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Sensor Management Tutorial 1 (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 1\n",
"\n",
"This notebook introduces how Stone Soup classes can be used to build simple sensor management algorithms for tracking and state estimation. The intention is to futher develop the methods explored here in order to build sensor management classes that can be added to the Stone Soup framework. This notebook exposes some of the logic which would otherwise be hidden by a sensor manager class.\n",
"\n",
"## Background\n",
"\n",
"Sensor management is the process of deciding and executing the actions that a sensor or group of sensors will take in a specific scenario and with a particular objective, or objectives in mind. The process involves using information about the scenario to determine an appropriate action for the sensing system to take. An observation of the state of the system is then made using the sensing configuration decided by the sensor manager. The observations are used to update the estimate of the collective states and this update is used (if necessary) to determine the next action for the sensing system to take. \n",
"\n",
"A simple example can be imagined using a sensor with a limited field of view which must decide which direction it should point at each time step. Alternatively, we might construct an objective based example by imagining that the desired target is fast moving and the sensor can only observe one target at a time. If there are multiple targets which could be observed the sensor manager could choose to observe the target that had the greatest estimated velocity at the current time.\n",
"\n",
"The example in this notebook considers two simple sensor management methods and applies them to the same ground truths in order to quantify the difference in behaviour. The scenario simulates 10 targets moving on nearly constant velocity trajectories and a sensor that can only observe one target at each timestep.\n",
"\n",
"The first method, \"RandomManager\" chooses a target randomly with equal probability. The second method, \"UncertaintyManager\" aims to reduce the total uncertainty of the track estimates at each timestep. To acheive this the sensor manager chooses to look at the target for which the estimated uncertainty (as represented by the Frobenius norm of the covariance matrix) can be reduced the most by making an observation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sensor managment as a POMDP\n",
"\n",
"Sensor management problems can be considered as Partially Observable Markov Decision Processes (POMDPs) where observations provide information about the current state of the system but there is uncertainty in the estimate of the underlying state due to noisy sensors and imprecise models of target evaluation.\n",
"\n",
"POMDPs consist of: [1] \n",
"- $X_k$, the finite set of possible states for each stage index $k$\n",
"- $A_k$, the finite set of possible actions for each stage index $k$\n",
"- $R_k(x, a)$, the reward function\n",
"- $Z_k$, the finite set of possible observations for each stage index $k$\n",
"- $f_k(x_{k}|x_{k-1})$, a (set of) state transition function(s). (Note that actions are excluded from the function at the moment. It may be necessary to include them if prior sensor actions cause the targets to modify their behaviour.)\n",
"- $h_k(z_k | x_k, a_k)$, a (set of) observation function(s).\n",
"- $\\{x\\}_k$, the set of states at $k$ to be estimated\n",
"- $\\{a\\}_k$, a set of actions at $k$ to be chosen\n",
"- $\\{z\\}_k$, the observations at $k$ returned by the sensor\n",
"- $\\Psi_{k-1}$, denotes the complete set of 'intelligence' available to the sensor manager before deciding on an action at $k$. This includes the prior set of state estimates $\\{x\\}_{k-1}$, but may also encompass contexual information, sensor constraints, mission parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figure 1: Illustration of sequential actions and measurements, [1]\n",
"\n",
"![intro_flow_diagram_f&h.PNG](intro_flow_diagram_f&h.PNG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\Psi_k$ is the intelligence/information available to the sensor manager at stage index $k$, to help select the action $a_k$ for the system to take. An observation $z_k$ is made by the sensing system, giving information on the state $x_k$. The action $a_k$ and observation $z_k$ are added to the intelligence set to generate $\\Psi_{k+1}$, the intelligence/information available at stage index $k+1$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparing sensor management methods using metrics\n",
"\n",
"The performance of the two sensor management methods explored in this notebook can be assessed using metrics available from the Stone Soup framework. The metrics used to assess the performance of the different methods are the OPSA metric [2], SIAP metrics [3] and an uncertainty metric. Demonstration of the OSPA and SIAP metrics can be found in the Metrics Example. \n",
"\n",
"The uncertainty metric computes the covariance matrices of all target states at each timestep and calculates the sum of their norms. This gives a measure of the total uncertainty across all tracks at each timestep."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sensor Management example\n",
"\n",
"### Setup\n",
"\n",
"First a simulation must be set up 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",
"Following the methods from previous Stone Soup tutorials we generate a series of combined linear Gaussian transition models and generate ground truths. Each ground truth is offset in the y-direction by 10.\n",
"\n",
"Ground truths are assigned an ID. This is later used by the data associator.\n",
"\n",
"The number of targets in this simulation is defined by `n_truths` - here there are 10 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 in the next cell."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1991)\n",
"\n",
"# Generate transition model\n",
"# i.e. fk(xk|xk-1)\n",
"transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(0.005),\n",
" ConstantVelocity(0.005)])\n",
"\n",
"yps = range(0,100,10) # y value for prior state\n",
"truths = []\n",
"ntruths = 10 # 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 a measurement model\n",
"\n",
"Assign a measurement model. This notebook explores the use of either the `LinearGaussian` measurement model or `CartesianToBearingRange` measurement model. This is changed by setting `nonlinear_example`. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.models.measurement.linear import LinearGaussian\n",
"from stonesoup.models.measurement.nonlinear import CartesianToBearingRange\n",
"\n",
"nonlinear_example = False # Change to True for nonlinear demonstration\n",
"\n",
"# Generate measurement model\n",
"# i.e. hk(zk|xk, ak)\n",
"if nonlinear_example:\n",
" measurement_model = CartesianToBearingRange(\n",
" ndim_state=4,\n",
" mapping=(0, 2),\n",
" noise_covar=np.array([[np.radians(0.5)**2, 0],\n",
" [0, 0.75**2]]),\n",
" translation_offset=np.array([[10],[50]]) # Moves sensor location from default (origin)\n",
" )\n",
" \n",
"else:\n",
" measurement_model = LinearGaussian(\n",
" ndim_state=4,\n",
" mapping=(0, 2),\n",
" noise_covar=np.array([[0.75, 0],\n",
" [0, 0.75]])\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the Kalman predictor and updater\n",
"\n",
"Construct a predictor and updater using the `KalmanPredictor` and `ExtendedKalmanUpdater` components from Stone Soup. The `ExtendedKalmanUpdater` is used because it can be used for both linear and nonlinear measurement models."
]
},
{
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the Kalman filters\n",
"\n",
"First create `ntruths` priors which estimate the targets’ initial states, one for each target. In this example each prior is offset by 5 in the y direction meaning the position of the track is initially not very accurate. The velocity is also systematically offset by +0.5 in both the x and y directions."
]
},
{
"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.\n",
"\n",
"(NB: Tracks are also assigned an ID, used later for data association)"
]
},
{
"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 manager classes. Two sensor manager classes are built - `RandomManager` and `UncertaintyManager`.\n",
"\n",
"#### RandomManager\n",
"\n",
"The first method `RandomManager`, chooses a target to observe randomly. To do this the `choose_actions` function uses `np.random.uniform()` to draw random samples from a uniform distribution between 0 and 1, and multiply by the number of targets in the simulation. This means a single target to observe is selected randomly at each time step. \n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class RandomManager(Base):\n",
" \n",
" action_list: list = Property(doc=\"List of possible actions\")\n",
" \n",
" def choose_actions(self):\n",
" return self.action_list[int(np.floor(len(self.action_list)*np.random.uniform()))] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### UncertaintyManager\n",
"\n",
"The second method `UncertaintyManager` selects a chosen target to observe based on the difference between the covariance matrices of the prediction and an update using the predicted measurement. This means the sensor manager chooses to observe the target for which the total uncertainty will be reduced the most by making that observation.\n",
"\n",
"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 `reward_list` function then generates a list of this metric for every track. \n",
"\n",
"The `choose_actions` function takes in the variable `action_list_metric` generated by the `reward_list` function and chooses the target with the largest value of this metric to observe.\n",
"\n",
"The choice of target which is to be observed, $N$ is found using the following equation:\n",
"\n",
"$$\n",
"N = \\underset{n}{\\operatorname{argmax}}(m_n)\n",
"$$\n",
"\n",
"where $n \\in \\lbrace{1, 2, ..., \\eta}\\rbrace$ and $\\eta$ is the number of targets. The metric, $m_n$ is calculated for each track using the following equation.\n",
"\n",
"$$\n",
"m_n = \\begin{Vmatrix}P_{k|k-1}\\end{Vmatrix}-\\begin{Vmatrix}P_{k|k}\\end{Vmatrix}\n",
"$$\n",
"\n",
"where $P_{k|k-1}$ and $P_{k|k}$ are the covariance matrices for the prediction and update of the track respectively. Note that $\\begin{Vmatrix}P_{k|k-1}\\end{Vmatrix}$ and $\\begin{Vmatrix}P_{k|k}\\end{Vmatrix}$ represent the Frobenius norms of these covariance matrices."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"class UncertaintyManager(Base):\n",
" \n",
" action_list: list = Property(doc=\"List of possible actions\") \n",
" predictor: KalmanPredictor = Property()\n",
" updater: ExtendedKalmanUpdater = Property()\n",
" \n",
" def choose_actions(self, tracks_list, metric_time):\n",
" action_list_metric = self.reward_list(tracks_list, metric_time)\n",
" return self.action_list[np.argmax(action_list_metric)] \n",
" \n",
" def reward_list(self, tracks_list, metric_time):\n",
" metric_list = []\n",
" for track in tracks_list:\n",
" metric = self.calculate_reward(track, metric_time)\n",
" metric_list.append(metric)\n",
" return metric_list\n",
" \n",
" \n",
" def calculate_reward(self, track, metric_time):\n",
" # i.e. Rk(x, a)\n",
" \n",
" # Do the prediction and store covariance matrix\n",
" prediction = self.predictor.predict(track[-1], \n",
" timestamp=metric_time)\n",
" \n",
" pred_cov_norm = np.linalg.norm(prediction.covar)\n",
" \n",
" # Calculate predicted measurement\n",
" predicted_measurement = self.updater.predict_measurement(prediction)\n",
" \n",
" # Generate detection from predicted measurement\n",
" detection = Detection(predicted_measurement.state_vector, \n",
" timestamp=prediction.timestamp)\n",
" \n",
" # Generate hypothesis based on prediction and detection\n",
" hypothesis = SingleHypothesis(prediction, 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",
" # Calculate metric\n",
" metric = pred_cov_norm - update_cov_norm\n",
" return metric\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an instance of the sensor manager\n",
"\n",
"Create an instance of each sensor manager class. Each class takes in a `action_list`, a list of the possible actions to select from. Here this is the possible target numbers the manager can choose to observe. The `UncertaintyManager` also requires a predictor and an updater."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"actions = range(0, ntruths) \n",
"\n",
"randommanager = RandomManager(actions)\n",
"uncertaintymanager = UncertaintyManager(actions, predictor, updater)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the sensor managers\n",
"\n",
"From here the method differs slightly for each sensor manager. `RandomManager` does not require any other input variables whereas `UncertaintyManager` requires a tracks list at each timestep. \n",
"\n",
"For both sensor management methods, at each timestep a prediction is made for each of the targets except the chosen target, which is 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`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Generate list of timesteps from ground truth timestamps\n",
"timesteps = []\n",
"for state in truths[0]:\n",
" timesteps.append(state.timestamp)\n",
"\n",
"predictionsA = []\n",
"measurementsA = []\n",
"\n",
"for timestep in timesteps[1:]:\n",
" \n",
" # Activate the sensor manager and make a measurement\n",
" chosen_target = randommanager.choose_actions()\n",
" \n",
" # The ground truth will therefore be:\n",
" selected_truth = truths[chosen_target][timestep]\n",
" \n",
" # Observe this\n",
" measurement = measurement_model.function(selected_truth, noise=True)\n",
" measurementsA.append(Detection(measurement, \n",
" timestamp=selected_truth.timestamp))\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\n",
" for target_id, track in enumerate(tracksA):\n",
" prediction = predictor.predict(track[-1], \n",
" timestamp=measurementsA[-1].timestamp)\n",
" \n",
" if target_id == chosen_target: # Update the prediction \n",
" # Association - just a single hypothesis at present\n",
" hypothesis = SingleHypothesis(prediction, measurementsA[-1]) # Group a prediction and measurement\n",
"\n",
" # Update and add to track\n",
" post = updater.update(hypothesis) \n",
" else:\n",
" post = prediction\n",
" \n",
" track.append(post)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot graph A\n",
"\n",
"Plot ground truths, tracks and uncertainty ellipses for each target. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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2A5Z35Au6D4h5NAUBjlIYa/Gt7alLtjufbPe+SRON4iULGXLJ+3DLinlqza9YVP9nABQaR3sMLd1Txd9ViqSrAE3OGt5IZRhgIvTfTy5Zzhgclc9vOxFIYCaEEEKId60rDFnS1oWrFKG17Aos1lheS6XQ1hLVTk9AZo2B15dDRwdqxlmoeBENM0fwyvYH2LZiAalcI5MHXMm4ylns7FjN0NJqBpVM2O/jRpTGdRX1mRwO0C8W2WdMmdAyMRk/YcpnSGAmhBBCiHelKwypbe1idVd+9yVYXK2Juw5jCmJsz/r4xhLVCqe5kfT85zHNjbgDhxDXsKNtGc+tuAVrQwCmDPssI6quxMfSPzGBpOcSWIOr9h9caaUodh22Znwqox4ZY0mFhgJHM6O44IQqmXFKBmbWWrpCQ9oY0qGhPQzJhoawe4pVA1GtSbiapOMQ1YqE4+C9zYrCGzdu5PLLL2f58uU9x26//XaSySRf+cpXjuyT6jZ37lxGjx7NuHHjDnre3XffTUFBAf/wD/9wwHPq6urYtm0bl1566ZEephBCiJNAYCxrUxm2ZnM05gJcrd6aT6ZhuKNpz2TZufAVMquWQSyON+s9RIcMZ1fHKp5Z9e2eoEyh0Tbb0xYxZw3bMjkUisqoS6nnvmW50lhLqrtrQLMfUOy6TEjGKPW8E64bwCkVmKVDw/asz9ZMFt/mu29pBRGlcLXq6cNlgZQxtGZCApulu6oK5Z7DwHiUUu/4/GsLgoC5c+dy+eWXHzIwu/HGGw95vbq6OmprayUwE0II8RaZ0PBaR4p0GFLmuRS5Dss7UrT4ARGt98ony5fDULkcZu1qEqPH0jmmmFVt82le/Xu2ND5P1CtBKw9jQ7T2KCuaSM6Y/P1QxLRDiGVzOkdLLqQ86gL539vWgsFgreK80iTDEwUUnCD5ZPtzfEYYvaDFD3itI4UCko5zWBF03AHIR+y2u1jdorYuhsQjjCyIod9FsbrZs2czc+ZMnnnmGVpbW7nvvvs455xzCMOQr371qzz55JMopfjUpz7FTTfdxKJFi/jyl79MZ2cn5eXlPPDAA1RVVTF79mzOPPNMXnrpJS666CIeffRRnnvuOb7zne8wZ84c5s2bxz333EMul2PkyJH85je/oaCgYJ+Zu/2NZebMmXzjG98gnU7z4osv8rWvfY1bb72Vl19+mYqKCowxjB49mldeeYXy8vJ3/PcghBDixLQ2laEp54NSNHRlSBlLOjTszAakjSGmNToIYN1qGDMBpT3s+65hi7+R55Z/FWN9AIaUncXF426hNbWBLS11VBRNpqxoLBborrKBIp/wH4u4pENDoaOpikVxVH63pQImJGNURCMHGu4J45QIzNKhYUl7iqSru9e+3z6lFAnXocDRbE7niGnN4Hj0XY0rCAIWLFjA448/zje/+U2eeuop7rnnHjZs2MCSJUtwXZfm5mZ83+emm27iT3/6ExUVFTz88MP827/9G/fffz8Ara2tPPfcc0C+Kfrll1/O1VdfDUBJSQmf+tSnALj11lu57777uOmmmw5rLN/61reora3lv/7rvwBYvXo1Dz30EDfffDNPPfUUkydPlqBMCCFOMZ1ByPZsjmebO/CUyve41Cqf9hONUB7xaPEDGjZtInjleejqJFZRSaSyiiAe4YW19/cEZaAoSo6mOXRR0dGMGzS2pzbZgXQEIQZFYC2hhX5RjyHxyAlRo+xwnBKBmeneousdgXYMSim0UuTMoVtZHajg3e7jV111FQDTpk1j48aNADz11FPceOONuG7+R1NaWsry5ctZvnw5F154IQBhGFJVVdVzvQ996EMHHMPy5cu59dZbaW1tpbOzk4svvni/5+1vLG/2yU9+kiuvvJKbb76Z+++/n0984tDVl4UQQpwcrM0vJa5NZ3CVYlDMo9EPenLBdjPZLOn5LxK8vhKvuIT4e99PuryCTS3LWLj2x3Sk61FoLOBoj4Elk0k6Dob8ylRLLk1ZxKU84mEB31gCa1EqP4PWHoRMLypgTCJ2QjcrP5BTIjArcDTDCqKsS2Uo9px3PGsWWkubH5B0HAbGDj1dWlZWRktLyz7HmpubexqOR6P5GTfHcQiCAMj/w39zQGetZfz48cyfP3+/j5NIHLjx+vXXX8/cuXOZPHkyDzzwAM8+++x+z9vfWN5s0KBBVFZWMm/ePF599VUeeuihAz6uEEKIk8sbqQxb0jlKI27P0mKLH7I5nSPh5Iu4htbS/Je5+E0NFE2eStnUGWzvXMbLy3/A5tblFMX6ctWUH4KKsrllCaWFkygqPI2UMexu3e0oxbaMT3tgGJWIUhRxKdAapaArNJzbp5BRidhx1fvySDolAjOlFMMLohQ6inXpHE25AAVEdH4K1u2eBXsz3+QL4uWMIbQWrRQDYxGGxmOHtUMzmUxSVVXF008/zQUXXEBzczN//etf+ad/+id+/etf7/c+F110EXfffTezZ8/uWcocM2YMDQ0NzJ8/nzPOOAPf93njjTcYP378W+5fWFhIR0dHz/cdHR1UVVXh+z4PPfQQAwYMOOy/tzdfC+CGG27guuuu42Mf+xjOSTJtLIQQ4uDa/IAVnWmssWzN+vjdifmhNfjGsL6lHRWL4bkuzpQZ4EboKC3jtc1/YfH6n5Pvh6k5d8yXGVFWQ1wrJlZMPuhjtvohVdEIjlJkjMFTitOLk1REvaPzpI+Rk2v+7xAqohFmFieYUZxgVEGUPp6LtdDmhzTnAppyfvdH/uucsSS0ZlAsSnVRgrNLChmViL+tshkPPvgg3/nOd6iurub888/ntttuY8SIEQc8/4YbbmDw4MFMmjSJyZMn89vf/pZIJMIf/vAHvvrVrzJ58mSqq6t5+eWX93v/D3/4w/zwhz9kypQprFu3jm9/+9vMnDmTCy+8kNNOO+1t/X2dd955rFy5kurqah5++GEArrjiCjo7O2UZUwghTgFdQcjyjjTzWzvZmMrSGRqiWlHiuRR7Dn08l5KNb5B4Yg4Va5ZT7DroyipMcYKlG3/VE5TttrF1FRtSWd5IZWnK+Vi757bQWjKhoTMIaQsC2vyA9iCk2HWoLkxwZp/Ckz4oA1B7/6WcqGpqamxtbe0+x1atWsXYsWMP6/7WWgKb39KbL6KRn0p1TtJp0nejtraWL33pS7zwwgvv+Bpv52cjhBDi2NiZybGyK4OrFYWOpj7jsy2bo9h1cJQi29rC1mefJrVjG4kBgxhw7vlEiorZ1LKcPy2/g5bUFoaUnUl9Sy3GBGjtMWv89ygvGkdgLZnQEncU/SMejqOIKk3c0USVwreWyqjLzOIk7kmWQwaglFpkra3Z322nxFLmoSiV73S/u16Z2L877riDu+66S3LLhBDiJNcehCzrTFPoOFgFHaEhpiCmFeu6soQb1hC+8jzKcSg85zyKRp3GurZlvPzq/dS3LqcoVsF1037EiPLpbGldzsbmOgb3qaZ/cT4Fx3bPonUGhoSjOS2ZzxnrDAw5YxkXjzC0IHpKTpBIYCYO2y233MItt9xyrIchhBCiF3WFISs7UqxJZfB6WiDli8RqBRVRl9bSMjr7DyJXcwaZWAFLNj7Kaxt+we5cspqRX6KkqJqsMQwqmbCfPpf5chcFjqYhF1CeC4g6mv5Rj37RCEUnUAulI00CMyGEEEKQDg1vdKVp9AMyocVaS6GncZTChiENdYvwOzsYcO4F9BnQH9P/fXT5WZ5ddz+vbf5fdueSWWB7+2piiclYLAlH0697uVLtzhci33zcWBiViDKjOEGR556SM2RvdlIHZvsrPSGOrZMhp1EIIU42HUHIkvYUSkF5JJ9gH3MU61M5aGmi6bmnyDQ1UjxyNNYY6ttXsnTr31jT+CptmR2MLj+T9c21hCbA0R5T+06nf2EcYy1dYUioYHg0SoGr8XQ+QOsIDUWuw/hknPgJ3ELpSDtpA7NYLEZTUxNlZWUSnB0nrLU0NTURi8WO9VCEEELs5Y2uNL4xeFrTkPVJh4a079NVV0vr4oXYaAxmXUhu6DDqdi3iz0u/2tN0/OIxX+D0odf05JINLa3eZ+ky6Tp0BiGNOZ/+ToRMYIkoGBGPMjAekVmyNzlpA7OBAwdSX19PQ0PDsR6K2EssFmPgwIHHehjHhLUW0/212v0hL0hCiGPEWkt7ELIj67O4vYucgZjOV+T3tIJ0ivZldRSOGEmfM2aRcT3Wtazk2RXf7gnKQLM13cnWdJbCxGlUJ/M77tuDMJ/g3710mQ4N2nEZEI1QHsk3PH83/aZPZidtYOZ5Xk+FfSGOhpzJ7ybKGEsmDOkMDR1BSNoYAgt7avmonq8V+R5zRY5DwtUkHYeoVkS0JqqlZIsQonfkjOH1rgw7sz5RRzM6EWdNVxYPS27jOgpGjkYVFjHy2v9DJFlIEGZZsO7XvLzh98QjRYTKw9gQR3uMKJtCzkIYhAyNR0m4uruIu8ZTimx3Vf/pJclTOqn/cJ20gZkQvc1YS0cQ0uTnG/pmQtMzA6YUPc19Cw/yztB2N+HtMobWTEhos91vMPPBW6nnUhnxKPZcycEQQhwRxlrq2lOkjdmnYOvQoI3aJ/5CZtdOcq5H2dBhRJKFLK5/jHlv3EOX38qUAZdx0ZjP0dC18S3LllljaPYD+kULiGpFV2jIGEOR6zIuGTtpmoz3NgnMhHibfGPZns2xKZ0lZ8FV+X6sSdfrvt0QGMhaS1suIGvzL4SGfO0e1R12OShcDTFHE1GauFZ42u0J7qy1dBnDyq40FijzXIbGo5R48t9WCPHONfsB9RmfhKPY4GdJBQGNK5bRsuBl0A6Fs95DR0UVOzvaWLzmZ2xsnAeAVh7jqt6LcgroWzSOvkXjgPwyZWAtIZau0LAhnWFwLEJV1KMyGqHQ0ZK28TbIK7wQb8OOTI7XUxmMtRS5Lsnupro7