diff --git a/LICENSE b/LICENSE
index 5c84d3dcd..fbfc42c67 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,5 +1,6 @@
 Copyright (c) 2009-2016 by California Institute of Technology
-Copyright (c) 2016-2023 by python-control developers
+Copyright (c) 2012 by Delft University of Technology
+Copyright (c) 2016-2024 by python-control developers
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/control/__init__.py b/control/__init__.py
index 45f2a56d6..40f3a783b 100644
--- a/control/__init__.py
+++ b/control/__init__.py
@@ -83,6 +83,7 @@
 from .timeplot import *
 
 from .bdalg import *
+from .ctrlplot import *
 from .delay import *
 from .descfcn import *
 from .dtime import *
diff --git a/control/bdalg.py b/control/bdalg.py
index 63cd9354d..ce8008537 100644
--- a/control/bdalg.py
+++ b/control/bdalg.py
@@ -279,8 +279,8 @@ def feedback(sys1, sys2=1, sign=-1):
         if isinstance(sys2, (int, float, complex, np.number, np.ndarray,
                              tf.TransferFunction)):
             sys1 = tf._convert_to_transfer_function(sys1)
-        elif isinstance(sys2, frd.FRD):
-            sys1 = frd._convert_to_FRD(sys1, sys2.omega)
+        elif isinstance(sys2, frd.FrequencyResponseData):
+            sys1 = frd._convert_to_frd(sys1, sys2.omega)
         else:
             sys1 = ss._convert_to_statespace(sys1)
 
diff --git a/control/ctrlplot.py b/control/ctrlplot.py
new file mode 100644
index 000000000..51f1342b2
--- /dev/null
+++ b/control/ctrlplot.py
@@ -0,0 +1,74 @@
+# ctrlplot.py - utility functions for plotting
+# Richard M. Murray, 14 Jun 2024
+#
+# Collection of functions that are used by various plotting functions.
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from . import config
+
+__all__ = ['suptitle']
+
+
+def suptitle(
+        title, fig=None, frame='axes', **kwargs):
+    """Add a centered title to a figure.
+
+    This is a wrapper for the matplotlib `suptitle` function, but by
+    setting ``frame`` to 'axes' (default) then the title is centered on the
+    midpoint of the axes in the figure, rather than the center of the
+    figure.  This usually looks better (particularly with multi-panel
+    plots), though it takes longer to render.
+
+    Parameters
+    ----------
+    title : str
+        Title text.
+    fig : Figure, optional
+        Matplotlib figure.  Defaults to current figure.
+    frame : str, optional
+        Coordinate frame to use for centering: 'axes' (default) or 'figure'.
+    **kwargs : :func:`matplotlib.pyplot.suptitle` keywords, optional
+        Additional keywords (passed to matplotlib).
+
+    """
+    rcParams = config._get_param('freqplot', 'rcParams', kwargs, pop=True)
+
+    if fig is None:
+        fig = plt.gcf()
+
+    if frame == 'figure':
+        with plt.rc_context(rcParams):
+            fig.suptitle(title, **kwargs)
+
+    elif frame == 'axes':
+        # TODO: move common plotting params to 'ctrlplot'
+        rcParams = config._get_param('freqplot', 'rcParams', rcParams)
+        with plt.rc_context(rcParams):
+            plt.tight_layout()          # Put the figure into proper layout
+            xc, _ = _find_axes_center(fig, fig.get_axes())
+
+            fig.suptitle(title, x=xc, **kwargs)
+            plt.tight_layout()          # Update the layout
+
+    else:
+        raise ValueError(f"unknown frame '{frame}'")
+
+
+def _find_axes_center(fig, axs):
+    """Find the midpoint between axes in display coordinates.
+
+    This function finds the middle of a plot as defined by a set of axes.
+
+    """
+    inv_transform = fig.transFigure.inverted()
+    xlim = ylim = [1, 0]
+    for ax in axs:
+        ll = inv_transform.transform(ax.transAxes.transform((0, 0)))
+        ur = inv_transform.transform(ax.transAxes.transform((1, 1)))
+
+        xlim = [min(ll[0], xlim[0]), max(ur[0], xlim[1])]
+        ylim = [min(ll[1], ylim[0]), max(ur[1], ylim[1])]
+
+    return (np.sum(xlim)/2, np.sum(ylim)/2)
diff --git a/control/frdata.py b/control/frdata.py
index e0f7fdcc6..b703a97a0 100644
--- a/control/frdata.py
+++ b/control/frdata.py
@@ -1,41 +1,8 @@
-# Copyright (c) 2010 by California Institute of Technology
-# Copyright (c) 2012 by Delft University of Technology
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in the
-#    documentation and/or other materials provided with the distribution.
-#
-# 3. Neither the names of the California Institute of Technology nor
-#    the Delft University of Technology nor
-#    the names of its contributors may be used to endorse or promote
-#    products derived from this software without specific prior
-#    written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL CALTECH
-# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
-# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
-# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
-# SUCH DAMAGE.
+# frdata.py - frequency response data representation and functions
 #
 # Author: M.M. (Rene) van Paassen (using xferfcn.py as basis)
 # Date: 02 Oct 12
 
-
 """
 Frequency response data representation and functions.
 
@@ -43,19 +10,18 @@
 FRD data.
 """
 
-# External function declarations
 from copy import copy
 from warnings import warn
 
 import numpy as np
-from numpy import angle, array, empty, ones, \
-    real, imag, absolute, eye, linalg, where, sort
-from scipy.interpolate import splprep, splev
+from numpy import absolute, angle, array, empty, eye, imag, linalg, ones, \
+    real, sort, where
+from scipy.interpolate import splev, splprep
 
-from .lti import LTI, _process_frequency_response
+from . import config
 from .exception import pandas_check
 from .iosys import InputOutputSystem, _process_iosys_keywords, common_timebase
-from . import config
+from .lti import LTI, _process_frequency_response
 
 __all__ = ['FrequencyResponseData', 'FRD', 'frd']
 
@@ -100,6 +66,10 @@ class constructor, using the :func:~~control.frd` factory function
     dt : float, True, or None
         System timebase.
 
+    See Also
+    --------
+    frd
+
     Notes
     -----
     The main data members are 'omega' and 'fresp', where 'omega' is a 1D array
@@ -120,7 +90,6 @@ class constructor, using the :func:~~control.frd` factory function
     for a more detailed description.
 
     """
-
     #
     # Class attributes
     #
@@ -206,11 +175,12 @@ def __init__(self, *args, **kwargs):
                 "Needs 1 or 2 arguments; received %i." % len(args))
 
         #
-        # Process key word arguments
+        # Process keyword arguments
         #
 
-        # If data was generated by a system, keep track of that
-        self.sysname = kwargs.pop('sysname', None)
+        # If data was generated by a system, keep track of that (used when
+        # plotting data).  Otherwise, use the system name, if given.
+        self.sysname = kwargs.pop('sysname', kwargs.get('name', None))
 
         # Keep track of default properties for plotting
         self.plot_phase = kwargs.pop('plot_phase', None)
@@ -280,7 +250,7 @@ def __str__(self):
         """String representation of the transfer function."""
 
         mimo = self.ninputs > 1 or self.noutputs > 1
-        outstr = ['Frequency response data']
+        outstr = [f"{InputOutputSystem.__str__(self)}"]
 
         for i in range(self.ninputs):
             for j in range(self.noutputs):
@@ -322,7 +292,7 @@ def __add__(self, other):
 
         # Convert the second argument to a frequency response function.
         # or re-base the frd to the current omega (if needed)
-        other = _convert_to_FRD(other, omega=self.omega)
+        other = _convert_to_frd(other, omega=self.omega)
 
         # Check that the input-output sizes are consistent.
         if self.ninputs != other.ninputs:
@@ -359,7 +329,7 @@ def __mul__(self, other):
             return FRD(self.fresp * other, self.omega,
                        smooth=(self.ifunc is not None))
         else:
-            other = _convert_to_FRD(other, omega=self.omega)
+            other = _convert_to_frd(other, omega=self.omega)
 
         # Check that the input-output sizes are consistent.
         if self.ninputs != other.noutputs:
@@ -386,7 +356,7 @@ def __rmul__(self, other):
             return FRD(self.fresp * other, self.omega,
                        smooth=(self.ifunc is not None))
         else:
-            other = _convert_to_FRD(other, omega=self.omega)
+            other = _convert_to_frd(other, omega=self.omega)
 
         # Check that the input-output sizes are consistent.
         if self.noutputs != other.ninputs:
@@ -414,7 +384,7 @@ def __truediv__(self, other):
             return FRD(self.fresp * (1/other), self.omega,
                        smooth=(self.ifunc is not None))
         else:
-            other = _convert_to_FRD(other, omega=self.omega)
+            other = _convert_to_frd(other, omega=self.omega)
 
         if (self.ninputs > 1 or self.noutputs > 1 or
             other.ninputs > 1 or other.noutputs > 1):
@@ -433,7 +403,7 @@ def __rtruediv__(self, other):
             return FRD(other / self.fresp, self.omega,
                        smooth=(self.ifunc is not None))
         else:
-            other = _convert_to_FRD(other, omega=self.omega)
+            other = _convert_to_frd(other, omega=self.omega)
 
         if (self.ninputs > 1 or self.noutputs > 1 or
             other.ninputs > 1 or other.noutputs > 1):
@@ -572,8 +542,8 @@ def __call__(self, s=None, squeeze=None, return_magphase=None):
         ------
         ValueError
             If `s` is not purely imaginary, because
-            :class:`FrequencyDomainData` systems are only defined at imaginary
-            frequency values.
+            :class:`FrequencyResponseData` systems are only defined at
+            imaginary values (corresponding to real frequencies).
 
         """
         if s is None:
@@ -638,7 +608,7 @@ def freqresp(self, omega):
     def feedback(self, other=1, sign=-1):
         """Feedback interconnection between two FRD objects."""
 
-        other = _convert_to_FRD(other, omega=self.omega)
+        other = _convert_to_frd(other, omega=self.omega)
 
         if (self.noutputs != other.ninputs or self.ninputs != other.noutputs):
             raise ValueError(
@@ -710,7 +680,7 @@ def to_pandas(self):
 FRD = FrequencyResponseData
 
 
-def _convert_to_FRD(sys, omega, inputs=1, outputs=1):
+def _convert_to_frd(sys, omega, inputs=1, outputs=1):
     """Convert a system to frequency response data form (if needed).
 
     If sys is already an frd, and its frequency range matches or
@@ -721,14 +691,14 @@ def _convert_to_FRD(sys, omega, inputs=1, outputs=1):
     manually, as in:
 
     >>> import numpy as np
-    >>> from control.frdata import _convert_to_FRD
+    >>> from control.frdata import _convert_to_frd
 
     >>> omega = np.logspace(-1, 1)
-    >>> frd = _convert_to_FRD(3., omega) # Assumes inputs = outputs = 1
+    >>> frd = _convert_to_frd(3., omega) # Assumes inputs = outputs = 1
     >>> frd.ninputs, frd.noutputs
     (1, 1)
 
-    >>> frd = _convert_to_FRD(1., omega, inputs=3, outputs=2)
+    >>> frd = _convert_to_frd(1., omega, inputs=3, outputs=2)
     >>> frd.ninputs, frd.noutputs
     (3, 2)
 
@@ -777,51 +747,67 @@ def _convert_to_FRD(sys, omega, inputs=1, outputs=1):
                     sys.__class__)
 
 
-def frd(*args):
-    """frd(d, w)
-
-    Construct a frequency response data model.
+def frd(*args, **kwargs):
+    """frd(response, omega[, dt])
 
-    frd models store the (measured) frequency response of a system.
+    Construct a frequency response data (FRD) model.
 
-    This function can be called in different ways:
+    A frequency response data model stores the (measured) frequency response
+    of a system.  This factory function can be called in different ways:
 
-    ``frd(response, freqs)``
+    ``frd(response, omega)``
         Create an frd model with the given response data, in the form of
-        complex response vector, at matching frequency freqs [in rad/s]
+        complex response vector, at matching frequencies ``omega`` [in rad/s].
 
-    ``frd(sys, freqs)``
+    ``frd(sys, omega)``
         Convert an LTI system into an frd model with data at frequencies
-        freqs.
+        ``omega``.
 
     Parameters
     ----------
-    response: array_like, or list
-        complex vector with the system response
-    freq: array_lik or lis
-        vector with frequencies
-    sys: LTI (StateSpace or TransferFunction)
-        A linear system
+    response : array_like or LTI system
+        Complex vector with the system response or an LTI system that can
+        be used to copmute the frequency response at a list of frequencies.
+    omega : array_like
+        Vector of frequencies at which the response is evaluated.
+    dt : float, True, or None
+        System timebase.
+    smooth : bool, optional
+        If ``True``, create an interpolation function that allows the
+        frequency response to be computed at any frequency within the range
+        of frequencies give in ``omega``.  If ``False`` (default),
+        frequency response can only be obtained at the frequencies
+        specified in ``omega``.
 
     Returns
     -------
-    sys: FRD
-        New frequency response system
+    sys : :class:`FrequencyResponseData`
+        New frequency response data system.
+
+    Other Parameters
+    ----------------
+    inputs, outputs : str, or list of str, optional
+        List of strings that name the individual signals of the transformed
+        system.  If not given, the inputs and outputs are the same as the
+        original system.
+    name : string, optional
+        System name. If unspecified, a generic name <sys[id]> is generated
+        with a unique integer id.
 
     See Also
     --------
-    FRD, ss, tf
+    FrequencyResponseData, frequency_response, ss, tf
 
     Examples
     --------
     >>> # Create from measurements
     >>> response = [1.0, 1.0, 0.5]
-    >>> freqs = [1, 10, 100]
-    >>> F = ct.frd(response, freqs)
+    >>> omega = [1, 10, 100]
+    >>> F = ct.frd(response, omega)
 
     >>> G = ct.tf([1], [1, 1])
-    >>> freqs = [1, 10, 100]
-    >>> F = ct.frd(G, freqs)
+    >>> omega = [1, 10, 100]
+    >>> F = ct.frd(G, omega)
 
     """
-    return FRD(*args)
+    return FrequencyResponseData(*args, **kwargs)
diff --git a/control/freqplot.py b/control/freqplot.py
index ea0e7fae1..a63ef20d3 100644
--- a/control/freqplot.py
+++ b/control/freqplot.py
@@ -8,24 +8,26 @@
 # charts is in nichols.py.  The code for pole-zero diagrams is in pzmap.py
 # and rlocus.py.
 
-import numpy as np
-import matplotlib as mpl
-import matplotlib.pyplot as plt
+import itertools
 import math
 import warnings
-import itertools
 from os.path import commonprefix
 
-from .ctrlutil import unwrap
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
+from . import config
 from .bdalg import feedback
-from .margins import stability_margins
+from .ctrlplot import suptitle, _find_axes_center
+from .ctrlutil import unwrap
 from .exception import ControlMIMONotImplemented
-from .statesp import StateSpace
-from .lti import LTI, frequency_response, _process_frequency_response
-from .xferfcn import TransferFunction
 from .frdata import FrequencyResponseData
+from .lti import LTI, _process_frequency_response, frequency_response
+from .margins import stability_margins
+from .statesp import StateSpace
 from .timeplot import _make_legend_labels
-from . import config
+from .xferfcn import TransferFunction
 
 __all__ = ['bode_plot', 'NyquistResponseData', 'nyquist_response',
            'nyquist_plot', 'singular_values_response',
@@ -33,6 +35,7 @@
            'bode', 'nyquist', 'gangof4']
 
 # Default font dictionary
+# TODO: move common plotting params to 'ctrlplot'
 _freqplot_rcParams = mpl.rcParams.copy()
 _freqplot_rcParams.update({
     'axes.labelsize': 'small',
@@ -53,10 +56,11 @@
     'freqplot.Hz': False,  # Plot frequency in Hertz
     'freqplot.grid': True,  # Turn on grid for gain and phase
     'freqplot.wrap_phase': False,  # Wrap the phase plot at a given value
-    'freqplot.freq_label': "Frequency [%s]",
+    'freqplot.freq_label': "Frequency [{units}]",
     'freqplot.share_magnitude': 'row',
     'freqplot.share_phase': 'row',
     'freqplot.share_frequency': 'col',
+    'freqplot.suptitle_frame': 'axes',
 }
 
 #
@@ -93,7 +97,7 @@ def bode_plot(
         data, omega=None, *fmt, ax=None, omega_limits=None, omega_num=None,
         plot=None, plot_magnitude=True, plot_phase=None,
         overlay_outputs=None, overlay_inputs=None, phase_label=None,
-        magnitude_label=None, display_margins=None,
+        magnitude_label=None, label=None, display_margins=None,
         margins_method='best', legend_map=None, legend_loc=None,
         sharex=None, sharey=None, title=None, **kwargs):
     """Bode plot for a system.
@@ -107,9 +111,9 @@ def bode_plot(
         List of LTI systems or :class:`FrequencyResponseData` objects.  A
         single system or frequency response can also be passed.
     omega : array_like, optoinal
-        List of frequencies in rad/sec over to plot over.  If not specified,
-        this will be determined from the proporties of the systems.  Ignored
-        if `data` is not a list of systems.
+        Set of frequencies in rad/sec to plot over.  If not specified, this
+        will be determined from the proporties of the systems.  Ignored if
+        `data` is not a list of systems.
     *fmt : :func:`matplotlib.pyplot.plot` format string, optional
         Passed to `matplotlib` as the format string for all lines in the plot.
         The `omega` parameter must be present (use omega=None if needed).
@@ -126,8 +130,6 @@ def bode_plot(
         graphs and display the margins at the top of the graph.  If set to
         'overlay', the values for the gain and phase margin are placed on
         the graph.  Setting display_margins turns off the axes grid.
-    margins_method : str, optional
-        Method to use in computing margins (see :func:`stability_margins`).
     **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional
         Additional keywords passed to `matplotlib` to specify line properties.
 
@@ -149,12 +151,20 @@ def bode_plot(
         value specified.  Units are in either degrees or radians, depending on
         the `deg` parameter. Default is -180 if wrap_phase is False, 0 if
         wrap_phase is True.
+    label : str or array-like of str
+        If present, replace automatically generated label(s) with the given
+        label(s).  If sysdata is a list, strings should be specified for each
+        system.  If MIMO, strings required for each system, output, and input.
+    margins_method : str, optional
+        Method to use in computing margins (see :func:`stability_margins`).
     omega_limits : array_like of two values
-        Set limits for plotted frequency range. If Hz=True the limits
-        are in Hz otherwise in rad/s.
+        Set limits for plotted frequency range. If Hz=True the limits are
+        in Hz otherwise in rad/s.  Specifying ``omega`` as a list of two
+        elements is equivalent to providing ``omega_limits``. Ignored if
+        data is not a list of systems.
     omega_num : int
         Number of samples to use for the frequeny range.  Defaults to
-        config.defaults['freqplot.number_of_samples'].  Ignore if data is
+        config.defaults['freqplot.number_of_samples'].  Ignored if data is
         not a list of systems.
     plot : bool, optional
         (legacy) If given, `bode_plot` returns the legacy return values
@@ -175,6 +185,10 @@ def bode_plot(
     The default values for Bode plot configuration parameters can be reset
     using the `config.defaults` dictionary, with module name 'bode'.
 
+    See Also
+    --------
+    frequency_response
+
     Notes
     -----
     1. Starting with python-control version 0.10, `bode_plot`returns an
@@ -217,8 +231,10 @@ def bode_plot(
         'freqplot', 'wrap_phase', kwargs, _freqplot_defaults, pop=True)
     initial_phase = config._get_param(
         'freqplot', 'initial_phase', kwargs, None, pop=True)
-    freqplot_rcParams = config._get_param(
+    rcParams = config._get_param(
         'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True)
+    suptitle_frame = config._get_param(
+        'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True)
 
     # Set the default labels
     freq_label = config._get_param(
@@ -268,7 +284,7 @@ def bode_plot(
     #
 
     # If we were passed a list of systems, convert to data
-    if all([isinstance(
+    if any([isinstance(
             sys, (StateSpace, TransferFunction)) for sys in data]):
         data = frequency_response(
             data, omega=omega, omega_limits=omega_limits,
@@ -448,47 +464,14 @@ def bode_plot(
             (noutputs if plot_phase else 0)
         ncols = ninputs
 
-    # See if we can use the current figure axes
-    fig = plt.gcf()         # get current figure (or create new one)
-    if ax is None and plt.get_fignums():
-        ax = fig.get_axes()
-        if len(ax) == nrows * ncols:
-            # Assume that the shape is right (no easy way to infer this)
-            ax = np.array(ax).reshape(nrows, ncols)
-
-            # Clear out any old text from the current figure
-            for text in fig.texts:
-                text.set_visible(False)         # turn off the text
-                del text                        # get rid of it completely
-
-        elif len(ax) != 0:
-            # Need to generate a new figure
-            fig, ax = plt.figure(), None
-
-        else:
-            # Blank figure, just need to recreate axes
-            ax = None
-
-    # Create new axes, if needed, and customize them
     if ax is None:
-        with plt.rc_context(_freqplot_rcParams):
-            ax_array = fig.subplots(nrows, ncols, squeeze=False)
-            fig.set_layout_engine('tight')
-            fig.align_labels()
-
         # Set up default sharing of axis limits if not specified
         for kw in ['share_magnitude', 'share_phase', 'share_frequency']:
             if kw not in kwargs or kwargs[kw] is None:
                 kwargs[kw] = config.defaults['freqplot.' + kw]
 
-    else:
-        # Make sure the axes are the right shape
-        if ax.shape != (nrows, ncols):
-            raise ValueError(
-                "specified axes are not the right shape; "
-                f"got {ax.shape} but expecting ({nrows}, {ncols})")
-        ax_array = ax
-        fig = ax_array[0, 0].figure     # just in case this is not gcf()
+    fig, ax_array = _process_ax_keyword(ax, (
+        nrows, ncols), squeeze=False, rcParams=rcParams, clear_text=True)
 
     # Get the values for sharing axes limits
     share_magnitude = kwargs.pop('share_magnitude', None)
@@ -624,6 +607,9 @@ def _share_axes(ref, share_map, axis):
         for j in range(ncols):
             out[i, j] = []      # unique list in each element
 
+    # Process label keyword
+    line_labels = _process_line_labels(label, len(data), ninputs, noutputs)
+
     # Utility function for creating line label
     def _make_line_label(response, output_index, input_index):
         label = ""              # start with an empty label
@@ -664,7 +650,10 @@ def _make_line_label(response, output_index, input_index):
             phase_plot = phase[i, j] * 180. / math.pi if deg else phase[i, j]
 
             # Generate a label
-            label = _make_line_label(response, i, j)
+            if line_labels is None:
+                label = _make_line_label(response, i, j)
+            else:
+                label = line_labels[index, i, j]
 
             # Magnitude
             if plot_magnitude:
@@ -805,7 +794,7 @@ def _make_line_label(response, output_index, input_index):
                 axes_title = ax.get_title()
                 if axes_title is not None and axes_title != "":
                     axes_title += "\n"
-                with plt.rc_context(_freqplot_rcParams):
+                with plt.rc_context(rcParams):
                     ax.set_title(
                         axes_title + f"{sysname}: "
                         "Gm = %.2f %s(at %.2f %s), "
@@ -820,11 +809,11 @@ def _make_line_label(response, output_index, input_index):
     #
     # Finishing handling axes limit sharing
     #
-    # This code handles labels on phase plots and also removes tick labels
+    # This code handles labels on Bode plots and also removes tick labels
     # on shared axes.  It needs to come *after* the plots are generated,
     # in order to handle two things:
     #
-    # * manually generated labels and grids need to reflect the limts for
+    # * manually generated labels and grids need to reflect the limits for
     #   shared axes, which we don't know until we have plotted everything;
     #
     # * the loglog and semilog functions regenerate the labels (not quite
@@ -884,50 +873,6 @@ def gen_zero_centered_series(val_min, val_max, period):
         for i, j in itertools.product(range(nrows), range(ncols)):
             ax_array[i, j].set_xlim(omega_limits)
 
-    #
-    # Update the plot title (= figure suptitle)
-    #
-    # If plots are built up by multiple calls to plot() and the title is
-    # not given, then the title is updated to provide a list of unique text
-    # items in each successive title.  For data generated by the frequency
-    # response function this will generate a common prefix followed by a
-    # list of systems (e.g., "Step response for sys[1], sys[2]").
-    #
-
-    # Set the initial title for the data (unique system names, preserving order)
-    seen = set()
-    sysnames = [response.sysname for response in data \
-                if not (response.sysname in seen or seen.add(response.sysname))]
-    if title is None:
-        if data[0].title is None:
-            title = "Bode plot for " + ", ".join(sysnames)
-        else:
-            title = data[0].title
-
-    if fig is not None and isinstance(title, str):
-        # Get the current title, if it exists
-        old_title = None if fig._suptitle is None else fig._suptitle._text
-        new_title = title
-
-        if old_title is not None:
-            # Find the common part of the titles
-            common_prefix = commonprefix([old_title, new_title])
-
-            # Back up to the last space
-            last_space = common_prefix.rfind(' ')
-            if last_space > 0:
-                common_prefix = common_prefix[:last_space]
-            common_len = len(common_prefix)
-
-            # Add the new part of the title (usually the system name)
-            if old_title[common_len:] != new_title[common_len:]:
-                separator = ',' if len(common_prefix) > 0 else ';'
-                new_title = old_title + separator + new_title[common_len:]
-
-        # Add the title
-        with plt.rc_context(freqplot_rcParams):
-            fig.suptitle(new_title)
-
     #
     # Label the axes (including header labels)
     #
@@ -945,11 +890,12 @@ def gen_zero_centered_series(val_min, val_max, period):
         # If we have more than one column, label the individual responses
         if (noutputs > 1 and not overlay_outputs or ninputs > 1) \
            and not overlay_inputs:
-            with plt.rc_context(_freqplot_rcParams):
+            with plt.rc_context(rcParams):
                 ax_array[0, j].set_title(f"From {data[0].input_labels[j]}")
 
         # Label the frequency axis
-        ax_array[-1, j].set_xlabel(freq_label % ("Hz" if Hz else "rad/s",))
+        ax_array[-1, j].set_xlabel(
+            freq_label.format(units="Hz" if Hz else "rad/s"))
 
     # Label the rows
     for i in range(noutputs if not overlay_outputs else 1):
@@ -966,38 +912,71 @@ def gen_zero_centered_series(val_min, val_max, period):
                 ax_mag.set_ylabel("\n" + ax_mag.get_ylabel())
                 ax_phase.set_ylabel("\n" + ax_phase.get_ylabel())
 
-                # TODO: remove?
-                # Redraw the figure to get the proper locations for everything
-                # fig.tight_layout()
+                # Find the midpoint between the row axes (+ tight_layout)
+                _, ypos = _find_axes_center(fig, [ax_mag, ax_phase])
 
                 # Get the bounding box including the labels
                 inv_transform = fig.transFigure.inverted()
                 mag_bbox = inv_transform.transform(
                     ax_mag.get_tightbbox(fig.canvas.get_renderer()))
-                phase_bbox = inv_transform.transform(
-                    ax_phase.get_tightbbox(fig.canvas.get_renderer()))
-
-                # Get the axes limits without labels for use in the y position
-                mag_bot = inv_transform.transform(
-                    ax_mag.transAxes.transform((0, 0)))[1]
-                phase_top = inv_transform.transform(
-                    ax_phase.transAxes.transform((0, 1)))[1]
 
                 # Figure out location for the text (center left in figure frame)
                 xpos = mag_bbox[0, 0]               # left edge
-                ypos = (mag_bot + phase_top) / 2    # centered between axes
 
                 # Put a centered label as text outside the box
                 fig.text(
                     0.8 * xpos, ypos, f"To {data[0].output_labels[i]}\n",
                     rotation=90, ha='left', va='center',
-                    fontsize=_freqplot_rcParams['axes.titlesize'])
+                    fontsize=rcParams['axes.titlesize'])
             else:
                 # Only a single axes => add label to the left
                 ax_array[i, 0].set_ylabel(
                     f"To {data[0].output_labels[i]}\n" +
                     ax_array[i, 0].get_ylabel())
 
+    #
+    # Update the plot title (= figure suptitle)
+    #
+    # If plots are built up by multiple calls to plot() and the title is
+    # not given, then the title is updated to provide a list of unique text
+    # items in each successive title.  For data generated by the frequency
+    # response function this will generate a common prefix followed by a
+    # list of systems (e.g., "Step response for sys[1], sys[2]").
+    #
+
+    # Set the initial title for the data (unique system names, preserving order)
+    seen = set()
+    sysnames = [response.sysname for response in data \
+                if not (response.sysname in seen or seen.add(response.sysname))]
+    if title is None:
+        if data[0].title is None:
+            title = "Bode plot for " + ", ".join(sysnames)
+        else:
+            title = data[0].title
+
+    if fig is not None and isinstance(title, str):
+        # Get the current title, if it exists
+        old_title = None if fig._suptitle is None else fig._suptitle._text
+        new_title = title
+
+        if old_title is not None:
+            # Find the common part of the titles
+            common_prefix = commonprefix([old_title, new_title])
+
+            # Back up to the last space
+            last_space = common_prefix.rfind(' ')
+            if last_space > 0:
+                common_prefix = common_prefix[:last_space]
+            common_len = len(common_prefix)
+
+            # Add the new part of the title (usually the system name)
+            if old_title[common_len:] != new_title[common_len:]:
+                separator = ',' if len(common_prefix) > 0 else ';'
+                new_title = old_title + separator + new_title[common_len:]
+
+        # Add the title
+        suptitle(title, fig=fig, rcParams=rcParams, frame=suptitle_frame)
+
     #
     # Create legends
     #
@@ -1036,11 +1015,13 @@ def gen_zero_centered_series(val_min, val_max, period):
             # Get the labels to use, removing common strings
             lines = [line for line in ax.get_lines()
                      if line.get_label()[0] != '_']
-            labels = _make_legend_labels([line.get_label() for line in lines])
+            labels = _make_legend_labels(
+                [line.get_label() for line in lines],
+                ignore_common=line_labels is not None)
 
             # Generate the label, if needed
             if len(labels) > 1 and legend_map[i, j] != None:
-                with plt.rc_context(freqplot_rcParams):
+                with plt.rc_context(rcParams):
                     ax.legend(lines, labels, loc=legend_map[i, j])
 
     #
@@ -1170,12 +1151,6 @@ def nyquist_response(
         curves for each system are plotted on the same graph.
     omega : array_like, optional
         Set of frequencies to be evaluated, in rad/sec.
-    omega_limits : array_like of two values, optional
-        Limits to the range of frequencies. Ignored if omega is provided, and
-        auto-generated if omitted.
-    omega_num : int, optional
-        Number of frequency samples to plot.  Defaults to
-        config.defaults['freqplot.number_of_samples'].
 
     Returns
     -------
@@ -1196,23 +1171,25 @@ def nyquist_response(
         Define the threshold for generating a warning if the number of net
         encirclements is a non-integer value.  Default value is 0.05 and can
         be set using config.defaults['nyquist.encirclement_threshold'].
-
     indent_direction : str, optional
         For poles on the imaginary axis, set the direction of indentation to
         be 'right' (default), 'left', or 'none'.
-
     indent_points : int, optional
         Number of points to insert in the Nyquist contour around poles that
         are at or near the imaginary axis.
-
     indent_radius : float, optional
         Amount to indent the Nyquist contour around poles on or near the
         imaginary axis. Portions of the Nyquist plot corresponding to indented
         portions of the contour are plotted using a different line style.
-
+    omega_limits : array_like of two values
+        Set limits for plotted frequency range. If Hz=True the limits are
+        in Hz otherwise in rad/s.  Specifying ``omega`` as a list of two
+        elements is equivalent to providing ``omega_limits``.
+    omega_num : int, optional
+        Number of samples to use for the frequeny range.  Defaults to
+        config.defaults['freqplot.number_of_samples'].
     warn_nyquist : bool, optional
         If set to 'False', turn off warnings about frequencies above Nyquist.
-
     warn_encirclements : bool, optional
         If set to 'False', turn off warnings about number of encirclements not
         meeting the Nyquist criterion.
@@ -1245,6 +1222,10 @@ def nyquist_response(
        response object can be iterated over to return `count, contour`.
        This behavior is deprecated and will be removed in a future release.
 
+    See Also
+    --------
+    nyquist_plot
+
     Examples
     --------
     >>> G = ct.zpk([], [-1, -2, -3], gain=100)
@@ -1295,7 +1276,11 @@ def nyquist_response(
                 "Nyquist plot currently only supports SISO systems.")
 
         # Figure out the frequency range
-        omega_sys = np.asarray(omega)
+        if isinstance(sys, FrequencyResponseData) and sys.ifunc is None \
+           and not omega_range_given:
+            omega_sys = sys.omega               # use system frequencies
+        else:
+            omega_sys = np.asarray(omega)       # use common omega vector
 
         # Determine the contour used to evaluate the Nyquist curve
         if sys.isdtime(strict=True):
@@ -1491,8 +1476,9 @@ def nyquist_response(
 
 
 def nyquist_plot(
-        data, omega=None, plot=None, label_freq=0, color=None,
-        return_contour=None, title=None, legend_loc='upper right', **kwargs):
+        data, omega=None, plot=None, label_freq=0, color=None, label=None,
+        return_contour=None, title=None, legend_loc='upper right',
+        ax=None, **kwargs):
     """Nyquist plot for a system.
 
     Generates a Nyquist plot for the system over a (optional) frequency
@@ -1509,24 +1495,13 @@ def nyquist_plot(
         List of linear input/output systems (single system is OK) or
         Nyquist ersponses (computed using :func:`~control.nyquist_response`).
         Nyquist curves for each system are plotted on the same graph.
-
     omega : array_like, optional
-        Set of frequencies to be evaluated, in rad/sec.
-
-    omega_limits : array_like of two values, optional
-        Limits to the range of frequencies. Ignored if omega is provided, and
-        auto-generated if omitted.
-
-    omega_num : int, optional
-        Number of frequency samples to plot.  Defaults to
-        config.defaults['freqplot.number_of_samples'].
-
+        Set of frequencies to be evaluated, in rad/sec. Specifying
+        ``omega`` as a list of two elements is equivalent to providing
+        ``omega_limits``.
     color : string, optional
         Used to specify the color of the line and arrowhead.
 
-    return_contour : bool, optional
-        If 'True', return the contour used to evaluate the Nyquist plot.
-
     **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional
         Additional keywords (passed to `matplotlib`)
 
@@ -1554,81 +1529,87 @@ def nyquist_plot(
         a 2D array is passed, the first row will be used to specify arrow
         locations for the primary curve and the second row will be used for
         the mirror image.
-
     arrow_size : float, optional
         Arrowhead width and length (in display coordinates).  Default value is
         8 and can be set using config.defaults['nyquist.arrow_size'].
-
     arrow_style : matplotlib.patches.ArrowStyle, optional
         Define style used for Nyquist curve arrows (overrides `arrow_size`).
-
     encirclement_threshold : float, optional
         Define the threshold for generating a warning if the number of net
         encirclements is a non-integer value.  Default value is 0.05 and can
         be set using config.defaults['nyquist.encirclement_threshold'].
-
     indent_direction : str, optional
         For poles on the imaginary axis, set the direction of indentation to
         be 'right' (default), 'left', or 'none'.
-
     indent_points : int, optional
         Number of points to insert in the Nyquist contour around poles that
         are at or near the imaginary axis.
-
     indent_radius : float, optional
         Amount to indent the Nyquist contour around poles on or near the
         imaginary axis. Portions of the Nyquist plot corresponding to indented
         portions of the contour are plotted using a different line style.
-
+    label : str or array-like of str
+        If present, replace automatically generated label(s) with the given
+        label(s).  If sysdata is a list, strings should be specified for each
+        system.
     label_freq : int, optiona
         Label every nth frequency on the plot.  If not specified, no labels
         are generated.
-
     max_curve_magnitude : float, optional
         Restrict the maximum magnitude of the Nyquist plot to this value.
         Portions of the Nyquist plot whose magnitude is restricted are
         plotted using a different line style.
-
     max_curve_offset : float, optional
         When plotting scaled portion of the Nyquist plot, increase/decrease
         the magnitude by this fraction of the max_curve_magnitude to allow
         any overlaps between the primary and mirror curves to be avoided.
-
     mirror_style : [str, str] or False
         Linestyles for mirror image of the Nyquist curve.  The first element
         is used for unscaled portions of the Nyquist curve, the second element
         is used for portions that are scaled (using max_curve_magnitude).  If
         `False` then omit completely.  Default linestyle (['--', ':']) is
         determined by config.defaults['nyquist.mirror_style'].
-
+    omega_limits : array_like of two values
+        Set limits for plotted frequency range. If Hz=True the limits are
+        in Hz otherwise in rad/s.  Specifying ``omega`` as a list of two
+        elements is equivalent to providing ``omega_limits``.
+    omega_num : int, optional
+        Number of samples to use for the frequeny range.  Defaults to
+        config.defaults['freqplot.number_of_samples'].  Ignored if data is
+        not a list of systems.
     plot : bool, optional
         (legacy) If given, `bode_plot` returns the legacy return values
         of magnitude, phase, and frequency.  If False, just return the
         values with no plot.
-
     primary_style : [str, str], optional
         Linestyles for primary image of the Nyquist curve.  The first
         element is used for unscaled portions of the Nyquist curve,
         the second element is used for portions that are scaled (using
         max_curve_magnitude).  Default linestyle (['-', '-.']) is
         determined by config.defaults['nyquist.mirror_style'].
-
+    rcParams : dict
+        Override the default parameters used for generating plots.
+        Default is set by config.default['freqplot.rcParams'].
+    return_contour : bool, optional
+        (legacy) If 'True', return the encirclement count and Nyquist
+        contour used to generate the Nyquist plot.
     start_marker : str, optional
         Matplotlib marker to use to mark the starting point of the Nyquist
         plot.  Defaults value is 'o' and can be set using
         config.defaults['nyquist.start_marker'].
-
     start_marker_size : float, optional
         Start marker size (in display coordinates).  Default value is
         4 and can be set using config.defaults['nyquist.start_marker_size'].
-
     warn_nyquist : bool, optional
         If set to 'False', turn off warnings about frequencies above Nyquist.
-
     warn_encirclements : bool, optional
         If set to 'False', turn off warnings about number of encirclements not
         meeting the Nyquist criterion.
 
+    See Also
+    --------
+    nyquist_response
+
     Notes
     -----
     1. If a discrete time model is given, the frequency response is computed
@@ -1684,10 +1665,14 @@ def nyquist_plot(
         'nyquist', 'max_curve_magnitude', kwargs, _nyquist_defaults, pop=True)
     max_curve_offset = config._get_param(
         'nyquist', 'max_curve_offset', kwargs, _nyquist_defaults, pop=True)
+    rcParams = config._get_param(
+        'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True)
     start_marker = config._get_param(
         'nyquist', 'start_marker', kwargs, _nyquist_defaults, pop=True)
     start_marker_size = config._get_param(
         'nyquist', 'start_marker_size', kwargs, _nyquist_defaults, pop=True)
+    suptitle_frame = config._get_param(
+        'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True)
 
     # Set line styles for the curves
     def _parse_linestyle(style_name, allow_false=False):
@@ -1729,6 +1714,9 @@ def _parse_linestyle(style_name, allow_false=False):
     if not isinstance(data, (list, tuple)):
         data = [data]
 
+    # Process label keyword
+    line_labels = _process_line_labels(label, len(data))
+
     # If we are passed a list of systems, compute response first
     if all([isinstance(
             sys, (StateSpace, TransferFunction, FrequencyResponseData))
@@ -1740,6 +1728,7 @@ def _parse_linestyle(style_name, allow_false=False):
             omega_num=kwargs.pop('omega_num', None),
             warn_encirclements=kwargs.pop('warn_encirclements', True),
             warn_nyquist=kwargs.pop('warn_nyquist', True),
+            indent_radius=kwargs.pop('indent_radius', None),
             check_kwargs=False, **kwargs)
     else:
         nyquist_responses = data
@@ -1765,6 +1754,9 @@ def _parse_linestyle(style_name, allow_false=False):
         # Return counts and (optionally) the contour we used
         return (counts, contours) if return_contour else counts
 
+    fig, ax = _process_ax_keyword(
+        ax, shape=(1, 1), squeeze=True, rcParams=rcParams)
+
     # Create a list of lines for the output
     out = np.empty(len(nyquist_responses), dtype=object)
     for i in range(out.shape[0]):
@@ -1794,12 +1786,14 @@ def _parse_linestyle(style_name, allow_false=False):
             reg_mask, abs(resp) > max_curve_magnitude)
         resp[rescale] *= max_curve_magnitude / abs(resp[rescale])
 
+        # Get the label to use for the line
+        label = response.sysname if line_labels is None else line_labels[idx]
+
         # Plot the regular portions of the curve (and grab the color)
         x_reg = np.ma.masked_where(reg_mask, resp.real)
         y_reg = np.ma.masked_where(reg_mask, resp.imag)
         p = plt.plot(
-            x_reg, y_reg, primary_style[0], color=color,
-            label=response.sysname, **kwargs)
+            x_reg, y_reg, primary_style[0], color=color, label=label, **kwargs)
         c = p[0].get_color()
         out[idx] += p
 
@@ -1888,7 +1882,6 @@ def _parse_linestyle(style_name, allow_false=False):
                          prefix + 'Hz')
 
     # Label the axes
-    fig, ax = plt.gcf(), plt.gca()
     ax.set_xlabel("Real axis")
     ax.set_ylabel("Imaginary axis")
     ax.grid(color="lightgray")
@@ -1903,7 +1896,7 @@ def _parse_linestyle(style_name, allow_false=False):
     # Add the title
     if title is None:
         title = "Nyquist plot for " + ", ".join(labels)
-    fig.suptitle(title)
+    suptitle(title, fig=fig, rcParams=rcParams, frame=suptitle_frame)
 
     # Legacy return pocessing
     if plot is True or return_contour is not None:
@@ -2056,7 +2049,8 @@ def _compute_curve_offset(resp, mask, max_offset):
 #
 # Gang of Four plot
 #
-def gangof4_response(P, C, omega=None, Hz=False):
+def gangof4_response(
+        P, C, omega=None, omega_limits=None, omega_num=None, Hz=False):
     """Compute the response of the "Gang of 4" transfer functions for a system.
 
     Generates a 2x2 frequency response for the "Gang of 4" sensitivity
@@ -2065,9 +2059,9 @@ def gangof4_response(P, C, omega=None, Hz=False):
     Parameters
     ----------
     P, C : LTI
-        Linear input/output systems (process and control)
+        Linear input/output systems (process and control).
     omega : array
-        Range of frequencies (list or bounds) in rad/sec
+        Range of frequencies (list or bounds) in rad/sec.
 
     Returns
     -------
@@ -2095,8 +2089,8 @@ def gangof4_response(P, C, omega=None, Hz=False):
 
     # Select a default range if none is provided
     # TODO: This needs to be made more intelligent
-    if omega is None:
-        omega = _default_frequency_range((P, C, S), Hz=Hz)
+    omega, _ = _determine_omega_vector(
+        [P, C, S], omega, omega_limits, omega_num, Hz=Hz)
 
     #
     # bode_plot based implementation
@@ -2120,9 +2114,12 @@ def gangof4_response(P, C, omega=None, Hz=False):
         title=f"Gang of Four for P={P.name}, C={C.name}", plot_phase=False)
 
 
-def gangof4_plot(P, C, omega=None, **kwargs):
+def gangof4_plot(
+        P, C, omega=None, omega_limits=None, omega_num=None, **kwargs):
     """Legacy Gang of 4 plot; use gangof4_response().plot() instead."""
-    return gangof4_response(P, C).plot(**kwargs)
+    return gangof4_response(
+        P, C, omega=omega, omega_limits=omega_limits,
+        omega_num=omega_num).plot(**kwargs)
 
 #
 # Singular values plot
@@ -2140,15 +2137,9 @@ def singular_values_response(
         List of linear input/output systems (single system is OK).
     omega : array_like
         List of frequencies in rad/sec to be used for frequency response.
-    omega_limits : array_like of two values
-        Limits of the frequency vector to generate, in rad/s.
-    omega_num : int
-        Number of samples to plot. Default value (1000) set by
-        config.defaults['freqplot.number_of_samples'].
     Hz : bool, optional
         If True, when computing frequency limits automatically set
-        limits to full decades in Hz instead of rad/s. Omega is always
-        returned in rad/sec.
+        limits to full decades in Hz instead of rad/s.
 
     Returns
     -------
@@ -2156,6 +2147,20 @@ def singular_values_response(
         Frequency response with the number of outputs equal to the
         number of singular values in the response, and a single input.
 
+    Other Parameters
+    ----------------
+    omega_limits : array_like of two values
+        Set limits for plotted frequency range. If Hz=True the limits are
+        in Hz otherwise in rad/s.  Specifying ``omega`` as a list of two
+        elements is equivalent to providing ``omega_limits``.
+    omega_num : int, optional
+        Number of samples to use for the frequeny range.  Defaults to
+        config.defaults['freqplot.number_of_samples'].
+
+    See Also
+    --------
+    singular_values_plot
+
     Examples
     --------
     >>> omegas = np.logspace(-4, 1, 1000)
@@ -2201,7 +2206,7 @@ def singular_values_response(
 
 def singular_values_plot(
         data, omega=None, *fmt, plot=None, omega_limits=None, omega_num=None,
-        title=None, legend_loc='center right', **kwargs):
+        ax=None, label=None, title=None, legend_loc='center right', **kwargs):
     """Plot the singular values for a system.
 
     Plot the singular values as a function of frequency for a system or
@@ -2223,14 +2228,14 @@ def singular_values_plot(
     Hz : bool
         If True, plot frequency in Hz (omega must be provided in rad/sec).
         Default value (False) set by config.defaults['freqplot.Hz'].
-    legend_loc : str, optional
-        For plots with multiple lines, a legend will be included in the
-        given location.  Default is 'center right'.  Use False to supress.
     **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional
         Additional keywords passed to `matplotlib` to specify line properties.
 
     Returns
     -------
+    legend_loc : str, optional
+        For plots with multiple lines, a legend will be included in the
+        given location.  Default is 'center right'.  Use False to suppress.
     lines : array of Line2D
         1-D array of Line2D objects.  The size of the array matches
         the number of systems and the value of the array is a list of
@@ -2247,12 +2252,17 @@ def singular_values_plot(
     grid : bool
         If True, plot grid lines on gain and phase plots.  Default is set by
         `config.defaults['freqplot.grid']`.
+    label : str or array-like of str
+        If present, replace automatically generated label(s) with the given
+        label(s).  If sysdata is a list, strings should be specified for each
+        system.
     omega_limits : array_like of two values
-        Set limits for plotted frequency range. If Hz=True the limits
-        are in Hz otherwise in rad/s.
-    omega_num : int
+        Set limits for plotted frequency range. If Hz=True the limits are
+        in Hz otherwise in rad/s.  Specifying ``omega`` as a list of two
+        elements is equivalent to providing ``omega_limits``.
+    omega_num : int, optional
         Number of samples to use for the frequeny range.  Defaults to
-        config.defaults['freqplot.number_of_samples'].  Ignore if data is
+        config.defaults['freqplot.number_of_samples'].  Ignored if data is
         not a list of systems.
     plot : bool, optional
         (legacy) If given, `singular_values_plot` returns the legacy return
@@ -2262,6 +2272,10 @@ def singular_values_plot(
         Override the default parameters used for generating plots.
         Default is set up config.default['freqplot.rcParams'].
 
+    See Also
+    --------
+    singular_values_response
+
     """
     # Keyword processing
     dB = config._get_param(
@@ -2270,8 +2284,10 @@ def singular_values_plot(
         'freqplot', 'Hz', kwargs, _freqplot_defaults, pop=True)
     grid = config._get_param(
         'freqplot', 'grid', kwargs, _freqplot_defaults, pop=True)
-    freqplot_rcParams = config._get_param(
+    rcParams = config._get_param(
         'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True)
+    suptitle_frame = config._get_param(
+        'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True)
 
     # If argument was a singleton, turn it into a tuple
     data = data if isinstance(data, (list, tuple)) else (data,)
@@ -2296,6 +2312,9 @@ def singular_values_plot(
 
         responses = data
 
+    # Process label keyword
+    line_labels = _process_line_labels(label, len(data))
+
     # Process (legacy) plot keyword
     if plot is not None:
         warnings.warn(
@@ -2318,22 +2337,9 @@ def singular_values_plot(
         else:
             return sigmas, omegas
 
-    fig = plt.gcf()             # get current figure (or create new one)
-    ax_sigma = None             # axes for plotting singular values
-
-    # Get the current axes if they already exist
-    for ax in fig.axes:
-        if ax.get_label() == 'control-sigma':
-            ax_sigma = ax
-
-    # If no axes present, create them from scratch
-    if ax_sigma is None:
-        if len(fig.axes) > 0:
-            # Create a new figure to avoid overwriting in the old one
-            fig = plt.figure()
-
-        with plt.rc_context(_freqplot_rcParams):
-            ax_sigma = plt.subplot(111, label='control-sigma')
+    fig, ax_sigma = _process_ax_keyword(
+        ax, shape=(1, 1), squeeze=True, rcParams=rcParams)
+    ax_sigma.set_label('control-sigma')         # TODO: deprecate?
 
     # Handle color cycle manually as all singular values
     # of the same systems are expected to be of the same color
@@ -2370,16 +2376,17 @@ def singular_values_plot(
         sysname = response.sysname if response.sysname is not None \
             else f"Unknown-{idx_sys}"
 
+        # Get the label to use for the line
+        label = sysname if line_labels is None else line_labels[idx_sys]
+
         # Plot the data
         if dB:
-            with plt.rc_context(freqplot_rcParams):
-                out[idx_sys] = ax_sigma.semilogx(
-                    omega, 20 * np.log10(sigma), *fmt,
-                    label=sysname, **color_arg, **kwargs)
+            out[idx_sys] = ax_sigma.semilogx(
+                omega, 20 * np.log10(sigma), *fmt,
+                label=label, **color_arg, **kwargs)
         else:
-            with plt.rc_context(freqplot_rcParams):
-                out[idx_sys] = ax_sigma.loglog(
-                    omega, sigma, label=sysname, *fmt, **color_arg, **kwargs)
+            out[idx_sys] = ax_sigma.loglog(
+                omega, sigma, label=label, *fmt, **color_arg, **kwargs)
 
         # Plot the Nyquist frequency
         if nyq_freq is not None:
@@ -2394,24 +2401,23 @@ def singular_values_plot(
     # Add a grid to the plot + labeling
     if grid:
         ax_sigma.grid(grid, which='both')
-    with plt.rc_context(freqplot_rcParams):
-        ax_sigma.set_ylabel(
-            "Singular Values [dB]" if dB else "Singular Values")
-        ax_sigma.set_xlabel("Frequency [Hz]" if Hz else "Frequency [rad/sec]")
+
+    ax_sigma.set_ylabel(
+        "Singular Values [dB]" if dB else "Singular Values")
+    ax_sigma.set_xlabel("Frequency [Hz]" if Hz else "Frequency [rad/sec]")
 
     # List of systems that are included in this plot
     lines, labels = _get_line_labels(ax_sigma)
 
     # Add legend if there is more than one system plotted
     if len(labels) > 1 and legend_loc is not False:
-        with plt.rc_context(freqplot_rcParams):
+        with plt.rc_context(rcParams):
             ax_sigma.legend(lines, labels, loc=legend_loc)
 
     # Add the title
     if title is None:
         title = "Singular values for " + ", ".join(labels)
-    with plt.rc_context(freqplot_rcParams):
-        fig.suptitle(title)
+    suptitle(title, fig=fig, rcParams=rcParams, frame=suptitle_frame)
 
     # Legacy return processing
     if plot is not None:
@@ -2426,7 +2432,7 @@ def singular_values_plot(
 # Utility functions
 #
 # This section of the code contains some utility functions for
-# generating frequency domain plots
+# generating frequency domain plots.
 #
 
 
@@ -2440,24 +2446,32 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num,
     on omega_num points according to a default logic defined by
     _default_frequency_range and tailored for the list of systems syslist, and
     omega_range_given is set to False.
+
     If omega_in is None but omega_limits is an array-like of 2 elements, then
     omega_out is computed with the function np.logspace on omega_num points
     within the interval [min, max] =  [omega_limits[0], omega_limits[1]], and
     omega_range_given is set to True.
-    If omega_in is not None, then omega_out is set to omega_in,
-    and omega_range_given is set to True
+
+    If omega_in is a list or tuple of length 2, it is interpreted as a
+    range and handled like omega_limits.  If omega_in is a list or tuple of
+    length 3, it is interpreted a range plus number of points and handled
+    like omega_limits and omega_num.
+
+    If omega_in is an array or a list/tuple of length greater than
+    two, then omega_out is set to omega_in (as an array), and
+    omega_range_given is set to True
 
     Parameters
     ----------
     syslist : list of LTI
-        List of linear input/output systems (single system is OK)
+        List of linear input/output systems (single system is OK).
     omega_in : 1D array_like or None
-        Frequency range specified by the user
+        Frequency range specified by the user.
     omega_limits : 1D array_like or None
-        Frequency limits specified by the user
+        Frequency limits specified by the user.
     omega_num : int
-        Number of points to be used for the frequency
-        range (if the frequency range is not user-specified)
+        Number of points to be used for the frequency range (if the
+        frequency range is not user-specified).
     Hz : bool, optional
         If True, the limits (first and last value) of the frequencies
         are set to full decades in Hz so it fits plotting with logarithmic
@@ -2466,22 +2480,22 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num,
     Returns
     -------
     omega_out : 1D array
-        Frequency range to be used
+        Frequency range to be used.
     omega_range_given : bool
         True if the frequency range was specified by the user, either through
         omega_in or through omega_limits. False if both omega_in
         and omega_limits are None.
-    """
-    omega_range_given = True
 
-    if omega_in is None:
-        for sys in syslist:
-            if isinstance(sys, FrequencyResponseData):
-                # FRD already has predetermined frequencies
-                if omega_in is not None and not np.all(omega_in == sys.omega):
-                    raise ValueError("List of FrequencyResponseData systems can only have a single frequency range between them")
-                omega_in = sys.omega
+    """
+    # Handle the special case of a range of frequencies
+    if omega_in is not None and omega_limits is not None:
+        warnings.warn(
+            "omega and omega_limits both specified; ignoring limits")
+    elif isinstance(omega_in, (list, tuple)) and len(omega_in) == 2:
+        omega_limits = omega_in
+        omega_in = None
 
+    omega_range_given = True
     if omega_in is None:
         if omega_limits is None:
             omega_range_given = False
@@ -2557,6 +2571,15 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None,
         syslist = (syslist,)
 
     for sys in syslist:
+        # For FRD systems, just use the response frequencies
+        if isinstance(sys, FrequencyResponseData):
+            # Add the min and max frequency, minus periphery decades
+            # (keeps frequency ranges from artificially expanding)
+            features = np.concatenate([features, np.array([
+                np.min(sys.omega) * 10**feature_periphery_decades,
+                np.max(sys.omega) / 10**feature_periphery_decades])])
+            continue
+
         try:
             # Add new features to the list
             if sys.isctime():
@@ -2571,7 +2594,8 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None,
                 # TODO: What distance to the Nyquist frequency is appropriate?
                 freq_interesting.append(fn * 0.9)
 
-                features_ = np.concatenate((sys.poles(), sys.zeros()))
+                features_ = np.concatenate(
+                    (np.abs(sys.poles()), np.abs(sys.zeros())))
                 # Get rid of poles and zeros on the real axis (imag==0)
                 # * origin and real < 0
                 # * at 1.: would result in omega=0. (logaritmic plot!)
@@ -2586,8 +2610,9 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None,
                 # TODO
                 raise NotImplementedError(
                     "type of system in not implemented now")
-            features = np.concatenate((features, features_))
+            features = np.concatenate([features, features_])
         except NotImplementedError:
+            # Don't add any features for anything we don't understand
             pass
 
     # Make sure there is at least one point in the range
@@ -2641,6 +2666,97 @@ def _get_line_labels(ax, use_color=True):
 
     return lines, labels
 
+
+# Turn label keyword into array indexed by trace, output, input
+def _process_line_labels(label, nsys, ninputs=0, noutputs=0):
+    if label is None:
+        return None
+
+    if isinstance(label, str):
+        label = [label]
+
+    # Convert to an ndarray, if not done aleady
+    try:
+        line_labels = np.asarray(label)
+    except:
+        raise ValueError("label must be a string or array_like")
+
+    # Turn the data into a 3D array of appropriate shape
+    # TODO: allow more sophisticated broadcasting (and error checking)
+    try:
+        if ninputs > 0 and noutputs > 0:
+            if line_labels.ndim == 1:
+                line_labels = line_labels.reshape(nsys, 1, 1)
+            line_labels = np.broadcast_to(
+                line_labels,(nsys, ninputs, noutputs))
+    except:
+        if line_labels.shape[0] != nsys:
+            raise ValueError("number of labels must match number of traces")
+        else:
+            raise ValueError("labels must be given for each input/output pair")
+
+    return line_labels
+
+
+def _process_ax_keyword(
+        axs, shape=(1, 1), rcParams=None, squeeze=False, clear_text=False):
+    """Utility function to process ax keyword to plotting commands.
+
+    This function processes the `ax` keyword to plotting commands.  If no
+    ax keyword is passed, the current figure is checked to see if it has
+    the correct shape.  If the shape matches the desired shape, then the
+    current figure and axes are returned.  Otherwise a new figure is
+    created with axes of the desired shape.
+
+    Legacy behavior: some of the older plotting commands use a axes label
+    to identify the proper axes for plotting.  This behavior is supported
+    through the use of the label keyword, but will only work if shape ==
+    (1, 1) and squeeze == True.
+
+    """
+    if axs is None:
+        fig = plt.gcf()         # get current figure (or create new one)
+        axs = fig.get_axes()
+
+        # Check to see if axes are the right shape; if not, create new figure
+        # Note: can't actually check the shape, just the total number of axes
+        if len(axs) != np.prod(shape):
+            with plt.rc_context(rcParams):
+                if len(axs) != 0:
+                    # Create a new figure
+                    fig, axs = plt.subplots(*shape, squeeze=False)
+                else:
+                    # Create new axes on (empty) figure
+                    axs = fig.subplots(*shape, squeeze=False)
+            fig.set_layout_engine('tight')
+            fig.align_labels()
+        else:
+            # Use the existing axes, properly reshaped
+            axs = np.asarray(axs).reshape(*shape)
+
+            if clear_text:
+                # Clear out any old text from the current figure
+                for text in fig.texts:
+                    text.set_visible(False)     # turn off the text
+                    del text                    # get rid of it completely
+    else:
+        try:
+            axs = np.asarray(axs).reshape(shape)
+        except ValueError:
+            raise ValueError(
+                "specified axes are not the right shape; "
+                f"got {axs.shape} but expecting {shape}")
+        fig = axs[0, 0].figure
+
+    # Process the squeeze keyword
+    if squeeze and shape == (1, 1):
+        axs = axs[0, 0]         # Just return the single axes object
+    elif squeeze:
+        axs = axs.squeeze()
+
+    return fig, axs
+
+
 #
 # Utility functions to create nice looking labels (KLD 5/23/11)
 #
diff --git a/control/lti.py b/control/lti.py
index 65a500121..2d69f6b91 100644
--- a/control/lti.py
+++ b/control/lti.py
@@ -386,16 +386,18 @@ def frequency_response(
     sysdata : LTI system or list of LTI systems
         Linear system(s) for which frequency response is computed.
     omega : float or 1D array_like, optional
-        A list of frequencies in radians/sec at which the system should be
-        evaluated. The list can be either a Python list or a numpy array
-        and will be sorted before evaluation.  If None (default), a common
-        set of frequencies that works across all given systems is computed.
+        Frequencies in radians/sec at which the system should be
+        evaluated. Can be a single frequency or array of frequencies, which
+        will be sorted before evaluation.  If None (default), a common set
+        of frequencies that works across all given systems is computed.
     omega_limits : array_like of two values, optional
-        Limits to the range of frequencies, in rad/sec. Ignored if
-        omega is provided, and auto-generated if omitted.
+        Limits to the range of frequencies, in rad/sec. Specifying
+        ``omega`` as a list of two elements is equivalent to providing
+        ``omega_limits``.  Ignored if omega is provided.
     omega_num : int, optional
-        Number of frequency samples to plot.  Defaults to
-        config.defaults['freqplot.number_of_samples'].
+        Number of frequency samples at which to compute the response.
+        Defaults to config.defaults['freqplot.number_of_samples'].  Ignored
+        if omega is provided.
 
     Returns
     -------
@@ -473,6 +475,7 @@ def frequency_response(
         #>>> # s = 0.1i, i, 10i.
 
     """
+    from .frdata import FrequencyResponseData
     from .freqplot import _determine_omega_vector
 
     # Process keyword arguments
@@ -487,13 +490,18 @@ def frequency_response(
 
     responses = []
     for sys_ in syslist:
-        # Add the Nyquist frequency for discrete time systems
-        omega_sys = omega_syslist.copy()
-        if sys_.isdtime(strict=True):
-            nyquistfrq = math.pi / sys_.dt
-            if not omega_range_given:
-                # Limit up to the Nyquist frequency
-                omega_sys = omega_sys[omega_sys < nyquistfrq]
+        if isinstance(sys_, FrequencyResponseData) and sys_.ifunc is None and \
+           not omega_range_given:
+            omega_sys = sys_.omega              # use system properties
+        else:
+            omega_sys = omega_syslist.copy()    # use common omega vector
+
+            # Add the Nyquist frequency for discrete time systems
+            if sys_.isdtime(strict=True):
+                nyquistfrq = math.pi / sys_.dt
+                if not omega_range_given:
+                    # Limit up to the Nyquist frequency
+                    omega_sys = omega_sys[omega_sys < nyquistfrq]
 
         # Compute the frequency response
         responses.append(sys_.frequency_response(omega_sys, squeeze=squeeze))
diff --git a/control/nichols.py b/control/nichols.py
index 1a5043cd4..5eafa594f 100644
--- a/control/nichols.py
+++ b/control/nichols.py
@@ -13,17 +13,18 @@
 nichols.nichols_grid
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.transforms
+import numpy as np
 
+from . import config
+from .ctrlplot import suptitle
 from .ctrlutil import unwrap
 from .freqplot import _default_frequency_range, _freqplot_defaults, \
-    _get_line_labels
+    _get_line_labels, _process_ax_keyword
 from .lti import frequency_response
 from .statesp import StateSpace
 from .xferfcn import TransferFunction
-from . import config
 
 __all__ = ['nichols_plot', 'nichols', 'nichols_grid']
 
@@ -34,7 +35,7 @@
 
 
 def nichols_plot(
-        data, omega=None, *fmt, grid=None, title=None,
+        data, omega=None, *fmt, grid=None, title=None, ax=None,
         legend_loc='upper left', **kwargs):
     """Nichols plot for a system.
 
@@ -67,7 +68,7 @@ def nichols_plot(
     """
     # Get parameter values
     grid = config._get_param('nichols', 'grid', grid, True)
-    freqplot_rcParams = config._get_param(
+    rcParams = config._get_param(
         'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True)
 
     # If argument was a singleton, turn it into a list
@@ -83,6 +84,8 @@ def nichols_plot(
     if any([resp.ninputs > 1 or resp.noutputs > 1 for resp in data]):
         raise NotImplementedError("MIMO Nichols plots not implemented")
 
+    fig, ax_nichols = _process_ax_keyword(ax, rcParams=rcParams, squeeze=True)
+
     # Create a list of lines for the output
     out = np.empty(len(data), dtype=object)
 
@@ -102,8 +105,7 @@ def nichols_plot(
             else f"Unknown-{idx_sys}"
 
         # Generate the plot
-        with plt.rc_context(freqplot_rcParams):
-            out[idx] = plt.plot(x, y, *fmt, label=sysname, **kwargs)
+        out[idx] = ax_nichols.plot(x, y, *fmt, label=sysname, **kwargs)
 
     # Label the plot axes
     plt.xlabel('Phase [deg]')
@@ -117,19 +119,17 @@ def nichols_plot(
         nichols_grid()
 
     # List of systems that are included in this plot
-    ax_nichols = plt.gca()
     lines, labels = _get_line_labels(ax_nichols)
 
     # Add legend if there is more than one system plotted
     if len(labels) > 1 and legend_loc is not False:
-        with plt.rc_context(freqplot_rcParams):
+        with plt.rc_context(rcParams):
             ax_nichols.legend(lines, labels, loc=legend_loc)
 
     # Add the title
     if title is None:
         title = "Nichols plot for " + ", ".join(labels)
-    with plt.rc_context(freqplot_rcParams):
-        plt.suptitle(title)
+    suptitle(title, fig=fig, rcParams=rcParams)
 
     return out
 
diff --git a/control/tests/frd_test.py b/control/tests/frd_test.py
index 25ecc5e21..e50af3c92 100644
--- a/control/tests/frd_test.py
+++ b/control/tests/frd_test.py
@@ -12,7 +12,7 @@
 import control as ct
 from control.statesp import StateSpace
 from control.xferfcn import TransferFunction
-from control.frdata import FRD, _convert_to_FRD, FrequencyResponseData
+from control.frdata import frd, _convert_to_frd, FrequencyResponseData
 from control import bdalg, evalfr, freqplot
 from control.tests.conftest import slycotonly
 from control.exception import pandas_check
@@ -25,35 +25,39 @@ class TestFRD:
     def testBadInputType(self):
         """Give the constructor invalid input types."""
         with pytest.raises(ValueError):
-            FRD()
+            frd()
         with pytest.raises(TypeError):
-            FRD([1])
+            frd([1])
 
     def testInconsistentDimension(self):
         with pytest.raises(TypeError):
-            FRD([1, 1], [1, 2, 3])
+            frd([1, 1], [1, 2, 3])
 
-    def testSISOtf(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testSISOtf(self, frd_fcn):
         # get a SISO transfer function
         h = TransferFunction([1], [1, 2, 2])
         omega = np.logspace(-1, 2, 10)
-        frd = FRD(h, omega)
-        assert isinstance(frd, FRD)
+        sys = frd_fcn(h, omega)
+        assert isinstance(sys, FrequencyResponseData)
 
-        mag1, phase1, omega1 = frd.frequency_response([1.0])
+        mag1, phase1, omega1 = sys.frequency_response([1.0])
         mag2, phase2, omega2 = h.frequency_response([1.0])
         np.testing.assert_array_almost_equal(mag1, mag2)
         np.testing.assert_array_almost_equal(phase1, phase2)
         np.testing.assert_array_almost_equal(omega1, omega2)
 
-    def testOperators(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testOperators(self, frd_fcn):
         # get two SISO transfer functions
         h1 = TransferFunction([1], [1, 2, 2])
         h2 = TransferFunction([1], [0.1, 1])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(h1, omega)
-        f2 = FRD(h2, omega)
+        f1 = frd_fcn(h1, omega)
+        f2 = frd_fcn(h2, omega)
 
         np.testing.assert_array_almost_equal(
             (f1 + f2).frequency_response(chkpts)[0],
@@ -90,14 +94,16 @@ def testOperators(self):
             (1.3 / f2).frequency_response(chkpts)[1],
             (1.3 / h2).frequency_response(chkpts)[1])
 
-    def testOperatorsTf(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testOperatorsTf(self, frd_fcn):
         # get two SISO transfer functions
         h1 = TransferFunction([1], [1, 2, 2])
         h2 = TransferFunction([1], [0.1, 1])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(h1, omega)
-        f2 = FRD(h2, omega)
+        f1 = frd_fcn(h1, omega)
+        f2 = frd_fcn(h2, omega)
         f2  # reference to avoid pyflakes error
 
         np.testing.assert_array_almost_equal(
@@ -121,14 +127,16 @@ def testOperatorsTf(self):
             (h1 / h2).frequency_response(chkpts)[1])
         # the reverse does not work
 
-    def testbdalg(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testbdalg(self, frd_fcn):
         # get two SISO transfer functions
         h1 = TransferFunction([1], [1, 2, 2])
         h2 = TransferFunction([1], [0.1, 1])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(h1, omega)
-        f2 = FRD(h2, omega)
+        f1 = frd_fcn(h1, omega)
+        f2 = frd_fcn(h2, omega)
 
         np.testing.assert_array_almost_equal(
             (bdalg.series(f1, f2)).frequency_response(chkpts)[0],
@@ -158,11 +166,13 @@ def testbdalg(self):
 #           (bdalg.connect(f3, Q, [2], [1])).frequency_response(chkpts)[0],
 #            (bdalg.connect(h3, Q, [2], [1])).frequency_response(chkpts)[0])
 
-    def testFeedback(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testFeedback(self, frd_fcn):
         h1 = TransferFunction([1], [1, 2, 2])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(h1, omega)
+        f1 = frd_fcn(h1, omega)
         np.testing.assert_array_almost_equal(
             f1.feedback(1).frequency_response(chkpts)[0],
             h1.feedback(1).frequency_response(chkpts)[0])
@@ -179,15 +189,17 @@ def testFeedback2(self):
 
     def testAuto(self):
         omega = np.logspace(-1, 2, 10)
-        f1 = _convert_to_FRD(1, omega)
-        f2 = _convert_to_FRD(np.array([[1, 0], [0.1, -1]]), omega)
-        f2 = _convert_to_FRD([[1, 0], [0.1, -1]], omega)
+        f1 = _convert_to_frd(1, omega)
+        f2 = _convert_to_frd(np.array([[1, 0], [0.1, -1]]), omega)
+        f2 = _convert_to_frd([[1, 0], [0.1, -1]], omega)
         f1, f2  # reference to avoid pyflakes error
 
-    def testNyquist(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testNyquist(self, frd_fcn):
         h1 = TransferFunction([1], [1, 2, 2])
         omega = np.logspace(-1, 2, 40)
-        f1 = FRD(h1, omega, smooth=True)
+        f1 = frd_fcn(h1, omega, smooth=True)
         freqplot.nyquist(f1, np.logspace(-1, 2, 100))
         # plt.savefig('/dev/null', format='svg')
         plt.figure(2)
@@ -197,14 +209,16 @@ def testNyquist(self):
         # plt.savefig('/dev/null', format='svg')
 
     @slycotonly
-    def testMIMO(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testMIMO(self, frd_fcn):
         sys = StateSpace([[-0.5, 0.0], [0.0, -1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[0.0, 0.0], [0.0, 0.0]])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(sys, omega)
+        f1 = frd_fcn(sys, omega)
         np.testing.assert_array_almost_equal(
             sys.frequency_response(chkpts)[0],
             f1.frequency_response(chkpts)[0])
@@ -213,15 +227,17 @@ def testMIMO(self):
             f1.frequency_response(chkpts)[1])
 
     @slycotonly
-    def testMIMOfb(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testMIMOfb(self, frd_fcn):
         sys = StateSpace([[-0.5, 0.0], [0.0, -1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[0.0, 0.0], [0.0, 0.0]])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(sys, omega).feedback([[0.1, 0.3], [0.0, 1.0]])
-        f2 = FRD(sys.feedback([[0.1, 0.3], [0.0, 1.0]]), omega)
+        f1 = frd_fcn(sys, omega).feedback([[0.1, 0.3], [0.0, 1.0]])
+        f2 = frd_fcn(sys.feedback([[0.1, 0.3], [0.0, 1.0]]), omega)
         np.testing.assert_array_almost_equal(
             f1.frequency_response(chkpts)[0],
             f2.frequency_response(chkpts)[0])
@@ -230,7 +246,9 @@ def testMIMOfb(self):
             f2.frequency_response(chkpts)[1])
 
     @slycotonly
-    def testMIMOfb2(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testMIMOfb2(self, frd_fcn):
         sys = StateSpace(np.array([[-2.0, 0, 0],
                                    [0, -1, 1],
                                    [0, 0, -3]]),
@@ -239,8 +257,8 @@ def testMIMOfb2(self):
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
         K = np.array([[1, 0.3, 0], [0.1, 0, 0]])
-        f1 = FRD(sys, omega).feedback(K)
-        f2 = FRD(sys.feedback(K), omega)
+        f1 = frd_fcn(sys, omega).feedback(K)
+        f2 = frd_fcn(sys.feedback(K), omega)
         np.testing.assert_array_almost_equal(
             f1.frequency_response(chkpts)[0],
             f2.frequency_response(chkpts)[0])
@@ -249,15 +267,17 @@ def testMIMOfb2(self):
             f2.frequency_response(chkpts)[1])
 
     @slycotonly
-    def testMIMOMult(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testMIMOMult(self, frd_fcn):
         sys = StateSpace([[-0.5, 0.0], [0.0, -1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[0.0, 0.0], [0.0, 0.0]])
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(sys, omega)
-        f2 = FRD(sys, omega)
+        f1 = frd_fcn(sys, omega)
+        f2 = frd_fcn(sys, omega)
         np.testing.assert_array_almost_equal(
             (f1*f2).frequency_response(chkpts)[0],
             (sys*sys).frequency_response(chkpts)[0])
@@ -266,7 +286,9 @@ def testMIMOMult(self):
             (sys*sys).frequency_response(chkpts)[1])
 
     @slycotonly
-    def testMIMOSmooth(self):
+    @pytest.mark.parametrize(
+        "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData])
+    def testMIMOSmooth(self, frd_fcn):
         sys = StateSpace([[-0.5, 0.0], [0.0, -1.0]],
                          [[1.0, 0.0], [0.0, 1.0]],
                          [[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
@@ -274,8 +296,8 @@ def testMIMOSmooth(self):
         sys2 = np.array([[1, 0, 0], [0, 1, 0]]) * sys
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(sys, omega, smooth=True)
-        f2 = FRD(sys2, omega, smooth=True)
+        f1 = frd_fcn(sys, omega, smooth=True)
+        f2 = frd_fcn(sys2, omega, smooth=True)
         np.testing.assert_array_almost_equal(
             (f1*f2).frequency_response(chkpts)[0],
             (sys*sys2).frequency_response(chkpts)[0])
@@ -296,55 +318,55 @@ def testAgainstOctave(self):
                          np.eye(3), np.zeros((3, 2)))
         omega = np.logspace(-1, 2, 10)
         chkpts = omega[::3]
-        f1 = FRD(sys, omega)
+        f1 = frd(sys, omega)
         np.testing.assert_array_almost_equal(
             (f1.frequency_response([1.0])[0] *
              np.exp(1j * f1.frequency_response([1.0])[1])).reshape(3, 2),
             np.array([[0.4 - 0.2j, 0], [0, 0.1 - 0.2j], [0, 0.3 - 0.1j]]))
 
     def test_string_representation(self, capsys):
-        sys = FRD([1, 2, 3], [4, 5, 6])
+        sys = frd([1, 2, 3], [4, 5, 6])
         print(sys)              # Just print without checking
 
     def test_frequency_mismatch(self, recwarn):
         # recwarn: there may be a warning before the error!
         # Overlapping but non-equal frequency ranges
-        sys1 = FRD([1, 2, 3], [4, 5, 6])
-        sys2 = FRD([2, 3, 4], [5, 6, 7])
+        sys1 = frd([1, 2, 3], [4, 5, 6])
+        sys2 = frd([2, 3, 4], [5, 6, 7])
         with pytest.raises(NotImplementedError):
-            FRD.__add__(sys1, sys2)
+            sys = sys1 + sys2
 
         # One frequency range is a subset of another
-        sys1 = FRD([1, 2, 3], [4, 5, 6])
-        sys2 = FRD([2, 3], [4, 5])
+        sys1 = frd([1, 2, 3], [4, 5, 6])
+        sys2 = frd([2, 3], [4, 5])
         with pytest.raises(NotImplementedError):
-            FRD.__add__(sys1, sys2)
+            sys = sys1 + sys2
 
     def test_size_mismatch(self):
-        sys1 = FRD(ct.rss(2, 2, 2), np.logspace(-1, 1, 10))
+        sys1 = frd(ct.rss(2, 2, 2), np.logspace(-1, 1, 10))
 
         # Different number of inputs
-        sys2 = FRD(ct.rss(3, 1, 2), np.logspace(-1, 1, 10))
+        sys2 = frd(ct.rss(3, 1, 2), np.logspace(-1, 1, 10))
         with pytest.raises(ValueError):
-            FRD.__add__(sys1, sys2)
+            sys = sys1 + sys2
 
         # Different number of outputs
-        sys2 = FRD(ct.rss(3, 2, 1), np.logspace(-1, 1, 10))
+        sys2 = frd(ct.rss(3, 2, 1), np.logspace(-1, 1, 10))
         with pytest.raises(ValueError):
-            FRD.__add__(sys1, sys2)
+            sys = sys1 + sys2
 
         # Inputs and outputs don't match
         with pytest.raises(ValueError):
-            FRD.__mul__(sys2, sys1)
+            sys = sys2 * sys1
 
         # Feedback mismatch
         with pytest.raises(ValueError):
-            FRD.feedback(sys2, sys1)
+            ct.feedback(sys2, sys1)
 
     def test_operator_conversion(self):
         sys_tf = ct.tf([1], [1, 2, 1])
-        frd_tf = FRD(sys_tf, np.logspace(-1, 1, 10))
-        frd_2 = FRD(2 * np.ones(10), np.logspace(-1, 1, 10))
+        frd_tf = frd(sys_tf, np.logspace(-1, 1, 10))
+        frd_2 = frd(2 * np.ones(10), np.logspace(-1, 1, 10))
 
         # Make sure that we can add, multiply, and feedback constants
         sys_add = frd_tf + 2
@@ -383,18 +405,18 @@ def test_operator_conversion(self):
         np.testing.assert_array_almost_equal(sys_rdiv.fresp, chk_rdiv.fresp)
 
         sys_pow = frd_tf**2
-        chk_pow = FRD(sys_tf**2, np.logspace(-1, 1, 10))
+        chk_pow = frd(sys_tf**2, np.logspace(-1, 1, 10))
         np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega)
         np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp)
 
         sys_pow = frd_tf**-2
-        chk_pow = FRD(sys_tf**-2, np.logspace(-1, 1, 10))
+        chk_pow = frd(sys_tf**-2, np.logspace(-1, 1, 10))
         np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega)
         np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp)
 
         # Assertion error if we try to raise to a non-integer power
         with pytest.raises(ValueError):
-            FRD.__pow__(frd_tf, 0.5)
+            frd_tf**0.5
 
         # Selected testing on transfer function conversion
         sys_add = frd_2 + sys_tf
@@ -402,18 +424,18 @@ def test_operator_conversion(self):
         np.testing.assert_array_almost_equal(sys_add.omega, chk_add.omega)
         np.testing.assert_array_almost_equal(sys_add.fresp, chk_add.fresp)
 
-        # Input/output mismatch size mismatch in  rmul
-        sys1 = FRD(ct.rss(2, 2, 2), np.logspace(-1, 1, 10))
+        # Input/output mismatch size mismatch in rmul
+        sys1 = frd(ct.rss(2, 2, 2), np.logspace(-1, 1, 10))
         with pytest.raises(ValueError):
-            FRD.__rmul__(frd_2, sys1)
+            FrequencyResponseData.__rmul__(frd_2, sys1)
 
         # Make sure conversion of something random generates exception
         with pytest.raises(TypeError):
-            FRD.__add__(frd_tf, 'string')
+            FrequencyResponseData.__add__(frd_tf, 'string')
 
     def test_eval(self):
         sys_tf = ct.tf([1], [1, 2, 1])
-        frd_tf = FRD(sys_tf, np.logspace(-1, 1, 3))
+        frd_tf = frd(sys_tf, np.logspace(-1, 1, 3))
         np.testing.assert_almost_equal(sys_tf(1j), frd_tf.eval(1))
         np.testing.assert_almost_equal(sys_tf(1j), frd_tf(1j))
 
@@ -431,45 +453,55 @@ def test_eval(self):
 
     def test_freqresp_deprecated(self):
         sys_tf = ct.tf([1], [1, 2, 1])
-        frd_tf = FRD(sys_tf, np.logspace(-1, 1, 3))
+        frd_tf = frd(sys_tf, np.logspace(-1, 1, 3))
         with pytest.warns(DeprecationWarning):
             frd_tf.freqresp(1.)
 
     def test_repr_str(self):
         # repr printing
         array = np.array
-        sys0 = FrequencyResponseData([1.0, 0.9+0.1j, 0.1+2j, 0.05+3j],
-                                     [0.1, 1.0, 10.0, 100.0])
-        sys1 = FrequencyResponseData(sys0.fresp, sys0.omega, smooth=True)
+        sys0 = ct.frd(
+            [1.0, 0.9+0.1j, 0.1+2j, 0.05+3j],
+            [0.1, 1.0, 10.0, 100.0], name='sys0')
+        sys1 = ct.frd(
+            sys0.fresp, sys0.omega, smooth=True, name='sys1')
         ref0 = "FrequencyResponseData(" \
             "array([[[1.  +0.j , 0.9 +0.1j, 0.1 +2.j , 0.05+3.j ]]])," \
             " array([  0.1,   1. ,  10. , 100. ]))"
         ref1 = ref0[:-1] + ", smooth=True)"
-        sysm = FrequencyResponseData(
-            np.matmul(array([[1],[2]]), sys0.fresp), sys0.omega)
+        sysm = ct.frd(
+            np.matmul(array([[1], [2]]), sys0.fresp), sys0.omega, name='sysm')
 
         assert repr(sys0) == ref0
         assert repr(sys1) == ref1
+
         sys0r = eval(repr(sys0))
         np.testing.assert_array_almost_equal(sys0r.fresp, sys0.fresp)
         np.testing.assert_array_almost_equal(sys0r.omega, sys0.omega)
+
         sys1r = eval(repr(sys1))
         np.testing.assert_array_almost_equal(sys1r.fresp, sys1.fresp)
         np.testing.assert_array_almost_equal(sys1r.omega, sys1.omega)
         assert(sys1.ifunc is not None)
 
-        refs = """Frequency response data
+        refs = """<FrequencyResponseData>: {sysname}
+Inputs (1): ['u[0]']
+Outputs (1): ['y[0]']
+
 Freq [rad/s]  Response
 ------------  ---------------------
        0.100           1        +0j
        1.000         0.9      +0.1j
       10.000         0.1        +2j
      100.000        0.05        +3j"""
-        assert str(sys0) == refs
-        assert str(sys1) == refs
+        assert str(sys0) == refs.format(sysname='sys0')
+        assert str(sys1) == refs.format(sysname='sys1')
 
         # print multi-input system
-        refm = """Frequency response data
+        refm = """<FrequencyResponseData>: sysm
+Inputs (2): ['u[0]', 'u[1]']
+Outputs (1): ['y[0]']
+
 Input 1 to output 1:
 Freq [rad/s]  Response
 ------------  ---------------------
@@ -490,7 +522,9 @@ def test_unrecognized_keyword(self):
         h = TransferFunction([1], [1, 2, 2])
         omega = np.logspace(-1, 2, 10)
         with pytest.raises(TypeError, match="unrecognized keyword"):
-            frd = FRD(h, omega, unknown=None)
+            sys = FrequencyResponseData(h, omega, unknown=None)
+        with pytest.raises(TypeError, match="unrecognized keyword"):
+            sys = ct.frd(h, omega, unknown=None)
 
 
 def test_named_signals():
@@ -498,8 +532,8 @@ def test_named_signals():
     h1 = TransferFunction([1], [1, 2, 2])
     h2 = TransferFunction([1], [0.1, 1])
     omega = np.logspace(-1, 2, 10)
-    f1 = FRD(h1, omega)
-    f2 = FRD(h2, omega)
+    f1 = frd(h1, omega)
+    f2 = frd(h2, omega)
 
     # Make sure that systems were properly named
     assert f1.name == 'sys[2]'
@@ -510,7 +544,7 @@ def test_named_signals():
     assert f1.output_labels == ['y[0]']
 
     # Change names
-    f1 = FRD(h1, omega, name='mysys', inputs='u0', outputs='y0')
+    f1 = frd(h1, omega, name='mysys', inputs='u0', outputs='y0')
     assert f1.name == 'mysys'
     assert f1.ninputs == 1
     assert f1.input_labels == ['u0']
@@ -523,7 +557,7 @@ def test_to_pandas():
     # Create a SISO frequency response
     h1 = TransferFunction([1], [1, 2, 2])
     omega = np.logspace(-1, 2, 10)
-    resp = FRD(h1, omega)
+    resp = frd(h1, omega)
 
     # Convert to pandas
     df = resp.to_pandas()
diff --git a/control/tests/freqplot_test.py b/control/tests/freqplot_test.py
index 5383f28a7..f7105cb96 100644
--- a/control/tests/freqplot_test.py
+++ b/control/tests/freqplot_test.py
@@ -7,7 +7,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from control.tests.conftest import slycotonly
+from control.tests.conftest import slycotonly, editsdefaults
 pytestmark = pytest.mark.usefixtures("mplcleanup")
 
 #
@@ -55,15 +55,19 @@
      (True,  True,   None,   'row',  True,   False,  False,  False),
      (True,  True,   'row',  None,   None,   False,  False,  True),
      ])
+@pytest.mark.usefixtures("editsdefaults")
 def test_response_plots(
         sys, pltmag, pltphs, shrmag, shrphs, shrfrq, secsys,
         ovlout, ovlinp, clear=True):
 
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
     # Save up the keyword arguments
     kwargs = dict(
         plot_magnitude=pltmag, plot_phase=pltphs,
         share_magnitude=shrmag, share_phase=shrphs, share_frequency=shrfrq,
-        overlay_outputs=ovlout, overlay_inputs=ovlinp
+        overlay_outputs=ovlout, overlay_inputs=ovlinp,
     )
 
     # Create the response
@@ -121,12 +125,12 @@ def test_response_plots(
 
     # Update the title so we can see what is going on
     fig = out[0, 0][0].axes.figure
-    fig.suptitle(
+    ct.suptitle(
         fig._suptitle._text +
         f" [{sys.noutputs}x{sys.ninputs}, pm={pltmag}, pp={pltphs},"
         f" sm={shrmag}, sp={shrphs}, sf={shrfrq}]",     # TODO: ", "
         # f"oo={ovlout}, oi={ovlinp}, ss={secsys}]",    # TODO: add back
-        fontsize='small')
+        frame='figure', fontsize='small')
 
     # Get rid of the figure to free up memory
     if clear:
@@ -150,7 +154,11 @@ def test_manual_response_limits():
 
 @pytest.mark.parametrize(
     "plt_fcn", [ct.bode_plot, ct.nichols_plot, ct.singular_values_plot])
+@pytest.mark.usefixtures("editsdefaults")
 def test_line_styles(plt_fcn):
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
     # Define a couple of systems for testing
     sys1 = ct.tf([1], [1, 2, 1], name='sys1')
     sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
@@ -181,6 +189,12 @@ def test_basic_freq_plots(savefigs=False):
     if savefigs:
         plt.savefig('freqplot-siso_bode-default.png')
 
+    plt.figure()
+    omega = np.logspace(-2, 2, 500)
+    ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0)
+    if savefigs:
+        plt.savefig('freqplot-siso_bode-omega.png')
+
     # Basic MIMO Bode plot
     plt.figure()
     sys_mimo = ct.tf(
@@ -213,6 +227,24 @@ def test_basic_freq_plots(savefigs=False):
     if savefigs:
         plt.savefig('freqplot-siso_nichols-default.png')
 
+    # Nyquist plot - default settings
+    plt.figure()
+    sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys')
+    ct.nyquist(sys)
+    if savefigs:
+        plt.savefig('freqplot-nyquist-default.png')
+
+    # Nyquist plot - custom settings
+    plt.figure()
+    sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0])
+    nyqresp = ct.nyquist_response(sys)
+    nyqresp.plot(
+        max_curve_magnitude=6, max_curve_offset=1,
+        arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], label='sys')
+    print("Encirclements =", nyqresp.count)
+    if savefigs:
+        plt.savefig('freqplot-nyquist-custom.png')
+
 
 def test_gangof4_plots(savefigs=False):
     proc = ct.tf([1], [1, 1, 1], name="process")
@@ -230,7 +262,11 @@ def test_gangof4_plots(savefigs=False):
     (ct.nyquist_response, ct.freqplot.NyquistResponseData),
     (ct.singular_values_response, ct.FrequencyResponseData),
 ])
+@pytest.mark.usefixtures("editsdefaults")
 def test_first_arg_listable(response_cmd, return_type):
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
     sys = ct.rss(2, 1, 1)
 
     # If we pass a single system, should get back a single system
@@ -262,7 +298,11 @@ def test_first_arg_listable(response_cmd, return_type):
     assert isinstance(result[0], return_type)
 
 
+@pytest.mark.usefixtures("editsdefaults")
 def test_bode_share_options():
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
     # Default sharing should share along rows and cols for mag and phase
     lines = ct.bode_plot(manual_response)
     axs = ct.get_plot_axes(lines)
@@ -321,7 +361,11 @@ def test_freqplot_plot_type(plot_type):
         assert lines.shape == (1, )
 
 @pytest.mark.parametrize("plt_fcn", [ct.bode_plot, ct.singular_values_plot])
+@pytest.mark.usefixtures("editsdefaults")
 def test_freqplot_omega_limits(plt_fcn):
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
     # Utility function to check visible limits
     def _get_visible_limits(ax):
         xticks = np.array(ax.get_xticks())
@@ -346,6 +390,195 @@ def _get_visible_limits(ax):
         _get_visible_limits(ax.reshape(-1)[0]), np.array([1, 100]))
 
