MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01C796E3.5E290E40" This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. Please download a browser that supports Web Archive, such as Microsoft Internet Explorer. ------=_NextPart_01C796E3.5E290E40 Content-Location: file:///C:/49579224/Farland.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Title: Assessing Your Students’ Understanding of Science as a Human Endeavor

DECONSTRUCTING THE DAST: DEVELOPMENT OF A VALID = AND RELIABLE TOOL FOR ASSESSING STUDENTS’ PERCEPTIONS OF SCIENTISTS<= /o:p>

 

Donna Farland= , The Ohio State University

William F. Mc= Comas, University of Arkansas

 

 

Abstract

E-DAST (Enhanced Draw-A-Scientist-Test) is a new version of a classic instrument that can be used to evaluate and encourage students’ knowledge of science as a hu= man endeavor. E-DAST requires that students make multiple drawings and adds a rubric by which the user of the instrument can extract additional informati= on about the appearance, location and activity of the illustrated scientist.  Both innovations greatly increase = the utility and validity of the instrument.

 

Introduction

All science teachers recognize that they must teach appropriate and interesting science content.  However, it = might surprise many to find that are also asked to help students understand the t= rue nature of who does science.  Unfortunately, many students view scientists as quite different from= the rest of us and often hold highly stereotypical views of scientists and the = work they do.  The lone nerd with extraordinary intelligence and odd work habits and even stranger friends has become a view held by many children.  In studies spanning five decades we find that students typically por= tray scientists as males confined to a laboratory surrounded by dangerous chemic= als conducting dubious experiments (Barman, 1997; Chambers, 1983; Finson, 2002; Fort & Varney, 1989; Mead & Metraux, 1957; Schibeci & Sorenson, 1983).  These inauthentic perc= eptions of science and scientists are held widely from elementary to secondary scho= ol (Chambers, 1983).  =

The reasons for these views are many, but are most likely due to the images presented in the popular media.  Each generation has had its version of the Nutty Professor and Dr. Frankenstein to help frame the views of members of the public.

The narrow and erroneous impres= sion of science held by students has, in part, inspired science reformers to cre= ate The National Science Education Standar= ds (NSES) (National Research Council [NRC], 1996) which contains science content, science processes and the human side of the scientific enterprise.  The Standards advocate that Science as a Human Endeavor should= be taught as early as the elementary grades, “in order to provide a foundation for the development of sophisticated ideas related to the history and nature of science that will be developed in later years” (NRC, 19= 96, p. 141). 

The Standards focus on the following four human dimensions of scien= ce:

1.      = Science and technology have been practiced by people for a long time.

2.      = Men and women have made a varie= ty of contributions throughout the history of science and technology.       

3.      = Science will never be finished. Although men and women using scientific inquiry have learned much about the objects, events and phenomena in nature, much more remains to be understood.    =

4.      = Many people choose science as a career and devote their entire lives to studying it. Many people derive gre= at pleasure from doing science. (NRC, 1996, p.141).

Issues such as these must take their place with tradition= al science content in the classroom and be systematically and deliberately tau= ght to young children (Roach & Wandersee, 1993). 

The Importance of Students’ Perceptions of Scientists

Several reports (National Science Board, 1986; National Science Foundation, 2000, National Academy of Science, 2006) have projected= a shortage of scientific personnel.  Part of the solution to this problem is to increase the number of minority members and women entering scientific fields.  A deterrent to this goal may rest = with the stereotypical images of scientists held by many.  Chambers (1983) revealed that older elementary students included more indicators of stereotypical images in the= ir illustrations than did five- to seven-year-olds, suggesting that by fourth = and fifth grades students already have formed their views of who a scientist is.  Unfortunately, there is evidence that students’ job preferences and career aspirations are strongly linked to their images of particular occupations (Gottfredson, 198= 1) and students often categorize occupations based on gender (Gettys and Cann, 1981). Clearly, career stereotyping may limit individuals’ careers considerations.


Measuring Students’ Ideas about Scientists

With any science concept, teachers must be able to assess students’ knowle= dge of that concept; the same is true with knowledge of the human side of science.  For many years, vers= ions of the Draw-A-Scientist Test (DAST) have been recommended for assessing stu= dents’ attitudes about scientists (Barman, 1996, 1997, 1999; Chambers, 1983; Finso= n et al (1995) and Mason et al (1991).  In its most basic form, students are asked simply to draw a scientis= t.      

Teachers and researchers alike have relied on these single drawings to make inferenc= es about what students think about scientists and their work.  There are two problems with the traditional DAST; students are asked to draw only one image and that may no= t be reflective of the range of views they possess and the standard interpretive rubric fails to provide as much rich information as might be forthcoming fr= om a more intense examination of the student drawings.

We would like to suggest several modifications to DAST that greatly enhance the information that can be derived from the instrument without making it more difficult to administer.  These modifications have resulted in the development of a new version of this cla= ssic instrument, the E-DAST or Enhanced Draw-A-Scientist-Test. 

E-DAST: Making Multiple Drawings

With E-DAST we have incorporated a multiple drawing task to gain a wider vide of what students really know about scientists.  The traditional DAST is limited be= cause students are given only one opportunity to represent their image.  Most of us, if asked to draw only = one image of a scientist, are like= ly to draw a stereotypical image to get the point across even thought we fully understand the limitations of that drawing.  With E-DAST, if a student draws scientists of differing ethnicities, of both genders doing work in a variet= y of settings it is reasonable to assume that they have a sufficiently robust vi= ew of the work of science and the true nature of who can be a scientist.  On the other hand, if a student dr= aws the same image three times, there is good reason to believe that it is the = only view they possess. 

E-DAST: Administration

 =

Teachers can apply and evaluate E-DAST in their classroom to gather the clearest picture of how students may be thinking ab= out the human endeavor of science.

1.   Give students a piece of legal sized white paper and ask them to fold it into th= ree equal parts.

2.   Instruct students to place the paper with only one box showing

3.   Read the following directions to students: Imagine that tomorrow you are goin= g on a trip (anywhere) to visit a scientist in a place where the scientist is working right now. Draw the scientist busy with the work this scientist doe= s. Add a caption which tells what this scientist might be saying to you about = the work you are watching the scientist do. Instruct them to draw a picture= of a scientist in the one box in front of them.

4.   Monitor the class to ensure students draw in only one box and are working independe= ntly

5.   When the students are finished, instruct them to unfold the paper and draw two m= ore scientists using the directions previously described.  It is important that students do n= ot know in advance that they will be asked to make multiple drawings.

Research Goals=

There were two overarch= ing goals for this study:

1)&n= bsp;     How valid is the established version of DAST in which students’ views are represented by a single drawing?  In other words, how often is the F= IRST drawing of a scientist a reliable indicator of future drawings made by the = same student? 

2)&n= bsp;     What kind of additional information about students’ perceptions of scientists can be extracted from that typica= lly shown with DAST with the application of a rubric targeting students’ views of the activity, location and appearance of the scientist?         &= nbsp;

To address the first go= al, we asked a random sample of third grade students (57 males and 49 females) = to complete the E-DAST.  Individu= al students made three drawings of scientists (but did not know that they woul= d be asked to draw more than one). Twenty-five out of 106 drawings were consiste= nt across all three categories. This data demonstrates that if we were to make assumptions ab= out what students’ think of scientists from a single drawing, we would be correct only 24% of the time.  In 76% of cases, the students’ first drawing is not their only perception of a scientist. This suggests that the traditional DAST protocol produces a reliable result only 24% of the time, = an unacceptably low value.

To address the second goal, students’ drawin= gs were scored by the DAST Rubric. Farland (2003) proposed that more informati= on could be extracted even from the traditional DAST if students’ drawin= gs were evaluated with respect to what they wore (APPEARANCE), where they work= ed (LOCATION), and what they were doing (ACTIVITY) of the scientist illustrate= d.  The DAST Rubric applied to evaluat= e the three domains is provided in Table 1.

The following pictures demonstrate the range of perceptions students may hold about scientists and is included for evidence that the first picture a child draws may not contain all their feelings and= /or perceptions of scientists.  

 

        =        Figure 1a    &nb= sp;            =             &nb= sp;     Figure 1b        &= nbsp;           &nbs= p;            &= nbsp;        Figure 1c

Figures 1a-1c:  One st= udent drawing of three scientists (Female, age 11)

<= span style=3D'font-size:11.0pt;line-height:200%'>The images in Figure 1 were all= drawn by the same student and may be used to illustrate several advantages of E-DAST.  The male scientist in= 1a is the first attempt at drawing a scientist by this student. This is a good example of how making multiple drawings enables us to see that this individ= ual does see that females (1c) can do science.=   If we only had a single drawing we would have reached the typical fa= lse conclusion that most students believe that only males can engage in scienti= fic activities. 

<= span style=3D'font-size:11.0pt;line-height:200%'>            &n= bsp;          Figure 1 also demonstrates how to evaluate issues of APPEARANCE, LOCATION and ACTI= VITY by using the rubric.  In APPEA= RANCE, Figure 1a scores a 2 for traditional, it scores a 2 because of the LOCATION= in a lab setting, and a 2 for the naïve ACTIVITY.  However, look at the lightening bo= lts in the second picture that score a 1 on the rubric for a sensationalized LOCAT= ION. If we only looked at the first picture we would not know that this student still harbored some sort of sensationalized association about the LOCATION = of scientists. 

        =    

        =        Figure 2a    &nb= sp;            =             &nb= sp;     Figure 2b        &= nbsp;           &nbs= p;            &= nbsp;        Figure 2c

Figures 2a-2c:  A stud= ent drawing of three scientists.  Female, Age 10.

 

<= span style=3D'font-size:11.0pt;line-height:200%'>            &n= bsp;          Figure 2a shows a non-stereotypical female in a traditional lab setting. The next drawing (2b) shows a sensationalized man who has tied down a dog and a cat = to two tables and is checking their brains. In figure 2c the student illustrat= es a woman working outdoors with a rocket, very non-stereotypical.

<= span style=3D'font-size:11.0pt;line-height:200%'>            &n= bsp;          Using the rubric in Table 1, the APPEARANCE rates a 3, 1, 3; LOCATION a 2, 1, 3 a= nd ACTIVITY a  2, 1, 2 for each o= f the three drawing respectively.  T= his student clearly does not make the same drawing each time thus helping to validate the strategy of having students make multiple illustrations before concluding just how much a given individual knows about the nature of scientists.

Conclusions and Implications

Science instruction must be exp= anded to include the human dimensions of science.  One of the most important of these dimensions relates to the nature of the scientist as an individual.  Use of the E-DAST will enable teac= hers to evaluate students’ perceptions both in depth (by having them draw multiple scientists) and in complexity (by using the rubric to examine the = appearance of the scientist, location of the scientists’= work and nature of the activity of<= span style=3D'mso-bidi-font-weight:bold'> the scientist illustrated.  E-DAST will enable teachers to exa= mine students’ stereotypical views far more completely than has been possi= ble previously.  This knowledge wi= ll assist educators in sampling examining preconceptions, for use in pre-instruction and post-instruction measures of teaching effectiveness and= , of course, in inspiring evidence-based conversations with students about their conceptions and how valid those perceptions are. 

 =

References

 

Barman, C. R. (1997). Students’ views of scientists and science: Results from a national st= udy. Science and Children, 35(1), 18= -23.

 

Bodzin, A. & Gehringer,= M. (2001). Breaking science stereotypes. Science and Children, 38(4), 36-41.

 

Chambers, D. W. (1983). Stereotypic images of the scientist: The draw-a-scientist test. Science Education, 67(2), 255-265.=

 

Farland, D. (2003). Modified draw-a-scientist test. Unpublished Doctoral Dissertation. University of Massa= chusetts, Lowell.

<= o:p> 

Finson, K.D. (2002). Drawin= g a scientist: What we do and do not know after fifty years of drawings. School Science and Mathematics, 102(7), 335-346.

 

Finson, K. D., Beaver, J. B= ., & Cramond, B. L. (1995). Development and field test of a checklist for = the draw-a-scientist test. School Scien= ce and Mathematics, 95(4), 195-205.

 

F= ort, D.C. & Varney, H. L. (1989). How students see scientists: Mostly male, mostly white, mostly benevolent. Sc= ience and Children, 26, 8-13.

<= o:p> 

G= ettys, L. & Cann, A. (1981). Children’s perceptions of occupational sex stereotypes. Sex Roles, 7, 301-308.  

<= o:p> 

G= ottfredson , L.S. (1981). Circumscription & Compromise: A Developmental Theory of Occupational Aspirations. Journal of Counseling Psychology, 28 (6), 545-579. 

<= o:p> 

Mason, C. L., Kahle, J. B. = & Gardner, A. L. (1991). Draw-a-scientist test: Future implications. School Science and Mathematics, 91= (5), 193-198.

 

Mead, M. & Metraux, R. (1957). Image of the scientist among high school students. Science, 126, 384-390.

 

Moseley, C. & Norris, D. (1999). Preservice teachers’ views of scientists. Science and Children, 37(1), 50-53.

 

= National Academy of Science, National Academy of Engineering, Institute of Medici= ne: Committee on Prospering in the Global Economy of the 21st century (2006). Rising Above the Gathering = Storm: Energizing and Employing America for a Brighter Economic Future.  Washington, = DC: National Academies Press.

 

National Research Council. (1996). National Science Education Standards. Washington, DC: National = Academy Press. <= /p>

 

National Science Board (198= 6). Task committee on undergraduate scienc= e and engineering education:Undergraduate science, mathematics and engineering education. Washington, D.C.

 

National Science Foundation (2000). Representation in science and engineering: In women, minorities, and persons with disabilities in science and engineering. Washington, DC.

 

Roach, L.E. & Wandersee= , J.H. (1993). Short story science: Using historical vignettes as a teaching tool.= Science Teacher, 60 (6) 18-21.

 

Schibeci, R. A. & Soren= sen, I. (1983). Elementary school children’s perce= ptions of scientists. School Science and Mathematics, 83(1), 14-20.

 

Smith, W.S. & Erb, T. O. (1986). Effect of women science career role models of early adolescentsR= 17; attitudes toward scientists & women in science.  Journal of Research in Science Teaching, 23, 667-76. 

 <= /o:p>

TABLE 1: The DRAW-A-SCIENTIST RUBRIC.  = Award points for each category by circling appropriate boxes.

                    =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;      

At= tribute

Se= nsationalized

Tr= aditional

Broader than Traditional

Can’t be categorized

 

Examples

APPEARANCE

 

Male or female who resembles a monster, or who has clearly geeky appearance (example: crazy hair, odd appearance, cape).

Standard looking wh= ite male or standard looking scientist unable to determine gender. This scientist clearly lacks any references that are bizarre (Example:=

humpback).

Female, person of d= ifferent ethnicity, or two or more scientists.

No Scientist

 

Historical Figure

 

Reflects teacher or= student

 

Difficult to discer= n

Score

       &nbs= p;           1

             2

       &nbs= p;      3

       &nbs= p;    0

LOCATION

 

Resembles a basemen= t, cave or setting of secrecy and/or horror. Often elaborate, with equipment not normally found in a laboratory (example: bubbling beakers). 

Traditional lab set= ting-a table with equipment in a normal looking room (Example: beakers without bubbles).

Anywhere other than= a traditional lab setting.

Difficult to discer= n

 

Score

       &nbs= p;           1

       &nbs= p;     2

       &nbs= p;      3

       &nbs= p;   0

ACTIVITY

With support

from caption)<= /o:p>

 

The scientist’= ;s work is either magical or destructive, or embellishes the drawing with a story= line that is about spying, stealing, killing or scaring. Often science done unrealistically under hazardous conditions (example: destructive, toxic, potions, or explosives).

 

“The scientis= t is studying or is trying to….” But does not show how the scientist is studying or researching. Student sees the scientist invo= lved in work miraculous in nature (naïve on the part of the student), = not destructive. 

 “The scientist is studying&= #8230;” and the caption or drawing shows HOW the scientist is doing this. Indicat= es that the student is portraying the type of work that a scientist might actually do with the tools needed.