swEdQX6LebjX+Urlu0j05JSRX8Ts+bCW0NqeWxSKmKNIOPlWJwlHkejeIdUVhNS2d1HhuYxJxInJDJoQ4m1ozAVsTmdp8gO2ZnM45BPzG5+fR/vrq0gOHEz/2RfgJZLUt63mieXfo6lrU8/9rQ15o2kxA0rGE9V7VgccFHGd323ZGYZML0rSP+ZJMPYOSWAmxGFan8qyLpWhj+f2zJrtygUYC46CqKNIuBqtVD7gwhIYumfK8p93v0zlZ8zAdRSu2jO9b60lwNLWvYMJFH1ch74xj0JHk3Ad2v18gDalqECWBoQQhxRay9pUhs3pHEnXoW/Uo9B1WNOVJmMshSNGk6iopGuAZv7OR2juqmfZjqdJRkq5cPTneGbtfYTGx9EeI8umkjWGqmi0Zxk07E7rCC3MKE7SPxY5xs/4xCaBmRCHoc0PWJ/KUuI61KdzNPg+rlIkumet0sbS7odkjSVrDNnQYtirkmJ3sn9RehFFuWW0xabTER0PKBwg4ihiWhPTiqjjENeKhOP2LGeu7kxT6DoMi0cp8hw6gpA3OjNUFxXIu1IhxEFtzeTYkslREcn/yk+lUix75mlihUUMnnE6DQMGsSHRxpyFNxNaH4AR5adz1cR/oyBSxKA+49nYXMeQPtX0LxlPJjQs70wxPIyRdDWuUgyKRamMuvJm8QiQwEyIw9DoBxhreT2VIRtailxNOoRtGZ+O7m3hurvxvaMh7mr0Xr1XLZaCzHJGNv4rigDb8RDr+v2CVGwChvzsWmdoaA0s1gZoBYWOQ0nEJaEVBY5LV2hY0ZFmZCJKsefSlPPJGkvMkcBMCPFW1lo6QsOmVJYtqRw7HJ+2rfU0P/c0YVcnRVNnkMv4BCbHS2vv7QnKQDGwZAKBTtDqhxQlxjIxMRZFPsc24WgcXEYnYgyIRUg4WkpfHEESmAlxGKJKsSGdJepoHK1Y05UltJaI1iRcjQECYwmsJRNYct0zZ6HZU7tsdNdCFEE+m8wGmM6FNOkxeFrhao0LJByNQmHIz5S1pbJ4WvW8+PnasC6VY2xSoZXClRdDIcR+7MrmeCOVJWfyifnGBux4tZbOpYuJFBYx5P3XUFDZj21tq/nT8jvY1bkBRX4FwNEe/UsmMzDq0bd7uXL3zLxvLK1+QP9khFGJmJT06QUSmImTRuvOHRSWleG43qFPfptKPBffGDrDkI4gJO5qoihSgTQ+5LEAACAASURBVKHJN2RC03Pu7oR/R4Hj6O6k/3wu2e7kf4Uho4roMAYT7GlTpRUUOA5JN7+cqbQmZw0bUxkqox7lEY8OE1Cf9plZksDV8qIohNjX7nzYEs+hsPv1MNLexnPL6kiMGkPJ6WezqWsVryz4LptbllIYLeOjU79PzEv2lMCoKh7PpkyO9sAwIOaRs5bAQkTBack4A6KS3N9bJDATJ4VsKsXvb/tX+g4Zxvu+/DW86JFt+xTTmn5Rl0XtKVylaM4GdHUHY55WPTXGwu6yGKG1ZA0YY3qS/6ekXwLyWWcGhWfbu/PL9ix7GiwpY+jMhGiVL5GRdB1c12FH1sfa/C7Qs/skGSgJtkKINwmMZWM6Sx/XIbCwtb6eon5VeH1KOetj15MoKeG5TX/mf1+7g/xOcM0Fp/0LFSU1KKWYlBwLWDqDEIVle9ZnQMxjcCxKqecctC6jODIkMBMnhWhBAWde81Ge+tWd/OE7/84HvnobsWTyiF3f1YqyiEcf16EhF9AahHhaoVFkQkPaGHyzb4N2le+5hAYq/JVUBCt6av9bXNYxnoZsPqdDa0WB1sQcTVQplKMxWHbl8gFg0nVIG0OT73NxeQnjCguO2HMTQpz4fGNp8X12ZHw2pDK0Z7L4C14itWY15ZdcQWzAIAKtWfDaL1m46bfs6T4Cjr+BiYWzesr47C7tkzYGTylqipP5Fk3iqJDATJw0Jl1wCbFEksd/8SPWL17AuFnnH9HrD45FecV2EnU0ZShafJ/W0JKzFkfli8U6aJSyWGtBqZ4yGaPTf+7ZCmBQrI1dTEdsIrGeY/nCjB1+gO1ezozp/Cxcix/gaUWx63J+WRHTihNH9HkJIU5sDdkcK7oyGAsxrRiS62Lh44+SbWmheEoN6cJ2Fq/5BWsbX6ElvZ2R5TPZ2LyE0ARo7VGYnIijwNOa0OZfi3KhpcRzGJ+MS1B2lPV6YKaUGgQ8CPQj/zvqHmvtz5RSpcDDwFBgI3CttbZF5RetfwZcCqSA6621i3t7nOLkMPr0s+k7bCQllf0A8HNZvEj0iFy72HUo8hyCHLSZHMrR9HHA2Pw7y3Ro6LQBfpjfZam633pWBSsZlnsG2F3PzKVWzWZ7NpsP6JQm0pOXpnF0vo+dtlAacYi4Lr41nFlSzPTihCwjCCF6NOUClnSk6eM5RLRm0/Jl1D39d7xolGlXXc0mr4EHFt6M6W46fs7IzzB1yLVsb11BfctrDCydjBcfzcLWLkYlYkS1om/EozLpUew6kkd2DByNGbMA+Gdr7WKlVCGwSCn1d+B64Glr7R1KqVuAW4CvAu8FRnV/zATu6v4sxGHZHZTt2rieOd/9Bhf842cZPfOsd33dpOtQ4ro05AKKXZeUNbTnfNIh5LD5xH2l0Y5CW7DKYi2MCVeiu/dmWhRvRC4iE5tIiQJjDWF3v0xjDRHXo8BROGgs+d1U5Z7HmGSMs/sUyg4oIUQPa22+44gxdIWKzsDQZSzF/Qcw9qJLaPa38btXb+sJyjSaPp5iZEGMEQXTsFXTUAqshWxoOKukkLgrHUWOtV4PzKy124Ht3V93KKVWAQOAK4HZ3af9N/As+cDsSuBBa60FXlFKlSilqrqvI8RhiyULKaroy59//D1q3ncV53z042j9zosfxhxNgaMpcjU7MyG+sUQcF8/Jt1Uyhny5DBPSZSy+gRBLR5C/f34CzbJVDydtLY4FR2kSrsIjP1MWWEMuhLKoIulEyAQhIwtiXFBeLDswhRAAdIUhO7MB27I5usKQbU3NrGrYReGQ4UQGjyA6YBAPr3uQhZt+S9RJ4igPY0Mc7TGlbw19uvvtGpuvn5g1hilFBRKUHSeOao6ZUmooMAV4FajcHWxZa7crpfp2nzYA2LLX3eq7j0lgJt6WovIKPnT793n2wXup/fMjNNVv5rIv/ivRgneeOD8oFuH1zjRxV6GMQyoISRlLJjRkrUWpfIvyAkejHIXCMi61YJ/8smI6cBVgLL41hFaTcMFFU+i4OAqygaGf5zEoEeHCimKppi2EAKAh67O8M41WkHAcMtvqaf7Lo1il6OiTYunOZ9nY/Crt6e2Mr7qYC0Z/noauTaxvWkJVcTWR+CiacgHWgqOhMuIxsDBCoSuvMceLoxaYKaWSwBzgZmtt+0HWrfd3g33LSUp9Gvg0wODBg4/UMMVJxvU83vOPn6Vi8BCevv9ulvz1z5x+1Yfe8fWKPYdCT5PJBQRhCAqiWhPRCgMYk29i7pv8LFoffyV9w313YzZGqkk6Gs9VeEpjlMU3hlxoSDiaEs8lNJZSz+WyimKS8oIphCC/dLmiM03S1XhKsW5xLcufe5Zkn1JKzx3JT5f8c8+y5RVjP88ZQz+MbwylsUmMLZuET7749cTCOFGt8xX8JT3iuHNUAjOllEc+KHvIWvtI9+Gdu5colVJVwK7u4/XAoL3uPhDY9uZrWmvvAe4BqKmpeUvgJsTeJl94KX2HjaBy2Mh3dZ1SzwU0Ua0JXSA0dBlDzhhCm1/KDLBYm58tG5t9rGfruUGxKnIhm/RpEBiUssR0fkNBueehFaRDg4OlPOrxnopiKqRWmRACyBpDV5jPO13dkabpuadoXfM6JcNGkJgxjF/V/ds+uWQxbRgU3/P6YaylxQ+piLhURI58EW5x5ByNXZkKuA9YZa398V43PQp8HLij+/Of9jr+BaXU78kn/bdJfpk4EqpGjgGgo6mRp+79JRff+E8UFJe8rWvEHE2pm1/C9I1FA1FX4YQuhnzjcmsBaynzVzIy90xPtX+LS1PiEqoiHtYqjDLkjCUdhOBYKiMepZ5LiOXSihIGROXFU4hTXUcQsi6VodkPsdaiAFdr6r0GdoxtJ1PyGmsW/ieek0ArF2sNjvYYVTaFVGh6Zu8BBsciDCs4MrvURe85GjNmZwEfA5Yppeq6j32dfED2v0qpfwQ2A9d03/Y4+VIZa8mXy/jEURijOIW0NzaweflS/vDdb3DtN75LLHH4hWhjOj9bZoASz6EtMKgwJDAGYy1G7emZOTSzBE3+HawFVkcuZDWj0b5PoeNS5Dj0jeTn07oCQzowVERcaoqTDJEXTyFOeW1+wKL2FDGtKIu4NG7ZjHZdvEgTT2f/m8DkYAcML5/Be8d/ncauLWxqXkJ50SRUdBQJrUlENMWuS5GriWhJ7j8RHI1dmS+y/7wxgAv2c74FPt+rgxKntAFjxnLFP3+duT/4Nn/8/rf44NduJxI/vA0BjlIUeQ59XJdN2SzWQkRrlLb4oc3XKdMWY8Fz8uVjd1fSzkVPozISwWDwbb5wbHsAg+JRBscjdISGKUUJqoukgKwQAjrDfDmdAsdh3eJFLHvuGfoMrOKlfi/ngzLyfXhrKqdxRlkVlFXB4Bl0hSEaxaQi6RByIpLwWZyShlVP49KbvsL2Nav5w3/8O5muzsO+b7nn0h4G9PVcCpx8df/AQGgNgTEEYX7poCr7Uk9+mUXhmFbAUug4VEU9hsajVEZdOoIQUEwujDO1KCFVtoU4hRlrafUDNqaybM3m2NGZ4vG5c1n6zNOkB8Z4tOgvrGyoRSuNRuM5EcaWT8NYSzo0NOV8jIVxyfixfiriHZKWTOKUNeaMs9GOZsHc/5dvoXSYCl2HyqjLxrRPVxDiKEVSKwLtEtr8rqchqceoDJa9ZTdmxloyfkgkMJRHXKo8D99CRdRhdmkREUfeKwlxqmrzA5Z1pskai6tAZTLs+ssctnasYt2obWwL1hP3S3jP+G8S9Ypobl/O1MoaSgvH0u6HFHkOQ2JxyiKuLFuewCQwE6e0UTPOZGTN6SitCXyfIJs9ZPPzQlfTHliKXE2xE6HBD8l278z0LfTJrWByx8+APbsxtxZcgimYSJVSOFpjrSUVGjqNYkRBnDP7JEhIWQwhTlmBsSxpTxF3NIWR/GuBdQrY2mc7zxe+AIFFK81na/6dsRWnY4BWv4Zix6GmJIGnlLRPOklISC1Oear7neXjv/ghc7777wS+f9DzPa0ZHItgLGzL+gTG4AIJ16XY1QwIlqIIe3ZjgmZ99EI6A5tftrSWUtdlREGMqKM5vSRB/5gk+wtxarPEtaY157Pylfm0tzbzx9fv56ngz/SU8rSKLW1vEHM0rgJXKfpGvXyeqwRlJw2ZMROi29izZ/Pof36X5//nfs7/xGcOeF6BVnSG+a3rIwqitIchLb7BN/kaQ2mKgD1VkTclroXEJMqVRgFZa2nxA0ZEYkwvKWCA1CoT4pSUDg0NOZ9dWZ/2MCSbzjDvyXtZ07WQXTva6DCNDCutYUvrUowJcLRL/5JqmnI+ccdhfDJOlZTVOelIYCZEt1EzzmTaZVey6LE/MWTSFEZMm7H/E5Wij+eSCw3r0jkyxuAAroKIUlSEq/KnkV/G9HWCXGjBMZR4Lv20Jm0tA6IRqgsT8k5XiFNQS87ntY40FihwNE5rM08//n/5W+Hj2GILBi4b8xlOH/ZR1jUvI9W5nLP7z2RiRTUJRxOXfNSTlgRmQuzlnI9ez6Zlr/H3X/0XA8bcud98s4hSeErREIQMjEXQWNqCkK7QEkkvo1/qb3stY7qk41Mo9BwcBakgJO4pxifj1JQk0BKUCXFKavADQizlEY+GLZt54s+/4vmKF7BqdwKEJuFqhhXEKItMpUDXMK1YSumcCiTkFmIvjutxyWdvJhovoK1h537PUUrhKRgei9AWBKxN5diZC0iFhqrUX1E9RWXzSf+N3jgcBUWuS1UsQsTRTCtKSNNgIU5Bvsnnmha7DhpY39nFXxr/wlP9nkJHXVztodE42mNgSTWNuQAHGJOIHeuhi6NEZsyEeJPK4SO5/j/v7NkUsD+lnsvC9hQFjkN50iW0oFKv0T/1BLBntixVdBmVkQihzfe56xuJMLmogBJPgjIhTiVtfsD6dJYWP//G7Y2tr/L80t+zLbqNplQ9Yysv4KpxNxP421jXXMeE8mnUVFZT7DkUuY7Mrp9CJDATYj+U1viZDCtfmMek97z3LXlgATC6IMbObI6N6Rzp0DCsoxZF0LOMubXgEpq8sRQqS6Hr4CjFoFiE0Qkp/ChEbwuMJWsNu0sUaqWIaoVzDAKcriCktj1F3FGUeg6vrvgrd77+TSwWQrh2/Oe5bPTHaPEDKiIVXDfizKM+RnH8kMBMiANYs+Blnrr3TrxojHGzzt/ntjLP5cXmDgILg2IeUa2JheXQsWc3ZrRgHGURl6wxhAZOS8aZWCgtUoToDdZaWoOQHVmfxpyPb9kr13PP1xGtqPBc+kU9ir2j8yvQ6Q4Ks6Fh4fy5/M+2n2PdPblk1hoyxmAsxKUw7ClPAjMhDmDs2bOp+/vjzHvg/zJw3ESKyit6bkuFhqHxKOkwZHPGpyXwGd3xGn3Yazdm0IJGURWLoCyMK4xLEVkheoG1ljdSGbakc0Qdnd/l2F1wtWjNEyTq59M25gpS/WsIjGWXH7Alk2NYPMqIXsrdCoylIwzpCkKWNy5l0Y4FrFi7iOXZWjw3gqNcrDU42mNQn2oKtcOowijlESl/caqTwEyIA1Ba897Pf5nf/OsXefKun3D1v32nJ++s1HPZnvNp90MKHE3/cCUVe+/GVC65gmmkrKHEOtQUJymVvDIhjrjQWrZmcrzc3IlWEFoo27GIqoXzKItuZuiWfN5n2dLfsO7q/yU9YDpF2iF0NOvTWQbGI0SP8CzVjkyO1akMobVsalnBL+bf1NN0fEhsDDfM+hHJsIG1zXXU9JvO1L6TJYdM9JDATIiD6NOvP7M/fgN/v+e/WPLkY0x97/sA6AgN/SMeA6KRfPX/zkXQvRsTFO3Jy0gWTaHSURQ4Tq+9KxfiVJY1hmUdaZpzPo6GrtAweFMtFz/9cRzro1T+jZICVOgTbHiBsGoaGWPz58YjRHohIHo9lSGiFBFt+dOin/QEZQrFzJHnUx4vQ1HGjYOmH/HHFic+CcyEOISJ519M266dDJlY3XOs2NU0+gFNfoCjFAknn9Bvu//sjIymLeczNhlnesnBe28KId6+nDGsT2V4rb2LIteh367FlG1+nkHrnuwJygwKqxwUFqM9lpROpSwXMCAaYUwiTqnn9EqB50mFBTyxcTG/evnrNLITlQ8NcbXHgJJqHKUYJ2/WxAFIYCbEISilOOcjH9/nWHtg6Bvx6B/zaPND4l0vsfvl3aKpcrsoiUcZFo9SJHllQhxRrX7A0o40mdCglUKteYUL5n0cjZ/P8dQai8Joj9fPvg2daaap/xlMHn464xJx9BFeumzxA5pyAW1BwMqGOuYuv5s1rUuJmAj/0O9T1Ew+k6W7FlFdWUNNZTUF0ttSHIQEZkIcpq7WFp598F6qL76c5NCRNPk+u3IBiexyRqRre3Z/WRy2u5OYUhBhSFyakwtxJKVDw7KOFFszOQY11DFzwZ8ZsPlxHMcHwKB5fcyH6Er2Z21FDQNGnc3QeJRBnkuyF94kbU5nWd2VIaY1S7Y+yX2Lv4XFolBcM/rLzJ54JTVFBZw3oOaIP7Y4OUlgJsRhisTibFpWh5/NMO5zX6Hc8+gfi+BvfoJ8nf/8n7r0Csr71DAgGsXT8q5YiCNlXVeGjZksOWMpWPMyZzzzCbQNwAGj8kGXdTw6x19LZ/8aZsSjDItHenV2ylEKa0Iee/3XPLH21/naZORn2lvcZmS+XLxdEpgJcZi8WIwJs99D7V/+yLj2NpqtS2dLHcNa/tKzG9Pi0lz4Xs5IxmUXphBHiLWW9iBkWWeKvjuXMGbto5Qt/SMOASgwSrNp7IdoLqgiOeJcRow8i6SjifRSTTBrLb7Nh2DtXRu568VbWNexhqGRUWwNNhOaANdxee/gM5hcVCDLluJtkcBMiLfhtLPOZeGjc1hTt5jSmjPoFy7Ddu/GVChU6RWUFE0h4Wk6Q0PWWMLuF3BjLaa7DLlWKv/BnuKTEa167ReJECeqdGhY2pGiKzSUvv48pz/zSZTN/58LlYvCYh2PtvHXUj7sTAbFI71WeiIwls2ZHFszWVY31vHkmgdY07gYN3SZ1TWbf73iK7TEW6jdWUtNZQ3VfasPfVEh3kQCMyHehorBQ4kmkrStXY2ZPAObc6nqudWyieGUh4a69hTWKvK/H/Ib9pWiZ4OAsXvus6cmucIBkq4m4TgkHU2B61DoOLIkKk5JgbHsyPp0bniZES88QP/Gp9E6H5QZ5bDptGuJlQ2mdNR5TB48s9dnplqCgBWdKTbsmse9i/ItlZRVXKAu45wrb6TvgEqGOEMkIBPvigRmQrwNSmtGnHUuW704mWzAgNTeuzEVCdrpG/OIaY1vbE8AZtmrnhL5P5TKt4fZ+9296V4iafQDtucsxliUUvTxHKoiHn0i7hEvhinE8WhjKsuGdJbC1x7jvOc/h8Zg9b6zZIlp/4fyEWcdtf6XSQ0rt/ye3664uyeXDKWIjK1gSt8yYvIGShwBEpgJcZg6g5D16QzB+z5MR1cGUnUUZvbejekSxGtY25Ulawwq/+uDPfNke7M9t3taU6AVccehQCsijsZTisLuHWTWWtLGsrIrDV2KgTGPQbEocUcCNHHysdaS2/wqdtVT9K/fwYgtv0NhQIFVDrsmfIR+FcPQw86hctCMXh2LsZbO0LB41xKe3fR3arfPZ1PHBqb3m87ShqX4oY/neFw97GwGxiK9OhZx6pDATIhDCK1l7V59+DCWdGjom6ljz25MyOlCItllECsnHh0I5H/JwF6zZft5Zx9YS9ZauvyA0NierfYRrSiPuBS7LgWOosDxMNayPetTn/EZEY8yuJd3nAlxNHUEIevfeIEJc65laJhFAc16MCV6J5gA5XiU11yHGnJ6r48lawyL27pYtmsJv3z1ixgbgoVxLeP42nnfpGtKk+SSiV4hgZkQB+Eby7KOFC1+gKcUm9NZGu/9GfEwwHzkfGz7g2D9fCil45Q1/hwaf06XN5zm+Nk0xs+myxsNPcFTPuHfcxQxpYk6iqjWuEoRUeC5ewpP+tawI+uzNZMjojWDYx4lnkuJ5xJay5pUhq7QMDYZk+BMnPCym14hfP0pBr/2J3R3UGbRbJ9+LamRsyne+gqZwWeS7TsFk85i96wk4iiVb1oOaAVRrbtbIql3vBHAWtjcvp4Hl9yeD8q6lQ8bSd+qKoq9QRKQiV4hgZkQB7Elk2NXzqc1COkMDDGtCDetRY+dTJM3ns6Kn1CcXUJbdAqdsQkUBNspS79In/QLDGz/Hwa1P0jO6Ut7wdm0JWZh8Ehkl9Ieq6Y9OoHOXH6npu1p2gKFnkOR61Dg7FnOzFnD2lSWmOMzIh6jwNWUR1y2ZXOUeQ6VsowiTlDtQcDqVc8zbe6HKe4OyAwaqxTG8egYdAY7yyZB2WQAdDq7T3LA7vxNa/e9bj6ws8R0/v/S7g01Cdch4eiD5qUFJuChlf/NL+t+iWMdtNX5tAMnwvVnXE2xJ786Re+Rf11CHIC1ltVdabZnfDQQWsPGlg7inR1kSvviaLCJSTQWTCSwoIylzelHY8EHMfEP4oSt9M3Op2/mJSo6H6O845HdpSepVBFer/wpmfgENLo7LAODJRUa2vz8O/Ri16E84hJzNBFPkzGGFZ1pRieiFHsuha7D5kxOArPjxO7NG4HNb/wIrCW0+eXwnDHkrCVnLL6x5KzBN/mgwuzusvqmpW/Ys/ytUWgFnoKo0rg6P9sa0Qqve8bI+f/svXeYZVd55vtba+19cuXQ1Tnn3K2EJEAkCUzQBVsYe4zHMwbsa/uaYYzH1+E6jO3Hj21szAwGYwZjE0wwMsFkEAKBYkvqHNXq3JXTqTp1wt57rXX/WPuEqq5utUAJ+ryPWqdO3Gvvc6r2e97v/d5PgBdHsXgCfCFecGpqYAz5SDMWRAwFIedKFXqO3FNTyQyCQ2vvoqV7OVNLbyFcuBvPNtoBGo4NTi1LCIG6jPE+MpaysUzrkNAYqpStzVN0+T4dvqLFU0xFmpEg5J4zX+dTh97PeHmYVyx7Ja9PvY5jg8dQKxQ3Lb7peVXJjHXD1wNjMfHXOSUgLSWppuf0JwZNYtZEE5eBsZZzpQraGCYii7aG5HQegEpLGxPlkMjM/pru8sncn/5QtnIufQfn0ncgbZmXjLydnL4IWIQNsYVHOWvXIXDdmVlPkZGSpBSkpPuGXjCGfLFMh++xIOm6PZUwnCoGbG1xatoL67T7kw9tLRUTEy1jmdaamchQ0IayqWfazaZYDZMhsLUMO9EQoyIBhGggZO7SlejqxD2yTkHV2pE/jYUG1bW+RXdLWklynqRFKjKeK/FVCd2zlfc13zGbCDX95QqjoSY0joqOVkJOlcosPOJIhRUgsBxtWcPQ+v9MRgkolGr7P3ffGsNmPAEpJUhLRUoKkkqSEpKUEnhSknZH071O/B6eLVc4OWMJreFiJeJE/xf52rH3ASBRvGzlW3jFiht5Ba94To7T5WCt5Xw54EypQmTdXifv+x7+ieOUb7yJaOtW2jyPDdkU2eZs3h97NIlZE01cBkVjibThbCWkTXkE1lIYH6UPKGRanS9MSQwmVkUcmdMWTMPrSGBp6ftk9cVZ8zSnU7vIKIXFKSzjQcQ44ElBu6fIepKMlFgpyIcRFWtZmkrgC8mMjShEmsBadrRknvNjcy3AWkvRGIraMBU58jWlNUHccRvV3muLjDPowBIAkTGExhLhVDEdq2h1GgHiCpTaxkSOuHNX4hQhT1BThxJCuutSxgqZwEfgS4EXq0fVhPqpSDNmIrRtJIoxaZOSFk+Ri0t8T0d5sdYRRW1dkLKmqhK6YxMZw1gQcqJYYTKMnFJoLGG8Z5GxVCzo5SH2XExOgdce+Qce7FrG4PKXI6WslfmFAMX8KmD1GOcjzZitR9VIoNWTtHmKnOeRVu75voTRwDAUhARac/+Zu9l35kM19mcxXJzcD9x41cfj2cJEEHHv+XG29rWiKhXk37wH9YXPA5D7108QfeAfmNi4icOFEje0557n1Tbxo6JJzJpo4jIoacOMMfgCTpXKZJUglW1hetctFNo6KQVOMbNznyhmq1gLggNsn35v9S4MghOJ23mCtSTCyJUpJfjKndo1htEgYjwU9PoeGU+R9TwKUcRoENGX9ImsZSKK2JRL051o/ho/E4iMI2LTkXYkOYwI4pO9BbAQYgm1ZcYYwlokShV1VawxDgWqJUZZo2R2DjkTtf85IlEtW0oharMWDY4EBtZiIssUBhsTIGZ9CsUs9SgjBUmlXPlTgR/n4NVIm9GMlaukzRG9Tt+jy1OkYy9WFCtMM9owE2kKxhAYUyebtXU41QsshUhzrFBmLNLYuIIooaYoe6cKiMmQ4a0tPLryRm67+E94OsJISUV4vOpbv8KJrp38+9Z3crZ7B76QrowrJWlPkInjZVLKwxfgSUdaxZySprGWsrXkyyGWAF9KFsS/M2fLFdqk5p7jf8uBgW80HEaBkglu6Lv+aX+OnkkExjAwXeH37j7A3jMT/PXaaZb90wfxBgbqamEYUXrkEfSGTSxrWhp+ItD8i95EE5dBSWsCA4GxtHvKfbNu7ca//WeQyqkVfqwuGFsnaI2nyN7wCK+e/h0XjokzNRt8zmZuBwFFa5gJXPkrrVypKSkEGSXRGAaCkC4s7Z4jaONBiLWWtJRc35plcTr5nB6TnySExilJU5FmNAjJR5qKMZS1oRx7wbR1BMtQV8eEhYi4jGgdkY4MaGNdWbFWUpytTgGXkvgY9ZDi2c+plkNVVTGToJCO6EnX2evLWC0T9dJkVT2ajDSj1mJtSON0iUxNQXJlv6yvCI2hbCz5MOLMTIWJKKJkLNYaklLG5UFFOi6tGhErZMapZjTsw1A54NhM2d0uQBtT0wARlsWHZ1h7/wQz3Qn6N2U53b2dv3vJP7J+5FGe7L2ei52bedHpL/Dqwx/g//3uL3Fw8cv58tbfpL9lFSUdMa7B2hBjLUoIfCFJSUhJSYuv6PSdLzMVq4kpxG3fowAAIABJREFUIUjFclxkLf0V5zcbmDzMp47/DZPFcw3GPsHyzt3cvva/QHodJW2e88zAGa05Xijz8Jlx/vE/jrH72EP88+l7aPnsEOWVq5h+12/R/oH3Y6MI4XksvfVmujpyzfDpnxCIqtn0xxnXXXedffTRR5/vZTTxE4a9+Rm+OjrJRKgZC0LSShIUCuS9BKFwHjBr7SXdYI24c+b/Y6V+DHBK2XlvJ3vTb2XM34QvRc0/VC1nRsaSVJIOT+EJR+eKkaY36VMx7jEv6WzhpR0ttDY7w542KsaQDyMGKiFDlZCysZSNqRGyEEewpBBEcamtbCxRQ2DwLGM+YhaZcuTb/asJWZcY+kVt8gM4FUk1KGQSd59Tl+q+sWqTgLVupJcjhmbWOrwqgYovfelIXLKhCcDEpLOgNWXjlK2i1kgh8RrWGQlHQo21hAYCW1X5nNm8SuqUcD45i1OZT86UuVgJUHEDgojVQoEgMoY1D02y/GCBoaVJHn9ZB8aXtf2bfZwgFZX5mTOf5k1PfoxUVOL+FXfy9c3/N6Vcn1MgrcVQJ6I2/lkSkzRP0ZP06PDdxIzQWM5PHuL02B5GCqc5Pvx9WpI93LT8Lu594sMYG6Gkz+/e+n7WdW9jKtIkhOC6tuxzNl0A4OB0kYulCn/5ge/zXx78FNed3efu8DyCv/8gA5u2sPvMSeTjj5O54XoyO3c+Z2tr4pmBEOIxa+11897XJGZNNDE/7hnNc+/YFJExBFj6KxGbPvJXhLk2jr757USWmvnfzinTLNTH2FL+MuvD79ReT+Pz+dxfMuBtxMTPEwIySpGUAhUrJCVjMNbS4Xv4QlLUmi5f0e57vKa7jW2t2ZqHqImnRmRc2fd8qcL5cshUFFHWrqstMK40GMZGfmJ1rKr0YCwR7mQfWhsHAF8ZslqXbHiL5sY7zL5iG2anXv41lRAkhSShLu3CrCpyGhuTFDANYcUCSHsSBU4VizRlrZnRhjD+cuGaGsCKuKQpBOmY3AkhakTTWhsrhhYhIKsUGSWZrmjOVSpMR6a25mr6lwWEtbzoe3mWnSnzxKYM+69vRUjhyrbSkVMFyPg2Idw4Mm0MmcoEdz3xUV5/5nMYofjiijdzoPs6Nk2f4MyC6xno3UlKxOuM9z+yscdPx407UmEqJ3j4yO9ibAgWFhcXc+etf4nfsYjpwlHyhYNs6bmOtV1ba8d+LIhYn02x+FkuExrrgqsHH9yDfXwPk5OTJP/jy6iZabBxgVwqpt/+Dtrf8XbWZFLPKVls4pnFlYhZ8yt3E03MA2MtY4EmqwQXQ0vRaLptRGZkgP61W2KjPxCrKhaBtgYDLAyPcWfw+3gEQN1XdtB7JWfkepQ1SOFKDhrLZBiBhZSSLr9MSIx0XX8dCYlAsimX5qd621mQbHpIrhZTkeZcqcKpYoWxIGJKazfux0JoHCEJ43Klid+/sME0XlW+XPdkY7nREQ5jYrUGM2smauMlNKprDlIQR6S4cqSkQR0TdQO8EnHXZkwWLZayNRTDS4mcLx1p85WLjvCFwPfcKxUjzUgQMVyImIq0I1XGghAkhfOcOQtYXHK1TgkuWRiP98XDKVBVBa4KG/+ejAUh05HGkwJj3XqkFSAc6asud6jbY7A3x/GNWcCiql9s4tgQR8jiYx7763wp0H4HH9vyW3xz1c/xc8c/xJtPfZy7Tn0cgyA8nuB3b/p7jnRso81XtHqSjPJIy/jIKhgJAs7OjDF89gOOlMVo69qKaO2l3VeI3EaWd2xhdTY169jmPMmFcvCsETNjLWdKAedKFc596352/M//gWciskC4bj3+W/8H0Z//GYQhwvfZ+NIX05pNPytraeKFgSYxa6KJeRAYy4yOmNKWFk/iG0Hx+AGEteQXryIhwAgIrESIqgFagoDrgnvxCGolKINA43PYezlWOwVGKUtKuDZ+IV0ZSselyhZfoYSgog1ZT/HSjiwv625r5hRdJabCiIOFEidnyowEEZExFK1TeKpKSmhMrXvWgFOCYh+Zxt3/VMWE2Yb9OltpzNlqfKxTnZziFlnnOSxHbuOX21aVvHk1L5lTyBKyTtosTv1rJG1VlTBfUwfrTQXVMqu1hrIFQ7UcakkIGStWgKh748rWMhNFgIi9bW4dReO+WFTVxiDuuvQNeAiUgR1Hiox3+JxZlmT/xozbegOzrJZuFXHjgJ1NEK129+8a38dd5z7HqumTtecqLNZEXDexl9Pd25gMI8ZDSMmIFk/SqpzXLJx+hKEzHyAMJ2vePSl9ula/CiUlxlpaPclYGNEb6Vqwc/U9eDbrSjPacHS6xCNff5wb3/9X+CYCwApB6WUvo3L7q9m8ZhXFR/Y0y5bXCJrErIkm5kFJa6a1U8wGy5qyMSx58gg6mSJauZbIilrII7XTLSwKj7Ep/AZQVVYUh/zbOeq/gjF/E6mqT8kSe8aMM/YrDxCUjSaBJa0Ui5I+P93XzppM+gUXEvpCxFQYsTc/w56pIgUdUTbOdB7iiIqulbdcacuY+gnX0JAc3xAGVjW1m1gVM1dRyrwcRNVD1qCYqWoJPH6AqvnMYj9Y/NkKrKEczSZwAlBxA0BCuQaAsrb0lwNGw5CKbnC/VX33NS9bVQmsNyUYa6mgQVd9YXHJNF6bjMlSZCxjxlKKDGVj8KUgtNSUtGoptXci5I6HZ+iZ1BxYn+aJJU5xqpK+qhJpYoOaxpU73WNsXKqVdFdGeccT/8BrL36lpj5/te92Xj78PTwTEUmPfZ27EEgynlMXA224MHmYsPAYYekExZnD5DKreJH3n9C+oL8lz9KunSxt20x/JWQs0ixI+ChgZg4xm44MazPPbJONtZaSsZT27mXs3vsYe+Awbzz+MEqAVcp9GH2f1PU3sDqbIrNzZ5OQXUNoErMmmpgH+chQMS6Fv8VTyAiSTxxmYsV6Cgg8YfGViMmXrGU3bQm/jkI3lC9v59vJ33AvagwJqUhJ5UzRcYmqYi0poMOTpHEnwy0taV7e2Upfs/39KTEdRuzJz/DgZIHhIARrqViBiY3hkXUEOIod5lVuo3Hm+avxjVVRVb4c5itYUouFaHx8o+riGj2cSlWJ5bpLxgnFXZcKFxEhY09Z1X9VfR2D84uNlyOGKyElbSBWn5SQSJyyWy8nOiXQ2noumBeXDxvJvzWWEEuRuDkhNvIjBOXI1BoiDBDEQbde/BrSwq4jJW4+VKKUFHzhxTnOLE4gjYm3YeP/RK18WvVmVgN3sfCm/i/xc+fvZnXhFLIhGdAgONeyiv9n+U+zc+xxDnTt5ljLJkSoScS+tlLhGFPn/wRwNoHOzK30rX4n0ypBqydZJwVlY5ECWjyPwBjOlStExnk7rXX+tulIk1aSRc/g7+FwJeBEsQL799P5G78GUcj1wOjGHWz+X3+NGB6msmcPuRtuILerScauRTSJWRNNzIOJKCKy7oQ0HkZE1jD2+p/HpDN0eB4z2lDSBhOXXhBOLdscfhuoljA9zqdfRVfcPaljU3lkDEZCu+eREgorXDdbSkoyUvDijhZu7cyRaLa+XxHGGPZNF/nS0CQXKwHaWIwQtTIlEBv3Dbqhe7ZWRYsJiyvt2bpRvZGoPYN1rCr5qeWUEfvI5PxKmbEWjSNBjWtQcWkz0prRUDMRRVTiUUMCauoT1Y5NUc27F7VO0GqfqLUQYBEm9nTNJUi4SJAwLitWo0GUFJgqAY2PYWBBWsuqgYAXHyxxdGmCb+3OUElKiO/zpPudQoCMyZmNt7Ejf4ibxh9DYbhj6F7WFU+7+4TiAyvfxtvOfBzPhETSZ0/HTo61b+NoxzYXVWMtKQwRknx5gGDgf+Pe/eox6yDtOXI1HkYuE00KImPxlMtuUwimjWaoEmJw49BaPMXWlswzarI/Vw6pHDlKxx/8ASJyfjcrJfLFNxH09NK3dAns3vWMba+JHz80iVkTTcyD/lKAEu4knlGSooahlRsoaYuNNBJLUrlv+tZCT3SEF5ffh6zpEs7sf5z1qCgirRQJIcgqiSdd51toLO2+IOMrpsOIBSmf2zpb2JBNIZuk7IoYqoT868URHs7PYKwjNVGskJlYvTRVgoKteclMg7fpMnoX0DC5wcy5Puc5ouFK1Ss1a6xSw7/GxoAoLn5bbeuti/HzlRDOnyVch6IvBTLW4ALtZkyOhxFFXW86mFserLIqGx+HKkUR2FpobVXJc6VOGyt5bqWKesitAUJrCHSdvMrYt9Z47DIlQyEtObkwwade1sKFHg8r6gG8GufdVNjYOxeXVoGXjD3EBw7+Lp51avOUymJw46uwlkhIfnnX+7hxYi97O3dxsG0LxhgkVQ+eoGg05L9OOPxJhHH1WAsI6aN6bqqRq7RymW0TYcSwEixMORJajAzL0gkSUnK+FFBJelzfliXzI3o7q2XL4t69lO+7j+zR46R/cB9FlcQoD2EN+D5Lb7mZnqT/I22riZ8MNIlZE03MQVkbSsZQjJzDp6wt6YN7SPQsJFy4xJVKIpd6HlhLd3iE1xV+B0UYO84EhgQX0nfQ5fs1lSGw7p6sr0gKDytcmnq3ECxvyfDGBZ10NlP8r4jIWL49NsnHLo4xEUb4OJN0VSHTsYHf2CpBpsaInIpZJ29XQiO5uZJWUnuVq4rRELWyoMSRLiHrChoN66tga6TNAIEJXeq+dqXXuURRx4upEicRR1nMXnu18cApt6JK5Bq7GOJPcFQtN8Y+fU29POvWWb/0teW2/UW2narw0dvbGGtVnOl1BMNjlmVv1nHbNnGINwzfw9LyRV46/ihevHqN4KsLXsGdg9/ANyGh9Hm4fScH2zazv20zIPBxA9qNtZRmjmMLD0HpEDY4T4tax87JXZxas5xJeR6V3kSYXENoNb6Ip2tYS3cigQVOFQM6PcWilFtzWgpuaM9hseydLrK9JUPHD5kZOBVpDk2XMAf20/nrvwqRo8gPLNlO9x/+EXe0lJum/iYuQfMs0EQTc1A2hqEgolUJRiPwrWbBVz7NxNYbGLj9pwnjM7DAnfw2Vr6JIqz5yi54O3ks9QuM+xtIS/Clir/7uwiM0Fi6ki6l3VrBrZ0tXNeWa3ZdPgUuFMt88Pww90/MIIVFSUEIGF2Pj9DYWoxJlTxciTBVvVem4VF1zjb/MxurWtWSJKKujM1VzOaSLh2X3hrZVSNpc94yCIBCpJmONBXj1KrGFVXJznzEsZGQujXMv+/OcO9IWlVFq5ZEQ2MbxbzZmWTxZfu05k0PFOib1Dy6NslUVtbusxDPxJy9Rs9G/Mq5T/PbZ/6P66oEHmrbzs6po3g2IpQ+d/fdwecXvoYbJ/fyaMcu9rVurvniVKw2awSmdBwx8Ge11dn2N2K77+LJdR4ISRvXE8YK3XigaYvPeIn4GM9oi4dTBNs9xepsmqxXP1oVY3g8P8MN7blZDQFXi4nAdcYu/MTHEDEp0wiWv+x6brl5HRlPNQlZE5egScyaaGIO8qEGaxmJDMIKUk8eRYYBwbrNNUWravtpDw6zIfxmw8nIY3/6FxnzN2IsTEUGKaDNF3T5Ck94zESuXNOb8LmlPce21mxtlE4Tl6KiDV8cGuOT/eMMBSE+AiMlka6rX41EBOpzJRsztK6EuaXHWTdeAabqTTON25m9RdcBWTe4yziFfz7SFmpDOXTKWDCPMjZ3C5bZW5tvya5BpX5/rTEhXk+1GSDCKW3Vxzduo/rcxtffcD7gtY8U0FLwmRfnOLkoUXvt6mU1mmNX/hAvH3+YtmiKV449xNLKYEOJU/L9zht4z6p3cOPkPh5q38He1i1I4LHWTXhC1sqQ1cgT98QJkkMfwFT3zgqESFBGkDSQVO69MXHkTWjhQjmkVQm6kz4pJVidStKd8ElI131Z1noWMUtKSaQsxwoldrc9/d/TpekE6rvfYfr792GFa2GQCZ9dd9xG9ocgek1cG2gSsyaamIOBSkBRGxYkPPorIS2PP0CUayW/agOVqDq8GcByQ+HjtY4xi+B44lWMJTaRk5KErM9MLGtDQUsWJRS+D8vSSV7b09H0lFwB1lpOTJf4+/OD7J8uUdEaKyQhQNwROJd8zRW7Gjshaz9fJVm7HKrdjBAHosY3yobb67pTlXRZdI281bcu467ICJf6XtLus9VIuBovG71qcymC5fIkqoqq107glLLGfbLUiVTjc8Wc51W3tWQkZLRV8fmbc+SzjmRE1AN5q8+9c+jbvO/Yn6MwCOBAdi0fX/QG3nX2X2qlyofad7C/fQv727fOUjytdZEnXjz6qfqa3vTjeCMfRNsSQkg3rUD6iMxGPKBgNEXjyp0JKSlZS1YKFieTZDzJslSCFZnZQbKtnuBMOaDFUyQa1OuspxgL3AivhU+zO/PI1+9D/M7vkN2+nZ53/TdK+w80y5ZNPCWaxKyJJhqg48HPIOivhKSmxkk/cYipm15OwvOcp8lKJJaFlYdYHj3acOL0GMjeQVK6MTIlC+2+IiM9lA9FbUgqybZshpd0ttDiNX/9LofpMOKz/aPcPTzJRNwVG8XHvUpAbHwCd519V6+OXQ2qZGsuuZnbpFktgVpz6balsHOS7OulTWe0d+Gi5ZiMNZrp576WnfOvtkaeHkFrJFaNt82a78nstTROPkiEllzJMNaquGd7xj22YRRAVQPanT/IWwf+gzUzZ9hZOI4FLrCQUyzl/tadfGjZz/NI23ZuzO/jwbYdPN66BWVcmVKJ+qrdvrjsNCUtonwcOfE5qBwjHbayQP1XTi9aBMEJSG8kkV6PJ4Qr0Qpo8RRrMym6Eh5ZT8VjzyzTkRtHlW0gYF6sYvZXQlbMyS3LKkV/JbhqYmat5XN338eKP3kXhUwnN77//WR6usnedNNVPb+JaxvNM0MTTTQg0IYL5ZC0gm7hMdl/DuMnmdj9YhdSihty3R4cYmfhr4DqiU1wMnk7o/5mUkIglftDHxqDVJY23yejLLd1trCtpTnr8nKw1vLwxDQfuTDM0UKZyEJoHWHWuNKymYe2VE/gdcL2TCzm8ncJUVfLqgpa9R2dXZ60l5C2yJp4JNSl3Z5zyVejSjaXsF2u1Djbxt+wZuaSytmPkfM8trEM2jmtefMPCiht+eBr2ohUff935Q9xc34fe3MbeNX4g7zt4t1cpI/TLOFTLa9m8fQI3+HFbruDkp4F4+xr38K+ti0Y4hORcLEZElvLVKvuiwG8sXsQ0/+Ce6clhQVvo9h5nQv+zWygNZ7ZmfYkPQmfVk+5XLKER2vDlyCBIKUkA5WA1enkrPy2rJKMBiHL0olZZcukdLE5xtqnLGdOlUP+7J/u5XUf/kNIJtnyiY+S6em+4nOaaKIRTWLWRBMNmIw0SQFnKxGBtaQ27WRq7WZSyoM4Sb0zOsKLp38biY7LPxKDz7n0q9DGUBbOT+bS/C1lY1jge9zYnmNNrjnj7nIYLId87