diff --git a/Documentation/User Manual/3-simulation-models/Engine_FC_Correction.md b/Documentation/User Manual/3-simulation-models/Engine_FC_Correction.md
index c5e35ced4cd917051e1cab8eabae95e110ef0c63..3b7c348517ebe34b41a4d7c4583774048b05bba6 100644
--- a/Documentation/User Manual/3-simulation-models/Engine_FC_Correction.md	
+++ b/Documentation/User Manual/3-simulation-models/Engine_FC_Correction.md	
@@ -12,7 +12,7 @@ VECTO currently does not model the vehicle's electric system. The power demand o
 
 ###Vehicle-Line Approach
 
-The total fuel consumption is corrected in a post-processing step according to the *vehline* approach. Therefore, for every engine operating point with a positive fuel consumption the fuel consumption is plotted over the engine power. The slope (k) of the linear regression of the fuel consumption is used to compute the additional fuel that is needed for the energy demand during engine-off periods and engine starts.
+The total fuel consumption is corrected in a post-processing step according to the *vehline* approach. Therefore, for every engine operating point where the engine is switched on and has a positive fuel consumption the fuel consumption is plotted over the engine power. The slope (k) of the linear regression of the fuel consumption is used to compute the additional fuel that is needed for the energy demand during engine-off periods and engine starts.
 
 ![](pics/FC_Correction.PNG)
 
diff --git a/Documentation/User Manual/5-input-and-output-files/VMAP.md b/Documentation/User Manual/5-input-and-output-files/VMAP.md
index 832db75b0e1474768c85893736d585346cda14da..553fa850acb76efb0e8fc7a4b2e126ee0a235ba8 100644
--- a/Documentation/User Manual/5-input-and-output-files/VMAP.md	
+++ b/Documentation/User Manual/5-input-and-output-files/VMAP.md	
@@ -3,7 +3,7 @@ The FC map is used to interpolate the base fuel consumption before corrections
 
 
 - Filetype: .vmap
-- Header: **engine speed [rpm], torque [Nm], fuel consumption [g/h]**
+- Header: **engine speed [rpm], torque [Nm], fuel consumption [g/h]**, *whr power \[W\]* (required only if an electric WHR system is used)
 - Requires at least 3 data entries
 - The map must cover the full engine range between full load and motoring curve.
 
diff --git a/Documentation/User Manual/help.html b/Documentation/User Manual/help.html
index 6138dae8f4f24a396679e98eccf8341a06101e6e..8b43e29a69e05bbf8a5c8648ac0a885f40f95dbd 100644
--- a/Documentation/User Manual/help.html	
+++ b/Documentation/User Manual/help.html	
@@ -189,7 +189,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
 <li><a href="#engine-transient-full-load">Engine: Transient Full Load</a></li>
 <li><a href="#engine-correction-factors">Engine: Correction Factors</a></li>
 <li><a href="#engine-torque-and-engine-speed-limitations">Engine Torque and Engine Speed Limitations</a></li>
-<li><a href="#engine-stopstart-fuel-consumption-correction">Engine Stop/Start Fuel Consumption Correction</a></li>
+<li><a href="#engine-fuel-consumption-correction">Engine Fuel Consumption Correction</a></li>
 <li><a href="#fuel-properties">Fuel Properties</a></li>
 <li><a href="#transmission-losses">Transmission Losses</a></li>
 <li><a href="#gearbox-at-gearbox-model">Gearbox: AT Gearbox Model</a></li>
@@ -363,7 +363,7 @@ Version: VECTO 3.3 / VectoCore 3.3.2 / VectoCmd 3.3.2</p>
 <div id="options-tab" class="section level3">
 <h3>Options Tab</h3>
 <div class="figure">
-<img 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" 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+<img 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n8FkAuBiV6hvwLIpVVgMjFt0gQAOTQGJgAAIDABAGiFwAQAoAUCEwCAFghMAABaIDABAGiBwAQAoAUCEwCAFghMAABaIDABAGhhbYE5GRdqdzBQA5l2C7U3sSsAAOiB9QSmDsvBoFBjF5KTPR2eu/NDU+rs7qmlcnWVbQEAWMIaAnOi9nYT4SghWoztQgKBCQDYIKsHphlN6tGlXfRceRxuZrlQxa47favXzZTZ9tpsa1cBAJBTY2Cmvj7JTUYcat5YFbWB6ULSlsu8rutGqZO93XJ02mbbQOoYmbZvAoAcWgXmXDbsFhthRqHXpo6oKwcsAhNALqsHpo6tue9htgm9NnVEXTlgEZgAcllDYGrRVbKTsQ40f5VseWrWn26Vj5/EoSfzqVOybbYFAgQmgFzWE5javM9hmgA063ZVUdjQ02G4Zy7ecadt9brExTyN29p6gCAwAeSytsBcCSNGrAmBCSAXAhO9QmACyKUbgQmsCf0VQC4EJnqF/gogFwITvUJ/BZALgYleob8CyIXARK/QXwHkQmCiV+ivAHIhMNEr9FcAuSwdmDs7OwtPQG4EJoBcVgrMRRCY2A8EJoBcOhGY42KgzL3WrcloGN0nVm7CPlSjLt0KaDJSw+Go/u5ETevXYT/2sWEITAC5ZA3Me++9Vx06dEjdcMMNc+tLQA59Gk7UaKiX9TQt0sEQ32jdhcVBhUbTfvfjuA76d9BBBCaAXLIFpgvLCy+8UD300EPz68vXg/knfD2a1PPjMERlfTgEDRGYBGaAwASQS5bAjMNSzA/Y8nsvTSS6cJQQsCHpR6AmGApVFHLKdqBDVs8P7VeCucDQdaZlxexp3KiN4WisRmZ+qAr/hZ7VNnxUB+UyAvb71Mc8lLpmknbKujMhVtm3bjfajzvW8pS0bcs/7qAtt+zai34HM9tvEQITQC4rBaacapVglIB0UmEp5gdmeRq2zAYbOHakOQnWmaCQ9zIl2Cqh4cIkqGsWw3WWayNsz837/cl7qrZFCZ8yAavllVFxwLUzb9+mjbpjDV48mPplsFbacsvxT7Mysb0p3w4EJoBcVgpMCUQJRheadWEp5gemPLeXI7xpiLj5KABcMKTCwgSEHW35KQglkWojnA/LRFO5nS/0sfp9puqLaJv0sZbBPNDtFXr0qxer2wm3HP8sV85uv0UITAC5rHxKNgzNurAUTYFpTmsWcnpxGgwmRHWZOzU7EzhxWMh804gq1UY4H5aJpnIzotNBZ0eetfVFWCbz845Vv1AoTxXrOnFb8T7i9SLc3hZtAwITQC5reQ/ThWZdWIrGwDTBo0dGLhyFnPbUZf7inzAYkmFRjq78+3bJIEm0UZm3bTSdkpVyU78cAU93aU/VNu3btTdzrDaATbHUmY6yfVV5cRHuo9JuanuzYisQmAByyXLRT0pz/TJAph8vEeGTvxYGQxAwsp0fSUm4mGU5LVl30U/cxux8fCGNIXXMqVN9nHr05upLSPpyOTUr5WF7TlxWc6yV9myhCWhTZkfdlX1Ufwep7bcFgQkglw4FJrA6AhNALisF5qITkBuBCSCXpQMT6CL6K4BcCEz0Cv0VQC4EJnqF/gogFwITvUJ/BZALgYleob8CyIXARK/QXwHkQmCiV+ivAHIhMNEr9FcAuRCY6BX6K4BcCEz0Cv0VQC5LB2bq1ndNE5AbgQkgl5UCcxEEJvYDgQkgl24EpvvarPC7MFX5dV+Vr9dqkvpKrVDT+nXYj304el/yPZn+t5ba934eTwcQmABy6U5gDqffL2lImYToIk/2TeGwH+Hh9rHIvhY+LvtiwnznJYEZIjAB5NKhwBypURF8WfS4UEP3Rcm2qFFTOOxHeLh97EtQyRdsE5ghAhNALp0KzImEpE3MsYTnOHiy13UKN6oaVkPClcso1Y9IpS0zApNpqMzZ3tpAKVSh92dGa9F+XIBPRrLetiWFcVtu2bXn2yjrzGwfiraVFw6V4661WGCOC3tMZgq26xECE0Au3QpM/+Sug6CyXJ6CLMZlFJjwKROwWq5DMnkKd6b9gJTpYBqZNqS9YJQbHo8LGFO/DNZKW245/mlWJrY35Va4jQlJ26qEfriPGYnA9IEYTFEb099f/xCYAHLpVmDqfyMZPZrTsfoJPQyS8Em/qdzOFzLiDEMjri+ibWYDRwLUvmeo2ytGY91OtJ1wy/HPcuXs9qHkNlq8PGOxEaYhy3Fg9wiBCSCXjgWmzMrFP/ZUpCtPPenPKzdB4kaNQXlcX4RlMj8vTCZje7pU14nbivcRrxfh9rbIqNsm1UbFooHpRuRmoZcITAC5dC4wzbwLAV/unujt037dKVkpN/XLIJm+/2hP1bYNlOmGdp0NYFMsdWQ+2oc7feq2qbSb2t6sKCW30eLlGYsFZp9PxToEJoBcuheYobBcz8cX0hhSx54+HerR2zQcphf9+IuBUvuJyyQI/X6qoevbs4UmgEyZHhG7K3p9e3o0adopAy21vee2mTmWxPFWLBKY5YuB8njLY44Pow8ITAC5dCMwgTUhMAHkslJgLjoBuRGYAHJZOjCBLqK/AsiFwESv0F8B5EJgolforwByITDRK/RXALkQmOgV+iuAXAhM9Ar9FUAuBCZ6hf4KIBcCE71CfwWQC4GJXqG/AsiFwESv0F8B5EJgolforwByITDRK/RXALkQmOgV+iuAXAhM9Ar9FUAuBCZ6hf4KIBcCE71CfwWQC4GJXqG/AsiFwESv0F8B5EJgolforwByITDRK/RXALkQmOgV+iuAXAhM9Ar9FUAuBCZ6hf4KIBcCE71CfwWQC4GJXqG/AsiFwESv0F8B5EJgolforwByITDRK/RXALkQmOgV+iuAXAhM9Ar9FUAuBCZ6hf4KIJdWgcnEtEkTAOTQGJgAAIDABACgFQITAIAWCEwAAFogMAEAaIHABACgBQITAIAWCEwAAFogMAEAaGE9gTnZU7uDgRoUY1sgJmpvV5ft7uk5AAA22/oCc3dXT0E4uhCVMrOe4AQAbK41Buae2it21Z5LxXGhdgs9EZQAgB5Ya2BOJCRtYo4lPMe2PBhhTvZ21UBGnoNdVbh0nYxVIadvXfmYiAUAdEtjYKa+DcJNngtEH4wSgMFyWD4o9H/tNma+fK+zEp46NP1IVUvtm4mpbgKAHFoFZiMfiBJ+OgTN6VgdizOB6S4EktGljspyyGnXAasjMAHksubAlFm5+EdOqwblcSjqUaS83zmQESaBiTUiMAHksvbANPPhaddKYIanW2W0KfPNp2SBtghMALmsPzBDM4EpRUX5cRM9uQuEuOgH60JgAshlPYEJdAT9FUAuBCZ6hf4KIBcCE71CfwWQC4GJXqG/AsiFwESv0F8B5EJgolforwByITDRK/RXALkQmOgV+iuAXAhM9Ar9FUAuSwfmzs7OwhOQG4EJIJeVAnMRBCb2A4EJIJcOBOZEjYaD8ttNQpORGg5Hs/enFfPWiab1y5A27T1wy2nIPW87iMAEkEsnRpiT0VANR9XwSZV5LhDrgjFHoMbbTMYm6GuPUSyzH6yEwASQSzdOyc4Ei4w6h6o2i1YNxGWCLLVNjv1gJQQmgFw68h5mFJBh0IyL4FSonAYN1of19HyhR3xSb6jbGtRuX54CNsuJbQfDIh3U4b48+e5O+92fbfaTeixYKwITQC6duegnPAVbezo2DkofYu59ULu9DiYfUqGZ7UxhfViHkuXRtk5yP5F567A0AhNALp0JzGmAzAZYISNGNzILgyj+aTephNG87V1dt85Pc0LQLpaCEWbTfkSqjl2F9SAwAeTSncDU0VEGZRgyEki6zF2N6tbV/SxrBeHWsL0rc6E3T7wPsch+6uqUS1gTAhNALh0KTMmQoQ6R8HRsOYJzi5ORPdU6E0jRKVndTjl6a9i+LC23nVZKB1lcbq+SLd+HbLOfmjrlItaEwASQS6cC04RSdDpUgsWdMvUX87ggCgPJbGvrFRK8ZXlyex1e5QU57nSqDjOzrKd5F/3Ydsqp+jnMNvtJ18E6EZgAculWYAIrIjAB5LJSYC46AbkRmAByWTowgS6ivwLIhcBEr9BfAeRCYKJX6K8AciEw0Sv0VwC5EJjoFforgFwITPQK/RVALgQmeoX+CiAXAhO9Qn8FkAuBiV6hvwLIhcBEr9BfAeRCYKJX6K8AciEw0Sv0VwC5EJjoFforgFwITPQK/RVALp0JzMl4+uXKtV/i3DXhF1j31YY9RgITQC7dCEwdloNBocbuWVmepAfD+aG5yhP5ukIgV5h0KaQWOZYOHDeBCSCXDgTmRI2GiXCUEC3GdiFhlSfndT2xdynYciEwAcA4+MCUJ1kZXdpFz5XHT8JmuVDF0J2+1etmymx7bba1q0pjVYQjW6nvji08ZazrmCx37Sf3Y5f1fHhcru3JaDhtK361UGlXb1MEdWd+UTXHbLct7LbD0di2I234g5j9nYmgfKhfzMz+nrSZ34e88HFtJervEwITQC7dCMzkE6wEQV1gujCx5TKv61bCSJKlzbaRcSHhUq7x7cTidur2o/9VRs++3D42V+bmnbDdMOAkpML9WMljttua4njeH9vAtz19rFG5jPQT+6wIj7epbmYEJoBcNnSEGT05t6kj6spDft1s2BUy2nKjqrCduL2w3I/C3CRt2tGYbq/QIz+9WNXUrl30fHlwzGHd1HzcVlO5XfR0ee3vw1Y5CAQmgFy6/x5m3RN4WN6mjqgrr3DHE9axpz2DU5mVduL2wvLUiwFn4k6Tzhlhptq1i1OJY26aj9tqKreLpYbfR1l6IAhMALl0IDA1CUczmiwXJ2P9xOtOIZon5+B0qzstGT45y3xYx59ebLFtgmwv792505wz7YzsaUrfTs1+9D9zenO6YVDfPb7ECwZXLz7OOcc9c8xh3eS8PbamU7JSPrPPpt/HwSEwAeTSjcDU5n0O0zxp29OZReHCSI/O5LSmfuIuT9vqdWZZtp8+aTdua+tVSHs+0EoSCu74/IUwQUCk9yMrdLj446qGjG8v3JFw7cYBFC+H4mMO686ZT/3OyrbssekRcGqfyd9H0+91HxCYAHLpTGCuJAwBbDUCE0AuBCZ6hcAEkEs/AhOw6K8AciEw0Sv0VwC5HHxgcjo1z+9gwTbH9o5A5kLZDUZgAsilP4G5bDtdCOwDD8zyYyIbnpUGgQkgF0aYXXDQgdmj/wcEJoBcOhKY05uE190EvPLZzEp59JlI297MDcvnls/ZTuh1M/uzq7zwc6RN+xRBm9PPMUYq+7WPf81tjs3o0i73YJRJYALIpRuBaZ70y6f26h1nUh/Er96JxpeHP4P2TJA1lc9bX7e/ciltwTbTNzivefwrtxnUcb/rNo9pQxCYAHJZOjB3dnYWnpLiJ+tKMLiRj5t0gMht8+rqhz/t6pn1tnjl7eyip8sbb0a+SJtSVnnsMrV4/La4ts02224wAhNALisF5iKWC8zEKcJ59cOfdnVtKKy6nV0s2XvDulHfOtqUsmUevy2ubbPNthuMwASQSzcCUweDO/VYPSU7SNy4vHpaMXlqNXzyb1tet75uf+WS1fJm5DVtmsc802bN419Hm2EdTskCQCsdCUy5wbc97VgJg/SNy8ttXLkNBvekHz/5ty2vW+/m4/2ZSlNNN2c3atqsu8F58vGv3GZ40U+wXaruBiIwAeRy8IG5InNxi79MNL/93h8WQ2ACyGUjA9PdlaYy8spov/eH5RGYAHLZ+BEmECIwAeRCYKJXCEwAufQzMHt0EUsr2/Z45yAwAeTSzcBcNQCatg/XryNsFtlfDrnb3yAEJoBctnOEud8BQ2DuGwITQC6dCEzzAXpzFeqw/KC+CwDzc3pj9uFobG86bu+lGgdFZTtbLjcacFe42u3kw/vlFa9RXT1fvTF5WVZ7s3Onst+4brQ/Wz/9+crgJvR6W381rqxzd/2ZeTx2vWt7yxGYAHJZKTAXndLKu+T4cJJ5FwBm2QZHPO8DKgiKunIntd7Pz7kLjg0+I3Wnn7DdVN1wf2Y/URgG27rb640LeYFgt3DHEgu39e1vNwITQC5LB+b62BGYDpFCjyAn8qyfCoLUfBwUNdvNvSl6ahvRVG4XjUXakHk/QnSTBGXNtvpfHLCNN3nfYgQmgFw6EJjWxJ1ujUaYLghS83FQzJS3uCl6qkw0ldtFY5E2ZN6NqEPxtnquDMqwvMXj2XIEJoBcOhCYNgTKNClDwo22wiBIzpenc93oS25bV90uWp+6KbqfL0e6yVOyrq6Il4Urq6tbKbf7mR5UeltNjkHuS+tOzZa/q4bHs+UITAC5dGKEWblxuaRBKkRq5isXDBWz7xkmb4qug6e8ECcYzdp2G29MHi8LV1ZbN9ifKdfB5/cTXvSTaNe/mCi1usn7FiMwAeTSnVOywBrQXwHkQmCiV+ivAHIhMNEr9FcAuRCY6BX6K4BcOhSYY1UU9gIWe1FLOJkLVsej6U0BLLky1pTNbGfvgmMq1beZXjfdVm4gUF1nJ1OhvGp1rCZqVCQ+KoJ9R2ACyKUzgSlXu/oAq73iUwIquGrUhJ278jTaLlye12Zinf94il0uuXAMBWWyTXg5Kw4EgQkgl44EZhA888JN6GAqP/LhPjdpy2e2a9lmcl1DOHphmRxPvB77jcAEkEs3AlNC0CVfU2Bq/jSpT0st2q4ySlwwMBcaYQb15LjCQ8L+IzAB5NKJwDR3tHGnMyXA4vcL46Aydaof6J/Zzt0QILXOTLbN1DoZKVbTUmsOzMrjwIEgMAHk0s3ArBsNGhJccms5CbogwOZtt8g6uaftMLhgyEsFplzsQ2B2CYEJIJeNC0xzOtammWzX6lTuouvCU8QegbkJCEwAuWzWe5hSrxJawYU/6wzMZDimyqp4D/PgEZgAculGYIZhJAEWv6eop+ForMaj4H1JR25kbr5HsyEUk23q2sntJIij90iTgRmWcZVsFxCYAHLpSGBKbqXeN9wgcmUtw8sDR2ACyKUzgWlGa8H7gZtF3stkdNkFBCaAXDoUmMDq6K8AciEw0Sv0VwC5EJjoFforgFwITPQK/RVALgQmeoX+CiAXAhO9Qn8FkAuBiV6hvwLIhcBEr9BfAeRCYKJX6K8AciEw0Sv0VwC5EJjoFforgFxaBSYT0yZNAJBDY2ACAAACEwCAVghMAABaIDABAGiBwAQAoAUCEwCAFghMAABaIDABAGik1P8DEo5oGJOxDu8AAAAASUVORK5CYII=" />
 
 </div>
 <p>In this tab the global calculation settings can be changed.</p>
@@ -371,6 +371,9 @@ Version: VECTO 3.3 / VectoCore 3.3.2 / VectoCmd 3.3.2</p>
 <dt><strong>Mode</strong></dt>
 <dd><p>Select either <a href="#declaration-mode">Declaration Mode</a> or <a href="#engineering-mode">Engineering Mode</a></p>
 </dd>
+<dt><strong>Output Directory</strong></dt>
+<dd><p>This input can be used to write all simulation result files to a certain directory. This can be either an absolute path or a relative path. If an absolute path is provided, all result files are written to this directory. If a relative path is provided the .vmod and XML reports are written into the corresponding subdirectory of the job file and the .vsum file is written to the corresponding subdirectory of the first selected job file.</p>
+</dd>
 </dl>
 <p><strong>Output</strong></p>
 <dl>
@@ -2365,7 +2368,7 @@ Version: VECTO 3.3 / VectoCore 3.3.2 / VectoCmd 3.3.2</p>
 <div id="engine-editor" class="section level2">
 <h2>Engine Editor</h2>
 <div class="figure">
-<img 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czs+bfd6KD9DxD7/p8z7NhrbH/99Vd6sFMn2nG77V3bmuCyH7jf/vRc9x721mZ9M/EbOkYYflUedVxqnfM5YJ996a4Od8r0hWa2Wc2bN5fhf/3rX6ljx47y9Vo333wzbbnlljK8bdu2dkq3cmG2//3vf8uwv/zlL3TKKackpH379vZWEARB0VVemm3XMHAhNsvaOj+/berBTsVst2jRwvk7AAAAADCpKrdmW9MDA2robzceKydJUz3ZymjrJlux+RUH0NCvP5FGXbePbHy4d5fN79/33EvCxlMt63B4eavW9pZuKVM6ZswYaVaVgVXbGZf33JN2+NNfqP6rr4zP+bLZfkiY7Z2238Epgx+c50H7HUDPG8y2KttbtW/R9n/6M+1jp1fbefNi/rbzLnTbLbfK7XTDne9mm8vPvcXbb7+9nCRNpWG4B5lf26U/U60rF2abJ1PjsMMOO8wOgSAIym/lndn2Gm2TfEy1WvcLTyb+/l+7luePAQAAUGzw34BUlY7ZPigls6055UPvPpd2uO4Il9H2mmyG43a7qYym/TzL3jKm6dOn0267/o323n0Px2hKA2owofvs1YTKW5vNNosnuDqsaTPZO6y24bz23p3zs5dt9Hy33GxzeSIsUxs7QGm2HxBmW+vZ1rdjOK8gPdtfjf9KGOidjdurPLxl23XHnaima42dg6Xvv/8+r832tddeK8Oee+45+ez0W2+9Rc8//zwNHDgwbnZyr3JhtquqqmQY92ybnrOHIAjKN+WV2fYx2myWYz3T1ozl0jy70tdRpZpQzS88ifj739QAA40H6iR6oE6iCeolc/gcpqq0zPYZKfZsq5nEz6i62jHabKq9ZntH+Xk0bXb5ftR5UHdatWZ13LDoKT/8IHuOlclk09n0gH/IZ5fvvKNSPnetuLOyA/V84UV7y3i1u7GtNOTKxKrPIw87jG6oqKAb2lTIXvT9/76PY2z3Fml23/X/6L+PVdm5xLTm1zX0uAjnPA9p9k86+MCD5LKeN68f+k8RJ8ze0UccSS/16m1v7dahYnt9O2bfJnvTIU2b0T9Fvvx5wL5Wj7xe/u3+uC3NnhX7kSLfe7Z5ODaH8fDxVJULs83v1FbhbPghCILyXflktr2v9yrRTLIrThsSLo24He7tzTaFJxKnNTXAAAAAFD78NyBVpWe2r05xGLltmH9aMJ32vOkEo9HmV33tyLQ5inYVnxOmfy+38WrK5MnymWg2lswuO/yVKtvfLifI+vXX1fIVWjosr2FnLVy0iI45spT2+L+/OWaWuba83E4R010dOtD/7bSzTLuHMNq8zD3T1vDl+LyVeNZ0nmBN7zk/49RT7diYVPnU5/APhzlD23kb/uQ82t5wA23YGHsf9Ksvv0xHHHqoU3a1j6uuuNJOkbue7b///e+0Zs0aX7znPNOeZGavvfaisrIyOv300+nCCy+kclFXnTp1Ip493qRcmG0Wv4aMw3faaSf67LPP7NCY+Cbh3vfPP//cDoEgCIqu8qpnu5HF3/2mBhhoPFAn0QN1Ek1QL5nD5zBVpWe2r0mxZ1szXsd1usQy2ra5toy3xc5tSumPrZrSJTW3yHdrW3KbNq/Z5uHkPBz7/HPOpcsvuZQuu/gShysuu5xmzjD/Wj10yFA5KZo+HJtnH1fiMqtyjxn9OZ11+hlyhnKerfyUE1vQMeJzhidvr8Gc/tM0OWO4brZPO/kUO9ZfzcuOc23DZTy75Zl2rFsv9e4tJ25TZps/eaZypVz1bCfjgQcesLewlK655Wfjg0xSc+ONN9pbxJQrs82vnfvb3/7mxPPz25dcIq65yy6jY4891pm4rabGPaQfgiAoioLZDi7+bjc1wAAAABQ+/DcgVaVltltem/oEaUo9P3qT/lj+T6dHWxpu22jvcl0pbdXqQOpdV2sl9phXltdsKzPKs5PzJGE6e/7f32iSMJsmvfj8C9TswIMck8p5XHfNtXas2zjz8ldfjqcxn39OM6ZPp19Xr7ZjEmtammabt+HycHou3z/225/ef+99O9at5cuX07Faeubgg5rasUTffvstDRo0yF7LXNxjy6+0UkbTjwcffNDewtLhhx8uw6urq+0Qt7h3muM5b/291t27d5fh//d//0fjx4+n2bNn09SpU2nixIk0dOhQuvzyy519DhgwwN7KUqJ9plsepblz59LVV19Nf/zjH539K3bbbTc5W3qyZ8ohCIKiIJjt4OLveFMDDDQeqJPogTqJJqiXzOFzmKpCNdvKvzZp15x2vJ5nIRdGm022zXZXH0pnPHoVTZk3zUpokMls8/Bunhxsl7/u6IIN+Pc+ZrvbM8+4eoTZELe3Z/PWpZ43HzFsOD0rtuEJzXo8252e6NKFpk9P/IxXOmabjb0y26ps/Iz2L78stVPEi5/91vfBPf1KPLyejWlja624QpcsWeL6EcMr/uFADf1nrVixwpmBfMiQIXZovI4++miZxtu7nWif6ZTHJH6U4JtvvqFPPvmERo8eTXPmzLFjIAiC8kMw28GFxioAABQv4Znt69I02/Zn50HP0l+uPsTp0d65zVHCbB9FW7c6SMR1cwyuSV6zzUb75BNb0E3CaN3arh3denMMNs9zlfnxmKo3X3+dDj/4EMfQ8udpp1hG2PtaL+6h5GHkf9p6G9p5h79K+GQ3TG2wU5iVbs92qTDPe2rb8PPbr7/6mozzmsPFixbR0Uce6duznc/iZ575PDNsfP101FFHyTR33mm9axyCIAgKLpjt4OK/NaYGGGg8UCfRA3USTVAvmcPnMFWlZbbPTNtsW0Zx9pL5tMXl+9MuwmwrdrzuCNr31pPo3S+HyzR+YrPNr9ZSJnnbrbah7s8+K+PYJHuR4Ybey4lfTxSm9gj5vm7Oi9mnSRN684037RQxPd/jOTkjuTK0/MkzgSdTumb7lBYnucwzL7c89TTneHRxTzv3ZKvzwfBz5YWgadOmyYua4YnSvD808PozzzzjpBk7dqwdA0EQBAUVzHZw8d8aUwMMAABA4cN/A1JVema7TfrPbCtd/HQ72um6I6XR3vX6o+lP5QfTTS91ovXabNsmTflhCu28/Q7Oe7V5qDj3bF/d+ioj1V262FvG68zTT3eZbZ5s7eCmzWSv+GejRtHHH31Et91yi3zNFhtmZWi5N/2+e+6xMjEYeaVUzbYyk2O/GEv/2Hc/Z3+ybOLz2quvodmzY8OU+UcALpsy5uo4+LhZiYZI54uuvPJKx0zzbOQXXXSRfFaaP/fcc08njmclhyAIglIXzHZw8d8bUwMMNB6ok+iBOokmqJfM4XOYqtIz2xXpm21l/76a/h397tJ9pdFm2HQ/PaSPHesvnl18d2F2lRFlmggzy6aZTa2X1tprsLwa+v771OwfB8ZMLecljCuvH3nY4XL2cX4vNuej9sXrfKJ5orRkZjbdnm3WUYcf7j5GUS7eN79K7ISy4+jE40+Qz5zrPeDMdtv+iRYvip/QK1/Fvfk9evSgI444gjbffHN57hX8PPeZZ55Jw4cnHg0BQRAE+QtmO7j4b4+pAQYAAKDw4b8BqSots32WMNvN0u7Ztl6ptWb9WvrH7afRTm2Ooh2uPZzO/O81NOcX9yuWTOJ3VzcXRnP3XXZ1mUwTbFbLW7W2tzTr9tva0x7CqOvb8CebWGW8VRzzlz9uSwNr37KGdCfpOM7EbM+fN4923v6vznZcDobLxPl5y8ZGnMvWu1cvO4fC6NnWxZOaTZ8+nSZPnixnJd+4caMdA0EQBKUrmO3ggtmOHqiT6IE6iSaol8wJy2w3Pev6TMy2JX6OeuDYD2jbq5rRrjccTfe8qV7BlNggsoHkWZ95AjE5BNw2m0aEGU1ktpUZ7XBHJf31z3+J295rZv+89Tb04P2d6Ndffw1kZNM12yrvuo8+krOse8ui0MP+uOUfqPODD8ntIAiCICioYLaDC41VAAAoXsIy2wedfUOmZtvq3V60Ygltf91h1KzDmfR+fZ0d5y/d4E6YMIFOan6inBxs3yZ7SzO8D39qcPjVV1nPL/tJ5flW7VvO7ORs4lXPsepFPuiAf1DtgNqUelPZbJcdfYzMR5ZRcMapp9mx/tKPk3vyDz/kUKccymQro81l5Xxfe/VVmV7fFoIgCIKSCWY7uGC2o0eh1MmGnn1oY8dOknVTphvT5IQFS539bqjuak6TIunUiSoDY4oHmYPvr8wJz2zfyM9sX2pnka5+o1Vrf6Vrnr+TTn8s/Qm9eDj3nNmz6acff5LmVuenn36keXPn2SnN8u5z4tdfU/Xjj9M15eV0teDxqioa/+WXdqyloOVct24dzZgxQyvbTzR71iw7NjXVffQxndWypZw4jV/t1VSYf/5xoMt/H6cNGxJPKgdBEARBfoLZDi40VkGu2NS8BV9gkvXD6oxpcgEbe7Xf3/ZqYkwTBqoMjCkegCgQmtk+p23mZlv51WWrV1DfTwdaKykoFWMeJC2nSZYuSBqlROmC5qEUJH2qeUIQBEEQC2Y7uGC2o0eh1Ekhme106kSVgTHFg8zB91fm5JXZZkXVIOaTcYXJhiAIgjIRzHZwobEKckUhDSNPB1UGxhQPQBTIO7MNQRAEQVDjCmY7uGC2owfqJHqgTqIJ6iVzQjPb595smW1TJAAAAADyB5jt4EJjFQAAipfQzPZ5t8BsAwAAAIUAzHZwwWxHD9RJ9ECdRBPUS+aEZrbPvxVmGwAAACgEYLaDC41VAAAoXkIz2/9qD7MNAAAAFAIw28EFsx09UCfRA3USTVAvmROe2b5dmO1TLjNGAgAAACB/gNkOLjRWAQCgeAnNbF9QiZ5tAAAAoBCA2Q4umO3ogTqJHlGsk1y8TzzfwL2SOaGZ7QvvhNkGAAAACgGY7eBCYxWA/ARmG2SD0Mz2RXfBbAMAAACFAMx2cMFsRw/USfSIYp3AbONeyQahme1/3wOzDQAAABQCMNvBhcYqAAAUL6GZ7f/AbAMAAAAFAcx2cMFsRw/USfRAnUQT1EvmhGa2L74PZhsAAAAoBGC2gwuNVQAAKF5CM9uXdBRmG6/+AgAAAPIemO3ggtmOHqiT6IE6iSaol8wJzWxf+gB6tgEAAIBCAGY7uNBYBQCA4iU0s33ZgzDbAAAAQCEAsx1cMNvRA3USPVAn0QT1kjmhme3LH4bZLjbuvfdeAAAAeYzpu52B2Q4uNFYBAKB4gdkGOYMbahAEQVB+CmY7O4LZjh6ok+iBOokmqJfMCctsN73yEUyQVmzAbEMQBOWvYLazIzRWAQCgeAnLbB90ZRXMdrEBsw1BEJS/gtnOjmC2o0eh1MmGnn1oY8dOknVTphvT5IQFS539bqjuak6TIunUiSoDY4oHmYPvr8wJy2w3bfVfmO1iA2YbgiAofwWznR2hsQpS5YgjXhXXTTX9/vdPys/NN3+CNtvsCVcYr3/yu334ApO0KLnBFe+3jVpWn/qyaZvNN38ybpsmJaJ9Z+93xu92kNsl20Zf5s9k2wQpu/jPwVT2HXfsTm+91WA8x1Fh/eChtKl5C8mGzlXGNCC/Cc1st+4SIbNdX0Wl4sBLymu18HqqKhVhpVVUr6fV4e0SxecSv32neyxekh1bGsfuNdtDKsrkBVdSUkYVQ+xAoVh4CZXVNKhQqrDD1DY1Q2qorKyGVApdQypKXHk21HCeFSIXJc5P5GHamNVQI+L19H7lisnah15GG1MZRf5lhrR6mSEIgqIkmO3siL/rTQ0w0Hhks064R9np5e3Zx5gmFTp0GCnKZ5nGZNSV6Gb7RldcYkSjXH4G249FbBvdbE8v2d4ON5HJfvyI5Sn+c4hPF2O33Z6nYcNmGc93Y8PXjCikZGOrcmOaxgTfX5nD5zBVpWW2rxL3WdNTLjdGho40jqVu86hMa5YNZ9bw23e6x+Il2bGlcewusz2kImZCG9j42saWwyuUOW2gmjJlQLU0SmxYfcw2G9+YIeZ8xLogFsRm15OflLVPvhFc5ty3XAkky8f79THbPmVPSdnKB4IgKIlgtrMjNFYLm/XD6hyzxL2TpjRBuffeUSIbyyAOHjzNmEankIaRp4MqA2OKf/jhL+zebsugH3roK/TOO8nPa5hE3WyDzAnLbB909VNRM9tVVFVeSlX1dlhtOZWWC7JsOLOG377TPRYvyY4tjWP39mzHZJlhUy+zY5pNpjKR0dTNPBt1sTxEN+DSPCdyywZzr8lt5s3innDf3neYZAiC8kww29kRzHb0yGadZMtsjxw5l7bYwhr+fOONw41pCplc3ScLFqylVq2GOkPMmdLS1+jLLxca04fNupnz5TXErJs42ZimMcH3V+aEZraveTp6ZrueTWlVvQyrZbNaqxlKjhMnh09QSUkplddq28l8aqm8lMOt7TmunIduc/rS8pjx1fHNU6QX+3eFe/Lk3mtjT3WQY/GUrVbb1pi/J708FrUftW0AfM02G88EvczSE8s03EtslSPegLOh5uHoyr56e8vFEqe3DXZys5zIbAfo2VZlc5VRU8LwCqoxDbEXcRWq110cq/wxwbWfINuJNIkOG4IgyEcw29kRfxebGmCgMMjGMPKZM1fRlls+Ja6VamrTpviMdhjwOT7zzIHy2XBlug8//FWaMmWZMT0A2YL/BqSqtMz2dc9G0Gw7BpKNs76eJH29ZbRjhpqfkdbW/fLR0fOUBts27Wya5bbWc9cqnM10QrPt7NN7LJ58qoSpls93++XvcyxOftq+k2Ay2+o5Z5PxlXHKkNqmUnppHnbOZrdGGU3LaLuziPWWcz6W8eR0nJ9/T3pM/mbbVS4fOftUZtgKjonDpSnWEfuT4dqPBkMq7O2VwbfDVb76p+922rGq9PYqBEFQUMFsZ0f8fW9qgIHGI0p1smzZBjryyNdEmaxni3ndlK7QCatO2HQfdVQ/sT+rp5snVisre12Gm9IXO/j+yhw+h6kqLbPdpnsEzbY0luVUywaXDahuKMUyG2o+QRJlOIUxLuVeX31CMhmuDJRCN+OxdMY8dROr1v3C1XpcuM+x+OWTKNx1HIynpzwgvj3bjpG0V4XYrCYyhTK+QhhK1dutb2zLMry62VTLiXqtlcxpkpXLEpt6e1s/cxs0XK0nC08UH1d/yX5ogCAIihfMdnbE38OmBhgAzP77vySukWraffcX5JBnUxqQfXgYOfd0W73cXeQw80svfQ91ALIO/w1IVWmZ7Yrno2i2rZ5eHkLtHiZeS+XSYNq9zS4jKgxtveoV1vLjcJW/kQR56iY2WbhaN4Qbj8Uvn0ThpmPxpg+Av9lmbxgb1s3LQXqOLbPNvcHxZl2Ke3dFGqs325I04LydwZy7FW+2g5SLpR+LWHGbYKWg4bppThSeKD7pDwsQBEHJBbOdHcFsR4+o1Ik187j1WqvPPptvTFMsNFadDBgw1e7pZtNtvT7svvs+E99zxTnCwAu+vzInNLN9w4tstq8wRoaObhx1c+mEszGOPXddX2UPsfbbTg3Jtp+ZNhvTAHkyzroy9Nrw77g89fT2ctyxGPIxDSN38vc5Fn0/AdHNtjSuwvBa5lAzyz7m0JVe9kyXuScfM27H6Ty93vz8tghL/Lw2y2O2A5tWvSedV7Uy6goa7qyrc2THqGHiKj7ZdsnMPwRBUBLBbGdHaKwCEw888Lm4NoLPPA5yC78a7O9/7+nUydZbd6Ubbhghvu9gukFmhGa2bxTXbyTNtk84m2E1nNqZPMyzncsAy+e47WG7alIxO50iSJ6usvGySl/ueb2XKb1fuFiOlc2zL1P+pmPx208C3D3bDcIvCgMt91fmmEF+P7YVFsMyxob0HuMY3/NsGU23sbaNuh5klNts+5fLa8qHUE2NZsn9zC2He/KTeXKvu55e317fRhh6eawq3rsf13aijOI8WNthgjQIgtITzHZ2xN/FpgYYaDwau0545nE1IdrVV39gTFNsROU+GTToJ9p++2dl3exa8gCN3HxfmrrbIbTp4kuN6QsdfH9lDp/DVJWO2W56U28qaRaVYeQgFNxmG8pEDdxDn3QoPARBUPYEs50dobEKdHj26y22wMzjUadr13o6+M8P8Q0smbnZDvTII2ONaQFIRFhm+6B2L8FsFxsw25kp1rOPHmoIgsJX4ZjtGdSrpf1dKmjZa4YdLmJ6tcxKeCJxWlMDDDQejVUnPCS5tNSaCXvXXXvIdVO6YiSK9wm/1k0UTDK9ZHvx0YX+8pdu9PrrU4zpCxF8f2UOn8NUlZbZvkWY7cgMIwehALMNQRCUvyoUsy0NcmWdWqGWJS1J+mS5XElWDBvyNMOTCI1VoDj44JfF9VBN//d/z+FVU/nAsjW0flid5KmLn6VttqmR9cc/ljRp0lM+523cDgCN0Mz2beL7pekpVxojQWECsw1BEJS/KpyebV0xk8wm3NtrzeuphicTzHb0aIw64RmuuWd0iy2epKFDZxjTFDP5cJ/wSIQ77xxJm2/+pCivNZHa/vv3pv79pxrTR4X1g4fSpuYtJBs6VxnT+IHvr8wJy2w3bf+KMNuntjJGgsIEZhuCICh/VZBmm3unW/YSlttglusqZQ94quG6WrRoIRtWXlSDC5/F+dm9+9fi0zJnJSUHBN4On9H85Hdxl5T8R74mjHu5rXptQ6NHz5PxQfMJ63NDzz68IOljiMdnbj+ZVJVWz/btr8JsFxvcUAMAAJC/mL7bmbw0265h4Lkx2yapRheIDmHWyYgRs+kPf+gq9vkElZdj5nE/8vE+4UcBTjqpP22+uTLc1XT44a/KSfBM6RsL3WxvbFVuTOMHvr8yJzSzXfkam20MIwcAAADynbwz2x6jzfKaZ7WeangyobFavLAZ+/3vrSHHF1002JgG5D9cz2Vlr8t6ZrjOzzxzoOwBN6UPm3Uz5zvPnq+bONmYBuSOsMx20w7iGmx6KiZIAwAAAPKdvDLbBqMt5Qqvo0rjxGkBwpMIZjt6hFEn/Hxvs2Z9xb660M47Y+bxZBTCffLllwtdppufz2/VamhkTHc64Psrc8Iy2wfd9QaVNIPZBgAAAPKefDLbdZXeZ6hjJpl7p1W4t9c6lfBE4rSmBhgobI4/XjR8heH661+fxczjRQZPgHf00fyKN8t08zDzhx/+Qnx34geXYoT/BqSqtMz23W9SyT9htgEAAIC8J696thtZMNvRI9d10qHDSNtkPUkDBkR7puqoUIj3Cb8abO+9X5TXArPttk9TZeWnxrRRBd9fmROa2b6nP5U0O+UyYyQAAAAA8geY7eBCY7W4eO+96Y65gtEGDJvuHXbo5lwXvPzoo18Y04LCIzSzfa8w201PudwYCQAAAID8AWY7uGC2o0eu6oSf2d122xppqK66aqgxTTZZN3aCM/HV2gVLjWlywrI1zn7XjxxjTpMi6dSJUwY+fkN8xmT5OJ966ivaemu+PqzZy//2t+eoS5cvjWmjAr6/Mic0s33fAMxGXmyYXiNTaJiOGwAQw3TfFBqm4y50YLaDC43V4oAnweJhwmyizj57kDFNttnUvAVfYJKcGU4D66ZMd/b7215NjGnCQJWBMcVnSq6Ok4eSq2ulZcl1NOr3+9GiZkfThs5VxvQgvwnPbHPPNt6zXVRwI7SQVayNbABSAd8DhQnMdnDBbEePbNYJ9y6vvft+6r7T+fSvkqtozz1fFPdIOBNhFZLZTqdOVBkYU3ym5PJHBb5GbrnlY6rY8nJnH2/96TgaO/ZnY/rGAt9fmROu2T7jamMkKEzQyAYA4HugMIHZDi40VgubDT37OGap35ZHhzrz+MZ2t0nDzUjTb0iTC/i9zWq/my6+1JgmDJwyCEzxmRLGcS5/+kXn+ulTcqT4qKYjjng1cqYbpE9oZrsjm+1z2hojQWGCRjYAAN8DhQnMdnDBbEePbNbJyyd1cMzSzJP/bUwDklOs9wkbeh6V8MugYdTh3BfEebAmUWPOPHMQjR+/0LhdWOD7K3PCMttNO/Iz2/++yxgJCpOUGtkNDfwvXn7hERDMNgDJyanZjsD3A8w2lExorBYugwdPo0NL2tODJafR9/9pR+sHvG1MB0BQeGTEqacOoM02e1Ia7s02e4JOOOFNmjJlmTE9iD5hme1m0mxf+oAxMi3qq6i0tIrq/cL94tOF8xMni0+YTnmtIW1Y6GUqLaWqXJYljfMZ18geUkFldnnLKobYgRxcJsLKZFyQ8KgocCO7ttyuo2xcj/VUVRqBaw/4fCeUZr9eEt17Ef8OYIxmu6GGKkoqSN3VDTV8r+vnkYnFs4ZUiO+Bmpizjsr3Q7F+D8BsBxfXk6kBBhqPbNTJ6NHzaJttuoq8ulDr1rmfebzQwX3ihs11aWk/cV6sXu7f//5JOvPMgXIiPlP6XIF6yRw+h6kqHbMth5H/87JGMNtpNhDjiFo+y2qpXDTsq+rtdc63pJxq49JliTTK7W5kDxGN6xKy2sQNVCMaznJZNLrLympEiB1eVkayPe0XHiGl1MgurzXHpUltOcx2o2O4J+pFXSe8T9K5/323if53AOP+HuB72WymXeL7XzfQvC62ccx2hL4fivV7AGY7uNBYLTzY8Oyww7Oibqtlr6MpDQDZgF8n17RpH3mtMVts8RT95z//C910g/QJzWw/OECY7Us7GiPTwq/hp8LTbBj6ku38MiXs8qSxP1cje0gFlTiNY00crjWqufdKrvqFR0gw20WO8Z5gA5zA8KZz3/ptkwffAYzbbCvxj2/+Zpt7rWPm2TLTFSLMMdsR+n6A2YaSCWY7emRaJwcd9JI0Pk2avChMzxpjGpAauE8S88470+jQQ1+R1x3DPd0PPPC5+D7O7cz3qJfMCc9sv04lB18SsFESBL3hJ5bL7SF1paWl1jA9Fe/6LKeqchEv0rmGe2rb81BM4zA/lY8xPIV8VT6BtxNpVM+VC2sYYXlVrchLC7fz1bd3Gv5++QYId86ryisAeiNbDhM1tIY5XB8aqtb9wqOklBvZqm5UnZvqyZOmVNSvdY3w9VHv5AmzHQFkXbnvCVfPNi9z3Uqs+lNDf517KaN7L/rfAUzKZtvVay3Ej5+I7w79OyBK3w/F+j0Asx1cXIemBhjITy699D1Rp9X0xz/WhDrzOADMc899Q3//e095DTJbbdVVmO7RxrQgGvDfgFSVltnu9CqVNMvmBGmyMcINP7vBaTdAuLEbb2rtT72h4jSK3dvH8tX2pcK5MeRCNIxSzVf/9N3OMzTUVB4ZpxpgXBZ7G5lvrLFcXyXiZW+KX76Jwg3nldMExGu2yyoqqMIeQqqGiPo1mqPUmPZTWo1sric+p371pNI4dakta+cfZjsCyPrRvw8EbJbUveRNy/Xnqscs3HsR/w5gUjXbfK/HfpcT6cqsdPp3QJS+H4r1ewBmO7j4/jQ1wEDjkW6d3Hrrx2JbHsr7JA0ZMsOYBqQH7pPU4Mn5dtyxuzhvXeQ1uf32z9Kjj35hTJsJqJfM4XOYqtIx26WPvkklTf91uzEyLVSjw9P4iAsPms4brtZTDU+Wb5B4USlcMTG0hrAPcT8yqLhk+dYmCDflo9YD4DXbJRX6M5bWsE+/RnOUGtN+SquRrc5honoypfGkh9mOAHF1KIyvMK1OvYj4crHu3Fem+nXdd0z6914UvwOY1Mx2zFyz9Pveb5nVmN8Pxfo9ALMdXHxPmRpgIL9gc6N6E/v1m2xMA0DYPPXUV/ZEfda1+be/PZcT0w3Sh/8GpKp0zPYZ1Twb+fm3GSPTwtQoMYUHTecNV+uphifLN0g895ir8MDYz4qmmm+icFM+epokuBrZPBTU1Dgupme29XPod3790njSw2xHAG8dMqqu1eRldq+wb/1m9d6L3ncAk4rZdptmfTK1GAm/NxpBMNtQMvF1a2qAgcYj1ToZOnQGbbXVU2K7arrqKsw8ngtwn2RGZeWn9Je/PCOvUWaXXXpQ//5TjWlTAfWSOXwOU1U6ZvvcJ9lsn9vOGJkWTqPDM9SRh+HpjRXvZ5LtuUHkSheXPkl4snxVfLLtqjyNdJXOxhpuGAt39WqJhnPcsETffJOEe88rpwmIu5FtN65lOzrWsy1a18UzG7lel3715E1jWhbAbEcAT51YqAnSrM9Y/Wr3p7NNZvdePnwHMMHNduL73GXE83k2cv0a8KsrbxrTsgBmOz+Exmp+w7M+77prD2lgeGI0UxoAogBPlnbJJf+T8wko073PPr1o7NifjemzyabmLfjLTrJ+WJ0xTSps6NnHyW9jq3JjmnwhLLN91hNsts+5yRiZFnENFqvXo7S81ApX8d7PJNsnnCBNpdEoLfeY82T5qng9Xdx2oqEuGrnWdrGGmJt60dASeTr5c4+WCJf5lGrb6/vwydc3PFZ+57yqvAIQ18jm3m07P/21PkXxnm1jA9pQT3FpDMsCmO0I4KkTCzaopfIeYoPt3D/O9woPNecwuyc5o3sv+t8BTGCz3TCEamr873PvUPG8fM92AX0PwGwHF9etqQEGGo9U6oTNCpuWPfZ4AROi5RDcJ9mDTfc113woZyzna7e85BL6ctt/0OrS46WJNW3jR9B6gdn2h89hqkrHbJ9S3Z/NdltjZJSQvUOyF8gcny65yjcOT2OsMTE3sn3U0MD/4uUXHgGl3MjWwzKsJ5ht4EuEvgOYlL4HUlUEvh+K9XsAZju4YCLyl7ZtR4j6q6bNN3+CpkxZZkwDQFThURkVFcPpwZLTHOP6xgGX0PjxC43pMwFm25+wzPbRT7whzPbZ0TTb3t4hp1cnQ3KVb0Ly1WznoVJqZMtrQKuXtOvJGtrL+cFsAyPFZLYjoGL9HoDZDi6uJ1MDDDQeQepEGe0//KErvflm5s++gsTgPskdy24Tf4fF+WXYeP/ud0/QhRe+G+gHJNRL5vA5TFXpmO1D2Gw3O/sGYyQoTNDIBgDge6AwgdkOLjRWw2FDdVfa2LGTZO2CpcY0QRk2bBZtvrk1BPfhhzGzM8hv1k2ZLnubZ7/6Hh2/e5W8rpktt3yKzjvvbdkDbtoOZIewzPZBXV6H2S420MgGAOB7oDCB2Q4umO1w+G2vJnyyJWwuTGkUiepk9Oh58j3abEauvHKIMQ3IPrhPwmPUqHl0wAEvyWuc2WKLp+iCC94V3+0b4tKiXjInNLP9OL9n+6zrjZGgMEEjGwCA74HCBGY7uNBYDYdUzLYf3MO3++7PiyysWZxNaQAoFAYMmErNmvWR1zvDPd333vuZMS1In7DMdtMqno38rApjJChMuBFa6JiOGwAQw3TfFBqm4y50YLaDC2Y7HFIZRu5XJ//4h9Xbt/feL2JobcjgPmk8amrqabfdrB+ZlOnu2fNbGYd6yZywzPZBj8me7TbGSAAAAADkDzDbwYXGan7QocNIaTQ22wwzj4PihHu69Xd077DDs9KIm9KC4IRmtrvwbOQw2wAAAEDeA7MdXDDb0cNbJ5WVn0pzwTOPDxr0kysuGetmzpeTTzHrJk42pskV68ZOcPad6aRwKbFsjbPf9SPHmNOkSDr3iVMGPn5DfMbk4Di9hHH9qAnS5D4CPGLx3/+Oo6226irvCaZ9+0+M6UAwwjLbhzzyEsw2AAAAUAjAbAcXzHa04ZnH1YRobLpNaRLRmO8Dzva7jYPChk3tl5+VN6UJA1UGxhSfKWEcZxjXDz9a4eyDH7MwpPHCk6W1afOh2MQy3L16WcPKQeqEZbZP69QDZhsAAAAoBGC2gwtmO3qoOvnmm8W0+eZPSDPRrt3HcemCALOdHROazn2iysCY4jOlmM22oqTkLEG1vE8mTlxiTAMSE5bZ/u8AOUEazDYAAACQ78BsBxfMdjThCdD226+XNBJ77PGCuK7jX3sUhPWDh0rTy2zoXGVMkys2trvN2TcPKTelyQU89Fntd9PFlxrThIFTBoEpPlPCOM4wrh829M4+xLIpTSKOPrqfvE923LE7TZmy3JgG+BOW2a6C2QYAAAAKA5jt4ILZjh5cJ02avCgNxJ57voCZxyMA7pNowvXCP0Txq/D4fuEfqNL9YapYCctsn3zPszDbxYbpFTmFhum4AQAxTPdNoWE67kIHZju4YCKiBw8ZZ+PAM4+PG7fQmAYAEOObb36h7bbrJu+bk08eYEwDzIRltg+7tw/MdrHBjdBCVrE2sgFIBXwPFCYw28EFsx0t7rnnM2kYmMGDpxnTgPDBfRJN9HoZO/Zn+QMV3zudOn3uSgf8CctsH9OhuzDbZ6a+Ichf0MgGAOB7oDCB2Q4umIjoMHLkXNpyy6ekWWjbdoQxDQDAny5dvpT3D/Poo18Y0wA3YZntXvKZbZjtogKNbAAAvgcKE5jt4ILZjgZTpiyj3//eesVXScl5xjSg8cB9Ek1M9XLLLXXyPtpssydlb7c3HrgJy2z3tMw2hpEXEzltZDc08L94+YXnQDDbACQHZrswgdkOLpiIxocndDrkkJelQeAJ0UxpAADBOeGEN+X99Je/dKNvv/3FmAZYhGW2m9/zfAGZ7foqKhUnjk+eRSmV19ab0waB8yutonpTXB5jbGQ31FBFSQUNsVdZQyrKnHNZVmM55YaaWFgMazsrfRmVcfqKWE5+4blS4EZ2bblV/qzXcT1VlVrnprzWFA9yStz3AMPfBYa0meD3/WD8HvKkiQDe74HY/V5G+m1q+h4QoeL7Qj/GMnKiIqKUvwcYn++C2nL3vVxfVSrSl1Otk6aWykU9V9U3/r0Psx1cXE+mBhgIj/326y3qoZr+9rfn5czjqJPogTqJJn71wj9g7babMHfivjrwwD5y3ZQOhGe2m973aoGZbb2xVF8rGz6lVcJwp2Oc09kmTNIsn7uR3UA1ZarBrJntIRVUUlEjYllWGqNPFiZdGmj+LNPT241vv/AcKqVGdnmtOS4LeBvoICQM90W9qOuE90o2vx+84Wnep7nG9T3A97u6TxvYSNvfBb7fA1qaiCrl74EE9cTmWv4dketsqMW6oKreTsPbaua7Me99mO3ggoloXNTM41ts8SR9/PEcYxoAQOrMnLmKdtmlh7y/Dj74ZWMaEJ7ZPuju1wrYbPuFBSWijWSHNMvnNttKiRvP3KMd69WKiXu9ZLBslMe2HlJhN8r9wnMomO0ix3hfcM+j3hPpIZ17yW+buPAk+24kzN8DLP8fxZzvAdePaNFUNs22TOPEifoUy7W6Afd8l8Bs54dgthuPRx4ZK40Ao888jjqJHqiTaJKsXiZOXOLMUN65MyZMMxGW2W52dyE9s52oka3FWUMAuSe3lMqdXu9y0YCye3jFsmwY6/mJxlRsaKg2LLSeG156uN34Etvq+ckeELWfcmv/pVW1VCWXg21npY3tRw1XTHUYdOpm26dnW2twe824WvcLz6XSMtu+9WgID1KPApjtRkLWj/uecPVsx93LhnvJdB96wrln03jvefbv2rcnX+d7xjHj9veVd9lUHrkfdR2qbYLja7b5vjZ+F2jfAzKNuJftUTG5vqfTUWZmm+97/X7W6kJPb39/uHu+YbbzRXztmhpgILeMGjWP/vCHruL8V9PVV39gTAMAyJyXX55EfUqO5C87yYaefYzpUoHzUPltbFVuTJMv8N+AVJWO2T74roI329aQv6paFeduwMpGrv2pGtTSjMc1vjSccKuRLg27DOe81XN7niGGnF7uxw73Ljv5+W/nNPxU493Zzk4fkFTNNhtkZ4ipJg5XBtzPVPuF51Kpm+1E9WgI52spYT2KZQHMdiMh68Q2pgo2tuq+8qaNu5d87kN1ndj3IZtoX7Pt2beVl2d79T2j74/zFNvI68a1X0N55H5EuGMIU8P0PcD3J+/fdI/KOGeoufVD2xBe4WHnfo+ZNKLSNtvyBzbtfEtidcD1Zt3XnM5QPwKY7fwQX+umBhjIHfxctnrFV5s2w+PiUSfRA3USTYLWy+cHnMGJJbMeftaYJhVgtlM324fcWXQ921aDt0Q0jsqrakXDyrCd0+jSwsUyN8C4YiTeeB0OV+kcuFHszi9uOch23vR6eEBSMdvSHBuHi3IDO5bez1T7hedSaTeyvfFBwv2WBTDbjYS33oR5YjPk1IWIT3gv87LrHmQS3IdqPdVwbV1dK7XlYj9VIpyvS3F9OvNNBClPipi/B1jxI1n8vwcshXFfp6q0vgfEeS3lvw/yRxB3Ostk68ZaLdt/Y7S0MNv5Ib6XTA0wkBt4oqbS0tfEea+mPfZ4Qa6b0gEAsgcbYnHTSa7/w+XyeW5TuqDAbKdutg/v0LdIntn2xnEDXA2/jIvzbqN6NFUPp0+e+vaexpcTrtKbloNsp697wwMS1GxzA9rUo82Ka1zn8zPbfucxSLjfsgBmu5Ew1Zuq6yD3Mi+nch/qaVIJ19elseYfAXjd6jGtUj2onC5IeVLE32y77+9E3wNKhWO2+e+B9YNs3L3LdVTOjxvFzrk04CJMDSdXwGznh2C2w+Xgg0WDUxjtnXfuIXu4TWlQJ9EDdRJNUqkX/mGLZ/zn+++YY/rJdVO6YiMss31cVSH3bMseLbvR48TZjW29Z0IOC040jNzquYjFi8aZDLcbZUmHH9v56OUzLgfYLlF4QAKZbd/nNlmGCZS057dd8X7hOVTKjezA9WiHJxuhwMsCmO1GwnhfWPdwrd+97NrG5z5U4fow8Lj96Ok94abtHZPG5bLfnCDWuYfbeaY76PdCiujfA9JQVwzR7lP92WzziBcnvZy9PPf3dapKy2yr88nL8nrR01p1FKszAW+r1ZsCZjs/BBMRDhs7dqKhR19FD5acRltu+SSNHj3PmA4AkDv4By4eUcKG++ij+xnTFBthme2DHis0s82NIQdr2J8TZzekuIGt0jnDNHmYqWjQyu30BpdpGx6C6qQRDTC1nWt/WrhrQqP4vN3LSbZzpbd+TEh1cqQgZpt7oK1jiuH0XInGdU1NvA3Py/dsq4ZzkHpU4UHqUQCz3Uh47xcJG1brRzbzvey5l0z3ocpbbSsMcfx+7DSmcDsulq+eRhlxe12/NuV2Ab4XUsT9PdBg36e8jzKqsO91/+8Bc/ooKSOzLYj/McWqI7exFvXCP8Cp68MGZjs/xNevqQEGsos40Q76zOMmUCfRA3USTdKpl3HjFmKGcg0+h6kqLbP9aCGZ7XTJsNGab5jNdpbU0MD/4uUXngOlZbZzAMw2iDI5/R6IgBrzewBmO1XNoF4tK6nOXmPVVbp/5Km0I2f0aumEtew1wwoU8gtPJE5raoCB7DF0qKgLcZ4VpjQAgPAYNmyWM0lhdfWXxjTFAv8NSFXpmO3DYLYFMNsFpZQa2eJGc/deZQOrB4zzhtkGUQXfAzZZ/R5o/Hs//8x2HVXy+S/RzTab75YU55ln9KKWTjotjV94EnE9mRpgIDvwRExbb91VDh8fdPAVcji5KZ0O6iR6oE6iSSb10rbtCLF9tTTdY8f+bExTDPA5TFXpmO1D73sZZrvYQCMbAIDvgcIk+2ZbmeFUcPdS+0v1aPM+9G3EesteItYt7r329mbzul94MnFZTQ0wkDk8AdMhh4gGpmjQY+ZxAKJHu3Yfi/vzCdpiiycznqE8X+G/AakqHbN9SIeXYLaLDTSyAQD4HihMcmO2g/UUS7l6mYPKY7ZlHpp5t413nImuq6SSyjrf8GSC2c4dJ588QJzfatp++2fpl0HDaP2wOokprU4262TdzPnOftdNnGxMkyvWjZ0QO+YFS41pcsKyNc5+148cY06TIunUiVMGPn5DfMbk4Di9hHH9rJsyPbYPsWxK40fQeuGyO/sQx6THqQnTjjrqNfH3I9gPYo15X2UbPoepKh2