Repository: slacgismo/solar-data-tools (language: python)

Issue:
Qss sara
Main new test module that includes all tests is `tests/solardatatools/test_signal_decompositions.py`. Files for i/o data for tests and for generating them are in `tests/fixtures/signal_decompositions`.

The module `solardatatools/signal_decompositions.py` is still a work in progress.


Discussion / hints:

Fix patch:
diff --git a/.idea/solar-data-tools.iml b/.idea/solar-data-tools.iml
index 292aeb08..c6753bf4 100644
--- a/.idea/solar-data-tools.iml
+++ b/.idea/solar-data-tools.iml
@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="pvi-dev" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
diff --git a/conda_recipe/meta.yaml b/conda_recipe/meta.yaml
index f94b0959..9d83ad46 100644
--- a/conda_recipe/meta.yaml
+++ b/conda_recipe/meta.yaml
@@ -29,7 +29,6 @@ requirements:
     - matplotlib
     - pvlib
     - cvxpy>=1.1.0
-    - urllib3
     - pykml
     - haversine
     - boto3
diff --git a/pvsystemprofiler/algorithms/angle_of_incidence/lambda_functions.py b/pvsystemprofiler/algorithms/angle_of_incidence/lambda_functions.py
index 934c00ff..0256224e 100644
--- a/pvsystemprofiler/algorithms/angle_of_incidence/lambda_functions.py
+++ b/pvsystemprofiler/algorithms/angle_of_incidence/lambda_functions.py
@@ -25,7 +25,9 @@ def select_function(latitude=None, tilt=None, azimuth=None):
     if latitude is None and tilt is None and azimuth is None:
         func = lambda x, phi, beta, gamma: func_costheta(x, phi, beta, gamma)
     elif latitude is not None and tilt is None and azimuth is None:
-        func = lambda x, beta, gamma: func_costheta(x, np.deg2rad(latitude), beta, gamma)
+        func = lambda x, beta, gamma: func_costheta(
+            x, np.deg2rad(latitude), beta, gamma
+        )
 
     elif latitude is None and tilt is not None and azimuth is None:
         func = lambda x, phi, gamma: func_costheta(x, phi, np.deg2rad(tilt), gamma)
@@ -34,23 +36,33 @@ def select_function(latitude=None, tilt=None, azimuth=None):
         func = lambda x, phi, beta: func_costheta(x, phi, beta, np.deg2rad(azimuth))
 
     elif latitude is None and tilt is not None and azimuth is not None:
-        func = lambda x, phi: func_costheta(x, phi, np.deg2rad(tilt), np.deg2rad(azimuth))
+        func = lambda x, phi: func_costheta(
+            x, phi, np.deg2rad(tilt), np.deg2rad(azimuth)
+        )
 
     elif latitude is not None and tilt is None and azimuth is not None:
-        func = lambda x, beta: func_costheta(x, np.deg2rad(latitude), beta, np.deg2rad(azimuth))
+        func = lambda x, beta: func_costheta(
+            x, np.deg2rad(latitude), beta, np.deg2rad(azimuth)
+        )
 
     elif latitude is not None and tilt is not None and azimuth is None:
-        func = lambda x, gamma: func_costheta(x, np.deg2rad(latitude), np.deg2rad(tilt), gamma)
+        func = lambda x, gamma: func_costheta(
+            x, np.deg2rad(latitude), np.deg2rad(tilt), gamma
+        )
 
-    bounds_dict = {'latitude': [-np.pi / 2, np.pi / 2], 'tilt': [0, np.pi / 2], 'azimuth': [-np.inf, np.inf]}
+    bounds_dict = {
+        "latitude": [-np.pi / 2, np.pi / 2],
+        "tilt": [0, np.pi / 2],
+        "azimuth": [-np.inf, np.inf],
+    }
     bounds = []
 
     if latitude is None:
-        bounds.append(bounds_dict['latitude'])
+        bounds.append(bounds_dict["latitude"])
     if tilt is None:
-        bounds.append(bounds_dict['tilt'])
+        bounds.append(bounds_dict["tilt"])
     if azimuth is None:
-        bounds.append(bounds_dict['azimuth'])
+        bounds.append(bounds_dict["azimuth"])
 
     bounds = tuple(np.transpose(bounds).tolist())
 
diff --git a/pvsystemprofiler/algorithms/performance_model_estimation.py b/pvsystemprofiler/algorithms/performance_model_estimation.py
index 2af350a8..f1fbf6a5 100644
--- a/pvsystemprofiler/algorithms/performance_model_estimation.py
+++ b/pvsystemprofiler/algorithms/performance_model_estimation.py
@@ -6,23 +6,58 @@
 """
 
 
-def find_fit_costheta(data_matrix, clear_index):
+def find_fit_costheta(data_matrix, clear_index, doy):
     """
+    Fits a 'cos(theta)' curve to the given power, which is assumed to be smooth, yearly periodic, and symmetric around
+    the summer solstice. Previous versions of this function did not enforce the symmetric constraint.
+
     :param data_matrix: power matrix.
     :param clear_index: boolean array specifying clear days.
+    :param doy: day of year (float or array)
     :return: angle of incidence array.
     """
     data = np.max(data_matrix, axis=0)
-    s1 = cvx.Variable(len(data))
-    s2 = cvx.Variable(len(data))
-    cost = 1e1 * cvx.norm(cvx.diff(s1, k=2), p=2) + cvx.norm(s2[clear_index])
-    objective = cvx.Minimize(cost)
-    constraints = [
-        data == s1 + s2,
-        s1[365:] == s1[:-365]
-    ]
-    problem = cvx.Problem(objective, constraints)
-    problem.solve(solver='MOSEK')
-    scale_factor_costheta = s1.value
-    costheta_fit = data_matrix / np.max(s1.value)
-    return scale_factor_costheta, costheta_fit
+    msk = clear_index
+    y = np.zeros_like(data)
+    y[msk] = np.log(data[msk])
+    y[~msk] = np.nan
+
+    x1 = cvx.Variable(len(data))
+    x2 = cvx.Variable(len(data))
+    x3 = cvx.Variable(len(data))
+    x4 = cvx.Variable(len(data))
+
+    constraints = [y[msk] == (x1 + x2 + x3 + x4)[msk]]
+
+    phi1 = (100 / len(data)) * cvx.sum_squares(x1)
+    phi2 = 0
+    z = cvx.Variable()
+    constraints.append(cvx.diff(x2, k=1) == z)
+    phi3 = 1e3 * cvx.sum_squares(cvx.diff(x3, k=2)) + 1e-6 * cvx.norm1(x3)
+    for val in set(solstice_centered_index(doy)):
+        constraints.append(cvx.diff(x3[solstice_centered_index(doy) == val]) == 0)
+    constraints.append(x3 <= 0)
+    phi4 = 1e3 * cvx.sum_squares(cvx.diff(x4, k=2)) + 1e-3 * cvx.sum_squares(x4)
+    constraints.append(x4[:-365] == x4[365:])
+    constraints.append(x4 <= 0)
+    objective = phi1 + phi2 + phi3 + phi4
+
+    problem = cvx.Problem(cvx.Minimize(objective), constraints)
+    problem.solve(solver="OSQP")
+    normalized_data = data_matrix / np.exp(x2.value + x4.value)
+    costheta_est = x3.value
+    return normalized_data, costheta_est
+
+
+def solstice_centered_index(doy):
+    y = np.atleast_1d(doy)
+    s1 = y < 172
+    s2 = np.logical_and(y > 172, y <= 354)
+    s3 = y > 354
+    out = np.zeros_like(y)
+    out[s1] = 172 - y[s1]
+    out[s2] = y[s2] - 172
+    out[s3] = 172 - (y[s3] - 365)
+    if len(out) == 1:
+        out = out[0]
+    return out
diff --git a/pvsystemprofiler/estimator.py b/pvsystemprofiler/estimator.py
index 6ca1e643..e4a62b21 100644
--- a/pvsystemprofiler/estimator.py
+++ b/pvsystemprofiler/estimator.py
@@ -7,8 +7,10 @@
 """
 # Standard Imports
 import numpy as np
+
 # Solar Data Tools Imports
 from solardatatools.solar_noon import energy_com, avg_sunrise_sunset
+
 # Module Imports
 from pvsystemprofiler.utilities.equation_of_time import eot_da_rosa, eot_duffie
 from solardatatools.algorithms import SunriseSunset
@@ -17,24 +19,39 @@
 from pvsystemprofiler.utilities.declination_equation import delta_cooper
 from pvsystemprofiler.algorithms.angle_of_incidence.curve_fitting import run_curve_fit
 from pvsystemprofiler.algorithms.performance_model_estimation import find_fit_costheta
-from pvsystemprofiler.algorithms.angle_of_incidence.lambda_functions import select_function
-from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import determine_keys
-from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import select_init_values
-from pvsystemprofiler.algorithms.tilt_azimuth.daytime_threshold_quantile import filter_data
+from pvsystemprofiler.algorithms.angle_of_incidence.lambda_functions import (
+    select_function,
+)
+from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import (
+    determine_keys,
+)
+from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import (
+    select_init_values,
+)
+from pvsystemprofiler.algorithms.tilt_azimuth.daytime_threshold_quantile import (
+    filter_data,
+)
 from pvsystemprofiler.utilities.tools import random_initial_values
 from pvsystemprofiler.algorithms.longitude.estimation import estimate_longitude
 from pvsystemprofiler.algorithms.latitude.estimation import estimate_latitude
 
 
-class ConfigurationEstimator():
-    def __init__(self, data_handler, gmt_offset, day_selection_method='all', solar_noon_method='optimized_estimates',
-                 daylight_method='optimized_estimates', data_matrix='filled', daytime_threshold=None):
+class ConfigurationEstimator:
+    def __init__(
+        self,
+        data_handler,
+        gmt_offset,
+        solar_noon_method="optimized_estimates",
+        daylight_method="optimized_estimates",
+        data_matrix="filled",
+        daytime_threshold=None,
+    ):
         if not data_handler._ran_pipeline:
             data_handler.run_pipeline()
         self.data_handler = data_handler
-        if data_matrix == 'raw':
+        if data_matrix == "raw":
             self.data_matrix = data_handler.raw_data_matrix
-        elif data_matrix == 'filled':
+        elif data_matrix == "filled":
             self.data_matrix = data_handler.filled_data_matrix
         # Parameters to be estimated
         self.longitude = None
@@ -56,41 +73,48 @@ def __init__(self, data_handler, gmt_offset, day_selection_method='all', solar_n
         self.eot_da_rosa = eot_da_rosa(self.day_of_year)
         self.delta = delta_cooper(self.day_of_year, self.daily_meas)
         self.omega = None
-        if day_selection_method == 'all':
-            self.days = self.data_handler.daily_flags.no_errors
-        elif day_selection_method == 'clear':
-            self.days = self.days.daily_flags.clear
-        elif day_selection_method == 'cloudy':
-            self.days = self.data_handler.daily_flags.cloudy
-
-        if solar_noon_method == 'optimized_estimates' or daylight_method == 'optimized_estimates':
+        self.days = np.logical_and(
+            data_handler.daily_flags.clear, ~data_handler.daily_flags.inverter_clipped
+        )
+        # self.days = self.data_handler.daily_flags.no_errors
+
+        if (
+            solar_noon_method == "optimized_estimates"
+            or daylight_method == "optimized_estimates"
+        ):
             ss = SunriseSunset()
 
-        if solar_noon_method == 'rise_set_average':
+        if solar_noon_method == "rise_set_average":
             self.solarnoon = avg_sunrise_sunset(self.data_matrix)
-        elif solar_noon_method == 'energy_com':
+        elif solar_noon_method == "energy_com":
             self.solarnoon = energy_com(self.data_matrix)
-        if solar_noon_method == 'optimized_estimates':
+        if solar_noon_method == "optimized_estimates":
             ss.run_optimizer(data=self.data_matrix)
-            self.solarnoon = np.nanmean([ss.sunrise_estimates, ss.sunset_estimates], axis=0)
+            self.solarnoon = np.nanmean(
+                [ss.sunrise_estimates, ss.sunset_estimates], axis=0
+            )
 
-        if daylight_method in ('sunrise-sunset', 'sunrise sunset'):
-            self.hours_daylight = calculate_hours_daylight(self.data_matrix, daytime_threshold)
-        elif daylight_method == 'optimized_estimates':
+        if daylight_method in ("sunrise-sunset", "sunrise sunset"):
+            self.hours_daylight = calculate_hours_daylight(
+                self.data_matrix, daytime_threshold
+            )
+        elif daylight_method == "optimized_estimates":
             self.hours_daylight = ss.sunset_estimates - ss.sunrise_estimates
 
-    def estimate_longitude(self, estimator='fit_l1', eot_calculation='duffie'):
+    def estimate_longitude(self, estimator="fit_l1", eot_calculation="duffie"):
         """
         :param estimator: 'calculated', 'fit_l1', 'fit_l2' or 'fit_huber'
         :param eot_calculation: 'rise_set_average', 'energy_com', 'optimized_estimates',
         or 'optimized_measurements'
         :return: None
         """
-        if eot_calculation in ('duffie', 'd', 'duf'):
+        if eot_calculation in ("duffie", "d", "duf"):
             eot = self.eot_duffie
-        elif eot_calculation in ('da_rosa', 'dr', 'rosa'):
+        elif eot_calculation in ("da_rosa", "dr", "rosa"):
             eot = self.eot_da_rosa
-        self.longitude = estimate_longitude(estimator, eot, self.solarnoon, self.days, self.gmt_offset)
+        self.longitude = estimate_longitude(
+            estimator, eot, self.solarnoon, self.days, self.gmt_offset
+        )
         return
 
     def estimate_latitude(self):
@@ -110,8 +134,16 @@ def _prepare_lat_input_data(self):
         return hours_daylight_filtered, delta
 
     # estimate tilt and azimuth with or without longitude and latitude input values
-    def estimate_orientation(self, longitude=None, latitude=None, tilt=None, azimuth=None, day_interval=None, x1=0.9,
-                             x2=0.9):
+    def estimate_orientation(
+        self,
+        longitude=None,
+        latitude=None,
+        tilt=None,
+        azimuth=None,
+        day_interval=None,
+        x1=0.9,
+        x2=0.9,
+    ):
         """
         Estimates tilt and azimuth. The intended use is to estimate tilt and azimuth given longitude and latitude.
         However, the algorithm will estimate any of longitude, latitude, tilt and azimuth depending on the input values.
@@ -149,8 +181,9 @@ def estimate_orientation(self, longitude=None, latitude=None, tilt=None, azimuth
         dh = self.data_handler
         self.data_matrix = dh.filled_data_matrix
         self.num_days = dh.num_days
-        self.omega = calculate_omega(self.data_sampling, self.num_days, self.longitude, self.day_of_year,
-                                     self.gmt_offset)
+        self.omega = calculate_omega(
+            self.day_of_year, self.data_sampling, self.longitude, self.gmt_offset
+        )
 
         self.tilt, self.azimuth = self._cal_orientation_helper()
 
@@ -178,50 +211,77 @@ def estimate_all(self, day_interval=None, x1=0.9, x2=0.9):
         est_lat = ConfigurationEstimator(self.data_handler, self.gmt_offset)
         est_lat.estimate_latitude()
         self.latitude = est_lat.latitude
-        self.omega = calculate_omega(self.data_sampling, self.num_days, self.longitude, self.day_of_year,
-                                     self.gmt_offset)
+        self.omega = calculate_omega(
+            self.day_of_year, self.data_sampling, self.longitude, self.gmt_offset
+        )
 
         self.tilt, self.azimuth = self._cal_orientation_helper()
 
     def _cal_orientation_helper(self):
         if self.day_interval is not None:
-            day_range = (self.day_of_year > self.day_interval[0]) & (self.day_of_year < self.day_interval[1])
+            day_range = (self.day_of_year > self.day_interval[0]) & (
+                self.day_of_year < self.day_interval[1]
+            )
         else:
             day_range = np.ones(self.day_of_year.shape, dtype=bool)
 
-        scale_factor_costheta, costheta_fit = find_fit_costheta(self.data_matrix, self.days)
+        doy = self.data_handler.day_index.day_of_year
+        normalized_data, costheta_est = find_fit_costheta(
+            self.data_matrix, self.days, doy
+        )
 
-        boolean_filter = filter_data(self.data_matrix, self.daytime_threshold, self.x1, self.x2)
+        self.data_handler.find_clipped_times()
+        boolean_filter = normalized_data >= 0.15 * np.exp(costheta_est)
 
-        boolean_filter = boolean_filter * self.days * day_range
+        boolean_filter = (
+            boolean_filter
+            * ~self.data_handler.boolean_masks.clipped_times
+            * self.data_handler.daily_flags.clear
+            * day_range
+        )
 
         delta_f = self.delta[boolean_filter]
         omega_f = self.omega[boolean_filter]
         if ~np.any(boolean_filter):
-            print('No data made it through filters')
+            print("No data made it through filters")
 
         lat_initial, tilt_initial, azim_initial = random_initial_values(1)
 
-        func_customized, bounds = select_function(self.latitude, self.tilt, self.azimuth)
-        dict_keys = determine_keys(latitude=self.latitude, tilt=self.tilt, azimuth=self.azimuth)
+        func_customized, bounds = select_function(
+            self.latitude, self.tilt, self.azimuth
+        )
+        dict_keys = determine_keys(
+            latitude=self.latitude, tilt=self.tilt, azimuth=self.azimuth
+        )
 
-        init_values_dict = {'latitude': lat_initial[0], 'tilt': tilt_initial[0], 'azimuth': azim_initial[0]}
+        init_values_dict = {
+            "latitude": lat_initial[0],
+            "tilt": tilt_initial[0],
+            "azimuth": azim_initial[0],
+        }
         init_values, ivr = select_init_values(init_values_dict, dict_keys)
 
-        estimates = run_curve_fit(func=func_customized, keys=dict_keys, delta=delta_f, omega=omega_f,
-                                  costheta=costheta_fit, boolean_filter=boolean_filter, init_values=init_values,
-                                  fit_bounds=bounds)
+        estimates = run_curve_fit(
+            func=func_customized,
+            keys=dict_keys,
+            delta=delta_f,
+            omega=omega_f,
+            costheta=normalized_data,
+            boolean_filter=boolean_filter,
+            init_values=init_values,
+            fit_bounds=bounds,
+        )
 
         for i, estimate in enumerate(dict_keys):
-            if estimate == 'latitude_estimate':
+            if estimate == "latitude_estimate":
                 lat_estimate = estimates[i]
-            if estimate == 'tilt_estimate':
+            if estimate == "tilt_estimate":
                 tilt_estimate = estimates[i]
-            if estimate == 'azimuth_estimate':
+            if estimate == "azimuth_estimate":
                 azimuth_estimate = estimates[i]
 
-        if 'tilt_estimate' not in dict_keys:
+        if "tilt_estimate" not in dict_keys:
             tilt_estimate = None
-        if 'azimuth_estimate' not in dict_keys:
+        if "azimuth_estimate" not in dict_keys:
             azimuth_estimate = None
         return tilt_estimate, azimuth_estimate
diff --git a/pvsystemprofiler/ground_truth_estimator.py b/pvsystemprofiler/ground_truth_estimator.py
index f6436b2a..c162f846 100644
--- a/pvsystemprofiler/ground_truth_estimator.py
+++ b/pvsystemprofiler/ground_truth_estimator.py
@@ -1,11 +1,7 @@
-import urllib3 as urllib2
-from pykml import parser
 import numpy as np
 from haversine import haversine
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-import os
-import math
+from pykml import parser
+
 
 def ground_truth_estimate(filename):
     def readCoordinates_onepolygon(child):
@@ -15,25 +11,25 @@ def readCoordinates_onepolygon(child):
         # Extract coordinates
         j = 0
         for i in s:
-            if j==1:
-                c1 = np.fromstring(i,dtype=np.float,sep=',')
-            elif j==2:
-                c2 = np.fromstring(i,dtype=np.float,sep=',')
-            elif j==3:
-                c3 = np.fromstring(i,dtype=np.float,sep=',')
-            j = j+1
-        return c1,c2,c3
+            if j == 1:
+                c1 = np.fromstring(i, dtype=np.float, sep=",")
+            elif j == 2:
+                c2 = np.fromstring(i, dtype=np.float, sep=",")
+            elif j == 3:
+                c3 = np.fromstring(i, dtype=np.float, sep=",")
+            j = j + 1
+        return c1, c2, c3
 
-    def coordinatesToCartesian(c1,c2,c3):
+    def coordinatesToCartesian(c1, c2, c3):
         # Convert coordinates to Cartesian
-        temp1 = (c2[1],c1[0])
-        temp2 = (c3[1],c1[0])
-        c1z = c1[2]/1000.0
-        c1 = (c1[1],c1[0])
-        c2z = c2[2]/1000.0
-        c2 = (c2[1],c2[0])
-        c3z = c3[2]/1000.0
-        c3 = (c3[1],c3[0])
+        temp1 = (c2[1], c1[0])
+        temp2 = (c3[1], c1[0])
+        c1z = c1[2] / 1000.0
+        c1 = (c1[1], c1[0])
+        c2z = c2[2] / 1000.0
+        c2 = (c2[1], c2[0])
+        c3z = c3[2] / 1000.0
+        c3 = (c3[1], c3[0])
         y1 = haversine(c1, temp1)
         x1 = haversine(c2, temp1)
         y2 = haversine(c1, temp2)
@@ -42,9 +38,9 @@ def coordinatesToCartesian(c1,c2,c3):
         p2 = np.array([x1, y1, c2z])
         p3 = np.array([x2, y2, c3z])
 
-        return p1,p2,p3
+        return p1, p2, p3
 
-    def normalVec(p1,p2,p3):
+    def normalVec(p1, p2, p3):
         # These two vectors are in the plane
         v1 = p2 - p1
         v2 = p3 - p1
@@ -53,51 +49,54 @@ def normalVec(p1,p2,p3):
         cp = np.cross(v1, v2)
         x, y, z = cp
 
-        #Ensure vector is in the positive direction
-        if z<0:
+        # Ensure vector is in the positive direction
+        if z < 0:
             z = abs(z)
-            x = 0-x
-            y = 0-y
+            x = 0 - x
+            y = 0 - y
 
-        return x,y,z
+        return x, y, z
 
-    def tiltAz(x,y,z):
+    def tiltAz(x, y, z):
         # Calculate tilt and azimuth
-        #tilt = (np.arctan(np.sqrt((x*x)+(y*y))/z))*(180/np.pi)
-        tilt = np.degrees(np.arctan2(np.sqrt((x*x)+(y*y))*(180/np.pi), z*(180/np.pi)))
-        #azimuth = 90-math.degrees(np.arctan2(y*(180/np.pi), x*(180/np.pi)))
-        #azimuth = 90-(np.arctan((y/x))*(180/np.pi))
-        #azimuth = 90-math.degrees(np.arctan((y/x)))
-        azimuth = 90-np.degrees(np.arctan2(y*(180/np.pi), x*(180/np.pi)))
+        # tilt = (np.arctan(np.sqrt((x*x)+(y*y))/z))*(180/np.pi)
+        tilt = np.degrees(
+            np.arctan2(np.sqrt((x * x) + (y * y)) * (180 / np.pi), z * (180 / np.pi))
+        )
+        # azimuth = 90-math.degrees(np.arctan2(y*(180/np.pi), x*(180/np.pi)))
+        # azimuth = 90-(np.arctan((y/x))*(180/np.pi))
+        # azimuth = 90-math.degrees(np.arctan((y/x)))
+        azimuth = 90 - np.degrees(np.arctan2(y * (180 / np.pi), x * (180 / np.pi)))
         if azimuth < -90:
             azimuth = +180 + azimuth
         elif azimuth > 90:
             azimuth = +180 - azimuth
-        return tilt,azimuth
+        return tilt, azimuth
+
     file = filename
     tilt = []
     azimuth = []
-    with open(file, 'rb') as f:
+    with open(file, "rb") as f:
         root = parser.fromstring(f.read())
     ch = root.Document.Folder.getchildren()
     for child in ch:
         try:
-            c1,c2,c3 = readCoordinates_onepolygon(child)
-            #convert coordinates to cartesian
-            p1,p2,p3 = coordinatesToCartesian(c1,c2,c3)
-            #get the normal vector to the plane
-            x,y,z = normalVec(p1,p2,p3)
-            #aa.append(x)
-            #bb.append(y)
-            #cc.append(z)
-            #calculate tilt and azimuth
-            t,a = tiltAz(x,y,z)
-            #gather all tilts and azimuths of same home
+            c1, c2, c3 = readCoordinates_onepolygon(child)
+            # convert coordinates to cartesian
+            p1, p2, p3 = coordinatesToCartesian(c1, c2, c3)
+            # get the normal vector to the plane
+            x, y, z = normalVec(p1, p2, p3)
+            # aa.append(x)
+            # bb.append(y)
+            # cc.append(z)
+            # calculate tilt and azimuth
+            t, a = tiltAz(x, y, z)
+            # gather all tilts and azimuths of same home
             tilt.append(t)
             azimuth.append(a)
         except:
             pass
-    #calculate mean and standard deviation tilt and azimuth per home
+    # calculate mean and standard deviation tilt and azimuth per home
     ave_tilt = np.mean(tilt)
     med_tilt = np.median(tilt)
     std_tilt = np.std(tilt)
diff --git a/pvsystemprofiler/tilt_azimuth_study.py b/pvsystemprofiler/tilt_azimuth_study.py
index 76cd982f..f46085d7 100755
--- a/pvsystemprofiler/tilt_azimuth_study.py
+++ b/pvsystemprofiler/tilt_azimuth_study.py
@@ -15,21 +15,45 @@
 from pvsystemprofiler.utilities.declination_equation import delta_spencer
 from pvsystemprofiler.utilities.declination_equation import delta_cooper
 from pvsystemprofiler.algorithms.angle_of_incidence.curve_fitting import run_curve_fit
-from pvsystemprofiler.algorithms.angle_of_incidence.calculation import calculate_costheta
+from pvsystemprofiler.algorithms.angle_of_incidence.calculation import (
+    calculate_costheta,
+)
 from pvsystemprofiler.algorithms.performance_model_estimation import find_fit_costheta
-from pvsystemprofiler.algorithms.angle_of_incidence.lambda_functions import select_function
+from pvsystemprofiler.algorithms.angle_of_incidence.lambda_functions import (
+    select_function,
+)
 from pvsystemprofiler.utilities.angle_of_incidence_function import func_costheta
-from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import determine_keys
-from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import select_init_values
+from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import (
+    determine_keys,
+)
+from pvsystemprofiler.algorithms.angle_of_incidence.dynamic_value_functions import (
+    select_init_values,
+)
 from pvsystemprofiler.utilities.tools import random_initial_values
-from pvsystemprofiler.algorithms.tilt_azimuth.daytime_threshold_quantile import filter_data
+from pvsystemprofiler.algorithms.tilt_azimuth.daytime_threshold_quantile import (
+    filter_data,
+)
 
 
-class TiltAzimuthStudy():
-    def __init__(self, data_handler, day_range='full_year', init_values=None, nrandom_init_values=None,
-                 daytime_threshold=None, lon_input=None, lat_input=None, tilt_input=None,
-                 azimuth_input=None, lat_true_value=None, tilt_true_value=None, azimuth_true_value=None,
-                 gmt_offset=-8, cvx_parameter=None, threshold_quantile=None):
+class TiltAzimuthStudy:
+    def __init__(
+        self,
+        data_handler,
+        day_range="full_year",
+        init_values=None,
+        nrandom_init_values=None,
+        daytime_threshold=None,
+        lon_input=None,
+        lat_input=None,
+        tilt_input=None,
+        azimuth_input=None,
+        lat_true_value=None,
+        tilt_true_value=None,
+        azimuth_true_value=None,
+        gmt_offset=-8,
+        cvx_parameter=None,
+        threshold_quantile=None,
+    ):
         """
         :param data_handler: `DataHandler` class instance loaded with a solar power data set.
         :param day_range: (optional) the desired day range to run the study. A list of the form
@@ -55,17 +79,23 @@ def __init__(self, data_handler, day_range='full_year', init_values=None, nrando
         # Choose day range from season dictionary
         if day_range is None:
             self.day_range_dict = {}
-            self.day_range_dict = {'summer': [171, 265], 'no_winter': [79, 355], 'spring': [79, 171],
-                                   'winter': [355, 79], 'winter_spring': [355, 171], 'full_year': None}
+            self.day_range_dict = {
+                "summer": [171, 265],
+                "no_winter": [79, 355],
+                "spring": [79, 171],
+                "winter": [355, 79],
+                "winter_spring": [355, 171],
+                "full_year": None,
+            }
 
-        elif day_range is 'full_year':
-            self.day_range_dict = {'full_year': None}
-        elif day_range is 'manual':
-            self.day_range_dict = {'manual': day_range}
+        elif day_range is "full_year":
+            self.day_range_dict = {"full_year": None}
+        elif day_range is "manual":
+            self.day_range_dict = {"manual": day_range}
 
         self.data_matrix = self.data_handler.filled_data_matrix
         if not data_handler._ran_pipeline:
-            print('Running DataHandler preprocessing pipeline with defaults')
+            print("Running DataHandler preprocessing pipeline with defaults")
             self.data_handler.run_pipeline()
         # initial values
         self.init_values = init_values
@@ -108,7 +138,7 @@ def __init__(self, data_handler, day_range='full_year', init_values=None, nrando
         # other
         self.results = None
 
-    def run(self, delta_method=('cooper', 'spencer')):
+    def run(self, delta_method=("cooper", "spencer")):
         """
         Run a study with the given configuration of options. Defaults to
         running all available options. Any kwarg can be constrained by
@@ -132,10 +162,13 @@ def run(self, delta_method=('cooper', 'spencer')):
 
         delta_method = np.atleast_1d(delta_method)
         # calculate hour angle
-        self.omega = calculate_omega(self.data_sampling, self.num_days, self.lon_input, self.day_of_year,
-                                     self.gmt_offset)
+        self.omega = calculate_omega(
+            self.day_of_year, self.data_sampling, self.lon_input, self.gmt_offset
+        )
         # fit daily signal of cos theta
-        self.scale_factor_costheta, self.costheta_fit = find_fit_costheta(self.data_matrix, self.clear_index)
+        self.scale_factor_costheta, self.costheta_fit = find_fit_costheta(
+            self.data_matrix, self.clear_index
+        )
         # estimate declination angles
         self.delta_cooper = delta_cooper(self.day_of_year, self.daily_meas)
         self.delta_spencer = delta_spencer(self.day_of_year, self.daily_meas)
@@ -150,7 +183,9 @@ def run(self, delta_method=('cooper', 'spencer')):
                 tilt_initial = [10]
                 azim_initial = [10]
             else:
-                lat_initial, tilt_initial, azim_initial = random_initial_values(self.nrandom)
+                lat_initial, tilt_initial, azim_initial = random_initial_values(
+                    self.nrandom
+                )
 
         counter = 0
         self.create_results_table()
@@ -158,12 +193,14 @@ def run(self, delta_method=('cooper', 'spencer')):
             # first quantile value in signal decomposition algorithm
             for x2 in self.threshold_x2:
                 # second quantile value in signal decomposition algorithm
-                filtered_data = filter_data(self.data_matrix, self.daytime_threshold, x1, x2)
+                filtered_data = filter_data(
+                    self.data_matrix, self.daytime_threshold, x1, x2
+                )
                 for delta_id in delta_method:
                     # declination angle
-                    if delta_id in ('Cooper', 'cooper'):
+                    if delta_id in ("Cooper", "cooper"):
                         delta = self.delta_cooper
-                    if delta_id in ('Spencer', 'spencer'):
+                    if delta_id in ("Spencer", "spencer"):
                         delta = self.delta_spencer
                     for day_range_id in self.day_range_dict:
                         # day range
@@ -172,62 +209,110 @@ def run(self, delta_method=('cooper', 'spencer')):
                         delta_f = delta[boolean_filter]
                         omega_f = self.omega[boolean_filter]
                         if ~np.any(boolean_filter):
-                            print('No data made it through filters')
+                            print("No data made it through filters")
                         # choose function and unknowns based on provided inputs
                         # choose range for each unknown
-                        func_customized, bounds = select_function(self.lat_input, self.tilt_input,
-                                                                  self.azimuth_input)
+                        func_customized, bounds = select_function(
+                            self.lat_input, self.tilt_input, self.azimuth_input
+                        )
                         # create dictionary keys for unknowns. If an input value for lat, tilt or azimuth is not
                         # provided, it will be tagged as an unknown
-                        dict_keys = determine_keys(latitude=self.lat_input, tilt=self.tilt_input,
-                                                   azimuth=self.azimuth_input)
+                        dict_keys = determine_keys(
+                            latitude=self.lat_input,
+                            tilt=self.tilt_input,
+                            azimuth=self.azimuth_input,
+                        )
                         nvalues = len(lat_initial)
 
                         for init_val_ix in np.arange(nvalues):
                             # loop over initial values
-                            init_values_dict = {'latitude': lat_initial[init_val_ix], 'tilt': tilt_initial[init_val_ix],
-                                                'azimuth': azim_initial[init_val_ix]}
-                            init_values, ivr = select_init_values(init_values_dict, dict_keys)
+                            init_values_dict = {
+                                "latitude": lat_initial[init_val_ix],
+                                "tilt": tilt_initial[init_val_ix],
+                                "azimuth": azim_initial[init_val_ix],
+                            }
+                            init_values, ivr = select_init_values(
+                                init_values_dict, dict_keys
+                            )
                             try:
                                 # estimate latitude and/or tilt and/or azimuth. If parameter is in keys, it will be
                                 # estimated
-                                estimates = run_curve_fit(func=func_customized, keys=dict_keys, delta=delta_f,
-                                                          omega=omega_f, costheta=self.costheta_fit,
-                                                          boolean_filter=boolean_filter, init_values=init_values,
-                                                          fit_bounds=bounds)
+                                estimates = run_curve_fit(
+                                    func=func_customized,
+                                    keys=dict_keys,
+                                    delta=delta_f,
+                                    omega=omega_f,
+                                    costheta=self.costheta_fit,
+                                    boolean_filter=boolean_filter,
+                                    init_values=init_values,
+                                    fit_bounds=bounds,
+                                )
                             except RuntimeError:
-                                input_array = np.array([self.lat_input, self.tilt_input, self.azimuth_input])
+                                input_array = np.array(
+                                    [
+                                        self.lat_input,
+                                        self.tilt_input,
+                                        self.azimuth_input,
+                                    ]
+                                )
                                 estimates = np.full(np.sum(input_array == None), np.nan)
                             # create dictionary with dict_keys and estimates
                             estimates_dict = dict(zip(dict_keys, estimates))
                             # dynamic results dataFrame based on provided inputs
-                            lat = estimates_dict[
-                                'latitude_estimate'] if 'latitude_estimate' in estimates_dict else self.lat_input
-                            tilt = estimates_dict['tilt_estimate'] if 'tilt_estimate' in estimates_dict else \
-                                self.tilt_input
-                            azim = estimates_dict[
-                                'azimuth_estimate'] if 'azimuth_estimate' in estimates_dict else self.azimuth_input
+                            lat = (
+                                estimates_dict["latitude_estimate"]
+                                if "latitude_estimate" in estimates_dict
+                                else self.lat_input
+                            )
+                            tilt = (
+                                estimates_dict["tilt_estimate"]
+                                if "tilt_estimate" in estimates_dict
+                                else self.tilt_input
+                            )
+                            azim = (
+                                estimates_dict["azimuth_estimate"]
+                                if "azimuth_estimate" in estimates_dict
+                                else self.azimuth_input
+                            )
 
-                            self.costheta_estimated = calculate_costheta(func=func_costheta, delta=delta,
-                                                                         omega=self.omega, lat=lat, tilt=tilt,
-                                                                         azim=azim)
+                            self.costheta_estimated = calculate_costheta(
+                                func=func_costheta,
+                                delta=delta,
+                                omega=self.omega,
+                                lat=lat,
+                                tilt=tilt,
+                                azim=azim,
+                            )
                             # calculate cos theta from analytical equation in case ground truth values are provided
-                            if None not in (self.lat_true_value, self.tilt_true_value, self.azimuth_true_value):
-                                self.costheta_ground_truth = calculate_costheta(func=func_costheta, delta=delta,
-                                                                                omega=self.omega,
-                                                                                lat=self.lat_true_value,
-                                                                                tilt=self.tilt_true_value,
-                                                                                azim=self.azimuth_true_value)
+                            if None not in (
+                                self.lat_true_value,
+                                self.tilt_true_value,
+                                self.azimuth_true_value,
+                            ):
+                                self.costheta_ground_truth = calculate_costheta(
+                                    func=func_costheta,
+                                    delta=delta,
+                                    omega=self.omega,
+                                    lat=self.lat_true_value,
+                                    tilt=self.tilt_true_value,
+                                    azim=self.azimuth_true_value,
+                                )
 
-                            self.results.loc[counter] = [day_range_id, delta_id, x1, x2] + ivr + list(estimates)
+                            self.results.loc[counter] = (
+                                [day_range_id, delta_id, x1, x2] + ivr + list(estimates)
+                            )
                             counter += 1
 
         if self.lat_true_value is not None and self.lat_input is None:
-            self.results['latitude residual'] = self.lat_true_value - self.results['latitude']
+            self.results["latitude residual"] = (
+                self.lat_true_value - self.results["latitude"]
+            )
         if self.tilt_true_value is not None and self.tilt_input is None:
-            self.results['tilt residual'] = self.tilt_true_value - self.results['tilt']
+            self.results["tilt residual"] = self.tilt_true_value - self.results["tilt"]
         if self.azimuth_true_value is not None and self.azimuth_input is None:
-            self.results['azimuth residual'] = self.azimuth_true_value - self.results['azimuth']
+            self.results["azimuth residual"] = (
+                self.azimuth_true_value - self.results["azimuth"]
+            )
         return
 
     def get_day_range(self, input_data, interval):
@@ -240,19 +325,28 @@ def get_day_range(self, input_data, interval):
         :return: Boolean DataFrame with day selection
         """
         if interval is not None:
-            day_range = (self.day_of_year > interval[0]) & (self.day_of_year < interval[1])
+            day_range = (self.day_of_year > interval[0]) & (
+                self.day_of_year < interval[1]
+            )
         else:
             day_range = np.ones(self.day_of_year.shape, dtype=bool)
         output = input_data * self.clear_index * day_range
         return output
 
     def create_results_table(self):
-        cols = ['day range', 'declination method', 'cvx parameter', 'threshold quantile', 'latitude initial value',
-                'tilt initial value', 'azimuth initial value']
+        cols = [
+            "day range",
+            "declination method",
+            "cvx parameter",
+            "threshold quantile",
+            "latitude initial value",
+            "tilt initial value",
+            "azimuth initial value",
+        ]
         if self.lat_input is None:
-            cols.append('latitude')
+            cols.append("latitude")
         if self.tilt_input is None:
-            cols.append('tilt')
+            cols.append("tilt")
         if self.azimuth_input is None:
-            cols.append('azimuth')
+            cols.append("azimuth")
         self.results = pd.DataFrame(columns=cols)
diff --git a/pvsystemprofiler/utilities/angle_of_incidence_function.py b/pvsystemprofiler/utilities/angle_of_incidence_function.py
index 9f0f6cd4..f31739c2 100644
--- a/pvsystemprofiler/utilities/angle_of_incidence_function.py
+++ b/pvsystemprofiler/utilities/angle_of_incidence_function.py
@@ -3,6 +3,8 @@
  processes. New York: Wiley, 1991."""
 
 import numpy as np
+from pvsystemprofiler.utilities.hour_angle_equation import calculate_omega
+from pvsystemprofiler.utilities.declination_equation import delta_cooper
 
 
 def func_costheta(x, phi, beta, gamma):
@@ -17,3 +19,56 @@ def func_costheta(x, phi, beta, gamma):
     d = np.cos(delta) * np.sin(phi) * np.sin(beta) * np.cos(gamma) * np.cos(omega)
     e = np.cos(delta) * np.sin(beta) * np.sin(gamma) * np.sin(omega)
     return a - b + c + d + e
+
+
+def costheta_calc_helper(
+    tilt, azimuth, latitude, longitude, tz_offset, data_handler=None, data_sampling=1
+):
+    """Returns a numpy array with the same shape as the data matrix
+    attribute on the input data handler. Each entry contains the the calculate
+    cosine of the solar angle of incidence for that timestamp.
+
+    :param data_handler: an SDT DataHandler instance, loaded with a data set,
+        and having run the standard pipeline
+    :type data_handler: solardatatools.DataHandler class intstance
+    :param tilt: the angle between the plane of the array and the horizontal
+        in degrees 0 <= tilt <= 180 (tilt > 90 implies downward component)
+    :type tilt: float
+    :param azimuth: the deviation of the projection on a horizonal plane of the
+        normal to the plane of the array to the surface from the local
+        meridian in degrees, with 0 being north and 0 <= azimuth <= 360, going
+        N -> E -> S -> W
+    :type azimuth: float
+    :param latitude: the angular location north or south of the equator, north
+        positive, in degrees -90 <= latitude <= 90
+    :type latitude: float
+    :param longitude: the angular location east or west of the prime meridian,
+        with east positive and west negative, in degrees
+        -180 <= longitude <= 180
+    :type longitude: float
+    :param tz_offset: the UTC offset associated with the meridian defining the
+        local standard time (e.g. Pacific standard time is -8)
+    :type tz_offset: integer
+    """
+    if data_handler is not None:
+        dh = data_handler
+        doy = dh.day_index.day_of_year
+        meas_per_day = dh.filled_data_matrix.shape[0]
+        data_sampling = dh.data_sampling
+    else:
+        doy = np.arange(365) + 1
+        meas_per_day = int(1440 / data_sampling)
+        data_sampling = data_sampling
+    delta = delta_cooper(doy, meas_per_day)
+    omega = calculate_omega(doy, data_sampling, longitude, tz_offset)
+    # func_costheta uses the convention from Duffie and Beckman that zero is
+    # due south, but the industry uses the 'meteorological' convention that
+    # zero is north. This converts between the conventions
+    az_duff = azimuth - 180
+    costheta = func_costheta(
+        (np.deg2rad(delta), np.deg2rad(omega)),
+        np.deg2rad(latitude),
+        np.deg2rad(tilt),
+        np.deg2rad(az_duff),
+    )
+    return costheta
diff --git a/pvsystemprofiler/utilities/hour_angle_equation.py b/pvsystemprofiler/utilities/hour_angle_equation.py
index 7cff63d2..ad6bb6e1 100644
--- a/pvsystemprofiler/utilities/hour_angle_equation.py
+++ b/pvsystemprofiler/utilities/hour_angle_equation.py
@@ -6,18 +6,19 @@
 from pvsystemprofiler.utilities.time_convert import clock_to_solar
 
 
-def calculate_omega(data_sampling, num_days, lon, doy, gmt_offset):
+def calculate_omega(doy, data_sampling, lon, gmt_offset):
     """
-        :param data_sampling: daily data sampling.
-        :param num_days: number of sampling days.
-        :param lon: longitude in Degrees (float)
-        :param doy: day of year (float or array)
-        :param gmt_offset: local timezone offset in hours from UTC/GMT (float or int)
-        :return: hour angle omega (float or array)
-        """
+    :param data_sampling: daily data sampling.
+    :param num_days: number of sampling days.
+    :param lon: longitude in Degrees (float)
+    :param doy: day of year (float or array)
+    :param gmt_offset: local timezone offset in hours from UTC/GMT (float or int)
+    :return: hour angle omega (float or array)
+    """
+    num_days = len(doy)
     minutes_day = np.arange(0, 1440, data_sampling)
     minutes_doy = np.tile(minutes_day.reshape(-1, 1), (1, num_days))
-    hours_doy_solar = clock_to_solar(minutes_doy, lon, doy, gmt_offset, eot='duffie')
+    hours_doy_solar = clock_to_solar(minutes_doy, lon, doy, gmt_offset, eot="duffie")
     hours_doy_solar /= 60
     omega = 15 * (hours_doy_solar - 12)
     return omega
diff --git a/requirements.txt b/requirements.txt
index fcfdec66..f06e043b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 scipy
-numpy==1.22.0
+numpy>=1.22.0
 pandas
 scikit-learn
 jupyter
@@ -9,7 +9,6 @@ requests
 pvlib
 cvxpy>=1.1.0
 cmake
-urllib3
 pykml
 haversine
 boto3
diff --git a/solardatatools/algorithms/time_shifts.py b/solardatatools/algorithms/time_shifts.py
index cb559698..320abbde 100644
--- a/solardatatools/algorithms/time_shifts.py
+++ b/solardatatools/algorithms/time_shifts.py
@@ -16,7 +16,6 @@
 """
 
 import numpy as np
-from scipy.stats import mode
 from sklearn.model_selection import train_test_split
 import matplotlib.pyplot as plt
 from solardatatools.solar_noon import energy_com, avg_sunrise_sunset
@@ -37,6 +36,8 @@ def __init__(self):
         self.jumps_per_year = None
         self.best_c1 = None
         self.best_ix = None
+        self.baseline = None
+        self.periodic_detector = None
         self.__recursion_depth = 0
 
     def run(
@@ -46,7 +47,7 @@ def run(
         c1=None,
         c2=200.0,
         solar_noon_estimator="com",
-        threshold=0.1,
+        threshold=0.005,
         periodic_detector=False,
         solver=None,
     ):
@@ -117,9 +118,10 @@ def run(
             )
             return
         # Apply corrections
-        roll_by_index = np.round(
-            (mode(np.round(s1, 3)).mode[0] - s1) * data.shape[0] / 24, 0
-        )
+        my_set = set(s1)
+        key_func = lambda x: abs(x - 12)
+        closest_element = min(my_set, key=key_func)
+        roll_by_index = np.round((closest_element - s1) * data.shape[0] / 24, 0)
         correction_metric = np.average(np.abs(roll_by_index))
         if correction_metric < 0.01:
             roll_by_index[:] = 0
@@ -137,9 +139,11 @@ def run(
         self.c1_vals = c1s
         self.best_c1 = best_c1
         self.best_ix = best_ix
+        self.periodic_detector = periodic_detector
         self.s1 = s1
         self.s2 = s2
         self.index_set = index_set
+        self.baseline = closest_element
         self.corrected_data = Dout
         self.__recursion_depth = 0
 
@@ -157,6 +161,7 @@ def optimize_c1(self, metric, c1s, use_ixs, c2, periodic_detector, solver=None):
         test_r = np.zeros_like(c1s)
         tv_metric = np.zeros_like(c1s)
         jpy = np.zeros_like(c1s)
+        rms_s2 = np.zeros_like(c1s)
         # iterate over possible values of c1 parameter
         for i, v in enumerate(c1s):
             s1, s2 = self.estimate_components(
@@ -170,6 +175,7 @@ def optimize_c1(self, metric, c1s, use_ixs, c2, periodic_detector, solver=None):
             count_jumps = np.sum(~np.isclose(np.diff(s1), 0, atol=1e-4))
             jumps_per_year = count_jumps / (len(metric) / 365)
             jpy[i] = jumps_per_year
+            rms_s2[i] = np.sqrt(np.mean(np.square(s2)))
 
         def zero_one_scale(x):
             return (x - np.min(x)) / (np.max(x) - np.min(x))
@@ -179,9 +185,13 @@ def zero_one_scale(x):
         ixs = np.arange(len(c1s))
         # Detecting more than 5 time shifts per year is extremely uncommon,
         # and is considered non-physical
-        slct = np.logical_and(jpy <= 5, hn <= 0.02)
-        # slct = np.logical_and(slct, rn < 0.9)
-        best_ix = np.nanmax(ixs[slct])
+        if not periodic_detector:
+            slct = np.logical_and(jpy <= 5, hn <= 0.075)
+        else:
+            slct = np.logical_and(np.logical_and(jpy <= 5, hn <= 0.05), rms_s2 <= 0.25)
+        subset_ixs = ixs[slct]
+        # choose index of lowest holdout error
+        best_ix = np.max(subset_ixs)
         return hn, rn, tv_metric, jpy, best_ix
 
     def estimate_components(
diff --git a/solardatatools/data_handler.py b/solardatatools/data_handler.py
index b3dca9e1..f8e7f298 100644
--- a/solardatatools/data_handler.py
+++ b/solardatatools/data_handler.py
@@ -18,6 +18,8 @@
 from solardatatools.time_axis_manipulation import (
     make_time_series,
     standardize_time_axis,
+    remove_index_timezone,
+    get_index_timezone,
 )
 from solardatatools.matrix_embedding import make_2d
 from solardatatools.data_quality import (
@@ -77,9 +79,8 @@ def __init__(
                     e = "Data frame must have a DatetimeIndex or"
                     e += "the user must set the datetime_col kwarg."
                     raise Exception(e)
-            df_index = self.data_frame_raw.index
-            if df_index.tz is not None:
-                df_index = df_index.tz_localize(None)
+            self.tz_info = get_index_timezone(self.data_frame_raw)
+            self.data_frame_raw = remove_index_timezone(self.data_frame_raw)
             if no_future_dates:
                 now = datetime.now()
                 self.data_frame_raw = self.data_frame_raw[
@@ -179,10 +180,11 @@ def run_pipeline(
         end_day_ix=None,
         c1=None,
         c2=500.0,
+        periodic_detector=False,
         solar_noon_estimator="srss",
         correct_tz=True,
         extra_cols=None,
-        daytime_threshold=0.1,
+        daytime_threshold=0.005,
         units="W",
         solver=None,
         reset=True,
@@ -310,13 +312,14 @@ def run_pipeline(
             self.data_clearness_score = 0.0
             self._ran_pipeline = True
             return
-        if ratio < 0.9:
+        if ratio < 0.85:
             msg = "Error: data was lost during NaN filling procedure. "
             msg += "This typically occurs when\nthe time stamps are in the "
             msg += "wrong timezone. Please double check your data table.\n"
             self._error_msg += "\n" + msg
             if verbose:
                 print(msg)
+                print(f"ratio of filled to raw nonzero measurements was {ratio:.2f}")
             self.daily_scores = None
             self.daily_flags = None
             self.data_quality_score = None
@@ -414,9 +417,22 @@ def run_pipeline(
                     c2=c2,
                     estimator=solar_noon_estimator,
                     threshold=daytime_threshold,
-                    periodic_detector=False,
+                    periodic_detector=periodic_detector,
                     solver=solver,
                 )
+                rms = lambda x: np.sqrt(np.mean(np.square(x)))
+                if rms(self.time_shift_analysis.s2) > 0.25:
+                    old_analysis = self.time_shift_analysis
+                    self.auto_fix_time_shifts(
+                        c1=c1,
+                        c2=c2,
+                        estimator=solar_noon_estimator,
+                        threshold=daytime_threshold,
+                        periodic_detector=True,
+                        solver=solver,
+                    )
+                    if rms(old_analysis.s2) < rms(self.time_shift_analysis.s2):
+                        self.time_shift_analysis = old_analysis
             except Exception as e:
                 msg = "Fix time shift algorithm failed."
                 self._error_msg += "\n" + msg
@@ -530,6 +546,10 @@ def report(self, verbose=True, return_values=False):
                 "quality score": self.data_quality_score,
                 "clearness score": self.data_clearness_score,
                 "inverter clipping": self.inverter_clipping,
+                "clipped fraction": (
+                    np.sum(self.daily_flags.inverter_clipped)
+                    / len(self.daily_flags.inverter_clipped)
+                ),
                 "capacity change": self.capacity_changes,
                 "data quality warning": self.normal_quality_scores,
                 "time shift correction": (
@@ -584,6 +604,7 @@ def report(self, verbose=True, return_values=False):
 quality score        {report['quality score']:.2f}
 clearness score      {report['clearness score']:.2f}
 inverter clipping    {report['inverter clipping']}
+clipped fraction     {report['clipped fraction']:.2f}
 capacity changes     {report['capacity change']}
 data quality warning {report['data quality warning']}
 time shift errors    {report['time shift correction']}
@@ -927,12 +948,12 @@ def auto_fix_time_shifts(
         c1=5.0,
         c2=500.0,
         estimator="com",
-        threshold=0.1,
+        threshold=0.005,
         periodic_detector=False,
         solver=None,
     ):
         self.time_shift_analysis = TimeShift()
-        if self.data_clearness_score >= 0.1:
+        if self.data_clearness_score >= 0.3:
             use_ixs = self.daily_flags.clear
         else:
             use_ixs = self.daily_flags.no_errors
@@ -1045,7 +1066,6 @@ def calculate_scsf_performance_index(self):
     def setup_location_and_orientation_estimation(
         self,
         gmt_offset,
-        day_selection_method="all",
         solar_noon_method="optimized_estimates",
         daylight_method="optimized_estimates",
         data_matrix="filled",
@@ -1059,7 +1079,6 @@ def setup_location_and_orientation_estimation(
         est = ConfigurationEstimator(
             self,
             gmt_offset,
-            day_selection_method=day_selection_method,
             solar_noon_method=solar_noon_method,
             daylight_method=daylight_method,
             data_matrix=data_matrix,
diff --git a/solardatatools/data_quality.py b/solardatatools/data_quality.py
index b6c9b7cd..3e677f13 100644
--- a/solardatatools/data_quality.py
+++ b/solardatatools/data_quality.py
@@ -57,7 +57,7 @@ def make_linearity_scores(data_matrix, capacity, density_baseline):
     temp_mat = np.copy(data_matrix)
     temp_mat[temp_mat < 0.005 * capacity] = np.nan
     difference_mat = np.round(temp_mat[1:] - temp_mat[:-1], 4)
-    modes, counts = mode(difference_mat, axis=0, nan_policy="omit")
+    modes, counts = mode(difference_mat, axis=0, nan_policy="omit", keepdims=True)
     n = data_matrix.shape[0] - 1
     linearity_scores = counts.data.squeeze() / (n * density_baseline)
     # Label detected infill points with a boolean mask
diff --git a/solardatatools/pvpro_post_processing.py b/solardatatools/pvpro_post_processing.py
index 40246143..cbd5b05e 100644
--- a/solardatatools/pvpro_post_processing.py
+++ b/solardatatools/pvpro_post_processing.py
@@ -1,8 +1,8 @@
 """ PVPRO Post Processing Module
 
-This module contains a class that takes in the output dataframe of PVPRO and 
-contains methods to process the dataset, perform signal decompositions to model 
-degradation trends, analyze how well the models fit, and visualize the trends. 
+This module contains a class that takes in the output dataframe of PVPRO and
+contains methods to process the dataset, perform signal decompositions to model
+degradation trends, analyze how well the models fit, and visualize the trends.
 
 """
 
@@ -21,10 +21,10 @@
 from solardatatools.utilities import progress
 
 
-class PVPROPostProcessor():
-    """ This is a class to process a dataset, perform signal decomposition to 
-    model degradation trends, and analyze and visualize the resulting trends. 
-    
+class PVPROPostProcessor:
+    """This is a class to process a dataset, perform signal decomposition to
+    model degradation trends, and analyze and visualize the resulting trends.
+
     :param file_name: Name of the data file to be imported (must be .csv)
     :type file_name: str
     :param period: How many data points in the file make up a full year of data
@@ -48,97 +48,117 @@ class PVPROPostProcessor():
     boundaries
     "type bp: bool, optional
     """
-    
-    def __init__(self, file_name, period, index_col=0, dates=None, df_prep=True,
-        include=None, exclude=None, verbose=False, bp=True):
-        """ Imports dats,  assigns index column and date columns, performs 
-        optional dataframe preparation steps, does preprocessing on the data 
-        frame, creates a 'preprocessed' data frame to be used in future 
-        analysis, and initializes other attributes of the class. 
+
+    def __init__(
+        self,
+        file_name,
+        period,
+        index_col=0,
+        dates=None,
+        df_prep=True,
+        include=None,
+        exclude=None,
+        verbose=False,
+        bp=True,
+    ):
+        """Imports dats,  assigns index column and date columns, performs
+        optional dataframe preparation steps, does preprocessing on the data
+        frame, creates a 'preprocessed' data frame to be used in future
+        analysis, and initializes other attributes of the class.
         """
-        
-        param_dict = {'photocurrent':(0.01, 10, 0.01), 
-              'saturation_current':(0, 1*10**(-6), 5*10**(-12)), 
-              'resistance_series':(0.1, 2, 0.05), 
-              'resistance_shunt':(100, 500, 1),
-              'i_sc':(-np.inf, np.inf, 1),
-              'v_oc':(-np.inf, np.inf, 1),
-              'i_mp':(-np.inf, np.inf, 1),
-              'v_mp':(-np.inf, np.inf, 1),
-              'p_mp':(-np.inf, np.inf, 1)
-                     }
-        
+
+        self.param_dict = {
+            "photocurrent": (0.01, 10, 0.01),
+            "saturation_current": (0, 1 * 10 ** (-6), 5 * 10 ** (-12)),
+            "resistance_series": (0.1, 2, 0.05),
+            "resistance_shunt": (100, 500, 1),
+            "i_sc": (-np.inf, np.inf, 1),
+            "v_oc": (-np.inf, np.inf, 1),
+            "i_mp": (-np.inf, np.inf, 1),
+            "v_mp": (-np.inf, np.inf, 1),
+            "p_mp": (-np.inf, np.inf, 1),
+        }
+
         if dates is None:
             dates = [0]
         else:
             dates = dates
-        
+
         def default_val():
-            return 'no_entry'
-        
+            return None
+
         self.df = pd.read_csv(file_name, index_col=index_col, parse_dates=dates)
-        self.period = period
-        
+        self.period = int(period)
+
         # dataframe preparation (column selection, time index correction)
         if df_prep is True:
             self.data_setup(include=include, exclude=exclude)
             self.df_ds = self.df_ds
         else:
             self.df_ds = self.df
-            
+
         # processing steps
-        if bp==True:
+        if bp == True:
             self.boundary_points(verbose=verbose)
             self.boundary_to_nan()
         df_scaled = self.scale_max_1()
         df_p = self.ln_df()
-        
+
         # attributes
         self.df_p = df_p
         self.df_error = None
         self.df_error_avg = None
         self.scaled_data = defaultdict(default_val)
         self.descaled_data = defaultdict(default_val)
+        self.processed_result = defaultdict(default_val)
         self.df_x1 = None
         self.df_x2 = None
         self.df_x3 = None
         self.df_x4 = None
         self.df_x5 = None
         self.df_cs = None
-    
+
     ###########################################################################
     # Processing functions
     ###########################################################################
-    
+
     def data_setup(self, include=None, exclude=None):
-        """ Adjusts time index so that there are equal intervals and isolates 
-        columns of interest. Creates dataframe self.df_ds with the selected 
+        """Adjusts time index so that there are equal intervals and isolates
+        columns of interest. Creates dataframe self.df_ds with the selected
         columns and adjusted time index.
-        
+
         :param include: Input to include a key term in selected column names
         :type include: str, optional
         :param exclude: Input to exclude a key term from selected column names
         :type exclude: str, optional
         """
-        
+
         # adjusting the time series
-        time_delta, count = mode(np.diff(self.df.index))
-        freq = int(time_delta[0] / np.timedelta64(1, 's'))
-        new_index = pd.date_range(start=self.df.index[0], end=self.df.index[-1],
-            freq='{}s'.format(freq))
+        time_delta, count = mode(np.diff(self.df.index), keepdims=True)
+        freq = int(time_delta[0] / np.timedelta64(1, "s"))
+        new_index = pd.date_range(
+            start=self.df.index[0], end=self.df.index[-1], freq="{}s".format(freq)
+        )
         self.df = self.df.reindex(index=new_index)
-        
+
         # column selection
-        rule1 = lambda x: ('photocurrent' in x or 'saturation' in x or 
-            'resistance' in x or 'i_sc' in x or 'v_oc' in x or 'mp' in x)
-        
+        rule1 = lambda x: (
+            "photocurrent" in x
+            or "saturation" in x
+            or "resistance" in x
+            or "i_sc" in x
+            or "v_oc" in x
+            or "mp" in x
+        )
+
         if include is not None:
             rule2 = lambda x: include in x
             if exclude is not None:
                 rule3 = lambda x: exclude not in x
-                cols = [c for c in self.df.columns if rule1(c) and rule2(c) and
-                rule3(c)]
-            else: 
+                cols = [
+                    c for c in self.df.columns if rule1(c) and rule2(c) and rule3(c)
+                ]
+            else:
                 cols = [c for c in self.df.columns if rule1(c) and rule2(c)]
         else:
             if exclude is not None:
@@ -146,123 +166,132 @@ def data_setup(self, include=None, exclude=None):
                 cols = [c for c in self.df.columns if rule1(c) and rule3(c)]
             else:
                 cols = [c for c in self.df.columns if rule1(c)]
-        
-        
+
         df_ds = self.df.loc[:, cols]
         df_ds = df_ds.reindex(index=new_index)
-        
+
         self.df_ds = df_ds
-    
+
     def boundary_points(self, verbose=False):
-        """ Determines indices of points on the boundary to a tolerance 
-        determined in param_dict. Creates a list of indices where data points 
+        """Determines indices of points on the boundary to a tolerance
+        determined in param_dict. Creates a list of indices where data points
         are on the boundary in any of the system parameters.
-        
-        :param verbose: A T/F switch to show percentage of data points on the 
+
+        :param verbose: A T/F switch to show percentage of data points on the
         boundaries
         :type verbose: bool, optional
         """
-        
-        bounds = param_dict
+
+        bounds = self.param_dict
         indices = []
         df = self.df_ds
-        cond = ''.join(df.columns)
+        cond = "".join(df.columns)
         bounded_params = [k for k in bounds.keys() if k in cond]
-        
+
         for name in bounded_params:
             label_selection = [c for c in df.columns if name in c]
             label = label_selection[0]
-            
-            if bounds[name][0] is -np.inf:
+
+            if bounds[name][0] == -np.inf:
                 lbpoints = []
             else:
-                lbpoints = np.arange(len(df[label]))[df[label] <= 
-                bounds[name][0] + bounds[name][2]]
-            
-            if bounds[name][1] is np.inf:
+                lbpoints = np.arange(len(df[label]))[
+                    df[label] <= bounds[name][0] + bounds[name][2]
+                ]
+
+            if bounds[name][1] == np.inf:
                 ubpoints = []
             else:
-                ubpoints = np.arange(len(df[label]))[df[label] >= 
-                bounds[name][1] - bounds[name][2]]
-                                    
+                ubpoints = np.arange(len(df[label]))[
+                    df[label] >= bounds[name][1] - bounds[name][2]
+                ]
+
             indices = np.concatenate((indices, lbpoints, ubpoints))
-        
+
         indices = np.unique(indices).astype(int)
         self.bound_indices = indices
-        
+
         if verbose is True:
-            print('Percent on boundaries:', (100*(len(indices)/len(df.index))),
-            '%')
+            print("Percent on boundaries:", (100 * (len(indices) / len(df.index))), "%")
         else:
             pass
-    
+
     def boundary_to_nan(self):
-        """ Makes all points in the dataframe at boundary point indices be nan.
-        """
-        
+        """Makes all points in the dataframe at boundary point indices be nan."""
+
         self.df_ds.iloc[self.bound_indices, :] = np.nan
-    
+
     def scale_max_1(self):
-        """ Scales a dataframe to have max value 1.
-        
+        """Scales a dataframe to have max value 1.
+
         :return: self.scaler, which saves all of the values involved in scaling
         the data frame
         :rtype: array
         """
-        
+
         scaler = MaxAbsScaler()
         df_scaled = scaler.fit_transform(self.df_ds.to_numpy())
-        df_scaled = pd.DataFrame(df_scaled, columns=self.df_ds.columns, 
-            index=self.df_ds.index)
-        
+        df_scaled = pd.DataFrame(
+            df_scaled, columns=self.df_ds.columns, index=self.df_ds.index
+        )
+
         self.scaler = scaler
         return df_scaled
-    
+
     def ln_df(self):
-        """ Takes the natural log of the scaled dataframe and makes all inf 
+        """Takes the natural log of the scaled dataframe and makes all inf
         values nan.
-        
+
         :return: df_l, a scaled to max 1 data frame in log space
         "rtype: Pandas DataFrame
         """
-        
+
         df_scaled = self.scale_max_1()
         cond = df_scaled > 0
         df_l = np.log(df_scaled[cond])
-        
+
         return df_l
-    
+
     def view_minmax(self, df):
-        """ Prints the minimum and maximum values for each column in the 
+        """Prints the minimum and maximum values for each column in the
         dataframe.
         """
-        
+
         for label, values in df.items():
             print(label)
             print(min(df[label]))
             print(max(df[label]))
-    
+
     ###########################################################################
     # Signal decomposition
     ###########################################################################
-    
-    def optimize(self, label, lambda4, lambda5, model, lambda2=0.001, 
-                 verbose=False, known=None, solver='Default'):    
-        """ Runs an optimization problem to perform a 5-component signal 
-        decomposition using cvxpy on one parameter of the PV system. Creates 
-        two data frames of the resulting components and a composed signal of 
+
+    def optimize(
+        self,
+        label,
+        lambda4,
+        lambda5,
+        model,
+        lambda2=0.001,
+        verbose=False,
+        known=None,
+        solver="Default",
+    ):
+        """Runs an optimization problem to perform a 5-component signal
+        decomposition using cvxpy on one parameter of the PV system. Creates
+        two data frames of the resulting components and a composed signal of
         the noiseless components. One data frame is in the scaled log space and
-        the other is in the original space. These resulting data frames can be 
-        accessed in the self.scaled_data and self.descaled_data dictionaries 
+        the other is in the original space. These resulting data frames can be
+        accessed in the self.scaled_data and self.descaled_data dictionaries
         using the key (label + '_' + model).
-        
-        :param label: Column name that indicates which system parameter is 
+
+        :param label: Column name that indicates which system parameter is
         being optimized.
         :type label: str
-        :param lambda4: Weight which determines the strength of smoothing on 
+        :param lambda4: Weight which determines the strength of smoothing on
         the periodic component
         :type lambda4: float
-        :param lambda5: Weight which determines the strength of smoothing on 
+        :param lambda5: Weight which determines the strength of smoothing on
         the degradation component
         :type lambda5: float
         :param model: Names the model to use for the degradation component, can
@@ -271,176 +300,244 @@ def optimize(self, label, lambda4, lambda5, model, lambda2=0.001,
         :type model: str
         :param lambda2: Weight on the Laplacian noise term
         :type lambda2: float, optional
-        :param verbose: T/F switch to determine whether cvxpy prints a verbose 
+        :param verbose: T/F switch to determine whether cvxpy prints a verbose
         output of the solve
         :type verbose: bool, optional
-        :param known: Option to input a mask on the data inputted into the 
+        :param known: Option to input a mask on the data inputted into the
         solver
         :type known: bool mask, optional
-        :param solver: Indicates which solver cvxpy should call to perform the 
+        :param solver: Indicates which solver cvxpy should call to perform the
         optimization problem
         :type solver: str, optional
         """
-        
-        acceptable_models = ['linear', 'monotonic', 'smooth_monotonic', 
-        'piecewise_linear']
-        
+
+        acceptable_models = [
+            "linear",
+            "monotonic",
+            "smooth_monotonic",
+            "piecewise_linear",
+        ]
+
         if model not in acceptable_models:
-            print('check model entry')
-        
+            print("check model entry")
+
         # initializing data and characteristic values
         data = self.df_p[label]
         y = self.df_p[label].values
         T = len(y)
         p = self.period
-        
+
         # applying mask if applicable
         if known is None:
             known = ~np.isnan(y)
         else:
             known = np.logical_and(known, ~np.isnan(y))
-        
-        if 'series' in label:
+
+        if "series" in label:
             decreasing = False
         else:
             decreasing = True
-        
+
         # components
         x1 = cp.Variable(T)
         x2 = cp.Variable(T)
         x3 = cp.Variable(T)
         x4 = cp.Variable(T)
         x5 = cp.Variable(T)
-        
+
         # weights
         lambda_2 = cp.Parameter(value=lambda2, nonneg=True)
         lambda_4 = cp.Parameter(value=lambda4, nonneg=True)
         lambda_5 = cp.Parameter(value=lambda5, nonneg=True)
-        
+
         # initial cost function to be minimized and initial constraints
-        cost = ((1/T)*cp.sum_squares(x1) + lambda_2*cp.norm1(x2) 
-            + lambda_4*cp.sum_squares(cp.diff(x4, k=2)))
-        
-        constraints = [y[known] == (x1 + x2 + x3 + x4 + x5)[known],
-                           cp.diff(x3, k=1) == 0,
-                           cp.sum(x4[:p]) == 0,
-                           x4[p:] == x4[:-p],
-                           x5[0] == 0]
-        
+        cost = (
+            (1 / T) * cp.sum_squares(x1)
+            + lambda_2 * cp.norm1(x2)
+            + lambda_4 * cp.sum_squares(cp.diff(x4, k=2))
+        )
+
+        constraints = [
+            y[known] == (x1 + x2 + x3 + x4 + x5)[known],
+            cp.diff(x3, k=1) == 0,
+            cp.sum(x4[:p]) == 0,
+            x4[p:] == x4[:-p],
+            x5[0] == 0,
+        ]
+
         # additional costs and conditions for all the model types
-        if model == 'linear':        
+        if model == "linear":
             constraints.append(cp.diff(x5, k=2) == 0)
         else:
             if decreasing == True:
                 constraints.append(cp.diff(x5, k=1) <= 0)
             else:
                 constraints.append(cp.diff(x5, k=1) >= 0)
-            
-            if model == 'smooth_monotonic':
-                cost += lambda_5*cp.sum_squares(cp.diff(x5, k=2))
-                
-            elif model == 'piecewise_linear':
-                cost += lambda_5*cp.norm1(cp.diff(x5, k=2))
-        
+
+            if model == "smooth_monotonic":
+                cost += lambda_5 * cp.sum_squares(cp.diff(x5, k=2))
+
+            elif model == "piecewise_linear":
+                cost += lambda_5 * cp.norm1(cp.diff(x5, k=2))
+
         # setting up the problem
         obj = cp.Minimize(cost)
         prob = cp.Problem(obj, constraints)
-        if solver is 'Default':
-            solver = 'OSQP'
+        if solver == "Default":
+            solver = "OSQP"
 
-        if solver is 'OSQP':
-            prob.solve(solver=solver, verbose=verbose, eps_prim_inf=1*10**(-6),
-                eps_dual_inf=1*10**(-6), 
-                       eps_rel=1*10**(-6), eps_abs=1*10**(-6))
+        if solver == "OSQP":
+            prob.solve(
+                solver=solver,
+                verbose=verbose,
+                eps_prim_inf=1 * 10 ** (-6),
+                eps_dual_inf=1 * 10 ** (-6),
+                eps_rel=1 * 10 ** (-6),
+                eps_abs=1 * 10 ** (-6),
+            )
         else:
             prob.solve(solver=solver, verbose=verbose)
-        
+
         # resulting components
-        df_components = pd.DataFrame(index=self.df_p.index, data={'x1':x1.value,
-            'x2':x2.value, 'x3':x3.value, 'x4':x4.value, 'x5':x5.value, 
-            'composed_signal':(x3.value + x4.value + x5.value)})
-        
-        
-        self.scaled_data[label + '_' + model] = df_components
-        
+        df_components = pd.DataFrame(
+            index=self.df_p.index,
+            data={
+                "x1": x1.value,
+                "x2": x2.value,
+                "x3": x3.value,
+                "x4": x4.value,
+                "x5": x5.value,
+                "composed_signal": (x3.value + x4.value + x5.value),
+            },
+        )
+
+        self.scaled_data[label + "_" + model] = df_components
+
         # exponentiating the components and undoing the scaling
-        x1 = np.exp(df_components['x1'].values)
-        x2 = np.exp(df_components['x2'].values)
-        x3 = np.exp(df_components['x3'].values)
-        x4 = np.exp(df_components['x4'].values)
-        x5 = np.exp(df_components['x5'].values)
-        
+        x1 = np.exp(df_components["x1"].values)
+        x2 = np.exp(df_components["x2"].values)
+        x3 = np.exp(df_components["x3"].values)
+        x4 = np.exp(df_components["x4"].values)
+        x5 = np.exp(df_components["x5"].values)
+
         ind = self.df_ds.columns.get_loc(label)
-        
+
         max_val = self.scaler.scale_[ind]
-        x3 = x3*max_val
-        
-        df_descaled = pd.DataFrame(index=self.df_ds.index, data={'x1':x1, 
-            'x2':x2, 'x3':x3, 'x4':x4, 'x5':x5, 'composed_signal':(x3*x4*x5)})
-        
-        self.descaled_data[label + '_' + model] = df_descaled
-    
-    def analyze(self, label, lambda2=0.001, lambda4=0.1, lambda5=1, 
-        model='smooth_monotonic', verbose=False, known=None, solver='Default'):
-        """ Performs optimize() with default values. All parameters and outputs
+        x3 = x3 * max_val
+
+        df_descaled = pd.DataFrame(
+            index=self.df_ds.index,
+            data={
+                "x1": x1,
+                "x2": x2,
+                "x3": x3,
+                "x4": x4,
+                "x5": x5,
+                "composed_signal": (x3 * x4 * x5),
+            },
+        )
+
+        self.descaled_data[label + "_" + model] = df_descaled
+        self.processed_result[label + "_" + model] = df_descaled["x5"]
+
+    def analyze(
+        self,
+        label,
+        lambda2=0.001,
+        lambda4=0.1,
+        lambda5=1,
+        model="smooth_monotonic",
+        verbose=False,
+        known=None,
+        solver="Default",
+    ):
+        """Performs optimize() with default values. All parameters and outputs
         are the same as those in optimize().
-        
-        :param label: Column name that indicates which system parameter is 
+
+        :param label: Column name that indicates which system parameter is
         being optimized.
         :type label: str
         :param lambda2: Weight on the Laplacian noise term
         :type lambda2: float, optional
-        :param lambda4: Weight which determines the strength of smoothing on 
+        :param lambda4: Weight which determines the strength of smoothing on
         the periodic component
         :type lambda4: float
-        :param lambda5: Weight which determines the strength of smoothing on 
+        :param lambda5: Weight which determines the strength of smoothing on
         the degradation component
         :type lambda5: float
         :param model: Names the model to use for the degradation component, can
         be 'linear', 'monotonic',
         'smooth_monotonic', or 'piecewise_linear'
         :type model: str
-        :param verbose: T/F switch to determine whether cvxpy prints a verbose 
+        :param verbose: T/F switch to determine whether cvxpy prints a verbose
         output of the solve
         :type verbose: bool, optional
-        :param known: Option to input a mask on the data inputted into the 
+        :param known: Option to input a mask on the data inputted into the
         solver
         :type known: bool mask, optional
-        :param solver: Indicates which solver cvxpy should call to perform the 
+        :param solver: Indicates which solver cvxpy should call to perform the
         optimization problem
         :type solver: str, optional
         """
-        
-        self.optimize(label, lambda4, lambda5, model, lambda2=lambda2, 
-            verbose=verbose, known=known, solver=solver)
-    
-    def sd_result_dfs(self, lambda2=0.001, lambda4=0.1, lambda5=1, 
-        model='smooth_monotonic', known=None, solver='Default'):
-        """ Creates six new data frames containing the six signal decomposition
-        components produced by performing optimize() with the indicated inputs 
-        over all system parameters. One data frame holds one component for all 
+
+        self.optimize(
+            label,
+            lambda4,
+            lambda5,
+            model,
+            lambda2=lambda2,
+            verbose=verbose,
+            known=known,
+            solver=solver,
+        )
+
+    def retreive_result(self, label, model="smooth_monotonic"):
+        if self.processed_result[label + "_" + model] is None:
+            opt = input("No data entry, would you like to run optimization? (y/n)\n")
+
+            if opt == "y":
+                self.analyze(label, model=model)
+            elif opt == "n":
+                pass
+            else:
+                pass
+        result = self.processed_result[label + "_" + model]
+        return result
+
+    def sd_result_dfs(
+        self,
+        lambda2=0.001,
+        lambda4=0.1,
+        lambda5=1,
+        model="smooth_monotonic",
+        known=None,
+        solver="Default",
+    ):
+        """Creates six new data frames containing the six signal decomposition
+        components produced by performing optimize() with the indicated inputs
+        over all system parameters. One data frame holds one component for all
         system parameters.
-        
+
         :param lambda2: Weight on the Laplacian noise term
         :type lambda2: float, optional
-        :param lambda4: Weight which determines the strength of smoothing on 
+        :param lambda4: Weight which determines the strength of smoothing on
         the periodic component
         :type lambda4: float
-        :param lambda5: Weight which determines the strength of smoothing on 
+        :param lambda5: Weight which determines the strength of smoothing on
         the degradation component
         :type lambda5: float
         :param model: Names the model to use for the degradation component, can
         be 'linear', 'monotonic',
         'smooth_monotonic', or 'piecewise_linear'
         :type model: str
-        :param known: Option to input a mask on the data inputted into the 
+        :param known: Option to input a mask on the data inputted into the
         solver
         :type known: bool mask, optional
-        :param solver: Indicates which solver cvxpy should call to perform the 
+        :param solver: Indicates which solver cvxpy should call to perform the
         optimization problem
         :type solver: str, optional
         """
-        
+
         # initializing data frames
         df_x1 = pd.DataFrame()
         df_x2 = pd.DataFrame()
@@ -448,49 +545,62 @@ def sd_result_dfs(self, lambda2=0.001, lambda4=0.1, lambda5=1,
         df_x4 = pd.DataFrame()
         df_x5 = pd.DataFrame()
         df_cs = pd.DataFrame()
-        
-        # solving for all parameters and recording components in their 
-        respective data frames
+
+        # solving for all parameters and recording components in their
+        # respective data frames
         for column in self.df_p.columns:
-            self.optimize(column, lambda4, lambda5, model, lambda2=lambda2, 
-                known=known, solver=solver)
-            
-            x1_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['x1'], name=column)
-            x2_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['x2'], name=column)
-            x3_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['x3'], name=column)
-            x4_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['x4'], name=column)
-            x5_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['x5'], name=column)
-            cs_data = pd.Series(data=self.descaled_data[column + '_' + model]
-                ['composed_signal'], name=column)
-            
+            self.optimize(
+                column,
+                lambda4,
+                lambda5,
+                model,
+                lambda2=lambda2,
+                known=known,
+                solver=solver,
+            )
+
+            x1_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["x1"], name=column
+            )
+            x2_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["x2"], name=column
+            )
+            x3_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["x3"], name=column
+            )
+            x4_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["x4"], name=column
+            )
+            x5_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["x5"], name=column
+            )
+            cs_data = pd.Series(
+                data=self.descaled_data[column + "_" + model]["composed_signal"],
+                name=column,
+            )
+
             df_x1 = pd.concat([df_x1, x1_data], axis=1)
             df_x2 = pd.concat([df_x2, x2_data], axis=1)
             df_x3 = pd.concat([df_x3, x3_data], axis=1)
             df_x4 = pd.concat([df_x4, x4_data], axis=1)
             df_x5 = pd.concat([df_x5, x5_data], axis=1)
             df_cs = pd.concat([df_cs, cs_data], axis=1)
-            
+
         self.df_x1 = df_x1
         self.df_x2 = df_x2
         self.df_x3 = df_x3
         self.df_x4 = df_x4
         self.df_x5 = df_x5
         self.df_cs = df_cs
-            
-        
+
     ###########################################################################
     # Plotting functions
-    ###########################################################################    
-    
-    def plot_sd_space(self, label, model='smooth_monotonic'):
-        """ Plots the SD of one system parameter in the scaled log space.
-        
-        :param label: Column name that indicates which system parameter is 
+    ###########################################################################
+
+    def plot_sd_space(self, label, model="smooth_monotonic"):
+        """Plots the SD of one system parameter in the scaled log space.
+
+        :param label: Column name that indicates which system parameter is
         being optimized
         :type label: str
         :param model: Names the model to use for the degradation component, can
@@ -500,63 +610,75 @@ def plot_sd_space(self, label, model='smooth_monotonic'):
         :return: Plot of the SD in scaled log space
         :rtype: figure
         """
-        
-        if model is 'smooth_monotonic':
-            model_title = 'Smooth Monotonic'
-        if model is 'piecewise_linear':
-            model_title = 'Piecewise Linear'
-        if model is 'linear':
-            model_title = 'Linear'
-        if model is 'monotonic':
-            model_title = 'Monotonic'
-        
-        if self.scaled_data[label + '_' + model] is 'no_entry':
-            opt = input('No data entry, would you like to run optimization? (y/n)\n')
-            
-            if opt == 'y':                
-                self.analyse(label, model=model)
+
+        if model == "smooth_monotonic":
+            model_title = "Smooth Monotonic"
+        if model == "piecewise_linear":
+            model_title = "Piecewise Linear"
+        if model == "linear":
+            model_title = "Linear"
+        if model == "monotonic":
+            model_title = "Monotonic"
+
+        if self.scaled_data[label + "_" + model] is None:
+            opt = input("No data entry, would you like to run optimization? (y/n)\n")
+
+            if opt == "y":
+                self.analyze(label, model=model)
                 self.plot_sd_space(label, model=model)
-                
-            if opt == 'n':
+
+            if opt == "n":
                 pass
-            
-        else:        
-            components = self.scaled_data[label + '_' + model]
-
-            titles = ['Residual', 'Laplacian Noise', 'Bias', 'Periodic', 
-            model_title, 'Composed Signal']
-            
-            plt.figure(figsize=(6,20))
+
+        else:
+            components = self.scaled_data[label + "_" + model]
+
+            titles = [
+                "Residual",
+                "Laplacian Noise",
+                "Bias",
+                "Periodic",
+                model_title,
+                "Composed Signal",
+            ]
+
+            plt.figure(figsize=(6, 20))
             counter = 0
-            
+
             for col, values in components.items():
                 plt.subplot(611 + counter)
-                if titles[counter] == 'Residual' or titles[counter] == 
-                'Laplacian Noise':
+                if (
+                    titles[counter] == "Residual"
+                    or titles[counter] == "Laplacian Noise"
+                ):
                     plt.scatter(self.df_p.index, components[col])
-                elif titles[counter] == 'Composed Signal':
-                    plt.scatter(self.df_p.index, self.df_p[label], c='orange')
+                elif titles[counter] == "Composed Signal":
+                    plt.scatter(self.df_p.index, self.df_p[label], c="orange")
                     plt.plot(self.df_p.index, components[col])
-                    plt.xlabel('Time')
-                elif titles[counter] == 'Bias':
+                    plt.xlabel("Time")
+                elif titles[counter] == "Bias":
                     plt.plot(self.df_p.index, components[col])
-                    plt.ylim((components[col][0] - 0.1*components[col][0], 
-                              components[col][0] + 0.1*components[col][0]))
+                    plt.ylim(
+                        (
+                            components[col][0] - 0.1 * components[col][0],
+                            components[col][0] + 0.1 * components[col][0],
+                        )
+                    )
                 else:
                     plt.plot(self.df_p.index, components[col])
-                
+
                 plt.title(titles[counter])
                 plt.xticks(rotation=45)
-                
+
                 counter += 1
-                
+
             plt.tight_layout()
             return plt.gcf()
-    
-    def plot_original_space(self, label, model='smooth_monotonic'):
-        """ Plots the SD of one system parameter in the original space.
-        
-        :param label: Column name that indicates which system parameter is 
+
+    def plot_original_space(self, label, model="smooth_monotonic"):
+        """Plots the SD of one system parameter in the original space.
+
+        :param label: Column name that indicates which system parameter is
         being optimized
         :type label: str
         :param model: Names the model to use for the degradation component, can
@@ -566,48 +688,58 @@ def plot_original_space(self, label, model='smooth_monotonic'):
         :return: Plot of the SD in original space
         :rtype: figure
         """
-        
-        if model is 'smooth_monotonic':
-            model_title = 'Smooth Monotonic'
-        if model is 'piecewise_linear':
-            model_title = 'Piecewise Linear'
-        if model is 'linear':
-            model_title = 'Linear'
-        if model is 'monotonic':
-            model_title = 'Monotonic'
-        
-        if self.descaled_data[label + '_' + model] is 'no_entry':
-            opt = input('No data entry, would you like to run optimization? (y/n)\n')
-            
-            if opt == 'y':
-                self.analyse(label, model=model)
+
+        if model == "smooth_monotonic":
+            model_title = "Smooth Monotonic"
+        if model == "piecewise_linear":
+            model_title = "Piecewise Linear"
+        if model == "linear":
+            model_title = "Linear"
+        if model == "monotonic":
+            model_title = "Monotonic"
+
+        if self.descaled_data[label + "_" + model] is None:
+            opt = input("No data entry, would you like to run optimization? (y/n)\n")
+
+            if opt == "y":
+                self.analyze(label, model=model)
                 self.plot_sd_space(label, model=model)
-                
-            if opt == 'n':
+
+            if opt == "n":
                 pass
-            
-        else:        
+
+        else:
             data = self.df_ds[label]
-            components = self.descaled_data[label + '_' + model]
+            components = self.descaled_data[label + "_" + model]
 