OIw94xPkQ8irBCE1hAhCO1sWnG13Gu+UuZcNJYdL1HI5tzgRhq5V6qWUecuSFSVMuKh9sJ5EivxoHR96VNq16vq1fz7cGXMp6Yx5/XknPvmErXLYf2FgDc8MoMRcPfNuZpKZoHr8of4t/3vYsh2cQv7aSfPPdzMA1znbPfTguNsbNioZUF+lOG2zlleNNd84Ih4Qtaz12xUpu/CP5G3D8zaOctFEvJ6UlKQUYo2z2NRyscXDSqYsowHERklSTTcnhCS6SiiYiypBsVPxhEbFW1Je/Xbq00RT/UeFIOIt7znm/zmF/+aDhuy+mOfILN25VM8q4kmZqNJzJpoogFDQcRgELE0lWRiYpQhP0MZRRTq2plzVeEziFpClmA0sYvTrb+El9rCQgtGWMqRwfddCaTDT/Kano5mFMZlEGrDt8bzfOzCKBdKFWfYxg0aDyxYY5x3qYElzSJFV4ErGfqrahvMo7bNud5Iutz1+svO9ZQZawm1vUQZu5Lq9UyKfXUF79IS5dVCGMvLD5a4+ViZ/g7F3bc4P9lL+/eweeQUQVLxiomH+ap9GQfZ4IjYZbomtBAIazFSMtjWXfOsVcli3UMWv7cCOkbvR098nLw3TbtezqTXj0UjhMei9m10ZFKklRtwVdKayFj8Bk99Vb0sa0PCu7Q31RGzS/b6kvchMIa0kk8ZNJsyEX/40EdpLY+z/MMfJrNp4xUf30QT86F5pmiiiRjWWgYqAR1K8EShQNcH/5Ke1RuovOmtRMa16C+avpsl4Q/c43G+sn2ZX2SU9fhBRE/Cp9NTGOXyom5oy7GjNUu6GYUxL05Ml/jIhWEezc9QjscmuXw4936ENlaR7JVzwszliNVcXEK04ktooFuz74N5ypOXVcripPyGeItni3w9FeaWQ3+o1xDQNxFxYEWJi73TLMwnecXpJ+kdm8KQwEPwPW6JdcS5pMz5/7SQfGX7rUigLz/KYFs3o22dKKgPVxduCoK28c/F/fjjn6EQnSNDC73ZX6W8+Da6g5OExSO0tWylr23zrLX6UlAwhrSazbS8ePxS69Xsr3V7kZxjNShEmlXp1LzPKVQi/uDzB3nL7sUsft//pO3JIyz+278he9ONV7HFJpq4FE1i1kQTMWa0YSSImDGw9MDDVCbHmFy/nclKiLHQGR5h0/TfA1UPjmAg8xqyuR2khJsdWDaGTiQtvsfO1jS723LP6DiXnxRMhSGfH5zg34cmGAtDDILQWDc70tpa/pWOT/c6No/9UIdyHhP/XDR61OZjUELYWeStsSvTETVLoF1X4nyq1HNFxi6Hp7v9G04MkjLDzKQ8RrqnWTk2wMJzdYpXJ2EGlY0YTHTRMzFV29bhRas427WAnkKewbZuhto6EVC7nBurUSW6Wms6hz9FpfL1mFBKEkvfSUv3TjqEhNRmbOsmAm0w2BphBqeohtrA02h0nkvAZoylw1eohtsDY/Dl/PMyjw1O8WufeJwzowXe+N1PUPjuPSz4/d+n9TWvufpFNNHEHDSJWRNNxJgIIwSWSrkI3/4P9PI1+Bu30icELcEhVk2/tzZyyZ3IJU8mX0nZaPoSPumET9FoWpTi9u42lqYTs4zFTTgT/56JaT5yYYQnimUiA5F1SfcYWxsdpK1Tn6x1/p7LlhqvBk/xnEbVrPrDfEZ+G19WZTBLPP6JS432V7HZFwwW5MdZOj5IxUvQUg7Y0D9Iwk41eLlszYEnMCxW/QzqBejYBRbOJOiZmWY418FkJseRRSsZbusCoL9zwSwiBnWFzMbqGLifW6fPkB74EBP++dm6mz6FJ3bXrtdKxvMZ8ef5dYvMbL8YQGBdh3QjMdPWdX8uziZqr1/ShuEgZEM2xWQU1Tt/reVLey/ynq8coyXl8ZnsMXJf+Cpdb387nW/9hac65E00cUU0iVkTTeBKGBcrAZOhJvm9rxMVpuCtv+YaAEoH2Tz2W0iC+GQrsEhOdbwLr2U7vrGMhAErVYqVqSQ3d+ToSyWfYovXHi4Uy/zLhVG+NzlFKXLzLCvWnfgDaxFxGdMpZNbNWWy8jfkT7K8W853DLzG/z2FYmktHG1VHFM3tavxxQd/kKGuGLxJJSWdxiqUTI/X9syCMj1HUKOl1HGI/G9EoFJr+1h6mS1my5QAt4FjfCg4tXkU+21p9iVkdonObDarXlQRVfgKvdJju/FmmzF5CJej2bmFU7wEbgfBIZ2eXLC+H0FpScnYZ08TvX6bBX1Yds7YqU+/ILMcErCvhcb4UMGMMJa0pasvipE9/JaK/EtY+M/ueHOd/ffEIm5a18251gtx7P0Lx1T/F6C/9MhemZmhRipyStHquU7T5Ba2Jp4NnnZgJIf4JeB0wbK3dEt/2x8DbgZH4Yb9nrf1qfN/vAr+M+9v3m9babzzba2yiCW1hoBySNpqJ/Y9Q2X4j0wuX4wlYVf4GkqAWizGe2M3J1v9MJb2VLqVIJxTTOmJ5JsktHS3kmoneNYTGMhGGfGVonC8MTTIShOi4VGliL5lA1MiXiefuGFv3lTWezDWzT/RXOt1dQpjmYVBzicNc9SyuoNaIWGPW148FrGXlaD+rRvoRFjqKU3QWp2v7F0o567EtIsGC9uOcmV5aI2KjHTneOPF1HmMrg/RQnMiiUx4PrFrLsb7lBH5i3qiOS44tdWKdURJZfgI7+BcIGzIhoMUupG3l/8DmltJdPE5QPIxMbyKdWT9rl6pkS81pBjHGkkvOpu4lbehNeKh4ywZLIdJ0JzwS0qnkw5WQiUjT43sYA2VhsNbiCcGu1hTtvlcjVmFk8D3Jyzb0ou60vHT0GInfeQ/mphfh/dEfklUeobEM6pDIGKwVJJRgcTJBb9Ijq5p/G5p4ajwXitk/A+8HPjbn9vdaa9/TeIMQYhPwFmAzsAj4thBinbVW00QTzyJGg5CKMZSkIv3bf0YmCMgkPPzSAfpmvtqgrngMd/4yXmoLnoWCNvQkfLa2tLCrLUtSXtsmf2Mt+UgzGoT0l0OOTBf5+mieC5WAUJs4zd0Rs8Yk/KoJPAKUnU1+7Dz/GgnAfJiPsM0lCY1WstrrzHlBV758+t2MzxUW5MdZlB+lP/Zx+VHI+sFzLB8bRBlN58w0KR0Cbl+Kft0nZYBz7UtYMXEez2qU0NzFZ1gyPcB5FnKOJWSY4cvhyzktViOtZSqX5oHl2zjb1YdtIEaNJGwuIZPUA3ZTcQdlJiwzk78HQRg/WFDufQmtuaUAJDPr8TPrCI1FzvGBBdrQ5qlZzRqBNi6xX9QJWFkbp1x51akEhmJk6E74aGM5VihTNgZjYWkygacEY2HEZDECAYuSCU4WA6CCtbD38DA/eGyAX7trEwvbM+ys9JP4g99Dr19P8Bd/ief5KEApgWsTiEu9xnK2XOFUqUyX57EulybTbAZq4gp41omZtfY+IcSKq3z4ncCnrbUV4LQQ4iRwA/Dgs7S8JpoAHDGbqpQBRd4IpvEhiNgx8bFZI5cuZF7NmLeJXqnwlaBiDDtaM6zPpa95k/9IJeDYTIXRIORiOeCSehfCAAAgAElEQVT743lOFgOC2LtjrfOQ6QbiZRouZXxZVcUaVZe5+VxP1Wk4H2Gbe9t8ylsjEXshKmONRExgeN3+B1DWdUQWkmlaKsVZ+zKZypLU9RLcme5FrB86hzIGz1r+r9HHWZV4lEKUYwUXWMwgd3e/krVjF3nCruAcS9AzgmN9yzm0eBUTcblyrjpWva3xZ4kbFu7FHkFPCHxrWXHhSdoHvsZj3Q/XX0l4ZHJbZ+1rZCwZKfEa8scCo0koSTImNhZLRRtSStaGjBucN6wj4dERE7iKNQTa0u15DAch40FE2Rg8IehO+EyEIUHgOkMXpHy6fA8/nmk6XQz57Lee5OjpCdYuawMpKJ86Rftv/xblrm4O/slfEEbQUijR5Xu0eIpUA/HypaBDulPtVKR5JF9gSzZNd3McWxOXwfPpMfsNIcQvAo8Cv2WtnQAWAw81POZCfFsTTTxrKGvDUBhhHv4+wT1foeWdf0xHLke2+ADdlYfqJ2jhITtfT0dCMaM169NpNufSrMwkr/kh5KeKFfbmZ+gvB+wvzLB/qkgh0tg4AkE7bz/gVLFG0lM9oTea6KvZW6bh/loHX8Pl5Y76lW6fL76iuq0XGhqJWDKscMeRPUhr4jULVMNeaCm52N7DosmR2vHr7+ghN1RCGYOWkml/EStLQ6xJ3MNKcY5lYoAhOugQ01Ssz33iBh7P7+Sw3Qqe4XxvF99ecSOVBqWtkZQ1zs+EujrmCde04cW/F0oIevPjrDj5DS4mHuPJrgH8xFqSPT9LWDmJl9lMNlsvWUY4pSzp1QlYoA2+krQqGbchOFLW4ilaYwJWtgZjLH0Jj6znEVhDRRt8KUgrwb5CkcAYMlLRm/BJS0FRWyoYOjxFq+/hCUEh0kghePJsni/dc4pKoLnzpcu5ZcdCWh/4AYv/4s9ACM6/571ke3tcV661nC1VsECbp1iYStTIYhWtniIwhv2FIterHK1N20MT8+D5ImYfBP4U97fjT4G/Af4r8/89nfeLqxDiHcA7AJYtW/bsrLKJawKBsVwsB5T23I/ItlBMZ/Cm9rFi7E+BaiiAYDDzGsqprXR7EgXc1J6jt/mtl5lIsyc/zaMTMzyYn2Y81ATGOiIm6gqZYf6yoGI2IYNLVZjLqVtzlTQa7psPc8ugLySPRCMJG8u1snbwHLc+eRBpqy0nszGRydFeKiCsI2XfXb8LgNcdGIOYiJ1YsIwTC5Zx/dAhXpI/wNuf+A5LE/tRwr0LGsGHe++iv7KQ3vE8WMFotp2Di1dxtmvhrHJlFXOJscS9h9UAfVVbrPMICgGbTx2lr/9+vrfwe27gOAK/9y3I7Bb87BayDaXJ0BiEFOTisqeJSVlKKVo81ydbMRprocN3vq0gHnOVVZLOlA8CpqKIlJL0JBQXSgH9lYCCNrT7HtpahiohYB2x8z00gslQ10vmFh45NkIi7fGqV6+iqzND9Pl/Z9n/fi9Yi/V9vIkJwkWLEUKQFIKklFhrKVvLkUKJpakEC5P+LPN/QkpS0nKmWGZba/ZH+cg08ROK54WYWWuHqj8LIT4MfDm+egFY2vDQJUD/ZV7jH4F/BLjuuuteiFWHJn5McLJYJjs+zMiFM6jX3kV3eIQVo+9EENViMRA+lfbXUtSaNZkU21uapYgqzpUrfPLiGKeLJcqmTsQ0gK2XJi/n1WokR3NLiHM7J+den089u5J2+UIqUdaJWBe5SomXH3usRsIaSWr1+kBLBz2FPNI60nXfup0AszxmAF/edmvttvFsC7/95Cf59cF/RmGxSbi/dQe7po5yiqU8wk6i/iwdcoYjfSs4tGgV47m2K6637PlkKyUudPYxEueSgVNEPajJnVJAwlqskAy3dNDfNxyTMgBBpXQCLyZlXkzAQmNJSlnrogy0+3S48qDLurPWklGKnOfCa2a0Jq0kPUkfK1yaf1IJen2PsTDigekiSSFQCLJKxeqZpEXJOM1f1lVACydPT9LemqSnO8Obe2foGdqHffAYvQ/+gO79+2qfNas14Z49FNZvIqtEfb6nEKRjonaxHFAxlpVzhqIrIYheKB/EJl5weF6ImRBiobV2IL76RuBQ/POXgH8VQvwtzvy/FnjkeVhiE9cIjLWMVULOPfwACIG3/QY6Jt8DcaKWRVBI7SZc8A46sztYqiQ3t2dJN0sQTEcR/zE0yVeGJzg3U2LKVENhZ6tZVbXsas5Dcx8zl5RVL+e7/XK+shfK+a9KbEZzrXTMTHPj6SPzqmEWGGjr5lxnL9efPYaM1a+HVjsP1lwiVr2sYqitk036Cf7oxIfYUjhBTk7VjkGBNN9RN/MV/1Ukw4iK77F3yXqOLlw+q1zZuOblEyN0VSzLBo/TeDR3XDjJV7bdyuicmZfSQntphhtO7GOirYsDqzcx1NmKP3MeTN1TlsxucVESuBBXIdwQ8YQScUcjZD1FUgq0tQTaklaKjBIY60z1KSloTXgo3HD4rFKkPEmgNU+WKoyFEa1SkvAU1lrGghBtBAGWMa1p/FpQLAQc3jPI0IVpVq/t4E3d09zy++9GRpFT6VpaOfOGN7L0619BRBHW9xncso3BUpmcp1ic9PEbmn+kELR5itEgcjmHMdnU1jIVaXa2ZJ7Ox6eJawjPRVzGp4DbgG4hxAXgj4DbhBA7iP2owK8AWGsPCyE+CxzBnRl/vdmR2cSziQvlACPAO/w4evka0v4p2sa+C8SnIOEx0fU2ptQmXppOsj6XvubHKwXasCc/w8f7RzhXCihpQ9ZTjAW6RsqgrmL9qMSokQLP98fgSmTu+UKVhA20dmCkYvXwBbZePI3AziKS1eNzoa2LvqmJmhr2yMpNDLV1MtjW/ZREDGB3/hA35/dxMdHDm/vv5aVTD7rh6ULwudbXc8P0o+y1mznIBsyEz2hbO4cWr+JMdx+2wVy/ID/GivFRZjyPZRODLJsYuaSWXCthGsOi/CgjDesRxrD9whNsPnscIyTnFywhJQWM301oCiR6344wU6SzW0hl16ONi75IK4knRG2+aEopEvE2tXHXPSFQwpG/jJIkpCN5CSHISIkvBRLIB5qhMCSnFBnpssTSMcHr9D1GAo0nXEkRwBjLE0dHObR3iA1jp/lZOUD3KZ/NH/oqKnJf0IwQnLzzTZz5T7/IwG2voPPAPsa37aC4cTOtQFEbTpUClqcTpBrIWVVFi+JPZVkbpiPN+kyK7ubs3CYug+eiK/Pn5rn5I1d4/J8Df/7sraiJJuoohBEnSxWin7qLFn2CxSN1X5lFELa9nr6O69jkK7a0ZPDntO5fS7DW8kSxzCcvjrI3X6SEwcRDurWQJNEU5j7nGdjuj8M3swX5cRZNjpBP5+goTrH73AnEHDWskYid6exj6eRwTQ3bs9KFqM5HwuYjYo24Pn+Az+3/7ySs677UthoZITgiVnM4WMEhuwaEZaSjjXtX7mY818aC/Di7zz5BW+SxpFCmpxQwUzmHFXY2FxNV5VHU/m+gNoy8ehJZMD3BLcceo7VY4HzvYg5v2A6pDH7lSYqT38Rvv4OWzleihPNvGWtJSEk1lN9iScYEDes8awnpFDUlwBcWhSQlBSnlLjt9n3bfcw0BAsaCkEmt2ZRNk5CSoSBkIAiRkaYck6SMFIzrCBNF+EoycHycA3sGuY1+3n3fB5BaI4CZzi605yGMQXs+RzdtY6RUobx+I5MbZ4feZpSkaAwjlYil6bryWNIGF61mGa2EJJVkd1uWDr9Jypq4PJqfjiauWUyGEeeDkI2ZJDNrJOnTf4fQYc1XZoXPaPbVvDibZGk6hXcNk7LRSsi/D01w71ie0UCDcNEXRevCyCrGkhRQtD96d2O1s++FZs5vxIL8OEsnBimrJJ3FKTYMnp03YNUCJ3oX82TPEl55dE+tO3LfsnXsW7buqtSw+bA7f4iXTzxMa1jgrqFvkbQuq0wj+EbmVWTKk+y1m8nTRkV77F25jqMLV4D0WTNt8PPjvH7/D/BsLUyOwtwAMurEOpKSB1ZvIxUFVLwEmShgqK2bsbiM6UkgLu/fv/1mJnoWIqXA2ohg8B8RXgfZ7p/FGItU9XBYEw+uB4ESgsAatBRkpcSPzfStStHle3T6kr5kgp6kT6evaPc9F0thYe9UkSeKZYqRJh9qImMRuBKoQjASaVLSbcMAoqLp2HeQ1eeP07p8FTcHx3j1PZ9HxZ42IwRHf+oNDG7ZTt+h/Qxu2c70hk14GAbKAV0Jj3Zv9ukzIaFijVP9jGEidOXZVekEWaXYkE26OZzXeAd3E0+NJjFr4ppFMTKce/gBpk4cYflNJ0gTxvcIwuz1JPp+le2du1mWSV2zf0wLkea7Y5N8fniSgVJIhI1zoixYS2DcaJ0onhn5wx4lQb1kOZ8vTFiNFXN8fW6QJgAb7QF2sJeQBAMsYoCFTNDFhOj6IVdUR1UNKyRS+NawbHSQ5RNDl/jCqkrS6e6FLBsfqpGww4tWM9TWOcuU/3SJWCPuHP42f3/0z1EYBDAR9RGpEiOinYfZxWOVzShrESlNf88CZtK7eMmU5Tcej9g4VcFazXt6DqFsnM8n6mu3QiBsgyomBE/0LefkgmU1EiZixUtaw9YLJ2krz7B33Q4qre3c+6Lba+U7Yyx64quY4Bxe37sIRIoE1m0QyHqSnPLo8BW9CY/uhEdf0qfb92j3fdp9RdaTpIXEl/KSL0aRsZwrB+ydKnCmWKEr4dPqK7KhUwyrpc60J8lFgolQE2jD+RMTmHse5te/90ESOuS2+L0bWLOOnjOnEUajPY+jGzaTX7uOs2s3uJmexh1vKWCwHCCS4EuJse53YkYbujyPgXJIUsKqbJJdLTk6E6pWNm2iiatBk5g1cU0iMIYDQ8MU//3j9G2fob10GKj7yqa63satvTeyKHVtDiKPjGFffoZP9Y9ysuRCYjWWojYIIQiMGzIeWYvRbqySlQKl7VWrXIo6GUvZIr0M0MMgCcqUSTNKN0+yFoRwpMzWU7MUmk0c5CCuM3E1JymTokySB7kVg6zPV3qaWJAfZ8XYAIHy6JiZYs3IxVlqWNgwj9EAx/uWsWb4Qo2IHViylgNL1v5QZcnL4br8QX5h4MusKZ7luumjNSIYWcndqddzLNFDrljCCoHOLOaelas51dnBz54N+MMDFQIBR9skn14wRaFwmL6pIgYX/NpYmnyoQRVLxarYaNx56eMOqRTQOznG7pP7aStMMdy1gC5PImOfly9c92NQeJjTE5+lt2Urb9vyehalfNp93+WFeYqs56EEP1QGYCHS7MnPMByEDFYCpo2hWApctIaxjAYhkYWUrHdLJn7wGG3fe4gJr4u3nL2PREPw7uHXvJ6H3/Eb9B4/St+h/VzcvJXxNRvwhaBdCRCyNrXCWqfoJuNstNBaylqwLpNkbTbNynSC3mTimveiNvHDo0nMmrgmkQ8157/0WRK5ERZsOdNQghLQ8QZ2LXwRC69BUmat5XSxwqf7R9k/XSKvI6SNS5TGxMPGLaG1SOEiDrSNy3ZXYSiTNmIlZ1jDE6zgFJ/hF5gWbSzlSZKU2c8OisJlO3XZYXxCQhL02QskqTBEHxVSaOHVSBnAl8WbGneiLutcxfu3aGKENcMXMELSUZxiUX7skgDVKnk5sGQNp7sX8roD99eI2LG+FRzrW/GMqGFzkY2KvPvMR/mVi/+GjHPBvtPyIq6fOsgBsZ5HxHbyUY4ObdgcrWZLtIh8ucRkepBkOc8FXeG313gURYWOmTwrxocop7N8a+MtGKni+IsE6ZiEVY38AhcS60lJqxAkpaDFEywyEUuO7idx7hQqm2Plq1/LHevWkVKKgUoI1pX0zuUP8XeH3oPFMD5znJniE/iZrazOpH6kWbJlbRgsB3x7fIqiNuQ8hY9AG0vGk3ixqtrue4wHEdPa9QNnH9zLz7/vj/GM67A0QmCli9vQns/xl7yMsjGcXbues2s3ILB48fYmrKUn4bxuSEloLUlcU0FKKVo9ydZcmuWZVHPUUhPPCJrErIlrEvtOPkl08iuseHUJMatwpli1+GdYlU0/b2t7vjBYCfji4DjfH59hUkdExhJZKNu4bBkTMmFxl8YRheoA8svFWdRgLQrN7XyZGXI8zE1MkcMAh8VWkrZMgpCEDQhEgjHRW1+bWHL1O1KbRH4pKVuQH2P1yEVC5ZEOAxZNDNNWro8xqqj6n0QDHOtbztrh8zUSdrp70TNalrwcXjv8Xd5x8d/YUjhJ1pQd6RUwSDcHw518T9zkYiY8j/WVLvA7ONymudc7R1v+HD0jlp6RS1/3eO9S7l+3A5Qb6z3e1okvIKMEaaVY6yl6fI/l6QRrsylWZdIsSiZYkPTIKkmpWOSDP/gmu1/yEm699VYSCWd0P1eqMBRE9CYlD174Fp888HfY2G1ojGYgv48VnZu5UA7YkPvhfrfGw4jHJ2coaE0+jMh4iqlQE1qNsZZTxTJJKUnE/2wQEU5UWDdzkZs++yH8mJRZ4MCdP8PZG29m0eH9nN+8jcF1G+n1FSnPhc9Wp1Roa5iKND0JD21dZlpGKta3ZliTTdOX9Gnz1DU/+aOJZxZNYtbENYeRSsjY459kzevOIjxLtXdOIOld9Qcs777+eV7hc4upUHPf6CRfGJ5kNIoItPPMFLSbJVjRpm7GN864LWNPmYw9SQZ3e6udooczHGcjplqobCBKoU3wQfHf3PVY0aqe0gKRohKPf/6hYS1Lx4eYyLZSSGVYOjbImuELIAQdM9P0FCbr21OKol/fngFO9i5hXTxLUkvJ8b7lHO9b/oyWJeeiGnXxQNsO0qbCu898lBunDnKehTxmtxGVV+GlTrGXzUzQDqVYEAT8KOKUGgIzBDPQMd8hoa74VXIttCVcWn6H75FRkm25FNtac6zMJFmUSsRKUDwKyVpOnDjBtw4d4k1vehO5XI53vvOd+L5PyVgqkStcp4XhxOA3+avjH2Vk5gJdmcWocAZrNUp6LGrfQbvnsSydnGeFV0ZJG86VKtwzNhUreYIprRkPNTnfTQxoTXgklGQsjBgtlDl9eIytX/syd528j8WFUYJkCqtclpnxfIZvvpWJdRsYWr+JyBo0ljPlkL4UpISsfcuYiUw8XUCSlIqdqSzrsi7qoknGmni20CRmTVxzOF+u0LJyDFGpZkoJouyNrFj5m6zvvfF5Xt1zh8hYHpmc5t8GxjlXDigbDUhmrEEJCAxoadHWElItV4bkzDhZpvHtDGWR4bxYjdIBv2ffzUIu8q+8laNi6/wbbTiZCSF+tADYuKzaXiwwkWulpVTgtmN7WTQ1hkVQ8TzSkWvosEDZq09qMMDepevob+/hdQd+UCNi1RFGz6Ya1ojd+UN87sB/J2GCeLyRpShTnGMhH+enifAgLYCF9d0Ws+dVVvdPzLlsvN0AVipaFy3hFd2tbMylWej7bG/Nsjw7PxkeGxvja1/7GidPnqS7u5tCoUAul2MwspwtTBNZODm2j++e/jfOThwiXxllaev/z957h0l2neW+v7V2qFzVOU5PzjkqWsGSA5YxtnHAGDDmOWDMxXDhwgEO+FyOL/CcAJgLx76AOb5Em+scZMmybCuOwkgzo8k59PR0jtVdeYe17h97V+iesSRbsmzZ9T5PT9VU1d61du3u3m+/3/u93zr+6JY/57Zld3Jq+jjHpw6xu2cv+7p3EZfyu7IGaK0ZLjucL1aYLDu4SpOxTbSGvkiE8YrLvOuTDDPNTE8zdnQa81uP8h+Pfo3VufHgjwnD4P7f+zClSJSBU8eZ27YTf8tWOsI/CpQOyvEF5ZOQkjY7UM1KvqIvYbMtnWR51KbNNok0TfxNvAJoErMmfqwwVnaYyx4m4nyrpjoIYbF+9W+yvOOGH/TyXhForTk2X+Dzk7NcKDgUfT9IVlcCXyt8pWjXV1ntnUdLk8PiNhyt+Y/u77KMy8iGQIzD3Mjfyd/Dx+IS6/gab+UxcdeLW8f3tniSlRKW57JmaoT+7BTdC1my8QQtxXzDGCMd+IioE5VLnf2L1LDRls5XpCzZCFNp7po+wZ1zR3isdSUfGP0cUVWpefQK6hYKlbdwxf4KnjCuKccuJWDVx2DxwHctBIW1m0jEYqTjceLKY2DFSt41MECbZZAJVaDrqT6u6/L444/zxBNPYBgGb3zjG7nhhhswDIMZx+NkoUybCV8/+w/ce+6fgUBvfvfmX+fGVe+hN2LTH4vSP7CP1w98b+pz2VccXShwNFfEEMGg8eGyy4zrYoaBuEEYrWKw4tJ75gTbvvoVbjx1lv7iDJV4ot5hqjVrr1zm5Dvfw8Wt28h5Ppbj0mObwcoFQeachjbLot00SFoGa+NRVkRt7KZvrIlXGE1i1sSPFa6WHWZPf4FWyw+vbAJa30Jf+74f+UgMrTWDpQr3TmR5dj5H0deUfYUCSkqx0XuCLd6jrFLHiFEAYEit5Tn7dqTWnBR7OC12M00neVKUSDArOgL1RsM/yw9ReQG2VS2JvihS1mjiJ7h4Sq24+/SzdObmgwyucF+GrzjftYy1U6OIMD3/4MrN3HLx2ItSw14yEdOajKvpL2n6Sor+ouLzAzYFS/CWEZefH3RodTQ97ik67T8EXH5pPFSydPCpKCweifw0927YSaxikhkeQWvwhUAgEFrV1a8GQiGEoGfHbnpScXrSadxymZUrVzIwMPC8S34+HDt2jC1btvD617+eVCpVezwmFGfHvsaXzvwzs6Wx2uMCQcH3iEnJQPTa0U4vFp7SXCiWGS47HM8VabdNbClIm4KoIRgtuxhCEJWCyekipw+PcM9TX+GuA/fX4m+fesd7GNy+m3f+2YcxPA9lWkxt24Elg9iNpGmS8zyShknUEJSUwhGaG1IxtibjdNgmadP4sWv8aeKHB01i1sSPDaYdj1nHpXj0DK17q+RAo2MbOZDNc2NL8ke2VDFVcXlgco6H5xZYcBWeKtPvn6TdPcWTkZ+lrBVrvAP0q3McEbcwKDczJNcyqnvQfkCAvirfAwS+Ml+pILcsZFhV9dEEnCXv3UjGnjd8toGIpYs5CtE4SkikVtx64Thly6ZvfpruhTnmYkky5QJCa3wpeWjTXiYybZzqm11EvGYT6ZeshkmtaXU0HRVNZ1nTWVF0VjRf7bcYj0neMObyRyfLJJbkhDzRZXLZMuhzT5GRz1JK+kTLDyBUUF5VGo4W3sYpfRNW9xnuXbGXJ7u2YUkwuZG+7jn652ewenpYFo3QNjdFdzpF3HPZvGY1lpQMDg6+ZBIGQdly//793HPPPViWxQc/+EEqhslIxWV2Ls+pqUM8fOkzDM6dZMGZZVPbFt694Wf4x+N/i6s8LGnyUytu4abW1Pfsvcp7Hk9nC8w4HqaEslacypWJG9V+VIHr+5wdzCIffILXH3qQt88NkXGK9dKtFLQlk0zt3cujf/bntB49QnHnLsT27bToYLpA2VcI06QrYhIzJL22xZp4hFbber7lNdHEK4YmMWvixwJKa3K+z/iF8yRbJ4Bq2Uci/HksIZDfczzqDy9yns8DU3N8e2YBtzzC8soTLPOPsMw7hkUFD5PDxt3kdQdfND+AJ2I4OujGNIRA64CAScASIsgt0xojnEvoh7lOKkz897g2/f66aIyy0JpUMU8xGkcJgdSKu849B8BopoPe+Wmkho78PEIrPCl5dMNu4IVHGH1HNUwHOWxKCFocxR2THq2Orn1lXM0/r7I53GZy87TPXx8uLdpcAadbDOZikom45IFlFmMxyWhMMBKTTMQkJVPwpukn+fDQhzG1j3BgjlYcYSK1wpMW/9/aO3lw4y5s81ZaLZO3p2LsSCdYHYuwLLaB7ohFzPjO8RIvlZB5nsf+/ft5/PHHMQyD3bt3MzAwgG9aPJvNI/H4+plP8MDFTwOBMvbWTR/ibet/jr0tSW7t3cPBiYPs7d7Lzq6d39MaCp7P5VKFc4Uyl0tl2iwLUwv6IzaWdskrRUwaWFJw9MmL7P30J/mJK8/UYi+ee/Nb2fbNB5CeizIthrduo6J8Khu3MLVhM57WrJYSpTRFX9NimWxPxlmViJA2jR/ZP8aaePWiScya+LHAYMnhfLFM5OT/S8uaXG3+H8JgdeetLG9J/kiVMmcLozw39jSXZg7ynHEns2Ilfe45bqv8A7Oij8Pm67lk7OKU3ooiCgKKOoahwaSaT6YxpASl8Br2rbWm0kDGGlWwwMC+WCELugfdIJi1ccCzCraUWnH3ucNoYCwkYq406F2YpWthDl9Kvrb9NcD1idgLIeZp7prwWJv3WVlQ9JY0vSXF36yP8IXlNu0VzX8+WQGgaMC8JcjagpQPNjCUkvz1pghTEcFsRJKNC2YjAkcI4kIw1GrwsRajxjW3ZU/w/kvfZEV5mFvnDiGrIa5acKpwD1/ZdQubxQkmem9g69rX8NOJCKviUTptC/kKkoQrV65w7733Mj09zdatW3nDG95AOp0GgiaPQ1e/xL3n/oX5cj13Ixhz5NIVqks7u3Z+z4QMYLLicGihEJRmtcZVsOD7GEJgAMLxWbjvCdaMnKCzMMvrDjyFVSnXS+FCEO3s5JE//e/0nTyO2r2b1NZtJLTGVZp5VxExBD22SVxKVsci9MbsJhlr4ocaTWLWxI88HKUoeR5zl++nbeDbQFXJEcTa386Kjn0/Eq3vjjPDpcG/Y2z6UVT5IgArMDkbW45nr+C03M1g4p+YUW1UtMYQAlcpXKVDdUxTpl6e9LXGE/WMMlcFERmhT7r2Vf3kfOresUS5CGgKkXgQk2GY9Ganmci0oRGLypNCa073rOC2C8foDonYfdtfg+ZaIjaZabuuP00AcVezZcFn07zPxgXF4TaDzy23MTR85ESZsoShhGQkLjnUZjCYksSByaTkHbcnyNoCZQZKYJVsJqUgH5fcu9JGiIC0Vo85Gibnb82eYF/2CIcz27gp+xz/2+A/1cJgr7jbWGaeQeLjSwvvXT/FW9bdQp7UBTQAACAASURBVH/0XSyLWj+wUT1aa771rW/hui4/87M/S/vK1Tw++RzPnT1A0c3x5PC3mClNsaNzF6/Z9H7+17G/qZUsf3rlrSyPf/exF42Ycz1Gyg4PzSxgCRmOT4K4lIxVXOZny5w7MUXvY4/w+89+Chn6CSd37mHs7jew7WMfRbiBQja6ZTuzGzdzdf1m2m2DdqXxdPD5r09F2JlM0GZ/52aHJpr4YUOTmDXxIw3Py/Ps8LcpTn2Zjtxj4VyZoIRpSJstK971qv5lXSoNUXGmSKV3c7agGB39LJPGOoai/4FhcxtTxiqynompIK8ilImEilaoiIXlSV9rLBn4uarRGAbBUGlPg6+v7f6D4LGW/AKW55KLRgMiBhSicUyvrrMJrRmYm+SGwVOc7lnJhokheudnaiRsItPGfDxN7wsoYjVSpjVJD/JW4En7X88U2ZZVta7MkZjgfFpiAY4lePdrEkzGBL4U9a5GEYyEUkIwFReYLO54lCIgpzK8LzT4hKOowsd2L5zgH47+NrZyEaEyVvM7CYO5jpu5uO73WGUep2PDa7lx+U21jLBXGlprTp8+zfLly0kmk7zzne9E2BGeKzo8Orifvzvwf+Dr4Jz1p9bxWzf/Z35x7R2YUnJTz/aXXLKE4PO8WCwzWKxgCkHSCJP0pUQKSJsWFz/9bQaeeITX5CfZMXmuPnlBSia27+D0Ha/FW9ZP/8lj5HbsIrV5CxFfMef69IdDzjssk2VRiw7belX/fDfx44kmMWviVQulg9mN1b+OJWALgapcYWbmEaZnHiabfRatXaQIO8WCuSpEMzexZc1vkcns/sEdwEtAdv4QQ0OfZGrqQaz4Rp7t/AQnCyUqmX9HC5sF30crje9DRSlUMM2SotZYYYxARQfEJJi5GMwYhOBzdUJ1rKoeNapULfkFom4Z0/fxpGS0tSssCzeMQdKKdRNDnOtZji8NpNb0zk/TkZ9nIZ7imVVbamrYZEi+xjNtjC8hYtVLaoujWL+g2DLvs3XeZ+u8Yiwq+JWbEyghONVicLDd5ESLwem0Qd4OSmFWuPaJRHDhlyHBNETwpXUweqixjF39TDRBo0PVX2dJQdqQ3JA7xaaZw5Dq5Y5LnyYSZpBpDYOV3fRHTiClj5YW+bveSHrFTViRN+IawefgKo0lX1mykM1muf/++zl37hy33XYbd999N5lMhmwlz8OX/p17z36yRsoEkp19d7G9u64kv9SSpa81WdfnVK7I4VyJhAQhJI7SjJYdLgxmWbU8w8Zv3c8H/u1vkOHP9PyevaSOH0N4HtqyyG7fiS0lJ1eto7BlG1FDoFTwR8XtbXHWJYKRT81SZROvZjSJWROvOvjhPMfhcjC0WOcPw+xXQJWgdAacYQASiXUMDLyfttbbuXLsKPPyYyjlIqXF1rW//aokZTMzj3Hp8l+zsHAEaaTJtv4CT8o3MZIrEEGQ901MA4qeBhFoW45WhAHteErjojFlEL9Q0YES5IXDnzUBwaiqYgpI5+dJVEokKiWiToUjy9ejRBihsISMVQmKoTTrJ6+ydmqEhzfuwfI8htp7eXr1NqbCcmSVkDWSPktp1uYVa3OKgaLiH9ZFEMAfnKpw90RAHAYTkqc7DI61GkESPPD/bIwGJC7cmSWojdUxRCPRCj1wYR1Whk9UlTEBWKZBxBC0mAZdlk1/xCJlSyqeZu3Mc7znyQ9iqGAAtoeJ1uHMRWFxZN+HuLwqzbqZQxQGbsbu2UNFa84Xw6yycGy4JSBhSBKGQco0iEpJRApsKV7W8qbv+zz11FM8+uijANzxutchVkb5i0MfI+fM8fDQN5krz7GhdROX5i/ga4UlTd6y/Gb2phMvS2REwfM5li9R9H0Kno8pIG2ZeJ7ixJkZRh7Yzy0n97NLTdM1fLmhw1IytGU7+hd+ifbjR8nt2EV0yxY6fU3MkCyL2qRNSbdt0RtOLGiiiR8FNIlZE686zLoe54tl2iyDSv4Y7sVfJXA4gZHYie56H3bmNdzUvQGAg1/7Eo/+633c87t/RKxzntbWG181pMx1s8zMPEpr601EIt047ixld55s1+/yGHcw5dloFcyudLWmqHxsNBKN44NlgC0FZaWxEEQklFWQau6pgCY4KiBgEuhemKV3ZpyyaRH1PSKVMvvXbmc6ka4RC8KAT7SmY2GWuUQm7KbUxCtlcvEEmVKBq+29jGY6yEeD8mY2GRjLRcNXtVHgNZMe7x102DLvEwkNXmUJn1tpk7cEn1lp8+UBiwtpg4K1eGKAQfAfFd6vcgkZqmJUk/JrhrhABauSoKQhaLcslkVtumyTFtsmZQiECDKu2sYP03byYSjNsW/ofsxq3AWCh1e8n4m57aztvcDc+tuQA/vAMBhcdROWCIhWVFxLtPxQOZt2PcacYPh3NRLCloJ2y6Q9DIGNvQRv1EMPPcQTTzzB2vXrSe/byTdnH+G+hz6B0sHPy4rMJv7bbR/llr69HJk88rKUK6vwlGbO83hsNheqsRpPaUqux78duMr5k5P85LFv8cenH6h58ubueC2Zp54Ez0VbFgs7djG9bgPmjh0oAaav6bAkd7Ul6YtGXtJn00QTP6xoErMmXnVIGRJXaZ6Yy9M//SXa8WuKyKRciZF+O7e3BIpOdnyMJz77b6zevY+Ne3+2pgBorak0mIR1+JgIx7QIwJICW4hXPGhSKY/p6W8zMvIp5rJPo7XP+vX/hVjXe3jcv5nHU9spach7PpbUzHkKHYawlkPCBVDRirIXHJyjFQsqiLYQBEROA33ZaVZMjuAKQaaYo39uCgEMtnVzunclE72t+FJC2ByQKJcoR6IoAiKmpEm6VCDmVSibEbLJNAYBCTsSErHQ1ocG0o5ie1axY85nR9bjv22JcilpkPA0CV/zxQGLky0GF1IGw3FRG9t0qiVQQ6rEq3ocJnWCZlaVsYZsNV8Hr5FAVErihqDFtGizTNK2JClNoobEkAJTCgwhKPmKiq8Zc1y2Xr6Ptxz4I6QOvsdmVR+2KCGEwjdsCttej9m/j0GCcrDn+EzqgLiJsDnAkpK4DIaEJwyJJQVmGJJ6PdLmKc2043K1VAm8bBoylkGbZdJqGbSY5vOm0ReLRcrlMgWzwGzvLHM3zPGZymcYfPK/LnqdQLK153Y2dwYk7KWWK6uojlK6WKrgKMVoxSEmJYbStJ8/Q9+hg7iHr/JbQ0fpnRtfpJDNr17L1Z9+F5mjR5jfsQNv4xbiWpMxTdKWQW/EotO2iDXT+Jv4EUaTmDXxqoNGEDMkmxJRdHbxL+iMKYnaJp0Ri0qxyJf//E8wTIudv/DLDJUccsqn4CmKvqI6vryew1WNnAUQSKGRCNKmQdI0SBqSuGGQMuX3zcPi+yUOPHMPpdIQ0UgfK5Z/ALPlTo46K3ns0iizFY8KwUW/6CtMJF6olhlCIIWgoDRGaO4v+mFXpRBo32fZ9BjLp0ZAQ7qwQGsxhwDG061c6ujjSls3o5kOZpKZGgMSOlDWDKWJ+j7RYoFISMRyyTQayBGQn0YDPVpjalBSsDrn8ydHSqwoBp+uK+BMRpL0ArL1zT6Lh/oWz7I0g9OAH165q7+sVOiNk2HkiQhJmgiJWcQ0iEpB2pR0mAattkWrKUlaVpBXdx0yFMSDKPKeom/yMHvOf5a2+YusmjtVnzepBSfV6xja9BpWJk4zuewm5nr2YCqNJSAiBFGzqgPW4WlNRWsKrsd4JZADgyx/SJmStGlgy+A8Fnyfkh94J3XIMjVwqaRxwgw5gaDFMui1LVbEbHqiNkmpOTNzhq8e+ipPXnmSmegMBRFMb0jZKXZ17eLNq38S00jwt0c+iud7WIbJ21feSov18l0GlNaczJc4WyjRYppIBCLr8tkDV0k9d4j/6/FPIDyPtwGV/n4m3vbLdH76X8ENFbKdO2ndvQtn905MX7PSkOxMx+m0LMxX2JfXRBM/KDSJWROvOsTC8s7ZokOELnqpqyZOZAO3puNUlOLLH/8os6MjrP6N3+dyJIUslfHDzjqlNKWweaDi69BTVQ9/qJI2VVVmQvWsWv7qjJisjkZos82XxRPkeQVMM4FhxOjpfivJ1CYimTt5Klvk8ekcw5V5DATFMHFfAyWliRJ0IlZ8RSY7Q9vMJBMt7Uxl2mmfnmDT5DAKiDoVurIzRPxAzRlLt3K+axmduTlGWzo50b+6VgO0nUp9YUIQcyq0lnJUzAjzoQpW9WMZDZ99wtWsy/lsWFBsyfrsnPP53AqLf1kdYTIquZqQ3NcfeMPOpQ0co36hraqUVfWyRvAaSViDcR8Cw74JRExByjRIhX6tmCGxhViUCTbvw3x47FIEpn5TgEQGjQBS4BSzvO7Yx3n9xX8PtS440X8XG0b2Y2gP37AYetNPMNK9iyF9G77S+KUqTQ5gG4EKFjWC7xOzWs6srqVB6VFak/cVZwplcp4PCOKmoMUwg+OQAltcO/hba828U+Dx0UN8KnuMyfkTTM6fwtPBeUtH09zUdxO3rLiFXV27WNOyZhEZ3du1+WUtWQIUfcVgqcJkxWWoVGGq4vLU1WlmHnyS/pOH+Qm3yF1XDyLDTl0tBONvvIeR9/48E7t2kTlyhImtOyhs2kKvIek1TbojJq2W9Yo3SjTRxA8aTWLWxKsSlhC0R0wilSNAPcU/JXKMlB2GKx6J176Jddt207lpOzOex7Tj4apa4SQMQA1ImqODeZGur1n8iuASLQmJgBREhORCocRho0C3bbMzHWNNPPo9e10mp77BmTN/xPbtf09LZg8dAx/iQDbP/iuTjJRdLAFlpbAQ5H0VGtUFkZlJEjOTTLd1EvV9dh/ej9SK9QjKlk3MrdQIw2B7DwdXbWT15DAlK8K3Nt+AEmJR+n71Nl0pMmPZtc/Bj0SZi0TRQMrTdJcUfUVFf0mTtQXf6LMQWvPVR/JEQ3/YRFRwsN3gbMrAAAqW4A92x2vU16De8VmLQwjfr0r2qr636usNCTEpSVomaUOQsCwSIemRUiJEXasyal6ya89JVRn1taLou+wavI/XXvosA/NnifiVhlgQyeXWrTy24ZdYP3OQqz03MtG5C4Og+1csKadpND6aklbknUCxrBL9iCFIGgYJw8CWwfaO0oxWXCSCbttCCIGHIu8r5j2/FtlRKJxlfO4AcSNGxZvnavY4Y7kLaB2QuQ67n+Xz/XQxwM6db2Hjpptos036bIuMZV6jEL5cJcsq8p7PgWweTdDQsDYeZWq6yJV/+ip/fOCfMMKmkNKKVajRYYTvoywLdu9ldSxCedduKjt3stww2Z1OkLGaJv4mfrzRJGZNvCphAuSPYhWfaUgBN9DxPZx+7hArduzGXbuBwf6VnMiXMAREpUBLQdb1yLl+jQjUSpoEKoarwVWqHhOhNSqUc6QAE4kpBcpxGSk7nMoV2ZmJ84aOzPOOz1kKrRWXLn2UwSt/Szq1Hd/o4LGZeR6aXWDC8aj4Gl9DBQVTEyRmJym2dDGcbqF3YpQdR59GaMUGAiO60UApZUOv41i6lW9svgEtBMf76spYuAhaizmy8VTgsQOW+Sm2T3oUTEFnWfFgv40CPvF0gc3zi6ddPtVh8ECfhRaCv9wcZdoWXEhLZiOy9pk2+sAgNOuHjzVGU0Bo2CdQKKNSkjACNSxiGNgCTClrvjOtA9Wz5PlUmz+WQgBCCtbPHGXT9CHOde5jLrmMrZNPs37iaTaP7SfjzIVKneTZwjvYGb8XKTx8aXGpZx+X23dwqmN7sMiyU9uvLQWWDMJRbSExZfAHg1kNSGuAhyLr+cw41UgKzbzr42pFVBrMoRcF9AqCfY1Nf5sjF/+q9owUFl3pzeztfxcDkdWsW3ErNhFmT58gumELiYhNu21SUpqThRJaQ8Y06IvatFnmy5ahprVmxvUZrzhcKVZ4fGiGwoMHWDd8lmW3b2fnkcO88ZmvIqudulIycffrWNgZqGNjW7fBhs1sNQz6IwYdEYsW02ga+ZtogiYxa+JVirRlYhQPEVCSwHdWTP8k2a8fY+rhB7B/54+Z6FuFENBiGeR9xaWSg6tUcDE1ApN33vcp+5rYWJn4SIlif4xSbxQAP4yWqJbQILggaeFjEBjFNQqJYHDc4Wqxwi8v7yL6PORMac285zNdnGbswh8yc/Uk8867yafewb8OaUr+DEVf0ZadQU2MUmrrxPM8Nj77GFIpuhFssCwibn1UuAbm40kypTxCa5Q0OL56M5nZKc72LGc62YKultK0pidfYDoeRwkwNBQSaSzgJ4Yd3jbssj0kXz5wskXyjX4bD7i/z+KRLs1oXDISk4zGJAVb1Dor7+u3aiRMNtxW0ViuhDC0lSDawjYECSmJGQaRsBRpyvrnaIrq51f3k1Wzx4JDqzZtLCn7oRmYeo7f2/9BLOUQqGjBCnJWhtloByknG3QFapjvbuHjm/+ONaUjXOzax3DHTmwE9hK+oNAoNGWlKPi6JjhKAXFpEAtLmZYMVLuAzIOLJut6LPh+cOv5RKTAFJKIIYjJgPRrb4HTI59iZPLrNNK19b3vYlthG+rpYxAdZ7TXoNUWJLdsp6SCPz6EEMQNQTwkYWVfcbZQQmlos02WhSTtex1BprXmdL7M1XKFc4NZvnFwBO+5I/z3/X+HpTx4+HMgJfM33Ej60CG07yEsi7abbsTcvBW1axebTZPtqRixZsRFE01cgyYxa+JViYLnI4wMUL1sacqjkqmHH2DZ3fcw2bcSW0JESqYdl/GKR8wURKXBvOczFioXlhSkJir0fWkM6Ws6mWN2RZS55TGMimK+L0quO1KbFamrXZwiMJ0Hf+Er0JqvT8/jafjgiu5FyoSvNXnPZ871OHD+ElPDV+lIncKaPcuJY29CKdCn78dfvQEnEsPKzdM6fAmhNT0sJTiaUiTKRMcylo0PIrRCSYPz2/bRP+czq3xMGWM8FeVQz8rqooEgfd9W8OHTkDdLHGw36S5rPrk+EkQVRCWfWWnz8YhgOiqZjgg8WR9R9OXldr2DlXrcRf0c1J9jyeMy/DJlQB5ihkHcDNQmK/SD6cYNAU/X1TknkMjqr2lkeA0QoX9MhkPpN8we5T3H/xK7GgKL5kT6Rp7y30duahnlm+f4nSd+FdP38AyT/Xtew/nW7TzLjmCHZQchCMg8QcisGXZVmsg62wyh0JTDUmYVUUMQNwwUMOe4GEjiUhKP2CQMn7yvQg+dZnz+OFOTXyGfP45SFTpab2U2+wxKe0gMuo5Oo/IHkctWENt9Ix5wuVjG0Zo+22ZF1KLiKyIN339RQxI1ZKAwKs3xXBFDCFZEI3RFrBqBeyFUlGLe9Rl3XJ6cy3HlC4/gP36A1elWfubyY1jKq52W8Xe9h6sf+CCxkyeIHD6MuXcfvTt2ssE2abFMUsa13rkmmmgigNBLfxu+CrF371598ODBH/QymngFMVVxOXHpY/hjH6tfDA724BTvYONv/AGnihVaLIOyr7hYLJMwgzDS8YpLyVdEDYnSmjnPp/PZOZY/O1+bEQkN44ckDK+PM7IuTrY7UgtfrTvQAARKK3wNMQl3d2T4lYHuIObh0mWOX7xEsn8ZrrPAxfu/AUqjw3KcfIGfPw3kki0kCwsIrTCQvNHdTb+f4ZGWEk+2K472JhlLpSj6qlamjLuKoilqnZWGEFi+rs0aaiRSjeSq+mWwmIA1ovr/xqkA1X1UPWMmQdxZREoiAiKGDMp8sn5BFkv2V1W8JNT8YiKUx2Tja8T11bHg/TXrZ4+yeeoQC8Lmg2f+J0aY2aUR+NriK7MfYURsYnJdgqs3tLCmeILN04c427mXy+3Xeq8UQTekD2hVP1+2FEGJVQoswLhOt6cmiGTxlGKs4galXSExRbC9KYLW0orWjE89wNjI39c+9WUDv0mm9Q6KhbP44/tZfb5AKrWZha27EV1ddNp2rbOzxTQxRZBRp4G0adATCQJYr1ce9LVmwfPxtabTMhmIRWgxje9IlqYrLk9PLfDgoRF2r2ln1dRF2n/jQ0gvDNq1baTvI7QGyyL3Pz9OcctWtBa0Rww2JWI/sLmgTTTxwwghxCGt9d7rPddUzJp41cFRipOFEkpm6pdmDb4XIfXeX4Hwgled8wggERQ8j6KvSBgGrlZMOR4IKA/EUYcWkH41KKOBfChYfqbIsnNFBtfFubI2xkxnXTkCAlKGxtIaoQX3TWY5ly9xu1/m7L1fBN8PQ0490OGPnNZU4kmipULgYZOS/JobuXsshpHPclAfQREQscLyHZxNGWwbGiep2/mbjhYWLDiYSgVrCLIcFnnHKlbDRTAkMr4hFh3bUvWr+rhqeLz6ukbiVYUfbmOKoBxphNlcS/1gtWU07K8Ri1W20PGn9DUK2lJU/WOBGheUKNfNHuMjB34dK1TIqu/pIxiu7OAR++d55tZ9jK2O4pkSrTQHU1s4mNoCgHRdTCFqXrFgXFNAbkyoeceqs0bznlcvY4aNIbEGVc1TQcdi0VdUVPC9a0mNR0N93JtjYfJT5LKPLjo6c/YSnYUV5Fdsxk9toJieYLazC4RkhWHQF7FImcaikmQ0XF9ZKc4VSlhC0BsJctsas88MIWi1TLTW5HzF4fkiSTMw7rdZdYJW8H0uzxb56MPnefzYBK7rs/rsYdrv+ycML+hy1UIw+7M/R27fjXDoIIl9N9C5exfLTZO0aZBoliubaOK7wgsSMyHEt4Df0VoffQXW00QTL4hqR99s7iRp6hffnhtXsGNFP+cKFdosybjjYRKoNJXQzF99bdEP6EdEGMx0Wsze087AhRK9ZwvIxf72QGRSsPpskZXni1xYG2WuzcKqKMa6bcbbgx+jCpresRH6xq8ifY9TC7MYfmhK1xorWsYtJwPVREvePbcRgC+vLHJ2VR9tbobMcJ7Zzh5ajZuZUXNc6O3lXGcrEVPyYLqNUcfjejBYbH8XgE29s3HRsYSvXUrMGtWypQpY4/uYBF2Ppggyw0zAaCBhWgddro6vvqMCE6wj9IiJhmkAQtRUssYuy+spZI25c8KvsG/8UX7p3N/Xy5YafCHRCDxp8sm9v8axVbtD0VBgc61JX4WEy1VqUVCtLQ0igloIraBO3Bq3rWhFyQ02rPiKitZEZJC7124b5H1BxddEhMQpnWJu6osU8icARSJzB8WFp9DaQ2rByvM5MslzxNZuwMfE6R8giiZpSiJCMFJ2SJoG7ZZFwlgchByVkmiYjTZcdrladmmzTHoiFgmz/h0hhAgy+kwo+YrnFopkTEl/1Ga07PDJb1/kwUMjbJy6yEemDrJmdoj05DiV1jaEaSKUAstC3nIL8a3bab9xb1Mda6KJl4gXLGUKIXYDfwFcAf5Qaz32Sizsu0GzlPnjhaKv2D+Xg6t/gpz7Uo1U+JH1mC13QuoWiG+n4CvGKy5zrsu8H+R9Lfg+WoOrIed6RA2DGdcNSYDAGinRfrXMivNFYiUVlhuD912q+GigHJ1iun2GYkyTrMwvMuW3+UnmjIWAOEjNwvYbefMxyZCVxUx34bR3cjUuGOuKUI4ZlLUm76lAXQuHPl8ou9ezUl2DCIsjJhqtWEvX3Ui6GkuVjSSuul01NNYyCEzwoUJTLSe+FNQbKoL/Pd9x1sqdYelPa82W7HHeOP4N2iuz7Jk9RNrNkSdDTOcRaDxh8s99v4FMFTnStpuTLdsW7VNKgUkwt9KQdV/aUtRKmaquqJphg0KQ4h/koTXC15opxwlI8BJFzRQwPfU1Jsf+keoZ6Fj223SKTSRP30fROU+LGiC36k5Ky9fSFbOJCknSDCYHNIYbV5SiEja0dFgGGcvAvF5JVWsKKiCcrZZBfyRC3Lz2dUprDg9l8ZMmBa2YOD7Olq98mj1PfC34OROC3C/+EpPvez/F48dZfeoELTfdSHrXLpJWEAfSRBNNvDCer5T5oj1mQoh3AP8n8EXgf2itSy/fEl8amsTsxw9zrsfw9LNMnHwfiJDMVJ8UUcx1f48T2cquTILJisupfIlT+RJTjses61FRClcHgokiLDEZgtmKhw/Iks/rHpwlXp4g25qjJZvEdlL4Zo5KdAaEj2vm0WZIxDQ4ZhTbK9cWYqbX09H3OOOlPi72v5Z0/zK0DuYjBiVAgas0C74PGiwJeV8x7fp4KiCRjSpY1dNVhcXiMuQLecWqJUlBvQzZuI1BUJI0JVgIDBlMMKwGtV7vV0XjQ9cjao2CmQz9Y9VzJcIXfCev2eL3CXPntKaoNNvnjvPJQx/C0h4aeKp1L0dLb6cwuQm16ipd6VMc6t3HmdZt4TF/B8IFqAbfmBk2IwTK2LWEq7adDnPKwk2Nap6aCLp5K37gXyQs8aKD7VxnktLkv+Dmn2nYo6Sn970sN26n95lHmd2wjfkVaymFZdFMOHkibZnEpLjusXgoSl6gb7ZaBu22+R2nUxR8has0bZZBX8QmZko8pfj22Sm+9vRVhkZz/NryPLufuI/2c2eI5XJ1VVVKcr/yqxTe937aTINNqdj3bQpGE038KOMle8xEoJGfBf4W+FPgV4QQ/0lr/a8v3zKbaOLFo9UyKRXa2f/V5XTclqCl/RRVS7vWDs7CM6xcsYd2y6TDtticiqOUYsJxOZ8vczxf5NhCkYulCrOuR8kLzPut2SnWjV8l4rs4iVmIVrA1FDNQbFyABukHzQDV62RqoYVKYhzQ+NLg22t7mMl8EFNKDKHpzJfpiZrEDYOC71PRirghKSvBkONS8BQ5VadeEVFXWyAMWaU+rLsxiLV6C4sJTiMpg8UETQK2CMuSMqAgUspaHEWwjwaP2HWYV7Uk2ZgF952gCAz0qMWesuuhugaJCJL6EbWRS7bW3DV8EFMHZV0fyeH2PXx+2R14tkSJHjT7gh154XD70DMWjK0CA1EnimGyvA7XV1aKsF8AKRU2AlMG5U8jVNXkkjKmp31mHZeSUggEcSlJmYKyDux/buEExZkv4hRPIYRJrOX1lOYfAeUhkUTioHe6IQAAIABJREFUW3BSnVx94ztww3PQZUhSpgECHK0ZLzuYUtBiGiRMidFAGk0kKVOiCEz9c65Hu20G3rIlxClhSLTULPiKmVyBqZE8Dx4YZnA8T7/l8pej32TTlx+qKWSX3/YOBu7/GobnIiyLgdfcQktL8kV3czbRRBPfHV6Mx2w/sBo4CTwNvB84A/zvQojbtNYf+L6usIkmrgOnXOL+j/0llVIv6ZXtyNxJoBbDyerOW1mTiNVer0OvzaVyhWk3UMV6cnOkR4bJE8fPF+kcGcTysg3O/+giA1Y+miJRyYUXLCjFOog4Y6CDImCs3EOs3I1jZxlvb6VrKs7ykRKT3TaTHQaTjoODIi4lWc9HKc2cr5j3l5jaqmumboGq+r6qalf9WOuoErHq3qolTUHwg26GliorzLoSoVcq2CZUj/Ti3LYXh4amCVF97+BOneA1PN+obja8thFV/5hC46uANBmeZt2lMpvOFkkW1kBbcB48aXGsfQ9e1AiP9Vq1raqK1sq5QmCEZUwhRRh0Gxr8RX2BPpoKmrJXP8ZqDp7ZUPrUOvBzxaWBFuACjq/RWlGavZfC1KdqR5vq+XXWzLSSHNfM2uOoll2MyZX4jkfcMGgzAt+X1VCStIXANsBHMe14zDjQapuklhA0STWaIyiFzzg+7bZJh31tidNXitmKx6cfusSeq8f4/cHHWDY6iOk4dSIvBKKtjTN/8Vf0njhG18030rN3TzMItokmvo94MYrZB4GT+tqa528IIU5/H9bURBPPC6013/zEx5gfH2Xg196Bkfuz4HEADHpXf5iVHTcs2uZErsTxy5cpnLyEXYzTnp1jLn+ShNYkquRBBRfV6s4WRDsJORaml0u6JnsoZAooFFoaPLtjHWuGull7cRrLacF0UwgElpthdQFWDxXRwFCHw6GNUQopydWMovAiWY+vFxMzWNwx2UjERMNjsuErSMwIFJ6qObymrIXeLtG4MYt4yXfE0nJjtdRYRc2YHy72+f1juqaOLYrJCN/H9sCzBCaafYfz5JIGX9+1iXcOwZNtN/CJNf+BY+kt4Adhw4YIy5BhmVQSEK9GyS9Q73QwoiuohNdVtYZOT6O6rRS17Vw0jqvqih4BgSuGBLtKWrQzRnnyH/GKi/um+s49wurpVSx0bUJvfi+5RJoeQ5KyDLzq/FatSRnB3M9G4mogiRvBOmYdjzk3UI9TpoHR8LpGgjbreGRdn76ISUJKjl2Y5Ylj49x09wr6Zyb5q1Ofov/QgWDNUnL2vb/A2i98Ful6CMuk8+ab6N2xA266gauuT3GhyOZU0+DfRBPfL7wgMdNan3iep9/8Mq6liSZeFKauXObsk4/T9eZ3kog+hCjUi3hO61spJ9/KqRPjWOMlIpNl1ESBr90gqDx0P0GaK4vrfUA030ek0sl82/GaAjYw2Ql04tpZLKcF201jewkcO8szq7s5kWjh5KYWNnWmWT9osOmyixEuZaTdZLDLJFrRPLg7jr/IAFaPtuixDCYbxkMZDcuqKl6Ny616w6pcCkI1jKAkKVjcIdmIYF8Ns0Ab65uNt9dB9e8yveR+aJ8Ky5mLCZ0I/1mqjtXVMxEeR6DSKRGcnuriVow4bD9bIl5SfOEn23BNwRd/spViQrIrexSG4DMr3s2p1m21X2TVRH5P1Q9ICmplzCpRq5IdQ4iG7QIzfTXX1hISS1IrX0KdiGkBReVTCRVGQVAeVULjFI7hznwRVT4HMoLZ8ia8+W+D9hDCIK36ObHvDvLdvZgIMoZB3Ag+GUsIrFAZCyYECNLXIWjXEi+PdsskaRqLCLMkUN8qvseDx8c5dXSSvstn+amp42x+OMvAmeOLzpcGyobF7P/9MVqPH0Hv2Yu1fXvtNR22Sdb1OLRQZHsy1ozCaKKJ7wNeUo6Z1vrSy7WQJpp4MXCVptLVT9dv/gyIJ4gU9tf5hDBpyd5J1+eOIsKLqxs3OJvM4jxxsnbV1wKKnT3EZqaCdn8psZ0uLDdNZnY7XkjELDeNBmw3XXt/y01juSlee0Lh985zoauNC52tzPdKLq91WXGhRHrB59/vSNXJWJUkac2qMYernRaeEcyldJQiJaC4hBQ1+sdouK2WNIOh3oFfql5kC+CH73VdVFlEbWnXIXDV6QbV2+r+GwhZ4+6C93z+3LHauwgdKnIiCCOlofypwfI0my6X2X6mTEvOJx+TnFgfwfF8fCnIRwPj3Yr5cwA8l1hDwfdrvrGaYhYeW5WIukrjNBK1qnet6hlrlAup+s00bmjyM4QIx0QFr60ohacCwz+i3hDgTn0ed+4L4cFKOlLvZ+uQIGu8hZMrLKz4Fq5u3AAiiGCRRjhuaslpMJAYSwhaJiRoS4lXlaBNOh45X9Fhm9gNZctSyeMzXz7DwkKFt048ywee/iwyPLeX77iL4dvv5Nb/8WdI10VbFta+vZxbu4G127fTal97iWixTAqez6GFInsy8WYnZhNNvMxoBsw28UOP6nzJk5cu8cz5izh9ij2lP0Xg1FQZrQXzqTczbW1nesNlxsQsTsqkODxIaWYaKx7HlTJgElKitu+mpMGcmiDX0cV4KUnXc/N0XQnIFywVknSjDR6tDTbNp2hZmyAqBVMVj/OdcK5VUvY1WtbJmAi7/gwFd54soylzpctkYMrjaoeBa0mudpmMdlg11amxa9KE0ABPzRvGorUtRqMyprVGaZ7HN1Z15dXJX2OzwNLXCeqxGtctdzY8eL1xTdU7IqxvCkFt4oKHZvVQhdufLTDWavD4TQnOD9hoGXq/lMYIP9fN+QvMWC1Mmm31NH6hw3KorpVxzTActnFd1XKkDlW1RkWtWg4UYQnTCMmdAkpKoXyNISR+2NXra40UAr8yhDv1j/ilMw0fmab78mOkSjvIrdlFpmMdERl0WFbh6WC4uakUyXCKQCPxaiRoc65H3g+CYa0lfjGJIGEYVLRiuOSQEoLcTIn27iQt507yW4c/z/LZq/SPXqmfDynJr1iJuu12rvR0kzhymMLO3egtW0loxblimZXKpjtqX3OaE6ZBwfM5ulBidzr+sg1Hb6KJJprErIkfYhR8n7Gyw6H5AmPT01Q+/l9JqAh37mtlbo1bq70EY5JMZv0b8SujnLnySE0dMxJJWm+/i/4Nm8gszDE3MkzHsmXEunoZc1zSm9bTahnkfIW3dxn5KzkKR2bx8i5OTOJ0RXBG87TMPIVXSVKZXgnA1S6TMysj4HhcqTiMNwS/tlgG837oshKCzqJHz7xix+ky/TPB6wbC2+XTQdyDknBkpc2pVRGmO6x66GpIxGoG/Wop9PmgNUKD8DRSaSwVqDOG0pgKemZc0kXFlW6L4Q4LCMzqVTqwVP+4HlHz+Q6ociQWk8tGT1yVJ2kBtqPYfa5CISI4sjbK8WU203dJRjrMRQTUC/cjVECkdmaPkzMSbFs4ydHM1rr9XQRdkL7WOIAUPpYQRBpKgXV1LLhRaJzgg62RtEZjf1Xd85TG11ARQRaeKp7FLR0FZwSKB0EmoeWnENmvo7WL1JJSx408tPy1SNsiJQWJJQTGFBLTCAma62FKQco0FilewTkJCJqrFRMVl7RlklxS3gTQFcWlM9OcPzWD5yt+YZvPGz7yB0gdfD+evfk21hw6gPQ8lGmR37GLNsuktGUrpS1ba/uxhCRjCgbLDqYQtEesa051wjRYcH1O5EvsSMWx5HWpehNNNPFdoknMmvihgqOCrrOTMznylxcwBudZN1qie+oMzxZyjPziL3N2/Azt2kSKgJzNzvRzdWgbudwp4NSi/bVv3krX5q0suD5eSzsb+/trF/z+0OS9pyVZe/1kKs7xlSlGC3Mw/SV067vQO1oYzm1jSvYzMuOTW3A4ngCNCzn3GuVoWdRmi2kw53msj0eJdglO50scsAUzgxV2XnQWKU4B4YA9lxx2X3a41G3i2BJTa4wGUlW771fvL741lEYqMK/f5LkIGrjpdJl/vTPFSId1zXPPh+tdfq+nl1QJ3dJZBUKD7Wr2nStzw9kyUVfz3OoIDoAhGOm0attX32vX/Alumj/CgcwOVhWHWV26igb+5chv876dH+W5zDaErnrtBAiBQqO1pqw0SiuMMLW/6sUTDURNLiFpFTSGCLohBVAJVTlTBlv6pTM4Y39KlaJakd20Jd9DrnU5KrqDzPh+nL7byGe2kRSBgukoyOMTM+Q1HZKNBG0u7M5MmOKaHDVLSAxDk3M9Cl5g/JcI8nmHs8enGLmURfmalt4E6Q0Zer/+aWRomlNSMr1qNVd/+p30njjG3LadjK7bQLLsMBC1r+m0NIQgbRpcLFWwDEn6On6ytGUw67hcLpVZ39AF3UQTTXzvaBKzJn7gUFozV3Y5V65wpejQc/8VNlwqIDV4Es7LYS57V8je8zPkDIMzl8dITd5Na+soXV2XyWQmmYwUyLSMM6s3UhiNglZIaZDsHwAgaUoWwqwyU0DZV+Q8nx2pOJ7SjFYcLhYrnJi5SE/u83QvfJVLehmPO9vIJFYzXOnnuXwpIC3JxetfGbW4Eib0GwLWxmwShkGvNhgru1wolin7CtlpMd1lM99a4o5DxUDVok6EBMF/+md8ihGFkgIlwZegpMCToGyBLwW+BL/2/OLX+RJcIXAbnlsz6rBuvE6RBAHJWznp1YhZo9Xp+cjZ9Z5bmqHWeF8sub/+qsPrDxVIVDRn+ywe3RpjotVctN3u+RPcMn+EpzI7SflF/vHEf8LU3qLGBwlYymXf3BEOZbYiNbhoTEQw3xJBteW0avL3lMYlKA0b4ddSU70U9VJwyVdBOZhAvTS1RpfP447/NTXdUAvWj1Xo4gJP3TCATG2hktm6xHsVsEEPzYLrY0tNzBDXDD43Q+JV9n3KSpA2NJGG/WgdqHauhrzrMl5wyEQtVNlj5FKWrpUZBja2kchEUFqTDCdRKClRpsXYlh2Mr17P2dUbAI30FHNeBdfXrI7btTDh+noESVNyPl9mSyp23ZJli2UyVHboi9iLyrRNNNHE94YmMWviBwKtNNmhLFOnZvAvzmPOVnji51cghKAQWeBC/xSVgX6GC+NEzpxGZyTmudO0nzuNAPK5TnK5Tq4U93Dr+n9j46bHAVguTvH0ug8zM5mgfdlyot09uFqx4Pp02zZ538dzIWYINiejFDyfb0zPs1BZoHfyT9lUfBwQPB35Of7W+Sn8koDSwjXrr5X9BOxIxVkT8ykqzbKoRUQIhooOw06ZgucjNcQF+FKiNZxcH2O61aB3wqNoC7rmPLZddhA6KGl+4fYkwx0WHgEBscL3qX12LyBpeQREqW/aZfvlCv0zQSm1sZxYLZ8OdtV/BbyI5sxFx954X3MtqVu6H6F1MNInIpjMGDyyLc5oh7loXQC75o/zuaO/jR2m+gv0ojFST2R2sjd3ClN5uNLi6czOYC4mQUOtFjqco1X1z1U7QeteM0UQlRGQNB2ULhtImh+WLVX4nrp4Dl08jOcMIctHQSQRWqJRSC0pt+3l6YFb8DQYKNAGXkgSG2EiQApcrXBdiBsae4mhXyD+f/bePEySqzzz/Z0TEblW1r50da3dtXVXt3qhuyW1hNACNgKEwSCEB2Pwhj1eZnzte31tj33xcj025t5hGeNh7AsejA02i7FBICQQloTQgtT73lXV1dW1dNdelVm5RUbEOfePiMzKWlpIICEw+T5PVmZFZEacjMjM88b3vd/7ETIMX/jvesQ1xAxBQWlWXA9XaVJTGRaGljCiJuqmFmJxg71v2o5l+XvMewpTCNpHLzHT0cW5W28nuXcfKzt3Ey8bj9IaW2uGsjkWXIeBeJRaa+20YAmJLRUTeZu+TaJiUgiiUjKcybOvOnbd/qgVVFDB80OFmFXwfUXeU8w+cw390BWMvEcUGKnPMlSXJDcVwjMgNfY4KA8WzhEWAgIndA0UErVY6RRC+15iyf4mXGlhCgcAgUskfJncwDu4JgWhjE3ckrSGLLZELBpM35pgKl/gycUVCvYUE14T9VaMS24Dj1gfYFq0ca0g17Q/2h4JMW4X8LQfbTmUiBE1JFtCFs1hg9ZQFWEBR5IrnFjJkXYVjtaYwsAVPglQSpd0V1MNFpMNvtjf03C226Fj1i0VAUh8QlbyKNOrj4sFD8AG9uMBrfMON1y22Xe5gFEWlSveK+B4T4hT3eEXnMYsPmf97suJFWXLAMK24q6TOWwLvr4/zlizxVjz2v3eufht7pv+KtVuhpuSp4hop7T9U/F+dmQvI7WHKy3ev+09CAhSm/s4XrO7FH0sEthCoDGUQFiKwJ9OP2f6UuC3oxL4TdiL4n+Ru4Ce/q/4taeg47fS5byazvEnGG7xyGy9jbnq3YTwe5Yi/POQcT3CUhKSG3twWkKihCbtKSytiRtyQ/TMQCIMTcpxmCsoHEeTmciQGlnGzbiYcZOqrgRaCJKOS9SQRJUuRfvqZqbZMnaJL7/j53jkdW/CkoJW1yFumKUIohSCqDAIhyQrrselrE1rWLElbK1JbVYZkiXHJeV6m6Y0q0yD+YLDiqc2XV9BBRU8f1SIWQUvGbTWOFczpM/Nk72wyPQdrTxdLahVBapb8gxbS9jN1RROPoPOevDIeYQ0EMpbfX08gZlN+5WNhuTavptIuw69K0forDnGq4xP+M/Fn3Y9LBasvexKRBmsirKjKkq9ZRI1JPO2w8WszVLB4erCt7CXvsEFp4bpxL1cyktmnZ8ujb0rbDFpO6UKxEM1MW43E4xk8rRHQtxaW0VrJESjZbDseDwws8y3UxmSrounFAqJpxUOAlcpFKKUEgOfjAmx2nLpaqNVqsosHT/WkqliIlLiB4TEuud6wN5LeV5/NLuh8TrBNrSABw7EONET+V5O7QYSVv5/+7xD16zLlSaTmpzix49liRU0T+6IlDzcDiZP89aZh7G0w4HUWXZkx0rv9VhiBzekR5Ba4UiL3+/7DQRwOEhtHqnZjQaerdldKlQoRhYFRQsM/14Hx9hTvk2HAWsiY/7x9EmaQlMIUpdaBOnS7FmY/SuKaUuBgHA74419TDVvQ4fCVJmyRHSKMPF92fyerMLXla0jZxJBWAocrVnxFFUGa7RnRbPZjKvIKkVhOEnmYpJwXZj6wXribVUI6VNNS0psT2EhiATE6PYH7wcg1dxC1JDklWIy71BtKmKGQbVpYIpVohoxJLbnsVjwm7M3hdaS55ghGc/Z7E7ENv1MGEKw7LgVYlZBBd8jKsSsghcdKuuw9PAVcucWYNl3j1puiXBhKctSJALGIiNz3wKl0JNlhqlaoyMRRDYLykNoTf7grdRHQqjpKa7VNDJTVUuHd4E7rb/BoBglA4UgGz2E0/Qe3tF8M7sTUQwhgspOh7GczclUkmzqBOPJEZKe4nHxbt8EY0VhidX4mAB6YhHuakhwKVugOxrilXVVtIZDNIX83psxKTidzvH3U/M8u5xhxfPwtE98ClrgKg9XCoRSeAjQukSsVNG6Qm+uzdoMAv/L6rHq/m+wlri1zTu8/mgWI2BJ5dWUChjdYnK5xSIdlfReLXxX51ZfZ6CNKY/mZQ/bggOXCsjgjUlgqt7g07fHGdRH+J/nH6QtP83BlfOlsc9btSUS6iJ5sPE23tvzn7g1eYInavZxtMavFjwWELLidks2cazad5gy6HJQqqikdHAVGjfwDTHFKkHTQUPyUlcFexgyR1D5UWThPJaK4Qm/D6UQBjqyE2EYaNOX5ttaE0KXSA6lMfpj8dBkXI+IlJs2/LaEKGnPqgyNKSUZ12N+PkvmUgqjNUZoSxSjs4qqhgjxxgiGkL65bcDse0YuMnDhDOf6B0mgeP39X6D/zAk0cO//+ACfqGtgon8Hec9DaL8faN5Wa5qdh4Qkozw0mqTjbSBmYSlJOh55T22qNYsZkmu2Q2c0vPmHpIIKKnheqBCzCr5naE9hX0riFFwK/XWM5/PUnJgl3RJldG8t0x1R3GuXWDl/Au9olrnk0hoxeIleGAbqljuoOfI4hYtn8W6/m+b2dppCJsnmZsJ5l7vVebYtfRQDp0wvJUCEqG//Nfa2HiZuSJKux1ze4fMzC1zK2myPRTi7PMOjqTa06IB1k+jeqhgn0lm/DZKAW+viHK5J0BQyaQpbhIP+kq5SHE9m+Mz0IidSGbJeYCyqV1NiOtAuCU+VbCWCzj8lQ9hyHdZmacDroeg3ViRj5d0BumbdUqRs9dj6MIC+aXdNAcBLD01d3RFqO47z7pFz9GevrNG4CfwG5F9pfBVvn3kIUzm40uLJgIwVCVnxvQhWDXaL4v+ir1mRWLnaT0X6BrM+OdqbPMOhpeM8W7efkzW7NxA0Tys0QZQtdx4x/eeAT2C2rWxjIHmIMx1xpmtXEPEbMKL9a3RUCk1OKSwhNk1bGvitpnJKoTWEjbVeZeBrzxyhmc07FKYzpC6t4C7ZCFMQr/eJTihi4oYNPMAqi/ptH7nIb77/jzCdAm8JjkvBWi3oMFyX7nOnmOjfgWUI0lpRL020hoWCuyZtGZKCecdjR2ijd5l/RgOSuQkxC0vJQsHFVTqoXq2gggq+G1SIWQXfFYppypUj0+ROzkHWJdscYajJ5EImh3WLS27sBM6lHO6RBYS36nwVbuugcG0KrTTCkMRuuhU3m0O2bkWmhxBPP0L74du5+Z57aA6ZLBYcjqdy9OS/QGz2LxBBJ0aNQGNC/U/Q2fpWOhsP8q2lNF+aXaY3amGnz/M/5mvxkLCwAliBm2kxfeNP8KYQ3NNUwzta67mUtXlNYzWH6xJr3q/tKZ5ZSvG5mUXOZ/KkXd9Zy9MKW/n+YsV36Ee1fIbkBpq00royrdjzJWPlKEaHvHWv1/hCftcAw/MjW08OhLnY/tzRi+9l+mxddBmcKLBlySVSxvd06cw43CX+mZaZi8xZtYHCS+Mh0ELiaY0rLT7X8lo+1/Jabkme4MmafRwLfMnKp/5yIlu8Gcql2UnSUFiivrBEY2GRvSsX2ZEeRiO4Kbm2R2Vehvn5V3yYkzW7S279buBPJtCQO4Y199eUzpYQpKq38cCu11MwTCLS90NbT6qKprau9nWEEbnRX0ziN0y3tUJ5ftpwtQG6pqA1addj6elp3HkbETOI7aoj3FEFpqCgfcIvgILWdA1fZM/Fs+SjMe547GEsp1D6TD178GYef/Xr+PUP/lcM18UzTc4N7EJp3xzXVR4FTxExDBw0LpoQAqW13+hdQ11oc8PYsJQsu96mvmbFM7RaZlJBBRV8N6gQswpeMFylWbh/BOepaZQhSG5P8GTVPEuZUdQXT1KYn4XCaqosVFuHs7zk/yMERksr8T0HsOau0d21jc7OdupNkxpT8OHf+kMs02FyT5omI0fWizOUtVlOPkPX9Pv8CdTfEKrqJqq2/iq9TYcYydh8ZXyOD1yZxtX+RKt1HZRpdsp1Y6YQvHNLHUIKfry+mlvrqzc1yMy6Hk8urfD56QUuZm1sz/OJhQYHhesp3GI6DJ9MONqfQIuG9MVoWRHrScZ3g2Lqrjz+NdVo8fd3JOiedRlrNjcI+18MdM467B+1qV/xaFt0ESU64N8MXG5LfJwV6ui0TrIldBEXyQPromJ/0PPr1LupUnRMAMeqd1HjpenJTtDoLNFUWKbJWaShsEyDs0RTYYkGZ5nGwhKNzhK17soLGrupHA4tHedE9S68oCUR+WHMzLcxc0MId5SYEyNnGiihQZjMbnkV0rSIBnq1vKcJSzZoygTCj76hyQWpPoONz7GEX5GJp4lITWa5wPJYCrO/GmFIot3VqC6N1xIhIiVG0AnA1AGJ1LDn9DF+96/+HwzPQwC2aaGCrhaeZfHw3T/BSE8/f/W7f0zf+bOM7NzNUM8AMU8RNQ0MKbA1SKX81l8KsngIBPWWhYHGVWJTczop/M/39bExGlhBBRW8MFSIWQXfEdrxyJ1fZPnINDN3bWUpYTElZ1nYOkwhKrEX5vAmMqXnG9XVeEViJgTWlq24Kyto5WEYBj++awf9XV3EzX1r9uM6Djcd+DGOM8xnx/+FL197iLt6f56+eCMds39cImVFE4Rk/S9w2e3hb4cmOZrMkvQ8P0UVPLNRZlnU8VLa68bqGHeZBrOOy85YmL6qGKYQ2FrzzcUUVuC63hyyMBE8vZzkX6aXGc8XKCiFUpoCAk8XyZgKHPN1abJy9SrZWu/z+t1GyUrnoezx9TxkpxqtF52Qdcw67L9s07G0Ql0yFMS9VltUCTx2Rr9OwpinPXSWxtAwn9ryBvbOjuEqiStNjiUGGY12cDB1hvlQHT25KRqdJQ4vn6TBWaYpIFshvXmqNWXEKUiLa6EmLsa3sWLH2btyflNjW/A1h59vfQNfbnkNf33qd7CUE1hr7C2dK5k7izXzF4jAgHX7Si/R8D2cb4wg1Cgytgsz0r92u0JjK40l/TToehJSbGSevw45A5Bas3w1RXZ0BXfBBgnh5jBmfQTZEvXT1VqT8RSGEuy4NMTuoXPkojF2jFzgpiNPYQRVp0oI7n/9T3J61x4GL57l3MAuxrf3gdZc6t3BeN9OtNbIIN1uaT/dnvIcmkIm9ZZFyBBUGQYxw8AQgrTr4T0n+dp8ndK+rYlR4WUVVPA9QejvZIr0Q4CDBw/qI0eOvNzD+HcFrTSZkWWWjk6jzy8y7cxzITLLQkuYfH4JuxgBA0S8Cp1JB/8IancMkhq+iPY8pGFw70+/06/ounKF7u5uOjo6vuP+zy0M896n34edeorfaClAiZQJhhngG5FfYdnoZsFOMu3FWG/gYCC4o66KqCFZdDy6IyHaAlGyxO9vaIi17XfC0jcAvbiS52gyzbKr0HgYQqIQ2Mp3ki/gtwZyKRYe+BV0QoBbxpjKU5Z6k8fF58DaxM/6FOXLgY45h32jNp1lZGytOYZbSgUbeOzZ+lEOqccJJPJcCzVR665QpXKbbj8vQsyH6pgL1TFv1TEfqmXeqvP7X4bqmbdqWQg8PMhsAAAgAElEQVTW9eTG+fSp3w58zQQpI06dtxotU8CjdTfxUPPt/NHwh0sk7Gf3f4gTNbvZlzzDjUvHebpuH8cSu9BaY2afJrTwtwid9zeiBbLqTaSb3uo3UzfkmrZM5Si2xrKECOw1Nn8OsCatqbXGzrvMP34NlXUhYhDpriLWkcCzBN665uuu0hw4e5zf/shqdCwXCnP8hn0cPHkMQ3m4psl/++0/YrinP0in+/5rlvTF/BFDBq2kfEuOsJSEBVhS0FcVoarMOqOIFdejLWJt8DMD33A3LiU9VRurejOuR7VhsLt686rNCiqoYBVCiKNa64ObratEzCoowXM8lpbyzEYF4yOXSX7hSTwBs5EMy3LZn4WngUiZyaQQNHRtY3HoPNrzI2JvvPlGQrfczNjY2Boi1tXZuel+tdZ84+MfZecr76BtxyAAnTXb+K2bP8i54T+E7JdKBOir4j7+kbehbQCXmFZrdGPbIiHqLIOWkEVLKARoGoIJZtYuNj0v2in4xEwAjlKM5QqM5PKkXV+kJXQg3hcq0OeIkkYMfMG5EAJPFRuGB4eE6xOqzciZXnf/cqF9zmHfZT9N2TnvBeMrF4ErJLpExl6Z+Dh5naAtdJZmdZGVsqjWaKyjRLbmrVqfhFl1zAdkK2NENxRgFAX+Ny8d5y2zXwcEHfY0Ny+fIBxE0jSayWgLn657I78w+XlM7ZvMfnTbuzlas4vhWBeHkyd4pm4/p2p2o7XmWPUujiYGEfYwoYW/I5I+g8s0ymxFuHOAQksTO7GXkJBIKfC03+i8XGRfRFFT5mi/DNfaJEIk8asx80phpBwKyQKyLYZtgNEQxuyvRrVEwZAURGClohSugIHhi7zmyUdpWphn98Wza6Njr/0JPvcT99J7aYhdF88yunM3Y70DfkVqYAfiaohJX4lpCb/YoN6ySJgGYSmxhCCnFFFpbCBlBEd5M0IKvj9c23X0ZXml6Yu/+OnzCir4UUOFmP0IQgf+SCnXI+165MeSmCcXiF1cZrEhzMM3m2Qe/CJF3wWlQ2VNogWtvX3MXjiHKhGxQ8jDN24gYs8nMgZw4qEvc/LrD1DbupWqnn4uZHJM5h1spfFqXofOPsBpdvEF3saw7i+NQ6BoNV3GPImHPyl1R8PUWiZKa+ad61cgGoHTe97zuGoXuJZ3sD0VtN4BpfSqNUQgFlMi8CET4Kmi5YVek1ZcHyUrHXNWydhmaciXi5S1zznsDchYV0DG1o5lLRm7JfFxctTwzY4OPl23g+7cVT5Q/7M8Wn8jnnjhPycHkme4a/HbpM0YDU6SuxaeZmf2cokWjEVa+UbDYV6z8NSqr1nvb3KsZjdfb7iVW5IneLpmH8cTu5AaTtbs5kTtbnSgoSq+FzNzkvD8B0ArXKBW38xE668QcUYx7AsQ3YlRlrY0gl6bjt6cnAkEMjAOFpINVhna84tjMpdTeMsFREgSbolgSkF0TwNov3K3mDK0pKQqleQt93+O1z7yUCkaeWpgkJ2Xhv3omGFyYfdeIkIw1beDS9v7iBqSKilQ2o/2uloQNyAsJIaEbZEIUUNu6IMZvInrwK8u3QwaQXwTn7Li+0gYFQ+zCir4XlEhZj8iUNpv5zKWtRnO2iw5DlvOJek/tkhdysG1JHO9CSZ7EziXj5SFfwSJnl6yI0Ol1OQNe/bQcugAV8fHvysiVo7JC2d59JMfo2XPK1g6dDsPzye5lLNZzs1xZmmciDvF3/EnjNJfssAXuGgtQUgi4S3caBosuB61ga9UynH9qsWgUrJq6jLV4yOkOntJt21D4hOy2YLDkuvhenqDhQWsHoKiRs0L9GNWkGqCtes3vL7s8fPoK/59Q9ucw94xm/qUR/cmZKxIPoXWWLjcmvhbsrqW+7cN8qWq7pJg/4XiQND/cijWRVTZ3DP3GK+ffxwR6NUcDGbCDSUC6yL5dOs9fKTznRxMnuFwkYQFxQLHa3aXHsOqWa8IzpMLSK3Ycu1fyRa+hBKqZGw2W9uBkBIn3AvR/k0jR8Wo1/XImd+L0ydn0lhdb09nSZ2cR9sKETcxdtYQbq/CFauFISIY58ClC7zmycdoWZhncPg8luuufmak5PTgHj77kz/FDUPnGRrYzVCvTx6VBqR/5JT2SWI8aDoeMYzAMJdNSZQKjre1yXv28Ju3hzchZrZSRAyxqVXGsuOyPRrZdF0FFVTwwlAhZv+O4SjNsusymrEZzuSZSOdpGkkx3R3HjJq0uIpstcWlGxtZ7quGkCQ1dRVn7JK/ASEQ0mD3nn2E9+9nfmKC+vZ2Co3NXBOC/Yfbv+umxVprRqcmeeC//RmyrpGjb3gHs1cXqDINxhbPcMoN/MboICxyAUuSSBSteorlleOE7GFU+69hWc20hCw8rckUjV7xb4mpMXb940cQWqMMkyfu/WUuN7WT9TycMjZSjHQpVif2tfWGZeQlSF1eb/16YvaDoIVum3PYc8UnY9vmrk/GpPZJ5rk2k5k6i2hsnkXRxRO1L4yMFUnYkzX7SFoJ7pt+kF+d+CcMVOl42MIkcKHDRfLBrnfxWN1BPn/qt0rVm0/V7EPik7ATwf7L+2YK/AhneXBTAWZuCDP7BIZ9nrR7jZiqI2uuoFEgTHR00CfYgqBgZGOlJfhaxaJH2mZBJIlfZWkvFLBCElll4UUNZHUI0VWFbAz73yMBlg6qaLWmeiXFfV/5Z+4ui449cfBmjuw9yK988m8wPRfXMDnTv4ux3gEu9e4gakhC2o/kKaGxkNQYJnWBSawpKPmruVoRuk7PSkdr4qaxqTbO9hS1lrFpv8usp9hRtbFXZkEpQlLSHtnc+6yCCip4YagQs39nyHmKBcdlJJ1nNGczkbepms2zayTNzSNpIo7mcUNyZaCaUzsSHKvPwtRpOBHBTCdxRy5SbJtTu2MXkb4BriXq6K+KMLC1rbSfjOtxPp3jUG3V8x6b1ppl12PRcRnJ5Hn4wYeYPLgPsaONR5cyqJIJRHtpphXa41Ye41vciasNTDz2mlcYqXsVhUwCYdSQDbyXPPwrfqVWI2DbTz+LCKIU0nOpPf0MS69qXR0TG9OImxGq4jLJRhKmWfUpU2y+vZcDbXMON1wpUL/isX3WfY7IWCD3Cgjp5S0mTwzGmKkzgQEeZuB579NULm+beYj3D38AQ7ul6BCUm8oKPrH1zXyx+U4+c+q3SyTssbqDHK3Zzdv3fKDUfuloWUSsfOzF7Wr8SFmpC4BSdFz7Okn3UyD8z0Eu8SaWa95CyL2ElT+Pjgwiov2l7ajAg6xoSLseIiBv61s5aU/jXMuQv7yCShaw2mKYe+shbhA61Oj7mgEI37ttcHSYO5/4N5rm5/zomOetiY6Nd3TzxE23sdy8hcGL5zg7MMjl7X2l6k9L+CbHlpBEpACtqA6ZxDZJH7pKk7A2v2jylCZubYxsqaBwoH4T0X/aU9RZxoZ2S57WLDse+xKxiqlsBRW8SHjJiZkQ4m+Be4BZrfXuYFk98BmgGxgD7tNaLwn/Mu3DwOuBLPCzWutjL/UYf5ihAr3YRM7mcq7AtF1gIm9jCQkFl1u/eI58ep56UceFthZO1S+TuvYMYtYgvJKE+dnSttYrssKJBI1b28grxZV1PfJcjT85PA9kXI+sUnx5Zoknkxm2hxwuzz3N/T37/ZSkKk6x/v0NYoiLqhsXn4i9ise4jcc4x24GuMijxrswrVbMSCthIck6yyhRhQ5qAhXab/qtIbwwvWYsdYuzm3qKrX9cOr5sJBbr/y8+73rb+H6ibc5h93iB+rRHz/T1yRiAkvDg/hi3ns8TzyuO9Ed4tjfCSuz5p6NuWTrGW2YfRgAd+WkOpM4RL6vEVMBXGl/Fl5ru5MMX31ciYV9ofg1Ha3Zz754PcEtAwk7U7MbEb710LIiOXe8Ttp5QC8+l/+ooNctfZShxLCgIAZAIGQIpcUN9eOE+n/4rFbRu8ntqFsmZJTZWWcrAAsPTuqTVyo+msC8lS+lKOVgLbVGUpvScwUtDDA6d5VJbFwfPHOcNj329LDp2OIiO/TWG5+IZJmcHfOPbc9v7GO4Z8Em+VkSkpNY0MKXfk7MY2bM9NtWCFdPsm/nyOVphSb9353pkXEVL2CS0rm2UoxWe0rTH11Ziaq1ZKLj0xyI0Xddw9ocXXuAdlx9L4Y2lCG+rJtdWRfryMvpKCqO7mupttSQ8F2lZiIq+roIXCd+PiNkngI8Anyxb9rvAN7TW7xNC/G7w/+8ArwP6gttNwEeD+wrKUExRXsnaXLULTNsOM7ZLQgiipy/TOHONuRu2YWczPGE/g7Z8CqEXDMSMS4Qg0mCFSjEqALN3APfyCCiFkAbxre0orcl7mqaQ/1EpKEXK8aixTHZuktYooqAUTy+luX9umc5oiFRmnL+asfxEltYg9vijCCYZ/6/C0g5v0Z9AAOfYxU7O0sMwCoOM2cMR6+fIhwaJBm2P0rlJlsd+j0jzz2LU3OFbBgRkNecprFx6zbhqUwvXJWbrsVm1ZJEQqLLHPyhkrC7t0fudyJiA4VaTrjmPLx+KM9QeYqzFIhuWFDYrLyzDgeQZXr34NGkjTqOzzF2LTzNQ1nx8NNLGP225m3mrjv888Q+YysWVFh9tfztHa3YzFW5eFesXTWXLWi8VScvmdYI+1hNjACM/RN+Vz5A0J7lWk0XKTrS6BnggTLzITkzhE/Xy1xcF6zIgXCo4Vpv5cAnAWS4ga0MYQqJtr5SuFA0hPyErApsMrdlxaYg/+cD/jeW6pdeXR8dG27t49MZbmWtsZufQWYZ37OZyTz8Gfh2sKQV+UMukIWRiiLVkye/buXn6taA0UcPwL842WdcaCW2IDNpKETYEdeuiZa7WpF3FQDyygcwtOC5d0RCd0R+MFKYOqqS/m9etBL8X7niK/PAiC7WaeTND+PQifcMWAkgLQarWomYx8GjULtNHP8q1qTNs++K/Ehl4/pHlCip4LrzkxExr/U0hRPe6xW8C7gge/x3wKD4xexPwSe2bqz0thKgVQrRqra+91OP8QYbWmqxSLBVcLmVtFl2XiVzBF+MuzJI7f5ntqRDxuUVOMooWGqbPEKacXGiUaWG4xbiYIN/RTezKJVAKpET3DqC29ZFYmKW2o4NsXRMp26E+ZBI3JAu2S8SQDMQjbAmHNk1dZD3FhXSOp5fT/NnlayX9zlrbBc221Dzj1U0oNAYeP6M/RoYqBjlLH0NooJdhNCYz8TdyXN7JFTkAUiICt/2CUnhWKzLcTn7+M8jYTdhYZD1dSi0uVjfRsDRf2nNVNs0N557l1OChF3QOBKtu++XE7OVC25zDrokCtSse/c8jMvZsT5htsy5NKY/BKZfxRpPlKn+iXa7aeKVf1IgNR7uIqTz3zD3K3QtPIIO9FDCYDjeWpScl/9T6ev6y850AfLPuQEljViReR4NIWPFTU36/GcEtj6Oybn2kYDNwbYyzTZqq+T9nOurT7UzNfRSq7yFUGMHIn8eL7ERH+vztFcmZDCwv8L9bCj/yDH5loyFWj4dWGm86i3N5BbVcQB5sRDVHEf3VGEFjekMIBi5dZM/QecbaOum4OsFbv3Y/oeC7poBvHTzMjSePlrRjF/sHEVoz3NPHcG+f7y8mJdHAzkIAnvB/oNeTMgDHU5vqxPxosSZhbnxNXnm+key6iFhBKzwN3dHQGqLnaU3K9eiJhqkpI2wqiJS1hkP0xiLfFRn6XqC035s0P5YiObJEsj1OckuU0FSG+EQa1VlFfYtJk20jsxmcZIrcMiCbMbdVk3r2a6QmryGXLUL5KgrZBdz6GsyBm6g5t0RIaVrRbHFyCCu2+v40RFcCoi38jqz6xruJtt2BUVv7fT0GFfz7xsulMWspki2t9TUhRHOwvA2YKHveZLBsAzETQvwS8EsAndfxx/phRkH5V3CT+UKJhI3mbNTsDHr8MsvSQKSWyVwZBQ3D5aEAggmtsQUW5/wIlZTkdu2j6sQz6ICIFbp7CPf0I2amcZq3oOsaqTVNIu0dCCHYGrHYGrZosixipiRmSGJSbvghXnRcxnM2Ty+l+cp8kvmCy5Sdw9UGxem120gy6SbwtMbQmpt6enhF/hIy9TV2cYY+hkrjLnZaDDe+mW1tb+U19Qe5OWvz6avzPLGUJul6GGikkH51Wv1Pw9U/JrvwBbK1962Z5J/dfxu9V84DqxP9DReOvGBiVuxP+XKibd5hcKJA3YpH/7XvTMYe2hcjVtDM1hjc90Qax4CT28Ic7QkH+jEfRRL2VM1eVswq3jb9IP9x8rNrhPp5YZX25iL5YPe7+GbtWqH+kzWrnRzWNyEvYr1+bz05Y91y1i2P5TPsmxhhYPoKhlJMmMcpGpboYsxNCNxwH244IGTFSJkI9l82ACHWEjRXgcTDUOCOpnHH02jbQ8RM5I4a3BoLpdSaysr+kQv82Yf+dE10bLS9i2g+j9AK1zD50l138+Br3sCui2c5MzDIxe39hITAEJqoNIibhk/8y4mRUsQ2STl6KOR10pG2p6ixzA3RsrxWmELQGF4XEUNhe5rtsTCRMsLmaEXaVXRFQjSWpSkdpVlyXPpiEbqioZeUlNlXUtijSXR3gkJbnMKVFMnhRZYsh/jVBerPayygUWvqRB6DQGqhNTq3xJybB+WCNJCJVhDj2ID0Gqg3tvrPNSFWvR1c0Gd902yfdEEhGiYcfOk1oCXM3dFK+6PT4CmEZdL9C/cS7a55yY5BBT+a+EET/z9XFmPtQq3/Bvgb8J3/X8pBfT/gab+Ny/CVcU6PXCK8dStmYwsXzp9DTlwBwJ1dwM34Px5rTlxw1KzObpypiRLxih86TF4pnOmr0LyFtrY2aGnBm7lGQ1s7HZ2d1FoGVXt3ETckCcMgYkhCUmzue7QJlNacSmU5l1riT8cWS1N5m1lg2g2hkZhC8h8SjYx/+mOMd+2g9bY7abMf5YbUB7HIAuWRPQuv7o3IujdysOOVpYmpNx7hvX3tnE1l+Iepeb6xkGLJK1BQ4Ji9xOO3EU59BTs8QCG6tzS+q1s6WUrUU7+yWFom3RdOsV4uUtY277Bzwk9TDlzdSMY0lPzWlISv743ROe9Qm1bsmHL4x9v9Zuyfuj3BZIPJ3sxZ7kue4Em5j5OJHdw381X+YvhDzynU/19bf5IvNt/JZ0/9HyUS9s3ag2s0Yi/UQmN9mngzIlY+Hst1uHX4JD2zU2gBwy0dnG5aQqZHA0JGKW1J2evL081F2dlmPxYicN138y4qaqABdzKNqLYwOmvRTZFSgQnAjtFhDp0+RjyT4bajT62Jjt3/6tfzibe/m/5LQ+weOsuJvp2M9gwQMSRjPf0IAdVCYEg/fRozjQ2tm1w0ISmwjI0pTMfT1JrGhnRk3vOIGQZVxsaImAC2hK01+yloRcHTdEVDa0heXinynqYvFqYutErKivKAfYkoTeEXJ31ZJF+hbdV4gHs5Sb41yvLYNWKPBabWaBwvhWXUkgCqS6/234sGP7xoBOdcgFsTxYhFMQwLJyuReVn6PKRbE4iIRdV4pvT85Rub0L111H1mGO1pMCTma7sRD02AqxBSEHnjNm68eStOXxP2aJLw9hrCXdVUUMGLjZeLmM0UU5RCiFagqECfBMrNsNqBq9/30T0HbKVYcZVvTDo5yczEOO1dXWzr7CRhbu6knfMUK67HvOOSVwpXaZT2b8lrV5mdmCC0ZSsil+HC1x/0U4sA0gC1eskW1hZu2WwV6ttJYXQoSEUaVO/Zj9izHzkzTVtXJw1b26k1JYl9u0gYkrAhCXduwZT7Nozxu4HrrjA//wjx6Qf51NKdKPwIhQRe29LB3Y3VfDuZYUvIZPZvPkifGObAXk0o+SVq3QvFt1WaKtKhnSQ6/k+i1fvZEYtsGi0YqIry9q0NuJ7H1+ZSZPAnw3Tdz2AUxjHt4TXEDECtS+uEC/kX5f2/VGibd9gx6UfGdgRkDNZVgq4T8NdmFImc4vazOeK2ZjkmGdlqlipsx1osbl06yqdP/w6WdtAIHGES0U5pv0Wh/r823cVfXvzzEgn7l+ZXX5eEXS8y9kKxWSWrBOJ2jmw4imOYJPJZzrRv53R7L3muUjPzEZzwDnI1b8Wyh3AiO1FBlGyzQo3STa+25QI/Xamnc6ixNORcuLPVf+EtzWhT+lYZwWstrXj9Iw/xi5/7B2RAeCabthDN55FBdOypAzcjtWZ4ex8Xt/eigRrL13wViw3Aj0qFhcTcJB2plKbKWpuq1GgKnqLKNAivE5rbyiNkSN/qouw1eaUQAlpDFmZZFC3r+cu3x8JrvMdWXA8pBLuqosRMidI6SGkq4obkUE2cxPOwyHGUZmUsiVskMFJQuJwivL2GUEeC/HSSq4+PEjqRLpGv1dpaTbzs7GkNJr6eVQTHYaXWpSpl+dY2piTzmn6qH55EexphGDT8/I3UbPPTi5mxJEsfO+NHuQxJ91t2IIH5j58BVyFNSf8rthLuqsaujq4hXXZn7QYSFu6qrhCyCl5SvFzE7EvAu4H3BfdfLFv+60KIf8IX/Sd/UPRlSmsuZfNM5B0EkLw2xal/+TxKKY4/abDnzfdSt3Urg/EoTWHLF8m7HiOZPJP5AghBRPrNgpeuTnLp4kUc28YeuUixX+kGSheJQDZoDi4g1r6NwvQoWvlGr4f276fq0EGSVyfo7Oqmq7MjSDXuXb+lFwVaazwvjWkm8Dybbz1xK66X4XPGL3GJPoxgOrSk4Ceba+mOhpmxC5A9zfbXzpLIXwDnfGl7a7RFwqJz+39hoPkmYsbGdGnW9TiRzPCvM4uM5GzSrqIpHqaq4DDvKJIyynLL/wXS74dp2iN4VitaxsmF48Cqzqw6vczW6XGubvnBSYG3zTvsmPI1Yzum3LIpyodiIxmL25qxJpOpJovD53O88nyBoa0WR/oiXG42uWP523zkwtcxlKLbvsqelYvBOfInt1OJfh6uP8xvjn9yg1D/WrjpJSNh5bheynLL8jwHr1ykeWWJT938WlzT4v59t/meYO4SNTMfQhk1ZBr/M8pI4EZ2rNneZnq18v14GpTtIiaz6PE02AriJmxP4CkNhkAFpGxwdIibTx8jns1y4Pwpts7NrEYUheAbt97O6f5B9g6d43T/Lkb7BjAQpY5TESkIy3U2E2gEgpCxkZQ5SlNlyDVEqkjKooax5oJFo7E9RciQNFirUTSNJhc0Um8JmxjBJ0qhybiKuClpj6ySNUcrlgsuESmJGoKhTBZHC3KeR0Fr2sIWrZEQV7I2jSGTWsssEbqieD5zeRk9tEwuKsnMZak7sQhaY687CyKguuGyZcX0sv+bIHBjeYxcGBAIUxJ+3U4KXx3zyZVp0PMf/IvLImnq7KrG3la/aSQr3l2D+Z4bNqxr+sWNy9aTrgoJq+DlwPfDLuMf8YX+jUKISeAP8QnZZ4UQvwCMA28Lnv4AvlXGCL5dxs+91OO7HrTWZIK2RVmluHJlnNOXRmmsrUWtJJk8eQLl+dEs5XkUpq/C1q3828VhmLmG2NJKqGkLw5kcoYkx8ldGfe3L8jLO4vyafRV/mhupY0knUSikkOw+fAtnHnuk1Pronh+/lZC8bUPrI/p7XrLj4LpplpaeZmHxcRYXv0nIauDgwc9jGGH6ev8Lf5/u54tXJe/a2sDbWup4Kpnh5uoYhfQJnnz2C9gTY2zpPk3RUKL4wwsGGo1A0rjlrbS3voWGugMb9p9yHL61uMJD8ynGsnns4Are0RrXEyANqkKSqOuyRNifBLRD9fx/ByTJpv+dhbpmOqavlPYvgV1Dx192YtY27zAwVaA27bFz8vmTsYlGk+qsov+qTSommWoCmoaJ7nmcrmiEQ6lFbh97lk57prS9M/FePtfyWn5y9hsY2sORFn+8/Vc5WrObJ2r3f19I2GZYozfTmtbkPAfGLtKWnCdrhTnSNYAKND8IgVAFEvMfQqg8Ky3vRRuJDVGxzbZdDlGs3ltx0cMpaAwjd1ehGsKooI+q0GBqxT2PPMR7Pr8aHbvU3sWn7nkr9z50f8nm4kz/IKO9A1zp3YElKOkwiw2h1ttP+E3QdaArE2uWO0oTM+SaiJgKSFnEMKgyi2UL/nLbU8QMg9oyUubhpyKrTYOGkLnakUApCkrREraot0y01qRcl2u2w7LrUvA03YsOiek8yS0RphrDDC677Jp3CW2vwe0wSHkesxkHEGyzJNVnJ0memSY7k6ImXYcAwsGtWCVZuvAMNH40WphqHFFdjTOe8GWBRbKpNNKUNL/bL8ZfE71qT2xKpIp4LhK12boK6argBxWi+KX5YcbBgwf1kSNHXrTtKa05m84xV/DTSEtjo5z+ypd87VYAs6ERd2EB/1dFUnPLbajqWla+9mXwPBACs7YON5X0/w9gRCJ4+bJUmpSgFIaW3L7lNmq7a1g0kvTs7KWjo4OJiYmNROwlhNYeIqhKGxl5P+MTf4vWDlJGqau7mcbGu2hvewcAH5uc4w+Gp7i3pY7/vrMTKQTjOZtLM0/ijvwyQgTvW6xGx0AgRIit234PU6/QXH8zNTWv2DCOGbvANxdTfGN+hYm8jdIaV0Nea7RS2H5Qg5zSpW07WqNcRQbQ9ijV8x9CKJsq743c++DTpeSIBoa7d/LFu3/6JT6aG9E279B/1aE27TE44awhY4LniIw1mywmDA6M2Lzp8gl6Oc201cSVNo/93pPctny0tK0VI8p0qIme3AQSjYvkL7b9An/Z+c41jvzfD+L1fCGA2kyKtx/5NzKhCCc6+jjf2oVnlF07ak1i4aOEs0+RavzfcGMHnlOUWqyiFcFrmbdhLA1Vvu+Y1hoyLl7cKh37XaND3HT6GFXZDAfOnWLr/Oya6Nin3nQf/3z3m+kfHeKGoXOc6x/kUu8AEcMICgxWSZPWEDHkGl2XwvfGikuJVUbYyklZxFglXx4Kx9NUmX6krLjc1QpHaWosk6pguUZjK995r8kyiAVVm5W7+48AACAASURBVH6UzCNsSOoMA1srlgsek7ZD/XSWXQsuw00hIsA7vjGPDMz7ctUmsWRR16hRIYlOhNE46NEhRMHBbOgDIQPBvA7GAcqaxXAbQUu0DI6L0mBKmn7xhhIhKmrMwtt9AX1Fu1XBjwqEEEe11gc3W/eDJv7/gUBeaWYKTkkMP1kU1AdoO3CI9K796OPPkDp9ArQi+cRjwSVhUWGsUYUCsr4BNbdq4ro938CovoZCIUyTnlfdjs7k6OnZzp7ebcTXaUc6OjpeUkKmtSabHWVx6QkWF59kafnb7DrwINqsJxvqI9LyM8jEYYyqfTgixIyA2VSGRxdXeP/lae6qT/AnfW0sOi6LBZcjFz5L3cz7sSLe6hVyMckpDBpb3kLX1rdQW7sxOqa1ZjJr88DcMk+n0iwUPJzAGkMDWaWQGnKBJsVVPhmTiIC4aaQhiSiFHd7Ocst7qZ7/CFn9GRaqt9GYWj2HkXz2JTum69E279B31aEm7bF7wllDEOH6ZCwSG+c2+SxPNu5jNjHA7//bM9xSeITdiYco9pdkAVJGmfEvkr/s+GmeqN2/acXk9ysS9rygNR2LszRkUpzo7GMpXs2Du25ksr4FTxolQlSkNZGVrxDOPkWm5l4KASm7XolKKU3uKeTVHOJKGpFx0WEJzZHgayrw4r64XSvFmx99kP/4+U+VomMj7V188g1v5b6v3Y8ZRMdO9Q8ihWCsdwdX+nYgAonC+siX0hBdR8q8IFIWW0fK3EBTljAkobLvf0ErtPKF/sUIWjF6ZkpBc9giVJaKLChNwjBoCBsYSApasVJwSXsuISnJeR65qSSdMzbLtSa7Ug5vPplGaj+lsUblpTSRhTQY4dLvmuutQHMtakVArBqrpoai8YgGv2eVxo94/eKPAXxH0rVZ6rCCCn7UUSFmmyBmSPZVxRjN2cwXXBLb+5AnT6CUQhoGTdt6SERC5KvipMpeZ9TWoZLLaO1bOQze+mPEa8Mc+fznUJ6HFJIdnTvY036QGZboHOihq6OD8LpUx4sBv7gguHIPlvkTmUAK33tpaelpzpz9LQoFP+XlmltYidzOp6eukjc9LHkT4fhhhBIYKZeo4RExJOfSOf56cp7dVRHe2drAtyYeZ2nxUartczTnj0FgEK4RCBFmy7bfJaJXaLxOdMxTivPpPA/PL3M0lWHZVbhaBREyhaEhp1ffh6M1ZpAqKrc+KEIKgYnGMBtJtbyX8MrXMPTTlLcSj9qZF/Nwb0CJjGU8do+vJWObRcbODyzRl57leGs9J7oHeOXiUf7Xmd/BDIT6rjAIWy5Yaysm/2f7fTzQ+Ko1JOyJ2v3fU8XkSw6t6VqY5sD4RZpXlklG45xq346SBmONWzd9iZU9Rmz5s9ixm8hV/8QaUns9giYAcTGFnMigqy3UDXXoLVGQq/L4Q6eO8ebHHqJ7aoKm5NKa6Ni3DtzMP939Zo4P7mHf8DnOD+zm0va+IDLm7yG0CSnT1yFlWvtNxYtifx1EyaQUVFtGSe9VjJJFDEk8tLq8EDjw11i+r6DfZN23uzCloNGyaJuzqZta5kyDyXhjiM45m8OTedyQpGbJZu9EYdPjpbUmr5YIW/UIBWiXpZUj1NceBiTKEFy7u5vC4FYi0qAptAtzMoP++wuB4F4Se0M3Zl49Z6qxggoq+M6oELProDFs0RiI+J2aPnb+zLuYvOKnFHu6ukg6Lhe2b2fi2W/jBRqwew/chXc5xZXLl9mSq6E3U0vNLV3s+pl3MXp+hO27+0qeaze8SOPMe4q88q+WM54i43qkPEVeeawKaxXkRyD1ODr1OJmaNzEe/XGSaUkrA1yN/RSz4f1oq40q08RCYHmavAdZPMKmwEKCFJxN5viHa4t0RELc01jNudmn2Tv7G8SKDZ1KaUtJde0tDPT8xqZkzB+7x+lkhq/MLXMxV2DF9fxOA0pjoMlqfzIrFJcJgat8wqaEbzHiBYRNa+1HJIIxaB1U0QlJrvpu0vEJ6laulPZtOs6LXgDQNu/Qe80nYzdc+c5krDwy9rGJXyekHNS4ZHx6K9vsyZKZq0ZzIrGDv25/O7a0+Ni5PyyRsAcaX/WSV0y+mGhcWeaOoeM0ppMkIzEe7d/HUEsn6joXJxowC5MkFj6KF+omXf+eUrpwU6H/ioMxlkZ1xaE6hO6qwtsSRdeF2HF5mH1fO8fp3p1EHZu3P/iv7B06758bIXjg1ju465knSyawJwcGMQWM9Q0w0e/bcBiAoxRSa6KmXEPKvKDfZnn60u8/6X92o+ZqBaaLxlOaqJREgu34VhgKIQQ1pkE4SFE62q/kjhqS6rDvUeZqjxVX0zZv0z9bYLglhOEofuyRBQwFN6GxdZYIqwapWis/7Rg8zi9cIFzfjxASLeD8gSpWutvZOp1Hd1ajOw4gZvNUT2aI9dQy2FVNVMpVa42BKPZ79lTSjxVU8CKjojH7HqC1ZnJyksuXL5P4ZpamdBwRkkR21BMdbCDSX4eMvfg95DKex0LBF+1mvCIB83/8Q4FzuCkAPNITH8Ze+gYUfN/eRbOPI6F7OWO+kpzneyWFpUQIMIV/9R81BFFpUGv6P8JhKZm0Hc6ms1zM2DSFTP5TZzORfBbz5K/RWHchSFv6on6BBmES7fv/OLD18IaI4Irr8sxSmq/OJxnPF8gr/6ofdCDu94lVXvkpG0cpHO1v2VEKj6C/odY4WvktcoJUZlHno5Tv/q8BB3j1Y19k3/ln10zmnjT47D0/x1RrF6USuheIIhmrzij2XCk8LzLWn5xmwPg2heoVWgoLvHb+CRrcZGlcKbeFCXcXA5HHEfhC/Xv3fKBEsn5QdWLXg1QeUadAJhylKp/l9aef4kRHHyPN7dclZEUIb4W6mT9CKJvklj9BmfVrCwYIROaLNsZYGjlvow2B3lmLbouVSNuO0SH+3w/9KZbj24NIIG+FCDkFJOBKyd+/8W2c6h9kz9A5zvQPMtw3UPpOQKCzCtLophBEy0T+ngZL+EJ/WUa+tNaBq79AIPACQialIG5ILOH3d3WVQmuIGQYxUyCRJUIWNiTVhkQjqJ/O0nYtz7xO03ttkZsWahF69bO7SsI02skgrHggwFdcSazQlqvFUBptCEbfto1G06RmKlsiVnHD2LTHZgUVVPDi4rk0ZhVi9gKhsg75i0vkzi3gpQs0/7JvTbHyxBRmXYRIXy1ZCSnHY8X1WPE8bOVfOUsEMUMQNwyqDUlNWcn588Gy4zKatVlwXJT20xcGglzgjeapPDL9bTx7hsn4m5izHXbP/BIZkWDYvIVh4xApGrBMQd7zXbNz2vdlqzYlVaZBXvm98WpMia0USdfX21wtuKVJ7nV1MbYeeZy2C5+n/+6hUi5JYxHv/D2El8SqPshKaDdd0TDbY35h/Eze4fGlFb61mOJK3sYNol22p3FEoHdzfTJmK1VqCF0IJi0pBV4ZMdNBdWbRF8pTupTaKpqBFl26tk6P8/YvfRwj8IUrn3o0Atc08aSBZ5i4hn/vPzbxiv9L/3HIlcQKEtMzaEiDwABhAgZCGGgMtDD9IgohcJqvUrDByE7QLGZ5beIsbbEVwNeHnY/18Ir0OYTWKEzeF/tzPnHDK9hpn/uhImDrYbkOg9fG2DN5icV4NV/Zc4u/IvBW+47QLjWz78eyh1lu+X28cG9pVbmo33h2HrlUQIckXmcVdMQgZLBzdIi9F8+SC4V5wxOP0H1tskSaHz1wmPtf9Rr+7CN/UYqQ/cFv/j7ntvf7nzXhNwgvtkMqpihN4UsBlIaQpCTGD0u/uXiRfKkgShYx/ChZOSGLCEEo+N6XE7KoIRDCv/jQGsJo2manaRwfI3xphK3zDrubXoelNk9FLqtlqqw6pPIvAh6/sZbbn00iAiI291N9tEUsYhNpIttrKxGuCip4GVEhZi8CcucXSH9rCvtyEhTIhEV0VyO1b+xBGKvl6BfSORYcD4HGlPL/Z++9w+u6zivv396n3I5OVIIEC0AS7KQoUqZkWc1FxT0ucY1nHMdJJs7nlPEkk89OJsl8duISZz6nuMZObMW9W7IlW12i2Ak2sIEAQQIgOnDrOWfvPX+ccy9ANcu2XDJz1/PwAQHy3nsazl5nvetdL64QnMoVOZotsCGdoDsVxzemcvPtiLt0p+JPGUxbRqA1x7MFDs8XKBkdJZhHhttgmljuYRLZB8kU9mCbIlnRwMeSn8FHoLVCI8kqjWfAkRCTkllfYQGTSlcUiJSUZBc1OTwVBLBr+BTXfvuz1N8kWbb6WGWxu5C8nRXd/4P6aOD5jB/Q6jokpODeyTkemZln2lP4xuBFypiO5oC6QlJUOowYEGF+EoQKQPA0ZEwQkrHy/zPRewoR+ZU1FBZte/voEDc8/B3axi9WtnmkqYPzy7qxVYClguirwtIBdrDw90TRJ1kKfxb3fECBUYu+BjxbSKF57bIjNCdzfDX2JoYuvYLHd19gc+nIf1gSthhxr8Sm4bOsv3SOmAoYrlvCoc5uhhuaf/yLFyE99S8ksvcw3/AOiulrKz+X2iDHi5jmeBihMZgNVbK2JCb6Xdx15AB/8U8fwtIKAeTcGLHARxiDbzv80e//KadW9rB+4BSbT52gr6eXE6u6MaY8GNxgIZBSVHikE6lnhgV1NmlZFZ+ZisqWthDELIlNqJiWCVlChEn+BkOgwus2aVnUeAUaRi9Rc+ECJ7bvRCQSXHXXt7nh3z6N44cDs5WU+FteTeOym1n8KKdEGO1hBNy3qx67OUnzaImJ9jjjLQnax0tsnAxYuqaR1IrqPMcqqvhVQbUr8zmAnvdR8z6Z53cS723AXZpBRJJ/TilygWbvTJYZpam1LSwBtlbcP5vjHy6MowBXCN6/Zim96QRgYYzhYtHDEtCdSjzpMz2tGSn6PDIzz8ViQK0tQiOwd4lJ6hj3Be3Tn2VF4QvMi0butl7HEbGTkt2KZQTjfkBaSpJqhFHVwMKEvycPGDJA7hlImTAGoqaBrtWrmX3j7zDbqemc+kMgLOG1+H3ES0fB3cK053OhUOKxqXn6c0VmAkUQlR6NgZwKZ/cVtAnb/NEUjUYSqhWLIYVALXqAKJevEGGIZ2AAbSoiTGCojN9ZjEuty/jR7tt47Tc/idQBWgp+dO1tT+kz65jw6bocMNxo0znhc/2xIsIB4YBJlMuVGiNEuCqKgOvS/0xJpbHVacZyHvPKZThfxxP1DW0Eg/k6liRKiJG1nOpwOJjZwAMtm572+P+y8IIxn5VZzd5Gm766H5/4DrBudJCtF05xrqmdQ8u6Gc/U/8SfG5//IYnsPeQzty6QMm2wLuWxz80jCgr/qkZUYxyWp+k9d4ot3z9GyY2x6Uw/uw/vxYquGSUEX3zhSzm4bgObTx3ncE8vp1b2IIHTq9bQvypUyQRgl0lWVBq3NThSIiPvZNnTaEekLS4lCoMXzaIMzf2RWru4ZCkhMTNDwY1jkgl6+49xw5c/T/3IJWpmpiv7/cH3fYBzXauIL2mh+ebXkWjs5mBHmj2rW7llUPGao/krmnmsyEuJgec/PsMjd6TIPa+Vpa7NGimhIcOQF9Bam/qJz0EVVVTxy0FVMXuWMNpUiFgZOaU4kS0yF4RupsFCiRPZIqfzJUracL5YYspfIEECeGtHE69rawDCm/yUF7AqGWdFVO4rY9ILeHw2y3DBo3++QMo/RabwMH7xIiOmliHnGmaddQTeBEVgztQw+8xiV3lPeKoeNgEsj9kMlYJK76IkVLRsrbjhoW+TXb2eFduvpsGxEUKwPO7iFvsQ8/uwZUDNzDcgGGc6czsPx9/KZZWhoBTKhKGd80pjiYgARtddThmkCLepoEM1sKySaRZUMlUO3hCyMs4KQMjQT6YJjdlCCFTkL/Oe5ghsPnmGdWf2c6qrgwMbFpSYMhkr2oIXHs5j6YVjs9gzhjBYRmMR8LzMJymaGjrdo7S4/fRNt/CD0dUQWfevLJmW30+yYUkzB9p38rkN25jKPDvC8/PExhnF9qmA/Q02U67gXf0lbry8MP/Rk/DOHcmnJGcJr8iWC6cZrWlkYEk7TuCT8orMJDM/1bY4xRPUXn4/Xnw9c0v+AIzAupTHOjePLCh0jUOwKoNeEipmO48c4C8XqWPF1l7G1l3H0r4fYabOEFg2f/T7f8qJlT0IwutaRvslCaMEZdStHDaShNshBMREWKLUEQGyhcCWVNTZhCUrJcvFCm6mkGfj4f0sHRqgbeAc7UMDpLPzfPpd7+Hg9qvZeeAUd+w5xnC8yOlGB1m3knanlZGWOmYdi+4JjxecLyGjbQkEOE+4Vfu2oORKUnkVNTDA0DXNZG5YiNcpKk1OaXbWpkg9i1FKVVRRxS8GVcXsOUCZlAXakFWKkjbsm80SmHAO3mRJcd/UPHdPzlde0xFzuLE+yQMz8wTRjb0jZjMZ+bUsAd3JOJ1xhx9NznHf1ByNjs2FosePJmZRwAp7ji2FT3LSrCEgw0Pit9BCQgAy8NA8lU9kgQIIo9nCPo6yBYUFwmCwK6ShyZb4JhwbY0tBV9whrzTLZidoO/IoU8KmbWoMu6eX5k1biUddXTWWZNoPcJxelrRsCjs2E69GTnyCNfNf5ebsfTwaewt75AuxhGReLSha+SiHCRP5bqxQ+SpPp5ECpJR4kU9Hi5AYK8rxH+UokLCsBVS+x4T/7+mGjncPZ7ntSAPC3ELXSdCJIqmSoegIXnjoSjJWPpIzjbPUTyUxRmILxfPTn6BoMnS4x2h2+7nL2sH3gi1cdQlOzC5ZuGZYIHSGcPvOdq1jqmUXH+/uYib9q7FQbpxR/MPePK4G+QQ6W862sjVsnwquIGYJr8iWodOsHxlAao2/3GZgSTu+7TBj/3RNLzK4TM3ER1F2M/ONvw1CgjHYZ+cxrsRbV8fauUG27H+4oo49f2gSd9UtBBOnoaaNhi1vpEFI1HXb+Krez109HZyMFLLyPikWAmjLNoLoUsKOlNjynEgQISGLBGcVKbKOENR5Hu3Dg7QPnadzaID57l1YTT0Mi2ne8A8fxrdtpnt2MXvNWzhYb+PXLOPFx7O8arABq+U62oAeKaibiHLpRhby9RZfO5eaXIYaHK45nUNqUJbg4MuWkbAEvV8dRKjQR2Z3ZSgqjWfCsNm4lGyvSVZJWRVV/AdCVTF7ljDGMFTwGCiWwhszhrvHZ7l/OstcoJgKrqQCEnhlSx3X1qc5Ol+kP1fgwZkctzfV0BmPsT4dpysRY99cjq+NTdOXLaKv0FieWtlaDJvFziaDwCCMRkaal8bCJuC/8T40kuNsZNC5lkmri7w2YSOCtCoLlm80NbaNIwUr7vkajScOMr7rJqa3X4uIxViTjNMcs0lbNjWORZNtERjDvtkcJ7IFRopeqCr4A7yw9I906T4uiFV81XoHZ1gdKQ9REGxlkHPIWGXUSVkul4bbbyICGy7OQRRHIKLAy7KKUX5tiachZMZw1ekS28/M0TQvkZTzoiI/WlkhWXQsK9G4wufl9e8FYNhbz3R9kcElcd448i3Gc0ken1rKQLZxQWbhyvBYkFj2CrSd4is3Xs25ZV3PeE5/ESirY6fTkvai4U0DHu3FK+8DX+x0uK/F5kMHCtgagicoZpsunObq8yeQ2nC6ZSkHlq1hNpn+mbZL6AJ1Y3+BDKaYL/0hjNXgXdMcMvWigphkx7FDvP/Or+I2rEZNnKJU107dpjcgonP6xBmrgYCzacnZjORs2oq+SiZjAit62LLCF1YI2cKpNDhCEAPqZqZoHRtBuTHOru7BeB5/9zcfodlqQo33o6fP4Xdup3b72xFItIDjNT4JEWfVjHrarDUDzMcE6ZKpqHj71qY43Z7g1+6fwNKEJffb2xlbEqNuNM+SkSJzS1PMtiURxrBkrMjSsRKmK4NYmiFty2gck0OdbVV+16qooopfHVTN/z8lCkqTVYqC0jw2neV7k7P0puLMBpofTM4xWAzVBQFcU5eiI+7wtbGZcPKIEPz3lW3sqkuTtgSzgeZdJwc5lV94TYtrM+oFPBsS9kQIoFdeol8vQSFxTMCb+BTzZOjlGBrJIXkNW/SjlDIvwjVZRp3N5GO9dCViCCmxCEmPBajzp8ne9TW6bnsFHes3EeRzWLZN3nKwhWBzJkmtEwqsvjYM5AvcMzHH3pkc4yWfGRVQMpq8CmMFMIYt+iFebT5DLdM8LG7m29abyIoafG2wygb9KKssLFlCEF2PUoQ/CyrX54K5HxY19UXdlwGL42PDsuSqEZ9YYFg77FGXj5S2yhIelUIXRSEINAKDheLazCeZkC3Y6RF2qR9iYfCRfCAabXT7vq+zNrrmnkily1tsOavwMlezd/1K9q2OUYw990HCPyl2jQd8+GAB+0lkNIQv4He2JzjQGJ7rxSXOM0kfP+pU7R67QMf0OAeW9zCX+NkIGQBGUzP6EVzvEPkLb0PlutHNcboT42w5f5JUqp3njRVZOXSO5LqXQTQ2bDERM8ZwNOnRXYrh6LC0d2+LRZ0Pq7KaJaWFe92sA+fSFucykpGEYoocWW+KkgUne3pZNx3w9h88THLwADUD+4lnOrFbN3KuIcmXX3QrmycVt58vVj7fF+CaK49myYKcLagvmUoZ/LFlMU62uLxx/zyWASXhu9tque3AHJY2aEvw0G0d5DtSdE14tI4UUF0ZSm0p4pYk7VikpMSWAjva/5gI/W4SUY26qKKK/yCoErOfENoYTueLDBc9BIK+bJ4PDIxWSAFAxpLMq5AKSODVLXW8rKWeQqDpz5fYXZfm6roUUgiOZwucy5f48tgUd02UZwUYXBGW68rfQ3lhX7i5ChYWewlsTNrEgwvsVN9jR/AdztDDcdbT5MaJxzrYMv/3SMK0eIniYsPvM1H7a5X3mw8UXckYqahdvzR4jpnvfoXiyT5kppb1b/hPNGzbSVaFYa9LXIeeVJyYlMx4AQfm83xrdJITuRIzfhCVTFTUYRmqDbYlMNoQCFhr+rhd/zsr6KdEkoe5iZxI0m82ck6uqaheFiCt0OQTRDUlWy4Y/yViUedlWK7EhHEYZZWs7BGzA821J0uhXw0oOUVcP0Y4NzBUO0XlSIfn0CJgc/0XsH2Lpe4xmtzTvH/Ff+KR2i2VVP3hYj0fFbdjzRVpnbj0lP4xAxghKTTexKNbd9LXFUNZv7zFcuOM4pqJAGFg7bzieeNhN+5iDCcE32+1KVmCx5/C5F/2kK2/NMDjXes40tn9nG9nauxOkqXvUBy7g0LqhfTWJ7htaIxb7vocbqaD2LqXU57JWIYxhpMJj5VeDEtDIA3/fXWJyfoGtk8FzOQvUpobJFMokM7naCwGpN1GTm99AauymuvODFNj1SLteOU9S6Vp+lub6J1RWDpUTvOmRFLGEU+gsYuJ+Ml6izP1Ni8+XwpLjRL+9rp6XAnvemAGqQ1aCh66vR1neQ1dEyUaLhaxV9aQWF5D8lKe4Pw87ooa4strqwSriir+D0eVmP2EyCvNQ9Nz1Ds2rpT8/0NjfPNyGAAqgBc11rAy6fKJ4UkCY3Ck4NMbVnBdfeYpb6iz+RHOj36Px8dP8xf5V6CMhS0Crs04PJCVUWkUupMua5Nhd+bJfIGSMrTFHJrUWbziEOvoZ6v6PrYpEIg4lilGT+KS/vTbGMi8gbR3lK7ct+ks3s106kVcWPJnFXmpPMx4VSpOXEom/vWfye19CJJpam66la4bX4zlxnEtQYfrkHYkJW04k83z/fF5+ubzjHk+JUOFIC3nJN36GKfEBs7KHlboftaZI4yZFurFJC8zX8DiyuwwDQS4/J315wyItQQqjMOwo2HHSoXEq9wlZ4xZZNQOy5aBCc395at3+ZjPrz8wj6XLg5TLXzW9ibs5XbgRhbWozCuxUDwv80n2p7bxmTW7SSUuXDHaqBzqesPZ+9hy8FGsiVxlP56ojgkEhZqr2Lu2lgvtK5/TiQI/DWLK8IbzHu8441W8VDM22AbSEZPVwMdXunxqlYt6ius27pUqhMzSitMtnexfvua5Uci0QY7kcYKzuKkHiecfY2X+pdw4cw1rR0dZF++uEKEyIS+HuRqIMt80Z/o/TyY3T0OiAz1+krniKHd86JMAvO/jH+H6A3sqH5lNJBlpbuXdf/LXIOCl936PxqlJRE0LiUQLKaeWBtKsybnUeaZyfmdcQa0XlhoV8O0ul8MtLu/Zl8VSoCy48+YmvI403VM+y0Y9xIoMjSvraXQd3Is5zPk5kquq2WFVVFFFiCox+ykwVvI4ky/haUN/tsBfDYxWMor+31VtvKipltGSz6OzOZ5Xl+aqRe3oWnvMzOwlmVxBPN7O5fG76ev7beLx5RyP384RsYO65FK6kiku+z5n8h4droMlBA3+MepLh8jGNiGMR/3Ml2goPbJgAnavYTD1KupiCdaOvRuMjxEOexs/yLi9Htcf5gVTv41nN9PX/DGMTGBEmIVWDBRNZ4+TWLcZZVmYPfeT9AqsvfklNGdqkBg8rZkLFKcLJc7lQtWwP1/EV4ZAK5aaU3Sbo5xmHWlmeIv+CBYKg2CENjq4CDxFACYwQz21TEcLnOTr4vV8X76qYuy3BFEsgVikmglMVPIMNFEDwIJKtm6oxI7TJTqmAuxKLVMTLuECm4CXNSx4xMYbAx6t3cytA/0sdY7QGB94ylT9o/5SrOFZarIzrLhw+kkeocWEDGcl51c8jwe2rmKs/pfXT7N1KuDlwz6NJcOmWUVSXdlNmrMhE8BAUnCi1uLLnQ5HnmF7bz3yCJ3TlzndvJT9y9f+zB4yADyFNZzHHsqyyYyxs+Y4U/YMPcXl3DpzXaUrt0zEjNEEI4exm9eDtEArPrAuTgqbmnN7aet/mKmaOqZq6pjO1DKbyXDfjt0YoHl6EkcpcskUpURiYcrA4icEwCmHw0b3wvXTiv/5eA5bg7bgW1szvPRgNiw1SsG+l7bTva6F2pECsQtZEivrSHfVRuX5qxa5gQAAIABJREFUqtJVRRVV/HhUidnPgLzSFYP7vtkc19SnuLYu86QbsO/PMjH5IybG72Fy6gGUyrFq5R/Q1fXbzJWyXJi/yOOFejwTEpApP2DCC0hKgRASS0C6dITesf8HGeXVhwuqg8CvKGNnMm/jcOL1KGNYQz/13iHmYlvJxjdg6RK9Y+8kHoxyfuknKTlLyQcaNT+He/hR3L0PoSbG6P7Pv0f3NdcRl4K5QDFSCpjyfIZLHoliH1Z2H/vUOs6YblYEj7FG76UkMmTMBJv1w+Vo2ycRlQIJEhSibRUc4Go2cRCLAIXNF3gbr+dTle//VryPs6y54r3KypgVdWlqormXUddlw4RP15iPEYLtgzPUzcUom/Vl1D5hodid+STDdgeDzTHeMvcpLKOuUMGeaazRpuN7ufmBb1ZmVT4VIdNC4mU20rdmK49uWoXn