bt0f79u3Rt29fDBgwAN26dcPy5csxatSoiO8rLS1FY2MjKisrw6bR9+7di9LS0qg/z+l0wul0Gr4vLQE3FiUiIiIiokzLd1jx8PlDcUyPtvj7ov/iP+v3YsJTS/H0xKMxqHNRtpdHRESUNf4EOtHNONEt8vHYdS5NuSLjshqid+jQAR06dEjqe/3+wI7tbrdb9+vDhg2D3W7HkiVLcO655wIANm7ciLKyMt3QneLTbizqsDJEJyIiIiKi9JMkCZeM6I7BXQox7dXV2H6gDuc8/S3uPnMwLjiWlZtERNQyeROY6PaaMI32KXUukV8Tl5kx/FfLiRR0xYoVeOqpp7BmzRps374dn332GS6++GIcdthhSiC+a9cu9O/fHytXrgQAFBUVYcqUKZg5cyY+//xzrFq1CpMnT8aoUaMwcuTIbN6dnGWzWsKmz532nHj4EBERERFRjhrStRgfXXccTurXAW6vH39+9yfc/PaPaPD4sr00IiKiJie6xW3W3NxY1BJjgt7HED11LpcL7733Hk4++WT069cPU6ZMwZAhQ/Dll18q1SsejwcbN25EXV2d8n2PPvoofv/73+Pcc8/Fb37zG5SWluK9997L1t1oFlyqzUUdVmuMaxIREREREaWu2OXA/MuPxc3j+sEiAW+v2omzn/4Wv+yvzfbSiIiImpTSiR5jg04zhtFKnYveui2sc0mbI444Ap999lnM6/Ts2TNik9G8vDzMmTMHc+bMyeTyWpQChw2VdYFNfdiJTkRERERETcFikTD9pD44slsxrnvjB6zfU40znlyKB88fgvGDO2V7eURERE3CG2Oi22biie5YG4sqdS4mnKBXYwpKhuSrJtGdDNGJiIiIiKgJjenTHh9ddzyO6dEGNW4vrn51Ne758H/w+PzZXhoREVHGid5wm94GnWYO0UUnuk6UaDXxBL0aU1AypEBd58IQnYiIiIiImlhpUR7e+ONITD2+FwDg+aXbcNFzy7Gnqj7LKyMiIsqs0AadOt3iIow24US3aA+xxgj/OYlOzYrLEWoAYohORERERETZYLdacNvpAzH30mFo7bRh1faDOP2Jpfh686/ZXhoREVHGiGltvY1FxWU+n/nC6Nh1LgzRqRlysc6FiIiIiIhMYvzgUnx43XEY1LkQFbWNmLRgJR79dJPp3xJORESUjIQ2FjVhGB0rRLeauIZGjSkoGeJyhibRGaITEREREVG29WhXgHenjcYlI7pDloHHl2zGpAUrsP+QO9tLIyIiShtZliFyZr2NRUUY7TdhGC1yfd06F2USvSlXZBxTUDLEZVd1olutMa5JRERERETUNPLsVtx39hF49MKhyLdb8c2WAzjt8a+xYuuBbC+NiIgoLdST2nobi4qA2mvCNDrU5R75NWswnTZj+K/GEJ0McTlVdS52PnyIiIiIiMg8zj6qKxbNGIPDO7bCvho3Lp63HHM+32L6f5gTERHFo65p0Z1Ez9E6FzPX0KgxBSVDCtQbi1r58CEiIiIiInM5vKQ1/jFjDM45ugv8MvDgJxvxh5e+Q0VtY7aXRkRElDS/P/T/ep3orHPJLKagZEi+amNRBzvRiYiIiIjIhFwOGx4+fygeOHcInDYLvtj4K05/4mt8/0tFtpdGRESUFK8qRdcLo3OhziXWus0Y/qsxBSVDChiiExERERFRDpAkCRcc2w0fTB+D3h0KsKeqARc+txzPfvmz6f+hTkREpBU2iZ5jYbSoc9EZoFd60n0mXLcaU1AyxBWsc7FI+psYEBERERERmcmAToVYNOM4TBjaGT6/jNn/2oCpL3+Pg6x3SYs5c+agZ8+eyMvLw4gRI7By5cpsL4mIqFlSd4br1bmYtVtcjrduEf6bbN1aDNHJELGxqMNmgaR3+oiIiIiIiMhkWjltePyiI3Hv2YPhsFmwZMM+nP7E11i1/WC2l5bT3nzzTcycORN33HEHVq9ejaFDh2LcuHHYt29ftpdGRNTsiDoXSdLfWFQMu5ptolu9Ht0JeokhOjVDrmCdCzcVJSIiIiKiXCJJEiaO6IH3rxmNXu0LsLuqARc+u4z1Lil45JFHMHXqVEyePBkDBw7E3Llz4XK5sGDBgmwvjYio2RF1LnrT3EAoWDddiK4Kx/UGcpUJepOtW8uW7QVQbhF1Lk67Nc41iYiIiIiIzGdQ5yIsmjEGf3l/Hf75427M/tcGrNhWgYfPH4o2BY5sLy9nNDY2YtWqVZg1a5ZymcViwdixY7Fs2TLd73G73XC73crn1dXVaVlLg8eH6a+tTsttERGZVYPXB0B/Ch0ITXl/vXk/prz4XZOtK56wGhqdtYv789POKkPrvnl8P/QvLUx9gQliiE6GdCnOBwCUFuZleSVERERERETJaZ1nxxMXHYlRvdvh7//8Lz7bsA/Lth7AaUd0yvbScsb+/fvh8/lQUlISdnlJSQk2bNig+z2zZ8/GnXfemfa1+PwylmxghQwRtQwdWzt1Ly8pDFy+p6oBe6oamnJJCWmdZ4PTFtlsIe7PgdpGQ3+WT/1N77StLREM0cmQbm1deOfqUegUDNOJiIiIiIhykSRJuGREdxzZrRhL1u9lgN4EZs2ahZkzZyqfV1dXo1u3binfrsNmwQPnDkn5doiIcsHwXm11L//9kM5wOWyoqvM08YoSM6RbEew69dDH9WmPF644Fr/WuHW+K7re7QvStbSEMEQnw47pqf9kJSIiIiIiyjUDOxdiYOemezt4c9G+fXtYrVbs3bs37PK9e/eitLRU93ucTiecTv0JylTYrRZccGzqYTwRUS6zWy0YN0j/z18zs1gknNS/Y7aXERd3hyQiIiIiIiIiQxwOB4YNG4YlS5Yol/n9fixZsgSjRo3K4sqIiIjSjyE6ERERJWXOnDno2bMn8vLyMGLECKxcuTLm9SsrKzF9+nR06tQJTqcTffv2xccff5zSbRIREVH2zJw5E/PmzcNLL72E9evXY9q0aaitrcXkyZOzvTQiIqK0Yp0LERERGfbmm29i5syZmDt3LkaMGIHHHnsM48aNw8aNG9GxY+Rb8RobG/G73/0OHTt2xDvvvIMuXbpg+/btKC4uTvo2iYiIKLsuvPBC/Prrr7