 
+def test_gangof4_trace_labels():
+    P1 = ct.rss(2, 1, 1, name='P1')
+    P2 = ct.rss(3, 1, 1, name='P2')
+    C = ct.rss(1, 1, 1, name='C')
+
+    # Make sure default labels are as expected
+    out = ct.gangof4_response(P1, C).plot()
+    out = ct.gangof4_response(P2, C).plot()
+    axs = ct.get_plot_axes(out)
+    legend = axs[0, 1].get_legend().get_texts()
+    assert legend[0].get_text() == 'None'
+    assert legend[1].get_text() == 'None'
+    plt.close()
+
+    # Override labels
+    out = ct.gangof4_response(P1, C).plot(label='xxx, line1, yyy')
+    out = ct.gangof4_response(P2, C).plot(label='xxx, line2, yyy')
+    axs = ct.get_plot_axes(out)
+    legend = axs[0, 1].get_legend().get_texts()
+    assert legend[0].get_text() == 'xxx, line1, yyy'
+    assert legend[1].get_text() == 'xxx, line2, yyy'
+    plt.close()
+
+
+@pytest.mark.parametrize(
+    "plt_fcn", [ct.bode_plot, ct.singular_values_plot, ct.nyquist_plot])
+@pytest.mark.usefixtures("editsdefaults")
+def test_freqplot_trace_labels(plt_fcn):
+    sys1 = ct.rss(2, 1, 1, name='sys1')
+    sys2 = ct.rss(3, 1, 1, name='sys2')
+
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
+    # Make sure default labels are as expected
+    out = plt_fcn([sys1, sys2])
+    axs = ct.get_plot_axes(out)
+    if axs.ndim == 1:
+        legend = axs[0].get_legend().get_texts()
+    else:
+        legend = axs[0, 0].get_legend().get_texts()
+    assert legend[0].get_text() == 'sys1'
+    assert legend[1].get_text() == 'sys2'
+    plt.close()
+
+    # Override labels all at once
+    out = plt_fcn([sys1, sys2], label=['line1', 'line2'])
+    axs = ct.get_plot_axes(out)
+    if axs.ndim == 1:
+        legend = axs[0].get_legend().get_texts()
+    else:
+        legend = axs[0, 0].get_legend().get_texts()
+    assert legend[0].get_text() == 'line1'
+    assert legend[1].get_text() == 'line2'
+    plt.close()
+
+    # Override labels one at a time
+    out = plt_fcn(sys1, label='line1')
+    out = plt_fcn(sys2, label='line2')
+    axs = ct.get_plot_axes(out)
+    if axs.ndim == 1:
+        legend = axs[0].get_legend().get_texts()
+    else:
+        legend = axs[0, 0].get_legend().get_texts()
+    assert legend[0].get_text() == 'line1'
+    assert legend[1].get_text() == 'line2'
+    plt.close()
+
+    if plt_fcn == ct.bode_plot:
+        # Multi-dimensional data
+        sys1 = ct.rss(2, 2, 2, name='sys1')
+        sys2 = ct.rss(3, 2, 2, name='sys2')
+
+        # Check out some errors first
+        with pytest.raises(ValueError, match="number of labels must match"):
+            ct.bode_plot([sys1, sys2], label=['line1'])
+
+        with pytest.xfail(reason="need better broadcast checking on labels"):
+            with pytest.raises(
+                    ValueError, match="labels must be given for each"):
+                ct.bode_plot(sys1, overlay_inputs=True, label=['line1'])
+
+        # Now do things that should work
+        out = ct.bode_plot(
+            [sys1, sys2],
+            label=[
+                [['line1', 'line1'], ['line1', 'line1']],
+                [['line2', 'line2'], ['line2', 'line2']],
+            ])
+        axs = ct.get_plot_axes(out)
+        legend = axs[0, -1].get_legend().get_texts()
+        assert legend[0].get_text() == 'line1'
+        assert legend[1].get_text() == 'line2'
+        plt.close()
+
+
+@pytest.mark.parametrize(
+    "plt_fcn", [
+        ct.bode_plot, ct.singular_values_plot, ct.nyquist_plot,
+        ct.nichols_plot])
+@pytest.mark.parametrize(
+    "ninputs, noutputs", [(1, 1), (1, 2), (2, 1), (2, 3)])
+@pytest.mark.usefixtures("editsdefaults")
+def test_freqplot_ax_keyword(plt_fcn, ninputs, noutputs):
+    if plt_fcn in [ct.nyquist_plot, ct.nichols_plot] and \
+       (ninputs != 1 or noutputs != 1):
+        pytest.skip("MIMO not implemented for Nyquist/Nichols")
+
+    # Use figure frame for suptitle to speed things up
+    ct.set_defaults('freqplot', suptitle_frame='figure')
+
+    # System to use
+    sys = ct.rss(4, ninputs, noutputs)
+
+    # Create an initial figure
+    out1 = plt_fcn(sys)
+
+    # Draw again on the same figure, using array
+    axs = ct.get_plot_axes(out1)
+    out2 = plt_fcn(sys, ax=axs)
+    np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out2))
+
+    # Pass things in as a list instead
+    axs_list = axs.tolist()
+    out3 = plt_fcn(sys, ax=axs)
+    np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out3))
+
+    # Flatten the list
+    axs_list = axs.squeeze().tolist()
+    out3 = plt_fcn(sys, ax=axs_list)
+    np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out3))
+
+
+def test_mixed_systypes():
+    s = ct.tf('s')
+    sys_tf = ct.tf(
+        (0.02 * s**3 - 0.1 * s) / (s**4 + s**3 + s**2 + 0.25 * s + 0.04),
+        name='tf')
+    sys_ss = ct.ss(sys_tf * 2, name='ss')
+    sys_frd1 = ct.frd(sys_tf / 2, np.logspace(-1, 1, 15), name='frd1')
+    sys_frd2 = ct.frd(sys_tf / 4, np.logspace(-3, 2, 20), name='frd2')
+
+    # Simple case: compute responses separately and plot
+    resp_tf = ct.frequency_response(sys_tf)
+    resp_ss = ct.frequency_response(sys_ss)
+    plt.figure()
+    ct.bode_plot([resp_tf, resp_ss, sys_frd1, sys_frd2], plot_phase=False)
+    ct.suptitle("bode_plot([resp_tf, resp_ss, sys_frd1, sys_frd2])")
+
+    # Same thing, but using frequency response
+    plt.figure()
+    resp = ct.frequency_response([sys_tf, sys_ss, sys_frd1, sys_frd2])
+    resp.plot(plot_phase=False)
+    ct.suptitle("frequency_response([sys_tf, sys_ss, sys_frd1, sys_frd2])")
+
+    # Same thing, but using bode_plot
+    plt.figure()
+    resp = ct.bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2], plot_phase=False)
+    ct.suptitle("bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2])")
+
+
+def test_suptitle():
+    sys = ct.rss(2, 2, 2)
+
+    # Default location: center of axes
+    out = ct.bode_plot(sys)
+    assert plt.gcf()._suptitle._x != 0.5
+
+    # Try changing the the title
+    ct.suptitle("New title")
+    assert plt.gcf()._suptitle._text == "New title"
+
+    # Change the location of the title
+    ct.suptitle("New title", frame='figure')
+    assert plt.gcf()._suptitle._x == 0.5
+
+    # Change the location of the title back
+    ct.suptitle("New title", frame='axes')
+    assert plt.gcf()._suptitle._x != 0.5
+
+    # Bad frame
+    with pytest.raises(ValueError, match="unknown"):
+        ct.suptitle("New title", frame='nowhere')
+
+    # Bad keyword
+    with pytest.raises(AttributeError, match="unexpected keyword|no property"):
+        ct.suptitle("New title", unknown=None)
+
+
 @pytest.mark.parametrize("plt_fcn", [ct.bode_plot, ct.singular_values_plot])
 def test_freqplot_errors(plt_fcn):
     if plt_fcn == ct.bode_plot:
@@ -405,6 +638,9 @@ def test_freqplot_errors(plt_fcn):
     for args in test_cases:
         test_response_plots(*args, ovlinp=False, ovlout=False, clear=False)
 
+    # Reset suptitle_frame to the default value
+    ct.reset_defaults()
+
     # Define and run a selected set of interesting tests
     # TODO: TBD (see timeplot_test.py for format)
 
@@ -415,5 +651,4 @@ def test_freqplot_errors(plt_fcn):
     # Run a few more special cases to show off capabilities (and save some
     # of them for use in the documentation).
     #
-
-    pass
+    test_mixed_systypes()
diff --git a/control/tests/freqresp_test.py b/control/tests/freqresp_test.py
index 18c59384d..555adf332 100644
--- a/control/tests/freqresp_test.py
+++ b/control/tests/freqresp_test.py
@@ -709,3 +709,25 @@ def test_singular_values_plot_mpl_superimpose_nyq(ss_mimo_ct, ss_mimo_dt):
     assert(len(nyquist_line[0]) == 2)
     assert(nyquist_line[0][0] == nyquist_line[0][1])
     assert(nyquist_line[0][0] == np.pi/sys_dt.dt)
+
+
+def test_freqresp_omega_limits():
+    sys = ctrl.rss(4, 1, 1)
+
+    # Generate a standard frequency response (no limits specified)
+    resp0 = ctrl.frequency_response(sys)
+
+    # Regenerate the response using omega_limits
+    resp1 = ctrl.frequency_response(
+        sys, omega_limits=[resp0.omega[0], resp0.omega[-1]])
+    np.testing.assert_equal(resp0.omega, resp1.omega)
+
+    # Regenerate the response using omega as a list of two elements
+    resp2 = ctrl.frequency_response(sys, [resp0.omega[0], resp0.omega[-1]])
+    np.testing.assert_equal(resp0.omega, resp2.omega)
+    assert resp2.omega.size > 100
+
+    # Make sure that generating response using array does the right thing
+    resp3 = ctrl.frequency_response(
+        sys, np.array([resp0.omega[0], resp0.omega[-1]]))
+    np.testing.assert_equal(resp3.omega, [resp0.omega[0], resp0.omega[-1]])
diff --git a/control/tests/kwargs_test.py b/control/tests/kwargs_test.py
index 8180ff418..d6bd06487 100644
--- a/control/tests/kwargs_test.py
+++ b/control/tests/kwargs_test.py
@@ -21,6 +21,7 @@
 # List of all of the test modules where kwarg unit tests are defined
 import control.tests.flatsys_test as flatsys_test
 import control.tests.frd_test as frd_test
+import control.tests.freqplot_test as freqplot_test
 import control.tests.interconnect_test as interconnect_test
 import control.tests.optimal_test as optimal_test
 import control.tests.statefbk_test as statefbk_test
@@ -193,9 +194,9 @@ def test_matplotlib_kwargs(function, nsysargs, moreargs, kwargs, mplcleanup):
 def test_response_plot_kwargs(data_fcn, plot_fcn, mimo):
     # Create a system for testing
     if mimo:
-        response = data_fcn(control.rss(4, 2, 2))
+        response = data_fcn(control.rss(4, 2, 2, strictly_proper=True))
     else:
-        response = data_fcn(control.rss(4, 1, 1))
+        response = data_fcn(control.rss(4, 1, 1, strictly_proper=True))
 
     # Make sure that calling the data function with unknown keyword errs
     with pytest.raises(
@@ -242,6 +243,7 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo):
     'dlqr': test_unrecognized_kwargs,
     'drss': test_unrecognized_kwargs,
     'flatsys.flatsys': test_unrecognized_kwargs,
+    'frd': frd_test.TestFRD.test_unrecognized_keyword,
     'gangof4': test_matplotlib_kwargs,
     'gangof4_plot': test_matplotlib_kwargs,
     'input_output_response': test_unrecognized_kwargs,
@@ -269,6 +271,7 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo):
     'ss2io': test_unrecognized_kwargs,
     'ss2tf': test_unrecognized_kwargs,
     'summing_junction': interconnect_test.test_interconnect_exceptions,
+    'suptitle': freqplot_test.test_suptitle,
     'tf': test_unrecognized_kwargs,
     'tf2io' : test_unrecognized_kwargs,
     'tf2ss' : test_unrecognized_kwargs,
diff --git a/control/tests/nyquist_test.py b/control/tests/nyquist_test.py
index a687ee61b..8354932d7 100644
--- a/control/tests/nyquist_test.py
+++ b/control/tests/nyquist_test.py
@@ -162,35 +162,35 @@ def test_nyquist_fbs_examples():
 
     """Run through various examples from FBS2e to compare plots"""
     plt.figure()
-    plt.title("Figure 10.4: L(s) = 1.4 e^{-s}/(s+1)^2")
+    ct.suptitle("Figure 10.4: L(s) = 1.4 e^{-s}/(s+1)^2")
     sys = ct.tf([1.4], [1, 2, 1]) * ct.tf(*ct.pade(1, 4))
     response = ct.nyquist_response(sys)
     response.plot()
     assert _Z(sys) == response.count + _P(sys)
 
     plt.figure()
-    plt.title("Figure 10.4: L(s) = 1/(s + a)^2 with a = 0.6")
+    ct.suptitle("Figure 10.4: L(s) = 1/(s + a)^2 with a = 0.6")
     sys = 1/(s + 0.6)**3
     response = ct.nyquist_response(sys)
     response.plot()
     assert _Z(sys) == response.count + _P(sys)
 
     plt.figure()
-    plt.title("Figure 10.6: L(s) = 1/(s (s+1)^2) - pole at the origin")
+    ct.suptitle("Figure 10.6: L(s) = 1/(s (s+1)^2) - pole at the origin")
     sys = 1/(s * (s+1)**2)
     response = ct.nyquist_response(sys)
     response.plot()
     assert _Z(sys) == response.count + _P(sys)
 
     plt.figure()
-    plt.title("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2)")
+    ct.suptitle("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2)")
     sys = 3 * (s+6)**2 / (s * (s+1)**2)
     response = ct.nyquist_response(sys)
     response.plot()
     assert _Z(sys) == response.count + _P(sys)
 
     plt.figure()
-    plt.title("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2) [zoom]")
+    ct.suptitle("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2) [zoom]")
     with pytest.warns(UserWarning, match="encirclements does not match"):
         response = ct.nyquist_response(sys, omega_limits=[1.5, 1e3])
         response.plot()
@@ -208,7 +208,7 @@ def test_nyquist_fbs_examples():
 def test_nyquist_arrows(arrows):
     sys = ct.tf([1.4], [1, 2, 1]) * ct.tf(*ct.pade(1, 4))
     plt.figure();
-    plt.title("L(s) = 1.4 e^{-s}/(s+1)^2 / arrows = %s" % arrows)
+    ct.suptitle("L(s) = 1.4 e^{-s}/(s+1)^2 / arrows = %s" % arrows)
     response = ct.nyquist_response(sys)
     response.plot(arrows=arrows)
     assert _Z(sys) == response.count + _P(sys)
@@ -222,13 +222,13 @@ def test_nyquist_encirclements():
     plt.figure();
     response = ct.nyquist_response(sys)
     response.plot()
-    plt.title("Stable system; encirclements = %d" % response.count)
+    ct.suptitle("Stable system; encirclements = %d" % response.count)
     assert _Z(sys) == response.count + _P(sys)
 
     plt.figure();
     response = ct.nyquist_response(sys * 3)
     response.plot()
-    plt.title("Unstable system; encirclements = %d" %response.count)
+    ct.suptitle("Unstable system; encirclements = %d" %response.count)
     assert _Z(sys * 3) == response.count + _P(sys * 3)
 
     # System with pole at the origin
@@ -237,7 +237,7 @@ def test_nyquist_encirclements():
     plt.figure();
     response = ct.nyquist_response(sys)
     response.plot()
-    plt.title("Pole at the origin; encirclements = %d" %response.count)
+    ct.suptitle("Pole at the origin; encirclements = %d" %response.count)
     assert _Z(sys) == response.count + _P(sys)
 
     # Non-integer number of encirclements
@@ -251,7 +251,7 @@ def test_nyquist_encirclements():
         response = ct.nyquist_response(
             sys, omega_limits=[0.5, 1e3], encirclement_threshold=0.2)
         response.plot()
-    plt.title("Non-integer number of encirclements [%g]" %response.count)
+    ct.suptitle("Non-integer number of encirclements [%g]" %response.count)
 
 
 @pytest.fixture
@@ -266,7 +266,7 @@ def test_nyquist_indent_default(indentsys):
     plt.figure();
     response = ct.nyquist_response(indentsys)
     response.plot()
-    plt.title("Pole at origin; indent_radius=default")
+    ct.suptitle("Pole at origin; indent_radius=default")
     assert _Z(indentsys) == response.count + _P(indentsys)
 
 
@@ -293,19 +293,28 @@ def test_nyquist_indent_do(indentsys):
         indentsys, indent_radius=0.01, return_contour=True)
     count, contour = response
     response.plot()
-    plt.title("Pole at origin; indent_radius=0.01; encirclements = %d" % count)
+    ct.suptitle("Pole at origin; indent_radius=0.01; encirclements = %d" % count)
     assert _Z(indentsys) == count + _P(indentsys)
     # indent radius is smaller than the start of the default omega vector
     # check that a quarter circle around the pole at origin has been added.
     np.testing.assert_allclose(contour[:50].real**2 + contour[:50].imag**2,
                                0.01**2)
 
+    # Make sure that the command also works if called directly as _plot()
+    plt.figure()
+    with pytest.warns(DeprecationWarning, match=".* use nyquist_response()"):
+        count, contour = ct.nyquist_plot(
+            indentsys, indent_radius=0.01, return_contour=True)
+    assert _Z(indentsys) == count + _P(indentsys)
+    np.testing.assert_allclose(
+        contour[:50].real**2 + contour[:50].imag**2, 0.01**2)
+
 
 def test_nyquist_indent_left(indentsys):
     plt.figure();
     response = ct.nyquist_response(indentsys, indent_direction='left')
     response.plot()
-    plt.title(
+    ct.suptitle(
         "Pole at origin; indent_direction='left'; encirclements = %d" %
         response.count)
     assert _Z(indentsys) == response.count + _P(indentsys, indent='left')
@@ -319,14 +328,14 @@ def test_nyquist_indent_im():
     plt.figure();
     response = ct.nyquist_response(sys)
     response.plot()
-    plt.title("Imaginary poles; encirclements = %d" % response.count)
+    ct.suptitle("Imaginary poles; encirclements = %d" % response.count)
     assert _Z(sys) == response.count + _P(sys)
 
     # Imaginary poles with indentation to the left
     plt.figure();
     response = ct.nyquist_response(sys, indent_direction='left')
     response.plot(label_freq=300)
-    plt.title(
+    ct.suptitle(
         "Imaginary poles; indent_direction='left'; encirclements = %d" %
         response.count)
     assert _Z(sys) == response.count + _P(sys, indent='left')
@@ -337,7 +346,7 @@ def test_nyquist_indent_im():
         response = ct.nyquist_response(
             sys, np.linspace(0, 1e3, 1000), indent_direction='none')
         response.plot()
-    plt.title(
+    ct.suptitle(
         "Imaginary poles; indent_direction='none'; encirclements = %d" %
         response.count)
     assert _Z(sys) == response.count + _P(sys)
@@ -429,6 +438,63 @@ def test_discrete_nyquist():
     ct.nyquist_response(sys)
 
 
+def test_freqresp_omega_limits():
+    sys = ct.rss(4, 1, 1)
+
+    # Generate a standard frequency response (no limits specified)
+    resp0 = ct.nyquist_response(sys)
+    assert resp0.contour.size > 2
+
+    # Regenerate the response using omega_limits
+    resp1 = ct.nyquist_response(
+        sys, omega_limits=[resp0.contour[1].imag, resp0.contour[-1].imag])
+    assert resp1.contour.size > 2
+    assert np.isclose(resp1.contour[0], resp0.contour[1])
+    assert np.isclose(resp1.contour[-1], resp0.contour[-1])
+
+    # Regenerate the response using omega as a list of two elements
+    resp2 = ct.nyquist_response(
+        sys, [resp0.contour[1].imag, resp0.contour[-1].imag])
+    np.testing.assert_equal(resp1.contour, resp2.contour)
+
+    # Make sure that generating response using array does the right thing
+    resp3 = ct.nyquist_response(
+        sys, np.array([resp0.contour[1].imag, resp0.contour[-1].imag]))
+    np.testing.assert_equal(
+        resp3.contour,
+        np.array([resp0.contour[1], resp0.contour[-1]]))
+
+
+def test_nyquist_frd():
+    sys = ct.rss(4, 1, 1)
+    sys1 = ct.frd(sys, np.logspace(-1, 1, 10), name='sys1')
+    sys2 = ct.frd(sys, np.logspace(-2, 2, 10), name='sys2')
+    sys3 = ct.frd(sys, np.logspace(-2, 2, 10), smooth=True, name='sys3')
+
+    # Turn off warnings about number of encirclements
+    warnings.filterwarnings(
+        'ignore', message="number of encirclements was a non-integer value",
+        category=UserWarning)
+
+    # OK to specify frequency with FRD sys if frequencies match
+    nyqresp = ct.nyquist_response(sys1, np.logspace(-1, 1, 10))
+    np.testing.assert_allclose(nyqresp.contour, np.logspace(-1, 1, 10) * 1j)
+
+    # If a fixed FRD omega is used, generate an error on mismatch
+    with pytest.raises(ValueError, match="not all frequencies .* in .* list"):
+        nyqresp = ct.nyquist_response(sys2, np.logspace(-1, 1, 10))
+
+    # OK to specify frequency with FRD sys if interpolating FRD is used
+    nyqresp = ct.nyquist_response(sys3, np.logspace(-1, 1, 12))
+    np.testing.assert_allclose(nyqresp.contour, np.logspace(-1, 1, 12) * 1j)
+
+    # Computing Nyquist response w/ different frequencies OK if given as a list
+    nyqresp = ct.nyquist_response([sys1, sys2])
+    out = nyqresp.plot()
+
+    warnings.resetwarnings()
+
+
 if __name__ == "__main__":
     #
     # Interactive mode: generate plots for manual viewing
@@ -472,7 +538,7 @@ def test_discrete_nyquist():
     print("Unusual Nyquist plot")
     sys = ct.tf([1], [1, 3, 2]) * ct.tf([1], [1, 0, 1])
     plt.figure()
-    plt.title("Poles: %s" %
+    ct.suptitle("Poles: %s" %
               np.array2string(sys.poles(), precision=2, separator=','))
     response = ct.nyquist_response(sys)
     response.plot()
@@ -481,10 +547,17 @@ def test_discrete_nyquist():
     print("Discrete time systems")
     sys = ct.c2d(sys, 0.01)
     plt.figure()
-    plt.title("Discrete-time; poles: %s" %
+    ct.suptitle("Discrete-time; poles: %s" %
               np.array2string(sys.poles(), precision=2, separator=','))
     response = ct.nyquist_response(sys)
     response.plot()
 
-
-
+    print("Frequency response data (FRD) systems")
+    sys = ct.tf(
+        (0.02 * s**3 - 0.1 * s) / (s**4 + s**3 + s**2 + 0.25 * s + 0.04),
+        name='tf')
+    sys1 = ct.frd(sys, np.logspace(-1, 1, 15), name='frd1')
+    sys2 = ct.frd(sys, np.logspace(-2, 2, 20), name='frd2')
+    plt.figure()
+    ct.nyquist_plot([sys, sys1, sys2])
+    ct.suptitle("Mixed FRD, tf data")
diff --git a/control/timeplot.py b/control/timeplot.py
index 58f7d8382..29691ec6a 100644
--- a/control/timeplot.py
+++ b/control/timeplot.py
@@ -808,7 +808,7 @@ def _make_legend_labels(labels, ignore_common=False):
         suffix_len -= 1
 
     # Strip the labels of common information
-    if suffix_len > 0:
+    if suffix_len > 0 and not ignore_common:
         labels = [label[prefix_len:-suffix_len] for label in labels]
     else:
         labels = [label[prefix_len:] for label in labels]
diff --git a/doc/control.rst b/doc/control.rst
index ce5073e07..efd643d8a 100644
--- a/doc/control.rst
+++ b/doc/control.rst
@@ -51,6 +51,7 @@ Frequency domain plotting
     gangof4_plot
     nichols_plot
     nichols_grid
+    suptitle
 
 Note: For plotting commands that create multiple axes on the same plot, the
 individual axes can be retrieved using the axes label (retrieved using the
diff --git a/doc/conventions.rst b/doc/conventions.rst
index 2844fd47a..680ba1ba8 100644
--- a/doc/conventions.rst
+++ b/doc/conventions.rst
@@ -61,20 +61,21 @@ Transfer functions can be manipulated using standard arithmetic operations
 as well as the :func:`feedback`, :func:`parallel`, and :func:`series`
 function.  A full list of functions can be found in :ref:`function-ref`.
 
-FRD (frequency response data) systems
+Frequency response data (FRD) systems
 -------------------------------------
 The :class:`FrequencyResponseData` (FRD) class is used to represent systems in
 frequency response data form.
 
 The main data members are `omega` and `fresp`, where `omega` is a 1D array
 with the frequency points of the response, and `fresp` is a 3D array, with
-the first dimension corresponding to the output index of the FRD, the second
-dimension corresponding to the input index, and the 3rd dimension
+the first dimension corresponding to the output index of the system, the
+second dimension corresponding to the input index, and the 3rd dimension
 corresponding to the frequency points in omega.
 
-FRD systems have a somewhat more limited set of functions that are
-available, although all of the standard algebraic manipulations can be
-performed.
+FRD systems can be created with the :func:`~control.frd` factory function.
+Frequency response data systems have a somewhat more limited set of
+functions that are available, although all of the standard algebraic
+manipulations can be performed.
 
 The FRD class is also used as the return type for the
 :func:`frequency_response` function (and the equivalent method for the
diff --git a/doc/freqplot-gangof4.png b/doc/freqplot-gangof4.png
index 538284a0f..f911e7207 100644
Binary files a/doc/freqplot-gangof4.png and b/doc/freqplot-gangof4.png differ
diff --git a/doc/freqplot-mimo_bode-default.png b/doc/freqplot-mimo_bode-default.png
index 995203336..86414d916 100644
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diff --git a/doc/freqplot-mimo_bode-magonly.png b/doc/freqplot-mimo_bode-magonly.png
index 106620b95..7fd5538ed 100644
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diff --git a/doc/freqplot-mimo_svplot-default.png b/doc/freqplot-mimo_svplot-default.png
index d64330e25..f546992cd 100644
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diff --git a/doc/freqplot-nyquist-custom.png b/doc/freqplot-nyquist-custom.png
new file mode 100644
index 000000000..06ccda040
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diff --git a/doc/freqplot-nyquist-default.png b/doc/freqplot-nyquist-default.png
new file mode 100644
index 000000000..ede50925b
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diff --git a/doc/freqplot-siso_bode-default.png b/doc/freqplot-siso_bode-default.png
index 924de66f4..3cf235a31 100644
Binary files a/doc/freqplot-siso_bode-default.png and b/doc/freqplot-siso_bode-default.png differ
diff --git a/doc/freqplot-siso_bode-omega.png b/doc/freqplot-siso_bode-omega.png
new file mode 100644
index 000000000..0240473ad
Binary files /dev/null and b/doc/freqplot-siso_bode-omega.png differ
diff --git a/doc/freqplot-siso_nichols-default.png b/doc/freqplot-siso_nichols-default.png
index 687afdd51..d8eab3feb 100644
Binary files a/doc/freqplot-siso_nichols-default.png and b/doc/freqplot-siso_nichols-default.png differ
diff --git a/doc/plotting.rst b/doc/plotting.rst
index 8eb548a85..a3cbc1797 100644
--- a/doc/plotting.rst
+++ b/doc/plotting.rst
@@ -107,13 +107,13 @@ each other).  The following plot shows the use of `plot_inputs='overlay'`
 as well as the ability to reposition the legends using the `legend_map`
 keyword::
 
-    timepts = np.linspace(0, 10, 100)
-    U = np.vstack([np.sin(timepts), np.cos(2*timepts)])
-    ct.input_output_response(sys_mimo, timepts, U).plot(
-        plot_inputs='overlay',
-        legend_map=np.array([['lower right'], ['lower right']]),
-        title="I/O response for 2x2 MIMO system " +
-        "[plot_inputs='overlay', legend_map]")
+  timepts = np.linspace(0, 10, 100)
+  U = np.vstack([np.sin(timepts), np.cos(2*timepts)])
+  ct.input_output_response(sys_mimo, timepts, U).plot(
+      plot_inputs='overlay',
+      legend_map=np.array([['lower right'], ['lower right']]),
+      title="I/O response for 2x2 MIMO system " +
+      "[plot_inputs='overlay', legend_map]")
 
 .. image:: timeplot-mimo_ioresp-ov_lm.png
 
@@ -122,17 +122,17 @@ instead of plotting the outputs on the top and inputs on the bottom, the
 inputs are plotted on the left and outputs on the right, as shown in the
 following figure::
 
-    U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)])
-    resp1 = ct.input_output_response(sys_mimo, timepts, U1)
+  U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)])
+  resp1 = ct.input_output_response(sys_mimo, timepts, U1)
 
-    U2 = np.vstack([np.cos(2*timepts), np.sin(timepts)])
-    resp2 = ct.input_output_response(sys_mimo, timepts, U2)
+  U2 = np.vstack([np.cos(2*timepts), np.sin(timepts)])
+  resp2 = ct.input_output_response(sys_mimo, timepts, U2)
 
-    ct.combine_time_responses(
-        [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot(
-            transpose=True,
-            title="I/O responses for 2x2 MIMO system, multiple traces "
-            "[transpose]")
+  ct.combine_time_responses(
+      [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot(
+          transpose=True,
+          title="I/O responses for 2x2 MIMO system, multiple traces "
+          "[transpose]")
 
 .. image:: timeplot-mimo_ioresp-mt_tr.png
 
@@ -146,11 +146,11 @@ Additional customization is possible using the `input_props`,
 `output_props`, and `trace_props` keywords to set complementary line colors
 and styles for various signals and traces::
 
-    out = ct.step_response(sys_mimo).plot(
-        plot_inputs='overlay', overlay_signals=True, overlay_traces=True,
-        output_props=[{'color': c} for c in ['blue', 'orange']],
-        input_props=[{'color': c} for c in ['red', 'green']],
-        trace_props=[{'linestyle': s} for s in ['-', '--']])
+  out = ct.step_response(sys_mimo).plot(
+      plot_inputs='overlay', overlay_signals=True, overlay_traces=True,
+      output_props=[{'color': c} for c in ['blue', 'orange']],
+      input_props=[{'color': c} for c in ['red', 'green']],
+      trace_props=[{'linestyle': s} for s in ['-', '--']])
 
 .. image:: timeplot-mimo_step-linestyle.png
 
@@ -196,7 +196,7 @@ overlaying the inputs or outputs::
 
 .. image:: freqplot-mimo_bode-magonly.png
 
-The :func:`~ct.singular_values_response` function can be used to
+The :func:`~control.singular_values_response` function can be used to
 generate Bode plots that show the singular values of a transfer
 function::
 
@@ -213,16 +213,68 @@ plot, use `plot_type='nichols'`::
 .. image:: freqplot-siso_nichols-default.png
 
 Another response function that can be used to generate Bode plots is
-the :func:`~ct.gangof4` function, which computes the four primary
+the :func:`~control.gangof4` function, which computes the four primary
 sensitivity functions for a feedback control system in standard form::
 
-    proc = ct.tf([1], [1, 1, 1], name="process")
-    ctrl = ct.tf([100], [1, 5], name="control")
-    response = rect.gangof4_response(proc, ctrl)
-    ct.bode_plot(response)	# or response.plot()
+  proc = ct.tf([1], [1, 1, 1], name="process")
+  ctrl = ct.tf([100], [1, 5], name="control")
+  response = rect.gangof4_response(proc, ctrl)
+  ct.bode_plot(response)	# or response.plot()
 
 .. image:: freqplot-gangof4.png
 
+Nyquist analysis can be done using the :func:`~control.nyquist_response`
+function, which evaluates an LTI system along the Nyquist contour, and
+the :func:`~control.nyquist_plot` function, which generates a Nyquist plot::
+
+  sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys')
+  nyquist_plot(sys)
+
+.. image:: freqplot-nyquist-default.png
+
+The :func:`~control.nyquist_response` function can be used to compute
+the number of encirclements of the -1 point and can return the Nyquist
+contour that was used to generate the Nyquist curve.
+
+By default, the Nyquist response will generate small semicircles around
+poles that are on the imaginary axis.  In addition, portions of the Nyquist
+curve that are far from the origin are scaled to a maximum value, while the
+line style is changed to reflect the scaling, and it is possible to offset
+the scaled portions to separate out the portions of the Nyquist curve at
+:math:`\infty`.  A number of keyword parameters for both are available for
+:func:`~control.nyquist_response` and :func:`~control.nyquist_plot` to tune
+the computation of the Nyquist curve and the way the data are plotted::
+
+  sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0])
+  nyqresp = ct.nyquist_response(sys)
+  nyqresp.plot(
+      max_curve_magnitude=6, max_curve_offset=1,
+      arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], label='sys')
+  print("Encirclements =", nyqresp.count)
+
+.. image:: freqplot-nyquist-custom.png
+
+All frequency domain plotting functions will automatically compute the
+range of frequencies to plot based on the poles and zeros of the frequency
+response.  Frequency points can be explicitly specified by including an
+array of frequencies as a second argument (after the list of systems)::
+
+  sys1 = ct.tf([1], [1, 2, 1], name='sys1')
+  sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2')
+  omega = np.logspace(-2, 2, 500)
+  ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0)
+
+.. image:: freqplot-siso_bode-omega.png
+
+Alternatively, frequency ranges can be specified by passing a list of the
+form ``[wmin, wmax]``, where ``wmin`` and ``wmax`` are the minimum and
+maximum frequencies in the (log-spaced) frequency range::
+
+  response = ct.frequency_response([sys1, sys2], [1e-2, 1e2])
+
+The number of (log-spaced) points in the frequency can be specified using
+the ``omega_num`` keyword parameter.
+
 
 Pole/zero data
 ==============
@@ -288,7 +340,7 @@ The default method for generating a phase plane plot is to provide a
 2D dynamical system along with a range of coordinates and time limit::
 
     sys = ct.nlsys(
-        lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, 
+        lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x,
         states=['position', 'velocity'], inputs=0, name='damped oscillator')
     axis_limits = [-1, 1, -1, 1]
     T = 8
@@ -310,7 +362,7 @@ an inverted pendulum system, which is created using a mesh grid::
         m, l, b, g = params['m'], params['l'], params['b'], params['g']
         return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0]) + u[0]/m]
     invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend')
-        
+
     ct.phase_plane_plot(
         invpend, [-2*pi, 2*pi, -2, 2], 5,
         gridtype='meshgrid', gridspec=[5, 8], arrows=3,
@@ -318,7 +370,7 @@ an inverted pendulum system, which is created using a mesh grid::
         params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1})
     plt.xlabel(r"$\theta$ [rad]")
     plt.ylabel(r"$\dot\theta$ [rad/sec]")
-    
+
 .. image:: phaseplot-invpend-meshgrid.png
 
 This figure shows several features of more complex phase plane plots:
@@ -341,7 +393,7 @@ are part of the :mod:`~control.phaseplot` (pp) module::
                 -x[0] + x[1] * (1 - x[0]**2 - x[1]**2)]
     oscillator = ct.nlsys(
         oscillator_update, states=2, inputs=0, name='nonlinear oscillator')
-  
+
     ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9)
     pp.streamlines(
         oscillator, np.array([[0, 0]]), 1.5,
@@ -406,6 +458,7 @@ Plotting functions
    ~control.bode_plot
    ~control.describing_function_plot
    ~control.nichols_plot
+   ~control.nyquist_plot
    ~control.phase_plane_plot
    ~control.phaseplot.equilpoints
    ~control.phaseplot.separatrices
@@ -428,6 +481,7 @@ returned values from plotting routines.
 