 

 

No answer to questi= on #4

 

Difficult to discer= n

Score

       &nbs= p;           1

       &nbs= p;    2

       &nbs= p;     3

       &nbs= p;    0

 

 

<= o:p> 

 

------=_NextPart_01C796E3.5E290E40 Content-Location: file:///C:/49579224/Farland_files/image001.png Content-Transfer-Encoding: base64 Content-Type: image/png iVBORw0KGgoAAAANSUhEUgAABTAAAAMdCAIAAAAj7c7nAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAO xAAADsQBlSsOGwAA/7VJREFUeF68/Xm0LXl21wdGnIg4853fffPLOSurKmuUKElVmtoIEAgWtmWa yRjkZYzsbjDgps1oGtToD4Ggl90ILWEDCwkEshBYQkgINICk0oRUpZqyKivnfPNwxzPHiYjTn8+O J2BhxLS8OgVVWe/de07Eb9jDd3/3d6c3z047ad4km7QqFln1xnz9VKezlzfnSXeYJ2m5SrppmvSa uu5kTc2PVtk6LTtpkTZJkybpJkk2m6TT2WRVss7yTqfZNBv+pEnSrK6TPE3WxaZIN6uy0+/UaZLV 6w0/ViX5pqmTDh+wqZJNsenk8fH1ZlMnSbdpqnWadfOUX6nyTdo0SVkXaa/Jyk2dVf55p1M0TVOn myxJO8l63SRVVnSTuqiLWb0u+kW63jSdJouvXKeFn52l6zor6k1Sb2o+o0iyhm9t8ryp0g5vkq/r xmfqbDr+cLrOO0mVdjtVmXbytG7KbpJWTW/TWXfSNW9a190kT3jhvNvZ1J06q/PlZrXJ+8N10nSq TSddsWLzTtqvqrrpdDKWg1fj5zspK85/1BsWyv+1TjcdVrJOsnTDsqasduNSJjxHXnV4k7ouc946 KdgBXrObVatOUbD2aTrvVMWm5uty1iNNWaKms0k31SbfZCW/nRabpuJjk6pOikFnU7KgNV/khq15 Gn6vU6Zpxit2siKtYgvTjB9PuylPWKQuVNqwclmdbdLa1Sj5Jp6P39rwqE2W8cQVP9Q07EfNyrOS PFLFmWCJmy5LWbNvTcme5GXSFEmn6qWdNevHwyRFknCg1hyKNM/4zWVa9dadssNXefiaZp1VnXKY 9BcsatXJOjz5uii6HJdeXU6TPEubdbnIk37R2bCldbWoq4zP5P8VaT2tm2zddPPBasOuDpJm6bt0 qqzibcqmznN2kGeP48w+LFldT3ee+xrlptvdsFLlsko4OVv8GzcgWZZNN1s1bA+HpNss10lWbeo1 55rDtE7r3qa3rKcNz1adNJ391XrWybJBNm7W1TIpWcIq6eUcQq4DB2+z6nSz2arpJqzOqEnn6SYv eaK66bGgcTHKzYrd2TRpxit0ZjXnuBmu02V/k6zW827eXfLmiQ/fy3KOerFu5ptFwQZu8prPrzJ2 NknSVcF/ld1Vh0dt0mWTFRnncdmkg2xRdXrpcsOHcv3KNYcvT7rLsuom2bpT7ebZZL2pmlWPc8j9 5sPqnFuZ8drdYbaa1U2apl3/hv/u8aM1e9V06tE0SXvZPFmP0rriqrCBXMn1puyUyaZ7bVM85P3z eoixqSpuY55nbEeecQ538vQ82eSLzqbgDnU23LnzTb9frbKCV53l3WK5Srtd1rPfydZVOshZ5WaV sHHsA+tRpUWP88CJXvv2Wb9aL3u9XlOm2aDcdLrpusixIClncMFpWnd6XQ59t+7V2ZxTwmd0WMwG K9NJR9iqqsSI1OteseVRKdfcrIIVTrtcp5T7WDd1VRVdLlAH+7npNvm6mxXNposl66S9qjMdZYNF vUnzdZIUPCz3qpPxwTw456+ouERZPqirdMOxWrNYq7zHcg/c22SZc0k7RcrGcdE3xbpbL5LudsJ1 xdal+WBdlUU/TzFBXDGMIJcbO5x1qqbuatXqrNvHOvDUbCQ3djTMN1XHL8+4Q7w5b4rhK7CWJcuZ YeK6XGlOaZr0+ZCwrP2ySAZ1g/XFQmm80nVW82MbDE9Z1Hmdc8QWCYenV6UVr18nTbfOSo69b+dW 1pgdzh8WFf/BZdQs8r1Jxd/4bFiPqs+pwzDU/mDNK2Fu+Js079YF74QJwO6sOTgb3gyrxS1hz8uk 4ODw+zkWizfHoFdlZ8PG4umalV/d4U21tQmmp8aiakVrzgCHdF1xA1hpLiQf3XCFWOhND2OgmywS z29ebtb9Rm/V6fEhHW7ypo/Dw6uxoHghTlPuImdseMVFqFge1gGfh/djvzlgS01f2q0GrC87jXXm d7MeHx9+yzXApOMZsd4p+8O3hctlAVgG1hj3xHlYb7B8OsGEG46p5+Zhx9mQes331v2qt8oWKYcg bTKehZfmCGwKXoJ9xIFxlnAW+uvNjK/PMJ4sYs3bLXheLg03jhesm24v51P5a6wWRtkDhEmquHdl 0u241J00KztJ1z/trdnqzSzdjFN+teC4hltjibOGb3U1Gy6LHtA/xcPrjTFGScnN3Yy6+bTibmCz 0xQHwAsu0zJdJT13uYsjyDEyyXLTDDiZ1TrBVvEeeHVO38ZIhQ9N+50N36UJYXHZbBx5U2ZND0ef curYPJwIt6ReV/xQzSdwbtKSzdgs+B/rslPwswQIWUa0sl6lw35nqbFvejpXTg6bmbMlHFkdH56k CqfhTcnznB/ZK9PzcPrdjtau4ZU1Wp1qiNls6nWGZ+1uWJWMrTWY4qziI9lBbse6ybocBw5wHdaM /ejkXEB8HA+eYcx1vmsCB5YOc8L/xixlKYER8U5Ze34yjl/Df+VVtcki9PB3MMv49GXWEM5xOYg2 PDFr9qNYJdOchTe2Ywe4rF3+jeAKC5c0G44sx4H7wkdkzTDJFoRO7CWPYMjCKd/0+ZGS5cHPpgRy BlT+Hxvt8nArOSEESeui1+PwdjuEnWwKu1hyrNgvri3LzVPm81WZacyrxbTT45GwEC5DxtP41vxT YH3znGNCpEPswIGcc2OzcWczK/PuEPe4qQjUWOMKU5TzDoY62I41BjdvuC98cTEvS/aBiMLj0u/k JaEbkdCs09nCoOkEu931ulNwv9N+2iEK5K7m686KiC5LuCkV/1VX2k2WoVflBKEsILtBrKQp42VY D2xip0sIwXdhf/DinPw0LYiHC65+hvdjYQiRiI5dR3ZJr9Cph+tsWTSDOuUKYN3w/pusi+dm4XPi NM5NgWfGs0fMiY3EIfOwLCgfRjjHPrK5GFN8cy/n6mySZYKByTlzrD9h+gofnRNheojXWAA8LSGe losL1XB9OZmYyRKDwVcTZxnDJ1wA/kJzwR5hPllNAsqs5iE5NmzUumj6HMc60deRMBDGJRFQRihI XsH9ZS3XWDyjty67zx3F/xtuF54UwssBoSCGRb+yaVasDweu28UHGok1xIRNUejaMDPGJLzsBm/u YzddHQLnxeSkwnYQva8I3XUnHSwnX0pAVWC5uJ8uNI5aN4Q341ivMezcFxZyXU2TbMi98U5ynvlz HqWuinSw4JcwwVXdLbh1BZ5Ls0b8RqAbN5YbhpnATxBFGJMTTTTYSkwk28UOcGXSdVVkRVmuykFG QLHAyXCDCDtXmyW7Ui8Td9tge85OYwiyrMBEEDB2Pb1+yaKe56ssGWjmNuwsJ5OThM9arTc97Q5/ wXqzsHnRw2rpCHlInrqz4o7xKcT4PT4UA4ypyfoJtsvsosYj5RvOERnOgFii5Ii451x/rCdJxarT I+qrWON1UvJgGVayGOIRM8Jlnx4jtubArfKSYAbzxJXh7nRxR+RvOCHD7YY96/eu/sg/+J6/+d3f Nt6/3sknZy+f/O0f/uFHpze9W5y1kqclrsRqc4RZuWaa1AMC/2ZNdIarIifEp+IICKeS7ppkjLNC VME/q2bVbfpPP3P9a3/n156cbo36s2X/5rv3f+M3/7k/fPPemxvCpy4XLCPqrTzqfLqequSgV+ym gYl5DjYE64BL4eZwfbBsHe85W9dp5tWmy0YTJNbNamu8df3S1T/zrT/46X/0PavRaTe7vFzdqeaD n3npzW/7/V/ykd/0J09mx1hQoqu4b2HwjWkWne6YRc3+0B/9Y5wdvpQ96BtaEOglOkeehbzImAXb wa/x5vw7nkwbywaZSGNxtBBGSeFP8N4cNPeM/+KO40jSzKgTZ0fOiVvmFvEA/IEnCefA7/I4XCqT eA4618RH5OUJqTiXeAQsOX/kgcQO8zP8+BoDQvqHwcIX8h/cLYwAz8blnxMjYQOxCWELzfj53SrH enYaYjgiFw2kq074gklJMO68I5/vl/AVmHbDH1xVsub2hJ3lf/PW5GxcMyJnvDirQrxrSuJHCgxw k/g0fKPHn1vOd+OCeDZzwoTLzoManfFxwhU+tnECdo/P5Lc1z4Zl7AILFOfCs6prxLbzd5w6fJZ2 WgjCFTRh92T44twx9gK76C5GSsg68n+uQhkuk4yStJezmpue8XPcD2x6ztXCBxpF6Fg4ilpqP44s w20R8eB1udcuqJ7C7J7VCsPLF+EA9PJ8F7/pEeFv8ng33op8lS3mhGP0MAUdTG5sc8rycSIrsq40 Lck7s3SARzWscIEIOCtMKZ9K2MHj8Amd1OjPqBpfkHddYE4kieQG4+hP4uM8XGycYZSnwPyap+R8 NE2ny8axp1i4jPfhPEQgq2f0IPpzQFDx8nwOf8PauIzEdARRGw6lv8i9I7fSuxAeuW8FsTemnzOB 2yPxIPjNhh5uwhrOTodYEbvf49IR7nCpGqI9/s4wmkivILQnUWNBSSxJCHg39rhKVnwM+bluT0fG ArGnJP16YfMWfo5VZsHTHN8r0sWL6iBFxLpmswQdRFpECwV/FOhXlfdJnFl43nmJry1y1xGjplng UJFH87ukKB0giqrLe3JY8FpJM8iJG/CGHOZuWW90CL6b2cG6nvFgZivGHHmPXMzAUSQMCMnl1xvh 4PHBIAvpPO/xB2lF3kOoaWTL180wpkam3G2MXJ8omR3CuhOeLTB1adL123KCyEiyQTpO+d0Sr2l4 yWJ6ObEpICBsO+CUMSSLR6TAaSMu22KJOd+bIhvixrKsAqQoQWh4bY0Mv7oxz+BDvEWRDhqy8M2s YN2N3IF35HTwiHH5ci5iRoBVdLkWPXALMy1PNteEzcQccpgKcJO8IObbEAzyRz3igRX/aqTDPeA4 YpG0GZgZDiEpnE6M+8Ld8LgYlhF2EnhueoOh5pGTI7AXh9+clDXZZHhiFpFQidBJO0EUJszHtTbO xo3z/BwbgC6ehTvR2UrAxfSJnCSOD2dRK6xRAgDjG00qNDMRSkecwTKkEXri1rG9BCIeX5aFQM2Q R3fBZnvn4s6aenFs3S8MnSaewF830xpf/o71w4TzAbh8HkFrx3eSqWM+ND9aUdbCS9hwErU84IoY iC5JsZkuwYPHesnLcafYmU1tYM33hJHvgQJqIfgdLCwr6Q7y/KIippwusPZA+5OIJPKpmEeekxgc g2eoQCBoAss78PqaYG4Oj8RBJ87k3fxVDQyfGdAkztHQklXvcnoNyBPsFakIX48b5H9iqyPW9mdN cAFNdJZhZXkD4Q5+yqXXmGjLWUuuafsnBlweDaBP/po/B7XW8gHIJoRyWG59p9AvJnsDFqrHxrAH kKXrx9kYb2ub9R28WO2XmBG7Mhpw3gFoGd9o2Ah+RJyBLcNQC8kT8hAe6tww014onIlrRfZmxOye mD+yb1wOrjuHgq32yBkXgrSzFPwgGJT74hawGlpGoSCSoHAxLI0eKbyri44B5mCaoPtdorYBCPDo rgGHTuPmiQUN4NdZHM+WkSs3nCzIu6A7xDmyyqZ02AdCTV1igAA8REQ8BowYK+sIbjEpryYBO6KR YVU8rfwPX1S/4UnwdPDH8W1YK1NaXhVkChuusxEkJ4vpYFB4EgsfAv+cNhImf5VAL2I+UBnTa5yz louEWqCetzM9IAjAXRgF6J88jbyyeAinmHAC+2bgiTEzTCRCMRk0HmP7WVyMmCGHGQorS+4HyqP1 ELiJUENXF2g4ToUj7UnztUWzeWvcN1aGwIiDqGXjRlm80DjyoyZU2CR+0ffiAUjG2Vo8BXdD9IaV 1dt7WUQL48pxTAgY+EpWir9kr8OKt+lQMeCWc03YZhwUT4tfcDv4Ec5SGCbMmDUCf4X118R4AkmF wqBSpADi46d10/pVni7HBGacat7LYoj2OXJJgQ1+UCASO8cPpNm8Jgc0rOUZ22zOCMQVxDKvweE6 Bdm8P+4VcjNcEf0PFovMz93kN6i9YBTib3CNBV8d0HQccHechxacw/76R97JiHX9F3LqsPwYdTGI QUmey4ZzF1yuNrDF8nrY2CbOa4apJfFJ5klnhGW1tkBAoUvw+8UgdNUaKNYXRyzSZUxt1CBYpO3h VHO4PF36Fc6heb/OFpvJdfOkYSH4/fj62G3fnM/TR3hrfUaSN66I3+nZHvBc1tq8y9xuLR274W8a DJv36mX5f2Ajnj/rVZiYMO0ed76/ATXXavFbwgsR9GAWLQr5PqQ1WA0LfmGb8TSW16xwYLXyikjD cJGzoUdgGeshjqb9Wf40HC83Tq+ELdENadP4cp0mSDr5qsAC8Db/hZHAZHF12MW6LkDiOC/eIYIY KzcRchsRAj5gx7ktPHjEP5wf3owjhE3FlIUZMciwAEXwYm5F9ECI5CXFXAKMBfoccQhbLXwAUCLc z1UoSAoMHj0ykWhFqko6SmhgQEaxLt3R34f5NFAR/GTLBKYJwACVXFURbZZAINPd5xtA8jyYJnX4 GCukrgw3koNMsOILuJYel4rMFXPISlJ2iHKCFoZoyxJKBCPW8wL15yQQtHfxGlwqXtuQzEssnM2S 6qL8vzqfPPfC1b/1d39ga7y/NleY9Tr7H/6SD51PJntZd4WV6PE8mEROLc8aCDI+CUPB4mic9dPx hQB4PFmXopFlY32oxnh/78JPf88PLIZn3XR3mG5vNePf8LW/9mwyNQAjQeEaeqs82GbCXFWvJZFf W1rGYeh5XEWuRrfLFxA4RS3Yk68ty1Zb3d4z1y9/7z/53O/7M9948tYPlHl/vqkHWfPqx49+0299 1zf+sd/3/Jf/jtn8QbgVcEiDd9cNwwRUQRSvFdBtc7Gx7GBfySwqlKBNC3aBxRIts1rIxdFgcR2i PEegw/8TMSJwjnJHeDqPhylUmBWPFrdJg45xAEvglvKeFEnATgJ4xYyz2REWhFV2E+NomNxiU7Dk ejG+lLiKI2XkE2aA8wLeK2bB34q54jP8QLELFopqqEeZt4o6M7k7Zx0Hz0kLL2bcyXPrFwy9jPB4 NSJO7FQZoZDXFUOjHwnHrrP1HGLmPdQpZTQSGk8GmRyPx3kg5MJYA9cYHeiPDGu4q9SE+AgMpBbe 8w8UH4EH5QjOI0GsWWTOQrNcLJ7ZNDEL5UaejowF0xnwDGbGk+HVisCVv8XQefk6OEzTLkrYAHhi