j99ttRXl6OI488EosXL47YbJSIiCjXSbIsy9lehJlVV1ejqKgIVVVVKCxkTx4RETUts74OjRgxAsceeyyeeuopAIG3b3fr1g3XXnstbr311ojrz507Fw8++CA2bNgAu92eltvUY9bjRURELQNfh4zh8SIiomwy8jrEOhciIiIypLGxEatWrcLYsWOVyywWC8aOHYtly5bpfs+iRYswatQoTJ8+HSUlJRg8eDDuu+8++Hy+pG+TiIiIiIiIqCmwzoWIiIgM2b9/P3w+X8RbtUtKSrBhwwbd79m6dSs+++wzTJw4ER9//DG2bNmCa665Bh6PB3fccUdStwkAbrcbbrdb+by6ujqFe0ZEREREREQUiZPoRERElHF+vx8dO3bEc889h2HDhuHCCy/Ebbfdhrlz56Z0u7Nnz0ZRUZHy0a1btzStmIiIiIiIiCiAIToREREZ0r59e1itVuzduzfs8r1796K0tFT3ezp16oS+ffvCarUqlw0YMADl5eVobGxM6jYBYNasWaiqqlI+duzYkcI9IyIiIiIiIorEEJ2IiIgMcTgcGDZsGJYsWaJc5vf7sWTJEowaNUr3e8aMGYMtW7bA7/crl23atAmdOnWCw+FI6jYBwOl0orCwMOyDiIiIiIiIKJ0YohMREZFhM2fOxLx58/DSSy9h/fr1mDZtGmprazF58mQAwKRJkzBr1izl+tOmTUNFRQX+9Kc/YdOmTfjoo49w3333Yfr06QnfJhEREREREVE2cGNRIiIiMuzCCy/Er7/+ittvvx3l5eU48sgjsXjxYmVj0LKyMlgsoXP13bp1wyeffIIbbrgBQ4YMQZcuXfCnP/0Jt9xyS8K3SURERERERJQNkizLcrYXYWbV1dUoKipCVVUV3yJORERNjq9DxvB4ERFRNvF1yBgeLyIiyiYjr0OscyEiIiIiIiIiIiIiioIhOhERERERERERERFRFAzRiYiIiIiIiIiIiIii4MaicYjK+Orq6iyvhIiIWiLx+sMtTBLD120iIsomvm4bw9dtIiLKJiOv2wzR46ipqQEAdOvWLcsrISKilqympgZFRUXZXobp8XWbiIjMgK/bieHrNhERmUEir9uSzFPkMfn9fuzevRutW7eGJEkp3VZ1dTW6deuGHTt2cOfxBPGYGcdjZgyPl3E8ZsalcsxkWUZNTQ06d+4Mi4UtbPHwdTu7eMyM4zEzhsfLOB4z4/i63XT4up1dPGbG8ZgZw+NlHI+ZcU31us1J9DgsFgu6du2a1tssLCzkE8EgHjPjeMyM4fEyjsfMuGSPGSfZEsfXbXPgMTOOx8wYHi/jeMyM4+t25vF12xx4zIzjMTOGx8s4HjPjMv26zVPjRERERERERERERERRMEQnIiIiIiIiIiIiIoqCIXoTcjqduOOOO+B0OrO9lJzBY2Ycj5kxPF7G8ZgZx2OWm/h7M47HzDgeM2N4vIzjMTOOxyw38fdmHI+ZcTxmxvB4GcdjZlxTHTNuLEpEREREREREREREFAUn0YmIiIiIiIiIiIiIomCITkREREREREREREQUBUN0IiIiIiIiIiIiIqIoGKI3kTlz5qBnz57Iy8vDiBEjsHLlymwvyTRmz56NY489Fq1bt0bHjh1x1llnYePGjWHXaWhowPTp09GuXTu0atUK5557Lvbu3ZulFZvL/fffD0mScP311yuX8Xjp27VrFy699FK0a9cO+fn5OOKII/D9998rX5dlGbfffjs6deqE/Px8jB07Fps3b87iirPH5/Phb3/7G3r16oX8/HwcdthhuPvuu6HeRqOlH6+vvvoKZ5xxBjp37gxJkvDBBx+EfT2R41NRUYGJEyeisLAQxcXFmDJlCg4dOtSE94Ki4et2dHzdTh1fuxPD1+3E8XU7Pr5uN2983Y6Or9up4+t2Yvi6nTi+bsdnytdtmTJu4cKFssPhkBcsWCD/97//ladOnSoXFxfLe/fuzfbSTGHcuHHyCy+8IK9bt05es2aNfNppp8ndu3eXDx06pFzn6quvlrt16yYvWbJE/v777+WRI0fKo0ePzuKqzWHlypVyz5495SFDhsh/+tOflMt5vCJVVFTIPXr0kK+44gp5xYoV8tatW+VPPvlE3rJli3Kd+++/Xy4qKpI/+OAD+ccff5QnTJgg9+rVS66vr8/iyrPj3nvvldu1ayd/+OGH8rZt2+S3335bbtWqlfz4448r12npx+vjjz+Wb7vtNvm9996TAcjvv/9+2NcTOT7jx4+Xhw4dKi9fvlz++uuv5T59+sgXX3xxE98T0uLrdmx83U4NX7sTw9dtY/i6HR9ft5svvm7Hxtft1PB1OzF83TaGr9vxmfF1myF6Exg+fLg8ffp05XOfzyd37txZnj17dhZXZV779u2TAchffvmlLMuyXFlZKdvtdvntt99WrrN+/XoZgLxs2bJsLTPrampq5MMPP1z+9NNP5RNOOEF5Qefx0nfLLbfIxx13XNSv+/1+ubS0VH7wwQeVyyorK2Wn0ym/8cYbTbFEUzn99NPlP/zhD2GXnXPOOfLEiRNlWebx0tK+qCdyfP73v//JAOTvvvtOuc6//vUvWZIkedeuXU22dorE121j+LqdOL52J46v28bwddsYvm43L3zdNoav24nj63bi+LptDF+3jTHL6zbrXDKssbERq1atwtixY5XLLBYLxo4di2XLlmVxZeZVVVUFAGjbti0AYNWqVfB4PGHHsH///ujevXuLPobTp0/H6aefHnZcAB6vaBYtWoRjjjkG559/Pjp27IijjjoK8+bNU76+bds2lJeXhx23oqIijBgxokUet9GjR2PJkiXYtGkTAODHH3/E0qVLceqppwLg8YonkeOzbNkyFBcX45hjjlGuM3bsWFgsFqxYsaLJ10wBfN02jq/bieNrd+L4um0MX7dTw9ft3MXXbeP4up04vm4njq/bxvB1OzXZet22pbZsimf//v3w+XwoKSkJu7ykpAQbNmzI0qrMy+/34/rrr8eYMWMwePBgAEB5eTkcDgeKi4vDrltSUoLy8vIsrDL7Fi5ciNWrV+O7776L+BqPl76tW7fimWeewcyZM/GXv/wF3333Ha677jo4HA5cfvnlyrHRe662xON26623orq6Gv3794fVaoXP58O9996LiRMnAgCPVxyJHJ/y8nJ07Ngx7Os2mw1t27blMcwivm4bw9ftxPG12xi+bhvD1+3U8HU7d/F12xi+bieOr9vG8HXbGL5upyZbr9sM0clUpk+fjnXr1mHp0qXZXopp7dixA3/605/w6aefIi8vL9vLyRl+vx/HHHMM7rvvPgDAUUcdhXXr1mHu3Lm4/PLLs7w683nrrbfw2muv4fXXX8egQYOwZs0aXH/99ejcuTOPFxEp+LqdGL52G8fXbWP4uk1EieDrdmL4um0cX7eN4et2bmKdS4a1b98eVqs1YpfmvXv3orS0NEurMqcZM2bgww8/xOeff46uXbsql5eWlqKxsRGVlZVh12+px3DVqlXYt28fjj76aNhsNthsNnz55Zd44oknYLPZUFJSwuOlo1OnThg4cGDYZQMGDEBZWRkAKMeGz9WAm2++GbfeeisuuugiHHHEEbjssstwww03YPbs2QB4vOJJ5PiUlpZi3759YV/3er2oqKjgMcwivm4njq/bieNrt3F83TaGr9up4et27uLrduL4up04vm4bx9dtY/i6nZpsvW4zRM8wh8OBYcOGYcmSJcplfr8fS5YswahRo7K4MvOQZRkzZszA+++/j88++wy9evUK+/qwYcNgt9vDjuHGjRtRVlbWIo/hySefjLVr12LNmjXKxzHHHIOJEycq/8/jFWnMmDHYuHFj2GWbNm1Cjx49AAC9evVCaWlp2HGrrq7GihUrWuRxq6urg8US/hJhtVrh9/sB8HjFk8jxGTVqFCorK7Fq1SrlOp999hn8fj9GjBjR5GumAL5ux8fXbeP42m0cX7eN4et2avi6nbv4uh0fX7eN4+u2cXzdNoav26nJ2ut2UtuRkiELFy6Unc7/b+/OQqJ6/ziOf8akacZKraSkEpTENoh2LLuoqPQiVLxJLKYFIluQFoKKNiKUKAm6MIRKQikoksg2ihaoaLeFsijabkaI9rRs8fu/+f+G//TzpIX+x9H3Cw4453nOc57zXPgZvpw5x21lZWX28OFDW7hwocXExFhtbW2op9Yu5OfnW3R0tF24cMH8fn9gq6+vD/RZtGiRJSQk2Llz5+zmzZuWmppqqampIZx1+/K/bwo3Y72acv36dYuMjLStW7fakydPrKKiwrxer5WXlwf6FBUVWUxMjB09etTu3btnmZmZlpiYaF++fAnhzEPD5/NZ//79raqqyp4/f25HjhyxPn362OrVqwN9Ovt6ffr0yaqrq626utokWXFxsVVXV9vLly/NrGXrk56ebiNHjrRr167ZpUuXLDk52XJzc0N1Sfgvcvv3yO3WQXb/Hrn9Z8jt5pHbHRe5/Xvkdusgt3+P3P4z5Hbz2mNuU0T/P9m1a5clJCRY165dbdy4cXb16tVQT6ndkNTktm/fvkCfL1++2OLFiy02Nta8Xq9lZ2eb3+8P3aTbmV8DnfVq2rFjx2z48OHmdrtt8ODBVlpaGtTe2Nho69evt759+5rb7bapU6fa48ePQzTb0Pr48aMVFBRYQkKCdevWzZKSkmzdunXW0NAQ6NPZ1+v8+fNN/u/y+Xxm1rL1efPmjeXm5lr37t2tZ8+eNm/ePPv06VMIrga/Iredkdutg+xuHrndcuR288jtjo3cdkZutw5yu3nkdsuR281rj7ntMjP7u3vYAQAAAAAAAADo2HgmOgAAAAAAAAAADiiiAwAAAAAAAADggCI6AAAAAAAAAAAOKKIDAAAAAAAAAOCAIjoAAAAAAAAAAA4oogMAAAAAAAAA4IAiOgAAAAAAAAAADiiiAwAAAAAAAADggCI6gL/24sULuVwu3blzp83OMXfuXGVlZbXZ+AAAdBbkNgAA4YPcBtoXiuhAJzZ37ly5XK5/benp6S06fuDAgfL7/Ro+fHgbzxQAAJDbAACED3Ib6FgiQz0BAKGVnp6uffv2Be1zu90tOrZLly7q169fW0wLAAA0gdwGACB8kNtAx8Gd6EAn53a71a9fv6AtNjZWkuRyuVRSUqKMjAx5PB4lJSXp8OHDgWN//XnZu3fvlJeXp7i4OHk8HiUnJwd9Ybh//76mTJkij8ej3r17a+HChfr8+XOg/efPn1qxYoViYmLUu3dvrV69WmYWNN/GxkYVFhYqMTFRHo9HI0aMCJoTAAAdGbkNAED4ILeBjoMiOoDfWr9+vXJycnT37l3l5eVp1qxZqqmpcez78OFDnTx5UjU1NSopKVGfPn0kSXV1dZoxY4ZiY2N148YNHTp0SGfPntXSpUsDx+/YsUNlZWXau3evLl26pLdv36qysjLoHIWFhdq/f792796tBw8eaPny5Zo9e7YuXrzYdosAAECYILcBAAgf5DYQRgxAp+Xz+axLly4WFRUVtG3dutXMzCTZokWLgo4ZP3685efnm5nZ8+fPTZJVV1ebmdnMmTNt3rx5TZ6rtLTUYmNj7fPnz4F9x48ft4iICKutrTUzs/j4eNu2bVug/fv37zZgwADLzMw0M7OvX7+a1+u1K1euBI29YMECy83N/fuFAAAgDJDbAACED3Ib6Fh4JjrQyU2ePFklJSVB+3r16hX4OzU1NagtNTXV8e3g+fn5ysnJ0e3btzV9+nRlZWVpwoQJkqSamhqNGDFCUVFRgf4TJ05UY2OjHj9+rG7dusnv92v8+PGB9sjISI0ZMybwE7OnT5+qvr5e06ZNCzrvt2/fNHLkyD+/eAAAwgy5DQBA+CC3gY6DIjrQyUVFRWnQoEGtMlZGRoZevnypEydO6MyZM5o6daqWLFmi7du3t8r4/zzP7fjx4+rfv39QW0tfzgIAQDgjtwEACB/kNtBx8Ex0AL919erVf30eMmSIY/+4uDj5fD6Vl5dr586dKi0tlSQNGTJEd+/eVV1dXaDv5cuXFRERoZSUFEVHRys+Pl7Xrl0LtP/48UO3bt0KfB46dKjcbrdevXqlQYMGBW0DBw5srUsGACBskdsAAIQPchsIH9yJDnRyDQ0Nqq2tDdoXGRkZeEHJoUOHNGbMGKWlpamiokLXr1/Xnj17mhxrw4YNGj16tIYNG6aGhgZVVVUFvgDk5eVp48aN8vl82rR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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import