/HIWZEsbdkwpXjvoce2EqhD4+5ZYHK63+J3THnakyPanJf+rJ8ajTdYik94CbBWw4eI5+luXUXDjNGRn0UIyk/rpYi+eiM2nc+w+Pccwhu6M4jX5mrBEXfb5LSJjGIMRAmMUxwa+yGDrUmjp5bHmGD9Y2Yq2Fsqt5fiLxSjvnRX9caKuY1eEg8cTUuJKSZtr0xRzSduShJSkrNC/1TxWYN1EgO6qIViaxh7Owvk5ckvTbOhtJvkTTOyooooqqngiqsTs5wRjFEJYKFXiwYeuQqk8rtvMkqabaGy6gVTtTk4XBRMlRVYphoseja6NHXUnZucOorP70LKetDpP4/x3sE1YLjPAWPJmLqfuYP3EHyMiZay/5SPkYhuY8wPaEzFiEjxtKASK9vG/pDn/A8baPoisvRbLK5L/908xdXg/RgU0dq+l6frV+M1zFJNXMWKtwy4eobX4KFM0UiiOsL74ZWRUSFRI7EV9jgqJjGJqNXCK9aziFBJVIV6v41PYBATY/A3vQwO9HOMU6zkj1rDC9NPLMU6L9ZxjTUi8IIoiCL1pJmIX5Q651gmfHUMXWV4cwh3uBRMu5jYFAmIQaXB1dYfoVidod47SGDvFG7pfzyNt73hWsyXbR4fovDRA3cwEG08dBJ66P1YDZ7rWsXfztVxqW/4cXk3PHpunA26/6FPnGbbOaOp8Q0mCo8MjEQAHGizWzClqAxhKCv5tucNXlsWe8v2EMfSMDnH1+ROkvCIPdG/mePuKn31DjeHmo5e4bWSezmyO5cnVEHkFgYVys9F4Q4/iLN0RjrEyivljX+auG27ne6tbK563ckn2iX8kkLIkcSmJWSKMdJFQY1vEpWR53CVt27iWiN5D4EVBx6uSsSu6FgNjyAaKdekE6UUREwWlKSjNVbUpMtXoiSqqqOJnRJWYPYfw/VnGx3/A2OVvo1WR7dvvBODSpS+TSndTk9lIScNsEPDIdI65ICBmCXxluFDyMAYabElj/vs0jP4VEIVDYjHvrifjHUeg0cLhcOMHqa+/inTpKE5+P3OxLUw7vcz4CksIWmJh0KsQgiVzX6Fm7G+xnVdhCstJLFfo1DZO/usncLsUxc4bSNUENI5/EEEASIqyibi+/CRFqKy6TNFMPeNIDArJo9zE1dxfUbw+zPsAWBURrwGxhpWmnx6O0c96zos1T8qMgoXuSZuF7DIjiEbgSDbN9LF96gCH0z3UTNn0HlgKprwYLrzLusT3OV24AYWFFvDVDZ+gze5jq27n/uaXs7fhuh97PttHh7jq4AN0D/UjzIKvaPEx0UjGl3RzuT7Dkd5tv1D/WKUrsj5UuV5/vsQtYwvK2J4Giy8td5i3BB89UMDVC4TlvmabT610OV779ERi2eQoOweO05ibYyxTz6Mr1zNa1/RTb++uS/NcMxkwbRyuvjTHDtJPImLaaI7pfawTW7GQoBXZRz7M37/mzaz2U5iJfr7fs5T+lT2h7xAAQca2SERlR0uEcReSMER1RTIWTsUAAhOGEzc6YV7fsrh7hXFfE5KvrkSctL2gfCljmA0UPckY9e5CDttcEB7vTZkkNVVSVkUVVTwHqAbMPgeYnHqICxc+w9TUQxjjE48vpaXldozRCCFpb381vjYczxYZLXloYKhYosaxsPJ9xPP76bZaKRbOU5u7h1RwYREJEhSb3kq28TfJZQ9j5Q8wE9vMuFyDEyiU24tv9+IbjUCwviZGTzJGTEqCUpHJox9Fqc+Rn0owfuRhOp//VYLQ6kXn1YSfUHoExhf2x6CRplj5XiM4Kq9jrX6sQrzula/i5fqTle/32zeyz9zISnOU02IDo3ItAsOQWlNRLcbMWi6asNsyGbXxQ2jWt6OuuvK8S4Vh6+xRrp4+xGN1Wxhxl/Cqke/wu4Of5WJpA2tzSxjx1+EZe9FWhrM/tYRVqYfpSdzPsL+Jj3eluNio6W/4IPc67c/qnG7p28NND3/rCv/YYmJqgLPL1/H41ut+KWb+jdMB/7ivgBMxWUmYy1XeRgXsa7Q4mbF483mvkse2p8Hiw2tjnH0WEwW6xy5ga8X3e3dwrqn9KUuczwTH93jV0UFuu+TRlvOprVlBOMzd4IsF36WKlFepQWjFyod/SJEfYjf1oCZOEcyeJzN6nE/c9kqS1nIE0MxC04ox4VDxhG1hLyJZAWFYsSXChg9Pa4QQNDk2nja0us4VpMxEpGyJ61xBynyjmQ80XXG3Qsq0MUz7ATW2zYZ0gni1fFlFFVX8AlAlZs8ShcIQuWw/nZ1voaX5NjKZjRUloKQ12UBzYC7HtB+QsSSOlCxPuMxP3k396HspK2MG8JPbmck8n5qZL2FMgBEOM/GdYXRGciPF+HpKWrPOcVgac0hYFhlL0uza1Dk2xYkxLhFjpBQw/t3309TxZYSAZGOB5TcsjO1eMKiH5G8ufjXp4kFEmXg5b+IW7+PIcHnjced29pvbWclRTtPLoLWOrFnJcnWEfrGBCbkWR0gOiQ0UjCaNQApB2oaCLs+5FCijQ+IVRWgIIYFwcLnEhJ4f4LbRH/DXJ/4nlgkY9dYw4q0nKae5q/hfGfR2VLZcRyOQJAG7av+FTzW/joeXdXC3884fW6JcDKENawYG6Rw+ROvlYVomR5/aP0YYeXHPdXdwpHfHz3DV/GQoq2MjcUFXzvDqCx4xvbBNP2ix+VqnUwl9VRI2TivecSacVfqddofPrHC5kHp6AmEpxdYLpzi7pIPpVA0P9mwmkPaCMf7HwRhqs/PMZsLuwi997F/oWPNahC0wNZEnT4T0eY/9MLv8nUjC8qR3+E6kmyaYOEUwfY4fXb2Faw/+EEsFKMvm1NoNSASeNrhyEZ2KcgGBMIRYLhC2ojK0xmw8YzDakLAs6hyJpw0pS5JcRL7KpKzetml2F259Ra0pKkNPMk599PO80uQCxfKEy4pEPJxUUUUVVVTxC0C1lPksobWPEPYVpv+C0pzKFZj0w1mZZ3MlTP4ITfmHEJRwC0eJl45X/r9BMFv/RqaX/HYYBpk/gsjtx05fRV3dVrQRIKDBtmiLuSxxbeKWJCgWGDp6hPOHD3D+yAFmx0bZ9cd/TmF5N+rs32DlPl8hX/nEDlLFQxgTECoXAozCCJt9TR9k1lc0eYe4ZG8mG1uPXeijMzjCmLuFU3TjRCpXSRlyWuNG+1tUmqI2lfgKX+uwkYGQjAmoJPM7QoZdlCZUygRgaZ/bR77HzZfvJ6kKrM4PUsq1ctFbjyPyPDz/NnQYmYkgwESWbo3hwMoYNdYEbbGjPLi062lJmFBZjLXQOSi0oXVasfHMAMsvDlA3Nw/Fg1eoYosjLyCkz0d7r+JYz9ZfqEp204jPX/YVK0Z9BZzMSHqy4fzQxcn7L77k8eYBn1VZTSDhGx0On13hMpp4BnJlDMsnR9l9to+aYp49K3o5uKznaf/7YgVRKsWmMye59vA+rjlyAEcFvOav/xcbJ3z+bt88GeGE800rg6dC/2XxwQ8hDdhNPVz2hni4s5WbH3sASymUbfHed78XKQTr+4/R19PL2dVrsKNQYUcKnEXTIRIyvBZSloUjBAEhmUpIyRLXJmlZZGyJIyRFo7GA9riLtUh1ywaKesemLeaE0yKMYT5Q2FLSk4yTtCWBDsuZSUuyLhWvhCpXUUUVVTyXqHrMnkNoY8hHJOXgXJa8MmHemZrCH/sMpcufq3Qt+s4yvNQOErPfQpgAIRz0yn8kH99ASYd5TB0xh1XJOHFL4kpBXEqkMQS+hxOLMzZwls//6bvRSuHE4nSu30jX5m307LoWk65hfHo/A8d+A6N9hHTYuvmzGAEz03sQ6e30zReYmN7DoNzAZWcdDpJR38cmXEKnfYUbqQE5FQbFWtFompIOPVeuCAljQWuMLs8SFCE50wYjwhFLSoclyk2zR7l2ai9Zp4Zaf57NM4fYMt1H3HiMemsY9jYwkGzh8viNoblsUTFRAxc7ciwdiWO0RFmCf31BhotNDs90pcaz91Ez9TViib9lrM7GcwQveuw0W44+hA7ORelXP36+ghKCh3fczJ5t1//U18izwcYZxfVjPgkFG2cVvXMLcwsU8C8rHD7WE78ieV8JeNs5jxdcDshZ8JVOh3/rcpl8hokCAqjNZ9l95gid05eZTmZ4dPUmRurDmZ5P9NOVGzvKeNFDP+Q3v/Z5avI51JIextdex2ONKcZq1vKuOYuFIrNBEXBk4t/pmUphjZxCTZ8LSaaAz916E1+84z+zfuA0m04d53B3L6dW92CLMLFeGYMrQgVWR+cqLmVlWL0twiHjrpBROIsgbUmWJWKkbVEZs1XUGimgLebgRJ4zH00hCJW1Rid8uCrq0MzfFnNoi7lA6Au1hGBFIkZH3K2MBquiiiqqeK5R9Zg9BzDGcLHkcy5fjMYECSYnH8CZ/hpzwThxrx8RdSyGkNBwB/N1b2Yq9UJqSodobdhFTc02MrZFyraoscOWfYDs1CTn+w4xeOQgg32H6H3+jVz/xrfR1LmMHS99Fcs2bKFj7TqsJwyHTi+5moatn2N6eg/19TtJpLdwrlBkX2w5RycLFI1CJrqY9BUpIcmrcNlzrHDRs2UYJSCFxJKSOV/hiJAvORLygcZDRyOTDL42lBDEZGi+3j57lE2T+zmZ7iZmAm4Zu5cXjt6DYGEMzal0Nycy3aTGarln9t2hGpa90vdjRDQUScI9a1thLXRdDhhotrnU5FTCPRfDCQxdl306JwK6Li2hZe5Psc08d16bJjF7mA2Hvol6FoTMEMZfgEFLmwvPRUfi06CxpHnTgMevD/qViIdzScGdyxxePuxX5lI+2Bye5746C1sbfvNMiWsmFXM2/PMqlzuXucy5otKVWG6uCMvJ4fuGjRWC9ZeHaJmb4sDqjZxauooSodIpWBj+bgC3WGDH8SM878h+vnDLHQx2dJJOLaWw+7cYaWhla6mWWmA1BjG3yExvFIXpE4i+b9M1f46HeiQnd2/mTXfb2IEisC2O9D4fgJMrezi5MlLqIq9hWKYUUTSKqRBwPwoxLitnCSmJW5KEFRLClrhDTJQHbBkKShO3JC0xO4oSNuQDhSUlXckYaUtS0pp8pIj1phPEpawQsu5knNaYW03dr6KKKn6pqCpmzwBPa7JK42vD+PQ+zo0/Qiqzkbiawp/4Eip3CAhLiLnMS5iJX0X7xPuRJgBhw6p/oqZmK63RU/lin4oKAiw75MV3vvePuXgyLHkmampZvnELa3dfz6rtVz/rbc0GimPzefbO5rjkeZQCw6xSxKVkxg8qC6CnDXMqwI0WtIJSeBpcW2Ij8LXCNwZHyijWI1TKtIFNM31smTrA3vqtXHQaedml7/D28/+CZRYIaSAsLKOicpzgn1a8hY92/QYvGDjNlsfSKFzCoUhhThUmTE7/wdYkyZJhsNlmuCkkJeWSngCkNjTMKTomA6ZqLC4scWiaDfitu+ZQAiZSw0zGLiB8D6c0Qcfo4I8nY4RKoJY2P9x9K4lingvtK57TEubGGcXu8QBbG3rnNNunwpFIZaUqAP6x2+UzK2NXqGN9tZJrJhVvO+exdVox5QruXO7w9eUuBTvqU1y0g8aEYbySMGNk+dgFim6csYZmLBUgA5+sE6uMwIKQkG0fy/O6Q6dJDB1gZd99JFu2Qed2TneuoMauZXl+YUDYQlitYUAdpUusCf1jWtE3+K98b9kBHltjKMQd8s3vYc1Fw6bTxznS08vpVT2AqHjFylDGYAlRUcsM4XD6mBSo6GujY1PrWDgy1KKVNjQ6NvEoyyxAU1KGjG3R5NpIQkXM14ZG16LJdVCR0p2wJEtjDnFLklfhzNaVidh/GELm67DBoWRMdC7Ds2nMwvkJBe6QtDtRdltMCqrht1VU8auDainzJ4QxhnP5EoNFD2MMZPfCwO9izKLgV5lG6mz0CgvT+k6CJb9BpzqJU9hPXe0u6uu2VW72xhjGBwcYOLiPgUP7KGazvPWDHwNgz9e+iJCSrs3bWLKsC/EsjdjaGCa9gAOzOY5lC1z2PHJKk7YsxvyAmAhDNae9gJI20bYYpn2FJSQyygnLKhUukNEPPKVZM32UHTMH6WvYTn/Del4wfDfvOfpXWCZ4yi5GjeAbHXdwV+stfOTgH+JoH186/OaWD+NfXs3u/Tm8eECsKDAmLFHetTVJumQ432wz+oRSpQh3kOuOFuiYDGifCogF4b893h3j7m0pMIZl4wGxmS+w5ehBll1OPSlo9IrzuuiPtpyfGxkDSPmGN573eNu5hZFIIzH41lKXwaTgz46VKurYf9mR5Fhd6IYSxvD88YA3n/VYO6cZiws+3+XynU4Hz46I11Og3LnYmJvj6lOHaZ6dZLB5KQ9t2BGVmCGVy/LiU8PsHslxNK0Y6N7Kn/cVsXVYvFRobGFXjtVly7BEEZYay1TWhF2V+Yc+xGQNjPas5etrm7i39yXYpdO4xROo+Dp0rPsK4qijrsonlgcDY4hLiStE5dwkIjW2NiJa5RiMklJYUtDg2DhCRuZ/jSUFSxyHpCUjQqZJ2xbNroMUVAhZq2uH3ZtGkLIlnXGXJa7zK0fItAnVv6IOR6hNq4BpXzHrq9ACYUxEYMPtluGFg0V4fG0hQiUciZSC8mzYhCVJ25KMtEja4TFPWAuqfRVVVPGLQ7WU+ROioA1nCkUanfCmPT/yQ0rGr+QpqfpXcjn5Qtou/R6YACEdVjXvprW+hri1C9h1xfsdf/BHPPzv/8rc+BgAzStWsXrHNWilkJbFzle85ifaPmUMp3MFDs3l6c8VmQ00cQGXSwG2EOSVz2gpLJVpBJ7W5JSqLHDaGEr42EicyE/mayhohQ1smT7M3+1/F7YOwoYCK05G5Sufr4GHllzL/S038IfHP4AdkbAvtryYY3UbecfWj3DV0Hkultaz5HADqy7mONfhcO819SSnA9ovB1yIlDEJCGVonwzomAxomwwouIK7t6dACtYOe3i2oK8rxsVGm+FGm6l0ebSOwHgHeOkP+5GknniYgAUiBqB+zmRs+2TAK4d9mkqGDbOKmL4y2uLrnS6fWxVDAONJi61TAQcabI7XWTjGcONowJvOeazMaoYTgv9vfYy72h28SCkSGiQmGlt15fifmO+z6fwJ1gwP4NkOj63ZSn/LMtZMBvzmg/tpHjhCm4rhrr0DpGRXHsSRKC5FhDMOLsctWoph16zCcDw3yXVuHQiB0IrSkTvxU2n21k/yjTuGOdVuQI4y3/weLEDHulHxbiwpsAlJf/kcWCJUxhxRdmCGzSExKYhHwa8xaZGyQvJZ41gkrXAugELjKUPSsqhzQpWsqBXaQK1jUefY+NowFwTU2BZtsRiG0CPpSmhxHVKRh3NpPEaza5P6FcsjKwSaEc9jMF/ifKnErK/JBwEFbZAGEAJLmNBLJyIfKOF1YMuQjDmRD8+JSsPl4OZy+G5JaeYCgSP8Kwr8NZakLe5SF1ksqqiiil8uqsTsKZC0JOvTydBPFhhMzYth4qtgNAgbp/EOdjddTV3L55ideZz6+p3U1m6rvH7q0kVOPHQfG2+4hZolzdiuS+PSTna96rWs3LqDVF39T7Vdc4HifL7I3pkclzwfrQ3jnk/Mlox5irHAxxVWNEooXNBiliBpO9iBwNOhb8wAMRP6zUpGs2NiHy8Z+T4AbYURNs0cxjGho8tgGEou48GGq3nrhTuxdUAgHf5t5Zt4LN3LsVg7O6YPsrduMwfrNmAhuOCtYfuxVjo1CHwOL3e5Z2cKIwSTTQ7jGYtSLLRrv+TxHOsHS5U5l3MJyZn2hVLmx19ci16kaCw2pgO0Xy48SUUyi75qaXHvtbf/zGRscYuCJCxRbp0K6Ku1yASGVw75XD21EInyg1aLfQ027z65oIwdbFz4dTtWZ3GszsLShtuGPd58zmNpwXAuJXnvxjj3ttpXDBbfMKPYFpU5TyxS16SA9TOKG89P4kxN4Jl6Nl22eMm54xzf0sytlwKksxaxZl3lfIrIz7Wv3mLTrMLSEAjDZ9LwByWN0CBMwI49nwj9aE09nBOn+OK28+xfbZFtfDUqcQd24QRBfB0m1o1kgQQrbRal6YdnwxJyocxmwDehub/GtklakrgVKWYR2QpN/hpfaWSkksWt8CGjTMhSlhU24wSalCVoiLnYURNK2rGoiTo122IuTa5NjW1dkfL/y8Z8EHB0Ls/euRxn8h5FpcLOZgg9nxEBg0XXn1BIBLaUxGTUEa3LHsPw6pMIHCsaOyVAGcgJjYrKnVJAnW3R4NqkLYkycCpXxBgqSmJLzPmJmx+0Mew5P8XjA1PsWtnAzq7G5/aAVVHF/yWoljKfAeUOTM8Y5mcPkpt7nLq6nbTUb3/STSs3M03/Iw9w4uH7GT1zCoTgJb/zbnqvu+Fn2gZlDFN+wOHZPMdyBSa9ICxXSrhUCpjyfYyQKGPIKhWl9AuCKO6iTCSsKOaid/ooz594hKKdoN6bYef4oyzPD1YIxWByOWdrVnHd2IPIaL7ku3Z8lKN1m+iZOsymyQPsq9/CoboNaGUQMvSJaSFoGy2x6UyJlnGfmsKC+f/QSpfZtEXbtKJtMiBZ0nz4lfVoKdh2ukhtXnOxwWak0WYuKSvkq1xg0TyZkC27cJAtxx6leTJP/fwMcGVumwKGlq7mkatufBIZW1yKXUy2FlMJs+jzI9tW5eebpwL+fl+hEm0hgIKE2KKRSB/vdvnsyliFUB1ssOmLCJUBYsrw0mGfXz/v0Vo0nKiRfHqlywPNNkYINsworpoMmHIFG2cVt18MKsfz0SaLgbTE13kynuIVoy6WWdjYspKmF+2fAu5rttg9oSpE8XeWCVadOscLZvPssaB27iivGTqP09hNMLG4q1Jw5/U2X79GgLCZb34POiJjZchFKo7BLKizmIqvrNyBCVBjS+qj8WTKGAJtcC1JxpJIIfAiQlZjWREhC0u1GdsK/VKEJbqEJbFFGF5c61jU2TYNjk1H3KXRtclY8lfGW2WM4bLn0zeX4/7pLCfmi8xrhU2oMEprodRbDtEtq6NShMqYgwwtBxEW61uWFMSEIGmF/lBLCHwdlj0tIahzLGpsOxrjFiqKAEtcm9bId1fOZHSlYFUiRksULfJMKPqKfWOz3Ht8mM/dN0ygDY4tee/rN/HSNa3VaQlVVPEUqHrMfk4wWiOkxCvk+djb34DyfZq7VrH22utZt/t60g0//RNjoDVn8yX6cwVO5YrMBoq0FAyVfKQxTPiK8SDACzQJ20IgCExognat8Gl7zVQfveN7GUx1klIFrh+9j2snHqkM5y4Jm4nYEtqKIxGhkHxs1dv57Io3sXP+GNumD7GvfhuH6zagtCIwYQnJjwI/fWVoHfdZN1iicVbRsWiIdnnepRFUSMNMSjLaYHOx0ebgqhi+LSoLS7kz8EqdZeHfNOH4pLWn99I83s/Sy7loaQ5RJi2mTEqkzZfu+A0uty57EhEDKnM4RbSdmnDxk5Fx2kTm6t5IGTtcb2FruHk04EWXfOKLgl+/227z3Q6HD+0vVEjP7+1IcrTOukK9E0A6MLxyyOO1gz4NnuFQncWnVro81mSxcVazczygoaR5xcWgQhYXEyyDIdA+lrCQ4soFbyESRKCAb3bYvGQkqGzTO69KgBBsnwo4PJ0ndvwQf/HYJ7G1qhybxaVXI8MTGdgO/+2338yZthx+pJCV98mKjmV5TBKEwcJhf4KoxFzEpcSRAgtBjS2JWbJCyGwpSFsWjgRPhYPMM7aFwwIhL8fJGBOqvnFhkbIFTY7D0rjLsrhLrWuTsiSxXyHPlKc1FwoeR2azPDCT5Uy+xJwf4EXxIOHvakgzJWXjfgghiAiWDH9PohNkLeJJAohbFglrobHCjwiXIwU1tkXaDkvCRWVQRhOzJK2uSypSKXNKo4yhNebQHnOxZKhMzvqKFFAbCKbmPUZmC4zNFRmdKzI6U2R0apbRuQITxScfbyng9t3L+S83rKI7lfi5Hd8qqviPiqrH7DmEXyxydv8eTj7yIIFX4tV/+j9wE0luefvv0rqqh8alnT/1e5toVt+Fgse+2SxTQdjBd9kPqLVtSloz7QdM+CqMuhCCEmFH5saoY3J/3RayVpI7Ru7itYP/Xhk6DlCUboXKXPTW8s3MWznZ2cz7z74TW/sE0uFw4zYC4OHMeg7UbcQVIgw4NVDUBh0Yuoc9WiYC0llFz6WgsqAvxpk2m0uNDlMZSdGVTDRYFJzwBl6mEzYLBAkWk48rydiKC6epm77IunOnKuSkvHwZwEiJMAYlbR7cfSvJUp5L7SuYaVtGPCrwlEdBaWMWFDkREkcdvZGK9rNMpzbNBHxkb0i2ytuWt+BwvcXWKYU0IeH5RqfLsTqL39uRrKhjx+usKxSllGf4tSGP1wx61ATwWKPFp1e6BAJuHAt46UWPG8ZUhYwtJkiPpbI8b87FCAuN5i7nIGNyjuGm5ZjGDfzJcQ9Lh8oWgrA0KeGbS12+vdRl25jHQd/nVN8E6zJjdBx8gE2TU2w8f6pi/NfAI2vtt5EDAAAgAElEQVThqjMCWxkCCz59i4Mrr+dg73WciCIuFquL5dKkLRaUHkVYXrOjTsC4FDgy1MlCP5kMM/JUOEqp1rYQIlSnfUWkgEmU0mgpcARYSGJCUGtZtMYdViRitMcclrgOKdv6lcsbywaKs7kij81m2T+bZbjoMx8EqEhyVSLMBjRC4mvwhak8LAFIKbARSANKGEQ0NaQMIcCVVnRswTOaghe+Pm5J0rZFLCoFT3oBUx7UuzYZ28LCxjOagXwR29ckfEMu5zOTLfGjuRKFvI9fCJie95icL1HwnhhSA3VJh1Z9mVZvkA1iitZEkaamRsZq1vGPJ2tR2mBbkpf0LKErEf85H+0qqvg/D1XF7Fli+OQxDn//u5zdtwe/VCRd38Ca3ddz/Rt+41l3UT4dAm2Y9H2OzOW5UPIYK/lMeYq0bVFUijEvwBA+wc56inmtiFvhguQojxsufJs/PPkhbBO2LS5WncLFXfDlZa/m7uYb+diB32eiuIJvT72PABdlCS7snqHb7GFfwzaO1m0ErRCBQdsWzJfYerRI05RP7bwi5l9ZCix/xmKVTFnwhRdkGI9iLyQgJAR6weOCKS/ii8ptkRrQPDrE0ovncAp5th59dNFw8QV/VIWMWTYPPe9W4hEZG2tdtjA0e1EmloCoQy387MUErUzceqcCNk8FTLiCrpzmpcM+magTVAP3ttl8YH2coiUqStrBBpujdVal3Fn2WpWPT31J87rzHq+44JNUcLDeYiAlOJ+SbJxR3BSRMRbtnzEajEZLiS8lf7JOcctj9+MuWUNOX+LR1hr2reglF08Coaq3fSpgX0P4nLVtKmBfrcXxYgnrYp6Np09yy9DjdObHWTexULY+sjzJuuF8SOQs+IvX2xTSN7BxyHBsmeDomusIInVMAo7girKWih4OYjJUv8ols4wVGfdF2I3pSEEiimsIdOiLS0oLgamQYiPCWrErJUkrzPhbGnfpSrgsT8Zpdh3qHbvyPr9K0MZwueRzNJvj8Zksx+dLTAQ+hSB8EDAYAsJrKCT+kXdskToG4bG1IuVMiic8tAiICYlrhbEigTFkx/Pkxws0tqaJScHMWJ6a5iTxOhc8jVPS+HlFPu8xn/Px8j5BUZHP+eTyoUeVKz4f0imX2rRLZ0rQxgQd/hBtuRN0zffRLiZp+aM9xNN1cPK7UJiCzl3QuKqS3bJ/cJrHzk2ya2Uj25f/dF7aKqr4vwHVUuZzgAPf/QaPfuVOenbuZu3u59Oxbj1S/mzeCV8bBvJFBvIl+vMFSspQ79pc9sKuqZiQ5AJFXzbPhB8QF4Ley3u48dLdGGPoKI6wfvY4Me1V3lMDP2q5kR+23cyfHflzbO1zMejln5f8V44u7aRn5jxdxyQmnw5JjoBLnTFyNTbp2YD0XEB6XnFoU4qLzTatwyWaxn26JlQYlcDiiIyQjAnCuY0/2pok5RmGm20mml0k4ZgeV4RlLG3CAdOukFhR2UpHi3ntpUEah84SCLh6zz3IKBvtis+KyJi2bB7efSvxYoGxpSuYaOuqqGHl53sRbRtEJEwvqHqWKHc0msrCd9NFjz85WsKK9jEAjtdJ1s0ujER6144kx+utKHLBVMzqi8uV5b+3FTS/ft7j9ig0diwuuJgQbJvWFcUpIIw4EIDRqrK3Gs0Dup+x9pXsbYkzwxRnOlaExvjcPDPpTKjuwSJSFxIlfM364bNsPnGUwrBm2/gpdl/su0LVDH1j8IXr4xxas5mt/fs53gmnOl3mm9+DinU/pQ9vIYdvoWRaLhvaAuJSkLIkJjLg2+WxShERLvOp0L8n/jd7bx4f2VVf+373PkNNmkrz3K2p57nb3e0ZbOMYbONAGBMC5CXhJh8gL+ElucnNvYSby/2Qm+S+5CUv92UihBBIQhLABkyM8Ui73fM8qKWW1K1Wa2gNpaHmc87e7499VFJ7APKAhwm1Ph9banVX6dSpks6q9Vu/tXAlVFgWta7DmqjLukSUtVGX5phLnWNjv4ZGki/Fku8zlvM4s5ThyEKGa7kiC74ZUaLDcaKEotKgNX5IuIrKxNLcdG7Fiv9uWT0EQ9TUXIHibJ54Qwy3NkJuJk9+NkfEkkyem0Or7+z3t2ULYnGHaNyhrtKlIhGhIuHgxCxaox571BXWLJ3l2oa3MeHW8frBz9H7/G8T2DGyzTtJteymc93d0HUX2JHvyzkto4wfJZSJ2fcAXiGPtKyXJe//WxFokyM2mi9wciHLUuDjIhnJF6gI4wGygWIyX6Bz9jQ7Jr/JoohQXZxj141DdGZHSxfZ4fgaXqjbz5yb5BdG/rq0MfmhPX/M2ZotbJk/x86RYTi3r8RSVr9D1xh1S4QmpqUqi5wr0EJwLSnZc6mAfAUytnwnSsKZWypRecV8i0u+JUqlbbMYmGR1EMz7fmlTzgvzmZYv8IHSxK6P0HvhOOsvnUCGY7XlY1z2jAkgCMlYvJBjoq2bqaaOsLpHlC5uMpQYtOYmgrZaQfNDpWxTKuD1kx6JQLN1XtGVVqXHGACf6Xb51LoIm+cDdsyaWIszNYaIy/COX0rLtYbWjOJD/XlunQlKf3+zd25FFZtMnSNZvw1bgSbgn5Nz5JLNHG2IcLEKto5eYuvoIHknwqP77sOzbNSqx7b8URcDNh89x86zZ4n4Rd558SmscPHDFwIrVByXfWNCG4XsN3/+Xk5t+zmcwiD2qvwx6yUszmJFzUQYUmVjlDBLiFKIaYn4opHLc2LM2M61JdWW2ZDsjrr0JqJ0xSM0RlyqbOs1p4K9FEGoig1kchydzzCQzTNd9Ej7Cq0VAeZNQ1GbqBElQIe1awqTIycwhE2Gvrtl/x1AYbaAP5vHrY/iCPBmCyBh4UKqZDKM1LgUUsVvcZQGyeYEzWursKMWTtyhvTpGxDWLJ3m/QHU0RnfmKnec+CMa5i5Sk75Wuu2BN36SS2130+unaPFT5Bo24mORDQLurK36fpzaMsr4kUTZY/Y9gBP57rwSvtJMFYqcS+fIBIpF32fBV9S5Dq7QVE+do2X8MAtVnfjFDGtH/pXdN1Yb9R3mYg3Ll3UCJE+2vZHPdL8XTymGKnewd+IY1zK7aTvYSoOdIpPoJLjSiB1eMTVG+bEVLFVajK2LM9XsEGQ84nM+RVtwx6ksUkHr1MvHlBoQlkDd2wS5gMkmh9lqSZUjqdCCSqFJ+5qi1viBQqvlTdGVEZjC+IsU0D5wnLu//i+lUaU5Ro0Kc5iUZXPojgeJFXJMtXYx0dxZMsLLkCAYNUyHUQA6VHZM36heNSpbVn3aCvDOoTxvHl2pRBqolHxujcNPXPNK/qwjDTYaOFdjcS7cpnTC86DCTYXSggNw73iRnxrx6Eu/dH90BcuqmHENab7YVcGpDXH2zPmcrrO5kEyitaZ+YZaHjp+iJrvE5aYOTvRtLZEyf/n5AJgvYl1J87rjL/Lrxz9XamAoEUwBz20V3HFBYweGjH3qPpvKnObCGpv+9XfjAET6CMKRpcCMH+3SOTaEblkRsxAruVnS+L+EMGqla0kczFZgrevQGnFYH4/Rk3DpiLjURpxSWv8PAxY9j+FskfPpLKeXckzkCyx4ZvlFCIWvTORLXguE0GS0MgXrYcesHy5CmDcEwHwBb7aAUxdB1kbJzebxp3NIoHB5ETTkXu1gNBQXvJu/tvxky5WFFSEFPdsaqK1zWDd9nIbpIXqvjNC2eJmmhcs8vvXDHF73UyjhUJ+6xFTdJo71vh2rbSeLjdvwIlUoz0dWt5Cz2wFYKPr0xssqWRll/P+FMjH7PmPB87mSKzBb9DmXySOBzulT9I2/yMmaHWQiCdaNPMa+S3+HWFVtVJCRklE/QPJX3e/jXP1e/vDoh3GURyAcjiZ34inNvU8t0D7exCIPUg2YwQn4lsd0s0vTeNEQiHCGtljvcPThBhbQWGNZHnx+CSvkE6uHVcujQCxB5vUNrNU2Nb2VXKqWnFnKcqPoU2kLlBbMeh4BYY2Vr0IipvGUwtcmHBMEgbdI48BX2XLhPB2Ty00KK2pLYDscuuMh4vks19u6udHcGW5LGuXHxvhrdEi2bCHBMhe/QGt8NL42c8tlw//tN4o8dM2jKafoSevS4zPnFp5ttvlsT4Tnmh12zvmcqrW5mLSMshcSL61vJnmb5gN2zPmkHMEvDBaofck1k/A2z9cL9qXAURot4O9bsuQr682CQHI7gYZLyZV8rZhX4A2nXyDvRPjGtlsZTzaVvEYBoJc8dESy/+IJ3vzkk7ROTdKRnr5J0VRSglYEFjy9XfD0dotNo5qTm+7l4vo7sfImf0xF+kK/06rnHKOQmUoko/jEpDQRDOGozRbGIxW1zMZfg+3QnYjQF4+wJhalNeZQadmvuUT9b4dsEDBV8OhP5zi9lGMk3KDMqgCUwA+lUg+FjSSnFJaAnFZEww1ZrTXFuQL52RxOXdR0kc7kUFpTvLxkxpwCRMxCZ19urH8ZQvIlpKBia5L0uRQ60AhL0LtekMgusiM2Tm3uBpPzDpHGCq40bMDy8/zqgV8AIONWM1Xdx8k1DzFW2UPcEixUdfKpt36dtB9Q61o0h0Xui35A0raotMzblrmiT6Vt0RZ1vw9nvIwyyngllInZt0CgNWnfvENetpE7wqz229/iopMJApa8gLF8kYmhF+iYPILduh+3ah2bh77A7Yc+jlA+t/FyE71C8JWud/B89ev4+JlfxlY+gbCxh9bTc6KJr/gfo809z6i/mYlCL3sOLeIWFEVH4Hor+4oaGNtZxfieGlI3itRezVF7cQkhBIuPtBEdStFzPkPLnF8Kd30pGZP3NeHkNWJNgvW9NVzNFXhyPs3CvCIGVFoSpQVD2Rx5pbCFRKMpaG2y00J/Ue3EVdpHB3AKKTqGjlCTcci5moGuGnpG00hljO4DG3YzvGEXMy3GwK+UUWLMGEiXRpbLFTOmhij0aYWbgFZI0Co8xd2THg+OFlm3pEqk5dEOhxN1Fr95Jl+KkThTZ/xMg7WCgVrbEBJtRq22MMRRCmH8bdqQsj86mi1FZqyGMe6b14pC8/U6n8/11rInZQjf+WTyJlVLCpPsXr80z3xlDZ4b5cCWfUxW11G0bHwNOuuz8dg59p05gZMvsnNhiJ7pMfOYhODZXfu49cxx7MAPVTGozEnO9vYw1DgGBAx02BSb7kRG+lDRvpICGJHhMgW6pCA6QmJpiNgS15LEpPmv2rVpdS16EjH6YlE6YhHqXZv4a3Az8juBpzRTxSKj2QJnFrMMZPPM+T65QFEMWxaKWmNLST6sQgqU+Vp6Lkd6OkdVQwy7NkJ6Jkt2IkMQaLyradBQYOGVv7EGXXzJi2f1iyJ8sQoJ27sWcfM5trij7Fnq5/iajTwWu594Q4x/Pv5mqj3zPfJWlMl4OwcSDzCKoGjH+J07P8lcZQdOZTNCSPIqwJGSFttCY363xcPaq0BrlnxFjSNZG4tQ1JrFok9zxGF9IvZD+fyWUcYPK8rE7BWgteZarshwvoAKHd4dJ/8cGRSZa7+ddMtuOqIu3fHIzVtqSjGYzXO9YOqbIiPP8sbHfxZLmdW+24XE0ivvkjWCs533czR5H+8585+xtIfSNrnTO+nKNvOxh/6YjenTzKQ2UBzvZrHdYaj6FgJnL82TRd70xGLp9/lCs8toW4SuU2mk0mhLYHVV0hxxsVpsqg6mkAXNCw/W0j6T57ZnVy4aKjTwa0sQeUMLTkFR7IhTbInhC02FZfGv0/PMeQEFFVDnONwoeCx4Pik/INAKkGGfIqHZGWqvHmbryadZc32ptDiQqqzk+dfdyuUNr8eTFgNT12gdH2GqrYvJJqOOaUxEhy1lWO1jyFYAoa9sWcUyHz0UQgt2z/g8fK1IfV6zfiHA0jDviJvUpKmY4LkWl+moZNdcwOlai/NJ23jTWFbhzAlZ7p8Uodqnw/vYMefjqBXn2MmkZPN8gBMoAqH5QnKOpboWTjRGuJC0UVozWGdjIXD0yvELIbAKefZcPsvaqTGe3XEHk7WNjNU1E2hN7+Al9jx1mMbZGe67dgwZktPFaPymx3S1UfHET9ezZfgGFzoFA+2CYuWbKCTfjVUYxMlfRMRM/pjAeMMEZrxWGtEisDAhrUnHoilisyYaYX0iytp4hKaoS71jQkh/WOErxYwXcDWT51Iuz1A6z6RXJBsY8i+1qWOzJXhaE5eSxekM+Zkcdm0ET2uKUzmCQLE0bNSv9MV5ZESiCq8+wi6hNHoU2OurCS4a/5glFA/XncAq+qxJzPPXPT9DYTbPxyf+gLdefxowb9huRBvRjRFe2FCLj+b3tn0UHa1iqqKDaTuJtCQ1ttl2LQaKkcZd1NoWPlAMAhKWpMG1yStNoBS1rk2Da5PxNUJo2qMONbbFoh8QlRbbKmM0uM5r3v9XRhn/3lAmZq+ArFL0Z/OmLFkK6k79De0v/Hc00GX9Xwy+7Z8YrN9BfViKfDVXZLboUTn0BLGLX6RLB9QtjVI1e+EmRWy6cSdXGu9i97k/xdImN+yLa9/F7FQ3VTMfozl+geH4Di53byNb43K1rpXhtl00Tfs0kCXpa5qHC9hzxdIoDjAkorsC65YkV9ZWUDmex1qTQDTHWCh41DwxSXyywD/fVoGftOm9mFu+GVrA4uZKZLVDtLsSrzWGLYxJu9qWpgJqIYNEY1mCqLCZLfqM5Ap4ypAlM2YLkELQNHaOtZefo3p2jLWTy/nsK2rgpY13cGnzPeb7a821xnauN3WsqDWY0Fnjz9GlceWKGmZGehtSPjtnffqTNjUFxUNXi2xNBSWy8o1Wmy90R4kGmt89nCmpY6drTab6haTxjpmxnPGraQjjDZb91gItdOi50mwZHmDn+dPcPjKN7H4nWloEQvOX66rQwJ45n1P1Dv3JupJq5giBjwnZNX74cGyrNWsmRtlx+Qyu73G+ayO1c7Psf/o5zibXsDE/xvsf+6eXbacGQvDcnlt5w+HncHyfwNIMNJzgcnOEy83hj7OwkYk9JKRAxvvwYialPy5lyd+mtSBmQY1t0xBx2JaIsa06xtpY1PQmOua1rbQmr7QJHPUDrheKZHyFp001UsgzkELgSlEqyI6ExeQ/SCK3HENzPV9kKFugP51nKfCZLQbYAnKBIm5JpqcyZKdzRBuiVNbFSU9kmRhbQgeahdElbv5hezletj+1PH4UGolCa4FNwIdiX2ZRx/nHXe9DVTv88o1PUkx57JcX6clPMhVt5FJsPTLpEklG+Eb9W3lGvI2JSCM3oo24bsQEQStT3H6+7S6zwKM0Ucv0jHpKI9BU2hauEOSVxpWCOtfCDheLErZFpeMihenoTDo2Fbb5GbOFYHMiRsN3kPhfRhllfH9QJmavgLiUrI9HGckXCHyoyKRKpnsRFGn92i+h9/4S1sIgNwJJbfYG664dwM1MAub3+GTtZm6sewc9l78EOkBpmxdG38nk6V7GnAbaIue5vP91JLtvo6VdM7bxIU7HHibhWFQLQQ2CplQBfXCG+NmF0sU5X++ycE8D8aRL9NFxdGDUsUxbjLzS0BpjrClCg2vzwnyapuPz7BzOc25XJV076uiMOVSvi8DxRbNqJ6DjlkbW9iapcSyaIi4VluT8UpZnUktM5YtoNHkFY7kiaRWQ8VWps9CSAj8o0Dw+xu7jz9FxfQijv8ibrmcaULbN9bZuAq1NmbUU2GrFYG5hdCklBEIbUuBrjSeClXwnKdg+7fHxI5lwqaGAADLWzZujVxOSgUqTpfVr+xLsmPU5U2dzudbB0caAr1je0tQlcibFctaZpnXyOo3TNzi6dQe+hg9++s9puTHBYHcfj8rTqJbNPNtVz2DSjIJG6h0EhkCqVcsOOjzu5ToiDew9f5TOqWvYuQLkoXF6gA88/Y/YKihNs0pkDNDSAkxu2ws7aznUE2fzlTTnOwUD7ZKg+kFUbCt2/iIiugk72heOpc3GpMT0S1ZZFo2OzfrKGLcmK+hLxGmM3LxprLRm3g+4nisyXiiSCxR+SIqXq5csYX55OJbEFRJbQi6AWe2b2A4EGk1MWrREHBpcm4rvczVPITABzJMFjwvZHFN5j4mCR0EpFr0AL1VgciJDXVMcz1dcG11AKs2NkcUVcmWxsu76rSAArXFEwHsTz/N3xTvwsXDwiWxpwvc0/1vmS7xu6lmeFrtZG5+lrspjMtKInXSRSJ7Y+laUhs9EGyhacfMGK/y5coAjNTtKyyyOEASBQktDgO1wycCRAteysIQZzUakwBHSvOOS5neZHYZEO9J0ktpCELcE1WFPaZ1r0+I6VDl2uE1dRhll/CBRjsv4FvDDrUJ97Qg1n3sLBB5aCJRwsIOV/amCW4mXaCaRumyIBZKTzs+QvuWX2NY7jh54jgPP1TPZvAvaYjgdCaaSNrMqoCL8pRqxTMVR8dICleeWcBY9rFmzGl/yjQmY2V/L9V1VWELQNusTvZYj6Eyg22J4gWIon6fetumMRbh4fIqt35gjvaGS3JuaaYm41LkWCdsmNp5n5F+GKcwXeei/7iWacHEFXM8XeXZ2kf5sgYiE6aIPCCbzRSaKZpOxqDW+UtSNHmd9/7PUzk3RPOfeRCZuyh0Tkv6Nuxhcv4uJppVmBCkIM84kVqhM+eHL0dTTGPP+upTHtmmPnITeJc3rJj3cVZVI/9rh8PU2h989ki0pY7++L86FpGM8aGLlyFa/2pf1gOWRZcP8LNsvnGVr/zm29p+ndiFFOp7g/f/nX+EJQe+1K6TqGshUVJYWApYN8742F0kRXgSXYca84EqT5K7Q9A0Psu/oQXKOw5u//jh24JfI2PJjOrJlBzsvXcAKfALL5pNveQPVc6e50L7EYEsGbdVDkDKPSNjQ9BsQW48EKmyLCkdSZVkkLYuYBT3xGBsqY3TEorREnDBx/2ZFxFeaK7kCh+eXmCz4KMyFHkQpCNgSppvStDWJVWqRKc+usiW1jk2VY+FISVEpsoHCV1DrWKyviH1PLv5aa7JKkSoGTOQLjBWKDGYLjE2kGbq+SENTgmjSZeLqIgsTWbJewPTY0svVrZfAjVkUc6UgktLbCwvF8ja0lhb12+rZMnWMn53/