-            titles = ['Residual', 'Laplacian Noise', 'Bias', 'Periodic', 
-            model_title, 'Composed Signal']
+            titles = [
+                "Residual",
+                "Laplacian Noise",
+                "Bias",
+                "Periodic",
+                model_title,
+                "Composed Signal",
+            ]
 
-            plt.figure(figsize=(6,20))
+            plt.figure(figsize=(6, 20))
             counter = 0
 
             for col, values in components.items():
                 plt.subplot(611 + counter)
-                if col == 'x1' or col == 'x2':
+                if col == "x1" or col == "x2":
                     plt.scatter(self.df_ds.index, components[col])
-                elif col == 'composed_signal':
-                    plt.scatter(self.df_ds.index, self.df_ds[label], c='orange')
+                elif col == "composed_signal":
+                    plt.scatter(self.df_ds.index, self.df_ds[label], c="orange")
                     plt.plot(self.df_ds.index, components[col])
-                    plt.xlabel('Time')
-                elif col == 'x3':
+                    plt.xlabel("Time")
+                elif col == "x3":
                     plt.plot(self.df_ds.index, components[col])
-                    plt.ylim((components[col][0] - 0.1*components[col][0], 
-                              components[col][0] + 0.1*components[col][0]))
+                    plt.ylim(
+                        (
+                            components[col][0] - 0.1 * components[col][0],
+                            components[col][0] + 0.1 * components[col][0],
+                        )
+                    )
                 else:
                     plt.plot(self.df_ds.index, components[col])
 
@@ -618,105 +750,138 @@ def plot_original_space(self, label, model='smooth_monotonic'):
 
             plt.tight_layout()
             return plt.gcf()
-    
+
     ###########################################################################
-    # Error calculations 
+    # Error calculations
     ###########################################################################
-    
-    def error_analysis(self, lambda4, lambda5, num_runs, lambda2=0.001, 
-        solver='Default'):
-        """ Calculates the holdout error, looping over system parameters, 
-        models, cost function weights, and 
+
+    def error_analysis(
+        self, lambda4, lambda5, num_runs, lambda2=0.001, solver="Default"
+    ):
+        """Calculates the holdout error, looping over system parameters,
+        models, cost function weights, and
         number of repetitions. Creates a data frame with error results for each
         iteration and another which averages
-        over all runs for each unique set of inputs. 
-        
+        over all runs for each unique set of inputs.
+
         :param lambda4: A list of periodic component weight values to loop over
         :type lambda4: list
-        :param lambda5: A list of degradation component weight values to loop 
+        :param lambda5: A list of degradation component weight values to loop
         over
         :type lambda5: list
-        :param num_runs: Number of runs to perform; more runs yields more 
+        :param num_runs: Number of runs to perform; more runs yields more
         generalizable results at the cost of time
         :type num_runs: int
         :param lambda2: Laplacian noise weight term value
         :type lambda2: float, optional
-        :param solver: Indicates which solver cvxpy should call to perform the 
+        :param solver: Indicates which solver cvxpy should call to perform the
         optimization problem
         :type solver: str, optional
         """
-        
+
         ti = time()
         period = self.period
         lambda_2 = lambda2
         lambda_4_values = np.atleast_1d(lambda4)
         lambda_5_values = np.atleast_1d(lambda5)
         runs = np.arange(num_runs)
-        models = ['linear', 'monotonic', 'smooth_monotonic', 'piecewise_linear']
-        cols = ['system_parameter', 'run_number', 'degradation_model', 
-        'lambda_2_val', 'lambda_4_val', 'lambda_5_val', 'mean_sq_error']
-        num_rows = (len(lambda_5_values) + 1)*len(lambda_4_values)*2*len(runs)
-        *len(self.df_p.columns)
+        models = ["linear", "monotonic", "smooth_monotonic", "piecewise_linear"]
+        cols = [
+            "system_parameter",
+            "run_number",
+            "degradation_model",
+            "lambda_2_val",
+            "lambda_4_val",
+            "lambda_5_val",
+            "mean_sq_error",
+        ]
+        num_rows = (
+            (len(lambda_5_values) + 1)
+            * len(lambda_4_values)
+            * 2
+            * len(runs)
+            * len(self.df_p.columns)
+        )
         df_error = pd.DataFrame(columns=cols, index=np.arange(num_rows))
-        
+
         counter = 0
         progress(counter, num_rows)
-        
+
         for label, values in self.df_p.items():
             data = self.df_p[label]
             known = ~np.isnan(data)
             T = len(data)
             indices = np.arange(T)
             indices = indices[known]
-            
+
             for r in runs:
                 train_inds, test_inds = train_test_split(indices, test_size=0.1)
                 train = np.zeros(T, dtype=bool)
                 test = np.zeros(T, dtype=bool)
                 train[train_inds] = True
                 test[test_inds] = True
-                
+
                 test_data = data[test]
-                
+
                 for m_type in models:
                     for l4_val in lambda_4_values:
-                        if m_type == 'smooth_monotonic' or m_type == 
-                        'piecewise_linear':
-                            l5_iter = lambda_5_values   
-                            
+                        if m_type == "smooth_monotonic" or m_type == "piecewise_linear":
+                            l5_iter = lambda_5_values
+
                         else:
                             l5_iter = [1]
-                            
+
                         for l5_val in l5_iter:
-                            if solver is 'Default':
-                                self.optimize(label, l4_val, l5_val, m_type, 
-                                    lambda2=lambda_2, solver=solver, 
-                                    known=train)
+                            if solver == "Default":
+                                self.optimize(
+                                    label,
+                                    l4_val,
+                                    l5_val,
+                                    m_type,
+                                    lambda2=lambda_2,
+                                    solver=solver,
+                                    known=train,
+                                )
                             else:
-                                self.optimize(label, l4_val, l5_val, m_type, 
-                                    lambda2=lambda_2, solver=solver, 
-                                    known=train)
-                                
-                            x_vals = self.scaled_data[label + '_' + m_type]
-                            composed_sig = x_vals['composed_signal'].values
-                            
-                            mse = 1/len(test_data)*sum((composed_sig[test] - 
-                                data.values[test])**2)
-
-                            row = [label, r, m_type, lambda_2, l4_val, l5_val, 
-                            mse]
+                                self.optimize(
+                                    label,
+                                    l4_val,
+                                    l5_val,
+                                    m_type,
+                                    lambda2=lambda_2,
+                                    solver=solver,
+                                    known=train,
+                                )
+
+                            x_vals = self.scaled_data[label + "_" + m_type]
+                            composed_sig = x_vals["composed_signal"].values
+
+                            mse = (
+                                1
+                                / len(test_data)
+                                * sum((composed_sig[test] - data.values[test]) ** 2)
+                            )
+
+                            row = [label, r, m_type, lambda_2, l4_val, l5_val, mse]
                             df_error.loc[counter] = row
                             counter += 1
 
                             t_progress = time()
-                            msg = "{:.2f} minutes".format((t_progress-ti)/60)
+                            msg = "{:.2f} minutes".format((t_progress - ti) / 60)
                             progress(counter, num_rows, status=msg)
-                            
+
         self.df_error = df_error
-        
-        # averaging over runs for each unique set of inputs 
-        grouped = self.df_error.groupby(['system_parameter', 
-            'degradation_model','lambda_2_val','lambda_4_val', 'lambda_5_val'])
-        df_error_avg = grouped.mean().reset_index().drop(['run_number'], axis=1)
-        
-        self.df_error_avg = df_error_avg
\ No newline at end of file
+
+        # averaging over runs for each unique set of inputs
+        grouped = self.df_error.groupby(
+            [
+                "system_parameter",
+                "degradation_model",
+                "lambda_2_val",
+                "lambda_4_val",
+                "lambda_5_val",
+            ]
+        )
+        df_error_avg = grouped.mean().reset_index().drop(["run_number"], axis=1)
+
+        self.df_error_avg = df_error_avg
diff --git a/solardatatools/time_axis_manipulation.py b/solardatatools/time_axis_manipulation.py
index 2acd5df7..f6ce9787 100644
--- a/solardatatools/time_axis_manipulation.py
+++ b/solardatatools/time_axis_manipulation.py
@@ -10,6 +10,7 @@
 import numpy as np
 import pandas as pd
 from collections import Counter
+from typing import Optional
 
 TZ_LOOKUP = {
     "America/Anchorage": 9,
@@ -138,8 +139,8 @@ def standardize_time_axis(
         s = df[power_col]
         avg_day = s.groupby(s.index.time).mean()
         # normalize to [0, 1]
-        avg_day /= np.max(avg_day)
         avg_day -= np.min(avg_day)
+        avg_day /= np.max(avg_day)
         # find sunrise and sunset times
         idxs = np.arange(len(avg_day))
         if avg_day[0] >= thresh:
@@ -198,7 +199,7 @@ def standardize_time_axis(
     # Find *all* common sampling frequencies
     freq_counts = Counter(diff)
     freq = freq_counts.most_common()[0][0]
-    deltas = [c[0] for c in freq_counts.most_common()]
+    deltas = [c[0] for c in freq_counts.most_common() if int(c[1] > 0.05 * len(df))]
     if len(deltas) > 1:
         if verbose:
             print("CAUTION: Multiple scan rates detected!")
@@ -259,3 +260,47 @@ def fix_daylight_savings_with_known_tz(df, tz="America/Los_Angeles", inplace=Fal
         df_out = df.copy()
         df_out.index = index
         return df_out
+
+
+def remove_index_timezone(df):
+    """
+    Removes the timezone information from the index of a pandas DataFrame, if
+    it is timezone aware. This function was written with ChatGPT and checked by
+    Bennet Meyers
+
+    Parameters:
+    df (pandas.DataFrame): The DataFrame to modify.
+
+    Returns:
+    pandas.DataFrame: The modified DataFrame.
+    """
+    if isinstance(df.index, pd.DatetimeIndex) and df.index.tzinfo is not None:
+        new_index = df.index.tz_localize(None)
+        return df.set_index(new_index)
+    else:
+        return df
+
+
+def get_index_timezone(df: pd.DataFrame) -> Optional[str]:
+    """
+    Returns the timezone name or offset amount (in hours) of the index of a
+    pandas DataFrame. This function was written with ChatGPT and checked by
+    Bennet Meyers
+
+    Parameters:
+    df (pandas.DataFrame): The DataFrame to check.
+
+    Returns:
+    str or None: The timezone name or offset amount (in hours) of the index,
+    or None if the index is not timezone aware.
+    """
+    if isinstance(df.index, pd.DatetimeIndex):
+        tzinfo = df.index.tzinfo
+        if tzinfo is not None:
+            tzname = tzinfo.tzname(None)
+            if tzname is not None:
+                return tzname
+            else:
+                tzoffset = tzinfo.utcoffset(None).total_seconds() // 3600
+                return f"{tzoffset:+}"
+    return None
diff --git a/statistical_clear_sky/algorithm/iterative_fitting.py b/statistical_clear_sky/algorithm/iterative_fitting.py
index 0b2cdbcd..6b21755b 100644
--- a/statistical_clear_sky/algorithm/iterative_fitting.py
+++ b/statistical_clear_sky/algorithm/iterative_fitting.py
@@ -7,32 +7,42 @@
 from numpy.linalg import norm
 import cvxpy as cvx
 from collections import defaultdict
-from\
- statistical_clear_sky.algorithm.initialization.singular_value_decomposition\
- import SingularValueDecomposition
-from statistical_clear_sky.algorithm.initialization.linearization_helper\
- import LinearizationHelper
-from statistical_clear_sky.algorithm.initialization.weight_setting\
- import WeightSetting
+from statistical_clear_sky.algorithm.initialization.singular_value_decomposition import (
+    SingularValueDecomposition,
+)
+from statistical_clear_sky.algorithm.initialization.linearization_helper import (
+    LinearizationHelper,
+)
+from statistical_clear_sky.algorithm.initialization.weight_setting import WeightSetting
 from statistical_clear_sky.algorithm.exception import ProblemStatusError
-from statistical_clear_sky.algorithm.minimization.left_matrix\
- import LeftMatrixMinimization
-from statistical_clear_sky.algorithm.minimization.right_matrix\
- import RightMatrixMinimization
+from statistical_clear_sky.algorithm.minimization.left_matrix import (
+    LeftMatrixMinimization,
+)
+from statistical_clear_sky.algorithm.minimization.right_matrix import (
+    RightMatrixMinimization,
+)
 from statistical_clear_sky.algorithm.serialization.state_data import StateData
-from statistical_clear_sky.algorithm.serialization.serialization_mixin\
- import SerializationMixin
+from statistical_clear_sky.algorithm.serialization.serialization_mixin import (
+    SerializationMixin,
+)
 from statistical_clear_sky.algorithm.plot.plot_mixin import PlotMixin
 from statistical_clear_sky.utilities.data_loading import resample_index
 from statistical_clear_sky.utilities.progress import progress
 
+
 class IterativeFitting(SerializationMixin, PlotMixin):
     """
     Implementation of "Statistical Clear Sky Fitting" algorithm.
     """
 
-    def __init__(self, data_matrix=None, data_handler_obj=None, rank_k=6,
-                 solver_type='MOSEK', reserve_test_data=False):
+    def __init__(
+        self,
+        data_matrix=None,
+        data_handler_obj=None,
+        rank_k=6,
+        solver_type="MOSEK",
+        reserve_test_data=False,
+    ):
         """
 
         :param data_matrix:
@@ -44,7 +54,7 @@ def __init__(self, data_matrix=None, data_handler_obj=None, rank_k=6,
         self._solver_type = solver_type
         self._rank_k = rank_k
         if data_handler_obj is None and data_matrix is None:
-            print('Please initialize class with a data set')
+            print("Please initialize class with a data set")
         elif data_handler_obj is not None:
             data_matrix = data_handler_obj.filled_data_matrix
             self._power_signals_d = data_matrix
@@ -76,11 +86,20 @@ def __init__(self, data_matrix=None, data_handler_obj=None, rank_k=6,
 
         self._set_residuals()
 
-    def execute(self, mu_l=None, mu_r=None, tau=None,
-                exit_criterion_epsilon=1e-3,
-                max_iteration=10, is_degradation_calculated=True,
-                max_degradation=None, min_degradation=None,
-                non_neg_constraints=False, verbose=True, bootstraps=None):
+    def execute(
+        self,
+        mu_l=None,
+        mu_r=None,
+        tau=None,
+        exit_criterion_epsilon=1e-3,
+        max_iteration=10,
+        is_degradation_calculated=True,
+        max_degradation=None,
+        min_degradation=None,
+        non_neg_constraints=False,
+        verbose=True,
+        bootstraps=None,
+    ):
 
         mu_l, mu_r, tau = self._obtain_hyper_parameters(mu_l, mu_r, tau)
         l_cs_value, r_cs_value, beta_value = self._obtain_initial_values()
@@ -88,23 +107,39 @@ def execute(self, mu_l=None, mu_r=None, tau=None,
         component_r0 = self._obtain_initial_component_r0(verbose=verbose)
         self.__left_first = True
 
-        self._minimize_objective(l_cs_value, r_cs_value, beta_value,
-            component_r0, weights, mu_l=mu_l, mu_r=mu_r, tau=tau,
+        self._minimize_objective(
+            l_cs_value,
+            r_cs_value,
+            beta_value,
+            component_r0,
+            weights,
+            mu_l=mu_l,
+            mu_r=mu_r,
+            tau=tau,
             exit_criterion_epsilon=exit_criterion_epsilon,
             max_iteration=max_iteration,
             is_degradation_calculated=is_degradation_calculated,
-            max_degradation=max_degradation, min_degradation=min_degradation,
-            non_neg_constraints=non_neg_constraints, verbose=verbose,
-            bootstraps=bootstraps)
+            max_degradation=max_degradation,
+            min_degradation=min_degradation,
+            non_neg_constraints=non_neg_constraints,
+            verbose=verbose,
+            bootstraps=bootstraps,
+        )
 
         self._keep_supporting_parameters_as_properties(weights)
         self._store_final_state_data(weights)
 
     def calculate_objective_with_result(self, sum_components=True):
-        return self._calculate_objective(self._state_data.mu_l,
-            self._state_data.mu_r, self._state_data.tau, self._l_cs_value,
-            self._r_cs_value, self._beta_value, self._weights,
-            sum_components=sum_components)
+        return self._calculate_objective(
+            self._state_data.mu_l,
+            self._state_data.mu_r,
+            self._state_data.tau,
+            self._l_cs_value,
+            self._r_cs_value,
+            self._beta_value,
+            self._weights,
+            sum_components=sum_components,
+        )
 
     @property
     def measured_power_matrix(self):
@@ -114,7 +149,7 @@ def measured_power_matrix(self):
     def estimated_power_matrix(self):
         left = self._l_cs_value
         right = self._r_cs_value
-        mat = left.dot(right)
+        mat = left @ right
         return mat
 
     @property
@@ -198,77 +233,110 @@ def degradation_rate(self):
         return self.beta_value
 
     def clear_sky_signals(self):
-        return self._l_cs_value.dot(self._r_cs_value)
-
-    def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
-                            component_r0, weights,
-                            mu_l=None, mu_r=None, tau=None,
-                            exit_criterion_epsilon=1e-3, max_iteration=100,
-                            is_degradation_calculated=True,
-                            max_degradation=None, min_degradation=None,
-                            non_neg_constraints=True, verbose=True,
-                            bootstraps=None):
+        return self._l_cs_value @ self._r_cs_value
+
+    def _minimize_objective(
+        self,
+        l_cs_value,
+        r_cs_value,
+        beta_value,
+        component_r0,
+        weights,
+        mu_l=None,
+        mu_r=None,
+        tau=None,
+        exit_criterion_epsilon=1e-3,
+        max_iteration=100,
+        is_degradation_calculated=True,
+        max_degradation=None,
+        min_degradation=None,
+        non_neg_constraints=True,
+        verbose=True,
+        bootstraps=None,
+    ):
         left_matrix_minimization = self._get_left_matrix_minimization(
-            weights, tau, mu_l, non_neg_constraints=non_neg_constraints)
+            weights, tau, mu_l, non_neg_constraints=non_neg_constraints
+        )
         right_matrix_minimization = self._get_right_matrix_minimization(
-            weights, tau, mu_r, non_neg_constraints=non_neg_constraints,
+            weights,
+            tau,
+            mu_r,
+            non_neg_constraints=non_neg_constraints,
             is_degradation_calculated=is_degradation_calculated,
             max_degradation=max_degradation,
-            min_degradation=min_degradation)
+            min_degradation=min_degradation,
+        )
 
         self._l_problem = left_matrix_minimization
         self._r_problem = right_matrix_minimization
         ti = time()
-        objective_values = self._calculate_objective(mu_l, mu_r, tau,
-            l_cs_value, r_cs_value, beta_value, weights,
-            sum_components=False)
+        objective_values = self._calculate_objective(
+            mu_l,
+            mu_r,
+            tau,
+            l_cs_value,
+            r_cs_value,
+            beta_value,
+            weights,
+            sum_components=False,
+        )
         if verbose:
-            print('----------------------\nSCSF Problem Setup\n----------------------')
-            msg1 = 'Matrix Size: {} x {} = {} power measurements'.format(
+            print("----------------------\nSCSF Problem Setup\n----------------------")
+            msg1 = "Matrix Size: {} x {} = {} power measurements".format(
                 self._power_signals_d.shape[0],
                 self._power_signals_d.shape[1],
-                self._power_signals_d.size
+                self._power_signals_d.size,
             )
             print(msg1)
             reduced_mat = self._power_signals_d[:, self._weights > 0]
             try:
                 real_meas = reduced_mat > 0.005 * self._capacity
             except:
-                real_meas = reduced_mat > 0.005 * np.nanquantile(self._power_signals_d, 0.95)
-            msg = 'Sparsity: {:.2f}%'.format(
+                real_meas = reduced_mat > 0.005 * np.nanquantile(
+                    self._power_signals_d, 0.95
+                )
+            msg = "Sparsity: {:.2f}%".format(
                 100 * (1 - np.sum(real_meas) / self._power_signals_d.size)
             )
             print(msg)
-            msg = '{} non-zero measurements under clear conditions'.format(
+            msg = "{} non-zero measurements under clear conditions".format(
                 np.sum(real_meas)
             )
             print(msg)
-            msg2 = 'Model size: {} x {} + {} x {} = {} parameters'.format(
+            msg2 = "Model size: {} x {} + {} x {} = {} parameters".format(
                 l_cs_value.shape[0],
                 l_cs_value.shape[1],
                 r_cs_value.shape[0],
                 r_cs_value.shape[1],
-                np.sum([
-                    l_cs_value.shape[0] * l_cs_value.shape[1],
-                    r_cs_value.shape[0] * r_cs_value.shape[1]
-                ])
+                np.sum(
+                    [
+                        l_cs_value.shape[0] * l_cs_value.shape[1],
+                        r_cs_value.shape[0] * r_cs_value.shape[1],
+                    ]
+                ),
             )
             print(msg2)
-            print('\n')
-            print('----------------------\nAlgorithm Iterations\n----------------------')
-            ps = 'Starting at Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},'
-            ps += ' f3: {:.3e}, f4: {:.3e}'
-            print(ps.format(
-                np.sum(objective_values), objective_values[0],
-                objective_values[1], objective_values[2],
-                objective_values[3]
-            ))
+            print("\n")
+            print(
+                "----------------------\nAlgorithm Iterations\n----------------------"
+            )
+            ps = "Starting at Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},"
+            ps += " f3: {:.3e}, f4: {:.3e}"
+            print(
+                ps.format(
+                    np.sum(objective_values),
+                    objective_values[0],
+                    objective_values[1],
+                    objective_values[2],
+                    objective_values[3],
+                )
+            )
         improvement = np.inf
         old_objective_value = np.sum(objective_values)
         iteration = 0
         f1_last = objective_values[0]
 
-        tol_schedule = [] #np.logspace(-4, -8, 6)
+        tol_schedule = []  # np.logspace(-4, -8, 6)
 
         while improvement >= exit_criterion_epsilon:
             try:
@@ -276,46 +344,64 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
             except IndexError:
                 tol = 1e-8
 
-            self._store_minimization_state_data(mu_l, mu_r, tau,
-                l_cs_value, r_cs_value, beta_value, component_r0)
+            self._store_minimization_state_data(
+                mu_l, mu_r, tau, l_cs_value, r_cs_value, beta_value, component_r0
+            )
 
             try:
                 if self.__left_first:
                     if verbose:
-                        print('    Minimizing left matrix')
-                    l_cs_value, r_cs_value, beta_value\
-                        = left_matrix_minimization.minimize(
-                            l_cs_value, r_cs_value, beta_value, component_r0, tol=tol)
+                        print("    Minimizing left matrix")
+                    (
+                        l_cs_value,
+                        r_cs_value,
+                        beta_value,
+                    ) = left_matrix_minimization.minimize(
+                        l_cs_value, r_cs_value, beta_value, component_r0, tol=tol
+                    )
                     if verbose:
-                        print('    Minimizing right matrix')
-                    l_cs_value, r_cs_value, beta_value\
-                        = right_matrix_minimization.minimize(
-                            l_cs_value, r_cs_value, beta_value, component_r0, tol=tol)
+                        print("    Minimizing right matrix")
+                    (
+                        l_cs_value,
+                        r_cs_value,
+                        beta_value,
+                    ) = right_matrix_minimization.minimize(
+                        l_cs_value, r_cs_value, beta_value, component_r0, tol=tol
+                    )
                 else:
                     if verbose:
-                        print('    Minimizing right matrix')
-                    l_cs_value, r_cs_value, beta_value\
-                        = right_matrix_minimization.minimize(
-                            l_cs_value, r_cs_value, beta_value, component_r0, tol=tol)
+                        print("    Minimizing right matrix")
+                    (
+                        l_cs_value,
+                        r_cs_value,
+                        beta_value,
+                    ) = right_matrix_minimization.minimize(
+                        l_cs_value, r_cs_value, beta_value, component_r0, tol=tol
+                    )
                     if verbose:
-                        print('    Minimizing left matrix')
-                    l_cs_value, r_cs_value, beta_value\
-                        = left_matrix_minimization.minimize(
-                            l_cs_value, r_cs_value, beta_value, component_r0, tol=tol)
+                        print("    Minimizing left matrix")
+                    (
+                        l_cs_value,
+                        r_cs_value,
+                        beta_value,
+                    ) = left_matrix_minimization.minimize(
+                        l_cs_value, r_cs_value, beta_value, component_r0, tol=tol
+                    )
             except cvx.SolverError:
                 if self.__left_first:
                     if verbose:
-                        print('Solver failed! Starting over and reversing minimization order.')
+                        print(
+                            "Solver failed! Starting over and reversing minimization order."
+                        )
                     self.__left_first = False
                     iteration = 0
                     l_cs_value = self._decomposition.matrix_l0
                     r_cs_value = self._decomposition.matrix_r0
-                    component_r0 = self._obtain_initial_component_r0(
-                        verbose=verbose)
+                    component_r0 = self._obtain_initial_component_r0(verbose=verbose)
                     continue
                 else:
                     if verbose:
-                        print('Solver failing again! Exiting...')
+                        print("Solver failing again! Exiting...")
                     self._state_data.is_solver_error = True
                     break
             except ProblemStatusError as e:
@@ -323,52 +409,65 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
                     print(e)
                 if self.__left_first:
                     if verbose:
-                        print('Starting over and reversing minimization order.')
+                        print("Starting over and reversing minimization order.")
                     self.__left_first = False
                     iteration = 0
                     l_cs_value = self._decomposition.matrix_l0
                     r_cs_value = self._decomposition.matrix_r0
-                    component_r0 = self._obtain_initial_component_r0(
-                        verbose=verbose)
+                    component_r0 = self._obtain_initial_component_r0(verbose=verbose)
                     continue
                 else:
                     if verbose:
-                        print('Exiting...')
+                        print("Exiting...")
                     self._state_data.is_problem_status_error = True
                     break
 
-
             component_r0 = r_cs_value[0, :]
 
-            objective_values = self._calculate_objective(mu_l, mu_r, tau,
-                l_cs_value, r_cs_value, beta_value, weights,
-                sum_components=False)
+            objective_values = self._calculate_objective(
+                mu_l,
+                mu_r,
+                tau,
+                l_cs_value,
+                r_cs_value,
+                beta_value,
+                weights,
+                sum_components=False,
+            )
             new_objective_value = np.sum(objective_values)
-            improvement = ((old_objective_value - new_objective_value)
-                * 1. / old_objective_value)
+            improvement = (
+                (old_objective_value - new_objective_value) * 1.0 / old_objective_value
+            )
             old_objective_value = new_objective_value
             iteration += 1
             if verbose:
-                ps = '{} - Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},'
-                ps += ' f3: {:.3e}, f4: {:.3e}'
-                print(ps.format(
-                    iteration, new_objective_value, objective_values[0],
-                    objective_values[1], objective_values[2],
-                    objective_values[3]
-                ))
+                ps = "{} - Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},"
+                ps += " f3: {:.3e}, f4: {:.3e}"
+                print(
+                    ps.format(
+                        iteration,
+                        new_objective_value,
+                        objective_values[0],
+                        objective_values[1],
+                        objective_values[2],
+                        objective_values[3],
+                    )
+                )
             if objective_values[0] > f1_last:
                 self._state_data.f1_increase = True
                 if verbose:
-                    print('Caution: residuals increased')
+                    print("Caution: residuals increased")
             if improvement < 0:
                 if verbose:
-                    print('Caution: objective increased.')
+                    print("Caution: objective increased.")
                 self._state_data.obj_increase = True
                 improvement *= -1
             if objective_values[3] > 1e2:
                 if self.__left_first:
                     if verbose:
-                        print('Bad trajectory detected. Starting over and reversing minimization order.')
+                        print(
+                            "Bad trajectory detected. Starting over and reversing minimization order."
+                        )
                     self.__left_first = False
                     iteration = 0
                     l_cs_value = self._decomposition.matrix_l0
@@ -376,16 +475,20 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
                     component_r0 = self._obtain_initial_component_r0(verbose=verbose)
                 else:
                     if verbose:
-                        print('Algorithm Failed!')
+                        print("Algorithm Failed!")
                     improvement = 0
             if iteration >= max_iteration:
                 if verbose:
-                    print('Reached iteration limit. Previous improvement: {:.2f}%'.format(improvement * 100))
-                improvement = 0.
-
-
-            self._store_minimization_state_data(mu_l, mu_r, tau,
-                l_cs_value, r_cs_value, beta_value, component_r0)
+                    print(
+                        "Reached iteration limit. Previous improvement: {:.2f}%".format(
+                            improvement * 100
+                        )
+                    )
+                improvement = 0.0
+
+            self._store_minimization_state_data(
+                mu_l, mu_r, tau, l_cs_value, r_cs_value, beta_value, component_r0
+            )
 
         # except cvx.SolverError:
         #     if self.__left_first:
@@ -400,14 +503,12 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
 
         tf = time()
         if verbose:
-            print('Minimization complete in {:.2f} minutes'.format(
-                  (tf - ti) / 60.))
+            print("Minimization complete in {:.2f} minutes".format((tf - ti) / 60.0))
         self._analyze_residuals(l_cs_value, r_cs_value, weights)
-        self._keep_result_variables_as_properties(l_cs_value, r_cs_value,
-                                                  beta_value)
+        self._keep_result_variables_as_properties(l_cs_value, r_cs_value, beta_value)
         if bootstraps is not None:
             if verbose:
-                print('Running bootstrap analysis...')
+                print("Running bootstrap analysis...")
             ti = time()
             self._bootstrap_samples = defaultdict(dict)
             for ix in range(bootstraps):
@@ -422,16 +523,22 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
                 r_cs_value = self._r_cs_value
                 beta_value = self._beta_value
                 # ti = time()
-                objective_values = self._calculate_objective(mu_l, mu_r, tau,
-                                                             l_cs_value,
-                                                             r_cs_value,
-                                                             beta_value,
-                                                             new_weights,
-                                                             sum_components=False)
+                objective_values = self._calculate_objective(
+                    mu_l,
+                    mu_r,
+                    tau,
+                    l_cs_value,
+                    r_cs_value,
+                    beta_value,
+                    new_weights,
+                    sum_components=False,
+                )
                 if verbose:
-                    progress(ix, bootstraps, status=' {:.2f} minutes'.format(
-                        (time() - ti) / 60
-                    ))
+                    progress(
+                        ix,
+                        bootstraps,
+                        status=" {:.2f} minutes".format((time() - ti) / 60),
+                    )
                     # ps = 'Bootstrap Sample {}\n'.format(ix)
                     # ps += 'Starting at Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},'
                     # ps += ' f3: {:.3e}, f4: {:.3e}'
@@ -461,45 +568,75 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
                     try:
                         if self.__left_first:
                             # if verbose:
-                                # print('    Minimizing left matrix')
-                            l_cs_value, r_cs_value, beta_value \
-                                = left_matrix_minimization.minimize(
-                                l_cs_value, r_cs_value, beta_value,
-                                component_r0, tol=tol)
+                            # print('    Minimizing left matrix')
+                            (
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                            ) = left_matrix_minimization.minimize(
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                                component_r0,
+                                tol=tol,
+                            )
                             # if verbose:
-                                # print('    Minimizing right matrix')
-                            l_cs_value, r_cs_value, beta_value \
-                                = right_matrix_minimization.minimize(
-                                l_cs_value, r_cs_value, beta_value,
-                                component_r0, tol=tol)
+                            # print('    Minimizing right matrix')
+                            (
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                            ) = right_matrix_minimization.minimize(
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                                component_r0,
+                                tol=tol,
+                            )
                         else:
                             # if verbose:
-                                # print('    Minimizing right matrix')
-                            l_cs_value, r_cs_value, beta_value \
-                                = right_matrix_minimization.minimize(
-                                l_cs_value, r_cs_value, beta_value,
-                                component_r0, tol=tol)
+                            # print('    Minimizing right matrix')
+                            (
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                            ) = right_matrix_minimization.minimize(
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                                component_r0,
+                                tol=tol,
+                            )
                             # if verbose:
-                                # print('    Minimizing left matrix')
-                            l_cs_value, r_cs_value, beta_value \
-                                = left_matrix_minimization.minimize(
-                                l_cs_value, r_cs_value, beta_value,
-                                component_r0, tol=tol)
+                            # print('    Minimizing left matrix')
+                            (
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                            ) = left_matrix_minimization.minimize(
+                                l_cs_value,
+                                r_cs_value,
+                                beta_value,
+                                component_r0,
+                                tol=tol,
+                            )
                     except cvx.SolverError:
                         if self.__left_first:
                             if verbose:
                                 print(
-                                    'Solver failed! Starting over and reversing minimization order.')
+                                    "Solver failed! Starting over and reversing minimization order."
+                                )
                             self.__left_first = False
                             iteration = 0
                             l_cs_value = self._decomposition.matrix_l0
                             r_cs_value = self._decomposition.matrix_r0
                             component_r0 = self._obtain_initial_component_r0(
-                                verbose=verbose)
+                                verbose=verbose
+                            )
                             continue
                         else:
                             if verbose:
-                                print('Solver failing again! Exiting...')
+                                print("Solver failing again! Exiting...")
                             self._state_data.is_solver_error = True
                             break
                     except ProblemStatusError as e:
@@ -507,89 +644,109 @@ def _minimize_objective(self, l_cs_value, r_cs_value, beta_value,
                             print(e)
                         if self.__left_first:
                             if verbose:
-                                print(
-                                    'Starting over and reversing minimization order.')
+                                print("Starting over and reversing minimization order.")
                             self.__left_first = False
                             iteration = 0
                             l_cs_value = self._decomposition.matrix_l0
                             r_cs_value = self._decomposition.matrix_r0
                             component_r0 = self._obtain_initial_component_r0(
-                                verbose=verbose)
+                                verbose=verbose
+                            )
                             continue
                         else:
                             if verbose:
-                                print('Exiting...')
+                                print("Exiting...")
                             self._state_data.is_problem_status_error = True
                             break
 
                     component_r0 = r_cs_value[0, :]
 
-                    objective_values = self._calculate_objective(mu_l, mu_r,
-                                                                 tau,
-                                                                 l_cs_value,
-                                                                 r_cs_value,
-                                                                 beta_value,
-                                                                 new_weights,
-                                                                 sum_components=False)
+                    objective_values = self._calculate_objective(
+                        mu_l,
+                        mu_r,
+                        tau,
+                        l_cs_value,
+                        r_cs_value,
+                        beta_value,
+                        new_weights,
+                        sum_components=False,
+                    )
                     new_objective_value = np.sum(objective_values)
-                    improvement = ((old_objective_value - new_objective_value)
-                                   * 1. / old_objective_value)
+                    improvement = (
+                        (old_objective_value - new_objective_value)
+                        * 1.0
+                        / old_objective_value
+                    )
                     old_objective_value = new_objective_value
                     iteration += 1
                     # if verbose:
-                        # ps = '{} - Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},'
-                        # ps += ' f3: {:.3e}, f4: {:.3e}'
-                        # print(ps.format(
-                        #     iteration, new_objective_value,
-                        #     objective_values[0],
-                        #     objective_values[1], objective_values[2],
-                        #     objective_values[3]
-                        # ))
+                    # ps = '{} - Objective: {:.3e}, f1: {:.3e}, f2: {:.3e},'
+                    # ps += ' f3: {:.3e}, f4: {:.3e}'
+                    # print(ps.format(
+                    #     iteration, new_objective_value,
+                    #     objective_values[0],
+                    #     objective_values[1], objective_values[2],
+                    #     objective_values[3]
+                    # ))
                     if objective_values[0] > f1_last:
                         self._state_data.f1_increase = True
                         if verbose:
-                            print('Caution: residuals increased')
+                            print("Caution: residuals increased")
                     if improvement < 0:
                         if verbose:
-                            print('Caution: objective increased.')
+                            print("Caution: objective increased.")
                         self._state_data.obj_increase = True
                         improvement *= -1
                     if objective_values[3] > 1e2:
                         if self.__left_first:
                             if verbose:
                                 print(
-                                    'Bad trajectory detected. Starting over and reversing minimization order.')
+                                    "Bad trajectory detected. Starting over and reversing minimization order."
+                                )
                             self.__left_first = False
                             iteration = 0
                             l_cs_value = self._decomposition.matrix_l0
                             r_cs_value = self._decomposition.matrix_r0
                             component_r0 = self._obtain_initial_component_r0(
-                                verbose=verbose)
+                                verbose=verbose
+                            )
                         else:
                             if verbose:
-                                print('Algorithm Failed!')
+                                print("Algorithm Failed!")
                             improvement = 0
                     if iteration >= max_iteration:
                         if verbose:
                             print(
-                                'Reached iteration limit. Previous improvement: {:.2f}%'.format(
-                                    improvement * 100))
-                        improvement = 0.
+                                "Reached iteration limit. Previous improvement: {:.2f}%".format(
+                                    improvement * 100
+                                )
+                            )
+                        improvement = 0.0
                 # tf = time()
                 # if verbose:
                 #     print('Bootstrap {} complete in {:.2f} minutes'.format(
                 #           ix, (tf - ti) / 60.))
-                self._bootstrap_samples[ix]['L'] = l_cs_value
-                self._bootstrap_samples[ix]['R'] = r_cs_value
-                self._bootstrap_samples[ix]['beta'] = beta_value
+                self._bootstrap_samples[ix]["L"] = l_cs_value
+                self._bootstrap_samples[ix]["R"] = r_cs_value
+                self._bootstrap_samples[ix]["beta"] = beta_value
             if verbose:
-                progress(bootstraps, bootstraps, status=' {:.2f} minutes'.format(
-                    (time() - ti) / 60
-                ))
-
-
-    def _calculate_objective(self, mu_l, mu_r, tau, l_cs_value, r_cs_value,
-                             beta_value, weights, sum_components=True):
+                progress(
+                    bootstraps,
+                    bootstraps,
+                    status=" {:.2f} minutes".format((time() - ti) / 60),
+                )
+
+    def _calculate_objective(
+        self,
+        mu_l,
+        mu_r,
+        tau,
+        l_cs_value,
+        r_cs_value,
+        beta_value,
+        weights,
+        sum_components=True,
+    ):
         weights_w1 = np.diag(weights)
         # Note: Not using cvx.sum and cvx.abs as in following caused
         # an error at * weights_w1:
@@ -600,15 +757,24 @@ def _calculate_objective(self, mu_l, mu_r, tau, l_cs_value, r_cs_value,
         #     + (tau - 0.5)
         #     * (self._power_signals_d - l_cs_value.dot(r_cs_value)))
         #     * weights_w1)
-        term_f1 = (cvx.sum((0.5 * cvx.abs(
-                    self._power_signals_d - l_cs_value.dot(r_cs_value))
-                    + (tau - 0.5) * (self._power_signals_d - l_cs_value.dot(
-                        r_cs_value))) @ weights_w1) ).value
+        term_f1 = (
+            cvx.sum(
+                (
+                    0.5 * cvx.abs(self._power_signals_d - l_cs_value @ r_cs_value)
+                    + (tau - 0.5) * (self._power_signals_d - l_cs_value @ r_cs_value)
+                )
+                @ weights_w1
+            )
+        ).value
         weights_w2 = np.eye(self._rank_k)
-        term_f2 = mu_l * norm((l_cs_value[:-2, :] - 2 * l_cs_value[1:-1, :] +
-                               l_cs_value[2:, :]).dot(weights_w2), 'fro')
-        term_f3 = mu_r * norm(r_cs_value[:, :-2] - 2 * r_cs_value[:, 1:-1] +
-                               r_cs_value[:, 2:], 'fro')
+        term_f2 = mu_l * norm(
+            (l_cs_value[:-2, :] - 2 * l_cs_value[1:-1, :] + l_cs_value[2:, :])
+            @ weights_w2,
+            "fro",
+        )
+        term_f3 = mu_r * norm(
+            r_cs_value[:, :-2] - 2 * r_cs_value[:, 1:-1] + r_cs_value[:, 2:], "fro"
+        )
         if r_cs_value.shape[1] < 365 + 2:
             term_f4 = 0
         else:
@@ -616,8 +782,9 @@ def _calculate_objective(self, mu_l, mu_r, tau, l_cs_value, r_cs_value,
             # difference:
             # term_f4 = (mu_r * norm(
             #             r_cs_value[1:, :-365] - r_cs_value[1:, 365:], 'fro'))
-            term_f4 = ((mu_r * cvx.norm(
-                r_cs_value[1:, :-365] - r_cs_value[1:, 365:], 'fro'))).value
+            term_f4 = (
+                (mu_r * cvx.norm(r_cs_value[1:, :-365] - r_cs_value[1:, 365:], "fro"))
+            ).value
         components = [term_f1, term_f2, term_f3, term_f4]
         objective = sum(components)
         if sum_components:
@@ -647,8 +814,7 @@ def _obtain_initial_values(self):
             beta_value = self._state_data.beta_value
         else:
             beta_value = 0.0
-        self._keep_result_variables_as_properties(l_cs_value, r_cs_value,
-                                                  beta_value)
+        self._keep_result_variables_as_properties(l_cs_value, r_cs_value, beta_value)
         return l_cs_value, r_cs_value, beta_value
 
     def _obtain_initial_component_r0(self, verbose=True):
@@ -671,8 +837,7 @@ def _obtain_weights(self, verbose=True):
         if self._state_data.weights.size > 0:
             weights = self._state_data.weights
         else:
-            weights = self._get_weight_setting().obtain_weights(
-                self._power_signals_d)
+            weights = self._get_weight_setting().obtain_weights(self._power_signals_d)
             if self._test_days is not None:
                 weights[self._test_days] = 0
         self._weights = weights
@@ -683,8 +848,7 @@ def _set_testdays(self, power_signals_d, reserve_test_data):
             m, n = power_signals_d.shape
             day_indices = np.arange(n)
             num = int(n * reserve_test_data)
-            self._test_days = np.sort(np.random.choice(day_indices, num,
-                                                       replace=False))
+            self._test_days = np.sort(np.random.choice(day_indices, num, replace=False))
         else:
             self._test_days = None
 
@@ -705,27 +869,26 @@ def _set_residuals(self):
     def _analyze_residuals(self, l_cs_value, r_cs_value, weights):
         # Residual analysis
         weights_w1 = np.diag(weights)
-        wres = np.dot(l_cs_value.dot(
-                r_cs_value) - self._power_signals_d, weights_w1)
+        wres = (l_cs_value @ r_cs_value - self._power_signals_d) @ weights_w1
         use_days = np.logical_not(np.isclose(np.sum(wres, axis=0), 0))
-        scaled_wres = wres[:, use_days] / np.average(
-                self._power_signals_d[:, use_days])
-        final_metric = scaled_wres[
-                self._power_signals_d[:, use_days] > 1e-3]
+        scaled_wres = wres[:, use_days] / np.average(self._power_signals_d[:, use_days])
+        final_metric = scaled_wres[self._power_signals_d[:, use_days] > 1e-3]
         self._residuals_median = np.median(final_metric)
         self._residuals_variance = np.power(np.std(final_metric), 2)
         self._residual_l0_norm = np.linalg.norm(
-                self._matrix_l0[:, 0] - l_cs_value[:, 0])
+            self._matrix_l0[:, 0] - l_cs_value[:, 0]
+        )
 
     def _get_linearization_helper(self):
         """
         For dependency injection for testing, i.e. for injecting mock.
         """
-        if ((not hasattr(self, '_linearization_helper')) or
-           (self._linearization_helper is None)):
-           return LinearizationHelper(solver_type=self._solver_type)
-        else: # This must be mock object inject from test
-           return self._linearization_helper
+        if (not hasattr(self, "_linearization_helper")) or (
+            self._linearization_helper is None
+        ):
+            return LinearizationHelper(solver_type=self._solver_type)
+        else:  # This must be mock object inject from test
+            return self._linearization_helper
 
     def set_linearization_helper(self, value):
         """
@@ -737,11 +900,10 @@ def _get_weight_setting(self):
         """
         For dependency injection for testing, i.e. for injecting mock.
         """
-        if ((not hasattr(self, '_weight_setting')) or
-           (self._weight_setting is None)):
-           return WeightSetting(solver_type=self._solver_type)
-        else: # This must be mock object inject from test
-           return self._weight_setting
+        if (not hasattr(self, "_weight_setting")) or (self._weight_setting is None):
+            return WeightSetting(solver_type=self._solver_type)
+        else:  # This must be mock object inject from test
+            return self._weight_setting
 
     def set_weight_setting(self, value):
         """
@@ -749,17 +911,25 @@ def set_weight_setting(self, value):
         """
         self._weight_setting = value
 
-    def _get_left_matrix_minimization(self, weights, tau, mu_l, 
-                                      non_neg_constraints=True):
+    def _get_left_matrix_minimization(
+        self, weights, tau, mu_l, non_neg_constraints=True
+    ):
         """
         For dependency injection for testing, i.e. for injecting mock.
         """
-        if ((not hasattr(self, '_left_matrix_minimization')) or
-           (self._left_matrix_minimization is None)):
-           return LeftMatrixMinimization(
-               self._power_signals_d, self._rank_k, weights, tau, mu_l,
-               non_neg_constraints=non_neg_constraints, solver_type=self._solver_type)
-        else: # This must be mock object inject from test
+        if (not hasattr(self, "_left_matrix_minimization")) or (
+            self._left_matrix_minimization is None
+        ):
+            return LeftMatrixMinimization(
+                self._power_signals_d,
+                self._rank_k,
+                weights,
+                tau,
+                mu_l,
+                non_neg_constraints=non_neg_constraints,
+                solver_type=self._solver_type,
+            )
+        else:  # This must be mock object inject from test
             return self._left_matrix_minimization
 
     def set_left_matrix_minimization(self, value):
@@ -768,22 +938,35 @@ def set_left_matrix_minimization(self, value):
         """
         self._left_matrix_minimization = value
 
-    def _get_right_matrix_minimization(self, weights, tau, mu_r,
-        non_neg_constraints=True, is_degradation_calculated=True,
-        max_degradation=None, min_degradation=None):
+    def _get_right_matrix_minimization(
+        self,
+        weights,
+        tau,
+        mu_r,
+        non_neg_constraints=True,
+        is_degradation_calculated=True,
+        max_degradation=None,
+        min_degradation=None,
+    ):
         """
         For dependency injection for testing, i.e. for injecting mock.
         """
-        if ((not hasattr(self, '_right_matrix_minimization')) or
-           (self._right_matrix_minimization is None)):
-           return RightMatrixMinimization(
-               self._power_signals_d, self._rank_k, weights, tau, mu_r,
-               non_neg_constraints=non_neg_constraints,
-               is_degradation_calculated=is_degradation_calculated,
-               max_degradation=max_degradation,
-               min_degradation=min_degradation,
-               solver_type=self._solver_type)
-        else: # This must be mock object inject from test
+        if (not hasattr(self, "_right_matrix_minimization")) or (
+            self._right_matrix_minimization is None
+        ):
+            return RightMatrixMinimization(
+                self._power_signals_d,
+                self._rank_k,
+                weights,
+                tau,
+                mu_r,
+                non_neg_constraints=non_neg_constraints,
+                is_degradation_calculated=is_degradation_calculated,
+                max_degradation=max_degradation,
+                min_degradation=min_degradation,
+                solver_type=self._solver_type,
+            )
+        else:  # This must be mock object inject from test
             return self._right_matrix_minimization
 
     def set_right_matrix_minimization(self, value):
@@ -792,8 +975,7 @@ def set_right_matrix_minimization(self, value):
         """
         self._right_matrix_minimization = value
 
-    def _keep_result_variables_as_properties(self, l_cs_value, r_cs_value,
-                                             beta_value):
+    def _keep_result_variables_as_properties(self, l_cs_value, r_cs_value, beta_value):
         self._l_cs_value = l_cs_value
         self._r_cs_value = r_cs_value
         self._beta_value = beta_value
@@ -810,8 +992,9 @@ def _store_initial_state_data(self):
         self._state_data.mu_r = 1e3
         self._state_data.tau = 0.85
 
-    def _store_minimization_state_data(self, mu_l, mu_r, tau,
-            l_cs_value, r_cs_value, beta_value, component_r0):
+    def _store_minimization_state_data(
+        self, mu_l, mu_r, tau, l_cs_value, r_cs_value, beta_value, component_r0
+    ):
         self._state_data.mu_l = mu_l
         self._state_data.mu_r = mu_r
         self._state_data.tau = tau