random\n", + "from collections import deque\n", + "import matplotlib.pyplot as plt\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.optimizers import Adam\n", + "\n", + "class EnergyManagementAgent:\n", + " def __init__(self, state_size, action_size):\n", + " self.state_size = state_size\n", + " self.action_size = action_size\n", + " self.memory = deque(maxlen=2000)\n", + " self.gamma = 0.95 # discount rate\n", + " self.epsilon = 1.0 # exploration rate\n", + " self.epsilon_min = 0.01\n", + " self.epsilon_decay = 0.995\n", + " self.learning_rate = 0.001\n", + " self.model = self._build_model()\n", + "\n", + " def _build_model(self):\n", + " model = Sequential()\n", + " model.add(Dense(24, input_dim=self.state_size, activation='relu'))\n", + " model.add(Dense(24, activation='relu'))\n", + " model.add(Dense(self.action_size, activation='linear'))\n", + " # Update learning rate parameter\n", + " model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))\n", + " return model\n", + "\n", + " def remember(self, state, action, reward, next_state, done):\n", + " self.memory.append((state, action, reward, next_state, done))\n", + "\n", + " def act(self, state):\n", + " if np.random.rand() <= self.epsilon:\n", + " return random.randrange(self.action_size)\n", + " act_values = self.model.predict(state)\n", + " return np.argmax(act_values[0])\n", + "\n", + " def replay(self, batch_size):\n", + " minibatch = random.sample(self.memory, batch_size)\n", + " for state, action, reward, next_state, done in minibatch:\n", + " target = reward\n", + " if not done:\n", + " target = (reward + self.gamma *\n", + " np.amax(self.model.predict(next_state)[0]))\n", + " target_f = self.model.predict(state)\n", + " target_f[0][action] = target\n", + " self.model.fit(state, target_f, epochs=1, verbose=0)\n", + " if self.epsilon > self.epsilon_min:\n", + " self.epsilon *= self.epsilon_decay\n", + "\n", + "# Environment parameters\n", + "state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point]\n", + "action_size = 2 # Example actions: [0: wait, 1: collect waste]\n", + "agent = EnergyManagementAgent(state_size, action_size)\n", + "episodes = 100\n", + "\n", + "# Waste management specific parameters\n", + "threshold = 0.7 # Waste level threshold (between 0 and 1, where 1 means full capacity)\n", + "\n", + "# Tracking metrics\n", + "episode_rewards = []\n", + "epsilon_values = []\n", + "overflow_events_per_episode = []\n", + "\n", + "# Train the agent in the environment\n", + "for e in range(episodes):\n", + " # Reset environment for each episode\n", + " waste_level = random.uniform(0, 0.5) # Start with a random waste level below the threshold\n", + " state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n", + " done = False\n", + " time = 0\n", + " overflow_count = 0 # Track overflow events\n", + " rewards = []\n", + "\n", + " while not done:\n", + " # Take action\n", + " action = agent.act(state)\n", + " # Simulate next state and