Cl6EgHVM4/kESy1E+lokSjHm7OkKI0sGEfgLt5bQivSSg2WYgS+oCLvEai3QGxz7x+C3REAC/Kye C7eIIBI8GsuPyTak80eJCSzBxIXknGjgzW54EbNZ4BPKHOwrcBy2xtiD346oF2S9nj8uZ+izALii yuprGzFxXUB2iUOFODo90G3st0eCPJPzYEzJ986A2kzCJVOQoVg9IKIUyMZo++dNZzAk2jFiWQP0 Ym3jb/1/5HLEZ1SLW4wFhIL6ETvHJ9VDYgigOTaWygSbA1xT9LRfnLcuC9ujPMp/Gp+SIrEfBQ7Z 6hnnli/rF3m/28Vysyi6f88nxqhP7bQqSzbYkkQYZeqjbHmv27PMw96QhBeC+tjJNBtWGVCsjuuU tcsIv3T7rGRSTkm+2Wk2jdCjSwkn7Y6NKPKsx/d2eTwun5FFfgrySRzPU5fVOTjBIBl2e+RwVJnM 2Mg+zdrIkEWgEypTPG1TDdlB6mCAgllRwUxgPUSKpMCM+hROoa/06hV+lIf1Dbd46+18TDaZUSIm ptRvFZZ3SoHOPh/Wz3oCQRzkcFScDCsa/VmPb+clmwmuoUi2uK4iuJgZXJKFPoOpOorTApM9PA4n yT9uVnitVQaIbNDLaSOVFKYYpMVCQ8aLkYTPk9UK977W7w0owXn7seFLPivliyv23KpvZCmCs32O DsaK9xMjzLrUXzdZj63us4SaRLk9ST3q1pA6ZHfkRbcglCTYMeCxzsD1A8YDm66odJM94zKxJhSP iSh4VU0DmwpqwdHh6804QWPM2FkfTniEj4ZBlCm5gtRvhYXAuAlhc3Yyhw+hbyV65bUxN8J+KfSR jnsGZAAtxk8k12AL+0k6W3Kb+Yss6+Gw2B/Aaz2pcbncIa6EGD/2iT+FfeONy620CECw0Hy8uIiV XfbF38O7sBCcf2oKGlvhk/CVcJpYH+rcpBaaJ703MR9lScNRrIqvjw2XfkPgGtZWuJatC4vEqSaZ BWPlihLswLNiUzTmMpAkllgoS4rhWjdhQGd0ok2rCcZMlUiIyNswQhHDiijoY1cRB3D2BgFG+cxy nMiYQfp5YrMS3sXiOv5PfKBDgUWkDiSGReIGG5eTdYpMYSX0pyYFfK9bZKrQyKTSipYwXUQ/icZ6 IqxEYMLtcIyiCuPi6Qo8d0T7ZomgV5s1ZpiTzxU3AfCnzAAFE1hDbiZJjrmPoCuF8KASmRu0UYPR O05A7pcRLiuMjeS7iKSiHh3Qb2TpLozYLY+Fm8fBY6U3lD4MfVljaD/pilyfCJcCi+GhBZcmWXJ5 NUWGiWwBRUGWDuAjoGmReQkQAIs+rrGEL+LZMvtmXfumspjTAhzM+jJZu6VuCVgEP+D4NXdOKpqB uB6wzCm2NRaTsn54JmMXnkO0Ua9M4OJKe+cNSDIupwEVEYJbYIzPPvH1UF0kh/Eini48FAZfAF3G RvAUOMBdiriRBVkIwhK4opLCsmQoQKpj9YgStrKufo8oCVUP6818Oi9rFTmy79Zj8yNyf4Qmpm1B WeTItQLK8xNEe6k/to9X4AlWWb3i3G8wR6yxGEQ4EiqWhHBkDRwKFlAbjCsCa7SMYygvOsAuc7kk yHFFyZCbZGWZ3QDQMIoyDhCxIQdYRR4+xooEbDUjCqE5rjhAszkGPrTg5FAJqBM8nIkoizkWei91 7N4APbzJjSVrEjIfxOTAuJt3BWXlOYnoPMtB4OOCsAL8sbZ52ckt+mFRE4rPArwGUVoDo1wCUI4K IQmPzieQ9pBCk9en/QR+khsjK0VLZYwGnI3fbbEZMHJB3RHGmQMjKE0dHf6aj2CeaZTVyRZi2Jap gM1l0hEEjUyUqV1iq6ltdpY6AWwE9hVbU3CxeXLs8QaiX5b2+ElunvFwmPpVPXJTscoRMHPmRVmo LOrteQEjNwIr4T98EEBrvVk2xFa8GcfFvTPFA7DltzkMFPUffPzbLWORhZkzmUZrtVgg74tBKv+U +SKyoCHVFGtGXF/JCPKFOHwB17AY5tbSweTtRDhuCY3LwxXjqLPq1DApoFm7JbbgrIuLaBGNgSWV UgQlRBWT5BrxMCVZHQYB4+unxE0nY/KqSKQI3ghWSLPL0ceJy3sjFcQhlkaMWJuA3kg1DJj4/2I+ /MvUAyWsIGxhLI4dXFOFtuad4nSxOtgEY3bcrtUgPAuYRHhIAERehNgFUwLGLgxoHklgoAstc4oK kXN01iSkLLXrw0EDHaeqKg2RR+cQkMnhGhYrya84ZrIWLilvAumDwik3r2uJTLx/bkZpNU1egIc7 IAYiKQ6bDieHkwMRlOXglTknCwz0muqGpSdLCdyeHm/P5woDSA7Gc3IzuK6YuiYf+rrNesAZxkYR H3hhhVvh6HGyuHQgCWQHBAGeKw65jMsZf0dWa3Ar2UQmEPFv0HjYOwhrHDKNnhfXDZOZxulkDSR7 ND2wZJl08AKIZbC3xJ64IyMu69zYhIBAdOlaSaE3NgtOYFTiDXwMirUJVit4k4KfcVOl6uFKghEr 2rXmXPMgPDNEwIt7h09tX+B3ufV7vaf+7vd8y8D9r8/redxxOS0EwsSQgoiGiARK/Aghie/Jh4QB w0jIpxjkA7I/Yuiuj9Obred3O4ud3vVlr6omzU/d+me8o4AMt5k3wqCYCbFYfZ5ZbrYlGYyxQJ/u LiuJcM0wrGYTh256Bai99QsirF7eDA92dq9df8fXfPN3/NU/9vDss0lxaZXn6+Ph3bt3/+H3/ze/ 83d9w/6Np2en96SFUman0gZ0pIs10oPxQPbBOo1Y7P/nH/ljHDbBJ+BcArlNsjcy1JFQIWIg/qIj jwoFZQeLDBqZwKR0imZN1mgjXbYMaAZv2iq6zT4Fh0tETDeulxXn4Nh4/62CBECtb3KDMSQitWZO OFlsQAsICAZEuGUgwf82A49QhsMgOsKesssGVvydB4ATyoEMDEkHJHPL4E3kXexYYrF0EQxjEFCM rKwU6E4xN8aXQvDmCgFbBA3A+IPN4JoNCFKFezAeJn/cTQE/X0y8nQ+XXxPFJWIJEi1cFzZUdgb/ Q6hJenfghS2NxZcka+mRInuicbeGoYZ/Apc6VWMXyxWBXtfr0iKy0Zlgpei8HyByxLuItssPM12y 1imOJfuep4rkOPZEyN7oLahGxgOCzQYFWPqlLk7DGPCFCxQlFFfYGENmnvFWABItk1fKCc/pn4lo mH8TUvKRYopWeHRW7DhPrDllTSSS+QXiIQE9sjrAHEbr1tR9N+wCxN+oVLBzBsJ4P6you6d3C9xR OAVbwt/H5ydw+WpCUBdgcXeWDbEbfLusQsGLtTQsk3teH9yJZMXanWUrLTOnininmwYBR8cCwxsL LFhWwL8heiHkA+iLGgElEH7MxIB368W6YS5a7MLClfG3W+ae4ztnK5n7bB/hK3/LihvBWTHfUE0H H9NLZATjNVbEQ+3O8KEWQHhXrLS/6m+2sFSHYiUgGuYB9xsHl2iGuMVLAcpAWM9yUDANgEeelacC /n8zxosIu7cchf4213mQdRaeJzwk6ILNDLwDJ0xrbZ5hPQkA3qPpM7JavaD1Y1UqECwei69gNcme SfuoSy5lTK6hWc0lteNDppHo6oCxdhZhms2SNIfDAJ+AmBmung+HJfTWZdmyhg8slb/PrrlBXkML oMZ/ZDkGO1ZU4ZpHNmVAhdXoYzETeVuChZvOnFclmt3cn3X2wSlpnKHUgKXo1L2BiD0wBSdeGJCj THKMd+TuAgdz1Gx8kaspN8Hbkhdw2AQDWBniVYJmOLR1KeZiPZ6ak64DVwTtIIy20YLJEk7VqAs/ 2g/AHK9qykxOmW6Ww2wnapLwMXlyqt4CpEL60hbdisc1dO0wcY9rYJpPzMnBEHEmFgqEULyfGML8 CMNhPSjIkwHf6qSATzB7rphUd/6z28X0B9hsXRSQSC4W78Q5kMDCJugBLNtbu/T/AKtwa1YoOBe4 fwJHAnSDz+hD8hnEVOWcGNpqzcXcOKx8kVmrpS7yK1bVO0n4AuRjvmu5O3C1uKP2/7inBpGWYdra sQF3kOTaqkxYcn9Dlq6IQDT8iJd2W3oSFsjvDOtpcE4dT+CU3xEENHHegJFALJSm69PLZCTXC+oP VYSIh8zNgx8VpWkzV5Fi/9zyByY98EvzGa+WloQaJfthlsevxtHilLZen63USmD+A4VvkzzxVfni 5sDBjvOsxZ9ZlwkAkw0SEfZ3rIBa2MVy8pV0rNh9EYh31MLaRQuOFj+rn/cRAh72thsR44/N6Vsa mMlW4MaPmX2RilJmiwYK825LVMb0/AKPqzHREfg6UYant6W0Xkz4G5+g1ZCezqGXhqrzolYplmGg LjgVPwJvgH12b6wKQSzpG2mF43anWHTvEhsMYCxDOiPzgiXM9wVfi8QnHJskNQto4XTN5m1z4gBD QpXZJFJKAsWZJX+3FB+ph3Ul41vbL2L5AsDgS3gA4HTRKjaUfos5hOpAgrw3xLbRXGB5mwsCNiUJ LnjwPnOAoLhEM+WWaMRfcLwD0eOeWhaPcmPEQfCgZNF7iKV/eHyw8Bw09lp0D2NidB8VUS8uZ0Gr HzWoMPxYXBFnUR/eLuitQVaXPS/kEDGeBdQAnznVeQPxkbDKQmR0wInwyGeRHBwPL20ER2SHHM8U ARLmxHsX9CIrGr63d8PEhKMlwB4JIW5G1+cRFpgx242bIsKNFRNn8QYD0mKAsfUGegGzmxBoy+Gx YNqMIewSBLm0DmOtk5iJFeSxuPsRInACWCmhnqDm8z+touIreRoj2ihU8UfedF0ZdroXm8c3YgCp E9oKwCL4cL6cVZpIEIBjrdXL0gneZpShPWoG6tE6QlLH8nu8sGV8vf0BFo2LDRRwiQvuuei9NySi VA2Uj2RRgqVuSTbClTpwrYmcCQPaiN+MWsH3rD+aPWhAfDnPx/jy+yO51dMZqBpjyUrXvMvC08nE 0dASik950Qh4LEjFyTPYiYMqcuoRhAVOxhVNmpGSExzg0DtkiiTrtB1pHeOku/3SuemxWmDcJXXz tkJDQDyYfbBcbg3JOzdVIoEVewPMqBz5SNF/KaEW5BHvRhmEjyWDMGIz+Av4lJOCTdOkcF/ZLi2W lD1jOHZZwJU/lLxDhk9ZmvvNrop44PHJu620ubSA1Z7IasV2u9e6S1eQT4uqCuvV5wzHk2mQBSUb HZrdq/ZymvQIHbknRrNRDI7oxlCVjg+2HyBd4lgUnzgckezq7WS2bVb2NkkF8SqJYMnilftrKsGv cbCJPdhGM/wgVKzBqaOGbKeJFs0D58KEebKqIMbqjYrj5pqxiNgCzXK0iMKc00qKROgJuNxeATu3 WEqpeTgh2hgEp9puVIpMmeuA5Q6Wj7wHnpoYN9ItjxGbDJ5GLAawwwcDfvCAwTsmKGOFQTK4SCZk BA68xcBwDAJ5134WOzm9QDwS0axUA/FYfkVamuidMWxf4mdwCbRr8A0821ZObrz/PT/8Pd/R3z7o 7nbeupsk20982YvvmFb01gQPTZZGBiKvwfG24mfXweWITNU/ZS+N/exnptFOZ6vBwjYd7G79wPd+ 9OXV3SerQVUs59Xqv/wdX3c+OTVXkENAGUmqGChk8OI8xB55ahZeMusY4Qj5UfMALbCsTUCWzo1r F8d7F77xf/2eb/p/f8P13sP9C08e5jcmncXyrflv+Zprf/7Pfsuke3VytlwtFlKE7ASUp+2n6YPp RBHdlJ4B/sdt+H/84T+K0+BUk6OymotVc3GY3lk2u3oU7Uf4kRbAp/5jYGSMY+enhWv+GoAThEDy CWG4LRz0Y9Ii3o8DIuLn2fcKtPlrpFuCfTBlhbUix+R7wHNZCKqUxGkSMrwhEWJ4sXTsJrQBE2MO gp5rJ0dUPY2qwgUbQIhtWNC3RyWqyiaTrJtxGD8njRv/YDLfkhEDqJJCaRFF8yBgyNtELGVjY0CS QTgxy5VmYApr5u69MHgFhdddmGIJFop+anVtpjbKsq5mgxMrLDFOeo6bzlp5Q+VUmQua1Lq4LdMp +CGREYdFMiLD0BDEa+IDppC9IFigDdYRe6+MWqEMBPEi/KzGQoyzTa8jVrcXQm6QkB33WUKZBRgq CtBnuJEBeIXPshBurq5FcImEC/2q2ISgLHIS/Byg45zmWwIms36p2sQK1k7pG/Y34la48/5KHJrw HYb55lLSFdwj4nOxywje43WjRmYzaHAsg2Rq5E2TF0vHKQunF7zNKKW1ILXHAKgl6Y/4z/BkMLb4 NHFN+klkSxPWhK8A71WfwAey6klMLwEkKGQ83BjqG7ZlTYMKFWsRAGP01jsY5XhLJbZgA6g419WK 1vSGyhs0hDg6mF3zdXFUomEI851BBimcfzQY0T7k8qzxMh5yMnV+TuI2K9rv9uXEy9SktAICyu13 2SMguFImM88RlU9eyj3Fj3OFzSD46j41+igSUeSijgt7QB5GZ5vG/UWyAPPEfxKDkigOKIQl1MLy LhfGnk+JUt0Nv0T1eo9ic5QgiH7z6aah4a+ACIS9Yq3gbkSiGiICZvBhqs2TJsmqb+s6qVdecpD0 1hTfdSOuFTw2WgK5fgT4+JO6WxVUIjiR9OgTePEia6hb/DCGdZwBC7Dj9H4ZwUA9JKpbFtVAFEhn zfYJK1f8RtuZERRI7gCgo9zuDftR1kO5C5Hh0VvA3/DJNuKbubKoBGlRnCdSwHJkPHN/jNuTDKgf 5Yhgp7WM3CF2InA8mrDZJJJY1pUnKXBLGZ5JKjk7HcxtMhgSfqy5HT7dLjTMlcVNuO+YfAmyPVQg PKky+fFXAtsySRvJ6GlPXEncrhjwwUgBiGBLq+yA/2P5KJGbOWnlAs3UivJpFipd5/hHQyWqyQdz zGSdGiAagvK7GD0yFUNtAnzOnxm7K6Td4qqY6SQUhyhMR6zpAdMIabhdN9t9zdjjVywwFQAdXG0/ w7hPKgw/KRXcMDdYQJpc4/io2gS1jPPqv1huNkXC7kZvlD05ArYch4htxBwjSpZpEzhtAKiyIjh6 FnyFHNlEsS5BUIrP5oiBEFAr4ktdosBsI/yNr7ePRj8evJ7AGD2mUhjBCvkPoWaMqwCniSFEEXkZ Rj78b5aRqEWIVBEPjqLdlFpzXJM1NzsFgiLPQQsQ2EXBvLKjXhxTFU2/ti+abTR7hkd4Fo0MQI+u JcxuMOc0jFH5BBnTOglns2BRvZJazgNi3a2fVLL28UKS7k0uWgRWj8PVDqqpPjEI5WYaOveou4WR j3aqtmdcyJsfJviyAVUavzRYW0Fl7+sCed3YARnLRBJ23ZIXRTqHoQjQwyewRFYmXC/PZpuWqoBC 2h8E5mgCtJQr+UAPZumNGxHKGlbNxQfN3e32Ddp/n1yLHcUGRd+CixR7a8FFt5QsYKQE6FtCP6HG Jf/GzE5HE+wJj4pVj0iD/V4xCSyt3xyBskcagwo+6V0m4cO9S7Zo6yNiUC3mJLnMtYnQQ3vYZkFy 7Qxygx3Iz4uTUz/07gVcZoWbjk8L2WJOMjt0caxf7AIVyyDFY+Lk3fOM7AvYIV/BmxgLGRmYHhmc 2biJFf3nkYOCOXwDm+tVjXzJYie0NGVDjGtbgj3P2ZfyKTUvWhNMXkxDpPuCg1hgCOIhO+59tCUu