Test patch:
diff --git a/tests/fixtures/system_profiler/system_profiler_test.ipynb b/tests/fixtures/system_profiler/system_profiler_test.ipynb
index 5f5f907d..e39bd8f0 100644
--- a/tests/fixtures/system_profiler/system_profiler_test.ipynb
+++ b/tests/fixtures/system_profiler/system_profiler_test.ipynb
@@ -26,33 +26,19 @@
    "execution_count": 2,
    "id": "8e4e1657",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[============================================================] 100.0% ...queries complete in 21.8 seconds       \r"
-     ]
-    }
-   ],
-   "source": [
-    "data_frame = get_pvdaq_data(sysid=1199, year=[2015, 2016, 2017], api_key='DEMO_KEY', standardize=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 3,
-   "id": "29c48c75",
-   "metadata": {},
    "outputs": [],
    "source": [
-    "data_frame.to_csv('./data_handler_input.csv')"
+    "fn = 'data_handler_input.csv'\n",
+    "if os.path.isfile(fn):\n",
+    "    data_frame = pd.read_csv(fn, index_col=0)\n",
+    "else:\n",
+    "    data_frame = get_pvdaq_data(sysid=1199, year=[2015, 2016, 2017], api_key='DEMO_KEY', standardize=False)\n",
+    "    data_frame.to_csv(fn)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "0ba6f413",
    "metadata": {},
    "outputs": [],
@@ -62,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "9dea1924",
    "metadata": {},
    "outputs": [
@@ -506,7 +492,7 @@
        "[160837 rows x 38 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "be4df0d1",
    "metadata": {},
    "outputs": [
@@ -539,7 +525,7 @@
        "              dtype='datetime64[ns]', name='Date-Time', length=160837, freq=None)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,30 +536,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-=======
-   "execution_count": 45,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+   "execution_count": 71,
    "id": "928143c9",
    "metadata": {},
    "outputs": [],
    "source": [
-<<<<<<< HEAD
     "# dh = DataHandler(data_frame, datetime_col=\"Date-Time\")\n",
     "dh = DataHandler(df1)\n",
-=======
-    "dh = DataHandler(data_frame)\n",
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
     "dh.fix_dst()"
    ]
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 8,
-=======
-   "execution_count": 46,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+   "execution_count": 72,
    "id": "dc869f74",
    "metadata": {},
    "outputs": [
@@ -581,37 +556,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-<<<<<<< HEAD
       "CAUTION: Multiple scan rates detected!\n",
       "Scan rates (in seconds): [300, 280]\n",
       "0 transitions detected.\n",
       "Suggest splitting data set between:\n",
       "\n",
       "\n",
-      "total time: 7.66 seconds\n",
-      "--------------------------------\n",
-      "Breakdown\n",
-      "--------------------------------\n",
-      "Preprocessing              2.72s\n",
-      "Cleaning                   0.16s\n",
-      "Filtering/Summarizing      4.78s\n",
-      "    Data quality           0.21s\n",
-      "    Clear day detect       0.16s\n",
-      "    Clipping detect        3.68s\n",
-      "    Capacity change detect 0.73s\n",
-=======
-      "total time: 13.36 seconds\n",
+      "total time: 2.92 seconds\n",
       "--------------------------------\n",
       "Breakdown\n",
       "--------------------------------\n",
-      "Preprocessing              3.81s\n",
-      "Cleaning                   0.21s\n",
-      "Filtering/Summarizing      9.33s\n",
-      "    Data quality           0.30s\n",
-      "    Clear day detect       0.22s\n",
-      "    Clipping detect        7.79s\n",
-      "    Capacity change detect 1.02s\n",
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+      "Preprocessing              1.23s\n",
+      "Cleaning                   0.07s\n",
+      "Filtering/Summarizing      1.62s\n",
+      "    Data quality           0.07s\n",
+      "    Clear day detect       0.07s\n",
+      "    Clipping detect        1.18s\n",
+      "    Capacity change detect 0.30s\n",
       "\n"
      ]
     }
@@ -622,19 +583,15 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 9,
-=======
-   "execution_count": 47,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+   "execution_count": 73,
    "id": "8f726246",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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OLwSpzpCQkJCQkJCQ8JVgl3crX5KycpPmuyfsEmysP9tnkJBwcZBsOSEh4ZnikmQOT57Y3enWhOcPfvan8mf7FBISLgqSLSckXETs8rLyJckcDoeJ25KwO/A1r6+f7VNISLgoSLackHCR4ME7dUE/VzoujXM4uhRHTUi4/PiaO3c5sSTheYNkywkJFxFpfF5CQkJCQkJCQkKLXR5rJcn8hISEhISEhISEFpckc3j61JWfMk1IeCb417+YkusJuwPJlhMSLhaeG6XhC0FaLRISEhISEhISnik8z4kpJxeCND4vIeEp8I53pg7PhN2BZMsJCRcRu7whJXEOExISEhISEhISWiTnMCEhISEhISHhK4FTF/bzDNA0DW9/+9t517veBcDq6irvfOc7efOb38w73/lOzp07d8k+3iVxDtOYpoTdgrs/muKnhN2BZMsJCRcJ/iL8PAN84AMf4MYbb2x///mf/3le97rX8eEPf5jXve51/PzP//xF+kBPxKVxDjeu/Hp6QsIzwd1/nHq2EnYHki0nJFw8XOoJKceOHeMP/uAP+PZv//b2sd/7vd/j7W9/OwBvf/vb+d3f/d1L9fEujXO4b39qSEnYHXj3/zB7tk8hIeGiINlyQsLFwgU2ozyDhpQf+7Ef4wd+4AfQeu6mnT59mv379wOwf/9+zpw5c8k+4SUJJY25FEdNSLj8SKMgE3YLki0nJFw5OHv2LHfddVf7+3d+53fynd/5nQB85CMfYWVlhdtvv52Pf/zjz8r5pTpDQkJCQkJCQsIzxUXQOVxeXuaDH/zgef/2qU99it///d/nj/7oj5hOp2xsbPC+972PPXv2cOLECfbv38+JEydYWVm5oHN4KlySsnJVXYqjPj9gSXzNKwnHjqbv46nwVPb6ZH+zqGTnzwKSLV8Yks0mbMMlLCv/g3/wD/ijP/ojfv/3f59/+k//Ka997Wv5iZ/4Cd74xjfyoQ99CIAPfehDfMM3fMMl+3iXxDlM4/OeiGeysMTn7Hzu+TbTwqfa/eXAv/ml7Nk+heccor0ar5/Upo1PnbOXG8mWz4+nC3C69vyVvDZhl+MySNnsxPd93/fxsY99jDe/+c187GMf4/u+7/su8oea4ynLyp/+9Kf5tV/7Nf7sz/6MkydPUpYlN910E294wxt461vfymh0fhLLwqLn1LFLcr7PGVgUdadfPfMGVEONbxeU7t/jY8ZrGuW2PZ55Q6Wa9pgWRYZmSvOk75dwcfCmb6r5nf+a2Bc70bXXWjXbHovI0OBp/x5R44mhzfnsNtnypUGy5SfHTps7nw3GNbe7fhuvMUCjXLs2J9t9fsBfpq/5Na95Da95zWsAKUX/q3/1ry7L+z5p+P53/+7f5Vd/9Vd5/etfz7/8l/+SP/7jP+Y3fuM3+Pt//+8znU55z3vew+/93u+d97X9/iU73+cMulkTANOJPM8XgRqvMV6ToZ/w9/jazJs2gs06X93OqPZ8mceErw4vfbl7+iftcpwvcx3tVT/J49FGd9ppPM7O18W/x2N0f0+4OEi2LOhWXWxnbY2/w/Y1Otpz9/cIHQL1J0Oy4YTnKp40jPy//+//+wlkR2stt912G7fddhvf8z3fc0nbqBMSEhISEhISrjh4nhPzkS8ETxrydB3Dxx57jLvvvhuAyWTCxsbGE56TsJ0zqHdk88xT/A0kAtUoTPg3Rrfd5+XMszV2R2ZSd7IyO7OWCQkXip2Z6a69Ppmtaa+2ZWm6GZj4uvNl0WMmZmcGMdl0wleKYkdGMNpuRFxPy0B0iKVhvcM+z5chB7HjWO3pPn/nWpxsd7fhAvmGF9jpfDnwtKzwX/mVX+H7v//7+aEf+iFAVLvf+973PuVrTpy48j/4xYLdsdDEf82ORciGRWjn31qyM4oc3W64EfF5uvMa2Vh3LEKBm9h9TcKF42d+8vlJ4t9p10/YHIOdZmhKPy9AZMGG408fuX6ZN09ZfovvY3fY/87GloSvHs8nW7Y7bOjJENfbfEeDX4lh4LNtzzGdtfipjvd075ts+bkPD3ivLujnSsfTOof/9t/+W/79v//3DIdDAK677rqnLSdnz5M1aOfGFReGyLXqZgtzTLuZgmQBgW18F41C++3HysNxTPhbPLYOzmD39e0xO+e1M5pNC9NXhgMHn5/k8vPxXnf+3rXJJ3tu3smwdLPi0Ua7GW/deQzYxr/dyfuKSPb8zPF8tOVoQ1nH+ct58mCjXdO9psFvC9btDq5hxISmtd147C4nN/5/Zxf/M3VgExKeDTytc5jnOXmet7/Xdf20B11efn4sQt2Ny4bMH4TsiZ8vKpk326LPuEluy/qF15wvSu0uRIU3lN6iO5nGPDiMtrMRd1/TZhzPQ/RPi9JT466/+fT2vhvRzWx3/w/zTGL70ykfN6FTc27j223SoOgj9hu78o3XrcMItAFSNwu585wiUlbxmeP5ZMvRGXxCUNNx2M7397hW5xgmqm7/vnNNbdf2ELznbFeZiM91+NaOuw5jDOyL0GQIaS1+TiGKYO/isvLT6hrccccd/OzP/iyTyYSPfexj/Lt/9+944xvfeDnO7YpDV6bAdja7Bg+dLEh00qzXWBV5WdtLc9orumuBw7fP6W6IuTc4PFVYeHY+Jy5SfZ+FY8j7N8pTMS9xR/kbPO0idj4nMckwPD/Q/e7P953naMbM7Wcn1aGb4XZ4GuWe8FzN05fiTHh95Hw1ncAp94aJarZt0Oez2Sink2z4+Y3zff87g2sIFB81t6nu60tkvdVhre9mDXPmFZqmI0kWnzPw2fmD8rBH7Az8Y8ax4fwSZsmOr3A8B0rDF4KnzRy+733vY2VlhZtvvpn/8B/+A1/3dV/H3//7f/8ynNqVgyfLtMWMXXeBMZ1FpLs4lN6Isxh+dkav0mAydyDj5hudRnlv3f5fe1mwYlTa/ZvtnFO7IHXkb9pMzXlKdd1yR4pkdx92Ug2eTIw62mEZ6BA77XVnEBNfY3Y8bkOZTXvVZtMrXGvfO49ldry2JfzvcFAjuhvqTntOdry7cT55pZ0ZQ+vnmT14YpASfx8GfmFblek0osRMokXWzW1Bj587fjFoMl7j8OToJ/DIu/bdXZvj/fVUww6SHV9Z2O2cw6fNHGqt+Y7v+A6+4zu+g9XVVY4dO4ZST/3B1s5dtPN71hE30lo1840nZEms1zglZYPmPNGq7SxIO8sS295jh2Mo7yuvnamG3JsnvD4ev0Y2R+0Btb27rtXu8hpUIxt0J2Ju8G22sbupdst9dDbfbiTb85ax2v1lqg//1u6YRLNzY9GoNtu983k1nr7PWFez9rndTHfMhMcMC8imGqkR3fcwXo63szS881+QjdYoOXa7YcYNV3Uasnbcj91jSVZnewYmPr/whukOQe7nE57rtrwzm9bNGNc7+IExEC6x1CEYsWruJDp863h1bTjH4HzHXgPVpx+arrSa3wsmrNtb1BReHMgaR6PcNo5j3CdiEgBof+95S9W5z6J1xs8Ws+jnE4oHkvD2s4ldLhv6tJnDd7zjHWxsbLC6usrb3/52fvAHf5Af//Eff8rXbG1d+V7x02EnvwrmjSZzwVTdZkTaxaMTaY583jqRWVhItmX/Ok5kiZ3/3cvvMOdwWf9E5zEeJ/eGHEPe5YJ51TazdN9nJ1dGo9pu0+7m+2SSOxGxDLjb8dm/2D2fsytQDdvLvjvFf7vNIdH24oZrO7bsOhzDmHXZGRjFf6NttyU2r1s7lXPQ5F4yNDu7nrul7G4DTPwM3S7nnU0scYONn/NKycBc7vN4rtvyzszazkaomOW2HVvJfYeL7Z/IYY2VnJlyrf3knbUtPj8jchHn9wTMg/ida/vODHj3PXUIzA2KMvBvWz565/7r8tjj+cvxtje5PNkanZBwIXhaq1pfX2c4HPI7v/M73HXXXXzwgx9sNQ+fDAcPpSgmYXfgff/L7Nk+hYSEi4JkywkJFwleXfjPFY6ndQ6bpuHEiRP81m/9Fm94wxsuwyldGTifgHWM7tru4M5Pn2xbh7JGMdyRSen+RGmbtlnFg/UKG8oYA2+xIcKEmFWZR6BSoogZQoNCYZhzXCKKkFXsdou2pTrmWnU7+WJdPcad46Vis0D3PRKuLOzk2+3sMO5+911E25nTFea227XtaKc6ZD+s10+wc5hnd7qPdxu0YjYxvk88doOn9Ka177aT38d7bzsXMdpr32etfEn8LF2+2U5ZkWcTKePzzNGtZHS1YeGJ4+x2/ttt4otZb6ClRcQMeIPHeuEbuu66i0EDpnuc8P++t0G+RjOjofSGGs/AZ9vuCe23Z8HjufW9fYKKBcx1a3fK58RMYXedjq/pdj4nXHp4py7o50rH065O73nPe/je7/1errnmGl7ykpfwyCOPcN11112GU7v82DlxBNjWcGJ9KHu1EgTzDa9700spQ7UskFgijguP9YpB3MQ6pYd56cKw4HPqwAmMf283Y29o8NvKH8Bc2sZrauXaY8UNuQxk6qx9nmklcLp6ik9wJHcsxLHpJi1EVybiBhKbjmDOx4vfc6uX6effa+FNSxfo2lu0ofnx44a5nbLQDXy6z4/3Sh7spvQ2PG/+fCBstPPX9sPzuu8Tnc0oqt1SIzrn08XOYCc+ttNJfDacxZ3nmvDk2Dk8YCfdB4Kz51W7PsW/xf+bjp12g5bSG4Y+azvnu99LjsH6eaAVO4wtOrxm3mBY49r3Ouj6ANudQj+/V+K6WwY+eZc29IShCJ1GMHiiCkBXQi2+PuEyYJdnDp+2IeUtb3kLb3nLW9rfr776an7yJ3/yKV8znVz4iV1O7JTEgLmURzezEgnHLc/EK2oVuYd0mj004NhUNX1v5wsAGoVCd2QQ5tqEslF5FCOfMdjBYZT3kmxKgWHKXOJDnEWHCscakHGSccvf6nnLmpLXd7kyGgV+HkFLw4ossFPVhEaW0JwSNs+uAxyvlzTknL9x5bmO+++98m/incjOk8ktQ/YNQCtP6QNRv/O9zh1DCTCMUi2X1YVgxIbHMjQz31CrefCy6HM2qbZtchEtH9Z7tpRkaOJGHTfVYZBjMuFcYnY+Hr/rHMbP44JNz4K96vNsoFG6RJqw5o87fLvxRimeaO+Xw4bP1319KfFcs+Vu00U3WI0NI7GZbuAzNlTVOl7dIKHvLRWOiVKto5h7DUpsZL/rbWtSckocOlnnVWu3U5rWPiO/HCXWGQPy+B4AE9VQYplQh2B9LpGTe81W2DeaYL/x80RIEmC+PcdMeeO7wxRcy98F2asqL5I4sUklNatcOjwXOo4vBE/qHP6Lf/Ev+O//+/+eH/mRHzlvd/L/9r/9b0960LNnn1sXLWpMZWimSPdb1CiM0R5qexOIiJsa8NtLGHFxKDCMqYM7OF+sKhp6WLKWjG8ovcHHjUpJ84rx2ydH5Bi2qHF4alzb6abDglQFp1V7RRZI/zHqXfI5x9kMi1QnkxQyjI7OAuWjxldwgj2tw5h1SuExywmSGZqEzuW4we6WBek//cfnzrif7kaQo5nhtjtI0Sny83JaNzPRzZp0nbIMjaMjQt3JgHS/58xrMiUZ5bjRZV619xZ0xYNNm7XZUrLhLvucNTVr3zva9DYNUD/PZta49m/doG2n1mLMUnazLPF5sWM/OtRRc67uOAyXaoN9unGCFxvPJVsGCXIcnlo12xpBmtDpG203Ool5R2YmZvs8czF1mE/sgTndYMHnZF7xsF5nyeVUyrUaxW0w4rP2XigwOOSeWvA5J9WYHpZZCLZAaBk2dDUXGGad4LnAor3YeeY1Vs0D9Vx5xtTbyt9i94Fioeb3QabEQex+/i1Vt/fzThvuainulvU54dLhSZ3DG2+8EYDbb7/9Kz7o8rLn1LGv/qQuF7oSCHTKTrEsEdXyu5nCmDXZpArO2/bONus1Sz6nxjFWtUSHODKvGJJxRk3E+Ws3LckEZj6W+XyITmVmrSHyDzUTmtaB7SHSISWWwhsa5ZmqhmFgIHZLKJG7BZL9jBt3ERa0eO5dpzB2QMPcmehy1GJ5EqRsvuGr9jpk3lCreldErd/27dVzZlNtJV46m+j8+5o78651IE27gQHMcAx91gYBEPQ00SgPVs0DH+WRzclLliQGLZJxr9vsSsyAZ17jlfyr1bxkJuW1kNXxur0XHJ4tVWO92uaY1tqx5EtKb9hQFfi5akBXXmfn7zuzM1rFLmvfdo6C2DpsdwgvVcCzUwj/UuO5Ysvx2scAB2g5ziWWWSjtRpH0JV9wlskTaDYR0QEDcfZ63nKCLQBGPmPRWx7WW+G9Qzd9+KqL4KD2kQzkwGfz9ZYGRczyGSAXPdsQPBdes6ZmIqFE09piz5s2c7mhqvk6G4L0mJ3v2mukYOwM5FHzDPhOOTTYHuho5lI5CReIOCFlF+NJncM3vvGNNE3Dl770Jf7hP/yHX9FBi/KCz+uSY6fjEhebeG+V3rRlNdvZYEc+wwNT1WBCU0fMLve9ZaLqJ3D+4uKxpeZOnZyDlKMd8wyNRTImUSw4C85fzF72vWVT1xRunjk54EoeNJtkXnPI98Pr5ptuEyLaARljH7KPKoi0etHg6gp3tyUbr3BqvsF2R/xFZzY2o0Stx5iNgSdmYM533a903HjTlX2u3dJbDBzaZqlIrFdiz/F7dHgmqpk7Y8HRnwV7AtlQ5fi6tfNuprjAoL28V41johxT5Rh6yzhkS2KJ2IT3abynH/hdeXASs1Dis4icyKLPW+uocWQYptRkSHYdAqWhm1UPZbo8SJLknaCuxslnV35bZjQ6gbVy20j/WXAa8WzT+IzXumu7F2rLl5tzeCXb8k5nPGr8RXpP1Csc+IwtqnZ9BbE1IDhodNZtRd8bxuEyR674IDQKTmi42smrH2YuVRYrPt3BAdYrKgWbqmpL1ef0jIlv5rxu76TBxOtttKDYwChBDu396Tr2DEJL2mAWroccY7Mzwq+t5HQD+c5eU3pDrua2PGZ7E0uO7tj9fKIQpEziV47nBm/wQvCUdQ1jDJ/73Ocu17lcFnT5hfH/seOsG3kWwYmLRHp5bO4oFhh62DbTkXtNL/jaZ/S0vYGXXN4uXv2o4eY1S07mVccFTkFbTu46WPG98rDRFd6w6HOWfE7Pi4MovBhCxlEyKnnYwGMGJ2Z/YllEe3m/lkcZmmViw0xc8LY1GnSacPLwe4ZmS1VtM0OXFB0Xpi62icOe5+8Jzxw7tQmjox5HfcUfmJdxs/BYWyLzpg0+KtW09h1tOUMz9DbM9Dato2dC4BGfB0KZyBDuYRE21uUYPITNKccw65SZCwxOiSN4TI/pe9tmY6KdFdh2g433Vc/PdUHjZytCJrS1XT8PznJMqyWad65BvFZtFj/YdLdpZV5afGIsfSH2223aeb6jux63NB0fu4vDhKlgD7CdJhEbr+LaBrTrngk2HnmtsiYaqpCRdECGoocNlBy1jcdXeFmvFaoNgPc4se9Z4H2XXniMlZK1OENLVhtJKCy7LOjczik/8fwiPSnDtA5ljZfAzYd1mjiub14JivZahvPuduSX3rYBT47YcIlpr1nXYdyJtB4/c3h/YT9XOp62IeVFL3oR7373u/mmb/om+v1++/ib3/zmS3piCQkJCQkJCQkJlx9P6xyeO3eO5eVlPv7xj297/Kmcw2NHr7zoI5YtIh8OJBPQ4OmTseWrbZF8ERpGHD5kDCXqK71hrGrJMHiFQ2FCw4ZFuC110GjbUPPMJMjIuQ1fh+xKLAtL+aDnhaNSKd+W9PpYSm/pe9OWng2KFVeQeYVQpBWzUFbwCpynjVozr7dlDrNQlgBo1LzrzXYibgdSvgslEkeFJhKcu6P5QhYocGn6ZMy8lIJQgCd0Fm4vLccyEXT0ya7gBpaf+PH82T6FbdhWRg5ZuTyUk6NkUiy1xtJT0drWnPs3odk+PsxLl3tLofBz6ZA8lHT7oRzXj9lfL9nw42ae7el5Qy9kFis8ZbCxAk3tPUW8p0ImMmaqHTBWtVAiOg0ysbM085I5P+QGNAi/tgwE/BzNki9Cidoy9nWbIcHTZtfls8q5Orx0pnZpFPH+91B1SsqxC3Tn99Al/u/MuDwTe77cZeUrzZa7aNcm5ShDM9/2So5hEhhzLpT+S2cCJWaeVY5Z8ALRG1xwlg1dt5nDaHMRJ3RF4WXMI8ga7ZhTEKLtA9jQKTxAM+lo2MZytEWzGfaWFV8wQ9bQSN+ovKzTY1WH79633EQfsuexHF4E3m/U+GzU/F5wgULRnqfqaI76eXb8HFNKb1uqRaRZQKBg4CBkOieqpg7cxZ1UoITz4PnMOYx4ulF550O/f2Vt9OLYiVMYnROZf6lbqZnIy4roYecluVBeigtLX0kjx4LPmCiH917m0TqD1opln7epf4AtVbNARsb2jrm4OcRFQEoAnolyson5eckiblVDL4LXAMZHTcTQpefn0jZ0uI4n9YTcazyyyMoGL58hkrXjhuwjwRnFgs+EUB2ug/ATaUsdsWs1llOilE/uDZWaMAiv7zo0XV5nbHJpuHKlF17ysuaKGjsW9d3qIJPUcuZ22G/GnAsaN0s87b/ryP9PqjELPmdVTYF5qbPbgV94BcTuSChbpx7KbkCFCZIejgEa5X37V3E2favtFjfMwne7TeclXO07m66Xs4mNNjXCTTyux8Kr9fMZ52LjsqwVoSmnqyIQ1QMkAIKtIA8VZX10uG5bSoWOf91uuN1Z4jWeMgRrU5q2dLepqme8wbrLbOdXii2f7x6PTR4NtPbcbbAovWHMPGiI3bwlJgTWwmVViO1sKGkYXEJTdxyp0mt8sJUmOJsDb9hSTmSR2O60FyEB4BC7r3D0UXxejxn5vF0/TbDDrcARjEHYos/JmXNrNYoxMPQZj6oNBmQtH9egyJVh2ZVkgT8b1+tI2YncQVEioOWTd7v4Y6d2bKyMY/psCMxqXBtUNYGr3IT9cWfAEoOg5/NM8vPB8zyWsok4c+YMv/Irv8Jjjz1GXc8Xx6dyGhcWL87JXSjiIrRTADciZkYKZNh6dIKiUyWk4Iy+twx9FoR2Jesy9HEDCtlDPKPwGhH9BRU6OQFGQbjaoCiYd2DGLNzIybFrFBPlqJSnxNLDMvCGKnTQ9cL/J8oxCpHwCV21nyc6nkOfBW6JZC6nqgnkfBF7HXrDUT1pF582c+h929Ayaj+joVKOAkvjxU3NvBJJBioaPAUW613b7NBqioVnROe8TwYhdxi16qooAaHkeajminEQ3/yWZ39D7TrXfSSzEcnm3a7yrv0OvOWEHrPXlS0vSisVggSPVbp9rO3ID1N6UPN7Y4ajDJyrmGk0nUAk2rHIwQhndVPVlD4D5Zh0SPeyKSuGPqNg7nzVShy1G5sRW6ppubkLPhc+GdFGPEb5NnMZg6Q8nLvMPZd8KcQABoZkbZPXRDUoL0FQvD/n8iS+bRqI64QNAeRENSIxss1xkGt/Ts3I0PR91soHNbinDXYuN+fwSrHlbvVm/rhu14poz92JUD1v2FDSJd+VrJG1Wq7jyFkqJRngMXV73Xvhe4rOYRt4K8OKs2yFLHHFnDMYdWTzEGAoL+u2RjHFc43r85DZou8NW74mQxzSuO6W3rDXZXzBrjPBU6DbDmjUPEj3+LZi5MIa75TsRWNfS0YxZP6iA73oMs7pCofHM5ek2lAVIz8Xa8rQ2xIfUaMx3m+x89siwdWY7ZUdmAejV1rQfkXg+e4cvuc97+GVr3wlr3vd6zDm2Y86nylixFOrpiXjxkYN1HaF/aJ9bC71kYX/73dl21yReUU/vG7gLZmXkXUNnlVdsxgyIpmX7ISk8eUmXkKzjpOFKmzQMZoVR2remDJVc4HrnrdkKHFKVUOF5zEz5oamTxkygPGcpTwsm+9+V3IuOI0Db9kKXcoFEuU65s6k9YqiLeXRCgYPvGHJF222Sbo3ZXHNmMuRyHlKV6BBJFF0aHCJ2cRYKhp0HM6YkZlGgYXQ1OK8b0t1z/cFKW6mJiz+UbzaqidmCnrh2g591hLUC0wgs4tWWrRr2Th0CBxkc+wzF6HOvGrlj4rwnb/U9/gUsN9btkLmLWMumeQVlCiudyJX4LzmhKplFB6KxquW/B8zh6Wfj4UsO0FcjuHGpsdJVbf2XymP874NWo4SAxXNpq4ZuYypaloneUNVLLtCBIm9oWDeUR/lmUBscawakRhhuzh1DJy22O7MSKZQgsajbDIITkF3nF/MHj6ZHbdiys8TdLtmx2y/Lq3IeaAbwNyBkuBeBydqrn3Z89KMEb+3q3zOQ2raBhYAJ1TN3lAd2lI1JZqtEIw6PEM0U+/bqk7MhMfvMg92m4egfhjUKvZ6y/04rvU556hCdh0WvQQiQ6+ZKpFJiiVhpcQZ3OMKBt5yWuVsKaEZqdChMNNRSkyzpl1wWKXxpMAw9Q0jbxkHCo/DM0PE6KOUlPEdZYFOBSFWn7RvWGbIlIY6XONJkB7LvQjdw1x8vAw0kUhXuthd+89VPBdG4F0InnZ1Go/H/MAP/MDlOJeLCtOJACPfAtg2VimKUPd3bAqx9BRRho0gTgmplGPgo+6WTF2QckKBRnFOV9zY9DmO3Jzn1IyegqmXLssSRRkya7XyNDj6CFexBoYhAp6F7k+gjVptOM8VpIQWpWrmJWXpFu15w5ZvwqLk2xmeEhkr7jcbITOzfYSZRzKHhTetozpykm08F0rEWXCAo2QPzJ0Q5WGzUxLaqeUWN0TpgpUNdYINx3HtNIv4HV7OiRVXIuJmmocu9PhdnUXGEEl3b5dOEKbteMV1zZA4b9v6uVOvvArOuXxHPW84p2Qyz1k13fY3EBs3QBlSHwcKx9GppkFRolj0eRvo5KjgMEKF6MjFxxvEjmUTU6wzIw/O4iYVOYoJ83vLhOdLkAHKe3zI0Jco7mcuVSN8yFCexKFQ8lmCUyGZT8WUOL8oZEdDqX2DihKR0ZGASUOQUym8acvWE6JskDgK/Y5DXis/53Gq6LjOhZy7GeDu97Wb0XUcYpBTess5Ndv2vJHPpfs3lFJBvqOej2MR5XuqOnJLUct1gGp5iDGT/aJmyKftSWbK0fdiYxMaShSboSqz5SsGCh5SNXtd1q7Nmdft5J8SRQUsK8XUy/4xQLGO57SaMGu5iRLs3OYLPhE+00Q5JqrhjJ5xoCnoampW4fgyj1lTBQ5sFIWPthHvwTi3WbLfbm6ToayNdzil2ufE6xMz7w5Y9rmM/1OSMFCAj5Nbwjo99BlrfkYT3iNSOdjhB3VpQAm7E09b13jDG97AH/7hH35FB73cE1LOZ6Alpl2ou4v4yGed180do34g7UcU4ZjDsFHGksQxPeNg06NEsYAOjp6UvfJQLhh5YXzpsIEAjDJHGaLPHEUvLAQa4fap8J6WeSk6KvdL2TZmImHRyTEdgQ8ZvsboIBbBASxCRnKfj7I8kp0pveZI0w+zbmXjLL2mCA5fEbI5Y+XY73pt1kXEVlV7rF5ncytDxF9gZOg8mkOuP+ewtdktE/61rRRQn4wynGNcaIGWY9OdgXu58cFfvXzZnSezY/lbV6piLppbBM6VZT7+bqxqRiFbEktuRee7L0M2rIdkYuJovFjed0r4dGK/VmxVwaPN3EHPgDzYcpStqRAbf1DP6Gmx2fiJinCfFPEzoGQzDJvxK+tlchQL3oRMoWI1lLR7wUGUc9csojihqvZ6FSiWXBYcCYIkjdhf7jUHXN5mKCOPMTqNsXknlrCjHcfMUeS1tQ5JsMXIVY7ryoLPmSC0ifhakA26nQ4TbHl+DS9vWfly2vL5kDOfSQ9suxbDUEWIItUgm1O09bguRYH0vrctX3VFR560wHrFXi3X/4W+oKfn900PWScHYV1eDXzxZWJjnjzXR35sWLMXrGPRSAZwZDyzYA+rxPsHbqqH1KERr0aoN0suD0kIadCKwflWh99XhnW4iE6sqsOeM8+e2pDp1/FahORGgW7/boNN97ysCXFNjde+723g+Zqwp5hWMgckaBmQtetu3EPzUHmDefY3NsOZzuPPK3iVZit/4AMf4Od+7ufI8xxrLd57lFJ86lOfetLXVNWT/umS4Lxiy17TJ+M0k7Y0BOIoxnmcMVsWScrSERydGMlK9MJmNaEOZVzDNT6jgbYb0yHdjVn4vpecxSOLzMhZHtKOxilKBcorciDXnqHPmNHwqhIeGscvQ3G76/E5PQnnJhzDQs0bWW7wOWdDkeBW1+OvzBb7XclUOb5stjjsyrZMXaJYMZ57vWOfyymJ0ycaDroCkCiyDKXuGlhX82tS4VjwFt+JQk3IDs0zVeJ4NMHRnNIwVvNFq1vCj46sDYuV8Qq0lKLjpjELZebtwtruWemkO37s8t3EkW8JtFF56S2bnSkKIDYRp/fkIfObeUM/OC7RkRJyvXBke+E7kFYl2bhKFIsu55SeiMAwNjQqNQxR8yy7Env1tWzC901hvwLnFQM9F3GfUlEqRAEg8/hKMQu1rEJB45EAymtKhKxfIt/zCM20zQgRymo1R1xOjdixRt5zjOecqtusiWyukhUVjTqxSRWCnWlwMuPmHF0SIeOLI7mqHbnT7WvnovS67YCNWe0GWs286JD3w/e0z/XabHqch9sKizNvYIl/25lNvJS4nLbc5Xu38+qDozEJJfqW1xkCzVgmjcHhHle0DRV9r6mUgTC7e0DGIVfwoLH0M491UkmJM96Pu3D/hGx34aVZqGc8WSO2JNUNOY+V3AkPLxhHy0dXgAfVKYD0M8eokcBggA4DARQjFHuKRrr40ezzhipku0sUoxC8aK+5tumxbquwN+k2Ky7ahipwch0Dn3MyvLHQLoTXGtUEVFiPHT6s46YtkUtCQ65rFtaECqn2bKma5aC166DdK2NjYuQzl4ECMlMNPW+pIkfXm3nXtd/etf98qfI87xtSPv3pT3/FB92/33Pisa/qfBISrij8vf+xuqIlQBISnimSLSckXEQ83zmHn/zkJ8/7+B133HHRT+arxfn0wqJcC9CSl4F28shCmH88UU2bSVhy8pw8lIsXfcGAObk3R/iENFKS2AwljcYLX8sqzzk1wwArSvGI12xoia6s8fQAXYdyifWYWjIU0zpIhGiYOTBKzl8mSYTytRZuy0EMf2Y2uKkRvbcTNLzW98gM/Lmb4UJWZY+3Ir+AorQOV3uWOlweDfRQLW8xcictMtP2MAXHVcUBJ7Oae16z7nM2VNVGynKdpHtWyiUZExxNiNYnYeJGd/B9JDXPo1povMWEbOE06JtZJZmaPEiRTNieadgthOjCm1YmYud4qyx2WXrfZlviSMR+KMPFbG4ZmjsyLxNI8lAiA+GNlh0bjuW6Erjd9fiDIHXU95YNVQdtN9o521Z5SusY1CFLhqaQtkpy7SmcZh2Rfyq0Z0+T0csb9i5M+IuTPSnpaU8Vnp97Qx5KgbmOJUX5HjcCHxFgXVWUFGyGzIsCGmBBqdA9LzNrCxQzfOjMl3tJoehByCYJR9CHfzWSwfcqZJfQnFVTbvADANZ0TTsRyKuQhQ0doVStrJVMjBG6Q4HmGjdiGDiWW0HSpjvCsJ1go6Q0F+khwGXNIF4OlN6293r7GKExpKOuEMuUUR0icmAtioOuYEtJ97fQABwvcEPO6CmbVFxlFNYr1maa3BqmeMaqCfqrkkFcKBqOb1kyE7LAxqMa2uaMCsk2nplpXObpGckgK2/RSlEaD43Ce1mXl3zBtJYsdw/LFEeBpqdg5iHPPM3M0wtZxyj7VSIZ70jFOGjgPm8k8x1sskFxXdPnhB63HPNll/GgDpxDNM47Ku0ZOEOlJEse55XfUA9FESBktGolGXPt5d4pvGKGpkHWCBkrKfdHnAldMlfV6JMxCOvMGjOibm2UXpPv1ISmGLmiMVO82+z5fHguTDm5EDytc/j+97+//f90OuWzn/0st912Gx/4wAcu6Yk9FXZq4u1sv4d5l2HsNG65K8y7OONs4txL+bLHPKXeQ7HkcyZ4ljoOYuENPS1i0ysmbFje88KmT5bJDfewmXCDLilqzYrLeFhvsHdhwpm1ghcZxVblWexXlOcMhTIcqzT7rUitunBji0ipNJQcsJAZz6jOWMOz6DMpTaMZ41ksGrYqw5KzrJkqcKpkoywUVI04gUMFlZfZo9e7kgZxHmODgDgEgcgcFu4+ur29+14zUTp0V9N26OlQoiSQmqURJeqRbW9I2VA1PW+ZqYZ+KHU4LSVBGTslx1v10nXovG/J5iJ1sV0K47nMdWlnZgdYr5nqGYs+D6LWptUyjIgC0lHfMHYSAkEiybfE+m67QwwEciWb3B6X0TOewniMC3qVoduxxjENDSZTGoZ5Q5Z5FmfBOVSQG7FXozyHXc4xPeaF9Yii8FyLol/U1I0OJdiwKSOv2z/LKKxsSD3jMLWi1GKbC4FrFcuLRaRiCHueBjjqXdtkopXYnofwr7xnLJ87RB4qBj+ll+vSQHCmNb3gUJpwzXIfZEdU5Kp1m3Okc7kOTQNFcG5yrzmqN9lf90LTi6KPFXF4YteyXNvYLHeVz9qGlRa7oAEr0nY2VLXNviPnLTY4dMXFS6SJSuTDYreylCzlWiqmXrM3rNFrasaZWjHLHI/6RhqCmEsTHdczjNdszQwm8LtR0MsdN3jNFwJtqAyOzqNK1BzKzKEnce/QFEZC6XOVZmTFaTpdK67NHH/mO/qsyvNpNWFro8TkiqGCnvGc8lNe2AwpFGGco3APjzXgjTxWInbWAKuhVBuDuYHvjoKEGZLEKNH0PO2oPw8sIJ3X7RjCQKuI4vh9hFpRhftanE2hRxmkIbIrV1XjGHoZ/XfczLmzUas0/n/WUddo6UN+rsea8NzE0zqHP/uzP7vt96NHj/JP/sk/ecrXbG1d2EmdDzsnQ9SqZuAzzqkZfZ+xqaod2STdzoIdkLXcoGj859SslQApUZxWjkNdZ0fTCpoWSjargZIsTE874aAo2Rxrp1jwCqVg2RXc2PRA+6BkH4Ss84Yidzy0nrEvd+zbs87WWknhDadVzY09z3hmqBxoBYuN8D3iDVxmjv2zot1IFpSi8bAv96zPDM7DAoZll3ODzyk1LDpLaT0na4UywvmKm/AAxWrgMjZ4egpqL9dZJB8kcxczLQ/oqfzupevTBg5kjPbLsLjhNSuuYKojb9CEuctiavtcwVEtGZWeNxxSmi3vaJhnxM6qqcgPBb5W7V0QcZU5ulEWB+azmmtVP8FRvBgb7Gc+ffGbBtppPeg2kx2DGICDbsA5M2sX4S01J33PwgSJ+LrCh/mzgW8oXYbBeVdEkSDKENBk2mMVXK0l4FifaSrjmCmRzYjOaBY2zQzNsC/XehwyXS+95ixHT44AjVLwqJbO04lylJniC1PFDUVN02geCXPG87DJFtYxm2l6maOZeQrrGFSG3Hg2asVeA+ca4ZQtuoxSy31XOQBF7eFxM+FQU3BOy3SeMjiOK9bzYO3phyupkbjk2lHFo+sZW8yd5OggRrmSgc9Y1TVHXMaJMHEIhCPbFfoug+ZhzCg2oYs596Ehx2vOKt9q8UnmRbWNGFFsWxpjaAWNW3hNo9wlybxcTFs+X7Y+PlZ6y7IvOc72jUCkiwylClObvN7mgLdriY8NabL2aC8NfBM/D1Q1iv3WhwBf1uerLfw5ImVUdL6zoXWYRmTHiqxBKc/KZsFjZoOegn0u45Zew5/Vil7RkE10EObXlIXDTRVfchUvCzI1J2k4pGF/1WOCdCBrBVMaZuHzl8ZzulaMrdyppfEMatU2Km6G4Mt2svAOWApSVWXYcxp80CK0obNfJNHycBzh48r0lSwGgMTpRLbNQE5CNk+cRU9sQpTZ0r7lavb9fKrWLOj5KqJklmZDzdomzjjf3HnJiG6oapuTWGKomGvWPpcrPE+A3/2cw694tTh48CD33nvvUz5n7dxXf9Gi89bFEzqkYhQfjHg+Iq6ThWEe3fS9bctNe1zWdliJiLNkCaMTCCFzaBybVJQoekayV33juaHp0S8c/aKhzBx55siNI9eexkl32qJSFNa17zlTjqbRWOM4RUNhHQsLm/RCSVB5xaCsyI2jNJ5cz2UOFlES+eY1KjhgNdI8M7TzG+0x57l5ccZrcinVFtpLycM4hkrKa1bJ40u5I9dwfeHJg/hqrsRRLJWU3EsFS6H0WCg4psehXCmdcJkSpzqW62OmKke1Oo1dcnn8fq5yJdbLuLMeijXv2y7U2Mm8ECRupJtaNtVIEBf5kXl5WTb3J3Y/Zuexo68Gv/NfL26HZ3fsmmRgA5kb35ZqYkd9VyTZenndLc2idDX6eTm/HxbrAvnccr1glDt6kZCvfWtbhQ0lICWbGsDAZ20m0wV9ttggsDAcUxYVfT23t9bujUjPFN6wrmqKrOGUrijyGq1dkH+RwKhfNBR5I52SuRNNwtxRthkamLiYeVC8plRtxrE0cxvd63JutNJApZFS9bL1GO05pStyoEQynKWCR9czciXXuEAxCJmoBUQpoERx0PW42ck0i34IQqL4b9yIYzBkWiddtdnzkrk8UKRRlF6mdkTJpvgD4vAMrXTE5uGYrY377V3NFys7frFtOSKeXzxng0yJiv+PyEOws+yF8NC9HnENjrJHxmtO09ALzym0UAK28G1G6/5a7HFD1+zxJVb71vneUg2Ncox6NS4oPlgUZVHzhXU7pxAFW88ysSVrHJmfS80Umazzry80+xdmoXtdUeSOm12Pa5Xhr48MpfXt/ZNjKIxjj/Us+IyrQ+UnZu4twZltpLe9NJ5Ce3IFkyDNVAQa0Rldt05kT4WgRsXytGRXY3fyLFy7Eugzp5FAHD8p9IyBjyP6ZO/L4zX2hsXO+L1aSUNm38swiDhSMMq65czXZxluMO/AjxI4u7qb2akL+7nC8bSrxY/8yI+ggu6cc4577rmHW2655Slfs2ev59Sxr+6Emk4JLSJu9DFjFA0wZqPON2lA+47um59Ho3EOcZRMOewGlAqOuEz4JQGFdWzVNYcKh1GesrH084Y7D2+wtiEiv9PKhEjUYMJCtaYr1hrLVbnjhVXGIyGrXuQV05lhUzUUuUeFjMNW4Ez1yoppFbQGPaxUlmVfcMPyFK09RV5T+Ixe2LAGxtN4WO7V5DNNMTYo5TmwZ4v7Hh+y1zrOuYYiVwydonYOrSA3Dms8VSPZHOc03ouGXeMluwjiTOhGFmUdsouDVipEYZVvS88aRU+DcYoZkpXNvGa/Kziup/RVxoorOKY228hZh8XpU2adW5ohjReHd6IkexVlVQY+QyvFLPRCd7/vOPmmW6aK87C72UV5zVcXtX7Xd1f8m1/Knv6JT4GdWaCWwxpLimGdaEs1sXzDfJxcfCzK0Sz4PIx4VIGzpNpOy0o5htZzcGWLk2d7MjYvOoTak2eOExNDZhSDzKEbFbryNcMgLHxN4Zi5MF5sMIHNklHuwMFoNObceg9yCVr6WyUlhn0uJ8+nWAyZrVlbL7nZ5dyNbLKzWlMWDWDplTVuUx63ZBS5w04Ni5ljXMsFWVmccnatYFJJxtF5GTO23BgW+zV2XbJ1hfGcnGnW8BglQV50fBXgnAR41BLotGVl7VloLAMtwY9BcavPucpnfDl8d5nXbRBUBRs0KPb4Mlx3+Y76aG52S/TQYZMt6HmRtYpBQD/QKWI3tNVhJrvX6DCfvVZOhJPxEPhbF4vHdaG23H3/7jmNfM5ZJRninrfk3jBy2RNeG+V/rm36fNbMp6JEkfRo4w3zDvaeDo6RdfSdIQtrQa4MBkJ1QaabrIxmuDWYecein9/v40Y4qsYr8qxmAcWKknvt8NKUc2dKtArzl/Ma1dkW86zBOUWlNUsLW7g1UaIosoaP203+f1d7pjNL4xQHpwUHg2xOkTsOjLa46fEhC6Ma7xXX5Q1rY4tvFFdpxSf9VOw32LbH04QFuLAeGtqM6pqakWvPoFE8rBp62EDZEKd7qhwP6gkv9T1xeL1qy+ZxytcIWZ9lFKBp6RjQVYgQRY84j7kMlKuBt6wrqWbIAIgoUQYo2uEG29bijvNvOhnxK3FM6lcKj0qZw9tvv53bbruN2267jZe97GW8733v4yd+4iee8jXZhe2lT0DOXAgYaP+NWasaF5T15zd11IUC2Sx7wUgXQrYuGrNCuCEHLQwy2UCHPqNfyri5xX5FkUuk2isarHUUuWRAHlnP5P9Zg9WeYa9hTc0YoCiyhsx41lQt2Z3eFKWkbGCN5+zZBbZUgyJEWdaRW4e1klVZDFnBg/vOMejNsMaxgOGFCzVn1YwiZCunM40JA0z+bDWjbgz7lCKzUo4o86Z1DE47ObYxnsKIvE5hRRahsJJlLIzI1/Qzxz6l6FlPaT0vrRdlIkyQWci0p9RwbTOQyNGKQxKzLHEmrozhy9pyeOvIeEOh4GG9Po+AEa6ijAt0GDQLPmu1wuJx2+zLTmcxRskdW5G/b7+Jv5Io9uChC1/AsiA9AzFDOA9u2tF3wVG0KE4rkTGKk0OiPl8suRVoeti540JoujJC3jdeMcgbjp7u0y/FQe4VkqUr8oYyr9FAL28YlnV73eL4MItmYVC1i/vjx5Yp8lpKbmiaWlPkjWRZsoaHzLjVXMtsw4Kz5EVF4xQvvmqNBZ+T5zW5dZS5lJ6KTGa75nndbrY97enljn4m2eylhU3yzLE4qMgyyXjmxnPzQi0BTbguhZUxfbcPJQuZa2l0Ka2nZx33MpPXIo1dPeMptWeUN6IHqj0vrhc4rsf0jWdo5Htopxd1Kgo14ryZwB/bQrifPUR/VCRLLPvDlJiBMyyFTLDM2TVthnBlNKPVqAvX3fp5FjFK3XQ32IivJhPz1dhyr7OmdjVHY9CSecPQ5yHDOm9WWO80nUCYRR2cjaGfCzznISgRbqk8tjckIwaoNmAvC5EKUyF7NQsl3BrHirOsqRmn13K0l6lWMdC69vAZDi9O2xnbeV5zw54gII+s38vGU+aBpmKbVqhde8izmsw25LZhVllKDAtossyxrio+99ASma1pGsVYNSyVYRpP1jCbWSZ4+oUcYzqTgMAo2WukpKvlvrSShFj2QSIp7EV7vW0z+D3rMCpIhwUblgYv0Su9yfUoQ4WgZ0Qqradkk2+U0ElicBSzmEWoAMVEVg/VNlVFutBMOVZc3pb9F3xOn9i8aZiFxkPoBLZ+rq8I2zVroy3tymziLsLTZg6/7du+rf3/uXPnOHr06CU6kSc3FOtl9mPcSGMZORresi/Y8BVrHdX9aNhxEYvO4XrgVux3vValPzNeSmSZg4mUm/O8oU+GNQ4dyhV53tDrTRlPMjLbcIqGPJNFxWhLbhsyr7lxZUKvrMi3Mio8tfKsrg0o8pqKjCKvUUFD0OHZpxR1rcmyhsYpjPacrnM2s5q1jR6D/pSmMVzXr5lMJYoc9XP+8mxOUxnu2DdGbfRY1TVVpekZz9ZUImrvYWMWOwFDpsY4vM/ZmmoW+g1+BkXupIGg8XxNNaTIKwaNOI9aw80oTk4NZ3FYwGpP4T2DWhaMwjpUIyTpMiz4GWEMm1KsBl5aGb7mhSAUnmNarUiDzKDuIXyVHOnorJTlUTbCjNsnNqK09hBI5/L3+UbabqrKPUF77VKi2zAFtE1Tc/Fbg+ssniNyaqRbXB4zbSdi5uej5SJZvVKOAZIVq3GUmaOohJPZKxr2Lo+pagNj+d6dVxjtKIuakfWUecPJc0VL5djrCjaVdOpmmWRwM685u5Fz5FDNeCrl49W1AWUxA3LZTMO81iUURV5zs9XkeUWRNwwHY25rlsnsmLoxDAdjFlgkyxoyn5Fn4kRkmePI8pQ8axif6TFRDY8dX6LI5Hty3kt3YKUZ9CrGE/mGX1wv0BvNODwzLC+MWdgYUliPRsrM1npGUyMB3kTKfVZLE05ZNBybVrzQ5hxoDA94Qy9vODcV+yVsxD0jnKwM3WZYXtBIxeFcJ9O+4ISGMfKGVV2xpWr6aAhOz8hnnAz3g0WTZ/NseLSLuZ6ibwOq2HAXNeV2juV7sozihWRn4jGHPmMc+Lwxa4rfXj4UO4mNJpLxP7tjAkoU4O+j20ap+Hmla30+zlCut1AItIayEWdrkBkenSr6RpyXQ+FIZ3QtZf7MoWfCryuC8zGrMq4+fIb6iyNxDrOKsqjYN7DYieLI4VPA3nCdTbDLeRYzyxrqRuO94tTZHisOtvAUWY31irETm/de9qlTYwNG7Pn0akGJ4sD+VU6cXGQyNRg8s0ZxdGJYVJZ1Koqw7muluHXQ8Jl14UaaiWT2ZjQc8H2xYafYdDWZsRQx4G5EiH6oJCDKA0VjVjl6Zn5dS+NxHqaNNFLmIbuunWKfKzlhtvhzs9HqM8fsokIyr3EO88jZdjRrnEm+SbWtwW6bkxjuoybYTxOqQjzFOvycyCw+3zOH73jHO9jY2GB1dZW3ve1t/OAP/iA//uM//pSvaZ5i37Wt8Wy/sN2oovsc24mso6r9COGzRFHobodVezwUg1CyaPAt92qBeWReBl5RmYVsWtiIxImSDXpzklEWciOUeYXRjtFgQllU9ELmI89qBr2aspBOsxfccJSynGGtb8nsRw6fIs9q1rU4lIP+RAjHynPV0pTFxU3yrMYaKW3kwCHX59TZHhubJZOp5fqrzqCUOA3WOF6+d8LNvYYvnOyTId2mK0tbPFh7dPhmF4YTjqyMWQpZjSxzKE0oh0NmnQh4W0eRSXZp7OXvmyFDVOY11nhuXJm0012skUzOZ+2alJOKRjiTVqYXaK/CtRauy5LLaZQsVvL9SIbHhqzBQNN+R3nYIEtvWPCWUeDRLfi8ncdcR0K3n4/+azMwoXs0Ik7FiDYVlf+fzBa72Fg/vw0/k9caLyXGfNuiqdqoujs+TWYdm7ZMB3HSjW4zpoWXqSa9wJ+rcG3HcY2jX4pjV4QGKK08ZR4W8aKmLCryvCHPa8aNYmlhzMN1HGUozU9xzvKwP23vhUHZkGcVVx1YY0hOvzejCHaf5zVLYd7sDMhszbBsMKYhsw1b44IlZ8nzGqMd/cGEA5kjs2HWd14zC5ut95K5McEx+vNzlizck3nWkFtHr2gY9qdynyASNr2yYlg29MpKGmmMo8gdWSaZ9Q0ltl0q6VZtMzXat5mbnvG8rBlS5I5TLjaqiCBxaZ1c3xCExLWl0L5tJelZz8NmQs+IjMkLmz631AvkEDLiipGbi2mPfEa/N2uzMXHCRfyRTlOzQ0B+nkWM9hU7g3dmFc/H3e7a8jOxYePnXLI4xjHeR5H3G4WYo7TSznuvm/WULLdiSUXeZYdiYn0rGD7IgxC8h+XhjEEIqKPTk3tp7LntiHyg7735LNYrJpW8/qamL13L3nDs1Ig8m08jyXPJ4g3DtfdOUeQVw4Gsx3lWiaQUngJLZuX5RVFzw9VnWPAmcBQbDrsBr7r+rNy/mWcBzaYXh6nIaq47co7bFis+fs8hsqzBWh8y4J7jIQA0XrKZce0tsgav5D7QKL7xlhNz5ytUo25sepSZY70yGB3H9xn2D2oJEENlS6MojJTTs2DHhRWnOzbJ5CH7uNcV5EgzYeQ4agJFwytudUX72MgbVpzM1or3ROwSjzxdjUjBdZ3EmFmOYxRjZvx89tf1BVoJqCsp0xgaUi7k50rH0zqH6+vrDIdDfud3foe77rqLD37wg9x9991P+ZqTJ678D56Q8Ezwsz+VRIMTdgeSLSckXETs8oaUp3UOm6bhxIkT/NZv/RZveMMbntFBh8MnljciupyD7ScS+GIhWu52dcaOqDj7OIrHxshjXVdMVL0tc1iETtv4vF7g8F1fzmUq2jm/RShVWhfeW1MUFdrDwxuWsqilu7ackeUVWV6T5TXXF5IByfOaXlkx6E8xKD79V9fQ70nmRXnh0TW1oVfOZH5xXnN2dRg+r6Ys6vY4xvi2VP3ypk/jFFVl2LdnjSyveWgiPLwsa1hcGLNvZcxZHFXgiCwvrzFWjgPLY+F5GIlyS6/ZDBmaMq8kGraOPBNuZcwqZdYxDhkdIES0TeBCimB4Hsp1RS5FUYeXMnwpkWuDdHeL1pxirOo2A18YKaVnXjEMGYJe6NbrWc8ChlLNy3ALaBaDGPRiEC4fhBGI0TbmdrY9+9LaXMgUt7YVsjHPhP/y+tc3582yPJNu6C4pu820tB3Y8nvkGZbeshTGWb2kXpBrhSHzEp1rpAt8EjrLc7RwY610AW8pyVznGBqE2/dbj/UpYta7qMizOmT8Kk56T683QUUb9IZ1JdIwdciHTVQjtmYcWV7RK0WD8fTZAXlRSYY7r+h5aQzZnzuyvGZrquU+yRpGozHLoUGlV1a4RtN4RZbX8vnymh5KMivG8cCxEXnoHH3TNWshyz4V3lfgOfZ6E7JMpDteszKThq1MKB83L85aHjBIhvulxlIWFaNMMjPTmWTEe2XFy1TeZmSO9GrJkkfbCqX8PJM1YyFwsWzo/OxZx1rQpSusYxSko0oUJ1Xdzl7vKXDKy3VAbHnB5wwG43a82c6frg3HTHPsEI1rY5d7282MZ16oBTtt9Gte/0TNue46u812/Xw8Y7Rl6yWjuZP3W9G0nwlEDL13nvsjXos9ZaSGhPU9lPOz8Jl6RaASAEZL5qvIK3pFw53Xr7LsC7nmudh7fyAjUjdrzSQIvo+8dNQ6p5hVlinSCJQXM7JMqi01jvG44EuPLXboDQ3RhV72uaz1WUOR1zxybLmdqZzZmqlqeOjxBbR2DHoVI0TfUHkoiopjp0YsL4zZCBnxvUtbFFnD0mBGz2uWQ9Ytsw1ZsM0saxhTk+cNxivGE+H6bamaIm/IM8frjmyQ2YZrVsYM+zUGOOgyekXIQGYNNlRoilwkeWrl2+rOwEiWNjOi65hrz6qusF4UAET700qpXgcBeiWKCdaHqlun+tHlRc/tSo5lmfNmS2+3cWrb+ePns79Y1g8UpSsR3l/Yz5WOp73q73nPe/je7/1errnmGl7ykpfwyCOPcN111z3la4aj7b93v/xuWaSLHN12KseNO0NKydarbSW4hVBeju35g7B5TpjXswtMW8J0ePphwSlyeY9FJwURjaIsKj65rhmEUprDUxRCFl80UmqYqobBYIy1NUUxI88qxrUOZYpAWs6ExL86NWjt2Lu0KcdToI3DZo00adiax04MiR3Tp1ZLmkY20DyTxWhohVS8b3nCddceRynP+nqPN7zgNENvyWzD6bMDMlvzgpygr6XZWO/z6qEns+IQTmeWE2f6aOCGUhoI+r0pWdawPjVt+SISr4u85obSkWc1hxZmlEUtC3Nec/JsKc6J8eRWFqHI2ywy2fyLrKEKHZcHC+Hv5F7KNMaLbhgE0evgBEijgZT6VmnacnMWhF+XlHxPC07kW+L3HzdRmeSxfXM1bLe5WNbo2tu2zfVJnL3X3tk8gUgdh9KLDZondSzjLN2uAwvSmGC8xivZSE24PgfDZnEs8DOH3nLIi0ancHWkdJXruSyISMTUVGrOf80xNE7ztuvXhN6AoshnrWNYFBWb4fkjpJGkFxpiIgesruW6VniOHDqDzWpGC5v0vcV7hTVNa/ev2Sv3Te2E07VZa6xtKPIZo9EGCinn9fsTvFesDGco5cWZKqcip5FJcPXiG0/QK2omqmF5aUM4YPEnbKJra4PQ0KL41JmCzIpjvLi0xupmFjZZ2bD3r2xxpg5dqn0JgBYGFWUhzuGt16y2JcsskyDnhmE9L+8izWWtUxjknB7XM4rcsRGmqRR5w8tMRm4cK5ljM0xeypVwu0BE6KPdjZyl3x8HqkRwEDtl1vje8f/AtqAmrh0x2Gk5gH7ewV/SDZwUX3PnXAkicm/NjudFxI07DhKIdmzPY9Px/GLDDQh/FeblZx1KymWQ/IqPRfSLOlB9JBg3KPYHqkCBERpPWbF379mw1td87IFluT5WOIdZKLEuGc/ecC+d3rKcPL0oTqM3WFtTljNZxzCs7F1l3ChUXHPyiip8/p6Pa3JFnlUsDid80a7LtS+qsG56SQ4UM4ySKSkARTHjC2uWvXvOiZ5l4JkL31zKutcXQumJpe4saygL+SxFCJ60homqGfqMIpf19S8eG7Uc4rKoaIAX9BrWNjN6ZSXXTzsmiLMojn1IiBQNhRG5nF6Qlioz10oAvcDnHNNbDL1l4DORaAOmfv59LaDZj9BlmtCQFTVpS29YdAXa0x5Tgvn5VKcYaIhurtkW2HTXzmh3vaAVej4nMuHS4Wmv9lve8hZ+/dd/nf/z//w/Abj66qv5yZ/8ya/wTbZzAYG226n7HOO36331gtOXYxiRt9mnQYePYJERQzvfRzgWomjvEPI9wOJQOtUWQlZGIR1pj5gt2fCCkUeeylV7x2R5zbqqxIHLa4xpyIsZN1+1is1i5nCGc5opDVcvT8iLGWVRyWYSnLbMirh0UVR8cTo/114hpGfhpUg28toDG2w0iunMUPYmrK0PWNvoMZ1m3OQKZpVlZWmLIq85OdO8dt+E0mu2xgWf2FDC+UKxd2WNUb9iiKZfBkc2LEbLAzl3oI2OM9tIhJnXrG0JV6wsJAt06wtO4JFmFGtkAdtQ4kQXITOVB66Lw7M8mrURpQndxkUg4edeJHR63lLkjjyTzOQyhsLIQt3gqRERWxcco1q5NnPc91a4RoFfpD2dTXWOuKnGf+tAmm8jV+ZaiefjIsZNOv6/Xdw6xziv3fu5LccNtMvTcXiWQ7ZQo9rQxkeiudcctGE8VjjWsvH0wjXM0OTWURQ1y64kz0WOo/SaYX/C1jgnz2WMW57X5IU4hnkxY1XJv1Hbs49uhbAdngMHzkhQ5TW93pQ8rzh1apkxNYO+ZNDzvMbYhvWtjBrHjYfPkeUVL7nuLFleYaxjz/6znMNhs5AdL2asj+X7UyjK3gSjCPeVD3wccX6PnVgODuj8p8hqvvj4AkVRUSJjKkVBoMI7xcwFW85qhn0Jgo6qqpMJr9FKMujiVE6F62tDwJPXjGdir3EtKvKGARkT1TAL12fZiRMau+hz61jo1eSZY9+iSJTsNZIVjx23ufaMkXVlwVuaxrAYdBC74+O6TmGXZxg5fVnn8Z3SX61dsF29YSdix7Hedvx5hjy+R3Qit2U0u1nLIB3lkO7YaNt7/HbJCukUh751bIVxoapz7xTFPKtZ5BUNIrkEEiRlgePqfZzoFIIZDzarKb1pNTJXG5FiqnD8JVNuvunhdpRfltdY0/B1N54Jwwkq6WwvZyI7lFVtdtBBu75r7eiVFRWOZRR5LmNET27a9jmnvWdgPJMQeGUofuMvrqGvfVsZykMQ/arlitoJT3xu35JgsF6R2eAsF1NmOGrlwxrdyDjJog57RUOF5/D+dQ7s2aII9+WgPxUR7JCBNGh6ZUwABIWKkGXMM8ein1MO1tVMePreYLVngIj7xAapvhLN3GhrtRLn0npxAIdItrMgZhXFtnPmnNsYfD1ZBrEfeLTdDOL5gphnE89bzuFP//RPs7q6+qQv/JM/+RM+8pGPPKM36WZs4pc99Nv5L93SiUHa6buiwEsub7WV+jtO+5yq2et62zZqhaKXN0FzLCcPZaatsbzvNGTaQEpuDk9eVCFikQhy2ResrskGq1F87t6DZFnVlpavufYYWVZhs5rNrRKbNfSxLC+OKcoZk2lG6aUpo9+fYLOaJTTaOCZhZqj2isxKR7Qxkm2RElvDGp7JzGJtw6HDJwB41ev+kje/8kEAer0JzivWcNxw/VEMipNnB1SdsvDZ1SF7VzY4rRq2JrLIOiel7QN7NqQ8QiOLXVjAZpVuy5F5Jp+1yCs+d98BaUgpmnZhgiAlZKVkGRt5YtY8C85Q3Gxic88ZXdEvajxSEsxCJrKn5pp8ouov02H63rIU5i0vhO5IoHVEc3ZsYOhtzl13MWqQCRZd59CeJyrtivp2O0ejTl2ccvJkEW08n3zH32OZuMa3Gc5h6N42XrcyS1f5jDN1dBbCWEPjWRhUspF5RZ43lDGLEex04HVwxhqyWC7LK/JcbDXLK1TIfuTah+YWz6qaMVWNiN/2J+35Rps/enLEGT1laXGjPWaWVUwqubbXXHuMLK+44QUPU/amFMWM44/vY6Ic1tbYTAKrR6eKXm/Cmq7IspqpD40sgzHGip1qFIcOnCHPa8m4h4x6ljVY5bGmYQZ87YuOkmcVo9GYhx4+xDmn2kaZmPF57UDK2P1SjrG0uEWWNRjbiLOc1/Tymixz5FnFjVethmyqFTK/lQ02rl3LvuRFpQ8NaGEsWl5LuTqT5oUSwyATeagoS1JYmccLsIymKKcsu4xFn7MYdCtj6S5mEqMdde0pzr6NIsOxcaZry9HZfDLEjbfEzAOdblOLl+DM42mUoww2q0N2r23wCBu+w7PfZTJf2xvWQsNF63h6Tc84ysJxr5t3W8fXx3XB4YUCgWZWaYaDsUi+hLXpscf3hkk0Mo3dKchCID8L2a0l45k2sh68yBcUpcycnylHXszEmSxn0viRVzQe8jzICmWS8dVecUKPQ5ATmrgmGTc2Q6wCYxoa5SUQymTt/NuvvY+Jk2uhjeNFeya86sg6tVch8y32Z7TDeVir5x3R7U8M1vOaobOcOruAw7PiClmLbUMfsbdhfxqkoDxHT474y8dGEshbcRxHPguVofCdGnmPPKyvQguRdbcJJeCXXH2ubRZZ9Dn9Usr0R4Y117qRKHmEQQqlN1zjCgkQFOzzfRZCpS8L94/2tPai2J4Rj/v7+aqJcc3UXrXyT/mTVHeeFXh14T9XOJ40tLz55pt597vfTVEUvOhFL2JlZYXpdMpDDz3EF77wBV73utfx7ne/+7yvXT11/kWpFzgH50u2WIJIpsuolaPPXBtv2eX0vGHTBM228Jro3B1xBYvesqqmbKiqjYrLvIGJCFwXuYxyOrjvHHpD8aAZc1VTij5bUfGiZh91NSb30m2bhS7P17zqPrY2e8J/G1RsbfboD0SUZLxVtjdzVRt6vQn7mv0cOvwI1tb0e1NqpMa+tVWyvHKOJeuxtmaPt2x54dOcWTccOSQL1azKsFnNiTNDhii2pvIZB8MtDh+UzriPfvpavu6VD9A00rW8hMY5zXVG8dCG5T2vvZe1tSEOGI22mM0yJsqxsigRqnMa8orHjw256vCYDTXnOepGo7U4hfsWp4EH2dArNQ9NRR7IGtd2VbdZuaymqX3oLpURUIN+zYByW5auDBmCoTf0e1ucOTehyDNMrckjB7FosDOZUjA0klE0M8UwLGpLSIk4zheOWUbZUEM3dOfmiwuSC5sqxLnPYovdjmd5rUgsaBT/6Rd628bc6eDQt1yaIE3yZOiW1UA278iH0sg4xvj3I7k4m7OQDepp+LKvWXKWVS0bYC+XTOHI5axr6Ugu8hlLviDPNsgDTzOzDRtbBVleo8nJskq+d2QzXXGZZJe1Z+IaZkGgNt5zf/Snt6A5Kba72Wdl3yqzShb14XCLXn/S2v81BzbIj1r+6E9v5uu/9h6U8hhbCwWjFH5VlteoymMzcepE57NmOskZasnQKO0By97lMeUxg3MSpHmvaOqgE1mJ/EaW11zdrzl9ZsSBfatUtWE2M2TQbsDeiwP88Ibl1qxifStncbSF1iL+bo3jzNkRw+GYLGvQWs4PYK/L2DKhNJzXVEqjHa3E0s3XnWRzq6QXOjLzrMahQmmwYuD6LAxE2N5oh64ku3jtrM8XTM2C8eR5xRKamZcqRqY0M+2oaMD79r6xHRuaP6bbEt3O+dtx4607IvDGa/71L9rtAZOf3xetjfoobq3b+2T+/C6NY84li9WWZeIUjvPcB4SpUUXF4bV++9jIZ5xWc0qOrL1Bh7CsMdYx8kacNO04frpPozbCaEeRYLGZlGDjXOx+5lgazhiv19x+9aoERQSH2TY89vhelhc3cMgwAofQfhxe7hMkiTGmJstqmrpCKc9kmnEDmYh05xVTGqpKqkn9wYRTp5ZZsI7aSzB09eHT/Pk9h5l4eX5VGYpixniSc8M1J2kePIDb8pJ5bDRNLX/PkCrTyBsePZWRGc1YBccur3ndDWfIrJSppzPRmFybal51/Vm535XHuoYaUS0Y+B4OgjpFTd0YbgzrRUQc0dnviZh5pHFl1rEnd5R5zfUbJSf0mJ6VTmlnhCrhlMc60Q5e8hmPw7x7HQ3eEWkQ4x0OYrSd7hoJSKaUeSe/QYXO+TFXCvxzoKnkQvCkoeVf+2t/jV/+5V/mH/2jf8RNN92Ec47hcMhb3/pWfvVXf5Uf/MEfZGVl5byvVWznD+wUp5Y33n5h2wkQRKX1+d+XvGWpzdiolkuYh8h5TQmXL8ouQCgrFxU5hg0cvTJw40KEKiLLisOuIC9mvHxlSt2YtlkiC87SF790DVlWcXM95MDec+w9eDpkDivyomozIntW1jh9eolX9D2u0S1Hqw6zbl/44nuFrN+II/Waq8+xrioKFPsWJ0wmOVleYU2DtTUnN+WKDErZrB64/2pco/jS527kESdZxjyXKHgG9PoTrto7ZhXHdFK0Y6DW1/v0e1NmNJxeLcjyirI3IbPCUclzaeax2VyaZHVsWuc4Zpq0kQaAkfZkmQtSJTUlViYW5BXaSPkkckDrxpCjGIbyL4jTaRG5C2sds1CCkdnTdYjim1AWMyz1hTfT95Yyk01pMQrjhokLi+2orvmiYzqZkG1yIX4ukN0t17WLIXqbbUZOV8x+z8WK5699MnQXwK7zOQhZmyw0ckTkxtHzltvCeLFCe84p4dZlPjiiep4Vzryi35NM843NUOwHmdnbH2zxqdMFWVZJc0Jeh+9SSrtDtJDzjWdDVVT4VtBco7j24JpcczTDhQ1sVrE6k+9E6BWShbS2Zmlpg543XH/4HMaKjE1TS4A1WtzgqJ6G+0T+lgMHDp7C4Tly/eMcXJC/Z5mUunudrI4NTmYWmqiMaVjsS5Dz0JZlOJiQ5RUPPrqX6687xtUjyZx4r2ic6C0uGNmANydGzjlkIsvehI8+vCjZlVD6i6/dr8JsdqTEd07N+JYDFQu+4LSa8OBjeyiLCh+yv0Ve87IXPkae1dS1Zk3XTCvTcnNLLFrDt1wjnLVR3pDlFUvGsxAkmxa9Zeht4PltLzF37ThWN+KmGZ8fEZ3G8yHyDFupnhg0+bmOZvfeiIGKlAktfW+JM7dj857xMnIxSlH1vWW/3071WfA5ZdG02VaQxo34nDyvZd1FUxSyVuc2NB951VZrjk4NNZ5lX2CB65s+WVYHvqxwJPctj7n9dplx8/I7PocJfx95g80r+r0ZJ08vSLnVODa8FycQj7FNaMoIHMusbu38wN5zOGBgPFqLDNnNL3wQG2zm3odXGPZCNaW2LCytc7JWog1ra9Y3+uRZTVULJz1SnLKQEbRZQ1EKfzfLK/Zh+Iav+SINnuuaQbgHajZD0GczcU5zFJuNZOOzsB+V5ZSeN+RZxUJY24b9SUsdWi5cSyUq8rqtys0qScpMVMMtrqBXVvRyR5Y59togPZY7DoxmwmnUPsj+GB7Xmyx4abpaCFSJWBnRKKY026k/nsBbnFdlIqLDOPRZy8W1V1C2zfM8LitHXHfdddx11128613v4ru/+7u58847KcvzxYZzLO7dzoPpRqFPtplqpCmg7y0LYXOURhTLIlrmFccycGemMISRayGihTkpO8+km21LudYp3NjstQtWSZj2kVV83df/OZNJLjpbXngkZ9WUO179OfKi4lEzYe9+icyyomo3WVk4GobDLRZGm1x1cA2bNdSVZWtLcpxegQ2EZKs81ja8+vV/wTSMO//8yT7DwRhrm9bxPDCq+IKesG95E2MbnNOcWR3xghd+mbFq6A/HssnZGgs0jWZlcQMDlD0pYQ/QjEZbGCPvk2eeLK/Y3Oxjs5p+Ka+PEXNcCNcauT6NU+3GbI00J/SsNLtY69osiw7X0GjpVjVetARPne1hgHEgg2foNtN6ewEq7A55WKCMabh575giLOb7XNZ28An527Gqpiz1pAzi8OzxBYebfjviKetsrO2ECU/7AzIZYScPMDpv3YkQAG/93s2OczkvT0eO7FM5h5HAv+yKdmErvGEQouVepzxnUJjgP35cSyNTL2s47IrW2dUozmzaoMUmHYORx9cL2ZUax2Im38MY6TLOvWntNMsqaRaJZcFMuFl9r9tsa4VjVomT0uAZDLewec3Mh0ks/QnGNBjjMLbh2hsepcZz6NApsQPryAp5n3IwZhTup2hLAyT46nvL4w8dYtCrsKGkprVQFPa5cptNxiCs5cjmFdf2aw4clPfcGBseePBgO7VlMBi3gdYNBzek3H3kXGi8qsNmHBzvfM5JtJk0ztz54keFF4sXKgaeh0/2281tz+JYnO1oF1lNUU7nHN3AnSzyps2Ia+0Zjcaigxca40ZFw6JSoSvfshA4iD0sJtjJnN/XEVAPtjPDBSHsOQXifDaZoXnHO2sa5WiU6+hqbg+GInY+1guO69BnFHQ6k0OGCODAwoxiRzYywiIl9zgpKmYdp2EkYV7MWn3BLBPO4b49a2jtqMKamecV09Ddn3nFJp4vmy1M4BzmOoh2TzKGC3IPnTuzGLLnikVvsFnDrbffx6NBaP2hB44Qu5gd8j7bJtRYuW9sVrN3/1keoWbmFFo7lIcHv3wVJtBrIpdyRM5oYaNd3x4zsh4fOHCGvJixONoSmw66jTFwyzOhWXikunT74XVWzy4EWgttgDWZiv5iUcy4+upjZCj2FEJNeuSxvWSZZDoNUib3CI95MBi33N9X3PYIRdzHsoo9gfs8nmQtLeGtr/9C60xmtuFULWL8RdZwdjOjxNKPlInggK4ouR8WnOWUnjAIAvmGOU/W+jllIuqy7rS5uOYt+YJJK/qeGlIuJy7NJPaEhISEhISEhN0Iz3OCN3ghuCyu+PlKdk+ILL3wTSJhe0vVgTfjWDSeYebarE4keOfhWLeHEkPMHGoUhRcdLOsVezocwhjNHXIyhGwWMgPLh09z6NCpcG4SpdU41s9JuW5NVQxHm9hI8C8qit5UIk3bMBgKl+mTX17B2prR0jrD4SYqRMcxs9B4yTJMNnutjt23vua+VvfQ2hqtPVcfPIvHc+ZcH2Majlx1nFll+OM/fAUOqCuL91DNMkoFo4UN9h04Q47i0UcPYEzDEMXhq45LWRiZXGOMY2n5HDZwVmwgc7dZSFtz7ahiMNziqsNn5LVanreOQyl47OSglTKJ2eBu1idDSqCDXo1RsOJyBl5I83n7PQj/BWibYTJbs7ohc3hnoWFn2Je50tJxXss0j94Mg2KmZI7vMEjBjHwmnY3BzqTb2c9LyuHxXpcPA215Lv4/2mb8V8rOdtuxMswTXrsTsfEEpNwFkvHphacbr9gXJD8Kbxj2ZuzzPW5rBmJjmWcBxTIqZKg0t16zis0aNoJUitaOLKtYQORjNAqrJUO8pus26xVLtlleYcN3kJczVrfm8WHGvFP1BS94FEfIyofMYRPusZPHhU6itZTLtjaEl5kXVSjZVuTlVEp0XnHYZ+19YmNJK6/oe8uxo/ukO7XRwfZlisqKs6EM7TBBBkqykg0vf8n9AKwsTvjDP70FmzV8ofI8dqbHMHRS20yOZUzDyvK6vO/yWkuTiOdiwj1XFlNZL0J2s9cfs66lcSezshYZFTuGVdt52s6/zmq+eO9V5HnFH9y7T7pz86bNVDo8Wvm2nFSG+2XUr1jKHYtavusFZxn5jJHLpFt0h56hDtnb2KlbqYYqlOtkHZXzMzs2rnbueFiLHZ5ByKDHrPn55pZrL5WYBZ+x4DPJznvNC5s+TaDMxGMf2LvermnljuMolIxtXNhkw8/vywWfk3uhOJRBXy9rs7iSTd7vrWQW8xkZ8tkcnaa3vKbC0UQpIO3JA41ofW3YrtkLSmFszb33XE8TutH7/Qk5si4C2LxCQUv2sKFxSSkp/+71Fhc6pL2CutZMJwVaOwrtqWvFJhVaO7R2vOUlj3JWTUPmU2xzcXED75WMtgz2FzPjWS7SPbEqc899Byi8Eame0Gkd121rhadbAhpp0tq7st5ydD2yxp4Odvz4sT2tZA5AWU5DGbrhvW/4AgCfOzqkxIYZ6iLlY7Qce0HTdu/fWzu5DzLhfU+VUIFWgoblApqzasooaIMWXsa5xrXTokXirbN27mwKnDcbmlYf8UrC876s/NVgvL79sHET7nW4ZzXbS89xsx0FmZKomWTRDPOGXu7aNGc7JD2kqZeLhlOqQ75GyK9lORNZARR5LotFL4hTH/aWDBghpPetc0Nx8FAc1+P25jx1YjkI/RryckpWCNfQ2Jq8nGFDx2aWV6zsO8t9ehY4ep7+YCwLpTctF+t1tz0ucgnTrD3Xq254jD171siLmXTGFTMeP7HINIy1M8bx4AOHOb2WszCa8LgZs7khpO6jx1fkpstrNtf7eODsWg9ra4ZGuDRZXlEpz9LCrHU+bZCFsDZoL+axtFdxwzWn8F6xZ+9ZrG2YTTOsrblKGTLtODHT8nmCMxLLQjqUGTMvzRZ7ljcYGOkKdwTeSHAIi9xR1WEmcuTC5TXHJ8KTiRp7i4ubWCtuWpY1DHzWNrUA3GA0Q6TrcuSF69IPXaZie9u1D2Pnb9SWg6623Hb9tgzNX/1RTzavjjOo/VyvTm+PcbahDNqBB13RluFyryl1HGpv6HfKcGVRs9eVfEvoRu8XFX3tmTBvsNnYKsL1gQLdbqAS5FQMyMJMYdkwbSD6i/Mvz7W5UBHycsbpZnsJcBYDLC0NKgNvKPpTjJ3fsafPDucdxEXF4p41AE4eX8HYGpM19BaEzrB+dsTbXvel4IzVZMUM7yVIu94VnD3XZ2OzpAi2X5QzimIqOoHazZ0507QOog1O55HDp7jxyCpZXjEJ3LjPPz5qHbwTp6Sk+KUv78dmNV9+8MC2rukYMEahY3msbh8/GyQ9slwE8R+vhddaYFheXpu/Ptw/958pybOalx7YYskb0U8MZeYax8riFuvrPTYRvUWtZfTf0mDG/lHFovUM0Sw5IfaPfNbSI+Lc2lhKjs0h5Y5AJ9p0pCxEaSaN4u6P6lZqRjTmxJaHPnsCR7Fr1xrFosvCecnotKuKhjpwzWKTVr83pfSGLdVwn55uO55H9ACtrdkkjtCcS0jluVAlalzbYW+0oyinXNWLpd2GQXDQxRmR+eLG1hxwvVZj0DlFFpzDF9x+P1lQowBZJ0FkhRzC1dbA+tpAmiiyhgFzMWibhfU9BlfAVUvCkV10GXtW1ljee5bBcIvl4Yybbzgh39EsC/Y8lfJ0VrMYgpStTaFlNaFBbBt1InTGW1tz6myPz0/mpdVo16ubecvB/NRnbqRvPLVTFOWUg4dPMhhuobSnH+gkJqzHOlzPzNZtMqPlMW6IKPieQrqQM6/4uY9fP++ezhpe/5KHpaycVxwIDl6/mHelT6hZGEjQt6BEpWHJiR0OvViYCt95P3QyyzoY9gHmMmLRptbUbJv+55UE7y7s50rHJXEOpxt6m6hw26na4Vjt/KIXOgth/HubdQndUhBmdBZR7Fc2tr+Yes7o2TZOgoF28V7DtUbe1IEvpmAUGhuyrAYvJGKHZxoWHIDlPecwWc2iz/FeYbKGrJhhA/dQNkmJ5sreFI+nNxiThShxK2rqZQ3GOv74c4exWcNwcZMtJdMhtBZhX4k2pYNOK7i66TGZiWN5x9d+hluvP0WeVRxXW1hb09SGW299gIODGlcbHnxkP2Mcj6/lwvvSnqOPHsAYx41Nj+Nnym1OoQtZTNtya2qMcTS1yGz0gvxO5CjetH+LymnpLjWuza6CRL+9voiEx29hNNwiMzLbugkZ2/h+Jzcy9uxZFQmFTPgzNqu5ac+EopDGirFqOHN2GDI3sSNUOqKjiCrAINjKgjcMQ+ZlJ7k+dgcL9yhvbStyEiMhO+9kajI093x00DpvQMs5i89/qsxh3Dj3MpenyREdR4CBm8vpNMpTljMyNJ//0hEAemXFcQdHVdUGQqPBhKKcsj8EN0aLPMcJJVI155SIM9u8lixMUbHg8+C01disah30yFcdkLGlZFZxETQVF5bXMF4xRHH8kQNtsFQgIsU2ky5imzXk5RTtYXVtIPdFaGCytmFhZY21tZG8v3WYMNGl6E05VDhe+9q/4obrj1L2pq3jtri8zsDPsyriGNZt5vDs6SUWl9fYd+A0o9EY12gyFB5YzFxw/hx7V9axWc2DWxZra/aubHSc5CpI/9A6n1kIjmKWUzhwYrNOSZVhU1XicMVGmWBb1ta89CrJTF516BQHjAQ9UQoqxwS5myysaTW90DndKytGgylLg4qFwD9c9obFkHmeS0HNA4m4Pvbazvd55tsijU7aiw3XeLRX3P3H27uau1JOTXjOXPxa+IVRvLjnDYsYRshkjzzzFN6003U0is2tkiUn2cWoJNFd5/OsYvXsAldrcVqj0PSATJwUYNkX7bq9uLxGnleszwwmfP8AfW/CLF9xlE3W8Aor3cqlN+xd2WjX79NH99A0RvRWFZis5prrH2Otlok3w9Emy4VjZZ+Iaxtb01NyTUoMJqzvxjasnllkDc8rXv4lbNZwjStFPDuTgP6lL36ALz+8D4Pm3OqCKFYMxiJTZRqqylJVlq1xgTENXz7Rb3mO0fm1ecWakrVQKfgbtx9DB6fZBM6wDq/JixlNA7VXbNRSvVnaey681lMpCWxEBkZz/HS/lYSaTTPhzId7bjiS5sk7XvJgqLAg07yyisXRmNnM8pHPXCO88cBjjGuxVPgkm+28NCAu5I4NJRWNPMhz7XVlm3n2ah5ox4BlG+cwiol3HMed1caES4tL4hwu7K+3axsGHa1ZIB/DvNQcseiyNqsTN+gCw4zQ7VfURNHUIiwe8QgOWmKzPC6bWswWNnjy0AEXdaPOeZn0sYbD5hWzScG+UFa+uulLtKng2lsekqyZyyn7E2wxwxZSmjt7fDlseiIPU83sttS4MY5GecaqweQVNqs4PKpQ2qGNjIsa4zFZww0veBhjHWVvQl5U3PyCx7i18Nx47Um0aXCN4ebb7uf4qQVyDL3+hIWldYxt6BU12jaMpxYHNJ5QNoYbbn2ALK+4qS/OYJbXDIabUtYrJCIu/byb1WY1p1dHTCeFlC9szfLKOaxt6JdTprWSpprQ9QlB5zCXaNSGLm+DOJh7RlP2Y1gNIrSSqVScdp6mkYaHdqPNKg7uWw1NFLIxP3B8SFnO6HtLkdfMVCPCxRj6ZDzUeExYUxaQjXXkTKurGPXcut2fsUzTJUVHgdWtTgY694Zv+f4zkhn1c+kO3cl4PFVEGzPfewrHwM3tPU7r2YPhdLguQ59RFCKy+8Et6WIsigqPlHYjhaKuRSezRLGs4ziwmi0lQUvP2/Z7vtFnmEzGI8ZNThsvDpwCkzUcV3XblVsGx9miyYoKr2ADz8r+s+jQzdnzhsdPDigG4swZWzObSGbjW/+7/yqbTynZQZtX9Be2+NjnD4X3rzh9fIVhX5pWjk41w6V1FpfXUNpLo0LY1F+9ZxYaq0K209Yh++649gUPY0yDNo7PfvEgw8WN1pnctzgNm61IdkgAIxv4gYOnWkfTWnm92GTdlqHb8l4IRKRhTegpG6phTU2plGvtfxpKZsY6VpbXpZmiMYwbhXPzCUozhIbxh8cka5RZoZYYIxnjQX/KsD9jMXMsaJkKtBAyz0Nv22s/nyahQxZGHLS4Ni74XEq7wd4HHTHqd/8Pkgk1XuOUDzqi82bBGEQBDLxln583761gWNSePTZkvXPRcpTGFsmsP/DoEi/KpPFDneeeyPOaF7/q8+wdhuDEy311uOlJNSa8f1ZIQ0rZm2CymqVeaFYyjnG4Z/c7uWMPuh42r9ioNUMrnfe3vOjLPHrf1XKvDbeophkFlk0HxjasrY7YVzRMaTj66AGuObAm9wQ6UCHkPWbI96xN02arFUjgZKWZ6Qv3HZbr5zQ3v/KL1LWiwTEYbIX7zbHkMrK85jN/cROzaU4RKAUD6yRwC+uuNDg2bQk5zxwHDp1k6DPJYGcSYNVOheasioVBxbng72dZxWRT5KVcEySHQoNNhuLY1LSBkdh90yoIfPovbgpqCHNB661o59YxHIw5NAyvMw1f0LJGLS1Iw94UUa5YXtyixrPYDw6+kgz3ojeMvJUGFZxk25HS8l4/b3DNOpluaViq27X0ykq2PY2G4TP5ucJxSZxDbWjb0+VNZJOeqea8iwaIXI0OkWRcsLIofxJEmCOiYGrcbm/RT+x22lfMBUUPYMjDaxZHUjpeVcIjuaWYa5uBaDa5sHEu+lxKpVnDWtxgQ1nOZA3LB0R30GQNs2mOc5oVb9na6Ifn1Kw4K8LJWUNezji7KZwaFXSc1lWDyWqO3PyoOJpGSoHD0SYbleZVX/9nwveqNd4rXnjzI4x8Rn84pj8aU80yHjtbUE0zmgbWdM3XvugoxjYsDSq21gZYW/OSFz3CViORsWzCDXuWJZNSYIMuXYhMlYi4lqEjuiinGNNw+PBJjBKuj3SrNuwJN7YJ3BXphNX0vebM6SWyrOHlV5/jrBb9rCi34qHtDp87pg39wSREx7Kg5SGTWgSHp8G3mcUa0XiM+odD61lQiiGaxZCVa5STspmfT2kpQzmtm8XeVLXoGwaJI5ANuDdyKFQr6RE30Jj5yNBPKs5atO/jWQnW2vdWZp96TU/BhhYbVcjIrSWXtVnWzNYso1hxds7XyRo++1fXs4bjv3n5g0JFyGqWgiO4xxVMZ7rNnJispsC2jpzNKrFhJU5iHMOXecm8RYc2yysG3jIJpWmb10yCo/K2t9+NsZI5N1lDbySdoc3MSiltOKEYimO3uTrkQargzHn2HznBo6d6GFuzqD22qDh1fA9Fb4o2jr/87M1o47jmyCmyokKbBm0k6946fbOMqsrI8hlf+6r7yIqKCQ6jYNCftkHOwmizXQOMrdtMeMxGKu0YGd/ybUECurqS7mqHbzdImR7jWfIlUxpxGEPmMMeQZcJTzPIa5xW3HtpgcVGUBrJcJiVlWcNU+ZAdqxkurzMYjGW0YSHjA/cuTBllDcuFcIYdnmEYGzn0ts2yqBBIO6IeHGFNFIdrFMp53XLzcESbZdRetbOMo/RN5HaBDB5Y9DkKmendA5bKhn7eoJCsdnw/DYx8Tu0UexZmbSDTXZO3lGSU7/nMLRzef45GuXYvGHhDFibe9L1uv4s4w75p5PsTh10Cz0hVmSpZkzc9jEpxdn//Iy9nceWc3MN5zeJ+4W8PtPy+sSEalCC8sVOrA9bOLITM4XwvELsJDlQ2Dxp1sJll4zmwR7KUWjv+9MOv5uYbTnDQ9fjyg4dR2vHZz7xAkh2mQWlZN0cLm6zsP0vlFIddL9AaapEgyyQwNrZmYTjl45+4dZ5giDzDEERp47j5pkewiOyVzWr++GMvxtiaPfvPtFWZiBXrt2XgI2XDZjWn1iUo7PUnrc30A13CaEevP+Xw/jWakMFcVzIkIq5pMbs5Gm5RekO/N6PEMsqlM36I5oi3DL3BK1lnCi8Z3UNN0dpKpeJc7TiXWbfSSc2V5B76xDn8qjHpZGDkjVRLTD3fLNrFYATLnbb2WIori7k8BDBf8MNhFBJ9bDBrX2sCKT/3mkP9uiUlHzp8Eo3iAbOBxnNw75Y4TFpKyaW37HMZxtY8pjYkS5jVjNCYrEZ3HEQ8QbIjzM3sTSlRrK2OyHozIcPHS+wlIh4VjSwm1rGlpAT74D3XUU1ytJ4bv3Oax5zn2P1HMEZumPXVEde98CFp/+9NOX18haqy5BoGC1sM+5JdHY9LrK1ZWRLh7yito8L/+wub2LxitLAp54I4uCZEpkp56sqysTrE2JqiNyUvZtx731XUHiqn2gxM5ELZPDgeWc0BDQveMhiMw2gyx5SGBZ+3C/+aalhcFM03Gxb+LKvYWO+3znruNTMnDuVUyYzrKDsBiHYjDf1CJjgs9mvJvCjFVM2Dk5GTiDXysLolLxOyLq2kBoaopzlvSAllnc5mGh3Doc9azkwXNmQXa+UZ9GpedWQdEFmfIowYNMC6mgc9xjTcoDUvqkU4vSxnrOP5uoMSpWsvpbkbrjlJhuLwNUdFny+vOeRtcFZCZimvOYWUl3P0nAZRVOisRivh/Y0QflWJNOL0Y3m9N+NAU8gkm6VNTCgljbxl+dAZKRGHAEEbx8BbHvn8tWjb8Pk/uU3GnFWGxQNnOBAcV2NryuGE2ssmPHUK7zRr6wOm4wJja1bXS2xWcc99B0WOI7zHvCxeM53mLCyvs35uxK2vuYeiN2VLObFv69r7cXNTnNAFJeXvxT3nAm8rHGdccHh50gY2vf4Yk0kp0NpGJH2YO44ubmoYJmOZvCGlTOGULe8512Z0rrn6OE2tWzkeuQ9r/tp+kR0qihlNFca3BTmRQX/CoD9lYSAZoYWQpVtAOHaDICVTBm5WFqSUouDw3Pa0NIB1nMOukxg33py53mY3IAJYcNttejGT5pkil4kZSsWRj56FsE5nRjiUp1XNWG13siKXsCyn7N17NshASXZ0rOT7zTWcUVXLCyz68t1szKS8GzO9pddUneSDyRo2cfQK0Vhdnxg++acvpuct9cyS92cSSBY1Jq+46dYvS+NSqG6c2bSsnxvKGmAbCi0yMH1v0LYJmUMRdH/FnmkIVmr2jWa86CVfor+0SV7OaGrD0WPL9Lzl69/8pxjTcNttD3DCTMXB1K7lIcZkhQZscNZW9p2Rz4nY69Y446brj7FJxcHgRNqsZmUkGcbJuGD/4ZMMtadnHXlRsbwwxuY1/eGW2IUVMe1rjeKepmkz8DHQihzeV976OCVS9lYoBmhe3JPydVXLcW686SEcsLlZsqqEX7pn39nWnmocm1ulTFgqZe9d7AsNY6ikwjcMVISRj/QfzfEwS96FBsIoERazh7HyM1NXkHNIcg6/KjSVjAaLKDFtRNBHRp8ddNJQER3FReO5phlQoEJUrNoFLs9kJmzkXUWRVAM0yjF2qhViBZlKsT4zZFlNrTyrE1lcAM6eWQTgJfUie5amnF0r5YbUrn1OXBZr5SBsZCeU8KhMXmFyyWiAOIfaOoaLG4yW13nx3jFX3/goTSXvOQMONCXZYIK2DVszg7biiMr4NMUtr/oid//uq9ts41ZoNjmjax68/yp00I1bXxuyemIZ5WH11CKzacbeA6dZ6NfkvSmTmWHFWX7vgaWWB7nv6hNo03DfgwdkeLppWq6YMeLoSjdd3W7CJ870OHjNMYretL0mAAf2n+NUA3fcfLxtMJiqRhaHrGb19BJZVjN2il74Xr1XvODmBykwMh4sFwf29kwzGInz2goqZzXjIAa+6KUFZKGQ8zVehQ7XrM3qADIlJAix7lncYtSrGRhxciZhWvFCiFgHLQk6cgXnDSpRS2vgszYT6PCsHp1nYOJClXvdBiojZ58wHi+i8KLpNezPGIWpOgNv27ndHqFDxCkYZ4JtLgfnVBvHFo5BfxrsUrOx2SPLK151YMz66iiUoiq+9TX3YfOafS5rS7rHtXBjlae1LcmCNIwbhbYNfQVbYUzhDOkyLMOmuAcpv6+dXEJnNQrZmDfOjMRxyxpsUTHbKoQ/NhhjsoaXvOEvUMozXF7nr/7kxZwmZP8y2XxnTiZSPIZ8t1ddfQxthIf4Td/8JzS15YU3Hm8zky3vK2gr7jtyEmNrrrn1QeqZ3K1Dr2k83PzCB4Rvm9dc/4KHMVnD0Hi0ach7U7kOwUHcc+g0+/astTzJqB06WNxohZNzDMaGWbR+e/OOCTqJku1qOHt6EWtrvFP0B2N6vWlbws4DT/HAvtXA26q2l7HDPTDoT4K9TBiEIGIQ7qWRj01XhWy4nZJxJxbCeJkuFO0tBnDHj8oIsshj7Ie/dSkSrXOI3D++83mLopYZ0hqWlzba+6uHwuM5O5Xu1hvI2gaqmDmMFYLhaLPNzhkvjq8KwWXPOFZiIIFq1+RRMc/c6fCzpRqGWppGtK05HUTHS6+5et8mb3zL3TikO1zZhlp5ilyO572SIARNfzAm154XvuoL4Ttt8G1Wltb+dJj7vT9oL5qs4dhawdFHDjLcvwrAZJqxb8+azG43Epit7D8rDXqm4bNHh/T6E5TykrUuGhZ9dHwbxpu9tnkmrtP33HeYiWpCxk5s6evf9AmhMjUapWHLKfqlrN1v+Osfo5pmGCuUJps1LKA4tDTljftn2zi3kS+fFzOW96wKJaWRDN0pVXHN4XMiOr+wIdnzaU6G5uDB0yHL56TUzVxUfe/esxL8BErXaDAVmlfm2MQzQNP3hoE3DIPtretqm600gaffrdJkT7LGPqtIZeWvHOOTWev02bAQRcRu5H7YgOMUi/0LM5bCYicSDor1sGG3gtPEMnPIEIbr+6obTzFWdVsS0SgmTmFD5lArceIA9u4TDtlpJeXT5YUJNq8Y7llrF6BzqiELZZOsN0Xbhq9fCBts3mByySBmpfAPo2xBNc14/GzJ5z71wnZx+PbXf4FG+fbY1kg2JBqH8YomiKearCEvKvYcOo1zmk1Vc+rMCGMajj1ygMkkp66lI3dxz5oITmfCyTRZzXf8d79ND8VrDoxR2rFv3xkmGyXGOt7yLR+TElpeU4aIfGFpHWOawHuZb8JnZkY2uIUtsrxitLxO2Z8wGGxhUCwsbkj5O5fJEdJwU7ccoX2FyKwcOHwS7xUHrj0uPD8kus3R9IsoJaLa7Ku1DctLG+RB5iRDsT6Vkt0slPeE3C2Lx1RJdqeqFBMlG+vSSDbVCt9unvuRecwDF7MpYpPDQObPghSOLER6Wxfon75/n2Ro/Hzm99BnYldesRAmWpwPBiVixtoznsxniee5NOXMQsOAQXHElRw8dJJ1p/hTI1nGopxiUXzmgfkkouuuf5wj1z5Ov5wyWlqnKMUBrKoMm9dsKYdDYYuKiWrQtpYMdSbZbmUcJpOpOiavedHhdcYqijlDhXS62qJiCy9NKcvr6JDR6KE5ePtDAGGT8/QWt8QZ3yrRpuHxL1yN7clG9Mpv/ISMOQzOnQrZcZPVPGg3OfX4Xpb2rLKw9xwma/iLT9yOazSf/PxhelEsOKvnDS1ZzcNfupr+whYP33OdZLlnljuWwv2VV23J24eA4tAecVrH6wNxMEMWs55ZxpO8DYx0qB7EwAckO22zii0lJc0FLzPe19eHbQWjhyIvp6yeXSArKvrDLfJiJkGNcW0TljENV11zVMqoeS3jL2OXasic57mM3RwOx+xdHotagxHh5wU0C96w4sQZXwzqD6U39JmT+qW7WYLsXmcm8r//pXxbJnHgDP2w8UYaUHR+Ryg2Qla78IaV0YyyqOiVFQovWbDgRMVZzw/7OOd73uwSUQZqQ2y6a4JU1f7Cyfz50ICRedVSIqRCU0lwbxuKIDuz4i2VciwWjYyBzBpO6gm5bVjwhrrWPHDPdUyVOFzaCM+5cQqd1Yw3e+xb2WRNzVjef5Y9C7KfSOdzw4GR9Ow3Spy0GDQr5Vnf7LXO3KZDsoX3XIN3mq/763ezuVW2lQmb16yvDtnnZH196WFZb70XZ3hrJvOhY9D2iU+8CJNXIhqdNaxvWV5441G2VCVNkmGd/6s/vxVjZQRkb7TJa19wittvfQjnNH/1p7czWllDm4a+Nxhbs7dwVLXmxusfC1nyOky0atpA6Q/uvpWBt+wPlbWzesYHv7woVbPFjdahHIQO6H2+x4SG9bUhHt8GIHVUoAgSWv3eFIdnaVBR4xkoaS6Jou+q0wwYOeBxZn3XnuXxKytzuNtxSZzDYql++iclJDwHcNs3rz7bp5CQcFHwDd9UPf2TEhISnhYema18IT9XOi6Jc5j13TYpmzLM8wQp/+RoToRupxjxLo7GPKrHHFUVwxDtHjNjKSsHLUGDRKStSKoSfoIPUXKcrRxLhVnI0DRekYVStA5Zp543Ql43DY/eezX1dK6v2CAZNus1ppDs3HSmUcYJX8uG7uOikqgvF15eORxzrlF807t+HZPVjPaeo66syEpYEZA+PtVzziJQoDh2/2EOHDjVXi+lZGTZdU2Pcxs5yjhufPH97Nt/hqKcsc/ljDdK6soIwXkwwRjH2aMrlCiJEE3Dgw8darMk933uRnSQcmhqicQX95xry7xV0DLUpmHqoRhM8Y1qhYy18Wxt9bh5acpguNVywdqRhdYFUXARTW3C51DKc+Lh/RgU62om3CKv+cZv+gRZMZvLgoQsQRRpXnKWGZ57XE1/OGZLSWbR4SnKKTVOvicUw76UaXu9KcPBmIXBrC2rgTSqDFGMQml4CU0fGVUWs4GxY7dS0q237EtKb7j6FWMpzSEluJGzjLyhTxyVNR//tBM2cLIePTnkzDmhCkxVQx74rw2SwWnwHDYwGZfcqydteaWaZexXmjPet1PDNzf65OWM6Szj4fuvbsu7p04tYvKKwqtQVpasri2lTBWbqOI9MMWT9acsLW60nCANPKwn8vyiYoyjQNNb2gwlVMmS2YVxy6m1hXTfFygevP9qtHUcuvlReR/bsPDCxxlG7pZtyPtTVpGGp+Nqi2tufYiTR/dKNtg01LWmN9ritutPi6RTyHTavG6zOA8+dIh6Zjl3dgGlJYOyb2UTreD08ZUgmSM6pMY2eCcNDbG0He0t70+555ElsT3btDSKyWYZuldNW1qMGbV+yJC88CVfkiwMihzVliujHFRvMGYwElWALDQZdBsbul3R8px5x2peVPT7E0YDWf8We6Ip2FcwDDYq2nEmkPbnFRmNjJZTYY3rZmBe/HKx7a6SxMBblsJrbadsvpg5JqGBMMdgtA/j3RpK4zl5egGgVRUA2jLmkcVp+w4xG1R6Q17M+Pznb2AwFDqJQbHYryhDWTk3joNqLgD90BevweY1q04qE1leYdR80xr2ax4wm+isDpxGxyED3/w3P8J4q8eiz1k9s4jJGzIMda3Q1rH38Cn27z/NchCgX17c4r7PvIAKsbXMSvNRRczWNWgrklHXXfd4oCY4MuDk6UX+y6+9DtfIuv7YiSF7vQ387Zrh4ibXG8kav/muPxDbCOMntfKUqKAZW7O+ZdFaxM2Nafj6/+avuPUVX0CH7yVmt19556dbfjde8do3/hkr+1bZ2ujzojs+HzLsDT0vlIgXXneSR9dyTp5cCbYueqNZUbVc8cYJR/rsqeV2bV1TVSuZYzPJdC94w/33X8UYWXNXV0fUCH++xrG0ck4a/rRjppxQK9AsLWxxjVEMraPwip6CJbYrl8SqTiwjWy/0tIGPDVlzGsWzjgvkGz5vOYfQ0dAKXB2RAzEMw1vGUtyKk27X4XDMC5oB62EyisO3gryiLF/P58EGR08+gGJpaV260aLAK8I/yntTWdy0b0VRe/0w7BzFbGYZ9Cfc9MovsnjVaeEPEuZYho5YkI326kPCTVKhM1ZbR1bOpNyWycY72ehxx7Vnef8Pv4NiQUpZg9GWdBNmQobeYz3KOJwTB3aK57pX3Metr/18W05xTrN84AwvKJxMGTGO3uIWxx7fx/Wv+QJT5XFO86LXfS44nR6d1XzqE7ejgPvuP4K2jqOnewz3niPvTXnhy7/IZiNlrmqat7wfW1RUNCweWBUuZHQgbMNgZR1tRXtLaZFnqBsdynzSQdp+33lFPZPSZr+UzbQcjkWtvzHiIIdNwKLFGQ3/N9lcnFmmeNQMg4u0pRxlKE3YrKaHlVIq80kmRjvysPksLG4wGk7Z4w3TQEtQwEDTcrdAqAtDJ9JDo04XaOwKnoTFD4RELU0btuUv5kh5ZFE/UZYJCHxS6Sp9dKoownUtvGlnRStELiMLAcyefWewKG5rBhivWd6zyouOrPG1V62300smkxxtHX91dMjmVugUz2pOnBli85pHzQQbuoDF7oRvFYn17XeLwuQ1Gxs9pko2/AZxXqc02HLGTDkWnBWObRE0OQFdBHmPvMbVsileU3i+/jt+X7qjF8YoLY7dX/3y/5+9/4637Trre+HvGGPOufraa/dyei/qXbIt25Jwb8IYO5jQDDi8CS0hFcglCRDIe+GThAAh5AViig0BjNyNsNxVrN6PTu9n97b2qrOMcf94xpr7HJrfG66Cb6L58flY0tllrbnGHON5fs+v3Ensi0FtLP1WKX/Nw66IMhbnFIVql6QXEScBxWqX+0+MMrRlGR1kXDy51R+2l6055ThxSpqfQrnHifMNAu0wgc2LwKgYo4OMpWZRfrf3lzOX8W0PbVvLx3WBb6R6HeEh110oudCB2HQUnSZRjkRZPvGxVxNEKXvrCZESsclqs5L/7KgYc/LkVin+CkKLGPjKWdhMaQnTfPQ8UEBHUSI+o6U+gVMiMkBRCawY2ztZmQ3Pm5UGx+fO+7UoYzvRDScq+wu82BAtdkguoMKmWjn2z8xQJWFfVkczsFkSQ29jMkLjqFV6/pmWEbT2vMMoTGh2/uIhrjx9ZHa5RKHUw3j3gJnJVSkOPe2n62QvMSjiOMoLc2lyMwLlGLCgi4WEFd3HFFKhhmSKyXrMn3z4m9i2+4LwNasdaV6cZrQhvO+gkDA7OyYitVqHTjeiWmuL1VqYYa0i824SeVNTjNlYr/KpBw8Qef74zuE+O7bP8po7jmCCjPX5BrfecIoLehAIYEkTw96ZplhK9UO0sTLSDTIKgeWs7svaDjPeeM/TBMVYivEw4/jRHUTlPqmyXJ1VvcduQlgUnnuhEINyVMbX6XULfOmrV6EU9Nsi7uopcRo4cnqcvkMSgrz4ShuXm8wHUUrfCtVm19Wn6KiUTDnfGMlnHnkv0CKKq64+yZCLZK/oiwXdwKbOmEyst/wZHRVi3j9hKZf6zAz3qJdT4Rsax1AoZ8LQZT6HoTNkynrRlSYh88JN9Y03VnZ/wz/f4NfLXhxefhUR5/SBOz+IAS9Audyjj2PYbnKzSk7i8wakWet/bjggJ/tfsbAwTIShy6ZbOwj/KEAReq4dSMEov1dRKfc4fWGMoBQTlvuExZiiE+897XlC8YYcZt1eiB78Xl8QhqVYDk4tBOM0Dmi1i7zxLY/I4Vnqs+3AObpKNh2lHCXPV9HG5ubQZ57cm2+OKIl+yjLDtqkmB/ZdIE0MSltGx1c589h+YixFT2wexIMp5Wh1IlLgicVSvok354fZWKkze2aaA5NtdJBRqov6tFTv5MT75fNjm/waxMKh3xJ+jU2FZ3Lu/CQFz4sc8HBilYnqLMxy3tjA5mLw+o69tDP/XHLkxKNnEcYT8uXgrA+1rkBkGjYQcjbeBsKFrK/WqflUh3eMb8ZBhVHKyVMz1KodmpdtJGP1PpXQUvbJJJGShIg6wi+sX8bZqrrAO/Xr3KanelnxV0eLn6LyIoEwyz0SL78C3wWDeH2dXJZ1VPOCGu244ru6VpTkwzZk0Poo5VhaK3Pg0GkCxMIjMOLLeUR3GR1dy0Ubb37L19BhxrLqkTnhVk3YoiB+TopEE4kHofJFhg4yLszXpGA1jsTLD3p+vRbdpkJQaUtJO3o4+rNDBKUYZSyFepe4UyDJFHG7KL+nkAiSUoyJin16yuaHbNKNcEhzNewiEW4F8vvKjTYr60UK5T5VFK2FBibI2HX1KbSxKO1yKyYTWG64/jg6yMjSgFaqiYz4hA5Q7X63gAkzLvWlATlzYrtvgNKcNzbcaOUHoNKy10zvuUQQJYzbAtPONzFOCuOKT9/5tu/5LCZMafcMJS2cYu39P8Mo4atfuJkwyDzilebIz7mT2widmNBH5X6e1DJADgeejlGUUCwJilyvipgpc4IiiqhPUzMODSinqPvVNNhXR4yj5u1CLp/iDBTGFqigqSHCqMiZK/bsajnmx151moHfbKkopv6FQoLRjmqtQ6asKFAD4aYqxH/v0K7F/Gfle7Gf+Lz65hP0u97rEcXo+BoKz7sLHF2Pcg9QKR2kjGh8k5pSDcXjM8MRxwEH0yFBqF2INo7Z9YjH1gIeeOAmiessSANfwJBZjQosk7cfZ2W9zE4X0t0okySGXTecIFW+cXeS6DJqI9nrjUyKokJCG8ejf3arWBQFGSvLDQ6+5jnqY2t8+fO3cMe3f551JcWf8jz0Wq2DDjI++Ud35cohbSxvfvNjEo8YCGd9eu9FbCqfgw6Ev6m0n3SxeQ8GXMEs00SlmMJQhyw1TI/J/l5utAnCLJ+A7dm6Rgaiwo8G3qQRoUfWgzDlRRdTdpq5U9OANBkt5VOzCkk+aQqB82dm8s+1WIxz5HCQMNNWWT61iwoxE2PrlCuixB+ud5lSmmpRimODyj1KByh36EzerKfK0SMjRDPhSn+p08nf1vUKcvg/cPXnopzgrFG5x5tG5Z50A2XxAB0MwwSnHNuVeK5dbkw8MPod9KODbFiQYvH0bJ01tZmQEjkthPsw892tIyjJkVuut9Eo2kjn9GRLEJTuck02BiWWFQMC/tDOBUyUEPqH3RQTEaQElv5GWQ6bSEx9h2eWGR1uUR9fRweWk187yPD+SwReMeespuXVymEp3iR/D29w5uk9OWk/KvepjjYJAkuShJQbMjp5/Kk93H//zRTRjGxZIksMylgmphdRCg4fEquBIjI+mRlvMX9+krgXsdGs8tLcANmwuTDA+QiniT2zgroEGWUtm2R5uIUJMiperDM9tcLU+Donj+7IVa/Wk5FNmFIeEmucbVsXxP+tGNPpFVheL+djhyCSOKeVxRHCSLyyTJDl4xtrZUTX94WKQexO5HCVOzYQ8IRo3v6+z1Gr9BjkB19/wzEKxT6JcnkWd73ao1qUg0UhJt6wiXg00FS9L1zdZ4EWnZD1l35xT66oTBGlZj1wlIy3GakkOQH9z1+DI3koyuj4TrGnxEsywqDBp3A42jiCMKGpUm6b7FL2yu7FdkCnVaKo5cCa2TZHEKVM2QKzs2OSYRwlDE2sSpODkfVelNG8CizDriANSZgS+IamEEhxuJHIYVQrJTlyCHIQFdCUnCFL5OcYBW0cv/8fv1UOLe1ozg5TGuqwnipBRrRFFxKyXoizCqUFjRWrqM2CXWtpjgrVHlt2XeKxz95GWIx579/9HCZK2D/So98uooMMPPJpQnnubnv1M5gooTG2jolSRnfO5a1At1OUwzYYCAky1rxaut8Pc+RQ+5H4+NRSrkYNgjRvAE2QUXCKirpyrLzdSrpOa6WGCTKWYy2Zy5EggANfvFqlJwbY/jAOkLFyvbFB1SNSxUY797gz4SAi7srxcoCmUu1ScSHbxtvEiUTZhWhqhUxQPRRVf9aEfq2G2lF1hgzrzbwNv/lLRQbpKS5/vuCs6fr1qKj6mMdKuY/xmeYDykYYJnke+uj4iveulTtfwOS+eCfPjVL6c89E0RmCMGHfDcfFQQHxMa3VW6xjMVFKuZBKY+ob+X0Hzghapv1+HGSUi7I+E2XpxiJaU9rliOmG53Jdd9UZNIokDlFBRsFpkkTue+vEJJVSwhpSvEVRylMP3CSiszClUpLzZasLrnCxUEqK15XVGvOnpzl8+AyT00uYSASGk+Nr9BZrQlEZvN5al1vuegKlLW9860M4q8kyyS1eXxliQ4swRBlL5rOWEzzFIjOyLp2mizhN9NsF73YhiPvXPncLNg6oDrXYsmUBE2SsXBqV9eWFPkfPDVP21lWDfbZca+OszhHZSRcy6QLOn/Wm3v5zv1yQ45yioODgNcflGXGKU+eH889YYgITxq1QlALfBK2uVYmimEq5R7nURytIUtk7NzPTyJXzA0stsR0z3m5JsSMrXdHovHK9vNfLY4IduStMsEcCUbOlWKql7LLlQO6AHhUShp2h6m0jQn8wgefnXFYQDlDA0Aj0XwozEmXzDVyj6HnelEERZzrfcOpTq5KTanoo7Tht2sTNEo3rzskozHeNxtuNoBw6zDh01UlMlKELCSpKUVFKd73sOYhSIBaqPTrdAhePbQVg983HOP7A9bJxKUA5OZS9se8gpL7bKnPqxHaxC1EOmymUclycH2JtrSYPcCElzjRDtZgER9oPWTo/QRClnDu9RYq5Spci8JYbzwq8H4eUK12GRtfR2rKMFIXNpboghpmkbVgFK+fHBd3RjqJHWvutkh9HyriutVEmTkKWVmr5eygORgKhJAuYwPLqb/s8EaIQt5miXhG0NnI6t6m45p4nAdiRiWF46FW3xnPMFnTiI9EcJZ95bcKUhhUD5IKTUfXnP3YnQw1R9wZhyuzFCRnZXcafqla71Cp9KiXxdBwpZ6zpmFrg/95I+oR2SlBeFNaji7WZHnWzqeSshRmVKCO23vm/1s29AS+/jNNitYQlCh23bm0S+FFyEArP06hNC5IBby1Tjm9621dJsETFmG3DPc6emxZ+LYpSreNNaDNKxZg0CTBRSmetglKOhotQClRgGcOgc9WyxURSEOnAEmpB4jLnDyMlhcLAM08FlrIfI5soQYUZWglyev31R+VZ8IXQyvkxYqQAVIGlMztMa6lOWIpJ4oBxFwj9QVtqE+t0PAd3xEZkcUBrrcbrvutP6bdKfOkzd6C0Y2ykJWNh7dDa5U2N1habKUxg+dQnb6O3Xqa/UWbXSA/rFLV625tnW86e2M7GUp0LposOLFPTS5vjyTCT/Fk/djRBKvtFYOUeeU+9gpbCNPbq2q1F4dlOX3VO+LuA8cjhrl0X88LzuVNjchgP+LROUSj12XfzSwwrb+jeC3P/Rikis5yzGHhPu0E0X+A09WqXM33xMCw7Ta2ceEW8ohba/BkroOReIJY2IeKFOD5l8/Fy0Qm2N1bM0Jc5J+R/X4z5b186JMpsYsrlrtA5QknuCKM0t3UaKstYt4QUA8aQG2FvIofy7I9deza3HzLA9L6LgnAVEwqFVOgOnidbG26ig0zoMKF40VZKCTXt6JJycO8lUkBFMgbdOr1EVcOaTjlw80tUXUi/F0ljg6bZlkLxwvM7qZT7xDiGJ1cpl2KGhpt+v4dqpYdxioqRJjnfF0s9xtD0+wHzl8ZpjK5x+tQWglqXpB+ytlalt1ZlxhZydDqJxWvRBJYvP3ArYRSjlGP54hgXL0yKgXQgTYVSjsJQR9KbjOP2ux9DaUusMno44T0WY9l7A5ngJGnA45+8nYVL45w9N4UOMrZcddZz6wWhvOuOo1y9pcnIxGqOkr/07P58cmTCjB9898MUFfR6hZx3X/BTna5XaCvl6Hs+c4alRMBi3wiggmPUFvJCOvA2cmGUsHXrPGGUUq70KBZjxquJeGYWZKoxoAlIItrmOh6sU4tj3EYMs5ng841wvZzIYb/f5z3veQ/vfOc7edvb3sYv/dIvAbC2tsb3fM/38MY3vpHv+Z7vYX19/WV7fy+PIGVkM+jcoBiuCmonwdx9YrVZOua8vjBlMoBupukpmydaDDruQUGoUXn8j0byQKfHW4BA0PI7dW4RUHaB2McUZcTcXasAcCAro5TYdnzlE6+BSDiBIPwYFUi82sD645pveQhlMlQx8V2sEOGVLxxMYHnp4as4MVtDa/HEmju6jY31KlYBThCI1b4fIxjHkI0IUYzvmOP1935JOFxW5wfpXDtgdb2MKcQobRmuxmzbskjZabobZSpDbZS2dLoFtBb/twx4w7/8fZSxJIlmet9FauNNtu89T9kjFlP7L6IDS7Empr/aIZxD38EWQ4sKMuKecH6CsqBTt7/hUcrlHrfcKqTnIMyEi4XZFA0EGc9++lZqgaO9XqGfGOk4PTKhAym6B2kaky5AhynDu+al+JtYle5X9b0Hn9xnySD1WbaeO9dTKacvDeUJFEGQcfDa4xgfLzdAp4vFmKFah6GqpLQoX2hVPQpRizLc5UZxkHevhW+7SC2SnN0BUliveHGTg2q1R+WveIwGPe6b3/I19h88I+t8MFJE03XiaKQRhCqMEgKnGd6xCEBUjGl3Aw5fdTJf+2ksZPcpF1Iu96gMtdFhxrOPXYUpJJSdoeMRkh/6wP3oQAyqdZiiPHqoAkkHUr4AypQj9BxUydglR9BuGEpZOTspNArjqCoY37YAyuGsYm2hweTBCyigPLIhh7jntRaG21SG2swE5GPl4ugGNTTKIxs2NdhMc+T+GwEYG11HacdV1x2l1ypuIoba5qO6cye30W8X+I6/9ykK3vR3anyN1CpKVb+mg4z5xQYj2xc5qzcEdelFmz8vTAmKMa1mNRcchMUk5yuaSDhbG5mgJxYZu1dLsn7DoQ46sGxgKUVS7K6uDOWv9d1ve4yp6aXcXxIE+fntX30XoR+P6zDLPRnz9JqBYGZgUIz2Ge0p/ThgyYcLWPAFjiUDKn4tb3Gh2MFox1Dgcp5WzYW8471x7tdZIqCLo1xM2eZ53/XLCP9RlEh8HFoSdjyaWSj2KRW92Mcf1nMbIQX/c8MokQSgP3eIhx6VW3hyN+112YNDFKuXRiV/PpKggzabtl+f++wdmEJKPZD7CzA60mTNiohsYmqJtkrFTskZprYs0LVSWFQn1hl3Jaa2z6N8wdJJpenpdws0N4po4OLJLVTKPY68sMf7C1quuu4oBsWFzPm92qKMZWzLImdVwu2veo4sNTz45RvYvmOWj/zct9PvFnjiQh3nxJ92YHF24fQWaXCClPf/xO+ijYQl7LjuFEYLgjt4NrSxlLesiM+oyaiNrwtyDowoWYfFWte/JuHaBiajNtQiywwHDpz1Uy35eR1P5zh6bDsrzRKnj+6QojGwXHv7c4Tlvv/3jI21Gudcxh33PJoHAUxkBbSxtFtlgiBleW6Ujp+4/fJ3PMSojaiaTUCm5GTaUUd8PwEvaBHxTLtVolDsc/XBc8xMrRLHhoLTNJXQiBIsidqk6SgUk7ZErCxblKER2W+c4tC9vGrlKIr40Ic+xMc//nHuu+8+vvKVr/D000/z67/+69xxxx3cf//93HHHHfz6r//6y/YWXzbO4eXoYKPWw/jDehBf1/f9wkCYEoYp5TDDKOHhDf67uMWLD97gsB2ISwIjzuxJaq5AijTkJOcMR6gtLhPlU8ur7A4NC5p1KCvzrp/+beKzo4KGeD6LLvjRX0E28c6FESnajENHcsCePbqd3ko1Lyp3Xn2Kdqb46oPXoJTj6PN7ePBrB5iwoXSvYcqq3wyESwJTSnP8qf3MnxQ433jCtAkzFp0lzRRf+qO76K1W0cpx3WueYZfRNKZWqY2vSUJGKEXd8ef2ooCH/v07hQepoLteBuV4+Ms3cseOVbSxrM+OiClwpYcylgoh1ZmVfNOplUWJrZRD+Y3GBJbJVx1lZbXG+bMzefcKbKqx/bh6bHqJWkkU3CCd+IG0JgeT55n8x59/L0EhYQUp/D//O29iEE9owjRPjzGex9RXcmhXnORKD8jLq4mWkHmvNJy/MCHjQVSOiBRL0rHWqh2MUzy2oYXbl8jYolpOcxP1DmkeBr/DxzLWK0nuhVir9KlV+1hfS9ZqLRr6ysJycAVKvLlWFxt5QdtRm9mmGYjqzyne+5YnBEFCUd6yAsjmoxTsuekYmY/7c07lKPiZ8xNeDJVy+PpjaM9ZNApUmLHtzc+gQu9FGVh0lJHFoaAhGpQn+Kc4atU+EaKEDZCmZyRwTIy22HLHUVSUMVyNMcoxfetxbGpordRZXRomaRcpKYhqPZoXR/nKn7yO0Z1znPrqYSYPnaOVKp8nbnGpZksghVKIojzWZHL3JY69tBuA297+EEEpZnFujCwbqJxtLpYKopSjp6a952gCVrFyfoyNVhmjZDSPEqT7rrc8RHG4RdWFUryt1v14XdZz0imwsV6V+zBoOAPri2fh5QVK/lvRi+m2bVnG4vitn/outMmooOnGMr77gyPjedzmRz91i/DdvPClQogJMt71zV9hrCrPt7OKQTb7oEA1PhZN++ZLOSnUAB45O0zFGS6ZHkWnKfvnq42lVpY9cTxwlNDUSim1ovDOBr6HsGn2HjpJ6JkYbWHA+3z6JgxFGCa879pZBqlW7Y2K50MK1y4q9gm82HDfZFuMt3E5H/zPnwNbrSjAt7zlGSb3XpK9Dui2SoJwhSmNxgbLKkUX5Gd8xz/7CMpkVIqeElFIaDYruQ/ezIHzNFyICjOGnSFJAoYCx5gLSPshQzaktVbNG50twz1UkHHNux5hqVmkpyz14Q227znPLa96lgjhdi9cGsegWNKJNFRezNVeEyXykef3MrcwTKcf0BhpMjWxSnujQllBa7nOlA2J2yK2OnTTEfodGQWf+ephaar8Z/HNv/krfNfe9Xx/VcbSPD5N6vfTpFtAaRFmGiS2b1DMyfpIqVS7zF+aYHm5Tq9bYJDiNThfTGBZXCtSilIee2HrFXSL4nDL/+4MazWXTDc/WwvOiOApTCl6Fb/SsjaCKKW9XiHBMdXo559zqiQ1aW+jnxf4xk/DBrGBYSTnyi8drYsPKYox35QYJLEqwxGrAfVBAg46Doar8RV1xd/65V4+E2ylFJWKNFFpmpKmKUopHnjgAe69914A7r33Xj73uc+9bG/vL+Z+vXK9cr1yvXK9cr1yvXK9cr1yvWzX6uoq7373u/N/f9/73sf73ve+/N+zLOPd7343586d4/3vfz/XXXcdy8vLTExMADAxMcHKysrL9vpenvi8tWDTygZFudTPOYiVapfAbdqQDGL2giihEFlamZIMWafpe8+5IPLKYCXReIPuRmsZSx88eIYihorvQAI0ZQWmIAjS5GgHHV2Z9XnTTUfpdIrce9sJMJZoy5oojgdqsoJwagack+L4BipKIczAowv1xgbV7UJI1kZUves4zq5H6DDllrueoBB57kc5zhXJxidVJMoRaUcSh2y7/hRnn9/l/z4B7WirjJGhDm/43k9THG7RTwxpHHDDgTkZuRlJh+j1IrSxrKwMUTWOm+59kKAQE3jhiwlT/vRSiXd8/yfRQUavXRKUoiAoxe60Snuu4dFAS+RfX218Le9Eg0LC8x95LXFsOHJiMld3pgyQpk3SdmutRppKh6mUIzAZo36p6UDQjB/5p39IqdGmhKa9UuOrL8zIyNlzYJpKPuOukiQai6iVh1GUq93c96qJo98rUHEiNhioPiuoXFxRLPVlHFbukynHJdNlvwp4MraMuiLVch+LrK2+2hzDAQx9YYRKWXJENYqx0XVq1Q4jxRSroFztMlH7y82FjV9LX3n4IEGQMWQLogAMBBFSIPwiFBM754i8aOrB33iTZNZOrDE52ubM03uInUI5KNc6mELC3npCmikZM4WZKNrDjDWdSocdpahhQYY1SlDdIMMmBmUcgRF+YOYUGZbQK8QHRHSbag7ukI2ndXoCZSz7916gHDqcFSXy2M45bnjrIxQaLZrOkfVDKuPrnL0wSljpU6p1SdpFFjwSMhCX3HbdWVlnKJqXRhi/4TTdXkih2vXqUMuDj+3j+ScOyfhb2/yPNpZeX1MebdKcH8Y5RVTus9IsUS5mZJnx/ogZk1efo79Woe5knDw6upYj3CiHKaRcf/eTnodleej+21BBRm+tkqPiK05+VqwsFRRra1UAvrQaYMKMqkJyho1lUcc5MvO+dz/E9K5LueeccSKcqI03ufWWlwiiJEcIB/Y3guhYn/8sI26h1Ug+9jfffpwEx4YSXXml2s3X0CBicc+WNYoo6tU+pULqbW4iik7z+U+L2MrihDqD8KYS/99CJ1xbEJrP9j3nqfpMcuVRWVHpCl/ZenrG4cOnsJ6zasKUvbtnqf05YGRH6DBRyuO/+maSbuTPBtj3mueF7xtl3stWlPYZ4pGqA8tQtS9K9TCT/GlElHPyqX00nKB9TSwzey8SGseoVvSbJRouYHFuDLyq+9JqERVaLjxygBN9OWOiUp+JXXNMHTgviF2YceHCJDEiXtOBrBWlHaNbl6igKJd73HrHs1TLCdXhDd74T/87vW6B/dMbjO5YIEIJX9tYkl7E0PQKOsjobpTzz1gHGfZ0g5fOjOVIn1KOwNt2KW0peSuxRGWMlmRapf10RntBYbnSZWFpiFa7yNjUMmpgbWacxM+ajP3bl0kzTdmLZAZ/Zp/dmf/zxJYFeiqjtyF+rEMu8CN+Qbit1Ww7eBYDua9vzRkCrxEou4DUq6PnmxHGUzEG2etBkDG5ZYEgkGf0n9+wwOJ6gchpLmpZu4OaYMBlBVjXkqyy4X0sL4+v/Nu8HH9zn8Ph4WE++tGP5n8uLwwBjDF87GMf40tf+hLPPvssx44d+5/6Hl+W4tB1rgQky2XxxIqcIYoEGh7yqri2j2gKCwlLrZAemzFmPS8OCAtJHn8H5KTXQph5q5uUERsx7k1Nlc/P1Z7QrbUlHBVeYnVUiMfVeotyuUehGNN+cQaX6NzDMLvMBsV6c+ysJ6MpFckIV4WWcq0rAhUtm8rzj1zN4WpGqMCUY6qTa7zn797PnE5kcysmHChtPpwWRzXKOHlqhsAHtys/ogwKCdtcyNZt8xz/4rUYbw+zfHGccxdHqYxssHBqGhOmpJkWvlm5Rzm09FaqLJ6bpNc3DO+aJyjGbKiM4w8eRgWW2qiMMuKWjD6KiCJYG9mQG3VveTPSyrk+QblPa6PCrbe/QL2S5IdajHwGyggHzYQZ++9+mkJk8/HJxNQyG/5zNZGMi88/v4tes8yqSrlwcivf+sanZJTSi8TYGIdRYmeUk6sDSwLE3lurSECIeFdaT9iOY4nXy9gcUxQKMYViTKGwOQKZrMf0lRCvK+VeLkRJyHJe1lmbMX0+YGR4Q7wACSTQvtJBeRJ2FCXs3r74lz4HoZbf9eY3PinFgD+SzWWjt8Fm97XP3YKJUqZcwMNP7gWg2yzTbBV58slDtJzfFP2Yc3y4xTVXnSGLA1SQcfDdj6D85wGgoowTv/lalKdKKD+2LM+sosKUwAjPayjKvG5axgiDGMGg3uPA4VOcuthg6OrzYBw7D55F48ja8pxpY7FJkK+RzlKd4vgG8xsRphwzcfA8zolgQnkFfFDps/fqk/m6G949T7xUEwPpy4QgANar/AcNmgmluIwzGXvXJ4Vg39i2xFJLCpjFS+Myfgssy8dmKAy3GXUFdJAxs2NOuItGeIcu0/SaZRGiBRnlkh+HKRGzabndohhFs0jGSrNEAcO/eu/D8hwrhKNrLA0f5aYDS6nWJvKNmfZq5ajUR2nLsZd2YgLxvRsoQTczfLM8JjAIMxTezN9pGiNNKmhenQ5hUBRLPb+GoF5ry/5W7YnoyZvjl11Aj5RxDEeelucmU5aaC7h2pE8hinnTnlU0isZlMadBkFGudzBors7KjE8JfzKKEn+fMgKnSXDMXRr3+6aIBxcXG4xXUr9cZQ9opjKivOatj1Lftiy/w/NeDYD3u7yku6hIRGvxhuxP27YuSGPuLXRGC5a6C9hoVkWtbMSE3QQZu2bWKWjHb//626igOXlWGhvjFKlTKD/mbKmMDZ1RrHYZPniRwkiLCiEqSjlw6DQ9JWp15Qt4bSy1rcs8YdocvvEldtxyjIXVIpXhFslylcO3v4BSMHTteeoa2mtVlLGM772UWzztuO4kJswYmlxBacuD/+UtHI3dFQXbyplJccvwTcrAVWLP9qV831B+fdtMY61i29YF1loRY9sWhLLkwYuqEzeLg9ccp1SUrOSB3yhOyc/xz1a5IS4ehUovXwNzup+bzOOEzmWQ17a6OIwBnCVvONpKnh2HnAMpoqo2fjTvnHxGpXKPLz25i1aqCf1ZP9iXB2PjzDcwsi8pYhxJovOI3G+E63+WlU29Xue2227jK1/5CqOjoywsLACwsLDAyMjI1/nu//HrZbnTwUzvCp+rgV+X+B5thmwPMm4DRJHXylSucrsiDD4S+5jB7RxwrkA4XRvrVe7duUHLk7XzVICBQrneyb9+bW6UAM3s+SlW16qMTS/x1T96Pb1zo3J4OVH46TCl6AxJUx7sjQujqDAFj7ioIJPNviAPhFKOqS0L3HDNKSItCuekXeT088KlUl7tVSxkeScaOk03MVxcLRJvlLj6DcI7mz+2lY25YXZ7XmQSB9gk4PCB87z0wh5GG22CYszEnktElT79OEBpR5oYCpHl7FN7+dinbqXZCTG1HkFREhqqDeGYnD+6HaUtG/MNUQMqy/zpqZxfuHv/OUGVYhEy9JZrmCjhhrc9gs00+/deyDmHqRJLEh1khGXZTNJOQd5rrYNWYK3ixtE+oRMzcIXii1+6nqXzE6zohPu+cpCTx3fIxubtFnbYGtbBuAsIS5Kkok1GC1GYRmjp9P36SpWVHN3tc5ggo43NN5IgSnJrkBRHw0a0uwHrSkj15UovT1QZcK80iqZOeek7F6jU2ogXnKZU7lGtt9mzfSn/2VNb5v/CM5ApKxw4NDMHzonYyPO5ep2iqK8v+3rJbE0ZMo63v+VRihjq0ytkVtHrm9wEuzraRJmM2cUaN77lUZK+8FnP/tl1vhnSpJlGFxJ+/iN3SjPj/PoLNhubMLSCgCtR57c6QtDXnhiuwpQLZ2ZIM8VH/9V3QpBx6sVdlIsZp756OH/d5Z1L6ELC1tDR2DeLCjLmxSiOoNon64WsqkzEXNqhAsszj1wt3+uLAx1m9PqB/HMkz9NYvS/irIEQxSMmzmpiCzYxnH12N0G5T1TrMj0UY/wzoLSgPff9/j2EQx1uD0OUdlw4PZMj5kpBVO0RlXs5B7jTFW5YZbyJ8ojIjCE/oHtKUEuAn/uDV6ECS0E7ZiaawmNEilmt5fC9cHSbt+OxLGnJHTeRZCcPUPUB38yE3ljcq3K1T4xxkCOIJkz5nrteYPdwnwqaQjGWZw+oe8V+qdSjd5lKv+z96lLgH/x4x/s0asZdwK7tCxw9Pc7xc2JHMqU2j4MgTFlbaOT7+NqK5OyaMCUMM6y3k4qVZXW9inGK26ZFKT42ts7h/ReveB5SJ81ENNnExoEXs8DGxVFij5QFYcqurJxPF9YujKGCjG63mPvEAiT+nJjZPssp3QPjqKAZvfE0lUqXciT58GUFb3/3l3LhVdlzxrfcepx1lZJgGdq6hMs0OhJ0V4cZpUoXhfjx4v01lbFsXBiVTPaRDY59+Rr+wY9/hKjaBav4wn2vY9eui3RPj3HKpdhMCoDWQoPScAscNOcbaG058fQ+lHY89uQe6pjcMF4pGNt3SZrtwPLEAzejA0uKY3ahwcCjUwpAQeD2XX+cme2z3HzdaaJyX9ZTKUZr7zZgLMVKj24v5KZXP5M3WkEhYWS7FJM6zOi3ixinaC4N5chyEUlYGZlcoVTv0F6uU1AQdwoUS31SHKsbhfwzbit5bWVfEEbInh168/TchN5kHHEJBS0irwidcw3HXInA6bwOGBSORTSTY01S9Y1jhO3c3+zPX3etrKzQbDYB6PV6PPTQQ+zevZu7776b++67D4D77ruPe+6552V7f/9TBCkFr2wrOoP2o8gBOjfmlXJBlBIDdeMIkQio0HsdBYUkH8lqVO5ZGHlUZHRilVe/4Wt0fHHoQAj8kU97MBn92SEA6uNrKBQXLkq3GxVjdh86zdKJadnYEcsdVUgpOENpt2weAMofqGiHSw3njm1DlRK0t/UoVnps23eBnlXoQsLK+TEO3/ms+Iplcj+WNqJ8tFVA07eKesGjPdpRKPc5/dJOVi6NctNNR+l2Slz9tkcxpZiRsTVmF2vML9dQRuxslHKStuJJxdYq/vRzNxAPgiSUo7tSZcaF/M4fvBalLdsOnBP06eAFdJhRcZpytZObc1/9fQ/kIx0VWBbPTHLyoasobl9BG6/KDKz3FwvIcuGK9aiNICpxt0AYZKyv1dmxdZG+ElFEgmW9EzC+Y44EywaOV7/ha3IPa110kDFuC6QOdpRE/TnYqAIGozDLkCtQ8O/R4qQrrXUFifHNCIi5dBimFIqbsV7lYpZ3qqVyl8wXmtp3sh2V0vdrtFjqi2WST2cplPqsb3jFZbQpvLn8Mk6jtYynzz63myBIWdcyMhoeXxULEiXqZYC5pXLeAKWxjP8Kw22MdrS7QW7qPRj9H+0Y2gtDIhoKMx76/M1gHFtsRDc2EFhRURvL4ayWHyjKKy8LXlUMgu6US/Juyy4Q7zqruTQ3TDdTrDdLqNCSxCGBsZw4sgtTiglKMdlGERVYRusxp798lQijjKBY7YsjRPUu0y7waIe8hnOz0u2GWpqqpZe2yigqSilMr2GKCTff8iLX3HBEXAM8Gqe8xUs1lP8W9yN0lOEyzdaZFSbHmqwsN6S4CjJuvvEY8XKVq/fNyVivW5R74IvHcKhDsdHJ0aGL80NX0CM0sH1cDIy1g4u6y3osJvk31uV3ZA6G6i1papwoSgdoy8T2eapj6/n3D0brew+eyT0btfE+qWHmE1o201s2bUEGyk/Z3+76pseZCBzFUj9HowvF/mVrVbFj5yxhkOU2S3/eHW5IbxaUM2MdYjJix2bKVJAKcugUJ3Sfxsh6boMShCnzFyaJfbxeYCzXmYCbb3sepRztVompLQt+v5bL4hXwCmwqXouhdky86TnGMKjQUq216SuHiqQ4TH3jE/fFgiYoJkxtWSBzMKwUaRyy2xbRUSrkeadoNsvMd+TJL2rH5z/9avCK2p3T62AsvcUaibKM2JDmJY+8KCcqWe1orQt1YE17UZ5vhE2UMGFDjj5+gNEtS5RmVgW5N5ZiMaYx0mT+pW2cNC1qo6Lc73eKpL2QtBcxcUhiJQ/d/gJKO2a7oukePJOmEFP85mNeuCcqZB1klFzApbWCNEduIO7KaK1V+a1ffyutZpWDN7+UW1gN3o9FUL7GzArlUsz0NWdyv9HBtEt5e6hTL+4iRNOYXM0V7j3vD1quiytGVOkxUpQC8Lc/cQs9Zdk+3cwV7RmSGmZ8gz/kvNtFKBZRSRzmXp67XEjJ71GDNXp51KPx4FFf2dx2aXjk5bNt+b99uZcXOVxYWOA7v/M7ecc73sF73vMeXvWqV3HXXXfxwQ9+kAcffJA3vvGNPPjgg3zwgx982d7iy1ccXvbmo0GGLvqK6DvYNN0NopQejpVMoZzKbRIGf2eiJOdwDdTBpYKMqEvVDkEhzo1QJeWEvNjrdgsUpqQKHz14gQDFarNEkhq+9qWb2PetD7PtrU+jtMs9nlQoxRtBhopSQcWCDEILQYau9dl3/XGUlk3POTGtG96yRNfJWK8xvcKpx/fjcKhSgjKWk5nNEToL9CzccM0ZKQz84aGN5Y8+cRtZaiQloJhg4wDnYGqsxbmW2JkknYKMFrzlThil9BLNct8wb6FcyFg9slWUexpOukz4ZpHYP4hJsRRFzZV6foC/+FuvRwUZzUsjqCAjjQPSxLD2zHZqI01mts7nY5CiM4zYAspzOoXXFlAsCAIShhlDjSYnzkySYTGVPkMuYLiSkPZDEmXZVXDUZlbykboO5VDLgMRqglKMQxAchyjw1lVMQoZR0Fyr5WkAg4P2cHXTT9MEsqEFoSAwXZWyc9sS+7Ky+HkV4xwtHKjkjVO5srNY7uUcmLCQEEYptUo3/9mDw/vPX0aJQXi52iEsxVRcmCtnAzTV0OYmwhuJwUQp1+1doNOWNJX/8n98N0o5dm1dk9QBJOtbFyRnfOHMFPWtEjH37b/+nwTdwNJPFRjLq3etooyT2EnjNv9oS2ZlZPZ0X9DMgSJ2yIaCyDa6PLZYQit42/segCgjTgJWNiK0EcKks4q0VQDtMNry0T95NaqQCjpV71HfO0dvrYJh8wBUxnKyGRKUYholr5wtxtx+9QVZk7FwBjea1fyANVF6GedQjJCDYszOq08RDnVQQcZ1dzzLWrNCtdbJi7Bup0g40qZabYtbwICr5RSB5y2feXyfPHcm4/xGmI/anFMMY9joiE+eRrGke7xEjAK+7fuEv9vJFHESooNMzNK9J6izatN2JMyoexpNUEyY2Dm7qSgNfOrFZXzbwZ8gSogQY+Gekhi+8ZlFPnf/LVSLGWEUey4s+USmWOpx9ViXNDFUam0UwiOd8A3ogDKxdbjHyMQqhcgSGEtXpbQH9mNOIhEn9l4iQBTO49sW8kz4xsg6xVKPDEfRaQ4ePkW9lLI0N0q50abbi0jiK6lF6553+qF/+AOk3YIUAAraT22npECFKXE/2txDcIxsX/TNxDAqlFz7NDF0M8V4MeW6f/QpdpRSMI4FMtaPzLC4Kk3b7rEOCmi2IzCWzKuEVZQR1buUXcCGzhg5cImsXWDuyd1oX3htNKU4jJWfEhmZ9FRnVglRtJpVMUFvdFmfG0EZx8TkEu2NMu21KhsqobVaQweW+sQq2jjK06v018ueAy4o+jKWon+/SlvCap+VX79FQhO0pdspCjcWxe7JtjRHpVjWWJSxsV7l7/ydLxKGKcN7ZwnKffHdjVKfJy3P3Wc+/AZe98av0Z4f8u9FmnggR/Pb7TJlF8h5giikG06cDaKSuB0EUUqllLJ4foJZJVZPxUI/P6erLsQUUlIrTVLkdO7zKclGLjd7/+bXvUi9khChmNfyLCoELLI4KqjcIeJtkZjbDA03r/BP/l/5OnjwIPfddx+f+MQn+OQnP8kP/uAPAjA8PMyHPvQh7r//fj70oQ/RaDRettfwshSHtqdz1EY7RRBmuefcgDtoPJScDBCRUFCTCLGhcTjKvqA0UUpQTHCDw97z74xPqaiONj1HSP6+pRIx5Q3kQI2TkAGN0PnF/97v/FNKhZgwSFFjXf79d/wjlC8eyi4AYyX5wo8apDi0EErhiLFM3HCapa/sFx5YmJH0Q7726TtYUimuH6DDjJde3E3FBb6IlAPE+RinyGlaOOqNDTK/maZxwGu/60/5plcdZXLbPNVKF11MMcWEdqvCgUOnKCnpWi4c38ozX7yBJDU5EbmfKpo4lpXwypbPj3Hm+d0kDr5pso8yll6rKGO0RdksAuDwWx8XLopydFplf0jKJrb16jOsLjc4+8xuhqZXGJ9ZlMizYOCNJwd3FkuiQHFqjXIppjLeZLjR4ujR7VRKki/bOj3BhAsYrnexmSbG8n0/9Cf8ys9+2xXcmwCJuRut9bnw/E6xtgkymipjfbmOQTipu0Z6TA5QCn/QGpPxgX/yB/nYOc9uDhNqNqSvLMdOTXD9aJ++yigUBNkeIIeZ35DXVJ/JNfF0C1B0SX0XnBIngu4Ffgz6l11Gy8+ZPT9FWIxZVzGTVnJPQ6eoeENlgJaVZ+DzRycZHlslcBql4OabX6LbixhzgRTGA888oL1RJmkXwDg+88++G+WR1aGSNAGdbgRBJuiqRwrVYEymACNoV8EZAs/9aniOGoFlXaUUtH+PUUazWWE+0UzNLBCvVtCBpbtco3txmMX1Iq+74yWe//07mbQROkpxVjFy3VlOq0QaKwWqkDIZyjpr1HqC4JVi3vh/fBhlMv7Lj/09VJgxuXWB+bOT8p6GOkLF8FyrTs+wcnKauZNb0OUYXUhpbF2i3Q255lXP+jGxE86ecpw+M4NSTriiHoHUYYYKM3bceMIT/R0/8RN/4O+RFJ/joaUUpVfwIAdRX7/xK+9CacfesS7jEyIECP0YT2kZ/9nUYFNBgXpKXktQ7tP3NieliXVBcTxKPxAqGB/XZoz4verAEmPptsQwvts3dGMtjQqaHpvpGoVSn+mpVfr9AoVCTMnviUPVhLPHfeODwjlYnh/hD7odLixKMTRhoOQks8KYjN56GYPi2qxM2ccMhoWEUqVLtdGS4hDFjqtPU6/EbNl1iaUL4ww3WnQ7Jb9O5fcv6gQdpmzfNk91+5IIYIyjuHWFWiRUh0KxL5Y7fl8vDLdRUcbESCtHuYMoZawoRtvH/utdnOsGoBy7tSaq9lhui5Bix/YFHDDfkoLfOIUxDrQjaRWpOLE/W3p+O+35IeJOQT6j0PLU89s9IrvZ1OggA2OZCeDO932eUq3L/JcPcun0DPjX1u0UqY+tETnNxJ5L6Cilv1Gmun0JXUjpN8tidj1YI8DO4X5efHYW6mwsitWaMo6Ll8bySUu92pUmRW9ahjmnaK9XiYp9XvqzGwl8NOYjv3MPymSUtBR/SWrod4q0lobQxtJZq9BZrRI3SzkYsXPvObFc82BBpqTwV9r5iFi5B/1Emt1f+9e/T4JQMfJzHwdW8XSa5Q2z8UWlCTMaY+v5Mzm1fY5eHLBK5qkREuM47CKaKiZkk4O4fWZdbK9q3Rxd/Ia4BnFq/6N/vsGvr3un/0cUMnYluiIk2wRpTnTXfvGN2IDsMv6ACTNqKMraEaEooEUN5c1TdSFF+UU0UB5bi+92hLcxMM9c0z6azngRhMlyXyHnN9FP/+FdGOMYHlnHlTICHydWwDBlCygj8VPKp6IE1R6EGS7wo2Vg4/QE1elVVCgE98pQm5NnJ6SoNI6N+QZveu8DmzdZOd7/uiPSKQaigGxjmb8k0nSlxZja9kOOH9/Grm95lCBMiRdr6GJMq1lh5sB5do11ePLTt7H90FkO3HCMJBVj514vwijJ8tWIv+SBb3uQ8ZlFWs7x6FwZpS09j0wNkIuGUkTXzuYjNeeJ26WGdKtZHDK1dZ4nnzxEd7XK+P5L+QEM4t2nws1RhbOKw9ccFwTGKoLAMj62TsUFNK47y0wxI7NaMkBR/OwvfAtvuvtpKcIrUsDW/OasjWPm4Hn6SErBdZFiZHKFYSdr7PChM7kh7+UeYKtnJnIz10EWqfEeiwWnqVdi3vruL7KhEgrlvowEEXPdQSRgiOHOc02iYkzdBvk6NWFGIYrz4nDgW/jnL2MElSuVexSHOlRdKJFWgajiQ2PzJipDaBCXVMKjD1+LVY4kkVSNd3znZ3IeGX50dN1Yj6kdcxT8WLRQ7EOYcdZ0/S+3FLzqfe9oTw6gwIK2YISKgHJsUxJPJXQPGEJUqBjLqk4oRxkXX9oOVhFFKWOBY2LrAmGtizLyWVcPzJJaRbnS5Zrv/CJ3jPdwqWHl+e2YqQ1BYJTLD/id002c1SyvFdGFlMrWZVa/tgdTSJlrFlDGUh9bZ2VpOPeoU14spbRlsW8Y2TNLsdIjaxUIhjsUxzcw2nH8qf2CMhrLLd/6JZxTLK3JOHl9vSZrxKN2yWqZtBtJsawttV0LOT1ioOTuew7joMi5qSb/3Eu9Alw5Tp+ShKJVfNSfR1tkVJhuCqwi7+HnFFGlJ02i3iw+BqlDg9coSlWbe2TOXZxgeOcCUSiG+CbMCD3CPTKzRICslzBImZsfoVTp5VxVrRyf+8MyygnZ/8xqkdpQizFb5O47n6fgDJXQ5vFlgn5KbFnNOLrrPnrTCzr6nWKezjJ7fCu3veoZZq45w8jUCvOLDUYnlq94FiJf5N71y/+NeE0UsaG2rDy9k1DLNMYElrKf1hgUhal1lLH04xB8ok8QpGyZ2KDVM5x8cTfLZKAdXW8snDlFAqyuSpG16mQtWwX9WJr+c0/twfjzYGj7IrUdS3z6k6+WZrKQsNQTNHbYhvlejXKEoy2sg43ZYebPTTJ6+AL7b34JHWRU6m2+/Pgexg9epKNSek3hTh57eh9H77+BYKTN7Ikt+TOgw4wUx7HVAnhaQ79VYuebnpEi1VjWW0W093g9P9fYpIX4JKJiqc/VP/Sn9DpFnnl6P71V8eyc2LKIMo7RsjTUi6slTh7ZRWPrkqz7XkR9ZpmiL7qVydj3+meZ1prhfbP5njTqx9smHPxex803nKBQ7lPZO0+IplLp5bzXAOGUD7iHGyrx4QiivG83y3lR21qr0UskfrHmqQwW8VwdcUWGws29+vzsEBqIexHRN1B83v/22co/9VM/xXve8x5+7/d+LydIfr3LjMRf/4teuV65/l9wPbiz+rf9El65Xrn+H7ne8K2dr/9Fr1yvXK9cX/f6f8LK5hv9+rrF4Uc+8hF+4Rd+gbm5Ob7lW76FH/uxH+PBBx/8a79HFe0VqiLJYnQM20Ku1Owoi3GasvfhDqKU6VJKLZLxzLYAxtC+GxGH/OKAJ3GZFYhGEZX66DCjZsV2ZtqWqXjlIEAcB7mopLBlFQt8cVHg8Eqtgzs2zA99/t/kxOUMB0HGjkC4WzYJCCoC/6PJxwhBKcYmxhOtHeWhNjMT64z527oyN8LIe54WJNFbMdz+ix8RZEI5eiojUY5r73hOlHhOPL5as8PcfOsLfOyffQ/dToHiVbOo0FIqdzn3wk5eXCxzw5se49yRHYxfe5bUyuscGVmnXk7Z0ejzgWvn6HQjOkenGNszyxrCR1PGksYBKrCk/VA6aZfRe3S7oDNGfBfz0ZuxRPUOu191hGKUsLFSl/Gx72IBuko8uEwkaOLSszuoj66TtAs0hpucWajw1EvTaBSf/Jn3UyuLHUZU7RI6zdZSyoVz0/5nJKjAUhL3BN77Dz5KFgfe+8tx07VnmTx4gVUV01Qxjz6xl7Akqs1Bd2tMxvpSI0d7BmMNbcTeoeYCGkMdTJjRIpYEHkT5Pm4jSi5gyKvoZ+sRQZTQ856bg054eGSdQd7z5WPHyy+jHYHTLMyN0m+WGHIF2irzmbyOfuKzw9FcOy7cuSFn2LfvPBbH1qkNnnr8MKWpNXqeVGGiBFPtE6eaHe9+TKLBtOWuf/6HqEDyiq0FjJMEjcDSbIf5+pNfmJEkBozlul3iZSg5vt6OxCPfVRswVIu57ic/BoGjVu3QTBXbv+shwrEWLjNkcYhLDUY5rnrXIwDMrZZwiWb0ujPYhSoLupsjfwAr62V0lNJNDGe+fBU2DuitV0A5tjR6oB33ffgNOKfQhVQQtss4i33AJgGt9QrBRAs10mHlpRn6ieHGtz3CIH4yXqvgEsNN155BBVYEKdriMhmXBdU+p5/ai46E76cb3VyZqrSlGFhGPXquUcRkfO8//wgaxbe956ugHN/+zz9Crx/lk4ABuqm0jNO0kVx041GYZL1ElhqOf/Vqsm6UK5OVtj7LfDD2l58TOp0/ZxcujvPH//le3vCGxzh04Jy4OKAwiE3OgCsI8IWLFcJIxnMAQ/Uu2/dllPx+23GwtlKnpRKeemo/GsVzPRFIaSS5pbteJkJTCiwf/f17fPZ0RmWoLfQKFFXt2Hb1aaYPXMBZTVTp0Y8DitVevj8DpMonz3QNnUURBwaB48kv3EixIJOHamODrud7GxS26wUpiQhWlLHUx9eplHu0MsXxMxMiukoMl1RKa6HBonO0sQzVW/SsYtzv122VUirG4CcRTgmdprNUBwd7dwg1JV2tYBGRlvFc3TxdaL3MYgaTr32JNA2wvZD6tiUILEk/pFFOef7+myi7gNGDIj5J04AXn92PCi3XftcXxEZnoYGKUvrK0RgItTyipyc3iJwgyuebYS4wGar2/ZpwOWK9bf95lj5zNRvrVeaXBanEKbbfekxoC95Jo1ZOOH1+zPMVLeVGm6RToDXX8Dx3y+zTu+laMDUROQ2Q1cF0Sfn88FazgvVnackZZueHUUiqWIog5SDK9I5KvQBG0PDhiVVRMRf7RMWYyFgO6uCKHOVxG6FRDFWEJtZVGUc7sk9GxZjoL0irXrleruv/rwH+zp07+dEf/VH+8T/+xzz66KP8zM/8DG9+85u5//77/8rvGRBHB3mbBpHGp0lwxch5IFzRYYr1/zxdEDPskh8LD7ybhqs++9iHzFurMWjKE+tokzHu4emyM+we7aFCsVkJw4xgn1iP2I6Qns+aNv04YGikie1EZHWHClOvkAaMYzFVMBSjiwm6Egu3LHQiSgGq++akAzAOHQlPaXhk3avGMqJizDP/5l3ybv0m86nv+vs5R62jUhIcW977KGGtR9opkPYiatuWKJR7dHsR6xsV5j5xvZjzdoscvucpvhAs89BHX8vUjjlWXpoRcYEWYcXMxDr9xPBNP/wnNNsh0XCbtfPjrOmEqwtKguUbMk44/9IO+T4Upx8+6M1TLSPjK2A8gdtYsn4A2rHRLlCqdvKCSClHqhyLqivecOW+iHJ2z9Nrl0j7IdpYvvP9X6CXCcF4bb3K8FCXh07IZlXAUCxk3PHmR6Rw7cpBu2xFbbdyepKnvnijJDNry7Y9F4jqHUa9p2U3MTn3ynirDR1kLC8MEw/4LF6RZ4JNB8sLcw0+8Qf3SOSTtyEBUE7RVikHdcCEV9KbMGPMBWJIHKV/wXPzct/Cyy+jZdxy8NoT/Okf3+WV8PI9GY5eImavAZqRRhsdWGa0ZnF+hLLPTj50+BTnHj5IcTAe9GKHTi+gd3xCCgy/HjGWCRcyOdoGI/FrGEfJixEI/Pg/ymj3hIt19fUvSfSgGUQhCnUDLSa6aappfvYgBJbRiRXmyXD1lDOfvZ7Ae6J9/pfuZSnRmJr8e7WYocsJKsqYf2Sf7AWez+r6AQsbUrKUwoyZq8/SPDvOxZNbQDvecu+XUdpy7TWnmJsfETNvfVnRpRwRongtlPqgHQufuo6oHFOrxL6J876bTpF1I/ZcfRKlHaWScBzFoikjGOpy9b2P5F8/4COi5HkuRJaV9XJOnwCIm2X6ZOx8zYugHcWrZhlpbEAmHmzKK/aVQp6JUozLNIMM3XijRFBI2HP7EYJaN1f3K+OYOzO1OVr2420Z+cs+aJ3i1EKFkS1L7Dx4FhPKehbqxCDjXZ7Nbz48TxClLJGRKTE5B/KDGCDuR9J8D4R+iKOE8Tzd0V3z0qRMbnB2QxpGnKLfKTLqvT0diiyRfGz8Z/TN3/lZVueHr3gWBvfGzVbz/S8wVlwS/L1S2vnGXDiWupRAlNLuBuA5f7WpVUbG1oid8AkP11LSFYlyGzlwkRkDdTSFYp+Wc2xY2Z8tjihMILCcfGI/INZEi2cmOf7567j7+z6FUxCMtPnm172Yv26lZe3qMMVliiGlUJWYIEiZf347F5/YA06RJCGNWo+N9So7bJWkWUYFGdt2i6XPwhcO0T46lceS6jDD4dgzvSE0JeUIizFP/fu3SZNhLO949dG8MWh1It+8W08PcXQ3SiydFUrSd3zfZ1BKXDSWXtoiIEMnQEcZG52QA3sv8cQnbxef26lVwnKf+tblnNYwNC1NYrJUzdXCjUDAgsHnpYxlo1Vm9twUKIlgLPhmJPdX1VYsbALxXM0j/4zkrQ88URcujTE9vsHe6Y187YVo6l4wFQWbNcKqEj1CWIhzd4e/9etlVit/I1xftzh86aWX+Lf/9t/y1re+lUceeYRf+7Vf4zOf+Qwf+tCH+Lmf+7m/8vsuD8g2wcAw1XL2zJZcnFLEsMcfwDqUzbheSXj7Wx4FoBJab4GQoaOUMBgY+sqCzKym4gI6CyKsADHMjJwmCrOcuyK/zKOIy1U0QkG0VnHp7BTBVQvwxWlUKLL5dS08ljUyXMF3vNW+GLWGLr9rndPj/NhPfAfKCBqR9iLm58dYdw5VSOl1ihx+y+O0VQaZvO5T58YEiShInq/D8fEf/CDR6AYf+Q/vyUPejcl47tww88tlatOroOCuH/gk0btO0lEp9/zQfVRGNmjsnSPzauVCsU+vF+UIjTEQDHeYvu04220B7dE1azUqyOi0SvmGvP2mE/nBWCj3ST0fC+OYPbIDFWTUKn0WLkyQJUFesKdYVnUvv/9KW84/to9Ko8XcyRm0dkxff5pIS1729m1zlMs9AWCLCSM2ZHEjYuSGMyjjyPrCARuUW2Ep5q4f+AR1J2TpsJCAk40kQrOWKmxq5PDJu2p4+MnduTlrzkU01ne4sHfnAp9aHSCLCcptilEyLFftXOEfv/p0vn41MGFLOQo5UD4PcrD/sivwG/v4rjm+6Z1foaVi1nScq+37qSJW4q05yLFuFFJuf+OjpFhGx1YpVbpM7ruYF4cqyNClhGzwPUbEUR//8e8GI75h1UoXNNSHNiBwVMsxSpMXhmhHq2cgyvjox++gr8Q2wwDzygu7IksDQy82dJbroB2FQkxTp6hmQHV0g43zY1iruO41z7CKZe2ZHZApVjoGVRGUoj6zQsEfFiqwmNEOLauwccBKT8QEq7OjzF0aF0XoWBOlHWdOz1Aqxiw/u0Ne92VcwKpxFMc3GJlehkwzdstJls5OMFxvk7YLuajJZYq553bw2Y+9VorCVNJhbGJIuxFZqwCZoKTaWFwnzNFJMWWGajkWNTZijfRbv3QvNRey9MI2UVevlNh3+BQYy4FA59xBpWUSYJOAx+57tXggakdlxxJai49qb7G+yTk0lupQe/Mw9Ui3doIWh2jueuMjnHKy/kb3zubJLUNW/OTEEzal1ytw9c1HMGHGJSMWN+s++aLgNAZFD8fE9BLGKXbvmiVVYko+UOhvCp8U19xwlHfcdiIX5pWqHQo1GVFbB+0V4XIGpRhtHEcfO0inLb9voNVyiOVSsljNPRID41heHJaccu1YujQmSKC/3y4WxHC5662QPJ/5+Rd2iXsBUIwyTK3HvO4RTa+zc6LNmJaDe1llbGBzgYv8Usf0rlmGnaGrMhZnx5jae1Hem9MQyrOQIZxzPApsyjGmHFM0jrlPX8ehO5/lzJGdDN7kpfOTfOuP/BFZZjhgC/SbJVBQqnbYu/8Mv/Yr76C9WJcJ2JCg0VVneO5C3e9bjpn3PJ4LefBuAIPp13XXyBob7BMD8GHfOx4nSUKeffBasiTA1Hqe7+rk+QI6iWZouMmOA+dy9DEoxZx9dH++55tIpKHhWIsUEWOO1mKPam8qm4+fG6Faa2M7oQiwtPgUxspKko5xTNiQQchDzqE14gigtaVc77Dv+uPUal22bZsTNwWkwKwoWXNKubyCiJVlJHAUa92cD/mNcP1vXxz+9E//NIcPH+ZjH/sYP/VTP8VVV10FwOTkJD/yIz/yl35PuBBtPoyIitQqCdHu9gQ1CFEUXcCAnWjClE7fEIUythiJMrZONmVTCSSyLvSHsAotAQrtVaXlCfHgqvrfmeGIwlTsD5wiMBn0jIgDDs0BcFNap1yK+fLje3Bly7HP3igWNUhHhHKs6RSswvVCqMtI4nI+rE0NP/0PP+aVlLLJv+e+X6TikZcDdz5PtH1VNtySvJ4brzsp36wcQy6i5gx3v1/Cs6+99gSlceF1nj2+nXtuPkWSKYpbViHM+PgvvBf35SnqLmLtyBZqOxa59MRetBLD7067RD8JKISCELV6RuL+rKKM4poDs4K0nJ8Qi5SrTovhMIbP/ObbcksFZxWzT+8Whbax7Hvno2SdiKuuOcHEtnmyXpijLD3vLYl2ohwNZGx96oVd1EeajPo0gGokytxrXvsMtVqLt956UsQIzrB1pMvyk7tEIT6ygdKOuldUFmtdkrWKCJGMw6YamxpWdJ8eGWseC7S4K8YfQ5WEoh+h6UDELNo3C+/dtcHwyDqrqp8ji+MYIicIT6wsD58cxTn4wCXxpCt5kdQA2Yn7oXjXhWke5/gXHi4jhXeWGHobYu5adoG36jFkbrPjvuX1T6KCjOnxFhNveg6L45b3f4FCqf8XNxJjGav3aM81CKo9VGB518//t5zu4JyCYsbxE9vAOKYmVuWUHpzUClqZUBEKgaWLt5lQMOnCHDkcMY5+rAmLMc7AzpuOMWxDkhcmGb/9ON1mmcpYk/Fvf5wEx8d/+82oUsobXnUU/Hhp/cKo/FqvyOwenRTrEm2JHZS2L9PvFNi+8xI4GNo/C8ZxarbO1PQixaE2zZdmZL15lMJ4pGnylhNky2VUYFmaHcvRTxQkrSJr58c5e2w7r77zaWlu6m1/GEpj0j4/SnhgIR8j227IINdQKcdCK8x95QI0MRnf9fc/TlulTHlU58FffRv73vykIPD+oB0gaBuLdYJSzGv+4cdysn7WLhD3IjqLQwTFJH89SlvGds7JGM+nKQ3QwLCQ0CNjdNccoy4gLPf5/O+9IR8h5zFvfh8tlXo0l4YwgRg9h85QiBJ+49/W6Ch5BiyOqBgz5AImppfQTkawLeU9QiOxEBo0Jdv3ns+ReWt1/vtiB2vzI6AdZx85iAoyHn5sH8Njq1csWYXcx+jmi1QnhZIRBJbGsOex+/daReWCP1VMoOQDVv3aNoWURr0jAi4FC+sFkqWqvKdOxJaZJaqh5ey5KVZ0LLGdSkaeE1PLYBwTN55iWCu0U9SGWtR3iWVOzcnn3/MKXIdvqLTjV/7hD7D41C4eszFnXtiFzQz7bjhOoSLRfmlm+PKHv4nGyDqjhYzS6AY6SlmdH+HQPU/zwR/4FOO3yt4vtmUZZbScf/78eOoX3s4Nf/QRodAYy4lTW8k68lpGxlfzRkIEVPI1Zus6cT/CWgEn0JblC2Pynp2sq75VPPvsPiojGzmCD9BcrUuF7YtTDaw8s8M31aC1u+J5UMZSKWbUR5q41GCBc/NVlMMXlAYcbAukkCwT5mKrgZG8DjLOHRdz+PnFBmNTy3lxmCjHrtEugVMM1UVYp1H0sezfviqpQpdNHf/WL6f+Zn++wa+vWxz+3u/9Hvfeey/FYvEv/N299977l39TJaXHJpqijaRVFL1lBkgBplF0ciubjDRTxInh+ccPc82BWYp+fJcnkgzGd76biiLZ/JoXRjHFhAu+UFnXCdt3zEJg6auMkdE13HyVFIddk84sBMIgJXUKEjFUVT5KbHBTFnQXlSjMWBtXk7GGQF4Oooy0U6D2T5+UFIpAxot6NZR3bhXFqTUe+Jn3ifze+8q92heTIGPlFDjx8CGydoGbvv0LwiFTyNjIZFRLm2q58fEVvvobb+YttsHxxw9iyjE7vulZMgcEljNnZohjQ5ppXKY5kVqs96Fr4hidkIzPsekl4RZ2I5SxPBI0ueveL+XoX1iK2fb6FyhtWZHO2ViCap9yvU1jyzJhxadKePPpmosEkcoEkdz31ieIooQ0DmhMLXPmocMc6QpyrI3lwE1HJTu2lLChMt7/j/+AL3/iNTkfDOW4fmsToyDuRGS9kEHe6LkT2wgqYibdcIUrEOpNfoxltRXJWGNAS/CHrgYePTXC6soQE7aY0xa2llJS5VjSMSmW2N+vY9UyOrCUtGNB97yHns3R2QFK9ZddRlt6SmLSpq46R4CoPwf8VxgkBWmGd86DRzyyXV2scgRb1zj54m7idpGuH/eqwKLKCZ1+SOPqCzJ6MxaGJSliq5EcVJTj0MEz4Ed3ROLR6XxR6BygYe/2ZVIlHLeycZKqMNisraJYsMTdAoSO2qFZNBIlSWCZvvU4WRKgutIxhR6tu/kPP4xbL3oqRUaWF6WO0oF5bj0wB9pRDxwu04xtXaQ+ti5F3XoZlGOq0ePV/+BTVA/MEtU7fg32wAlapYIMNPzqP/oBcDAysYpziqDS92vBMXntGX77a7tZW25sIuaD9JEopbZnXniYvqCz/TA/fHvzQ5x1GctrPmLS22qN3Hoqfy/KWNbX6nQvDdO5NMzpvs9i9/xKbQRxeuq/viHPVk7bBZJ+SG3HoozM/b1XxlKeXM+RHOefpcgXNwCr5yYoKygMt7GZzlNZiqjNqFDPrawNNwl8E2KQNXHg+vgKo+G4HzLjwtwG55pdK2TKeRRe7sN44Lh0dpr5C2IrpLRjZW7kMhsriIp9T9uQ4nHLRIvKUDv/PeCRQ+1wCml2gUKYcvt7vsw9b3gcjGSkb6mmEHjud6sIlUSaAePQpYTSthWyzNDG0rVwKYXCwXkyHBtHp+n3I7ZNNVnbKBI4RaQBLY14p10CK44VU3UxxA+jlEd///Wc+9JVVJzxljpxHmspD7JjrRMID9VpJrfP89X7XsvY1ec48sRBdK0vsYLVDlv2yBj5Y796L84p4jikuHOJqbc+g+2Kx2kWB/zk9/0wATBTHCRmWU6d2E7phEfOjGWtGRFtXRMbrbYY0btMS7KRljH0x3/wg2y0ytz4+icp1Nso7Tj09z+HCjLauJyzeMMNL7GxOITSjrhZlhx0//VKOfrrZW7asUqh3pZGG+jFJn9ejB+Hv+VtD0l+uRErnsA4EiWj5AEv/PCuJaEIIa9Te6so7ffg//7gfp588Dq0clSHN+ggHFMFXHXVaTSKc5eGyHCUXUDZGR4+NYKJEio5i/Zv/3L2b/bnG/36usXhmTNn+OEf/mHe+ta3cs899+R//rorqdjcVmZwOA+sbEoeZRn4yi0PDDCDjEJg6cWGIycm2HP4FBvtkiy4UDg5pWLsx8pyZ7NM0VcZYzeeQZdj1nxO84iNWJwfFa4ISpIAopQARfv0OBrFk6ZFqdRnsiZjo7mLExBuJrcQCqKCHw9lNdksnAEXWOxSmcaNZ+Rr/RgoKCRsfGmvFIdGUlZuv/erkrThvd0e/o/vlCK0KgWOU46D3/Q0LtN84VffiQoz5p7czdTVZzlw01Gmx5uostyjdqvC157ezY37Fjh5Yhtpu4DthsS+8HQO4lRjncIlmhQfIeg9lfbf/iIYR68j1h61CTmMuqRcOLKDQVSUyzSm0aW/ICOPpUf3osKMcye2sXp+PDcZHnTz1gt4BgemLiUcO7YDZRynn9vDxTPTtHx2cWm8yfgtJ5m9NIHtRLRVylOfup3/dLog4ws/NhkaalM2jrmTW3IPSGUsN77pUWxsSLCMuiKHqt4+BJVzcnRg2eWd+w3KW5c4bykCo5HluWNTZGpz3GSMdL81J5tPF0djapWv1ifQoaRybHi/PuULY+0P48s5aVc8XP6/99tFTKWfc3lAumTnC8MSAT/7E98FQLnUxxkouoDm13YxvyjxWz3lE1wKCcSGNJW8ZOc5VW6pJCOzRp/MalzoWFluANBplfP1p8oJaOTQVI7GcJOeRw4zBwnCS3OhY6KaEAUZ5ZENnH8vbSXv3zaL6KLkE9PXNNB5wRwtWVRdijSbikn6YG242NDrS7yidUAqxU+h1PcikR7KWN7+vgd4+D+/FaymtGMJlOOB/3QvOrDUCpk0ZGHKXa9/CgLLgR/8M+IkJNmQ5i8a2+DEl67hNz/7bzlzekYKYZ/pbAqSda4b3vZHCXJiqn1vFC4N0SXTZbkXkE8k0bi+F0d5U/uR0TWWTk5TGG7zXLDBwGQY5RjeuUAw3GZm70X5nsASVnvC+VUDrqHNeWVAXpRpbxljnCLth0RoRnbOkzl45pO3ce1tLzCImbMIGiVcwZTAZHzus3d4Kxxv6q8cr3lrn6JPnUpwRIWEeZVy/uwMVjnWm6W8KDJhRlDus2tqg+WVOhvr1bwRunB2mmhsg9jvlaWaePDteN0LZEnAzl2XWPQJVIOr4QJBBMNNS6MoSinuW6A2ui7CP6vo9KX4MSjC/Yv0H9kuaU/GQiaN1WCdBQqmDFCPaamE3kaJh1+YoVzq0+kLGLF7op2/hiee3QEagrEW6+2QVR1jgpQ7fvl32fWux3GeGzu5a5YMR3MwFVGOb33Xw4xec46tLqQ81OLYqXF0IeHWtz4MhZTrbn+OV/9/PsXI3lnO9DXv/ekPoYOMXdeeFOpCZD3NwOAyzc/+9r/HALWyrMWBkXz/sa0yOlWSoHLmvpsJnaHXFUPsgUgQYylU+sxsm0cpx5nnd/lFanELFWlwcHmE7MrSMKdf2imj/W6ETQzlesevdcfG0hAnLzao7psnQOf43GAtmihFhxlf/LNbKZT66HJMBc1qX77W4lhXQiXZtfccWltpzoNMOPh6szkzKFqdIltmlpg/N5V/Pg6Y3Co818FIfMQG1JzhA+989K+k77xyvTzX1y0O/8W/+Bd827d9G8YYfvu3f5t7772Xd73rXV/3Bzfspjmm8QkD6zrhqhuOAuTGlwOOiwosw7U+/VizFhsKpT5bZoT0rLSFMCMaZCpHGcZpWu2iFACNLkpbWr44HHYBSRqA9kWodqgxD1P7grHgJImhH8stuOZVz8khgWJdpfmYwymwGwWSIfk6FwCBQw93UduaFJ8RgQtOUdmxxOwLO0RZahx6vI353uMs+fxPFWU8/tReANaf20aIRjnF2qlJTDnmiee35whjcdsK549uZ3i4ydpjuyE19HoRGsf6Rpn3/+Kv+/vmy1m/YbZ7hlKU4lJDT1nUeCffjP/sI29AGcvw9AoqyqjNrAjfBM1Pf+YaUQdqMSWOzzeIRtoSwzYpI424H+GcIhzq5FmngdO0VZIrYZVxrD69A2sVtbF1et0Ck1sWmPZFV/DmM9hOxOve/lXh1KA4fnwbLZWw9MwOBoawSRwQGcvKUsMvVOGhOKvpr1TF3xLYMrlOsd7JI6dQoLXl0fNDRD6vU+fpGrIxvRA7JoZ7OacwiFIutERwMjg875zukHgKhA4yWl48MhitjU0t+/f71wlSLCUXkPRDHv7w3f59QHliHRAELHLyc+edRVf6jE2sYJ6rU3SGp+6/hTgWpbxxEimpjOXkf78drcH2Qtn8B+s7sKxsRCyvVlA9zVv/5YdxoaPT8aj/ZUp7hYPAsW2PqCo77TJGiRgsxICB8eE2S80CcVu+X21r0lGWwo2XUIWUbKNIbdcCVFJGNFx740tky5WcepGulilUekzZKF+D/fMjdHshWS+S3O3UEHcLFIdERGOGxfD6F3/l7dSHm3TPjeb38w+f3oqOUvbvXpAD1Wqu/rFPo0LL5/7Rd5OmmovP7srHxF97+Fp+6Vv/Gdfd+BIox8xNJwQx96M6VY3pP70lRzdWnt6RNxGXntvJkIuYrMrhXXSCjPx/v++HBQmNhS85tWOOjZU67bmG91a1OaqWdiPWnt3OH/7Om0iUoD66kHqhl8s9FrWfFihfpGojvFAZZ0Oh3kUjhZFRsPv6E9hUpgMVZ+hyeXHpWF6ps9yM0MbSVLFwwhI5bCOn0U5xoABHn99DH8eWbbMAHDp0loHxsNaW/mqFMMyYmVlkZbWWv7f/cLxK6otwA2z7Jm88HmW0F4d4/Km9rC5vPrcAW7Tw51wozWeEJgoTvvhz7+HB+2+X/VE52qneVNUXM7rLNUluMU7EV0FGmum8yRqtJp6jqCkPt9g52mXrjllC46g5w8GDZ6Aonnvygi0f/YnvpplqUhzzFyewX5vC9UJaKtt0rFCOCRfmDfOhD36ehSf2cM1Um4f/9HbKxQyzpUl52zJox9a7n0dXEgpbJUVl9ZnttM+NEpbFf7T39BayniSIWKtRpYR5MlZbEfhpRBBmHH/g+hxxs8DEwfOUXcDyotzPgUhQaYtNNVv2XSBNNfNzY7l4Jr1jVe6X/3qj4PjJGV77/Z/GJYZio0VlzwLjey9JMxJklGodTqeOC1+4Kl9L7Z74BysvmFHGsnPHvETZRhkxjjbCOSw6I5NSY7lwbkaQfgYItFfjdwu5s0O/H1CttTl3Zoae2gQZirUuBQwdB6HTbNeaKppupyjNyWWTor/V6xVBCvT7fe644w4AtmzZwg/90A/xyCOPfN0fPCjUALQR5DDBcvT5PcID9JtQX4kZqDLCKTzX19x7z7PCmUiCHGJXgc3VzANOSrUisWYu0T7uboBWwspaGXxGZ6ddwhW9/YxXWB6yJZxTLPYCMDC0dQm8QASA0NJwBQgcqphQeiqEgpUHQAHehqR/TT9XdLlMM7xliTElnK3sYp3uf7iWFIszgq59+9/7OEo7avvniLEUULnJ6Hd/8JMAjB+6wIUHrmbvTccIw4TCUBtVlcJh55Z1ev2Q1Sd20l+u8k+/94eJBgIU7SgElqVmAR2lTLgAVxW0a1EnLK+KXcj0ex+/jHgvI4Qff+MLuNSggoykFxLtWiZZK4N2lA/N4aymWOpRqrdZPTbjETTrFbh+xOppAJWpNfpxQGl0g7gfsfuOI/S9OAQAp1i5OErQ6FBzAfd+7ycpu4CJVx0n2ZBx5DU3v4hSiPVBrZsXRkpb4WOhuCqtkySGoBgTk12BxrQQo2njR7HaH7Y9ZdmuDL2+IUt1PkIbCqQAaCuJfbrplhevIGI7z9MajJVtJsWCjrIrkJ/BlSlRSGdKuF23vPMhQbFdQNYP8ohHjdhAbDOCiPd7UrhYBRcvThCGGS7VVNAkSsRQe/7+F7AWzOSGjIsH6y90nIvFuNk2UtpHRVXY64eQCY92gCCWAvmemetP5yPGwDhCbWmrBKcdK+tlTqSWcy/sxIXQOSj2SwNUXAUZ8UoFYk2kwWaGYKKF7iiwinDbKsWRltwQf7gUr73E6MgGphRz7d550vUSzaUhvvDR16O0JVsTkdQqliPP7ZNiryQFWleJanRyehHnTY+TrSlEKZVqh3Kpz9CE52ZFGR/4tf/E2Y2Q5lpN1qc/5IJSjI0Nrh0y//x2eZ6VY/b4Vgaq2U6zwnYb5SkuQ05cFk7Hsg6O33crKrSceWkHe+44QlTp5Q0nyuWxec4qxhrtzeZlwAu1Sri7eVNlc1RMKXmtyjgSJc1N4DTWavaOSQb6i08fEJSGIFe39jxSY0zGnbeJncmsbtNwRSbH1/K9Uf7fsbZe5Vu2t+h1JbIs7od5nr0ylrNP76VW7TA6scpQvZNz3S7pNqa0ybN1iYxcVZjS2LFAu2dEDHXZtWSlcXSBNA0ZjkIh4ZZ3PEy53JNG3lgpaIy3U1or0HjzEZnE+N+tqzHFQkqMCJMKYYYritm8Mpa3fssX5blz4gwwPL6GM5Bgudg3OAPv+JE/JnYy6l5ebrD49C6e+/07KTsDVrG+0CB0ShjLfm91KyWaS3V277rIhg8RSM8OY0bbUMzQw10++1Pfjqr32FVNaa/UePHL15HFAX/4T76f3/zZbyca30AZT8dpFWjqlMCjhiA0p6v/6afEyiYS/vvSsS30VcaZi6Obe7aSPSmNA+ZPT2GtJhhwEf1UTRkrCvkwJdKC0utSIpnoxYSnfu/1rJ0fy/euuFOgj0zJAjROOVYSKdSVt8hRYcaew6dkkrZSpoulqlSeRFWzMjYfGV1DaceQkzCJQkPG142JNa9mhvf8vY/TbpVZWqkKZ9bvg6UhafQVAuwYBXWl2HvzUTH+/gYxwf7fwecw+HpfEEUR1lp27NjB7/7u7zI5Ocny8vJf/z0b3ziKoleuV66/yfXWzjl4xQf7let/geuRPxhi7G/7RbxyvXL9L3L9v6HA+5tcX7eK+/Ef/3G63S4/+ZM/yQsvvMDHPvYx/t2/+3d/7feoVBOrTX7AwERWo5hfaFAkIEKhHXm28kCy38Ry4FXPM37w4uYP9DwgecEKCl65ZpzwIzoybpj0o+wQxVCtC6EIAoqlXk6az7pR/nt6vYih0OK0o7htxQtYbC4gGXaRzE1SzZf/1ftyxBINVGOyI2O0/v31AMQrEl3UWq7zPX/nS2Asc1/bz9DffdqrP5GOKxZFnBnu0PU+h6uXRlHFhMl3PU3WjYgm19n6/Q/y0iOHSZKQ0v55ACa3LnDj7c+y3IwojzdJ2kVO65iqV9S1uxHlYsbJWO5ZAYXKFP2FOhM2zE2XWSsI96RVhEzRJ+Oq3/goNhUvsiDMBJEarP1SIh1fIcE5xann9uReW7HKGHaS74v23NHdS1RKMevnx0jSgBNfvZomlkRZ7Be3gHLMXZgUFNMFzB/bypCTzyXwiMSeb36UcjHlqlc/R9qNfD61BM4XxjYYsRF1NH/n//z/iXn44MX6DvrNe1aJEcJ+UIxzixvjFLfsW+D5tYjfeHiPeHIZR6AdIZqOShl3JX7vE7eycG6SCSVj+a7/6EUxmFEo97BqkyP25y/jNMY4NlTC8M4Fil6hWUQR1ntXCGmsN7hGOZ56fjvZcoWiM7k6/3MfevNlD5eDWKM1JGdGBLUeIJfG8e13HmWjG6BiJaPHwFEp9wSK1E74T4GjXk5kjUzJiLvbKaFxjAwJFxYF8xsRSzqhUIzBgOk5uqQ4BWpMxr86EjV8IbBc870PgHK0DyhR9yeGsN5lTWU5n7T//BS1WguXaupDLUr7F2htVHjdO78iqNQjklU+wGIbUyv0z4j68r/8zIdpXRjlqjc8ifacP9OCI79xF3f8yz9icnpp8562CzgL/+74T/IHXz6ECjIuPrpPRpDFGFPpg1McffqAF5o5JnfNyv01jlKtw2RocxHcLRX5uSdMG+MUc+cmAbjtm78CylHZucioLTDwTAShIxSGOowMb+QjZ9sNKVS7OKtoL3srm4Eg4aFDOfLtMlkffeS+1Z1YVM1MrRLVeiwsDkmWuEfHzUC5rBxrzTJbdl9Ce4VoTIYJMtZnA4p+y1cKzs5XGW60KJZ6aBTnL0zm6L7Sjj13vEi7U+S5p/fz7LFplI/0qzqhMwyQncHePXgO5hNNVPBccv95NLFe/CNeghmOqBATb5TEZ1Fd9gwNrGeiDKxXMGtyCxytxa2iFGZ8bamAmitSdgFpL6LTLDN3cRJrZTScJgb8s11SQOjE7kWJ6XKrXWTx7CRpamir1PuDppRcwApZ/ll+7dffjNKwbe8Fmq0CrW5A68w4i186BFHG2T+8jeZGhfnPXsOWyXVOPL+HY8e2U5lexVoYGeoIP5jNPaNqAwkw8PtWtS78yFSJFRrA9m99lATLD3/05wXVVh5pNA4TZlzz3gfpJwF7Dp/Kkenw6doV97NgLI1aT9T4gC6kXPuORxg7eDGfOojzhGXm9mOyTeBYQyY1WIWzkkoQ9woyHi6KiXfVeGUzYi/mrOLZ53aLet9psYjypvMSvQg7G31662VaG2VKxYS6RwMV8MwXr/dIpCC7W0a6FPSmwKvgvnGAp//VkcOve6evvfZaKpUKU1NT/NzP/Ry//Mu/zPXXX//Xfk9/+MoEE6WdmA2j2LZlyVuGbC6owUggMJYIRXXfPNH4BkGYigWCH38GA6uKQaHoM0t1SVI1Er+xBUjh6Iwnyq7WsQ0ZeZZvP4PFEXoO23KsQcH689tAOw5nlfzgLjmDCx1qqMcdH/5t73Eof3fuw7fTmx2m3yqBdkRjG6go474/ei1Ls2NCqg5TnCaX36tyQm3rci5lFyIvjGxdRIUWO1vDVPq41ND67AFWlhuyGcy0IRIX/m67xDvf9ij/5V9/J2G5zy/+3a9S9MVyminKJTFHcYmmoCA7OsrGpREcwiFBiZ0IfuSlCqLS/aPb/4m8RmOJKsKRbM81ZJPxAe79XkRjxyJb91zIDwSAEe9VOdhkiDKmppbpt4tkqWb2/CRtnVFyAX/089+GMpaJmUVRto132P/Df8aYDXMehzIW1wspF1POPreb0swqw744HJjtbuiU07qPbUlRGWEYJAco5bj7W75A1fMcn/nMreI9ZyxVpxmbWGFVpfSUkKaVtizEYkYt1h+KG3etsGX/ef5b6aAUxoNn2fPC4l6U3y/9V3AOlXK5vcPyszsAqClwmRhtO+cteFBEvsj7fBwTXLVAiGa9WWJxtcTBa4+T4aef2pGdGiFOFMfuvwEXG1E5e9rExQuTxBZsxVH+DjHzHRlbg4qsf7cuhXwUCNVh9qsHCdCsrVfInOLFuQodleBCOGuF47MwOyajvpYTvt3FKqwXybqRCFJaEdVSCkW5D5XP1GSNVxLaF0dkk1EIZ67S53X/6g8I6l2+/PgeCDIao2vyXBjhbg3GbLOLNXobJQozawCsnx/Li6DBmFwvFDj0fV8gOzPMzO6LjN14BrRj/eQkyw/u57/d8RPMqUQ+s674GuqC2EqRaS/okQOwvmMxL3CqwxuM1GJJPzGWC62ACEPLixRufsfDOKs4/+Q+nvnTW9ClhG0uxFm1qeRPDU995lYO3XREqBdW0Zkb9qp6y8ZyPV9DaKEfKCN0BDHTFlqC0mKvFBQTFpaGWD07zhve/iBKO+YtDDmzWaAZy9T4OqM75/J1qFFEUcybfnQZA1jluPOOI0wPd7nm9udZWhhFO8WuXRfzvU8HGWGjg7XCVzzXV3nhOmFLefER+cJOaUe6VpbkJxxpeuVQqoB8XuZcEbN9TXJzizFhpUep3MWFjjBKuOBEhW7xRVCmmPAeteFwBwoprXaRCKEEFfGNCDKq77bKzGybY8t4m7oz0oQaKXZWnMMZh+1ElLT4rlbKfa79559kzy1HBRQwjtFdc2hg1vR942C5/Uc/QRjFJP2IZiegn2pq++cIijEsF4nKfd7w3geY+J7HWFqpcvfP/S633/k0qyenuOaGo5y82BAQQzuKjQ5ZJyJEcWjX4pWFcSyWSSrK2NXo8xNv+wnKBCQnhXurfeqJMpbVS6OYRpdysS+Fs5Lie/nBvblKWdf6jA/3KBVjktUKGEe8XsLUe/RWqjlVYGzfJboqxfYDrG/OKuh8bYLoAh7+yvUiHgsk11v2Xp0rnNNWgTvueB6Uo+OflcGeblPZo0eGOixfGCdJA7ZtXaA2WHOonAMdKBHtza6WxHYLKarr7htHrfy/+vVXFoc/8AM/8Nf++fo/+DKLEV/MlZzhhWPTOacgQIsaD+FK7D94RoLincJMbDA2sSLqYeXyjk6jcsJ74pEw24rAd7KDK8sEKSl6HpDuCAF5QDjeUXBMTi8xXUpxATTe/SxoaJKJObVxFJzCaUBBUhP+hdPgQsdX/+w2Tjx0mJnvfETik1bLJIs1fvQL/4ZbvusB0HDpxBZ4ZELEwsaBg+jtJ+X91MRKYeDSr6KML/2ndwrpttrnxS9ex+jYKko5nvg/vgUsXDi9haQfcvg7vsQ9dz9JFodse/URoiDLD/zAWCaVxqWGiVJK1ikw+cbnKaJ47e3HUMZR3L6C6wXit1VKaNiI2aVKHlO2ekk2os5aRTrnSDbndrskKFqrLP5fA8Wu3/jJVI6u1hviV1gsxiRpwEXdIVGW21//pBD5910EBeOjTZ7+2XdS9UtRBxIH158dYqjWYXrPJVwSCJtUOVyqc4+oFd3HjHRFfefRroH/4u/+53cx7ApoFIde81xupTCCYd8Nx9ivAna5UIysS7EcaIj1kQW++Z99hG5T4CKlHadJc5NXZYRzKGvbo2d/7sp8lFriydYVL0KpRcKH65PRxOW8yG9+18MQZfRVxrnfeA0pjiiSAvLPPnsbUf5gWR76rTeyHhsOfcvD2A1RAtuqxfmIu0ZRDtiFX3wVKGiMNDeR3XKCi2yOIvdagrqsb5SIjGUeMaxH/keCFPJY0M8OYZwiPj+MSzS9xTqlg3NkSxVWW6EYzYcZj/zK2+RQDS3VnYs+hkw+l6wb8fyvvhGA/VvXOPJbr6dU63L2awckiWi4C8ayTSu6sWF1fsQb0Dsa2xcpT6/ytT++E1Xro6u+KBjv0bkwQlhMRFSlHMPXnmPsjS9wernEhJOEnzPHtstn0wvBQrJQ454f/RNvsm3pLdVkDRnL6tyIJHiUJBmpicvR7Uw5iu84jjKW/e95mOmds2QbRW7YsiHeqqEISlyq6XWLbP+eh+gjMYC9jZL4FxpLa60qCIIvrpL+nzv4BkWD8k1EmLLeKlCbWOfCya0oY2ljGVaKoNbL99wkDQRNvyxlYoBUxDi0UywujHLHa56hNr2CMd6H9jIEfhDnaS2kHrETlAnuHVY5slX0AjIdZqwckalAhkRwwpWClEERjvf/DMOEyr55ypUuaEel1iZE4bTbjEmzikJk830b7YgTQ4xj0VmWVYYrew76tmWu+d4HeP7ZfYyPNjH+fTstz3bJI5C6HDOXiSF4mmr6uzLOPrWXijMQWkqTTTSIGjsXx6Qo7Zg9N8n2yRadTGH7AVkSkJwfZuTAReaOz+BeGOGF1QhaEfv/zkN88ROv4fD7v8IHPvhp+gtD4kWoHMFIm7fuWWWosZGjk6MTy8S7E1Is2UqJ3dsX+cmf/y0mbFmcIwZpJf41vfDkQVCOQpRy4qXd4BTxiTGqW1ak2fB76pve/UWGR9Zznqgyjn/9gR+hs1zL19na2QlxUHAqBzMGxYFSkn6VdSPGRpuUGm1p9FB0M+Hg9jxXO6j2mb80jg4yyi7wwhk5gxs7FlDKMTa2zokju2h3CszsmBPk0SlaKuEL99/GsC3Q97e9mymJVPT7xyAF7Rvi+r/jafiX/fkGv/7K4vADH/gAH/jAB9i6dSvFYpH3vve9vPe976VSqbB///6v/4Mve/Na2/zfjWFToeyRQ/kiy/D4KnWUmJ9aJe75XvlLKAkgAIRSlCSp2ErYfiiCFP87RgKH8RtZgGbr7ouC+gGsy+j54M5F4jhkelwI87O/eQd2OCEYFJiazX/uB5je5kbtQrj2hqM4Byv3XQuhjJI6C0PY58ZRN0tO54Wz01x6+EAe+ZNeaGBOio8bfSOGyMChH/wzUZmlAdZ7jh2681kefvQAUSHh5n/2MdBQqbXp9woc+d3XcvzILur7Lwmp3b/MrTPLbLQLZA56i3WGajGP/sHrILQMacTc1VjUtiZ6qEc40gYnivF/+PRPSbFobH6QBNGmlQ+BZc+BM3I7OgUG3nCB03mHqEJJfiFTFEt9tr76CIVinyQO2FDiH3jsmX0i8PDjlHf81q/yHz9zvRQQCm/VIJ+ddYrGzgVOPHAtDaVyNAZgVneYzIq4RBOWZRRq4yD3jhsfbueUhdLEet5tLyBJG3/v+z/LES3f17w0Qgasq5guKRroXBym4SOllHbM6x4JNkcnBybYGMvpBw/9hfVvnCYwGTGSHNFZrqEdhHoTaSyiCJwQsT/7mdtASwN1/Lm99EnZtesSlVLK7u2LV1hL3PCmxzDKkb1mGTPakRHTpQIulGetUkqxIdx/3+tk3Qy1cAW7Sc/QjvkNKXTG98zKJq4cYegYxjDiitgQVnRKiKa1XgUN7beIyCB63TnsRoH6VRfpPD+DvmaRxCpO/dfXglXc/sOfkPG1hbNfumqwIeAyje17gYgTW6u99zzLxP6L7P97n5fXFkjxHGmolRKu/df3SVKGsZT2LqALKXNzo7Se2i5NYSVBbQQknQL1Lcui3lbQOTOGaxb40R+9j5//2d8B5Th/cQwVpoIcRhnh7Rcwe1bldxrL0PXnxAPSKhZnx4giEXcM0MTBZxA6zZP/6FsBcLFh1/sfIm0VefZiTQrgXog2GWee3MfM9lmykVRy1FtFRg4IXcZ2Iia2i6H8QGyRpZu2OQO7pAxH4C280l7EaicgqnWpVDtyaANXbVvLnycdWFbXy2SxeciGQwABAABJREFUJArJ63bMzUnDN65kv73jLQ8xMrNMNCx7SuA0I+Orm8VhPp2BO+96kobySG2Q8fb3fCHPP5YFkeWUmsHV7+XtDABDPj853deF2GflFmJe+MidLC+OgIH6WJMaOi+G5z56o9xjJ/ZhytMsCtFAAgejHilIsGSdiGypwtJamSQxpMBNb3gMDIISKhhMJWOkAB1utFj9+VvZ+6oXBRSIfJMA0tj54pfYMHHwAktLw7z/j3+BgnaEM+tsLAxh6l3i1QoPfeV6zj9wDbvLGf2zIzLiDWXUO/pPH+bhP7mTsNqTfaxd4NqbjhD6PfaxD9/F8sIoG798g4Ame1cwQcbGxVGaKqZ6wznZi7Ug0Ec+ejtDjQ2ytRKlUp9jp8ZF1HnHBYK3nAUrdkX98yM4q7jtW76CLiYyaQDe9OqXmHrVMbkZTlGoiuhv6QVpoMQoHT+uENTfxobXfevnqWxZ5qe/+0fRQMk4+khU3oQLcFax3qzirBR7KAFFlHJ0loRG0WkXueFVz9LpBQxNrOaOyAmWTj8gRKZeZWdoOkGixR4KZgrfIAaB/zurlW+99VZuvfVWjhw5wn/4D/+Bu+++m7vvvptf/MVf5Iknnvhrf2gYX8nBGsjaOyrjnnueyDuTvPhCupnKyAajgxxYJ8VAhTDnWAx845wR5Wu+STiF64a0PEpTjjJBDpFNJir3yRryvctflpD5rx6dpFCIed1bHgZg/LozqKNDLOiEWMkBmqOfI2KD44oOFyhsCFd91xc4eXwH4U89AxaiiSY6yDj9wLXoNeluqrUOaRzmPyeYWcdNdz1PzFF3IZHTrHz2KrAKYzJ5eJWjfvNZ8cBSsjGhHFt2X+KFF3bRaZV56cwoLjWEI22qZRl/7Dpwll4c0MRRPTjL4lqRR5/YS+eprTQKKff86w+Ddix99HouffIGgpE26ZKoxTZ+5xrxEzSOcl2KxoE1wsB78PwpUXMefufXGDj0x8q71g8e2khsgA6+5XE650cZGm4yMrqWf+afe2YbWSdi+LpzAJz6T3fzA3cdYWiw8flDKWy0uTg/JJtKq0zLkR9OUpQqWjrlwV96R46QSGEgRevd7/qy+Bg6RbxWyTmHAGk/ZGVuhJZKiFXGY1+4iTqSK6v9pvj4Z26nOd/gaieGrusqzkfQeAUpyKhj1x0v/aXPwcAkPBjqMnLdWVLl6KeatFWUUR9QIKCAYbYVgLF0Vcbdv/ab7LZ1Oq0y1XLM/muOs64yabACS+k9L7FhFcGqEn9J7bAT0jjdeNeTBP5+vPv7P44zsOXW4x6xsbh6csVrTDoFxm2RYkESisYDxx3pMM5A5kfs87NjuACiZV9slEU16lIxYj72C2+iFFhWF4fFK3SpiisKF3HL9afyps00uiwe2cq5s1P5/Ql3LZP2QlwtRRVT4mMTUEqZGu5SKSVyOA1Mw1PN6ktbmJ5e4txTe3D9gI37DwDw2J/exsbssPzcMKN2xynINM999VouPbcTgLPLEunWuTAi62GuhPPcWbQTFNYjFK1WBaN9JrESTmhbJYzbAiGan//cIX/wKZoP7ealz13PGX8YDjb+amODC6e3cOnf30mKlSLPI25PfuxVJP0IU9y0WNl9x4u5h19u/aMyPv1f3yG8wTBl50QbpRyznrObArVal4sPHfQNlnx/e6UK2tEjo+4ihhstTjxVYLoqSv/yRJOTz+5FB5aJ6UUCNLWRJgpyM+P2pWFanQKdjRLX71phYNB96oVdEGT0yERtC6BgdXEYZRy7Qwgui98DqJSEz2dDwErxERVjDr37YdbWKzhvJ1TC24WBUHZCS5wIT1bVexAImttHCuerhmPUBflcbRJgdqyRporF5ToVFENXn5fPwgUsOAsG0pUKAYqaNayuVWkt1ynvWRDKhIbK7gUs5D6OA15eb6XKkyfH4HyVmeEeS188yLY7j/A7/+SDlLeu8L3/9ZfY8W0Ps31mjS/+5lsAaLWLnP/kjUQvFbnpDY+hgox+s4wuJpw9sY2h4SZYxR0/8GmsVQxfd04a3bMNgiBl/Dsekz1ptXwZt9hy8qWdGJOx/uIWwiihXMzyQs5+XnLKN1TG6QcPMX9miv/4k9+VP/PNi6Nc/+ZHWX12W75flsebdFXG1K3HhRvtNBWPootbSEZQ7VO75zgqyvjhf/JHKCAyQu2KvY+t7YUM1VvEzTI9JY2+Kchz0ZxvoI2j3Skyunc2L5IGBv+xsjS7AUWnKWpHyRmmAiizGY1aK125f/3tXf/rq5W/LudwZWWF8+fP5/9+/vx5VlZW/trvKXT1lX5EapN3t+/uZ0ix1JUi9D508kocKxfHKBcz5r54GNcLqHq39gFJPI/H0rLpFKIYBehKHxebPIanVBDO24C7Enc2PRerM6uETrOKpbVRwVpBrMydFzl13y1kWEGcFMJhVECiMZ+aIp7Qm2jwrhaHrjlO+b+L2asylvLWFfbc+xh0ZDyxZecsQZRQIsAFDjfWwz43nnd/1n8Axx49JNY3mcnTM7LFCocPnxFXf4DAUaq3eXqxxKHXP8N6oukv1lHGiuAASBNDnMgLzNaLJJninfc+yE/+i+8GIDkvm/fQ/llm3vIM8cUG4VSTCE35+58jGhUU1Waa9NQIZsArbInlRpwEKGPZODUJ2oKGyBkRH/lD6f3v+Ue4bsBTf/Qa6q8+SbneIfIWNxmOn/2l/4JzSsjRxjK2e45TJ7dy0/5576Mmh7UylrVOQGHLKlEhoQD5BqG0jDE6pLzmx/9Ybg86R+SU3hyRBWiihhC9lU/r6LeLfO2ha/I1ESfh5atVOtdiTLnR5pvUWRnbsnloK2NprtW8j5fLLXz+/DWIPzv1wHV5xx4aRzjcIULTA+pO0l9myaAXMGmL9L+0k5iMF45NUyombHvVUeZ1X1B27cjKsLWU5sbUTkHrjw+DguqPP4Hxn83q+XHQEM6sbY7H/BNfCS2EjgtHdjDuApLEUC2mrKRqU4fkAkIU7U5R/D5LwstUPXI/zsLuRXbf9SxKwfPP7ZXCfUZG6BQspcOz0rT5QqqxbZGxMfn7k+fGILQsnJihc/8+Np7aTrRzGZQj9D6aL/ybd4jhtZFkmPmTM0xvn+fpxw+jDy/xid94O0//n29n39UnSHoRybrw3kg13TOjbN19kagoRvff+rbHUNpRmlznzGdv+L/Y++8wu67y7hv/rLX3Pv1Mn9EU9S5Lliy594YbxsYGjIG4ACEJJZCEJAQCIUACSZ7EhN4ChtAcDKG6UNy7LVuWZKtYXRpN07Qzc/oua/3+uPc5IycQeN88uR7e3+N1XXNZkufsc87eq9zlW7B1V6ZtjFMNZ9PEhTVKpRQvDLXIM9Qwq0SOa1YFTKs63/3uJ0SaJdK0nHWAtp4CBx2prHq5Gkpb5p++hwt+9272bJVOi26I+CrLaTfeS3kme5x8jWVyf9+cLFIsUm6Bl//eT8REIO2zfPnR2HlDIA4ZNKvX7+Heu8+K/ZsNpYpLuZBHa0Munl8Llw2y+a4sjpa5XJ/Osm/ffKJKgtHhHlpsgslh4TKHStbY3ifX4GjDjudWNINktGXDJc+gtEAnOtvqMbQiZGZa9qOzTt5PIvHiNeHECb51FWRlnXrJAGf+DCeetAfrSadi05JpQPbt5zavlfnrWmKwJMTQiWktoduu6SRmYZmcdVFOhBnL0tVepbWlSk/CUNi+EOuILWA7GuuA113EQTBtQ8fyTMX2fwBq1kO31jAQi2LL+VN5vh8nEdKejNj1rXNZtnSYrgt2460b4+avfBLdUoO2OnYmRTIZcNEPvwhAteYx/8Id7P3aeaJ5GImkkam7rDrpBZaesUuStkjT2lakfKCHFC566TRHBudRums1XcdhugGUhqs/+S+sOmU3+55eRS5f5tKXP4b2Qqxr+caHbxYTBGVYc9NDdPZN8qY/uAN/ModOB3SuGOa5X5xMSwPvqKE60SKasHlJMiMsee84WILR6ERI4adrCKYztCwfFTiYkrM4wDCFBKgvHOjDSQXNPXP0edEP7Vk1hNKGU8/exsygzLVaaU54HaAWKTJWiztTzE0I42RFKcupp7zwH7fZl8b/0PiNRLBvvPHG5s/NN9/M+9///v/yNZXsf6gcKinhK6B0pEuIKEoO4WZrwjEc2LOYMNL0Xf0sqqOK48yJXjYAy9pKa8BBMTElB7QzMIteUqAWB6CpZEhn9zQ2Zql1LD6GrkmlMowDRQfFCaftJKglQEOYh6XvvpcW61FXgtma1vEGFykGH1lD6nHJUK0Daspj0cn7CC8dl9aDY/CWycFmJ6V1/PjDJ2GNamozqbJLFGPEpDIpmMPZQh6M4tTLnmyyHXf96AyODvYyPt4WM4cth55fyqKUIdFZYsPCAomOEjbSZLKixVYpZfAjTR7FfZ+7mr72KvO+fA9vfe0jtLX43PqXb6Zh1QQwum0JNhJ9qp1vuxY39rN0XIO7elwCZ2D4JxsB8H0PFGy9f2OTfdrEBsWH2auWFDHTGbZuW0F4pA0vGVCYamWBEf9T97xBEr2z8nqr2PXIiXhexMvf/T25t432WCYgtCL8q50IP/5/jfcFsR8kERH5rpBPrGriopLZOn6svxXVEs0D2LOKh35xGpHRTcLKowc6mtlrGP/OyHA3JlJ8m9UC8jcZCYwcgTmUSllJeOK//7LhOhEpHEYHeySIRBFZBdqQtA4zRLQaDwdFWUWQCvn7N90n91xXaGvx41a6ZlYF1JVoSzplabnjEFe94OhzSwSL+9UlkpkqGHj/A1gHqheWmy45qioC1/l0gHXFGWhWRVR9l2LVJQRm4kZPMq6ihpHGeorUUykSaFRVCw4wxlo6Zw3huZZSRdZS6VlpTdmEIVpaiYMNWeN+KU0UOiKibRQEGms0k/v6yJ+7L8ZGWjJpH0dbpidb5Tu6hmCojUTKZ+XrH+WKN/wCNZbB8yJOevddVMtpqrNZRrbJ9ycS/FP34jFuiwXI2xvC5dqy4Jyd1A93SrIX74JNkpUSWMp+GwnbFXGGkXsi+pbDP19POJ0BA2agRs/qQVqs4Mka7EzlWAYfX8WCZUfJEKsUNOz9EhEDqwdxUkFz/tTL/4HYhdiMuq1VfBXhtlTZv38AkMBauxEBlt3bV9LRWmp+t5m6Q669CMqSsx4OGjcRcvmbZ6j7EjTUZjOcsPYADWcOg2Xz5hPwMTSsKk+8YjP9fZO84X/9C14ipCFQ39ByBMik6s3POzbWIZWxWpLOxr2O18LQtDh8WEdBKRbET4RE/T7di4Q84yYDdh9pQxkJOIqx0PbyxcfEpy8mowFkrUukLL2eQYWKvHUpHJqHrXmccc421m54AUdZShMtTWKiD4INUJZKzIith5qW9lmmnlrKPOtiuuvNRK6BlQWoz2QpTbQQWcXUeDuF6RZKWxcQzovY8smXQ6ixkynqgx1MTedQP+8Dx1Aoe+z+wels27yWv/7gDYSVJImWKlPbFtHxyUdILZxsBqb9q+aKMAB13+UnX34FLdZD5+vNxAXkXhQnWjn1n26nZ/4xrFWUjnYS5WHBwDjKsSSsJJNf+tolzLv8OdkftcCGTn/nHeIr3qjILRsV8em982IcOXTkGxhF01wbm+8+AyfjU9g1INVfzzSDu4IWBYEzTtsFFtqtOCHt2rYKFE14Rs/yYY7uWUDd1yRSvtg/xhCbjlRICw6etlgspVAsdhsdo9aOWX4bhuX//+3zfi2687zzzuPnP/85Bw4cAGDp0qUkEon/8jXmtwgz+tJ4afx3xjGy/6c/wkvjpfG/ZbT3vWQ/9tJ4afzvGv9faA3/d8ZvJBqUSCRYvXo1q1ev/rWB4dyFVfO/DfupCEsm1nsrG9GZSjeqao6lu2dSHAlijEkqU5NEMQbRZ3KVF71HZ3tJ3sUzc4QTIJupx+QLqBFRK2SbQOTM5XtwULTHn69v/SGMB96EwrqWggrIWBfrWaoqQkWAVWx7ch3Dd2/AuqDrUHlqEaneAu4vuppge8qx3dJCYYe2t5boWj4Sa5wBVZeffeFqsIrKlgWAVA43XvAsAG1XPY/bViWayHL4wHxWrj5AIhESDbaBZyiXMlx+6TOYSoJiWfyRUZa+BaPs+bdzmgzBE/uLXPqhb5NKhtTefwrzN+6js63I/impTOgYt7H3ueWoVEjWumzZsibGrFi6TzwMIJqMRtH3+4/PlQAsLF+/T8gjyZCE1fSZbIxLNLzm77+C0zfLdEkkQJKZGlPTeTpMUtwXBjOoXB0T+yW3dsxw1sueYuSpFS96tsF4joxr0AOzeMlAMInHjQZDzhZSaE/0DKvjsTSIsqQ6pcXnE+Gk/GblcFaHnHHONuqBI1Z5MeYlhWoSa7LWob19dm7xK8hbFwc9J6OkhGnc8Dn9ZcNxpTI4MdGOzvjUCNExM7+mIooqatoKlrRgNVv6pkmtHOOksJ1XXH8PQeDgT0uAmrQOOAZnf5Z6qLEezeey6JS9oCEopaSqqBWlW9fLZ210+CKR6rDaSpsPuOhtPyHAkkkGZFMRNcSVAyBlNR6KBfOFYHXk+6fONYCsaroEWU/uuRtj0jLX7m6yUp1xj5bjssVaMc2Gc7e+iK238q33cmDnEvAdHvnYq/Gf72Xj6c+xZOkQZ33pGzTUCnTKZ8Epe/GPttH2mm2gLZ2dBb54/Z+z9vfv5fC+BdTLSazvYKseiZOHSHfNiqYjsOXhk+T7G8Vn3/1WYb7HFe+okkDH+D+lLUHgEGCZmBLNuHrcVq6piDQuH/nSZbidsW9vBInOEr0m1cRKazfihZ9vZOHZu0nnK5TwsUZTPSztNBtpQv+4LFrZJoyjQdhqWKiNPr1M9sxD3cwfGEdpQyaWDYqw7D3YzSnnbxGIjhJG/LEjUq3OIvOr8V7Hqi4Rls33n8zocLeQq3wh9q094dBcJ0dZTN1tttvvfb4f4ra411rBhhrHxrZ8XtRszWsvoljK0D4wpzkJUI5kPliHZvXPcSLUlg6SuRoomRs6JvxpFGEk871WS0CompVqLxHQZuV7DAYKVfBwrRbmdWeFjvnjrPiHu1i1ZJyf/ug8ud9AIn5rpS1JNGUV4TqWg3sW8/BPziEE1E6B3oQxft2mBdPbfvZeipMt9HZUWLT6MNOzWdxsndKt69n01p9jqh6mkCa5YIpjUxncE8cIDndQDRVt86Z5+eZP0uFKRdZGQgA59tYL+ezb39nsPLWuHMF5xwvSkbAKRxuu/9k/0mOSRDFetlG1taUE1VIGIhjcN58nH96IX03ijThc9M4fSZVeyd706su2sO+b55DoKs6tu7qLv3Ne8/mUDvaQNy5/+YEb5dmgqPkvdiNRiYhKOSUEUEek4xrwnQjp0qnjqntuLIXzlZ1dotxQToIW+8pjY11ERlEvJ2lsTy6abDqk27W0paMY9ynt/0a3LZWt8lsx/m8mpPy3RsRcuxEB7QvQXwRoDQKkzllnzg7HMdRrCeq+yDAwmyDbWhbB3VhOJgxiqYf4Jclk0GQC41mRIgBy2SozhTnmXLq9RJSP29O7OiSQQHxd9z28DmXA7wUViJ1fLr5OUvoR4Fiu+dpn6H/N01gHyt9Yx9iuBRQP9kCoiQ50yAEzncHMppttgq6eab7xT9c3WbPkAy6+8WfYQJNZM0rKij7U3i0r5TUVYXsduGcDp1/6JOtf/zC1WkLaHIHm4n/+V2amWxh7bhHLlw6L16i2zEy1suoP7sVaRcIxpFMBppikXE2w68k13PXlq4TBm4yYemw5g/eeCI6RYNIo+q2H74seXwPTB5DISAts7NbTsKHmjIuelvvbNRsHxAqjoM9kmvf6uX+9EBRkkhGccYzdz6wmmQwYdipkcNn11QuwNRdvgWCLapUU/a9/invvOKd5aKAs9akcCcdgp9KYUHPDax9uYglxRJoiGwdWQvyAREtF5HQQLcHqcTpbjVFSIXffdQbnn7uNBtIwikWofYRc46B49NnFmMAli9+8H9mY1Ytj8BqYKm0IZ9O/dBlobfBQJJMiOO2rCD9ujTlWESjxJs1YV+aaVWy7dxP1jVWGnBqf/cxVGKPZ/+RqNHOL9e7330TaE4HgBpmiNi0JUL2UFsyhsXzx468BIMjH0ioNj1Sj8EMHZRTuKSMEyrJgYIKZiouPoYbBuooECscqXC/EasXY4Dw8qwieHoBkiI3EI1iFkHANN/3FbfJZBjQqUHIiV8TezcbtvPlXPUvHquGmFAqeofLEYs79vbv53jvexp2PriaxZJJH7j+FBSuP4Ix5bPv8ZShHVAmM71LY28ehT18I7XXOf8dPuP4PfszB285kZibHvBXD6FSAqSSY/P4GUmtH6OksSYs1L4LmOhHyR9+4hfSGWATYKtyWarOVa408sxbrNC07g7jdOqOEOXzLR74116oPFe7ADEmrmi04N19jzTVPcu8XX0HH4mPNYMM5Dp8aVJJNH2aAh+89NQ6y4hacEkKKjoklX/3n6/ASATZyOBgneoGyLF88znTMRlbKYi0sPUWYqHXEhq1z/gSVoqIzGRFgmvaD1irSmTqhsqy/+BlKKiBhHZRj+dEXXkkUOkzvnB8zV6WtfOTx1bG9m2JsMgPpUPDdRtqYhZkMTvx8GyoVubh1bjWQD4gQEeej963j2P5+AAZ3L6Qr70v3E8XhsZxoQ1ZjzHhdzgbHiSirCIUwcqN+Hw0iE5MIefgn51Dr1SxcMsSqFdKqTaCZjVOb+kgbPrJnOMpy9rUP0j9/jCkizIbp+L6JLzrHFR2S6ToD/RPMW3+Inq4ZvI4y2flT2EUlaqNtTGxdjJlNUQs1TKbwlk0QWcX3vnUJyRHDuz/6NVFj0Jb8iUfJd8+ya1ZIgDbSeIuncEtC3gTIpH10WclxF5PMmphUxzCw5jDRs/NIZ2ssXXaUY4d6CXd2ozOCsU1ZYX47jmHe6qM0LBp1MmDo/rUklk80k8socEigKWIExYGir3vOAlHFyYK1irCaYNfj62jHwXXkbHdQLDQJTOCwa9sKvGydeZHM0bY4OWwoPQDs3t+DG0NyLHPFpEPTKbpb6gShtLYjBFrR0GPc8fQJ/LaM/2uDwwYj2ff9X/Urv3KkC+6LPRDjKotGURlra06Eduu+KFMdH+9gqii4nYaw6VzlEGamW2joHDoIKNtBDluTtM0vk0zVOXykB5Dfi3wX48WVzB7B5pQxdK4+ysjReVgNicGYUYymoo6rBMV7w6cv/ysqJweYOHve8vh6kUgBnBWTslmmAj77rrdRf3IBRIrTv/xNytW4OmDAHmtITFjI+QSIJ+jW7UvldyKFToaseMsDdL1iO2EhzdhkBufUUdAQ7evg4SdXUirkOHSkFycVYI3i2GgnRArPE9ZasZxCD8xSDxymp1o58+LNjI63ccPv/pSOi3fRu/4Q1nfItxaxvkObtrzxA98UvJ+yBDNpASnPZMCxZLtnsb5D56ohTOCy96nVmKqHDR1cq/AbYgTacuJND4C2hKFCz2omJtqp1RLMKHmei770U9S8CtGYVGRybSUING94/zdlo4h1DLPLx0glI0Z+cSK5jiJHDwzEOEV5qwSOaGDGxIgEmok9A80ZbYLjqjJmbo4lrSabCrj/wZPIWaeJhR0nIlQSAOTRhFZEf9+WfoaGdptqTsgYR2v1r8QbggSHRRXRP38UUxS5kGUDhRdVGosqItMoazuW9edvI2hVbIgyHDGGTLrOvCWjsgFbBVZx3uvuZajqomsxO9WBQ9uXAeCXU6JLpxWZZCTMz8b1XQuRaHg6Me7UJi3awvwlIrESIlVMHVjhAAAd3dMoI2LyPSZJYq3MR91SJxhuRR9Nc9ZZz5E++xBEmuSoweSErUwmJGU1SkuSV9q6gMpwOyhLV1sNPEPm5CN89b1vYfnKQ3z4U18kXFmh7rs4sS7emsslKXE7yxSOdNN9xl4cN+TQl8/F1Dy2P3ASu7euIgw11WkB3ddHW+h85XP89L03M3/BKEpDe2cBlQwxvktUSGPLHiZtm2QZlYgkiI037mUJKFVEfN1DkzoOhZN72R55jlahahoizapYKd2fyfD0d8/jc3/0Nh58bj7//rlr5F6HmuT86eZcLM9kRcoGCQZ3jYiEirU0iSoBhsqsJF/rVg8yNtpFdSpHfz5AeaLJmstVOHxgviQYQEs2YM8TJ4i4sRVmfK2U4jufytOaC8T3O2aNKjfihFN24VqFm/ab1wBIp3xMrM3Y7c5ha/OdsyLyj8KgYFZw1FoLNmz16iNNYl1jrF0yCa54K1ORfd9L+XSvFpICFtp7pqX6rIXAuLRvFqwik66DZ5tKDlEkXSgfw6yK0BVFGw7Z/ilQsPNgF96MZeJYJ5lcFTTkjEsmPmuSiyaxCLa4FmpM4LLp+oeEFRtAtLKIUXM6gU3B70yd4ZFO9tyzkSs/8nWIFLMHesBAesGUiFL3zVKOFLavSnnzYtYtnGb1shGcGc09/3o51ijqhSzl3X38/SdeyXicwKIsg987FfehLtlPa44QACsONSyPfvEKmZsxJlelQlJtZUaeWkEUOhzYP591r34Ut6dE4dlFEBM6cAzr33sHd3zlSvyJvCg6RLpJkpLk0pCdN0PKapESAjIoqvHcbAQySotAe6p7lpa2IosyIflsHYWi1SapYfHaKhL8GCXJt7L8y9c+DYCXFeJkYaSDcqDJpMLm3G5U5S855QDtrVVWLzsmBKt4DyJmTvfNnxN3f2n86rFr1y6s/dVn028yfmVw+NGPfhSA66+//v/xRf20aYL9QVrGDSuz7MBU/GdY4NkXsZWPDLdhrfzZ5sIXZdVWi7YciNSBRto3GaulDe3EivZAMuUzPJ3COuCrhiitsJ6ClXUsUFAh+x9ax+lXPob1FMWNktGHGDqNvE/GapSZk4dorOPcsmMsWjKEUhanS2RfCAWkH4YK78LDcmg80sP+qhOzTEGlQsrjLZIFxq1FgPPO2waO4dC3zxLCSinB9M9P4LFvXYxSsP8fX4ZpCdnyrQvY70O2tUyt7giLU1vOe+VDYBWL1x0kn62jleVHb3sr4yWP3v5x2haOMz6TpPfi5yEVUTjYg8oGBL6HygbSyrnxcLNC1shOE7FETvJNu9HpgMe/cwHaC9n0ugchFsYNG6SUxmvjlrUfadR4WvTGIk2HSWKBza+4kSO3nt08PEYO90Kk2H//iS96b1t36e4o42XqJNI+W59fhLWK3Y/I7zUq0Y1Ay8c0BYVBKjStNoFR4qDTqHp0mwSJRMhkRdpSDTD1jI7I2YSIYgNJLcb2jwbzEfstaTurmGneyIAbdmy/bGhtKKmAqYl2xp9dgq8iBkdFzLaupKJzXl7at9m4St12/TbS3+viQbdAKw6OEzF5tJu89WSrdGQ+zzSq0b6D1ZZNHxPW9vCBfiKjcSqWfEaqMPtfc00TdmGzMudyGV/kQ2YcstZhzesexVgBlZeVWORlG9n8PiGYdPVMMq59bD4EA1s/fyneiWMc/M6ZAuOYSINjeP7PrpVWcwTBjnkMKKf5XGaHOxjatRCsIpX0semI+u55XHPTT+lfMSTtylnFDX/6HVoGJsF3KA93xGQjg5fyKe3pZeFrn2Lx7z2E21Vi1Sm7hbymoWfTAcJChvQl+6Hs8tSOATp6pmOSibTZdCLEWTqFXVbE2SMBWVhOzlVllMVzQ1YtmiQwsXoAIp+Utx515vYllMUmDTbQIpWkwC+mOe2Ge1nYN0XVwu/8zVfpMznCWgJT85pzxxpNZaKleW90PJ+sUU0CgIdm/qb9OCh+8fQSTty0m0z3DJs27AcENB74Ht/Z0dNch9YqFp1wCID5UUrgHI5h07l1WnLSluvumWSiIHCFdFsJD0echFBN69Mt+3rEeMCNaMsGTamp5x6XzkPaulgL93/sOpS2dHbMSiEgcOaIZfFtWhFrpGb2WpE7wuIkAszvH2TxeTtASRBirBLZJBzyeYERSUUyvtBMktmZHK7VsdWpwiYtBSKcfJ3arl7mtdVIPJlDa8PksQ6sFkvViJhMmJCqY9Y6TIeKljXD+JM5fAxqX0u8rKVVqnyF0jD18Eq8lM8zw3mUNhSvLKBbanRetEt+v6XGjm2rsOUEfbkAkxZdP9c1nHHVoxBo7tzZi5P2SfcWMIHDq87dTVHL3rT3R6ez4FVPY0+bkMphpMhm6gzfdhpJFCdf+WQzadnzrXPk+dx1On1//jCzhTxhpAln09i+Crnf29bcFyvPLORL17yXlpaKJMyx1mlLW5HS04ua0zgoJ6kpwye+80+ANOfGJjMSiCo751xlFVE5ydIzdpFMRM35FGKYVCHKjRhYKAFc2sp8NDWvWfkDYamfse4omXSA60ks0CgYPbdrIX39x2hpKUphJ67wNmwDF204wG/L+G2uHL7//e/njDPO4E1vehOf/vSneeSRRyiVSv+PrvErg0PXdXnf+97H2NgYf/u3f/uffv6rEWUMqePbykoO4QCDTgaEyhABi/tm8VBNvcMDFYdyKKVwNEyPdor2VDxmCnmpHCpZvNqxTVV7q+Z8mjPZCjPRnJNKIlclrnDjPSdHnkKx5oaH+OFXXgFAywOyUUYIS0qPJoXtFbfu1qwYQn1jEU7FwVkxSb2WxMn42HmVJkPWVhKUfIfgvsVSgZvK0RXfYesAXVW61guej7psbgolbD0FPasHcXJ1bKTYdv9GTrpoC+0tdZa/9X6mv76JzU+voqwMua4Z1q05jPJCydBi5ft6JUlrvoLWhj2HuigbOPV3f86xPQPUjUL1lSHQzLtkBwDlcgZSETORIvVAvtliq8+Kpla6/bjJlIgYWDIstkxWoTPSbq0RMmC85r0m0uA7JBwD7XVqMW6p0yYxWHbsXsjCa5/G7SlJC7Kaws6k8GvSOrJxEKBifNHf/vM11CtJ9lVECHnnjqVgFX0mIwlCKgSrSKC5586zJCh3DH4x3axe28BptulyaM675ClaElGzndHIlivScBO3FAvlQp7HwwXNW9BsrTmGMIh9fpRtftbjh0ah45bLxEQr7cuk+rd84RTEup+OVZy4RuaDYxV4hvqAwlk6RbdJklXQPW+KfMds/MkkMG04Hdi4igAwffsGwRz6HmGkiFKwYsUgysK8JaNzmNG4Py33GWzKkEMz/uhKvBgbXCXEJDVpR6rx6XQNqxWrLthOoAw2JQd8W08BKg4Lz9tBKl2fC9RjVrtV4K0ZI9GQ3Iljr7V/+lOpGudqclON4odfv5xEvkpy0xCq4uC9fi9hNQEW2jYeFj1Ho/ibT11Fbt0Qn3vdnxO90MmROzaRyPi4XkipksTpKuHka9TuXYote3zwvg/z/R+eJfO9FFcp6i42H6GH09hFJbBKBOFdYVUrz5DNVVm7aTeOkmAtH0vCtJoESRwoJOfup5EvO1KXeZTumqU22kZP7yRVLFu/cx5Jq4lqHuFsqjlH2nqnSHfMrbGu9HFSTJHGWkXSOqRXj+KhOapCcu3iPPTE0yuwsRzWmdc+zKtXTgrLWFk62ioc2L5M7CnTUg1PpH02nFfn9PME37xg1RGZe0ahFAREnHrDfXjMyYudfsIwbR0zzE60UqyKDqdSlhUn7hNJFqQ63cA6W+kHMzHRTrL1xfjwY6Pijz3+jVObVo9uKqD4/tPiNijM33CgWdEG4mDbkotxZqbqQSJCa0MeTaAMCavRQymmdEBtqJ0jT67inPO38O/veQteIiCdqaJCqYi7SMKCspSVFCs8wJ4yQRS4jGsfFhUpf+eEeJnMHeAf/l+vQSkYUj5rXvUEh697FaWd/dz7129g8tubJAgyGtVR5amSIvzZYg5uX8pp523h2L5+bMrwp697uLlXtFy2m6Gj85r3um/NYYZ/vBHnQFre1yj2Hmmn/5ot1JQhvXBS5pkSm0WMol5LcttFf4ExGmOE9a8mk5iUBLQJNPd/+xKemExw2bu+T+FIdzMR2XDN49z9tZc3uyqzw53U4o5ZqGQPLDQwh8pCJPqssyXRaBzdsZBi1WPzXunQhUig3sD0Km3Z58RzO4bShLFqREvvNM+90I/nhpRnM9RipQhtoVRzWLD8KIeP9AlMH4uNA0OlrGjx/hYMy2+3zuH3v/99HnjgAd761rfieR7f+MY3uPTSS7n66qv50Ic+9Btd41cGh1/4whc455xzSCaTrF279j/9/LpxVJePe5dYDBgtmAcUSWDR4hFSds7WTqMoNmLBCFxvTisJB2oNe6nY39hakduwCWlFZGL11FSmhkXmdNa6RHUPJ96rwlVltFUkUez41wtpbyuBOQ5XgiO6bJ4hHWdwaFi04gj7HlpHmAsor7P0LxnGn8oR9hppGUcK3IhKpEj0zUil0I34y1u+LEQHDfgOTr4up2bjPljF0RcWQqTInCmBgppXYfUpu2k7/QBdnbPYbETn5Tvo7pB7uvvJtaw963kwGlvzCOsik/HUAyeTTAZkM3U2nXiEGQzhOVMMfOQ+fAO3/86fSGXBdyg+uYRiMY3Nhijgx391U/MeFMfasAlLbtkxwWNNWyYeXSlVPqsoHelCpSVLrDXKqfGebn0H0iEdrTXscI5M2mfZsiFajEudiJve/V3ec9VfMvnocgCWrD7E1FNLeeKJtYJhizcigHIlyZtf/gxaW/EWVpaZUhIMLIrSFFQdW3MJax6OVaSSIf54vpnpziqZPzoZNIOjDg1Tox1kUiEXzi+RwKEnITibelwxWZaOyHqGdD7WR4xbyM3FoizJVF02cW3x5s1hc44fDdF2z4tI9M7IIR1jzgwWhWJkpIs6Qn4C+KeBDzN822lclPSYtpZFaw7Rf+q+eAkobCWB/8YhZuLfJyGanF4skeK6IZHRWFex/mKBhvRetAMVNcBF8p9SJYHVUP3FMnwsnRsOkXQNrdbBQfTgGkHdsrWSrYe1BF6jRZ0wLLpKrv/Cnadw+OCAiNGnIzZe97DgV0MNqYjxCAksNPTf9AQPv/f1oC2VSorKvSvwOsvMlj3artwhbe+EYfKW05ke6mLHv1yMyvjgSdXv0hNGqbzQy9vu/ijDj6/i7h+dy2N3nsXBQ734voPK+QKxaC9LmzhlGPTlOXX1TUDgEMxmmPrWRmwxSZSX7xjNprFxGw2jqFZTjB6ZJ0L02tJmxHM25q+w/bOXvYhUAzCm5Nkme2bRXsj255bSrRRLN+4jZz20F5LoFbC+0paJwW7c/BzAvqs9Fts/riVrlOXQv58GwJkthqF9A9QLWUZqDto1BMpSn8kwNZNrviabrTbJLUdjWEthrB2AaimNYzXp1gr9i0fQyZCgGpMMHUuVsEkssFahtSGoJ5qyJtYq8t0FsJDCZbbq0tkzhXIjqfABj+3rajoZNVQzc3npsHRuOtgUwXYSIdnuGWpDIrpen82Qz8k81ig6u6QFn0zVpcqd8SERkW8p064UFmhBY47lCJQhqCY4NtzF+GgXk9NZXDdi4YpB0DDsVJggEueyuuh3VlVEUlsKX9nE0W1L5LMaRcslgtf0rMJkBC/7z9/8OMoxtFmX+z/1Shw3ZPJgL/c/s4TR/f0Qac6+5EmoeGx3C3z2/W9k586l9J/5AsXpPDZjuP8Xp1HYPUBUSbD/cxdSqiSZVnWIFC2nHqIyk4UWEdvHQndrHdtVJ2cd6iOtNNyqFr/pEcgHbDhvKxe/4hHqdY+OtiJO2mf/N8+WANgxlFXEgkXDANz3uaspTuZpuPG4XSWuu+Vf5O+OJdNeokYkQvBAwNz6l7lhCItpCsUUtfE8S16+hem6ZsJayirAxEFnMJ4XxxuYcz9rdHNiHdrKdI58JiCKHNq6CgRIZzGBw/tu/SRt819MZlJ2rlOTyP2WEFL47a4cAqTTaU4//XRuuukm3vjGN/KGN7yBarXKww8//Bu9/lcGhx0dHVx55ZV8/vOf59prr/1PP79uNIU7oenBG2Iw8QbsKOhdMCotp3gsywcSKGqL9SzHhrub1RnrgBMHkNaZy+pSKFTVwWqx23GsJttW4jjokOB34gDQ3ZMRPInVPPrIeoZHZdMkJxu7YxULjzf3NmCzISv+8F5aYhaWE8/P1IIpqn0RxftXiD9ra52ktsLecwzJ5ePoRDTXOncN/nkF+T7tdRTSUiwXBdtHzZH/jmfofPcTPPB3r6GtfUaYUS0Bh4dbWa0dwsDhXz93NSpbZ3L7ImbH2iHSHB5qo7V9Fs8Nufif/5UAi7Iw84lTmcWy60gbE3esR3VXyL5yF7lcVdpgWHSDdWsVnUtHKXxvPWrTseYsyfVOk87U0Ckf7UTNKlDCOnQ05MeOq6K1t5aYfW4+dd+ju1/8dSNlybxyNxHQsnwMEN/gjpMP0ts903xWKIsNHSo1l/5Vg6TbSvjxPWxvEdzKTWfuY2XUBnFlMlCGUiVBIgZRRzVPxL2thznOs/asE49y170n8bJLN0srBsP11z/QrFBrFK5rOVDTL9qkgpj00nCOMUY3D76o8KsJKRnrkorbzs154FhWRhk0cGSsRVqDymIVzISKfO80R6suVQzptjIo23QYUskQ53NLpIrYWL0WHvneBQAcOdxPGMrnGnpuMVZB5YVeqTLGbU/rgOcalJHWpgX8yTwtWam9J60mOWzIpSOyaGYnW1GRZd/ja/CVQdViGIVjMfPqTYeYaCqDzRgqNx+N30sCfS8OolEWO5KlVk1JAFbzyJ51gGAyy++/79+g5mDHcqhQk+8t0NorVVMSEXY2CaHDpnO2kVl+jB+/8R0seNXT/MFPP8pZVz7G+Iy4vJCJsJHGXViAfAAHWrnh/F2CS44cTMUjNX+Kb37xaqaeWkojxna7i0xvW9i8n5VKip7+cVatONp8dgmrceKKVdu8Kfl+jUJX4DCh5TkHhSxYxc6pFBuXTdD+sl3Cdg5cbD2ufCjLyGBvs9VmjaJQlD3TxnMs/ihYq3Ct5qFZRb2eIN0zwzSGwt4+NDC0eyGvuP6e5rybms6z+EQJ6MtYImXxY1LH6FFhqCbbSgR1D50MOHZY/u3I/evIIMxltOXKD36L4kyOei0hBJi4yrTtwY2QMIQYfKNYc+FWUHD4qFQHj6oA5z+IYKczNXAs5deNStIAOKmA4R2LyZ4uihGliRbCUMedIWElEynCwEUZRXl3H6QiOrqmOWYNCuhwLPbkCTLW5YXH1rJwheAyF82fQGvDkpseRUXEYuDxMILfrSuLo6Dzkp30rRmkw4gbl78o1u9Eo/bnURp0b4mpo930a83uvQOc8NZ76D95H1nXsOKzd2ANzHvXo9hA41rFm//4+5x38VM8+KUr8RIhQbsinQpoWz1EfSbLsjc9THfnjECLtCTVldjL3WAxk1kWLxoDC2PaJ7Vwao6wF6/vx+46k+73PMpMMUNrWxGdCFly+VbCnGo+q0/fuYk3nrmPSz/2DXqWjcjU04I9NqVEXPUGN1OXz+KapqVn0rHNqrhSltT8KWYrLsnOEtXDXUzEJK2s9UjEXZqwmiAymqCSnMOvxnO50fHAKs4883mqNY/W3mmyiPamg+LeT1yLmwzo75sQMh9KKofxcH5Jl+b/yLDIvfnv/PwPjp/85Cd85CMf4fWvfz1ve9vbePTRR1m1ahXf/va3uffee3+ja/xatnJbWxvveMc7OPPMMznrrLN45zvfyejofw0KVROJF7WVG4dbgGkSBXwroqfhccnJ0oUTc1IZjrSRkzFuASss5DlTcGmHtDiWaF4Q233Jq7MdooCPFh9U5Rr89nhr0MKOakOz8aS9DBVSoJUA6JHq0RQRJCK6lBLpg0KCH775HYwc6mvetd51h6ke6kQZTdQIPqziT953O6pXWNZ4BnXpUdls4zk9+4+nyGKb9Wg1EsD09I/L5yolqOzrYefXz+PAe6/AGE0uX+bxP30DGJioOWxaPcoJZ+xk1bJj1A910X3GXqJAmMaOI1n26HgbR79+BgZwn2gn2VKmiuGP3ns7nTdsgUhx1++8g0ymxtjXTmObU2RkrB1TEmB5Zs0ora/bhn1W2hCH/v5iwloC3/cwtQSlY23xcxU3BRtn3A1cGAayuSqt5+wjDDWV2QwFHUoVr6x50yufbArN9vfMoDorBKEwERtta2fRNEEoDN5qIUdRRYKlWjiCjTT3P7WCaV2XYBooEzA+m2xafKGEWVpTIRPPL2xK2QShi6MsTzyygfMueQoXTecSUftvjO72MkUMiXyNG1PbQFmyVktAFl8711IWQgxwx9//clyujsWFHW0wdU9wVlpkmZalpa1VD+WQCjDYtJF5q2CnU6GkDIl8lcpwOyFGWrqRQr3xoGCtjlu9l/7R9wFYsmxQCA2eYsl5OwhzGjdTb1qSWVfWUsKLIBLGrALS64coVjwcoNUmCNtgaNZjacqw+JQ9oGDXjmWx8DdM372WiYdXova0krh6P51dBZS2PPOnr6X4rvNxjjWqcE1xAWykOPTDU1m6+iDKM7iOIVgkwrnZV7wgraszpsExZM/dT7qzyD/eegl4hsKWxcLQfMOTqETE1R/6JrbiYZOG9it2iFODNthshEpEhIfboejB4hnysUC0NRCWUhApXv36e+g45SB6Wjf3hfJUvjmva3UX7c6xK31lcBBsqEax8OotmHIybisLnljeRJJhazQLUhGP7esiOtJKRYVMHeluOlCgLN3zJglLyWY1p1R1moeGjXGtFRUy/wxxhSjogKefXYaT8XGAthUjeFbRu2yIrlMOkLEuYSVJJl3nuUfWg7Jcu34IjSLwXe7+Sp59B/qJlGH0+UW0905hagnKs1k0im1PrJtTVgBKu/tIpet8674TaW24ZRhF7/wxUJaS8kloy677TwILk0VRT/CVwY+JBo0kvi3GfQLYkayQDZRl1RsfwIzm4vmhOWHdvsbbSDLuSAXTakt2tQQ3i9YfJBXj0bQCFcMhl23cS9v8CcJQc9lXvkhrxwy1rQNgIG9cWtDyEbQEzCmrSbuG+ol19jx+Au3xTB396/NQKBJWc89HXyef25X9o2rg5Vc/ApmIykg77/v+3+POWGzVw79jGaaYwleGPU+uYckf3s+jzy4mky+TfD7JDR//IhhFfskxqLpNdxEM/PwjbyCdrzB7igRQuq3KxgufQdVFAJ/YHKFZ9TDw8nd/j9nPbWJiOs0pVz0uMIPTJzj8lsub+2tJRZxx29f58k3vJr9oojm3omKqqRjQwIdmrMOjX7wiLuRY/GgugLExrj4Ru1hlVo1SVhE+ljrx3g64aZ8o1NQKWbLWfVEgpNw5TO/0ZCt+qIkCwX5PqzpJ65BvkbU6OtqJAvLHEVatVZTGWnlp/PrxwQ9+kK1bt/KqV72KD33oQ/zZn/0Zl1xyCd3d3b/xNX5tA/9973sfr3jFK/jkJz8JwI9//GPe97738dWvfvX//Sd/abw0XhovjZfGS+Ol8dL4/+j4bZajefrpp9m9ezfPPvssn/nMZzh48CDd3d2cdNJJnHTSSZx55pm/9hq/tnI4OTnJq1/9alzXxXVdXvWqV/1ab2Xb5c+ZliMM1IQVX1Z/Jo22ipy25Lpm8aFJSDnnmofiF0ilrbUtxnLF7bCmtzIxWzVSIpRcVVhX0RaXR47uXNysA2kUTiLAqcX1ixjPk1aw5rztpGMQfRQXDQIEx2MTlo5U2AQBX/rEZ/jFw4K1VKElvWmQyngrP1n1Hlpet735uT//v66bu7OBxilIO12FQCSSEhhF4aGV1JQhATx4/8kQKaYeX05qYJpVr3yKe39xCoHvMXK0l188uhploQq87J7P07npICOj7ez6xUZUzmfv88vBsXhuxH0Prme27DHvpEOirba8QLq/gIsiHQtDM5Ni5bp9+PUE835nM7PK5y33/i21ox1NvFP48AKiYgo0vLB9JUpZ+heNMP7cQu7+9wvkOQdiJebGUiAoSziax9/TQyLpY2fFPslNhJRVKLphRvHgAxub7+MHYnt36rnPzk0go7DTKfxAkchVxYM2Btv7vlQnDkSGHiPsWGvFtP21r35ErmsVjieixQZLPbZMVNoyPNrOa179CIsXjXBo9yJCDLseXt8E4hukZZxBY0LNN2obAGE7mkYrV/Mi79j+BWO/dB1ox+ChWbVhDzrji91U0gfHsmigIJdSUFVis4eCt7/pF+x7fA3Xtko7NtFZwsuIr3KAEHWCnG2KxzfWR6MKUCmncV2pMHidJTKPpYluHJT5Z2liL+u+6BwmWyvMqohH/vY1OI4lxNJhPBIvyD0r+prqZB4VQbGcJMJS+8VSZsfa6Lp0B6aS4Nl3XseJF21BZeucfMvt7N6yCjuca64Dt5H8p0LSLRUWnrOr+dHD7y2jPNwhrepIMfVxwddVHl3CsV0LuGD5JDYd0Xbtc/Idix7RVBobr5eZ206CREQtVLiOQR3NoByD01KDSGPThue3rQDHMjbUQ6JvBpUM6TtrDzZwsLmYRDPYTrWUiaVsBN+889lVMs+N6LilrcOYrok3+xPLKB8UQWsVKaJCmqVRBpTop1ojVokdrqU+1kq7SZLM1mPrLI3OCpZxaNvSZmUliKvv1sgctlaRsS7+VA6NokrI0YpozZ3aKRWmBJrWxeOMb14qcmGJkHrd45TLnwRgzabdZKxLJl/lit8tMjQtz7Vj4Tj1SgprFJWykCBaWkvCVkYqpurdLzA63M3Nlz3LKy5/mobneef88ebz68j5jI/KfXCPk3VqYGAbo+eEwaYGqeoQXI5KhNh5NWzoYD2Fmwg5clDsAV2rmH+SMLInJqVapNIB1rNUpnN4yDMxFoLuuDq5apiw7mGtYvq7G5gY66Q41AlabnH2uCpUIi67z+usoGuW0/7kJzSAUAve8RAaxYD12HSh4GoxgoEPgIO7F1P46Rru/Mbl2J4a2//0VVIJf81+TDlJyjqc9sHvw5EWkq6lWk5ju+qoZDjHiO+qMq9vnFlVBw2X/e032L19BbnbY2HqnM8d37oMmxJS29Gfr4/Z4hZ8LeLcVvGFT15Loe6SWSLPxPxsATNTrVhf5H6+9fXPkDjscs1NP517GBaczjLVfT00DkovVyNtXWZm8ng41JVl46rRZksYoxjbvBylwO2doX64E4XYftaUyICtdhycbB1jNMl8haOOYLbHty+S+T2bFp1E3+Wc37ubbDrAGMWsiug2aTLW5cSXbcFagZz4WFbOq+DZuCtlFDPH2vntGL/dhBTHcVi7di033HADt9xyC1/60pc477zz+P73v8+b3/zm3+gavzY47Ojo4Ec/+hFRFBFFET/60Y9oa2v7tRc+3kwbFaulQxNLk/HELaV4XBD5rc9cSw0JNJRR9C8eEcZpfKlkqi6kFq2E7eka2vM+ajoBCpbEp9Dqlz2LgmbbTSeipkuEKaQJMRyzhvtuexmlGP8S5eZuxZiuYz0RoyVSos/lwBOmhjKaY399DniG0mQL1z30j6iKxsauLje96efY0SwYqDw3wMwP15FuFGin0ux6eg1oS9ulu8hYjQ+8cftHwShyA1P4Yy3c88+v4u3f+ieSqTq1WoIPPvnBmI1tGfrTi9DLpwgjzdREO9Z3ODYhm2drrs6WumVvTeMtmRQdvrrm4a9cLm2c1xxGlR1IByx/38+YiVs/LTZBbb4ms2YYpS2Dd56Ec9EgRx5bDcDlX/0CpWOtpPJVxo/MY+GCcXlGGZ8aEYPR3LN+2++/ncT6EcLAJRhtIYzEP9OzEiRbx/LWT35eNslIMzKeB9fQ9YmHXoTDePrzV+A6lv2bV7Hn+eUoxGf5tCsfA2C3W2SJSaKSc5JHqUxNCA1WUS+lmnPwa9+6EJBD/shUipnJVg4f6ePM6x8A4OCBAbyY8axR9PePU1Bh04dbaUuHUk3pIZSlXMo0W2auG/7SNaCUaAju2b6CH37gZgCKs+Imc+7LH8OzitlQoRAM1PBnzqFwrJ1D+xdw2mk76bQOpu6SWTdMzsgKisby5J92KOiwKa2EhtqV01gFjzy1gpassD91fxEzkyKz4zgpEAsqFCkbDCQv209RhZzzZz8kihQh0ImDv8bHVRBaxci+gSYbvdukcF5/gN6NB/jJu38XvWqS8dEuMgsmUekQXdac+eoHUQuK4IvOWkIJ3tGWkiRzNaZ2SQDQ0zONc/1+gkpS2L+BJjdPsKfpC/fTOjDJK+/+BJPf3YgKFA/87WuxVQ80PPkvlwvsYpEcin1tdSKjGb5rg2CM+4QNr3a2cygG8zdaYyoREUzkQBv0YBqU5ZFvvexFz64eOLS0lmhtjwkkVoKRipIWWtsrnyO/YTBOZGUedCnVZGwrx7DNN6S9iMy6YVaaNC0Dk4SjrdL2r7oobehfd7g5N3UDI32cfBbAI9+9gJoKhSUNONk6HW3l5ns+/YOzuecHFzQhN5MzGRItVbCKbEeRTceJ1DfeI7tgkmxbqRmsNPRmj5d3cr+0iKUrjqC1YfhIbzOxOPT8UsQbXpFwDZfd+kWsUbRmRHd1kUnixWzlxrQ7/ORKIR0GHrW1ocjEaIvJ2aZ+6aFdi9l4zjaB+WCbQtqASIpFIq4ugs0CGbKA1Yop7aPciMJQJ2HkUBjqZHK8nZG9Awi/Q0htx5/JnlUM9Me4Yi2C9BjFbdf+GQ6KeZ6h45SD2FAzeeeJTI51EmE58ezn8ItpVq4+hE0alp22W1qlnzwNb51ArsI9nRy+ayOvvf4BtDZCfNKx64wbEe3s5uRbbm9+Fv9gJy9/z+2U9vXQuHFPj2SZ+OYptJkE/WcKtKDRLlbTAgF67WsexjdgKpI0Fw93c8aHv4utuwRY9LJphr51BtqNMKGe22ONJJ6NZ3r4Cdnri8UMaeuggdbWYrM4YnyXrrVHsBZMMUWyr0BNiRxbQ1YsMBDMpImMeIvXkQD2hn85T75jfN5kO4pE5aSIuxczBEoMAhwUN/+VQHSimIlfqnhzepPA+PBv3hb9nx7W/vd+/ifH7t27ue2223jPe97DJZdcwmte8xo2b97MDTfcwO233/7rL8BvEBx+7GMf4+677+bss8/mnHPO4Wc/+xkf+9jHfu2FW+2LbfY8pHKYaK1glGXW1+hE2JQR+U/DiOVTgwWFFjsyL2ZSNg7m7s5Z7ExSsqEG8DVXI9ajxbMKJ1tvStmU9vUIuBzFVe+/jYS2qMjitwbNhZO0GpuEhCf4N8ou6nNLKakQb8ah45ZHOfzVcyhN57GOwn9igWCO6pqBVz3N/n8/XbSJs3Va1x2lxSZo0Kcv/evbJJD0BcA+q0KG33chWEVy9RjJvhmSqTpkQyYn2zBWMXXbRkAy3/07l2L2dXDVdfeTzVUgcPB9FyJFIhEyrusM6hrUXRIxEeDM6x4kwnL0fRfx3ZvfJXZNVU0UOQx+6Vw8NA9e+AcQOqANbfMn0LMarQU/6PdC78U7ObhjKYf2z+fiP/n+fwbVxs/phIwB32FqqlVwdkbFTiECNH/qL1/Lrh+cIZuSUfT3zILv0NAdD0sSkJ36Zz/GdS0rztxJrZZokhqchFh1WSzXnL4PrEK7gu373FcvkYtYRXU2S94m0Chak1EzW5u2lp75x3h2KM/MISE8/eBILtaxE8eaZ59bRFGFHNoiln7WKMatbUobKS1abiomSzm/wj5PKYtRMK//GC9/249wUMwUM+Batjywibqy5Bz5LjUVMf/aZ9j81AlMFbI8++xqIqA23oKZSVGLN0+nq0ywS+YwGmHxGkgNGtCKc0/fQxQJJmn4O6dh6y52OC+OJYCKweyplI91LSpSdFgPs6REe0udLJogDg4cJVXh3mUikL165SCRsui7exl+ejmZTI3w2T6u+PyX2HPnKXKvPMux3QtETzFe2o5C5n8kdlnzLt4JyrLm5F0k9iWozGSxPUJYyG46Ilp0vsZNBjgTmq5XbqP8wHIuuOUb2LqLzvj0Lhyl68Zn2PuLkwBYsWyI2VKSzpXDwqLfvAAcQ2HbIvb4MteOHetsHobjLwxQPdSNGZAq1nlv/qmQX+LhxXJFSsl8ipRg6TLWlUQ31M0uBBp0MuCojeTfLJjAIRlHIpMPrWRhOsRN+dQm8igFNnSYnmibYytb1RRNaCTQWOlkXPiWu4iwtJkEpy+Z5tgTK3jNp7+AjRQtCcOKjXv4nS9/Cp9IGP0Vl9JIu1Q8EyGDhDhxAjO/o4pjNUcfX0WqTfBdi1YcwQBbn18YS5LEhJFrDpJtLXHSH9/FivX7JAhWVsglRpGyLpFRPPO+14hVX0aqmaXYgeX4seySbbJWA9BBTP5KhKhAAh5l4ORXP8yB55eiIkuehEgZATOlJNax1A93QKSZf/Fz5FwxEMgnIpyKkBmmnl/Ikms38+hQjq6v/BzXi5iaaEMZqKiIhbmGVJBgSCNlCXwPHcDh754uhQkj2HbHKlKeoX5UKlX1copUus6JnXW6L32ezk0H6eqbQFU1LWcchFAzMdgNVZeSCoiqCeafvYvQd/nk98/EKSo2f+pKbOhQG23DWT6FLjriIOMY3vGu30OdJEm3aXwOpei+YjsaKO7rBdXQbIXCo8uZ2LaYRX//c7mX7RKMd/zOsxRPDZpajpQ9vnrrZeSXHmvacNpIozyDc9JYs/BSjgO31//gn2gI7u/Zu2AumlaQWDaBQeF0lrFG0WM8KirCjbsuu22IkwoIQ42TDEgjVe4f/uVPZD4lglggW/P4ty+iXElQLabJGoeyCrBYPvbaJ8HC4gXjOMDqZSOkmZtLs4U8L41fP973vvexd+9eFi1axNe+9jUeeOABPvGJT3DzzTczPj7+6y/AbxAc9vf384UvfIEnnniCxx9/nM997nMMDAz81y8qus3s83itKIDp/b0AlK2wl0aPU4jv7Z4V0D+xnd1x2TNGNNwagOlGWyCZjBeCgYO+vJeTq8sXixOlYGrOWzm7cBKAOpZ/+eO38upXPPWi97AK+kwSovj9DZAOSV+2XzyX4wrKod2LSGZq6LpFX31Y3igV8ceXf4C2vimsgvpEHlYVpCwO4Bh2f/McVCqkumU+9ZjSMP9syTzxJTi74MP/BnWNow2plE/nNdsFE63gtCueQLlyCExOtDe1xzCKZMJndZQhUmJt14ImajMkrtxPDctD955Gwgvke2YMQegwfLCfnPVo7yzQcIrIX7CP4OmBpiOCvqOP4rb5AGw8a7tIiwBEmrqKmNRhcxPp6ag0q3dT+3sJQofCRBv1+LmtOmsH3/zR6dQnpe141sue4uBtZ+LfIi17N25HFR5egecY9j2xRsTG4+ub+EDuM5mmtl5DiiPR0JQ0ih3PrBFANDBUnYPW+lgmhrvYOFCkZcFkE0htlJCRQgxd7VWy1sXxQs50xX5rSoVSAY7fY9+B+SJr4lhKxSy/bCglIu9+PcHg5hVNEhXAndsHmnM9YTVFFRAc6uCp8TRXXn8vh8fTFFREsrWCSgcSXAM21OgLhqXFHCiouGCh3qexGpZv2MfOcdnoWxfKJqD6izKPQ4WK10gQOM1AqdU6PPVHr8dxZNXmlMJ4Ck9DyrEkMj5Y2HjJ03Qal9pYKwsvfo4zX/UQblsF0xqxb/cSzITch3vuPAvqSlpgkSbpyvxU+TrDe+cz+UgcdFvF517zF9LCLMQElkhh97ZhsxHp1+5GVR2CHfP42F/dyODXzsJGmqiQZuRwH2okxeOPbMAezdPaPouxiq0/OQOVCkkuHYdIk50/yWAsLzBVyGBjP+i2+RNkLtuD2tku0jd9s029UBR4bsTA4uFmYNx4TtNK5me0p4OGpiYAjpAcjm+YvHLlJH/06c+Tai+TSkaY0CE1b4YocAgLaY6NdWJjyRdrFWGj5dRsLyvabIKvf/gmAgwpqznz4qf4y8++nCf+1zUAdLfVCOsJjtx+Ghk8rFG0ZwO2PrgRaxWOFzKm66RyVbY/lCIyUh1Pt1R4+u4zsJEm8BNYLGefsRuNagaH7pTi2FAP4aEO5q0ebH6vE294EBBtu8goOnsnpTJsAauoYwlmYi/gRnW9pQoGEt/rAWNxEK1Uv0ejW2pYLexlQAoDGOrxNa553b1YFyojEqjpnI8fB5+tOR93RjGjfCLfpbpnHkkUymiODndw5vUPYB2RNEslQtniAk3KalqtQzpTRVcUXctHJDh0LVf++2eIlMVxLLVCFuVYvvXVy+hacIzJ2QT3fvANOIsLRKGDqju88NULsEZx9OAAZEMpgpw9iE4HLDp7F0Na5sxd959IVHfJrB6h+MByaits0yrvprP38vBbb6ZayOKi2fIPV7NuQAg5EZbWkw/JvbECv9BOxLZHNvD4625GKdj82ZfLegsUubtbmgk0vqYUKryV403IDdoy8ot1qKLbvOZz21eI6UNNFBxSaN788S/K/zZiuXfnX7xJ5v5EDqzi3PlFqnElPUKSp/p0tilp1Nizs0sl6PXyteYcemHvAiKjyLaWKemQZVEbdWXYvW0FNtK0tBXRwKHBHmrYZht2wZK59fh/ckj177e3rfyDH/yAD37wg9xzzz0vEr++4447+PznP/8bXePXBof/b4YuOQTqxdWUjHUJMXSfcoAaEWklh3ivnZugqZTPjJbWna5bwtCVYDAWGCuVMqSsg4pEZsB1Imq1BNGyMhhLKt6IVL5OELfPLPECb+zhPSU8NJM64LTTdrLoth8D0PJ0onk3Qiy6pEm4c++99+8vY0LXCFoCVAT1WpIlFz6HdcGJoXzhC128/fqH6XzDFlQU+6zWlAQpDpCKaOmeAaP40eeuEZyPdSjsHhC/4GM5Dt61CTuTIjzczmkXPU06XYOyi8kYulMhLzxxAv5YC/1Xb8FzQ/Z9/3SR8ADyLWXe+dpHSVjN/n8/nbQCk4Z/f8Wf4ivDla+7h1d+8QvgWEwKEl7I6W+7m4TVZLJVaYkpi7+9l8TJQ9TLSfAM93z2leTP2U9bZ4HQdxm8Y1OzauKi2OPMNMXC3/CJL0hVIZZvqQWaJev302pdHKtou3wXH7rlKyTapS028Ipn6d+0n7/66GtlGlgFFkZ3z6dQ9mifN83CxcPNZ2sjOTgjDPc+cgK2lAQTwwyUbfrFDg51EsR2eO9/z/fiOSbts8//bANB6GBDqWa/YqBKQOyligROGpgab+csZwhrNL4yMhdjtnIQNCJRRbUyJ9v0H0fKao6NdZGIPcXLFbGH9GI25EwkHsspHHTG59NHPsjIvgFOWT2KZ5UEOb4j0htEqEzAty76C3lrz0LMkjVJUAbal42SUFKdSQ1MU79mnGB+2MRKNu7DyHhs1aYtMyqiq29CWNVxsIyGiVBRjTTFY60oA5n5U0zogNwph9FrJ8icdQg1UEQPCXbtOx94I2ooy2zZQ/lzW4sXM7SxioGVg2R6ZrGh5on7TuVtX/kEHYvH5NDKhFIZXFpg/y0vQ5cUdixHebCT937oW/Sfs7tZVbvvsdUUHltOd+cst/3p77PpXXeR9CL27V0o6gRHOkTP81A3pVh/0A9FZxXHkD/5MOpYCnvCNGgo7+xrPk8sdHUUWbD+ANluqSZGWEpxIltVIU987VKR4zncJXNfWTqt2/QsBvj6vjaM73J06zLGZhPMHOmmOtSOmwwIimn6+mOcWCxvU4sFwY9vObUah0ULj6FRdOBw/11ncX5fhbseWou1ilymzrOPbmD3ltUikx5pZisuA4uGhUF8aB456+ImAzY/4lGqyHvt2ryGobhF99Rj6/GJ6OydxEE15/nhz1zAoYP9mLrLwadWNZUBZp6fD1Y1A5slNzyKaq017dY6cJpJXmNUDki7tHrDCInBuT3fOIg2JeAuKIikFnJL925eDVbR0j8lUIiFE5LYLixjrPikz5QkMfSJ6Fw7yPieAVa11XE/vZxnp5LMHuluzvuGlanK+SQQncQwdAk6LPff9jLBaDsQ3bcgxjhD9i3PgbK89S//jb4bnuRQoDjj2oelWFH3wMDyv7kbjKa7d4InP/gaZlWdb1/z56iV0ziXDIrOLTAVKZI9s5jpDDNDnfH3lHs6OS6Bb+eJR2i3Kb7+s410dc7ww7e/VT5zMpyrVFvF1OF5bDh3K+f/zb9hLQwN9jZllSq7+sGIyUJD5PzAV84jt3KUhrvTYz8/nXB/ZzNgPO+Sp2jD4cvXvBeQ42r0sdhiL57PJ566A0+b5nxdtFg0FBtwhqqKqBVyRHFwWFGC2beBbib1jfc/OJWio7VCUBfnJ5+Iigo56cznZP8+0kcEdLSVhbEcJ0yLNu3lt2X8NgeHjfGpT32K9773vezfv5/bb7+db3/729x6662/0Wv/R4JD01OndrzF1FyXBP9YCwZxoIiqCerYpoyIH8t9oCzKQL49jrqstMlqtaRUX4wcYI4bEYQuuiRfo6MBiG4Rt99GtVBp2/yzWVLGQdFlPPFqDhyMpyhtsE2x7pfNL4O2tORrmG4fO5Zl5TvuYUrVaNmcRIVw5jUPUzrUjd+hUVVZYG5vkZU3PQT7W7GO5fn7NxLt6cRXBiIwI3lGD/ahPMPrPvN5OmySBALQJdRMbFvM4ku2s+UrL8MdmKHvdx/HSwSER9rQUx6dbTUKU60k5s0SDLZz/pt+SlD3GBg4Jtp5J+2lNJOl1bosv/EROlIhzixc9+1/JlCW8nSO6KBsQsEPl+G6hgduuZaqCtm9c5loFFpFfToHZQ/Hi8B3ODLUyfgdGxg9Oo+OheNk2kvigewYHCvWeIBggtoE95dK1WlfOkYYKZxUwDLiylCg+ObfvaE5NaKpDKWjnTznlGTzi+QA/+FPzmQm0GgnYvW5z5FFNzeyxuG7LzQCbo8DBt9Iy8KGmo62Cj4C5h5+YQE2mnvdpA55Ziwd68cpspla003FYJnXM0OXdalVU3yhvknkJ6wjm52VTapcFQxg/WAna07e/UvXgVKWvHWo1RLMv3AHGkVfTwG0EKIg1qFDCCZOpwTMJ1y6hav+8VbSaNS8CiMPreGYrtFv0uA7dLSX5uAWnkUZRWaPRQWW4a1LafUMxlNEsymUAZNSIqVk4oCyWeURLNeIrjFyRKq8GbQ4dRhZryPGUKukwFiqNw7TbjyGf7SJ0p2rJVB2LLbdZ/6iEdrbSoTHcvzxFz8j2m0JaXkXfalSRlMZDj2/FDcnFYQXjrRTumJWKgqJCEIhmdiRLPNP2YcqePjH8gztWkju0hdwlgsRzuks82f/cCttl+7iyRd6ufLmuzEDVVLJgP7+cfzBdg49dkJTOqMsWSI7im6MzQJbTFLd0S9f0kB64SRdp+97EVTiiR+fQ60g+GED1AmpqBCN4oHHV4GFsCaEKus7ggi0Chs5mNCh1TgoN6J7yQhbqFMrpQiqCWrFNKmBaQaWDs1JQBmFD3OHRixZUlGGNSfvImNd6lgGj+W44e++QncugEjT2lJieLSdiclWDBblGgKjOLBnMSgI6glmVYCX8nndO0scrbok0NRrSeZ1T2N8l0uueRCNojyTI8A0JcGCukdPzzQP/etlbHtmTfO+TB3qgZSIFmfTAQ0Jq0w6QLkRg9rHxNi4Roc8e84Bgc4cE5HzRkfJJMFMzCXv8wbGm3/OtxWh5vCvt0jiGMyInqjd2U7Ok0Rmsuaiig5Ggbdigta+Kd7wth8xtnMhO50iub4piCBrHHKZWLqllJB4HpiaasUtQrUq3ucmZUmsH8EAsxUX5ctzyF+0l/HbT6ZDKX78xauxLaFgm7cOYDxQbsTtP9vE0UHpjK1Zux8VKPSWdooqQPmKVW11dDogmMkw//qnGPqTi6jGgeMrP/wNZmdzuAsLtNsEf/77P2X0WDtXf/dTVFTE7FNL5jYWx1Aq5Og5Yy9Rfx1XwbWf/qK46iQNH/7AjVij6DAetupRspYlr3ya0UdWQ1zx6u6eIihkpMJoFJMjnVyydoTVqw8TKJHUyvUU5rCvkWbha55iyfxpigd6wCraOmZf5MW9KsrgpX2iBtmw8SAbc7oRB1jFifNnqNYTDB3sx2JJ4xJi5yrxSJfHGE1fWu41yjbtU38bxv8XgsMFCxbw8Y9/nHe+85387Gc/49ZbbyWf/81a8/8jwSEOzWwJ5CZGWBTg5mqkcOjwDPd9+UoqjXRHWUqVFItNEiKN82yrAFfjGaUi0THTKKyOPRcjTRhqVFUmaNDQCMtENN69zbiku+ewRCaGQvpYtmxfjPEkiE1OzE3KbKYGoSKTqRE9Ml/aqLMJQmUoniyVw9yVuwnrHv7fncjm918ni6yUoPTMIupH21EWFq05hLNyUsSLQ4XO1zl8QNqzB28/A8cqZjEkc1XRD3v1MzhdJU66XsgZNmE5509+xLt+/x1y2FrFA9sXEBYyRMUUydVj5DtnxXVDW4qTeSrlNPOsC3WXrvYqetLjvne+CYulY9EYhR0D7PjMpbj5GplMjW8/tYROk+CEE/dipjNgFYmWCjYXsOzlWwRjlAooTeYpFTO0rD0qjMC6C5GmzSal3RrouYMuEdHaPkvxaCdpL6I41sa4tdKuijQ9XTN87cM3yWZhFS1Lj8WBF80AcEFPSRKI0CUzMEUCwELkezEuJx6N9yVOnOPK4doNLzTbtk88uj6eiIpJFaItzHPi97YJiuW0YBoR3cZSKUWnVpxyyWbK8s54VrJbDMf5xiqSy8fxay/G1zaGUmLTVSqLpliXSZNO15ufVQNVDNOqToilvrFK4Z9O5dDja6ifVCODgukk9UqKKL5WNJHlyg98WzZlA0xK1dLvA5NUdH3+fpbNL8QfoLEAj/tz/Nln6sJWbtzIkeEeanWXXs9QC0Q0fgrRMVt+1WaUBW/GoaYM8971KJlFk9ixLMwmqD2whBOvfpJL/uj76NNGCc+LQf6eVAsDo8Az6FydU37nfpyzh1COpRopkl9eyNTePuxYluIjy9jx2Dr2ffdMHvrGJdiOgLFti1n7e/eiLBTvXSmew77ThKdsWjZB68mHuf8tv4vnRnT3TjL67BKefvxEbKjpvGgX3SYVV5dpVn5JRJSGOySxA2zdbWoV2kjjBx7P7ppPabIFkGphQ8hfo3j3R78GQMuJR5uvr8SVu4ZG4e+cLK3F7Q+exIzyGThlH6n2ssBAQk22oyhzyTHS3kXa0g2co7WKgg6Yt/GgzEsV0ZIJ2frt8zn/3G1YqwhClyDUREaq4Fhoz4YsX3MQgMmxDhE0bqmSzVvGEVH+yamWJgs41VYmicO2rSsFb4as4+FDfezcM5/zb/8yF1/zYHMNLbrweQgUFQRfhhL860D/OA190QaZqzG2/N3VYKDeYwmf7o+Z/wbjHSeW7Ri6l4qWYZ2QniWjQvQox8YJSUnOZnfMJ5uKSFoHV1lMW4hPRNRfx1pF69XPM/8v76fVJkivGxb7PGVwXYnyVazZWMLQ3T1Feali9dr9Ulyf0YQvdBFhqMTuWDZSECja1xwlsnDZ6+5h/OunsujkfSTOPYw7FbdPHUuxJAHspg/8CGYSqHydtWELVkHSi3joU1dL+7zqcvRADM+KNGrRDK/4m69TOtunP0rR+0ePsGc0h4oURlnyGwaxoY7ni2Xndqnq/fPJHyafMDz8N9dJp6quWdTqC74TmqYEz/3rhRSnWuT1RvHdx1cQ1hLNZHd6so3d+3v44iMrpIuCIjMw3VwPKMvgD06hVvdoXTWMjTSPPbq+SWRK4TCpQpJtJaJIUZ3MCwHRKqk0HhcYWquYnk0zXkjF25RCIza6R15YJFuHGxJgqfsurTm/+fqhI738tozf5uDwqquuav68613vYmZmhqNHj3Lddddx1VVX/UbX+LU6hxMTE3z84x/n2LFjfPnLX2bfvn08++yzXHfddf/tL/DSeGm8NF4aL42XxkvjpfHS+N83vvCFL/y3r/FrK4fvfe97Oeecczh2TKzUFi9ezNe//vX/+kWBauIQACFL4JC2DuWhDlyr8Y3imV39TcAyVjE6keGCFeNi7bN2hmIs0SLXAK0ss0pkOhqyDtYq7GQKZWE09nSzKTEOVyHUlSXZX2ha3lkXktYhgaJQd3ninN8HBX673IoAw+h4K6rusPWFXtzThrC9VcKRFjpMCh04qMhSPDGkfe0gX/nEq7j1vnUQanAN7/ng7zC4eQUEGjchOJE6MXOzvcaKNQexBjJtJYo6IIumNN4q/ZeaK0ziZIgppGF7J8VtC7jx/F3gGjo7ZqlbKOzt48jmldhCmpbeaSYn2iDQjB6dx4qNe+hLCaO3o73Et2/+YxatOEIdQ8sle+g8bw8nXP8oleF28vkSJ2QMWTTr3nQ/uqeEciOSyyYo/WwVtaE2AK458DH2PrecdLpOMJmja/3hZrshZz0h3OjjwPhWUasmcRJBzFa2FGM9LDOW5aI33MNVr71PfLZTAe7pQ/hETbygNYqdQy0oYOeW1eiWmjBeiYHAkaamImZ0hJlK48ZEgv68tLiUsuzduZSKCnGt5rVv/4G0m63YMbVbl+EITKiZVT5bh/IUVYgfS9W0tlSYMbB/ywrmqZJckxhjFWfafqRjVqEi03qcj/hxw4nbmlobTCVBTUUsX7tf5jKiUbfU0VRUKBin0LJ/23JWvPJJHn/VzSKtYRX9G/dTJqCmIvypHCwviDRRpKRdri06sDhlS+nPz2DPkXaxm8zUURG8CP4bP6eiiVvNkaKkQgqzafLZOsYqjgYKXbPM6pAEiuhMwXz5n1tDxmpuv+LP0BvGY901Q+qiAxT29VLYOZ+9H72CH130bqxrmq5AFpoSLwfvXU/1jpXgRnSlQ8JSisJYO6qvzPe/+Eoq5QyL/+7nDI92YjKWKHQhcKDs8dj3z2ti13pO2wcljyBwOHrXBozReF6ElwzoP30v17z1R2AUk/fF7VDPcEJrIJW9WL5q9+bV0nWIq32bP3uFfN5IU615LOgpMjXaAUCnSeAhup51ItInjGCNIpzMQqgwda9p8dhojT26dRE20uIoglg62kj2BWsUU0NdzUoOxJ3/wH0R5tCJKy8+hikVMFz0mLdolMUn75F1UEswXfZYtepQk6RXq2u2PLkWLBwd7KXPpBjf28/4yJw/eCIRkstW0YkQG+mmZ7ZBKvzWKo4M9tLdXsI7kGw+SBtp9v/iJGw+JGc98UKONF+/+Y85+bKnUI6l1SZI9cQSQPH+PnioH4xCBw6VkbamlI1T81DZOhiLOZZjdI9U03xlmB7uBNc2WdzVcaniTg91kUkGBMrgW4XJCO7NmXDJ9MzIM32wF41i7zfOlfmuQmp1OTPKO/rJxBXSxasPkz1gue/+jQ3oKPrMUUJlqdoYmhRpfvz2P2DPTzcxgaF942F6rtrG4WeWC4ZuMonxXd753u8wMpkmZxNNeaP6vm4+U/gwaHhiPEUy5eMXMhBpTr3rG3OWmrMJJh5ejflfa2hBo0saRwkM5BSTwRTSKCeW/XEsF137ANFMhkqgacv6nPtnPwTH8txHruZtD30EgIIOm/PqxA/8mL41hyHGA1axZJeMNyFbSlkqgaaggybuuDFfieflo784jXTKZ//PN0KkmC17tNpEE3s6pQJs5BAZjeOJxI2NRIKsUVFv7J9bZ1zmtVdp75qRbSmuHq4583msla6dAY4eyzarbDbUbN//2yFl0zin/js//5NjYGDgv/z5TcavDQ6np6d5+ctfjtYxAcF1m3/+VUNNJJp4gwbgtslc6p+mriICozjn5P0MOzGDySh21mFsoiW+CHMMueNYy502hQrEM1hpK5pK7XKNYny4W01TjqOoQlRLDacmfw//fRkZq0mh2LB0gmoliXVVU2jbYJmpSBvDN4pwcz9qxsNdNUHOetL2iT3/gkKWwZrDF+4SaR+VCmlBseLGh4U0EToS6GKxOpYnaC+iEhHtf76ZCiHtSrHj6ROaB4SteKhERPVwF5NblpBbMcai1YdBW6YLOU5dWCCRrTE+0oUpJWg5YQgdbxjJlM/MWAeZVIiteqRSdS577b0s/sgvMMqy5zMXQ4uP6qjwt3/7OlLpOqdu2idSAd0VackaxfYvXkL22l24ubq0Bd9/Ku1dBWq1BNVjrZi6i84EjDxwAqGyAnwmbrfWHEhFmEiT658mmTA4Xkg5ZgI/+KlXkjv5ME8/sAmsYuKpZdxx09v/0xx62akigNs3MEZUSJPW0rIzsaUeCJZMaYsJNRnrcmRW2JrGaHYf6pI2MJDI12RzsiLaWlDSWrNGkcZlfkZahj6GlHUpV1IUiFi45jA3JJ4XQWNsE5eIgXr8XAFeeGbVL18HytJiXYLAobinVwRvATR0pkJSKNESjOVD1E/72Xj141QPd3HsWCezKhQwt++SRgg9qflTTH93A23GxWqL6qyhjEKFoKtwaMcSFvSU0IFldm8fqqFB2VhC8Tyrx4EtQEBEuSqyE462ZFBNVr6DwtstrbL8CcPN71a/dwk/+8gbBCd6OM+2h05i8mg3K95/N9d9+dOoSIkHbKjoTMVgeqNYcOpesqcfwlYSlH2H1FV7WbhxP4e/cg43/vTvueS2L6BC2D2apf6D5aRy1eY9P/GM53EzdVQ65Dt/9UYAXveVT/PYPadz+pWP4WjDqnfcQ3WkTSRx6i5B3aOiQkZ+vJENJx4Q+RRHsHlnveVnQrKw0mo89QM/kIA3UiS8kJWrDzFbyGMjTRoJrCoqJGUdgiMdYLS0uePkqNzQwbQyB684/3nCqSyrXvs4i6Is2jVEvksUiFXk9LF2aR+HouXaCBSiwJW5HjgYZfFnMgQqwijLhRuO0LZgoknMmSpkWT5Q4L5H1uIrkZAxKE46ZSfWKB4/0EG7dcl3z3D7V1NN8ejpmQyJRIjxXZyEBFpLlw7F8yEO5EdzrFx9CDOaY+xAf3O+LL9qM8Hj8wmVJeEF2EKKV7/jB1Sm8tjAIW8cjj6x8kVrwR6XQDaSORs6aB/wXUliMj6l2VzT77qlu4A50iqoiEjgLlRcBk7ZS0dbGY1iWWcVZ0LOo2hfBxO75/P8Jy8HhAw2b+VRlFGEyhLGMkN+MU1aQQrFwoufI+hQbCmppoevnnEoKrkLd17xLjCK0y95imIhTzta5G0SESv//k6mvruB8tOLUNrSetZ+AiOFEf+5Xog0e+47idSQQZUdysqwbONeglIKtCV9KF6UjsGWPbQrUjAtGtCW3pY6299zLdPWgnvcHmug6+KdVMdaeP11D9PRViE4SfD56z76Q/TBHCiR5rKRokMpvn/ju+baxEbzZ69+HN01l9SecMouhiKY0DVp8SpRE2kEMtqN2HT6DtadvIuFp0liEhpFJpb/SlgHD02tkCGMFKmu4/gCDY1RK2QrEymqSood2hEiSpWYSR7PsYNHekiiqEWKYsVr/vtpa3872Mrw291W/t8xfm1wmMlkmJ6eRsUaHFu3bv21gEbbGv4nCZuSClAovP4Z2m0KR1nOf/cPcf8DWPVArOBvPWJWp24GAw1pGxXKvytl0coS7uvEKtGyAsCZ+2IVFUIyIkrNZR+iZwft7bNsOncrqSfT8v6BHPgL582CskwYiz5/CKzivj+7SUxADOg6zL7zfGoTeQn8OmOP33TIX/7VbYzevR4cS+eao81FLZ/L4iYCyAbs+cOr0CiyjiXfWgSr2PaZy7E1IacAtK85yvDDq+lcNQSBxvddTli3n9zicRYsH8SflIMrDBxwDKvP3MH0eBsvFMSvtVZLYiIH5YO2isce3CQYPd/hnW/+OdYqTv/XbwIw89M12KoLRpNrK6LLmoMPrQNl+ftbXsXYSDfasSRbKjFxBUqFHPNMWg6TOMNsiLQuWHaU6f29pBIRfjUhIqkY1py6CxRMTEsSkO0pMLBgVLQgG/PAKpavF4/VRMqnMtiJVrIYJw73SMUGI1KJdY/KZAsZ69JALyk15wuawMGEEhhipErWb6XKE/kentXcVwvJHweszmZqtONQns7xc39pfB2Fh9PcpGomZnVqy8bLNv+nNeDEGohdShGEDjsfFSB1FAlW8KR1R0gDvpGsOWE13vIJtt95Gs/94mTmLxjFbcg19RdoN0my1kXl64zt66dfacEMhnMEBpOFdXfdRjZTRwWW9nP3YB0FRgJBZUGVHVSoiBAcr4rmEri+vgmKgW5SyRp4ouKFFawLX3/3H9CKZtPpO6hOtJBI+kTHcqiOKu2dBaLAQU8kOPLNM48Doas5MXmr2PvAemYfXAGRohwpdFHj5assfMeD/Nsr/px/f8WfUvnGCSS1JdldpO/6zRJUJA39l2/ni399M9bAlTffjS0nqK8OeO3Hv0RuxRhaG6bvXkv2ml3YYhIbOsy7cTNnhW30Xf0sRwfniU4cQCLCmT/TrPBEpSQTP17fxM1aqxgenCfPyyp8RPQ5Zz3hX7RW5OAM3OY1glgKqoE59BLiiVt+oZdhp8que0+iPN5KFDoiXRNKQN6Ynw4K5Uhi1ai21Igojkj10rWK2WKW8X39QrKLFFPFBLsG2yj4urmX5tNBs2p9fNH4/CvqOEiik/AiMtkK2gvJDExTJxJpKESTFqNY0lvCWsXmWy9h/VvulYtoi26v4g0UhMRSSaE6qmRPGqQw2s709gVk0czEeMbGPlyYFZkjZWL/XSz1kVZSRy1hTDQBREMRMU2Yd9EuVLbOQHtNErKZrGidXjpIR2cBgE0n76Z6/zLZXwpZ/FqCndtXYi4fI28d8msEw2iBXCYQRv/5L6DiNf3CD09j8m/OJIWm3cbe1mWXogpIAbftbRd2e980rhfSnTAkF02K+9Uz7WR6Zvm7v7oRtIF8QMKReeLNm4VERO+yYZz9WYg03dal6+w95JZIF456Y+0qVCKi87KdpAamyXoGQkV7S5VlZ+xmj1PBaa1hAy1V6VBDMiK/ekRwzZUks5/dCFZx7MunSzBWd2VPCx3+9hcfQStL6VB3c/9aftmz2JlUs5pnjKKoolheTQijOuNzvJ7t0pdtZdlrnuDIk6uwkWbEF5xsRQW4iO+4l/aJjMbUXVJW5vnkrvlzQVHcHVrtOGQyNarltBDyYoTbD//1CjCKtScckvngWsq+ExO9NOOTx3UT/w+P/+uDw/e+97287W1v48iRI7zuda/jL/7iL/jABz7wX78oEzVZaoA8WAUWy+SjyyWgQjH4s/VNLTqAfuVIKw05tCrjrdLuiAMPpWwzw3Dj1kc+V8HZcAy0epF8TuPWFxuu7PFIdBVpQZNBUamkmX/TE9Q3VVBGslMXTUd7sSnb4Ey54BlOPGe7XCB+i2xHidJEC//0yX9B1TREimiohcTbd9H7uqebv2ePtJDGRYUKc7iNIy8swgznWXTyXjw0nmNlU1aWledvR7fWsEYxO9zB4MMnsO3x9SRPPiog/GKCxZv2MvTYKmrltGR2vkMYH2Btr3xO7NMAlCWZ9Om+5HmcmZg9Fkglk0jTvWoILxGw/z1XkFCQu+F5VDwbMm1lKHqMDgorLQI6OgtYC2M7FzYZxSte+xgdJkGfiaVcjIbQYfa+FZQKeYK6x3QpwYKXbyUZZ5i9lz1H4dHldLXPSkVmxTE2ve/HbIjyYqMVb0RP3HcqAAdeWCTaX7EUytR4h1h+qZAaEdZ38NJ1OmwSD8k8baTJxUBsg32RHVnj33LIRjurfDbYFAmrm0r8y9YcJMISBi7PWWljdqJJW0eupaFq42v7DiM7F/7SZaCUpSVh8EPNWX/57wBMjIjV2OPPLsEAM0YSkoqKqD6zgFPe/AtmC3lWbNpDNk6Motk0WesKaabFZ/MTJ0o1IW4LEwFa1kzuR20cGW6TD9BYNzFTWSidUhnzAJuwEIlMTz4TMFPIM2ktIypAWUhaxSLPkrlNLL1O2vgCS1t9TKRo23SIwPeIiiluvugDVCsp7rjzdKLDbdRKGWEqx/Owq706lyQpy5afnwoKUtpiR7Io11B/YAmjE1lefcctbPnZabzykq2ExZRUomMQPjmft9/yRZRnaD1/L2hLcrdHeEKR4KJJqrUEbZsOcfST51N+oRflhahZj1y8/uu+JwL0OR8zmSE6OnfQ+NNZOs/eIwevEqvGKHKYncmJtmK8L7XZRotVQahxYicQJ1cTAlSs8WlCh9lCnmAmw/jefmoqIheLTgc1T+Se0rW4QijkLi++rolJWbWZLFUV8dg9YimoUFRrCbY9sU40AK2iGGjOXj9I2hHhaBs55LM+ft3DGs05yyYxSEC2bmNEgzo1f2CcvfvmYwKXqCLJW7korj+ulRZgMhES+B5fvX9tUwMVoyhuXoxqrxFimJxJQ6Cx5QRdC8dJtZdZlLTSRj5udHXOxtVjmuLW2ouoLlS4HWWBPtTdOImLt+zuCtFEjk2xGkDku5ALUE914bjSiQgDFyftiwwNUJ7JcvUffp9aT8iorjPyoMAKstZlspAUQlnFo2ihhuXuu09jYrCHSRUwqeIqsCd2r62e4du3fh6sIrPhKDPTLRzyhUCEa7Frp9nzwHoOEKsY1Bxe+YonRIy8vYadTfLjb10irj6BpoYlnMhx9NE1FJ5cytGvnE2gomZyUXx0KcFlk9RCRfhsH22tZXKXviDOTI5pBkg4FtsWoFprtA9MMFNK8sw9p0qCGDr468tElYTI9dRd/J3zuOTGn5HumWnCbqJSiomnlsl8jTTf/dbL5vR4ibeU1jldQmsVd95yHfVDXfSvOwRW2NepOIHNWY+yiggqSYxRzWeMVSSzteOUJuT3s4mIKHTwPCkklQmIlCWT9okCh00vf7LZ/QPifddy/mWP89sy/q8PDteuXcs3v/lN/u3f/o2PfOQj3HHHHaxevfrXXvj4lrq1qum1vO3BjYQYagaefXRD0w8ZIKHF4khaOxDWPYo6iDcmOWy94z6yiQRn1BgN3IxVwkZWNq4cBnOvUZ7BRQKedLpGsKMHFVqx1ws1SRz2HZoHvmZ1PuY8GxjavYDX55IirJ1WpHtm6Fo9hP97h8GXoEbna/zTgg/LawKNk6vzzT///biyplCrpkQ+p79I7srdJK2mEihOes0j2EiR3jBEfW8Ptu7S94anCHwPRxuYTYBnSCYMracfoDjVQr5rRqoHYcMzWoFrmb90iKWt0rapVpKQjDjy5XPIWIe2lqpIhlhFcmCaSjlNtr1Eq2c48tGLYt9XcVUg0oShVEWWt9V5ZutySqU0nUuFQWh9B1NMMaZrvODMNlm8j33mFeSv2E0qV8UaxURdo3I+eesIBsV3aDv9AOe9+kGIFHd88EbscE50DOPWAwaSCR8fcfLI90/hR1JhybeWwCi6rNjjGd9FuwafiFZXgh0TadatGiaF28S+NgR6AYZ0IEzguN100lIRRo8QUdrBAwPklKJWjqUzQk1CM5d8RFJ9CWPh8ofuPeVXroNKIBZj1W0DaMTHFqsYD4VNWI7Zr2UV8qW/uZHxJ5fTv3CUp+89RSSeAoeRZ5ZiseK4UXV5/Qek2ttwPQFwnm2FCHb827mkkxHKwpEfnkyUEb1PZWk6XGCkatLYextV/sXLBkVOAmlTt8drU8UuLMvP3kG17lCvJrGrCmw8/1mGnlrBV7f+NR3d0zja4nRWyMc4osZi7OudhLoDyjJv6Qj/9uQySIRccvZuVHuNY9sXkZw/zbu/9w84IwnOvfVrdPVPkFw2QXWraOrhGHZ9/mKcfJ2D3z2d2tYBUJao38cpK5J7XKLI4dCdm/jBdy8ke8IwwbEWSETkE0bcVzJSGRu/cz3vvPGPsb6DnU41MYeEThNjFUUOdd+lFGtYRkokt2aVj2MVP//H67BGUR9ul7XtWLJWJGxsfNgfOtyHtYq2gUk6TJLhQ/L3ZLaGDTXpbLXZsmviqozGRHKNWjFNhOH0C5/GsRoPzeGxHJvO2doUjC5ZMEYz0F6T9RBpcpka2ZYy1ipOO2+LBJ3xhhzLfDM21kHQ2Du0zOWObqkGJpD7sO1QuwSwgJlNxe4vits/ey31rYJbao2hJ7buUilkcXM1Lr1wK+Mxy7ux89ZqiWbCnIrbm4neGYH7xEGjqXus++CPUZFU/O1wDrenxOR4Bxw313V7hbHRLtLW4cD++SSXyPpNLpgina1ifIfk55aRti6t//AEGETgWckHEgtVSwuKfVWHdR/9oXgYWC1Y2ZRUzzryPs9840JZM4HDyZc9xRm9FSq7+7AtIQ//wc0sPW03JzgOtu7y4F+9js75E2TwGPruqaicj7UKlZYCxZCu8YN/eD2zE62k2soUJ1tkjVtJ2H/85atIPtTCWARhMcXYeBuq7FImIBg+rmIWKe75/bdgJrN879Yr8QPFy979fWzo0H3iERI70pjAxQOUF6Fcg5ev8vzdknCHlSQPfv1SKtO5ZtLsx9AJD0f2NaC4ZVEsDSbz4Yo//CFO2md2WBL0Q4UkWTTazgmn14oZglBRjp+/NYo7vn1ps6JujcYaTV93keHRTnxfIFw565G0Dus37cYa3RRAL/ia+V1xlT50OByzmV8a//Pj1waHURTx4IMP8vjjj/Poo4/yzW9+k69+9av/z97FyEFogAeekTbd4rY6dT/BMe03f6cUKarHtSgP7Vgih7sVr2XHFfuqxlDKinWcARWJnVLjW2mAKHZScSzaj/Ekq0VmY0gFjI11cODeDU2MC0iAOVtxwXfojoWa8R1O+vM7uODCLagQEluypDYeJbV6lCfP/b3488tnfv+3/4niPSvBKgo7Brjxa58QOGMEqujK5625bH7Pa6krQ2gVX/qLt6A0RCMthNWE6LlVXVa88ikGFo1gK5KF9XaVoL3O00+uI5GpiTtIISNuF5HGepZEysd1DN/587fQ2lbEziQZPdRHp/W4/Ma7sekIG2j+7o1/wqFD/ThuRNI1tPQUmoDnzpMPyDNaeVjmgNFceMGz+IFLqqvYBPQDTa/ZRmD3zNZlKAtLztsBiJiqv6ebghaHEVNM8cBHryO/5BgmdLj8D38ootzQzHxt6DByrI0alkTSR2mLpwUjODOdxxpNEAdVjbZISYXkU1ET+Hx0pItEXK00wVxFMsIyrmtMNmQWsOwbbKemRN+tQsC8gWNMW8uqS7bIl7SKaRPrcTYA2kibDwXnXvTML532SlkG2usoZUn0CAYnmao3D8mIhvygJYHmDz/zWfZvW8HksXbOvPoRwUUGDscG5zGlfWrKQKB55vbzJK5rALyNELhUAKNDPeyYTqAr0PvBhwTvNGdgI97CceUQo7BpAY6Xax7G6Be1IT0ULZmQ0r4elIHUvBm626t09E4xcetp9Fy0k0WXbUVXNMVCnosu2Mbw3RsY3L1QEjxXAtFCIc/InSeJW0q+wpqc2MxlshXQMLx/gDv+5g2Qk0PUmXY4sncBtNZJ9hcEquA7LL90K6MPr2b/zqUUDvWIBdgxDyKY/vkJREax+PJnaW+pUtw+H+VJIvTmd/1AqkFeABoy3TO8+dJtOC01/MOdch8VUlU8bviBi+tE8TyXfkfGugTK8Pnnu+ds7nwXU07QaV15FpFAGe4bSTO0fTEf+vvrKGifjZc8TeFYmwR/oUP/CUeaWFiRspGEVynbDFJrKiIRt3tTVoTeq8UsYTVBWPMoYoiM5k3/+C8C6ncMlWoSHcvjRKFDBASx3FJDOqy7q8BEMSGfty4H9NR4m7QU498ZtkLw2e7OUD4gRAAbKVavPsLhJ1bjIlJis0/LgV2czvOPf/p7fPmnG5vi1M19opqI93FweovSQs/5JB5rQWXlDNCpgOJP1kh1EcPoAydAxmfl+r0oC/kFsURSOkQrS966lCtJokJaIBihZnaqhczaEdyOMnnrkLmzFfN8Fy3WIZ0UKSqd8WlRcUdEKWb+bQMdxhXstQYCCZJ6Oousv/KpZlA8PdhNR1uRsZ0LOPq5c3j46eXkTzvEX37tnwnLSU679hGOHZ6HwdJ/8fOS0CdDbM3FziYxwBVvvAulLUeeXsGyz9wl38cIceMNP/hHnvv6BRxTAelNR5kopLjnL24SOEsgyZXSEqg+uX0haMObPvyvKAUPf+5KWd7lJGagSlTz8IGj965j7NklfPlDN3PyH9/ZxO9f8Lt3S1ISaWzg0NVSwwPS1okhQJBbMyzzI5BkpTQootnd6w+DhQkMrUrRYVPUieg2HqmWMi3ZAKUNTtzdu/7dt2NinVkTVyq1hpHJNNMTMufSuHhoVr9ic7PyvlhrZrEcncg0YUF79iz4pXvt/4nxf33l8K1vfSs/+MEPKBQKlMvl5s9/NZzxF28MApKW8aGvfxyNolZ3aG+fncMJRppJDEUdgVE4+7McOdwfBwBABFEclBwnoUhXzzQM5cAchzlENnNlIIsnmX1MIqk9tISMgqV4bB9sZXKsY47NaaQdPb+nDImIo2N5bEo+jxnOk0j5qAiCDSVqzyzATGZ5+rmF4BnBg8yr8NO/eQP5Uw/FlTRN/fneZkuqvqOX0fFWbCnB5+87AZD2ZPNhZOs4qYBn/+5qCLRoZF33qLQUDCS8gB3/dAWnn7WNdEcJ6zuM754vdkXKoofS9JwwiOcZrr/lX3hh7wLssiKn/e13aUGJByiAVbz/e/9AsZyg46+f4OwzdtF59XbwJHA8et86iBT5rlkIHGYqLqtv+TFKWQ4/skYyydBBOeJpKiLfUvF7+y1fhLJHebid2clWylhGty5hiUlSJ6JyuJP+xSNir2UUs/vngVH0JqNmFmsjTbnqUsVQmG6hXsiS8gzWaO58dDU20pRUSLuVw21mtJ2KCvED1QQ+7z+WoaQCqUA0Mta4FVwnaoL3fSJOXDlKNfYuNsrSteCYaMHtns/vGlHsL8cMPmsU/mSOiBjaEGpGj8z7petAKUsqGdDeWuXbH3gTAIfjdtv8VPSi3w0w7Pnxqaw4+QVOOHMHX/yn6+hA7BRPeded1FXUZH5mslXKjd6bspLcxEWg0658jEO6jq4qdr39Wuo98Zr5DzAPpeLKo5Z7orVl4QmHcZgjRhgsSxeO84tvXyrz86Rj1Oou89/9IJ3rDzP7zCLK++dBzWHwSB8/+vkm+q/YxuNPrUHVdTN4nS2l2b99GTbUFAa7ueyyJzl85yZGjvYy8eP11KpJXvH1z2HHskQHOqDksf6iLQQLQ0p7eqkd7mTyJyfitlRpXzZKOlNj4mg3tpQQdv+MR9trtmOMZvLppdz02c/hJEN0MqD4wHLaX7sNlKW3fxwbaJLds3QPjINjCCtJbKTIDExhY4cPrMLzIl7/wW/Q3laSAzQmJK2L2qgS8rGrn/1PzzuIkyQVt2CvXDJLvrPIh/7iewypEpHvSdU10tQLGYJyCuWaGF8o1WgbCZkFq0hkaySspjqbxYsJMQPdZbyET+i7uGlJGJ7Z1YduET9gE2laW8soLfO7OCWs/+pMhls/NefkMzLWQTWaw3trFGNjXc3g0FrFlA5JxE5H+VMPYQ0oxzI53kYUSuLluoaWTUekDV5JMRjCjIqYqsVV54YOXuzipCKZjA4KWuuoVIgtJVAWaoMdTB4SHbskDo4bYScztPZNgVEkFkxDjElXyhIiiev09oUkcCju6sevJ/jFh95A+XWjsi+tKKDXTTCuwiYhxfguDuABY9YwO9ouDjgYcfbxHVqsR0triRfuPUkYt1WXo/sHcFzD+HA3Sln+/JZ/4b0vfz9vueGPCWYyHHh8DffeezIhhpntC9h363mMTmSwvsPnf/9dlFRAqqvI89tWsPjsnSTGxGGqAfVRZZd1H/shWSsY8lqo2XiBJKjevFmBmFQShMdyVCPF4Z+dROL/x957h1t2VGfev6q998nn5tA5J3W3pFZLakUUEEiAEMmAcRDBAY+xPxingTG2x2NjG88YZzDGRGMwAgshCRAWiijHVmd1jjfHk88OVfX9sfY9LXnMMDMMA55RPc991Oo+96Rdu2rVu96wfopSPuGhp9fhnCJ4/VFcIE4MGWDZdXvpXjrDez79Z4TPD7/glAjZQpi2e6EZBh3FsE55qWP3bTm7dhqP6fTaLLS353TMnHPMq5C6ilmqFUo7SoWQ6VPD9LoMLhFhFWnB56zGGM3ek730d0WEUYCXcq4LziOuFAT0aWZ52xsewwL15GyX47Ird/03990PY3y/heG/heLwe/ocjo+Pc+edd/5PPakL3Pd+0EvjpfFvYExSoI/qD/ttvDReGt/3GFxkYfaH/S5eGi+N/zvGv4UC7/sZ3xM5vOqqq3j44Yf/p57U9sQv+n9nFV1OTgimmpdkiYxlZHRAfAsBnOKinphK+v/NK1ucHJWoPZVJ0Tur5aS1IPZwigtf9yiqO0RZOs+lTBrdY+kIWpyfnmBXzdDlW5Z3h6zua1OrlFKyvjxnlws4OFoGDcYpXE5I5npVBS8wKOtQEUzuX46pZylkjZDlNdDwueKNDwr86BTdayf4lV/5OUJlOpyf8UoW1w5Yojx6bIYmjuGBmiCPGcPUgWX8833bhMfYCqjsX4oqS1spMR67njmHydFBjj0uROv+dWOUii1pfRQTcitneGIiD7HH8EAFlSjue9876fYdBz55rRCpY80jH3kDPeU2ft2SL7Y7vBeconel+F8VBuS0mvMtk39yBZkg4fDeddhEYsFQjpqKaZNmaBotbY96hmf+eQeZXEQLxyP37EABOedRXDfB3GQv47tX4YzmDz/yJkw1xy9+8B9TmxrEdy1wxKkwQPuGMM3F7Q4szsh8GrYZXOJRm+2iqRJueMVOOeU7haeEt9TvcrTnSykXTBMqsU4ISa1xcLz6/bfQUpK40OWyjB9dQoIj31vnTr1WPhuuExW1+1sXi71GOp/nU+X1vxzOKYYH52m2MmzecpSc8zk0WhbvyxQFn9dCyM46j2w+pD7TRbbUYvuW0xQ12EYWM1Mk5yRfmcRj7RfuEIQ9jRvEKolxTBTq/QcY001M2bHx2l0EFYuKkcfFujPPNUCisVk5yGV8Q/87n8JDUjncvYLah2HAqdEeVAxJr8NYxZ/v+F2aZ/rouvow+Z88QP3htaxcPSJ+dPmYt71roV0m/9m08SSX/PR9KM+ifcOXv3Y5q97wNK/+8Gd59r4LaTXzzH35fGp7luEtrkKs+eR/fSvBrhJdVx6hOdHN/FgfeI6gv45JNFt/9S6mHl9P86HV0Ayo3LaVwDcMvHovzij2fPtCVMbwjc+8BnegDxoBJ08sFeujMKB31SRmPk/lTD84RVwpUD0s+crOaHLZEO/SUbK5ELegTAde1ptQJGDR2lFMM0Ncy+GMojnSJ5Gd6fxDOZYtHydbarH/0a1k8Jg8tpgk9jFGkym3GT20jMZk91l0Buk2mPgs97HLZRhcP0q/y2OBlSvGOXVkuaiRnSA8pYxh5pENKb0Dpme6qM7I8y495yQOqM+VuemtET6iQp+czWMceAWxK2mTMDg4i07Rc6XEXaA8UGFDUoY0dcNZRb2RJw4zNIkJUisfZzStpiSg/9TWCabdiwGC4eFpiDyUdYT7FktHqCCdimhc+LJ+MWT1ux8UdA945K7LcFZx7Nn1aWfiLC95Zq6EBhHNtDMMujyHnziHOPZZvvoM/p9uYl7F1M5NUJGipmP6uluoWFE7uJi6g3OXVelFc/+3LmNigd4UamgE9NkMqzacZHJ8AJxi1+evodXMk8nE4h156SGCvgYv2zpKpCzFdROsf/kuLtx2BB9Nz1WHWHfzw2QzFlPN845f/7JomBpZCvmQ9nRZ7M3ScfqubTz4gZ9ERWknppJjoBzR+/ZnMQtUK6PwChGtsV5ec+XzkrCiBPUHcJGHPxLAY9LJ8FC42COq57DzeZ6+7UrpjmkrHY+jS1LfT49FAzXCFB1f8MucG++TNTmN2GtUC7hEUzs5gLPi+lFXhkgJNaU7a7CJRyaTcP8DF6DdWXGW8Gh1R7H8iksOUSqEZIKkoxXwnaicnZV9xPMtZRTt9J50VvHYw+f/q2vtS+N///iexeG2bdv45V/+Zc477zy2b9/OBRdcwPbt27/3E/+LNpa8mGL+0BIiDPW2z/hsvkOGd0bx7j/+JAXn44wiN2KZjzwh/o92o4xsthmnOxxBazVRNY851Y2KEbNpEO4apIuME/uFMFVBl0MqsSaMPILAUqmWxOLDLrTSoOYA5ZiPNAtC3P0feRVDG0ZQcfrBlGNq1yq2bjqN86WtHO5bzBf/6seoPrYGYo0uhnzsG38on9EowukyVSOijDUDTVoqoYjitb90myywiWbo3JOE6WMASqumOoWbp634Fm46yc5nzpEiqtyiHWblMbEmmSswo2Iah2WBUBM5rv3LzzKXKP7uq5fzmzt+H6Udl//721m6dJL6Z88lbGcgUbhGhuqRRZRufB4Xa/bedwEAr7rhaQa2nsLzHNf/+1s7nEOXeJRdIMWvkw2uNdqLizWtVg4TewwrzY/9xi3MIgsIVnPRO+6lZ9k0zio+/NGPozKG/FJpHZnYF7V2U6xVy10NsgM1+U6cqDNxipzzOl6FC3SD6ck+nBH7hIFiTN75ZJ2HNRqbcsF8NOckPXQ7T9p3QDjW0xFMrTBFgmxMXVl23Svz3DkxwY7TVvT8bHdHSadzEZnMC3gO/2IcPLYI6xS3fXsbJReQ81ynx6sQr8Z22tLOFELuu+tSRvevYHKyl5aV1555drUYZSsHnmXmV152Nrs8vdGyd/eAg+JhR1uJIGVszyqhTCwccLVLrSkgTue4VxELlMRoTOpQtchk0deM4gOzlRKn04zZ+t+dT29Xi/d86HPiVRd5eDXFs3ft4KI/+gorB1vUn1jNE9/eIVQOI/N+++sfxd8hptFeYMj7Vua3VfT0VUiMxz996rV0XXUEM16GwPIz//6rHL91BxhN345jrH3HQ5KL7Fmu+LlvQSVLedkMf/m7N+NaAU9+4zI8zxDuWQyRR7HcRGcStl+yT1SXuYRdx1NLmJ4m5esOUTm0mIEtp8EqsitnOPLMhs4G9roPf5bqV86VOWKFcN9UCTtns3hO0f/yA5h2hiQMhD+5dJY8qkPeB3j8qfW0qgVufWgTJRcwtHaUfKrcdFYxl9pyLNjWGJxspFZ3OLIDNss9n7+e1aZMSxnyhbYIxZSo8NvKUgk9WtUCRjmaM2Wm53PUKyI20J4T8VE6FuyJIqMp+WlRYeWQNLh4WopDxJduic2ShAFDeLislUNS7LFo0QzVSknmsHIiZIs1p08t4tzumN2HFhOm99PCBtNq5sWzM1Cd+W93D2LPqZF5mXCbw5kSbrSUWi1Z/vnwAHq4wd4960SZvWcZo988XziBgWHA+WSChEVXHyDnPBavGSUKMzzx6Pl87W/eAEDxC4tlacQS+HKI7T73NA6YreZJgFNTBWJlySMFGIEh4zQrrtrPyz//dzijeeTRc9l80QF8P+HpowN84bffhS5F3PjRT8hBM/EYeWo9y9aOiB9qonGVHP3dLaK5IlEtz5wKCSsFrnnL/YzuW8nk311Kzonn5Yo3PcW5V+2CRDGnBVyZbwTosRy9Lks8XUqtkzS10T6u+NCXuOh1j0IrIE40b7/5HlzicfgT11I7PoTyHCUlwEzPJom723LVLpmf6VpdrZTkMO4UZ8a78IDlNoeXXrXD+0UfEDezOKvY+obHcVYzd2YAZzQrXCAiPqc7nrJ+NiaXiwg8i0m5swu/76zutJYXTOu7e6udNnbOacKq5D1HafxiGycUGoSP/vDUi2MZf5jj//a28vcsDj/84Q/zpS99iV27dvHss8+yc+dOnn322e/5xP9SrWxwGCy53nq6+MD5G8fEWDr9hWe+dDWl1Jtp/KOXY5HF7Pj95wraEvlYUnEHafJE4uEtqr3Irw2NnLZI/64VnH1fS1vM41izfIYTUwVedeC/4LyUC2M0QzZ9bDVDBPhTUoxs+PA38N95CNLXX/HjT9CzcpJrPvlpHnv/Twgqkos5OZ8lTPM1bTvADbUF8XGK2kSvCAE8x89++s9JlKT/Pn7LNQCM3bOV7MpZPvBfP8XIt8/FRR73fux1spgaxYrVI4w8v4LqbBd7Rsr4PS0pXNIF2pzsIVg+R4Aiv3QWazV/8ePvR1UDGjgO6zbv/un75DMEllYzz198+K00agVBKo2ia/0YY5+8DIxmyZoRnIWN1+/kHz50M9lMxOc/+LMpB0V3kAvzAqHG3334bZ0/l/urtBzsvuNSACn8I4/5vcvE6sAqnvnKy3Cx5sS953XEFS7xCNMJtHj5OIfv3UacmrH2Zg0m9pnQLbaWLDb2qVVLcmhQruMRd+Xle4lTz67qdLdwXox4/fWloiZnJBu6cmoABYTK4KEp90kbeXjpZPo4RZz63GEVzx9dTKSscBCN+E9+t9Foe5SLbbQScUFkxQKllDPktPh/LiQMjJ9YzE994B8pdDU5MV6mnX6vhYEqFsl3fuwjr6f/zx6i2/mdeYFT6GKMms+gDnVTdCLS2PPkZvzRNNfUyvwmkQ3QgXzfTZ8sPrWWTzCh8Uj9+qwszLVGRqxtQk3PG/fQ11sjd844++7ZjqtnSB5bylX/8VbwIEk086cH2HTeEYhkgyT2ePzLV6Pm5Ts6c2g5xinwDTMPbuTCj91CNiuoTfT8UMeH8OMfeTNDG0fkvSuHmykQTZaxrYBTD2zFNYTXvGmViBTK3eLJF84XcEZz7ru/LfNny0k++Pr/SHKkn5oD08zI842W6N12Er+nCUZT37eUbW9+WA45sUfl6VU0prrTQu2sOGREC4r/nQ+9Bb+r1dmwAdanLgFxUw5rOy48wsCGUVZ3xUTKEjVkY4tDSZ44cXIxMyMDHTTFkvKzk4VECY1VcMkrn2SdzVJ0Hv2LZih31WWuJ1Kw7ncJfavSuZpuOseOLsMaOWBOq4SwJa+dSTfiplFcev5JTDMjXqnKcuTAarkOCA+yD82JA6v4pXfcyzO/+eaOzc+iZRPMTvcQKsPIZLmD5kWxz3OVFye8LIx2muOrDMwdXYTF4a2do33bBtRMVjo/I/3yGunvbyyIgGTHZbvFwaCvTqG/hqvk2LDhFD7ymmppjRDDPXe8jIGhWXzP8tbf/jyhsswfWIpKNMM2RxgFKANH7rgYD+gutfGAoa6IGCtFiFMQyR155pFNeMcKkGj6upssvvJ54ijglRcdY2SqKNc9sLz38mOiXG9mWXb585KkM1NEDTZYvnycmSOL+eLHX8ekapLrafDE1y9n0zsfpNCf0lWMxoU+ud46tbs2UVERzkJkFG42T4/N8Nxtl7NgmL73iS3QCDjxxEZcMyDwLaePiFCjNlem8PN7UMqJ3ZVT1I4O46ymPtl9NkjAKhq1Yofj/a73fZUhDz78/lsJkW7GxHSXrC3aYmKfpJpn/vgQxd46ODiiww6nNINHLfIwkc+SZeNcdtleEizWaDLlVno4Fz/F6kw3MzM9zMydTT9pp4iPTTT1mS6CXETUznRM250TexzFj05R9aOckPK/Y3zP4nDVqlVs2LChY4L9PzJUxe8ghzZt3yVYLDB5cCmhMoRG8cjepWd9Do1m8zW7mNWxWJVsPk2ghBy/7o1PghX1oIgfXvx6ZqwLFavOJuuUtPyUlQQMsmeRHT0jxd8jB4eJLHQ9nsM/HQiyaBTBwvTLGKpYwsdXyKaqoflXAmkrA7WXtcktm6O+0XD5+7+KjX3qpwY4d1mF+792NSQe888vRYUak262hb4aY+mbn398rQTHA+vOOyqKbl98+x743A2063mqB5Zyw+98UYpbqxhcNsn0ZB9bf+EedntNpp5eI8TsRNqL3/zTN3Pqn3YQpifzcleDKy/fC+WYLIpp3Wb1O4QicO/vvQ2AYmBZtHzy7JfpFEtu2ANO0b9mApzinz/2es499whRHHD19U/I5pWmQyRYMmhZKK1iuuXjYg9rFNXpbqpY6tUio17ItBYPx/LKabxUSfq1B7aQ1PKsedOTLzKrVmnRufaaPUyMDoqqN/GQ+DxB8m644Qms0RRLTbJ47NyzUgj9RrPp6t0AtJRh7OSiTmujgM+kjqiki9GsavOrH301wzaHh6jqi711fOCpx8/jmviMREIpUaq6xGO4vyYoD4K6ZLMvplF0vkqnWLmoJq0VJ+rmhRjAsbqPcVIwJ4hS+tiRFfzmr/0s/X/2EKGVuWgjj+poHz0uQ6wce/auITdqhbTulETLxYpkcw1izfyzq1huCqgYrn7zA8zccR4qUVKsRbrTnvNADLRrWTRiGUEjoIUTv74nhiijmGr4jOgWKtLs/6PXMD7ZC8WY/kUzuHYgSRi9IU/86tsYHqiw7KeeoDZfwlUz0sZ2inq9CC0fF/ss23CaV79iJyTSZn3oHe8EYHiggmllSOYLYBTzkeaZb1wCCsLTfbjEo3JqUGwwzjsBDnIrZ7jshsdR2YRl609jrSasFQQdqWWZeGQjLtHsVC38FfOc1hHNiR4ATtx5IbaZIZkr4Ix6kVels4pcf42Jk8Md38wEh+8Up3Wduor5ymPrcbFHYbCKSzxs5PNLX/yvQrnwLGEjx6ceXk/tTD9PVzXawYm9azomwDbxWL/+NIs3nMFE/tmYssTDGK/TXm5jmDi2hD5frHIO7NrI5mt2YROP+TMDeEgkZKa3QZAekNqJFiNiozCxx4QOGV41zsGv93QQ75YD30+I04JVO8XSFeNi1ZNuxqt6Qh55agPLr93LBT/+nbPrw+ZTQt1AkQlsx/c08A1diDnycKqQUunmP7RoRh7nQXGwKgfKWOO/8zDx0QFpK2fj1FJIfmfLhlGIPDa999tgFeFsSUQ8+bgTiHDe+YdwnsMp2LjxJIs3nGHreYe5/+M3kUtNmTGKwClpgcea+ekeBgNLOwzwlaCfsRKkawHV1EDP0hnoF6T3bX/2t+j+BtlcyN1PraGUN7RPDEA1w+V/93mUZ1m54xDeQJ0MmnimCLUsrWaeZx85n1+65b9glCVuZjlwZAmj39jGnZ9+7dnFIvYonDNO+dXPEzjN7GPrmDBQP7SIQeeTyUYdVPuVv/2PuHqGtdfsIZ4u0Y489uxdjU08tv/W7fjzUmS10v78A7deg20FVKelOIwbWbl+vkmvnaK8dgJfQXnNBJGy+MDLrt4pnpvpUJ6lvGhepoGRg0lLmY73Z8soolaWfXvXcezwio7ob+bI4o7XrKzDmqmZMvONgHZ6aFFAXUviVSYngqtGrUCP58ghv6eUY6X3I1JUuZeQQwYHB7n55pv527/9Wz7zmc90fv57ww9f/LQLVbIGtC8K15ZRjLgXVHkOjj6+iRCDM4rMhinKgRQfLt1kmq2MqDZTzyulHF4uRve0xKLgBVY2bWUglEXBzhTQC/t35JHg6MtYjqgI15VgB9N/TDdNDyCwEjG28JlOB+x6YJs8j4HiUUU8UyQ/5nH0C1eI2syz/OQtH+HiK3fiFhTQUZrBazS9r93LlJbFSqWKvbx2wgExiqEdRyHRXHzDEwytH+GTH3kz9334LdLGNYrnHj0PpRzHv3QZbQyzo/2Ec6WO12M2E/O3n32lbO7A+Vc9x+GDK0n2iL3CiK5jTvTijOa6P/oHSl11esshzVpe0KTE63BMUI78SkFl8vk2W14u6sz9z26Sx6SjrQwZ53XQwg/8py/gjCaTSZif6enYC11sRT3sEo+//eDPcOjJTWkChHBgfunNv9HxbFyYLxaY3r+cjRcclKLQakZC2TizTjOwchKXaBatGifEsP28E2Ijkp6Og7RBsnrzcYw5mzhhEU6qtYoCAb/zpicoOmnrKRRhQ7gEB0fLbInnsUaSAGIsNtGs33jy7Lw1utOe/tdGPhcRRj4Z7URRbaWtNo9kt3ZbUQka5bj6tQ9xWsWYj5zD+Wum6fHExzHX1aSQttHH57OEt69jRscQn6UfeDUF5ZiPf+THKDoPFWqKK6bJD1RTNaTYtbwwx1e+a9JWooyWMuSdhz5/iioOrWBSt3C+Zf2ff51D49KurM12oXIx81M9MJfl4nfew2ve91Voe+zftxZVlHtKOHjpk6cv0rdkmmikh7+/7XKqlRJxFPCab/0Vxx/bRLBqlo/91K8TAUHars8umwNg0Sv2kVTyqbWGRpVCBv7dk7jIozRQJUlRVhd5zO9egZdJyC+eZ06HzN2/iQgj3YZGltXveoikUkCl3L2FRI8F5ejjt1zD9EQ/zaZwCpda2cQiZcni8YqNk2LSrZAWotFMfOvcznWfGRkkg6a8dIamsnS5DBsv3c/0ZJ8olq1i5MwwQTHERGIjFLCgqhf00MRy3N29awNb104SYVm+cpQzu1aTRD7ZQkjJ+jSV5dgD56Y+hykdpdDuFJgL/odjO0tU0+hIAzz27Bqmjy5GafH31Np2DirOKK66ahczoaZ1qr9zX9rEozlTJk78ToLPAnJaa2SZxBDiWD/YfNF9UEl5ucrAsSc2SXHYCNBtR/10PyoWdbbSDpVGUx4/NYSZLvGfX/6fwCm6Lj5Befm0KLuNNMjP+czXUKHwcVduPkGrUuDUiSVkMpHw53qaEIuH3/IV4xB5bP+JByhkLVOVLF2+5cRcTmyqIPWsFRXxoSfOORv008zg6lmSxKcNFPMxubWTqbOGGJLf8mdvhRd4R7rIY/maM/T2VXC9kXhVFkLO1H3idobLX/702Xsk9pi8bzOq4VNXMUef3cAVy6uUzhljTiUs33ICEk042YXqbfP8l64gs2oWv6dFteUz0CcG61QD9FQWk/oHouGfDvfx/Le3s/nf3QMO+TcHtWrxbJs3DAitIpzqIsGSU7D57d/pcGhd4hHOFdFBQruexyYeJedTUTGJcqINULKW+57h0Kl+SVsxHp/5+1cIYpnOzWqlRFepTSFraDTEz3BOhSQ4tGeZGRlgZnSA48eWMmsUcxi5552it/DdKTz/J4cD2ZO+j58f9fE9i8Nly5Zx2WWXEcfx/7CVTTwUvbitbM+mlzzziKBvZ1RCCc3z3rw8JvHI5KIOL43IY+WSqpw+Eg9ijbGKXht0Tpbas9RG+2TDM2dfUJk0ri/ReGiiya6zCn7f0lSWnmLMJjKoMwVUrIS0j5jE6hd8M16pjbMQ7lrSeW6VKJIHluMVIvzHe1n7nvtBOYrLZwgfX0EcCQ8pbmWY/vp5ImSwUL17Ixe4PM5obvmbN+A5RSmw1OekNeNSM+0Hb7ua3ECN4f4mvi85yRjN2GQP571sF0suPEIWj/27N3Di6Q1ilO0UV7zlAbYun++Q5wevfh7ft8w9v5QMioaK+bNffo+8ViXL0NIpli6eZXqyX147lKQGW8+cJY0azeDwDJmBGuVSk/6BOVlQYg/TyNImSU1zBWHJLargYk3fwJzEq+E4dGQZq/olI9cZTRwryj01bKK5fPUcLvHYVE7OuugbzbSVS2CN5lOfviH1gFMsS70Me12Wx751KSb2KQ5WiJRl08UHOu24kT2ryZDyuIyXcrkUoTLMpRnHzmqqKqRn0RyF9IJ7TjFxYhG+06zoa/PRwlZciij3uAzOak6fWEKSThFnFCfPDPyr94FzijDyyWXjDkrYskAzw5BWhAga5DuF5xQD20/wxmVNlG85cKK/8321K8XORjxjFN7NR4Wq0AjkOsUpItjwaSSpV6GWODEgbSmn90ia9x1DpyVdcgEhQMun6FLGkYUalthBU8Uoo3jip25m4+IaZiim2C2b0ZqLDuFiaYvtuf1SiDWXvmzn2fekHN3dtc7hYWZkgL1PbCW7eoZf+5XbuOlDf0+QiZn/i4sZXDXB8S9dxnvu/CNiHO1Wlpl7z+Gbv/+TqMDwFzf/Gn65jV+S5Ibw4DDerI9rB+x58HyqjSzFC05j2gE955+i77xT+L1NWiSdxXght7j11ApOP7OOg/+8Xebfy/bQHu/uFED1epFcvo1JEY+12bMOkDGW1/zKrZjIJ2llOoeZwzs34NJCcXDFBEUUp59dJ1xAldCqFPH9BBNLvvLQ0AxPf+NS4naASzz8F/h22sTrtIJffuMjhFEGD8XBA2u4/euXglNMnRoi7zxe1Wtp1vI4BZl8yOkEZuZKQrGwcg9EzSzX/PYZGirBQ5HgCDxH/+px4laGHD7Hjy4/ywG3mpUXHubKTeMAVI8s6mRAP3nfReTStTqMNFixK1FKUJ4sqmM4vrDBLJhij/76tWTzafJGbxv/mW6KS4RvHBRD8Vw0Yqcy38gQTZZpGAWRxkwVyV10WmgA1SItHLmdEnvYUoY4DIhaWY6fHmBqqo9EWVqTUvQXUERhSilIPOJEcSSx9JZiVvXKkbFDj0m8jmdk+OhKoUU1sthaliSWTtfbf//T/N273yfF5KIWLta88+N/BUZ48Zllc2AUa6/Zw8NPr2Pn77xRDoGRzziGlT/1GKv+/f2dqEKU459vvVZAEJWw4zdv4/hYF6q7TZ/z6d12EmcV2f46tH3+4bbLsZWc2Holihtu/pagqbHsNUk7I0AE8HM7TrDhmt1Eh4ZwTlFaPMvffein8TyJQXVGc/rRjUQWRnavRqPIe454otyhEDkHhcXzkkvd1cB2Ymhd50cDzbkSl137DM6JV6VNND15Q5J2k6zVlMpi0J4kimOnBoixtFNRS9zK0jM8i1KOXad6aeGoaSP0jn8DaNv/TeN7Foe//Mu//K/+fK/xwlg8Z3Wn5fvYUdn0QhwDHmdbwbFmfqZH/pyqMDefe1gWqxQ5M1aRdx4qFSDEUcDM6ICgdrHqkGIxqVdbWjBml82hF5KAfEdLGbZsPkE5YwkvbIIB3RYvpc70C8VXzCuHYDSPfelant29Kn1+RVzNC3/Js4z/48XCzcgmjD63hrXX7wSjufv2q8j31WUBSDTlNxzgt//kUzinuPwKaXtOhR7LLzwi7S2rsM0Mb/zrj+MVQ37yDz7NOTsO0D7dB07x7GyWfH+N5mgfdRUThgGbX/84g8MzYBQ779pBYsSjyyUe5GMeOtrPA7dfRQAEzuPXvvAnjHznHIg96vMlevvnmZvr6kTn/c0Hfjb9jBrbCqRodIqdX76KcnedfKGNTXlBOptglRN1mxXPvwW1Wc/gPMMrxxnRId+Z9UmMzAEXe9xww9McPbgKZzVfOCkGp0drgWysKUKYS9GsM4eXM2ok8cY5RZgIGjKt25x78f4OP0ujaM6VUsTF42+/cgUgJt3OqQ6aaJGkk4zTTJ8ewuDoWjpDG0nfaamEO799gSCOKycAsEYKJpf62K1ccwaXchCdUxQLC7kT/2I4xYHTPZLso+nkR7u2zznL5olw5F5wC5p6ludHuvnbP3qb5A4rUL7FT9vWI7pFn+fIfKdH2srBgpu2zH+KCdvWzBApi4oV2UUV9t93gXTK7FnhF0aUsQtq5+aCcahVdKM55gnq01KGQCHiF6M4enQZu0bLzHx8B8suPszDf/E6isulXeivnSFXENX71++4vHOfKA3HTyzpIJyZbIzWlif+8kZKv7ETt77K9HQ3fVtPUfzgs/SvGmfsHy+mjWPj9oP0X3eA697xLZ773LW88S3344xm9uBScIoj39mKPdnF6EObmJgYoB1qoiMDYBVTj60H5bDtgAuSXj78X97MYpvFz0V4XS2yS+fIFtqsvXw/WBjbt5LP/ulbU/4cJLHHc3tX09dXwTlFV1G4octtiQiDV2rj5yN0Gms2vW8551yyPyXbK9r1PE97Ve67ewfd1qPLZfAzCcZ45MtNbOyRycZksjFekGDTtcoaTa1SwsY+nmcoO+HwTc8VWKI1l17zNFdcfBhjNMNrxtDAMzNZapUSEZbGnPx3ZC4nc94IrWV2su/sPEs38nYiqtDmTBnfKXK5iCxehwr05b9+I/uODuFlYx6+40oWotYWojQtjkLq1+kSj0I+ZpWvuHHbKearhRfdCq22FH1Lth9j6XknpJtSybLnc9di2hlUrJg5ukgOMrEkVc1HGr+7JXPVT0+LbelS7DywjJoyEHl0bxjDYDlxYBWNSomb33M7V934CHMqYuTAig5ifeKkiPRs6BP4jpoy9PU0WbdmrCPCcImsewqoVUr8zW+/Q36nKYeAfEEOuf7SCnP1DPNPrUbNimvC3k+/nCN3XEzB+Tz2F6/jm3/4E2TXTNMyivPf8jA5PAEzADtRZOwTl3UEKSjHW3/tFoi08PCbAUcSS2v3UuaUYfLx9Z14UdfyuflNjxDPFtFdLSyQ7a9LK/nQMK4rJmxkRUAXeezev5JDD5xHsLgCVhHXc+y48BDegsemVTzz2Pks7Yr57a9cAkDguY4DhSDQPnvuuITbPnkTQT7qrIkWR4TBR5PRjkJPg2JfjVYiNC9nNW96270sJP/YRDM92c9MJc9Iy2eyLgjngl7g+O41WOMxN9NDT8aSQWI8F1TVJ2pn9QM/3KGw7vv7+VEf37U4/IM/+ANATLD/tZ/vNV5opLtgGaJQHNFhR5k0ZxRld9Z4dstVu0TpZYSwPrRq/AVPKCcNs+BzkI7piT7Z8Jw6a2WTOuyTKAou5cMtwLhpxmsuH3L1ZQfw510HicQqzug2cwvEZEB1tXGhzzW/9488GBpULOjhvZ+/nnC2BKurLLrpuXTTVRS663grKgAU8hHFLaNnT6THSxx+4DywiqFV42ighcO++zg6Jy3XA3degjnTjUs87vzDn2Dopl14uRgXa8Z1xNShpWR7RNSzfPl4aiEhApgrf/1rWKsYcpJRaka76fMcb/nYxzAIckXks+x6KUzjKMA5xchEmdYhyRD+qXffSevEYKewdolHqadG7+Acc7Pd5Irtji2BhKv7UoCli8ier1+SLqTCfxq2Gd6wpoJ1qVg28Vi5/QhLlk2IIMT61Md6aWI7ajabaF62QYyod+9ZSxFFHREfjSRSrHlOMTPWjzUes6ckvaFZL8gCZDUzGBrEWBwm9khiH2s1MYYYS4/zuP+BbdJCcwttbLHPOWFksuzav0y+A6PJOE1TGWyiWbvjeYxyYjocBnSX/1skfYG3VbeKrnKDY6kJewMHTtrNBphVSQet+U/v/QWmU9X8lLP84r//Ki7R+LlImIAKQVEyhk3B2TnLQmJN2+PJo/1iBm8Vlf1L0dqmyLru2N50Hm+UGEFjRdloFBmF3JNOhCn1hXvNKK5+5ROsKCX857+8iSMPnsv51z0jhxDtiA4Mky/Lxvkrt/+R2DulyQqvfP13IPKw7Qxnji9BKcfyDafJ7Q5QbU02kzC7dwWFA5rypjEGzz9BgKJv/RiuniG3cYLz3vYQy6/fjc4k7H9yMyqb0Ltolm/84U+w9OX7MEaRCRx+j7yH8tIZSDS6ELHK07z/12/lwt4YHRhc7BOO9BK1srTnSrjEQ3uGbzRibCRq+cR4NNoe7ZagM9VGQB6fl/tnjaRt4mEiMYR/5jsXMDsy0OHj5spNJnWLQ3NZlmuPBMup/YJCNSpF4ehl4g6KstBys0YsYWyimTgzzFoC6rNlnpr3eUI16V46w779q0iis5vkMa9JkviEJLTrBULlsI4OcqjSP8u8TA/WQHdB7tuZkUEiJQkyCwlUzinumQ64csdhnFVc8dpH0uJEs2vfapSSg33gC69MKceSxdN88BsfYnqmm8F+SQRa2GAu2CbPo3/sOEmqRB371nls/tOvkRmsgVGcObqMiSfXgxProLwWG6vzltbEdin0IRSBVZyKxJLJEjN7lxOgOXVyMe1WljP7V9L7vieFQwhyeAJmq9kXdZisgnKpya59y8mgmdUvPuTFUcDiwVqnSPJKIRfe+AQzGFw9y2BPW4R1KVf6r++4mP/ypSsZtBmmJ3s5cXqA8W+dx2AxwVs5T875jB8TMRvA0w9sZ5EtdNry2dUzkDUMuBxmtkAfHvkLznDUqzO47YQcgjMJhD5/fuvlVI4uQgkdnmf+6WU4q5k+tAR3pJe4nUmdPzQ95TabXrGT23/zndjYZ/LIEs67/mmWLBtP11uPdhjQDD16nU/B+bzskoOClhrhrmrPcPu3t7F4eJbRg8tTzqFJ004dZZfBOqjPlqlPd6EVDNgcNvHY+8RWSV9Klfjtdob5psT7FX1LqAybTa/QYbSsx9VKicQo5rFidp7uC4c7KM8Pf/zfzjn8rjLL173udQD8zM/8zP/0k3qxLD4vjZfGv/WxIqr/sN/CS+Ol8b9lTB/Kfe8HvTReGi+N/6Hxb6HA+37Gd0UOf+d3fgeAHTt2/Ks//72RqXkdaXqygAilbbufP2cWD0VDG4YDS19K9LaJx91fvJ4sAnM3di3nO9+4At+JQS2xR7SgnEovirGa+bkuCTY3EhgP4DQdb0GVeo+pBXSwmsF3mvHRQQaXTKHr6XOmVh9VFYuSNZa2Mt2iEosODVFTMSpWHPyzG7jpQ38vv3e4B9o+NpX63/r5G4gPD2DbAcuWj/P3v/QeQiVmryqX8J++cT44Rd+W05KVimLil4RrYkOfRWtH0d1tlGfZ8fKnOfEPl+MVpbXdZ33uuv1lFLaOolH4gSGp5+RUFfm40Ke/r8bqUoJSjiPfvJCfeut3cNUsHtLWdInG1rK0T/WRzYWUuhosGqiTGahhI5/ionlyi+Y7PCoXaxZvPcnqV+yi0chz5MDqDjriIo9FttB5nDOaY0dWoHxDs5bn+H7h/V3/U//MSEWuszOKv/i9n2LvnnU4q3jPtQco9Nc47AmPZeEkOzHdhULRjjyW5AxNZTGJh4co8Xw0+WILm2iSOMDiyObD9Dk0NW06iJzWC6iktJ+zeBTRfKcuyELSyqQ3gyAWORSxcnjacf38hPjzQQchLSybJcRSQDw51YtMPc+Ohd9rNPIdr7kFIG7nkcGOKnCBr3PDjiN85I8/SzXyyKM4+uwGdGDoOfc0czoicJrTKkblY3ztYCGCL+Xc2okil6ybFpubeoBSju7+SodbuPDj4rMiqYV2s6eA0GfaWXqsD4kot9sLFj5tn7vuvIJDdY8//+jfEmQSsr0NzjyxAYDGSB9dQ3NijP7IGuxsXniobZ/vpGbGABu2HeKVv3kLpeE5zvzTxTTvWUcuF0nrvBZgIx9/1SwZFLu+fglmrtBpSe/70pUAXP62+3Gx5pnvXEC9nhfVcD6kHWuOfP0icGIW7FKLEAVkSm3O2XhKbJ9iaaf2Lp0m1ysWOI99Z7ugyIlwDNvtDFrD3Lz4y9VjQY9PhmJS7EK/g5Y7o7j2bfey8nLhvLYqhU6W8WsvPsbm5XPEyrJ3zzoajbxwfJ0iiX3arSxRQ5DCBIdJPI4dX4w1HkNLJ3lOtehfPk1f2u41kU/gG9r1nCCOiNH/yvWnyOBxxx2XEWHpLyZY42ESTQEf7Vme/XLa0uxI1qAy1sc9915AgiVfFNTVpCrTtrI8+vR6nPHY/+jWVFSgyWcSMoFEU65ZNY6L5J7N5UOe+PAb+fqZImH44vbfqq3HhVc5j4ikgGJ/lXvf+gvoghhsX3Dj48xP9kiXCMuygRY28pmey4PRJJU8rpHBhgHGwYwO0YWI2nQ3BedTKrbIZCPWXPI8wf4C3Tbg+JHlkGjaOJIUNZ/au5LAN3Rbj9HxXna21FkbNKuIJrvQwKHji9h7qhecQmUSmseGKF5+jKeCaWwti7NIUEDkd9TTdWXoQfO6T/4NhyoZ9jx2Ls1Q46YKZPDQ2tHvfOaeW8mS5ROC1Kamz84ieczOY+cXrkEDv/eq32KFKfL81y8GYO7AMj73q/8OjaBpErsome029lj26ufQPS1a9Tw1FeMSzVve90/YxGPpinFcohneeIaokWPDlfs6avw3/sLtWAs3v+wgeXyKpaaomlMhiTUeb7zhWcYnewlb2Y6NUsH5JMrSUDF1exYBz2hHv81gjOaj+/qE7pPIcz17cBGVFMGdi+V5miqhy3lksxHTowO02hl8zxEpS8GlwiqrO6jrD3u4/5fVyu77kNO0Cu6slU3qx+WjRVG25gwgxP+5FwbdG809xwRadlZT3DrC2FQXkbLUTg9ILmskij5SNbRJNM2UtE1ytjhUSdo+MZKJahvZs21lB1kUs3Nl/uqL14jAZMHiwylCZck5DflETG2cLChnHt/AGlOg8fWNbHz7g3ztP74LLxejBpu4lviWNSd6ZELPlsAoDhxYyRvffQctEin+Zgsc8irY0Me0A4poGlh2PblV3rdnCfKhCFA8R2Wy56wfkoONGeG3mZmiONL3VaTVoCXb2NYzmETTjsWPbOWOgzy/ewPJVJnuwDLsxObDhgHNyW6SxKdQblAstNE9LVwipuILpG2Xkq/v+fsbcIlHvZnlmp+4R9rKiQYlGahtlaRCEsXg4CzOKeZmeih31SmiePRrV1HyLVl8nPG4+eZ7yARCzJ+e6sMa4Rgt2Bw4o2m2/U5baLqdFv1Gs7UvlPaJbtNq5HFOEeSiVC0n4pMkESslD5l32WJIEotFyFJbpEVCl4ary4oMupOtanG0VEJVLai/Df/ctVgEOOn0sYlHNC1u0dl0rn43lyfnFFMY+vrnMTiqKqIHhW0F1Ky0xSop3y9Ulmv+6Asce+wcstqRR3H7/edK0TBXEB4hMKtj7vvdn6CZaKbvOE/meKIhFk5OLheJV2g+oT7dRf/qiZQrpjp2QxgRrbjUAmmBAuKMJsIRoJi77TzJ9cXKBqYdxXxMAtzz8Zs4emC1CH+OLcFWcsydliSJ9qFhCstnJNUoEj7VQyd7pID2bKe9mV8yz5Hd6yhuO00YBvRefZDmrqUc+cZF0JQ7OV9oozKGyQc3YduBWFzUcx0vzLUbT/CyGx7HRZ4YwQPz0yJAcOljXKypG0Vh5Qy9/RXxa6sUCAbqjB5cgQkDXOxx1SufoK3MWWqDU2gc7SjAWcWshZoSqZdFxD42EeqESzy8bExzokdUx/mIiZOLmNJNzr1iD4fO9Mg1jnxqtQJLV45iE4848llxzkkyhRBrPOKUgnPuuUcxicbzJU/bJJozqWKuXS1w/rbDeL7lyLMbaCsh8x8/uErcD7LyO92l9KBkPGIsp08s4by3TothPdDGUmn6FHrqnG76DLo81Ur57DJpNC1laKZcwEt+4gEWcnjXrhllvlKmSUxv/zwulkMSwJZrn+O4V6edtr0XfOl2PniBCE0mspjIR6PoevUBBoZniEZ6IFGYMKB7sALp4WXTxpPYdkA1lOtoE8kcNrUsxskzTz6+nkwuwkfzipvvJsgkNCa7iU70k8Fj/TnHhFeLFR6d0QxsOcXiwSqDeISxRx8egdMi5HOK7OJ5WXfqPr/xwS/hrGJi1yqyg1Xuet/PMaWbzO5bztt//7O054tybyXiglFyHuMq4fRnL+eNlxzBGs2JWDH52HoADh5YQwZFEgZse8tDnPBqOKtoHhti1ydfwcMffBsaOPe1T1DQjt+58w8BeOzh83FW4ecixmby/O5776Rv/Sgn77yQJo4L3n032reQeIx+YxutRr5jvfU7738Xhx44j74hWZu9TCJ5x2nyiXOKqJ5jNhaxSOA0m298qtMGtomIKz9y1zZefuMjDK8clz0gnSsxlnpK6ZoYEYpPy2jqWg7Tm20eY9J8ZaM4aA0VLA0cc6nFXYRlWIs37vxsN41mlkasO36ywhnXZ4v4l8YPfHzX4nB2dvZF1jX/8ue/O3K2o1bOODl1LNA/1l62v/PCrRdcaJtobtw0QcmJEpaMoRkKV2f00DKIRUwwrgVFW4jmCWOfZKoMRp0VwTjIO1HWxTjiag5VSU+ysUfeiQ/YPq9xNgfagrMQY3DK4YqyabvJPM4qvnrrVSzRmuJrDhIeGSTIJHiZhF965W+JCMMoWvNFPG156s7LwHO8+sceoHzNERIli9LccytZYUqYdoBtB/QpRRtHV3ddNrLE4+gzG4jGu2me7Oect3+HgXVjjD+9FmcVg91t5qo5SKTQrsx2gxMDYBsLehknPnOhJpouc+Shrbz2rz7B7PNLGOwOOc8UOrZApWUzVOa6WLL9GM4pdn3yFeAUQbmNbWeIZ0qdwrRQbGPaAVo5nvvWjtT+QGEjnxgrqrVIhCiLl4+D0SxaNsGiNWP4wAO7lzGVIrE20fzxZ17B1JzwrqxVuETzcl+4XaLklGIwwrByySwFT5A1Yzw8naZI4JgcG8TGPp/6/MtF3TjdIyfMRFN0HhkE7TGxzMEkEjVugMeS7pAtG08D8MlPX0+I63gXdqUxeT3lNqdzBYmEQhTFzmqSVkY4h6n/1qq1p//V28A5CNMospaylBE0yTSyXLxCCsYZHUq9hsP2x6y7eg+9pYhRlTDqpFgZ37lGlO0uIMFy7Qe+Qt0oQZwaGfEwdAqvu82r/8OXxcw3RXOKafLMQjSiiz1c5Mkha6GwBEljSTzySOHY+7ZdZJwmVGJzglFsu+AgJ70Wuw4u5obf+IpwB9ed4aFPvIY1Py8+eEF/HX3BVEed7IzmEX8GFLjYY/zEYv7sHb8CwJotxyDyieKA8PAguaXzrLl2N7YRcM5Ai+nJPlCOR791GVhFqbeGl4s5/tgmbDPD3992GV/90nXi+VfNU87HLFk70iGvO6s49eBWGjh0NmZo1Tinn1uDVwyFL1tskR2o4RLN12+7ih6b6RD0AYr5BK0cNvbJIIK3AoqCk8Ods5rmbBkbe+IXuFCQWsXg0ikSHPWZMkeNZU6FzNUztKMAY0Q532wUMLFH1MxKsagEOZyd7sUmwpPNOk1jtkykLIHTJJFPZa6LdiPHyJlhjJKOzKoNJzE41q8ex+EwVhB1gD6bpVRusmjDWU6dUY6GUalYUAreJJb11nNyH8ZYWlYRhwH10/3YyMcmHmdGhhifLpEoy+iZYdm0Y5++wVke/PLLWWTzaPXiTXxw0TQ29onOiTqejsxnufB3v0o4V4RE88itV9O1ZEbEfSiJ4wsMi7pDSDy8XEwyWyScK5H3LAM2y+BFx1IE1fHAl66j3Fele+046rWncTjWXf8cLk3MChPduUZLlk103tu6rphYicXaAr8QYKgcU1o1hUs0YTNH5fAijpwcIsFxz63XoHMxfdfvT+e5YlBpimhOeg26F89yzYf+kdm5MjGO0cPL0A6WLptgTMWUl8zy6f/4c/IGEk3lTD9HD68kk4mJsWRXztJfTHDzOXJOc9XLnwGjyXY1+cCX/itDN+6icnyYxx/YjgOqO1diQh8zn2fJTTtRygnf3imOq4ilm051xFNf+ou3COe1q9URicycGqKBY9MNz+ChmNq9EqVt54BhjWZ7wXHXbVdTm+nCxB4tlRBjO6k7bRzlrrog7RbqKsEkHl2+8L7jUOZ+hJVDTdqViDFM6haeki4PwJnZPNPWUVGx3BexT9jIscT86FAjfpDI4djYGDfffDOvfvWrufHGG/nc5z4HwPz8PO9617u4/vrrede73kWlUvmBfb7vWhxaa19kXfMvf77XWKjwE2wn3ixRlua0WBoEqRq1J00kcYnHc4eHuW7NnGxYoU8t9Qi89VsXduBzC5BosapJk0HmDy6BRJN/gUK6qRIwmppOiOr5s8pOz9KLRNGtNHkO/8n1kvkZ6c6GVlMGu3+AtrJM3L8ZnOI9v/MPGAeq5eGX2+RyIaee2sDrzj8jJGHgls+/ksRopqd7O8Rne6pbvovUruDHhw0qJXF7yuGU45pfvkOUlYnHV+7exi1/+lZmTwxz9MuXitI1F4HVVBsZesptVFbsKNaef5jK6QFmU5U3VtHbW8VT4OVigmyMm80zuOMopWKb1T2htEh8w21//mYmJvp4+rYrqNYLfP6uCzvIgM7Gkv2Zto9Hx/oZfWYdl17xHI89tV4KOuNhI4+C87DKdSw8htaOSYup0CZqZXDAu97yCNX0hAhQxXKsFmATzZf3DzFzcpibXiOG1guFXGQ0iXI8fmiY+QVxjFFMVbIkscewzTM53Y0xGoMUkvXUt8sYj2VKMkKjtB0dRz5JHNBWli4X8PRcQByJx+Ca4QaJsp0UnwEtLVWT+hdaq8ghNjgL/nMJVnwPYw8/+O4MW88p5ma7semfAVTG8ON/8BmCdGFM0gVWnyoQ9Dd4zxf+hIYyXFAWEU6u3CKLR5/1KbmA+afWMINl+OIjJI1sx+rJtX30ulnqOhFVp3JUjw2dTUWJzka7dWvX+bsATQxEk2WmVUyMQ43liJSlNy2UiXw++c3tFJzPj7/tAdAWnU2Iw4DLfvI+Wk+soDFXQpdSm6BEd/w58/iClrcyaM8y2ZL71M8koBxx7BHOlHj+tkvQ2RispqvcJpcPwSj2HZe4rsFzTxFXc6y48Ag4xYbhJquXT4PRTFaylAoRy974DAupD8qzLLvkEE0sn/mVX+STf3sj0+P9Imywip7Fsx37pBvf8BABqeI+tT1au/Js6siWYVFw+4gQYyFpwsskHYN1Px9h04Jrwfuya2ieZcqjpiJKuYSxmTxRO4NNPPEVjAKidkY2z7StbF2qrk9bdLOTfaJIReYegDGaTVuOArDB5FOfRE21VsQqiCKxwgmbOVa5gCCdo4ETakXgFKGD2ZEBAgUtEsYmewiVFd9Lo2krSfE5dWQ5hcXiNWkTj1e95T6qLRGjlcsNEXxoh/YcR04MkXGaSj01107vg1ZTWsP+nCO3aB4PxdF/uAJmctSnuiHWjE/2UlwjBwsfzYmTw1SODbN9+yFxiKjlqB8fQnuWYk4i7ponBphMXQeeObCYuYle2lNdTH34Csn+PSVuFrFy1BLVOQx5vmFpKSGMPLZsPC3+usgBRmmJHFy5bAZdlk7F4s2nyPfXOH/LcXwU+VxEMleg/uga7LwIAuV7dKw0RcrnjGJO9rB23WkiZbng391NohxL14ww6rXIr5nm3Z//CLOq3bEvMsZjx6/fIfOwFfCWm+/mwFcuZ6XyWHXlvrM0hmaGvR+7Hi8Tc9PPf508ijN7VzH/AkutsJ1lTsfY0Oc9F50hU2rxz1+/IhUpIYhgaqHkrCZsZTFIhKHBkS210L7t2IAd3LmRFUvmWbp4llypLfM3xYV9pym5DAlQrxU78zXEkMQ+kZXPZhI/PWhr2mmF0Ebm22VxP4lTNKsFvvXsShb3hFSVIVSGNpaoLfNpVv+oqBn+11JR/kcTUjzP4wMf+AB33XUXt9xyC1/84hc5cuQIn/jEJ7jsssu4++67ueyyy/jEJz7xA/uE37U4HBwc/K42Nv8jVjZ+uhEmSiLOxCbEMHl8EQBNJSbTuQUuVuTxth97hLGpMi7xMPN5otQu4XXX7cLFPgmw1ubSm92KItembdXIp6ESjLKolu6YZ9cXDAzTFraLPIqeY82aEa5c1OK2r11BdGBINtfQp0hA0Xl4gw0aytCqFrGRz+ieVTzgCX9LnzvFM3tXsP7Nj3HPruUpb8Pj+uuf5sILD3Lf0T5ROE51Y9u+WL04iJo5puby2MjHK0QkTlqLT3/uOnlvRnHlljHe+itfIVtss+fpzdz12dfQs2YCF2uWLariadfxpvvGl19BcbDKzKy0OV2i6eqpkfek0Hzm8a3Yepaj37iQYrHNpvUjHZf9N/3alxmfLnLlh24hm0n4yG1/3PF4e/oL15JfNN9BmpqtDJ6fkC2G7G+rjqJ4/uQQG2yeNqaDmEyfHMYlmtnJPkwscYfFnhoDaBoqZurwEt7/umfo0qI6DlDkuxos33ZMHp/6ao2G0uYxqb2QQjyyijmDSXx80hSIxOMYMTl8BoZnMYmHiX22r52mpmLaJPzB568hCjOE7YwojFOUsLd/njaGh8YKNJXF4phXIdPWETiF76WRTrFPXkn7xMS+WPmQon2xT6HrxYa/C8M58XwrdzWY0RFGOfKpRUQ8Xeq03OwCr68nkrlUz5J3HivSDbk4PN9JotlGhuLSWWIcteND6NSOAofIwZup2jT06Rqep2/7CWnTRRJdKAkpmrzvOmh1jKWO5fn7zxduI8IVnNcx/TrN2jWKCJhWbbF6mZO26sx4P36pzeiu1eAUs4+vpXbb5hdxHH9xyKN1qh+lHF19VbYsreFij+5lM/zxj/8HtLbse3AbSzad7syjXSd62fHT96ICaQc6qzDtAJXO7drJAV7/09/i3v2LO9ej1shiZ/Opd5vqHISayvLghGwui1eO0xzvxTSyeEFCe7ILG3v0Lp8SCkrKea03MwwtnpJCLfZYMjwr6lxFZ02xiRYbmsTDxh6P3Hp1B6He/+wmAMJGjt/6489QVzGJ0VQSRbuVI0kRU2clqcIYT4zZjcdzzy+WA0jsYRUUyw3aSlT2YTPLkWOLxbMPmZNzytCoFPGcYvGSKXGDyCZEoRSeTVwnC3mBs5Vd8PX0DFUn3K7pWuZs7KjR9NksE9Zx8WsfozXZjQ1l7n/+b28kthKZdvrMsBgUW8Wpo8s4UPf43Tc+JWkkLxgHDqyUNnxb4w8KuPDck1sh8UjCABf6NFoB9SNDknKEY7qaI9fT4Jv3nytc1HxEtruBziT4nqWhDTPHF9FuZSk6n0U9IZW5LhpT3Sy68nk0MHN8GNIW+Zr+tlAqIg/PM2QDQyawfOaplVgcOSft69GHNpEDJia7+fQvvweXaLGKiX1WbTqJ5zTXvOV+gsEaNhaajnizOmZVQuAUpi5osPYsy5yPmStQdAG5cpOKirCtQLLJUbjYZ2Z0gGtuegiXeo2OP7SJvnVjfOn2yzhGkgYUSGvVhT63f2MHJ3avpTbWSwKsuewAzarQhsxkmSQRhM7EPpe95UGU56g0xS5saLCCTTSjz53lj8dhQB4lhuw4Rp5fQWu2LIftRLNm83F2HRvgC/uGuO+uSzFpGz1AEypDhKGmDKdPLaK7v8IbLjmCU2Bij/WLa4JERz5J4pF1wukuolOQyHHQq3PaGsZOLyKjIIw92lhyTu6BOAwImzkKC4fVH4Hxg0QOh4aG2LJlCwClUok1a9YwMTHBvffeyxve8AYA3vCGN3DPPff8wD7fD4RzGI6+OBzbpCfBwGlu//olaBQNlaStvPT1jMf46SF6u1IfPXuWJvj005s6cv8BTxb+ME07AJg5M4iLxaYEAP+shYFGMhmJzk6qgm85cWIJW7Ye5bqr9pBZOy1mppFHznmUnYeZlLzeldc/Bw6qs12c1hJRxoku3vt7n0NlDGXPdbh51nicd9Pj5JGW686ntjD13GqKyOKR62oy1vZQKerScmJ5snTdGUwjS2Oyhzjy8XIRg9uPMV8tcnqsCxsLd2RgYI4gMJhmhkEbcGKq0DkJkxZ9udQE1yWagf4KJJpCV5NyuUGQiTsFn84mzIc+5kgf49MlbEPeozOaDZccEFQ2bTuemMsyPTJIeaDCe165R1CFSpFWtUC3Jzy9BTHJ4KoJaT2dlOSFOo7Bradokfqi9dapzpfZtFQWqCVK4wWG3HCls/hZo5lUEvSVUYI0gpx255vSdp5TIaen85jYo6YSfKc4cXQ5SSwL0LYde4kwRMrw5vNGCdsZKRyV65iEP/XMRjwU4zrsmLC2VEIbRya9Nd66b4Yk8mkvmLoajU18HKRt5X/dnHXBA3GJ1ixdNYZCkMPhrgjTyvAnv/FzaM6m+ijATRTEiBxoKMPGrUc6RU5NxYzriOV9bTIbJvGBe2+5DpsWfOGhIUFXDg0ypyKIPXIDNVR3m4VM5aQu1hK27ZPxbIca0FZSQNUqJRyC6jefX4zF0ZNN0rayoPglF/CFf7xW7vOZEt19FeL5IksvOEbvqkl6toyQSTOH5b7WvP2Lf0ph9RTKN9TnS0SRcFbjVob+ckip1EIpx3P3bceGPko7Igf+yjnC8W4uOvekbMCJxrQDgkLInZ9/FV1rJolwVI4u4oJNY8zUA0ylAArOPCdUjKSaJ1KW33rbI2xcMUexp46XjRjZuYZMqS3WNkYzun8FvtOdU32z7VGvFUnSttq2lz2HxdF2dMyMrfHEWzP2MFHAVT95j/y91Rw5IdZQ8xO92DR7e+OacSKgkVouNZs54jDT4VOFymASzaZVMx0bGofjq3deyohu0GszxFGG2ZocdFrNHA7HGd1iz+71VFVEFAZooNEKCNtZ2q0sTSzWap78vVWCduMoImIp7VtiIOc82qkQJE65l68sCn3jt37npxnZt5KFCLSjLY+KVRSd2DgtFMczM1385V99glvvuIzTlbOxaAC1ZoCLfGn9pC3n1/+HL+EiiRt0rYBaK6A23gdGUNB8xjB7fJjJF9SZSSuD9g2Nlk/gFF6QMDvTQ8l5XHfD4zQbBb556zXCn1WWTCHEJSI0PDiTE/FM7DM1PkDgG45WMlzaLS9QSCMpuxbPAtLafN1P3y084/kCmZ4GD/yz5MSbdsDpe84j21fH626SNHJUlSFR0jKNZkqMPr6B7zy8hYGcxKJm0OR7GjSJMbUcj/7p6wUltIooDOjdOIprZGkow66Hz+f4I+fwmqv2sRqf5kQPCxnfjZMD/OYXPsIX7t5GttyS4uqB8+hfMSXrZ+iTxD5LTT5dP6A21stAlyB+S5ZN4Kymf/VER7R06vhSAqA1V8IoR//S6U78ojVSTP/6B/+RVy1usXrVeCclq+AE+AFoK8vYVIkvfuVlNBoFKU6Nx5nJoiRUOUUcBZ2DeMzZGuOEV2Vax/h+wssvOMlswxdBojI4HK1mjrCZY6n7UfE5/D83zpw5w4EDBzj//POZmZlhaEjWlqGhIWZnZ39gr/tdi8PPfvaz/8tP6hWM8OzSYROvE8v0bdvEd5qck5Pygm7OJpqTpxYzOl2UlmUYCFcIRSYji5UHZD0HkZjKxokYxC499wQu8jqvqUKdqpWFm2di76y/ldG0Eklx2PbGR1myZjT1gFPY2KfbBnSjMM0MPhDPyIn75LFllFwG2h71A4s5+MB5uMhj2UCL1uk+TOQxdnoYnYuZRgq4uWqerqUz9FlRfUWNrLQunPiCzaTF7OjRpaAdhYEqdx7ux8Y+tWPD1JoZKqHXiQtbvvYMK1aOsv+OS9iUc7QtNGfLlIttbCQJBUo5GspQG+ulUGpKW7Krwfh4v3j9JYIimaYgD0ktx56ah21mBBWLPcqrJwWVjaQoreLYcMU+WtUCs1O9mMTDzyTse3YTsVUM2jwu9T50Tq7lfLVIHAaUUHzpwz/JPFJ8ZYshceTT3d3AWc3WFXOYNG3FGo8kDkgin1OpEXPRF25KjAibLtgoZOgIQ2QVSRxIkYbj+OkBWeyMnNijdBFasfY07VaOZiNFORUkwMZ1owRoelxAXcV4iI+mQ77DsakSh0ryORo4+p0UVwuRZCAFa/NfGP6CGEc7p/CVozLTRXdKn5ioZsAqdpqYnBOEGyTb9qmPvRob+aiMwWCZnpC5cOTRzXS7DG1lODKdZ//HX4FCce6FB2RjNZrqqUFsGPCx//DzlJyPS7S0nNt+p+W/oKhManm6S5H4s1nVuTfXnX8ERSq4aGZpK8N8Wqy6WPPbP3MPVjkOJ3Jyq471serSg+IBVxbPt6lnVtOeLXUi5VzicfQTV6fkd025t0YmYzDtgOpoH6/5sQcYGp5hbGSII0eX8s8ffx0oRxuHnSzh5SJ2vPbRzmcIylJ4/vSHPg2epYQiiXwufdVjzBrF2DOSN77iIvHVO/7YJlrKMLBqAq0t2rMcemwLyy89yLEnNlGf7JbDzImlZFMOqTMa4xSnTi4mjGXzPL53DQBjKiHnPM7sXYVNNJWpng66krSk0IvDgDUrpii5gHyphQl98s5n0wUHCYB8oSWcrXYg6KDVmMQnwpLEAUuWTpLEvrSYgadsRDVN9Xnmqc08Hzsa9SKNWoGc88jisWL5BBmEDwjQbHu0mjnarSzTOmZ0spvFF9Q783YgsPQFch2TF2zSiXKpalqzdLhCgOJ9N9/Hs09tQacisjBdtTNoKrWUyhH7GOPxG//fL1DMGPaqF/vRmbToVg2/4zYx9dxqMZxO4zpjo8iWJG0kcJrJekDfqgmaWDnctDPErQwm9EmsYsD5PH7fxRiryTuP3mUS97loeB5dbhNiqc104SJpgS741yZhwJmxASr1DJMqYfPG0/joVJCkyC8SHlfbKk7uXS3zIZS18IprnpF1ackcjUoRv7cJVjN5aClTOkIhPrO10T4+9/nraMUeN930uKwVOFrzRUHiY48L3/gwJSfm2sMrx4mrOWwroKUM177rLnoWz7LlmufYvnaaqeOSD3/0iU385R+9jWikh1dtE/X9PJaV5x/DC5JOeIEfJKxQHrvv287PfuBmssW2AAuxR7m3BsoJdzrli0ZxIPScdkZ4hJmYqZOLhB+baIzxqIz1cf9oga2X7ZXDC65z2NDp2un7wmW9Y+9iYgwm9mmncYc28YhTIMXhaKTcw0hZMk4SgmSdzkkIADI3LdBuyUFnRP2ItJX/N6iV5+bmeNOb3tT5ueWWW/6bl2k0Grz3ve/lN3/zNymVSv9HP+J39Tns6en5X3/Snh+N/MOXxkvj+x3Prcyx+dAP+128NF4a3/9YftM0h55b+sN+Gy+Nl8a/+bEA8nw/o7e3l69+9avf9d/jOOa9730vN910E9dffz0A/f39TE5OMjQ0xOTkJH19fd/197/f8V2Rw+93LETZLZCbHaKQe6Uu0OOyTOs2bWUJUzTDxD7VepbEpKHzjSxReqJ9zVvulSxfYDwS5Kvk/E7eYlTLg9UdniPJAudQTtWZQtQRm5BoEqd48uFtmHbAPXdeia3kOgkSsXK0cJh2hsUuYD7NFN249Uia3iLt0zvu3k54sp8DEwW+/fkbsMbj0pseYeTRTXLSmSuRDQxeJpGos0QTtbLccP5p2nNF5g4s60SynXfjkzijeeqOy3nDpkkqJwaZPCJZzuWMiBJcolnz6p30Dc+ydNMptm4Yo+g5suUWXd11ITS3MmQLbSGET/QxPjYIVlGd6KVSyzM/14UNA5RvCGdLVKzw6U55bQ5/azs4sdNxiUfl6KIOP9ECcSPH0qsOsHHbIVGf+4bFyyYpZ2wakyQIbdLOpOidYnJkiJyCnWMl+vE6yuEoyuBp+Vy3nSoR1vMce3izfE4n6E2MWLfUE00PmkRZksSnr78iKI1ydGXOip3aylBtpryW2CdqZ/ERBHns9CJarSy1apF4QfWpLGdS24X9XuVF6MmC+u54S1on7aYo5CJlxRInPf36yGeem+79V++BOApInKJQaqUJKwmjRhaVOFU7J6kaWAObrtyDiz2i0z0A7Nu/CgClHINWeFUHdMjmn7+PGEeQjYlreVzi0a7lcZHHsbq0eVzoM3tiSFCa+KwQxcUefqnNsiUzwkNMhEphcEyfHsIhUYW5PjH/PmmNIB2Jx7KX72XQZAXxd4qBTSMc/s65zJ8YIp4roJRjbrSfrs0jRLOl1PtS0bNkhrnnVuKM5syxpWjlcMZjYP0oS173LI16gUuue4otW47zxQMDuMijhWN256qUa+V11oiF3GGXeLRGennHGx5nYPNpSstnMDhKA1VUJiFpZUjqOcr9VWoqpmf7CV71zrvQnmXt9sPUz/STK7WEU5xotl66l5oyHcW8UnDFdU9RyMbYxGP5hlP0uixVFeOhmRobkJxmpzqCgmfuugSTvte5+RJFF1CfLxPWxVYkV24ykLWCChqPmXlpK0uCj9cR77WaOfm7RPJMXtMjHNmleKxcMc5lvQnNVpbR0UECFGXrMz7eTxaPI2OCLsxH0rZu1sUGKU5ezNXqKiRMxHLfxgjn0FOugyyaxOfkSA8rs444zLBk8TThfLFjRbRwv2w657i0+yOfWkNaydmMYTJNsligTSQLKuB6hnhCONJDO46gPEuzUsLF4kdZHJ4Hq5nXEYmDU8+tpanSmDejyKfJK1lfuJalYotFi6fPzo90nLrzQsrOZ9OPPdZJ6AoQO6fmdBeTczlKhYQYx+DiaVTqbepSoYYBIgd/fs+W9Bpr8CzZYvq5goR8ucU9f/YmbNsnamVJsPRZsenpXjXFji0jLB1osvGnHmJ6z4rUbku+k7mji/ivv/FznT0lW2qjPIfX0yLvPFqT3dz91WsobRhnbr5Iz6JZXKJZe8nz/NKv3cpHP/hODhxZwqEnzqGhDSMHVsjam0hr3FlZt3/zsSXM6oiTeyXVxqaWMpWJPpJUfW6MJpcLySrp+nQ7eZ5csSVCUCPdki/8/SuZ0BH7Ht+Scg4tQbrGJkjSSRhpqjjaaUckSTze9KpnSWKfMMzQbuXodT6Jcp3iI8HS47JknKZWLxBGGXoD2+ni1FVMs16g1czR474rnvV/fPwg4/Occ3zwgx9kzZo1vOtd7+r8/ctf/nK+9rWvAfC1r32N66677gf2+X5gxeHCItMkIY4C4tSn7abXPEXJBZRdQFMlnTfgEo93/9GnaBlZ/FszZeZTD7hPffy1uBe08kiLA5PmLZtYiMQLCxFp0P2CeW67lsfWZOFK5orkPcvw8Cxf+dO3AtA4NpSqFDUGy4iOMe0AH+jbcgZnNL2LZ5lXwl/Z+e2L8BSYMGAaw7KVY9Jay8WMHl/COy86Ra63juc5bJxmUCYenme56BVPiWVMPkSh6HI+088vxRnF8rVnuOEX72Dq1DAH96xDK0dPKcKknKWxhzdRmyvjZ2OWrR6RFmDqtWZj2USz+ZAymiVbT/Dg0T5wiq/dehXNtk+lVsDGHn/5H36W/OJ5ZlRCXMsROMW6V+zqcA5t6ONl4lRoo1jswfjhJczsXMXseB8mkTbLOVft5kiYXu849fzTUkgEgYStLyrH9HrSJszjE9bzDC2a5ol9S0lise+oz5f45l07OpukMV5HrFF1wo+y6XeolEvtEOTwYRK/41H4tI2EH9jK0qzl0U6slKqVEo1mDmM8yk7a0BWVYIzq8A9Nqhi2OCrKMK9ihtN1qN3M4SHqO5P48vnVAh3CEy7nvxi51HNzzFqOPL+qYxfSjXCAPBRFFL4T2w4PzfMPn4tNPO775I2EyjLfCDCRz7or9lFLjWLLzmPkaxcy7rU5fmCVXPvUE88lHh/63S9gEZ+/6ky3cGnTlqxpZzq8vcFF0yLY0NLWWTCtj5Ulhya3fpJFJk+Js0Kud/30+0iUZWWa3X3qyQ1kC20+9LHXoDxH90XHWXvtHk7fey7tmbJw5hKPrtVTlJbOYmOP0dFB+npr2ETz9J2X4ao5tLbMjvXT3VfhfVcfEv6tkra8CUWI4JyiMdlNOC/WQqYd4BdCDuxZj13g/ALFRfMi0rGKyqlBhs47SYJj8qGNzJ8a5OMfvUk29v4acRjw7KPn4awmW2rRZX3h/6VOAjNj/XieFAztep4AzYwOyaB56rnVWKPpSr0TrdGs2XIMm3g8cP92li6ZZpktkSu2SMKAUBmOPLeexChmZsRyaaot/NhapSzzSjmSxKfVypEkcsgZsAHLlsxSdAHHVcz0dC/WQbudwffFAaDoPKbmSoQY6laRIJzNdjtLq5UVR4HUKiBOBSn5XEIFEcJYIO98tDq7bttEM9MWW6zJ0QEy2aiTsduLpoUjxNBq5KRItpq5esC7Xv0Mc/UMIS/uHsk6LaIoHci/NU8McOb+LcJBNIpCxpC0MsILxBEBQ6smWOkC4aJ6jse/ehUmDOjvabMSn7HJXjzP0FaWTLFNGAaMT/Sw7Jp9zOsYd8EsLvbF2gw5DI+fWERiFdWGiA8LpSZFJ8WpcBI92ikf/pWL2h1aT1LPiXPEwp5lFFf/9Ld5+FOvYvGmUwAivMPSni7zsh+/D8+zPPOxV/G+j75KQIN6jgwe/ZvP8P6//DglF2ATj67l00zsW0F4qo+M02S6mtQaWZJqHmMV/eef6KjwqxM9/Pz7b+H1P/YgvUNzFJzG9xOsVZ19IAyzJA5KLiBwmmw+JJ9LbYSUI1to88LM+eFF02SUw1pNyfkkkU+hu0GSCMfbJD5veuMj/ML6KmNjA5hEuMpF69FEIkAjbCeoogvJiE5in4FlU8TputxuZxnWIuZTKPJOHr/Clig7j0YzS5JohvukCI8w5J3HA7uXU6+WGNXhf7PW/nDG99dS/l6o4zPPPMPtt9/O448/zutf/3pe//rX8+CDD/Lud7+bRx55hOuvv55HHnmEd7/73T+wT/gDKQ6j2ReTRqN2hhYiGvjT2y8mVmKsaxBVKIiqyR+qsXXFnKibCyFhupDp1ObFkCpEEzHgjGKfJEW3XOJ1ckOZzQtimfrhhY1cBznRhYjAc2y/7mnixGPNmhHK55/uIIehspStLBAA2hf7kju+dB0DTiwL+odERRrV8lS0IZ9uAO25Ind8ZzNRKH5p5VIL5Vsx9o5Exbj7ofMB+NLHX09VJfSmyKqNfQo9DZ796pUEmYSbPvxZDlcyhOnC5IymMtVNubfGrvsvoKu/whtf8zRPf3sHfQNzHUsOyQ+WJIVWytPbtvUk1dAjl5WF7eafu4uZfcvFXiWbEKCZ2r2ywx167s5LO2bAzmpWDTU4vH8NR3eup9jdwBiPsJ4nbmappkq1BZVn3MpgjSKfi1myagznoBgIbzDEoH1Drthm6+oZnNVoB8XuBg9GsZxGo0AWo9TiIAIO6pAYS5J49A7MpSpoR28pShWdjhw+vTag3crRbuWE9J+e0nceHaTezHLf4QF6rE+IIYfHdDUnnmKc5cSCCDIyeEwksOFxIf77KHLOI25nOq9fdB4m8cj8K8UhCB+xpSy7jg7SVpZp3RJUOgwoW4+CgiKiDrQ4qhXxzFucerD5WhZrlGNSt4mwKKf4+j9dg0ax9bK9spkmHrWZbkwzw4lnNlB0HrYd0DM0J36VaYG44M23IF6y6d8BVFVMvVakQUxNGVoHFrFZ+ZTTIt1ZzU1r56krw6ZFDVqT3eSKLdr1PH/xtx9j9shiXCOLaWZYdvV+cn01Qdljj+l9y5nYvUr4rKUm+w4vIm5mOXNmmL/+hfcCMgeG14zx0GOb8AoRFsj1NHDG4w9/6+3MnRgSxWMj11GI+sWQ6dmSqGjDgDKKnXdchgoSknaGdj2Hl4/QgJ+N+atPvIpCYJk4tAylHeuu3c3L33w/NvEYO7SMImcTevJZQ6NWIEo5h2OnFkner/OpERGmm6r2LCb2CBs54nYGazXnn3ucKMzgoYjaGTnQ4Djn0n1kPEup2CKJA6rO8dRT59Cs54nTA2AS+9RqYnfz5DMbqaZFcjs9KO850ScHvXqWZisj5uo6YdnwPArV6UZEQLOVodbIM2wzzLV97v9ysXOAPjmV7yBlRjm6XEDOP4ueGyMHuwkMvm/oG5xj/PASTKJZnBfErVNIxh7WKFpGc+bUEo4k9iz/O32+cj4RDnPkMbNrFQZHVM+x5JLDxKEk4yRGEaeejxaHBySRzxQm9XEV78f6ZA+rVo6xvDtkyfAspe468ypmZN9K5qtFxudz2HZAjGX27y7uqNk3l4zY7ijHyuE62Ywlj0J5jqLzxTs33Sf6NGSAB8cKLJjzj+xeTd85I/L9tDMs3XGYP/7Auyh31/HzEXWV8Ju//Y9UlCFuZ1DacvUNj1EoNelysjsFuViQ+rYIAL30sKgCg5+J+bvfv5my8/jjD76DMJL3EgSW6edWiZ/nSD8D60fJ9tdpVgvMjPfTxnJg94bO3K2eFmcADzmkfuhV+yh0NTn/4n1YIw4DJhZhzsLBZv2O53Eoju5fQ1MZola2I5QyRjwKn3tyK9qzbDzneAfpjpQghgmS0Z44GNZy+C25gCTxGT22hFYzT6NWpNnMsXqo2enOLCDQI7rJlI5phwFz1TzWKImBV46889m8pMb0bJcgrf8PjIsuuoiDBw9y5513cvvtt3P77bdz9dVX09vby+c+9znuvvtuPve5z31f9L/vNX4gxaGJVGcR0ijazVwHgs6hmFFtGiohVpZuFlpGHq1Dw7TDDEnk46wim3py9ZZiMZrFMezL6W5h0dFafADFkiOVteVjUYvGHgXnE4dBB0V0sZDNRw6sZOdEnqVrRmXzDAViD9KTTGu+SBWHNyAb1N7ZLOcngsQMLJ/kda9+miTy6bM+667ch03ErHYwl9DTJ2hCX19FlIyI5YmfSXhm9yqcU7zjP35RCP846nNl2vNF8r11jh9bSr7UJDrZxykdcbgihOVHvngd1nh0Dc5z/nXP8qnPvpK+xTNccPVOksRPUTtp97VxnXYwwNLVoxinqNTygi6WW7SqBQKnUJ7Bd5qxY0tShaRiy7XPkSm1OirE5csmueat95HNhdTmxN5gbryvQyzvcqni0ui0mPZJEs3I8SXMNHzmQvkOmiqRQ0AQMzNXwsRiZWNij1kdih9h7GMSP0WVpeU1o0JBVWKfuele4oXisLtJkohZb9H5XDHYptnIU2/kqcx3kUnnz0Axpt70MYiHZckFLLUBhayIZNYlZXlvOIyyRFi0gxEd40aKNOsFqhiyeLTqeVGY4sg7TRIFJMm/vmDNTPdgAeMUYdqyq2NJQknHyXtnN1gfxeVvlhSKNTuel2LYSgH36D9dQ4xBISfyQ7M5Bm2GQ89uoD7Zg408zhxfgvIs3YNzdDlZ+BdfcRCvEEprNvbT1BAp2GqVkhSJRhZ5H82mS/cR4DGlQ6YPL2Fpb4inUl+/2OPCy3fRVoZnxguMHlxGsbfOoedXk1TzDJ57ksc/eQM29vnr/+89KN92FOwPf+syfuOz1wCwZPmEtJWt4rXv/CZvf++tjI4O8k+3XM38WB9vfuuDmFaGWDnaaRvzJ256kvLwPId3raMy3puKRuRA+OC8JmrkmDu6CKXgm9/ZgsqIsODg7g2MPLKJvPN57oHtjGC48tLnyeRD9t9zgaStpEWys5p1OdspBPK5hGd2rSFJfQuf3bUWg2PYZmmqhF/9/c91DjM28ZgaGSSJ04NNovnynkXSFrOaVrVADo/qRC9KyaHWJJLasWnDabQnrWaLI459KrU8rWaeTetGqamEJD2oxspR8C21SBKEGq1AkHeVMDbVRUWFjKmEljLEOOZrOeZrOXpQ9BcSpsbOCqBqVlFThupcuXNvvrCtbBNNuRDho/j7BzbTbuYYWj2OSXxm2z4+kkC04cKDnbZrZGH1+pNM61jsu14wsoHpWCcttIbHDi3HRj5BJsGGPr3lSK5tLAKSeSzz473M6kQEVu2AVRtP4vmGudlulg7PMz7Zi0pblJlcxGwlS8MoopkSBefxuY+/tpMIFMVyrVdsOYGnLVP1gByK4XNOpXsNnfukJ2coeY5fuHbB5FozemIx0WxJ7NI8y+P/eC3/36/dyvpLDtCY7KGlErpefYCWSogaOaoj/dzyhev4zgPb+d13340FWpUCeXxak914pZAZ1U4dBAKK/TV+6a8/SlNZXv/K57jkoiNgFVo5nn/qHGzscebIcpzRPPz3r8Q5Rd/QHLFyLF81KgKm2KMwUBPqCtBtPYbXjBJkI7522xUsJPNYq+VgbTQm8Zg5OYRxsCI19FfKpWvb2QLy+Gg3/QPz/P3d2zoH5LM7sWwH0wbqVjGDeBRGYcA9D26l2czRamWpN7PsGy9KLC6OLAsuCD4eilboU236TFWyeE7a1VO6TRj6sm68gP7zwx7/z8bnfT8jvyg62wIGWs0cVkFTGcZ01EFKEmwnJcUaj7iZpdESc2QTBvQ64W/UW37anlUs6W912kiedjgn/I+FiD4AV8l1isOmSsiXm2IUDLjYx1pYtuUEdSyD60YJT/d12gl9ToySM4VITu3p+3nIn+Hi/ggb+QxfepjNP/YoIAksUU1yTrPFNr/wW1/kju9sJmlmpUCLvQ5npn/jCKNtQeMOP3guLWUIsay9Zg/Z7iY29njljz2An42J5oqM6aYUekaz6cLnadbzDF+7nz0PbGMPMV5G0La5mR5MJKbHSeTTUsJ1/MgHbpWs0FaWjHaiGDSKTLlFz9IZYuXQGcNlOcW5r30i9fDz+L3f+Wka091S2MY+l7z+EdrzJfoWzfKlu7dhjOQam9jr8OYWTq1AJwdzwWpIIWicn5r4LiQ3GOPhO027maPPZoUDY2TR6nXZjt3BQgs3jgOM0SSxzItqLUcUZiQiz3lkMoZGI0+tkaNWy6cTXGGMoh55PO81ZKFRhjllmG3IPOrDI4ffmbMKObUutgGHf2yOZjNHHvFdbDVzVOfKnWSAJPIJ2y+2bgJRH9dqBXqcR28uwSHoUZjGrQ1LgE+nJZ53nqj0E01Uy9NShp5ijIkCDhxe2vGnK6DYvqxKjKPVzJPvEdX3eVfuJq7n2P/0ZubSDGjbzFA5uCTdlHUHgY5qefwFf75Epyioj58TlM3i8HxDKxQLnwV15X3fupQQw1suO0z/0mnGjy4hm40IKwWeu+1yRkeGsInmvZ/+M0maiCR/ePN5h3jjijom9tn6qqdZPFjvZA+XN40yOlViPvLIdzUp9NRpnOnH4Di5dzUm8vnYHRdLrNzLdpErtTGRz/MPnA/K0e18MsU2+d46ee24evtxwrFuTOyzbNUo/evGyKDZetkefu/tD1IsN6lM9XDu657oFIYmCkA51q2YkdZc4lEuthltpq1Wo6m0xAbp4pws6lG1QBL7HN6zDpN4NBt5oUQkPj19VX72ysNM6hYm8WhURaFanemmGWsaDWkbz+qEbC6SIjOROR1HASfmRFlfrRapq/QghWNWhZTzCaFVVCOPZlsy7EMMI5WscN2c1+GN1toep6sBnoKTdZ+3vq/WQccbOGoqJmxnsUBFRXQV43Qu+iSJz3PjRXrR7PVqNGqFTnpR5KRQbagYrV2aSuThKfi92y9kWrfJpof+hQ3G92zHo29k9yoAhlaN46wiyMrhPpNJZOOMPXyncEA2H9Jn/TR7WLPhbY9iEo/RyW6uftOD/MT7v8iKbUelZdvIExkp8urjPXLASNHxtjIcCGVOohzNVoZpe3aPGvGaeKSH29intytk3ih2/PI35B6xmmwu4sijmwF47s5LOXBwBSYKaMx00aoUmFdt3OkuLHB0zxpa1SK12OPBOZ+hbccxWIJ8hEZx5JkN3PdXr2deh9KyTvPdk/k8bWXZ8qqnGR8b4DO//S7mqln6BuexxmPrNTtR2vHoztVkchEPP7idtjJMjA2mFBOP8QPLZd7h2FZyJGFAfbbMReefwBjdQbvFHUIO4/fdfQlzzqWUHvACQxJJYRjHPlEUMNDVpqu3yqSKOwfinNP4CBhkXzC3coj9VqNRYO2yCrVGjpm5EvVmwDjiZpJD0UjVznNKukO1tkc90ZyJVYqAG1rKMDGf48xUiVl9tq3/wxzOSaLa9/Pzoz5+YJzDhWFxtBqyUSukfWVxjOmmtI4421aePTWEp11nERIH9YSxtocJAwyO+WpGvN2AMPYpFWJpObwg6N3M58WTLpHiMGpmJRUBMdtuJJpbP/laAIqbR8mumpZoqNhjgxYeXGF4nqxTPP7xV2MTjz6XY2w+KwhjKyCZKzB7ZpCmspgwg7MiAjn97FrqzqGDBK0tM8cXCa025ektePY9+cQWCs4nj2bkyfXEjSwmEhuXIBez+54LscDrzx0FoHv5NFOT/fzxT/wGl7ztATH8TlGLvoE5cIrGRC+TqZWFiXzKqydBO7RnmEuURM/FvhQHuQjPiUnwq171FHEt3/Hs6/Id7Xpe3PFjj4/9/k+S66kLH8VznfzYVrXQSf1IIl/SStICc6C/Qndvjcu2jtBykHnBdTaJplIXlOXVy+v83dcuJU4FJ0J+9sX8FEeiLGGKCCexR6uRJ0k8MniMzOY7qIpRjodHStTreerNgGo930Gvq6FHzShe7cs8XPD1qxiFh6LfEwNcs0BjQBa9MF3sao08tZRw3Wzk+fK3zydBMj8Xov7+m3mvHLWGpA5sWjtBgiPrPNrpa4yZs/cHQMEteDx6lFdOoYE4tUUZqUpsXlMlVHG87T9/FoNj7TnHxDoo9sj11rGxx7FTg2hEWHT7R95KceksNo3Sc06RtAPalQKV+a4OP6nLZeh2PiYK8BFCuvYNUw0fj7OJIP29IlKZm+2mZ9UkfUtmmJsvMXd6kMp8F8uWj/P5P38zn/6lX6Ix3tOZT5Ojg5wcl8PGgW9fIB/cSWJQPNnFTOjxe5/5C2bH+jGxT7ZHXufZZzfijOaV66chLQ6SlNv6qX/exnN3XMbPX7+L5761A2cVoVWsXH+Kx269Gmc07VaWqFrA4Djw5GaGzzlFO81ir50cYHz/8g4qorWlp7cqrbbEY3h4miWFBK0dcRSwdrGgXcbKRpiEAdZ47DmwlCQKyOVDtLYdbpU1mjYJYTtDkEloY+jqr+ApKORDktinoiIqc10o5YjTvOFmM88jukqjnqeW8hynKkJ/aKqEe2ugFNQtzKbrYIAWjiEJEY5QGRpK7vnTKqbgObo9mWcLXN0MYkXTbOQl9cUpGi2fJI2RM7HPxcuqJOn1j+MAaxUm8bli4wQ5J4eKmTMDqXm94sqtI5JEouKz/O90TFdzsp5EPtlCiIeiMFDFtgO0J0lDc9V8p9AUHq6iWSsKR/jUAM4pWoeGOPTsBn7+w5/im/9wA9lVsxx8ZCtGOWqVEsYqstrRd84IPTbg7Tff00Fp9vripVoZ6yNKJDe8pB1P3PYyQfixTD+/TO6nbMQJHVHfu6wTMZcvtFiz43kAlqwe5VWve5ji0DzFgQqV6R4qKiKZLmHTWMmolcU48SwdfXI9kbKcObwc5cTY/MndIlKxqaCnNVfCpvvcz/7qu7BW844P/gOe53j0kXMxiSaq50naAWMJfOfeiygX27RIuPqt9zF2fAk20fSvmJT5jGK+5XH60Aq6h+d54Ok1KW8b4bRGfmc9PjWbE2P9+TJzSsz4JadeCkilHPumCrSbObqdT5xmZy+sZy2VCNcVaGDJoairmEYjz4FTPdQaWVqRT60lqLN2iiKaOX0WSDI45hJF1YkoNFauM48iC7VIdzizPwrjJeTwf3G8EDlsNPIdTtcOW8JHU1UhBkttQdUUBZT6agJnhwFJGDCjYxIlN7BJ27NHWl4HOWyHHnEsN5aN/E7LZEGdZmOPEEPYyp4lrafcnrVrRgUhqmfZ/alXdFCy00a8m1ozZSrakMu3cUbT4zJ8S1Xkdap59n3rIsZPD+FwtGuyqJUHKjzx6Pm8ctMESSvDY8+uodhbl+zhxCOqFliXkYLigZlA/J1Q9Cyd4bn7t8vN38wStwPOuXwvBsuug4vlJg59Go08s6lau65inJOibGxkGGs1IweX0agV6HM+7fki9VMDuMRj+bZjAGxYNyIiAauoT/ZQQPOdz93AN+/awYmnNnRMVt/3W19MW23CwRkL5TtvVIrc+PLdmMRDa2k7AILyvYAvmMQefpAQZCMmp7upYIkW0OJYNlXjlKAl2rJ9SY126omVJB5RFJB33lmlY3q6jOOAdjvTWZhqRhFFcuLOOY8L+0NqjRzVls/4fLbDJawYRQ3Hk5HwGLttBofjsG7joThhUq6hE9d/DXJaTU+p1XqWLIoWCbVqkb26leYhQxxlvuuNnhj5BNYpKipKOZSGVqXAhrz4NC6cuNvKcuzJTZjQZ+7wYrLOY/OGM2LyjSOTIjFHvAYzu1YxqyOUdiTtDEk7Q3H1FH4h5Bc/+RdMpGjEje++AxsGnVYZCEJfGq6glRPvttQovtfJBijZukqur0O84ZA2ox+IR+RHjuRlM6sUGJkqsuTCI0xN9eCcYvFglbf/7t/LnI3EuPrU6WEeikNs4lGZ6+LH/+AzeIFhZO8qkqZsoO2RXnY/u4nyIuHzaiS2EKcYXjzdKWRBxDaTKmHX7nVsueEZCsUmJgpQwIlDK1m+9gzWKvKFNqQbzOjoIO25EvlCmygKqIz1pa0zOczU5svkC+0OMj011UeUilNsolmz9gzKwe5Q0OgDT2wmiT26S5Eg6L6hXi2JD2CY4QuPrSdRljgO+JuvXIFG0arnyXiW6bku4thnXoU8uneZJKYksi7V6zliZanW81QbGSkYE7lGWUSINeWsRFAi7dsYSwOHVY4IlxaSKc9XOUKr8NMputBdyaBEtZ+mGFklKmOb/n6SSEqMRYrPyUlR5MeRz+cP9dJUQkWYnerrtNcXLxsXLiHmBSj8wr2giNuSCDO8WcQbUT1He7ZMdaZb2tKxzDubig+vWz1Pu5VlAE1p0Twm9jj9zHoeeGodLvb4+pkijf2LCYIEiyPIiK3yuWummT+0hM3a5+47r+z47BacjzMejzxwEb1dLVk3PMcjz66hmKpgF+bDfLVA2XrkF8+dPbT11gTlw9G1aI4lFx0haQc0Z8ssWiuH+GiuKHnz2ZjF55zkCAKG7H5S0i76F80QoNm/ewORhR6XE95n5JPvaXDs0XMoOM2Q8wn8pMOB/Pq88H5r09381Yd/nAQ4Ml2gVGwRoNn17YsoddVlfTcSo7iiR8Qbhw8vpzFX4s03Po01mlK5kR6KvLRIDHhCN4hwPPLERvEbbORSJFwEgu1Wjj5f4h2LKOLYT4MtVIc3aLBS+EFnXavVswx3RVSb0i6uJYq6smkm+NmxMC/nkLldUzKHNIqQhJZVJK7jn/4jMX6QauUfhfEDKQ5d4/8N0uhL4//+oXZ2/7DfwkvjpfG/ZRza+f9eusRL46Xx0vhfGz+Q4jCpnF2ENIpaI9fhGR4mwkdLGgCWyVSanqRIRLURCLHWeJ0TybgFm8gJd14ZTCTKMuEcSgvEvCAhRWdjMnjipI/FC0wni8/GEn3WaEiqhdKOzTc+1cnHnNSxkJM9yTLdvWtDp3WYddIWcVbx9Xu2MTIyxK9ffRg/k3T8yl5+4yNcev2TWOMxEWvmRvvT1xWl69WXPY81mm4n3I4ZDNWJXpauGiNuZ4jaGWoz3USNLAEaP22nOasJI58GjnCuKJ/TsyjlGBiclUxN41GrF3jV5nHiMOCLH30DNvQ5+cx6uj3Htb99C9YotGfxfIMHXHDNs9wRN1i5/UgnHeLjf/Q2jPGkZWk1kyqhMdPF6WPLeOiRLR3fK5tINiaQ2lyIqs0kPplsRKmnLuktQCU9CSYpsjgWCZq68ZzjbLvgoMTapS29pGNNIu74WUTZnsQ+UzNd4h+IFfJ9S7iJTZXw2ExApR5QTxRThg6heQ5DVRlmdEisLD1OUgiE05K69Kdoh4Q2KVHsKUvjjsVUmz5FxD6p3sizweZZyGdut7KEKX/uhSPGYqwiwrHz+cViWYETfy/PEgQOf8HrEEtDGVacdwyb+GS7WizE7xnT0eCjU95mrrtBXcUdBMZEPqaWw6TUilnVxrQD5o4tEhQmRd0WkDebaDzPYI3H3ofOI8Ey6EESBp0T/MyZATJa0NEEyUd/9a9+BYsjVJaolieJA2Zjscg5MtrF/v2rmJwpc+Te89KoNfGe237RAa7JiHdaubsu/F/l2LPzHJJ2QF5LIsuSJVN42ZhwvsSADYiSdE6laI61InywRvMfrjvAu/7g00SVPCvOPd7xu+wfnOWJh7d1eISjzy/Hc4pX/+S3aVeKNOt5fD9h1xNbefrRbSSRZBjXa0XOednuTvt9aqbMdKglIcV4PPjYJtrKEKT8qkOHpCWttKxdpa46v/rAKpJEUJaqEjSrVimxY3mFLpfBJD79PSFaSas6UpaqlfZUuy3zuN7ISbu5nmOmIfO+YUV1uvDdz6uEmjLEqQo6j09VGQouoJHeZ6EyHQpLZGEmUez8ZlcH0Wsj3oa1uqyDVRVKTv0CWh/7HJjNUU/b2c4pwroI2jJosk5Q8CCICRuSFQ3wtmWtzn37wpHxJJ3EhgFeVpTXOk3saNaF0tJKtLTrYw8fWe/q1SLzC12gep6H7t9OwbeEMyXaynL/F1/JzFQvdRWz4pyTAOTzIRPHF1Exijf9zDcwkU+sLOtMERN5vOYn7iaKfXwEwdm2cZwwtXLqXT6FNWLLc1lvwsOfelXaVvboGqqQpPdY3M5w7ydei5+LKQ5Uuf0fxKR48tBSHDA7OkBUz4tTBY7LX/0YCZZ8uUneefie4RfefVeH/uSMpjbVxbJzj2OB4zpk37FBnFXMNHwWWXHACLIRP/cL38AHLt0wwVf2yNqybps49S9wCZutDHP1DG2ruOSK3YStLNm8iP5On1yStou9Dh3o/dumiJXjrliun/n/2fvvaFvTq7wT/b1fWGvttfM+OVXOCqWcAKkAIYKBFmAaX4SRMGC77cZtG2gudtvNGD0YeFx8DW17YIuWjY0Bc4lGIIKQUM4qVamqVDmcOvnss+NKX3jT/WO+77e+vetUFVCqoNKeNfaos8PK85vvnM985jNtyr/5/dfLwF+dUVc53/L1d/GvP3wjm0wHqCIfPHKn41rRKny/PeqwOugwMAlbRjHA0w1CZYOWn6QocmQH9lZiGCvT8KxBeIwDfBOfnnf7MmxIeaHbs/JOJwcqXNSBQ7hX0jqzvCbP6AcIPwnDDCB6dT/5X27j87U4Xj3p0g0Tb7cu1c3ww1CZRvfPe5FMcVZI5UlIAFVXJl1dGFzIO7qB522VkSq48WUPCa9rfRY96DU7iodKM+tTNk8dbF6PrXM8nm9JZ/FGtPze8tqHefs/+H1e+V2f4PxDsnVguL7A/+/X3kqaG3SZU+P5r7/9tZTKBWFd2RGpS9ncq3EUyvLbv3kbaWbYOLef1bMHKcc9hmuLVFi6HdcIQx8/JrtkP/NHb6LnUx65+1oArnrJY1idsv/oGonyLK9sk2aW7/r+DzA4t8Kxm09x1aERD/63N+NsSjXq4ULL8+6Pv5wZMln95RTWpPS6jv7CmO2LK2FVHKydOcBv3n2Y1UKCialFAHgmJDcxKFVFF6tT1lZXWDu3n83BLLMoEbHGyZqwqsMktK7qqoPWGZ3Q4jJGCNBxX2cCIniOp64z7r3UR5uMMkwXj0YzjVbhpaRmq07ZwrOuZFrOKMd2Yhkqw6zP2FSV7PzEy1SnsgyUplQWqxyplwClkKQo/9HH2NYJp5KKzCuG4y6PJVVodUMx7jGZzFz2Oujmsoz+5quklWSUQ+PQVc65YR6uD7kOFDK8ZE3Cp//wazA4/uT2q0S0NlxHYzQHbZf7P/FSFIr1R46gS5nur9alpbnxqeswSoqm7lyBHgXplyAF4rQMfl114+N4m3DVzScBeO0t56hGM9TIdODiwS3R01PSivdOccdvvgWNEx20KmcynGlWsN1y1QY33HCaN77xHv7j771JOKthenIymuFcKcXRlS99jF/7Z39Hdo13K7KeZibz4BRf+8N/iik6pN2a/+Ndf8FcX8tEe0eSCaczqiBIfv3r7+P/+4/+PqbskKRO2pIeTtzyOKcuzmFNwvrFFeaWRiTAR//H13Hh5GEunDvAow9fwQOP7+eVr79bCkuTccc9VzA4vyKxRKdsjmRlonOS4F6qE+ogywvwDd/6aYxJeWS1L3Idp45wwsqQSl3n3JxIYnfx0hJXXX2OsdKURRfv4M4Ls02BAzAOk5wGz2CcM1I1m+OMwgnvbhUpVE0oSDaSWuJgKIoANpSW6yu00Wscg0QGoVZxTPB809/ZmsZboMBw5sJ8E4uMUSRe0UNUAxJkun/WZxw9eom67GBMSt+LBEtGwtziSKR6jFy7r3nTXWwmZaMcEQ+YowfG2CBtVIdhsWJzjt7SmGLSwxRdHrHTWJeguLgu7+0rlmtslXP+5BHe8DV3cd0VG+hJlyWX8TXf8XHuvu8KHDSJ28fvPs7RG08zwfPoHdfjTcqy67KeaJxNGa8vAJIgrxrFuQvLFBiM8rKeT2fUTvH2d7yfW2+7sxH4v+8zN/Mf/s33yPunU05cfZaf/xc/iNUZn1iTAvHf/+o3oIC7vig0nVKJrE/aMc3nPeMz5uYnLF1xiQx5T0ydcccnbkWlnu1E82//zoexXrR0K6eYJ2n2E88f3eCqrgAD33H9OhYXdiRnmKrDn/zWN7A17PG22+7mxGKFCxP3cVXjqbNCBbBGppatTen2ZCmiR1q8ps74gdc/iq4ztJYVhx/9+EuY97GtLMXuXJCWKZS8f3W4FxsSua0qY65j2fay5m9LWSo8iVdMgr+64LNLLmMz0YyUZjPE2FpZSmXZUIZ1NX0Pn2/zPLs6hy8Ee1aSwzSfSiIkKAaTrOGynKwlmYjBJUrH6Dpjvxd00NQZl8L2CoDrr7nQVKZDJROcIOTwsgrCtTrFtd5vh2j9OTyTYb9JKGWqzgt5HC+7bFMhM9haxunjm9LzKefX+8JDQapfU3Swdcb7P3M99/7FK+kcHHLw6vM4I1sTrj2xwYVHjqLLDiWet772EUolWonlcIaHHz2K94peQCC08txTweLhTZYObnLDa+7n9ttvZvHwJqWy3PaWO0V/sM44dfoQOYrXfstn6PuM49eelam0zTmcSZldGbI5mG3eA6tlB7KpM173xrtYv7gieomryzxy13VMcFz38oc54DpNUDZ1xpEDAwC21hbBK2Z9Qn9hzN9784O8/bZ7Gh6KrnKq8LnaphIVTuG51UXmwmBBDxGbroO0QV3njJUTcXQt5OarbR9jUjk0g/ivB5Z9xoIXEWFtMq6c1w15f86nDSpdYZkoy5b3ASWs2VZ14zNDpZn3GRN0s72hVnLgFkGnCxCV/5A8JijyoxWbiJxOgmJrnPOmXA52j2c4nOPxM/suex30uoYcxa2v/VLzXBIU1bjH1SslxquG65R6mXx2OsO6BK8QoeGJTKGWQWh2lBg+9ImXsOQ7zK4Mm9uACMkP1xYpkQ0h/UPbfPp9bwpbP8L+Wy08w/1XXsRUOUkiyBPQEHouJBMunjxM5aHrg15Z2eG6QMY/6LpkHcOpx45xZF9BPZzh1V/zRY5eeZ7JeIb/+evuZ+WK1YD2iUD3//4P/wiVOCZbs+zfN8DUGceuuMBjn7uBykqw/ON/8zephn3wig/+yZvo5gbvEh4/eURQK6dYOLgp10OV8Xf/8e9RDvqUA9GiGyF85XMG8MJHndsn+NnXf98H6XQ1s3MFWWqZaMXs0qhJBr/+zXdz/xduCtIeGZta9EJrm2BMymuukMTqkXRMiWG8PYvRGVngLZ58/BDffc02dZ3zsXuO8pKr10lRHD64SV2J8PraxX2sLI1JEDkbh6cHbA/7jMY98ac6Zaw0W1ZhkOGprUR0NIuw17lQhpGSQ7RSVjZIhIOzCAiYQzZLrKuKS0nNtjKsHLGNb46Ubcj9Ua56rchEMzQMHERd2bEynDp9iHI8g7UpY2WFD47j4fuuZjKcxeiUNLX8wf/4GgyenJ3UojvPLEhs0KnEUER/ttruMx73Kbb7lEp2+jojW3vWq5TxaIZeVySwVg5ucuzmUywuDaknsqmnGvXYDpuM7v38zRRWccHLDu15FP/Pn78cZ1UoDGspwGxKf6bmQlryaFLy3e/8ExKUgBZeeNa1U/zp73wDabeW7VNhX/sjRq6RTr/id9/3Wn76F36ZL330Vt71BuF1/+C33U5Gwtd+4+eEQx5iiS7kOq4nXY76nJe96S42Tx5i1mfNRp6bb32I03deQ4llZmHCDce3+D//5Q+QKbh6QQsnu875vV/6Lq67YoNuV3Pw8DqVcnziz95AXeVYk/Cmt3yBQSFF+LVXrrK1vtTI1+AVxiYNImjDxPJMv2y0KyssWudsbiw2YuxV2eWGay7xD77pbgo8dTiDl1qDR9LBkWK8E0CDgZNzeqCkezNsLb4oA2DgkCGmDooJhpEyFJhmbzPAZqLZTnSDVL4QbC85fIbm8Gzq6YqdhZAUdX3aoEMgCWGNZ5BojM65eP4Aw0TC1v0PH8UE9K5QMmLvlG9InauPH5ILJ1TXvsrIQrvZKE/eMU3LrWkLVLmsU7IJZtJBh9ZI7hMM0J0tMcpJQlhnWByjMPVpqwzt4f/64E3UFxYkUJUdTlx/hsfOLPPog1fKajM8r/vOT9L1QmheO3OA+9d7KOXpJ548VOApiiRxjeD2/GwpiR0JZx8/IsvRTcqZS7NYhJDf9ymDtUWRvnjwSpxNyLoarRMunD+AqXIevus6QSu3ZpmZLxiP+pLY6ZRi0uN8Wskhn0wYr8+LJJbWjjQAAIt3SURBVMf2HFWVs7W6TF3nOJswT0LWMXzokzfR7VUSUGpBXNbDwWUCwfnDH70VXWdsjnPK0Qwb211uPjRpfCG2jcswnVwWPbTOOKASabXVcjBdSGoMTlDH0H6o64y1cU4ZPsNrOzAY51RKWl9bqmIzIIEFhonSJF7J98pyJpnQI2OgajZVFfT9FKWy1Gp6cOpwf3FKeksZqfDxbNUJEy1IaIeE4XCGR7af2FYG0eC0eMpJr0F0EkQYea5f4bwc+C5U2yZofL7qzXegvMgkOac4kInIN8CCy/gb3/x58nCIxQGgU3dci7OKpeNrjZ9vPHyEl7/prmb6vxG11RmzRzal3VblVFiRwQgoZYeUybhPGdrsNaIbeO7eK+iSciYtqMZdPvPAYeqARG5eXOZzn7hV6ATz46nItk757OdukuLLpGxe2McnvyTF0/EbT3H25FEUcP6B49z76EGqkWzcuOOioEvOpHzp9BJJJgNLn/nz12GD4Pu5B06Ea0ZazhrP6QeuYDW0Qa97ySOUwz6Vsly4/woevv8qur2KTsew2LVceOxI2HgjqP6Ja85idI7RKRWwnhhMWB/222f6zPsQQ5SjGM+gdU4dBquuu/Ysf/6IIDKb4UDr+BQdELWOT/ncvUe5uLbAgXzalksVGJMwGHeEAmHF3zexrGMpMJQhAYyFxFBpdECh4yEafSu292KnxisYK8NaGK6KRdAkqADUVhBt2VgR/DZI6gxx5ChSr9ge9ZiMZiQh9gmZT+j7jEdO7WcympH1p1vzvFePgw8lzX0BaGgkyvSk2xz63it6vYpOv2KiLJ/9wGuxYeDuNIb+bEmSOJyR1ub64we5/6FjZB3DRDn+zb//DjqpXD+PPHaYsYcbu1NJlTcfH+Ks6Kn2SJtrQYdhxW1V84HfvU2GH7ycC97DXOZ438UO2+f2NUNJs3MTXrcs8U4XHeY7lkc+/DKuvuUxBoM5uj5lJqzKzDpTECP3MtyXoNBVzrXzgmA+eOf1LLtus5Vn4cAWK8fWZJCuyjl69BJrWAoPHxn6hrZz19l5Tlx1jrX1RcZDoQW87FX3s7G2jKlyOr2abuo49fhhVteW+H8+chNp6sIQiqKTuWbNqNFxS88cLmzIKZVsvpmbnwQkPKMoupw5t8KD919FFyXJZkCl21Yqx1Bpuohw+wDHvRVsK8N2otlWNTokeHXwYYtIaa0nJkrnyu+CX8bkcRJ8fc+eG3t2BlKm5yxWOTZD8KqVYy71bChBbiJXDETDroPiopqg64x7HzrU3Mc9Axm7r5TnoOs1HEDnFFvjDh/52K0NsgiCoAxULZp5OE49crxBDq1O6QSu4iGVsH1mP1m/xpQyIT1SWjYl2ISRMizMauHBAHd6LVVvJcnASOkgfZEF7mPK1cc3Zf1VLeuwxquLjBKpfO+/9xrOK2mDFwHmXHEZR1XSVIWn77+SudkCXXZYcV3ec/dBNi/sw5qUVSO3UYls5xgNZS/s8r4tnE3QZYev/6bPsTA/xpqUw8cvomNSXGes7N9sdgEniWNb1WyeXyFBMbtviNEZjz90gkpnpKnlwJF1Tt57NXOJBM3v+96PcvHcQeEcWplkW0sqauWaJGV5oZTdy8DF8we4WKZc3OiJhlhoR9RVR9qrdcZo2KcquyzmgiTWOqeqcw64Dlo5UmAxHIrapLw3WWMcDpd+17JZifh5lOjYSGrZ9hESw0pZNFaS+9B665AyUrrRfJNVXdOgEw9Xr6AaJmwnuhF73cIzdDKJdzSFwWgGzROr2XjftXKUkx4nbF9eM47xcJaXv0pQuCLgNtuJphjONALwcqg76qpDaQXViFPNnV4tvMmBtN2cTTn96HGqYZ+HPnNzg5rPHtgm6xhBS+o8cHMl4X/804KSmVpa+GXZwRnRiJv3OcWkx6EMCuVkMrvOOXDVRZSXw25waYlu4tkcdagnXe6/9xpu+46Pc/HcQVYv7Ge8ttBwtWZndCO7tLB/m/PeUU16/MFvvI3XfctnBEFdHKPCwVANZ1hXlm5XY3TKP/xf3if+VWccOSaI5K+9+zv5iT96GT/777896HvmsqKxX7KeVJg6Y7C+yNqZA3R9yvKxNU6dX8bojENHV6ltQjGRzSTWiG6nd9LBMCajwrOmSkZWYbSI3WfIfuzMi0yO0Sn7uhZd50F8Xj6v63uOex4TNFnXMr0M0M+dtKtNQlnlOOWxHkaTjK2J+Pg2lgzFRmK4mFZMAkqYBWQr7rJ1CKrolEwTO3yDLMYpTxP8ugiFzmQ4XU5QBq25D9lS/FLZRgS7Q0pVS/G86KWYOj3IRcvRpPzIGx6lDgXU/uWCquxgbUJVdps1jOkuZORY14a2Z857f+ObANh3zQXu+PCrcE66KhZPrydC2BkJY2Wpqg5fPDePqTNmZgvOPX5ENB0zabWvdBzdTLYc3XDdWRH/rqUQejQt+MKZhWZDyqITNQXvFddce4Y5Lxzbb/n+P2eGjGFi+OwHXouzKYuzmlPpmN58EaR6Eg5ddYG/9ff/AIBzDx1nYVbzu+99I0luuPr6U/TJWDl+iZyEOz/5cnQVVRdge3WpeS+qOkEXHRaWhsx64dPW4x5J6uivDDGBenLv/VcyTIQbrsNqu8mgjwMmoxmKUmJl32cUox5VJR0gXeV0O477zyzy/rN93n7TKoPtOcqii7UpJ46tS4Fvskaqpix6pF5RKVkAsLW5wMfvvIJaZ2gjX49vdfmVcxkPpwW1zmUCP3zecz5vlA5KZbkQzvXNAB4MlSSGI6UFEVQ+UHl8SADFJ6O/JqHbFDnZJvh0zBdeCLY3rfzXsNHFvDkcDZ6t4EAGRycVjoELeliRdFpXHV59SOQFdJ3zLW/7Ag3pH0HvUq+4wYVdnoBzsFolPDaSoBMDnzeyUs2E6uaBh482yaPVGSvzUkX/n//23czuG4RWcY6pJTncVoZR2BwwnORBoDZo0TkhTVfIwV9t97nw+GHZNTnp8dJX38fLXnkfzklLoRzOMFYiOPyyVz7AaiIaZ+OQGFs8Kz3D4NIiddHl4QevYHsggrM5CVf4DrOLo4YzeX3Pce7+E8ygcE5Qiytf9iimyim2+9Rlh05Yk5d1DINLS5STHhdOH+bKlz2K1Rn92QlX33ySBMXKsTUSFBcePIbVKQePXOLQgS2yjsE7uPueq3GhotZVzsfuOhHQR+EGGhzrStaBGZNx7bVnMDrlklGMhn32dx0Pat9sTKirXNo6PkXrnJ95VBDDN7/hAWotyGFd57zlyASNC0MRggyU4TMejOWwfWSYs+l9cyDlpAwDf7AOXLk2klIr1/w7+mPeWscUB6DimjTl4YP/90HGYa9xl4xNZfhEtoXFs2YRvcYnuQ4mRR6S4JyhMs3uZqMzTj58BTOpb55nhW2kJc48KEMUS17asrWnWRvV8wn/+be+lqvIKEczTdK0vrGASh1XvexR4fsqz+D8MnXgUJnAZzS1FFrj7VnqoiuIIXBudVFkhpDHWlgcNq/DIahFmhsq5ej5lHI0w1kLB5ZKJtuznLo4Tz3u8co330GnU7c01FKWFsYNcjkZ9DmeJOg641u/6yM8due1nHeO3/rvX8+h5QJT53zq/a/nQmj9E7iTps5xLuGuu67DuYQf/If/A4vnVFI3m3qs8uRdKQA2VldYvbCfuaUhh12XetxjeUHuM0kcMx1HVXYaGkSnV1MVvfBeCV9w0XdE8LnOuLEriVb0obrqUNcdzpdpIOx3GAfqw1gnXHlA0PKyypvVeLdXjks6IVFQ1eIbDyQV62UmbWTl2Qrdkm1Vs6GqJgaaEC9KZRtudfRloac4yrB1qlkGgBc+GoKG//q/m22QQ6tkCcGSky0rBo8N72PiYTzuMYsMVB1zsi2q0xXR7LLoMetFa3RjWxDeOAz301cbMlQz4BGPwDTxspNcp7zsZY8IaHDyIK94yx1BZ1Je382vuQ9T5SHJheFwhjNKuKeDjQXWLy0z9ojkl3K85Y33M98XuZhbXv8lEqCTyNDXhqpkiYBLWPLTTVWmzlhc2WbeZ4yVQaWOg060JK+69gy6zDl6cBuNRU+6jS8Xgz6f+qOvAeDgFatMqoy/9b0f5d7PvITFA1ss+C71pEvuFVkYFvTKk5IwuzhuzqezlRTXf/CBVwgXO4AdZx44wUOfvgWNoxoLt/Yt/YSOEiDC6pSNtWVu3FfyZ5++geEkZ31jgRWX0V+YcObcinzuXoT/zwWR7wOHNvBeUYcz7tCRS0LpCV2auuowGs9glRQYtXKs7N9iJpP2cV1nlFUu0lZKtrpUgdoTU7Uy0Bpq5VhxXbpeYu9AaR7OxkzUtE3c8yllQMVjzK0wbCf1jkK/8X0VR12mSPQLwbx/Zl8vdHtWksPOvN2xVmcrqZu23AP19HCusY1uUVl0+eTFnsDJOhPx2NB2rZXD6JxxIimnrqRKqW1CT0n7zbR0DuPWB1NLMpEFSB3AmpQ08dRll7/3Yz/C7X/2On73P7y9QVEmyrDfZ/T6JQZHqaWFtc91OBgRrDJnEhzdlrns+zVJaEmoBkW0yjMZ9Dlke1iTcvHsQamWdNpshtHKc/zwkK21JVTi2B7OcHF9vklsf/zv/qkgMC6uxYNPf+Ll3DBn2X9oXTTaLi1hrQyEjAeznDp9CGcTOr2a3/ydr0Upz4Xz+7nn4y8XPcblIXUh64lUOCQeuOc6WcPnFXc/eDiQzzO+6Vs/xbb3XDx7EOcSznmHsemOTSXxfTUhCTEmY0ZBUXbQVvGaRRkM6fi0aR2nSLA64Gcow/5ZXWdUoY1xcP82AOecYzPwm4oqo1aOrVII6/enE9ZDJZmgWPIdDD4IVAtKkqKadglIS3CGrNHh6pBQBAZhNKtk2tMqzzVvHmCQFpzFsZ1M9yifTCq26pSFJwlYkzL4S501wc0qz+Mnj3DtjY+JKHnr5yZMgF/98kdJUfzw274o+5mZ8gJ7JNyXVDyAxtQZOrSiHzi7wINfuIH7Pn0LDs9jd1/L5z76KnnNddYQ1p1LqMY9TtzyuPioTTngulS1IL8JkpicPnWE8waOBp93NuGDv/MNdH3Cy90MX7r7OpZRpImgHJ3Uic/UGefOHeDMwydkCMak3HzrQw3SPDNb8LiT4D9YW2TfkXVKPP/gX/wGc7MlziS84g13o5Xj/KUFrE0ptuZwJqEquvzKVg0ehpcW6PoEi8PahHLUE55oKvc9Oz+mP1ugq1yQeZNw/7l5ur2a4WAO52g4r0anlBPZx22MDGMAzPkUgwgGv+xG2bSSeMKBLknkANfoby554XFdfWhEnoWp4TqnKDtNwqaAdecZTgQ9HynDpvesh0NxW2lmA8d2EHiq0UcKDAMlsdQyLXTa/htVIOLvXEgQLZ7Xfq0OvuZC10Y3vC+AQKeTKf1Jly1kiv4NSVe2WQz7TMY9tMkYKC2ai2OJGbrOqaqco8cvkvonHitv/toviaJDnfPgg1fKD5XnzH1XNKvcRkrzqQ++DqOl85P7sCs+bP6YjPvNqrxqNEPmFefOHuTCVk+4wwPZHV07xWRjnpwEDxTDPkMcvTDUYU1GVXTp+YSuT3jgky9hzmUoYOnQBlsXVxiOY9KbtDolGb9192H5uU5ZHeXMLo9kAt+KeHwx6DNWhquul+trJqzlM7VQnZxLeDiR7VILHaHAOCto5rkzhzl+4ym0ciSJ45u//RN8z//rA1zyAUiwKfsPbvDWb/kUNx4ZUtuE7VGXGRKKYZ8/2PLUZafZTLWlLEcQzVkTuN1bGwtcurC/oTuYgAzefXKliVElhrXVFbqZo6pymexOLS8/NuSNZplaOepaYvAwxMOez6iVxMih0qJlGNrBQ+pm97JT0/jqlRQwaaD2xKGguB2rPbfQ9ucXgsmGFPWMvl7o9qwkh935aYsui6hI+GAXdxGV++HQGw373NTzsgu5ztF13kw8j+IgA5ZVZdBBckNbxVJHDgNdd3YghzagHQ6ZAIxcQ6sz3vR1d5Jmlpf04GO3X8OdF2apJ12skdbRnBJeGEBh5WC5Nk34YjrCVCK1UyiB0Cfbsww2F9hcXxKUp+hy6pETcqgpy8P3X0MP4QxevLgiF4AWKRWrPMorlhZlF6apM97y9bfz2EgO865P+aPfua0Z9OgAr37545zdmOHYoW2sFcFoH9biRZLr+tYMl84doC473HTFFt4lXH3tae6570qsTdm8tMzHPvQqMhI+/f7XY5XnTd/y6WaAQCkoxjJtWhWy5WN9fYmPfPiVgpyEVpsggBmzPhyyYQ2YtNlEBNp6WB0KIlUHZKXWOYfDQZqg0CZjuD1HVWfUdUqtM774wBESFNvKUiI7kIeliABvhQtrweVsJTqggpYtVe84NHcgh4onBhwFfS+rtdoVaeS1VFhuevMkPHdpSw+VZtkLod7i2fKei0+CHW7G5FALLzLzgiafvzTPpz7xClQraU29bJ8wOudf/eL/RE7C8oFNrE1YUIqOT6RFjeMdV07YSCrh9QX0eNspyqLHycePAFJsPXZGhIsjWhi/AH71P3wHddlhtDWHQgXBZwkHOQm3vvZLbCrLVUtVmDJPueqaM3RJ+Xs/+BeMJ10GeE5fmmV7YxHnFb/039/C9qUlTlxxgX2HNgSt1Ckf/YtXN9PsaW75YrYp6L9XFENBLHTR4bEzcg2luWHFdXn5LSfxVjWFCl5apZOwys0hk5/RbCgSF30HZ1P2HVxHJZAnwqdcD+V6t1tjrGI46odp+ZzNtSWqILBuTSoC50ThdhkSKjBsJTLtXocipgwo4ub2HG8+OqYOqyGH465wN8uMNPUs+i635imLqRde7CRrkrStIN9hEB9b8r3Gb+OhmoUBvqqFqkQfj92X1MsGCaM8Wu1MEA2OV7y5at6rJJD/k+DbAAGrZaRqBuMO64lmrAyrRlbZpZnF2pQ0cajQ7vNI8lxXOcYm/MaHXtq0veVxxH7tz2+V+FBnfOnsPAD7rz/HZz97C8ZK3KyV48abH8UGQfAln1GUuWyDMimjwSyDUZ80+rTyvPk7Pk5pFRbH1uoSDs+hOU2SC1L1xmMjJoN+4LdJkjke9rnrizcwR8J+32N2YcxqWsk1bTLmVwasbUoJ+b//p29ofLeucv7Xt90NwBc+cWvznlz3mgewJmXR5XzsL16LA47ecgqjc26eESpJOenJmVV1Gt788rzsYLI65aN//nqOnThP1tXCezcZd376ZdSTLhvKCEc60BeK8Qzf+O2fwFjFVcfWKRBApVTSur/3zpskecFzfF43KhDCH+xw7sJykBSTWKzrjP2zmoHSZOEMWVgcsW+poNYZVZ3x0NlF0sTx6sMTAU3qLLT+TRMzLbLVJEVxKQ79hdhtlG8GRmMb2SASYnH4L/ppgqLnRZqufR1EtPuFYnsDKXu2Z3u2Z3u2Z3u2Z3v2VWPPWnIYCf6RUyAPpjiV1HR8SuIVtbJcZQW+H41nWJjVpISp1Uo4OTWOrk+oq06YSnVSfWEorcKhwhqz1vq8QEA2oYrv92yDmOgqZ7g1j9Epf/cnf4ulvuH7v+FuBuuLGJ02fAfv1XRyquyybVWDTJhakLAExWBtkZnZkvnFEdak/Mp7X8ev33UkTF87vIeOF67N/HzBRBnhXqnw2lCcOnOAB++/imI8w6lHTjBAeChWeX51UDW7PXsIj896mJ+bhNeY8fCXrpWWsFVMxjPcOVHMzsvv3/q9H8RZaWe40O7u9Uucl5brf3loiaHSnL7/CoyR1t/9lYj0xhYMwJGjq1wYdNH4KZk5ILMd0qZtUVcdjE7pJLCyPKKwitOtBfe6zpgUHWaU8LFyr3js3CK/d+cJqjqn0ilVnXG+FvW/QaIZKGlLbBvFWGk2wmd0Jh2zFVpvUrm6BiXc3VqToRMRCm633ha8IMTtdkWNlUnOgKjEdktEaoYqtFJI2VaWjeTyJOkzTqRFdJ03vMgE6PcM91zsM6wTOkh7JifsAq5y1gIxe7CxgDUp5720aqJY9i+cFiTHuUSmvaucW/aVXP/Sh3nwvHBl09RyxZEBn//IqwSRDLwpHTifWeaZjPr0+iUTZVirhBbhEO3FC6cPieyEipzDjHd/4KUy7LM5z803P87RDGY7sm9aW8WMEmT+ld/yOS6d3yeTwDbl0rbwtuoqpxzPsBWE7//wj1/L7PJI2k8Xl6isTPXrssOSy7jmpY9itHBdpTWeNAFrMuzL3nTlKEcz3H/39eQoNteWycI0ZZpbTj1yHOcFafrfvvtTFJOeCDrbhGPHLwripTPGw1nxXSNc2DoO4gB12Cvs8JShC1LXOTpwkes64/x6n3Nrs5RVhrEpw0lExWUydqhqvIduJjysLSMoqEeGqDYDvzByCiOCYsLzaPto9F0TJuAn6AZ9ib+LiEvkSD4ZV6sKMRZkGAZkMGBYybWtlecl+0q6CC98czBLkrhGx24uE26dCZSF1xwbSvt91+Pcn07k/Q2yYAD3f/hWTm/2WksPHEsHN6UFS0KGYlJlHLZddNVhaWWbs5dmBXXSwmt/76+9jaWuSFFluaBU62P57HMS7jg7T3emYqgMB71cA+fPHmRzIOfOtqr53KdfRu5Vg+jpOuf8RDjf/9tbHmwm2J1JOX3yKAAr+7boJJ47P/oKzj9wHF11uNHNoELL9PRd1+Cs4tA+6Tw4k8jZprOmRfzWv/FJcqRt/Yavu4PN9SV02ZE1iWWHh0+tUJcdej7hUjLB2nD7UZ/NCytYDxfXFhkpR1V2hf5Sdnj3fcsUQRd4bZyzdkmUJ6o6J881va6hDO1iXUtb+WMjAq9a+PonTx0mzyxVnVLplA2rBC0PKhpFlZJ51eKwuiYOl8qylpR0SJ7QsYmSdh7fxFitbIMwAozRjR5nOy7L1HJ9WT9+7u2ZDaN81Q6kDC9lOz7UNhy8reoQ8OT3a+Gg3Rr22B7lVBiqOqeuO81Qy/V0mmSx5xPqOpf79LBeSSDSrYGUuAki/sy6aZvY6pTVi/tkn2mV80M/8VuURZetDdHhqpWVjQ2B1zibC6fprnTMiu9KW1mn/Pg33dOIeVdlh2LSw7mEqxY0x0jRYULxiqvO4ZGdwhcuLtH3GRfPH6BSlgSYQfHKVz3AZ88sSIvLCK/OmmmyW1cy7NFPPPc+epBLjmboxNqUzU2ZyHNOkuiNRDfq9+ONeYrxDN4rxqVwZ/CKE0fX6fkUhWpaCbH9sBx5KkELaxZFmjpuObEp5HUr/LR4iHZ8EtrK0mrTOmcmFb5KARxRrUO96GJMQulF6209qaiM4ovZNlWdUWrZSrGUiIzKRTWhVDJZuY60aOKydounYqcw6m5uilU7h1BEPHvnz/o+23F41oGH1fMpH3zPUpNYtrlfIIFwIwzA7LYUxUESGY4JhUIvbHpZG3Q5rzRn/TQoeoTDouucrUQGC8ajWYzJuKXnWUtKHJ4eCS81C2ypiu2NxaaVP5pIS/q1N1xkxstu4PtPLfH4uRUpaEyGrmVLx4VTh9i3WARNSfkM0+BLFkcC3HXP1Xg8j2+JPpupM14+L5PfZ04eZXF5wBkDK4slK/s3efmNF3jJ8W2Z2t3qMzs3aaRsHtXSelTK48NHVdc5R/dP0IVcJ52ZSkR/jRQkh0gbHmrkTEYuYK9fYmrh5f1/vufzOJvypw/tl89yNhzGTjiKH77jSv6nb7qTuuzw67//Jj7+havROuPovoJi0guFjvh7HaU9Qsv6iMuFz6xTFheHooAQEq2qEu4sQFV3OLp/zOlaNT8rwp7qok7QYbcvQIInJ2Gt1R7eVrUUjWFaeCN81nFic+q7lhxpt8XDE4QDWYbNKNE3I7+rTan4zf/cbbiPebjmyzDNDyJ4HWPutpV2dYXli+tdLga5o0tbPUbjGUmKsbz0+ouieVrnXNrs8TvnOyz4bnM9xatq2eVNjPj+b70dgLvvup6//Tc/waToyi5fHI8/cAWmlkUJZ5OKqk4YJpaq6LJ2aQWlYEZJkalxfP3bPkO/Z3AKBlvzKOBBb9CTLmM0S6lne32RnIRFpTA658przmAtDe/75bc+yIyXjSmRL34eS9enVGVH+Ohhsvd9D4qfHTx6CefhZW+6m3vuvIm67PDT//T3+bpv+BxzPmewNU+SOSnIlRTU3dYua1Pn5D0pemPRNtMvGW/MC5dWZ9x49SV03aEXJs+tFckZ75TICnnFuMgYJprP33Gd0DPqnO85aKmc4k2zivncMh73ggai0IaqQAmqqg5lJdJgS2EAaMbL+/rJiz2G4x6VTtFacV86EbmloBk51knw3+CDxMEpxxjho9atIqZNkRgGDrlp5QXt5HGWnIGaUiDq8Lc28BNfKLY3kPJlsMgJAzl4Ey9K/BrHxVS4CZvjnF5XJu2KQsby4wH+ju/8TKPI/qquog6Jl1LyAhRS1cbA54xUTXVIDpWSgw+k4jx3YRljUk7fczXF+jw//5kr2HdwA2ME0TxpvXDugOMHRozDiqnzyURI1SbjwLFLzPqMRx+8sjlQ1i6tsDHOeO11a9S1CN/agBJak3DLLSeZ81K9lU2V7pmdH/MNN14UDp4SIVFdZxxyMrGm6xxnEvbNGm57031oRIrnzGPHsEbWTBkjmnXHrjwfkBPhp5gwJHPh3AGW52qszpiMZpq1Wf/wVWfDpoNxEERN2UI4ZoIehkS7zrjlpY8IqmEkWFqTsuQ6DFQdBk1EisboDOcV28M+YxxzmfDtHB5tpBK9qExzkO6b07zNLVLWKaVRlFVCHQ6vfb5HjSXzqpnk3EzqBsGRg9KRI5y8GIii30VyfPSlWNlGlCUGqbZd7mCNiHL8e4eIA28ndTNJ3DaLZ6kjyWytc9mQERLZ5TnBakbKMQ6IIAg3sa5z/say3P/a6jK6zunkEmSFc+b5zlsuytaF1AakNmO7ShlszodrzDIczXDlwTE3X3dOkqsgUl5XOXMLYzod0QJ89MErAJhJPWdPHW6S7te/7j4UCuvl9Zs6Y/+SoNF33n+ETq8mBwbjLnXV4Z6HDlPrjIcfvJJ7P/VSTj12rCHy39gTovxgUwZMomj6Qxfm2Ly0TBRELqzwLnWd8fff8SHWzhzAu4TxcJay6DIJQwLFeEbWFmKbtXnjqIhgwjadqiODMonnc5+7GecSji9VpAqqssuBfdvceY8MQ0SeodEydVxVOaVyVAHZq3XOeCTi8nEKVxvhxtbKUdUZD1+Y57GkpKgzTl7qU7vAEbSKzUGXsdKMTMJ6lVLhGrTZKvGjWGCIr4n/JUErTg5WH0SuTePLkbO1oxAK/rr7OhDOl2v8OC5llM090+QzaiZu4xrZkDccGzFMLCas1Nza7jNShkpZ+v1SRJKrnPUq5R1XTni5Wd4BDgDcmofuQpWzcngDgIcuzHH25BGMTbh0UaR/FpaGGJ2z4HNZH5rKtafrjMNHV5mf0Xzjax/BhmG4R++/mtkZwaKEwyyF2WhzHqM8t/uaj3zspUyUYeLB6JTf+bNXcaoQrVWA+++9hq2kpusFsTM6b2LK4ROrjdyLtSlbSqSGklQkrSZbs9x0yyPUVU5vaczv/s6bOeByfvWjNzEZ9Hn84hybqsLqTFA5lzDrJdG8ePKw8AStcMr3HV5nsLFAHhDGK64+i9EpC+GzsiZldm4ixeBwllR5rjmxzraqedWtjzBGMx71ueWWR9Ee5mZq7qrgV850hc8dZMS2h51m8E+4symvWZFJ4ZvtLLKSUTGaZFR1QmUSVlxOrROqWmLdtlXNYFTbtJpO9D/Z15BQDDFNDo2aJokGH/jZO/m1T4WAP9cmCd4e5/CvbPMHTAMzgxyUMejsniSNzrFhFI8NJAEcDHuUpejcWeU489gx6qqDVZ4vlYKkOUQjbBiCkNay/QJkj3GNaI7VWIxRmCBIWlc51slAxW/9yasZXFrkew5aNteWm+nb02lBVXYBGaqoq47sWw7tKl1noDxzPuGuBw83FfHs7ITrjw44fuKC6A7ipO2nBDlMEkl+Y+tBEgRLXXYZjWeo6w4bm/NsqxprU37kNafkAAxt3O94+8f444++hNfuq7n/oWN86p5jeK/Y2BYR2tOPHueLX7iJfS4PE4BB41BLIidJcsLMbMlgKIlnb0a01IpRTyQ56g7vfMMjdLq1oLV1jkaS6/NnDjXIoa5ziqIb9mGagEJJC63WkuTOdGu2leWiThoR6KrOqOqUDaWpwmq+oko5sVJS1CmlUxQmYdNPyfYmyL1stTaeVCHBiwmfQ+RymoQPud1u5DD+bWy/1a0DM1r8/UQZvumHt5qgFIn2TUBD9Lc2W1VutBTFMFTXq+tzgLRtMhLmZ2uu8PmOgOjwrF7YR111+NS6+PdnHzxEXWcMCkl4OqRsJBVLywMcXlANJxs8Xn7NOiCr2Goc3Y4mSTwf+uIV0pqNyG4orA4dWqOucg4c2hDZnBnLvgOb5Ehh9chDV9LzKdshKTU6Z2FhwoLv4LxisDnPKw4UrI0yzp4+zNJszWvfcDfOJRw4vEa3qwXNtilvet2D1JVILEWhbWtTznnHr77/FVjl6c2VvOz4QBLYssMVb76X82cO8/D9V+GdwtmENJX3qwibOhySdHoHiwH5iDI0d919DVXZZb5jOXZEBmAqnVI7OWTvf+QgaSpT2EZnVHWOsakcoFoE9yNSUdUZw1F/R8IjGnPyWqoqY+AQFLyWISwLzHvZbTupRfD/QTSbeFaTovGZti/uHiBpJ4UgSX+NnU5+4ps1kjH2NX4d29/h7x2e7/+haSFShXWRRShYuqHAmQ7JiGaiVo73nusx61K2t2dZmZdhlWYzy6QXJlpzlIJLG7Ncw3RIKB4wnzG6SdhPPXRCYk8q76dSCNKM59LF/RJjlWfRpRgr2nvjUR9d52yMOtx93xXUdU6hDIePX2RrKKjjhz93HR0EzarLTjP1/ZIbzlNhOas0ps75RDKkQNr7Fs/tpxd5JNmW9ykUURPlBHQoO2EjlPjJnE/IQxJ5QRmcS5hfGYgczOoiH9fyeVzVCa8NkaHROpP3MxTERmfMLw8lvutM9t2nlpnZgtWkxHvFJz75MozJOB+SdNnbLfqa//ftx+gkXhQhcPzCp66mVtLir6oOhYfVrR59EuZdxmjck9hb5Xyy8BSlJIZap2iTcmFbJoRfslSTonjNkTFpKgh4EWTUVsuU2spWs02srF3dlRw2AyS7Ym/7q2z7cdhSFdFBh3SDoi/bcD9tn9+z58aeM+TQttAXkP2NAjuLrSOL4mvl6M/UjMa9psVRFN0GZVpLdCOivIFrdJZ0nTfIYSN4bYXPtbxQUYW2sq5zlhcKrE35dDLiX/7m12CtYqZfcGl1H6512Hs8952fw3tFB5keNrXwnz7yZ28kQ/F5J6jZ6bDu74abTor8SNgBW1c5fR+kGoaiM6a1bKXweCrlKYsunz0j+z4vrvfx0LQApLJMqMsO6xf2UTjYtzxibdjhxEqJMRkPjqQ19om7TnDv2QU50MoO1iSMB7M4l7C4NOTAyjBsJely3bVnqZUjywwlhi984eYGDRwM5tjeXGhaKRbPpUsr/NkXTzTPRxuZ4BwG6Q2ZausESkDOhk7oz1Ty9yGBA+SAtglrSUlVJ3g82ioqLahh5aFy0maNB1e87UDVTYEBNAfgVFNzGoyq0GYzrcM2Wrxd+xBtBx0TWuUNWthCYSzTZDJWsSM1lbeJZvFseo8KraoakXdSKB69MMfd6YQK19xPggqaeDnHlCQ6CtAmI08lMXR41lVJnk9b6UXRFc7cRBC8l7/yAXmvRj0evzjHyAuisr250OyxtjZlPJqlrjvN+9rrGIqwI3rRd7i4PofHk4XnZ3TKaCSbXr7trXeg65zZfoXxijS1bI46/NGfvI7rbjjJuVNHOHjkkiSldcbG+lKD+lmTCDJSZxTKsq4MqVekueHkhXm0lvegWptne9DnqmtPC0ew6lCVU0UCF4h4SkE56dENk+kPPngFTnluvvEUdZUz0SmPnd6P1hmveOljjJxMxx9YLslSkWXSJqMsu1irmgInijjXiGyH1il98qkPmYSyzuh5oUHsz0QypjIJhYfCB6UGDyOr6HuZut0KPMnoM+1DM7bh2r4c/wamhY3b5dOWnQdmu4XnQuuuZCr5BDS82R2t8lZBH4XfKyzrSUUXxbjoMC4yzkxEHzBDMRz2hb8Wp+DHE/bNPJGDqxGaidY5P//JqwE4b+CaWx5rtBJNSG7i9piRcmzUKduqJs8N25sLbBrFQ8O43s+zvbHIhXFo5Vsp3obKSPxD+JQPPnaYSjmWAmIXVQyigPPBbtBEVb4polKkuwU0MkzGpDyeFnRI+JX3vo5SOcbbs4HznVIO+4JaKsdb33K3tIGNaqb94xKAKqCF/+k33yKv26SoRCaaH3nganIvaLZcL9OuhA5anzoU/bWT2JKS8NYFzyh0cCajPiMcoyDpNlGWbtdQVRlF2WEjqTA2afiElU7pZwJcDIucrk/ZGvYo6oTKKUoHR33OR7ItxqHQ30pEUeByfteOp7tjbFOwhISvvYigDPPvQiNyT7xvtXNZwfNte5zDL4NN+TY7RYlTFHOBt7ee1OhQAd9+epHNwdTx/vChfVS1aNi93PekyifKL4gbRkQECDIXnslY5AiWFsbNBaV1xkxPLqKffMkWf+vGNT5+SQ7WmRlpcackTTJZIBdl1ytO2BnZBaxzZnqSqGThQt63NGQ4mOWuu64PgwWCfHovMiQmHED7fJey7DZ8i6HS0q7ACtrogkajSTlwaB2HF6kar5iZLdjfdZxbXWQmc5zb7GGCILg1KUcWaw7PaxZQVGWXyVj2QheTHlluSBLXDI5AuOBMRqEMRw5uBmmDjEdPHSDv6Ka1XCOt5K+7/pIkbCYJgT5tpDXqOg+oYUpV5ezrOLaHfUmSlG3aCGWVUBrFhWRMoYWLVVrF+UFAWfAUXj5XuQ0Nz2TSEpIGGu1MkFVl8T01appQAg3H1ShHFQJPFGKP4q1tpFszbY20/RfYwS+ULStPbEvH93agpACYlPI6UxQay4ZVIs/RkmpQwEyvpqpybj42oMJwPzV1nXGuCq0oHEu+K+gBjt/+1PVUVYfhYJZ7zs03bWOQJPyGE1vo4L/Dgey/VYGXdNeDR6gq4fIa5fn0hiT1CdD1KSsLBTmKCWG7UdUhTeX99V7xqc/ewGA0Q+llY8+xA2OGdcLq+QPc8IoHOX/2UCOXMRr1GI9nOPnocYrJDFm4HobK8P/+ztvRynP2/isorNAhNteXWHvoqGyfCeLqVSnbHfIwnGZNygwimH768aNN2Xni+Koc/BNB4r/nOz/FpZEknMPhHPtzoWycWeuzPsqb96wM9AttMoyVYagbO4pKeSqdMhxPV77JdaModUJEvlfmamrl5DXgGSDcqzGeNe+YKEm2JqFoiX60O+Fz+AZx2c3HEkqEEf/FN7zty/levE2y6xCK9J7ITYxcxVhkpT7BKscgJI+1cmwo2bu7OepQ24QvpRMmaOZ9h25XU1bSprQelnyHSdDAgynncKKsFI9lp9kzvqEM7/nVbyRLXdMNmox7EksQbtrIy47oupJOxQBHifBAVaAOLXXER9dx1EicsCalS8ph1+Prvuau6ecWzob1RFDDHikPV3DAzzDn84ZiAIR92CsNr9forNne8a2vPsk+l5Hllhte/pBsvRrO4PH0fMJoMEtZ9AQND4CAw+MCCjce9TkTVokZnZF3RKLtwMF1ochUOXN96SDFQsWYrInhpbJ0U8+lzVksjsEkZ1vV1Drj9794nIlyjLxsOMpJmBQ5tUmoamnZR253VSeUOuHhSmg5Yy1Dco+NMiaBG17gKfFsqpJ7qNE4tpVmU1UNIJOFAjfGzR0Fe4i98WuiTEAKp8lhRAedCjQCYqcxoOfqhYcc7nEOge3tbR566CFOnz6Nc0+fuRfDnQEpIi9yCKomuIGgFCA8so1EY5RjhGeznO5K7qEoyrwhTtdh+lNWSIl+VVXlTeBrNA2NoC1F2WkOzbrqcPbiEkXR5bqXPMKnHjjMu97wqNxnaCUfdTNUpRwG8wqGo35YVO8bDcYbbnqMWRTXBtTMuYTzq0scOrBFVXabDQgmDJjoOmd5ZZsFlzGZ9BpHT1DCP0mrMBUZBZEzLpw9yLLvCndQZ4wHs9z2pnuZndF0ctnL6luB40ubHVYWCuZTT5pZQayqDmXR448/eSP7DmxiTMrmxqIQkrHcfc/VdH1Kb6YUNNCkvPoVDzMZ9eV7mza6bqvr8/LcgjBsGVr9GUlQ0c/QJhHR1MSztjkTNuHYgBw7CThOsa1qKif6V5VTfDwZUniZnhziGCSSTMbPPCZ+MZmC2NaV97FQgoBG37JqZ1Xa5qvENXZxGn73IRtRbqc8d3y00wStpvINflYquwOV2W1rYT/052tJSrPAf5wg7bx4lcTnJqsDOxw/fhGn5EAt65xTSdWQvW+xcw0P9Lbr1puW3ulQZIxHgl6My5SyytinFFpnzIcCqa466DpjvUpwVpCMjk8YKcdg0G+GIB48Ny9JXHifq6rDseMXsXg+//mbObx/zImj64zx5JnlS2fnmYQho2rcI1G+Sewurs+zvLLNQ6fkoO0hfraeVGysLtML6+hGgRPmvaKa9Lj15Q9z8tHjzZpFgDmfhXVtKYtexNM3NufpK4QjGd6bwXA2cNw63EFJWXTRdcbirEabjJuvWidRNKvBqjBlHP04ARlkw1HVKfYyorWlUQwSQ2kEcVn0nVDYeMZBxHrYSgK3Vc1Q6R2J347kLByeEFrIMZ6paSu4DAdnFfiBDU1CTakOMXFs+61Rjk9+LGmQl4kypAEdnyKHkVfrGSvNTGg1x6n9zVDYxYRT4xiNeyLorRPuSQqWXE6idiLxAIdcV4rHoPMHQql5BE2tp2hYWXUFOVaWeSeUFof40mA0QxEoJlrLeVBXHdYqua6WwpS0R/i7y67DlWRkHUE5DT60oy1pmE7OvGI1JIp9LxxZrTO6XrbO/NvPH2f90nIzrPe1ZomOT9nYWOSaHJLUcv7kEYzJuHR+PxUyMHf6zCHqKmdhYbxjC0rk3FZVh286OgnxP+NP/+T1fORjt/Kpz96MRYZPHrwwh3OCaIKce6ur+9AmY8GJluzCbE2pLDdffan5m2+6bh2Lpwu8ft6zkVSUdYY2EhMcoK2iqFMqk1AZxYWkpsZSOAFxHklKCjdNDNeU4aib5cF0m5HSbKlqR8fEhER7d6FhlGMY0OmpFuJOLmG8fSzqY9K4Gz2ssTuK+OfbXuycw+zJfjEcDvn1X/913ve+91HXNSsrK1RVxfr6Orfeeivf//3fzxve8IbL3rYY7ckn7tmLw+78WO+5gdf3bM+eZfvkxzNmnu8nsWd79iIwD18RreFnYk967v2jf/SPOHz4ML/+67/On/3Zn/Hf//t/5/d+7/f4yEc+wo/+6I/ywQ9+kN/+7d++7G2XDtodK5TabTuPbyZ1Lb6ZiKuwbCU1NY4LSc2a803FemXXh4ontBzLTmjJTtXVq7rTVCFWS1tjHHh/28OZpu1cV53At8hIM8tnkxFfuOtq6qrDYNCnT07PJ8LlwnHNwQlbgxlq5VjyaRjSyKiqDgr4nrfeJe1XkzGc5Kzs25JJxtD6rqoOk1A5rl7YxwmVUpShxaCk+jZaVitVVc45rZo2xO9/8TiHAoppgt5gXeeMi5zZnua0keplLgzKdFAsLIxJFQy256jKjiAmOuXqlZJzZw+h65y5uTHWCEn+qhOXpN1v00bzSteyDszoNKC0jo3tLjO9MBVoE4oylxYy0zVh2qQUVUpVp3yphFEtbcC1pGw4gLVT1AFUKEIRWCBoaYlMag+UZdJC56K5UHXGz7lUtkH92hVnREciclhHhLCFZkQivw4ITFuPM6KDGsf//I+Ggqx41fAdbUB5YCcfsW1WOSbITua4NaCHaJyNw0SfB3k9SsjxVUCtLl1aoUTazuMiR6Ea2aRD6c7J/LLK2B7M8B3HJ01LLLbhH7kwR+Hl76uy20zjap2zhmNSdCkmgpZnQKcju061kp3WhM+lF9YeqvBaf3fLsbI8xNiEAsfC0pBDfcNV85oDBzcYbs5z4PBaM6R0ZpQx3J7HhsGSeS8t3G1Vc/r0YfZ5oT4c71lZvdgv+JM/fgMba8vMzRYYI223u+87znHXY319CV3nzHjhgt1000mK8BGYIHfT75fUdcbWxgKPpUMeeuQYlzbmmetLq35xcYTzyGRyJa3kL52dp6wyahOGW8J9ViZpqAHR7isEAc98QmEVY52w4roUeEbBf2UFnuahdEypDCOlKdROjmvkWUWURLd8d7cfR7+K30fkMLaho+9G9Lz9f43j7/+vdfN3EYWJLb/2/cf72Q6T9AbHRqJZxXHRwoqfyoaVlbzfZZ2F7S4Zf+xHjX82ElbKUQYd1EJNW9qivEBDdfniyeWGU94NEk8JqunqlDg6iE8L2p4xCa/p6iXhii95QceXXYdz3vLoA1fi8CwgfjwOLX6LY8HnzPuIeAW9XJ2hAucw9Yql5UFAlXNWcsc+3+O9J+d5wysfYzycFV/emidJHZtJxbLrsBCmim3gbOugyxjPrm635rrrzkh8qDPuHKZcGmfC81MWozMeRcvgX0R0dYoxwtf9vmsGjE3Cxc0ZHJ6HHt8vHNfA/0yAz6cjPj6EITWlTppW8j6XU1hFZRSlVVRO8Ugq7e9B6Jp0fcIkoIYlchZXoVOSkTBRptGfjZaFgcAYp6OvpmEzVJt7LtqGO+k8lumAVaEMk1ZHJvpxO07v2bNrT5oc/sqv/Apvf/vbWVhYeMLvXvayl/HP//k/53u/93svf6fpVPIBplOdVZhSiu0Th2/W7xgcQwIXJamanbkZ4sSTMsMpqPAUpQSSuJexg0wPNnt+dUaHRDT1cGyNZe8nCIF/u5Bhj4unD3Gl7XNyIglRFQLUUBmKsoNTsDXoUOmUnk85m9TUdadpgXigPzdBm4ytIKpaFl1+9b799HoC9z96aj99L22TzcEs8x3HhY3p5KPB4ZzwPLxXrIWLw+iMB9Mxx9w0UdM658obH+f9I8ukylhNNCYIA2udc9WC5tTZfSzP1RSlJAR5x5BmjqWFgkkh68GiYG2CCpplwvXSQafws1+8mtF4hsFoJnCBYGmhpj9TS1vXKQbjLqMg9Fsq05CbtVWUdcZ2YjGhsjqdDJvPpvbwcBBBLsMhOcahPIxxQtpPplOVMRmbBOlUmHKwIjfFhsAVW27tQZT4920ejG39PvXJDn4iTA9gi2d23jf8xIxkB4m63ZJ7MjM45l3aSDNICzwk1EgRFdviRSmB/a7HZDAq94qqltuNQjtyYMMBgGVru8/GoEt/puZDp+ep6pzB9hwlltIqTqyUnEyizFDWHBy6lmlcbWQK1+AYKUueTfUbs1SGUWoc19h5ksTxbz96PQ7P2WTMeCJyU6VyDT+3qhMm4xlOP36UC+cOSrGhc95662msE1mSsujR99LCnSjNF8IgltY5pZGJUFkLCJtbc3R7lfDEnGJcpxxJYXl5QFl26PiE//yhl2BtykyQtdKBSqKDdMfmxiL7XY/3bBdsjyIfM6MoulgvyU1Zifj6Fn7Kw1KWT1LIHlirME7tOAh7KAofil0PjznLI+lADtOgO2jxDXfP4MPP3I77qUNx4Fq+Gf+vg2xKOzls+/V0kjNMe4aiJhZDMcEskecyNz+9j3jQxmujaOLw9JoZheQwQTFRhvVEs64MN6dTTuFgnDOYiD6pVZ4UuMrMPmGK9UJSUFYpZdlpJqtj4bdRZk18/nQyoqpy+j5jO/CUo0pEJzdUyrGSgNZS3NZ1Th1ew/Y4xyIajlrnPJgNmSjHkeOrDTda1zklUrDlJBz0GZ2gRGFxYQhPEt4E+PE3P8zDjxxD1+KDF7TEi2tUxkOPHKMsujzwwJWkqW0SwRyR8XI2mdJ+dIZVHucDlabqcN99V8oQic4ZKMNvZOe47sQmXS9gwwkvsmDNcJTOQxGeiZyYhzQUxzdduyr7jXVGUXVwwKvtHGtJhVGe0qimlTwTfLdyisKpprACkYSqlaUKUk6FsowSQ4VoXgrPXjXc7lrt/JzbE8jRj9tTy9Ffp4OCrcInfN8ML7ZyiOjPLxjz4N0z+3qh29N2zN75znf+pX72VGaU7AaNRP9oVk0TwvbuxTGajST8HM+fqQFboXIv8EwCilgqUVgv8JTVdJIwbiepa+E3VVY1O0+1ySicYjju8tBDV7CaVPytt9xLVeWY1nRXp2NIPNxdyAH9g9cMWXBCgK+qDlsbizgI4rwp57e6GCeDIINEc+bcChOlObA8FpHoOufIwU16ubz+jITMyxaWOPBS11mQUFBonVIow4982xeEh6NzrEn4xf/8NtmWYRSJD/xLJPicGuQ4rzi11WVtq08VSPbWJJy5MM+J46uN0K8O03O9Xk3fhz2xlaB/n9YaHaRvyko4ad7BeCLJtjYKY+TD6vnAodGpVKdWiPorLkPhRdC1FQRKhLAtn3OUjHGcSoeMEiPbI1TdEJsjghfJzG1/argpEUmJ1araqdoff9auWuPPo6THDn9lOigCUzRnFAXcmUqMGBwdnqhzCOCDVt1GoumRMWhtc5EDyjWJqMExLkRu5aZj24Bw6LSdJp6SnIqQeIJiVMiAwPlLc4yVoNTaZFTKsuHk4E4DIh0lWoRfl1Mpz3Dc4fzaLEaJyHZZdUWEXlkW+5rl8LpGiaEqu5xJC0lQVcVsvxTRdeUYbM/x0CTlqmMign31dae5/5GDDYn/E3efIM8M+xeE1woytVxiedutp0mRa+CtX3uvcFyrDovzNX98ZjYMiYhg74GFioFVTMY9jE3JUZxNSu750tUUniAZJQNaw/EMWqdsD/sMVM23uiUcKkx7JqxeWiRPaKY1S51wy7yRxzJJSOxqSeqcYmySBqW2yvH5bDsg3ZYxviloioAaxsRnoGoeSreCX03jX8MtJErPuB0HplZ2xyHatjZ62E7m2vfnWj7c9u+Y1DXFWuvQrZBkNN5fHoqhDimlsmwkFduJ5kvWUoZk8uHA96uMxPcZFCezcXOfKrjvPtelqtMdcbrCMkwMm95T1YIEfudMj6ru0A+C8THxM4FL2vUJHkVZyTVQ1xmjUGw9qCU5nShLXedsqpIuim5X4sk5peU6CINiGQlxjFEkuWxAuwV508pz7uxBjh3ZCNdNhw3ks33UGz69LhqjL7/1QeFwl12utHPkKK677gzDUZ+i7MggmplyDuU8yFiYl3FKowX4cMrT64luqtEpm4hGah650mF7lBQ0HWogz+R1bGzOiWh3naK1+O91K2XjOaUTlLA0igfTCeNQ1FRMi3QBa0KRrUSLc6Jk+CSK8BtcM+EuftsCgWI8bflm9Od2XG77cdvacXm3Rc7iC8eeGd/wK4Fz+KTJYVVVbG1tsbm5yfb2NltbW2xtbXHmzBlWV1ef8k71LmWP2LazSmRA2i3ntvhrrDa7iHyBQ7ZkjNFshcq9UJYi6osp+X6oLGWdTqUHTIZWlrWBtJVLpygCulFWGSkwLjKy1PL3b97iC3deR1F2OL3Ra9AhYxIZ7VeWyiTcd3IfVvlmsGVzcwGLZ/X8fso6ZzZ3PFB7NrfmKDBsjTvTZAQvyVfZwVohA3e8iM+mXnFpfZE5L3pTGseMT/mlhxZYdt1GgNvojHvvv5JDQXah15UgVVV5g5ikiMTHAE9Rp0GPUBCUC0XGAw8fbSaSx+MeCphMepJk65y6lmnjm7xsMZHvM7zylHXGmUuz8jkFFKcyCTPImqmqFrK68YL0jpVl6BQdElIUcQVdgedMOgmf/c5W77bSjJRmW1U7DrnoH+0WRURLLtfSNfgn/D8i15f7/WV9OCSNq+enWyrKViAU/5tqyF3Ook9vBwkeeUyHVpL0xucFkmSPa2nLX1ibxSjHAdcJKyJlgj4jkWGWMqdHRq3lPleLjDfO+x1bOwo8F4qw3k3nFKVM5FZhMleuBRE17vmUTVUxHHWZKEPuE3pdw2LqqZTjX37N49Q646idCbqQjtW1RSZFhwrH2sY8AyUbNB4/fYjRYBZtp2j0A16zPZjjgUt9rEmY8aInqJXlsVMHKfFonXL7HdcH3bWM1U25dm2gaDiXsH9lRIkkEjpoZE6U5Ypja9QxwQmv/9TFOWqdcXG9T60cL71qkysODWQ7T50xqXLWjBLEuxZSflUnFLW0ieU9lAS+8jRUiKmv1hQ4ukhrXYfYVioZNhoHdCxO2Ed/iAh4jA1ulx9f7mC8XPESk8fdf9MukOK/o104L9cj0Phy2kImJ8rsSDZTr5rp0QIjPqI0F5NS0GllSBEJmUnznnn2uy5qF5qulaPUMqw2fR0yuZpAQxtyjsaHt5MaHRLyouwwKbss+4w156nChhTvFYbYHZBYawnIcWhbnz9ziAlaNnPodAcCtYajg2LRdSiVZTzpoXWK8oofu2mb+bkJn733aIO8n0sLDI61pBaUreqwvbmAdQm1zrna5ywi8k55ZpqOljXiBcYkLHop4j7w6HKT4F7rOvR8xvb2LFo5tJGkdxKoUd14PhhBIydlhsYzLFMSD9ujniRnYaBLK5ku17EI8VD5KPfjKSPQggutY4NTnrPJpHlvhkozVJoNJSs24wBeVHuwTIfzACaYpuOy2y+bqePW73bH36eKx5dLLp9Pi5zDF7OUzZMOpPzmb/4m//W//ldWV1f5ru/6rubnc3NzvOMd73jKO91c25lztp1lBzemlRDG9kG8yKNIpjzJhI3wfRUgcpLIuYEZlTKp8qayHw/6pF62E5AglWkVOXsZhYc/qSu+azDD/HzBsMhYmMs45zwmdcL1mnSBUi4InzMK66TKKse6hLXNPgMcH7j9aq5aLilNQqkMhw5t8PpT1zDT1+jKcd+ZJawychjqlDsKxUu7nlmbc4mClB69bs08STjYZOqrxHLcyyYIayWAHDuywUuWB3zmU1dzbruDUwXGJmEXc8YAzxGkEtyqE7ROKYsedZ1xw/6Cfcsjqjrj0sY8nVwm9coqY6hGaN1DG5nKvF9VnNApSZiG1niqOqHfsTgNxim0A42iR8oEQxGmkGsPOMVC4MgNlWlaqlYJShiDQKHCLtSkJvGKgapFtkLZRtcvtiVsaP9a5SAUF1rtTNb+unyUJ7udwfMb/6UDyjEOCWni1Y4g5ZTHXKZH0A6Eo7AdJQ8H8xPa2OHvto3C+YSLFujAhaTioJshSaSKm/M5Y+WCvp4USFnqWU6l1T8cd+l1TXOfR/qGe0rTyA6pBKwSEfahMmjXYVgnTDIjn0+gN2wlNRc2VkSWJXXM9Avq+gDvuHGd/+ORgNrHBFMZLm330VicS1iYKzh/9iBXHBpR15G+AKNJl3PKYGzKsuugdbUDTa51Tq8j18mH7l+hRlYF2jBBrE1KnimWlKDq1iVoRAKoDvt6Ew9l1UHhObRUUtUZszOG3igVHUgtyXAZxMl7yL+dF97VhSrhUCqHqEukM6G9HJ5p63Mz4TAtgy9PWmhgRA1LZZudsQ2SEoqhrEVlEBTwib74VIfo5X52udvFCV2AFM+v/ZecuVb8jdamuLR/Fs+v2KURCohuWtCzPueSMvS8tHNjEnw6GdMNqHM8CUZKU9p+w7eO99twLyvZeb5WZCxU0vnZVjUamaCvqoyH12YolAmdjpRSVZR1t7U/errOraxyEi+akpNJDxdQ+1rnpKFbVeN4PJ1wje2jw2fpvfDXrZJiy5qElVlBlNPUcToZCZ9dGV6NKDrsO7jBuXMHSBPHJo4rM8XjJ48JNWLYJfHSucq8oqxzLBZjEl53oOAjW8LzPqM0fZ8xN1twSaWUVZcFn/HYuUUyLyhuWcnGkiyVRQIDHC9d0DAArWWyfgqcaDbqLiaTLkUZgBaQRLNWjtRH6Rmas3Pey3W9qgqWfZeBqhvRamCH/IxMH0+TnErJysF2fPzrxOQn8/eMF1ZC9ZUgR/NM7EmRw3e+8538xV/8BT/1Uz/FX/zFXzRf733ve/mBH/iBp7zTucXLw8WR69K2tgRDdECNnWrWhQCymdS4sGpqbKWlOFA1Wjk2VU1hpvp3cfikcEogejxFqEYnATm83vb5+Lao/l91ZJvhuMONvWmwGhVBwgQYW8Xt6Uh0tXTGpOjQyaRFdM5bQdUMrHjRY1vF0utIMnR8uQz7oOV2g7A26wYz1yQ/q+tzrCqDsVKpL7uceZ+zpTRnzh4IC89lA8H7PnFjw77rIeigQxATBQzLlA6KTtxkYuRgNVaSglpnzM7UGCNabsamLPquPIYRBPBsMsE6Wa5eaQkktU2CrptHO/Aoaq+mA0WBu1IgQ0PnkooKGW7okTWUgVK5RiIjEq0nyjQE5zoOrmCbQJS2EsOIQDf+9NdMCJ/O4v1+/bfshMHbiE0kWMcW227TQQ7HqCC1EzTqdEDfQAJ1PCQzJT67LwmIYFJQIqjhnM/ZUhW5VxSVvH8uBMuxTviLiZUBi3A4GGBQpaLlWAlSVtcpw0lHUBflqIziqn3SKnbKszGcaoUaF3XjHBfOHmJ72JfWn5eE5sDKkHEhYshDnbCVaMoq496TK5JkhkRPmwwDjIucCpHoeOXMtE0IMvTym4/Oc+elGeo647ZrNrnt+IjrOtJCr3VGUeakqedNLzknaLYXiZVSWcqqy8FERJzj63dehNU7mSMNB+tokomkRyDja2gI+bWHS8rIwFT4bKMuayTl7+Y8lUp0SktlmSD6d7HAkVWJO1vCbemk3Qfos+HHu+/zm77FNL7aII241vW0s+hZV2Xo+ky/4p7zmOQOEs0lLBPkWh2zk1MZbag0lRPEq5HRCQnKeuj8ODwPEzcnycYjGx5rEor7sbK85tiw+ZtxIbI3Lgx9FUgiVeuMjIRXqA6DkfDB+z5rkGUTksmJMozC4JDFYa3IppXK8b6HV5gUXVYWCmqdUtcpGQlHnXRQai8JQjHp8diFeao643RasmYUZy7MS4dGC+Kvgy5jWaUBGUw5enhTzr1Wa3w86QnH0qRcSjRLs/V00C5QHso6ozLy/m2NZTitCDSkiIDrgAxGSZgKTxWQQkEBIwXCNRzo6A8AOSnrqhRfDnu/q8BnjUMjsLPIiH71bMfkPXtu7Gk5h9/zPd/DL/3SL/Ev/sW/AODkyZN86EMfesrb9PtP/rvdCue7q8gEtUPMOP5uMxHxzbPJmFH4u1JZxmis8hRu2h7JOzqgTXKwnklLxmVISOqUjoIVpej5hI3BDOtbs2yPcwa1DJ44vCRCavoGvdHNsREGKYoy5+T6DIrpvt/VRFPhubS+wOuXDOsDEe3WRraA1FomInMvvI+/des5QNotC3MVGt+0la8h528simCujivpaiHRXwwJ9MALd0/rRLQeTUYPRTd13DxnmclkCtAEnop1QpLWOqXXqynqLLQ0UmosVZ02ulddUgYTCUBFlUrrXifCu8JjvKjmV15oAT1SSg81O6fbVpWhpL3H2Dc7kUFaWxkJQ2RlU9wjGzl4zbSmipNyT98O/nLbra90Ow7v9mNrNdVvvJxFzcC45WU6OPDEVrRTNJs1Ism8Vo4BTpC3EKRBpmdTEk5ViolWnHGOg074opMyI0MQgYWu8DXLOuxI1SlaK8o6Fb4rsDmUwyXxiiyZbinYNIpRmLjdHvTxDobjXvMeWJeETRka7aUFW1Y5S33D4uKo8dnYIjRGiah20WF2xlCHJO6G684ConN6rBOuAZ1y39kFvuUb72BtY447zs9hnayx02H9onMJfS+rvMoq43En10UVlAp0KHQmlQgXVzrlkSKl1koSQKeo8YGUL+jlmXRM6WUwCqRjYVUYMNn1ecWJzS1VBZRQdsaPW3qbMC0g4r9jS/m59mWrXOPL7cfdMTiodiKHIxUG0Foany6gplYJF7HAME4sYxX1RacKFDAVwc68okCQ2g5JM6SglWPepw3idbXPmZQZ8z5nFhmccojo+CaekTJkqcSrBMWkEh3WxCtOpCGZV1aQSGU5aR2jSYe+z8m9avwOIA/+k6KmXEuXYKx0btYTTa0zzlyaQ5sEYxOWfZfDrsctZp5NHGXZZbA9TzcVLViN40vpmJXFipMX57hoJQ5UWq5LYxMqJK4PR30ZINIZX9+Vae8yrnmtU65zHWqTNp9HVcsqu6JO2KpDFyK0iatQfVdOMdRpKESmJrvCHUWIpYUSCkSN/DtSG6qGB+3YSipKZaZT8UzpDLv53Y0PfbUkcP7F31Z+2uTwn/2zf0ae59xxxx0AHD58mF/8xV/8Kz3IbpmQBNUcqPH/zdaKGAxoTYUqaTHYELC21HQqtVaO3CeMW5X9ZDwjSv/ATByYCJyYiVbMpIJuPZyO2BxnTMqUJIHzThbXWzyTICmTkjDCM5eJ7MG4yCnrjAOzphmsKOqE88mE67tw77l5yiplQydySIfkLU7zfueRijULH75L9otW2MBLQQ4u5bhm/4Q/24a+TxkVMi0tAqayWm3Op01VWOmUM8mEuk55y00XuP7KdWa6hiwRHtfq+gJVEGD9owtdKp3y4OMrkiwo2/wuThrXTjHvwlSnVQ05fxwSlxKD9rI7tqQl5hsTQ+WEg4XjUlLu4FsBzUEKUdRXJD66Pm0SrTiA0kacX4jWnva8nD1Zuy6iNRbXHM4lhnGo9kunAjIj/jBQWjaqIK3WiZXJyn2JTBtqPGPlGJUpkzpl3neweB6eCNJRVJL0F3XCqE6ptfBdS6eobNLI5OiAyMvz8RgESRiMZrAuYVzkzUDFXY/ta7hKG4i8xep2l/1LE4pJl8GoJ4LYgWPW7VgSBNn8/Ib49IzPuHhxhVI5vnuuw4FFaQUXZc5ix3L+zGGsU9yeDcJQSiYIZpD0yHzC9851MTblS9l2kLRKmv+XJqGoUyZoaq04l9RUNqF2NGLVBVPfzX3SXFexQHHE6ePQEm5xRyumLeRNVeGUZ0T9xNZbiw8Yxaufa3uqayj+rl3ktOWfJqFgiMLa0RIUF5IJ26puprK3lJZi4zItwBJPYZPmvWkPxZRhwG0VWUG4lpR0fEKFJDDrZca5pGJTVZy6uCBJpheuaLTtQP0Zhb3tACPlqOokoJ5SKCUhGUxQHHOCKjYrOkc9dKAdbCQVVZ3xQEVTXOU+YcGnrCWaG7seaxX9fsF6lQYJGbl252Zl29ahFDrIkIiItMu5UpuE85dEaL7WKfuXC5n+rVMmoZh5VNVUdcKVodVbmYTCSVz+UiL3PwodsygLtuodIydo/EAJXSTxKmyccpRMxf8lWYwDKHHN6XSQqolV4XrYzXN9Njs3Xwn2Vb8h5dSpU/zoj/4oWSZBvtfr4b8SXtme7dme7dme7dme7dmX2Twv/g0pT5scdjodyrJEBU2CU6dO0el0nvI2q6tP/sJjOyGSmtsSDO0Jvnb1XWObdlfiFRtJ3bRoHJ6jrkfRQnAmoz5WyXcHnLTCBgE53HKKbaPYxOEVfIAhW3XKRCt6of1ZY5k41dz/EIf10uYdF6LpNSpFSmPJZRRGCXLgoJPAg4VQng2eTu4wiEhsUacszBUc63gqT9NuLeuUr1uUxwA4engTgNeqHqNSkJ6qzrjr7DweIV1rZHCnqhOpcHVKVXdY25inrFNOl4JUvndT9hmfG3TIEORmbsbyeJngAW2UoEt1ShGQQ5D2RO2Ej5WiyAmSB0oQpYi2OKDvc4qA4pZh6KRSos03CbuLI+JQhDYFCA9JtLJs04KN0hsl9gmcrefD/sO/y5/y9081RVcrt+M1RYvvRUTh4l7RsXJMcFzyUtnnpFT4hpc463MeSscUXqQ3Bk6kKPaRsuBTPu0qagdLrkulxO9mfaAHWMXEJFRO0OASy4b3jIzimOsLDy74X4msG3tpIissiyoTTlhQDOj6lDUn8+e1slxKhAVbWGkbxxV2VS08LY8Q/R0wKTOR3alTeqScXZ0XBLxOeHhNUEHrElarlM2tOWZnNK81C42A7z3n54QXVmfiO1qoFaeSIVe5LlXYXXuhyNBOJFYSBCHVyB7vAtgk0hw8NUKJyEga9FuraftfWm87dQBN4DuCtNQnSjdr7aI8TIa6LAer3cp9Lu3pfPlyQyqNKHbs6qhA/QnIaqlMgxyWQR9vQ5WNRFmc8jZK+G4jNx1cmfM5GbK6sbAqxADxoyJwOGOXYdU7+l72JX++Fh3PBIlP8bQ51ZaKqgRZPhpQt3H8fGr5uUIFPmPQ9gv0l9ok1CaVgRSks/LKBUttA00hTGg/kg45emCMtbJ3eR3XoJVzXqbrz1lpqwtSKFPFka5U1SkXxgK4VDptOLiVFrS/1hJ3KyNdsZykaR0XVoX32rOGcN/btIcoTTMKUm8NBas9cYxrUO/oj5qpT5dh/3F793fbvpoRw68We9rk8Md+7Mf4kR/5Ec6fP8+P//iP8653vYuf/MmffMrb5E8Rg8yug7KtyG/xTatxt4xJ+4DdDEHAhYAe92Q2unY2JfUS6B9PR4yUaaQu1pVhhOz7HSPJi/aSDJ1NNDWWGTLWAjeuVo5RYliZE87jSItky7lAq1kgYRIOTeEywZrSzIeINZiEDQI6YaIVX3r0AIVRIjuDDCtUgT9SW8Wsz9janmWiLK+4blX2D1sh1x+ZMZzxluuYtvcqm7CpSrRJGQx7rA+7bE4yHk1KJmXGgs+prTRwvm5RWjZllXB/Ks2iyiRMlKYyQsovvfAgayeBvXSQeCHvV02rLEgghNZoRtIkhVHOY87nzUBPFH8GaaPH5z5BNy0OE7ku4TCNLeXn6yCNdujwUwfBp2qtRG7WDlFYNdW6a2uEgQzoRC5Qc/8hCYktOJkYlUPNA0McIxynkpLVpKRyqjlUl1GsuEyS/vBZOqZyFkNlqUFEgL0UA/FzUsD1RweiI1pL8WAtDe9Rhceucc1A0UXvmOlpRpMeF4N4fKVTFnxGraftxJ5PMFYx5zssz4chG51yxlvKKkGbhKsWa2qdMRh3GeCE8qATPpcORU+zitdMwqTM6JGKZFR4DamC7VDcdEgpQ3FYedltfTYVykOJtNpqJeM9w/D+R/Fn2TnsdsQXoJnAjwLlMSFstu7sasFFX3k+7el8eXdcjooSbY1QabOLD9ShFRmHyUpagwoh/lXh/gaqolSOcUO1iAMkws8sWs+j8MKxjXzNjIQH0hHzPmXRTYcNU5RwnUMCdCMi/tzzqdAHlOWWlZLVImPRd8MQVsKS78pZgyRMm6rGBJ5vVUsSWGCY87nQE6qEwihqK5zrCs+qmmBswnDcZXNzgeUg5xXPqq3BDLfMG+53NnC6JSnU4Y0pdcKBnmkec2NbNsBsFaLnW9lEiv8wIAVy/cZi5oSdYawMOiTpRYwv0BTtuuXHlXLM+kyGT5T8rlBGEuAgLydFuWm40fHMbcflPZua88/s64VuT5scfs3XfA3/7t/9O/7Vv/pXfPu3fzu/8zu/w+tf//qnvM3y8tMfqLv/HXW5QA7E9mqdyEeM/x62RDijyHDZct7VtUWRSFGGs8mIQpkmKD2UilaaVZ4ZMg66GcYI9yjxNGufHgvJU5s8rpWjcorSBUkNPAtKLt4emegpIqLBnURucybwCKugAZgoz9Aphky5lbVNeGyrS+UUR22PL55aAmBze47S0VSt62XGMLF8PplQxaEFJ4hbqRM+tNahMoqTVqYoKyNJWx1i/saoI4LgNpH1SMpI4hATW2imMgcBVamQSn+MTLwlXmFBJIVUe4pR0L5CTQWAo4ZZ3DACYcIU0/xeI5/7MAgOPx9DJ09l3/29l59E/stY1ADbrY8Yk8KyhUY55Zv3rmSKrle45uAFmPMZJY6x0sLhCjqfw0TjQ+I453M0jk+nQzmAvUyRD/CMvKDWfUQuZBASowTF2NEkRCnw0dMLchhaEeOu7TRcHEqn0+Y9L5OY64mm0hnWJpyxXvi2OuE1S1ompxFf9ngqK5yxLBOe73vZ5iBp4+sfH4gG4WODnGWSsO5LuGy1UxQmHJ5GNDdfbQ7Imk0nnOHKwx/l5ym9PL+JhwWfMwk+djYZsx7iRvxa9B3Wk0oSQabDI1Fsv2198jC565iE7TXt1Y7RXih+DE/vy7vj8u7VZvFrGIrzuHmlxkqHQDm2VLWjsI8FZRmKwgk7OYsGmVie4CTOJDZ0ISxrgVcXQYCLSU1OwpxPqJBCJ/KeExQDLxPOVUAiLaL1N8SzGHi4hY1bjhxdgtJD+NwGSjiplZFBpA7y78Ikot8aCq8yJFaDcZdJmfLo2UWJiSYJShIhhhnF1YjeYuRuF6F4KWzCJ8tQ4JuEB0r5+bYVhY3SKh5PR2gvhViKoiJex1KIbKoKB038BRk8qUPRHlUh5OdWrr+Q9FfYZilF3Ur+4jBRWyPzq51b+GT2Vc85vP322+l2u9x2220MBgPe/e53c/bs2S/7E2k7X6xaTCuwtKc924hMHaq/SQthytLpsEojYBwcfS5UTxuq4qVmgQvJpBEDdYpQhYkmnVMyTaeV47GtLqlXlAj6sOUlUBZephtnfd6Q3IdKM7SR9CxJUJTLsCG5GqnpFK8NAaD2MIPiYVUz6zM+e6HPTOKbVqAOb1GBwYf/ahemeY1iqCyFU0yUa9oQJZbay2MvzBhJYFsiv5VHgqAnVKHS+u0FWYgmWQyISoboywlhvdW2QNoTtXJNNRr3cO7QM8M0SdPuAPRisyjW3dZjbMs5tQcTNLI6sFCW7SSuXJsmJrGQOOA6Yf90xVjZIIExFRReU4aDridSFsqyjGrQhgLHurJMmE5Yj5Ul9XFvtCBiINtrUqRYGlvZrvAhWza/72a+aQ+CBJLMy9DKuMi5JpdDrzKysaeberqIhMyptKC0ihkEGayxdEJLt3IK7aTtN5zkLKaeM0pTezhdKxa97IUdWcVWojlnRWrquOuIvA1SxE2Q9W+rWPo+o8TTDUlIpaabGyIVogrvR8+ngdLgGorJ1J+nPjpQVaNhWNLqWij3ojlM24WaVraZXG0LZUcKUJSgMkHyJFInqpaPl2o6GQsiLVMqK7qyLTS9YLo1SIf4cqWVwZFKWZGEIVKOBCVLUJxJaqySoYoyvP3bOuFcIo84UDWlgy5Z8zzSQGfpeVnnWFqJjwahc1ROcdaC9lA7WdAQqR5R0eH33QCLFOqxtW5sRBqlpV3FpDA8r9LBfi/avJVTrCWCPs8oueaqgJ4WHg4lYb807eEoea/Gicj4NEm4clxMqpAAuuaMqZEBwarVEStDly4WnsATY9WLwI+fDfN+b1qZn/mZn2FmZob777+f//Sf/hNHjx7lp37qp56VJzNF6Ha2cKLeWPxdWyYiJocFO6tiqxyrSdkgIXEdX6z8LiQTLiWaOZ+Hdp5UfLUSeZVDrrtDguLhpGpEjCd4LDIRNkEuzHi41eGCXMeRe6lMNS5IvQic3EUO5RhcJ1bRUZJYboeL8zrX4cP5JYwXqY3KKRaykBKq+Bq9rM5D2mkWx0zimfcyVWc9fEOnI0kpsDXJqL20bV7hZ5goQ40c6o0EDRJ4oi5WiYiCD5WhUCZskPHTINgk37bZntHWzdpWtSSoRJ6dbZC0y6nmv5jsyaatm41BrZ8LOqsZBV6QDYl4ETibRUBmDDR7e8fKMlSCGMag/0g6wirxkSXf4XPpKCT58rn68LluqQqFIvMJa+Ew0cEfMxLWleFQKsl8ghRE19mZZhLVWGnrpeH66nrh4OapY3WYc0EHfmNAyWurmPNpc4DWHl5rFhtKxU1mLhywUBq5Rksjielxn6OBh1MpizbD9Vdg+HS2LqLVwO3aNj5cE9qWypKTsKlkn65upGnkfY7IYKVsswUiFjlRViu2j9tWM10V9tWAsMTXtpvmEfnBkRYUE40GOQwdgyS0kCet2DAI8kzrYSOSxbOdaEbK7VA5iMXknE8DxUX8OcZcG5L4kTIN97EICZzxsJqU6MAprJBrIE7mjsP1BZLEFV6KdI+gkLWD1aSmAjSw33Ub/mntYE0n3GDm5Tk51aCoZZUyCfuPTSji29SQR1XN0TScDY4mZpZ+mgiCIIUbbrpJp8KjgopBh5TUyzXccAWx5GFfexQuF51DWYsZ/y76dK3sji0nL7TOzQvZvuqRwyzLUErxgQ98gL/9t/8273znOxmPx095m8H2M3tSlwuy7YomJhpR8mSo6h3aWkUppMeCOMRCc2/SrpNA8WC6LULDgS/nkETwCjdH7lUjrWLxXEgKDtteaK+KXEsZ/m2UQ3lBZiolkhgJAZELCEZMvm73dRiembbwvpSUbHpBd84lpewGBc4moyapLJ3o3zkg9YpIw45BpPISYConq+tmvWyCeayUADrGs2VVo0X4qWQk3BamRHyNb4K5JCGuWQe2nlRsJnXgs/mwmH2a4FYqIiwGHXhYTk0PC90EJcfuzSYvZHv/n1x+b/Jfxp4swLb5W9Fs8JOx0lQhEcm8oBqTcIiOlaZWjrVExIkHoZ0ZhYn7PmPFdxkoTe4TrrUzsuYtoL61cnR8Ej5XSwZsJZotVVEoQ4HQNzISDvis4e9Z4AKWBTXlJG6bacE270VzbqgMj252qb0gL4UTDusAx8QqTmRSmESU8kjXMdYJOQk5StBpYGQVsy5tqA41gmpXWLo+IUMQI60crzIrlHhJHJUkhTa0y1IUp9IxGsdQGaqQUNcI2hLR1ciDjQnfDn5dSCLjoRqlbCKSFhHIr4TD9Jn48pNZo3nHznVqzdBV672sgt/FZHpbCSOxUq5JGqM8UOZFPDryDnWIfdtJjVe+QdOjX0fRbqM8a6qgCF0LhaDBAyUt6TJcZ1ZNqUK1cs3qwBofWtaCdK/iuMZ1m/i9qWpGkQ4SqvSrk5TtUPA4Jdd3odNGPxOgAOHWhng5TMRjNI4KudatEu7gSGkKplqvjyWlJN7h/Yu0h7iWNCOZctCDr5qwMAICiBKKncj/buhATIv1Pduztj1tcjg7O8u73/1u/vAP/5DbbrsNay3GPDV3ZTL58kKm7UO03aaLVesk7ORt/j4cTjEQ1cqx1Zpkq5sLwwQxW9u0rqXFkLCdhIEITNMeXSChCAmTENhVk0DZELDGSiYbzyUVJVYOI+UaFO6hdBuNBAoTLtgMSdpqhLMzUYbHkoo5n7OGZRgCyEWdcNh1duwqjoM2sZ09k0qCt89nTPA8khbUeE4lZUNWrvFcafvSvmjaZ9MWm4/oS2y5qWmAjwhTRUAOQ0spBvXYrpiEg7NUZgeZ+fmePv6r2l13fvkP1HYLsvlZeK9GYcNG5P+UrWRFfibvtW0OWt+Q6bukbKiKg07I7YOIRrR4dVuJoQzotkKxkVRNInUpqeVQ9sIx3Qzl7TAclCM/pQc8HFp1lini6PGcx1KEYmxCW/sS5mcMBbKCLR6a2oVrFc9GUlOHQqZUjtPOhsM6aC4qx3YQm490hwRBxQ+4DnMuI4omVwgfeb/rCa0jvF8VU3+O16wOhc6GKptEUVDDaTt/N3LY5kp/pdiX25fbr333VGuj39mK1duhCIlmcHR8Km3gEKujwkHGdODHhthZhs8pJkqxPRofN6JrVjmGytIjY8R0qt6E+4lJqSCJdsfzbov4z/iEDoo1bIh5PmwVEdrRA9Tif57GJ+Pr2jaq6TCB/F4RAYeAtIaEsMSzHAZtmnjeJHueDqnQhIK/xhWN8eyKrwOEU1iE2BuvhdiiryJaiHTfKmWbLt1XQnHzQrOveuTwF37hF+h0Ovzsz/4sBw4c4OLFi/zwD//wU97m8JGvgFe+Z3v2l7Cf+On66f9oz/bsK8D2fHnP9uzLZc+Mb/iVwDnMnu4PDhw4wA/90A813x89epS3v/3tz+ZzeoLt5rnsqHKUQ3uHayGHZWtFkiHyEnW4r6ksA8CK61JhSZQ0aq2Sdl2cxJTJL6nsziXT4KpQsgov3F/kMmocy66LU54eaUMOLsP9lIisTpwkE16ZpVDyvUzQySTaIddnPalZdh0mODooNgIp3EFYORf0A0ObYdMottKaI+SMcFxQEw7QpUfKqtLNQMESSfO8tpNqioAGBOBcWjXjBhYne0njIEVoK+s21yiQ0NtV+HTTxPTzer6laV4I9mSr+Jpvmz3Uhp7PdnATx8o0OnNDpen5lG1VE9cTTpRhLanp+ZRH0lHThvLhbi2eLgEdDKhD1Hi7lFTNANFGotFetATPJzV9n1CimqnzEksv+KwOg1sjDLMqY+QVKySiJQoME01lM3zgi82SU+JZqxLS8HrXEx1QFc9EOYaJxvqMNGyAccCy66CV/D71QrQXCkUqHF4F/cC3qpUjC6vRJspw0M2gW/w4mLY8LVPdtw4isSTIlQM1XYO4+zP7akdbdvhxiA1xECS+XzFmRKuYXv8JKiB6WTPwU2KafeWZT0iCvm4VEN4yDGHpEGtjPIl8vkpZZr1Mko+QbVrRBKGcDrvIcMlUJgoEOUzC61pLNAsu41xast91ZOBKaTIvHFurPBMvCGCdOKoAB2Uk3JVOOGF7zautES74uaRqWr+FpUEz+wGniUN/G0rvOFOidJLGoZQMBSovyGzNVKuwjYSq1uua7sWebr7ZQwz/+ubhRf/OPW1y+P73v59//a//Nevr63jv8d6jlOILX/jCc/H8ntYMccXP9EKIJPep2LDFhQu3Qg7XsdIs+x6Vkt21kf9nkD3OPZ8GHTSDoRP2PU8P2WblWGhTxemw2KJa9B06Pm3aKFWE+cNaI6tEFqbjZX3ZltLM+IwF3+Gg7VIoi04dj6ZDbmKJxKWsJ9OVfZWSKcyYOERtwW1k6rVCyPevMiuMA0dlIzEshdV4ZWhrV8qHFrwEWg8s+I5MZrdaQ3N+uudUeDImkPWlJVkTksPA5YpTgF9Jbbfn2xouZog6UYA5WoZioOodsio1NgwJuYaYv65KDvs+m6F9Fg8DoNE8c4pGn8+EA2jB51xSExbpkoeiJfGSXM36LluB4yiyLY5uSMS6gcZBOHgrfDPp7gP3qcAxqYTzteQ6jJXFe89MHPJShm7gQ9Y4Mi8ajY1ER+CddbwcjLrFjZ0oGT5bdB1KJbyvqA05DEWhDavfil3JAtDscu+QNiR9vHweqVdfcTzZ58OaJGMXZSLGkFikJ0o1f1uGlZCx5Qkie1WpKCLOjoE3HahAw5A4tfevO3wzoCF6uRLPIi8XaGgyHWSKOGmeu2PBd5qd5NEmyjKr0vAcwSnHBE1HpVTKco3rshkoObHQjknZUGlqujhEFLtWjtmQUGqEox6hhloJlUm0eeXx40BOjSVF+JeVktiOF3F6r2h0IrWfTntHfx6qekqNUI7Sm4ZfuFekP3P7StAqfCb2tMnhz//8z/Mf/+N/5Nprr/1L32lVPqPn9Fc22VM7tcIkkE115CyeJCIegasR0b5NVTLn86DtJuEiDxqAUc7GegkvkVeToEAZfDhggaaKtfhmqjkjYZ/rsplWlMrikYRwlBj2u51bZiZhqtLgOELKp5OxTJ2pSPQW7cLDrs8GWbPzMgG6XgSAI5K4ELYC1LiGvL2WlByxM81h64AJpkk+IhG/7e/tAGLVlCsTSdwxcIME2LjVJibrL4bK9JGHnhv43+B3DDsADXIVfw/ib/E9d3gRG/aBNxr4n7nvodXUV9s8rxmfNdwjjwxNtZF0oxzeCwKZhdZHnPqd91lzGPdaGmqVksIHxLd7LqH2UVNNfEMrzzmdYJSl71O08hgvQtqRXL/g86ao0Wo6uR8P7GvtDA+lE0CS3IgQ2nAYX+ln2Wpdj/JeTnXdBOWeottGtblqUxQ1DlY0heeLwI/hufHl3ah4Gyk0ylH76fclUzmvmETKZyXoYaoUSdzao2RpQRJ8MUPJppFW98IhU/o5CRUST88lE15iljiZDkL3wzY8R82UR93seVYRIRZR9IlyrLhOo5BhlGPBdXDkjBAOYscnTVHk8FxSE26yyw0vvevToGgh+poJioOux3q4LkvlUERtWHk9kXdZKUfPK3nuYeAvUTSvNycI2O9OxpXDtTjCMZbsFet79pe1p00O9+3b91dKDAE2N5/bfnr7YAUYWgUZDaomCZvYRGlp1SlH4qBM5CLMSMgCuhgHAUBkXqL2oVXTQGK8TLjF1gQQDhJP6mV5+qKPa8wEUbShuo0T0CCohUxNGvo+xQPnlGGkNMu+Gyb2HJVSHHBdziY1dQhcGkeikqYynWAYqpR5nzNWNlTgigoXEs1pwmfDoRfbPo0chZoegnGFlUOGCCL5WzMVvwZJZqpAto7tCnzyojhQf/93nnrl2JfT4vu1I0n0O4d44qRt/DcAKpW1hgE9KIL4eETC2q2znOlnXgVkLCZnMaEqlaXvMrZUvQN9i7+P99XenhFbWILuyEHUTFCGwaxuoGEccR1Oq4olcu7MhvR9Rt+nQc5DfDNezbVyrCYFB91MmKiXpCFapSyZl6Qu9wqX7Fx3F311ogyqlVDK85/6cERW2gUO8KJJDOG59eVopdqZDLbjyzRO7BzMiqLMpYdESZFZETbRKEuBoeMTQfHC4Eq0IbXI3QQKgVGenGmSWSNt2wYt9iLNFIv8qMvoQiFfYznsu5xTVYM0zpAxwbARfGwzrRoEtO8zJoluCvXUKw75fhD/n077jxKDDgVW7DiBFDMDVbGoOuF38p5lJKLfqUzzWozykgAq3yTdsWCP10mcwO/6tNFd3bMvj3me2zznubanTQ5f+tKX8o//8T/mrW99646dym9729ue9DbLy561C1+eJ/iXtbbTN5OQrQs9wunxIjYBOWgqRix1g9JMD42odVYhbbvGFKwlBQedXPiJ38mviTpaOnAI2609rRyrSdU8Vq0k+dSqwz7XpecTjro+m0ncBCOBddGnFEpeT4oEwLjVRePYSCr6LguyCCJv4EOgrJQNSGF4jkqStyaxDYlf5A9GZAXiDtXpGrzmPWwO2enasPi9eZG0Lb7rb+rn/FDdyeXaKb7cRnPr0PaNh0FcOymtZqEs1Gq63UN83TRJPtB81p3QRrZ4JmiGSu9A3bqk5GRNcRTXzaUtjbWYeFXKgZ9uUCkbNESkn9aVafbBdkJyMAjP8Xwy4Qo3Jz4W/HasDBYnaxuVIQ2H4wE/gw1oUWzrTYK6wDhMbIIkfpOQUETEOyoWNAUOQol4MbeQnw9fnjDlglu1K4Yy3SseXTxO88YYkvuprqrcn6FPvmPj0s6YJAU4TIurgTJ0EDmbyOmOMkVZSMJGQc2iCnSEBBVawOK3UYy741MWfIeh0hTKkJKwqgqWfFeKcBI6QdppokTcfc7nUoih2Od7rKmioWvEx7ThutItf4ygQvtnQpHKGoWCkTJMMM1mL9N6P5r3gCd21/bsmZmIYD/fz+LZtadNDsfjMTMzM3ziE5/Y8fOnSg67vWf+xJ6JxVV5O6QW1BTdayMPsDMZBC7bMq0wzb5QmKI5UbJFuDSajk+ALFS5rjl02od6gSENAc54F7TsDNpLwnUmEZ26iNzF224nltWkpBsqThtacJFHViJV5XqQwREdSBU4gsJRHIeVVDEYa+XIQlCKsh3xvmNFHn8mbUxpK+9ecfhkgs9f6Xbt9c/v67mcgHbD58LSUXIoxL3kFk+P6YAQ0LRMAVA70YUo+tzzKVVg1dYtKSKADVUGDuJU4Fi3ro8om5EEWsRYGbRKmkIk6rBFXx4pTU7CybTgUlKy5DqhhWabA7L9+JkP/q3k+XbD4e/wTaLYI2M70Y0EzcRPNSBLLBkGVNYUOPH2u6kQu/34xeTPz4cv75SueeLjx61XEVF0TLUSYVrgR05z/Hc7Njs1pQlErt2i72JCEjhKTHO/lbfToioib+G+0lA8RHAzFs5FoNyI7JjwbF9hltlQ0pie8zk6FEsVIqFzISkYqpoTbi7QFRwJoj8LUjBF+pEOSWCndQ2oQHFoo/mxixNfZ3y/2htp2oCIxPMXnx+/UOzF/o4+bXL4cz/3c8/F8/iy2nhXgK8CRwVo2hXA9OJTO1t3U20zRxaCQt0SAG5bRGp64a2M7V0R1bUNJ2a38LGgfqqFjghSM0DjoQlEcX+pwwc1f08SAlg8SPs+aw5r0XKTyU1JU5MpShSeT3xPYBoA2+jPQFUs0WuSCNfiG8bHbCOh7fd6z55da7c5IzISyesRvXV+Kj4eh1Vichi/j5tq2kWRU9MhjKj3FxEP66cHUjyk4oEeE9E8EP1lkMs2rbLG/9V0n/ZMQCFlFWZOEg7pWZ9j8U0BFFFPjWOQaCIKHy0+xqLvMFaGoTBtKZQJrTTbfC/cLPHzNCSR8b5i623Pj7+81taf3W25TzHKBJ+dqqq1p/On22ckCcxDPIuFUBaGomzg2Blc4GBLzI9xPK73izG/00ouO6RNIR65efExALbRO3wZoOcTbCLIYtQZVNBs1IkLADSORZ+zlsj12CHBKtGzzUORUykprzo+bV5THJaKyOhI1fR8Nk0QW/E3FvcwTQ73uIV79kztaZPDn/7pn77sz78Sk8Y927M927M927M927Nnal/1beXbbrut+XdVVXzgAx/g4MGDT3mbC+efX6JmfZmq6XKaXLWSFVjs0pIrozxAGLSIE2G7kcMGYQwTcA0q2GrRxufSJk2XypKGSnna3gs7PsNb1/MpPZ+ypSpp4+LJvGuqYcKCduc9XdLQlkibtt1cQBaHSjeyO3HiGGj4LnW479h6jPzM+LqixfenvYLpxUTWfzL71z/Xefo/eg7tCTp7AXVp7/eNqEeKAk+ziQegDm21iEa3aQRTHmLwjYBaR/5TbG0J+uF3DGJFcrxvPX4zdMWUO7WpKhZ8JyAtiqXQ/otqAhWmaZV3fcqIMPSlpih8nDiWYbDpv8doTJjubz/2dBJ1ypmV30/13uDFL+/xQvPlDgkFO2OyVW5Hv66J2y3uXVRwaHd4YNpKHSlNn5xZnzcdlxLh/7WHkToBWe60aAoRsYtcb4vDKYNHJGoiv/tiUjNQNTM+YxAGt1DCsayVZcHLliIdXk9EJI13pD5pYjSIpNIseTNA1iFtOIUx1sbByIh+GrULKWQnlerFHpdfCPZif4efNjn85m/+5h3ff/u3fzvvete7nvI2/f7z+7YVTxPkG52tpqVqmmQNaHhIQoQ2pCHIxBZrtEiCjmTimHg1K50Cp7C92xLifucpF6dGksXIaxmj6agkrPeywkXEUajp8AnhnlDTNVAwJTIPVE2fvOFTGuVJncIkU/kOeS+mwTImh/H7dsIcHzMNwwhfDYkhwMtfYZ+VFXpfLosH6+6hFVkFCT1Fo0UJUyrCbo5p2vLfmGDGrz5Zw3NqS2MYXDNQUGObVvFImUYgPT5mWwInbflrRoJHpuEzEubpUOOY9VloQU+LlkiRkBYjzLmEcSJyTPN+yhWO7XVDMk1mQ9ET/x1f21dT++2F5svjy7Sc24li26J0VxzGmxanO4uiOExSY5s2rFB2LAuuM/VxJcmhUSIwbYOft/nmcUd3VLFIUMz7jgxuKR+GCKdi2hUyJW8Jg1ZKJoRTrxCJdy/yPOwEGmrl6Pq4sjHuug/7m5lSOWplhbsYKE5t1YB0d9G4Z8+qeV78yOHTrs/bbSdPnuT8+fNP+TcLi3/t5/Nlsb/sIvEicPiAIOsynWiGqaRI5KzE6rX9ZVqV6O5drBGBm05IT38fL36ZyvSN3ldEYSwSfKIyftzUErl+NVH2Q/hZuxHKHQdzO1nlicM2IMGy0SpsHbLNV/h5HV7bV0sAetu3vvDRpN2fRTOcoqYHarOtJvwskthjsRMPn+Y+g0/ayI3F7bh/mKLOQNBNjILGgXOrpnqYhTJBlHeK+kyUaXa9Rn8co6fi1EzvI/5tfG5tPzc4htTNfenWdbybo9iWW4oFzp4vPz/2ZNtmLvd5tD+3OAgXVRKagkVNt4O041yMtaWaShXFmCyPJ12ivs93DL7IjvO4WSRqzYq3DqnDTvKEodLN0FVboixB4fHMhvgsQ1aCmLbRQRmi0dNFAkyHckwY/ivDgJYOXOE6nBV7iOHzZ/4Zfr3Q7WmRw1e+8pWo1nTvgQMH+Imf+Iln9Uk9U3s65HC37b6gdiOJ8YBJg5ZfNGmBTCvxOD0a9ebaxOGIzLVbXBCI8cR2ibT9Oj5pVkUVQYYhHqYdHyULpM3sEBmS5nHUFN003jUbNGCKKjo8C77DOuWO9kSuIjLjm0R3t+2eJtyzF57pXXSAqCcHO7elxGIEpj4ebcdU766pThtQlfbEpFGu8fsoXdO2ePDJoJUIGEd/BRrt0ChSP6BuEKCMhDKsDYxFlByUEW2ZSti05ZXktU0Hsprn/1WCer+YrO3Tbd9q/JvpSsdKWXqkdHzarOFrfh9iaRxIEu3ZKaoeC6bMi891fNoMccW/mShDz6fh+rGUmB0xOEExopYEsJWA1lhWfLdBFuOwTjshbZ8Z7S5VG/1v+/NeUrhnz5Y9bXJ4xx13PBfP48tqFebp/+gprN2qywJ6Qosj2P67BkXh8hqAToVJOvyOciHqXMWgUiiD8Y5MhelSb5vDu81XrJVMRjfVsfJ4P20Ly21VgwxF2QYQTayY9E2YypKABNm09XraQbd5TwBa09579sK0Nr82bSVEQOPHsJPHFfX92khgPJx6Ptvh91IgtJLDqA+6W5w7/r3yOyYtMxImGMZKk/hpwWSU8H17ZOLnPn3CoZm1DuF2sVPvQv3ja0789PqNWm97B+lXtrWT+7Zfxkl7mIpdN6LXeFKmcS33U3pOE6tbCVm7C9SmA8UYOxcpO83fBxFsMkpECgoPHZWykejGj72aCn3Hoj12dHphLV7kVGpcI3fT91lAMp9II9mz596EsfzitqdtK99+++1MJrKy6g/+4A/4uZ/7Oc6ePfuUt3muN6TstssNpPxVrX3Axk0Ql0PSIrS/uyVtW7eJKEYMODEIxWSyjeQ0Wl4tknxEZtocsRiYYgLZvo8s8Kraf+uQtVTxMQdB9DU+351E/Ygy+ie03Qz+RU/cb9vv/fbT1k8vaKtCwtemT8REsV0AuN2fc8vf2+2ueB+7eattf3PKN+2wNu0iPk5EFke7WmnxYGyj4G1R3+nP3Q6KRNtnbet1RB6aYQ9hga9sX27HovZn2E4U28V6TOTaCV+kWcDUV9rFRCzw277daCi2fLQIFId4v811oBz9Fj89FjJDpZtroQgxOFKD2s8l+n97nWVMEjOSPR9+gdmLva38tMnhz/zMzzAzM8P999/Pe97zHo4ePcpP/dRPPeVt9JNLWz0nFjkmX05rHzDtAxbigTdF4toDLTuQjF0Bp7nvduUagsYOoVflmj2kETUECWZxytkEPkw7sMUkLj5e5MS0t5nsfn7tBGDP4OKFF8+KpN0Ha/sQ2p3wt1c8VoFvG1Fw+f1lDkemSWObrxoLo3j4TdA7Esn24Rvvr97FG7Stgir+TdRXbBdkuwu49oH61X6ovph8OVr7M81aIuYwjattP3d4cp/uKIpiARGn+NtJ447EsHV/ccd5vA7i/USebruAioLuNcI7FN6kfQKgEBPDNs87to7bnN89e2GYe4ZfL3R72uQwyzKUUnzgAx/gB3/wB3nnO9/JeDx+ytscPPj8BuFnC9kyzYX6RCSmnTy2bQeS2Kpm279vB6IYjNqtuR0yI5FbxbS1ZtXOA7QdxJrJTOV3HKTRdqNBe9XpTvtffux5rnSeBWuLwz/V5xzR4zYvcYreTFvSpuVnu5M1oStMpyvbqHm8z3ZyGX8Wv28f7BEhj77ePpjj7XZfg3t+PLUXoy+3bXdcll3OUx+KPHLhee/k0MK0IJpuzPE7Ere2r8ZEsl3ETwcP5XlEhHESJpjlOcrvdyOEMO3exOtl93aTPV9+YZn3z+zrhW5PmxzOzs7y7ne/mz/8wz/ktttuw1qLMV9+ZG7P9mzP9mzP9mzP9mzPnn972uTwF37hF+h0Ovzsz/4sBw4c4OLFi/zwD//wc/Hc/tq2uyL7ctru6u1yHJj2ROju/wNPaBtEm+4InXK1mr+PgyShjRwRwnaVGyvOOI1n1c5KeCe5/4mly94U555Fa6NweeCwtqeZo+/Fv40/MzyRy9W2NhITpaLaft/miDVyNS1kvNnpHf4uylZdDjXcs69ei8gd8KR0oOZnl4nRbRH3NsoXqRXRB+N1sJs/DjR0ijbNKQ63NINSux532g1qiYK3brdnLwyLAykv5rbykzKUvfcopThw4AA/9EM/1Pz86NGjvP3tb9/xN7stzK88b/Z8HRIxsUqf5KBqt8Savw8TlfH38fCLUgs771+Ws+/mKMa2hQSZpEkUd4ujNodsO/HcayM/pX3xjr+yFOiLwtqFwkhpZnZNLO+QxFFRT87S33WI7T74QA7N6J8xkYzi7o3AMZ5c7VQIiI/R/j5yzGo/FQTes8vbV6MvR8WJ9r93Fy4N/zpMtbcTMRvSgN236YRJ6Pg3bU5gFn1bOWqvSJR6QgK6e+f0br3C9vNv327PXjj2bH8iP/3TP82HP/xh9u3bxx/90R8BsLW1xT/5J/+Es2fPcuzYMX7xF3+RxcXFZ+XxnzRa/OAP/iD/7b/9N86dO7fj53Vd86lPfYqf+qmf4vd///cve9vB9vNb5TzfB0T7YN1dte7mruzmvrQTyCdDHSP3KgaURlB7VxUbLd5n5OC07fl+r17o9ud/+pU74flMbLdf7C4q2t+3ke02BzDqsrWRPosMb+0Wpm7bDh9XbsfftjmyIjIf1ACehj+5Z3u+HP/d9qHLiW/v5sPGmDrl2E5/Vjc/2ymplLXkb9pc2XYS2T4jdoMGu305rjvdsxeOPdvI4Xd/93fznve8Z8fPfvmXf5k3vvGNvP/97+eNb3wjv/zLv/xlejVPtCdNDt/znveQJAn/9J/+U772a7+Wb/u2b+Mbv/Eb+eZv/mbe97738a53vYvv/u7vvuxt9+3/6g7ST6X2H1sGTza8EtX/dyd4QNNui21kzc6puNRfXvA3/r+dkO7ZX85+4F0vbhL/X9Z2H6Q70OdwmMW1fTA9FEGQmZ2STTuLnYiY754UjRaTxLjF6HIDYHs+/fS258tiu7slT+bX0dqx1Ab0sa1OEYuiOPyyQ1qn1bbesYXrMu3tJ6P7tJ/Dnn312Gtf+9onoIIf/OAHm87t29/+dj7wgQ88a4//pKVkt9vlHe94B+94xzvQWrO5uUmv12NhYeFp7zTPv6zP8UVhTZV4mQAQA4WIuE7z9XZQibuXY0K4g7OiplPU3ZZMQ/v+98RT/3p2+Mje+wWX59pGewKf9jLodBRYfzIurkZEuPHpFDH0kRqx8zH3pjf/erbny2J/Wb+5nB8/GZ+9KYR4Et8Psbe8DALY+PNTqGx8NWnLfiVY5Bw+E9vc3NwBsH3f930f3/d93/eUt1lfX+fgwYMAHDx4kI2NjWf4LJ7c/lJ9hjzPmye0Z8/M9GUu8suKuipH+y/bCaH83jYcGlkd9tStkt3/3rM9+3LZ7nbzk/7Nk7jfdH/zlCMbfTz+fs939+y5sBh7///t3X1M1dcdx/EPV8X1BjtBvaKJbUK1yWLRaWyaGtu0UEDFFtRZ+yDJSBvcGkdbspJoo3+QNKaNaYIPWUOsnan9y5WHiLhuNX1YbcPWZqt/oNEuJakEuEoVpHRwuffuD8avyhAv3HPuw+++X4bEC+Hc8yNffnx/53zPOTe+Hq0hlEaStNEH8MG0oDN9rPDN33ur0UinLj3smTAZROKL9o6UmZmp+vp6I32xwUqFcpCYv6WJ/sjdWGsy9v+Sxq2rGp3Ok3TTTWz0NaLTfz3ePUh84z3cjD3NYuw08I3lDqM1g2O/xgihWcRyZG43Qn3jKN7Y2vLxYnW8GkJW1ie/eKxWnjNnjvx+vyTJ7/crKysriiuYmJXk8LKfpATu8NbB9Hh3ATCCWAbMCEsKR/lvKvLy8tTY2ChJamxsVH5+vrmLGsNKcpiRwZO+aWOfPm/1hAqzVq9hleBk3C4GbzUqeKuyCJhDLNsx3shgJN8DTKSqqkpPPfWUvv32Wz388MM6fvy4KioqdObMGRUWFurMmTOqqKiw9v5W9jbImGWjVUwWN6DorX4opM8/i3cv3CGSeCRm7SGWzSFOYbsw4M033xz380ePHrX8ziNSc+MrAACAKXL74wHJIQAAQIRMbGWT6Kwkhz1XWJACd3j3HZ6f4A7EMmBKWOE0d48dpt5hmwAAALglK8lhqh+fB/coK2eFJ9yBWAbMicc+h7HEPAMAAECEqDkEAADATaa6kXWysDKtzDFNcIvP/0ZZLtyBWAYQKSsjh/39rFaGO3z+GYPrcAdiGTDH7dPKVh4l5/ncPdyK1PGbHUPx7gJgBLEMmBOPs5Vjycqj5LRpNloFYo+jIOEWxDJgBgtSAAAAcJOwy6vnrEwrBwI2WgVir6vT5XcApAxiGUCkOD4PmMCxP86IdxcAI4hlwJxQEtQNRsPKyOGdP3f3Dw2po2Atp0rAHYhlwIzRmkM3n5BiJTn0em20CsTe8hXJ8GsM3B6xDJjDamUAAAA4on3USvRNXdgyHwAAAA4rI4d+PwtS4A5/OEARP9yBWAbMCCsc9YKUlBw5nME9CC4xPzvxa0OASBDLgDnhtOg+Ep2V5DAzk5sQ3GHTFlZ4wh2IZcCc0P9GD6f6keioOQQAAICD1coAAAARCktJsR1NNKwkh329NloFYu8vpxK9bBiIDLEMmOP2XUOtJIcDA0lQbQlE4Oy/+IMKdyCWAXOSoW4wGlZqDrMXuPuHhtTx+51D8e4CYASxDJgRNvCR6FiQAgAAAAcLUgAAACKVJoXSkmH8b+qsJIeD/7HRKhB7/75I/SzcgVgGTEmOvQqjYSU5vHqVmxDcoeFPHPcDdyCWATOSpW4wGpyQAkxg468C8e4CYASxDJjDCSlTMPNnNloFYu+eJYn/SwxEglgGECkWpAAAAExCMoz+RYPkEAAAIEJhcULKlHR1siAF7rBvb3q8uwAYQSwD5rj9bGUrNYder7t/aEgdy34ZjHcXACOIZQCRspIc3vlzG60CsVe4jj+ocAdiGTDH7auVqTkEAACIUFhhTkgBAADAT1iQMgWckAK3qD/O8xPcgVgGzBhZrezukUMrNYcBNuKHS3R38aADdyCWAUTKSnLo87k7o0bq+O3veNKBOxDLgDnhKP8lOuYZAAAAJsHt08okhwAAABGi5nCKBgZstArE3tf/tPIrAsQcsQwgUlZGDvt6KXyGO/z1zwyuwx2IZcCU5NjIOhpWHiXnzHX3Dw2pY9uvKeKHOxDLgDmckDIFM2bYaBWIvewFif9LDESCWAbMCEsKu3yClHkGAACASUiG0b9oWJlWDnK+O1yi/3q8ewCYQSwDiJSVkcPLfpePtyJlvHUwPd5dAIwglgEz2MpmijIy3P1DQ+pYvWY43l0AjCCWAXOCCkf1kejsJIezbLQKxN7qh0Lx7gJgBLEMmBGOcqVyMow6siAFAABgEpIhwYsGW+YDAADAYWXksOcKC1LgDu++w+A63IFYBswISwqmubtMg5FDAACASbC9IOXTTz9VUVGRCgoKVFdXF4MruhnH5wETKCtnhSfcgVgGzAhHmRjeLjkMBoOqqanR4cOHdfLkSTU3N+ubb76J0dWNYOQQAAAgQZw9e1Z33323Fi1apPT0dBUXF+v06dMx7QNFKAAAABEaqTm0N0Pa3d2t7Oxs5/X8+fN19uxZa+83HivJYSgUUsH6f9hoGoipr7+WCtbHuxdA9IhluEFHR0e8u6C8/F/o6tW/R9XG4OCgNm3a5LzeunWrtm7dKkkKh/8/8UxLi+1CXyvJYWtrq41mAQAA4urtt9+22n52dra6urqc193d3fL5fFbfcyxqDgEAABJEbm6u2tvb9d1332loaEgnT55UXl5eTPtAzSEAAECCmD59uvbs2aPnn39ewWBQmzdv1pIlS2Lah7TweJPbAAAASElMKwMAAMBBcggAAABHRMlhZ2enysrKtG7dOhUXF+vo0aOSpGvXrqm8vFyFhYUqLy9Xb2+vJOnq1asqKyvTihUrVFNTc1NbZWVlKioqUklJiUpKStTT02P4koBbMxnLQ0ND2r17t4qKirR27Vp98MEHMb8epC5Tsdzf3+/cj0tKSvTAAw/otddei8s1AUgMEdUc+v1+Xb58WUuXLlV/f782b96sQ4cOqb6+XrNnz1ZFRYXq6urU29urV155RQMDA2pra9PFixd18eJF7dmzx2mrrKxM1dXVys3NtXphwHhMxvL+/fsVDAb18ssvKxQK6dq1a8rKyorj1SGVmIzlG23atEk7d+7U/fffH+MrApAoIho59Pl8Wrp0qSQpIyNDOTk56u7u1unTp1VaWipJKi0t1YcffihJ8nq9WrVqlWbOnGmn18AUmYzl999/X9u3b5ckeTweEkPElI37cnt7u3p6erRq1Srr/QeQuCZdc3jp0iWdO3dOy5cvV09Pj7Mxo8/n0/fffx9RG7t27VJJSYkOHTo07k7gQCxEE8t9fX2SpNraWm3cuFGVlZW6cuWK9T4D4zFxX5ak5uZmrV+/PuanMQBILJNKDn/44QdVVlZq165dysjImNIb7tu3TydOnNB7772nr776Sk1NTVNqB4hGtLE8PDysrq4urVy5Ug0NDVqxYoVef/11Cz0FJmbivjyqpaVFxcXFhnoGIFlFnBwGAgFVVlbq8ccfV2FhoSRpzpw58vv9kkbqXyKZVps/f76kkWmQDRs2xPwwacBELGdmZuqOO+5QQUGBJGnt2rVqa2uz23FgDFP3ZUk6f/68gsGg7rvvPmv9BZAcIkoOw+GwXn31VeXk5Ki8vNz5fF5enhobGyVJjY2Nys/Pn7Cd4eFhZ4ojEAjo448/jvmu30htpmI5LS1Njz76qHOO+BdffKF77rnHWr+BsUzF8qjm5mZGDQFIinC18pdffqlnn31W9957rzyekXyyqqpKy5Yt00svvaTOzk4tWLBAtbW1mj17tqSRG1R/f78CgYBmzZqlI0eOaOHChdq2bZsCgYBCoZAefPBB7dy5U9OmTbN6kcAoU7G8ePFidXR0qLq6Wn19fcrKytLevXu1cOHCOF4dUonJWJak/Px81dXV8ZADgOPzAAAA8BNOSAEAAICD5BAAAAAOkkMAAAA4SA4BAADgIDkEAACAg+QQgHHhcFhPP/20PvnkE+dzLS0teu655+LYKwBAJNjKBoAVFy5c0IsvvqjGxkYFg0GVlpbq8OHDuuuuuybdVjAYZD9UAIgRkkMA1rzxxhvyer0aGBiQ1+tVR0eHLly4oGAwqB07duixxx7TpUuXVF1drR9//FGStHv3bq1cuVKtra06ePCgfD6fzp07p5aWljhfDQCkBpJDANYMDAxo48aNSk9P1yOPPKLFixerpKREfX192rJlixoaGpSWliaPx6OZM2eqvb1dVVVVqq+vV2trq7Zv364TJ05o0aJF8b4UAEgZ0+PdAQDu5fV6tX79enm9Xp06dUofffSRjhw5IkkaHBxUZ2enfD6fampqdP78eXk8HrW3tzvfn5ubS2IIADFGcgjAKo/H45z9u3//fuXk5Nz09QMHDmju3LlqampSKBTSsmXLnK95vd6Y9hUAwGplADGyZs0aHTt2TKOVLG1tbZKk69eva968efJ4PGpqalIwGIxnNwEg5ZEcAoiJF154QcPDw3riiSe0YcMG1dbWSpKeeeYZNTQ06Mknn1R7ezujhQAQZyxIAQAAgIORQwAAADhIDgEAAOAgOQQAAICD5BAAAAAOkkMAAAA4SA4BAADgIDkEAACAg+QQAAAAjv8C3j13KUYVtPEAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
+       "<Figure size 1200x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -647,11 +604,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 10,
-=======
-   "execution_count": 53,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+   "execution_count": 74,
    "id": "7663bece",
    "metadata": {},
    "outputs": [],
@@ -661,29 +614,17 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 11,
+   "execution_count": 25,
    "id": "7d99d6c5",
-=======
-   "execution_count": 54,
-   "id": "34d67a6d",
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-<<<<<<< HEAD
-       "39.47786774926718"
-      ]
-     },
-     "execution_count": 11,
-=======
-       "39.477867597485165"
+       "36.36875214063194"
       ]
      },
-     "execution_count": 54,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -694,77 +635,80 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 12,
-=======
-   "execution_count": 55,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
-   "id": "46786552",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 42,
+   "id": "676a128b-7339-49e1-8e26-2f61173def0a",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-77.02149061524531"
+       "36.80399911266385"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dh.estimate_longitude()"
+    "dh.estimate_latitude()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "id": "eaf33f52",
+   "execution_count": 37,
+   "id": "08aca47b-5944-4233-9d8e-07690a78a04c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36.368752170381086"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dh.setup_location_and_orientation_estimation(-5)"
+    "dh.estimate_latitude()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "4133ce50",
+   "execution_count": 59,
+   "id": "15a71ac4-ff7b-4967-9dad-a4cccc701b9a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "39.48011432047507"
+       "-76.98637699846356"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dh.estimate_latitude()"
+    "dh.estimate_longitude()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "c06aad4f",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 54,
+   "id": "36876e1b-dc37-4d57-a2e9-3f8e9d300d66",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-77.02773892637121"
+       "-76.98288034218749"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -775,24 +719,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "86dbb1e6",
+   "execution_count": 49,
+   "id": "a00e6627-a55d-40c8-a213-3f5962e81b6e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-<<<<<<< HEAD
-       "-77.02148972778593"
+       "-76.98637700537415"
       ]
      },
-     "execution_count": 12,
-=======
-       "-76.99497716211415"
-      ]
-     },
-     "execution_count": 43,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,17 +740,14 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 13,
-=======
    "execution_count": 44,
-   "id": "12299aa5",
+   "id": "86dbb1e6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-76.99497716211415"
+       "-77.01556086465614"
       ]
      },
      "execution_count": 44,
@@ -827,68 +761,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "941495ff",
+   "execution_count": 61,
+   "id": "cd17c244",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22.445143825420143, 0.289549939361291)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# actual site location \n",
+    "lat_actual = 39.4856\n",
+    "lon_actual = -76.6636\n",
+    "# run orientation estimation based on actual location\n",
+    "dh.estimate_orientation(latitude=lat_actual, longitude=lon_actual)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
-   "id": "cd17c244",
+   "execution_count": 70,
+   "id": "3885f9da-41a6-4eb3-8727-03a3bc9d6679",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-<<<<<<< HEAD
-       "(31.737784658389096, 1.6540872355437344)"
+       "(22.445170556838107, 0.28954902242283853)"
       ]
      },
-     "execution_count": 13,
-=======
-       "(37.83464950799888, -0.9222505405437297)"
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh.estimate_orientation(latitude=lat_actual, longitude=lon_actual)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "cc81549d-616d-4e44-aa3b-2e723e9b15d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22.445209394406703, 0.28954772898832193)"
       ]
      },
-     "execution_count": 11,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# actual site location \n",
-    "lat_actual = 39.4856\n",
-    "lon_actual = -76.6636\n",
     "dh.estimate_orientation(latitude=lat_actual, longitude=lon_actual)"
    ]
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 14,
-=======
-   "execution_count": 12,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
-   "id": "63a3b49c",
+   "execution_count": 65,
+   "id": "b04cb5b6-53be-4afd-bc08-cc393c068859",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-<<<<<<< HEAD
-       "(31.58596842585692, 0.9873311837872928)"
+       "(22.44515535597386, 0.2895493319809174)"
       ]
      },
-     "execution_count": 14,
-=======
-       "(37.834704856913405, -1.5056045273181193)"
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh.estimate_orientation(latitude=lat_actual, longitude=lon_actual)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "63a3b49c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(19.40498281092358, -0.5961302217652527)"
       ]
      },
-     "execution_count": 12,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -899,31 +870,17 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 15,
-=======
-   "execution_count": 13,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+   "execution_count": 27,
    "id": "ab379a03",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-<<<<<<< HEAD
-       "(39.47786774926718, -77.0258469334807, 37.83310641907293, -1.5126372211437822)"
-      ]
-     },
-     "execution_count": 15,
-=======
-       "(39.339893264226376,\n",
-       " -77.02669236001245,\n",
-       " 37.69707468705119,\n",
-       " -1.5188203396734161)"
+       "(36.44476945682727, -76.99412125928257, 19.40503936064613, -0.6184148510653438)"
       ]
      },
-     "execution_count": 13,
->>>>>>> 6869df8e6ec610bcc759f5e665909dbd1a4e5973
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -949,7 +906,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/tests/fixtures/tilt_azimuth/tilt_azimuth_estimation_data_creator.ipynb b/tests/fixtures/tilt_azimuth/tilt_azimuth_estimation_data_creator.ipynb
index be92a4ed..5e8723bd 100644
--- a/tests/fixtures/tilt_azimuth/tilt_azimuth_estimation_data_creator.ipynb
+++ b/tests/fixtures/tilt_azimuth/tilt_azimuth_estimation_data_creator.ipynb
@@ -82,9 +82,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-
-      "CAUTION: Multiple scan rates detected!=======================] 100.0% ...queries complete in 26.2 seconds       \n",
-
+      "[============================================================] 100.0% ...queries complete in 20.9 seconds       \n",
+      "\n",
+      "CAUTION: Multiple scan rates detected!\n",
       "Scan rates (in seconds): [300, 280]\n",
       "0 transitions detected.\n",
       "Suggest splitting data set between:\n",
@@ -122,7 +122,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dh = DataHandler(data_frame[0])\n",
+    "dh = DataHandler(data_frame)\n",
     "dh.fix_dst()"
    ]
   },
@@ -136,17 +136,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "total time: 6.77 seconds\n",
+      "total time: 3.30 seconds\n",
       "--------------------------------\n",
       "Breakdown\n",
       "--------------------------------\n",
-      "Preprocessing              2.16s\n",
-      "Cleaning                   0.11s\n",
-      "Filtering/Summarizing      4.50s\n",
-      "    Data quality           0.15s\n",
-      "    Clear day detect       0.11s\n",
-      "    Clipping detect        3.71s\n",
-      "    Capacity change detect 0.52s\n",
+      "Preprocessing              1.29s\n",
+      "Cleaning                   0.07s\n",
+      "Filtering/Summarizing      1.94s\n",
+      "    Data quality           0.07s\n",
+      "    Clear day detect       0.08s\n",
+      "    Clipping detect        1.48s\n",
+      "    Capacity change detect 0.30s\n",
       "\n"
      ]
     }
@@ -247,7 +247,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "scale_factor_costheta, costheta_fit = find_fit_costheta(dh.filled_data_matrix, dh.daily_flags.no_errors)"
+    "scale_factor_costheta, costheta_fit = find_fit_costheta(dh.filled_data_matrix, dh.daily_flags.no_errors, day_of_year)"
    ]
   },
   {
@@ -259,7 +259,7 @@
    "source": [
     "# exporting inputs for tilt and azimuth estimation\n",
     "# costheta_fit\n",
-    "np.savetxt('./tests/fixtures/tilt_azimuth/costheta_fit.csv', costheta_fit,  delimiter=\",\")"
+    "# np.savetxt('./tests/fixtures/tilt_azimuth/costheta_fit.csv', costheta_fit,  delimiter=\",\")"
    ]
   },
   {
@@ -389,7 +389,7 @@
    "source": [
     "# exporting inputs for tilt and azimuth estimation\n",
     "# boolean_filter\n",
-    "np.savetxt('./tests/fixtures/tilt_azimuth/boolean_filter.csv', boolean_filter,  delimiter=\",\")"
+    "# np.savetxt('./tests/fixtures/tilt_azimuth/boolean_filter.csv', boolean_filter,  delimiter=\",\")"
    ]
   },
   {
@@ -407,7 +407,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "omega = calculate_omega(dh.data_sampling, dh.num_days, longitude, day_of_year, gmt_offset)"
+    "omega = calculate_omega(day_of_year, dh.data_sampling, longitude, gmt_offset)"
    ]
   },
   {
@@ -429,7 +429,7 @@
    "source": [
     "# exporting inputs for tilt and azimuth estimation\n",
     "# omega_f\n",
-    "np.savetxt('./tests/fixtures/tilt_azimuth/omega_f.csv', omega_f,  delimiter=\",\")"
+    "# np.savetxt('./tests/fixtures/tilt_azimuth/omega_f.csv', omega_f,  delimiter=\",\")"
    ]
   },
   {
@@ -479,7 +479,7 @@
    "source": [
     "# exporting inputs for tilt and azimuth estimation\n",
     "# delta_f\n",
-    "np.savetxt('./tests/fixtures/tilt_azimuth/delta_f.csv', delta_f,  delimiter=\",\")"
+    "# np.savetxt('./tests/fixtures/tilt_azimuth/delta_f.csv', delta_f,  delimiter=\",\")"
    ]
   },
   {
@@ -522,33 +522,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "double-easter",
+   "execution_count": 43,
+   "id": "b6dfc488-3842-4524-afe1-fd9d20612be0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "estimates = run_curve_fit(func=func_customized, keys=['tilt', 'azimuth'], delta=delta_f, omega=omega_f,\n",
-    "                                  costheta=costheta_fit, boolean_filter=boolean_filter, init_values=[30, 30],\n",
-    "                                  fit_bounds=bounds)"
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(boolean_filter, aspect='auto');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "id": "clear-retention",
+   "execution_count": 45,
+   "id": "double-easter",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.654457422429566"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (9916,) (1,1096) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m estimates \u001b[38;5;241m=\u001b[39m \u001b[43mrun_curve_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc_customized\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtilt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mazimuth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdelta_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43momega\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43momega_f\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mcostheta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcostheta_fit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mboolean_filter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mfit_bounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/github/solar-data-tools/pvsystemprofiler/algorithms/angle_of_incidence/curve_fitting.py:27\u001b[0m, in \u001b[0;36mrun_curve_fit\u001b[0;34m(func, keys, delta, omega, costheta, boolean_filter, init_values, fit_bounds)\u001b[0m\n\u001b[1;32m     24\u001b[0m costheta_fit \u001b[38;5;241m=\u001b[39m costheta[boolean_filter]\n\u001b[1;32m     25\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([np\u001b[38;5;241m.\u001b[39mdeg2rad(delta), np\u001b[38;5;241m.\u001b[39mdeg2rad(omega)])\n\u001b[0;32m---> 27\u001b[0m popt, pcov \u001b[38;5;241m=\u001b[39m \u001b[43mcurve_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcostheta_fit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeg2rad\u001b[49m\u001b[43m(\u001b[49m\u001b[43minit_values\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_bounds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mazimuth_estimate\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m keys:\n\u001b[1;32m     30\u001b[0m     popt[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrint(popt[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39mpi) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mpi\n",
+      "File \u001b[0;32m~/miniconda3/envs/pvi-dev/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:870\u001b[0m, in \u001b[0;36mcurve_fit\u001b[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, full_output, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_nfev\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m    868\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_nfev\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmaxfev\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 870\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mleast_squares\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjac\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    871\u001b[0m \u001b[43m                    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m res\u001b[38;5;241m.\u001b[39msuccess:\n\u001b[1;32m    874\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptimal parameters not found: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m res\u001b[38;5;241m.\u001b[39mmessage)\n",
+      "File \u001b[0;32m~/miniconda3/envs/pvi-dev/lib/python3.10/site-packages/scipy/optimize/_lsq/least_squares.py:830\u001b[0m, in \u001b[0;36mleast_squares\u001b[0;34m(fun, x0, jac, bounds, method, ftol, xtol, gtol, x_scale, loss, f_scale, diff_step, tr_solver, tr_options, jac_sparsity, max_nfev, verbose, args, kwargs)\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrf\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    828\u001b[0m     x0 \u001b[38;5;241m=\u001b[39m make_strictly_feasible(x0, lb, ub)\n\u001b[0;32m--> 830\u001b[0m f0 \u001b[38;5;241m=\u001b[39m \u001b[43mfun_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m f0\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    833\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`fun` must return at most 1-d array_like. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    834\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf0.shape: \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(f0\u001b[38;5;241m.\u001b[39mshape))\n",
+      "File \u001b[0;32m~/miniconda3/envs/pvi-dev/lib/python3.10/site-packages/scipy/optimize/_lsq/least_squares.py:825\u001b[0m, in \u001b[0;36mleast_squares.<locals>.fun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfun_wrapped\u001b[39m(x):\n\u001b[0;32m--> 825\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39matleast_1d(\u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/pvi-dev/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:501\u001b[0m, in \u001b[0;36m_wrap_func.<locals>.func_wrapped\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapped\u001b[39m(params):\n\u001b[0;32m--> 501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mydata\u001b[49m\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (9916,) (1,1096) "
+     ]
     }
    ],
+   "source": [
+    "estimates = run_curve_fit(func=func_customized, keys=['tilt', 'azimuth'], delta=delta_f, omega=omega_f,\n",
+    "                                  costheta=costheta_fit, boolean_filter=None, init_values=[30, 30],\n",
+    "                                  fit_bounds=bounds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "clear-retention",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "estimates[1]"
    ]
@@ -563,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "id": "young-universe",
    "metadata": {},
    "outputs": [],
@@ -573,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "wrong-separation",
    "metadata": {},
    "outputs": [],
@@ -583,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "built-latvia",
    "metadata": {},
    "outputs": [],
@@ -593,63 +621,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "3bb3554e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "39.4856"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "latitude"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "editorial-messenger",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31.736919707713444"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "est.tilt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "id": "suspected-virginia",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-      "1.6570329881770878"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "est.azimuth"
    ]
@@ -671,7 +666,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/tests/solardatatools/test_data_handler.py b/tests/solardatatools/test_data_handler.py
index ee0b8e80..77311d70 100644
--- a/tests/solardatatools/test_data_handler.py
+++ b/tests/solardatatools/test_data_handler.py
@@ -22,30 +22,28 @@ def test_load_and_run(self):
         scores_path = filepath / "fixtures" / "scoring" / "clipping_1.csv"
         with open(scores_path) as file:
             expected_scores = np.loadtxt(file, delimiter=",")
-        np.testing.assert_array_almost_equal(
-            dh.daily_scores.clipping_1, expected_scores, decimal=3
+        np.testing.assert_allclose(
+            dh.daily_scores.clipping_1, expected_scores, atol=1e-3
         )
         scores_path = filepath / "fixtures" / "scoring" / "clipping_2.csv"
         with open(scores_path) as file:
             expected_scores = np.loadtxt(file, delimiter=",")
-        np.testing.assert_array_almost_equal(
-            dh.daily_scores.clipping_2, expected_scores, decimal=3
+        np.testing.assert_allclose(
+            dh.daily_scores.clipping_2, expected_scores, atol=2e-3
         )
         scores_path = filepath / "fixtures" / "scoring" / "density.csv"
         with open(scores_path) as file:
             expected_scores = np.loadtxt(file, delimiter=",")
-        np.testing.assert_array_almost_equal(
-            dh.daily_scores.density, expected_scores, decimal=3
-        )
+        np.testing.assert_allclose(dh.daily_scores.density, expected_scores, atol=1e-3)
         scores_path = filepath / "fixtures" / "scoring" / "linearity.csv"
         with open(scores_path) as file:
             expected_scores = np.loadtxt(file, delimiter=",")
-        np.testing.assert_array_almost_equal(
-            dh.daily_scores.linearity, expected_scores, decimal=2
+        np.testing.assert_allclose(
+            dh.daily_scores.linearity, expected_scores, atol=2e-2
         )
         scores_path = filepath / "fixtures" / "scoring" / "quality_clustering.csv"
         with open(scores_path) as file:
             expected_scores = np.loadtxt(file, delimiter=",")
-        np.testing.assert_array_almost_equal(
-            dh.daily_scores.quality_clustering, expected_scores, decimal=3
+        np.testing.assert_allclose(
+            dh.daily_scores.quality_clustering, expected_scores, atol=1e-3
         )
diff --git a/tests/solardatatools/test_system_profiler.py b/tests/solardatatools/test_system_profiler.py
index dc10e649..91bf39fb 100644
--- a/tests/solardatatools/test_system_profiler.py
+++ b/tests/solardatatools/test_system_profiler.py
@@ -24,19 +24,29 @@ def test_system_profiler(self):
 