HI0VBV6QOzgW9FFX5XFg85vQVoy4t0BOuORlBEeKkNwqZKgPB9qMEHXpjUIYKquW94sJg4v1Sgk85s2QJUxGnCuNomxaFkxeYGJV+n+lY1FjW1TbFknHodaxaHAtYpZNVJocuh+2JY0yyvj3gHKO2fcC144QjHyTK023IC9/g7VH/9hoAkKSufM3OXm6jf3zv4yFT4DNc/bv0/S6NxDdVctY3mPBD7iaK1AXFjejNVMjC+RHMojGCNaSj30qhTVdLBGc7PZqgjUJKh+fKAVJ5d/RSdAWY8HzaY26uFIwlvc4vpjhXCZPQWne31JL3yLk/3aISEucvf9hE5UxhwCYGlpg5NIcXmeceMRm5s8uEVtTQeINrfRXGWKmgRrHZqroMVYokvYDfK3JBRqpCnRe/DzbTl+kIbWsIxosHzeAbzthCGyWqy0mkX+5TH2FpBiyaWEuLmY7MPSTaYHUmoeuFPjAxXwpTy1jwZlai90zK5VIv7k/wWCty/o5jy2zPqdrLS4k7XAkaUiXFGasuHpkGc1m2NJ/jmNbd+E5Nu/9l8/y1ie+zHxlNec3bObchi2c3biNqfoGFGYr0wnLubUweWCGmJnIjoi1XK69Ak9rIgIqM0tsvngCXVB84DOfxPbNGufqonYdBqr6ls1v/8p/RqHZduk0F1uuMZg8ab4vApF8B3bNw9jeZcj3E0tsoa5yI8mwg3JtLEqjawz5Ectic0WMtbEI8bAg/KXIBwEXM3menV1kJFvAtQRRaa0UaN/0r82zLBHMzmSZnUizrrOG9W1VCKCgNfnADILrXZv2qIMTPq5FPyDQmp2Vcaqdf5tY7yvNCyOzHBiaoaO1ktF8gYHRRaJ1EYJCwJXRRYqeYmx04duSr5uhsMLVHoeAj9qf5qPq5wi0wBaKj8lPMea00F2xxEy0nmNeD9M9WxEtjfiehyctXCFRmNesJUPSqjVSGMVWLL85ECIcUZiFAimM38/REikhJgWOZRYvXMsiuhxJIgQRSxDF5MHZQMSycDDEKmGb59nGkLOYLU1dkxC40nSMuq/hsN4yyvhRRJmYfQ+hlWb60NPUPfluhPYQdgTxvsf4xjcqqOMCaxIXSOy4l3zXPk4sZpjI+8Qsgac1I7kCjhDUODZieInsP1wFtWI6D+IWMhuU1LHM7fXU3tWMGM9SvJLG64iRb4mx5AUEGuJS8OXZBUbzRsnaURnjJ/wIraN5Rg9NIV2L1kfWMDm0QC5hEUl7FF+cQYfdj6uZlLIFl97cxEyjS9o3St71fIFcEKCLI9ReG6JvcJaa2Qlq56Ze4h1bpY5JycX1u7i0fidzLWtwpFFYLMwGoWBlixIMQSqEfqUNcz47Zn3SrqRrKeD2SY9kYdX5AT67LsLn18VDEuZxImlImJTGmya5uRtzmYT5aAgUG68OsfP8abafP03vyGWk1vzOR/4L5zduJTk9RaKY53pb54qCET7vywQvZklW/8ws378lTH/h8kKCCRXV2IHPhpF+7jz4DN2Xh6ianae6kF25rZBoYQJ0P/2u91GdyXBh/SYGmxcJUl9BF0aA1VkckmTTu2hv+gnaIhG6Ei5u2EBRVJqC0vQmXFpcl5aoS71rv2qp97znc2YxwwupJaaKAXOeT7xUqr0CM04zJGBhOsvsVAZbCg4dnUAp4z1805t62dRRTZ1jI8Nznw4UthCsS0RLXrNcYBTE/TUVr3hMptJnhm1rkhSU4rnBGZpbKphOF/jM44ME3zbpfmUpozohWMwEaCTSaF1GiRICLSy0UkTw+FDl17hhNbKmKkt9nc0TFT/G4FIlTQ0RmhrjCCeCBSQsaciSJYlIqLBsElJgSUlUGpXKFebNR0waJdgNR7/L414pTD2VFO3hbGcAACAASURBVBK0aUcom+vLKONHD+WA2e8BZsaW6D84yeUTN8jMC1oi/5WN7cNseMfboGMv9/0MKL2Rq7mHOZcvkJvPcCGdpcq2yI/lCK5kqGmJkkrlme1fJHYlWxp5CSCytx61sYriZ0fQoTq21BrBDRSVbXFka4wgUFzLFYgKyZsba0jYkqfm03xkTS331lVyY3iRkb+5xPDyPNDXXPn0YOl7FFl92QK/wsZaMmM0Ak18LMd4JXhBnonsGfzFw3Rd6mfzsE0y7a5Sx1YuJBqTPXbgtjeRKOaYausm1bIGLTQRbbxIxpckcdAohBlXhpEaUghiSF5/rcAvnsmWKpGKAg412QxXW7x7sGBKuiWcrHfw0VxI2lxMGiN/RBgDtBElNGgR+pygenEeSylSNbX0Dffz8d//GEoIhrp6+eKDb+XMpu0MdPWigZmGRuYwip55cCsblIoV1W15JKoJa5WEKI38hDBerJ7BS9x6+Ju03LhO+/XrJFOpEokNpAQNvm3zqXe9l2Q6zYUNm7nQ1YcojiNSn0NPngrPsGRD67sYnPwiSnlY0uGetv00VFWx6CvqbBdhGfWuyrbYUZUI64xe/WI/WShycjHD0VSGGc9jKVC4QpJVhrELjDLoSIEtYGE6x8xkmkIhYPji7MsUqSDQTE9luNGYIB0oOqIOtpBU2hZpX3E979GTMAGlrhQseoEJQ10mJIFHJrvAExfH+I9fuo636o3DCgle/XhWv4qX6bMokS+FhbAk+zZV8NzROTxM9tr7esZZkEnWru9DJRq4cn2RrWuS7Fv7QKm/MmpJbg/L1yUrYbrLZP97izIhK6OMMl6OMjH7DjF6YY6zz4/RuamOW9/SQ9e2u3Bj5vQprckGirNLWcYLHjWORcySrIlFmBxIof9x1IwigUpAVDnojVXoS0smZt0SqE1VyLYE7nu68K+kKbTFaO+soDViczlX4MhClmOLWdKBYndVnD+oM/Uoz+7dAMALqSUWhxcNqQuhxKoxlAC5sRo1YEz/2hKM7ayi86AJrAyCCbypc8SrerAn/4m+/lGSiy6JQuJlJv7V6tjIxj1c27Sb6ZZOBOZCXh2O+8xGnzJZXKgwesKoKevnitw57pPwYHPKpyWrblLg/rk3wmfWxVBoTtba7JjzOV/vMFJrE0WYBH69nKxu7ldp49fpu36VXaePs+PMCbpHLvOv976Rv33X+xnqWc///MD/zqVN28lUVi6fFmwwhn1W4iMQJi1NYyp20OBa0hj4hQ6/DrY2RmqJINCKjK/Yc+B5PvTpP0WGW6UZN7riE5SSZ++8l5naeoY3bWFi3QYoXMZfeJ7k9D8xn74A4maTvJYRHtz+e8wsnqW9ZjstNZvI+Ipq26I15tIcsal3jW/s1TbplFKM5Aocnc/Qn8kz7XkmjFZpCgocS+MiyAUBUSmYmc4xPbZIIesz8yqbiatLtm3HYv5GlhPjC6xtrSRu2YyNL7HPuohbTPH4nKQ6mCOV9VjXVsH/s/7tnBqapfvsX7EUuJxR3ZzXa1BhkZUu/c8k1u0RFzmt+/AJx7FSGrUuTBpWGmxL8pHbEizJWvatb2NXZ5Lzuxc4MjLLrd31Ly/V3vGKp6qMMsoo4weK8ijzO0QhZ7LIIrEVLuspzdVcgev5IgGagUye4rUs7tUM2BI9lSO4uIhYNX6Ru5JE3tiGLSXBWIb00CLxrkoau6tNjAGCiBC0Rx3aoxF+a3CML96Yp8KSPFBfzZsba3hdbeXLPCO5QHFpYJYX/9d5lK+QtmT9I2sZePQKKlBIS3LPh7ZxMZ1l5FKKqSYX3R7DP3SCyIlnqJ4aQuqbMy5fyTsW2A7H73qQSC7HdHs3S21rcYRxbvkYTxXaKEvLCkOgtYnW0JrWTMBbhvLcN1o0XhxgoNriVL3Fj48US8rYb99ayaVa+6ZRpKfBRpiR0PLIMjwuQzIUH/9vv0H36AhKCIa7ejm5bTdHd+1lvK0DgYkacMLNNXPfK4n3Ra1M7ZAwR1ZSxZQmIiXRcIyp0XhBGOVhybDdADYfPMg7H/0cbdOTpWMLhOD5u+7ltoPPYwc+gW3zud/6OPMbN+ErzczMYwxf/5vSGd7R+kY2Nb+Oz5/6KEGokD2y4/eprdzIukSMQri12Rpx2VwZI/ZtKo4yfsDlbJ4XU2nGC0VmigEVlmTBD/DRRIQg5fvMT2dZmsyiHMnSTI6lscwr3l+YGYyUgsa+Gib758zrGhWOm5efERm+doJw63b1s8VNn0dkwKaaPM1xyZPjUZQWyLDoWymNbQk+9dNbsNw4R0ZS3NZTD8Ch4Vn2d9fd9PnLyFcZZZRRxmsQZY/Z9xhFpSgozenFDPO+ImlbxITg2guTjH11dIXFuBJnbQJvKA1KI2xB28/0EbTFSYc+sdqIRV88StK2iUjBscUsfzc+y/9Y305LxOVgKs2NoscD9dXfUSbU5PAC1wdStK1L0txdzeTwAlf759AdcQ5VaGaLHumhfrL932QhGGLXiXlk2Ez+UjKmAKTxwigpGdt8C1Nb9rDY2oXSmpwKKGizALB8ZMtF4QHG8+VIwf7xIvddLdCQCWjNqFXDp9A3tiHK53ujbEz5bJv1OV/ncLHWRocO+2UDuhACLxy3Rf2ADf3n2XvsIO3Xr/Gx3/zvIAX3P/lVcpEoF3bsJlOdDJUvQ+6UNuQrEpKv1XuaQXg8yxuDWhkFbZmUubY5P8VA4WNGY+2X+ll/9hRaCDYfO8a668PmthjPmNQKZdsc/cT/xNOKjvPn0Lt2calNc2T0C0wsDpDKja969iRb1ryX13e/h6nFC4ylTtOa3E5FYgNtEYdNFXHqXJvkdxBrMFv0uJDOcWg+zZIXkPJ9Km2z0OGEhPnqxBJzY0v4OZ/Z0fSr3NPKZqItAj4Se5Q5q5YX9/4MV4fnWBxZ5OUjOf0KX3tlCAFvu3Mtv3pvH00RN/SYlQlXGWWU8e8bZY/Z9wgzBY+RXIHFwKgDQwOzZE/OYecVjGVhdfedgMTtjXBHA/krS8jRLJ3rkzR0V1NhmyiDWsdkOxWU4otTKf7s2jT9mTz1js3lTIGWiMttyVc2Sb8amrurae6uBgxJKrREuRSpZDidZWg+ixy7xqZ/+FMsf6WnElZUo8Ayhd1CKbRlc+n1jxDJZ0l19DDZ3AkCXK2RQpCwbKKlcaXZ1Fuf8tk063O1QtKQCbjnaoG1YbG3Ah7vjnKhweHDR5dK0RZn6xwsBP21NheSNhFhPD5miCUIlCbA1OF0Xr/GfU99jX0nDlOZXiIXjXF6xx6qAp+CHeGZ+x8yypdlspyWyZc0nm9cLYjbEqFlmPZvAlctDClb3tr0hTLZX66FLaGoBG2XLtJ18SzXOrvovnyRB778hVIBuy+XFSIzrhy8735oasbas4fIlq1YSnFqXS+nrn6a/qNfKL1ItrU+wIXJZ0rqWE/tTqpsizVNO9nduANbCvpiJn3/23mcPKWZyBc4uphlKJtnpuiFRn5BJtD42mdhMsuVoRRezmdhKrvq1prlseGP2wd5VN+JUhqXgN+2PsWE3UxvRYq6qoC5qgYuNEQQwy9RwZa3OJefO6WxQq+bHxgFUobmeIEgUArHlrxrUwtNYVfj7jXJm0hYmZCVUUYZP2ooE7NvAR16x4pas+QHnFjMUi0l7mSO8QOTFE/PrZzAtQmCNXHkCzOIwKhj9etqqIxGaN5ewZr9kVdUvLKB4s7DF7le8NiYiPJHGzr48cbkd52YnvJ8jqTSPDM7zciN57l840l8VeS2wZ1Yvs/qvTsNKGlxdfMexjftQQP114eYaesh1b7WuH5Cw76nNNnQuO0gcKQgYkkiGjaO5/k/Di9hrVpqSEXMdqPEELNpF15sckjdXsWWGZ+L9TZjtQ5RVkaWRa3xAohKo5LVZJbQUpKNJ1g7Nc4dhw9wYsceTu27nbNbdlB03OUDNCXbOgxVDeduGpPg7ytNwraICAstTcaYF5jxZcIy0QZFZUxrCdvCkSYZfb6o2XbqKD/7J7+HFQSlx7baEzdx6620HjuG9DxwHPIPPMjCxk2ks/0MXf4k6eIcgzcOkPMWS+ddIKiKtfPWnX/AWOo0Lclt1FZspCnisCYWpTrMn/pWRn4wMRRjuSIH59PMFj0mCx75uRyj19NE6qNQ8BkfWiC7mGVxaVkbXIEgwAoXM2yh6emKsn9tGxNTBeprNQdb/wdpbXFGC2odQ1TX2Ta9O9v4u9ElgsCMG3/roY1k8j77u1951JiMu6SyxbIaVkYZZZTxLVAeZb4KxvNFhrJ5PA2gmDs+y9TRafRcAZEJXmbCqruvlcSdTVRO5rGv5Wjuq6Glu5q4JV+mdAxk8jyfWuLn2hsA+JOrU2yrjHNXsuK73vzKB4pjC0t85dpJXhz9Eqn5F6ifVTTP16MTPew8Plaq+VmGAq5s3ceZ+95qohWEQCtNhWORCwwZs8O1fzCEpnOmSN+0x2CVpKGgueN6kW3T/k25XF/tjnCoNcJHDy6W1LHfub2SC0kbB4GUoLRgORBDElIGrdFekT3nTnHrgWfZcuY4X37kHXzl4bch/SKO55OOREk4EqHD2qfQBO5rHSabWyGXVCg0hUATs0KfGOCFCesxy9QHeYEi0GBLjZSSotLsPPwCO559ipq5GeqvXb3pfA129NAzec0QNcdm6g//hLzWJE+fpLhrN96WrXzz2jN84ezH0GE/alv1Vra03sdTl/4XgfKQ0uE/7Psj1tdtRWtNxLJYH4/QEKpH3wqe0sz7PqcWs/Rncly6tsCV8SXWtlYyW/A4/NQo6mXREquHyCvmfVvC1lsaWcxrqhvjtDdXEKBxhelWbIw4tEccOqIuVY5NrWOTCFP8V48eywSrjDLKKOM7Q3mU+W9ENlCcS+dIOhZVUjJ+cJKpL5oLsxBQcVcT6ZYI4gvX0L5G2pING+vorq2iqjEJ215+nwWleHx6gc+Mz3JwPk1MCh5prKHBdfjwmqbv+pg9pTl44zLPL2gGCxa5s9+kbfAkHZENbD+7gFAKGC0RsuXRJUKgLJvLG3bhaU3WC5ACqmyJrzRRaSIO8sooWRLYfMPjV15YvEkZm4xLnm13uWPcGPh9C850xLhe5/CJO6pYP+Nzrs5isNbFwvjQXC1JSEBYKG0In9Kat//9X7P/4PNUZNIsVCd55g0Pcm7PrSZDTEYIXJcYkJBWSMoUGoEXpuxX2rKk0HkahBbUuTYWK60FrpRELEMGY0JQn4jRYWk6+i/gvHCA1oPfxL1xw5wrIbi+Zz8NJ45h6wAlJZnbb6Hivt9k7tgJsjt3Md67nqmiz1JTwODUk8y/+NeMzh0LSaehnZuabmVvxyM0V/YyNHuC1pod3NG8k8aIS9ySVLwCiX8plvyA8XyRI/NpBq4vcGp0npqozcFD4wSB5vyJKUyKWsn1x/KzJJZnjZhx46Y9TQRFRXdbFY1NCRocm65YlIaIIWO1jkVEfmvF7qWjxzLKKKOMMr47lInZKyAmBV0xl2t5D4Fiaal4899HLfbsaaOms57JwfmS0f7VcHQhw/vODjPnBayJuvyn7hZ+sqWOeve7P/3T2Rk+N/g1Hhv+CvrqZbqKd1AR6WHroaMIVYkgVfq3y4GmoFGWzcm7HsLNZZnt6Cbo6KZeyrBySZFTZnxrAX1zPptuFEEKOhZ8do8Xw55KQ36e6Y7yd9srCBA8N5unb9rnXK3NlRqLCJrR+gjX6k2OVRyTORYI402TStKYmmbdxbMcuuP1WEJSuzBP/5btvHjb3Yzv3EMgTTZ7FGPSzweaSsvCFiJsDxChqidJOhYSgYdGKI1rCzwlSPs+QggSUrJheJANgxdIb99JoqOdjmNHqTtyiMjRI5DNEtgO07EaGgl3C4XAqq/m+Xvupi01T9fDD3Hnmx/hTDpP1aatRKVkHZrBya/whXN/iFkbgI7kTsYXzqOUj5QOG+t30uA61DVs5/Wtu+lLRKn6DvojC0qx6AecXszw4sgcl64tUBBw4uhEGLi6YrbXaDYwymXRafYhxYqiKKXktv1tSF+zoaOaO7tqaQ8bAWocu5wOX0YZZZTxGkCZmL0ChBD0JWJ0xiIs+YrJzfV887lJVKCwLMm+rU2sqYxBZYy2npqbbpsPFIcXMjwxs8Ce6gRvbUqyJuqytzrBQ/XVbKiIkQsU/ZkcOm3YjSMliTA5vMKWVFrWt/WYzRfzfPCpDzJ56QxNsy5rqGLjYCtCDyEYKv07DeibyNjDJPJZMmv78Du7TI2MVkQ0YWelwMUirjWeH3DXQIZ3nM+WKpHSDpxodtg55SFDZexAexQf06c4Vh/leoPA1wqhjGHfktIQJR1WGQmIeQG3nDzM7c99g/XnzwCCKzv3sFhVzWc//GsUtMIWFnFpVC2lTem40lBl2yTC82NiOCDhWESlia5QaGIItIT5QFEpBfvGrtDbfw5RXcPGP/sThOfBZz+NCEf5qrGRF3tu4Yl4N2eb+nikMsd7/un3IPCRts1l22bN/ffTve9WUsLiymKWQ5OnmFk8jSsdhmYOMrFwbuU1hGRL4y08uO7n6Z89SVftdu5p3U2141Bpy1dN41+GCn2N4/kiXxm8weXReabzAadOT6JeIXRVhmTQkrD73p1sjjcwNr7Epo5qGlyH8Yk0t/XUcXdPPREhv61vrYwyyiijjB8MfqDETAhxBVjCCCG+1nqPEKIW+EdgLXAFeIfWOvVq9/H9RERKIq6kfmM99b+y86YYipfi09dneHJ2kRdSaXLKGMqTjsWVbIGr+QI/3VqPFLDgBzhSEJeShaEB5i+dp2rdZnR3H3N5PwxM1dS7Nn2JlcLnrJfl6dFnGV66xtZgD8eOn6B2LMuOgSaEXra86ZL1bZmMactm4PVvJl7Ik1/TS9DWyURg/o3lq1Lgq681mUCxcdbj7tECVQVFz6xHonhzJdLX+uI8tj5Oz2yRTTM+w40uU40REsrkjHlao5WGsOfPUxrHggTLHZOCtedO854//j0S6SXmaut5+i3v5Phd97BUVcNy4ruDRY0tw1gLTTH0hDW4Dq4wqllRm5Lt6jBqpMK2qHNsvECT14rEhfM0nTuFXVPL5mUyxgqd0Vpztm0jaz/xW0Q2bCB1ZoK+QsAndnfgpuc4Xz3DLjdCYt9e3rZ+PafzPnOYRYFi7hxPnPlVAm3y7eJuku3tP8G58S+jlI8tHdbV76YnuYW72nbTE49+R+XdGT/gwMgsj1+6QUt9nIGJRZ49MobSq4/crBwsjz1tKXjg9nZyRcGt3XXc1VNHlW1TvVcSXc45K4epllFGGWX8UOC1oJi9Xms9s+rPvwE8pbX+XSHEb4R//o8/mENbweoYigXP5/lUmrF8kV/sbATg85NzzHk+726p5Z7aSrZVxBjIFjiykC4tAEhMX6RGMj9wnnN//Al0oJC2za5f/Sg1PesAQxgWA8Xh+UV09ixfv/JVnh59mpyfoyvbSvH5ryKUYi0rQ6zlRH6tNVg2k/e/hUgux0JnD+mWNfiWITlVQNzSZIOAvDIRF47QbJr1ufPSEjuur5Son21yuNTo8siFTCn4dbQ5SsKSXGuIMlyv8JQi6gfEwtLleFijo8Mk/oL2aB8YYMfJI1zt6eP87v1MtnUysHUnh2+/m/Edu3BtB4AKrfCUBiGpsS0sKSiE4a0VtkUkDK11LUFn1GFt3KE3HqPasSgGmqFsAX32FP6x47jV1az7s//75WQME/qKNmXhX7/9Ee5v6KTBC9ixoZEmfM49/w1OnDhBZWUlPe98F1OJCm7kfJ4aO8bE/HFUkObcxBMlUiYQ3Nv9du7tfS/n2+5hNHWaBztvZVfjDhLWt9+qzAWKQyNzPH5pivlcgaePT4SVUi8NZFWl2FbLtnjPPT3gKe7ubeCWtUni8tt71Mooo4wyynht47VAzF6KR4DXhZ9/GniW1wAxu5or8LXpBZ6cXeTwQhpfQ71j83PtDThS8A/be6iwJNcLHldyBU4v5TibzlJhWSwMDeIP9WN19aLyBYqnDuOdOgKBubCrwCd16XyJmIHpPfyHC3/LV/r/jCq3itd1PoBK3Ers8DCor91ExtCgLYup+9+KyqSZbe/F61hbukh7QcCCHyAERKVFVArWz3nsGMnhFhRdM0WSOYW/6pqugMv1Ll/tjTJc57J+xmOgweFKnYONwJWYTkrbIu8rIgIQJnPMBzacOMqmE0fYeOII1fMplJQcetOPM7rvNoK6JJ//0EeIWhY1lsnwL2hFoAQJxxRC+5h6pXrHptE1G4HtMZf2iEOt65CwLIpK0Z/JczlTYKxQxDl9ilv/06+9IhnTMtzGFBZfuvfdrIkFeLfv4+7dO+mqiNLo2IyfP8fjzzxNLpdj3/79VO/ay1XLJuoFDM8c4iunfx0VblhWRlvxgiJaB9jSoaduNxaC+9tvYW3fHd92FJ0LFIevzPH4hUmuz6Z48dLCK6hisG5tDSPXFgiUxrFsPvLG9RQLAbd117F7be3/p9dyGWWUUUYZr138oImZBr4uhNDAn2ut/wJo0lpPAGitJ4QQjT/QIwzxpal5PjEywYZElF/saOQNdVXsqkqU1BBbCE4uZhjNF6mybeK2ZE00wuTARfJ/8fvg+y+/07AQW1g2id5enrvyGM9e+TIP9P4UfY23c2fHj7G7roe66j2cTHu4QjAXmTCkTAiwbZYeeDsqk0Z3r0d2dIPWRPwAL1BYwuSM9c75rJnK019nsyB97hnIcNvoijJ2pd7hiR1VzLqCnz+QwgogsGC4KULCsRmuU0w2RohZFgllku/98CPaBLjWTIzTOjnB5d17iaH5sS/+A/XXxxjYvovre/dzdddeshWVuFrhBZoa1yUmIR+YTs8qyyIZtaiybRojNl2xCK2uQ0PEoeIlHZBzRZ8j82lmPI+pI0fpeepJNi0tkTx+1OSIsaKMaQ3Kdhj54IewFxbxd+3iob17aIq4JG2LCsf8CPi+z6MvHqSmto4H7v8xhvV1PjvwGZorO5hInebg6KMlUiaQ3NP1MC3V27k2d4qH19zGjsYdRKQgpzTpwJj1hQBfwbmxFGeuzLO3q5bmigjfHJ7m8RMDHL8WvKSC0jwjljDZa44l+S/3rycuJYdG5sqRFGWUUUYZPwL4QROz27XW4yH5elII0f+d3lAI8QHgAwCdnZ3fr+Mr4Sdba3lLUw2dsUjpa4HWXM0ataagFP3pPN6Vy1jDlxCJCtTkdYpHD9xEymRLO2piDHfDNhL3vompi4e4WjnG3/f/Enk/S1PFGgpK0RaJkLabGRG1HF7I40hIaw1TEwSOy8LtbyDftZ5sexcLnuk9TBSLuNLClYIqKcn6iobJPO9/ZhZLwRtYVbUUQgEnmlyOdhoP1KfvsVhzo8jlRpfxWgdLaZQULPgBlgBLSFygMTVL9+kTdF44S+eFs1Sl5vAdh7/81OfJRyI89iv/kVR1LdXxGK4UFJQmCAIiQtAQjZCwIGHbtEZd1sYitEddahyLCinR4SLBajJWVIqxvMd4ocjIwUO0PPE1GibG6D17tkQw0z19MDKMUIrAsvn6G99Db1zTc/+d7N+zh0rHIrGqW3JxcZHHnzzAfffdh+047H/7O1l0Ihybv8CfvPhhfGW2cQUWbck9TCycRCsfSzr01e7itpaddPbdji0EQ1mj3PlohNZcHl3gxUvTLOR9BoZSYdn3ynjSpYjGKf15OSPOsS0++tDmUhDrMhErq2NllFFGGT8a+IESM631ePjxhhDii8BeYEoI0RKqZS3AjVe57V8AfwEmYPb7fawNrlP6PNCagtKcW8wy7XnU2DZJ26L17BGGPvuX+CqkPpaFu7aP4pXLoBRCStTkdSJ9G6l4/wfxpM3XJv8b6cIMd3bcxyN9P8GWuh1M5T0enZpjrODhBQFTnk/CsnAIWHfpDKm+LQztu4+YLXE01DgWeaXIBIqGG3n6popMV9nUL/ncMZDFCQ9HA+fbIhzvTfDuAymk0gRSMN4cIRM2HBTrXC7XmYBTR0BECiSSivErbL54juG77yEbr2DnC89x+99/mnR1DaObtzG7bTsTW7YTRF0CrSg0t9JomS1PS0BrxGFt1KUrHqMhYlNtW1TZFkIIUp7PeMHjaq6AOHsG98QJirt2obZsxQojPJaOHkU+9hg1k+PsPrdCxkqQktw99/Bi8ufYMT5I7/138cHdu17Rd1UsFnnxxRc5cOAASinaenqZSSzylasHqXIrOTj6zyVSBoIH+n6Se/o+wMTCBXKZc+xpuoVbmnaQU4ov9E/yzcsz2HVRHA3nL8wwk8oykyq87DUk0LzBOc0v902Tbb2d9zwbxQtMLdErkbEyyiijjDJ+9PADI2ZCiAQgtdZL4ef3A78DPAa8D/jd8OOjP6hjfCkWPJ8ruQJzng8I+s+fJXvyCHYxjxoZRM2u4pBCUHnfw9j3v5lgaBB1+Fn0ycP8v+3dd3zdV33/8df5jju197Qsb1uWlzzjONuZEKDsWSirlEJLAwXKo3T8+mtLB6ushlDgR8MIIUBCyMI2SXAcb8lbtmxHsmztrTu/4/z+uNeyldjEgSRS8Of5eCjWvfd7dc89/vrqnXPO93N0VQlbV/Xx2ZIcyoMRFm/4V3LDVYzqCHtHYvyw7RSdcYe01oQNRWFXO6XtbYzOmE1O3xmsRIxYcXnm6kfHJ2j6JLIbLK3udnjDE4MTpS0AuvJNIilQGnxTsb0hl2NFAf772mJm96Y4URaksyRAgYK051FoG1jKwIzHqN21g9q9u6jd10zu0AAAseoaOpY1cfjaGzi0Zh2J6hqiloWbHQwqNAwqwhaVdpDacIDyoE2xbREyJpdo0NmrQE/EkpzYvpOC/c3YwSCF3/haZoTRNOl+89sY7TpD/pkz1LYefk4Yy2yybmb2wrRtGq/ewFVNKy66AF5rTUtLC5s2bWJsbIzaufOoWHclO70OvrL5o3g6Mw1qGWEMZaK1RKLbSAAAIABJREFUxjJsGsquZHYkyDVFawkY6xh0XL594DSPtJxh56H+bIX980tYTC5nYRgKfB/bNHnvn3yQRfWZLYvunitV84UQQkw2lSNm5cBPs79ELeD7WuuHlVI7gXuUUu8FOoA3TmEbSXo+jtaMux67RmNETYPgQA8dj/6C+JNbgMxid6O2HpbcCk8+Br6HMi0qG5eRGwmRDmj2t+xgJM/jwUW7KfNn8eSuJ0k+08dQTT1Jf4hY2xFOV9czUFJNaXsrszqOYSqDmpZtKN9DZ8qEooGabb+iu2YWRYFqlvU4BDTMHnKo73MmbYm0bWEODyzNo7Y/TX1vitYSi/7iIKUBC6fK4lBVFA9NgZ8pl1FwupOoZRKrriWvr4+bvvg5Erl5tDcuo3/pcgaWr6CvtALla/zSUsrsCooCASoDFrXhAGVBmwLbet46WUnPp2Usxojjk27ey5w7PjqxNmxiwb7nUfn972W+P++5Wim0MjDQWIEAZZ/6FP7ICJHVq4gsX/68f5/Nzc2M5Y1jNUVIVlRxcHQrPzv8tYlQBoorZr6Z6qIm4uMHmV28nPrCBjoSKX5ypJuD7cP4fpptu/uetVj/nMym6ZnN0C3T4I5b5pNMeVw5u+Q5G3RLIBNCCHE+2SvzIvpSadoSaRJeZtPngT3b6XxiE/5gH/T3TD5YGZS/+g1Ern8VOadPYp48Su7cRQRmzqblkXvpue/HpC2PEw0hZqTrMKxiIgd2wdkpz/NGWS4WZ84fgzHNMFUNb2HtWNnEzod9EYMDpTZXnkphaPANxU9uLKe7NICHwkPj+D7jnkehbaEze3VTcrKNWdufon77U5ScaufkdRvZ8bFPkHY9okeP0lc/h6JwgJBhEjYU1eEAdeEAlcHMlZFh07jkivF9qTTtiTRd6TRDm7Yw6xc/J//wQax4fOI9Zkp+ZL43dKY8hIfCMI3Me7Vtyj/9KbzhSwtj3d3dbNmyhZtvuYWhQJjNJ5/ia3s+geOnJ3o1L1TFeKoXX/sYhsV1Df/K/JJGoqaJpzV9SZfRk218f/MInp581eRZZnbDdd/X2KbBp287u6G3jIYJIYSYTPbKfIHins/esTgFtkVxwKbz14/S8b93ZR5UipyrNhKvm4v+4V1oz8UwLeYuXsLc4jwKy1ewb3Y9W7tbqDoQo/8n92KiCDsWDc0ucDz7laGz/z0bzWL5RURGBs8t0lcGxcFKKsL1ABQHyqkI12OOmZMKv+6aE2XH4jyO96eY3ZumryrCmZIgjvYnNgk3lSKgDHLMTJX8qz/5McoO7cc3DHoWNrDvAx+m64r1BJRBQchi5pqVVAYtSgKZ6cg825q0IP9SnN1suzflcOjJp6h++EFmnDjO7LZjE+/Zy05HYtsMfvgv+NWmQ1TOqGDDI98Dz8V6gWEMYHh4mC1bttDS0kIoFKKzp4fWaICHTn4Hxz+3/quy5Hoqqz9MjnuCofEDVBYspTK/ASeVpKjnac4UN3Cw1+Lwtn48ffbCD03mQmKFYSjWNZRxxcIycm2Twd74c0bGhBBCiEslwewCQoaiJhikO+0wjsfoyMi5B5Uit6CINRuupmhOPV2H91Pb0EjVvIXEnTjfPvhDvn3gO8TdBB/133/Bn392ZEhp0EZmzEv7Htq0eGb1tSzYcj/KczGsEHmNb+WGoZLsyJhi3HB4qtKnr6aYV+8axvAzo2NdlSGCpkF/eYSOkiBVoQAl2YKzPproiTYqHt9CzcED7P78F4lraL/+Bto33khq/QZKy0opt01WhAKUBCwKbfuCbb9Urq8Z2b2btt88RbK6htxtT9Hw0IPPWSfmoXh4xirCtdW860N/xKLly1n/vsy6rPjbriO+Y+clhzHIrCPbtGkT27ZtQwMzm1bRX53k00c+S+fIUTztosjsJqCUTSj/BqK2RTi0kILwXBq7t7B019cY7exhlzOLg1UJHu8sQ+tzV+NalsEN62eQSrmsri9iXX0xSinGXI851XmsKMj5vfpOCCHE5UuC2QUYSrEoN0ydF2Dc8ShYspzuR36O77qYlsXqFSuYnRdB5S9kxoKFjKRG+Hrz1/n+ke8znBpmfdV6PrD0T8kbsPnFYw/juy4qW+BUez7KsvBuexOpsTHM2fMxlcJ65ijpurksNspZNj6LnNEEpako9tC5Igs+sL2xiM0NuYSVgVkaoqY7TX9VmGB5iCoNjvYZc6HAMsjp6qLykQep2Pokeac78Q2D0RUrqUkkKa0sp+bd76QsYBO5hOr0L0RbLEn3rt0UfuTPyE2nyePZJToUWmUuWMCyuPoj76bplqsws204OygXWb78kgOZ4zjYto1Sing8ztxFDTiL57Pl1LfYuf+xzM/FYFbdHdjBUlLjBzAiDZTmLsJH4SXHuPmBj7AnOYPvmHP4cfo9+Cg4NblfaqpzedUVtcypzsfTmhHXI+b55FgmEdNgxHHPK4ohhBBCvDASzH6LqJmpe1W+ZAkVf/t/OXXw3OgYgOM72IZNX7yPr7d8natrruZ9S97H0tKlAOhSzTWf/Ada9zej6+dxOukQaW8jOHcBdt1sEu2jlJyJ018Rxp5dyfwDI9Se6s2O6ERpr43QVRNm1Y5BjGw9Ma8uj4XREMOOi1MTpqMms22R1j4JzyNy7Cg1xcUEqiop6Oli7r33kF6xAvs976boxhupLy5mjaFe8JTkpdJa05lME9v6FIVpZyKgJM0AYe2itUZZFr1/+lFynTjJ5U14jY0MOS4lwRc+SpdOp9mxYwdbt27lHe94B9XV1dx0y0388Og9/PeeDzKWPjfaqQEn3UNewZVERgqJ7G5hsf0EP695C92tQ/ws9leZ0Txvct+c7SrTUCxfUUG4OHzusezPTXqZ9XsLo+GXrG+FEEL84ZPF/y+Q67s80fkEPzjyAwqCBfz71f8OQHesm4poxcRxSc9n31iMU0mHiFI4WtMWTxG2Mmu8irrjNN7XgfLOLfp3DTD97OiYgt1NhfSsLqWgO0F+Z4zTFWHOlAZIa43jaWrCAZQC6/RpKn+9idpfbybc0Y75zndR+ImPE/I8wskEVuFLv97J931OJR06U2k2H+hm6/ce5XO/+QaW74JSGPrsajrAsnD/+5voJUsASPk+cdfn6qLcS97rMZ1Os3PnTrZu3ZoZIZs7l40bN7IvsY/P7/4Cp8Y6qCtqorz4Wna1fRlfuyhlUZ9+B6uG+/l//atwMLOr+y5wZWX2LsNQLFpZQVRDdWUuuSVhSgMWEdMk7nmUBSyKbJuIZbIgGqLQlv/XEUII8dvJ4v8XQXesm/uO3cdPjv2E3ngv5ZFy3rbwbROPV0Qr0FrTl3Z4JpFmyHU5OJYkz1TonhTFp+NUVATpdT1qTsRYcGwMwzsXVrrm5XF8QS5rHzwzMTp2siyI4WliVRHGKiOkfU3U8ygwDGaGgxTaFkV3/CXGtqcAiKxcSd77/oS8G2/EDNiADeHQS9Yn8bTHprZeHj3Uy9ajfdxePMLakQ5m1dRTYnVhaS8Te7TGVwqlsxc5+D5q9y7cxkbink/C85kZDlxyKPN9n2984xsMDg4ya9YsrrnmGkYjo9yx4xM09+2hMFLHhoX/iBFeRmo4SZH1cRL+EQaOl9GSqKOFufy2yUZlKuasKAfHJ1oaorg0TI5lkdbga0j7kGsqluRGmBEKUhSwyDFlA3EhhBC/Pwlml+ie1nu4a/9dXFF1BX+z+m+4uvZqLONc96V9n9bxBCcTaXItk3zToi4UwDs1yuqfd6I8zRyyo2GGor8sSHFvMlP41VC0NxYQr4yw57UzKDgd41R5EMrD1IZtbGVgplKUHdxH/s7t2GdOU/eNr2MoRe+SRowr1pJ/223YVVUveT9orRlMOvz1vfvo37aL+d1HOVk+l5sjKd5w910YnscMzk3xnd3Xs/emWyjdsgnlOGjbZmjJMpTrURSwWZRjU/Q8I02pVIoDBw6wfPlyDMPg6quvprCwELPQ5HO7vsiWjocJ2QWsmv0RHGctw63dRAceY3N3BR65ZDaVOE82Q2WylMpMsSqomJVPeX0+MypyMFAUBEyqg0HqQgGqwwEKbZNCyyZsXVqJECGEEOKFkKnMS9Sf6CfpJqnJrZm4z9ea7pRDZzLNuOdzZDxOfneSyq4k8VyT6FCaGQeGCSYzS9810DM7l6PXV0LIJPdMnEBHjLHaKIG6XJKej6fBUlAesKiLBOHpHaS+/794O3dCMokOBnGXr2D0Xz6HDgZRSqFQBBRELZMc0yBimgQNRdj47YVeL1Xa9dnS2svxwRhLFpeR9jX3fP5e/uxnX8DyMlOV6rzzSCvFwBXrKdy5E+U6YNv0fPHLBA2TopZmStatJXfFcoKGmhhl8rUm7WvSWpPyfVxfZ7a+SiU5vGcPB3buIJlI8OZ3/TGzZtYxnBjhS/u+yS/bfgTAourXYzrXMbDjCEdixZmF+5Pi4eTpyTkryjFcn5LyKChFoi/Bgtp81s4spDY7GllsW0RkJEwIIcSL7LdNZUow+x1orXG05sBYgu6UQ75tkmOZ+Af6KX6gHeWfiwTjpUGiA6nMDVNx8o31dJUFibs+Kd8nYposiIYpClhEFajWIyQ3byH26tvpLykl+NAvyfnWN0ldsR7vivWopibMUJjzs4LWZAvIZtrlZ/9KFVBkm1QFbQpt+wWHtP2nh/nu9g4e3t9Fzek2rhxqY9mScoq7Osn7zZMY6cx+khoYbWgk51grhuuibZueL/4XUdOgdP8+CtauJrp8OcHzCtGmPI8Bx6Mr5XA6lWYw7eFnS3sA4LrE9jczcqAFP50iv66e6qbV6JIi7mv5EUd6f4jPGLX+fNK9N9DlziI2drZ6/3PDmDIUS1ZVYLswozqXphmFzIkGKbRtyrJrxuwX8cpUIYQQ4mIkmL1Ixl2PU8k0vSkHD83A8WEKj48S8qGoJ0leb3LiWA20NxXRuq6UcGeMwjNxcuYU4FbnEjYN8i2TsoBNvvZIbt/O2KbNjG7egt/XizYMBj/7D6Q3bgTPx89u76NUpkisZShspQgoNVFi4kK01sR9n6TnYyrFwmiIsmDgt75Hx8+UgPjpd3/JsV/+mt6cYq52u1jTvBmya8ScnFwGFi6ibO+ezHZRtk33F/6L0oBN4b5mIqtXkbdixXOCjuNrelNpdo/GODyeZMx1cXTmfSmtQSlM7WOZJqbn4z9wD0ZxCXbjCtqSNsc6fkPM+T4p1Y8bm0Wq91b8ZM2F3whgmopbNszAcDXr6ou4ZnYpedmyFjIKJoQQYqpIMPs9OL4mrTNXDe4ajREyFAUpTXR7D+Edvahs940V2HRXhZnVOobha7Sp6HzTLOJVUXJNg9nREHmWiaUUJJO4AwMEampwh4Y4tv5KdDBIf9MqBtauZ2TdOty8gok2nM0Qmb+qc5sz5R46QGFLM35TE5Hly8m1jIuWanB8zZDjUh8OMic6+YKAkUSaLT/dTMem31B83Vpmj3eT96UvZvb8zB4zsV7MMBh+7/sZe9e7mX/8KObePYRWraKoaQXmb3nt47E4vxoY48B4jAHHxwJMpcjU11UEXIfc461ETp2k77rbcAyTzmd2MTy4l8Kkyyn/KFb0BH6qhGTvrXjjCyf64dn9ZBqK25ZV8YamGq6oL75ou4QQQoipIMHsdzDkuJyMpxh2M+HEPj5MYO8AwbE0RQPpTNV+zi5uh9715TzTVEJ1X4oZPSnM+jzsujxysntJas8jvnMnIz+/n7FHHyXU0EDZt7/NsXiC/p272Vc1g/xohKBhoFQmZJioC47sqFSKonvvofx/vgla49s2B/7t8yQbGqnJVu6/0PO01gykPdYURIkYBr8+2sf3dnbQ/9RO/unxr2L7XubnM3nh/tj1G8l98nGU64JtU3rXXRQ1rXjeel2Or2lPJHmwZ5gdw2OMeB4JXxPIvkENmOkU4YPHcTsHqGKYgbwyjtmVRJ0Whoq/D8rNtN0Lku6/CWdoDSgTNCgjUwxY+xrLNHjPDbPpi6V59YIKrp1d8vv89QshhBAvGSmX8QIlPJ/do7FMvTHbJLS3n+hDHROBpWthHh2VYVY/0QteZnQsMqeAq4pyKK4oxFgyObAM/u/dDNx5J25vL0ZODjk33YRzy6080DNE3PNx5i7g9HiSkWT6OW0JGAblrYcpb9mDGQ6Td+I4eVufxIzFJsKT4ThUHNhH95KlnEykGPd86iPB5/wspRSxlIOrNb7W/PW9+4i7Hv/Uux07OzqmgfGVq8nZ3wKui7IsZr/7XUTe++5L3h7J8TXPxJP8qm+EXeMxziRSjLmZHTs9IKXAGUrgnxmhvv0wu5xafCpppgoj9gx2wd0k849jGtlQpsEZuhJn6AoMU7FubRVO2ienNMzMcBBnIMXiugIW1OQzkHaYHw3/1vYJIYQQ05UEswsIGIrSgMVA2iXhKYrH0kTPPqjALokwc00VxfPK8E6OEZyVT7Aub+L5qbY2Rh96mKL3vBszJwcUhBYvJve22xhct542FG2xFCnHJc82CSmD6rDNiOORY5rnRrs8j5oHfkrDnV9D+ZnrDJ1QCDcSxYzFwDTRWqNtm9iyFVhKUWCZ9KUdygI20WxJh7GEw5MHe/jV/m6Gx9Js/9R1WKbB3e9bQ+TJRxn/0Q60ym6TZNvM+suPEjaN5wSx5wtkad/nmXiKzQMj7B+NM+B4dKfTJH1N2vMzo2RdwyTaRxkZADQMhDVW3uO4iRqsnCMEiraiVCaMaX12DteirHYtwepSqqpyWD6jCJfM