# The Environmental Aesthetics of Generative AI # Introduction In recent years, aestheticians and philosophers of art have turned their attention towards generative AI — e.g. whether AI systems can be authors or co-authors, whether AI-generated work has any aesthetic merit at all (Wojtkiewicz 2023; Cross 2025). Carlson's aesthetics of natural environments, we argue, offers a productive approach to this territory and opens up the possibility that LLMs themselves can be appreciated. Two temptations should be resisted. The first is to appreciate LLMs as persons. Users talk about a model's 'personality' or 'vibe', and it is natural to respond aesthetically to these apparent traits. But LLMs lack the temporally extended life, the stable dispositions and projects, that underwrite person appreciation. The second is to treat LLMs simply as designed artifacts. LLMs are artifacts, but their aesthetically relevant features — the patterns in their outputs, their characteristic 'feel' — emerge from training rather than being specified by designers. Order appreciation offers an alternative. Carlson argues that we appreciate nature by attending to patterns produced by natural forces, guided by scientific knowledge — geology, ecology, and the like — that makes those patterns visible. LLMs call for something similar: attention to patterns produced by training, guided by what we call semiotic physics — knowledge of how mechanisms such as embeddings and reinforcement learning shape generated text. This framework applies at three levels: outputs as specimens, chats as environments, and models as the ground of order. The result is an aesthetics that treats LLMs neither as quasi-persons nor as ordinary tools, but as generative systems with their own characteristic dynamics. The paper proceeds as follows. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers how person appreciation might fit into this framework. Section 2 describes what LLMs are at a schematic level: token-based predictors trained on large text corpora and shaped by reinforcement learning. Sections 3 and 4 develop the negative arguments: §3 argues against appreciating LLMs as persons; §4 argues against simple design appreciation. Sections 5 and 6 develop the positive account: §5 introduces semiotic physics as the right kind of knowledge for order appreciation of LLMs; §6 shows how this framework guides appreciation at the three levels. --- # 1\. Appreciating Design, Appreciating Order Both our criticism of agentive views and our positive account will draw from Carlson’s environmental aesthetics, as laid out in his 2000 book *Aesthetics and the Environment*. We start with Carlson's general recommendation for aesthetic appreciation: take things as what they are, and look at them in the light of the right kind of knowledge. as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (Carlson, 2000, p. 6\) This captures something quite intuitive about how we appreciate nature versus how we appreciate works of art. Consider what goes wrong when we depart from it. If we accept, as a majority do in the 21st century, that mountains and cliff faces were not items crafted by some divine artisan but by natural forces, then appreciating them *as if they were* God-crafted artifacts, seems wrong-headed (cf. Carlson, 2000, Chapter 8). Similarly, if someone were to study a painting by Rembrandt, believing that it was in fact the product of natural forces slopping paint together, they would be seen as appreciating the object in question in a sub-optimal way (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object in question really is. Different sorts of thing, Carlson says, require different modes of appreciation. Things like artworks and non-art artifacts, (e.g. laptops, hammers, washing machines), merit what he calls *design* *appreciation*. Things which are not designed, primarily for Carlson, the natural environment, warrant what he calls *order appreciation*. For both works of art and everyday objects, Carlson talks in terms of *design appreciation*. With paradigmatic artworks,[^1] we recognise them as creations of designers – objects where "every one of their features is the result of a decision by the artist" (Carlson, 2000, p. 109). Our appreciation centres on the relationship between the initial design and its embodiment: we consider whether the artist succeeded in their undertaking, how they worked with their materials, what constraints they faced, and whether the outcome realises their vision. This same approach extends to designed artifacts more generally. Carlson is explicit that functional objects are properly appreciated by seeing how their forms answer to what they are for: This is in part the point of the much-repeated phrase ‘form follows function.’ The forms of all functional objects – buildings, airplanes, and appliances as well as landscapes – must be aesthetically appreciated in terms of how and how well such forms fit their functions. However, the cliché is frequently interpreted too narrowly. With anything functionally designed, not only its form, but much of its aesthetic interest and merit, ‘follows function’. (Carlson, 2000, ch. 12, p.188). So a chair, a kettle, or a bridge invite the same style of attentive appraisal as a painting – guided by knowledge of ends, materials, constraints, and the fit between purpose and realisation. In *order appreciation*, we face objects that show order but have no designer behind them. Natural environments are the main case. Here there are no intentions to recover or evaluate. Instead, we find patterns and structures created by forces – geological, biological, meteorological – operating without purpose. Our task shifts from evaluating success against intention to understanding how these forces have shaped what we observe. Carlson describes its general form: On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them. Moreover, the objects are selected in part by reference to a general nonaesthetic and nonartistic story that helps make them appreciable by making this order visible and intelligible. Awareness and understanding of the key entities – the order, the forces that produce it, and the account that illuminates it – and of the interplay among them dictate relevant acts of aspection and guide the appreciative response. (Carlson, 2000, p. 119\) In design appreciation there is a split between the planner and the product: intentions, plans, and constraints precede and shape the artifact. In order appreciation there is no such split. In design, form precedes matter and is imposed upon it; in nature, order is immanent in the matter itself. In both modes, however, appropriate knowledge guides acts of aspection – what to look for, which dependencies matter, where to set boundaries, and how to draw contrasts (Carlson, 2000, p. 50). But the character of this knowledge differs. In designed cases, we need functional and technical understanding: what the designer intended and what constraints they faced. This knowledge shows us how ends and means relate. In natural cases, we need the appropriate scientific account – geomorphology, for instance, reveals how landforms develop over millennia. Any number of natural sciences might serve this role, and they are not mutually exclusive: the same landscape might be illuminated by geology, botany, and ecology together. Without such knowledge, natural structures might look accidental or chaotic; with it, we see them as effects of identifiable processes (Carlson, 2000, pp. 50, 60–61). Selecting a particular viewpoint or timeframe serves only to reveal the order more clearly, not to impose our own design. Once a specific scientific account is in play, some cases will show the relevant order better than others, preventing the worry that everything becomes equally appreciable (Carlson, 2000, pp. 118–119). The fundamental rule remains: do not project a planner where there is none; where something is made to a plan, judge it as such. It could be argued, however, that Carlson’s approach to aesthetics overlooks another important category of object of appreciation: people. In ordinary life we not only admire landscapes and artifacts; we admire people too – their wit, their manner, their steadiness. Some philosophers have taken this practice seriously, investigating the aesthetic appreciation of personality – sometimes termed “beauty of character” – and asking whether traits such as kindness, wit, or courage can be aesthetically as well as morally valuable (Gaut 2007; Paris 2018). Carlson's second recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge – familiarity with a life (real or fictional) and a sense of the values and dispositions that organise it. We do not admire kindness in the abstract, but this person's pattern of generous responses given who they are and what they have faced. As Parsons stresses, such knowledge is typically built up through direct interaction, careful biography, or the more precarious route of gossip (Parsons 2023, 297–299).[^2], [^3] Although Carlson does not consider person appreciation, it is not difficult to imagine ways in which his framework might be extended or modified to accommodate it. One option is to treat “persons” as a third category alongside natural items and artifacts, with their own distinctive mode of appreciation anchored in their status as subjects rather than as environments or tools. A second option is to treat the appreciation of character as a special case of order appreciation: we focus on the psychological, social, and biographical forces that shape a life, much as we attend to geological and ecological forces in a landscape. A third option would be to emphasise the ways in which personalities are, at least in part, self-shaped, and to appreciate them as self-designing projects – a thought that has obvious attractions for existentialist traditions. On all of these views, however, Carlson’s second recommendation still applies: aesthetic appreciation is guided by substantive background understanding of what persons are like and how their traits hang together over time. For present purposes, we need not decide which of these options is correct. It will be enough to note that person-based aesthetics, where it exists, presupposes a rich conception of the subject as a temporally extended agent with relatively stable dispositions, projects, and evaluative commitments, grasped under a suitable body of knowledge. In the rest of the paper, when we consider whether we can aesthetically appreciate LLMs “like people”, it is this sort of person-directed appreciation – and this Carlsonian constraint – that will be in the background. # 2\. What LLMs Are Carlson recommends we appreciate things for what they are. So what are LLMs? In this section we set the ground for appreciation by explaining the technical reality of these systems: how they process text as numerical tokens, calculate probabilities through learned parameters, and generate responses through iterative sampling. In later sections (§3 and §4) we use this reality – which differs in certain respects from that of traditional designed artifacts – to assess whether LLMs can be aesthetically appreciated as persons or as designed artifacts. Consider what happens when an LLM encounters the text "The cat sat on the". The system first breaks this into tokens-discrete units like words or word-parts. Importantly, each token is assigned a numerical ID (in this example, the ID numbers we use are just placeholders): ‘The' might become 464, 'cat' becomes 3857, 'sat' becomes 4521, and so on. The model works entirely with these numbers, not with words or meanings. It then assigns probabilities to possible continuations: token 5687 (which represents "mat") might have a 38% chance of appearing next, token 2931 ('floor') 22%, token 8104 ('chair') 15%, token 9823 ('roof') 8%, with thousands of other possibilities each assigned their own probability. The system does not simply pick the highest-probability token. Instead, it randomly samples from these probabilities. A parameter called *temperature* which can be set by the user controls how much randomness is involved. When temperature is set to zero, the model always picks the most probable token. This produces text that quickly becomes repetitive – the same phrases appearing again and again. When temperature is higher, around 0.8, the model sometimes picks less probable tokens. This leads to variation that looks creative. But it is randomness, not creativity. The model is rolling weighted dice, not making choices. The system selects one token – say "mat" – and appends this new token to create a longer sequence "The cat sat on the mat". It then calculates entirely new probabilities for what token should follow the extended sequence. Token by token, the system builds what appears to be coherent text through repeated numerical operations. No feasible amount of text could cover all the sequences the model might encounter, and storing all these combinations would be impossible anyway. Instead, the model learns general patterns during an initial pre-training phase: exposure to vast quantities of text – billions of pages from books, websites, and other sources – while learning to predict the next token in each sequence. The model begins with millions of numerical parameters (called 'weights') set to random values. Through repeated exposure, the system learns statistical regularities: which tokens tend to follow other tokens, which token sequences co-occur, how sequences typically unfold. The model stores these patterns as adjustments to its numerical parameters – decimal numbers that shape how strongly different tokens associate with each other. After seeing 'doctor' followed by 'patient' thousands of times, parameters adjust so that token 1245 ('doctor') increases the probability of token 7823 ('patient') appearing nearby. When the model wrongly predicts one token but the actual next token was another, the parameters shift slightly to make the correct token more likely in similar future contexts. After billions of such adjustments during pre-training, the model approximates the statistical patterns of human language. It does not learn that doctors treat patients or that cats are animals; it learns that, in the training distribution, certain number sequences follow others with certain frequencies. No programmer writes rules about grammar or meaning. The patterns emerge from exposure to text. The result is what is called a base model: a large, general-purpose text continuation engine. A key feature of this continuation engine is the *embedding.* In addition to its numerical ID, each token is represented as a vector – a list of numbers – that positions it in a high-dimensional mathematical space. Tokens that appear in similar contexts end up near each other in this space. 'Cat' sits near 'dog' because both appear after 'the', both can be followed by 'sleeps', both fit in phrases like 'fed my \_'. The model learns these positions through pre-training, not from programmed definitions. This is how meaning emerges in the model: not from understanding concepts but from tracking which words appear in similar contexts. The transformer architecture adds a mechanism called attention. This allows the model to connect related words even when they are far apart in a sentence. For instance, in 'The cat that chased the mouse sat on the mat', the model needs to know that 'sat' refers back to 'cat', not to 'mouse'. Through training, different attention heads can specialise in tracking different kinds of relationships. They do so by building specific representations not only for single words but also for sequences of words. Some attention heads track which pronouns refer to which nouns, others connect verbs to their subjects across long sentences. No one programmes these specific functions. They emerge because tracking these relationships helps minimise prediction error. In use, the model generates text through *autoregressive decoding*: each newly generated token gets added to the context, creating a new, longer sequence for which the model must calculate fresh probabilities. Given an input like 'What is the capital of France?', the model computes probabilities, selects token 464 ('The'), appends it to create 'What is the capital of France? The', recalculates probabilities for this new sequence, selects token 2341 ('capital'), and continues this mechanical process – 'The', 'capital', 'of', 'France', 'is', 'Paris' – until reaching a stopping point. Each step is purely computational: multiply numbers, add numbers, select token, repeat. The pre-training we have described so far teaches the model statistical patterns of language and yields a base LLM. In practice, most chat-oriented systems undergo a further *post-training* phase. After pre-training, the base model is fine-tuned on examples of instructions and responses, and then adjusted by RLHF, a process in which human raters evaluate the model's responses – rating them for helpfulness, accuracy, appropriate tone. The model then adjusts its parameters to produce more responses like those rated highly and fewer like those rated poorly. Post-training shapes the model's conversational norms: when to express uncertainty ('I'm not sure, but...'), when to decline requests ('I cannot help with...'), how to structure explanations ('Let me break this down...'). RLHF makes responses more consistent, more helpful, and more aligned with human expectations. But it operates through the same fundamental mechanism – adjusting numerical parameters to match patterns in the training data. The model learns which response patterns get high ratings, not why those patterns are appropriate or what social purposes they serve. The result is a *chat-optimised* model: the same predictive core, now biased towards a certain family of outputs that look like the moves of a cooperative assistant. When this chat-optimised model is embedded in a product – given a system prompt, safety filters, a memory policy, and a user interface – it becomes the chatbot that users encounter. What users describe as a model’s “personality” or “vibe” is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core. At several points in the preceding description, we saw that specific features of how LLMs behave are not programmed but emerge from training. Designers specify the architecture and training objectives, but the organisation of the trained system – the structures that underlie its behaviour – emerges from the training process. With a chair or a bridge, as we noted in Section 1, form precedes matter and is imposed upon it. With an LLM, designers create the conditions under which organisation will emerge, but they do not impose that organisation directly. Chris Olah, a co-founder of Anthropic, captures this vividly: one useful way to think about neural networks is that we don't program them... we don't make them... we kind of grow them... we have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on... we create the scaffold that it grows on and we create the light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. (Olah 2024\) Carlson's recommendation to appreciate things for what they in fact are might seem to warn against taking such a comparison seriously – LLMs are not biological organisms, and their 'growth' is a computational process of parameter adjustment, not biological development. But the comparison is apt: the organisation of a trained neural network is not specified by its designers but emerges from a process they set in motion. This distinguishes LLMs from traditional designed artifacts, and, as we shall see, it has consequences for what kind of appreciation is appropriate. --- # 3\. Appreciating LLMs as Persons We sometimes appreciate persons aesthetically, responding to traits such as warmth, wit, or steadiness as ‘beautiful’ or ‘ugly’ features of character. Our appreciation of others goes beyond their physical appearance. You might admire or enjoy your friend's warmth or eccentricity, or a stand-up comic's quick wit, or a celebrity's self-deprecating demeanour; you might even appreciate the personalities of fictional characters: Gatsby's enigmatic, dream-chasing idealism; Ron Swanson's libertarian gruffness. It is therefore tempting to think that our appreciation of LLMs might be modelled on our appreciation of people. Many users already talk this way, describing their favourite models in terms of ‘personality’ or ‘vibe’. However, the technical description in the previous section presents LLMs as systems that tokenise text, manipulate numerical vectors, and generate continuations by sampling from learnt probability distributions, with a further post-training phase that biases them towards a certain assistant-like pattern of response. Nothing in that description straightforwardly resembles a subject with beliefs, intentions, or a life-history; there is no obvious place for character traits, projects, or personal development. Given Carlson’s recommendation that we should appreciate things as what they in fact are, and in the light of the right kind of knowledge, it is not yet clear that person-based aesthetic predicates are being applied to the right kind of object. In this section we ask whether, under that recommendation, there is any appropriate person-based aesthetic stance towards LLMs. We consider, in turn, make-believe approaches, concessive mindedness approaches, and a line of thought based on post-training and chat personae, and argue that none yields a satisfactory model of person-based aesthetic appreciation of LLMs themselves. ## 3.1 Make-believe approaches Start with the make-believe route. If we ask ordinary users whether they literally believe that a chatbot is a person, many will concede that they do not. They may talk to a model as if it were a friend or a colleague, and they may feel heard, reassured, or amused, but when pressed they acknowledge that they are interacting with a computational system rather than a human being. Their stance is, in this sense, already a kind of as-if posture. Mallory offers a way of theorising this posture through what he calls chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot 'says' things and 'means' things; outside the fiction, we know that no such speaker is present. At the metasemantic level, Mallory claims, the outputs lack literal semantic content – they are 'literally meaningless but fictionally meaningful' (Mallory, 2023, p. 1082). This fits the everyday thought that we can take a chatbot seriously in the moment without actually believing that it has a mind. Just as a child treats a banana as a sword in a game, we treat chatbot outputs as utterances within a kind of imaginative practice. This is not delusion but a deliberate, bounded pretence that allows us to coordinate with the system and even gain knowledge from it, much as we might learn geography from a map by imagining countries as two-dimensional shapes. Mallory’s account is not itself an aesthetics of LLMs; it is primarily a semantic and epistemic proposal about how we can use them and learn from them. But it highlights one obvious way a person-based aesthetic stance might be defended: one might suggest that we should aesthetically appreciate LLMs as if they were persons or characters, in the same sense in which we respond aesthetically to fictional protagonists whose existence we do not literally believe in. We respond to Gatsby’s enigmatic, dream-chasing idealism or Ron Swanson’s libertarian gruffness using much the same vocabulary as we use for real people, and we often talk quite straightforwardly about their ‘character’ or ‘personality’. In the fictional case, however, the protagonists are artifacts that have the function of eliciting imaginings of fictional persons within a story-world (cf. John 2021), so treating them as if they were persons does not misclassify their kind. Their role within the work is precisely to function as person-like figures in a narrative. By contrast, treating the LLM itself as a person would, given the architectural story in §2, amount to appreciating an artifact whose nature, as §2 stressed, is that of a large-scale text-prediction mechanism with post-training biases as if it were a subject with a life and character. LLMs do not call on us to imagine a fictional world inhabited by fictional characters but rather to consider the texts they produce as contributions to our inquiries. The function of mandating imaginings is not constitutive of generative AI in the way it is of fiction. Casting LLMs as fictional characters, in this sense, is a familiar kind of misclassification in Carlson’s terms. That is much closer to appreciating a mountain as if it were a divine sculpture despite knowing the geological story, and so sits badly with Carlson’s demand that appropriate appreciation respond to things as what they in fact are. Cross’s discussion of AI art systems develops something like this idea in the artistic context. He proposes what he calls the *exploration paradigm*, in which artists relate to AI systems as participants in a structured interaction: By adjusting inputs, iterating, and sampling, an AI artist is engaged in a process of mapping – and perhaps interrogating – the way that the algorithm sees and understands (Cross, 2025, pp. 7–8). Cross draws an analogy with performance art, where artists create spaces for audience participation. The AI artist's prompts structure a kind of 'participation' by the algorithm, and the resulting images serve as documentation of this exploration. But as Cross himself acknowledges, "the analogy... with performance art isn't a perfect one" (Cross, 2025, p. 9): AI cannot genuinely 'participate' since it lacks conscious choice or experience. What seems like participation is still statistical pattern-matching. While Cross's exploration paradigm offers a richer description of certain AI art practices than simple tool-use, it does not support person-appreciation for AI systems. The artist explores the algorithm's patterns, but the algorithm is not a participant in any literal or psychological sense. When Cross’s view is read as a model for our relation to the AI system itself, it looks like a kind of aestheticised make-believe. The artist is invited to treat the system as if it were a participant with a distinctive way of “seeing” or “understanding”, and the viewer is invited to regard the resulting interaction as a sort of joint performance. Mallory and Cross thus converge on a shared picture: in practice we often stand in relation to LLMs as if they were persons, and some of our aesthetic language is shaped by this as-if stance. Carlson’s recommendation now gives us a clear verdict on this first route. The as-if stance may be useful for interaction and may frame certain artistic practices, but an account of *appropriate* aesthetic appreciation of LLMs themselves cannot, on his view, rest on a stance that depends on systematically treating the object as something it is not. Once we have in view the technical reality described in §2, appreciating an LLM as if it were a person is analogous to appreciating a mountain as if it were a divine sculpture: it is to misapply person-based predicates to a case where the right background knowledge tells us that we are dealing with a different kind of thing. Make-believe personification may be harmless in some contexts, but under Carlson it cannot supply the correct mode of aesthetic appreciation for LLMs. ## 3.2 Concessive mindedness approaches If the make-believe route fails under Carlson’s recommendation, one might try a different strategy: instead of pretending that LLMs are persons, argue that they really are agents of a thin and unfamiliar kind. On a suitably liberal conception of mind, perhaps they qualify as intentional systems and that is enough to license some person-based