         estimate_orientation = dh.estimate_orientation()
 
+        # Based on site documentation
         actual_latitude = 39.4856
         actual_longitude = -76.6636
 
-        actual_orientation = dh.estimate_orientation(
+        estimate_orientation_real_loc = dh.estimate_orientation(
             latitude=actual_latitude, longitude=actual_longitude
         )
 
-        np.testing.assert_almost_equal(actual_latitude, estimate_latitude, decimal=0.1)
-        np.testing.assert_almost_equal(
-            estimate_longitude, actual_longitude, decimal=0.1
-        )
-        np.testing.assert_array_almost_equal(
-            estimate_orientation, actual_orientation, decimal=0.1
+        # Current algorithms output after changes in PR. Note, this
+        # changes slightly each run, depending on the results of the
+        # sunrise sunset evaluation, which uses holdout validation
+        # to pick some hyperparameters.
+        ref_latitude = 36.6  # +/- 0.3
+        ref_longitude = -77.0  # +/- 0.03
+        ref_tilt_real_loc = 22.45  # +/- 1e-4
+        ref_az_real_loc = 0.28  # +/- 1e-6
+
+        np.testing.assert_allclose(estimate_latitude, ref_latitude, atol=0.45)
+        np.testing.assert_allclose(estimate_longitude, ref_longitude, atol=0.2)
+        np.testing.assert_allclose(
+            estimate_orientation_real_loc,
+            (ref_tilt_real_loc, ref_az_real_loc),
+            atol=0.05,
         )
 