    ~control.combine_time_responses
    ~control.get_plot_axes
+   ~control.suptitle
 
 
 Response classes
diff --git a/examples/cds110_bode-nyquist.ipynb b/examples/cds110_bode-nyquist.ipynb
new file mode 100644
index 000000000..eb0988e1c
--- /dev/null
+++ b/examples/cds110_bode-nyquist.ipynb
@@ -0,0 +1,1254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8c577d78-3e4a-4f08-93ed-5c60867b9a3b",
+   "metadata": {
+    "id": "hairy-humidity"
+   },
+   "source": [
+    "# Frequency domain analysis using Bode/Nyquist plots\n",
+    "\n",
+    "**CDS 110, Winter 2024** <br>\n",
+    "Richard M. Murray\n",
+    "</center>\n",
+    "\n",
+    "The purpose of this lecture is to introduce tools that can be used for frequency domain modeling and analysis of linear systems.  It illustrates the use of a variety of frequency domain analysis and plotting tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "invalid-carnival",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "python-control 0.10.1.dev32+gdbc998de\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import standard packages needed for this exercise\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "\n",
+    "from math import pi, sin, cos\n",
+    "\n",
+    "import control as ct\n",
+    "print(\"python-control\", ct.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "P7t3Nm4Tre2Z",
+   "metadata": {
+    "id": "P7t3Nm4Tre2Z"
+   },
+   "source": [
+    "## Stable system: servomechanism\n",
+    "\n",
+    "We start with a simple example a stable system for which we wish to design a simple controller and analyze its performance, demonstrating along the way the basic frequency domain analysis functions in the Python control toolbox (python-control).\n",
+    "\n",
+    "Consider a simple mechanism for positioning a mechanical arm whose equations of motion are given by\n",
+    "\n",
+    "$$\n",
+    "J \\ddot \\theta = -b \\dot\\theta - k r\\sin\\theta + \\tau_\\text{m},\n",
+    "$$\n",
+    "\n",
+    "which can be written in state space form as\n",
+    "\n",
+    "$$\n",
+    "\\frac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\theta \\end{bmatrix} =\n",
+    "  \\begin{bmatrix} \\dot\\theta \\\\ -k r \\sin\\theta / J - b\\dot\\theta / J \\end{bmatrix}\n",
+    "  + \\begin{bmatrix} 0 \\\\ 1/J \\end{bmatrix} \\tau_\\text{m}.\n",
+    "$$\n",
+    "\n",
+    "The system consists of a spring loaded arm that is driven by a  motor, as shown below.\n",
+    "\n",
+    "<center><img src=\"https://www.cds.caltech.edu/~murray/courses/cds110/sp2024/servomech-diagram.png\" alt=\"servomech-diagram\" width=\"240\"></center>\n",
+    "\n",
+    "The motor applies a torque that twists the arm against a linear spring and moves the end of the arm across a rotating platter. The input to the system is the motor torque $\\tau_\\text{m}$. The force exerted by the spring is a nonlinear function of the head position due to the way it is attached.\n",
+    "\n",
+    "The system parameters are given by\n",
+    "\n",
+    "$$\n",
+    "k = 1,\\quad J = 100,\\quad b = 10,\n",
+    "\\quad r = 1,\\quad l = 2,\\quad \\epsilon = 0.01,\n",
+    "$$\n",
+    "\n",
+    "and we assume that time is measured in msec and distance in cm.  (The constants here are made up and don't necessarily reflect a real disk drive, though the units and time constants are motivated by computer disk drives.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e476db9",
+   "metadata": {
+    "id": "3e476db9"
+   },
+   "source": [
+    "The system dynamics can be modeled in python-control using a `NonlinearIOSystem` object, which we create with the `nlsys` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "27bb3c38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<NonlinearIOSystem>: servomech\n",
+      "Inputs (1): ['tau']\n",
+      "Outputs (1): ['y']\n",
+      "States (2): ['theta_', 'thdot_']\n",
+      "\n",
+      "Update: <function servomech_update at 0x12005ec00>\n",
+      "Output: <function servomech_output at 0x12005eb60>\n",
+      "\n",
+      "Params: {'J': 100, 'b': 10, 'k': 1, 'r': 1, 'l': 2, 'eps': 0.01}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Parameter values\n",
+    "servomech_params = {\n",
+    "    'J': 100,             # Moment of inertial of the motor\n",
+    "    'b': 10,              # Angular damping of the arm\n",
+    "    'k': 1,               # Spring constant\n",
+    "    'r': 1,               # Location of spring contact on arm\n",
+    "    'l': 2,               # Distance to the read head\n",
+    "    'eps': 0.01,          # Magnitude of velocity-dependent perturbation\n",
+    "}\n",
+    "\n",
+    "# State derivative\n",
+    "def servomech_update(t, x, u, params):\n",
+    "    # Extract the configuration and velocity variables from the state vector\n",
+    "    theta = x[0]                # Angular position of the disk drive arm\n",
+    "    thetadot = x[1]             # Angular velocity of the disk drive arm\n",
+    "    tau = u[0]                  # Torque applied at the base of the arm\n",
+    "\n",
+    "    # Get the parameter values\n",
+    "    J, b, k, r = map(params.get, ['J', 'b', 'k', 'r'])\n",
+    "\n",
+    "    # Compute the angular acceleration\n",
+    "    dthetadot = 1/J * (\n",
+    "        -b * thetadot - k * r * np.sin(theta) + tau)\n",
+    "\n",
+    "    # Return the state update law\n",
+    "    return np.array([thetadot, dthetadot])\n",
+    "\n",
+    "# System output (end of arm)\n",
+    "def servomech_output(t, x, u, params):\n",
+    "    l = params['l']\n",
+    "    return np.array([l * x[0]])\n",
+    "\n",
+    "# System dynamics\n",
+    "servomech = ct.nlsys(\n",
+    "    servomech_update, servomech_output, name='servomech',\n",
+    "    params=servomech_params,\n",
+    "    states=['theta_', 'thdot_'],\n",
+    "    outputs=['y'], inputs=['tau'])\n",
+    "\n",
+    "print(servomech)\n",
+    "print(\"\\nParams:\", servomech.params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "competitive-terrain",
+   "metadata": {
+    "id": "competitive-terrain"
+   },
+   "source": [
+    "### Linearization\n",
+    "\n",
+    "To study the open loop dynamics of the system, we compute the linearization of the dynamics about the equilibrium point corresponding to $\\theta_\\text{e} = 15^\\circ$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "senior-carpet",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Equilibrium torque = 0.258819\n",
+      "Linearized dynamics: <StateSpace>: P_ss\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "States (2): ['x[0]', 'x[1]']\n",
+      "\n",
+      "A = [[ 0.          1.        ]\n",
+      "     [-0.00965926 -0.1       ]]\n",
+      "\n",
+      "B = [[0.  ]\n",
+      "     [0.01]]\n",
+      "\n",
+      "C = [[2. 0.]]\n",
+      "\n",
+      "D = [[0.]]\n",
+      "\n",
+      "Zeros:  []\n",
+      "Poles:  [-0.05+0.08461239j -0.05-0.08461239j]\n",
+      "\n",
+      "<TransferFunction>: P_tf\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "\n",
+      "\n",
+      "         0.02\n",
+      "----------------------\n",
+      "s^2 + 0.1 s + 0.009659\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the equilibrium angle to radians\n",
+    "theta_e = (15 / 180) * np.pi\n",
+    "\n",
+    "# Compute the input required to hold this position\n",
+    "u_e = servomech.params['k'] * servomech.params['r'] * np.sin(theta_e)\n",
+    "print(\"Equilibrium torque = %g\" % u_e)\n",
+    "\n",
+    "# Linearize the system about the equilibrium point\n",
+    "P = servomech.linearize([theta_e, 0], u_e, name='P_ss')\n",
+    "P.name = 'P_ss'  # TODO: fix in nlsys_improvements\n",
+    "print(\"Linearized dynamics:\", P)\n",
+    "print(\"Zeros: \", P.zeros())\n",
+    "print(\"Poles: \", P.poles())\n",
+    "print(\"\")\n",
+    "\n",
+    "# Transfer function representation\n",
+    "P_tf = ct.tf(P, name='P_tf')\n",
+    "print(P_tf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instant-lancaster",
+   "metadata": {
+    "id": "instant-lancaster"
+   },
+   "source": [
+    "### Open loop frequency response\n",
+    "\n",
+    "A standard method for understanding the dynamics is to plot the output of the system in response to sinusoids with unit magnitude at different frequencies.\n",
+    "\n",
+    "We use the `frequency_response` function to plot the step response of the linearized, open-loop system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "RxXFTpwO5bGI",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[list([<matplotlib.lines.Line2D object at 0x120111730>])],\n",
+       "       [list([<matplotlib.lines.Line2D object at 0x1207e9250>])]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Reset the frequency response label to correspond to a time unit of ms\n",
+    "ct.set_defaults('freqplot', freq_label=\"Frequency [rad/ms]\")\n",
+    "\n",
+    "# Frequency response\n",
+    "freqresp = ct.frequency_response(P, np.logspace(-2, 0))\n",
+    "freqresp.plot()\n",
+    "\n",
+    "# Equivalent command\n",
+    "ct.bode_plot(P_tf, np.logspace(-2, 0), '--')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stuffed-premiere",
+   "metadata": {
+    "id": "stuffed-premiere"
+   },
+   "source": [
+    "### Feedback control design\n",
+    "\n",
+    "We next design a feedback controller for the system using a proportional integral controller, which has transfer function\n",
+    "\n",
+    "$$\n",
+    "C(s) = \\frac{k_\\text{p} s + k_\\text{i}}{s}\n",
+    "$$\n",
+    "\n",
+    "We will learn how to choose $k_\\text{p}$ and $k_\\text{i}$ more formally in W9.  For now we just pick different values to see how the dynamics are impacted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8NK8O6XT7B_a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<TransferFunction>: C\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "\n",
+      "\n",
+      "s + 1\n",
+      "-----\n",
+      "  s\n",
+      "\n",
+      "<TransferFunction>: C\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "\n",
+      "\n",
+      "s + 1\n",
+      "-----\n",
+      "  s\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "kp = 1\n",
+    "ki = 1\n",
+    "\n",
+    "# Create tf from numerator/denominator coefficients\n",
+    "C = ct.tf([kp, ki], [1, 0], name='C')\n",
+    "print(C)\n",
+    "\n",
+    "# Alternative method: define \"s\" and use algebra\n",
+    "s = ct.tf('s')\n",
+    "C = ct.tf(kp + ki/s, name='C')\n",
+    "print(C)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "074427a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qGzdu9HoPwRdffJFVq1bxyCOPMHbsWG688Ub27NnjipSOGTOGTZs2kZmZyRVXXMGMGTP44IMPOtw/sCMuueQSnnrqKX7/+98zadIkXnnlFX772996VeawsDC+/PJLli1bxpgxY/jlL3/Jn//8Z6699lqv7ApCf0dRO5ugIAiCIAiCIAwoJGInCIIgCIIwSBDHThAEQRAEYZAgjp0gCIIgCMIgQRw7QRAEQRCEQYI4doIgCIIgCIMEcewEQRAEQRAGCeLYCYIgCIIgDBLEsRMEQRAEQRgkiGMnCIIgCIIwSBDHThAEQRAEYZAgjp0gCIIgCMIgQRw7QRAEQRCEQYI4doIgCIIgCIMEcewEQRAEQRAGCeLYCYIgCIIgDBLEsRMEQRAEQRgkiGMnCIIgCIIwSBDHThAEQRAEYZAgjp0gCIIgCMIgQRw7QRAEQRCEQYI4doIgCIIgCIMEnb8L0B9xOBwUFBQQGhqKoij+Lo4gCIIgCEMYVVWpqakhKSkJjabrmJw4dq149tlnefbZZ2lsbCQrK8vfxREEQRAEQXCRn59PcnJylzKKqqpqH5VnwFBdXU1ERATZ2dmEhoZ2KGO1WsnIyGD+/Pno9foey7ijPxDwVz16O9/esOeJjZ7ouCvbnZy36QMFf9TDF3l6a9PX7dJdeekzW5C26bn+UO0za2pqGDFiBFVVVYSHh3cpKxG7Dmgefo2KiiIsLKxDGavVSnBwMNHR0V12Up3JuKM/EPBXPXo7396w54mNnui4K9udnLfpAwV/1MMXeXpr09ft0l156TNbkLbpuf5Q7TOb7bozPUwcuy6wWq1YrdZO01rfeyrjjv5AwF/16O18e8OeJzZ6ouOubHdy3qYPFPxRD1/k6a1NX7dLd+Wlz2xB2qbn+kO1z+yJbRmKbUXzHDu73c7p06d59dVXCQ4O9nexBEEQBEEYwtTV1XH77bdTXV3d6UhiM+LYdYDJZCI8PJyysrIuh2I3b97MokWLuhxW6EzGHf2BgL/q0dv59oY9T2z0RMdd2e7kvE0fKPijHr7I01ubvm6X7spLn9mCtE3P9Ydqn2kymYiJiXHLsZOh2C7Q6/Xd/pA8lamz1qGqqlv6AwF/1aO38+0Ne57Y6ImOu7LdyXmbPlDwRz18kae3Nn3dLt2V91ZmsLRLkLbpjf5Q6zN7YlccOz+xetNqLpgu8PamtxkZMZKR4SMZET6CEeEjGGYchlaj9XcRBUEQBEEYYIhj1wW+WjzhUB3km/OxqBYyyzLJLMtsk67X6EkNTSUtLI20sDRGhI9wPQfpgrypUq8jiye8szFUJwL3BTJB3XN9WTzhW6Rteq4/VPtMWTzhIX25eKJRbaTcUU6ZvYwSewlljjJK7aWUOcqwYetUL1wJJ1YbS6wmlhhtDLGaWGK1sRgVo5ySIQiCIAiDEFk84SX+XDxhd9gpqisix5RDtimb7Opsckw55JhyqLRUdlrmUH2oM6oXnsaIMGeEb0SYc1hXp/FdYFYWT3hnY6hOBO4LZIK65/qyeMK3SNv0XH+o9pmyeALYvn07jz76KFqtliuuuIKnnnqqxzb8MRFYj540QxppkWnMY14b2aqGKpezl12dzbnqc2RXZ3PBfIEaaw1Hyo9wpPxIGx2dRsfw0OFt5vCNDB9JWngaIfqQLsvdE2TxhHc2htpE4L5EJqh7ri+LJ3yLtE3P9YdanymLJ4DRo0ezbds2DAYDt99+O0eOHGHy5Mn+LpZXRARGMC1wGtPiprV5b7FbyDXluhy+5ivHlEO9rZ5z1ec4V32unb244DiXo9fa6YsNipVhXUEQBEEYgAxax27YsGGuZ71ej1Y7eFeZGrQGxkSOYUzkmDbvHaqD4tpiV2QvuzqbbFM256rOUd5QTkldCSV1Jewp3NNGL0QfwoiwJkcvYqTrOSUsBb1mcPynLAiCIAiDkQHh2K1du5a33nqLkydP8uqrr3Lrrbe60kpLS1m9ejUZGRmkpKSwfv16FixY4Er/5ptvKCsrY8KECf4oul/RKBoSjYkkGhOZNWxWm7RqSzU5phzOVZ1rmctXnUN+TT611lqOlh/laPnRNjo6RUdyaHKbKF9KSAoNakNfVksQBEEQhE4YEI5deno6zzzzDI8//ni7tAceeICkpCTKysrYtGkTK1euJCsri8jISIqKivjxj3/MO++844dSd43mm5cYXn4C5XwMJEyAoMg+zT/cEM7U2KlMjZ3a5n2jvZH8mvw2c/iarzpbnWshR0Z+Rhu9v733tzZDuiPCRpAankpiSCIaRdOXVRMEQRCEIcuAcOzuvPNOAH7961+3eW82m/nggw/IyckhODiYFStW8NRTT/HRRx/xne98h9tvv52//vWvxMfHd2nfYrFgsVhcn00mE+C7fewAtF/9iWnmItjwTwDUkDjUmHTUmLEQPQY1ZgxqTDoYE6AP57spKAwPGc7wkOHMTZrreq+qKiX1Jc7Vuk1DujmmHHKqcyhtKKWsvoyy+jL2Fu1tY8+gNZBiTCE1LJXhocNJC0sjNSyV1NBUwg3hHpdT9rHzXK4/7MnUF8heYZ7ryz52vkXapuf6Q7XPHLT72M2bN481a9a4hmIPHjzIkiVLKCkpcck8+OCDBAcHk5aWxrp16xg3bhwAv/3tb5kxY0aHdp944gnWrVvX7r3P9rFTHUy68CqhDRcIbSggyNr5NiZWTRA1gUmYA5OoabrMgUnUBsRCP4mENagNlNnLKHWUUmovpdRRSrm9nHJHOXbsneoFK8HEaGKI0cYQrYkmVhNLtDaaaE00OmVA/M8hCIIgCD6nJ/vYDei/nmazuV0Fw8LCqKqq4v777+f+++93y85jjz3Gww8/zAsvvMALL7yA3W7n7NmzviiyE0XD0eQ7XR919nqMDQWENl1Gi/MeYilB76gnqi6LqLqsNibsih6zIaGd01drSMDRxwscApVAknXJJJPc5r1DdVDlqKLMUUaZvcx5b3o2qSbq1Dry7Hnk2fPa6CkoRGginE5fk+MXo4khWhtNmBImQ7uCIAiC0AkD2rEzGo2uYdNmTCYTRqOxR3YMBgMGg4HAwEA0Gg19HcS0aYOoChlFVcioNu81DishlmKns9dQ4IrwGS1FaFUr4Q35hDfkt9FRUag1xFFjaHH4mu82bd8eR6ZRNERpo4jSRjFG33bFrkW1UGGvcJ640RTha3b6LFiodFRS6ajkDGfa6OnRE62Jdjl7re+BSmBfVk8QBEEQ+h0D2rFLT0+nurqaoqIiEhISAMjMzOTee+/1yN4DDzzAAw884Dp5Yv78+Z2GPG02GxkZGcyfPx+druOvsSsZd/Q7w+Kwo1Tno5SfQVN+Bk3FGdezYjFhtBRjtBSD6WAbPYcxATV6DI7odNTodBxNF8ExHs/j86YeHaGqKhWWCvJq8sitySW3Jpf8mnxya3K5UHsBq2qlyFFEkaOonW6UIYrhocMZHjqcFGMKycZkko3JpBhTuj1jtzfq4YmNnui4K9udnLfpAwV/1MMXeXpr09ft0l15f/aZ/Q1pm57rD9U+8+IgVlcMiDl2VqsVu93O4sWLue+++1i5ciUBAQFoNBpWrlxJVFQUTz/9NJs3b2b16tWuVbE9pS/PivUJqorBVu0a0g1tuOAa4g20VXeq1qgNaYnsGVqifHUB0f1mHh+AXbVT6ahsO7TbdDer5i51Q5VQojRRrjl8zVeUNgqDYuijGgiCIAhCzxl0Z8WuXr2aDRs2tHmXkZHBvHnzKC0t5a677mLbtm0kJyezfv16Fi5c6FV+zRG7wsLCfhmx84iGamdErznKV34GpeIMSlUeCh03AVUXiBo12hXZa47yqZEjQBvgn3o0cXG+ZquZ/Jp88sx55NXkcd58nnxzPvnmfEyNXf+nE2WIItmYjLZGy/TR00kNS3VF+4x694f15b/P/oVERTzXl4idb5G26bn+UO0zTSYTiYmJg8ex6ysGfMTOAzSORowNRYRa2s/j06gdr2h1oKHWEH/RSt1EagxJ2LX9b55bnaOOCkcF5Q7nSt0Ke8tznVrXpW6IEuKM7F0c7dNGy5w+QRAEoU8YdBG7vmZQRux6isOGUpXbJsrnultrO1cLG+aM7EW1jfIRHN2rxeut76+msYbz5vPkmnL58siXBMQFcKH2AvnmfCotnW9DAxAREOGcwxfqnM83LGgYRSeLWD53OVHBUb1ej8H032dfIFERz/UlYudbpG16rj9U+0yJ2HnIUIzY9RhVJdBa2bI1S8MFQpu2ZzHYajpVs+hCW63UTXTN46vXR/fpBsw9oUFtaBPdax3t625OX7AS7IzyNUX3XHP6NFEEa6RNCYIgCO4jETsvkYid+7Sph9XUfh5f+Rk0pvOd6qv6EBzRo1vm7zVF+9TINNB4/h+WV/Vww16ttZbz5vOcrz1Pfk0+583nyavJ41zFOWrUzh1cgLCAMFKMKaQYU0gKTsKUZ2Lx9MWkhad1eRrHYPrvsy+QqIjn+hKx8y3SNj3Xl4idOHY9QiJ2vkFrt2C0FLZbqRtiKUHTyckUDkWL2ZDQtFK3JcJnDkzErunfq1gtqoUKR4Vrn74KR4XrJI7unL4gJagl0tcU7YvVxBKrjSVACeijGgiCIAj9CYnYeUlzxK6srKzTL9BqtbJ582YWLVqEXt/xSQ9dybijPxDwqh52K1Rmo5Sddl7lzjvlZ1GsHS9qUFEgYjiOqNFkmwNIuXQh2vjxqNFjICjCP/XogY16Wz35Nc7Vuvk1+eRW55KZl0ltQC2l9aWd2lZQSApJItQSypWjryQ9Mp3REaNJDUvFoG3r6HZXDm/TBwr+qIcv8vTWpif6PdXxtj90R2awtEuQtumNfk903JUdCH2myWQiJiZm8B8p5mv0en23PyRvZdzRHwh4VA+9HhInOq/WOBxgOg+lp6H0JJSdcj6XnUKpr4SqXLRVuYwG+OzTFr2QOIgdCzFjWt3HQWiC2/P4euPn0d3Pe2LQRCbGOetstVrZWLmRZcuWYcXq3KbF5NyQOc+UR64pl3PV56hoqOBC7QUATp486bKnUTSkhqUyIXoCE6MnMjF6IqPDRrtVF2/TBwr+qIcv8vTWpif6PdWRPrNnSNv0XL8nOu7K9uc+syd2xbHrAqvVitVq7TSt9b2nMu7oDwR8Vo+QROeVNrflnapCXRlK2WkcxSfIP7SVtJAGNOVnUWoKoLbEeeV81caUagh1RvRixqDGpKNGj0GNGQMRqaDR9lo9PLHRWkev1zPCOIIRxhHt5CoaKjhVfopP936KPlFPdk02WVVZ1FhryK7OJrs6m0/OfQKABg0xmhh2fb2LqXFTuTTuUkaEjUBpcm67K6e0zf6Vp7c2vW2XvSUvfWYL0jY91++Jjruy3vaJffHz7IltGYpthcyxG7jo7PUYG1rN47O0zOPrbANmu6JvmseX2Go/viTMhgQcmv4/n01VVWrUGgrthRTYC7hgu8AF+4UO5/EFK8Gk6lJJ06aRpksjUZuIph+dKiIIgiB0jsyx8xKZY+c+/qqH2/naLFBxzjV/z3mdgYqzKLaGDlVURQMRqagxzsheS7RvDBhCvS+ThzruyhaaCnkt4zUChgdwpOIIR8qO0GBvW9cQJYTZKbO5etjVzEicQWRgyxF80jb7V579fR6Tu/LSZ7YgbdNzfZljJ3PsvELmi7iPv+rRbb56PQyb4rxa47BDVR6UnYbSUziKT1B1dg+R9lIUi8m5qKMyG8583lYvNPGiOXxjnfP4QmLdL5Mn9eiBbGJYIuP041g2bRl6vR6r3crxiuMcKD7gvIoOUGur5bO8z/gs7zMUFCZGT2RuylwWpS5ieMhwj+vRH5F5TJ7ryxw73yJt03N9mWPXOeLYdYHMsesef9WjV/INTXZeI67BarXy1ebNLFq4EL2loinCdwZardZVzMVQU+i8sre3MaUGRqCJTueShmDUr7OwxY9zRvjCU6CLIc++mi8yIWICEyIm8N2x36XeUs+/Pv8X1mQre4r3cKryFEfLj3K0/CjPHnqWtNA00hrTSCtNY3zMeNfcvIGGzGPyXF/m2PkWaZue68scu+6RodhWyBw7oSt0tlpCLYVt9uILbSgkuLG003l8NiUAc2BCy7m6hpZ5fGoXGzD3JTWOGk5bT3PcepyztrPYW+0tGKuJ5ZKAS7gk4BLCNZ1vniwIgiD4Dplj5yUyx859+v0cu76wZ62HiiwcxSfI3vc5o8PtaCvOQkUWir2xQxVV0ULUCOxRozln0pN6+SK0ceNRY9IhwOhVGb2ZD1LTWMO2vG28fvB1suxZNDqc5VdQuDLhSq4fcT3zU+YTpAty99vxGzKPyXN9mWPnW6Rteq4vc+yG8By7/Px8li9fzvHjxzGbzR4d8yHzRdyn386x6wt7ej0ET8MaP4lT50MYtWwZil4PdhtU5ULpqab9+Jzz+Sg7jdJohvKz6MrPMgbgk49a7IUlQ+wYiBnrvMeOcz4HhPWojJ7MB4nSR3HD6BvQntYyZ9EcMi5k8MHZD/im5Bt2F+1md9FuQvaHcMPIG7hl7C2Mjhzds+/KD8g8Js/1ZY6db5G26bm+zLHrnEHr2MXGxrJ161ZWrFjh76IIQxWtDqJHOa9xy1reqyqYCqDsFPbiE+R9s4XU4AY05WegttS5ObPpPGRtbWNOFxzNLCUGDVsgbnyL8xee7PYGzD3BqDdyU/pN3JR+E/k1+XyU9REfZn3IBfMFXj/1Oq+fep3L4y/nlrG3sGD4AvTawfHHVhAEYSAzaB27wMBAAgMD/V0MQWiPokD4MAgfhmP4bA6XJpO8bBkavR7qKpoieyddp21Qehqq81DqyomhHA6eamtPHwIx6a6VukrkaIwNReCwAb3jbKWEpvDDS37Imqlr2FO4hzdOvUFGfgb7i/ezv3g/0YHR3DzmZm4deyuxwbHdGxQEQRB8woBw7NauXctbb73FyZMnefXVV7n11ltdaaWlpaxevZqMjAxSUlJYv349CxYs8GNpBcELgqNg+FXOqzWNtViLTnD4i7e4JDkYbcUZp8NXkQXWWig85Lxw/lIvANRTv3RGC13bszQN7YaneVw8jaJhRtIMZiTNoKi2iHfOvMPbp9+mrL6M5w8/z4tHX2TZiGXcNfEu0iPTPc5HEARB8IwB4dilp6fzzDPP8Pjjj7dLe+CBB0hKSqKsrIxNmzaxcuVKsrKyiIyM7MCSIAxQAkIgcSrnoy4wZd4ytM3zLexWqMhuiuw5o3xq6UnsJSfRORqb3p2EEy2mdCgsDIhGa/p/EDeu5Uzd2DEQ5P7vTUJIAg9c8gDfn/J9tuZt5ZUTr3Cw5CAfZH3AB1kfMCtpFqsmrmJG4owBu2WKIAjCQGNAOHZ33nknAL/+9a/bvDebzXzwwQfk5OQQHBzMihUreOqpp/joo49YtWqV2/YtFgsWi8X12WQyAbKPnTsM6H3setme3/ZkihjhvEYvdaVv3vQ5i6+cgL4623XiBuVnnM/1FYQ0lkHWFufVCjUkDk3UaKbUB6LuzsMWP9558kZoQpfz+K4Zdg3XDLuGw2WH+feJf7P1/Fa+Lviarwu+Jj0inTvH3cnStKXoNX27crr1faDm2d/3CnNXXvrMFqRteq4v+9h1z4Da7mTevHmsWbPGNRR78OBBlixZQklJiUvmwQcfJDg4mHXr1nH99ddz4MABLr30Up544glmz57dod0nnniCdevWtXsv+9gJg5EAq8m5B5+loNV+fAUEWSs61bFqgtqdqVsTOIzagNgON2CusFewy7KLA40HaMS5ZUq4Es7swNlcFnAZekUWWgiCILhLT/axGxARu84wm83tKhgWFkZVVRWBgYFs2bKlE822PPbYYzz88MOuzyaTiZSUFObPn9/pF2iz2cjIyGD+/PmdbqXSlYw7+gMBf9Wjt/PtDXue2OiJjruy3ck1p0+58X5XugrUWcxoKs6ilp4k/+AXjDA2oq3IQqnKQe+oJ7LuHJF159rYUrUG1KiROKLSUaPTcTRdatRsbtHdgqnRxHvn3uP1069TYang4/qP2enYye1jbuemUTcRog9x63vyBH+0TV/k6a1NX7dLd+Wlz2xB2qbn+v7sMz1N7w2aRxLdYdBG7H7/+9/32L6cPCEI7dE4rIRYigltaBXhsxRgbChEq3Y8PKCiUBsQ6zpxozwwgS16M5+rJ6hSqwEIUoK4KuAqZhhmEKyR3zNBEITOGDIRu/T0dKqrqykqKiIhIQGAzMxM7r33Xj+XTBAGDw6NnpqgZGqCktsmqA6CG8s6cPgKCLDXYWwswdhYQoLpEOnAVcDPgffDY3kxLJh8XT0Zlgx2Wr7iKt1UrgpaQKi26w5LEARB6JoBEbGzWq3Y7XYWL17Mfffdx8qVKwkICECj0bBy5UqioqJ4+umn2bx5M6tXr/Z6VWzzkWKFhYUyFNsNMhTrnY1BOaygqlBbiqb8DJry0yjlZ9CUn3Hea4sBsANfBAfxQkQ4Jw0BAASoKjfZDNxtHEtMzATUqNE4okajRo4Afc+PMJPhLs/1ZSjWt0jb9Fx/UPaZbmAymUhMTBw8Z8WuXr2aDRs2tHmXkZHBvHnzKC0t5a677mLbtm0kJyezfv16Fi5c6FE+MhQrCL5FZ6sl1FLYFOUrJMRSwBG1mH+H2DkUaABAr6rcXGPme1UmEux2VBTqAmIwGxKbFnAkYjYkYQ5MwKIL98mpG4IgCP2JngzFDgjHrq8Qx04Q/ITdSkHDQTZbv+YM5QDoVVhhruf7lRUk2O0dqlm1wdQ0OXytHb/agHhUzcCO6giCIDQjjp2XNA/FlpWVdfoFWq1WNm/ezKJFizo9nLcrGXf0BwL+qkdv59sb9jyx0RMdd2W7k/M23Zeoqsr+kv08f+R5DpQcAECv0bMi7iruDhlNUk0JStkZlPKzUJWLQsfdl6poUSNSKbaHEzNuBkrsGIh2rtwlOMpn5ffFd+etTV+3S3flpc9swR91GYptczD1mSaTiZiYmMG/eEIQhMGFoihMj5/O9Pjp7C92Onj7S/bzVtFXvKfZxfKRy7l7xlMkhSSBrQEqslGa5u8p5Weg/KzzubEWpfIciQB7D7bJQw2ORo0aDdGjUWOczp4aPRoiUkGifIIgDHAkYtcKGYoVhP5Hti2brQ1bybZlA6BFy7SAacwNnEukpoNFUqpKoLUSo6UQY0MhoU13Y0MBwV1swuxQtJgNCa4hXbOheT5fAjad7/bbEwRB6A4ZivUSWRXrPrIq1jsbQ3WFlyccLD3IP47/g/0l+wHQKlquT7ue1eNXOyN4F9FhPRrrUCqz0JSfRVNxFqUiy3VXbA2d5q0GR+OIHIkaNQpH5AjUyJE4okahRqS1WbE7FFceuisvfWYLsirWc/2h2mcOulWxfYVE7ASh/5NjyyGjIYMsWxYAGjTOCJ5hLlFaD+fPqQ6CGssxWgoJbShsifY1FBBoq+5StU4fRa0hAXNgAmZDgvPZkECdIQZVGdgOiCAI/QOJ2HmJLJ5wH1k84Z2NoToRuDc4VHqI5488z+6i3YAzgnfdiOv43sTvkRKa0nv1sNRAxTlnVK/pTkWW83ND506fqtFBxHDUqFGoUaMgaiRq1GjU6FEQmtjhGbsd0d8nqLsrL31mC7J4wnP9odpnyuIJQRAGPZfEXsL6a9aTWZrJ34/8nd1Fu/nw3Id8kv0Jy9KWcde4u3onI0MoJE5FTZzadg2uqkJ9hdPRK3c6fWr5GWpzMwmzlTqHdivOoVScAza3ManqgiBqhMvpU6NGQtQo1Mg0CImTvfkEQfAYidi1QoZiBWHgkm/LZ2vDVs7YzgCgoDBVP5V5gfOI0cb0bWFUB4HWKoyWIkIsRRgbijBanFewpRQNHe/LB2DTGKgNiKPOEEetIY7aAOe9zhBHXUC0DO8KwhBEhmK9RIZi3UeGYr2zMVSHFXzJkbIjvHD0BXYU7ACcc/CWpC3h3on3MiJ8hE/zduu7c9igKs81tOsa1q04B6YLKKqjU/uqooXwZNTINOfCjcg01MgRzkhfZBoEGD0rk5c6MhTbM2Qo1nP9odpnylBsL6HX67v9IXkr447+QMBf9fA0X1VVsTtUbA4Vq91BvVXF1AhldXYUDVjtDleaza5iczTf276z2p12rHYHFquNzCKF0v0FqGiwOVRsdgdWh4q9Sd/abKsprdFqJ++8hk9Nx7GruGzZmuzaVRWN4tzfTUGlolzDGyWZaDUaNIqCRgGDTktQgJZAvZYgvRaDFvLOK5TsLyA2NIiIYD1RIQFEBgcQGRJAQNOqre6+u4HaNi9NvJTnEp/jUNEhnvziSU7ZTvFpzqd8lvMZS0csZc2UNYyMGOnTMnT93ekhfqzzuhhbI1TlQWU2VGRDZTaO8izM+UcJtZU7h3erclGqcoHt7fVDYiFyBESNcN4j01BCkwhqLEOvVXr88+xpG5A+s2f4oy6+yNNbm57o90THXVlv+0Rf/jx7YlccOz+xM6ucY5UKgadK0Wq0qDidDecdQEVVcX1Wmz47U5yyXJTWIu+0Q+u0pnzVi9+1yrN1/h3lQZs05zu7zc7J8wo5286h0WhcaQ5VdV12R9PnJkfF4VBxqLR6biXT5HA55S+SUXE92+wOysq1bLiwF4dK27wcKtZmR6zJsbLZWxyzZoesPTo48KWXP1ktZJ/qoY4Gyovdlj1j6nwvttbl+CS/43KEGLSEarS8W/4NyZHBDIsMIjkymNGxRkbGhqB1u9z9m4nRE/mu8bukXZXGP479g2352/g0+1M+y/6MJWlL+MGUHzA6crS/i9kWXQDEjHZeTditVjI2bmTZtUvRN5S7HL529/pKqC11Xuf3tpgEFgPq8f+E8GHOjZgjhrdc4SnOe9gw0MqfBEEY6MhvcRdYrVasVmunaa3vPZX56dtHKDVr4eTBdmkDDy2f5J/1Q74K1FT1qkWdRkGnVdBpNOi1StNnjfN+cdpF77UKlJeVkpSQQIBOi06roNcqaDWt7Wmcn7UKeo0CqJw7e5qJ48dh0OvRapQ2+WqUFkfZarNz+PBhJk6ajEajcTnHjTYH9VY79Y12GqwOai1WzubkERGTgKnBRkWdlaq6RirqrDTaHNRa7NSiUHS6rF39NQokRwYRpmo4qjnJlJRIpgwLIzE8EGWATehv/r1LD03nqdlPcbLiJC8cfYGM8xl8lvMZn+d8zsLhC7l30r2kR6T3ap5d9Qse27TZISgWhsXCsCvaCzZUQ2U2SmUOSlUuSmW2M/pXlQ/VeWhUe9PnvA7zURUthCWhhqeghA5jbLkVxzfl2CJTUcMSITQJAjreqNnb/tAdGV98t/7CH3Xxadv00KYn+j3RcVe2Ozlv03uDntj2aI5dfX09//3f/81bb71FRUUFJpOJzz//nBMnTvCTn/ykp+b6DX25eOJvJzSYrQoKLQvgmv9stv7c+k+p873apVzrv72t3ykdvGuXhztyykVl6iQPRQFN8/umZ02TjEZplncOM7Z5z0Xyrey47hfJdySnVVS0GmeattV18WfXO02L/mBFVaHRAVWNUGlRqLRAhUWhshHKGhSK66DO3vEXYNSpDDc6r1FhkGZUCRigob1CeyHbGrZxzHrM9W6CfgLzA+eTqE30Y8l8SNNijuDGMoIaywhuc5US1FiOVrV1a6ZRG0K9PoqGgEjq9VFtnhv0UdQHRGHTBnVrRxCEnuHzxRN33303VquVRx99lNmzZ1NZWUlhYSHz58/n5MmTHhe8vyCLJ9xHFk94Z6M/TQRWVZXy2kZOFlTzyY4DKJEpHCus4XSxGZujbTeh1ypMGRbO9LRIrkiL5LLUCIID+tcAQHffw5mqM/zj6D/YkrfFNdVgfvJ87pt0H+OixvkkT3/YdEtfdYC5BKXaGd1zVORy4dguhoc60JiLoKYAxVLjVn6qIRRCk1DDksCYiGqMB2MctqAY9h7P4fJ516GLSOrxQo/B0meCLJ7wRr8/9Zne1KOn+HzxxCeffEJ+fj4Gg8E1PJOYmEhhYaEn5nzGww8/zL59+5g2bRp/+ctfeqwvE4HdZ6AtnvClvYE8ETgxIIAYowHTWZVlyyah1+tpsNo5UWjiyIVqDuRWsudcBUWmBg7kVXEgr4q/fZlNgFbDFSOimDsmlnljYxkdZ+w3Q7edfQ8TYifw1PynOFt5lr8f/juf53xOxvkMMs5nMC9lHmumrmFi9MRezdMbfD5BPSrFeTETq9XK4ZqNJC9bhrZZp8EENYVQfR5MBU3XBTAVoJouYC3PIcBe53QALadQytrO8dQCswHO/Nr5IsAIxjhocvwwxqMJjmV4eREBuXp0YQkQHO28Wg0BD5Y+E2TxhDf63vaZdrvdNbxpt9vR6XTY7XY0mvYbh3ub3lP0ej1arbbdO3fxyLGLiIigtLSU5ORk17vs7GySktqf1+gvvvnmG8xmM1999RX3338/+/btY/r06f4uliAMOAL1WqYNj2Ta8EhWzUhDVVXyKurYc66C3dnl7DlXwYWqenacLWPH2TJ+vfEESeGBzB0by7yxccxOj+l30bzWjI4czR/n/pE1U9fw98N/57Psz9iWv41t+duYmzyX+6fez8QYzxy8QUVgmPOKbb+a12a18unGjSxbOAd9fZnL4cN0AcwlYC7GUVNMXUk2IQ4zirUWGs1QYYaKcy47WmAaQN4/22agC0QXFMVcmw5t1T8hJKbF6bv4CoqEwHCnM9hP/rkQ+hdms5nz58+3WiCokpCQQH5+fof/kHqb3lMURSE5ORmjsX1U2x086m3/4z/+gxtuuIFf/OIX2O12Pv74Y5588sl+Nb9u165dLFy4EICFCxeye/ducewEoRdQFIXU6BBSo0P4zvQUVFUlq7SW7adL2X66lN3nyimobuC1vfm8tjcfg07D7PRYFk+MZ+H4eKJCAvxdhQ4ZFTGKP8z5A2umruH5w8/zafanbD+/ne3ntzN72Gzun3o/k2Mn+7uY/ZsAI4REQkz7xSh2q5UvNm5k2bJl6B0WMBe7nL7my2EqoiT7KPFBDpT6CqgtA7sFbA0oNQVEAGR3vPijHRqd08Frc0W0f9fsCBrCwGB01iHA6HQMdQZxDgcZdrud8+fPExwcTGxsLIqi4HA4MJvNGI3GDiNu3qb3BFVVKS0t5fz586Snp7eL3LmDR47dAw88QFxcHP/85z9JTk7mL3/5Cw899BC33HKLJ+a6Ze3atbz11lucPHmSV199lVtvvdWVVlpayurVq8nIyCAlJYX169ezYMECqqqqGDVqFADh4eEcO3asM/OCIHiBoiiMjjMyOs7I964eQX2jnd3Z5Ww/VcoXJ4vJr6hny4litpwoRqPA9LQoFk9MYPGEeFKi+t/JLiPDR/K72b9jzZQ1vHDkBT4+9zFfXfiKry58xaxhs7h/6v1MjZ3q72IObAxG5xU9qs1ru9XKnmbnT693rvix1kFdObbqIvZ9+TnTJ45EZ6mGuvKmqwzqKpzPtWXQUOXcBNpha5HxFI3O6eAFhDbdQ9o7fwEhzmd9IOiC3LujQ2u3OOc2Cn2K1WpFVVViY2MJCnIu9HE4HDQ2NhIYGNip4+ZNek+JjY0lJycHq9Xad44dwMqVK1m5cqWn6j0iPT2dZ555hscff7xd2gMPPEBSUhJlZWVs2rSJlStXkpWVRUREBCaTCXBOOoyIiOiTsgrCUCcoQMv8sXHMHxvH2hsmcLKohk3Hitl0vIhjBSb2ZFewJ7uC//n4OBMSw7huSiLXTU4kLabjrTT8RVp4Gr+++tf8YMoPeP7w83x87mO+vvA1X1/4mhmJM/j+lO9zecLl/i7m4EZRXM6TGpJISVgh6uRl0NV8o2ZnsKHaedVXtTw3VDsdv9b31umNtc7LVu+05bC1pPUieuB6gMP3gTagxeHTGkCrd77T6i96DnDzuemu0TkvRQMaHYoKKeXHUI6YQW8AjRYUbZOctoPPuqbPHaU1fbY7CLRWQk0RBBicedG87YGm5U7z51bvFI3TsfXT4Vf9ZQ5wR3hbNrcduz/84Q9uyf3sZz/zuDCdceeddwLw61//us17s9nMBx98QE5ODsHBwaxYsYKnnnqKjz76iBkzZvD3v/+d73znO2zZsoXVq1d3at9isWCxWFyfmx1CX+5jN1j2ZPJXPXo7396wJ3sydczomCBGz03jh3PTOF9Zz5aTJWw5UcK+nEqOF5o4Xmjij5+fYkJiKMsmJbB0UjypvRDJ6616JAYlsvbKtdwz4R7+dexffJz9MbsKd7GrcBeXxF7CvRPvZUbiDBRFGZJ7hbkr36d9phLg3PMvKBYiuxdvh8MGjXXOeYCNtSiNZtcz1lqwNM0TtJidnxtrnaeCWOvB1uC8rA0otnrXs/N9vfO9o1Ud7I3Oy9K7zuPF6IBLAdwcyXYHPbAE4Kjn+ssB9ZCC2trha+MI0uadTlFYarWhO2VAbXYUm51JaPOsBRbVN6DN+i+nLKAGJ8IlP0cts6HqNc0ahDocKBYt7dxMBRS1Kb2xg/QmfaPdgWoMwZ0YrMFgYOLEiTQ2Nrp8ldaRPofDgaqqbSJ2PtnH7u6773Y919XV8d5773HllVeSkpJCfn4+e/fu5aabbuK1115zO/OeMm/ePNasWeMaij148CBLliyhpKTEJfPggw8SHBzM73//e37yk59w4MABpk6dyv/93/91aveJJ55g3bp17d77ch87QRjqmK1wpELhULnC6WoFR6sdElNCVKZFO7gkWiU60I+F7IAKewU7LDs40HgAO3YAkrRJzDPMY5x+HBrF+6EYYZCjOtA6Gp2X2ojWYUXjaESj2tGoNjSqDUW1tXx22FzvNaq9bVpTett3VhTVgaI60OBoerajuJ4dTc/2Vs8t7zWqHbrVdQAqiqqiNLkzSoduT/+iwZhC9qw/M2JYLIG63o3aVQWlNjmhXTN27FhOnTqF3W5n+fLlfP/73+fGG290pTc2NpKfn09RURE2m3N/yZ7sY+d2xO7FF190Pd9888289dZbLF++3PXuww8/5P/9v//nrrlewWw2t6tgWFgYVVVVADz99NNu2Xnsscd4+OGHXZ9NJhMpKSnMnz+/0y/QZrORkZHB/Pnz0ek6/hq7knFHfyDgr3r0dr69Yc8TGz3RcVe2Ozlv03uTbzXdK+sa2XKyjM+OlbAnp5L8WoX8Wi0f5sHkpFCWTIhjyYQ4hkW47+X5sh63cAsl9SW8eupV3jv3HgX2Al6te5URoSO43H45Dy5+EIPe0Ct5eVsPX7dLd+Wlz2yhuS5zFl7bZ3Xx9PtToVN3rZ1NtUlaVZuGWZuu1u9oSbPbbOz46iuunjUDrVbrdBYvlm9tCxW7zcrePXu4YvrlaLUXD+c6Hc2W8lk5cOAAl192mVMWsNhAbTTiCB+Ow+BcyKWqKnV1dQQHBzuDfRdVWEWlvq6OoOBgOnIFVVWlvr6O0NAwtxbbKIpCaGgoADNnzqSgoMD1GaChoYHAwEBmzpyJweDsR5pHEt3Bow2Kw8PDKS8vb9M4bDYb0dHRVFf7Lpzc04hdT+nLkycEQeiYGiscrlA4WKZw1qS0OmsFUo3OSN60aJWI3vGbvKbWUctOy052W3ZjwTmlI1oTzRzDHKYGTEWnDGwnRBAGEzqdjoSEBFJSUpr26ez9BSyBek2X8+SaI3b19fXccMMN/PSnP2Xp0qWu9D6L2LVm0qRJPPnkk/zyl79Ep9Nhs9n4zW9+w8SJfbvXU3p6OtXV1RQVFZGQkABAZmYm9957b5+WQxCE3iNUD7PiVWbFq5gam5y8coUsk0KuWSHXrOX9XBgRqnJp03BtmB93UAnRhLAoaBFXG65md+Nudlp2Uu4o573699jasJXZgbO5LOAy9Mrg2FRXEAYLDVYHM57a3et2dz18FUFdnLlYUVHB7NmzAVi0aFEbp6438Mixe/nll7n99tv585//TFxcHCUlJUyYMIFXXnmlVwvXjNVqxW6343A4sFqtNDQ0EBAQgNFo5MYbb2Tt2rU8/fTTbN68maNHj3LDDTf4pByCIPQtYQFwdYLK1Qkq1Y2QWa5wsFzDuRqF7BqF7Bot7+aojA5TmRajMjVKxegn/ylIE8T8wPnMNMxkn2UfOyw7qFar+bj+Y7Y1bONqw9VMN0zHoPSTUKMgCH4hKiqKr776ymf2PRqKbSYvL4/CwkISExMZPnx4b5arDatXr2bDhg1t3mVkZDBv3jxKS0u566672LZtG8nJyaxfv961MbGnNJ8VW1hYKHPsukHm2HlnY6jPsfOUwuoGPj9ewqfHSjhS0HKGqVZRuGpEBEsnxjN/dAQHdu/o03q0/u7sip2Psj/i5VMvU1RXBEBYQBi3pd/GytErCQ0I7cZae5syx65/t0t38UddfJFnf2+bHclaLBYKCgpIS0vDYDBQb3UugDLXmDGGdn7SQ0/Sg/TaLodihw0bxoULFzpNb2hoICcnh6SkpDZz7BITE90aivXIsWs9p+1i4uLiemqu3yBz7ARh4FHeAAebInnna1s6U62iMi5CZVq0yuRIlUA/+QI21UZmYyZfWr6k3OHcLNeAgasMVzHTMJMQTf/av08QBjOt59gFBPhnDkfzHLvO8HaOnUeOnUbjnBjYrNraM7Xb7T011++QiJ37SMTOOxsSsetdcsrr+OxYCZ8eL+FMSa3rfYBWw5z0KK6dGMfc9BiCu5j/4g1dfXd21c4X+V/w0omXyDJlARCoDWTFyBXcPuZ24oPje2zT2zL1lo5E7HqGROw81+/NiF1gYMsq+5qamjYrUy/G2/Se4JeI3cUUFRXx5JNPcuWVV/Ld737XW3N+QyJ2gjB4KKyDg2UaDpYrlDS0/PMZoFGZGOmM5E2IVNH38bZzDtXBKdsptjVs44LdORyjRcvUgKnMNswmVhvbtwUShCFEf4jYdYdfInadFWTkyJGcP3++N8z5leaIXVlZWadfoNVqZfPmzSxatMh5pmEPZdzRHwj4qx69nW9v2PPERk903JXtTs7b9IFCcz0WLlzI2fIGNh4p5pOjRZyvrHfJhBi0LBwXx7LJCVw9KpoAnXdeXk++O1VV2VW4ixePv8iBkgMAKCjMT5nP3RPuZmL0xB7b9LZMnup42x+6IzNY2iX4py6+yLO/t82OZBsaGsjPz28TsVNV1RVx62hunLfpPaU5YpeSkuIqo8lkIiYmxnfbnXTEnj17XJ6lIAhCf0FRFCYkhjEhMYxHFo3myAUTnxwpYuPRIopMFj7ILOSDzELCAnUsmhDHdZMTmDEiCp3Wt6E8RVGYmTSTmUkzySzNZMOJDWw7v42t+VvZmr+V6fHTuWfiPUyLmubTcgiCMLjwKGI3fvz4Nl5pXV0d5eXlPPPMM9xzzz29WsC+RIZiBWHo4FAhpwYOlms4VK5gsrb0aSE6lanRKpdGq4wKU9H00XnhJfYSvmr4ikxrJo6mY5qStEnMMcxhgn6CHFcmCF4iQ7GdsH379jafQ0JCGDNmTLeZDRRkKNZ9ZCjWOxsyFOs7elIPu0Nlf24lnxwp4rNjxVTWtRy4HWsMYOnEeK6bnMC0lAg0XXh5vfXdFdYW8u+T/+a9s+/RYG8AIMWYwuoJq7luxHUEaN3/gyRDsf0PGYr1XF+GYn00FLtv3z5++tOftnv/1FNPtTlzVRAEYSCg1ShcOSKKK0dE8d/XjWNXdgUbjxSz6XgxpeZGXt6Tz8t78kkIM7BsUgLLJicwZVhYr3TiHZEYksh/Xvaf3DvxXl47+RqvnHiFfHM+/7P3f3juyHPcOe5Obh59MyF62SpFEIS2eBSxCwsL6/BA2ujoaMrLy3ulYP5AhmIFQWiNzQGnqp3n1h6uVLDYWxy5aIPKJdEql8Y4GBbs1tnfHmNRLey37Odry9eYVGffG6gEclXAVcwwzJC98ATBTfrDUOz58+f5+c9/zvHjxzEYDEyePJk//vGPREREAH08FPvmm28CLSdBtFbNycnhhRde4MyZMz2tY79DhmLdR4ZivbMhQ7G+o7frYbHa+fJMOZ8cLWLryRLqWx0ePiI6mGsnJbBkfAw5mTt9NtyFBjbmbGTDiQ3kmHIA5154y0ct57vjvkuSMalLfRmK7R/IUKzn+gN9KFZVVa688kp+9KMfsWrVKgDef/99Jk+ezKhRo1xl7LOh2Oeeew5wepPr1693vVcUhbi4OF566aWemOv36PX6bhuOtzLu6A8E/FWP3s63N+x5YqMnOu7KdifnbfpAobfqodfrWTZ1GMumDqOu0cbWkyV8nFlIxqkSssvrWL/9HOu3nyMhSMu5oDyWT0tmZGznRxB5kr9er+fb477NTWNvIiMvg38c+QdHy4/yxuk3ePvM2ywdsZTVE1czLmpcp/qe5Nmb8tJntuCPuvgiT29t9mWfabfbURQFjUaDRlHAWofD4QBrHYpVi0bTfoFSj9P1nYfwt2zZgtFoZPXq1a53N910UxuZ5kMgWpe7J99Pjxy7jIwMAJ588kl++ctf9kRVEARh0BAcoOP6KUlcPyUJs8XGluPFfHy4gO2nSymqh2e2ZvHM1iwmJIZx/dREbpiSREpU703r0CgaFqQu4Jrh17CvaB//OPIPdhXu4pNzn/DJuU+4KvEqVk9czcykmb2WpyAMOqx18JskNEBEF2I9Tv+vAgjoeHrE8ePHmTbNt1sYue3YlZWVERMTA8D3v//9Ts+LHchnxV6M1WrFarV2mtb63lMZd/QHAv6qR2/n2xv2PLHREx13ZbuT8zZ9oNBX9TBo4LpJcVw3KY4yUx1/eXc7+cSyK7uS44Umjhea+MNnp5iSHMZ1kxK4dlICieGB3Rtuort6TIuZxrPzn+VExQlePvEym/M2s7twN7sLdzM6YjR3pN+Boio+a5fuykuf2YI/6uKLPL216Y8+02q1oqoqDocDBw58sYGQw+EAh6PztFb3zmRUVcVqtaLVOo8/7Ml35PYcu9DQUGpqaoD2Z8W6jCnKgD4rVhZPCILQW5itcLhC4ZsyhbMmBZWWoZkRoSqXRju4JFolrJfnb1c6Ktll2cV+y34aaQQgTAljhmEG0w3TCVTcdyoFYbDRZvGEXg+2+u6VepxJUKdDsRkZGfzv//4vH374Yafq/eZIscGELJ5wH1k84Z0NWTzhO/rTBPXSGgufHStm49Ei9udWud4rClyRFsnSifEsnhBPXKjBbZvdYWo08c6Zd3jt1GuUNZQBEKIL4abRN3Hb2NtICEnocT28kZc+s4X+1Db9aXOoLp6YPn06P/nJT7jzzjsB+Oijj5g4cSIjR450lbFfHCnW38jPz2f58uUcP34cs9mMTtfzqspEYPeRxRPe2ZDFE76jP0xQT4rSc89sI/fMHkVhdT2fHC7k48OFHMqvYk92JXuyK/nVJyeZnhrFtZMTuHZSIgkXDdf2tB7R+mi+f8n3uWPcHfz+w9+TGZDJuepzvHzyZV479ZprocXYqLFu16On9faFzGBpl9A/2mZ/sOm3xRNNCyGah0Wb31+Mt+kX8/777/OjH/2IX/3qVxgMBi699FLmzp3r0u3TxRPN5Ofn86tf/YrMzEzMZnObtOPHj3tisteJjY1l69atrFixwt9FEQRBcJEYHsS9s0dy7+yR5FfU8dnRIj454nTy9uZUsDengnUfHeey1EiunZTA4vGxXuUXoA3gMsNl/PLaX7KnZA8vHXuJvUV7+fjcx3x87mNmJM5g9aTVzEic4bMNlwVBaGH48OFdDsV6i0eO3S233EJ6ejrr1q3rt3PQAgMDXSFMQRCE/khKVDD3zRnJfXNGcqGqns+OFrHxSCEHcitd15OfnCDVqKUwPIfrpgzzeHWtoijMTp7N7OTZHCs/xoajG9iUu4ldhbvYVbiLMZFjWD1xNUtHLO3lWgqC0Jd45NgdPXqUHTt2uBVydJe1a9fy1ltvcfLkSV599VVuvfVWV1ppaSmrV68mIyODlJQU1q9fz4IFC3otb0EQBH8zLCKI7109gu9dPYKi6gY+O1rIxiNF7MutINes8LvPTvO7z04zJTmcZZMTWTYpkeHRnjl5E6Mn8oe5f+A/zP/Bv4//m3fOvMPpytP8147/4ulvnuY76d8hzDE4zv4WhKGGR47d0qVL2b17NzNn9t4eSenp6TzzzDM8/vjj7dIeeOABkpKSKCsrY9OmTaxcuZKsrCwsFksbBxDAaDTy8ccf91q5BEEQ+pqE8EBWzxrB6lkjuFBh5n/f2ko+MezLqeTw+WoOn6/md5+eZGJSmNPJm5zIiJieHys2zDiMn1/xc9ZMXcNbp9/ilROvUFJXwv9l/h969Jzae4pVk1YxMnykD2opCIIv8MixCwoKYunSpSxevLjdvnWtT6ToCc2rQ37961+3eW82m/nggw/IyckhODiYFStW8NRTT/HRRx+xatUqtm3b5lF+rbFYLFgsFtfn5nNwZR+77pF97LyzIfvY+Y7BsldYZKCG2QkqixZdQrXFwabjJXx+rJjd2RUcKzBxrMDEHz8/xbiEUJZOjGfpxHhGxbY4ee6UKVgTzF3j7uL29Nv5PO9zXjnxCqeqTvHO2Xd45+w7zEqcxe3jbueqhKs6nIcn+9j1jMHSNgf8PnZNix6aNwdpfn8x3qb3lD7bx64169at6zRt7dq1PTXXhnnz5rFmzRpXJO7gwYMsWbKkzYbIDz74IMHBwfz+97/v1E5DQwPXX389Bw4c4NJLL+WJJ55g9uzZHco+8cQTHdZJ9rETBKG/0rxPXma5wulqBUerffISg1SmNu2Tl+hBF6aqKjn2HHZZdnHCegIV55+JOE0cMwwzuCTgEvTK4FiZKgwt2uxjF9DLm0j2Et7uY+dRxM5b560nmM3mdpUICwujqqqqS73AwEC2bNniVh6PPfYYDz/8MC+88AIvvPACdruds2fPelpkQRAEn2PUw8x4lZnxKrVWOFKpcKhc4VS1QmG9QuF5LZ+dh/gglUuiVC6JdpDY+RGWbVAUhRG6EYzQjaDCXsEuyy4ONB6gxFHCB/UfsLlhM9MDpnOl4UrCNDIXTxD6Ex45dn/4wx86fG8wGEhOTmbBggVERER4Uy4XRqPRNTTajMlkwmjsvcO1DQYDBoOBRx55hEceecS1QfH8+fM79YxtNhsZGRnMnz+/0z3yupJxR38g4K969Ha+vWHPExs90XFXtjs5b9MHCv6ohy/ydNfmiqZ7db2VjNNlbDpeytfnKiiuh88vKHx+QUNadBBLxsexeEIs4+KNnW5vcnGet3ALZquZj7I/4o0zb1BYV8h2y3Z2NO5gUcoiVo5cSeGhQo/7Q3dkBku7hKHXNntT39s+02KxUFBQQEhISJudM5o3GO4Mb9NbM2zYMC5cuNBpekNDA4GBgcycORODwblp+cV+UFd4NBR766238t5773HllVeSnJzM+fPn2bNnDzfccAMFBQUcP36cd999l2uuuaanptsNxZrNZqKjo8nNzSUhwblT+pw5c7j33ntZtWpVj+13hRwpJgjCYKLeBkebInknqxRsaosjFxPYEslLDnEvkgfgUB2csJ5gp2UnufZc1/tUbSozDTMZpx+HVtH2dlUEoVfoD0OxY8eO5dSpU52m+2Uo1maz8c4773D99de73n3yySe89NJL7Ny5k1deeYWHH36YQ4cOuW3TarVit9txOBxYrVYaGhoICAjAaDRy4403snbtWp5++mk2b97M0aNHueGGGzwpuiAIwpAhSAfTY1Wmx6o02OBYldPJO1GpUNagsKVAYUuBhmiDypQmJ2+4ETRdOHkaRcPEgIlMDJjIBdsFdlp2csR6hFx7Lrl1uYQr4VxhuILLAy4nRNPzlbqC0FeoqkqDvaHX7QZqA/262bdHEbvw8HAqKipcqzXA6exFR0dTXV2Nw+EgIiKiR6HD1atXs2HDhjbvMjIymDdvHqWlpdx1111s27aN5ORk1q9fz8KFC3tabLdpHootLCyUodhukKFY72zIUKzvkOGuzvVrG218eaaCz4+X8OWZchpsLSv54kMNLBgbTXRdHvfcOA9DQPeLJAprCvnfjP8lU82kqrEKgABNgHOYNn0l4yPHS5/ZCmmbnuv31lBsWloaqk5lxuszelzu7th16y6CdEGdprszFJuTk0NSUlKbodjExES3InYeOXYzZsxg2bJlPPbYY+h0Oux2O7/97W/5+OOP2b17N7m5ucyePZu8vLyemvYrMhQrCMJQw2KHE1XO1bXHqhQs9pZIg1GvMiVSZWq0SnqYirabPemtqpUj1iPstuymwF7gep+iTeFKw5VM0k9Cpwxsp0wY2LQeirVr7Cz+ZHGv57Hpuk1dOna+Hor1yLE7ffo0t99+O6dPnyYuLo6SkhLGjh3Lq6++Snp6Onv37uX8+fPcdNNNPTXdL5CInftIxM47GxKx8x0SFem5vsVmZ9e5Sj4/XsLmY0XUtXLywgJ1XDM2hkXjYpk5KhKDru2ITes8VFXlaMVR3j77Nlvyt2BTnX+cjIqRb4/5Njen30xcUFy7/IdCnwnSNr3R782IncFgcA3F9ubiie6GYvtlxK6ZnJwciouLSUhIIDU11VMz/QaJ2AmCIDixO+CMyRnJO1ypYLa2/KEyaFUmRjgjeeMjVAxdrJUwO8zsa9zHXsteatQaADRomKCfwFWGq0jVpvp1PpIwtBgKiye8cuzq6uooLy+ntYnhw4d7aq7f0ByxKysr6/QLtFqtbN68mUWLFqHXdzwHpSsZd/QHAv6qR2/n2xv2PLHREx13ZbuT8zZ9oOCPevgiT29t9ka7tDtU9uc6I3mbjhdTbGo5qSdQr2H2qGgSbEU8ePM8Io0dD0HVW+r566d/5VTwKQ6WHXS9T49I55Yxt3Bt2rXoVN2g7zNB2qY3+t72mQ0NDeTn55OWluba7kRVVVfEraN/MrxNvxi9Xk9iYqLr89/+9jeWLVvm+twcsUtJSXGV0WQyERMT47tVsUeOHGHVqlUcPnwYwFWRgIAA6urqPDEpCIIg9FO0GoUrR0Rx5YgofnntWDIvVPP5sWI+P17C+cp6Np8sBbS8/qcdzBoVzZKJ8SwcF0dEcMsfXp1Gx6SASTw0/yGyzdm8cfoNPs35lDNVZ3hy75M8c/AZrku7jnh7vP8qKgh9gK+PkvMoYjdr1iwWLVrEo48+SmJiIoWFhfz3f/83o0aN4gc/+IEvytknyFCsIAiC+6gqXKiDzHINmRUKxfUt0QoNKqPDVS6JVpkcqRLWwahXvaOebxq/YXfjbiodla73I3QjuCLgCsbrx8tiC6FX6Q9Dsd3hl6HYiIgIKioq0Gg0REZGUllZSWNjIyNHjuT8+fOe1aQfIUOx7iNDsd7ZkKFY3yHDXZ7r91SnWX7EJTPZcqqcTceKOVlsdqUrClyWEs5wTQU/WnE1KdFtJ6HbHXZ2F+3mzdNvsqNgh+ts2qjAKJaPXM5No28izhA3KNolSNv0Rn8wDMV2h1+GYiMiIqiqqiIqKophw4aRmZlJVFQUZrO5e2VBEARhUDI6NoTxSRE8OH8UueV1fHasmE3Hizl8wcT+vGr2o+Xdp3cxNTmcJRPjWDIhnuFRwWg1WmYlzeKK2Ct46/O3qEyu5IPsDyirL+PF4y/y0vGXuCrhKkZaRzLfMR89A9uxEwRf4lHE7sknn2TixIl861vf4oUXXuCRRx5Bo9Fw33338cc//tEX5ewTZChWEASh96mwwOEKhcxyDdk1oNIS1RgWrDI12sHUKJWEVt2tXbVzynqKvY17OWs763ofpoRxueFyLg+4nDBN15ELQbgYrVZLYmIiMTExBAV1vtecP6mvr6esrIzCwkLsdjvQh6tim8nNzcVsNjNx4kRvTfULZCjWfWQo1jsbMhTrO2S4y3N9T4di3ekPL7lqDhlnK9l0rJg9OZXYHS1/gkbFBjM6wMwPrruSSckRrmGt/Jp83jn9Dm+ffps61blAT6tomTNsDjePvpmrEq9Co3Sze3I/Qtqm5/re9pmqqlJcXExNTY1LTlVVGhoaCAzseP85b9M9ITQ0lPj4eJc9nw3FTpgwoVuZ48eP98Rkv0av13fbcLyVcUd/IOCvevR2vr1hzxMbPdFxV7Y7OW/TBwr+qIcv8vTWpq/bpbvySVFGVs+KZPWskVTUNrL5eBGfHi3i67NlZJXWkYWGz5/fR2p0MEsnJXDtpESmJo/gJ5f9hJFFIwmYGMA7We9woPgAGeczyDifwTDjMFaMXsGK0StICEnoUR39ibRNz/W96TNTUlKw2WyuhQlWq5Uvv/ySOXPmdPrPrjfpPUWn07XbfLkndnvk2GVnZzN8+HDuuOMO5syZI5tKCoIgCB4TFRLALdOHc8v04VTXW9l8tIANWw9zukZHbnkdf99+jr9vP0dSeCCLJsQRXqPjh8OXcEP6DWRVZfH26bf5IOsDLpgv8OyhZ3ku8zlmJc3i5vSbmZMyB71m4P9jIviG1s6TVqvFZrMRGBjYoQPlbXpf0yPHrqSkhHfffZdXXnmFl156iZUrV3LHHXcwZcoUX5XPr1it1k73m2l+39V+NF3JuKM/EPBXPXo7396w54mNnui4K9udnLfpAwV/1MMXeXpr09ft0l357mSCdbBsYiz6Agcz585iV3Y1nx8rYdvpUgqqG9iwKw/Q8eoft7NofBxLJ8bz4yk/4f7J97M1fyvvZ73PgZIDfHXhK7668BVRgVFcP+J6VoxaQVpYmtt17wukbXquP1T7zJ7Y9niOXXFxMa+//jqvvfYatbW1vPHGG24N1fZnZPGEIAhC/6LRDqeqnUebHa1UqG91fm2ITmVSpPNos7HhKlVqGd80fsM3jd9gVlt2aUjVpnKZ4TIm6ScRoPTPvcsEoSv6ZPFEVVUVb775Jq+++ioXLlzgvffeY9KkSR4VuL8hiyfcRxZPeGdDFk/4Dpmg7rm+LxdPeNNnfvr5ZoyjLuWLU+VsPlFCZV1LFMNo0HHN2FiWTIxj5qgI9pfu4v2s99lRsAOH6nDK6I0sTV3KitErGB853m/TiaRteq4/VPtMny2esFgsfPjhh/z73//m4MGDrFixgt/97ndcddVVXhXYV2zfvp1HH30UrVbLFVdcwVNPPdUjfVk84T6yeMI7G7J4wnfIBHXP9X2xeMIbGZ0GrhmfwJIpKdjsDvbmVPDZ0SI+O1pESY2FDw8X8uHhQoL0WuaPi2XppF/wyGXwRf4nvHvmXc6bz/P22bd5++zbjI0cy03pN3HdyOsIN4S7XcfeRNqm5/pDrc/02eKJ+Ph4EhISuO222/j5z3/umni4d+9el8wVV1zRE5M+ZfTo0Wzbtg2DwcDtt9/OkSNHmDx5sr+LJQiCIHiJTqth5qgYZo6K4YkbJnIwv5JPjzhX2F6oqmfjkSI2HikiQKdhTvol3D1hMTGx5/k870O25G7hVOUpfrv3t/xp/5+YlzKPFaNXMDNpJjqNHGEmDGx61IIjIiKwWCy89NJLbNiwgYtHcRVF4dy5c71aQG8YNmyY61mv16PVav1YGkEQBMEXaDQKl6VGcVlqFL+4bjxHL5j49Gghnx4tIrusli0nSthyogStRmHGyJtYM/67OEIOsCX/Y05WnGRz7mY2524mOjCa60dez/LRy0mPTPd3tQTBI3rk2OXk5PioGE7Wrl3LW2+9xcmTJ3n11Ve59dZbXWmlpaWsXr2ajIwMUlJSWL9+PQsWLHDL7jfffENZWdmAX9whCIIgdI2iKExODmdycjj/uWQsp4vNfHa0iE+PFnKyqIYdZ8vYcbYMRYnh8tSHuH10PfWGPWwv+JzyhnI2HN/AhuMbmBA9geWjlrNsxDIiAiP8XS1BcJt+FXNOT0/nmWee4fHHH2+X9sADD5CUlERZWRmbNm1i5cqVZGVlYbFY2jiAAEajkY8//hiAoqIifvzjH/POO+/0SR0EQRCE/oGiKIxNCGVsQij/sTCdnLJaPjvmnJN3KL+KfTmV7MsBmMqkYbO4YuQFqrQ72V/6NcfLj3O8/Dh/3P9H5iXPY/no5cwaNkv2xhP6Pf3KsbvzzjsB+PWvf93mvdls5oMPPiAnJ4fg4GBWrFjBU089xUcffcSqVavYtm1bh/YaGhq4/fbb+etf/0p8fHyn+VosFiwWi+uzyWQCZB87d5B97LyzMVT3ZOoLZK8wz/X9sY9ddzK98d0OCw/gezOH872ZwymsbmDziRI+P1bM/txKjl4wc/RCOHAto+KXkpZ6mhL1a3LMp9mSt4UteVuICozi2tRruWHkDYyJHONxOaRteq4/VPvMPtnHzpfMmzePNWvWuCJxBw8eZMmSJZSUlLhkHnzwQYKDg/n973/fqZ3nnnuOdevWMW7cOAB++9vfMmPGjHZyTzzxBOvWrWv3XvaxEwRBGPzUWOFIhXOvvNMmBYfasg1KVGgBUbHfUG04SAO1rveJ2kSm6acxJWAKRo3RH8UWhhA92ceuX0XsOsNsNrerSFhYGFVVVV3q3X///dx///3d2n/sscd4+OGHeeGFF3jhhRew2+2cPXvWmyILgiAIA4RQPcyMV5kZr1Jng2OVTifvZJVCRU0SFTVJwLWERZwiPPoApoCTFNoLKbQX8lnDZ4zSjWJqwFQm6CfIBsiC3xkQjp3RaHQNjzZjMpkwGnvnvySDwYDBYOCRRx7hkUcecW1QPH/+/E49Y5vNRkZGBvPnz293WK87Mu7oDwT8VY/ezrc37Hlioyc67sp2J+dt+kDBH/XwRZ7e2vR1u3RXfiD1mcub7rWNNnacrWDziVK2nSnHVDUBU9UE0NYSHnOU0OjDVKtZnLGd4YztDEHaIOYOm8u1qddyedzlnW6dIm3Tc/2h2mde7AN1xYAYijWbzURHR5Obm0tCQgIAc+bM4d5772XVqlW9lq8cKSYIgiB0hNXhPNrscLnCkUqFOptzuFbRlxEceRBDxEGs2gqXvFExMiVgClP1U0nSJvntlAthcDBgh2KtVit2ux2Hw4HVaqWhoYGAgACMRiM33ngja9eu5emnn2bz5s0cPXqUG264wd9FFgRBEIYAeg1MinSeTWt3wNka53DtkYpoTCWLqC1ZiCYwn8CIb9CHH8asMbPTspOdlp3EamKZGjCVqfqpRGoj/V0VYZDTryJ2q1evZsOGDW3eZWRkMG/ePEpLS7nrrrvYtm0bycnJrF+/noULF/qkHM1DsYWFhTIU2w0yFOudjaE6rNAXyHCX5/pDfSi2JzhUlUP51Ww+Ucrmk2UUVDcAdrTG0xjCD6ILPYGqtKxonBozlcXJi9Gf03PdguukbUqf6RYmk4nExES3Inb9yrHzNzIUKwiCIHiKqsL5Wsis0JBZrlDSoICmAV3oUQLCD6INPgeK80+uFi1j9GOYqp/KWP1Y9Irsjyd0Tk+GYsWx6wCJ2LmPROy8szFU//vsCyRi57m+ROy8R1VVssrqmiJ5pZwsMqPoqtGFZaIPP4g2sNAlG6wLZu6wuSxOWcwV8Vf45Lzaodg2B1OfKRE7D5GInSAIguALyhrgcIVCZrmGHLOCxlCELuwg+rBMNAFVLrlgJZhJ+klMDphMqjYVjaLxX6GFfoNE7LykOWJXVlbW6RdotVrZvHkzixYtQq/vOITelYw7+gMBf9Wjt/PtDXue2OiJjruy3cl5mz5Q8Ec9fJGntzZ93S7dlZc+s4X88hqefX8H+Wo0+/MqITAPfdghdGFH0OjMLrm4oDgWpy5maepSxkeN92pl7VBsm4OpzzSZTMTExAy8VbGCIAiCMNhJCAtkTqLKokXTMDWqfHGihE3HL2PnuRIcgVnO4drQo5TUl/Dvk//m3yf/TYoxhSWpS1iatpSR4SP9XQWhHyMRu1bIUKwgCILgL+pscLxSIbNC4US1HTXoNLqwTHShJ1A0LStr4zUJTAmYzBT9FNk+ZYggQ7FeIkOx7iNDsd7ZGKrDCn2BDMV6ri9Dsb7FnbrUNdr48kw5m44Xs/X0eSwBR9GHHUJrPIOi2F1yk6Inc23aUhYNX0RMUIxXefqiHr2tP1T7TBmKFQRBEIQBTHCAjqUT41k6MR6LbRI7s6az6XgJW07nUKs7iC4sE23wOY6WH+Fo+RH+dODPXBZ3GUvTljA/eT6RgRLJG6pIxK4VMhQrCIIg9GfsKmSZnKdeZFbX0BB0BH14JtqgfJeMomoYoRvJ1IDJjNePJ1gjf8cGOjIU6yUyFOs+MhTrnY2hOqzQF8hQrOf6MhTrW3qrLg6HyqHz1Ww6XsynJ49Txl50YUfQBha4ZDRouTzuCpakLsRxysGNS24cMm1zMPWZMhTbS+j1+m5/SN7KuKM/EPBXPXo7396w54mNnui4K9udnLfpAwV/1MMXeXpr09ft0l156TNb6I26XDkqlitHxfLL6ydyrGAJnx0t4qPjmRTa9qALOwyBRewt2cXekl0oqpbN27exYsx1zE+ZjzHA2C/qIX1m9/TErjh2giAIgjDAURSFScPCmTQsnJ8uGcvZkmv59EgRH504RE7DTnShR9AGFrO35Gv2lnyNVtEzPW4GK8YsY17KPEL0If6ugtBLiGPXBVarFavV2mla63tPZdzRHwj4qx69nW9v2PPERk903JXtTs7b9IGCP+rhizy9tenrdumuvPSZLfRFXVIjA1kzJ401c9LIq1jMZ0cLeW3f15TojjgjeYZSdhd/ye7iL9ESwGWxV/GtMdcyO2k2wXr35uT197Y5mPrMntiWOXatkMUTgiAIwmCmuhEyK+BAdQmF2iPoQo+gMZS50jWqnjTNGKYHTWasfgwBSoAfSys0I4snvEQWT7iPLJ7wzsZQnQjcF8jiCc/1ZfGEb+kvbbOitpEtJ0p4/8R+jlZ9hSY0E01AhUtHi4FLY2aycuwyrh42i0BdYK/WQ/pM95HFE0BBQQHf/va30el0hIWF8eabb/Y4+iYTgd1HFk94Z2OoTQTuS2TxhOf6snjCt/i7bcZH6LljxgjumDECU8MKtp4o5u0je/imYhtKSCYEVLKvLIN9ZRnoCOTSmFncOvF6Zidf3cbJ6+9tczD0mbJ4AoiPj2fHjh1oNBrWrl3LJ598wsqVK/1dLEEQBEHod4QF6lkxLZkV05Kpb1zBtlPFvHVkF/tKM3CEZGLTV7G37Av2bv8CLQamRM1g5bhraVQb/V104SIGrWOn1Wpdz4qiMHbsWD+WRhAEQRAGBkEBWq6dnMS1k2/GYlvB12fLeD1zB7tLtmIPOgz6Kg5WbOPgzm0oqp53P9nKbZNuYGHqPLcXXgi+Q+PvAjSzdu1aJkyYgEaj4fXXX2+TVlpaynXXXUdwcDBjx47liy++cMvmjh07uOyyy9iyZQupqam+KLYgCIIgDFoMOi3XjIvn+Vtu5psf/pXn573LPOP/oKuZj6MxElWxcrR6B7/4+ufMePVqvvPeD3j71IeYG83+LvqQpd9E7NLT03nmmWd4/PHH26U98MADJCUlUVZWxqZNm1i5ciVZWVlYLBZuvfXWNrJGo5GPP/4YgKuvvpoDBw7wpz/9iX/961889NBDfVIXQRAEQRhs6LQaZo2OZdboFTgcy9l3rpSnPnuXLOUU9QEHIaCcE6adrNu9k1/t0jE69DJWjlvGdekLCQvoesK/0Hv0G8fuzjvvBODXv/51m/dms5kPPviAnJwcgoODWbFiBU899RQfffQRq1atYtu2bR3as1gsGAwGAMLDw7Hb7Z3mbbFYsFgsrs8mkwmQfezcQfax887GUN2TqS+Qfew815d97HzLYGmbk5OM3J6SyMKF3+VMWT2vZ+5j24UvqNEeQGMo44x5D7/Zv4ff7PsVI0KmsSJ9CTeOXki4IdzjMg3VPnNA72M3b9481qxZ44rEHTx4kCVLllBSUuKSefDBBwkODub3v/99p3Z27NjBL37xCzQaDVFRUbz88sudrop94oknWLduXbv3so+dIAiCIPSMojqVXVWlHLUepS7wKFpDy99vVA1R9lFcGjiR6cHjCdHIiRfu0JN97PpNxK4zzGZzu0qEhYVRVVXVpd7VV1/N9u3b3crjscce4+GHH+aFF17ghRdewG63c/bsWU+LLAiCIAhDloRghW8Fx/EtrqG84Rp2VpZypPEYNYajaAOLqNCdYYvtDFuqPyTCPoJLDJO4Mng8odreObt2qNPvHTuj0egaGm3GZDJhNPZeAzAYDBgMBgIDA9FoNPSzIKYgCIIgDEiiA+GGxFhuYB7VjfPYVVnGIcsxqgOOog0spEqXxTZ7FttMHxJmH8GUgInMDJlAmDbU30UfsPR7xy49PZ3q6mqKiopISEgAIDMzk3vvvbfX83rggQd44IEHXCdPzJ8/v9OQp81mIyMjg/nz56PTdfw1diXjjv5AwF/16O18e8OeJzZ6ouOubHdy3qYPFPxRD1/k6a1NX7dLd+Wlz2xhKLfNbzfdK+saeftwJm8d+4jKgKNoAi9g0p1jh+McO0wfE6MbyzXJ13DnpGuJD47zKM/B1GdeHODqin4zx85qtWK321m8eDH33XcfK1euJCAgAI1Gw8qVK4mKiuLpp59m8+bNrF69mqysLCIjI3u1DHJWrCAIgiD0LQ022FtVyYH645TqjqAJOt8mPdiaygT9ROYYJxCli/BPIf3MgDwrdvXq1WzYsKHNu4yMDObNm0dpaSl33XUX27ZtIzk5mfXr17Nw4UKflaU5YldYWCgRu26QiJ13Nobqf599wVCOinirLxE73yJts3P9equdj0+c5L0zm8iq2w2BOW10wpRRjHSk87OFdzMqKq1XyjcQ+kyTyURiYuLAcuz6AxKxEwRBEIT+gc0BmdUmdtcdp0BzFAJzUZQWl8VgTWK0diJzQicwTB/rx5L6ngEZsetPSMTOfSRi550Nidj5DomKeK4vETvfIm2z5/o2h4OMs+d4/cTnHK3egRqY3cbJC1KTuCxmNt+ddC1TY8ehKMqg6jMlYuchErETBEEQhP6NQ4VTNbV8bT5JLsdwBJ1FURyudK0tmlQmcrVxIumGJBRF8WNpeweJ2HmJROzcRyJ23tmQiJ3vkKiI5/oSsfMt0jY9179YR1VVvjlfxL+PbeJA2Vc06E+gaGwueY0tgskRs7htwjLmDr8UjaLpcTn6Q58pETsPkYidIAiCIAxc8ussfGk6zVnHMRoDT6FoWo7iUmxhJKnjuSp4IlOCU9EqWj+WtGdIxM5LmiN2ZWVlnX6BVquVzZs3s2jRIvR6fY9l3NEfCPirHr2db2/Y88RGT3Tcle1Oztv0gYI/6uGLPL216et26a689JktSNv0XL8nOqeKSvnNpxvI0ZzFpMlE0bacCa9xGBkdciU3j13KspFXsX3r9n7dZ5pMJmJiYgbHkWL+RK/Xd/tD8lbGHf2BgL/q0dv59oY9T2z0RMdd2e7kvE0fKPijHr7I01ubvm6X7spLn9mCtE3P9d3RGZsQyy3xE1i27KcUm+v554FNfJG/hQr1IA6tmdP1X/DbQ1/wu2+CiLaNo/Q43HnJEgJ1gR7l6cufZ0/simPXBVarFavV2mla63tPZdzRHwj4qx69nW9v2PPERk903JXtTs7b9IGCP+rhizy9tenrdumuvPSZLUjb9Fzf0z4z3hjEf81dzn+xnDJzPS8d3MrmvC2U2A+AzkxZwEGeOXaQZ478D8mGS7lu5CLumLwYY0Bwv+gze2JbhmJbIXPsBEEQBGHoUGdzsKMqj8zGY1Tpj6Poq1sSHXrCbOlMDpjInNCxhGg7juT1STlljp13yBw795E5dt7ZkDl2vkPmMXmuL3PsfIu0Tc/1fdlnzpo7j3dO7OfDs5+TZ9kD+gqXjOrQEqOdxILhC7hn6rXEGaO9qkdPkTl2vYTMF3EfmWPnnQ2ZY+c7ZB6T5/oyx863SNv0XN8XfWZ4SDA/mLGAH8xYgMVq562je3jn5EbO1u5C0ZdQrmbyZm4mb+Q8TaRmPPOSr+G+y5aTGBzlcT3cRebYCYIgCIIgeIhBr+XOaTO5c9pM6hss/PrNDZwOKOSUeScOfQFV6jHezz/Ge3n/RxjppNrTmVZzFcOj4v1ddHHsukIWT3SPLJ7wzoYsnvAdMkHdc31ZPOFbpG16ru+PPlN12JkeHs9/LboTnU7HprPHef3YRo5Wf41dn0cNpzmqO82FqrtIDI1yuy49QRZPeIgsnhAEQRAEwR1UFU7XVvKV+QQVahk/S7zRZ3nJ4gkvkcUT7iOLJ7yzIYsnfIdMUPdcXxZP+BZpm57rD9U+UxZP9BIyEdh9ZPGEdzZk8YTvkAnqnuvL4gnfIm3Tc/2h1mf2xG7703AFQRAEQRCEAYk4doIgCIIgCIMEcewEQRAEQRAGCeLYCYIgCIIgDBJk8UQHNC8UNplMncpYrVbq6uowmUxdrvDqTMYd/YGAv+rR2/n2hj1PbPREx13Z7uS8TR8o+KMevsjTW5u+bpfuykuf2YK0Tc/1h2qf2eyPuLORiTh2HVBTUwNASkqKn0siCIIgCILgpKamhvDw8C5lZB+7DnA4HBQUFBAaGoqiKJ3KTZ8+nX379nVpqzMZk8lESkoK+fn53e5J099x53sYCPn2hj1PbPREx13Z7uS6Spe22f/y9Namr9ulu/LSZ7YgbdNz/aHYZ6qqSk1NDUlJSWg0Xc+ik4hdB2g0GpKTk7uV02q13f4Qu5MJCwsb8J2UO9/DQMi3N+x5YqMnOu7Kdifnjh1pm/0nT29t+rpduisvfWYL0jY91x+qfWZ3kbpmZPGEFzzwwAO9IjPQ8Vcdezvf3rDniY2e6Lgr253cUGiX4J96+iJPb236ul26Ky99ZgvSNj3Xlz6za2Qo1k80H1vmzvEggtCXSNsU+iPSLoX+Sn9rmxKx8xMGg4G1a9diMBj8XRRBaIO0TaE/Iu1S6K/0t7YpETtBEARBEIRBgkTsBEEQBEEQBgni2AmCIAiCIAwSxLETBEEQBEEYJIhjJwiCIAiCMEgQx04QBEEQBGGQII6dIAiCIAjCIEEcO0EQBEEQhEGCOHaCIAiCIAiDBHHsBEEQBEEQBgni2AmCIAiCIAwSxLETBEEQBEEYJIhjJwiCIAiCMEjQ+bsA/RGHw0FBQQGhoaEoiuLv4giCIAiCMIRRVZWamhqSkpLQaLqOyYlj1wEFBQWkpKT4uxiCIAiCIAgu8vPzSU5O7lJmUDt2paWlrF69moyMDFJSUli/fj0LFizoVi80NBRwfoFhYWEdylitVjZt2sTixYvR6/U9lnFHfyDgr3r0dr69Yc8TGz3RcVe2Ozlv0wcK/qiHL/L01qav26W78tJntiBt03P9odpnmkwmUlJSXP5JVwxqx+6BBx4gKSmJsrIyNm3axMqVK8nKyiIyMrJLvebh17CwsC4du+DgYMLCwrrspDqTcUd/IOCvevR2vr1hzxMbPdFxV7Y7OW/TBwr+qIcv8vTWpq/bpbvy0me2IG3Tc/2h3me6Mz1s0C6eMJvNfPDBB/zqV78iODiYFStWMGnSJD766CN/F00QBEEQBMEnDNqI3ZkzZwgPDycxMdH1burUqRw7dqydrMViwWKxuD6bTCbA6YVbrdYO7Te/7yy9Oxl39AcC/qpHb+fbG/Y8sdETHXdlu5PzNn2g4I96+CJPb236ul26Ky99ZgvSNj3XH6p9Zk9sK6qqqj4riR/56quvuPvuuzl79qzr3S9+8Quqqqp49tln28g+8cQTrFu3rp2NV199leDgYJ+XVRAEQRAEoTPq6uq4/fbbqa6u7nSKWDODNmJnNBpdkbdmTCYTRqOxnexjjz3Gww8/3EYuJSWFxYsXdznHbvPmzSxatKjL+SKdybijPxDwVz16O9/esOeJjZ7ouCvbnZy36QMFf9TDF3l6a9PX7dJdeekzW5C26bn+UO0zL/ZnumLQOnbp6elUV1dTVFREQkICAJmZmdx7773tZA0GAwaDod17vV7f7Q/JU5ltp0s5XqmQWFRLdGgQ4UF6woP06LUDc9qjO9/DQMi3N+x5YqMnOu7KdifnbfpAwR/18EWe3tr0dbt0V95bmcHSLkHapjf6fdpnqipauwV9fSn6mlqwmKDB1HSvRlNXxfiCg+h11/rs59kTu4PWsTMajdx4442sXbuWp59+ms2bN3P06FFuuOEGfxcNgMc/OE6RScvfT+5t8z44QOty8sKa7p1dkSEBRIcEEBUSQHCAVjZTFgRBEITOsFmgrhzqK6G+ChqqoL4KTW054wr3o/n8S6ezVl/pSqOhCl19Fdc7rHC4Y7NaYAxgtdZCQEBf1aZTBq1jB7B+/XruuusuoqOjSU5O5s033+x2q5O+YnxiKFpbAwQEYaq3UWOxAVDXaKeu0U5hdUOP7Bl0GqJDAog2Gohq5fBFGZufne9jjAHEhQYSFKD1RbUEQRAEwfeoKljMUFcGteVN97JW9wq05hJmF2Shy/5vp0PXWNOhKS0wFqCo46yaQyaqRocSGA6GMAgMa7qH49CHkFNUQUo/WbIwqB272NhYNm7c6O9idMjzd17Kxo0bWbZsDnq9HpvdQU2Djep6a4eXqYN3lbWNlNc2YrE5sNgcFFQ3UOCmQxgWqCMuLJD4MAPxoYHENt3jwgzEhwW6ngP14gAKgiAIfYTD4XTOaorAXNx0L4KaYjAXoTUVsbA0G92R74Ot6793GiAKoK7VS0ULQREQGOG8B0XiMISRW1zN8LFT0YZENaVFuuSseiOfb9/Dkuu/hb6DiJzdauXIxo2kGLrfPLgvGNSO3UBCp9UQGRJAZEjPwriqqlLXaKfc3Eh5rYWKJmevoukqNzdS0ep9mdlCg9WBqcGGqcHM2RJzl/bDAnUkRQQxLCLIeY9seU6ODCLCMDDnBAqCIAh9jK0Ragqg+jxUX0BTmceU/J1o3/g31JY4HTlzCaj2Tk1ogJDWL3SBEBwDIdFN9xjnPTgKW2AkB07kctnsxejCEpwygRFw0bQlu9XK4Y0bSZ63DG1Hc9msVuzawHZ6/RVx7AY4iqIQYtARYtAxPLr7rVlUVaXGYqPE1ECxyUJJTdPdZKG4poESUwMlNRaKTQ0tDmBRDSeLOg5h67UKYTotrxXtY1hkCKnRwaRGBzMiJoTU6BDCgwbHJGdBEAShC1TVOTetMhuq8p3Om+lCkxPX9GwuAVqGK7XACICyi40pEBILofFgTGi6O59twTHsPHyOGYuWow9PhICQTh0u1WqlqHAjasqVMEgW3LiDOHZDDEVRCAvUExaoZ3Rc52FjVVUxNTgdwAtV9RRUNXChqo4Llc3P9RSZGrDaVcrtCuXZlZBd2c5OVEgAqdHBpEWHOK8Y5/PI2BBCA4fOL5ogCMKAx9YI1flO560yB035Oaaf24PuH3+EqlznwoPu0BogPBnCh+EITeJMcR2jLp2LLiK5xZELiQVtx+6JarVSeW4jRKYNKWetJ4hj1wVD/eSJYB2kRQWSFhXYYbrN7uBCZS0fbvmKpPTJFNdYyausI7fceZWaW4aED+ZVtdNPCDOQHmckPc7I6LgQRscZGR1rJDSw+2YpJ094LtcfdlHvC2R3f8/15eQJ39Kv26bDBlV5KOVnUSrOQvlZlIoslMpcMF1AuSjilgRQ3aKuGhNQI4ZDWBJqWDKEDUNtuggbBsHRrgib1Wrl5ObNpExehNraSXOo4PC+fxpMfaacPOEhzz77LM8++yx2u53Tp0/LyRNeYrFDaQOUNSiUNkBpveJ6Nlk7n6sQEaCSEKQyLASSQ1SSQ1RiAkEzMKY3CIIg9Hv0thpCGwoxWgoxNhQ575YiQizFaLqY42bTBFAXEEetIbaDeywOjf+3+xiM9OTkCXHsOsBkMhEeHk5ZWZmcPNENntajut7K2RIzZ0pqOVtq5kyJmbMltZTUWDqUDwnQMj4xlAmJYUxIDGVsXDDZmbtYulhOnuipXH/YRb0vkN39PdeXkyd8S5/WxVKDUnYKe+FRzn+zibTgejRlp1BqSzpVUXVBEDUSNXo0atRo1OhREDkCNSLVOUzaKuLWn9vmYOozTSYTMTExQ/tIsd5AdlF3n57WI0avJyYsmKtGt31fXWflTEkNp4prOF5g4liBiROFJmob7ezPrWJ/bpVLNkCj5Y3iQ1yWGsWlwyOZNjyCaGP7E0R8WY/esiEnT/gO2d3fc305ecK39GpdbBYoPQUlJ6DkeNP9BFTnAc4/9qMu1glPgejREJPuvDc9K2HJoNHg7iBJf2+bg6HPlJMnhAFLeLCey9OiuDwtyvXOZndwrqyWoxeqOVZgct3NFht7sivZ02rRRlp0MJcOj+TS1EhmjIpmZEyInMghCMLgwmKG4qNQeBgKM6EoE0pOdjovjdBEHLHjOFdjIO3KZegSJ0HsOOeKUmHQIY6d0O/RaTWMiQ9lTHwoN13qfGexNPLiu58SmjaFzAsmvsmr4myJmZzyOnLK63j34AUA4sMMzBgZzcxRMcwYFU1KlMyZFARhAFFXAUVNDlyzI1d+ltbbhrgIjID4iRA3vuma4HTggqOwW60c27iR1KnLZDXpIEccO2FAotEoJAbDssuTuWOGs5OqrrNyML+Sb3Ir2ZtTwTd5VRSbLLx/qID3DxUAkBwZxKxRMcwfF8us0TGy5YogCP0Hm8XpvF3YDxcOwPn9zq1FOiI0ERKmQOJUSGy6h6cMmE10Bd8hjp0waAgP1jNvbBzzxsYB0GC1801uJTuzytl1rpzM/CrOV9bzxv583tifj16rMD0timvGxTF7VBSyjEgQhD5DVZ2Rt+ImR+78fig60vFwauSIFuctocmRM8b1fZmFAYE4dsKgJVCvZeboGGaOjgGg1mJjb04FX50uI+NUCdlltezMKmdnVjkAMYFajutPc8PUZCYNC5O5eYIg9B6NdU4HLm832txdXJu7B/2h2vZywTGQfDkMuxySL4OkS51nlgqCm4hjJwwZQgw65o+NY/7YOP77hglkl9Wy9WQJGSdL2JNdTlkDPP9VDs9/lUNKVBDLJiWybHIiU5LDxckTBKFn1JZB3m7I2+W8Fx5ybv6L87zTAEDVGlASpzY5cpc57xGpMpwqeIU4dsKQZURMCN+7egTfu3oEleZ6nnljM0UBSWw7XUp+RT1///Icf//yHClRQdw0LZlvX5Ysiy8EQWiPqjrnwrV25MpOt5cLTYLUGdiTpvNVroVZ37oPfaCsTBV6F3HsumCoHynmDv6qR2/na9CoTItRWbRoAlZVYfvpMj47VkzGKaeT98wXZ3jmizNcOSKSm6YlsXRiPMEBbX995Eix/kW/PrapD23KkWI+QFWhKgclZwea3B0ouV+jmIvai8WOw5F8JWrKlajDZ0BYMigKVquV6tLNWB0K9FF9hmLbHEx9phwp5iFypJhwMY12OFyhsLdU4XS1gtq0ZadBo3JpjMrsBAfD5B9uQRj0BDWWEVNzghjzCWJqThBsLW+T7lC0VAaPpCJkDOXGMVSEpGPVGf1UWmGwIUeKeYkcKeY+/qpHb+frjr3C6gbeO1jAuwcLyK2oc72fnhbJd69MYe7oSLZt/UKOx+knyJFinuvLkWJATRFKbquI3EXbjqgaPeqwy1BTZ6GmXo067HLQB7llWtqm5/pDtc+UI8V6CTkex338VY/ezrcre8Nj9PzHorH8eOEY9mZX8P925fLZsSL25VSyL6eS+DADV0QozLYrBAfL8Tj9BTlSzHP9IXWkmMUMOTsg6ws4t639HDlFC0nTYMRsSJuNMvwqFC9PbpC26bn+UOsz5UgxQfAhiqJw5chorhwZTWF1Pa/uyeO1vXkUmyx8ZNKy7c9fcteMNO6eleb12bWCIPgIh8N5okPWVueVt/uiPeQU535xI+ZA2hwYfhUEdh0pEYT+gDh2guAFieFBPLJ4LD+6ZjTvHcjnfz87SnG9jf/LOMs/dpzj1unD+eG8UcSFBfq7qIIg1BS3OHLnMqC2tG16ZBqMWgCj5kPa1RAU6ZdiCoI3iGMnCL2AQafl5kuHYSjMJGDEZfz9qxwOn6/mpZ05vL4vj7tnjWDNnFGE93CIVhAEL7BZnNuPnP3C6cwVH22bHmB0RuRGXeO8okf5p5yC0IuIYycIvYhGgcUT4lk2ZRg7zpbx1ObTHMyr4rltWbyyO5cfzB3F3bPS2m2VIghCL6CqzrlxWVudzlzODrDVtxJQnMdyjV7gjMwlTwddgN+KKwi+YMD/dfnd737HY489xq5du7jqqqsAWL16Na+99pprsmFqairHjh3zZzGFIYaiKMxOj+Xq0TFsOVHCnz4/xaniGv74+Sn+vTuX/1o2nuunJPq7mIIw8KmvdC52yNoKZ7eC6XzbdGOCMxo3egGMnAchMf4opSD0GQPasbtw4QKvvvoqCQkJ7dLWrVvHo48+6odSCUILiqKwaEI814yL46PMAv606RTnK+t58LWD/Ht3Lr9cNtbfRRSEgYXDBnnfOFevZm2FCwdAdbSkaw2QOrPFmYubIEd0CUOKAe3YPfLII6xbt46HHnrIKzsWiwWLxeL6bDKZADl5wh0Gy8kTvWGvOxvXTYpjwdho/rEjh79/lc2e7AqWr9/FrDgNV9XUExXaO2UcCLuo9wVy8oTn+v3u5InqfBynNzP93JvonvoRWExtktWYsThGzkcdOd95woO+1cbyNptbdehLpG16ri8nT3TPgN2geNu2bTz55JNs2bKFtLQ0Xn/99TZDsR999BEAY8eO5Xe/+x1z5szp1NYTTzzBunXr2r2XkycEX1FhgQ9yNRwq1wAQHqByy0gHEyMH5K+jIPQqWnsDMeaTxNUcIc50BKOl7XFdjdoQSkMnUhI2mZLQyTQERPmppILQNwz6kydsNhvTp0/n5ZdfZtKkSe0cu4MHD5KWlkZISAhvvfUWP/zhDzl69CgpKSkd2usoYpeSkiInT7jBUDp5whc2vjxVzM/fOkSZxTlUdOOURH6xbCxRIe0ndA+mXdT7Atnd33P9Pj95QnVA8VHUs19QfeBdYurOorTaU05VtDiGXcZpezIjFt6LNvky0Gjdqkt/RNqm5/py8sQAPXli8eLFfPnllx2m/fKXvyQ0NJSrr76aSZMmdSgzbdo01/Mdd9zByy+/zObNm7nnnns6lDcYDBgM7TeSHdC7qPcxQ+HkCV/YmDM2np9PtXNSP4oXd+by4eFCvs4q5w/fnsKC8fFe2e/Pu6j3JbK7v+f6Pj15wlTo3EsuaytkZUBdGQCxzYIRqU2rV69BGTEHhzaY0xs3Mnr4FYOiXYK0TW/05eSJzumXjt2mTZu6TF+xYgVffvklb731FgClpaVcd911/OlPf+Luu+9uJ6/RaHxSTkHoDQK08OjSsdxwSTI/f/swp4pr+N6G/ayakcp/LRtPoH7gRiYEwYW1nljTETRbdkP2Nig53jY9wIgjdRZH6+MYf8OP0MeNbbvoYYDP+RSEvqJfOnbd8dJLL9HQ0OD6PH36dP7+978zb948AN555x2WLl2KwWDgnXfeYceOHaxfv95PpRUE97gkJYIPH5zFHz47xT93ZPP/duWy+1w5f7ltGuMS5CgjYYChqlB8zHXSgy53JzPtFshqFlCcZ682bw6cPB27qpC9cSPjo0bJSlZB8JAB6dhFRES0+azVaomKinItdPjf//1f7rnnHhRFYezYsbz33nukpaX1fUEFoYcYdFoev34Cc8bE8sibmZwuNnPj/33Nk8sn8a1L2m/rIwj9CnMJ5O1oObLLXOxKUoB6fRSGCdeiSV/o3FMu+KJFDxKVEwSvGZCO3cXk5OS0+bxjxw7/FEQQeom5Y2L57Cez+c+3Msk4VcrP3jnMgdwKrpBRWaE/UV8FOTvQZG1j/omN6A9eaJuuD3aeuTpqAdbUOWzac5pl112HZpDMkROE/sigcOwEYTASYzTwz7um82zGWZ7acpo39p9nt1HL9NkNDI+RP4yCH2isdZ69mv2l8yrMBNWBFnBNFkiY4lr0QMqVoGtamGa1gnLGTwUXhKGDOHaC0I/RaBQeXJDO5ORw/uP1g+Sabax4bhcvrLqcy1Jl7y7Bx9gscGFPiyN3fj84LhoujRmDPfVqDlSEMG3Fg+jDO17NLQhC3yCOnSAMAOaNjePdNVex6u9fcaHWym0v7OFPK6dy49QkfxdNGEw01sL5fWiydzDzzMfojvwAbPVtZcKHw8g5MGIupM2GsEQcViuFGzcy7eI5c4Ig9Dni2HWBHCnWPXKkmHc2eqKTGKrnPybZ+aw6ga2nyvjxawfJLqnh/rkjUFqtIBwIx+P0BXJskxv69ZUo+budV94ulKLDKA4bWlr2k1ND4lDTrsaROht1xBzn/nJtM/D9kWJu6g8UpG16ri9HinXPgDx5wlc8++yzPPvss9jtdk6fPi1Hign9EofqPI5sW6Fzf8YrYh3cOtKBVrZrFLohsLGCaPMpomtPEW0+TVjD+XYydfooyo3jKDeOoSJkLDWBSbL1iCD4mUF/pJivMZlMhIeHy5FibiBHinlnw5vjcV7Zm8//fHISu0Nl7pgY/nrLVIICtAPieJy+YMgf22StRyk6jCNvD2WHPiXBdh6NubCdmBqdjppyFY7hM1CHz4DwlL4/UswNmcHSLkHapjf6cqTYAD1SrL8gR4q5jxwp5p0NT47HWT1rJKnRRu5/5QDbT5fxvZe/4R93TSe4yU5/Ph6nLxkSxzapKlRmOxc3nN/nvIqOgMMGgGsmpqKFhEmQOguGz4DhM1CMsShARwFfnx4p5qHMYGmXMETapo/05UixzhHHThAGMPPHxfHv713J3S/tY19OJbc+v5t/fnda94rCwEVVoaYQCg45txspPOR06JrOWm1DSByOYZdzwmxk7II70aVcDgEhfV1iQRD6EHHsBGGAc3laFG98fwar/rWXE4UmbvvHPlan+btUQq+gqlB9nsSq/Wi2HYLiI05Hrra0vaw2ABKnQvJ0SL7ceQ9PwW6zcXbjRsYMnwmDJNIlCELniGMnCIOACUlhvL1mBt/91x5yK+r4v1ot8+fXkxYrf8gHDLZGKD8Dxceh+CgUHYbCTPR15VwBkN1KVtFC7DinI5d0ifPM1cSpLZsBC4IwZBHHThAGCWkxIbz5gxnc+vfd5FbUcec/9/Ha92eQEiUru/sVqgqmC04HruQYFB9zPpedbr/5L6BqdJgMSYSOmY1m2DRIvATiJ0KA/FwFQWiPOHaCMIhIDA/i39+7nG/9dTvnqxq49fndvP79q8S58wcOh9OBKz8DZWeg9BSUnHA6cw3VHesEhEL8BIibAAmTIekSbFFj2LZpK8uWLZMzVgVB6BZx7ARhkJEQFsiDE+y8mBtGTnmdOHe+xloP5VlQdhpNyUkuy9mO7h9/hIossNZ1rKNoIWZMixMXP9F5hae03zNuEGzIKwhC3+GWY/fmm2+6ZUyr1XLzzTd7VSBBELwnwgD/vudyVr14gHNltdz2wm7eWjODxPAgfxdtYGKpgcocqMh23iuznc8V56AqD3BuB6oFklvraXQQNQpi0p1X7HinMxczRubDCYLgE9xy7G6//XbmzJlDd3sZ79u3b1A5dnKkWPfIkWLe2fDl8ThRQVr+392Xccc/95NbUccdL+zh1XunExagdGlnSLZNhx3MxSjVeVCZg1KZg1KV0/Lc0VYirVADI1Cj03FEjeJUucroK69FEz/OefyWtoPhUxW3I3H9/dgmd+Wlz2xBjhTzXF+OFOset06eCA0NpaampltjkZGRVFZWup15f0OOFBMGIxUWeOaolqpGheQQlQcm2AkeYpMwdPY6ghrLCW4sJ6ixnCBrhfPeWE6wtZzAxgo0OLq0YdEaqTPEUWuIozYgjjpDHGZDPGZDIo26UDl2SxAEn9HrR4qVlpYSGxvbnZjbcv0dOVLMfeRIMe9s9NXxONlltdz2j32U1zYyLTmM25IquH7pIDhSzN4I5hIUcxHUFKOYi52RN3MRak0htQWnCXVUozR2/4+pqmghLAk1Mg0i0lAjR6BGpqFGpELkCAjsujOFoXlsk7vy0me2IEeKea4vR4r10pFi7jprg8Gpa40cj+M+cqSYdzZ8fTzOmMQIXv7eldz6/C4OnjdRV6Nh2bXa/nmkmN0GdeXOkxRqy5x3cyk0OW/Oe9NVX9GlqTbdX1AUhA9zLlAIT251OT8rxnjQaOmNuNtQPLbJXXnpM1uQI8U815cjxTqnxwMy1157LUoHQw4Gg4Hk5GS+9a1vcc011/TUrCAIPmZCUhgv3XMFd/5jD6eq4aE3D/PcnZeh03Z0SmgvoarOhQcNVVBf1eSsXeS01ZY5HbnaUudzQ1XP8tDoITQBjPHOe2gCGBOwBcew9+QFpi+8CX10qhylJQjCkKDHjt3ll1/O//t//4+77rqL5ORkzp8/z8svv8ytt96KoijcdtttPProozz00EO+KK8gCF5w6fBI/nbHJdzz0n42nyjhZ+8c5k/fnopG00WcymGHRrPTQauvanHSGqqgvrLtu/rKi9KrQLV7UFIFgiIhJAaCY5z3Vk5bm+fgqA7nt6lWK6UFG52rUQdJhEcQBKE7euzYffrpp2zZsoX09HTXu+9+97vcdttt7N+/n5tvvpmVK1eKYycI/kJV0TganU5WXSM01kFjDVicztnMuir+mriHoyX/v737Do+qzB44/r3Tk0x6ISEJhBogNFEpFlARUCyLa1sQRdeKICroKgICiu6ubW3o2nX9SVHsgkgTQUU6CaGGUALpfVImk2m/PyYMRALpmUlyPs8zz8zc+77vnJu9ez3c8h4LfklmtuVquTBKg2IphcoS1BUmRhZkoUl53NXHWtb4mNR68AkC31DX6/SE7YzvYa5kTaVu/O8KIUQ7U+/ELjU1lejo6GrLoqKiOHToEACDBg0iN7eGAtVCtGdOJ9itYKtw3fBvqwCbBSpKCSo7jHL8D3DaXMtsFa5Jb61lrvfKcrCWobKUMfDYAdRff+VqU1m13lruelW63jXWcq5zOiCx5lA0wNXA1Sf/359b9aqiAowAlj91VGldyZkhqPq7T/CflgWf2U7rI0+NCiFEC6h3Yjd69Ghuvvlm5syZ474Uu2DBAq666ioAtmzZQufOnZs80D9bunQps2fPJjMzkyuuuIKPP/6YkJAQAMxmM/feey/ffvstwcHB/Pvf/2b8+PHNHpPwEKcTHDZX4mSvrPpcWfXd6qq/aa903ZRvr6zxu1JZQaf87ai2ZwOOs/SxnvqNk0mazQJ2C9gsqK1mRhTkoEl7tmrZaQmcrYKTk9ieTguMADhY+2aqgc4A535eoPrN/yqtq6aozh/0/qA34tAZySwoJapTD/YVwpojZsqcBi4f0I1hvbtgU/vwx85khgwfhdYvuKqfv0yoK4QQrUC9E7sPPviAp59+mvHjx5OVlUVUVBQ33HAD8+fPByA6Oppvv/22yQM93b59+7j//vtZs2YNAwYMYPr06UyZMoXFixcDMHfuXAoKCkhPTyc5OZmxY8dy/vnn07Nnz2aNq16KT+BryXaVHVKrwelw3cvkdPzp5axhWWPbNNHvOGyobFYSTqSgWvUrroTI5toOh931O+7vthqWnf79bMuqxvxTP43DxrV2K6pdVbE0kgY4DyCt4WOogCAAcx0aq3WgMeDU6DFXOvDxD0LR+lQt14PW13WWS+dX9dkXu9rAgcNpxPc7D7XB3738VDsf0PpiVXSsWv8ro8f+Ba3hzHkY7VYr21asYOzYsSRotfy0+iDvrk3h3R3wWs+BjO0VQX6KzVWrVO5NE0KIVqXeiZ3RaOSVV17hlVdeqXF9TExMjcub0po1axgzZgwXXHABAE899RSdO3emrKwMPz8/Pv30U7755hsCAgK46KKLuP7661myZAlPP/10jeNZLBYsllPXnUwmE9C8lSfUH41hVFk27K3DBnsxNdAdql3KawlK1W+fjVOlcZ2tUmtdyZJK43pXV72rtDjVWncbp6Imt6CYsA5RqLT6quU61zhqXdU42tPG1LoSM7UeNAbQ6LGjZnvSHgZdOAyNwYhTo3claeqq96p2qHWguJ5Ere+cTCnlq4kbWPucTDa1L1YHNVY3+PN+OXVEHIWlFXy6+TgzPk9Ed0vfautbK5ndv+H9pfJE85J9s+H9pfJE7eo0QfGfLV++nGXLlpGbm8sPP/zA1q1bKSoqYtSoUfUdqkHeeOMNNm7c6K5hm5GRQXR0NDt37qRz586EhIRQVlbmrhrx8ssvs2XLFpYuXVrjePPmzXOfcTxdc1aeGLn3cfTWYlBUOFFwooCi4ESFU1FB1TKnogCqqveTy+q4vmo83O1Ojl/1ueq3a4rhVP+zrXdN6OpQVIAKh6J2jaeocHL6ZxXOqnWO0z6f3E7XS40TVaPHcigaHIrGNdGs3M9VLw4n/N8hFdvzVGgVJ5P72OlW+3y8QgghWkB9Kk/U+4zdCy+8wKeffsoDDzzArFmzAFfJsalTp7ZYYjdy5Ehmz57Nli1bGDBgAP/85z9RFIXy8nJKS0tRq9XVErKAgABKS0vPOt7MmTOZPn26+7vJZCI2NpbRo0c3X+WJUaNYcdq61pqGSOWJxo3hTbOoj7E7eHDRLtYfzOPd/Wr+7+8XMqBTSJ22wxvJ7P4N7y+VJ5qX7JsN7+9Nx8zGbEd9nbySWBf1TuzefPNNtmzZQmRkJLNnzwYgPj6elJSU+g51VqNHj2bDhg01rps9ezazZ8/m7bffZtKkSeTn5/Pwww/j7+9PdHQ0RqMRu91OeXm5O7kzmUwYjcaz/p5er0evP/PGcJlFve6k8kTjxvCGWdS1Wvjv7Rcw8f0/2HasiPsXJbFs8kV0CWvdE/vK7P4N7y+VJ5qX7JsN7+8Nx8zGxFRfzVp5wm63ExgYCOCuQFFb4lRfq1atqrXNhAkTmDBhAgCHDh3ijTfeICYmBrVaTWRkJLt372bIkCEAJCYmkpCQ0GTxNYXxP44nqziLj5d/TIA+AKPOiL/OH6PWSIDu1Hd/rb9r+Z++69X6GiuACNEYBq2adyeex3X/WUd6WSUT39/MssnDiAr08XRoQggh6qDeid0NN9zAAw88wMsvvwxAaWkpjz/+ODfeeGOTB3cuO3bsYODAgWRmZnL//ffz5JNPola7bqefOHEizz77LIsXL2bPnj189913bN68uUXjq012eTbFzmKKi4sb1F+j0hCgCyBIH3TqZQgiUB9IsD6YIH3VZ0MwgfpA13ddIGqZ9FXUwt+gZXIfO+8fCeBofjm3f7CFz+8fRoifztOhCSGEqEW9E7uXXnqJGTNm0LlzZ8xmMx06dGDSpEk8//zzzRHfWU2ePJk9e/bg7+/PAw88wMMPP+xe98wzz3DPPfcQFRVFcHAwb731FvHx8S0aX20+Hv0xP/38E/0H98fsMFNSWeJ+lVpLz/q91FqKw+nA5rBRUFFAQUUtk5qdRkHBX+dPkD6IUJ9QwnzCCDW43k++Tl+uVbeNyx2i/vy18PGd5/O397ZyKKeUuz7awmf3DsWor/chQwghRAuq91HaYDCwcOFCFi5cSG5uLmFhYR65JHiuM3A+Pj589tlnLRhN/XXy70S0JpohkUPqde3c6XRSbiunpLKEYksxxZZiCi2FFFuKKbIUUVhReMayoooiSqwlOHFiqjRhqjSRVlL7hG2B+kDCDFVJn28YHXw7EOkX6X6P9IvEqGq6S/DCu0QH+fB/9wzm5v9uIvFEMff9bxsf3nkhBq2c9RVCCG9Vp8Ruy5YtZ1135MgR9+fBgwc3PiJxToqi4Kf1w0/rR6RfZJ37WR3WU4lgRSEFFQXkmfPIM+eRX5Hvejeferc5be72qcWpZx1Xp9JhxMg3a74hyhhFB78ORPpGEu0fTYwxhmhjtJz5a8W6R/jz8V2DmfDeH/yems+0xTt567ZBaNQqT4cmhBCiBnVK7G699Vb3Z0VROHHiBIqiEBoaSn5+Pk6nk5iYGA4fPtxsgYrG0aq07suttXE4HZgspmpJX055Dtnl2WSVZZFdlk1WeRZ55jwqHZUUUEBBTgHknDmWgkKkXyQx/jHEGGOI9Y+t9jlQHygPgXi5AbFBvHfHBdz50VZW7c1m5le7eeGm/vK/mxBCeKE6JXann5WbP38+5eXlzJs3Dx8fH8xmM/Pnz8fPr3VPiSBOUSkqggyuhzG6u+pK1Mhqt5JuSufbdd/SpX8XcityyS7PJrMsk/TSdE6UnMBsM5NZlklmWSZb2XrGGIH6QLoEdKFrUNdq7x2NHeVBDy9yUfcw3phwHpP/bztfbD9BoI+WWdf0luROCCG8TL3vsXvjjTfIyspCo3F19fHx4dlnnyUqKoo5c+Y0eYCe1JwlxdpKeZwIfQRxmjiujL7yjHsFnU4nBRUFnCg9wYnSE6SXppNems7x0uOkl6aTa86l2FLMrtxd7MrdVa2vTqWjU0AnugR0oXtgd3oG96RHcA+ifKNQFKXJ/35NMV5bL49zRc9Qnh+XwJNf7+H9X48QYFAzeUTXWuP2FCnb1PD+UlKsecm+2fD+remY2ZSataRYjx49ePPNNxkzZox72erVq5k8eTKHDh2qz1Be5+RDIXa7nYMHDzZrSTEBlc5K8h355NpzXS9HLnn2PPIcediw1djHoBiIVEUSpY4iUh1JpDqSDuoOaBR5WrOl/Jyh8M0x19nUm7vYuSSy3lUJhRBC1EN9SorVO7FbsWIF48ePZ8iQIcTGxpKWlsbWrVv57LPPuOaaaxoVuLcwmUwEBgaSl5fXfCXF2kh5nObYDrvDTmZ5JkeLj3LYdJiUwhQOFh3kSPERbM4zEz6tSkt8cDx9Q/uSEJpAv9B+xPrH1usyoZQUq9/vvLImhbd/OYKiwMs39eO6/lG1bGnLk7JNDe8vJcWal+ybDe/fWo+ZjWUymQgLC2ueWrFjx44lNTWVFStWkJmZyYgRI1i8eDFhYbXflN/aSHmcumvK7dCipYu+C12Cu3A5l7uXW+1WDhcfZn/Bfvbl7WNT6ibyVfkUVxaTnJ9Mcn6yu22ALoB+Yf3oH96f8zucT//w/vhoaq+e0J5LitVn/T+u6o2pws5nm9N4bNluVCo1486Lrj14D5CyTQ3vLyXFmpfsmw3v39qOmY3VrCXFAMLCwrjjjjsa0lWIBtOqtcSHxBMfEs/YzmNZkb2Cq6++muyKbHbn7Xa/9uXvw1Rp4reM3/gt4zfAVamjX1g/LuhwARd0uICBEQPx1cpl9oZSFIVn/tKXSpuDL7af4NHPd1Fpd3DLBbGeDk0IIdq1Ok93snTp0lrbTZgwgUWLFjU6KCHqSlEUYgNiiQ2IZWzXsYDrzN7BooPszt3NzpydbMveRk55DjtzdrIzZyfv7X4PjaIhISyBiztezCXRl9AjoIeHt6T1UasU/n1jf3QaFZ9tTuMfy5Kw2h3cNqSzp0MTQoh2q06J3XfffccXX3xBbbfjrVixokmCEqIxtGotCaEJJIQm8Ldef8PpdHKi5ATbsrexLXsbW7O2klmWSWJuIom5ibyV+BZB+iA6OTrhOOJgeOxwQn1CPb0ZrYJKpbBgXF90GhUf/XaUWV8nY7U5uPPiLp4OTQgh2qU6JXZDhgzhrbfeqlM7IbzN6Wf1buhxAwDppen8kfEHv2X8xqaMTa7SaxSRtCkJZZPCeRHnMbLTSEZ2Hkm00TvvHfMWiqLw9LV90GlUvPPLYeZ9v5dis41pI7vLPHdCCNHC6pTYrV+/vpnDEKJlRRujubHnjdzY80asDis7Mnfw6cZPyfbNZn/hfnbk7GBHzg5e3PYivUN6c2XnKxkTN4bOAXKZsSaKovDkVb0waNS8tjaF/6w5SE5JBc/8pS9qlSR3QgjRUmTyL9HuaVVaBkUMIssni7FXjyW/Mp+1aWtZm7aW7dnb2Vewj30F+3hj5xv0D+vPtd2u5aq4qwg2BHs6dK+iKAqPjupJqFHH3O/28NnmNPJKLbz2t/MwaKWKiBBCtARJ7IT4k0i/SG7rfRu39b6NgooC1h9fz6qjq9iUuYmkvCSS8pJ4YcsLXBJzCdd3u57LYi9Dq2ob0y80hTuGxRFm1PPIkl38tCebOz7Ywrt3nE+Qr87ToQkhRJsnid05SEmx2nlqO1qqpJi/2p/r4q7jurjryDPnsfLYSlYcWcH+wv2sP76e9cfXE+YTxg3dbuC6ztfVO6a2Wh5nVK8wPpw0iAc+28WWowX85c3feGfieXQLb7ma0lK2qeH9paRY85J9s+H92+oxszbNWlKsLZOSYqKusu3Z7Krcxc7KnZQ6SwFQUOil6cVg/WC6a+TBAYCMMnjvgJoCi4KP2smkng56B8khRwgh6qNZS4qZzWaefvppvvjiCwoKCjCZTPz000/s27ePRx55pDFxew0pKVZ3ntqOpv7dho5ntVv5+cTPLEtZxracbe7l3YO6M6n3JEZ3Hn3Oy7TtoTxOfqmFqUsS2XasCJUCM6+OZ9LQTs2e+ErZpob3l5JizUv2zYb3bw/HzJo0a0mxBx98EKvVyg8//MCll14KQP/+/Xn44YfbTGJ3kpTHqTtPbUdT/25DSihd0/0arul+DQfyDvDS6pdIciRxqOgQczbNYWHSQu7ocwc39rjxnJUu2nJ5nMhgLZ/dO5TZXyfzxfYTPLfiAMkZJTx/Qz/89M1/N4iUbWp4fykp1rxk32x4/7Z8zDzb2HWlqu/gy5cv54MPPqBv377uf3FHRUWRmZlZ36GEaFO6BnblWt9rWTFuBdPOm0aIIYSssixe2PoCV391NZ/s+YQKW4Wnw/QIvUbNCzf1Z861fVCrFL7dlcH1b/7KwewST4cmhBBtSr0Tu6CgIHJzc6stO3LkCB07dmyyoIRozQJ0Adzb/15W3bSKp4c9Tax/LAUVBby07SWu+eoalu5fitXe+m8Ary9FUbj7ki4svW8okQEGUnPL+Mubv/Hl9hOeDk0IIdqMeid2Dz/8MNdddx3Lli3Dbrfzww8/MH78+DZ3GVaIxtKr9dzc82a+Hfct8y+aT5RfFDnmHBZsXsB131zHyqMray3T1xZdEBfC8mmXcGmPMMxWOzO+SGTa4p0Ul7e/ZFcIIZpavW9wmTJlChEREXzwwQfExMTw+uuv8+ijj3Lrrbc2R3xCtHpalZa/9vgr13a9li9TvuS9pPdIL03n8V8eZ2D4QIbZhnk6xBYXatTz8V2DWfjzIV5bm8J3iRlsOVLAizf359Ie4Z4OTwghWq16n7EDuPnmm/nxxx/Zs2cPq1atapakzmazceONNxIdHY2iKGRlZVVbP3fuXGJjYwkICKBHjx589NFH7nXr169HpVJhNBrdr40bNzZ5jELUh06tY3yv8Sz/63IeHPggPhofduXu4r+l/2XeH/MoqCjwdIgtSq1SmDayB8seGEaXMD+yTBXc/sEW5n6bTJnF5unwhBCiVap3Yvfqq6+SmJgIwObNm+nRowe9evVi06ZNTR7c8OHD+fLLL2tcN3HiRPbv34/JZGLFihXMmjWLPXv2uNf37NmT0tJS9+vkE7xCeJqPxofJAybz3bjvuDruapw4+e7wd/zlm7/w7aFv293l2fM6BbN82iXcPtRVh/eTTccY/Z8NrNuf7eHIhBCi9an3pdgXXniBu+66C4AZM2bwyCOPYDQamTZtGlu3bm26wDQaHn744bOu79GjR7XvDoeDY8eOkZCQUO/fslgsWCwW93eTyQRI5Ym6aOuVJ5pzjFBdKPMunEdsXizr1Os4VHyI2b/N5ttD3zJr8Cw6+Xdq0PitYRb1P9Mq8PQ18VzeM5Q53+0lvcjM3z/extUJHZh9TS8i/PX1HlNm9294f6k80bxk32x4f6k8Ubt6T1AcEBCAyWSisLCQ7t27k5ubi0qlIjAwkOLi4noHW6cgFYXMzEwiIyOrLf/Xv/7Fs88+S3l5OYMHD+aXX37BYDCwfv16rrrqKgICAggMDOT2229n1qxZqNU1FyKfN28e8+fPP2O5VJ4QLcXutPO75XfWVqzFhg0NGsb4jGGIbggqpUF3TLRaFjusPK5ifaaCAwWD2smYGAfDI51o2tefQgghgGauPDFo0CD+8Y9/cODAAfbu3cvSpUspKCggPj7+jGlQmsrZEjsAp9PJli1bWLNmDU888QQajYasrCyKioro2bMn+/fv55ZbbuHuu+/m0UcfrXH8ms7YxcbGSuWJOmjvlScaO8af+xwvOc7zW59nc9ZmAIZGDmXe0HlE+Ea0qVnU62Jvpok53+4lKd11Br1TiA9PjOnJqN4RdapaIbP7N7y/VJ5oXrJvNry/VJ5ohsoTb7/9No888gg6nY73338fgJUrVzJmzJh6jTN69Gg2bNhQ47rZs2cze/bsOo2jKApDhgzh008/5YMPPuD+++8nMjLSnQT26dOH2bNn89Zbb501sdPr9ej1Z17qkVnU6669Vp5oqjFO9uka0pX3Rr/H5wc+56VtL/FH1h/csuIW5gydw8iYkfUa35tnUa+LAZ1C+XrKJXy5/QQvrjpAWoGZKYsTGdo1hKfG9qZ/TFCdxpHZ/RveXypPNC/ZNxveXypPnF29E7shQ4ac8aDEhAkTmDBhQr3GWbVqVX1/+pwcDgepqak1rlOp5PqNaD0UReHWXrcyOGowT218iuT8ZB7f8Dh/7f5X+jn7eTq8FqVWKdxyYSxj+0fx3/WpvLfxMH8cLuD6N39jVJ8OPHplT/p0PPe/XoUQoj1pUKHGxMREfvvtN/Lz86s9wff00083WWDgukR6cnyLxUJFRQUGgwGA999/n5tuuomAgAA2btzIZ599xuLFiwHXdCfdunUjNjaWlJQUFixYwMSJE5s0NiGaW5fALvxv7P94J/Ed3k16l68OfcUm9SbOLz2fLsFdPB1eizLqNTw2Jp7xQzrx0k8H+GZXOqv3ZrN6bzZj+0Xy8MiexEf6ezpMIYTwuHqfynrzzTcZMWIEv/76K88//zz79+/n5Zdf5tChQ00eXHx8PD4+PgDExcW5PwOsWLGCbt26ERgYyIMPPsiLL77I2LFjAdi+fTtDhw7Fz8+P0aNHM27cOKZPn97k8QnR3LQqLVPPm8p/R/2XIH0QmfZMJvw4gbVpaz0dmkdEB/nwn1sHsvrR4Vw3oCOKAit2ZzHm1Q3c88k2thwpaHfTxQghxOnqfcbu5ZdfZt26dQwaNIigoCAWLVrExo0bef3115s8uKNHj5513VdffXXWdTNmzGDGjBlNHo8QnnJRx4tYfPVi7vv+Po5bj/PIz48wecBkHhjwQLt7ahage4Q/b4w/j6mXd+fVNQf5MTmLNfuyWbMvm4GxQdw3vCtX9Az1dJhCCNHi6v1fhIKCAgYNGgSATqejsrKSSy+9tMnvmRNCVNfBtwP3GO9hfPx4AN5OfJvHf3kcs83s4cg8Jz7Sn7cnns/aGSMYP7gTOo2KXceLePCzHYx69VfWpivkl1V6OkwhhGgx9U7s4uPj2bVrFwADBw7k3//+N6+//jrh4VLfUYjmplbUPH7+4zxz0TNoVBpWHVvFnSvvJLusfVdp6BZu5J9/7cdvT1zBtCu6E+Sr5Xihme/S1Fz64i9MW7yTzYfz5TKtEKLNq3di9/rrr+NwOABXebGff/6ZTz/9lHfffbfJgxNC1OyGHjfw3qj3CNIHsTd/LxOWT2B/wX5Ph+Vx4f56po+O5/cnr+D5cX3o5OfEanfyXWIGt777B1e+8gsLfz7EicJyT4cqhBDNot732A0dOtT9uU+fPqxbt65JA/ImUlKsdlJSrHFjNKY8zoDQAXw65lMe+eURUotTuWvlXbwy/BUGhAw455jeUB6nuWkVGNe/A37ZSUT3HcoXO7P4PimT1NwyXvzpAC/+dIAL44L5y4AorkroQKBP003Yevq7N4wpJcW8j5QUa3h/KSlWu3pXngBIS0sjOTmZ0tLSastvueWW+g7lVRYuXMjChQux2+0cPHhQSoqJVsHsMPNZ2WcctR9FjZqbfW+mr66vp8PyOhU22FWgsC1X4ZBJwYmreoVacdI7yEn/ECd9g534tY25b4UQbUizlhR74YUXmDdvHv369auW9CiK0mbO3plMJgIDA6WkWB1ISbHGjdFU5XEsdguzfp/FuuPrUFC4xuca5lw7x2vL47SEc21HZnEF3ydl8u2uTA7mnPoHqlqlMDgumFG9I7iydwRRgYYm+83m2I7m6i8lxZqXlBRreH8pKdYMJcVeeukltm7dSkJCQoMDbC2kPE7dSUmxxo3R2PI4Wq2WVy57hX9u+SdLDyzlB/MPRO2L4qFBD521rqq3lxRrKjVtR6cwLVOu8OfBy3uwP6uElclZ/LQni/1ZJWw6XMCmwwU8s3w/faMDGNEznOE9whnUORitum63JbfHsk11bS/HzFOkpFjD+0tJsbOrd2JnNBrp1q1bfbsJIZqZWqVm1pBZhOhCeHv327yX/B42bDw66NGzJnftnaIo9I4KoHdUAI+O6smx/DJ+2pPFT3uy2X6skOR0E8npJhb+nIpRr+GibqGMiHclerEhcpuGEML71Cmxy8nJcX+eOXMm99xzDzNnzjxjipOIiIimjU4IUS+KonBvv3tJO5TGcvNyPkr+CKvdyj8u/Ickd3XQOdSP+4Z3477h3cgpqWDjwTx+OZjLxpRcCsutrNqbzaq9rqllooN8GNIlhMFdQhjSNZS4UEn0hBCeV6fELjIyEkVRqs0BtWjRomptFEXBbrc3bXRCiAYZph/GgL4DeH7r8/zfvv+j0l7JrKGz2mWVioaK8Ddw4/kx3Hh+DHaHk+T0YjYczGVDSi470opILzLz1c50vtqZXtVez4Wdg/EpU+iUbiIhJhidRv7eQoiWVafE7uS8dUKI1uOmHjdh0BqY+/tcPj/4OVaHlbnD5no6rFZJrVIYEBvEgNggHhrZgzKLjR1phWw5UsDmwwXsOl5ETomF5clZgJpl//0DnUZF344BDIgNYmDVq1OIr5w5FUI0qzrfY+d0OnnvvfdITk5m4MCB/P3vf2/OuIQQTeCGHjegVWuZ9essvj70NXannTkXzvF0WK2en17DpT3CubSH63aUCqudXceL2HQol5+2p5BZqaPYbGNHWhE70orc/YJ9tfSNDqRP1X19fToG0DXMD00dH8oQQoja1DmxmzFjBosXL+bSSy9l1qxZHD58mAULFjRnbEKIJnBt12vRqrQ8seEJvkv9DjVqznOe5+mw2hSDVs3QrqGcHxtAV/MBrr76ctJNVhKPF7Gr6rU3w0RhuZWNKXlsTMlz99VpVPTsYHQne/Ed/OkeYSTcXy9n94QQ9VbnxO7zzz9nw4YN9OjRg/3793Pttde2+cROKk/UTipPNG6MlppF/YroK3juoud46ven+Dr1azJ0GYyuHN3omLyZJ2f3t9lsxATqiAmM4Jq+rofKLDYHB7JK2JdVwr7MEvZnuV5llXb307en8zdo6BbuR5dQH+wFCprkTOKjAokJ9kGtqnvCJ5UnvI9Unmh4f6k8Ubs6T1AcEBCAyXTqwBMSEkJBQUH9o/NiUnlCtHW7KnfxZfmXOHFykf4irjZcLWeFPMjhhAILnChTyChTSC+HrHKFfAvuyhh/plGchBkgzOAkVF/1ftp3eV5DiLanWSpP+Pn5sX79eveTsaNGjWLNmjXVnpQdPHhwI8L2HlJ5ou6k8kTjxvDELOpfHvyS57Y9B8Bdfe5i6oCp1ZI72Tc9/5sWq52j+eWk5pZxMNvE78mHKdf4cyTfTKXt7A+zKQpEBRjoFOJDbIgvMUE+RBi1pKckc+0VFxMTakRfh8xPKk80r9a8bzblmK3lmNlmK0+Eh4dXqwUbEhJS7buiKBw+fLgB4XovmUW97qTyROPGaMlZ1G/seSNJe5L43vw9H+39CB+tD5MHTm5UTN6sNc7ur9Vq6etroG9sCFarlZ6Vhxg79mJUag3phWaO5pdxrKCctPwyjuWXk1ZQzrH8csxWOxnFFWQUV/DHkcLTRlTz5t4/AAj319Mx0EDHIB+iAn3oGGQgOsiHiAADEf56wv317til8kTzao37ZnOM6e3HzKZa3xjNUnni6NGjDYlFCOGFhuiH0KN3D17Z8QpvJb6FVq3lnn73eDosUQu1SqFTqC+dapgM2el0kltqIe20RC+9yExGYTkpGfmYbGosNge5JRZySywknig+6+8EGDT4KGqWZG+jQ4CBcH89Ef4GIgL0hBv1RAToCfXTE+ijRVWP+/2EEM2v3iXFhBBtw8ReE7Fj57Udr/HajtfQqXTckXCHp8MSDaQoiiv58jdwQVyIe7nVamXFihVcffVoSiqdZBZXkF5kJrPI7Dq7V2Qmo8hMtslCbqmFSpsDU4UNEwrZh899H7VKgWBfHcG+WpwValYU7yLU30Con45gXx2hRtd7iJ+OAL2KSpnDXohmJ4mdEO3YPf3uodJeyduJb/PithfRq/X8tdtfPR2WaAaKohBq1BFq1NM3OrDGNk6nE5PZRnphKcvXbqRLn4EUlFvJLbGQU2Ihpyr5yzFVYKqw4XBCflkl+WWVgELq3pwaxz1Fw6wdawgwaAn00RDgoyWw6hVg0GLUq8jIUCjbfoIQo4EAg9bdxlfjethECHFuktgJ0c5NHjAZi93Ch8kfsmDzAlSo0KHzdFjCAxRFIdBXi6/WSM9AJ2MHRJ313p5Km4OicldSl2sqZ+2vW4iLT6DIbKewvJKCsuqvwvJKrHYnlTYHeaUW8kotZ4lCzbfH9p5lnYaZ29dg1Gsw6jX4Vb2M7nf1aZ9PX6/GqNfip1fjo1Xjo1Pjq9Vg0KnQqVXyZLhoUySxE6KdUxSFRwY9QqW9kv/b9388u/lZbvK9ibGM9XRowovpNCrXAxcBBrqH+VC438nYIZ3OnghWVvL19z8yZPjllFuh2GzFVGF1vVe9Csss7Es9hjEkglKLnWKz1d2uwup6GrjC6qDCWkleaWWTbIdKwZ3s+eiqEj+tGsPJBFBX9VmrPqOdXqNGr1Gh17oSRL1WXfWuci3XqNBr1Oj+9FmI5uS1iZ3NZuPWW2/ljz/+ICMjg8zMTCIjI93rjxw5wv3338+WLVvw8/Nj6tSpzJw5073+448/Zvbs2ZhMJm688UbeeecddDo5CyFETRRF4R8X/gOL3cIXB7/gy/IvGZw2mKu6XeXp0EQboSgKBg1EB/mcc7qTFSuOMHbsoDPalJktfLt8JUOHX4bFrlBmsVFa9XJ9tlNW9bmk6r3Mvd7ublthtWOutGOruq7rcEJZpZ2yFrwBUKNSUKFmXuLP1RK/09+16pMvBY1ahValoFWr0KhV6KqWadQKOrUKjUqFVqOgVbmWnex3sr1WpaDgYF+hQvDhfAw6nbuvWqW44ql6V5/+UhQ0KhVqtevzyeUqBTnL6cW8NrEDGD58OI8//jjDhg07Y91DDz1E165dWb58OSdOnODiiy9m8ODBjBw5kt27dzN9+nRWrVpFjx49GDduHAsWLOCZZ57xwFYI0TooisLsobOpsFXw/eHvmfnbTAw6A5fFXubp0IRAp1Hhp4XYYN+mmbvS7sBstVNRacdsdb3KK6t/N1f+6b2qfXmlnXKrnUqbA4vNgcVqp9LuwGJ1uN5t9lOfra7vp98f6EoqFSrLrUBLVtJQ89/925tkpJPJIA41s3asQ6OuShCVsyWKKtQqUKtUruWKgkpxUlCgYlnudtRqFSrlVNKoUlxPgbs+u76rFAWcDjLSVfz6zR40VZfRT65Tnd5WpeB0ODhyTMX+NSlo1erqY6lOfXY6HezPUMjZdAytWn3G7zocDpJzFCw7M9Bo1KgUBaUqTgVwOuzsylcYZXfgDTPxeG1ip9FoePjhh8+6/tixY8yYMQOtVkuXLl245JJL2Lt3LyNHjmTRokXceuutXHDBBQDMmTOHe+6556yJncViwWI5db/HyQobUlKsdlJSrHFjeGN5nJmDZnL0+FF2W3czff10Xh3xKsOizvzHlbeTsk0N799eSor5qMHHR02wj7rJxjwbm92VBFbaHZSZK1n3ywYuHHoRDlTuBPH0RNDmcGK1O7HaHdgcTmx2R7XvVrsDW9V3q8Pp/myzO6m0O7A5Tq23OZxYrHYKi034+Bldy6vGsDuc1V9O17vN4eRc5QtsDmfV0ywKlRZbI/4yKg4W59e7z+bc9Dq3XZNxpA7t1Hxz7MA51y9KTT7n+gfNFrTq5rnU3iwlxTxJUZQzLsX+97//ZefOnbz++uukpaUxcuRIli9fTr9+/fjLX/7CmDFjePDBBwHIz88nLCyM8vJyfHx8zhh/3rx5zJ8//4zlUlJMtFd2p52l5UvZa92LFi23+91OV21XT4clhGhBDic4neCo+myv+m6vyulOLq/2qnGZcmZ/Tn12Vn0++e6o93el2vdz9jv5+eTy09o4ahjjz8vBNQ7V2rkuSz/Q246umf6NUJ+SYl57xq42F110EW+99RZ+fn7Y7XbmzZtHv379ACgtLa224Sc/l5aW1pjYzZw5k+nTp7u/m0wmYmNjGT16tJQUq4WUFGvcGN5cHufd69/lyU1P8mvGryy2LOati95iQPiAOm2XN5CyTQ3vLyXFmpfsmw3v783HzOYuKVZXHkvsRo8ezYYNG2pcN3v2bGbPnn3Wvna7nbFjx/LEE08wefJkTpw4wbXXXktCQgI33XQTRqOx2h/h5Gej0VjjeHq9Hr1ef8ZyKY9Td1JSrHFjeGN5HF+9L69e8SpT107lj8w/eGj9Q7w/+n0SwhLqFKe3kLJNDe8vJcWal+ybDe/vqWOmRqOhwl5BmbWM0spSymxlFJuL2Wfdx9Waq1tXSbGmtmrVqgb3LSgoICMjg8mTJ6PRaIiLi2PcuHH8/PPP3HTTTfTp04fdu3e72ycmJtKlS5caz9YJIc5Or9bz2uWv8eDaB9mevZ37Vt/Hh2M+JD4k3tOhCSFEvdkcNkoqSyipLMFUacJUaaKwvJCtlq3k7M2hzFaGqdLkamMtoazSlcDlmnJ5YdkLlFnLsDtrfoJ6in2KV8wB6tWXYi0WCydvAbRYLFRUVGAwGAgPDyc2Npb33nuP+++/n4yMDL799lumTJkCwIQJE7jsssu499576datG8899xwTJ0705KYI0Wr5an1ZOHIh962+j6TcJO5edTfvjnqXPqF9PB2aEKKdcjqdFFuKKbIUUVhR6H4vtBRSVFHkfj9acpSPV3xMqbUUk8VEua387IPuquVHT5s6UUHBT+uHn9YPX40v1jIrVod3PAzp1YldfHw8x44dAyAuLg7AnegtW7aMhx9+mCeffBJfX19uvfVW7r33XgD69evHyy+/zHXXXeeex27WrFke2QYh2gI/rR9vX/k2D6x+gN15u7nnp3t468q3GBgx0NOhCSHaiHJrOfnmfPIq8sgzn3qdTNQKK1yvbFM2c5fMPeuZszMUnbnIV+NLgD4Af50/Ro0Rc6GZ7rHdCTIE4a/zJ0AXgFFnxKg1YlAZSNySyKgRowj0CcSoM+Kj8UGluJ6APVmP2V/n33R/jEbw6sTu6NGjZ1134YUX8vvvv591/Z133smdd97Z9EEJ0U4F6AJ4d9S7TFk7hR05O7hv9X0sHLmQCyMv9HRoQggvdfLMWnZ5NjnlOeSZ88ivyCe3PJfc8lwOlhzk3e/fJb8inzJrWb3H99P6EaQPIlgfTJAhiBBDiOu7IRg/tR+pe1IZPmQ4Ib4h7oTNX+ePRnUq/TmZmI0dNvasD0fka/LpEtilVdzf6dWJnRDCuxh1Rt6+8m2m/TyNzZmbmbxmMq9d/hoXR1/s6dCEEC3M6XRS7ijnYOFB8ivzySrLIqssi+zybLLLsskqzyK7LJsKe8W5Byo59dGgNhDmE0aYTxjhvuGEGELcyVqIIQSjxkjy1mSuG3kd4cZwdOqz39NmtVpZkbKCYVHDWkVC1lQksRNC1MvJe+6mr5/OhhMbeGjdQ7w04iWu6HSFp0MTQjQhp9NJoaWQ9JJ00kvTOVF6gvTSdDJKM8gozSCrLMuVtP1Y+1ghhhDCfcIJ8w0jzOBK3EL0IaTtS+PKi64k0hhJuG84vhrfc5Yrs1qt5KvzifCNQKtuP8lafUhiJ4SoN71az6uXvcoTG59g9bHVPLr+UZ4e+jQ39rzR06EJIeqh0l7J8ZLjHDMdI73UlcCll5xK4sw2c61jBOuDifSLpINfByJ9Xe8dfDsQ6RdJpG8kEX4R6NVnTilmtVpZkbqC8yPOb1dn1JqbJHbnICXFaiclxRo3hjeWFKtPTM8New4ftQ/fHf6OeZvmkVmayX197/OKAuFSUqzh/dtLSTFPaeltsTlsHC8+zkHrQQr2FnCi7ARpJWmklaSRWZaJk7MXoFJQCPcJp6OxI9F+0XQ0dqSjX0c6GjsSpgsj6fckxo6u+d40Nwc1PjHaHo+ZDdXmSoq1lIULF7Jw4ULsdjsHDx6UkmJC1IHT6WRtxVrWW9YDcL7ufK73uR610vz1N4UQp1Q4K8i155JtzybXkUu+PZ98Rz4FjgLsnP0JUj16QtWhhKhCCFYFV3sFqYLQKHIOyNPqU1JMErsamEwmAgMDycvLk5JitZCSYo0boy2Vx1mWsox/bfsXDqeDi6Mu5vmLn/fo4/9Stqnh/aWkWPNq7LaUWks5XHzY/UotTuVw8WGyy7PP2ken0hFMML2jetMlsAux/rF09u9M54DOBOuDG3SW3dv3TW8/ZtaHyWQiLCysbdeKbQlSHqfupKRY48bwxpJi9d2O8X3G08HYgX9s+Ae/Zf7GXavv4o0r3qBTQKc6j9EcpGxTw/tLSbHmVdu2WO1WUotTOVBwgIOFB0ktSuVQ0aFzJnARPhF0DepKt6BuxAXE0TmgM3EBcYToQlj540rGjqjlsmkzbEdz9G8Lx8z6aBUlxYQQbc8Vna7gk6s/Ydq6aRwuPsz45eN5+bKXGRo11NOhCeHViiqKOFB4gAMFB9zvqcWp2By2GttH+ETQLahbtVfXwK4E6gNrbN8W7k0UdSOJnRCiSSWEJrDkmiU88vMjJOUl8cDqB5hxwQwm9p7oFQ9VCOFJTqeT9NJ0kiuTOZp4lJTiFPYX7D/rWTh/nT/xwfHEh8TTPag73YO60yWwy1kTOCEksRNCNLlw33A+vOpDntn0DN+lfscLW19gW9Y2nrn4GfkPkmg3nE4n2eXZ7Mnfw568PezN38ue/D0UWYpcDfZUbx9jjCE+xJXE9QruRXxIPFF+UfIPIlEvktgJIZqFXq1nwcUL6BPah5e3vcy64+vY//1+XhzxIv3D+3s6PCGaXL45353E7cl3vfLMeWe006g0RCgRDI4bTO/Q3sSHxNMzuKfX1BoVrZskdkKIZqMoCrf1vo2BEQN5bP1jnCg9waQfJzH1vKncmXAnapVMiSJapwpbBXvy95CYm0hSbhJ78veQVZZ1Rju1oqZbUDcSQhPoG9aXhNAE4oxxrPlpDWOHNP2DDEJIYieEaHYJoQl8ft3nzN80n5+O/sSrO17l5+M/8+zFz9IlsIunwxPinJxOJ1llWSTmJpKYm8iunF3sL9iPzVn9wQYFhS6BXUgITSAhLIGE0ATiQ+Lx0fhUaycPMojmJIndOUjlidpJ5YnGjdGeZlE3KAaeH/Y8QzoM4eXtL5OYm8jN39/MlAFTmBA/AZWiatLfk8oTDe/f3itPVNor2V+4n6TcJJLyXK8cc84Z7cJ8wugf1p/+Yf1JCE2gV3Av/LR+1Rs5z4xZ9s2G929Px8yafqMuZILi00jlCSFaRpGjiK/LvybVlgpArDqW63yuo6Omo4cjE+2RyWHiuO04afY00mxpZNgzzqjUoEJFpDqSTupOxGpi6aTpRJASJA82iBYhlScaSSpP1J1UnmjcGO11FnVwXd76KvUr/rPjP5TbylEpKm7qfhMPDniQAN25D1x1IZUnGt6/LVeesDqspBSmkJSXRGJeIkl5SWSWZZ7RLkgfxICwAe4zcn1C+5xxSbXBMci+2eD+7fWYKZUnmojMol53UnmicWO0t1nUT/pb779xWafLeHnby6w8upLPUz5nzfE1PHTeQ4zrPg6NqvGHKKk80fD+baHyRL45331vXGJuInvy9lBhr6jWRqWo6BHUgwHhAxgQMYCB4QOJ9Y9t9rNxsm82vH97O2ZK5QkhRKsR6RfJiyNe5KaeN/H85uc5XHyY+Zvm88meT5g2aBpXdrpSLneJOrE5bBwqOkRiTtVDDrm7OF5y/Ix2AboA+of3Z2D4QAZEDKBfWL8z740TopWSxE4I4RWGRA1h2XXLWHJgCe8mvctR01Gmr59Ov7B+TB04lWEdh0mCJ6opqigiKS+JXTm7SMpNYnfebspt5We06xbYjYERA11n5MIHEBcY1+QP6wjhLSSxE0J4Da1ay+19bueG7jfw8Z6P+d/e/7E7bzf3r7mfPqF9uLvv3YzsNFLmv2uH7A47WfYslqUsI7kgmaTcJI6ajp7Rzk/rR7+wfu5Erl9YP6l2ItoVSeyEEF7HqDMy9byp/K3X3/hg9wd8mfIle/P3MuOXGcQFxHF7n9u5tuu1+GrlqfW26qxn47ZWbxcXEEf/8P4MCB/AwIiBdAvsJom/aNe8NrE7cOAAM2bM4I8//kBRFMaMGcMbb7xBcHAwANOnT+ebb74hNzeX+Ph4XnnlFYYPHw7A+vXrueKKK6pNVfLjjz9y6aWXemRbhBANE+YTxhODn+C+/vexaP8iFu1bxFHTUZ7941le2f4K13e7nlvjb6VbUDdPhyoaweqwklqUys6snfxY9iPvff8ex0qOndFOh46BHQYysIPrbFz/sP4EGYJaPmAhvJjXJnbFxcXccsstfPbZZ2g0Gu666y4ee+wxPvjgAwACAwNZtWoVXbt25csvv2TcuHEcO3YMf39Xrb2ePXuyf/9+T26CEKKJBBuCmTJwCncl3MVXKV+x5MASjpmOsXj/YhbvX8x5EedxbddrGRM3Ri67eTmH08HhosPsyd9Dcl4ye/L3sL9gPxa75VSjqrlY4wLi3E+qJgQncOC3A1w78to2MZOAEM3FaxO7wYMHM3jwYPf3e++9l+nTp7u/z5071/355ptv5pFHHuHgwYOcf/75LRqnEKLl+Gp9mdhnIhN6T2Bz5maWHljKz8d/ZmfOTnbm7OSfW/7J8OjhXNP1GoZEDPF0uO2e0+nkROkJ9uTtISk3iY0lG/nnF/+kzFZ2Rlt/rb9rrrhCH24cdiPnRZ5XLUm3Wq2kKCktGb4QrZLXJnZ/9vvvv5OQkFDjuqNHj1JQUED37t2rLYuIiCAwMJDbb7+dWbNmoVbXfN+FxWLBYjn1r0WTyQRISbG6kJJijRujvZbHaQoXhF/ABeEXkFuey8pjK1lxdAUHCg+w7vg61h1fh06lI04VR+mBUi7vdDnhPuHNHlN7LNt0sp3VaSUpJ4nDJYdJKUzhYNFBUopSMFWazmhvUBvoFdKLPiF9SAhNoE9IH2L9Y7Hb7KxevZrB4YPRqrTVfr+17Jd1ISXFGt6/vR4z21xJsV27djFy5Eg2bNhwRnJntVq58sorGTFiBM888wwAWVlZFBUVuS/H3nLLLdx99908+uijNY4/b9485s+ff8ZyKSkmROuSbc9mV+Uukq3JFDoKq62LUkfRTdONrpqudNZ0Rq/oPRRl6+Z0Oil2FpNjzyHLnuV+5TnycOA4o70aNZHqSGLUMXRUdyRGE0OYKgy1Ig84CFFXraKk2OjRo9mwYUON62bPns3s2bMBOHLkCMOHD+eNN95g3Lhx1do5nU4mTpxIZWUlS5cuRaWqeV6iJUuW8NZbb53192o6YxcbGyslxepASoo1boz2Wh6nuTmdTg7mH+SDDR+Q6ZvJnoI91dZrFA0JoQmcF3EeCaEJ9A3tSwffDo3+3bZUtmnlqpX0Htab42XHOWI6wpHiIxwxHeGo6WiNc8UBBOoC6Rnck55BPV3vwT3pGtAVrVorx8zTSEmxhvdvr8fMVlFSbNWqVbW2ycrKYtSoUcyZM+eMpA7goYceIiMjg5UrV541qQPOuQ5Ar9ej15/5r3dvLI/jraSkWOPGaG/lcVpCfFg8lxkuY+xVYym2FbMlcwubszazOXMz6aXpJOYlkpiX6G4f4RNB37C+9ArpRffg7nQL6kYn/04NKmvWWso2VdgqSC9N50TJCY6XHOdE6QlOlJwgzZRGmikN+0p7jWOpFTWdAjrRM7gn8cHxdAvoRvrOdG695lZ0Ol2jt6M9HDNBSoo1pn97O2a2iZJixcXFjBkzhjvuuIP77rvvjPVz587lt99+45dffjkjKVu/fj3dunUjNjaWlJQUFixYwMSJE1sqdCGElwnzCWNs17GM7ToWgBMlJ9iatZWkvCSS85JJKUwhx5zjvj/vJK1KS1xgHN0CuxHjH0NHY0eijdHEGGOI8otCq/beBKPSXklOeQ4Zpgx2V+4mf18+eZY8cspzyC7LJqM0gxxzzjnHMKgNdAnsQtegrnQNdL26BHahk3+nattutVpZkbhCKoMI4QW8NrH75ptvSEpKIjU1lRdeeMG9vLS0FIBnnnkGvV5Px44d3eveeecdbrvtNrZv385tt91GUVERERER3H777dWeqBVCtG8x/jHE+MdwQ48bACi3lrO/YD+783aTUphCalEqqcWpmG1mUgpTSCk882lMBYVgQzChPqGEGkIJMYQQrAsmtyIX8yEzgYZA/LR+p14aP3RqHRqVptpLrahRK2rsTrvr5Tj1bnPaKKsoI9ueTVJeEpXOSsw2M+W2csqt5ZgqTRRWFFJkKXK9KoootBRSVFFEibWkesA7a/5bGLVGYv1j3X+TGGMMHX07cmjbIcZfMx69Tu5FFKI18drEbtKkSUyaNOms6891a+CMGTOYMWNGc4QlhGiDfLW+DOowiEEdBrmXOZwOMssySS1K5UjxEdJL012vknQyyjIw28wUVBRQUFFACtUTv1Vbar/VpN4aMKROpSPCNwKNWUOvmF5EGiPp4NuBCN8IOho7EusfS4Au4IwzbVarlXxVvtRTFaIV8trETgghPEmlqIg2RhNtjGZ4zPBq65xOJwUVBeSZ88g355NfkU++OZ/c8lySDiURGBGI2W6m1FpKmbXM/aq0V2J31nzf2tkY1AbUDjWBvoH4an1dL40vPhofAnQBBBuCCdIHnfmuDyZQH4jNZmPFihWMvXhsm7k3TQhxdpLYCSFEPSmK4roE6xNabbnVamVF5grGjjh7EuVwOrA77dgcNtflVocNu9N+6rKsSu2+PKtSVKcSs7GSmAkhaieJnRBCtCCVokKlqNCqJEkTQjQ9uYFCCCGEEKKNkDN25yAlxWonJcUaN0Z7LY/TEqRsU8P7N6SkWG3t5Zh5iuybDe/fXo+Zba6kWEtZuHAhCxcuxG63c/DgQSkpJoQQQgiPaxUlxbyZyWQiMDBQSorVgZQUa9wY7bU8TkuQsk0N71/fPo09HtalTVvZL0H2zcb0b6/HzFZRUsybncx1zWbzOQ9A5eXlmM1mbDZbvdvUpX9r4KntaOrfbYrxGjJGffrUtW1t7Rq7vrXwxHY0x282dszm3i/r2l6OmafIvtnw/u31mGk2m4Fzz+F7kiR2NSgpcc3YHhsb6+FIhBBCCCFcSkpKCAwMPGcbuRRbA4fDQUZGBv7+/uesfXjhhReydevWc451tjYmk4nY2FiOHz9e62lVb1eXv0Nr+N2mGK8hY9SnT13b1tbuXOtl3/S+32zsmM29X9a1vRwzT5F9s+H92+Mx0+l0UlJSQseOHVGpzj2hiZyxq4FKpSImJqbWdmq1utb/EWtrExAQ0OoPUnX5O7SG322K8RoyRn361LVtbe3qMo7sm97zm40ds7n3y7q2l2PmKbJvNrx/ez1m1nam7iSZx64RpkyZ0iRtWjtPbWNT/25TjNeQMerTp65ta2vXHvZL8Mx2NsdvNnbM5t4v69pejpmnyL7Z8P5yzDw3uRTrISefvK3LEy5CtCTZN4U3kv1SeCtv2zfljJ2H6PV65s6di16v93QoQlQj+6bwRrJfCm/lbfumnLETQgghhGgj5IydEEIIIUQbIYmdEEIIIUQbIYmdEEIIIUQbIYmdEEIIIUQbIYmdF/vll18YNmwYl1xyCdOnT/d0OEK4HT9+nEGDBmEwGFp93U7R+k2fPp1LL72UadOmeToUIQDPHiMlsfNi3bt3Z/369fz6669kZWWxe/duT4ckBADh4eGsW7eOoUOHejoU0c7t2LGD0tJSNm7ciNVq9Uh5QyH+zJPHSEnsvFh0dLR7XhytVotarfZwREK4GAwGgoKCPB2GEGzatIkrr7wSgCuvvJI//vjDwxEJ4dljpCR2TWju3Ln06dMHlUrFkiVLqq3Lzc3lmmuuwdfXl/j4eNauXVvncXfs2EFeXh59+vRp6pBFO9Fc+6YQTakh+2lRUZF7tv/AwEAKCwtbPG7RtrW246fG0wG0JT169OC1115jzpw5Z6ybMmUKHTt2JC8vj1WrVnHzzTeTmpqKxWLhb3/7W7W2RqORH374AYCsrCymTZvGl19+2SLbINqm5tg3hWhqDdlPg4KCMJlMgKu0k5xJFk2tIftlcHCwByKt4hRNbsSIEc7Fixe7v5eUlDh1Op0zIyPDvezSSy91fvLJJ+ccx2w2Oy+//HLnjh07mi1W0b401b55+nhWq7XJ4xTtW3320+3btzvvu+8+p9PpdE6ePNm5efPmFo9XtA8NOX564hgpl2JbQEpKCoGBgURFRbmXDRgwgD179pyz30cffcTevXt59NFHueyyy9i0aVNzhyramYbumxUVFVx55ZUkJiYyZswYNm7c2NyhinbsXPvpoEGD8PHx4dJLL0WlUjF48GAPRirak3Ptl548Rsql2BZQWlrqvgfkpICAAIqKis7Zb/LkyUyePLkZIxPtXUP3TYPBwJo1a5oxMiFOqW0/ffXVV1s+KNHunWu/9OQxUs7YtQCj0ei+B+Qkk8mE0Wj0UERCuMi+KVoD2U+FN/LW/VISuxbQo0cPiouLycrKci9LTEwkISHBg1EJIfumaB1kPxXeyFv3S0nsmpDVaqWiogKHw1Hts9Fo5Prrr2fu3LmYzWa+++47kpOTue666zwdsmgnZN8UrYHsp8Ibtbr9skUf1WjjJk2a5ASqvX7++Wen0+l05uTkOK+++mqnj4+Ps0ePHs7Vq1d7NljRrsi+KVoD2U+FN2pt+6XidDqdLZ9OCiGEEEKIpiaXYoUQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQQggh2ghJ7IQQwoPmzZuHVqslMjKyyca87LLLWLJkSZON92evvPIKfn5+GAyGZvsNIUTDSGInhPC4uLg4fH19MRqNGI1G4uLiPB1Si7r77rurFRJvDn379uXo0aNNMtb06dPZs2dPk4wlhGhaktgJIbzCunXrKC0tpbS0tMYExGq1tnxQXqAptvvEiRPYbLZ2lzAL0R5JYieE8Err16+nV69ezJo1i7CwMJ5//nnMZjNTp06lY8eOxMTE8O9//9vdvqysjAkTJhAUFMSgQYN46qmnuOqqq6qNdTpFUdxnyQoKCpgwYQIRERF07dqVTz75xN3usssu45lnnuGCCy4gICCA8ePHU1lZ6V6/dOlS+vbti7+/P/369ePAgQM899xz3HXXXdV+7+KLL+arr76q07bHxcXxwgsvEB8fT58+fQB48MEH6dixI0FBQYwePZq0tDR3+61bt9K/f38CAgK4//77cTgc1cb76aefGDNmjHt75s+fz3nnnYfRaOTxxx/n0KFDXHjhhQQFBfHYY4+5+/3www/Ex8fj7+9PbGwsixcvrlP8QgjPkcROCOG1Dh06hK+vL5mZmTzxxBM89thjFBcXc/DgQbZs2cL//vc/vv/+ewDmz59Pfn4+aWlpLFq0iE8//bTOv3P77bcTGxvL8ePHWbFiBTNnziQxMdG9/osvvuCrr74iLS2NpKQkli5dCsBvv/3G1KlTeeeddyguLuaLL74gICCA2267jW+++QaLxQLAsWPH2Lt3L2PHjq1zTN988w0bN25k9+7dAFxyySXs27ePrKwsYmJimDZtGgCVlZX89a9/5aGHHiI/P5++ffvy+++/Vxtr5cqV7sQO4Msvv2T58uUkJyfz9ttvM3nyZL7++muSk5N5//333dt+zz338OGHH1JSUsLWrVsZMGBAneMXQniGJHZCCK8watQogoKCCAoKYubMmQD4+vry5JNPotVq0ev1fPTRR7z88ssYjUY6duzI5MmTWbZsGeBKvubMmUNAQAC9evVi0qRJdfrdrKwsNm7cyPPPP49er6dXr15MmDCh2tm1e++9l06dOhEUFMQ111zjTnw+/vhjJk+ezMUXX4xKpaJXr15ERUURFxdH3759WbFiBQBLlixh3Lhx9XrY4NFHHyUiIsLdZ8KECQQGBmIwGHjiiSf49ddfAdi0aRN6vZ57770XrVbL1KlTiYqKco9jt9v59ddfueyyy9zL7rnnHjp27EhcXBznn38+o0ePJiYmhpiYGIYMGUJSUhIAWq2W5ORkSktLiYyMdJ89FEJ4L0nshBBeYfXq1RQVFVFUVMQ///lPAKKiolCr1QDk5uZiNpvp2bOnOwF86qmnyMnJASAzM5PY2Fj3eKd/Ppe0tDTKysoIDQ11j/vOO++QnZ3tbhMREeH+7OvrS2lpKeC6d61r1641jjtx4kT3k6mLFi1iwoQJdf1TABATE1Pt+3PPPUf37t0JCAhg8ODB5OfnA2dut6Io1fpu3ryZvn374uvrW+P2+Pj4EB4eXu17WVkZAMuWLeO7774jOjqa0aNHs3///nptgxCi5Wk8HYAQQpyNoijuz2FhYRgMBo4dO0ZgYOAZbaOiojh+/DidO3cG4Pjx4+51fn5+lJeXu7+f/gRqdHQ0QUFB7kSpPmJjYzly5EiN626++WaefPJJtmzZQk5ODldccUW9xj5923/55Rfeeecd1q5dS/fu3Tl48KD7nsGoqChOnDhRre/p3/98GbY+hgwZwvLly7FYLDz99NNMmTKFtWvXNmgsIUTLkDN2QohWQaVSMWnSJB577DGKiopwOBzs27ePLVu2AHDTTTfx3HPPUVJSwoEDB/jf//7n7tuzZ0/y8/P55ZdfsFgsPPvss+510dHRXHjhhTz99NOUl5djs9nYsWMHe/furTWmO++8k7fffptNmzbhdDo5cOAAmZmZAISEhDBixAjuvPNObrnlFveZx4YoKSlBo9EQGhpKWVkZCxYscK8bNmwYZrOZDz74AKvVysKFC90xQPUHJ+qjsrKSRYsWYTKZ0Gq1GI3GRm2DEKJlSGInhGg1Tk6M269fP0JCQrjjjjsoLCwEYO7cuQQGBhITE8P48eO5/fbb3f0CAwN5/fXXueWWW+jSpQuDBw+uNu5nn33GsWPH6Nq1KxERETzyyCOYzeZa47nooot49dVX+fvf/05AQAA333wzJpPJvX7ixIns27ev3pdh/+yqq65i2LBhdO7cmX79+nHRRRe51+l0Or788kv+85//EBoaSlJSknt9fn4+mZmZ9OvXr0G/+8knn9C5c2eCg4NZvXo1r732WqO2QwjR/BSn0+n0dBBCCNHUPv74Y5YsWcLKlSs9FsOmTZuYOHEiqampZ22zYMEC/vWvfxEUFHTGJdXGWrx4MatXr+bDDz9s0nFfffVV5s6di6IoFBUVNenYQojGkTN2QgjRDKxWK6+//jp///vfz9lu9uzZlJaWNnlSB67LwQ8++GCTj/vII49QXFwsSZ0QXkgenhBCiCaWn59PTEwM/fv355133vFYHA19aEII0XrJpVghhBBCiDZCLsUKIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQRktgJIYQQQrQR/w+Yr86HtC/uJwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Loop transfer function\n",
+    "L = P * C\n",
+    "ct.bode_plot([P, C, L], label=['P', 'C', 'L'])\n",
+    "ct.suptitle(\"PI controller for servomechanism\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Bg5ga11VuRtI",
+   "metadata": {
+    "id": "Bg5ga11VuRtI"
+   },
+   "source": [
+    "Note that L = P * C corresponds to addition in both the magnitude and the phase."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "UmYmSzx2rTfg",
+   "metadata": {
+    "id": "UmYmSzx2rTfg"
+   },
+   "source": [
+    "### Nyquist analysis\n",
+    "\n",
+    "To check stability (and eventually robustness), we use the Nyquist criterion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "Qmp59pmS9GLj",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=[7, 4])\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "ax2 = plt.subplot(2, 2, 3)\n",
+    "ct.bode_plot(L, ax=[ax1, ax2])\n",
+    "\n",
+    "# Tidy up the figure a bit\n",
+    "fig.align_labels()\n",
+    "ax1.set_title(\"Bode plot for L\", fontsize='medium')\n",
+    "\n",
+    "ax2 = plt.subplot(1, 2, 2)\n",
+    "ct.nyquist_plot(L, ax=ax2, title=\"\")\n",
+    "plt.title(\"Nyquist plot for L\", fontsize='medium')\n",
+    "\n",
+    "ct.suptitle(\"Loop analysis for (unstable) servomechanism\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "s4dDf4PrZqU3",
+   "metadata": {
+    "id": "s4dDf4PrZqU3"
+   },
+   "source": [
+    "We see from this plot that the loop transfer function encircles the -1 point => closed loop system should be unstable.  We can check this by making use of additional features of Nyquist analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "K7ifUBL0Z3xN",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N = encirclements:  2\n",
+      "P = RHP poles of L:  0\n",
+      "Z = N + P = RHP zeros of 1 + L: 2\n",
+      "Zeros of (1 + L) =  [-0.26792107+0.j        0.08396054+0.259999j  0.08396054-0.259999j]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the Nyquist *response*, so that we can get back encirclements\n",
+    "nyqresp = ct.nyquist_response(L)\n",
+    "print(\"N = encirclements: \", nyqresp.count)\n",
+    "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n",
+    "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) > 0))\n",
+    "print(\"Zeros of (1 + L) = \", (1 + L).zeros())\n",
+    "print(\"\")\n",
+    "\n",
+    "T = ct.feedback(L)\n",
+    "ct.step_response(T).plot(\n",
+    "    title=\"Step response for (unstable) servomechanism\",\n",
+    "    time_label=\"Time [ms]\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "p3JxLilMxdOE",
+   "metadata": {
+    "id": "p3JxLilMxdOE"
+   },
+   "source": [
+    "### Poles on the $j\\omega$ axis\n",
+    "\n",
+    "Note that we have a pole at 0 (due to the integrator in the controller).  How is this handled?\n",
+    "\n",
+    "A: use a small loop to the right around poles on the $j\\omega$ axis => not inside the contour.\n",
+    "\n",
+    "To see this, we use the `nyquist_response` function, which returns the contour used to compute the Nyquist curve.  If we zoom in on the contour near the origin, we see how the outer edge of the Nyquist curve is computed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "R5IBk3Ai9Slk",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x580 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=[7, 5.8])\n",
+    "\n",
+    "# Plot the D contour\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "plt.plot(np.real(nyqresp.contour), np.imag(nyqresp.contour))\n",
+    "plt.axis([-1e-4, 4e-4, 0, 4e-4])\n",
+    "plt.xlabel('Real axis')\n",
+    "plt.ylabel('Imaginary axis')\n",
+    "plt.title(\"Zoom on D-contour\", size='medium')\n",
+    "\n",
+    "# Clean up the display of the units\n",
+    "from matplotlib import ticker\n",
+    "ax1.xaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n",
+    "ax1.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n",
+    "\n",
+    "ax2 = plt.subplot(2, 2, 2)\n",
+    "ct.nyquist_plot(L, ax=ax2)\n",
+    "plt.title(\"Nyquist curve\", size='medium')\n",
+    "\n",
+    "ct.suptitle(\"Nyquist contour for pole at the origin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "h20JRZ_r4fGy",
+   "metadata": {
+    "id": "h20JRZ_r4fGy"
+   },
+   "source": [
+    "### Second iteration feedback control design\n",
+    "\n",
+    "We now redesign the control system to give something that is stable.  We can do this by moving the zero for the controller to a lower frequency, so that the phase lag from the integrator does not overlap with the phase lag from the system dynamics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "YsM8SnXz_Kaj",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change the frequency response to avoid crossing over -180 with large gain\n",
+    "Cnew = ct.tf(kp + (ki/200)/s, name='C_new')\n",
+    "Lnew = ct.tf(P * Cnew, name='L_new')\n",
+    "\n",
+    "plt.figure(figsize=[7, 4])\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "ax2 = plt.subplot(2, 2, 3)\n",
+    "ct.bode_plot([Lnew, L], ax=[ax1, ax2], label=['L_new', 'L_old'])\n",
+    "\n",
+    "# Clean up the figure a bit\n",
+    "ax1.loglog([1e-3, 1e1], [1, 1], 'k', linewidth=0.5)\n",
+    "ax1.set_title(\"Bode plot for L_new, L_old\", size='medium')\n",
+    "\n",
+    "ax3=plt.subplot(1, 2, 2)\n",
+    "ct.nyquist_plot(Lnew, max_curve_magnitude=5, ax=ax3)\n",
+    "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n",
+    "\n",
+    "plt.suptitle(\"Loop analysis for (stable) servomechanism\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "kFjeGXzDvucx",
+   "metadata": {
+    "id": "kFjeGXzDvucx"
+   },
+   "source": [
+    "We see now that we have no encirclements, and so the system should be stable.\n",
+    "\n",
+    "Note however that the Nyquist curve is close to the -1 point => not *that* stable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "GGfJwG716jU2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Step response for (stable) spring-mass system')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute the transfer function from r to y\n",
+    "Tnew = ct.feedback(Lnew)\n",
+    "ct.step_response(Tnew).plot(time_label=\"Time [ms]\")\n",
+    "plt.suptitle(\"Step response for (stable) spring-mass system\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5114fa7-6924-47d7-8dd2-f12060152edd",
+   "metadata": {},
+   "source": [
+    "### Third iteration feedback control design (via loop shaping)\n",
+    "\n",
+    "To get a better design, we use a PID controller to shape the frequency response so that we get high gain at low frequency and low phase at crossover."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e6da93a4-5202-45d7-9e5a-697848f4ba71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QExPjsiPy3bt3kZqayj8+duwYhEJhlTUFNenUqRPu3r2LjIwMflv5gSSuREdHQyKRuHx/SvXu3Rt2ux2ZmZmIjo52WsrXDoaGhmLOnDnYsmUL/vznP+M///lPlfk8efKk07aaOrPXV5cuXZzKB8CVoY4dO1b6kauJQCDA0KFDsWTJEpw9exZSqRRbt26Fv78/goODcfv27UrvT2kn+epMnz4du3fvxuXLl3HgwAFMnz7dKf/nzp1zGqRx5MiRBpUTAOjTpw9u3rwJPz+/SnnW6XTQaDSIiIjA/v37632OP/74A0OGDMHcuXPRu3dvREdHV6rBrI+GfDdro0ePHkhOTnb6DpTXGGWqtEaufA1fVRpaBmr7/SWtD9XYkRZLr9fj3LlzTtu8vLwwd+5crFixAvPmzcPLL7+M69evY9GiRViwYAGEQiFUKhX+9Kc/8SP/wsLC8PHHH6OwsBDPPfec0/H+9re/wdvbG/7+/li4cCF8fHyqnPx3zZo1sNvtGDhwIJRKJdatWweFQoHw8PBavZ7o6GjcvXsX33//Pfr374+dO3di69atldLJ5XLMnDkTn376KQwGA+bPn4/HH3+83hfb0aNHo0OHDpg5cyY+/vhjFBQUYOHChQCq/s9erVbjueeewxtvvOH0/pSvsenYsSOmT5+OGTNmYNmyZejduzeys7Px22+/oXv37njggQfw6quvYvz48ejYsSPy8vLw22+/VQquS82bNw/33nsvli9fjoceegi//fYbfvnllyapLfjzn/+M/v374+9//zumTp2KY8eO4YsvvnAasVobJ06cwP79+zFmzBj4+fnhxIkTyMrK4l/j4sWLMX/+fGi1WowfPx4WiwWnTp1CXl4eFixYUO2xhw8fDn9/f0yfPh0REREYNGgQv2/69OlYtGgRZs6cicWLFyMrKwvz5s3D008/zTcp1sf06dPxySefYOLEifjb3/6GkJAQ3L17F1u2bMEbb7yBkJAQLF68GHPmzIGfnx/Gjx+PgoICHDlyxGUtrCvR0dH43//+hz179iAyMhLr1q1DXFxcrYLdqjT0u1kbw4cPx7333otHH30Uy5cvR3R0NK5duwaBQIBx48Y1Spny8/PjR0SHhIRALpdXOb1RQ8tAbb6/pHWiGjvSYh08eBC9e/d2Wt577z0EBwdj165dOHnyJHr27Ik5c+bgueeewzvvvMM/96OPPsKjjz6Kp59+Gn369MGtW7ewZ88eeHp6Op3jo48+wiuvvIK+ffsiLS0NP//8c5XzQ3l4eOA///kPhg4dih49emD//v3Yvn07vL29a/V6Jk6ciNdeew0vv/wyevXqhaNHj+Ldd9+tlC46OhqPPPIIHnjgAYwZMwbdunWrc8BRnkgkwrZt22A0GtG/f3/Mnj2bf6/kcnmVz/vkk09w77334uGHH8aoUaNwzz33oG/fvk5pVq9ejRkzZuDPf/4zYmNj8fDDD+PEiRMIDQ0FwNU8vPTSS+jcuTPGjRuH2NjYKl/L0KFD8eWXX2L58uXo2bMndu/ejddee63aPNZXnz59sHHjRnz//ffo1q0b3nvvPfztb3/DrFmz6nQcrVaL33//HQ888AA6duyId955B8uWLcP48eMBcE3R//3vf7FmzRp0794dw4cPx5o1a2oVxAgEAjz55JM4f/68U20dACiVSuzZswe5ubno378/HnvsMYwcORJffPFFnfJfkVKpxO+//46wsDA88sgj6Ny5M5599lkUFRVBq9UCAGbOnIkVK1Zg1apV6Nq1KyZMmICbN2/W+hxz5szBI488gqlTp2LgwIHIycnB3LlzG5Tvhn43a2vz5s3o378/nnzySXTp0gV/+ctf+Nq1xihTYrEYn3/+Of79738jKCgIEydOrDJtY5SBmr6/pHUSsNp0piGkjTl48CDuu+8+5OXlOc2T5m6LFy/Gtm3bKtVUNrYjR47gnnvuwa1bt9ChQ4cmPVdDPP/887h27Rr++OMPd2eFEEJaBWqKJaQd2Lp1K9RqNWJiYnDr1i288sorGDp0aIsL6j799FOMHj0aKpUKv/zyC9auXdug2kpCCGlvKLAjpB0oKCjAX/7yFyQlJcHHxwejRo2qcTSuO5w8eZLvBxgVFYXPP/8cs2fPdne2CCGk1aCmWEIIIYSQNoIGTxBCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2BFCCCGEtBEU2JEWLSIiAsePH3d3NkgVPv30U3z44YfuzobbrFmzBuPGjavTcw4ePIjHH3+8iXJEDh48iE6dOjXJsV9++WV89913TXLs1mDx4sWYM2dOnZ6zZs0a/OUvf2miHBFXKLAjTaItB2Q5OTn405/+hODgYGg0GkRHR+Pll19Gamqq2/L0yy+/IDo6GiqVChMnTkReXl6VaePi4tCzZ08olUoMHz4cd+7c4fctWrQIoaGh0Gq1iImJwerVq6s8jtlsxueff46XXnqpxvwlJiZCLpdXmyYrKwsPPvgglEolYmNjsX///irT1pTPNWvWICQkBFqtFs888wysVmuNeWwsn376KZYsWVLl/hEjRuD27du4dOlSrY63Zs0aiMViqNVq6HQ6DBo0CMeOHWus7NbJt99+C7VazS9yuRwajYbf/8ILLyAwMBBarRbdu3fHjh07qjzWrFmzIJPJ+GN17dqV3+dwOPD2228jODgYXl5eeP3118EYa9LXVpOUlBTs2bOnVkF5bYLLBx98EH5+ftDpdBg4cGC1n2l1343CwkK8+OKL8PPzg7+/Pz799NPav6hG8PLLL2Pt2rVV7p8+fTo2bdqE7OzsZsxV+0aBHSF1UFRUhBEjRiA1NRUHDx6EwWDA8ePHER0d7bZANjMzE9OmTcM///lPZGZmQqPR4JVXXnGZ1mKx4JFHHsErr7yC3NxcDBo0CE8//TS//6mnnsK1a9dgMBiwa9cuLFy4EJcvX3Z5rC1btmDgwIHQarWN8jpeeuklBAUFITs7G//3f/+HKVOmVBmgVpfPixcvYsGCBdi2bRuSkpKQmJiIpUuX1ioPxcXFDX4du3fvxtixY6tN8/jjj+Obb76p9TFHjRoFo9GI7Oxs3HvvvXjkkUfcEuhMnz4dRqORX+bOnYtJkybx+xcsWIDExEQYDAZ88803eOqpp6r9J2PJkiX8scqXs6+//ho7d+7EmTNncOvWLRw9ehRfffVVU760Gq1duxaTJk2CSCRqlON9/PHHSE1NhV6vxzvvvIPJkydX+ZlW9914//33kZCQgFu3buHUqVP497//jT179tR4fofDAYfD0eDXsWfPHowZM6bK/RKJBOPHj8eGDRsafC5SS4yQJhAeHs6OHTtW6/Tvv/8+CwgIYGq1mnXp0oVdvnyZP84//vEP1qlTJ+bh4cHmzZvHP+fGjRts2LBhTKfTscDAQPbWW2/x+1avXs1GjRrFZsyYwdRqNevfvz+7fv06v//ChQts2LBhzMPDg/Xp04fFxcXVKp//+te/WHBwMCsqKqoyzaJFi9hTTz3FJk6cyFQqFRs1ahTLyMhgU6ZMYRqNho0dO5YZDIZavzc1+fLLL9m4ceP4x7du3WJyuZyZzeZKaXfv3s06derEPzYajUyhULDExMRKaW/cuMH8/f3Zzp07XZ53xowZ7PPPP+cfZ2dns7FjxzJvb2/m4+PDnn/+eT4PMTExDABTqVRMpVKxlJQUp2MVFBQwqVTKUlNT+W3Dhg1ja9eurfH1V8znm2++yebMmcPv379/P4uMjHT53EWLFrFp06bxn9WBAwfYTz/9xLp168bUajWLjo5mGzdu5NMbjUb25JNPMp1Ox3r37s3eeustNnbsWH6/yWRiAQEBrLi4mF2/fp0NHTqUaTQa5u3tzRYsWMCnO3bsGOvcuXONr40xriyXP8elS5cYAJaVldXsZa08u93OAgMD2e7du13uj4uLYzKZjP8uVzRz5kz24Ycfutz3yCOPsFWrVvGPN2zYwIYMGeIyrc1mY/PmzWNeXl6sY8eO7IMPPmCxsbH8/uq+60ePHmVdu3ZlGo2Gvfjii+zee+9l3333ncvz3Hvvveznn3/mH1d1/SkuLmZyuZwJBAKmUqmYTqdzebxSDoeD7dixgwFgRqOx0v6avht9+vRhu3bt4vd98MEHbNq0aS7PNXPmTDZv3jw2fPhwplAoWEJCAvvqq69YdHQ0U6vVrHv37uzAgQN8+szMTDZu3Dim0WjYiBEj2J/+9Cf24osv8vtv3brFevTowRjjynSvXr2YWq1m/v7+bNmyZXy67777jo0fP77a94E0HqqxI2537do1/Otf/8LZs2dhMBiwefNmeHl58ft//vlnHD58GBcvXsT333+PP/74g9+3dOlSZGdn49ChQ1i/fj22bdvG7ztw4ABGjBiBnJwcjB8/HtOnTwcAFBQUYPz48XjttdeQnZ2Nd999F5MnT4bZbAYAzJ07F3PnznWZ1wMHDmD06NE1Nitu27YNf/3rX5GZmYn8/Hzcc889mDdvHjIzM2E0Gqusrfnoo4/g4eHhcpkwYYLL51y5cgXdu3fnH3fo0AFisRi3b9+uMa1KpUKHDh1w5coVpzyoVCp07NgR4eHhuP/++12e9/Lly4iJieEfOxwOvPzyy0hJScGFCxdw6tQp/Otf/wIA7N27FzKZjK+dCQoKcjrWzZs3odPpEBgYyG/r2bNnlbWF1eWz4mvs2bMnEhISUFRU5PI4W7Zswcsvv4yCggIMHjwYWq0WP/74I/R6PT7//HM888wzSE9PB8DVMOXk5ODu3bvYsGED1q1b53SsAwcOYNiwYRCJRHjvvffw4IMPQq/X486dO5g6dSqfLjY2FteuXatzDaHVasXatWsRHBwMHx8fAM1b1sr77bffYLfbMWrUKKftc+fOhUKhQP/+/TFu3Dh06dKlymN88skn8Pb2xpAhQ/D777877WPlaq8YY1WWha+++gq///47rly5ggMHDjj1gavuu26xWPDoo4/itddeQ05ODnr06IGjR49WmdeK5R1wff0RiUT45Zdf0LFjRxiNRuTn51d5zAkTJkAul2PChAmYP38+VCpVpTS1+W7U9r0CgO+//x7Lly9HQUEBQkJCEBQUhP3790Ov12PevHl44oknYLFYAHA1hQEBAcjMzMT777+P9evXOx2rfO30q6++ijfeeAMFBQW4evUq7rvvPj5dbGwsLl68WGWeSOOiwI64nVgshtlsxpUrV2C329GpUycEBATw+1999VV4e3sjJCQEI0aMwPnz5wEAMTExuPfeeyEWixETE4Pp06fj8OHD/PMiIyPxzDPPQCqV4u2338aVK1dw584d7Ny5Ez169MDkyZMhEokwadIk+Pv7831cVq1ahVWrVrnMa05OjlPetmzZAg8PD6jVasybN4/fPnr0aAwePBhKpRIPPPAAYmJiMGzYMMjlcjz44IO4cOGCy+O/+eabyM/Pd7lU1V/JaDRWag7VarUwGo31Svvmm2/CaDTi+PHjePjhhyEWi12eV6/XQ61W8499fX0xYcIEyGQyBAYG4sUXX3T6PKpTl9dQUz4rHqt0vapj3X///Rg1ahQEAgFkMhlGjBiB2NhYCIVCjB8/Ht27d8epU6cAAJs2bcK7774LrVaLTp06YebMmU7HKv9DJ5FIkJCQgPT0dKhUKgwYMIBPp9FowBiDXq+v1fuzf/9+eHh4IDg4GCdPnsTWrVv5fc1Z1sr79ttv8cQTT1Rqmly1ahWMRiP27duH4cOHV/n8V155Bbdu3UJaWhpeeuklPPTQQ0hKSgIAjBkzBv/+97+RlpaG7OxsrFy5EoWFhS6Ps2nTJrz++uvw9/dHUFAQ5s+fz++r7rt+7NgxqFQqPPfcc5BIJJg7d65T8FRRxfJe0/WnNnbs2IGCggL8+OOP6NOnj8s0NX03xowZgxUrViA/Px+JiYlYs2ZNle8VAEyZMgV9+vSBSCSCWCzGgw8+iLCwMAiFQjz//PMQCAS4efMm7HY7tm7dir///e+Qy+UYMmQIHn74YadjVSzv169fR25uLjw9PdG7d28+nUajqTbAJY2LAjvidtHR0Vi+fDkWLlwIX19fPPfcczAYDPx+Pz8/fl2pVPIXtJSUFEyePBkBAQHQ6XRYsWIFcnJy+LShoaH8ukwmg5+fH9LS0nD37l3+h7J0uXr1KtLS0mrMq7e3N197AwCPPPII8vPz8frrr8Nms7nMs0KhgK+vr9Njk8lU27enRmq12un9AgCDweD0I1TXtAKBAAMHDkRaWhq+/vprl+fV6XQoKCjgHxcUFGDGjBn8oIUFCxY4fR6N9RpqymfFY5WuV3WskJAQp8eHDx/G0KFD4eXlBQ8PD5w6dYp/HWlpaU7lqvw6wPU3Kv2h+/jjj1FcXIxevXqhZ8+e2L59O5+uoKAAAoEAOp2u2tdXauTIkcjPz0dWVhYOHjyI/v378/uas6yVMpvN2LJlC18LXpFIJMKoUaOwf//+Kvt79e7dG56enpBKpZg+fToGDx6Mffv2AQBmz56N8ePHY8CAAejfvz8eeOABBAcHuzxOdZ9Jdd/19PT0Sp9fVecAKpf3mq4/tSWVSvHoo49i2bJluHr1aqX9NX033nnnHURERKBz584YP348pkyZUu3rqFjet23bhj59+vDvT2ZmJnJycpCVlQWHw+F0rPLvl9VqRVxcHIYNGwYA+O9//4urV68iOjoaQ4YMcRoMUlBQAA8Pj9q/KaRBKLAjLcLTTz+NY8eO4ebNm7hz5w4+++yzGp/zzjvvwNfXFzdu3IBer8err77q1CSRnJzMr1utVmRmZiIgIADBwcF48MEHnWooTCYTpk2bVuM577vvPuzbt49vtm1sH3zwgdOow/LL+PHjXT6nS5cuTs0ct2/fRnFxMaKiompMazKZEB8fX2VzmcPhQHx8vMt93bt3x/Xr1/nHy5cvR25uLs6dOweDwYDly5fzn4dAIKj2dcfExECv1zsFzefPn3caKVmd8vms+BrPnz+PyMhIKBQKl8+tmLenn34azz77LDIyMpCfn49+/frxryMwMJCvVQLgtJ6QkACpVMr/cAYGBuKbb75Beno6/va3v2Hq1Kn86Nxr166hU6dOVdaGNof6lLVS27dvh5+fn1MtpCvVlZ+KhMKynyORSISPPvoISUlJSEhIgL+/P/r16+fyedV9JtV91wMCApyuEQAXrFWlYnmv7vpTU3l3pbi4GAkJCZW21/TdUKlU+Oqrr5CWloarV69CIBBU+V5VzJvFYsGTTz6Jjz76CDk5OcjPz4efnx8YY/D19YVQKHR6T8q/t4cPH0a/fv0glUoBcM2tGzdu5AdzPfXUU3zaa9euOXWPIE2LAjvSZKxWK8xmM79UNQLr+vXrOHjwIKxWK5RKJWQyWa1GnhUUFEClUkGtVuPSpUuV+n/cvn0ba9euhc1mw4cffohOnTohIiICEyZMwKlTp/Dzzz/DbrejqKgIu3fvrlWz2MyZM+Hp6YmpU6fi5s2bYIwhLy/P5X/a9fH22287jTosv/zyyy8unzN58mQcP34ce/bsQWFhIRYtWoQpU6ZAJpNVSjtixAgYjUasWbMGFosFS5cuRb9+/RAeHg6A+687Pz8fDocDhw4dwrfffosRI0a4PO+4ceOc+kUVFBRAoVBAp9Phzp07Ts3ZPj4+sNlsVdaKqtVqPPzww1i0aBGKiorw888/49KlS3jooYdcpq8un9OmTcPGjRtx5swZ6PV6vP/++04/MjUpKCiAl5cXxGIxNm/ejNOnT/P7HnvsMbz//vsoKCjA9evX8b///Y/fV3E07I8//ojU1FQIBAJ4eHhAIBDwP6p//PGHU9rFixdX+T43lfqUtVLffvttpdo6o9GIb7/9FkajEcXFxdi8eTPf59CVzZs3w2Qyobi4GD/88AMOHz7M95PMyclBYmIiGGM4deoU3n//fbz55psuj/PYY49h+fLlyMzMRFpaGr744gt+X3Xf9cGDB8NoNGL16tUoLi7Gl19+WW2tvavyXtX1x8/PD5mZmVX267xz5w527NjB9/X74osvkJycjL59+1ZKW9N3Izk5Genp6bDb7di3bx9Wr16N1157rcrXUZ7FYoHVauVreT/77DNkZWUBAN90vWjRIpjNZhw/ftyp1rl87TTAlYmcnByIxWJoNBqna3jF8k6aFgV2pMkMHz4cCoWCX7Zs2eIyncViwRtvvAFvb2+EhYVBp9NVOV1Hee+99x72798PrVaL+fPn49FHH3Xaf9999+HAgQPw8vLCjh078O233wLgmlR27NiBzz77DL6+voiIiHCaSmHOnDlVTsKpUChw8OBBBAYGYvjw4dBoNBgwYAB8fX3x7rvv1vataVR+fn749ttvMXfuXPj4+ECv1zvVeI4fPx4ffPABAK5JesuWLVi+fDk8PDxw5MgRpwEAu3btQocOHaDT6TB37lx88skneOCBB1yed+LEiTh16hTfd+aVV15BSkoKPD098eijj2Ly5Ml8WpVKhb/+9a/o3r07PDw8XM75t2rVKiQlJcHb2xuvv/46Nm7cCE9PTwDcj0b52rvq8tm9e3csW7YMDz30EEJCQhAaGoqFCxfW+v385z//iZdffhmenp7Ys2ePUz+xRYsWQafTISQkBE8++aTTVDEVA7uTJ0+ib9++UKvV+NOf/oQNGzZAIpEAADZu3Ihnn32WT5ucnIyhQ4fWOo/ulJeXh19++aVSYCcQCPD1118jJCQE3t7e+OCDD7Bhwwa+pqbiZ/iPf/wDQUFB8PHxwfLly7F161ZEREQA4KbwGTVqFFQqFZ566iksW7bMZdADAC+++CKGDBmCTp06YcSIEXjiiSf4fdV912UyGTZv3oxly5bBy8sL586dQ//+/V3+QwQAM2bMwE8//QS73Q6g+utP586dMWHCBISEhPCDXCp6//334efnh4CAAPzwww/Yvn07/P39AXC1qeVrTav7bty8eRP9+/eHRqPBX//6V3z//feVBidVRavV4pNPPsHo0aMREBCAnJwcREdH8/tLA05fX1+89dZbTp95xfK+a9cuxMbGQqPR4PPPP+fnlrTZbNi1a1etWkRI4xAw5uZZHwlpAmvWrMH333+P3bt3uzsrbdqyZctgNpvrFDi1RTabDSEhIbhz506NI6YPHjyIlStXYtOmTfy2vn37Yu/evfD29m7qrJIqMMYQEhKCbdu2OfVhLG/evHkYNGhQlX0L24u0tDQMHTrU5cj7itasWYPLly/jk08+aYacEYACO9JGUWBHmlNGRga2b9+O2bNnuzsrpA4OHDiALl26wNPTE8uXL8eXX36JW7duubXvY2tw/fp1XLx4EY899pi7s0JcoKZY0mweeughlx21Dx486O6sEdIg/v7+FNS1QpcvX0bPnj3h7e2Nbdu2YcuWLRTU1UJsbCwFdS0Y1dgRQgghhLQRVGNHCCGEENJGUGBHCCGEENJGUGBHCCGEENJGUC9RFxwOB1JTU6HRaOo1gzghhBBCSGNhjKGgoABBQUFOd2lxhQI7F1JTUyvdQ5AQQgghxJ2SkpIq3e+3IgrsXNBoNAC4N1Cr1bo5N4SQls5sNvO3LVu/fn2NkxQT4g5UTlsvg8GA0NBQPj6pDk134oLBYIBOp4Ner6fAjhBSI5PJBLVaDYC7Z6pKpXJzjgipjMpp61WXuIRq7AghpIGkUil/b0ypVOrm3BDiGpXT9oFq7FygGjtCCCGEtBR1iUtouhNCCCGEkDaCAjs3yS7Khs1hc3c2CCGNwG6349y5czh37hzsdru7s0OIS1RO2wfqY+cmz+x+BncL7sJX4YtAVSAC1YEIVAUiSBVUtq4OgkpCnVsJaenMZjN69+4NgDqlk5aLymn7QIGdGzDGkGvOhYM5kFGYgYzCDJzLOucyrUaq4YK9kuAvSBWEAHUAv81b4Q2hgCpeCXEngUCAoKAgfp2QlojKaftAgydcaI7BEw7mQK45F2nGNKSZypZUYyq/rrfoazyORChBgCrAqabPKQhUBUAqotFPhBBCSGtVl7iEAjsXWsqoWJPNhHRTulOwl2pM5baZUpFZmAkHc9R4HB+FDx/kBamD+ECwdF0r1dJ/b4QQQkgLRYFdA7WUwK4mxY5iZBZmOgV+aaY0p1rAouKiGo+jkqj4mr7ygV9pDaCvwhcioagZXhEhhBBCKqIJitsJsVCMIDVX8+YKYwz5lnynYC/VlOpUC5hrzoXJZsKt/Fu4lX/L9XkEYvir/J2aecsP9AhQBUAhVjTlSyWkRTObzXj66acBAOvWraNbNZEWicpp+0A1di40S43doY8Bhx3wCAU8wgBdKKANBsTN2x/OXGx2WdNXGvhlmDJQzIprPI6X3Iuv6Stt8i0d6OGv9Ie33Juae0mbRbdqIq0BldPWi2rsWoOT/wFMmRU2CgBNIBfs6ULLBX1hZdukykbNhlwsR6QuEpG6SJf77Q47souyKw3sKP/YZDMh15yLXHMuruRccXkciVACf6U/AlQBZYsywOkx9fUjrZVUKsUXX3zBrxPSElE5bR+oxs6FJq+xYwz4YxmQfxfQJwH5SdzfYnPNz1V6lwV9ujAu8CsfCMo9gGYOjgxWQ1lTb7nBHaW1gNlF2WCouZgpxAr4K/3hr/KvFPSVPlZL1c3wigghhJCWgwZPNJBbBk8wBpiySoK8u2XBHv/3LmAx1HwcqaZcoBfmvK4LBdR+zR742Rw2ZBVmId2Uzi2F6WXrpnRkFGYg15xbq2OpJWqnmj9XQSD19yOEENKWUGDXQC12VGxRfuVgr/RvfhJQmF3zMUQyQBfiupnXIxTQBAGi5m+ht9gtyDBxkzWXD/rKB4EGay0CWwBaqdapps9P6VdpoWZf0pgcDgfi4+MBAB06dIBQSJOGk5aHymnrRYEdgNTUVDz22GMQi8XQarXYuHEjlMra9U9rsYFdTayFgD65co1faQBYkAbUNO+dQARog8pq+MoHfbowLiiUuGckVaGtkA/0MkwZSC8s+VsuCDTZTLU6llwkh6/Slwv0FOWCPpXzY5rcmdQGdUonrQGV09aLAjtwNzsWCAQQCoVYtGgRunXrhilTptTqua02sKuJ3QYYUio0894tq/HTJwMOW83HUfm5buYtfSx333tWYC1wCvQyTBnIKspCRmEGMgszkVmYWas7epTylHnyAaC/0t9p3U/pB1+FLzzlnnRbt3bOZDIhODgYAJCSkkI/mKRFonLaiBwObgBk6e+oMQsYNKfJTkejYgGIRGUT6goEAsTGxroxNy2ESAJ4RnCLKw4HYMyo0MxbYd1m4gqzKRNIOeX6OHKPcoM7QivU/IVxA0CaqBlUI9VAI9UgxjOmyjTmYjOyirL4QK+qxeqwIs+ShzxLHm7k3ajyeGKhGH4Kv0pBX+lS+lgupjmj2iqVSoX8/Hx3Z4OQalE5rYMqK0JKHuuTAbvV+Tl9ZjT6zBX10Spq7BYtWoRNmzbh2rVr2LBhA5544gl+X1ZWFmbNmoUDBw4gNDQUq1atwsiRIwEAhw8fxiuvvAKFQoGdO3dCp9PV6nxttsauoRgDivKqCPpK/hbl1XwcidJ1M2/pY00A4OY7XTDGoLfo+Zq+irV+WYVZdRr0AXB9/6oL/PyUflT7RwghzaGqrkulfwtSa9F1Scj1Sy/97Rr3EaDybpLstrmm2PXr18Pf3x/vvvsuXn31VafA7vHHH4dOp8Nnn32GvXv34tlnn0V8fDw8PT35NJ9++ilEIhFee+21Wp2PArsGsBiraOYt2WZMr/kYQgmgC3bdzOsR5paJnKtis9tc1v6VDwIzCzNhttdiKhuU1f5VFfj5q7gRwRKhpIlfGSGEtFKMAeZ817NLlD6u1WBDaeXfn/KPtUFcS1gzaHNNsU899RQA4P3333fabjQa8dNPPyExMRFKpRKTJk3C8uXLsX37dkydOhUymQwAoNPpYLfbqzy+xWKBxWLhHxsMtRt9SVyQqQG/ztziSrGl5L8kF828+ruAIZXr55eXyC0uuZjImZ/epWSAh7R5+o5IRJJqb+sGcLV/BqvBqabPVRCYa85FsaMYqaZUpJpSqzyeSCBCgCoAIeoQBGuCEaIOQYgmBMHqYIRoQuAp86QRv83MYrHgxRdfBAD8+9//5q89hLQkbaacMgYYMysHa+VnibAW1Hwcp+nBXLQgqfyAVjhyuFXU2JUaMWIE5syZw9fYnT17FmPHjkVmZtkdHObNmwelUomHHnoICxcuhFAohJeXF9atW1flqNjFixdjyZIllbZTjZ0bOOzc6F0+6HNRTV6viZwr/Lflhomca2Jz2JBdmO0c+BU51wSmm9JhsVuqPY5KokKULgpRuih08OjArXtEIVgdTM28TYRGG5LWoNWUU7uN+ye/qto2fTJQw3UQgPPvgEd45QCuBf4OVKXN1dhVxWg0VnqBWq0W+fn5uOeee3Do0KFaHeett97CggUL+McGgwGhoaGNmldSS0IRV+OmCwHCB1fezxhgynbdzFv616IHCnO4Je2c6/O4/E8tDO6cyFkilCBQHYhAdWCVaRzMgZyiHCQbk5FckFz2t2Q9szATJpsJF7Mv4mL2RafnykXc7eO6eHfhl46eHWlKl0YgkUjw8ccf8+uEtEQtppyaDWUDEMoHa6WPazM1FwRcU2iV/bWbr+WmpWnVgZ1ara7UbGowGPj/SGpLJpO13irp9kYgANS+3BLc13Uas971wI7S4M+UxVXTZ17hFlfKT+RcMejzCOWagpupb0V5QoEQvkpf+Cp90duvd6X9FrsFSYYk3NbfRrw+Hrfzb+O2/jYS9Akw2824mnsVV3OvYvPNzQAAsUCMaM9odPHugh4+PdDHvw8itBHUlFtHUqkUb7zxhruzQUi1mqWcOuxAQXpZkKYvF7SVBnC1mXJKKCl3Da4ww0JpX2s3XINbg1Yd2MXExECv1yM9PR0BAQEAgPPnz2P27NluzhlxK7kOCNABAd1c77cVlVxk7lQ9GspuAXLjucUVgRBQB5TVLlZctCGA0qvZa/1kIhmiPaMR7RnttL3YUYwUYwpu5N3AlZwr/JJvyce13Gu4lnsNW25uAQB4yb3Q2683+vj1QR//Pujk1QliYau+VBBCGovVVHVNmz6ppJ90cc3HUXiWXC9LArbSa2dpP+lW2r+tJWgVfexsNhvsdjvGjBmD559/HlOmTIFUKoVQKMSUKVPg5eWFFStWYN++fZg1a1alUbF1RaNi27n6zF/kilhRLtgLdr54aUu2Sdx3X1vGGNJMabiScwWXcy7jbOZZXMy6CKvD+bVpJBoMDByIwUGDMSRoCEI0IW7KccvlcDiQlpYGAAgMDKRbNZEWqcZy6nBwLRql04A41bSVXPuKajHFk0DE1ah5lLvmVQzgZHVrWWvv2tx0J7NmzcLatWudth04cAAjRoxAVlYWZs6ciYMHDyIkJASrVq3CqFGjGnQ+CuxItRwObqh8+SYGfUrZY0MKN9FzbSh9XAR95R6r/Zv1v1ar3YorOVdwOuM0zmaexZnMMyioMLosTBOGwUGDMTRoKAYGDoRS4v4JOd2t1XRKJ+2aKS8LfWMCEKIVYOeGLyEryqrcXFqbf1pl2gq1bOWDtpYxF2lb0+YCu+ZGgR1psGILF+DxQV+yc+BXehePmgglgDaw7KKpDS73329JrZ+8dhNv14fdYceVnCs4mnoUR1OP4kLWBRSzsmYWmUiGwYGDMSJ0BIaHDoePwqfJ8tKSmUwmeHh4AADy8/MpsCPNz1ZUMpK05BpjSOGuPYaUsu3m/JqPIxByfYidatpCyg1OCGnSaw5xjQK7BqLAjjS50gk0K9b2lV6U9cncxZhVPf8iT6Z1Dvq0wVwwqA3iZkXXBjXa/XuNViPi0uNwJPUI/kj+w2m+PQEE6OHbAyNCR+D+0PsRqYukQRiENIZiC3c94IO1kutD6bo+pXZNpAA3I4AuuEJTabkat2acdJfUHgV2DUSBHWkR7MXcnTpcBn6l/V1qcQs3gLuYlwZ72mDuP/LSdW0g97eO9/BljOFG3g0cTDqIA0kHcDnnstP+CG0ExkSMwZjwMejo2ZGCPEJcsZm577khzfmfOn49hev3VhsSZck/eMFl/Xi1Qc7rVNvWKlFg10AU2JFWw2qq3L/PkFq2FKRy07/UhkhaEvAFuwgCS34U1P6AyPUI2QxTBg4lH8KBpAM4kXYCNoeN3xehjcDo8NEYGzGWgjzSPjjs3JybBanc9B+Gkr/84zRuvrba1rSJ5WXfQ76GvqTmrXS9FU24S+qGArsGosCOtCkWI/cDUj7YM1RYTJk1HwcomebFn+scrfZ3XtcEcFPAaPxhlCpxKO0Y9ibuxeGUw04jbcO14RgTPgZjIsYg1jO2TQR5FouFn+R8+fLlNC9mW8YYYDFUEaylct+1gnRuqU1XCoAL2jQBFWrZgsuaRhtp+iQqp60XBXYNRIEdaXeKrSXNQamVa/wMqSW1C7Wcn6qUwgvQBMCo8sUhhQR7WQEOWzJhLfdjF6YOwZjIcRgXMa5V1+TRqNg2wFbE3X/UmMmNajdmcOumituygOKi2h1TIOTmY9MElPR5DSjp9xpYtq4J4OZ0a4ayT+W09Wo3txQjhDQSsbTs7hpVKZ3jqnQ6l4L0kh+8dKAgo9zfDMBh45qYinKhBvBgyWISCHBIqcBelRKHFXLcNSbjvxf/i/9e/C8imAhjRV4YqwpHjDYCUPkAKl+u75/Kl3us9OHy2sJIJBIsWrSIXyctgMPBDVAqzOWmJyrM4ZpGTVllgZopqyyAsxhqPKQTuY7rplC6aAMrP1b5Vdl1wR2onLYPVGPnAtXYEdIADgc3qMOYXhIAZrgI/tJhMmbgkIRhr0qJPxQKWIVlNRYdrFaMNRVirKkQUbYKtYQyXVnQp/IpC/iUXlzNR+ki9yhZ9wDE1OTUqpU2f5r1XNkqzOECNlNJwMYHbjnlHufWvim0lEhW0sXAr9xfv3KP/blyp/Zrt/chJe5BTbENRIEdIc3EYgSMGTDq7+JgymHsyTqFIwWJsKHsBuAxDiHGFlowLj8H4bZaTJ7qikTFBXh84OdRLvDz5KaDkWkBqRqQaSovYjl1Sm8IezF3f2arifvMrUYuSDPnA0X51ayXPtbX4qbwVZBpuaBf6cPV/qp9uZo0p8CtZF2mpc+ZtEgU2DUQBXaEuI/BasDBpIPYnbAbx1KPOU2I3MkjGmP9B2KsrhNCmYBrSjPlcH+L8pyX0uAAjXCJE4pLgj5tuYBPzU0vIVGAiRWwOoRgYjlkag8IJArudnESBRcU8usKQCLnJp4WSbjjiiRVP27sIMPh4PpJOi32cus2bvqN4vKLhet/Vmzh+pZVfGwzc4Ga1VgWtFlNgKWgbL3Y3Dj5F8m4oLy0hlbpXVJj6122reJjqq3lMcag13Oj5HU6Xavt09oeUWDXQBTYEdIy6C16/Hb3N+xJ3IPjacdhL9e01tW7K8ZGjMXYiLEIUge5PoDDAVj0XIBXKejLK9tuMXCBiKWAC05K1yvcTq3ZCURlAR9KfoT5H2OB0x/nbQKu+ZKVC+TsNjRKkNsQQgkXEEvVXB81ua6k5tSjwnrJY75mteSxG++t3BbQ4InWiwK7BqLAjpCWJ8+ch/1392NP4h6cTD8JR7mmuR4+PTAmYgzGRoxFgCqg8U7qcHC3fuODvgqLrRCwFcFaaMCy/1sKhRh46YVnIUExV6tlK+Jqq2yFJTVhJTVcDhsXaJUGXA5b/ZsaG4NQXLaI5SW1jCV/xTKuplEsK6l1dPG4tAlbqipZLwnepGpum0zDrbfAgS/tCQV2rRcFdg1EgR0hLVtOUQ72392P3Ym7cSr9FFi5mqhevr0wLnIcRoePhp/Sr1nywxhDcTHXZCwWi+vXxMU3k7oI+kqnmal4uXZ6zCpvE4qcgzahmNtWWgsoFHNTclCTXLvQKOWUuAUFdg1EgR0hrUd2UTb23dmH3Qm7cTbzLB/kCSBAH/8+GBsxFqPDR8NH4ePmnBJCSP1QYNdAFNgR0jplmDKw784+7Encg3NZ5/jtQoEQ/fz7YWzEWIwKHwUvuZf7MkkIIXVEgV0DUWBHSOuXbkrHnsQ92Ju4FxeyL/DbRQIR+gf0x7iIcRgZNhIeco8Gn8tqtWLhwoUAgPfffx9SKfUlIy0PldPWiwK7BqLAjpC2JcWYgr2Je7E7cTeu5Fzht4sFYgwMGoix4WMxInQEPOWe9To+dUonrQGV09aLbilGCCHlBKuD8Uy3Z/BMt2eQZEjCnjt7sCdxD67lXsORlCM4knIEQoEQffz64P6w+3F/2P0IVgfX+vgSiQSvv/46v05IS0TltH2gGjsXqMaOkPYhUZ+IPYl78OvdX3Et95rTvk5enXB/KBfkdfTsSCMICSFuQ02xDUSBHSHtT4oxBQfuHsBvSb/hdMZpp3nygtXBuC/0Ptwfdj96+/WGWEiNHYSQ5kOBXQNRYEdI+5ZnzsOh5EP47e5vOJp6FBa7hd/nKfPE8NDhuC/0PgwKHASlREnzg5FWgcpp60WBXQNRYEcIKVVoK8SxtGP47e5vOJh0EAargd8nFUrRP7A/BvoMxIujX4Qt24aCggK+gzohLQkNnmi9KLBrIArsCCGuFDuKcSbjDH5L+g0H7h5AqinVab85xYwZ98zAuI7j0MuvFyRC6qBOWg4K7FovCuwaiAI7QkhNGGOIz4/H7ym/48CdAzibcRYCUVnTlkaqwdCgobg35F7cE3xPvadSIaSxMMag1+sBADqdjppiWxEK7BqIAjtCSF2YTCZo/bTQdNfg+Q+fx/GM48i35PP7BRCgh28P3BN8D4YGDUUX7y4QCUXuyzAhpFWhwK6BKLAjhNRFxSYuuUKOi9kX8Xvy7/g9+Xdcz7vulF4n02FQ4CAMDRqKwUGDEaAKcEe2CSGtBAV2DUSBHSGkLvLy8uDlxd1/Njc3F56ezs2u6aZ0/J78O46lHsOJtBMosBU47e+g64DBQYMxNHgo+vr3hUKsaLa8k/bDarXigw8+AAC8/fbbdEuxVoQCuwaiwI4QUheZmZnw9/cHAGRkZMDPz6/KtMWOYlzKvoQjqUdwNPUoLmVfcpozTyqUoo9/HwwJGoIhQUMQ4xkDoUDY5K+BtH00eKL1avLArqioCO+99x42bdqE3NxcGAwG7NmzB1evXsWrr75a33y3GBTYEULqIjc3F97e3gCAnJwcvvauNvQWPU6kncDR1KM4knoE6aZ0p/2eMk/0D+iPAQEDMCBwACK0EdTpndSLxWLBggULAADLly+HTCZzc45IbTV5YPfMM8/AZrPhzTffxLBhw5CXl4e0tDTcd999uHbtWs0HaAZJSUmYOHEirly5AqPRCLG49jPFU2BHCKmLxqoJYYwhwZCAY6nHcCTlCE5lnEJRcZFTGj+FHzd3XsBADAgcUKd72hLibna7HTabzd3ZaNEkEglEIufBVU0e2Pn5+SEpKQkymQxeXl7Izc0FAP6kLYHZbIbZbMakSZPw66+/UmBHCGkyTdXEZbPbcCnnEk6mncTJ9JM4l3kOVofVKU2wOpivzRsQMAB+yqqbgQlxJ6PRiOTkZFAPsOoJBAKEhIQ4TXRel7ikXjc89PDwQFZWFkJCQvhtCQkJCAoKqs/hmoRcLodcLnd3NgghpN4kIgl6+/VGb7/eeLHni7DYLTifeR4n0k8gLj0OF7MuIsWYgq23tmLrra0AgHBtOHr79UYfvz7o698XoZpQarolbme325GcnAylUglfX18qk1VgjCErKwvJycmIiYmpVHNXG/UK7F555RU89NBDWLhwIex2O3bs2IGlS5c2Wf+6RYsWYdOmTbh27Ro2bNiAJ554gt+XlZWFWbNm4cCBAwgNDcWqVaswcuTIJskHIYS4YjKZnNabqlO6TCTjauYCBwDgbnd2NvMsTqSfwMm0k7iaexV3DHdwx3AH225tAwD4Kny5QM+fC/RiPGJoDr12ymQywcPDAwCQn5/frIMnbDYbGGPw9fWFQkGjvqvj6+uLxMRE2Gy25gvsXnrpJfj5+eHrr79GSEgIPv/8c7z22muYOnVqfQ5Xo5iYGHz22Wd49913XeYlKCgI2dnZ2Lt3L6ZMmYL4+PhK0w0QQkhbo5QoMTR4KIYGDwUAGKwGnMs8hzMZZ3Am8wwuZV9CVlEW9t7Zi7139gIA1BI1evr1RF+/vujj3wfdfLpBJqJO9O1FcXGxW89PNXU1a+h71KqmOxkxYgTmzJnD19gZjUZ4e3sjMTERgYGBAIB7770Xs2fPxowZM/jn1NTHzmKxwGKx8I8NBgNCQ0Opjx0hpFYKCgr4a4XBYIBGo3FzjjgWuwUXsy7ibOZZnM48jXOZ52CymZzSSIQSdPbujB4+PdDTtyd6+vZEgCqAfoDbIIfDgbS0NABAYGAghMLmm0bHbDYjISEBkZGRbusmJZFI0LVrV1itVgwZMgRfffVVs74HteXqvWqSPnYff/xxrdL95S9/qe0hG+zmzZvQ6XR8UAcAPXv2xOXLl2E2mzFhwgScP38eY8eOxeLFizFs2DCXx/nwww+xZMmS5so2IaSNKf/j0JJ+KGQiGfoF9EO/gH54Hs/D7rDjRt4NnMk8g9MZp3Em4wxyzDm4kHUBF7IuYP3V9QC45tsevlyg18O3B7p4d6FJk9sAoVCI4OD2O4ra29sb586dg91ux8iRI7F161Y8+uij7s5Wo6t1YHf16lV+vbCwEFu3bsXAgQMRGhqKpKQknDx5Eo888kiTZLIqRqOxUuSq1WqRn58PuVyOX3/9tVbHeeutt/i5fYCyGjtCCGlLREIROnt3RmfvzpjeeToYY0guSMb57PO4kHUB57PO40buDWQVZWH/3f3Yf3c/9zyBCB09O/KBXnef7gjThtHEyaRVEolEGDRoEOLj492dlSZR68Bu9erV/Pqjjz6KTZs2YeLEify2n3/+Gf/73/8aN3c1UKvVMBgMTtsMBoPTEOHakMlkNFEjIaTerFar03prmdFfIBAgVBuKUG0oJkRNAAAUFRfhas5VnM8qC/ayirJwNfcqruZexffXvwfA9dXr4t0FXb27oosP9zdEHUJNuC2Y1WrFZ599BoAbBOmuW4oxxlBkszf6cRUSUa3KX1FREQ4cOOCy335bUK/BE7/++it++OEHp20PPPAAnn766UbJVG3FxMRAr9cjPT0dAQHcTbTPnz+P2bNnN2s+CCHtW/kJV1v75KsKsQJ9/Pugj38fANyPcLop3alW73rudRhtRpxM5+bXK6WVatHVuyu6+nTl/np3pf56LYjNZuO7S82dO9dtgV2RzY4u7+1p9ONe+dtYKKVVhzU5OTno1asXAC5mmTBhQqPnoSWoV2DXrVs3LF26FO+88w7EYjGKi4vxwQcfoGvXro2dPwBcYbTb7XA4HLDZbDCbzZBKpVCr1Xj44YexaNEirFixAvv27cOlS5fw0EMPNUk+CCHElfKDs+oyGXprIBAIEKgORKA6EOMixgEAbA4bbuffxuWcy7icfRlXcq7get51GKwGHEs7hmNpx/jne8m90MW7Czp7dUasVyw6eXVCqCaUmnHdQCwWY+bMmfx6e1Pax66tq9eo2Nu3b2PatGm4fPky/Pz8kJmZiS5duuDbb79FdHR0o2dy1qxZWLt2rdO2AwcOYMSIEcjKysLMmTNx8OBBhISEYNWqVRg1alSDzkd3niCE1AXdXJ27S8bN/JtOwd7NvJsoZpWn11CIFejo2RGdvDoh1isWsZ6xiPGMoQEabVj5kZ4ymcwtTbEBAQFIT0+vcn9L0dBRsQ2a7uTu3btIS0tDYGAgwsLC6nuYFocCO0JIXVBg55rFbsGN3Bu4nHMZ13Kv4XruddzMvwmL3VIprVAgRLg2HLGesXzNXievTvBR+Lgh56SxtYTpTtpLYFevutjMzEwA3G27IiMjnbb5+dF9CgkhhHDTrXT37Y7uvt35bcWOYtw13MW13Gu4lncNN3Jv4GruVeSac5GgT0CCPgG7E3fz6T1lnoj2jEa0B7fEeMagg0cHaKX0Tzepm9YQ1DWGegV2AQFcZ9jSyr7yVZ92e+NXrxJCSEvWXLcUawvEQjGiPKIQ5RGFB/AAvz27KBvXc6/zNXvX864j0ZCIPEse4tLjEJce53QcP6UfYjxiuIDPMxoxHjGI1EVCKVE290tqNUwmEz+PXUpKCpXTNqpegZ3D4XB6nJ6ejqVLl2LgwIGNkilCCCHti4/CBz7BPvzt0QBu6pXb+tuIz4/HrbxbuJl/E7fybyHdlI7MwkxkFmbiSOoRPr0AAgSrg/lAL8ojCpG6SERqKeArpdfr3Z0F0sQa7ZZiVqsVUVFRSE5ObozDuRX1sSOE1EVLvaVYW1VgLeCCvfxb3FIS9OWac6t8ToAqAJHaSC7Q00UiSscFfT4Kn3YzHYvD4eAn5e3QoUO7u6VYa+GWPnaunDhxwu03FyaEEHdoqbcUa6s0Ug16+fVCL79eTttzzbmIz4/HzTyuZu+2/jYS9AnINeci3ZSOdFO601QsADfRcmmwV34J1YRCIpQ046tqekKhEDExMe7OBmli9QrsOnfu7PQfTmFhIXJycvgZrUnrwxgDYwArWQdK1wGGkscM/N/y20qfw1C2H1WkcTDur71k3eFgcJSs2x2MT2Pnt7ve53QMxkqOU3kfY6zkWGXnspek57eXpCnbDtgZ459bfnv5tPx+h6tjOB+b319u3encVWx3lByz/DmcXwcgFgkgl4ggEwtLFhHkkpK/UhG0cjE8lBLoFM6Lr0YGf60cfho5pGIKRkjr5yX3gleAF/oH9Hfarrfo+YEZpctt/W0kG5NhtBlxMfsiLmZfdHqOWCBGqDYU4dpwRGgjEKYNQ7gmHGHaMPgp/WgePtJi1Suw+/LLL50eq1QqdOzYkZot6+DBz/9AVoGFD544ZcEVUBYsMeYcbKGqNBUCqdJEDFUHbaT1s9qBQmvDBi35qKXw08gRoJMj1FOBCB8VInxUiPRWIdhTAYmIfsSq05buPNEW6WQ6lzV8VrsVdw13kWAoC/ZKA7+i4iJ+vSK5SM4FetpwhGlK/pY89pZ7t9imXZvNhq+++goA8MILL0AiaVs1koRTr8AuLi4Or7/+eqXty5cvx4IFCxqcqfYgq8CCzILKczm1ByKhAEIBN5paJODWhUIBhCXrIqEAgtJ1Abde+hyhQFCStmRdIIBQWJau/PNFJfv4dBX2iYRlxxKVHJf/K6y8TShwvV1Usi4oOb6IT+t8PKGgbB+/v9z28scrfX1Ox6pw3NLzFdsZLMV2mG0OWIrtsBQ7YLZxfwutduiLbNAX2WAosiG/0Ap9kQ35RTauDBossNodyDZakW204kqaodLnJRYKEOKpQLSfBl0CNegUqEXnQC3CvZQQClvmD1hzq3ivWNI6SEVSbioVT+eJ9RljyCjMwG39bdwx3MFdw13ub8FdJBckw2w340beDdzIu1HpmCqJyinYK1/b5yH3aKZX5prVasXLL78MgJv4nwK7tqlegye0Wi0Mhso/AN7e3sjJyWmUjLlTcwyeuJ5egGKHAwJwP9Cl/+Dxj0vScdsrbhNAgMrPQfltVaQpOVwV5yp7jqAsUY1pyp+7/DYByqbC4Z/TQv+Tba8YY8g1WZFuMCPTYEGa3ow7uSYkZpuQmF2IxBwTLMUOl89VSESIDdCgc6AWvUJ16BXqiWg/NUTtMNjLycmBjw83kW52dja8vb3dnCPSVGwOG1KNqU4BX2nQl2pMRVmnlMq0Ui3CNGEI1YQiRBPi9Lc5mnfNZjN/T/d169Y16yCGljB4IikpCfPmzcPFixchk8nQu3dvfPHFF/D09KzxuYsXL0ZAQADmzJnT5Pls1sETGzduBAAUFxdj06ZNKB8TJiYmwsvLq675b7diA2jUHHE/gUAAb7UM3moZugZV3u9wMGQUmJGQZcL1jAJcTTPgWnoBrqcXoMhmx7mkfJxLysd3JfeBV8vE6B6sQ68wD/QK9UDvUA/4adv+CLjyP1Q04q9tkwglCNeGI1wbXmmf1W5FckEyEg2JXNBXUBb8ZRRmwGA14FLOJVzKuVTpuVKhFMGaYIRqQvklRM0FfcGaYMhEsgbnXS6XY9OmTQ0+ToMxBtgKG/+4EmVZTUOlUzJMnjwZ8+fPx7Zt2wAA27ZtQ25ubq0Cu9akToHdv/71LwBcde6qVav47QKBAH5+flizZk2jZo4Q4l5CoQCBOgUCdQoMiS67tZPdwZCQbcK1dAMupRhwLikPF5L1MFqKcex2Do7dLqu5D/dWYkCEF/pHemFgpBfCvJRUc0vaJKlIyk++XFFRcRHuGrim3KSCJCQVJCHZyK2nGdNgdVir7NMngAB+Sj++dq9i4KeT6VrXd8pWCHzg4j/Jhno7FZC6nnR5//79UKvVmDFjBr9t0qRJLtP+8MMPWLJkCSQSCTp27MgHw6dPn8Y999yDtLQ0rFy5EuPGjUN8fDxmzpyJwsJCyOVyfPPNN+jUqRPWrFmDXbt2IT09Henp6fjzn/+MF198EQDw/vvvY9u2bbBYLJg3bx6ef/75Rn0b6hTYHThwAACwdOlSvPPOO42aEUJI6yESChDtp0a0nxoTenAXaLuD4WZmAc7dzedr8q5nFOBOTiHu5BRi02lujks/jQwDIr34paOfhvrqkTZPIVYg1ou7D25FxY5ipJnS+KCP/1sS+JlsJmQUZiCjMAOnM05Xer5GokGIJsSpeTdYHYwQdQgCVYGQiKgv3ZUrV9C7d+9apV26dCl27tyJyMhIpwmd79y5g0OHDuHcuXOYN28exo0bh8DAQOzfvx8ymQwnT57E22+/jS1btgAATp06hfPnz4MxhgEDBuChhx7ChQsXkJWVhbi4OFgsFtxzzz2YMGECAgMDG+211jqwy87O5vuQvPDCC/y9YStqS/eKNZlM0Gg0/H9CVqsVNpsNYrEYMpnMKR0AKBQKfg4rm80Gq9UKkUjk1DRTl7SFhYVgjEEul0MkEgHgmsEtFguEQiEUCkW90hYVFcHhcEAmk0Es5oqA3W6H2WyuU1qBQAClsmw2d7PZDLvdDqlUynfKrUtah8OBoqIiAHC61Y3FYkFxcTEkEgmkUmmd0zLGUFjIVfsrlcpKn2dd0tbms2+McuLq82yMclL6eTa0nFT8PEvTRvso0SlAiycGhMFutyMr34hzyQacTzMhLiEX55PzkVlgwY4LadhxIQ0AoFNIMDDSC/3CNBgY4Yke4b78+97QclL+82xoOanq81QqlfzzACArK4uuEXSNqPbzrJhWLBQjVBMKL6EXeuh6VEqbacxEWmEaMq2ZfG3fHf0dpBhTkG3ORoGtAFdzr+Jq7lVUJBQI4Sv3RZAqCIHKQPz4zY9w5Dmw9vO1XN8+lR9UyrL3p6muEWazGQ6Hg+vCJVECb6dyU1iV3Mmq9Fylnx1jjBsQV/I+1Cptyd1FXKUt7Tpmt9trPO6QIUPwwgsvYNq0aZg8eTJ/rgce4G6B16NHD9y9excAV57mzp2LixcvQiQSwWw28+nHjRsHpVIJoVCIkSNHIi4uDocOHcL27dtx8OBBANydQOLj4/nYqfw8mDabDXa73en11gqrJbVaza8LBAImFAqZQCBwWoRCYW0P16Lp9fqSmdjAMjMz+e1Lly5lANjs2bOd0iuVSgaAJSQk8Nv+8Y9/MABs2rRpTml9fHwYAHbp0iV+21dffcUAsIkTJzqlDQ8PZwDYyZMn+W3r169nANioUaOc0nbp0oUBYAcOHOC3bd26lQFgQ4YMcUrbr18/BoDt2LGD37Z3714GgPXs2dMp7fDhwxkAtnHjRn7b4cOHGQAWHR3tlPaBBx5gANjq1av5bWfPnmUAWFBQkFPaxx57jAFgX3zxBb/txo0bDADT6XROaWfOnMkAsI8//pjflpyczAAwsVjslHbu3LkMAFu0aBG/LS8vj/88rVYrv/31119nANjrr7/Ob7NarXzavLw8fvuiRYsYADZ37lyn84nFYgaAJScn89s+/vhjBoDNnDnTKa1Op2MA2I0bN/htX3zxBQPAHnvsMae0QUFBDAA7e/Ysv2316tUMAHvggQec0kZHRzMA7PDhw/y2jRs3MgBs+PDhTml79uzJALC9e/fy23bs2MEAsH79+jmlHTJkCAPAtm7dym87cOAAA8C6dOnilHbUqFEMAFu/fj2/7eTJkwwACw8P57cVWYvZyCdeZLohT7ARizezzu/+wsL/usNp6fv3fWzehjPsuxN32CNPP88AsH/84x/8MRISEhgAplQqnfIwe/ZsBoAtXbqU35aZmcl/nuW98sorDAB7++23+W1Go5FPazQa+e1vv/02A8BeeeUVp2OUv0ZkZGTwj+kaQdeI8pr6GnH89HF2M/cm++3Ob+zl1S+zwKcCWb8P+rGHtz7M+q3rx7qt6Vb98nU39uCWB9kLe19gS44uYd2f6860/bXsvzv+y3KLcpnD4WiUa8STTz7JfvnlF5aamspvMxqNLC4ujp0/f94p7c2bN1lcXJzT729hYSGLi4tzuiYyxlh8fDyLi4tj6enp/Daz2czi4uLY6dOn+W179+5lgwcPZnFxcU55sFqtLC4ujsXFxfHbHA4H27x5M3vqqadYZGQks9lsbNGiRWzlypV8Wn9/f8YYY++++y5bsGABO3nyJDtz5gx/vVu9ejV77LHHWFxcHLNarexPf/oT27ZtG3vhhRfY3//+d6drAWOMnT59msXFxTGz2cyKiorYlStX2JdffslfI0rjEr1ez2pS6yE4BQUF/LrD4YDdbofD4XBa7PaGzaVFCGnb5BIR1EXp0B/9HtOCcnB+0RhsmTsEM3t5oCjxLFixFdlGC34+n4o3t1zE6aCJCJ7zNfbq/bD1bDIyDOaaT+IGNGCCuItMKEO0ZzTuC7sPfdEXaevT4HfYDz9N+gknp5/EgccPwLLagqQvk/CQ50MY7jUcXdRd4CHwALMzQATcMdzB0dSj2HRjE9gwhrCXwrAiewXu/eFeDNowCCvyViBsfhhs99iw/sp6HLh7ADfyboCJW89kqKNGjYLBYMCuXbv4bdu3b8ft27crpU1ISEC/fv0wb9482Gw2p/inooKCAvj5+UEgEODHH3902nf06FGYTCYUFBTgt99+Q//+/TFs2DD89NNPsFi46c6uX7/uVMvXGBrtXrFtSemw4tTUVAQEBFBTLDWzUFNsLZti61tOSj9PS7Ed17NtOBafjaPxOTiXlI9ih/MlKspHhQEROgyM8MSIzkHwVElr9dk3ZVNs6edgtVohkUggkUjoGkHXiCo/z5ZyjTAVmZBtyUaOLQcpxhQkG5NxN/8uUk2pSCtMQ1ZRFmriIfVAsCYYwepgBGuCEaQMgq/UF8HqYET5REEq4t63/Px8pKSkICoqii8/rLGbYmtIm5iYiHnz5uHq1auQyWTo06cPPv/8c376kNK0kydPxq1btwAAU6dOxTvvvIPFixfD398fL7zwAgAgODgY6enpuH79Oh577DGo1WqMGTMGa9euRWJiItasWYN9+/YhMzMTd+/exYIFC/Diiy/C4XDg448/xrfffgvGGPz8/LB9+3b+8xIKhbBYLEhISEBISAiEQiFEIhGsVmutpzupV2CXlJSEv/3tbzh//jyMRqPTvitXrtT1cC1Oc8xjRwipmclSjLjEXByLz8HR+BxcStVXumNKl0AthnTwxtBoH/SP9IJa1mi3wCakXbPYLUg1piLFmIKUghQ++Esxcut6i77a5wsggK+SC/JiNbEYpRmFiIgIqBQqSEQSSISSNntrtjVr1uDatWv46KOP6vzcZp3HrtTUqVMRExODJUuWOP2HRQghjUklE2NErB9GxHIdi/WFNhxPyCkJ9LJxI8OIK2kGXEkz4L+HEyAWCtAz1ANDO3hjcAcf9An3gExcx47H9WCz2fDtt98CAKZPn04z+pMWqa7lVCaSIVIXiUhdpMv9RquxLNgrCfzKL0XFRcgszERmYSbS89MxKHoQMgszIbSVBXNioRgSkQRSoRQSoaRsvY0Hfk2p3neeyM/Pdxq90ZZQjR0hrUNmgZkL8m7l4Eh8NpLzipz2y8RC9I/wwpBobwzp4IPuwbomuTOGyWSCWq0GABiNRqemP0JaiuYsp4wx5JpzuRo/UwpyDDno4OgA3xBfMBGDzWGDg7m+q015VQZ+JesNCfz27NmDv/71r07bxo0bV69atsbklhq7cePG4fjx4xgyZEh9nk4IIY3CTyPHxF7BmNgrGACQlFuIo/HZOHKLa7rNNlpw+FY2Dt/KBnAdGrkYAyO9MbQk0Ovor26UiV1FIhE/FUKdpyYgpJk0ZzkVCATwVnjDW+GN7r7d+WAlWB0MuVwOxhjszA6b3Qarwwqbwwarnftrs9v4wK/YUYxiRzGKUOTyPA0J/MaOHYuxY8c21VvgNvWqsZs5cya2bt2KMWPGVJq3rvwdKVorqrEjpPVjjOFmphFHb2XjSHwOjt/OQYG52CmNj1qKwR18MLQDF+iFeVPXEkKaQl3vFesq8Ku4Xt8aP4mwrLm3JTb1uqXGLioqCn/+85/r81RCCGkWAoEAHf016OivwayhkSi2O3A51YAj8dk4Fp+DuMRcZBut2H4+FdvPpwIAQjwVGNrBB0OivTG4gzf8NDSNCSHuIBAIIBaIIRaKoYCi0v6qAj+bo+SxvQ41fi769rXkwK8mNN2JC1RjR0jbZym24+zdfBy9VfXUKjF+agyN9sHgDt4YFOUNnYIGRRBSH3WtsWsoPvArX9PnIvCriUgochnwNWXg55Yau48//tjldplMhpCQEIwcORIeHh71OTQhhDQLmViEQVFcwLYAgLFkapXSQO9KmgE3M424mWnEmqOJEAqAbsE6DOnggyEdvNE/wgsKKddPqbCwED179gQAnD9/nmYLIC1SeyqnTjV+4mpq/GoI/OwOO4ocRSgqdl3jVz7wC1QFQix0/3RL9crBmTNnsHXrVgwcOBAhISFITk7GiRMn8NBDDyE1NRXPPfcctmzZgvvvv7+x80sIIU1CLRPjvlg/3FcytUqeyYrjt7nRtkfjc3A7y4QLyXpcSNbjy0PxkIgE6B3miaEdfNA7WIlbtxMAhx3UCEJaKsYYP/FueyynAQEBSE9PB1D3wK9bx244dOYQRBJRlYFfsDq4uV+SS/UK7IqLi7F582ZMmDCB37Zz506sWbMGR48exbfffosFCxbg3LlzjZVPQghpVp4qKcZ3D8T47oEAgDR9EY7F55SMuM1Gmt6Mkwm5OJmQCwDo+OZP6OQtxrq4NNwT44sugVoIm2BqFULqSy6X4/Dhw/y6uzDGqqwBawiFWNEoo9yByoGfUCBEoCqQf98qBn7FjuIW0x+vXn3sdDodcnNznYZLFxcXw9vbG3q9Hg6HAx4eHjAYDI2a2eZCfewIIdVhjCExh5ta5WhJoJdXaHNK46GUYHCUN4Z08MaQaB9E+aga7UeHkNamfL8xh8iBgRsGNvo5Tkw7AaWk6ubl8jV2VbHb7Zg1axbOnDkDoVCIt956C9OmTUNERARmzpyJbdu2QaPRYMeOHfDw8MCXX36Jr7/+GhaLBQMGDMBXX30FoVCIESNGoG/fvti/fz8kEgm+++47REdHIyMjAy+88AJSUlKgUCjw9ddfo2PHjk55cEsfuy5duuCDDz7AW2+9BbFYDLvdjo8++gidO3cGwN1yjPrYEULaKoFAgEgfFSJ9VJg+MBwOB8O19AIu0IvPwYnbOcgvtOGXS+n45RL3QxKglZcMwvDC4CgfhHo1Xu0CIaRxnDt3Dnfv3sXly5cBAHp92W3ToqOjcf78ecybNw8bN27ECy+8gMcffxxz5swBAMydOxc7duzAww8/DIC7Tpw7dw47duzAa6+9hu3bt+O1117DokWL0KdPH5w4cQILFizAjh07GvU11CuwW7t2LaZNm4ZPPvkEfn5+yMzMRGxsLDZs2AAAyMjIwIoVKxozn3WWlJSEiRMn4sqVKzAajfzNqQkhpLE5HHZcPrIHOgBfPTUZTCDEhWQ9PxDj9J08pBvM2Ho2BVvPpgAAgj0UGBjlhUFR3hgc5Y1Qr7bbkZ20DMXFxdi6dSsA7kb37vpdVIgVODHtRJMct6GioqKQlJSEV199FZMmTcKIESP4faUBW+/evXH79m0A3CCUd955BwaDAfn5+QgNDeXTTZ06FQAwYcIEPvj77bffcOXKFf6YTTFRdL0+1Y4dO+LUqVNITExERkYGAgICEB4ezu8fMGAABgwY0GiZrA9fX1/89ttvmDRpklvzQQhp+ywWCx5//HEAZbdq6hvuib7hnpg3MgZmmx2n7+ThWMlEyeeS8pGSX4QtZ1Kw5UxZoFc6rcrgDt4I9mj4jxQh5VUsp+4K7AQCQbVNpu7k6emJ8+fPY9euXVi8eDFGjBiBxYsXA+Bm/gAAoVCI4mJusvPZs2dj9+7diImJwSeffAKTyVTt8QUCAd/M21Qa9Kn6+flBJBKBMYa7d+8CAMLCwholYw0ll8vd2jmUENJ+CIVCDB8+nF+vSC4RYWi0D4ZG+wAACq3FToHehWQ9UvKL8OPpZPx4OhkAEOqlwOCoskAvUEeBHmmYmsopAbKzsyGVSjF16lT4+Pjgn//8Z7XpCwsL4evrC7PZjI0bN+LBBx/k9/3www/o378/duzYgd69ewMAhg0bhv/+97944YUX4HA4cPnyZXTv3r1RX0O9AruLFy9ixowZuHDhAgDw/USkUikKCwvrlZFFixZh06ZNuHbtGjZs2IAnnniC35eVlYVZs2bhwIEDCA0NxapVqzBy5Mh6nYcQQhqbQqHAwYMHa51eKRVjWIwvhsX4AgBMlmKcupOH47dzcCw+BxdT9EjKLUJSbjI2nuICvXBvJR/oDYryRoCO/nEldVPXctrWZGVlISQkhH/81Vdf8ffOLZWcnIxnnnkGjDFIJBKsXLmy2mMuXLgQffv2RXh4OB+8lZJKpRg0aBDsdju+++47AMA///lPzJkzBytXroTNZsOMGTNaRmA3Z84cTJw4EceOHUNgYCDS0tLw3nvvoUOHDvXOSExMDD777DO8++67lfa99NJLCAoKQnZ2Nvbu3YspU6YgPj4eFovFKQAEALVa3egdEQkhpCmpZGIM7+iL4R25QK90suTjt3NwvCTQu5NTiDs5hfg+LgkAEOmjwqByffT8tBToEVIdu91eY5pevXrh7NmzlbYnJiby67NmzeLXX375Zbz88ssujzVjxgx88MEHTtv8/f35fo5NpV6B3eXLl/HHH3/wVblyuRxLly5FVFQUXnzxxXpl5KmnngIAvP/++07bjUYjfvrpJyQmJkKpVGLSpElYvnw5tm/fjhkzZjTKfx8WiwUWi4V/3FqnaSGEtA0VJ0suMNtwKjEPx25zTbeXUvRIyDYhIduE705ygV6Ur4oP8gZGedF9bglpp+oV2Hl4eCA/Px9eXl4IDg7G+fPn4eXlBaPR2Nj5w82bN6HT6RAYGMhv69mzJz8UuSpmsxkTJkzA+fPnMXbsWCxevBjDhg1zmfbDDz/EkiVLGjXfhJD2o6ioCIMHDwYAHDt2DApF4/aH08gluK+TH+7rxAV6+iIbTiXmcn30EnJwOdWA21km3M4yYcMJrr9ztJ+an1plYJQXfNSyRs0TaX2aupy2Ni+99BKOHDnitG3VqlUYMmRIg4/tzibvegV2s2fPxqFDhzB58mS88sorGDZsGIRCIZ5//vnGzh+MRmOlyfi0Wi3y8/OrfZ5cLsevv/5aq3O89dZbWLBgAf/YYDAgNDS0znklhLRPDocD58+f59ebmk4hwcjO/hjZ2R8AoC+04WRJ0+2x+BxcTTfgVqYRtzKNWH+cC/Q6+qv5/nkDI73gTYFeu9Pc5dSVlnQrs5r6z7lLQ9+jegV277zzDr/+/PPPY8yYMTAajejatWuDMuOKWq2u1DRqMBigVqsb7RwymYwfxkwIIXUll8uxd+9efr256ZQSjO7ij9FduEAvv9CKEwllgd619ALcyDDiRoYR/zt2BwAQ66/h++gNoECvXXBnOZVIJBAIBMjKyoKvry9Nzl0FxhiysrIgEAggkUjqdYw6BXZdunSpMU35ifcaQ0xMDPR6PdLT0xEQEACAmxBw9uzZjXoeQgipL5FIhNGjR7s7GzwPpRRjuwZgbFfumplnsuJEQg6O3+aCvWvpBbiewS1rSwK9GD+uRm9glBcGRnrDV0OBXlvjznIqEokQEhKC5ORkp4EIpDKBQICQkJB6T15cp8AuISEBYWFhmD59Ou69995GjbhtNhvsdjscDgdsNhvMZjOkUinUajUefvhhLFq0CCtWrMC+fftw6dIlPPTQQ412bkIIacs8VVKM6xaIcd24vso5RgtOJOTixO0cnEjIxbX0AtzMNOJmphHrjnOBXgdfFQaWNNsOivKGP426JQ2kVqsRExMDm81Wc+J2TCKRNOiOFAJWh8bcgoICbNmyBd9++y1u3bqFKVOmYPr06ejRo0e9M1Bq1qxZWLt2rdO2AwcOYMSIEcjKysLMmTNx8OBBhISEYNWqVRg1alSDz1mVutxslxBCiouLsWfPHgDA2LFjW90tDHNNVpxMyOVr9a6lG1DxlyHSR8UHeQOjvGjC5FaotZfT9qwucUmdArvyMjIy8P333+O7776DyWTCDz/8UKum2taAAjtCSF2YTCa+32/pLcVas/KDMU4k5OBKqgGOCr8U4d5KDIzkmm0HRnkhxLNl3iKKlGlr5bQ9qUtcUu9wXSaTQaFQQC6XIycnx20jbAghxN2EQiH69evHr7d2FQdjlE6vUjog41K5CZNL74wR4qngg7zBUd4I8VRQB/kWpq2VU+JanWrsLBYLfv75Z6xfvx5nz57FpEmTMG3aNAwaNKgp89jsqMaOEEKqVmC28bdAO3E7FxdT9LBXqNIL0smdBmOEeysp0COknpqsKdbDwwMBAQF48sknMXr0aJft8wMGDKh7jlsYCuwIIaT2jJZinL6Txw/GOJ+Uj+IKgV6AVs4HeYOivBDpo6JAj5BaarLALiIigv8iCgSCSpPoCQQC3L59ux5ZblkosCOEkPortBbjzJ38ksEYOTiXlA+b3fn3wlcj4wdjDIryQgdfNQV6hFShWQZPtGUU2BFC6qKoqIgfqf/rr7+2+1s1VWS22XHmbh6O3+amWDmblA9rsXO/bB+1lO+jNzDSGzF+agiFFOg1JiqnrRcFdg1EgR0hpC5otGHdmG12nEvKx4mSCZPP3M2DpUKg56WSYkCEFwaW3B0j1l9DgV4DUTltvZplVCwhhBCOTCbD1q1b+XVSPblExN+39hXEwFJsx4VkPY7Hc330Tt/JQ67Jit2X07H7cjoAwEMpQf8IL/5et50DtRBRoFcnVE7bB6qxc4Fq7AghxH2sxQ5cTMnnb4F2+k4eCq12pzRauRgDys2j1yVQC7GIpvAgbRM1xTYQBXaEENJy2OwOXErRc330EnJwKjEPRkuxUxqNTIx+EZ4YWFIT2C2IAj3SdlBg10AU2BFC6sJut+OPP/4AAAwbNqxB93kkNSu2O3A51YATCdw8eicTclFQIdBTSUXoG+GFQSWDMXqE6CBp54EeldPWiwK7BqLAjhBSF9Qp3b3sDoaraQYcv83d6zYuMRf6IucbzSulIvQN9+RugxbFBXoycfsKbKictl40eIIQQpqRQCDg75VNc7E1P5FQgG7BOnQL1mH2sCg4HAzX0gv4e92eTMhFXqENf9zMxh83swEAMrEQvcM8+D56fcI8IZe07UCPymn7QDV2LlCNHSGEtB0OB8ONzAKcKOmjd+J2LnJMVqc0UpEQPUN1fKDXN9wTSinVfZCWgZpiG4gCO0IIabsYY4jPMpYMxuAmTc4ssDilEQsF6B5SFuj1C/eERi5xU45Je0eBXQNRYEcIIe0HYwyJOYU4WVKbdyIhFyn5RU5phAKga5CO76M3IMILOiUFeqR5UGDXQBTYEULqoqioCA8//DAA4Oeff6ZbNbUBSbmFfG3eiYRc3M0tdNovEACdArQl97v1woBIb3ippG7Kbe1QOW29KLBrIArsCCF1QaMN2740fZFTH73b2aZKaTr6q/mm2wGRXvDTyN2Q06pROW29aFQsIYQ0I5lMhvXr1/PrpO0J1CkwqXcwJvUOBgBkGsw4mZjLB3s3Moz8su74HQBAlI8KA6PK7o4RqHNvDRmV0/aBauxcoBo7QgghdZFjtCAuMZcfkHEt3YCKv65hXkq+j97ASC+Eeindk1nS6lBTbANRYEcIIaQh9IW2kho9ro/e5VQ9HBV+bYM9FCWBHtdHL8JbSfPLEZcosGsgCuwIIXVht9tx5swZAECfPn3oVk2kEoPZhtN38vim24vJehRXiPT8tTIMiPTmB2R08FU3aqBH5bT1osCugSiwI4TUBXVKJ3VlshTjzN2yQO98kh5Wu8MpjY9aigGRZX30OvppIBTWP9Cjctp60eAJQghpRgKBAOHh4fw6ITVRycQYFuOLYTG+AACzze4U6J29m49soxW7LqZj18V0AICHUoIBEWV99DoHaiGqQ6BH5bR9oBo7F6jGjhBCiDtZiu24kKzn++idSsxDkc3ulEYjF6N/hBc/IKNbkBZikdBNOSZNiZpiG4gCO0IIIS2Jze7AxRQ9X6N3KjEPRkuxUxqVVIS+JYHeoCgvdA/2gFRMgV5bQIFdA1FgRwghpCUrtjtwJc3AB3onE3JhMDsHenKJEH3DPTEgguuj1yvUA3IJDZhojSiwayAK7AghdWE2m/HEE08AAL7//nvI5S3rjgOk7XM4GK6lF/B3xjiZmItck9UpjVQkhKQgBerCNHy04DkMivaHQkqBXmtAgV0DUWBHCKkLGm1IWhqHg+FWlhEnbufgeAJ3h4xso8UpjUQkQI8QD76PXt9wT6hlNKayJaLADkBqaioee+wxiMViaLVabNy4EUpl7Wb5psCOEFIXNpsNa9asAQDMmjULEonEvRkipALGGG6k6bHiu51IKpIiW+iJdINzoCcSCtAtWMcFepFe6BfhBZ2CynJLQIEduIkYBQIBhEIhFi1ahG7dumHKlCm1ei4FdoQQQtoyxhiScotwvKTp9kRCDpLzipzSCARAl0AtP4/egAgveKqkbspx+0bz2AFOM2oLBALExsa6MTeEEEJIyyEQCBDmrUSYtxKP9wsFAKTkF3HTq5QEeok5hbicasDlVAO+OZIAAOgUoOGbbgdEesFHLXPnyyAutJgau0WLFmHTpk24du0aNmzYwHdEBoCsrCzMmjULBw4cQGhoKFatWoWRI0fWeMzDhw/jlVdegUKhwM6dO6HT6WqVF6qxI4TUhcPhwNWrVwEAnTt3hlBIU0yQlqeu5TTDYMbx29yI2xMJubiVaayUJtpPjYGRXhgQ6YVBUd7w19LAoabQKpti169fD39/f7z77rt49dVXnQK7xx9/HDqdDp999hn27t2LZ599FvHx8bBYLE7pAECtVmPHjh1O2z799FOIRCK89tprtcoLBXaEkLqgwROkNWhoOc02Wrggr2TS5GvpBZXSRHgr+abbgVHeCPZQNEre27tW2RT71FNPAQDef/99p+1GoxE//fQTEhMToVQqMWnSJCxfvhzbt2/HjBkzcPDgQZfHs1gskMm4KmKdTge73e4yXWlai6WsE6nBYGjgqyGEtDc+Pj7uzgIhNWpIOfVRy/BA90A80D0QAJBnsuJkYi7fdHslzYDEnEIk5hTih1NJAIAQTwUf6A2K9Eaol4JuZ9bEWkxgV5WbN29Cp9MhMDCQ39azZ09cvny52ufFxcVh4cKFEAqF8PLywrp166pM++GHH2LJkiWNlmdCSPuiUqmQlZXl7mwQUq3GLqeeKinGdg3A2K4BAAB9kQ2n73CB3vGEXFxK0SM5rwjJecnYfCYZABCok2NApBcf7EX5qCjQa2QtPrAzGo2Vqh21Wi3y8/Orfd4999yDQ4cO1eocb731FhYsWMA/NhgMCA0NrXNeCSGEkPZKp5Dg/k7+uL+TPwDAaCnG6Tt5fNPtheR8pOnN+OlcKn46lwoA8NXIuP55JQMyYvzUFOg1UIsP7NRqdaWmUYPBwPcTaAwymYxvtiWEEEJIw6llYgzv6IvhHX0BAEVWO87czeMnTT6XlI+sAgt2XkjDzgtpAAAvlRQDIry4PnqR3ugUoIFQSIFeXbT4wC4mJgZ6vR7p6ekICOCqe8+fP4/Zs2e7OWeEEMIxm8147rnnAABff/013VKMtEjuLqcKqQhDo30wNJrr52e22XE+KR8nErg+eqfv5CHXZMXuy+nYfTkdAKCVi52abrsEaiEW0ajz6rSYUbE2mw12ux1jxozB888/jylTpkAqlUIoFGLKlCnw8vLCihUrsG/fPsyaNQvx8fHw9PRskrzQqFhCSF3QqFjSGrT0cmotduBiSj6O3+amVzmdmAuT1Xngo1omRr8ITz7Q6x6sg6QdBHqtcrqTWbNmYe3atU7bDhw4gBEjRiArKwszZ87EwYMHERISglWrVmHUqFFNlhcK7AghdWGz2bBy5UoAwEsvvUS3FCMtUmsrp8V2By6lGvg+enGJuSgwFzulUUpF6BvuyU+a3CNEB5lYVMURW69WGdi1JBTYEUIIIS2L3cFwNc3ANd3ezsHJxFzkF9qc0sjEQvQO8+Br9PqEeUIuaf2BHgV2DUSBHSGEENKyORwMNzIL+Hn0TtzORY7J6pRGKhKiZ6iOD/T6hntCKW3xwwsqocCugSiwI4TUhcPhwN27dwEAYWFhdEsx0iK19XLKGEN8lpHvo3fidg4yCyxOacRCAbqHlAV6/cI9oZG37CZpgAK7BqPAjhBSFy29UzohQPsrp4wx3Mkp5GvzTiTkIiW/yCmNUAB0DdLxffQGRHhBp2x5gV6rvKUYIYS0Zkql0t1ZIKRG7amcCgQCRPioEOGjwtT+YQCApNxCvjbvREIu7uYW4mKKHhdT9Pjv4QQIBECnAC0GRnphUJQXBkR6w0sldfMrqRuqsXOBauwIIYSQti9NX8TX5p1IyMHtLFOlNB391XzT7YBIL/hpmn+eSmqKbSAK7AghhJD2J7PAjJMJufyAjBsZxkpponxU/J0xBkZ5IVCnaPJ8UWDXQBTYEUIIISTHaEFcYi4/IONaugEVo6YwLyUGRnrh9bGx8Nc2TW0eBXYNRIEdIaQuLBYLXn75ZQDAF198QfeeJi0SldOG0xfacDIxFycTuD56l1L0cDBuEMb5RWOabIQtBXYNRIEdIaQu2ttoQ9I6UTltfAVmG07dyUNitgnPDI1ssvPQqFhCCGlGEokES5cu5dcJaYmonDY+jVyC+2L9gFh356QM1di5QDV2hBBCCGkp6hKXtK1ppwkhhBBC2jFqiiWEkAZijCE7OxsA4OPjA4FA4OYcEVIZldP2gQI7QghpoMLCQvj5+QGgTumk5aJy2j5QYOdCabdDg8Hg5pwQQloDk6lstnqDwQC73e7G3BDiGpXT1qs0HqnNsAgaPOFCcnIyQkND3Z0NQgghhBBeUlISQkJCqk1DgZ0LDocDqamp0Gg01fZB6N+/P+Li4qo9VlVpDAYDQkNDkZSU1OpH3tbmfWgN522M49XnGHV5Tm3T1pSuuv1UNlveORt6zKYul7VNT9fMMlQ26//89njNZIyhoKAAQUFBEAqrH/dKTbEuCIXCGiNiABCJRDV+iDWl0Wq1rf4iVZv3oTWctzGOV59j1OU5tU1bU7raHIfKZss5Z0OP2dTlsrbp6ZpZhspm/Z/fXq+ZOp2uVuloupMGeOmllxolTWvnrtfY2OdtjOPV5xh1eU5t09aUrj2US8A9r7MpztnQYzZ1uaxterpmlqGyWf/n0zWzetQU6yY0CTJpqahskpaIyiVpqVpa2aQaOzeRyWRYtGgR3YSZtDhUNklLROWStFQtrWxSjR0hhBBCSBtBNXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW0EBXaEEEIIIW2E2N0ZaIkcDgdSU1Oh0WggEAjcnR1CCCGEtGOMMRQUFCAoKAhCYfV1chTYuZCamorQ0FB3Z4MQQgghhJeUlISQkJBq07TqwO7cuXOYO3cuLl68iOjoaHzzzTfo3bs3AOCjjz7CsmXLYLfbMXv2bPzf//1frWvfNBoNAO4N1Gq1TZZ/QkjbYDab8dRTTwEA1q9fD7lc7uYcEVIZldPWy2AwIDQ0lI9PqtNqAzubzYbJkyfjrbfewnPPPYcff/wRkydPxo0bN/Drr7/iX//6F06cOAGFQoGRI0eiU6dOePbZZ2t17NIAUKvVUmBHCKmRSCTCvn37AAAqlQoqlcrNOSKkMiqnrV9tKqhabWB37do1FBYW4oUXXgAATJ06Fe+99x4OHTqEdevWYe7cuYiKigIAvP7661i/fn2tAztCCKkLqVSK1atX8+uEtERUTtuHVhvYucIYw+XLl3HlyhU8/fTT/PaePXvirbfeqvJ5FosFFouFf2wwGJo0n4SQtkUikWDWrFnuzgYh1aJy2j602ulOYmNjIZfL8eWXX8Jms+G7777DrVu3UFhYCKPR6NSEqtVqYTQaqzzWhx9+CJ1Oxy80cIIQQgghrVGrDeykUim2bt2KdevWISAgANu2bcOoUaMQHBwMtVrtVOtmMBigVqurPNZbb70FvV7PL0lJSc3xEgghbYTdbse5c+dw7tw52O12d2eHEJeonLYPrboptk+fPjhy5AgArsB26NABffv2RZcuXXDx4kU88MADAIDz58+ja9euVR5HJpNBJpM1S54JIW2P2WzmR+QbjUbqlE5aJCqnLtiLAYuBW8zl/xaUbC8ArEbAauL+WkrXTYC1oNy6CfhrIiCSuPsVte7A7uLFi+jYsSOsViv+9re/oVevXujWrRueeuopvPTSS3j88cehUCiwfPlyLFiwwN3ZJYS0UQKBAEFBQfw6IS1RmyynDgdg0QNFeWVLYbl1iwEw68uCNHOFIM5W2Hh5sRoBhWfjHa+eWnVg980332D16tVgjOHhhx/GmjVrAAAPPvggLly4gP79+8Nut+P555/HM888497MEkLaLKVSiZSUFHdng5Bqtfhy6nBwwZgpCzBllvzNdg7aivKAwtyydXM+wBwNP7dYAci1gEwLyDTO61I1IFUBMnXZulRdYbuKS98CCBhjzN2ZaGkMBgN0Oh30ej3NY0cIIYTUl72YC9IK0gBjabBWErCZskq2lawXZtc/SJOouNoypSf3V+EJyD0Aua4kSNM5B2x84Fay3gKaUKtTl7ikVdfYEUIIIcQNGONqzAypQEE6F7jxS3rZdlNm3YM1hSeg8uUWpTe3KMoFbEqvco+9AIUHIKZ+8qUosCOEkAYym8383Jnr1q2jWzWRFqlO5dRuAwwpQH4SoE8q+Xu37LE+BbBbqn5+eQIRoPYH1H7cUhq08YuP83oLrz1r6agptpyVK1di5cqVsNvtuHHjBjXFEkJqxWQy8VMq0WhD0lI5ldMCA1SOAiD3NpCbAOQlAPnlAreCtNrVtCm9AU1gyRLA/dWWfxzEBWtCURO/uratLk2xFNi5QH3sCCF1YbPZ8NVXXwEAXnjhBUgkVONAWgCHHdAnlwRvt2HPvoU7Zw9CZ8+Bl8AAQXFR9c8XyQBdCOARCuhCAY+wkr+h3HZNIDWBNhMK7BqIAjtCCCGthq0IyL4JZN8Asq4DWde49Zx4wGGr+nkCEReseUUBnhHcukcooAvj1lW+gLDV3segTaHBE4QQQkhbYzUBmVe5pTR4y7rONaGiijoakZQL2ryiAM9I7q9XFOAVyQVv1J+tzaHAjhBCGsjhcCA+Ph4A0KFDBwiploM0BGNcsJZxCUi/xP3NuMT1hasqgFN4Aj6xgG/Hkr+xgE8M13Ra0r/NqZx6RlI5baMosCOEkAYqKipCx44dAdDgCVJHdhtXA5d6Fki/AGRc5haLwXV6tT/g17lCENeJG6BQw90kqJy2DxTYEUJII9DpdO7OAmnpHHau+TT1LJBypiSYu+h62hCRlKt18+9WsnTl/qp9G5QFKqdtHwV2hBDSQCqVCvn5+e7OBmlJGOOmDUk6WRLEnQHSzru+N6lcBwT1BgJ6AAHduQDOJ6bR+79ROW0fKLAjhBBCGspu45pS754Akk5wAV1BauV0EhUQ1IsL5EoXr6gam1EJqS0K7MopP0ExIYQQUqWivHJB3AmuVq7ivHACERDYAwjpDwT14YI4nxiarJc0KZrHzgWax44QUhcWiwUvvvgiAODf//43ZDKatLXNMeuBO8eAxD+4Je0CKo1QlXsAoQOB0AFA2CAukJO2nAEKVE5bL5qguIEosCOE1AXdUqwNMhuAuyWBXMIfXDNrxVtseXUAwgeXBHMDAe+YFj2hL5XT1osmKCaEkGYkkUjw8ccf8+ukFbLbgORTQPxv3JJ6xnUgF3EPEHkvED6UuydqK0LltH2gGjsXqMaOEELagbxE4NZ+LpBL+L3y3HGekc6BnC7YLdkkpN3V2B07dgxDhw7FBx98gDfffBNr1qzB7NmzIZfL+TRXrlxBWFiYG3NJCCHErawmLoC7tR+I3w/k3nber/AEou4DokcCUSO4G90T0sq0+sDO4XDgtddeQ//+/Z22jxo1Crt373ZTrggh7YnD4UBaWhoAIDAwkG7V1JLok4HrvwA39nBBXfnJgIViIGQA0OF+IPp+ILBXmx6xSuW0fWj1gd1XX32FgQMHQq/X1/sYFosFFkvZl91gqOJWLoQQ4kJRURFCQrjaHeqU7mYOB3dHhxu/ANd3AxkXnfd7hAHRo7lgLvJeQN5+uttQOW0fWnVgl5ubixUrVuDYsWN47bXXnPYdOXIE3t7e8Pf3x/z58zFnzpwqj/Phhx9iyZIlTZ1dQkgbJha36stp62YrAuIPANd3Ajf2AqbMsn0CIVcr13EsEDueu69qO54MmMpp29eqP+G3334br776Kjw9PZ22Dx8+HBcvXkRYWBji4uIwefJk+Pv7Y/LkyS6P89Zbb2HBggX8Y4PBgNDQ0CbNOyGk7VCpVLDZbO7ORvtiKQBu7gWu/Azc3AfYTGX7pBquabXjeCBmDKDydl8+WxAqp+1Dqw3szp49i5MnT2LlypWV9kVGRvLrAwcOxPz587F169YqAzuZTEYTNRJCSEtXlMc1r179mRsAUb6/nDYE6DwB6DiOG8Eqlrovn4S4UasN7A4dOoQbN24gOJgbfq7X6yEWixEfH4///Oc/TmmpgyghhLRSpmzg2g6uZi7hEOAoLtvnFQV0fhjo8jB3y6523MRKSKlWO49dYWGh0yCHV155BTExMXj99ddx/Phx9O3bF76+vjhz5gwefvhhLF++HI8//nitjk3z2BFC6sJisfDdOZYvX04tAA1VlM8Fc5c2A7cPAazc/bt9O3OBXOeHAf+uFMzVAZXT1qtd3lJs1qxZ6NSpE9588038+c9/xrp161BYWIjg4GC8/PLLmDdvXq2PRYEdIaQu6FZNjcBayI1kvbSF6ztnt5btC+wJdJnIBXM+Me7LYytH5bT1ancTFAPAmjVr+PVly5Zh2bJl7ssMIaRdkUgkWLRoEb9OaqnYyt314dKPwLVdzgMgfDsB3R4Duj0CeHdwXx7bECqn7UObqbFrTFRjRwghTcThAO4eBS78wPWbM+eX7fMIB7o9CnR/DPDrQs2shJRolzV2hBBCWrDsW8CF74HzPwD6u2Xb1QFA18lcMBfcl4I5QhqIArtyVq5ciZUrV8Jut9ecmBBCSjDG+Lvf6HQ6CCg44RTmApe3Aue/B5JPlm2Xabk+cz0e56YmacO38WpJqJy2D9QU6wI1xRJC6oI6pZdjt3ETBp//Drixu2wQhEAERI8Eej4BxD4ASBTuzWc7ROW09aKmWEIIIc2HMSDtHFczd3ETUJhTts+/OxfMdZ8CaPzdlkVC2gsK7AghpIGUSiWsVq5mql3di7MoH7iwETi9Bsi8XLZd5cc1s/Z8Agjo7q7ckQrabTltZ+iTJYSQBhIIBO1n+gjGgKQTXDB3eStQbOa2i+VApweBnk8CUfcBIvp5aWnaVTltx+ibRwghpGaFuVy/udNrgezrZdv9ugJ9Z3I1dApP9+WPEAKAAjtCCGkwq9WKhQsXAgDef/99SKVt5Ab0jAGJh7nauas/lw2EkCi5iYP7zAJC+tEUJa1Emy2nxAmNinWBRsUSQuqizY02NGYB5zdwtXO58WXbA3oAfWdxAyHkdG1sbdpcOW1HaFQsIYQ0I4lEgtdff51fb5UcDiDhIBfMXdsJOGzcdqmamzy47ywgqLc7c0gaqE2UU1IjqrErp/wExTdu3KAaO0JI21eQDpxdD5z5H5B/p2x7cF+gz0zuFl8ytfvyRwipU40dBXYuUFMsIaRNc9iB+N+4vnPXfwFYyd12ZDpuEETfmTRNCSEtCDXFEkJIM2KMobi4GAA3P1iLvVWTPoWrnTu7DtAnlW0PHcg1tXaZBEiV7sodaWKtppySBqHAjhBCGqiwsLDldkq3FwM39wJn1nJ/mYPbLvfg5pzrOxPw6+zWLJLm0aLLKWk0tQrsNm7cWKuDiUQiPProow3KUGPKysrCrFmzcODAAYSGhmLVqlUYOXKku7NFCCFNL+8OVzN3dj1QkFa2PfweLpjr/DAgkbsvf4SQJlGrwG7atGm49957UVN3vLi4uBYV2L300ksICgpCdnY29u7diylTpiA+Ph6enjSJJiGk8SiVSuTl5fHrbmO3cX3mTq/h+tCh5Jqt9AZ6TeMGQ/jEuC9/xK1aTDklTapWgyc0Gg0KCgpqPJinpydfaNzNaDTC29sbiYmJCAwMBADce++9mD17NmbMmFHtc2nwBCGkVcm9zY1qPfstYMos2x45nOs71+lBQCxzW/YIIQ3T6IMnbt++XasT37hxo1bpmsPNmzeh0+n4oA4AevbsicuXL1dKa7FYYLFY+McGg6FZ8kgIIfVWbAGu7eDmnUs4VLZd5Qf0ng70mQF4Rbkvf4QQt6hVYOfr61urg9U2XXMwGo2VolqtVov8/PxKaT/88EMsWbKkmXJGCGlrrFYrPvjgAwDA22+/3bS3asq+yTW1nv8OKMwp2SgAokdytXMdxwEimnyWVNas5ZS4TZ3nsRs/frzLIdIymQwhISGYPHky7r///kbLYH2dPXsWY8eORWZmWbPEvHnzoFQq8X//939OaV3V2IWGhlJTLCGkVpr8Vk02M3ev1tNrgDtHyrZrAoHeTwO9nwI8wxv3nKTNoVuKtV5NOo9dv3798L///Q8zZ85ESEgIkpOTsW7dOjzxxBMQCAR48skn8eabb+K1116r9wtoDDExMdDr9UhPT0dAQAAA4Pz585g9e3altDKZDDJZ8/Y/+eiXazDb7NDIxVDJxFDLxNDIub9qGbeNfywXQyYWNWv+CCG1JxaLMXfuXH690WRe5Zpaz38HmPO5bQIhEDOGq52LHg2IaNYqUjtNVk5Ji1LnGrt+/frhu+++Q0xM2ciqmzdv4sknn8SpU6dw+vRpTJkypdb98prSlClT4OXlhRUrVmDfvn2YNWtWrUbFNsfgif7v/4qsAkvNCUtIRUKoZCKo5WKoZRJoZGKoZCJo5BJ4KCXQKcoWD6W05G/ZNrmEAkNCWgVrIXB5KzfvXNKJsu260LLaOV2w+/JHCGl2TVpjFx8fj+Bg54tKYGAgbt26BQDo06cPsrKy6nrYJrFq1SrMnDkT3t7eCAkJwcaNG1vMVCcv3huFvEIrTBY7CszFMFpsMFqKYTQXc39L1k1W7lY/VrsD1kIH8gptAIrqfD6ZWMgHeh4KKbQKCTyVEnippfBRyeCtlsJLJYWPumydagkJaUZpF7hg7sImwKLntglEQOx4rnauw/2AkL6ThJDq1TmwGzNmDKZMmYJ3332Xb4pdunQpxo0bBwA4efIkwsNbRl8PX19f7Nq1y93ZcGn2sNqNVrM7GEzWYphKAr2CCsGfocgGQ5EN+UU26ItsyC/k/pZf7A4GS7EDGQYLMgy1ryXUyMR8kOetlsGnZN1PI4e/Vg5/rQwBOjl81DJIRML6vhWEtF9mA3DpR665Ne1c2XbPCG5Ua6/pgCbAXbkjhLRCdW6KNRqNeO+997B161akp6cjMDAQkydPxpIlS6BWq5GcnAyLxYIOHTo0VZ6bXFuax44xBqOl2GXAl2uyItdkRY7RghyTFTlGK3JMFuQYrSh21L5YCASAj1rGBXpaOfy0cvhr5AjQyeCnlSPEQ4FgTwWUUurTQdomk8kEDw8PAEB+fn71ndIZA5JOcvPOXd4C2Aq57UIJ0HkCF9BFjgCE9M8SaVx1KqekRalLXFLnwK49aEuBXX0wxmAoKuaCvAqBX7bRgkyDBRkFZmTozcgssNQ6CPRWSRHsqUCIpwIhnsqSv9x6sIcCKhkFfqR1qtVoQ1MOcOF7LqDLula23SeWC+Z6PgmovJspx6Q9olGxrVeT9rEDgJ07d+LHH39EVlYWduzYgbi4OOTn52P06NH1yjBpWQQCAXRKCXRKCaJqmJrQ4WDIMVmRYTCXLBakG8zILHmcpjcjNb8IBnMxFxyarLiQrHd5LG+VFBE+KkSWLFE+KkT6qhDhraLBH6RFUygUSE5O5td5Dgc3efCZ/3GTCdut3HaxAuj2CBfQhQ7kqr0JaWJVllPSptS5xu7jjz/GunXrMGfOHCxcuBD5+fm4du0aZs6ciRMnTtR8gBZs5cqVWLlyJex2O27cuNFua+yagr7IhpS8IiTnFSI5rwjJeUVIyS9b1xfZqn1+kE6OSF8u4Iv2VSM2QIvYAA28VDTBJmmBDGnAufXAmXVA/p2y7YG9uGCu+2OAXOe27BFCWpcmbYoNCwvDyZMnERAQwN8bljEGb29v5ObmNijjLUV7b4p1B4PZhrs5hUjMMSEhy4SEbBNuZ5twO8sIg7m4yuf5amToFKBBR38NYgM0iPXn1hVSquEjzazYCtzcw92v9eYegDm47TIt0ONxLqAL7OnePBJCWqUmbYq12+3Q6bj/NEvvQGEwGPh2e0LqQyuXoFuwDt2CnWsxGGPIK7QhIduIhOxCxGcZ/7+9+w6PqkwfPv6dmpn0QnoFEkILIFWaAioodkX5iahgV3BF1y4KrN3VFQvrq+vapSy6lhUVQTpSpffQ0ntPZjL1vH8MDAkgBNJmkvtzXXOdM+c8c+aZ5MnJPU8lvaCK/QVVZJWaKaqyUFRlYXV6sfs1ahUkR/iTFhtMr7gg0uKC6B4dKM25onnkbcfxxxdYNn+Jb92piBIGQ987oPu1oPdtvfwJcYzVauXtt98G4OGHH5Ylxdqoc66xmzp1KlVVVbz55pt06dKFzMxMHn30Ufz8/HjrrbeaK58tSmrsvEO1xe4K8vJdgd6BY/vF1dZT0mrUKrpEBtArNogLEoLpnxRK53C/0y6PJ8RZVRfBzoWwbS4U7HQfzq1y0uGSqegHTILw1NbLnxCnIYMnvFez1ti98cYb/PWvfyUxMRGz2UxkZCR33HGHe2FhIVqKv4+WCxJCuCCh/qTTBZW17MyuYEdOBbtyKtiRXU5xtZW9eZXszatkweYsAEL99PRPDGFAUij9k0LoEROEXitTTIg/4bDBgcWuYC59MTiPdRHQ6HF0uYLZKwrZbY7g/UtmQgsvUShEQ2i1Wu644w73vmibGjXdSVFRER06dGhztR5SY9e2KIpCfmUtO7Ir2J5Vzh8ZZWzLKsdid9ZLZ9CpGZAUyrDkDgxL6UC3qEDU6rZVtsU5UhTI3+laq3XHf8B0osmfmL7QZwL0vBF8Q1svj0KINq/JB09s3LixQW88cODAhuXQw0lg1/ZZ7U525Vaw6Ugpm46WsTmjlHJT/ZG5YX56hh4L8i5KCScqyNBKuRUtrjzT1dS6YyEU7T1x3D8Seo13BXQR3Vovf0KIdqXJA7uOHTueeIFKRXZ2NiqVirCwMEpKSlAUhbi4OA4fPtz43HsACezaH6dT4WBRNWvSi1lzsJj1h0swHVun97i02CAu6x7J6B6RpEYGtLma6nbPVAp7vnPVzGWuO3Fco4cul8MFE6HzJaCRJiwhRMtq1ulOZs2ahclkYubMmRiNRsxmM7NmzcLPz4/nnnuuURlvbTKPnTjOaneyNbOM1enFrD5YzI7scur+pcSHGhndPYrR3SMZkBQqTbbeymaG/T+7aufSl4DzeK2tCpKGuaYp6XYNGIPPeJmamhpiY2MByMnJkU7pwiNJOfVezRrYdejQgfz8/HodL202G9HR0RQXF5/hld5DauzEyYqqLCzbV8CvuwtYfbAYa53+edFBBq7pHcO1fWLpFi01eR7PYYejq1zNrHv/B9aqE+ei0iDtZle/uaDYBl9SRhsKbyDl1Hs166jYkJAQfvvtN8aMGeM+tmLFCvfCwkK0ReEBPowfkMD4AQnUWOysTi/i190FLNlbQF5FLR+sOswHqw6TEuHPdRfEcv0FscQEy5I9HsNhh6OrYfe3rqW9TCUnzgUlQK+bXAFdRNfzurzRaOTAgQPufSE8kZTT9uGca+x++uknbrnlFgYNGkR8fDyZmZls2rSJr776iiuvvLK58tmipMZONFStzcGK/YV8tzWXZfsKsTpcNXlqFYxIjWDCwARGpIaj1cg0Ki3uTMGcb5hr4uC0m11rtarl9yOE8FzN2hQLUFxczE8//UReXh7R0dGMHTuWDh06nHeGPY0EduJ8VJhtLN6Vz3+3ZrP+8Inl9aKDDNzcP54JgxKIDJSRtc3qeDC35ztXM+vJwVy3q6H7dZA0XAZBCCG8RrMHdp7k1Vdf5emnn2bdunVceOGFAEyaNIl58+ah0+kASExMZPfu3Q2+pgR2orEOF1Uzf1MWCzdnUXZsGhWdRsXVvWK4e3gnusdIuWoyVhMcXg77FsGBX+oHc8ZQ6H5NswdzNpuNDz/8EIB7773Xfe8RwpNIOfVeTR7YjR8/ngULFpz1jSdMmMDcuXMbntNGysnJ4YorrqCoqIhvv/22XmDXtWtXnnrqqfO6rgR2oqlY7A5+2ZXPl+sz2HS0zH18aHIY9wzvxMVdwmWwxfmoKXEFcfsWwaFlYK+zRqsx1FUz1+P6FquZk07pwhtIOfVeTT544ocffmDhwoWcLQb86aefGp7LJvDXv/6VWbNm8cgjj7To+wrRUD5aDdf2ieXaPrFszyrnX6sP8/OufNYeLGHtwRJ6xwUx7dIujEiVAO+sSo+4Arn9P7nmmVPqrBwSlABdx0LXKyFhSIs3s2o0GsaNG+feF8ITSTltHxpUYzdixIgG/dPR6/UsXry4STJ2NitWrODFF19k6dKlJCUlMX/+/Ho1dv/73/8ASE1N5dVXX+Wiiy7602tZLBYsFov7eWVlJfHx8VJjJ5pFdpmJT9YeZe6GTMw21yTIEuCdht3qCuDSf4WDS6FoX/3zUWnQ9SpIHeval5+bEKKNavN97Ox2OwMGDOCLL76gZ8+epwR2W7duJSkpCT8/PxYuXMiDDz7Irl27iI+PP+31Zs6cyaxZs045LoGdaE7F1Rb+teown6/LcAd4/RJDePbKbvRNCGnl3LWSimzXRMHpS+DISrBWnzin0kDSUEi90lU7F5zQevkUQogW5PWB3ejRo1m1atVpz02fPp2AgAAOHjzIu+++C3BKYHeyyy+/nJtvvpk777zztOelxk60puJqCx+uOszn645Sa3M1L17VK5onL+9KfKhvK+eumdktkLXBVSOXvgQK99Q/7xcBKZdB8qXQeSQY22nAK4Ro17w+sDub6667jlWrVqHX6wEoKioiODiYN954g8mTJ5+SfuzYsYwbN+5PA7uTyeAJ0RryK2p589f9fL0lG0UBvUbN5GFJPDQqBX+fNjI1h9MB+Tvg8Eo4vAIy19cf+KBSQ9yAY8HcZRDVyyvmmDOZTKSkpACQnp6Or28bD8iFV5Jy6r3afGBXXl5ObW2t+/mAAQP44IMPGDFiBL6+vnzzzTdcfvnl+Pj48M0333DPPfewY8cOkpKSGnR9CexEa9qdW8HLP+1l7UHXtB3RQQZmXN2DMT0iva//naJAySE4ssIVyB1ZDbXl9dP4RUDnUa5grvMo8A1thYw2jow2FN5Ayqn3atYlxTzBycuXaTQaQkND3d8+3nrrLe68805UKhWpqal8++23DQ7qhGhtPWKC+PKuQSzbV8jM/+0mq9TM/V/+waXdIph5TQ/iQjz4W7aiQPEByPjdNfDh6FqozK6fRh8AScOg0wjodDGEd/X6gQ8Gg4GtW7e694XwRFJO24dzrrEzm808//zzLFy4kNLSUiorK1m8eDF79+5l2rRpzZTNliU1dsJTmK0O3luezoerDmNzKBh1Gh4bk8rkIUmo1R4QDDnsrqbVzHXHgrn1YCqun0ajdy3b1fFiVzAXc4Gs+iCEEOegWZtiJ0+ejM1m46mnnmL48OGUlZWRl5fHyJEj2bdv39kv4AUksBOeJr2gime/3cXGo66lygZ1DOWNm3q3/OAKcznk/OF6ZK6DrI31R64CaA0Q2x8SB0PiEIi/EPQeXMsohBAerlkDu4iICLKysvDx8SE0NJTSUtc/muNv2BZIYCc8kaIozN2YyUuL9mKyOvDTa3j+6u7c3D++efreOWxQsAuyN7sCuezNUJJ+ajqfIEi40BXIJQyBmD6g9Wn6/Hgwm83GV199BcCtt94qSzUJjyTl1Hs1a2DXpUsXli1bRlxcnDuwO3LkCGPHjmXv3r2NynhrmzNnDnPmzMHhcHDgwAEJ7IRHyiip4bGF291LlI3qGsHr43rRwb8RwZTTCaWHIX87ZP8BOZshbzvYa09NG5LkqpGLH+SqkYvo7hUjV5uTdEoX3kDKqfdq1sETDz/8MFdffTXPPvssDoeDH3/8kRdffLFN9K+bMmUKU6ZMcf8AhfBEiWF+zL93MP9ec5g3Fh9g2b5Cxr69mnduuYALO4Wd/QJ2KxTthbwdrv5xeTtcNXMnN6kCGIIhth/E9XcFc7H9wK8B79HOaDQaxo4d694XwhNJOW0fzmu6k4ULF/Lxxx+TmZlJbGwsd911F+PHj2+O/LUKaYoV3mJffiVT527lYGE1ahVMu7QLU0Ymo1GrXCNUa4qgcK/rkb/TVSNXuA+ctlMvpjVCZA/X4IbjgVxYZ68fsSqEEN6uzc9j19wksBPexGS188rXv7N/50a6qLO5OLiYi0NK0JfuB1PJ6V9kCIboXq4JgKN7u7ZhyTJaVQghPFCzNsXOnj2bkSNH0rt3bzZs2MDEiRPRaDR88sknDB48+LwzLYQ4C6cDyjNdE/6WHITSQ1C0H9+ifbxQXQDHu9jVHHsAoILQjhDezVUbF93bFdAFxUtNnBBCtEHnXGMXExPD3r17CQoKYtiwYdxyyy34+/vz3nvvsWnTpubKZ4uSGjvRahQFqgtcgdvxAO74tuwIOKx//trgBGqCUvhfXjAbqiM4ok7gjqsv4/pBKS2X/3bKZDLRu3dvALZv3y5LNQmPJOXUezVrU2xgYCCVlZWUlZWRnJxMUVERarVapjsRoqHM5VCeAWUZdbaZrv3yTLCZ/vy1Gh9Xv7ewzq6m07BkV21ceCr4uEa7VdXaeGTBdpbuLQBg0pAknr2yGzpN+x652pxktKHwBlJOvVezNsUmJyczf/589u/fz6WXXoparaa0tBS9Xn/eGRaizXDYXTVulblQmXNiWzeQqz3LFyCVGoITTgRuYcknArnAuLNOLRJg0PHhbf2Y/Vs67/yWzqe/H2V/fhVzbu1LqJ/8nTYHg8HAmjVr3PtCeCIpp+3DOdfYbdiwgWnTpqHX6/noo49ISUlh7ty5/PTTT3z55ZfNlc8WJTV24rSsNVBdeCxwOxa0VeTUCeByoTofFOfZr+XbAUISXQFccOKx/UTXHHFBcU02we8vu/J49D/bMVkdJIT68vGkASRH+DfJtYUQQrQMGRV7nmSC4nbIbjkWrBVCzbGg7fjz6gLXdCHHj51unrfTUWshIBoCY449Yl2DFY4Hb8EJ7mbTlrA/v4q7P99EVqmZQIOWD27rz+DOMhedEEJ4i2YP7LZv387atWspKSmh7suff/75c8+tB5IaOy/lsIGp1DXFh/nY1lRne/Ixc+nZm0VPpjWAf6QrWDseuAXF1Q/i/MJB7VmTf5ZUW7jn881sySxHp1Hxyg29GNcvrrWz1WbY7Xa+/fZbAK6//nq0Wpk2RngeKafeq1kDu/fee4/p06czduxYvv32W66//noWLVrEtddey+eff96ojHsKCexaiaK4asVqK10B18kPy2mO1VYcC9rKwFJ5fu+r1oF/hOvhd2zrH3nimH/kieM+AV47TUitzcFfF25n0Y48AB4alcyjl3VpnnVm2xnplC68gZRT79WsgyfefPNNli1bRt++fQkODmbu3LmsXr2ad95557wzLLyYwwaWKlf/M2v1ia3l+P6xcyc/dx+rqhOkVYLiaGSGVGAMAd8w8A11bY2hx/brPj923i/clb4dBDcGnYZ3/+8CEkN9+eeKQ7y77CAZJSZeH9cLg86zahi9jVqt5uKLL3bvC+GJpJy2D+dcY1d3WpOIiAiys7PR6/Uy3YmnUBRw2l19xxxW19Zmci3mbjO7HvZa1zFbbZ1zJz8/yzlrtetxpnnVzpdaB4agOo/Ak54HgU+dc3UDNUOQxzWDeqL/bMrimW93Yncq9E8M4V+39ydERswKIYRHatYau9TUVLZt20afPn3o06cPr732GkFBQYSHh593hs/HggULmD59Onl5eYwaNYpPP/2U0NBQAMxmM/fccw/ff/89ISEhvPbaa9xyyy0tmr+z2jbP1XRot4DD4lqY3WE9EYy5j51ua3HVlP3ZOVphPIzGB/R+rkEB+uOPMz33c219Ak4N2rSGdlGD1ppuHhBPbIiR+7/8g80ZZdzw/u98OnkAiWHSNCOEEN7snGvs1q9fj16vp2/fvuzZs4epU6dSVVXFa6+9xqhRo5orn/Xs3buXwYMHs3TpUnr37s2jjz5KcXEx8+bNA+CJJ55g165dzJ8/n127djF27Fg2btxIly5dGnT9Fqmxe6OLa7Rlc1NpQOcLOoNrkXed0bWv83UFUOd07thDazwWmNUJ1DS65v8soskdKKhi8iebyCk3E+qn51+396dfYkhrZ0sIIUQdbX66k3fffZc1a9awYMECAPLy8khMTKSsrAw/Pz+io6P57rvvGDRoEAC33347ycnJfzpq12KxYLFY3M8rKyuJj49v3sDuh4dcfcq0PqDRH9v6gFZ/0vbYeY3+1GN/9lqN7sQxWdRdnEVhZS13fraJXTmV+GjVzB7fhyvSols7W17FbDa718pet24dRqOxlXMkxKmknHqvZm2KBcjMzGTXrl1UV9ef1+vmm28+n8udl7rxqKIo2Gw20tPTSUxMJD8/n7S0NPf53r17s3Hjxj+91iuvvMKsWbOaNb8ns1z5BlqVFo30BxOtLCLQwIJ7B/OXeVv5bV8hD87dwrNju3HXsI4yYraBnE4n27dvd+8L4YmknLYP5xzYvf7668ycOZO0tLR6CwirVKoWC+wuueQSpk+fzsaNG+nduzevvPIKKpUKk8lEdXU1Go2mXt4CAwNPCULrevrpp3n00Ufdz4/X2DWnq769ivyafLQqLXqNHh+NDzqNDh+Nj2tf7drXa/Tu83p1nX3NSfvq+scNWgNGjRGjzohBY8CoNWLUGjFoDfhqfdGqtfJPW7j5+Wj54LZ+zPrfHr5Yn8GLi/aSWWpixtU90KilnJyNwWDg119/de8L4YmknLYP5xzYvfHGG2zatIkePXo0R34AGD16NKtWrTrtuenTpzN9+nTef/997rjjDkpKSnj44YcJCAggNjYWf39/HA4HJpPJHdxVVla65+45HR8fH3x8mmYJp4ayHhtNalfs2O12TPYzLPzeDDQqjTvQqxv0GbVGV0CodQWFRq0RP50fAboA/PR++Ov88dPV2er93ftatTT7ejOtRs3fru1BYpgvL/20l8/XZZBbbuadWy7AVy+/2zPRaDRcdtllrZ0NIc5Iyml9TsWJ2W7GZDNhspsw2Uyu58f2T3esbvq6+7X2Wn684Ud06tbvb37Ofew6derEnj17PCraP3jwIMOGDSMnJweNRnPOfexO1hKDJ0w2ExaHBavDitVhde07rfWfO6xYndZT0/3JcZvThsVhweKwUGuvxWw3n7K1K/Zm+TwABo2hXqDnr/Mn0CeQQH0gwT7BBPkEuR76oBP7xx4+mpYNrMWZ/bwzj2kLtmGxO0mLDeLfk/oTEeA5f/NCiPbN7rRTba2mylZFtbWaals1VdaqE9uTjtVLe2zfbDc3aZ7W3rKWQH3zxAxNPniisLDQvf/999+zcuVKnn766VOmOImIiDjPLJ+7LVu20KdPH/Ly8rj99tu5+uqrmTZtGgCPP/44e/fuZd68eezevZvLL7+cDRs2kJqa2qBre/U8dmdhc9gwO8yYbWZqHa5gr+7jeAB4fL/GVuN+VNuq62+trm2to7bR+TJqjQTqAwnyCSLUEEqYMYwwQxhhxjA6GDu498MMYYQYQqR2sAX8kVHGPZ9vprTGSmywkU8mD6BLZEBrZ8sj2e12Fi9eDMCYMWNkqSbhkTyxnDqcDqqsVZRbyqmwVlBhcT3KLeX1tsf3K62VlFvKqbHVNFke1Co1vlpf10Pni1FrPLE9dqzu9vj5uumNWiOpoanNVmPX5IGdWq1GpVJxpqQqlQqHo7GrBjTcoEGD2L17NwEBAdx///08//zz7j5jZrOZu+++u948dhMmTGjwtdtyYNccbE4bJpuJKmtVvcCvylpFpbXS/UdZYamo94d7/LlTObdOvCpUhBhCCDWEEukbSZRfFJF+kUT5RhHld+Jh1MqIr8Y6WlzD5E83caS4hgCDqx/ekM4dWjtbHkeWahLeoCXKqaIo1NhqKK0tpbS2lBJzCSW1rkepudS1PXa8tLaUKmsVSiPmXjVqjfjr/PHX+xOgC3Bt9QH4605s/+yYn84PP50ferXe4/uct/npTpqbBHYtx6k4qbZVU2GpoNLi+iZWWltKsbn4xA3BXEJxret5WW1Zg28CwT7B7iAvISCBhIAE4gPjSQhIIMovSmr9Gqisxso9n29mc0YZOo2KV2/oxY394lo7Wx7FbDZz0UUXAbBq1SqZRkJ4pMaUU0VRqLBUUGgupNBUSJGpiAJTgXu/2FzsDtosDsvZL3gSP50fwT7Bp3TdOb5/8rFAfSD+en+P6NPWEpolsFMUhX/961/s2rWLPn36cOeddzZJZj2RBHaey+F0UGYpcwV75mIKTYXk1+STb8p3bY89zjYYRavSEhsQS3yAK9DrFNSJzsGdSQlJIcgnqIU+jfeotTn468LtLNqRB8Ajl3bhL5cke/y3XCHE2SmKQkltCXnVeeTW5JJfk0+BqYAiUxGFpkL3vtXZ8CUkfbW+hBpCCTWGEmYIc3exCTWEurvWhPiEEGwIJkgfhE4muT+jZgnsHn30UebNm8fw4cNZvXo1d911Fy+++GKTZNjTSGDn3RRFocpW5Q7ycqpzyKrKcj0qXdsz3aDCjeF0Du5McnAyycHJpISkkBqa2u4HeDidCq8v3s//W3kIgHH94nj5+jT0WllMXAhP5nA6KDIXkVOVw5HSI+Sb8im2FpNXk0dutSuQa2hf6RCfECJ8Iwj3DSfSN9K9H24MdwVwxlBCDaHSFaaJNUtgFxcXx/Lly0lJSWHfvn1cddVVHDx4sEky7CnmzJnDnDlzcDgcHDhwQAK7NsqpOCk0FZJZmUlmVSaZlZkcqjjEwbKD5NbknvY1WpWWlJAUenToQY+wHvTs0JPOwZ3bTTNAXV9tyOD573fjcCr0TQjm/Yn9iAyUEbNCtCabw0ZOdQ6ZVZlkVWW5729ZVVnkVOWcdUYEFSrCfcOJ8Ysh2i+aSL/IUwM4Yzh6jb6FPpGoq1kCu8DAQCorK93PQ0NDKS0tbVxOPZTU2LVfNbYaDpUf4mD5QdLL0jlUfoh9pfsos5SdktZH40OPsB70j+pPv8h+9Anvg6/O9zRXbXtW7C/kL/O2UllrJzzAh/dv7Uv/pNDWzlarMZvNXHrppQAsXbpU+tiJZuFwOsipzuFwxWEyKjPIrDwWxFVlkleTd8aBaFq1lghDBAc2H8BWYmPandPoHN6ZGP8YYvxjiPKNkuZQD9YsgZ2fnx8rVqxwj4y97LLLWLp0ab2RsgMHDmxEtj2HBHaiLkVRyKvJY3fJbnYV72J3yW72FO+hylZVL51WpaV7WHf6RfbjwugL6RfVr0033x4truG+L/5gf0EVWrWKGVd3Z+KFie2y352MihVNyeqwklGZweGKwxwuP+zaVhzmaMXRM3YjMWqNroFigQnu/sPH98ON4ZQUlxAZGQlAQUFBi05RJhqnWQK7pKSkM96wVSoVhw8fPreceigJ7MTZOBUnmZWZbCncwub8zfxR8McpzbhGrZEBUQMYHjucYbHDiAtoeyNJayx2nvhmh3tQxU394njhup4YdO1rDWS73c6PP/4IwFVXXeUR84MJz+dwOsioyuBA6QEOlB3gYPlBjlQcIasqC4dy+unDfDQ+JAUmkRiYSGJgIgmBCe4ALswQdsb/0xUVFQQHBwNQXl5OUJAMFPMWMt1JI0lgJ85HbnUufxT8wab8TazNWUuhubDe+Y5BHbk04VLGJI2hS0iXNlOzpSgKH646zGu/7MOpQGpkAO9NuIAUmcxYCLcKSwUHyg64H/tL93Ow/OCfTg3ir/OnU3AnOgXVf8T4x6BRn98XJ6lZ9l4S2DWSBHaisRRF4UDZAVbnrGZNzhq2FW6r9w08KTCJyxIvY0zSGFJDG7Yiiqdbk17MtAVbKa62YtCpmXl1D8YPiG8zAawQDVVsLmZ38W5Xt42SPRwoO0BeTd5p0xq1RlKCU+gS2oXk4GQ6B3emU1Anwo3hTf63I4Gd95LArpEksBNNrdJayers1Sw+upi1OWvr9ZPpGtqV65Kv48qOVxJsCG69TDaBoioLj/5nG6vTiwG4slc0r9yQRqChbXfKdjgcrF69GoDhw4ej0bSvpuj2rLy2nN0lriDueDBXYCo4bdoYvxi6hHahS0gXUkNSSQ1NJT4gHrWqZaYMOv6/DZD/b15GArtGksBONKdqazUrs1fy69FfWZ2zGpvTBoBOrWNk/EjGdRnHhdEXem1Nl9Op8OHqw7yxeD92p0JssJHXbuzFsJS2uxSZ1IS0DxaHhT0le9hWuM09kCqnOueUdCpUdArqRI8OPege1p3UkFS6hHZptgXiG6qwsFAGT3gpCezOk8xjJ1paeW05i44s4vuD37O3dK/7eOegzkzoNoGrOl3ltVOobM0s4+H528gsda0CMmFQAs+M7Ya/T9sbWGAymRgwYAAAmzZtwtfXO39nor4iUxHbiraxrXAb24q2sadkD3bnqfPBJQYm0j2sOz3CXPNcdgvrhp/O84L7oqIidzBXWFhIeHh4K+dINJQEdo0kNXaiNewv3c836d/w/cHv3UuiBegDuDHlRm7rfhsRvt737brGYue1X/bx+boMAGKDjfx9XC+GJLfd2jvhnRxOBwfKDrgDue1F209bGxdqCKVPeB/SwtPo2aEn3UK7ec0yhFKz7L0ksGskCexEa6qyVvH9we+Zu28uWVVZAOjVeq5PuZ47e95JjH9MK+fw3P1+qJgnvt5BdpkZgBv7xvH02K508G+78/wJz2Z32tlXuo/N+ZvZVLCJLQVbqLZV10ujVqlJCU6hT0Qfeof3pk94H+IC4ry2m4QEdt5LArtGksBOeAKn4mR19mr+vevfbC3cCrgmQb6689Xck3YP8YHxrZzDc1NjsfPKz3v5akMmigKBBi2Pj0llwqBENGrv/EcpvIfdaWdvyV42FWxic/5mthRuocZWUy+Nv86f3uG96R3hCuLSOqThr/dvpRw3PQnsvJcEdo0kgZ3wJIqisLlgMx/u+JD1eesBV4B3U+pN3NfrPsKMYa2cw3OzNbOM6d/tYneua4nCtNggZl3bg74JIa2cs/NnNpu55pprAPjhhx9kSTEP4FSc7Cvdx/q89WzM38jWgq3uLg7HBegD6BfRj/5R/RkQNYDUkNTzniPOGxQXF7v71RUVFdGhg3SJ8BZtIrCz2+2MHz+e9evXk5ubS15eHlFRUe7zM2bM4OOPP6aiooLIyEieeeYZJk+eDMCKFSsYNWpUvQ7MP//8M8OHD2/Qe0tgJzzV9qLtvL/9fdbmrAXAV+vLpJ6TuKP7HV41yMLhVPhqQwZ/X7yfqlpXZ/SxaVE8PqYrHTt4Xy2C1IR4hrzqPNblrWNd7jo25G04ZY3nQH0g/SL7MSBqAP0j+9MlpEubDuROJqNivVebCezmzJnDoEGDGDx48CmBXXp6OjExMfj5+ZGens7FF1/MkiVL6NGjBytWrOD+++9n37595/XeEtgJT7cxbyP/+OMf7C7ZDUCYIYxp/aZxTedrWmxOrKZQVGXh74v3sfCPbBQFtGoVtw5K4KFLUryq/53dbmfBggUAjB8/XpYUayHV1mo25m9kXe461uet52jl0XrnfbW+DIwayKDoQQyIGkBKSIpX/X00NVlSzHu1icCuLpVKdUpgV1d6ejrDhw/n448/ZuzYsRLYiXZBURQWZyzmnS3vuAdZ9A7vzbODnqVbWLdWzt252ZdfyWs/72P5/iIA/PQabh+SxN3DOhLmRQGeaF5Oxcmekj2szlnNutx17CjaUW9FF41KQ88OPRkcM5jB0YNJC09Dp27bk2OfC6lZ9l7tJrB79dVXeeGFFzCZTAwcOJCVK1diMBhYsWIFl19+OYGBgQQFBXHbbbfx7LPP/uls8BaLBYvlxHp9lZWVxMfHS2AnvILNYePLvV/y/vb3MdvNqFVqbupyEw9d8JDXTMNw3O+Hinn1533syK4AwKjTMPHCBO65qBMRAYZWzp1oDRWWCtblrWN1tmt5vtLa0nrnEwMTuTD6QgbHDGZg1EAC9LJG8Z+RwM57tZvADly1Fhs3bmTp0qU8+eSTaLVa8vPzKS8vp0uXLuzbt4+bb76Zu+66i0ceeeS01585cyazZs065bgEdsKbFNQU8OYfb/LzkZ8BCPEJ4YmBT3Blxyu9anoGRVFYureQd5eluwM8H62am/rHMXloRzqHe94oRYfDwZYtWwDo27evLCnWCHXXWV6dvZrtRdvr1cr56fwYEjOEoTFDGRwz2Cun/2ktsqSY9/KKwG706NGsWrXqtOemT5/O9OnT3c/P1hQLMHXqVNLS0rjvvvtOOTd//nz++c9//un7SY2daEs25W/i5Q0vc7D8IADDYofx/IXPE+0f3co5OzeKorDiQBHv/pbOlsxy9/ERqeHcObQjw1M6eEzAKjUhjVNjq2F93npWZ69mdc5qCk2F9c4nByczPHY4w+OG0ye8DzqNNK+eDxk84b3OJbBrtR6+v/76a5Nez+l0cujQodOeU6vP3FnWx8cHHx/pxyPahgFRA/jP1f/hk12f8P+2/z/W5Kzhuu+vY1q/aYxPHe81ncdVKhUjUyMY0SWcdYdL+HjNUX7bV8CK/UWs2F9EcoQ/tw9O5NresQT5tu4/epVKRWJiontfnF1+TT4rslawPGs5G/M31luqy6AxMCh6EMNjhzMsbhix/rGtl9E2pG7ZlHLadnl0U6zFYkFRFIxGI0ePHiUyMhKDwdXP5qOPPmLcuHEEBgayevVqrrnmGubNm+cePNG5c2fi4+NJT0/nxhtvZOLEiTzxxBMNel8ZPCHaisMVh5n5+0z3BMd9I/oyc8hMOgZ1bOWcnZ+Mkho+/f0oCzdnU21xBQJ6rZorekZxc/94BncKQy2THXskRVFIL09nWeYylmctZ0/Jnnrn4/zjuCjuIi6Ku4j+Uf3x0ciX7aYmNcveyyuaYhsiKSmJjIyMeseOZ/eGG25g5cqVWK1WEhISePjhh7n33nsBePPNN/nHP/5BeXk5ERER3HbbbTz//PMNnoJAAjvRljgVJwv2L2D2H7Mx2U3o1XqmXjCV27vf7rVzeFXV2vjmj2zmb8piX36V+3h8qJHrL4jjmt7RJEdIJ/rWZnfa2Vq41R3M1V17VYWK3uG9GZkwkhHxI+gY2FFqkZqZBHbeq80Edq1FAjvRFuVW5/K3dX9jba5rcuO0Dmm8MPQFOgd3buWcnT9FUdiZU8GCTVn8sC2XKsuJ5ryuUQFc3TuGq3pFkxgm/8BaislmYm3uWpZnLmdVzioqLBXucz4aHy6MvpBRCaO4KO4iOhhl5YOWJIGd95LArpEksBNtlaIofHfwO/6+6e9U2arQqXU82OdBJvWYhFbt3ZPqmq0OFu/O53/bc1mVXoTNceLW1isuiMu6RTKqWwTdowObvGaotraW//u//wNcg7WOdxlpL4rNxe7+cutz12N1Wt3ngn2CuSjuIkbFj2JwzGCvWiGlrSkpKXEvI1ZcXExYmHctR9ieSWDXSBLYibYuvyafv637G6tzVgPQI6wHLwx9gZSQlFbOWdOoMNlcQd6OXH4/VILDeeI2FxNkYFS3CC7pFsngTmEYdI1vjm5vNSGKonCk4gjLslxNrDuLdqJw4mcc5x/HyISRjIofRZ+IPl7/paGtkFGx3ksCu/M0Z84c5syZg8Ph4MCBAxLYiTZNURT+d/h/vLrxVaqsVWjVWh7o/QCTe05uU7P1F1dbWLqngN/2FbImvRiz7cScaAadmgFJoQxN7sDQzh3oHhOI5jwGX9hsNj799FMAJk2ahE7Xdn5+xzmcDnYU73D3l8uorN//uWdYT0YmjGRk/EiSg5Olv5wHKi8vJyQkBICysjL38mLC80lg10hSYyfak0JTIS+se4EV2SsA6BbajReGvkBqaGrrZqwZ1NocrDtUwtK9BSzbV0heRW2980FGHYM7hTE0OYwLO4XROdy/XY+yrbXXsi53HcuzlrMye2W9VR+0ai2DogYxKmEUF8ddTKRfZCvmVDREe6tZbksksGskCexEe6MoCouOLOKVDa9Qaa1Eq9Zyb9q93J12d5udDFZRFA4UVLP2YDG/Hypm/eFS9xQqxwUatPRNDKFfQgj9EkPoHR+Mn0/bblYsqy1jZfZKlmcu5/fc36l1nAh+A3QBDI8bzsiEkQyLGYa/3vNWARF/TgI77yWBXSNJYCfaq2JzMS+se4FlWcsASA1J5cVhL9I1tGsr56z52R1OduRU8PvBYtYeLGFbVnm9ZlsAtQq6RQfSJz6YnrFBpMUGkRLpj06tYu/evQB069btrJOie5rMykyWZy1nWeYythVtw6k43eei/KIYFT+KkQkj6RfZr00107c3VVVV7v9plZWVBATIlEDeQgK7RpLATrRniqLwy9FfeHnDy5RbytGqtNzd627uTbu3zdbenY7N4WRfXhV/ZJTyR2Y5fxwtJfekplsAnUZFcrgfGxd/gzX/IL8u+De9EsM9umbPqTjZVbyL5VnLWZ65nEMV9Vft6RralZHxrv5yXUO7Sn+5NkIGT3gvCewaSQI7IVy1dy9veJklGUsASAlJ4cWhL9I9rHsr56z15Jab2ZJZxs6cCnblVLArp5IKs+20aWODjaRGBZAS6U+XiAC6RAaQHOGPUd86k0JbHBY25G1w9ZfLWkmRuch9TqvS0i+qnzuYi/GPaZU8iuYlgZ33ksCukSSwE+KExUcX89L6lyizlKFRabiz553c3/t+9Bp9a2et1SmKQnaZ2RXk5VawM6eSPbmVFFdbTptepYKEUF86h/uTFOZHUgdfksL86NjBj5hg43mNyD2TCksFq7JXsTxrOWtz1mKym9zn/HR+DIsdxsj4kQyLHUaQT1CTvrfwPNLHzntJYNdIEtgJUV9pbSmvbHiFX47+AkBycDIvDH2Bnh16tnLOPFNZjZUDBVUcKKzmQH6Va7+gijLT6Wv3APQaNfGhRjp28CMxzI+kMF/iQnyJDTESG2xsUNPu8fnlVmWvYlXOKrYUbMGhnOgnGOEb4a6VGxA1QILzdkYCO+8lgV0jSWAnxOktzVjKC+tfoLS2FLVKzQ0pNzClzxRZGqoBFEWhuNpKekEVh4tryCip4UixiaMlNWSWmLA6nGd8fbCvjthgV5BXN+CLCFSTZ93FtuL1rM5ZVW89VnA1oY+Md00W3D2su/SXa8cksPNeEtidJ5mgWIizK68t59VNr7Lo8CLA1aR3d9rd3Nb9Nnw0Pq2cu9ZRW1vLXXfdBcC///3vc15SzOFUyC03c7SkhqMlJo4W15BRYiKn3ExOmYnK2vrTsKi05Wj996H134fG7xAq9YmaQJWiJUjVlQRjP3qGXEhySCKRgYZjDx+CjDoJ7topWVLMe0lg10hSYyfE2W0p2MLrm15nd8luAGL8Ynio70NckXQFGnXrDBBoLc1dE1JUU83yIxtZm/M7O0o3UGw9Wu+80xaEvbor9upUHDXJoPx5E6teoybMX0+on+vRwd+HUD89Yf56wvz0hPr5uPfD/H3w02skEGwjZPCE95LArpEksBOiYZyKk0WHFzF7y2wKTYUAJAUmcV/v+9pVgGez2ZgzZw4AU6ZMafSSYoqicKDsAOvz1vN77u9sKdhSb6JgtUpN7/DeXBR3EcNjh9MxMJniaiuFVRYKKmsprKyloNK1X1BlOfa89ox9/P6MXqsmzE9PkFFHkFFHsO/xretYoFFH8MnnjHoCDNp2vWqHJ5IlxbyXBHaNJIGdEOfGbDfz5Z4v+WzPZ1RYKgBXgHdnzzsZ22lsu22iPRcFNQWsz1vPurx1rM9dT0ltSb3z4cZwBscMZkjMEIbGDCXYEHzO71Frc1BcbaG0xkpJtZWSGislx5/X2S+utlJSY6HWduZ+f2eiUkGgQUeAQYu/j9a99auz7++jw9+gJeDYcf+T0vobtPjptU0+Wri9kj523ksCu0aSwE6I81NtrWbevnn1ArxQQyjjuoxjfOp4Inyl6ee4nOocNudv5o+CP9hcsJmsqqx6541aI/0i+zEkZgiDowfTObhzizeJmqx2SqqtlNZYqTDbqDDbKDfbqDTbKDfVOWayufcrzDZMVsfZL34OfLRqjHoNvjqNa6vXYnTva+rvHzvn2j/5vBYfrdr10GlO7Gs16DSqNt/kLIGd92oTgZ3dbmf8+PGsX7+e3Nxc8vLyiIqKcp8/cuQI9913Hxs3bsTPz4+pU6fy9NNPu89/+umnTJ8+ncrKSm688UY++OAD9PqGDe2XwE6Ixqmx1bBg/wLm7ZtHfk0+4JoEd2TCSK7tfC1DYoe0qaWpnE4nmZmZACQkJJyypJhTcZJRmcGWgi1sLnAFc3k1efXSqFVquoV2c9fK9Q7v7bXTkVjtzmNBnpWqWjvVFjvVtXaqjm1rLK5jx5/XPe8+V2vD5mi5f08qFe4gzxX4qTFoNfjo6hw7fl6nrpdWr1Wj0xzfqtBp1McedffrP9drVWjVJ/aPH9dqVOhPek1TBZyypJj3ajOB3Zw5cxg0aBCDBw8+JbC76qqriIuL49133yU7O5uhQ4fyxRdfcMkll7Bz504uvvhifv31V1JSUrjuuusYPnw4f/vb3xr03sd/gLm5uURFRbn/qKxWKzabDa1Wi