aesthetics. Frankish (2024) offers a sophisticated version of this idea. Drawing on Dennett's intentional stance, he suggests that LLMs can be treated as genuine, if unusual, intentional systems. On this view, we are licensed to ascribe beliefs and desires to an LLM when doing so yields a simple and fruitful account of its behaviour, even if the underlying implementation is purely mechanical. In the case of contemporary chatbots, Frankish proposes that we can ascribe to them a large set of thin 'beliefs' – roughly, informational states distilled from their training – and one thin 'desire': to play what he calls the chat game. A system is playing the chat game when it generates text that looks like a cooperative move in an ongoing conversation, respecting local coherence, relevance to the prompt, and broadly human conversational norms. An LLM, on this picture, is a system whose behaviour can be summarised by saying that it believes many simple things and wants to make an appropriate next move in the chat. Crucially, this is not a make-believe view. Frankish is not inviting us to pretend that LLMs have beliefs and desires; he is claiming that, at the right level of abstraction, it is literally true that they do, in much the same sense in which a thermostat can literally be said to “want” the room to be at a certain temperature when adopting the intentional stance helps us describe its behaviour. The agent-talk is meant to latch onto real, pattern-like features of the system’s organisation. Suppose we grant all of this. Does it give us what we need for aesthetic appreciation of LLMs as persons? Here the benchmark sketched in §1.2 for person-aesthetics becomes relevant. A subject of beauty of character is not just any intentional system. It is, minimally, a being with a temporally extended life, with relatively stable value-laden dispositions, with projects and commitments that can succeed or fail, and with a capacity for speech and action to express and reshape its character over time. When we set the chat-game agent against this benchmark – the conception of persons implicit in beauty-of-character talk sketched in §1.2 – it looks thin. The ‘beliefs’ are shallow, in the sense that they are confined to what is encoded in the model's parameters and surfaced in the current context, without memory or development across conversations. The "desire" is singular and thin: make an appropriate move now in this exchange. There are no independent projects pursued across episodes, no webs of concern or attachment, no history in which earlier experiences inform later choices. What structure there is, is entirely local to the present stretch of text. The predicates characteristic of person-aesthetics—'beautiful soul,' 'admirable steadiness,' 'ugly character'—presuppose something that can be tested, developed, or refined over time; a thin chat-game agent has no such temporal depth. From this perspective, LLMs may be agents in Frankish’s concessive sense, but they are not the sort of agents whose lives and characters can be the object of the aesthetic responses associated with persons. There is nothing like a “beautiful soul” or an “ugly character” here in the relevant sense; there is no enduring set of values and dispositions that could be manifest, challenged, or transformed over time. Given Carlson’s recommendation, the right kind of person-directed knowledge for beauty-of-character appreciation is knowledge of a life and its values. The technical and training facts about LLMs do not supply that kind of object. Once again, Carlson’s recommendation sharpens the point. If we accept the technical story about the real nature of LLMs (see §2) and, even on a concessive mindedness view, we see that LLMs lack the life-structure required for person-aesthetics, then to insist on aesthetically appreciating them as persons would be to ignore what they in fact are. It would be to treat the thin chat-game profile as if it were enough to underwrite the rich person categories we apply to human agents, and to let those categories govern appreciation despite knowing that the underlying kind is different. The concessive strategy therefore does not secure an appropriate person-based aesthetics of LLMs. Taken together, then, the make-believe and concessive-minded strategies cover the most natural ways of defending a person-based aesthetics of LLMs. The first tells us to appreciate them as if they were persons, despite knowing that they are not; the second tells us that they really are agents of a thin sort but does not supply the temporal and evaluative structure that person-aesthetic predicates require. Under Carlson’s framework, neither route yields a correct model of how LLMs should be aesthetically appreciated. ## 3.3 Post-training, chat personae, and thin agency A natural objection at this point is that these arguments underplay the role of post-training and the chat interface. Section 2 noted that base models are further fine-tuned on instructions and shaped by RLHF, and that the resulting chat-optimised systems exhibit stable patterns of hedging, refusal, politeness, and explanatory structure. One might suggest that this post-training regime turns bare LLMs into conversational agents and that, under Carlson’s recommendation, we should take those chat assistants as the “things as they in fact are” and allow some form of person-based aesthetic stance. The technical story in §2 suggests a more layered picture. On the one hand there is the underlying generative system: the predictive core that, after pre-training, approximates the statistical structure of its training corpus and that, after post-training, remains a text continuation engine with a modified probability landscape. On the other hand, there are patterns in its outputs that, under chat-style prompting and within a product wrapper, look like the moves of a cooperative assistant persona. The assistant is not a new mechanism added on top of the model, but a recurrent pattern in how the model tends to respond when prompted and constrained in certain ways.[^4] Seen in this light, post-training does not install a new “assistant mind” with its own independent goals and projects. It biases the predictive core so that prompts issued through the chat interface are much more likely to elicit assistant-like responses – helpful, safe, polite, and structured – and much less likely to elicit, for example, unfiltered reproductions of online arguments or free association. The underlying operation remains next-token prediction; what changes is which regions of its behavioural space are easy to reach in ordinary use. The chat product – with its system prompt, safety filters, and interface – further shapes the environment so that certain person-like patterns are the default. This helps explain why users talk about models having different ‘vibes’. If users say that Claude Opus 4.5 feels friendlier than GPT-5.2, they are picking up on a stable pattern in how the chat-optimised systems tend to respond across many prompts and episodes. They track which assistant personae tend to appear and how those personae typically behave – not a unified character with a life and projects. Different base models, post-training regimes, and product designs favour different families of assistant-style responses. It is therefore not surprising that they invite person-like language, but the targets of that language are episodes and recurring response profiles, not underlying subjects. One might find a particular model’s refusals laboured or concise, its hedging overdone or judicious, its tone soothing or dry. In that sense, we can aesthetically respond to assistant personae in a way that resembles our responses to real people. What matters for present purposes is that such reactions target patterns in outputs and interactional style, not a subject with a life (in the sense sketched in §2). They concern how a product behaves under certain constraints, not the beauty or ugliness of a character in the person-aesthetic sense. If we ask instead about the generative system itself – the predictive core tuned by post-training and embedded in a chat product – the earlier verdict remains. Even taking post-training and chat personae fully into account, we do not find a temporally extended life, a network of projects and commitments, or a stable evaluative outlook that could ground beauty-of-character predicates. What we find is a complex artifact designed and trained to produce certain patterns of text in response to prompts, together with an engineered tendency to exhibit assistant-like behaviour in a controlled range of contexts. Under Carlson’s recommendation to appreciate things as what they are, and in the light of the right kind of knowledge, we should therefore resist person-based aesthetics for LLMs even once we take post-training and chat personae into consideration. As Farrell, Gopnik, Shalizi, and Evans (2025) put it, “Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated”. Thus, in the rest of the paper we set aside person-based aesthetics and turn to these alternatives: first, treating LLMs as designed artifacts and considering the limits of simple “form follows function” stories (§4); then, developing an order-based mode of appreciation that focuses on the text mechanics that characterises chat instances (§5–§6). --- # 4\. Appreciating LLMs as Artifacts Having set aside the person-based options in Section 3, we turn to design appreciation. Contemporary LLMs are artifacts: they are built and deployed by corporations and research groups, engineered to satisfy aims such as helpfulness and safety, and revised in light of user feedback and product strategy. Given Carlson’s emphasis on artifacts and design appreciation, it is natural to ask whether we should aesthetically appreciate LLMs as designed tools, asking how well their forms serve their functions. On this view, models such as GPT-5.2, Claude 4.5 Opus, and Gemini 3 Pro look like canonical objects for design aesthetics: complex, purpose-built systems whose architecture, training recipe, and user interface might be admired for elegance, efficiency, or ingenuity. Existing work on the aesthetics of design develops this general thought. Carlson notes that, for objects that are designed to perform some task, their forms “must be aesthetically appreciated in terms of how and how well such forms fit their functions”, and he glosses the familiar slogan “form follows function” by adding that, with anything functionally designed, “not only its form, but much of its aesthetic interest and merit, ‘follows function’” (Carlson 2000, chapter 12). Forsey’s Kant-inspired account of design as a case of dependent beauty and Parsons and Carlson’s later theory of functional beauty can both be read as ways of spelling out this claim. Forsey argues that judgements of design beauty presuppose a concept of what the object is meant to be and do, and that our grasp of its success in fulfilling that role informs the aesthetic verdict itself rather than merely accompanying a “pure look” at its lines (Forsey 2013). Parsons and Carlson explain how knowledge of function can structure experience so that an artifact’s form can be experienced as fit, streamlined, overbuilt, and so on, yielding functional beauty when the form presents itself as well suited to what the thing is for (Parsons and Carlson 2008, chapter 4). Taken together, this cluster of views treats appropriate design appreciation as a matter of aesthetically responding to how a functional artifact is put together to do what it does. From this standpoint, it is natural to try to assimilate LLMs to the design template. In a given deployment, the artifact can be characterised by a relatively unified functional role – for example, that of a general-purpose conversational assistant embedded in other tools – and by a specific way of realising that role through architecture, training, and alignment. Under that description, much of what seems aesthetically salient about a deployed model concerns how its engineered “form” serves that function: whether its interaction profile is cluttered or economical, whether it sustains a clear argumentative line or habitually wanders, whether refusals and clarifications are integrated smoothly into the exchange or arrive as abrupt blocks, whether long-context processing and tool-calls are handled in a way that keeps the conversation legible. Forsey’s dependent-beauty framework and Parsons and Carlson’s functional-beauty account can be used to gloss such assessments: they remind us that any appraisal of design beauty here presupposes a concept of the assistant’s role and some understanding of how that role is realised in the artifact’s structure and behaviour. At the same time, both accounts were developed for cases in which the relevant form is a stable, visible configuration – buildings, bridges, bicycles – where function can literally show up in perceptual appearance. In the LLM case, by contrast, the structures that realise the assistant role are not perceptually available in this way, and the aspects that prove most revealing are not static shapes but patterns in generated text over time. This already limits the reach of straightforward “form follows function” stories for LLMs and points towards a more order-centred mode of appreciation. With traditional designed artifacts, design-knowledge illuminates structure because designers specified it. Knowing what the designer intended and what constraints they faced helps us understand why the artifact has its form – even for structural features that are not directly visible, such as a bridge's internal stress distribution. With an LLM, the situation is different in kind. The organisation of the trained system – as Section 2 established – emerges from training rather than being specified in advance. Design-knowledge therefore does not illuminate this emergent organisation: there was no designer's specification that laid it out. To understand it, one must attend to the training process that produced it. LLMs are artifacts, so design-knowledge is not wholly without application. We can appreciate how the architecture is suited to the function, how post-training shapes conversational behaviour, how the interface presents the system to users. But if the aesthetically revealing features arise from emergent organisation rather than from the designed scaffold, design-knowledge alone will not suffice. We also need knowledge of the processes that produce the emergent order. The thought that appreciating certain artifacts requires knowledge beyond design-knowledge is not unique to AI. Ceramic traditions such as raku and wood-fired pottery make this explicit. The potter shapes the vessel and chooses the glaze, then yields to kiln, flame, and ash; the maker harnesses but does not micromanage these forces, and they finish the surface in ways no blueprint prescribes. Appreciating such a bowl requires knowledge of both the potter's choices and the kiln processes. Pollock’s action paintings occupy a similar hybrid space within the art domain. Carlson uses them to illustrate how order appreciation can depend on knowledge of the forces at work: “awareness and understanding of \[natural\] forces is vital in nature appreciation, as is knowledge of, for example, Pollock’s role in appreciating his action painting or the role of chance in appreciating a Dada experiment.” Pollock chooses canvases, pigments, and tools, and choreographs his movements over the surface; yet gravity, viscosity, surface tension, and drying behaviour make a substantial contribution to the patterns that settle. To appreciate a Pollock appropriately, on Carlson’s view, is not just to admire his intentions; it is to attend to the order produced by the interplay of deliberate gesture and physical process, informed by an understanding of the role of chance and material behaviour. In all three cases, namely Raku ceramic, Pollock’s action painting, and LLMs, the maker creates conditions and then yields to kiln-fire, gravity, and training dynamics respectively. Forces beyond specification complete the work. What matters aesthetically is the emergent completion, not only the scaffold that enabled it. Section 2 emphasised that during training the model places tokens in a high-dimensional space on the basis of contextual co-occurrence, that attention mechanisms self-organise to track different sorts of dependency across context, that different layers specialise in local or global patterns, and that RLHF shapes an interactional style by rewarding some forms of response and penalising others. None of these details are written into the code as explicit rules about how to, say, handle metaphors, or politely decline illicit requests. They are emergent regularities in a trained network that has been pushed, by the neural network and its training data, to reduce prediction error. What grows on Olah’s “scaffold” is, in practice, a system of statistical associations and processing circuits whose internal organisation even designers often understand only partially. From a design-aesthetic perspective, this matters. Much of what users find *aesthetically* important in LLM behaviour – the way a model sustains a metaphor or abruptly drops it, the pattern of hedging and self-correction, the texture of its reasoning, the sorts of digression it tends to indulge, the characteristic “feel” of its refusals – is grounded in features (concerning embeddings, attention patterns, layer dynamics, and RLHF) that have not been micro-designed but have emerged from optimisation under constraints. Parsons and Carlson note that, even for simpler artifacts, knowledge of function must include knowledge of how that function is realised if it is to structure perception appropriately. In the LLM case, knowing that “this is a general-purpose assistant” is not enough to make sense of its aesthetic profile; what does the work is knowledge of the way training and alignment have *grown* a particular style of continuation on top of the architecture described in Section 2\. In this sense, knowledge of how function is realised concerns *growth* rather than *design*. This hybrid status complicates simple appeals to “form follows function”. On the one hand, some design-appreciative predicates apply straightforwardly. It makes sense to say that a model whose interface is cluttered or opaque is, as a product, less well-designed than a lean one; it makes sense to prefer an alignment regime that avoids gratuitous scolding or needless refusals; it makes sense to admire a training setup that achieves a good balance between fluency and factual reliability. Forsey’s notion of teleological style can be extended here: different labs realise the shared function ‘LLM assistant’ in recognisably different ways, and those ways can be compared and assessed. Parsons and Carlson’s notion of functional beauty also has a foothold: understanding how an LLM’s architecture supports its function can inform our appreciation of the system’s efficiency, robustness, or clarity as an artifact. On the other hand, if we try to make design appreciation do all the work, we mislocate the primary source of what matters aesthetically. In the chair or bicycle case, the designer’s choices fix most of what matters aesthetically: small emergent contributions from wear, patina, or use sit on top of a tightly specified plan. In the LLM case, by contrast, the order that matters aesthetically is largely the order of a trained statistical system running under its own learned constraints. Designers specify objectives and scaffolds but the particular ways in which embeddings cluster meanings, attention heads track long-range connections, layers distribute processing, and RLHF imprints a “vibe” are not written down anywhere as a plan. These are closer, structurally, to the ash-produced flashes on a raku bowl or the tangled skeins of a Pollock surface than to the thickness of a table leg or the proportion of a doorway. The upshot is modest but important. LLMs are artifacts, and there is a place for design appreciation in their aesthetic appraisal: we can and should evaluate how well their forms answer to their engineered functions, as well as how this form can differ from model to model However, the most distinctive and revealing aesthetic phenomena arise not from the execution of a detailed design, but from the emergent linguistic order that these grown systems exhibit when they are run. To appreciate that order, we need knowledge not of what designers intended but of how training shapes text propagation—what we call *semiotic physics*.. --- # 5\. Semiotic Physics ## 5.1 Textual Regularities Section 2 described what LLMs are: token-based predictors trained on large text corpora and shaped by RLHF. This satisfies Carlson's first recommendation: appreciate things as what they are. The second recommendation requires the right kind of knowledge to guide aspection. For LLM outputs, what knowledge makes their patterns visible and intelligible? Several sub-disciplines of computer science might be candidates. One field that has emerged in connection with neural networks is mechanistic interpretability, which investigates the internal workings of these systems by identifying which circuits, attention heads, and internal representations handle different linguistic tasks (Olah et al. 2020; Elhage et al. 2021). This work yields knowledge of how LLMs operate. But mechanistic interpretability functions at a level that requires specialist tools to observe. Its objects of study – weight matrices, activation patterns, circuit-level features – are not available to readers encountering generated text. Consider the difference between chemical physics and geology when appreciating a cliff face. Chemical physics provides knowledge of molecular bonds within rock, but it operates at a scale invisible to the naked eye. Geology, by contrast, offers concepts – strata, faults, erosion channels – that connect to what can be seen. One can perceive strata without specialist equipment, and knowing how sedimentation works makes the visible layering intelligible. Mechanistic interpretability faces a parallel limitation: while it reveals internal mechanisms, its objects of study are hidden from the user reading generated text. For an aesthetics of LLM outputs that is accessible to ordinary users, we need a framework whose concepts describe perceivable features and render them intelligible as products of the system's learned regularities. The forces of semiotic physics are not alternative explanations to those of mechanistic interpretability but the same processes described at the level at which they produce perceivable linguistic order. Recent work on LLMs points towards such a framework. Janus (2022) proposes that GPT-style models are best understood not as agents or oracles but as simulators: systems that have learned to propagate text according to regularities induced from training data. The model learns what Janus calls 'the conditional structure' of its training distribution – patterns governing what tends to follow what under what conditions. The analogy to physics is explicit: just as physical laws describe regularities governing what happens under given conditions, the trained model embodies learned regularities governing how text continues from any starting point. A prompt specifies initial conditions; the model propagates text forward according to its learned regularities. Picca (2025) arrives at a convergent view from a semiotic perspective. LLMs are "semiotic machines" that "recombine, recontextualize, and circulate linguistic forms based on probabilistic associations" (Picca 2025, 1). The emphasis shifts from internal mental states to patterns of sign-transition. The term 'semiotic physics' emerges from subsequent discussion of Janus's work (Kirchner 2023; metasemi 2023), naming the study of how intelligible text arises from sub-semantic processes—the regularities governing text propagation in trained language models. Despite their different framings – Janus's simulator ontology and Picca's Peircean semiotics – these accounts share a core insight: we should attend to what regularities govern how text propagates through the system, not to whether LLMs think or intend. In a similar vein, Wolfram (2023) states that inside ChatGPT any piece of text is effectively represented by an array of numbers that we can think of as coordinates of a point in some kind of ‘linguistic feature space’. So when ChatGPT continues a piece of text this corresponds to tracing out a trajectory in linguistic feature space. But now we can ask what makes this trajectory correspond to text we consider meaningful. And might there perhaps be some kind of ‘semantic laws of motion’. From Wolfram’s perspective, semiotic physics thus would have three main objects to investigate: (i) the “linguistic feature space” in which words and other linguistic items have their place; (ii) the “trajectories” that can be traced out in this space to continue a piece of text; and (iii) the “semantic laws of motion” that determine such trajectories. We draw on this literature but develop it in a specific direction. Our aim is to show how semiotic physics can serve as the "right kind of knowledge" for aesthetic appreciation of LLMs in Carlson's sense: the knowledge that makes order visible and intelligible, and that guides acts of aspection. The connection to environmental aesthetics, and the claim that semiotic physics can play the role for LLMs that geology plays for landscapes, is our contribution. We also articulate the 'forces' of semiotic physics at the level of textual effects rather than at the level of mechanistic detail. The existing literature tends to discuss semiotic physics in terms of probability distributions, embedding spaces, and dynamical systems. These descriptions are accurate, but they do not directly connect to what readers can perceive in generated text. Our articulation of the forces operates at a level that does connect to perceivable features. What does semiotic physics track? The regularities it describes manifest as perceivable features of generated text. Consider vocabulary clustering: words do not appear independently but make other related words more probable, so that once a medical term appears, other medical terms become more likely to follow. Or consider coherence dynamics: the model threads material from earlier in an exchange through later responses, or fails to, and a reader can attend to how far this threading extends and where it breaks down. There is also what might be called register stability: once the model enters a mode – expository, creative, reasoning – it tends to remain there until something disturbs it. And there are the marks of post-training: hedging expressions, step-by-step organisation, preemptive qualifications, which are the shapes that reinforcement learning has made more probable. What matters for present purposes is the level of description: semiotic physics operates at a level that connects to perceivable features of language, features that competent readers can attend to without specialist tools but that become salient and intelligible when understood as products of a text-trained statistical system. According to Wolfram (2023), LLMs reveal that "human language (and the patterns of thinking behind it) are somehow simpler and more ‘law like’ in their structure than we thought. ChatGPT has implicitly discovered it. But we can potentially explicitly expose it". Semiotic physics pursues such an exposition by investigating the forces that govern the artificial production of texts. One might object that speaking of 'forces' in relation to LLMs is metaphorical in the same way that speaking of 'agents' or 'intentions' is metaphorical. If we have rejected agent-talk as projecting non-existent