 
diff --git a/tests/statistical_clear_sky/algorithm/test_iterative_fitting.py b/tests/statistical_clear_sky/algorithm/test_iterative_fitting.py
index a2365bf4..fe3acfcd 100644
--- a/tests/statistical_clear_sky/algorithm/test_iterative_fitting.py
+++ b/tests/statistical_clear_sky/algorithm/test_iterative_fitting.py
@@ -1,19 +1,21 @@
 import unittest
 import os
 import numpy as np
-import cvxpy as cvx
 from statistical_clear_sky.algorithm.iterative_fitting import IterativeFitting
 
-class TestIterativeFitting(unittest.TestCase):
 
+class TestIterativeFitting(unittest.TestCase):
     def test_calculate_objective(self):
 
         input_power_signals_file_path = os.path.abspath(
-            os.path.join(os.path.dirname(__file__),
+            os.path.join(
+                os.path.dirname(__file__),
                 "../fixtures/objective_calculation",
-                "three_years_power_signals_d_1.csv"))
+                "three_years_power_signals_d_1.csv",
+            )
+        )
         with open(input_power_signals_file_path) as file:
-            power_signals_d = np.loadtxt(file, delimiter=',')
+            power_signals_d = np.loadtxt(file, delimiter=",")
 
         rank_k = 6
 
@@ -22,39 +24,61 @@ def test_calculate_objective(self):
         tau = 0.9
 
         initial_l_cs_value_file_path = os.path.abspath(
-            os.path.join(os.path.dirname(__file__),
-            "../fixtures/objective_calculation",
-            "three_years_initial_l_cs_value.csv"))
+            os.path.join(
+                os.path.dirname(__file__),
+                "../fixtures/objective_calculation",
+                "three_years_initial_l_cs_value.csv",
+            )
+        )
         with open(initial_l_cs_value_file_path) as file:
-            l_cs_value = np.loadtxt(file, delimiter=',')
+            l_cs_value = np.loadtxt(file, delimiter=",")
 
         initial_r_cs_value_file_path = os.path.abspath(
-            os.path.join(os.path.dirname(__file__),
-            "../fixtures/objective_calculation",
-            "three_years_initial_r_cs_value.csv"))
+            os.path.join(
+                os.path.dirname(__file__),
+                "../fixtures/objective_calculation",
+                "three_years_initial_r_cs_value.csv",
+            )
+        )
         with open(initial_r_cs_value_file_path) as file:
-            r_cs_value = np.loadtxt(file, delimiter=',')
+            r_cs_value = np.loadtxt(file, delimiter=",")
 
         beta_value = 0.0
 
         weights_file_path = os.path.abspath(
-            os.path.join(os.path.dirname(__file__),
+            os.path.join(
+                os.path.dirname(__file__),
                 "../fixtures/objective_calculation",
-                "three_years_weights.csv"))
+                "three_years_weights.csv",
+            )
+        )
         with open(weights_file_path) as file:
-            weights = np.loadtxt(file, delimiter=',')
+            weights = np.loadtxt(file, delimiter=",")
 
-        expected_objective_values = np.array([117277.71151791142,
-            478.8539994379723, 23800125.708200675, 228653.22102385858])
+        expected_objective_values = np.array(
+            [
+                117277.71151791142,
+                478.8539994379723,
+                23800125.708200675,
+                228653.22102385858,
+            ]
+        )
 
         iterative_fitting = IterativeFitting(power_signals_d, rank_k=rank_k)
 
         actual_objective_values = iterative_fitting._calculate_objective(
-            mu_l, mu_r, tau, l_cs_value, r_cs_value,
-            beta_value, weights, sum_components=False)
+            mu_l,
+            mu_r,
+            tau,
+            l_cs_value,
+            r_cs_value,
+            beta_value,
+            weights,
+            sum_components=False,
+        )
 
         # np.testing.assert_array_equal(actual_objective_values,
         #                               expected_objective_values)
-        np.testing.assert_almost_equal(actual_objective_values,
-                                       expected_objective_values,
-                                       decimal=8)
+        np.testing.assert_almost_equal(
+            actual_objective_values, expected_objective_values, decimal=8
+        )