vlpYDcDSaxhYqcRd3iUYztZS3Qi7S8nBOYy2yEUUQ+hgehXcb+OoKDTwO6IthijB9Ya0Bh2X62MQ FWGMdA/gv22lZ7BxFJbU+rCuGscad8ghYTlYy6DautcYjuibSRY2P1sTlBUKFZqTXdgrHVGjDIi2 bhQ9iHE4uEct1z8e0FgIiyt10fXxe2AiQFGKFDeBFMdhtb1BOj9/UJr9i+H49GruRPDm5raBazQn mNSHGIUQuAea9IKkwo228OKVESuPfsxWIwBvF/081hTlQYRJD1gRKZpI1iikgRL7mUa0cmJIR9o4 hvNirdykXw5NNIcImmlHvMWBr4TVt7wZFypUOwTQNHxWiDE7bJPiBW0tBdZnPM0KANSw3z3ighAE WUPh6ABkRLMQnBbihSCjCp0YSYdvsDHEX+Hqy61lSQIa56AJLtnlx6sH4uA7eSGJU8Cx2+OjF4iq iGaVIIKHJwUhFBNMVfBDlDmaB8XdTHwCNdZXBYvbtIZf9Lhrk72Da5bCTMHwwZqZ+TMcMusDrrCB vcvlfTPsCCDZBtWEa+VxC4JoODJY8tw32FriFETicN40F/afWLHmr4TP4jUCCrPyvNSbSF52Ty1A CmMQ3gvFxnnmUMGnIGzgh6EABegY/FHXOLrRRatNwYJwR2fqYzxBUoPXPfpSZHZheFl6HRaRvzzh ltDMkWRxBRQNqjHOAlRedZYEFxBBuhxXK7XRIev+2RGpkoNhm7x5pW1MNdsf9gkNTbndlO0fAzWD 6GyKJIIAkBNlUUvTYeCelvPFpSuX/tEPfH9vsPv6LHl6P/vB7/qu/+zrvyYpxU3AA3hgzgLHjp+1 fTOS2ghPAk32kLuF9KzrlIHktAKYV4RwOgfb+z/4Tz+6ufXWejyELTA7u/+VX/TVnfGA9EJnaPmL X3EbhToEzGEU0vYZuauQJJJSkun4jsHAHhCCu1Gvf3jY+1/+98/+Z7/zD2S3X728u8/BoXz22t1P fcGVy//D//1rf+1v+/23J3cq2KsQMc0TcXVWh73jVsblCthnxIHhCazzeygi2fmlfz43SS5tJXv/ 8h/9n/3vUQH5d/oHVQMYo8F65h8T//bfPCCz+clouBeiA8itII4Fe6JHSbtawgHuplvnyXKb2hSa WfwlHrC202MCJ2mBFkPGue6TNbi8VX8zWm/m62QIUaiPaovwnLxfzvdSGBC7u1jVRX+ez3uIBvBR 4FtqgGCW4K/OZTIm9vl35sQY0I+QcNrUs3QwEmOeLGajQXfWnPZWF/Jes6yWgw2d0BxEe8fBrGgW pjQuOx28j6JlgMyVbaHz9aQq+sY+vXq87K9RhoOkCouqm+xMk9UW4cwSLjvqE4MV3z/b9MY5/62R IeBXpkpnbWRNb/Kql4+X3WV30S+Xs+54ZEyxmFFQlKebTE77vYEv2V+fTgfDgRc9GqxZVaxqsRoY WS+T1bjZhRgHmd6aEMtqxYlwpUfVtI8aT1osm95g8Gg9s2RX9Yrk+DTrjAbbdGvsZjzIZpwVU9h4 p/hJ+T/RtJ0uxciwLz0sr+GkZQGLfTJSAzyCPh3FHFEj2+QgRdgaqQ7XCj6PvdmqPujIsTu0gMhe M8wq5a1GsyXrgFBFxnYb0lko5gcg4yyFUmSHEyPSvBIU3PBzNpIRH0B2W5LOdjJkJKjscRxs1CAA CGE+joMEPItycZMM3w1OZfw+1jxbL/n0Tcm1ayiEEnOYcNp/JshVr4jZ6bsmCsM9oM5FiA/sR0hZ LsH4ie3MTZRiikCQUvJ6wRuRY5PbV+gPcfpMJyyiUaGjaaVHYGC01BkasVbk9sP56lEnH7OVNG73 yZUAzpoVNW8YTTTFkWRyuVb6G7vw+mU2UfUOdQ6cj8ofeOKdZDPVPbI+XUj2ypz5i3bRzEg4FWHg laEg+PHZsld3VxAtCC5Rg+QuYTdLKl/G1hCEhmU9JY7iHFFVp4IDwopcCYxJTqoNPDpeoqZCWDRP R5Z33Fc40HiSfjHg4berzYmAETKH9F8MCPaAK43cBWQGRS4h09YmwACtaWeYoQVoHi94IE1oGeJD qLiomheExOVOJ5t5IL3hkJOkyRKVw/+3HuAbh1oS5lRIIri+tiLaMmw1n8S5Q2QF7QcjpdRHiiJC EL8sNwI3LAGzbYGJpj8LLyqsScPmlJ5lzV7sYJRYRaFwnj0IiCn0fQCb4Hv5V4QCCPgYC7BYIc7D OSWBtF5uMgNLCiaAh92HB3MxXw7WLLBCNE/KWuaLeC3BhLCt4phi3F55w1qaxixF6ZBtHvBcR0EY eP1xF6qxsrsrfG8dpUXMoNdijDUaZkmaeSu6BjnV1jqb2XinwCbiboacMgkiV5IFg6qW8SDxS1Qc SEmNrmyDNwg0bYBnpxKZYlSaSSmFpgmRC+AFgvAdzeR2V1PWA9nqG66FwxADE160wYYEA0HHkFOK LkkqvFwiVpbIA21EM03piLhxKXKB38vugLiwpcCoT2MY4D6qxVGi8GY+BfBkyuhxc73axnllENXe siwAMQEMWZ0F4wYjXgXVrKT7v9ujpdnhfey4U6UoHJ1mxmIhS8qHLUGabN0XpBcxsLXbHDZ4YaGZ YzXPsqn4rToSQeL1U6Mua3QZKYPIpJaF0Aryjmsp2iE28hgdaIMPrD7PY/IrLmVsG2UNsddANlgn MPQlOiySK8xFLSEiHmFVAsVLOKLrBDADekA0rxNrKmaG38PKWjgy8zCEk+kRKR2hli2AFFdU/jSQ sXzlX0cNlWthJkp0KoMYGxjlJ/B0O1xbijgLyQk0l+70CxI5Y/qItYwCAio1DwvpUXFZjZflHsoN uRpynC9+WJ2tZkEZhNqIwCEBNSQ6Ae1IS/kR4Ftvn8z7iI0trYUQGqcbb0nVUaER2mKqfNHjurdH TuXXHvJ0YlqWxKIa4AeoGUvnRaRRtJQRBtsqJZ+GIqBle+28Kji4GNU3BK7jPQjrZfJHYUqJI5u6 PRjcCNoBDPKEtTR+AjJd2B1SzngMwhgQazJnHyIaP9uMSIE40hutiHVsD6EEG7mwSuGxP9iKdQec mohVJ00X24qGKHj2GpkeNGzzfRlD1gyjDIevMCsHFzEkjaIieGyfgC3pr2o6OWmEsjbDmtqy2uQr xex8vyCSmkwSBIsGyzAUS7GuhulUy82KLApmUYf2+yDJGfZvhtKY6hV4elTzm24flUYJBHrsMOCk QmbKlldcSh6QitV5Z4lE5rkilFHNkD/K+rOgAa5Km4RwasdcECyxZqBj8M6EbTVofqD2XRhygzBo NSpRTgnmjKen7aUxZYsKSnsRgPIMh2Cgafa1xpH0CVZB2hIKtY4WnXaROAEM8wxi8CZHSn7hyYRJ Hlte+1dkLgTvAksVObaJBj9r5RLHJ9VcKdm4O4q8cGUwFxac1Quy65t3XiqiWuPWbZ8BpqEF1Isq zgX+jsAbPPC6TxzkldFLUTKRlkygQWEqK3uP6zn6Zj228IP+kusVUYN4cBxsqGEFVs9fNXMBwIIt Iq9XTlf0LYUNIwFleVhhzI71Agnq0qGU1CBmqFS7JDJExJUH5xpagNTnRy3FyI6IUviT8ImXDdAD 70BQ1yjQZvBoZQicVVSXLABLph8kAA38k0ULC2hCzu+L2Cj2G8bLFdNJR7+/baqINnJbVNULohKp Cp3+uAPJJZ5iTTq7s2hKJOOQm5YEaAe+dH6JurprrATERlZRpccWjYEEEEZHRpaVBs2jtwXT0UuH dAlFVVo6LiFaFDUIZjyz+hnqR22T9eNCaIB9YhSsBY/Jolg7j8K5/dpsT4XMG+fWMASKeonGVNxD mh09PsZmHjM3SRFHH5EoCy+kLhw9m1YWtGUBhNlCFa0+fjvHZM515zMh7gFfwBe4tH/xm/78X/jY P7xdPPkAmdTNyfKLvurX/dHf+9tv3blNKMInATwLrqE5rJ/D8YHURCURYFv+HJ3jvqHtP8iID+GV y28i3D/cu/rX/9Jf/raP/+xzyXqcj37+05/6mZ/9maMHt+DMRHO2FFxtDOxb1PBoP6T4FRYi/Ixa BYBNmMCSfovx7riZXLp46SdfLb75T/3xvP9GAgN2sDdZ1FOO482X/7+/77/6st/6u986P04nU6ui CJH7UNHmaSwG/BQyEqAz4cR1E6DpnHfX6okvSC4kyd1xMppHrzT989WFcvtRvpVk95JZN+mNt4fo v60ms2arWKMNO1wOz3Y2yfkyKXaQs2wmi/7Fw2Vzd3f03rPVnaTYH89P0pNk9K53F/zCrTtHi2r/ xuXF0ed7w2ePN48OdzfDzoujReeTq19817Uv+qovuvIjnznfw6zv957vHLydrc+KcvHm9Nd+xeHV nZ23TtdPHCBim0/zzqX+8Pj4/u0kff+Fi7DtV9VkuVzV/d09I8MBZmmbfDDr7BS9WW+9erjYukSX 7E5xukz30mK+akaAnc0Qk9obr6anq2LRHey2SD5iwsbK5A9Vd2ovWC89XXV3OIJF9WCZHw4JrtfT TWd4Nn9UjLd3e53VksJivnh4luwS9o4RW9ibLe8fJZuLSb/up7PTan/Yf5jMhtwRjDdZR7U7S+eg C6v67LC4Ujdn8zKbd+v+vNjbQvdue50vZqenW9uj8Sx52GOLoA8kl/NLx+t7RbXzsD652NuZLe6m 2Q5e9GxzfDC4TgUaicNHTbnXBxjb3sweNMMLy7Ozgws70/MTTHU1Ww3601VvdDprdsbD1WrWo/RZ jG6e3N1L9+he2j7kSu+vl4tkWia7Fn5BLyZl3e91BsvTOj/YpNMuSEHWLJfrwXib9JVTueDnlpMe irx1t7+3T+8scIA1sqXhUr57eD55+2Jxidok9md28+bO7tVmp7N72H342UeDvdHxuvrCp7dmvHOR 3D5Prm+3HWD+Q50dGOrf+R+LuqvZWd3tLua6kHK2nGOItVqGOZgKBUFzGtZXtL6SbtvFbF3XSlLI Nqjm1sbsMpYwR6EzT5ddhPfijlY4NEJRfLXJSOx2vUL3davlJFiMCLqZly0SERIEbTIOIph1Gjty YWuTTCJAbZT0g2eQi8SBVLGW9KEFd6VJ0PiyTKa0Y0NzzqslcUYHUdRgMih0LbBPJm4I1alAS6jg WGIOTBs1//W5wYua+nJKVklzkBYTbq2FchYDAVlhUWF9m27xdITMC0wkXU14LzCUiTkdTotEoxpz HTQW0Y2ItYgQhXbVpR2S5nK4RpIb+47hoqroTUKFpCoxApEwABn8fzwqQcVO00zEUISs7cLDrdKV o0R4xf8Rfyh4g++TDSXUYk8U7xO9aHDRJLkpXkOpmOzAjIP8DkI74jQrGqKIDat1ORCdkI0S1D5w jRJwo+4MbGQWSDajCx23IUUekVPzJD2E6bWRlUEx2Sj9aGZA1pmCu2y/CYwGA6l+2l2mFPnzebVG DJ+uftJeK46KDOIjyBMku+KVyZIjBZZ3zseaGZvf9vEjPalbVlZ5X1ofiCbkYLVNJGapbXMUpUMb scm98MwJbYxAd3i1KDXgP4heeGmLma0KolMKwpsH/dSie+RL8fVmhTLC1XFp667WlkCYkQ/G0wT3 yRzUlw7JdNvtrUNZtDOxMYIKjg0KuBYnzYAey3BY7DAEibqSLBIpouo00YmNe5ayYzhECCHPsyX6 Srqh0qVuAQdXbjU7YKc2bGcZdpZELCAgD6BqbtQieQBi1iBOA3rYP81D2FNg16oNA+Yi4vPWB1RL jMAdPxc044i2vV4OC+C2iO9Y0PE8xrWl7qVARlCsVG+IkpllDPnVBs1cX9rczCgshcoSJPQpLRUJ x9uEx7wALrWBdyvxQfOgQxekvqwJUPwbyftR+NFIRAQbls8gxSww4mfTOHPqyJixCZ4jGc4sgBms 3ENjSlMks0DEcGmbcFsIvszorXP6V+L9GAcrHsHuD5zC82hAhGiIrASpnCqbWgqUNWOZlODbFI54 m2CfX6LeOlRVRo6WhTKyuGhjsMTDU7nZlnAdBCFRyyyJqI076sOxS+RHUipl+1cK76ZINjL3QRTH AhoMGWM17G1b1Qkijdr+iKhLoOqiay4CId4S0R8iT8hdGwIb2gdepLSdOS1qyytvUjpE27WzBv9q C96OJrBSJOwSnfat2oncF8g62DSXRi11Fk2GjTAMpkpBkxgB0+ngl/gcgnyJHryMAbWMAQtz5iCq 1yiR6MGQthK8dYEFj1DQbuVckRwG/hYTV/w3DCnvrOh4NJU4z6LtEpCPTQQQhdOAqrG/KmEjkxGU 6VBBVKZHZobahkJ1smGDtwhtpG/KKVtE5gK2C39guc63COaDnwuPD/6wTtK8UG6HPsLxKdhLUnFd pTlMHHgzPz1KOB6TLLlxnmTRUxEqu4IFAzdzMyHzdCm9Ar5G8y3dXO45LjYIL9BHoCCo3WDpK8rC nkJujWQf41+ejO4RHlUJTJxnTG4JFwviztlG5wlA2czViyCmL0SmP1YlwKobwIYV4FY6NCbZ8D5c ZHM0ImQT3qjOCY6DiJG9t3kC1wBXY+HBfWrTOQhiqrkqlhI6SCFDHpTJaA2JWjyHCyNAfVpjrrmz zCmiwRpyJjFcwS+UPIFN5i5xbDlE0Uwi4Uwsny+QQ2ElgLMW2mZRPlerDANW3kmLd23QV8H+AWlE OUAeVnsigzXGmsi1kpkRNIu2LcUti2/hJ1eg29HRGRczXLJPhHHxLeIPUXWVqmAXf4jyRJdo8NKl yzTQK4AheKRgRpiVVsKIj/k3uk8uIgmx18Gituk1RooDpaXn3RDs5j84WtDwZGEJQFIpANewjVE8 28qbqIkvaQ4tCRLmHaw2UQ9tshgOSRmpbB8RXEhnyrYJQciMdDcIozWqQppxnkIsjyxIRmO0B4em os3jYGQ2gdtRJd6nQbTpRbjSmrb2oFymNAsHSSUUn9qOW2AA8i2bFyj24JgLdPWIbVwmoKIS7SBN iwgCK2FlQnl2s9CIT/0Kagf8N+8StzHmm5TJ0kcUTaAawCFRXQopB3tQBQQVvQ0+TaiXcbZD6UJ+ jpSS0IGWsSaGJjecaMcIQAzYTlvMJO9kCyTHjesJ8KlziYgxfsX2ZVvBwjDiDL0FQA0cOHVTqdmg YMbNcpKJECotd8qoB+daenggty17JiySh9sCurV3QEfIhZaLrcnbU9ZibFoVy87perS9/Rt+/Vfv XHyKj++n/ds37/3uP/VHf/NXfOTe/YceVaxStPkGcUAGlrokyicR2PFm+ZrSks04QWuT3BwSNXYF 5p//xZ/+7//0Nx48/9TZzd3f+msOfu8f/FP3j44tliHdh6iVxCzMl96CiE8v4bAmBRIp2gR/Co+y 3Rss9/e3Pn/76I990z98/fW/+cT2hc14ZwTYt+rcmdz62q967+/+g//jdD06ObqnGCKvhTgF2Kbq r9F80BpfKaCRcljhD1/PidcvFelf/8b/4n3vfOLW7IxhHCeLs0sX331juPqpj50Wl5v37R3mg+xj H7vdvdYd9Mm6mrObmzVNrudngyd2zs/r1Vtnu88cZsO9a+P+m2+/8ej23WvPXPvcvfsXBoPZ/WSy PrtXri/115cPrj+8f1QWVXVUXX+ud+el+ceS0Reuj96+QFq3XN/vHnXnl5JJM+guTodNfvNh+WSW vbKeZK/cXxxkF/d2F6fVepVtzc4X22n30qr/SnGrml3rJ+fD4ejebJ71BzWY5u4rY0Z2LDhP28xp SlaLwWhv8WidjEZJ/34vubqa80zXkuuQapGNOMQUDCfVnDgknyfTUb79TNWcJp0TS94Hc1lpZdo/ Xy0Pe0kzow6YDC+xRMzP2kx3P/zbvuynv/97kxwkY9Bfl8vm/nD+3Pzi28mDbVD7pHeS189U268z vCo5Jg/buzx6+95pkYyQhFklJXL9qMOwLb3eo7PViD+/khy9lexYUkzOB2olYgTTRcLcudlUDiwX bwvpT+Ds7d5yvuJ3l9eT2a2if75eXUiGCzKphLlSxDl8Gmy4eS+5cppM0MM6T845Zn2ZnTNoBOTw rycXR5e29uYHW5MHt5IT3mucLFfJHs92L7m5nexdPLxwd3anmHPei2kCtNuDm3A96T5MFlQld5Ml 7YunyfpGsv9Kcu9GsjWhAmJdk8lLzodgTMXdZHIjOTiiPr46GSWHq+R4mdTvSPbOkuMsGU+S9RPJ 5PPJ9SeS+wSBdxxHMb7qyzZHRfnkplduJt3OtaZztrvq5tfJdkfHw+zaG3fOnv3gu/rnTbaPVTtM d7Iroxdf/BWHWN6tfv9rfs37P/WZW+/+wuvPb1khLpbJ7sj0flvDFNHf4yxfvqS8xnW1Wk4BD5LN Qp+FbV2tlovTZrFejIpmosXSemlMOPhEvcpUMMeDAFjKl9w29RwMsbL1UKl10VxTOrl3xAwrtYAN otPFcrmd5bADrEsXwyy1RVEgs012tESmTWB5PKtiKUBqlolpFqWKu7CDD5COo9E0Q5hCOBkMqgFT J1kuaQhiTgXU6rImR4WgYQGUijGV3gLqiLOk8AxW8LF6M+VN05IEHBETadqW8WNChSpUJt4mghTt +elhaLUSBD/Kmu3OZrVO99P6VEjU0I5YgO4//gXfTEY8JWjGQS86S5Cc4H2VGOBgJFO4h7q2gvmB I5sU5fZ6SC5uvy/uGsY60nr2QrMaTCkKx0XXWgw4S5N763qfwiLnnyNfJXAsGEckNN0Up5t6lz+H 4U/rB/m8kwZiaEWnthyLpE9BsSFf7XDC0mLbdjVJipIkJV/2z+GMJLvd+rQeRraF1D3OR/GR9QIL vkblB+9HTM8agWKRHkdwbJTkgtFyhMvtU/iWMIOGGy86qlDsKvE8BFTQcVhFxQFW5Xm/u03FrJcv 8XwRG4UOWYhDAAIYXmeDKFQ4EQjXToBGTKeX0ecJBa3AFeRKqo8rAKMsvEG+2YDAEaGRmWyK5htr Q73CCSTBUBVrx6BEpcAX1+zzkdiXujNuqikv5OFo4EsRu3sinTMSlRUTpijY0/5ufcDexRE9Zf5d lB0tRQU3XWEMxkF2wbAhnFBRsTpnAGdnitxPZX7gmQVBzakWqCKIfEk35JSx0+L2hsk0jbCa3Q1F APly5vneKW4dA+es0Jrh6vhD9QC8JfB6LmlVNiE5Zq804JaU57Z3oe1xdyDhagH+4JQy18JOmEhs yCpNk/TBACr8jVoVjsNw5hnzD9rWGXr8KKrYLmp1OioT2GWppGJIzpfDVpCfOc7CBmVlnJ2NGGRX pzRJNOd5zW5cmShIyEEM/ovphUViPljRHt49Wp7sWhIxUDPDsmAoc21GPLDUUiq0yspgoRx6KF1F 1GlSp3secytabjKFINJVq6rRsOAUPIU5I0xzUtqKkWGAQ/6GJS8rN23fnyPztGVy0OT18EoUW6wu E7OwitRpVDExHZMLIfdHYvMyRoR5y4jgHRLkWvLqQt6SWLS/mCYiJ2EVWld4RkYs8sE9xfTs444Z UZ5YKSPAOgDlsBDpdEk7I4YDIeHDbCtY6Zwd5G7BHA3eJRZamyStGjSDWbJSV5EHFD6M4FV9aSrq ULxVk0arwfTbOMqOcypXdBsJaNq5oFZW0EewwQRyGFtDP/MabQB/51yLyHgrB/jwlzKdOQ6wTuYh BUA+zB4RoVc5xLZBKRIAWCYkvGCYllGvobJ1v7aCSMGcok9gI1jaATJqhJW2ZBOwqXOOueDxBt4a vti+dHNeMSeRA6u38rIwlToZZnT2gnDhXE+HdGCjSuR78AgkWMJOoQMQG47+W+sdJZZ6VBHF465A lpIB6tXDCsG9EA1iDhDsy5HKozHOVNUEr04wCVlNMjpH6dB0g5PMl6ed3kXHdnhBpG3Di4hykeJB UAqLDg0F2BbbN8GroTmx0BhQgQ1nJbJlMt49m21yb2rLY4nGmB0uSMHUUkaBTrvlYQYMQEIrIwg6 r5tR1osOkJatoaeguketSwZ9CGWDSYav5taRKVhlxiLo28TJrVSDnG/6kORthLE7lVRXimkP1Jk8 hlsTFJboNfHOm+CE4L/0pJgKEJXKVjmErXjcncxaYkKdg4LxjK5VQRURDxvOnZ5kku1kU6vZgXsE W9vkWtFScAh8l8YzzFoPjo6bGTMtBPPEPTjepn8qJyvwTl4cotvSYOzgXs8Qs5eqLXIAvuaUzxBK IBVzDk60SIdWA7fWsR/uK79mQiFfXWqUXYBw04MdzPYTP7lqlKMi3cJpxLgtpdjCQXFo1HJioRm+ qQeLEAd1V2ExaU0cHyrTtBniXCt6trLkcN2c2Lgkbc0u0AwdlmrGsliccDMIBYg35BbJoKmX+WZL cXNzYSjfFH5tFaG3SRwbn8qptDtQAJ0fx1LHQC/ZZ60vxnQknbHipFaDWRKQDaF9Lh1taVj9GEDL flqcBznC/8uj5mTY7CokgoAPVRSpM97PEBYIq9RWQLzpMWqjbYnk5slrcBIxBr4DGjQM9jJXJ8Qa 0OYnqoNNoYvt12y89WWlAdW50ykEDA66RCF84PkJKxcdI7y0Y5nZqlLbARKqcgK2awmRBBpwhVSv HekezRg/IJ9GynvQV3gX1tKhcbbBg+Njq0XI6YvAz/B1kObD8wRNw0NOoQkkw8qEAhbBhnAalp0l YHy8DykvRj86+WEMEAuTpyqFa0+moY74Ma6BY9u9MDp78NIf+J1/sHj+yeHqRnYwf/37Pvrff/tf /dovfe/JveNFMHY4FNuE0ZykTjqTGdkQxOF5oBI45sxUHCIhGhJYJkIYxSmmm/rFq9e/6298w7f8 +R/64Jc//xf/p295/faJ5yGqPqjsedy9bPNQxqIwGZMziQgMMdJuvzMeXqB//cd/cfMnvv43772T 8b8XLh3ON/ngUXfy7Dy7+dkHf+t/+yP11a9cHp1Ml+fdHO24UMEzFQ9GTowu4Quh9GL67DlRxYbT LVcxJiGxbmOSAwlGrCGVSczf56vkHXlyLjqlqVGAUBQ8JPhgaMdPitb/0n+yOhTWFaOIX///4z9t MRWrJ6yeJKfmiv4D9+U06Y7jYY/lgWs10YjmH/JJNp1kcmwjhjUUuPkP4m/LzRLeFBEMkRwk2Yf+ YcaHnMUn8C8kpXyOI0yr44/n++9PqkdJfunozifJZ7vVeLmZjg5eTCavJ1sHQcnn5y8lycN6epaN n+Y5X/3kzz73vqeTDaqJ4/Obn+qjp4W+5AEPwF/dfvLy1WLHdjxakbPRbnJyPJlNt24cVGdVvjNI ahCE7eR0Yn3g4sXk0f3kwu4Gyf4tXnnEm95/65OXRtvJhcP6+Fa2/0Jy9plk5/1JeT/pskSzO28+ vHr9YkCF2Qp1gePj0c5Bpy+6Rg4KrabXhyjOpE2IujDFklHaeXQ8QdsvO9xJSnLCaTJ18t5yWjL0 A65/cVouYRIwp7ZYn82KYS+fL5rDg9EKXv389hFFpsWMQ7WYNlCYkTtnWllzRgTUlPVJr79NXe7+ 8RkhyGw9uzoYv7ksncNAjLq6v5hssq3x8ujmZv5oTnxzfpdiycmds9HOYNBUn3p12t05/cyn02Jr pl7YfPPq8YNzzvpxsp+NHy6nnEB5SPYpbJIBBqaX9HaS3qENSHlnb3Rt5yNf9AHSlnfvv38bf9od 4XVZWkqrWb699zyqHFefvnHlcG+rmFzduTR23mj7Dx60mi/Oq3OwhSMs5d1ZRcJKZy5+b11xA7jN imKL8Ycfww4tyknR3WNioulriBwYyYdGU9Af0iV1a1YXlfWo8FjNoLCYnmfNCOfBxxBEyehn1ygn ps35CgriZic6NbFii2ZJPwH9yWCGmCnnPMK/xK5bCAM3dRKxOkGC0HB2+vlmQerLL5+umwOEthkC qTQdzpCZ2QWi/6RB5j/m4REEOeq3nVaXLzeLIYVoYADTXypCBCuQHyn3w1wk6sP0C3373dEktlRB s97u1Asbb4kKZTPDtQramPr3uBaLaEGe9IPkwPM7OkeTIldD6pO9AGSZITAhAdscqrOUKEn8yPnV jEXFPMqLYlxSqAVf1F9xRIEyPdb0grAlFU1ku1byQL1y1wCBT5vFHWTQyYbWC5z9hie3bCK0oTYs Wy0PciCV3VoUzoflodYSjWbWNoJxG0OfALJi/liM28E74x3UcgsyoF6LWI1ii1VICZzy5/gJ2/1U UrGqFepovI8pX3QSkL+4MhbiJYbJc4/ECZaHI8MDHZKMSMjCZljZENE1ImilLGXmtSlgFNZiFrKh gqdDCoBfomoQq2pNzM5R9VisbQt6sGAEzSyW2IALFJZecTV52qj9I3jQTpGK9jLLEdbnQxgokDCB FgpJ4kIxxoYoAN64DBUZCDbYhedyhh78POcJSTiRbm1dgcBAXQV+eGkToZSW4NME09ZOaH6XU2UT nC2P8XWC70EkVrTCzFB68tz5fYo7e13k1jqp1wKhY3WDRhm1T04WSWwkswZ3BBkkOs6ion7wWLFS QD+QPYP5Vv2AtMBmFCX3yVWsNLi3akqH5o9O1zZBGUZBKrCUKbbl4JxoIQ/0n9fycvDhygERyyhV F41yEmIZEhtkYtXvleYyxo0eeWtXYoJG7nx3aLOEMhVvShRgT2WU5XlRNRBARGScOrrP0lTkY5JB 1FRzOdv6lJSBoNOEETF+VLzU0C7a+hnYjXVS3ztIrKTMEeOq8KM4Mw9kk6old7jMjk4Sd4yqiQVS wkDncEcgC63XqRgKT9jHah+zspjWxzzpLo1WNcSWLMLjWx/X+6K6a2uvTecWzY1fRSaiEGj/AnIY pAK4e7sFbMlUn9hipIMrxCkEvOU/2gsZmgVqJlkpUmGczzOBVYMoVHjbhkVSOBg85iwCL+q0/pJw XOiTRpblFjsopNWVCo524fDtsEIQPSRa02GEA8CQkIxHc7iJQgyZiYqefU1BCOWE8MyWkUMtR+6+ 7b5xA4OlLk9a0SEbpkMGxiqsdHEMGCwqUD/qWqRRDBMm28HlcNyjIMuSCO1Z9KRWhq8OTXohPLOF SOGsvpqoYdtDs9aTIhVGEYiog2uiQx5AOW0r5ZI/Y2hCTDdvO9GDyMPqw9Uiv1dyhj3x7+R7BQck VtbD3goXBS/aKxlVX9feSSzgdbJ41bGPx6P25PhETmb0EPEMpP/Mk7beGy+ltbQiywpYyNX4iz9Y TuRNQ2w6NCTCkHg6OB9+rNiU5cz1BjogYWFou4XeVTSYujLSoESs1aL39b0G/FYoxsYcnXifIHs9 LkQbn0umDwNpvzjLBk7iMsVcW4eZiylGH7V9dmEq4rRaEWTvWS/oA9gYU/dW/DFkLFV9iCmQqOQp LG5OHGLxrayJ5KS2EwQgjHMhjYQag/PlDE5s4QAFU5zOfptQUwc5ajrIO/uHir8Er4rkiyZ8UzbZ F4R4ukjFiryi400yiaYT++SlUwlVqVcWKq4iC/aGpEsTzWiilmqorbG/3xsSxdJg8UevEzrols6V R9Xbec5MSnQAQWaJnn/p7ZqnKHaHMIHyKR7+6IGKInvkiMpv8SaItQu++kvyC/IOnXldfLSa7kFv pFWYlkYKWAQ1omj2qBOftbKlMR4h8GO+Laq/plrC4rFujgiQLRDaurCpYBk49URIJsAz4gCJmWHU xOPUMAJR0nNipJUIwZqAmhGuABW4zzIUtCStUJu9Jg46iWq/Pe1mw8L7WCSpCLbq+FzOE5Yqw0sq uCBNzrxG8WlCHUVoA+nlZJC8BndPlEpxuFY5R46BE27jvhgg2ClqM2aYDdknRGw8u32mRG4WylsI QY+IGeGjormEBfLqhuqMMQqqe5bJabxSqUL1Y1CTKEkFT4O9oxaF9x8O93/gh/7Gt37z33nhPdcf NqudzuL1z1Vf91/9lq/7HV9zdLSel+dAi0zhVYAW9xOtS4yowel4bxTvoIFR8btg/kVAG4KZXOf+ cHs5ee3C+MmzqhREdZRQcBLDL+E2UEi27EZHjtAWbkUw9vDSBdqC/9x3fM+PfOd3I/n+/JUbzXC2 oQTKaNNXH33tr/+Cdz33/Ie/+qvfmmXJ6UOWgtpAweCi4NVGEUH6YNw/EAzWTAZuhFbtYTYThQ7p uCQe/+bRSVgG4q3Ox8ry0aT6sn1nj1rF8S2lReLh5GRpXUOnxJCGPXRPDByEbA1/IwgJyJTtoURl LyvlZcAYletgzOudDXmcAy9CJuXNIdRQz9QQiA53STDmCXySLXneKPFfR8OJJKoVz7WAz7ayW6MZ NRuWBtA9vEA5KmFRZwR1VGLElZrxIpntJOkkDCUWYpQmE7tWFImguLWHzhc2d0Ol1PaOhYaREVAL NAToQlGwiZCDm1v1+/lCeRtjmugcEUnHYA5Q0R+uSD6IIparjG1YnNaDcbqaNukWRk3cujNMyUf7 B5qN5Xk6ItYkcAYjqRdF018VZQ8rAIcHFA7ujAGkO4JZI71ZT0jgvVOECPud6iyQEQ8OuEHWnM4p vFX91bgczLlW5QrMmNOgzahnLA6kLdDsHkRmWpgtW52tqvlu/+pc3Ot01N89X9RbxJO8AEW9YnMO SwSZL/KJQbo4odd73CRnxWCr7DMm0Fasmn7f8w7seAqeZKlGKjQn5Ht5NludjwTVRx0INzLcSMUp 4E6Kk+Hy4mpAc9yEedUj+TcQvJ3zS8pMphvTEMVRmqRXJuf9f1HTZrkeyWQQCAEhAhRpsaGW3P6v /rO6Tdnm/OGyX5yf3P8sheLk9uLm0Vv1o5O/9eP3rlxKZnffuDWhkn3yuZcfAlQk6zvHJzur3vpy Ou4cndwZLPAm9HrNtjrJhGEKV5Pl3SRn5TvJC1/Rpbl8ce0//k++5OC9B1/89DNXDq/duHa0l/ev dC6ARBwtH9zY699Z96/U2XlzhtGaVEwqnCb94aNbbw8HGaoF1WSxnBEHAcRByDPIImuryzW8Y64J 4bVrxtEvy1VQcLgJFNTpO8a4UurB5C3XnTfPlofdZhgEaidY2rXOJ5JIEyzbC2bfMJ+3piFt1c/H DjOn7dd8JXfuUjFkUxdMd6tZpf6ynDuKwMgacTgMwjLjkBTlbrb7qDwib3HWuPTncd2cOnXPoTqM CMcs5CghOF6M0jD1epXENbrOReE56mScwZs3FKqACJiObETN2ZYfJQWMC7NOZkE4xovXyYRCBi5X yjhZQz4vFjvU7pEGFivGIcm+JgJjtnpJCEA7oI6pWlJtwWWHcIsjJBxRIyWBXIU5b/ps1PicSZuh