9HKRbbF2Xhm2YTDmetjKeN7SG0IIIcR0Jb/BLsBUiiU5EcY9nxHHJTWnEJ7uQXsaTIO6BSUU5EUw8hXMzKwFc3p6GP3FLxi5/wFSra2gFOFlS8nZsIGit78d681vYe9ojJ5kGtMwGHNdRj2fuO9joQhky1uolmbmPb6J8PgYxQf3ExwamjSt2LnxZmofeYj+5U0Mv+e95O5vIbZsBYmGxcDZKUiFARw7M8q9T7Wz89gAnq+ZWRrlvetn4voay4Typ35F12f/lsiaNZR88IMk9u17QUHsrKTncyKe5DeDYxwcTxDP3naypS9ig0mMzhEi/cP0xEKc3evACLcTmXEXKJcATAQyso8HravQfhFlJcuoqFnCqKcpDNqMuy6GMqgI2IRNg5TvM+x45FsmDXkRQqbUGBNCCPHKJMHsIpRS5FomuZYJC4Kk3r+E1ImR54yOATinT9O28UbwfcJLl1L+mc+Qe9ON2GVl9KbSnEyk6Uo5HI8lKQhY2Non37YY89KMOC4hQ2G1HqPxvnupe2LzxJRiT+MSel/1Whb96O5MGQrLpmzn03iRCFs/9tdUVFeRWrJ0oh1x1+VY9xgz8sLEI0E6R5IcOjXCa1bV8JaVtayqKZgYjRv68Y/p/uzfEV23jpqvfgUjHCa6bu0L6qOY53FsPMHTIzEOjSXRaI7HEqR8zWBvjHhXAnMsQbw/U8ZilDCgMcLt2AU7MSNtoNxzgcxuQjn7gcz2ScHqGyjKWUjQMoh7EFWZPT5zLBOfTGmQmoBFjmkyPxqk0DZlob8QQohXNAlmlyhYlzcRyNLt7Qz/+Mdox6H805/Grq6m/G/+hpwr1xOYORMA19fsG41xLJ4ixzSwlcJUmUKqhqEIGQarnnqc3F88QOh0J5GBfvzz6nxpw6B3+UoG3v5O9i5fSd6+veS0HqZ621Y2/cM/M5RXSI7jEXddTg/GaW0b4vjxIYaGU7xuXS3XXF/IvMZK3rusmhx78sUAQz/8Ed1///dEN2yg5r++jBF6Yds2jboeh8bi7B6JcTSRItYX58ipEVxTMT6UxIk5pPpTk55jhDuwIkdRwQHsvOZzYUwZmVE+w8IsezUGt0PyEIHIYuzwPApti8JAZspWoagPBykNmMwKh6iPBCmybak/JoQQ4g+GBLNL5I2NMfrww4z89Gck9uwB0yTv5puzW/koit7xdgAG0i6nkil60y4HxuLkmibu4QPktTRTtHgpnaEwdU8+zszNj5Lb052Z1lOK0299B90NS1j6j3+L4Tr4lk3HosXYGtKNjfjDA1R/5y6OvP5N9C5fRbVl4vs+P/rFUU6dGQdgfm0+t6+uZfm8YlrjCcZdzbjnkvQyhWwDhkHNAz+j/Iv/iV6/ntDnP48XCHIpE3++1gymXfaOxmiNJdnRPsRob4wkPq17+9D++UefK+mh8LBz9xOovgfwJ01XKmVg5V9LMFBKOLKYkvyFzAgGCVqrwM/UYKsM2oQNk9KAxdK8KFUhm1xLRsaEEEL8YZJgdokG7ryTgW/eRWD2bErv+Cvyb38NdnnZxOOe1hyPJTkaTxIxTfIMRW0ogLevhcWf+BjKcagDVmmNVop4WXlmsX12g283HMFdu5bW//gCuc176Vq8FL1gEaWWhT7TyZz//ByDc+azae1rGdzRxcbb5hM2TDpmFrF+XinL55XgBw0GHZeDyRSD4y7lRw5TfXAf441LaZ89h1Xf/Rbl9/+U4YZGdn3y7wgNxSmPO6wtiF608Gza9+lPOWwbjrG7Y4B97SMEDcWePd3PCmNwfsV9O3wSM383RqAfM9I+ac9wpUxAYxgW62fcSnHOQhydWatWE7ZJ+RrbUlydG2F+NERJwCbHMl/Mv04hhBBiWpJgdokK3/pWcjduJNTYODEtqLWmL+3QlXIYdFwOjyfJO3QQc38LQ3X1RLrPUHXvjyZtXTS0Zi2df/UJQn19zL7jL9DZTb1VUxMhQ5FsaCTZ0Ei5bTHTUBzrHWH23/4druPxtcLVVH/7e/TVL2TzijIirYeYt7+FM4sWs/lMinl7djH3YDM50VwWjgwzf9uTKP9cejq7di2v7Sj1HScYWtRAZzLFPd0p3l5VQuF5VzOOuR5dyTTbhsdpPT3CE3u6ON0xynPL3vkYgJ9dzG9F2lChfgJ5zRNH5OY2ERvfj69dTMNm2cwPMpoeoSJ/KXMLF5PwMwVn50cy05MzwgEqgzZBU8KYEEKIy4sEs0tkV1VhV1VN3Pa05sh4gs6kQ46lKLAtVhzYS+4nP5EpxEomCCWrq/FNE3W2rMUfvwevrIye4hKG//XzlO5vIW/NKqqbVrDQNDF92PHMACesFN7RA8z9xp1Uth/l0fnr+eSeH2D6Hrr1UbqOP0xV6+GJzcOfPbHnZUfjzoaxWGUV0e6uzPGOQ6R5L/0LFmEbBqVBi10jMeZEgkRNgxOJND8/3M3+w330Dyfo7o3znA3Bs9uEm0pzxcwhnrF6GLTuBp49jGZQW7iY3Jq34iYOUlmwlIKcBYy4moqgRUXQpioYYFYkSGlA1osJIYS4vEkwewHins+ZZJqetEPK9+neuZvcHdsZx8c6dpS8XTsngpJWijOvewMnP/QRwgf3k9O8l/y1qzEbl1KjIN+yKL/2Sopuuhbf93niaD9fbznNpsO9JNMet5SP8eH/+WcMJ3NF442tW881xPOoOHZ0UvAaqKqmsOsMhtZ4KB6euYaNnXswPRffsnjiVa/jxu/eheFmRuiGliylOGAx0Btn26l+Kiui3Dcc4+jRQWJjDt39Mc6FsfNpTHxW252M1CrSJa100Mvg2IFJxyplgtYYhk11/lLMyDwqS5eQY5pELYON0RB14SB5liVhTAghhMiSYPY8fJ0pyxB3M7sBmApy02nyfnA3FXd9E7KFX1NFxZy59gYqn/w1yvPBtgjcdBMzQgGCq1cy95orKbAtAtlNyM+KpVzW/csmRpMu4aDJgtmFBCoiNG59AJXdDspHcXLhIuqOtWL4Pp5l8ci73s+N3/0mpuviWRaPLb6e1/X8ANNzcQ2LTbUrGb75Fq5On2Zk6TKKFzXQsmgRMw/tJ7pmNTMbG9l6coAv//ggrqc5f8H++RSZixsyV0VqZkT3Ea7qYqhYc3rkSRjLPK+2cCVnRvbh+y6GYTN/xvuJqjiV+csozV/IjFCAhpwwJQGbfMuc1AdCCCGEyJBgdhGjrkd7PMWA46LRqN27MH76U+yhQcKHD2GkUpM29k684Y0MvfUdGIffwIxDBwisWkXp8mVETZOwkdnzMu36/Katjwf3dZF0ff7jzUtpdxyuW1kNeTYNdfnk2BbjrsdYqgn/Z/dA9grNrvf9Kd1A8b4WBhqXcFCXs3mdorH/OPtKZnMkMpPt6/NYmr3dVjaL6zcsYqQqF4AcIL6kkSfnzqPQtvFOtNDy4DZcbwHPnQgFw1BoDZZpcs0VtQx3dBIwdnAg+FMG8GBk0tHk5y5m3ax3cWqoheK8RqryG1iUE6YqaFMRClBgmRfcv1MIIYQQ58gm5heQ9HyeGh4jaBhETAPr5z/D/L//dG7a8MqrOLN0OYvv+gbKccG2SH/165StbKI8aD+nlMOOk4P8cGcHvzrUw2jSJTdocc2icq69qpZxX+P6HkdjacKmQpEZnRp2XaKHDjLn8AFO1M7nQLSWnIjN6HCKY8eHiMWdSa9hmYobb6gH22CsL87SuiLqKnPwYwNEO5+moGsnFb17eLxyI/cVvp7x4z20P5MADM6uHTMNha81pqlYPtvGOdVJZLaDV3qCwXgn3SP7OH+60lCZ+mKGYXN947+woaKJkGlQFbSZHQ0RlcX7QgghxHPIJuYvkG0oCiyLIdfD1R65vb1MRAzDINDQwIx3vJ2qK1bh7tpFdNVqIivObV+USHs8frSXDXNLiQYtdrcP8atDPWxcVM6q+aWUVeVwMpXidMol3zYJmgYlAZO4r4kaRmbXAdtifOlSfhasouWpM2jdDWS2LaqpzWNBURF7WnomrpK86fp6Zs8sxPccektCpA3Funtuo2boMADb9SK+F7iKU4O1HGo+hfY1TFQwy15liqa2OsgMdxvOyAHSFQ7H3TboyrxIXeFKOs+brrxlwZ8TS48yt3gZt81YTXl2iyRDRsaEEEKI34mMmF2ErzUjrsew4xHbuxfjz/4UXBdlWRTedRelK5smjYwlHY8tR3r5xb4uNh8cSd0sAAAW+ElEQVTpJeF4fPVtK7htSSXxtEtaa47Ek3QmHSxD0ZFIEfN8wqaBBViGYsTxOX5mhNPPjOI7Ht29MYaGJ1fQn91YQlFNDoe2niE2mqa6MsrNs0bZkNpK3ZmnSJhBPr7mK9gjDvUtj5AT8ThgzqP5ROC56/ifRaGZm/ck3VUPoZV+1mMGV895L+UFjXQMtrCirIlbaldTYGe2rZIwJoQQQlwaGTH7HRhKZbYDsi1Yv5b4d75NfMfOSZt8n9U1kuCG/3ycWNqjJCfAH62o5rbGSlbXFzGQdmlPpGhPpjkeS1FoG5jKIGoaDDguMc8nZCiGR1Ps39dL65GBiZ9bVBymcUkphw724/sa01BEUex5tB07YPLmupNUdjWzYaCZZcZxfhC+lXv1NTzzmy7i3XFaWHjB93a2+r4yIL88wtjIIay8vRiBAboix0E99+pK07BZVbGSteXLKZ23YVLNMyGEEEK8OGTE7HdwajDOj3edwtOaT9y0AK01//FoK1fMLmFNfRGWaeBrTet4ktZYgrCZ2XS7NZYk3zQJWwoTxb62QZ7c383QaIqRZ42MoaCpqZJrV1XS0TXO0ZNDDHR0cWrIIpxjUjgzn56D/XhaoQDDVnjOBZvLrLo82jvH8LLhrnFVBb3jhwnTQmjsKB3hExNhrCJ/Gb2jB9HawzJsXtfwUfDGubFmLWsqV7y0HSuEEEJcBmTE7EWQdDwePdTDPTtP8Zu2fpSCWxZXTOyV+YmbFgAw4rh0JlJ0JdPsH0sQNQ3O9AzTfnqMsvIIJ0IGnc+McvLoIENDSSCzwmtlYynllbk8vOkkvp/Z6DxYHOSZQ0foO9BKx2A+gzoP0CTGXRIHBjm7RkwDAdMk4XgT7TVU5n7TVNy0ugZjDRxsH2ZOTT6x9NOcGP43xvAhcu49KgyWlK+mfsEHGBs/wNXVa1hTsVz2pRRCCCFeJhLMLtHnHzvKnU+coLogzMdumMcbVtZQXRCeeFxrTXsizaHxBGFDkWca1IRsDnSOcPfPjuB6etIG3qHQeV2vIC8aZM38UqryQhzvHMEvDGLHenjgiWFS1HCupMW5kHQ2L1mmwa1XzOD+J9rxPB/bNPirW+YTS7g0zMinsiKHp7r3Eo/+kq3H2+hMHpk0XXn26krbtHl13TrWlq8gZG54iXpSCCGEEBcjwewSvW31DDbMLWH97BKM84qjDjou3SmHgbRDy1iCM91jdHWNU1mWQyyW5ondZ7IFXDOhrL46l9uvqcd1PO687zCu52Oaisa6AoLOODf038urdT9bZ/4Vu/amcBgHMtOVS+cXc+j4EO554SuZdFk7u5jVdUW8bX4lT58YYG19EQtq89lyehcPnnqQVGcv2049iM6u/i9TMxhQXWjtYRo2H1h+B3jjrK1cxbKyZVPQu0IIIYQACWaXbGZJlJkl0Un3tcUSnIiniFgmUcvEGEjyo58fxfPPjUZFI9bEtKJlGty+vo7SojBxz+OPbpvDma4Y11TCq05/nZKH78ZMjeLVXcmS2hJ2aJutu7twXB/bMvjMtfMwrlOZ8DWrmKa6wkntaaorpL4yh0OxJD86uZ0vPfVhfO1OOkZhsGbBLVxVtYbO4RZWlq+UMCaEEEJMExLMXoCk59ObduhNOYx7Po+19XKic5SobTLQn2Bf2+CkULaioZRbr6mjvWucts5RNswuZkFNPgpFrmlwW1khZZH7sH/+YdA+LHoNrPtzzJrMesDVM4u4+31rnxPEnh3IzjfoeIy7HscH9pwLZRoMZYBS2IbF62deSVP5Mqhd9dJ1lhBCCCFeMAlmz0NrjU9mA/M9IzE8rQkqxRN7z/CDTcc5m8MCAYO62jzaT42itcYyDW5aWklNKEBtfTHvbaimJGhjAcHOp1GBKBQsg7r1sOp9sPbPoLDuOa/fVFf4W4PYs9VHgqjxMbYeGEKZCq00lmHz8dWfIuGMygiZEEIIMY1JMLuImOvRmUzTm3ZwNBzqGOahli5SSYeunhixxLkpQqXg5lU1rGuqpKc7xmhfghUzM4EqxzKJmgZGahT2fA92fgv6DsOi18KbvgsFtXDL5160dptKUR4NUzKexx1L7+DLp77M0rKlvH3Bm1601xBCCCHES0OC2QUkPZ+dIzEMpcixTDa3dPGVB1snHq+rzmVpdS4793TjeT6mabBqVhFXFORSVVGMbTyrvMSWf4anvgJODCqXwe3/BYtf/6K1V2vN/v37OXz4MG984xvJycnhIx/5CFopBvaM8J0Dd9HSf5S6/FlA5rpOQylspQgYSqr2CyGEENOEBLMLsJQiYhqMex4xD7qHkxOPGQqaZhVx+7oZ/OnSWprbh1g7q5jVM4vO/YB0HA79PBO+rAAE82DxH8HK90B104va1q6uLh566CE6Ojooq6zk8OAwaTvImOuT0j4zK1+Dffh7fLH5Tt65/G+zz1Ln/RfCpkGOYZBrmbLFkhBCCDGFJJhdgGUoVuRFGXY9+tMOTbOLuH/7KVzPxzINbptfzvqCXOwixVWzSzJP0hrO7IV9P4KWH0JyGEJ5sOA2uOLPX/Q2JpNJHn7kEZr37sUKhSjZcC3GnPlsGU+jlIOlVLYwbISGqlezu/MnNM18J1W5tUQNg5BpEjUVllI4WjPqe/QnXbw42AZUBwNUhGyipvm8bRFCCCHEi0OC2UVYhqIkYFESsFiwKMy894cvWqaC8V74zm3QfxTMQCaMrXpfZmH/i8z1NaeTKdrGYuw7fgJnfgOJxctwgiEsx8VSCstQBJRB0FAEDZN1M9/EvtM/Y/szP+DGhR9nWHv42gE0uZZJecAi37aImplRMk9rOlNp2pNpFkRDVAZtlIygCSGEEC85CWaXaNLVkalxOPwApEZhzQchWgoVSzJXVja8FsKXfhXlpXA8n65Umm3NLRxrbiZx9Y0Mo3CvfxW5gSAhQ6GUgdIKpcAHYp7PqKuzJTNymFl2Ey2nHyJo5dFQcSW1BYsBSPo+bfEUOZZLfThIyDQwlaLAtnB9zcHxBJ7W1IaDL+p7EkIIIcRzySbml8r3of030PyDzPqxswv5P/Drc3sjvYhc36c75dIWi7Pr8BHG9+6CwX7cgiK8K6+jNxAhYiqUkdkwfWTsCCNj+8nPbaQ8fxFRQ9M/tJVTA09jGjYDY20MxU8AYBlB3rXq8xPhDGDc8zGAxTlhzPMuXnB9zbjncXVR3ov+HoUQQojLkWxi/mLY9A+w9YsQyIXG18PSt8GMtZNCmdaa7O5LGIoXvIDe8Xy60mlOxtLsG48zHo+R2PQQxkA/OprLYNMV6Pq5+ED30EFS8QOU5i3FYJz9x/4FrV1AEQlWEk91A+c2NQ+YORPfu77D4b49k4JZjmkw7HjEfZ9c49y6srTWhAzjBb0PIYQQQvxuJJhdqiVvhvLFmfVjgcjE3aOuR2/Koc9xSXgeJ4cO0jawh7nFy5lT1EhJwKYsYFNkX/hKx8xuAmlOxtMcGosTdx2Genpxi0sJWQGS4She01yGSj26RncR7m/Hc7o5deZeND493Xc/6ydqQFOYs4Ch8UOARmFQX3oNbb2P4fsOhmFTmrdk0rNSvo+pFKHzRstGXQ9Pa1bkTd6KSgghhBAvDQlml6p8UebrPB3xFEfjKWwDIqZB/+hBvrLtz/F8F9sM8Mn1X8EoaOBMMk1p0KIxJ4KhFOOuR3/a5UQ8yf7xBN0jB2nr3knpSA5lJ2IYToreq+oYiO/Bqw4wnHiMkdYDwIWnnSOR+SQSx9HaxzAslsy+A4AdRz6N77sYhs2cio3MqdhI53AzxbmNzC/JBDNXa2Kuj2Uo5kWDKBTDjourNYWWxYKcMBFTRsyEEEKIl4MEsxcg7fsMpF360i6jnsevOnfRObSXvGABo/Fn2Hv6YVzfAcD1XFoH9jC/ZAkBpTgRT6E0tCfSbO9upnukmVDOYsyxPn79zL/ha49WFLnlhcSMMbxnnInXtcwI50KZoqhgPcMjO/C1i6EsZtW+h5inceOHKM5bgh2Zi+vDojn/B508TE3hUqKR+bjaZ0FkHmXBACnfZ9zNTHUW2iZFtkXK1/jaoypoUx4MkGsacjWmEEII8TKSYHaJEp5H81iChOcRNBQ7Oh7kpy2fw9eZcGMoi/K8BrpH9qPxMQ2LaHQxO4fG2d/fwuBIMzNyqzk93smeju+jtTf5BRRoNG7IJj8wh8GxI2TCmMGMkmto7/sVnp8JYvUVryFc/Vq6h/cRjjYQji6gxFSQ34CjM6NgQUORX7AYnwZ8IK19IoZBvm0SMhQR06QsaFFm2+TZJkHDyN4vYUwIIYSYKhLMLiLh+XSnHHrTDnHP50h/M7/ueAzfj9E7cpDBxJmJYxWKa2e9g1k1b+NXB/+Zk/2/prbkGo7072XgxHc42r8L0DSffYJmoux+ee4C+sbb8LWPadhct/DTxFyPxw9lpiFNw2ZOxQ3Ul91A/+g+6oqWU5u3CNMwsCtWkGNZ5CpFNGAQUQamgrBpEjIMbEMRMoyJbZeCsv2SEEIIMa1JMLuAlO+za2QcD4iaBgfOPMyde/6Js9OJJbnzWFL7Vg6d/slEeCovbGJk/AgdA1sBONH7CCd6IWhFJ56HhupYNd3RHnwyQez6+R8mahr0jOxjQckKZhU1kmsa3Fz2dVr797KsrIklpUuxlYFtXI+hIKCUjGoJIYQQf4AkmF2AgcIyDNKuh2NousY7ORuuFAZrqq5h45x3YS24iYN9e1hVsZLZBQ18qeWbE1ObCoOb5ryTK2s28Pe//hCedjGVxcdu/DQFuXns7tnFqoqVrCxblg1ZV01qw5K81dxau/plfudCCCGEmEoSzC7ANhQr86IMpB360i6Ly9fxcNv38XwHy7S5vmYd6wtzCZlNXF11blPy181czy9a/wfHdzB8xYbgPG6vW0Pl9d9gT98e1lavZVnZMgDWVCyfqrcnhBBCiGlKgtlF2IaiIhSgIhSgMW8di3LuYlfPLlaWr5wIV2c5jsO2bdtobm7mitgVDEYHWVezjnV16wBYXbOa1TUy+iWEEEKI327aBjOl1M3AlwATuEtr/a9T2Z5lZcsmBbLR0VEGBgaor6/HNE327NlDfn4+f7LhT1i0aBHBoOwtKYQQQogXZloGM6WUCXwV2Ah0AjuVUvdrrQ9NZbuGhoY4fPgwhw8f5tSpU0SjUe644w4Mw+BDH/qQhDEhhBBC/F6mZTADVgNtWusTAEqpHwKvAaYsmD3xxBNs3rwZgIqKCq699loaGhowsvtISigTQgghxO9rugazauDUebc7gTXnH6CU+gDwAYAZM2a85A2aNWsWpmmycOFCioqKXvLXE0IIIcTlZ7oGswsV6Zq0UaTW+k7gToCVK1deeBPJF1FNTQ01NTUv9csIIYQQ4jI2XXen7gRqz7tdA5y5yLFCCCGEEH8Qpmsw2wnMVUrVK6UCwFuA+6e4TUIIIYQQL6lpOZWptXaVUn8OPEKmXMb/aK0PTnGzhBBCCCFeUtMymAForX8J/HKq2yGEEEII8XKZrlOZQgghhBCXHQlmQgghhBDThAQzIYQQQohpQoKZEEIIIcQ0IcFMCCGEEGKakGAmhBBCCDFNSDATQgghhJgmJJgJIYQQQkwTEsyEEEIIIaYJpbWe6jb83pRSfUD7y/BSJUD/y/A6rzTSLxcm/XJx0jcXJv1ycdI3Fyb9cnHTuW/qtNalF3rgDyKYvVyUUru01iunuh3TjfTLhUm/XJz0zYVJv1yc9M2FSb9c3Cu1b2QqUwghhBBimpBgJoQQQggxTUgwe2HunOoGTFPSLxcm/XJx0jcXJv1ycdI3Fyb9cnGvyL6RNWZCCCGEENOEjJgJIYQQQkwTEswugVLqZqVUq1KqTSn1qaluz1RRStUqpbYopQ4rpQ4qpf4ie3+RUuoxpdSx7J+FU93WqaKUMpVSe5VSv8jerldKbc/2zY+UUoGpbuPLTSlVoJS6Vyl1JHvurJNzJkMp9bHsv6UDSqkfKKVCl+M5o5T6H6VUr1LqwHn3XfAcURlfzn4e71NKrZi6lr/0LtI3/57997RPKfVTpVTBeY99Ots3rUqpm6am1S+9C/XLeY99XCmllVIl2duvqHNGgtnzUEqZwFeBW4BFwFuVUoumtlVTxgXu0FovBNYCH872xaeATVrrucCm7O3L1V8Ah8+7/TngC9m+GQLeOyWtmlpfAh7WWi8AlpLpn8v+nFFKVQMfBVZqrRcDJvAWLs9z5jvAzc+672LnyC3A3OzXB4Cvv0xtnCrf4bl98xiwWGu9BDgKfBog+3n8FqAh+5yvZX+H/SH6Ds/tF5RStcBGoOO8u19R54wEs+e3GmjTWp/QWqeBHwKvmeI2TQmtdZfWek/2+zEyv2CryfTHd7OHfRd47dS0cGoppWqA24C7srcVcB1wb/aQy65vlFJ5wFXAtwC01mmt9TByzpxlAWGllAVEgC4uw3NGa/0EMPisuy92jrwG+H8642mgQClV+fK09OV3ob7RWj+qtXazN58GarLfvwb4odY6pbU+CbSR+R32B+ci5wzAF4C/Bs5fQP+KOmckmD2/auDUebc7s/dd1pRSM4HlwHagXGvdBZnwBpRNXcum1BfJfCD42dvFwPB5H6CX47kzC+gDvp2d4r1LKRVFzhm01qeB/yDzf/ZdwAiwGzlnzrrYOSKfyZP9CfBQ9vvLum+UUrcDp7XWLc966BXVLxLMnp+6wH2X9aWsSqkc4CfAX2qtR6e6PdOBUupVQK/Wevf5d1/g0Mvt3LGAFcDXtdbLgRiX4bTlhWTXTL0GqAeqgCiZKZdnu9zOmecj/66ylFKfIbPE5O6zd13gsMuib5RSEeAzwGcv9PAF7pu2/SLB7Pl1ArXn3a4BzkxRW6acUsomE8ru1lrfl7275+ywcPbP3qlq3xRaD9yulHqGzHT3dWRG0Aqy01RweZ47nUCn1np79va9ZIKanDNwA3BSa92ntXaA+4ArkHPmrIudI/KZDCil/hh4FfB2fa7u1eXcN7PJ/E9OS/ZzuAbYo5Sq4BXWLxLMnt9OYG72SqkAmYWV909xm6ZEds3Ut4DDWuvPn/fQ/cAfZ7//Y+DnL3fbpprW+tNa6xqt9Uwy58hmrfXbgS3AG7KHXXZ9o7XuBk4ppeZn77oeOIScM5CZwlyrlIpk/22d7ZvL+pw5z8XOkfuBd2WvtFsLjJyd8rxcKKVuBj4J3K61jp/30P3AW5RSQaVUPZnF7jumoo0vN631fq11mdZ6ZvZzuBNYkf0MemWdM1pr+XqeL+BWMle+HAc+M9XtmcJ+uJLM8O8+oDn7dSuZtVSbgGPZP4umuq1T3E/XAL/Ifj+LzAdjG/BjIDjV7ZuC/lgG7MqeNz8DCuWcmeibfwCOAAeA7wHBy/GcAX5AZp2dQ+YX6nsvdo6QmZb6avbzeD+Zq1qn/D28zH3TRmbN1NnP4W+cd/xnsn3TCtwy1e1/OfvlWY8/A5S8Es8ZqfwvhBBCCDFNyFSmEEIIIcQ0IcFMCCGEEGKakGAmhBBCCDFNSDATQgghhJgmJJgJIYQQQkwTEsyEEEIIIaYJCWZCCCGEENOEBDMhhHgWpdQWpdTG7Pf/pJT68lS3SQhxebCe/xAhhLjs/B3wj0qpMmA5cPsUt0cIcZmQyv9CCHEBSqnHgRzgGq312FS3RwhxeZCpTCGEeBalVCNQCaQklAkhXk4SzIQQ4jxKqUrgbuA1QEwpddMUN0kIcRmRYCaEEFlKqQhwH3CH1vow8H+Av5/SRgkhLiuyxkwIIYQQYpqQETMhhBBCiGlCgpkQQgghxDQhwUwIIYQQYpqQYCaEEEIIMU1IMBNCCCGEmCYkmAkhhBBCTBMSzIQQQgghpgkJZkIIIYQQ08T/B5IdW1elghQAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotterA = Plotter()\n",
"plotterA.ax.axis('auto')\n",
"plotterA.plot_ground_truths(truths_set, [0, 2])\n",
"plotterA.plot_tracks(set(tracksA), [0, 2], uncertainty=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### UncertaintyManager\n",
"\n",
"Here the chosen target for observation is selected based on the difference between the covariance matrices of the prediction and posterior, based upon the observation of that target.\n",
"\n",
"The `choose_actions` function from the `UncertaintyManager` is called at each timestep. This means that at each timestep, for each target:\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",
"- The difference between these covariance matrix norms is calculated.\n",
"\n",
"The sensor manager then returns the target with the largest value of this metric as the chosen target to observe.\n",
"\n",
"The prediction for each target is appended to the tracks list at each time step, except for the chosen target for which an update is appended. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"predictionsB = [] \n",
"measurementsB = []\n",
"\n",
"for timestep in timesteps[1:]: \n",
" \n",
" # Activate the sensor manager and choose a target to observe\n",
" # i.e. {a}k \n",
" chosen_target = uncertaintymanager.choose_actions(tracksB, timestep)\n",
"\n",
" # The ground truth will therefore be:\n",
" selected_truth = truths[chosen_target][timestep]\n",
" \n",
" # Observe this\n",
" # i.e. {z}k\n",
" measurement = measurement_model.function(selected_truth, noise=True)\n",
" measurementsB.append(Detection(measurement, \n",
" timestamp=selected_truth.timestamp))\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\n",
" for target_id, track in enumerate(tracksB):\n",
" # Do the prediction\n",
" # i.e. {x}k\n",
" prediction = predictor.predict(track[-1], timestamp=measurementsB[-1].timestamp)\n",
" \n",
" if target_id == chosen_target: # Update the prediction \n",
" # Association - just a single hypothesis at present\n",
" hypothesis = SingleHypothesis(prediction, measurementsB[-1]) # Group a prediction and measurement\n",
"\n",
" # Update and add to track\n",
" post = updater.update(hypothesis) \n",
" else:\n",
" post = prediction\n",
" \n",
" track.append(post)"
]
},
{
"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": true
},
"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",
"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`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metrics\n",
"\n",
"Metrics can be used to compare how well different sensor management techniques are working. Full explanations of the OSPA and SIAP metrics can be found in the Metrics Example. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.metricgenerator.ospametric import OSPAMetric\n",
"ospa_generator = OSPAMetric(c=40, p=1)\n",
"\n",
"from stonesoup.metricgenerator.tracktotruthmetrics import SIAPMetrics\n",
"siap_generator = SIAPMetrics(position_mapping=[0, 2], velocity_mapping=[1, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The SIAP metrics require an associator to associate tracks to ground truths. This is done using the `TrackIDBased` associator. This associator uses the track ID to associate each track to the ground truth with the same ID.\n",
"The associator is initiated and later used in the metric manager."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.dataassociator.tracktotrack import TrackIDbased\n",
"associator=TrackIDbased()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The OSPA and SIAP don't take the uncertainty of the track into account. The initial plots of the tracks and ground truths show by plotting the uncertainty ellipses that there is generally less uncertainty in the tracks generated by the `UncertaintyManager`.\n",
"\n",
"To attempt to capture this we can look use an uncertainty metric to look at the sum of covariance matrix norms at each timestep. This gives a representation of the overall uncertainty of the tracking over time."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from stonesoup.metricgenerator.uncertaintymetric import SumofCovarianceNormsMetric\n",
"uncertainty_generator = SumofCovarianceNormsMetric()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A metric manager is used for the generation of metrics on multiple `GroundTruthPath` and `Track` objects. This takes in the metric generators, as well as the associator required for the SIAP metrics.\n",
"\n",
"We must use a different metric manager for each sensor management method. This is because each sensor manager generates different track data which is then used in the metric manager."
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": 20,
"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": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x282aab41b88>"
]
},
"execution_count": 21,
"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": [
"import matplotlib.pyplot as plt\n",
"\n",
"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`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SIAP metrics\n",
"\n",
"Next we look at SIAP metrics. This can be done by generating a table which displays all the SIAP metrics computed, as seen in the Metrics Example. \n",
"\n",
"Completeness, ambiguity and spuriousness are not relevant for this example because we are not initiating and deleting tracks and we have one track corresponding to each ground truth."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x282aabdb508>"
]
},
"execution_count": 22,
"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_managerB.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."
]
},
{
"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": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x282aabe4488>"
]
},
"execution_count": 23,
"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` peaks initially then remains at a constant value. This peak is because the priors given are offset but have a small uncertainty meaning uncertainty increases when the first observations are made. 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 is reducing it by the same amount each time. This means the total uncertainty in the system is constant."
]
},
{
"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
}
@apiszcz
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apiszcz commented Feb 22, 2021

This looks great, detailed and will take me some time to review.

  1. Sensor manager, is there a possibility of measuring the resources of the tracker to maintain real time updates with varying numbers of targets? (latency of tracker in milliseconds)
  2. Interested in understanding the impact of tracking parameters, approaches, (JPDA...) on different configurations.

Plot Graph A and B remind me of what I was creating last year.
image

@apiszcz
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apiszcz commented Feb 23, 2021

Resource measures/thoughts

  1. CPU FLOPS/etc required for a range of target set sizes
  2. Memory required, data structures, state, etc. Sizing for 1, 10, 100, 1000, 10000, ... targets
  3. Real time ratio, for each given target set size how do each of the methods under consideration maintain tracking with respect to real time, 0.1x (10 times slower than real time), 10x 10 times faster, for each target set size.
  4. Consider performance counters for measures
    https://pypi.org/project/python_papi/

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