zfdidLwqz3TL1DUH+gkY2AADfA4VJ9s12GPKYbTbL2jo/v23qwU7FbLdo0UI2rLyoBhc+s/l5gaBaUGV8Zjt4Ppl98tsDxIqE3wccdLtsfG5q3oIXJC0M8bn6ZMMmFiTTDfFhfYr/HIKkT/UzjOMM4/rhRyvEguRBQ3w2PvuIT7Ei4Xdk6/E8Q3lJyV0Cvl/bJ8xHfTbmfZXtTyZVpWO2S+/vAbNdbHAjtNAxHTcAIIbpvik0TMdd6BSE2fbKx1Srdb/wZFKNLpA9eLZxq+Fe7cw8LlYcvOm9ZLNO2Fio/bIpMKXJFbrZ5h5AU5pcoJvQ3/ZqYkyTKunUiSoDY4rPlFwcp5cwrh/dbAeZ00AnaL1w2dU++Ji88V9+uZA23zz4DOWNeV9lm7DM9uEdamC2AQAAgEIg52Zb9jq7e4ctUh06rsttttksx3qm+blue4Zx1xB1bXi7X3gScblNDTCQHu+885OcEK2kpIu4Dj90wtl4KvT0uWb94KHOfjd0rjKmyRUb293m7JuHlJvS5AIe4qv2u+niS41pwsApg8AUnylhHGcY1w8bV2cfBiOcDbjsah98TKY0PEP573//pLh3q+mRR8Ya0yga877KNvw3IFWlY7aPvR3DyAEAAICCILdmm41s7DVc2VN8z7br1V/aDqURt8O9vdmm8ETitKYGGEgdnmDpT396RpzTajrnnLeNaYKAOokeqJNoku16qaz8VN6/f/hDVxoxYpYxTaHB5zBVpWO2j7+dJ0iD2QYAAADyntyb7RQmS4u4YCKyw4IFa6hJkxdlQ50/ed2UDgAQbTp0GCnv4y22eIqmTFlmTFNIhGW2T7y3D8w2AAAAUAjk1mzbPcjZ79puFMFsZ4ezz35bNtD//OdnMn6FEOokeqBOokmu6mX//XvL+/nAA18Sf1MK+5V9YZntw2/vDrMNAADFhmlCsULDdNyFTq7Ndm6e2W4ccblNDTAQHH422+oJe5I++miOMQ0AIH9gg73PPr3kfX3AAS8Z0xQK/DcgVaVjtg+5A89sAwBA0cFmtJAFs50L5eqZ7cYRzHZm6DOPDxgw1ZgmVVAn0QN1Ek1yWS+jR8+jrbbiyQ6r6dprhxnTFAJhme2D7+orzPaZMNsAAFBMwGwXJrk323hmG5B8f/ZWWz0lG+OXXvqeMQ0AIH/hGco339yaofypp74ypsl3wjLbzTq+DrNdbHAjFIB0MF1PID/h+ixkFev1mluzLWS/87oQFGWzza8zUq/X4dccmdKkQjZfMbRgwVrabrtushF+xBGvGtOkC34AiR6ok2gSRr20bTtC3udbbvkUvfPOj8Y0+UxYZvuge96A2S42Cr2RDeVGMNuFRU6/Bxoa+F+jCmY7F7KGkXMDJR48s51NfturCRdQsm7KdGOaVNjYsZOTHy+b0gTBep6zp8imWj7XWegTKAFQ7CjDze/hLrQZyvlvQKpKy2x3gNkuOmC2oXSU1+alvopKxZdqSXmtFl5PVaUirLSK6vW0KVJbrhsODde+oof3e2BIRZlT9rIaj1NuqKGKkgoaYq+yYunLqEKLsMLLqIzz0SNCVuDrtbbcOg6+DuR1UkpV9Vo8hwW6RuzrSeRVXmuKD4ec92wXkLiuTA2wKBBVs33FFUNEFvwu3ty8GijKdVKsoE6iSZj1whOl8X2/227PZfzGgSjB5zBVpWW27+gHs11swGxD6SjvzXZpqds0KQOeodmOUUvlJeVUa4yLHq7vgSEVVFJRQ5bFbqCashLbQFvL/AepRDfbnL7MTt8wJGbEhSkvU+Fy2zLy+vawlJLZVj+MmK6JwGbbgn98gdnOD0XZRKwfOYbWD6uTrF2W+Xur2bCr/NI174U+pBQAYIYfHdl55+7y/m/WrK8xTT4Sltk+4h6Y7aIDZhtKR/lvtquoqlzrtRQmq7RcALMdp4aaMk/vtmao46SZamnaY6mGVCjTHr7SNtv2deIY5qI324mGjutgGHkho0+W1K3b18Y02QB1Ej1QJ9Ek7HqJzVDehc4+e6AxTb7B5zBVpWO2D7/9BZhtF6pnQ6O0qt6c1g+fxllUhpu6GtncE+UqDw8JzUFXlKvHy62GmopYGUSj3dl/gm2g8FUIZrueDbZ9P9ey8a7V7lWOc+4D22yxEVPx8rshkZk2mG2xTbk9tLik1I6TZSmncrH/Ek7vSiPCeH+qvFo+zronT9eQ5xTwN9t6z7ZSArMtv0OsOK9Jjzft4SkTs+06/64wVW/8d6FWmnLrWon9jSiunu0Z1KuldS3m45xpXG5TAwy44ZnH+T3abLQL+TVAAIDE8I9um232hPwuKIQZyvlvQKpKx2yf1OE5mG0XeiMrXZLm0bg9YHFmWze0DUNkQ1s2kLNpdn3y4sY4D0d1/L0ckmr3ksFsR0oFYbade1Pcg651n/Rimc2TNFbCCCc2Ud772nqGVxmx+iphzNjUcd78XLAM5zQxs8Y/BiQ221Z6x2D7lT8AfmZb3ZPu+85stmVa8cdKGeqCMdtimX+MkfWtwvhTPc/tXdbqoFjM9oxeLWXd5/PM5DDbyeHhozvtFN7wUdRJ9ECdRJPGqpfKyk/l9wFPmNa79/fGNPkCn8NUlY7ZvqXXAJhtF56GU1okzSPCZpuVC5NrzDPJM50w25FSQZhtaVbFvce92Mr4qntVLJdzz7L48pU49zDfr2I96QgUz33t/R5Q6559Jk3jDVflc9DMdwqYzLY0x8Z7LtkwcqsnvJDMtqxPvS70OL9lQcGb7Rm9qKW87vL/fdt8/5gaYMCCZxrfY48XxHmqpn/84yW5bkoHACgueIQLfy9suWV+z1DOfwNSVTpmu+3zMNtuPA0nY5inoRU3pNOUhwt3o9zp8bLXZWOtivMod+WtN+IzGUaa1GyrhrUeJ5Yr1ERJZRWOQebGtAzj4eexwPi0pv0Y960pWTyUkcaOHWsvxcsUVxhm27rfeLI0V6+lvCdVb7M7vVyW91qie5oJy2xr+8gAr9mW97Lv/ZbIbFvbSlNdIM9sq/MurxX1XL8e57csKGSzXVdpfa+3zHeXbYuPxdQAAxZt2gwX56iaNt/8idAa1KiT6IE6iSaNXS9HHfWaKEMX2mmnZ/N2hnI+h6kqHbN9fieYbTeyMWsbRYlo2HoaU7HGlc+QTm/6OAyNcmfdjrPDVN4xQ575MNLkZtvucR6i4jw90M42WgOcw+SyT1rTfoz71pQsHkpby5cvpyuvvJIGDhxoh8TEYRzHaXQVitl23W9OuHXfxe43ezi3NsybhxUnnr/Bc1/LbX2GkTv3LG8Tu5/lM+Wm8jjhdp6qHGnc/4q474EEZtprtqUxFy7aujdjPdvue9bzXRCysmG2rfqxf2jR4/yWBQVptusqrb+HLXtRYdhsS43dWI0ybdpYPVd/+ENXGjToJ2MaAEDxwiNd1CMmhxzyslw3pYsyYZntM+/De7bdmBqv3jC1zp/SkOtw75ghDxfeRrnWQFMNv1T3qcx3AJKbbU/PtmyIe/fJjWirkS0nNasRDW/OxC+tY9w1GfetKVk8lJFMhtvPaLMKxmz7hLPBVvcW93yzwap1jTpxG+N44u9rzj82CsXev7csbKTVfrXZ0aU5l+HC7OuzpteL/WQwskWhfw9wD7S1rxju4d/enu0GsY1hVItQXr5n29ds2/XAYXqc37Kg8Mw2ZiMvNngSJG5AM/fcM8qYJlegTqIH6iSaRKFevvxyIe2wQzf5XXHSSf2NaaIMn8NUlY7ZPucBmG03hsZWXJha509v41qP94Y7GBrldoNPzpDMjedU95kCSc22CtM/E/V68aRqsoFtG3RTWtN+xBqe2W5c6YY7kdFm5bXZzheSfndkD9f3QLbV0GD9+NaISstsZ4FCHkZeaIKJiIcbzzzxETeeKyqGG9MAAIBi5Mi59LvfWT/OVVePN6aJKmGZ7fPvfwVm24WxsWuZ48BDOpM2mA1mm8O4t0ptZ5tqZ5+uYeSGfTr5JCeh2bZnI3cPCbWHiSpX7IRzb5cyy8o4+6T1Mc7qOVHMRt54UoY7kdFmwWyHQBr3c7rk1GxHQCmZbfEH1/nuTRvru5nzgtnOD8Fsu+GZx/fZp6dsNP/9772MadJh/bA6B1O8TjbrZN3M+c5+102cbEyTK9aNnRA75gVLjWlywrI1zn7XjxxjTpMi6dSJUwY+fkN8xuTgOL2Ecf2smzI9tg+xbErjR9B64bI7+xDHZEqTCqbzwq8B4+8N5umnJ8RtE1X4HKaqdMz22fe/CrPtwqexm9KQzrTMttUj4jwTKvMQ+3Ly1vLLcBhpnNmWx6Xwec81m2AeMs5pXBOkxd6R7Qw7NaVNYJxd79n22z+UU7HJTmS0WTDbhQXMdmGSfbPNw8hTmHlczlaeH0PK+W+OqQFWrDRp8qI4J9l//lJk6mCKzxUbevZx9ruxVbkxTa7Y1LyFs282JaY0uYANm9rvb3s1MaYJA1UGxhSfKWEcZxjXz8aOnWL7EMumNJnCZVf74GMypUkFv/Ny7rlvi6Bq2nrrrjRx4hLXNlGF/wakqnTM9pG3vgSzHQ3YQGsGPIc9XIXeyIZyI5jtwgJmuzDJjdlWP4YGBWY732jX7mNxPqrlEPJvvsluQ1lk7GCK18lmncBsZ8eEplMnqgyMKT5TYLZ5k2DnNiyzzbRsOVAEV9Mf/1iTFzOU8zlMVemY7aa39IXZbnTsYYyumY5htqGICWa7sIDZLkyyb7YLV9k0dvlMefkHsoG85ZZPycnRTGkygY2nwhSfK9YPHursd0PnKmOaXLGx3W3OvnlIuSlNLuAhvmq/my6+1JgmDJwyCEzxmRLGcYZx/bBxdfaRBSNsgsuu9sHHZEqTConOC4+I+etfn5XfJ2Vlr8t1PT5qhGW2/3nHazDbxQbMNpSOYLYLC67PQsd03IUOzHZwwWwTDR48TTaMmVtv/diYJkxQJ9EDdRJNolov3KO9++7Py++Uo4/uZ0wTFcIy2wfdgWe2iw5uhEJQqipW8wJAPgGzHVzFbiLGjVtIm232hGwU8zByUxoAAEiVESNm0+9+Z323dO78hTFNFIDZBjkDZhtKRzDbAEQfmO3gKmazzTOP779/L9kY5onRTGkag2L/ASSKoE6iSdTrZcCAqfL7hamu/tKYprGB2QY5A2YbSkcw2wBEH5jt4CpmE9GkifWKL37VFxtvUxoAAMiEK64YIr9ntt22JpIzlMNsg5zBpgmAdDBdTwCA6ACzHVzFarZzOfN4phTzDyBRBXUSTfKlXi6++H/y+2abbWoi98MezDYAAAAAUiL3ZnsG9Wppv+KrZS+akUfv1faqGE1EZeWnsuHLRjsXM48DAIAOz0i+447d5fdO8+ZvynVTusYgNLN9O8w2AAAAUBDk1mxbRrtlrxlEdZWW2ebQXi2d5XxSlM02v85IvV6HX3NkSpMK/FqhZYcdS59uti+Vl1xCt94a3rufU6EYfwCJOqiTaJJP9cI92rvtZs1Qzq8EM6VpDGC2AQAAAJASuTXbdVRZ0pLYa+tmW7jtvOzdjnJj9be9mnABJeumTDemSYWNHTs5+Q0pvcqYBgAAcsVHH82hzTeP1gzlMNsAAAAASInG6Nmuq7SHlFuJ8kbFarZ52ZQmCuRTb12xgDqJJvlYL/p7/bt2rTemCROYbQAAAACkRG7NNkt7ZluRh0abFeXG6vqRY2j9sDrJ2mVrjGlSgQ27yi8b5h0AANLhyivfF9+91fSHP3SlUaPmGdOERbhmu+W1xkgAAAAA5A+5N9uFoyib7WIFdRI9UCfRJJ/rpU2b4aL8luGeMmWZMU0YwGwDAADICePHjy94TMddDMBsBxdMBAAAhA/PSL7HHi9Iw73//r3luildroHZBgAAkBPYjBayYLZzJZ4gzTOE3AETpIHMQZ1ED9RJNMn3euEZynfayXolWLNmfY1pcg2fw1QFsw0AACApymyb4vIdFsx2uOIJ0irz8EXbMBEAANB4DB06g7bY4klpuK+9dpgxTS4Jy2w3hdkGAIDiAma7cGkMs41Xf4FsgTqJHqiTaFIo9TJs2CzabDPrlWDPP/+NMU2uCMtsH3R7Pzbb1xgjAQAAFB4w24VLo5htfg0YzDYAAIA0aNt2hDTbW275FL3zzo/GNLkgNLN9y2sw2wCALDJ7LA0aNJZmm+KiSpAyc5q+famvxqDhw9M/1mydpzTycZvtMdSzXTtq5/AIvdsQv42k4V2RtieN0cLG9HzE2a7nmOThuYYV2GxPGh6rz2xfsyLvQYMGSdQ+hk8ypMsyuTXb/s9sYxg5yAaok+iBOokmhVYv1177oTgmnqH8qdBmKOdzmKrSMdsHd3gTZhsAkEWyYSL1PFLJL919B9nOlCbd/TGZbKuTRj7xZtttoONpoHcfUWZcSzumJ7V75F1q4OUGLR+/8BBgpWS2h0/KXl1ozJ40ycqPTffY2TRpeCGY7cISTAQAAESHAw54SRru3Xd/jhYsWGNMk03CMtuHtO8Nsw0AyCLZMC45MD8JCbI/U5pMypmtY0wjH5fZbniXHlHGOCmJjDMbcqtXvOHdR+iRdxucOF4Pq3ebFQWzbTGbxg4aRGNnL4PZjqCibLbXjxxD64fVSdYuy7zRuW7KdCc/XjalyRVqv/JYDPE62ayTdTPnx4554mRjmlyxbuyE2DEvWGpMkxPEtaL2y9eQMU2KpFMnThn4+A3xGZOD4/QSxvWTyX0ZtF647M4+xDGZ0qRCLs8Lz1C+887WDOWHHvqKMU02CctsH9PhRZhtAEAWUcZFfg6nscPVMNpBMbMh4oYPUsN3RRphRqzthtNwYUrUsFs5tFc3QtxLqOL0/FLa96TYvmX47Nh2el5eTGnssEljxX7YtNnh0liNFXF9h9MkGSb26V1OWEbD+fELN5UrCXFmu50wx3bPtW6S40lgtmU+hp5tNuE9k+WbPViZmW2+Puzrgtdd51zUFaeT6flatepu0NhJdj26t+M0k+y6KwyznejVXzr58fw2l9XUAIsCv+3VhAsoyYY53tixk5MfL5vS5Aq1X8YUnys29OwTO+ZW5cY0uWJT8xbOvtmUmNLkAr5W1H75GjKlCQNVBsYUnylhHGcY108Y9yWXXe2Dj8mUJhVyfV5Gj55HW2/dVWRfTWec8ZYxTbbgvwGpKh2zfWE1hpEDALKJMi78qZsPNsrS0MR6+0zpx3rNr76sYwr35GXeNxthZYjY+PI+ffahI/O0TZdEGWa1P6+x1o6TTZ3YRpqtQGU0nJ9E503G2+EBMPVsj+HntHnItzDd/r3QZrPNPddeo+48s/1IT+oplvPCbMsfYrRzrK4Xl4G2zzlfN5zOu2zXBZtrNt/8zDZfb4XSsz2jV0sq8Tygza/+8oblg2C2w0HtlzHF62SzTmC2s2NC06kTVQbGFJ8pMNu8SbBzm29mm+EZyn/3u2qxi2rq1m2CMU02CMts39xzAMw2ACCL6IZEN4F6uMu0WqbEZXj1bT3LbIac7fT89bT6NonCvfHecB1TGi3MMVPKwGlhk4aL4+Oebg5nU81m35ufWudP17lh7PPjF56s7B5cZtuDdwi4m2TDyM1GfUw+9GxLYyzOqV13kkR1pML9lj0UTs92S+o1w15Vwqu/ss6miy+Vho3JxtBPbiCr/LLR4E4FtV/GFJ8r1g8eGjvmzlXGNLliY7vbnH3zkHJTmlzA14raL19DpjRh4JRBYIrPlDCOM4zrJ4z7ksuu9sHHZEqTCmHdV5WVn0qz/fvfP0nPPTfRmCZTwjLbXQbAbAMAsonJkHjDnV5gDT29cVn1Qnt6GdX2epg3zi/cG+8N1zGl0cNs4yaNteoZlcaafyDgNNxrOlaYbu7N9myr58Wffucn2XkLSG7Mtt+2/iY8F7DSM9vi3M5WPdl2vPfcqnU93G/ZQ2GY7RnUq2V8LzZ6tkG2QJ1ED9RJNCmGerniivfFceZuhvKwzPYT/WG2AQDZxGRI9PC4odyG9MZla3i2MrKzxwqjpNIoTHnp4XH7Vgbek96EN8+4MDbTfT1l4vz5mV5rf2zE+VlraZgDlzFJuDefAOhmWw4B7zlGmzk8wau/PGbbta1uqvmZ7Z7WM9sNvJzAoGcbVlpmW51DXnZ+1FDm2z7n/OOJ95z7LXsolGHkjuEWjRSHjI025+nuGZfD1e38W2pd6amGJxKnNTXAAAAARIOysjfEd3UX2mmnZ7M+Qzn/DUhV6ZjtB/vCbAMAsokyHF7j4TIltjHlodDGib4mSZPjei5ahLPBVkOp5fuL9fxlvinuWxhtaaQ8+45NZqbhzdMQxoZKGWsLZdbsdWXueDloGdX58Qs3lSsJ7p7tBvc7sZ2eaVMvtjfMb9s8es+212wLZvOEd05diDj7mnNPkKbFm5Y9FI7ZzrbUpGua2XYNS2cjbg9dTzU8iWC2owfqJHqgTqJJsdTLsmUbaLvtuonjraa77hppTJMuYZnt9j0wQRoAAGQBNsIGkx5B3Ga7sGClbLZNcQZmp5hep6Cf2U5bqkeb842Zbe6l9vZa83qq4ckEEwEAANHnqae+kmb79NOzOzt5WGb7yi49YbYBACAj2IT19fZqRxeYbRu73uJGSGjIof9Oz7Y2yiAw1ugG3r4Qerb5+ezsP56d2GyLncqh6qmGJxPMdvRAnUQP1Ek0KaZ6mTlzlTjeatpqq67yXdymNOkQltm+5Km+wmyfAbMNAADFAsx24ZL7nm3rueh4YmY5deXebLdo0cJQZquxik984hOf+Iz6ZzsBvw7s0IDpk38yqSods/2vrq/CbAMAQDEBs1245LpnOzfCMHJggTqJHqiTaFJs9dK58xfimKvplFMGGOPTISyzfe4T/WC2AQCgmFBmu1AFs51vcptt4Za1Cc84zjQRWoDwJIKJAACA/GDixCXSbPN7t03x6RCW2b6ommcjh9kGAICigc1ooWM67mIg92abJzXzDMdu2UuEZiKP2Rbi3mmVv7fXOpXwROK0pgYYaDxQJ9EDdRJNirFe/vKXZ8RxV8tnuE3xqcLnMFWlY7avrIHZBgAAAAqC3Jpt22h7nofmSdMyN9zhCyYCAADyB56NnM32gAFTjfGpEpbZvrj6FZhtAAAAoBDIrdn2GaLtGsadP4LZjh6ok+iBOokmxVgvHTqMFMddLZ/fNsWnSlhm+4qumI0cAAAAKAjQsx1cMBEAAJA/cI82m+3zznvHGJ8qYZntq2tehtkGAAAACoHcmm1WLp7ZbhzBbEcP1En0QJ1Ek2Ksl4n2JGl77fWiMT5V+BymqnTMdtvur8NsAwAAAIVA7s124QgmAgAA8ovttrMmSVuwYK0xPhXCMtuVfTBBGgAAAFAQwGwHF8x29ECdRA/USTQp1npp3vxNcezVNGzYLGN8KoRltjv0zobZrq+iUlFgLrSitKreP21pFdWrT794b3gmhJknh7vORSmV1/qciwypryqP7as0d/sBAACQH+TWbGOCNAAAAI3Heee9Lb67q+mqq4Ya41OB/wakqnTMdqes9GynYmZV2kRmNSxjnAlBy19fS1WlCX58SJP6qlJhsKuott4OE/spF8a+Sq0DAAAoOnJitqWZ1n9ENoAJ0kAWQJ1ED9RJNCnWernnnlHi2KvplFMGGONTgc9hqkrHbLd5vBfMdlqkUv6s779eGHgYawAAAG4apWc7TwUTAQAA+cXYsT9Ls33AAb2N8akQltm+tGs23rMdxGCqde+nvo2ezhBeXmr/gl5aTrUqvFYbSi2HbMenLxXGlHuB4/I0bSv3X05V5WKbdPI0lp97nUWZ7bzLZd5iPUDZeXh43H6M+/CJU+uufVv7c8w6x3F57GX9PMPQAwBA/pBbs11Y4r9zpgZYFFg/cgytH1YnWbtsjTFNKqybMt3Jj5dNaXKF2q88FkO8TjbrZN3M+bFjnjjZmCZXrBs7IXbMC5Ya0+QEca2o/fI1ZEyTIunUiVMGPn5DfMbk4Di9hHH9ZHJfBq0XLruzD3FMpjSp0Jj3lWKrrbqK468Wf/M2GOODwucwVaVjti98+o1cPLOtjKWf6dM+9Xz0dK5w7sktcZ5JlkOoy2u1eBtnW096YWqNxlhHL5c0vvawbzbEqeTpW35hbmutvKtMz1b7lN2Yn3EfPnFqnT+1fdeWx4a2x86nXU7dhPvtBwAAQOSA2Q6udExEWPy2VxMuoCQb5nhjx05OfrxsSpMr1H4ZU3yu2NCzT+yYW5Ub0+SKTc1bOPtmU2JKkwv4WlH75WvIlCYMVBkYU3ymhHGcYVw/YdyXXHa1Dz4mU5pUaMz7SlFa2k/svppGjpxrjA9KWGb7oqdy9cx2ItOnf+rb6OkShenrYrmce3/FCZOY8jblaYcH3jaFPOPD9Z5tdx5pld24D584te6br2awOUyVxUEz3wAAACINzHZw8d84UwMsChSr2c5mncBsZ8eEplMnqgyMKT5TYLZ5k2DnthDNdps2w8Xuq6m6erwxPih8DlNVOmb7/C7989ls25OCeXuBfdPb65IUtw2Up0+4MY9Myu7pgdbx2z4uH5WHFs7Lajg5AACAvANmO7iibLY3XXypNGxMNoZ+cgNZ5ZeNBncqqP0ypvhcsX7w0Ngxd64ypskVG9vd5uybh5Sb0uQCvlbUfvkaMqUJA6cMAlN8poRxnGFcP2Hcl1x2tQ8+JlOaVGjM+0rRrdsEabZbtcrseMIy2+dWZ2sYeZwhtHpzlSHkYdcu02fcRmAMdw+tjg179uyjSg3tNqSPy9NnW+/+nfUgeerp1bo1G3nseXAVF6zssWHsVjqF2n/8bORJzrsnD372PDZTur1vtW7YBgAAQHSB2Q6uKJvtYgV1Ej1QJ9GkmOtFTZJ2yCEvG+ODEpbZvujJ13Nltm1DKA5ETv5VnoLZlttosLEW4bGJu2Lb6e+adk1apuVTWs6mMn5fxm295dLXA+QZX37t+W9P3kHKbpwgzUbfXt9PwvOu5yH34+khZ7PunOeYaQcAABB9cm+2Z1CvlvbfCH7dV56+Y5vFx2BqgAEAAIguPDHaFls8Sb///ZNy2ZQmCPw3IFWlY7Yvr87GbOQgZ8iJ2EyTwQEAAAAecmu2LaPdkt/9VVfpvFt7Rq+WeM82yAqok+iBOokmxV4vf/7z0+IcVNOIEbON8UEIy2xfUd1HmO2WVxsjQeNQ67yeC73LAAAAgpNbs629Z1sz28Jt52XvNkwEAADkJ//5z/+k2ebnt03xQQjLbF/21Msw2wAAAEAh0Bg923WV9pByK1HeCGY7eqBOogfqJJoUe73wTORsttu1+9gYH4SwzPZ/qnvDbAMAAACFQG7NNkt7ZluRh0abVeyNVQAAyFf4Hdtstvmd26b4IIRltstrXoPZBgAAUJwMHDgwJ+Q6bz9yb7YLRzDb0QN1Ej1QJ9Gk2OtlwYK14hxU01ZbdTXGByEss33XywN4grRyY2Qi7r33XgAAACBv4b9lbF5nzpyZVZQhzmXefsBsBxdMBAAA5C8HHNBbGu6JE5cY45MRltl+YkAGZhuCIAiC8lEw22mKn9UWDZR4MEEayBzUSfRAnUQT1AvRxRdbk6S98spkY3wy+BymqnTM9v2Z9GxDEARBUD7KZLa/eq8DjR1wJX01sDVNfLecvn/vapr0/jUufhh6Lf0w7Cb68et3XSZYp3DNNs9GXkKV+fhSbYPQWAUAgPylc+cvpNlu23aEMT4ZYZnta57pD7MNQRAEFZdMZpuNNq3uQgun3E1TP20r16eO7kS/zOkvthjvsGb5B/TdkBtdJljHz2zPmDGDfvzpp6RMmzbNtZ1O45tt+9VfBSCY7eiBOokeqJNognohevnlSeI8VNOuu/YwxicjLLN96VOvw2xDEARBxSU/s71p1eO0aaXFul8eo58n322Z8OnVRDNsltbS4HcupAP77Uh3D2/nMsOMn9ke/vFoeuGVT6jbS6Pomd6fUk3PT+ipFz62qRN8Sj36fELvvvcRTZ061bWtonHNNr9SuyWVFEjXNhqrAACQv8ycuUqa7T/+scYYn4y8M9tDKspkoUtKyqhiiB0oFAsvobKaBhVKFXaY2qZmSA2VldWQSqFrSEWJK8+GGs6zQuSixPmJPEwbG6TKVFHjs88G/7JAEARB+S8/s71x+X8lG5YxVRIOX/f17bTp20qi7ztI3hl4Ae3bdwfa7aU/03mvneLkoRtir9l+tudgurrTDDrntgV05i3z6Iyb59BpbWfRqTfMpJOvnyGYSRfcNoXuefRt+vyLr1zbKhrXbFvDyGN/u3XwzDbIHNRJ9ECdRBPUi8Uuu/QQ56KapkxZZoxPBJ/DVJWW2X4yG2Z7SAWVKHPawMbXNsIcXqFMawPVlCnTrKVRSmBw2VzHjDrnI9YFsSCxrTc/X2n79tsnzDYEQVBBy89sr/+lSvZor1sSg8NXj7+N1k643THb9YNa0+AJA2nXF7elHbpvTR0H3xlniL1m+43+79A3P66j0RN/pdFfr5Z8NmGVZFT9Sho2ZhW99L81VNVrBn348QSaMSO2raJxzXZhCY1VAADIb8477x1ptgcMmGqMT0RYZvvKJ7M+QZplhk29zI5pNpnZRAZXN/NslsXyEN2AS1MfzGq79gOzDUEQVJTyM9trFz1Kaxba/Pwo/Srg8GVjb6XVX91Gk8dfS+cMbErnvtZUbnNJ7fm09Utb0J6P7RRniL1mm9d/+41o/foNcaxbt4G++2kjteo4ix7t9imNGTvBta2eh/632AvMdnDBbEcP1En0QJ1EE9SLRceOo8W5qJafpvhEhGW2y6tfy7LZZqNq7GXWerZlGu6dtoafxRtwNtQ8HF3ZXW9vuVji9LbBdvd8K/E23t5vfcibyE/fp1iuUOUR+46ZewiCIKjQ5Ge2f13wCK2e35lWz+tMq2w4fOFnN9OXI1vTibX70aAl/emsQSfKbc7s1UKa7W07bRlniL1me8CAAbRp0yb69ddfXaxYsYIWLlxMI8cvpMvvniI+F9Hbb79LU6ZMcW3PhG+29UnRMIwcAABAdOAebTbb3MNtik8E/w1IVemY7du6vZo9s209S60/mx2TjNOMLZtc6aV52DmbcPX8tFz39ozHess5H2couszPvyedn/VWZZH7VybdUw4rD/4hwE7r6kmHIAiCCk1+ZnvlnIdp5eyHaQUz6yFaLuDwug8vo38POYHeXjKAbv26Ne3dfTu69o0raceu20izvc29W8QZYq/Z7tevH61bt46WL1/uwOlmzVlEdV/Mozffn0l9351Hsxb8RuO+nk99X+nv2p4J32wXrmC2owfqJHqgTqIJ6sWCn9Vms83PbpviE8HnMFWlY7afejMnw8jVs9mWZM9zAvMq4ysqYr3d+sa2LJOtG2u1rPV6e+Uy0/Z2JrOth7G86xAEQVBByc9sL5/5EC2b8SAtncZ0ol9+6kS1A86ni4aeQHWrh9EN48+lY9/fnZp024526/4n2ulVy2z/ocPv4wyx12z37duXVq1aRYsWLaLFixdLfv55IX36xXS65sGZdMoN0+k/lQ30zsjfqH2XH6h77/dc2zMw29kTGqsAAJD/bLfdM9JwL1iw1hjvR1hm+/kBWTfb7FVjw7plj3IS4xoz2zy0O96sSw0R8SKN1ZttSRpw3s5gzi0pk20w2PoyzDYEQVBRyc9ss7le0vAALZn6AC2e8gDN/fouavnuofTJr8Op7fiWdPrwfaTZ/ufbO9Pe/beTZnub57agEx4pizPEXrPdq1cvWrp0Gc2ZM5fmzp3n8OO0efTpuJ/pw9EL6fZOtfT0qz9Rx6pa+v6H+PdtN67ZTjSMXJE/w8m5vKYGGGg8UCfRA3USTVAvMQ4//BVxPqrle7dN8X7wOUxVaZntN4TZbnb6VcbIROhmWxpqYXgtc6qZZTathl5nV3rZMy0Msf7qL+N2nE78IdeNNQ/3FmGmYetK0sgLw+2k0Y20s6zKbKWR5YPZhiAIKlj5me3FU+6nRZPvp4WTOsr1L/pfSafWHkI3fHEGXfTpgXFme8c+29BOd+1AH4wdGmeIvWb76W4v0rhvl9MHoxbQh58Jc63xwSiB+HyoehC1v/sJGjV6rGtbReOabWG3K8XfYc97tvUw+R7ulr1IPuIdcUW5sfrbXk24gJJ1U6Yb06TCxo6dnPx42ZQmV6j9Mqb4XLGhZ5/YMbcqN6bJFZuat3D2vX5YnTFNLuBrRe2XryFTmjBQZWBM8ZkSxnGGcf2EcV9y2dU++JhMaVKhMe8rP5o3f1MUp5quueZDY7wfYZntF1/LgtkW9lT4XmFQRaHle7ZtY8vPTFthMSzTa0jv6U2ON7yWIXYba9uo60EyTDPq0rhraYxm215WZRRl08sCQRAEFZb8zDab7AXf3Ufzv7lXrg/peTk99/w5dEKPveiMIfs4ZvvAN3aknbv8kfa5dy/6YFzMaOuG2Gu27+7Ug1rd96N8t7YfHP9U94E0atRnrm0VjWu29cnSNM3oRS1Vj7a+HHHx33tTAywKFKvZzmadwGxnx4SmUyeqDIwpPlNgtnmTYOe2GMz2K69MFsVJfZI0PoepKh2z3TsbPdsQBEEQlE/yM9tssud9fQ/Nqb9brr/T4xJ6s+ZiGtz3CjqhZi867u09pdne8YFt6NGuJ7iMsMLPbA/98BPq/tII6vpinS8c//a7H9APP/zg2lbRuGZ7BvVqWWLu2Va92XWV6NnOAsVqtrMJzDZ6tjMh6mY7KMVgtidOXCKKU00HHNDbGO9HWGa7+kWejRxmG4IgCCoimcz2uIHX0eSPbqLZ4++mWePukma7b9WZ1K3jKTSg63n0bq8L6IiHd6VD39iVdrh3a5r6/RiXEVb4mW1m+vQZSfFuo9O4ZtuSNNeikeKgzLfs1S4hjxePrKJstosV1En0QJ1EE9SLm3QmSeNzmKrSMdudn3sFZhuCIAgqLpnM9oxpU2jiiMeo/p2rqeHT9vTV21fTuwN60KhRo5w03039ls6tOYsu63aJE+YlkdnOlCiY7UIRGqsAAFAYlJb2k2Z72LBZxngTYZlt69VfMNsQBEFQEclkthVTJgyVvdzcs/3NhNFx8cmA2c4PwWxHD9RJ9ECdRBPUi5t27T4W56SaqqvHG+NNhGW2737+DZhtCIIgqLiUyGxnSkGbbX4mWzRQ4smfV34pcblNDTAAAAD5Rc+e34rv9Gpq1WqoMd4E/w1IVemY7Tt6vJ6+2QYAAADyFf5bBrOdiqz3bOfLM9nJBLMdPVAn0QN1Ek1QL27Gjv1ZnJNq+sc/XjLGmwjLbN/VO81h5AAAAEC+w+Y1F+Q6bz9yb7YNr/7KU6GxCgAAhcGyZRvod797QlAtl01pvIRltitf7A+zDQAAABQCuTXbPOF4y7hXf+WrYLajB+okeqBOognqJZ4993xBnJdq2cttivcSltm+pVuas5EDAAAAIFrkvmfb9Lw2g2e2AQAANB5t2gwX3+vV1K3bBGO8F/4bkKrSMdt3dE/zPdsAAAAAiBa57tkuJMFsRw/USfRAnUQT1Es8PBM5m2023aZ4L2GZ7duf7gOzDQAAABQChWK26yrdPedq5Locxm6HtdQeHvcLTyROa2qAAQAAyD9Gjpwrvter5Tu3TfFe+G9AqkrHbN/T/TWYbQAAAKAQyL3ZnkG9WtomuGUvmjGjF7XM+hBy3odhIjbXvrQ0fuFJBLMdPVAn0QN1Ek1QL/HwxGhstrfaqqsx3gufw1SVjtnu2ANmGwAAACgIcmu2LaMte475fdtstjmUe5Xt5eyojioN+fF+vL3ZvO4XnkxorAIAQGFxyCEvS8MdZJK0sMz2/ejZBgAAAAqD3Jpt7dVfmtkW7ja7vdsyP7v3nNFMvctEcxkq63zDdbVo0SKWn4ZqcOEzGp+KoOnxiU984tP9eYWgWnBk0vRMqkrHbN/9dF+YbQAAAKAQaIyebfl8dTZ7tjlvzbxz/rzPTMy2SarRBQAAoDDo0GGk+G6vplNPHWCM1wnLbD/Sd0AWzHZ9FZXavxAoSqvqzWn94DxKq6jeEFdfVR7Lv7SUymtTzDufUee2vFYLr6eqUj4X5vMFAACgOMmt2WZZhlv/e5/dIeQG+Zhqte4XnkxcdlMDDDQeqJPogTqJJqgXM717fyfOTTUdcEBvY7wOn8NUlY7Zvisr79lOYJQD45NHfVWpNJW19SpdLZWXlFKVWo8S+jFk45wwMp9Sd14cJi4QmG0AAAA6uTfbuReb5VjPtNab7hqurg1p9wtPIjRWAQCgsFiwYK0029tt94wxXicss93+yWy8+isbxtKYB/fgRtRYm8iWwdax86wq185DbTmVlgtgtgEAAGgUgtlmuV79pQ0Jl0bcDvf2ZpvCE4nTmhpgoPFAnUQP1Ek0Qb34s9deL4rzU00TJy4xxiv4HKaqdMz2Hd365chse8P0dbFczsOgxUGWlJZbJjJIHl48+dTaYaWcpzCnMryEh51b6WUvuQrjYe7e/NW6nUe5nUdpVa2dnz2EXcW79m0P7ZbrKg87b7GcSjldqHzYYNtD82vZeNdq+XOczEPLx1hGLV8AAAAFR6GY7TDEfxtNDTAAAAD5y3nnvSO+36tpwICpxngF/w1IVemY7dt6vJmLZ7aFsVMmUU8j1z291Srcm961jRbmYJlb9fy2NNL8XDNvo0wxp2MjKvPg4eea0U1URjsP50cAfdmJt38k8O5b5ecsp1pOK08HfZ8yXhyHaz1BelMZvekBAAAUDDDbwQWzHT1QJ9EDdRJNUC/+dO78hTg/1XKyNFO8gs9hqkrHbLd7LlsTpHmNnzfMZQJ1Y854emr98kgUp+dvClc9zzzBWlWtSBcwD9NyKtsFSauHq/W4cC5/OdXKIeTxxr5cHJdzPlPJHwAAQMEAsx1c/PfS1ACLAr/t1YQLKFk3ZboxTSps7NjJyY+XTWlyhdovY4rPFRt69okdc6tyY5pcsal5C2ff64fVGdPkAr5W1H75GjKlCQNVBsYUnylhHGcY108Y9yWXXe2Dj8mUJhUa874KCvdos9k+/fS3jPEK/huQqtIy2y80itk2DGk2mkFPL7hOovxN4c66GhKepGdbhZuWU9kuSFo9XK0bwrl3midLiw0T53B7wjjVQ55q/gAAAAoGmO3ggtkOB7VfxhSvk806gdnOjglNp05UGRhTfKbAbPMmwc5tMZrtmTNXieJV0y679DDGK8Iy2zf3yJXZtodtO8OYnWHS9rBq9WowP3NoI4c/i/D42cgTDM/W83HW1XYcrky8Txn1PEzL/Gkaom3cLtVy2uumcF5WP1Q44Z5jqCqP9WybysjbAgAAKEhgtoMLZjsc1H4ZU3yugNlGz3YmRN1sB6UYzTazww7dRBGrpfE2xTNhme3bu+fMbNsGTxyInLRLnz2bTS4P6eY4niSMDaFPHlY+YluZj52X1osby0fl7clHW9fzUZONGcuo52Falp8ivXffwvjKoepsiD3bpVJOuZ5CuOu4eDg5h8t4UxkBAAAUKjDbwcV/G00NMNB4oE6iB+okmqBeErPbbs+Lc1RNNTX1xniGz2GqSsdsd8iK2S42/AxwlMiHMgIAAMgqMNvBhcYqAAAUJm3bfiTNdseOo43xTFhm+4GXYLZTB2YbAABABIHZDi6Y7eiBOokeqJNognpJjJokjV8DZopnwjLbd3R/A2YbAAAAKARgtoMLjVUAAChMpkxZJs32Xnu9aIxnwjLbtz+fjfdsAwAAAKDRgdkOLpjt6IE6iR6ok2iCeknOdts9I85TNS1YsNYYH5bZvq3H6zDbAAAAipOBAwfmhFzn7QfMdnChsQoAAIVL8+ZvSrM9ePA0Y3xYZrv9s2ma7XvvvRcAAADIW/hvGZvXmTNnZhVliHOZtx8w28EFsx09UCfRA3USTVAvyWnX7mNxnqqpunq8MT4ss/3Y6zxB2mnpmW0IgiAIykfBbENorAIAQOHyyiuTpdm++OL/GePDMtt3dusHsw1BEAQVl0xm+6v3OtDYAVfSVwNb08R3y+n7966mSe9f4+KHodfSD8Nuoh+/ftdlgnVgtvNDMNvRA3USPVAn0QT1kpyxY38W56ma/vjHGmN8WGb7jqdfZbPd2hiZCJhtCIIgKF9lMttstGl1F1o45W6a+mlbuT51dCf6ZU5/scV4hzXLP6DvhtzoMsE6fmZ7xowZ9ONPPyVl2rRpru10YLazJzRWAQCgsNl88yel4V62bENcXFhmu/I5no0cZhuCIAgqIvmZ7U2rHqdNKy3W/fIY/Tz5bsuET68mmmGztJYGv3MhHdhvR7p7eDuXGWb8zPbwj0fTC698Qt1eGkXP9P6Uanp+Qk+98LFNneBT6tHnE3r3vY9o6tSprm0VMNvZE8x29ECdRA/USTRBvQSjtLSfOFfVNHLk3Li4sMx2++f7Z8dsD6kok4UuKSmjiiF2oFAsvITKahpUKFXYYWqbmiE1VFZWQyqFriEVJa48G2o4zwqRixLnJ/IwbQxBEARBHvmZ7Y3L/yvZsIypknD4uq9vp03fVhJ930HyzsALaN++O9BuL/2ZznvtFKMh9prtZ3sOpqs7zaBzbltAZ94yj864eQ6d1nYWnXrDTDr5+hmCmXTBbVPonkffps+/+Mq1rQJmO3vi9oe38QUAAKBwaNNmuPiuN0+Sxn8DUlU6Zrtdd56NPFOzPaSCSpRRbmDjaxthDq9QBrqBasqUadbSKDX4m2021zGjzvmIdUEsSGzrzQ+CIAiCfORnttf/UiV7tNcticHhq8ffRmsn3O6Y7fpBrWnwhIG064vb0g7dt6aOg++MM8Res/1G/3fomx/X0eiJv9Lor1dLPpuwSjKqfiUNG7OKXvrfGqrqNYM+/HgCzZgR21YBs509wWxHD9RJ9ECdRBPUSzC6dZsgzlW1NN3euNDM9rNvZHsYuWWGTb3Mjmk2GesEZttl5tmoi+UhugGXph5WG4IgCAomP7O9dtGjtGahzc+P0q8CDl829lZa/dVtNHn8tXTOwKZ07mtN5TaX1J5PW7+0Be352E5xhthrtnn9t9+I1q/fEMe6dRvou582UquOs+jRbp/SmLETXNvqeeh/i73AbAcXGqsAAFDYqEnSDjnk5bi4sMz2rU9no2dbl28vs9azLdNw77Q2vNxlttlQ83B0Zb29veViidPbBtvd863E2xh6v+V+KqjCHt5eVjOEauSytj+xjzIRx/HOsHi1nV3mErFs7R2CIAjKN/mZ7V8XPEKr53em1fM60yobDl/42c305cjWdGLtfjRoSX86a9CJcpsze7WQZnvbTlvGGWKv2R4wYABt2rSJfv31VxcrVqyghQsX08jxC+nyu6eIz0X09tvv0pQpU1zbMzDb2RP/Lfc2vkDjgjqJHqiTaIJ6CQZPjLbVVl0l3knS+BymqnTM9p3d5Xu2s2O2rWep9WezY5Jxykzbxlp6Wx52zia8xgprkOvenvFYbznnY3lsTsf5+fek87Peqixy/8qkKxPuXXbMviYVLtNWOPtx8oMgCILyTn5me+Wch2nl7IdpBTPrIVou4PC6Dy+jfw85gd5eMoBu/bo17d19O7r2jStpx67bSLO9zb1bxBlir9nu168frVu3jpYvX+7A6WbNWUR1X8yjN9+fSX3fnUezFvxG476eT31f6e/anoHZzp7QWAUAgMKHe7W5d5t7ufXwsMz2bV1fy8Uw8vgJzYxG1paMr+DeZLu322BiLZOtG2u1rPV6e+UYaG073VQnWGbDz5Ug4XA9nuVdhyAIgvJGfmZ7+cyHaNmMB2npNKYT/fJTJ6odcD5dNPQEqls9jG4Yfy4d+/7u1KTbdrRb9z/RTq9aZvsPHX4fZ4i9Zrtv3760atUqWrRoES1evFjy888L6dMvptM1D86kU26YTv+pbKB3Rv5G7bv8QN17v+fanoHZzp5gtqMH6iR6oE6iCeolOOed9444X09Qx46jXeF8DlNVOmb79mf6Zdtssw+NDeuWPcBJTGnMbPMw73izLsVDu0UaqzfbkjTgvJ1vD7My2cEMtrVsDz9XQ8pVuJ6W5V2HIAiC8kZ+ZpvN9ZKGB2jJ1Ado8ZQHaO7Xd1HLdw+lT34dTm3Ht6TTh+8jzfY/396Z9u6/nTTb2zy3BZ3wSFmcIfaa7V69etHSpctozpy5NHfuPIcfp82jT8f9TB+OXki3d6qlp1/9iTpW1dL3P8S/bxtmO3tCYxUAAAqfK698X3zfV9NJJ/V3hYdlttt3z8IEadJQC8NrGU/NLLMhNfQ6u9LLnmk2t5p5NW7H6Ty93vz8tggzDVtXkkZeGG4njW6SjctWT3ksuT05m12mWDiGkUMQBOWr/Mz24in306LJ99PCSR3l+hf9r6RTaw+hG744gy769MA4s71jn21op7t2oA/GDo0zxF6z/XS3F2nct8vpg1EL6MPPhLnW+GCUQHw+VD2I2t/9BI0aPda1rQJmO3uC2Y4eqJPogTqJJqiX4AwfPkucr2r5zm09nM9hqkrHbN/a481s9GwLi6q/Z9t2pPzMtBUWwzK9hvS68eUUbGa1dd6GTbzbWNtGXQ+yzbJjg6VJ1tIYDbZ7mQ22miCNjXpsGLkoKw9z5zhX2SAIgqB8kp/ZZpO94Lv7aP4398r1IT0vp+eeP4dO6LEXnTFkH8dsH/jGjrRzlz/SPvfuRR+Mixlt3RB7zfbdnXpQq/t+lO/W9oPjn+o+kEaN+sy1rQJmO3viv+V6wytK/LZXEy6gZN2U6cY0qbCxYycnP142pckVar+MKT5XbOjZJ3bMrcqNaXLFpuYtnH2vH1ZnTJML+FpR++VryJQmDFQZGFN8poRxnGFcP2Hcl1x2tQ8+JlOaVGjM+ypdFixYK4pbLSdJ08P5b0CqSsdst8tGz3ZRSDfmEARBUF7Lz2yzyZ739T00p/5uuf5Oj0vozZqLaXDfK+iEmr3ouLf3lGZ7xwe2oUe7nuAywgo/sz30w0+o+0sjqOuLdb5w/NvvfkA//PCDa1sFzHb2BLMdDmq/jCleJ5t1ArOdHROaTp2oMjCm+EyB2eZNgp1bmG2LvfZ6URS5miZOXOKEhWa2n+0Psx1IMNsQBEEFI5PZHjfwOpr80U00e/zdNGvcXdJs9606k7p1PIUGdD2P3u11AR3x8K506Bu70g73bk1Tvx/jMsIKP7PNTJ8+IynebXRgtrMnmO1wUPtlTPG5AmYbPduZEHWzHRSYbQtrkrRqeuWVyU5YWGa78rksvvoLgiAIgvJBJrM9Y9oUmjjiMap/52pq+LQ9ffX21fTugB40atQoJ813U7+lc2vOosu6XeKEeUlktjMFZjt7irLZLlZQJ9EDdRJNUC+pwTORs9lu1+5jJywss31TTVae2YYgCIKg/JHJbCumTBgqe7m5Z/ubCaPj4pMBs50fQmMVAACKgwEDpkqzffrpbzlhoZnt7ujZhiAIgopMicx2psBs54dgtqMH6iR6oE6iCeolNaZMWSbOWTX96U9PO2Ghme1MhpEDAAAA+Qr/LYPZLl6hsQoAAMXD5ps/IQ33N9/8ItfDMtvteqQ5QRoAAACQ77B5zQW5ztsPmO3ggtmOHqiT6IE6iSaol9Rp2rSPOG/Vckg5r4dltm9/Ic2ebQAAAABEC5jt4EJjFQAAigeeHI3NdocOI+V6WGb7xq4vw2wDAAAAhQDMdnDBbEcP1En0QJ1EE9RL6vTs+a04b9XyNWC8HpbZvu3Z12C2AQAAgEIAZju40FgFAIDiYezYn6XZ3mWXHnI9LLP9UF8MIwcAAAAKApjt4ILZjh6ok+iBOokmqJf02GqrruLcVdPMmavkOUxV6ZjtDt36wGwDAAAAhQDMdnChsQoAAMXFIYe8LM324MHTQjPbd3Z/FWYbAAAAKARgtoMLZjt6oE6iB+okmqBe0qNNm+Hi3FVT585fyHOYqtIx23f3wDPbAAAAQEEAsx1caKwCAEBx0aXLl+K7/wk6+OC+oZnt6v54ZhsAAAAoCGC2gwtmO3qgTqIH6iSaoF7So3//KeLcVdP223eT5zBVpWO2H30VZhsAAAAoCGC2gwuNVQAAKC6WLdsgzbZFOGb71mfwzDYAAABQEMBsBxfMdvRAnUQP1Ek0Qb2kj5okLSyzfdPTOTbb9Y+V01HiYPiASo46ispr643pIk/9Y7HjsDnqsTw9FgAAAAUJzHZw8d9xU0MMAABA4dKq1VDx/R+e2b6l+5u5M9v1jx0lDPZjVFtvh9XXUnnJUfSYWs8VbIzFfuu9y5mQjXyyVRYAAADAQLGa7Rm9Wjo/hLfsNcMOTSyY7eiBOokeqJNognpJn+rq8eL8hWe220mzfXorY2Rm1NNjR4VgrE3kwtTCbAMAAIg4RWm2Z/SiliWVVGetUK+WLSmI30ZjFQAAio9hw2aFa7afzZXZTmYsRXz5UfaQ7KPKqdYOO0osl5cf5YRbyyLek94x8dxbrsJLeJh6rTD5Kp3Yv16OBPt8TO1T5mHnraPno1OrDZPXt01WLpkm2TkQ5eLRASoPDFsHAACQgGI029yrrfdme9f9xH9bTQ0x0HigTqIH6iSaoF7SZ8GCteL8hWe2b+vBs5GnYbZ//PFHX2QaP3Mq4V7vEuf5bTncvLzW2kaYysc4XF/29pI7edv5KBOqhqnXavv2pvXZp/MsOZtnU7llOtsYS2xz7E0TtFxBzsEy3k4z4Z59ms49AACAwkf9HfACsy1UV0kllVY/dyJFubH6215NuICSdVOmG9OkwsaOnZz8eNmUJleo/TKm+FyxoWef2DG3KjemyRWbmrdw9r1+WJ0xTS7ga0Xtl68hU5owUGVgTPGZEsZxhnH9hHFfctnVPviYTGlSoTHvq2xywAG9xSGI40hR6Zjt9j36p2+2TeEOjvEMEKfW9XDvsjghMaPLeM2rhikfPSxIuFoPEF5+lOp9Fpjy1NI64d40at0VbhlyObHcY7Uizk4LAACgaIHZdiuI2W7RooXWfojBja4ofupmu4khPtVPvVH/oCE+l59iwSFZekWydEE+y8WnWJGwKQi6XTY+dbPdwhCfq0/dhE43xIf1Kf5zCJI+1c8wjjOM6yeM+7KP+BQrEjbKQbfz+2zM+yq7n5fI5VSVjtlu9/RrOTLb0ihqvdE6QYymdzlhT3KCcLXsTZssXK0nDFc91qoH2ydPhR7uTZNo2/pae5i74RwAAAAoKmC23SrEYeTo2c4c9GyjZzsT0LNtplB6tidOXCIOQRxHikrHbHd8KYNh5KZwHf/ZyBMMoTaaUe+wbBXnDU9zGLmzHz29vZ4w3BrmrX5Q4NecWc9jByhXoPKo86XSq2UAAADFCsy2R64J0uqosgQTpOUrqJPogTqJJqiXzAnLbN/9Qg7NNuN6z7Ywjs6z0cJUxiYHs82lbjS95pYNq5M+0QRpnL+ajKxcTqym55lwn6b1JOH68R3Fw8mdfJOUS6ZJXh5X/sq8AwAAKFpgtuPFvdnW31u8+gsAAEBy+G9AqkrHbN/xbL/cmm0AAAAAZA+Y7ewIZjt6oE6iB+okmqBeMicss33zky/BbAMAAAD5Asx2doTGKgAAFC9hme07n0vzPdsw2wAAAED4wGxnRzDb0QN1Ej1QJ9EE9ZI5YZnth1/N8TPbAAAAAMgeMNvZERqrAABQvIRltm99Jmev/gIAAABAtoHZzo5gtqMH6iR6oE6iCeolc8Iy27d0wwRpAAAAQN4As50dobEKAADFS1hm+9Ye/WG2AQAAgHwBZjs7gtmOHqiT6IE6iSaol8wJz2zjmW0AAAAgb4DZzo7QWAUAgOIlLLN94zNvwGwDAAAA+QLMdnYEsx09UCfRA3USTVAvmROW2b7lOe7ZPq21MTIRMNsAAABA+MBsZ0dRbqz+tlcTLqBk3ZTpxjSpsKFnH9pwfAv65ZAWVH9LHxo2jOidd4gGDSK5/PHH1ufw4URjx5rzSJf1I8fQ+mF1krXL1hjTAABA2IRltts/D7MNAAAA5A0w29lRMZntjR07Ofk9WNJJLWbE734Xv8yfm21mLf/+99bnrM1jx3LodtNpr72I9tyTaKediPbfn2jnna31ffbhJLNpv/1IpmnWjOiCC4hOOono4ouJ/vMfojPPJGrThujcc4kuu4yodWuiq68mateO6OmniXr3tn5AeO45oo+enECLmrWgBQe2oJkX3iZ/XBgxwvpRYcoU83kC8aAHNZqgXjInLLN9W/ccDSOvryqnUnEQfCAlpaVUXltvTBd56qtix2FTWpVnx8LHUFpF9aa4RKS7XSKylWegfOqpqrREXHuecLUtf3rqllHp/a7h2nJ3eofyWmu7Wn27cqqq9+xfkWT/AABgAmY7O+LvW1MDLArk0mw/vnUn+vvfLZO7yy5Eu+9OtN12RH/7mxW27bZWUt1MKwOto8cnYnpJ7FialEx3xeWSFiV1zkpdSQtXnAnT8fIPBptvbv0YsOOO1o8AfL722MP6oYA/DzyQ6MILiU4/neiaa6wfAr7bqYWT2bB76+jVV60fANjo8zL/IPDeezD9ADQm/DcgVaVjtts9/Ur2zXZ9VakwGVVUq0xGfS2Vl5T6m45soRuwQGYsANnIJ1tlSZd0959su3TyzWQbfduA+fC16P1xxAlLkEewa5jDyqnWWRcIo13CYc52Yh9+137AY0hKtvIBAOQFMNvZUZTNdrZhw66GcmfDvKfCwlMupZ8PsoawD35xvjSZbDa5F5rNJ3+++aYVVlLS1omrrrZ6sS+5xOq95l7s886zDC33cHOv9xVXEDVvbqXhnnA2v//4h/V58c4xs/3F1i1cPy5suaUTFfhHg1Rgc69W2PTrccnQy8Pl5B5/Nvpcbv6RZLfdSPb+c48/G3w+B6edZp2fVq0s49+5s2Xu+bxmauqL6T7JJ1AvmROW2W7/bNbfs829iSEYaxO5MB3ZyLOxzVC6+0+2XTr5ZnIu9G2D5hOXTrs+ffMIeg17zbbPdmzA7V5vF5mcCwBA0QKznR2hsVrgLFga+4Fh7ARzmgAsWGD9KDBggPWDQP/+1o8BbGJ56DqbWv5RgE0u/xDAPwJ889eY2e54fB3tu2+sN5xNPxtnHkmw1VZOstDZZhurHKosbOD5hwv1Iwb/gPHUU9axqh9GeCi++JoxnicA8o3QzPYzr2bZbCczECK+vNQeMltqGxW5TTmVl5c64dYy9xC60ztGhnsaVXgJD/GtlUOGrXRi/3o5EuyzSu1T5mHnraPno6MPFda3TVYumSbZORDl4p5VlYdp2Hrcfuw0pnLpx2DaznuMal0Pj8vXGqIt132OK1ZXsfBSYUad9Arej2NadQNrL8tyiHOj788OS1p/XgMst4uV13XcCr/wOPSyCng7fT1IuO/+fY5NxMXOg30uVT6Bt9PqBgCQd8BsZ0f8fWhqgIHGA3XiZubMmMlVvdSqx/rWW61n2VXv/+WXW+snn0x0zDHWkPf/+z+ivfe2HhPQe/OzgeqB57x33dXaDw+3Z9OuJ+Sy9eoV+5GCj4ePy3S8IDi4VzKHz2GqSsds3/lc/zDNtmXQlDGUQ3W5x4+3EcagShk/texrlOx8lAlVQ3xrtX170/rs02VSTeWW6WyTIklkmgKUK8g50E2cDPfu02c/XgOlypVS+Uzb2eEJ4zlvrQzefarj5V7euDy1bTlenGfXjwSm/fFykPoTOMPGPctWHnrd2vWr7ychWj0xvtt50ikS7d94bO5zaTw/vtuZ6sZeBwDkFTDb2RF/55oaYAAUAzzrvHp2nHvo+Vny66+3erZ5SDr3ch9+OFGTJtZkdjyEfYcdYs+zJxp+r69oi0Y4P86fe9eZ446zhsXzjwft21uPE7BBZ7PulH/ZGmfUAs92rx8XAEHhvwGpKh2z3aEHT5B2Wkhm2xunmwQV7l0WJ8JtRgzmUGHKRw8LEq7WA4SXc8+iKpcpTy2tE+5No9Zd4ZapkpNyVdWKODutdxs9TIvzLZffdt5wU3qxnPB4eVnFOSQw8mrdhicdY4NdWy62qRJp+AcINotsjNU23v0FyNcdF9B0JsrLhcFs+5pqK9yZXE39wBJk/2o9WXiieFPdeK8rAEBeALOdHfF3oakBBhoP1En0SFYnI0daPe9siJVpFxs5sHFmw85D6XkIPfe4s2nP5Fn5vX833VmZtXkT+cMAP8fPz6zfe69VDi4P/5hgKnMhgHslc/gcpqp0zHa7mpeF2T41pGe2E5kBFe5d9jMvej6mcLXsTZssXK0nDFc9wj49jK60Wrx3WV83bVtfaw8J9pwDv/0kK5ffdt7wuPQBjpeXg9SVd10hjTUbent/4pOH0ht7uE35+OUrUddk0G0SXMMuPGbbbztxbCk9s+1XzmThieJNdQMAyEtgtrMjNFYBiAb8SjY1RL5jR2s4PM/qzkPieUI4ft79r3+NGXSe1V4uCHi2e3sxIdyDznnwUHfOL5lB5+f8eRZ/Zv2At+PKDPKfsMz2bc++TiX/PPVKY2QiEv2xl0OjRSM/fiZnNiPakGJO5+3hcxkGO70+7FfGecOVGTTlE2CfrvT2esJwy2QpU8WviLKGRgcoV6DyqPOl0nsNnM9+5KehXN5zkWw7NfzY2S5ZvhzuzduzT/14nW10eB+x16pxD7f7eXbP/lz7Nqx74P3y8+LOEHImwTaqnJnORl7PdR+3nY3f/n2PzX0unWHiKj7ZdnF1Y6cDAOQVMNvZEcx29ECdRI8o1sm6mfNpycEtaPE/W9CUwy+VBp2Hv7NBb9HCbNBTpbykj7PSf5ty2SuvTyLHM+RXVsYmzePe/TBf5YZ7JXPCMtvt+ZntZiddZoxMRKI/9ozrHcXCbDgGQTT0XRNd2WFO4z/OMAhD46TXJnbSw5381WRkbHbceSbcp2k9Sbh+fK5Jv5KVS6ZJXh5X/rpBVBj341MuPW/f7URaFVbuMXF++cYdl19diXzUtsJEm85n7EcIe13vDXbK4VO/rjT2uhdZBo/p1cqlo8637zXsII5XHbtGpu/ZLlXnX0+n1vVtvPWrp4vbzqduAAB5B8x2dsTfh6YGWBTYdPGltKl5CwkbC1OaVOBnW51eOrFsSgNAoTNkSOwZdWXQGe5B5x5vnuSNDTr3gutmu49o69mLgdliC2u4PA+f54nq2KjzZHFnnWVNase9+B+2HypfibeoWQta06nKWGaQO/hvQKpKx2zf/sIAKmna/N/GyEQkM9sAgNwiJ5szDU8HABQ0MNvZUZTN9m97NXFa7dl4LzabbJUfL5vS5IpUjiWbdcL7Uj8wbOjZx5gGJCfK90kuUcPIp7XuROM7vi1nUudZ4NUkctyz3bSp9R50fiadTfrWW6fWk24y9Gz0eYZ37knnHnp+XdzBB1uvY7vsMqIbbrB60ktKWsth76ayg2CEZbbbPvMalTQ74V/GyETAbAMQPnJ4vfhykKCHGoCiBGY7O4qyiShWs51NuAdf7ZdHCJjS5Ao298roh3nMIDrw6814aDn3pPOkcTyrOr+HnXu2+Z3sbNLb/Smz3nOFMvh/+YvVi37UUdaM7vyDwIh/3kaz920he9DnD03/ffOFSFhm++ZuPBt52dnGyETAbAMAAADhA7OdHUXZbPPrjNSrjfg1R6Y0qdCYw8gbq2e7Mc0270/tO9TzvWCpU88bqrua06RIlO+TfIcfEeHrg1k3cbITrl7LxiadP2+5xZrIjSeOO+QQa2h6Scl82ZvOl1mi3vS6kti12KKkzgnfaiurR557z8vKrNe98TD37t1jz6AvWBBf5kIiLLN9+/M8jPzYM4yRiYDZBgAAAMIHZjs7gokobBpzGHljmW0+ZrVf/pHDlAYULjysnIe7//e/lnHmHvSvt49diyf9Lma2U4GHtvMz7Ty0/c6dY73x9YeUy2fPufd++HBzmaJMWGa73TPcsw2zDQAAAOQFMNvZEcx29CiUOmmsYeS5MNvp1An/2KAwxYPMCVov/Oy56j3nkQ8qfOLEWO/5VVdZk7addJLVa86TufHkbval5CLIxHF//jPRrrtaE87pz5t36mQZ86j0modlttv3eJPNdktjZCJgtgEAAIDwgdnOjmC2QcGRg2Hk6aA7L1M8yC/YGKte888rYmb7vZ3K5SvW/vY363lx7gHXqj4QvA1vv8su5hnbBwzI3SvVwjLbdzzXD2YbAAAAyBdgtrMjmO3ogTqJHunUiXRRNqb4TMFweT706N4rI0e6nze//HKi886znhHnieF23NGpvpThV6rxbO3cA8/vPOd3q3vfeR601zw0s93jVZhtAAAAIF+A2c6OYOwAyA1q2HKunleH2S4s9BnbuUebe7bPPtvq6eZJ4JhUXqmm4G1427//3ZoI7qKLrNe2VVRYxjwss333C/1htqPAvffeW/CYjhvkDlMdFBqm4wb5ial+Cw3TcacDzHZ2BLMdPVAn0SOKdRKG2ebn7tU+NrYqN6ZpTIr1XuFXqvHQcn7n+SOPWD3bPJP6AQdYk7jxsHaead2uuiSI/1JUOma7Q/fXYbajADfEClnZbGiCYOCaAvkErtfgwGxnRzB2AAA/wjDbPHmZesZ+/YC3jWlAZnAPNj9vzr3m7dtbE7Xxq84OOyw8s33H06/AbEcBNDRBtsE1BfIJXK/BgdnOjmC2owfqJHoUa52EYbYz2QfulcwJy2zf+Sye2Y4EaGiCbINrCuQTuF6DA7OdHaGxCgBoTMIy9Kr3PMzX0OUDYZntji/i1V+RIO2GZkMD/4u8YIzCB9cUyCfSvl7zRNm8XmG2syOY7eiBOokeqJPckckw8qD1wu86F4kluZqwLl8Jy2zf2XNA7sx2bXkJldfG1uurSsWBlVOtk6aWyktKqapeLNdXUWlpFdU7ce4wzotPShzltXbe5VSqwkpLxX7rY/k48P70/fuEmcqSY4wNzYYaqiipoCH2qldDKsrE8ZZRmTjmsor4VEMqRHhNNFxTroxR5K4xzk/fVsJpPelSJY1rEtdUZkT22rK3sainqlLeJrVrI4qkc73mk7J5vcJsZ0d8v5kaYAAAUChk22yvHzxU5sls6FxlTJMv8N+AVJWO2T609X25M9vcOC2tUo1GbhSKdYFsnHKYbDzajdckjVUnzGCOZSNYpKt18tUawVq6/DHbDVRTZje8/RqaohFaVlYjUsoVkb6MXB6I48X2hW6MIneNGfKrrxVGKtPrKY1rEtdUZkTz2uIyaHnKMiQx28ZyRI+Ur9c8UzavV5jt7AhmO3qgTqIH6iSaBK2XbA8jD2Poe1jwOUxV6Zjtgy66I4fDyIXJiDUCRQNSLNfqDViOV700aTdWrUZwvLE2Ed/QNYY1QuPU3dBUGuLfqzOkgkq0nkfucYytWkapoqKs8I1R1K6xQPmlQRrXJK6pDInotVVVrqUXZSgtT/JjThrXTmOQ8vWaZ8rm9QqznR3BRAAAQGrAbKdhts+6PpfPbGsNS9Uw5Yaf3UB19Ryl21hNqSFpMj12mMynnMpFQ5ZPfoln2dmGG7ccJrGHB9vbciPYFe7aT2JSbWg21LhNj2tdmCYeAuxN05jKnTGK2DVmSOv0bHvj1Lr8VNeb2K/vNRawDDa4pjIlotcWXx/2fmvZeNdqeYg05TysnK8d+b3FZl6t+6VReWvfeU4Z9fK6z4f3GpU99Pa5scrmHoafjFSv13xTNq9XmO3siK9fUwMMNB6ok+iBOokmjVUv62bOl8PRmXUTJxvT5At8DlNVOmb70LNvyKXZjvXacEPManiJBpts9Hl6dLixJw6aD9yFaiA6ZNBY9W6rh8n9c8NVNGLtZfXcpLcR6aD27UkvG