IufgdHv+ZK9CiRwTcQSTAEdZoo3noB2Dm/PuZjvp0caJQQfIpM9aHR9Jzgq26chNcO1p7Mshi8Gf ylYZRM67nW3A8hjmKpuOAgFryLwWBeLlo4fqiONJZFiYACsLJ1Mdz9wHCeODs2GQ1kxPol0Zw531 yL9UjAt1SK2joiOSKoIhjpkH/g6FdVNapXzMaLCpxG1ECDKvUsbTwJEJTU3CKTnePAPBwwrKp2GH 41UM4SJ9N7yw15nSC0BNL6uWpvE8DtPfqF+sAc4gqPERxOxYESr6sIGzQbWCLNzt7TNWptc7LyYX F72zcbZroRjcDfQtn02INRDJXQx2e5A9luPFqNymYnnWqRiwTrl11WVjmtM6w0avUqxnNjPbgSrL AQ8MniYzesLMcgz67H80MwmNZsycNe6VJFCgFYMVaWcR3Stnaz+uMSHHFG3wEK7zIgaFHDsMpwgK um0HdmJaEY4IEBZJd8ZEO9dFnXsL8qLcthPyFRGgKoSEb6fuQk0B9iRJnNFWsNPUlZ4QI0sEMHmw GBusNXY52J2y8yO3Md9Bt8ZpCyHMS0CADjxBczTqCUI1FOa4MdDiOUDMI1Abw1TU0AaRQUxA6CqG cJsSi6ZkirKDJQkVTtfNUF1v/Xbb+035j0mO3B1yTsmqVGXx6FHpUf85JjjQw0KMwiOxHrxFEA+M DXX9CqE4MsgeDayZmXP0+ADxUWJoO84tOYQcgbBGTkljC/zfWJmiFTx+EydyUWtofD+LxwmTNQIC GU01MVB4lFbnWhNDrRDtMROJIpk/oa4Cp8P+lsgqQ0hIAYLYIDWzHA/k7eY9CGeUMLQY4xWUYh6j Lg2tVSET3yOUUYKAkIbo3BTPM8z28qxxOaIpgzknnS6iaJS8FSiOcZfRjOrsAIoyUrt7iY3wiK2A kPAoUFn8bVXdAIOAbyQUt1VT0Q9DZ76e8NjdDYqs8If4Cakxx4WuaqEX6cXCWK1GgIRboy2EMxxR I+DHcSht3aF1nzPT5eKvZInOiXiAE/lzLBJWjxq6m7wAccd6kOjagmqSozrovFKXw1nW0J34WIu9 kjkEQpREQfyAoihHx2KdfVnmkdFijz3mHEGyIRDj9KDmzZ/G1gh+qh5nRZqqnf9qGmcx1wmQklhR 2XQLqDO2TdieN1II1sd+r4AopbWbWT3OQOw2jzZ1W7DpVy6kMsJOcs/MM3RLpJ4xNFZyCntMwIkn CJUwJz+aIgvz0SlLV73ECALJLp2i2Mu8wnI6Y054xNNJiaCvMjbpnssRuvEqhIFPGMe6BRxQWU+m caEyFWPq2jwxILho9BALEGjyaus/Q5EGup15jdlfi1q1ZTX5bpzjirZW1lyZkXwsrUH8hl0pPS6S TaKhI2S4XW9s2WYzR57GHM51ltxDiixx3GFAXDbZ3tENLf0LHR9DQAfshoR8MHhZZZny0YKtKrbz WQidHGogH5l1l+lmkEzLG1cmZCZthOH0dBhVJQj5mCpCwxjn3vCedxetJqjWzrHz6uNgP00T2/Qi AEfjHwpEwI5kC3SHrRvEavm16SobAznEUXMALswc8QAezBYR1rYcAk1yJ6klZHCMMbIglZBkOBR+ X9AgLGmzJqG02VKaxLxDKyYmN8l+gHaCA1SonRdmF4Hd5g1zwAQ8VbAj+lBnUK0jGgGdrKbgFEC7 3DAB1zydl+WWbVk6rsDizZM5MEGG5zTbNYA17SZbiwbdHhY1RtGFAKfTFSG8VsVEA9cmL4OiM2P4 S7gADJSNifioZZIPYT7Z3r0GBOhRohNl7pzTwo04rzijmLM8pxADxKHCnXQSEq8BXTuG6mptZUKE IIjhuIonXFTsmwRNp6dLxIB1FCoPsNxtXoJAuOwgopn1wbaD0mNcq1hiHBiKJgoWKfAekq7ZfLNC 2Fhj0J4AEX8AGweTSeUPW0ET0bDODq9c+q6//1f/4l/4sadeGGCgaL26c3bn5s/3fv6l7yF6u/no NrHHTPUqPFQLZqK51tlqaoqAG0L6Zl6AD9uSiHYdMQu7RD6n4HzUg2Rt9Rnng6Iy22A8JhEsOCCy LHmU3avXB+VsXuSPTvp/+C9+54N//N2DnZ2DJ9f9dBfm7ydPHu4+Wn3hi8l/93v+b8XzX72+fzKj XxcgU0dFOGGVhmpFKyMbwoWt6g3OhgxPtlBkuQpkgX/BH+FeRhNqkt46mjDzObSzO3ud+semyYfy elkUpLlbgnPA3OJn0d/uoD6PMdkOuwM1MbRvpfyBMqm/5WUMdIi4XDJsDEsIYU5RJIwtpyxmSQHQ zc97gy0LKB5eUT7DiWA3UsAKQ2HhSl49+YQlOVG+4EHQfijLwVY/mWvoM0lBFOm1wYE6hHMwLd/h 9qKjz7KGYK9VD5v424a96Ai2O03sN9QTQmdehLcB/FcVjRBZTopXWU3y0A2ObsoAqJU9cXyWw5K0 t+A7DhkIbamYRxqClniv3hL7H5ESdJc17scjaW+NGKGvLm4kz9evcHahwLDbJutPsIQLpDqV6K2l y6AURrDtlju+SGMoNSb0gVrNCd2UJl3BSa0ruQFmzQGtw4JCS4xg9PNEjlpxbxg8KBZA6jQYs1GL lVuWp0XnQKFyw8zQkheWUEDMoxeCNHH9MDGCKCLdxBpsKKYSpnJn1rbacBChF8oWMzrSLISGkSkP umF4EmU81wT17UwL4+jQ3xRNNLCSRca6EBRaJQvuoOdFApZUW3jS23MQyQWdSSRLxRjsinwmr0eO en7M7Aj+m31vnDp4I93ppB5Q9M4nm8X9h/c6mLJ+dvrwQVotbpfH2Vs33z77/PLRrXW6+xM/8/H+ dvenfu42ocP5o5BT8h+KG9ATSECJYJ88/ODz164c1vvTbNp/fu/J8X56eRv/nmyNPrT/3MUXL23d uDZk+3ezwXbx6GzaPFycrE9kGa+Yvz6bMRaE16cPRmKV3KzGUZLB5BLBxTKuSqaBKRLLWiE9Qhe5 kVkkHSirVSu8V1u+g/4H95pIyLqeDggpQIdnQhiI0QBmIJwYjmlpxxGH1oDMRQmEXr6hDaRgYDoi YKqmIdqbsSvGD0y09dyFxKL4qVtjJqCGpxp/BPDcX5AA/nvTOc0AbwTIiZsq7BQ5kJSrhEZz9buC e42UwYBCem+DsWMyGe7Hvl8fgTNfI5LYI8DMNOUXOE5b1dasmEE/UAiLMxMpqgdOFrIqUrCgVDiL vCSiFJWROLu8JBAapS2CeEjVTtDGM7EMjOB2kJmgVwzrMceCQLuS3UmkC4tMuAh7au3fogoVOgox 8sIQjbRpSv6FUYTIfrS9mnBoynSAFjFiODSgcxQGiY7xWmyfTU8qTmqzfBZHOtNfWjPfPurZmvdW vdtJvpb/TeAVJhBK8PdEIIJ2rT5OaHk5ihMPaolD9Zno4AtRbTN1YBcn60D9F0ccrKuZST4edKvO T7aa7XXGjPhtzD7ZeNB8/u3/AJntJ8l9jh26kugJLpPPfOb43c/s3zlL7rz8eXrh02bxzHPvPl8n g2Fy9TC58zC5/7m39594oruVvPbx2y++6/LRJruyb5sR7TX728lkkzydSlu6GLyUf0FS+bc/y7/5 Jx5rgopS1YhzrvpcrmUxHwIWHAB32S9NYmdEyeEdLTeriANWUHFIyYaQkDuzhbR7qHejZZ/fJAa2 FGslPXwKxX8Y8YQ/jVaUADMEh3Syj7WyQme1NHy0oVULr8dx15xYRxpivu6yh0c03JaiK5lQU2hn clBZrWW0KtIBVFmDkJVvhyzxDD9COKqPcvhhqDXHp+oerDV5eEIWue3o59pGv7wl+BgjCkUirKsu O7pyrSipwyTP3d7r0P7z46QzgrtBrbLjxiqWZXiXS3xO5oQ2QQCoHXUWdWNSnKg84hB12KEQF8hu K8hngNbGIo9JHCzwrC6JnQz1+AffQSMJ5CAwrMhIeDXr4bQ1QjTCzTtEA5pKTEe37Oi0LvERe7oN Ah3oFqJrzt+SCmD5ggYRwifVgn0Luzv5HDlNUabHCjkOnCyO5Do4SxamvYDSnhXDU7YpRAgdhKNE Qny+Q8eCI0DU5f5FHGp1yi5aGT8hYkFggcki1yzoYwIZ05mGYrnlQIvVoFVk6AHiCbcI5JCnsr1W VdS1Fjwwjud0BbueNyLEFQB1DHTIMNiKxJ9isvh1i/vabOekGq7w+oYNHjAju9BO8FFt8CelUQ0+ nEyofGv7I2WQpxOYKshODB5wrEVAYM7Oi5TUEc7BOFI1yRf3ZPPUHAG5seD34Ghqc1h9tCumrTBL 3jBZklviQAJZGzw6S4TvketCWdYRpFHg0iCaWkqD8An1Brbox3QXeTUsCvwymo9AZmirgTnlQnK8 FDpT0FZsTgpWzC/QRLLa2Ah/P9qO21CMA80zQlH2zIV/AQMDTjIBjUGJrr9JCiiSTTyCXhI0pLsE 8UbqvZU624b4bnj0pummd1FsBY/2UCl7IHIAkx8V0tyaBWQ/Fk/xD4ypkTzEWRsoIC0ix0BrGNdL L2lXFsUa2rRUaeH+4RsVbwtei6wLMhQJ2mYs5griK9FY7wxtJ8i2xVRAKJbFKr5PS3VH4W7lBIJ2 pBAmohBcV1NnY0eiC5wIIIhxmf6PXId6m1LtkYgoxuYJt5HcQ2bFm78ir7AIbAMIfA32U6KEorki vSSZ/JeKce1APx5mla6dmWpmIBSiNtDjSYBRYIXTzrsaYbbxudh+8OXMcEPV1B4e/s25u2EXzDAj rjeHElQivOQ1Tdr9ONUWFNWPOUmOC281f7R74sEx4FGGFhtDEBGKc5HLxK0lMybIt28lYE2TMoh4 THcLqiBZgfOknHEfou9WlAOBknNtsTlSJnFzVWdIRkicSbZZYolcMUNA+PyxMBvPL9xngsRFN/IA HZEC7dEMw2+S7dLDZ3QGG3dG5C9mIZZYdkVnnBskWyrYFnYcqPUYBKcg2Jvc+WmR9xgmubWqL7QS KR5KSU/Yb6yGDBJL/boqRxvZ7SXFSCsQzVrR7sHX20KtOoaBGF/DstiGzx0IduTmYHv0nf/zH/9r P/zmuy89U/bmFXjycn3nrU9/6Cv+89/zX//eFy41d4/Op4voF7DjTfyZBe5YjrFwGkdN0RKzQ0wd 2JXcHfDn8AFATWTB4cDC34VMUF1ujwaHu9uLVfb3fvDVn/j0D/7oT/3MB/vl+sIuBpXRwuNe995p cv/49d/+/nd++W/81e9495fOpv3T6n47XsCTgbUKmYSA2J1QJEQNMtlBinjTBSwzKzQmCz2Y0AKR F2AmCWwu1+bm6VkI+LHHdH0nn7p7/CtuDIn5cWM0C0kNdYiIVCHPv1pX0d0jASOIP0pp6m/lfGGx WnUrjJR0FaC3dsKjhl6vmWacQZqcCtvbjEcU9E+XZE6BZ7OZts/FzttKY0HeKYm0LMTgRmkmpaPh nKsXsLVEEs6ZUhAxBoiGW8atLayu+xXqTypW1FEp2vH1hBAOM1alzDWM+lSMNnC0JpfKfhbhKf2k Jl0ES+qaoCVkL2zb42GXUtc5vw7UslhinIIoFYmTYpYgxK55CExg8LKiXK5z2u4iP7ZU6XQihU8c dUnOo145F4IdwBjZMxh5dhS9IuDSLWiDbeMJrX8pKXr6qgvwGBi5fbAYJPECDLRd3VptPaeanfph wfHVohgMsHNQh+1MJNWLTg6Pv3GI2aUWhzdAv9o6j86QklBdzolLVmW5M0aVXZdpuUTGEGCZkuRY ADbJdM9WJLzcEiYnuZpuTFtHRR/3oP6qo7u5c5RB+DOrchD5wG0JbQSmyCq5ls7PYcvqBcZOkSk2 OyyCGnuhR1NuVhZzxRGI2dUKCzvmeGB8I5Bd5B1QQ/hWureRj1cjO4SL1YpSnVpTUJcT6nl7dBmu p8XhpV51OmmGWzZ/pDY0bm1tYbMGuAhcOfncyXRvT8gnn7598uh1YLdbH7u9mix+5u7nJufzye3J z51Qi6h23pjc7RZnx6/dvVcsyAGzW7ZTtP9sD5Plfv6lX3dxsPj1X/WRL3n28GAr36b75TB75+5T k+W9nc6FRb3o7Ywent++XOcPFg+nAKRFAex61iyPq82+J8i6ibAesU1Muowr40VbWAXWiWjtWozJ SYVsiF5NE0kzo7xZrje9RTldeMgH2BYLDR3x/pSmvhh6azwIfLgC8E3KVbReO1g+A8fQmXGCyrls E/aPyXaD6HLF8FJzyM+baQ+v3+Z+wdXiHq94sHyGF4kClFRo7EjP6YgAbKX+wJK2tUKemboSkWxD LWIzHxWDgipUF1RYW8F4RVtiVZUOT0b+ADQc/z+EP9tOfwvJCiBIvJIALUvPwnTSzfu0jNL3Tcu0 FEfQCJTlwo7KM8gWXZxoOHJdCIZF3ir1B59MwTBcIkg3h5TGVFr5QIsRe8TSi41x9uwYV6jGibs2 ZglraSUExDiCtOoa2YZMG5dL06LsO68uM88h5qZSPVoAtlmQSKuVLiNJrJG0TOHwtFbGAFSWEK0Z FuBsThYITgaD4aXBIOnTyN5SKVkSJHCoYCtMMJl1UIrDbZxtJo5PLbsn9LyMWbLl6nT11uzs+Nam vz06P1v+5N3PvvIPTnuX17ff/CdXs+f/2ek/GN0e39tLB/XpYM44zfNk6/nO7P5kcgoHJJnZzXGA ATjc6TQ3m+XVk8FiC+2C1d5wb+7kUy745PiUYuzqfLVdjGeDtDymXFfTJzTfQoGiW10sH51iLErK T0VvyXyGvVly8kKyewtdkWRvkZxe9YuqN5L0g4616E+Bh0bL/dkBzXGj/vpweeP82cmzr188yI4+ /vz21TcmD5fp4gPv/E27Fy58/sHx83vZF37k2Uk5Hdb9l9+cfvkHnrh4Cfh7581bZ4NOv3st2ym3 Vqs7z199ZheWGQnMOB3MIH0rasBoglBMGLCpY/YmVZaCWvPeWZpfUD/WtDVgPv1EXHHCoLZ1K7C/ f/M/9lvVyVkn2QldqzBXUSxEDwLxRLzBrGY8BVE5V5VhBBz0epBlc+I948NoVwhwWQIbZBfvJmZ+ WZEJhR8hcqUp14QQlEhJXMvXBliWUDhfQG8W90JCGRtMKVZsX9UB5dVDI9gWUfAqK9c1OUDkMPpI /wYeolB0vZFFINIfskiW/3SAWic/38zfbjx6yaKrkQ/CuWD27VDRxxnTqftLvBjdwg7U4YEt+4oM CEvwJyRK/YTpKEFft1IfF4TbIfxgO5b08zar8BVtAtHOhOQ/9StwLtsS8F5qL+EtCBeC0xoQpVRS iQX21gdAbkE4ptxj/GjyNHpURtC+ItK/ABKNqs3k5TBEq2CpBpq+motqzKuyJgp5S9utrUZb07Ov RRxbhbK2rZToqR3y5/BBW5VNM+0A5+pEmIubUy6abVadzykWtohKfmCajDJaOPZWtt9kxMe2QSk0 LAqCK6e0tDp4amXzASyhkxrUzcJvWtrnT5Bx5vEYwJTRm0sOHqP1jORiEoBjvf0TVNJsunIwCw5F cpTvEsxYiwlmZR589UGX8PVNqGNGCtSPqFNAkSUa9xAFbUjuv5irI30DseTHpLQKvMjotcvDJC+c v6lA3CiCWsEN5boiCRJLGSjCZyoRHF3K2ywASxPz9CTPyxHxyR5/hGUQ6t32dgfn3gglXkszb6OB 