4/PiaalmpoaAIxGo/uGbrPZsFqtaDSaeiPkziWtyWRCURQMBgMajcb9c7FYLKjVaoxG43mlNZvNOJ1OfHx80Gpdc2M5HA5qa2vPKa1KpcLX19edtra2FofDgV6vd/cxOpe0TqcTs9kMUO+bpMViwW63o9Pp3MH5uaRVFAWTyTUxq6+v7ym/z3NJ25DffVOUk9P9PpuinBz/fTa2nJz8+/yztNWmalblrmLh4YVsLdzqTh/iE8LliZcztvNY0jqkoUJ12t9nY8tJ3d9nY8vJn/0+fX19MZlM7pqQgoICnD5ODlQdYEfRDnYW72Rn0U6qbFXUpVVp6RrSlb4RfRkUO4gLIi4gQB8g94g6ac0WGxZFjV1RYbY6qLbYKK8yUWtz4lTrMFnt1NocVJos1FhsWBxgdYDJ6sBktVNTa8Vsc2A5dsxsdWCxO7DYHFgcClb7+Tc1tyStWoVOo0KrUaNVq9Co1WhUoFGDVqNBq1GhVatQq1SoUdBq1Oi0GjRq13GV4kSjVqE4HSz++WcUxcF111yD0UePVqPG51hzt1atxulwoFGDj07rvoYaBcXpPJbWNapao3KVHxUKPnodWo3G1adSUXDYbWjUagwGH9Qq0KhU2Gw2d1qd9lj/S8WJ3WZDo1ZhNBjQqF1BrN1mBcWJj16PTnesKV5RsFktqFUq/Hx9j6UFm/VEWr1eh+uvVnGVVZXr716tUqHC9bfsdNjR63T4+PigUoEKMJtNqMD9NwxNf49obBxhtVq9P7CrS6VSnRLYpaWl8c477zBy5EgAbr75ZoYPH85DDz3E008/TXl5Oe+//z4Ay5Yt4+677+bw4cOnvb7FYsFiOTFTfGVlJfHx8YBrFFF4eDgAL730EtOnT+fuu+/mX//6lzu9n58fJpOJI0eOkJSUBMDs2bN55JFHmDBhAl999ZU7bXh4OMXFxezatYsePXoA8K9//Yt7772Xa6+9lu+++86dNikpiYyMDDZu3MiAAQMA+Oqrr5g4cSKXXnopS5Yscaft0aMHe/bsYfny5YwYMQKA7777juuvv54hQ4awdu1ad9oBAwawefNmfvzxR6688koAlixZwujRo+nduzfbtm1zpx0xYgQrV67kP//5DzfddBMAa9euZdiwYSQnJ5Oenu5Oe+WVV/LTTz/xySefMGnSJAC2bdvGBRdcQExMDDk5J+bXuummm/j666957733mDJlCgDp6el06dKFoKAgysvL3WknTZrEZ599xuuvv87jjz8OQE5ODnFxcWi1Wmy2Ex3Cp0yZwj//+U9mzJjBzJkzgfodhq1Wq/sfyuOPP84bb7zBY489xt///nfA9Yd0/I+4bufimTNnMmvWLB588EF3J3kAnU6H3W4nOzub2NhYAP7+97/zxBNPcMcdd/Dpp5+60wYHB1NRUcGBAwdISUkBXFPsTJ06lXHjxrFw4UJ32tjYWHJzc9m6dSt9+vQBXLXQkydPZuzYsSxatMidNiUlhYMHD7JmzRqGDh0KwMKFC7n55pu5+OKLWbFihTttnz592L59O7/++iuXXXYZAIsWLeKqq66if//+bNq0yZ126NCh/P7773z77bdcd911AKxYsYKRI0fSvXt3du/e7U572WWXsXTpUr788ktuvfVWADZt2sTAgQNJTEzk6NGj7Cvdxw+HfmDulrk4DCea6kJ8QkjzT+PLWV/ik+9DYVah+9ytt97K3Llzeeutt5g2bRoAR48epWPHjvj6+rpvhgD33HMPH330ES+++CLPPvssAEVFRe6Rf3VvddOmTePtt9/mmWee4aWXXgL+vJnq2Wef5eWXX+bhhx9m9uzZ7mscv1HvzthNjjWHSU9MQhutxZBkQB9xak2b0+okLTyNoYlD6R/ZnzX/WcMTjzwh94hWvEc8//wMrA4nhSVldExOQaXRs2vvfhyoqLU5eee9f/Kf/37L9TfcxPhbJ2KxOzFZbPxl2qOotHqefGY6Kq0Oi83Jug0b+WPrNlK7deeCfgOwOZzYHAo//bIYVBoGXDgYlUaLzaGQX1hEYXEJ/oFBBAaHutLanVSbzKi03llL21aoVKBWqXA6HK4gV6tBr9PiCg0VaqqrAYXgoCBUKhVqletLk6mmhms1W3hv9pt1ruW6RzRFHPH+++83OLDz3FWqz2LKlCnMnz+fIUOGkJmZyfr163nuuecA2LNnD2PGjHGn7d27N0eOHMFsNtf7pnncK6+8wqxZs1os70K0R11Du9I1tCtrX17LskPLGDNtDLnGXMosZayyrCJhagKKU+HGH26kb0Rf+kb2xWK0gCd0e1JBra6WDXkbyKjM4EjFEZKeTMIQb2D88vEAhF4bWu8lHYM6ktYhjV4denH/dfdTfrCcRQcXuW/aG9jQ0p9CnEStVmFQawg0aHHWlAPQOdzP/eUvgnJqD/9BLCO5to/ri5vNZmPy5u8BeHD4hye+/G1ZyK8/v0Ofjg/y3oT73O+hu2cIdrud//fGSV/+Xj725e/vn7rTHv/yt3fffhI7dsbmdPLBhx/x9LPPMfaqq5n9zrs4nAp2p8Klo8dQXFzCZ198QafOKdidTn7+ZTFv/OMtBgwYyLPPPe9O+8gjj1JQVMyjjz3OW7PfRqXWMPG22/hy7nw6Jydz1933YHcqOJwKH370bwqLirlx3E3ExSdgdzo5cjSTxUuWENYhnLFjr8ThVHAqCkt/W0ZJaSkXXjiYyKgonAoUF5ewYdMm/Pz9ufDCwTicCooCO3btoqKiks7JyYSEhuFUFKprTKQfPIhe70On5GQUBZyKQl5eASazmZDQMIy+vjgVsNpslJWXo1ZrCAgMwqm48mCx2nA6FVQaDU1xs1AUcCgKqNSotGqcUG8QkdrHVatcf25JHRq/YKjFI3htjd2OHTuYOHEie/bsweFwMHPmTGbMmAHAJZdcwuTJk5k4cSJwohambtRc15/V2ElTrDTFSlNs45piz/a7RwPbCrexPHM5q7JXkVGVwcl8tb4kByeTGppKYmAi0X7RhGnDiPaLJjo42v07akxTrNVhpchUREZpBoXmQiocFRSYCsirzuNoxVGyqrOwOE6//qtGpSEpMIkuoV1IDkwmOTCZXuG96BBwYjUOuUec+ruXe0TL3yN8fHzc/+v0ej1Wq9Xj7xFn+92fnFZRFBwOJzVmE4oCRl9fFMUVsNVaLNjsNrQaHVq9DsXp6v9aYzqR1qko4E5rR6PRotXp3EGnyWR2/SwNBpRjtXgWqw2bzUZqVBBG46l/y+2mKXb06NGsWrXqtOemT5/O9OnT3c9PDuwcDgeJiYk8+eSTPPDAA2RnZ3PVVVcxc+ZMxo0bx7XXXsuYMWN48MEHgROzbZtMptPW2J1MBk8I0TqKzcVsKdjC1sKtbCncQnpZOjbnn8+9plVrCdIHEewTTJBPEIH6QHQaHTq1Dr1Gj1atRVEUbE4bNocNm9OG1Wml2lpNpbWSSmslVdYqzHbzWfOmUWmIC4gjISCBxMBEuoR0ITU0lc7BnWU6FyFEszqXuKTVmmJ//fXX835taWkpubm5PPDAA2i1WpKSkrjuuutYvnw548aNo3v37uzcudOdfvv27XTs2LFBQZ0QovV0MHZgdNJoRieNBsDmtJFZmcmBsgOkl6WTXZVNTk0OudW5FJuLsTvtlNSWnDLn2/nQqXVE+EYQ6RtJpG8kEb4RRPlFkRDoCuRi/GP+dCSvxWJh6tSpALz33nv1vokL4SmknLYPHt0Ua7FYUBQFo9HI0aNHiYyMdFcvJyYm8tRTT3HfffeRm5vLFVdcwZQpU7j//vvZuXMnI0aMYMmSJXTu3JkbbriBoUOHnvOoWKmxE8Jz1dprKbeU13tUW6vr1c7ZnDY0Kg06tQ6tWotOrUOn0eGr8yVQH+iu5Tv+ON9pJWR+MOENpJx6L6+osWuI1NRUMjJcfW6Odzg+Hod+/fXXPPzwwzz11FP4+voyfvx47rnnHsA1YvbNN9/k6quvds9jd3yUnBCibTBoDURpo4jyizp74mam0+l48cUX3ftCeCIpp+2DR9fYtRapsRNCCCGEpziXuER9xrNCCCGEEMJreHRTrBBCeANFUSguLgagQ4cObX7NUeGdpJy2DxLY1TFnzhzmzJmDw9G0C1gLIdo2k8nkXuFCOqULTyXltH2QPnanUVFRQXBwMFlZWdLHTghxVjU1NcTExACQm5sr/zCFR5Jy6r2OL5xQXl5OUFDQGdNKYHca2dnZ7rVihRBCCCE8QVZWFnFxcWdMI4HdaTidTnJzcwkICDhjH4QBAwbUWzT9XNIcj77bQq1gQ34O3vC+TXG987nGubymoWnPlu5M56Vset57NvaazV0uG5pe7pknSNk8/9e3x3umoihUVVURExPjXnLsz0gfu9NQq9VnjYgBNBrNWX+JZ0sTGBjo9TephvwcvOF9m+J653ONc3lNQ9OeLV1DriNl03Pes7HXbO5y2dD0cs88Qcrm+b++vd4zz9YEe5xMd9IIU6ZMaZI03q61PmNTv29TXO98rnEur2lo2rOlaw/lElrnczbHezb2ms1dLhuaXu6ZJ0jZPP/Xyz3zzKQptpXIJMjCU0nZFJ5IyqXwVJ5WNqXGrpX4+PgwY8YMWYRZeBwpm8ITSbkUnsrTyqbU2AkhhBBCtBFSYyeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYOfBVq5cyeDBgxk2bBiPPvpoa2dHCLesrCz69u2LwWDAbre3dnZEO/foo48yfPhw/vKXv7R2VoQAWvceKYGdB0tOTmbFihWsWbOG/Px8du7c2dpZEgKA8PBwli1bxoUXXtjaWRHt3JYtW6iurmb16tXYbLZWWd5QiJO15j1SAjsPFhsb654XR6fTodFoWjlHQrgYDAaCg4NbOxtCsG7dOi699FIALr30UtavX9/KORKide+REtg1oRkzZtC9e3fUajXz58+vd66oqIgrr7wSX19fUlNT+e233xp83S1btlBcXEz37t2bOsuinWiusilEUzqfclpeXu6e7T8oKIiysrIWz7do27zt/qlt7Qy0JSkpKbz99ts899xzp5ybMmUKMTExFBcX8+uvv3LTTTdx6NAhLBYL//d//1cvrb+/Pz/++CMA+fn5/OUvf+Gbb75pkc8g2qbmKJtCNLXzKafBwcFUVlYCrqWdpCZZNLXzKZchISGtkNNjFNHkLr74YmXevHnu51VVVYper1dyc3Pdx4YPH6589tlnZ7yO2WxWRo4cqWzZsqXZ8iral6Yqm3WvZ7PZmjyfon07l3L6xx9/KPfee6+iKIrywAMPKBs2bGjx/Ir24Xzun61xj5Sm2BaQnp5OUFAQ0dHR7mO9e/dm9+7dZ3zdJ598wp49e3jkkUcYMWIE69ata+6sinbmfMtmbW0tl156Kdu3b2fMmDGsXr26ubMq2rEzldO+fftiNBoZPnw4arWagQMHtmJORXtypnLZmvdIaYptAdXV1e4+IMcFBgZSXl5+xtc98MADPPDAA82YM9HenW/ZNBgMLF26tBlzJsQJZyuns2fPbvlMiXbvTOWyNe+RUmPXAvz9/d19QI6rrKzE39+/lXIkhIuUTeENpJwKT+Sp5VICuxaQkpJCRUUF+fn57mPbt2+nR48erZgrIaRsCu8g5VR4Ik8tlxLYNSGbzUZtbS1Op7Pevr+/P9dccw0zZszAbDbzww8/sGvXLq6++urWzrJoJ6RsCm8g5VR4Iq8rly06VKONu+OOOxSg3mP58uWKoihKYWGhcsUVVyhGo1FJSUlRlixZ0rqZFe2KlE3hDaScCk/kbeVSpSiK0vLhpBBCCCGEaGrSFCuEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEEII0UZIYCeEEK1o5syZ6HQ6oqKimuyaI0aMYP78+U12vZP94x//wM/PD4PB0GzvIYQ4PxLYCSFaXVJSEr6+vvj7++Pv709SUlJrZ6lF3XXXXfUWEm8OPXv25OjRo01yrUcffZTdu3c3ybWEEE1LAjshhEdYtmwZ1dXVVFdXnzYAsdlsLZ8pD9AUnzs7Oxu73d7uAmYh2iMJ7IQQHmnFihV07dqVZ599lg4dOvDyyy9jNpuZOnUqMTExxMXF8dprr7nT19TUMGHCBIKDg+nbty/PPPMMl19+eb1r1aVSqdy1ZKWlpUyYMIGIiAg6derEZ5995k43YsQI/va3v9G/f38CAwO55ZZbsFqt7vMLFiygZ8+eBAQEkJaWxv79+3nppZeYPHlyvfcbOnQo//3vfxv02ZOSknj99ddJTU2le/fuADz44IPExMQQHBzM6NGjyczMdKfftGkTvXr1IjAwkPvuuw+n01nveosXL2bMmDHuzzNr1iwuuOAC/P39efzxxzl48CADBgwgODiYxx57zP26H3/8kdTUVAICAoiPj2fevHkNyr8QovVIYCeE8FgHDx7E19eXvLw8nnzySR577DEqKio4cOAAGzdu5PPPP+d///sfALNmzaKkpITMzEzmzp3LF1980eD3ue2224iPjycrK4uffvqJp59+mu3bt7vPL1y4kP/+979kZmayY8cOFixYAMDatWuZOnUqH3zwARUVFSxcuJDAwEBuvfVWvvvuOywWCwAZGRns2bOHsWPHNjhP3333HatXr2bnzp0ADBs2jL1795Kfn09cXBx/+ctfALBardxwww089NBDlJSU0LNnT37//fd61/rll1/cgR3AN998w6JFi9i1axfvv/8+DzzwAN9++y27du3io48+cn/2u+++m48//piqqio2bdpE7969G5x/IUTrkMBOCOERLrvsMoKDgwkODubpp58GwNfXl6eeegqdToePjw+ffPIJb775Jv7+/sTExPDAAw/w9ddfA67g67nnniMwMJCuXbtyxx13NOh98/PzWb16NS+//DI+Pj507dqVCRMm1Ktdu+eee0hISCA4OJgrr7zSHfh8+umnPPDAAwwdOhS1Wk3Xrl2Jjo4mKSmJnj178tNPPwEwf/58rrvuunMabPDII48QERHhfs2ECRMICgrCYDDw5JNPsmbNGgDWrVuHj48P99xzDzqdjqlTpxIdHe2+jsPhYM2aNYwYMcJ97O677yYmJoakpCT69evH6NGjiYuLIy4ujkGDBrFjxw4AdDodu3btorq6mqioKHftoRDCc0lgJ4TwCEuWLKG8vJzy8nJeeeUVAKKjo9FoNAAUFRVhNpvp0qWLOwB85plnKCwsBCAvL4/4+Hj39erun0lmZiY1NTWEhYW5r/vBBx9QUFDgThMREeHe9/X1pbq6GnD1XevUqdNprztx4kT3yNS5c+cyYcKEhv4oAIiLi6v3/KWXXiI5OZnAwEAGDhxISUkJcOrnVqlU9V67YcMGevbsia+v72k/j9FoJDw8vN7zmpoaAL7++mt++OEHYmNjGT16NPv27TunzyCEaHna1s6AEEL8GZVK5d7v0KEDBoOBjIwMgoKCTkkbHR1NVlYWiYmJAGRlZbnP+fn5YTKZ3M/rjkCNjY0lODjYHSidi/j4eI4cOXLaczfddBNPPfUUGzdupLCwkFGjRp3Ttet+9pUrV/LBBx/w22+/kZyczIEDB9x9BqOjo8nOzq732rrPT26GPReDBg1i0aJFWCwWnn/+eaZMmcJvv/12XtcSQrQMqbETQngFtVrNHXfcwWOPPUZ5eTlOp5O9e/eyceNGAMaNG8dLL71EVVUV+/fv5/PPP3e/tkuXLpSUlLBy5UosFgsvvPCC+1xsbCwDBgzg+eefx2QyYbfb2bJlC3v27DlrniZNmsT777/PunXrUBSF/fv3k5eXB0BoaCgXX3wxkyZN4uabb3bXPJ6PqqoqtFotYWFh1NTU8OKLL7rPDR48GLPZzL///W9sNhtz5sxx5wHqD5w4F1arlblz51JZWYlOp8Pf379Rn0EI0TIksBNCeI3jE+OmpaURGhrK7bffTllZGQAzZswgKCiIuLg4brnlFm677Tb364KCgnjnnXe4+eab6dixIwMHDqx33a+++oqMjAw6depEREQE06ZNw2w2nzU/Q4YMYfbs2dx5550EBgZy0003UVlZ6T4/ceJE9u7de87NsCe7/PLLGTx4MImJiaSlpTFkyBD3Ob1ezzfffMNbb71FWFgYO3bscJ8vKSkhLy+PtLS083rfzz77jMTEREJCQliyZAlvv/12oz6HEKL5qRRFUVo7E0II0dQ+/fRT5s+fzy+//NJqeVi3bh0TJ07k0KFDf5rmxRdf5NVXXyU4OPiUJtXGmjdvHkuWLOHjjz9u0uvOnj2bGTNmoFKpKC8vb9JrCyEaR2rshBCiGdhsNt555x3uvPPOM6abPn061dXVTR7Ugas5+MEHH2zy606bNo2KigoJ6oTwQDJ4QgghmlhJSQlxcXH06tWLDz74oNXycb6DJoQQ3kuaYoUQQggh2ghpihVCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCMksBNCCCGEaCP+PwXWcC4Tl5c1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Design parameters\n",
+    "Td = 1                    # Set to gain crossover frequency\n",
+    "Ti = Td * 10              # Set to low frequency region\n",
+    "kp = 500                  # Tune to get desired bandwith\n",
+    "\n",
+    "# Updated gains\n",
+    "kp = 150\n",
+    "Ti = Td * 5; kp = 150\n",
+    "\n",
+    "# Compute controller parmeters\n",
+    "ki = kp/Ti\n",
+    "kd = kp * Td\n",
+    "\n",
+    "# Controller transfer function\n",
+    "ctrl_shape = kp + ki / s + kd * s\n",
+    "\n",
+    "# Frequency response (open loop) - use this to help tune your design\n",
+    "ltf_shape = ct.tf(P_tf * ctrl_shape, name='L_shape')\n",
+    "\n",
+    "ct.frequency_response([P, ctrl_shape]).plot(label=['P', 'C_shape'])\n",
+    "ct.frequency_response(ltf_shape).plot(margins=True)\n",
+    "\n",
+    "ct.suptitle(\"Loop shaping design for servomechanism controller\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d731f372-4992-464c-9ca5-49cc1d554799",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Step response for servomechanism with PID controller')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute the transfer function from r to y\n",
+    "T_shape = ct.feedback(ltf_shape)\n",
+    "ct.step_response(T_shape).plot(time_label=\"Time [ms]\")\n",
+    "plt.suptitle(\"Step response for servomechanism with PID controller\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "JL99vo4trep5",
+   "metadata": {
+    "id": "JL99vo4trep5"
+   },
+   "source": [
+    "### Closed loop frequency response\n",
+    "\n",
+    "We can also look at the closed loop frequency response to understand how different inputs affect different outputs.  The `gangof4` function computes the standard transfer functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ceqcg3oM619g",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ct.gangof4(P_tf, ctrl_shape);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gel18-iqwYYs",
+   "metadata": {
+    "id": "gel18-iqwYYs"
+   },
+   "source": [
+    "### Stability margins\n",
+    "\n",
+    "Another standard set of analysis tools is to identify the gain, phase, and stability margins for the sytem:\n",
+    "\n",
+    "* **Gain margin:** the maximimum amount of additional gain that we can put into the loop and still maintain stability.\n",
+    "* **Phase margin:** the maximum amount of additional phase (lag) that we can put into the loop and still maintain stability.\n",
+    "* **Stability margin:** the maximum amount of combined gain and phase at the critical frequency that can be put into the loop and still maintain stability.\n",
+    "\n",
+    "The first two of the items can be computed either by looking at the frequeny response or by using the `margin` command.\n",
+    "\n",
+    "The stabilty margin is the minimum distance between -1 and $L(jw)$, which is just the minimum value of $|1 - L(j\\omega)|$.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "m-8ItbHwxLrv",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gm = inf (at nan rad/ms)\n",
+      "Pm =  47 deg (at 0.15 rad/ms)\n",
+      "Sm = 0.6 (at 0.19 rad/ms)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bqiyGOXPmwMzMDHPnzi2zXOfOndGoUaMSyQ8hBK6urujduzc7LSEhAUOGDIGBgQGMjIwwdOhQ3Lx5EwzDYOfOnWy5Tp06Ke2XOSgoCE5OTgrTPr59n5eXh6+//hrOzs4QCoUwNTWFl5cXO7x1UFAQNm7cyM5b/Cir3+biKhRXr16Fj48PdHV1YWtri4ULF0Imk5W5fwDg4cOH6N+/P0xMTCAUCtGiRQvs2rWLff3SpUtsUjVmzBg2ptKqJSxevJhN3OfOnQuGYRT2y7Vr19C5c2cYGBhAT08P7dq1K/FZL642cvbsWYwdOxYWFhbQ09Or9A8jhmEwdepU7NmzB+7u7tDT00Pz5s1x4sSJEmWfP3+OESNGwNLSEgKBAO7u7ux7AxQdQ1ZWVpgyZQo7TSaTwcTEBBwOB+/evWOnr1q1CjweDxkZGSrHXLwvlD0+PA4LCgowb948tqtCW1tbTJkypcQ65XI5Vq5ciUaNGkEgEMDS0hKjR48uMXhM8XEVFhaGdu3aQVdXF05OTtixYwcA4OTJk2jVqhX09PTQtGlTpf2+f2ofFnv69Cl69OgBPT09mJubY+LEicjOzlZ5X1UI0QBHR0cSFhamiVWrbPfu3cTY2Jj88ccfJCsri8hkMnLjxg0yYcIETYdWLlwulyQmJip9LS8vj5ibm5P09PRyLatjx47kzz//VGN0lVdYWKjyPAAU9smePXvIl19+WeY8iYmJxMrKikgkEpXXERYWRnR1dcnp06dVjlVdZDIZadOmDfH29iYrVqwotVzLli3Jtm3blL724MEDYmJiQsLDw0lGRgbp1KkTWbhwISGEEIlEQoyMjMiePXuIXC4np06dIiKRiGRmZpYrvqo4J8TFxREbGxsik8nUEsPGjRtJy5Ytybt378jTp09JvXr1yL///qu0bHh4ONm7dy/53//+R7p3717i9Y+PwfJS9Xh//fo1EQgECtP+97//lXkMEELIrVu3SMuWLVWO71N27NhBAJDw8HCydu1aAkBhH3bs2JE0btyYfX7s2DECgJw7d05hOSdPniQAyMmTJwkhRecyd3d3YmRkRNavX09CQ0PJtGnTiIODAwFAduzYobCOjh07logtMDCQODo6KkwDQBYtWsQ+//LLL4menh5ZtWoVuXjxIjlx4gT58ccfyfr16wkhhLx48YIMHjyYACBhYWHso6CgoNR90rFjR2JmZkZsbGzIunXr2NgBkClTppQZz9OnT4mBgQFxcXEhu3fvJidPniTDhw8nAMhPP/1ECCEkMzOT3e/fffcdG1NcXJzSeOLi4sjhw4cJAPLVV1+RsLAwEhERQQgh5NKlS4TP5xNPT09y4MABcvToUdKtWzfCMAzZv38/u4zi9dna2pIJEyaQ06dPk0OHDhGpVKp0na9fvyYAyM8//1zqfireficnJ+Lt7U3++usvcurUKdKpUyfC4/HIy5cv2XKPHj0iRkZGpGnTpmT37t3k7NmzZPbs2YTD4ZDFixez5YYNG0YaNmzIPr958yYBQHR1dcnevXvZ6T179iTe3t5lxrZo0SKiLKV6//69wrEQFhZGVq1aRQCQyZMnE0IIkcvlpHv37oTH45GFCxeSs2fPkl9++YXo6+uTli1bKhw/EyZMIADI1KlTyZkzZ8iWLVuIhYUFsbe3J8nJyWy54uPKzc2NbN++nYSGhpI+ffoQAGTJkiWkadOm5M8//ySnTp0iPj4+RCAQkPj4eJX3YVJSErG0tCS2trZkx44d5NSpU+SLL75gP3sXL14ssU8CAwOJvr5+mfuzvGpFEuvo6EhWr15NGjVqRIyNjclXX32l8PqGDRuIq6srMTMzI6NHjyY5OTmEEELatm1Lzp49SwghZOfOnQQAeffuHSGEkBkzZpAffvihzPXKZDJibW1N1q1bV2Y5AGTTpk3E3t6emJmZkf3795N//vmHODs7EwsLC4UPtzp8mEwGBgaSmTNnks8++4yIRCLSrVs3kpaWRgghRF9fnwAgenp6pG3btiWWc+HCBdKqVSuFaUuXLiUODg7EwMCA+Pj4kPv377PTORwOEQgERF9fnz1BfqisWGQyGRk4cCCxsLAgJiYmZPDgwSQ1NZUQ8t8XbUhICKlXrx6xsrIiu3fvVrrtxWXXr19PrK2tSVBQEElJSSHdu3cnZmZmxNzcnIwfP17hQ79t2zZia2tLrKysyJYtW0okECNHjiSHDx8mMpmMfPXVV8TMzIwYGBiQli1bKpwU3NzcyK1btz79BpGSSYqXlxf5+eef2fg3bdpELCwsiJ2dHbl48SLZtm0bqVevHrG3tyeXLl0q1zpUsXnzZjJt2jQSGBhYagLz+PFjIhAISEZGhtLXv/32WzJx4kT2+b///kucnZ0JIYSkpKQQhmEUvqQsLCzIgwcPlC7r5MmTxMXFhZiYmJBFixYpnBPy8vLIlClTiLW1NbG1tSU//vgjO19WVhYZMmQIMTIyIi1btiQLFixQmiQSQsjvv/9OBg0apDBt0qRJxNramhgZGZGuXbuSN2/eEEIICQ4OJgzDEF1dXaKvr6/0M+vj46MwfeHChWTMmDFK111sx44dlUpiL168SNzc3Mj8+fOJmZkZWbx4MXn27Bnx8/MjRkZGxNramsybN09hnmXLlhELCwvi6OhI1qxZUyKJ9fX1JRERESQvL48MGzaMGBsbEyMjI9KuXTu2jFQqJfr6+uz5Ul0+TGLFYjGpX78+8fLyInK5nBBSMomVyWSkfv36pH///grL6dmzJ3FxcWHn27x5MwFAjh07plBu/Pjxak1imzRpQgYMGFDmNk6ZMkVpMlOajh07lho7h8Nhj1Fl8QwbNowIBAISGxurMG/Pnj2Jnp4e+1kODw8vsR/KUlpS6ePjQywtLUl2djY7TSqVkiZNmhA7Ozv2/Sh+n0ePHl2p9X0MALGysiJZWVnstKSkJMLhcBTOa927dyd2dnYlfkRPnTqVCIVC9ntp27ZtBAC7/5YtW0YaNWpE+vXrx362CwsLib6+Ppk/f36ZsZWWxH7s6dOnxMzMjPj7+xOxWEwIIeTMmTMEAFm5cqVC2QMHDhAAJCQkhBBCyJMnTxSS32K3bt0iABRiLD6u7ty5w05LTU0lXC6X6OrqKiSskZGRBIBCrlPefTh37lzCMAyJjIxUKNe1a9dqSWJrTZ3Y48eP49q1a4iKisL+/ftx9epVAMDBgwcREhKC8+fPIy4uDhKJBN9//z2AopGNistdu3YNzs7OuHbtGvvc19cXsbGxMDY2RmxsbIl1RkdHIzExEf369ftkfNevX8ezZ8+wefNmTJ48GX///Tc79OnUqVNLvS1kbGxc6qM41k85cOAA1q5di+TkZEilUmzYsAFA0bCgAPDy5UvcuHGjxHyPHj1CgwYNFKZ5eHjgzp07SEtLQ9euXTF69GgAwMKFC+Hn54edO3ciJycHc+bMUSkWoGjY0devX+P169fIzs7G0qVL2dcKCwsRHR2NN2/eYPfu3ZgyZQry8vKUrqOwsBCPHz/Gq1evsHnzZsjlckydOhXx8fF48OAB7ty5g82bN7PbOHv2bBw5cgSvX78usU8JIbh48SI+++wznD17Fjdu3MCrV6+Qnp6Obdu2KdQndHNzQ1RUlPI3oQw3btzAo0eP0Lx5czb+mJgYxMfHY/r06Rg5ciQeP36MN2/eYM6cOZgxY4bS5RQfq6U9lB3DAJCWloY1a9Z8slXz3r170adPH3YEt489fvwYTZs2ZZ83b94cr1+/Rn5+PszMzDB06FDs2bMHMpkM//zzD/T19dGwYcMSy0lOTsawYcOwbt06JCUlIS8vT+FW2Ndff43MzEw8e/YMt2/fxu7du/HPP/8AKKoekJWVhbi4OOzfv19hhLePKTu+fX198eTJEyQlJcHOzo4d0Wzbtm1wcHDAhQsXkJOTg6FDh5Zr+yszUEOrVq1ga2uLoKAgpfXVi7148QJ6enpITExkb78vW7YMKSkpuHz5Mv744w+25fipU6ewZcsW3LhxA5GRkex+K5aZmYnXr1+zt5xzc3ORkJCAlJQUhWomXC4XLi4uFTrey0tHRwfLli3DnTt38Ndffyktw+FwMHXqVJw4cYI9vl++fIkzZ85g8uTJ7HDDFy9ehIGBQYlz9YgRI9Qas7e3N06fPo1vv/0Wly5dQn5+vlqWW1rscrkcV65cKXW+CxcuoHPnzrC3t1eYHhQUhLy8PISFhaklPgDIzc3FrVu3MHjwYIWW5VwuF6NGjcLbt28RHR2tME95q72pwt/fHwYGBuxzKysrWFpasr1dFBQU4N9//8XAgQOhp6cHqVTKPnr16oWCggK22lCXLl0AFFWFAoBz586ha9eu6NKlC86dOwegqBpWbm4uW7YykpKS0KNHD1hbW+PIkSPsCIcXLlwAUPS+fejzzz+Hvr4+/v33XwBgG4x9XM7b2xvu7u5suWLW1tbw9PRkn5uamsLS0hItWrSAjY0NO93d3R0AKrQPL168iMaNG7Pfb8XU/dkrTa1JYmfMmAEzMzPY2dmhU6dObIOA7du3Y8GCBXB0dISuri7mz5+PQ4cOASj6wipOYq9fv47Zs2fj6tWryMnJwePHj9G6dWs4ODggIyMDDg4OJdZZ/MVSr149dpq3tzeMjY0hFAoV6szMmTMHQqEQgwYNQkZGBiZPngw9PT307dsX2dnZSEhIULpdGRkZpT58fX3LtW+GDh2KJk2aQCgUIiAgoNyNJTIzM0t0cxEQEAALCwvweDzMnz8fDx48YJPhysTC4XAwcuRI6Ovrw8jICDNnzlRIKAkh+P7776Gjo4Nu3bpBKBTi1atXStdBCMGSJUsgFAohFAphYWGBPn36QCAQwNraGl9++SW77EOHDmHw4MFo3bo1dHV1sWjRIoVlRUREwNnZGUZGRuDz+cjKysLTp0/B4XDQqlUrhf1jYGCgUp0oNzc3mJiYYMyYMVi+fDm6du3Kxr9gwQLw+XwEBAQgPj4e3377LXR0dBAQEIBHjx4pbWhXfKyW9lB2DAPA/PnzMWPGjE82zNi3bx+++OKLUl/PycmBoaEh+7z4/+LjY/DgwZg9ezYEAgGGDh2KzZs3Kx1W9tSpU/D29kavXr2go6ODxYsXs3WyCSHYsWMHfv31V4hEItjY2GDSpEnsZ/rw4cP47rvvYGBggIYNGyIwMLDUeJUd3yNGjICRkRGEQiHmzp1b7h+KpW2/Kp+ND125cgVv3rxBZGQk8vLyMHbs2FLL6unp4dtvvwWfz4dQKESDBg3QoUMH8Hg8NGjQAF988QW7HQcPHsSXX34JV1dXGBsb49tvv1VY1vnz5+Hv7w+GYcDn85GcnIxXr16Bx+OVGKJZ1eO9IoYNG4ZWrVphwYIFkEgkSsuMHTsWurq62LJlC4Ci4aV1dXUV9llqaiqsrKxKzPvhuVsd1q1bh7lz5+Lo0aPw9/eHqakpBgwYgOfPn1dquWXFXtYPnNTUVFhbW5eYXpyglDWvqtLT00EIUWl9yspWlrK2HQKBgP1BkZqaCqlUivXr14PP5ys8ilvFFw8n7ujoCBcXF5w/f55N+ouT2OKk/Pz589DV1UW7du0qFXd2djZ69eoFiUSC06dPK1wsSE1NBY/HK9HgiWEY1KtXj92vxX9Lew8+3v+mpqYlyuno6JSYXpxMFxQUsOsp7z5MTU1V+jlT92evNLUmibW0tGT/19PTY788YmNjERwczF6N8vX1ZXeur68vwsPDER8fDz6fj/79++PatWsICwtDixYtPtlqt/jD8mFL9tu3byMjIwNisVihsUFxfFwuF3w+X+FgFAqFyM3NreQeKF1p++ZTjIyMSlS+3rp1Kxo3bgwjIyPUq1cPhBCVToSlxSKVSjFjxgw4OjrC0NAQgwcPVliuQCBQSBDK2g4dHR2F/ZudnY3Ro0ezDY5mzZrFLjsxMVHhKsXHVyxCQ0PZhjedO3fGpEmTMGHCBFhaWmL27NkKX6zZ2dkwNjYu976Ijo5Geno6oqOjMXPmTKXbqqurCwDs9ujq6kIikSg0mKqMe/fu4fbt2xg/fnyZ5W7cuIH09PQyuz4RiUTIyspinxf/LxKJ8OTJE4wZMwbHjh1DYWEhQkNDMWrUKKVXhz9+T/T09NjPWnJyMvLz89GwYUP2Mz1//ny2Z4ikpCSFectqOa7s+F6+fDlcXV1haGgIb29vlY5tZdtf0b4O/fz82PPEunXrcOrUqVLfc2tra3C5XPZ5fHw8Bg4ciHr16sHIyAhr1qyp0PE+atQodO3aFQMHDoSdnV2JxmWqHu8VwTAMfvrpJ7x8+RIhISFKyxgZGSEwMBDbtm1DWloaduzYgREjRijEZmZmpnBRoZiyXkiEQqHSBkbF3xtl0dfXx5IlS/D06VMkJSVh8+bNuHnzZpmNIcujrNjLGs3PzMwMiYmJJaYXXzQxNzevVFwfKm7wpMr6iq+UVycTExNwuVwEBQUhPDxc6ePD81znzp3x77//4vLly5DL5ejUqRPc3d1hY2ODc+fO4fz58/Dz81P6g7y8JBIJAgIC8PLlS5w6darEecvMzAxSqRTJyckK0wkhSEpKYvdr8bFQ2nugrvdblX1oZmam9HOmSg9AlVFrktjS2NraYu/evQpXpIoTRhMTEzg5OWHt2rXw9fWFnZ0d0tPTcfr06XJd5XRzc4O1tTWOHz9eZfF/2DL840fxVeSq0rRpU4XbPzExMZg1axb27NmDjIwMJCYmgsPhsMl6ZU5Ie/fuxdWrVxEWFoasrCwcOnSowt2tfBzHqlWrkJaWhsjISGRlZWHVqlXssq2trREXF8eW/fB/ADhz5gy6d+/OPp85cyYiIyMRERGBs2fPKvTG8PTpU4XbydUtNja2zONFWcJ4+fJlPHv2DLa2tqhXrx4OHDiA5cuXl0hq9+7di8GDB5d5ovbw8FC4vXz//n04OztDV1cXDx8+RPPmzeHr6wsOhwM/Pz+4ubnh1q1bJZbz8XuSn5/PJmHm5uZsR/jFn+esrCycPn0aQNGv+w+rHnzcIvdDHx/fly9fxm+//YbTp08jMzMTt2/fVij/qeNb2fY3bty4zHnK48Or0Mp8HNd3330HCwsLPHv2DJmZmZgxY0a5j/fi26VA0Y/BpUuX4tmzZzh//jzWrVvHnnNkMhlevnxZLcd7ly5d0LVrVyxdurTUH67Tpk1DSkoKBg8ejIyMDEydOlXhdX9/f2RnZ5c4V+/bt6/EspycnPDs2TOFRDY1NVVplauyWFlZISgoCMOHD0d0dDRb/an4M6RKVYPSYudwOOjQoUOp83Xu3BkXLlwocadv9+7d0NPTg4+PT4Vj+pi+vj7atGmDw4cPKyxHLpfjjz/+gJ2dndLqQ9VNT08P/v7+uHfvHpo1awYvL68Sjw9/GHTp0gXv3r3DmjVr4OPjw1ZV6Ny5M44cOYLw8PBKVyUIDg7GpUuXcPjwYTRr1qzE6507dwYA/PHHHwrT//77b+Tm5rKvf/bZZ0rLhYeH48mTJ2y5ylJlH/r7++PRo0cl7gAr++xVBV61rEWJwsJC9tI1UHRCLU83Tx8LDg7G8uXL0aRJE9SvXx+JiYm4f/8+e7XBz88PW7ZswdatWwEAbdu2xbZt27B3795PLpvD4eCnn37CtGnTYGZmhr59+0JfXx/h4eEqx1mait6OVAcfHx/Ex8cjLS0NpqamyMnJAcMwMDMzg0QiwaJFi0pcbS6rq5iyZGdnQyAQwNjYGCkpKWodmSU7Oxu6urowMjLCmzdvsGnTJvbWeUBAAHx9fTFp0iR4eHhg2bJl7HxZWVl48eIFWrVqBaCo6yFCCFq2bAkDAwPw+Xz2ClhSUhIyMjLYsjExMXB2dsbr169LdMtTVRwcHFQ+XiZMmKAwetr06dPRoEEDfP311+w0qVSKv/7665Pdxo0YMQKdOnXC+PHj4eLiguXLl2PkyJEAgBYtWiAqKgq3bt1CmzZtEBYWVmqS16tXL3z11VcIDQ2Fv78/lixZwlaf4HA4CAwMxNdff42ff/4ZhoaGiI6ORnZ2Nry9vTFo0CAsW7YMBw4cQFJSEvbs2YNGjRopjbdr166YO3cuZDIZuFwusrOzwePxYGZmhtzcXIVjAfjv+C7+4v/YyJEj8fPPP+Ozzz5j60zv2bNHaVm5XI7CwkJIJBLI5XIUFBSwd2kePXoEqVSKJk2aICsrCzNmzEDXrl3LfaUnOzsb9vb2EIlEePjwIf744w/2y2vw4MGYNGkShg8fDnNzc6xcuZKd78mTJ2ydOKCoLpulpSXc3d1haGgIHo/HHu93795FgwYNFO6sVKWffvoJnp6eeP/+vdJjpmHDhujRowd7AeLj+nejR4/G6tWrMXr0aCxfvhwNGjTAqVOnEBoaWmJZo0aNwm+//YaRI0di/PjxSE1NxcqVKxXuBJWmTZs26NOnD5o1awYTExM8efIEe/bsQdu2baGnpwcAbOL/008/oWfPnuByuWjWrBl7y1YZMzMzTJo0CbGxsWjYsCFOnTqFrVu3YtKkSaVWEwKK6oifOHEC/v7++P7772Fqaoq9e/fi5MmTWLlyJXvL2sXFBbq6uti7dy/c3d3Zqjof1ossjxUrVqBr167w9/fH119/DR0dHWzatAkPHz7En3/+Wekrr1FRUWzVoQ+1bt0ajo6O5V5O8YUrPz8/TJo0CU5OTsjOzsaLFy/wzz//sHVQgaLEsLg7sA8HqejSpQtbXakySezPP/+MPXv24KuvvoK+vr5CN36Ghobw8PBA165d0b17d8ydOxdZWVlo3749Hjx4gEWLFqFly5YYNWoUgKILaxMmTMD69evB4XDQs2dPxMTEYOHChbC3t1e441dZ5d2HM2bMwO+//47evXtj2bJlsLKywt69e/H06VO1xVImtTQPU5GjoyMBoPA4ePBgmeU/7M3g4xbWmzZtIm5ubkQkEpEGDRqQX3/9lX1t7969BADbEu+3334jDMOwLePfvHlD9PX1FVqAfuzIkSOkTZs2RE9Pj1haWhJfX1/y119/sS0x8VFLY4FAQF6/fs0+NzIyIk+ePCnn3vm0j3sn+HBffNwa+uPYPjZ16lSyZcsW9vns2bOJoaEhsba2Jps2bVLYlqtXrxIXFxdibGystBVpWbFkZmaSHj16EH19fdKoUSPyyy+/sC2BlXUDVFoPFsrKxsbGkrZt2xJ9fX3i6elJFi1apND6OCQkhNja2hJLS0vy22+/sfvk8OHDZPjw4Wy58+fPkyZNmhB9fX1iaWlJpk6dyra2X7duHZk9ezZb9urVq8TR0bHULo9K2+8fx5+YmKjQojU9PZ0AIPn5+UqXW1nKeic4ceIEsbOzU9oVlb6+Prly5Qr7fMeOHcTGxoaIRCISGBio0AvEnj17SMOGDYlIJCKurq6ldtVFCCHHjx8n9evXJyYmJmTx4sUK73dubi6ZPn06sbOzI0ZGRsTLy4ucOXOGEPJf7wSGhoakZcuWZM6cOaRfv36lrqdPnz7svBKJhIwYMYKIRCLi5ORENm3apLDv//77b2Jra0uMjIzIgQMHSixLJpOR6dOnEyMjI2JhYaFwnvn4PHLx4sUS57jAwEBCSFGvDg0aNCB6enrEysqKjBw5stReAIp7J/jQ/fv3SdOmTYm+vj7x9/cn06dPZ5dNCCFLliwhFhYWxMHBgaxdu5Y93latWqXQk8HevXuJq6sr0dfXJzY2NmTp0qXsa7NmzWK7jVKnD3sn+NiIESMIAIXeCT5U3MNMab29vH37lgQEBBCRSEQMDAxIQEAAuXHjhtJW+bt27SLu7u5EKBQSDw8PcuDAgXL1TvDtt98SLy8vYmJiQgQCAalfvz6ZOXMmSUlJYcuIxWIybtw4YmFhQRiGIQAUvg8+Vtwjw6VLl4iXlxcRCATE2tqazJ8/v0SXfh/HQwghUVFRpG/fvsTIyIjo6OiQ5s2bK+2F4M8//ySNGjUifD5f6XI+VFZvAVevXiWfffYZ0dfXJ7q6usTHx4f8888/CmXKep/LWl9pj+LtgZJuxwgp+r748DNQvMyxY8cSW1tbwufziYWFBWnXrh1ZtmxZiflbtmxJAJDr16+z0+Lj4wkAYmZmxn7Xl6W03gkCAwNL3a4Pv6fy8/PJ3LlziaOjI+Hz+cTa2ppMmjSpRBeYMpmM/PTTT6Rhw4aEz+cTc3NzMnLkyBJdpn3c08eH+6p3794lpivbt+Xdh48fPyZdu3YlQqGQmJqakuDgYLZ7PK3sYouqOeLi4kjz5s3L3ZemNpkwYQLZtWvXJ8tJpVLSrFkzkpSUxE774YcfFJJ/SjO+/fbbEt3NfCg8PJx07dq1GiOqubp160YuX778yXKZmZmkcePGVfZDqqIGDRpEbGxsVOortzg5Km/XUppQWrJB1S7FSaxEIim1P9y6TiaTEYlEQkaPHq22JFZj1QmomsHOzq7c42lrm1atWpVrDGcul1uivs+8efOqKiyqDHFxcYiPj4e3tzfu3buH7du3l1k1yMvLC2fPnq3GCGuuzp07o23btp8sZ2hoiIcPH1ZDRJ8mFosRERGB27dv48iRI1i1ahX4fL6mw6KoUvH5fOjr62u0qmBNNWvWLKxduxZAUR1rdagxSWzfvn3ZPtA+dOLECaVDBFJUZX355ZeaDoFSkVgsRnBwMGJiYmBhYYE5c+awDZWospXWt3NNlpiYiHbt2sHQ0BBffvklvvrqK02HRFFKTZgwAX369AEAhd5EqP/Mnj2bbUehrn3EEFLBJuIURVEURVEUpSG1vostiqIoiqIoqu6hSSxFURRFURRV69AklqIoiqIoiqp1akzDrtpELpcjISEBBgYGGhlWj6Komo8QguzsbNjY2FRoIBdtQM+VFEWVR0XPl3U2iR04cCAuXbqEzp07Kx0hpCwJCQklxiSnKIpSJi4ursRY6XUFPVdSFKUKVc+XdTaJnTZtGsaOHYtdu3apPG/x2MpxcXHlGqqQoqi6JysrC/b29uz5oi4q3vaYmBh2KGhtIJPJEB0dDTc3N63pToluU80W9Ptt3HmTjp8DmsKRl6EV2/Sh9PR0ODk5qXy+rLNJrL+/Py5dulSheYtvixkaGtIklqKoMtXl2+jaeq6UyWQQiUQwNDTUmkSCblPNxtfVB0cghp7IACKeVCu26UMymQyA6ufLWllR68qVK+jbty9sbGzAMAyOHj1aosymTZvg7OwMoVAIT09PXL16tfoDpaharLCwEIsXL8bixYtRWFio6XAoiqIoSkGtTGJzc3PRvHlzbNiwQenrBw4cwIwZM7BgwQLcu3cPfn5+6NmzJ2JjY6s5UiA9txDfHLyPxwlZ1b5uiqoMiUSCJUuWYMmSJZBIJJoOh6Ioqs6io1IpVyurE/Ts2RM9e/Ys9fVVq1YhODgY48aNAwCsWbMGoaGh2Lx5M1asWKHy+sRiMcRiMfs8K6v8CeneW29w8O5bHLz7Fr6u5hjn54yODS3q9C1Gqnbg8XiYPHky+z9FURSlWTR1UKR130yFhYW4e/cuvv32W4Xp3bp1w40bNyq0zBUrVmDJkiUVmreTmyWeJmXj9MMkXHuRgmsvUtDQSoRxvvXRv6UNBDztqdNCaReBQICNGzdqOgyKoiiKUqpWVicoS0pKCmQyGaysrBSmW1lZISkpiX3evXt3fP755zh16hTs7OwQHh5e6jLnzZuHzMxM9hEXF1fueJrYGmHDiFa4/E0nBPs6Q1+Hi2fvcjDn7wdo/+NFrP/3OdJzaX1DiqIoiqKUszAQwNZYF0I+vfD1Ia27Elvs49v1hBCFaaGhoeVelkAgYK9Kbdy4kW1Fpwo7Ez0s7OOB6V0aYP/tWOy4HoPEzAL8eu4ZNl56gc897RHs6wwnc32Vl01RFEVRlPbaOKIVgKJW/E+epGg4mppD667Empubg8vlKlx1BYD379+XuDqrqilTpuDx48dlXrX9FEMhHxM6uODKHH+sGdoCjW0MUSCRY8/NN/D/9RIm7L6D8Jg0EEKrcVOalZubCz6fDz6fj9zcXE2HQ1EURVEKtC6J1dHRgaenJ86dO6cw/dy5c2jXrl2llr1x40Z4eHigdevWlVoOAPC5HAxoaYsTX/li3/g2+KyRJQgBzj5+h8+3hGHAphs48SABEpm80uuiqIqSSqWQSqWaDoOiKIqiSqiV1QlycnLw4sUL9vnr168RGRkJU1NTODg4YNasWRg1ahS8vLzQtm1bhISEIDY2FhMnTqzUeqdMmYIpU6YgKysLRkZGld0MAEXVHtq5mKOdizlevM/GtquvcfhePO7HZWDqvnuoZyjEqLaOGNbaHmYigVrWSVHloauri7dv37L/UxRFUZrx7d8P8DgxC193awhTTQdTg9TKJPbOnTvw9/dnn8+aNQsAEBgYiJ07d2Lo0KFITU3F0qVLkZiYiCZNmuDUqVNwdHSs1HorUye2PFwtDfBjQDPM7uaGPTffYO/NN0jKKsDPodFY++9z9G1mg6B2Tmhqp54EmqLKwuFwYGtrq+kwKIqi6ryXyTl48DYT2fkSmNK2XSyG0MqXKiu+EpuZmVmlQymKpTKcfJCInTdi8OBtJjvd09EEge2c0LNJPfC5WlcjhKK0QnWdJ2qy4n2QlpYGExMTTYejNkWNa57A3d1da4b+pNtUs32+5QbCY9KxcXgLOHLTtWKbPpSeng5TU1OVz5e18kpsXSHgcTGolR0GtrTFvbgM7LoRg1NRibj7Jh1336TD0kCAkT6OGO7tAAsDWtWAUq/CwkKsXbsWADB9+nTo6OhoOCKKoqi6jQ52oIgmsSqo6uoEpWEYBq0cTNDKwQQLerlj3+1Y7L0Vi/fZYqw69wzrLzxH98b1MMLbAW1dzOhoYJRaSCQSzJkzBwAwefJkmsRSFEVpCL1nrhxNYlVQFQ27VGVpKMSMLg0xuZMrTj8sqmpwLzYDJx4k4sSDRDiZ6WGYtwMGe9rBnDYEoyqBx+MhMDCQ/Z+iKIqiahL6zVRL6fA46N/CFv1b2OJhfCb2h8fi6L0ExKTm4cfTT/Hr2Wh086iH4d4OaOdiBg6HXp2lVCMQCLBz505Nh0FRFEWx6Hf5h2gSqwJNVSf4lCa2Rlhm2xTzerrj5INE7Lsdi8i4DJyMSsTJqEQ4mulhcCs7DGhpC3tTPU2HS1EURVGUCgx1+TDT14EOlwFo1QIW7Z2gAmpDq+PHCVnYHx6LIxHxyBb/11m9t7MpBrW0Ra9m1jAU8jUYIUVpt9pwnqhqtHeC2qMqt4kQgmyxFPmFMlgZCtnpN16moEAig4DHha4OF7r8ooe+gAdTfR1wK3kHkb5PtQftnYBS4GFjiKX9m+Dbno1wOioJh++9xY2Xqbj9Og23X6dh0fFH6OJhhYBWtvBrYEG76qJKyM3NZfuJjY+Ph76+voYjoiiqJpPJCc4/eYcniVmISclFUlYB3mWJ8S6rAHmFMng7meKviW3Z8tP+jERKjljpspzN9XHx607s8103YmAg5KFRPUO4WOpDwNOeBI6qOJrEajk9HR4CPO0Q4GmHxMx8HL2XgMMRb/H8fQ5OPkjEyQeJMNLlo5uHFXo1tUZ7V3Po8GhCSxXJzMz8dCGKouqcrAIJbr1KQ75Ehn7NbQAAHAb45uB9ZBUoH6r6445zGtsYIi23EIVSOfIkUuQXylEgkSG3UFqi28hNl17gXVZRwsvjMHCxEKGpnRG8nU3Rtr4ZrSpXR9EkVgU1tU5seVkb6WJSJxdM7FgfjxKycDgiHsfvxyMlpxAH777FwbtvYSDkoauHFXo1sYZfQ3P6a7cO09XVxbNnz9j/KYqqu2RygvCYNFx9nozrL1Lx4G0G5ASwM9Flk1iGYdC7mTUKpQQNrESwNhLCylCIeoZFf3V1FL9Pdo31VrouqUyO3ELF79meTazxODELTxOzkFUgRfS7bES/y8ahu2/R3N4Yx6a0Z8sWSuVadzFm4dGHePYuGzO7uEKk6WBqEJrEqqAmdLGlDgzDoImtEZrYGmFBb3eEx6ThdFQiTj9MwvtsMQ5HxONwRDz0dbjwbWAOfzdLdHKzRD0j4acXTmkNDoeDBg0aaDoMiqI0bN2/z/HHzTd4n61469/ZXB/tXMwglsrYCx4rBjWr9Pp4XA6MdBWT0MX9GgMoql+bmFmAJ4lZuPMmHbdfp6FtfTO2XK5YirYr/oWXkym6NLKAM792XnT62KOETETEZiA9TwIR7aCAVSVJbEFBAYRCmvDUBlwOA5/6ZvCpb4ZFfRvjbmw6TkUl4nRUEpKyChD66B1CH70DALhbG+KzRhbo2NASLeyNte6XLkVRFAW8zyqAhYGAHTgnJUeM99liGOny8VkjS7RzMUN7V3PYGFf/HRqGYWBjrAsbY110drcq8fqt16nIKpDiwtP3uPD0PfgcoHu0FENbO6Cdi3mlG4tRNYvakli5XI7ly5djy5YtePfuHZ49e4b69etj4cKFcHJyQnBwsLpWRVURDodBaydTtHYyxcLeHniUkIWL0e9xMfo9IuMy8CQxC08Ss7Dx4ksI+Rx4OZrCp74pfOqboZkdTWq1jUQiQUhICABgwoQJ4PNpbxYUpc2eJoux+X4kzjx6hwMTfODlZAoAGN3WER0aWKBDQ4saf573d7PE2ZkdcPZREk48SMTTpGyceJCEEw+SYGMkxE+Dm8GvgYWmw6TURG1J7LJly7Br1y6sXLkS48ePZ6c3bdoUq1evpklsLcPhMGhqZ4SmdkaY1rkBUnPEuPI8GReeJuP6ixSk5Rbi2osUXHuRAgAQ8jloaW+CFg7GaG5njBb2xrT6QS1XWFiIqVOnAgCCgoJoEktRWkguJzj1MBHbrr5CZNx/DTmvvUhhk1hXSwO4WhpoKkSVMAyDhlYGaGhlgIkdnHHi+n3cTdfB8fuJSMoqgJPZf72syOSk1lyZpX2hKqe2JHb37t0ICQlB586dMXHiRHZ6s2bN8PTpU3WtRqNqe8OuyjATCTCwpR0GtrSDXE7wIjkHN1+l/v8jDWm5hQh7lYqwV6nsPFaGAjSzM0YTGyO41ROhoZUBHM30a81Jo67jcrkYPHgw+z9V96xYsQKHDx/G06dPoauri3bt2uGnn36Cm5ubpkOjKokQgkvRyfjpzFM8TcoGAPA4wIAWthjrWx8eNrW/b2OGYeBqJkBfX3cs6O2Be7EZCr0YfPVnBIQ8LmZ0aQgHs9rRuwH99lSktiQ2Pj4erq6uJabL5XJIJBJ1rUajtKVhV2VxOP/90h3d1glyOcHz9zmIiE3Hg7cZiIzLRHRSFt5liXHu8Tuce/yOnVfA46CBVVFC62ymDwczPTiY6sHRTB8meny2DhaleUKhEAcPHtR0GJQGXb58GVOmTEHr1q0hlUqxYMECdOvWDY8fP6b9BtdyhTI5FhyJQkJmAQyEPAS1dYS3qRjtWjXRyh+tQj4XbV3+awD2LqsAZx4mQU6A4/cTMKS1PaZ91oDeQaxl1JbENm7cGFevXoWjo6PC9IMHD6Jly5bqWg1VA3E4DNzqGcCtngGGezsAAPIKpXiUkIX7cRl4mpSN6KRsPH+fjQKJHA/js/AwPqvEcgwEPNib6sHWRBdWhgJYGgiL/hoKYWkggJWhECZ6lR/FpaYSS2XIKZAiVyxDjliKHLEUuWIpsv//b65YComMgMMAHIYBwwAGQh7M9AUwE+nA0Uwfpvo6mt4MSoucOXNG4fmOHTtgaWmJu3fvokOHDhqKiqqo1BwxTPV1wDAMBDwu5vRohMeJWZjU0QWGQi6ePHmi6RCrjZWhEEentMcvZ5/hyrNk7LtVNMLlFH8XjPOrDyG/ZiXyAh4Hunxu0fcfrVvAUlsSu2jRIowaNQrx8fGQy+U4fPgwoqOjsXv3bpw4cUJdq6FqCT0dHttIrJhMThCXlofod9l4/i4bMal5iE3NQ2xaHpKyCpAtluJxYhYeJ5ZMcD9kIOTBWI8PY10dGOvxYaRb9DAQ8qFXPHShDlfh/+K/XA4DPpdT9JfDAZfLgMcpfnDA+f82C3JSdLuNEEBOSNFz/PdcJicQS+UolMohlsohlshQKJNDLCl6XiiTQSyRKySjOWIpsgsU///wNYms8mcmCwMBPKwN0d7VDB0aWsDNyoBe3abUpnjwC1NT00+UpGoSQggOhMdh+akn+L6PBz73sgcADGhpiwEti0blq4vV5JrZGWP3WG/cfp2GlWee4s6bdPxy9hn+uvMWIaM90ahezalSsX9C0UhnRcPOpn6idN2htiS2b9++OHDgAH744QcwDIPvv/8erVq1wj///IOuXbuqazVULcblMHAy14eTuT66N66n8FqBRIa36Xl4k5qHhMwCJBcPV5hdgPdZYrzPLkBKTiEAILugKAGMQ74mNqPK6ekUjR1uIOBBX8CDvoALkYAPkYALPpcD+QeJdI5YitQcMZKzxUX7LVuMy9nJuPwsGT+cego3KwN87mWHzz3tYaSnWsOsvLw8tp/Y58+fQ0+vdtQZo6oGIQSzZs2Cr68vmjRporSMWCyGWPxfX6JZWUU/SGUymVYlScXbUhu2KSY1FwuOPsLNV2kAim6dD2xhXeLHbW3apvIq7zZ5Ohhh/3hv/PMgET+ejkZ+oQz1DAQ1cl9o4/sEVHx7GEIIvTCtouI6sZmZmTA0rDm/1LSdRCZHZr4EGXkSZOZLkJlfyP6fnidBrliKvEIZ8gv//69EhvxCGft/gUQGqbwo+ZPI5JDJCaRyAqlMDnk5PgUMU1SpnsMw4HAYCHgcCHjc///LgQ6PAwGfCwGXAwG/aJr+/yeiBgIeRAIeRMIPngv/f9oH0/V1eBWuLpErluLZu2xExGbg2vNkXH+ZikKpHAAgEvAQ2M4R43zrw6ScVQ5yc3MhEhWNDZOTk0PrQKpI284TU6ZMwcmTJ3Ht2jXY2dkpLbN48WIsWbKkxPSwsDD2WKKqByEEp5/nYNvddBTKCARcBiObG6NfIwOtrZKlDvkSOeKzJHA1Kxr2lhCC+0kFaF5PSO9qVaGcnBy0bdtW5fMlTWJV8GHvBM+ePdOaLyeqqJsZGSGQygiYD+qcchgGHAa18uSVmS/BiQcJ2BP2hm19bKzHx5zujTCstT04n/gik8lkiIqKAlDUVZ42NvaoStqUxH711Vc4evQorly5Amdn51LLKbsSa29vj+TkZJiYmFRHqNWi+DugYcOGNfJzkSuW4rtjj3D8fiIAoJ2LGZYPaAwH09LvptT0baoIdWzT4Xvx+OZQFPo1t8bSfo1hINTMQKfLTj7Bq5RcTO1YH7r577TqfQKA9PR0WFhYqHy+rNS7YWJiUu4v97S0tMqsqkagvRNoLw6HAQcMalhd/kox0uXjizaOGN7aAeeevMOqs88Q/S4b849E4eDdOKwZ2gKOZqVfXeVyuWjRokX1BUzVOIQQfPXVVzhy5AguXbpUZgILAAKBAAKBoMR0LperVV+4xWrqdj1MTMc/DxLB5TCY28MN4/3ql/u7uqZuU2VUZpsy86Xgchgcv5+IyLhMrBveEi3sjdUbYDlExGXiflwGvvB2gC60732q6LZUKolds2YN+39qaiqWLVuG7t27o23bogrIYWFhCA0NxcKFCyuzGoqiKoHDYdC9cT10bmSJ3WFvsOrcM9yLzUCvtVexpH8TBLSyrZVXmqmqN2XKFOzbtw/Hjh2DgYEBkpKSAABGRkbQ1a3+IUep8mnnYo7venugmZ2RQuNaSnXj/OqjpYMxpv0Zidi0PHy+5QaWD2iKIa3tNR0aBTVWJwgICIC/vz87wk+xDRs24Pz58zh69Kg6VlMjaNNtQqrueZueh1kH7uN2TNHdkaFe9lg6oDEEPMVfwhKJBHv37gUAfPHFF3TELhVpw3mitB83O3bsQFBQ0CfnL94HaWlpWled4MmTJ3B3d68xV8P+uPkGnzWyhI1xxX5c1MRtqix1blNmvgRzDz3AmUdFP+SC2jnhu97u4HGrZxje/huv435cBkJGtoINUrXqfQKKqhOYmpqqfL5U294PDQ1Fjx49Skzv3r07zp8/r67VUBRVSXYmevhzgg9md20IDgMcuBOHEVtv4X12gUK5wsJCjBkzBmPGjEFhYaGGoqU0qaibuZKP8iSwVPWQywmWn3yM744+RODvt5FXKNV0SFrJSJePTV+0wswuDQEAu8JiEBGbUX0B0OZLSqktiTUzM8ORI0dKTD969CjMzMyUzEFRlKZwOQy+6twAvwe1hoGQh7tv0tF/w3U8e5f9XxkuF7169UKvXr206hc/RWmLQqkcM/+KxNarrwEAAZ520NWmiv01DIfDYHqXBtgy0hMLernD27n6q2rQml+K1NbMbsmSJQgODsalS5fYOrE3b97EmTNnsG3bNnWtRi1OnDiB2bNnQy6XY+7cuRg3bpymQ6IojejkZoljU9pj3O47eJWci8+3hOH3IC94OppCKBTi5MmTmg6RoiglJDI5Ju+9i/NP3oPHYfBTQDMEeCrv+oxSrx5NFPs5f5dVgOwCKVwtaTdy1U1tV2KDgoJw48YNGBsb4/Dhw/j7779hZGSE69ev16hbT1KpFLNmzcKFCxcQERGBn376SSt6TqCoiqpvIcLfE9uhpYMxMvMlGLH1Fs4/fqfpsCiKKoVMTjDzQCTOP3kPAY+DbYFeNIHVkIy8QozafgtDfgtD1NtMTYdT56i1w7M2bdqwDUFqqtu3b6Nx48awtS0aaq9Xr14IDQ3F8OHDNRwZRWmOib4O9o3zwZR9Ebjw9D2+/OMufh7cDINa0S9GiqppVp2LxokHieBzGWwZ5YlObpaaDqnOkhNAyOciLbcQw7fexO5gb7RyUH8jxmNTfQHQYWc/prYrsbGxsWU+1OXKlSvo27cvbGxswDCM0l4PNm3aBGdnZwiFQnh6euLq1avsawkJCWwCCwB2dnaIj49XW3wUVVvp6nDx2yhPDPa0g0xOMPuv+3DpMhINGjRAXl6epsOjKOr/jfRxREMrEdYNawl/msBqlKm+DvaN94FPfVPkiKUI+v02HiXQK7LVRW1XYp2cnMrsa1Jd4/zm5uaiefPmGDNmDAICAkq8fuDAAcyYMQObNm1C+/bt8dtvv6Fnz554/PgxHBwcoKxHsYr2kZmbmwsDAwN2/sLCQkgkEvB4PIUOv3NzcwEAurq64HCKfjdIJBIUFhaCy+VCKBRWqGxeXh4IIRAKhWzDG6lUCrFYDA6Ho9CPoypl8/PzIZfLIRAIwOMVHSIymQwFBQUqlWUYBnp6/40QU1BQAJlMBh0dHba7JlXKyuVy5OfnA4DCEKhisRhSqRR8Ph86OjoqlyWEsEmanp5eifdTlbLlee/VcZwoez/VcZxIC8VY1NMFQh4Hf9yKhcxrON6npiEnJwcMw1T4va/scVLa+1nZ4+TD97Oyx0lp7ydFqZu1kS5OTvMDv5q6d6LKJhLw8HtQa4zefht33qRj1PbbODDBBw2sDDQdmtZT2yfg3r17iIiIYB+3bt3Cli1b0LBhQxw8eFBdq0HPnj2xbNkyDBo0SOnrq1atQnBwMMaNGwd3d3esWbMG9vb22Lx5MwDA1tZW4crr27dvYW1tXeY6xWIxsrKyFB4AYGNjg5SUFLbczz//DJFIVKKvXEtLS4hEIoUr0hs3boRIJEJwcLBCWScnJ4hEIjx58oSdtnPnTohEIgwbNkyhrIeHB0QiESIiIthpBw4cgEgkQr9+/RTKtm7dGiKRSOGq9IkTJyASidClSxeFsh06dIBIJEJoaCg77cKFCxCJRGyjvWI9e/aESCRS6Jni5s2bEIlEaN68uULZgIAAiEQihSonUVFREIlEaNCggULZUaNGQSQSISQkhJ328uVLiEQihSvpAPDll19CJBJh7dq17LTExESIRCIYGxsrlJ01axZEIhF++OEHdlpmZiZEIhFEIhGk0v+6p1mwYAFEIhEWLFjATpNKpWzZzMz/fm3/8MMPEIlEmDVrlsL6jI2NIRKJkJiYyE5bu3YtRCIRvvzyS4Wytra2EIlEePnyJTstJCQEIpEIo0aNUijboEEDiEQidlhYANi7dy9EIlGJH3fNmzeHSCTCzZs32WlHjhyBSCRCz549Fcq2bdsWhgYG6KCfiDHtHAEAZt2nwLXXeHTo0EGhbJcuXSASiXDixAl22tWrVyESidC6dWuFsv369YNIJMKBAwfYaRERERCJRPDw8FAoO2zYMIhEIuzcuZOd9uTJE4hEIjg5OSmUDQ4OhkgkwsaNG9lpsbGxEIlEsLRUvEI1depUiEQi/Pzzz+y0lJQU9v380Ny5cyESibBkyRJ2Wl5eHlv2wyvTS5YsgUgkwty5cxWWYWNjA4pSl+svUnD2//soBUAT2BpGT4eH38e0RjM7I6TlFuKLbbcQl6a+O1g/nn6KyXvv4mE8vcr7IbV9Cpo3b67w8PLywvjx4/HLL79g3bp16lpNmQoLC3H37l1069ZNYXq3bt1w48YNAIC3tzcePnyI+Ph4ZGdn49SpU+jevXuZy12xYgWMjIzYh709HamD0m4Mw+D7vo0xsaMLAMC0ywTkObTXcFQUVTfFpuZhyr4ITNhzF+doo8say1DIx64x3nCzMoCAz4FYKlfbsq+/SMGpqCQk59A+uz+kthG7SvP8+XO0aNGiSm6tMQyDI0eOYMCAAQD+q+96/fp1tGvXji33ww8/YNeuXYiOjgYAHD9+HF9//TXkcjnmzJmDCRMmlLkesVgMsVjMPs/KyoK9vT0SEhJQr149Wp2AVifQmuoExe9ncVlCCH49G40NF4uuDs/v1QgTOrio/N7XxeoEiYmJsLGxqdUjdlUWHbGr8nLFUgzadAPR77LR3N4YByb4QFgFfcHSEbvUJymzADwuA3OR4NOFy6nv+muIis/EttGeqCdP0ar3Caj4iF1qqxNbfIu9GCEEiYmJWLx4cYlbxVXt4zquhBCFaf369Stxu70sAoEAAoEAGzduxMaNG9n6vfr6+grL1dHRYb/wPvThl2MxPp+vdBhPVcp++MVfjMfjsUlCRcsqGxOdy+UqjU2Vsh8mVhUpy+FwlJYtfn8qWpZhGKVllb2fqpQFlL+f6jhOlL2f6jhOFOrHSqU4cuQIHAFM/6wp1l54iR9OPQWXw0Gwr3O1HielvZ+VPU5Kez8re5wAyt9PilLVkn8eIfpdNiwMBPhtpGeVJLCUetUzUjwvxaXlwd605HmYqjy1JbHGxsZKk0d7e3vs379fXaspk7m5ObhcLpKSkhSmv3//HlZWVpVe/pQpUzBlyhT26gJFaTOxWIwhQ4YAAHJyckDAYN2FF/jficfgcRgEtnPSbIAUpeXOPkrCX3fegmGADcNblkiOqJpv7603WHL8MZYNbIIhXhWvikhAh51VRm1J7MWLFxWeczgcWFhYwNXVVekVn6qgo6MDT09PnDt3DgMHDmSnnzt3Dv3796/08j++EktR2ozD4aBjx47s/zO7NoRUTrDp0kssOv4IXA6DkT6OGo6SorRTSo4Y8w4XNdyc4FcfberT4dtro7ScQhTK5Fh49CEa2xiisU3lLoDRUWcVqS27ZBgG7dq1K5GwSqVSXLlypUTL5orKycnBixcv2OevX79GZGQkTE1N4eDggFmzZmHUqFHw8vJC27ZtERISgtjYWEycOLHS66ZXYqm6RFdXF5cuXVKY9k13N8jkBL9deYXvjj4Ej8NgmLeDZgKkKC124n4CUnML4WZlgFndGmo6HKqCpvi7IiI2HRejkzHpjwicmOYLQ2HJal9UxagtifX390diYmKJbm0yMzPh7++vtquXd+7cgb+/P/u8uEujwMBA7Ny5E0OHDkVqaiqWLl2KxMRENGnSBKdOnYKjY+WvGNErsVRdxzAMvu3ZCBIZwe/XX2PekShwOQw+r8RtMoqiSgpq7wxLQyGczPQh4NF6sLUVh8Ng9dAW6LP+GmLT8rDk+GP8OqT5p2ekykVtvRNwOBy8e/cOFhYWCtOfPXsGLy+vEg2/arPiK7F1udUxVbcRQrD4+CPsCnsDhgFWDWmOgS3pELUfoucJ2jtBbUK3qWqFx6RhyG9hIATYMtITPZrUU2n+AokMhABchuBZ9NMasU3qpLHeCYoHHWAYBkFBQQotemUyGR48eKDQ3RVFUbVDfn4+O7hFWFiYQg8DDMNgcb/GkBGCP27GYvZf98FhGPRvYVva4iiKKod/n7xDc3tjtXbPRGleaydTfNnBBVsuv8T8I1HwcjJR6T0u7pWC3glWVOkktrhuKCEEBgYGCl90Ojo68PHxwfjx4yu7mhqBVieg6hK5XI779++z/3+MYRgs7dcEMjnBn7fjMPNAJHgcDno3K3sEPIqilHubnofJeyOgw+PgxFe+cDSj3bRpk5ldGyDsZQq6N6kHE72S3fJRqqt0Ertjxw4ARcOlfv3111rdNyJt2EXVJUKhEGfPnmX/V4bDYbB8QFNIZQQH777FtP33wOUAPZrQRJaiVPVzaDTEUjla2BvDgfYrqnUEPC7+ntQOvAoMGbzqbDTiMwoQ3J72CPMhtTXsWrRokboWRVFUDcDlctG1a9dPluNwGPwY0AwyOcHhe/GYuu8eNn7BoHtj1ep8UVRd9io5B//cTwAALOzjUaLfdUo7fJjASmRyyOSkXANYnH/yHo8Ts9CnmRUsPlm67qhUEtuqVSv8+++/MDExQcuWLcv80EVERFRmVTUCrU5AUcpxOQx+/rw5ZITgWGQCpuyNwOaRnujqUflBRiiqLth48SXkBOjcyBJNbOmdPm0XEZuOeX9Hwb+RJb7t2UjT4dRalUpi+/fvzzbkGjBggDriqdFodQKqLpFKpQgNDQUAdO/e/ZODlnA5DH79vDkIAY7fT8DkvXexZaQnOrvTRJaiyhKXloejkfEAgK86V+8w7ZRmpOYUIvpdNl6l5GCEtwMczGj1kYqoVBL7YRUCWp2AorSLWCxGnz59ABQNMlKekfd4XA5WDWkOOSE48SARk/6IwJZRrfBZI5rIUlRptlx+CZmcwK+BOVrYG2s6HKoadPWwgl8Dc1x9noJfzkZj3fCWZZang84qp3rt4k8oLCzE27dvERsbq/DQBhs3boSHhwdat26t6VAoqspxOBx4eXnBy8sLHE75TxU8LgdrhrZA76bWKJTJMXFPBC5Gv6/CSCmq9tPhcTC5k6umw6CqUXE1guP3ExD1NrNc8zB04FkFaktinz17Bj8/P+jq6sLR0RHOzs5wdnaGk5MTnJ2d1bUajZoyZQoeP36M8PBwTYdCUVVOV1cX4eHhCA8PV+g6rzx4XA7WDGuBnk3qoVAmx5d77uLys+QqipSiarflA5vi9vzO8KlvqulQqGrU2MYIA1rYAAB+PPMEahp7qk5RWxI7ZswYcDgcnDhxAnfv3kVERAQiIiJw7949rWjURVGUavhcDtYNb4nuja1QKJVj/O47uEITWYpSylhPh/ZIUAfN7uYGHS4H11+kIuxVqqbDqXXU1sVWZGQk7t69i0aNaCs7iqKK8LkcrB/eClP2ReDc43cYv/sOtge2hm8Dc02HRlEa9yY1F3mFMrhb181hiSnA3lQPQ1vbY8/NNzgVlYh2LsrPjX+ObwOZnECPz8HL5/RiQDG1XYn18PBASkqKuhZHUZSG5efno3379mjfvj3y8/MrvBwdHgcbR7RCF3dLiKVyjNsdjhsv6LmConbdeIOea69i1dloTYdCadDETi7YEdQa/+vfpNQyxno6MBMJoMNTe1OmWk1te+Onn37CnDlzcOnSJaSmpiIrK0vhoQ1owy6qLpHL5bhx4wZu3LihdNhZVejwONj4RSt81sgSBRI5xu4KR9hLeuuMqrsIIQh9lAQAtF/YOs7WWBf+jSxpdZIKUFt1gi5dugAAOnfurDCdEAKGYbRigADaTyxVlwgEAhw5coT9v9LL43GxeWQrTNxzFxejkzF2Zzh2jGkNn/pmlV42RdU2D+OzEJ+RD10+Fx0a0jGYqCIFEhnEUjmMdPkK0zdceI6krAKMauOgochqJrUlsRcvXlTXoiiKqgF4PJ7aBzEpSmQ92d4Kxu4Mx84x3vB2pq2yqbrlzKNEAIB/I4tyDTtKab+/wuPww+knGOplj3m93BVeO/EgEU+TstHN3RL0bPkftSWxHTt2VNeiKIrSYkI+F7+N8sT43Xdw9XkKgnbcxq6x3mjtRE/NVN1x4WlR45xuHvU0HAlVUxjr8ZGRJ8GBO3GY2bUh/XFTDmpLYh88eKB0OsMwEAqFcHBwUMstSYqiqodMJsPVq1cBAH5+fuBy1XdCFfK52DraC+N23cG1FykI+v02dtJElqojMvMkeJpU1FaknSutTkMV6exuBVtjXcRn5OOf+wn43Mte0yHVeGpLYlu0aFFmpWQ+n4+hQ4fit99+g1AoVNdqKYqqIgUFBfD39wdQNOysvr6+WpfPJrK7w3H9RSoCf7+N34NoHVlK+92OSQMhQH0LfVga0O9DqgiXw2BEGwf8HBqNg3ff0iS2HNTWO8GRI0fQoEEDhISEIDIyEvfu3UNISAjc3Nywb98+bN++HRcuXMB3332nrlVSFFWFGIaBh4cHPDw8qqzVrK4OF9sDW8OvgTnyCmUI2nGbdr9Fab22Lmb4PcgL33Rz03QoVA0zqJUtGAa4/ToNcWl57HR2MC/ag4ECtV2JXb58OdauXYvu3buz05o1awY7OzssXLgQt2/fhr6+PmbPno1ffvlFXautVhs3bsTGjRu1oqcFivoUPT09PHr0qMrXU3xFtrix15id4dgW6AW/BrTFNqWdRAIePmtkpekwqBrI2kgXbeub4cbLVByLjMfUzxpoOqQaTW1XYqOiouDo6FhiuqOjI6KiogAUVTlITExU1yqr3ZQpU/D48WOEh4drOhSK0ipCPhchoz3xWaOiARGCd93Bpej3mg6Loiiq2g1saQsAOHwvHoS9BEspo7YktlGjRvjxxx9RWFjITpNIJPjxxx/ZoWjj4+NhZUV/fVIUVVJxP7JdPaxQKJVjwu67uPD0nabDoii1epdVgDXnn+H8Y3psU8r1bGqNWV0bYs3QFuy0XWO9cXWOPzwdjDUWV02ktuoEGzduRL9+/WBnZ4dmzZqBYRg8ePAAMpkMJ06cAAC8evUKkydPVtcqKYqqQvn5+ejXrx8A4Pjx49DV1a3ydQp4XGwc0QrT/ryHM4+S8OWeu9j0hSe6etAfv5R2iIzLwJrzz+FhbYgu9LimlBAJeJjWWbEaQT2jogaAtDqjIrUlse3atUNMTAz++OMPPHv2DIQQDB48GCNGjICBgQEAYNSoUepaHUVRVUwul+P8+fPs/9VFh8fB+hEtMWN/JE5GJWLSH3exYUQr9GhC+9Okar+XyTkAgIZWIg1HQlG1n9qSWAAQiUSYOHGiOhdZZQYOHIhLly6hc+fOOHTokKbDoagaRyAQ4I8//mD/r058Lgdrh7UAl8Pg+P0ETNkXgXXDWqJ3M+tqjYOi1C0hIx8AYGeip+FIqJpMLic49TARV54lY1Hfxth29TXyCqUY5UOHnf2QWpNYAHj8+DFiY2MV6sYCYG9L1hTTpk3D2LFjsWvXLk2HQlE1Eo/HwxdffKG59XM5WD20BXgcBofvxWPa/nuQEYJ+zW00FhNFVVZSZgEAwNqY9g9LlY5hgB9PP8Xb9Hx0b1wPe2+9wftsMfo0rQfaydZ/1JbEvnr1CgMHDkRUVBQYhmFb1BX3L1nT6nH4+/vj0qVLmg6DoqgycDkMfv68OTgcBofuvsWM/fcglxMM+P/WuxRV2yRk/H8Sa0STWKp0DMOgk5sF/rgZi0vRyZDKi3IqHpdBzcqmNEttvRNMnz4dzs7OePfuHdu/5JUrV+Dl5aVysnjlyhX07dsXNjY2YBgGR48eLVFm06ZNcHZ2hlAohKenJzs8JkVR6iGTyRAeHo7w8HCN/gjlchisDGiGYa3tISfAzL8icejuW43FU5eU51xMqSYlRwwAdKQu6pM6NbQEAFx69h4SWVG7BD5XbWmbVlDb3ggLC8PSpUthYWEBDocDDocDX19frFixAtOmTVNpWbm5uWjevDk2bNig9PUDBw5gxowZWLBgAe7duwc/Pz/07NkTsbGxbBlPT080adKkxCMhIaFS20lRdUVBQQG8vb3h7e2NgoICjcbC4TD4YWBTjGjjAEKAbw7dx4Hw2E/PSFXKp87FlOpyxVIARS3QKaosbV3MoMPlIC4tH9kFRccNj0MrE3xIbZ8imUwGkaiotaW5uTkSEhLg5uYGR0dHREdHq7Ssnj17omfPnqW+vmrVKgQHB2PcuHEAgDVr1iA0NBSbN2/GihUrAAB3796t4JaUJBaLIRaL2edZWVlqWzZF1VQMw7ADmFTVsLOq4HAYLB/QBDwOg91hbzD37yhI5QRftCk5yAqlHp86F1Oq2z+hLbLFErbLJIoqjb6Ah9bOJrj+IpWdxqVJrAK1XYlt0qQJHjx4AABo06YNVq5cievXr2Pp0qWoX7++ulaDwsJC3L17F926dVOY3q1bN9y4cUNt6/nQihUrYGRkxD7s7e2rZD0UVZPo6ekhJiYGMTEx0NOrGS2pGYbBkn6NMaa9EwBgwZGH2Hn9tWaDoigVNLUzQjsXcwj5XE2HQtUCxVUKitHqBIrUdiX2u+++Q25uLgBg2bJl6NOnD/z8/GBmZob9+/erazVISUmBTCYrMfKXlZUVkpKSyr2c7t27IyIiArm5ubCzs8ORI0fQunVrpWXnzZuHWbNmsc+zsrJoIktRGsIwDL7v4wE+l4OQK6+w+J/HKJDKMbGji6ZDq/NKu2slk8lqXOPeyijeltq0TYQQREZGIjo6Gubm5rC0tERISAgKCgowa9YshIaG4uTJk7C0tMS+ffswf/58mJiYwMfHB02aNMHbt2/h4OAAIyMjTW9KudXG9+ljfg3MFJ5zmaIGXrV5m5Sp6PaoLYnt3r07+3/9+vXx+PFjpKWlwcTEpEpuRX68TEKISusJDQ0td1mBQACBQICNGzdi48aNWnfwUFRtwzAM5vVsBAGPg/UXXuDH008hlsgxrbNrjaj6UFetWLECS5YsKTH9xYsXbHUzbfLs2TOVyktkBKeeZ4MQoK+bQZXdGs7MzATDMLh69SrOnDkDPp+PVatWYefOnbC1tYWxsTGkUikGDx4MgUAAmUyG7t27o0ePHgCAJ0+eoEOHDkhJSUFqaipu3bqFo0ePIj4+HuPGjcOjR48AAC1atICzs3OVbIM6qfo+1SSEEPw+0BYMgNQ8GZJiX4FhmFq9Tcrk5ORUaD6GFPeFVUFjx44tV7nff/+9QstnGAZHjhzBgAEDABRVJ9DT08PBgwcxcOBAttz06dMRGRmJy5cvV2g9qsjKyoKRkREyMzNhaGhY5eujKE0oKCjAsGHDAAD79++HUFgz6/BtuPAcv5wtOqFP7uSCb7q71YhEVtvOEx+fi5VRdiXW3t4eycnJMDExqYYoq4dMJsOzZ8/QsGFDcLnlrxaQVyhF0yVFo+A9XNQVujrqqVJACMG7d+8AABMmTIC+vj7mz58PW1tbGBgYgM/nf3IZqmxTQkICbty4gbi4OIwZMwbjxo1D586dMXToUJiamqplm9Shou9TTaaN2wQA6enpsLCwUPl8WekrsTt37oSjoyNatmyJSubD5aKjowNPT0+cO3dOIYk9d+4c+vfvX6XrpldiqbpEJpPh2LFj7P811dTPGkDI52LZySfYdOklCiRyLOzjXiMSWXU4c+YMRCIRfH19ARSdh7Zu3QoPDw9s3LixRiWHxXetPsblcrXqC7eYqtul80EuKQNT6X1CCMGaNWtw5swZ+Pn5Yf78+Thy5Ah0dHQqvMzybJO9vT2GDh3KPt+7dy9OnTqFmJgYhIaGQk9PD7179672kf5Ko43Hn7ZtU0W3pdJJ7MSJE7F//368evUKY8eOxciRIyv9SywnJwcvXrxgn79+/RqRkZEwNTWFg4MDZs2ahVGjRsHLywtt27ZFSEgIYmNjq3zI2ylTpmDKlCnsFRaK0mY6OjoICQlh/6/JxvnVh4DHwcJjj/D79dcolMmwtF8TcLSgJe8333yDn376CQAQFRWF2bNnY9asWbhw4QJmzZqFHTt2VNm6P3UuplQj4HGhw+OgUCpHdoEERrqfvkL6MblcjhMnTmDr1q2YM2cOxo8fj5kzZ7Kva+Kzqqenh8GDBwMAnJyccODAAcycORO//voroqKi4O3tXe0xUXUEUYOCggKyb98+0qVLF6Knp0c+//xzcubMGSKXyyu0vIsXLxIAJR6BgYFsmY0bNxJHR0eio6NDWrVqRS5fvqyOTSnThg0biLu7O2nYsCEBQDIzM6t8nRRFld/+22+I07cniOPcE+TrvyKJVFaxc5A6ZGZmquU8oa+vT16/fk0IIWTRokUkICCAEELI3bt3iZWVVWXDLFN5zsVlKd4HaWlpVRpndZNKpSQqKopIpVKV5/Vado44zj1Bot5mqDRfVlYW2bFjB8nNzSW//vqr2vdpZbapNOnp6WTGjBlk4MCB5O3bt2pbbnlVxTZpmjZuEyGEpKWlVeh8qZa+GgQCAYYPH45z587h8ePHaNy4MSZPngxHR8cKVdbt1KkTCCElHjt37mTLTJ48GTExMRCLxbh79y46dOigjk0p05QpU/D48WOEh4dX+booilLd0NYOWDWkOTgMcPDuW8z6KxLS/x/pprbS0dFBXl4eAOD8+fNs94KmpqZV3md1ec7FlGqKr75m5kvKPU9oaCiGDBkCfX19CIVCzJo1q0ZVIymNsbExVq9ejfXr18PExATz5s3Dw4cPNR0WpUXUPmQIwzBgGAaEEMjltfvLg6LqMrlcjidPngAA3N3dweHUjv4JB7a0g4DHxbQ/7+FYZAIKpXKsHdYSOrzaEf/HfH19MWvWLLRv3x63b9/GgQMHABS1uLazs9NwdJSqLA0EePE+B0mZnx4FLzQ0FGFhYZg3bx66detWa+t529raAihqdLZs2TJ07twZw4cPr7XbQ9Ucajmri8Vi/Pnnn+jatSvc3NwQFRWFDRs2IDY2Vqu6Vdm4cSM8PDxK7U+WorRJfn4+O1xzfn6+psNRSa+m1tgy0hM6XA5OP0zCpD/uokBScxunlWXDhg3g8Xg4dOgQNm/ezCYEp0+fZrtEomoPOxNdAEB8RtmfqXXr1uHChQv4+uuvIRAItCLhc3Z2xvbt2zF8+HD8+OOP+OWXX2p0o1Gq5qt0F1uTJ0/G/v374eDggDFjxmDkyJEwMzP79Iy1mLZ1nUNRyuTm5sLJyQkAEBMTA319fc0GVAGXnyVjwu47EEvl8GtgjpBRXmrr1uhT6Hniv31Q3Ge4tpDJZHjy5Anc3d1VblX9OCELmfkSNLASwVyk2HpfKpVi5cqVMDIywuTJk6s1ca3MNlUEIQTbt29HWloa5syZUyXrqO5tqg7auE1AURdbpqam1d/F1pYtW+Dg4ABnZ2dcvny51H5aDx8+XNlVURRVjfT19ZGcnKzpMCqlY0ML7BjTGuN23cHV5ykI2nEb24NaQyRQe00qtcrKymJP5J+q91pXE+TaysOm9Pdr0aJFcHd3xxdffKEVV17LwjAMxo0bBwBYvXo1DA0NMXbsWK3fbkq9Kn0mHz16dJ056Gg/sRRV+7RzMcfusd4I2hGOW6/TMHr7Lewc6w1DoerdG1UXExMTJCYmwtLSEsbGxkrPseT/Rymk56Pa79atW7h37x6WLVtWZ75PPzRt2jSsWrUKy5cvx3fffafpcKhaRC2DHdQVtJ9YiqqdvJxMsXdcG4z+/TYiYjMwctst7B7rDWO9mtn/7YULF9j+ti9cuFAnExttdiwyHo8SshDs64yLp47i77//xtatW+vs+8zlcvHNN99ALpdj06ZN6NatG1xdXTUdFlUL1Ox7ahRFaUxBQQGCg4MBANu3b6+xw86WV3N7Y+wb3wajtt/Gg7eZGBZyE3+Ma1OiXmJN0LFjR/b/Tp06aS4QqkqEXHmFh7EpkLy5hwkD/DFkyJBa0/tHVeJwOOjXrx+CgoKwcuVKtGrVStMhUTUc/dRQFKWUTCbDvn37sG/fPq25Zd3Yxgj7J/jAwkCAp0nZGBZyE++zPt3VkSYtXLhQ6f7PzMzE8OHDNRARVVktbfSQfOwnPInPgLW1NU1gP2BnZ4dDhw7B0NAQb9++1XQ4VA1HPzkqoF1sUXWJjo4OVq9ejdWrV9f4YWdV0dDKAAcm+MDaSIgX73Mw5LcwJHyiuyNN2r17N9q3b4+XL1+y0y5duoSmTZsiJiZGc4FRFWYheQ+DVr2RYd5E06HUSMbGxnBxccHcuXPxxx9/aDocqgajSawK6IhdVF3C5/MxY8YMzJgxA3x+zW0EVRH1LUT468u2sDPRRUxqHob8Foa4tDxNh6XUgwcP4OTkhBYtWmDr1q345ptv0K1bNwQFBeHatWuaDo9SQWFhIYKCgtC3gyd0nVvhaVI20nMLNR1WjcQwDHbv3o2IiAjExsZqOhyqhqJJLEVRdZK9qR7++rItnMz08DY9H59vCcOrZNWHya5qRkZG2L9/P6ZNm4Yvv/wSa9euxenTp7F06VKt6idS2xFCEBgYiICAANS3tUQDy6KBgK48r93d2FUlLpeLVatWISMjA2fOnNF0OFQNRJNYiqKUksvliImJQUxMjNYOIW1jrIu/vmwLV0sRkrIKMOS3m4hOytZ0WCWsX78eq1evxvDhw1G/fn1MmzYN9+/f13RYVDlJpVIkJSVh+fLl6Nu3LwCgq4cVAODso3eaDK1WcHNzw/r16xEZGanpUKgahiaxFEUplZ+fD2dnZzg7O9e6YWdVYWkoxIEJPnC3NkRKjhjDQsLwMD5T02GxevbsiSVLlmD37t3Yu3cv7t27hw4dOsDHxwcrV67UdHhUOSxevBiXLl1C/fr12Wk9mtQDALzNyEclB87UegKBALt370Z0dLSmQ6FqGJrEqoA27KLqGj09Pejp6Wk6jCpnJhLgz/Ft0NzOCOl5EgzfehN336RrOiwARVfxHjx4gMGDBwMAdHV1sXnzZhw6dAirV6/WcHTUp9y5cwcpKSklepJoamuEC7M74tiU9nW2f1hVmJmZYciQIRg/fjyys2ve3RJKM2gSqwLasIuqS/T19ZGbm4vc3Fzo6+trOpwqZ6yngz/GtYG3kymyC6QYtf0Wwl6majosnDt3DjY2NiWm9+7dG1FRURqIiCqvpKQkuLi4YN26dSVeYxgG9S1EGoiq9mIYBkFBQQgODqZXrykANImlKIpiGQj52Dm2NXxdzZFXKEPQjtu4FP1e02GVytzcXNMhUKWQSCQYO3YskpOTP9lFXVaBBNkFkmqKrHZr3749ZsyYoTV9V1OVQ5NYiqKoD+jp8LAt0AudG1lCLJVj/O47CH2UpLF4ZDIZfvnlF3h7e6NevXowNTVVeFA1U0hICAIDA9GwYcMyy2269ALey89jz8031RRZ7deuXTvMnTsX8fHxmg6F0jCaxFIUpZRYLMb48eMxfvx4iMViTYdTrYR8LjaP9ETvptaQyAgm743AsUjNfGEuWbIEq1atwpAhQ5CZmYlZs2Zh0KBB4HA4WLx4sUZiosr24sULBAUFYejQoZ8sa64vQIFEjr03YyGRaWcvIFUhODgYM2bMoNUK6jiaxFIUpZRUKsW2bduwbds2SKVSTYdT7XR4HKwd1gKDWtpCJieYcSASf4XHVXsce/fuxdatW/H111+Dx+Nh+PDh2LZtG77//nvcvHmz2uOhyiaTyTBp0qRyf2b6tbCBuUgH8Rn5OPEgoYqj0x4eHh4YN24cJBJaDaMuo0msCmjvBFRdwufzsWzZMixbtkzrRuwqLx6Xg18+b47h3g4gBFj773PkFVZvQp+UlISmTZsCAEQiETIzi7r/6tOnD06ePFmtsVCftnfvXgQEBMDIyKhc5YV8Lsa0dwYAbLn0il5ZVEH37t2xfPlyJCVprroPpVk0iVUB7Z2Aqkt0dHSwYMECLFiw4JMNU7QZh8Pgh4FNML1zA/wxrg30dHjVun47OzskJiYCAFxdXXH27FkAQHh4OAQCQbXGQpWtoKAAAQEBGDdunErzjfRxhEjAQ/S7bJx+SBMyVQwaNAgLFizQdBiUhtAklqIo6hMYhsHMrg3hbF79XY0NHDgQ//77LwBg+vTpWLhwIRo0aIDRo0dj7Nix1R4PVbpFixbh7t274PFU+6FjpMvHWN+iq7G/hEbTurEqaN68OXx8fOgV7Dqqei8pUBRVaxBCkJKSAqCoKyfaIbtm/Pjjj+z/gwcPhr29Pa5fvw5XV1f069dPg5FRH3r16hVevXqFDh06VGj+8X7O+OPmGyRk5uNRQhZa2BurN0AtNnbsWOzevRuBgYGaDoWqZjSJpShKqby8PFhaWgIAcnJy6sSAB7VBmzZt0KZNG02HQX3ExMSkUiOoGQj52DC8JVwsRbAyFKoxMu3H5XJx8uRJ+Pv7w8HBQdPhUNWIJrEVUHzbIisrS8ORUFTVyc3NZf/PysqinYurqPj8QG9zar/U1FSsXbsWS5curdRy2rnSwSsqau7cuTh8+DBmzJih6VCoakST2AooHrfZ3t5ew5FQVPVQNuwpVT7Z2dnlbqlO1U47d+5Ue68111+kQMjnwNORDmhRHp6ennB2dkZaWhodBKQOoUlsBdjY2CAuLg4GBgZsPcHWrVsr9Frw4fOsrCzY29sjLi4OhoaGVRLTx+tX93yfKlfa66pML20fVsf+KytWdc1X1fuwth6DqsxbVjlVX1PlGARU34eEEGRnZ9MfAHVEr1691Lasv+7EYc6hB7A31cWpaX4wENbNLu5U9ejRI4SGhmLZsmWaDoWqJjSJrQAOhwM7OzuFaVwuV+GL7ePnAGBoaFhlCYSy9alzvk+VK+11VaZ/ah9W5f4rK1Z1zVfV+7C2HoOqzFtWOVVfq8gxCKi2D+kVWO336NEjDBs2DFwuV23L7NGkHtaef464tHx8f+wRVg9tobZlazNfX18sXboUhBDaELWOoF1sqcmUKVPKfF7d61f3fJ8qV9rrqkyn+7By+7C27j9V5i2rnKqv1cRjUJmgoCBcuXJF02FQpfjf//6n1gQWAAyFfKwd1gIcBjhyLx5H7r1V6/K1FcMwWL9+Pa2HXocwhL7bVS4rKwtGRkbIzMys0iuJ2oruv8qj+7DyNLUPAwICcPLkSdjb22PMmDEIDAyEra1tta2/Mor3WVpaGkxMTDQdjtrIZDI8efIE5ubmmDlzJv78888qWc/qc8+w9t/nEPI5ODSxHZrYVt2V/eJtcnd3V3tSXp2SkpLw559/YubMmVqzTR/Sxm0CgPT0dJiamqp8fqVXYquBQCDAokWL6Og6FUT3X+XRfVh5mtqHf//9N+Lj4zF16lQcPHgQTk5O6NmzJw4dOkTHjdcwCwsL7Nixo8qWP61zA3Rys0CBRI7xu+/gfXZBla1LW1hZWeHUqVP0amwdQZPYaiAQCLB48WKaQFQQ3X+VR/dh5WlyH5qZmWH69Om4d+8ebt++DVdXV4waNQo2NjaYOXMmnj9/Xu0xUcC0adOQn5+vMO3KlSvo27cvbGxswDAMjh49qnTeTp06YcuWLWUun8thsG54S7hY6CMxswChdEjaT2IYBn379sW7d+80HQpVDWgSS1EUVUskJibi7NmzOHv2LLhcLnr16oVHjx7Bw8OjUh3tU6ojhODFixclqknk5uaiefPm2LBhQ6nzpqWl4caNG+jbt+8n12Mo5GNbYGusGtIco9o6VTbsOmHq1KlIS0vTdBhUNaBJLEVRVA0mkUjw999/o0+fPnB0dMTBgwcxc+ZMJCYmYteuXTh79iz27NlT6Y72KdXk5+djyJAhJab37NkTy5Ytw6BBg0qd9+TJk2jevDlbt/n48eNo0KABdHV14e/vj127doFhGGRkZAAALp84iLGfNcGJEyfg5uYGPT09BAQEIDc3F7t27YKTkxNMTEzw1Vdf0UFJUHQ19ptvvtF0GFQ1oF1sURRF1WDW1taQy+UYPnw4bt++jRYtWpQo0717dxgbG1d7bHVZbGwsBg4cWKF5jx8/jv79+wMAYmJiMHjwYEyfPh3jxo3DvXv38PXXX5eYJy8vD+vWrcPWHXsw/2A4Tm5bgAEDB8LUxASnTp3Cq1evEBAQAF9fXwwdOrRS21bbMQwDLpcLqVSq6VCoKkaTWIqiqBps1apVGDJkCIRCYallTExM8Pr162qMitq6dSs6d+6s8nxisRihoaH4/vvvAQBbtmyBm5sbfv75ZwCAm5sbHj58iOXLlyvMJ5FIsHnzZiQRI7zXTwXftS0uXr6EuPgEWJubwMPDA/7+/rh48WKdT2IBYNmyZZDL5ZoOg6pitDpBDZKdnY3WrVujRYsWaNq0KbZu3arpkGqduLg4dOrUCR4eHmjWrBkOHjyo6ZBqnYEDB8LExASDBw/WdCi1RvFt3gYNGmDbtm1qW65UKsXYsWPx4sULtS2TUo+CgoIKdbV24cIFmJmZoWnTpgCA6OjoEkPWent7l5hPT08PLi4uaO9qjl1jvKFraAaOgSWC9z7E2/Q8AEUt89+/f1+BrdFOtH9l7UeT2BpET08Ply9fRmRkJG7duoUVK1YgNTVV02HVKjweD2vWrMHjx49x/vx5zJw5E7m5uZoOq1aZNm0adu/erekwag2pVIpZs2bhwoULiIiIwE8//aS2RiU8Hg+Ojo60nmMNNH/+/ArN92FVAgBKR5dS1j0Un//f0LNtXcwQ4GkHHp+Hx4lZ6LfhOsJepoJhGHr18f/p6uri5MmTmg6DqmI0ia1BuFwu9PT0ABT9ypfJZLSvOxVZW1uzdQYtLS1hampKW6mqyN/fHwYGBpoOo9a4ffs2GjduDFtbWxgYGKBXr14IDQ1V2/K/++47zJs3jx7HNcjr169x+/ZtlecjhOCff/5Bv3792GmNGjVCeHi4Qrk7d+58cllWhkI4m+ujsY0h0nILMXL7LcSn539yvrrCxcUF6enpmg6DqmI0iVVBefr/27RpE5ydnSEUCuHp6YmrV6+qtI6MjAw0b94cdnZ2mDNnDszNzdUUfc1QHfuw2J07dyCXy2Fvb1/JqGuO6tx/dUVl92lCQoLCCFp2dnaIj49XW3zr1q3D1atXYWNjAzc3N7Rq1UrhQVW/p0+foqBA+cADOTk5iIyMRGRkJICihDcyMhKxsbG4e/cucnNz0aFDB7b8l19+iadPn2Lu3Ll49uwZ/vrrL+zcuRMASlyh/RifWzSS14AWNjAU8mAm0lHL9mkDDoeD7du3azoMqorRhl0qKO7/b8yYMQgICCjx+oEDBzBjxgxs2rQJ7du3x2+//YaePXvi8ePHcHBwAAB4enpCLBaXmPfs2bOwsbGBsbEx7t+/j3fv3mHQoEEYPHgwrKysqnzbqkt17EMASE1NxejRo9VaP7EmqK79V5dUdp8qu1vyqeRDFQMGDFDbsij1yMvLg4uLi9LX7ty5A39/f/b5rFmzAACBgYGwt7dH7969weP999Xr7OyMQ4cOYfbs2Vi7di3atm2LBQsWYNKkSeUaWENXh4vVQ1sgPiMf383chwIUXfE98SAR3RvXgw6v7l6rGjp0KNuAjtJShKoQAOTIkSMK07y9vcnEiRMVpjVq1Ih8++23FVrHxIkTyV9//VXREGu8qtqHBQUFxM/Pj+zevVsdYdZYVXkMXrx4kQQEBFQ2xFqnIvv0+vXrZMCAAexr06ZNI3v37q3yWGuDzMxMAoCkpaVpOhS1Sk9PJw8ePCBSqVSl+Zo2bUoOHDjwyXLLli0jdnZ2FQ2PnH2URBznniD+v1wkF568I3K5/JPzSKVSEhUVpfI21WRBQUHk8uXLWrVN2vg+EUJIWloaAUAyMzNVmq/u/kRTs8LCQty9exfdunVTmN6tWzfcuHGjXMt49+4dsrKyAABZWVm4cuUK3Nzc1B5rTaWOfUgIQVBQED777DOMGjWqKsKssdSx/yhF5dmn3t7eePjwIeLj45GdnY1Tp06he/fumgi3ytAqKorGjRwJo/XrgcTEcs9TWFiIgIAA9OzZs8RrmzZtQnh4OF69eoU9e/bg559/RmBgYIXjk8nlMBfp4FVyLsbsDMeATTdw4em7OtfGok+fPuByuZoOg6pCtDqBmqSkpEAmk5W49W9lZYWkpPKNd/327VsEBweDEAJCCKZOnYpmzZpVRbg1kjr24fXr13HgwAE0a9aMrdu4Z88etjsbbaaO/QcUdZwfERGB3Nxc2NnZ4ciRIyW6AKoryrNPeTwefv31V/j7+0Mul2POnDkwMzNTWwwymQyrV6/GX3/9hdjYWBQWFiq8XtUNvspTRaWuEWdlweHkScjGjwfs7Mo1j46ODhYtWqT0tefPn2PZsmVIS0uDg4MDZs+ejXnz5lU4vh5NrNHO1RwbLrzA7rAY3I/LwNidd+BqKcKw1vYY294ZHI76qrzUVI0aNUJKSoqmw6CqEE1i1UxZVynlrR/n6enJNgaoyyqzD319fet8FzOV2X8A1NqyXlt8ap/269dPocW5Oi1ZsgTbtm3DrFmzsHDhQixYsAAxMTE4evRotdT3W7VqFYKDgzFu3DgAwJo1axAaGorNmzdjxYoVVb7+mmhEjx6AGq9Gr169GqtXr1bb8gDAUMjH/F7umNChPrZefYU9YW/w4n0O/nmQiHF+9dlyT5Oy4GIh0spW3v/++y8EAgF8fX01HQpVRWgSqybm5ubgcrklrni9f/9eqxpmVSW6DyuH7j/1qwn7dO/evdi6dSt69+6NJUuWYPjw4XBxcUGzZs1w8+ZNTJs2rcrWXVyd4ttvv1WYXloVFbFYrNBosLh6lEwmq/193SYmstUHmP8fHY3cvQt2q6ytix41jIkuD3O6NcSkDvXxz/0EWBoKIZPJUCCR4ezjd5j51wMwDCDS4aJFPQG2N2yo6ZDVxsjICMnJybX/2PtA8bZo0zYBFd8emsSqiY6ODjw9PXHu3DmF8bTPnTun0LE1VTq6DyuH7j/1qwn7NCkpia0OIxKJkJmZCaCovt/ChQurdN2qVlFZsWIFlixZUmL6ixcvIBKJqizO6mC5aRMsN28GAMwDsBKAzaRJ7BVMsaMjCp2dNRVeucgJQVq+DGl5MmSKZZB/UEX2HYAUUws887PQWHzqlpycDIlEgmfPnmk6FLXTtm3Kycmp0Hw0iVVBTk6OwvCPxf3/mZqawsHBAbNmzcKoUaPg5eWFtm3bIiQkBLGxsZg4caIGo65Z6D6sHLr/1K+m71M7OzskJibCwcEBrq6uOHv2LFq1aoXw8PBydcGkDuWtojJv3jy2Symg6Eqsvb09XF1dYWJiUuVxVqn58yEbMwYA0CQ4GIFRUdjSqBF+XbQITVxcauyVWABIyMjHzhtv8HdEPLLzJeADMAdgbSSEt5MJbE10YSHSgak8Ew0bNtSaxlBisRjx8fFatU0ymQzPnj3Tqm0CUPGBKdTbSYJ2u3jxIgFQ4hEYGMiW2bhxI3F0dCQ6OjqkVatW5PLly5oLuAai+7By6P5Tv5q+T+fOnUuWL19OCCHk4MGDhMfjEVdXV6Kjo0Pmzp1bpesWi8WEy+WSw4cPK0yfNm0a6dChwyfn19Yutl4dP04IQOJOnSKDBg0iv/zyS43s8iguLZfMO/yAuM4/SRznniCOc08Qnx/Ok19Dn5JH8ZkKXW9pY9dNt27dIhcvXtSqbdLG94mQinexxRBSx/rcoCiKqsVu3ryJGzduwNXVtcoak32oTZs28PT0xKZNm9hpHh4e6N+//ycbdmVlZcHIyAhpaWm1/0rsB74fPx5Ltm2D/PZtcLy8sG3bNhw7dgwbNmyAk5OTpsNDXqEUmy6+RMiVVyiUFTV09alvigkd6qNjQ0twlfRMIJPJ8OTJE7i7u2vNFb558+ahdevW6N+/v9Zskza+T0DRlVhTU1NkZmbC0NCw3PPR6gQURVG1iI+PD3x8fKptfZquTlETJeTm4uHo0fCwtgbDMBg/fjw+++wzTJ48GZ9//jmCgoLUOmqbKkIfJWHJ8UdIyCwaFtenvilmdmmINvXV1+1bbZGRkaFSQkTVPjSJpSiKquGePXuGS5cu4f379yW6kKvqbraGDh2K1NRULF26FImJiWjSpAlOnToFR0fHKl1vTdayfXu8sLGBxwd1YF1cXPDPP//gl19+wbBhw7B+/XpYWlpWW0xZBRIsOf4Yf0e8BQDYGutiYR8PdG9spbGEWtMWLFhA+4nVcrQ6AUVRVA22detWTJo0Cebm5qhXr55CQsIwDCIiIjQYXdm0tTqBWCzGgwcP0KpVK6W3dO/fv4/Zs2fjq6++qpZeLO7FpmPqvnuIz8gHhwEmdHDB9M4NoKtT/tvN2nibev78+RgxYoRWbZM2vk9AxasTaGP/xhRFUVpj2bJlWL58OZKSkhAZGYl79+6xj5qcwGqzu3fvYteuXaW+3rx5c5w8eRI3btzAuHHj2P5yq8L+27EY+ttNxGfkw8FUD3992Rbf9mykUgKrre7du6fpEKgqRpNYiqKoGiw9PR2ff/65psOgPuDs7Iy3b9+WWUYgEOCnn35CYGAgBgwYgMuXL6s1BqlMjoVHH+Lbw1EolMnRo3E9nJruBy8nU7Wup7YSi8Vo3LixpsOgqhhNYimKomqwzz//HGfPntV0GNQHLCwsyj2UqZ+fH44dO4Z9+/bh66+/RkFBQaXXXyCRYfLeCOy5+QYMA8zu2hCbvmgFkYA2cylWWFiodOANSrvQI56iKKoGc3V1xcKFC3Hz5k00bdoUfD5f4fWqHHaWUo5hGAgEAsjl8nLVSzQwMMBvv/2Gf/75B3369MHPP/+Mli1bVmjdOWIpJuy+gxsvU6HD5WDd8Jbo0aRehZalzUJCQtCsWTNY19ABKCj1oEksRVFUDRYSEgKRSITLly+XuCXNMAxNYjXk3r17ePjwoUrJaN++feHj44Np06ahadOmmDNnDni88n8N5xVKEfT7bdx5kw59HS62BnqhnYt5RcLXehEREQgMDFQ6PDKlPWh1AoqiqBrs9evXpT5evXql6fDqLB8fH/z7778qz2dhYYF9+/bBzs4Offv2VRjyuCxiqQxf7rmLO2/SYSjkYd94H5rAluGLL77Qqh4xKOXolViKoiiKUlHr1q3RsGHDCs3LMAxGjx6Njh07YurUqejduze+/PLLUvtzlcsJpv8ZiavPU6Cnw8WOMd5obm9ciei1W3p6OqRSqabDoKoBTWIpiqJqmFmzZuF///sf9PX1MWvWrDLLrlq1qpqioj7E5/Mxffp0bNq0CTo6OhVahqOjI44dO4Z169YhICAAGzZsgI2NTYlyv5yNxplHSdDhcrB1tBc8HekVxrIcO3asRN1xSjvRJJaiKKqGuXfvHiQSCft/aerqSEw1haOjI27evIkOHTpUeBkcDgczZsxAt27dEBQUhHHjxmHIkCHs68ci47Hp0ksAwMrBzdDelVYh+JQLFy5gw4YNmg6DqgY0iaUoiqphLl68qPR/qmbp27cvkpOT1bIsDw8PnDhxAsuWLcOJEyewdu1axOdxMOfQAwDAxI4uGNDSVi3r0mYSiQQ7duwAl8uFTCbTdDhUFaNJLEVRFEVVQNOmTXHlyhXIZDK1DAGqo6ODpUuX4tatWxgwaBAyG/SG2NQdnzWyxDfd3dQQsfbbvXs3DA0N6QAhdQRNYimKomqwgQMHKq02wDAMhEIhXF1dMWLECLi50SRHE+7cuYPc3Fz06dNHbcts06YNPMetwLa1P6Fhd1esGtIcXA6tOlIe//zzD/bs2aPpMKhqQrvYoiiKqsGMjIxw4cIFREREsMnsvXv3cOHCBUilUhw4cADNmzfH9evXNRxp3TRmzBjs3r1brcs8//gdDkelwqzzOKwL8oWxnvKGY4cOHULTpk2hq6sLMzMzdOnSBbm5uQgKCsKAAQPwww8/wMrKCsbGxliyZAmkUim++eYbmJqaws7ODr///rta464JgoKCYGBgoOkwqGpCr8RSFEXVYPXq1cOIESOwYcMGcDhF1x3kcjmmT58OAwMD7N+/HxMnTsTcuXNx7do1DUdb95ibm2Pnzp1qq1KQK5bi+2MPAQAT/OqX2hdsYmIihg8fjpUrV2LgwIHIzs7G1atXQQgBUNS4yc7ODleuXMH169cRHByMsLAwdOjQAbdu3cKBAwcwceJEdO3aFfb29pWOuyb4448/0KtXL02HQVUjhhQf8VS5yeVyJCQkwMDAgLYOpihKKUIIsrOzYWNjwyafFWFhYYHr16+X6JP02bNnaNeuHVJSUhAVFQU/Pz9kZGRUMmr1ysrKgpGREdLS0rSq43mZTIYnT57A3d0dXC4XDx48wP79+/HDDz9UetnLTjzGtmuvYW+qi7MzOkJXR3liHBERAU9PT8TExMDR0VHhtaCgIFy6dAmvXr1ij71GjRrB0tISV65cYbfByMgI27Ztw7Bhw0psU22TnZ2NwYMH48yZM+z3cm3fJmW0cZuAor59TU1NkZmZCUNDw3LPR6/EVkBCQoLW/HKlKKpqxcXFwc7OrsLzS6VSPH36tEQS+/TpU7b1tVAopD+oNahp06aYPXs2xGIxBAJBhZfzOCELO27EAACW9m9SagILAM2bN0fnzp3RtGlTdO/eHd26dcPgwYPZHwuNGzdW+PFkZWWFJk2asM+5XC7MzMzw/v37Csdbk1y9ehVTp06ln4M6hiaxFVBc3yYuLk6lXwwURdUdWVlZsLe3r3T9vFGjRiE4OBjz589H69atwTAMbt++jR9++AGjR48GAFy+fBmNGzdWR9hUBTAMgwULFiA7O7tSSeyK008gkxP0aloP/m6WZZblcrk4d+4cbty4gbNnz2L9+vVYsGABbt26BQAlOvtnGEbpNLlcXuF4a4q8vDy4urpWeAQ1qvaiSWwFFP/SMzQ0pEkspbUKCwvZ26Pz58+v8KhEdV1lrwytXr0aVlZWWLlyJd69eweg6KrazJkzMXfuXABAt27d0KNHj0rHSlVcp06dsGHDBgwfPhxmZmYqz3/9RQquPk8Bn8vg2x7u5ZqHYRi0b98e7du3x/fffw9HR0ccOXJE5XXXdiEhIbCxsaFJbB1Ek1iKopSSSCRYsmQJAOCbb76hSayGcLlcLFiwAAsWLEBWVhYAlPjx7ODgoInQqI+0bNkSixcvxvr161WajxCClWeeAgC+aOMIBzO9T85z69Yt/Pvvv+jWrRssLS1x69YtJCcnw93dHQ8ePKhQ/LWRRCLBiRMnEBoaqulQKA2gSSyl1XLEUrxJzUVsah5i0/KQnC1GWm4hUnMLkZ5XiFyxFBIZgUQmh0QmB5fDQMjnQsjjQqjDhakeH5YGQlgYCGBlJISLuT5cLUWwMBBofd0rHo+HyZMns/9Tmkfv/NRs7du3x+XLlyGVSlX6zNx4mYr7bzMh5HMw9TPXcs1jaGiIK1euYM2aNcjKyoKjoyN+/fVX9OzZEwcOHKjoJtQ6hYWFOHDggFY1cqLKj34zUVqBEIKY1Dw8jM/Eo4QsPErIxJPEbKTkiKtkfQZCHjysDeHpaAJPRxO0cjCBib52XakUCATYuHGjpsOgUNQf6F9//YXY2FgUFhYqvBYREaGhqChl5s+fjwMHDmDo0KHlnmfzpZcAgKFe9jAXla9Orbu7O86cOaP0tZ07d5aYdunSpRLTYmJiyhtijRQREYEtW7YgJCRE06FQGkKTWKpWkssJnr3Pxq1Xabj1OhW3X6chJadQaVlTfR04mOrB0UwP9QyFMNHXgam+Dkz1dCAS8sDncqDD5YDPYyCVEYilMhRI5MgrlCEtV4z3WWK8zxYjPiMfr5JzEJuWh+wCKW69TsOt12kAAIYBWtgbo3MjS3zWyAru1rT7NUo91q1bhwULFiAwMBDHjh3DmDFj8PLlS4SHh2PKlCmaDo9SIioqCiKRCL179/5k2Yfxmbj2IgVcDoNxfvWrITrtIJFIMHfuXOzbt0/ToVAaRJNYqtbIK5Ti6vMU/PvkHS48TS5xlVWHx4G7tSEa2xQ/jFDfQh+GQn4pS6yYAokMr1NyEfU2E3ffpOPOmzS8TM7FvdgM3IvNwC9nn6GBpQgBnnYY2NIWVoZCta6fqls2bdqEkJAQDB8+HLt27cKcOXNQv359fP/990hLS9N0eJQS8+bNw8yZM9GrV69P/pjddzsWANCzST3Ym366Liz1n61bt8LCwkLTYVAaRJNYFWzcuBEbN25k+2akql5WgQShD5NwMioRN16molD6X3cwunwuvJxM0MbZFG3qm6GZnREEvKqvFyXkc+FubQh3a0MMaV3UX3BSZgEuRr/Hv0/e4+rzZDx/n4MfTz/FyjNP0b1xPUzoUB8tHWpXZ++5ubkwNjYGAGRkZEBfX1+zAdVRsbGxaNeuHQBAV1cX2dnZAIq63vLx8cGGDRs0GR6lhL6+PkJCQnDkyBH069ev1PqauWIpjkcm4P/au+/wpsr2gePfJN1NFy1QSksHZW9aRplFhOJgqCxBpIIMAX0RFVFfBP2BW1FkOZDxiorKUEEZKnuWUWSUVUZLB9AWumdyfn/URmILNF1pyv25rlwkJ885uc+TkNx9zjMARnSSwXmldfz4cRYvXsyiRYvMHYowM0liTTB58mQmT55sWIVGVI6cfB3bTl/jp8h4/jxzzShxbVDLgd7N6tC7aV06+tfCxqrsKyFVJE8XOx7v2IDHOzYgLSefjX8lsObwFQ5dvsFvJxL57UQiHf1qMbVPo9suI1kdFRQUmDuEe56npyfJycn4+vri6+vL/v37adOmDRcvXkQWXKze0tLSePPNNw2zfPzbxuMJZOQW4OfuQEiA6dNy3YtycnKYNm0aK1euNHcoohqQJFZUG1EJaXxzIIb1R+NIz/0neQqso2VgGy8eaOVJw9raat/X1NnO2pDQnklM54tdF/gpMo6Dl1IY8cUBejWpzcsPNKWpZ/UeaW5vb8+VK1cM94V53Hffffzyyy+0b9+esWPH8vzzz/Pjjz9y6NAhHn30UXOHJ+5g9OjRvPzyy6SmppbY8LHuSBwAQ4J9qv33WnWg1+spKCjg008/pV69euYOR1QDksQKs8rKK2DDXwl8cyCGyNibhu1eLnb0b+vFwDb1LXqQVBNPJz4Y0oYX+zZh8fbzrDoQw7Yz19lx9jpPdw/g+fsb33FpSXNSq9XUr1/f3GHc8z7//HPDqkoTJ06kVq1a7N69m/79+zNx4kQzRyfu5t1332XTpk20bt0aLy8vw/aUzDwOXEwGYEAbr9vtLm7x6quvEhQUxJAhQ8wdiqgmJIkVZhF3M5sVey/x7cEY0nMKW12t1Cr6tqjL4x0b0LWhB2q1ZSauJfF0seONgS0J7+rPe5tO89uJRD7feYFNJxJ557FWFtXFQFQttVqNWv1Pt5mhQ4cydOhQM0ZUc4SGhtKyZUsAvv76azQaDc888wz/93//h0qlws/Pj6effpqzZ8+ydu1a3N3dmT9/Pp06deLZZ58lIiICf39/li1bRnBw8G1fx9/fn/DwcL7//ntDP/Pfo66iV6BZPWcZ0FUKa9euRaVSSQIrjFSPDoUVLDc3l7Zt26JSqYiMjDR6TqVSFbstWbLEPIHeg/66cpPnvj1Kj/e28fnOC6TnFNCglgPT+zVh3yu9WTQyiO6NateoBPZW/h6OLH4iiKWjg6nnYkdMShYjvzzAR1vPotNXr/6NeXl5vP/++7z//vvF5iYVVSsnJ4eDBw+yYcMGfv75Z6ObKJ8VK1ZgZWXFgQMHmD9/PvPmzePLL780PD9v3jy6du3K0aNHeeihhxg1ahTh4eE8/PDDREREEBgYyJNPPnnH/slNmjRh7ty5HDp0yLBty8lEAPq18Ky8k6shTp48Sd++fZk7d665QxHVTI1siZ0+fTpeXl4cO3asxOeXLVtmtM64DNKqXHq9wu9RV/ly10UOXvpnSqCQAHfG9fAntHGdGpu03k7vZoUD0+ZsiGL1oVjm/3GOiIspLBjRDvdSTnZe2fLz85k+fToAkyZNkmVnzWTTpk08+eSTJCUlFXtOpVLJbCnl5OPjw7x581CpVDRp0oTjx48zb948xo0bB8CDDz7IhAkTAHj99ddZvHgxwcHBhIWF0bhxY15++WVCQkK4evUqnp63T0g7dOhAXl4eo0aNYv6Chew5X9iVoE/zupV/khbs6NGjvPrqq6xdu9boioQQUANbYn/77Te2bNnCBx98cNsyrq6ueHp6Gm4yaKVy6PQKPx+Lp98nOxn/v8McvJSClVrFo+3qs+HZbnw7vjP3Na17zyWwRZzsrHl3cGs+HtYWBxsN+y4k88iivURfzzB3aEDhUrOjR49m9OjRsuysGU2ZMoUhQ4aQkJCAXq83ukkCW36dO3c26nMfEhLCuXPnDHXbunVrw3N16xYmnK1atSq27dq1a3d9LRsbGyZMmMCAR4eQlZODi701TT2dKuQ8aiK9Xs9nn33GqlWr5HdalKhG/TJdvXqVcePGsX79ehwcbt/HaMqUKTz99NP4+/szduxYxo8fL3/hVaACnZ5f/orn0z/Pc+F6JlC4TOsTnX0ZHeKHp4tM/n+rQe3q07K+M08tjyAmJYtHF+3liyeD6ehfy6xx2dralrh8paha165dY9q0aYZkSVQta+t/FkspSnZL2lY0+O5uunXrxgPjX2HxnnjaNa5Zff8r0urVq3FwcJDufuKOakwSqygK4eHhTJw4keDg4NuuCf1///d/9O7dG3t7e/744w9eeOEFkpKS+O9//3vbY+fm5pKb+8/qUGlpaRUdfo2Qr9Oz/mgcC7ed51JyFgAu9taM7ebP6C5+uNhX7MpZNUlgHSfWTerK0ysOERl7k9FfHWRpeLAM+BIMHjyY7du307BhQ3OHUiPt37+/2ONGjRrddoGCinAhx5H8lDgivlhCTNjXNGggCx3cavHixRw7dkwW8hB3VaoktlYt01qEVCoVR44cwdfXt0xB3Wr27Nm3nSi6SEREBHv37iUtLY1XXnnljmVvTVbbtm0LwJtvvnnHJPbtt9++awz3srwCPWuPXGHh9vPEpmQD4OZgzbgeAYzq7ItTBS/7WlN5aG35bnxnJvzvMDvOXmfM8gi+Gt2BLoGSyN7LFixYwJAhQ9i1axetWrUyagUEeO6558wUWc0QGxvLtGnTmDBhAkeOHOHTTz/lww8/rLTXUxSFIzE3sfNuzjsT72fVqlXMmDHDYqcRrEh6vZ6oqCjCwsKYOHGi1Im4q1IlsTdv3uTjjz8u1QAoRVGYNGlShfXVmjJlCsOHD79jGT8/P+bMmcP+/fuxtTUeFBMcHMzIkSNZsWJFift27tyZtLQ0rl69etvLda+88grTpk0zPE5LS8PHx8fEM6l5cgt0fH/oCku2RxN3szB59dDaML5HACM7+eJoW2Ma+quMnbWGz0YFMfHrw2w/c50xKyJY9XRngnyrfsnazMxMwzyxcXFxsuysmXzzzTds3rwZe3t7tm/fbvTDrlKpJIktpyeffJLs7Gw6duyIRqPh2WefZfz48ZX2etfTc0nJzEOtgj6dWtO/WzsWL15MRkYGL7744j2buF29epUpU6bQr18/xo4da+5whIVQKaVYt1CtVpOYmEidOnVKdVAnJyeOHTtGQEBAuQMsrZiYGKPL/PHx8YSFhfHjjz/SqVMnvL29S9xvwYIFvPTSS9y8ebNYAnw7RcvOpqam4uxcvVddqgw5+Tq+OxjDkh0XSEzLAaC2ky0TezZkRMcG1XbyfkuSW6Bj/MrCFlk3B2vWPNOFgNraKo0hMzMTrbbwNTMyMiSJNVFFfU94enry3HPPMWPGDIvru19UBykpKbi5Vf0fYncTGhpK27Zt+fjjj03aT6fTERUVRbNmzUzudrDz7HWe/OogAR6O/PliKFDY+LNgwQLS0tJ47bXXTDpeRSnPOZVXUlIS+/fvp0GDBkYD6crLnOdUWWriOQHcuHGDWrVqmfx9WaqmstJ2WC+Snp5uUvmK8O8+RUU/vg0bNjQksL/88guJiYmEhIRgb2/Ptm3beO211xg/fnypE9h7WXaejlUHLvPZzgtcTy/sI+zpbMczoQ0Z1sEHO+ua8x/K3GytNCwa2Z7Hv9jPX1dSCV8WwbpJXap0+i17e3vOnj1ruC/MIy8vj2HDhllcAitKdv5a4ewjjer+80epSqXi2WefRa/Xs3DhQgICAnjggQfMFWKVSU1N5fnnn6dZs2a89NJL5g5HWKB76lvR2tqaRYsWERISQuvWrfnkk0948803K7X/U02QkVvAkh3RdHv3T+ZsjOJ6ei71Xe2ZM6glO6aHMrqLnySwlcDR1oqlozvgU8uemJQsnv32KAU60/6gLA+1Wk2jRo1o1KiRJFBmNHr0aFavXm3uMEQFuXKjsOuVr3vxKxtqtZrRo0ezY8cOXn/99aoOrUplZ2ezceNGxo4dKwmsKDOTOy3eboUYlUqFnZ0dgYGB+Pv7lzuw8vLz8yu2gkq/fv2MFjkQd5aWk8/KvZf4cvdFbmblA+BTy57JoYE82t4bGytJbCpbbSdblo7uwKCFe9gbncyHW8/ycr+m5g5LVCGdTsd7773H5s2bad26dbGBXR999JGZIrN827dvr/LXjLtZOHNLfdeSr25otVreeecdCgoK+OGHHzhz5gwvvvgidnY1Y2rC48ePM2fOHDp27MgLL7xg7nCEhTM5iR00aBAqlapYgli0TaVS0a1bN9avX18t+0CJu0vNyuerPRdZtuciaTkFQOFyqZN7BTKwrRfWGkleq1Ljuk68+1hrnv32KIu3R9O+gVuVrPKTn5/P559/DsD48eOLJU+iahw/fpx27doBcOLECaPn7tVBQJYs/mbhOAKv2ySxRaysrBg8eDBr1qxh0qRJfPHFFwAW2w/y7NmzqFQqjh8/zjvvvFMtGruE5TM5id26dSuvvfYac+fOpWPHjgAcPHiQ//73v8ycORMXFxcmTJjAiy++yNKlSys8YFF5UjLzWLr7Aiv2XiYjtzB5Dayj5dn7Anm4tRcamZTbbPq38eJozE2+2nORl9f8RRuf7tRxqtyWmby8PKZMmQJAeHi4JLFmsm3bNnOHICrQjaw8ANy1d1/GWaVSMXjwYAYPHszZs2d55plnePjhhxk3bpxh3Ed1p9PpmDhxIjk5Obz55puMGDHC3CGJGsTkJPY///kPn3/+OV26dDFs6927N3Z2dowfP56TJ0/y8ccfM2bMmAoNVFSe2JQslu6+yOqIWLLzC6dGa+rpxLP3NeKBlp6yokw18fIDTdh3IZmohDRmrDnO0tHBldoSp9FoGDx4sOG+uPfMnTuXjRs3EhkZiY2NDTdv3jR3SBYvNbuwa5api780btyYLVu28PPPP5OXl8e8efPo378/gYGBlRFmueTk5PDLL7/www8/sHTpUmbOnCkLOohKYXISGx0dXeL0B87Ozly4cAGARo0akZSUVP7oRKU6EZfKZzsv8OvxBHT6wu4hLbycea53I/o0qyvJazVja6Xh42Ft6b9gN3+evsa3B2MZ0anyfhjs7Oz44YcfKu344s4effTRUpVbu3ZtpcWQl5fHkCFDCAkJkStrFUCnV0j/u4tWWVYw1Gg0PPLIIwD07NmTuXPn0rVrV8LCwlCr1YZ5nc1Br9eze/dubty4gbu7O0lJSSxevBgnJyecnJzMFldNEZuSRT0XO+RX2ZjJSWxQUBAvvfQSK1eupHbt2gBcv36d6dOn06FDBwDOnTt323lZLdnChQtZuHBhhS3kYA6KorDzXBKf74xmz/lkw/ZugR5M6BlAt0AP6WdXjTXxdGJ6WBPmbIzi7V+j6N2sDnWda8aAD2GsNIvLVLailQqXL19u3kBqiLyCf2YXsS/njC7t27dn2bJlKIrCsWPHmD9/PvHx8axZs4bLly/TpEmTSr+CEhcXx969e3nooYeYOnUqXl5ejBo1ioYNG9KtW7dKfe17yca/Epj8zREebl2PT4a1MXc41YrJSezSpUsZOHAg3t7e+Pj4oFKpiImJISAggJ9++gkonBh95syZFR6suU2ePJnJkycbJvC2JJm5Baw9Gsf/9l3i7NXCeQo1ahUPt67HuO4BtKxvWedzL3uqqz+/HIvn2JVU3txwioUj2ps7JFEJli1bZu4QRAXT3TIguqLGGKhUKtq2bctXX32FXq9HrVazfv16du3axQMPPMCDDz7Ijh07aNasGe3atSvTnM9FA7kPHTrEvn378PX1xdbWljVr1tClSxcURTEMAhUVb97vhfN1b/grQZLYfzE5iW3SpAlRUVFs3ryZs2fPoigKTZs2pU+fPoa5JAcNGlTRcYoyir6ewf/2XWbN4Suk/z1Yy8FGw7AOPozt5o+3m4OZIxSm0qhVvPVoKwYs2MPGvxIYHHSNXk1Kt5qeKbKysmjUqBFQeHXFwUE+K+LOcnNzyc3NNTwuWkVRp9NZ9BWsfys6F1PPqaCgwHBf0eupjCrR6XS8/PLLvPzyyyiKQkpKCi4uLvzxxx94eHiwZs0adu/eTd26dfn88895+umnsbOzo1evXtja2rJq1SpSUlJ47rnn2LBhA7t378bNzY2vvvqKDRs20KFDBzp37oybmxt9+vQxet3qpqzvU3WSnlNgWCADICuncGCgJZ9TScp6PqVadvZ2cnJysLW1vecuP1f3ZWdzC3T8EXWNbw/GsOvcP32T/T0cGdXZl8eCvMvUH0tUL3M2nOLL3RfxdrPn92k9K3zBCVl2tnyq6/fE7NmzDd0EbiciIoLg4GDD4+XLlzN16tS7Duy63bH37dtnMaPpK1NGnp7h38cCsO7xBlhrzPfbqdPpUKvVpKamkpeXh42NDWq1mvj4eNzc3PDw8JABndXA3pgs3tp53fD468HeuNrVvPclIyODkJCQyll29lZ6vZ65c+eyZMkSrl69ytmzZwkICGDmzJn4+fkxduxYUw8pKsjJ+FR+OHSF9ZFxhsUJVCro3bQOT4b40S3QQwZr1SDP92nMr8cTuHIjm6W7LzK5V8WOUrazs+Po0aOG+6JmmDJlCsOHD79jGT8/vzId+5VXXmHatGmGx2lpafj4+BAYGFij5g3X6XScPXuWxo0bm5ToFfaJLUxifRs2qlaNCUXnFBwcXGOS17K+T9XJ/04bzw3t6+dPamKMRZ9TSW7cuFGm/UxOYufMmcOKFSt47733GDdunGF7q1atmDdvniSxVexaeg4b/0rgh0NXOJWQZtju6WzHo+3r83jHBvjUksvANZGjrRXT+zVl6upIFm07z9BgH2o72VbY8TUaDW3btq2w44nqwcPDAw8Pj0o5tq2tLba2xT+DGo2mRv3gFjH1vOw1Gmw0avJ0erILFGpVwzqpie+VpZ5T0UBso20Udtu01HO6nbKei8lJ7MqVK/n888/p3bs3EydONGxv3bo1p0+fLlMQwjRJGblsOpHIhr/iOXAxhaIOITYaNX1a1GVIkDfdG9WWxQnuAQPaeLFsz0WOXUnlo61nefvRVuYOSdQgMTExpKSkEBMTg06nIzIyEoDAwEDpHlBGjrYa8rL0ZOYW3L2wuKedvZpBQmoOtlZqdHqFAr1Cvl5/9x3vISYnsXFxcSVOrqzX68nPz6+QoERxV9Ny+CPqGr8eT2BvdBL6W3oyt/Vx5ZF29RnY1gtXh7uvAiNqDrVaxX8fbs6QJftYHRFDeBc/mnhWzJyM+fn5rFq1CoCRI0fKil33oNdff50VK1YYHhctf7tt2zZCQ0PNFJVlc7G35kZWPjcy88wdiqjmtp+5BkDnAHciY2+Smp1vmNNdFDI5iW3RogW7du3C19fXaPsPP/xg+IIT5afXKxyPS+WP09f48/RVTsSlGT3f2tuFh1rV48FW9aS7wD2ug18tHmjpyW8nEnl/8xm+HB18951KIS8vj6eeegqAIUOGSBJ7D1q+fLnMEVvBPF3suJScRWJajrlDEdXc9jOFA7pCm9TmZHwqAAU6SWJvZXISO2vWLEaNGkVcXBx6vZ61a9dy5swZVq5cyYYNGyojxntGQmo2+6KT2XM+mR1nr5OU8c9UNSoVtPF2pU/zujzcuh6+7jJSXPzjxbAmbD6ZyO9RVzkWe5M2Pq7lPqZGo+HBBx803BdClF89l8J5WuNvShIrbi8jt4BDl1MACG1Sh892FK6Imq/TI9/G/zA5ie3fvz+rV6/mrbfeQqVS8frrr9O+fXt++eUXoznjaqKKXrHrenouBy4mszc6mX3RyVxMyjR63tFGQ4/GtbmvaR1Cm9Sp0EE7omZpWFvLoHb1WXskjo+2nmXFmI7lPqadnR0bN26sgOiEEEXquRTO9JGQmm3mSER1tud8Evk6BV93B/w9HLH6ezq2Ar0iSewtTE5iAcLCwggLC6voWKq98qzYlVegJyohjaMxNzgae5MjMTeITTH+ElOroFV9F0IaetAt0IOO/rWwsVJX5CmIGuw/vRvxU2Q8O85e5/DlFIJ8a5k7JCHEvzT4u/vXvxsthLiVoStB49oAWKn/SWKlOesfZUpiRelt/CuBZXsucjwuldwC41GFKhU0qetEl4YehDR0p6N/rWo1b6CwLL7ujgwJ8ua7iFg+3HKWb8Z1NndIQoh/KRp4eTox3cyRiOpKURR2ni3qD1u4GqOVprBBq0AnsxPcqlRJrJubW6lX5UpJSSlXQDVNRm4+hy4XTuLrYm9NuwautG/gRrsGrrTxccXZTpJWUXGm3BfImiNXDF1UQhq6l/lYWVlZtGlTuE73sWPHZNlZISpA47qFSez19FySM3Jx10q7mjB27EoqcTezsbNW0zmg8Dtc83cOJuO6jJUqif34448N95OTk5kzZw5hYWGEhIQAhUsKbt68mZkzZ1ZKkJase6PafDikDe0auOLv4XjPLdErqpa3mwPDOvjw9f4YFmw7V64kVlEUzp8/b7gvhCg/R1srfN0duJycxenEdLoGShIrjK07cgWAvs09sbcp7AErqUPJSpXEjh492nD/scce480332TKlCmGbc899xwLFizg999/5/nnn6/4KC2Yl6s9jwV5mzsMcQ+Z2LMh3x2MZc/5ZI7G3KBdg7It92lnZ8fu3bsN94UQFaOllwuXk7M4cvkGXQMrZ/U0YZnydXp++SsBgEfa1zdzNNWfyaOGNm/eTL9+/YptDwsL4/fff6+QoMrqyJEj9OnTB1dXV9zd3Rk/fjwZGRlGZWJiYujfvz+Ojo54eHjw3HPPkZcnk06LmsPbzYGBbQu//BZtjy7zcTQaDV27dqVr164yxZYQFahTQOGgywMXpfudMLbjzHVSMvPw0NrSXf7AuSuTk1h3d3fWrVtXbPv69etxdy/7pcvyio+P5/777ycwMJADBw6wadMmTp48SXh4uKGMTqfjoYceIjMzk927d/Pdd9+xZs0aXnjhBbPFLURleCY0AJUKtp66yhkZQCJEtdLJv/C38tDlFPIKZKCO+Me3B2MAGNjWyzCYy4h07TJi8uwEb7zxBmPHjmX79u2GPrH79+9n06ZNfPnllxUeYGlt2LABa2trFi5ciFpd+MYvXLiQdu3acf78eQIDA9myZQunTp0iNjYWLy8vAD788EPCw8OZO3cuzs7OZotfiIoUWMeJfi0KV/FasiOaecPamnyMgoICwx+sjzzyCFZWMpmJEBWhUR0tbg6Fy8/+deUmwX4yHZ6A2JQs/vx7qdmRnRqYORrLYHJLbHh4OHv37sXV1ZW1a9eyZs0aXFxc2LNnj1GrZ1XLzc3FxsbGkMAC2NsXroxS1K9v3759tGzZ0pDAQmE3iNzcXA4fPly1AQtRySaFBgLw87F4YpKzTN4/NzeXoUOHMnToUHJzc+++gxCiVNRqFV0aFl4q/vP0NTNHI6qLrw9cRlGgeyMPAmprzR2ORSjTTPqdOnVi1apVHDlyhKNHj7Jq1So6depU0bGZ5L777iMxMZH333+fvLw8bty4wauvvgpAQkJhJ+nExETq1q1rtJ+bmxs2NjYkJibe9ti5ubmkpaUZ3YSo7lp5u9CjcW10eoXPdpreN1atVtOzZ0969uxp9MehEKL8+rYo/C3adCJRZv8Q5OTr+D4iFoBRnX3NHI3lKNUvk6lJW3p6xfXBmz17NiqV6o63Q4cO0aJFC1asWMGHH36Ig4MDnp6eBAQEULduXaNBKSVNcaUoyh2nvnr77bdxcXEx3Hx8fCrs/ISoTJNDGwLww6ErXEszba12e3t7tm/fzvbt2w1XNYQQFeO+pnWw0ai5kJTJ+WsZd99B1Gg/HIrlRlY+9V3t6d2s7m3LyZ87xkqVxLq5uXHtWukvedSvX58LFy6UOahbTZkyhaioqDveWrZsCcCIESNITEwkLi6O5ORkZs+ezfXr1/H39wfA09OzWIvrjRs3yM/PL9ZCe6tXXnmF1NRUwy02NrZCzk2IytbRvxbBvm7k6fR8sati/k8KIcrPyc6abo0KuxT8duL2VwJFzZev0/PZzsLv5wk9A9CoizeqyRzzJSvVSA1FUfjyyy/RakvXRyM/P79cQd3Kw8MDDw/TppkoSki/+uor7Ozs6NOnDwAhISHMnTuXhIQE6tWrB8CWLVuwtbUlKCjotseztbXF1lYmpBaWR6VSMblXIE8tj2DVgRgmhQbi5mhj7rCEEEC/lp78efoa647G8ex9gZKo3KN+ORbPlRvZeGhtGBosV3pNUaoktkGDBnzxxRelPqinpyfW1lW/nOqCBQvo0qULWq2WrVu38tJLL/HOO+/g6uoKQN++fWnevDmjRo3i/fffJyUlhRdffJFx48bJzASixgptUpvm9Zw5lZDGsr2XmNancan2y87ONlqVT7oUCFGxHmpVjzd/OcXFpEz2XUg2DPYS944CnZ6F2wpXRhzbLQA7a5mT2xSlSmIvXbpUyWFUjIMHDzJr1iwyMjJo2rQpn332GaNGjTI8r9Fo2LhxI5MmTaJr167Y29szYsQIPvjgAzNGLUTlKmqNnfzNEZbvuci47v442d39j0y9Xs+xY8cM94UQFcvR1oqBbb1YdSCGbw7ESBJ7D/rx8BWir2fi5mDNE53vPq2WjAE0VqMmfly5cuVdyzRo0IANGzZUQTRCVB/9WnoSUNuRC9cz+Xp/DM/8PeDrTuzs7NiyZYvhvhCi4j3esQGrDsSw+WQiSRm5eGil69q9IjtPx7zfzwIw5b5GpWpcEMZk3hwh7gEatcowb+zS3RfIydfdfR+Nhj59+tCnTx9ZdlaIStKyvgttvF3I1yn8b99lc4cjqtBXey5yNS0Xbzf7u7bCSm/pkkkSK8Q9YmBbL7zd7EnKyOO7v5c2FEKY3/gehVdGlu25SHpOxQ2MFtXX9fRclmwvnL/7xb5NsLWShoKykCRWiHuEtUbNhJ6FP5af77xw1zXbCwoK2LhxIxs3bqSgoKAqQhTintSvpScNazuSllPASmmNvSe89WsU6bkFtKrvwoA2Xnff4W+KzBRrRJJYIe4hQ4K8qeNkS3xqDuuPxt2xbG5uLg8//DAPP/ywLDsrRCXSqFVMua+wu8+Xuy6QmSt/NNZke6OTWHc0DpUK5j7SEnUJ88KK0ilTErtr1y6eeOIJQkJCiIsr/CH83//+x+7duys0uOpm4cKFNG/enA4dOpg7FCHKxM5aw7juAQAs3hGNTn/7v+rVajXBwcEEBwfLsrNCVLL+rb3wdXfgRlY+X+2+aO5wRCXJK9Azc/0JAJ7o5Etrb9dS7SdTCJfM5F+mNWvWEBYWhr29PUePHjW00KSnp/PWW29VeIDVyeTJkzl16hQRERHmDkWIMhvRqQGuDtZcTMpk4/GE25azt7cnIiKCiIgImSNWiEpmpVEb5nBetD2a+JvZZo5IVIaPfz9L9PVMPLS2vBjWxNzhWDyTk9g5c+awZMkSvvjiC6MFDbp06cKRI0cqNDghRMVztLViTNfCpZjn/3Hujq2xQoiqM6CNFx383MjO1/H2b6fNHY6oYIcvp7BkR+FgrjmDWuBib/qUWjJPrDGTk9gzZ87Qo0ePYtudnZ25efNmRcQkhKhk4V39cHWw5vy1DNbdpW+sEKJqqFQqZvVvgUpVuBTpwYsp5g5JVJDM3AKeX30MvQKPtq9Pv5b1zB1SjWByEluvXj3Onz9fbPvu3bsJCAiokKCEEJXL2c6aSX8veDBv61lyC4rPG5udnU3Xrl3p2rUr2dlyaVOIqtCyvgvDOxTOGTpz/YkS/28KyzNn4yliUrKo72rP7AEtTN5f+sSWzOQkdsKECfznP//hwIEDqFQq4uPjWbVqFS+++CKTJk2qjBiFEJXgyRA/6jrbEnczm+8OxhZ7Xq/Xs3fvXvbu3SvLzgpRhV4Ka4K7ow1nrqbz0daz5g5HlNPaI1f49u/v2PeHtMZZVuaqMCYnsdOnT2fQoEH06tWLjIwMevTowdNPP82ECROYMmVKZcQohKgEdtYanr2vEQCf/nmerDzjaX1sbW1Zt24d69atw9ZWlsIUoqrUcrTh7UdbAYVzOku3Ast1OjGNV9cdB+C53o3o0tCjXMeTPrHGyjRvzty5c0lKSuLgwYPs37+f69ev83//938VHZsQopIN6+BDg1oOJGXksnSX8bQ+VlZWDBo0iEGDBmFlZWWmCIW4N/Vt4cngIG8UBV74IZIMmTvW4qRm5/PM10fIydfTvZEH/+ndyNwh1ThlnvzRwcGB4OBgmjZtyu+//05UVFRFxiWEqALWGjUv9C2c1mfxjmgSU3PMHJEQosis/s2p72pPbEo2/113HEWa4SxGXoGeZ74+zMWkTLxc7PhkeDs0sqhBhTM5iR06dCgLFiwACgd+dOjQgaFDh9K6dWvWrFlT4QEKISrXgDZeBPm6kZWn453f/vljVKfTsX37drZv345OJ4NLhKhqTnbWfDS0DRq1ivWR8SyVRRAsgqIo/Hf9cfZGJ+Noo+HL0R2o5WhTrmOqkAS4JCYnsTt37qR79+4ArFu3Dr1ez82bN5k/fz5z5syp8ACrE1mxS9REKpWK2X9P67M+Mp7Dlwv73+Xk5NCrVy969epFTo600AphDp0C3PnvQ80AeOvXKHadu27miMTdLNoezfeHrqBWwYIR7Wnu5Vxhx5a2eGMmJ7GpqanUqlULgE2bNvHYY4/h4ODAQw89xLlz5yo8wOpEVuwSNVUrbxeGBfsAMPvnU+j0CiqViubNm9O8eXNUMr+LEGYT3sWPIUHe6BWY8s1RLidnmjskcRsr913i/c1nAJjVvwW9mtYxc0Q1m8lJrI+PD/v27SMzM5NNmzbRt29fAG7cuIGdnV2FByiEqBovhjXByc6K43GpfL3/Mg4ODpw8eZKTJ0/i4OBg7vCEuGepVCrmPNKStj6upGbn89TyCJIycs0dlviX7w/F8vpPJwGY0iuQ0V38zBvQPcDkJHbq1KmMHDkSb29vvLy8CA0NBQq7GbRq1aqi4xNCVBEPrS3T/17L+91Np4lNyTJzREKIIrZWGj4bFYSXix0XrmcyaulBUrPyzR2W+NtPkXHMWPMXAGO6+hsGzFYUuRhWMpOT2EmTJrF//36++uordu/ejVpdeIiAgIAa3ydWiJpuZCdfOvrVIitPx6syGlqIaqWusx2rxnXGQ2tLVEIao5cdlKm3qoFvD8YwdXUkegUe79iAmQ83q7wuWPKdbKRMU2wFBQXxyCOPoNVqDdseeughunbtWmGBCSGqnlqt4p3HWmFrpWbXuSQ6PT6VPn36yLKzQlQT/h6OrHq6E64O1kTG3uTpFRFk58nsIeayZEc0r6w9jqLAiE4NmDuopYwhqEJlSmKvXLnCokWLmDFjBtOmTTO6VZa5c+fSpUsXHBwccHV1LfZ8cnIy/fr1w8vLC1tbW3x8fJgyZQppaWmGMpcuXUKlUhW7bdq0qdLiFsLSBNTW8nyfwkthiV7d2X74lCw7K0Q10sTTiZVjOqK1tWL/hRSeWHqAm1l55g7rnqLXK7zz22ne+e00AJNCGzJ3UEvUMhdslTJ5GZ4//viDAQMG4O/vz5kzZ2jZsiWXLl1CURTat29fGTECkJeXx5AhQwgJCWHp0qXFnler1QwcOJA5c+ZQu3Ztzp8/z+TJk0lJSeGbb74xKvv777/TokULw+Oi2RaEEIXGdQ9g2+mrHLgIQc8uRFHLil1CVCetvV1ZMaYDTy2L4PDlGwxeso8VYzpS39Xe3KHVeFl5Bbzw/TF+O5EIwIwHmjKxZ8NKfU1JjUtmckvsK6+8wgsvvMCJEyews7NjzZo1xMbG0rNnT4YMGVIZMQLwxhtv8Pzzz9928JibmxvPPPMMwcHB+Pr60rt3byZNmsSuXbuKlXV3d8fT09Nws7Ep3yTEQtQ0GrWKtwc0RZeVSmKuNe9tOm3ukIQQ/xLkW4sfn+lCPRc7zl/L4NFFezidmHb3HUWZXbmRxWOL9/HbiUSsNSreH9y60hNYcXsmJ7FRUVGMHj0aKFxbPTs7G61Wy5tvvsm7775b4QGWVXx8PGvXrqVnz57FnhswYAB16tSha9eu/Pjjj3c9Vm5uLmlpaUY3IWq6us62JP/6CQArD1zhl2PxZo5ICPFvjes6seaZLjSqo+VqWi5Dluzjj6ir5g6rRjpwIZkBC/YQlZCGh9aGb8d1Zsjf82tXFRnWZczkJNbR0ZHc3ML56by8vIiOjjY8l5SUVHGRldHjjz+Og4MD9evXx9nZmS+//NLwnFar5aOPPuLHH3/k119/pXfv3gwbNoyvv/76jsd8++23cXFxMdx8fKr2QyuEOeh0OrKjD5J6oHA56Rd/OMZfV26aNyhRZS5dusTYsWPx9/fH3t6ehg0bMmvWLPLypO9ldePlas+PE7vQwc+N9JwCxq44xHubTlOgk77sFUGnV1jw5zlGfHmAlMw8WtZ35ucp3Qj2k66I5mZyEtu5c2f27NkDFM5I8MILLzB37lzGjBlD586dTTrW7NmzSxxodevt0KFDJh1z3rx5HDlyhPXr1xMdHW002MzDw4Pnn3+ejh07EhwczJtvvsmkSZN477337njMV155hdTUVMMtNjbWpJiEsERFS83e3LGCrv4u5BboGb/yMFfTZAnae8Hp06fR6/V89tlnnDx5knnz5rFkyRJeffVVc4cmSuDiYM2qpzsT/vcE+4u2RzPyywNcS5f/r+WRmJrDyC/388GWs+j0Co+0q88PE7rgJX2PqwWTR2t89NFHZGRkAIVJaEZGBqtXryYwMJB58+aZdKwpU6YwfPjwO5bx8/Mz6ZhF/VybNm2Ku7s73bt3Z+bMmdSrV6/E8p07dzZqrS2Jra0ttra2JsUhhKUzTBOj6JnzUCDjvj/D+WsZjP7qIKvHh+DiYG3eAEWl6tevH/369TM8DggI4MyZMyxevJgPPvjAjJGJ27GxUjN7QAuCfN2YseYvDlxM4cFPdvP+kNb0aiLLn5pCURR+OHyF/9twivScAhxsNLw5sCWPta9vnim0ZNquEpmcxAYEBBjuOzg4sGjRojK/uIeHBx4eHmXe/26KJmov6v5QkqNHj942wRXiXnbrUrN13Jz4anQHBi/Zy+nEdJ5afpCvn+6Eg43MWnAvSU1NldlcLED/Nl4093Jm0tdHOHM1naeWRfBIu/rMfLg5tRxlIPPdxN3M5pW1x9l59joArb1d+HhYWwJqa++yZ+WTtQ6MlfkXKC8vj2vXrhWbP7JBgwblDqokMTExpKSkEBMTg06nIzIyEoDAwEC0Wi2//vorV69epUOHDmi1Wk6dOsX06dPp2rWroTV3xYoVWFtb065dO9RqNb/88gvz58+vVgPShKiuGrg78L+xnRj62T6OxNxk3MpDfPFksCSy94jo6Gg+/fRTPvzww9uWyc3NNWo0KBoEq9Pp0OlqzoT8RedSnc/Jr5Y9ayZ25qPfz7Js72XWHY1jx5lrzHy4Gf1b1yvWmmgJ52QqU88pN1/HF7svsXhHNDn5emys1EztHcjYrn5YadTmrZu/s1e9Xg/qmvU+QdnPR6WYuK7k2bNnGTt2LHv37jXarigKKpWq0io2PDycFStWFNu+bds2QkND2bZtG6+99hqnTp0iNzcXHx8fHn30UWbMmGFYHGHFihW8++67XL58GY1GQ+PGjZk6dSpPPPGESbGkpaXh4uJCamoqzs7OFXF6QlQ7mZmZhlX5MjIycHR0BOBozA1GfnmArDwdwb5uLA3vgIu9dC34t+r6PTF79mzeeOONO5aJiIggODjY8Dg+Pp6ePXvSs2fPO3a/ut2x9+3bZ7TCo6haZ5Jymb8/mcs38wEI8rLjqfZu+LlKqywU5i/7r2Tz5eEbXM0oXMa3RR1bnu3kjrdL9fhum/ZbAmeT85gZWptO3g5338HCZGRkEBISYvL3pclJbNeuXbGysmLGjBnUq1f8r7k2bdqYcjiLVF1/nISoSMnJyYbuPklJSbi7uxueOxJzg/CvDpKWU0ALL2dWjOmIh1b6jd+qun5PJCUl3XUmGT8/P+zs7IDCBLZXr1506tSJ5cuXo1bffjxwSS2xPj4+XL9+HTc3t4o5gWpAp9Nx9uxZGjdujEajMXc4pZJXoOeznRdYtD2aPJ2CSgWD2nrxfO9G1Hezt8hzupu7nZOiKOyNTuaj388RGZsKgKezLS/3a1Jia7U5PbZkH5GxqSwZ0RZv9Y0a9T4B3Lhxg9q1a5v8fWnydcDIyEgOHz5M06ZNTd1VCGFBbr2q8u8rLO0buPHd+BCe/OoAJ+PTGLRwD1+ODqapZ/VJ1kTJTBmLEBcXR69evQgKCmLZsmV3TGDh9oNgNRpNjfrBLWJJ52Wv0TC1TxMGtK3PB1vO8OvxRNYdjWfjX4mMCvFlQg9/wLLOqbT+fU6KorAvOpn5f55j/4UUAOys1Yzt5s+k0EAcbatjF6nChFr19//BmvY+lfVcTH6nmjdvXi3mgxVCVK5bV7IraVW75l7OfD8hhKeWR3A5OYvHFu3l4+Ht6NO8blWGKSpJfHw8oaGhNGjQgA8++IDr168bnvP09DRjZKI8AmprWTQyiMjYm7z722n2XUhm6e6LfL3/Mj39HJhaK53mXq7mDrNS5Ov0bPgrni92XuRUQmF/bRuNmhGdGjCpV0PqONmZOUJhqlIlsbeuUPXuu+8yffp03nrrLVq1aoW1tXF/kep02UwIUXa3/t/+9//zIgG1tayf1JXJ3xxhb3Qy4/93iAk9GvJC38ZYa0yehlpUI1u2bOH8+fOcP38eb29vo+dM7IUmqqG2Pq58M64TO88l8eGWM/x1JZUt5zPYMn8P3Rt5MKarPz0b10atrj6X1MsqNiWLtZEJfB8RS+Lf81zbW2sYEuzNxJ4NZc5XC1aqJNbV1dWob4iiKPTu3duoTGUP7KoOFi5cyMKFC2v0OQphKjdHG1aM6cicDadYse8yS3ZEsy86ifmPt8PX3dHc4YkyCg8PJzw83NxhiEqkUqno2bg2PRp5cPBCMp9sPs7+2Cx2nUti17kkvFzseLiNFwPaeNHCy7la9RG9m/ScfDafSGDlrkT+unrZsL22ky3hXfwY2akBrg6WM7DNgqq+SpUqid22bVtlx2ERJk+ezOTJkw0DNoSoyW6dPu/fU+n9m7VGzRsDWxLS0J2X1xzn2JVUHvhkFy/2bcLoLn5oakBrjhA1lUqlItjPjVd71MbJ05evD8TyXUQs8ak5fL7zAp/vvECAhyP923jRt0Vdmnk6V8sW2mvpOWw9dZUtJ6+yNzqJfF3hFQOVCroFejA4yJt+LT2xtbLcvqSKohR1jxWUMont2bNnZcchhKhmsrOzje47OTnddZ9+LevR2tuV51dHcuBiCm9uOMX6yDjeeqQVLevLH35CVHfebg689lBzXujbhO1nrvHzsXj+iLrGhaRMPvnjHJ/8cY5ajjaENHSna0MPuga606CWg1laaVMy8zh4MZn9F1I4cDGF04lpRosB+Hs40MXLmolhbfFxlyneaqJSD+zKysripZdeYv369eTn53P//fczf/78Sl1xSwhhebxc7fl2XGe+jYjhnd9O89eVVAYs2M2wDj48f39j6jjL4Akhqjs7aw39WtajX8t6pOfks/XUVTb+lcC+C8mkZOax8a8ENv6VAEAdJ1ua1XOmaT0nmtdzpqmnMwG1HSusX7xerxB3M5sziemcTkwjKjGdqIQ0LlzPLFa2jY8rYS3q0re5J/7u9kRFRUmf1xqs1EnsrFmzWL58OSNHjsTOzo5vv/2WZ555hh9++KEy4xNCmEnR4gb/vl8aarWKkZ186dOsLm9sOMXGvxL49mAs64/GM667P+N6BOBkVz0mERdC3JmTnTWPtvfm0fbe5BXoOXblJnvOJ7H3fDJHY29wLT2Xa+nX2XH2nxks1Crw0Nri6WJHXWc7PJ3t8NDaYm+jxtZKg62VGltrNVZqNTn5OnLydWTl6cjO15GRU0BiWg6JqTkkpOZwNS2HAn3Jgwkb19XSyd+dTgG16Ohfy2iGARm/UvOVOoldu3YtS5cuZfjw4QA88cQTdO3aFZ1OV6PmKhNCVJw6znYsHNGep7qk8NavURyJucn8P8+zfO8lngzxI7yrnyySIIQFsbFS08GvFh38ajH1fsjKKyAq4e8W0oQ0TiekczoxnYzcgr+T21wgtdyva61R0bC2trDF19OJpvWcaenljPs98v1R1FlD5gUxVuokNjY2lu7duxsed+zYESsrK+Lj4/Hx8amU4IQQNUOwXy3WPNOFzSev8sGWM5y/lsGCbef5cvcFhgb78GSIH4F1pM+aEJbGwcaKIF83gnz/WZFNr1dIyszlamouiWmFLalX03JIysgjN19HboGe3ILCf/MK9NhZa3Cw0WBvrcHeRoOjrRV1ne2o52KHp0vhv7W1tljJtH3iX0qdxOp0umITnltZWVFQUFDhQQkhzC8nJ8fovqldCv5NpVLRr6UnfZvXZcupqyzefp5jV1JZue8yK/ddpnNALUZ28iWshSc2VvJjJYSlUqtV1HGyo46THa2QAZ2i8pQ6iVUUhfDwcKMlBXNycpg4caLRj9vatWsrNkIhhFncadnZ8lCrC5PZsBZ12RudzLI9l/jz9FX2X0hh/4UUajna8FCregxs60X7Bm7VciofIYQQ5lfqJHb06NHFtj3xxBMVGowQovq427Kz5aVSqega6EHXQA/ib2bzXUQs3x2M4Vp6Lv/bf5n/7b9MfVd7Hm5djz7N69KugZvMNyuEuCcVTWGmKMg8sbcodRK7bNmyyoxDCFHNlGbZ2Yri5WrPtD6Nee6+QPZEJ/NTZBxbTl4l7mY2n+28wGc7L1DL0YbQJrXp3bQu3Rt74CyzGwghxD2t1EmskGVnhahsVho1PRvXpmfj2uTk6/jz9DU2nUhk+5lrpGTmsfZIHGuPxKFWQcv6LoQEuNM5wJ0O/rXQ2srXmRBC3EvkW98EsuysuJeYsuxsZbCz1vBgq3o82Koe+To9hy/f4I+oq/xx+hoXrmfy15VU/rqSymc7L6BRq2jh5UxbH1faeLvStoEr/u6O0p9WCCFqMElihRAlKsuys5XFWqOm89+trq891JzE1Bz2XUhiX3Qy+y4kE5uSbUhq4TIATnZWtPF2pbmXM03qOtHE04nAOlrsrGVeayGEZfnnz3GZKfZWksQKISyOp4sdj7Tz5pF23gBcuZHF0ZibHIu9ybErNzkel0p6TgG7zyex+3ySYT+1Cvw8HGnq6URgHSf83B3w83DE390RVwdrs6z/LoQQomwkiRVClKg8y85WNW83B7zdHOjfxguAfJ2es1fT+etKqmG99TOJ6dzIyufC9cy/11xPNDqGs50V/h6O+Lo70qCWA16u9ni52uHtZk89F3scpc+tEEJUKxbzrTx37lw2btxIZGQkNjY23Lx5s1iZiIgIZsyYweHDh1GpVHTo0IH33nuPtm3bGsocP36cKVOmcPDgQWrVqsWECROYOXOmtMAIUYNYa9S08HKhhdc/fdcVReF6ei6nE9M5k5jOhaQMLiVlcSk5k4TUHNJyCjh2JZVjV0peItPVwZqOfrX4/MngqjoNIYQQd2AxSWxeXh5DhgwhJCSEpUuXFns+PT2dsLAwBg4cyKJFiygoKGDWrFmEhYVx5coVrK2tSUtLo0+fPvTq1YuIiAjOnj1LeHg4jo6OvPDCC2Y4KyFEVVGpVNRxtqOOsx09Gtc2ei47T0dMShYXkzK5lJzJlRtZxN/MIf5mNnE3s0nPKeBmVj5ZeTIziRDCfBTpEmvEYpLYN954A4Dly5eX+PyZM2e4ceMGb775Jj4+PgDMmjWL1q1bExMTQ8OGDVm1ahU5OTksX74cW1tbWrZsydmzZ/noo4+YNm2atMYKcYvc3Fyj+9W9S0F52NtoaOJZOPirJGk5+STczEEvvyBCCDOQ9KRkNWaB8iZNmuDh4cHSpUvJy8sjOzubpUuX0qJFC3x9fQHYt28fPXv2NFo6NywsjPj4eC5dumTya2ZmZqLc8qOWl5dHZmam0Y9/UbnMzEyjaYry8/PJzMw0Wp/e1LJZWVlkZmYazVtbUFBAZmam0chyU8tmZ2eTmZlJQUGBYZtOpzO5bFZWllHZnJwcMjMzyc/PL1NZvV5vqJ9b5ebmkpmZSV5eXpnKKopiKFvS+2lK2dK89xXxOSnp/ayIz0nR+6nT6Yze09TU1HK99+X9nNzu/Szv5+TW9/NOZZ3trGlcV0sDZ41J770QQojKU2OSWCcnJ7Zv387XX3+Nvb09Wq2WzZs38+uvv2JlVdjgnJiYSN26dY32K3qcmJhY7JhFcnNzSUtLM7oBeHl5kZT0z8jn999/H61Wy5QpU4z2r1OnDlqtlpiYGMO2hQsXotVqGTt2rFFZPz8/tFotUVFRhm3Lly9Hq9UyfPhwo7LNmzdHq9Vy5MgRw7bVq1ej1WoZMGCAUdkOHTqg1WrZtWuXYduGDRvQarXcf//9RmV79OhhqL8if/75J1qtlpCQEKOyDzzwAFqtlnXr1hm27d+/H61WS5s2bYzKPvbYY2i1WlatWmXYdvz4cbRaLY0aNTIqO2rUKLRaLZ9//rlhW3R0NFqtlvr16xuVnTBhAlqtlk8++cSwLSEhAa1Wi6urq1HZadOmodVqeeuttwzbUlNT0Wq1aLVaoyTrtddeQ6vV8tprrxm2FRQUGMqmpv7Td/Ktt95Cq9Uybdo0o9dzdXVFq9WSkJBg2PbJJ5+g1WqZMGGCUdn69euj1WqJjo42bPv888/RarWMGjXKqGyjRo3QarUcP37csG3VqlVotVoee+wxo7Jt2rRBq9Wyf/9+w7Z169ah1Wp54IEHjMqGhISg1Wr5888/jVbpCggIoEePHkZl77//frRaLRs2bDBs27VrF1qtlg4dOhiVHTBgAFqtltWrVxu2HTlyBK1WS/PmzY3KDh8+HK1Wa3TVJSoqCq1Wi5+fn1HZsWPHotVqWbhwoWFbTEwMWq2WOnXqGJWdMmUKWq2W999/37AtKSnJ8H7e6uWXX0ar1RquAEHhHwNFZW9NvN944w20Wi0vv/yy0TG8vLwQQghRecyaxM6ePRuVSnXH26FDh0p1rOzsbMaMGUPXrl3Zv38/e/bsoUWLFjz44INGLUj/7jJQ1KJyp64Eb7/9Ni4uLoZbUXcFIWoyGxsbc4cghBDiFtKhyZhKUczXySspKcmoJbMkfn5+2NnZGR4vX76cqVOnFpudYOnSpbz66qskJCSgVhfm5nl5ebi5ubF06VKGDx/Ok08+SWpqKj/99JNhv6NHj9K+fXsuXLiAv79/iTHk5uYaXSpMS0vDx8eH+Ph4PD09DQlwXl4e+fn5WFlZGXVZKLqsaG9vb4gtPz+fvLw8NBqN0fmZUjYrKwtFUbCzs0OjKZzAvaCggNzcXNRqNfb29mUqm52djV6vx9bW1tCKrdPpyMnJMamsSqXCwcHBUDYnJwedToeNjY2hlc+Usnq93vAHya39M3NzcykoKMDa2tqQeJlSVlEUQ8uag4NDsffTlLKlee8r4nNS0vtZEZ+TovfTzs4OtVpNVlYWBQUFaDQaNBpNmd/78n5Obvd+lvdzcuv7Wd7Pyb/fz4SEBLy8vEhNTcXZ2Zl7UdHqhikpKbi5uZk7nAqj0+mIioqiWbNmhv9Tlk7OqXobumQfBy+lsODxtvhpbtSIc7rVjRs3qFWrlsnfl2Yd2OXh4YGHh0eFHCsrKwu1Wm3Uolr0uKiPYUhICK+++ip5eXmGH6YtW7bg5eVV7DLlrWxtbY1+nIo4OjoavZ6NjU2JrVclDYixtrY2ulxblrK3/vAXsbKyMiQJZS17a6JSRKPRlBibKWVvTazKUlatVpdYtqT3x5SyKpWqxLIlvZ+mlIWS38+K+JyU9H5WxOfk1vfz1qTNw8Oj2NWKqvyc3O79LO/n5HbvZ3k/J1D959YVQghLZzF9YmNiYoiMjCQmJgadTkdkZCSRkZFkZGQA0KdPH27cuMHkyZOJiori5MmTPPXUU1hZWdGrVy8ARowYga2tLeHh4Zw4cYJ169bx1ltvycwEQpQgKyuLOnXqUKdOnWKDr4QQQghzs5gptl5//XVWrFhheNyuXTsAtm3bRmhoKE2bNuWXX37hjTfeICQkBLVaTbt27di0aRP16tUDwMXFha1btzJ58mSCg4Nxc3Nj2rRpxQbj3E1RD4yiAV5C1ES3jq5PS0szmt1A3F3R94MZe2yZ3a3flTXp0qdOpyMjI6NGnZecU/WWn52JPjeLzPR0Mqxqxjndqqzfl2btE2uprly5IoO7hBClEhsbi7e3t7nDMIsLFy7QsGFDc4chhLAQ0dHRBAQElLq8xbTEVideXl7Exsbi5ORk6IbQoUMHIiIiDGVufVw0ECw2NrbSBnj8+/Urer+7lbvd86Zsv10dVkX93SnWitqvsuvQUj+Dpux7p3KmPmfKZxBMr0NFUUhPT7+np9qqVasWUNgdzMXF5S6lLUdVfSdVJTkny1ATzwkKp7ps0KCB4TujtCSJLQO1Wl2sZUWj0Rh9oP79GMDZ2bnSPnQlvV5F7ne3crd73pTtd6vDyqy/O8VaUftVdh1a6mfQlH3vVM7U58ryGQTT6rAmJW5lUTTLhouLS436wS1S2d9J5iDnZBlq4jnBP98ZpS5fSXHccyZPnnzHx1X9+hW9393K3e55U7ZLHZavDi21/kzZ907lTH2uOn4GhRBClJ70ia0CRXMl3svzRZaH1F/5SR2Wn9Sh6WpqndXE85Jzsgw18Zyg7OclLbFVwNbWllmzZpU416y4O6m/8pM6LD+pQ9PV1Dqriecl52QZauI5QdnPS1pihRBCCCGExZGWWCGEEEIIYXEkiRVCCCGEEBZHklghhBBCCGFxJIkVQgghhBAWR5LYaiQ9PZ0OHTrQtm1bWrVqxRdffGHukCxObGwsoaGhNG/enNatW/PDDz+YOySL88gjj+Dm5sbgwYPNHYrF2LBhA02aNKFRo0Z8+eWX5g6nWrp06RJjx47F398fe3t7GjZsyKxZs8jLyzN3aOUyd+5cunTpgoODA66uruYOp0wWLVqEv78/dnZ2BAUFsWvXLnOHVC47d+6kf//+eHl5oVKpWL9+vblDKre3336bDh064OTkRJ06dRg0aBBnzpwxd1jlsnjxYlq3bm1YuCEkJITffvvNpGNIEluNODg4sGPHDiIjIzlw4ABvv/02ycnJ5g7LolhZWfHxxx9z6tQpfv/9d55//nkyMzPNHZZFee6551i5cqW5w7AYBQUFTJs2jT///JMjR47w7rvvkpKSYu6wqp3Tp0+j1+v57LPPOHnyJPPmzWPJkiW8+uqr5g6tXPLy8hgyZAjPPPOMuUMpk9WrVzN16lRee+01jh49Svfu3XnggQeIiYkxd2hllpmZSZs2bViwYIG5Q6kwO3bsYPLkyezfv5+tW7dSUFBA3759Lfr3zdvbm3feeYdDhw5x6NAh7rvvPgYOHMjJkydLfxBFVEvJyclKgwYNlOvXr5s7FIvWqlUrJSYmxtxhWJxt27Ypjz32mLnDsAh79uxRBg0aZHj83HPPKd98840ZI7Ic7733nuLv72/uMCrEsmXLFBcXF3OHYbKOHTsqEydONNrWtGlTZcaMGWaKqGIByrp168wdRoW7du2aAig7duwwdygVys3NTfnyyy9LXV5aYk1QmksU5b0sc/PmTdq0aYO3tzfTp0/Hw8OjgqKvHqqiDoscOnQIvV6Pj49POaOuPqqy/u4V5a3T+Ph46tevb3js7e1NXFxcVYRu8VJTU6lVq5a5w7hn5eXlcfjwYfr27Wu0vW/fvuzdu9dMUYnSSE1NBagx/390Oh3fffcdmZmZhISElHo/SWJNcLdLFKW5LBMUFETLli2L3eLj4wFwdXXl2LFjXLx4kW+++YarV69WyblVlaqoQ4Dk5GSefPJJPv/880o/p6pUVfV3LylvnSolrBejUqkqNeaaIDo6mk8//ZSJEyeaO5R7VlJSEjqdjrp16xptr1u3LomJiWaKStyNoihMmzaNbt260bJlS3OHUy7Hjx9Hq9Via2vLxIkTWbduHc2bNy/9ASqtTbiGo4RLFBV9WWbixInK999/X9YQq73KqsOcnByle/fuysqVKysizGqrMj+D92p3grLUaUndCVatWlXpsVYXs2bNUoA73iIiIoz2iYuLUwIDA5WxY8eaKeo7K8s5WWJ3gri4OAVQ9u7da7R9zpw5SpMmTcwUVcUq6f+0pZs0aZLi6+urxMbGmjuUcsvNzVXOnTunREREKDNmzFA8PDyUkydPlnp/q4rIpMU/l2VmzJhhtN2UyzJXr17F3t4eZ2dn0tLS2Llzp8UOFiiLiqhDRVEIDw/nvvvuY9SoUZURZrVVEfUnjJWmTjt27MiJEyeIi4vD2dmZX3/9lddff90c4ZrFlClTGD58+B3L+Pn5Ge7Hx8fTq1cvQkJCqu2VElPPyVJ5eHig0WiKtbpeu3atWOusqB6effZZfv75Z3bu3Im3t7e5wyk3GxsbAgMDAQgODiYiIoJPPvmEzz77rFT7SxJbQSrissyVK1cYO3YsiqKgKApTpkyhdevWlRFutVQRdbhnzx5Wr15N69atDX0b//e//9GqVauKDrfaqahLg2FhYRw5coTMzEy8vb1Zt24dHTp0qOhwLUJp6tTKyooPP/yQXr16odfrmT59Ou7u7uYI1yw8PDxK3Xc/Li6OXr16ERQUxLJly1Crq2ePNlPOyZLZ2NgQFBTE1q1beeSRRwzbt27dysCBA80Ymfg3RVF49tlnWbduHdu3b8ff39/cIVUKRVHIzc0tdXlJYivYv/vCKYpS6v5xQUFBREZGVkJUlqU8dditWzf0en1lhGUxylN/AJs3b67okCze3ep0wIABDBgwoKrDsijx8fGEhobSoEEDPvjgA65fv254ztPT04yRlU9MTAwpKSnExMSg0+kM3+GBgYFotVrzBlcK06ZNY9SoUQQHBxtax2NiYiy6r3JGRgbnz583PL548SKRkZHUqlWLBg0amDGysps8eTLffPMNP/30E05OToY/ol1cXLC3tzdzdGXz6quv8sADD+Dj40N6ejrfffcd27dvZ9OmTaU+hiSxFUQuy5Sf1GH5SP1VPKnTirNlyxbOnz/P+fPni10GVUoYHGcpXn/9dVasWGF43K5dOwC2bdtGaGiomaIqvWHDhpGcnMybb75JQkICLVu25Ndff8XX19fcoZXZoUOH6NWrl+HxtGnTABg9ejTLly83U1Tls3jxYoBin6lly5YRHh5e9QFVgKtXrzJq1CgSEhJwcXGhdevWbNq0iT59+pT6GNXzWo4FuvWyzK22bt1Kly5dzBSVZZE6LB+pv4ondVpxwsPDDV2l/n2zZMuXLy/xnCwhgS0yadIkLl26RG5uLocPH6ZHjx7mDqlcQkNDS3xPLDWBBW77f8dSE1iApUuXGj53165d4/fffzcpgQVpiTXJ3S5R1MTLMhVN6rB8pP4qntSpEEJYqHLMjHDP2bZtW4lTrYwePdpQZuHChYqvr69iY2OjtG/fvsatplFeUoflI/VX8aROhRDCMqkUxcKv5QghhBBCiHuO9IkVQgghhBAWR5JYIYQQQghhcSSJFUIIIYQQFkeSWCGEEEIIYXEkiRVCCCFEpZk9ezZt27at1NdYvnw5rq6ulfoaovqRJFYIIYS4B4WHh6NSqVCpVFhZWdGgQQOeeeYZbty4Ye7QTDZs2DDOnj1r7jBEFZPFDoQQQoh7VL9+/Vi2bBkFBQWcOnWKMWPGcPPmTb799ltzh2YSe3t77O3tzR2GqGLSEiuEEELco2xtbfH09MTb25u+ffsybNgwtmzZYlRm2bJlNGvWDDs7O5o2bcqiRYuMnn/55Zdp3LgxDg4OBAQEMHPmTPLz80sdg06nY+zYsfj7+2Nvb0+TJk345JNPDM/n5OTQokULxo8fb9h28eJFXFxc+OKLL4Di3QmOHTtGr169cHJywtnZmaCgIA4dOmRK1QgLIC2xQgghhODChQts2rQJa2trw7YvvviCWbNmsWDBAtq1a8fRo0cZN24cjo6OjB49GgAnJyeWL1+Ol5cXx48fZ9y4cTg5OTF9+vRSva5er8fb25vvv/8eDw8P9u7dy/jx46lXrx5Dhw7Fzs6OVatW0alTJx588EH69+/PqFGj6NWrF+PGjSvxmCNHjqRdu3YsXrwYjUZDZGSk0XmJGsLcS4YJcS8ZPXq0YVnTdevWVcpr9OzZU/nPf/5TKce+nVmzZhnOa968eVX62kKIshk9erSi0WgUR0dHxc7OzvB/+KOPPjKU8fHxUb755huj/f7v//5PCQkJue1x33vvPSUoKMjweNasWUqbNm1Mim3SpEnKY489Vuy4Hh4eyrPPPqt4enoq169fNzy3bNkyxcXFxfDYyclJWb58uUmvKSyPdCcQ5XLrwIBbb+fPnzd3aNVWv379SEhI4IEHHqjS1w0NDWXJkiWVcuwXX3yRhIQEvL29K+X4QojK0atXLyIjIzlw4ADPPvssYWFhPPvsswBcv36d2NhYxo4di1arNdzmzJlDdHS04Rg//vgj3bp1w9PTE61Wy8yZM4mJiTEpjiVLlhAcHEzt2rXRarV88cUXxY7xwgsv0KRJEz799FOWLVuGh4fHbY83bdo0nn76ae6//37eeecdo3hFzSFJrCi3oqTs1pu/v3+xcnl5eWaIrvop6oNma2t72zKm9CcrjZSUFPbu3Uv//v0r9LhFtFotnp6eaDSaSjm+EKJyODo6EhgYSOvWrZk/fz65ubm88cYbQOFlfijsUhAZGWm4nThxgv379wOwf/9+hg8fzgMPPMCGDRs4evQor732mknf999//z3PP/88Y8aMYcuWLURGRvLUU08VO8a1a9c4c+YMGo2Gc+fO3fGYs2fP5uTJkzz00EP8+eefNG/enHXr1plSNcICSBIryq0oKbv1ptFoCA0NZcqUKUybNg0PDw/69OkDwKlTp3jwwQfRarXUrVuXUaNGkZSUZDheZmYmTz75JFqtlnr16vHhhx8SGhrK1KlTDWVUKhXr1683isPV1ZXly5cbHsfFxTFs2DDc3Nxwd3dn4MCBXLp0yfB8eHg4gwYN4oMPPqBevXq4u7szefJkowQyNzeX6dOn4+Pjg62tLY0aNWLp0qUoikJgYCAffPCBUQwnTpxArVab9Ff/pUuXUKlUfP/994SGhmJnZ8fXX39NcnIyjz/+ON7e3jg4ONCqVatiI4ZLqquSbNy4kTZt2lC/fn22b9+OSqVi8+bNtGvXDnt7e+677z6uXbvGb7/9RrNmzXB2dubxxx8nKyvLcIwff/yRVq1aYW9vj7u7O/fffz+ZmZmlPk8hRPU3a9YsPvjgA+Lj46lbty7169fnwoULBAYGGt2KGir27NmDr68vr732GsHBwTRq1IjLly+b9Jq7du2iS5cuTJo0iXbt2hEYGFjid+iYMWNo2bIlK1euZPr06Zw6deqOx23cuDHPP/88W7Zs4dFHH2XZsmUmxSWqP0liRaVasWIFVlZW7Nmzh88++4yEhAR69uxJ27ZtOXToEJs2beLq1asMHTrUsM9LL73Etm3bWLduHVu2bGH79u0cPnzYpNfNysqiV69eaLVadu7cye7du9FqtfTr18/or/tt27YRHR3Ntm3bWLFiBcuXLzdKhJ988km+++475s+fT1RUFEuWLEGr1aJSqRgzZkyxL8WvvvqK7t2707BhQ5Pr6uWXX+a5554jKiqKsLAwcnJyCAoKYsOGDZw4cYLx48czatQoDhw4YHJd/fzzzwwcONBo2+zZs1mwYAF79+4lNjaWoUOH8vHHH/PNN9+wceNGtm7dyqeffgpAQkICjz/+OGPGjCEqKort27fz6KOPoiiKyecphKi+QkNDadGiBW+99RZQ+D3x9ttv88knn3D27FmOHz/OsmXL+OijjwAIDAwkJiaG7777jujoaObPn29yi2dgYCCHDh1i8+bNnD17lpkzZxIREWFUZuHChezbt4+VK1cyYsQIBg8ezMiRI0ts8c3OzmbKlCls376dy5cvs2fPHiIiImjWrFkZa0VUW+bulCss260DA4pugwcPVhSlcIBR27ZtjcrPnDlT6du3r9G22NhYBVDOnDmjpKenKzY2Nsp3331neD45OVmxt7c3GqxECQOjXFxclGXLlimKoihLly5VmjRpouj1esPzubm5ir29vbJ582ZD7L6+vkpBQYGhzJAhQ5Rhw4YpiqIoZ86cUQBl69atJZ57fHy8otFolAMHDiiKoih5eXlK7dq17ziYYPTo0crAgQONtl28eFEBlI8//vi2+xV58MEHlRdeeEFRFKXUdZWTk6M4OTkpf/31l6IoirJt2zYFUH7//XdDmbffflsBlOjoaMO2CRMmKGFhYYqiKMrhw4cVQLl06dId4/P19ZWBXUJYiJK+jxRFUVatWqXY2NgoMTExhsdt27ZVbGxsFDc3N6VHjx7K2rVrDeVfeuklxd3dXdFqtcqwYcOUefPmGQ2yutvArpycHCU8PFxxcXFRXF1dlWeeeUaZMWOGYZ+oqCjF3t7eaIBZamqq4ufnp0yfPl1RFOOBXbm5ucrw4cMVHx8fxcbGRvHy8lKmTJmiZGdnl62iRLUlU2yJcuvVqxeLFy82PHZ0dDTcDw4ONip7+PBhtm3bhlarLXac6OhosrOzycvLIyQkxLC9Vq1aNGnSxKSYDh8+zPnz53FycjLanpOTY3SZqkWLFkb9OOvVq8fx48cBiIyMRKPR0LNnzxJfo169ejz00EN89dVXdOzYkQ0bNpCTk8OQIUNMirXIv+tKp9PxzjvvsHr1auLi4sjNzSU3N9dQv9HR0aWqqz///BN3d3datWpltL1169aG+3Xr1jXM8XjrtoMHDwLQpk0bevfuTatWrQgLC6Nv374MHjwYNze3Mp2rEML8br3qdKsRI0YwYsSI2z7+t/fee4/33nvPaNut3b9mz57N7Nmzb7u/ra0ty5YtK3Zl6+233wagadOmRl2bAJydnbl48aLhcXh4OOHh4QDY2NhY3GINomwkiRXlVjQw4HbP3Uqv19O/f3/efffdYmXr1at31876RVQqVbFL2bf2ZdXr9QQFBbFq1api+9auXdtw/9/zBqpUKsNghtKs/vL0008zatQo5s2bx7Jlyxg2bBgODg6lOod/+3ddffjhh8ybN4+PP/6YVq1a4ejoyNSpUw2Xz/59/rdTUlcCMD53lUp1x7rQaDRs3bqVvXv3smXLFj799FNee+01Dhw4UOIgPiGEEKKySZ9YUaXat2/PyZMn8fPzKzZQoCgZtra2Nox8Bbhx40axNbFr165NQkKC4fG5c+eM/lJv3749586do06dOsVex8XFpVSxtmrVCr1ez44dO25b5sEHH8TR0ZHFixfz22+/MWbMmNJWxV3t2rWLgQMH8sQTT9CmTRsCAgKMkvzS1JWiKPzyyy8MGDCg3PGoVCq6du3KG2+8wdGjR7GxsZHRvkIIIcxGklhRpSZPnkxKSgqPP/44Bw8e5MKFC2zZsoUxY8ag0+nQarWMHTuWl156iT/++IMTJ04QHh6OWm38Ub3vvvtYsGABR44c4dChQ0ycONGoJXHkyJF4eHgwcOBAdu3axcWLF9mxYwf/+c9/uHLlSqli9fPzY/To0YwZM4b169dz8eJFtm/fzvfff28oo9FoCA8P55VXXiEwMNDo0n55BQYGGlo/o6KimDBhAomJiYbnS1NXhw8fJjMzkx49epQrlgMHDvDWW29x6NAhYmJiWLt2LdevX5eBEkIIIcxGklhRpby8vNizZw86nY6wsDBatmzJf/7zH1xcXAzJ1/vvv0+PHj0YMGAA999/P926dSMoKMjoOB9++CE+Pj706NGDESNG8OKLLxpdxndwcGDnzp00aNCARx99lGbNmjFmzBiys7NxdnYudbyLFy9m8ODBTJo0iaZNmzJu3Lhi00qNHTuWvLy8Cm2FBZg5cybt27cnLCyM0NBQPD09GTRokFGZu9XVTz/9xEMPPYSVVfl6Djk7O7Nz504efPBBGjduzH//+18+/PDDKl+wQQghhCiiUkrbsU4IMwoNDaVt27Z8/PHH5g6lmD179hAaGsqVK1eoW7fuHcuGh4dz8+bNYnPcVpbWrVvz3//+12gKs8rk5+fH1KlTjQZ1CCGEEJVBWmKFKKPc3FzOnz/PzJkzGTp06F0T2CIbNmxAq9WyYcOGSo0vLy+Pxx57rEpaS9966y20Wq3JS00KIYQQZSUtscIiVMeW2OXLlzN27Fjatm3Lzz//TP369e+6z7Vr10hLSwMKZ2P494wEliolJYWUlBSgcNBdaQfPCSGEEGUlSawQQgghhLA40p1ACCGEEEJYHElihRBCCCGExZEkVgghhBBCWBxJYoUQQgghhMWRJFYIIYQQQlgcSWKFEEIIIYTFkSRWCCGEEEJYHElihRBCCCGExZEkVgghhBBCWJz/B/RUkgb9RJvGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[7, 4])\n",
+    "\n",
+    "# Gain and phase margin on Bode plot\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "plt.title(\"Bode plot for Lnew, with margins\")\n",
+    "ax2 = plt.subplot(2, 2, 3)\n",
+    "ct.bode_plot(Lnew, ax=[ax1, ax2], margins=True)\n",
+    "\n",
+    "# Compute gain and phase margin\n",
+    "gm, pm, wpc, wgc = ct.margin(Lnew)\n",
+    "print(f\"Gm = {gm:2.2g} (at {wpc:.2g} rad/ms)\")\n",
+    "print(f\"Pm = {pm:3.2g} deg (at {wgc:.2g} rad/ms)\")\n",
+    "\n",
+    "# Compute the stability margin\n",
+    "resp = ct.frequency_response(1 + Lnew)\n",
+    "sm = np.min(resp.magnitude)\n",
+    "wsm = resp.omega[np.argmin(resp.magnitude)]\n",
+    "print(f\"Sm = {sm:2.2g} (at {wsm:.2g} rad/ms)\")\n",
+    "\n",
+    "# Plot the Nyquist curve\n",
+    "ax3 = plt.subplot(1, 2, 2)\n",
+    "ct.nyquist_plot(Lnew, ax=ax3)\n",
+    "plt.title(\"Nyquist plot for Lnew [zoomed]\")\n",
+    "plt.axis([-2, 3, -2.6, 2.6])\n",
+    "\n",
+    "#\n",
+    "# Annotate it to see the margins\n",
+    "#\n",
+    "\n",
+    "# Gain margin (special case here, since infinite)\n",
+    "Lgm = 0\n",
+    "plt.plot([-1, Lgm], [0, 0], 'k-', linewidth=0.5)\n",
+    "plt.text(-0.9, 0.1, \"1/gm\")\n",
+    "\n",
+    "# Phase margin\n",
+    "theta = np.linspace(0, 2 * pi)\n",
+    "plt.plot(np.cos(theta), np.sin(theta), 'k--', linewidth=0.5)\n",
+    "plt.text(-1.3, -0.8, \"pm\")\n",
+    "\n",
+    "# Stability margin\n",
+    "Lsm = Lnew(wsm * 1j)\n",
+    "plt.plot([-1, Lsm.real], [0, Lsm.imag], 'k-', linewidth=0.5)\n",
+    "plt.text(-0.4, -0.5, \"sm\")\n",
+    "\n",
+    "plt.suptitle(\"\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "WsOzQST9rFC-",
+   "metadata": {
+    "id": "WsOzQST9rFC-"
+   },
+   "source": [
+    "## Unstable system: inverted pendulum\n",
+    "\n",
+    "When we have a system that is open loop unstable, the Nyquist curve will need to have encirclements to be stable.  In this case, the interpretation of the various characteristics can be more complicated.\n",
+    "\n",
+    "To explore this, we consider a simple model for an inverted pendulum, which has (normalized) dynamics:\n",
+    "\n",
+    "$$\n",
+    "\\dot x = \\begin{bmatrix} 0 & 1 & \\\\ -1 & 0.1 \\end{bmatrix} x + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} u, \\qquad\n",
+    "y = \\begin{bmatrix} 1 & 0 \\end{bmatrix} x\n",
+    "$$\n",
+    "\n",
+    "Transfer function for the system can be shown to be\n",
+    "\n",
+    "$$\n",
+    "P(s) = \\frac{1}{s^2 + 0.1 s - 1}.\n",
+    "$$\n",
+    "\n",
+    "This system is unstable, with poles $\\sim\\pm 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ZbPzrlPIrHnp",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.05124922+0.j,  0.95124922+0.j])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ct.set_defaults('freqplot', freq_label=\"Frequency [{units}]\")\n",
+    "\n",
+    "P = ct.tf([1], [1, 0.1, -1])\n",
+    "P.poles()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "W-sBWxKi6SPx",
+   "metadata": {
+    "id": "W-sBWxKi6SPx"
+   },
+   "source": [
+    "### PD controller\n",
+    "\n",
+    "We construct a proportional-derivative (PD) controller for the system,\n",
+    "\n",
+    "$$\n",
+    "u = k_\\text{p} e + k_\\text{d} \\dot{e}\n",
+    "$$\n",
+    "\n",
+    "which is roughly the equivalent of using state feedback (since the system states are $\\theta$ and $\\dot\\theta$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "hjQS_dED7yJE",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<TransferFunction>: L\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "\n",
+      "\n",
+      "   2 s + 10\n",
+      "---------------\n",
+      "s^2 + 0.1 s - 1\n",
+      "\n",
+      "Zeros:  [-5.+0.j]\n",
+      "Poles:  [-1.05124922+0.j  0.95124922+0.j]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Transfer function for a PD controller\n",
+    "kp = 10\n",
+    "kd = 2\n",
+    "C = ct.tf([kd, kp], [1])\n",
+    "\n",
+    "# Loop transfer function\n",
+    "L = P * C\n",
+    "L.name = 'L'\n",
+    "print(L)\n",
+    "print(\"Zeros: \", L.zeros())\n",
+    "print(\"Poles: \", L.poles())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "YI_KJo0E9pFd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Bode and Nyquist plots\n",
+    "plt.figure(figsize=[7, 4])\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "plt.title(\"Bode plot for L\", size='medium')\n",
+    "ax2 = plt.subplot(2, 2, 3)\n",
+    "ct.bode_plot(L, ax=[ax1, ax2])\n",
+    "\n",
+    "ax3 = plt.subplot(1, 2, 2)\n",
+    "ct.nyquist_plot(L, ax=ax3)\n",
+    "plt.title(\"Nyquist plot for L\", size='medium')\n",
+    "\n",
+    "ct.suptitle(\"Loop analysis for inverted pendulum\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8dH03kv9-Da8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N = encirclements:  -1\n",
+      "P = RHP poles of L:  1\n",
+      "Z = N + P = RHP zeros of 1 + L: 0\n",
+      "Poles of L =  [-1.05124922+0.j  0.95124922+0.j]\n",
+      "Zeros of 1 + L =  [-1.05+2.8102491j -1.05-2.8102491j]\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/murray/src/python-control/murrayrm/control/timeresp.py:1027: UserWarning: Non-zero initial condition given for transfer function system. Internal conversion to state space used; may not be consistent with given X0.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Check the Nyquist criterion\n",
+    "nyqresp = ct.nyquist_response(L)\n",
+    "print(\"N = encirclements: \", nyqresp.count)\n",
+    "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n",
+    "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) >= 0))\n",
+    "print(\"Poles of L = \", L.poles())\n",
+    "print(\"Zeros of 1 + L = \", (1 + L).zeros())\n",
+    "print(\"\")\n",
+    "\n",
+    "T = ct.feedback(L)\n",
+    "ct.initial_response(T, X0=[0.1, 0]).plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "VXlYhs8X7DuN",
+   "metadata": {
+    "id": "VXlYhs8X7DuN"
+   },
+   "source": [
+    "### Gang of 4\n",
+    "\n",
+    "Another useful thing to look at is the transfer functions from noise and disturbances to the system outputs and inputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "oTmOun41_opt",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ct.gangof4(P, C);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "U41ve1zh7XPh",
+   "metadata": {
+    "id": "U41ve1zh7XPh"
+   },
+   "source": [
+    "We see that the response from the input $r$ (or equivalently noise $n$) to the process input is very large for large frequencies.  This means that we are amplifying high frequency noise (and comes from the fact that we used derivative feedback)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "YROqmZTd8WYs",
+   "metadata": {
+    "id": "YROqmZTd8WYs"
+   },
+   "source": [
+    "### High frequency rolloff\n",
+    "\n",
+    "We can attempt to resolve this by \"rolling off\" the derivative action at high frequencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "vhKi_L-F_6Ws",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<TransferFunction>: Cnew\n",
+      "Inputs (1): ['u[0]']\n",
+      "Outputs (1): ['y[0]']\n",
+      "\n",
+      "\n",
+      "  800 s + 4000\n",
+      "----------------\n",
+      "s^2 + 40 s + 400\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Cnew = (kp + kd * s) / (s/20 + 1)**2\n",
+    "Cnew.name = 'Cnew'\n",
+    "print(Cnew)\n",
+    "\n",
+    "Lnew = P * Cnew\n",
+    "Lnew.name = 'Lnew'\n",
+    "\n",
+    "plt.figure(figsize=[7, 4])\n",
+    "ax1 = plt.subplot(2, 2, 1)\n",
+    "ax2 = plt.subplot(2, 2, 3)\n",
+    "ct.bode_plot([Lnew, L], ax=[ax1, ax2])\n",
+    "ax1.loglog([1e-1, 1e2], [1, 1], 'k', linewidth=0.5)\n",
+    "ax1.set_title(\"Bode plot for L, Lnew\", size='medium')\n",
+    "\n",
+    "ax3 = plt.subplot(1, 2, 2)\n",
+    "ct.nyquist_plot(Lnew, ax=ax3)\n",
+    "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n",
+    "\n",
+    "plt.suptitle(\"Stability analysis for inverted pendulum\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "WgrAE9XE7_nJ",
+   "metadata": {
+    "id": "WgrAE9XE7_nJ"
+   },
+   "source": [