6KBy2SRPWMktimztikOYxSxa8y0DzY0XAZvPmpdbmNfe3pe3jRBy2CDaypTInhtua4FuyzOOpeJDa7ne8u1jwRpnGtQOzb+kUEchzx2Vz4aKlzmoY7Nc5wBSPV6zTdl83qF2c6O+No2NcAAAAAUPvw3IFWlY7abnn5dLs22aqTqDVO1HKDRaWzcZdBYNTYA7TA9H2+enrhyUX5XYzpR+oBkyxjp4cVijCJ1jcVdC7WyLEazotYN4UmvsQDgmsqcaF5bXAaRB/+op5tpb16m8CBpBKpXWvacV4k43o/Wo87bxV2j2nbOSAA7vyCker3mm7J5vcJsZ0d87ZoaYKDxQJ1ED9RJNEG9ZA6fw1SVjtk++l+35NZsywZaebndG2SFyQYsP2eoN8a8jUC/sDjDrDeEk2FIa2psevfrrPO+xfZ2r5BxW9P2AUi5oWl8vlafVChGFMxRTo1RlK4xU37KePhdJ67wgNdYAHBNZYGIXltcBp4szfUjjnd/pvAgaRg+bmGyreMWZRafVfKHB473uUbt7fhalyY90HdyjJSv1zxTNq9XmO3siL/LTA0wAAAAhQ//DUhV6Zjt5pfcnmOzLRtmooGuN0y5QSbCnF4SxtvY8wuLa6xaDU/uWUk+G7nqeYntl59/jDNCvCz2obaX+cvyW/uOhYtteRtvOY3lTkzKDc1kM0cLFU8vZISusYT5ea4fNjSc1rWNJ43fNRYAXFPZgOsjgtcWL6t8nHA27j5DxJ1yBEnDWMetPxsem7vC5xpVcfw8uCuvYKR8veaZsnm9wmxnRzDb0QN1Ej1QJ9EE9ZI5fA5TVTpm++q7nxZm+5gzjJGJCG62rcadq2EqG2uexmRcY88nzNBYZVzvqRV564bajSiPaDhar9ARlNv56/viZZFHKTcaOT+tDPp+eHujETKWOzHBGpru9WTvRC4eYxSha8yYH5fPKos0N2p77h3ltJ5tAl1jAcA1lQ2ifm15wsVybPIzlZYfZeCwmDmPSxOXt3XcVk+2QP0oaccbr1E7jn/QdJ+vYAS7XvNX2bxeYbazI75+TQ0wAAAAhQ//DUhV6ZjtOx7tnWuzDYJgbmgGUEMD/4u8wjFGQAfXFAifWmHk439MCELa12ueKJvXK8x2dgSzHT1QJ9EDdRJNUC+ZE5bZ7lDVB2Y7CqChCbINrikQKqbh9SmA6zU4MNvZERqrAABQvIRltq+5uwZmOwqgoQmyDa4pkE/geg1OsZjtukr7cQebyjorfEavlk5Yy14zrEAhv3A/cTpTAww0HqiT6IE6iSaol8zhc5iq0jHbp111J8x2FEBDE2QbXFMgn8D1GpziMNszqFfLlhTnmWf0opYllWT5bi2NX3gCobEKAADFS1hm+4hzK2C2owAamiDb4JoC+QSu1+AUh9muo8qWvYRtdot7r7292bzuF55IMNvRA3USPVAn0QT1kjlhme2DTr4CZjsKcEOs0DEdN8gdpjooNEzHDfITU/0WGqbjToeiMNuyp1obRm4b7zgTXVdJJZV1vuGJhMYqAAAUL2GZ7YMvuRVmGwAAAMgXisJss1l2hoVbz2+berCDmu0WLVrEjDsAAAAgSFXpmO1bew4QZvvYs4yRiYDZBgAAAMKnEM22PhmasUPax1Srdb/wREqnoQXlVqiT6Al1Ek2hXhpHaZnt59hsH3++MTIRMNsAAABA+BRDzzab5VjPNE94Zs8w7poIrY4qS0wTpGnhCYTGavSEOomeUCfRFOqlcZSO2b6lO5vt5hcYIxMBsw0AAACETzGYbZbr1V9ad7c04na4tzfbFO6nJk2a2EtQVIQ6iZ5QJ9EU6qVxlI7Zvqn768Jst/iPMTIRMNsAAABA+BSL2YYgCIKgKCkts/30y1TS7NQrjJGJgNkGAAAAwgdmG4IgCILCVzpm+65urwqzfXprY2QiYLYBiDamVw8VGqbjBvmJqX4LDdNxpwPMNgRBEASFr3TM9nMDBrDZLjdGJgJmG4Bow437QlY2zQtofHC9BgdmG4IgCILCVzpm+9n+bLbPuMYYmQiYbQCiDcwLyCdwvQYHZhuCIAiCwlc6ZrvrqzwbectrjZGJgNkGINrAvIB8AtdrcGC2k0ufyTzIjOWpzmQOpSf9POdihnkoFfEr9dTr8izhvmlsxdcJ7ploKR2z/YRltlPfEGYbgGgD8wLyCVyvwYHZTqK6Sipp2Us0W1nceDW9iztAOJRd8Xl21UsJSe+AemkE8bvo2ZBpxg73TSPLUCe4ZyKndMx29esw2y7qq8qp1P5FqKS0lMpr643pJPVVVFpaRfVBw5fVU21VLdWb4n23CQBvq8qsUV5rSBsFvOUtLaeqekM6RSbnJpcYzntpVYLrJWSM5qWhhipKKmiIvZrPyrnZNt5X/J1gSJsJcfvJ3j5S+j5LhyzeA7hegwOznYq4AWs1OLnHx9sDxOt+4VCWFWccUC+NIz73bMj43tCMnUu4b8KVT53gnomc0jHbj70Bs+1QX1UqGqRVVKuMX30tlYuGr68R9DOBfuG1ouHLDVFTvN82ikTxybb1I9Xt0t2PF86npJxq1XptknyztV9FNo8j03yyfWwabvPSQDVlyhDBvATCUDf1fA9nu768+8nSNZHy91k6ZPH6xfUaHJjtYOKGJl9DqrEZ1/DknrzKOt9wKAeSPW98X8d63FAvjSWz2cZ905gy1AnumUgpHbN9S7fXYbYt6qmqNMWGqF9D0xjO+dsG0xSfrNGaKD7ZtlGDy6ub7WVsAvR1D1E9vmyUK4fH5jYvSkPQUxgUY90kuVbTIW4/2dhHGt9n6ZDF6xfXa3BgtlMR9wZZxgEN1EaWa4irf88p6iUsmc22Jdw3jSNDzzbumUgpHbN90zNvwGxLkjUaRXx5qd3TYjLNWnypaORyj5IrL9Wr7d1O4ZOXtS9uOKt1QxlN+Tnh5VReLsrD5aqqpSq5bA8n1baTvWCcP8fZ5YwLS1jG2P6sfdjbmYbDcjq/nm0+T3JbbXt9v77xPvvl3jxVThmuHUfC7fTjE/Fq/zp6uXRMZZTpvWUR9eGtV9d+3efVqkftvCUB5iVDDPXr6tn21JVjbF3hPteOjmc/Sfch0ye5Hjx5muITX2dWeNJrLtl+UgDXa3BgtlMTN0BNDVG17hcOZVmuhj+GxDa+Eplt63zjvglbnjrBPRM5pWO223XnYeRnwmwnbjRaZlc97yhNaHmtto0nXjSW3Q1srVeb4e24MevFlFfcvlSeGsb8xP5kuN275V3mvJw8tZ40mY6XDWFO+gRllCbSCpem01RmmU4vq2ZIvelc5UwQb9yvXU7nRw4+JnEOlLlPtp0KT7R/13HY58ubJkhZZPrE57VKlScgMC8ZEle/Ar6PpanmurLvJ5U2lWtHx7sfsQ8rX599yPRJroeE+w1wnQXZByPTaWU33QMBwfUaHJjtxOIGpruBappUKNZL5BsOZVepnn/US47F51SdXz7duG8aX+46Sfnco05yrnTM9i0w2zaJGqfeOLXu/fRLzyZOmSxTvB7ml5dpG2+aROGmZSfManzLCZTkBG68vSEsbjtOFyBcrevheqNcXxfL5dwb6BgPT77J4lV+pvBk8cnC1XqA8KRl1NI64d40at1v2yTAvGRIXH3wSAT7hyGOU/XrwObUpw7Vugm/NEH3YSLRfr1xal0PT7S9TtB0AcD1GhyY7eTSX2EUMxDcFrWeR2W8PUCmcCi70s+zVi2ol0aRx9gJ4b5pbMXXCe6ZaCkds9225jU2222MkYkoOLPt7UXSSdY49YuX65yvp7fHm14P88vLtI03TaJw07J3OzYT3mGjepjvdknC1boe7uoBU+de9fbaP0zE5Zss3s7PLzxZfLJwtZ4wPGAZFXq4N02ybZMA85IhpvPOo1acXmDPfc341aGexotfmqD7MJLB95kpjR9B0wUA12twYLYhCIIgKHylY7Zv7fEmzLZCDqcUDcf42XutXl7jsEvZ0DTEqwaot1ebMTVQE+Xl2pe2jcIvTg83LTth6jh5O4/x1cOcHrWAZfSu6+EuQ6/WeZ+xZ1/5tUXuXuFk8Vp+ct0uZ6Jh5Im2U4bZGV5up4tLr4f7lDFZWeS2Ac9rQGBeMiRB/ap5FGL1meK1o+Nbvz77CHg9qO+h1L/POG2wfQROFwBcr8GB2YYgCIKg8JWO2b7t6VdgtnVc76UVDVOn0SwalbEJhQwNUl62tystL7XDuVEbsGfKk1fcvkQjnxvIvs8F2/vWKS3XGvqe/OWyFqYft/pxIC7Mk0fC82Fa18PtfC3sobkizrVPYfBl3lo+yeLj9ssGQ5VT1aeKT7idWLb3k+oEacYyyvSGsnjrVeSZ9LwGBOYlQ4znne9p+0covT6d56w53O/aUUbdTqdIVL+mfcSl98lXoF+LKX+fBdmHb9n9y+QHrtfgwGxDEARBUPhKx2w/8Cy/+gtmGwBf5IR33PNniIsyZvNSOMq52c4C+XrtNAa4XoMDsw1BEARB4Ssds90RPdsAxFOrXn+k9yga0kUZmJfGoRCuncYA12twYLYhCIIgKHylY7bbd+0Fsw1AIQLzAvIJXK/BgdmGIAiCoPCVjtm+67lXYbYBKERgXkA+ges1ODDbEARBEBS+0jHbD/WS79mG2Qag0IB5AfkErtfgwGxDEARBUPhKx2zf3Q3v2QagIOHGfaFjOm6Qn5jqt9AwHXc6wGxDEARBUPhKx2xXPt0PZhsAAAD4//butTmO4t7juN6PBAYcFFK5nDrnBHAIOZAEclG4iEsMxtjGIC4hBGEwlq/Y2IrvxrIly5JcuCqP/YDyoxRVKT00D/KEFG8g76DP/98zPdvT2z27Go1WO873X/Wp3Znp6ZnpWUnz0+xKbUHYpiiKoqjBV52wvesE//oLAIDWIGxTFEVR1OCrTth+9TSf2QYAoDUI2xTVgvr2otk2MmW+yifvrvrKTI1sMxe/zSf9ssed/fvLqa6Dj6/37cVtZlu0s4rqc3y/msr/Fee2i2aNW6CorqoTtrefJWwDANAahG2Koja3eoXtWAjWdTT4dq+ngbg7mDdYX00RtqlGqk7YfvkMYRsAgNYgbFMUtbm1trCtd6717vK2qSlZFq6nfW3wOwAI21RDVSdsv/TX64RtAADagrBNUU3Xt+bitvztxn7wy4PjxTws2sAYJMzibcp22cVOCPVDZ89+EtuPle2rs83OHeGsjynZh87ysK+q7VTvQ/I4w4qF7a++kt71iS4L1tMgXBxE5Bh0mbbxp21Tbzu9xpewTTVUdcL2dv71FwAA7UHYpqhmywbJPMTZu7ClQOcFNxv6OkHStvVCXBZIU2E73U9y+12lYdQLq6UQ6cJysH99bqffZVql4wzLP+6w7LLyerqtTjDOj6E4pvyt56XptY+vnSZsUw1UnbC968yyhO3HCNsAALQBYZuimqzgLdFdIS6xzK4Xfta4KgxW9ZNaFlYeRssbzStbVrqja/t2+1i1nTUs0wqPx6+q/e9aL/jlQX4MncOrmPa3E/Yb7gNhm2qo6oTtHSf0/2wTtgEAaAXCNkU1WVkgLd6mbOXBLQxtpelICPXndYXBqn4S249WuX14V7icw/15VdupWGb3teI4wwqP1a+uvrQfv+16wnZqfKUI21RDVSdsv27/zzZhGwCAViBsU1STtYbgWJrOAmo53Hp99R0GK7bfq0pvl46FbX8fq7az1mUV7cNj9csu89bT/S/tMGGbGu6qE7b3cGcbAID2IGxTVLNV+kyyH2B7hDj72WYvxJU+y7yGMJjcflcFIbcUIrMg6q8b3b/EdnouSx1nWOGx+mWXddbTftwmsyJsU8NddcL2e/b/bBO2AQBoBcI2RTVdGmI1QAYhsleIk8qCZ6bXXyNP95PYfqxsEI61zYPolL+8vK/V26neh+RxhhUZo6LsMree7m/YB2GbGu6qFbbP8QfSAABoDcI2RQ1rBXeeB1phMN2kqgrbm1WEbaqhqhO2d52aJ2wDANAWhG2KGobKwq3/17/Dt20PtgjbySJsUw1VnbD9Cv/6CwCA9iBsU9SwlP/Wa7WZIXOYwnY2Hpu+L1LF298J21QDVSds7zjPZ7YBAGgNwjZFURRFDb7qhO0/nlgibAMA0BaEbYqiKIoafNW6s32SO9tAI/7xj3/c9WLHDWCwCNsURVEUNfiqE7ZfnyVsA43QMHo3F2EbGA6EbYqiKIoafNUJ27tOE7aBRriwHVvWdlqEbWA4ELYpiqIoavBV6872WcI20AjCNoBBIGxTFEVR1OCrTtjecfo6YRtoAmEbwCAQtimKoihq8FUrbM8uEraxTt99bW7OzZm5wK07kbZ3sWjY/uffzIVdF8zf/XktpEXYBoYDYZuiKIqiBl91wvZrZ7izjfXSsH3za/NdbNlaNNVPzEb2nSuH7X+av+3fZXbtUoRtAM0hbFMURVHU4KtO2N55lrCN9RpAkG2D6J3tf/+dO9sAGkXYpiiKoqjBV70727yNHOuVCtt2/i3z9a2b+VvLb3beWi7Lbt3M33J+U5br+q6fvteTNt9VzM/7ueX6WVPf+T7p/D4RtgEMAmGboiiKogZftcL27DXCNtZJA6wNrr5b5o6dr2H2u6zdnVtZmP73d+ZrCbXFfBuAg7CdXO9mKWBXzs/7+Vr76avvyD7p8z4RtgEMAmGboiiKogZftcL2yQXCNtYpFUzD+X7grZpftbwr1GuYrpjv+umn79h8N90HwjaAQSBsUxRFUdTgq07Y3nGSO9tYr1QwXWuw7We53jF38/3lqfmuH7+PVN+x+W66D4RtAINA2KYoiqKowVedsL3zJH8gDeuVCqbJABu8Zdu9ldst77Xe1+FbvRPz/X5i82J9dL29PG/XB8I2gEHoFbYBAMDGiP3srbL71AphG+ukgbXrbdxz5uatILCG4de1jf6BtNR6dxJ/IC0yP9xez77lebhPrl0fCNsABoGfvwAAtMMbsxK2xx7bHl1YhR/2aMp3d26ZuVt3oss2S519ioftu4MWYRsYDvz8BQCgHXafXCZsY/DuFP96y7sTHWk3SOvdJ8I2gEHg5y8AAO2wV+9s8zZyYP0I2wAGgZ+/AAC0w27+QBrQDMI2gEHg5y/QkNVVs7oamQ8ADXnnAne2gUa4sH23FmEbGA5r+vm7OmPGR0bMyOQNb/6qmRmXeeMzZtVvuw43JqU/3U6otN3NdWNyvNiv8ZnV8nIZp8mRSXPDm9dpP24mb3hti+WRfpQd83Ez44c4nbfe8R7QuSy2U4gf/yClzkX8nN6Qc1ne/9K5KK07bo91PHydVpwvPe/+PqzOaD/+a0e3H9/mUFnLa1La+l8f2TH7Y6yy5cmvmxuTxeuqa7yHUPw4Iq+tG+v42g7GVa3OuHGS7fZ4TVedh2Ib2HSvHr1E2AaaoGH0bhc7bgCDteawPS6Bwr8Y1Hl6UdZkQCvoReEQXuzJhf7IpDveLKBmF9B5WA0vUrW9G5/VyDHlY5gO28H42vOwzvEe1LkM97WJfV+P1LlIntM+XoOlY9J1+//liAacznnP1tXzUqxvz8mAvwbqnKO+1kl8fYS0Lw3Qya8bfe59zQW/sBg6lccRjEOdsU+Nq3vt6GvJbte9riLbjXHnIbYMm2bPqXkJ2z8jbAMA0AZrD9szcnHrhQG5kByfFGu+QOxHnxeFm6wcmFTVfodhLJuelDFNhu18zItAUeuCPDCoc9m1r8N0TsNz0VGc037G2gb1TigJ71ZX9uEHMR0beX7Dfz0FfQ9EndfXmtapfg3oXeDuc+Kdq9KYDb/w+4NO29dHbMzqjH0hGNegr+J12ec24ucBm+2t0wuEbQAA2qJO2F7VUJZfPNoLMv+tj7rM3mFR+UWlf3GsffQdtiIX5bL+pLuLM54vs/s1aQOrvbNTaiPzdNtu371+iumgz84FZj/BMLurVApXVevpdv1lOl4SproDe87tZ7C/nXnuuPXO+A0bnrNxd30l9sX1sdHn0t9vYbfnrR89l0Wf/r57z4P17Pmy2/FeA/2cu9K2fN45tW3k3OTbi52jWJgqtbP75sZA9kt/uRI7Pzq2Gqy1fR6wy31Fjik/7uy86z7m58lv48vbF+PUdY6zY8/GNn6eiq8Pb77eje8E4F5jX7Hc7l/n9VKan6+jY1L5C4jSMWb7HH2deW36/trJ1+t7vJX/NaOvLfeREXtMwWursv+1jqv3Cwq9s+0fe7jdYp2c3Y/gPOT7tv5xxXq8OXuVsA0AQFvUCtvFhZhewPnTifbyPPtMslx42aARtEuKXTxqCMou2oqLbt2OXMzN2PnZBWbRxl3ohvtYTHsXpKX5+XQPdh+62scvim3b0sVt5wK4HKo8pTHMx87N00c9bhc2/ee9jsHvw7bdoHOp6+nFuCPHm4114lz650PPnaxTOubU+bLbkfl9XtB3n4tgmRuDvP/irbh2n7vb+/10nctiH3V9b9+tzvHoelnf+bkIjzUmP+4iyGh4dvseUzVOxX7mj3Z+YrztfO/8lQJlL3J8idDYGYPyPP9c2fGd7AS9rrc6+8don3d+QWD78r9n6PzweR/j1/d45/Rr173+NYjaY8m3VXptzdTrPxMZV7sN2Z4oxjW23ciYh/PcsW/IuKJvb59eJGwDANAWtcK2vdCWizq9EHQXWO5CSp5roLAXlvbi0l1g6YWgTIcXxpWCi8fwgs1NB9vv2Sac7/a1kF8gurYJ9qLf77MQuegtuJCSr+8HCD+gOaX91gtj71j8ZannKUWbDT6XqX0J53vT7u2u9k67hg/dju6fCyjFeXI0ACS2U6lzLty89Dn1lgfnKZzX1cbus/YbHzNtr+GqE2rd86rXUa5iHLvaqkj7rnPst9HnpbFWkfHutd2S1HHp67u/r5v45+tz4f7H9jPVJmwfSvXnt+kh+2VV99e6fd2EH+NYU//BuJbW9V9fZV2v19R58PtLjUOqTdgete05yZ1tAABao17Yzi/Q5OLNXuQW8/ViTy/E8wu38GLLXcy7/nqqunj0psPt9GoTzu8VaCLsBb/fX0mw34Hs4lbvDIchJnIRHux36YLcX5Z6nuK12dBzmdqXcL4/bYO1hkCd1gv/GQnd3v7Fxja1nR6yc5EdY/U5zfjtC7K/foguPhvrlrt9Xo0EQ2V/0aF3ajvb1u1M6nnu9QuNqnGMKS1PnGO/jT7vZ7zD6Urxr4/o2HqK5e4XL+F81zbc/9h+ptqE7UOp/vw2lRKvAWGPo8GwHY5Lanz7bVc5Tm461SZsj9rePLtM2AYAoC3qhm373F3YFfOziz1390T/7UwWXrK7Knr3Tu9WRi/kosKLcnehmq2vF4XFWxeLC7k8QLh90Avz2L4V8/M+3T71c1HoH3tU90Wv7mfWZ/xiu68LXEv7zoOuvyz1PCVs7/a3mB+MV91zmdyXxLm0y7JjdH3bt+AWd9oS5yu5nbLkufDHINXe3mmO3B0sbTsbn1Ibf3l0O/k5LY5faICP/fIlFB53OB0qLU+c41KbxHjHzl/Vdkuy7ZbHoHvckufKrW/b+vNz/v7r89IxZn12tYk9jwmX92qv9Fzmd+Lt2+3zY4++tpp8x4Buq1i3M07Vr+nu81AIx6nJcUXfdp66RtgGAKAtaoftxPzO/3WVoCAXbXqxp39d2V6I2fb+xV1wcdglsly21fmjPIkLOQ3Sbh+8O0X2gtDOz+8aFuvLdoo+OxeQqf3TO5dZPx3lUBSulwXTrK1sOxKgdN/6C9v5ceg8f1nqeWqMI/2G8xs5l6nt5Mu6zqUVBCgbVrwgGjtfXdtJ7E/iXKTPaerclfvP2sg51PX8fVXBvhXnr2iTHW/3aygMPZFjCo+7arxV1750n2P9RYvuT/FvpFJfH9qXW1eOv9Nvauyd2HHINmeCcav6uil9jVeMt30u6xb778/v8byJ8c6VjqP4xUDk+Cr7X/u4dvr3x6liXKPnwS3z9sU+rzuuWI8dJ68TtgEAaIu7/ufvf/xFnlyAc5E7GKurZrUUjpvGuVyzdX39M95JhOdNs/vMCmEbAIC2IGwDdyO9y5nfdSypujN6FxrY1/9/2HjzfXXTbD++RNgGAKAt+PkLAEA77P6rvbP9cnRhFX7YAwAwePz8BQCgHXaftP9nu17YBgAAgxf7uQwAAIbL++dr3tkGAAAAAABxH5yzn9kmbAMAAAAA0JRPL3NnGwAAAACARn2+RNgGAAAAAKBRn10jbAMAAAAA0KiZOcI2AAAAAACNmj6/TNgGAAAAAKBJ754hbAMAAABoqZH3vkEPsXHzffnll+ghNm697D55jbANAAAAoJ1i4RJlsXHzaZj817/+hYS6YXvPaT6zDQAAAKClXKCMhaT/dITtZtQN27tmeRs5AAAAgJYibKcRtptRN2zvnl0ibAMAAABoJ8J2GmG7GXXD9p5T1wnbAAAAANqJsJ1G2G5G3bC99/MFwjYAAACAdtrwsH172mwdGTEjautWMz0XaTOkNits356eKI3ZxNztaLtG6XnaOm1ux5atU92wPX1Z/0DaNsI2AAAAgPbZ2LA9ZyZGJGDfzqdt8J4wc13thtNmhO3b01slYE+buWLMgjHcKEMYtt+YvUrYBgAAANBOGxq2NzDADcLgw/ZtM613/zc6WMcMYdjeMztH2AYAAADQThsatm14HDET03Pmdhgg57y3So/oW6V1fsWdcHk+IX1lb62eGEggHXjY7hV4Y2Nm15HxmNgajKUuk/F0Y2bn529Hj41lr22vQ+0726ds2H4puhAAAAAAhtnGhm0hgc8PgtGQ7AW9uYkRs3U6C4X2LdUTc/I8uOO7gcHQN3Rh2+fa6qMfpDWQ2z7cLzpcwHa/yEiM5QaOad2w/faZa4RtAAAAAO204WHbc1uCoH4e2QY6CXcTEvqyEK53WDvzO2ExD4U2UObtCong3qChC9uxMQvXcdOpvnR+aRyVjOVcj22vQ92wPXVa/xo5YRsAAABACw0ybGdvE9e3hed3Wb23NXeCngvZ3jwbEAf/h9UGHrbDO/gliTErjV3FfH95bCxT7RtQN2zvnZ0nbAMAAABop40M29nbwDsBrnNnOwvdLlTqv7oq7mzn622V0OneTt757HcsnG+cwYftfMzk2Lr/GnlizMKxKKbDMXP9JMZyCMP22+f1X3/xf7YBAAAAtNBGhm0NyXPF57XF1okiRPr/S1qDtR+2s7uvwR1eDYvhH/VyyzbIZoRtVfo/2zIO7vPY0TFLhm197o2Z1090LIcwbE/ZsM2dbQAAAAAttLFhu902K2zfbeqG7b3nNGw/+mJ0IQAAAAAMM8J2GmG7GXXD9hv2zjZhGwAAAEALEbbTCNvNqB22zy0RtgEAAAC0E2E7jbDdjLphe+/pa2ZkjM9sAwAAAGghFyiRFhs3H2G7Wt2w/d65RcI2AAAAgHaKhUuUxcbNp2ES1WLj1ssncyuEbQAAAAAAmnRo3v7rLz6zDQAAAABAU45c4m3kAAAAAAA06swV/kAaAAAAAACNml/OP7P9zTffAAAAAACAddKwvbjCZ7YBAAAAAGjUF3pnm7ANAAAAAEBzDl4mbAMAAAAA0KjXT1wmbAMAAAAA0KTnjl4hbAMAAAAA0KTfHVuQsP3oC9GFAAAAAABg7f5wbImwDQAAAABAk547oX8gjbANAAAAAEBjnjlylbANAAAAAECTXpnlbeQAAAAAADTq1Vn+QBoAAAAAAI16d47PbAMAAAAA0KgDy4RtAAAAAAAaNXPdhu3J6EIAAAAAALB2H88TtgEAAAAAaNQ7Z5cJ2wAAAAAANOm1z+ckbD9C2AYAAAAAoCnPHjpP2AYAAAAAoEkTJxYJ2wAAAAAANOnpY9cI2wAAAAAANOk3h+cJ2wAAAAAANOm5E/qvvx5+zpQ9a0Z/+kyZzpNlY48830X/mnlBgvvooy+a0W0xL2V+9nLgj4Fwec6tH4q1fUz6Sfn59rLHAsWyVxJerfb4jrKfv5Z5PGZnl7Endlmj//d6gi6rsnt9nnD2dHtSveHZm/ml781uT3piy8XYL9+yYsvW5Ndve94pjD31rjX66/xRPf1ex1Mqn/+UrPN0/qhcf25+FbeO3Zaumz93nv5TpbHfvC+PcbosVF7251L7UV2+Vr+VPgofdPzO+YsZ/f1fzNjvP7TstL/stzqty6atUdvGkemJfWZM/eFja3Qi94dPZPoTedzf7ZmOsWc+LcTmaz/l9Q6UPeuez5Q9m/DcQWvs+UPisMzT6UNmVKZHdfr5I95jbvKoNfbCsYKb1+WFwIuf1XC82wvh/BNm9KXPK5wse/lU5o85N/3yrEyH/prZnj/GbD+TecXz6llxTpzPHl/R52KHTDt2eTZv7LULnWlfrL3lzY95zZF+1U59vFjPztzrl6q5dv20jdn1RQ+XO3ZXmcvsyR/9aeeNKx17natlb87nFjrP31oH24f2GyPL7DaDeSHbl+yPGJvKjPbr7cX+TV3rKOYF/bjl78jzd66b0XeXxLIZfS/3pxVPMP2+58+596WNctMfyPPc/e9fMT/98LTZt7JiZi6vmCPXV8zTn63I/DmzZVraTEt7+yj7MC378uGSueeDFWv0w3yZPm40ux8JHwVibXxh+4/kGJx9Mr0GYx/3Nur7pEyXb3GkP/f8Hp+0U2NuHe/56CfLZmy/TO/XR8+nS0IfM6MH5PGAtJfH0QNyLtWMzJuRdjPXhbzWLDnHB2WZOiRtD8k6+nhQyXM1I9uTx3vEFnl+//4ls3X/dbP1U7VoHjhwzWw9sGC+N7Ngxg/Om4cOXjU/OjRv/vvwvHn48ILZJh4/cs08eXTRPH1s0Tx77Jp58bMF8/LxRbP9xKJ55fg189rni2bP7JKZOr1i/nxhxXxwccVMX1ox++U1evCKvE7nV8yxBbEoz6+tmM+WVsxxeTwpr99Ty9njGZk3J6/rlZUF65I8331W9ukD+Z74tny97b4qPx/k+9oO+R6+Q7/3y8+WV06be+Rnzth2/TlzwYy9etI8/NYx87msuyAWxVnZ5s5D583vD140Txy+bB4//IX5xcwl8+uPvjAT++bMxIcXzK8+vmR+eOCyuW/miozDonlIxvAHBxfMvfu+MPcelfETOpb3HJHHozLWR2Tsj+i5WTAPHbhixj/5wmyRcbxP2t0r6/1AxuqhD8+ZR/adM09+dMo8+u4h89j+i+bH+y6a7++7bB78dMFs2S/t9XzJ4xbtV87Fvfqo8w7n25R9uU/O7b1yru6T7Wv7B8T9slz3ZUzcLx4QW/RcSx/3y3ra5nsyPS62StsHhD4+KO0elGXflzYPiQflOMcPLcn+rpgfHhPy+KPcjx1Zz/mJrP+Tw0uB69Z/yXP1P7JOZtn6X3nu+2nuYUe2a+XzH5Hnvkf1Uedby4VtMq1+Jssfy/1cybLH5fEXx4V8b3z82LLMWzK/kPlPyHRBlqsn5XnBm/7lZ8vmV/IY+o2E6qcOXzXPH5s3Lx2fN88cu2p+d1SmT6yY/wd9CpBfATttDgAAAABJRU5ErkJggg==" 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/>
 
 </div>
 <div id="description-10" class="section level3">
@@ -2416,9 +2419,17 @@ Version: VECTO 3.3 / VectoCore 3.3.2 / VectoCmd 3.3.2</p>
 <p>In engineering a single correction factor for correcting WHTC, Cold/Hot Balancing, … can be specified.</p>
 </div>
 </div>
+<div id="dual-fuel-engines" class="section level3">
+<h3>Dual Fuel Engines</h3>
+<p>If the engine is operated in dual-fuel mode, enabling the checkbox “Dual Fuel Engine” shows an additional tab for providing the fuel type, fuel consumption map, and fuelconsumption correction factors for the second fuel. For dual-fuel engines the result files (.vmod, .vsum, XML reports) contain the fuel consumption for each fuel separately and the total CO2 emissions.</p>
+</div>
+<div id="waste-heat-recovery" class="section level3">
+<h3>Waste Heat Recovery</h3>
+<p>In case the engine is equipped with a waste heat recovery system (WHR) the WHR type can be selected from the drop-down list. For WHR systems that generate mechanlical power no further input is required - the WHR shall be considered in the fuel consumption map already. For WHR systems with electrical power output the generated electrical power needs to be provided in the <a href="#fuel-consumption-map-.vmap">Fuel Consumption Map</a> of the primary fuel. The final fuel consumption is at the end corrected for the electric energy generated by the WHR system (see <a href="#engine-fuel-consumption-correction">fuel consumption correction</a>)</p>