7VCYAL6EyTgQGZ0SokgAMgGm0E4kF82SGGHEEhNDQhWDr3I2LWGZYQmRJGNZeoS1MONJDqIkFIKE Yu0xTjOKVTYcU48IcTjLTurOKVlojm+Gay0kBrr6S7YYsGC21Zh1B/BEzMcaB3tK5rOnGjCwVV2P hCc6wpXv5gICK1FgV/YkquRBczC1joQ1Cs3kN7LaORhmu+JjMXzDRhoRE25rxKMmYoE7hMgjL2uE xTVT5YvHF00hjIJnANTIc/OH0TTbypnYjkSYGvKK0ZNjVdCKDvAiRVbTCLMZjZox9QK6ncVBu+BD dNNcx1o+/BzgQk9nFxiF04RVBISQ2hpxctQsvXRmm600b4NEVJ/+EUvSkaNGU44sd2dxyeJjd4gc CXItOWiQuN/BIguREtENpSopFYcgIAcWa2MDdCgKEAWsYOe1Qq2Gog5+8CrbdiFHRygj8l5JLZLv fUZh1tCCM2URSYKiRlt8DB5X689uhra1PZTL7c6kPI+j7fZRjHceDk9PHAhsgXwFcZIDp6XaSECR siyTXchVBX1qBjagUaaRN6vJpIeLpYyE3JjMAxnSl8Liyiuy9pglbTLoELgzAXt7v2lEpBPSZEkc yjaYmOYC3SrPaUWdK3EbVpll49SabkXHGgtgbuHSYcglxEjNsE/MAbAltL5e/vTh4Xf87T/7nd/1 I/n64tbl0cmq3u3VDzePjt54efvgD33rX/h1V/a3t/vbd5fHqzPEw6jGrJZA4ZI9OqNuThXU4Cnq Fs4KQbrGXIapojHTxBytpPoxPDxgGRgTfuvew8+/dv+1O4tv/vb/8Vcn76yuvz3sXijXg964Th/m ryWvPL3z/NVh/Wf+6O/O9z50dz6pVlNQbJEcHZbVEDXvIDU6hVGeIjHDhJ4IIXn62ddAABFcE64i 8l3QD09EEaR5hxeLSGNm3zw6Jgu1KFkh9mTS2wvcCM0N+hE18IrvEQi22WkrbGJ3W1BvnB3uUQsp COc6ma1bOhYchNoKmV7V6uAUOoIIEmZcEAlbIsbm3FHANbPWXAo+4z5JLzh8tp/Uiy7WXyg1LBG5 W0wY88RKVlRHQ51LSXEG2EE5cl670J+qywYDUYUgLulbb5dWT3ol49EunRA3sZYcIi7aKF8NJ0Xo HcN3TSoc5yHpgC4srVQoeLbFfz12tK+6Co4xf4wRihJFFxmWguHGkJPNHUC4AlyOljvTk5DCNJLw 8xVB1QQKAoZuaxXtwtJ5g0AZNiWydLWN2tk30lp15BEcsmIC0TGOWJ5gy4uzemjCBmu0RkKOj4d6 jomhTHicoKXvxItApRxwEVMghDwVDQpKuhk12AGfRxnJ+FqABQwkdFAxQ+U6H3QZZB0TmGyJjNKH pSGYwpx86PzYQ7J/ixwWHQr69ZnhiY0J6RxbC0OFKKAutrDmDkMsGyJl7ptEVcjuH8I4K8fa7GDQ absF+IjTpGE5ILMNTyVuhlyS/XWW4HW36FkENuy+t2kQbGwcSnTEhMgk0LaJjrFIzC1ng7R66pdz nW3PDLkow9oB+Fp/Oz/dVNeGu8xt6VOP7u/0ylmnX80fTsG2e0WznD0qz2sE66uHj5Ly7r3l4uFn fvKls9Of/u6f//z6/j2KgPSe+y7wRBbJwTPJ4Wa8dwjr6R27Tw97T+x9wYtfcWm8f+PJDz9V5NsI qp1vynPH7TT58fliXs3mmsHm7Hhur2s0XuHlnecmR0EWSNnL0YegtQDhE3sr0+WcsVGQem0wh5QW Pg30ioxa7colE8idkgEIQ/6qUgdFxe6UWYHinmy8fWrxVdzFjBYv0AcpFzYt4pxYGVHe86Tck2ZG pBd5bAf6DbO9u7D5o2k4CMQKhMA4ag67A44RGY6tL0Ly0qss/9hhJd+d5kBLONTPTV7B5aUAtGzf aHOXx22ZMbBGrlfhmHUAtyDJWJFzMgs5guPrBZzJVAQL2E7BHdA1hWT01xZqgtw1q6E+ETkRvHPQ sZZDfbA4m51YAgl8KJI6su8dYEFpIowbTqbvRC79KY0rAADG/QpLaS2F3LVSYA9kGlannGWqCobT 52QFGYm5pPIP7X0srJr24OlbxtvqQvelrYbhiflwQMtHflhsM4pwl7yb4z7fdN66lXz69qOX33xj cvNz/Q7DFSav/OxZ58LD+dng+OToPQeDT7188guTW8/sdp9rnvhHr/500t9NXv+cLI9+79l3DifL 3sX96rWXX+k24w996PLk9dunu8P3ptfPxmdPv/DEzgLn2Fx9Zn9n79Jed/jw+P6T7//i6WQ+Ljo7 48PueDx+6ombr93/4FNPlt2RVIVO5+TB2/lgfHH7+r3ybNRTapLzccoasYlZer4+H2zq83x3++QO zJTl3uzk4eRg/MLJ4uOd5Aq1E15y0dyvjlbnd+7nu5fW88/3NpfuVw9eOHzy5vHi8x/7qc5OPlvO 4FmueI6jOWXbs5v3+/tPvvzw9sW9g0cvHb3ev7033emMVm+ut4bVyfHDpDM47077J9PVw0590E/O VigATTbcQSzGHgIjluGYs4FKQzI+Rd0nmRwnmyd0SoACM3KrUbL9HGhcZ5Y0J28/9ZVf/uZrnxtf eXKYzqd3LwyuzK7cGKL7v1ON1oPVZ6veO1aLa/3BPfhF2bN0cdT0CnUf1qODh6c7T43TSV0d1Bdn e+MPP/El8+7mtbd+8crFSzuHh3lx8c7DTz4xuEEnY3dn/z/+yNZb58kTY+BVH5OKz1n8J6nV/3n/ xLAFUZvqeLbaGoyo4zCFtxmTVSlURX8JLMVxnsElWE+h9R8XypQ1xaJeDaGLkK3PzZnpNaiTaYdC lpV5q8YhxIJjVJko9Ml1RWDOrQfVDjuY3cIzGXyrWB4seCsbQvx4lzD2dimZk8tUc3qyRCKjXbNU ZaCirGMGiRWzq1wbQ7mDufJg3DgMU/QISBT4MGHV3YQLsLyjMyXehvVsCyFGzZhfgkrkQTbDoPjO G8h7N0y1esuPWpItKI7R0kL2pVye9DZNpI2NQS2Xq0ykhaq8zYjS+aCgcL9toWUViE9Cx3W0WU/R yQDRM883WDffBKJnNYACBDY0ZL6F7Q8prV9AovZaKDtEnlOj/aFtMiTiwaIpOQZ6ESUglVaWCNNA WOPJLESoChIl0BjGJB+BiMigTBohPJ2mPukwhRHDZFAWc8k1ZeQnKpaphqH1xtsqcxVzIJYVAzWE C41UQpDQmmj0wod0sH3OJlususTm1usptMHru76+MxEEo1Oz+Zr19M75Y9YYkWqSpRBggImtk5yJ SwlNYCtA9LSYjS+jx6ckMoG3xyEK2p+Wll8hDobrHG3G9OvK9BbOjkCKpJTlCzFov8D812e1/ynG Vxn4SBw3QGxH75EH818DDn7furF5k8MXLLjqMFlOKVriBKGhpYKFi8zuEX/oRSF++GWW88RdVCm3 F0rBAKkazj+wwgretGJBQP+Cs8Fjm0KI4ATIzCfzdMF9Cm5pjIpk+5ye4C9z/KgqOfiuLY6a7kUb JCs57uQ0M4YOomUwUBB73gOyAJX7pQczkXeyChg7RaPHmEGwtkzHyfB4Ho853kqqBgGzPy+XW1CM lbUYH8QQyt1S3kT7zMcs2tnjK7GEqlvQQLyJfrDX2P/NoYhB2E7I0wawG1oL8XrcpEAMo9acbBNM bKv+WxtjqaiLChVYO6Zob/ZnhOluxvB5PkfUxnResCyAxxAys5+WEx5FQKLFtsHG/5ZrJnBCji0H kKzAklPUgXhXcr3gpAVdM7gmUZ6Dww7Z3X4aqBNaJBmCEZBbaePu0NmnNB38cs1KxC7uYvSWBwnZ YrdkXQvMAREiODnlRAEJwWlScR2Z03Q1IOdSEw7qg8fM3CPqpiKSLqP4MCfdYrjLx05D+MWyBzFn tupvoWBERGgCxetzfWRQOcmVY0FlghaJ3K5StQks+TqwLDRpPE7Ruy2zA8sHVBCjxgmgyRRCk8LB GUgTGW23CqZuX2iNW3VpdSYsWQTV1QntUCkZxOMamdk7xpgT080Gu/3h6fTlP/Knvun+m5PhpcPB gB/IZxWtpfnLZ4+eHl18//u+7Mt/5W/6wPtHh1lyvJ4ny+k5DMYatmsytQFhPSizZb+3v9k6rR4y CK1K51c6F3tjoy/C1p/97OyH/sHfO37rk+ez87defXXV37nRORs/9dRyeX7Sv3j9XvF29+SiknPn 3/QNf+LSjSfzYuvOOcbmzC5lb5HUUtRvZWxpuKKFx+ZtC4ytxEuPoo7aVjEEkwtlEu4YDX9aYE72 iVdR2ChJX7l/MuYk0yyIyLgKzusLNGs6MlNgAwVLO8U8woo1YhLMc4Pxxo2hdwK71Or0kG5TA3Zp JYgAvtvGiFtxswRF1dETEtXYKLYEvhtWS8/SVvZD3QgfS/GOjlM7KCRfKb0rv8FI3pOtqGrwo7Rr kQbI/pIWI93Qf/FUO84gXEmI5xKp4+clBsaVF1b0oCtnIwwNI15r2srcBTprk6b12+DQxVgjG00A DjjuxPv6aLciGBrcLNErR9rYgSP6Ja5mrU8xVgUaQvhBvScCe3tMUcu0qhG2M4h40l8oDscQHqUU 2wmbrWQDfYsJotgugibLwN7L4NYHlV4gQSqTWaXARwvx+xASpaRvaAWpVqvNqNEx02knMjvBVHaJ VUm5mqJ30djVitX4VRRraLJVeFQmWpRJeNecer8yGS17WU6kHD3BH24XKpXWQoU5/OQQz4geYTEy 8D7nZ4duhzqjzgqWG2Z3nnay1U5hVYsBp+e0ah4smgtZc9kaqYxf8maKkgD1VoPaYVusK5PduRQt JTvwIBMhF119nZj913YyikvawO4kr3W3P6ZxjOwsyj+WUK1caDudsotnM9ST+KeFoARZL2g/js2H sO255LbEsAKyjJjrsgB2ob4UYzIcGoEgf05c319susMOQzNGo70+5fsrFw861fR0fr6zPr9//Obd z73x6mRy84h0I32lvNOZ5Z94/Rfr+3defYUurFAtbP/pvTO5fPXquz80em/xdc8+tUXDfj240L8x 3jvfPyBln2+jTzduFi9TbdyczdAZiNjEIM5qD1PBI6uW9xciEmBsdsyZfkNMGGfTe4vevvNKaUqT 3+Fao5q3GNkmxDFgmtoodzwPuFKotTIi0BGwOCeq6NUUwTMhUrfDOE5YqZXgBQhp6VgGIfwJxBWX h5tRdemekxpoECe0Tw1QZQ7FEzQ7ckesSgWB0yFfZdEvljy+WHNcB/8lYnZ7O/QSDrZVdofoWlVL 1MYsylGiUu2J3lT+mv1krltMXU4z0psL2DE1sexmlCVpyJaTaCxoEhXBsbqkrJsO37H1nUEhxY+f 76G6wNLQWzDSGluTV+SLTwFH44mlmxCvOQVqSTNn62rENyV5GZXi+eSL8En9YS+Z7/d3y610d0iP dW/CQp4vj0kZ15MHN9/YZNvUaM7mZ5eG23/79c9fmHX/xo/+4hydijt/5/g8Se6hQP8WR3046L64 0ymHh9tV9+IHL9A0cmn4HsQtdp9697gz71/q0VTWSXfT5vj6aF+iZdp5z1MXs/2t8fZB1nvSApuU qn99uheIWwRBETFT3QiDmyzPk/GwXsLZLxdpiigAW7HcdAei9zmz9SD/n43Wu6A7Y+7QesFyZWg7 oWJAkkvvImPrq/6iN99Z9E82ItbOfASqwDZtm2YxG+osT67ibttE1BllYXXCWk9ikkL7z79DUfqf F7CNA36JEU/gSxYwT9Zvg9GUn317eOPw/Hh2+9XbhFYv30FJFWHN/O3zia7WAVoIO3fPlov8iHLM ymkJnc7D5NbB/Jl79a3p5AyN4IOmf/zg7W42uslRuHszqyc37w2uPjd849FNAJ3l66vOTn9anSyG nbtvnRzOrzc37iO3MZsv8slWM1pRsj8j7Z4GA/LxP9YSQkMjlCtB8XZ3k1POL6KUB8nWerTanl2b JuWNfn5hefJoZ+vKWb3cQmipc3V6eOF6fnQ5fe7uYfKeg62qt5OP+0/spQd7W3eYY/np6Vf9J+// yU/c/G1f+e4Hq2oP88qH5ulgUqaDrVEXWM68jWymV8BXubhsEObcw2pvXx2dT8tL+93J3RKuw+Ve l0R82Mqn/of8w5vyXnMSc3wNyOLCsYnrZmYddy4AzvEzAzBiDjElZWIseltV1f57rajy4OgjqMc6 +xhqCxsr2h4k61ROt7rF0oeIMKOvV1JrpLch2a+kt1K4AnoE7E6nVoEfHbIx4Y76OzSKR4+IDtEs JViQkvYsXms7o2IdfMigbukdaAdD/DxIEDiP6IE3L8BExCBtazgkjmb74vrt2ZS4zr476VJCV+ij tQltFFJtpg/CsW4+hiRh+oLxHv3WNt8E3VTJmbi8XthQyVZjy2zCzBlusGwKFQH5EZMALaQtOYWP Ftm0nc5B+pWNZSJtX7tzzGzgRaJm43iHIDOHQCQxOnUhk3YTVBtwTAKI+J0EKphOjueMDxwRrpwF stZs7on9JEUhp7UHUyCcmcBM/YRzLivKrDYlE4Gc1o8KBufESpHNA5ER56tymnWGip512MI2YZVc JRtYIgaB13qIcmfUVuXPR6eTGZXUYoBiYlJbutVbknhgrGO5yAjVvSak4u5Lto2J5tYmPDuhrB5L bIgdQBN/x/vKGlR7gr/V4lsbMkX0xwEQQECIUe2Dt0kzwCIXR6jASMrUfkncH+7YiJPsAqZUiQCL +x85nsmPMgfGkDHKygK2ySznv7XTUZcIZbYIymzL8OTIyAe5QcjBp46oL1pYvVnEy8BN8hyi3UYG ijGUzPFgF6vHR5hg+OtRtGnQ8hUH3sqdfQqsu3NMKfaYy4ULthTXihHGiAeyUgm+is9zp53U1Q5w AR0gEVTgQfUCT0vQxzn5Mr/Mz/3huKbsq8oXbHB06AftgpiNjCOiXqNli/H4eRdRfg4XhfMC67EG yi2X/U5/KRTIrY9ZdzEyVYEJDrsN6TgUO+jtQEFXv2UzCMGYXQpNPZ6oqXqEfA+z6bYxwxFyyEex ABEh2BbRSqQ62Q3uCsZFhE4qaiA+ilm0D0+IHARYq4bBO2gJUP6s50sdAEQajHphm/fbUqUlK4cD SN2Reh1DUiIzUs6P+qorIzQh7MTmiBIxWreGLOt4UkniPpvJhkiWUAt2ImJxIK+ubeRxOpQONtBt zZ7JlIwdz7zHyWMYJc2gE1oCD9tbOpkPQqKnlx1WJDDIuRYqQsPD2I1+khXct6UBn2N7YGtRYFfE 1GCwO94ebe5/61/65u/4iaN3DfaLMbz0/mqGak+vnnbm6eub9fn5+aBz/at+63/64ScODsln1wer i8XezugpOuQulFceLj9/Nj/fHV1KqsnLLz3K9ovv+es/9MqDtye371+7MU9Hu1tbYAeXJkVzqTiv EeXKO6/dHzyX3P/Ih/cPhy/8lq/7yk3nyqPVdjo7Dvss9hkdxBhXJ67LmsBDikSK8gWdRzPN+xBQ E+ZKnTclwdg+hpZ4aTsiPa52EfFjzBvQDr318CGBKrcH+e9PLJfX6OUXylLegfLM47tqDqvuAsmv MgbeSbnyXJfQJLKLQcHUQikITowLrkehvwSGOYdaJBpcG4MCrICwkdw0p+tY75fhFD3dyueG1bHx