reward (replace with environment logic)\n", + " waste_level += random.uniform(0, 0.1) # Waste increases over time\n", + " if waste_level > 1.0: # Overflow occurred\n", + " overflow_count += 1\n", + " waste_level = 1.0\n", + "\n", + " next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n", + "\n", + " # Reward structure\n", + " reward = -1 # Default reward for time passing\n", + " if waste_level > threshold and action == 1:\n", + " reward = 10 # Positive reward for timely collection\n", + " waste_level = 0 # Waste collected\n", + " elif waste_level < threshold and action == 1:\n", + " reward = -5 # Penalty for collecting too early\n", + "\n", + " rewards.append(reward)\n", + "\n", + " done = time >= 20 # Example end condition (episode ends after 20 timesteps)\n", + "\n", + " # Remember the experience\n", + " agent.remember(state, action, reward, next_state, done)\n", + "\n", + " # Move to the next state\n", + " state = next_state\n", + " time += 1\n", + "\n", + " # Replay experience to train the model\n", + " if len(agent.memory) > 32:\n", + " agent.replay(32)\n", + "\n", + " # Store metrics\n", + " episode_rewards.append(np.mean(rewards))\n", + " epsilon_values.append(agent.epsilon)\n", + " overflow_events_per_episode.append(overflow_count)\n", + "\n", + " # Print progress\n", + " print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}\")\n", + "\n", + "# Visualization\n", + "plt.figure(figsize=(15, 5))\n", + "\n", + "# Plot Average Reward per Episode\n", + "plt.subplot(1, 3, 1)\n", + "plt.plot(episode_rewards)\n", + "plt.xlabel('Episode')\n", + "plt.ylabel('Average Reward')\n", + "plt.title('Average Reward per Episode')\n", + "\n", + "# Plot Epsilon Decay\n", + "plt.subplot(1, 3, 2)\n", + "plt.plot(epsilon_values)\n", + "plt.xlabel('Episode')\n", + "plt.ylabel('Epsilon')\n", + "plt.title('Epsilon Decay')\n", + "\n", + "# Plot Overflow Events per Episode\n", + "plt.subplot(1, 3, 3)\n", + "plt.plot(overflow_events_per_episode)\n", + "plt.xlabel('Episode')\n", + "plt.ylabel('Overflow Events')\n", + "plt.title('Overflow Events per Episode')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/jsconfig.json b/Reinforcement Learning/Waste Management through advanced RL techniques/jsconfig.json new file mode 100644 index 00000000..508e58d2 --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/jsconfig.json @@ -0,0 +1,13 @@ +{ + "compilerOptions": { + "module": "Node16", + "target": "ES2022", + "checkJs": true, /* Typecheck .js files. */ + "lib": [ + "ES2022" + ] + }, + "exclude": [ + "node_modules" + ] +} diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/requirements.txt b/Reinforcement Learning/Waste Management through advanced RL techniques/requirements.txt new file mode 100644 index 00000000..e63a2d28 --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/requirements.txt @@ -0,0 +1,4 @@ +numpy +random +tensorflow +matplotlib diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/waste mngmt b/Reinforcement Learning/Waste Management through advanced RL techniques/waste mngmt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/waste mngmt @@ -0,0 +1 @@ + diff --git a/Reinforcement Learning/Waste Management through advanced RL techniques/waste-management.py b/Reinforcement Learning/Waste Management through advanced RL techniques/waste-management.py new file mode 100644 index 00000000..e2b8be1e --- /dev/null +++ b/Reinforcement Learning/Waste Management through advanced RL techniques/waste-management.py @@ -0,0 +1,298 @@ +# -*- coding: utf-8 -*- + + +import numpy as np +import random +from collections import deque +import tensorflow as tf +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import Adam + +class EnergyManagementAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=2000) + self.gamma = 0.95 # discount rate + self.epsilon = 1.0 # exploration rate + self.epsilon_min = 0.01 + self.epsilon_decay = 0.995 + self.learning_rate = 0.001 + self.model = self._build_model() + + def _build_model(self): + model = Sequential() + model.add(tf.keras.Input(shape=(self.state_size,))) + model.add(Dense(24, activation='relu')) + model.add(Dense(24, activation='relu')) + model.add(Dense(self.action_size, activation='linear')) + model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate)) + return model + + def remember(self, state, action, reward, next_state, done): + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + if np.random.rand() <= self.epsilon: + return random.randrange(self.action_size) + act_values = self.model.predict(state, verbose=0) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = reward + self.gamma * np.amax(self.model.predict(next_state, verbose=0)[0]) + target_f = self.model.predict(state, verbose=0) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + if self.epsilon > self.epsilon_min: + self.epsilon *= self.epsilon_decay + + def load(self, name): + self.model.load_weights(name) + + def save(self, name): + self.model.save_weights(name) + +if __name__ == "__main__": + # Environment parameters + state_size = 4 # Example state: [current energy usage, time of day, temperature, price] + action_size = 2 # Example actions: [0: reduce usage, 1: maintain usage] + agent = EnergyManagementAgent(state_size, action_size) + episodes = 100 + + # Train the agent in the environment + for e in range(episodes): + # Reset environment for each episode + state = np.reshape(np.random.rand(state_size), [1, state_size]) + done = False + time = 0 + + while not done: + # Take action + action = agent.act(state) + # Simulate next state and reward (replace with environment logic) + next_state = np.reshape(np.random.rand(state_size), [1, state_size]) + reward = random.uniform(-1, 1) # Example reward + done = time >= 20 # Example end condition + + # Remember the experience + agent.remember(state, action, reward, next_state, done) + + # Move to the next state + state = next_state + time += 1 + + # Replay experience to train the model + if len(agent.memory) > 32: + agent.replay(32) + + # Print progress + print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}") + +state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point] +action_size = 2 # Example actions: [0: wait, 1: collect waste] + +# Environment parameters +state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point] +action_size = 2 # Example actions: [0: wait, 1: collect waste] +agent = EnergyManagementAgent(state_size, action_size) +episodes = 100 + +# Waste management specific parameters +threshold = 0.7 # Waste level threshold (between 0 and 1, where 1 means full capacity) + +# Train the agent in the environment +for e in range(episodes): + # Reset environment for each episode + waste_level = random.uniform(0, 0.5) # Start with a random waste level below the threshold + state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size]) + done = False + time = 0 + overflow_count = 0 # Track overflow events + rewards = [] + + while not done: + # Take action + action = agent.act(state) + # Simulate next state and reward (replace with environment logic) + waste_level += random.uniform(0, 0.1) # Waste increases over time + if waste_level > 1.0: # Overflow occurred + overflow_count += 1 + waste_level = 1.0 + + next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size]) + + # Reward structure + reward = -1 # Default reward for time passing + if waste_level > threshold and action == 1: + reward = 10 # Positive reward for timely collection + waste_level = 0 # Waste collected + elif waste_level < threshold and action == 1: + reward = -5 # Penalty for collecting too early + + rewards.append(reward) + + done = time >= 20 # Example end condition + + # Remember the experience + agent.remember(state, action, reward, next_state, done) + + # Move to the next state + state = next_state + time += 1 + + # Replay experience to train the model + if len(agent.memory) > 32: + agent.replay(32) + + # Print progress + print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}") +plt.figure(figsize=(15, 5)) + +import numpy as np +import random +from collections import deque +import matplotlib.pyplot as plt +from keras.models import Sequential +from keras.layers import Dense +from keras.optimizers import Adam + +class EnergyManagementAgent: + def __init__(self, state_size, action_size): + self.state_size = state_size + self.action_size = action_size + self.memory = deque(maxlen=2000) + self.gamma = 0.95 # discount rate + self.epsilon = 1.0 # exploration rate + self.epsilon_min = 0.01 + self.epsilon_decay = 0.995 + self.learning_rate = 0.001 + self.model = self._build_model() + + def _build_model(self): + model = Sequential() + model.add(Dense(24, input_dim=self.state_size, activation='relu')) + model.add(Dense(24, activation='relu')) + model.add(Dense(self.action_size, activation='linear')) + # Update learning rate parameter + model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate)) + return model + + def remember(self, state, action, reward, next_state, done): + self.memory.append((state, action, reward, next_state, done)) + + def act(self, state): + if np.random.rand() <= self.epsilon: + return random.randrange(self.action_size) + act_values = self.model.predict(state) + return np.argmax(act_values[0]) + + def replay(self, batch_size): + minibatch = random.sample(self.memory, batch_size) + for state, action, reward, next_state, done in minibatch: + target = reward + if not done: + target = (reward + self.gamma * + np.amax(self.model.predict(next_state)[0])) + target_f = self.model.predict(state) + target_f[0][action] = target + self.model.fit(state, target_f, epochs=1, verbose=0) + if self.epsilon > self.epsilon_min: + self.epsilon *= self.epsilon_decay + +# Environment parameters +state_size = 4 # Example state: [waste level, time of day, weather, distance to collection point] +action_size = 2 # Example actions: [0: wait, 1: collect waste] +agent = EnergyManagementAgent(state_size, action_size) +episodes = 100 + +# Waste management specific parameters +threshold = 0.7 # Waste level threshold (between 0 and 1, where 1 means full capacity) + +# Tracking metrics +episode_rewards = [] +epsilon_values = [] +overflow_events_per_episode = [] + +# Train the agent in the environment +for e in range(episodes): + # Reset environment for each episode + waste_level = random.uniform(0, 0.5) # Start with a random waste level below the threshold + state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size]) + done = False + time = 0 + overflow_count = 0 # Track overflow events + rewards = [] + + while not done: + # Take action + action = agent.act(state) + # Simulate next state and reward (replace with environment logic) + waste_level += random.uniform(0, 0.1) # Waste increases over time + if waste_level > 1.0: # Overflow occurred + overflow_count += 1 + waste_level = 1.0 + + next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size]) + + # Reward structure + reward = -1 # Default reward for time passing + if waste_level > threshold and action == 1: + reward = 10 # Positive reward for timely collection + waste_level = 0 # Waste collected + elif waste_level < threshold and action == 1: + reward = -5 # Penalty for collecting too early + + rewards.append(reward) + + done = time >= 20 # Example end condition (episode ends after 20 timesteps) + + # Remember the experience + agent.remember(state, action, reward, next_state, done) + + # Move to the next state + state = next_state + time += 1 + + # Replay experience to train the model + if len(agent.memory) > 32: + agent.replay(32) + + # Store metrics + episode_rewards.append(np.mean(rewards)) + epsilon_values.append(agent.epsilon) + overflow_events_per_episode.append(overflow_count) + + # Print progress + print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}") + +# Visualization +plt.figure(figsize=(15, 5)) + +# Plot Average Reward per Episode +plt.subplot(1, 3, 1) +plt.plot(episode_rewards) +plt.xlabel('Episode') +plt.ylabel('Average Reward') +plt.title('Average Reward per Episode') + +# Plot Epsilon Decay +plt.subplot(1, 3, 2) +plt.plot(epsilon_values) +plt.xlabel('Episode') +plt.ylabel('Epsilon') +plt.title('Epsilon Decay') + +# Plot Overflow Events per Episode +plt.subplot(1, 3, 3) +plt.plot(overflow_events_per_episode) +plt.xlabel('Episode') +plt.ylabel('Overflow Events') +plt.title('Overflow Events per Episode') + +plt.tight_layout() +plt.show()