+</div>
 <div id="chart-area-2" class="section level3">
 <h3>Chart Area</h3>
-<p>The Chart Area shows the fuel consumption map and the selected full load curve.</p>
+<p>The Chart Area shows the fuel consumption map and the selected full load curve. The fuel consumption map of the primary fuel is plotted in red and if provided the secondary fuel is plotted in green.</p>
 </div>
 <div id="controls-6" class="section level3">
 <h3>Controls</h3>
@@ -3025,7 +3036,7 @@ Example: “Gears\Gear1.vtlm” points to the “Gears” subdirectory of the Ge
 <h2>Advanced Driver Assistant Systems, Eco-Roll, Engine Stop/Start</h2>
 <div id="engine-stopstart" class="section level3">
 <h3>Engine Stop/Start</h3>
-<p>If engine stop/start is enabled in the Vehicle, the engine is turned off during vehicle stops to reduce the fuel consumption. During vehicle stops the energy demand for certain auxiliaires and for starting the engine is accumulated. In a post-processing step the final <a href="#engine-stopstart-fuel-consumption-correction">fuel consumption is corrected</a> to consider the energy demand for the auxiliaries and engine start.</p>
+<p>If engine stop/start is enabled in the Vehicle, the engine is turned off during vehicle stops to reduce the fuel consumption. During vehicle stops the energy demand for certain auxiliaires and for starting the engine is accumulated. In a post-processing step the final <a href="#engine-fuel-consumption-correction">fuel consumption is corrected</a> to consider the energy demand for the auxiliaries and engine start.</p>
 <div class="declaration">
 <p>In declaration mode, the engine is switched on after a period of 120 seconds of engine-off.</p>
 </div>
@@ -3356,16 +3367,28 @@ Example: “Gears\Gear1.vtlm” points to the “Gears” subdirectory of the Ge
 </ul>
 </div>
 </div>
-<div id="engine-stopstart-fuel-consumption-correction" class="section level2">
-<h2>Engine Stop/Start Fuel Consumption Correction</h2>
-<p>The energy demand of the auxiliaries during engine-off periods as well as for starting the engine is accumulated (see <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">Engine Stop/Start</a>). The total fuel consumption is corrected in a post-processing step according to the <em>vehline</em> approach. Therefore, for every engine operating point with a positive fuel consumption the fuel consumption is plotted over the engine power. The slope (k) of the linear regression of the fuel consumption is used to compute the additional fuel that is needed for the energy demand during engine-off periods and engine starts.</p>
+<div id="engine-fuel-consumption-correction" class="section level2">
+<h2>Engine Fuel Consumption Correction</h2>
+<p>The final fuel consumption is corrected in a post-processing to reflect systems not directly modeled in VECTO (e.g. electric waste heat recovery sysmtes) or to account for systems not active all the time for different reasons (e.g., engine stop-start).</p>
+<div id="engine-stopstart-correction" class="section level3">
+<h3>Engine Stop/Start Correction</h3>
+<p>As the energy demand of auxiliaries is modeled as an average power demand over the whole simulated cycle, the energy demand certain auxiliaries during engine-off periods needs to be compensated during engine-on periods. This is done using the <a href="##vehicle-line-approach">Vehicle-Line approach</a>. The energy demand of the auxiliaries that shall be active also during engine-off periods as well as the energy demand for starting the engine is accumulated (see <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">Engine Stop/Start</a>) during the simulation. The final fuel consumption is corrected for this “missing” energy</p>
+</div>
+<div id="electric-waste-heat-recovery-systems" class="section level3">
+<h3>Electric Waste Heat Recovery Systems</h3>
+<p>VECTO currently does not model the vehicle’s electric system. The power demand of all electric consumers is calculated to the equivalent mechanical power demand considering the alternator’s efficiency. Thus, the electric power of an electric WHR system cannot be considered directly but the final fuel consumption is corrected for the generated electric power. During the cycle the electric power generated by the WHR system is accumulated. This electric energy is converted to the equivalent mechanical energy that the combustion engine “does not need to provide” considering the alternator’s efficiency.</p>
+</div>
+<div id="vehicle-line-approach" class="section level3">
+<h3>Vehicle-Line Approach</h3>
+<p>The total fuel consumption is corrected in a post-processing step according to the <em>vehline</em> approach. Therefore, for every engine operating point where the engine is switched on and has a positive fuel consumption the fuel consumption is plotted over the engine power. The slope (k) of the linear regression of the fuel consumption is used to compute the additional fuel that is needed for the energy demand during engine-off periods and engine starts.</p>
 <div class="figure">
 <img 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nusrRMeal2o4hkL0tLYaqWvBNWFBepVu+++rAFLicCGKAhsAKrgB6Uqe6f8w5p+CNSyQpU9l5Ufyvx2C3rh+mEPnn9fu3DYqbzDn1alD4O+/HL673PPpdNKca5aFAQ2REFgA4DFo561/BkLWm7Ftg561TRUx5vfPB3aEA2BDVEQ2ABgcSiszQ5qaXU8F+jOnYyvVhMENkRBYAOA6mw/+nxuQAtrzrHVFMze/37nBgezBtQFgQ1RENgAYOF0BejAN07lhrOZ1XJX7ZqY+zCoAtuFFxLYaojAhigIbACwMAeO/zwnmIVVIqhpaI5LLnHu9OmsAXVEYGug8Ioj/wonf6Rt/0qkqtrLIrABwPxVdghUDhyYTAOTcUDnq6G2CGwNpIDmhzSjNruUXCHLxvqpqr0TBDYA6JwOga7b234Q3Mu2nkyCWtvppfQdvGVLtjBJQ3ag1ghsDaRApQopWPm9YVrW2EBVtXeCwAYA5ZUJalalZi344Aede8MbGK6jixDYGkhhzQ5X+ocsw2ClkbZ1X1XtnSCwAUA5GqZjeZvZCtJquSt3nJq7V8160p55RodI0tvoCgS2htPUJwpYQmADgO5S2blq6knTeWp/8RdZA7pNtMB27tw5Nz4+Ti1BqZdN/65evdqNjo5OtWv58OHDlbXbcplSYMtrpyiKosbdt46cdCu35YeztGZOOTW4pzVrGxOtlvvJY49NLf/sttvcczt3zliHWppS5lkoetgaTj1s6gETXSiQd7FAVe2doIcNAPKV61VruVNnX00OgRYeBtVFBepV01Wg6HoEtgZSQPPPYfP57f5hzarayyKwAcBMZYOa6q5Dz2aPauPJJ9NDoDpfDV2PwIYoCGwAMO1AqUFwT7iV2+aYC/Sqq9IrQNE4BDZEQWAD0Os0VMfVe8Zzg1lRzXlxwZo16Vygr7ySNaApCGyIgsAGoJeVG6rDquVWZIPhzjpX7Ve/cm7VKuf27Mka0FQENkRBYAPQq8oO1WHV9jDo2bPOvelNzi1fnjWgqQhsiILABqDXqFetfyQ/lBXV8i0td/CHwZAQuohgw4bpWQpeesm5115Lb6OxCGyIgsAGoFfoXLXr9pWbVsovhbXc4To2bpz86z3553t0NGtALyCwIQoCG4Be0Nm5alat5DDojJ61kyed+/7309vqTdPYajp/DT2DwIYoCGwAmky9ajfsfzonjM1duVeC6spPTdaOnkVgQxQENgBNNvCNU7lhrH213GcfGJ8+DKqJ2q0X7bvfdW5kJL2NnkRgQxQENgBNdOD4z91lHR8CVQUXF+jqT/WoacgOYBKBDVEQ2AA0yXwvLFBQyx1fTQPfMr4aPAQ2REFgA9AUCmtX7DiZE8bmqpbb8vgL2VYm6arPCy5I5wAFAgQ2REFgA9DtdAXoqu2ajD0vjLWrgl61nTud++3fdu6pp7IGYBqBDVEQ2AB0M/WqzedctVljq919t3NXXpktAMUIbIiCwAagG83/XDVVzqwFH/lI2qsGzIHAhigIbAC60S0PnskJYnOVdwhUw3R89avOHTuWblCD4OoCA2AOBDZEQWAD0G3md3FB0KumCwo0rdSaNVkDUA6BDVEQ2AB0C11coKA2+5y1uS84SGYt0CTtBw/OHARXE7YDHSCwIQoCG4C6U4+aDoF2fnFBOmPB0IEz6WHQDRvSXjXN/wnME4ENURDYANTdfM9XSw6BqlfNzk175pk0tHGuGhaAwIYoCGwA6my+56tNDdnx3vdy9ScqRWBDFAQ2AHUW9q4tn/OwaHBxwS23ODc4mC0AC0dgQxQENgB1od60G/Y/7fpHWkld+82J5N/8YOZXKxkIVxcWjH//RDqtlAbCBRYBgQ1RENgA1MX8zlU74a7aNZFtYZKu+tRh0PvuyxqAahHYEAWBDUBdzDew7brhb5z7i79w7rnnsi0Bi4fAhigIbADq4tTZVzuYxL2V9KxpyI6fXX/T5F/RyT+jR45kWwIWD4ENURDYANSFzmErF9ha7rEvbJx52FPDdwBLgMCGKAhsAJaSDYKrUo+aUfvKkbxwNruSc9YuvNC5t70tezSwdAhsiILABmAp+eep6XCmuWbP+IxQNrta7v51f+Nu+8YP0zHWnnrKudOns0cDS4fAhigIbACWkh/YNGSHBsXV8B0zw5lfLbdi60l39IHvpOepXXVVtiUgDgIboiCwAVhKOgyqnrXLR+aevWDVpuPu8xuzCwk0nZTGVqNXDZER2BAFgQ3AUjt6+uUSE7m33HcuGUh71Y4dyx4JxEdgQxQENgBVKrqoQCFNhz9VK7bNdSVoNr2Uvp8+9SmuAEWtENgQBYENQJX8c9T+avTpqfC2suRwHWfe9V73yz/5ULY1oH4IbIiCwAagSn5gm3ui9pmluUDdsmXprAVATRHYEAWBDUCVdBi03ITtaQ3c87h76g/+xN33hR3pcB1AzRHYEAWBDUDVdJ5aXjjLqzu+8qBzb3iDc7fckj0aqDcCG6IgsAGo2pqdp3LDmdXQjfvctz/xGXftjqfSXjUN2QF0CQIboiCwAZgv/4rQR0/9D3fD/qdLHQ599EPLJv/qTf7Ze/LJbEtA9yCwIQoCG4Ay8obr8C8wmGsg3NsGdyQ9a1fuOOVOjP/cuSPZgLhAlyGwNdjIyIjr6+ub/H6a/oIaGBhI2harvSwCG4Ay/HBmc4D6bXNdEfrq+W9xL/7rf5M8DuhmBLYGU6gaGhqaClQKcMPDw8lttel+qaq9EwQ2AGXkBTabZqroMOgXb97vVmz+8eTtlju28QF94SSPA7oZga2hFKgUpvzApmBlt0XLExMTlbV3gsAGoAwLZ9ePPp2Uf2h05fbZYU2HP3We2ugla13/9pPJekATENgaSGFKQU3CwOYHK7uvqvZOENgAtBOeu6YLC8JwllcD9zzmjl70H93mO/czvhoaJVpgO3funBsfH6cWoVavXu0OHz6c3B4cHHSjo6Oz2v37qmq35TKlwJbXTlEUpVq/d/pw5+CeluvP6U2z2nnpevdPv3mBW3vX99yVIy136AencrdJUbFKmWeh6GFrGPV82cUAfo2NjSU9Y35PmPWUVdXeCXrYALQTXliw3AtoYe3ou9GdufA97oa/+bvs0UDzENgazj9cqfPa8i4WqKq9EwQ2AO3oMGi7ITs0ptr+Tw5M3lZPXCuZD5RDoGgyAlvDheeX+b1ufq9YVe1lEdiA3pU3tlpI61zeZjDcn/3WO92pd/1bd9ehZ7NHAM1GYEMUBDagd4QBzT/caUN1iNbTxQUKauH4ap+59wfu0MUrkiE7tHzF/ce4ChQ9hcCGKAhsQO8IA1pRYGt3Jeg9azdN/sU6z+1edn0ya4EexyFQ9BICG6IgsAG9Iwxo6mXTmGo6R03/2mHRcCDc6zc8lAQ1W77lC3vcZ+7/+2RdoNcQ2BAFgQ1oNv8w6CMnfpEENb9XzA9xCmort88+X+2xP70k6VVb/6WxdD0OgaKHEdgQBYENaCYLalfsmL7C0z/saYoOf2ostTUbjya31cO2ffnNk7db7rMPjHMIFD2NwIYoCGxA8yis+UHNyg9sFuiK5gHVZO2nf/ePs+V0yI7BvT/JHg30LgIboiCwAc2R16um0pWe1+yZmDEH6HX7JmasE5bGVtvcf/vkbYIa4COwIQoCG9Ac/vloYfmD36qn7bJguI4vr9+ZnKd275qNWVvaq3bwhwufygdoEgIboiCwAd0vr2etfzKg+SHNv71y2+zDoDpP7bm3/5776rqtk8std9OBZ7KtA/AR2BAFgQ3oXkVBza4C9Yft+Nw3xt2q7dPrqXQhwRPv+2gy+O10e4uLCoA2FhzYNO2RP01Ru9I0SYAQ2IDulXcIVOHMxlOTdodJNQ+oLi7Q7AUKalwBCsytksBWJoiVXQ+9gcAGdC8/jC1PzjlLbyu06T4Ft6v3jE+1q3YvG3T3r747ub1i84/dqk3HJ29zCBQoa8GB7cUXX3THjx/PloqVXQ+9gcAGdAcd/tSYaRqGo397y/3V6NMzBsLVoU8/mKnUHs4F+uvXvT6ZrH26jUOgQCcW5Rw2hTP1qFkR1BAisAH1p7CWN16aAplRb5qW/YsLVOpB0/Ac6WHPE27d7YeyAXFbbuW2k4Q1oEOVBzYd9uTcNcyFwAbUV9G4alZ+YDOjT/xsxjoKaxquY+el6712xlYD5qvSwKbetIGBgaSHDWiHwAbUk8JaUVBTac5PfyBcrb92d3q+mq76VE+abqtnbUffjVO9airGVgPmr9LANjEx4datW5ctAcUIbEC9HD39chLUwnPPFNCWZ7dXbGslMxfYfdZu9Xf/7t8n56rZYdC0Wu6uQ89mzwJgviq56MA/X02HP4eHh2e0cQ4bQgQ2oF7yetV0BWh4Dlt4rppfd16z2R26eEVyFaj1qm15/IXsGQAsxIIDm8KYQlq72rRpU7Y2kCKwAfVgPWt5ASwc8DZta0216xDoP7/5re6xP71k1npX7ZrIngFAFSq/6AAog8AGxGUXFoRze6bVmjVkh3rbVmybuZ560lq//4FkjLXpdgbCBRbDggObHQYdGxvjYgOURmAD4tLYan748kvTTCnQacw19abp3DU7X23oxn3up+94t/vizfuDxynQneTCAmCRVNLD9vDDDyfnrfX39yf/ahloh8AGxJF/CFRhKw1cCmv/5b+eLTxMqknadWHB1wb+2mtnXDVgsVV6SFQ9bOpp05WiGt5D567pylEgRGADlpZ6zIp61VZuPzm1Tt74a5pS6sCfr5nRNl3MWAAshUU7h01BbWRkJAluCnC7d+/mkCmmENiApaMgVtRjplq7eyLpecs/n+1Ecp7av/zGG2cN17F21wRhDVgiS3LRgc5z06HS2267LWtBryOwAUujOKxND9dx3d6nZ62jQ57Xb3goua3Bb21A3OTw6VYOgQJLrdLApl41HRINKbDltaN3EdiAxVcU1nS+mn8FqG77vWvqSdO0Usf/6MNTbWkxCC4QS6WBTcFMh0B11ah603QIVKWLEXRYlNAGQ2ADFle7w6C6sMAmbVd9dufJZIgO/8rPTVfc4dZ/acx7HOeqATFVHtgUzvSvLjiwGQ8U4HTlKIdEYQhswOIKLzDQOGp2+5rdE+66fRNJr5rNZLB9+c1Jr5qCmq2Xlob0YB5QILbKA5vCmX87/BcQAhuwOOxKz5kXELTcjv96Njn0qamlVm6bOd2USueoPfJnn3YD9zw21XYZw3UAtVFpYLPDn+pdUzhTj5pdKaqrRNXjBgiBDaiWBbWiw6CD+55O7vfbNFm7ppZaten4jHYFPE0tRVgD6qPSwCaaW1QhTQFN/yq8qRTamAQehsAGVKvdzAUqHdbUzAV+2/5PDrgf/eEHvcDWclfuOEVQA2qoksBWNogR2GAIbEC1/J41hTMd/gwPfX5u2w+TMdV29N04oz2tlhvc+5NsawDqZsGBrey5aZzDBh+BDaiW33umcdXCw58qjaemQ6A6V23mfVxUANRdJYGtr6+vVBHYYAhsQLU0TEc6WXvLLfcuONDQHI9+aJn7/B2PeAHNikOgQLeo/Bw2oAwCG1CtonHXdPhTw3XMnKxdRa8a0E0IbIiCwAbMj10NqlKvmrXZeGqqL6/f6e5ds3Fq2R8QV0FNteXxF5LHAugOBDZEQWADOpM3bIdmKQjDmuqn73i3+/XrXj+jLS2mlgK6FYENURDYgLn5vWnhkBwqjZVmt2/5wu5kDlDd1nlrX123deo+BTUNgsshUKB7EdgQBYENmJuCmoUuzVAwHcBm1tq7vpecp6aBcGff33Jrd53OtgigWy04sE1MTCQD5LYrJn1HiMAGzM0PbLr6c+X2MIxl92067h76WL+7bXCH156eq8YhUKAZCGwN5Q+novfIaMYJa9eQLKaq9rIIbEA+/zDoIyd+MaNnbYU3EO6WFbcm56npAgNrm64WQ3UADcMh0QZSSLYQpds2h6t/W/crdElV7Z0gsAH5wsOgRXOD3rN2kztz4Xvc9Rse8tpbbsVWzlUDmqjywGZzifpFD1s8ClZ6D0TByoKcaFm9b1W1d4LABuTzA1tYhy5e4Z76gz/JvY/Dn0CzVRrYXnzxRdff35+EBP0RV1DQsgUGLB07XOnPLhEGK91nPWRVtHeCwAbk09hqGq4j7yIDXVTw3Nt/b1a7whq9akCzVRrY9Efb/njrX1Hv2m233Zbc9p07d86Nj49Ti1wbN250g4ODye3Vq1e7w4cPT92n9tHR0crabblMKbDltVMUNe5uH0uDmC4mOPzhS92d12wOAppVemHBPQdP5m6Hoqh6lDLPQi1KYFPvi3rW1OOmwGbhDXGop03UM+b3hFlPWVXtnaCHDciftUAskGk8NQ3XcfSiT061TVfL9W8/lT0CQNNVGth0/pr1pumwqB2We/jhh5M2LA0/ICswK1CJ3pO8iwWqau8EgQ2Yeb6aDoPKoe8+lcz/ecX9x5J2XVRgty2ordp2kqtAgR5T+UUHPgU49bJhaSmkWVi23jXjt/u9YlW1l0VgQy/Lm2bKxlgbvWRt0qumq0CnQ5oVFxYAvWpRAptCmnperBTcAB+BDb3M71lbvvWEu+4rh9zn73gkWdb0UpuuuMOt2PzjqXXsXDUuLAB6V+WBTYfj/N4XFeewIURgQ6+ZMS/ot2bOC/qPb/sd9+r5b5nRNl0td+WO8WwrAHpV5Rcd6HwmDoNiLgQ29Job9k+HtM/uPJX0pNmyZi3QeWu2nFbLLd/CuWoAUpUGNp3LtG7dumwJKEZgQ9OFV4D656utu/1QMq3Utz9xpRfQ/OLwJ4CZKg1s6llTYNMVhJzDhnYIbGg6/zy1a785kZyrZsvqXTv1rn/r7l9991RbWvSqAchXaWBTMNP5amFt2rQpWwNIEdjQdH5gU+mwp2YpUO+a355WelHB2l2ns0cDwEyVX3QAlEFgQ9PpMGj/iIJYGsp0jtprbzzfDd24b6rNwhpDdQCYSyWBTeeu6dBnOJyHFYdEESKwoWnsnDVdXKCrQDWumkLaN//TtUFAmw5qqi2Pv5BtAQCKVRLYdu/encxwwCFRlEVgQ9Nct28iCGQn3M9+653uF2/917PaFdTWPND5gNMAeheHRBEFgQ1Nc9nWE8lgt+pVs0FwB+55bMbwHRbW6FUD0KlKApsOhdp8obrNIVDMhcCGbqbDnzr0qaE6dAXoX42mY6x9ef3OyW/V89zhD18ahLQ0qGm+UK4ABTAflQQ2naemQ5/hbaAIgQ3dzB8EVz1odiGBetjuXbNxqofNgpqKcdUALASBDVEQ2NCt1Lumw58WyB76WH/Sq6betemQZsXhTwDVqCywacBc/asLEOy2FYdIESKwoRsprK0aOeFWbTqelELZF2/en4Q2W7ag1r+dAXABVKeSwKZAFk747hc9bggR2NBtFNZseqkzF74nqemA5heHPwFUr5LABnSKwIZus/qB6UFwNQfoI3/2aS+kpUGNQ6AAFsuiBDYNpDs2NpYtAbMR2FBHNvitTdhu7vj6o8mYarPn/pwOa8xWAGAxLUpg03lrHAZFOwQ21JE//+flIyfd53adSm5fv+Eh90+/eUEyxtp0SJsOa/SqAVhsBDZEQWBDHfmB7c5rNrv/9oH/4K64/5gXzmbWim0tLiwAsCQ4JIooCGyoIx0GvWr3eBLGdJ6ahutY/6WxWUFNvWprd00Q1gAsmcoCmwKahvTwadYDzTGqfwEfgQ11YeetaTDc+9Ztcves3ZSEMg2Cq0OhYVhbsZXhOgAsvUoCmwJZf39/bjAbGRlh8nfMQmBDXfiHQV89/y3uH9/2OzMC2nS13HV7nyasAYiiksDW7pw1zmdDHgIbYrOetTvW75ga9PaWL+zOOQSaDtfBVaAAYqoksOmctaIeNh0m1WFRwEdgQywW1PpHpidr1/lqM0PadFhjEFwAdVDZOWwKZepJU4+aKLzpvDYFOWsDDIENsSisacJ2BTL9e+DP1+Scq5b2qq15YCJ7FADEVVlgU0DTuWr+lFQDAwNcLYpcBDbEsHLkRBLQ/uU33ujW3X4oCGnTYY3z1ADUTWWBzadDpHmHRwFDYMNSu3pPOgiuZis4/kcfnupls5BmddOBZ7JHAEB9VBLYFM4efvjhqduaDB5oh8CGpaJetaf+4E/c4Q9f6gU0vxTUTmZrA0A9VRLY/CtBuSoUZRDYsNh0cYHCmkLZ6d/9Y/d3/+7feyFtOqyt3XU6ewQA1BeBDVEQ2LCYvnPoSffE+z6aDNMxO6SlQU21nJ41AF2issC2bt265F8N42G3rThEihCBDYtF56rdNrhj8tvtvMLJ2jkECqDbVBLYFMj8q0PDoscNIQIbqmBjqqlG7tnvdl66PplSSsFs4J7Hpm77QY0rQAF0o0oCG9ApAhuqoKBmgezoRZ9MetXy5v+kVw1AtyOwIQoCG6pw54ZvTQW0NRuPujuv2eyFNAtqDIALoPsR2BAFgQ3zYYdAr9kz7pZvPZEMgHvmwvcEIW06rA3u/Un2SADobgQ2REFgw3z400qpdi8bLOxV2/L4C9mjAKD7EdgQBYENnTpw/Ofuq+u2Tn5rnee2rLg1CGnTYY1eNQBNRGBDFAQ2dEJhTYFs7V3fS6aVGrpx36ygprrr0LPZIwCgWQhsiILAhrIe+t8/5376jncHc3/ODGsrt3EFKIBmI7AhCgJb7/LHTjt19tWsdbajp19OLiz49ieudD/7rXe6K+4/NiuoqThXDUAvILA1lD9w8cTE9JAGAwMDU+2ahcJU1V4Wga13+WOnDR04k7XO9N2PfjoZBHdmQPOLw58AeguBrYFGRkamQpRu20wTuj08PJzc1v0KXVJVeycIbL2rXWCzc9VePf8trvX7H5hab7rSXrWbDjyTPQIAegOBreEUqCywKVhp2WhZvW9VtXeCwNa7dBhUQU01NU3Uyy+7Q5dek0wnpWCm89XyDoFyBSiAXkVgazj1hKlHTMJgpSCn4FVVeycIbDD3Hv6HZJiO9pO1t9zBH57LHgEAvSdaYDt37pwbHx+nFrFGR0fd6tWrp5Z1+/Dhw1PLg4ODU+tU0W7LZUqBLa+d6o36f/7+lLv1/u+5uwa3JqFMk7Tfs3ZTbq/aPQdP5m6DoiiqW0qZZ6HoYWso9YDpggCfesbyDmVW1d4Jeth623X7JtwT7/to0qumsdVmhrQ0qGmydnrVACBFYGsgC2thiNLh0byLBapq7wSBrUedPetu/OZTSSjT4LezrwRlqA4AyENgayCdU2ZDblhZeMtrk6rayyKw9Z4f/P1P3WtvPD/pWZsZ0qZr+RYGwAWAPAQ2REFg6y0aBFeB7OhFn3Sb+28Pglraq7Zi68npq0YBADMQ2BAFga03vDD2nWRMtTuv2RyEtOmwdhm9agAwJwIboiCwNd+B4z93tw3ucP/0mxe4r65Lrwb1g5pq7a7T2doAgHYIbIiCwNY8Nkfo/mu/5o588JIgoIVFrxoAdILAhigIbM1jU07pogIdBtVsBbODWnoIlHPVAKAzBDZEQWBrlnO33eXuvC497Llq0/HcAXBVzAEKAPNDYEMUBLbm+Mru45PfJOe5H/3hB4OQNh3WmAMUABaGwIYoCGxd7pVXnDtwwK194P9NQpkuLiiasYDZCgBg4QhsiILA1r10ccG+lTclvWqatH12SEuDmooZCwCgGgQ2REFg60Ivv5xU/8gJN3DPY270krXJv3lBbc0Dnc9+AQAoRmBDFAS27vOL/+1jydRSsy8o8MMaw3UAwGIgsCEKAlt3ufK/nHDbl9/sDn/4Urdi849zghqD4ALAYiKwIQoCWxd45RX3/7/9ne7bn7gyCGh+0asGAEuBwIYoCGz199BjE+7Mhe9pE9gYVw0AlgqBDVEQ2GrqyBH3j+//kFv/pbGcgGaVHgJluA4AWDoENkRBYKunfVfdMfmtcF7OZO1+WOMQKAAsNQIboiCw1cjBg+74TV+bCmVrNh6duj1daa8a46oBQBwENkRBYKuPs7/zB+7Xr3t9MgdoUVBbuY1eNQCIicCGKAhskU3+/J/90Wm3ZudJd/2Gh9wXb94fBDUrghoA1AGBDVEQ2OL5hxPPJOepPfG+j+YENKv0XLWnnv9l9igAQEwENkRBYFtiv/qVc889l8wDevmWH7sdfTe2uRKUCwsAoG4IbIiCwLbENm5MetXuvGZzEM78Ss9XY7gOAKgfAhuiILAtHfWq3f7Fnckh0M/f8UgQ0qbD2nJ61QCgtghsiILAtgQ+9Sn3/73r3TnhzK+0V21w70+yBwEA6ojAhigIbItLvWqPfGyFa/3+B4KA5hfnqgFAtyCwIQoC2yJ4+WXnPv5xd+jG+4JglleENQDoJgQ2REFgq963vnnU/ctvvNE9+qFlQTjzKw1qXFgAAN2FwIYoCGwVOX3aucFB94X//P0kkH3m3h944SwMavSqAUC3IrAhCgJbNf7vv7x28re4zHAdBDUA6GYENkRBYFuA73/fHdz87SSMrdj8Y/fl9Tu9cBYGtZbr334qeyAAoFsR2BAFgW3+/uGd73avvfH8IJyFRa8aADQJgQ1RENg69Nxz7o6Dp5Mw9tV1W929azZ64SwMai23dtfp7IEAgCYgsCEKAlt5f//wf3e/ft3r3SN/9ukgnIVFrxoANBWBDVEQ2Mq5+cGn3RX3H3NHL/pk0rNWHNRa7qYDz2SPAgA0DYENURDY2tu19m73z29+q/vizfuDcBYWc4ACQC8gsCEKAttsmk7qun0TbvnWE+5rA3/tnnv777nrNzyUE9LSoKa669Cz2aMBAE1GYEMUBLZpR0+/7FaNnHAPfay/xHlqqpZbuY1eNQDoJQQ2REFgS6lXTT1qCmJnLnyPe/FfvT0IZ2G13FPP/zJ7NACgVxDYEEWvBzYFtQ2jJ93/9bH+qfPUVm06ngyEOzukpUHtql0ThDUA6FEEtoaamJhwfX19bmRkJGtJDQwMJO2qI0eOZK3VtZfVy4Et6VWbDGHrvzQ2+Rt4njt08YqcgGaVnqu25fEXskcDAHoRga2BFKAUqBTW/MCm28PDw8ltW0eqau9ErwU2hbRbHjzjrh3+XjLorfWkqXdtzcajXkCbGdSu3DGebQEA0MsIbA2mYKUyClYKWEbL6omrqr0TvRbY1u2dSIKYetPUq8b8nwCAThDYGiwvsPnBamhoaKqHrIr2TvRKYDtw/Ofu6ru+5wbueSwJZJ+/4xG3o+/GgnPVWm5w70+yRwIAMI3A1mAEtnh0CPTyHWkQ03hqP/utdwbhLCz1rDFUBwAgX7TAdu7cOTc+Pk4tYm3cuDEpW169erUbHR2dsXz48OHK2m25TCmw5bU3oa4YmRnG1KO2e9ngjDa/Ltvacod+cCp3WxRFUVT3lzLPQtHD1mBhD5suFMi7WKCq9k7UtYdNg9heseNkUo+c+EXWWo7m/VQA0+wEmqxdQS0MZzOLoToAAOUQ2BpobGxsasgNK+O3+Yc1q2ovq46BTYcxL8sGsVX1j5Q7RLky6FFbd/sh99N3vNvdv/ruGe3TxVAdAIDOENgQRR0Dm4bd8IPVXIFt+9Hnp9bVUB3H/+jDBUN0hNXKtgAAQDkENkRR98C2fDJUHfxh8TkHdvjTavSStclh0OLJ2lX0rAEA5ofAhijqGNhOnX3VDR04k1TReWVrdip0pQFs+/Kbk7LlK+4/NnU7LE3WzrlqAID5IrAhirpedFBEFyPYJO1W//zmtyZDdvht09Vya3dPJIdVr9v7NGENALAgBDZE0S2BTUHNLkTQYLeb+2+fOk9Ng+DqAoOZQS0Na3cdejbbAgAAC0dgQxTdENjC89RuG9wx+RtznnvoY/0z2mdWy63ddTrbAgAA1SCwIYq6Bjb1qPWPTJ+ntmrT8akLCXRb56ypZ206oE0HNS4oAAAsFgIboqhbYFNQW7ltOqhZ2WTtc139uWIr00oBABYPgQ1R1CWwabDcdXsnckJYWl9dt9U98mefTnrXZt+f9qpxvhoAYLER2BBFXQKbf/jTShO1axDcsH1m6XH0qgEAlgaBDVHECmzqUfvcrlNB+JpZ/+0D/yE5FJp3n/WqcWEBAGApEdgQRazAlhfWdH6a5v6885rNs+6bLoIaACAeAhuiiBHYvvK3M+cKtfry+p3JtFIaYy3vfgW1mw48k20FAIClR2BDFEsZ2O49/A+zQphC2rc/ceXUxQT500q15pxTFACApUBgQxRLEdiKetRUj35o2eSn/7yCmQpU9KoBAOqDwIYoFjOwrd+fP0yHhugYunFfcls9auu/NDZrnbQYqgMAUC8ENkRRdWBLB77NC19paR5Qnaemiwvy7p8uLiwAANQPgQ1RVBnYDhz/eU7wSuuLN+9Pwppu37tmY7IcrpNWy332gXH31PO/zLYKAEB9ENgQRRWBbeVIXvCarnvWbpr8hJ/nRi9Zm3t/WulwHYN7f5JtFQCA+iGwIYqFBDb1qC3fmhe+ZtaajUfd4Q9f2rZXbc0DE9lWAQCoLwIbophPYNMsBVfvGc8JXtOlgPbPb35rEtby7rceNU0rxeFPAEC3ILAhik4C21wTtPu189L1rvX7HyicrL1/+6lsqwAAdA8CG6IoE9jKzPup0kTtGgQ3777pIqwBALoXgQ1RzBXYdJ5afvCaXS/+q7cnoW32fenhz8s4/AkA6HIENkRRFNi2H30+CF2za+1d30tmKiiepUDVclsefyHbKgAA3Y3AhijyAlvenJ95temKOyY/uee57ctvzr1fYY3BbwEATUJgQxQW2DRDwWUlhujQ0BxbVtw6taxppWxA3JnFUB0AgOYhsCGK0e8cKXVBgdUT7/to0qumw6F599v5aswBCgBoIgIbltzNDz4dhK380jlqn7n3B8lt9bDdv/ruWeuk1XI3HXgm2zoAAM1DYMOS+crfnskJW/mloPYvv/FGd/p3/zj3/ulqcQUoAKDxCGxYdGUvJgjrwJ+vSSZsz7svLXrWAAC9gcCGRdNJj5rKJmtvH9JUTC0FAOgtBDZUTjMU9I/kBa32NXTjvuQQ6C1f2J1zv80ByvhqAIDeQ2BDZRTUBr5R/spP1e5lg23m/rRKe9QAAOhVBDZUYr7nqT32p5e4V89/y9TVoDOLHjUAAITAhtLUg3bD/qfdFTtOur/61tPu1NlXS00lFdahi1e0GaJDlQY1BsAFACBFYMOcLKiVmZFgrlJP2q9f9/pkINy8+xXUBvf+JHtmAAAgBLYek9dLlsfWW7VdvV15wap8rdl41O28dL274v5jybIGxLXb05X2qnGuGgAAsxHYeswtD84camPowJnsnmkKawp0/noLKYW1dpO1948wRAcAAO0Q2BpAAUtBTFXUY2bCwObX5fMYiqOoNOfn9RseSm6rN23TFXfk9qpdtWuCsAYAwBwIbA3gh7C8HjPfo6f+R3KYc3ly+NEPT53Vp68fdn19fUl9+uovzbr/R3/4weRctaKrP7+894nsFQEAgLkQ2Bqgk8DWroetdN3zcBLUbLlv1Wr36Q3fTHrQVmz+cdL2tYG/dt/8T9dm69j5aS133d6nkx61Rx99NHtFAABgLgS2BtBhUAU1lX94Me9QqR/Y/o+d41mQyoJYyVLvmt+rpuUV197hXnvj+e7RDy3z1tXQHOO5hzwJbAAAlEdg60JhELPldXsnXP/2lrt8pOWu2j3edhiO5QsYoiMJbJM1tTz0n91lA0PJjAXf/YwuLEh70+469Gz2imcjsAEAUB6BrQv5vWTqVfOXl6IssN02uMP99B3vdmsHvjzV4zbXIVlDYAMAoLxoge2ZZ55J/mhTnde1O384FZ6ueeBHM5aXopLANhnQdJ6aDoN+bvUNUz1uej15r5miKIqierWUeRaKHrYuFJ6zZstX755wK7efnKz03DGNb7Zmpw6NTp/0r1qx9WR2lWjntWXFl93/ecnl2UUHaVt60cHe5HbZITr0AQYAAOUQ2NCZ97/fuTe9yQ3feefUsB4jIyPZneUR2AAAKI/AhrkpkD35ZHr7pZd0PDu9vQAENgAAyiOwob3nnpv8lEx+TJYtyxqqQWADAKA8Ahtme+UV577//Wxh0sGDzp0+nS1Ug8AGAEB5BDbMdt99aa/agQNZQ/UIbAAAlEdgQ+pXv0p71kTnqH3+8+n5aouEwAYAQHkENqQ+8hHn3va2bGHxEdgAACiPwIbU3Xc7t2pVtrD4CGwAAJRHYOtVL7/s3LvfnZ6vFgGBDQCA8pYssA0NDbmBgYFsKaUBV23w1eHh4ay1uL3b+ful8gec1c/G2o8cOZK1LiKdn3bBBc7dcEPWsDTGxsam9lOfiabz91flf561/9au9ZrE9tv/LE9MTEztr8oUtXez8Psu3Ef/vqZ93/n7qf02Rd9xS/7dt4iKvt/8fVTZz0X7a23+Z6JbFX2n6XNt7f7fvaL2blT0Pebvo8r/ufjt/u9KkSUJbPog6kX6H0jbOaP79OEtam8CfSDzPpRqsy9q7eui/eJq8Nsrr3Tu7NmsYenZB1M9bPrl9j+8TaT9y/sjrHb7Qg8/893OPs/aP/9313+//c98UXu3Kvq+y/u9btr3nd4/e/26bZ9x/331v+OK2ruV7a9oX+xzrdt6r0P++62fg34e3Ur7Ya/ffy/D99X+BhS1dyu9f/b6/fdSt+1z4PPX0f3+Z6fIkvWwaUf8N8f/RRUtW+W1N0HRvvi/tKLlRfngbtgw+Y5PvuWRDkf6H0oFNi3773UTFe2jH1JEy/5noAn8fdLn2Q8m9n1Q1N7twv0o2q8mf9/pvbffd+173ndcUXsT6H213/G8/dJ+q934P69u53/X6+fgf6bt51LU3gTad9uXov0KA6r/PVgkamDz3yz/DcxrbwLtl94UK/uiCn+Z/T90C6ZwZuOpaeiOigfA7YT/XiqwNekLqoj22X/P7Rc3fI+b9GVl/H0Mf/9FP4+i9m4X7peW/c+B/R40+ftO+2H7VvQdt6jffZHZ51u0n/be2+ci/P4LPzPdSPtj+2nC7zb7zBe1dyv/d9z/HdZta1fZZ0K3feHvQp7KA5v/4vw3I/wwhm+OfVEVtXcj+zmEb4xov+znEb5RlX5pvfe9yWTtdeC/l70S2HzaX/sshO+xfi7+70sT+PsY/v6LfhZF7d0ub7982kf9bJr0fefTvvn7X/QdV9Te7exvWR7to+7Tfuq2mesz0020b/Z7HH632We+qL0JtG95v8faP3vPw++58HchD4dEI7I3TD8X/0uqzBvXlqaV0lWg8tRTM6eZiki/nPZh7ZVDoiF7b/Vz8L+stNyEP1Q+f5+0z/4XlH0fFLV3u7n2Q597vf9N/L4L31PRzyLvO66ovZuF72nIvve03/5nRMv2/dgE9vuvffU/0/bZL2pvgqLff79dvyP+Zz38nckTLbDpjfRfoO5TW1F70+iDab+c+qCqRPua90aXpoCm89R0cUHN6DNgH9JeuejA57+3+qKy999+Lk1jX9hG+27vt/9Hrai9m4XfdyH7PWja953/O+4r+o4rau9W/u91Ef97Tz8re7/1c/ADTLfRa7d98T8H2lf/fZ2rvVv573vR58D/ftP99n7rZzHX50aWJLDpjfDLPqx64dbmf1CL2rud3hDbL5XPb5/Xh9Z61F57LR2q49ixdLlm9H7aftoHt8n8z3L43urLytq7+Y90KNxn+yLSPlqb/0Vd1N6tbF+s9H2nCttMk77vwu84lX3m89qkqL3b6LX7+6LSzyNs97/3/M9FmT/YdRbup/+d5n8u/M9+UXs38v+2+d9j/ve83x7+vMpYsh42LCINfvuGN6TDdnQJ9bABAIByCGxNoPBz8cXppO1dgsAGAEB5BLZu9alPObdsWbbQfQhsAACUR2DrVh//uHMf/GC20H0IbAAAlEdgQxQENgAAyiOwIQoCGwAA5RHYEAWBDQCA8ghsiILABgBAeQQ2REFgAwCgPAIboiCwAQBQHoENURDYAAAoj8CGKAhsAACUR2BDFAQ2AADKI7AhCgIbAADlEdgQBYENAIDyCGyIgsAGAEB5BDZEQWADAKA8AhuiILABAFAegQ1RENgAACiPwIYoCGwAAJRHYEMUBDYAAMojsCEKAhsAAOUR2BAFgQ0AgPIIbIiCwAYAQHkEtgYbGhpyAwMD2VJqZGTE9fX1JTU8PJy1VtdeFoENAIDyCGwNpaA2NjY2I7BNTEwkAcvoviNHjlTW3gkCGwAA5RHYGkzByg9s6hULe8msqmjvBIENAIDyCGwNlhfY/GClHjgFr6raO0FgAwCgPAJbAygs6RClSuHJENgAAGiGaIHt3Llzbnx8nFrEOnz4sFu9evXU8saNG92tt946Y9mqinZbLlNPPPFEbjtFURRFNa2UeRaKHrYGC3vYdGFA3sUCVbUDAIDFQWBrKAUqv+xQqX/41D+sWVU7AACoHoENAACg5ghsAAAANUdgAwAAqDkCGwAsIrtIxy4A0jmfVZz3qe1pu1zwA/QGAhuAtjQnrV1gorILWOoifH3+mIB6rf59unJ6qSlQ6TWaosAWXm1tQc+n/fG3pdsENqA3ENgAtKVQYCFNgSdW8Cnivz6fQpEfePSa/bCzVMoGNgVNv92uxPb3LVyHwAb0DgIbgLbCQFR23D1/6Be/10vbszCl8gOInsfareYKh+HrM+1epwUfe45w2R5nAdXKD156jP96/ft87QKbtmGHSrUtfz21636VUZv/89D6BDagNxDYALSlUGCBSOFA4WSuEOWHErFwI9qeBRMLRMbftv+YdrQtC00qPSbcbkjbtvttXXtNYXDy+SFQ27CwJbqd93qLApsqfB69Dr0elYUzew5tx38+0eMJbEBvILABaEuhQEHCqkxACB+jsgAXhgwLJqL17LbWKxvYwvW0DW2rSBgG/dfghySxcGdlrz3chvZPbSGtr9dotJ6247cZ2xeVbUuvRduwkOfT+mXeDwDdj8AGoK28QDSXdo8JQ4YflnSfBSPdLqPoufxwFSob2LSOH978176QwKbSdsPXrWVtw9+2bmt9/7lNXhuAZiKwAWirKBC1YwEjTxgyLCyFPVtlFb0+C0U+e01h2LLXIGFgs8eoPexh87ev23mvoyiwSRgq7bn912OP17ohtfuPB9BcBDYAbSkUdBrYxEKGlQWLMGT44US3/ccoFM2l3evT4/3t2fOUDWzivyb/tWsb/vaLXqsFLuMHNt3nvy7R8/nrS9H2w58lgOYisAGoBb83SxRiFFTqKgx9RcLAViUCG9A7CGwA5sV6lsKy3qP5CHvYFIi0Pb/Nr5g6CWx6rX6vXRXsZ0VgA3oDgQ0AAKDmCGwAAAA1R2ADAACoOQIbAABAzRHYAAAAao7ABgAAUHMENgAAgJojsAEAANQcgQ0AAKDmCGwAAAA1R2ADAACoOQIbAABAzRHYAAAAao7ABgAAUHMENgAAgJojsAEAANQcgQ0AAKDmCGwAAAA1R2ADAACoOQIbAABAzRHYAAAAao7ABgAAUHMENgAAgJojsAEAANQcgQ0AAKDmCGwAAAA1R2ADJo2MjCRlxsbGXF9f31SJ7vfbhoeHk/ZQ+Fh/u0tlaGgoeR0LFe7zXNus4nmPHDmSbCdP+Hrmes865T9+YmIia0VsdfidyqPvgLq8FjQfgQ2YpC9d++JVYNAfBaNl+2Ke68vZ/rD4tLzQEDMXbb8oQC6Ev88KMOG+zcdcr3WuwJb3HhS9Z76BgYGkvYj/x1evseg1YOmFnxm91wRq9BoCG7rWmZf+p7vlwTNJnTr7atY6P34QKOolKgoLvrzH+n/8LRToD47K354ChbXbHyOtr7J17bZK64u2b20qPVbr2esIn9PaLeRY2fZ8elz4Gu21+a/Xf652z6uyZVXeH935BLai9yyk9YqeN2zXMupB760+T0bvox++7X1VqV3voy1biT472o6WbXvhY8X/3bD18tr8z6O/Hf8zauvk3Qd0gsCGrqWg9pdbTiQ1dOBM1jo/9qUqfijx+V+6Kv8Phsl7rJYtDOmLXl/sYn9URO22PbXbOv76Id1nISX8g+bfp9v2/HoOu61/7Tn1HEX7bD8X+4Ml2qa1237o3zLPG77WkNYt2ufwPbDn0rbzXn8RrW+vx9i+mU63icXjf2bs82b0mbDPgeh90+fH/xza51zr+u973mNFj7fHmLw2Pd7K/0zr9fnPaa83fO1AJwhs6FoxAputUyTvsVq2PwT6Uvf/QNgfAd2vL3K/JFxf6/rr2H361/+D4T8u3IZt23+tuh3+MRL7Y2Plr+/vp+1HmecNX2tI29H28hS9B+HrmYv9vH3hcqfb7AmXXOLcsmXZwiTdXrUqW5ik21UuZ/SZsc+gKvzs+fep/M+z7rfPYfj5yXustm2fe/95itpU9vk3/mff1jF8rjBfBDZ0LR0GVVBTPfX8L7PW+fG/VMMvXxN+8ebJe6wfUMIQY+sXfYmH6/t/iPz7/OcQ/75wG/qjI/4fK/+xvqJ9Dl+v7UeZ5w1fa0jb0fbyFL2evJ97HtvnvJ912G6vF54wsF18sXMf+Ui2MEm3q1zO+J8Zvf/+5yfvvdf9ev9U/mcp/PzM9bnR5zz8rPpttr1wO7rfPvvhcxb9rgNzIbABk/wvVX3R+n+s9eWqL+DwizeP7tcXsk/bsi9zbcf+gGi79jxqy9u2/8Uv/pe9btt9/h808R8XbsOeM3ydeYr22X+9/n6Ued7wtYb0s7KfUajo9Wibtn2x98yn/W33x9nfJ22v6DVg6YWfGb3X9nug9rz3Ok/4+cl7rE/PEW7Lb7PthdvxX1/4nHqs3Qd0gsAGTAq/VHVbX7pWeW3tQoW/nh9a7Is97z7/MbZtreuvo9u2jr748x6vPwb+48JtaB3Rc9hjVP46Jvy5+PzH+oF0rucVe1zeH675BDYJf+6d0mtZyOOxePQ50ufJ6L32l/W7YO+bbus+/720z0ze5yd8bNhmn+G8Nn97efdL+Jxaj8CG+SCwAUsoDDGx6A+GH4rahaSlVqfXgu5kwUv0WfeXgW5FYAOWUF0Cm4Q9bHX5Xz+BDQsV9rBZDzDQzQhsAAAANUdgAwAAqDkCGwAAQM0R2AAAAGqOwAYAAFBzBDYAAICaI7ABAADUHIENAACg1pz7X4SJMFtwYqsxAAAAAElFTkSuQmCC" />
 
 </div>
-<p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="\Delta FC = k * (E_{aux,ICE,off} + E_{ICE,start})" title="\Delta FC = k * (E_{aux,ICE,off} + E_{ICE,start})" /></p>
+<p><img style="vertical-align:middle" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAdcAAAAUCAMAAADoUYddAAAAM1BMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADxgEwMAAAAEHRSTlMAEN27iTKZIkSrVGbNdu+R9mHAxgAAAAlwSFlzAAAOxAAADsQBlSsOGwAABnBJREFUaAXtWduiqygMBcUbSMf//9pZKwmIvdh9du15meFBbICVkCtY577efPo6i/8ZNBpIvvnx6Wt4DTZ+iv0fWX+VnnwD5LtdeX7sthGt37IQfVjGnN2kv/Z5zduw3ppfx9c8yG8/3gy1P45/69e3GV6NP4WrNDHvlvJ9i7ouwiJHdqETw4RuPuEbdcWzGWshGurQuFMZ+k7/bYbX4ofpMi1UlXs3bE0i3cSgbqZBewuvYTtju7ysoamOGKrbvekM8Rdj6c7zvs3wWvwL05gGJDQIA/Z7GM20MUgzuvFW7H3K93Z0tib4+zJSUJ06zS/s9m7JnV2/zfBa/CZ5vtvn2/HBsidVP2zFAC4wjtU0c/FJd2qOCeU1pj0XN9W6Ft5A4u48b4X78wl3dv02w2vxR8ZPysmntEjh+/P97ytM6ZK/+mqWbg2LnZn6apZ90ZM3mDT52eYuweFnCe8KANTx9jft+m2G1+JT+36eNpStWOvjE1X/iKQ1k/mWAWtu4vmS9MdWjV3xxqW2mm77mtE5LWNCtvQ9ldD1G7xnfpODp+Um1deHnLMcwEnJwU4oVYYhiFzWFfIxXluGD7hPYYdScgrgA6EMsN/xfX9Lblo7bG5ZobeJUSEPPCH++IMTRaRmZheprhoTbkZ9OxUCs5+Nd7ITU3Y5IUVa244gFrYg1CyN94e2rbHVSe7qdbbaNUrRxqy7w80RK0tARynKq5y4lFI9SKd3XmS2DjRxtnWlx5WpB4YPuM9g1/s9FsLQ17abqMGfGWCZGUrVkOl18nDChxZ/0yx+RkovD52PbFeEeAXwbFzsWkwyMKLQxiYNaPiAKGdjGX7yGG5u2E9OqRvGYTX1+pKHC+p0useethw0xrOomRTvxMSVs92220s3x47xemD4gPsMtoha+TwQ6gheGnzsn6HKRCozhJteEXpu93bqy1ziza4yc79R8gB0KgTBuf6uCa3qrFeDdsXjMXm0Mul3P32Sh+ma8EmTHh2AylYK24LKgAxpRMdVAaknjRGfPbTJ4c3c2yrBhNJtoy4nLEByWznfujJ2b9eWoXvABWGHdTnieDCuLB4imw9jyv8oYcc/vjX4EzJcnOGNpkrhpo7O11R2lxM/0nATwiDpk7BZ1UUgJPgihsvZpEJZ8rYmhzmNdHmRtIxXTKKJrXOcrWU5/E4WtjLBdSLTUCRT4v2TGbMbzFk5WIyJV7vnFNQMp+kHCAwdIFusEzLqkkrdFcf3WvWVybBlOXfKrwXOLwVIxan60qnHeG0ZIptgSot7gHX41AOFi3OpbNFtPu7eZviH7oC/eR553KSuCHCcCUQH0y1l/a6DxRAfbimbUAb6JKyFT2Jk8dikYpSOfRzqmgEmrVNE6gaTaHLPuW17g9LCuh2+PeUFZxiVl0ueNqbzRF6l1QADXchAXUIISwdfpJIX+KeH/LL5rtY1OTHQ8dkEhJQCLPmZRh81l1snk/lo83DLECPUW4vbwiLytx7yQHlVtoELSHjVjvjbBBm3Iqhwi2Iq3v2k+gJHy4ttQhg4fUIAuyeIDnFCKCqCekSqLkUIuK/BTahM0XHAbxUN6eeV2NfRveyvxeNe6QkR8sugGFdmLAxEXobRtOwLxRSWsHtRkh0V7k8MrV0Foj4ecFtYzPIJPMm2yCZRsMtVgV68dPT8rSR2AddDMIts8SeKDw66CWGgKQLE0HoQaqyJQfVQiKhm39eAWCQVqVtMzGfUf70dZCY32rND0g5ISzmi9W6e5OOWu8n+GDxu4APbIoU3HlzGZogvMWQKt07m8dFkiUrTlwfcCktcqm9h+khVNn4aEMId0IufKxVfz5gCrtmXz7ziMkHx4c9+sE3ot4fyBaL1/QkpzVSUQhQhmFehMJ0NDczLNNsUGT9gQlUlwb2Q9iKyOlsDNqZIl55GHpliQAZH3uhx5Rs3PbKFnPT+DApKlXzLxDjcQJzkxXG44XB8fcBtYMnXp8h4G8nTZBOPF8IR6sUv+Vhk+xRuU+BWPMTHPnskEhWfRUk3oSFlgXU4vkiNVTGgIZUqJK7U2fABzzRgkoqQLSa4vpDyYvIP/1d/VxP2cbonmnWfSrvjfop0uv6MjbhFWR3gCh+1Ug4+ArlssX5peA1Xx6dRLgXWvV7ww5GK+8P5v5x2yuYvBdgvRf9s2Zk/E7mOT/o5xrrPmLa4HyOdAlTxT2ddN/gv4HYyuVl9++YAAAAASUVORK5CYII=" alt="\Delta FC = k * (E_{aux,ICE,off} + E_{ICE,start} - E_{WHR,corr}/\eta_{alternator})" title="\Delta FC = k * (E_{aux,ICE,off} + E_{ICE,start} - E_{WHR,corr}/\eta_{alternator})" /></p>
 <p><img style="vertical-align:middle" src="data:image/png;base64,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" alt="FC_{final} = FC_{final,mod} + \Delta FC" title="FC_{final} = FC_{final,mod} + \Delta FC" /></p>
 </div>
+</div>
 <div id="fuel-properties" class="section level2">
 <h2>Fuel Properties</h2>
 <table>
@@ -4451,7 +4474,7 @@ Example: “Gears\Gear1.vtlm” points to the “Gears” subdirectory of the Ge
 <p>The FC map is used to interpolate the base fuel consumption before corrections are applied. For details see <a href="#engine-fuel-consumption-calculation">Fuel Consumption Calculation</a>. The file uses the <a href="#csv">VECTO CSV format</a>.</p>
 <ul>
 <li>Filetype: .vmap</li>
-<li>Header: <strong>engine speed [rpm], torque [Nm], fuel consumption [g/h]</strong></li>
+<li>Header: <strong>engine speed [rpm], torque [Nm], fuel consumption [g/h]</strong>, <em>whr power [W]</em> (required only if an electric WHR system is installed)</li>
 <li>Requires at least 3 data entries</li>
 <li>The map must cover the full engine range between full load and motoring curve.</li>
 </ul>
@@ -6130,9 +6153,12 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <h2>Modal Results (.vmod)</h2>
 <p>Modal results are only created if enabled in the <a href="#main-form">Options</a> tab. One file is created for each calculation and stored in the same directory as the .vecto file.</p>
 <p>In Vecto 3 the structure of the modal data output has been revised and re-structured. Basically for every powertrain component the .vmod file contains the power at the input shaft and the individual power losses for every component. For the engine the power, torque and engine speed at the output shaft is given along with the internal power and torque used for computing the fuel consumption. See <a href="#powertrain-and-components-structure">Powertrain and Components Structure</a> for schematics how the powertrain looks like and which positions in the powertrain the values represent.</p>
-<p>Every line in the .vmod file represents the simulation interval from time - dt/2 to time + dt/2. All values represent the average power/torque/angular velocity during this simulation interval. If a certain power value can be described as function of the vehicle’s acceleration the average power is calculated by <img style="vertical-align:middle" src="data:image/png;base64,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" alt="P_{avg} = \frac{1}{simulation interval} \int{P(t) dt}" title="P_{avg} = \frac{1}{simulation interval} \int{P(t) dt}" />. <strong>Note:</strong> Columns for the torque converter operating point represent the torque/angular speed at the end of the simulation interval!</p>