DDjQq0A6BzgQ7Wsy3mLOrwQVWfBkFTSSCDg4ySgEg8n1BDsd8SYjDU+jfRIjEF1qi/HcUHISrngg earZYDUj8JPfIKJl722NoA5aS+p1QteXZ81aA6hZAycTW8+KwQX6WMhkadvxetrcItss6CiY0Cji aVZxZWjIyTXVPVAQ1nNr70JECUTbI05Rgp+inKdUshgirG2KD9GCh9EG3pYNzv0yNgoAnvyF+D6G SESbvHmjtlekL5pZPSFEkHQcsFfcupwZrTnZqQZJUp6Men4Wo2d3bFBzVJe0nR1uOUKIJkrkWs6W AQAw5QnUFOpARqPRkI5zwMWYNBToIGYvLAnEyqEsYi5tRYm7r04MJ9MniVwfvBcEx8saFD7SAro6 bK4zz1LLqOnR3Mkr315As8GqVHcXqxf3UO/To6ELINLeap+EAXGcs8URtREU4eJ/U7VoxrToaLzt CWCYQPe8VpybVUOHHJA7dbhVBEX20FVzrBs4O0AkuwnC4iyrZkrfDiPZCGZANIF07AEOV5duFqt5 OGYCQEyNaRbQNUgSfAAY1sBUWBiI2pyUbrcv9S5nnpqemt1zonWQ2wyUOJ7D7i5Cf3vw+Rihc9of jBfrm53mmXT99nLYGd97+PGbH3v5pbdfu/8zP/nSWyefGdwvH8QUBP7BDbPkzyXbrybrw2sfeMez T335tRf33rX9/HNPP/GO5/os3vz8zTy9kC8mby+PugzNXjePElTTPAWSDtgVPGunz7xwZIqCrcFz FyMyccgsTA7kDYRVO2W5AMoJMpxnGsCZ/zKaEDNSzM1GU3LeCnLPlKXDb8y5MIYNHB2ujSe7ahYZ vTROwOK2j+gjhY0jZEV0hvKw5Bavb4oinqM1eVgE7CYEEyB7MssAfXrJuO5NJVBBooGFNOzDZcmb gVM1ovhhoydfZQuo3B0DKRpOVL0RbcLIc7es5USNRJ6HhSdkpsilHfKGxEONfr5HMTo+qNarPABc DMKhrzT4AHtn4t08TQDme8vuemjziZR3+2fs14jmT41WtJcI9Q23OqvukMijc7BzCNXkaH9+fbO9 yffqYpTO7jMN8Bc+vn7j0ZuffPO1X/joPzg5XQyXt/pF8/Ipxun0+kl9K0OuYL6aHV8eXjrrn+/2 9z50efvCM8kHnvuC973wzIvvfWE9urG99yxsh/5wVHTJiroE+ARug+XinOVnGGGSbZW2GBJYn/Fi C2TkVxB4qAszBjIIaCAxk6Qcs8+SRUTOsVfR+CNcBiLrFB7nwDmTQT4ohjxmihmCG0IoC0A9JVR3 jWVlxMkasORntoy5hUoyUEUJSFXxpjANrLXIqfUQAzRKbowXgK2qPlvI49sSZbeO/MfQz7CwKcYW fFakqokh8AxOmaKOhxtHujZoUdpRx7PYscADYyiketKhGR9LHMWC+WKdzgDj7qhJ0IyiC4c2GxDZ c5iBnDMm4TC2UR2DGOzR5vRUZFT0/6XMU251/M9/GSDgLxcQLoLQ1kpX/jL/MDnS9C9BHKc+zrL9 UM2rVuX9dXkyHr+Dfodm+fmTu6+fn6+evZIt1ns373z+5Zc//u6n3zeePlpduvHaS588O3r06O3j 4dNXzm7fvvnGrefe8+Tnb7/+1PY7Ht5dLh8d3d++d/fmem97+dLR7PnO8OP357zR7nr7vL++c5Qc posJ5XAQlxmIR4j6JM8kyevJgMGBlG4nbl5/4AjPbQrVBOVo3ObJlSeS8+P9/fz47Sy5MEp6d5Py 6eRqk9xPk51r7xydfK7eS4ZvXnnwruXFe9V59UUf/oJ/8rFfvHZ4/clLX7V/Y/eVNz67u3Vxca98 543Ls7vZxQ8cXtm/MevVz1/eHaaXsovlle3uO58p6Nl7atelRq4jEpvHrQH/puX85f+Ot6ZdDSxw RqV/1qAPpU5ksmDUGBNBsuS8SLfP6/VuvZwhIgUlrEdaxzyLVXd9IcuOAWDWAFvpcOEYLj3uuotw GcqpUUVVBBYvCmKJqcJmiADISgVbpopaMompa7DEQWP5LNhx5gl3iUFo18AOMk5lGci+ULX/4sAE S58K1ASZ05tkx+tjVl9wfeUGcnOi/5WyIV0qUbgxedQVSaN1RJq14pwITXElwfwQ6bLUZwtTEAww 4LJi+TmrwmZsMXnLmAxEVq6RvY3AuiphdfHsaA0SbtoFQCRYWEBX3Ieb311LnEBcwNcxeHe5SGNt BaAk1xJ5zeDEDCyISl83YDfthSQ/W8H81BBIKnbsqBIzGJwVFIbo/gZiHYw2SzXFTGvl8Zt+O8vO VnCJCnL4uEKBwhA1WOtR14jo0BmnVMAttCp0YBRqSIFUZ21Rmr+ItiCfVEMW+hiYKyNJYYzgLduk EMiwWIi89RgJADJtJOPrGt7oRYjS4qC0UtUx5Mku82CQq1Xnzpg1mvKqOA8Kj6qovo0pHjH/AQzZ 5B+Fvk6G0yFOZ5Fg8tAAaPHHCpPsLbsITXhs2rNMIhok2MHpGWzWIKToyjhaKLQXiMFQtWNvHIDI dpIRBTk0KGo2P/KctjoR/SgPZ/KvE3CYl92dPBvFVegIvEhPZTM7LFn/CKwlGtoGLZ/CEihlO57N hVCq3h5tyS9SNiP9tRwSmb3diO6RtR5l+gVYjbmC1EpGrfarvEyjBRUOQH34YNEK61tM9gXFW8Fm sc3BEixJAV0bkTYSHRpQhKCGYzAi2W/FxluaDpmR6UekmCA+6UBKZMgtqjfP59ObFkxY5zqqokXA TdBx3hVZU8GFOANJPNfJDJVyB9g9hFLGcaGnAxTAjBsLIRJeggoYylVyLZmKawM6xegWRpEYIsYY c0vnRGjOZrcIp1YO82Pxk6pKClrJJSGqVOhPY0Jg2XMH7MYwLYhZEa3CD+GXJUvyFnv61OoP3qlK C07JVjTCHt6Q4PQiGho7UTLEoezXEGiCK0GkzvaYBcl/8h6HZTLqt/0B3juzZp17ZzVFyF7lasa1 SehhO6yoSVX2pqDJopmJQUUWb3Kkj/g4sj7prtaUxV3MojQcaXdw4dJeNv253/K1X785fD4bHQ5m r3YHz572dJbKk1HwWiav3Hvj4tb2w+V6tzN4s3zw9PbFk2W/Gs53zsvzo6J37aQ5ttA3feqgSBcX +1eLDcrHD7aL0XRWnXZXlxe3GLR9XC9f2FT/1//8i3/1V31d2buGKVut59PZwiHHLJ+BhOkTR5ME OR5WcFA0wxqCAIn5qWNsnYYuSko+rRZDX3dBA4CMfi+I2rIWOfkNdOKkEiBblt58cEr8uVRMODmi ppQmIyksVPeyXVmbcgh0PsEPa1kVmChry3JETdRC6Swqh5I6MAE2SNuwxn1QZ0pSC3wpeexOsFH0 xTxZT9Bqlxjmw4Khxz4GJHGn6ZkF8NZW20QiMokpcNBVEJkM/WBrBAYs8i0DPK4+8bFJq5/Woqle QOAGS+QyguwQtzCtCp3qFJ57FQxMJf0MG/PN7ZUOMWaPlDksWEgiiTApKmLw5QaY2Kpg6CuaY6iE YM4WOo022rd+zVdmfSwby8ZqA1zx0mBxYNy5+Roj+7kFyFuGnmAni2LXn3XuGNPiJIPon3FaKbZN yxMDldp2e4APmVCtmY7anll2EFGkRizmvR7YsJcNDS3yaYd6xtAI6wPWJdAUxV6aQThpk0vPIwQX WH0GBUOdZ0D0wPDaDUJzUbFWgw5BshjRZN1dZpQO3kEwPq8UZDsEQxEr2mWskwr3CRmEdwh9N7c5 FEEyxtGvkNAvzuZzHmCcprep9ya9A2q1SrAQ0lsgUGxHVLtcO7aVyEDdgJZazM2WveZUTKmM/Lz9 /UoSsmkI5Kpd0w4j4nSGvCwmZhWKNJIXXAvZFP4IgpZyECxXeDRNxm278DjocTyPgGTsBX3pfJSs PbmRnCRTfZjWpgJYnJqSICnDqmHSGnO+xYZZX1FY2Vxtn7+PL0tM5xela/YVXeKDfFhvw5Mpi+39 6klK2+vxyYOfHQzzl25/5o3PzO8AG1Rvbs63P/7mj9557eTNzzGx75fS9Sc//OyNd7744a0LxfBg /wlgxmcP37dXbK48NTisJ29Oeb7y7buLMi0HNqnU88hOePyYYGUyG/2gdqNiHyAL8kpMJrBggOiX IrTtXLXoJhPp8yZr19eOQ0elK6rZnDMOo7UZtekyZ30iT6wEWhxlFh98Iya4ON0H5mRARXYfRBeL zgYQy2GNHFESRBQQCJZgjaDmr5LqgMHhwQqnni2OSP2K0cGAT/bwsdUGhPgScW1OHR1m6EmoUBqo cfSM2IKjrFQcUdmFEAUYQCwpEa8YvVa2WypujACKPRaWvQEXxrwXf6q+k8ETFNZmNCiWdefdWzvF 1nBZQLnMkbWoF5PXHmxuPrh5fOuTTPG4Wx6hTv+//MRJfvbaw5d+1Ert/BFT+ugw2rp48Tc+vTe/ dPldF7dGV9+5NbrUGY4OtsfPDNOd6xf3Dg+ZcLBz4Vneo1R1Mjklr17PHWjbdAar6dxMFQ8c4FWQ UaMi1ZaADLRYcTr8YyquFCXtlNw59TOVdZXqwmo5X9TAR5BFoTCrZYnj2oXttC4hi2IjiNKWEgvs liGOl7RrzcNgnu8nqeCPsCVtV0jI3WjcgqXkzgR3JYTQgwoUPdUOIxLUtAIj9BsPFB0DogQxe8rq B1CJvD5srgO3dcrSnx5P7ghWRdwC/zikgURjsT6Ou5TQ58Tg0MgI0Y6Q+NJjWSKzUKcQteXTGGrO VYBKI8XOOEN+GpXbyNBN6tXviLkjyjuDZKlXRB9uzEOSteXLYOAxL5ZELRuFGLGNJV5y5V7Ygy6p jZVJAMqYtLFM+4gnnPeBbHR7K9iP6q2VybzvryrAa5HBphysHOn7busb/8Py1H/1t1i7O40dQ9vJ +dHDo7uI2YBF8Nzni5NetnXr/hnDFj/1xuyZLH1Z6iIsf5TRq3vkmVUGI3bz6R/vveeD0+mjzdHr 3etfsLp1O93u3/rpT178yOGP/PWfWubT5MG6/9TVQXMPBapPn0zeKKfjze50czo+6z0AG62rJYSK dgfdVr7doKNtLUgS+PyzZDlOr1zdrI6S3vPjzaq8erBFR09++R0feOdRXl0vDonht7YaavnvGndv n6dXrvV3i82wh1nqFv3FQcO8RktB6+TiJjvb3x2Lia+2lFfsboFeX3/++dndm9tXbizvn2yN9p54 Jrk/SQbMS1WbToCA//cf+g9gjkQC4hAu5BImAnarmJ1OO0ibTzVAZsXWSnHWTpQ2E7AEHhUox/JF XVcVQvMI8uRyhfHiKoveq+G8KRhSocqI1FvPhSLhdgHaPx/SOFHGlC4sLY6cyK54zW2bV9oqFh2K Xon2XhF/0IzKNB/FKTEOgfpHF4LVLiPzqGyjEYOAd0yZxqo6OMqvEBVIc6Z02mEkrCsuQZHBX1Hf RlarMrOcpRhexbVDYNLKnnwiu/xCRsp+KRojVQayjGzzOBROY2ITOB89TF07+Fr+J69OJTek9jIE ROoNsoyObrISoetuFc5CNyisZASTPpABE0GAU0McnOZIgpjuF6IotstZESJuxNxhIrG6VitIU6UY xmAdzCYsAHzitE7HBE6Gu8Il0XMdck+aUGeU0PAbOvS6VdVaZVj9UsNhsInhrmO5CQpkn/nl4Z39 E78Zxp9lVF+5bU02FpVvrwHV3oqgG95GNcS8T0KBJwB6MnMHZJzwuvZ16CBNyENbzu6/FkT1ZyMA N2WVKdqaX5LYyHcNMM22fbXg72jlXOEwjqyMLRIG0SFzY7xppdxBQcFzDR1I0Sl7NKQjqhkdclcW ghTKDkaIXtkJLjyB/SVRUW4NvIFcy0mwSUIk1zKVrSUO+eTI+MMEgRht5i+aCkBh7Mr9UIuWWFfJ F4TZ1sxxsXPHU22uKb2Yz5A8rNg8PXoU/zyM0T0SyhGoW4n+ODZAbipK8rqukIy02sLLe0k8/w6U CtJHlN1bTaTwQ0bIcSP5AxYgJOypZsLWhp1lYdP8z5+1TYCXC4lyI3upGCFFJ14hkUYhymBf0BqD ypZ4Q8x/CZaiDsOIWPavu+j1sMUQ4YEQq+e4FtTtB6A/KmpF4V7mgQI5kcNEvJ0whgqOoZGBDByn 2NGp5+zZiDZaWnCONmWPepbC77lNlc6Xj5YEyRTSgzxWodXOyjlaN9ogWHRop34OMzlN3oy1lxvG 2Rtqx/xFamb7Fy4NP/OzP/ITP/nRf/Tjv7C4MOqeXRj1lqMd2Nnru2CohKk5JR+KMF0mFinXQ3S0 uV90rxqQqy5R57v1uD6g8uDY4k128uj0waT/xNbt9XL4q37N+1947oUPf+QrCbXPl/XZ6cMaJ0gp W7TMqbUWdYIYGFJ3dApxfxSSDGSOoqN0nhW5N0sVl8XwI7CndnCfyWZbOA6UyLVhwZWcFGa1pYAg 9NbpacsTzqvlL3a6l8rkQCSgc9LZXA6xBmm7nH5thTIh0CXQPQg1bFSPbIFsx9491nsA1hHp48Ph U24rHR7treomc7idR0r92e6yLoVQZmMYyy/MRYNWY48OBdjIaSMSNwbHDwQnSfNCtykqYg5aUNoY xAYUSri1a+k+lPzFkuIQCgWY76sGG8vlteQk2RbCxegJ40QRW6BUa4L/4CrTCiewGYCRx5b/7rJy tm+t6cCYEs5SRIsxUKK69nqar1b8TcL4HsdOknjZgCypy+YTquAhmsGeEKjFJBSHMhh9Slfl9yXk e2fxTig7C161U11pQcUs2EViyqCZBJ5UFzr6umRKmgFyIvj3uH4hqcYiCuEE373lPOoeKRpVOTid iDcpJcVc9GYkvmtMUPbqm2eSAIty8JBUt0S+NEI2+fiuMSkPQpkIQEpiZofE0ubAggZTk1nSntCj ELQ3s6UqwNEIweFo5POQWrki1w9FGDXABDh4UV8eQdeCeXODLJ9TuhuMPvPW2ZdexWyPPnY+2Svv P7H9HF1VrFHB7nNaO2gi6JfN9kFdBC70x6TF3RAq4emAAqTTm2SH5J/znkjoIb3LVNRhVCs0vo2V ia8Lh51Y5lBpnxwb8i3cZqa0qxXoNSSXSzMmwmh1gnZsvqjnQHrZPj+VE0St+9XyBNYNo9j7JCPL fEk5zYE4NBoGn5v0BzOEsF4fbTVCbSRDrUbqkwVYONjC6HJhbAmjRrPEHMw39eW8OOGNDTWAIQfj 3nKnGCxn3Xx7MS9gp5aTs+a5C/XJYn766R/76Zde/8e/+HM/9Pqni8/ury8cO0Eu2YkWVP7pJ8+9 P/nQF/+3z7zng++99PS1y0V9UnYPOxnE4TmH7+EMCfjz7TqdOWIJy4y8WygoOo8BP+qlAqFHzTWg aIgNlGI4nrrmQOI426RG3XK9lWVoVpHXIDTSjkdELVU2i3fbmpJVn5YMFsWQadpcSjtTDnJwUfqA 8f1lejfdAGbSv8SEj6Crs6dkPfPQH7EBxx6S6NqJQm7RpU1O985JR7UNRWzEdVU0o1wS9VAprXIY sCMGudw9NeSMbaPTQd23OFuA7ZpVKRCGDwMG0jozR5GTfn9nF2xqNMy3x6OmHDC/bVkep/lrnz15 ZXH+d19/5Se/6/uTeZm8/Ylk/1Zyb+CAIcw0ic2NTvLm7MoLF5+4cvYrti8+92Vf8eF3v