+    "While not (yet) a very high performing controller, this change does get rid of the issues with the high frequency noise:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "FknwW6GkBLLU",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3b44OHTogPj4eK1as0F4BlMvlWLx4McaPH4+AgACkpKTg3XffRZMmTTB27Fg9o/GgNWvWoF+/fujduzeWLl2Kzp07Q6VS4dChQ/j222+1V/H0uf3ap08fvPXWW1iwYAHu3r2LsWPHIjAwEDdv3sR3332HAQMGCJrSxN3dHW+88QZee+01aDQaDBgwADKZDMePH4ebm5v29nRkZCRWrVoFHx8fREREaNd9/fXXDbo9TUi9LNWZjxDCWEZGBnvppZdYy5YtmUQiYW5ubqx3797s888/Z8XFxdrtVqxYwQICApizszMbOXIk27hxY7UO6LVNGVJzQIBSqWQvvfQS8/DwYN7e3iwqKopNmDCh3qk2Kqc0kUgkrHnz5tUGWzAmfKBEQ6Y0YYyxDz74gPn5+TFPT0/22muvsZdeeumBgRKvvPKKznIwVvtAiapTmkgkkjqnNImPj693/7VNaWKoxMRENnLkSNa0aVPm6OjI2rZty77++usHtnv//feZSCSqNoUNY4y99NJLrHXr1szR0ZE1bdqUPfvssyw3N5cxVvcgBplMxl555RXWvHlz5uTkxFq1asUWLlzIFAoFY6xioMeIESNY06ZNtefE9OnTqw10qVTzPSrjGBMTo/NzZ2RksHnz5rHQ0FDm4ODAgoKC2JNPPllvvvr8+uuvbNCgQczd3Z25urqyzp07s6VLlz4wpUlV//vf/1hoaKh2WaPRsFWrVrHw8HAmkUhY06ZN2ciRI9mRI0e02xQWFjKRSMSeeuop7brK8+6bb75p0GcgRBeOMQM7yBBCbJpGo0H79u3x9NNP48MPP7R0cUgDzJ49G1lZWdi7d6/gPLGxsRgyZAgKCgoE3Xo0RM33iI2NxdixY3Hr1i3tYA9CiPHQ7VdCGonU1FQcPHgQgwcPhkKhwDfffIPbt29j8uTJli4aMZBUKsWZM2fwyy+/YM+ePQbtIzg4GKNGjcLWrVuNWrYOHTrg1q1b1db9+eefePfdd6lBR4iJ0JU6QhqJ9PR0TJo0CZcvXwZjDB07dsSnn35abXAAsS2RkZE4ffo0/u///g//+9//9MpbWlqKu3fvAgDc3Nzg7+9v1LKlpqZqR5u2atXqgcEXhBDjo0YdIYQQQogdoJ9OhBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCCCF2gBp1hBBCSCMwY8YMfPrpp5YuBjEhatQRAqBFixZwcXGBm5sb3Nzc0KJFC0sXiRBi46heIeZGjTpC7jt8+DDkcjnkcjlSUlIeSFcqlWYph7nehxBietZSr5DGgRp1hNQhNjYW7dq1w8KFC9GkSRN8/PHHyM/Px6RJk9CkSROEhYXhxx9/1G4/Y8YMvPrqqxg8eDDc3NwwefJk3Lt3D8OGDYOnpyemTJkCtVpd63u1aNECn332GcLDwxEREWGuj0gIMTNz1ivJycno168f3N3dMW7cOJSUlJjrYxILEVu6AIRYs5s3b8LFxQWZmZlQq9V47rnnIBaLkZaWhps3b2LYsGFo164dBgwYAADYsWMH/vnnHzRt2hTdu3fHE088gY0bNyIwMBA9e/ZEdHQ0Ro8eXet77d69G3FxcfDw8DDnRySEmJm56pXJkydjxIgRiI2NxYEDBzBhwgR0797d3B+XmBE16gi5b/jw4RCJRACAuXPnYuTIkXBxccHbb78NkUgEnuexc+dOJCcnw8XFBZ07d8bMmTOxdetWbeU7ceJEtGvXDgAQGRkJNzc37ZW3oUOH4uLFi3U26l577TU0a9bMDJ+UEGIulqpXUlNTcfnyZcTFxcHBwQFjxoxBnz59zPjJiSXQ7VdC7jt06BAKCwtRWFiITz75BAAQEBCgrZBzcnKgVqsRHByszRMaGoqMjAztctVGmbOzM5o2bVptubi4uM73r7pfQoh9sFS9kpmZiWbNmsHBwUG7LiQkxHgfjFglatQRogPHcdp/N23aFDzP486dO9p1aWlpCAwMNPp7EULslznqlYCAAGRnZ6O8vFy7Lj09vUH7JNaPGnWECCQSiTBu3DgsXLgQpaWluHz5Mn766SdMmjTJ0kUjhNgoU9UroaGhiIiIwMcffwylUom9e/fi9OnTRio1sVbUqCNED6tXr0ZZWRmCg4Px5JNPYunSpRg4cKCli0UIsWGmqle2bNmCv/76Cz4+Pli/fj3Gjh1rhNISa8YxxpilC0EIIYQQQhqGrtQRQgghhNgBatQRQgghhNgBatQRQgghhNgBatQRQgghhNgBatQRQgghhNgBatQRQgghhNgBevarEWg0GmRkZMDd3Z2eCkBII8YYQ1FREQIDA8HzDf/NTHULIQQQXrdQo84IMjIy6Jl6hBCt9PR0ozzLl+oWQkhV9dUt1KhrgNWrV2P16tVQqVQAgB9//BEuLi4WLhUhxFJKSkowa9YsuLu7N2g/VLcQQqoSWrfQEyWMQCaTwdPTE5mZmfDw8IBKpUJMTAyGDBkCsVhcbRlAtTRjq/nexs6nazt903TFSciysRmyf2PETVe6kLjVts6csbOnc67mNoB+31eZTIaAgABIpVJ4eHgIjoWu/QmtW+g421fdIjQf1S2G57Nk7ADT1C3UqGuAyl/TarUaSUlJ2LJlC/2aJqQRKykpweTJkxvcqKO6hRBSleC6hZEGk0qlDADLzc1l5eXlrLi4mO3evZsVFxc/sFwzzdgvQ/cvNJ+u7fRN0xUnIcvWEDtjxE1XupC4WTp29nTO1Vyn72fLzc1lAJhUKjV73ULH2b7qFqH5qG4xzTln6tiZqm6hPnVGJJFIIJFIdC7XlWbqshg7n67t9E0TEjdrj50x4qYrXUjcaltnztjZ0zlXuc6QMpqCNX1H7Ok420LdIjQf1S2G57NE7AwpoxDUqDMipVKpfVUu1/a35r+NXQZD9i80n67t9E2rL05C4mhMhuy/ctvy8nIwxuqcdqK+fdeVLiRuta0zZ+zs6Zyrb119THluCqlb6DjXvs4W6xah+ahuMTyfpWMnpIz6bkd96hqA+r2Qm1Lg0F0eN2UcOADtvRlGNdegmbOlS0YsgfrUEUJMgfrUmRH1qTMszZb7vZSVKdinB66w0KjoB14dPviTHbmWaTV9N0wdO3s652quoz519nmcrblu0Tcf1S2mOedMHTvqU2cDqE+dYWm21u9FrlDhtV8TcCgxCwDQr5kG700cAMbxWLz3Cs6kFGDuLxfw29x+aB9Q/RcV9XsxPB/1qbOO74g9HWdrq1sako/qFsPzUZ86UivqU2f//V7SC0owZ/MFJGXL4SDmsfSJcDhnXUILb0dIJBKsm94Dszedx4lb+Zi5/gz2zesLD2eJxftu2Gu/F+pTR8fZXuoWQ/NR3WJ4PkvHTkgZ9d2O+tQ1APV7aVxuSDmsS+JRrOLgIWGYGa5Gi1om9y5RAV9cFCFPwaFHEw2mtdGYv7DEIqhPHSHEFKhPnRlRnzrD0myl34tCoWDfH7nBWr2zn4VGRbMnvvqXpeXKdOY5lZyt3X7n2VTq92Ij51zNddSnzj6Ps7XULcbIR3WLac45U8eO+tTZAOpTZ1iaNfd7KVao8P7uK/g9/i4AYEzXQHw6vjOcJCKdn6F3q6aY/3AYVv59A0uir6F3i76Cyk79XgzbjvrU0XG2tbrF2PmobjE8nz31qeMFbUVII3QmJR+ProrD7/F3IeI5vP9EBP43sesDDbq6zBsShk5BnpCWKvHenkRQRwdCCCGmRI06QmooLFFi0Z7LeHrtCaTllyDQ0wmbZ/bBzAEt65xcuDYSEY8vn+4CBzGP2KRcnMoRnpcQQgjRF91+NSIa/WrbI9SKSspwJJPDByvjIC1VAQDGdw/EwkfD4e4k0fszA0BLHye8OrQ1PvvrBn5P4TEztwihTR4cXUEj1Azbjka/0nG2hbqFRr8ahka/Pvie9aHRrw1AI9TsQ6kKOJ7FISaTR5Gy4mpagDPD2JYahHs2/OuhYcBXV0S4XcShracGc9trwNNFO7tEo18JIaZAo1/NiEa/GpZmyRFqCoWCnbiRxV7ddp6Fv3dA+zSIru/vYz//m8RKSsuMGrcraTks7J19LDQqmq2Lu2nWUVbGjp09n3M119HoV/s8zjT6leoWS8eORr/aABr9aliauUaoKdUanEnJx1+X7+FgYhYypWXatLZ+bni+XygcMhIw6qEWeu+/vjK18ffEk8012JkiwvK/bmBIe3+E+roK3g+NUKPRr9YyitOejjONfqW6Rch2tjT6lRp1xG6VKdW4kiHFyVv5OHU7H2dT8lFSrtamuziI8HinAEzq3Rzdm3tBpVLhwL0Ek5VngD/DXc4bJ28X4I0dCdj2Ql+I6D4sIYQQI6FGXRWvv/46zpw5g27duuGrr76ydHGIQEq1BncKSpGSV4ybWXIkZspwJUOK5JxiqDXV+8R5u0gwrL0fHunoj/5hTQRPT2IMPAd8MrYjnvjmOM6kFODno7cxe1Ars70/IYQQ+0aNuvvOnz8PuVyOuLg4zJ07F2fOnEGvXr0sXaxGT6UBMqVlyC8tRmZBMY7e43Djn5vIK1HiTkEpUvNKcLew9IHGWyVfVwf0auGDPq188FArX4T7uYO34NWxYG9nvP9EBN7+/RI+/+s6erf0QZcQL4uVhxBCiP2gRt19J06cwLBhwwAAw4YNw8mTJ6lRVwfGAJVaAzXUUGsYVBoGtYahTFGOQkXFQ+814FGm1KBMqUaZUgN5mQLnczmUnr8LJeOgUKohL1PiShqPM9FXIVeoUVhSjtRMEb5JPoaiMjVkZUqUlIuBU/9WeXcRcPvWA2VykvBo4euKFr6uiAj0QIdAD3QI9ISfh6Nec8uZw8ReITh8LRsHE7Pw4i/nsW/+ALg7WFcZCSGE2B67bNQtWrQIO3bswLVr17BlyxZMmjRJm5aTk4MZM2YgJiYGISEhWLNmDYYOHYrCwkK0bt0aAODp6YkrV66YpGw/HUvB7kQeO3LOgeM4MAYwsIq/Vf8NAPeXAWjXMcbu/63cpuoyg0bDIJWK8O3tE9r3rLnfyn1A+x6ARsNQXCzCF9fiwIBqjTWVWlN9WSMGTv5dxycUA+eP1pEmAm7UjCsP3E2vsswBRcXV98hzaOruiKZuDtCUFKJjWHP4ezoj0NMZob4uaNHEFc3cra/xVheO4/DF010w+ptjuJ1bjFe2xeOHqd0sXSxCCCE2zi4bdW3atMGqVavw/vvvP5A2b948BAYGIjc3FwcPHsSECROQnJwMLy8vyGQyAIBMJoOXl5dJynY9S45rUh6Q5plk/xU4oKTIsHyKUoPflecADgwSsQiOYhGcJDycJSI4SURwEHMokUkR6NcELg4SOEl4OIg43Lubjo7hYfB2dYSrA4+biRcR2b83fNyc4SwGTsXFYvyoR+Ho6AClUokDBw7gscciTDpCzRw8nCT4dmp3jF19HHE3crHqn5toZ+lCEUIIsWl22aibOnUqAGDZsmXV1svlcuzZswcpKSlwcXHBmDFjsGLFCuzbtw99+/bF2rVr8fTTT+Pvv//GjBkz6ty/QqGAQqHQLlc2BoXM+j6+ix9citLRqWNHiEUigOPAAeA43P9bsYz761BlXcU2nHZbcDXTAJVKjQvx8ejevRvEEnG17avmR41llUqFs2fOoHfv3pBIxBDzHMQ8DzHPQcRzEIk4iHkOTK3GsaP/4uEhkXBycNCmi3kOarUKhw4dwvDhDz/Q6FIqlffTOmvTKtalYvjgUEgkFU9sOJTF0DPEQ7vsKgHUahWUSs4qZ31vyMzlrX2d8dHoCCz47RK+/fc2poRxGE6zvuu1nb09UaIhdUtdZTcWezrO9EQJqluEbEdPlLAykZGRmDNnjvb2a3x8PEaOHIns7GztNvPnz4eLiwuWL1+OV199FefOnUOXLl3wzTff1LnfxYsXY8mSJQ+sp1nfiSH2pfH4+y4PEccwt70GbYzwFAtiGQ19ogTVLYSQ2gitW3gzlsni5HL5A8Hw8PCAXC4HAKxcuRJxcXE6G3QA8M4770AqleKLL75AeHg4wsLCTFZmYv8eD9Ggm68Gasbhp+s8sgy/A05sHNUthJCGsMvbr3Vxc3PT3s6oJJPJ4Obmptd+HB0d4ejoiAULFmDBggWQyWTw9PTEkCFD4OHhAZVKhZiYGAwZMgRisbjaMoBqacZW872NnU/Xdvqm6YqTkGVjM2T/xogbAPQfqMDT3x5DipzD+ttu2PxcN/h7OAmKW23rzBk7ezrnam4D6Pd9rVm/6KshdQsdZ/uqW4Tmq2+butKpbrFs7ADT1C2N6varXC6Hr68vUlNT4e/vDwAYNGgQZs2ahWnTpum9f3roNjEmuRL432URcss4+DkzzO+ghrttjwdpdBp6+7US1S2EkKqE1i122ahTKpVQq9UYMWIEZs+ejQkTJsDBwQE8z2PChAnw8fHBypUrcejQIcyYMQPJycnw9vY2+P0qf01nZmbSlbpG9mva2L8Iw7v3xYxNF3FPpkA7fzf8OLkTzp2Io1/TNnSlLiAgoMGNuqr7E1q30HG2r7pFaD66Umd4Plu7UiekbrHLRt2MGTOwYcOGautiYmIQGRmJnJwcTJ8+HbGxsQgODsaaNWu0kw7ri35NE1PILgVWXRFBruTQwo1hboQaTuZ7mhlpALpSRwgxhUZ9pc7c6EqdYWn28GvaVL8Ir92TY8bGeMjKVGjhxrD5hX7wcXOqc1+N9dc0Xamj42yvdYvQfHSlzvB8dKWOVEO/pokppcmBbxNFKFFzCHFlmNteDVfqY2fV6EodIcQUBNctjDSYVCplAFhubi4rLy9nxcXFbPfu3ay4uPiB5Zppxn4Zun+h+XRtp2+arjgJWbaG2BkjbrrSzyVnsYiF+1hoVDQbuSKWZRbIjRJLS8fNWs+5muv0/Wy5ubkMAJNKpWavW+g421fdIjSfoXWLkLhZOnaWPOdMHTtT1S2Nap46QmxNO383vNxBjSZuDriWJcfkH88gQ1pm6WIRQgixQnT7tQHoFgkxl+xSYHWiCIXlHDwlDHPaqxHoaulSkZro9ishxBTo9qsZ0e1Xw9Ls4RaJOS/zp+bI2NAvYlhoVDRr9+4+FnvljsGxtHTcrPWcq7mObr/a53G2hbpFaD66/Wqac87UsaPbr4Q0cgGeTtg6qze6h3iiVM1h9i8X8eeVLEsXixBCiJWg268NQLdIiCWUq4FNN3lczOfBgWF0qAaRAQwcZ+mSEbr9SggxBbr9akZ0+9WwNHu4RWKpy/y/79rNonacZ6FR0Sw0Kpq98et5tvP3xneLhG6/0nG217pFaD66/Wqac87UsTNV3WL8WRYbMYlEAolEonO5rjRTl8XY+XRtp2+akLhZe+yMETdd6TXX8xyw9MkOCPPzxLIDV7HjfAYuePAYNBRo4mKZ2NnTOVe5zpAymoI1fUfs6Thbe91SWKLETRnQPLsUnZs7Q8TXfTneWHWL0HWN5ZzTld6Q2BlSRiGoUWdESqVS+6pcru1vzX8buwyG7F9oPl3b6ZtWX5yExNGYDNm/MeKmK11X3FQqFaY/FIJgL0e8vuMSbsiAp9aexA/PdkeQh0Ot+U0RO3s65+pbVx9TnptC6hY6zrWvs7W6pVihwid/XsfO8xlQacT4+spJNPdxxidjO6B3Cx+99m1I3dKQWBqTJc85XenGip2QMuq7HfWpawDq90Ksxd1i4IdrIhSUc3ARMTwfrkEbT/pqmxv1qSMNdacY2JAkQnZZxVU5H0eGEhVQpubAcwwz2mjQxZe+240N9akzI+pTZ1iaPfR7saa+Gxu372ajvvqXhUZFs9bv7Gdvrd1j9/1eqE9d4+jf1BjqFoVCwb4/coOFvbufhUZFs4c+/pvFXrnDdu/ezbILpGzupjMsNCqatV14gJ2/nSt439SnzvDtqE9dI0d96gxLs/V+L/rkMWXfDQ8H4JeZvfDunqvYl5CBX2+J4HgwGR+M6oDKzey13wv1qWsc/ZvstW7JLy7HW78l4O+r2QCAERF++OypznCVcDhwA/BydcZXz3SHYtM5/HMtGwt+u4TolwfAxUFc777rS6c+dfbVp47mqSPEjjhJRPhqUle8OjQMALDxZBqeW38G0lLT9BUihDTMsZu5eGxVHP6+mg0HMY+loztg7bM94OXiUG07sYjHl093gb+HE27lFmPZ/qsWKjGxZtSoI8TOcByHeZGt8HxbNZwlPOJu5GLC2lPILrV0yQghlcqUanwYnYgpP57CPVkZWjd1xe4X+2Na3xbg6ph00svFASue7gIA+OVUGs6lFpizyMQG0O1XI6LRr41rhJo+eSwxyqqLL8Pjkd3x0q+XcTuvBCsKRWjZIQuD2/kJ+3AC2dM5V9+6+tDoV8PzNaa65UhSDj7cfx2p+SUAgGd6BePtR9rCxUFc77nYK9QT47sHYuf5DCz8/SK2z+6hs+w0+tXw7Wj0ayNDI9SILZCVAz9dFyFFzoEHw9gWGgz0pydQmAKNfiW6pMuBP+7wuFJQcZPMQ8IwqbUGHbz1+29YrgSWXRChRMVhTKgaQwLpv3F7R6NfzYhGvzaeEWq2OMqqvLycFUiL2FNf7NU+gSLqtwusuLSMzjka/dooj7M546ZQKNiRxLvs8U//+/61fmc/W7r3EsuTlRgck19O3GKhUdGs3Xt/sHW/0uhXY59zpo4djX61ATT61bA0WxmhZow8lhqh5gpgSmsNHu7eDp8dTMK2M3eQkleCb6f0gLdr9Q7ZhrKnc65ynSFlNAVr+o7Y03E2Zdyyi8oQnZCJLafTcDNbDoAHzwFPdgnE/KFt0Lqpm+B91VauSb1b4Pf4TJxNLcDvKTym0OhXg/PZ0+hXatQR0khwHDBrQAuEB3jg5a0XcPJWPkavPoYfp/dEWz93SxePEJvGGENyTjFir2fjz8v3cC6tAJWdm5wlPLp6q7B40kCEB3oZ5f14nsNHYzviia+O4mI+j8PXczCyY6BR9k1sFzXq7ktPT8fo0aORmJgIuVwOsZhCQ+zTw+388PuL/TBrw1mk5Zdg3Jrj+OqZrnjYyAMoCLFnpeVqXLsnw+W7Upy6nY+Tt/KRK1dU26ZLiBee6h6Exzs2Q9zhQ2jV1NWoZWjn74Hn+oXih6MpWBp9FQPbNqs2dx1pfOjo39e0aVMcPnwYY8aMsXRRCDG5tn7u2D2vP+ZuPodTt/Mxc8NZvPNoO8we2KrO6RQIaWwYYygoUSIlrxhpeSVIzSvBrVw5rmTIcCtHDk2N8QmOYh49W3hjRIQ/RnTwQ4CnMwDTjaoFgJeGtMJvp2/jbmEZvvrnJt5+tJ3J3otYP2rU3efk5AQnJydLF4MQs/FxdcCmmX2waO8VbD2dho8PXMP1e3J8PK4jHMUiSxePEJMoV2mQX1yO7FLg4h0pSlQMObJS/JvB4dJfScgrViK7qAw5RQpkFpahSKGqc19N3BwQEeiJHs298VArH3QJ8YKTxLzfHRcHMZ5qqcEP10X4Me4WxnYLQrg/dadorGy2Ubdo0SLs2LED165dw5YtWzBp0iRtWk5ODmbMmIGYmBiEhIRgzZo1GDp0qAVLS4h1chDz+HhsR7Tzd8fS6ETsPH8HKXnF+G5qDzR1d7R08YgeVGoNFEo1ytUVtwZVjAcDA2MAQ8VVp4q/qFgB1JnOwKpsU7GuXKlEgQLIKCyFSKxE1cmwKvPU3BcAKJUq3CsBbmRXdGup3FatqXip7v9VlCtxvZCD241cgOO161UahvJyJc5nc5CfvQPG8VCpNVBpGBQqDYrLlLiawuP0vqsoVzOUqTQoVSiRnsljU8ZpKFQMJeUq5BaK8PbZv1Gq1NwvmRi4cKpKBEVAakqtsQ3wdEJzHxe08HVFaBMXtPf3QIdADzTzsI4LAR19GIa3b4ZDV7OxcNclbP+/vuB5uuLeGJm9UVdaWooPPvgAO3bsQH5+PmQyGf766y9cvXoVr776quD9tGnTBqtWrcL777//QNq8efMQGBiI3NxcHDx4EBMmTEBycjIUCkW1xh8AuLm5ITo6uqEfixCbxXEcpvdrgVZNXTHvl/M4l1qA0d8cxQ/Te6JDoKeli0cEWvXPDXx9+CYAMd48/Y+J3kWMxefjDMr3ScJxAduJgKvn605LTqwjjQcy0x9cJy2ssswB0GiXnEQMPm7O8HCWwMNJDFVRHrqEt4S/pzOaujuimbsT/DwcEeLjYvarb4Z477FwHEvOw9nUAmw6mYrp/VpYukjEAszeqHvxxRehVCoRHR2NgQMHAgA6d+6MV155Ra9G3dSpUwEAy5Ytq7ZeLpdjz549SElJgYuLC8aMGYMVK1Zg3759mDZtGmJjY431UQixKwPbNMXuef0xa8NZ3MotxlPfnsD/JnbBIx0DLF00YmYcV9EEquxfyd1fp9FoIBKJtMscuAe25+5n+C8/g1KphKODgzad4wCe4yDmOYhFPMQ8B54DSorl8PbygFgkgpjnIOI5bVp+bg4C/P0gEYkgElWsdxKL4CAC7qanIqJtGFycJHASiyDhgeuJl9C7Rze4OTtAwgEXz53Eo8Mi4evmAkcRw19//oHHHhsEiUQCpVKJAwcO4LFHw006XZIpBXo54+1H2+GDPVfwyR9XMahtU7RsYtyBGcT6mb1Rt3//fqSnp8PR0VFbYQQEBCAzM9Mo+79x4wY8PT0REPDff0RdunTBlStXdOYrKyvDE088gYSEBIwcORKLFy/WNjprUigUUCj+G+Ukk8kA0GPC7O1RPsbMY+nH0QgtZ4iXI7a/0Buvbr+IozfzMGfzebw6NAwvDm5Z5wAKezrn6ltXn4aemw2pW/5vQCie7RWAmJgYPPzwEDjcb5xw4LSNLlRpVFUezaoNLV2DZJRKJQ4dOoThwx/Wq+HzX74BOvP9t13PB7b7L61jHWm3MXxQqDZNqVTiUM5FDAv31Tba8q8B/m4SSCQVt4Rrxq/qX2Mz5Xek6jYTuwfij0uZOHErHwu2X8CWmb2gUatq3Qc9Jszy9bKQMuq7ndkfE9a2bVscPnwYwcHB8PHxQX5+Pm7fvo3HHnsMV69e1Xt/kZGRmDNnjva2alxcHJ577jncvHlTu83ChQtRWFiI1atXG+UzLF68GEuWLHlgPT3Kh9gLNQN2p/D4917F44y6+2rwTGsNHKz/LpRFNfQxYVS3kIbKVwCfJoigUHN4srkaQ4PoEWL2QGjdwpuxTACAV155BaNGjcJvv/0GtVqN6OhoPPPMM3rdetXFzc1N++u2kkwmg5ub8Nm76/POO+9AKpXiiy++QHh4OMLCwoy2b0KsgYgDxrfUYGIrNXiO4Xwej6+uiFCoqD8vMRzVLaShfByBsaEVfQf3p/O4W2zhAhGzMvuVOgDYsWMHfv75Z6SlpSEoKAgzZ87ExIkTDdpXzSt1crkcvr6+SE1Nhb+/PwBg0KBBmDVrFqZNm2a0z1CVTCaDp6cnMjMz4eHhAZVKhZiYGAwZMgRisbjaMoBqacZW872NnU/Xdvqm6YqTkGVjM2T/xoibrnQhcattnTFjdyalAK/suILCUiX8PRzx/ZQuCKsyiao9nXM1twH0+77KZDIEBAQYfKWutv0JrVtM/R2xp+NsC3WL0Hy1bcMYw7xtlxB7Iw8tfJzxYlgRHhlmfXWLMT5/Q/JZsl4GTFO3WKRRZwxKpRJqtRojRozA7NmzMWHCBDg4OIDneUyYMAE+Pj5YuXIlDh06hBkzZiA5ORne3t5GLcPq1auxevVqqNVqJCUl0S0SYrdyy4Dvr4mQVcrBRcTwQns1WtJUWA9o6O3XSlS3kIaSK4HPEkSQKjn0bqrBlDBN/ZmI1RJat5ilUffZZ58J2u6tt94SvM8ZM2Zgw4YN1dbFxMQgMjISOTk5mD59OmJjYxEcHIw1a9Zg2LBhepVZH3SlzrA0e/g1bQu/CI0Vu8ISJeZuvYiEuzI4inl8Ob4DHg5vYlfnXM1tALpSV8mejrMt1C1C8+na5mxqIWZsjIeGAR8+0RbjuwfpzEdX6oSlN/ordc8995z23yUlJdi1axf69OmDkJAQpKen4/Tp0xg3bhy2bt1q6qIYFf2aJo1NuRpYf4PHlQIeHBgmttKgr59NXuw3CbpSR6zNX3c4HEgXwYFnWNBJDX86jWySVV2pq2r8+PGYNm0aRo8erV23d+9ebNy4Eb/99ps5i2I0dKXOsDR7+DVtC78IjR07lUaDxdFJ+P1CxTRELw0KRVh5Mh5+2PbPuZrbAHSlrhLVLYaz1JU6AFCUK/H0mn9xQ8ajpa8Lts3sAXen+s99ulJHV+oE8fT0RF5e3gMB8vX1hVQqNWdRGox+TZPGijHgQDqPg3crBtAP9NdgXAsNGvuTiehKHbFGsnLgy0siFJZz6Oitwcxw+q7aGsF1CzOzfv36sUWLFjGlUskYY0ypVLIlS5awvn37mrsoRiOVShkAlpuby8rLy1lxcTHbvXs3Ky4ufmC5ZpqxX4buX2g+Xdvpm6YrTkKWrSF2xoibrnQhcbNk7H769yZrERXNQqOi2StbzrKS0jKbPudqrtM3brm5uQwAk0qlZq9bTP0dobrFOmMntG45dSOTtVl4gIVGRbPP/0i0+rrF0uecrnRjxM5UdYvZ56nbtGkT/vzzT3h7e6N169bw9vbG/v37sXHjRnMXhRDSQM8+1BzLx0aAB8PuhHt4fccllKtolB0h1qZjoAc+ejICAPBN7C38fS3HwiUipmCxKU3S0tKQmZmJgIAANG/e3BJFaDC6RUJIhYv5HNYn8VAzDhFeGjwfroHE7D8ZLY9uvxJr93sKjyOZPBx4hlc6qhFMj4e1CVZ7+zUrK6vOl62i26+GpdnDLRJbuMxv6thV7vuvhDTW9v7tnUlrj7OCohKbO+dqrqPbrw37fljrcbaFukVoPn3rlpLSMvbM98dZaFQ06/zePpacmWfUWNpK3AyJnT5xqi9WdnP71d/fHwEBAfD399f+u/JFCLFdA8J88dO07nB1EOHErXw8v/E8ispM84B0QohhxCIe30yqeCqMVMlh7taLKCpTWbpYxEgs/kSJe/fu4aOPPkKfPn3w7LPPWrIoeqNbJIQ8KKUI+O6qCKVqDiGuDC9GqOFi/FkirBLdfiW2Il8B/O+SCDIlh3aeGrzQTgNRI+wyYStMdvs1Pj7esPsIOigUChYUFGT0/ZoL3X41LM0ebpHYwmV+U8eutn0npOaxrkv+YqFR0WzUV/+yXKl+54ulzrma6+j2a8O+H9Z6nG2hbhGaryF1y9ebd7N271V0mXhzezyTy+VWX7eY65yrL3bWevtV79/PTz75JNzc3PDMM89g8uTJaN26taENT61Tp05BpbL9y78SiQQSiUTncl1ppi6LsfPp2k7fNCFxs/bYGSNuutKFxK22deaMXdV9d27ugy2zH8LkH07i4l0Znt8Uj00ze8PDSdhnq2//DU0TEhdD4maO2BqybMqyGDsf1S2G5TOkbglxA/73dGe8uOUCtp+7Cz8PR4TVsW1jPed0pTekXjakjELo3ahLS0tDXFwctm7dir59+6Jly5aYPHkyJk6cCH9//3rzt2/fHhz336yHJSUlyMvLw6pVq/QtitVRKpXaV+VybX9r/tvYZTBk/0Lz6dpO37T64iQkjsZkyP6NETdd6ULiVts6c8aurn2HNXHGhhk9MW3dWSSkF2LaT6fw87SK2ez1KZM5z7n61tXHlOemkLrFEsfZWPmobjEsX0PrlkGtvfHBE+2xeN9VfB1zC+NbcBhu5XWLsfJZul4WUkZ9t2tQnzq1Wo2//voLb775JpKSkhAZGYnnn38eEydOBM/XfnP+yJEj1ZZdXV3Rtm1bozxSx9yo3wsh9btTDKy+IkKJmkMLN4a5EWo4iSxdKtOgPnXEVlU+IxYAng1To2dTeqazNTH5lCYXLlxgb7/9NgsNDWVdu3ZlX3zxBdu6dSsbNGgQGzVqVJ35Pv/881rXf/nll4YWxeKoT51hafbQ78UW+m6YOnZC9h2fkss6LfqThUZFs3Grj7L8ohKrPOdqrqM+dQ37fhjrO9IY6xah+YxVtygUCrZw5wUWGhXNWr+zn/116a7FY0d96v57maxP3dKlS7F161aUlZXhmWeewYEDBxAREaFNHz16NHx9fXXmf+ONNx5Yv2zZMrz++uv6FseqUJ86w9Lsod+LNffdsIZ+L11DffHLrIcw5ceTOJdWiP/75QJ+nNpNrzJRnzrr+I5Q3WI4a+tTV3P9+4+3x7VbaTiby2P+tgRsntUHXYPca83fWM45Xel20afuzp07WLt2LQYNGlRrurOzM44fP/7A+u3btwMAVCoVduzYAVblrm9KSgp8fHz0LQohxIZ0CvbEppl9MPXHUzh9Ox/ztyXgSfraE2I1eJ7D5NYauPs2Q8z1XDy//gw2PdfT0sUietC7Uff999/Xu03Xrl0fWPftt98CAMrLy7FmzRrteo7j0KxZM6xfv17fohBCbEyXEC/8NKMXpv18Ckdu5ELqy+NxDYPpro0QQvQh4oGvJnbBzI3xOJ2Sjxnrz+GFNpYuFRHKbFOCxsTEAAA++ugjvPfee+Z6W7Oi0a+Nb4SarYyysqYRat2C3bHmma54YXM8LuTxWLj7Mj4Z27HaqHih+6fRr9Z7nPXNR3WLYflMUbeIoMF3U7riuQ3nkHBHitWJIvS/W4iIIK9Gc87pSrfb0a9C5ebmokmTJgCA7OzsOrdr1qyZqYtiVDRCjRDDXcjjsD6JBwOHyAANxoRqUEe7zmbQ6FdiT0pUwJpEEdKLObhLGOZ3UMPP2dKlapxMPvpVH25ubtp/cxzHeJ5nHMdVe/E8b46imASNfjUszR5GqNnCKCtTx64h59w73+9hoVHRLDQqmn3551WLn3M119HoV+McZ6pbrH/0a13r7uYWsn5L9rHQqGjW+6NDLDE9p1Gcc8aInU2MfgWA1NRU/Pbbb8jIyEBgYCDGjRuHli1b1rl9UVGR9t8ajcaQt7QJNPrVsDR7GKFmzaOsrHmEWp9mDK3Cw/HRgev4KiYZnq6OmDmg9rqERr9ax3eE6hbDWfvo19rWNfVwwYsRaqxP88SN7GI8vykBs1s1nnNOV7o1jn7V+/G90dHR6Ny5M86dOwcHBwecP38e3bp1w759+/TdFSGEYHrfUCwY3hYA8GF0IvZcuGvhEhFCqnKTABuf64nWTV2RKS3DN4ki3C0stXSxSC30vlL3zjvvYM+ePYiMjNSu+/fffzF37lyMGjWq3vzp6elYunQpEhISIJfLq6UlJibqWxyjOXLkCN5++22IRCL07t0bK1assFhZCGlsXno4DAUlSvx87Dbe2JEAH1cHDGzT1NLFIoTc18TNEVtmP4SnvzuB1PwSTPnpDLbO7osADxq7bk30btTdvXsX/fv3r7aub9++yMjIEJR/4sSJaNOmDZYsWWJVHX/DwsIQGxsLR0dHTJ48GZcuXUKnTp0sXSxCGgWO4/De4+2RXVSG6IuZmLPpHLa90Bedgj0tXTRCyH1+Hk7YPLMnxn99BHcLyzDx+xM0j52VEdyou3PnDoKDg9GnTx8sXrwYixcvhkQigVKpxJIlS9CnTx9B+7l8+TKOHj1a57NhLSUoKEj7b4lEApHITh9OSYiV4nkOXz7dBQUl5Th2Mw/PrT+NnXP7IdDDwdJFI4Tc5+/hhJc6qLEhzRPJOcWY8tMZzGxt6VKRSoJbVpWPAlu7di1iY2Ph4+OD1q1bw8fHB4cPHxY0KTEAPPLIIzh58qRhpa1i0aJFiIiIAM/z2LZtW7W0nJwcPP7443BxcUF4eDj++ecfwfs9f/48cnNzqz36jBBiHo5iEb6b2gMRAR7IlZdj2s+nkSdXWLpYhJAqPB2Azc/3RFs/N2QVKfDNFRFuZsvrz0hMTvCVOnZ/OrvmzZvj2LFjSE9P145+DQkJEfyGzs7OeOSRRzBixIgH5qWr+qSJ+rRp0warVq3C+++//0DavHnzEBgYiNzcXBw8eBATJkxAcnIyFAoFJk2aVG1bNzc3REdHAwDu3buHl19+GTt37hRcDkKIcbk7SbD++V4Y/+1xpOaVYNameDwbbOlSEUKqauLmiK2zH8KUH07iWpYcU38+iy2zH0K4v7uli9ao6dWnLj09vdozWwMCAsAYQ1paGoCKBl99WrVqhQULFuhZzAdNnToVALBs2bJq6+VyOfbs2YOUlBS4uLhgzJgxWLFiBfbt24dp06YhNja21v2VlZVh8uTJ+Prrr+Hn56fzvRUKBRSK/64eyGQyAPREicY467utzFxua7O+ezuJ8NOz3THxh9O4nCHDumIeI4cr4KrHPmzxiRINrVts7TgL3a4x1i1C81mybvFwlODnZ7vi6W/jcKe4HJO+P4ENM3qifYBxGnb0RIkH37M+gp8owfM8nJycUNfmHMehpKRE0JsaU2RkJObMmaO9AhcfH4+RI0dWe3LF/Pnz4eLiguXLl9e5n2+//RZLlixBu3btAACffPIJ+vbtW+u2ixcvxpIlSx5YT7O+E2JcqUXAN4kilGs49G6qweTW1v3UiYY+UYLqFmKLqj55wkXM8GJ7NULcLF0q+yK0bhHcqHN3d682ibChPvvss1rXOzo6Ijg4GEOHDoWXl5fg/dVs1MXFxeG5557DzZs3tdssXLgQhYWFWL16dYPKXqm2X9MhISHIzMyEh4cHVCoVYmJiMGTIEIjF4mrLAKqlGVvN9zZ2Pl3b6ZumK05Clo3NkP0bI2660oXErbZ15oydqc+5mOvZmP/rZWjAYX5kS8wd1ELQPgyJU81tAP2+rzKZDAEBAQY36hpSt9j6caa6xbB81lK39Og7EC/+egUX78rg7ijGd5M7o1tIw0avW/Kc05VujNgBpqlbBDfqPDw8tLcCGmLSpEnYtWsX+vTpg+DgYNy5cwenTp3CqFGjkJGRgcTERPz+++94+OGHBe3PWFfqDEHPZyTEPI5lcdh+q2JE+tQwNXo1Nfkjqw1Cz34ljVmZCvj+mgjJRRwceIZZ4RqEe1nnd9XWCK1b9B4o0VAqlQo7d+7EE088oV23f/9+rF+/HsePH8cvv/yC119/HRcuXDBo/23atIFUKsW9e/fg7+8PAEhISMCsWbOMUfxq5s2bh3nz5kEmk8HT0xNDhgyhK3WN7Ne0LfwitIcrOIiJgUuzYKw/eQfbbokxvF8X9GrhbZVX6ozBkLrFHo4z1S3657O2umXYMDVe2X4ZR5Pz8UOSGCue6oCh4YZNJE5X6v4jtG4RfKXOWDw9PZGfn19tHjiVSgVfX19IpVJoNBp4eXnV+wGUSiXUajVGjBiB2bNnY8KECXBwcADP85gwYQJ8fHywcuVKHDp0CDNmzEBycjK8vb2N+lno1zQh5qNhwIYkHhfyebiIGF7tpIafs6VLVR1dqSMEUGmADTd4XMznwYNhSpgGPa306rqtEFq3mH0G4IiICHz88ccVv74BqNVqfPrpp2jfvj2AihG2QvrUzZ49G87OzoiLi8O0adPg7OyMf//9F0DF1Cjp6enw9fXFG2+8ge3btxu9QUcIMS+eA6aEadDCjaFEzWHtVRHkphmsSAhpADEPzGirQa8mGmjAYfNNHsezrHiEkz1hZnb9+nXWo0cP5u7uzlq3bs3c3d1Zz549WVJSEmOMsVOnTrGdO3eau1gNIpVKGQCWm5vLysvLWXFxMdu9ezcrLi5+YLlmmrFfhu5faD5d2+mbpitOQpatIXbGiJuudCFxs3TszH3OZeYXsf6f/sNCo6LZk1/9y7bvNN45V3Odvp8tNzeXAWBSqdTsdYu9HeeGpNlD3SI0nzXXLWVlCvbuzgQWGhXNQqOi2bcxSVYTN0vHzlR1i/E7D9Sjbdu2OHv2LFJSUpCVlQV/f3+EhoZq03v37o3evXubu1iEEBvh6+aIH5/tjok/nELCXRm4Uh6PmrcXCSFEAJ7nsOiJdnB1FOH7uBR8+mcS5GUqvPxwa3DWPDeRDTN7n7pKJSUlyMvLqzYAQ8jkxdaE+r0QYjk3pcCaqyKoGYehgRo8GaqxdJGoTx0hdTh0l0N0WkVf+sgADcaEWveck9bG6PPUGculS5cwbdo0XLx4saIA94+qg4ODRSYvNobKEWo0T13jG6FmC6Os7HlU5O4LGXh373UAwLIn22Fs1wCd+ax9nrra9ie0brHn49wY6xah+Wypbtl8+g4+/vMGAOCpbgFY9Hg4RHzdLTsa/fofo89TZyz9+/fH8OHD8fbbbyMgIACZmZn44IMP0Lp1a/zf//2fOYvSYPRrmhDL25/G4+BdHiKOYV6EGq0b3pYyGF2pI0S3k9kctiXzYODQxUeDaW00EJt9yKbtEVy3GKU3rx48PT2ZWq1mjDHm5eXFGGNMoVCwoKAgcxfFaGighGFp9tCZ2RY65Jo6dpY+537ftZvNXn+KhUZFs65L/mK3sqQGx6nmOhooYT3HubHVLULz2WLdsjc+nYW9u5+FRkWzSWuPs/yiEqs750wdO7sZKOHl5YXCwkL4+PggKCgICQkJ8PHxgVwuN3dRjE4ikUAikehcrivN1GUxdj5d2+mbJiRu1h47Y8RNV7qQuNW2zpyxs9Q5x3PA5091wt2fzuJKhgxzfrmAnS/2g+P9bQ2JU+U6Q8poCtb0HaG6xXCmjJ0t1S2jugbD180JszeexYlb+Zi27hzWP9cLvm6OBn22ulhzvWxIGYUwe6Nu1qxZOHLkCMaOHYtXXnkFAwcOBM/zmD17trmLYnRKpVL7qlyu7W/Nfxu7DIbsX2g+Xdvpm1ZfnITE0ZgM2b8x4qYrXUjcaltnzthZwzkn4Ri+ndwV4787ietZRZi/5Ry+mtDxgXz6fDcN+b6a8twUUrfY+3FuTHWL0Hy2Wrf0CvXEpud7YubG87h0V4qnvj2OdTN6IMjrvxnFLXnO6Uo3VuyElFHf7Sw2+rVSamoq5HI5OnToYMliGIT6vRBiXVKLgK+viKBkHB4O1GC0mUfEUp86QvSTXQqsSRShoJyDpwPD3PZqBNCp/gCr61PXvn37el+2ivrUGZZmD/1ebKHvhqljZ23n3O9nU7WTnb7z/R7qU2enx9mY3wdrrFuE5rOHuiUtV8aGfhHDQqOiWefFf7JTydkWP+dMHTub71N3+/ZtNG/eHFOmTMGgQYPscuJB6lNnWJo99Hux5r4bja2v1dgezXE7vwxf/XMDv97iMSpDjr5hzXTui/rUGV4WY+ejusWwfLZct4T4SvDb3H54bv0ZxKcVYvq6c/h2anf0b+XdoH1bc71sSBmFMFujLjs7G7///jt++eUXrF+/HhMmTMCUKVPQuXNncxXB5KhPXePr92IrfTcaW1+reYNa4FpGIQ5ezcGLWy5g55w+CPF2oT51DSyDIfumuoX61AnhKuGwfnp3vLQ1AXE38zBrw1l8MqY9HAzYt63Uy0LKqO92FulTl5WVhW3btmHr1q0oLi7Gr7/+ioiICHMXo8Go3wsh1qtcDXx1RYT0Yg4BzgyvdlTDycQ/Y6lPHSENo9IAv9zkcT6PBweGcS00GBRAjwG0uj51VRUUFLC1a9eywYMHs7CwMHbp0iVLFMNoqE+dYWn20O/FFvpumDp21nzOrf91N+v54UEWGhXNnvv5FCuSy6lPnR0eZ3usW4Tms8e6paxMwd77PUHbN/bT6EtMoVCY9Zwzdexsvk+dQqHA3r17sXnzZsTHx2PMmDH49NNP8dBDD5mrCCZHfeoMS7OHfi/W3HejMfe18nIEvpvSDc/8dAaHr+fg27g0tK1le+pTZ3hZjJ2P6hbD8tlb3bJ0TCd4uzrgq8PJ+DYuFfmlGiwb2xFikfDHT1hzvWxIGYUwW6POz88P/v7+eOaZZxAVFaV91tnp06e12/Tu3dtcxSGENBKdgz3x6bhOeH17AlbH3sJzbTk8ZulCEUJ04jgO84e0xr2UJPx2W4Rfz6YjR67AN5O7wcXB7FPs2gyzRcbLywsKhQLr16/Hhg0bwGp05eM4Drdu3TJXcQghjci47sFIzJDhx6O38ctNHuPuFaFTiI+li0UIqUd/P4YhD3XFq9sv4vC1bDzzwyn8PL1nnU+faOzM1qhLSUkx11tZDI1+bXwj1GxllBWNigQWDGuNxIxCHL9VgDm/xOP3OQ/B3YF7ID+Nfq27DIbsm+oWGv1qqMp9Dg7zxqbneuKFzfFISC/E+G+P46dp3dHcp/bBQ7ZSLwspo77bWfyJEraMRqgRYluKlcCXl0TIU3Bo46HB3AgNREacMpNGvxJiOlmlwHdXRchXcHCTMMxpp0aIm6VLZR5WPfrV3tDoV8PS7GGEmi2MsjJ17GztnPt2y27W/v0/WGhUNHvv9ws0+tVOj7Ot1y1C8zW2uuVOXhF75H9HWGhUNGv//h/s7ysZJjnnTB07mx/92hjQ6FfD0uxhhJo1j7KiUZHV0wJcgC/Gd8KLWy9g06k7ULXm8BiNfjW4LMbOR3WLYfkaS90S5CPB9jl9MXfzeRy9mYv/2xyPT8d3xlM9gnXmE7p/fdKtcfSr8LHBhBBiJ4ZHNMOrw9oAALbf4nEhvdCyBSKECObuJMHPM3phbLcgqDQMb+xIwOqYmw8MwGyMqFF3X0ZGBvr164dBgwbhiSeeQElJiaWLRAgxoZcfboPh7ZtBzTjM25qALFmZpYtECBHIQczjywldMGdwawDA539dxwd7rkCtadwNO2rU3efn54ejR4/i33//RY8ePbB//35LF4kQYkI8z+Gz8R3h78yQXaTA/206hzKl2tLFIoQIxPMc3n60HRaPigDHAZtOpmLu5sb9PaZG3X0ikQg8XxEOjuMQHh5u4RIRQkzNzVGM2e3U8HQW40J6Id7bfZlu4RBiY2b0b4nVk7vDQczjYGIWpq8/B7lpZqexejbbqFu0aBEiIiLA8zy2bdtWLS0nJwePP/44XFxcEB4ejn/++UfQPo8ePYoePXrg77//RmhoqCmKTQixMk2cgJVPdwHPAb+du4ONJ9MsXSRCiJ4e6xSATc/3hoeTGOfTCrHysgipeY2vG5XNNuratGmDVatW1fposXnz5iEwMBC5ublYvnw5JkyYgIKCAty7dw+RkZHVXk888YQ234ABA3Du3DmMGTMGP//8szk/DiHEggaE+eLdx9oDAD75Mwk3pEacvI4QYhZ9Wvli59x+CPJyQk4Zhwnfn8K51AJLF8usbHZKk6lTpwIAli1bVm29XC7Hnj17kJKSAhcXF4wZMwYrVqzAvn37MG3aNMTGxta6P4VCAUfHiseOeHp6Qq2u+568QqGAQqHQLstkMgD0RInGOOu7rcxcTk8aqH1d1b/T+gTj8p1C7E7IxLokHqNzihDk5SSojDX3b6iG1i10nGtfZ4t1i9B8VLdU18LHCVue746pa48hvViJyT+cxBdPdcIjHfz03j89UcICIiMjMWfOHEyaNAkAEB8fj5EjRyI7O1u7zfz58+Hi4oLly5fXuZ+jR49i4cKF4HkePj4+2LRpU50zuC9evBhLlix5YD3N+k6IbStXA6uuiHCnmEOwK8MrHdRwEAnP39AnSlDdQohxKNTAhhs8rhTw4MAwOlSDyAAGzkYvwgutW2z29mtd5HL5Ax/Yw8MDcrlcZ74BAwbgyJEjiImJwc6dO3VWoO+88w6kUim++OILhIeHIywszChlJ4RYloMImBmuhquY4U4xh19v8TDnz16qWwgxDkcRMDNcgwF+GjBw2J0qws4UHvY+44nN3n6ti5ubm/aWRSWZTAY3N+M9IM7R0RGOjo5YsGABFixYAJlMBk9PTwwZMgQeHh5QqVSIiYnBkCFDIBaLqy0DqJZmbDXf29j5dG2nb5quOAlZNjZD9m+MuOlKFxK32taZM3b2dM5VbpNXFotvr4pwNpdHiBvDksmRgj5bzbpHXw2pW+g421fdIjQf1S115xv28BCMFImw7kQ6vvg7GXH3eIg9muHzcRFwcRBZNHaAfm0BoXWL3d1+lcvl8PX1RWpqKvz9/QEAgwYNwqxZszBt2jSjvjc9dJsQ+xWbyWFXigg8GOZFqBHmWX+eht5+rUR1CyHGFZ/HYfMNHirGobkrw+x2ang4WLpUwgmtW2y2UadUKqFWqzFixAjMnj0bEyZMgIODA3iex4QJE+Dj44OVK1fi0KFDmDFjBpKTk+Ht7W2SslT+ms7MzKQrdY3s1zRdqbOvc67qNpGRkVi4LwlXU7Pw4/MPwc+z/kaVTCZDQEBAgxt1VfcntG6h42xfdYvQfFS3CM93Pq0QL/16GYWlSgR5OWH10x2QeumUzVypE1K32GyjbsaMGdiwYUO1dZUVcU5ODqZPn47Y2FgEBwdjzZo1GDZsmNHLQL+mCbFvSk1Fx2ORwN7HdKWOEOuWXQqsvSpCroKDs4hhVriwq/CWZvdX6qwJXakzLM0efk3TlTr7OudqbgOY5te0UHSlzrA0e6hbhOajukX/fPnF5Xjp10u4cEcGEcfw0ZPtMLpLoOD90JU6O0W/pgkhVdGVOkJsQ7ka2HyTR0J+xWX4x0PUGB5kvVOeCK5bGGkwqVTKALDc3FxWXl7OiouL2e7du1lxcfEDyzXTjP0ydP9C8+naTt80XXESsmwNsTNG3HSlC4mbpWNnT+dczXX6frbc3FwGgEmlUrPXLXSc7atuEZqP6hbD8xXJ5WzGyr0sNCqahUZFsze3x7Pi0jKzxM5UdYvdzVNHCCGEEFIfnuMwpoUG7z3aBjwHbD93F/+3OR5FZSpLF81gdPu1AegWCSGkKrr9SohtupzPYcMNHuUaDoEuDP/XTg0vR0uX6j90+9WM6ParYWn2cIuEbr/a1zlXcx3dfrXP42wLdYvQfFS3GO+cO3c7h/X48CALjYpmvT86xM7cvEe3XwkhhBBCbE2nIE/89n990KaZK7KKFHh2fTwuF1jpyIk60O3XBqBbJISQquj2KyG2r1QFrEvicV3KgwPDuBYaDAqwbFOJbr+aEd1+NSzNHm6R0O1X+zrnaq6j26/2eZxtoW4Rmo/qFhOdc6Vl7I1fz2lHxr6/6yIrLVMYLXamqluMP8tiIyaRSCCRSHQu15Vm6rIYO5+u7fRNExI3a4+dMeKmK11I3GpbZ87Y2dM5V7nOkDKagjV9R+zpONtC3SI0H9Uthuer/fMCy8Z0hCL3DvamibDxZBoypGVYNakbHO5v25DYGVJGIahPHSGEEEJIDRzHYWgQw1cTO8NRzOPvq9l4eu0JZMnKLF20OtGVOiNSKpXaV+VybX9r/tvYZTBk/0Lz6dpO37T64iQkjsZkyP6NETdd6ULiVts6c8bOns65+tbVx5TnppC6hY5z7etssW4Rmo/qFsPzCY3dsHBfbHq+J+b8Eo8rGTI8tfYUprVoeOyElFHf7WigRANQZ2ZCSFU0UIIQ+5VXBqy9JkJWKQdHnuG5thq09zZPE4oGSpgRDZQwLM0eOjPTQAn7OudqrqOBEvZ5nG2hbhGaj+oW05xzdaXnSIvZ098eZaFR0azV2/vZuqPJBsWOBkrYABooYViaPXRmpoES9nXOVa4zpIymYE3fEXs6zrZQtwjNR3WL4fn0iV0TiQQ/T++J59YcxOkcHov3XcXdwjK8MSyszn3RQAlCCCGEECvkIOYxubUGr99vyP0QdxsvbUtAudrCBQMNlDAqGijR+Doz00AJ+zrn6ltXHxooYXg+qlsMy0d1i+H5GhI7jgNm9QtBiLczonZdwaGr2bjuKkLvAXIEervVmp8GSlg56sxMCKmKBkoQ0vjckgE/XhehWMXB24HhhfZqBBr560oDJcyIBkoYlmYPnZlpoIR9nXM119FACfs8zrZQtwjNR3WLac45fWOXmJ7Dei7ax0KjolmHD/5khxMzaaCEraOBEoal2UNnZhooYV/nXOU6Q8poCtb0HbGn42wLdYvQfFS3GJ7PGLEL8/PEax3V2JXbFGdSCjB703ksGdUebrXkp4EShBBCCCFWzFUCrJveA2O7BUGlYVi4JxH7UnloNObr5UaNOkIIIYQQI3AU81jxdBe8OqwNAODvDB6vbr+IMqV5hsZSo66G3377DSEhIZYuBiGEEEJsEMdxeHVYW3w+viNEHMMfV7LwzA8nkSdXmPy9qVFXBWMMO3bsoEYdIYQQQhpkTNdAvNheDU9nMeLTCvHU96dxr8S070mNuip27dqFxx9/HDxPYSGEEEJIw4R5Attn90GorwvuFJRi5WURTt7KN9n72WzrZdGiRYiIiADP89i2bVu1tJycHDz++ONwcXFBeHg4/vnnn3r3xxjD5s2bMXnyZFMVmRBCCCGNTKumrtj1Yn/0aO6FUjWH5zacw87zd03yXjY7pUmbNm2watUqvP/++w+kzZs3D4GBgcjNzcXBgwcxYcIEJCcnQ6FQYNKkSdW2dXNzQ3R0NPbs2YORI0dCLLbZkBBCCCHECvm4OmDDjB6YtvoQzufxeHvXFYwI4vGokZ//YLMtmKlTpwIAli1bVm29XC7Hnj17kJKSAhcXF4wZMwYrVqzAvn37MG3aNMTGxta6vytXriAmJga7du3ClStX8M477+CTTz6pdVuFQgGF4r8OjzKZDAA9JqwxPsqHHhNmX+dcfevq09D4NrRuoeNc+zpbrFuE5qO6xfB85o4dDw2ebaNBr/YtsfZoKg7e5bHuWAqeH9BS8Geqj80/JiwyMhJz5szRXoGLj4/HyJEjkZ2drd1m/vz5cHFxwfLlywXtc8CAATh69Gid6YsXL8aSJUseWE+P8iGkcWvoY8KobiGkcTiZzeFkNo8X26vhIKp/e6F1i832qauLXC5/4AN7eHhALpcL3oeuBh0AvPPOO5BKpfjiiy8QHh6OsLAwg8pKCCFVUd1CSOPwUDOGlzsIa9Dpw2Zvv9bFzc1Ne8uikkwmg5ubm9Hew9HREY6OjliwYAEWLFgAmUwGT09PDBkyBB4eHlCpVIiJicGQIUMgFourLQOolmZsNd/b2Pl0badvmq44CVk2NkP2b4y46UoXErfa1pkzdvZ0ztXcBtDv+1qz7tFXQ+oWOs72VbcIzUd1i+H5LBk7wDR1i93dfpXL5fD19UVqair8/f0BAIMGDcKsWbMwbdo0o7736tWrsXr1aqjVaiQlJdEtEkIauYbefq1EdQshpCqhdYvNNuqUSiXUajVGjBiB2bNnY8KECXBwcADP85gwYQJ8fHywcuVKHDp0CDNmzEBycjK8vb1NUpbKX9OZmZl0pa6R/Zq2hV+E9vxr2hqv1AUEBDS4UVd1f0LrFjrO9lW3CM1HdYvh+WztSp2QusVmG3UzZszAhg0bqq2LiYlBZGQkcnJyMH36dMTGxiI4OBhr1qzBsGHDjF4G+jVNCKmKrtQRQkzB7q/UWROpVAovLy8kJSXB3d0dKpUKcXFxGDhwoLZ1XrkMoFqasdV8b2Pn07Wdvmm64iRk2dgM2b8x4qYrXUjcaltnztjZ0zlXcxtAv+9rUVER2rZti8LCQnh6egqORV30qVvoONtX3SI0H9UthuezZOwA09QtdjdQwpwqf02Xl5cDANq2bWvhEhFCrEFRUVGDGnVUtxBCalNf3UJX6oxAo9EgIyMD7u7u4DgOANCrVy+cOXNGu03lskwmQ0hICNLT043S56Y2Nd/b2Pl0badvWl1xqm3ZWmNnjLjpShcSt9rWmTN29nTOVV2nb9wYYygqKkJgYKBRniGtT90C0HHWtc4W6xah+ahuMTyfpWJnqrqFrtQZAc/zCA4OrrZOJBJVO1A1lz08PExWedR8L2Pn07Wdvmn1xam2PNYWO2PETVe6kLjVts6csbOnc662dfrEzRi3XSsZUrcAdJxrW2eLdYvQfFS3GJ7P0rEzdt1id5MPW4t58+bpXDbnexs7n67t9E2rL07mjJuh72eMuOlKFxK32tbROWd4nMx93umDjrOwNHuoW4Tmo7rF8Hz2Fju6/WpmlVMUGGvKg8aEYmc4ip1hbC1utlZea0FxMxzFzjCmihtdqTMzR0dHLFq0CI6OjpYuis2h2BmOYmcYW4ubrZXXWlDcDEexM4yp4kZX6gghhBBC7ABdqSOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQPUqCOEEEIIsQNiSxfAHmg0GmRkZMDd3R0cx1m6OIQQC2GMoaioCIGBgeD5hv9mprqFEAIIr1uoUWcEGRkZCAkJsXQxCCFWIj09HcHBwQ3eD9UthJCq6qtbqFFnBO7u7gAqgu3h4QGlUomDBw9ixIgRkEgk1ZYBVEsztprvbex8urbTN01XnIQsG5sh+zdG3HSlC4lbbevMGTt7OudqbgPo932VyWQICQnR1gkNpU/dQsfZvuoWofmobjE8nyVjB5imbqFGnRFU3hZxdnaGs7MzxGIxXFxc4OzsDIlEUm0ZQLU0Y6v53sbOp2s7fdN0xUnIsrEZsn9jxE1XupC41bbOnLGzp3Ou5jaAft9XpVIJAEa7VapP3ULH2b7qFqH5qG4xPJ8lYweYpm7hGGOs3r2RWq1evRqrV6+GWq1GUlIStmzZAhcXF0sXixBiISUlJZg8eTKkUik8PDwM3g/VLYSQqgTXLYw0mFQqZQBYbm4uKy8vZ8XFxWz37t2suLj4geWaacZ+Gbp/ofl0badvmq44CVm2htgZI2660oXEzdKxs6dzruY6fT9bbm4uA8CkUqnZ6xY6zvZVtwjNR3WLac45U8fOVHUL3X41IolEUu0yam3LdaWZuizGzqdrO33ThMTN2mNnjLjpShcSt9rWmTN29nTOVa4zpIymYE3fEXs6zrZQtwjNR3WL4fksETtDyigEzVNHiBFlSkuRnl9i6WIQQghphOhKHSFGUFSmwltbLuDvq9kAgJ6h3vh8Qhe0bOJq4ZIRQghpLKhRZ0RKpVL7qlyu7W/Nfxu7DIbsX2g+Xdvpm1ZfnITE0ZgM2b9SqYSGAS9uicfJ2wUAAJ4DzqYWYPy3x7DpuZ5o6+de777rShcSt9rWmTN29nTO1beuPqY8N4XULXSca19ni3WL0HxUtxiez9KxE1JGfbej0a8NQCPUCAAcy+Kw/ZYIDjzD/A5qeEiAH66LcKeYg5cDw4JOang4WLqUxBxo9CshxBRo9KsZ0ehXw9LsYYRabqGMdVi4j4VGRbPvj9zQrs+RFrPIzw6z0KhoNm71UVYgK6IRajZwztVcR6Nf7fM420LdIjQfjX41zTln6tjR6FcbQKNfDUuz5RFqey+lQa7iEOzlhOf6t4JYVDH2qIlEgh9n9MKY1cdwLq0Qyw8m4yExjVBrSD4a/Wod3xF7Os7WXLfom4/qFsPz0ehXQgjUGoafjqUCAGYNaKFt0FVq3dQNXz3TDRwHbD1zByey6IHshBBCTIcadYQY6NStPNwpKIWziGF896BatxkS3gxvjAgHAOy4zSM+vdCMJSSEENKYUKOOEAPtir8LAOjmy+AkEdW53YuRrTEiohnUjMP8rQnILiozVxEJIYQ0ItSnzohoSpPGM+1AmVKNA5czAQA9m2rqzbNsVDgu3s7CvSIFZm84iw0zesDV8b+vH007YNh2NKUJHWd7q1v0zWfpaTns9ZzTlU5Tmtgpmnag8YrP47A+SQQfR4b3u6nBC+gul10K/O+SCCVqDm08NPi/9hpI6Fq5XaEpTQghpkBTmpgRTWliWJotTzvw/LpTLDQqmi3bd0mvuJ1IymQR7//BQqOi2ZQfTrD8ohKTD503dezs6ZyruY6mNLHP42zNdYu++WhKE9Occ6aOHU1pYgNoShPD0mxt2oH84nIcScoFAIzpFoQbZ5MFl6lHC1/8NKMXnlt3Bkdv5uHZdWfx8/Re8HaW6HxvmnaApjSxlu+IPR1na6tbGpKPpjQxPB9NaUJII7b/UiZUGoYOgR5o08xN7/wPtfLFthcegq+rAy7fleGxr+IQdyPXBCUlhBDSmFCjjhA97b4/6nVst9qnMRGiS4gXfn+xH9r5uyNXXo7nN57HtmQeecXlxiomIYSQRoYadYToIS2vBOdSC8BzwKgugQ3aV6ivK3bP649pfUMBACeyeQxfeRRfHryOPLnCGMUlhBDSiFCjjhA97LlQcZWuf1gT+Hk4NXh/ThIRlo7uiG2zeiHIhaGoTIWvD99Ev08P46Ut5/H31Wwo1A1+G0IIIY0ADZQgRCDGGHbdb9SN6Wr4rdfa9Aj1xhud1RC36IEfj6Yg4Y4U0RczEX0xEzwnwtbMU+ge6oOwZm4I9XZCdikgLVXCRyQGX2M+FY2GoVihgqwcSMsvgULNobC4DFcKOKgSMlGqYpArVJCXqSBXqCArU2r/LVeoUFRW8VIo1eB5DqL7LzHPwdVRDE9nCbycJfB0kcDfwwkBHo7IKOQQkVeMVs08IRIyvwshhBCjo0YdIQJduivFrZxiOEl4jOzob/T98xzwSAc/PNElCJfuSrH3QgYOXMpEhrQM8elSxKdLq2wtxrILMeA5QCziIeIAjVqEqLN/o0yp0W6Dc0er5BEB1y4ZvdyV+/726jE4SXiE+3sgIsADHQI90KuFD9o0c3ug4UkIIcT4qFFHiECVjwUbHuEPN0fTfXU4jkPnYC90DvbCWyPCsHnXH3Bu0QU3sktwM0eO2zlyZMsqrsBpGFCuqmzEcYBGU21fzhIero4SuDuKoCorRrCfL9ydJPdfYrg5iiv+Vvm3u5MEbo5iOElE0DAGtabipVJXXOGTlpajsESJghIlMqWlSMsrxrX0HBSqRChTapCQXoiEKs+49XaRoHdLH/Rr3QRD2zdDsDdNoksIIaZAjTojoseE2e+jfJRqDfZeyAAAjOrkp3eZDH0cjUqlgq8TMLxTM+08RUqlEocOHcLgIUNRrAJUag0U5UrEHT2KgQMGwMPFEWKOIS72MEaOGA6JRKLNM3x4F6PPJVW574eHDkFmkRJXM4tw7V4REu5KEZ9WiIISJf66koW/rmRh0d4raOfvjqHtmmJoW1+dMakvNoak0WPCGlYGQ/ZNdQs9JsxQ9JiwB9+zPvSYsAagR/k0HlcKOHx/TQQ3CcPS7mqIaIiRICoNkF4M3JRxuFrA41YRwPDfrdgAF4beTTXo0YTB08GCBTUSekwYIcQU6DFhZkSPCTMszZYe5TN30xkWGhXNFu2+aHOPozF17PTZ970COfv1VAqbtf40C3t3PwuNimahUdGs5dvRbNpPJ9mhy3dZWZnCYudczXX0mDCqW6w9dlS3mOacM3Xs6DFhNoAeE2ZYmrU/ykdaqsTf13IAAE/1bC74cTFC9i003V4e5ePnJcHTvV3xdO9Q5MpK8Nm2v5Gs8sW5tEIcScrFkaRctGziimcfCsVTPYPh4SSs7PSYMOs6zg3J15jqFmPma+x1S0Py0WPCCGlEDlzKRLlKg3A/d3QINPyWGqnO01mC/n4M22b3RswbkZg5oCXcncS4nVuMpdGJ6PvxP/jkj6vIpYmYCSFEEGrUEVKP38/fAQCM6x4EjqOpOUyhZRNXvP9EBE6+MxTLxnZEWz83FJersfbILUR+GYffbvPIKCy1dDEJIcSqUaOOEB3S8kpwJqXisWBjGvCsVyKMq6MYU/qE4q9XB+Gn6T3RNcQLCpUGcfd4DP3fUSzcdQnZsjJLF5MQQqwSNeoI0eHXs2kAjPdYMCIMx3EY2t4Pu17shw0zeqCNhwYqDcMvp9Iw6PMYfPbnNUhLTTP9BCGE2Cpq1BFSB6Vag+1nK269PtO7uYVL0zhxHId+rX3xUgcNfpnZE92be6FMqcGa2GQM+iwGPxy9DaWm/v0QQkhjQI06Qurwz9Us5BQp0MTNEcMj/CxdnEavdwsf7JzbD98/2wNtmrlBWqrEZ3/dwCcXRDiUmA1GU24SQho5atQRUodfTlXcep3QMxgSmm3YKnAchxEd/PHnq4Pw+VOd4efuiDwFhxe3XsCzP51GUlaRpYtICCEWQ/9TEVKL9PwSxN3IBQA804tuvVobEc9hQs8Q/PVKfwwP0kAi4nD0Zi4eXRWHxXuvUH87QkijRI06Qmqx9XTFVbqBbZqguS89nslauTqK8URzDf58uT9GRPhBrWFYfzwFQ788guiLmaA7soSQxoQadfelp6eje/fucHJygkqlsnRxiAWVKdXaRt2UPnSVzhY093HB99N6YvPMPmjd1BW5cgVe23EJ313lkZZfYuniEUKIWVCj7r6mTZvi8OHDeOihhyxdFGJhuy9koqBEiWBvZwyP8Ld0cYgeBrRpggOvDMTrw9vCQczjmpTHY18fx5rYm1CqaZgsIcS+UaPuPicnJ3h5eVm6GMTCGAPWn0gFAMzo1wIinp4gYWscxSK8PLQNouf1RRsPDRQqDT778zoe/yoOF9ILLV08QggxGbts1C1atAgRERHgeR7btm2rlpaTk4PHH38cLi4uCA8Pxz///GOhUhJrdK2QQ3JOMVwdRHi6V4ili0MaoGUTV8yL0ODz8R3h4+qApCw5Jv5wGntSeZQp1ZYuHiGEGJ1dNuratGmDVatWoXfv3g+kzZs3D4GBgcjNzcXy5csxYcIEFBQUWKCUxBrFZlZcmXu6Vwg8nCQWLg1pKI4DxnQNxD+vD8bYbkHQMOBwBo/Ra07gXCp97wkh9kVs6QKYwtSpUwEAy5Ytq7ZeLpdjz549SElJgYuLC8aMGYMVK1Zg3759mDZtmuD9KxQKKBQK7bJMJgMAKJVK7atyuba/Nf9tTLW9lzHz6dpO37T64iQkjsaUeLcA16Q8OABTewcLeh9jxE1XupC41bbOnLGzhXPOzUGCz8Z1wNC2Pnj394u4lVuCp747jml9QtBRIzy+Qj+ToRpatzT241zXOkvXLaaMHdUthuezdOyElFHf7Thmx9OwR0ZGYs6cOZg0aRIAID4+HiNHjkR2drZ2m/nz58PFxQVLlizBE088gXPnzqF79+5YvHgxBg4cWOt+Fy9ejCVLljywfsuWLXBxoekvbNXGGzzO5fLo4qPB8+HUqd5elaiAXSk8TudU3Kho6sTwTGs1WnsYYd8lJZg8eTKkUik8PPTfIdUthJDaCK5bmB0bPHgw27p1q3b533//Za1bt662zbvvvstefPFFvfZbVlbGpFKp9pWens4AsNzcXFZeXs6Ki4vZ7t27WXFx8QPLNdOM/TJ0/0Lz6dpO3zRdcRKybMxXUmYBa/l2NAuNimZnbt4za9x0pQuJm6VjZ6vn3F+X7rJeHx1koVHRrEVUNFu69xIrKilr0Pc1NzeXAWBSqdSgOqshdQsdZ+usW0wdO6pbTHPOmTp2pqpb7LJPXV3c3Ny0tzMqyWQyuLm56bUfR0dHeHh4VHsR2/btkdvQMCDCS4OIAHdLF4eYwZDwptg7tzf6NNWAAfjpWCrGf3cS17PkFisT1S2EkIZoVLdf5XI5fH19kZqaCn//ivnHBg0ahFmzZunVp67S6tWrsXr1aqjVaiQlJdEtEhuVVwZ8FC+CBhxe66hCC2rTNTqX8jlsS+YhV3EQcQyPh2gwJJBB3xltGnr7tRLVLYSQqhr17dfy8nJWWlrKBg4cyDZu3MhKS0uZWq1mjDH21FNPsRdeeIGVlJSwPXv2MG9vb5afn9+g95NKpXT71YA0a7lFEvXbBRYaFc2eWXtM7/3bwmV+U8bOns65zPwiNnPdKRYaVXEbftzqOHb9Tq5Zb782pG6h42x9dYs5Ykd1i2nOOVPHjm6/6mH27NlwdnZGXFwcpk2bBmdnZ/z7778AgDVr1iA9PR2+vr544403sH37dnh7e1u4xMRSUvNL8Nu5uwCAOQNbWLYwxKJ83RyxZnJXfDiqHRx5hnNpUoxZewYnszkw+72hQQixI3Z9+9XU6BaJ7duQxON8Ho92nhrMjaARr6RCXhmw+aYIt4oq7r8+31aNLr71V5V0+5UQYgqN+varudHtV8PSLH2L5EJqrvZWW3xKrkH7t4XL/KaInb2ec1XXyYrk7KuDiWzkx3tZkVwu6DPR7VfbOM50+5XqFkvHzlS3X+1y8mFChPjy0A0AwOOd/NEh0MNkE48S2yTiOczsH4rg4iTwHD0DmBBi/ej2awPQLRLbdUPK4ZtEEXiO4d0uajR1tnSJiD2g26+EEFOg269mRLdfDUuz1C2SktIyNnJFLAuNimbv7kxoUOxs4TK/MWNn7+dczXWmukViirqFjrPl6xZLxI7qFtOcc6aOHd1+tQESiQQSiUTncl1ppi6LsfPp2k7fNCFxM2bstp1NwbUsOTydJVgwsl295RHCGHHTlS4kbrWtM3bsdLGnc65ynSFlNAVzf0f0KYux89ly3VIfU8aO6hbD81kidoaUUQhq1BmR0Idu1/y3sctgyP4by0O3C0rK8cXB6wCAV4e2hrsD16D928qDo+31odvGPufqW1cfU36vhdQtdJxrX2fJuDVk/0LyUd1ieD5Lx05IGfXdzux96kpLS/HBBx9gx44dyM/Ph0wmw19//YWrV6/i1VdfNWdRGoz6vdie7bd4HMviEeDC8GZnNUTU/50YEfWpI4SYgtX2qZsxYwabMmUKu3TpEvPy8mKMMZaRkcHCw8PNXRSjoT51hqWZu99LfEoua/l2xRQmcdfvGSV2ttB3wxixayznXM111KfOPo8z9amjusXSsbObPnX79+9Heno6HB0dwd2fJiAgIACZmZnmLorRUZ86w9LM0e9FpdZg4Z5EaBjwROcADGjrV+e21KfOMPZ0zlWuM6SMpmBNfcPs6ThTnzqqW4RsZ0t96sz+mDAvLy/k5ORUW3f79m0EBgaauyikEfkh7jauZMjg6SzBolEdLF0cQgghxOjMfqXulVdewahRo7Bw4UKo1WpER0fjo48+srn+dLWhgRLW2Zk5Ja8YK/9OAgC880hbeDnxepe/LrbSIddeOzPTQAk6zjRQguoWQ/NZOnZCyqjvdhaZfHjHjh34+eefkZaWhqCgIMycORMTJ040dzEajDozWz8NA1YninBTxiHcU4O57TWghwMQU6GBEoQQU7DagRL2iAZKGJZmjs7Ma2NvsNCoaNbuvQMsOavQ6LGzhQ65DTkvGts5V3MdDZSwz+NMAyWobrF07Gx6oMRnn30maLu33nrLxCUxLRooYViaqTozX7snwxcHbwAA3n08Aq2aedabR5/9G5KHOjMbno8GSlhHh397Os40UILqFiHb2dJACbM06q5evar9d0lJCXbt2oU+ffogJCQE6enpOH36NMaNG2eOopBGokypxitbL6BcrcHQds0wtU9zSxeJEEIIMSmzNOrWrVun/ff48eOxY8cOjB49Wrtu79692LhxozmKQhqJz/68jutZRWji5oDlT3XWTp9DCCGE2Cuzj379+++/8euvv1Zb99hjj+HZZ581d1GMjka/WscItYOJWfj52G0AwCdjO8DTsfbRrvqUv6F5LD3Kyl5HqNHoVzrONPqV6hZD81k6dkLKqO92Zh/92r9/fwwfPhzvvfcexGIxVCoVPv74Y/z55584fvy4OYvSYDRCzfpklwJfXhKhTM0hMkCDsS00li4SaURo9CshxBSsdvRrcnIy69OnD3Nzc2OtWrVibm5urHfv3uzGjRvmLorR0OhXw9KMPUJNKi9lI1bEstCoaDZ+zVFWXFpm8tjZwiirhpwXje2cq7mORr/a53G2tdGvWflS9uelu+xYUhYrK1PotW+qWwzfjka/CtCqVSucPHkSaWlpyMzMREBAAJo3t49O7DT61bA0Y4xQY4zhg52XcT1LjiZujlg9pQdcnBzr/Rz6lr+heWiEmuH5aPSrdYzitKfjbAujX1OKgI9Wn0KOvBwA0DXEC99N7QF/Tye99k11i+Hb2dLoV7M/Jiw7OxvZ2dlwcnJCy5Yt4eTkpF1HiKG+PnwTuy9kQMRz+PqZbvDzcKo/EyGEWLF7sjJ8f02EHHk5mrk7wsVBhAvphZj840nkF5dbunjECpn9Sp2/vz84jgO735Wv6qhEtVpt7uIQO7A3IQMrDlU8Bmzp6A7o29rXwiUihJCG+/SPJBSrOHQIdMeOOf2QJy/HpO9P4lZOMV7ffgE/T+9l6SISK2P2K3UajQZqtRoajQYajQZ3797F3LlzsX79enMXhdiBc6kFeGNHAgBg1oCWmNIn1MIlIoSQhruQXoj9l++BA8PHYzrAxUGMEB8X/Di9JxzFPGKv5+CHuFuWLiaxMnpfqTt9+nSdab1799a7AP7+/lixYgVatWpl89Oa0JQm5p124Ea2HLM2nEG5SoOh7ZrijeFhDYorTWliGHs65+pbVx+a0sTwfNZUtxibIfv/NuYGAKBXU4Y2TZy1ecOaOOP9x9vhvT2J+OLgdfQKcde5b6pbDN+uUUxp0rJly2rLOTk5KC8vR3BwMG7dMuxXQ1xcHCZMmIB79+4ZlN9SaNoBy8ktA1ZdFkGm5NDcleGlDmo4iixdKtLY0ZQmxBhySoFlF0Rg4PB2FxUCahx6xoAfr/O4XMDD35nhjc5qSMx+342Yk9mmNFGpVOzDDz9ky5cvF7R9u3btWPv27bWv0NBQ5ubmxn766aeGFsViaEoTw9IMnXbgVmY+6//J3yw0KpoN/zKWZRcaJ5Y0pQmdczXX0ZQm9nmcrX1Kkw/3XWahUdFs6g/H68yXmV/Eui89yEKjotn0lXupbjHyOWfq2FntlCYikQjvvPMO/P398dZbb9W7/XfffVdt2dXVFW3btm3Qr1prQVOaGJamz1D5QgUwc/MF3CksQ6ivCzbP6oOmRh7pSlOaGMaezrnKdYaU0RSsaWoOezrO1jiliUqtwZ6ETADApF4hUKVk15rP31uCzyd0xvPrzyI2k8eZNBkGt/PX+72pbqEpTarRaDTYunUrXF1dBW1/5swZDB48WPvq2bMnPDw8sGLFioYWhdi59IISfHVFhFu5JQj0dMLmmX3QjKYuIYTYkbgbucgpUsDH1QGRbZvq3Pbhdn54plcwAOCt3y9DWmKaPoHEdujdqHN2doaLi4v25ejoiHffffeBK3B1Wbp0aa3rly1bpm9RSCOSnCPH5B/PIE/BobmPM7bP6YsQH+pjRAixLzvOpQMARncNhIO4/v+i336kLZo6MWTJFFi4+5J2ujDSOOl9+/XatWvVll1dXdGkSZN6823fvh0AoFKpsGPHjmonXkpKCnx8fPQtCmkk4tMKMXfLBeQVl8PPmWHLzF4I9qYGHSHEvhQUl+PvxIqJ+Cf0CBGUx8VBjGfD1FiVKEH0xUwMa++HMd2CTFlMYsX0btSFhho2D9i3334LACgvL8eaNWu06zmOQ7NmzWieOlKr87kctq47i3KVBh0C3TE5sICeFkEIsUv7LmagXK1BRIAHIgI9BE9jEeoOzItsha8OJ+P9PZfRq6UPgrycTVxaYo3M9kSJmJgYAMBHH32E9957z1xvS2yURsOwJvYWNtwQAdBgWHs/fDG+A478c9DSRSOEEJPYcfYOAOCpHsF65507qCXibuYhPq0Qr/96Ab/M6gOxiOY5aWzMcsRzc3O1/37hhRe0z3qt+SIEAKSlSryw6Sz+989NAMBz/UKx9tkecHU0+1PtCCHELK7dk+HSXSkkIs6g26diEY//Pd0VLg4inLqdj4W7LlP/ukbILP9LtmzZEkVFRQAefPZrJY7j6NmvBOlyYMyaE7hTWAYHMY+xzZV499FwiHgOGjo9CCF2aue5iqt0D7drBh9XB4P20aKJK1ZN6ob/23QWv55NR4CXE+YNbll/RmI3DGrUpaam4rfffkNGRgYCAwMxbty4B540UVVlgw6omALFXtFjwgx/lI9KrcG3R5Kx+rIIalaGYG9nrHyqA+5ePmnVj/KxlcfR2OujfOgxYXSc7eExYUq1Brvi7wIAxnYJ0KtcNbeJbOODRU+0x6J9V7Hy7xsQQYPmteyD6hbL18tCyqjvdno/Jiw6OhpTpkzB448/jtDQUKSlpWH//v3YtGkTRo0apc+ubB49ysc47pUAv9wUIa2YAwB09tHgmdYauNDdVmJj6DFhxBCX8zn8cF0ENwnD0u5qGKMr3B/pPP68U7Gjx0LUGBlMt2JtmckeE9axY0cWExNTbd2RI0dYRESEoPxpaWls1qxZrFevXtUeF9a+fXt9i2I16DFhhqXlFMjY7K/2srB397PQqGjW8YM/2cIf9jC5XG72x9EYun9beByNqWNnS+ccPSaMjrO54yZk/7M3nGahUdFsyZ5LesdO1zb/O3iNhUZFs9CoaPb2jnhWUlqmV9wsHTt6TNh/L5M9Juzu3bvo379/tXV9+/ZFRkaGoPwTJ05EmzZtsGTJErv75UmPCROWJhaL8cfle/gwOhGZUh4AQ2R4U3z4ZHucP3oYDg4OVv8oH0Pz0GPCDM9Hjwmzjsdd2dNxtoa6Jb+4HDHXcwAAT/duLvj7L2SbV4eHw1nC49M/r2Pr2btIL1Tg62e6wbtKnz2qW+zrMWGCG3V37txBcHAw+vTpg8WLF2Px4sWQSCRQKpVYsmQJ+vTpI2g/ly9fxtGjR8HzNNS6MTqenIeVh5MRn1YIAPBxZPhwXFc81jkIKpXKsoUjhBAz23vhLpRqhk5Bnmjnb/xnoD/fvwVybl/FltsOOHozF4+uisOKp7ugV6in0d+LWJ7gllVERAQAYO3atYiNjYWPjw9at24NHx8fHD58GN9//72g/TzyyCM4efKkYaUlNokxhpO38vHNFR7T159DfFohnCQ8XopshXe6qDEiwg8cx1m6mIQQYnY7zhk+N51QnXwYfp3dG62auuKerAxTfjqFZQeuoYxmFLA7gq/UsfvjKZo3b45jx44hPT1dO/o1JETY40yAimfHPvLIIxgxYgSaNWtWLa3qkyaI7VOo1NiXkImfj95GYqYMAA+JiMOUPqF4cUhreDuJcOBAkqWLSQghFpGYIcOVDBkkIg5Pdgk06Xu183dH9PwB+DD6KraeTsP6E2nwlIjg2OIeRncLph/WdkKvPnXp6enV5pcLCAgAYwxpaWkAKhp89WnVqhUWLFigZzGJLbmRJceei/ew8/xd5MoVAAAnCY+ePip8/GwkmjdxB2C66QMIIcQWbD+bDgAY1t6vWj83U3FxEOOTcZ0wsoMfPthzGWn5pXh1+0X8fDwVrw1vi/4tvUxeBmJaght1xcXFCA8Pr3OGao7jUFJSUu9+Fi1aJLx0xGbcKSjBH5cysPGiCOknjmvX+3k4YlrfFpjQPQAnYv9GgCc9t5UQQkrKVdh5vuLW66Te9V8QMabI8GY48FI/vPnzQRzJdsDFO1I8t+4MugR7opszh+FqDUw4XoSYkOBGnaura7VJhA312Wef1bre0dERwcHBGDp0KLy8vBr8PsS01BqGxPRC/HMtG4cSs3A1U3Y/hYOY5zCkXTOM7x6Mh9s1g4OYp6tyhBBSxb6EDBSVqRDq64KBYU3M/v6OEhEeCWFYNGUAfjqeho0nUpFwR4oEiPDHijhM6ROKcd2D4OdGrTtbIrhRZ6z77efPn8euXbvQp08fBAcH486dOzh16hRGjRqFjIwMzJw5E7///jsefvhho7wfMY5ylQbX7slw/GYO9l3jsfB8DOSK/0ar8hzQI9QbIcjFW5OGwt/L1YKlJYQQ68UYw6aTqQCAKX2ag+ct15/N180RCx+PwOxBrbDx2G2sP5aMLJkCKw4lYcWhJPQM9UIrEYeHisvh50UNPGun90CJhlKpVNi5cyeeeOIJ7br9+/dj/fr1OH78OH755Re8/vrruHDhglHej+hPWqrErRw5EjNluHxXikt3pbh+rwhKdeU5wANQwd1RjH5hvhge4Y+H2zWDuwOHAwcOwNcMfUMIIcRWJdyR4vJdGRzEPCb0ED7Q0JSauTvhlaFhaFmaBBbcFTvjM3Hydh7OphbiLETYsTwW3UK8MLhNE4iLjdcmIMYluFFnjFuvAHDo0CH8+uuv1daNHDkSkydPBgA888wzmDt3rlHei9ROpdYgu0iBTGkZMqWlyCgsxc2sIpxLEmHpxVjkFZfXms/DSYweoV7wKMvCjEf7o3NzH4iq/MKkW6yEEFK/zfev0j3RKcAsAyT0IeaBx7oG4qleociUlmLX+XRsOZqEO8UczqcV4nxaIQAxfk4+gj6tfNCjuRcUxRVdcug6nuWZ/emaERER+Pjjj/HOO+9ALBZDrVbj008/Rfv27QFUjLC1VJ+6119/HWfOnEG3bt3w1VdfWaQM+mCMQaHSoFihQkm5GsXlKuQXlSEhj4P87B1IFWoUliiRX1yOwpJy5MrLcU9ahuyiMmhq/ZHFAaho0DVzd0RbP3d0DPJEp/uvEB9nqFQqHDhwAB2DPKo16AghhNSvoLgc+xIqnsA0tW+ohUujW4CnM2YPaIkg2VV06/8wjiYX4O/Eezh6Ixt5xeU4cOkeDly6B0CMNddj0DHIA52CPLX/b7TwdbXoreXGyOyNug0bNmDy5Mn4/PPP0axZM2RnZyM8PBxbtmwBAGRlZWHlypXmLhbOnz8PuVyOuLg4zJ07F2fOnEGvXr2M/j7Hk/PwbyaHzGMpUDMO5SoNlOqKV7lKg3I1067T/lVroFBpUHq/4VaiuP+3XA11ra0zEZCUqLMcEhEHPw8nBHg6IcDTGc29nSC9cwPjhvVHmL8H3J3oNxchhBjb5pOpUKg06BjkgW4hXpYujmABnk6Y3Kc5JnQPwN7oAwjq1Bfn0mU4dSsXp5JzIVeocPJWPk7eytfmcXMUI6yZ23+vphV/Q3xc6KKAiZi9Ude2bVucPXsWKSkpyMrKgr+/P0JD//u10rt3b/Tu3dvcxcKJEycwbNgwAMCwYcNw8uRJkzTqdl/IwK4UEZBi3El3nSUiuDqK4OogBldejJZBTeHj6gQfVwm8XBzg4+oAbxeHikaclxOauDpW+wWlVCpx4EASOgZ5mPTZh4QQ0lgplGpsOJECAJg9sJXNTvgr5isGxj0U1gwvDAjFvv0HENZ9IK5lFePS/X7YVzNlkCtUuJBeiAvphdXyO4h4BHk7I1j7ctH+DfRygq+rIxzE9ChRQ5i9UVepWbNmEIlEek9eLMSiRYuwY8cOXLt2DVu2bMGkSZO0aTk5OZgxYwZiYmIQEhKCNWvWYOjQoSgsLETr1q0BAJ6enrhy5YpRylJTl2BP3E6/i9DgIDhKRHAQ85CIeDiIeTiI/vv3f+s47bKLQ0WjzdVRXPHv+39dHMTaXz0VjbMDeOyx7tQ4I4QQK7I7IRO58nIEeTnjsU4Bli6O0Yg4oH2AOzo398HTvSoGfqjUGiTnFONmtrzilVPx91aOHAqVBrdzi3E7t7jOfXq7SNDEzQGcgsc/xZfg5+kEH1dHeDiL4eEkgaezBB7OEng4ie//lVBDEBZo1F26dAnTpk3DxYsXAfw3VYqDg4OgyYuFaNOmDVatWoX333//gbR58+YhMDAQubm5OHjwICZMmIDk5GR4eXlBJquYa00mk5msX9+UPs3hnXcZjz3WiRpdhBDSSGgY8POxFADA8wNaQiKy7waIWMQj3N8d4f7u1darNQwZhaW4U1CKOwUluFNQivT7f+/klyC7SAGVhqGgRImCEiUAHkkXM4W9J8/BWSKCo4QHU4rw9c1jcHEUw0ksgpODCI7iikdVinkeYp4DxzFk3uFxet9VOEhEEPMcxKKKNBHPAYzhxh0OqUduQSQSgeMADhw0GjWu3+WQcTQF4vvrNRoNrmZyyD6RWrEOFesS73EoOJUGkVgMjVqNK1kcpGcqniRyOYtDd1kZQnyN1xYwe6Nuzpw5GD16NE6cOIGAgABkZmbigw8+0F4lM4apU6cCAJYtW1ZtvVwux549e5CSkgIXFxeMGTMGK1aswL59+9C3b1+sXbsWTz/9NP7++2/MmDGjzv0rFAooFArtcmVjUKlUal+Vy7X9rflvY6rtvYyZT9d2+qbVFychcTQmQ/ZvjLjpShcSt9rWmTN29nTO1beuPg2Nb0PrFjrOta+zhrolsYDDrdwSuDuJMa6rv6D3ElIuW6xb/N0l8HeXoGdzjwfSNBqGwlIlcuUKZBaWIPbkefi3aIv8EhUKSpUoKlVCVqaCrPJvmUo7Z6pKw1CkUKFIAQAc8nLqvhL4Hx7Hs9N1pIuwP/1mrev3ptXsRiXCrpTrD6z77fa1asvbb13V/vuRezL4e9T/pCWh8eWYmSeb8fLyQn5+Pnieh7e3NwoKClBeXo5WrVrhzp07Rn2vyMhIzJkzR3v7NT4+HiNHjkR2drZ2m/nz58PFxQXLly/Hq6++inPnzqFLly745ptv6tzv4sWLsWTJkgfWb9myBS4uLkb9DIQQ21FSUoLJkydDKpXCw+PB/7DqQ3WLfWIMWHVFhNtFHIYGavBkqMbSRbIrGgaUqYFyNVCuAZT3X+Ua7r/l+2kaBmju51Gzyr+c9t+V69Ws4rgBAEP1f2v/svtplesEbFN1OwB4NESDIAFz9QutW8x+pc7LywuFhYXw8fFBUFAQEhIS4OPjA7lcbvL3lsvlDwTDw8MDhYWFACB41O0777yD119/Xbssk8kQEhKCIUOGwMPDAyqVCjExMRgyZAjEYnG1ZQDV0oyt5nsbO5+u7fRN0xUnIcvGZsj+jRE3XelC4lbbOnPGzp7OuZrbAPp9XyuvrBmqIXULHWfrrVvibuTg9snLcBTz+GBiPzR1dxSUT0i5qG7RlS9S4DlX+3amjB1gmrrF7FfqPvroI3To0AFjx47FDz/8gAULFoDnecyePRuff/65Ud9L3yt1+lq9ejVWr14NtVqNpKQk+jVNSCPX0Ct1lahusR9Vr9INDtBgXAu6Skf0J7huYRaWkpLCLl++bJJ9Dx48mG3dulW7XFRUxBwcHFhmZqZ23cCBA9mGDRsa9D5SqZQBYLm5uay8vJwVFxez3bt3s+Li4geWa6YZ+2Xo/oXm07Wdvmm64iRk2RpiZ4y46UoXEjdLx86ezrma6/T9bLm5uQwAk0qlDapTDKlb6DhbZ91yODGThUZFs7C397GUe/lGjx3VLaY550wdO1PVLWa7/RoREVHvNomJuifMFUqpVEKtVkOj0UCpVKKsrAwODg5wc3PDk08+iUWLFmHlypU4dOgQLl++jFGjRhnlfQkhhJBKjDF8HZMMAOjnzwTfdiXEUGa7/ers7IzmzZtjypQpGDRoUK2TLg4ePNgo7zVjxgxs2LCh2rqYmBhERkYiJycH06dPR2xsLIKDg7FmzRrtpMP6olskhJCq6PYrqSqxgMPaayJIOIb3u6vhaV2PeSU2xOpuv8pkMrZ+/Xo2fPhw1rJlS/bWW2+xhIQEc729SdHtV8PSrOkWiaH7t4XL/KaOnT2dczXX0e1X+zzO5ohbaZmCDf8yloVGRbNFuxJMFjuqW0xzzpk6djZ/+9Xd3R3Tp0/H9OnTkZWVhW3btuGFF15AcXExfv31V0G3Z62dRCKpNqFwbct1pZm6LMbOp2s7fdOExM3aY2eMuOlKFxK32taZM3b2dM5VrjOkjKZgTd8RezrOpozbrrPpSMqWw8NJjHlDwnA89rZJY0d1i+H5LBE7Q8oohEUeE+bo6AhnZ2c4OTkhLy8PGo19jAaiyYdtf4JQffdPkw/b1zlX37r6mPLcpMmHbaduKS1X48u/KiahfTGyFVwl1csllL1OPiyUJc85XenGip2QMuq7ndn61CkUCuzduxebN29GfHw8xowZg8mTJ+Ohhx4yx9ubBPV7IYRURX3qCAAcvMNhf7oIPo4M73ZVQ2LfTwQjZmB1feo8PT1ZeHg4W7x4MTt27Bg7derUAy9bRX3qDEuzhn4vDY2dLfTdMHXs7Omcq7mO+tTZ53E2ZdzScmUs4v0/WGhUNPvtTKrJY0d1i2nOOVPHzub71Hl5eUGhUGD9+vXYsGEDWI0LhBzH4datW+YqjklQnzrD0qypv5Ch+7fmvhuNod8L9amj42wtdcvyvy6huFyN7s29MLZ7CHj+v5keqE+dYahPnRX2qUtJSTHXW1kM9amzrX4vQsporDyW7rthr/1eqE8dHWdrqluOJ+ch+mImeA744PF2UKtVUKtNGzuqWwzPZ+nYCSmjvtuZ/TFh9oT6vRBCqqI+dY2XSgN8dlGErFIOA/01eKqlfQwAJNbB6vrU2TPqU2dYGvWpM0/fDVPHzp7OuZrrqE+dfR5nU8Tt67+vs9CoaNZt6V8sV1r/99hYsaO6xTTnnKljZ/N96hoD6lNnWJo19RcydP/W3HejMfR7oT51dJwtWbfcypFrHwf27mMR8PWo/aoq9akzDPWpE1630EBrQgghxEAaDUPUzotQqDQY2KYJxncPsnSRSCNGV+qMiAZK2EZn5rrQQAnD2NM5V9+6+tBACcPz2WrdsuFEKs6kFMDVQYQPn2wPlUqlV/l1oYESNFBC3+1ooEQDUGdmQkhVNFCiccktA5YniFCu4TChpRoD/Om/U2IaNFDCjGighGFpNFDCPB1yTR07ezrnaq6jgRL2eZyNEbeS0jI2dvVRFhoVzZ7+7hgrK1NYJHZUt5jmnDN17GighA2ggRKGpVlTJ3BD92/NHXIbQ2dmGihBx9ncdcvXsUk4n1YId0cxvpjQFY6ODvXmoYEShqGBEjRQghBCCDGJ07fz8c3hGwCAZeM6IcSHbo0T60BX6oyIBkpYb2dmIWighGHs6Zyrb119aKCE4flspW6RlirxyrZ4aBgwtlsgHo1o2uDvf0PyUd1ieD5Lx05IGfXdjgZKNAB1ZiaEVEUDJeybhgHfX+NxtZBHEyeGNzur4SSydKlIY0ADJcyIBkoYlkYDJczTIdfUsbOnc67mOhooYZ/H2dC4ffZHIguNimZtFx5g8Sm5VhE7qltMc86ZOnY0UMIG0EAJw9KsqRO4ofu35g65jaEzMw2UoONs6rrl4JV7WB17CwDw6fhO6BrqW+9n0af8Dc1HdYvh+WigBCGEENJI3MyWY8H2BADAjH4tMLZbsIVLREjtqFFHCCGE1CGnSIHn1p9GkUKF3i18sPDx9pYuEiF1okYdIYQQUovScjVmbTyL9PxSNPdxwZqp3SER0X+bxHrR2UkIIYTUoNYwvLwtHgnphfBykWD9c73QxM3R0sUiRCcaKGFENE+d9cwlZQiap84w9nTO1beuPjRPneH5rKluYYzh/b2JOJSYBQcxj28nd0WIl6PBcaV56gxD89Q9+J71oXnqGoDmkiKEVEXz1Nk+xoDfU3j8e48HB4bpbTXo5kv/TRLLonnqzIjmqTMsjeapM898SKaOnT2dczXX0Tx19nmc61qWy+VsWfRlFhoVzUKjotnWk7etPnZUt5jmnDN17GieOhtA89QZlmZNc3AZun9rng+pMcwlRfPU0XFu6PeBMeCbI6n4Pi4FAPDhmI6Y1Ce03vLqg+apMwzNUye8bqFGHSGEkEZNo2HYeZtHXNZtAMB7j7fHsw8Zt0FHiDlQo44QQkijpVJr8NbvlxGXxYPjgA9Hd8RUatARG0WNOkIIIY1SUZkSr/8Wj9jrOeDB8Pn4zhjfs7mli0WIwahRRwghpNHJKwMm/nAaN7KL4STh8WwrJZ7sEmDpYhHSIDT5MCGEkEblTEoBvrwkwo3sYvh5OGLLzF7o6EPTlhDbR1fqCCGENAoaDcOa2Jv48mAS1BoOHQLd8dP03vB1ESE9wdKlI6ThqFFnRPRECXqihKHb0azvhm1HT5Sg4yz0OMuVwKxN5xB3Mx8A0KOJBmundYOni8gq6xah+ahuMTyfpWMnpIz6bkdPlGgAmvWdEFIVPVHCOl3M57D9Fo8iJQcJxzC+pQYPNWPgOEuXjBBh6IkSZkRPlDAsjZ4oYZ6Zy00dO3s652quoydK2PZxvptTyF765az2CRFDPj/MLqbl2UTdIjQf1S2mOedMHTt6ooQNoCdKGJZmTbPlG7p/a565vDHM+k5PlKDjXDVNrWE4lsVh8ZpTKChRgueAhwM0WDmrL9xcnLS3smyhbhGaj+oWw/PREyUIIYQQK3T6dj4W7bmMq/dEAJQI93PHsjERuHvxGBwlIksXjxCTokYdIYQQm3fxjhRfxdzCkaQcAICziOGNR9pjer+WYBo17l60cAEJMQNq1BFCCLFJjDGcSy3AD9d4XD5xCgAg4jlM6BGETiwFTz/UHGIRD6VGbeGSEmIe1KgjhBBiU8pVGhy4lImfj93GxTtSADx4DhjbLRgvDw1DoIcDDhxIsXQxCTE7atQRQgixeowBVzJk2HsxC3sT7iJXXg4AcBDz6OGjwuJJAxEe6AXAdPPNEWLtqFFHCCHEKjHGkJQlx5+XMrDtogiZJ09q05q6O2LaQ6GY0CMQp478jVZNXS1YUkKsAzXqCCGEWI1ihQoJtwtx5HoODl29h/T80vspHCQiDiMi/DG+RxAGtmkKiYinq3KEVEGNOkIIIRaTJSvDpTtSnL6di78uifD6qRioNf896MhBzKNfKx/4q7LwxqRhaOJBT9YgpC7UqCOEEGJypeVqJOWU4HZuMZLuFeFyhgyX7kqRU6SoshUHgCHY2xl9W/liaHs/DGrbBBKO4cCBA/B0Nt3EwITYA2rU3Zeeno7Ro0cjMTERcrkcYjGFhhBChCotVyNTWop70jJkSsuQKS1FhrQMKTlyJN4R4ZUT/9Saj+eAsGZu6BTkASdZOmaPjkSLptWfbUm3WAkRhlou9zVt2hSHDx/GmDFjLF0UQggxO5VagxKlGiUKNYrLVf/9LVehWKFGYbECJ+9wSPjjOgrLVCgsUaKgpBwFxeXILy6HrEylY+8cAMDbRYJWTd3QqokrOgZ5omOQJyICPODsIIJSqcSBA2kI8nI2zwcmxA5Ro+4+JycnODk5WboYhBBikINX7uGfq/dwK5XHwe0XodYASrUG5WoNylUa7b+VKgalWgNFlXWl5WooVBoB7yIC0lPrTHVxECHA0wkBns73/zohwNMRWTcSMHnUMDTzpBGqhJiSzTbqFi1ahB07duDatWvYsmULJk2apE3LycnBjBkzEBMTg5CQEKxZswZDhw61YGkJIcS0Lt2V4tezdwHwQM49g/cj4jm4Oojg6iiGS9W/DiLI87LQKbwVfN2c4OMqgZeLA7xdHODtIkEzDyd4OInBcVy1/SmVShy4lwBvF4cGfsL/b+9ug6Iqwz6A/1nkbVl2NzGSt0cEUcfAZhh10iSw8RE1a5pGnJEUsSzzdXxrKh3Fl6jog2HlFB/StAk0R8fS8Q0DjNLUkUYtTctRQAURkl12BVzc+/ngw8lFXJbDLrt7+P9mzrjn3Pd9zrXXOV3du2d3IaLOeO2kLj4+Hps2bcLq1asfaVuwYAEiIiJQV1eHI0eOID09HVeuXEFLS4vN5A8ANBoN9u/f31NhExG5xJi4fujjA1z5+xISE4Yh0N8PAb4q+PXxgZ+vCv6+Kvj1efCvfx8V/HxV8PP1gb+vCkH+vgj27wN1gC/8fVWPTMyA/5+cHTiAyWmD4efHLywQeSKvndTNmDEDAJCTk2Oz3WQy4YcffsC1a9egVqvxyiuvYOPGjdi3bx8yMzNRWlra7WO3tLSgpeW/b2wZjUYAD4pe29K23tG/7R87U0fHcuY4e/262tZZnhzJozPJ2b8z8mav3ZG8dbStJ3OnpGuus22d6W5+u1NbRvyPFs+EB6Ho7l/43xER8iZeworWx9yGVdJ59oba4ug41hb549ydO0di7Go/HyGE6Lyb50pNTcXbb78tvQP3+++/Iy0tDbW1tVKfRYsWQa1WIzc397H7aW5uxpQpU3DmzBkkJSVh7dq1SE5O7rDv2rVrsW7duke2FxQUQK3mbygR9VZ3795FRkYGDAYDtFpt5wPaYW0hoo44XFuEl0tJSRGFhYXS+s8//yzi4uJs+qxcuVLMnz/facdsbm4WBoNBWqqqqgQAUVdXJ+7duyfMZrPYu3evMJvNj6y3b3P2Inf/jo6z16+rbfby5Mi6J+TOGXmz1+5I3tydOyVdc+23dfW51dXVCQDCYDD0eG3heVZWbXF0HGuLa645V+fOVbXFa2+/Po5Go5FuWbQxGo3QaDROO0ZAQAACAgKctj8iIoC1hYi6R3G3X00mE0JDQ1FRUYH+/fsDAJ5//nnMmTMHmZmZTj325s2bsXnzZty/fx+XL1/mLRKiXq67t1/bsLYQ0cMcrS1eO6mzWCy4f/8+JkyYgDfffBPp6enw9/eHSqVCeno6+vbti7y8PBQVFSErKwtXrlzBE0884ZJYDAYD9Ho9rl69ipCQEFgsFpSUlGDcuHHw8/OzWQdg0+Zs7Y/t7HH2+nW1zV6eHFl3Njn7d0be7LU7kreOtvVk7pR0zbXvA3Ttv9fGxkYMHDgQDQ0N0Ol0DuficbpSW3ielVVbHB3H2iJ/nDtzB7iotsj64IcHmDVrlgBgs5SUlAghhKitrRWTJk0SQUFBIj4+XhQVFbk0lrbPvXDhwoULAFFVVcXawoULF6cvndUWr32nzpNYrVbcvHkTISEh0u87jRw5EqdPn5b6tK0bjUZER0ejqqqqW7dn7Gl/bGePs9evq22Py1NH656aO2fkzV67I3nraFtP5k5J19zD27qaNyEEGhsbERERAZVK1Wn/znSltgA8z/a2eWNtcXQca4v8ce7Knatqi+K+KOEOKpUKUVFRNtt8fX1tTlT7da1W67Li0f5Yzh5nr19X2zrLU0djPC13zsibvXZH8tbRtp7MnZKuuY62dSVvzrjt2kZObQF4njva5o21xdFxrC3yx7k7d86uLd1/KUkdWrBggd31njy2s8fZ69fVts7y1JN5k3s8Z+TNXrsjeetoG685+Xnq6euuK3ieHWtTQm1xdBxri/xxSssdb7/2MKPRCJ1O1+1vx/VGzJ18zJ083pY3b4vXUzBv8jF38rgqb3ynrocFBAQgOzubv0UlA3MnH3Mnj7flzdvi9RTMm3zMnTyuyhvfqSMiIiJSAL5TR0RERKQAnNQRERERKQAndUREREQKwEkdERERkQJwUueBqqqqkJSUhMDAQLS2tro7HI+3bNkyJCcnY/Hixe4OxavwOpPv2LFjGD16NMaOHYtly5a5O5wu4Xl3HGuLPLzG5OtubeGkzgM9+eSTKC4uxrPPPuvuUDxeeXk5TCYTysrKYLFYZP05md6K15l8gwYNQmlpKX755RfU1NTg/Pnz7g7JYTzvjmFtkY/XmHzdrS2c1HmgwMBA6PV6d4fhFU6cOIHx48cDAMaPH4/ffvvNzRF5D15n8kVGRkq/L+Xn5wdfX183R+Q4nnfHsLbIx2tMvu7WFk7qnCA7OxvDhg2DSqXCjh07bNpu376NF198EWq1GkOGDMFPP/3kpig9n5w8NjQ0SL/GrdPpcOfOnR6P2xPwGpSvO7krLy9HXV0dhg0b5pHx0QOsLfLx+pPPHbWFkzoniI+Px6ZNmzBq1KhH2hYsWICIiAjU1dUhNzcX6enpuHPnDmpqapCammqzTJkyxQ3Rew45edTr9TAajQAe/NmV3vrqUE7u6AG5uaupqcHixYuxZcsWj4uP9cUWa4t8rC3yuaW2CHKalJQUUVhYKK03NjYKf39/cfPmTWlbcnKy2LZtm8P7s1gsTo/T03Ulj2fOnBFvvfWWEEKIefPmiZMnT/Z4vJ5EzjXYW6+z9rqSu6amJjFu3DhRXl7ukfE5ur/edt5ZW+RjbZGvJ2sL36lzob///hs6nQ7h4eHStmeeeQZ//vmn3XHNzc0YP348zp49i7S0NJSVlbk6VI9mL49JSUkICgpCcnIyVCpVh6+IejN7ueN1Zp+93G3duhUXLlzA0qVLkZqaihMnTnhUfPbwvP+HtUU+1hb5XFlb+jg7WPqPyWSSPpPRRqvVoqGhwe64wMBAHD161IWReZfO8piXl9fzQXkJe7njdWafvdzNmzcP8+bNc1NkD7C+dB9ri3ysLfK5srbwnToX0mg00mcy2hiNRmg0GjdF5J2YR/mYO/k8PXeeHp83YA7lY+7kc2XuOKlzofj4eBgMBtTU1Ejbzp49i6efftqNUXkf5lE+5k4+T8+dp8fnDZhD+Zg7+VyZO07qnMBisaC5uRlWq9XmsUajwcsvv4zs7Gw0NTXhxx9/xB9//IGXXnrJ3SF7JOZRPuZOPk/PnafH5w2YQ/mYO/nckrtuf62DxKxZswQAm6WkpEQIIURtba2YNGmSCAoKEvHx8aKoqMi9wXow5lE+5k4+T8+dp8fnDZhD+Zg7+dyROx8hhOj+1JCIiIiI3Im3X4mIiIgUgJM6IiIiIgXgpI6IiIhIATipIyIiIlIATuqIiIiIFICTOiIiIiIF4KSOiIiISAE4qSMiIiJSAE7qiNxo7dq18PPzQ//+/Z22z9TUVOzYsaNLY5YsWYKgoCAMHTrUaXEQkfuwtvROnNSR28XExECtVkOj0UCj0SAmJsbdIfWoN954w+YPO7tCQkICrl279tj2vLw8HDx40KUxEPU01hbWlt6GkzryCMXFxTCZTDCZTB0WCIvF0vNBeQBnPO/r16+jtbW11/0PjQhgbXkc1hZl4qSOPFJpaSmGDh2KVatWoV+/fvjwww/R1NSEhQsXIiIiAlFRUcjNzZX6m81mZGRkQK/XIykpCStXrsTEiRNt9vUwHx8f6RXsv//+i4yMDISFhSE2Nhbbtm2T+qWmpmL9+vUYMWIEtFotpk+fjnv37kntO3fuREJCAkJCQpCYmIhLly4hJycHs2fPtjnec889hz179jj03GNiYvDJJ59gyJAhGDZsGABg/vz5iIiIgF6vx4QJE1BZWSn1P336NIYPHw6tVou5c+fCarXa7O/w4cNIS0sDAGzZsgUDBgyARqNBXFwcSkpKHIqJSClYW1hblIyTOvJY//zzD9RqNaqrq/Huu+9ixYoVMBgMuHz5Mk6dOoXt27dj3759AIB169ahvr4elZWVKCgowLfffuvwcWbOnIno6GhUVVXhwIEDeP/993H27FmpfdeuXdizZw8qKytx7tw57Ny5EwDw66+/YuHChcjPz4fBYMCuXbug1Wrx2muvYe/evWhpaQEAVFRU4MKFC5g8ebLDMe3duxdlZWU4f/48AGDs2LG4ePEiampqEBUVhcWLFwMA7t27h1dffRWLFi1CfX09EhIScPz4cZt9HTp0CGlpaTCbzViyZAmOHj0Kk8mE4uJivsKmXom1hbVFsQSRmw0YMEBoNBqh0+mETqcT7733nigpKREhISGitbVVCCGE1WoVQUFB4tatW9K4zz//XGRmZgohhIiJiRFlZWVS26pVq0RaWpoQQoiSkhIxZMgQm2MCENXV1aK6utrmOEIIsXz5crFmzRohhBApKSli06ZNUts777wjli9fLoQQYs6cOWL16tUdPqexY8eKPXv2CCGE+Pjjj0VWVlaH/bKzs8XcuXMfycf333//uHSJv/76S4SGhgohhCgtLRVxcXFSm9VqFVFRUaKwsFAIIURra6vo37+/MJvNwmQyCa1WK3bv3i2am5sf2W9HeSLyZqwtrC29Dd+pI49QVFSEhoYGNDQ04KOPPgIAhIeHw9fXFwBw+/ZtNDU1YfDgwdDr9dDr9Vi5ciVqa2sBANXV1YiOjpb29/BjeyorK2E2mxEaGirtNz8/H7du3ZL6hIWFSY/VajVMJhOAB58niY2N7XC/M2bMkL4lVlBQgIyMDEdTAQCIioqyWc/JycGgQYOg1WoxatQo1NfXA3j0efv4+NiMPXnyJBISEqBWqxEcHIzCwkJ88cUXCAsLw9SpU3Hz5s0uxUXkbVhbbLG2KBsndeSxfHx8pMf9+vVDYGAgKioqpAJtNBqlb1WFh4ejqqpK6v/w4+DgYNy9e1daf/jbYJGRkdDr9dI+Gxoa0NjYiK+++qrT+KKjo3H16tUO29LT03HkyBGcOnUKtbW1eOGFFxx/4rB97seOHUN+fj4OHjwIg8GAU6dOSW3h4eG4fv26zdiH19tuj7SZPHkyiouLcePGDQQGBmL16tVdiotICVhbHmBtUR5O6sgrqFQqzJo1CytWrEBDQwOsVisuXrwoFaGpU6ciJycHjY2NuHTpErZv3y6NHTx4MOrr63Hs2DG0tLRgw4YNUltkZCRGjhyJNWvW4O7du2htbUV5eTkuXLjQaUxZWVn48ssvceLECQghcOnSJVRXVwMA+vbti5SUFGRlZWHatGnSuwJyNDY2ok+fPggNDYXZbMYHH3wgtY0ePRpNTU34+uuvYbFYsHnzZikGwPaDzLdu3cL+/fvR1NSEgIAAqNXqbsVFpASsLawtSsJJHXmNjRs3Ijg4GImJiejbty8yMzNx584dAEB2djZ0Oh2ioqIwffp0zJw5Uxqn0+nw2WefYdq0aRg4cCBGjRpls9/vvvsOFRUViI2NRVhYGJYsWYKmpqZO4xkzZgzy8vLw+uuvQ6vVIj09HUajUWqfMWMGLl682OXbI+1NnDgRo0ePxoABA5CYmIgxY8ZIbf7+/ti9ezc+/fRThIaG4ty5c1J7fX09qqurkZiYCACwWq3Izc3FU089hbCwMNy4cQPr16/vVmxESsDawtqiGO7+UB+RK2zdulX6MLO7HD9+XMTGxtrts2HDBhEcHCwiIyOdfvyCggIxe/Zsh/ouXbpUhISEiOHDhzs9DiIlYW1hbfFkPkII4e6JJZGzffPNN9ixYwcOHTrkluNbLBZkZmYiISEBq1atcksMhw8fRmhoKEaMGOGW4xMpEWsLa4sn6+PuAIiUpr6+HlFRURg+fDjy8/PdFsfDH2ImIu/H2kKd4Tt1RERERArAL0oQERERKQAndUREREQKwEkdERERkQJwUkdERESkAJzUERERESkAJ3VERERECsBJHREREZECcFJHREREpACc1BEREREpwP8BR2zaFSlI+C0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Check the gang of 4\n",
+    "ct.gangof4(P, Cnew);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "wJHJLjXwCNz-",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[list([<matplotlib.lines.Line2D object at 0x12095f9e0>])]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# See what the step response looks like\n",
+    "Tnew = ct.feedback(Lnew)\n",
+    "ct.step_response(Tnew, 10).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "WUhz529a-w3q",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/steering.ipynb b/examples/steering.ipynb
index 217e3b2db..ebad51185 100644
--- a/examples/steering.ipynb
+++ b/examples/steering.ipynb
@@ -90,9 +90,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -452,9 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1067,7 +1063,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1081,9 +1077,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }