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Sensor Management Tutorial 2 (old)
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sensor Management Tutorial 2 - Multiple Sensors \n",
"\n",
"This notebook follows on from Sensor Management Tutorial 1 and further explores how existing Stone Soup features can be used to build simple sensor management algorithms for tracking and state estimation. This second tutorial demonstrates the limitations of the brute force optimisation method introduced in Tutorial 1 by increasing the number of sensors used in the scenario.\n",
"\n",
"## Introducing multiple sensors\n",
"\n",
"The example in this tutorial considers two simple sensor management methods and applies them to the same set of ground truths in order to observe the difference in tracks. The scenario simulates 12 targets moving on nearly constant velocity trajectories and an adjustable number of sensors. Each sensor can only observe one target each at each timestep but a target may be observed by more than one sensor. \n",
"\n",
"The first method, \"RandomManager\" chooses a target for each sensor to observe randomly with equal probability. \n",
"\n",
"The second method, \"UncertaintyManager\" aims to reduce the total uncertainty of the track estimates at each timestep. To acheive this the sensor manager considers all possible combinations of target observations for the given number of sensors. The sensor manager chooses the configuration for which the sum of estimated uncertanties (as represented by the Frobenius norm of the covariance matrix) can be reduced the most by observing the chosen targets.\n",
"\n",
"The introduction of multiple sensors means an increase in the possible combinations of target observations that the UncertaintyManager must consider. This brute force optimisation method of looking at every possible combination of observations becomes very slow as more sensors are introduced. This demonstrates the limitations of using this method with a large number of sensors.\n",
"\n",
"As in the first tutorial the OSPA, SIAP and uncertainty metrics are used to assess the performance of the sensor managers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sensor Management example\n",
"\n",
"### Setup\n",
"\n",
"First a simulation must be set up in the same way as Tutorial 1 using components from Stone Soup. For this the following imports are required."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from datetime import datetime, timedelta\n",
"start_time = datetime.now()\n",
"\n",
"from stonesoup.models.transition.linear import CombinedLinearGaussianTransitionModel, \\\n",
" ConstantVelocity\n",
"from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState\n",
"\n",
"from stonesoup.base import Property, Base\n",
"from stonesoup.types.hypothesis import SingleHypothesis\n",
"from stonesoup.types.detection import Detection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate ground truth\n",
"\n",
"Generate transition model and ground truths as in Tutorial 1. \n",
"\n",
"The number of targets in this simulation is defined by `n_truths` - here there are 12 targets. The time the simulation is observed for is defined by `time_max`. \n",
"\n",
"We can fix our random number generator in order to probe a particular example repeatedly. This can be undone by commenting out the first line."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1991)\n",
"\n",
"# Generate transition model\n",
"transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(0.005),\n",
" ConstantVelocity(0.005)])\n",
"\n",
"yps = range(0,120,10) # y value for prior state\n",
"truths = []\n",
"ntruths = 12 # number of ground truths in simulation\n",
"time_max = 100 # timestamps the simulation is observed over\n",
"\n",
"# Generate ground truths\n",
"for j in range(0, ntruths):\n",
" truth = GroundTruthPath([GroundTruthState([0, 1, yps[j], 1], timestamp=start_time)], \n",
" id=f\"id{j}\")\n",
" \n",
" for k in range(1, time_max):\n",
" truth.append(GroundTruthState(transition_model.function(truth[k-1], noise=True, time_interval=timedelta(seconds=1)),\n",
" timestamp=start_time+timedelta(seconds=k)))\n",
" truths.append(truth)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the ground truths. This is done using the `Plotter` class from Stone Soup."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from stonesoup.plotter import Plotter\n",
"\n",
"# Stonesoup plotter requires sets not lists\n",
"truths_set = set(truths)\n",
"\n",
"plotter = Plotter()\n",
"plotter.ax.axis('auto')\n",
"plotter.plot_ground_truths(truths_set, [0, 2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create sensors\n",
"\n",
"Create a set of sensors. This example explores the use of the `RadarBearingRange` sensor with the number of sensors initially set as 3. Each sensor is positioned along the line $x=10$, at distance intervals of 10.\n",
"\n",
"Increasing the number of sensors above 3 significantly increases the run time of the code due to the increase in combinations to be considered by the UncertaintyManager. This is discussed further later in the tutorial."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"total_no_sensors = 3\n",
"\n",
"from stonesoup.sensor.radar.radar import RadarBearingRange\n",
"\n",
"sensor_set = set()\n",
"\n",
"for n in range(0, total_no_sensors):\n",
" sensor = RadarBearingRange(\n",
" position_mapping=(0, 2),\n",
" noise_covar=np.array([[np.radians(0.5)**2, 0],\n",
" [0, 0.75**2]]),\n",
" ndim_state=4,\n",
" position=np.array([[10],[n*10]])\n",
" )\n",
" sensor_set.add(sensor)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the Kalman predictor and updater\n",
"\n",
"Construct a predictor using the `KalmanPredictor` and `ExtendedKalmanUpdater` components from Stone Soup. The measurement model for the updater is `None` as it is an attribute of the sensor."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.predictor.kalman import KalmanPredictor\n",
"predictor = KalmanPredictor(transition_model)\n",
" \n",
"from stonesoup.updater.kalman import ExtendedKalmanUpdater\n",
"updater = ExtendedKalmanUpdater(measurement_model=None) \n",
"# measurement model is added to detections by the sensor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the Kalman filters\n",
"\n",
"Create priors which estimate the targets’ initial states. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.types.state import GaussianState\n",
"\n",
"priors = []\n",
"for j in range(0, ntruths):\n",
" priors.append(GaussianState([[0], [1.5], [yps[j]+5], [1.5]], np.diag([1.5, 0.25, 1.5, 0.25]+np.random.normal(0,5e-4,4)), \n",
" timestamp=start_time)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialise the tracks by creating an empty list and appending the priors generated. This needs to be done separately for both sensor manager methods as they will generate different sets of tracks."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.types.track import Track\n",
"\n",
"# Initialise tracks from the RandomManager\n",
"tracksA = []\n",
"for j in range(0, ntruths):\n",
" tracksA.append(Track(id=f\"id{j}\"))\n",
" tracksA[j].append(priors[j])\n",
"\n",
"# Initialise tracks from the UncertaintyManager\n",
"tracksB = []\n",
"for j in range(0, ntruths):\n",
" tracksB.append(Track(id=f\"id{j}\"))\n",
" tracksB[j].append(priors[j])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create sensor management classes\n",
"\n",
"Next we create our sensor management classes. As in Tutorial 1 two sensor management classes are built - `RandomManager` and `UncertaintyManager`.\n",
"\n",
"#### RandomManager\n",
"\n",
"The first method `RandomManager` manager chooses a target to observe randomly. To do this the `choose_actions` function uses `random.choice()` to choose a track from `tracks_list` for each sensor to observe. It returns the chosen configuration of sensors and tracks to be observed as a mapping."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class RandomManager(Base):\n",
" \n",
" sensor_set: set = Property(doc=\"Set of sensors in use\")\n",
" \n",
" def choose_actions(self, tracks_list, metric_time):\n",
" config = dict() \n",
" trackIDs = [track.id for track in tracks_list]\n",
" \n",
" # For each sensor, randomly select a track to be observed\n",
" for sensor in self.sensor_set:\n",
" trackID = np.random.choice(trackIDs)\n",
" for track in tracks_list:\n",
" if track.id==trackID:\n",
" config[sensor] = track\n",
" \n",
" # Return dictionary of sensors and tracks to be observed \n",
" return config"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### UncertaintyManager\n",
"\n",
"The second method `UncertaintyManager` chooses the target observation configuration which results in the largest difference between the uncertainty covariances of the target predictions and posteriors assuming a predicted measurement corresponding to that prediction. This means the sensor manager chooses to observe the targets such that the uncertainty will be reduced the most by making the chosen observations. \n",
"\n",
"For a given configuration of sensors and tracks to observe the `calculate_reward` function calculates the difference between the covariance matrix norms of the prediction and the posterior assuming a predicted measurement corresponding to that prediction. The sum of these differences are returned as a metric for that observation configuration.\n",
"\n",
"The `choose_actions` function selects the configuration with the maximum value for the metric generated by `calculate_reward`. This identifies the choice of track observations which results in the greatest reduction in uncertainty and returns the configuration of sensors and tracks to be observed as a mapping."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import itertools as it\n",
"import datetime\n",
"from collections import Counter\n",
"from functools import partial\n",
"from stonesoup.models.measurement.nonlinear import CartesianToBearingRange\n",
"\n",
"class UncertaintyManager(Base):\n",
" \n",
" sensor_set: set = Property(doc=\"Set of sensors in use\")\n",
" predictor: KalmanPredictor = Property()\n",
" updater: ExtendedKalmanUpdater = Property()\n",
" \n",
" def __init__(self, *args, **kwargs):\n",
" super().__init__(*args, **kwargs)\n",
" \n",
" # Sensors must have a fixed order within the manager\n",
" self.sensor_list = list(sensor_set) \n",
" \n",
" # Generate a dictionary measurement models for each sensor (for use before any measurements have been made)\n",
" self.measurement_models = dict()\n",
" for sensor in self.sensor_list:\n",
" measurement_model = CartesianToBearingRange(\n",
" ndim_state=4,\n",
" mapping=(0, 2),\n",
" noise_covar=sensor.noise_covar,\n",
" translation_offset=sensor.position) \n",
" self.measurement_models[sensor]=measurement_model\n",
" \n",
" def choose_actions(self, tracks_list, metric_time):\n",
" # Generate a tuple of dictionaries where the dictionaries are potential sensor/track observation configurations\n",
" possible_configs = ({sensor: track \n",
" for sensor, track in zip(self.sensor_list, config)} \n",
" for config in it.product(tracks_list, \n",
" repeat=len(self.sensor_list)))\n",
" \n",
" # Select the configuration which gives the maximum reward at the given time\n",
" config = max(possible_configs, key=partial(self.calculate_reward, \n",
" metric_time=metric_time))\n",
" \n",
" # Return selected configuration as a dictionary of sensors and tracks to be observed \n",
" return config\n",
" \n",
" def calculate_reward(self, config, metric_time):\n",
" config_metric = 0\n",
" \n",
" # Create dictionary of predictions for the tracks in the configuration\n",
" predictions_updates = {track: self.predictor.predict(track[-1], \n",
" timestamp=metric_time) \n",
" for track in config.values()} \n",
" \n",
" # For each sensor in the configuration\n",
" for sensor, track in config.items():\n",
" # Provide the updater with the correct measurement model for the sensor\n",
" self.updater.measurement_model=self.measurement_models[sensor] \n",
" \n",
" # If the track is selected by a sensor for the first time 'previous' is the prediction\n",
" # If the track has already been selected by a sensor 'previous' is the most recent update\n",
" previous = predictions_updates[track]\n",
" previous_cov_norm = np.linalg.norm(previous.covar)\n",
" \n",
" # Calculate predicted measurement \n",
" predicted_measurement = self.updater.predict_measurement(previous)\n",
"\n",
" # Generate detection from predicted measurement\n",
" detection = Detection(predicted_measurement.state_vector, \n",
" timestamp=metric_time)\n",
"\n",
" # Generate hypothesis based on prediction/previous update and detection\n",
" hypothesis = SingleHypothesis(previous, detection) \n",
"\n",
" # Do the update based on this hypothesis and store covariance matrix\n",
" update = self.updater.update(hypothesis) \n",
" update_cov_norm = np.linalg.norm(update.covar)\n",
" \n",
" # Replace prediction in dictionary with update\n",
" predictions_updates[track] = update \n",
" \n",
" # Calculate metric for the track observation and add to the metric for the configuration\n",
" metric = previous_cov_norm - update_cov_norm\n",
" config_metric = config_metric + metric\n",
" \n",
" # Return value of configuration metric \n",
" return config_metric"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an instance of the sensor manager\n",
"\n",
"Create an instance of each sensor manager class. Both sensor managers take in the `sensor_set`. The `UncertaintyManager` also requires a predictor and an updater. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"randommanager = RandomManager(sensor_set)\n",
"\n",
"uncertaintymanager = UncertaintyManager(sensor_set, predictor, updater)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the sensor managers\n",
"\n",
"Both sensor management methods require a list of tracks and a timestamp at each time step when calling the function`choose_actions`. This returns a mapping of sensors and tracks to be observed by each sensor, decided by the sensor managers.\n",
"\n",
"For both sensor management methods, at each timestep a prediction is made for each of the targets except the chosen targets, which are updated. These states are appended to the tracks list.\n",
"\n",
"The ground truths, tracks and uncertainty ellipses are then plotted.\n",
"\n",
"#### RandomManager\n",
"\n",
"Here the chosen target for observation is selected randomly using the method `choose_actions()` from the class `RandomManager`. This returns a mapping of sensors to tracks where the tracks are selected randomly."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"\n",
"# Generate list of timesteps from ground truth timestamps\n",
"timesteps = []\n",
"for state in truths[0]:\n",
" timesteps.append(state.timestamp)\n",
"\n",
"for timestep in timesteps[1:]: \n",
" \n",
" # Generate chosen configuration\n",
" chosen_action = randommanager.choose_actions(tracksA, timestep)\n",
" \n",
" # Create empty dictionary for measurements \n",
" measurementsA = defaultdict(list)\n",
"\n",
" for sensor, track in chosen_action.items():\n",
" \n",
" # The selected ground truth will be:\n",
" for truth in truths:\n",
" if truth.id == track.id:\n",
" selected_truth = truth[timestep]\n",
" break\n",
" else:\n",
" raise ValueError()\n",
" \n",
" # Observe this ground truth\n",
" measurement = sensor.measure([selected_truth], noise=True)\n",
" measurementsA[track].append(measurement.pop())\n",
"\n",
" # Generate clutter at this time-step\n",
" # Skipped for now\n",
"\n",
" # Do the prediction (for all targets) and the update for those observed\n",
" for track in tracksA:\n",
" # Do the prediction\n",
" new_state = predictor.predict(track[-1], \n",
" timestamp=timestep)\n",
" \n",
" for measurement in measurementsA[track]: # Update the prediction \n",
" # Association - just a single hypothesis at present\n",
" hypothesis = SingleHypothesis(new_state, \n",
" measurement) # Group a prediction and measurement\n",
"\n",
" # Update and add to track\n",
" new_state = updater.update(hypothesis) \n",
"\n",
" track.append(new_state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot graph A\n",
"\n",
"Plot ground truths, tracks and uncertainty ellipses for each target. This is done using the `Plotter` class from Stone Soup.\n",
"\n",
"The positions of the sensors are indicated by black x markers."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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sWPsN6YbD4V7nnU4nALquY+z9hwvH9Diwu+dR44P14cD3P9De/sCh70lRBrq9Xy/VlmCU20F0+xZsct/XcWlpKdXV1ViWhaZpjB07Vj2NqQx6QtdxDB2KPT8fs6OD8LZtNPzwh0QrK2HvLyy6zo5AOyt/+xh2u4OzvvgVRhSPwOzsRFfBTDmcL3zhC9x11108+eST3HTTTQAEg8FDtj/zzDP5zW9+wzXXXIPf7+e9997j4YcfJhaLsWnTJiKRCOFwmLfffpszzjjjsO995pln8vzzz3P33Xfz97//nba2tmO6l+LiYlatWsWcOXN6hc3k5OTEk2WKMphJKeno6KC7u5tYLEaXhAdbQ+yqr+eiHRuIBroTbXVdp7S0lNLSUhobGyksLFShTBl0pGEQranBaG4By4wXKNNtCLuN6K5ddC1eTOCDfyOEIH/+Q+iZmQRXrKBOWHy4fCkjJk1h5lcvxZuVje5yoX3K7NLJRAWz40QIwaJFi7j99tuZP38+WVlZeL1eHnrooYO2v/jii1m+fDmTJk1CCMH8+fPJzc0F4mvhJk6cyIgRI5h8kKrGB7r33nu56qqrKC8vZ9asWQwZMuSY7uW+++7j+uuvJzc3l+nTpyeOX3jhhVx22WW88sorPP7448f0HopyMpNS0tbWRigUYo2p8WBbhJzdlXy5cj1R4uvIpkyZQiQSISsri5KSEjIyMtB1XU1dKoOSFY4QraxEeJPQ9paGsSxCGzbS9JOfxBf/C0H69ddjpqfTgUnhd75Dpq6T/sU5DBk/sW9v4DgSA2G6aerUqbKioqLXsc2bNzNmzJg+6pFyOOpzowwUpmkSCAQwDINYLMaPa/y8v6eWM6q3ktnenGi3d93l+PHjkVKSnJxMXt7JVVtJUT5vsdZWYtXVWIEACIHhb6PpwQcx987yaBrm7Fm8H/KDhK9dcyPeiZOwpZ98JTAOJIRYJaWcerBzasRMURTlKAUCAWpqapBSEovFSF+/ngurq9i7QnNvGQxN0xg/fjzFxcVomnbI2oCKMpjYMzKwpadjBYK0L1pE8yOPxE/YbGCaWMCKXdvwFBcz86xz0KSMh7gBEMwORwUzRVGUoxQVgj9V7SF1zw6CTY3s/8iNEIKSkhK8Xi/Z2dnk5eVh368EgKIMJrGmJoym5vjTlpqGsNkIV1URXr+ByPbtBD/4APeUKRQ88jCta9ey4uGf0JHmo+zqa5hw5tlomobmdO6b9hzAVDBTFEXpEYvFiMVimKaZ+ID4gv2968GklOzYsYN313xMc/VOHNIinJLK7NmzycjIYNGiRYkdM0499VSKiooS1yvKYCRNk8i2bVjRGHpyMlIIIlvX0fjTnybKYHhOOxXnlVciYzGyZs2iwAjxxZmz8aYO7NGxg1HBTFGUQS8ajdLY2EgoFEqUihFCJD7q6+upqqoiFArR1NyMaRgEHC6aikqZO30K548blbguJSWF6upqtSG5ovQQuo67bDKxmhqM5iakZdHx+uv7apMJwZ5gF6v+70nmfu2b+JJSmDTtNOyDMJSBCmaKoigEAgHa2trwer2J+nt7/7tz506WLVuWaOvMK+C1rGLOGVbI95Ns2Hv2vMzIyACgqKhIBTJFOYCe5EUfPQojO4uGe+4lvGYNCIEELAHbg+1MPHM2Xk8SSIncu0H5IKSCmaIog1pHRwctLS1EIhHa2toSi/U1TaO2tpYtW7b0aj88PZVHxxVT4nVjWRaRSISOjo5EMFOUwc5sbyfm94NpgqahORyEt2+ne8lSut99F9PvJ/O221i5eS2xj9chxo7h/P/+HzLyCoD4CJsYxOsxVTA7TlpbWzn77LMBaGhoQNd1sno2U125cuVRb1D8zDPPsGHDBn7xi198bn1VlMGsra2NSCRCWloaqampRKNRotEo27ZtY/PmzUB8E2UJ2DQNt9uNVV/LLrud/Px8MjIySE5O7tN7UJT+JFxZidHcgu7zgaYR3raN5vnz40ENSLv2WnznzCHZo5Ny/gVMPPtchKrnl6CC2XGSkZHB2rVrgXiB1qSkJL73ve/1arN3jzxVYFJRTjzDMGhoaCAcDhMOh2lra8M0TYQQ7Nixg127diEBQTyUpWVlM3XiBHJycrAsC13XKSwsVE9aKsoB3BMmEKuvx6ivx2hvo+0Pf0iEMgl01NeRVN/A5MJSHEOHqFB2APW3sZ/gmjW0/OZpgmvWHLf3qKysZPz48dx4442Ul5dTX1/PDTfcwNSpUxk3bhz3339/ou2KFSs49dRTmTRpEjNmzPjElk6vvfYap59+On6//7j1V1EGKtM06erqwrIsUlJSKCgowG63s27dukQog30jZaNLhmG323G5XBQWFjJs2DAVyhTlIDSnE2dxMeg2mh58CKO5GalpWIClCay87HjZDKdjUE9ZHsqgGDFr+MlPiGzectg2Znc3kS1b4vt1CYFz9Gj0pKRDtneOGU3uXXcdVX82bdrEH/7wB5566ikAHnzwQdLT0zEMg9mzZzN37lxKSkq48sorWbhwIeXl5XR0dPTaIPzll1/ml7/8JW+++SYpA2TjVkU5UTo7O2lsbARIjJY1Nzfz8ccfJxb9761Jlp2dzaRJk8jOzkYIQWZmJp5BUEtJUY6E2R3AbG+Lb6Fks6HZ7YQ2bcb/hz8QXLECLS+XDaeV01lfx+jMPMZefyNpp5/e193u1wZFMDsSVmdnPJQBSInV2XnYYHYsSktLmTZtWuL///znP/O73/0OwzCoq6tLbFo+ZMgQysvLAXqFr7feeouVK1fyr3/9i6Tj1EdFGaiklIl1ZEIIkpKSiEQibNu2jQO3qNN1ndGjR5Obm0tBQQEOh0MtPVCU/UR3VRPduRMtKRl0na4lb9Px4oL4z1NNo+OMU2ht2MOc+x6gdOqMvu7uSWFQBLMjGdkKrlnD7mu/iYzFEHY7+Y88jOcINgw/Gl6vN/Hn7du389hjj7Fy5UpSU1O5+uqrCYfDSCkTdZEONHz4cCorK9m+ffsRbWquKEqcYRjs2bMHwzDQNI1YLMauXbtYsWIFQGL6UhOCYcOGMXz4cHJzc8nIyMDlcvVdxxWln3KNHAk2G+1/+Qvdi9/GaGjodT4/Khn21cvxYDvszzVln0ERzI6EZ/Jkhvzh9wRXfoRn+rTjFsoO1NnZSXJyMj6fj/r6ev75z39y3nnnMW7cOHbt2sXq1aspLy+ns7MzEeiGDRvGT3/6U+bOncvChQvVhuCKcoQsy0oEspaWFpqamqisrOzVJjc3lzGjR5OTk0NxcTFut7uPeqsofUOaJjISQVo90/oC0DSE3Y6wxWODlJLw+vW0vbiAzjffRIbDOMaNo6Mwl6RVH6NJiWaz4S2fjLOoCFt6ugplR0gFs/14Jk8+YYFsr/LycsaOHcv48eMpKSnh9J65d6fTyZ///GduuukmwuEwbrebJUuWJK4bO3Ysf/zjH7n00kt54403GDZs2Antt6KcbJqamvD7/ei6jt/v55133sGyrJ4ClwJNSmy6TvnkyRQVFeHxeHqt61SUgS7W0kKsuhorFAJ6VveI+HrLSFUVka3bcIwYgdXRQdfbbxOtrES43aRccAHdk8az+O036Pa3csrXr2B4SibJp512wn+mDgTiwDUVJ6OpU6fKioqKXsc2b96sRpL6KfW5UU40wzDo7u6mtbWV2tpa1q9fT3NzMxCfvkwtKWWI10NGRgZjxowhJyenbzusKCeYNE26P/gAzeNFO2DaPrx1K4333derGr+9oADvrDNJOv10WjVY+NTPySwayjnfupW8EaNOdPdPOkKIVVLKqQc7p0bMFEUZ0ILBIDU1NViWlRgpMy0r8dSlTdeZOXYMpaWl2Gw2NXWpDEpC13EUFhLbswczEkFLSkLoOgDdixf3CmVJc+aQfv31dLb50VPTyOjq5OzzLmLcZVdgT1LFlo+VerxIUZQBzel0kpWVhcfjYUdDY69Q5nA4mDhxIqFQiNWrV9PW1obNpn5fVQYXaVnIaBR7YSGuCROw5WRjBbqJtbbQvmAB3UuXxheaaRrC4cA2fRr/fPlPLHj653S0NiN0G6VDS9FMq69vZUBQ34EURRnQdF0nLS2NNZbObxxpzNI0bJaFEALLslizZg1CCKSU/Pvf/+a8886juLgYj8eD2+1W5TGUAUlKSayxEaO2FisYipeKEQIhJZEdOwhv2khkx04iGzbgmT4d7xdmE9leSbNTZ/Fbr2IYMabOmElycjK21DRsmZnoqqbm50IFM0VRBryFDX6+s3k3ozKzmTL7bMzGepxOJ11dXWzfvj1Rv8yyLOrr60lNTaWpqQmfz0dRUVEf915RPn8yGiWyaTOaz4eWmpp4YjK8dSvNjz6amLr0nHkm6f/xNSTw7rb1VG/cSt6I0Zx703+SUaC+No4HFcwURRmw9tZNmmaTXO3VmZdqwymyacBi6dKlWFZ86mXviJmmaWRlZeFwOPB4PPh8vj6+A0U5PjSnE+eI4USrq5HhEMLlRjiddC9dum89mRA4CguxZWQC8f1ic3PymfzlC/CoUHbcqGCmKMpJzzRNotEosViMSCRCNBrl1bYgr3dF+GVeElY0yhUiTGdLF0II1qxZkwhlACNGjMDlcpGWlkZhYaEaJVMGPCkljqIibNnZGM3NGK2tdL76Kt17yzIJATYba+uqGb51E3n5RUybehqYJpql1pIdT4MymEkpsQJBZCSMFQphdXdjhcPxvb56tpEQdgea14Pm9aI5nWgez2febLW6uprzzz+fDRs2JI7dd999JCUl8b3vfe/zvi0AFi1axMiRIxk7duxh2z311FN4PB6+8Y1vHLLN2rVrqaur48tf/vLn3U1F+VyEQiG6urro7OxMTEeGhMZ8f5jFgRjlLhtBBGluNx6Ph+7ubt599138fn+vUbLs7GwKCwvx+XxkZGT08V0pyudPmiaxujpiDQ1Y4QggQYLQBJGd1bS/tIDo9ko8M6aTMvcy6pYuZtWeKlq7WsmNRbDlZKN5veguF5oaST6uBlUws8JhYg0NGPX1WLFYfE5dxKsZY7PFf0MQAiyJDIUwOzuRRiwx966npWMvyMeWltbHd3JwhmGwaNEizj///E8NZjfeeOOnvt7atWupqKhQwUzpl1pbW2lqakpMVxqGwZZwjJ8GBQ0WfMNhcYkeI9oSZmt7O7W1tdTV1RGLxTjttNPIyMigoaGB/Px8srKyiEajdHd3E4vFyM7OVmUzlAHFCgYJb9mCnpaOvt+askBFBc0PPZQYlLCfeirvrniXytpKsoeV8PVbv0dG4ZA+7v3gMmgeNzLb2wmuWkWsthbh9mBLz0j8A02Mijkc8Q+nE83tRvf5Eu201DSsQIDQxx8TqapCHuNQ7llnncUdd9zB9OnTGTlyJO+//368n6bJ9773PSZMmMDEiRP51a9+BcCqVauYNWsWU6ZM4dxzz6W+vj7xOnfddRezZs3ioYce4rXXXuO///u/KSsro6qqit/+9rdMmzaNSZMmcemllxIMBoH4yN0jjzxyyL5Eo1HuueceFixYQFlZGQsWLGDEiBGJopyWZTF8+HBaWlqO6e9BUY5GIBCgsrKSjo4OOjs76ezsJByO8FhIEJHwcLLga14dl8NOR0cHH3zwATt37iQSiTB27Fh8Ph+GYVBQUEBycrzuksvlIikpCYCamhoikUhf3qKifK60pCRco8cgw2HMNj9mVxehLVtoeeyxnhL/cY2rK9hRtZWpp87igvMuIjVVjSCfaINixMwKhwmtX4/wxAPY0RBCILxehMdDtKYG4XTiKCw8pn4ZhsHKlSt58803+eEPf8jixYt5+umn2blzJ2vWrMFms+H3+4nFYtxyyy28+uqrZGVlsWDBAv73f/+X3//+9wC0t7fz7rvvAvFN0c8//3zmzp0LQGpqKtdffz0Ad999N7/73e+45ZZbjqgv999/PxUVFfz6178GYMuWLTz//PPcdtttLF68mEmTJpGZmXlMfweK8lkFg0F27dqVWEtm6TqGaeIAbpQCl7Tw+SUtmobf72fbtm3sv8OJZVm43W6klJimSUtLC7quJzYqt9vtRCIRIpGI2pJJGTCEEDiKCrHn5mB2dtL2yl9p+eUv0VwupM0GponQdQpPOZ25GfiZpEkAACAASURBVKmkpWWgud0Im97XXR90BkUwwzTBsj7zGrGDEUKApiOj0SNre5jjl1xyCQBTpkyhuroagMWLF3PjjTcmilymp6ezYcMGNmzYwJw5c4D4qFpeXl7i9a644opD9mHDhg3cfffdtLe3093dzbnnnnvQdgfry4G++c1vcuGFF3Lbbbfx+9//nmuvvfaQ76sox0MkEmHXrl00NTXhcDiojxr8wvKSi8l3CFBo1+no6Ka+vZ1QKERDQ0Ov6zVNw2azEQgEcLvd2Gw2bDZbIqBlZWVhGAYOh0NNZSonNSsaJVpTg9nqB9OIH9RtRHZU0b7gJSKbNuGeMgXbf97Mvx//OZ6GZs64426Sy8vj66x1HeHxqI3H+8CgCGbC48E+dCjR6mq0ZN9Rj5pJ08Tq7ED3erEXFHxq+4yMDNra2nod8/v9iQ3H9/42rus6hhH/wtm7XqbX+0rJuHHjWL58+UHfx+v1HrIP8+bNY9GiRUyaNIlnn32Wd95556DtDtaXAxUVFZGTk8OSJUtYsWIFzz///CHfV1E+b6ZpUl1dTSQSwTAMNoYNfulII4LgnFAb/mAHgUCA3bt3c7A9gNPS0hg6dCjJycmJ6U+fz4fNZiMWi2EYBlJKsrOz8Xq9agcA5aRmdXYS2boNW2Ymwu0BILhmTa/1ZLV5Waz42QN4fSnMeHA+qZMPunWjcoINijVmQgicxcW4xo9HWBamvxWjvQ0zEMCKRg+5XkzGYlihEGZHB2abH6urC3tBAe4JE44o3CUlJZGXl8fbb78NxEPZP/7xD84444xDXnPOOefw1FNPJcKR3+9n1KhRNDc3J4JZLBZj48aNB70+OTmZrq6uxP93dXWRl5dHLBb7zEHqwNcCuO6667j66qu5/PLL0XU1xK2cGLFYjJqaGhobGwkEAqxzp/CgMwM7kh8Yfk6zg81mo6Wl5aChTAhBZmYmLpcLKWXi325nZyc2m420tDQmTpzIsGHDSElJUaFMOenp6em4J00Ew8Ds6CC8aSMtv/pVYj2ZtCyaK1YyfOxELrnoa2QJ9W++vxgUwWwve2Ym7qlTcJdPwVlSgi01BYFMBC/DH/8w/X7MNj/SiKG53dgL8nGNH4/3lBk4S0o+05Toc889x49//GPKysr4whe+wL333ktpaekh21933XUMGTKEiRMnMmnSJF544QUcDgcvv/wyd9xxB5MmTaKsrIx///vfB73+yiuv5OGHH2by5MlUVVXxox/9iBkzZjBnzhxGjx79mf6+Zs+ezaZNmxKL/wEuuOACuru71TSmcsJIKamvr6e5uRmn00lDZxePRzSGmlHuDreQG4vQ0tLCunXrCAQCies0TaOkpIQhQ4Ywbtw4UlNTicViOJ1O3G43SUlJDBs2jDFjxjBmzBjS0tLUtI0yYAhNw5Gfj2fGdMy2Nprmz0doWrwCgaYhdZ3iL3yR2bO/hCczE/t+y2OUviUO9tvlyWbq1KmyoqKi17HNmzczZsyYI7peSgmGAVIiIb7Bsa4j1IjQJ1RUVHD77bcnniI9Gp/lc3OsZCwWr2Ldsw8cQiQ+t+rz2/+ZpkldXR3t7e20tvpxOOzouk5VzCLLiEIsSn19PdXV1b0KxiYnJ5OXl0dKSgperxe73Y6UknA4THJyMoWFhaSmpuJRa2iUAcLs7ia2Z8++mpy6TqSqivaX/kL4449xjhvHjlPLGT9pKrYd1XimTYuPqGma+hroA0KIVVLKg84dq7FLehbj94yCqX+eh/bggw/y5JNP9uu1ZdI0Mfx+Wn/7W5CSpDPPjB8HolVVhLdswTl6NK7SUhACPTUVPT0d3ZeCnnTotXrKiWdZFh0dHdTU1CCFxtO4yQhEOCfcCl1d7OzuJhAI0NHR0es6TdMYNWpUoiRGJBLB4/HgdDrJzc2lsLAQl8vVR3elKMeH0dREpLoaPT0DzeEguPbjfevJhGClFqWuYjmZw0cy6frr4qNnSr+kgplyxO68807uvPPOvu7GYcXq6wlt3ER4/QZCa9aAZeE7/3zCmzfT9PAj8UfC7W+S/T934hw5EqurG6O9HWGa2IcOxVlc3Ne3oPRoaGigs7MTYbMzPyBZLu1c4dJwWEEqKyt7jZDtlZycTH5+PklJSTidTmw2Gx6Ph6KiItLT01X5C2XAcgwdivB4iO3ZQ7iyktZf/zqxnsySEl9XgKnXfYsshwfT78emSh31WyqYKQOKLS0NW3oaaddcgzQNjPYOguvWEVz+YbxsCvHpzY7X38D3JTN+TNMQDifYbCqY9QOWZdHZ2Ul9fT1tnV38WkviQ2nnPwhySkMtqw8IZftvrTR27FhSUlIIh8P4fD7S09Px+Xyq9IUy4Aldx5GbS3TrVprnPwSA1HWkaYKuMfWa6/CNHY+WnIyutlTq1wZ0MDtY6Qmlbx3vNY2a14tnyhRCGzbgu+ACNJc7/hCHywW6nghn4TVrkIaBp2wSjtLhaElJWIEAsdZW7GqvxD7V1tZGXV0dXq+XB0IaH8YE87Qw01sbWHfA08iaplFaWprYRqmoqAghBAUFBRQUFKinK5UBK9bYiNHcgrRMhKYR3l5Jx8KFhCoqcIwcyZAnnyBcV8eeV16m+LIr8ZRP7usuK0fouH/XEkIUAc8BuYAFPC2lfEwIkQ4sAIqBauByKWWbiCepx4AvA0FgnpRy9Wd9X5fLRWtrKxkZGSqc9RNSSlpbW4/7+h6habjHjEFPSiK0ZQvRDTvoeutfCJcbufepPSmJrFtHZNMmUi65GHtePlp6Om61DU+fsSyLlpYWWltbaW9vxzRNJggXY7vayK/Zyfqe7cD2Sk9PZ9SoUWRkZGBZFrquU1BQgNfrxeFw9NFdKMqJEd2zB9PvR/MmEd6+neb588GykEKwygUZu3fhHTmKsT99qK+7qnxGJ+LXSQP4LynlaiFEMrBKCPEWMA94W0r5oBDiTuBO4A7gS8CIno8ZwJM9//1MCgsLqampSeztqPQPLpeLwmPcyupICLsdZ0kJlmWhaRopc+fS8ZeXP9nQMIjuqcE7cyaYJuHKynhBRvWD/YSSUhIIBKivr6dVt7MnOZ2h7c0UbltPbW0tTT3t9p+2HDp0KD6fj9TUVJKSkkhNTVWL+pVBwzNpErHWVsLr1tP6xBPxJzGJfy3lmALD30Zk8xZsM6Z/LrveKCfOcQ9mUsp6oL7nz11CiM1AAXAhcFZPs/8D3iEezC4EnpPxOa8PhRCpQoi8ntc5Yna7PVFhXxm8XKWl2DMz49PaTicdLy9EhkK9Nu0NrVyJo7gY1/DhCLsdszuALV0FsxPFsizq6+sJBoOsjkke8EfI62jlix9/8Imp78LCQnw+Hzk5OWRmZiZKX6hRcWWwEXY7sV27abjnHqxoFEsIkBJhszH527fimTgB4faoUHYSOqELMIQQxcBkYAWQszdsSSnrhRDZPc0KgD37XVbTc6xXMBNC3ADcADBkyJDj2m/l5CWEwJaainv8BHSfD+fIUfifeSa+o0NjY7yRaRKprMQ9YTxWdzexpkZs6Wl92/FBwjRNQqEQ7e3tPNcR4Y8RQVlHM6dvWU3sgFCmaRoFBQXk5+dTXFyM0+nE4XCoUKYMeDIWI7pnD1YwCAjCldvpXPQqwZ5fKreVjaFt+1ZOGT+FggsuxDNZrSc7mZ2wYCaESAIWArdJKTsP8830YCc+sWJcSvk08DTEC8x+Xv1UBiZHUSF6ehpm4APS580j1tyM/6mn9j0MUFFBaOhQvDPPIFbfgD0nB1uaCmfHW319Pe1dXfyw0+L9iMYFjTvI37KO2H5thBAUFxczcuRICgoKyMzMxOPx9FmfFeVEk4ZBtLYWGQoTramh+dFH49+7hMB3wQXMGD8WPS+X9NIRfd1V5XNwQoKZEMJOPJQ9L6V8pedw494pSiFEHiSWkdQARftdXgjUnYh+KgOb7vXinTaN6J4aROV2HKNHE937lJ+UdCxcCDYb3tNORUajSMNARiJY0Wh8ZwhI7B4gbDbQbWgOu1qPdhSi0ShdXV1EIhGCXV1kNvm5Yec2tHZ/r3a5ubmMGTOGoUOHMnToUDU6pgxKmttN0owZxPx+giv2K/0jJZHqalJKS7AbB9/zWTn5nIinMgXwO2CzlPJn+516DbgGeLDnv6/ud/xmIcSLxBf9d3zW9WWKcii2tDSwLMyWZpLPmUPrpk371ptJSceLL9L56qvoaWnY8/Ox5+WiZ2Vjz85Gz8rCaGwkun07zlEjcZSWIgAtKQlbdjb2rCwV0o5Qe3s7r+xuIC3YSbBqOzm7diXOaZqWeMryzDPPZOjQobhcLhXKlEFNOBw4cnPRzzgd64U/o0kJNhspX/0q3rJJCDWKPGCciBGz04GvA+uFEGt7jt1FPJC9JIT4f8Bu4LKec28SL5VRSbxchtotW/lc2TIy8J52Gu5JkxCWpOWXv4yHM00j+bzzMFpbMNvaie7aRWjVql4PCgDxETO7jew77sQ1bizStIhW7cCoq8NdVqYW2x5GIBCgqbmZ37R084+aZi74eFn8B0wPIQQlJSUkJSVRWlrK2LFj+7C3itK/7Nm0nr89/wxFhTkM39NA/o/uJ/n00/q6W8rn7EQ8lbmMQ29BefZB2kvgO8e1U8qgp7lcaC4XWTfdiHf6NOrv+yHR7dvxnXcurvHjCa5ahdHYiDQMrGAQs62d4OrVxKqq4pvdxwwCy5ZhGQZC09CSvNhzcvr6tvo1wzBoDwS5f9NO2upqOb9pT69QBvHRsjPOOINitQODovRiGjH++eQvcCf7mHb7f9D23e9hz8vr624px4Eqi60Mep4pUyhZ+DItzzxD0syZCJcrvmWJpiNjUaz2DjSPF3tLczyYQXwUTdcQSITbDaaFnpGhRst6SCkxTRMpJbW1tWzZsgVcbl6oaaa4ciNDkAhITE/u3U6prKxMhTJF2U8sHMbq7sKsrePcORfg8XqhIb4ku/V3v8Nsb8czYwZ6UpL6/jNAqGCmKMTXb2R9+9sAmN3dxOrq0ZKTMOriyxultDCbW3pdE2tsxAoGEdEottw8dLUpMAB+v5/W1lYAmpubWbx4cWJvy5ID2paUlJCcnMzEiRMpKTnwrKIMbm0Ndbz2yAMUFAxh+vhyMoYOQ+g67R8sByCw7AOCH64g87u34xoxEsewYhwFBX3baeWYqWCmKAfwP/ccLb/8FalXXIH3jNPjT2haVny/zf1oHi+O0uEIPT6ypqvfVgGw2Wzouo5pmtTW1vbacDwvL4/GxkaklOi6zsyZMxkyZAiapvVhjxWl/9mx+iPe/NUjCE2j9KprcKZlEqmsov3llwm89168kZRI0yS6YyfO4SOIVFWph5AGABXMFOUAmTfcQLSykvYFC9B8Pmw52Vhd3Wg+X692mtdLtKoSzevFVVaGlpTURz3uX3w+H8nJyRiGwYoVKxJFCO02G7NmzULTNOrr6ykpKaGoqOiwr6Uog420LJa//ALLF75IVlEx5579FfQVq+j2evE/+yxGYyNJX/wigffeQxoGwmbDM6Uce0E+drWd3ICggpmiHEDYbOT95CdEtsfDWdb3v4/dl0J486Ze7WQwgKOkBKHpEI0h1KhPghACv99PZVUVOzLzGTOkkK9PnpAIYiNHjuzjHipK/9S8aQMrF73MiFHjmDF0BG3fvwMZi5dc1rMyKXzicZJmziS0bh3BlR/hmT5NVfofYFQwU5SD0FwuCn7+M3ZeOpfORYtInXspjuEj4O0lieKOwY8qcJWV4RwxAldebh/3uG+YpkkgECASiRCLxTBNE8uyaG5u5q133iWiaZhTTuHm8cNwOhzxPUtVPTJF+YRAexve1DTS84u49MprSU1Nw//cHxOhDCDpjJmgaQRXrcJTXq4C2QClgpmiHIJz+HDyH56PnpaG4fdjz87GNXEi4TVr4g0si9iu3XjGT0A4nX3b2T5gWRaNjY00NjbidDoTo2Q7d+6ksqesiF0Ivml109jQAICu66SmppKWlqYCmqL02PbhMv7x5GPMue7bjJk5m7zTTqfhJz8luGxZvEHPbiOOkSPjRa1drvguJMqApIKZohyG75xziDY2YrS0oiUl4Rw1al8wkxLN60EKgWYbXF9KhmGwe/duIpEI4XCYzs5O2tvbWbt2LXK/2mSalLTW7KEwIx2Hw4FpmjQ3N+P1enEOwjCrKPszYzHe+7/fsvqtN8ktLiWjvZua//ovgv9ejtnRQcYNN+A943RCq1fjmTIF16RJCCFUWYwBbnD9NFGUo6CnpOD//e+x5eVhtrX1Ohdatw6RlETg3x/gPeUUPJMno7ndCI9nwI4IBQIBmpubaWpqIhAIAPESGdu3b+8VyiBenyw5OZmmpiaysrLIzs7G4/GoUKYMeoH2Nl5/9CfUbNvM2HFlTE7Po/WOO6HnKeb0668j7etXY8/Kwjt9eh/3VjmRVDBTlE+hu1zomZl0L1mCu6ys17notu1Et20HoOOlv5D13dtxDB2KlpKKp3zygAlnUkqCwSAtLS3U1dXR2tqKpml0dXVRU1NDV1dXvF1PewGkp6eTm5tLUVERPp+P7Oxs3G53n92DovQndVs301BdxRevmkdBfQstTz2VCGVoGkZDI+GNG9GnT0dT+2AOKiqYKcoRyLvvXqrO+xJWIAB2OxgGaBqO0lKi27YBIA2D8KbNuMZPwJ6dNWBCWTAYpK6ujo6ODpqbmzEMA13X6ejoiFf0Jx7I9t6ty+Nl/JjR5Obm4nA4cLvdRCIRampq8Pl8pKWl4VCP9CuDkJSSlj27yBpSzIgZpzEv52cEnnyS5ldfw1ZQgNnchDRMhN1O8nnn4ikri+8sogwqKpgpyhGw5+SQc8cdNNx3H2lf+xpmKIRjSBGxpqZEMENKhNeLNGLYsrL6tsOfAykl3d3dbNy4EbPnSVSHw4GmaRiGkRglg3go2xvOIsEApmkihMBms2G327Hb7YnX6+7uZsiQIdjVOhnlONs7td4ffkkKdbTzz8ceZufmDVx+w3+ir1hJ+4KXkJEIGTfdRNZNNxLauFGVwFBUMFOUI5V6xeV0LV1CYOVKip56EqOlhbYXF/RuZJrYMjIQut43nfycSClpamqiurqa9vZ2DMPoFaR0XU+Meu0NZPv/6ItGo9hsNgKBAF1dXaSlpZGcnIzb7aa7u5twOKyCmXJMLCtGKFRNNOrHkgYAnZ3riIQbSEs7hfT0U7DZfEDf1xds3rWT1x79CR1NDUybdjrWK4vofOut+EmbDVtGBtI08UyerAKZooKZohwpIQT5Dz6IEILQ5s1YnZ2IA7Zpsrq6sEyTWHNzfLpTaKBraHY72GwIuz3+cEA/Dm6mabJ7924aGhro7OzEbrcTjUYJBAJomoau61R2BQnV1gL7ApkQAiklmqbhdruRUuJ0OpFS4vf7sdlsWJaFy+VSa82UY2aaIQKBnei6B5stiYaGv1FT+ywAu+tewMy4kulFXyI5aQwOR1qf9XPD0rd4+3dP4kxK4rL//TG+mjrqnnluXwPLIrx1Kxzw4IwyeKlgpiifgS0t/g3eUTyM4OpVnygl1PWPfxCsqMBdVoZ78mTQNCLbtuEcOQJn6XAAhKZhy0jHnpeHnpp6om/hsEKhELW1tVRXV+N0OrEsKxHIYrEYLW1t7AhGSOqMP526fygbMmQIUkp8PVtXtbS04Ha7MQwjEcZSU1NxuVxqb0zlqElpEQrtJhptQ9Mc+P3LaWl9i2Cwar9GFk3tazHzzyQWa+uzYCYti7Y9u8grHcHsU87CWPhX6l99DXthIUZTU3xLJbud1EsuVgv8lQQVzBTlKLT86ld0v/02eT//GR0LF8YX7Oo6SeeeS2z3brrfeYfuxYvjjXvqDuXcew+uUaORloXR2UWssRHXmDHYc3L69maIT12GQiE2btxINBoFoK2tDU3TCIVCNDQ04O8pFbJ3R9C9oWzvE5cul4vk5GR0XU+EsdzcXDIyMvD5fOj9eJRQOXlIGSMU2o1pRQkGqtm952nAQgKGjE9capqNr4z7LikpU7DZkk94H1tr9hAOdJNhczAhdwijDZ2W//5+fBQdSLnkYpwlJUR37MDTU2ZHUfZSwUxRjkLaVVfS8corRDZsJPvuu+leshR7fj72YcW4J00i+dxz8P/fc5hNTSAlMhaj++0laElJ2FJSEQ47MmBhBYN9fStIKWlsbKS+vp5oNEpXVxehUIiOjg6klOzevRtLSg62fFoIQUlJCcnJycRiMXw+HxkZGWRkZJCSkoJtkBXeVY6f+C8PNRhGG5ruwt/2ITV7/gDES0xYEurIpyy7nILsc8jMnN0n/dz0/lLe+u2vScvJ42t3/QhbYyP+115LhDKEILanBsfQoSSfdx7OYcP6pJ9K/6W+ayrKUXBPmIBnxoz4Juf/8z+kXHopVjCI1dUZ36qpoTEeyvbSNOwFBUR37SYSq8QxZAieadOw9/HTm7FYjJqaGtra2vD7/TgcDgzDYP369Vh7ayrR+6lLTdMYPnw40WgUt9tNfn4+Pp+P9PR00tLS1KJ+5TixCIdrCEcaaWh4hba2D/Y7J9A1GxdNeojU1Clo2okvYByLRlj6h9+wfsm/KBg9jvP/8/tEtm2n8f4fEa2uhp7pe2Gz4ZkxHfe4cdj6wWi50v+oYKYoRyn1koupu+NOYrt2oblcSNPEkhLNNAi8916vtu7yyXimT0O4XMhIBFtmFo7cvt34vKOjg6amJmpqahIlLRoaGtheVYVpWb3CGIAmBDk5OaSnp1NSUoLb7SYtLY2MjAwcDke/KEmgDBymGSQY3Eks1oGUBt3d22hu/hfdgS2YZgAp924XKUhOHsfQITeQnn5an/Q12NnBX+67k5baPUw5aw4TswponPdNotu2YS8qouiZZ9C8HlUKQzkiKpgpylHynn46QHydyMyZyFAIuruJ1jQQ27VrX0Ndx1FSSnj7dmyZWXhPPaVPQ5lhGHR2dlJVVYUQgkgkwq5du2jt7qazpSURxCTx0bFhxcVYlkVhYSHZ2dmkpqaSnZ2dqGmmKMdDNNpGMLQbhz2Dru5tVFY9CEgk8HanjVnJJnZA05yMGnkvKSnlfdZXV1IyGSlpTBk1gdQtlTQ+9lT8hKaRdu08PNOmojmdKpApR0QFM0U5SrbMTIa8tIDY7t3Irs740+4OO4Fl7+9rJMBz2qkknzMHaZpomo4jN7fPNiE2DIOqqiqi0Sjt7e3s2LGDhoaGT+xxuXdLpbFjx5Kenk5KSgp5eXk4nU61iF85rqQ0CQZ3Eom0IC2DuvqFNDYuYu+GX5aEYSnDKB7yVTSznZycL/VJKItFIyx74f8o//KFpGTncPYV36DhnnvpWreuV7vw+g3I888HtT+scoRUMFOUoyRNE0IhNLcbS2jIQIBoZRWxndX7Gmk69vwCjD17cE+diqOoqM9CWXNzM+3t7ezZs4eqqirqewLZ/hOQ2n61yMrLyykrK8PhcKgtlJQTRkqDcKSe7q7NNDcvpqNz1b5zxJ+4vGTC/5CWNh1d75t6eP66Wl7/+U+Jbd5C5rrNZLg8dPztb2huN+nXX0fbH/+EjMUQNhtpcy9F7ykhoyhHQgUzRTlamkb3u+9idnTimTEd3eUksGzZvvNC4J05E9+cOcjY/2fvvOPkusr7/Zx77/TZme1VW7TaVe/dTbhhBYxtsOFHgIApIQUSCAFSKalAgAAJIRCwwYAJxWBcwN3Glots9bartrva3men11vO74+71ZIbWFrJuc/nY2s055Zz79gz3/u+7/m+BbSyMpR5eGqeWmE5MjJCb28ve/ftmxZkc0SZorB27Vq8Xi8LFy5k0aJFTqrS4ZySzQ6Qzw+STnXS2fVlpDTn1JKFgitYUP8+ysq2zUtNo5SSI488wGPf/w6lmTwXnxqGjgHigP+ii6j70hfRysspuvJKp57M4bfGEWYODr8lRjRK6vEnQFXxbdpErr0d/dSpmQ0UBfeiZkQ4hG9hM2owcG7nZxgUCgUGBwfp6uqip6eHnpFRtElRNt1KSQiWL1/OqlWrWLRokbOq0mHeMIwU0ege+gd+gCXNOfWOquJm8eLPUFy8Yd7md/jRB3n4lm+woLKWdUOd0z1kEQKtuholaLv8Oa2VHH4XHGHm4PBbIlwujPFxPEuXonjcpJ54YtagwH/JJQTWr0f1+8+6KLMsC13XMQwDy7Lo6+vj2LFjWJZFz/AIyfExAFRACoGQElVRWLZsGevWraOlpeWszs/B4cXI58dIZzpIp0/SdeorgAXSdihThEZl5Xbqat81b6LMNAxUTWPJJduQO5/Fd99DmIk4qCpIiXC5KLr6KoST8nd4FXCEmYPDb4GUktzBQ1iJBK6aarJH2uauxFQUgtsuw7d+PWo4fFbnous6fX1900/vHR0d7N69G8uy5thdMPl6yeLF04X99fX1Z3VuDg4vhmGkyOdHyRcmGB19kKGhO5gyjEVAuGg1SxZ/et5WXOrRKHvuvZMT+/fwxo2XEf/e93D39+Nas5qGf7oNK5N2UpYOrzqOMHNw+C0x0ykAtLJykg89NDMgBIFLLsHT3IwaCCDOUp2WZVkkEgmSySQjIyMYhkE0GuVIW9u0D9nzUVWVrVu3stBxG3eYZ6SU5PMjpFLHGY88zvDwL2aNClTFzaJFH583UZYYG+VX//ZPjJ3qZHM8z/j9j9sDqkrxO9+Jp7UFoShnTZDljBxpPY1EogqVoxNHOTx2mC01W1hbufasnNPh/MARZg4OvwVmNIbe1w+ahj42ht7XNzOoqhS/9a0ENm48a6kN0zRJJBL09PQQj8cZHx8nEokwODjIlPHFHHNYRWHp0qWsXbvWEWUO5wXx+D503fYqGxr+xZwHiaKilSxa9EnKSi855/OSUtK+4zEev/W/qR+KsHY8gchmZ29Ads8eiq+9dtrN/9VGN3X2juzFkhZCCDpjnXx171expMW3D32bL277IpctuAy36qROX4s4wszB91T4KwAAIABJREFU4bfEu3Qp5R/7C6K3fnfmTSHwb9mCd9XKsybKEokEw8PDAEQiEZ566qk5PmRTP3DV1dU0NjYCsHLlSidt6XBe4fXWYRhpdvT8iro5OXeFmpq3zosoA7AKBXq/9S0uPdaFO5sjsO0yQm98I8P/8I+2BYbLRfGNNyLOYh9YTdGoC9bRl+wjq2f58bEfY0q7VMGwDB7vexyfy8eq8lUUuc99k3aHs4sjzBwcXiFS19EH+sl1nGT8a18DfaY5sXC5KPvDP8RdU/OqnzeXy5FOp0mlUvT09HDq1ClGRkfnirJJHzJVVdm+fTuNjY2O5YXDeYdlGcSSJ/j8/u/SFeniI5UCMR3rFbhcxUhpIsTZNTM2U2n04SHQdU498hC+I+0Ujp+gaXgY7+rVVH7i4wQ2bwbA3dh4zurJhBA0hZuI5qP8y7P/wlB6CBVhpzUVldaSVgzLwLTMszoPh/nBEWYODq8UKZG6TuLee2dEGeBZsoTwTTcS2PjqrxyTUjI6OkokEiEWi/HMzp3IyeJ+SwgUKdFUlSuvvJJ8Ps+iRYumo2UODucbQij8oONhHhvayweXvoVSVw+x2M7JUZP+/ttRlQClpVvPqomsGZ0g2dbG4TvvoPq5vUwlLENveQul73k33iVLprc9mxYYhmVwfOI4sXwMgJ54Dw/1PMSB0f2UaT5u+73bUITCc0PPsbZyLavKV+FW3WiK8xP+WsT5VB0cXgFmKo0xOoJwuzEnojMDmkbpBz5AcNtlr6qzv2majI6Okk6nMU0TTdMYHh7BmlXcX1VbR2U4RGtrKytWrHBc+h3Oa6SUCKHwoQ1/zcbaS7m4ai0T0aeJx/cgpQ5APL6bI22HKC+/iprqGykpuQgpDaTUsaQJ0sSyDBTFBUJBoCCEiqK4URQPQry8KHFPfw8nvvV1mk4NzLypKAgkVjoDlnXW6shmI5Gk9TSWtBhMDfKlPV/EkCaKlHwyZYK0aC1d4hT9/x/BEWYODq8Avb+PfGcnwuXGGBqaGZASfWiQ7N59+Ddu+J1bsFiWRSaTIZFI0NvbyzAKj00kWR8ZJDI6Oi3KBLCseSGtra0UCgV6enpoampyTGIdzkseOPUAPzvxM7519bcIuoNc2bgdgNqaGwn4m5iIPks4tAGwGB65h7GxBxgdvQ9VDVJcvIni4i0EAy2AYqftkSCZ8xoh0NQALlcJbncZmhaa7hIgpbSL9w8epOMbXye/azfNBQN11SqsEyeQhmHXkN10E/6zEPl+IVyKi/VV6xnPjvNUx712ilLYnoNPL7uabbkow+lhFhUvOmdzcpg/HGHm4PAykbqOCATQysuJ3vnLuYOWhTE8jOfaa1ECv5uZrGVZjI6OMjQ0RHssyVMDwwzmC6zp66D7DNtHIhEaGhoQQhAKhZwm4w7nHRk9wxd2fYFfdvySNRVrSBQSlPvK52wTDq+ftsYwjCSWpVNachnpzElisV1MTDxNJPIb3K5ySkovoaz0MqQ0SCSPECpaSTC4FLDFl5QFcrlBstk+FMWF17sAMZRD7x8iuXsXie/ehsuy0IDSD3+Iqj//czL798+rJ5mmaFQLN2/Yewd3lgbICbvq7sqma1hftZ6A69x2DnGYPxxh5uAwCyklslBA6gbZQ4fI7ttrr7Jcvpz000+TuO9+8ieOU+g6NXdHIfCuWo27ru4lz2FZFrlcjmw2Sz6fJ5/PYxh2rdrExAT5fJ6shP+JpFlxeDeV0qLqeceYXeTf0tJCU1MTmqbNS/9AB4cXY9fQLj7zzGcYTA3ywVUf5ENrP/SCtVGGkaRQiBCL7SVfGENVfbhcJVSUX0NZ2eWkUsdJJo8wMnIPIyN3T+83KFy0tv49odBahBAI4UFR7L60lqWTjhwlfvcOYr/4DYHITAmCEAJzeBip6/PbRmkqZeovZe3ln+U7xdX8Yuhp7uq8i3ghTthzdk2qHc4vHGHm4ABI0yTf2Ykxucox39nJ2L9/BXQdvvHfdhflyS9Pz5IluBob0Xt77Z0VhfKPfoTw9mte8jzZbJbh4WHy+TxSymkxpes6w8PDHO7uISgtstksS2NxVGlN7zslxhRF4dJLLwWgqamJhQsXOoLM4bzEkhZf3vNlVKFy2+/dxvqqFzaLTac7yWS6EUJBAopQURWfXS8mVARgelNIK0cwsJix8QeR0n6gkdJgfPwJSOm4lBLojKEf7kApDqMf7yLz8DOIgg4eN/rmjbgPHJpOWxZt3/6q1oW+Yia64OcfgNf/Eyy8DDa8l7XAmubttE+0c9uR27h+0fUoL7NuzuHCxxFmDg7YwswYHUOaJsLlotDRaYsyACnxLFlC8JprcNfVEty2Da2k5BWlPizLIp1Oc/LkSTKZDNqkB1I0GmV0dIyObB5leAABTD3Pu5krxpYuXYrX62Xp0qW0trY6YszhvKUr1kVVoIqAK8BXr/gqpd5SfNrpqyulNNH1KLncKBMTOxDCi1AUpGVS0OOkMz0oQkUoLgx9guGRe5lu2YQGKIBECI0irRXzyCD5U7vQv/8kGPZ2piIYKA6S37iGLZ/4BFUNa+Y9bTlN2y/hno+AUMDIzRkSQvDeFe/l7576O54aeIptC7bN0yQdzjWOMHNwABS3G1dVJbnOLsyBAcxkYs64e9EivEuX4mlZhHC5kFK+7NSHZVn09PSQyWRQFAXTNDFNk2QqzZ69e5CWZTcXP8O+tbW1FBUVUVlZyYIFC6iurib0Oy4scHA4W0gpuavjLj733Oe4oeUGPrX1U9QFz5zeN80sieRhTDMDaChqEDDQ1CBCE7jdxaTTJ0kkDqMbcdLpE8yIMgiF1lAUXE5BHyPgb8Hna8RamsR47uS0KAMYaqwmfN2lrHzHuwlMFs/Pa9oSwMjDg38Pu78DdRvhbd+D4obTNvu9hb/Hf+7/T7575LuOMPs/hCPMHBywf1DMZBLF40apr0c+/sTM4KRxrMxmEarK0Kc/jTE4RNNPf/KSx0wkEkQiEYaGhqbtLtLpNAMDAwyPjyMnbS/s1I2YYxarKAo1NTUsX76cYDBIOBzG4/GcnRvg4PA7kigk+Oed/8wD3Q+wuXozf7z6j8+4nWGkMYw4yeQJUqljaJofiURaJvn8GJbsQggP+dwwY+P3M/XIomllGEYMsBBCxeutBSw8nmo0LYRpJrFCEGkqIqgIFEuCS2PjX32Soi2XoPnPoweaQz+zRdlFfwZXfRa0M1vcuBQX7172br6050scGjvE6orV53iiDvOBI8wcHLDTBlp1NWYiSXrvPtJPPTUzqCioxWEUvx8jHif9zE4CW7dgJhIoweAZm5Sbpkk8HqezsxOXy0U2m2VoeJhO3UL2dM2xuwBQFYWWlhby+TwlJSXouk5DQwMrV64k8Duu8nRwONscjRzlY49/jOH0MB9d/1Het+J9qMrpq4MzmR4y2W4AhJAoigcpFTTNh9AUdCNGInqCfH6YXL5vzr7B4GKCgaXk8n34fQvx+RoASb4QxeMpI5uA3T//DX0Hu2lZs4zF+9sp+8M/pOSKN5yDO/AyycbAVwzr/gDKF0PDlpfc5abFN/GtQ9/itrbb+MrlXzkHk3SYbxxh5uAwiRmPI3M5CseP2YX+UygK2f0HMCITWKkUVjyOVltH5sBBhCJwLViAu6FhWqBls1kGBwcxTRMhBH19/ezdvw85+5iT1NbWEg6HKSsrIxwOEwwGWbBgAV6v1/Eic7hgKPYUU+wp5t+2/RtrKtbMGZPSxDAS6HqcSORJhKIiJh9JFEXBMBLkC4MkEweJxp6d3i/gX0om2zHdmsnrrcftLiYUWgNCYpk5pDTxeqrpfjbGrjvvxTJNNl/0OlYuWcXw/o+R3vks8dZWAhddhBoOn/Eh6pxgWfDkv8Nz34QP/gZKGl+WKAMIuAK8fcnbufXwrfQmemkInZ7ydHht4QgzB4dJfMuWoQYCeI4dI/3U02AYoCi4amrIHT5M7uDB6W3dC+rQSkuRpkmhqwuhaYiqqjmpy0QiQU9kgu7RMQKznPpnF/RXVVWxfPlySkpK8Pv9eL1ep7elwwVBRs/w0+M/5eYVN1MTrOHH1/74tAUpppkjkTiIaWYRQsPlKiOfHwShUSiMkU6fwDTSpNLtGEZy1p4Cr7eakpKLyOZ68Pka8XnrMcw4iqKgaSFc/hLc7lI0rZQndv89NU2LuHjdFooCISZuvx2A3IEDDLW1UfGXH8O3ahW+NWvO/QrMzATc9adw4gFY9f8gUP7S+zyPdy59J99v+z7fb/s+n77o02dhkg7nE44wc3CYhVpejnfFCsJveQt6fx+u+gbcDfVM3HIrZnTG/yj1xA7weNDCYdRgEFFURH9/P6lUimQyyYmuLgZ6egCYnYhUFIXly5ejqioLFiygtbWVUCjkiDGHC4qh1BB//tifczJ2kvVV61lTsWaOKLPryFJks30kku24tPDkuILbXUE0touhoZ8yVT/mcddSUn4p45FHkNKwI2S+BsrKLkXKi7FkAaRFkXsZweBypKWy5547Wbv9TWheLzf+3T/i9niJ/vCHDH3+i1iJmcU70jTJnziJb/VqkGdaYnMW6d8Dd7wPkkPwhi/B5g/a1jsvA93UyZt58mYeieTqxqu5q+Mufn/p71Ppr8StunEr7jOmjB0ubBxh5uAwCzMygZAW4RtuQGbSmPE42bb2OaIMVcWzfDlCUe3VlbW1ZEZHyeVyPHayk8jhgwgp59SRNTc3EwgEqKioYMmSJVRUVDhizOGC5NDYIT7y2EfIm3m+cdU3TktdptNdZLO900JMU4MUCmPkckOkUm3kC2PkcrPrxwTh8DrKy6/E56snk+3C52vC7SpBCBWPpxqXqwRNC6BpQXqPHOThb3+d2MgwgUARLSWVxH/1KzK7d6P39uLftInwTTcy/A//aHfr0DSKb3wL/g3nrsXSNHu+a38BvP9BWPDyzp/RM3THu4nkIvY9lICALdVbuP/U/dxy+BauW3QdQtptqEKeEBW+CsKeMH6X/6xejsO5wRFmDg6zUIvDSCC7by9S10FRSD7wwJxtfOvW4WltRfd60UuKGR4fZ9/QKEc7OgjEJlCYEWVgR8nWr1/PokWLcLlc0x5mDg4XGvd23stnn/ksVf4qbt1+63TvRssyMIwYppknkTiElCaq6kcIDbennInoU4yO/pqpCJnf30I22z1dP6a5Qqiqn5LSiyhhKwIVn68Bn2/BdEPyTCLOwz/8Cu07HiMUKubaG36f8qExej/+N9ORsJIPfIDKj/0FiqbhbmycH6+ybAyyUShdCG/4Ilg6+Epe1q5SStoj7ZiWSYm3ZE4UssRbwsaqjfym9zf4NT+rK1fTWtxK3szTFe/ClCbV/mrqQ/Vn9IxzuHBwfiEcHGah9/cjhIJv7TpkoUD26NG5zcpVFd+WLcTSKbpMg4noBCfHxsmNDFM0uYkyq4Zs2bJlrF27ltbW1nm5HgeHV5PaYC0X117Mv1zyLxR7iwEwjBTx+EEkBgIFTSsil+snGnuOWGwPhcIolpWddRSF4vAG6mrfQSJ5GJ+vCa+3Bn9gEW5XMYrinRR1c1N+O370PY49vYPN19/EujWbIBol8t3vzaQnFQVjaAgjEsFdVTU/XmWD++FnN4M3DH/0BHiCL7lLzsgRyUaI5qOkC2naJ9oxpUnAFUARCi7hwu/yM5weZjQzStbM8ouTv+Ceznv41EWfYnHJYryaFyklkVyEifwE6yvX41bPbMHhcP7jCDMHh1lo5eXk+/rIHT2KpReI/3Jus3J13Tqi9QvYlc7SdXT/nJoVgV3Y39LSQlVVFUuWLKG+vv4cX4GDw6tLV6yLnUM7edeyd7GhagMbqjYgpUTXE1hWnnx+lGyuH70QIZvrweOuIhbbRSy+e/IIguLwVuKJPZMRMgWvdwGBQDOBYCsurRivtwa3u/S0c4/39aBqGiU1dVz6++9h03U3UrbAXpUodZ3oT35qb6goCJeL0LXX4io9/ThnHSlhz63wwN9CoBKu/Yrd+/IlyBpZDowewJIWXs2L1+VlTeUa+pJ9JPIJBpID7Bvdx3B6mGg+Ot1jVCIxLIP2SDuLSxYD9ndPkbuIidwEOTPnCLMLGEeYOThMIqUk19GB0Fx4WlrInTiB3tk5s4GisGfFSvZ09eBPxtBmm8FOPt2rqsq2bdscQeZwwTPl4v/5XZ/Hp/l4U/ObCHvCk8bJh9D16GRUS5DLjdDT81+AObn37GiXwO9voqLi9SSShwkEFlNXexOaFkYI7YytxfRCnufu/Cm77/kFC9dt4s2f/BTBklJ8ikq+qwuZyyEti+yhQ6jl5QQuuZjiN7+FwEVbz8GdeR6FNNz7UTh8B7S8Hm78NvhfWByalknWyJI384xlxxhOD1PhqyBv5DFVE4/qodxXzgOnHuCpgRk/xS3VW1gUXsTPTv4M0zJRFIWFoYVk9AyGZWBKEykltcFailxFL3h+h/MfR5g5OEwihO1Jlm8/SmF8nOT99wN2Vcx4WRnPXnwJ6XyWUD5r1+MqCkiJqqpcffXV6LpOU1OTI8ocLnjGs+N8YdcXeLD7QbbUbOHzl36eoOamUJhASpN8fpRk6uh0kf/4+GPMiDIoLt5MPL5veoVlOLyGUGgNFRVX43aXoaovXAPVc+gAj9zyDWIjQyzfdiWve/cHADCiUbL7DyC8XoSmkXz4YcyxMQCS992Pd+lSlFAR3tZWhPscRouECpFOuPJTcOnHXzRSFslGOBk9iSENkGBhYVgGj/Q8wmB6kBJPCb3JXo5OHMWUM/dTQaE2WMtFdRdR4ivhZPQk5b5yGkINFLmKCLgD+DQfQVcQr+Y9F1ftcBZxhJmDwyRWJkPmZAemqtA3OkK7olDYvJnxygrSwSAWM3EARQhWrlhBZWWlI8YcXlMUzAJvv/ftRPPRaRd/vTBKNPqsLUKQJFPtdHf/F1O9KxUlAJMdX4VQKS+/moryq0ilT1JV9SbKSi99Wec++vQT3PefX6Kkppa3ffpfaVg5s+JT8flQi4rItLWRuPde8m1t02PSNMmf6sZVW4eruhqtrOzVuyEvxOGfQ8vVtpP/Bx4C9cX90Sxp0R3vRlXUORGtsewYd3febYs1QBUqq8pXUROo4eGehzGliaqorChbgU/zsap8FSvKVhBwBVhVvgrXS5zX4cLDEWYODpNEs1kGLZORvj6eGhuDJYtBSkojEdYVCvivvIrHjhzGNE1UVWXz5s2OIHN4zXAieoLW4lbcqpu/3vzXtBY3syBQjmnEmIg+x0TkN7jdVWSyXUQiTzDTUFxQWfkGPJ5KdD1GedkVhMNrEMKFpgUQ4sV9tqSUpKMTBEvLaNmwhcve+V7Wv+F6tOdFvQpdXYzfcgvpJ55ACYUIbt9O+rHHkKaJ0DR8K1fgXb0Ktbj47Nyg6Ylk4P5Pwv7b4fK/hcv/5oyiTEpJJBthJDNCLB+zU8D5BLtHdjOYGqSlpIVkIcmD3Q9OizKB4Nrma7m2+Voyeobm4maOjB1hQ9UGVpSvwKt6CbgC+F1+itxFKMKx3HktctaFmRDiu8CbgFEp5crJ9/4B+CAwNrnZ30kp75sc+1vgA9hx8Y9IKR8823N0cADIDw8zMTjIc4PDc94vjsVpveQS6q66ivoN6+nu7naiZA6vGcYyY3xt39e4p/Mevnr5V7m68Wquqr+UWGwP8fgAqdRJuk59CTkpHkAlEFhCJnMSKe2G4sHAYsLhdQSDrWjay69vGu/r4dFbv0k6NsF7vvhfuLxeNlxxDfnjx8klk0jTJP3cc6TufwB9YAClqIjyv/gopX/wbtRggMz+/efWEmP0KPz8AzDaDts+CZd94gU3TepJ2iJtBNwBwh7bYHcsZ0fHdEvnmaFnACj3lpMzc0gpcSku1lSswaN6UITCyrKVrCpfxaqKVYTc51ETdoezyrmImN0G/Bfwg+e9/1Up5ZdnvyGEWA78PrACqAUeEUIslnJWst3B4VUml8uh6zoPDwzzTCxNVT6PoswUJMuKcnILm1BUhfr6ekeQObwm0E2d24/ezrcOfgvd0nn/ivexsWIFuh4lFtvH6NgDGEaGaPTpWaJMUF11HeXlV1HQ4xj6OMXFmyku3viSkbHZFHJZdv78x+y7727cPj+XvfO9qJP+fvrAAMbwMLKgM/H975M7dMjeSVEoff/7KLryStSg3U/jnFpiHLsPfv4+cAfhXT+H1qvnDEspSekpUnoK0zLJm3myRpa0nqbUW0p3vJvvtX8P3dKn97mq4SretuRtHBo5RNtEG2vL17KsdBlCCEq9pQS0AGFvGI/qOTfX6HBecNaFmZRyhxCi6WVufgPwEyllHjglhOgANgM7z9L0HP6P09HRwYEDB4hGowwMDFADICRY9opLxbJYUVHBoiuuQPE4X44Orx0+/OiH2Tm0k8vrL+cTGz5BkTmEkTlKX+IIp7q/zlSqUtOKEUJFSokQGsUlW/AHFlHmrUPTXtqn6/nER4f5yT/8DanIOCuvuIbL3nkz/lB4elx4PMTvvpvUo4+dVkhf6O2D+TJorl0Hy66D7Z+DYOVpwx2xDobSQ6hCnV5p2pfo45HeR+hP9ZMoJKYjYUjQFI2Lay/Gq3pZV72OFeUraAw30hhqPNdX5nCeMZ81Zn8mhHgPsAf4uJQyCtQBz87apn/yvdMQQvwR8EcADQ0NZ3mqDq81IpEIu44e49lHHwVpPW9xPyzs7CKQyVA5OkrdVVehqk4/OocLFCnBMgBBvJDA7wrgUt28a9k7ePvit7CtbiuJxGGGhu8mnekimTzC7PqxsrJthEJrMM0MpSVbCYfX/1bTMHQdzeUiVF5J48q1rLryGmoaF4Jlkd75LKknd1Do7iG1YwdCVSm5+T34N25k8OOfQBqG3VrpLW/G29z8at2Zl+b4/bYNxo23QKgGbrpl7jVZBpa071XeyCOwvcTihTg/OfYTdvTvAGZqx7bVbePo+FHGcmNsqt5ES0kLqlAp9hRT5i0j7AmfNgWH/3vMlzD7JvDP2E4E/wz8O/B+5prfTHHGrrNSym8D3wbYuHHjOe5M63AhM1bQ+fJQjLbOATbMEmV2XzqJAizs7qY8ErHf97jJtrfjbWlB8Tu96BzOQ0wDCkkw8pBPQz4OhRSYOlgGlpT8cnQX/9F7HzfXXcEH6q5kU7afRL6TY/23MFo4yNRXbZFvMancKaQ0URQXVeXbKU1pqAMHQDVgSjv07YLuJ6HpMqjf/CJTM9jzq19y4KFf8+4v/Af+UJjtH/ywXR+2Zw+ZAweJfPObYNkCx79tGzX/9I+4q6sB0G773rlvrZQcgQf+Gtp+CVUrITM+J0qWN/OcjJ4klo8hsHtWdsQ6eLL/ScYz4/Qke+bYXQgEfs1PdbCakCeEV/WysXrjubkWhwuOeRFmUsqRqddCiO8Av5r8az8wu4BnATB4Dqfm8Bqkr69vumC/zRPkb/e0UZSIsjU+NudJYGVDAyEhKHrgAcKTogwhsFJpzPEIZm2tI8wczh9S45AchFgfJPpBz4FZsMcUFYQGisKRfIR/Hd3Bkdwo633VbCZP58gPGTNOkrZGn3dQQZFVwhJzMdbIPjwE8B//AgwfBGkhhUqm6gq8oQBqx69tMaW54d13QeNFp01x8MQxHv7OfzHe203LpouQk+ILIbCyOWJ33EHqscemRRlCoIVCqL4Zn7NzWkdmWbDv+/DwZ8HIwhWfgks+al/jLFKFFMPpYSr9tli7u+Nu7jhxB3JS3K4tX8uS0iXc2XEnpmWiKRpLS5eiCY3GUCMVvopzcz0OFyTzIsyEEDVSyqkGhG8Bjky+vgf4XyHEV7CL/1uBXfMwRYcLnEKhQCQSoaenhwcfemj6B0EoCjdMvRZiTjhWNQzWIhg/cHDWmyq+TZvwX7R1zo+Fg8M5x7IgOQQj7TCwG+KDdkRMUW3hoGggVEwBppnBwuLHhaMcYYAGl8bN4WIC7mFGrS4oCIqVBbS4r8BDkKOF+7EwUaSgbihPqO0HyMkUnWQmlSGkSWD4EZi9cNkowKGfzhFmlmXy2Pe+zcGH7yNYWsYNn/gULZtsV34zFiNy63eZ+NHtyFwe/6ZNZPftm7a9KHrjG1CK5sm53sjBU1+B6lVw3deg3O5xmywkmcjarY6EEHREO9g5uJPx7DhtkTai+ej0IRQUFpcuZvvC7dQEayiYBS6qvYi1lWvn55ocLjjOhV3Gj4HLgXIhRD/wWeByIcRa7P/nu4E/BpBStgkhfga0AwbwYWdFpsMrIZ/PUygUaG9v56k9e4lFIqhTT+NAWWkpmzZtoqmpiVwuxw9/+EPbl0xRWFZTg/70MzMHEwL/1q24a2tQzqWTuMMrRkqJZUnk1KINVZmzsvaCRc/ZEbHBvdC7C+K9to+Wxw9awBZl0rAjZZOeVuOFcSwrg+UzaAj3UA8IYSDkCEGjnApWUikX4MYLWQNUDZ/rWjITB/AMFHBH903XXUoJszsmWcLFSMOHKK2QePZ/y65dU12w7E1zpq0oKrlUknXXXMvW7W/CpbmJ/frXxH78E3JtbchcjtAb30j5hz+Mp3nhube9mE0hDc/9D2z9U3D74X0PQKh2+sINy+DQmL0y1K26eW7oOb5z+DvTtWV1wTquariKezrvwbAMVEWlpaQFn+rj+kXXU+F3omMOrwwh5YVfnrVx40a5Z8+e+Z6GwznGsizS6TTpdJpsNouu65w4cYJDnV3koxMI7BJmIQQC0FSVd7zjHTQ3N8+smpqV5qyrriZ+zz0M//2nAHt12IJvfpPAls0Ip/j/vENKSSqW4+SeEYY7E2huBVOXuDwqpm5StShM7aJigiVefEEX4kIRapkojHdA/24YPgypYbt2zDJt8aV5QbF7VE6JB13qxMwMPyLFQV+M60ImYffkM60AJNSnKqlPlaF5ynCpAbLjo6THouQND3XudshFmfo5kCggpuovBUIodh/I5ddB1Qp7Pn27IdYDK2/aZGncAAAgAElEQVSExouJDg3w+A9uYdsfvJ+yunqkZVHo7SWzbz+JX/+azNNPT85HUPHJT1L+/ved2/v6fKSEY7+GB/4G4n3w/34Iy68H7ObiuqUjELSPt/PkwJNkjAztkXbaIjMdBwSCty5+K1trtuJSXHTGO9lYtdGJjjm8JEKIvVLKMxYaOs7/DhckhUKB7u5uMpkMsViM8cmasLYjR5j9sKEAC5uaqK2tZenSpad5kFV7vZSXlGL1DxB/7jmGP/sPALhbWih55zvQSooxIhG00lLEfC3Tv0CRlkQvmJiGhWVKLMMCRaBORrNUl4LqevHIVjqeJ5fSMQ2L8b4ko31JvH4Xw91R0oks2WyB/MTU1nOP07l/jDXbq6lsKKakvIjy+vO0sbNlQnwAhttgcA9ETkEuakfCJGAV7HoxIwuaB/QMU4X6FpK4IrjDE6E/mKLOY/Euz6wFLdLesm4wR9PwIDlLEE0ncVtRStVevEhb2+Umtxf29kpJE/jLwFME3hCUNkOgEnyTzbk1Dyy8FDLL0aWbXT/7EbvvvgPV5WZisN8WZvk8yYceJnLrrVjx+Mz1CoExNMS8MnEK7v8rOPkQVC63o2STqdhT8VP0JfpQhcqx6DG+vv/r04X8QVeQqxuu5on+JzAtu1XSwvBC6ovqaQo3cUXDFfN5VQ6vEZxfGocLCtM0GRoaIplMEolEGIxM0HZgP0h5muUFgKqqtLS00NraSmXlXO8hK5Mhd/iI3RTZ7SLynVvAtL+ACz09CM2FmUiij46hBgP41q+fjrQ5vDCmaRHpSzE2mELPmUhLkoxkSY7ncPtUTENSVhekrD6Iqgq8fhehch/ewExUyzAMIpEJOg73EY1NEB3JkDgcZOqTlbOqA8Ws98Ss/wosy+KpJ3fStKaYq6/afu5uwMshG4NoLwzsg5EjkB4FPWvXjKlu0NN2ZCwXt1OFVsEet0ymRJkuJZaV5QeleZZVF1iX0CkZ1YmGNMJJg6qxPJbupSSfnj6tmxghBfuJZZLZNWSAHR0rXWTbQ1SuAP9kiyNp2REyTxF4bP+yUz0RHr3ju8SjcZZe8jq2rt2CtXs/ww8/TuL++zDHxglccglFv7edkX/9HFLXES4XoTe+4aze3hfCtExMaaLd8+eIwX1Yr/9n1K1/Oqelkk/1EcvH2DGwg8d6H5sWZQLBtgXbuHnFzdy84mb2jOxxomMOZwVHmDlcEBiGQSwWY2Jigv6BAQ7EU/T39uGJT+CZFSETQkwaYQqam5tZtmwZra2tFJ2hmNgy7QhO7thRsvv2o/f0zD4hyccfJ3zttShFQZTiYkeUvQSWZREdydC1b5TxgRRG3iQRyWPqJqmJPLOrJk4dHGfV5XWU1HkZHI0wtneUTD7OG994LQKVxx5/hP0HJ8sTJJRMrEebJR8Ep38Wz39PURU2XLqC5hVVlNW+ciPUVxU9Z4uvSCf074V4D6QnV/7qeXB5IJ+wI1FTthdSgJmfrB8DS89hFlLE9DSn3AaNUidUMHinpRDuMnCfsRo3faY35zBz1xSoWWMXvocX2HPIRWeEmVBA1SAzMS3Mek92oaiCG951MyUpndE//dD0w4172TIWfPWr+Dfa2RpPS8u81JFl9Ayj6WGMAz8iUrcG3V/KwNLLaW9spb7IT6jjl6hCZSI7weHxw0TzUQ6NHwIJS0qWcDJ2EktaqIrKyrKVBFwBlpctdwSZw1nDEWYO5zWmaZLL5RgeHmbX8ROYExGi0SjxSIQQk7GDKf8xRWH9+vWYpklraytLly5FUV64ya8xMoyVzeJuaGTsP/5z7qCq4lu1CqkX8C5ejGvSU8nhdKSUJKNZjjwxQN/RKKZpkU/qZBL6C+5jmZI9+3YRa+uYfk8TXn7z4za8Hj+5eBGNXIzH5yE7LsjpxtTHfEYURbDm6no8fg1vwEUurVO3uITq5nky7JQS0mOQGoGhIzDaBpmIHSlTPVBIgOKGXMyORKVHbOHmKQaXH3JJMEbtcTOPBExp4gIqgIrczKl8eesFJvEyKGm2/blMHbzFULvWFodgr/QsZOdsrucK7Nq9nwXLdRoXL+Liq7awZc0SInfuZOzBB6dFGUIQvPTSaVEG59j2YpKMnqHj0O00P/ttgmMnKdr6QZ5puYR/O/m/GJaB6HuMan81ST1JspCc3m9j1UbevuTtXNlwJe2RdnYN7WJ99XrWV653Goc7nHUcYeZw3qLrOvc/t4tdvQP0p3M0DHRNZ2CmnvIVIVi4cCHFxcUsXbqUlpaWFxVjU0hdR+ZySF0n+ehjkM/PGfetXo1nyWLc9fVopaWv7oW9RrAsSSqaY/BElLYnBxnuSrzgtlOpx5molkQkwpQo61CFhooPBYXReBah5MinTEAjw0wBe/2yUhDg9qnk0gaqKqhdXIy0mF8RNoWetWvFot120X68H/Ip2+zVHYLshJ2eNPN2nZgese0ZcknbjwxpC7kzIADXGUdsptKRz09Lnkaw2k6VYkFxM5QvmjnC80QY+RQEyu1Ry+LEkRM88UQbyWSWLS4fVYU0E/c8QeKJPUjDwLt6Nfn2dtv2wuWiaNu2l7xlZwMpJRkjgzmwF+2Jf2P1qafI+8vouOKvONWwiR8c+uZ0v0opJclCEp/LNy3MBILaYC1F7iIMy2Bt5VonOuZwTnGEmcN5R1dXFw8fOMSOnEnzySOoUtLE3BYQU2lFVVV53eteR2PjK+svJw0DYzyC8Pow47G5g6qK/7LLUHw+PC0tTgrzBWjbMUDnvlGyyQITQ5nTxuWcT+y0SiY0M8CU7rIACwsKL3AyCXreoHFVOW6visurEa7woect6peVzM9nZBTs9OPYcYictAvKk2N2/VY2bqf78pNRGEu3o1LSsoVacgiEG1yTUbMzNzg5Iy+05fQd9peD6gU9Ba4ASNP2QKtonSzel3Zq1V82s2chA56QHS2zDFs4esLgL2N8cJDfPHqQ3r4IFdUVXLNiCeLhZzh16ATC56X4bW+j9D3vxt3UNL+2F0A8F+dE9AR5M0/r01+jdPAAd6+6ljs9MDr+FAPdPwVsrzGJxKW4+MuNf4klLb6w6wvTZrDXN1/PxuqNqIqzGtvh3OMIM4fzAktKHhoco9Dfy4EH78OyLFqY+1OuzBJj11xzDfl8nqamptNWWr4cCgMDSNMg195G5pmdMwNCEHrzmwldfRXu2lpHlDFViB+h8/AgyUGL4nAxQ11x+o9PMPUJnbnmC/zlgtKqMNIUDByLnbbNy0Uo4Au7MU0LwxC4keQzBqEy77n7jCzTrq9KjsDwAUgM2kKskLDTj6lR8BbZKjM9akfO/KWQS9jiS3HZQi595qgYzIgr247NbvYDnBYGGy1zkfWoFGVMRsrdZP0aS7olHoIIlx/NW2xHxoSwBaGnCFw+WyBahi3CVJctyGAyheqx56oo9t/dPsyJEXqODpM/OcQ1uoVn4DjZB59Fq6qk8hMfp/htb0MNz0Qq5yNdOc3wYcxHPoN39Y3469YT3fYxfjK0k1uP3o5M2fdxQ+UGLq+/nP5kP8PpYbbWbqUp1IRLdfG5Sz9HR6yDS+sudSJkDvOKI8wc5pXBRIrbugd5tLuPmr5OmiZG0Kac+ZkbGXv9619PoVD4rcXYHIRAcblIP/30TF0M4Fq4EE/zQvI9PVjZLIqmgceDEgyi+nwoPh/C9WJJpQub/uMT9ByZoGxBgMOHDzHaFyduDiBNQTi6clKA2dYHYjqxLHl+REwosH57E6suX0Ag7GG4K85dnfsxDeuVBIcoqfbj8qoUV/gorQviC7nwBd0UV/jxhdx4/GfxK8w0bEGVHLaNXYcO24X4ucRkRCkE2Yht8pocsoWYrwTMAnJgL7MvdLaumk47SogZtYS1IRQhJ6OGthyz0NhlXcVi7RRF2gTSU2AknKcobQuxsQofhmvuQUdqvCwYdYECimWg6Hk7Qqe57dq2bNyeq+oBK2qv/FS9k1EyHfCA2490hzjRm0J6oE6roHz/XjYc77NviRCEbrqRms98BsXjOXv3/mWS1dNk2n6Jd893CfTvpTtQxF1eSWbiIB2xDjrjndPbKii0lrayoXoDS8qWIKRgU/Wm6ajYmoo183UZDg5zcISZw7yg6zqfPdnPD0ditAz2sO3kgdNiLlPF/Pl8nmXLlrF8+fJX7fyehQtR/H6sxGSqacqos6uL9NPP4FdU0juexLt0Ke6FC5EDA/b8hECrqMC1oB41GHjV5jNf9B+boKdtAtVn0NXeT7Rj0rgUsKuayggzlfI6c2RKURWklEjLFmTLL6ll6UU1c2q+qpvDvPlj6xg4EcUbcDHWlySTKOAPuamoLzrt75lEgarmEBUNRQhmnPwVVaCoZ6n42tTtxt+JSSGWGrHTk0LYqcCx4/YqRW8xRiqOmY5RkAHIxQgWOoAZy5bpSjo584YERFEtMjmKlBYSlSHPOsLWKBKTgoC7S6u4xHJT5ynhYiVCHjdSqcWUBeJlcTqbC9PFZN4s5D324k1FQlEhiOX3obr8CHexHRHTvOAO2NEyT2jS6iJgpzS9YTtK5p6Mprn8TIxHefT2H9B/6CBrfCGUUwOYY2Mz90gI1KIQ4jzohJHKJ7FuvZqy0WMUAhU8sP6t/G1sL0a6E9Kd+DU/ayvWciRyBMuyV1U2BBtIFVJU+iupCdQ4qUqH8xJHmDmcMwxL8mgkzrJcknwuh8gUuGG8l/KTB864fXNzM0uWLEHTNKpf5VWR+sAA2b370AcG8G3ciFZRjlZdQ6Gzk/SOHWSeeQaEIKFpVHziE/hWrkB4vEgpMaIxjLFx/OvXoQQuPHEmpeTUwTH2/6aL4RPpWSsdxRlSkvbfPUENPWdiGTOpNVURLL2khqVbawAYOBF90SL86ubw/Bfoz8Y0IBu1xdhIuy3Exo+j9x5kXF1DXnfhjR0mTTmqHqPRehQxWTmnTf7jAUxLBSGnWxdl3PV4CkMomEihTO4zqVrLllAoaeREspf/9Qqe8x7l4dxqtOwEwl/GW/zFoKfICYu4z2DUnyanFkh58sgpt1gAAWVF6wm46sh7BCXhTRSvWolwFaF4Qrathea106cvsRhGWhaFaJTnfvELOu+8g+axBCt0C5Hqwr1xA6E/+RNGv/SlGQ+y7dfMX4o/OYJ18MeYW/8UXRqMtV5J74rreMjv5Wcnf44xy3PsTc1v4rpF17FnaA85K8fWmq2srVyLKlSnRMHhvMYRZg5nnYRh8r3uIb4/HGVQN/mb5CCegR5KMxlcyeQZ91FVlc2bN9PQ0IDL5XpZKy1fLtI0yXd2Evv1r8Aw8K1fj7uuDjQN4XaTPXQIKxYDKZGGQXrnTjv1GSrCXbcAtagIY3wMIxrFfR4IM8u0OHVwnP7jUVRNkIoVKF8QpLY1jNunIS1J9/FhetpHSabjhDwVDJ9IMZVUm7JnfbH1fI3Ly1h1+YLpiNeZ7CjOK9H1fKS0a6vSETKjQ3QcSJAYGkONnSKQP8FApoX1zSeoityJJi2q5M7JVPqsQzD3tR24EhRCrXjTJ+yOE0KgVpRgmEEUI4/lDZFWM1iFGAl/BT8SMX5txkgXqyx1FfORktWYFRsRngBpxhjJ7EFzLyRZ6COa3jt9vqKiNQQCSxgZuQspDRRFo2TJeykr24am/W4dDfSBQTofvp+x23/AlsHIdGS4/KMfpfxP/hghBN7ly+a1qJ/Ro+Se/HfcbXchLIO2YIjdwuTB7El6R3rJmlmq/dXkjBxSSlRFpTHUSEpPcWXjlSwML3TEmMMFgyPM/o9hGAamaU7/aU3Wc039OSWAFEVBVVU0TUPTNNTfoldkUjf40qkhfjQ0QTAaYfPEAG/LpoiNzRQ/V1dXMzo6imVZCCFYv349gUCAlpYWGhoaXoUrPh19bIzCxAS5g4dQq6owcxmynZ2YExFS991vp63A/lVWVbzLlqGGw1ipFLlDB3E3NaGWlaE9r5PA2UYvmJgFC9O0GOtNMtKVoKKxiOhImufu7kLOsrPq3Dv6AkfxkSE1+XqWtaiq2CampkQRtifYocf6MA2JqglWXb7g/It4vQhWPsdE/wRjPQkKR5/AFTmAr2ExC4va0Ua6KXTVU6UNs8jzDEKTrA4B4/a+YvpfNhIQ5UsREx1gmbaUnSwSE4qKu76VrKxDSY5hlTZglS+ybSa8YaJ6PyejP5iMs40TjancVLSQDeWLqfWXgyzQJTvIF0aJRB4HrDN4wioEAi3UVF9PdfV1JBMHKSnZQji8/ne6R9lUkoFj7TQ2NlN04DDLByMzg0JgRiemxcy8FfWnRuHuD8PJh3CpHkaXbqd31Q18b+A3PDXwlD1VBG9f/HYurr0YBLRH2llXuY7VFavxal5cymu3JtThtYkjzF6jSCkpFApks1kKhQL5fJ58Ps/zm9ZPffFO/Tk1/vztFEXB7Xbj8Xjwer14vV7cL1BnMlbQCSMZ6uvj18MprkhFWHBgBwCz3cKEENTW1nLxxRcTjUZpbm7+3Yv6Z2EUCuj5HPlMhkI2QyGXRc9kSLa3kz9xAuXiLSiKQkIvIA0d7+5dqNasPoMVFbhWrqCQiCPGxhBCoPgD+FatQq2oOKdP4GM9CWKjGWKjGXrbJhg+lXiBIvqZyFd1SxG98cMErRoKkclCbQEL15TT2zaBZVoo4oXTkc1rK14yPXm+kE+myUfGCAUNjJM72H/nThJGOSF1hA2BXyKwoNve1g1sDO6abOA06yaWNkO0B6RltyUC+7WiwvIb7PRgpBMaLgZ3EHNgJ7nqxWSqFpBOH0doAYTmJZvtZTyxm66xQcJGDwF16hwm1xabwHFk9DgD0Re6GkE4vIFk8jCWZaAoLmqqb6KkZDNCCMpKL/6d7pVlWRz/4ffp+vHt6IaBVZCY4xFC119H8sGHkIaB0DRCb5iftklYJkycIhOuJYvFqfQAu9a8gdLmq3lq/ABP7/k8hmVMby4QZM0sHs3Dusp1XNlw5fzM28HhVcIRZq8x8vk8qVSKRCKBYdhfXpqmIYTA7XbPaVkEM4LspVKFlmVhWRapVIr4ZENiTdMIh8MEg0E0l4tHIgn+u3eUrmyeZza24tE0flDt49DACMdnHWv2Sst169a9qmJMz+VIRMaIDg2SmoiQz6Qx9YIdTRISM5Ol0N2NoggsRSLzWTwnRwh296KO292wpX1DSK9dhaiuRhYKiNEhStatw7+wmVOnOhh+9H6qFy2murmFsd5uBk8cZcHy1TSsXIXyKhcUTwyl6Ts2Qd+xCfqPnm45ESh1kY4WpmvFBBLVpXLJjYu5ceFGRk4luPur+zFNC1VVWH9NI+uvaTyj6Hr+6/NVkE0MphnqjDHSFWW4K0F0JEd9bYbrLz2I9uSX2RSYCR+eOUkrEI0XQ98uWwioLtj6Z6BotidZ8+V2bVbfLlh0JdRvnrO3rseJVhehKB401UvQvY5sbpD24adR4g8ikNQKSAsNiYVAIoRKQ8MHKQqumCz+NxDCTVHRUlLJY7S3/yXWZJqyfsHNuFylJBL7X5XoGPD/2XvzOLnu8sz3e/baq6t63xdJrdXWYsmWLUsGGbAJi8HGSQxDhgkkk5vAzWSST8jMZG64GTK5WbkJgUAgQHCMQyA4Nqux8b5bsvZd6lar9+7autZTZ/vNH6e7uluWLcmWvNHP59OSuuosv1Oq6vP0+z7v8+BZFlNPP82uO79Gz5PP0zubLyu1tND1z3cS3rSJ8h13vH4tS6sMe++Cp/8O1yqx+/YvM1ga5XOBCk7+EOw9hILCjs4dbGvbxl/s+gts10ZTNN7V/S62tGxBV17/oYQlLOHVYomYvUVQrVZJp/24IvDJj+d52LaNbdu1VuEcKTu7IqZpGqqqous6mqbV2phzLUxZlpFlGVWdf8u4rks6neZ7I5P8U9HlRNWh3dD4ZFcTqqrR1dWF4zjk02mOHfOpmaqq3HTTTZimeWlsL2ZhWxZjJ44ycvgA5VwOx7Zm9UGST1g8gfBc3EyG+qefR3JdptetImhVqX9hP9ICiw4BFFqbKchgFtJUbAdX1yj++B6scoVKIc9cuUoPhbDKvhfUru9/j+vv+BhrdrydUOzSEJrceIbxgxMM708zcuocEUeSYFR6HqleoqOun77+bgJahI6V84SrpS/OLbMTkQuJ2BuVdC2EWbRJjRRID+ep5Cts3RmGao7Hvj7N6LCEoTs0N1ZYsd6kNZmBIz9gUU83uQIpN+g/JimzJmGeT8Ru+LT/95mnoWf7i8gX4JOyWQjhYpoTVCrD2E6ecvkkCIlydZJq5TSOk4f8fmRp/rPV2bCdULCHSmUIw2hG1xtQ1DCqEkHXGzCMJhQlQDjUSyDQTDb77CIilkxuvXSvZT7PPV/6HFeeOIMs5idIg5s21SxjXpeWZTkDz30FnvuyH1vVvpnyjk9zqjDEnUfvwhHz1bGbe29mZ9dO1tSvYWVy5VKQ+BLeklgiZm9yeJ7H9PQ0Z86coVqtoij+xJEkSSiKUmtBvlzbTQiBEALbtqlWqzW9GUAgECAcDhMIBF6kM1MUhaOSxh+mS3SqEv8zafCRvk7qovOB0Zqm0TSrxdq0adMlr5AB5FMpdv3oHrIjw7X2lEAgXIEnPEAgSwrCddAHTlM3NEK6o5VypYx+4iQDDTHqixXqyn4moSfLjDYmyM5MU52RsF0Hp2qe89yuNU+WXMclfWQQ+e3veHUX5DpQGMccP0VxYJLCaYmRUwstK/wb/6o1Llp9FlONcs3qTuJNHRBv9720zsIbufo1ByEEkhBgFdnz4Aj7Hp0mYh6iXT/EqLWWotrH1dL3kfPD3FCfRO5tIsYwUnECgvVQzMP04fkDyhpc91t+RWx0N3RthYaVcObJxUSs+/ytQSE8yuUxCsV96FqSgNFAyVN56Pg/0GU/jzL78TK0BlwnC3hIkkoysZVgsIe6xBZkSSESWUUg0DbfKl2AeHzTJamMnY3cxDjRSJTSt7/NDUeGkEyzNqkpqSqR7dejL1t2nqNcBswOS7hje1Ae+d/ku6/l4RXbub8ywpnBbzFUGCKiRVAkBSEEqqyypWULq+tX0xxqpiPasUTIlvCWxBIxe5PC8zzy+TwjIyNMTk5iGAaqqtYI1lxFzPO8WptSkqTahONc9WuOxGUyGVKpFA0NDdTX+yRACFGrigGEQiGisRhPVD2mHY9fToTYFNT4XHucbWEdXJfJsVHchgY8z6NcLuM4DkNDQ4Av9Hcch5GRESKRCIFAAMMwXpVWa3p4iEe+8RWKM2mU2TZG7WizY3XSrGWBly/Qd+Qk0/EwBxujeKlJRpt8siILwZZTY2TqYgwlI9hYSKaNJ8RLJmcrksrG+E72ZH6GJ1xkWaH/yusIhCPn3P6l4HkCr2oiKjNIhWHk0d3IbgW74OFOVRkanoubkmjWjtKuH8SsX87GG7YSDCfQAyoSrp/FWBiHaCs0r108UvhK4bl+BqRr+VONnuNXnmTFt39QdN+WQbmIHyXDz8Gph3FbN5FyV5Db/QgMP8fx9Dpu3DFNKPMszeU+tsaq9If/zb82ZKRgHez3280JgInznKf3Bj+UG6BtvV+NaVsP3RdehXKcEqXyALadxXUrmOYIJ8cf4FjqeUwrRb/hLrTVpSG5jWCwh1LpGLrRTCy+AUNvRFWjqGoEWb78rTbhuri5HJVclhe+/EV4+FGaHA8qJrF3voOGT34Kr1x6/VqWEwfhqc/74env+l9UOq9m761/w325w/zk1LdrUV47O3fyiXWfwMVdqowt4ecKS8TsTQjTNBkZGWF6eppMxp+cmtOTSZL0snqxSqWyqI0pyzKlUol9+/b5Y+aKwrZt26ivr0eSpNpUpucJnihZ/HM2w4An02+o3F4XRJEkrg+qWKZJoVBgfHyckydPkkgk0HUdRVGYnjWoNE0TTdNwXZdUKoXneUQiEZqbmy966lMIweDe3Tz1nbsopDNouoonz7U8ZqfmkJEkcGwbx6yQHJ1kb3OcmXDQJxzq/Dk9YHdvC85cq3a2iihLCh4uZ6NjzRWsiGyk77qt9ErXMz52nM51V9LWv/qC1u+6HqWsSWkqh8icQM0PkE7rpCYdmuIl4glBISdIj+fodI/TGqqjWTtJf+AJQOBWdUaf/xSV5ddgRMMkW8Oz5qFRyI/65Cxcf951vAhWySdihUnfyb4w4ZMwWfXbfrLmC+BTR0EN+tFD0VZo6IdyyhfPd22F3u3zxqWju2HgEaxgB5KZRXv2rxGeA0Khaq1hhX4ICY/+KLDHX0YbT9K2aGHefO7kHAIJ3/AVAAnar4LxfT55lFXo3eGTMSF8Mtm01l/PBUAID8+zKRaPY5qjBAItaGqMbHY3lfQP6JFBGCDpbUjOJEL4FbJodB3hyDLq6jYRDHYRDHacszp2OeGkUuz59l2M/eiH9J8eq9lf1H/yt2j8rd9aNGn5mkEIGHgEnvpbOPUQQguxe/2tPLPn76g4FR4YeoDx0nhtcxmZmB4jHozTFe1aImRL+LnCEjF7E2FORzYwMIDruhizkSjnmo6ca2WeCzMzM2SzWSRJ4vTp04uImuu6TE1N1apmACccwRdKcMRVaZUFv61ZfKA+SLVSIZfLcebMGXK5HJqmEY1GKZVKTExM+BOP+XxN9/bII49gGAadnZ1os7FGhUKBYDBIIvHi9ttLwa5W2fvgjzj08IOYpSKecKmaNpIkI8uKHw/oOLiOg+c6OFV/FnQ0do6b8oJrd85R9YlrjeTtFO6sziUWa6R945Ws2roN1QgQW9VOndJFFxd+kxNWhel9BymPDqI5OQqTWU5O9GLlUrTrBzl0ZjWR6AjLxAGu1p5CCvnmpZ6Ydc6SQBYO9vhxlFiCqrKOasVB1RQUr+RXstQLqMw4tl9lq+ZhZgyyp6CS97MfS2nfKT5Q5xO07IAfjj191CdlZ0PR/aoawKF/869TUvFQkEUVBOgLCngSoEguHcZBXxh/rvXVr4DMgK8Pk1XovxmO/2rKsgIAACAASURBVNgn1bIKfTvh6H0+EVNUWPFOWHkzpAd8cth5je96r4d9l/sLHMool4epVIYAF9e1ODT+MybST9FpgG2naoVISZJpbdhOONRHoXiQaGQd7e2/iCRpyLL2mhMy8H9huffLf4v60CP0T2QWVI8l3MxLjoFefjz0WXj8LxGRZkau/lXub+zm80e+jpf2p7W7o938zqbf4Yv7vojjOaiyyq39t9IVvTyWOUtYwhsZS8TsTQAhBOl0mtHRUUqlEvl8HlVVMU0T0zQpFos1Uf5CYf9c9WyhH9nExASnTp16yXPNtTjLFRPPMIjIEhqQEfApw2UHFvlsht2nj+M4DpZlMT4+Xjunoii4C7MnF+RKep7H4OAgnZ2dtX1VVSUYvLAqBkB2fJTnv/89Ro4exrZ8TZjwBK5t4dk2zCnM3BdXuRa8oGc9IHFuZgBCg2C4DiFBfXsH7StXU9/ZjR4M09y3DPlCK31WCcppRGGSmRcewc3kyWTrGcs0MJLqo0PfyweTf4rMXOVzfqlzpOxIeSerQw+D8HBRkZIdGEoV1yqSHcgh4SJCSZSmVQSKKoZw0AOqL3Z3TD/fsTgJxWko53yylR2E3Bm/+pXshfK0T75k1c9UzI/58UQvAwG4QkPBqr2MXsMa9p7up1U/TIt2HEnyzVjt+Er0wqnZSUgVectvwK4v+7o6WWbWTM2vzt30J36la+R5XwfWeQ2M7YXTj/sase6tflt07vtzifcvEH6FrDpbJTtKqXScU+k9DGYO0qNM0yKDbUM0uoFi8SBCuEiSQix6BbHYlTQ13YxhNL0mrcpzoZjNEI7X4WYyrNlzCGkiQ2jbdVR27fbtLzSN+Pve+9pZvBSnYNfXfaLcuh6uuB0S3Uwv38lXD36F+47806z+07e7WN+4njtW38Gm5k1Lbcsl/NxjiZi9geG6LqVSiZGREaampjAMozZdOVclU1WVfD6PEGIRCQJqthmyLGOaJrlcbhFpAmhoaGDt2rXk83lyuRyReB0vxBv4bElhedXh0yFBo23z36dGyedyDAWDzMzM1NqT50NjYyMTExM1rVtLSwulUgld10kmk0Sj0Ret+1ywq1VO7nqGQ488RCGbwqpWsMsVBALXsvAc57zHeCmt2LlImSQrRJL1JHq6CMeThOvqSDS3EEnWU9/RTTheh3S+NAKn6lsATB+B3DAeMrm0yehpj8HUKmKlvWwyvsP19VkS6ugiMnbKvIZj5bfxzsTnUISDi8oR80YiappW/Thj9b9EvL0VkehFiTYhBeoQehRXDmFX8lSHhsAqEZl6iHBhP2rbWv+mnDvjV8DUgK9HmzrMvCGajN/UnV0HIGSdufTMBWlAtXUKJFw0Hs9+lO2xr6FIDrKiIr/7TwlPrkBXjsEDd4BrIyka+oc+7++8kEytfd/892c/B7BiwTBF99bFGrHOq18VIQNwXZN8fj+uWwFJYij9LNmxryMDfepCuziZcHgZzU2/QLF0jKbGm2lsfH09s+yqya7v38Nz932XG699O/q3vo2cL9D8v/6Yug99iMreva+tlmxsDzzzJTj0PXAtSgjGA2Gemn6Oe0fu5dTBv0Yg2NK8hT1Te3CEgyZrvH/Z+wmqQTY0bVgiZEv4uYd0tm3CmxGbN28Wu3bter2XcUkxpyMrl8ukUilkWcZ1XVzXJZ/PL2pVyrKMZVk4joOmaWiaRjqdronuXwqyLLN+/Xri8Tg5IfEABj8VOgVJZn12gs1Tw6yNhWvasfNBkiRWrlzJ8ePHa0Tsne98J5qmkUql6Ovro7u7+6KSBDzPIzU0yMFHfsaZw/swCwUc26ZaPHeU0yKc9d42bIeqpvolqLlS1FkwwhGSbR0kWtsJxGJE6xtItrZR19RCOJFEDwTPT8jMGUidgHIWPBvG9+OUsjgj+5nJyZRyFh36PnS5WiM3Q+ZVdAb21fIV7838v4xbq2jRjrI28QxWpI9p/Ro2W3+GLlcZ3PB1HClAaGY30cwT2NGVyA3LiaR+hj5zFDfYhFzNEBh5CJ9sSaAEwK0s4qHz0UJQlZIYYr79ddD5IEdnruaW5B+h4OAh11qQSBKjwXfgqQnc5vXonWupN8bRiyeRlr9zMXm6RFWtSw3HKSGEjeOU2D3wVQrFw7QYIbLZZxDCWrClDAhkWWfdui9QF9+IogSRZeP1Wjr2zAx77/lXdj36M4ypaa7Im0SmM2idnbT/xV8Q3LD+tV2QEPCtX4IT94MegQ0fhqv/M49URvjyvi9zMH0Q8LVjn77m03x41YfZO7V3qTq2hJ9bSJK0Wwix+VzPLVXM3mAwTZNsNsupU6dqVbBqtYosy2iahq7r1NXVUSgUauTH8zwqlQqZTAbbtjFNszYMsBCBQIDW1lY0TaNYLBIIBFA1DcdxeCBfZTA/wWZFYk0+gzvtj7wdP8fkWzgcplwu++J4WWb58uUYhsGqVavo6upiy5YtjI6O0tvb+4qtMYQQZCfG2H3fvzO49wWq5QKWWb6oY0hC0GwbTOg2IHxS9hKoa2lj+TXb6F2/gUAkRigaQw+GUA39/IaxngelaV8Ib87Age9CbhgalgPgDjyBMn0QFQgAJa2OrNNJo3YKWRIIIZGWWzkhrWVz6AjlQB/N9Y3oRZtQ3RpmkpsBhTpFoB2ZxvUExrFv0RzKkTzzLRAuIGEpCXQ3c86urBCCsqUTkiu1zKERrqFZvICCX5HbVfoQW8PfQMUGWSXSv56VxjKmpc8Qc46hLbsOPWz4VZGurbT3bPOHAZTZr5fCJahqXWqUSoOk80d4dHI/B0Z+wjuCw37LvgS61ojtZGuC/va2DwOCpqabSCSueb2XDsD3/+bPOX1wLxuKDq0DY/6Dskz89ttxcjm8chk5FLq8i6gW4PC9sOEjIEl43ddR6dxMad2t7J0Z4O5df8KB1AEs11q0W7bia92WqmNLWMK5sUTM3iDwPI9UKkUqlaoRq7nWZSgUqrUr56pN8XicUqlEoVBgenq6JrAHCAaDKIpCtTofgNTc3ExTU5Pf1hRwQjYYLZbpOT1Ms23SmM3SOLvt2eqsUChUm+aUZZkNGzYQDofJ5/MsW7aMFStWLNo+Go2y7BX6IlVH8xy7+yGOm7tIjY5QyqQueN9ooIGAFiYaSMDJp9FLJcZa2oFzVMdmv69raeX6Oz5G/zXXXZz+xrXBKoJjwf5/9ScPW64EMwcvfNMnS6ceBPwqwRw8IbO//B5GrXXckvwjEA5CUoh0rcMLbeKo9lGEEgVNpUGRKM+USZ8aRFUsrNQYm0L+TXjZ9OcXLUcIgbCqfnFH8itgKXkDCffQbBVOZSh4Kyvtu1BwQNGoe/snMCWZQG4f6vIdXN9zLYx9AM48A73b6amRqZsWX/u6D1746/QGguuWcdwywrP5wdF/4NjovSQUm3cGPaSazFAimbyecGQlnlsmkbiORGLL67xyH0P799KyvB9NVbkyVMfqkRxkMou2sQcHiVx3LdIFyANeMYpT8MwX4fmvQXXGH9Louob0po9w36n7ePjpz7AvtQ/w9WMfWf0Rvnv8u9iejSZrbGvfdvnWtoQlvAWwRMxeZziOQ7lcZmhoiEwmg67rvlt+Pr/I40tRlFoUUrFYJJfL1apjZ6NSqdT+raoqsVgMWZY5Oj5JynbwLIu6SpH+2W3OntU6m4ht3ryZRCJBOp2mt7eXnp6eVxRqfi4IIRg5epCRQweJuQlSj59i9/T9iAVapwtFUmmhyegkaAQYKz/E0dZ6kKrUVFFntS8VVePdv/VfL9jiAvCF6blRSM+GTI3shsf+3Cdih+9ZfG1AKb6J06UrWGnfXatMjVrrGJVa+GfnI1wZncFL3Ew+cAVCCMyMjW2Xsco2+bSJVXYB3xdtU/hhYH4YYNJdQ5Ny3Bf9SypDdR9hefEbyMJByBrljZ9EaYqhT+7CarmWSHg9Fe9WIrnnoWc7kRrxum1+0T3b/K+3GPJmmnuO/AOb6pcR9fLUz3yHbRHfMSscWk6lMjQr6Fepr99Bff12NO3CJ4UvF7xKheHnnuHpH99LZf9+NklBgukMbi5HcP16op/4BNN/+7cI20bSVOo+dBvBdesuz2IqOX+6cs+dvn5yzS1w3acYr2vnhwe+yn2n7mNwZhB5wTSqhIQnPL7yrq8stS2XsIQLxHmJmSRJDwK/K4TY9xqs5+cKc7YStm2TyWQIhULIskwgEECSJIrFIuVymXw+TyQSwbZtxsbGKJfnW3rRaBTwbSfOBcdxal5nJd3A1gwinruo3ZVMJsnlcnieh6IovOMd78AwDCYnJ1m2bBnd3d3nPPZCCM/zBwtmyY8sy8iK79htV02qpRLVSpnJgZNMDpykvX8Vwbo6Tu/dze777qlNaF0INCOAfQ4n/qHSQYZKvpaF1uRLi/2B5Vu2suX9t10YKTPzkBvyo2PGXoDD9/mVMjPre4YtRLIPcmcQnoeQFYal7RwzN3N0ZlXNwX7C7qdcf4h46Cb2piM4Yy6uM8H5XoJJsQ4HvTYMMNb5SbLxIJGZXcyEr0Lt3cpI/u2EMs9RTl5NzriCQHMcp+VaHNsFy8Po3wbGjvNf85scQvhWFy9M7eLfT/2AB4YewnRNfm3le7ha2lcLL5eQiUWvoKP9P+C4BZKJrZfFff+VYHpokMf+8UuMHtrPmqkZ2qazSIArSdT98i/R+Lu/ixqNEty44fIK/KsF3x9PC8HJB+CK2yle/es8UDzFDw79Pc9PPI9AsKlpE79y7a/QFmnjtx/6bWzPRpVV3t377qW25RKWcBE4r/hfkqRNwF8CQ8B/F0KcXwX+GuPNJP73PA/TNGvi/DkX/kKhgGmatWnLOTf+Y8eOLfIZ0zQN256PAYpGoy9JygRwuL2PDyVCtDQ2MuhJ1DtVnGKRAwcO1IjYLbfcQjAYZHx8/IJ1YXbVpFIoUMnPYJaLfjSRRM27ybFtirkUI4cPkRkdpZzL4lSqWNYcqZRQNBXXXpz/WN/eRXZyDG+O5AGSLNO2cjX17V2svWEntmXx3c/+4SLiJUkSb1t/Nd63v8vJhjijyei8yH9uG1nmihtvZu0NO1+ekLm2T8asol8R23e375WVH4fUkfntom0QboKpgzWvLWfnn5CeUTGHjjJcXsvxiT6qZRvPnfs/EbiyiSYHEecZJFVUGdf1/E6sDD3r6omZB4gVd1Nt2god8y02y3SIJAMEQtrs9y5aQCHRHMIsO8iyRFN3FCN0GVtcryM8z8Jx8lSrKWw7i+1W+PXH/5yBwihBxWBn21W8u+ta2kSKM8N/D8ylYSisXPlZmhrfgabVvb4XcRa+/we/Q+Dp52hP55EWakZlmfgHP0jzf/sDlMjFpUxcDNyhp3Ae/0uUyUP87IN/zcHMUSJKgJP50zwy/Aima9IV7eK9y97Le/veS2d0/ufGkrB/CUt4ebyc+P+CpzIlSboN+H+A7wF/LoSonGeX1wxvFmJWrVYZHx/HsiwqlQqlUolQKIQkSaRSKUZHRzEMoxaDNBeFNIeWlhYaGho4fPhwzTYjkUiQWaAzGW/poHFqDNnz8GSZyvot3NrRTERVCIfDRKNRotFoLV/zYoPEbavK1OAAU0ODuJYFeDBr7CqEx+ixI4wfP4qi65RyWRzzpd8mmhEgnKxnZtL3QVNUjVt+73+gGgZjRw8TjEapFAp0rr3iRURq7PgRHrvrG2RGh4lHomzZcDXSnXfjjI6SDRk8u6wNT5ZBkmiKJWjoXcb6237xpQmZ58LQU/6XFvQd408/AaML3leKAe6sbk+SEVfeQXH1r5E/M4Jz6ilO51dzcqqHUFwn2hAgP10mM15BOluOL4GiSugBBSSJSv4c4eRAvClAIKLj2h7RhEEo5pN21/WrkrGkPxUoANt0iDWEUHWZasVGlmQiyQCKJlPXGCRSH0BRXnvD08sN1y1TKp3CNCc4lhtkV/okH11xE5KkcueJ+2kMJLihbSMhNUK1Os7RY39IMNhNR/tHKRaPEI6sJBTsQggXVQ0TCHRiGE2vizlsMZPm6e98iw3LVmHecy/Fhx4CVaXuA7cQvPpqJv7n/0TYDpKq0vX1rxHadBkqe0LAiQfgic/BmaewjAgPr3wbf1A8XAsTNxSDt3e+nTtW3cHGpo2vnTfaEpbwFsKrJmaS/8lbC1wPfBYwgf8mhLjzUi70leKNTsyq1SqFQoGhoSFc161ZX+RyOWRZplKpsGfPnkWVMV3XiUajZDKZmtZr9erV9PT0kM1mSaVShOJ1HLQ93P0vIDwPSZZ5cP02Nige6yp5rl25ghUrVtQMZl8uqul8EJ5HZmKMk7ueJTs2jB4IIssKxVyGqdMDlLI5rHLxoo6ZaO+kdVk/siqj6QFWbdtxcXovoDo4iHnsGMUnniD/3X+rPZ6NhnDu+EWWvef9tK88xzHnLBy6r/dd8vfcDbu+OjvhOHvNsgaePWsnIWM234Qx/RCS5yCpGu6H/51/+CsTzxUggRFUCcU09KCKEnAZH5/Ey0UWE7OFhmAvA0mC5t4oelhHOC6hOgNF9o/jAa7jEUsG8FywTBtVU9CDKpIsE00aJFtCRJJBjKCKor31CJnnWZTLZ5hKP86jE/v54dghjuXHMWSNr279OEkjUvMNFgLK5ZOMT3wPkFm+7PcJhftQ1QjSgsEM163iukV0vYFodDWSdGl0lOeDbVXZfe+/MfSNr9M9niJeNlHq6kh8+A4SH/4wakMDAOU9ey6/J9ngY/BP74NYB+61v8nIirfzj8f+hXtO+vpJCYn39L2Hd/e+m6uaryKshS/POpawhLc4XpVdhiRJTwB9wCHgGeBjwFHgtyVJ2i6E+PVLuNa3FGzbZmJiojZlWS6XCS4waJUkiXQ6zfT09CJS1t3dzfr162uVs1QqRSQSQdM0zKrF6WCUh9vqeEZoVJD4lWtj7HTLdPf08HtdXQQDgVdFws5GaSbH8OEDnDmwD8cyyadSFLNpzGLxwvzEzoFgNEbfpi0kWtqINzXTtmIlevDixvuF4+CWy1QOHiT/vXnhfbDBou36FuK3XAO9syMOruNPTJZScPRH8Mj/ng3klnyX+wUj/QJwm99GOXIrkZO/BzgIVGbO7CTjvI1g+Aj1y2+gOtzJ+hWThC2PakjFihvYssWJqUNM5AaQ5RB10vpZIiahaBLCE7W2JoAeVLBNtzaXEGsIIKsyoaiGEVTwhMARAtt08FQZ1xE4tocRVCkXLBRFJpoMkGwNE00GiCQDGEENSX5rVjGEEFQqwxSKh3lh7HE+e/in5G2TtmCcTyzbxo0ta4hoOiCQJQ0klVz2aaamfoAfZ6VimqM1oX8w2I6mN/j+bIqBohhYdopqNU0g0PSq1yosC2HbcJZ9jaRpSLrO8Sce5eTn/oqWwRGutB2Ujg4af//jxG+5BfmsRIzQxo2XnpB5rm95UcnAlk/4XnO3/xOs/AUUVacb+KD3Qe47dR+ucNEVnXf3vJvNzZsJaZfZjmMJS/g5xYVMZf4GcEi8uLT2KUmSjpxrh59nCCGwLAvTNGsVsrkw72g0ypEjRzh+/PiiCKPGxsYaOZMkifr6eizLQpIkYrEYoVAIIQTxeJxPFWVOOB4hWeK9DVFub21gWzKKchnaCZZZYfzkcc4c3k8xlWZmapzsxDjlbOb8O58Ha3bcSM+VG0m0thFJJFHUi9c+FR57jOw3v0n5mWcJ1luEmqp4HjSvL4Ccgjs/AME6n5RZL6HDE4Ky2slE6G10F+5EFg4eKtWuj1M500XF+hMC8gGq3hXYYhVRRQJzLTN7BewdoAO/ikAGzPEiluJylGmSci9huR2tOYCZt6mWHVz7rI+QBMm2EJquUs5bBMIqakDBtVzCMR08cFwBqkAPakiSwAirROsDxBqCxOtniVhIQ36LErE5COFRraZ4dPDfKJZPs7lxJS26ytpoA+9qWcWVdW34L0EV2zIpVwYoFA5h21k8r7LgOC6mOUIkshJPuJTKpwkJgWE0LjgZr7g9JzwPd2YGZ2oKJ5XyK9ksnkORJHCyWYo/ewj7oYfos23k/n7afvv/JvL2t5/fwPhSwHPh0D3w6J9D6pgfAL/54/7i1n5g0aYbmzaypWULh9OH+cKNX1jSjC1hCZcZ5yVmQoiDL/P0ey7hWt70qFarTE5O1vzDSqUS6XSaqakpLMsim80u8hYD6O3tZfny5eRyOVKpFNFolJ6eHnRdp+J5PGnBQU/hL/vbCQaD/MfxDElN5eaGOKHLpBlybJv0yBlGjxxi6MBeyoU81WKB7Pjo+Xc+B/RQmLb+1QQjUWRVYc2OnXSuXveKb0BOJkvh4YeZ/MxnELZNsLFK19vSzMmC5u+pAowYXvs1lNUOCpUguYKBkxlnnfOPSJ6Dh8aPxz5BIXQlyxo30Rk8hOjaRud1NxKMpcmfWY3lrsb35194s/YzOeUFjwVcQcBVeC+bOF31yDkCQ66ScgQL/9fjjQE0XSEQ1ZBVCSOoEouqyFV/clLyBLrpoVUsAraAZAC90SDUEqGuO0ogHkB+C+rFXgoFc5p/Ofj33DP4EMPlNOtizayPNxDRDD7VuwqASvkUlp1GlnXK5SEsa25GSSISWUOpdGzWMFYhFPI99mRJQVVC2E4Ow2hECBfbnkHT4mha/KLX6VUqmIcP4xWLoBvIkSjSAlsZ89gxMg8+SPbIYcJTfqRZePNmIjfuJNjTg9re/upeqAvF6SfgB78DqePQuBo+9DVY84FzJmHMoepW6U/0L5GyJSzhNcCr8jETQgxcqoW8WeG6LtVqFdd1OX36NIqi1Jz1T548uSgwPJFI0N3dzcmTJ2sTkT09PYTDYUKhEB0dHSTr6zmCyr2ZIj9KFyi6Hl0BnbJmEFEUPtHR+DKreZXX4tjkUymGD+xl5Nhhxo4fYWZq8mVtJ+YQiEbRAyFaV6xEksAqV4jUN5x/AvJi4LngmNjP/is8+jUSfRkCCYdoZ4U5c34BiFltkKRonOr+Y376YNzXgQGaoVDffj39t70fY/JprPpreG/3NQTCGr6Ech7a9g709gi5+09jDxUWScP8/Mj5G5lYRNwkenQZdH89HvB0ySXjCCQJ6lpDhCI6nudhVQUqELI8PFegyBAOGxgVG2N61hIkXyXUFEStODBSgoAGobc+MXOcAncd+gpfOng3RcekP9rC76zcyeZ4fHbgJEgg0EahcJhs9nEWZ37OQ1PraGm+jYp5Bl1vIhj0h108z8W2C+h6HZaVmSVtvQSD7RetLxNCYB4/gZPLoYR9fZswK6AoSKrG9A9/QPHOf0YCQoCzehXdn/wUWnNzbX97ZAS1rq6mKbukcG3f9iKU9K0vZM1vWa5+/2x4/Px1lJ0yFadCwSpQdao4wmG4MMzq5GoGZwaJ6lGCapCgGlzkWbaEJSzh0mDJYPYVQghBJpNZNBE5MDDA4OAgpmm+yPhVkiQ6Ojro7e0lkUhQLBZZvnw5HR0dGIaBpmkYhsGP03k+fvA0YUXmfY113N6S4Nq6CPJlnHwaPnKQwReeR5JlCpkMQ/tfoJw723bW93wSzLZb27tQDI14Uwtrd9xI9/oNKMplejsNPwdHvg+RZpzxowQO/DOBhEBK+veb0oRBuM236/CEymMzv8rmHVFim99FqNrPei9Fc2+Mhs4IsfrgvP6q/zqCL3PaasVhynTJr23ADKiEpyvopo1edhE1Hyyp1oIWZ9l3CCH8/zchMIKCtYpCTFcpq5CVBCFPEAqo1MseoaKFETPQ4wbu4Azu9IJpVg8qh9IEZXBSFeQ6nUDXxVd03iw4mTlKVLKpFl5Aqo6zLt7Ce5q6WFPXiSSBbReZye2hYg7jeSa2vTgdIhDowjTPAB6SJKPp9WhaDFVdi+OWqVanEbhISASCXUSj6zD0en8Y4CKIhnBdv205OYmTTmMND+Pmciiz3oIA1plhJn7yY4zZChn4ti0NGzfVSBngx3oJsYgkXRLYpm8I++TfQvd1cOuXoXU9/F9PvqhCVrSKHM8ep2SXkCQJVVZRZRUhBOlKmunyNE+NPUV3rNuPi5M1uqJdNIWbUOWlW8kSlnCpsPRpughYloVlWdi2zenTpzl27BiNjY1UKhVOnDhRc+YHv0UZjUY5ePBgbaqyra2NtrY2Vq5cSSgUwgN+mprhm4PTbKuL8MnuZnYmY3xxTfdlbVXOYeTIIfY98COOP/OE7xv2MpAVlU073ocIQ+/Gq+hau/7yj8lXC3D8p/DvvwGuhcB/w84J5YUHqSNRUodjnLn2VpINOayWrdRdtx1pSzMkA7QCrcsvzp/KMh0yY0WmRwoU01WCUR3REmY6oFE1HfThGZqKJhFJrRE0Ifx/pR2PhCqjAEi+jaksSWxRVEACy0MMFDiFxUkJulSVXtsPbRLjZSzwCz6GAtUFE6IFG/NwBrUtwoVMUr8ZcSp7ki+88Fc8OPIkv9K3nQ+2r+RtrVews209ZjVFuTyIaQ5j2zlMc6i2n6Y2YDsZfHG/bxgbjazDNIcIh5cRCi3H9UzwHGLRVYTD/RhGK7regKq+HDV/aXiWhXnoMO7MDFIwiByLE1gVwRoZwTxwgOK+fYhUCmd0FC1gkO3tIjE8Bq6LpCgE1q5FuK4/HDBrKWMsX46SuERpA+YM7P4GPP0FKE5Cx9VwxYfmn5/97FacCiW7RM7MsXtyN65wCagBZEnGUAw0WePBMw8iEBzLHuOvd/01f3jtH9Kf6MfxHAZmBpiuTHNF4xVL1bMlLOESYYmYXQA8z2N6erqWVzkxMcHDDz+MEIKjR48CYBhGbfs50f66detYvXo1U1NT9PT00NXVhSRJ5B2XLw9P84+jKYZNi1ZD4531MQCCisytzZcvCsa2qgwd2MeBn93P4AvPI85jNy8rKmtvuJF1b3/HpWtJvhw8zzd2HXgUnvk7xMRhpLmJSQEzxUYioQyy5CI8qEwaSEBfyKHn9/8YozH5ik9tVRxmUmWGj2bxXIEEmCULx3JIlsfVogAAIABJREFUl8cZHZyCqflWcpfm0qbL5BwPPaozkrPIOIKkJljZFiJcdQmZc+RqsULtbZKOB8i2QJ5tg87RrWejEj8tl/kf6EgL9vNyFm7IPGdI+ZsZp3On+MKev+H+M49gyAq3dm3mnc39WFYa28qApFEo7Cef382LvUYkQqE+NP0qLGuaaGQthtGC65YJBFoJhrrQtQSBQCuBQDu6nnzVNhjC87DHJ7CnplDjcf83BctCUlVKhw8xc+c3kYS/0tDmzURuvJEWVcUeGcEaGkJtbERJ1OEVCyjxOGp3F0pdHfKsufQlwWN/AU99HvreBrf9I/Rc/6IK2emZ0wwXhpGQ0BQNWZKpOBXGimPsmd5DvppntDiK7fk+ewKB4zkcTh+mP9GPKqvEA3FyZg7Hc9CVS7j+JSzh5xhLxOwC4DgOuVyOYDCIqqqMj48vqlqsXLmS+vp6nn322Zp2bN26dfT19aEoCqtXLyY0nzoyxP2pPFvjYT6zvI2b6uOol3GqTgiBVSlz9InHOPCTHzM5Osi5zLRk2Tc8FcJDlmRWbtvBymu307JsBbKiYpsmsqoiK8qlrZYNPQNDT0LDCj9A++QDiNTxeUKC7N/8XMg8I8iSINRUpTxlUEnrSKpC58due8WkzLFdUiMFsmNlbMulkq8SCOnIKkymJpg6nUfYGqq7WN93xhGcsV0kWWJ5T4xY1SVgusSagnj1QSQE0lNj4AqkhVN5ikTzf1yD0hCkcjhN/oeDfhVw9vnOliiFAnx7zOSX0RHM0zp3rEz1dJ5A9xvLpf6VwJ9gTvGZJ3+fg5lBbuu6hve2dNMQakVWdAr5MdLZZ7CsKYSwztp7NnJcUgiGetG1OKFQN4bRjiwphCPLiIT70fV6FCX8qt+vwnVx0mmcqWncbBavVKR6aoByOoU9No6na+T27sGYmm+rSrKEsWIFoQ0bEI6D0d+PW8ijBAKENm9GWpCF+6oxvs8nYhs/Cn03wNbfhHW3Qdu8vYbt2eTMHOlKmhlrhv3T+8lX85wpnGF53XIQ8MjoIxzPHq/tszKxkhWJFfzk9E9wPRdFVlhRt4KiVfQJm4BldcuWSNkSlnAJsUTMLgC6rtPa2ko6naZardLR0VGLSpJlmb6+PlatWsXq1as5ffr0Ijf9qudxfyrPN0dT/P+ru+gI6PxeTwu/29PCldHL7wMkPI/JgZO88JMfcOTxh865jSQrdKxZR+caP/w4PTJMQ1cPDR1dAEwOnJz/bVvy/9B0Hc0IYIQjBMIR9GAQ+WKCzYef8zMnKxnY9y++r9TsUxZhNDF3LpkxdsDIBPbRFGbavwFUZv9Ggtj73kdoy5ZznOTl4Vgu6fESxZxJ6kwRI6SiqjJmyeHknklyuRyKE0IhBggSbSFyExWEJ5BkiZaeKEpAIZo0CMUMwnGdho4IkXiAQExH0xWqaxuoDswgh1TssaIfmr2pGaPbr5Bq13cgawq5e0+BJ5A0mfU7e/lWd4zKjMnhbx6icbTEwqnQ4qOjiLKDV7KxpyoEViWJbmtHNl4bQ9RXC9u1ufPgl7m+cRkRTD7es4lo/06CXo5i8TAThd1UqyPY9px+UyIW20yhsHfWf0whFrsKRdYJBLsJBjtQ1Six6FoCgVZUNYosGy+7houBcBwqBw7iFvLIgSByXR1KIoGdzpD+4hdhVgYgKTKprjYaRif9yq+iovf14RYLSEKAomD09WF0dCBdiuqYEDDwMDz5NzDwCOhR6J3NQY21+V+zqLpV9k3tw3ItDNVvUZqOyZ1H7sQVLg8N+z8blAXVRAmJ7lg3G5s2EtfjnJo5xbWt17IisYKYHiNuxInpsSVStoQlXGIsEbMLRCwWIxaLYds2nZ2dNDc3Mzw8THd3N729vUiSRDwep7OzE08Ins0V+e5klvumcsw4Lp0BnWHToiOgc8VlJmTC86iM5Tj+/ceYOH6MfJ1vEHs2JFmmqXcZzX0rSDS3gCz7uZT9qwCJfGrajw9SFBRVRwsECITDKLqOLCu1vMy5dmgoHieabCAQib40SasW/ciX7/0a1FokPgfzhMT+0nuZjt3ITvFpZByQFAJSB9NPHgdmbwCzUUsIgaTrJH7x9ot6fayKQylXZfR4jkqpSqVokx4p4bgWhWyZyiwfUAkxX8fybS2ueFsb2YkyobhBQ0eYZGvEj2CqC2CEX+zFZnTHaiTspRC5phWtJUx1YAajL17bPhgPsOm3NjH99QNYJ2Zqr5VTtik+Om9dYp8p4Oaq1L2nF9l4436khfDYM/E0n332TzkxM8TM8p18qHszrYZBsXSAkfTP8GdYQZIWv5aSJNPe/lHKpQEMo4VQuIdYdN2sViyJqkYvS4yScBycTAZ7chI1UYckS0iuQ/7gQaY+/3nUBdrM4PYddL///VgDA1SPH0fv6yW87TrkcBglEECORBbZZ7xq3HW7HyoebYV3/jFc9TEI+EMhQggyZoaJ0gRFu0jOzPHk2JOkK2liRoyZ6gzPTTyHuyDpYm39Wq5svJLvHPsOrnBRZZUrG6+kLdJGf6Kfzmgn9cH6S7f+JSxhCefEBWdlvpHxRolkKjouEVUhZTmsf+oguiTzC41xPtScYMdlMoFdCLdgUdw1zszzI2TGhnl4/G48zi3qT7R30NTTRzRRDwtVS5KEoioomo4eCKAaARRFQQiB5zo4jgNIaIZOOJ4gEImiapo/7l81sa0qiqLS0NlNKF4336oZfg4x+DiF6RyBQ99E93K1UwrJPz6yjvWL3yOwahti6Bmc3feSP2ky9fX7F1l2hLZtI/6BW3BGRght3XrBbuiO5TIzVWF6uICiSlTKNlMDeY4/NznbmpYQeItieuZy0CVZYt0NbXSsTBAIa8Qag4Si+mviJ1YdyjP9D/vB9QcM5gYOFnqoSRGNlv+6CSX04uqFcD2E7SEcb473gAySKiNpCpJy+VVrBTPF557/E7478DOSRoRf67uWq+IJ8vkXsKxpqtWxRdtHoxspFg/UKmTt7f+BZOI6FCVCINCCYbSiXKZKjXAc7OlpnIlJvGIB4XkUn3gS6+RJHEMnf/gQgakUliKjzbagJVWl+Y/+CL23F69cAk8QWLUSrenVpQcsQiUH+/8VNv8nUDTYcxcg4IrbQZ3NTRUCRziMF8Y5kTtBzIihyzqPjTzGVw9+FW/2lygZmdZIKxOlCYQQqLLKf9n0X2gONzMwM0DWzLKjYwfrG9cTUANLU5dLWMIlxquKZFrCy2PEtLhnMss9k1nimsI9G1fQoKv8y5XL2BQLEVYvb3vJSVVAlfACguzBIez7p6koeY6az70kKdMCvv9QMZ3Gcxz0UMRvTQYCyKqKhIRrWxTLZcBv0RjhCMFIhGDEtwJwHZuZ6SlmpiaJN7cQjsXRA0H0QBDXtpkcOEk4kaSxrYXq/vvRf/SfkT2LmASmG8JVVF/0rmpIN/8ZUiUNPdsJdF4NgGhaj5k0KR2/a7GPmiwT+4V3U/e+9134a2S7pEdLmCWbQsZkcjBHZqxMpVilkKoy50oGgmR7kJkJC88VyIrEqutacC1B15oEvesbUY1LrK+7ABjdMRK3raDw2AjORPlFoegCoGhTem6C0IYm1LqA/7gnsFNl3JTffgWwpyrYU2X09ghak1+5VWI6WkMQSbu071VfQ5amWh3jL3f/HfeceZo72paxs87AtfcwOjo/WRkK9VOpDNSIWDJ5HfX1OyiXT1FXt5XmpptR1QiyfPEJERe75srBg3j5PFIojJJIYh47Ru7uuxGOgwTIiszEyj7atm4jUjYRpRLBK69Aa2pC8lyM7m7U+nrk0CWqjGcG4Jkvwd67/MGY+mWw/EbY+JHaJkWryHBhmLSZBgF7p/by1PhTuJ7LRHmCjDlv6yMhcVPPTVzbdi2HUoewPIv1Teu5ouEKImqE7R3bCb7CadUlLGEJrx5LxOwV4ifTM3xpeIpnZkoAXBUL8b7Gupqn1fZk9DxHeOVwixal3ZOUdk/gTpmUO6oMqgep5ItIQbBUE7NaRcrNe2xFGppwq1WQwLVt0qPDNcKjaBqBSJRAOEIgEiWSrEeSZcozOeKNzUSSSaqlIpX8DKF4HeFYHEXVUFQN4brkJscxi3mSbR3IsoIkqxSyAdJjKbTsSeLP/BmyZ/nVJ0Be936Ua38Vhp7ws/k6r6ZsOZQtl+cPjLN3OMfGmGD1kROUn3xy/sJlmYZPfYr4BZKyStGiPGORGi0yeixDPmVSmrHITZRr21haDt2pAyEhKzIrNraCJFHKVem5sp7O1fUo6utvAxBa34hkKGTuPgrOrEUH85QSoPjUGE7aBAFexcHNVXHzVfDAq7rgzE/gmgdSxH+hF70ripu3EJZ33pbrhUIIgedVOTL5BKXyafrifXx0xXvYFpxBLe+mVHjxZGUkspLGhp2UyqeIxdbT3PQ+NC2CokSQX6NqjbAsvHIZe2oKJRxGUmQqhw+R/uLfwywpE0Dk5ptZ8dFfwbNt3OlpjBXL0Ts7/fzLS9mqrGTh3k/C0R/6ea7rboNrf9P3IcMX89uuLwfYM7UHGZmyU+aHAz/k4eGHa5XV5lAzmxo3sT+93x9OkhXWNayjPdLONa3X0BZpe8klLGEJS3jtsUTMXiGGTYu07fDp3hZubU7QHbx0YuOXgus4pO4+jHV4BklASS+SiUwwPTOOZ3hUzTKyqmJXqqiGTrKzC7tSIRiNE6qr80mT5GvQirkc5ZlsTQtWSE1TOkcGpqworL3hHUQbGn3t2swMjlUl3tiMJMtIikIwEqVSKDByeJLUaZeRkx6N7Oea5L9TpxxBdquIWeNMSdHRt3wYuq7xv4CjE3kmZkxOThX5/358FMcTqJLgpvQoO+u6WJP1Kyvh7dup/08fe1lbAb+l6jK4b5rBfSkSbSEyYyUGXvCn5XyHfgAJJFi+poemjjoK6Sp9GxroWJlE1eU3XOSRpMiE1jag/NqVlHdPUh3N44yWF23j5W0qe6eRDAVkaa4IiFe2XzyE6wrMoxmUhIEkS8jBS/OjwHEKnJp+iruOf5/7hh7n/fUR3lWnYFnTqC+arJSZCxavT15PXd3Vs0aw8desKilsG3t8HHt0zA8bl6B64gSFRx6hODKMnp0BwwBFASGQVZX/w955h0d2lmf/954yVaPe6+5a23v1rns3YFwwYGxIIBSTEEJI4EsgfEm48iUhlAQIIaE4BNNt4gDuFRdct3dtUdlVl0aj6fW09/vjSCPJu2uv12tsk7mvS/bO6Mw57xTNuc/9PM99V65eg5NKIiV458/DO28eQj9LSp5VgHCXO03prYBsFC78NGy6FUKN7iaORU+8h8ncJFJKdod388TgE4SzYRJGYs7uFBS2NG/h4paLyZgZhtJDrG9Yz5r6NXjVszgVWkIJJZw1lIjZGeKDLbV8pLX2Nf9iy0+mSewcJB6MEBkZIDCgIwOScW0Q02tiFfJYlg05MHM5dJ8XcLALBmYhjzdQhubRSYTHKWTSeHx+pHSIj42+7LEBHNsmER4jVFuHUBQ8gQCFbBozn8MTCAKgGgmO71BQJvawMvgA51UPUM6wywtscGoWotQsBF85tG+B6gXuCUjzIqUklbeQSPYPJbCmSm6WhPur1vPQhev4+N5f8NbhndR+5CMoPt8p12oWbML9ScL9SZ7/ZZ9bvtvl/m46MskdKnXfM1VVWHlBB80LK/H43hx/Ct6OcjytZeQOR4n+9BDM2KQR+L2lhOoD5I/GMMNZ8t0xnMSLydAUFIGnzS1nqmUelFfx/KV0sKwEfbGj3LX/y8STBxECPt/qpZwx3HhYQUXFRpLJ3VOZlRptrR8AAbU1l1NV9cqnas8Gcl2HcBIJlPJylFCIxEMPEf3e9xBSogMTHS20v+smvOEIMpcjsGED/tWrUEMhlFAIxXuWLsjSYdjxfdj+n2AX4FOHwBOEDz4AQlCwC+QLrsXF9rHtAOStPL8Z/g3DaXcQRCC4qOUiGoON/KrnV1iOhaZonN98PhubNlLuKS8RsRJKeBPgzXE2egNi2nfMcWyMXA7HsnAcByEEQlHQvV5076lJxKkgHYdsNE5i1zCFfZNoEQWBYLjpABmZYtLvnolzqSROziITixYVsUwsSiYexbYscsmZK2dFVYvO/pmTPRePB8uYOYFXtbQSHx1BOg5CUQjVzvXvcmxBeFBlckSyYn2K8tQOlnrDLKn5bxRONKxVJrsh3g9v/5rbHxM5AtFj0LoB4Qmwpq2SSKrA/Lo4miJcciYBoWAj+ebqG+nvaOKWaA+bmdsr6TiSdCxPNmXSs2OMY3sipOOFYk8VSAreSbyGO02mqiqbr5+PaTq0LammccGbL9qoqJ59dDWZXeOEk3k+fWiYDwxFuaI3TqE3jj2eO/mDFfAuqyG0sRHvwqqZeKpXgWRyP6YZY+vR77GRPSjlLvd1Y6mKq8bva6Wx8XqMQpjq6guorFz/qo99JpCO4zruWzZ2OoU0ChiHuoj/4pcU9s9MLwshWHj+xZSvWoNTyBNYtx61LHh2FxPpht/8Mxz4H3dKufMK14NMD0wvgtH0KD3xHg5EDvCdfd+ZM0lZ7pkpPwsEVb4q3r343Vyz4Bp2hXexoWFDKXi8hBLeZCgRszOElJJEeJz42EjRbHa89yixsVHqOuZT3dKON+Cnrn0eHv9LNwFbRoFMIk4yEiG65xhV+0MojkLEGGTY6UaGBF6lnOR4GCOXw18WIpuKExsZhql1zCZfLwdfKEQhnXb7zxSFyqZWJgeOu0qGolDd1Ep1UyvpyQkClVWU19YjJaSiOpEhneRkLY6t4PObBHofIWjto0rNIKZIWdGCbNb/cSwY2QMNy0H1QmoUCknwBNBVhaZKP5+4bBErmiv54XN9PN3jlmkQAgeFu6vO46EnJd+dF+bixfVYhs3w0TjH9oVJx9y+scREjumZxWllQFEVrn7PZsqrygj3J2lZVPWmJGMng7ejHE9biD/+zvMENYXzDqXIjmVPvrECgbX1BDY04Jv/6s1pc7lxnhp8iMd6fsJFwTxVqkGLMTHrDRdUVmwkntjmGhYrGk1N76a6+txXfewzhVMoYAwOYo2OAZJ8Ty/Z554l090DY2OIQIDMkoUEeo6B4yBUFU97OyIQILB0ydkjZZbhNvEHqiGfhMP3wYYPueXK2oXkrBzpXIR9E/vYMb6DjJGhK9rF0djRYt8YwCWtl3BJ2yX807Z/KqpjNyy8gSXVSwBY17Du7Ky3hBJK+K2iRMxeASzDwMjnsEyTwYN76dn+AvUd8ynkcvTt2EpszB35V3WdKz78x6hqHRMD/bQsnuv87zg2RjZHMhohPTxBfm8EQxQYzB0lMTZGh76EsD1A/8hel5yMCrzDZRQyqVOu7ZSuJ0IQrKohNTnh+n4JQbCymqqmVgrZDP5QBb5gEM2j41gWoZo6AhWVWIZBdds8/CG3hJmP5+nZVU+z9yBr67YjahqoVPponHgcXqSSTTdJC2+FewKSDigq1C6CzJQzevUCCNTOeZyqCK5Y1sDqbQ/xo71P8e8rrsdWppqphUJBwmd/vpdbvBkWpgfpm1g+0zslbCTKVLlS0rYyRMuCujlErG3pmcc1vVGxd/84qf4k/0EQTkbKVAisayC4ofFVN/c7jkU09gK7+u/g18O7qCPCWwI2wgbDhmBwKdlsD1LaKIpOW/sHma9+gkRiB1VV51JR8foShfzRbuxYFLWyitRjjxG97TaYyjjVL7qQhlveC5YJuk5h/34CGzcS2LQJcbaCxZMjbn7lju/Doqvh+m9C63r49BHwlgFg2AbPDT/Hw/0P89Cxh4pErMJTwaVtl/L08NPYjusxdu0513Jx28WcU3kOO8Z3lNSxEkr4HUGJmJ0GpJRERwZJhMMIYPxYL8/990+QjkPPtucB0Gf1PtmWxdChAyw+/yIqG5vcbD2jwMDBffTt2E55fR3erJ/kgSEiE4N4RYCkOslorAeAcY6+eAUYublFSFf1ykz16wgq6htJjI8UpzDLG5qwDQNvMOj6jQWDZJNJdJ8Pf6gc3evFG/AjHYnj2FQ1tqCoCrZpkYqq5NLzCKZHaCp7gkCDjelr4NLOrSxN3wE4MOmuQxRXCLa3GrUQAyRC0WHN7wE2JEfdzL72zRCoAW8IPCdXEfM9PUx+4xu8NZtlXnyEx9rW80jHRixFc8s6GYOvpXVWGAtZqdi02CoSiV0WQ8/WgAOqprLpLYt+Z5SxUyGXKvCFJ7rZJDSUkxBzvSOEf3kt/kVV6I2vTu2xrBSDQz+lp/crKEiuCs5VRkGhsnIDCxd+jlRy7xwi9nr1j4Hb3C8Nwy3Lez0UDh1m8r57cbp7ZtYvBP6aWhRFoNTV41+1kvJLLjl7ixjc5oaJH7rXvUhZeBWsuBFwQ8TH8xG2HXuAJ4eeJFFIcDh6GMOZaS0QCC5uu5jPbPoMffG+E0jYmvo1JUJWQgm/QygRs9OAZRSIj43hD5Wjahrjx3qQzoxKtGDdRiqbWtj78H3YlgVSkkulGDy4nyPPPo1QFfKZNL3bnp+VsTmtK50cwaoasvFYsbzYunQlQ4f2uycYoVC/YCGObZOJTuIJBPCXhfBXVJBNxPAGAnj8waLfleM46D4vFf4mhBA4jk0+lUJoGoqiYNsWtmmQz7aTTbVhmT6aPAe5turvUIUJkwJTC6FbyblELNCAmou4cp2qom34IOh+yE5Ax/kw73y3gVk/PU8kaRiMfuazyHwegKWxfpbG+rliZDffuuRjdJvupKEUsN9jc8Bjc2VWYZ2l8M7fX4coq2P4aOx3qlx5MjgFC3Myx+C2UTZPGFzh9yOyJ/b26XUB9Ho/jv3SQfWngm3nGU/28tihf+EcZZh89hiKmPn8VledRyKxA8exUBSdpsbrqKhYR031ea/i2Z0dOJkM+d4+nHgMKQSZp35D8uGHsScmMDSVsboq2qLJ4qRl6MIL8K1ahVpRcXYUMjPvmr4Kgdz3c+h7kp1r38m22nlsWnA1S2uWsnv4GR47/hhPjzxNOBsuPnRd/Tqu7riar+76KqZjois6Ny68kXJPeYmElVDC/wKUiNlpQPf6qJ+3gNjoMIVshubFS+nbua0YWN6xai2tS5cTrKjk6AvPMt7XTd+ubfTt2nbKfXq9QQqFdPF2bcd8JgcHpvpxFNqWrwQgG49RUd+AN1ROWU0NmViUioZGQrV1KKqGEAqKECiqiqZ5wJ3qR1EVdI97YhCAomlomg6KWrztOILxYwZt88FjTjD89C7K9fuo8CepcI6hGOYUfZRoqsCpXo4SOYKQDkLR0M7/BJQ3ueP97efB/AuLDuSvFFJKxv7xC+QPHqT6Ix8m+l+3g2MzUbOS6LKb2RjzcazMmLbwcgka8GjQxs8O1ozG2Hz1zb+zhExKiTQdcvsmGNozToWEQG+Sm/DASUgZqsC7sBJPawgl8MqsHOLxnfSM3MPesefwmcdo0SU5XOVGCG0qI1Zj/ryPoyg6sdjWN0SpcjaMgQHsaBQrEiF82204/VNmtqqK533vZf3GTQSqa8hu305g08bTTo94WSSGYcf33JLlTT8k1rCc3qVX0dW6iK/t/RZW7AW+3X0nMPV3pWjU+GZijhQUVtWt4palt7CsdlmpRFlCCf8LUSJmp4lQTS1l1TWYhTxNnYupbW1n5MhhWpevoH35aiyjgGPbREeGGO/rftGjBR2hpQzlunEcG0VV2fCOd7D1f+7Eti0UVePCmz+AomkMHz5Iy+JlNC1agqqqIATKVDbkdNnl1Y68RwbidD11nPTeJ5mvPEnoWJ6Q1U9NasR1ksgD3koQituXpuqIy/4aFlwMyWEY2OqGJc87/1WtYw4cByNfYOgdt/JcvA193fvBqSEVmgdAiw2fX9vCzrzFPYfHi11tEsHdrOPB3wh+0Blhyzm1pzzEmcCyHQzbwXam4pDcTCQUBXRVQVcV1LMw2Tgb0nZwchZOwcYYSGIMpt1p3aEUzlCGABLrRe7/xZuKILC2nuDGU/eUOU4Bw4gRi20jmdxNWdliPN4GjEKYsfCDRKNPI4B5AkxdB8yi91tT07vx+1rmELE3AiGTjoMsFMju3EVu504IBIjdew/20W4sRaAy9RJJSUU6Q8WKFeiNjQTWnQVCJiUMbYcXvoXsuhuQGJ2XsS3Zy3399xLOhema7MKSlrs5kkWVi7h2wbW8Z+l7OBI9wq2P3FpUxy5vvxwhREkdew2Qs3KkjTRpM43t2Agh8KgefKqPoB7Er/lP+f1q2iamY7I/sp+d4ztZULGAhmAD3dFujsaPsqFhA1uat5RsSUp41XjNszKFEP8FvB0ISylXTN1XDdwJzAOOAzdJKWPC/TT/K/A2IAv8gZRy18sd442SlSkdycjRQ9z1D3+NZU73iAhUTeNdn/sHFF1h8OB+2pavpHnRUkaOHppz+7VbmIRCkvyBRxm9/w4KmQL1nj6qtKGZRn1PORjJqSUrcO7HYNFbYPAF152/Y8trsjTHkRzZNchzD+0jGcmi5131QCBQzSy25nPXg8O6K9vYfOMi/vPpPr740BFsZ+azqwj41DqVP8l/B26544yVO8t2iOdMJlIF4lmDvGljOzPGtHJ6bYpAEQKJxK+r1Jd7qQ56CXk1lJcgalJKpOEgDRunYIHtkgocQHH3nz84iTGcxsmZmMfdgY/pfqhpP7apF8mFqlDxtnnIgjMnBP1kiMW2Mzr2P0jpMDb2S148uOGucdrqTdDUdBPj43fjOCaKorNu7Y/eEERsNqx4nMKRI+S7ugj/y1fBcgmQoQqON9dRf8FFVP3iXqRlIjSd9v/6HoH1Z9GqwzLga8vAMogvv44dHeu5L7qXxwcen+O+H8lFkFKiKiqf3/J5Lmu/jJDHTQjZE95TUsfOIkzHREpJ2kwTzUVJGSnGs+PsGNvBaGaUJdVLWFa9jGOJYxyJH6GzspOFlQsJ6kHOqTwHXdVJGkkORg5yKHqI1mArHtVDV7TGTTUhAAAgAElEQVSLO4/ciS1tBIL2UDsDqQEkEl3R+bN1f8aqulUsr1mOrr628WElvLnxUlmZvw1idhGQBn44i5h9GYhKKb8ohPgsUCWl/IwQ4m3AJ3CJ2bnAv0opX3a+/vUkZk7OItc1SW7fBAC1H1zByNFD9D76PGUttRgyT9viRTS3t4CRcS0ibGNqUlFz/Yqmm+G9Fa4UczZgGTjHniG161GMrEVd9mnkxKEiuZDeKpRCzN1WKLD6FjjwC3dtqgc+cA9M5VaebTiOw6FDR5joyTCyNUVs0imavhYpiHRoHnmGscbNOEJFVeGqq31UXLCKbWNZfnNkgp9tH5yz3+YANOWOUNXQSlXLYqqCHqoCHqoCOpUBD9XBmX9XBnT0We7+pu0wMJllKJYllTPJ2zamBYZtT6lkM9Rs2p9L1xT8ukq5T0VRXOVMVxUW1pdRXz7Xw05KiZ0yMMM5ZMEC23Hfa1VgRnJYoxmErmDHCxSOxE54zaYJWbFhXVOouGY+Mm+/LBkDyOfDTEaf5MiRv0VKc+77IWFXVuOGNf9EfVkLe/Z+aA4RA96Q5cpp5Ht7ST/+OJM/+jFO2O3VkkB6zSo6P/EJqs+/gOzu3WS3nVnZMmNmiOQiZMwMXZNdHB7byQXxCc4b7SbxvjvJOQa7D97Jo6lexvIRuuPdxbBwcIn8TYtu4u3nvL1Evk4TUkoc6aAI5WXVJ9MxyZpZHOmwbXQbzww/Q2dlJy2hFp4afIrB1CD1gXomshPsCu/CmboYUYU6xxNuGqpQURUVFZWcfQo/wCkoQsVx7KkgEYUbOq/n0rZLWdewjgrv72ZbRQlnB69riLmU8jdCiHkvuvt64JKpf/8AeBL4zNT9P5QuW3xBCFEphGiSUp6eTf1vEfmjMdLPj5A/GgNbolZ6CaypQ0pJ86KlrgJmFSB8CDITMDrhWkaoHtdgdXQfNK6EmoWQHJpqoNehdrHbt3WGkFIS6epm9IlHWDHxt5RjuzFMqmfGW0yoiBU3wN47ZojY+j9wf44/XcyvPJsY6YlzbP84cXOU7sO9OJEKvEYVs+f63MKkRDoOirRpGt9G0/g2YpWLaF3dRH3N+egel/g8eTg8Z3zinLogSxrLifWNMBSOcSATJpqzMKxTN76HfBpVUyRNFQIhwKMqaKqCVxNoqoJHVfCoAq+uksyZjKcKtFT6aa8O4NEU8rpKPAcqgpBfp7HCy4GRBOeYNh017iSktBzy/UmMgRRCAUVTMKN5rMkc6AqFg5NzhKsZdczlq+61kzv4IBRBcEMDgXUNp2V/EYttJRp7FtOIE5l8vEjKpGRWOVhhTeef0tlyPaqism7tj04gYm8kQiYNAyefJ7tnD8kHHyK1dw+y7ximqqLPik5a9Pvvp3ydq4wF1q49oz6ySDZCV7QLTdE41vdrvtL7c2wp+TnQ4FVJ/upacnZhjr/YpsZNXNlxJf+845+L5clrzrmmVJp8CTjSYXd4N8+PPE9LWQv1gXp64j0ciR5hRe0KNjdtJpwNc2DyAMtrltNS1sL+yH72hvcS8oSo8lVxOHqYe/vuxZEOAkFAC5CxTmap7SKoB0lOVwmAcpagWx3kTIP6CoHwHeNY8hjg/r1YqeXY2Q689Y8gFBtd0bh50c38+NAdSFwLk81Nm1lTv6ZEykp4VXi9eswapsmWlHJUCFE/dX8LMFsGGZq67wRiJoT4KPBRgPb29td2tSeBOZbBHE5TtqUZ/6paPG2hmSs7qwCFNPQ/D1YWPCFQNUCB48/AU192s4pUL7z9q67pKoBtwuheV0krqzvlsV+MadVTCMG2Xx1lx8PDXF15H4rPvRqUCMTyG6Hr7hkitua97s+LidhZJGRjfQkGD0fJpQ32PzFU9FrTmY9Qp9ZdTOGWKDgs6r0LQ/ioindTMfWlWJEdpOX//AuB9etQAgE6y+Gmje3cvXcE03LQNYUvv2s16zuqYMwD374A1t6KfOuXyZk2saxJLGMQyxrEsibxrEE0YxDPmsSyBuPJPP2TWTIFi5xpY9ovrSLvFDGuXtZIc5XPdU5ToDrgRS1Y9IYtOhuCjCbydNQEsRIFCr1xjPEMWA5CVzGTBXJ7Iq5UNQsS6PFAmyHRcElYaEsTwnTQW0I4Weu01DEA284xMnIX3T3/OEshm/oMIjGRDOgb2VC3gs7mt80hXhUV695QRGw2zLExCt3dJJ98kvhPf4aY8iEbaaqj+pN/yvyWeRR27XpVDf1ZM0ssHyNjZXhu5Dme6H8CIzPOofQQtiLcSUtALWtiZVkrkVyE3kQv4DbvL69Zzs1LbmZJ9ZKSQnYKWI7FnvAeto1uo7msmVg+xjf2fAPLsRAI6vx1hHOu+nlv3700BBqYyE7g4KpoLWUtDKWG5hDi2ZBSzlHDpBSYidXo5QdBWAhcEvX4wONYjo2UGpmJy6nROukI+risuYlzlya59ZFbMWwTTdH45LkfZkvLJsYKN9Kd2MPmlk2srV/LVfOvKr3PJZxVvOalTIApxey+WaXMuJSyctbvY1LKKiHE/cA/SSmfmbr/18BfSil3vtT+X49SprQcUMTcSBsjCxOHIBMFpKuWxfthsgesPMQHXMf7aQgFNnwY1r7Pve1YrgFr8zoINbzk8R1HMtY9ybFdwyQPbOWCVX2EWpuJT1qYA3uojdw9o0UpOrzvLrdc+hopYqO9cYaPxGldUkVZtZedD/dx4MmxkzqCtC2romPgUZ5LrEKqOgJJc2o/Dd2PFskYAKpK6OqrqLrpJoKbN5+wn539MV7om2TzghqXlE3jgb+AbbfBJ3a6EVAvg7FEjm892YNhORRMh5xpk7dsCpaDYTmMxHOMJApzHjOvJsDatkoURaEqoKEIQZlPo9ynUxfy0lDmZYWiYQymcFIGwqNipw2saAGzP4kszC2h2EhM4HNanovqynlLRZD6c6rwNAbxtJSd1mSllBLDiJDLHccw4vQe+zqZzGEELgc8YDXwgY1fJZncTah8PTXVr02p+rVE8sEHidx2G4WuQzPKohBUvvcWmv/mb171/jNmht3ju1GyMZ7Z+5/8ONtX/Ah3qmUcd3I4U31in930WS5svZCR9Ah/9OgfFdWx2666rXSCfhEKdoE94T3sDu+myltFX6Kv2KsFTJXpZ74sdEXHdGZK7gIVOR0MKwGpgjJz20yuwsl24m24B4SNIjTet/Rm7jxyJ6ZjoQiNdzR/jnKvj9FCFxsa13PdkvM4HD3M8yPb2NK86aTvWan3r4TXCq9rKfMUGJ8uUQohmoBpE58hoG3Wdq3AyG99dacBoU31JzmO625vm9D/nKt5+yvcfrKRndB1D0V2UtYI8y+BY0+6txUd6hZBLgaO7RK1hhVQVn/iAR0HspMU9j3IxDMPMxELYpkO7fphWr0H4JCEQ3Bi2I5wJygbV0Kw5qwTMsd2OLp9nCd+dBjHlmy9B15sPQpT/fvSLeOtu6IF6//tYktZlIhZS9mBJ6hI9M3aWOBbvZr6T/05wU2nXu/6jqq5hGwal38e2s49LVIGUFfmRTqS/sksqgKOFMUJzFzBPoGUARQsm3TBotyvE0kXqA16GE8WkA50BDwsSNrkx5MoPhUzVsCeyOJkTJzUVClx1uvimV/BM+NJqPXxjQVt+D0a3vYQWrUPNeRBqC/ddyilxHHyRCKPMx5+iLLgIvaOPEyocHjmWELhgo7rCIWWUl19Isl9I0JKiZNOk921i/i99xHfsxttaBi1qoryd7yD5H33gW2j6DqV17z9jI+Tt/JEc1F2hXexo/cBUqO72G0lmdDU4jYCwbJ5l/HR5gsYSg+xsXFj8UTdFGzitqtu+19/Arcci93h3ewa38W5Teeyqm4Vw6lhnhx8kmeGn+H50edPqXBV6PUkzQgODkJqBNLXkw/d65IzqZIZfTu+hnsRwkagsdBzE/3yLkzHRFN0vvy2P+TK+edzYPIdbB/bXnx/rpp3MjXrmuJxX668XCo/l/B64PVSzL4CTM5q/q+WUv6lEOIa4E+Yaf7/hpTyZZnE69L8LyUkBmGy11W6EHDkATj2GzBSkBpnrlykuG7fjSvhsc9D/TLY9FFo3QB60N2ftGF0DwzthLaN0LCS/Egvya6d+OJ7KU/tROaic0xe0csQ5rQfmoBzLnNd9h//BzcUWdXhPT+FzstnNdi/eji2g1GwGeiK8Pz/9JGOFabWJHF8Keo7A8QO60hbomgKF960kFzKoHlegMZ5IaK//CUT//TF4gRdEUIgvF7av/9fZ8db6tjTbkD01f/omt2eAnftHODOrQMUTIeCI6esSWAomiNtnNggvLQxRG3Ig2VLPKpAV1VWNIZ4W2sVdRN5VCEwhtMYQymcmDH12rj/FQgsJPuFzfq1jQTKfSheFU97OWqlF7XCi6Kd3hCIZaVJpw+RSOyjp/dLwOzyzcx0ZXX1RSyY/6eEQstRlDfHtFi+u5vIj35M8r//u1iyjHXOY/Odd6EGg6+qoX8apmOybXQbzw4/y08O/Qhn6kVbhJfVzZu5e+x5LMdCV3W+cdk32NK0pWSFMAXTNtk2to3d4d1U+6rpT/bPmVjUFX1OgsFsBOylmHqPa1mBSvL4RwDQA31Uq0vprFjBx9+icSC6m1bfCjorVhC1jrJvciaYvaRmlfBmxuuqmAkhfobb6F8rhBgCPg98Efi5EOLDwADw7qnNH8AlZT24dhkffK3X94owHT5sG25JsOseaFrjlikP3+uWLqfRshEqWt2AYmm7fWNNa9zGf4DV74XmtW7P2eEHwVPmbtfzKFLasO3bCMA39ZMVDVBZichF3ccLBXHhp92y5E9vchU7VYdLPuuqYu2bz3rZ0nEkA10T7H2ul0RhnOhYEn2yhdnqmKop3Pjxi2leWMVYX6LoxF9XrzH6uc9hJhJYX/oihb374MWh64pCYMtmaj9y69kz/Bze6Zp9Hn/GtdGo7TzpZssayynzqlhSolqSginJFMwTSJmuCJoqvIBkMmXg9ygsqqvgusV1LMw4WMfTGMMpZMHGGpmbXWlPUTMFUITg/PPaCK6uQ63yowb1uWXxl4CUEstKEotvY3z8QXoTx5G5LsqVmbWGylaTznQVcyvb2z9EefnqNw2pyO7eTf/f/R0cPlK8TwhB5yWXowZdgn2mDf15K08sH2PH0NM8uu/7HJV5hvOR6YMgECw95y18aPUfcV0u8jtx8jdsg7ydJ2fkSJkpUmYKx3GwpFWcIBUIvJoXv+rHr/nxaT58mo+AFuDg5EF2jO9gZe1K2kPtbB/bzp1H7mR/ZP9JVTApJfX+Fj686v0cHy3nP548RKD9B25/l9SoyL+dT1+0mMHcfho9ywie20lHTYDWKneoZhqbW2bbmqxjU/NM72NJzSrhdxW/FcXstcZrrphJCdE+iB1z/z12AJ74hymlbAp6AMypE7FQYOV7YNm1YBZcMtawHOJD8Oj/da0yADQ/WHPHsYt9MxLGxDpidddQvvFqqpcsQgvvQr/jHWAbiNmWFoPbXsNpyhjDR+OUVXnZ9vxOUkf9TBMx8aJyZW1bGee/s5PWJXPDwgvd3Qz96Scx+vup/4u/oPoPPkD62WcZ+qOPnaCYCV2n7tOfouo970Hxn16U08ui7ym4a4rj33ibqx6+CJF0ni/c38VILEveksSyJiPxPOaLGvQX1AQIelX8Ho0FtUHeuaaZJbaC3Z8ifziKNZpheqZhtueYxI2S8q+rRfNo+JZU41tUfUZEKZncz9jErxno/6ZrACwgYwsC6tQshdBZsuQLeL0NJ+RWvlHhFApknnuO2D33YvT2Yh49iqioYLKxlupjg2DbCF0/K0rqEwd+wn17vssj1mRRRd5Qv4H9k/uLfWLfvuLbbGg86cXsGx6OdEgbaRJGgqHkEAPJAZJGskigFKGgCQ1VVfGpPryqF6/mxaf68Kge10YCG9uxMS2T3eHd/Ojwj7AcVwmr1JqIWcNzjiklWOlFaME+EDZCanxq1Zf44PorGYplOTCcpKD2MZzfz3kt55YIVQn/6/FG7DF7c8HMQeSoG8Ct6jC8fS4pO+dy1+Zix/dmyocr3uGWFKeNTsOHp9Qzl5RJwLC9eGQOIdy+pmz5aoKZg0jHRqoa2vr3Emq+FNtTycRAGsQiPBf9CP/EC9it5+MJrsJv2Ghtm84qIYtGo3Qf7aFrez+ZriqEnL6CDc6hYlVNARITuWK58rwbTyRlyQceYOT//jVKMEj797+P8OiMf/FLOJnMiYoZIG0b4/hxhHYWP5oLLoYPPwo/uxl+fCN84D43PmoWyn0eKv0eIukCqYJBf/RE/6JKv0Z9uZeO6gBvWdnImrTE2RUllzEwjsaZPWQ6Wx0TCviX1xJYX49/8SsnY25T/ySGMcHAyD3EIvdTKIy4rmZT0VQLmm+gtuZScvnjVFdtKRKxN0Ju5cvBGBpi5P/9Pdnf/KZoExK68kpavvRFlEDgVZcsc1aOaC7KfX338vDe79Hn5LCFKJIyBYWVdSv55PpPvinVsZyZI2EkmMhMMJAeYDAxSLwQJ1lIIhSBX/Pj1bwIBB7Fg67pKIqCR3gQCAzHIFfIYZgG/akheqPDBOUCFtV2kBFHeaD3EUzbcq1bkEwWxhHK3M4ITfHwiY0fp6nCx3DuAFtaZprpW6tcJQwagTf+57GEEl5vlIjZ6cATcJvyJ3vcJv22LXD04RmT2IVXwqK3wfIboP+ZkypXKaUdz8J34u26G2kZWFLjhdQtnB/6LxRcMpfb8jcYmoo+9jxm0xZk/XrSQ2nChyepbw9R01oGZedSaN+EbTqkRzMgJeW1firrA6ddCjsVuru7uf/++4nH4wBUpJfgkTP7rGoMkIjMELGLblmEpqmnDA53CgXC//JVfEuW0Pz1r5HfvZvhv/hLMOcanAJFY12h61Rcdz1CP8t9UDXnwB8+DfvugHkXuPdZhSJx1lV3qtLv0RhNpE66i2XNId61rpWLmqrx9idI3HtsjuXFtEoGboxW+aVt4Eh8S6pPy+LiZIjFthGZfILxZA/h2DYqxKx+QtdzBFXRaW56J9XVr006w9mEnU6T3b6d9LPPgWWSO3CAwoGDc7YRQuDr7EQJBIAzK1lO9x+tqVnJ3kP/zU+ju4tB4YoQ/NGi93J77y+KCtll7Ze9KUpjtmMTzUeJF+KMZcYYSA4wmZskY2ZIGAk8mgfLsshaWXyqD01o2NLGkQ5e1YuqqBimxVi8QN6MkBfDDKQGGQgHMbJ1yIrHQdjA0zwbrsNSx4FZiV9onN/wFrZHH8GSFqpQuaHzBq4757pZr12JfJVQwqtBiZidLirboLzF7TFrWQd1i2FoG3ScD/MvdolFdQe0zwQVREcy9O4O07dngshgmgtuWsbqD9yLcegJhgorqPCv4Fj6AsoS20hXbCRbWAx5EOWdqAWV9N4Jdj08gONIVFVw8S2LqWktQwiB5lHRPCpSShITeSzToa4tdNpPZ2JigqNHj3L06FG2bNnCOfMXkglDMDOPjo5yairr2P/IRHF7RRFsum4+ZZW+E4jYbEImpST9+OMEt2xBCQRou+27CK+X+M9/TvT2H8wlZbPIWMNffRY7kTy7gdIvhu5zTXTBLU3f/nZ421dgiTul5dNVUjmTaPZE4njFklo+d/liWnSN9JODxA5GEdNDAuAqVxKEKgisrid4btMZkzGAQmGCcPhBjnb/A1K6JsFex3UJKOZWNr6TgL/9TVGqBDDGxoj+8EfEbr/dnTIGzFAZvU3VlC9fQfPTL4DlliyDF1340jt7Cbww8gIf//UfYzhmsTegQgvMsmRQ8JbVveEnKW3HJmtmmcxPMpGdYCw7xmBq0O0Xs/IkC0n8mh8pJdFCFK/qpWAXSBVSBNUKHKuMpDXOeHac8dHlZBIt5Ao2htaLEujF709jeaamob3uz8xlmIOthmcWI2B++Xwuar2Iy9ovY0tkCRkzw3nN570hX7sSSngzo0TMXgkUBXxTJ9tFV7k/J4FtOtz5he3ERjMgoGlBBefd2Enb0irGC+XkmxdgJg2clIFZt45E4zoS4Ryx7jhVDQEQMNaXZLQnjjNldmrbkvHjSVc1mwUhBP6QRjZZwHHKXjKn0TRNnnrqKQ4dOsTk5CSaEaJKb+GYL0ZkXw/7nhxBOuWMDMIIE3Me276iGt2jUT+v/ARlbBq5gwcJf/krZLdupeFzn6Py5veQ3bWLyLe+jTU8qydFCITHQ8NnPoOdSr22ZOxU0PwQaoQ73gc3/Afm8veAlPRFTnQK//2NbXz6nAaM+44zOpFFy9pT4UjgIFBUQeiqdoQD3gWVr4qQTSOfH2J48vkiKZNAY8NbSUafKMYltTS/+01ByACsaJTJ73yX+B13IOX01Kug8m1v4/wPfoD6eQteVclyT3gPW0e34nEc7jp4O4ZtFGtt11St5D2bPsVHH/tYUSGbJmNvFFJhOzYFu0DSSDKaHmU8O040H2UsM+ZuICFaiFKmlSEUwXh2nIyRIW7EqfBUkI0vJJGqYSIbJiUnMfMOvmAvIrTDJaOeLqjVEIrFdIqsdZJ1TJNXVahsatzE9rHt2NJ1tX/3onezom4FmqIxv2I+Gxs34tfOUh9oCSWUUESJmL1KOI5krDdB394JjKzFZe9fiqortC+vZuXFLSxYW4cvoJPPGowcjWMUbDRNwbYcchkTy3LIZ0wOPDmCdE4xiCFcxUr3qziOnEO+pCPJpU1C1d4TSJllWRw/fpxMJsPSRcsZ7UnQ9WQYj7+ehf7lxKccPfrGc8DcZl5/SCefsZBSoqiCtmXV1LadnPiZ4+NMfPWrJO6+B7Wqioa//RsqrruO2M9+RvjLX5nbS6YoBLdsofZPPv7bJ2OzUd7k9pr97Ga45xPkQp30RnTGknPH+y+oDPCJSUhvPwqAgpzKG3dfB19nJeVXdpwVMgYQzUfZMbaDK9ovpbP1JvZEHwMcVMVLZ8eHoONDb+jcymlIKXFSKTLPPkv0Jz8lt28f0jQJhwLUprLuZKrXS+0NNxCYtwA4s5KllJLbD97O13d9vThdGLQdVFVBItA1Dzdv/kvW1K95wyhkUkqyVpa8lSecDTOYGiSai5I1s4xlx9AVHb/uJ5qN4vV40YTGUDLKYDRDNDeKzLeRKOiYoW1uoz0CafbgiDxKeRpVwLQDW/EbRUCNvwKP6mE0M2NyPdvYVUHhqo6rcHCYXz6f9op2Ois7ieajnNt8LouqFmE5FrFcjHkV80qkrIQSXiOUiNkZYqQ7zqHnRzm+L0I+baJogo7lNUhHIhTBBe9aiG05xMYyRIbSWKZNfDyHr0xjciJHfDyLx6cxOp4gMpieQ8pqWoIkI3mEAku2NJKJG1Q1BvAFdVdBkxLbdLBMByEE1U0BQjXul2Q+n6enp4fDhw/T3d1NoVAgFKhk5HlB354IKo3YQPxFz6e6OUB8PFcsm150y2I8fpWxngTNiyppXVzNqTD6139DdutWam79CDUf/ShqKIQ0DIy+vmLJCigqZa87KZuGJwA3/QD+bQPykZ8wMHndnF8L4H1xMOKJWfdNpT1IidCUs0bKEoUEPzj4A3586MfYjs3Gdz9Gbe2lrFj2VQ52/RnNTTe/IXMrTwVjeJjxf/wCmSeeAFyCsG9BM/U33URDx0LE0Z4zVkp3ju/kueHnMB2TncPPsi9+tGjaJhC8f9VHOK/90hNI2OuhkEkpyVt50maaaD5KJBthIDlAzs5h2AYTuQnKPeWkjTRd0UPUetrAbKEneoxwfoD2Oolf93FobJKCMgSA8G6jKHvh9jV6fWlUFLKzroFmxxipQmV13WoAxrPjONK978KWCwnnwliOxbLqZaxvXI8ilGKpdGXdSlrLWsnbrgmvrugsrFpIU9mZ5/mWUEIJL40SMTtDjPUl6Ns9QceKGuavrqVjeQ0ev/ty2raDmbcZ7Y1j5m08fg1VUwjV+hg8FKV72zizXUrKqjxk4oZ7blEglzSwLYdVl7ZSVu0lUO7BNh08Pg0jb6HpCt6ATlWFF29QI5vNFPMyH3vsMXbs2EEgEGBRx3L0VAOjXTn6+iJz1t+xqoahrhiO7aBoCpe8bwlCiBP6x9qX1pzw3KVhELvrLkKXXore1ETDX/0VwqPjaW0tbiM8Hsqvu47Er+5GFlzz2bLLL6fi+uvQW1qw43GE14vw+V5Xby1HhMg1fpx01zqamOvw34xgFxYOsFrRiskFFW9f8IpyK18KiUKC2w/ezs8O/4yMmeEt897Cx9Z8jCqfm2jQ2Hgt/QPfJZ7Yjm1n3QB6oSPE6RnQ/rYhHYfUQw8x+uUv44yNz/xCCC6+6jqa/uAP3duXXvaK950xMvz7nn/nx4d+XFR5WkyLW/IGvwiVYQnQFQ/ntV/6WydhUkpMx8S0TZJGksn8JCkjRSQbYWd4J8OpYRqDjXg1L4ZpcTwRJpv1U11uEDX2ciR2dOpveC8wVYXV4WjcvRjQdS9i1jVOra+WaCGKI93syA0N6zEsg50TO4v3dYQ6aAm0kLNyVAeq0YWOJS02Nmwkb+dpCjRR7aumKdhEmaeMoBYkmosipSSgB6jz1xWDviu9lSyoWECFtwJNKZ02SijhtUTJx+wMYRZsFE2gzorLsQyb2FiWbMrAcRyiIxlSkzkSEzkcG1KTeVKT+Tn7aV1SyfxVdaSiOWLjWeLjORLhHKsub6Vpihz5Qx7Kqn0Eyj2omoIQMDYa5nDXYY4cPcJYeIRrLnknulnBUG8YKWyMpMpYXxJFCM5ZV0fDggqe/2UvzlSZ5/o/d5WKU01UngzSskjcex+Rf/93zKEh6j79KWpvvXXuNlJiJ5OY/f0YIyMYvX3k9u8n8/TTBDasp/ojt4J0imRMaDp6SzN6YyPC43lV78krQdawSOVMJkeTKA8OUDae5wAWnyCLxazIJMADfH3TAs6vKjsrZAzc10kIwUh6hGt/eS2XtV/GratuZVHVoikD2QT5/CiGGSUcfoCRkTuorZVVFq8AACAASURBVL2KysoNBIML0dQQPl8jHk/t6+7kL22b9LPPkrj7btI7dyHHxkj6PJRfdy3invuRpnnGHmTTvWOOdHhq6CkOTs5McCpS8iehZdx69TfZkx1h+9h2ltUso7WslZyV4+DkQQ5NHmJl3Uo2NW4i5AlR5il7iaOdPnJWjoJVoGAXGM+OE8lGEEKwJ7yHg5MHaQu1EdJD7A3vY8/kbrfMKgXYIaSafMkQDp8SIu/MTAZ3VnTSWdnJowOP4kz97ZzbeC4hT4jxzDgV3goCagBd00kWkoSzYSq9lVT7q/GpPlShYtiG24+q+YteZapQ0VWdCm8FIW+IMt0lZ2V6GZX+Svyqa7PhVb0ob9ALgRJKeLOi5GP2GkD3ul0cUkosw8G2HEZ64khH4vWrIBQmh9N0b5uZbPKV6TQtrGCsN1ns3WpdWo23TEPzlJGMFlxSdlkrG946D1VTiAymGOmOU9dejmM7dG09xvbn95IWYyAFIaeZjspWBnZlGe+eKCpxuldl4YZ6Otc20LGyBlVXaJhX/pITlS+F5AMPMPFv38Q4dgzfsmU0fvc7BC+8sPga2IkE5sgIhb4+CsdcGwknmcQaGcYzrwNMk8yzzyJth7LLLiO4cQMIBWlZFPr7McfH8a9cieLzncV36STPI29ycDhB30QG7/ERzukdpibVwAEstmKxEZXt2EzPZUrABI6Wq7z10rZT7/g0MZga5PsHvs9EdoJ/u/zfaC5r5uF3PUyVpwzTjJJMHiQ88SDJ5H6CwYWEypaiKi6ZiEQeYXLySRYv+jvUgJ90+giqNkRF+VqU10nFMAYGCH/t66QefBBwX6/jLfXU/snHOeet12K9451n1NCfNbM82Pcgf7/174tB142eCj6w7APcceQOLNtA17xsuPCvSHp81Mk6pJT8ovsXLK5ajIPDd/Z+B1va/KLnF/z5uj+no7yDprImOis7X5FKW7ALJPIJclaO3eHdPDfyHPMq5jGvfB7PDj/LoeghglqQeCHL8VQ3INk6uvXFcbGARChzJ37X122gJdTM/cfux5Y2qlDZ3LSep0eeLt5eUrWEprImru64mpHMCB2hDuZVzCuWH/2an6AniFf1uo79qs81jtVmjGO9qreodE3bZihCKRGuEkp4A6JEzF4F0rE8sfEsjuUAgsGDUYa745gFi0ysMKdciYDWJVW0LKykuilINmHQtqyahnnlxCeyDHRF6Ns1wYI1dbQtqWLng/1k0hl6t0fd/jMBFXUBEuEcOo1U0cj0N382AVnmThMuOreBJZsbyactLNNG1RUaF1ScNhEDtywlpiwt0s8+i9A0Wr7xr4SuvBIAO5XCGhqiMDRMoacHkBiDQ5i9vYiyMnJbXwDbcXt/pghXbscOcnv24Hz0o4QuuxShaWhV1VgTE1jxOJ7GxjN9O14WiZzJww91ow6l8RaSbI6o7KeGfyHDs0VLWOgIeriiLMgPJ+LYUqIpgk3zT91jdzromuzi9gO383D/w6hC5bpzriNXmERIE5HtYyw6iKJ4yOdHGBj4LlLaxGLPoevVmOZMGVpKi1T6IKHQUhTFg2FGkdLgt/mnbMViJB98kOQ995Lbs8ddFxQnLddd/04a33ETAJ5X2NCft/LsHN/JHYfv4PnR54ukTJGSdycSfHT9n3N5x+W8MPICmtD4+ZGfs7ByIfFCnB90/QBHOjzS/8icfZqOyQPHH+D3lv4eIiOYVzEPXegnbLMnvIedYztZXL2Y+kA9+yb2sXVsKwoKZZ4yRtIjbBvbdsogbmmroMiiGua3ltJZ1UJX+gm3p0tRuaDlXJ4ZfqZIuhZVL6Qh2MD7lryPkcwIy2qWsbhqMZuaN3E8cZz1DetZXrscXejoqo5H9aApGpqi4VE8b5qIrRJKKOH0USJmrwCO7WDkbRxbMnQkSt/uCeo7QjgO9O6eIDIwU36obS8jVO2jf/+kO0mpCuo7QlS3lNG+ooayKh+KIji2b4Jff/9Q0RZjuDtG357w1Ilu+kvX9UtIhHMzt1+Eqia3eX9aiaus82MUbGpay/AGXlmpS9o2yQceJPKtb9H8pS/iX7mShr/6HMLnxcnlyB86hDk2RqGnF3NoCHNoEHQPGAbZrVvnNvzD7DRtF7ZN/uBByi68ADQdJ5VCeD1o5WdnsvGE5yMl1mSO3t/0c+62CC7V9HAPBl+hcMJptqYmgNpRyfs7yxlJFFjXXsXqtsozPv79fffz2ac/S1AP8ntLbuZdC64g4MRIJXYDgtGxX5LL9SIdi3xhxM1KBcABbMrL15NK7UFKByE0gsFOTDOGlBDwz0NRXpvpOGnbOLkcuT17yGzbjqJr5A92kX7mGbAsCtWVNH3yT/F3djL8f/4CLAtF1ym/8Mw8yIaSQ/zngf/kF92/cMmPBG2KBmmKxqZrvslgZoSCVcCn+vjX3f9aDMx+MVlqDDQSzoXd0h+CA5EDfPbpz7KgYgFXdVxFY7CR4fQwC6sWYtgG28e2c1/ffVNEUOBXg+Ts9NwFSvc4YsqzzjFqUT0R1+sXQaN3FRP2fhxpo6kaH157OYuqFjGR28KxxDFW1q1kWfUyblx0IwcjB1nfsJ419WvQFA39TRIsX0IJJbz2KBGz04CUkmTE7f2SwORwihd+dQzpSHp3uX5fqjaLeAiobgqyYG0dnevqiI/naF5YSVNnJao2t3RwfH+kSMrA9UBzdzEVTjMdG6MIll/YTNczI9i2dCNREMgp0rfu6g5UTTAxkKKxo4zmeQE0FQR5rJhL6ISqIjTNjTvStKIaNvt5ph57jMg3vkGhuwfvwoU4uRx2PI4xPIwVDpPv6cUcHKTQfxx7IoJ5bCo/9GSYPoOpKv61a13SZtugqngXLcRJpwGBVl+Hp7397GVjTj8fR+JkTDK7xsm8MEptrPD/2Xvv+DjPMt/7ez9lqmbUm+Wi4t7i3uL0SkiADaHDLktnWfawy8LWF84C5919OcsBFthdSGgbDiwkEEJII05x4jiJpbjLVZJlyeqj0fTylPt+/xhpLLkkjlMIMN/PR7Y08/SZ0fPTVX4XGoJfkecnWPSfI/IhgDKPRiJnUxP08oblFaxvribgufCPSs7JcX/P/VT7q7m86VI21i7n48vezk1N6yF/kpGe/4UQOq6bIpvtm4x4FfB4GrCsUUAhhEF93VvwemspCy7CcRNUVmwgHF6Fx1OJaVaj697zH8jLwB4dJX/sOOmODqLf+c5psR0qY3zxfA5ZKey6Wv7omqsIL1yM8YPvX3TK8rnB57j7+N0cjR5mJDvN1Bh4Y+Uywg2rmB2ax4ODT7Gj/Z9xlctwergYTVMoqn3VxPIxpJIYmsHNrW/Br4UZSg9x5byNzAnN4ccHHuHJwYf5z/3/+YLHpJQinQNhFN7CAsGlsy4lPrKKTvfbKAq+Xu+95DZ+cvz7RW+0L131CbyGl/bhdtY3rD9v80FzeTNXzbnqgq9RiRIl/rAoCbMLwLEk44NpAiETTdcY6krMsLdoWlxBZUOQQzsGka5C1zXmr6ljzpIqDI9+1vYymQxdXV10dXXR0z2Ej4WAwDB1Lr1tPjvu6pos0tfZ+vYF5NJ2sS5s4YaGYp0YwKlDERpme6mrdnAmJmisTaAywziHThtITummqaCVmsw7aYaJHg6hVVWhlZVx6s8+Qba9HU9LCw1f/AKehQuREzGG/89Xsbq60DyegkDr7z/ndTJbWrD7+griy9AJbL0MmU7jmTsH3/Ll+FcsxxocIrhpE6HLtiK8XjS//xUfv6SUIt8dJ7VrCKs/iTtR6LY8gMO3ybEfed5151b5qS7zsXJ2mA0tNcytCuIxLqwOpz/Rzz1d93DXsbuI5WNc27SBOfYBIpGHWaOgv/shLGu4uLxhVGCalVjWVPeiIBxaQSDQiu1EqShfT0XFWgwjhGGUv6Z1ZE4sRuL++4n/6ldFUaaAY0GTgbCXdbe8i9U33ozHf3Fjk5RSPNr3KN87+D0ORA4AIJTiTd5GHnYnsF0bTdM4IHOowWcYyfySjJMprl/tmcWENYJEYmoGH17xYXYc1ugY3kcm3sxXOwv1gLMrm/j81isAOHpsFceOz8ZT+yCe6u2Fz4OCDY0b2NCwgf/Y+21cXEzN4M1t7+e+U7fjSBtTN/noJR9lVd0q9o5unGHDcU3Lpee05ShRokSJi6XUlXkBKKVIjmeJjWZREqJDSZ6550Qxbbj5LW00r6whm7QYPB47q8tRKcXw8DDhcJhgMMiePXu49957CQQCtLW10Vjeip4LM29pDQ2t5Qz3xF+wW1IphRuLkT9xAnd0DOW6hVCPYRSiYkKAaRaiY7qO0HSExwRtpkiU2SzpnTvxXrISq6eH5AMPYDTOwrdoEant2xG6jhuLYff2nl5J188yjJ2KilW+770gBG50gsC6tfhXXoJeHkYLhdA8nld2MPk5UFIhMzap54ZIbuujkA1THMDlYWx+jc3ZY9NP856Nc/ng1hbmVvoxjLMF9flw3QxfeuYLdPTfyyq/pNZfyZJQNV53DNseLy6naQGknBIXgrq6mygLLuJE7zdRykHTTFau/A5VlZsR4sL3/0ohs1nS7e0kHniQTHs7zsAA5pIlON3dKMdBCkHqA+9j1cc/iXdyjuWFknNyjGRG2HZyG4/0PsJQeohoPooOuEUPMmgONCB1L0PpYSx52r6k0lvJRH4CAKUE1tj1OJlWPMET3Pmed7C+cR3f3t7NgYE4tSEvVQEPIZ9BdZmXWy6ZBUB/tHDt+9KH+B/bP4YjHQzN4I7r75gUXXtniKwzfy5RokSJV4oX6sosCbOXgJQKO+cgJ8cjDXfHaVxQwZzFlWj6zKjKlOv+4cOHOXbsGMlkkptuuokNGzaQyWSIRqPMmjULTbvwrihl29gjI2T27sUZGgJNQ/N4UFMpQ6EVq8+UKjQkTC/tEv4ARkUF0soTv/deMs88i8rnCVx9NZnt22cKrilM8/R8SyHwLl9G/vARkBKh61R+8APgugQ3biSwZg3C73/NC5KVq7AjGdxYHulIMs8N03FsjL249ODwyAvIMQFct7Sej17Rxtp5lRe0v+FkP4/3PUznwP28pa4eTeUYS3ahO0OnqwKFB9OsmExLAmhUV13BRGxncaTSJSu/S2XlJhKJPb91N3+rt5fY/fcz/q1/L0bIIqtXsNtwec+HPglHjl30qKSOkQ6S+SRPnHqCnnhhNqNAcDUhUtkI7X7PjBimUiDtCjQzhhAFR/o3z38zv+5+AFvZGMLgowu/zNY565hTGaD8JdZQTj+ukugqUaLEb4OSMHuNyefzfPWrXyWXy2GaJvPnz2fhwoUsWLCAsrKX7qMkLQurt5fcsWPkjx1DOS56IABTYtAw0UwDYZgIQwePB6Gfjk5ZJ08WuiaVItPRgZyYuLAdC0Fg00ayz+9GOQ7CNJn9H/+B5vdd9EzDVxKZc3CSebLdMZ7ri3HPcIy/X97EM50j/P1A5ByzAIu9gwBsaK7kb96w5EUFmevm6B1+mI7eH9OVGCSVH2Wxz6XJPN2Bp2k+pJzyqBM0Nr6LutqrOXDwE0hZiIatWX0nwG9dhJ2JUorxH/yAyFe/hrIK9W4S6JpVQ8UH/pSNb3k7vpf4vrVci8f6HuPvnvo7HFV4JXQErpIUh39C8eXw6T5y7tT101hXeSP744/iSAePbnL79bcDlMRUiRIlfi8oCbNXEcdx6OnpobOzE9d1ue222wB48sknqa+vp7W1FfMiaqiUKviAWf39ZNrbkbk8rusgI+PooVAhKjUZbVOuC0ohXQd3eJjc8S6EpmHOnk12925k7MwBTDPRa2txo9FCpETTCjfOyYjY7DtuR/N4XhdCDArXRdkSazBJZCjFvb3j/OTYCKeyNjdoJpd5vPx7Ls3geSwNWiv9rK8N8daNc9mwrP68+8m7eTqGOwhoOhX5ffT0/G+m1IREoOthlDs1qkmjoeGtjI7eV4yGrVl9J+Xla4jHd7/uhBgU3jNuIkHigQcZv+MOnKEhzLY2cj09CKXA0Kn7xr9R+yIO/ZZrFed7HogcYGn1UpZXL+f2A7fzcO9vcNRktPXMQsdpaGjctvA2ftX9q2IhfUmIlShR4veZkjB7Fejv72f37t0cPnyYXC6Hz+dj2bJl3HzzzS8rladcFycWI3vgIPapfpyxMWQqjR7w46bSuIk4elkITBPN6y1EzRTkenvJ79+POzLy4juB0zdIXafuM59BKysj19mJb9kyAHKdnZhz5lD1rne+6qavF4qbtrGH06isw0DvBH/9dDcblEGtrrNO6ZyQLn9F5rzr68CdNy1n/aIajNpAYeblNPqT/Tw98DRPntrOruF28m6eWxvbuMp7ArdonSCYN+/j1FRfwZ69fzJDiMHrLxp2PpLbtzP2zW+RP1AovEfXmftfP+T4rp1UxFPU3XDjeUW4Ix0yTob24XYePPEgg6lBDkYOTrOsKFTVKwViWmcxQGtoAZfO2URLuIUvt3+5JMRKlCjxB0nJ+f9V4MSJE3R2drJ48WKWLVtGW1sbxssoble2jTU4iN3bS7anB5XOYNZUAwItEACvF80wcVOpwsijaBSru7uQljxXbdgUQhQjYMWfDYPwLbegMhmM+jo8rS0YlVX4Fi0qrmbW1WI0NPzWRZmyJU7Swo5meHp7H/ZEjk3zqwkNpfmGCgIgXMho8N9nzLqcIigEsyv9fGTzPJZf0oAZLlhMRLIRTsROcEl1K3krwpce/yhBZwiPVsafzGpkoV/DyB7EdU8LDiFMqqu2UlGxjjWr7zxLiL1eBZlyXTK72kk+uR1ncJDU40+gbPt0clcpsh3Ps+rjnzzvNjpGOth5aic+w0d3rJsHex8822xVFf+Z1GOi6Mfn0T18/tJ/ZE194RotqFxQ6mgsUaJEiTMoCbOLZOPGjWzevPmi0pRTTKUr7UiE7O7dOGMRhM+H09eHCAaw+jO42RwykcDN53GG+pGuhTM4DPFzR4ZmlO5oAs+bt6JrPuRQFE/DbMjk8bS24pk3D2VZKNtCn6wfUkqhMhlkNotRV4u3peWiz+3lIi0XN2WR2D1M55EoO8YSvCuvYwCZyPDpc6Rwzgc9ir7cdIF6up4srRRdsSy9+QTfO7iLuNzLYPQZauQweUw8rVeQy/Xx9rIpG5BY4Ss76acwubeysqXMmvWOGSLs9SrEpmMNDZF48EHG/vUrRYFutbZwSLNZ0T2ABmgek8CG9TPWmyqQbytv42DkIN89+N2idxic7RuMAl1J/j6W4ss11ThKYmomn13/WeJW/KxI2Gs9aLxEiRIlfhcoCbOLxOu9eGNP5brIVIrc4cM44+O4uRzO6BhmQz3CMMn39GDt2YsWDuHmM+S7jsPYC9eJFf1odQ2tpQER9KEvn4NoqsDOpTBWtuAGy9CVH2WV4SSi6KYPz5w5yFweUimUUuiVVQQWLUSvuHin+5eDsl2swTQy57Bv/zD1z0doBlrQp01CAL0hgDuaBakQumDO6hqs9jQ4LueajOBKyfbOh9jY8j0WeBWXBuWkqHCIRB5C16fbPwgqKrZQXXU5PSe+glIOQhi0tPwFNdWX/9aHhr9UUo8/zvi//8cMP7ITmTi+m26g7i8vRevqnlE/qJRi28ltfPapz+LIc7RQnFkuNpmy1IG/qVzL29/xNRam+kppyRIlSpS4CErC7DVEWRbW4CDO4GDh/9FR3EQCq+cEGDr5I4ex+k9hHT06001/ms5QANV+xEQOpkxudQGbWhBpG6NtNnpjDbgu0nFQkRRmMIjI6JDKYGtx8p5hjKpqwv7FaIEAWmUlRjiMFgy+4u77F4JyJE7CworleHZnH43DOapMnaaR7AwxNiU+0QXB1XWkzSPE0+1UNW1l/dIlvEvv5Fs7fJyOcs3swqzU4mwKGZhGGNseK2501qx3U1N9BQc7P1msGWtt+XMqKzdQWbnmd6ZubDqZ9g6STz5Jvrub9GOPoVVWFqxPJicvLPvYJ1jwzncXlr18M6OZUZ7s/C+2n9pOykpxbOJYsZtSKaj1NjBujRRHEk3nKulhZd0lrFv1IVbN3gLAqkBVSZCVKFGixEVQEmYvASUlMpNB2Xaxg1EYBloggNDPbQg61f3mZjIkfnUfuUOd+FatRq+sJNt5iPTjj589W3L6+rqAWWUwmCoIMV0g3rQIhI7afQqlXOSicuw2hR2WeK0EvpwP0w1gemvQQ5WIYAAjFEIrK0P4fGg+H0q4OFqeQEUzXm/1q3XJzn9eSqHyLvkTcYY6hugYTyPHMlzqFiJjDqCHTGTaKSgDTRDc1IgwNYxWgyHjQfp6/xWluwwMfw9PtJo1gQjvW7KZ+3puJJav4LSSA124bGnqoLX5zygvX8++/X9atLGY1fgWysvXsGb1j85ZM/a7JMgA4vffz+Df/C04BWGVm9/K02EP19zwIWpzDoEN6xErFvN43+M83vc4o5lRTiZPcip1qrABBU5mDrp/ECFchBBEpk0suCZrscNn4mg6pubhA2+4vSTCSpQoUeIVoiTMLhBrYACrtxflShSK1LZHUbaFd+EifAsW4Jk3F3P27BkdmTKfJ7f/ADKbIdt5iMg3vwmuS+qRbefch7lqKayowf7RUyhVSNGJP1kNs8NwYhxOxhELGzEWzCqk7BbOAb+XlK+HCXcnoEgLg9n178MMzALdwBdowTTPnZYUbp5stuc1FWb5kwny3TG0kIk9miXx1AAmii0IXHSKdWGVh0gv3Yu/phFSAjs8xqA+QDRxGPPU6SL/yf4/TLOKcPkmrlAPcGBsCXvz089Z8u7FP2dB1SBVVVt+r0TYmTiRCGNf+3pRlCngZCrGvKtvpvbG6zlsn+CBnp9x7NfdHIsfLq6nMzM131geZG7FZjonOqb5i4GuFMuCs3n/8vfQobuse4GZkCVKlChR4qVTEmYXgMxmyR87hl5RiTBN4r/+NfGf/QwAYZrUf+5zqHwOvbISZdlY/X2kn3mG9I4duOkMKp0uzJCcjmEUb54AaIL8eh3RKJEfWo4amkBbXAdtleAx0ea3oHk9oOtk7AlyVi8Sl1yuf5qVAyjlkneHCXmXIZVDOt1FIDAPj6f2XGcGvDb1UsqRZPaPEb37GMjTCcZ8eRfZqiP4o4vx1TUQs3YRr99BvrwHBExkAR28TgN+cx5HrADSmEODv456axcoia55aG79AvcfreL2J7cwnA9yeg8C0MjoV7J42Z//XomwKZRSpJ99lok77yT97HOofB6lFQbcK00j8P5beGa+yy/3fp5dw7sm1ymsW5gXKZgXWE9//jlsaYOAMecIYxFoCbfwzpo1fLXrbmxNYOpe1t/wlULh/m/vlEuUKFHi95aSMLsAhM+Hp6UFu78fJudUTqFsm/Hvfx//8uVkOzvJHTpM7uBB3EikuIxeU4M5bx72wEAhbSkERl0dgcu34M7y4AwO4Szx4V+xGNMfAo+HXH4A10mh62UIIVBKYVmjxOO7SGeOnuMoNUAhhEEg0FZ4RBhghMjmBs8SZo6TQimb8vIlr8YlK1wbV5I7NkHsYISuoTjhoSyBcA+ZqiP4ogtwasYYafkBSjiAAFFQC7ZjYhQeQSoY92/inVv+LwBrlCpGJUfG2rl/zy46hls40pGkJzIKnO1Qr2sab930JmbV1rxq5/rbQknJyL9+hYnvf7/YJpn724/yWPQgDfsm2Nm6gCf5b+hyZ9QqCiHQRGHOqaZpBAIxWrwtdMWOIyfTvxqwrGYZ167+GEvb3kDH+IFSMX+JEiVKvMqUhNkFIITA29yMp6kJmU6jXJfkgw8Wa83snh7snsIMQDwejMqZI37cSAQ3EqHqwx9G6Dq+davQVzQRS+1FUxYe2nAyveTkILbjR7gaQnhBGMQT7WTSPThuEtdNc3bHoaCq8nJ8viYcZ4JAoI1AoLn4rHSz6MaUHYaL66aR0sEwygiFLjmjG/HFUa5CZm1yxyew+pJ45obwtpQjTA03ZhEdeo5YYhfB+BL0Y3Uk5WHStfsoN8IkFo0Qmf0EiClPtRlbJhxaRadsRmh+Zqd+DsrF0E3esOzTM14LgL6xBJ/9hc1zJ5onvbQy57g2oAn42BUtrG2+sDmYv0tk9+9n7OtfJ/30zmKbg0Rxz847+MVm4EqB4OiMy1IQY6BrOiuqV5CyUxyPHefg+EFQiiV5i26vB0cIDM3g+nnXUxOso66sgVWzNv6WzrTE7wpSOkQSndhuFl9oXVHk60LgEQK/rqG/xrN0S5T4XaMkzF4CwjTRKyoIXXUVc3/wfTK7dpE9cpTUQw8VoxU1H/8YvmXLOPWRj06uJCi/9VZqPvoRjNkNpNMnyOeHyaf2YNvjhfovYSCESTrTiceswTDLyWR2k0wexnWTxf2HQivxehuJRB6BybHPVVVXEgqtwHXSVFWvKtRcKRdXWjh2El33YOhl2PYEoOPx1uDzNmAY4Zc8oUBaktSuIXJHo1jdMZAQOfQUmdojBMYW4xoZBlf9G0pzIKShL6/C9UVmdpVOel8pBacsD7NMG00UonuNjbeysvYGvN4a4vFbz9kNmbMcbn+qhx8+00skZXO68/Lsc1nWGOb9W5qpC3vpj2aYXxd6Sef7esB2bWxZ+NKVwG7fTd+9PyN+6BC1PSNkfD62rxZcfUBhuODocHDuVFRR0RiYxVhuFCklhmbw4ZUf5sj4EZ449QS7x3bP2JeGYE3jBj616oN0xrpYX6ofK/ECKKXISEnWVfSd+hET0SeRqefRZArHvwKn7Qd4NDEpxE53mZfpOrN9Huo8JoZWEmklSpxJSZhdJIHVqwmsXk2mvYP0tm2F6JkQBDdtIrB6NZXvey/KdSm/+Rb8q1di23Ei49tx7Dia5keIQjoRBJYd5dSpH6DUTM8oXZ+elhNomg9N6IBE14NUVV2J3zcHyxpH173Y1jhSOQgh0LUyQqElBALNGEYQTfOi6wGE0F7yuSqpcCJZsocjJB46WfwdA9L+MAAAIABJREFUmy3vom/9v4BwUC0zzUYVkrQZpTg3QAkqvFuI2btAFbohb173H2iah1isncrKTVRWno7InFkD5krFoYE4n/35fg4Pnxar5xJkAEsaQvzzW1fguopoxsLQXvp5/7bI2BnGsmMMpYZwlMORSA+7evZzTWeaFf93Ox6lqAF+s0rwkyts0gGdHcsVy/oUo4tqOVEbR5MSUzf54tYv4NW9/LLrl1iuRf9AB3vGnsfltFGsLnSUUpiGyQ2bPs3qutVsmXvVb+8ClHhdknZd4tkooxMdjMc6SFlxInWfJee4NA7dxyknSKf5FzQHK2koX0wum+dwJkfMdriuOszKcBClFHmpOJLO0Z+zWB0O4Pkd+myWKPFaUBJmF0n2YCfxX/ycxEMPo2wb4fEQvOJy/KsKEYaGf/gHpHTIZvuIRnfiuBky6WPoejn5fBeZ7AlMo4p4fDfJ1MEZoiwYXER93S3krVEGB3+MUi5CaORyg8TjuwgEFtBQfzMgUEoSCMwjEGjFMEOYRgjDCKPrwZc1s3MKN22T3D9M+vlB1LhdFGWWN8rQ0u+Bdtrr6lSqlabgScAFoaPXvRUR/SVqUojNX/Ep4Ox5klVVW86/f6mIZSwe6hzigZ37ODzy4m9ZQxNsbKliPJXH1DUW1odoqnjt/dkuFKUUSTtJ2krTG+9l28nH6RweQmUW0x/Nkwj9kDXdDgvulcXrLwVEygVeX5i8yNI9W9I3z+Tb132Fjwu9aO66vGY5dx29i1923YMrC10XNY6DYRhIBKbu4bPrPkvCTpTqx0oglcKSha8JyyLhSsYdl/aT93Ei0ctKdwcLOIZE55j5RnZF4jhKkTL+iYOWg7JAWOCP22TkUHG7j0dT/MuCJpaHA/h0gU/XGMvbjOZtZvsv3qy7RInfR0rC7CJJbX+C2C/uoeyqKym/+WaCl12G5vEAoJTEddMkEvuwrBiGGcLQy/B6ZxGL72Fo6GcwLWJhGBUUSq0loFNRvhbdCBAwmpnd9D6SqaNkMl3k86eoqbmOxoZbMYwwXm89Hk81hhF6RUSYlBbD4zvo6P0JJ/M6if4yVo0FaPLmCCaX4M22kq46SGzuo2SqDqM0F5QAAZpm8pbL/wUhxAzhFY+/7aLmSU4Jsp/uOkn30QPoYwfZmTn3elNnrglYM7eSa5fWcePyBir8XoJeHUN//f1FrpQi7+ZJWSmORI9yeDCBJkzGZAd3d/0MV7rAE6yISt7zC0nbMAxVQHUSdFlIWR5tNvj6G/8dOHv49/xwC4+1f50vP/m3HMoM46qCKBPALUvfy9Vtbyw58/+BY0lJxpU8H8/w1ESS2UaWZg5zLDbA/pTFAudZkg1/T7fl59HYEiRLuUfcRLXuEHdNbAeIpc/argJqPAYBXeNIumBt4yjFvlSG5eEArlIkHRdT06gwS7egEiXORKjpDvO/o6xbt051dHS8pvt0EwnQdPSyYPExKS2y2ZPkcsMoJUmlDpPLDZHN9oMQ5HODpDPHmV5vUV6+gbram8jnT5HJ9GKYVVRWrEPXfSgk+fwY/f3fxbIiLJj/DzQ1vQMhPBctxKLRXXQN30+UWrqzFrF4ByLfS2VwPlc3LGTg1J1I5aCUIBhdTLb6KFP1bEitoAoApQRltX/JnOoaYvF2Ghtuo6pq08VeziKuVDxxdJT7DwzhGT3AXafKcdE4bRY787xNXfDeTXPRNY0rFtayubX6dSnEphPJRDg42sXuk1m2dR3gZPoAjgtloTFss6c4GHzBKcUX73QRgKvBF9+pMTtRzlXuPE6uqWX9Ne9h4xkF+Xt7fsO2Pd/mwPghdnsmfeEEGJqBUgpDM7jj+jtKYuwPjLyUJByXZyZStMfTzBIxqmU3nXIB3x2xcJRCU5LZ9NHPPJQ4d90mQK2pYwjBkFWIlgtgRZmPQ+kcbsEDm/fNqsaRip8MR3EVGELwj62NLAh60YWgwWMyz+/F9zr/rJYo8WohhHheKbXuXM+V/ly5SPRwuPi9lHmktIjF9iBlDsMoR9MNstl+Bod+StF9Xg8QCq0glepEKYkQJjXVV03WgDXj883B52vEH2jGNMIkEvvp7f0mQmisXnUnlZUbLvj4orkoXRNH6R16mFyyHcNTz+rKJgYHf4pSBalTacMiE/CAcEY4deppoFArJoQiW3WEyUGIhU4+qwrHF5l8XhA2+vB6lzF3zocpK1v4sq7nrp4IT+w9gjNyhO/2NeIqgCqmizFNiMkhAIK3rWuiIexj64Ja1jVXvax9v9pYrsVwepju8Qjdyd1sP7WdIyfDpOQAerAbrRw8gNesoLlsAd2xbqSSrOpVTN22hIIlA4JLP/M5Lp13GUEzOGMf49lx9j7wF3w6ua8Qi/UYxfuqJjRunX8rjWWNpQjZ7ylKKZ6JpXg2nubS8iBNPg/H01l2TMTZn7LAzZHN9fC8PQeFmPxYzQOsyS0IpNAYFvNQ6rRYWhr0MdfnYVs0URRdN1SXA4qfjsRwlUIXgq2VZVxbHeZUzmZlmZ+FQR9BQ2N9eZDD6RxbKspYEw7i1QQBXUMrdWaWKHFeSsLsZZDPR8hmTxaL+CPjjxGPP4+UeXK5U0iZn7a0oKrqcirKN5EuW0ouP0xV5VbKyy9BN4KYRhjTrETXC+XyA4M/5ejRz+H3N3PJyu8QCMwDIB7fXUwNhsOXcGpkG72jj5Iz53HpvFuIRp/m3s6vM5qL0uSRtHomZxtmjzCYnTwSATrQ4POiph2jSM8HXy9KuAhlUH3k3Ywv/glKOAh0aquvZyR7d3God3X1FVRUrC16rb1Unu+NsnP3HoyRffyfk23YhaOasczUL3BT1/jbGxeRtlw2t9Wwdt7r1/5CKUXaTvPsYDv3Hd3BqdEQvRGLjLEbI9xZWCgAXoxiQlsgWFu/isuaLsNRDicTJ7lMafDUDwqy3tC59R2f45L5N07tBAaeh/0/Q279FEeTvfxSZHCnvQ6GMApTETSTW9puKQmy3xNcpYg7Lo+PJ3hyIkmDx+RwKsO2aHIqto2Bg3OGebTOrIIom/QMXOjJ0xqs4NFYblJgadxWX8HdIzEcpTCE4ENN1ayrKOMdjVXsSWRYXx5kQ3kQUwj+qKGKZ2NptlQE2Vx57o7nteVn+wqWKFHihSkJs5eAUgrXzaCUjW0nSCT2YRhhcvlBRkbuIxZ7rrhsINCG19tELLZzMjqmUx5eQ1lZG9XVW/H7Z6NpM39xOk6SkZHHONn3bZLJgwSDC1i44P8hm+1leHwn+fShQsQLF6VgzDGoNZyiWcQzQ18DJItNWGyCEh6EOv0XcU31tUSj25GTxfhtbX9Dd/f/h5Q2Shp0HbyVqzDJVh3FH11MzfLLqdU3kKk6TG3LFZSXr2F2/I9ekaHe24+O8qEftmNLBSxketpkqoPe1DU+f/NSJrI2m1qrX/dirH24nfbhduoCdWw7fpinIz9nKg0sqsBELyaxBYJLaldyYPwArnQxdIO3L3o7mxs3c3THdsJ7+lm14TL6+QEVt91GxVtvJbB6NYx3w/6fwYGfQbQHdC9ay2Usa72cxlnr4WgXGhqmbpaK+n8H6Iin2RlLsaWijGVlfnZMJGmPp9lYXsZlVWWgICslQ3mbx/p3sCOhSNs5jtsVRAmfY4uF8HarNkpI19htN6AQaMBbamv59XgcRyoMTeNLS5awubyMXYk0z8bSbK0oY2NlGe+eVVM8pnXlhcjswqCfN9TOHO12aWWIS88jyEqUKHHxlGrMLpBcboh05gQoG6kU4+PbiUQew7bHkTLL6eJ9AI36+jdRW3MNjpsjl+ulonw9FRXr0DTfOaNLth1jYOAuunu+PG0750cpcNAxhFuUNKZZjW2PF4+hoeGPGB29HyltNM1kzeo7gUJXZMxdzn+1h/hIwyD5Y9vxjy/GH58/o6okdNUcym9ofsnXSimFIxWuVNiuxJUKZWUwuh7Ge+QXuFUL+LvEW/nl3sHiOlNizKNrfO6WZUxkrNe1GNs7urdYPB/Qqvjasz/lqciPTpvnnsHaurVcM/cavrb7azjSwdRN/vPa/8TQjOJ2Wmhk2x3fonffbmYtXMK1S1YT+cIXaXv4ITzz5kE6Av+6oPDit1yGs/xWhudtZNjJkHNz3H30bh7tf5RNDZu4fPbltJS3UBuoZU5oDgHzpRkJl3jlcaTiuXiKXbE0S8p8HE1l+d8nR3BV4bdHk5Gl3/FNE+8ShX6OLSlqiBOhHCZF19saKrl3ZAJbgakJ7l41H4Db9nZhSzXjsTNFV4kSJV57SjVmLxPXzZJMHsI0K9G0EMPD9zEw8F+Tzwpqam7EY1YwNPyLYpqvrvY6qqq2YhgvHMqfyE2wd3Qvz4+0c5V/hKl6NKngUM7DkN5Kbdk8GkPzuax+CV1HPo2UDrpusnj+P3K860tF4dXa8pczfp7d9E5mN71zWupzNU8dH+OhB7I0DGfZIBz8+0OUyZtPH9CUMtM1vIteWBS5UpGzXXK2S8ZymchYxDM2WdvFlRKFoGKsgzm9v6Bh8GFMJ03OV0e/OZ9ZFUWHs0IB/8a5JPIOSxvDLKwvw2/q+D06I/Esfq9B0GOgv07MKJ8eeJpPPvbJwlxJJZCuH83IzKiVXlq1lOOx47jSxdRNPn7Jx9k4ayMralec1Q25smYFex9+gB/+5EsgBFf/6UdZde0NRP7fvwHA2P7X8Md3QbAGeet3SNYvZ9jQuL/7frr3fou2ijY0NB7vfxyA3aO7uantJip8FUTzUWL5GOsb1hdc/0u84iilcFXhk6uJgsu9UhLLjjKUHqdHNvJMLMWjw8c5ZE1FuWa+l11gxNEmP/2FqNcSY5StYairWMOT0SRPxVKFfSB4Q2Mrd41MFEXX+2bV8L5zRLruXjX/rMdKgqxEidc3JWF2AWiaj0CgmWz2FAiw7CjTuwQNI8icOX9CY+OtxGK7XjTNdzJxkm/v+zb7I/s5mTgJFLrmNm7+FJr2EFJa6JrJH196J5UVa2esG/bXz0glhkKLXvBnKNhTWGmLv/rKDjZGbD6GgYa3UNBfF8Ady4JSCF2j/JZWZMbB21qOd97MVIlSiozlEk3nGYhlGUvkyTkuyawzKcYUhq4Rzg2Q8s8GFK3Hfkb92KN01V5LV8NNjFWtRdN1MpEMACuawmxpq2FBfRk+U8ejayRyDhnLQcsIHHk6ohvymlSVmVQGPIR85msm1HJOjl8dfZSfH30AWzoMWwcKoqxwVQiZ5ayqvZpdkYeLQuzvNv4dcLaNxaq6VWelFZ18nvb7fk7ToiVce8t1lHX/Cr7yl9jPWuj+AK5WTXrfbuLCplt5yEx0cSB+mB8f+TGucnlq4KliJyeAIx0OjR9iQcUCUGDqJuI8HXZ/iDhSkXFdEq4kZjvkXMnkLHd0ITARVJg65aZBma4hhEAqxa5YIe24KuRnQcDk2cgpdkTHaK1oAwV7x/YQzB3Ckg69qo4+1UxMVAKFcW1BMfXHiECgWOsdZ59Vhas0PJrgn1pn8/meyKTY0vjyysuLImpTRRm7pkW/3tFYzTsaq19UdK0rD5aEWIkSv2OUUpkvASnzOE6aeHw3Bzv/ohiZumTl96mqOnuO4NQNctfwLnaP7ObGlht5U9ub6E/2874H3scltZewonYFq+tWs6x6GT7DN6O4/+XUcAGMHorQ9fwQK5fVkuuMku0cn3l7FhC+fh7e1gryPfFzijGAVM6hdzzF4cE4Q/E86bxTMKJ0XZyCPRamclmU3Mn6yD20Jdv5zsI7GAwuJSyjWFoQaZyOkAkBHb0T7O6P8T+umU/Ia6DrGjoCSUEAgkDXBGG/QbnPIOgtjDXPOxJHSnRNUBfyUh/2Ue43X5aPW9bJEslGaB9u58DYAZrKmqgJ1PD0ycPsGT7CSO44Sk8yNVmmKdTEcHoYqSQe3cO3r/s2a+vXzkhvvlhNl3RdDjz2MMuuvA5NSiYOHcSTzOA5eRee7jtx6zZw8p4cTsrC/uDbSM6q5JloB51uP1KDI2KElJspbq8+UE8kG0GqwuilT635VDGV2Rxuxjft+