+<p>Every line in the .vmod file represents the simulation interval from time - dt/2 to time + dt/2. All values represent the average power/torque/angular velocity during this simulation interval. If a certain power value can be described as function of the vehicle’s acceleration the average power is calculated by <img style="vertical-align:middle" src="data:image/png;base64,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" alt="P_{avg} = \frac{1}{simulation interval} \int{P(t) dt}" title="P_{avg} = \frac{1}{simulation interval} \int{P(t) dt}" />.</p>
+<p><strong>Note:</strong> Columns for the torque converter operating point represent the torque/angular speed at the end of the simulation interval!</p>
+<div id="quantities" class="section level3">
+<h3>Quantities:</h3>
 <p>The following table lists the columns in the .vmod file:</p>
-<p><strong><em>Quantities:</em></strong></p>
+<p><strong>Note:</strong> For dual-fuel vehicles the fuel consumption columns are present for each fuel (e.g., FC-Map_Diesel CI, FC-Map_NG CI).</p>
 <table style="width:94%;">
 <colgroup>
 <col width="25%"></col>
@@ -6363,14 +6389,24 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <td>Power demand for every individual auxiliary. Only if the run has auxiliaries.</td>
 </tr>
 <tr class="even">
+<td>P_WHR_el</td>
+<td>[kW]</td>
+<td>Power generated by an electric WHR system, interpolated from WHR map.</td>
+</tr>
+<tr class="odd">
+<td>P_WHR_el_corr</td>
+<td>[kW]</td>
+<td>Power generated by an electric WHR system after applying</td>
+</tr>
+<tr class="even">
 <td>P_aux_ESS_mech</td>
 <td>[kW]</td>
-<td>Power demand for the auxiliaries considered during engine off periods, multiplied by the engine start/stop utility factor. The final fuel consumption (.vsum) is correctedd for this power demand in a <a href="#engine-stopstart-fuel-consumption-correction">post-processing step</a>.</td>
+<td>Power demand of the auxiliaries considered during engine off periods, multiplied by the engine start/stop utility factor. The final fuel consumption (.vsum) is correctedd for this power demand in a <a href="#engine-fuel-consumption-correction">post-processing step</a>. This power demand has no influence on FC-Map.</td>
 </tr>
 <tr class="odd">
 <td>P_ice_start</td>
 <td>[kW]</td>
-<td>Power demand for starting the engine after an engine-off period multiplied by the engine start/stop utility factor. P_ice_start = <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">E_ice_start</a> / dt. The final fuel consumption (.vmod) is corrected for this power demand in a <a href="#engine-stopstart-fuel-consumption-correction">post-processing step</a>.</td>
+<td>Power demand for starting the engine after an engine-off period multiplied by the engine start/stop utility factor. P_ice_start = <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">E_ice_start</a> / dt. The final fuel consumption (.vmod) is corrected for this power demand in a <a href="#engine-fuel-consumption-correction">post-processing step</a>. This power demand has no influence on FC-Map.</td>
 </tr>
 <tr class="even">
 <td>P_PTO_consum</td>
@@ -6483,29 +6519,29 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <td>Fuel consumption interpolated from FC map.</td>
 </tr>
 <tr class="even">
-<td>FC-AUXc</td>
+<td>FC-NCVc</td>
 <td>[g/h]</td>
-<td>Fuel consumption after <a href="#engine-fuel-consumption-calculation">Auxiliary-Start/Stop Correction</a> (based on FC)</td>
+<td>Fuel consumption corrected for different NCV values in VECTO and VECTO Engine (FC-NCVc = FC-Map * LowerHeatingValueVectoEngine(fuel) / LowerHeatingValueVecto(fuel) )</td>
 </tr>
 <tr class="odd">
 <td>FC-WHTCc</td>
 <td>[g/h]</td>
-<td>Fuel consumption after <a href="#engine-fuel-consumption-calculation">WHTC Correction</a> (based on FC-AUXc)</td>
+<td>Fuel consumption after <a href="#engine-fuel-consumption-calculation">WHTC Correction</a> (FC-WHTCc = FC-NCVc * WHTCCorrectionFactor(cycle, fuel) )</td>
 </tr>
 <tr class="even">
 <td>FC-AAUX</td>
 <td>[g/h]</td>
-<td>Fuel consumption computed by the AAUX module considering smart auxiliaries</td>
+<td>Fuel consumption computed by the AAUX module considering smart auxiliaries. (FC-AAUX = FC-WHTCc if the AAUX model is not used, otherwise the fuel consumption as calculated by the AAUX model)</td>
 </tr>
 <tr class="odd">
 <td>FC-ESS</td>
 <td>[g/h]</td>
-<td>Fuel consumption considering engine stop/start. During engine-on periods equals FC-AAUX, during engine-off periods the fuel consumption for supplying all auxiliaries at idling speed multiplied by (1 - engine stop/start utility factor) - see <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">Engine Stop/Start</a></td>
+<td>Fuel consumption considering engine stop/start not always activated. (FC-ESS = FC-AAUX if the engie is on, FC-ESS = FC(P_aux) * (1 - engine stop/start utility factor) if the combustion engine is off - see <a href="#advanced-driver-assistant-systems-eco-roll-engine-stopstart">Engine Stop/Start</a></td>
 </tr>
 <tr class="even">
 <td>FC-Final_mod</td>
 <td>[g/h]</td>
-<td>Instantaneous final fuel consumption value after all applicable corrections</td>
+<td>Instantaneous final fuel consumption value after all applicable corrections. (FC-Final_mod = FC-ESS)</td>
 </tr>
 </tbody>
 </table>
@@ -6514,10 +6550,13 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <p>P_loss_total = P_clutch_loss + P_gbx_loss + P_ret_loss + P_gbx_inertia + P_angle_loss + P_axle_loss + P_brake_loss + P_wheel_inertia + P_air + P_roll + P_grad + P_veh_inertia (+ P_PTOconsumer + P_PTO_transm)</p>
 <p>P_trac = P_veh_inertia + P_roll + P_air + P_slope</p>
 </div>
+</div>
 <div id="summary-results-.vsum" class="section level2">
 <h2>Summary Results (.vsum)</h2>
 <p>The .vsum file includes total / average results for each calculation run in one execution (ie. click of <a href="#main-form">START Button</a>). The file is located in the directory of the fist run .vecto file.</p>
-<p><strong><em>Quantities:</em></strong></p>
+<div id="quantities-1" class="section level3">
+<h3>Quantities:</h3>
+<p><strong>Note:</strong> For dual-fuel vehicles the fuel consumption columns are present for each fuel (e.g., FC-Map_Diesel CI, FC-Map_NG CI).</p>
 <table>
 <colgroup>
 <col width="6%"></col>
@@ -6593,154 +6632,164 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <td>Average fuel consumption before all corrections, interpolated from <a href="#engine-fuel-consumption-calculation">Fuel Map</a>, based on torque and engine speed.</td>
 </tr>
 <tr class="odd">
-<td>FC-AUXc</td>
+<td>FC-NCVc</td>
 <td>[g/h], [g/km]</td>
-<td>Average fuel consumption after <a href="#engine-fuel-consumption-calculation">Auxiliary-Start/Stop Correction</a> (Based on FC-Map)</td>
+<td>Average fuel consumption after correcting for the <a href="#engine-correction-factors">net calorific value</a> (Based on FC-Map from .vmod)</td>
 </tr>
 <tr class="even">
 <td>FC-WHTCc</td>
 <td>[g/h], [g/km]</td>
-<td>Average fuel consumption after <a href="#engine-fuel-consumption-calculation">WHTC Correction</a> (Based on FC-AUXc)</td>
+<td>Average fuel consumption after <a href="#engine-fuel-consumption-calculation">WHTC Correction</a> (Based on FC-NCVc from .vmod)</td>
 </tr>
 <tr class="odd">
 <td>FC-AAUX</td>
 <td>[g/h], [g/km]</td>
-<td>Average fuel consumption after Smart Auxiliary Correction (<em>still in development</em>) (Based on FC-WHTCc)</td>
+<td>Average fuel consumption after Smart Auxiliary Correction (<em>still in development</em>) (Based on FC-WHTCc from .vmod)</td>
 </tr>
 <tr class="even">
 <td>FC-ESS</td>
 <td>[g/h], [g/km]</td>
-<td>Average fuel consumption including fuel consumption during engine-off periods (Based on FC-ESS)</td>
+<td>Average fuel consumption including fuel consumption during engine-off periods (Based on FC-ESS from .vmod)</td>
 </tr>
 <tr class="odd">
-<td>FC-ESS_Corr</td>
+<td>FC-WHR_Corr</td>
 <td>[g/h], [g/km]</td>
-<td>Average fuel consumption including fuel consumption during engine-off periods corrected for energy demand during engine-off periods not accounted (Based on FC-ESS, E_aux_ess_mech, E_ice_start, k_vehline)</td>
+<td>Average fuel consumption including fuel consumption deduction due to electric power generated by an electric WHR system (FC-WHR_Corr = FC-ESS - E_WHR_el / eta_alternator * k_vehline)</td>
 </tr>
 <tr class="even">
+<td>FC-ESS_Corr</td>
+<td>[g/h], [g/km]</td>
+<td>Average fuel consumption including fuel consumption during engine-off periods corrected for energy demand during engine-off periods not accounted (FC-ESS_Corr = FC-WHR_Corr + (E_aux_ess_mech + E_ice_start) * k_vehline)</td>
+</tr>
+<tr class="odd">
 <td>FC-Final</td>
 <td>[g/h], [g/km], [l/100km], [l/100tkm], [l/100m^3km]</td>
-<td>Final average fuel consumption after ALL corrections. Value for calculation of CO<sub>2</sub> value. If Loading = 0[kg] the column [l/100tkm] is left empty.</td>
+<td>Final average fuel consumption after ALL corrections (FC-Final = FC-ESS_Corr). Fuel consumption for calculation of CO<sub>2</sub> value. If Loading = 0[kg] the column [l/100tkm] is left empty.</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>CO2</td>
 <td>[g/km], [g/tkm], [g/m^3km]</td>
 <td>Average CO<sub>2</sub> emissions (based on FC-Final value). Output for [l/100tkm] is empty when Loading = 0[kg].</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>P_wheel_in_pos</td>
 <td>[kW]</td>
 <td>Average positive power at the wheels</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>P_fcmap_pos</td>
 <td>[kW]</td>
 <td>Average positive power at engine (all non-negative values averaged over the whole cycle duration)</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_fcmap_pos</td>
 <td>[kWh]</td>
 <td>Total positive work provided by the combustion engine.</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_fcmap_neg</td>
 <td>[kWh]</td>
 <td>Total energy</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_powertrain_inertia</td>
 <td>[kWh]</td>
 <td>Total work of engine, torqueconverter, and gearbox inertia</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_aux_xxx</td>
 <td>[kWh]</td>
 <td>Energy demand of auxiliary with ID xxx. See also <a href="#auxiliary-dialog">Aux Dialog</a> and <a href="#driving-cycles-.vdri">Driving Cycle</a>. In Declaration Mode the following auxiliaries always exists: E_aux_FAN (Fan), E_aux_PS (Pneumatic System), E_aux_STP (Steering Pump), E_aux_ES (Electrical System), E_aux_AC (Air Condition)</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_aux_sum</td>
 <td>[kWh]</td>
 <td>Total energy demand of all auxiliaries. This is the sum for all E_aux_xxx columns.</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_clutch_loss</td>
 <td>[kWh]</td>
 <td>Total energy loss in the clutch</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_tc_loss</td>
 <td>[kWh]</td>
 <td>Total torque converter energy loss</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_gbx_loss</td>
 <td>[kWh]</td>
 <td>Total transmission energy losses at gearbox (includes loss-map, inertia, and gear-shifts). E_shift_loss is already included here.</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_shift_loss</td>
 <td>[kWh]</td>
 <td>Total energy losses due to gearshifts</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_ret_loss</td>
 <td>[kWh]</td>
 <td>Total retarder energy loss</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_angle_loss</td>
 <td>[kWh]</td>
 <td>Total torque converter energy loss</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_axl_loss</td>
 <td>[kWh]</td>
 <td>Total transmission energy losses at the axlegear</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_brake</td>
 <td>[kWh]</td>
 <td>Total work dissipated in mechanical braking (sum of service brakes, retader and additional engine exhaust brakes)</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_vehicle_inertia</td>
 <td>[kWh]</td>
 <td>Total work of wheels inertia and vehicle mass</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_air</td>
 <td>[kWh]</td>
 <td>Total work of air resistance</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_roll</td>
 <td>[kWh]</td>
 <td>Total work of rolling resistance</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_grad</td>
 <td>[kWh]</td>
 <td>Total work of gradient resistance</td>
 </tr>
-<tr class="odd">
+<tr class="even">
 <td>E_PTO_CONSUM</td>
 <td>[kWh]</td>
 <td>Total energy demand of the pto consumer (if a pto consumer was used).</td>
 </tr>
-<tr class="even">
+<tr class="odd">
 <td>E_PTO_TRANSM</td>
 <td>[kWh]</td>
 <td>Total energy demand of the pto transmission (if a pto transmission was used).</td>
 </tr>
+<tr class="even">
+<td>E_WHR_el</td>
+<td>[kWh]</td>
+<td>Total electric energy generated by an electrical WHR system</td>
+</tr>
 <tr class="odd">
 <td>E_aux_ess_mech</td>
 <td>[kWh]</td>
-<td>Total work of auxiliaries during engine stop and thus not considered in FC-Map and FC-AAUX</td>
+<td>Total work of auxiliaries during engine stop and thus not considered in FC-Map and FC-AAUX. Considered in FC-ESS_Corr via <a href="#engine-fuel-consumption-correction">fuel consumption correction</a> (Based on P_aux_ESS_mech in .vmod)</td>
 </tr>
 <tr class="even">
 <td>E_ice_start</td>
 <td>[kWh]</td>
-<td>Total work for starting the combustion engine, not considered in FC-Map and FC-AAUX</td>
+<td>Total work for starting the combustion engine, not considered in FC-Map and FC-AAUX. Considered in FC-ESS_Corr via <a href="#engine-fuel-consumption-correction">fuel consumption correction</a> (Based on P_ice_start in .vmod)</td>
 </tr>
 <tr class="odd">
 <td>ice_starts</td>
@@ -6750,7 +6799,7 @@ CycleTime,UnknownCycleName,3600</code></pre>
 <tr class="even">
 <td>k_vehline</td>
 <td>[g/kWh]</td>
-<td>Slope of the regression line used for the <a href="#engine-stopstart-fuel-consumption-correction">fuel consumption correction</a></td>
+<td>Slope of the regression line used for the <a href="#engine-fuel-consumption-correction">fuel consumption correction</a></td>
 </tr>
 <tr class="odd">
 <td>a</td>
@@ -6797,6 +6846,7 @@ CycleTime,UnknownCycleName,3600</code></pre>
 </ul>
 <p><em>Hint: E_shift_loss is not taken into account here, because it is already included in E_gbx_loss.</em></p>
 </div>
+</div>
 <div id="application-files" class="section level2">
 <h2>Application Files</h2>
 <p>VECTO uses a numbers of files to save GUI settings and file lists. All files are text-based and can be changed outside of VECTO <strong><em>if VECTO is not running</em></strong>.</p>