v8h4UhF2nWJ2Q5DeZvhvE1/zuZoJsdw3qLV72We38NQ3mbEclgfCrIo5GUka9OZjHAyHWduWT0JKbhrOIoLaEjmqRP00loopj/jvachadKTLPU7rCzzsKlhJcvLghzP5F80tTi95uxMQfVCz5UoUeJ3ixdKZZaE2UXyQgLKkQ6ffuLT7BreRcpOAdBS3sL7l72fWxfcytQ1fyVMYc8k1xvnxO5hdvXHuHwoPxkSPXe8RJgaNR9acU4xBtA/nmZnzzgHTsVIZB2YjMu4siCehBCE3RhvjH6fNcntBGWCCaOWnRW38EzFm0gbp4uFdV3D1AXeyf/v2TuIoQnevLoJNS0q5jV1yrwGZV4D09BwHInlFhzv/T6d+jIv5QEPmhCk8w6W61LmMWmpDVIVvHB/N9u1eW7oOXYO7iTn5OiJn+D50ZnvIYGOtGrwGhqWVnAx19D45JpPsq5+3UUbtI70dPHI7d8keuIob7tlGXWDvyE/6824zTci7BS2HWds/3Gc238EgGto3PnHTTxYf9pJvZIAcXIoFLqm8+5F7wYB8Vycy2ZfxrqGdQTMAH7j9Tvx4JXEVYq0K3kmlmT7eIpWv0nUdnk+mWGW10AqGLMK4iwvXXpzNpLCHxUVumDCBaZVd70wChN3sot45rICeGt9BR9oqmVZyI/3jHFDJXFVokQJKNWYvSpMzXJUSnF4/DCP9j1K1snymfWfwdAMJJIbmm9gY+NG1tetp0qvQDkSO5qbVDaABprHQBgCYeoI8+XVAB1rH8D8eTdlwNXMlGPeJZXkj8cL+9YEwXX1BNbUnzNdeXgwzq/3DbJ/MI7jKjy6QCFwlcIrs6zI7wMBe/xbyCmTFckd7Pet5dng1Rzwr0eigQ2G42AaGoYmULg4riCLg+UoIimLpQ0hxhJ5Ah6dgEfHYxTOfyJjM57Ko2mCCr+HioCB1zDIOy4nxtPoE1lmlfupLvMQ0kxytsv+gTghj8GixhAh38xu1yn2ju7luaHnaA4388zQM/zi+D0oThvmwlTwQ/C2hbfxZyv+mrDPx6Hofj78mw9jSxtTM4ti7KUKsnwmzY7/vpP+J+5idd04y5cOox/fia3PIn10iO7t/0aXP8ncrjg13dHC8QDClfi6B6BeKz62LriEuc2XkLRTbGrcxLqGdZR5yjC1c5/7a4lSiowreS6eZle8YKewtTJ00d5V08XMypCfZ2Mpnp5IsTToJ+26PBJN4BMCS0kejCSRnOuPzTP3LYrHakmHwq9CAUiW6QMoYXDIqWfqU7Q2FGB/KoujFKbQ+OTcBr7RP4ojFZooLOWqQiTsXY3VrDmP6CqlFkuUKPFilITZRdId6+b+nvt5uPdh+pJ9aEJjc+PmYiTpG1d/ozD8O5rFHcyTlwmgcOO3R7PYw2nMhiBG7WREQ4EWMDBrA2j+C3tZMpbDtsOjBD06V7XWEHxqaNKnaJLJb4WuEb5yLlzJeVOWjivZfXKcu54f4MBAHKTCa+oIAd5chEutZ9ho7WKZfQATh8PGEp7WNwA6H6n+Ia4wEBoYlkLXFYYmcITEsc7uUhxLFiw8Gir8eI1CejKVtxEIfKZG2G8S8BqgIJa1GU/lKA94qC7zUOH34krJqYkMg/EszdUBKgNefKZOxnLo6J1gSUOIhmmzMV3p8qvuX/FPz/wTrnLPOh6UoNHYTER2IFWhY/JNbW+ipqzQybiqbhW3X3/7yxthJF0e+Ma/0rOng48sPUmZivJ8wwaeq21l4TGDWT/ZRlgq1gJZE7avgC2HQXcL45fc5iZMRnGRmMLg1g0fYMucra+bgn5HKp6OJflNJEG1oTOUt/jx8AQS+Pc+wefbGlkQ9NGRyLC1oowNFaebYqYLr7VhH5aT5unxKE/HUrgyx3dHDZzJzsWFngRHrNAZ0uscomsqEzCZfy7XNeKuy5T40gCFQBdwWUUZ22K5SYd6nbc1b0AHvtQzVBBimuB/LmgCZqYdr6wOF38+87kSJUqUuFhKqcyL5Ft7v8Xt+29nY+NGbmi+gavmXEWlr9DFqJRCWRKrP4GbttH8BkIToGvYw2ni9/UU2i4NQeWb52M2Fn6RS8tFWRLv3BBa4NyRD8uRPHZklPv2D/Lo4RHabMEHQmVsVgYyZTN5x0EYGuU3n7+Qf4p0zuaZ7jF+sXuQY2NJ8pbE1DU8mo0jPGgI/mfmS2xxdtGnzWaXuY52Yy2dxlIcUajt0gUYmoamnY7RiWn3RgDTEPgMHVPXODiYIJLKs6W1Cl3XCHgMgh4dQ9Ow3ULq0qNrVAZNAp6CSM3ZEtt1qQ/5qCrzogmBIyXJnM2sCj+N5f7CY65kImMxp9rg+egj/Or4Qwwlo0Ss3oJ9/iRr6tZwMHKwaF1xx/V3AGcX679cxo92EO6+B+PILxm54jvkRmKEe3exZ6yPpwZ2IVFcv1tRXch4I4GfXyo4tLEeORZhcZ/k6FyDT7zhnwiWV7M70cn6pk2vC1+yjCt5NpbivtEJRvIOT0wkz2v0MttjMGQXahM1FDf7jtAoxui3PDzkbsRFQwNa1FFShBmh4azaLQChXBTa5HNTycjC+25JQOd4xsJFQ6Ami/YFhhD8yawqfjgYLRqn/vncWlKOZHU4yIqQn2PpHPtTWTaUB9lYHiSgazyfyJTEVokSJV4VSjVmrwLRXCHVVOU7PQ5IuRJnPIcbz6NciTWYRibyONE86CATNvZgakYpS3BjI8F19YX1lcJN2niaghjl5y7W/uD3dzF8NMobDQ+LfV4WpGRRDIWunYtvQeULFvJPEUnkePzoKPfvH2RgIkvOUTjKpVme4lb5CDe42/mQ7yuM6nW0yl5cdE5qcwqHLcSkLYDG9BKaqT7VqWW8uoZpaOiioENdqVBKcWAwQVXQw+o5lYVUkitRSlHmMwl5dTShYbkOOccl6CkINEPXEWhkLZeQz2RWRQB9slsulrWYU+FHmqMcmdjHvvHdHJrYRcZNTl5X0PMLUb4eQOJ5FYXYFPn+fYz/9K+pS7Sjawq7dguPN27hQaeLikMDvPVnA+iTjRMTAQjlCrrR0eGf3+3hz9b8OV6Pn73eUTYtuPp1IcRcpXgymuTR8QRp16U7k6c9kTlH4nBqjJaaNlHrwtKYNVoOryYZcPzFbWiT65pC8NezfXxlIIctFfrkdqdSiGcW00///sUK60uUKFHitaRUY/YqcKYgU64i3xtHWRIRMBBeEzeRJ/Pc8Gm1YgiMhgDO0GQ3nS4wGwOFSJmjQCrMah96yAsU0ou/OTTCj549yTfetZqqgIcPV5XTRB7hAKlp8QkBypF454XPK8iUUvSMpnno0BA7jo8wlrSxbBdD5rjJfZKb3cdYwXFsDB7TNiOlQ15JDjO3IMZchSYEmlbwbXJxYVpmUBeiUFNWGKaJ5UpyjkvWdrEdRWXAnIx8KYSCWCZPmc/Ea+hIJYllsoxnLLweF01IhICJhGI4BZVlOpoAv+Enm/Khay6uOUhfupPe9AF6ezrJq4IQE26ImkC4KMx0ofHnm29kfcP6c9pXvJIoKTn20I9Y8NwnqVUa94eXsa26mj4tzVDkp1y7R3HL0xJjai488Mg6nWvmXYf35DBH19TymS03sXruJrRwmE3aa5eqVEqRcFySjsuueJrdiQyXBA0qtCS9Tjk/H5lgbzJDcXYpbmGu4mRkSwck8hzp9KnlCxhC8I0lc7m0ooxn4yk+cbgPZ7JT8QerVgDTjVE1vji/iQnHLQqqzbXpF0whThddZ35fEmQlSpR4vVMSZi8DJ2XhRnPIrANA9vA4Vn8SXIU7kUflZ9YzeRdV4mstJ9UxijuUJnzDPIzaAJrXQKs00AImmlcnlXf4aXs/33/6BKcmssyp8tM/kaUq6GGxZpCavlFNFD3I/Euqz3mclu2wr3+ChzpH2N83QTTn4DoOZSpFRoUJuFk+497BSRr5qvbH3C8uJybKEbLgt6QJAZrCBVwUOKe3rWsahg765M03lrHIORJDiEKHpIBI0kIBg/Fccb2RVJ7KoAdHKjyGQ9ZNYck8tlSInCDkU+S1U2S1XoLuIsz8PIT3FD2ZY4w7XcRjfcjJAczCqcFKLcTNNjM/tJIrWpbQ3DTGl/f/VaFmTDNZ37D+ogr2LwQpXVLHH+LZ7of48fAAud5hKirnc7zcZFRPsKorxnufk7QNgd+GrgZoGRNoCpSucePad7N8zXV4589nfeVrP+kg6biM5GzuHh2nc/wYdn6UJ+35hUosxaTwSuOf5hsnkFzv7eJxaz6O0ooCak8izd3ThltPj2idKbAAbq6rpMHruSBj1CnOFFglsVWiRInfJ0qpzJeAUgplS3AVMueQ70sg/DoyaZPdN0b+WKy4rF7tQ6/wYp1IFHJpmiB8YzOehiCZA2NkOkZp+l+XnmXvEM/aXP7lx4lnbdY3V/LBra1ct7S+aKaaP5lg7Dv7maxUpuKWtvPWkU2kLZ7tGeORzhF6ImlSOYcqd5TrnCe40XmcMSr5mPEFbFfSrAboVo1oomCoqQGn68pnJCkxhEDXRfHRnO2SdyS6gHjuHMX152F2hY+AP0cil8Lnkxi6wNEGyGt9mJqfmOch1PSQ3GT0xa+aCIlmVtWuQ88v5Pkel2WzwmxqqWLF7IKwyVgOJ9OHSHD0VZsVuatvO8/t+wH+/l3sIceTAX8xeuTBoCXu5Y3b4mw8VlheCvjOG3R2XmLyjZHraXHKCaxbR3DTJrRQ6FWxTzkX7bEk20Z6mScPMZI4ytPZBga9W+jJFSxRppsng2JzIMVHWldQbei8fX/Pi474mZ4yPNfzJUqUKPGHTimV+QrgxPM4kUwh5SgEuWNRcofGkWmnEBmbfk8V4J0TwrukisCqWtxoHm9zGE9zOcKjke+OU/ClLKy0/1SMjt4JPrC1hXK/ycevbGNDSxVr5laiZEEMSqmQlotQ/a/NAAAgAElEQVTw6IRubCZ5/wl8i6swZ5Wh+XT0oFlYFkVvJMP2oyM83T3OaCJP3nVZbe3mrda9rHX3oaHoEMu4T7sa23FRCLplI1PT+TQmM5RFTaQwdIHrFurBdA2UVWhEUIDlXri4n37LzxMll8ug4SGjTqE8B8h59lGcFTp1TRV43BaM1GXExttIWl6qZmksbGmmriHM5nkQz1o0VZ6eB5mxHa5u2UBN2WUv8ZV+YfaO7mXnwE5G+ndwb3R/4RIFNTQZAAELTim2HpKsG/NT058oGOZSSONpCK6Xi3lP29tZf9sb0YKvnVBpj6V4fCJJJnGQO6IVOOr/Z+++w+Oq7vyPv88tU9WrLdlylxvGcgUDBkIJJaGHLKSxIYFNNpvwI42UTTbZlM2mrMNmExJSCClAEgihJ/QOrnKVu2XJkqw+Go2m3nJ+f9zRSLJlsE2Rjc/refRoyp2Zo8tF+viU79GBGSCyG8inBrtBB4cdve+G0PjE1HmcW1qIkQ1jR1ptXgUyRVGUw6eC2WFwMw5W2wBa0EDzayQ2dBF/cWgDbv/MYvQ8k8T6Lm+Wuy7wzyklWFuMFjj4FNt9KZCSR/++k9t3d2Lti7HMMOkpCFMwIZ/rp1eQbu6n+8V2jFI/ZnU+meYYVtsARnEAsyKECBlk9sWwWvoxykJEO2Jsj6Z4tjfG9vYYkXiGWruBbm0ylhNggtPCBKeVO/T38ZA4i2ZZiSa9ydmaJnGEhpAujgMZ6eIi8GlejLIciSsl1uvvrX4QARSHDHRNIz9g4EibnngCoaXB34htNoHrwyl4BnBHhDHkYK0pg0jLhchUDdXFBhNKJBOK8ykJeX/w05aDoWsUBr2VrJFEhuKQn+KQ78gbPBopSbet48+v/Dc/jG8bVidL5JLmcjGb8NqtfOQJxxuiJEZw0SLaJ+WR/9ALSEeimT7Ofv9nCS9b9pb1jtmupM92eLW3j5e79zDRXs+O/m7+5FyEt0ykbKjdQzPzEQzNATM1wTenVRF13IN6utQ8LUVRlLeWGso8DNKV2F0J7L40SEhu7CSxrst7UkBoQQV5y6tx0w6Zxv7XLk+xsZPeu7eD9AbpNusuJzu690dRgF7sB0PH6RzabgefBqPUAwOQumDzgmIejw3Q0peizGrmTF7irNSzVLn7+VHgU9wvzsUnLQZsgSY0XOlV8E/ZLpbtomkC6bo4rgAhyRz+aOQIAigMGjiuxNQFCI2gqeM3BBIXhxRJO46lN+GGNiHDo/eOed8NSpxzkSJFPDKTsDaeWeODFAZC+EUBU8uKKQ75SVsOGcdl5vh8AoZOJJGhIGAwb0IRpv7GJs5nOrfy0Cvf58GuNTQLm25jWMiW3irKmW1euYtTOwowuvuGoo4mKLzsMoqu/QAynSK5rp7Q0iWEFix4Q20azZponKd7+pFSsqE/Qjq2gVecmbjDJuYPD2GDZ0U/oDDqaHPAFEVRlDefGsp8g4QmMCvDGCUB3LSDtF0SG7u9eV66RmhhJb7x3nyawOTCEa9NWQ4rG3t5oqGd06aWcup2r1yGQGAA8x09d6yUkI5mwBC5zV6G/pwOa09Qx006XqlMR9K8vZeOQIzvx7/CFLcRF8F6/SR+53sfT4tTka4kIwwMA5IZbz6YdCVJO5eCDvh+ZMI+DU0IAqburbDEqxXlM3Rc6ZCwk2SMHTj+3WCauIVPMVrvmASQGlZ0MTI2H6M4SEDPo6Ysn8klxQSNMNI1GFcUpCDgI5rM4DM0asflI13ojaeZWBJmUmno6EPZQCfkVRBJ9vKD+6/kIb8OhkAIg3PLlvFC71ps18bnwHfXzqD66S2Dyx7wz5lDZtcupG0jTJOCSy4ldLK3yjC8ZMnRtecQ1kTjPNPdQTLRxO3dBdjerMDss3PwLjJvHuD8vCAN8dSoAQzUHDBFUZRjyZgGMyHEXiCGN5vJllIuFkKUAH8CJgN7gfdLKSNj1cbhhKmjmzqhk8rQbzj5kPXCpJT8/tUmntnWySt7ekhZLkFTp6ooyMwpeZjrO8GRSF3Qu7ickjVdbHYs6oXDlAXjqM0LUP58O7gSqUH71HzG7egHV+IKeDYgOTMp0ZGAy05DIypD7Dam8ag4hxeM09kvi3Gky0DKQQibtCVxpCRtH30PadDQkIChCXQdbAd8uoaug6lr6MKrbiulRNMFKTtFQtuOFV4L+et5rd6xZMclaHoC065hcngSVRPDBIw8SvOCFAX9ZGyv6GxlURCJZCBtMa4gQMivk8w4FIZM5laXHHI7pkNyXWhdC9sfgW2PQLybzn99iS+8+GXWBUb2kBU9uZtfF5xNx76tTGjoRuvbMvS8phGsq2PcV75MYu26w+4dO5x9Uwcn0xcbGu0Zm8bOlfwtMdHrEaNo1EKsOl7Q9WmCr02vwq9pr7nKUVEURTk2jOlQZjaYLZZSdg977PtAr5Tye0KILwHFUspbXut9xqLA7HD9KYuXd3XTGUvzkWWTAbjwx8+TshzOnlnBWbVlzK0qZOv+fnoG0pRFLcKdKZLjQ7jjw7Ts6OEbL+3BkhLT0PjOZScxFx2zNc5AZYCdhqR5UycV3XFCxmZq0/cxI5nAck+i04zyyfAn6Us5hHw6CEl/0sZ2JLH00Y1J+o1sj56m4boSy3UxDYEhsn/uBRjZICazG5sHsvtcOlLimLux/NuwHQFFo/SOZUODkBq+zMmErUU4iVqmloUpCYURaPgMb06ahkDXBPkBg4BPRxfe7Ty/ScCnUVkQoKooSL7fOPJ5W5vvg8e+BPFOpNBxJy3j5Zo6/r3zBZJOig/MuIbfNfweW9os2C255a8uIrvhulFdRf4FF9D3x7uQloUwTWp+e8chw5jlSiKWRZflkLAdkq6LI71t4XUE+aZOqWHQks6wJhqnxNTptTIY6Ra+12ZgjegRGzyRQwVYJUOLDHxqWFJRFOWYdrwNZV4GnJ29fSfwLPCawWws7OtN8HhDB09t7WBVYy+2KxlXEOBDp0xC0wR/+pdl5PsNWiIJ9kWS1DdH2N4eozDkY2syTWM6wYSoS6IzygMb2shkA7LtuGxoiZI3vZS9RYJtjZ1E4hYOcLn8A+/r/zNdegW/CF7Jo/Yi4kYJbd3JoxqELAx488Gyu0VhOaB72Q7DECA1bNfBRMfUNBzHxZYSHS07POptfuM3NGzpYmm7sfNX44bXcGDvWC7/S5NUx3sxjARlxiwm580mGPDhL/HKdJiaRsDUMDQNXROEssOj+QGDwqBJUdhHZUGA0rCPgqB5+EOWdhr2PAsND8CSjxEpnUrUzZBXOYuXK9/DKr9Be7qPVfseoiqvim8Z/0Ts63/l+nCIcZpgTkMkF8oQgvx3nUPlF75Awfnnk1i1+pA9ZGnHZXMsyc54iqR00REIIdmfzLAvbbGwMIxfE6zpjGNLl/s6ozjD/7EkfQf1iAm8Yr5SMqIAa7GhqzCmKIpynBvrHrNGIIL3j/1fSClvF0L0SSmLhh0TkVK+ZtXNsegx++E/tvN/z+xiRkUe586u5JxZFSyoGZpwnrIcNuzroyuWpjBgohuC1kiCTS39/HFVM447+nnXNcG5MysoJkpd76OU9W/lduMaBsITMdMR4mmbHllAX/LIe8MGV0hajiQvYBDQdSzXwXElPl3DzvaO6QiEBnZ2NaaueSHAdaW3UEBKb5dCAa7RhBPYiStBFj8OuLkcISUIBoc3NZz+xYTSSykzZ5Ln8wKX36fjN7wespBPJ99vUBA0KQyYFIZMKgoCVOT7KQj6CPt1gqZ++D1jjgU7n4CGv8H2xyDdj+PLY8OSj/BAyEd7vJ2QEeKZlmdym5svKK3j0+nTCf/H/4HtZCfya4QWLSK5fj3ScV63dwwg6bhsHUiwJhqnNZUhbOh0py0aEml6MzZ7Uplh/13ksGB94GR91SOmKIryTnPM7pUphKiSUrYJISqAJ4BPAw8eTjATQtwI3AhQU1OzqKmp6e1qNgAd/SnSlktN6VDtLNtxaY0kaYsmSVsuW9r66ehP0dKXwG/odPSn2NgSxT4glM2rKgAhaelNMsPo4GL3BdpSBnHp4x73XJyDhrFGN+JPuoDqogAZe2gvzaDPwNC83hZNE2RsF1e6GJqOlC6OC0iJi0BKB6HpuFLiui66pmUXLUgssxHbvxvXDZAu+Bu5gmej9I6VW1dgGBnKtDlUB2aRHzTIC/goCGrkB3wUBEyKQialYT95AYOgqeMzdUxd4NO1Ix+ejDRBrB1qTgE7jfzBdFwheKpqNo+Hg+RVnERTvJV1Hetwh225PaNFMrdZUhrP4/y9Olr30LTGwiuvpOq73yFRX/+avWMAcdthRzzJK30DRGyX5kSShkSahO3Smhm2ZcJB+0l6twe3gR8thKkeMUVRlHeGYzaYDSeE+AYwANwAnC2l3C+EGA88K6Wc+VqvHes5Zo4rsRyX+uYI/SmLgoBJyGfw0Po2fvNyI4M5zNAFhQGDnriVe+34Aj8BU2dvTyL7p3pwr8HXDiSDKzY1AROLAmi6RqHfJGFZDKQdSkI+/IYg43qT9aWUOK7E0DWklOi6wBAauld0C9PUMTWBqesETG8IMeDTCftMwj6dgM+gz9nJS733si26Eok7csmoHNpuWhMap5ZfwPXzr+aU6kVv+vkeIZOAppdg15Ow6yno2YlTNoO9H/oTT+x9gobmZ0n4QqzqWDOiX2q42n2S/7jbwcjWCXZ9Bprj/XzCNKm54w5CC197Iv+AZbMzkeKFSIymRIb1Awl6LIf2XBgbVvsM0KWNFN6WVwINFw1TE/xntn6YCmGKoijvXMfkHDMhRBjQpJSx7O13A/8JPAhcB3wv+/2BsWrj64kmLJp640TiFiB5amsHG1u8OUL7epP0Ja0Rxwd0bUQoA9jfn2J4CDswlAkxOCSYHTr0dnfi5OoCkpZLWdhHVZEfIXRAEvJ5w32mrhEOGBQHffhMjaCpURD0EfIZ+HSB39QIGF7vlN/Q8BveJuHGKHO21neu564t9/L35oe8QJZrK2jZMGYaJv9vwRdIubGj3gJJSknGzZB20tnFBwZ+3Y8mtOEHQaQRSqZ69x+6CTb9GWn4WVk9lyfGnUkir5KOld9hTfvoYawmv4bWgVZwHE7bIvno4xJzcGRYCIo//jGKlp/1ur1jABHLZnc8xT37e9gaT9Getob1jA2FMSG9IV6Jji7gw5UhhFHAOSX5hHSNNf0JFcIURVGUMZ38Xwncnx2qMoC7pJR/F0KsBv4shPgY0AxcPYZtPEjKcrAcl1jKZn1zhLyAwUDK5v71rTy5tTN3XHVRgJnj8ljb1JcbuhwYUbl1eA+Km52LhbdhONkNBDTBBXMrcV2YN6EAXRPs7Y6zsKaYhZNLCBheD5dP9ybMa5qGqYs3XFV+fed6Xm1bhWnNQNfj3Lr5a9jSPug4Q9P56ilfJZqJvqH9KC3HonWglfZ4O7Zr0xhtZHtkOzOLZzK9eDpTCiYzLtoGW+6HrQ9DpBHnU6vpCRWxqno2fxfn0q7BjuguZKIbEnsP+gwNbwWpoRlc31fHpKd9GDub8EeTUFwMbgxcF+HzUbT8LEILFhwykLlS0p2xaBhIcldrG2v6+mlzfBzYyylwc0OThqZzU00FNnBOSQFLsjXEBp1WnH9U505RFEV5ZxmzYCal3APMH+XxHuDct79Fr60rlmJPV5yk5YWrV3f38I+GdiLxDH3JkaFFE7B0cglnzSzn3XPGcc/qfezsHMg9L3AxsUFo2BiYusFN504nkXFZMrkYU9dY2xxh2dRSFk8uedt+xoG0zZ3rnuUXO76IKy1AZCfyH9zrpKHxpaVf4uqZR5+bE1aCnkQP9+26jx29O5hcOJnWgVae3fdsLkR9s/ZD1L3wERjoRGoGr9TM54EJk4is/g4tqS72xfYd9L4Cwfyy+Wzp3YLjOpi6yU0LbyLa2sTMnz5L9ba/ZvdDF5R//nOUXn89yQ0bXreHLO26dKYtbt/XyTOdTXRZLlEKvJWT3gdnz43H1Ay+NnU8ccfl9OJ81RumKIqivK5jsVzGMSeZcdjYEqUw6E1Sf2hDG7e/sAfw/hafMb2M8jwfD2/a783j0jTeNauCs2orKAyZzC6Bj961Fdv1esGuruzkytNPgrJaXt3Tw6lTS1k0aeT6htOml70tP5vjStZ3ruOubXfx1NZOLN9ONMPKBbKqUDWdyQ5c6aJrOqdXnU55qJxLp1161D1kaSfNE41P8Fzrc/g0Hw83PowrXV5oe2HEcZZr8eO9D7G5uJCGiZNpx6Y91QUDXTAAIWNo4YXA225KSompm3xuyecA2P78g8zc2Id7332Y23diuEM7XaJp4LgITXvNHrJoJsEf96zk3s4krVQRdQDyYHCLcjG036RfE/zn9Cr67IP3mVQURVGU16OC2WHwGxpVRUE6+lNeuYp4OvecEDCuwM9HT5/CB06dxKrG3qGgleiFp3/Gaa/+nLsC03h12c85dca4ESHswED2VrFdm5SdwnZtYuk0T2/r4KmGPrZHtpEu+5lXLiIEZb5x9FlJpJT4dB9fXfYVDM1gU9cmThl/ylGHMYCMk2FnZCdrOtawYu2KXImKQUJKzo0neCEUxNI0EBp9mX7uMjKQah9xrIbGuye9m8f2Pobt2piayReXfHHEsGrsuefxf/1PSNebF5eaNIHyj1xH3w9+lCsKG1o6cqukwSr7S/L9bO1ay186Y+yzQ3RTARQzcs9JDS07B1AXcM34Ut4/rkSFMUVRFOWoqWB2GDRNMHt8AZNKQ8SSFvF0BQ9v3I/luJi6xhULJzC32tsjc8nkEoh1wOP/Dmt+C5kYzLmcRWfdwqLK2W9bm6WU9Gf66Un20JnoJJKKsKvD4ulN0NCskbYF+QFJ1ZQdtGUDkkBw+oQlzCyZya7ILmYUz0BDw5Uuc0vnYrs2OyM7KQmUEDbDBIzAYbWlJ9nD8y3P82jjo0RTUZr694wIZVq2DIchNM6eeSUXTz6TnQMtLK5YzJP7nuSebfcgkdkg5M0VMzWTq2qv4qraq1jTsWbEHLfMwADb/+PryPsf8OaNAWgaNVdeTdkHP0TBnLkHDVuuicb5c3svd+/vwZaDg7el2a/BKmICDZELY2rjb0VRFOXNpoLZEQj5DEI+g4tPDlJZGDx4GNLOgOGD2H545Wcw9wpY/lmonPu2tM+VLt2Jbrb2bGVPdA/dySh7WkOMK7Eoy4d9kTy2t5Ywd3KKeZMzTKm06UiUc892A1vaCCEo8hdhCIP55fPJ8+UhhCBkhPAZPmzXpjfVS0e8A4kk35dPTUENRf6ikSsn8YYhX2l9hSeanmB3ZBebezdnNymXTLYsWkwTR2gYmsGNM96P7QtxatVpLB7nrR4+P/s+PsPH/Tvvx3KtUXvFAOoq6kjU19N572107tqB9fTTBFIZ9NmzcXfvzhWFHewdCy1YQMPUWm/vyGic7mSEj2/txM5N3h9Z5FWFMUVRFOXtcszUMXsjxrSOmZ2BbQ/Bql9C0SS48hfe47F2yB/3ln+8K106E51s7d7K2s61tMX20x0pprW9mub9pVi2zpLZHSye3QWuhqZrHFgQozvZzSONjzCndA5X116NT/fhSIe0k8Z2vIUNITPEuPA48n35uVWfKTtFwkoQNIJMKphEWbCMtR1rebH1RXZ1b+H59pVD5SqkNxdLk/Dh4pM5dfY/sTHRwmlVp73u8Oj6zvUH9YoNF1u1in0fvR4cr1J/Khyi8LM3M+UDHyS5fv2I3rE10Th/2t/DPe0RbOkNccpcOVfvnkBgeqsDcKRUYUxRFEV5Ux2TdcyOe5EmWPtbqP89xLugeDLMvXLo+bcwlDmuQ1eyiy3dW6jvrGf/wH4GMgMIofHSynOIxUMYus3E8d1Mru6kojRKX8p7raEZ+HSf1wum+zB1k4AZIN+XT9yKsze6l4ARoDRYSsgMIQwvhGWcDHsie/AbfsbnjacoUETACNAca2ZT1yaqw9W0dW7g3tZnvIK6B1Ts0IWOxKt3dt6yL1BXUccZeEOubjKJm0wibRuhaQifDy0UQhje5VlXUXdQIEvU1zPwwou4sX6if3sA4QztPjDh4x+n/IMfAkb2juW3dPIfu9rI5P4t4pXpnW700eyU4EhvV4TBuWKA16umwpiiKIryNlHB7Gituh1e/RnUXgiLPgrTz/NW+b1FbNemO9FNQ28D69vX0xjpprkjn86eMtLpchbUPY9P9zF+fBPTAgkqyjrwmQJTM9EIYOomhmbgSpeMkyFhJ0CCX/dTFCjCr/vpTnYTs2L4dB+t/a2YukllXmUuxPl0H5Zr0RhtpMKqYMAa4FuvfgvbtXM9YoPfvR0AdIBDDkE6fX2kd+/GjcdJ7dlDevt2AjNn4ps8GZnOYFRWgOPg9PV5X5EITl8fyR07ib/wAkivHK9RU4ObTHr3TZPwqafmJvHn6xpf39WKJYfXjRu65dc0fnzyKcDoIUwFMkVRFOXtpIYyj1asA1wLCie8ZR+RcTJ0J7tp6Gpgc89m2hPtNLYUs61xAtGYt52oz5eisGgftbXrCJgmPuHD0A0MzfC2YZKOF5zwAlKBvwCf7vMWBSQ7KfIXkcwkWdu1FgBDGFw2/TLGhcdhORZpJ01lqJKiQG77UnbufZa1rS/SKNPEGJrEP99XRoPVhyNdfLpv1DA2yOnvJ7FmLVooSLqxkY5vfwec19+YXRo6jpToztCk/vKbbiK0dEluyPKliVO5fvPebBgbuW+UF50FhlpFqSiKoowRNZT5VsivzN10pYvt2rjZOUu60DF186jeNmWn6E52s6VrC1t7drG+Kcm+jmKmTmqjKCyJpdMIzWXalAZKStooK0oTs/q9eWGuw4AcAAuSTpK0k6YsWEaBr4CuRBedyU50dCxp0ZXsGvXzHenQOtDKuPA4TN2kK9nFpu5NFOtBurq30p3spW3wqpGgadnaYZrJVUs+w8VWkn2xfVww+YJR54NJx8Hq7CS9bRv9Tz1FprGR9JYtI0KZf+5cgiedhG/KZPzTZ6AXFTFgpXnwF7fS19vNlKIyZr9a79UgM01enTaTP+IjUzeLllgfTZsaR3ymNjhvTNPUXDFFURTlmKaC2RsQy8RojbXSm+rFlW5uK6EZxTOYWzqXynAl48PjMbTXPs0JK0FPqodNnZvY2dvMyt1pWtqLae2agOMYGLpNeXkL5YVpaia0UTu5l/aBdrpSXTjxQoQQ9Kf6STgJfJqPjJuhI9EBwN7+vQd9njhgAlhZsCz3M2hCozqvGoBdPTt4fN+T3uT4wY4nfagHSgjB2RPOpiJcwZzSOdQW1xJJRnhXzbsYHx5/0OdKKUlt347d1U2qeR/9998P2RpjaJo3FGkYFL/vKvLOPx+tqIi+9jZKJ9TgdxwqZ8+l7LRz2Fs+gYGGLfStXMkLkybwgl4AfRbIfGaIRi4NRfl7ckp24r4KY4qiKMrxQwWzI2Q5FpZrEbfibOjaQMgIkXJSPLznYZ5sehLwhgxvWXoLSTtJ3Iozq2TWQe/Tn+mnO9HN5u7N7O7pojnSQ0F+nEw6wMvr5xEu3EP55BeZXFxKVYlDW7yZDV0xBAIXl5ZYCxJJJ14vmMOhhwH9mp+0O1QUtypcxf74flxcdKFzctnJAHQkOnBdh+2tr7Aj0c1WN43UsiEuV0liqMK+oRmcXXM2tcW1gBcwTd2kNFB6UBvcVAqrp4foQw+T2rSJ1KZNI0JZ6NRTMKuqKbrsUgILF7Lt5ed55d67SCcS3PCTX7Mh7fDE+Vdxb0c/zsB+CJbC2RczfLsoXQjeN+U0bpo8LjfHTIUxRVEU5XiigtlhiiQjNMWaiGViCCGo76jniaYniKaj9KZ7RxxruzZburcwLjSOgO4VYZVS5mqA7ejdwdqOjdS3ttLbNYP9bbMYXxVhzsxWwnqYukXb2ZNYRxxJQ1zQlA4zYA+M1izA67kavp1lSaCESCqCzA7hVYQraB1ozfWIjc8bT2mwFMu1GB/2bvfE2rC7d7CdTG4S/yxXZ4cmGdzESCAwNIPzas4j7aRZXr2c2uJaXOkSS8fQNZ15ZfPw6b4R7XOiUZIbN5Hc2kDkzjtH9pKB10v2T9cQOms5W19+gVU3f4K+jv0k5p+Cdea7+dK2VdwdycObVTayt+/AGmOnZzcDX1wYVoFMURRFOe6oYHYYUnaKjd0byfPlURIs4R+N/+COLXcAXlhZVLkIx3VY37U+F16Wjl/KSWUnkbSSbOjawPP7nqehp4Ha4lqe25pil30/CBvyX6W0dhwxrY2V+w/+bIkk6SRHPFbiLyGSjuSq4U/Mm8je2N7c/bJgGaX+UuJ2nJAeoiJcQWWokkgqQnGgmIAeoNBfSLmvgKKBXp6NbWJb37bB6hHezyU0QuPruKJoCtF0lLAZpj/TT3VeNfPK5lGdV42LS1+qDxeXCeEJTMifcNDcOjeRIProo/Q/9hipjRtH9pKdcQaheScRPuMMQgsW0NKwmcd/8b8kFyyj4aobedY1cS0JET9DY6nDt0QCn6oxpiiKoryDqGB2GHy6j4pQBT3JHpJ2ku5Ud+45gaA6r5qElQDgkqmXsHT8UjriHXztpa/h0/10x9Ksj7wASF5se9F7Xa6yhoslRu4DOT40no7sxuGa0JhfOp/1PeuH5oCFq5lUMIm4FSffl49P80pZxO04JX5vu6SMm8mVuPAmvpuUh8rRpUaobx+9/S0krAR/95n06cPKfGSLx2pCY3bpbCbmT8zdD5th8n35mLpJv9VPnpFHTUENpQGv5tlw0nFIbd/OwHPP0/2TnwwFMl3PzSUr/NAH2Rntxt69jWULFlBeO539n/kEf8xUI93hVfglhhDZPSm9OmPz8oIqjCmKoijvOKpcxmGSUpKwE/Rn+tnQuYGvvvhVb/Ns3eRf5v0LP1n/k9zQoamZZNzMAa/PZR7Kg+X0pHqQUqILnVPHn8or+1/Bld6cr3dNfBdpJ01nopOKUGNv7r4AAB0VSURBVAUVoQr2x/fTmegk38ynNFCKoRs4jkPKSeHiYgoTIYRXrd+10YSWm3vWnewmmo5Sno6TTkfZZerZ4UqoMcIYwQr2DjTh4qKhMatkFrNLZzOtcBqFvkKCZpCAEciFu+JAMXlm3kFDloMS9fUMPPMsVmcnsccfRya80IqmUXDJJWjVVTRbKdY01JMaiJF59xmsnD2fbekgfRQxvFfMqzWmesUURVGUdw5VLuNNIIQgbIYJm2HGTxnPuPC43DZBL7W+lNt6SCIJGXmk071eEJOC2rzTaEyuwnEddE3nA7M+QEAP0NDbwJSCKUwqmMTiysU09jcytXAqk/InYejefxpNaAgE0pVIITF101sAIF0cx8Fn+NA1PVeuQxMazbFmNndtxo+gef8qdiZaAOgECgwjN01LExpLpl7IldOvZG//Xhp6GphTOoe68joviOkmuqZjaiamZr7u6lKA/ueeo+3fPo20LO8z8vORhgGuizRN7p09j9bNq7FFhl0XXEtkXA07HQlpgUByTUmKv/UFsVyJrmqNKYqiKCcYFcyO0uA2QVJK9nTF0fDhYuPXTT6z8N/47srv4Uob0zD52pmfAD7B6vbVLBm35HX3hjwSg/tI1pXXoWs6D+96iL/svDcXFDXXzU2y19C4cNb7eHD3g7lNwS+ffjknV5zMyRUnc+n0S4+6HYn6ehKvrqT/ySdzoUwCm959Mf4F8zEatrJiwnQ2TpoBNbMB4fXaOTA0Z0wwtWgK907OUysqFUVRlBOSCmZHqXsgzd/qW7l3bQvb2mME8m5g/vRuvnzOe6mrqGNG8YyDNt5+swLZYBgTCH5S/xMceUCpjMENw4HLq5bzaNcaLNfG1EwumXYJl0y75DU3BT9S8VWr2PfxG5AZb/hWAq4QWIbJz6bMpqFkMiyfMvSCYQsrNRixqnIwjKlApiiKopyIVDA7Src9u5tfv9jI/IlFfOuyuVw6/90UhoZWJI628fabYU37Gm54/AZsaY94XCC4eMrFXJSRfG7/E9jSxdRMrljwCa6AtywkrukboO+nv6Aqk0EALvDI6efQXlbO+hmzaZg20yu9wS52MwUHDUN4ZS+8ArBq/piiKIqiDFLB7Chdf8YUrlkykRmV+W/r5/7fyv8eCmVSogMIgan7uGbWNdRV1PGrzmvfsiAGsCYa56VIjPLnnqHot79hyv4WXCFwAcsw+fuyM3OBTAB+XeOHdRcBQxuFD7+twpiiKIqieFQwO0rVRcHcbSkltrSR0luVqQkNXdPfnA+KdcDOx0lMPp2f7/kba/u2oUmJEN7qzy8u+ixRN31QCHsreutW9w3wq6ZGHu6x+NDD93H+o38FwNZ0bn3/RyhIJthQO5sdU2eiA7omDpq8PzyEqUCmKIqiKCOpYPYGxK04bQNt9CR7sKXNnr497IjsoLa4lnll86gMVVIZrjxkSBucKzY8VK3tWMure59kUrQDo3UNu6J72e0z2dhQRqfjlZ3QdR+XT7+cS6dd+pYEMCC3pVGxofNyX4yNkTb2WEGWbVjHr++/m0kdbcOKWkgKEgnuuehyfJrgv9TQpKIoiqIcFRXMjpDt2tiuTdyKs7FrIz7dh+Va/HD1D2mKNeXqmN2y9BZiVox+q3/UvTJX7l/JJ5/8JLZrowudZVWn0p3sYWvv1qGiZxpoxUXUhMaRZwbpjO4BwJUuVXlVRxTKpJQ0JjP8pb2H7fEU7ykv5IrKErRscbXBILYgLNnSs4NvteUxc89O6nZsZef02ZRkUtz45CMs2brJOw+ahqtpGI4DhsEZ553N5KnjVRhTFEVRlDdABbPDFE1H2de/j75MH0IK1net57HGx2iKNY04TiK9vTJ7tlDqLyVlp3i88XGe3vc0QSPIgDXA9sh29kb35kpa2NJmXevLFIQrvTcRAoHg2lnX8tnFn8Wv+1nfuZ4bHr8hV+ZiceWodekO4krJ/R0R/tDWw854im7bW8H5aHc/n9m6j3KfQVjXaEymcb0fAEQBc/bs4H9+/B1My8rtUJk2TCTZlZRAbPlyauvmE1q6lHkLFrzxk6woiqIoJzgVzA5Dyk6xsWsjQTNIkb+IP2z9A4/seWTEMZPzJ9M80IyUEk3TSNtp/rzzzzR0N9CX6csdl+/LZ0FwPHWEeVjGcAATwc/HnwcLPjwifF005SL8uh/w5o398t2/5IW2lcyrWESvPpUfNbZzVkn+qD1UjpTcsa+LO9q62Z0cvguBNwApcJnJDqb5y9lsjc9uEA5CSBaFbBZvXIsvG8pc4Kklp/HIsrP4/m0/RLguumlSd+MNhFQgUxRFUZQ3jQpmr0NKCRI6E5082vgo7fF2mmPNI44ZFxpHU6xpqAfMtXmk8RHKg+WUBEuIZvqyPU0a1868lk/FkmjN93Dl9HNZU1jK4hlDc8V++e5fHrLGWG3pyXRrU1gZHeDW7XtwgRVN7XxzejVTg37W9ScwBKyPJWiIJWlKWyNeL3DQpNdKQ7h8smQf506sYbdWw9UbdmO5ElPT+M/mPfiffxLw6pHZus6WyZO4prqAyb/+JZn6DYSWLlGhTFEURVHeZGqvzNcRTUd5YNcD/GDNDw55jC70EUVeL5x8IZ+b/c+M2/sy6zffxQ2iEytb0uKXF/yGutK5oBlDm2e+jgHboSdjs74/we0tXdTHEriv+yqZ/fKq/gvAJxy+OC5G2lfDmaWVI3raBueYnfHsEwT/6zuAF8r2leQTOP88lvy/zxMuKj6s9iqKoiiKcmhqr8w3oNBfSH+mP3dfIDhv0nksrFhI60AryycsJ2SERgxBfrD6bMbddgZIl7qKOfxy2sWsKShh8bSLjngV5ZponIc7+6jvj7OyP5FtA5hC4EqJLgRLCkO81Bf3XpBdOCBw0RCAd8zr7Tk5Z88Oqv96P31//WvuMU0IZl9yBZO+9KUjarOiKIqiKEdHBbPDcEb1Gdy55c5c8PrInI8MBSwpoa2eX4ZOYo2Ms3j5V6grnw/nfRNmnA8Vs6kDjrSohSslf9nfw+d3tGId0KspgOntu6hwG7lkejszJt3CP/UnSDk23oAp+DTjsCvqJ+rrafrIdbk9LtF1b8K/aVJ+wQVH2HJFURRFUY6WCmaHYXDi/Yi5X8kIbPwLrPsddGyizghSt+BDMBjYTv/MUX/emmice9t7ebCzb0QoM4Q3583A5uqyXzNT205/Wz5zZn+cM559mPu37+Fdixdy/pVXcXrx6IsCDuQ6Djt/99vc6ksJFFxxOYGJNWoemaIoiqK8zVQwO0wHVdN/5r9g1S9gfB28539g3vsgUPiGP+f3rV3csqM1N4dMz6YlUxP8+0SDjXvvYL6xnzMnnsXv7hzHt799BzAbgJtuuokVN/8r4jDnrgE8+7tf0bhzC6fqOsJ1EBJ8ZeWU/cuNb/hnURRFURTlyKjJ/0crshdSURg//015u6e6+/nf5g5WR+O5UKYB7wnuolJPcHntVSwuDBOJrKKoaBFC6LnSHINc1z2sUGalUljpFKHCIqKdHXQ07mKCGSKxciXRxx8ns3MnRe+/msJLLlE9ZoqiKIryJnutyf/aaA8qh6F48psSyqSUfHd3Gx/ctIeVUW8CvykkGi6GTLM4+Xs+XLiHRQUh72OLl+ZC2c033zzivW6++WZeL2jvXruK337+X/n7bT8GoLCiktpTTie0cAFln/wERZdfBrZN311303zdP5NYt+4N/4yKoiiKohweNZQ5hvYm09yyvYXnIrHcYwLJcvcfVOoJ3lUxjQun3YHPVzLidYOh7NZbb/WGL1esyN0HWLFixUE9Z/3dXTzz29vZtfoVSifUsPSy943aJplKe2U8pETaNonVawgtXPgm/+SKoiiKooxGBbMj5EqJJSWuBE2AIQT6EczpAnixN8YP97aztj+OX9P4UHEv9/YVY0mBqQk+XD2Ld0+5CD1b9f9AQgiKiopyoUwIwYoVKwAoKio6KJQ1bVzPAz/8NlJKln/gn1n0nsvQDXPU9w4tXYLw+ZBpL6CFli45op9NURRFUZSjp+aYHaZ+26EpkabHsnGRbOxP0phKMycc4NSiPCYF/RSZh865gwVc21Jp7mzrzZZ+dfmc+F/q3BeIlH6StqIPH9Em4FLKESHswPtWKoUZCJCKD/Dsnb9k2fs+QGFF5eu+b6K+nvZvfZvMnj3UvvIyWjB4WO1RFEVRFOX1qQKzb1DKcVkdHSCgaRSaOg939fHT5q5sNX3BN2ZU0ZWxWVacR1jXD3r9mmic963fRcodDMHefpUg6QktZ1Htv1FYuOiIVlMCBx0/eH+gt4fn/vAbevY18aHv3UognMeF/3rzaG8xqtCCBVR+6RaaP3Idsccfp/Cyy46oXYqiKIqiHB0VzA6DqQmKDYOI7WBLh+60DXjxypKS+v4E86pC+MToayme624j7bqQ3Shcy77apxlcOfMaig6zh+z1OLZF/WMP8cp9d+PYNksuvQrXddFGCYuvxz97DnppKZ0rViCCIfLOXI4WCLwp7VQURVEUZXQqmB0GXQjqCkL02w4Ry+H0kjzu64xge7sfcXlFMQsLQmjDerBcN0139zPsb7+fcHcLJl/HFj5A4CIY7zP49KRKJgV9dGdsNAF+TRDStCPuOQOIdrZz33e/TmR/G1MXLuFd191I0bjxh/36xLp1JFavJrRkKVowQPfPbsPp6QGg9XOfo+KLX6DkmmsQ5uhz0xRFURRFeeNUMDtMmhAUmQZFpsGUkJ+pwQCf295MYyLNtJB/RChLJltZtfoSbDuKz1fBeTVXMCuvjDXJMFOCPrYMJPlDWw9f3dnKA50Rqv0+Ts4PUhsKoguoCpjUBP34tdeuZiKlxEqn8AWC5JeWUzphEu+67kamLBh12Hr097Asuu+8k+4VPwbHGf0gxyGzc9dhb7quKIqiKMrRUcHsKC0uDHP3ydNYvmobt2zbzXeLnkEImDL5UwQCVfSUfIwd+mKWltfSZZhYts2ANcDPu/ooNHVOygvycjTOymgCSPBwV5T/njmBmeEArWmLrozNKUV5h1zx+c1//yq0NFIZMLh+xc8xA0Geaemi/oGH+cYhgpmUkvhLLzHw3HNo4Tzsrk7ir7yK3dY24ri8c86h4D3vYf9XvoK0LIRpUnjZpQhDXS6KoiiK8lZSf2mPkuMk0foe54PmHm7vexefizhcWhCltzjOXW3d3NN1Og4g2psoNHT6bSdX0R+82WYBTeQWBNhSsjGWZHrIj5SH7pxKxvpZ/dBfCe3aiGs7tGvFuO7IumaDqzMT9fUkVq3GP2sWMpmg7/77iT/3/FAbwiHyTjsN/ayziN57L9JxEKZJ6Q0fJ7RgAWbVeBKrVqs9MxVFURTlbaLKZRyl7Tu+QUvL79ljLufr9k1IDj3MV2XqFJk6WxOZ3HrMc0vymRb0c0dbD7aUGELwlanjmJcfotrvY5zfJKCPHMrs7+7kzs9/ikwqxcxly3l2515+9NPbcs8P1jVzYzF67vwdPbfdBu5QHBSmibQs746mUf6ZT1P2iU8A5EKcCmGKoiiK8tZS5TLeAhMnXEdF+UVsjk5CNLbnAtdlhUFOd9L8+4CDDczbs4Nv7tyAUzuLD0yZh4XAFPDJmnJOK8rn4vIiXooOsKwwzClFeSPmqgFE2tto37WD2WecTUFZBYvfeyUzTjmNsomTeI+U/Oint3FaKMR78wv4sGGy9+r3k2poGBHIEIKif3o/he95L8033JAbngydckrukNCCBSqQKYqiKMoYUz1mb9BgjTLLlZjA7+hnUShAvRFg97p6lvz4BwjXRZgmfV/5Gi9OmMJSK8nSqnICM2eO+p5SSvZt2UT93x9k15qV+AJBPvHz32EGAiTq64m/8gpaQQGP3X034S0NzPT7EUIgAf/MmRScdx56SQmd3/8+0rYRpknNHb8htGCB6hlTFEVRlDGmeszeIm4yycl93fzeb/FqyuGUV55j0o6tdOsGVV1dlO7YAdngK22bSTu2Mv+k2bgJF7u3d0Slfuk4JDduZP9DD7Bp+xa6+yPkGT7OrJ1DdfVEelesILVjJ4lXX829Z52UJAoLEZkMSIkjJS+4Ltf926cQQhCYM/ugEKZ6xhRFURTl2KWC2RGSUoJt4yQSpDZuBKDOMJn+7D/o+9OfSBzYA5kteSF0HV/NRJzeHrS8PAJz5uRCWW9bK7H77iPxs5+DlMwEcn1pWxuJAiIU8lZFZt/fBeqnT+Oqb32b5uuvR1oWUkpik2py76tCmKIoiqIcX1QwO0zOwABWSyt2Tzc4LoktWxj4xz9wk0ns/fuRmczIFxgGhe+/Gl/1BJzuLoILFhBauBAtHEYLBpGuy971a1n32IP0vvwSpzZ1og+GOiEouOS9lHzgA+hlZRglJWihEIn6epo/6oUw3TS56lvfJrRwATV3/MbrGVuyhJMXqiCmKIqiKMcrFcwOg5tOk6xfD4aBlpdP7Kmn6L399tzzvunTMarGk3jxJW/SvRCUfepTFF5xOWZFBeKAQrENzz/Nq/f/mUjrPqYnHU7b045eWIgcGMiVrCi+9lqCdXUjXhdaMCyEqeFJRVEURXnHUcHsMAhdR8vLw+mP4kqJ3dk59KSm4Z8xg0xTE7gu/rlzqfj858lbduqI9+hq3ktJ1QR0wyC2ejWT9rayTA/AzgbCy5dT9f3/JrN37+tOzFchTFEURVHeuVQwOwzCMAiePA+nvx87EiG0ZAmxRx/N1QQbeP55sG3Gf+c7+KZOIbFqNVrAjzFrJttffoE999yFtn0H0997GeUl5RTc8QcKbG8j9Lxzz6HqRz9CDwQwiotV6FIURVGUE9gxG8yEEBcCtwI68Csp5ffGtD26jlFcjFFcTGDqVGQiTsd/fQ9cFxmPI8Jhun/xC6x9+0BKJGDrGprjUjv4Jr+5k64RbyrQC4teozTtaxu+qnO0+4qiKIqiHF9ee5fsMSKE0IGfAhcBc4BrhRBzxrZVI7mJ5Ij7orQEy8rkVk0CuGWlGFOnDDtIEKirA9METUP4fBRdcQVaIHDEn/+Nb3yDm2++mcE6dFJ62zJ94xvfOKqfR1EURVGUsXes9pgtBXZJKfcACCHuAS4DGsa0VcMEFy8Cw0BaFq6Al30uoYpSFkT6kJaFZprU/vhWgNxKSmGaVN7yRYA3VORVSklfXx+33uq9/4oVK0bdK1NRFEVRlOPLsRrMqoF9w+63AKcc4tgxsXrLOnbXlFOetAgsXsjpl17O1EVLsbduOyh0HWol5dESQrBixQoAbr311lxAG9wrU4UyRVEURTk+HZNbMgkhrgYukFJ+PHv/w8BSKeWnhx1zI3AjQE1NzaKmpqa3tY1dTY1E2tuYMn8R5lEMRb4ZpJRow0pxuK6rQpmiKIqiHONea0umY3KOGV4P2cRh9ycAbcMPkFLeLqVcLKVcXF5e/rY2DqB80hRqTzl9TEPZzTffPOKx4XPOFEVRFEU5/hyrwWw1MEMIMUUI4QOuAR4c4zYdMwZD2eCcMtd1uemmm7j11ltVOFMURVGU49gxOcdMSmkLIf4N+AdeuYzfSCm3jHGzjhlCCIqKikbMKRucc1ZUVKSGMxVFURTlOHVMzjE7UosXL5Zr1qwZ62a87VQdM0VRFEU5/hyPc8yUw3BgCFOhTFEURVGObyqYKYqiKIqiHCNUMFMURVEURTlGqGCmKIqiKIpyjFDBTFEURVEU5RihgpmiKIqiKMoxQgUzRVEURVGUY4QKZoqiKIqiKMcIFcwURVEURVGOESqYKYqiKIqiHCPeEVsyCSG6gKYx+OgyoHsMPvdYps7J6NR5GZ06L6NT52V06ryMTp2X0R3L52WSlLJ8tCfeEcFsrAgh1hxqr6sTlTono1PnZXTqvIxOnZfRqfMyOnVeRne8nhc1lKkoiqIoinKMUMFMURRFURTlGKGC2Rtz+1g34Bikzsno1HkZnTovo1PnZXTqvIxOnZfRHZfnRc0xUxRFURRFOUaoHjNFURRFUZRjhApmR0EIcaEQYrsQYpcQ4ktj3Z6xIoSYKIR4RgixVQixRQhxU/bxEiHEE0KIndnvxWPd1rEghNCFEPVCiIez96cIIVZmz8ufhBC+sW7j200IUSSEuFcIsS173Sw70a8XIcTN2f9/Ngsh7hZCBE7Ua0UI8RshRKcQYvOwx0a9PoTnf7O/hzcKIRaOXcvfWoc4Lz/I/n+0UQhxvxCiaNhzX86el+1CiAvGptVvrdHOybDnPi+EkEKIsuz94+paUcHsCAkhdOCnwEXAHOBaIcScsW3VmLGBz0kpZwOnAp/KnosvAU9JKWcAT2Xvn4huArYOu//fwIrseYkAHxuTVo2tW4G/SylnAfPxzs8Je70IIaqBzwCLpZQnATpwDSfutfJb4MIDHjvU9XERMCP7dSNw29vUxrHwWw4+L08AJ0kpTwZ2AF8GyP4OvgaYm33Nz7J/t95pfsvB5wQhxETgfKB52MPH1bWigtmRWwrsklLukVJmgHuAy8a4TWNCSrlfSrkuezuG90e2Gu983Jk97E7g8rFp4dgRQkwA3gP8KntfAOcA92YPOeHOixCiADgT+DWAlDIjpexDXS8GEBRCGEAI2M8Jeq1IKZ8Heg94+FDXx2XA76TnVaBICDH+7Wnp22u08yKlfFxKaWfvvgpMyN6+DLhHSpmWUjYCu/D+br2jHOJaAVgBfBEYPoH+uLpWVDA7ctXAvmH3W7KPndCEEJOBBcBKoFJKuR+88AZUjF3LxsyP8X45uNn7pUDfsF+kJ+J1MxXoAu7IDvH+SggR5gS+XqSUrcAP8f51vx+IAmtR18pwh7o+1O/iIdcDj2Vvn7DnRQhxKdAqpdxwwFPH1TlRwezIiVEeO6GXtgoh8oD7gP8npewf6/aMNSHEe4FOKeXa4Q+PcuiJdt0YwELgNinlAiDOCTRsOZrsfKnLgClAFRDGG3Y50Il2rRwO9f8UIIT4Kt60kj8OPjTKYe/48yKECAFfBb4+2tOjPHbMnhMVzI5cCzBx2P0JQNsYtWXMCSFMvFD2RynlX7MPdwx2E2e/d45V+8bI6cClQoi9eEPd5+D1oBVlh6vgxLxuWoAWKeXK7P178YLaiXy9nAc0Sim7pJQW8FfgNNS1Mtyhro8T/nexEOI64L3AB+VQ7asT9bxMw/sHzobs794JwDohxDiOs3OigtmRWw3MyK6a8uFNsnxwjNs0JrLzpn4NbJVS/s+wpx4Ersvevg544O1u21iSUn5ZSjlBSjkZ7/p4Wkr5QeAZ4H3Zw07E89IO7BNCzMw+dC7QwIl9vTQDpwohQtn/nwbPyQl9rRzgUNfHg8BHsivuTgWig0OeJwIhxIXALcClUsrEsKceBK4RQviFEFPwJryvGos2vp2klJuklBVSysnZ370twMLs753j61qRUqqvI/wCLsZbBbMb+OpYt2cMz8MZeN3BG4H12a+L8eZTPQXszH4vGeu2juE5Oht4OHt7Kt4vyF3AXwD/WLdvDM5HHbAme838DSg+0a8X4JvANmAz8HvAf6JeK8DdeHPtLLw/rB871PWBNzz10+zv4U14K1vH/Gf4/+3cMUrEUBSG0f+ClViKMIuwcAc2Ija6GEsLRV2BlRuwdQviKkSYwj2IhVUsjDAMgt3kSs6pAnnF5RHCRxKywX1Z5vu7qZ977/3K+otxX16TnEw9/6b2ZO38W5Ld/3it+PM/AEATXmUCADQhzAAAmhBmAABNCDMAgCaEGQBAE8IMAKAJYQYA0IQwA1hTVU9VdTQe31bV3dQzAfOw9fcSgNm5THJdVXtJDpKcTjwPMBP+/A/wi6p6TrKT5HAYhvep5wHmwatMgDVVtZ9kkeRTlAGbJMwAVlTVIslDkrMkH1V1PPFIwIwIM4BRVW0neUxyPgzDS5KbJFeTDgXMim/MAACa8MQMAKAJYQYA0IQwAwBoQpgBADQhzAAAmhBmAABNCDMAgCaEGQBAE193iskuHK7NlAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.lines import Line2D\n",
"\n",
"plotterA = Plotter()\n",
"plotterA.ax.axis('auto')\n",
"\n",
"# Plot sensor positions as black x markers\n",
"for sensor in sensor_set:\n",
" plotterA.ax.scatter(sensor.position[0], sensor.position[1], marker='x', c='black')\n",
"plotterA.labels_list.append('Sensor')\n",
"plotterA.handles_list.append(Line2D([], [], linestyle='', marker='x', c='black'))\n",
"\n",
"plotterA.plot_ground_truths(truths_set, [0, 2])\n",
"plotterA.plot_tracks(set(tracksA), [0, 2], uncertainty=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In comparison to Tutorial 1 the performance of the `RandomManager` has improved greatly. This is because a greater number of sensors means each target is more likely to be observed. This means the uncertainty of the track does not increase as much because the targets are observed more often."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### UncertaintyManager\n",
"\n",
"Here the chosen targets for observation is selected based on the differences between the covariance matrices of the prediction and the update of predicted measurement.\n",
"\n",
"First a list is created of all possible sensor/track configurations. Each possible configuration is a mapping of sensors to tracks. \n",
"\n",
"At each timestep, for each track in each configuration:\n",
"\n",
"- A prediction is made and the covariance matrix norms stored\n",
"- A predicted measurement is made\n",
"- A synthetic detection is generated from this predicted measurement\n",
"- A hypothesis generated based on the detection and prediction\n",
"- This hypothesis is used to do an update and the covariance matrix norms of the update are stored. \n",
"\n",
"The metric `config_metric` is calculated as the sum of the differences between these covariance matrix norms for the tracks in the possible configuration. \n",
"\n",
"The sensor manager identifies the configuration which results in the largest value of this metric and therefore largest reduction in uncertainty. It returns the optimum sensor/track configuration as a dictionary. \n",
"\n",
"The prediction for each track is appended to the tracks list at each time step, except for the observed tracks for which an update is appended using the selected sensor(s). "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 4min 31s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"for timestep in timesteps[1:]: \n",
" \n",
" # Generate chosen configuration\n",
" chosen_action = uncertaintymanager.choose_actions(tracksB, timestep)\n",
" \n",
" # Create empty dictionary for measurements \n",
" measurementsB = defaultdict(list)\n",
"\n",
" for sensor, track in chosen_action.items():\n",
" \n",
" # The selected ground truth will be:\n",
" for truth in truths:\n",
" if truth.id == track.id:\n",
" selected_truth = truth[timestep]\n",
" break\n",
" else:\n",
" raise ValueError()\n",
" \n",
" # Observe this ground truth \n",
" measurement = sensor.measure([selected_truth], noise=True)\n",
" measurementsB[track].append(measurement.pop())\n",
"\n",
" # Generate clutter at this time-step\n",
" # Skipped for now\n",
"\n",
" # Do the prediction (for all targets) and the update for those observed\n",
" for track in tracksB:\n",
" # Do the prediction\n",
" new_state = predictor.predict(track[-1], \n",
" timestamp=timestep)\n",
" \n",
" for measurement in measurementsB[track]: # Update the prediction \n",
" # Association - just a single hypothesis at present\n",
" hypothesis = SingleHypothesis(new_state, \n",
" measurement) # Group a prediction and measurement\n",
"\n",
" # Update and add to track\n",
" new_state = updater.update(hypothesis) \n",
"\n",
" track.append(new_state)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot graph B\n",
"\n",
"Plot ground truths, tracks and uncertainty ellipses for each target."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotterB = Plotter()\n",
"plotterB.ax.axis('auto')\n",
"\n",
"# Plot sensor positions as black x markers\n",
"for sensor in sensor_set:\n",
" plotterB.ax.scatter(sensor.position[0], sensor.position[1], marker='x', c='black')\n",
"# Add to legend generated\n",
"plotterB.labels_list.append('Sensor')\n",
"plotterB.handles_list.append(Line2D([], [], linestyle='', marker='x', c='black'))\n",
"\n",
"plotterB.plot_ground_truths(truths_set, [0, 2])\n",
"plotterB.plot_tracks(set(tracksB), [0, 2], uncertainty=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The smaller uncertainty ellipses in this plot suggest that the `UncertaintyManager` provides a much better track than the `RandomManager`. As with the `RandomManager`, performance has improved from Tutorial 1 due to the additional sensors."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Combinatorics\n",
"\n",
"The following graph demonstrates how the number of possible configrations increases with the number of sensors and number of targets. The number of configurations which are considered by the sensor manager for $M$ targets and $N$ sensors is $M^N$.\n",
"\n",
"In this example there are 12 targets so the number of possible configurations should be $12^N$ where $N$ is the number of sensors. This exponential increase means that as larger number of sensors slows down the run time of this Jupyter notebook significantly because there are so many more iterations to consider. \n",
"\n",
"Changing the number of sensors to $N\\geq 4$ leads to a much longer run time for the notebook - note the wall time of the cell where the sensor manager is run. This highlights a practical limitation of using this brute force optimisation method for multiple sensors."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'log of no. combinations')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bUV1X417IPf0UiX/9IdbCInreIaSHMEIarcd6OPZ//gluLFr2bsKvpIMyhf+h4jjOpvv1X1+77a5KCHFWKfX86kLan70gSXczj20jrV5+hep/StebZVtPpeuTbTqdLhL71atXt/SiLCVPv/14cnKSSCRSdBaU3n7vZdKthKCtbX5+ntOnTxOLxSrOcvNzeYOa8VbTx3YT6RYGnyf9+L9SmJnByRZwCw5OwUE5GqFD+zj2wT9CX+2WLPVllzbJlEo7UkpCoRDpdLpix6KfQrYLcb8Q4nPAu4G2FxTpliPbSrfyjSJdn8B9ArRtu64s21oq3Uwmw9DQEJlMhjNnzqwh9q0Sny8v+ME6g4ODRKNRjh07Vlycmp+fX3f7nc/nsSzrplRnO4HgeVWa5RbM5V1YWCgbeOMT8k619dYK398bhD0xSvpfvk9hdgblCqQjkbbrfbkQPXmUYx/4Y8QG51SpScaXdkZHR3Fdl9HRUbLZLEqpos7u68yFQmG3pov9T6WUEkIMAtru/J9tMGppaAAaKi+4rssvfvELdF3n9OnTddtZqql0M5kMg4ODxSq6p6dnWwiuUChw9epVotEod9xxB7FYrDimpbSrzK/45ufnGR0dZXh4eF3Ft9WIw92Aaj5MKuXyBv2yMzMzpNPpTf2yNwtSyuIxOLNTZB//AdbkOK7l4NoOSmpIy0HaEqU0Wl98nsPvfkfd+/OlnXA4TFtbW/H1Vaqzf+tb3+Ib3/gGy8vL/O7v/i4XLlzgoYce4uzZsxtu/53vfCff+c532L9/P8888wwAb37zm3nuuecAz3rY0dHB9evXGRkZ4fbbb+fcuXMAXLlyhUcffXTTc1BK/dQ/HaXUV29p0q2noQEaIy8sLS0Vg8PPnj3L4cOHt7S9jSrdoGRx5swZuru7t4VsFxcXGRgYwLIsLl68WGxQ8Cvf0n0GGxlSqRQ9PT10dnYWKz6fjIeHh9dkBwRvv282yVSLrVTw5TIWgs0LlQKC/LbfnQwIcl0XLZ0gc/VHWOMjONkC0nZwrdXK1nFxCw6u5dD5yt+g5w33N2S/pQtppTr7hz/8Ye677z6++c1v8pGPfIRnn322qtfO29/+dv74j/+Yt73tbcXH/uZv/qb4/Yc+9KE1H5Jnzpzh+vXrVR+38F4UB4Be4P8SQvz9LUe6W21ogPpJN5hlGw6Hue222xgfHycajda8rVKUq3TT6TSDg4MUCgXOnDlT1/idarC0tMTAwAChUIjz58/z9NNPr+sI2wxBTbdcxbcRyZTatWKx2K6TKBotm2zUvOBrn9PT08Xbbdj+gCA3lSBy9V+RyWUsBNJVSEfiFGykq3BtiSzYuHmb7t++n67XvKph+65mIW1lZYWuri7Onj27aYXr49d+7dcYGRkp+5xSim9+85v8y7/8S62HG0QIeBnwRiAOfPCWId1GNjToul7ThAV/MWloaIiWlhYuXLhQzAzYjvHpqVSKwcFBbNsuku12YHl5mYGBAUzT5Pbbb1+nUZZiM+LZSE/eiGTKBd/slEOgWuyUVh3UPsPhMBcuXAC2NyBIZTMUfv441tDzaItLSMNEoaEchWs7SKlAaODaSEdy4K0P0nbvvQ0972o60hrdGPHv//7vHDhwgL6+vuJjw8PDXLx4kba2Nj796U/z8pe/fLPNuMAzQPfq9z/f86Tr275mZmbo6upqyNwkwzDIZDJV7TuYZXvXXXetWz1tFOnquk4ikeDatWs4jrOtZLuyssLAwAC6rnPbbbdtSrb+9d6MVOtBJbtWqUNgfHx8zbBIy7JYWVkhHo/vyKLUTi8QlkpNtQYE+T9f6qQInoOyCtjXfowz/BxuPo8sWDiWja40hHRXK1sL8AoCqQSH/o9307L6QdBIVOPTbXTYzTe+8Q0efPDB4r8PHTrE2NgY3d3dPPnkk7zhDW/gxo0bGy7eKaUc4DngOSHEV4CuPUu6pR7boaEh2tvbG6IBbiYvSCmLt3abZdk2gnSTySSjo6PYts2dd965bb3liUSCgYEBhBCcPXu2oSvBjbaMVXII+FkLCwsLzM7OMjg4uGZRKkgyjdRBbwbpVnP8lQKCfEdNuQnH8UiYzulBwnPjCNtBKLGq2zq4to3SQuC6yLyFdFwkOtJyOPS+9xHt7d2W861GXkgkEhw6dKhh+/u7v/s7nnzyyeJj/p0YwKVLlzhz5gzPP/88ly9f3nR7QohDwOuBN+450q3U0GCaZsMcB5VIV0rJ5OQkY2Nj7Nu3r6os263Yz/wpvlJKDh48iG3bDSHcUoIIkm1vb++2pDTtlE/Xz1rw58PB2lZV3wPq38k0SgfdadLdaguwYRhrAoLAy0ewn/059rM/x8qkyWdyWAXLC+h3JJqrcByFa7qIgo0UGhgGKl/gyPvfT7jGYPtaUM35NlJe+MEPfsBtt93G0aNHi4/Nz8/T1dWFrusMDQ3R399fbIeuhNVIR4lHuPewF6MdXdfFtu11DQ2maWLbdkMWrUpJ13VdJiYmmJiY4MCBA9xzzz1VW5wMw6h5Aq9PguCtlnZ0dLC4uMj8/HxN2ykHn/yEECSTSQYGBlBKbRvZBnGzmiMqtaoGddBUKsXMzExRLy61tG3WxLBbK91qoJREDj6DHHgGmc0gLAdT09HDEcK6ieso3EIBu1DAdSS5ZBY7l8dRAt00Cb35IZZNk5ZUquqAoFpRzbWth3QffPBBfvSjH7GwsMDRo0f5i7/4C971rnfx2GOPrZEWAB5//HE++clPYhgGuq7z6KOPViPx+Qd+EHgCSO450vXn3ZdiO7rI/CzbqakpDh06VFeWbS3ywsrKCoODg2UrzkYuyCUSCUZGRnBdl97e3h1JZdptbgNYq4Pu37+/+LifzZtOp1lcXCxKO34TQ1CiKLUx7RQaRbpy+JeooRvIdNKraG0H5TgoR6KkQroSLAtl22joaLZNWDMIxVrQDJPO9/4RhXC4bBfZRgFBtaKaD+x6SPcb3/hG2ce/+tWvrnvsgQce4IEHHqhp+4B/4IvAEpDdc6RbCX6l2wgopUin0zzxxBMcOXKkGK9YD6ohy+XlZQYHB9E0jb6+vrJaaiNSxlKpFJlMhv7+fvr6+nY0d3QvtQGXZvP6CFraghMYIpFIMfZwpwZrbpV01dQQavAXqFQK6axqtY6L66ySrvT+r5TjYlsOmhFG5bIoy0br7gCpOPTBj6C3tROHmgOCfEKuxklR7bkmk8ldl6W7Ki0A/L33TzVzy5BuIyrd4ABGYF28Yj3YiHR9stV1fdOFq61Uuul0moGBAWzbJhaLcdddd206xLJWZDIZdF0nHA6XfRPtJdKthEpNDPl8nuvXr5PL5dYM1vQJxq/2GjnLrW7SnRuHoV+gkgmk7aAcG2nZKNtBuhLlOEjHRimBlCAdF6EbSKlQSuBqOkZbO/ve/V70Cl7tjQKC/MW7UjlnI/vfXs/SFUIIpdS0EKJVCHF6z5FupRetYRh1V7r5fJ6RkRGWlpY4ceIEvb29/PSnP21ItVKOLP1uNdM0OXfu3KaWLKiv0g02T/T29tLV1cW1a9e2XDEHkUgk6O/vLzalWJa15jbcfyPdCqRbDr5v1jAMTp48WXx8o1lujWiBrpl0l2dh+GlYWUC5XueY9AnXdT3Ll22jLMuTFtBQuTyuZYERxk0kcJQGZoj9f/hf0aK1B8tsNGppo4CgUCiElBLLsjbU1m3brnpI605hlXCVEOJleItph/Yc6VaCaZrk8/mafsfPsk0kEpuOqakXPukqpYpkGwqFqvK/lttONfAzGPL5fLEt2EcjZApgzSLc2bNni0lQQoiynWWFQoFQKEQ+n98TI9W3io2sWo1oga6adFNLMLJKtrYDjoN05Kpu66CkBCVQUqFsG4UA3cRNp0AzEOEoTioFLW2EImFy9/56XYS7ETYLCFpYWMCyLG7cuFE2IGiro5a2GQJP1/0c8P8BX9q1R1oJjah0M5kMw8PDpNNpTp06VTHLthEr0pqmkcvl+NnPfkY4HOb8+fM1t9D629mMLIOBN5UyGOoNIfeRTqfJZrM899xz9PX1FW/ngte+3G34+Pg4rusSi8UqOgX8r70efrMRttoC7S9IbUq6mSRi9BlYmQfHRtouynVQtr2a/qWQlg1SIZWXDKaUQAkNaTugGSihg1VAuZJQdzetb30H0/0DO3CVPPjXSkqJlLLYGVYaEPT444/zhS98gVwux0c/+lHuuOMO7rvvvk3tXOXCbv78z/+cL37xi0VZ5DOf+Qyvfe1rAXj44Yf50pe+hK7r/NVf/RW/+Zu/Wc1p+G+2Z4H/Rym1tOdIF8rrg6Zpbqrp1pJl67+w611A83MYBgYGyOVy3HPPPXWRrY+NKt1sNlsM19ks8KbeEPJsNsvAwAD5fJ5wOMylS5dqur31XSf79++v6BQorfyCRLyToS47jWpboOfm5shmswgh0HUdTdNYXFxcq4EWsojxX3pygmWtVrYuyi6gXIWyXa/CtW2UZSMlICUql0UqDSUVbioFkTjuyhJKD6G3tdP9jj/EUuqmBBCVarqlH+rnzp3joYce4nWvex2vetWruHHjBv/5n/+5KemWC7sB+MAHPsCHP/zhNY89++yzPPbYY9y4cYOpqSle9apX8fzzz9dyPeLA54QQj+1J0i2HjRbSkskkg4ODOI5TdZatv71aX2RKKRYWFhgaGiIajXLhwoW6AmJKUe54gyHlZ86cqSrKsVZ5IZfLMTg4SDqdpre3l+7ubp544omaq+VKZF/OKRCcSOvbtsotTsXj8YoLd7cCNmqBHh8fJ5VKFTVQN5fhoJuk3cmiK4mBRAM0KVGOi3QkKIVSrGbdOqDpXuVbyIMZgVwO5UpEawduJoNoacXo6KTrHe9DC4dxVxdLdxrVLKSlUim6u7t5zWtew2te85qqtrtR2E0pvv3tb/OWt7yFcDjMqVOn6O3t5erVq9y7ScaE+tUbxQVuA/7qliHdcpYx3/cK1Jxl65NutcJ8aejNnXfeuW0p9j4RplKpdSHlm6Fa0i0UCgwNDbGysrJuH9u9KBas/EqbGYKLU5OTkxQKhWLeQnDh7laG7xLRNI3jhw8ipgcRS4vIvMSRAseyKeTy2JaFa9vorkTgNRPpAMr15AMJKAVGCIVAKuVRdSGPm0wSOnaySLiwGut4E+42qm0BbpRz4fOf/zxf//rXuXz5Mp/73Ofo7OxkcnKSK1euFH/m6NGjTE5ObrotIYSplLKVUsVOiz1JuuXe9D5JKqWKVizDMCr6XjdDtQtXQbKNx+NlQ28aBSklN27cIJlM1ky2PjYjXcuyGB4eZnFxkdOnT5edFFzPYlwjiLrS4pSftxD0z2YyGX7xi1+sIeLdGAlZN1yHtuwc2nOjKKcAjoOGi4nC0A2IRlCGgXQd3HwBu2BhWwUK+TxuLo+LQHNdNMdBhKKQSqK1deIuzqG3d2Hu20/Xu/+oSLhQ3Xy07UA1xU8ymWxIR+X73vc+PvGJTyCE4BOf+AQf+tCH+PKXv1z2tbvZa0kIEQfeKYT4PPAXeA0SmT1JupXgOA5Xr14lEonU7A4oxWa+X6UUc3NzDA0NVUwYaxT8kenZbJa+vj7Onz9fN3lUIkzbthkZGWFubo6TJ09y9uzZDXXheuSF7aqO/byFYKVz9epV+vr6ihKFr4cGu9B8Qt7qnLIdhZSwNEHH4iCaZYGpIxwH5ToIxwHpLZghFdJ1kZYFSAwzhLZKXjLSgptcQYYjOJaNnS9g6WHkSgLlKISmo9//JlYyWeJCK16fraxxbAXVxDqurKw0hHQPHDhQ/P4973kPr3/96wGvsh0fHy8+NzExUe1ggnG84nY/0AK07EnSXRM/t0p+w8PD2LbNpUuXGnJ7WYl0S+McX/ziFzck76Ec8vk8w8PDLC8vc/r0aZaXl9e8KOpBKek6jsPo6CgzMzMcP368qoaQ3Ua6lfbn585WioQsbfEtXbjbVVMrlIKlKcTyDMLOIfI5hHJB6SAdWG3hlY7jabdSrnaVCRAmyrVB15FoCMdBM0MIIwy5HHooREjmkYaBao0Tfut7yEq15vr4eQNCCBKJxI7OcatG022UvDA9PV1MKvv7v/977rjjDgDuv/9+HnroIT74wQ8yNTVFf38/99xzz+t/eWkAACAASURBVIbbUkql8TrRAP5QCNECxPYk6YJHfjMzM4yMjNDe3s6LXvQirl271rBqs5R0g2Tb3t6+YZxjpeOttjr19dTl5WVOnTpVvMUfGhraso3NJ13XdRkbG2NycpJjx47V1Opcrlqu4lZrVzRHVPKEBhfuxsfHixkCpQt3jewqqwpKQXIesTwNVh4cG1wbLAshAF2hHAdcp+hKQOGRbiGPciVK01C5jJcK5ro4mSwiGsNZmEXr6EEmliESwwhHaf/9P0SLt1JaM1qWxdjYGLlcbl0LdOlUj0brvtWQrj/LrBaUC7v50Y9+xPXr1xFCcPLkSf76r/8agAsXLvCmN72J8+fPYxgGX/jCFzZ9vwQaI+LA7wC/CcT3JOkuLi7y7LPP0tXVtYb8SsedbwV+JKNP7sPDw5tm5260rWqOy7IshoaGWFpa4uTJk+v01K3a2MAjP38S7eHDh7n33ntr3t5uIdBGIhQK0dXVtcayVZpCNj09va6rzK+KtwMqvYRYmYVCBuFYXjXreg0OSjngKhSGJyk4XiuvkAopXWQuB2goFCqdgkgMMhnQDLSWVqRtIVraUFYBN5dH00O0PfQutHh5SS4UChGJRIjFYsXb6tKx6dvVAl1tgHlvjVm+5cJu3vWud1X8+Y9//ON8/OMfr2UXGp5r4RXA+4CHgf/Yk6Qbi8W4fPnyOi3Or04bRboLCwtMTEzQ2dnJS17ykrrzCjYj3eDi1Uadcf526iFdKSVTU1MMDw/T2tpaV2Kaj70gLzQClVLIgl1ls7OzpNNpMpkMTz311Jr23nqrPplNwMocWiENroNwbVitZoXret+vxpsqpYG7KiUIDaV5nWWYYe96WxbKDINUqHwOWtpw5qfQOvbhLkyh7z+CEW+h7e1/hN6+sbun9DUclHDKTYHOZDJlW6CDH1jVNMLstHuhUVBK+SvxCvhnpdQ/wB51L8RisbJ6q28b20qYi5SSmZmZ4iDGaoLKN0MlJ0QwYOfkyZP09fVt+CatxzWglGJ6epqRkRF6eno4ffo0UsotfTAFu9qCC4qu667rLvNN+3uRdCuhtKvMdV2uXbvGuXPnynqLyy3clftQlfkMKjmHyKXRpI1w7OLiGI5HvGJ1oUw5NuhGsfpVjgMIlOOiCjmUEUZl0ijwnnNciMZR+Rxa1wFvWy1tIF1af++96N371h1PKar9wA+6TIJrEP6HVSaTYX5+npGRkTV6erAyDu6nmpbnRo/qaQSEEL8OXMFbRLsghHgH8LM9SbobtQLXmzQWHMHT3d3NbbfdxtLSUkMCNEpJN+gUOHHiRNVpZrXkL/ga9NDQEF1dXcUPj+np6WLMXr3wGx38jrt4PM6dd96Jpmnk8/l1c8tM00TXdZRSpLYx6PpmwZ9q4Aell6v6ys1yK344RUyiTg6tkPXI1rUR7ipROg5IZ9Wh4IXT4NgI2wYhwPK0W4SOtPLgOhCKoNJpiMRQVgGhC5QRQaUSKDOCOzOBtu8wIpum5U3vxDh4pKrz3Kq0Va4FGn6lp2cymbIt0P5ooY0yeROJxLaH8NeBFuDFQAKvwP0wt5plrJ5MXf+2e3R0lJ6enqJskUgkGj7+J0i21ToFgqim0vU74gYGBmhvb18nizQi8MayLJ599llisRh33HFH8Y3hOE5ZH61lWUxNTRVJJ5vNopQiGo0Wf761tfWmTvPdCjZa3NzQW5xYxsks4yTTJJw8wrExlcIQYAgwNdCV/JWWqyh+7yqBKUHhIkIRpGMjdB2pGZ5TQbqr9rIFVGcPcnEe0d4NuQxa136EdIk/9IeYJ85UfZ7b5dMtp6cHW6B9Wax0urEv4YTDYRKJxG7M0v0u8N3gY0IIc0+SbiMq3dJ5Z3ffffcajbiRkyiEEMW2zXrI1sdmla5feba0tFS0sm2FdFOpFP39/aRSKXp7ezlypLoKKRQK0dbWhuM4xcUOf5EqlUqt6y4LyhObpW3tBtTqKHEdG5lZIepm0Q0N0RJBcw2EayPtAm4hj1uwyFgFpFVASImmCXShYUiJoYFU0rOMuS5KB5VMoCIxVGoJQi2exitdVGcP2A6ioxt3YRbR3o0cHaDlgbdj9p2v6Tx3sjnCb4GORqOMjIwUrVtSyqJEsby8zFNPPcWHPvQh8vk8n/rUp7h48SIvfelLuXjx4obbLxd285GPfIR//Md/JBQKcebMGb7yla/Q0dHByMgIt99+e3Hm3pUrV3j00UdrOZce4NeBDiC3J0m3EqqpdKWUTExMMD4+zv79+9eRrY9GkK7vgZ2enmb//v1bDkWvRJjLy8tFDdqvPGvdxkYIht309fUxOztbs25equlWGhleKW3LD7nejZkL1ZKudB0KmRSikEFzLDTloEvPAqa5NrguhlKYmoYKG6BrEDJg9S7JtQq4+TxZ10XkcmQRCEPHcBxEtAXNtiDSCpoGtoWKtqAmx2H/YVQqgejoBtcl9vq3ELq4cWZAOdyMjrTSfWqatsby19vby7Vr13jZy17GW9/6Vm7cuMH169c3Jd1yYTevfvWrefjhhzEMg49+9KM8/PDDPPLII4A3q/D69es1HbsQQl9dTPsI8FJgFnBvKdLdaAhkkGyrGS65FdJ1HIexsTGmp6c5evQoJ06c2PKMKPBecMFK1x9gqWla1R14tZCu3wnn23H89LK5ubl1i2KN8umWi4X0q+JymQu7oSrejHSllOQyKbDy6HYeXTloq0QrlFtsbtAcTxZQUiJc6flxXRcFmMrF0HVobSds5Ui6kmg4grQKOK7ESSchk/GaH4RAoKFls2j7DqKSCURbB+7As5hXXkn4ym/UdZ43g3SrcSP5mvp9993Hy1/+8qq2Wy7sJhiUc+XKFb71rW/VfLylh7b69yuANyqlpmCPuhcqvcDLVbrBSb4HDx6sepJvPRWhP8hycnKSo0ePFhsOxsbGGqIP67qOlLJ4m1/PFN9qzqs0f6E0b7ieTN6tNnT4VXFwNdy27aJboLQqLhQKLCws7EhVXIl0pVJkMxmUlUNzLQxpoykXXVqe/Us5HrlK6bkSVuWC4jaVQmkaOC5KaCAUSrqQz6OUQk+voMc70HMpwvFOlBlGOQ6ubiInhii07YP0EpbQYWkRdeYO5Itehl1nR9luJV0fjfw//vKXv8yb3/zm4r+Hh4e5ePEibW1tfPrTn66K3APz0f4KeEAI8TNgeU+SLmwcegO/ir+bnJzk4MGDNftSa9LoAt1dR48eXddwoOt6Q4Zmuq7L0NAQhmHQ29tb18LBRqQbbAk+ceJERQtbvZm8jbaMmaZJZ2dn2ar4qaee2rGquJR0lVJkcnmcfA5DOZjSRigbzbHRlINwHYT0/kZ6NjDfh4v0IhiFXfCsYEJH5NMgdCjkQQhUKIKWWUa1tiOUAmF4HWm5NAqBJjREzyGijg0HDmP0P4t27DTO6x8knS8wMzNDOp0udpQF7Vob3ZE1cux7taiGdH3bWaPwl3/5lxiGwe/93u8BcOjQIcbGxuju7ubJJ5/kDW94Azdu3KglSEsCfwyMcitNA4ZfTY8YGRkpBlJspQlgM/jEPjExUZwaXG5fuq7XPEooCD+kfHl5mf3793PbbbfVva1yhCmlZGxsjImJCY4dO7ap9rybmyP8qtg0Tc6c+dXK/EZV8Va1Yp90PbItYFs2QhbQpYMhbfRVYhXSWbWEOb/68uWEVWkBBRRyqzemGqKQQ4WiYNsoIwQIyKaRKEgtoVo6PbIWAtHSjlqYhXAL5NKevcyMYJ46S+S/vBvR0kpHyXH7Fj/fO+uHpJfzW8POjpmH6hsj6kkSLIevfe1rfOc73+GHP/xh8Vz9mFGAS5cucebMGZ5//nkuX75c7Wb/b+ABYApPpd+bKH0TO47D9PQ0CwsLdHR0VCTARsCXLMbHxzl8+PCm+/JbimtFPp9fk5vb0dGxZbtXUBoI2uUOHjxY9TWrV164mc0R5apipVTFfF6/+mttba1iXplCN2MsLKfRlY2hHHQl0WUBTTpoq4QrkAglve8dB5Sn1wrH9kJqhI5wLdBNFMpriJCe/EBqBVraILEIrV1o2SxE4v4BIDNJL6LR0FG2BR3dMDMFiUXCD/4RomW93l9rKFAul6O/v7/qOW6NwE6G3fzTP/0TjzzyCP/2b/+2prV7fn6erq4udF1naGiI/v7+TadS+BBCaMAPgEGl1DzsUU03CH/RampqisOHD9PS0sKpU6casu3SWVTBxbhaSMowjJo03WCA+OnTp4tRjlNTU1t2VPiLcTMzMwwNDdHd3V3RwVEJ9cgLN5t0y8Gv6CppxeXM+sHqLxKJkM67pHKKUNjEEC4GDrpre4tl0kW4LkLaCCRaMXZRgli9Fo4LeJOUURKsPEo3EPkMUjMQqz5d4m2eBBFrhVwaKRUszcOBoxCNIvIFEBroBmpuAnHkNFp7B8Yr7kfbd6im61IpFOiJJ56gu7t73XXZzlCgajJL6mmMKBd28/DDD1MoFHj1q18N/Moa9vjjj/PJT36ymLT26KOPrvEUb4IIsA/4ihDiu0Biz5Kur2/6DgFfR52ZmWnYPvwK1TCMNWRbq2RRbSdZcAErmC4W3M5WKl0/4H1lZYVYLFZ3nsRuJNBGolJV7DsoUqkU03MJ0MOETEFIl2jShrxC00FTclVGcNGkt2CmOQ4ob8HM6zhzPZK1CyCVR65WAWWGEYU80ggDAtwcGBpqYQY696MSS6hIK0rkoLUDlU54o3Yy86hwDGKtCN1ETQxi/G9vxDh9e8Oui6ZpZZsY/JyFSqFAWxk17zjOpq/RehLGagm7eeCBB3jggQdq2n4AOnAdyADngNY9S7rj4+MYhlFXSla10HWd8fFxZmZmqrKZbbSdjUjXtm1GR0eZnZ3dMIOh1DJWC5aXl+nv7yccDhOLxTh/vjZjfBClpFsoFBgYGCCTydDa2romfWunRvxsN3yzPloE1+gk3GZjai66srELGZysg5W3Kdh5NNfBFApTExgoQhpeRKOSCMdCSIlSIFwbpZuAg3AV6KbnZihkEWYUrKwXVuNY0L4PZRU8SWF+Ahlq9RwQoQigUPsOw/gA4mgvwjARF1+GeenXGnb+Usqy1WvwbqFSKFBw4Gg4HK4pFKhaTXe3daP5UEqlgP8mhDgGhIGVPUu6p06dqkhAW82c9bXO5eVlQqFQ3WTroxLpBv281Sxg1UO6yWSS/v5+NE3j9ttvp6WlhSeeeKLmcyg9DikljuMwPDxcDOw5cuRIUQv0F2X8NthQKIRlWQ1LgdtpZAuwnHFxXQdTdwlpLhoOppDoAhxd0RqLoCkTzbG9MTlWHteyyNoWrmOjSYkpQNMEhqZhuC4YGiKXATOMyKWRkRYvO0Epj2CX51GtnbAwBR0HvMq46yDhpXnoPgDTo6ieQx5ZHz+HHHoWcfwsxm+8saHnX6tzodKo+UKhUJRuSgeOlgtKqkZeSCaTDVtIazSEEO3A7wH/O9CKF1O0N7Edo9ODC0v79u3jwIEDHD58eMt2lFLSDboegn7earZTrbyQyWQYGBjAsiz6+vqKt19KqS1XnH6+w/j4eDEAHTx5JBaLrQl88SuepaWlopXLdd2iPupXxrupwyyIbAEW0wrbdgmHXEKag4GDoTzS1ZQL0kbgoLnenDKB16prGDqaFkaEDK/SLWRxHBfpWLi5PFlXYbgWUhgYbh5DKYSU6MklaO1E5DOoljavSaLzAGJ2DNVzFJFdxtZDRPJZOHQSRp5DtXdDOIZ2sg/jtb+PaPDdXyM8ukKIqkKBJiYmiguafjiQEIJ4PF72GBKJBMePH9/SsTUaQght1ad7L16A+fuVUs8IIU7sWdKtBL9BopYXiFKqSLbBhaX+/v6GNTW4rrtmIe7QoUM1Oyyq0Ybz+XzxVt/vIgtiK8TmX6fBwUHi8fgabbvSh4Ff8YRCIdLpNHfeeecafbSSl9Z3DdyM6bMAeRvmUxqW7WAIl7BhYyiJIVyvfRcXXbme20A6GNLLQhCujSZdQHrPrXaUCTuPMExMpUCPQChMxLHBNnClRFoW0rGxrBSSEMbKEm4oRktqGqetBx0Hug/ByjyqrZtoYhR0AQjEsdOo2UlILKD/l/eVdSpsFdvZGLFRKNAvfvELhBBMT0+TTqfXpI/F43GSySRLS0vcdddd23JsDcAxYEQp9QyAUmp0z5JuI0Jvglmz3d3d64LR67V6lUOhUOAnP/nJlrThjRobglMn6p0UXAn+xOPBwUE6Ozvp7e0tViDVIqjp+vpoLBZbowMGvbTj4+NkMhnAu/UMasXbOUiyYMFcSiNrSUzN021DuoMuFLrwiNazgTkeyUqJJh2kdNCk5i2IoTxLGBpowmuC0AxPx7XzKCOCll5BRVu99l8zhK6bUMgRischm0RpcRwlKLT2YC7PkAq1YjgFdFeilubJxtpos7OI1BJq3zG0noOI2y+jHTyxLdflZnSjmaaJEIJjx44VX2vB9LF0Os2f/dmf8fTTT/P973+ff/iHf+Dy5cu8973v3XC75cJulpaWePOb38zIyAgnT57km9/8Jp2dnSil+NM//VO++93vEovF+OpXv8pLXvKSag7fv518Fvh1IcR78BbUwnuWdCuhGtINjuAJZs3Ws61q9yOlrNmaVYpyla7jOIyMjDA7O8upU6cqTp2oF/4CXDQaLSaXzc7O1tzsUc1CWqUOs2w2SyqVWuMZDYVCa+SJrY5XL9gws6KRtSGkS8KGV+EawkFDoisXHRdDuQhcT0ZQEk1IpJJoUoIC4R00IBEScG2v4hUaOBYqFEM4NioU8zrPXC93AcOEUNjz5ra0I+bGMGLtGCET2rpoy2WhoxuZTkJyibweJRdqwREh2qaHSR48Q6b1IK3bNDTyZo1fL91v8AN73759PPbYY7z73e/mgx/8YHHhezOUC7v57Gc/yytf+Uo+9rGP8dnPfpbPfvazPPLII3zve9+jv7+f/v5+nnjiCd73vvdVtSayOhtNKKX+lxCiE3g/8E72snuhEjZKGgsGe3d2dm46FWKjAJ2NEJym0NHRwaVLl3jyySe3XKEFK12/9XhqaqqqRbhakU6nef755wE4f/78mlu/nexIK3frqZQqBl+nUqk1i3Z+Q4Of77sZ+TguTC5rZCyBhsTUXEzN9QhXl2hINF/Ddb0GB23V+qUpd9WL6xGz5tqAQrPzKM0AOwtGCKUMr9VXMxBWDoSGyCZR8Q6v2yyfBV0H23M2SE1HtO/zGiFa2jwHQzoB+RxatAU30kLbzAi0d6AMHQ7chXbp1YgKLb7+VzQarfuD6WaRbjWL4olEgsOHD3Ps2LGqusTKhd18+9vf5kc/+hEAf/AHf8ArXvEKHnnkEb797W/ztre9DSEEV65cYWVlZc3E4E2OXQkheoEfKKW+4z++Z0m3FnkhOMm3o6Ojan+qYRjFW9xq4C8wDQ4O0traWtcQy43guxfGx8cZGxsr6sKNfDPkcjkGBgbI5XL09fWVteLUS6CNsowJIYqtmUHN2nXdol/UcZx1i3ZB875UgskVjURWQ9cUpiYxdRdD80hWxyNUTbjoSHS/q0x5RCz8zASlPHnBl06kRBnmarXrQcsmUdFWRCGNNCJe669helYw5Wm/0o0gWjtQiQVENoGKtUMo7M070wR0H4b5CVT7fsglSbcfIJ5c8Py/r3oT7R09ayb4Blt8/VluuVxu3VDNeDxeVVV8M3IXqkUymdyyZWx2drZIpIcOHWJubg6gOC3bx9GjR5mcnNyUdAMLae8Bfg78rRDCUEo5e5Z0KyFY6QYrznrGptcyHscPEI/FYtx1111lJ8Ruxcrmn0symaS7u3vLNrZS+Jrw8vIyZ86cYd++fZs6REqx0bnthDNB13Xa2tpoa2tjamqKS5cuFcknlUqRSqWYnJxmxWrFNTqJRkNEIzqGqWHoAm1VNtCFRMeXFSS6shFSFgkX10bg/V9qyvuA91LBpDdEUuE5DoTueXGjrV5ko2agZVdQ0TaEWHWRGCGUEUIk5lCdBxGRFi+LIbOCau1CS8yjQq2oxAJq31FEchHNyhG2JaK9E+58GaJj/XyzSi2+pUM1BwcHq6qKb0alW+2HtD/KZ6eOocrXsv9DLwH+h785uEUr3Ww2y9zcHIODg7S1tVWcorAZqtF0gwHiFy5cWLcC66Pe8fDBRayOjg5isRh9fX01baPSdoUQa5LFNppEHMRuDrwpt99oNEokEiWvHUJqGm3Cq2xRBTRlIW2LTMFCxyVkCMKGhqYrdMOrgjW5Wt0KhSYVsEqwSoJTAKUwlQWsfghqGogQopBB6SG09AIy3oVQFsqMeIlgZgSxMo/sOOA1OOgGYnkG1X4AkUt5lfHCJKrrIOQyCAEsz0IkhhtpQ1+aRnQfhDN31nQ9Kvlny1XFQWknl8s1ZF5gLajG+um/prZahR84cKAoG/hDB8CrbIM6sR+kVQX8F/sA8GtCiBEgK4TYgy71AErfyEopMpkM4+Pj7Nu3r26y9bER6dYaIF4P6S4tLdHf308sFiuey49//OOazqEcfNP51NQU4+PjHDlypCZNuBKBblTJ3yzSVQomlzRmUjooCJkKU1MYGuhCw9TD6Ojoeng158BCunlcxyadKyBcF11ThAQYmoYp5GqlohC4oIfAziLV6rUr5FChGJqV8Vp6pURF29GyKVQo6kkH0Q7QQLX3oC1NIjsPI5REtfUgFsZRHQe85LBIDJbnwAx5Pt3EAioUxZgeIdvSiXHltQ25RtVUxclkEsuymJmZaahWvBGq6UYLnsNWcP/99/O1r32Nj33sY3zta1/jt3/7t4uPf/7zn+ctb3kLTzzxBO3t7dXquf6t4P8L/AVwAJgA7t/TpOsjqKWapklXV1dxptJWUI50U6kUAwMDSClrChCvRapIJBL09/djGMaG1XM9UErhOA5PPPEE+/fvryv6sh55wd/3TmJqSTCxbKAQGJpaJVyJJlxMXWEIhRAOYtWJoCPRDRCaiWZoaGHTkw9cB+nYKLtAwXHIOrb3swJ0TaA71qqk4HjEqlxUOIaWXEC1dCCcAioU8VLF4l2IpSlk+0GE0FCdh9EWxlBt+73rF++A5CJoOqq1C6EnUGYUMT2MbO8BIZDt3SycuMRxc/usc7C2KrYsi3g8Tk9Pz6ZV8VayFoKopkiphZh9lAu7+djHPsab3vQmvvSlL3H8+HH+9m//FoDXvva1fPe736W3t5dYLMZXvvKVmvallPpPIcSfAK8DeoCP7mnSDZKtr6VKKRkYGGjI9oNEGezwqidAvBrSTafTxYaMvr6+ho+UXlxcLG7/4sWLdZN5adXqui6jo6PFSQ2+las0e2GnMLsimLJOYS0ahE2BoXtka2gKo7hY5i2YeX9Lb+EM6eUZINGEQlMuYnUUjGGaCKEQkTCikPV8t4UMthIUXIXhWKwULNqERUELE9IEWjiOXsgiQzG05Bwq1uFFOHYdRp8fR8Y7vRjHjv2IVAKURLbvQ2i6p//OjaLa9gEC1X0AclnEyhyF3stYbT2bXodGwtd0N4qD9InYX0fxteJgRGYt1r5q3Cf1tACXC7sB+OEPf7juMSEEX/jCF2rafsnvC6XUHFBk6z1Nus8++yyO46wZxlgoFBrW0OBPfHj66afJZrNlO7yqxUZShe8YyGaz9PX11RIbVxUSiQTPP/88oVCIu+66i1/+8pdb8nH6pOt3qI2MjHDo0CHOnTtX9NQuLCysyV6Ix+M4jrOtCzJzCRie1Sk4GiEzRMSkSLam7skEhqbQNektmgkFUnoLZwKPYAXoitUROt5EXVBQ9No6YIQQKG9Cr2FiOHmUHiYWCiNdl2g2QU6ZhJwcGUeiqxxKb6E1OY8TjqEZYWTnQURqERwH1bEf1Sq8qQ8L48i2fd6EiO5DkFxGK2SRnYegRYeDJ8icfjEimdqWa1gJm+mruq5X1Ip9R8nc3Nwaa19QoihXFe9klu52Qa1WJ2L1k0YppfY06ZbO7oLGjU73hzLmcjnOnTtHT0/Plqq1cpVuMDe3t7e36n1U64LIZDL09/fjOA7nzp0rVgT1hJCXIpfL8dOf/pTOzk7uvvtuTNPEsiyi0eg6G5fvp7Vtm2vXrhUzWP3GhtbW1i15mBeSMDClkXMMQiEIhxW2LYiYCl14VWuxuhWejKCJ1bZdTaHjfa8p17OBocCxEJrmuRRQaMIA5a4OknS98TlCR0iFLUxidhrMELqSEI3T4tioWDftiXmUHsZBkNM6MDMrIBMkRBRTj6DpEFucxGntRmgaqvsoYmUOYReQbT2o1k5U10HE3BgCkFdej8q7O27fct3a9xmsioNZC761r1ICmf9VTTt/PVm6NwMq8Ibb06RbLhx8q5mzhUKB4eHhYjvt8vLymtuoehEkXdu2GR4eZmFhoWxu7kaoJtDHj1pMpVL09fWVzV+o9xolEgl++ctfks/nueeee4rWuEokHqyApqenuXz5cnGOWSqVYnl5mbGxMWzbXvOma21t3XSBJpGBX45rpAsaoZAgGlEYOpg6OIbE0Far2NUmB0OTCBwEeIQrvBZeIVgdlSPRhPDG6ui6N39MOmhCQxUyaOEIILw/QoCdRxkhNCWxjQghK+dpubnU6pyzPCreiUgvY9oF9JYuz97lOnQm5rDDnbiOQ6alm0hqCU26pEUYPRRDtXQRTc4hdAOVXPI03gv3QWsXbmbmppBuo+5QgtY+H8EEMp+ME4kEQgjy+XzFqnivkK4PIYTY06TbSASJMGidGhwcbMj2fanCD14/fvw4V65cqfnN45N3uTdA8ByCEydKUc+k42w2S39/P5Zlcfr0aSYmJsp6katBcLqvD7/LLJVKFXVB38xfGoKTLej8ckywlNWJhgXRiMAw8LRbHUxdYmqga9KTEsSqZiukJyH4li/poOtilWxBg1UHg+NVsq6Fphko6SIME6U8ksZ1wBAQjiFyKWCVoIVAs3LISAt6Ngm5LCra5jU6uA56HoJgbAAAIABJREFUch7Z2u0thnUfwViawlAKZcagYz9SDxFfmsRFRyTncIQgJcKEDB2MEPPhbuLLyziOs6dJtxzKJZCNj4+jaRqtra1lq+KrV68yOjqK67p1Hd9zzz23ZuLv0NAQn/rUp1hZWeGLX/xisdj6zGc+w2tfW79TZHVkj1BKucDhPU26jVicCWYXnDhxYh0R+lXhVl7kUkoSiQTLy8ucOnVqS11k5Qgz2BJcDZnXQrqWZTE4OMjKygp9fX3F1etGOxGCXWal0ZB+9TM4MsXATIysihNvCRONhFCYGIZB2NRW9VqFLhSa7mu1nsQAEpTE0EFDgQJNgYZEuban77ouCBCa5nWKaWL1OQGugyZMLyPBzoMrQNNQegi9kEOaYVAOKp9BExoyGkeYYbTUIrLF0+jdrsNoy9OA8Bbi2no8iWJ5BqVpaCuzYEbRWjshHMHQdLoWplCaRvJFr8FQBvPz8ywuLiKlZGlpaU3+xHZZt+DmjV+Px+MVq+Lp6Wl+8pOfMDAwUGwW+t73vlf1Ive5c+e4fv064J3fkSNHeOMb38hXvvIVPvCBD/DhD3+4IecRsI/BXh7BvhGqIcpqswt8jbgezTG40BSNRjl+/DgnT56seTtBBGUKpRSTk5OMjo7W1BJcDen6joTp6el1Ekg9lXK9MAyDcLSd52Y6mMtqtPVodIcEQkiEsgCLQi6DnXO90TmmQTikrboSXAxNea4DpbwGB6VQrI7NEQppWRiGDtJFEyAEKKuAZoZQqxWlAjShUHYeYYQQRsjrONNCCF3HEArXdVDhCEI3EekliLV7628dB9ESc9603rxAtXYDApGcBxVBW5pEReKocMzbt2GiLYyjzDDKBdl1CHWkj5aDx2jBa1EdHx9H13W6urqKenmwzbd01Hwjwm9uFumWO3a/Kv6t3/otnn/+eV73utfx9re/nVwuV3fb/Q9/+EPOnDnDiRONTWlbDbu5b/Wf08BztyTpmqZZkShrDRCvh3T9lt3BwUG6urq4++67WVpaqinHoRL8/IW5uTkGBgbqagneiDSDRO5POi69PjvV6GDZkqcGvEAa09QJRzVCpkbYVOiajq5HMHRWXQkSj6UslLTQhEs6vYIhJKapYxoaIV0gNTCE8prGlEJoAlAox/JCZ1wXzQh5mqzQkIUcejiK0k2ELHjtvLrh5eNKFzSdvAgRkzlQYZASt30/WnIBhIZWyHhWMYHnVgC0lRlktNVrAW7r8qrl5WlUNI5IpZHdR7xQnOUZhHJxTq71nEspCYVCZQPB/YYGf15ZJpMp5k8E4zHrCY2/GePXq7GMXbhwAWBLjVCPPfYYDz74YPHfn//85/n617/O5cuX+dznPleTRdRf6BZCnMTLXrgHaAGiwD/tadLdqBXYj//zIaVkcnKSsbGxbZ3k63th4/H4mmCdWpojNoLjODz99NO0t7fXPViyHOn6nueBgYGiI6HSB812k67rKq71u4zO6RimTiSqY5qCsLlKsKv6rddVJtE0T0IQGhiGiSZ0pFOgsz0OSqKkhXJtCnkbJZ3VRTUNUwdNCEKGhqZ71a4QApXPokWioGnohomyLe955bUAowREWlDZJCIUQ0dS+P+5e/cYy87y3PP33dZa+1q3vnfbtG8NtuMb2HgyYo44ykAyDDgQTkiUSAlKAhFhIkggCaORCX+MchyNSDIaNBrCQARIIJGZY4HOKDlxOBACCdiADcHY7m73xX3vruu+rtv3ffPHt9fuXdXV3VXVbYP9Sq3evat61a59eda7nvd5njduE2f9EdAGLhc89JdCpu7KSKcrFb4R/pYr53D1aUTaw83uCRsoskEI0Vk8BUB5538HcvVJ70pXcZez+V4uNH4SiH+SofHr1UaMD51O55oHaXme85WvfIX/+B//IwDve9/7ePjhhxFC8PDDD/OhD32Iz3zmMxs+3oS66G3Ajd77N1VfE0L89csadC9Xk7KxyUv8HTt2bLor3GiQ+fLyMocOHSKKIu66665VQ6LqONcCut1ul4MHD9Lv97nlllvYu3fvlo+1VjJW6XjjON6QdfrFohesc/zwsOX5MwIpNXGiiCOJMpLIEMwNmhFvS+Bu5egPPhgapAt5tyM+V0hAKpQR+FKhdeBpcQ5nS7wNdt9EC4qixGhFJCWkA1QU47xHChGGaVENsi7oUe5tfRo/7CAE6LyPTwLIU+YBaDsL+Pp04IZrrTCw687jGjNQZLjZ3YhhH2FzvCA42IQKj23bPvzUNvzcpT7/zc4YrhQaXw0uJ0Pj12poX8zQ+CvVRtevX6tO9+/+7u947Wtfy86dOwHGfwO85z3v4a1vfeumjjfx2ZoFTq358umXNehertOtNKNnzpzh6NGjq1bwbLaupvvtdrscOnQI7/0VMxi2CrrD4ZBDhw6RpikHDhzg/Pnz1y2Xt1IkFEWxSsd7tbrena73nh8dLnnuJHipiBODiQLQGi1Q2mMkyFFmghCMTQ46OBmQeLQahdCIMEzzeFxZYkYryrQWCDzeebSWoSs1MbUkRjpLJAYhZ8yV5EWJzDNK62logRcSLRwibiKzQXgQww7EDcp+D1M570axjMI7fNxElCkiH+LqMwhbYmd2I3tLiCLF6Qgx7IQQHOdwc3vAgVw6jc8N9u5/v+7zdb1iFivL/KQZxzk31tBOhsYPh8PxmqbrERq/kdqoOeJaYx2/+MUvrqIWJvNyH3300U1HCkx05/8V+C0hxB8D3wf2AHe+rEF3vapcMM888wzbt2+/alD51epyoDsYDDh8+DBpml42d3Yjx7lcTaoGJo0T8/Pz19xleu85efIkRVGMFQmbqev5YXv6cMYzxz0FhjjRRHGIWow0GCVQihBQo0HJ0OUKEWgFLT2iSv/yDoHH+QDQEORfWgfOFm+xeBQglQiOMymxWYoxJgzL4gRZ5MgoASWQUuGLDOsFokzJS4fOluk5gZFQEx5XZCQU5FGDSAh8UkekPUSZ4xtTeB/ham1kd36Uv9sIgFtrg1DBaWYLZHcBH9eh3wHA3XQPxOtL8l7MbNtKojXZPDjneOKJJ5iamlp30/Nms3k3Whv5Pa8VdAeDAY899hif/OQnx/f98R//MU899RRCCPbv37/qa5sp7/0/CyEU8G5C9sIS8MgrBnS99+NMWwiRbDfffPM1H3ctWFZOtU6nM7YFbwSENtrprl2/s9Y4cS00RaVIeOGFF8YnpOsBoNZajh07xunTp1dt+W21Wpcd2Bw8PuSpg5A7RVKPaBiNiSXGBLDVKvC2UlwEWyUDhYAI3S3ehywD4VHa473DqADAUgik9JRFUDUgZHCdSbBFMeJoQUcRLs9QWoN1SKXxRYbUMb5MQUl0kSNqdYy1kDnaJoZsQEoNbI73iiTtAJ6hiNC6hqy10dXgTGtwFtecA+9xrTlEkSMGS7j6dABcwEdNiBt4HeFuuOOyz/dLHSjunMMYw7Zt2y5xlk0G31TZvOuFxr9YXXGv17umQKh6vc7CwsKq+z7/+c9f68OarO8R9qQNgRKov6xBt3ohFxcXOXz4MEmScPfdd7O8vLylNTvrVbUGOs9zjh49ysLCAjfffPO6FuQr1dXA0jm3SlVxOQnbVvjUtYqEm2++uZqubuo46x234sv37NnDa1/72rGuttvtcvr0abIswxhDq9WiKAp+dHCRHx81DLwhSSIakQrdrZJoLTAqiAiUDgleslIajOy44JE+mCDESGyrZeh2vQvfU1gHo3Dx6krPuxIpRaAWTITNU4zWeOeR2uCLDGEivM3DsCvvI0aX/Z4caS2+zBG1NrgCohpJ1kN4z0BIXG0aLyT1wSK2LPFpF+c9HSJip9BxCzNYQboS3wguNS81KI2b2YMH1PJZAMo7Lh2eTZZz7iVVElxOLna5vIVqaFcpKNI0vWTTc71ev+qQbKOpdT9Nwz9YpV54G/ALQAtQgANmXtag673nySefRAixao9XxUldrzp//jynTp3iVa96FbfddtuWXuTLgW61kfjo0aPs3LnzqlGLm+l0JxUJs7Oz4yHiqVOnLrtHbqNVluU4e+H+++8f75PTWjM9Pc309DRSynFQ+rPPL/KvPx4i4xRtCmr1iEI5Im0QxERGojRoFSL3lQzaWCnC0EyOul3wY+1toA2C0aGwjsQI8IH3hQCwo2ciDESLDKMU3jmUNrgyRyqDcwVCKSiGoCPkqIkWzuLLAhk3cK4AEyOHK+GQcQMaYVtsvb+EswWqGOJ0jEiayDIHZ5kedBG+JHMG4Up6XmN7A+KoAVJT6y3gEYgRyLr2dvzcvqu+ri8l0Gwmd2Ejm55PnjxJv98Pz129vgqMq3nFZhqLl1rKdrWaUC/8r4R0sW8QIhcM0HxZg64QgjvvvPOSwdL1CL1xzvHCCy9w/PhxarXaNS9+XKsYmATE6elp7r///g1xz1LKDQHmlRQJ16I+6PV6PPfcc+R5zute97qwb8y58SWo9x7n3DiF7My5Af/6g4KlQYKqzbFr704So1GRgFHQTFmm9PshBcxEGmMUwmuiRI3AlxBYI8COullrXZB8qYCQRkJpHUZ6Sg9CeJQSlLkFJF54tNI4m6OUxnsXXGFlhtQG4TzeC6QtwrqcuIl3BcLEkHbxQqJMgquFIZlIu2ASGHYZojA6QSoNUiJ7CwG0TQza4GotImfxmaCuI2R/CSsNMg9JYSteI0uPSKZZrO0mmp8fA9B6gPKToBeu1RhxpU3PvV5vVQZHFEXU6/XxUK9Wq637+77UHf9Ga+Kx/gvwGe99Z/LrL2vQBcYf+sm60kbgq5VzjtOnT3P8+HF27drFXXfdxenTp6/rm3wSEO+5555NZRhcrdMdDAYcPHjwkmSxydoK6FYhOr1ejwMHDvD000+Pj7OWqlBKsbSc87XHu5xdAqQmtznTs21qiUYpSaQFSiuUisKwTI9cX97iXAkuo9fNEUKgjSLSoKShFgOIsNNMWorSExsoS088ejeH1Tqjrlfr0IVWdjNpcGWB1AopLc6CskUYpEUJviwQWuOzXrDomgifNJHO4rN+kI3lA3zSAjwiquHyEp2H78eXuKQVADkfhnzefIjIh+E4vUW8EIj6NL7exjpHe7iCsCVFewo9vX2VlraiZiZDgF5q0H2x3GhX2vS8sLDA0tISR48eZTgcIoQYS9mqDI5+v3/VjS0/4ToAfEEI8Z+A08ACsPKyB931aiud7uR69kmJWa/Xu275vNbacbThRlb8rFeXA8z1MhI2e4zLPebJwd7tt9+Oc45du3bxgx/8gLIsxzGNrVaLKGnw7e8POXzSoxJJYXOSWLJn53ZqsUbp0RJIPcpIkGKclyCkRyBBR2hpgNpIj1tiXQkuZ9DLEdogbYo2MZH22FKiRADeSDMC9cDXCuFASJyzAWhFINaELYIMLIqxRYGSIUkMZcJGh6iO9CU+HyBNgrcl1NrB/ksI8xbDLiiD8SW21kZqhRj2AI8YLAd6Imkjegu4qA4mDlm53iP6iwBBXmZLPCBuvIsdtdaqy/LJEKAqo7jKX26322MAup6qgfXeAy+VBbjK4KjeT3fcccf4MVS04fnz53n00Uf54he/SFmWfOxjH+Oee+7hjW9846aUDPv376fVaqGUQmvNd7/7XRYXF/mVX/kVjh07xv79+/nSl750LeqIHwN7gd8CmgRu9+U9SIP1+ZzNgO7k9ol2u32Jy2uzjrT1Kk3TsbzszjvvvKaQ8rWdbgWKZ8+e3XBM5EZAd3JItnfvXh588MHxz6ukNDfddBPeewaDAZ1Oh3/6zgLPHpkHHRElBiMM09Mtmq06cWyQOkjAlAzAqGSgAaQUCAkCF/aYaYeshiTKE1JtDbFR+DhCS4/3GucshXOU+QDvPZFRFLnFaEXpXABapZDKYUuQZYkDtDHYskALSZkNkdqAAGcSlLO4PEUag/cSUWvj8mF47l0J+RCvY4S1wU3mSnRZ4HHQD3SBiGp4WwZXmvfYWiuE3/RGQGsC1eN1hOgv4WtTuKkdULv0JBxFEXNzc6viOZ944gn27NlDv9/n7NmzdLvdcUbxZADO9Vok+dOQu7A2DvJP/uRPePOb38xf/uVfcu+99/LUU09tSLq5tr72ta+talAeeeQRfu7nfo6PfOQjPPLIIzzyyCP8+Z//+ZZ+B+/97693/8sedNerjQ6blpaWOHTo0Fj1sN5l/rXww1WU48LCArfccst1EXJXgLlWkbAZzvlqIeYLCwscPHhwbAeezCheSyUIITh+2vMv35dkfgetbRbnCqZmpohijcCRZ33yAoyRxJEhNhopdRheEa76jfJA6Hol4IUM8jAfBvlaObwLFERWeuoGnJRoBypqoGTgeYUvsbbE2ZK8VOESX0i0AJQiUoIiz9Ha4PGgY4TNsc6iTISXEkyEy4bgbAgltwWYJGgnalOIMsWVBUJpRNoLz6VzkLTxyoSBnAguDjHs4IUcRUCCixshv2Fq5HrqL0GZ43fs39T7YGpqapUTqzr5VfzoiRMnyPOcKIpWAfFWTA0/CdDdiBut2+2yb98+3v72t/P2t7/9uvzcL3/5y3z9618H4Dd/8zd54xvfuGXQFULEQAVE46nuKxJ0r/am6nQ6HDp0CCklt99++xUv87eii51MMJtUPBw7dmxLa9gnS0pJv9/n29/+9ipFwmaPsV6nW1mNlVLcfffdq4Zk60nMzpxL+dq/rrDY11ghKO2Q1lSb6altKB0st0oJlBbjrtbZEu8tWdYnTS1KSmIjIVIgDI1EUlhBEoVEMIdHSRhm0KoJnJNEwmJHyyatDwM554PCwTpFZAS2LEiSiLJQKGFxzpLlObl3I+nYMASWC4HQEVJIbNYHBIqg+VVxHY/HK4PMBzjvkCYJICkk0jtcY4ZOp0vLgysypLch+tEkMFgJVEatjatyG8oiGCeKDDmSnLmZ3UENsYla+1pUnGej0RjbWCt+tJJvVaaG9TKKrwSqW9kaca31UqzqEULw5je/GSEEv/u7v8t73/tezp07N3aj7d69m/Pnz2/6uNVJynufCSHkmmjHl3+nu5mz9uRyydtuu21DL9hmjj85hFsvoWsra9gna3Jrw4MPPrjlVKW1oJtlGYcOHaLf73PgwAHa7fZYhbAe2HZ7BV/71yWOnxM4qcjzlEarxvbZWYzWGCORSqIUo4FXCBmXEqSJUMqjVEJoED1K5OQWlB3QWSmRSmNzB6pGIyqxVhEbyEecbVEKjBx1xir8O1EehERRUFiP0ZqydCGkHI1AkkQC7x2RDp27tSUaFwZWvsR5cB6M1kidICpaQesQnKMjvBD4WhsB2LQH1lJXPmyjiOswXMGP+Ft0FMwTWT/Yk+MmIh8AILRBpD50zzuu3cSzXk1mFE/SE5NJZKdPn6bX662Sb1Vd8aR868XkjNerlyLs5lvf+hZ79uzh/PnzvOlNb+I1r3nNlo9V1dNPP803vvEN3ve+9yGEeLP3/h/Wfs/LHnQvV5OZuhWn2uv11l1fc601GeW4bdu2y3afW3WTTSoSbr311rGMbatVPTeV++38+fPjTROTne3a7sZazze+s8iPjhS4kSIh1podu7eRxBEmCmA77nBlCIuRo1SwYGcIQGMtIKAWgScabX3QFA5qxlGWFnxBWnh82UeoCC1KrAoxjVkpqRnACxIjyAqPL/oIIanFUVASEHJuBRAZifMG5R1lmYcBnvAgDbGOcM6ivMeUKd5ZbJHiBQwLR+RAqwTtPS5PEdoE2oIAnmWeI2utYNaImwilkcORSiiuI3xIDqMMhh2nE4RQ2OmdocNdh8t9MWu9JLJJ+dbi4uI4cyGOY6y1tFotBoPBixqUPlllWV71Pb68vHxNq7T27AlhQjt27OAd73gHjz/+ODt37hxnL5w5c2bVUHMjdfDgQf7mb/6G3/u93/tt4DeEEN8B5oA+kAODlz3oXinecTAYcPLkyfG+szvvvPO6v2EWFxfXjXJcrzYLupOKhAMHDjA3N0ee59ecvSCEoN/v853vfId9+/ZdMiRbr7t98kcdvv2jAaVVZNbivWNudppaM0FridISJSuwDZ1t2FISzA4eQekgMWGQVh3deoK0SwHCUzOQFVCPJVkR0Yg9ggiBIy1M6IqLkA42cHVE2QMEZZFSb7SIjAxdkpYYJSmFwQhHXhRh7Y2zSBMj8JReYKTAFgEMpbd4HQdTh7N4W9I0DpylsA5DkCF2+0OU1iitiZzFxHGQgWUhpUtWv5zU+GEXonqgG7wLnK82MFiBAth5yzW9lterriTfOnjwIEVR8Pzzz68KSq864qvRE1upjVwRVlb8rVS/38c5R6vVot/v8w//8A989KMf5aGHHuKzn/0sH/nIR/jsZz/LL/7iL27quPfeey9ve9vbeOKJJ34d2AX8EUG5kAIx8M2XPeiuV2VZkqYpTz75JLfccst439lWa71NFBUvrJRatQL+SrVR0L2SIuFaIyLn5+d57rnnKMuSn/3Zn73ikAzg6Ikh//TECp2+IrdgXcbUdJtWu4HWCqkExgiUkqPuOMyQQDBaG4bRgPDEI6ODdSElzBPiGZXypIWgEUNaQBKF74m0YJhDM3Z4L6hHkmGe0Kp7nE8oy5RS1xCuIGm0KPKcIivRJsa5DKsMWnqcFOgowdoC6yWxFJR5EcDRe2RcR3hHmefhZFDkgEdpBXmBEwqTGCxhu0NDBrdj6RwCR2ktsugipSD3Ybgn6rNhzU/aC7vVbDkasCkYDdWotcOfn9KapCd27NgxpuMmrd6nTp1a5S6b1BRvdtYwWS92wti5c+d4xzveMf5Zv/Zrv8Yv/MIv8MADD/Cud72LT3/609x444387d/+7aaOe9NNN/HhD3+Yj370o/8ZmAEeBXYCdeAGYPEVBbqTAyxjDK9+9auvWS0Aq7dHTC5oPHDgwKY4pasB5qQiYe/evesqErbqJut2uzz33HMYY7jzzjt59tlnV3G2a8F2cTnjH7+1zKn5kNyV2wHNRoupmTm0UShV8baj/0+QfSHCh1WPbisVuNysECSRp7SMwshhUAgSHQC3Fnmsg1h7+qlnqgbOh863m0qmEodH0Ig9vczjiwFKK2qxQghFXjoaDUVROiIN1gpsWeKkREqBzVaI4xjvHWUpEDpGYCnKEi08ftTtGiVxaKTSODzOCJSUuDwNHbkYDaGVJnIpXip6qaXdnqZwLuTsYimyIdoVKBGAOCkCHUGUIAZpuD1zaVbuT2OtVS9MWr2rquiJbrfLwsICx44doyxLkiRZNbTbaPjNiz1Iu/nmm/nBD35wyf1zc3N89atf3dIxq6rVanjv/0II8SoC4PaAZ733/y+8AjjdqguttkJUu8IOHTp03YK2tdZjQfrKysqYF95s93yl/IULFy5saP3OZn9mxWcPBgMOHDhAq9UaX1ZVRo1q+V+r1cJEdf75iS7PHSvwQjDMMpJ6zM5tOzGJQcnQ3UodXGFCCIQKdAIClLg4NMsKgTGOLJfUYocQkPuw3ywtBM3IBaA1nm4KM/XQ4TZiWEkFUwl4PK3E080C1zvMM8oio15vYH0YjOWlpx7LkBqmFXlZAJI4iShLRxwpvG6PpX+lE+SDLvUkwjtHYQuUjsLGZlvggAiwRY6Q8iIgxzG+zEIaGIJCaFxZ0kiAMkeoCDVSCEVxDZGOQneEx3lInSDv9lAqQZqIwdDR1JvjSV+KNUlrayPqhcvRE1mWjc0dZ8+eXTf8Zr2NFS9Vlu71romwmzuB3wBeQ8BZJYT4AvCFlz3oVsEr27dvXwVW1Z6063H8fr/PD3/4Q2699dZNp4tN1nqa35WVFZ577jlqtRr33XffNQ3IJmtySHbLLbewffv2VUOyalJb+dtXVlZ47J/P8KPDBZ4YHWlMHNGemQqOpygMsIQKnZ9UgUqQUoz2lAm8AOsFGk9WCBqj7tQFRy6FEzSMxzlBYhxLA8m2psVaQTP2LPUFs02PdZJW4uik0IqD0FH5lGEmkSqi3ogQCBqRDxIx7RlkFik8kXRopdAq5DQgBHkRdM2J0RRFMFu02m2cA1yJd5bSOYpR8lhRFKjg00Apg9ARDkaArFHI8SDNeocZkbiScJL3hE0TrjYVwmxsTmkNJkqIRrxvGrXoDwacG62ar4Bokie9XN7ASy3f2mr2wuRa9cmB12T4zdqNFdXvXxTFS7Kq53rXxHP1PmAA/Kb3flkIcS/wvwEXXvaga4zh9a9//SVnxWpP2larCrw5efIkxphxdu611GSnWykSrLVX1QpvpiYpirVDMriUt5VScvKs4BvfFQzSWertEmstzakmkTE44en3u8hUEMWaKIqIY0Mca5RQ2FHgYukABPXY4widbmkDcNVMAKNEOxb7ku1tR2kl0zXHcl8y27SUTtCue5YGknYSjBCtGPo5eFtilCSJDEZDWkKiPf0s2IeNhtgolAhUhVZQ5uHklkRgy5Df4AAvDVJBmpcoIcZAGUcRpfAIaYiUJrcOZy2uKBBCkOcZNRNAIBv0iUOUGYkEpEaYBIfA63D5XKZ9sCPDRWEDuW2rE64gmdvNPqXHSWxVNm232x0DUaW9rSyxzWbzJwK619sccbnwm8rme+HCBXq9Ht///vdJkmTVHrdJeqLb7f7Uge5EvQr4v7z3ywDe+6eEEEvASyy+e5GqSrearK2C7mTUYrXA8siRI9eFqlBKMRwOeeaZZ1bRFNerKoqi2kB8tSHZmfMp//jNJc4tC5yDvBzSardotZsorUahMAKlFMaAd47SlZRlSWclxSGJo/DHaI0yhtKFYBkjg73XKM9iX7KtZSmsYKbhWRlIphvh3+26Z6kvmamHS/B27OmlYBT4YgB46vWErDAo5ehlHiODLtbo4FpLi8D9DoqwPUIL8MaghCcvHdY5YucprSPSF1PajBY4IoSQFNZSEhMriS2yEFGYRJR5oBZqUdj5M8wyBII0D48hiWOkEChHkIR5j65AUUj8WJcbXdTo1tvIKB5qmbXMAAAgAElEQVQnsVWv0aQSoALi4XBIt9vl7Nmz4xyQoig4fvz4JXraF6teCkfa5MaK3bt3s7y8zAMPPECappdk887Pz/OVr3wF5xxPP/00d9xxx6aGdidOnOA3fuM3OHv2LFJK3vve9/KBD3yAj33sY3zqU58ad+V/9md/xlve8pZN/x6j+lvgPwghbgCOAtuABDj+igDd9coYw2Aw2PD3r41anNypdj3yF6y1zM/Ps7CwwGte85oNZSRstCaHZPfccw9xHF/RSdYblDz2zSWOniwonSPNcmrNBjvmZtCRRmuFNkH6hZQhT1sEWRje0GwEEVhYY+7IraOwjqwYIEWJUdAVbaaTHGsNMw1HLw0dbFaKQB0MJVOJxTpBK4HlISQaYu0QLqWwIHST6Zqln0E98niCxEtIRzeDVuJJ82AbFkIQGY0Unn7ukMJjpMM5Ty2S5EWJEDJ8KEwMHgYj8E1MiIUEj3cjHlZKijxDKoP1nv7QYuKEpNUIuQ3eo3DgA6hLGzritChp6NHzLeVIEzcyQ8gmXmr09A7UBEhUoGutvQSIa7UaSZKwc+dOpJTjQW4cx+sOrCogvp7bGl7q/N6qhBDUajVqtdoqemJlZQXnHN/73vf4+Mc/zjPPPMO73/1u3v/+92/ouFprPv7xj/Pa176WbrfL6173Ot70prCw9w/+4A/48Ic/fE2PGcB7/zkhxBzwP3AxxPyPvPc/fEWA7nqLEjeTmbC8vMzBgwdJkmTdbbgb3Qi8Xk261Kampti7d+9YlH0tVQ0pDh06xHA4HA/JruQkK0vHP317iR8eGmKdJM1SdBSxffd24ihGaYFUKuhtZeBvEeCcDNdEXpBEF11gSghKL2nWg1W3k9aYrjnyEmIKhqVAuS6DNCGWGSs2ph45siL8vdiXtJKwaqceCfLCkllNohRKR8TakVsJePIyUBitxJEVUDPgvECIwOn2s7BNonJc1gw4F2gF68AiiJVgkI1ohxHmKSHGnW80uvCTUYzHU7hwpSBcQVyroWSwFwOYyIR8BkK3W91u1BKcLSkcFFkBwoQrhaIMEjLvMGt2n1WAthbYqhNn9ZqWZcnS0hLGGObm5sZ784BVdt/JjR2Tq5NeimWS16OuNiycmpriLW95C3/+53/O5z73uU0ff/fu3WOrb6vV4vbbb+fUqbVLe6+9vPd/Cfzl2vtfEaC7Xm0kU7fX6403+V6JV90KVbGeIqHb7XLu3LlNHWe9klJy8OBBFhYWxksrr+Qkg2Bu+Jfv9RjkkBYlQkB7bpZ6LQnSL61GllmBG4W16NGSnCQOA7O8DB/Y3lDQboSdZXlWXeIHWVduBUns6QxjpuuW0kUkxtFJYxKVMygipOuSDT0owzCP6OcSVS6jTcRUXdDPE7zzFFkA1qmaw3tIBKSlJLeglWWYQd3AcHSpHxuBG8WJ9HOL99CMIS0sQvhRNwuxkRRlgdIaLQXOaQrryJ3HOoeSHj/qXJVgHMyjlaSsLnhsGQJtVLAGk7RwCIo8BaXRsUYWQRqmJhxsK2nJM9/9LsAYENvt9rpLHaWU49dyMBiMr2YOHDhQefvHHXHlMpuZmRmfcMuyvCQWsrqM32juwk+iNjK46/V612UOcuzYMZ588kkefPBBvvWtb/GJT3yCz33uc9x///18/OMf37I6QoSzW7BFhj/Ce2/hFQy6V+p0KxlVlTWwkU2+w+Fwwz+76pxrtdoql9q1GhuqLb7dbpcdO3ZcdUgGcPTEgK/+yzJLXU9RFGRFyfTsNI1mHSlViFuUCpB4JEKCFKGjxUMxGpB1B4KZVrjE1zrshuxnkmbsQvaBcSz0FdvbFmthuu5Y7CtmGpZhIWknntxGGAPOt0eDspzeYID0JS6eoSh7rAwFeIsxOUoZjBKsDGXYKqHCcK6VOLIygKyQAiElRjm6aeiQ6iaAtFGQjsC3FimcByEF1ntyJ1Al5OFzQBJd5HrTQZckCVc7WgmsBYHEe1BRuD+zFisMkdbkIzNFbEb74RmZQEavgfSOaiKwZ//N3HBbPM6H7XQ6nDlzZlU8YyXfa7VaaK05duwYFy5cuOx7dbIbngRiIcQ4DrEa2DnnxsqBSWPDpHKg+rk/qdqoXGy9gP7NVK/X453vfCd/9Vd/Rbvd5n3vex8PP/wwQggefvhhPvShD/GZz3xmS8f2oV1fF4BeEaC73iXTep3u5HLJW2+9le3bt2/ocmujVMXVFAlbpSkm+ea5uTmmp6fZuXPnFYdkSysF/+WfFjh+usTjGGY5jVaDmW3bkEYhlAofRClwSLSSRBF4H47lEfQzwXQzyL2SQIPSzwTtmsMhacSO813F7mmL84JtTcv5jmZnu6SwMNe0dIaSWHtWhpKa8SN6QeDckJWsTiMB6zU149D1OmkBGsfQ1hFFNur0BML18ERESjHIJYXVRNrRTUPcoxgl59UiT1F6olFYemEV1gae1/lAO+TlqOPVgqyAKqMBwDlLvV5HKn0xo0IYjJZjDjg2EutGioQqQMp7bJGH1wMuWoulwlW3TYwy8fi9MJkPG372RYPB/Pw8Bw8eHOcd7NixY+y0XLthueqGJ7vDtUA8CcaNRoN6vc7u3bvHv2M1sLtw4QJHjhxZtdW3LEuyLLtu+bxXq42C7rUoF4qi4J3vfCe//uu/zi/90i8BjNPZAN7znvfw1re+dcvHF0LcCKwQut0CyADnvS9fEaC7Xk12ldXq8TNnzrB//34OHDiwKW7raoO0PM85fPgwnU7nioqErQzkOp0Ozz333Hi1TxzHHDlyhCeffHLcFVV/jDHkheMfv7HIj4+kWAv9NCWpxczt3EEUaaRUI65WIo0Ik3YhkEogBAwzQbspKC00E0AER9h0PVhx2zXP+Y5mz4wlLwU72o6zy4o9MwF4d7ZLznclM3XHMBcjCy9kZViJvtBTaLtIFNeYSiyFNSNHmmN5EPS6WWGItadmDIUN0Y1p0aawoMs+XtXwZcog1XggUTZk2wpBXnqyUhELz7AIQNyMYZhXz2i4TwnIihIpJXmekqcFJmkQxzHD3IINcrOiDP9HVVcTMD6ZKyUvcrxGB9caISTd5WV4TDoKe9KQmPqVV4VXBgNjDAsLC9RqNe655x6EEHQ6HVZWVjhx4sQYAKtueD2+9nJAXP29tiuu9LQ7duwY5y1nWTYeWj3zzDPjfN5JCduLEYCzEdBdXl7eshvNe89v//Zvc/vtt/OHf/iH4/uroBuARx99lJ/5mZ/Z0vFHuuP/hRB08xyh4xXAj4QQ//aKAN31XvRquHbixImxZnVt1OJG63IdalmWHD9+nLNnz25oLftm6IU0TTl06BBpmnLgwAGazeb4Q3LTTTdx0003jTc2zM/Pc+TIEX70nOXImTpShQAWHWnmts1i4hip5AhoQ9qMVhKjQ0eb5pKmhrwUNBvh0rifSWaaHucCVbDSl2ybCjrYbS3HfFexfSrIvnZOW852NNsaJQWwveXoZ4LCCgSOYSGYTjL6vRSjBKWaYWhBW8cgl0zXAwXQioNsrHCSugzmCSEc9dHQqxE5rK+hlUDFCcPcY52nV0goJT5fQccB2KS3WALfnBUXuVxE4FiV8PRzC06ghEPEdaz3FKMuWEkoipG7TKsxuEZGje83UlBYQlcsJTKq45DkQpDLYNGLvKd6yZvJlY0vFX108uRJbrnlllUJV7VabVUnlmUZnU5nPCeogmgmqYm1BouNDOy89+P3qDGGdrtNvV7nrrvuAhjzxJM/d6PGjo3WRmIdr8UC/K1vfYvPf/7z3HXXXdx7771AkId98Ytf5KmnnqLajPLJT35yS8dfWloCuBN4FlgC9gH/HvhvgdlXBOiurWrfWb/fZzgc8uCDD15T+MZaemHSdny5jIT1aiOgW5YlR48e5cKFC1cdklWh1d1Bm+8+s8RK3yNMRmktcRIjhKQ7GBDZEmMiklqEVhKEorQhN6GwglYFtLkgjkNXOt3wDHNBq+7pDCXtRtDPNmuefiqZaTjOrSh2tILsa1uzZHmg0BKSyBEpaNcsg0ygXJ+ltInWEUaHPdTtxGK9pB55uqnEOsFsw9JLJUY6hnlYud5KwDqJUZBZRVYKGrGjX4ST21TN0c9CaGRca1I6kD4ny0OugrdFAGJpQtaCUwgcghIh1Xj1D0ASCYoRQEdakI/YKSl8cMUpFbZQmITSwdB6Cq8QFqStAnwAH6gHJcWYtjDGXLF7q65opqenef3rX39V0InjmO3bt1/i9KoA8dixY/T7/XH3PDmwW3vsyYFdVc45FhYWOHz4MDfccMO4I5ZS0m63mZ6eHtNaGzV2bLTh2UjC2LWA7hve8IZ1FRKb1eSurcoCfObMGYAXvPe/U31NCHEb8D8B/8crDnQXFhY4dOjQ+JLrtttuu+bLnwp0JxUJV8rNvVxdaU3OJJDfcMMNGxqSnZ9P+buvLXL6QkleWvKyoNFs0WjUkVojpCCOFd47itKSlZZsJaewklYDBIaSBOskuVNM1cOgKolgvivZMRWGZO26Z6Er2dby9FNo1UJ2wnQ9cLq1yNNOYLoeQnIre+/5jkb6IY0komEcWjm6aZB4XeiFTnCmXmKdpBE5ihKacZCf9XOFd56lgcd5wXTN0cvC7x/4W0GkRgM1LTDaU1qNcxBHikEOIgq2YOsI4eYuvN3z4QpJI3CpipKSEeB4jxAgR5GU2kQ4J+gXnsIZjBD4kXwh1oLShqvGyEiKYgSuWl7shLVkRPMSXybysyxLnn/+ebrdLrfffvuq7ILNljGG2dnZVTv4rLVjID516hS9Xm+ctzFJT0y+j6s4x7Isue+++8aD4MsN7ICxY6wa2Hnvx0HplbHDWrtqienljB0vxdaIF7NG9NMNQogHgVMEPve/B9owXlr98q6K8zp48CBaa+666y4ajQaPP/44ZVleU5cLocPJsownnnjiEkXCtVY1JDt06NAYyCeTxNYD28HQ8l++Ps/BY8FEkOYZca3G3Mw0SiuMCUMyhAg5ryJwi7N1gUdSOtAygFhNp3Q7DqVgsTvFzvaQNDNMNzSnFhU3bHdYK9gx5ZjvSuaaocOdaVi0DHIu6wXnu4qdbctiX9IwQzrdlEglRElEN1PExjLfC+YFoyAxnkZkQ1aDDCE0g1zRShzDNIDeTN3RTcNanmEOkfYk2uO8DHkPStJNJQJHYkZrfrRjMAK6ZuTHt7X0lKPzXavdprQevCUrCsoiGBxyUwcZY5RlUIbHUI8I6WFApCEbHW+yMZx8dSY3s7iJq5q1Q6gq+P7IkSO86lWv2vScYaOllFo3EawCxCp8v9rqDKHrvummm9i7d+81DewqY8euXbvGA7sqAGdhYWEclL7W2FGFp1+pOp0O+/fvv15P03Wp6rkaccGPAr8PPEWwBN8A/O/AzlcE6FaOrLVRi1WHei2g2+/3x1GO991333XLSIDVQ7L77ruPKIqu6CRzzvP1f13i+0/3SHPPIE1ROmJ6ZgYTRwilMCqE0TgCCDUiifOCWhKOt9SFbVPgvGamKVjstdg958kLQeIdC4Mas/U+vV5GQ3lOLswwV++RFRHbmsG8EGlHL1PkPdg17VDe05oq6adhB9oQjWWWnY2Shb6ipsOHcKrm0Mqz1FcIAYUN9MZcw7IyDCYICJ1sI/J4D40oxEMuDxSu8Lg48Mqx9iPlAbSTQI0ARGa8oIHSeaTwFPmQXECcRAghGJaKwoYh3zBPwAQ+uRq2OZuCCh/6wXCI1OF2ORbpesqiOil68lGXK8XF71FCYG14gNqYVe/BwWDAs88+S5IkvO51r3vRbbxra9JyWxl1BoMBTz/9NEIItm/fzrlz5zhx4sQ4+6Diitc63TY7sKu2G1/O2HHmzBlWVkIMZ7fbveyg8Ke10/XeV+uu/kII8e+AnwO+DfzP3vsevEIkY+12mwceeOCS+yvZ2FaSu7Is4/nnnx8rEgaDwXUD3DRNOXjwIFmWXTIkWw9sAX74TJevfWuJ7tAzyDKEhNb0FHGSYLRCa4n1gswKYq0onaDZCPvJVjqCuUiQF4LZlmCYCxo16GfQqkF/KGjWoZ8qtk3BhU6b/dsdpYMZkXGuUydRGefyCEFG02QIaWjWFWdXEppJUB54NLP1DC8UsS7JrRgH0pzvhi42HUg8gm0ty/JAhixbB+3EYZSnn0vyUowca4FzFcKPTBKe/ohiCFxwUDaULlASYW8aJEbgcawMBV5ENIwgtYY0g6nEMbr6n6B6/EilMJKfyTjws9LjxajjsjlWqNHNFFVZxGUgO6SUI1rCYH1gjdMycL/TOgCuc26suX31q1/9UwEaVbDT2bNnL8mfrhQM1cDu9OnTDIfDVQqGamC3HhCvxxOvR09U+byVsePw4cPMzMygtR7L54bDIUIIWq0WX/va1zh//vx1S+T7+7//ez7wgQ9greV3fud3+MhHPrLlY4mRwzGKov8A3A6cJ2yM2EtQMrwyQPdytZX16ZtVJGz22Gma8v3vf38sLbuak+zk6ZS///oC5xaCsSEvSprtNs1mDaUVXoSu1npBVgraDYWQgjwNEYPdgWBuSmB9MBIsdmFuerQx3MByH6Yagu4A2k3PMBPsnXW8MK/YMeXInGHHlMP7iM5QMt2IuNCtMZ1kLAxiFAOKQQ+lIpJIs9BvIiU0Ys8gl2xvWZYGoYuNdaAWImWDjRhPI3bM9xVSeGoR42yG7ohiaNccy8Nw243ANZKhC65H4dK+lykKK5lpWOZ7ow98uYKIwknSi5G9VziGo+44Up50dLtmgtQMAp9dddCxGQXbCKjXY7wL5hClmhSFJbcSO8xRUdgaotxg3CHXorBZAqCeKBYXFzl48CC7du3igQce+IlkGaytTqfDs88+O3ZMrn1Mk9GMk0qKPM/pdrt0Op1VG4YngbjidyfravRERXssLy+ze/duarUa7XZ73IhUxo7qyvb3f//30Vrztre9jT/90z/d0nNgreX9738/jz32GPv27eOBBx7goYce4o477tj0saoEuC996UsAvwYcAhaA/xF4mxDij7z3h18RoHs5UNwM6F5NkbDeyp6NlnOOkydPcuLECbTW3H///auUDOt1t91+yf/3jws8f2JInpUM84Jmq8703DRCKYRUOAR5Lmg0An8bjTjcC8uePdsU1gX97fwK7JoFh0dryfkl2D0rQHhmW4ILHdg1AyfnJfvmHN4Jds+ULHRDboGQkuW+ZO9cSVkKdk1Z0lzgbBkCxoud1JSlb4MELfZLpGlMLArSoSSREbVYcr4TOr7ZBnRSSc1cBNSZhmNlOLLbSo+RYJTHOkE9CjRBJ1WUVjDXtFwYget0AsUon6GiB4QvEFEYSNWjkO0LIRy9+p7YeAYjGkJJxpnAQkBkNNYJ0hKGpaB0gsJ68lGHHGtL6QwIT5QYgrvYgQrdr/eONLMIGYaYP/63J1FScvfdd495059kWWt5/vnnWVlZ4Y477tj08K6iCNZuGK4GdidOnKDXCyuNNmJ1rv4+ceIEp0+f5vbbb6fdbq87sGs0Gnzwgx/kscce4ytf+QpTU1NcuHBhq08Fjz/+OLfeeis33xw2Mv/qr/4qX/7yl7cEutWV05e//GWAz3vvHx196f8UQvzfwBuBVwboXq42kr+wUUVCBZKbAd3JY1ch60899RSnTp1ienp63W7AWs8//PM8Tz7dp8gtgywjihPmdswRRzroQK0kUQFoa7Ug91nseXbOSAoL9UTSH3pqiQgdnZB0B9BsCJqJRynJqQXYs03iHOzb5ugOBdvanhPzkiSCWhRW47TrjrSQbG+VnF5UQU8bDRi6BrONgm7aoJU4pmoBlJQsWexPh3zcZs7iIEJiSUdcZyK7dNLwIa9F4diRdqPONaSDLfQCPbKtVXKhF4B4pmYprUCIIGWDsNqnO6IbIoaULlxutuuK3ghQjfRkVJuJQwcKAuvCGp+sFCwMoHQSJRxSerwXaBkcbBAGeGPANX6cv5AYQVH6idvhsSSRHNMVedqjXqthreWHP/zhKi1tu92+5NL8xa5K3bN3797rouypSmt9SUbupJRs0uo8KSVrtVoURcGPf/zjcbpf1QmvxxOnacpf/MVfcOLECeI4Jooi9u7du+XHferUKW644Ybxv/ft28d3vvOdLR2rei5vueUWgH8nhHiG4EqTBKPEKXgF0QuXSxq7EuhWGQn1ev2qioTNDuVWVlbGyWWTQ7IDBw4wPz/PsWPHxpdllaPs8AuSx58a0h96esMUJSWz22ZRSmO9DB2dECRRiF4cZgKtRdgp1pRYD2k52lUmgxHAI0gi6GWCvITtM2H400gEJy4IXrXTceycZHvboyXsnA4AeL4j2TPrOX5BY7SnHguEdzTUEnHUoqmD5rW0nkg5TozW7c21XOCTY8fSIDxX29owyDVGWoyKyPIUa0sudGp4FDW5wiJBwjXXLCldANdBHk5INePopBd1ub2sUhaEiMjw+ghQ4XcIX4PSCnqZYliEIJ9eFqRmifFjXrcROfJRJ1yPGQN6LQqcNwSNz2D0/ZG6CLpaQvXumryNG+2XB3Zvn2Xb1O7x+6LS0nY6HY4cOTIOoVkLxNebfqhkYNZa7r333uumvrlSKaUuu+q9shw/88wzZFnG1NRUaB4WF2m1WutanZ966ik+8IEP8NBDD3H06NFrViXB+olmWz0RVeqNd7/73TzyyCN14A+Bk8ADwEHge/AKAt31yhhDmqaX3N/v9zl48CDOuQ1vbdgoVTEcDjl06NB4SFbtJKt427V7pIqi4Oln53n0/1niwlKJdQ5tJEmtRhTXsC44yYRSJIkcXerCIA9Aq7RgadGzMwLnYaoOJ+YlM81wKV2PR1scSo82kufPCG7ZDXkJN+0M5oV65BkWggtdwY3bHKeXFdONsK9mx1SJtxlLfYlWmr6bo9MV7Ju1LPUlRnm6WehIdrRtcJ/VS2omGAyshfMrQUmxo205343H35v2VdDc6gaUoEXGyiAQtRE98nLUERtLXmrMCFRbiaMsLUvLfZAxUicspnWcl9QjN05Dm6rZMUDP1O2YJ64ZN6IkAq3A2ARx8XWc/CxO3nYT/7Bu/dtplo8zFhrJ6o/Yelra6tK80+lw/Phxer3e2NQw6TDbChBXofzHjx+/xOX2k6jq96rcort372b//v1jKdlaq/OhQ4fG3POTTz7Jpz/9ae6+++7r9nj27dvHiRMnxv8+efLklqJXvffjzvzVr3413vvfFUL8NwQ32n/y3v+o+l5xlezKl34L3harKIpLtjssLCxw4cKF8T6wSUXCgQMHVr3xr1Y//vGP2b1792UTycqy5MiRI+MwnWpINrGo7pL/s7xS8OXH5jl2YhCyZIuMxkiWkxeOvPBExmKdxxIRx4o4jkgSTS+VzLRkkHrFghMX4MadYVhmtOCFC4J6HKbrSQTtpiDNg+vq2HnJXNvTzyTWwat2QGFDWExaCLpDQS2Cpb6kFXUZujbg2TENiz3JVC3oeiPlSUb/JysEzsMgl8w1LSvDADa7pi3nOyHhbqbh6WWSxLjRUknB7MjJBhdB20iPlgV57smKQCsUPkEJC8oAAuO7OBVAebZhWR7xwduaQRVR3b8y6oSnapb+CICbsRt1v2GFUD7Kd4h14JAFDiXDSUyKEJLuGSkbxMiZJfxYnxtuByuzFGE7hRjFY75qe7SlzmnS1NDpdFZxpJNAfCWXVyVNq9Vq3HrrrdelM7zWcs6NQ6eu1vBkWcaXvvQlPvvZz9LpdMYqh89//vOrKIFrqbIsOXDgAF/96lfZu3cvDzzwAF/4whe48847N32sd73rXRRFwfbt2/nUpz71MeAEQb3QI5zav+29t6/4TrcoilVLGm+66aYtKRIuZ+GdHJLdeOONvP71rweu7CTLC8ff/dd5/u3ZPmleMEgzms0ac+3t5CUopYmVoN5QaD3S1rYFWeEQWE6cE8w2+ywtGxp1yWInZs82xdGzsG9b+Bn7tsEgg4VO4HIPnxbcuB1WBrB3zmF04C3rieDYOUkj8SxLSVaELvbMskb6glotoaUsiQndcKIcUgjmVyStmud8R+G8YO9sBa4XhyOR9iyOBl7bWmFQFmvPbN1ROIEAhPckKjjczi0H/WwjdgyLwM/ONS2DgQIB9ShlYMOwSgk7jkscZjkQvj8tLrrWKnmZkp7B6LZR/iLgGj/uiuuGEfgGauGisiGckJSEaKR/9ggkgb7ISxgOeqAbOKFoxoLFsGeRdhLkblup9UwNkxzp6dOnV7nL1nbEL7zwAufOnfupkaZBUEs888wz7Nixg/vvv/+KnXuWZTzyyCN885vf5K//+q/H4TNLS0vX5NpbW1prPvGJT/DzP//zWGv5rd/6rS0BLsBDDz3E6dOnWVhYgGCIeC0wCzQJq9gPAL1XTKdbluUloNjv93nqqafw3nPDDTdwww03bJkre/7552k0GuzatQu4dEi2f//+qzrJAP7liSX++fEVOoOcIs9RRhMnDaQygX9NNMZILiw7dszqMJyRgm4fdm9XpEU49vFzcNs+wSC1RLqgPxQgHIOiiVKeqaZAKclcW5AWAiXhzJKgKGHfdsnZJcGumWDvldJz43bISo8rhgwzh5d1ZqcUpxcVkfIYE/jO3TMh7AZg92xwp2nlR1KrkJ9gncRoRy0KoFaWYRvfYk/iGeUajKRh/RFvu7NtWZzoeOd7FzvXpdH9NZ1S+BghPJH2lHY08ArwjXJDhA5cZSyHlITb0xM0w3TN0s2CC66ZOMoR0GoZulzrxSgMR5CXkmbsx130zETnPFOzdEZ0RSvKGJahi5wauegA9k57bpx7cT9Clcyq0tIuLS2N4yB3797N1NTUJTbfl7qstRw5coTl5WXuuOMOGo3GFb//e9/7Hh/84Af55V/+ZT784Q//RLN9r6Eu29W9LH+bq1VlsTx8+DBFUfCGN7zhml+4SU63Wpter9c35CQDOHxswH9+7AILKyVFkVJaqMNzIJwAACAASURBVDdbo11ZklpNkRfgEMyvOLZNhxwBB/QHniiWLHU8s1OSYQ637JEcPuW5dZ/h3JJm+7QgjiRFEfaVnV/WbGv1eO5kG6NKkljineDmXcFye8O2ADKJcUw34fCZ6pLc0C1i5lqeM4vhvh3TntNLVWJXiGFsxiGBbHurJNYhFMfbwOFe6IZ1P7EJQPz/s3fmYXLU1fr/1NJ79/SsmUyWmeyTkASSSUKAC1xFQC+rsgaEcNFcBQXxp4ARvAjqVURkucjlorKJklxkC0uIIBBlcSYBDIasM5ktmX16Znp6X6q+vz+qq7oGZpIAk4WY93l4nu/TVKqrerrfOt9z3vOeAo9OPLflHxvU6IkYfwufWxAzu8Dyfz3LY0GVBQPxXPTpyJLRjTxp0K0TSZk6XpGXnQWcJLM6EjqyriOyMTJZjd5YJjet14HQJAbTDoSQSWuaVYgzyDK3dudJ2qXmryxje6YPxhKgGOQhyfnvlmbLcAXc+z5mMbvLPB4P0WgUh8Nh+XaYOlqzzddUDZhR8f7ohBsYGGDr1q2MGzeOhQsX7naHmUwm+dnPfsbf/vY3fve7333siPNgxyFHuh9UJPz9738flSelqqrE43Hee+89MpkMM2fOtEZi745s+wYyPPFCJ22daRKpFJlMFn8gALqKx+tECIl4CmIJ8HqM6buxhI7LoeP3q3hUg7iiUYHfL7OzW6eqQiGVEcyqkmjvNRoG+qMS0YRgSoVCd1hmfBk4HQECfoMFesI6Tlmjsd3otioriNGfCOBWNTr7AGQmlup0DrhwKDp+j4RTNdp2s1ko9Oh4XdDWbziCeRzQ1mdMdPC6ZeIpiaBXpyeSI+oC3Uo3+D2CeG67bhawZElYuVe3QyecWxd6dQaTOcmQNoCuBnGrOgUeQVbPTfuVdXxOw5wnmZZIp410gCwUKxIt8DisHK7HaQzEBEFG1xDIIHQicQHSB1IRuSgXcmmJ3FqW81I1hIakeHN5Xj2vDybfcAGGQ9r+gOndMXHixCEeDvZtuBDCsgINhUI0NTVZ3Zp25cRoGZVrmkZDQwPRaHSv9Mnr16/nO9/5DhdeeCFr1679tEa3e4VD5s7i8TibN29G1/WPJfjeHTKZDF1dXYTDYebMmUNpaSmapqFp2oidZOm0zqqXunl/a5RkOksmm0JRPfgLCpBlCZ/bMKXp6dcoKVTJakYjQzQuGFem0tKpIys6Xq9C0C/wuGW6+jQmlKtsatKpGqvQPSAo8EgUFhh60fElEm29GkKDwZjCYFwwuUKipVtBkcEXdBDLSIwt1kmkPPgdEVQpgRAOBNAfdiM0gc8taGg3tqOVZdAWkpEkQWFAQtMNI/OuAeOHPbZQ0BnObbM9eQ8EMz+qyIKBWL6ANRBXUGRBsU8jqxvjftwOQVY3xsDr2TTZRNYoHPr89EfM/vw8oRo5XyPydqhG5K7KgsGcUMXt0C3C9dmKZgG3IJ42vvIFHkEsbZC7U06QEQYpyHoMXTYiWK9DJ54xjnGQIoNBSH63lJeWOcgRutHhl9Lya3Ufjx5LpVJs27YNgJqamt0Spmm16PP5LKNuIYQ1MWJgYMBSDbjd7g8R8UepgZjdd+PHj9+jkU8ikeCnP/0p69ev5/e//z2zZs3a6/f5tOKQId1sNktVVdVHUiTsCbqus3PnTnbt2kVJSQkej4fi4mIrzTBSdPvXuj5eeaOPbFYjlkgicBIMFiGQcbkUwlGdEq9CMikIBlTqWzPMnu4mlYZgQKYvLBhfKtPRL3BGNEqKjOr/1AkKqbRg+gTo7NOIxqGkQGVri6CiVKK129jyTx6n0BMWVBQbngWVZUazQziu4XXAYFQnklQp8qv0xkoAickVhtTM6OzKGXbLKdpDxha0PJilN2asgz5hkZpJrqqiE08bdo9uhxH5jynIosqCVFZCAVyyoD9pmKV7FEFXGECitAD6csTsUiSywm+Qddz4PN0OYcxOw2gLNrf+QVtUXOjVrdxvwC2sYpbXqZPIEafbkXcdc6n51Ibf66I/915+j5PBnD43lYiAahShtGwScsY3DkWQyKXsnIqwSNehCot0fftwso0Qgvb2dlpbW62xUx8HkiTh9Xrxer2WQboQgmQyaakm2trarBFBJgkXFBQMO+I9m81a06mPOuqoPXoj1NXVce2113LxxRfz6quvHtLRrR2HzF0WFhaO2Ahhyrb2FmZOeMeOHYwZM4ajjz6aeDzOhg0bACzB9wcji/qmOE+80EEsrhGJJRBCQVZ9lBV7AImBqCASy1Ja4iKrCRSHRDShUznOycb6DHOmOQlHchGlkBlXIshoMjs7NWZUqby/QyPglVAdMrGkoLpKIZ7UmVQhUBUZVQKvR6KlSyOVgQKvRGOboNBvFMGyGpQFIvQnDbG6x+VkMC7hcQk6+w0iG1cCnQPGfVWUOggNgiqn0bMJ/Erc2JYPqgQcDpyqRCTmQEtrFBdCe49xjsoywa4+o7g1rgi6cpFwkd8orMlyXtXgcQj6YrlI1JUmnjHeu9hvGN6AQahmHtjrFFa3mcsB5KJbZYT6qF1EaJPSoon890HT82sh5cNTWXVawyWdTjfJ3Mkig1FwGM0cmWwGMB5G9m+Yz7lv8rmxWIytW7fi8/lYtGjRqBOVJEl4PB5rLpsJ0/jGHKRpN74pKCiwRmJNmjSJmTNn7jG6/clPfsK7777LY489RnV19ajew8GOQ4Z0R4Ip9drbL6e9SFZTU4PD4bCmtC5cuHBIBGBuxZADrK3V6Q4JUuk0mtARkpeiIhfprOFClUgJggUKjTsFbo+G16uiSBD0KUTjgpmTHLzfkKayQqVhl0AInSkTHKQ1wcwqGU0XTK4w/AF6wjpjghLbmjV0AVMnKGzbpeNzgxxVSGVgcoVEa7dxTwEvRBK5seVaAI9TUBo0qv/jisHrhlTG6LSSBPgcGukMtHYY+tvSoMTOsJGuqSwTtOUKbKX+KAMJg3CisSSGbEvQnyNRpyroyaUHgl6dQVNLG9AJ5RQQCoOAEU0W+BTiA8Y1O4ZszfM/4KyNRU25F+SlYpDvKgOIp+Rh1vlcLeQlZB/8t7rksvQ7mpQvOjk8AdJmm685/QqIxpOgGNGdW80ymj8vXddpaWmhu7v7gMjAhptUkU6n6e/vp6mpiVQqhcPhoK2tzbJkHK7N+W9/+xvXXXcdl156KbfffvtBN/59f+CQId09md7siXTj8Tj19fVks9kRi2Sm45L5xUtnNJ5e3UHdu/1oIks8oeF2KUiSA1Sd1vYUUyd5SacFPp9KX1inarxK064sPg/4/Qq6plFRrpJKC+ZMc5BIGgRVGFTZ3pKhtFBmR9gwz5452UHDLh2nasw404WgslymqV3H44LKsQqRuKA0IFAkiQmlAi0dZ6BfwudwUVrkYEebQdRel0xHKOf65ZRJpo3mh129xuc4qRx29uS2yh6JPkObb6UTZEmQ0HKmMi5BJGVUjfzOBLGUkRP1OeIMpoxjAh6sCFWV81Gg1+sjbp7bRn5xG/mZqgJJEpbaQJGFJc1yKHnFg1s18r1g5HbNrb/HqZO2jMmF7XVBKve6W82SzQpkBB6XnPu768ZcCVngVAQIo3NNUc17cCFhDAXOCg/phEYmq9PQt5F6LWUVquzDQz8qwuEwW7duZcyYMQeNQxkY19XY2MjkyZMpLy+3bA0/2Oa8bds2Xn31VRKJBJ2dnTz66KOHrDJhb3DIkO5IMBskRuo1z2QyNDY20tfXZ9kt7qlIBlD77gBrXguRSKboG9QoLHRRPsaLwynTHcqgpXVURWL7jhhjyzRiCQd+r4N0WqFqnEIsIdHenWHWFBfvbU0TDMgoqkw4Kpg52Un9Tg23U1AYkHG7BQGPhK7pTKkAt0tiMKahFghiCR0tI/D4ZTbV6whgeqXClmYjJKwodtAXc+BUIZk1Bj+WF0t0hIz7mDhGoiUXEfs8EqGIsU7YqvbdA2Y+VdATNmVk0DNovF5aAG19xrqo0EM8d26nw2kMKgEGBhMYGnHoG8wAqkGiSeMrOEQelpubBkbLbyIto8qCAo9OSpNwS8Yo94wwmhScqm60SyNwKIbKAWGcU5WMAp0DQVY3ct66LIjHjW48yQO9EWPqr1eJkxBG6mVMgZ7XCgc0+nLrYr9mddAFvRrRXAu036VbXhFuh87nF80fohjo7e2lqamJbDa710SczWZpaGggFosxZ86cPepb9xfS6TTbtm1DCPEhE/YPtjkLIYhGo3R2dlJeXs6sWbNYunQp119/PRdeeOGBuoUDikOedEfyTLAXyaqqqpg+fTpCiD0WyVrbEjz5fBft3Qn6w0micZlpkwMkU+BwyKRSgrFlLjp6smR0nZJiF03tGaonO9jarON1pSksMLb2k8bKJJMwbYKC0yWTSAnKi2U6u9Og6Ywvc7KtKYWiQmmhg94BnbElMr1hIz9ZPUmlYWdu0q1TRqDjUAU7uw1SKy+W6A4bP+iJ5TJNncY9+L15cjWlUqpCTjoGRX7oDRuvjyuG9n5jXVwAsV7jGJcDQKAqIDAUD4oMuiYoCejIEqiqTHlQAwS6piLifagKxvggLQkiQzbrRJXVXBeYA4SMw6ETjhqOaU6go9fMFeu09RkkN7FMZ1fIeH1iqaC9D8DQH7fn8tPjizVrPaFEp2PA+LfjijQrp+xSkqSzxgO5oMBPImzcnz1HrNrWTttu2OXIR+/2Il3QK6zv0HCKgQ9Kt4Yj4oGBARoaGqisrKS6unq/OpHtDl1dXTQ2Nu6Vj0MsFuOWW25h8+bNPPTQQ0ybNm0/XeXBjUOGdEf6Un7Q3nG4Ipksy7tt2wWIxTWefrGL9zaFSWdShCNQUuIlmdFp2pmicryHnr4MpSUOkilBWbGKJEmk0oJ5M93s6sricwuKgi52dWWprHDQ0qGj6YLKiiTbdzpRFJ2gTyYcU6golalvNR4A0ycakS+AyyWjCx23E9q6DcIdXyazK7cuCSTpjvhQZMOcPJUhR4xQXmRsy3VdMLHMyJsKoVNZCk6HIJM1/BM8LoFblYxIUAgCuVbYaARIQyYraG2XCEeNUTrhAZmsBgVeaMlt+ccUQiiXzy32xQkn/ICPyjGC1m5bCiNHqGMCcXqiRhFNS8eIpIxCldA0yA2O1G3Fr6ytUcHsKoN8+gOG5nxHWieS+QeyvbFBsxveMPzabpBj3xAFPUM9QOzYExF3d3ezceNGhBAUFhaSTqfp6+v72KmJ0UIqlWLr1q0oisLChQt3ey1CCN544w2+973vsWzZMu6+++5Rz91+5Stf4fnnn2fMmDG8/77hJdPX18eFF15Ic3MzkyZN4vHHH6eoqAghBNdccw2rV6/G6/Xy8MMPU1NTM6rX81FwyJAujGzvaEavH2ycMItku2tuEEKw9q1+/vx6L9FYnExGQ1HdTBjvob0rTVFQJRITbNqeYO4sPxs2Jwj4Ffxele5QlhmT3by3LYkQMHOqh/qWFEG/MbeswC9RWqwST6pMGCNRHFQJRzR8rgwOOUtZwHDVikUdTChV8XsdJNMaE0oFBT6ZUDiLWxHo6QyKDgKFWMJBMprE45LY3GAoFsaVyWzabnwu0yYqbG411jMqFXa0GetJ42RaOo312BKFngFjYoLHJZFIgccFvQMSQkBJ0JhAATC2WKLDjJADEpGcmsDnwYqmvW4n4YT5R8p/tnbVgKzm5UWyw2elJfoj+YLUQDRPwIOJ/L8djNvyv/bXbWsz/2scb3MHk32YEoWYrehmL6gl08OTur2oZ1dG7I50h4Mp3erv72dgYIDZs2dTWlo6bDOD1+v9xDnijwIhBJ2dnTQ3N++VPC0ajfLDH/6Q7du38+STT5resqOOf//3f+eqq65i6dKl1mu33norn/vc51i+fDm33nort956Kz//+c958cUXqa+vp76+nrq6Oq688sqP7Zk7GjikSHc4OBwOS+6laRqzZs3C5/PtkWwBGltirFzVSVPLIA41i6K68fm8eDwqyZTO+Aon2Sx4PTB5ootoXGPGJMNVKp7UmTHJQV84jc8tqKxwsbUhZsi2JnjY3pxAVSSErtPbr1FWpNDWaURw06uc7NhlXFP1ZAfbW7KAztjiOD1hJ5Is8LkhlpTxujKkNaOzrapCpbUrF/2OVazUg98rQ8hglkyuMwugb9C4T1WB9l7jtUK/MXkCYGyJRGcuP1teLLEzl/sN+rBI1+XMf3b2j3EgHMPM4Wb1PDFE4rZjYua/E1ahTpGF1Z3mUATJrBH9uhw6iYxxHoecJp4y8ohuNUMyY6z9HkEinW/EMH0d/O68VleVkmR0I53gceSnSjiVfMeZKuc71GQpX6QDQcxG3nZlRMpWBDTTC3uLWCzGli1bCAQCQ2Rge5Oa2JdEnEwm2bJlCy6Xa6+i29dff53ly5fzta99jXvvvXefFvxOPPFEmpubh7y2atUq1q5dC8Bll13GZz7zGX7+85+zatUqli5diiRJHHPMMQwMDNDR0WF9rvsbhxTpfjDSNTvJIpEIc+bM2esiWSKp8Ycn23jiuS5cLsGYMieNuxTKyyQGIhl6++JMm+xle1OSTFZw5Ew/72w0GGTmNC9bdyRwqBKlpU66Q1mKCxXqm1NkNZhS6WJ7sxHGzZjsZssOY11S7KAvYqRBYknjHlxOidYOgyzLSxR6cjnJynKJ1q5c4Sqg0ZnLuep6GvNPGs9FnBLQGTLI1+2Etl5jXVYk0zdovM+4Mpmd3ca6tFAmkjDWPleeoBU5v7Zv7c2RNwADUYEZyqb03NwwOR/xuhyCgZyczO8RFmkFvRDORauFfqxjCv3QmyvWFfqMghdAcYFKd+6B4XPpDJhRdDYCBHPvlSWWNsjY4xTWbLQCr2I1QvjcOumcFtjv1i1jG587X8jzuvLX6XEIUpYPb14BISGGRMYB995Fuuagyt7eXqqrq4eYfQ+HT5Ij/ihELISgra2NXbt2WcXl3SESiXDTTTfR2NjIM888c8BGo3d1dVmfS0VFBd3dRpQw3HSItra2w6Q7mjAnnLa1tVFSUoLX66WoqGiPRTKAf2we5LZ7G+nuzVBSCIrqpK9fMLXSRTpj0M6RM70kUjqTxjso8CskUllmTnHi8yokkhozJzsJBFQGIzq+8SqFBSo9/RqZjMDplAgGjDE5bV0GExQXKuzYaayrxjvY2Wmw2qTxDra3mMeo9A4Y159KpSHXkqpLfkBHlqEnbHZeZenoMZoTSoIafbncakWpQktXLqINSBbpOm2/R/s2OZnOryO2LbkZ5SpyPlp2qhrhuPF1CvogmosIiwNY71/oh+5coSrozc8r83uERbpeVz4Cdtuuy2VbO2zfWq/HYZFuYdBPoj93H9kUZtNCPDYAlObOIw17HqcDSJjvJaw0ids2S83jFKRyx/icgmiOaN1OQSqTj85de8FvAwMDbNu2jfLy8j3aHO4Oe0vEexsRJxIJtmzZYunSdye1FELwl7/8he9///tceeWV3HfffQeNnM2O0ZwOMRo4pEjXzD81NjZSXl7O4sWLLXtHSZJG7CQzkU6naWhoYfa0DFXjfXT2aOxsT+J0ykRiOl09aVTF8Dno6knjdECB30FvfwavR8ahKgxGNYIBhWRGkE4LKsY42FqfQQiYOsnDhn8YjDJ7hpctDVFUVaJ0opdENI3TIeOSBWUBHVWREZk0VWOMIk18cJAyfxafz4PT5cbvk1BVI+qsLJfxeIzCVlaDsmI3O7t0MlnweYRFkolEFPDl7lXHjEqjifxn0D9oVt6x0gwuR55cg34YzEWKJUGJUO71Ao9GKKZax0TN3K47T9Jum6mV00Z+imxf2/4ge/G7ELbimv235XS5rbyw3xcgmSPySDSK2YyRTqWAnOeC7b0U29quXBhK0gJyDw0jTZF7rz04i2UyGRoaGkgkEsydO3efDKr8OEQcCASIRCKWB+9IZv0mIpEIP/jBD2htbeXZZ5+lqqpq1O/jo6K8vNxKG3R0dFjqitGaDjFaOKRIt6GhgWQyOWwnmdnCuHPnTtLpNF6vl2AwaHXNdHR00NHRwXFHT+ZLZ5ZbT8L2ziSNrQne3xple2MMp1NhwyaDRWZM9fH+VuPXPG2Sl41bDTaqnOixXi8udNLRbfwiVdsvOJ4wtqDZrKCtM01ffwZVkRiMaCSSOgG/TEOzMWq8OKjRl4tip0/O8N6mHHFP97B5RzK/rjfYU5nqYVejwTh+yUW8L2I0QKgKanoARdbp6RQ4AZcqkYw4KPM68HpUkFWCHgmXQyKtGf63HpdELAW6LhHM+QhlsgJJM/O2EoGAi1CO2FQbg9kjCjsp2pUCdsvEtE3dZ8+TpmyKg1Rm+OPtqoRILAUYkZyQ8hGdx1tALPcQ0EX+7xEejGKmJjLZ4dMD9uBoiKTMVpj3u0ZOLZiqmaqqqj22yo42dkfEPT09bN68GTBqIK2trQwMDAwbEQshWLt2LTfccANXXXUV999//0ET3Z511lk88sgjLF++nEceeYSzzz7bev1Xv/oVS5Ysoa6ujmAweMBSC3CIke60adPQNG3ETjLzyWd+2cLhMM3NzfT391uibnNelTmpd9xYN+PGujn+aOPJr2mCpp0Jtu2I0daZIhrTaG1L0tuf//V39+b35bs6DFJ0qBLNO411YYFCS5tBiuPHOmnrNI6fPNFNQ4vx+rgxDrY3G6+Xl3npCxuvu2xCUTs3xOJ55uobMOVv0NmTIZ4UaJrOjsGsoT4oVOgbzBXcxqi0t6SBNBUlGTpCxg9s0jhBS6fxYzpimputjca1zJzsZGuTsZ42UaG1LYrTIeHHRaJPx+GUCTscOFI6qioTDysEHRKKLKGlZMb4jJFCZGXKC3I2jwLKAoaiQ+gQ8AjLtlGSjOm8dlXCyOv856FL+UaCWCp/TNy21m2tvaorYPk4JFJZzNRENJ7E3B3YHxT2B4idOoeLdJPJJNu2bUNRlA81Exxo9PT00NXVxVFHHUUwGBwxIn7nnXdobW2lpaWFeDzOc889R2Vl5QG77osuuoi1a9fS29vLhAkTuOWWW1i+fDkXXHABDzzwAJWVlfzxj38E4LTTTmP16tVMmzYNr9fLQw89dMCuGw4x0r3xxhvx+/0sXLiQBQsWjDh/yWxXbGtrw+/3M2fOHFRVtdoXW1tbiUajQyb1BoNBPB4PiiIxbZKXaZPy28JEUqOhOcG2HXF2dSTZsNkoxU+ocLGrwyDLiePdNO001mPLnAxGTQJWLdJ1ueyRcBzzz2Nvj+0fzJN7V68R8ikytOeiaZ9XpqfPOKas2MglA5SXOmltN96nuFClb9BYBwIq9BjrokI/HSHjGlWH4WwGEA4PQm4KQ/9ABDOfnNUVhNBJpQUDAxrdoVzOOZmlN/e+wYDCYMxgK69bIZkyzqkqRipEkUFIhhTN6ZCsqNfjgkQuPRAMSAgh43LJBP0yCWScThm3RyablnDIkM3GyaRU/H4vDlVBkmV8DglJAkmW8aiGB4ZAwucyutQyWQlZErnZbjYDHCkvX9PJdzIafzODgA1iNh6AdjL224poQgh27dpFW1sb06ZNo7S0lIMF0WiULVu2UFRUNKS1eLiI2PR9WLVqlTVU8swzz+TBBx9kwYIFB+T6V6xYMezrr7zyyodekySJe++9d19f0l7jkCLdZcuWUVtbywsvvMCPf/xj0uk0c+bMYcGCBSxatIjZs2fT2NjIrl27KC4uZtasWUN8d82cr1npzGQyVlqiq6uLRCKBy+Wy0hLBYBCn04nHrTB3pp+5M/PnGhjM0NiSYNP2ONsb40iyZJGuw5EnV/sszWg0LwWQZC9m0rAvV0CTZCxiC/hlwhGTUB0W6Y4tddCUK8oVB/Ok6/fm39OeT5VscZp92pEQKuaYXEnxYvp1pbP5rWZvXwpTN9sXNv6xLEH/oGa9j0m4fq9MPKeKCAYkwpFcQa8gr6IIBiR6c3lko0BpnMfrluns1YnEdByKQlfIuK6KUpmOnBpjTBH0hLPAIGNLZbpCxnnGFMt09xnHlBbmc9CFAYnBXDok6DM8eV0uGb9HQlZkI7/uhIxDwaFKKKqEUCUcUgyBTjKaBRFDCEF/Wsfh8CCrKqqeQAin5QZWUFDAokWLDhpjF7tiYm8mYYfDYW644Qa6u7tZuXKl9dswd5OjiTvvvJPf/va3SJLE3Llzeeihh+jo6GDJkiX09fVRU1PDo48+elDtFD4ODinSra6uprq6mssuuwwwtnUbNmygtraWO++8k7/85S+4XC5OO+00jjvuOAoLC/F6vSPmpBwOByUlJZZkRghBKpUiHA4zMDBAS0sLmUwGn89nEXFBQYExVLDAQc1cBzVzC6zzdXSn2NaYoK0zha7DjtYE/eF85NoXNuerQXeOWNwuyTqmpFAlNGAcUxRUicbN6DY/R9zlzN+Log6fT83Y8qCJZP6HE47lWbc/kn/dSJ3IyBJEchpal1OyNKtet25JpnxejUhOhlUUzBNe0C9beewCX/6BEfDK9OVI2u+R6R0wiTZ/7R6XjEn6XpftIZGJY0bgfq9KT+7z89ikbh5bzdTrlgnl0io+j8RgzDjG7ZbpCunEEhoyEqHcAyTol6yioduZV3PIEsiSZCk9PG6JZNrIbZQc20zztl40TWPMmDEEg0HS6fSw/rP7G5FIhC1btlBaWrpHxYQQgpdffpmbbrqJ73znOyxdunTI8bIsj2out62tjf/+7/9m8+bNeDweLrjgAlauXMnq1av5f//v/7FkyRKuuOIKHnjgAa688spRe98DgUOKdD8It9vNMcccw+LFi3nhhRf4r//6L84++2zeffddamtrefzxx2lpaWHChAksWrSIBQsWsHDhQoqKiob9gdjzw3bTZ3MwYGdnJ/X19QghCAQCFhH7/X4kSaJijIuKMQYLfPmLEI3GeaO2gZ2dgkS6kIaWLLFEiuIilVAuQi0rdrCzw/i1FwZUQgPG2ufJE609cpVt163Z7xYQpQAAIABJREFU9F/pTJ5E4zaijcTy6/4c2aiqZK19XkjkNKvFhYqVNigOKnT2GuvSIic7u7TcNTry59TzpKgqBnGDSaK5aNjWXGG/D6ftgWFXDdgDxkDAQ3dYWNecP374tf38bqeNmG2B0xCCd0tW95rfK5FM59YeyVJ8qMpQaZ1ClPFVVYwdO5ZoNMrg4CDt7e2WEbhdsvVRJzJ8XOi6TmNjI/39/Xs1VWVgYIDvf//79PX18eKLLzJ+/Ph9fo1gGPwkEgmroamiooJXX32Vxx57DDAaHm6++ebDpPtpgCRJvPTSS9YX/Atf+AJf+MIXgPx2q66ujrVr13L77bcTiUSYNWuWRcJHHXXUiC5lkiTh9/vx+/2WDEXTNCs/3NzcTCwWQ1EUK33h8/no7OwkFAqxaP40vmATnydTOk2tSbY2JqhvSpDJCot0XW5bFGsLMuxRbNZmGpBK59fm1h4gGs8T7WDUID8jkjNJKEs05/5V6HeQSOZTFCbp+rx54vS482ufR8VMixQXFtCde0jI6JikG4tFMfPCks3NYAgBjcBF0UgEU+ZlbNlN7fXw/3SIFMz2mSk2XZiq2Ik5f8wQYrZFzz5PXmZX4JPoz3XTuVSNmvlHWhMT3G73kDxuMpm00lW7du0aMhrHTsSjCdMW0tQD747khRD86U9/4uabb+baa6/lkksu2W/KhPHjx3PttddSWVmJx+Ph1FNPZcGCBRQWFlpaYbOp4dOOfwrShZHF0LIsM2XKFKZMmcJFF10EGLncjRs3UldXxyOPPMI//vEPVFWlpqaGmpoaFi5cyPTp00fM0ymKQmFh4RCj6XQ6bZmfh0Ihq0gXDhvdAqY0x+2SmTXdy6zp+ULdwGCW+qYE7d1p3E6F+ubkEOVCKj18FBu1RbHhHLnKcp5o/V6ZWI48Aj6FZDq35fc7iOa64tyu4aNGu0xKHiIRy79ur+O7XB7MyNzt8WKS9GA0jknAhlLAOHEma3945NMebq8fcikWe5rE3iVnL2zZmz2GZCDt6oMhxGy7R7su17Y2UjjG2RxK3huiOKjudkTNcCoak4gHBgZobW215IwmCX/cqb2aprFjxw4GBwf3yhayv7+f5cuXMzg4yJo1a/a7jrW/v59Vq1bR1NREYWEh559/Pi+++OKHjjvQKZrRwD8N6X4UOBwOi2CvvPJKhBBEIhHefvtt6urq+PGPf0xDQwNjxoyxouGFCxdaRs7DIZlM0tzcjM/n4/jjj8fhcJBMJgmHw4RCIRobG9E0bUh+OBAI5PLDKouOMgoeZ59inK+zJ019c5L65iRdvRl2dqRJZwSDkbxczMybuhx5XXAwoDCQy9cGfAqxRI6t9ASmTMrrcWBGqw57E4ONaEe6T3ttxZ7esEfgms00xuH0YUarWS0fVfUNxK3rCQ/mu8vsW3lTCWG8PnyEb9f0DiFp27XZr3m47iUY+mCxpzsk8qRbGPhoUaF9NI49XZVIJD4k2fL5fEOIeHdtvf39/Wzbto3x48czffr0PUa3L774Irfccgvf+973uPjiiw+I7vbPf/4zkydPtgx1zjnnHN566y0GBgasIQQHuqlhtHCYdPcCkiRRUFDASSedxEknnQTkhwPW1dVRV1fHfffdR29vL9OnT7ckazU1NYRCITZu3MjYsWOprq4eUi02f3Bjx44FjFRHLBYjHA7T3t5OJGKo+E2lhH38ydgyJ2PLnJywyCjUaZqgpT1FQ0uKhuYU7d1py+OhoEClJ6d68Htli3RdtjlewUIfPWGDoewR7ZAoluHDWN1Orva1LVpN2yPXzPAEmdUUzAhSF/noLq3lv6aDkQxmNBxN5NkyZk+fJIZPqySS9vfNX7/9euzErI1AxqlUEvMhEPB76QqbxcJPHoXZh0Wa3wu7dranp4cdO3ZYD2g7EYPRIBSPx/dqMGRfXx/f+973SCQSvPTSSwe0YaCyspLa2lri8Tgej4dXXnmFhQsX8tnPfpYnnniCJUuWDGl4+DTjMOl+TEiSxPjx4znnnHM455xzAGNLt2XLFurq6njiiSdYtmwZmUyGk046iX/5l3/B6XQya9asEfvZZVkmEAgMIWZN06w8YGNjI7FYDKfTOYSI3W43iiIxZaKbKRPdnHq88W+TKZ3GnSl2tqd5vz5BQ0sKty0vnEknMLf2Dlu33EiBkS6Gj1ztRJVOj0Cu9qjUtk6kdNvaTpD5HHAma3fzyofbRm7auNiIbR21+UREbPnr6JC1jZiT9ojZdi8jRMma7d7tcXHhKJDucBhJO2sScVdXF1u2bCGRSBAIBCgvLyeVSuF0OodNgQkhLFnlDTfcwJIlSw74tn3x4sWcd9551NTUoKoq8+fP52tf+xqnn346S5Ys4Qc/+AHz58/nq1/96gG9ztGANNJ2KoeP5lF3GBaWLVvGlClTuOKKK9i0aRPr1q2jrq6OrVu3EgwGLe3wwoULGT9+/Efa0qXTacLhMIODg4TDYVIpYxaXXbY23PZTCMH2hjbefq+LeLYUJB9/2xAnGtc5YqqLzTnHs1lT3WxtMtbVk93UtxhMNGWig0abMU9ru8FE48c6aO82GzIUevqMlEVR0JbK8CpWNOpySlaEqzokNM3QIBuQUOR8lOlQ8yblLlvzhMuRTx04Vcjo5uw0HV3khl7KwvLslWWjMcJ4B6OQZgblTjXfTmzkuY3/UVKY1xAX+bP0R42HZWW5YpnGTx6v0NxhrC882cnxR+1fo/FMJkN9fT2pVIrq6mqy2az1kDZ3Sn6/H7fbTVtbG1OmTOGmm24im81y7733WmmNwxh1jPgUO0y6+wgjjX0XQtDb22ulJdatW0dbWxuTJk2ycsM1NTUEg8G9jj7MPKBJxIODg2iaht/vt4gYoL6+Hr/fz9SpU4eQcmdvhpa2NFt2pNjRmgIJqwV5ykQXzW0Gu40vV2nrMtipvESlO0euxYUK/Tb9q0WuLol0xoicZUlGiHwnGhjqgFSO7FwOzUojBLyS5WoW9OebGOwNDYUBiYGI2VQh5xsdbMd73TrxnNzNfn63M+/TYL8eCZBkyVKDuF2SRexel25ZPJYVyoTCeVvMjlwjxte+6GLu1P23eezp6aGhoYFJkyYxduzYYb8vmqYRjUZpbm7mxhtvZNOmTRQUFPC5z32O008/nTPPPHO/Xe++xki/uQOEES/kcHphH2GkP74kSZSVlXHGGWdwxhlnAMZWsaGhgbq6Ov70pz/x05/+lHg8zuzZsy0injNnzohyInse0L79jEaj9PX1sWnTJpLJpJXj6+3tpaCgAK/Xa+SHSx2MLXWw+Kicx4AmaO3IsKM1RXt3Fi1nQ2nPj8aGyadKUn6r7lAla3vudUskrIkSEuFoToblgVTOfMbnVUnn1h53nnSNIZy5tVO2Otzsci63E4to7cf7PIp1PT6PStoySs9g5mSdqm4V8NwuWwOEPLQIl7KlOBKp4dMRQd/++cFnMhm2bduGpmnU1NTsVmamKAqZTIY77riD0tJSNm7ciN/vZ8OGDSSTyf1yvfsDmqYNSaUcZAQ8BIdJ9yCALMvMmDGDGTNmcOmllwJGCmHDhg3U1dXx61//mvfffx+32838+fMtIp4yZcqIaQlJkohEInR0dDB58mTGjh07JD9sFlxMwb4ZEbtcLhRFYvIEJ5Mn5ItZyZRO0640O1rTNLSmaGrLEktoOB0S6YxJbJKVK/V5beRqI123S7bka0JPYeaUvW6F/ki+fdiE4X/7Yd9fu6rC6bBJuNT88S5HXj/sdkmQu56Az0UibaY68kbsEnmfYoecIa2bxJxPP0hA0mbcbldN2Lvo9hVMp7IpU6bsMTUghOCZZ57h1ltv5T//8z85//zzLSI67rjj9tk1DgwMsGzZMt5//30kSeLBBx+kurp62PllowVFUejr6+Ouu+7i6quv3uNYoQOJw6R7kMLpdHL00Udz9NFHA8YPaGBggPXr11NXV8czzzxDY2Mj48ePp6amxuqoKy0tZd26dZZ/sH38i6qqQ8ZjA1ZbczgcHqITteeHVVU19MNT3cyamm8SCUc0mtrSNLRm2LEzzWBUt1qTPTZytXd5OdQ8SRUGPIRj5uu2e7cRqmGHmTtmiAH58BIu+9per3TYyNu+9rhVq+XZ73VZxuouJ6RzDwpVyZLOqvnXcxGwJA2NdPcl6abTabZu3YokSXvlVNbd3c13v/tdHA4Hr7766n4loWuuuYYvfOELPPHEE6TTaeLxOD/96U+HnV/2caHr+pCA46mnnmL58uUsWbJkj34SBxqHSfdTAkmSKCoq4tRTT+XUU08F8mPka2trefPNN/nFL35BU1MTRUVFfPnLX+bYY49l4sSJKIoy4lbL5XIxZsyYYW0vu7u7aWhoQAhh5YfNjjpZlgkGFObN9DBvZl6a1NmbpXFnmo7eLO83pGhpzw4hyCGKCVuEam/jHdo5ZluPYHY+pKFBGf4Y++uOEZoespk4Zreb3+cikjS70RQrGpZt0bBTFdagSmOIJ6MOIQRdXV00NTXt1dhzIQRPPfUUt912GzfffDPnnHPOft1mDw4O8te//pWHH34YMIIHp9M54vyyj4Ndu3bR09PD/PnzyWazRKNRnn32WV588UWmTp1KMplk+/btzJgx40PkfDDgMOl+iiHLMlVVVVRVVXHyySdz8sknc+eddzJr1izq6upYsWIF3/ve95AkiXnz5lmNHNXV1SN209nlSaYQXdd1q625paVliO2lGRF7PJ5cflhlbKnxtTr3FCM/vHFrN+9s7CWSKsLhKKB7IIUQQzXAQ9aSfW27Nttvx84j8l68Lg8hadsNi3xuOljgoz+Wz0lbOWNXXj8c8LtI5kYCOVWddC4f7FQ0GhoarM9kNHwVkskkW7duxeFw7HEwJBgzwr773e/i8Xh47bXXDoiVZGNjI2VlZVx++eW89957LFiwgLvvvnvE+WUfB93d3Tz66KOsWLGCP//5z7z00kt0dXXx1a9+lUmTJqHrOk899RR//vOfOeaYYw66/O4hRbpr1qzhmmuuQdM0li1bxvLlyw/0Je03lJSUUFdXZ207582bx9e//nWEEESjUd555x3q6uq49dZb2b59OyUlJSxYsIAFCxZw9NFHj1j9BoPcd2d72dnZOcT20iRiXdfZtm0bsiRxyZeqrYLPsrROU1uWju4swYBM064MkjS8K/he2DEMgTTC2s6zdlJPJvPRreHHIHLr/PEfTn0Yx3i9KqYbZ8BntH7bDW5MXwXz89jbdl4hBB0dHbS0tDBjxow9DobUdZ0nn3yS22+/nR/96Ed88YtfPGAkk81meffdd7nnnntYvHgx11xzDbfeeusnPq89Yq2pqeFb3/oWTU1N3H333ZSWlrJy5UqeffZZ5s6dy7x586iurmb16tUcc8wxBxXhwiFEupqm8c1vfpOXX37Zcg0766yzOOKIIw70pe03DPejliSJQCDAZz7zGT7zmc8A+Vly69ato7a2lgceeIDOzk6mTZtm6Yfnz59vuaMNh93ZXvb19bF161ZSqRTBYJCysjISiQSqqqIoCi6nzMzJTmZOdvLZxQbhDUY1GtuyNO7KMBjTaevOEo2LIS26Q9t1ba+PsLbrHe3rTCaDScOBQIBOa9KGLTUxgh+Dw0bMTls07PPIlJWVWblTu69CX18fzc3NH2rnNfPldpiDIT0ez5B8/Ejo7OzkO9/5DoFAgLVr1+6RoPc1JkyYwIQJE1i8eDEA5513HrfeeuuI88v2BnbC3b59OxMmTODnP/85v/nNb9B1nd7eXkpLS7n00ksRQvD000/z+OOP81//9V/75B4/KQ4Z0l23bh3Tpk1jypQpACxZsoRVq1b9U5Hu3kKSJCoqKjj77LOttkpN09i+fTu1tbWsWrWKm2++mXQ6zdy5cy0iPuKII0bc4pq2l8lkkv7+fioqKqiqqrL8JTo6Oti2bRuAZXtp5oclSaLArzCvWmFetREN//tZ0N2XpbUjy+TxBhnbiVMbyeBmpLXtHyeTKcDIQ9vPOeTxMiTazh8lD8kT59cfLKKN5Ktg2oCa+XJd1/H7/RQUFJBMJgmFQlRXVw8pdg4HXdd5/PHHufPOO/nJT37CWWeddVBEdGPHjmXixIls27aN6upqXnnlFY444giOOOKIYeeX7Q1kWaazs5Orr76abDZLQUEB3/zmN/mP//gP7r//fsrLy/nXf/1XAG6//Xaeeuop7r//fo455ph9dZufCIcM6Q43276uru4AXtGnC4qiMGvWLGbNmsXll18OGDnFv//979TW1nLPPfewadMm/H7/EJOfyspKZFkmFArR2dlJKpUa4mpl2l6anqx228umpiZisRiqqn6orVmSJMYUq4wpVlk4m9y/Fezq1mhqy9Ad0klnBO09GtkR3Mbs60QyjenZ4PH6LKeykYjZjiF5YnsEPIR09/wZD2cDakZq9fX1SJKELMs0NDQQCASsz8R8MJno7Ozkmmuuobi4mL/85S97JOj9jXvuuYcvf/nLpNNppkyZwkMPPYSu68POLxsJH8zD3nTTTZx11lmcf/75nHTSSdxzzz08+uijPPfcc7z88su8+OKLRCIR/vM//5PrrrvOOgccfM5khwzpHmyz7Q8FuN1ujj32WI499ljA+IxDoRDr16+ntraWlStX0tLSgsvlIhQK8cMf/pBTTjllt2PFd2d7aRr9mPlQe37Y4XCgKBJVFSpVFebX1kcqLWjtyNLYlqWpXWMwajdutxvc6ORtI/PXY/dR0EZ0HrOt7Tcj2fK7H0MuZs5Qa29vZ/bs2dZnYtdTmw+mUCjEs88+i9/v59VXX+X222/njDPOOCi/4/PmzePtt9/+0OvDzS/7IMwmB0mSiEajdHd3U1lZiRCCgoICTjnlFI499lh+8YtfAHDttdeyatUqVq9ezXXXXWeZBH2wWeJgwiFDugfbbPtDEZIkUVpayr/927/xb//2bySTSU4//XQmTJjACSecwHvvvccDDzxANBrliCOOsCLiI488ckQTeDBy0aWlpVa13cyHftD20tyGB4NB/H5/Lj8sMb3KwfSqfNpjMKbTtCvD+9tDyEKnP+pDVlyYCgQ76Q6xexzBYcy+HilR/FFJNxaLsXnzZgoLCz80Q01RFIqKioY0D9TX19PX10dTUxPTp0/nxhtvZN26dfz4xz/+SO97sMP8HF544QWefvppJk2axA9+8APC4TBLly5lw4YNTJ48GYD//d//5cwzz+SrX/2qZYRjRsgHK+HCIUS6ixYtor6+nqamJsaPH8/KlSutMR+jhZ07d7J06VI6OzuRZZmvfe1rXHPNNfT19e3TbpuDFW63m9/+9rfWj8BEOp22TOAfeughNm7ciMPhYP78+VZ+eNq0abvtptud7WVbWxuRSMQqEn7Q9hItSiq8lWOOKOGi0ycjyzI9/RpN7cZ/PX06fYM6mexQJ7S03S1tLwzSxccgXXOybnd3N7NmzbJ8MXZ3/GOPPcavfvUrfvazn3HaaadZ0W0ikdjtv/0k0DTNMmN6/vnnaWpq2mcDIu2Fsmg0ytKlS3E6nWSzWV566SWOO+44brvtNt566y2am5uJx+PccsstpFIpy+Hvg+c5mHFIGd6sXr2ab3/722iaxle+8hVuvPHGUT1/R0cHHR0d1NTUEIlEWLBgAc888wwPP/wwxcXFVrdNf3//J+q2OdQghGBwcNAygV+3bh07duygvLx8SH54zJgxH2m7bG7DTaOfWCxGNheuVlVVMWbMmBEjbE0XtHXrtHYaaYnmdo1ECvpzJjplRTK9OVObsSUynSGDbe0OY5PGKbR0GutLvuBk8ezd62jNwZAlJSVMnjx5jwTR3t7Ot771LSoqKvjlL385JCWzr3HHHXfw9ttvMzg4yPPPP88FF1zAOeecYw2IPOqoo0ZlVpmdKEOhEKFQiKuuuoqXXnoJgJ/97GfWVIvXXnuNNWvWUF9fzymnnDLqv+9RxmGXsX2Bs88+m6uuuoqrrrqKtWvXWnKYz3zmM1al/jCGhxCCtrY26urqqK2tZf369YRCIWbMmGGZwM+fP98y5dkTQqEQ9fX1lJeX4/f7h9hemuNvzIh4JBlWMi1o7dRo7tDo6NWp36nRNygoCkgWGY8tlunuN9YTymXaenIOY2e7mDtt+PPquk5TUxOhUGivxp7rus7vf/977rvvPn7+85/z+c9/fr/mbnft2sVll13GjTfeyB133MFzzz1HWVkZnZ2dqKrK3/72N26++Wb+9Kc/jcr7hUIhvvvd7yLLMtdffz1f//rX+c1vfsOMGTPYsGED//Ef/8Gll17KN77xDVRVpa+vzyoeHsS528MuY6ON5uZm/v73v7N48eJR7bb5Z4EkSZam89xzzwWMH9DmzZstE/gbb7wRIQRHHnmkFQ3PnDlzCGmm02m2b9+OpmnMmzfPimztelnT9tI+dcGuDvD7/ciyjNspMaNSZUZl/vyRmE5Lp0Zzh05zh0b/oL1Ql7+fkdILg4ODbNmyhTFjxuxx7DkYhPetb32LiRMn8te//pVgMPjRPthRwLe//W1uu+02y483FAqN6oBIuzKhubmZK664giOPPJLbbruNzs5OjjvuOP7yl78wY8YM5s2bx6RJk2hsbGTLli3MnTuX4uJiq3B+kBLubnGYdD8GotEo5557Lnfdddcec3KHsfdQFIW5c+cyd+5cli1bZvlAvPPOO6xbt45f/OIXbNu2jaKiImpqakilUnR1dXH77beP6Li1O9vLcDjMzp07iUQiyLI8JBo2I+yAT2bOVJk5U/Pn7Ok3CLi9V2dHm86ubh2vZyjpappGY2MjAwMDezUYUtd1fve733H//ffzi1/8glNOOeWAKBOef/55a/af6ZUwWsoguzLBXBcWFlJWVsabb74JGDrfhQsX8sc//pHa2lr6+/spKytjw4YN7Ny5k7lz537s9z9YcJh0PyIymQznnnsuX/7yl60k/ifptjmMkWH6QJx44omceOKJgEEA69ev54orrsDhcBAMBjnzzDOZPHnyEBP4goKC3bY1mx1hJsyJC6bRz0i2l2Dke8uK8hGrposhe8mBgQG2bt3KuHHj9jj2HIwC7dVXX82UKVN4/fXXD+iD/M033+TZZ59l9erVVkfdt7/97VEZEGlGpXfccQcbN25k9uzZXHvttdbI99/97ncsXbqUs88+m8WLF/Pkk09SXl7OkiVLuPjii/fYnfdpweGc7keAEILLLruM4uJi7rrrLuv16667jpKSEquQ1tfXx2233TZq77s/K8mfBvztb39DCGF5wpom8LW1taxbt4533nmHZDL5IRP4j/oZmaRjknE6nbbaeIPBIIFAYAgRaJphehONRpk1a9Zu9crmdT/88MP85je/4Ze//CWf+9znDqoIbu3atdx+++08//zznH/++Zx77rlWIe3II4/kG9/4xh7PkUgkLPP8eDzOxRdfTCAQ4Cc/+QlnnHEGX/rSl/jRj37E//3f/7FixQruvPPOIWqY1157jR/84AfMmzePe++9d5/d6z7A4ULaaOCNN97ghBNOYO7cuVZu7qc//SmLFy/mggsuoLW11eq2Gc0uof1VST6UkEqlLBP49evXWybwNTU1FhHvjYLADrvtpUnGuq5b5Nvb20tlZSUTJkzYI3m2trZy1VVXMWPGDG677Tb8fv8nveVRh510GxsbrQf9/Pnz+f3vf7/biRVCCDZs2MCGDRu4/PLLef3115k8eTI7duzg2GOP5frrr7e6HV955RWOP/54vvGNbyCE4L777rPOs3LlSqqqqqwGnU8RDpPupxX7u5J8qMI0gTcHhK5fv97SdJskvGDBAkpKSj5StJlOp9myZQvRaBSfz0cymRxiexkMBq22ZjCi2wceeICHHnqIX/7yl5x00kkHVXQ7mujq6uKSSy6hs7OTE088kR//+McUFxdzww030NnZyYMPPsj111/PypUraW1tpbW1FVVVGTdu3EFnx/gxcFi98GnFvq4k/7PANIH//Oc/z+c//3nAIMDW1lZqa2t5/fXXueOOOwiHw8ycOdNq4jjqqKOs7fEHEQqF2L59O1VVVRx55JEWSQxne/nkk0+STCbZsGEDNTU1vPHGGwdldDuaSCQStLS04PF4+MlPfkJRURHptDFqw3S8Gzt2LOl0mtdff50TTjgBOLjnm40GDpPuQYx9WUk+DKOgNmnSJCZNmsSSJUsAgzA3bdpEbW0tf/jDH7juuuuQZdnqplu4cCHFxcU8/fTTHHPMMcyfP/9DDRgftL3MZrO8+eabvPTSS1RXV7Njxw5OOOEEHn/8caZPn77f73t/YdKkSbzwwgs8+eST3HTTTdxzzz04nU48Hg9vvfUWd9xxB//yL//Cxo0bh4wTOtS/z4dJ9yDGvqwkH8bwcDgczJs3j3nz5nHFFVcMMYGvra3l6quvZsuWLSxYsICOjg4WLlzIokWLKC8vH5YsmpqauPrqq5k7dy5/+tOfLOmYZvemHEUcLK3qZrQ6ffp0Tj31VG699VaefPJJzj33XD73uc9RVFTE8ccfzyWXXAIYuw5Jkg55woXDOd1PDUajknwYnwwvvvgijzzyCHfffTeaplkm8OvXr6e7u9sygV+4cCFHHXUUK1as4NFHH+Xuu+/mhBNO2C+Esj9b1ffW6yCVSrFq1SruuusuHA4Hxx13HD/60Y8sb+aDuKvsk2DkP7YQYnf/HcZBgtdee02cfvrpQgghduzYIRYtWiSmTp0qzjvvPJFMJkf9/fr7+8W5554rqqurxcyZM8Vbb70lQqGQOPnkk8W0adPEySefLPr6+kb9fQ9m6Lo+4v/LZrNi06ZN4sEHHxRf//rXRVVVlTj//PNFLBbbj1f4YZx11lnipZdeEjNmzBDt7e1CCCHa29vFjBkzPtF5NU37SK8LIcTTTz8tfv3rX3+i9/0UYURePRzp7kPs2rWLFStW8JWvfOWAj1H5qLjssssbWm2BAAALE0lEQVQ44YQTWLZs2ZAx2oeNffYO4iAoBjU3N3PiiSfy/vvvU1lZycDAgPX/ioqK6O/v/0Tn37p1KzfccANz585l4sSJLFu2DNi7CPjT4gj2CXA40j0Q+M1vfiMkSRL333+/EMKIMB5//HFRX19/gK9s9wiHw2LSpEkfiuxGO1o6jH2HSCQiampqxJNPPimEECIYDA75/4WFhR/pfDt37hS/+tWvrN3Ne++9J+bOnSt+9atfiWeeeUbMmDFD/OEPfxidiz80MCKvHtKPmgONt99+G6fTSTweB2DHjh2sWbOG7du3A8bTXtO0fVZU+biwj9GeP38+y5YtIxaLHTb2+ZRgd63qwMdqVf/HP/7Be++9xwsvvAAYrc6nnHIK3/zmNzn77LP59a9/zfe//32SySRCCO666y5ee+210b2xQwSHSXcf4qWXXuK+++5j69athEIhBgYGCAaDlkxIlmUURTnoigjmGO0rr7ySv//97/h8vlEZo30Y+x5CCL761a8ya9YsvvOd71ivn3XWWTzyyCMAez0Y0uzoAzj11FM5+uijWb9+PVu2bEHTNNasWWMd+6//+q8sXryYDRs2kEgkOPXUU/nsZz87ynd3aOAw6e4jpNNpent7ufzyy3nttdeQJIldu3bhdDqpqqqivb2dyy+/nLPPPps77riDZDK5x3OK3effRw3DjdF+9913P3G0dBj7Hm+++SaPPvoor776qiV9W716NcuXL+fll19m+vTpvPzyyyxfvny35+nt7aWmpoaTTjqJFStW0NraykUXXYTP5+OPf/wjn/3sZ5kwYQLf+ta3ANi8eTOhUIjKykq8Xq81hXt/fWc/TThMuvsItbW11qiZiy++mN///vf09vZSUVGBrussWbKEY445hm984xu89dZbvPXWW3s85/4qzNjHaAPWGO2PEy19FNx5553Mnj2bOXPmcNFFF5FMJmlqamLx4sVMnz6dCy+80OpoOozhcfzxxyOE4B//+IflfXDaaadRUlLCK6+8Qn19Pa+88soevUFKS0u5++67SSQS9Pb2cuGFF/L2228zZ84cNE3jmWeeYeXKlTQ0NHDJJZdwwQUXcN55531IM36gi4kHIw6rF/YRbrnlFrZt28Zjjz3Gb3/7W+677z4uuugiZs+ejcvl4vrrr7cmpj722GOsWLGC5557bsSqdzabZc2aNcycOZNp06YN+56jWRHesGGDpVz44BjtfWHs09bWxvHHH8/mzZvxeDxccMEFnHbaaaxevfqwuc8BxMKFC7n00ks5+uijWbFiBe+99x4LFy4kGo3ywx/+kHHjxtHQ0IDf77eCjMMADnsv7H888cQT1oTSadOmoWkab7/9NkuWLOGRRx5h0aJF1rGhUMiyHdR1/UM53r6+PlauXMkLL7xAOBxGURQefPBBpk6dysDAAJlMhrKysmEJ1yzSfdS88ScZo/1xkc1mSSQSOBwO4vE4FRUVvPrqq9aA0csuu4ybb775MOnuRzz88MMcd9xxvP/++/z3f/83t912G2vWrGHt2rVUVVVx/fXXW0HAP4EMbFRw+BPaR/if//kfli5dChjmHj09PXR2dlJYWEhFRcWQfv01a9YMMWH5IMzC1hlnnMEbb7zBH//4RyZMmMCWLVu44447OOOMMzjuuON46KGHAMMH1tyGD1eo0zRt2Pc5kBg/fjzXXnstlZWVVFRUEAwGWbBgwT+9uc+aNWuorq5m2rRpB6SYOWfOHK6++mq+9KUvAXD99dfz1FNPccstt/CVr3xliJ/wYcLdS+xOT7Z/ZW2HNnbu3Ck2btwodF0XmUxGfPGLXxQnnHCCOO2008SNN94oenp6dvvvH374YXHeeeeJ3/3ud0IIIeLxuLjkkkvE17/+dSGEEK+//rr4/+2dXWwUVRTHfyctIjxIq9IK/Qgt24Bmu4lrpaWIsokLDQmWED5KE60WAw8qEROjpYX60iI1aaLxiaCUhqSGFBEeihqggcRSwJQYLJXGllpLG76mhbTVLrscH2Z2pSBCgN1+ML+XnXt2du6dzczZs/977pmSkhJVVd22bZs6nU4tLy/X1atXa319/V3HFwgE/nc1UbgxDEM9Ho9evHhRfT6f5ubmanV1tc6cOTO0T2dnpzqdzhEbY6Tx+/2ampqqbW1tOjQ0pC6XS5ubm0dkLOnp6VpUVHSbfSSvmVHOHf2qLS9EiGBGAEB0dDR79+7l9OnTdHR0sGTJEsCsKtba2sqGDRuG6br9/f0UFBTg8XhYuXIl165dw+VyYRgGbW1tzJkzB5/PFypw0t7eTlxcHF6vF8MwqKmpITs7G5/Px65du2hpaWHevHl4vd5Q0ZM7RSkaoZVVBw8eJCUlJVRtatmyZTQ0NDzSxX1OnDiBw+EgNTUVgLy8PPbt2xfKDIgkVVVVlJWVDZMQVNWObu8D2+mOIMGHMAYdW05ODk6nc5iTCwQC1NbW0tXVRU5ODqtWraKlpYWFCxfS19cXyno4e/Ys3d3dJCUlUV9fz6ZNm8jIyGD69OmsW7eOy5cvs2PHDvx+P7Nnz6aqqgq/309+fj5nzpzh2LFjJCQk4PF4hj0R4FaHGy7dLjk5mcbGRgYHB5k0aRKHDh0iIyMDj8dDbW1tSAt/2BkTo5nz58+TlJQUaicmJobyZiON2+1mz549w2x2ZsL9Yf9MjQKCF290dDQzZswY9l5UVBRutxvDMNi8eTOnTp1i48aNxMfHExMTw+HDhwGYNWsWHo+HoaEhLl26RFZWFgC9vb0MDAwQGxvLlClTaGpqYsGCBdTV1ZGfn09zczPFxcUcP36ciooKSktLuX79Or29vTQ0NHDu3LlhK+bCFdlkZmayfPly3G436enp3Lhxg7Vr17J161YqKytxOBxcuXIlNDn5IBQWFhIXF4fT6QzZDMPA6/WSlpaG1+sN1SVQVdavX4/D4cDlctHU1PTA/d8rOgprJ4+21ZNjkbuljNmMYkTkFWALEAM0Ax8CzwDbVdUp5h26BChU1aUiMhl4G0gFjgDfAeVAQFVLrGO2A4uACcAB4CdgLvAS4AKeAH5W1baInehDRkReBvqBalV1WrYKwFDVT0XkYyBWVT8SkcXAe8BiIBP4XFUzIzTOucAnqrrIahcBqOqWSPRvEx7sSHcMo6pHVDUbeB4oVtUOIA4ILnqfAGQBHVZ7oqp+ATQBJcCzwFPAjwAi8iTQAEwFkjHztNcAacBzQAHwKrBbRNaIyOhav3yPqOpRwLjFnAvstLZ3AktvsldbkyONQIyITIvMSDkJpIlIiog8BuQB+yPUt02YsDXdcYCqDgGt1vZ+/r0xA5gOtkdE3ECliLQCg8BRYDLwN2bkexRwWm2ABOCAqv4FICJxQIKqzrfaU1V1PP3XjFfVHgBV7bHOF8zv4c+b9uuybD3hHpCq+kXkXeAHIAr4WlWbw92vTXixne44xnKKtcG2iLwJzAeSgG9V9TcRyQBeExEDyAf+AH7BjPB+tT4nmHLEiyLyGVCjqpETN0eW/xJRI6bJqWodUBep/mzCj+10HyEs+aHjFvM3wHXgdeB7qx0DvIAZYYGpb3YB71t6Z5mIfKCqLZEYd4S4ICLTrCh3GhCsW9mF+SMVJBHojvjobMYNtqb7iKOqfar6laqusF4HgKvAdiC4DjhFRKpE5B3gd2AAmHiHQ45V9mNq1liv+26yvyEmWcDVoAxhY3M/2NkLNndFRB7HnL2fizmpVg6c1DF68YhIDbAAeBq4AJRiZnLsxpxA7ARWqKphSStfAjmYWvhbqnp7UQobm3vkHyxzG5UoOAb1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"nsensors = np.arange(1, 100.0)\n",
"ntargets = np.arange(1, 100.0)\n",
"nsensors, ntargets = np.meshgrid(nsensors, ntargets)\n",
"ncombinations = ntargets**nsensors\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(projection=\"3d\")\n",
"ax.plot_surface(nsensors, ntargets, np.log10(ncombinations), cmap='coolwarm')\n",
"ax.set_xlabel(\"No. sensors\")\n",
"ax.set_ylabel(\"No. targets\")\n",
"ax.set_zlabel(\"log of no. combinations\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metrics\n",
"\n",
"Metrics can be used to compare how well different sensor management techniques are working. As in Tutorial 1 the metrics used here are the OSPA, SIAP and uncertainty metrics. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.metricgenerator.ospametric import OSPAMetric\n",
"ospa_generator = OSPAMetric(c=40, # 'Max distance for possible association'\n",
" p=1) # 'norm associated t distance'\n",
"\n",
"from stonesoup.metricgenerator.tracktotruthmetrics import SIAPMetrics\n",
"siap_generator = SIAPMetrics(position_mapping=[0, 2], velocity_mapping=[1, 3])\n",
"\n",
"from stonesoup.dataassociator.tracktotrack import TrackIDbased\n",
"associator=TrackIDbased()\n",
"\n",
"from stonesoup.metricgenerator.uncertaintymetric import SumofCovarianceNormsMetric\n",
"uncertainty_generator = SumofCovarianceNormsMetric()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate a metrics manager for each sensor management method."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.metricgenerator.manager import SimpleManager\n",
"\n",
"metric_managerA = SimpleManager([ospa_generator, siap_generator, uncertainty_generator],\n",
" associator=associator)\n",
"\n",
"metric_managerB = SimpleManager([ospa_generator, siap_generator, uncertainty_generator], \n",
" associator=associator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each time step, data is added to the metric manager on truths and tracks. The metrics themselves can then be generated from the metric manager. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"metric_managerA.add_data(truths, tracksA)\n",
"metric_managerB.add_data(truths, tracksB)\n",
"\n",
"metricsA = metric_managerA.generate_metrics()\n",
"metricsB = metric_managerB.generate_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OSPA metric\n",
"\n",
"First we look at the OSPA metric. This is plotted over time for each sensor manager method."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x19df00aecc8>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ospa_metricA = {metric for metric in metricsA if metric.title == \"OSPA distances\"}.pop()\n",
"ospa_metricB = {metric for metric in metricsB if metric.title == \"OSPA distances\"}.pop()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.plot([i.timestamp for i in ospa_metricA.value], \n",
" [i.value for i in ospa_metricA.value],\n",
" label='RandomManager')\n",
"ax.plot([i.timestamp for i in ospa_metricB.value], \n",
" [i.value for i in ospa_metricB.value],\n",
" label='UncertaintyManager')\n",
"ax.set_ylabel(\"OSPA distance\")\n",
"ax.set_xlabel(\"Time\")\n",
"ax.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OSPA distance starts large due to the position offset in the priors and then improves for both scenarios as observations are made. The `UncertaintyManager` generally results in a smaller OSPA distance than the random observations of the `RandomManager`.\n",
"\n",
"A larger number of sensors results in improved performance for the `RandomManager`, in comparison to Tutorial 1 where there is only one sensor. This is due to the increased likelihood of each target being observed randomly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SIAP metrics\n",
"\n",
"Next we look at SIAP metrics. We are only interested in the positional accuracy (PA) and velocity accuracy (VA). These metrics can be plotted to show how they change over time."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x19df0224548>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2)\n",
"\n",
"for metric in metricsA:\n",
" if metric.title.startswith('time-based SIAP PA'):\n",
" pa_metricA = metric\n",
" elif metric.title.startswith('time-based SIAP VA'):\n",
" va_metricA = metric\n",
" else:\n",
" pass\n",
"\n",
"for metric in metricsB:\n",
" if metric.title.startswith('time-based SIAP PA'):\n",
" pa_metricB = metric\n",
" elif metric.title.startswith('time-based SIAP VA'):\n",
" va_metricB = metric\n",
" else:\n",
" pass\n",
" \n",
"times = metric_managerA.list_timestamps()\n",
"\n",
"axes[0].set(title='Positional Accuracy', xlabel='Time', ylabel='PA')\n",
"axes[0].tick_params(length=1)\n",
"axes[0].plot(times, [metric.value for metric in pa_metricA.value], \n",
" label='RandomManager')\n",
"axes[0].plot(times, [metric.value for metric in pa_metricB.value], \n",
" label='UncertaintyManager')\n",
"axes[0].legend()\n",
"\n",
"axes[1].set(title='Velocity Accuracy', xlabel='Time', ylabel='VA')\n",
"axes[1].tick_params(length=1)\n",
"axes[1].plot(times, [metric.value for metric in va_metricA.value], \n",
" label='RandomManager')\n",
"axes[1].plot(times, [metric.value for metric in va_metricB.value], \n",
" label='UncertaintyManager')\n",
"axes[1].legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to the OSPA distances, positional accuracy starts as quite poor for both scenarios due to the offset in the priors, and then improves over time as observations are made. Again the `UncertaintyManager` generally results in a better positional accuracy than the random observations of the `RandomManager`.\n",
"\n",
"Velocity accuracy also starts quite poor due to an error in the priors. It improves over time as more observations are made, then remains relatively similar for each sensor manager. This is because the velocity remains nearly constant throughout the simulation. This is not likely to be the case in a real-world scenario.\n",
"\n",
"As with the OSPA metric the larger number of sensors generally results in improved performance for the `RandomManager` in comparison to Tutorial 1. This is due to the increased likelihood of each target being observed randomly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Uncertainty metric\n",
"\n",
"Finally we look at the uncertainty metric which computes the sum of covariance matrix norms of each state at each timestep. This is plotted over time for each sensor manager method."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x19df026d988>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"uncertainty_metricA = {metric for metric in metricsA if metric.title == \"Sum of Covariance Norms Metric\"}.pop()\n",
"uncertainty_metricB = {metric for metric in metricsB if metric.title == \"Sum of Covariance Norms Metric\"}.pop()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.plot([i.timestamp for i in uncertainty_metricA.value], \n",
" [i.value for i in uncertainty_metricA.value],\n",
" label='RandomManager')\n",
"ax.plot([i.timestamp for i in uncertainty_metricB.value], \n",
" [i.value for i in uncertainty_metricB.value],\n",
" label='UncertaintyManager')\n",
"ax.set_ylabel(\"Sum of covariance matrix norms\")\n",
"ax.set_xlabel(\"Time\")\n",
"ax.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This metric shows that the uncertainty in the tracks generated by the `RandomManager` is much greater than for those generated by the `UncertaintyManager`. This is also reflected by the uncertainty ellipses in the initial plots of tracks and truths. \n",
"\n",
"The uncertainty for the `UncertaintyManager` starts poor and then improves initially as observations are made. This initial uncertainty is because the priors given are not correct. The uncertainty then increases slowly over time. This is likely because the targets are moving further away from the `RadarBearingRange` sensors so the uncertainty in the observations made increases.\n",
"\n",
"There appears to be a periodicity in the uncertainty metric for the UncertaintyManager. This is due to the fact that this simulation is quite clean and the uncertainty of each track increases by the same amount if left unobserved. Since the sensor manager is then making observations based on this uncertainty, it rotates through observing each of the targets in the same order creating this pattern. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"[1] Hero, A.O., Castanon, D., Cochran, D. and Kastella, K. *Foundations and Applications of Sensor Management*. New York: Springer, 2008.\n",
"\n",
"[2] D. Schuhmacher, B. Vo and B. Vo, A Consistent Metric for Performance Evaluation of Multi-Object Filters, *IEEE Transactions on Signal Processing*, 2008, 56(8), 3447-3457.\n",
"\n",
"[3] Votruba, P., Nisley, R., Rothrock, R. and Zombro, B. *Single Integrated Air Picture (SIAP) Metrics Implementation*, Single Integrated Air Picture (SIAP) System Engineering Task Force (SE TF). OCT 2001."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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