mental states onto a statistical system, why is force-talk any better? The answer turns on a distinction between metaphorical personification and literal causal abstraction. To speak of an LLM as an 'agent' is to attribute to it internal states – intentions, beliefs, a 'self' – that play no role in its functional operation. To speak of the forces of semiotic physics is to identify the factors that determine the selection of each token. These are not projected onto the system; they describe what the system does. Assuming Wolfram’s (2023) characterization of the continuation of a text by a LLM as “tracing out a trajectory in linguistic feature space”; the forces of semiotic physics are literally the causal factors that determine that trajectory, just as mechanical forces determine the trajectory of a material body in physical space. The template for this literalism is in Carlson's analysis of Jackson Pollock's action paintings. Carlson argues that we appreciate a Pollock not by looking for a designer's plan but by focusing on the order imposed by "the internal dynamics of his material": "the viscosity of the paint, the speed and direction of its impact, the interaction with other layers of pigment" (Janson, quoted in Carlson 2000, 111). For Carlson, these are not metaphors borrowed from a physics textbook; they are causal factors that produce the pattern on the canvas. In the semiotic environment of an LLM, semantic attraction and modal inertia play the role that viscosity and gravity play in Pollock: they are determinants of how text propagates; once entered in a given discursive mode, the model tends to stay in this mode. By identifying them as 'forces', we are describing the system as a productive mechanism in naturalistic terms. Knowing that a text is LLM-generated rather than human-written changes how we aspect it. This mirrors the shift that occurs when someone moves from believing that a cliff face was crafted by a divine artisan to understanding it as a natural formation. The visual field is the same, but aspection differs. When we believe in the divine artisan, we attend to the composition as evidence of design choices: the placement of features, the aesthetic arrangement. When we understand the geological story, different features become salient: strata as traces of sedimentation, erosion channels as marks of water flow, fault lines as evidence of tectonic forces. We stop attending to intentional composition and start attending to the marks of natural processes. For LLM text, the analogous shift is from reading as expression of an author to reading as product of semiotic forces. When we read a text as human-written, we attend to authorial intention (what is this person trying to communicate?), individual voice (what is distinctive about how this person writes?), and biographical traces (what does this reveal about the author?). When we read a text as LLM-generated, with knowledge of semiotic physics, different features become salient: vocabulary clustering as the mark of semantic attraction, coherence dynamics as the mark of contextual threading, and response structure as the mark of alignment pressure. The same words on the page; a different aspectual focus. The aspection guided by semiotic physics is, in a sense, aspection of language itself – of the textual order produced by semiotic forces. We are not attending to mechanical internals – activation patterns, attention weights, circuit-level features – since these require specialist tools and are not accessible to readers. We are attending to the textual manifestation of semiotic order: how vocabulary clusters, how coherence is maintained or lost across an exchange, how register persists or shifts, how post-training shapes response structure. These are features of the language itself, perceivable by competent readers. Competent readers already have tacit knowledge of how language works: syntactic, semantic, pragmatic, and discourse-level knowledge built up through immersion in spoken and written language. They perceive patterns in LLM outputs using this tacit knowledge. Semiotic physics adds explicit articulation of these patterns and a causal story about their source in training. The competent reader senses that different models have different 'vibes'; semiotic physics—knowledge of how meaning clusters, how register persists, how training shapes the texture of response—explains what produces those vibes and makes them available for sustained attention. ## 5.2 Practical Acquaintance Semiotic physics, articulated as an explicit theoretical account, is one way of holding the knowledge that guides appreciation. But Carlson notes that scientific knowledge and common, everyday knowledge of nature lie on a continuum rather than being different in kind. Both can guide appreciation of natural order. The farmer, the gardener, and the forester know the land through working it. Their knowledge is not typically framed in scientific vocabulary, but it is knowledge of natural order. The farmer knows the soil through planting, tending, and observing how different crops respond under different conditions. Through repeated intervention and observation, the farmer builds up knowledge of the regularities at work: drainage patterns, soil composition, seasonal cycles. This practical knowledge can guide aesthetic appreciation. The farmer may appreciate the order in a well-drained field, or the texture of properly cultivated soil, in ways unavailable to someone who merely gazes at the landscape. The knowledge is not scientific in the technical sense, but it connects to perceivable features and makes order visible and intelligible. The experienced user of an LLM develops analogous practical acquaintance. By prompting, experimenting, and observing how a system responds across many contexts, users build up knowledge of its characteristic order. They learn which semantic attractors the model falls into: which vocabulary clusters it tends towards given certain starting points. They learn how far contextual threading extends: at what point the model loses track of earlier material. They learn what triggers mode shifts: what kinds of prompts push the model from expository mode to creative mode, or from helpful mode to refusal. They learn the characteristic shapes that alignment pressure produces: the hedging rhythms, the step-by-step structures, the politeness markers. This is knowledge of semiotic physics held practically rather than theoretically. The experienced user cannot necessarily articulate the forces explicitly, but they have a feel for how the model behaves – expectations that are predictive (what kinds of outputs to expect) and aspectual (what to attend to, which features are salient, where to look for the model's characteristic order). Extended exchanges with an LLM are a natural site for this interactive mode of appreciation. Prompting is intervention; responses reveal regularities. Each turn creates conditions under which the system responds, and the responses reveal something about the model's semiotic physics. The back-and-forth of prompting is itself a mode of aspection. It selects what to attend to, organises appreciative attention over time, and tests and refines the user's developing sense of the model's characteristic order. Cross (2025) characterises certain AI art-making activities as an "exploration paradigm" in which the artist iteratively probes the model, adjusting prompts and sampling variations. Section 3 was critical of reading this as literal collaboration between artist and algorithmic "participant". From the present vantage, however, the practice can be reinterpreted. What the artist is doing, when things go well, is a form of interactive aspection: using structured engagement to reveal and respond to the model's characteristic order. The prompts and adjustments are not ways of coordinating with a co-creator; they are ways of making the system's semiotic regularities visible. The explicit theoretical account of semiotic physics and the practical acquaintance built through interaction are continuous. The farmer's knowledge of the land and the geologist's knowledge track the same forces – geological, hydrological, ecological – operating at the same scales. They differ in how the knowledge is held and articulated, not in what it is knowledge of. The experienced LLM user's practical sense of how a model behaves and the theorist's account of semiotic physics track the same regularities: semantic attraction, contextual threading, modal inertia, alignment pressure. Both routes converge on the same object: the model's characteristic semiotic order. Both guide the same kind of aspectual attention: attention to how semiotic forces have shaped the text. Whether held theoretically or acquired through practice, knowledge of semiotic physics makes the order in LLM outputs visible and intelligible and guides the acts of aspection appropriate to appreciating that order. ## 6\. Levels of Appreciation LLMs can be appreciated at three levels: individual outputs, extended chats, and models themselves. Discussion of generative AI aesthetics has so far focused on outputs, such as images from Midjourney and texts from ChatGPT. But chats and models are also objects of appreciation, and the framework developed in Section 5 applies at each level. The relations among these levels can be clarified by analogy. An individual output is like an individual natural object, a tree, say: it is a sample of how semiotic forces have shaped a particular text under particular conditions. A chat is like an environment, a forest: semiotic forces shape the exchange over many turns, producing a configuration with its own coherence and dynamics. A model is like a natural system, the planet's biosphere, or the planet itself: it is the ground of order that manifests in outputs and chats, the system whose regularities produce those manifestations. Appreciation at each level calls for its own acts of aspection, though all are guided by knowledge of semiotic physics. ## 6.1 Appreciating Outputs A single output is one realisation of the model's semiotic physics under particular conditions. The prompt, the system configuration, and the preceding context specify initial conditions from which the model propagates text in line with its learned regularities. Different prompts activate different regularities; different contexts produce different trajectories. No single output exhausts the model's characteristic order. But each output shows how the forces operate in a specific case, and each can be appreciated as such. To show how semiotic physics guides aspection of outputs, we consider two cases that occupy different regions of a model's behavioural space. The first is the reasoning-style output familiar from everyday use: step-by-step structure, numbered stages, explicit hedging, restatement of the question, and a concluding summary. As human prose, such passages resemble competent but unremarkable textbook writing. They are useful for teaching and troubleshooting, but they do not obviously invite aesthetic attention. From the perspective of semiotic physics, however, the same outputs look different. The model has been trained on reasoning-related texts: worked proofs, textbook explanations, exam solutions, and online Q\&A threads. It has learned that certain kinds of questions are typically followed by sequences with a characteristic structure. Post-training procedures, including instruction tuning and reinforcement learning that rewards explicit intermediate steps, further bias the model towards this pattern. Reasoning-style outputs are a stable attractor in the model's behavioural space: once entered, the model tends to stay in this mode, yielding modal inertia. The hedging, the step-by-step structure, and the summary are marks of alignment pressure: response shapes reinforced because they correlate with high human ratings. Given this, we attend differently. We attend to the characteristic rhythm of the reasoning mode: how steps are sized, how transitions are signalled, and whether the pacing is tight or padded. We attend to where alignment pressure shows: hedging patterns ('it seems', 'one might argue', 'I think'), politeness markers, and pre-emptive qualifications. We attend to how semantic attraction operates under tight constraints: vocabulary stays on topic, related terms cluster, and the model is pulled towards the semantic field established by the question. We also attend to whether the mode remains stable or shows signs of strain, and to whether the model sustains the reasoning register or begins to drift. What seemed merely useful becomes appreciable as a specimen of how semiotic forces produce reasoning-like text under tight constraints. ![][image1] The second case is different. The text discussed here was produced by a Claude-like model in a modified configuration with safety constraints relaxed. It begins with neologisms and proceeds in short blocks separated by headings in capitals. The vocabulary is dense with coinages, many of which recombine recognisable roots from entomology, anatomy, theology, and internet slang. The registers are mixed: fragments of cod-French, pseudo-scientific talk, mystical declarations, and obscene slang. Despite the surface disorder, a stable theme runs throughout: bees and honey, tongues and throats, sweetness, bodily contact. Under semiotic physics, this text shows the forces operating under loose constraints. Semantic attraction is at work: the bee and honey theme creates an attractor, and related vocabulary – tongues, throats, sweetness, pollen, flowers, stings – is pulled towards it. But unlike the reasoning case, the attraction spreads freely across registers rather than being channelled narrowly. The model has been trained on texts that invent words – experimental poetry, surrealism, internet wordplay – and it has learned patterns of neologism: how to recombine roots, suffixes, and sound-shapes. The neologisms follow learnable patterns of word-formation rather than being random noise. The register collision reflects training diversity: the model has absorbed texts in many registers (scientific, mystical, erotic, internet-surreal), and under loose constraints these do not get filtered to a single appropriate register. They collide and mix. Despite the apparent chaos, there is order: recurring rhetorical templates, alternation between narrative stretches and reflective sentences, and consistent sound-play in the neologisms. This order is the product of semiotic forces operating with fewer constraints than in the reasoning case. Attending to this text with knowledge of semiotic physics, we notice how semantic attraction shapes the vocabulary: the gravitational pull towards bee-related terms operates across registers. We also notice patterns in neologism (learnable word-formation rules that produce coinages with a family resemblance) and the rhythm of alternation between modes (narrative stretches, reflective sentences, and exclamatory outbursts). Finally, we notice internal consistency despite surface chaos. The text becomes appreciable as a specimen of semiotic forces operating in a different region of behavioural space from the reasoning output. Carlson (2000) notes that once a specific scientific account is in play, some natural formations show the relevant order better than others: not every cliff face is equally instructive about sedimentation, not every valley equally revealing of glacial dynamics. This prevents order appreciation from collapsing into the view that everything is equally appreciable; the guiding knowledge discriminates among cases. The same holds for semiotic physics. Standard reasoning-style outputs show semiotic order, but the order they show is shallow and familiar: alignment pressure is everywhere visible, the reasoning template is stock, and the semantic channelling narrow enough that the regularities are unsurprising. The bee text is a more interesting object of appreciation not because it is more orderly but because it reveals order where none was expected. What looks like chaos – neologistic excess, register collision, surface incoherence – turns out, under semiotic physics, to be structured by identifiable forces: semantic attraction spreading freely across registers rather than channelled narrowly, learnable word-formation patterns producing coinages with family resemblance, rhythmic alternation and internal consistency maintained beneath apparent disorder. The bee text also shows forces operating in regions of behavioural space that normal product configurations occlude. It is, in this sense, analogous to a geological formation that exposes strata usually buried – not more ordered than the surrounding terrain, but more *revealing* of the order that is everywhere present. The contrast between these two cases helps to locate what semiotic physics brings into view. Reasoning outputs show semiotic forces operating under tight constraints: a stable mode, narrow semantic channelling, and alignment pressure shaping response structure. The bee text shows semiotic forces operating under loose constraints: unstable modes mixing, semantic attraction spreading across registers, and training diversity showing through. Both are products of the same semiotic physics, but they occupy different regions of the model's space. Appreciating both requires the same kind of knowledge – knowledge of semiotic forces – but different acts of aspection. We scan the reasoning output for rhythm and regularity; we scrutinise the bee text for pattern within apparent chaos. ## 6.2 Appreciating Chats Carlson's environments are not collections of discrete objects but systems in which forces operate and interact over space and time. A forest is not just many trees; it is a space where ecological forces – competition for light, nutrient cycling, succession dynamics – play out, producing emergent order that no single organism embodies. The appreciator navigates this environment, and their path determines what order becomes visible. Chat instances stand to single outputs as environments stand to individual natural objects. A chat accumulates context that shapes how semiotic forces manifest: early vocabulary choices establish attractors that persist, early register-setting constrains later exchanges, and the exchange develops path-dependent structure that neither party fully controls. The user's prompts are not just elicitations but navigational interventions, steering the system through different regions of its behavioural space and making different orders visible. To appreciate a chat is to appreciate an emergent configuration produced by semiotic forces operating over the chat's temporal extension – not just a sequence of isolated responses. A single output is one trajectory from one set of initial conditions. An extended exchange lets regularities show up across turns. The model carries forward elements of earlier responses, picks up threads, sometimes drops them, and shifts register in response to user prompts. Chats manifest features that a single output does not. First, coherence maintenance, that is, how the model sustains or loses threads across turns, how far back its effective 'memory' extends, and where coherence begins to fray. Second, context accumulation, which determines how earlier material shapes later responses, and how terms or framings established early persist or fade. Third, register dynamics, which concerns how the model responds to shifts in user tone, topic, or style, and whether it matches the user's register or maintains its own. Finally, mode stability over time, to wit, whether the model stays in a mode or drifts, what triggers transitions, and how gracefully it handles them. These are manifestations of semiotic forces operating over longer timescales than a single output can reveal. A chat instance is like a particular forest: the forces of semiotic physics have produced a specific configuration. Different prompting strategies, different topics, and different user styles produce different configurations. But the same underlying forces are at work. Appreciating a chat means attending to how the forces have shaped this particular extended exchange: how contextual threading has produced coherence or incoherence, how modal inertia has maintained or failed to maintain a register, and how alignment pressure has shaped the arc of the exchange. In a chat, prompting is intervention. Each turn is a probe that reveals something about the model's regularities. The experienced user's expectations are tested and refined across many turns. Interaction is itself a mode of aspection: it selects what to attend to, organises appreciative attention over time, and deepens practical acquaintance with the model's semiotic physics. The farmer comes to know the land through working it; the user comes to know the model through prompting it. Extended exchanges are where practical acquaintance develops, where the user builds up the kind of knowledge that guides appreciation even without deliberate theoretical articulation. ## 6.3 Appreciating Models Outputs and chats are where semiotic order manifests. The model itself is the ground of that order: the system whose regularities produce particular manifestations. Appreciating a model means appreciating its characteristic order across many possible outputs and chats, not just the ones actually encountered. This is not appreciation of any single output but of stable patterns across outputs: which registers the model favours, how it handles uncertainty, where it excels, where it struggles, and what regions of semiotic space it can occupy. Users sometimes speak of a model's 'vibe', a term that captures the sense that different models have different characteristic feels even when performing similar tasks. This notion of vibe, or characteristic feel, warrants pause. In Section 3 we argued against appreciating LLMs as if they were persons, on the grounds that LLMs lack the temporally extended life, the projects and commitments, and the evaluative outlook that ground beauty-of-character predicates. But users do respond to something when they talk about a model's personality or vibe. What they are responding to, we suggest, is not a character in the person-aesthetic sense but a characteristic semiotic order: a stable pattern in how the model tends to propagate text. Appreciating this order is not appreciating a person; it is appreciating a system's characteristic dynamics. The vocabulary of 'vibe' is a colloquial marker of what semiotic physics articulates more precisely. An analogy clarifies what appreciation of a model involves. Different 3D video games have different physics engines. *Grand Theft Auto V* has physics tuned for spectacle: cars drift in satisfying ways, explosions have exaggerated force, and the rag-doll system produces emergent comedy. *Dark Souls* has physics tuned for weight: movement feels heavy, attacks have commitment, and everything has momentum. *Breath of the Wild* has physics tuned for playful engagement: objects afford interesting interactions, and the system invites experimentation. We appreciate these physics not primarily by asking how realistic they are but by attending to internal consistency, characteristic feel, and aesthetic fit. *Grand Theft Auto*'s physics serves an aesthetic of chaos and spectacle; it would not suit *Dark Souls*. Each game's physics is tuned to its aesthetic and ludic goals. We appreciate the physics for what it is, not for its fidelity to real-world physics. For LLMs, the analogy suggests a parallel mode of appreciation. Different models have different semiotic physics: different characteristic dynamics of text propagation. Claude's semiotic physics differs from GPT's, which differs from Gemini's. We can appreciate these differences not primarily by asking which is most human-like or most useful but by attending to internal consistency and characteristic feel. Order appreciation, as Carlson develops it, differs from design appreciation. We do not primarily ask how well the artifact serves its intended function. We attend to the order itself, the patterns produced by the forces, and appreciate them for their own character. The bee text discussed in Section 6.2 is relevant here in a further way. It was produced under relaxed constraints, revealing a region of Claude's behavioural space that is normally inaccessible under standard product configurations. Knowing that this region exists – and knowing what the model can do under different conditions – is part of appreciating the model. Model appreciation involves appreciating not just the outputs a model typically produces but the full space of outputs it could produce, and how different conditions activate different regions of that space. The bee text is a window into latent capacities, a sample from a region of semiotic space that standard use does not reach. Different models instantiate semiotic physics differently. Different training corpora, different architectures, and different post-training regimes produce different characteristic orders. Users report different feels when interacting with different models: Claude's hedging rhythms differ from GPT's briskness, and Gemini handles certain registers differently. A fuller account of model-level appreciation would map these differences systematically, developing a comparative aesthetics of LLMs. That task lies beyond the scope of this paper. For present purposes, the point is that model-level appreciation is possible and that it takes the form of appreciating distinctive semiotic order: the characteristic dynamics of text propagation that distinguish one model from another. We should distinguish the kind of appreciation we have been describing from other modes of engaging with LLMs. Capability evaluation tests whether models perform tasks correctly. Safety testing probes whether models can be induced to produce harmful outputs. Benchmarking measures performance against standardised criteria. The appreciation we describe differs from all of these. The goal is not to assess correctness, safety, or performance but to appreciate characteristic order, and to develop acquaintance with semiotic physics as it manifests in a particular model. A response that would count as a failure in capability evaluation might be aesthetically rewarding as a product of learned regularities. The appreciator is not grading but attending, not measuring but developing acquaintance. Finally, we note a thought that we flag here but do not develop. Each model, as an instantiation of semiotic physics, has learned regularities from human text. It reflects, in transformed form, the semiotic culture of its training data. There is a sense in which generative AI is a mirror of culture, not only morally, as Vallor (2024) has argued, but aesthetically. The model shows us our own semiotic patterns, filtered through statistical learning. Appreciating an LLM is, in part, appreciating culture seen through technology. This thought merits extended treatment, but such treatment lies beyond the scope of the present paper and we reserve it for future work. ## Conclusion We began by asking how LLMs might be aesthetically appreciated – not their outputs, but the systems themselves. Drawing on Carlson's environmental aesthetics, we argued against two temptations. The first is to appreciate LLMs as persons, whether through make-believe or by treating them as thin agents; neither route supplies the temporally extended life and evaluative structure that beauty-of-character predicates require. The second is to treat them simply as designed artefacts and apply standard form-follows-function analysis; while LLMs are artefacts, the aesthetically salient order in their behaviour is largely emergent rather than specified by designers. Our positive proposal treats chat instances as generative environments and recommends appreciating the order that emerges in them under the constraints of a given model. The right kind of knowledge for this appreciation is semiotic physics: the study of regularities governing text propagation in trained language models. This knowledge, whether held theoretically or acquired through practical interaction, makes the order in LLM-generated text visible and intelligible – much as geological knowledge illuminates the order in a landscape. Appreciation guided by semiotic physics operates at three levels: individual outputs as specimens of how the forces operate under particular conditions, extended exchanges as environments shaped by those forces over time, and models themselves as the ground of characteristic semiotic order. ## References Abell, C. (2020). Fiction: A Philosophical Analysis. Oxford: Oxford University Press. Carlson, A. (2000). Aesthetics and the Environment: The Appreciation of Nature, Art and Architecture. London: Routledge. Carroll, N. (2013). Andy Kaufman and the Philosophy of Interpretation. 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[https://doi.org/10.1093/jaac/article/81/4/454/7571331](https://doi.org/10.1093/jaac/article/81/4/454/7571331) Wolfram, S. (2023, February 14). What is ChatGPT doing … and why does it work? Stephen Wolfram Writings. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ [^1]: We will consider some non-paradigmatic artworks in Section 5\. [^2]: Carlson stresses that ordinary descriptions of environments and more theoretical scientific, historical, and functional descriptions lie on a continuum, so that scientific and historical knowledge can deepen rather than displace practical familiarity as a basis for aesthetic appreciation (Carlson, 2000). By analogy, one might speculate that the sciences of mind and behaviour could relate to folk-psychological and biographical understanding in a similar way, so that in some cases empirical work on personality, emotion, or cognition might feed into the aesthetic appreciation of persons alongside the more everyday forms of knowledge stressed in the main text. [^3]: This personal appreciation also scales up to what we might call performance personalities. We respond to a comedian’s improvisational skill or an orator’s gravitas much as we respond to character in our friends, but now filtered through a public persona – genuine to the individual, yet artfully composed for performance. Recent scholarship has engaged with these performative dimensions, examining how performers construct and present public personas (Carroll 2013). Here too, Carlson’s knowledge requirement bites: to appreciate such personas we need to understand both the individual and the conventions of the performance context. [^4]: This way of distinguishing between an underlying generative system and the agent-like patterns it instantiates in particular episodes draws on work that treats GPT-style models as *simulators* of text worlds capable of generating agent-like *simulacra* without themselves being agents (Janus, 2022; Bereska et al., 2023). We do not use that terminology in the main text, but the present discussion adopts a similar two-level picture. [image1]: 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mUTruu3ZYI2bt2UuWNr5o6szKzt6GoGTl+1pI0dbzN/gxjW6PDna/X7GxSbaoEl+vbQatUQRZsnZ1FsXoVk4K6VlE0gV5ewUHdgoaxZRSuUNYNsbRcV4aHjhBXRbJJd5FSOV2STIhglu8VHhznJZhVtuqbGkFGS7C6+PvbWjYq0b1VAU904BbmbJMlVlaBhmY34b3E1ACjssmKD5qQZFMATEsqcalR0zU4XWLBYOUsBu+jdtV2RDm3f5zXew8KXKdtmeqwOZ7KN1zesSLxJhiSqbNX3i0gw2xVFsuDGLCqaSRVkq2KytIkMgfoMKAR/i2BFXmyizVNTLB5G/xIVhrXvfqVLl28b9rjbod+tHv2/bNLtcqeB33Ud+GXHHl90C37WJfhJ+x5PWnS53zno296Dn7TueqdHv2/a97rWrte5Tv3uuhoqeHgYUSdQFVHV0MdpQwytXNPiNe3so+01jtVhWnSm/Tq0Tk3vS/jnc2HpnmN7tmm1Wny9f9Au8Cqfe2wPao9akcZOfJDYxkZayEYIbUKkkRL86RAaJznp3035Ad9r0sxmnuMEd3f39M0ZGzak79q6dWPaunXrUrYwpNuwJQPXjG2bQTr2bd+6IWtL8vq05PXrFavewag/5GH7I0G3ApTYTJq7WawfMrT55MjGE0Jbx0U16j/QTdeJAAR2WNl6r+AVg6K1jIpwEUWTommKLUDyNli8602KAMbpAKd4ooebgNDobx4iTDx9o4Zsk8w6DkLVYlaz9jcj1zhkRPOpE5pFjWs0friblzf4g6GLqm/WEziZFxT9QOKyFURNz1rzkMFH1yJNskHR5xmhPHqp3n9TlJoTRrWNi24QOa59TNT7Fl++ao2mE6N1pJNsAE0d+wwioBPABwJ2cybRbvVGFoJiscueHjabKnk3nhQGfATeASw01VMWLLo+K+hY4G6s1uejA+26P27b5WHb4EdtOx+dmnC/x4A77fs8atPlXsLMCzFTdk+efrZnr4Md21+PnXZp8vTT8dOvd+i8vUefY0F9r3Xq+rhKzQkib+cMVgwWoqRv87bbfGjTj175DOYI9QR2jotj4wttl1HYDmraKqhXTp7TSWnPDW3ZIYY2qzfbkSPQxzyoOrQivAWjgff1KUKf7lGmKjtPjDKS2GZvwsG3snCSk/5NlC/w6cqRPnHGcUCmjE1pqUnLtyTOH9e29oiW1baumLM7bfWmrEyizdu3gDZsz9qwYysIOiDgb01acvduvd5SEPIhUYbCBV3sL0W8G0SHGK1+NqtPAbOhU0xYQU8rz1ugE7l5en0gwepVONHaMiraxmnAKQ+bZlAtf9Fs9SZNgPKFPuYiK4VU9X3oezZv2er1gYkDshRWlMKcYMq1dj+waq1jQ32aNxVtRVxhh1apUKJVM/3wOsUbeXl4ewHseM3rPVlrFR31niIV5kVAkpuquft4uXKiBXqKLAOmEadpQpS5TDnVILjarf7tmxdr1ESpXrPFhHDgLLTFQpwg+PgWNHEmxcIrFkAHJ2tGqLeq1c3EA1sBiwU8FYNoqz4lDGYvqtpVUQtbLZAWYAHgg07kYWrUpdfRtj2fdOl/tXufW6tX3s/5IaffkH1dgu/07nPn+5ycs+de3L6d8/KHnGEDr77MeXjq7DfXbuR8lZPTvefJXr1y2nS/1rN/hhG6m9mT5g1I2ypWtFTxYqVpYyOgJ2RsmL9fMQBWkYASAtt4SJuxmzRuUfB9lwb1m5QsUVZiG60RoWiRkm+eei+wnYxenn7lylZ8U1usUrlm0yat8Cqh8QH1WjRvC1QlYMU1wL84cqTth0BDsLWwcxUJGX/mJImTnPQLKR/gMwuqi0Eo7GEqH1g6Ze3iVUsStibN2b526f70uZ9lrD22Nn7D+kXbd63buiNty+6NW3dt3LYzfcuuLUTbdmVlAgqzMndlbakQWMaALix4AbOgceTNyFW1s6l6rzYRkW5VK8my6iJrnOYncDZ/zaeQams3JqTa5LBG0THv+QVIBluziEhO9eY8vD8cNKJ2XETj2Am1o6OBdwUVa6240BaRYS0+mcSXKu2qaW0TpjefHNUiMrRiXKSLzQqDFwDkWbpi/QlhLibJw0c1K/birsA7mysnNJgSXnpeVKWYcU3i4wpYtSbTIspMCw9MCG01dHgBQakVH94wOqxpbITdZHNVFEW1FdRs9adG2K2eUA5tZhvnDu4WzwpV606K/rssSpUqVo0dWydiXImY8QXsAe6ardakUBjvMqe0ihn/d6u/weJXeeK4JhMiak6NahMd62f0+kD1bJAQU3vyhDoTQtyKl5RFRVLc3IwtO/S43rH3iw5BN4KC9zzLyen/8Zovv8vp1vt6x0HXvvkuJ7j3mf7tb32HkAGbv87JGTpyV//Oz1+9zBk5PrtFx/sdB3/Rvf9uQfCCEa3PfsqwLS3FS5StXqNuQJGSDRo2E/XP4ywTIuM83KGyybExk3XznJ2TOGrkOFxHjxoPFP548Aj6Irtxoxb6lIIK+9cCRIOKivBeQf3Klqlot/mOHBGCQETo2SO4RPGyUL3Dw6IRMyw0CuFjRodCDcTNoI+G4cbfr3hYaIzV4otrZMREo0ECxcVO0UFfX3dykpP+7ZQP8LFPJniTu8ua5fO2pC1NWz796KZVJzYmnU6ftXfV/NNrJm/dvDJrZ+qOPRu37c4A4Wbnzg1Ee3Zt3LUjfdeOdft2bd6yeS0URkGACmPVlY48GbF5N9Ug25tHhv3Nx9fXTW0WH1Z7YliVqNEePr5tZ0wqNiXMwEtGu3+76dF/tygtJ0YFmDX/nh1qTAz9Gye952aqMWey3V0r27pVya6dC2neBWSt+cRIqCzNJ8T8xcuzoNW7fsJk2dtXhNUpyb61P2yQEGcwa168Vi8+FNDZZFo0zNhCRlUopP6XWWkcGemhebnYPZuHh9vd7Lyb1n5i9H9ZPT/wCShQwq99cH93SbZ5KG5W32rR4z15m0GforR6yIpq0SxlyzWeEi3RyaBevi6KrcGQIfamzQpZPStPCTMqni6qWn9yGICvRuRwn4b1LUbbXwW53rQJimeReiEjLU0a2bmAYn17tp82UeYCJJnzMNXr3vds+17POgU9zD6a8/UPOYN6H/v2u5yePU9273v/5devnufkfPVtzq1HOR26ZX+Tk/Pkm5wXOTnPvskJ6n+qQ4/77fs87T4wU5BEmTexdWrZzMlWm0+PnsEdOnbz8y9u1r+T86xerQ6wBvA0fNiYXLNUiAiPAfDhHiEdO3QDHnl5+pcpXaFL557AO7IGkApwiZgiO4U7asJEwBz+EngBCgGpQEBwBkNEgA6ItMF9BhLbeh82LVa0bHhY7NAhYzq07z7ooxFVq9QiYM3bTpzkpF+d8gE+i6R5anLPjs33b0nauX7x9tR5RzJWHlm7OLRFyREtal5YO3XX1jXbs9bs2qNDHmj3no17d6WA9uu0Zt/OpM92rd6blXxgz7q+fToynmwfcp6MdDtXkgtr1tbRkcWatfAz2P6uqq52/9qRYwvIcqsJE4qMG6GaeOhotSeN/n8WsV1IiLdJqhMzus2kSMVdn9qrOnG8H+fVeMzIOrHh9aPD60+cUDcu1MQrTcInuGk2N6NSKy7a6lcE6qbIS6aSpapNCneXPL1dpL9r9r8GFKs9OaKYrVhhv6J9xkd3jomuNS1a8PTVdbRp0bLZ5iJZobu1igjtGDUBcBwUHmqUVbur5CLZG0ydKMvesiwLRtHIySYzb6lSoeaksMKF3P+iCk3GDW+XENskKtTQ9MOCNk+Ea5y3q2KpOyXib9Yi9RLGva/IUKutqk+D+PHIpX7U2OpTwjtNmFA5enSzqHEyV1ySFA9znS69D3fo/XmHnne/+yEH0PbdNzB2vx884HC3bje/fZmz8/jzzD0vgwbs6NLz+Xc5OYeO5uw49Kpj74z23a91C37WofstBnxmWfDQd//wVpi9msVrytSZ0THxUPdwH9SzL9AKWhtgq22bTlMmzyDjNDIiFlBFOh3u6eBFIGOf3gMG9P9YXyN6fbiOHVbtsKGjwQHJgYMVK1RDQovmBdT7sG6jli3ahY6fgJiAs8GDhkO1/Gjg0M6deoAVdD2L5jNp4nQf72JQ+qImxCOvCoFVnRqfk/4zlA/wCbzFKgtZ61eu+jRuV8ay/VtXHcxauX/17DOrQi9u+HTf3GF7d6zevzNp357kfXvT9+5JBd59tnst6MDupAN7Eg8xOrxz+eGdS/dsTfSyKDbNm/l2eDsjvUOKPCephQOKNooKLWCzcjbf9zlLg0mRZl//TqGRleMj3VTtH15+TeLDC1ttDWIjBcVSs3vPJlFh78lWweZXezLsKRsUKL8enUycp6oEuGnehRWlRWysqyhLqr1GfJRappRBkb2AUJK1xaSoyv2C/6LIotFWKKBE3ZhQSbJXiw4pUNTfVfWqOSWCg8bhxjeKHOdh1niTpUNMxH95eqm8dyHJ5sEpRv34QItB8W4SHuHbqa3Fw86LPn4d2lbr11OtXBVoKxotDceP/q8yJTnJp2jXLn9v3xRqXYO4cM1arJC3V+2JoS62ojWjR/s0b2xSfP5q96qWEGkzaVV7dfOoXkX0sBbw8/yHl12VFU22f1Codq++Zzv0+KJT8OmvcnK69dzQI+jAhLg9UPSCBh559SKna9/bHbs969j7btuel17+d86wUbu69/i6fZ8b7Xs/7NTjZbeed4P67zfpyw76GTCoatyHjIuwWL0FUYuIjJUVG/AOQDMxbiqMXMAZ4V3tWvXx193NHFi+ytAho8ivALDM0+6HyDBISWUjaxdXEIEjYoLwFGyhu0HRA5ZNjp9OSmKpkuWBgH2DPwJzcHMkRPxJE6fFRMfjBmYyWw95u504yUm/OuUDfKJoK1/Cf/3KhN0bZu/MmL9j09LPNs/fvmzKjk8G7V0UdXRuv+2pU7J3LNy/ef7h3SuO7EnM3rns8K7FgLmjOxdn71pyZMenx3cvOr59XvaOOYe3L6xYwtsmq2r+J7tZFUG0mlVR9Qkc1Ld5REjN+HEdEuLrTwovIBj/JohtQkKax0Z0nDTpb6JmE71qDh3cOHSY7F+6WnC/Fp/Et54+sVFsrNEkiJ7eNcKGtw0LqREzpuaw/h9IUqdxEa6q5M5xLeNibIHlDIpmM+tLvQUKuXeeMrFJbEiDuPEwnBvHhxt4uUHsuA9jw3pGTWgYG+biaXXjxXrhY+pHje49bORfFPXDqeENI0JaToutULSC1eYJ85aXbAUKezSfGF5vZmzjSbFNI8MLFCrs5uXfLDq6Y2SEb6NGDWJCu8TGBIaP8G/ctCAvNIwJrR8f0XxKaLMp4bwlwMXu3TRyfLOQsa2jI1pHRWmitZCfV6XI4fViQuqFDa/Wvg0vuCui3WSu2bHL1q59Xnbpf+rjUad69v2iSYfrHTrf7z1iT4suZ/sOyGzf93qHnrfbd7/foe/9fgN2NWu3r9fAF217PunY61GnoEedez5q0Xq5brHqywX6VAOQrmy5SuERMWHh0eXKV8YbgfpmMopAOkAVMAiKGCAJOhrQBxAWHhYdPykhZGw4dDSobNFRkxANNq/Bgx8XEgH8ApAB3cAEhq3MTpYFLELXA4EJsAxxoA8iMsIhSeFCHgMHDAFnQB4SgsBTYFtegLOQBHYxs6PzthMnOelXpnyAzypJqxfE7UyK2pM8Yf+GKVnrpp3eMj99+riItmVDmlY8GtfuVHrc4YxJhzMTDm6ZcSRr1rGsT05tnfqaNk/NXj/xTOb0U1mzTmxLwNOstCWayHF5NkOALCY7J+mfowHUXBSLTfH08C9iknwMHqKvYpVk1axaC3r7FBZtokmCrmX0tKq8WFiChenJGSWzzWZSfMHCy+4LM8rNJLxnUaxGRRYsRjerSf/aXnpf9PQwiSwj2SzpSpyHYnexWYyyxnuoH4iKTbYA+1RbgFHfMhhg0iy8gSvEKYU8PV09BEG2G41qQZNk9ipS0F2QTILK8wpkEa0GTtS/FZG9oGBaZMVHtsFm52QNxOsBVheD2STIJleDpviabL4Cr7hrWkFwDCgq2L0MdjvkhP3rI3v62wNkzVvSfK0WX07fx+ctmK1QOQPK9mvb9X6Hnje7d3/Rue+t9r1eBQ38vGPQpc69HnUIeti8x5MevT7v0etu2/5Xu3R83iroScfetzsHPezU+0ZH6H29npiEqprFQ+LMguAliLKbuxlKn93Tz2AUFNXeqXMP0sWAZQAjQB5tABozOlRgyxQAJtQqwmlHHmAL2AQlzs3VBJQkJRGPcGOz6oeGAb9obYQWQJAQEXx9iiICsBLgiBvijBtPu6/IVoFp4x5bcdZ3/DF18u124iQn/eqUD/BpojF93qid83ttn9/zs+UDDyUOO5USnB7R7Oz8cSeTw7LDmmWnjDuQNj07NfJ42qgTm8ad2BZzcOuUw9tmHNs28+L2qXtWjjq1c9aZrZNOZ0af2xZzLGuuKvzdQ+9jb2fkpHxJEfQd26I+5+BdtUrnwR992rdv2r+mfsm5N0l9+y9XbS3ycnaSk5xElA/wSaJ5/aIJ+xPHbV825kBq3I6lYUfSQ1MiOp9fNOHI6siD4e2PbZx5Zj1QL+rEpjk7Vk9PnBG/aEbMkhmxK6ZPSIr5OCm81+HVU/aun3s8c+7ZbXOzMxfazAXNpnw0PiflS6+/KRb1NRMTb+WlIrws/UsSQfrX0xYZyClD13TPy9lJTnISUT7Ax3PyrNixCaH9p4QNDR/ed1jPjqMGtg/p2CC8QWBwvZLrhrZNiBwzN3LUzPAxE4YOiB7Sd+KgoDHDB40bNTx81NBJw3pN/Kh93LCuMR91ih/YbnlU37RZkQG8u2T88eBfJ/00SZyXxFv1KQBFPzGFE8wiZ/mXJHOqbLbJZk9cbbI3277nJCc5KX/KB/g40ZoQFz0jLiY2MiYmIjpidMik6MiQbp0SurUY1anJsn5NI0aMGjd0SMjgwSH9ekcOCo4f1H3pgFaJw4NWjghOGd03dezA1KjRmZP7gdIm9p0d0tduKsTx+azq/hKik4rpa2L6+DRvnH8T0Uepjkx/5vFciO/mavw5Xyboi90//tXP5hJE/QAF/fQESYEa6OZucPx945FZ0LftiIKkk/7LZcI+I/txHyUk8XA303dm/xmir0R4dtY/fbGbN84vIZ4def3m95ESO2H7p7+Y/F8RfbRHVZf36z0n/eEoH+AzmsTZCQmzZ0yfP2/Rok+XLp03DzRpyMCNcSEr48PTx7TfuGLVsrUr01Yv2rYoIWPy2OTRPRcNa7E8tNeikOC4ns0iOzdYEPrxpoQB2z4ZtmdJ1I618zWzKx2a8iuS1eKlsNPk7TYfxwf2/0kSmKch+tA179O8RB/n//SpDa/p9eGD7IwsaH+cNyfaicyCDVeDWeMlT9xIqo/jkX4ADDuYC+MWxw47cDAE6pEzJgeR67i38/23kcOrCXlKekuYX04Kc5zyZoiQ60ggb+R3I8dhDT7eAb8iWyf9VpQP8MmKdXJc7Iyp0+bMmffp3Fkr5k9fPe+TKUMHbJg6LnXulPSRjZdNitsQ0jFxQP3p3WstD+uXtnx+esrS1NSVaclJWSkrJw/vv2xyxI6Nq3duSNyRvnrv1gxVlPSv1vJk9EuIcARdOmpCHH3rnjfOv4lo5EeO4WFRpMXkjZOXwkInVK9W+2f3GU0S9JOyJEFEZq91QP0EQNVgMFksNjOwXpA4TnCE64f00ZmDjNgxha+5QVTk7viLqqtfr3Ho+Mg8mf67iCAPFTV+XISa63HpVySZeaerVfNDRwiyiIyIcRy78MuJVMimTVripkf33nkjOOmPRfkAH8yrlg0az0iYOSVh1oI5s5d9MjFxQuS0bi0WflRvUbvKs1oXn9m5xqpx/TYtnpOyOjFp9Yp1a5dlrJ69ee3CjJRl61YtXD1/2s7kRVvWr9m6YTEoqH1n2fj6BNCfIDrGihxsq8wvIh2LVPB93emXwDyoEVErp4NA1qVuePrkOZ6SE1s6eUlgzn0wRMvMuxAdsiSxE5boKBH8rVqlJkLwl5AIRqjAPtHnmVegN49vMjF3lGQbGpkHSIdbyJs37joOXFLYKU+OhCKDGzyCYG3bdET4F59/JbL+4yivwtwBU75QYEeOGEuBQT2DL1+67u3lTyoGnkIAkZ2GgrKg4K9efvf8i5co+Jcvvi5apCQNAGBFKjCu5Fyc5EEqRDh75iLJiZKCFaSKiZ6EmxfPX0GFUdhZLAo7KAVpER/c6GQBI/PQRpKQ8DJz5kmBlJ2Q69KIqpRwjdzaIeSDgq4KG5YMzC2nQz3H9cH9Jybm1Ik8xpH67FLYw5GpgyFqA2VBLSECeQ2Vcv09kRF98MBRMKEmhOpC5Lt3HuLaN3jg+XOXBXbg1aKFyyR2BBbqAbhPTYjmH5AWf4kt6adkJpuYP1IqyPas3fS+UGkCc9opM6USj1xdDPRanfRHoXyAz8Ab0WzjR/Sf1LluVItqka3rTmldcVaz8pNalJ3ZvVnSx12SZ0QtnT8jLT152fKFq5bO2pw4Zf26JVvSF+5Km/dZ6ifHNsw9nDbnYHrSgY0rdm1YrpgMvFnQP3PKk5GDqPXA/iJoQxulniwwg4WaIPV/nnmnJRsTrQ09P8C/OJosnpqZUzTq27hHHGqOpG6kJKeT0kF21v17jxXmEFJi3shUdjIdCYNoCAeZmAdI6k5enn50oAhBG/isWL6aBIa0yBF54Up+1EhswhGJaaa4IkeJaQ0iw0TEJ6jCX3R1/H344ClJSwWhvgr+1LFlNjkIDh8NHIKYHHOZhsDHjz6nKkJMOuZEZj4kqcfSKJK1bRd1ZsIsZHrv7iOSU809R4/IyHwKE7J46rsj9eNSyG2mkfl4I/khMCqEiolS45GrvmtRP3wFmeKRwjwTUIUT8iL848HD69ZpUOgDNxpgFDYmQQBwUJnDYomdT6Uw+KZaUpl6iMh0bAz9dZSFXo2fb1FcUQ/InYpDrC5dvEYSkpv5b77+AcaBzJxzQuCdO/YiEKBML4XCUeEWttOeciFvojwDQTABTJNIGHLoBSEJ2iRqg1I56Q9E+QCfhHcqWUa0b7i0e6U5bcpENas+tn2TsS3rd69XJrhhlTV9Wu1eFLctcW7kqGEjho0cOnR46OjRw8aGR0+ImhYVunx67JoFs5OWJ2as+DRzzeL0pMVoUJKkmMWfmttCG0JnhhrSqGEzE3OuiPuvX31/6GA22hlaJ5oagAMjtsC0sFUr137+7MvNm7YB+NBt0LGpz4PJkcPHMSB/tv8waUyIcOP6nS2Z26dMns4xD5DIDoFIDo0Aet+tm/fOnb0EBQoN/fix02jcCIeaibTghizu3H5QsUJVdBV/v2IIwd8SxcsgMm6Qaf9+g9AloDShzyAh+IMDhIcAiAPJU1PWI9/A8pWTVqe8/OpbIA7iW5mHcuiAKBfPlBSkhTaE3ovOhpKKTL8b9NFQsIKoKCOikatyyHDgsyOAD0QY0H8wdF7k3iuoL7ihb5vZkaJgiGhg/o/3CiEtNMRj2adQLdu27uSZbQ62SD52TCjKUriQ+6OHz4iQF+pt9679qHwIg+yAeqiiYkVLgRvCd2zfg5urV26KTPUbPWockuAvDRsoOwqOrCsEVkFx8PpwvXD+CmEcXsTWLTtQ1VCBUZ81qtdBAVWmx+GNQPjSpcqDyaSJU1EbVIEcU+gIcJEQrCAPQQ/5ZUd9oiBVKtdACO5RBIK5gQM+hjmPfJEKoIlHKD5eGW4QE8UHIQQRMJCgpOAMtuCJIqC8337z38gd1QXBILPKvCSDud47RO3okRNoEp06dqNRkxpY3lbtpN8z5QN8FjP0DnsZxTSjY2BmRKdjifE71y9eHPrRkMalo7rVSh9W59iWqHNpE05lztqZNi8rbcmmtBWJC8IPbZl7PHP2qR0LVyycGD11YvyM8KjYcKuXLx0Kr+/PyJORg8qWqTB+XATa0N49B9DE0cLQMdCg0ZnRu9AJ0dzx9PvvctDUEBgyNgw3wEFYH2jlaMHUBNGaFWacovkSRuhnpZglNF+0Y3QJkWmOa9esAzTgEWkKyAhJKAICZ30yr96HjZAvuAnMWTj6JNg+efyFmZ3KCeURN+jwAjtRblxIOHogOjNYOQoS3GcAetHFC1clZluhv3Xp3IM0FGAQwAXMSeYzpy8gkJhrzJDHPSRBofAIf6OjJjZs0JRQm8IBZMgLhZocnwAZPqzbEF0aEVBLa5JS9+z+DAiLhNBJqfgQlZTo27fugwnGAAAThIEMZBiShoV8gaGoUqhmBLIAIFQXkqN0wEfckJ6IF0SFQgTEHDZ0FCRECFAGEhJg0UtBoWBlC8wWRgiJgTeIR0gOYRACoCG9D+KJTBmHGMB0jBbAd9LggFakmiWuSMIbQUxCcIgEhqgKhED1Qw2cPHEWgweyQKbNmrZCBDOb60AuNMYgL9QeVT4SQjFEBFi+LVu0xQ01IRpmAMRICOyenjAL8QH6EA8jK1n6qChCZORFTctJfyDKB/g03iIJngon9GpYeXiLcpO7VvpsYpejnwTtTRqYnTTmyoohF9ZFZW8cl70x9Mym8LObwi9mRJzfFHFxY+i1TRFXN8dczJqxc3Vc4oqYT+cluBQ2MS8Tsu4vLU9GDkK7wbB56uQ5sj3RyjHMoh+ig6GZouWtT9+E/nPzxl1EADi+/4/CaP0YeEkBRDum2TqEoCFCj0PzJR0B4UAZNG4ocVruwcJkqhjZhB16I5o7sA+6z7Wrt9CIASsIAbpBJI4ZQdAoIQY0CEQuUzqQLNzTp84jGphAWUDvhWwETFQQygt9WGBTeMhRZIotYl65fIM0Dihr6K4cOx+UZrsQB6pEXOxksAUuyGzek8vdliGwiU70ZJFZxATNQF4UH8mT16ZhhKDqQpyMjVtQfFLZoLcCC5AWWQu586HggIQ0AYdAJJGZuYenGlt7RSBqAzWDkuKKoiE+6V94NXiKQqFoyLF1q/aQGWmpOEiOp9CjcQ/M2r/vELLbt/cgaoOmC4AgFFlkk4zhYVEWNsfHMas2++hJvHeybUmRRKU1qN8ExSkSUAJ/Y6InoZYoPpRNwBDwi0qKVHgdEIBmfpEE+iP+xsbEQyvk2OCBUY2ADwkxQtDsRJvWHWAIIwnqUGIHryIQwwn4ADHRhHBPgSg7x+xcDKuIufDTpdWr1aYX5KQ/EOUDfILu2EzkBcVbElqWkGPrW0Y0Lzc9qGZUUMmNcUE7VwzZmRJ9IDE8e03UieS40+umnElLOLBx8uGN8YfWTzyzJeH0pqnnNk07unGGv+EDO697reZks1H+15sn0Dk3ZWxFg162dKXKJuYRCEukW9cgga11IASaFIZxGroBDWiRsI6HDxuNBr1r5z40TbRyNM3ly1YBodAoof4M+XiEmU2fC2wmEWnRt4n/hMhYdCETm70mmEBy9HaOaZ1keaFrUWcmIcEcFiJghdPPbtb1KfSi+EnToHbxuZNlKAjpXEATKA7I/fKl6xJbXUFkSEIWJXiSPghJAM00h4UI4FO8WGn0WJnNOkFVJNhS2GJOtaq1rGwrT6mS5SAkuKEHqmzSkzok9GLCd4FNQgHseDbdBq0NUgEaUHwT2wKZnpaBhKg3Uovs7JBkFJ8KAgSHdQ/AgtqFyL179UPlm9msHGxYcIBupbJJN1K7KleqHhY6gWD64IGjpFDTsIRiAo6B0YiG+jSziTMgI6AKwhvZHkPgEXjinlYbUM9UM4SVKpu8A5NRI0MgP0RFIAYkiX3tS7VHQyapchgdwRxZ04tDQqo0Go1WJq4Rme4PnVpkk4kQA3+nTpmBl46mhQon6ISoNHoBW1G0iPBoMnhBVFFkFtDqk5P+QJQP8AH1BLYjzK4oFpNr33bNYpp4TW5undu9WN9qXFTfVksmjk1fNHXbihl718TvT445mjo5c8WCLUum7lwRe3pzwuktc7M3L0bTpHn0vPwdhDaNaLC80EBxv3jR8q7dgjwM3Odf6KYQdCi0bHQDKGJovlD6Ro4Ya2T2GroBtAaabIZCgZZHKpLANpqge0NDQS9FhKZNWqK9OhYKqW9jVKc+if5gZHP2CERPU5jtg6aMro4bhc2yE0qi/+/csRfJoQ8SoulzSYJitXmDCbAGDJHKURAIg44EnMLfgQM+hnEN8w2QDdsQMZEcPU1hE4I8mzuHWQogI1Sys7VU9DdkBLvb0dNAYAtuPXv0gX4HSY4fO42aIRNSZeuYKAuufXr3Jz0IHRjRALKEj1D9wBkQj8jQm2j5ghDfAe6oB3RjmMwoFASAhDJbqIHiQ6sQ06bOhHaMmEgY4F8c6h7qExyAhjQ4AYPABJiC+7R1G69fu83lKokIAUP8BUpGRsSACaSCjkZIB2HwcjEUgS3p7DRWAUa3ZG4HWyoRCoiBAXWL2kac0aPGoXpdXQwdO3QlvRWvHlIJbPmVy7VGkXWlitUEZqfDlq9dqx6k/ebrH1BRGFmRHclgYitCQ4eMpAVo5IVKE5jVj3A0RbQ3A1vWBx+YwIhJUJu3eTvp90z5AJ+iA58qCPr8C2fwUAzuLQO9etcOmNC+QlT7ckPrew1v6FO7XOlPp01cMSc6ZWn8qlmxK+YtTFowd/X8aVuSPx3at7NF0pftqM3l5e8gjS1E2pkHHLRF6jPU3KdMnaFqdlnRZSBwRDiwAxGgyk2OTzAzM1Nji6poxOiTJjZPR2uI6NJo67hCZ0lckQRjjWKa2YYJdK0mjVugxZuYdUnaBCxZCkQITeKAFboTwZ/I5vJhMdEkkZGtdWiKTRJUdDZ0YzMnGYw6hlJBzMx87t6tF8c0IHCDBte8WWs1d83Xz7fomNHj6RGYB/UMhh3HM9XMkru+iS5dvlwl9Z8/SBDZhwoG5pyMlERk0Td4IDQywi9E6NSxW53a9c1sRhJxUD8YM1BFyBTMUVKBzV1CS0WVSmxfCMBRYs6hcIVWhXCVrV9T7oBCFBn1SYqzlrtcO6D/YDB31DxSkfxkI0+Mm4J3R8Uh3VZim0UEpkm1atlOYB9ClCxRFhUrsoEQbFEPgz4aqryxJm5li62wiDW2xo2YaA8KW5T3YA6M8HTwoGEwh41sVRrxUdV4U3jR4EaSQysXmZYHDuNCwulF4IqBBLYqYbrGNhXQDa6oUioOOACLBbZNqkXzNsQHUIgh1sLWc+klOukPRPkAH9P49A/XdA+wml3EqCsJislUQhHqBCjdahdpW1kr4+9XLqB4pTJFgru3XThr5vwZC5ctWDYtZqJVklQYWbK+34pMlbz8HURdQmbbL0RmcfBs6g0N19snAKiH3Klv06BKqOTJTjSiQOr21BmoHSM+kstsCglqFMAFwIQuQX1YY6sHGtsPQd2PZzstSFqBGdSEWXRPcEn3pM6IuVsOEdi5XZfbN+/tyNpNK8hQAB0FIfEoLxKberXE5vsd6wkAVgJf3CCaB1tmEdmQILJNZBLbT+OoMY5tx7GxHWdmtunMMcDQhhKwJSMaMtACBeVOOELFpwED4VQPCvvsgYYEle3b4NhuGAI4RCYAUpjtTNBMKo8Hc41GyE6ierDdjlCFoI874IMKTjUm5O7gI0nIqiWjmDKVGLKT/CIDFIXZp/RaoVNTkalVkMAoiMC8xJGEYm7boCJTNSIt3eNKu50MbHugA5RxjxAY4ySJkjvaUTSNgbWFrU2h+UVNiMNQigaG+FQWktZJfyDKF/jYt6jM2v2RdNcZ+om7siwriuRn1Yp7+5UrGVCyiLe3xaaY9YPtZP2wT/0rAv0L0zxsfw6RE24QlCmZU3R/uLl7UH4+UasVGJY5eo7I+oPCFuwIwqhB501OJDO/i/mO5Ca259ad7ZLVtSrm/VIyy+af8znazyBkWriQO83rEab/UUhhOxwJwoxvfB38pyHHOEEbqv+UZfw/QvkAn0jYR1+MMl8ZurNX9hEoL9nIeZAmycA4mTcpGCDdAQ+agDYBWyYPq/8VcdJr7NNRz6yoRr2dcT9pL+cljumAHxR0ldkyJakGArOqSAsgjYDG9rzJiRxNPO8jGv/R9I0mQVFtKDWk1XgNAueN/A4EXHYAB+QnHfAPQaQ9mdhnrQDuvBH+6ERatsg0dIXtxckbx0l/CPofgQ+k6B+KivrnovoJcbJ+UJIkWGE9cQrUJl7yRL836j4zvHS/tbxihK0HfNTBKx9F6ecQzCYkB3rKJnnURyPGDRljZIt6eWP+BL1pBDkCYaFEhEcrzF4eOWKsne3yJ3MmX0LXLVa01JtmpoPIMtLhlUmL1v9h9Q/nT5szoGtw3sjvQBB+TVJqwwZNcV+9Wm1ImzfO75PMuR/PEPbljfAnII7NxqB0LoU91J+cyXHS75nyB77/DEFnhDELXZKXLHZRa1Ptw1t7Yh+eDN48O+jSqtTbq9IfpWRdWJmuFHQDvhgVXf0kM1xkqlDZcpV8fIuKkj43r+prdtX9fIspso0OKYGW6m31NXjwIKvVfu/elXNP7t94+uWx7NNbzxw6fPysgal7eAQd08jrQMarogfPG2UVxnGXYL/FU2oHWApUa+1Zv29Nm12S9DFABlvOLPsqXukzF748Ou75oeBbu+bfXrH5/obMWyv33Nq4w+pu5FUdwcXcSQNGWpmyFcuUqSxJdiiJkDawfGWNbWdx1IbJCMtaMRpQNp/bL769eu32s1t39p46u+f49UZNW3l6+ZMHMtSY4nRF5iQn/TL6LYFP3zDIjhKRDfy5zTtvrtv2xZ6p35zvlbPvoy+S1x6JmJGTlPE0ZXv6lDkWTndhC0jKPbNE//oV+kX5wMolylYoV6FqidKBvn7F9Hk9hnp0ppMq2UxmqW2Hrqcu3jx37tntp59fuXn5zKmrn51/fOb8NSALx+vzfRarF27MVmV4jbpbB46Z16RdLa5Qv48KXD/S6fvjgw+u+LBtcFXgI+WrcqqfaL2xbe/VxLTn26O+OtUr51DUy+Sdt6cmfrF64/Nlm1fHJuiKsKwDOhCKpdILC1ArElCydJkKZStWLRtYhdMXChRZstLJKyyOVtjFMHDw8IvHzp298/jGo1eXb35+6vKFkyfO3bzzcHy47p9bZDGdwOckJ/1C+i2BjzkU1w9Q0gzc5bQtn6/dej056fv983J2JXw/f/mpfmG3hobeW7Pp/LpMH6Oo8jKzgnXTG2hiNusmcJXKNUoGVi9apmKp8pWAJvoUnq5kvQY+D3cOBvjJc5eOX7588vj9lw/OfnV949V9qdeOHr5199Hte49v33kAhVGC2S4ogpd9YZOOF7sOu9Ll45XNGqbOq/Iwe+BXx4MvLinXL7iGLCqkvgGCixrF++uzXqzbcmc1lL6px8Jb5qxZfyx42PMFnz5N2no1Y4fdKFh0Q09jnoMIqXXbuXKl6mXLVCwRWKl0YGW/IiWBjLKgz4q+JtFy4vSF0xeunDtx6eqdq19dO3TjSObdU+kP7tyEqFdv3L12/Y74I0o6yUlOenf6LYGP1ovRjbl/FL6dvvXLlZvOR08/2n/MreiE7U3bnB0w6oelabdXrbuwfO3Z5I0nN2yTXDjYiQApkEmQYauWKluxeLnKJQOrlg6sWrRUOUmF3aq5e0CVsnl5+gNKXD24IyfOHL2Uff3sucOzGlydV/zS7Jr3FrW+effezbsPHj55rgsgWhDfKMjLevU73W3Q1S4Dswf2yd7U7Ksj3Z4davvwYvemfUpbJCtzf66b52unzHqUvOXZ6g37g4bvCR54qGOPI8H99/cb/mh5xsUVyfc3brudnpURP8fGaSa7p6rZjSZBAiKr9lJlK5coXTGgVGDlWvXdzbIg2wXByssaiqMpNpFTk5LTj545f/TCg1tnNj+eUeHBjMqXPql6//b563ceXLx2KzZuCq38OIHPSU76hfRbAp/ErF2gia/N+/GuQy9Ttn2xYuVng3qfDRq3tFS5kwMGfT096WpEwqXIhHuL1jzevLeYYDEL6rnL14+dPn/4+NmDx8/uPXxqddqGExcub9mx78zF62cv3Th26vLh46ePHDtNnl5lxRYdN+XSzcdPrly9u6Du0xkBdz+pe2dauQuXLl6+fitlfaZRdxJuV0SrO+9WQnjv4JCBp/r32xjRZ1A3v8zJtR8fGDNwsNasd1lV5CxmMmBVfzfpfur2B6lbr8YlZPcddy54dEqRCi+mLHwxe+2JweHXps67k5j6KHO3p6vZ6MFdvHYn+9SFg9mnj5+7dvjkxdXrNp+6eD05fTPkP3X+6rHTFw+dPD1k5Bh9e41R0ixeALiH1++f37ngyczAu3NqP55d6sSGWZeu375y8y6D9bfr0ElOctI70G8JfNCeCPhgxE4K6v9F8rYnG7JeZSy/1C0kqWyVS/0HXuw0+myfUaf6h7xasf52UkbLqnX2Hz5x7OzlwyfPA0SWJKV9mph8+PipZavWLliycvHK5C27Dh47e/34+UtnLl4drn/fJnC88uDhs5t3Hr68e+Hm7DLPPvG6Pqv6s8UVTp4+dfLsudMXrhk5VVM9rapXsWK2XkObDW9bKaZrnbiINp8NDt/bp01oUPnuobVafFRF9RasDPjMkurlJtxdt/PehqwHyZt+WL4lO3jYVt+KF4IGne3e905w2L4+w5+t23w/c0fbqrWvnLx4+tKtA8fOZZ+5snhV+pzFSdv3H09K3bh4xZqlK5Oz9h46evriiUuXz127YdE8YfKHhUbdffD01u2vX97Y+3x6mTtTK341t8zNIxsB5cfPXKhbv4kT+JzkpF+Ffkvg49jSJ+DPS1Ce7D3yeO2W75Myr0VPPhH88blew0/0GZqzLOnZnDXfrd3xdM3GB5u2tK9Vd+W6TcvXbpq9KGnHZwdPnTl55sTJeWOb3jw9tm/PlldvXd53/PSm/TsWrE5duylrzbr1sqJvsrl97+mdl08v3Ht458yB61mLnmVvfXrt7pFzF05cvHHhyl1EgMpXpIh/wsSKj08G5Rwe+vWONnMHeZ3q9dG5HsPvdRsdO7Bm0/4lVMv7giTqk4y85i9Yb2TsfLFx9/eJ6y+OiT07aOxnbXtdGx31KjH50Yxlr1J2PEla/zR9Q/ta9RLTdy5NSVm0ZuXug8cuX7px9WL28cPLbh2cEhUx6NSFCycv3VyzcdO8hUlLV6SYOLNLYY/nX31/+8HjS3eeHj995vrhLdczFn9z8cDhS8ev3b539OSZYyfP0Wq109R1kpN+If2WwOdwqaO5ireSs75ZkXXg41EnPx6Ts3z9q8SNB3oPvTks9EHK7seJux+u2HwrbYunLFevU+3kuUsnzt88ev5qQLnKgTWaVfywakDJGpUrNi9Tus7wkdEnzj44dOICTEiLVT+Nw2L1Bo0YEnnm2oOTF26cOH720dMXl548u3L11pKliVar3WA0m8ySonhZlQLPjw55eXbYq+yga1lBV7oPvdp52I1ewxdP6jR6eec2Iz4URR34PE1iVJde91M3P951eH/fIXs69321Nu37NRuO9BpzY/Kn99L3Pkra+TA569K6TVxB98ZNuxy/cH3f8ZOHzl0pWaWxf+magbXqlw6sWrJCk1KBTQ4cOXfo9BkYvCtWr1UUhSGat9Xqd/Xk3ewzdw5fvXn84oV7d+4/ePT1tWsPmjRu7e5uEEWe+U/7c26Rc5KT/mP02wOfKFjtvOVOxs6vVmfcXrXhq7VZj9Zsu5e67ejAkVeCBz9OSrmQkd60WgkLXxi4wwneIm9XBXN8SLv7R6NfHBv7+OTwL7LHPT82/NnxsOtnx+HR6zUTXjO780ajWZIUQRCyLzzed+bm/rOnrj+5ffXBk8iwBIm3w8TWvTAy/2TVO/tcO9zv+ZnO964EdQsSV83rv2xi96TpvRek9IhO6xb6aVdVn7KTRF7pWLX656mbHi7Z9mXitq8St91J3vxizabsLiOO9xr9LD1z6+RpzevW9pCN7oKLTXKH1il4iFkpMfeOD/7i+MdfHpwAaZ8eH/P4+LjrR2fYVMPr/d6irEqeIqefwM5x9qNnrh28fP74tfM3Hz+6cPOFh4eor8Dom2PYrOh/0EGak5z0p6TfGPgk5gzMqsgHkhPvbV73at2ezxM3PVy79cn6HTcnf3JrXOTLlG2Hd66dsWb0tJWjZyyK43h/AJaFNwV6FXh4aPg32b2/PNLv5dHuL7M7vTw55MLR7t68fvSpqFvQmp9ngA4oFs1DLDxs7KRFSSkeFleLr2Hz5s2i3ebiYbCoVpE5peUkuc6Qos36aCGxpdqNUNsMrVRngF/n4eVnpvaLWtMual2X+HXBDRoHCpr+dYpiLHwxNfHpioxXq3c9Stp+P3X7o9Ubr8bPOR8x+fmarOzMhXGJg2NSQuNmx3oKmtkgWk32Xk08X14I+vpUlx8Oj3h1pPeLY10/P97z7uHoUr70KbS+5UWW7JJsEVTOoFoOnz5Vv3G12fMnDh03JG37ZyaLh2gVraqXpJ/n+k+uI53kJCe9A/2WwAeE0vfiiryguU/6ZPDO/bNOZCw5kbno/KbEu6kbnyZtfZaY+Shrx5SFA6M29Ji6YZDNxwCNS08o2L39/cpWCChfoUiZCpXLVyhXqXyZ8iVLlvCXOXcdR0T6vtisiJJi8/aKTewyN6X/3JShxoAChawFpi8dH7Wy35QVow0Sc+HGvj6Gimbx1I8/4kyiZpNMFje7za1Z/+p9JzWKzOgzMq1zyMoeFn8PTRKHhvaZuXzoZxnzzmcu35ax4FLq2sfJGZdSNn6esu3elh0JK3rHZHYLXdKDV41mo40323mjHZKUDyxRNrBoyXLFy1YsXb5i8UoVilg5F4tkYzqvXht2T1/VV5uVGB+f2nnq8v5tuldLWBLyYTef+RsGxyd9NHn5GE7mdTe7gl5jeSvTSU5y0s+n3xL4JN6qe4MVVDNvqFS/1OTFYeFbOo3PaB67Omj/rrWntqae2742PqXb5MSBg8b1MIkGi+zPsRUG2HoGo9kqmKPGDloQW3J2VKWEyFYtPgxQRXdBeO378U1q27fGpFUfTVzfJ3Z9j4RN/RLSB85cM7JZ15q6bggdSrc080KJ7icEQGPiPFoEV4xd3SNkQ/sO46sqVjerIg6L7BGR0n5CZvcRaS3Dk4KO7l1/bNvaE4dXT1zXPm5l3yFhvcyKWeS9FJO/yLYrQk0zuP49YnSHhOhmC+Pqzozq7a0adf5vnMgvsEOfPNzNsWsHRCUFRSd3jknpMnl9cHxa//mfTpUUD7PZ/Boldfv87TI66U9JeT8H5pmHuZ84X8NJP4d+S+CjlQ1cgWK8xWCwFJqyZnj0qr6Tl4+y2RXe/YO+PdsOj+vtov6XYDVygtmmeQvS6yk5/fASs0kyFvIS/l9gEdlblWHbyqIPJ+RzYAYvKIWFwn/lC7h7/aOg/FfB2yh4m1zNrpLIFC6gnqSfZ/lPxLx0A5t4XvTQXOQShnZhFWMXfWyxi4ps4zW+cc8qM5PGRq8YEBze3kuRPGWhddcPe45uIRRx5aw8bOfXpQNGQWbBqspWWOgW/q8VAtw0oXDhQgXfsljp7APccF4872U0eRaOXxCBOuG9TRYbZzC6slPwNAbTTuD7v0J0ntCbJz7Q991/1jMg/mP0WwJf7iEumv7JqkVWbBIvFSwsFnCVC37g6iFpMASBPKpoUSVNxc9k4qjPCzrwCbBJBZNREa3+vqVF3psze5o4uym/nW42wd+ieZllHgS27m5mSfIyGCT9s39S9/JqfKL+YZy+nsBr+nEGNk+zb4G6LcvzImcyWDQtQLFrmuJmkN8raHpPNlsU1eaqGY0KDzuZTknQoVPk9VNdJf0EGyThjJpoEAUjcFaRLXZ9N88b67N0ChavO9Jmg4HB0KVLF0TQKwdcRFFTPfWDDp3A93+J6BygN4+kJMh783gLJ70D/abAx9Q3hhE2WbJzRsls0D/INemHoOgnhpoEXteb9Mg6FrB79ukrr1ktvjxv4XgFWMbsXxncoLhJYj5uXxhu8IJkBtGOEDpoS58x1EFEN0XfTsWWEXTs0z8NVvV7XX3TV0KgvuGpfrQMh1S0gmyHwPoahe5F00G0emPVgU82s9VbVmrBSy+1pAOras6Try4tncLAS6KZoNNk1Ikzq7kan2xkDtfppAYyfPQ8/jinlpK0+R53+EuIzkmmY6N+yWGodIqtmKuG50UZPPVgju0df3/6lLP/idRc//R5HxHZ7L6Vq9SEUqBqngqqS7JUrlQ9rzxEHDug28c74M1AOoBSZP4DfhM9kY5M55jDwl/xBEMq1/9UFf+SfkvgYziiA5/F6i0LFuhNgmIxS/pSg7fFE8hisdheHwalx9fhiXZ+iNRiVLvweiRk23r1GUM76O1cmIbIiVZYwfoh0jpZCVYASUzr1IHs7VS5wKdvYBF09JHM/q8VLia5KJn00/lpjVWUVV62mnV3Jf8ssEZziHpGkq76CbKBPaVoKoPOPNK+lpMxZ6TDK6/3xtdZi/ox7hbm/oI6G8fOf1dyz17/Q9CbWsyvRVLuifMyc5D0C7OgrkWnz7/1iHRz5AKgoacKO9z7fzv2kJeonwBoXlDRO0qVDvTzL+7lHYAb8oWQb4cngd9sAwrzQGBmbubNzENe3lT/AdK/yGR6a96afGdSch085H30c+i3BD6F7WUB9gEVOE4ILFehfr26VqufJNlV2fr81XecxYcASI/PTDwz6U0irFbd7H324vnr00oIDnTVL7+Wx2xDgCMpero3JcHKiXazqB9qoOtlQp5Ur5VB1aHBGRWdP9M37QLvbVB0tH29OCPrAKqaSdTcN5FrxrIkVonz1hVDQdb3rzA4gw5olvPkSwlfE/14s9lstVpl+TXog9LWbbQxZ0kK80fx7OkLchv0RzmuGXICuEeNDMn76JcQGD7/4uW9u4/mz1vk8JHyDsQzD0dBPYOLFS3l0JjeJJU5Av10wRLUf8cOXcnnUc0ade3/S8/igLy6dRqQD5N8ST++TFADK1QtU7ZiufKVywdW+YmxDbDSqmW7N9U6jrnuIglpLiVvqn83KbneCunczLwR3o1qVK/TvFnrn6iNn6bfEvgchBfPMxelaEwiG7hA5LBZZn4zHDE55s9h7pxPzcw5zhef644oUbNG5l7L4faFnFvyzBsZpaIW4OAjM6ccaNyURGaOb6jV4i/uER9XWmmloRL8zbkufhwyIw6IlAvSAt4cV8EEej5pH7gxMR9yoIoVqlIgnS+Ne5fCHuio4Ek2rJDrqlVhStyD+0++fPG1xAxblAs3GzdkSrkey0Tm6Q3M6cRpmTkzo8hEMrOFabxFFvSXdBPywgMmri4GqmeOnTCM3KmAAnPJRC8CHMy5br+pAqk4iInkVGMyOyvfwvw0SWwNitdnLV978CE+HDMeJebOzcA8wZuZexMi8ulD5aI3QpER0qljNwhG93TIs8Qm/ikCQl48f0U1MGf2AsdLdxyjjQhUww75Tcy9Ec/8jpJsFCIwR/Ujho/BcCKw7kq1Kud6YsE92urSJYkoJm6oeeAdXbl8gyTUmDclkTkSoBojeejlkvBU4bt27qOYRFQQPQIv6nvveQNsoIAqRe1lK5SsXNOnXElVgG5vxeBtVVT9W3T23aejgHduP6C8vL3YjgLWutBNcDUwD3O4ocI6UkF4skZlZrBTMSXWfoiVxNxIdesaxLMJBDJXqZaogZFbK3rqYCvk+pbiWadA76bJGap5M3MBSG2eokm5frXAnwKRNbnlojbGM0dXhOB4imGmdav2xJ8aDD0V2UnglJdDmLz0uwA+Qquvvvzm5VffYsgidR29HW8IVfne3z9wxMQbCg+Lun/vMfWiVy+/M7OpIlQW2qiZqfQicwBG3bVwIXeNeQWTmf+NN/lQBDxFd0U1oYoJGhRmHSBQZH4mLcwDBvVqgTnw5ZlDCerYIqtlyKkxj2LICFkbmO9HcmOG9kdZIyG9cmQ9oP9gjXkyhHimXIdhSAL4MzFXjQrT4yg+tUXKmtooOK9P3+RoQyIDPrqhPkbxPyjoSkWjdiCytqWxc+GpA5CbNBozTEx5lNkAQC3J0V1xpaytzD8nyUYdG2wRn+qBY1YMqo4e+fsVQ8yhQ0ZSf6OKQkJAJFKhFIgZOj6SY9NSfr5FIQyKb2Vuv2m4IpsOrAhK8BdjIb1NE4MnwjWqOghZ8H0X8q2OJPPmLtSYdkOSUy8i4KZK45lPFZXND9ALMue63BMZ4CLhRwOHkB5NJRVzcYSwgIAPqR49fEbhYNW9W69qVWtJDIhVZt8hIdUYNSGqBxKbEGRL5nb+jaGddW9Av+Lmbm7aon3DurVGDx23KnHDxIipkSGRG9ZnfjxkZMuWHWH2Sgw4zOYfzWRkBGFwhfzoTclr00SGvOSkmF49x+ba6I04UlHjQSoyvanmqcnhvVAfQZMDKytz9YnSUVOXmNpBqESv6U22GNg4pqkg1ZPHX+D9UuciOCMgs7CRgMSjNkANniRUmT8vRwOme2qTDuBDi6IGQ72S3i+9U4cweel3AXwoRu9e/YBoQz4eQeMnKgg1hXZ888ZdvEJHzDatOwDsYM6QQ24MZejzgMhvvv4BLwlVgOTnzl7C09279iMEdYG3jgjkutvB5+mT50hCeSEyBRJPVDrwl9r3jet3EOfunYdmBq+4hxmFq6Nl4KVCALBCLiCRDZsQGBLeunmvaZOWSIW2CKRGcRABCb9+9T05FKfuh0YAIaGqQEKEkwqGyBSf3Frv2f3ZqZPn8Oj4sdNQK8AQ8lO/oiaLhAhEku+/y6G2C4aICTHwFHyuX7sNVvU+bISGhUeIj1J06dwDcWTmAxONHgILzAk3JEHCWZ/MK/SBG8/8ne/fdwjhCEQWZ89cxEtBcoWpsdeu3kJZwBxCQlTEQWGhd8AMRGRUHWIiF/J+iQbarm0nPIUAqORBHw0F6kG2nP/OQSroFJAWuYMnFRMckPy7b3Mg2O1b9/HqUXAQsiOtHLJduniNuh94UqVdOH8FGh+SILsZ02eDG6qlYYOmKAtyodaVuCJJYcCBjFA08ERkAk20Q3pleNcB/sVRqLDQCWCLaOhpbwGfyBAfdY5Wh9whAxii4eFNIT6Y4C/VGJ4iC1Q1WP3wfQ4qbcrk6RAyNWX9m3iBKgWinb1wfd+R0weOX9x/4fqqrbvmLNx84eKBrTtSPlmSnLHz4METlw+funLo5OXD2Wf0FY/ctJAEmcIARIuiJgdpG9RvQk5mUEaEoNphJL7pjobABcXBi0AqgTnSQg3g3eFt9u83CEzwChABfxEzNiYeN+h6uKJKkYrel/s/L56gmIiDYuKKCkG+iIP6uXzpuoUpehAVgXinKDIqkGMaMSKgMaB+INX2rN1Ijmh49ZAKXQAigRv+osYcwIcIJAAaMOSHnKhtgniHMHnpdwF8qAi8FY4ZhmijGvNXjRrx9SmCpzfYycNEeAfDh41G7fDMWsEN3ihurl65SSYDGhyNVKgIxEHFcWyoB6S+Ocqh+tBzSAtDhaLzINqapFR0V1QZ6g5X9EBqkXiFLVu0jYudrDsRZ9YlkE7Jdb2KLGRmI0RHTUSDK1+uUq+gvhLTUFAoGoIIT9HsRNayYUM5JOGYGi+y+XJkBLihVkJJyBX6wQNHjx45AYYolMIMZHJcKzIdRGTApzBbG9fsoydFpj6b2LIm6hAKJiKD0JoxqMpsMERCZIebQwez0WQzN2eRdrN2zTqKQOUi/hCApg5QJ1Uq10DVoSyE+1R1KAKY44aqHQnp3Y0dE0q1RIT4qBMP5rYYEfCU3o6VKXp4KeCDyidAT1qdQlp8ndr1MS5CfmokMtO/8LJQXuTu6MNy7kQSaP68RShLhcAqkEdkeh8qBBGor0L+1auSHfjlwdwZq7nO3VEQ8ERk4EWRgBJgArZUfLwXUkYcwEd1RVqtjfk7RhYQ4Mjh45Skb/BAqjHcb9yQiUyztu0yMl/DYIuEAL438QJV4WHgd+0/siQpbUFi6sGj54+fOXHpzMFrp6dnrB176eypM1dvrduyZ3FyxuL/z95ZR8tVZP/+n/feb9Dk5t6Wc07rvQGSQIIECUESEtzd3d3d3R0Gd5fB3QZ3DQkaXGM3NyS4vg/f76vzetIBMgOBZui1avXqPl2nateuXd/ae1fVrmtuvf3uh2t3g1IvIAvu777b3pANu/bZ+wB+0rp55p5/qy23g+eMFyhsq1lOgfNMRWVZMKYcVgD6BRn+iJYh6dZb7rTwgy+J7HEgj3cpzZe1T5b4i9pTTJ8gbwxlelA8cP8jdCLTof9iUJSF4KAYz+3PYVBbv4NRZfmRGF9Q7lk/BT6PcQhDJrFyeAXi4ynt/a5NDQF8JGCOHqIDkNQN1t+EJzSjJKX31VfeqM3JaDGC8BcyanmlM8ACZiq4AB89+Vvbgk2MKJLvLXOiIp4jgnzvP88CcHaXnfeA3UgznzAO+WCQM/lQIPPMmWecyyvU4mnT+pqHNN8RVjoVM6df33muv+5mnpCHnM42b/8B0MNPinXn1QKfC6GPmayo8fTTzjIkuadBeXr38ceeZoQjSWB0JMuOWtzZUQC+gnw3fBo4IMAmCTn5bj4gVbALQIESK0eUOS6ovdSIzkVOyOAvyvRIiAV8VXlekHJgiOfMugXtTjj2mBPzsojR+Gja7bfdTckMibJsf+satd1HR8ThmAoDo6IlCDo91cqh02oviJCVw5Thim4IPBn4ErkRaAufxu6UkynwYerSNNANvKPXqJRmMmZQt/nkL3DT45lmlsJN80X5OmijsWzrrbaHkssvu5p33ZuURqWekwx8tvTJRjOt41hugY+qLkrffLOtzTGqgDkoUGSjNGqhvSA7OmmKDj+2olgtV2Z5+rnhI98b9dIbH7z2zqcjXv9oyNBFBs430/xz5RcaOPj54SOeGfHKw88Mf/z514a/8qP2lL4L8ZtusiWFQxvVgbNUYRynvTaPEDO+uFudaA7yYP67I6yIRPKaUZQ7C+KN19bEyUkez2Srr7Z2bRenxNT2/gR5YwxP1Agn+cKopx9RIyzM0GlqLXh8h0uWWypaZ+0N0AR5C2qTGo3PA5AnMLZ3r74Wp0jXydZTlaaGAD4GAGp/olvu4TXzakmrZnk5YpjE0py0Z++99qfxOemxNJXOQEZfGvHa888Nj6QP/2gshJU4mNIqR2lJKk9ajsUFiUQ1gEGMDXLmpSHuu8+BXpGA7y6nIH/EEYcfM2e//v4r1uJGCnyRnLXrr7fx/PMNnG/eBfkCeTPN2GKXBD2Xl38XAKUuSqCKVPLIBjYZtmgOKictoqddOI1CDlDK0ON40V5qhODeex7wDOlykKpWORYp/8VhLyfyfmbky2Pmh56KnJgAE39Rpkdap4wv6L/qyn9YT6FqRkhRymxVHj2XD/DxOgUiUkOHLEVzaIunHKarSEsx1vgwpfPS+AxS9Ky97E7mBnkMXgfsfwiEMaMk8m+6N6GnogUBKKnKC9Z3jrl33GHXWN2dl3LkEYKKjQ1eDHtWaoEv1fhol9UEEhy2/kJm9BePrnGy2fPyjuekflIyBFAac2Gq8UUynKFnMuArhEUe8CInbdTIzlwFT3iCaW+Owa4777iXFy+95MpIs5cpj+T2TVkE8MVJuVTuGLrUsosvvdziQ3rP13+2AQM7Fhs476ABvZZeqvdCA+ZccMC8e+1/8Gprb1gqzVoq/H/QzMoh0yXfzo/3PrfmDcTUjuhutulWJtXynL5FNvIk8pO2ynNH6pJb0MOwoLUOixxVGKeg2Uzj51prrpeWlib+qu19cx6eWIZRU+KwVpmVdxXGlqTUF7VUSB4wOpYlFKtzR8vpT2Y/T4GPycwCY0ONJ4aOepJqU0MAH1zu0DqshwTDPpJPJ5EDG3MvzWkVgOHHeHNTi3KXohAx4HOarifKA2j+IrjIMagBmNqidKIbyOaJkbqsRfL6df+4ifwFub2oHV4zLKEnK+uGL6hmPGQ+j+WatKvC2hnmGMAHdvOkU+4el0lFWOt8MsvxE/WeKQs1lgFJsW3a0EBO3oIPqJaMLmhAnqxilGXqvvnGu3Qqn24gNJS1NlKSUkxDvv3mh/fe/QgEL8thbMHNy1tvhlAFEuPJHAJARiSG15kkzQfLqMshD//GshcoHwbm5YqmURhNfIESAwcl2833xsh3yPOpHIvU/srLI/NaoqUtnu1pKa+gw1q/gNTDDj2qIJcotPEWVRt3YjmqUbUYgXCDQQtvPaStaxS1LsGXkhYuPE4SOWet0PEWjaKQiy68zNou75Lh5JNOhxhEC+CONf9BSSoV9CZPbr7pdoihUfw1e585eX7ZpVfx5OuvvkeTzWunC1VccP4lVOcBBm1UYT6YqkcfeTKvaXvddTY0x/gJ8EHeRLlKKc2KDHzAMk1p+HGH/I/TUrmQz1UKLd+P2PrrYVt1Pr3RhGEbTHp+o0+f3bLr+c3OPHDpSt7h10qVmj3wMAHponDGgmWYYeLpzX1HH42Xk7S9xsdnNOyU9xbiV11lzURumUjLLxBpBIcbfnjhBZfyhREHD2kgTFhxhVXT0mqJ4S1YzQgqyaSINCdZay5Ll0RI6J3jjj0p1Vs/ldPZeiKjjLfgD1XzcJK8zOQ3rlE4JRdk4sQSGBsB0GyV1oj5U6khgK+k3QB5LSa6w0pyuBak9bTV+CMY6jTMzz3heOaM5G8qyWbxK4nWKzNa/kMa3HlpOTmtIpXDOtcM03fLSykjsxencrKDMtqZEWvBiyr69O5XktqYyMfhetu0zaWktTATxos9tLYLtR1amI7DXoFWbafIaDeDu59PMnv8uFJrdmaI25WEjSxFrUvmtSRS0o3pRreiVtY8IBNtC+ATIMvJguiuDTfGKTvyUiZAXk4Tu+nv0GqmiczKJery3VgbFDmpRVRR0XpxWTOHy3dDcnL5VWUaFzUtxWFJ121JZBtShT07OS19Oo95bhQzK+Kg1abZZpyhO3QuvNBgBnAheCGL0p6onVfMtDZtFbJQmfKsNl6gP0ZyU3jw10pXXnoZJaS7i3iFRlW0MkulzKOuywypyE43Ve7NivbiuKXFoLmYY+4LM83CSb+kwJ3SEICvGmeSH48EVVoWHTDHoPl6D12gPGRAefCAnovON2Ou2/9KotlycUdb8cdjS+m7Ja13IXVuII2l4Qh/9K8bhlKZTFtteY6DCuYvsXyyVW35so/S2drCUm9R008qvZMleFjQ8nG1ZvuLO8v0mKsWiUgD2YMrp71WntR7zTYH36nd3Qq6mZPtcsJ6gGQFFBYYk+08/v5TqSGALxayWCbcpFhMz8rUSgdqHLZ0Gd34yzODsdKscTb3XyRUYtI2Z2s1vljc4a02uf8LmmQsoLYHDb5tcmpMP93MLhlJcjYTZjrbg0vCElMUBiVCnzaZVxb9Vi3586+FxoOwh5YaqnIG8Tnrj7eG/zjNMrN5B5lxwbBb0AaUOOzzcF202hBJzhYtq+Vky8QSaA88l2/JiKWeZAToZmw2bOhDUmmLKyqGPXcu3wT30KJE2lISQGB5Tdnr9rZqx0naO7GUqThIv2GoLJ3aA9Kv0xFusrEjkf1r1pFsa/MF4PvHtTeCevvvd3BKpwmLNAzioMVYoswB/+VCEk1vkSx0D8s0WRJcLDk9waT4Zaw0eUVNe4YSMhtNDD2e4bJS51OOOWcu7DfyTGNBmgz4fEjpxyOS+WT6Uo9Z+vdfaMEVB/YfusAAvs0x13z9OjpmL0R9oqiaLWbbiv+yB95dlgvzOgSktWQ1PZNoVC3cu5vigJvulKomUc+sRUF8VfN6oo0siYYhL/bQrrpaAtJE/hZtEYu1vSzRTEzyAIk0B/i5GRJJpD0Z5KUAuS7yo5ekIu1327WOFAVZdY+4x3OaYPJSDuqpSlOjAF8zpSkvFDO4N9MUk8HRCsJ/WUojd/gUUEexd69K6cBdh7w74sj3X75k7+3Way3+eF9zMV8o5eMfr+uqP2beTFORmsDXcMnjudYL00yTpbyU2fyULKw/ffLhy/+XSvnWuIptWuzo17ujkuTak3YdsowVvUJHIX+Mi1FXSDP9UmoCX8Ml6+31z5spTbZx/juBL3Lwjv938LxYzETYcFGlW5S0ZLLZXFLMzfLjfTKFAuZsLlYY3foSmumXUoMCX1HuSXslbN6nyTZ8/Sv1iRe9laH+rz822XmRetn+a5L9WfXPf/OUkdO9Hvjsn/KXNm1hqX839UnZ4RVLTvIhsIpdS/Vv/WKyy28q320LRwDtzKpvSH0qh0Wk+r9+22SHqb2TOR3crs8zjVI+LFfW//WbpwYFPntwouAIT5/b1T0ZFP5USrR62GieIOi3GRv90h7LP11KFzSmdUq08lAPsiUt66drC/UZ4rCI6QHmRUP7y5NwwNYI+O8mY+7UD9pEOxljLS5NzVslLR9PsUW/bfKcEYcZ4vccPu6X3wHc44YFvkcefuL6626+/LKrywpAkD63AuhzSL+Yxo7pmmzNrhGSx9hzz744lVP9nyhddOFlv8PIJL337kfWlSZ7bmjwntulllxuySWWrX/XPAegvRVx2AsvZRV5wSPc0U3q3/rF5DXQ558bXv9XfVp4ocGvvvLGffc+6HX2qQE+WtSp0zj1f/22iYa4H0895Yz6f6dpYgaa7JjWtEsNAXyeLdu0dyyjbVzGLM/eluZbb7nT9m9R8Quy2geU1R4Lb00o6EhDrXfMm5btMpt1lj7eUpBoI4X3EHlm8zY37zIphb0LFW3jsn1hO8h6eCzkcgklbe7Nh51rjr3hn/UVeRdFrKV9/n3wgUct9HFNmEYTn2hnok2hgnZC0cAZZ+juJXyrVG5yMURYK2k7VVEbxzyMY2nHNql6dvTqoXA1lOANKybJI9z7AAqKy/LO2x8UtDco5eHPJM/PPqhgPpx/3sXGo2rYpmd9qtvMrR0KhWQ+J9LX+HSsYJdjtkTaq+W3Upaab4k0Ml6h2NdefdONNZ89hVhTS7T51sw0VXvvtX/vXn29EEwD010OPCQPM5AJcy2jPh7nLxntZUm1P3eQ5SFb44GpaD9jXltVaOZLI15zF0Q6LNRDG7DdkDZt3HPXnHP2BbEM3qr22eS105suTuuyjJl+N5yiJn7yuXdTuQmRdnXktc5jGsracMfDNu1jLWq8VBRBxyU7eZiQfN5uhum7ucmplnDB+ZfQuqOPOj6jDTqWRg+xnCIymF25EF4MqsjgnUwev0Xt37QE+vWZZ+rBGNxrz/3y2pLiAmP1Vy5sHoTU4S++UpILohzirFjm3xj5jiu1SEdyqpjVbot5ZWbSy5bqVEd2X9QyoSGAzwNv330OnH66H+97pDGWXSOC89xz9/30qPcB+Qz/0kst7z5uVYzcrbbczmiVFvvxR2Np7corre7BXNB5cn56G2pW22VJm226lUWnIP/OvP0H9J9ngUzY6ZbVHt2cNkYlGqImqU/vfj4Y6ELItvtue1fCRtz6ihIZVnP2629JevKJZ7NhT+maa6yLCNoqd2kWuOWWXYnB4K6lXWutuV6/vvOYXZSMJFkmYjmASgLKWJJEaeTfdJMtW7UNaputd8iHXbXk33yzrY2bfnHnnXa3eOV0EqZd26RTHv5Mcgd99eV3kcL58R1NId27Bzx54NEvlvV0FEEn/TJ40OIuYacdd/McFotX/Ez3xEEnP4FCM4oXF1l4MUp+68338jUTTJvceS4hEfC5rxP5UvfZ+4CCgiYUdSiVBlpg8sJKn/DzoIIbvEvt226zY1k7itLhl1NQKU9FcXDSlQR/nkJy2r738kuvl2RoU52PiBW0cZ2GLLvMipSGzFDIWWeeZ0BMhNpDFlsyq4ncCGI+eFaGWmNZpMA5ZN5t171ymmiLwv2tt9reYukedDCVqvai88TCX9uhRc093sXt5rTK97LqKmvSiba+DXxn/P2cVrmVXLLZW9SMggittOJqOeEavcMThN/TWLu2tZYVY6q7TqAuusgQs5FKebFQE5DRWLnaqmvx10cfjqE5Lzw/Apmv6IRlOhbI6anOXbbA/AvBTPOZWiBgnbU36NCmQkMq008sHKfetdda3+xyx6WpIYCvrBM/RcXnuOLya+DC6FGdtA1rJdFchM3y7DPDllh8mcWHLk3jGZ+77LzHPHPPP6HrU3gEmjASaPDrr73F97RYCvHpekzjDoXMGzN6PIw75ugTzJrHH3v6E0UEIadBCjLAU6obo9gk337zg0WTydbi5Z8QgARjj3cqFMKVV1wL2fD9U4ULpSFTrAiCr73mBiwseoIn3RW2E2mm584+6/yrrvxHTx0EphbI4MWcDhK1SmllxAL6TMIjhr9KfqQkkd7k46tmoMc5n19/9f3tt90NRvhkXkFhbCJto+VFPn04HI5RPj+POvI45hUaTiFme30f1SdepzsgAID21Aob4TakfqIAsfz84P1R0MZPn8Ew93h+3LEnIay8yxwz91zzffbpVzCNWd2nkTp1dJRXfCoT+5FJMaNIITBqoYGDKDCnaMMnHH8K9d7/z4cduchV+GQV4229dTeCMF4ZOmSpxQYvARhhSSBF/CyFACTw013m/h2tAA3QjLxdcvEVDGOfSs4qlCafDO9lll4BXjmCE0UBLgClzyyjsED/pZdcecvNd8SKyFDUJnMrkk89+RzqJ9J17z0PwGQaTnXY2q4lI12J1yHSh8B8RjivoDj+6XOcYxSjl5L9/N13PoSBbTrmDF6sv97G5oCzoYYjWmmvjVN0KVgB4l926VUUgnV57DEnIlRIb1nKvoEP6eVzjdXX6dTJS9J7737UptBBkLThBpvSKBhCG8crrgQ1TtSJ71heJj432XgLeEhd7lCw9aQTT0PAzGrepTmjFFuTEmLN6LCxqnNyVmjIQIsQsLffeh+2z95nTphAOQMWWJgX27QCAxP4AktJQAeMpXZegXi4Cm8ZcV01wSycGgL4aF67DmzQDEdW6FJsu9rkA96J3M+W+5wCReR0ZI+BBE9JnQrc4uQziXTShRdcCpeRMJtyeZ2xZ0g89uhTnrX4PPywo8mM4cNYOv20s3iXDMABooCqNSbE7IsF03Rnm0JaTVBYlImKXof0HH/cyYceciSzVn1F7TJUYw0GjwGaeded91HXIQcfwetuS0VBOt9/7+OqlvxuvOFWsvE6AhFLk/IM4YhjAC75EXSqe+bpF4rBzgXXYkHAWMXIjOT24hONrBAiCXcqiAjtOuzQozKyCzKK3+sSkBUfimRGYZamg7oU1I93fSKYlFd8J8cudHXnnnNhVUYu4zCrEG8nn3T6aaeeSWPbZHxZJzXMUSPvepx4AMOZU07++5lnnHv9dTfPN++CPAHr4QwCTb/0UPwF088Ml9HZqS5FdnIvpGRYBtAoUaWLCo4Uh3NvxkejiacKZiCDiKXLBz/bZKCZ1T4DT6fYc1KSnUUTIlmjpP33Oxj6XQIgEmn8F2qOe7shgKMbwkimjWl1dNZcc85ryotyQSBFlIB2j7ZiR6RPXgMHifQAH2gF0QrSnjIKkUK933z9AyV78oOHsI5EF8DYFp3WgCTg/uKLLrco+vysI5iZRY8+8iTvIid8nnjCqe06T2nzy2fhQZ8jDj8mze8hgPFh+u33qOioOH3NFAWpgFGLwtOifLgvnCyEO2y/S1Wna/NS3j0PYXUZfNGg3WSHwPJMnwvRvBEt4K8g+xpSoSQr3xeZ3d08QQPIymbK/esJtoYAPno0J0uBxljafgr4Ihkd1r9oDy/CNXqOXgEdYNMdt9+TvuKznORkxuPLyNffRkeLpLXxIqLMrF6U44Z/Dz7ocGwT9Cxgi4GKlmGjA0AhMwOylhj0O480kCLWLPrKyyN55YnHn0G+KaS+olyI2O5hiSpKvfQQf0E8idcrilDGi4yxGabvVlRgpVi4QMnuS5/x3mvP/RgYqYYFu/hpfT6jeIKx5LJTzlDqterBYDCemjkO4OEz/Ggu0ENzLFXujhadgTOrW3RcLy+Pkk2kWHaNB3ksOWbU5WX0eQAwpCEbLIAh+XAuLSs/RkYOpvTguhU9BsnTTz0PNJCArbKiitGh6LkOvXP5ZVdntQYKHBdkwaXSkpEDN64DvlhxzIxWsYDPHWEOJP8KfAVFBMnL4xaH6DWeMzzdJkIrpisgqRwCZcMB7Ay/TksLwoIkuJz41w2BZjeE5ynwwcBdd9kTRhk6q4pvnJOm/9CDj+V1HwOU3HzT7RAAbS06BEaPkw2lslWOnUgRnMiANuqT/Afsfwgzk4UZAbOcxxJdZmIQ0zrspwqe6AaadbYkUuAzZ3bacbdBiw6FSN5CvNEBPQyNuWMUvtevf6Iz5jxEgHsqVDhqBBJrgwzVD0PNZDgxE9Bwuhga/vf/mi4JwBdJj0NxLsvLBJH28VUVe8LvwmdedKdU5OIcq/AiVOS1L3OGXqZRMNO8SlNDAB/GCFoxX1DNumS7/QzwlRVnIhajJyluCqMFKzKW8bXpJlumrzi0CQ2+5urr6YzVVl3LGlBWZgsMBSstATwEStJhzE8gKdbMT79OVGijtNiiFCILOkgRCV8S+Wg7FGMGZb6+IrTRKCgaUMXUyk/UcgenJCdfTC1FfaoovtDsIFGrrLyGVU5mM+TPvYuBY02nS2HgPAiLcvmPU0i+suzfnPwpngNKilccS2nlXT4ffODRqoLf2hZmtBgCyiFCfRwi4vQIdx3k5HovChwT6SzFcIiCWbosNYd+IT+fWLJkZuSUtbZgDQLmZOUG7VJAwFhGHG/BZwYGX9AU+DzrzPOwCqkFS5wBbNblhMjjFYqSPHAGLCtqRXWKwAcb6Vn0R49tMpeC47ge+DxOVlh+lUhRiD2oxiuqGFwytTzniQGoqDCckY6dJgIy5r9ICz4luVw3WH8TQMQNgQNHHnEsZgcZUuB74P5HitLy3JWGEt5l2Ftrhoeot2Y1aOIvtmRpsmeU3r36miEIrV34vE6NKDuxBj+4ac7wOoJkoS0r2k1edrSHAKMP6yEJwMdkb4nis1PhWNxMz2oU9e03P+Q1p2bDeppZSpqgyLiMykhBtzw/USP2kAdRLMXQRcVaz7XkG/hycjePU5ipvNQFm+1ue0FuR9jCqDzn7Au23mr7kpZAPwlBWRBjd7GnsYELLuopNq06bhDgK2oSY7QzH7rjbSqmIN2is+VQ72E2RjFXZ5qxxbLIRMcXHo5W+Jq0WETnf/7PDIARPdquO8mQWrqEzC6QeYC6eEhFTJKwbJutd+Bf+IXctGsNFOkZsMDC5ZqFTliJjQm15Jy9z5xwn0qZZHiLIe3IWmlFkeAPcaf8UYoGHgn+Eo0oaEO77FRA8HKIU1JSEDQb4OiqzJMeeDTwqy+/szJCwq5xGGEmQ5SaWvLsljLwWVw8hlFM0FUnKSS60QpTerRCbyXSUAoy20GZtLSfSWUZelhSvL7ggEV4ct65F0E2bYfzifTEt996n6Yx4Re1Vg5zeMX2JhofVGW05AKdsJGmYdHTTEc5jRWfle/rrL0B8ERpq6+29keKh37dP24yNBcV0jIliWEPuzDeybbwQoOxyzIyysYrFlMskxMejlcUaPgAPdZ5cwqoU5DGx2Tjnsophhi4+YnimFIRYkNHf66A6ZAx/XQz08V85wkoYBufZIymWIg3YTSEAtOGoAe5vT0ViYe/0PvyWgIysmPrUUKf3v123GFXW7i2640vtM7IC8Mh1d5GJASj2w5W4wUgghBaPSzI8ULhiA3DgVfGay4sCvHpQRrovTh5RaWk4/gOr1oVFYKqS1qYYkSgo3TpogXIg4GurqBFHnCN55T2kSJW0TrrDUsvtXz6c4LiLbvfjW4OGW+jlWFV1dZdDGRm5Yx2a0I2/375xbeMRMdtJBtj0Eol6I8S4NHUpsgxvjFi/vkGouuQkx7ppXjgtakhgC8ntdlreT104Yjhv/SvoUOtdKRPiooU5HnSy2G29dIM7SFmZCzdrRBuQfVD6wiZoNfkpMggjp7AbRKWpRl5kTQtlr/sFfagysoMhLOQh4hU5cZKK6JSJmTEnb/I0K5oi1EINmWqGF38BMfz2rZdlDJl2opagzNVhRDlvF0RkHLaWODSamezvBQfS3mLQmyZaZZ+63ctikpiUktamqzIym5TOJm2qdus7xerIfBJPkQ9iqVcZLUU6JHsjptZd82Y+R6Zbp1pK8myLmmp1DR7hnCr/dDJJUSKEoZyffhhR6ckJbpeyi+6g+CVLXRzKQoRtEyby59h+m4eYGWtRXrgWTCqYeuGdVWzvaxNLXRKTqq6M5iwNq3w0tcZXavUJg+96bGS64a405MQ2MbVxeH4hwXSyJ7qknEQUbc9V7MBMNFml6rCgEeyyj2UCgHsXEKrFtZBJRRzP3E55onByG2xbMyiGFAm3nhk14fFg+8oHNSVEYTlhNcV2ek5bdKw1JVlAZilkaS0oCBDrneUFq94ERQDqix7rYqKZq25EG6GMh/y0gDod5fs0FUWm1hjvEORdVKqonD9dBzGRZoaAvg82Wblg3Rfml+mOA7HaNyFFgjAKKNNHqlMl0JEubTYjPzf9Ch/WS8ryczJaptYLAXbbCoILs2vOGzaKinUJcZIXLPFycRgC7jqqvDRcJZXpDPnSSvyILGHztJW1WaOohwWVpoyGmMtYXNWFGCxQzsS3OUWnTZdPtldwdEKQnzGj6UzJS8f0CeVMwtiRijgF9NRYcpTqjw4DUa/mMw0XoSe1BXgYi1tKSW5mmst84IAj7FYHZ2VndWm/SimzRjtPvUIicPGb/OB0mAOvWNNNiXJFZnPpYChJjIVoXSUFoXCPcNKejGgcCoGWfkTzX8TVtVM4z6NhVM5KenmqgXAkmOx9Eiub0gcIjwaSnKaitJOcVvMlli4VlVoKbfLKS9kn0UnMg1J5XChZTZsn+rQrWkmoCyPZE6mLlp/HCZCd4R7ISOkKGq2bgkbnsZpFXjwoMWduaQokAXNlJ4GzElzzFUUND27KPdyImSvaNOSxSMJjrzxuhkCbc47bfkLA3m8ws0XNZEUQuCpvBQR8z8vFaGgEe02mhgLgCv1KzmN5aLujDVjnRoC+BowRWEoTvEvy8TUpJJ8iNa8ot/rHOJ/fWrXvjZDWIoUzTQ1qahp0hD588nwYVCzGNfn+TUpo0tKKdaqZUGqvb944pxKy+M/S03gm3LyvFqr6KXJwDeV481zkSEvKwWwPk8z/bvJSpb166nUT5vJKdUK6/+aLKUTvDW43M8GNP7PUiL/QDrKrKharU5C3NxplP5MwFerLtmJVp+nYVOkZbh6VdEiWPs8F8LS1Bfyb6VItkYK0EWZYAb03LSJuvGLs7Tt+l/ftF9MHlEleZ3c6vo8tt1sef25ZKmZfn36cwCfz77Ui++fSF4ZYJMtzjgV5FOrnYFz8txNjTHy88luprTGopYX8iE2yVRqrP9WMojUP69NtmXqn/+2KZI7Lx/ilU6xxrK21NqUK8o5VZ+nmf5b058D+GabdXa7ftMnjN7DDzt6WozeaZSOPeZEO18ne27jd9llVkyfWFuxt/jXJJ9nTGeLo486fsEBi3hVpxYQf8NEG13+T6V11t7AC1n1f/22yYKx2aZb7bD9LpmfDpY1x+xzvTHynZNOPM0OpvoMzfTfmhoC+BiEBx90eBwWpL1OZIvMs/EyS6/gnaKemW2vden8nVeIvNZT1u0qPXU83gPbmS3T/pKEDWteFYq1/pDX6SsPj6IWgPJasY3DDbwmKRfCZnhdya7ZrBZevcjFQHK2kmKieJnMS4rezR+H/cBlrcGZPB4edeRx/mnjK9Fps7JW4orhDrmc1sty4e6hnuHyeb/VpmPzLp8MNOH0084ye0taUqTAyy+7uqhdkAV5ke3EcTO765KXYojO1KqzWSayEM7hu1O8MOcazS5zwzR7k4c7qBTOS6Rk3HLzHcVwW5tLdvNTPZG/es02h1dm/XpBS67O5p5N+9GdmC4RelLJajHUDbzrzvvSM7x5LfC1abdEHOJ6emdcQZtLQEkvnpqwnBwOaR+5wJLun0o0M1lQqT2j3QUFbUsohb04Xvo0W6zO57S8mNdKesqTZvqjUkMAX06n0DzkqtrkUZTK06bTCx5aHkvknHWWPv5p4PNzi74l1bt7fLudPUp5mZkW5Vo3bU4qmLUD7wi1dHpniXUTi3tPXVLuRfqMFt0jbe6P5NVyonBa0a6AMeT3kCiHyFQp8JmMvHzz3oFJNtSxinbSGXzJ42giOe0aMxlGn4w2VZge0+z8eRm2psQ4cvFFl7cosFKLrnMb9fG4C86/xA1Eg/aw9M9YU05WcatMZFmbA/wQNDcG2TDMaydXUXdlpZw0YT6Em2jvQiFsH6mGO+HIc9ONt7mXc2FvR6J5yJ64rPbEFYXIfMKN6f42k0XCDI9rnKEu3J/uQW+3dI/75z13318LfAb3nDagwF6Y4IMcZu+tt9xpnrjLXKYhOK/NE6ZhMiisaqOfC4m0K9ud7rcsvb179U20FamqTZQuuZn+2NQQwNc57hNE0KdSMD1iCVyXDntF2uUYacsPPxlaH304xicKDXxRCMjTr+884zsnfvjBaMMl+X3M/r57H0wU6gM76/PPvvYJJ8rh9WEvvFRVKAgLrs+1MB5Gvv42r/B5xOHHUOaRRxxrWd9i822++foHn8fwkLvj9nuo5YH7H+Hfd97+oEunPhhvCPceu+9Du3ho5E2Bz0MIIn3SY911NqTq4487mXrJ42x9evd7/72PY+kvn0z4jErXWnM9b9SEyI8/Gktmylxk4cXIkF5r3anLttu0jkwh5517Ee/6vEFeZwkuufiKGWfo7oMKJZ0P8dE6uL3zTruT4VNFeLXiU5QqBD9plE9KTD/dzC7w9tvuhsOwFP2OVkCPd8PRL7AUCh9/7OmC5iTjY1nhnr74/Jvbbr2rqg2D6627EbW7OhL6PmQ88/QL9D7wuuIKq264waZ955i7pNNpE3U0O13O4gs0QAl1xZqx1l9vY4j86svvvFcLqigcMsCyFPh4F6ZRI3+9/db7OR2KouvJ1lMnCsbrnnXPWD7pMW//AZFinEB/euQWFKMtr7/2Vk/d4rjQwEHw36cOqOXll15fc411ybbVlttRwsILDaYK2PXwQ4/z1ksjXrPY1Ap/M/0hqSGALx8O50Y6r46EXXThZQiKj2o64I9P2CCFFR2rSIEvkSlRUUCItmDIFIJpyfhkLCF/LtkKS0+FSIoVKYBBSE6q4wtyv/RSy8cK6kA577370XLLrhTrwFNJZuA4nYE1ClMXz33S9orLr5lFt/F2KXKRAe6Mv5/D59prrW88TYGvIEuKoeJFXpq58UabH3P0CQxj1/KBQg8Z97/84tuq9mcCCrz19Vffe7u8DzkOWnSoT9eSk/w2+hii8C3RgVkD9Cc6yQsZAF8sfKSoY4850fQcduhRic60eTvVoosMKciENG3ACsXyIn/NPdd8GW2Ih3ge8uInOpRudTIOUUN4l6pTjSnWUdyc9Fzr9ZR8w/W35GSz86WgQ7twg0/jiyOaDFhgYbrAHBsx/NVU4TLEQHMa07BTZ6XdU7GCjpRlfYOPKfCV5Rbw8xeeH2ED3ydkLRXuyrJOsKJs5hQ8htYB/RSYkdeCApnqzG1PMBMUEYRGeTocrahWtnyBvwsvuDTR8VLgkreAy1jOgbxM4NK0X91upp9KDQF8HmOJLFkGHhM4IwdViIcYR3kZcQY+hMmWRQp8tiYQvoMOPIxx+Owzw0raeT9OAT9sXLzy8ki+g255beYu6mw5Ak35/Byy2JI+Hs/YG6dInCBgokgV1gQZz3PNOS9fLr3kykQmDH+ZZnAHwoCns886P5LPyBmo96gjj7O2wifkpcBnAniXkdOuoJhQe8rJf6cuRzb1iWO3DjOTEmg1eEchFMW/MAcCGHULDlgETTCWFtmlKHLkeezRp95950OeXH7Z1YmcAMBHLfABUh7GXTrgzBeUZVj07Tc/0Jx2neXw4LQKSaUoUwCljVnr0WQDhSkw0QmBnCy4cYqcwZfhL75SDJfvUBpTl41fVDB390YbbkbV9NcTjz8TKzxPLHUJ1TKj0EOxYH3rrbY3qaYzkro080w94Am1dyoYAQ8vu/Qqr4xDgGeFggzSyXx8QCfNcYtqgS8WttoMT9U9kmGXqjfdZEvjJu/SRxT41pvvVbXdzEHraOajjzxZVaSc88+7OJEhzItkhiq6zLbznXfcG2k93dNnrqn6/XGpIYAPUUCUY8kfIsLQQvTRy/hk1rXoG/isWVi1MTR4aOVCmOmSouAathLZRyuvtPo5Z1/Av473beOxrAWKM8841yoMhSOmeR3P3nefA+1E71SQUQMc2gdVMIGXQjg2/oK2Ofv1J/MM03erKqCugc/1jleINMpk/LQrWJiBrywPpo9/84TqcgpAktGRsoxiMbh1RdmJVZ2Km6BwFw5+Z52XNHjQ4quvtnYinZe3jPhjFcqJioxKiXRAF2vgI8N0f5sJpIjktrN2VpRT1UpZJSw10HBIooGtOgYESz1WHYOEwW+M5i/HfRyl2JAGPjfWn526LILCb77pdoracottx2rphhpfHPYyZKMyn3rKGe3y+qPbPvfsizykdcB6JPRMwxYUpMuX5YKk+4yG7pdEQUFgC7wt6Fz29dfdnAJfSQcQM1o7YgJzA2s1PjsoYoUFdKVmMnxjJjaL4JV7zXGWeBd7wpOrpZGWOrSJuWH+G4jRnR968DHX61D4pabG98elhgA+T48lubf5jgJ1y813WHTefuv91HAohEBGXgHwhO/xYHhiiB5x+DHXXnNDRkGGHfz9M8WM5C2m4ljA16WoKtZoigo0hJlpK4kZ28aLodaiyResvETGIwPYzjheRz1xSB80GhSiWOGAoG2lFVfzWx4qExXqCvr79O5nDzev3H7b3Q6mz26rRtYAAIAASURBVPPll1sZjS/SerH1nbKcSh4z8883sP88Cxj4OhVfz9hNmWjHa66xbk6rENCw2aZbQR5NsN1tg53vLgoN13jUqejqNPnoo46HAKu96Er8RV1k9izSa7Y5sENvvOFW6qXw4487GVIrCo9u2xluu/mnnXqm47uYyETBhdw17TrbT+dCBnwjP83cYvNtzHn6l5ITzRMGR889iyy8GBzgk9a576x8RSHSF93nOMM5LU8BZMYviIcqmgnZFiqHWfYqDdMG8oCyyRcv+3hy8oTB9zZtKqCZqMClEPwKuGd2oRaa0CVXBnkcEBCL2L1cCAFiaeDFF13u6vhuS9+uTP61u7ksj6dRvpn+qNQQwNddl54ccvAReS2EIZReiIzDUkCrFhDt0U/CWRa+rLjCqknYy4IEYx0DdkVZvp0KAHXA/oc4rJM1EWsuVLH/fgdjSRW0BOwSHJzKMTNyYa3T360VAlgYj4CaPfF2fvHviSecyidDsaSoGIcdelRBR3z49C0cVgb5XG/djaCzRcfRjSw02QOJQQUBsXzw7fLi52Xg8wXtYKcdd2M852WnQzlKIgX21HYZR8HidUpeeqnl+cs85CflgA4HHnBoQXoxtczbf0BWTrFE6gzPodY10l6I2WbrHTz3WM3kc4nFl6EVRpyy4qkNXHBRKrURR0X77XvQMkuv4BJMP4RZY/L4t2pJ4WiF/OyuGyQo3Gw0apjgJBy3yCnmWA9dI1UKsQZawv1K7r6Kztg7xVrQcKXOs9DAQUOHLMUrhbAxhb/oXOYnK7AFRUgu6w4/U4gswQ0HZ0UnpV09tXRu+fQUaBHKyVB18BXKoWl777V/FIIyeP6Ogn3dKldGh26EiKSf8hZTnXuhfiw00++TGgL4Sopz6fGQ1ya7NoWU8PCzXVOWA8syl9OiZFZ7UzyAM2EvVUa7GciDjVOVF4aimOezUpSK2hRS1XKBd5xlw4kxO6qKiniRhM0KholMCNlir1lBZks+bILhxZYQg6Cq0EA8981VHl3+4oqs1BR0WsN0eiAZQz3y84I8m0JF3YLkQe6R5mx+Mc0Zh41pprOgxe6Kwr0lii9iq60ib0BRW0YyIeZVrOPiRqh8uLIr0q4dNzMj91xFu3NMahTiPiVaPjI8xVLxqLFDscLcNYbRFF9i7cUjQ0XWtAlu0bUyphy8q2jJ2z/NZDPWhaTdl2YwJSmH3Yn+WZF+2qFtjzk5B938jJwnWV3o00O3vpUU0yxSqKuy9g8ZK5MwC7qEXLiowJ3oHoykM1py3FnmofvdTUh7PK/gNCbVU10z/SGpIYDvt02Gy8JvvRHfSqhFtpmaqZn+1Om/EPis1hV/6yNZBlMrKc3UTM30p07/hcCHMdWmPVb1f/2aZKOmOqVAVc3UTM3050oNAXxoUt11yWwu3LXGw266byzWOqy9RXYqFYI/2969ntqN7Mx2vuQUhdVeqtQ1Y5dZWqP9XIVw4s2OGztf7H4i/8wz9bDby/4g+33ssYpl+doR6SVmJ7uH7JBKtMskq92tPJxt1tntUcqE07gpJa0622A3ZVked7/SI8Q6n3WWPrmf2PPVp3e/dDGhpIUgE1kI1ymkHLNXtKzNNPy0T9MV+Ym5miYz3ystbkguhEvr3auvuZRRKGy7sfwZy49ZDMFO3AVJiJudD4Fzy1rkMYXt2sxY37RmaqZplxoF+Ko681iQLWlkSVHPiJAN64MVXb5hb3TqI5+lZ297ju3q9vM4uPyTcKzVQOC/knD9dqItxx7YZd1zltWySVZLkLHWXlKS7C/3QkpBLvxsjdfPSzRZLdSCVlWtrsRCyVZFS3eYmTYtMUchoGmHbkiIdc4hBSYvYhoyilrrnKLx3qbYBF6dSMJdFkaojJYvZpqxxWCak5PeQBxr2mhRvHKX4/JNUqSVE+Ov+Qx7axdY23Ue2R2U10mYgrz4frckl39WU4g51qbVz4zi6Rv4KiHuub/POEP3+qY1UzNNu9QQwGdNxFcojO+ceOklV5aknpx5xrk+beZxwgDbacfdfFjC58+cEt1Y9t23Px48yGn3SSyQ+lR3kxs4rrryH37FMOf4ApQ/QcdjR4/q9Kjm4ddfff/5Z1+TOsd9wlt27bmKsWO6yOwtbzx8/rnhY0aPB1nShnj7fqJNc7Tli8+/mWH6bh26OuOrL7/zsQcynHfuRRM/+Xycrpjq1CHWeeaef8ACC/OTen3eNhamkJ+cFDVGZ/XqWXfvPQ+8OOzlOGwVJv8Y3Ws16uNxNNxrlLxO4smBBxxqYCID2cj89lvvV7TQvNSSy0GGSXIDAfGRr78NZ95958PHHn0K5md1E2s6DTz80OM+KbHSiqtR4KSJX/hdiu2hW6gL0iVPP+0sH+R6792PcopZAG56R2SkXUHbbrPj9tvtXN+0ZmqmaZcaAvjQKXbbdS9AzSZqZ83Ry0jHM/mJrvTcsy++9uqbHv9955ib4eQdHoxqVDNbZBN1By7lPP3U8wx176LgFQCFJ2mNZR0IZehSAibtmmusC1T9n/89/Rsj3xmtq52tN1nJKshO9IEBnoPFAEGsCx7n7Nc/DtfB5HU4lC/jdVc3NMzeZ84xigH17Tc/JNI6KdMn4aBqmaVXABQSqZxbbL4NlHvDR1kXg7Yp/BHE5LWfrqjtvs5svChpp86XX3wLMFVDBBTvvk60hcXXnvomZut6lAajdth+l6223K4kBXnTTba8/58P8y88TNVn649A1QrLr0L+ijZFDx60OID11pvvZbXFp6QYB95CnJeRSzafTPBkM+yFl3gXEPTd6rGC8fE6beTz1VfecGSHSEEofsqQb6ZmmkapIYAvlqpSkBOtrBgYDI+ll1oe/cK6VU+FbxqlMC3Z4Gwap3AGn+laVdQuv87Diratede+1RlG9XrrbrTuOhum1aHL+CCXC2zXVeJA1Wgdj/fQjQV5ifbWLb/cyo7pVFJQOWso0GmANpFWZNDvgLa55py3VfdA+jlU9ZptjtS4s2o5TqeJDbIfvD/KcJPTLjzgLJLZ+OQTz+bCzWHjdWjPVFW1xuLTZg4NQDng1GmnngmveijUM6hEmZN0K2tJlizgntPZ+4IMTKpjOnn8saczOrdQVBQ5X30LbSOGv2ozOeXDs88M4zsZeBeEpSjfbm6OgbCZcKcf7/Ii6qSZ7K7hCx2R0+XlBtZiuBO5vXmMoZl+39QQwFfW1dekCbr/OJbLie933nFvUQ6vnM7Jbr3V9lFwexk4Lr/sah9otz1r/CL/QgMHXXvNDZiBZEORvPqq61AVPUSdgAMP6TjEkkPJYjSCZXff9U/Sgw88aniKhWsQY/LAWbQzXtlm6x3QUo3U9vqBrXvsvo8N7TG63dyvQxU4ZSveYQiK8qb5lJ5NPx/JcuLfsYochVoEN8BEPql3u213MjGxkIiGH37Y0ZQDkEEAqivWdFZLQAAWpG680eaY6j5GVtId7S52vO7um6D7zn3ULJFqaUO1S3FH+LeiGHkmNaOTFfyV0ZkTlLV99j6Av3xA1XnA2ZLOrmRlEVOIT6q5X8ryGHrqGq9rzqk6q0NjLzw/wo1qpmb63VJDAB+DAXst1iLgJRdfAWa16VL6okKQ5nTr6FNPPufMidxPAA1wgGbBWI0UE9Q5bc1RGj+vvOJannyim95T89mJkV/VSQababxOUdiwjNUXh70MRqCtdNdFH0beCbpWnHKAkvE61ImCYy9VFNz5QKd1K38eeMCh43X+Nxa2kgFbD2Kmn27mgpY1od924oorrHrsMScWdH7OulinAhlRKbbwSSeeZqTw6q0jJvXp3c9usrz8gBlFxzNy8Ra448B2TANQRWkw03qWlTXKPPOMc63l8fCQg49Ya831bFzHQlXotD3r3rFmSn6DuIvi4XPPvpjIZ4o2TdUFqbTzzD2/lXF7ThMdfsgpYCJMnm/eBekXctLqgw48DAqXXWZFzxDN1Ey/W2oI4Dv3nAuHLLak7al2xWVj/KDI5EKcXoYWKlgkdY+hCw5+/dX3YB86XadiqOR1GvfQQ44c/uIr8/Yf4IO6a66xLuiw4w67FnRePVXxYgU4wGRLB/Z4RY6ygWk06amAlP730kuuxKbzUGfYOxqdtaeyFkxjQTbjHAUHTEkXQAEg4BKI8RItJfBuN10BjsaEhpjIyQVSnH3W+eRs0cl5sAAYgviJiuecV2iWm268zQjbqpNq0PPP+x567NGnnnj8GUM/ZExSRNWy4u/zyfRg69iqro1NbGHw3UCMhoilGSteniEvbYvdoz7xtusuewLZPEc1KypGy6233Ent6H1MD9bpoOGKy6/JyRUAtY88/EROkT692SWj9VwzGb61aXs5VBmp20LI6GZqpt8tNQTwoY9gxm6/3c7DXnjJysK22+zIUD/s0KMO2P8Q9BEbU4wchuIO2++CPnLC8ad48PAdxYHh99ab7zkDJfCE8ebgP7F2mRhM0xpff+2tCVqsOPKIYwGRNVZfJwWOSEpcItdeUZvsvvn6hxtvuBXbFpOZYQ9IFRSkj1r23efAvfbcD4y46877KJAXGdjPPP0CTw4+6HDy8DrFokJSFJR3KSRnThfaR9pjaFAg52KDl1hi8WX4gtnrFRuHGAEUbr7p9i6FbAGsY8VNImdGu1ViuRppLPAEJbGQOpH1yosOIpJoRyFNA1iduUURFuzy66nwXwAxbcF4X3ml1fkL4KMWWoGiPUZhssC4RLdqWHfOK5r/CsuvQjOLsnlbdLC3IBvcm10w1SkHbRrll0JAc2hAz/V8U1LgvIsvujyaNjcfNVMz/UxqCOADsBhOANajjzy51JLLMRRHvv42Oh0qGJj43bc/MEh6zTbHMUefQDaGEMpF+i5DmpwoR+Qs6XIM7/ywCuNUUsT22id8B6oYlgBZXjbdjDN0H6Vgf/UJRKMKRixmI7gAHMw913yfKXi9E5AEiKAYzjB9NxS61FlmjLPPCwonKjyUdxSCtt4HA0bwZPCgxb3F5Pnnhme0Fw91DDSvapM2DXeMIyrCQuRd291tumFjlKLgAeWt/7oNGEgC0KNgRVI1GNQll6gTKhsE85k2hI5YZeU1yAZD0J0hm4eGM+gpavEXkmhjSTFUmFroC4iBjRn5ATsU4M97v3lyz933wzqeWHXluResfSMKTLNq3Dyu30y/c2oI4IvDlpE4BCju07ufPeIdujDMK6T+13s+0uQRWNDO50K46SqSPz7Nk9fG5hQCYsFNWe52PyyGiB21JTtFMjC9PluSIWlgsgnplNXZkmLYBNOmXXgu37pVogWZrDaa5LVZuqrtL6Ct88TaUewa3QQTnBr7cdgdnVGMo7xMe5++iLS7Ja5Z7XUyZ3JhpwgEYNjWttEZzDenRKY3gGsy/LND0U2ool1XEVk7sy4MYGVCEBdjtGlO5Ndzw3sqfBYttfbt/s3JS0DJIC993TR1m+l3Tg0BfAYmj9JSONIUayR73KYDoxSu70vfzYRYSX7dA88AUVuFNY70Z0mnu5y/TecKDEa1r6Q5XVQig7EjXCo4WbaCwkbFIZaByU6/xEKKrMK0GW0Nf663olhGhqQoXKXookxAFHDcUGU3XxwubDNKuoraVqeuNP+sKKxhqebcXkFQXvukpH3j/m78MmSbhnYdSjF5fjEKVqr1Vr+bNtO46XYZ94tyWbhq/5VibkpDMzXT75AaAviaqZmaqZl+z9QEvmZqpmb6y6Um8DVTMzXTXy41BPCliwPdZm6taBudXeBe0KhN9ij9xy4hl5w6xSbzA9Ymu//sapwsRdqRV//890ymf4pOyfo0GbU57aHxCkx9zoJCztT/Na2T/Zg/3yLIa1Ec7KJizdb3jv2VXur5fVI9M+0Y9SqWPbZl7TNNRfdnpG5qkhn18xJY1OmdvFbAitqVVfs6Q6xd0cYi+ZrhZ+qJjrWZ38TXF+vmTLa6+FPJmROd5qz/9w9PDQF8q6+2dg9dfcAn2LfuOhumfv3JctKFRx5x7PvvfVxfyNQk37lFIY89+pQRsD6PU/GnI0HRo0MWW3KN1dep/+t3S59/9nVey0H1f9Wn9EStk9F/iuLo1YZOXV/3OyfqfeftD3wF6E+lovbT9AhBEuuBz8vcRx15XP270yjVM3OH7XfpO8fccTjg7Idt2qRNr3nlp76cqU8FnSOqX16rTWnwCABuMu2hGEIlxppILEVff/V9msFrTb179a0v9uijjve+/fq/6pPVi6++/A4mMGbH6c7lxkkNAXyTJn7hgHrIBMy9794H23R8qh536O9jjj4BbtYXMjWpa/wkz2+vvvLGFEd+mjyTW4Lr06KLDFl7rfXrn/9uyffMTuUQ8pm29Od55150zdXXT3FKz2llnHFV/9e0Tt27tY3vnOibf38qgWsOV0M/lqa0tm4V5vjjTq5/dxqlembusvMevWabw7RZvRq06NAoXJJZ1sHK+nKmPmUViSOF1CkmpjoyXHD+JQ69UZsZSmbvMyefs806+9lnnU8GvjOdpBmqivQzRZw65eS/+whT/V/1yVjvg4zHHXuS79hsnNQQwFdWzDgEYuCCi8JZ6+cZnYdPFPGJDJYkfp5+2lkAX0YHHrKKETDjDN1t/qDeR9pIgbHTXSGdfbY0o7u6Ix2rcI0vjXgtBQ5vQ/NOPW/X8KyIxHTq+tfJbAHeWmzwEmutuV5e9outM4t+RXEDS9puTYGlcO9loqCnRUUDtFZbUngCiIzC9kPrDj507EsOc9pQQv704JfhONIhFlNSy8NIG0Qy2khs/rjqka+/7cYyjffQfUmutKTNMa4or93F3ijjuAYZbdCjFS06b2c9yxun89JxIDLd2eethXHYb2TNi4p86yPtdU/F0jiScP1YUWexC4o1DTcANcZqRRdv2n7MKLKL1RYT6RPKNi1t9nqgVhXzNRLwnXD8KVRBc6o6jm1WmH7vyIEeE+b2WkKyOtJnjrlbydNTV8t3KMh2VttxcuEWt5L2XSUK3+2+qOgw5Y477AqyVHXLuFuHDkgJZUXi8AzUomv5Zpi+myW2qnvjXAI1knxq2zaHjaGcLFPLM+WY4RbXguJpe5j4gLnPBfI9FU5K7hauTsXuKcr+NWMLOtDpPi3rKrucNsZWFarWPIkV5PykE0/r1C3MsSYhG0z821M3dpql5EfM3Lnk6dJpSJ83N8dK2h+W0c2oaf9abBINEw+NSPvM3LpYMEpmOMZbCy802K+XtUfKnhmzNA5XwrqBRdlt6RipTQ0BfBD67DPDNt5o888+/WrxoUvHMuUwfJByD1E6Bg5aEzn5pNNhIj8ZJwvMvxBPmFXI8OEHo8nAJ70+XoH8KJZ+mqA4o6uusiYClALfi8NehmX0EE9cFNxfZOHFvvn6h7ffet8TJgRQ7Ecfjnn8sadr3V686LCdZIZCXyVORWuusS79ffllV+d1JSbTO7MclNAuyqciaCM/ffbKyyPHKsIohZB5icWXITNNjuVhoVKmgUcfebKg418FbQZ0UNVY2jFCUA98Wd0yntNGSIo1CjgADO9C3lhFUUUUkJvVVl2LQniC5mh90CdnIIlyeJ1XTC3kPfXkc8NffMVDCCuSLzScohwzFUq+/OLb0bpZ3KETIIMyPZ7heUFncsnPwz1232fUx+NIaNyUvOEGm/IiDOdJUQd+z/j7ObAOqtyhjzz8hHHfR+V4Mk6xvLoU0qqoMDbuwQMPOJT5LwrA9+47H8JSRvic/frDk2eefmGS4qS++ca7lH/aqWfy3ScU+/Wd57tvf4CA22+722OMGjt0aTolk61Dl2EimZBqAYM//PvkE89SFMxcZeU1oASx9PkciEGZ8jw0i+4gt7giYP5CXRTlSZ1yeMKn4cODv13Xq0M8TIjDMXC3fYP1N4Hmt958z5TQBCSTeYL8Lhx4Irmjhyy2JPQUdav9OMW4XWjgIL4j0hMVBxdV1II0TuFjeU4JFTnZRys+G5JP53oUwJZa4POc7Sbz+sMPPR5pzsBqpvssgYjEJEVFM/DxIqofVdC0sTo66dNQvE45DEZyIvZ8h0V0n8mwZBZk4NML/PWp4rCh/XhyhTzede1t8vx6iopCOPF0jNSmhgA+sxLWoEvT65i6xXC97DiFTYY7ed1TYQnzuIKz8CVRYD4378orrvUXpJlpdt7+A9Zbd6Osgtx5ZMLNqq7vAHoqCoKSagTZcB9FXrFGBg9anJxG3pShZd2kQYahQ5baZOMtPB2N0+FiuM9bOd296xZR8m677oUplA93sDLq5ph9Ln6S2X3j03UI6AH7H2Khd40VBUG48YZb/3762Qy8a6+5AaQY9sJLlPPgA4/6XxNmkgyO55x9AbVcesmVVOSAAgx4KvKEQbbXXn0zCuoqTz5VuEN3AfVmZcJAno/WwRZPABSFglySaXnE4ce4p1xgahTbVrrt1rvmnms+y5z71Mn6NcnSH8m2MqxXtHP75ptu5zlohanL62+MfMdMm6Cof3TrfPMuaCmH25Z1Ax8sKsofwnPrJnkBX0EaJXwbr1A6E3SMmi8TFbUMW3jRRYa4BEA8kaLhVjs0dxxcXTyhB8mWtjR1gHpyRU58LnCMYnBRxZ577Nuh2FwmjId777V/IQTd8bt33/VPqD3yiGPdcYlk2BzjOyQts/QKfr0gLLYKs+kmWwIrhaCgWQ+ld8yriq46YQRZ46O/gDmQgopoWkmnj9xST4ckZtwuaWQeR66LzqI5RsxS0GqhHOADtsZKdbDYkO2Ky69xuCC4QQa6laHnQTRi+Ks9FCYjL7zrVHRIALRNJjBdbAEw95AcaCjIv5xRqCEAjhdhC1OXu3WiYjtSF4jPE4aw+wiSKoo55Nl9s023mqCw6nx3oBPzfLLUEMBnFjMO0VdpM4PQycGUEsEBfDnzjHNpMBrWODkg0g6rB76+c8wN8F1z9fX0MayHZV4NMIzyCipMovm2qpOnFZ1q4Ak5PXMuv9zKWUWLK8gO5V9bNBYvND4y5GVrwN9sCKlCKxi6pXC8BPo9f5J2321vz5b8RfnIHI0F0OlLgA9tsairQmjvLLrpHIDzrHvZpVdBNmoFUxwTvvV8sqXWpZts6+CG628BrYB7qIIDqGCxzhrHQrSXX3o9qgE+D92iDDck0tKcD6YuXzoV5IZpH32cLxgahx92tF80/ycDvoKO3zKNL7nEspkar2I98EFkUbiPfE/UKWby8/OiCy8j29FHHR/JaHrowcc8vdksalfknqyC3MAiuAGHqY7afVjYIwTgi6UCMyz5l4dAiQ99QzAFMg5LYeWKwpEuK3F0RCR85F/ErEumwPnnXWyGuy2TAd/ABReF25Fm6HadDtppx90Qv2K4FAEk3W7bnWzJpu8yOfHX66+9NU7GymiF5m/TCSJgi9ZldcNMURY3xFx/3c0QgLQYwflEeFoUBMh9QYu+/+4HmH/iCacahigf44mSKYf5ntbxiYBZoc7JcKa94+QunCQzok2XYXXpjHmXvKj8xcChWIikOhS3CSHEdyzBo79SMKJwqsvpbqwOHdmGeAM6PeJJjl7gOSPdSpyBj6KY+Ry9wphFdWiaFIIYl8OaOJRQL1MCqo85c8vNdxx80OHzzD2/ZZhBatvcQyORl+yntig0BPBZnxofPCBPP/V8VV4PJKmig2U0gwbQu4x/a3yx+D5K0djrgQ/FascddmXyAf5LOgNfkmcBhtqJgO5DsXRDGgKPhyNff7tVtwiBC0BbLD3IFRV1NNX8hZgFByzipWcYzZDzZL7LzntQJmgby9XFWIpCkCvLIoiWkXcDKdx1lz3PPefCNt06RGnYnrFAx2BHXYip9aAJMiqpGuDzqnRBc76psmYRaYdNJN8fIOLqPGekg5aiDPcp8PE8H/QyvhfkBzTeOb9hAlMXYhIFGTzk4CPinwY+D8VI+kKpxrdSD3x8oYs/VtBZuh5jDebQrahXtN2RYKAcqKItWAD951nAL1pz58WyroWC24nceYUQD9XDzN0E8BkQrWLn5YJAqECHSEaQx3mbvJz+2UM3291z9/1mIw9PP+2sbNCa4zrgQ+NbdZU1K9LBM/KK7rfvQbQlH65qYSLffrudPQjTdy++6HL+Rc0np92OJthc/UTRdwpSBqGNidkdR0UWSAT+b/8zIxngG+h515338QrTEhww8Bme0Go9T+TDWXXb1OYhryMJntq7FIrCMy6aFCRN0MzqDoqkz5INddJPUuBjmreEk4H8DChUfgxPMntsfiL3Cyq2gW+0QjTCZBjleg18aO70FCwCE9vljq/IXc7rFnLeIsOpp5yB+jyLIqFFmgDoJvugW3X1mLs4kfbaqgP+DQ18sYa68ctNQoyef244bDrj7+eAYkgMzZ6kIB9MO1bczNy8wKKgqRLliDEAm2bvM+e22+zYvVsbfLGvxGJ61pnnMYZLCohEIbCsU54jYwSmBz8xuNCMAD7KfOThJ8j25BPP5sPCfEEoidpFsZBEsV7UY7CNlrsKm7QiJze0QTCf997zwASFO3aidqZcKiI/Cj9tR8qRacgGqZm+xspHabcafUwTMAQgfq8992MazGsVYqxchHznSVZ3P+YDvGJrM1pQMD1IjC+GLd+YwVgF+LIyGOOw2jBBqkRJfuVJckTCauSYMmEjTIMJELzvPgcauGMBFjTEQnkLNKoNTRunuziqisJgqmw9kd/wmij8Mp/rrL2B7wyx+F54waV2epIN3pIYhMYj2Ii+M0auXjvgrEONksdwrJyVNv0gDwmxIIEvEEa9MHOMHFVWLckQy37klfnnG0h7+dcqmw2xFZZfBUqYCKETCaQo87AQPBuJIolRlB2mtP2mG2/jLzhGF3hpi397aO0Fmj1R8UpZDhMIc/hF8ncq4JBn4lhrCHQ6HIAt9pRBJFyCALjhTmcitJJrjweaHcKJ7MEErKJ2XXVAhqFDlvIGmgmKH06ig2gLzade8qDxUQJPPlUYHmugkVSHMYpfSb/b4TtaF9FACVMRhRhGy1JOIYxPDNt27ZuhZF8HFsnJjm3uTqei3XbdC/IgG3mg2FjqNpIPM6+95oaPFZeXcjrl1BsjL9Cncm330K2KtMiD7p/3PeS+eHHYywzVyZBkKlNDAJ9thGI47l5WEM1yUHEzul6yImccrXWGomyxWJNPNdy6m9EOWAs0M21e6q4nEKtaVklK4TJZV+d5taQV2ETLf2Udy89pocC1kPmH739wX3picd9bGjq0Zmf0KWiVqqK4A0nwwSXhuL5fufqq67KyFx64/5E4bMLorqssbe+4nLxWcp2Bt8iAfuqGZLQKyRNGZiFEpvGKHonB0x7WW9N6c7rhrEPLf3Hwqxa1TOkWZbRcW9SM4udJMH4NBwBuUSgZaeHCVHkmqISID5FMRf6dbdbZ3VkeDzmF0XYPutdigbJZ7VYX5AmFeEMY1bVrvc/ER/JpGlBMXklKZQ8tevKkp265NLfLisaaSG2kNDOzqk3ySViHTeTwcmflwuXLzhmHKAxmO5S4aXHY423hdO9YkCiqVd4Sy2deCpRpdme5dQUFsyjKrVEKS5lmYDpVVOV+sZpj/vfQGpGFHBmwyJkYC39GfmqLSotufDb90AAKG1Ir8tLkNSjc125sOnGae2Ut7FoSrLq6Q0sKgmsRKkgbpa6rrvwH2VoUgigl2GPKbDRbYk1IXvPxw5QkZ7MgueoW7T9Pd2i4LxwE0/EorRVCxmT3SfxbqSGADy54o7l/0nj/zMuWTKXEwp2XElHQaPdAomvbQiSSkqCqXav+3XQHW7vW7C30VW1c8HfvEUm0WNFNgVXatWeiJUQZ6aGdH64rrwDxLr+iPQdt8olYMkxeQUPXiGCQtbDmpX575JBmnKE78yFKO6bxKEUAdOflhaQtuus20nTqeiPBoh9aXHro3ri8RBNt0eMnHeqW+JJM+4p2flBmVWBnG8HDkiZb+j3mXbIpcV0eUZRj3cScyWmXTyxJRfISbR1AoPMaPybJBMfhuvG0FeZGRROYR7LfKtagWEnzDezlRSg3nw1MLiQR9lV0Fa/HSVEOYgOcYdS9EGuAmV1mZkXWU6u2sPjuJ1da1WTpxpokqnCxBqC85LCqi0PzkpmCMN1FxUF5dMN5t7s2/Rgu2+TNMDHmWJtQ3pk9V7m7fRdoRp5K3mUIJHIRuhVmkVsBw3nRzO+pCwlKmjzc2JwQ3B1a1kToJsQCSn56V5MlwTqH2VKRp7sYFALjo+mnxor8gwZZ9xQ5L7rwskhuFqN8VT61Ft0iHYdDLGZpVruITBv/esSZ/y3aiULzM1rW6KHrsw2m7lMLDxofdswhBx+B2rv8cit7pdjN/A9SQwBfMzVTMzXT75mawNdMzdRMf7nUBL5maqZm+sulBgW+1ElhD0J9hl+ZIrnb7Kew46k+z2+S7FupaJ3Xddm5E/3EhvL/ONlTFqtp9jPW5/k1yV62n3epuNdycntPi177A5OdYqmz7N9K9qXad9aAKRWVqZEZO2Hrn099yspvPjV1TWUqasUmrx1vmbDI84upQYGvJIf98cedbI94fYZfmcyd4449KV8Xlue3TbV4UdGy2sorrR5PKfDMr08UvvlmW7tpXqb4DRNS5TOw9X+lyb226CJDplGv/YEJ3j780OPXXnND/O/3HTyZvc+cJ590ev1fjZNmnKF7z45e5bqAN7WppJW9n5eBX0xek1lpxdXq//rPkgtEPq3K1GeYYmpQ4Eu0cObdlV5F+m2TV/Q+0ckNLznV5/lNkiFgjdXXcZfQN+eec6FXqeoz/5rkBbvHHn3K3f/zEvwfJEa+d03W/5Um9xoNnEa99gemu+/6Z09dtexNM/UZfia1V2cdtOhQb6tswBRprXyO2edabtmV6v+tTUWdp/6VqquNLW+l/E1SSRuefO9VMqWQTlNMjQJ83rxTUKCOnBbpac/ETz7vNnOrOTX9dDMbO8ratxHLiiyEHWc2V1sUZyUtx4M/r70I3k3mfQAuZLq/zYQs8rr3/fMX2WaYvlteWz3gYK/Z5ihrwyDvMh9anfY+Mu/SiMMOGJvMpZprkhJtduuhiCCxtoO26nLIWEfQqmGflGl26MeM9tnYFubTmyFsXiXaq5jR9j3XEslednu9+SNS6I4nHn+mXfE2/KKtALfFJMWKq2GESgewpwEo7KlgJKaf171Vwo3qqaOEWe35SEKgykR70Io6nuUNH3xecvEV7rV27cJz61xsTueZ8iGsiFlN+b5pM6+9b2ZvUUdlcuHK9qK2HxnQ06ZldfdTrAFpoS9oE6V7pEMHZt2oUogI4kHuFCmIQKRN/0mNFsPr7risNt91KEjBM0+/kEizMG9dwiy6/5MmmGBvvKhov0468ZCNfxcbvESXIggUtA8mrctheNxS0+9yKNZbQ0yYB0VJW4sonN6MtAMmr31OFOsv/OvmF7WpyNuPitqOl552SPlT1oGZgrb+lHRgdOedds/JCE35aQZmtGnMnPdRM/MBzrRoz6CLtSC54SYv5ZjVMe9N8a6gww49qqQoSlXt5cyGvVAV7QekTIDYnHc28vgYuLvbtGVlNVe1pWmMzi/79US7u9p1bKO2Z2tTQwDfGyPfyck3lJ5mnaSgDu+9+9E7b38wdkyXEQEWvP7aW/QQD9t10GeD9Tfxrn3ePfWUM3gdAS1IyXL/jVVIko8/Gnv6aWcxAIYOWcoBHmJhH7L40ojXKO399z72ID/zjHNRM3389sILLqUuCrcJ8OEHo0k8OfSQI195eeQ3X/9AT2S00Qk6eWu0Dg5D/xeff+Mx4HZR/mhFZ4ESRPPSS6789psfwHTvAUy08478a6+1/kcfjvnh+x8PaVECdVn6swpxwbsbb7Q53/ff7+B77r4f+Xv3nQ+p/bVX3/xYIS623GJbev2f9z20yspr0C7IjiUiyy+3MkW9+ca7Udg+TZn8u/de+6PwUhocgxhUxVgTw+GHHe3t+B6HfB+jaDGJjh9lpVc6ZoTFjp8ONuPTvnCDBrrXfARqh+13GbLYkvAnPZcO8Tdcf4sHxmqrruUOhbavvvyuS2fmGZB82XOPfakdbg8etDg9++QTz3rT2YIDFnn7rfd5At9ATPLACoikjYyQ+ecb6JNMVW3lg0iqoNPTIy600Qdp6NY+vfvldfKHf40LBU0GQMnNN91utpQU94HvZNtqy+3MQ1iNRLnTeYV5keomKjQL7WWSbtN9xz66Q35ohmDI9tGLQgiJ5hMv5uqdd9zbXYdk4OQoHdeldfRdXjPch7r9vaDzlHwnj1Fg4IKLTtSNzDQBGPX+x/7zLACFsMVDI6t5wgeNaHKi6QrFHM77TNg4RWfxwRjKvP22u80xGA4rrr/uZktLTnFuQOETjj+FzkIMPLskmth84IfXEcK85mMKRJBoeO9efS1UPjNHCSeecKpnuOefG17SXL7euhvRFtjIQIDsCTowXpbX+FOFF6I0n88x248+6vg2hd6irkcefoLayTPy9bf5edWV//Ch4Ej7KOsBJ24Q4KOHoO+0U8+ktTQePr715nt80kIfeKY/4MjDDz0+YIGF+cLAuObq62HK5599beuDNgMBFAXXNtxg02JQlDzBejgdfNDh8L1VGzWpsbsiX1r+9tpzP0ScgUp3GrN4cfPNtubL5ZddHWkan6Dzj0xEll06z+euHn/s6b5zzJ3IjzNah7R9SjfSwXW/S0UWvpKuNrcE0zEFbeI18CGXOZ0PgwZo3mnH3Xwwi3cRPlo9QXF34RIZSlLQIp2CKkqtm6Qz4c89++LFF13Oc8yWc86+oIcCZsRhxSMJ7j8eOsARsluUvuazYmTjLRgOek7QMT7LH4qwgQ/N5euvvk80k+elAt9374MMRdCEbA6YARPca55gmJBcC6N3rKKGwGeG1oorrApJdHRBag6SamWcL1lFYWEkeDjde88DNJzaPUIs0BBAR7SFPds33nDr+uttzBckIZE2x79WMTyJDn/xlQk6ludgjpRsxiJU5j/zSklGU6QYCnfdeR8Pd9xh17PPOh9WG3ajcFiF71BelRpuG9b+4rwCYWQVRmGuOecl8+JDlz7v3Ivm7T/AMkwb00klUdQTj9WPdUSfAQxEVhVGcLyOrpt4N4fGZnX+zGPBfyFO1rlefeUN8Nf9y0yDhLuPkM+Kzn5ZAi3hiyy8GIhjyYFgXqS07bbdCRqWXmp5z4K8BVUMrmEvvETzkQEzqqg42Iaksi5HLurSCDJnpbgxA0EhVYxV3BTSUksuN1ZxqLIK5wG1AF9Jm66RlkhKNzO3ww2MU9wEco7WmWKLPXVRAk37WFEn3F6bFEXNWEwqZW1TX3aZFW+79a68DpzABMt8fWoI4EMRQ2ph/ZprrAtfmGxtg9jH54FKC9EL+JdsfMIOxOLKK661IJKB512KfUYn+SxHVjP8Hbffs+kmW9I9YxQGgww+DAhrkIZZdEo80sltGIcC6FHNwysuv8ZnBhGFSCeiLa+myqDAF+tWHorjFDZmoix0I0VB6ud4RWGMhKcAU1FzdTpE/RwFsyB1g66FNmZmyMvp+C1WOX+hYG6/3c4A3/gQjC8Kx84phzkZ5Y62J8GUc8A4dCuaBktbtSk/EtCnRzI9w/OdVvhfJlJrTFZg07pyitgxSbqn3y3J5vV84DweA46wYkbx3BEByjqT4BOasRQxoIHntNr0Gw7MtChEWs3r3C5YwHdrlJBx7DEnJlItsetbtOkfoWdU2K5BQ4xlYD715HMm7ILzL6HALTbfhjIZQlZ86FZ0q6LCfDJ9jtGxZTcqrxPQc/br7+6YqABNTz/1vDM4IRuTFHlwog6DV7WMTiHffftDp0LaWD8yi/hJaeMVoCEWzwvS+BJZZJgOcMajmn7xKyU5oCsKfpGT6T1ek7Tlhy/Myh8rUoPnSyj08Uen9GoEcpIhVhAKSrBmh67KUMqHYzaxLAOo2nefA/kCoMchfjK0kQFOuhWmv4ciO1AUBDDhVXWRPKwGUvOaO+kO0+BOJNnSz+joHv1+/nkXI8bOPCbEj8DQhktU5NMg5iGvfKzAHEylILVFiHFh/Y65s6yzOpRgFZhE7ZGOTnnyMM31qSGArxTC0RRkW1l06E4DBwktwIzwcb+8jAXSZZdeVZEXAPkD3dp1NM2KgzO4p6s67f/oI0/Sqa2KzmIPQiruic6ckz8FPnKOGP4qFaF3AHxlHbBP/hX4KLlDB8gtkUVFzqEPuuTNqSqkcKzRO14hczPy4k098HUp3CZt9yFZXkS1dIyGOADfGEXFoEWIBaWh8SUB+BgJbTpvRLFMvx7/rvqngM9UwSsLN7yqBT7eokAmG7TCrPxfiZwSRZl+ZICrvFsPfIlmXU/vGYU/6Nd3Hiw7KP+3gA+uQtKLw16miysK9QE/F15oMAjoMUzhqBux/HQMgJKcwm44doBbbTHDGq1oDxOKjNVG2hvJu5pIEUPS7E4Zq2jGkwHffPMuuNmmW8Va7DZWmg+eHnLS+gtyeFklGbToUNpVD3yUfM/d92+7zY4rr7Q6TyjEfhW+o1lTe6dCRkJMPfDxFzlpL4KNWt2j5g6geuAbo/AqhjPIYCJEqnNykO2/38HwE+ZABhXxV1leiKKUYt5NgS8jx5+VPj8xM00YIlSVe4EhzIQ0ReCDAOhhTCHGEAyHRynCGKVtsvEW9cDHT4pFmz7l5L/bo20Nt4d2F5kedxlWRUH206233NlDsSESxW1qa2RTlzYgxJg/iBENcCgu891DaJzCTA4etDhcQ5KYmmxGMfHm5Bw85OAjKIHXb7n5Dms9BS0sIrXuIUu5nVPYcS6ZV2ATrAGwVl1lTb6kwIciba0Q+Vtm6RVizZkuNgU+XmcoLrrIkI8UUQcDwbExyIlJ7qBAee1l8ahwcOmpBz7aSIuoBQXWz7sp/PdExUTxi3zn39VWXcso8MLzI5IAfIxViLF1+dCDjzExmP74p4GP8tFZOhSPCMC1ouEMFqOSVgk8lopywTBiPbVcf93NQC0E1wOfBxLfUbIYXVaBY21w+7eAL6PQb4g+dKL32fpDWkxhLL8tXWlPnHWoOADf1lttP17GI2y/6cbbEsVerCp0yrrrbMiLnSG2MAk7N52Gsb9ihUrzX06RTAS+gFOj5eYbI+c6xr4NtCOPOBYLEWoBJsYt8jBRHpL4X4EvEtS6IfyFEGL5QiTW/cknnd6mKNkgiONHWU5S4HN3WLXM/+vNWfXAx7g495wLycnPY44+gW4aq8gu2BBGZD4RfqqeacYWhgldDxKhGFJjCnwIRqvW6CiET6TO021Rl97BMYwSyBgrF3k98NGP8883kH8pfKcdd2M4UxejdeONNi/KHV8PfGSgmfP2H5DXwhf9jkayz94H8J3CKc0LOJAN+lP7csuulJMPBDLQ8TF1yz+xvaEhgK8k9d7aaRyGGW1uk/Miko7t3oVBiFFOPrLsv25DKSoYmefYOKymFXSomw5DGeEvVKc999gXYc1ogS+SIoNxxBPGABaldcaSDvnTDY4tSj8BLnzC7qyWTSjKRlYmrN8ddeRxzJY5LYpR3W677uWcbYpUgbhgQlKLMxS0lOaZyhjqoAwZnWz3qDAT2uS+RVvxLrBER/TTtVdo+/abHxCLXXbeI6vQlWUt7VkNwTowM7fZeoeFBg6qZZTX+Aohhl1GUQNK2qUF4CKUrXIgxiH+R0nONZjA93Yd4G+pucKRh1hPc805L4VAf4vio0UhpFqKJrEWlLHH99v3IOdpUxgC90vvXn3dxZbjSDd1ZLSe6NL4gnZW0T4y7Hp0rh46zR6pExMBK+VT5pJLLEsGetNrjhYq9yk5ec6QY8CbA21aQKTJtS2CGBrFHFNVmBn3RaFmF0siRWP99TamOX/7nxkhmx603yofwp/wlmMsl2WjxGGDd1qaRZRumiRvr+UW9sJMhzLt0NLBrrvs6VYngpiM1BwzikRm445dE0W57doUmcJvtShkALUAExTlAWKgZJ5eZ+0NYgkJ3N5g/U1m7zNnVmvle+y+D2ysyENXUWAek+eE/IBl5If+rFYdWxWpF7YPHbKUJQSG+BW6GINjvIwe7Nl27Yvg+QrLrzLjDN15HeBrVzChjFbeq+G6EhIVjdM6clnX8jgEHOi20YabtWulyxOG0xGHHxNpfZmiUIHXXGNdMyrNUJsaBfgMZFGISuLFmrzMNIO68cX/5uSDc/iKtJCq1sXN00QTVLewFSYVlGIIaWsBysvb2qZ7TKxjtiikSixdo13R2fIhNFYUYmBYzkxJUbsueoSoQbY6e2gzREXbGpw/ErjkwsmKOCz8u1431iOhIEXVRVmIPT6NkkYWN8c2y8dyivtnOrYTOYnatE8iDnvlU0YZXypa+C+EkOWWs0hzjAcM49lSxcjPCN/NSUOJ/QlOHoqJ1goTIYJ7zZ1Sa4Lx3a3LKRTYrLpZySPEMG3mm9XuxHT8Z7QiEQc3nD8ZVB54absKUjaLNbfMlBUOpCAAzWp/Rl67/M1AjzdXl9LpPFkFFGFwulsNoE5uQlHhgtrlI7Zd7Kr9V06GpB9aaGm+ZxQTQzlAT5cidFrA8gEfs8FtWpQX0pQnIc6Y/zKdAAqWI9P5ePmzzEALZ3u4xMdSmtcQiLSJx/3uitynLeFkkSnPasJLZP24IzyynIoydZ3f0mhOuvkFbSazSJiNWD+2tZ3B7zpnRZf8egzyZPrpZvZfJS2kVOSnY6bEUBin+09cS6viiZXk3Uup8gpbSTEly2HtxSytT40CfB5UZqUhvxg2Imbl+zT3Ew1pj+c2BR1LC3EP+btzFoQXtc8LghUPm4KAJgoHvHKaP93T7doK5zJNkjMXQoQlZ7BUuUDz12PPcmak85Bzv1ocXY5rccNdSyLIyAmh/GkOlGs2Bjqza8+GeSLlmyHMrhAPDG8oMUNSRhXCNkA/LIZAVf7pJuSkUydSMM09S5jluDUEL3JyZhfbJg3X+fMBhmqr9k+LtWs0AJmBBXWQeWVuuCtdmhXzouYh/1UMAQdNQ17B7Fy4O8LFmvNpp7uD0tnLT5zTyXJlwagqGLjbnmZIn7vJSRj8BotI2q7FJqdwh5GAIxLkeZ6IQ6AqMySnvat+133qmcztioMAmO3uIz8xape1udXPTZLpyUmq3TqXn9VM7wYarD3iPCGZacUQ4c1PzOq07U45wXopRPAvaeNkpBmrQ3fsmUsFQVuqo7hPLa5GUtfof62hxyEoVhTCfLkt7mUnsyIfgD4licyWK7/l6W0yyp0aAviaqZmaqZl+z9QEvmZqpmb6y6Um8DVTMzXTXy41BPDZy2AvUmrw/xmTPQuT+R3i4CD7fVL3bm2pTz1b4wesz9lMzfSXTQ0BfPZc2uE6aNGhyy+3cn2eP0VKN1ikT6695oaRr79d7xuedslOdDvyC2H18Kknn6vP2UzN9JdNDQF8sbYWJ1ohXXzo0muusW59hj9FogkffTgmX7P8d8vNd/iceX3maZRKOoFrZnaO+2STjbfg4Yjhr9bnbKZm+sumhgC+ovZtewfAQgMHrbTiaossvFhW+zNK2pu25BLLrrbqWrVbTLParzvP3PP36zuPdwB5T2PvXn0z2hy3+257eyNIRvfaDR60eC7sSKJA7/jtNdscPiqU7g9YZ+0NQN689hK36Va9HXfYNVb4oDlmn8tBL6q6BI6HW225nbdWbLnFtt6w4rV5W5dk45Vbb7lztG6zT7RnAsIghhdzurSsqC2BW2y+TUU74yCMf3t29Npwg029UWDVVdb0rmlvfs6GWzRbtUWZ9lJymzbW+TpzE2BmWv206T38xVfq2d5MzfSXTQ0BfF99+Z3PezNKQah33/lwfOfECV2fMv5BkBNPOPWzT78CPjrDRfSxNnyR5wPdzL3brnt1jZ+EYoVeU1Uopy8+/+b11976/LOvvVeOwiltrOJDPPH4M7w4SrdQjxs7AQXN57dADUqgNOq6794HAZfnnxvOT6oAod58491xui56g/U3AXQm6MbrCQp4NWb0eDQs3vWmyvRUOc0Zp5PCr736pgGODCAm+X3ttKGWPJ9O+hKCi9p7SXVQSwa+P/rIk5Q2XkfrDaZZnfFmDuDL5ZddjRHdU5FpSJMmfsFbbYrmYmZSAhwAypvA10zNNFlqCODLKeRWTmeVBi06dNNNtgQF9th9H0cE8ynOWNGr0lcYzG+MfCcOxzl90svHPIGAHrp5ds899gWz+s4x95VXXBuH8wDPPjPM2uJmm2612OAlcjp7j0EKrFBgpC2XPmPrs65lHWmws4xPn1L0wclYgXnt1AN0ttl6h6KiXFAmNFd0bv+f9z0EPTZ1u83cajwqhK22337zQylcBAxI0XzHSqDA/fY9aLyuDY0UicCqHNkobZWV1+Dh9dfdDDFGOm8TnaAAPjwxM/nCi6BkE/iaqZkmS40CfA66AE6h8a291vogHZ8GEfSp5559EcB6cdjL6StkQL9LwrF2L6QaAvgc9sJLjz/2NMi18EKDgYzttt0JnQilDGThoWEI03jggovynecAH1BCRU89+dyTTzxLXWR45ukXrGfx/aURr0HhSSee5ogR6SH/Ky6/xl/Q7HbYfpdIp+sjnQz3SeGHHnzMUTYj7SanRaVwziarqHNFbZRPFFjN6lte90yjzaHkgsWQ9Oorb6QnFni4/nob8/oN198CwFEsrxR0/KBLYQcpNmUmKAkb4SEM8RkSY25JRwJ8nLP2SFkzNdNfJDUE8GUUdSvRMZ2hQ5ZadZU1U+BjuDqaBem6f9yUvvIzwEf+vfbcD5QB9Yo66lhVtBxQYMYZujuEUVwHfIBOl0JF2uaNA/AZ16xDzTbr7HyZGuDzJ6+ghWF0G2oTBc+x/tiv7zzAGcTM2a9/Tr45q3WOmUHOpZZcboyiZkHzjTfcGmt6yCrm1R2339PRPhsaosmYDPisb5qZlOaoM6+8PNKrvXkdxfUZeCubZl0zNdNfKjUE8NmCc2jyIYstudKKq6XAZ2cW0AYYASLpKz8DfPPMPT+GJ0MdXMjppCQZULvsVkOhy+iQ4GTARzmjPh7HK1ijdiYa+Aw3lICh7b+mBvhmnaUPSl+X4jWiuxV0ZjMfItCCaJTjs5M0HDR0sVnFm0108DNR0GBIourHHn0qowOefJ54wqmAL69MVKDzeuCzomdmkgdmQgyvgOmozKh+IC/8geYXnh8B8lqlbaZm+kulhgC+skIpMGjRg2bp2TvWhjjrJomiEqQaU/oKKk8PBSMshKgbWQWKmGnGlh6KsERyIJOiwszZoqRAx0aOtZRcUWSIfE0o2jbFbY7lOnT5bYp6WNBm4LZwutsBRSg/q9sSWhSiygW26ax4pIPiPHHQkVpwiXSwvEMXtUCMTU7X6BfTnO0K71NUEAGbro5O4eXaUoiT7qLsOvTyccpMt9SrIllFMXEy0yoKG5WbZldrNlMzNWxqCOArKSpRQR76tv/L3nlAWVVl69r37ni3bRUJVSftE6vIkqMkIxjADOacMWurbY6Yc845YCaKYM4RBRFBzIACSiyS2Zb38f/37HuagzQqtmWza8xRY5+9V5gr/WvOtdacS44Z0tLUGM+xokOkKt1MFkZJl3iasoNiA4qxIFN0o2LRCWhIFl13xYruoYy2djTCcwO5DLIWHMhlCNHt266+LnkyCgdyeRJohY4EyciOicyDmc8VfQGldJlA/p8NJww6mZLbttLyapvU0p618pDqFa9Pc0mN0Wa4Wn7NjHSpovc6sx1WZphIWK6g6A8jWbzypvRTRBGtJlQrgC+iiCKK6N9JEfBFFFFEqx1FwBdRRBGtdlQrgM8OYCvkVzldvCM9W3QDm5T/VS+oeV3MK3cFORxO6ahdtui6OatTvl7U8yp+VfHKiIRcUXvPwfsD3iqt0D32XhbM6TIdbzL4Ooh80XmvF+a8ueE1O/t3Jd/69WLeEsnKbbcfMkVfu5miz+GYbpCIF/05O5gX7GJyO+xFScf1ApwPZjtMRnsXcVnyhaxCtmkLdECaMPz39o5T470vTHAKgRY3s1qOpGYcJWwIXnpzJtBaZOm+hxcrvRiaKu4mpXTyJlCLJIvXZaS1ROsi5OSI3zxXauMoqVtjAp3O8c6PyWV0jnH56XURvOjpSghveHCOpZxHFNEvoloBfF65T+nsSEH2uTGZ32aLNyckdZmLR11aV7QQsqEubDQeeZx4pPHed/d4BHqohIMkryuQ/TUn9/yk2UjXD9l4Nlu8zwWq0p2hIQ8Jub12XN/G4DTj8qPt6HltxRgBQZykjrCEySaF4B7SgfamQYEK3cOQlC1HpWx+M7p1BASvlpd56qRZ05Y5zQGuK38y1qdLbjAIiheYGEyNF5WylnO+lTLsNXTGtDcS7rEEJa75A4F76U5LVvshieIMYSroIifyclW4tpPFDfGC7np3udxGMR0k8gznvfIw/UBNYybDEiWKd62ki3dRkT41RlX7to3S6BFFtPJUK4CPwcN/n8hdMP+rt94cV6XrcYGDrbfafo6upjc0fDZ1RlA8HfLlF3O8s1kzb9HHH02Z+eVcb7xu1qv3vLkLv//up+nTZvLp0EOOPO7YE/nqg28G1kDDb/asGlvOLl70LfTkE88yFC+79Kr5NYttJOuQjDTeWFTMycHBwQcdRr5t23T85usfbK4bCHEI6dsFXa4rLr+GMCldRUgu5E4uPnyXlqRJsosWfkNGpONTihQcrmZMnwUD6zVv7ZnAgMtQJxHyIsdwZxmGt9u2n231AgEEbNTI4iVTlBln647NSl3E9e03P1LPH304GWkrrjOSXdbvETYEgT/84FNycQ2E938Hwn2bHvMfzJr2+Ze+h3uRrhinLUh/ke7Ypjhj3nrHgmdd3S88X5eOp2RN6DLCHlDuO0JN746fRHYE+O7bf7iqaWUYprBEJ9P3J31MqRfqpnOzMWTwo2H0iCL6RVQrgI8ufszRxx991HHWcQC+TTbeLK5zeQxC+rph4onHn2napEWmeOWYkYJhxkuGJYkQkui8GfHo40HxZhzCAzf1dZ/hc8++9MzTL4TnPALBny+9NVIw2h8b8YR1rr477MxPPjG8AdxXXxmd1hkawvMAt/N0bSijFOCu0QW1OV0qaiHRiJyWJlgj45NK3W4TZm2l8pqrb6CMFboE6+abbl+g2+PhtsV6bYxfSel0cdlsGP3hf47u3HOp7aEg0AlBwk+c8AEh99pzv/q6dYiQIFSVDgOReEF3a+26y55kWq3rnA3uprQs55ISS32KMPwE3CR1Qqh5s1Y2lYPPd8ZNjOmyG5h5dPgogMnIe9edA8879yIX5OKLLn/pxdf22fsAEqHSLrn4ioLO3MCAA8eKt+W6WckFrrJajqBBXW8V8kYzZfK0Tz/5LJBkWiELvJC9iCL6RVQrgC+ty7mtu/FzwNnnX3ThZQwAkIVRaqBhqDCGPVTo9Btv1At5Kq1L7xkhDXQre1wLT8aRtM7xBkJVv7EW6WEWZk3E0Hvdm6PfZnxWaikqoUukYIBPYJltSwhvPEJcIlZOGmuokTGeQbE+vbcNEzcQ8+baa27MSOu0bBuWOhBwH3boUebWBbRSSWrj3p6QlRlvUmopcGOQ8tdKnRlEIDJ+ZaQRkwjsEeumG2+Dt08+nnrP3fcboAEppoqEDFHyRT8LITSbiAg4NtQtWcsgi30oGAoNRm4R/3RSyeLaK9V4y813GMiM0cOHjXTWP3y/pFvXDZ2vm8PFIV/XCWW0m5kLL7h08836uL0sz7o50lKoXQ8hexFF9IuoVgAf3ZoBjG5Ln952m74WmujlTO/0e9tXpWXdldKyOv+te4aCDGFatWznpSU0ph9/WAIgAqZ5HR4+8YRTQQ3EwKQu2SvNGjnOmldaJmteOzOiWY5LyvsLvPGV1BA6EKlIhIdASvrTTz3/4guvgsKEJHwo06GoDh40nGQpCAom/+FnmYJTBJIlNbLbeafdH3pwcLp4mDlVdMqSkhGIpR44AUxRn+NaVQQXjL+k7IXL7t02GjpkRFKWef0PPvz++x6mJse/815C18t37bIB6SCXkekrL79BvqNGPmVMMR1x+DFkOk+Ord4e+26pxJeV4xmaydMGPy+95EpaJK5FPXR/YqGlUvkUdpFuyCaYIYyHyZ9+jmBO0f7x4xJDMwEa6kLClK48J1/aiIKg0hZkUkKCXgeAGd9PSBEIQ/cAhQ30y9RnRBGtJNUK4IPAqTnyTMeoQCLIyNeARwWfrE+BOA2KrkQsIFjJSsmxioWI0049a8d+u8a1ocGQq7tuhbcOEjLdZ/hZZLD8lZak6SFqLdWaYE52XW1ad0Dm6tSxK0IT0bfcYutTTzmT8FtvtX2vnlsikzIaef/Rh5NJ+cwzzrHk5cFM4sg45t+SWr54hWtYZMMKUaznXn/dzT26b5zX3khBq/h8slCT0Eqf2aYSvGbHJ1v4EgBARMhKSQSDDaAc3RwoScmKA1apBxCENwvkmhBxjIyeevK5hroqO1W8wJQcY9oS8Z3Tfm+By/MN6YdiKQ/PPP1CXi5XqX8IKPzwg0+36rMdkEr0sWPGW1snQI2WAtISn5s1bVlaCTAwY/qsTNGcGaxMaQ0UmLPw6+br3Knbrbfc6SpNF/edwnQiiugXUa0AvttuvYvRki5eomy1DpmFkcPgnC0HoldecS0YhOSV1D3Bs+XFpHWr9gwJm+Iyqnn2EEppBYpgcW0CxrXSR8qIGwY140sgyCCAhxMCS1Z6a0EHZRh4DuDFuIz8/QF8/ETRfuH5V3y6onmzVjCT014k4GJNmcBIK8S6+abba7TAZ2nU4pKJn88/9zKCqgERVN11lz0rdRV0SnA5dcr0mLYyCWxlOa/FR+vasPfB+5+MfmPsE48/w/OwoY8R1+uhpACfQCGBkXOz2jFHgquQTV5CaixJTZ8200ufBrKYfF4ltW7oYHkZ+cL5JRdfkZNH6IwcHySlnHprwvqscc16Kw3hJqA27AfhtVff9P6Pq6i06d3oJJXQNj1NCZoDwUipTCdZbUmnip4QM1rWtA7uiipNKqKIVp5qBfABLkkt4dObERbAwcdHPe3TKoy3d8dPssRB1w+knAIKXu0GcaZ9/uU6a9dDlOMrb0jq5JNOP+P0AaefdjbK4x6778OQQwxEyiAwUklS2m5Kx/SAD7RUDyfeoFkDJS1btEVvJTAwRCyv02UkIX7/3U/IZR7eBLjwgktBXmtkjFVSgJmddtyNTAECNE2/GfnYkzAAVyf8/RRLeab6cqMSvsnJ/fIdt99DwRn8AJZLTdawQS6ffDyV+oFJYoGD74ybaLSFH/RWMOuA/ftfcP4lRiWDsveIc9oWMOYaRyq1bUpdmbHjjj2xfbvOACh1xc8TTzgVCooneEgNKYxJyCuDwGVWOw9Upk8XEdI77xYeYZ4H6q0gZ3/GKSrEHhlou1BsdxZJLVbsted+Z515LljvJV3+X3bpVbABPyedeJq5pQKZHiBU8rTWBEp7UUQRrTzVCuBrIIfJaalyVTpYl5J2YzRhFBW0bM/zgLPPBwWSWuxP6JAHP6+5+gaPn7+uWad0Ca9C58XAF8RJxnaptOX1e/IiSkGH2pD7cjrWB2Ki5HrhLyWltap4Vo5gjF4LHTB5SP8j0HBJykjKS+AGOQtqsV4bD0sz5vBGsZAHQ3DpTwgB8JwBF5i38JPlvqZNWiB5JeTNJS2FPa1l/pRO+Tq7ZdJP6jg0aSZLhKOcdodgNXyT0XJbrmR7lwBr/mUdH2/OyWspiYBEOXlRTWoTI1U8QMNXS80ODFmQDFNL6ZC5m8yVGX6KFfc3/NIiXiiPm+LajfFaoSmpaZLuEb6JKKJfRLUC+HJy8ZTRWX8PXQ+GrJbhPCC9RmbhyxBphTQM6YG3TLJ+01COm0o/JXQMOK9tBMNHXDu59eQ2OYwYDsKkZB8kxKRMJnzUwwhijPZ2rUd4UsdB/Ml4FNOFR+l/PhK8XvPWhuaQ4vL4kpUVSqlYlCtafZhzzwSWeT0xkDsad/DPG50pCYwh/IXvSdnchmSIj5ecBy5oeyFf9CuT05RQWTxc7WnArVCpjZdA6BnXlkuoUIeppXXeO6Zj4XEd5y79RNO4OVzJ/l8KfK5ndwOTc4/pOHREEf0KqhXAF6grx4pOpUrlgnCIpuQkKpAEZ/GB/35j9c1ygSVHU1A0fkpKQiwFBY89j65qnYLO6GhYVgYDPo8W6KhtSlsNRkB+VshMLYTCnKQhS3NOM641r5yWES3j5GRxwU8rfSEPIayYvCsSFC3hSj9ZwAmKBgzh3EAKTBiOBc/l6/0UzahUiiPEqrNO/UZyg2rKSWV2+qaUBNhM0cFfpmi+RuXX021wrsyUVgwoOP8tRRqFnUKYo5u1IDOPjBYNSjkMVGTjqf+nNUOEzBgWCRm+yRYtZMJ0IoroF1FtAb6IIoooon8bRcAXUUQRrXYUAV9EEUW02tGfDPjyOmJWus4daI1vmb2LiCKKKKIV0J8M+LxqbiM2EyDYq+eWO++0e3ngiCKKKKLl0p8M+Cpk3WW/LCZ+Any77rJneeCIIoooouVSbQc+H5FDyttt171atWwXSnwovH132NmnN3p033jHfrsW8o0269W77roV5YlEFFFEEZVSbQc+YK5jhy4ffzQln2v4+WdftGzRFoD7Ysbs6669KSeTtZSclc6aOW+nHXeDpk+bWZ5IRBFFFFEp1Xbg87FVJLvTTzv7lZffuOnG29B2581dmJRDc7s/6t5to2223iGvq2bnzllQnkhEEUUUUSnVCuBLylTWh/ht9Wkbspw8gqTlPGryp5+fd+5FN1x/S0rOkWwMYKcgG2/Ua+eddk/JmsLOrJo2aWGDjUD7IXGZ6/vZNmHlPEQUUUSrD9UK4MsWvQ91aL8+wGfrripdmVZ33Ypxb0+w6dVTTz53/XU3p+T/YwXAly9a9eZ1K5iTMpJu0GOTWGWq1GA2oogiWg2pVgAfiurzz70MzPl2iK36bIdWy09vYgTyWwWitWvb6dprbgzkgnwFwIdY9+oro7fcYutA/kr5+cbrYzbcYFPQ0+GXMWiNKKKIVjeqFcCXKLl4MFt0upuRM8ugaKLvq3PK45ZTVt5NQl8ggRwHGCgDabv5yI9bRBGt3lQrgC8rnyiBDDOSxftwk0V/StZbq+QgrzxuOTmWFwcDIZ0x1OkY/iKKKKLVmWoF8CXkZc8OnQJBlVEv/Grw8td/SRbojHSO6FW/jBwiZeXRqDxWRBFFtPpQrQC+iCKKKKJ/J0XAF1FEEa12FAFfRBFFtNpRrQC+bKaqzjr1q3TNkBfmLr7oct/HWB545alhddNmTVsuN5GLLrysY4cu5e/zuYbEOvqo40pf+oxheeAV0NZbbV+hyy3LP/3eRL5Nm7Q4+aTTP/5oSrhl1KZ1h2Xc2f9qiuvKpAsvuNTOwcrPBpHpCX8/ZcVl99UcpRceRRTRv5NqBfD5mPFJJ54W3q82+dPPc8Ubf341kcIP3y+5b+BD5Z9+zrItmcjUrxfzbeWmlK6d9B2yK0+LF30biIHyT783wX+jhs2owIxuzPDdFLNn1WR0i+ZvrNJASEcDza9ZzAzhPfdlAtRdt8JXRJbHDckb95tusnn5p4gi+jdQrQC+6qomTRqvF17L/fiopwNt5iZ1XVlOd4z5REugC8AYb+ElRElde+YdYV9+ltK9NsZNJ+KdXBIBEfzeuODByXubxxkX+OlxmykeguGrU7Yxia/7qdKNjiSY1D22Pnvo8dy2TUeAz0CDUENcP7sgKV1ZaT4ptdnwrUbhfUMuV1YGLUmd70noSiNf/QO05XQvHYxV6J62lA5vp2TqR8iZX87N6jhkXvcZEQaccgFde4HEYd77Hkizl9KdcInihWdhambSGOf/223b77BDjwqDwbzvckrocvc7br+HecVp+uJQS3/eu0cGD+9LosUpIJy4+dLFa4/MpFvKTWOFwLdxuiBB8cCm83VVEyCm+yrzMtwOKzOiiJahWgF8KGKM1W+/+XHO7Pn0+5p5i15/7S3QZ8K776+9Vl369PRpMwkw7fMvA/V7BswnH08lMD/HvPUODy++8Cp9/ZKLr+jUsSuj5di/nfDQg4MZFah7jO111q6HEEcKaNAMEjRZZA2PFgIfftjR8+YuBBriuqeN0fLC86/Aw6yZ8xiloAMSH2yQuwcw4238O+/xnuFNsFtvuROWeN5i8634edCBh8I8HPIMZXRZJWX5bOoMGPBl2998/QOJ83XY0McoI+/J4rtv/wE/MExgmOElcRcu+JpPX8yYTQ1QEP5TWMTPo448NgRWHsiOlHt035gx76y/WvwdzMd1XyXoQ2pEJAvgAwZgZvfd9qYsd905ENwBI7bqsx1RXKWNGzVHVSdTkjKyozvDEj+N0Z9/9oXBK6Vj4fAAz9Thttv0JTxFu+3Wu0iTpuE9mSaK93ZSnB9/WEL6BCMv+JwxfRbzBFXKJ4rQZf0ehnLKyBtfAE/utAgt+ObotynOu+Mn+eJ2Gmv0G2PBZSLaRIei9T/4cDIlbk5XYpZ3togiCmoJ8EEMact0dF+GEOOqZYu233/3U7Lod4AwZ5w+YI/d90EcsK0ugfm6fufuRGEUAUAW1pDmGAaMpfvve7haV0futed+lh2ssZJgtng/Ie8JHJdpx4EHHML7D97/5OCDDgMsiLXLznuQ3YcffAroMMZIigCkz39ytOcYhDsCgwsQggnhQZDXXn2TjK64/BqDUV5XU5IRY54c+U+OBIvJXoVYYOUjDw9N6/bIUEBLSetnqDtkTorzM0+/QNa+1zynC3ZBASM45SLYhRdcaqzxeiUBjjzib5Pe+wiAgG1SpizgoKVLqhrmO3boAgAldfE5b6hS0DaQVkt4uHrwgUF8BddANF7CQLZ4PtwNBD/k6JYiBd/OTjrwQKw777iXNzRNVqIuyEjg4cNGFnRPPOnQLm4LHty+CR01ZwLbfLM+KbmlyEjepA6TEjOpduoQKLz0kispwn77HgRtv92OtA7MUNsP3P9IeTeLKCJTrQA+evnXX33vnk2/BzV4g1DGvO2+bllsx367nnvOhUMGP8potMPRG66/BTGEgcT/uNwQICPcfdd9hiRGEYOEAf/Si6/Z2pdgHqhpOTTNyfsL4fmKGAjEkAKjztFBByDpqSefAyl4f+YZ58BDn97bEphYBBs18qn3Jn4IpsCJZdWcrtn14OQlDO+w/U4GLJjJSI/m06BHhsGDxTG+vj32XQAlIbsUwgBzfDIokCOlY2AnpfBOnPDBLTffYcQxAcqBpGCjP7Eo4/77HZwt2dIB1DxzOAz/qZC4tEvPEJSFWiIX82D+12veOtDNwrxEDoUNIxdJUZaErAz5imJLjo8OH0W1nHzS6aQDk64Ky6HgIHnx3KH9+q4KNzQJJnXf8fuTPjbqwTxZkDJQzv+4lFySosXNGFhGlxg7Zjz/TzrxNAOiJTuiWNoFEGGGh5W3cYxoNaRaAXzr1mkAgiS1WMOMbaChTw+890HeoFd6vDGAQQoGUudO3RBYmjdrdc/d96ekbTFg+H/6aWcDQ4xbBgMhGR4Ax8jHnrSwA5A9+cSzZPfcsy9Z0Ai0Y2sweuP1MQS+8YZbicVPGLD8Ahv2ZAUa/u2Yv/OTr3vvtT+qIipYoF1OCHkwrZU4ZFK03YT0aAahAcsseQXt8VFPgzsF3a7NVz6hgJMC+jgKIEx6u5Os27TuAAiaQxiYOmX6BedfkpGGG1Ydwp1hmsEP58Q1KhliTLw30JPRs8+8CM5aB9ysV++bb7rdWJ/Urg4aNG2BoO0VQApCdlS4PYbxwPsBZ5+PQpotrj+ieKaLdjVk4TUHgjGT8RN1m1je7iBB0iFNTzmU1BeQe0GQMjLHkDWy4TFHH5/TAt8B+/cnimeCuOQ71GcAmrpybbuw5GghlJJSsRndDc+DBeqIIiqnWgF8dNxhQx9r1bJdQd6V81qSZ6jktXtgNWfPPfa1/9Fvvv6BYAweYOiaq2+guxOS6AT+bOoMPi35aQmSIMl6VQ6Bgk/HHXsiEAMCnnrKmShEVXJURVxEA1L2OhEvEVtIk9wJRkYkQuKMnxbrtTEWF4qbA4xGwg8dMgIF/KYbb2vXtlOgTQZ42GnH3QgMpjAsydoSYlKyJ2BBsuCCUYMAZ591HtmBoeAOUAs2UWRCduu6IdDQulV74jK2eUmO/CdlRn5YdUid1117EwEs88IAMu8ya1vUG9mddea5pEBNwqGr+ttvfkxo7wK84P0pJ59BFVFYmiCljReEMiCGYLDKbEF0ioMWDKzEtN3kVnjrzXEUwWKaHecAXuQC5+As0JzR6h6zVEbLkQSmCExjLVu0BeBeful16grhmnxz2rhwjd038CHvQU2fNjOl3RsaEZZ4wxwQaFeKWY3Ujj/uJHgjd9KEbd6QC5hbOkNEFFEp1Qrgo1ujnSHBlX+Ka9vu8suu3nyzPqUTeFa7q4ceciTCV0rbqeVxUUsteZ126lmWKbLaB9xm6x0snqSkYQETl116lVGjPBELDvwP3yS1oAYUonentPW5TBRwjU8e1eUJrlqiRL16bgkiBDrB9/BDQxLaPE2UOHSA4XMGXAA0GwgAwfPPu3jjjXqVBkDIsiMv0xGHHwNQxnVkDxWV4oSfwCxyzJfYOyMAHn3UceSeljscaoaXXbtsQNNU6gikxcMNN9gUXZiHQGyD7MwQSa1U0rjkGNOGSUaKMC3eo/vGVnXDjAKpyddec2NCinZ5o/OGqljmGGZEEZVTrQA+D7ByBAm0epUpHiuxqGVKF08/eC1suRCTlYKclssDZ2G8CwctPy2LFYrHX8oTsYhXmrXx0YJYSlpb+MlD0dnxyRri701GcOjww45GPgVZvFgZBrCEaCgMA5eihgtSmqYrzTVjJC39mi3ubJiSugbA01Jaaq8jWusMWzAv1zsOYx6cclIHdxwxbIWkzuKUwrfzDbOwbFj61bESxWNPEUW0AqoVwOeuv1zwSmoLb5lh6fceP461XNDMaL3cn/zsKMYjD13LgMuNbvJQLB1jHnXWxzP/LFuZfu7970HkQrl8VJABn5HEhNZcitRxed53EYwXKU0kYYC01stK36Qkybq6AgmJ4SdPA6XSt2GOColrO8KStfNyPYTNZKkwKSnPrAbFRVKz4QdDpNE5PLDpjPI6j+myoNWGn0x5ueEpFc8jimi5VCuAL6KIIoro30kR8EUUUUSrHUXAF1FEEa12VCuALxFPhytBjRo2K104N/HpjNMH1K8Xq1e38tBDjiwPUEqE3GbrHVawbLcC8pYFdP55FydLvED/FvKqVvn7lScv3j024ol3x0+qrEi2btU+rW2Z8qXP5VKzpi1btWwXrvGF76nM//5/a5Wu362A6q5b4bVLr7KVBygnr3JSk6VvktpkLw/87yf3t8suvSqpbfqVrMx/SYV8o4oGibPOPLf8U0jUZPduGy2TI8yss3a98sA/R3EdgF9xPy9dYDV5hZTy2jVO+cJ6TofwvTuX0gZUebIhkQ4dY8WLqt45bNyoeQO5ICkPEFK6aLRe+pK4nTp2LQ/MMI/JzKn808pQrQA+KmXe3IWLFn4zZ/b8L2bMLsc1mmrunAWUk7p7f9LHK8aRBfO/uuXmO1ZyPC9DCZno8/D9dz+tksFAucINll9N9NE669T/8os5gx4Z5j0EM7biegipR/eNt99ux6T2mks3ml3PK1lRTZu08G3FVMvzz71cPmCWSznZKZe+SeokZnnIfz95C+Wrxd95F+U3tlFI7jYrLiNhjjj8GLp9+MZNs5INGlJqeaepSimuwwylwOQo997zQJXMQ8uBw7NaSp4+nnryuRWfTEjo5Gb5LlMpefPq7bHvZv55k7Cc1q3TgN544gmnhm8yOos+4OzzywMvkAulXz1CawXw0QUfuP8RT0177rHvSy++tkwAyj/zy7kxOd6YOOGD0i3LcrLJx4o7xM+Rxai4jBniOkJYHuYX0fyaxT4RUv5p5cmbttdcfYNn1xnTZ6WLjkzKA5fTJhtvtvNOu9tRyt+O+Xv4nhRIefCg4eVRyokiTJk8zZi7XvPWKwmXCRnwLfPyl/r4+v0oI7sUd5UVd6qVJysKNbrI9OeIfGmIUkxx0/yiTktgpsPS0+zlVCn7JVSl8E1eJuroVUTk/3KBIyOHZoy1x0c9XXpgs5zuv+/hF194de7POHkzxWRmTmGtNJQHCCn+z4fzg6LDuuOPO6k8MMM83N//FVQrgI/av+fu+ymhTzkYLMaOGT9r5jzIE7JNO6kI3jOcpk+bWZD9Bk0LaKID7rLzHrNn1Rx26FGIGA8+MIjGQGoLhBrUJjMwPeDMM86xqrhjv10zOk5BRKp72udfPv3U8wzRyy+72mduGQ+IV2R61JHHGm6uuvI6GPvg/U+Yl+CT0Wsb4T69t5386ec833nHvfbyREbwQMrwCUwzH8ZkRmpLjECiVoWuOR/9xlgCkyw/Kd0+ex9gnyggy9VXXW+RhAa2BxdKRKb8JwDByHTc2xNyMjf+bOoMCk4uBINbysvA+/qr7y2XbbrJ5jvtuBs1RizeU9iczj/y3yI2hc1Ih506ZTr83H7b3W6IDz/4lHLtsfs+/KRyyBrquekWH304OSeFaLdd9yJZuxigGqlDEkQlR7ku6BAfQg05IqSTKXgdaBjYhI7wn37yGc8L5FiFkl57zY2EITVq2AWHMUpE/VTJCdiPPyzha+dO3ague7WgjHAYdqS0zuVQIqKs37l7QoaJLvX++x1My5LOIf2PoBREjMsgmpBMk/DA6PLMmtOJHBh7681xfN17r/2dzkw5+4rLgIcapkS0LA0Nq3SSFuu1+e7bfxDG/oToe2QHz2j6YWdw65Pyt9/8iL7fqmW7kHMz+flnXxCF9qXTwtuokU+584eVT1uQbEKexyj4O+Mmjnj0cRfcEEbNPPH4M4ixTz7x7EYb9oRJcqfSzj3nwrvvuu/oo44jcRqI2Y4o7dt1dvORclZe10ic8PxklA0fNhL6OWAlZEHuJNIy2nHu1MOrr4xevOhbikMRqD1aMNCkwsChaCedeFrbNh2pQEpU0AnZsKGHDhlBzaNV3HTjbRmZG1BG3lNLxAKj4Zly0UOIRTEpSDlXK0+1BfiYOtLS8Lt13ZDxltXl4rwZ+diTDz80JAQ+Qn780RTqhRqn6qlcmjknP0vU8gY9NiE1YwQ1RTdq07pDWgZMO2y/E83MJwIwdHfdZU+PPSqdYUZHJH0qnVY/9m8nZOV8hegEcAtVyRKW/2REXzE6o0I2b9aKiDTMcceeCMOEpGvSaciFn7S0hwF9q/eW2xCLsUGbQTDPAJ4jN1B030ceHpqTuyp6JzhFyNtuvcvH8ehDG26wKTw/OnzUVn2226xXb0qH6kqCBx5wSL++u5C+i5wqOhwlNcCLuLBKBzLwde2ywe677X3RhZeFFhqwQbLorXYnRe72L7DkpyUo13F5FkjJWpmCkB2lILzhksS7d9vI6RvQYYNOCdJRM4xqC5gwQz2g5oARvN9i863clAxdGnrSex/xE/ijmYhOK5x+2tkIp7acQ1DdfLM+N95wK0UzgIKnGVm/wdsP3y9ZKB8WPIQdyYlT/244SvH3409mnNsksVpOFWGDzgCHDCrKwqD1xEPTG+5jsgmh7MwrhGcYt2vbCYYJEJdCagNhEuQTHcCQTZ0gf/GSbgOfdsjYsLqpxVsqAewLZOm49VbbU9sUtlRDdNNQP3QMWPKCBjWTlEtKygKOIxxQCTlZN9NkPPz0jyUpGfMR3iDSd4edbd9t20H6CRXC3Nyh/fpAEiUlJFBC0SgjrckbOsYVl1/zzNMvgN20IDyAd7QsJb1v4EON5ANxGTLqJeSPh3yZIQjGw2WXXjVfjoWYlem3HTt0IQV7Agb4YI+MaDUyeuH5V5hpYIbBfs6AC5jpqShypM/T4pSddJjLEVCoWHpFWt7qYJgUqBZ3znLGVp5qBfDlZLLuZb4F8sVC067113Ut09mZSijxvTfxQ8LTF6+/7mZ7WKJ/E576zcv7EzXiSSOQi/m0zH4DLc+TIIgAECAeGmfnyjHJbHnEJAoyF9Ua9nI3LcmChqSJFHbdtTfBSV4WuJ7SwYuU1oDp3PBDKQiGeMhU/+wzLyZkXEWjIrXR4QDZgs5RGybgnCgxnfhl7N180+0x+bCCk7vuHBjX6rVHLF8RYzNaJfGIbSC/pIvknWG/fQ+ybkVgotP1EV0pOy8Z4Qa+lLweUJCYLMPS8vdJFogMnq7zEjABXIqW0C4EGRE3K/XfbZSW46wQEFFzaAUKS51kZfiMfBQUrfrSEkPgISnTjoI8x2SkXWZ0sBn+EZNfevE1C9rj33mvIAPBuXJb4PnfIMtLoIS8Lr3kSvKqkOcu5hiC3XH7PWFHYvgxrm65+Q5CAh9/+e+1A8mY/Jwrf4UXX3S5VyqNFAATI9zNQQCgNq01BON1ICQdMvjReTISJ9+Qvbi0BGCloJPbAF9Gpkf8nCP/N7Q4IZGwYM+dYb68lhlkaXQUW/u/Mblp3Gnt24LUrI4EWs9KyEEZTZCQ9xr7AVsgR2dTJk8Dbaln0KRTx65kTVJVcqvjHu5We+3VNzNFExrqnAcm1LnyAEZDAFUEc2BP5/CAOJJcni7pwtIhESqTcrHjJgOVkAySMi7gOSVxlTc8uJ4ZGlSCU4AfEqEDmyvYsMMLen5CtucMlqzk0AFnn0+lrfmXdQDlp558znD/nwB8DGzUjVjJXiFiHY1BvdDAto33AKAWPvl4akIbr7ynBzBu6RC0kz210PN4trxDOtdcfQNVNkduDowv6MW9em6JWBGXmQE9JiMHcwnZvYEgJEu/tH86eJgrSeqYo4/PSDW2fO6B4SihnsgcHmg3I6MlEss7gaZHa2p0R9qMh0q5e3HfShdvT4c92A6Kew6IfkYoF58odJqG8vZOT8rJ/UxCKg/Iy0s6DQLvAfv3B0x5Y9R2Rsz2lDchfwT2nGykSwv6EZmr5bWQkYPsyTMDiUrzeEA0pnKYouFklhwEEIvK4eEfPy5BbSRMlTw+JKQPMmZI56EHB/MeTuB8gTxf8YlEDKkWIffacz9UYApCbQN/JDLmrXe8vF0jB67EYgznpJUHRQm6gXxQ859KQOohLgpB2G0q5bswrd1nEIFaRVuEPB0i6oJBVIJxmSL/1//9Cx2gUg4jKBTDuFK+cCh+CHNMqMSlLDDjqqN6qSXKhXiVksCFpOMmzsi9I7HgljCIKjSZO0NC67yeR+GBHlVqnO6mgXMygpO41lKpn6wctcGM/bDZFwMiLZ2NjDbeqJeR0UReedn5IWrN0hoRtUeO1BIlQrTPyLwHhplj+MpMsFAeW5kwpk6ZzhuaYL6c4FJX5EhqueXtYsEnUi2Y9crLb5iQu8mCLjpf61QEYIpKaV8YWS+Qsw8YYzzG5RwEllDG+UpBUvJqwUtkGiIy9aYFwbQFjFEhVKPHuHeiPfN5Ei3nbSWpVgAfBVgG+OgTM7WbwVzKyIzL0Z5Hl6/jSGkZLlweYj659ZY7HYA+B2r4GbHZIIXSxFSPEpfSyi7DJiM/JVQiIWl+mienlSxEpIKciboPeSoLhGi0Da01TY6gF8lrS6boboTAFm3ovkj+JIVmOvqNsQmdJyAw0zsPnrqNuQhTLuPQISPolLyH7XTRANkdFySiJwXaJaBDkAufeImeiLxDd0GQpOtkNGe6E9NFmAMYriSFZn3hBZei4/MmUH8lvEcvw7KRvCu7IElpmuedexFp0ol5A6xQ54ylOfJ5R5+bL6ekWa0D8MCos1xz9lnnWdK0mEw9DHpkmIe6wQs1Ki1XgBapUILIDn38gvMvQUIhWeRo2KO6eA8DFi1JzZBXpSMOpIPInJG/Vd7zE/UwKakk7DYN5GsaPa6BVnVhibwY3uiJoAk6Jm/mydMq2q5b1jhl0QPg45mSeuAxpwLEhN9u235kREMTHZHWUhJsU0WpIvAhpmXlO9K+beg/SRliwo87w5dyyOhZhHRorFKJL2waOKG7ujcaxPnqsgRaCkwWvdcktfKTLdmToeknvfeR3VaTXaVWq2dpYYGivfH6mLSuZ+AnsiF1iADlEcTQsDcduCLA2mvVBfUAF1qkanmGz/CWk8ZtaINQV5GmoTnykkvXQsh1TwOm4cc7Y1RUaIMID3zlPUMmp/WTQOI5DephRcPRdWHspBNPo1FoCJIFHKlqeLPXpVLc/0VUK4CPYiPsuI1NTAhMQbTZXLmKo4IYIQvlPYl+T2dKylOmJfa8vHWi4GQlEn74wafojGlJH2edea6T/fGHJfQnhHNLN+izVB/BQDHSpNPbXzwBSAFp3FlYdaUDkcjgQcOJMk/eAGlaOp9FAzgsSCWnCQnPM50JIJ4tP1E5WfWSJhHpx6Cqu2xSQgdxYQA49tRqVTcp5YLwlJfWdQ+mu4AOFh5dCRSBYYmKZE48YIyA1gQpFADEMyjgcWgoDDXlnOx252q3BAbOPedCPkFUoAOTy/ff/cTkYel7ntYiGEIwRkeEmaOOPJa4rkNSo4uTPl2TSoZPsiZZ3szVRhNVmpLfbCpnoZweIjOSIALplVdcS3YW8ymU9fe58uw/RwvhFppo5a/k3Zq8SGG3XfcikdLLpJKyGiY6EZl+LGSRArGoRsY5EW+4/hZwZI48jHmazMpOHPZQBZxIhTZPPvpwMlmfcvIZCR3iYb6hBgDQBdqnJguQNwS+Pr23pXvAYU7bPkzD5EhhGcbuDBTTTUNV8KbL+j3sz9Hkppmta2dARh7mab0/pV0jkqrQuRPiegqhCLPldbXUltmt8PbYd6kBWsG80RNg+OSTTgfFCJzW/g8TLU3D9Ek10o6ebJLFxWKy8+xCDZQyWcotrdB7y23S0lcgRgRc0Y5fyoc2WQCCHnr21EtxEBIvv+xqSySEGfnYk/STSq1gUm9gPWIvZFU3rS0d2GN+PeHvp7g3UmRmoy+1mUY/JEfLgL+CagXwrTzltQ5VkL5JZS135fWXErW/SLcLuZ0iiqiB7hIwbv5LIhjjufz970fgeE4aKKDPNFwe4FcQGO1pnuKAj8OHjdxwg01Lxcn/MPrzAR9gx+w35q13aJjqFZ6uXBmyrORJxqJWRBExs95z9/0ro0bFdG3eHrvvU/7pd6K0toxQAL0ouapma6fDfwQ0RP4NemziN6nlHfT7D6A/GfBZR8sWPSOt5Jy8AkpqVyGhdbfyrxGtnoTs4zW1lSHvjZS//50ooZ0993zLfauE0lq9tVJfv+S6klWiVNVC+pMBX0QRRRTRb6cI+CKKKKLVjiLgiyiiiFY7ql3A54M5sZ/3ehQvuilPFZ2n2xLWB75+BfnMREon7H51IitD2UzV+eddvPIrRyumxrqYfBUu8fwceW378MOOpp4TOrDtLfX5ukA9WGnnLitPBXnSX8HqbUrncn3OY5UsvVfJqsTnRX6OMjq5Tb4EKz1B8ouIOhzx6OP0Nx/5nDpluo/C+TBdQgfOy2OZMrJ1WW7/yckFQPn730g+GpWTLww///jDkmmff7ntNn3LA/8W8t5xXPZaOVnBX3bpVbTICmrjt1PtAr5A1W1EWy7ldHKSnnr6aWcHRb9Ml1x8xa/ed/cBZqp4ue6wVi1decW1q2oVHJ733efA5R4uXbWUldHFYYceZaNU8mXkn3nGOVtvtT3DmMGwglnq19G33/y44skvENp6QAIEO+24W3mAX0S777b3lMnTVtyFMrIkMdraROdXkE81JmV+S4KLF30LloFZtvL2ubzyWKZUyW1N5cnG5X6q/NNvoaQMDa+95saUzgOmZDKQk2e98sC/hcJJbs7s+RWymbnzjnt/7/3GWgd8Rx157Aqmcfq6jxP7BKw3oT795LNfLfu8OfrtBvI/QZ/7E0l89Ehkh1Xe15dL9L9jjj7e3T0jY+Ebb7jVxx5ddauWkKcysvkr/+SOkZN1SlXRRGz2b7PZDIrT53IxJaQd++2al0Mn8v3V4lVKp7JtUEF2b705zgjIgI/LI9MKGjQtnxrLvTlzh+13YhJa5b3XIogNKnzqfuaXc2F4uUeafwvZEIC8kPXiMqOyodGqGizLpVoBfPXqVlJaevCihd/YAAPUZ3qh0ocMfpQAH304GWBCI7C5mA/iI6Ndd+1NPtxPxE032ZwewMt//LiEeZWO8sbrY1DHSPbuu+77/LMvSJBud+std1pyIZdvvv6BpGxGxv+crOIJOWP6LNszIdrYq8rjo56eKa9Ekz/93E7KkvK55kPnPlAK8TxHjp6Islmv3gQmzPRpM5FJiXLxRZfzhvThqnWr9q+9+iajqO66FQwk2729P+ljWJorQ3FikTjRE7I38iF160d5WaoQ0qLHbHmpokL23+9g6wvUkk+9Z2SRSlxCfvjBp2AKEQlMXLqaJwzI7pXWrdOAkO9N/JCsszIRvf66mx2eBG1fBWOMPVQeivDQg4PJDn4IwHOFbKIXyc1Eo4bNyJcHuKLamcD5T3akZlO5jKxW4CcnY+f1O3e3+W1Gbm9sGPvxR1NIGWmIHAlJMd1J5sr7W14GhfQK26LwKSY/MXxdKLdXMTmYSssiAj6ffeZFNzQMEIyadxXNksX0bbfelZH5mluQXMgxLmMYO26ibjOyyM7IPYwtdqgWm1gc+7cTws581ZXX0cqIkOR+zoAL/vLfa5Mptfr8cy8TktKR/qiRT5EID3NkDvjD90tNd159ZTRQmJSj1pnyOmVvBXmZ2dlu4Ybrb8lI64QQw+fJ0gY2CDZ2zHj3Z+vRr7/2Fs1Euz/80BAi0uuoXgpI355XtPUExS695ErCz5W7FzIijOt5nvwyUHwalDcUaqGcrblueUNGME+PpSa36rPd9tvtSJ+5954H+LTzTruffNLpZnKRfN6QPk3pjh1W1O233U0ii+Q1Ky1teoFMYig7HWYF0s8qoVoBfPSParkMmiMjUJ7HvT2BjmsN1B0uISuWWfKMwsCokT8cT5gM6YwkERq+oOtc6Vh8pSdZMDEkkWCVrD4tL3gKJSPraz6pT1fjZ5116nfq2HWR/EbAAAnSNWfLSxpZMETz8jcVCAjmyPrSAzgmj0b0NrrRBj026X/w4U78++9+gme0hoKM24A8W/YQF7YvuvCyjh26wPCLL7wKBmVlMhloms1pTa1Xzy1hLyVlx+jAezsQzcnWKqc1r2p59YENWwIBHPw3Hlk0Jt+8bONOPOFU2/3YR8PLL72elknQ4Ycd7ULdf9/DfJ0vF0PUFf04BD7omqtvMPTDIQPJOu8iGeqSHXMVySbkYAM5kRSoEIsMdiHlY2KoqOiYEJx8KZvfubKBNRLBM4PcarWtqcAvwIiHENDdMcglq3OddIMKuXRkLqGZkrI8C+TdwFX6xOPPkD6TqEUtIDuQ1kxSAF9O5qI+wQf/TIE0Rz25z5ktS7K47ttMa04iawruc++nnXoWnSTszFdecW3PTbdIaD2OFg+E0R+8/wnAR0YMdU+6sGebfGM9b8a/897ECR/wEyBLyMyZ6uK/PR0QHlwwHNiAb56Mx2GMgsdlHLnO2vUo8gK5bEGVsQyYlyeh3XbdKyWvB2nZtjPiiNu8WSs6PG+Yilz5sJeVyv/Si695xPG+gRxDuKdV6QLljDzOu8kI78vjeaDpbb9oYIUfqvq8cy/KyXCTypxV4jyR6CRC/dMicd1Dv9oBHxM+7VQptxyBlI53xk3MygmKnQXw7Nl1tgxgPV2ni2fNGeHul7alpTHcJ+yHx0MUxZCkjE10iKTOLVO55Ot03LkJQHh7wuCZJuc93cKzYiAHQSlZhht9SMqrNlmt9zMAPOUym9Eb6G0Vukl2gTyUXHbpVebnkYeH0roVsqhNyFtJRmqFHYKnZJBvPh1+k403my93RjxbTCPNYUMfS8kbDcKFp/28dLHZMrwlMANyl533iMtrAKM3Ibt6ni03eUr3BO5ZZJYso+fKdHfMW+8k5PolLhUMBF8G+MKKfXf8JFdmjVyq8EBr1siT2BzZwyblByGrw7E2DbQ8CH3y8VTa7o7b7yFks6YtB5x9PkWDSaIAPbxnqBx80GFGOsKs9dd1yXT0G2M9naS04jZLRvhJGaUam3jPTJPS9ldSzgVcFuYDpCQK2L3bRtSzbUhdafZqsUBSv7GD92efdZ7dWM2VwbhbPCHzXiMC45OHvfbcb3aJuo1072mGKERPSoOmmPS0jBTzCnljhIG3x77rOpwtN18oKEChJ3XHmq/tI+Zg4lKBNBk9MyVfTwn5fKdZEUXJMVkUDOfItJmfnvVzmhFhuO8OO9MWpF9fvnJ9YcXAex+cL0veOXIFmpHHMEcZPmxkQsekDW1Gw1laEIfzPffYl+f5UqeIRX3aRdigR4YtkmsJN8pjI54gKXCZlxDD0yUyucuR9Vx5zyzIyD1YrYAPmOjXdxcmWFqlTesOWc1+bjZgxXIWHaJCC59uhkWyY09oI8zO6DPSnrJybEdg2tUOQXnz1zXrGEToN6V9FGKEZzVtLpCXDjc/Eh9zLFkzJX4nt7r0LVRpxJNS4yR3tbhW4ivlOA+WgAkYQAhi2qd/ZLRqs1C+M5HsMnJsRYdDaiD6o8NHHbB/f+CSr/xk+MFqXMscFj14ZvYGQy220OcQNGJa+Ke6UvI0Q2HdS+bIcxfIG5OWZ28cCclQBW2VMtKq5KYtkKRDLLKmsy6UTw76JQAXaOUhrX0D67zwwOhiQvaIZVAB0HkpfSRlB0e89CIp+EUprLzMlfsGD4NANtFUV1oyjokoDHIDjXHTWGyl+5ab7yAWsIKQWyl/U6irF15waYW8VWeEvCn5Qcnr/iOgn2CujW905YVLWpBXd6YBsoYBxKhq+VAABegPOa0Y2i/OQrm34WG+nC0CN7TOfQMfogka6Q6sanm4mCX/SHCIxEf6Bx146BwthJmQ+KrlZp0uRLdp3ao9/CDUPPnEs2Tqu1wCraAhDXlt7ks5vHrl5TfAx7j8xKSlqtfI7Si8IR0/PurpqqL7r0DiEmWBAbRseIvLcyeVT14Wqz09VMplJOmQApUGG2REeEuRQCqVRll4jklZmaPVRiIOHTIiIamT/lbQBiuc0F5x7aJ4Kk1JJyAMGgmVwE8kPlIg/e227YcMyFgj7qmnnNm2Tce8xBemT6fv7koipIYcmpJePEt+z+h1CPsFOToMa3WVU60APgDFwpqdrAVySkz/pgMxeOrL2+iPPyyhuufJk7V7Ob2kQm7j6DfIQUTcZ+8DeJ4vh2K0IuMcGErIFShDiyxoCV96EmaNqkv69D8Sn/zp5x3ar08YJBFLKzRV507diEify2mpojQuo4s+SnaAY0Le6xg5MGB5wa6SScc6EVkgQRiSAI5ZWu0iWaCnSi7S6BBMdF5a8hBF6rGsFGjy5yfwZ/dBMSlNgA59nbFEImRE9EBeQ7weukhe0bPS8pLy7BCXfEQ/I3Hv1uXluCmnraGElFPS5I19Dj/z9AvUJJnOk/rskUBSl192taGH+mf0GokKcotCo1jVIrzlvnTRD1WlFi6sN1noo0RUNQ+U2utZSd3+Q0g4AfgItt++B82WMktlwjxpAn/kSBaWjqkZGo5Y/KyR0xpGvkdyQboeORLRgWmpE/5+SlJ7lGRt9ASkABcCfy3no3ktocLDgw8MIlnGIbHsNT4nGX++3HPx8v77HqZOgGaLKiakPKMDzHtRFYSlCM89+5K5pWY8zkE69zHYhhmCIRy5z1NAPrkb77rLnnQhGpGi9d5ym4TEybgEeQLw3loLdYvsDycGfXd+ylKtXeOt+myXkmu8uES20EvQddfeNFsLdkQHARfLuRYh7fWWkLxhrIHgDJMQFsnCoyyc7On2jNkaLbjTNAQmU7T7yuL+NfVAc8Oqp6u81ihIhHHNezJFLybMqJFPIQZZUCgda6ucagXweYmEXkX3sthfHubPRRRhgx6brPikRUbSn7drViHR8+ivVT9/0iUlBdkDgL6OJFIeplYR43P77XZkdszLHSHjJCbXEunf7fhRRodXgLwKLRrGtARsGP03U1oLEYH21tEhvPgL9iGH0tA+Y8TLo486Lv57Hv74z6NaAXxMF6++Mnq3XfdiZrA3yvIwfy6iCEh89sP+c/TSi68x0yIUFOTptzzAryOLVxbifo6ssQ4fNnK+9kzKA9QqaiAnz8yIKFDHHXtiqGf9fpzHdcEe0uugR4ah5tM/keMqf8/ztD9HlotfeP4VRgfSHBJTQgfcUB6Rj44/7qTLLr3Kqx8rbvGIlqFaAXwN5VE9ppWyQtFj+5+a4tq5a7jCI/6x4t0XXlcqD/DrqGmTFjmdUCn/ZEprXy+r3Qav75SHqVUEt9Xa9PeY9woa0t8qrLRlyCuYFbocI6+VVrJe5Qd3V4ZoIMprUKvQzXzmLavzRqni0WJv+5ZHj+jnqFYAX0QRRRTRv5Mi4IsooohWO4qAL6KIIlrtaPUCvqTMe3/jTkKjhs28H1/+aWUoqbOmXptfwd7rKiSvIcZ1uL/86yon51L9aw1aV0BOOa4z1Vl5T8jqmGF5yBVQQudyV1wVjRs1J1mfufldt499Wq00fT/ndUGVV+5yuuIurkt7y1MopbBflX+qJZQocbqT1IVWv3rl1B3gX9ZJg+UZfQerG/AFOny34k7/LymTLjRv1upXAx+IQJN//91PWR26Lg/we5AXyPM/495j1VJSN7f9xtlluZTU4cGf/rFkwNnnZ3Xuf2W6/jKU08WeG23Ys/xTSFSXT/z06L7x9tvt+PtVWlZH8UuBD2h44/Uxp5x8RkEHv88+67yhQ0Z40vJWRnkiIZEO/eo3du/fj7yrFv60FFJ69vsXUVz2piue9tZr3roUaktp9QK+jGxvfyPcVFYk6Ze/cWAv0JWJP9cqq5a8V75QZvz/BnGA6p2p+4LLP/1GsvxVM2+RTy/XyIPDL23NlAzdVnw2xZbUSHybbLyZ754vD7NKKKaD6KV15fmpfr0YmX5TvE43W7zxdsXNF5Oxx4rD/IEUK16daqJQedk+lYdcGaKBXnv1zRU3zVwZIJe/D2oJ8FH+tm06Ikb5fFlGJp/HHH28RfdKGVfRIfbb9yDrKVtsvpUl5ITsqInS/+DDwREoKROCvjvs7Pc+AHHE4cfEdRA8JZ9i1Hizpi1J1mdoWqzX5ti/nVDRIEFH583aa9XdacfdrIO4bZKyKGjSeL1OHbt6cj7xhFOdPn3Rb5AgCNm0SYujjzouK1sLSyJk+rdj/u7eXNB9o/yMyygtJWPSbl033G3XvRzFRLKwZEsDFzDQ/FaQs8yYrkbiJc9bbrE12lBClqqEh8PNevXmEw8Z2YRRkKwO6Ffq9tKkbgq2NuTxH9dB/IwsAZwscXfYfqeQHyMLFeuS+mfPTbdI6kAMLZKULlaQsVrnTt1isnwC+AoyFOM/XzM6tbN+5+7giDP12PaodkOQss9q9Ou7S1qKeVrGbbw59JAjYYCU89IK3ZvzOvrvbk2www49qlLmWQZ3/u+z9wEZmU+4Nc0Dn8h3rb+um9JiRVqWYYTMylzBwYwyaTkv2XCDTffea39C9uq5pWvS6waB5Bcz7A6Wlo3Knnvs6wCEJGt+OotA4zzshxlZ+x1/3Ekws3jRt+7nRKRBHaBSvtdsXunWmS0TbLdsQfYecdlKZtSrXZN0CTjnmZrPyLLY5eJnXt3YQ4nK/PvxJ8P8unUauL0OOvBQp5PQQhDDgSyq5MiDNLMyEq3UNcpuESaDgiTiCrmBcF9NyYjYkmmf3tvCqgcgb4484m90VDt34CeDnR5utpFqeThg//4EcGUWdEznkP5H2CKoe7eNENyS0ovdSahVs/rm6LdzuhvatcSbjh26eACaE1caDyROYcMhFtQS4Ltv4ENASdcuGzDFeQ6kOgARxg+dvqHMyW1iAgAAgABJREFUZmfLiQDl8UXxY8eMp3kSsld75ukXUElsqFtfvjR4//JLr++x+z50PhLxIAk0jEmZ3nzN1TfY/uz++x6+954HaFS7xGB4z5g+i3RuufkOMg00A9erW0nvv+P2e0iNr/y//rqbgRhaNyO7bhigxulP83QtKZ3PtkHzaxafesqZTpNEEjIsD2QgzAMNPHXKdAsUPp1veujBwdQG4O7hZ12AgTdLnmmoK1KrlEMHquiuOwcOvPdBfvJgfy1ff/U9nePxUU+fdea5IFSNboa2mwMYtq8BkwdbVhq38Qgl65GHh267Td8a2cm/9eY4dyDovHMvysiPCMmCFNQnfZdqnyP/AvyHbSaGr3RzOZyTQl5uTlq2aEubUlKaA+xbIE/XNTLRZZzz01jjGvDh5JtuvO3R4aMK8s350YeTPeAN2eZt8836wO1MOQpjgvz4oyl0ElqQLlQlBxaffvJZRsbzc+UKiU/t2naqkqdlWoHa2GvP/eDz229+PP20s2kCW2obgKh5WgferBGjPO6y8x6g58QJH9CUPh+elFMyKpPyogvTEIzqu++6j2fekCPzmdv63fGT6MMFGbqF/TArCzxSo3Q18hBB4B9/WLJBj02y8lFEa5I7Dd17y23atO7AVETr84YKdNvZUUJWmrJBinxhnrjuyRaHk7Kh5isTA3nRjrxnyNAb58ksPScrdYJ9NnXGJx9PpRppO8Ifd+yJtH4gMS0nu293P0rhAO+Mm0gd8ob68cqATSQ33qgXOWZkOE+XcCPCJ/2K+qHR4ZPOzBicI9Nyoi+Qu7AJ775Pqd2xPRlAJAJK0sPpz5Ru9Btj6eSOSxaAAP+pYdchgw6EhRlbK9P6lHS7bfuRnae0sOcHtQT4zjzjnPnygFQtZ20Uo1ImflQHXZPyUzVeGgskIBx+2NHPPvMiVc/LhXKOxsv3J32clZnRddfexCg9Z8AFdpRERV915XW0X1b+C+bI8xUg5VU2fgKCRLn2mhtPPun0EY8+TqwrLr/m6quu5yGQ4AZLDOk5cp1WJbtaBn/o/J0uWC1/UFmJLcQF1xgeCa1fuN/YkJ5Be8P1t9DniDhLdvsvvvAq0EBDWigwMRSJ7sm5IAt298tBjwyDE7oRw3vkY09edulVpAbnVBEhqYfzz7v4ogsvg+0BZ5/vuPQ2Bg/ZARxz5JyKCiFxE3hNh6ZvkcWUydOoHGoeTgCyhO66f/WV0Z6BqTGKzH8bCVD2RfLf55FmK07rZRZV7ComLav+K6+4dputd0DoCCTae8QyczDGKDv9GEBhWNI7eYYTwl94waVktOZf1jF6BkKKoOgGda58edIQM+UlyUgU0+l3oidkcZyWzFhf/q9OOvE0ZsFLL7myII8JddetANoAO8KPf+e9gsxmPAsmJM/yEyYNggxj5Iu4tlPsFwPGaFDmSzsQmy//OrBhvwykBr4UZGZbKJrZ00AFuR4J++Ftt94FovHgyTItGdk1VpC/KQIYW3lDPZO+gyUl+MA5EECHR9EBc2ll1wYJAhlk0aplO2YaAiOjkSm1TfHNkufgjNz3kvutt9zp9V+7mdp/v4OZdej8hJ8tFwMUmeae9N5HBWkwJEj3i+t8Pu2VkvUx6fAAt7ynxujnJAVXdIOCXLrVk4cRBmxGPtDot+RCtbhHJYtKnqGQ/0Y91/CBBxzy1JPPzZMrmi/kI86iLg9UApkOHzbSh/AZZQyHyy+7esjgR5lu3dncbTxywyEW1BLgg2/YorFtqEsPS0hCpsYpDAUOu1dCIh6NRI2A9FQBNUKdQvZ3QtWADq5Ka2ckSL17giUYdeHxQzqeXhJSn206Bha4chNSpjySk9Jz4fPccy6k9xD9lJPP8FBMynOcR3hODsvIa6s+2y2S/0Ub3geynyUMUoM9L8Xk4aMgNZCI++5z4PwSdz1xaXl0VveAYUMfS0vroTaAe0JSQB7MWFwqKmWkoxvsTAAlYqPHWIX8WdXI1Uf18jZb40WFl77o8V8la9+3x75LePcqgI9n2HZtz5P/rnXWrucBn5ZO5NmisqhWZyVLXnzR5QCfpe+UPHAEUo6AWsONxUNyefKJZy2p1ZcTLYLRppUli3Hx4iUBGYmoRExIiPb4hAHLMp6iGsibFtnltM5AfQYCUIIh8QFn8MAUBay4G2RKJIIv5EyBkIi0fXfY2ZBEE3g+tkdP88PLKqmEnmAcl5Lal0RCqwdmuFKeY9wPGfbW+gvyyRiTEw23ZkqrMUBDXutfKW1oeE5y86UlgzeSBz2IsUCpUSwQP8liYdEzOVVHSLSitOYANyvPdkPgWHALJ61btc9I+ybY2Wed52cPigZyc0Aw+hvTIfki/HZZv4e/gi98/VKOHdPFJQgk5d123SuQ4hLOpmQ6U14jIdTqpETmtFYMLGa6t8yRmTkhA6ntadkpJ+SByaPVGqF7CJnSPxGHGRFAQUK+hJ1OSr4pq4tuMvLy1FAbgW/33fZOacmJgsXlVg+goZxIudZbGU4uMHCOBMsDGATee64uaMlj8qefUzYELjpBhYx7Lrn4CioasYKSIwNuvdX2npkzWs6w6GFZMpBKyzyJ8LVQbpF4Y9dYaU25aLJ5iQD0J/j5+/Eno3fwQP/zVBwU3QiSDhOdZ7N58m6WlT7iJa0aKXSwYSi0Z2aSXVxy2QJ9i0/0BsI0kDtyGtgTOAkyVgnPgKRjNZAR62MjnogVXRh5PnDcKvkgYECSdZ116lOT4BTP5fWf1YJRCI5GcxSZp596nukdmQJdgwon2QVyLEiFU+1xWY8RhWTJNKVlFyaqjDY34hIKAnkrScnlukv6he42qZb679l+sRzz8BWFzq6zGZZM46RgKTXk02Nyjry3VsuRclLeZ6u01lYtzTEjJYvOw09akPZae626r7/2lt2aunRolEglPCBsWiq0xh1mRAqkCf+EhKu0lEp3jGppUgvlLCAtz7gJzRNW6FLypViQ3Bc6s5qtlSaqKOyHeTmUhR/GLcWhYsMay8iL55133EtGdN2cRJWU3Cw2b9YKbj0QAi0LmE86GxCTFsB5qOfk1rBaTrTem/ihcQqI8XCAPXrOXHnATsuvala+qoz+H37wqaFz3NsTSDyjRT27OCM6oNa+XedAFyfRRubWg9d1wvCkxuCT/mntmLJ49kpIjTCu0WfgOalVESokpbVXf3J1EXLzzfpQXYTZsd+ujNYKeb32YGdA7b3X/mgklfIjSxR3Wg/YjTbsSReKyxt5XNI6WVf882VStQL4qF9qn2K3a9spkMhKT50tt6N8YtBSpJT2qmgeKpqQu+6yJxCQ16JDUgvJH380JaZdPyp0sTyS855pEChcJOdRMbn2d+14bkxI0CPBBfIH575Of+WZir79trtdWUlNUIvlPM7bBQWtUgFwntBcs4F8KLqxDWTWuTJSbLPaSAHpYIAxwPhPScjigcBoHGFtODzJvvD8Kxm5BqJC6MQGAhj2M8X/Sn7zPbroN4hL1pKIQilIlsng4IMOS2jFmvKCEccde2J5/QfqlNVaTKBQZM28ndQ20UJ5Ie/WdcMzzzinoPUaC9RWcKjGxfLRxpwxV67Ygcu4/NHHtd3G8EAlZ8AcfdRxi+SKEk7IiHJ5koMOP+xoUMBMIn3Ml2tMjzcvG5XWTE5ydFpKH1jmxSAYni+nmJVStO2RP6ZdoNnyy09E2DA05+UxzLMLfYZSAGdWY8OMmGl4A2QgvHtWTspdaFabXXPkeckCrHWRjFY/UxI07HSeABZpF2kNDmLmCPshX1984dXFcskBt0ndKOAa45l2pGhJLUHGtYHg5mOeoPly2iyCE2TwsWPGE2zQI8NqpGYmtLriMs6XS0Ge77pzoKsUMYKfP/6wJCO90toJjCH0zdBdC/a/iwILY7xpsV4bT5kEZgrJypse8q8lPhJB4vMnApMaI44woBXSNDwDizSruxBNQIAv5V4XuYTwVOb22+1YKS+WDz4wiJ4zT7uOca2Mp3TENSGfr/w84/QB9pRTIY2N1Pr03pYyMkCqpWmhM917zwMV2tshZSozrT0l9G5+Mn/Hys4Y1ArgKyVYXKSTTWlNs+UBIoroj6KYHEfP+7UnMFYrsmi2/34Hn3bqWRltqYNZ5QD0R1HtAj7XC7BdqaX98gARRfQHEjKL98rKP0W0DKWL6z/IeogyqNhvvTnOy0q1gWoX8JnsENyzRPnXiCL6Awm9eJnVooh+jjyE0ZcbyTl+I52TLQ/2h1BtBL6IIoooot+VIuCLKKKIVjuKgC+iiCJa7ahWAF9aVnhxuaBIyhzSawEJHc31V5M/Jf7ZuU1eZyyJ/tKLr1XLesabwnGdBPaGyScfT60qNPapyAbL81TjkKRWd92K4487aeaXc79a/F2pfR/v0zoOXR43pJhOzDjrhA6IlKbvgnjPHma8gZPRmaykHIg7ZE7nPEv3vxI6hWQOnUhGB2IdJa3TOQ7mGvN5BW8+xmS76iM4/homm5Tpris8rlsmwkXVKt1kGCseGaunCyeXaaMwnUrdPeiyl6afKt7/Gb5JFw2oG1Y39ZmM8JMTLG0aZxSTqUNSJ4qSxTvCfVAj5DYr20xq21ZW4ftAtiJEcXM4YvjJW7TuPKawDxx37IkxmTHYx30YIKL/GKoVwJeRKYWXQj2qfcKrSmfi00XL54zOry5a+M0pJ58RxjUWBAI+H3yL6VSzB0YIK75L0CN2ueDlc0MJnVb/onjjYiklda6tFI/KKaOj0bmimfoyKBMIC7I6pOryejAbm8KQHqUOHybrevCzyQEKsuBx5SQ0MeR1TjjQKUKn7Pr0+0KJE6eUzp3y3tkZaAKNf0a+zbbiOk/uRjHPDu+KNSU197i8pVhWIYuR0oJ4KkrLC9uJJ5y6DEoG/+yvLaHzaCmdoXPpnFf4xpCU1rGnCtnST5zwQZiUyS3iM8ClWOn0l2llVyzpnH7a2QUZCzrH+ApdgET0Z6RaAXwW4g4/7OiYLFuz8gbRvdtGW2+1vbs7n/IyG8rr+KgNP02tW7Xv0H59D4OXX3rdg9zWiDzwdb99D8rKriOnneKddtxtubtyhx16VFCEGB9GT8r8JQzAm636bFc6jMup7w47pwR8PGR1jr80OrTRhj0tFjVq2Kxli7ZGATLdZOPNPAhTknD5f/BBh4Vxe2+5jZO1aX2qCHNbbrF182atLO84fSoqXvT/USNHA0ccfkxCYqaLdkj/I8JkK2VEZQ8iPO++2962ToNO+PspCIxJyYCZojsQt1HbNh3dRmE6lGjHfrsmBeKlF8vxnnrIlEAkbQrkpYSPiNWl9bPxRr0oS2lg3hRk7pqUkNiubadePbd07VHGDXpscughR2YlDAaCP7ga89Y7TRqvx/swEQKTaVKwTu+ijOEnEqFvdOrYtTSwr3Wm+IGOoyfl0aQULiP6z6BaAXx0SmZXRDkkNcBlnbXrzZg+67OpM2rkXMy2Tcgd6GXTp81kQBLS1pSff/bFN1//QAc96MBD6Z1vvD4mU7RjNzQgtvDVxj38/OD9T+z6xR3akg45kvj8msXff/cTY2PK5GkkTpRJ731EIkADqDRXdpG8PP+8ixkw5517UVDUsJ579qW0rpemCD59TqZAJwkyCA06aRlskgIBCIYsxn8yNVoB5QTmU/t2nQHEhQu+5tO33/wIzPEebikCzPBAynxtsV4b0pz2+Ze8JyRQQvo+nc+befK6US27WlLm/2KZZxKMNIluWM/L9QOB+Z+WDxgqmUTOPus80MGaPoEpgi29y9vINUBZnAXRf/h+yQxdfZ3WegKJwyQB7KHI5gFTp0zvsn4Pph+yINN58mdDGDNmzyKffvKZjRxs0fzXNevY6naxLkqn/rfbtp9rkjBVJTaYpOMwNCLpvPrKaDoMBXnowcH77nMgxafDzJW/lrjsI/lJ+HMGXACHe+y+j3sLGSGNJuUjwGhe3mMj+rNTrQC+Ce++z2zPyMzLpR3owwBIarmNzp2XkzXbviRlr3rs305gYCekCmUlHoJZIfCRoE2d6MFeqwJNwJ2sTPwy0n0ykhBJEzBdIPtTS0MGUNuQOhhZb7rJ5majQsb522+342WXXmUpgP/PP/dyoeh0iFgXXXiZPRoQfaYMs50Oad55x72EHHjvgymJk8Dc0UcdZw6zsoVyqW1NmSyabQfStt6b+CHJEpiM6qxTv1K2qNQP+DJ2zPhA5qXGenKZL5cH8EwKADfjuSBjeKS/pGQfMiIi7HmhkEp45OGhBTlNMByfdOJpnnXIdJ5M5cM2IkG3UVo6Zo3cXtnVitcBXJ9nnXkuJbLfwzlyIpQurhLOkFstpKoqWRMbhV1RnjlsAEsxzzh9gF0T8gaG4dPCvgsIDRv6WKn8bjvTnJwvEf61V9+0sSN1Rbly8sv29FPPvzNuIg8PPzQEoZvwXtmokaUqqcHzySednpXnFc+jYfoR/cdQrQC+2XLKkpLik5ReY3cLdLvZcsPnzmcc4X+oFDNO6Nk33nDrLHkhD4GvRqbaNpVlADDYLEow3j75eCoYwdCNa2ksJqPFcBFn0CPDkjLLNRj5P7rSYtneMsIBkXvuvv+6a29KSSeFXnj+laBo1s5PtC2QaPw77/HfZt5ZqWMwcPttdxPg7rvuayDXA6jhVvcQguD/gvMvmS+7Y9tduh7myoc7sRCCrMMaFIykFPDccy58e+y7SRnPBkWNDwzKyOuJUX62HD1Y0PMbnkGchfLoxacDDzgEYIWo9jdHvw0MwZgxlwAL5C4tbKOUUDtR3EWZL2N43gA63rJgdqHIiKij3xhLam+9Oc7QTD0DMVdcfo3XW0FGAw3YilwGz1ddeZ3h25hL1aG0tmndISWfLuTiqaUgFwkkDpFyomRZkCLAAxX16PBR8EAA8DqrFUC6ysQJH7zy8htU18svvY6S+/6kj2km2uilF1+LyRjWnYfsvIhsfwqusTCLiP4zqFYA3zVX34CIQSe7+qrrrZd9/NGUlBwNMRQLcjpkCYVeyCAhvGXA1197izeIIfaNA/Ah3FXJqj8tDzx5rcT367sLcRk5DAwv2dDjvejjYbZe89akP/nTz62UzSsxxiRZJD6GfVwbmoxAHk495UyEEaIfdeSxduIyRy6kSLznplsMHzaSRNat08BiFCkTEbbtIQqJz7jWqmU7om/WqzdqMmHQ/hjPCDUe5GlJlE6WwAzgjNzqWSJD5Nxwg01JBwETcAkk5CIcwRKlIK+4/O4ZcykOjFEEwAgGKCxhsvLdZkkzIS9GHuHgF3V42qlngXcwk5GrFaKHbcRXR4xpS7RG/mYIRpox7aVYt4W9kY89WaH9jVEjn2LysNSGvPylPFkBaui8DeQ0m/YikW236btAnoVIKq99FWTwDu3Xr5RXIhJnupovpwAGcRJBNKuQzxW7TqAmLZAyP/EfgKNy0lJs0QlIKiEP2HvvtT81TJr2VfPRh5MpDsjbe8tt6Dz0IqTRjPyY2htKttbYG0S0qqhWAB+9n64f+gUx8NGbLVYAB8g79PiGcrNOVyYkP+mUhIdAh4Xy5M7I+fabH7Nyb5spepcF7BgGjCXiEpGMGLc9um8c18YxoLnnHvsyVhn59rZiGTDkzaoug9YR77j9nqS2FAmPzEXuqLpJqdUeYE3k8rNGq4qoWoCRNVOGmR1I2J2UEQTRlTfkDtwATwY+QlrITcvrUVoaIjWQ0p4AJULDtQZKLX02dQb/0/Jk6SJQM2v+ZR2izJCHOD5ZlknIuxcZGRDjcmUY164u//mEIDZl8jRyrC9nZwvkx7ixfI7CZNhG33z9g9soofM6NboBg0RIOS3llwCut9nyr0OlIfSBR7BH+mRqn10AE19pmpx8KIE7XkB0qUmQKPvuc2DHDl1IDXynpKRgEfiwQ4+iUORIdIufnmBIxF2FFCgywjgTZ1q+AvfZ+wCiUEbS6dN725jcXjrThbqN5LhjTyRxKpPogGNaXl6y8umdjLTd/ziqFcBngAu00WZY8X+PwKB4TUlchyEsI9DRm8hnX0LLRgltU2SK5zYyWpvzqn9G+ktC6iFj7K9r1slqvzWpeypS2iIMioe/0rpIIVdyyoE3vXpuaSU05C2pMyj+75SDkrN4KS1aWUyo1Bk30oxJY03pUIXzdS5ZbTsYmLK62SCjExXmp0oOn728VaX7BuM65WP+LexkJKnNlH/dtMA3LE5KurwDgLNw1UDOckknp4syzEZM7rzMW1j5Bse8joyYPZcuKynSb7xEWE+XH/qTH1z/ea3cVemcUEp3p4R1ntZxH7eXRaqM5NlE8URRyEOYtSmtacBNY1bd3GGluURp7VPHJIEWdOInp4M+JhcEtu3UiASbyNlvSmua/PRlFAnhqWvPOUb0H0O1AvhqM9Hp0dqQNco/1SryFoTxLqKIIloxRcD3L6hKpyUsvtV+iq3wfHVEEUVkioDvX1BGPpD/FMrOnwWdI4roD6c/EvhSQSadyiXSucoMP/NVsXwim/5LJpGIBfWyDZtWxM7t0qtBOqiXT1ZXVqUTjSuyGaJkk/lsoiqVDhKZZDqZL2QbpbIy0kj+nylPnxGLLQeh0plCs+at+J/S/gBiUfsOXVJBPrVUMfwfildkGiWb5LPNMlVNM6lG6fjStaeGmTWzqYBcssll04RatmjbtEmLpetQ2SoSTyQzbdp2isXTwdIlv/9JNhXkUplYEGSCRLMg2TgIkrlck8bJ/F7dC6OfvbSwdAVNi3FBjqrIJZZSIpOvCqoKSxe5WuTX+mvDJJ8odYZEqCs4qY4ty0lEEUX0i+iPBL6lcJDKVWRzDXJL4a8qnmmRKGSCpoe27F6ZrjqquvXd7Zo3blDZKGicSwbxTBKMaJBpnEpVZZdiQRAsBcFcVb04CHjnRT3nDtvt2gM7VgT/a4saUiwedOjYpVDVeCn2Bbk2bTs2a946mcrFl+7W/Q9Ctc3XmfH22YX6VEisfqyQ4K9+xYIXz2gWXxO0LcSXs3YG5HXu1K26YVPShP+WbTs0X69NIpldin3FZHPxqnSyammATBKwJlbjQmbG40fPHbFX51RFZYxiZIRrS4FvKUoGmUyh4VYtOjdce+0GifQFnbvvnGlG3Hg6l042rK5sTEbrVEWbjBFF9JvojwS+BDgUzyHo8T9VEWuUyaTSzVLppg+1Wb9/quldbTa5uWuTVsl8g0LzqlSiab21OtXNxNNVwrtMRTxfkVqKAq2Sf5k49Igfh227cPhhiGlBtjJM33t8EBiXq2rSueuGnbps0K5zt9Yd1s9kqysqkeUKyQQSYp7/cyac9s3YY6e/c2y/LdpUpOrUSaTyFX/56fGdd+7SoO7Snd//ddFhsREVmOj5QuM27ddv17Frhy492q/f/X+kyKX7gDnSzKSrALVEkK/I5oMczMQbp4PZT58wZ/DWHw89FDSnBqrjAWBXp5DLJ/ON6yWqMqnmqYa7tu16+/o9t/+/9e5v0+GCbpvWr6gOso2qgso29et2iGWbx5ZuIkcUUUS/mv5I4KvMBggy4MJ6jVvs1mmjfRp36LBuslmdygfbrn9fl54Pd271073H91hznbZ1Ks5ov9GZ7TbcrcXSe+3SQRJxL5FqkgJ9Yv9nyNmbzxm+w8JBfS/cuUPdikyi6p+cIAU+KxPk07mGS4Gv24atOnVp320D3iCapZce320Uj6UbVje79tRNFr9z3L698tk69YJkk4p0rnVijR9H9P1k5KnJbFVl0KyUcx/CiKdyhYbNwDuSbd25a6cNNgbynGxlRWqp8l5oAkzHc3UpaT7etEmszpiR5341bNt5T+/VtO4a8RQoH88lM/FMpm4+37qy6rD1uu9a2bDDmvX7N24+uGXbu5t3uLFD/vKuHdtUVG3y18pL26x/9Sabt0oFscKynmMiiiiiX0R/JPChrC5d4EtX5aobd8oU+nfa8Kl2Wz7coeN9PZv845YTFg06dvrde9/ZIfbYZq2e7b1D/3yLbCrIJqqs5KIYto2vMX3o/osf3W7RA9u8/cCRhcSascpGoE6Yvo93LD34Vt00X90UhFqvfafWXbq17b5Bg4rkUnFvqRVa41y2IdJZPhEbdFf/bINkEDRZe+1EskGDaSMPr3mk3xdD+lbFKxNV/4s1SZ0iJPGKZCbfqFnbTl079tgI1AP7QD0ESS8dIu5VVy11doICC+dN11ljxhMnzRnSa96Dvc4/tE0AfCeagXoNskuFxEK8Kla3okf95DVdtrihecvb2ze+f+vCwttPqhn+t8Gbtxq4Sf71fpve0r5r11QmmS3EUpGqG1FEv4n+SOArJJHdMlYP00EhGU8f1aTH3e22WXLXOd+Mv3X21/OnTZlcM+zgt47o13LtTDzbNJ5t2DrbCuU1la178EaVX4/oN3fYDrNHbF/z9AGbxdbo3mCNdok12sTX2CizRrtU/XiDFmMmfDphyoQxH40d99HEJydMvGXg/c+MGvXkY0/cOXjUi+9NePnDsc9MePG5caNvuWdovdh6iF1opOlsJsiu1S2/xuJhvRc8uvn8obvNu3+DyXdtvU+n/8rlMvlsizr18qPfHT964ttvvfvFa5Mmvfje5DvvH3XFWRfcc889Nw0aPPrdz8a8//H7k2e+9faUO+8Ykk42TGQ7ZNO5Q7qsMXvE7jWP9f7pwa2nDd+rW8UanRqs0S27Ruf4GkHuv7K5daozhXSwXryiSaGi8YgW6794wH6T7u+3ZNYTM2t++G7qU3MeOHTwzptWrROL55s0bZAKqiL3cBFF9JvojwS+bHLpNmVlJr90qSuVqW5QcUKndtd1ajxqp2Y/jrvgH1/P+XbxknkPHHl95/hGqXVaVFQU4vk1Y+lsxV/OO6jjjME7fD18r4XDN18yfNuvB+0w/7Edakbu8fWoYxeM2Pq1IUcXKtaoUy8x6NGX7hz41F33vXrrjUNHT5z93nvvP/f42UMGHzVh3IKXnhp/562PPfjQqyOefH30uLEVQYOluFuZfnPY8fOe61/zwM7fDt5q8ZC+8x7dZvHwvtC8Ef3mDO37weBzxrw4etCjL9z8wMP3PPDIfQ8OfQtsff+5KZOuvuCcfV8aPX70mPduvGPgLQMHDxz+5DNvvVGRjzUJ1rng4A6Lnjrw26F9vhq6JUx+O6L3vAf7LBi2xewh23wxZPeBA85qXK8OsFtRmWxeEWtXt85dbXvd1TmzZPCBS8Zd/9PiH5eMv/3DAT0Hbtykw1/XrmyYSyWC6mR0bCWiiH4T/ZHAhw4YTy99aFiZ6rp27MwNet/drd2XZx645JaTFw85cP4rA5csmPLlfccuuemCu7t37F/duFCZXCeRvffi/p8O3HXRkD41w/b8esSOix7Y6vthfRcP2/LrR7ea+9B234/coUn9NTIV6yYzjZ97+fWx7056/e0Pxr72wdjxH+27x043Xb7vxef22ajbxuMnffLSGxPfGDfl0Sdeee310UhzVYVMvEHdAf27ffJI/28f2eWrYZsvHrzbgmHbLxzRd/6jWy4etc30x3YaceXeJx1xzAsvjX9h7ITREye9N37q6NETDjxgi2su7H3hWXt26tTp3Y+njvvokzc/+PCx51+d9OHMZLzF4CsOmD5o16+e2A0k/WH4ztNG7Dj/vu2/e3TPrx/a5odHtvxo4KEVsXpVhVw+lW1YUblfw0b3bbDp7W27f33rSTPv32vqiAPmjRvx3qDDF93Vf8ntF97RoWePOrFsrlA/GW1uRBTRb6I/Evh0zG3plu7OXTYesOFW13Xb6qKu3e5p3+32zvmpjx78zfD+NcO2G9G3zY1dNh2w0ebndd/6qJ590vH1CsFa1ek1kOlaFdZoFv+/V++91pcPbfHd4H7zB+34wfCD8nX+qyqeSyerECTT+dzd9wy8854HmndstHQZrnP3zp3aQe079ejQqX2rDu1ufeDea2+9PZdrVsisF0unM0tdwjWryjRsEl/jooPqTh20y/cD+y0avvWDF23cdN016mUbVqYbLl2/y6Tvfeih2x8c0rJdpy4b9OzQfuOubXt27ti1XZd23Tq1O2fAxe+99+nNt9xRCDINk4Xq1FqN02s0DtbIVv6/lon/nvr4ITXDt/x86Gazn+gzadBB/5+9t4Cyq8jet+e31v+bYYCQdPeVY1e6OyHuQNDg7u7BCe6DOwMM7u7uboMM7u7uGnfcvqffd86ZnlwmE5hAGri9avU695ySXbv2fmuX7aoEs5TzQVII5unWd/fFVztq/mWPn3/JU+brc+mQub+/fOfPr994/C2bf3XtphcM6XXMwgscPfdyhy+0wiIDBoaN9aFuPdTD/xRmJvC17WKJ2nYvdw6DKCkWCw39GstHLLTsW8NXvW/N/pcu3vX7y7Yfed5O/f9vtrhLtZK0JsVcz1zncqFbLurRUO7avdpjcPkPU25e7utbVp9y46rvX7VV34YuhbYdJFEYB4UkKkWtSTBnFMw54alTP31l/XFPDJ/yzG6TX9h8yjN7jH54+5dvP6BX0jmfi/JRqSksVhqTQq7nmkvO+sGTh3WZo6lnpenVW3f/9pqh42/dume5pTEY1KOxbachY3PQrDHX1K9ry/gX1hr35CaTntxmygsbT3l+y8+e2WXUU1uce/jwrknvLo2V2cPGpubGUtC2bF0Mu3WJ5qx2/sNn16098aplR122wglbDS4WyrmmbkFzOQiCHrlSJZcU2igJl/rD/ztnwXm+v2SX0xdqvX3jRUbdvMHZ88zf9Y//L9fSrWtchnHRr+EYST3UQ0cOMxP4vKG3FFTiQqlUKHWrtO7QNb64X/9j+i+8SH72gwcudkPvZb6/ZL+1o2JQquYqxSRXmT0ZXA6buhXnyBVam5M/j75l+8+uX/Sdq9dtbfwDuNkYFMKg6g3DhK5JzygflyvRgD5NW6238PB1Ft9yw0W2HDZkm/WHbjVsySUXnbtUbqlUerbELc1B3JQUW/KzT3phr2/e3KlP+IdKpdJttj98cftqL91wQLnSGsWFLuVKrpTEQaUcdI3zYFlu2KpDt1lvke2HLUy2wzecf9sNBm+38fwtUdC2iyUpdi1XK126QUa3fCEKm7sns7z99+0ZjE++fcdy4f/CMG76Uz4PuaU5y3HXICzlgjBsyjc35C7vsfhtS86/19B55/rjLKfOvcyNCw64Y/1llyl0qpS7RkFrWCgl0T9vxqiHeqiHnxZmJvBhDfnMQxxVKvlSS2Ny6jyL7b/gMq1zFIOwnG8MF19w8Vv7LLFPv7k6N+e75srlYmX2cq9S1NSST8rR4B5Nf/jupqFvXL/tIZsvXQkqhbiClRf/0MT/HF2b+w9dYPB8cw+ed9Dg+ReZe4FVe/boG2m/y7+iNXR96+mLJj+0y5cPb/vuk4f/qTHXkswx7uoV9thg4ULSIwnbrpiYKsxe7d5r/qUHL7AMo+a+8/Wea7HBPbr3mdo/StDapZIUo+aBnf/w3jXLTLpmmYGtUaFLl3xToF0v5bbzcMXmOGjNlSvdG3LL/qnp/F7z7zhggcak3Km5Za6G4JL5ln50yTUPr/RraC727kIdE6pZS0w91EM9TH+YmcDnQ1oAXxHQCipzdR+wSqW1b6fGOBdj4zVHLaDhZlG3E+daKooCRsQAX7WpX1jqXEiCls4N79+9yztX79qv+Q9x42wNXcoMctv2AxenLoXQ2tjSPFunEc/dOPLF895/9thNV2+087u20xdpnGpzkpu16e7L1h334q4tTX/IB5XmOH792h37hLMV8q2V+F/7ov8VZuvSI5htxCsXffTCCe8/fdWGSw0NajxWMuhuLJe7NzaPumOrN65bYWAXqO0EPjPEbtvEE7aNneM2+NOkZOeGbYcu2aOpsaUQRVFLXO5eDau9CsVT5lvi0MGLRtVy9yYGxDquV0tMPdRDPUx3mPnAR4hk/XUvdau2NEdxKZ8veVsfI9dupXj1HgN7NCXaulzu0WXOpnKhczXqW/jjW7ftXm1s7NR2hLdvc3UAplMcFX7Qm0BjpdClkAvDsKWlmsStcdQnc2b5L2LKrUBP925zXHf50aUw6tEyZ7GhMmdpju7l1mrcEgQ/AHyz5jvHrXM2NVYHDpg7X2xMWtu8eLadkGsPf3GhUCr0zs9y21EbDC7+X1AuN3ZpKhSTLk3/BL5cqe3IWttZ3XIraFfp0hSGUNhSipqDfBIG5SSZs2WW2dbtPW/XUtdyoRkYbS5MDa/1UA/18KPCTAa+SNjnZ4w1n2CLQoarlTZLME6iII7jcM5CNewURnGlFCTAUy5u7laco2fx/4CMttO7QSvGYDlI2o6yRf8a6pbTK3qxktqy5S/Og0ShnCNMHYI5Qag2MBKAloOgVGzVoLJS0mrJ1PHDMoYnRLbRT57/Idu2A3ZxQzXs3FJsqOa7RXFT22m5qJkQtnlkaRu32ktNWx9QquZyhd5Rr9awpVSKse2a40qvsCcRejSWq4W2RXCA78d6Z7GP4tr39VAPv9sw84Hv3960OYBqg55I7qrarrrHhkviYqU5Lx/oXaqFYtQaB23enIpBn2ICQmEcBgSAD8Rsw8E0Nzt8lzP0SlzsBpDJCUqbY6taYlTMnG3PcZvZWIwjA3Ek91m50g+AWluGbXkmpbDNQVZc/LfzvA5xlOdrLuzXuQJQJi1NPXxSxXBv1yyO2ebRPqRkMLwZxE+SKJdrBG675BnmtyUxPU0lHn6A/mkE+3yvfV8P9fC7DTMT+NoHj3nT0Kal6Hm17Qh/CeOrUIwb/pyL26wjGW5R2+qqPOW1hmE3rLlCjBnV2mb6tRuT+s6EtlsmMMfCZoaQ/wSd8F+uVtoRgM2oDIM2+OtUnrO5UK4WI1CyMenWFP/r1uostFlzQXNU7NGWedxmuNXGKWKtRt1KUUPXHPm3QRhmaRra4lcLzYzii7qDomH2LnFcKmOlRs35praFkahYjYIStl6by8IoAjrJoT24T0/wNRS17+uhHn63oUMAn2+E8UUwka6vDXSddpsRpDu0Ro0cx09fteOXDz7wKG9mn62Lbyzz5TUErJsovfcnTK/0juRI2TfjNHTJNzUWjQXZvUKBrjTztbChrh8yPZHMRl+as+UW21CW76/hZ+c5mnjuNHsDZPh2Gx5MTJBeReR8InnuI44v4unSOVdML0jyjbS+9+fTKV8ed+xJTz35nHlCknFjJ/nKm6LuAPKItaJLLJ2zyyrqoqVYN+qaFaFu9vHqDfUt67bssm7s9vWbvgmI+hLZz64p9NiNsy81d56mkKr5iqKybudpP3YO5LXBpLqOcXonVLOuSyf/OTo1krm92lR0ExMU8vKeux/wjT9lzUu4uFhX0NlQDeq2aj38DKFDAJ/XWJF1X4hlZIl1K2AkzPrk49GoQUUXIVqrfdmzQYFUxheee3TvE6V+WUg4ZfIXvobC97GVdBGXtTGfXntmVbcyWzl9d3WmxqEutz72mBOh6vLLrn7v3Y+KumDsvHMv8u1rcbqFJUPARJd+GWsKugic+GSS6GIw3vser0S3kRlxwGgjr2t06SVXUsduXXsaasEv+OO6kHOzLi93uUZDs851LGrpxphoNoKhrizvfQWt8cgEZ3Dffc7eTkXRkGRqXWJBXY7vvTO+Z803Z7deZV3VVpJ3azONjsEQ6ZyhAQp9QaivMeGZh48/GkXnQbluskguv8q6EY2mNP1ZQfVQDzMqdAjgQ9axg1ZcYVU0AaVCSaz/yy6zIg/zzbvQQQceOqD/XGgsaoD++KJrmwPLL7dy/36Dy7pSOkqHt2VdurjxRpuvs/YG1tg111iXnHm22qNXa625nqGHlyghP419KKFv111v3WHGU8cJZMd9/tnXUEJWABloMnDA3LFMRVLtsP0uRjobUJtusqUftt5q+6rcWJGJIaNZF0X+Zbe9iOArIiHPd7lCnlFm4oRPl1h8GV5SEHEoHRAsCugNfIHM23XX2dDdgzF9j933IZqtUcimaFu1b77x7hqrr9OrZz9DbSCD2jyBmQV1AwavssxPYkLbH/+/WXmz1fDtyNPozMuNhm2W6B6SrPkgA1JhNbkZ3XwhOlU268hz6aWWz8vWi9VdbbbpcEOqTWaet99u50hoaHCEDwZNiq4VmHqoh/8xdAjgQx++/up7pPy5Z18CTVCDt958j4eVVlwNtfnyi28xQDAr3nj9HaCQaKAP/8E7rCQ08I7b777k4ivIZ8QnY0ptvkVbMcRISCoUDxUiK/Tni8+/efWVN/n62adfHX/cycAiConag7mgDNFeevG1nXbcjZjYiX+/7S7UmBFioju5eSDnxRZdaszoCSssv8rCQxdfasnlGJwCu0susezYMRNPOfkMCOCB5ET+6MORGCygAM+Qys+555qvpPEdBDOQJKZv1OZlv76DyBZoKGu4Z1zmDcNeUMCV5afxiKwuu/QqwI46XnvNjRQ6aeJn5En+PJAWJgAZwC6VApeh8MILLiUymRx5xLFPPvFsUVMHVJBSqCnv+UlBABMlUgoMT2SxktV1197EG1+5fcH5l7zy8huUCAObdVuuTUX4DGFwidx442vRoZDWmfXPc5A5EA/STZ70OamI/O47H0KY2Uu2pP3wgxEQAMGnn3b20089H8iIJvCmVL9BqR5+htAhgO+1V9/CRrBNhNog9yCCRR/tAhM93kFtbv/7P/bea38UBqUFdyqa8LKW7v6XvYEk9I3cSE5WqBxfHS668DLUGGDlGQBNNDysaKxnFT3n7Avy6eCX/IsaLQJnACIJsZjQf9T1qCOPK2vCa94hC4Ij5MN7AweQx8CN/NHwvK4z5z+Ye/BBhyWaa7PNGAmgmzXcvvWWOwxzX335nSEv0PXhgwbOAz2mwQyBpFiDaBAQChlxgxcGBYqgdKpAhpROBGpkbKJcYN3oFqmDoWhG6EMXWoyOhE8AIpHBGhLyhp85TSMUNIfoViAVwEcO5FPSKJ4WyaVziKDqQgsuWtTYn0KJBlpBMLB+y823Y9ABasQ8/7yLyZz4tBo/KRrG0hWRP3Xv2aMvpdB78Yzh3LtXf36C0bagawWmHurhfwwdAvisaTY6wA7UBgUINemDkqD5zVph2HyzrUC91197+/HHnkYlUCQwwkNXFMmmIu/R/A3W35jcMI5IuOsuexAT/eQnthVDqmWWXqGqmXsDq4djJMcSAa0YyQI6nst/792P0Gf0H8iDkheef8XgRRKePRZD7W3BAX9eHKA6hlSiMQakRuAmFHogj6l4+GFHBZr4hySgAUwkrRHQ2PfQg4/lNCkJc+7+x/28wdTiPyYe5FEEMAcZnpEkLSgM6AC44GNVC9kwgfxLmnQDayK74JftCanGa9LCGZhw9VXXE+Hbb76/4fpb+El9A60OvfjCq5C06iprUiOSg1OkpTpbbL41Rbi/ycw9foKqNk4plMhA6jFHn0ASqOJlpNlSfpY0Koch8JkBsqGc5ND2/HMvU+Khfz2ChqA6PllYD/Uww0OHAD4MCswNP+y265483Hbrnc1afGTIxpjUcID+AAegjNHHhow1jfeotyGMsSHKg2k237wLoXvgAko71+B5PdxDUdvPT1nPPb1lnSQCBhFFAKk8u6CCFlVQbECqpCk2m5PkgGkDcIRamIYA0jJednUYsJP/HJ0aydkoxs8F5l8Y8zPS1Bh1IeFxx54EsGYkZTUlNxAz0jL3AfsfQj6gs+NQF0M2sIjZCPR44ds2INQCIpGWHfgPRttkpqbADTGJwJsDD/grCEtxq6y8Bs/77XsQxKy37jDP4nlUCxmALINcaKD68JM3XkFyMHvJDcMWe5xnaNtl5935ydf99zuY3Ph6wvGnwMlTTzmzf7/BppNOBbYPHjQkM8wNjuTw0ouvQSqMMkRmZdVDPcyo0CGAD8sFaKCHx8hC995+6306/FCrBEAM7wlgUCQ9RPn9fMXl1wAQqBkIRWT0n0EWg1OAD+VhtIgidZ6jCfTBSBm+5bYgI5pm8ycLJCQmeZIPCGtkxPRDG413JPnyi28ZDPKJ95R48UWXgwtYQ8A0JlhJ+04AGnKwxUrpHtiuuca6gAVf0WGSlLV1pqg1UyJDbaSVEwpqTxIQAzcwqTz5SJ7Ump9E+/MsnQzu77z9AdDAVwho1a6UrYZvR55Yyjyvvdb62LxlLaqCy8ArpJKEZ3IjwE+49OADj8IfOAycUUfKLWn0ivFIWsjOaQOQ5w0AuzvvuIciSEvtyJl8vDYCn2ELuc3yp9lJCFBCfz5drYZIvu67z4EAtycBbQ6TClKJAOUQQ3GeNzTCbrbpcJurYX2oWw8/Q+gQwJekS7TWNPSQUNB+i1A7OWwjoBJFLWtWtFrqaUHSJlqp9IOVLRK+OBMviTqOdcnJHbxe6dwAUHQb5PLOsrjd9kCbHqHO4ToTBy/vlrSiEmgjSEawddhlZQQEmrb3y5z2qTGsA5UK2gKScSPW4rXjQ0yztn0wwOSBVPff93BZuwJd/aqWniMhZkHr47PP1sVLvcW2sy//XDCJNa4PNIal1uY28Zu1MwZUqmifIEaiGevKhto8VNF8qKvsyrohApnepiTWvIT5k7Gron02idZA4BVJWrVlh69YfInW7nMa1DsHnrGXgVpTbrIzztRDPcyo0CGAryMEtLegTSQ77rDrL7OFItFMP9bfSiuuVvqhtUvbm7fcfDvGGsBnZKlowdcgm/zaTuAa4LAHse+mIt5dBdYiFrrBujZ5PdTDjAp14PtnsJGFPVLRSnFthBkeDLW2BG2lThVKOgpS0IZq8MIGoE0tm2m/OmsosyJNf/tP1BFjs6QFXxvItcnroR5mVKgD379CohG3Uab26wwPHhL6of1QNwseQgIEkSAD8hjAehRpuPxlAHoGBmO9pyx9CCT7lGjuzwzhq+dD66Eefqbw2wG+Fp17LdY42pt2KGmJ9semmiHBRde+/1GhoJnQGW4fQZuh1qA87Z7AFmiztg1OpxGaaKnnf6/+fw3uxtxVeFGlNs4MCT9N/H7DwZPpNt49aqmNMxPDbwf4SlqRDH7kmfZAu+dmOHBMT3DRte9/VLDZOG1g+gnBqwrZksW04ayig2heXJ5OSkJtVv8FYMJ2pSEvLzcKtXFmSPhp4vcbDl4N84bZso4J1MaZieE3BXzw2tvHpj+Q5Oijjv/5DIFpBBdd+/5HBbR6o2Gb5XUgZAYGKzBYts/eB7Tq7G1tnCxAA3GOO/ak6UQ9B5J4m+HPGozaK6+0uhfHp43g/0v4aeL3Gw6BfPbsted+7g5/lGz8AqFDAB/98Lixk8aMnjBxwqdTJn/hTRswzmc5PDHkjRSl1PWIJ4NCbRvuNHtDRaf9+Tlh/JRmnVhItM+jKNdPZR2i4GXPHn3L8vzhlQFHGz9u8lprrldKT/77K2m9T4WXoTy4BPJo4vP/lNJ9zt5edfUhf8/ZQ0NRu3k9Am2WgwAb/Fll/zxLp0032dJ7OL75+ntytk4W5QymoLNivCGrWJtpEm3TsX3kWmQbREoahz7x+DMF7Urhvf04NMnLgw2cZh0X8SCUB3Pbu2H8MtCWFM8kuvrl1GUL+XvXt9+bgKwivAe5bBX6pG2gXd82fEKNlKlIeyPLJJEVTUaEA/Y/xHtcZp+tSygbsKINTLF23pCnuWRKIo3rI3mXidLFKBfhnAnHH3dyXlv/zHzTTBEnnXhapO0+5Blof5Jb30VU5d3H7iHcgpY3T6EWtZrk/Ms6Qtdeh8mKJB7nEmDX2mutb7EhE2Tjk49HZz+L2gDktovSuV0XVNC+q4p2/+S1iuWfFlF3zJkgBRLFrC7u9kxet649SzoSHugUU1HS6FbzdAT5O2cLlVntUojTpB2db7353uhR4x9/7GkTkFV2yy224etnn36FtvqEuBvamVhIvDZlAcunB7EydQvTNToXTRUo0St4Lsic5JMdkcRymxak7ub69R0EYaEWx/bYfR9naEl2u1TlDSQjeBqhQwAf1bvyimut6m6AULvhMk0uCfLgaZO25pWk/LRxoo1gZj0/eYn6uRngF5G9pW6OTo08WOv23edAI2MkL1JJCnyRWh1cC6WxpsqaZkZbsILUUWAiYPVGa5di7ifCqdVWXYtCEx3RdYSssjTttdfcGAjWP53yJS1qgh2TIiy1VfmYaZULvEZ5PXGJFp1MUPgP8Fl0LB+Zcrp2kSTexJhaS5KrH0mdqtoJ2CDPAoV0/cHYkQGfAWiq5ZT77n3IGfLe53lz2olpXSprdFlpN5zvKs9aJfVeRNtzj32r6pyMHWajm9s6TJwe3fuQJ02fCE9NvHN2zLy2cLoU+s5K6gDGqmjKsUZj9QdWJBNghls/iwI7q5zzb0kdzLg6lXQnI5CNkGQ1slSYA2SLWq6z9gZucW+otL1s1sXawun/3kDqhptt1s7OKpKwmfLMP43lrX0TJOoJXPFIgmpMdLkZA4vq4dzxF9X9JOonGrT/3DWK2s01W8YWWnBR1/T7776nY2tvlW+26XDv6jeHnTCU9mW8jWVTl7UhKdSefwtkWVO6VpOKOiQDcSTBi9IVrVAa1ChnjtYCV9k9qDnZLCthnLxVWlCDdKdtSaCfETyN0CGAj3D+eRdbskvSZ5/BIgxdaDF4ClKcfNLpwMRll15V1QEDnzNDtl55+Q0i83LppZbffLOtYMdNN97G148+HJnXFAO9hI9hkM/CQxcHGX3Qwv1VkgKffcBYdXmPGpNk6622twiS+eRJn5NtJOmhHyvJXwB2Hx3g22+9n8jCokskDgTwEvm44PxLeMCsa6/8I0eMpSDyT3Rm46orrxs7ZuKHH4ywNKMPYI19uiAQyy+3MmWZWhobi4Yk9twXqeeHCU8+8Sz0Q8x337bZj5EkyacphsyzQEkHS4wvkPTC868gVZROfX3CDMmGPyM+GUN8BraWxffe/Ygcbrj+lgz4iMOz83fgDflAHmnLOrDhTEwY3KBSZPLaq29lSUoaD/qcxlyD5/3rIX8r6JT0m2+8Sz5kgi1MDq++8mYoPCWyD0HznyagoYlMCy6+2NKmivdQtcTiy8AQSoceeHXH7XfTiOTDV7KijsccfcKA/nO5TY2nVryLL7r8ogsvg8hvv2lrI4ihAyYroA1Sd91lD0o89ZQzyZNmJf5GwzYzMVmN+vcbTHJoQFBDnZ5cb91h/KTu5jkPxoLDDj0S4p995sWc7LuyzsYQE2GA7bQ+OfOGupAKaolATT94/xN3ReRGZOg3OtCzQhLxK9p9deYZ5yJpSIK1A37y9bRTzwpkhT315HN8giHzzbtQSXbu32+766svv7v3ngeNX/ff9zCZU6+KLO6iAJeyqhr3ZJXdYvOt0YJAvaYBCFl66cXXaNAJ8odk8XBWtj0hY//9DuYN4tEgn7ixpvxIyEuqfPllVxugaR3LoQGdUiiLfF584dVI/UFJHQBtwfP7731M3W1O0kzE9MHHJp1WyAieRugQwGe9wny484570EYqRk1gOvLnHoYWQghosC5yX5zI197KK63Og+vPJxhHzeHm4EFDSPvoI0/GMtmQbOwdUmHAF2Xxue0NRtYQ+iVaYoH5F37n7Q8CHZUtqQOBv7DykIMPz/T5+edejmRZ8JVWISExIYC+HWJoXYqGJJAa04CHkoyL9o1BQbfdemdZxiPVHDRwHh6QS1NrQOfZDWwB4g2YDsHQE8jxyRgdT27VCb/HHn3qjNPPsWnsI7rj5crFbAS7/3HXfc2aPaCT4AFGlWQR0IusuMKqZZ3J4ysCxyc6Vaoz75AFyWexRZeC7fDc1lyomQSzriQbHIUx2VD1sTzTkIpCYQXKZq8tQIOdhtmg46vtPnqLgw48tHev/j6xxyeaDI7BcHgC5hZ17DdQz099oRNuFDQkRFoi2aol9fAUba2boPOLJKSPtALvvdf+cACL7+WXXo9lGcHhXHpWBNZZeMjcHhYY39mc4RnykByY6ebjv8+DF2VO2usEPAzVWY7VcWzLEjGXWnI5MCunI+RkDsGvv/Z2qPORTz/1vGNCM2I8QScdJ8tnl1uH5IsusiQEGziQWyiEMwWtjZqecXLJQRi24aZgJcBNWvpdwyIy45Pdw7fc1q1mfEQ+oR8kYuxPTKoPYccec+IuO+8eyKJ0F0uJxPSos9hupgJOuqehOXww1J5rkfnVV1ubTitMffPwhr4TFSD/DTfYhJgnHH8KlbUtYt0paDgFBC+z9Ao8kGEgQ8RaY1JhMsm3Gr5dRgP9BE1MoKCiei8Y4lH/wAFzh6k72/8aOgTwQStdVqJBYij36A8/9LjtWIwCWggtQg4KGpW4vUvy2gKCgEQ0gzWqWUOzRh0js9/NojpPhMm9EGXt/pe9bS5Z1SN5RoC5Zc3WTZLjOXQALMBSow1Cuf+jCCQYXlvlDHz0sZSCfFx/3c1FHVCjDfr2GUh8u2tGaskEMSq1M5SIaa1GFi2+lLjJxlsg6zfecKsNUrTRDQ9mIUC33Hw7NF991fW0OqJJXz1Gvqds5CO+Zc0klmTc0VuQnKJhCDRj4kHYxzpBDAcg29rryTXQh4SQTS0y4LPuRepmfbrWXTps4QGd8VeSI7Wt8hvqHjun0dxY+XogJmkxQuEYlWrWmAgOj5arq4JG0/a9ChuLskZtF1BHciAtn8COSFMQ1hP4VtEskps4UH8J8dS0rDGd/WIRf7y81MSaRSLm3w4/muEkP2EOLCqmTv9hHXhkI9cmA+BraDPw8fDG6+80am6U+IxLPAS2FUbm2NQ+jGw8otYrLL9KSeNQhoqRHENU5biskk6rYQOSIf+BpwsvuHSUvDzwk1rQTJ6rQRhWXWVNElJlmGDB5oEmcCNi1Rp9ktQxGhEO/esRlEhNoRkGUlN6RMolT0okEzv14b/7rVjGFxIF8bB9nCxQKOTnTjvuRj7u0jLRBWQRHqthTnOjvIRyC+1ZZ57HWIcHi5YH1FgS7pNIcuQRxzZp9hkeUpFAMzO8R0GIA8G0LKmggWzJEzqhDZ4gwxkN8LNJR1qpsmEOk5N2/1hHm0LNSmWRpxE6BPDBAoyCYru+hZogo7ADvOM9AgrAldutvSY674Wy8fKB+x/BWsQkDjT2MZfXXWfDRRZeoqoT7yUZyfARfu2x+z5VTZ/F6XwiHFx/vY3KmgdB+CLhmhUp1HQp9ia2kpGlR/c+iVzhV+SlDukkTwCLn7Q3WT3x+DPEyWuKvaiDqJGM/4xyXmLVEp/8EdyS5isZq2Je0WVlxiZfqxorEZMR+heff7PgAovQpQfqFSm0WWuU5E+vkE8PzJKcTx6PxJpRhipDG9U//LCjyNCqVZCjeSgPZVeWdevFeJmKhvuqJljH67IOGGJVDzUpZv7zgPolGqGTijiegrGRzk83KKV7Nq1Bk7YYRGYC9GN9Ww8rmvkiISPNnHy1mjw4fNed9156yZWe40PHzMMrLr+mpKkGj6cmyLkWSYgfa6JnnCaA3Ar8pPs0McSZLCcLJc1PwTowwvUaK8cQDz34WKROkTdY+uRAhKoWBMjhvHMvsiwV0wUBSvS42Dzk/zprb5BoVn6MThaOkcsZFBgZy6xRiuY/ozw6PHcJJDSOFLUcMf98Q8kHtjB0GClvu2PTcWizFm0sBqEsLHdaJLS5gDUQah4AtjBOD3XzjJVigiYu7CHRDYcA33rLHbGsJPdbho+mH9p9wrBjgqY7DOLEpArmBj/PPut8xsLjNUvDT0ZaJjuviT/+02XGmkKlINq6qN4OnkBwlDocapFXi7zcmhXVoRLZy1kObutIAwt3frGWuUoyEgvTvUeqQwAfdF9w/iXt38CvJp1SwPKi8gxAXnv1rfZV8vjLHRTDB9gNcEQy38wvWnS+eRcqyF0db4A/GMr7lVZc7aorr2M47JYLNRAYr5EsncYxR59Q0vhiySWWtdozZKBj9/ANe94witQG8nWMNJMD3TJx3PFGqS2Zl58riqZEOqWM8rImd6q6UAkAtdxssP7GGPyo0Gj5/kPnMevgAD/R0nvufoCfxohmzRB/LrfMFn2GyWPkn/mwQ4+kg6X0HbbfxSsevMdmpIibb/q7WeqeAACNhPjOjdqF6jypF8SgLRBM5DffeBf+8x58hGkIJbzCmA3TmVCeN9t0+PLLrYwmUHQsvCZb6ojqYqGQ2wH7HwLZls5EPvvA8ZLcfwHEvPRQl9oxZHPnxFiMaBjsY7RYYagN5TjniL8dM2e3XjRlzx59J2lhkT6DEuGS5y4RjOWWXem6a2+CaW5csj39tLPDdLHv/fc+tnrbvqPK0AzG+X4lagSq9us7iDYCl6Ef2WvWVQFQgpSG6WSTaaaHbtW9BcbcyZqWIgKG5IEH/NWaHwpDkS6S0IPSJRflBDvUNqCi1p2Jhrx55gvuLb7Y0muvtb4579lqywwS4q4IapG9Zg30oAo5gTzyhHvQj+nHyAMuYfERZ5y8mVXlSZu0SBd0ljRF46YxWmGrerwPDQxiMonNgmdg99pzv7332h/mLDD/wvQESB3dD0XwnNcy7lyD511t1bUMTB64GKaPOvK4giY3i7IEkZxmWbJmJg3hdhmhKWNGZnRL5EDn+szTL2Q0wPBAYz6LGc1NiXPPNV+f3gOob2W696h3CODzHFx7iqkYcsMIJZFRlsiez7fbrZaTB7fm9PoeWO83SE9ZU6fuFnyLDR1+SVsHDE80G++L6lpj2UQk2X67neeZe/6yxozkudXw7Q4+6DAIa9ReEPiLNmb9ZKCJNne8DekFIBsN2wyZ5nneIQt6EhqCjz7qeAYI7swdEE30hNo1yHWVraG8HL0EWkihX116qeWL6kUDXUbhGTdjEzxBQAN1dPnUJw1fSeLSS1pNY4CPsmWMAkTOOfuCQCBLqmWXWZHut5Q6vAm0SDLLn2aP1KPAKPqME0841ZlbzjCf/7LbXi7XbcFQiBywoCNBQKKOxPZLVXuMAvmeadJ6n7GyqCEkuEwTUJDjl7V4ndPMNFnRWKaTzMFfNzH64whDF1pszz32DWX4QKfraOExiNufIw9oPihZluUeydarylkO9ribhkIffOBRlAp+Yl7ZpiNn2GgHqK54VZtdKL1BC6ZEaNE6aSBXsjwDAc6/KBOGaAAEQ2CbsVF6Go9PnrfxhNQkrW7l5e4/kpcgeiMG1xa/kqaGi+rpq+mlg9ttu9PWW22faKhIznSQvLG5R+bgkduaZ0TR+ZDWY3wLeaMWkZs03vQeUhfBzxOOP6WsbcZlOUlsaLcPIQvuXRw/r8Vxcttwg0123WUPS7gFA4mFAPon89Ol9+7VP6ctB45J62AQwDrbyxBZ1kJwa+q8h7JgL72mi8toaNUdVQQ0hdaHYCAP02f4ltuWNH4PfkWLG7/2UNSA1P0eHfWzz7w4Vv7iSx3DxcglF18xWuvRv66A5mBfMBjcdpsdbZLP2FDScsrLL71e++nnDgVNaKKrZ515nq312jj18LOGOvDNgGDLyDBnszTQpip3pzM92Ditfd/Bg22ZMN0a+XMws/LvG3R/sRDI0qenjNMNcbVx6uFnDXXgmwHB+lnW+kBOu209K9RBevKqJtdq33fw4MGsh2y2qWvj/C8hTifaaz/93KGizSXeUTidQ7N6mLGhDnz/DD/3yNSzYJ6njyT6tXH+a0g04+aZo5kebNX+ApBqaJjOgmKt6ta+rw2ldscbPHFZG2eGhDB1Z137acYGz6IWdLLCPXFtnHpwqAPfPwNmWjh9p/x+WsjrHEJRKyqhVgZr4/zX4AWQDD1nbiilJ1trP83Y4IKmp9buEqaTpEBLKO5IHGrjzJDgRYPpBO7/JZTSU7r++bPK86891IHvn8GoVPt+RgVbIt6+4KWu2jj/NRS0odTLlDM9lP6bu6oZFbzyOz0WU1nHk9tv+5pGaJFvCCNFrK28tXFmSPBqcqu2zf+sgRZx3d2t/jKt8ysNHQL4KjoUXUrPrrvZjA5FnYjwLvAoVbaSfLJ7Zi3rt52kVV7hvMDvnraUbtowtDXriG4xPcSe0TBFh3ktKyWtizugSK06GVbQhvVYE0MlLeQXtPHV2x0iybcHGlVtpwzkYCNJ3ah40ePjj0a55/dIpH2f7Pp62BVp10tJY7Fcuq00SB2yj9UxhjjdnOyEBV0zFKZH3DfeaPPNN9tq2IabElxZc8BlmfJIJqSrUJY7jSjdQOv/nmIryolAkJ6Tr8gRjpvj8ceeNnsjwZMRxO1oQDHxLs4cQDPnnmu+iy687OSTTjc/Y+0kd4Yl7cVxTV0vk+SdaIEG15HGdM6zoi0jocaS5vMnuhHU9cp42z5U5ejBkwYTxk8ZrYuNEnlAyQaJ3efsnZMvgEh7rdwu+dS1TFfd6helXmAtuoG2IrnuLiLLwbvzXJdECzXeM+SEGTGm3wyvyHMEgleStWsOu4Ei6UuSet9xtiWpTKOu4hs1clwsFTAnXbtKu+2QWaNYYl20v5a13cSMNTGugkOszY9uMge/L6Z70fzS1JoV3vHTXvBco2Ztv88a1BGsJq5XKN8/bg4TX0o9lifT7e92GqFDAJ+7xJJAzZjVpC1dFm6LXSHdk20ue0+QZcX9fDl1RBGlrjHdupF00lzrPEeT90ChSEhVJklRelqjkh7SPuzQI3nj3UwuqCX15WsCTjv1rF49+1mOM4U3MRnklXQ8wG+MyFYzN1uD/FllBMSSqrKAz/l4fOfkVW3msg74YNakiZ95R1hBhxzKOnLXovMeKMA4eQEYMs8C8w5Z0OeIDd+R4MNiFKYbaMva4ZXVPdQuuZzOJJnsQBv9zDFogADj8gP3P2LlITK8dRxzCZJK6h7MVSsYby65+ApvbF5n7Q18WC2vLXUlYUdVF8U5B8Oi293A5zomOh4fCf7KWlB6/72Pr7v2pqLw4ssvvjVX3S4Ze7Pg6QJiLrLwEquusmaUokaoDqCYdhJ/+uNsuXRbaChsMpT7a16+f6y9FW2aNSJEkj27nLD+Wwy8nahVh82J9tab7zlmWYJdEAiGOi9hkc52d7qybiMT6RLd9G5QtybFnX/exX4ua+7Sqcx2x3QVDC6udVn7H0OJt5M0aSufaxr/u2fZOO2lzFgL6jgdr45TT2Jm/rrrbEjX26JzgRaVIL1osJDeIeOquRP1+1AnWFyjZu3fJOZfdtvL+eQ1WdSsgysZST85dAjgiyQEO+/0F+Ss0+zYcpFvaHWbRakrCIMjDEWpMumHcausvEazVlG32XoHM5S0oNJ22+5k3lmYdtl5d4SjWQ6mfDCztd3oAyXkzZJLLGuZzusCoFZtrO3dqz+Fuml5KLYzMA0NSy+1/KCB8+R1jtiOrYjTo3sfC0r/foOhEMOhrDMbiZY4qOkaq6+TtOtOeeO2X3ONdYvqpfm/6CJLWtWd1corrT7rn+cYo/uCzZ/W1Etok676zsL4cZOdeZKaTsSEA/4UqttfasnloH/ypM8H9J8rJ/c+GImWeJ9a9xZrMqFcu94zwk6c8Gkij1tPPP4MkUllnS9o+y70zz/f0EhdFPlTcXJr0CmxinwEWEOMGpGGnPxfa8314IB7GupC82291fawlJoWU+ArySKwoiItZXV45Pneux9ddulVjTqOisnDm7332r+o46IZe7NAhpSLmNFqK624GkhkgCPnbl17WtuNWRusv3FBnUoi1DMCxuqGYYtBM5TlTh1RdVrc3WqYuoeiIrvusgc5wPMMU/gEkTnZg3YQySeKNvbt/pe9u+v640SIWZbHmpx8XkWaKSbDVu3ydZeww/a7VGSXQZLPhMXaFk6N6FogyWrSLE8q7o0ItBGCZJ3ivcWbTGgFt0uiHt2HQzLWFbXzhjpusfnW0ADxsfryKD2YGEkGMsk3bw3TBlb+o5XkCSWt6nRJsv12O1N9civp5F9ReO0auYKB+t2KjOu111rf9a1t2R8VOgTw0d6fTvnSboWQOdoeAIILDz/0OJ0ndWakcMvNt6NmKMDIEWO9670sV0hAGGmxI2AZObz91vst8jhGzFHyRmUmkgS88JH7CboVm/j2T+nAT3Ijq3E65nnsMSfyhsgoOYG0zsEEQLDP6ld0jAmxhk4PZz6VX5CCzoSSLdbN5599DWG+NdF2ZVXnxsjKh2qjtPMkB6KNk+eC8Tr3zpt33v6gJFt4svxiMVj2bl5jwW677nn2WecX5YAgUY/q3CbodHAxHeHynrQMAyn3nLMvgIZnnn7how9HYh+9/trbLtqjJCpLTGimJ4hl65HzJDl0AvtQmLHyE0Va+EyLEPPddz70waNERyxGyb3Svfc8CAF33XkvCX0ey93PHrvvAxnLLL2CLVP+H3fsSeRGQmIutuhS5LP8citPkM+ikTpUz5uJunx9rFyD8Ga8DtXzvMLyqyAkPiTvTeNwmDrCNHKzKpow/y/J9jHokD81HS3HluN0PDnWAVv+U98333iXbEs6z2crhjeJ4J6vY3VDpiWBWtDK5OPDZM4kVKdoYmARX0uyfw3isJ1Po+R1jVrAavI895wLyWGUXDMlglrijJSzL4sEgZhj5cGhKMPcXEIkBg8agmVEtCnyTRDoZLoF3uLNSzLnP12C5eq8cy+qyonRBHlbmSQPY+ZqWXa0mUPke+5+oKBBfVWzOlY0z7fAIrvwoAWXWHyZl196fbScZS08dHF6gq66qDrQCINsqdRXX343Qr7L6HV4SY9Lia5mWX4uJukcqg/SUUfqSyDORPligc88mMIWTdFa4H9C6BDA98rLb+Tl0JG+YpSOu5pNZkFV7kURL9TMqtiv76B99zkwlPsz9yo+d21R4729sOXkv3CMRqxWYzKx4T1ZzpEs0A6UldNA4KILL1t9tbUz4EN0WuQPebI8ZZGWliP+kUccG6trJX/+U8SBB/zVzj9CDVIm6STpk088ayE++qjjW+SUgqx4CVg0yftmdvaQ6lOL44872XBg+nmg1XmDqjhbohlSDXw5nffmmY6hkh5UNPCNllPCkTr9+rfDj/bg6KUXX8tpUAZAR7KzsNdCnXWH+QV5hahq399RRx5HBCwplKqsMexEuagK5e3KvHru2ZcSeUyAhscefYqX5G90cJfz+GNPO7cwdYHny85vvOFWMmGIShHQZlt+tlk70/rYYqN18pQkmIoYF+Rm0McMJE8M8J123C3RWJKX8JxqXnH5NabEEBnpEDvUXn7Z1e5C6FGwMkbJyaOPhWLwrrjCqqEmB6cCPtLSmkVZpnZmEwn4PEQYL0cslHXmGeeCvC++8CoK6VUFJ7cQQgnVp+34BIUFjfrzGmN+nPrRwjSmrDidQyxozAtbnn3mxaJcV8UyPxES8iEV4JLTSU0kB0GtaL41pyOxJDzl5DMKGkxQXEnWMcnp56wFoSbLkEAPqiilpMmKgqYvIGPggLlhpl2HUhd7/SAttYvTpTlYWtWsCzSgAoncWbpHwVhBZnKafgX4UCJDM1+BTgiYZ+75MS0bNMMzWcfDkW13zMRv0uzWmNSVbCKTlmwBdHdX2DQWGIt3VXOR4X+Yyf2voUMAH1yw6MfqbN3X7bXnfog13Ic1Lzz/Crymd2rStBdM94lONwkRpqSOYd0R0bpWs7KOcxMTrYZZ4zS1R4lEaJErkYwGvrpJiHDpJVe2Bz6McEiiZ3PbfCJn4kTgJyY6Mooy5zVQQm6QHpqfURLDKKx0TKRIJzEjjQKoXaNOpEPAa6++9cjDT9z9j/sL8p1d0SFzxr+xhgn2UHTG6ecQk2z572k4CoKSKAU+qnb6aWfbBHARUSoZgUYKzjyU4xY73YzlrZqff7/trjHyHeKeI9HohtqZpfvtexAVBC8MuDAT8E1kOkGYgQ/FDjT58vxzL99/38PO9qknn6NqfCrL45vpMbfzmqbxqN8kkRz9sRBX5KeLTKhgQTBBKRDprh6sxLaFJLAYnoO5Tz/1PP99bv+2W++saqY4w5HR8mxY1rDUVTC4dJGjF0AQqxPYijUynQr4wJHVVl3LwkajVDXyBfgceYx81VR0SBFRITJGq7ltgTTKgJiMy5o1Q2drNCdLE1bA7YoGgAstuCiVrcqD1uGHHUUS6kuGdgs4Rl4Xy5ohCWTEhZqWbdAMIAiVaNwTyD0f1CIJxkF+ojj0YbQFL5vlC8ASQqpW3ehAHIpAAhvl4wsyhsyzAHEYAUQahIGekIHSweec5k8sgV98/s1NN97GUINBD4OA9979KJZJASgjvYZFngE+3kNAWV5/yBBeociUSKvRgrGUvVFTrpYNirBiWm4d9tn7gFhTvVjx9EZVnZseJ99ifp+p8I8KHQL4GKhiGFN/ho2WvDdefwcW0EXwjPlmnaQtjfG03EEHHsoDwm1+OVUgnw3EpJ/fbNPhsAZDgDh9eg/ArChq7oAhHv+RsJImUDIaRmuqApECcWhdwMvWmaW8mHa/FOeyaPWijDILUEWzV02axEHK6aJRb0t/RWtSI+TWZaJmxzDr3EVHshxtRpEJwucpDDTqUzmwJH/kjJhACRYKOWAtutVtFvHA8GHxxZZ2QU3ytl/WqMEVLMkQfujBxwqpa3UeVll5jSm624T+wP2N3b0gczA5kel0wP6HgA4rr7Q6jRKo5/cQA5K+/eb7RH0GNnhFEy4INNjHJzS/SdPVI+VWwA5WMyZXNVAyEFMuLCXOiSecas+syMAoOYxx10ImqFyLpmhtxwFqJxx/CvkjErZoiBDKzxIDXgPcBI3xE81RTCVmUwWgjW4przVEQzANMUYnZ8fpzHWso/Iwas899m2Rf1YDtGHI9PAf/oyT78xYE148jJLnFRQVJmB/VWW2G3ccPIGDzmPxmc4/z9KJJsBosmUNMwNNdxhHDHlkSzs2yyENhdI0V115He+Hbbipza5TTzmzqzxfUVyU3tHBaJpWniTnMYnmfAxh5EkcO3M0B4hJBPvWJZ9xmvPhmYpYJolG99akVR0oQQUMoFZPOhJPT8fyJ7jeusNyclMET+xehThGSXJDnityFEhMopGb8xkh9zDuJi0kWw3fzpLP2ByaIaMgj4FhuvhT27LTEzoE8MXyRYM0uzGoz4ILLGKf3RhTcKRZE65okcWL2qKWVXk0imVNuJ9P5KTPyMgnDONRWtqvyJhy/nbTSnwkAFZmNKCQWDSeQKnKPRHMJeEEzdzFGv64UZEMKDzib8ckMn/s2sjTTyAFYkEpRG7V+iZAgBR+Lq+cpEJeR8px/AT59QTUkJ5EaxdF2QXrrL1BUSsbxEdiiGAIC4XydilOWqo8RXNYaGP/foPJGeihamamdWas5uaQLfpJVGW8RnwwEwEtaKxBDmQF7rho4yZSbhU9+KDDLHlk4nmZeYcsWNLy3AhNWdIodtlEbsAlKlESskMS8GEXp+hVe9FMZGpR1kQ57yQfrGlMEvSBDCloo2GbUfo2W+8Ana+8/IbHpBmUbLftTtBc0p4VkvCSB/JkiEduHt9NlgOxvNav4mlueoAPWCV5DTBJRRV8/YDtVgx29xNgK18hz52WAWiUZuI8LgNWRmvulVaOZRKOT10qQD8ZktyWdRaOOfqEUXJhj2XkqpFwlOZGqSBJjOOTNJdd0ASZuxO+2h1pKDfLkIRIUMrgQUP4yvigUX6JmuWuiqbkk1eloM0QD5cqmkbw3gBa0FINkdgHFOd7FDyBYD+gZJWXecH7xRZdip9kixacfNLpxqCJmgdfYP6F6S2KmnwEQwHBqmZsPpJX8KJWuqCcZiUJukmGxKHWZEWGSFdRE6Ao/njNluY0aqZGZi/1/Vh+tKxogWaHapt1OkOHAD4q7MU7W0wFjc6KWgNq1fJfkI7a8hpzuakatNyZ11pYZ3ktdQ/TlO6SD7UtwxyspEvged0XE2uVM2w3QZDXJHpFi1x0dznZ9oE2WPhrQUPpigab7pGcJ4Ll/j9I9/chrwB3UfagaWjUtjKTWtRqo22HIN2al9OJUUcLNS/eTZfRWEYbtLqa1zjRb/Ja7ixpha5JeyASLXPn1UW3aJ3UYpFoNGRJIs8WzSoiOmeeca4ray1tlLuhOPUtbJ6YPF56zdo2oxlYSi+acUGJ+gCM3KqmQWONfbxKm+UWpW6L3JqE2WbtTAS7R3Xm5rb5k9euC9cuY50toGK6ndD0WNVbtKiV0Zz/b9PeQbqtp0ErzmG6VdPqagLKmkRv1fqj69ss93ZeuAy1pGtRjNNNSFW55LJ4JJqIpKUw6DIJjLRsTbYWMFctkgxgcpZkoZsGfsIim1rmDzlb/hP50C5o2scZWv79s6xBfUZbJF1wG1kaq9o2RHw6HitRQZM8lsYmzeF01YV5Va1BVTTvRoY2YBNN6pGcFofIcnptrgmzYFtt3YgWCWtHpHX8Vvk39BSQeWVgddpGOdGykJAzpZhv6Jrfu77tRevHhg4BfL+ZkJfzUbv/S1JPeTM9IChD5lnA9iOhRRtKQWf3rltsvnVtkl8swCKAz+ra8UNBXfJU5ls9/BpDHfhmZMhsGfeuwQ9toP3lA/2kzY043eRlG80GS238XzIEMpNnOhnTGSrpLY61n+rh1xXqwDcjg8fageaGwv9hrX3GhmJ6aiKnjSweenSTT6Tm9G7TmRWykUvtpw4YzMlfC0zXwzTC7wv4PE8RT3PO+7+GZPqmkDpa8JxOUZvvPbdVG2d6Ajl01VWWtZ+ykOG+Z6xqI7SfCSqnx4T/U/AMlycufzDYvi5pnfe/5vbTQsa6ykzyXTrDg3von4NXv4rw+wI+lKdL51zt+x8VPNEzbc3vgMET0p6l9kJBbZzpCWXd8FL7Pgv59LiVWTSNHoKsOs3e4Dn+2q9Z8IJG+8WB2uDpVIN77df/PWSsi7X+XhvhVxcsBu4zOshk9C8Zfl/Ah1ZM1OWKtZ+mP3iv2TT0uWMGT06BNeeec6FXh2vjTE9o1Z1z07AUbMoFOiXyn8b7NpnRN298m7YNBdAcecSx0yA4Tu8/8maXaef200LGOi9x1kb41QVq0X3O3ltusU3HWYX7JUNHAb7MDKlou6+XCDyZ4p42Sk+efvjBCJ+26TxHU6JF/ao2vle0v6GSOgKxUcbXknYqZCNc78Yq6JhOoLWIsva7WEWr2v3vyKecfEaoLQ7Z+n1Zy/Zjx0zE5GG451MiZW0SbtJJOBuD1nw71Ym1it+gK6O84Z4HKLfyJNpr4qk3VyEjwMOQUro67JpmMQn9+g7q3au/CfNgMK/NVnHqBqqoewvLOr2w6SZbUrp3t15y8RWFdDNzs7ywOAdzyZuB8trVkZ1PKqf+b4o6Tv/lF9+6ydCcKN314qLbG1xvv/W+Wy3UjpyyTmt4hnHddTZ86833KnKmwhsIgyeGyya5+jB/bOiR9vjjTg41cHYrUC+Md/PHlLg6k3QffJTe15GT94GKdiDFgloX4fa1YCTaBjRHp0Yrf17bL1wX/wy1yc4xL7zgUreak59+2tlXX3U9cbx8ZPmJtYPED+ZYtk2kLFcoptZSmtOpxFiC6uYoyeWaSw/lr8VyHkkR3KwWS/9v0lGNZvky8U74Fm35tl6UtR2HOB99ONK1qOqOYBcKc8bo8t/whzqn33boEMBnvfKDmxPRsehbXg0WlvtYao/KefMU7WrpL2gjEiLYqAO5BpGiNtMlqb8zMhmVXn2bKWpJm3JJOMufZs9rQxMJ99n7gG+/+d6i6U4eqSpqexc5oPyUxX8K8o4kE5lo+yH/u8nZhg3DbFNSov1KzqQqtyVVzX9ZWEtafOgqn25WTu+Q6i7fcK67k/P+zjvuseZUtM6Y02ixnDpTcxWatZGqqo11X3z+TZye7zn1lDMdzdJf0r6/kibj/DKSbhd0Kstg7WjmIS+9bTjQ+NTYV9GeNROTNesrL79hOIsFZBm3XVyTNuiN+GRMVf2KEcGlGBfMzKJm1o4+6njzp6IptpKQ2qhk8lyKN5/7vdniokM5JjCdlrGcNsdaEtzoifas+X2i7qRJRxSatX0vFPDdcP0tLsj8ieS4u6iRb7M2XTZryjJjdSgA4r+vTzTNZoJluyR5Nt8SdZlRio/e6Ef8Ht37GKqatMs1L0u5RR7YSqm/qbyQ3Tqy2657Nsm9kMExlr707NG3oK7RgGj0LOswnONkrfY7CR0C+JZbdiX6/48/GoV+/vWQvwElqNPXX32PVozSdeAP3P8IzwyLLrv0qvvufcjnqDEWsCnG6XTn8C23nTB+ik9QGG54IP5Y3aLdKE+QfCW3yXKIsv12O0+QZwtvvqfhX37p9Yly8oFWr7H6Onz97tvvx+pyaP7H6i191NwHqnjz4guvWg9Jha0BPbYvLI7j5QPDpwtIddKJp02SVw9S9ek9ADJ8HCKRJfWJvGLw3xc5b7jBJtnhBOu/jzqM1ZHPE0841ZmPldcTPpEVGb7z9gehzmyBOKPldMTqNEE3pk+WQ5pIQ/WJcnFx9lnnQ+oKy68CE/g0Rp5j4N4br79DQ0zQ2S+q/MH7n/Cc7TUt6RyFq8wn3tBwVvUN1t/YV5U7jNAl7rbQDUa8GTli7M03/T3Q8US0FzpHyo8Iudlu+kSuOHjjQ5pm5nHHnsR7iKHoZZZe4cAD/jpsw00jTb2RZwZ8VJPcEI8p8krCy8nybsB7EHys/MrwfP99D1NQi47uv/7a2+Rs8/zzz74mPpXdY/d9iIzA8NU4ZeAj28/la+feex6EIQsPXdxnsVddZU2ypSCkN9LRbGpNgDNLLrFsF116PUrOZiI5o6Vlv/+u7dgfZKy+2tpj5MTlkIMPh+AF5l/YDP/qy+8G9J+LCAgM3J6g84JJugmZOpKhPZd4K/43X39vgadjg0U8Qz8vS+owJshZAETCZ9jIQ2e555qsU3cG4t9V6BDAN3Shxbbeanv3nLQxymyFB7N23GFXHh584FHe5DVK8l2osc5F2ubK69w+INhZvvD4RNN6OPn8cy8DB5tsvMUxR58QylrxoVT3wxR3y823hxpD+fAs4YzTz6HEPffYF+FoSr0S9O83uKJNv406QBPKBABfKMt+U1rkJGf22brEOhOCqH0q9ydUivHRSiuudsTfjhkrX2PkYLSlrO223alJI257pkToyTzRgUobhuCgT00++cSzBY1r3M///ba7yhojB9ocb7OO/A184ELUzs1yTjObtqEiOVxx32A4Q4UM6/Qu9q3izCOduHKjEEBJ14uCPD5Fn6nFM0+/QAXhj3Gnve1AbrEMdp8bCXSSOpaRTnEAX1FHlKzPzz7zYiCL8sYbbrXR5AOqFVnuJxx/ik/I5HSOHbJhFHHmnms+KpsBn3Mr6mAP2B2mfhAIiMdrr75l6xL6HTmQmYw1BBIFcsBV1ZGGEXIJAeWAVFnD/1DAd+01N0ZabJkod0HgC30kbPEwgtbnPdD/2KNP0XPbduYh1jHHRPMVE+QswGAEbbBilKaMKZQabbbp8EUXWZI8yfxPf5wNwQvkJtoGmlmXqKd0F0URE+Sn69C/HuEBNQS3pu5pKWKLzbeed8iCpBqnk7DjdMqbr/fc/UBJi+BApw3/rNV+J6FDAB9Gh51ZokujddTf1gTP9Eh8feThJ+ymhThIFc+0Kz0qCEIT0pD0jXZVhjHVKAfcrXJiceQRxxLn9r//o6hBU6N8+xQ1r0enjWr5gCcydMnFV3i0kmgcsesueyCR7gntd2i+eRfCECBOBnxPPfmczR+IQRUrGlIFGuuRA5kHmrGCtnPOvuD4404eJ38qVR3UJwDBBIwF6AEj8pqcouLk5gOJtiKhmaqtu86Gln4UCS6BTZEsGp7RFrhEvaiLZx7XW3dYIZ3/MvahkKGGwCQB+CoaMo+TSw9SoeeYty6RVECS4YOsQATgj080gfExTv1IF3WmkKLhM/wpy0HpVMCX0+D0rjvvdUKgp5QOcg18H8sXPyPB/fc7eP31Ntp0ky3Hy7khLehDzWa15/ha5Ldjgg6uwva5Bs8LhTmNZw18Y+UBASa0yKdAqEPZZgK5MV5u0vknLD5SGTpDzZqRZ0lOE/IaITLaMLd5cIOGAj46RSLQBFDIfwjAXqPPniLnQNQUYaOvMuaCg56NAVhfeP4VEvJzlEYwobpempWRB2kDIT4UAvqDBw0BrWKNpm0hYgmSLcJmriYakpMnX8fJ6SRFDxo4D5WKNK8aaiBPi9i0R31KGpQY+Jo0aXjpJVeGklUaN/iZ7xfsmKFDAB/DAfQ/1My3bRNkotRuUgmL77lnX/I0EBYfYlTRiJL4WIvY9mF6r8UUOWgaq8EpD1h8dLaI5mGHHhnIXkDc+Q+gnHfuRaTiv4EP/a9qMhiZICZ6OEL+VCqaf0HIyDMvkzMDvqefej6WY7uypttLcljmKb+y3GGZhrPOPA+VPvaYEx2zIo8JsXpvd7ZIJIhgBftEfjVcRKJJdPfqDz/0eKRZoXnmnt9QYtaRv61jSmdESf789Nn7qmYSExnOIJqTE4EqGwFhcqIDWKbBRigRINgzQaBGWTNiaKxtZFuRRhMTz7iYB/gD/mIBFdtt9WCcaHtnivy/kpbmwHDzHLyBD8ICzejRvhXNOVxx+TU5TbR10cUU7oqwl4vpYpclhJ4JMtDkLnLmbuBzZxMK60ExdyEWFRCBgUKoSUxS2WoraCaObCGV/8YvMs8qbnB0npBx7jkXem7XOSy+2NIrrrBquZ0LLAqld3zzjXctAzTx8sutTOmISlVTqGM1WWGGE7mrfOdVdGgaq3+3XffE6qffcnEfp/5gTCQsymuKsCLfGaH0hTh8pU2xE++4/W6feadRYk0dolYrr7R6Tj6EDHwVTURed+1Nlq5QitO+1X4noUMA31JLLjdWc0yI6eWXXU2TDBwwN43EMApZxBx4/LGnkVpEvFHuSIEJhI+2R5otc6edetY4Tc8xsDVYoFE2GC1nE+SMj0BvTIkPPfgYck9xlviynDlj3QCIlqRIPhEpFNVFGU484VRPsiAlmG82eeiEEake3fsQc7xcVmBNBJqCSeQWiSTff/f9ZPkLOe7Yk6bI4TgJGaDx9dVX3vzm67aJHmDFTtBiuWDjYdttdsQYRH88eLRd5jkdunoi2K2FzYfRcjjKM8Q3yaPkWmuu1/Lv233JClgcIde+0F/RCrgNtI032pwH+GanWHDY84xwmMiYPHaw3Ktnv4oW3ENd90Fun8pbdUFbiz3tGMqsq2hJhAeaA65C1Qg5+Io1KxrLJIRFF190eax1WDfcJPlNiuSzmmEvP8en3hNgqZ3lUE3ew5yKFr48Niym61c5eScbJ8fI7jk8h1hJF0MnyDkr1cQUrWqtgJg08VhNfRKB5B5LwhAb4O5BK+r8iupQPVMMBoFZDAJgdb++g+DhGHlUPvus821rT9CMLcTQUpROjZrlb93gCAE2tSjLTqWo1zVX30CJDHXt4wRph8mhvGCN0tRk71793TfAkxHyDUOYKK9WG26wCUaoXQGBdPQT5E+evKQXLMiroDkQa/KB3gX699n7gElyAGNs/V2FDgF88883dJ21N/DcRClduo3kJcJr+bEmuZu11JVPL7ty50YcmrBZ2zJyWgWLtYxlkOom711VTdxEqZHi5IHmd5wnzyi27Z2yZqPy2vpAfJd49z/ub9TirJNTIkJc0U6oznIMY9FhdBPIcEs0m2ObC6gqapCOKFNHX4Ng6zXQ1VzNunIlq2ZZ67mBru+ItWiQpLsZuuruG+uku+5sm0irHKyWdW4h1DaIjL2J7EFSoaJ+TwSyokZedS1o9tBK1Sp3OLZtXVlX3DQY1uP0XjS/J8AfUClIF2Sr2gRDQleqRTdRZAvEibbLNGkDUF43DZnzNvSidK4d2vhU1f4el95F3raTdPl1pByUR5pYcGOV5Sm6Sb5PzMOS9hiFslW7yt1IJb3ADBzJa8nFNfXqjS2+SNsVo/R0s0XIfEtkuppRxt+q/K84KzdKTlcvhalTmYqsP1e5SSvFXXX0BSIRBsstpedS3yqWgarmbUMNit1fFjRtV0ivD6zIQm/S2p07CZLMO2TBSdrT47q36q4oi19JQyhXqiJ3sG47rwVbVH4/oUMAH61lf620geXVU2aB7rsxKFhbHCwH1thAA9jmdOuGbQ1bK7GQxUMhTw9ZcN3MVomsyYua+y/I102zdjAUtPwPWDxw/yM2HDIM8kYtUxIIQENt0MtUlIp4MOIhsPWzpE15FtmSBtFRerFeXnsUXMdMsgPtVjEQW39MrUlt1K4dfzUWRFrHcIYmw8HlBkIl19GqFUkHXJc47SooCJXorht2Es2jt9dDJ4e9QbqoQq1PP+3sbPjfJMsup70jZSF4VZsnUPUgPcJs8HLOZogrWJTt5pZ1cgNHqNmoUuqDi2cMFq/yh9qTYagyQpmARK3fafYGtw5kNMiJWSTwirWFzaUbIqupl7CuunXI7ZKkmzdjwVxBAFpR9+C6u5RQfZ4FpqyOJ5+ehnbTm7BIYGoRSoS/ptxSmsjs8icLak57S4uaSM1YV1ZP05Be/mdbIS/ng3fdee+662yIefjgA49G6qIiyVXW1Tlbk1RKd25G6YnATFp+J6FDAB8NaWnrgAFNsLLVfppGaNZ+LgvWbz4YB6caXP+sAdWds1svG2X1kCFvS3oDam2cepgqdAjgcwfYMRsMYfJYr/bTNIItCHe2v/kQy1r8sSz6X0IpvbG39tPvMFQ1o+L9W7YNa+PUw1ShQwCfxw5+cLN5XJk1oY38UEtyNtrbp/Ubj8I8qVHQTE0pvcbYFn57qy17Lmiw7MzbG/weDzqti2hfogdN2QDEwyKPTUxM9tKR43Q4Y4zwWM+VDTXA+cH8K9p0ksWMNCpxVo4WaELN77Pkzspjf2fuHNpHaB8/SgfI7XEkTBnutG4Lt5HHku2T10M9/OpChwC+vFYkDA2RJi/KmgDK9NOQZLxI0tNCDtZYI4611Kky+AjS6aH24GIDM0O99qU7JJpqcRfqnLNPUTrL5umSQDNxofAxr0ll02+QimSeuHRP0mVZhenGOudWm7+JTASLYbrtwBzIUDUjsn1aI7LT+qF9BL9pD3Mm29S2f9Me+LI2coS6tVUPv+rQIYDPIOLVq0hQVU3XYR3yWvVL0hn6qYDPCJKk5z09VW9AsTIbxdorf5CioaPZlmmPjHG6xSkbgxt9Qi3VGSaCdmsFTTpNaQguyuIr6nR6qEWPYrpZP9Sktcs1qUbDH8w/o9MPiYxKI04GZ868PW46fpanH6ZipjnZvsQM4h2i9Ny0yzXOuo0yerIM66EefnWhowBfqLNWEyd86ksh4vRS0a6tPYCP9979yDtdx+oELjG9ww4lvPCCSwcNnMd62KzLpXhYeaXVx+tGsRGfjFln7Q2q2tZniOH/A/c/8tab7xW0OaBb156TdIsYeS6+2NKxVvopsUWXf3t7gbdoebvsON1I/+gjT5Zk7o3UEYuvv/o+MxuJ6T2DUyZ/sc3WOxhHxsqPS6K1VyCSnInw2adfLb/cypHOtGb5j9TB1ccfe9p4GmjvNFkRecg8CzRpP8RZZ57nra3GJm/ozVCYGpGDN5EZ8WHUOB0acfBY9fPPvnZ1xukcaKyL7rI3saw8b64k/6uvur6qHSQ+c+I2MoK3h9R6qIdfS+gQwBfpqJatIbAGPZ/1z3Ogbz5ugZ77MrqrrryuWXuD+Y8qHn7YUYAUMNSqM6Rk0rfPwBuuv6WojZpe8g+1/zPRLnnvI1l7rfXH6+hor579UNrnnn2pu66qI7Tfps/zE48/s966w2JtjiE+uOA8SQVtvPnqy+8AIyL37NGXEqFq4IC5b77p7zbuPNvobEfrBG4sK9JHTSvyiJftT87yD7T/xlhW1rbqqvbEkRyoMtry4G4gy5+v3lbWu1f/8bqs1jYan6CwpP20Gauhf4vNt/bOVQcyufWWO3y7oAM5/+Ou+3x0v1E3oOd0ttRTh26jP/1xNhNQ25r1UA8dPHQI4MvLy0BBm7A86kTnn3/uZcwNdGyMrvSu6qRRkE7ng1Y33nAr/8fLwUms2a4HH3jUlojPSGUwSp7HH3cykEdyHzvPy2dUJGurqu2sxpS8rm0kmu8hveD8Szzuq+jy467yZ0cE7xH1waZs9AdgYUv6OdKmLVKdcvIZY+WRJdZewvPPu/j6625OUp99O+6wa5iednD+Tu4qv/7a23vusW9F26T5+e47HzZp0y80f/jBCE+6DdtwUzqARENdkkNYc7qX2+ZhVdvoXnv1rYzblIg9SA7ZG/L08bXsTVnHsBLtK4TOk048bcsttsHQq2gTtdvIcJzVtx7q4VcUZibwhel2YpTzvHMvGisnS5EWMazhk3TsLEwdzNmpHPq21JLLAQ2h/KP4/mObadg1xCQrEoI7IBS48KmuGM/rFLBPjwfpVl5bW0S2cw5PDhblA8r6DPgapBZcYBFAMLN3QFgyfOrJ54h58EGH2epxETlN5HkerZwe/7z8squN1z4dVdEe41DTf7awsvwrWqt5+KHHYzk7cRzvVACqoIpBOuPiQLdNk7MnBBpTb6wkwUwraBG2nK4R+YxqxvaK3MxgPl926VUXXXhZpPUQWHflFddefNHll1x8hYF4lHw0FLV44hPQZ591PhUkjttoqtash3r4FYWZCXxJeuwJi6ZJHj3fefuDEbolvqpt8ejegP5zGRqIgJIfe8yJL734mk0/dO/bb7736Z9AS6uT5FxklByfgXeem2tKLyTm5WqrrtWiW2UL2gWW0zmBLnKvNFau98jn0UeexM4CPkjy5Rff8rKrfN7l0+NNxtyy3A0Q/2+HHz1SDpTIE2AFK8EmjDXeQENB50BWWnG1TTfZkgwnyMWQ58USHRsoacVgqvwN31SzMXUNwIMNTHoC79cfK08ko1JvXSbeLjogjP6AXuGxR58KNBZu1H3hDmZUUWfRuumyauJgEjZp1yGcp+4UPVqe8jz7WdUJjbJO3cNDt1Ftg9ZDPfxawswEvoLm720i2bLIy3ebjbKSZqbKWjRA8U495cxll1kx1MDwE3kAjuS8zxYZ0fr0HoC1QsLMF0glvVnCyMhozoZeFvzTZ7a2Gr7dttvsOP98Q8GgUXIHQACDEg2ZeRnIqIQqBss5eQyPdGgh0gSloRlQrmiVOdQ2eoAP7Biv0/VnnH5OQcsFBppQu1sqmonjZ5Y/wcB6w/W3UH2PwavavWgvcl694SVJDjn4cCNpnJ7WssOYqtY9Djv0yL59BsZyBNAep2AjA/9SutoLrJ980ulLL7V8lO5MzMmZx8orrW5Du6BVoDjdHtSsFaQ68NXDrzrMTOCLBEmolieYKhrlAVuxDpCut+6wTKsDuWM0NAQa5ZU0JQeyoNto5tCFFgMvijoez7jVSJqVUtSBxyefeDZ7Y8T5/LOvwQ4bUO+/93HvXv0Bpv79BocyOdFwQ8YxR5/gZVkC+Og1UDuSwzgi54lycIShZ4QybVTKNpQD9lciF74Q3CSHdF7Z4AH0zPInLZadzS4ejJKrr7a2saZH9z733P2AUfuE40+BkrXXWj87qNRp9gbMtBbdscB/L4CQD/1BqL7BRdheNsi26AizYdd9jO3fifLfZ3CMNfLN2iiWm6m4voe5Hn7NYSYDnzUWPQeDMI4m6iagSBYN2vWPu+6zDTL3XPPxtSRnHvzHgMrLW4FdQhIT0LENAi4AWDaUslIwWBjKeUreIUl3pT3+2NMTtI3m6KOOpwggxjgSCS49q0iE/fc7GMhgyMxgnEGo1R4EGSM/SCbY0JzI2Hzi8Wf22fuA9iV6sjLWNh1i2leV36y7zoZZ/tT0xBNO9ciUr8AN+T/80OPN8uDy5hvvmvJAZ2PBfUAzTs+cd5Gfu0lycM+AHbYAhXQD41I38aBnUc6XJsvBOqwjfyzlMXKr5f8knG/ehc45+4KctoubjEhD4KnaqB7q4dcbZjLwOdiOSDTnlZkhqHGTNqCE7ZYjrPO2XwLt/Ci3c+DjuTxPlrWffbcOtzdSrMyBPCQnWrskq+bU7VWcnm0oahNyQVNsUXoCN6fNInZ4ZTOqlPordRHl9ABce4BolqNwSjFkV7Sp0PkncrXi/P3JScrymlXQSoV30tjWK+hkXlN6dCyv3d1FTR1iEjoTr1mX5COkqB3OVW2LcW6xjOiSBtE57bwJtSATqS1y2t44FceyNrK52vILuiSoh3qY4aFDAF891EM91MMvGerAVw/1UA+/u1AHvnqoh3r43YUOAXxeT4x02qGo8/y1cWZsiNMLw2o/OZTketcTXl4sro3z00JjQ8FTbBDQpXOuNsJPDp6VC7UtPPihk2Rh6oDgtxoqcivf/k2Yrk1PIxS0a8ezxtMQifahpN2doVb/Pb9sOamN+UsGzyOHuorPU9W1cTIZ8Ax1bYSZGGKtc04nG2NtNvCkvCfia+NMI3QI4LPGdtXtE1VtXa6NM8PDtFWi/QqAlxRq4/y0ANh5WcMLF7URflrwQk2rLqnwz6kilLTJuf1C828vmJ9TgXsxvZ7tP4WcjjaGOiTjFq+NM1Ugsre4G1wsS/+pv/nFQpPu34hSn2w/SEyn2RvC9La8nNxM1MaZWcE4MJ26Zs57afE/VXYaoUMAH7o6eNCQB+5/ZNllVmzSif3aODM2oAxLLL7MSiuuVvvJgc5kySWWXXml1UPdUT+djTE9IdFuOK+o1n79ySHRKQ60sap9J7Xdppd9P9U157/hEGubYfYz0LHraXcw3pLJg0/mtd8P8J9CUa7MPvv0KzJ3g1pOatn+S4ZQm0P33GPfUNvjf7Ailj0POxKdqqyNM7PCbbfe6Z0MtZ9qA+q52abDN9l4i4ED5k50yqA2zjRChwC+Ddbf2P4/rrryOqTQQ48oHW8m6T0ss8/WxTCfSy9UcwSHJp3JD7QRN5LLeO9iKerq65L2wTi+LaOFFlx0/fU2qsojXlH7hN0BwneMMhIutuhSa66xbiSXATYiTImzitP9LqYtEnz7U1F365jURHtlJoyf4oZxTOK89+5HOR1JdmeV1x1DJPdJW29qIeevvvzOJr1HAU06t1fU3XJmXUGuTHv36k8EgM9FZId8vT3ohutvcQ8PwWN0g2I+vZ7Vl0ZmrMtaxMXZLLU1xM8e3fvEcrXgyubTbUOh7nizYWW98oaksvb6uCndXnkdYfYOns5zNFGo26ihS76gPUNmL9yjCcjknbc/KMlQjdJNQuaY2V7V3VIlmc/+H2iHU3twh+CJEz4lGrlh7FR0C5rbqGePvgXdjTl61HhH5sH5IGmWE7eyxcZIYWKoyJNPPEsjxnJs4Zp+8fk33kj051k6uTXJqkHX4LXK0ZklNhJuturCkFBbtSwJVN8satTtdAVth+Jni+5ftlSU0uvY87prMNSVx2aaf06e9Pn++x1c0vmiRLgGhW7EOPXjTVZAfKhN/rEOp1PfRNaWBRj+F9rd6JaJn5uvJAvLwl9I3aSXtGkfOc8k3xGy8YfFL0g9ovvUQFEbrcLUie+tt9zhaFnzTSNcecW1VKpb155nn3W+0SPQbi0TaR6S/8svvW7apkLGDgF8vizcQjNHp0arIu299177ox7WXkJnXTXJVyTYCakP2tiia/qGb7mtoQFRsFgvMP/CPt5vMfIuv3XW3gBmkRu4hkFHnEiCaLybb96F1l1nw0jwMRXwma2ZcGy+2VZVXXRQ1SkLSu/XdxCEkbkhY7ttd7JgQYmPdlQ0A1XWxkPKMj2Zeliredh6q+0rupo20q3YsIWYLpRnn9IzRpA52A14Wb6nAj4DGT/vvOMeWASRoXZEk9UyS6+Q6RulZ6xr3yi8X3qp5fOaQIk1dbjw0MUtuAUddCP5Tjvu5pxh1CILL9GoO3DN8I032txYtsbq65AJqhUJLunkqBpIzU/Xa6Nhm8Eing00VN/6X5B/GmsmrUNWWw3fzjUyeaTKC3z5P/98Q+G81XvkiLFZLSAGLCDnbbfZMdBsQBfdqUa3Z9Av6mJiR/ZJISihppaTOJ2NrejO7x132BXKrWNPPfnkx6IXAABP5UlEQVSc7+cN1SnCB59HRDBm+dPszXI/UZClSd2r8gDEy/XWHVZQX+WObZedd4d7CBg84esqK69h+XHvwjN1hNVHHnEsuQ0aOM+wDTfNUNhqTyZ9eg+ApEUXWbIoZxMH7H9IoBM7ViWYs+UW27heiXodcuZrs265DOQiyNyGJwaLqqabTOGcugB+qSWXM+xGuhps7bXWtw4C8U3yqDTX4HnJwadCy/J+lAFiqF2fLborDjPNLbjF5ltbo92tYrj17zeYHtotmzXfNMJ1197kyHk5B/HOVpdSUK9j+H7zjXfdo0yFpx0C+EAfexOwthdkniCOvKTj8iEq1+eZp1+wBHzy8Wha/fTTzqbvos5Ewzrw6Q5iIi52mYnx7E6ebGlgn7UYL79yYBx69eEHI95+6320MdQhWfKxT9Ba4HOAfXzFECPaRDlxoYHHyaso2WJ1IxAHHXioffCRsCwjy6c17E/FHSaZQNjDDz1OhFn/PAfPyGuiC7zJeYRu4IaYsXK8uuIKqxqYKIWKY1kQhzZ+6833KNruZ2qBz/J36iln8tMnQCj9u2+/h2z/DNXnk3/GuqxFqOD7733Myw/e/8Q6TBWgn7RvvP5OrBN7JBwv338QOV5XYl926VUw30XwkjiIndsxkaVANF6S5z13P0CNxusWdhISwTrmMy0kv+D8Sxg5jpGbCfonaj1G981vuMEmdtNAXWhQGrpRt8qa28AfBfksjYPbiwxp6I8+HJnIviBDyKBoeA5XM+DjDVXbYftdfEy7PRuJBkPclGjUjTfcyldXPJHNTnMw7CUrcqb5SPXlF99SR8gDInk49K9HUDuwEkqKGlDDB+JT1kS5d4RsciAt6EAEk0HF4QCi/tqrbxFhrFzDkicxbUT73Dq99Sh5wCU5bInUZRpBTA+FNujm1YK8ZozXie9xcjoLGWRLPkTzJAn9IiRN0P3r555zIc9wfvnlVoZIRpdkCGE+tkjkF194dYI878JnmtKpKA5bpCQ/tWbvOLnm9al2c4lnG+zYPVSKJMih4XJ6wqWXXNmg2xlLMm/5P2SeBVZbda2irA0bvL7znkotvtjSwb9PAnYI4EtkOWOUwjVgKFLfa+Gee675qKHjUCVal/6T/9QHWSQaQmx8iWWN0wx5XTNa0YoPNa/IFwCfkHuU1icZYBNKgqWDZGNXkpsNsVhnJMbIN8FUwBfL1uOnz7Ei/Ta+Lr7ocnd3sTwIUBaFBhoLX3vNjW5+3hvvTGckOeDhicefcdtT2f32PQiJHC1PKuX00jLKai8KJbnqCmR2gdqvvPxGWQOx0br29913Pky0DmONzVL9/ba7YplscAaBCDTEQNChmcFaojGOWZcloVy4l8jnK0mKGryXZGs487HyDdOQ3i/crKvNQWTyJEJVo34yOefsC3iwkxgopN9qlZvCifLfRSauLI3efc7ed9x+N2/4SRPb5EETItn1Y+WyzH2PG5dCMRMG9J8rkD+xooZdFvcJ7byu8pOauuHIAd2O5BwsL6PS6IBWBzLrxsppGGyEBsuJsYP/YDqt7HYZr0PWDHXR2KwgSicfHyUywtJSd915L2lBBL4ec/QJge6JphSgEGOHaFQfCWGIN++QBbHLQo0B7XGWVJH2ABA/1HKERzauAjK25x77GgExQsfJkQ+FAkn77H1AKP+7/k9FmjQ6DtNxcSTxs6CWNLcD32KZkI8+8iTvBw8aMjK9rJ22iwWXE+VAyFMTVIqRptv07n/cn5fdbfm3nPOffgJW/OOu+7Cs+Tk2dULOAz1cItuT8QqFWhf4eu89DzrPSnpMyGpl+7Gc3mrvQIs8/dTzCC3yAKRSBGYpdrqTOE/iQ0aWpH3oEMDnHpU6I/3oMA/GGsMErRJpMbSgA/ZuMIxtexs2WDz7zItYT0AnJp51A0xBLuELPVUgJ3q0GUAG3xEpnrH4Vlh+FXQG7tOokfxWkRVWpGes/xPwUehss3aupF6wMLtK6b3RE3SJ+Oj0WC5d0G677omUWKOK6TVAUQp8tFwgS5a6/O3wo80H9ME5RDXAF+ooblmzJ9SCJJH2ABlwydNi95+Az7hpIYOr8AROPv/cyxnrsiQT5Igh1lK7+/ZRsr7/esjfxsmesvJbIuEPPQqKDd/MroIGuSSxl2zsO7OIT4D1Iw8/AQIa/sA4Hl595U0i0MRDF1oMnBorF2FRCnw2yrIe2+YhLQhS0HsV5PMGs4XxINFgzlTAN1GGeaL5b9JiM8IECKBQ6h5raGzgs/UEGVBoOXEfQz7+ZB5im8Q1wGfCTOQLz79S1IwbmYBWZ5x+DkWfeMKpVJbkWMGUTkzyAT6Ma1dcfg3CwHvS2psGGIT0QrzrDt+APLR9os5KA5Q0BCNfxHv77XZ2Z4wwkxsdeZgCHz9jjdY9w+UZkkjiV9S4OK8x4AT5D+cZFIOfiyy8xGhNd/LmgfsfcbdHizOaWXaZFcnz7LPOh9QWzUGdd+5FzjYW8JldocZPcTrr53mARl2qwxtwn+Qwf+ed/oL00hBmIHZ0kO6jKMpdk7MyzQVdnZhxG1ZEMgXIAUCnj0HdUFhbDGPVMXd04ENi+vUdZOGbpMsxkMWC5kdgq8ehboY1Vl8HCUCRQrkdPeXkM6gn8te3z8BI45pNN9mSJuRTSXNnmBLmI5+OOvI4DASjJy0BrgF5fMVmnCCXczSVBd2y9Z+Az4rtqRlIXWXlNZBao4ljGv6IjLaEmh6iGTxx43yi1KHWnXfcA0m8vOrK6/bd50DeA6PQAKnLLbtSVAN81IKssIJ5edaZ5yFn1jQPokf9+1A3S5UBXyAnN1FqQxXkTGHQwHky1mVJKNej0Y032hwUMDoQf//9DvbSgeHPQulaUzsIgOfj5dmfsojD+I5PCGVZawXZ7BvtMkenRg+syBbc5CXcXmnF1fJybe1aG/jC9P4Av/xY943wDFjT7ksvtfzxx51McfT2ozSmbg98lAvBnpVjmEZ9m+XTu6LZPQNue4uPbAHrksZoFNos3xBksvpqazPysGljlf5B4DPb7YmHOLZHEo2FAb5Q62AMWmEI0TbfbKtIk4NmI7hmAbORhcaaOcYCw3dZMyfl1MwnFGRG+aWx7KADDzXHQt3HUtDkMowFKQzHkZoPntjiCzXza1hBIPmJgNGILvqhBx+L1QUSB1LdEPDfwkYmgKDbKBbwgT6RBOzUU86EquuuvSnR+e7xGly7e6YUehcDotXNyj5GIy0iL7TgopEsdA8mPKJqTbdqOVx/3c2uC5p4370PYWEQk0EDOSw8dHHjXdLBgY/6I3xwdqwm5uAa5jEMQh8myZd6FrMkP52GufGaYArkv4DOk4Qk8eQxEXhDTKuWBe7oo44nDirnJa1lll4BaUBcMB4ny8cnGXomyPshYDStSxHQZt0INHHrTQ95DahzWl+mXFSUn7QQFJ55xrkU5DeBZn8YPVEXd91erxyr0VZJJhj6w/8D9j8kr7ULCEC3GQqFWtXleckllqVcvyETIljQx8ulitWYPBlXUiLVn6D5xP+/ve8Al7K61j5/bmIHzjkzX28zcyqHcugdVEBAUbBLBMWGiL3HeNWosddYYo8NFQSkI2rsJiIqIk0RsSsi5VDtJvzvvG++uScMItcSjtd5nvXMM/PN/nZd+91r7b3W2rlOwyqtXRXwjVRdNxZPNuq63CvoEDRT23wqF5MEUhWUWXQpOBKDBR0KIgzqjBmrv5AYvYGfqM/22zVCEZAQI2pDO2zfGP0JFse/yBxLQg58TYqf+BfyC6QhiDxoEQpFKWgplHHUX3KuR+UFi99aRqBBPl277CwsQA/jCT7RD0C3TxgaFsME2XwZN221oYZJgj6Hjowcvv5qAzoK1QMioPkoThusWFG+/OIf4hOPV464tA1G5RfMfwNlYa0KeTUCfuZ6DBVG08C36BC8m+R9eDYjLTpc/K65+nqTh6RoI9YSi3oAykUCyH0uz5QV/wasguKQ20ru8OKJ8AIc9c3XG/BcgjZkPR3+oOZnnP57dOk63lf1xwsvdblL63GpRhvxl9AfhIGweSWWwNHl7EOjUjx2g2ZqUuJDep2GQeJL07oW+SAlkEVbmVh1pEXdN3J0GYMhZXg/13Ju6YY8gtdGCkoMGB7YosYjCQNrA0bEoeQufkYCrSgYX6E8xghCCRq7khsdeIjxzXX4tKmPLuOWF97SzIp4iQ14CcMn4QNNQG3xluzkcu+6DQT4LO5HhtxYCanJm9SVtB3r1RN5BD3QiF0qyFqv0OMQH5A+t3giQQ4uI+4X2NQxvfj+b7wrqcHl6hTQqg45aOUJ4vhXZTwCw8Ntt9nRjy/K8BlFxudGRsDDI49GHk4c804D7HItUrkmzzQ0ydPcDsNQiX1DbhvpVg3NVYkVOvdM0NCkkrayIDw0eFpfXdUcyOIQ0BOMYYM88SlXkJChknOdhjQ6oEzSUlccoK7YqOtyr4DJIoZHNHmAq2zTvMoSmehnknfgqXUmzQgsmrD4FHZCSiVObHgRxCcn2mayGH3HpExhc+crQdMW1DOI73FX/yg3/aURFGkKqWNVVpIGOiW0jFFxKlqSQoI2TAaNRUp4qVPEEzOfhjXqAUG8wZ0s8UkQX+4svrK464dkIU88ct2lslLc7hReoLb4Dj3mwgsuUSvUM2ovngCzxOcBRU5laFDAAVKgFC1UyApaLXAtw8jhTXghTClPz8884+ym1S1S3E5Vn4iFynng7pG3c8VpCNS3IpfzTu9q6NXtERVM5KDZp/4XjwWcJiFDl6doB4bGimM1yzxuFOJFsehqWnEpB5/BKy0eGWsUNFOQp8wbzNhkQn0i5knF58uqpOomUpciAborpM2Nw2NiVc/hbqkTB8dE72ku56hBAJ9NQw2Xs9GkAVHAQE+aThoVkbrGja1ANHPElCFNH7X4uMS7iGcadnyFrvpUPSjyue+TY0qUVUKrKxVaxm14l4u5vng8BNCQazqBv2XREsX3muNT5xUhJRSznmOQxtKnbC9do4zWTAHZMeI9PmIvjZZPbDXia3A1A4sZPkvMpFJcsqlG1+UU9f/d1cSJLbY87soVMzSW8tyo63KvQEYwiBpIU8ZL7DQuDnlR00lDJt4KqWep5iFhCJVJc9tI1S6nW06uJkqm8UV3SapSJuhPj5NWTdBsiQhSGlCfG0AeJ7O+i0PUInWUykozXL4bu7U4MZhqytnxTSlBHONamWiSi09URDK+7lndJawR7+V6TB2VoG2HR9xfw4NR5WBSIdWYesQRl0L3J7yeFNqiE8cxU1kRhRfJMpC7VWf1m0NoW84jdfGtOsHiqlPMmIxiRZ9nU7me9Lh+RFQONEZKo7/UNCXWz4ix13Jdmtsf9DlfQsqzaXofYQTFgR4XpAw1YjRfC7ZmogYijK0jtf7pe24EUzykAgNodNSiFNVbM15ccx2u9Bo49blH0zGbG5oh1/6IGxrq0o3ebRDA94sizQ0NZ4MlMWJOai7QlpOmseZzxMU7P43NE61EbL6+kTAiEraasfSaIyGd/tV6kP/uVqQgNqUWAgrvGiAVgO8/TZKYGixDiHxqPVoqC/S/IknrwjuJe/lpHIrJQgfJ0flpJFHasalz7rkVX7Gi1xsaI8mgRxWTnJifpiFQAfi2AgXUbfOfNzTa5IQs0ObJjj0jnVh5zCdpixZ3xzYS6HJURsc+h3prfXQL4nDf+tfaMj+H/xgF8UaNQz3a22KD5P8w/UKBL6CDh85S0jy0yk/zPUiLsFg2FR+e/BQk1v/RmV4iRsitYrRCs1cbXptMnOD1x/l/bSFF8SVzRhwrf5OKYT7Z3LDXTpkba5ebH0SbKmH9M58tIbXdp3EfitCpwg/fAciBFxpeTs+q/DQbkcGb3QUrUnLz0/xEpGFSP0iTlTSan9KNt+nFFfqe083zE/8Qsrnlmv98C+kXCnyHHzYMs3rffQ4UD/1Yo6K17orLr9H2zU8n1l180eVuvGn9I5LDc97Ro8a9/tqbApdXZ8+/6I+XbVLn3WP3Adqczv/rOym3KuDzskuvStBlWCJMfuJ8wqhdftnVua0xvbj5d5X/JRdfkf/XZkinQCju1FPOXPj64pJi49JLrtwSnNo8CaZPPun0d9/5UCZQ+Wk2IoPRCjAWqM/5f7goB/r/AXJ4CqETA4zXeede6HIJkeHBRqR+vvCCS1weZQDWdR71w1eLjahJ4wTG4ntPgV8o8L2x8C0M4dVXXZdk/Mjv3X35FNBkCYyiI6f8BD8KLV+26kfnJJcIYtEI9i933ANx+Pm/vzjqgbEP3D9mkzPz7rvuk0iY/9d3Egrq26d/n932AIh8+MHSptUtdu7RC0i6hY1y6JyjcBIuJxsmWH2Xu3zyeNKas6DeQlpNDxaXngAyxl5Gw90fSEnan65Z/SkAomOHrlvCJ+ixrl12VhSAJR8t+37d/r0JNdSd0ejDtWs+g+SLygwcsF9+Su1IrqTrIcB6xfLVwugfcYqJ0jR1/N7ZNgjgk0icpJWZQZugxo1KJdjjU3E7DFo5Kb2eaCWRxBvQ7CNiCA2TBsz1OSPigbrO8qVVYZLgxWuuvj4nZXg87H9twSKpHpj2ygormwR7HdjvuEMTCRf616ZiiJ9PPfkcOMPiATxmF/4NuNFTVdlM9kQmT/FKGQcNRVhxJCWHVhd4LmMuN54VKXp6JOg2oCoh/Wmn/k71+YyX9kpXqqyoUaME4lgJgSNBbPegEA8GzUrQKKnhUtkc7sKoCX4sn+LFF2e+EtLcQb4EAfUygyY+8iSR2gVwRFkmLROTtEBSG/FQ/RnEtnUGrQ2c2AcxN176LrPVNO1pbJqnFTNWqxvvlyEHGcH63EBwaGkESDpkyOGqM5402qlEBjR4BauC+gE/NQouhUSUWB/4HG6QJWmSlqTDP1Ki6yLaQKhdGg6fzjAmzxNk9CuLP3UO3ipl/A83Dk0mfvaojNtUCdXJGm6FR8YTdC+KQM39WBNUlSzu/VVXNZcgLGMR/LvLzr3r6Om4Io6aAW50YzMgj2b/YoYU7Rbx/IP3P9YXh1sW+Ffc5deLxOd/SzA7h2YiU6c8olFo07qDuGgl/S9RmQMPODjHmQ5l2CSt/3w6z9jcipGvgUXzlJBbKD7txioZONKkKUyClkbiDWQl8yaL2zhKoOcRNwcUMayCYWnkoWRSAMfoazj04ndSgwA+VHfG8y999OEnjz36pHZh5s557csv/vHxkuVoZOtW7dF9a+i0iH/Xr/sCpNECD6Hx4GbIDuqvgw4cLBOn+sdJ6K+vv9qAtwRJ6Kx84HMpRq1b+zm4avd+e6FcpAG+QBETN4845gT8O/6hyfLiGHbUCDAu8kSVzj3ngg3/3ACmxBNUA6MOhIIgc+stfxHQHDviRGT1xeffaFSg4OCJAiigIOA48sRCipUcWkyKVvL4CZ4LeD+kTesHZI4OkbMBygVGI4fFb76bpA4CSQT5owlgo4kTpqIyf//bTBQ9+OCh6JyQC/XMF2aB56668k/oN+E4snri8Wc+/+xrWdujbpgqSIkOhLaLslbSbVbsiIeqw0q6l95/34MYMr3ocQMIXYdmDhl8mEdjDjH0SoZpQZ7Tpj4KERI57LrLbsiwtmXb/nsMdGLgk6Nlh/ZdVjLkCbooYCAJtAhVUg+gsUMPPRIVOPGEU1HnQQcNQQ3fXPSOps0aXouuOqM/odUCnp57dkYdLyYOedSIsnJcgRYhZ3T+9ts1wnP0DNgsZJSUT+iij5FFWeCor778JzocNXdi4LMY2gDfwajoTAwZUqJ6YAOg9qOPPIG/5PMnsNOcRyeAqzWBkRvGCx0YUUQyGDEBzUdZ77z9Qc5oTp4bUE3cPOAz6TepcT/9tLMCLlQQIcGleAK2wU/khsQoxaXQKrPBKZOnC5XQA1rbkDjXLTnK0L8IDV/DiBJooMuppOgJAj5hKPpNziqoEiR3iMZ1dBtPMMIQEHzAXvui3G++3hASozFfwCeL3ng74GI5YfwUcDIaIihEhU8+6XT8RD6zXp6Dbnn2medRGVQSYiZSCnlzwIeKpRlTDjmPGzvx5wR8N95wC4AAvYAZi45Gb4Kf8BNdhmZgerwya24pbetXMfwDOkirOtqpdQb9CIZA18jD0WN4hlz+YE2tvUjvcg3PBz4NoUAtRa+adm074TtGVIIJikYFUO7jf30acFZHX0hxG14HZCOlBJA6eo8n6Y+tcjH8ISV/sD4ahcyBSqqbIEPcrMqgCI/WmA+Nm7T3wP2FUDYjrGHC25QgljPUD56/9+5HAaWnFfQ6Muj0BsS0GNfo+utu8hmZWbhQR0dLecIGFKB67toHUO5SaljL+AUguaB6dCnTF5GmvRNbvS5nRICAgTfwBOCCeWtRInhw9EMORQwkQP94NPxG/jLxXcOQEJo5OeCTZ7TDYDkBJQiTvncqWqOJz1UMxyAIk/sdEBNrjOpj83BG7nrqDUyVFAPPiCtW13PjdegeK4FFVerUsZtcO5EzXkkxcJYaq66IGFjBpwm6pMsTjj/l9tvuUjPdWKFbSYd8LAxquMMjr7V0QscT1FM2uvW1ZpuuzS+9ODvJKIer6IW9ku6beB3gjkr27tVPHSLgQ1kARNW/jkEHAHBgGPx8YcbLPiUDILtyCOmzHFCFsgm7KBQ6Cpa3vn36n3TiaeAWLO1IhhadesqZmI82wREo6cR+uDkecOPhy9UfHSIHjGUMA76EgUUCxiUSy7nkMbziM0AL+gfrMb7j86+PPRVwcRWs4xPJkGA1QyfgicLVYMEGjie4ey4nbo0a2gKORd/KD1Lr93dSgwA+MG6GTmDoVjnSouOwnrvU5NHFAwfsB6EaCZ55+u+Sv9A74B5pkffe88BqOsdgqO78y70ap/rA5xFc7rj9bqzJyAp9+m3AhwTiDPSpRfkcdMvNd2huoHOXMe4LhhaiBDj+2mtuEPtika8PfJhyQGE5e6JpwEoMGxKvY4QiJEjF2zqqm8eA6SFVTs0Qm2cLKCukPGUT+CBIqrZ1jLOAh6NHjXM535CzRY+FT+LIengo/l7NEAyYPOAbvSvlFJXEDNEUSjJIlNBw88CninmMkqBSMKNMBmX607U33nD9zdCUhWXSNNczZJDF4AVajaVvfhvwKS6hch7z4HhVD9IB+gSohLGWUomm6XW1XXNMM3wV42KV0HcNJV73pz9jeVCG9YEv4G5sSEkQYIR/dRtBkpHAl9OncCPg0wKTpHc2VFSB6Sq6369hIC+PEw//Akeiek44d991Xy4rfAETiodzlTEZt04d5XBLAa+rDjY1zXvuvn8jiU+cs8fuA7DOLacyhEo2YeDew4YeBRDHi/KHVf5i6YC6eQ5iMEYYMowIUAO4ieIkYi9hLDIQ5HSVtXngw+Rq2aKNJBKT8XLUwxoL5AbwEttE9DvG1EvxgAvcoodiV3wH/8ufvY7B4jCOqBhqgkFEPTEZMaBaa8Wx7dt1hoSLlJB+tNrlarUZahDA93EcMQ3Dhmajc6HJ9+u7JxoG1QaaEUBQGgGEXvGBNpvAAb8/61wM3jqGDkUaTAwzbzcHo6Ll97eDDkG2SPltwAfwsrn5JfHe4Z4LXo8YYQWkqdK8WSsFCrbi6NuPTH8cq5A4u45ikUtXf2EZ1k/hl+ojthCJfb04pJXeimJXm9mvzKsPfGeecbawGPwEYAVzYIqKmyXzugxxIXyx6YtuMs64NuYhO0M+Wvj6Yj92hoMqIZk0N/H87wI+VRI/scZobUDNQ8b71NoTMaK9OseNI8c4jIrucydR2X4b8J31u3PS9MlDZXKhhyCYaFJhfNXJ4Jn99j3IpeCGtgvvBDSqj0NBCd9ROqaKiq4PfKgtFE9xi2SH7t121UE/lC9MfpcgpeJUZ0FegrFG5O1vUy7TZ4rHWepM7UICuCWwj7x3lHJwGLxnn70PECjkKoM8BSgmvdS1+Gko1cn3jRy9EfAhB2jW8v2S6LAyjnkH5oT0HXKPT1yh8XVi2zpoVCY3T5Lc4gyIzkbs+KXe05QEYztbAHw+YQtLoEIkSCPJtdHnvt5Nf75NvB3SnVYaADgwyc19pJEIr5XSplCfpms5cpNWjnHRYhMxSLumjMOQTi7jtajmuVpthhoE8KXjUIhAq6eefA6aKb6gI/723AtHHH50l849tJtgMoyShmQ1d0/RR5hjQwYftiLeu8HSqk6sDy5vLX6vR/eeIKTH5I8Y9gp99Lsz/xtyvknHIJPnGHUM6Nisphav462QkT9KeY6hsbR5ypFmDNuAG64SN2679U7Jdx4veXDrjXqagT1SVMn1BRM1V7dU7OwNpgFzYO6BWQGUFlUhj9vkqfgYR/oXvqD+CR6Wob3KZzX3/pAeoiheRJ1RMe0BQ5BZSaXJjbUDkRgIkx/NHHbUCF0DguJenT1fLUWTob84sRtpCYNoDj/6OHQaGqX1wGPARNQK3Ytlo5xRNFZRuNOcX8pwxPiJZVmwqN2cbl132X+/QQFFQqTcuUcvjDK+7Nl/b8BEm9YdHO4PYi1EGlRSUw7tNWhiso6RSCLG8rvwgktsCukukUWNbdumIxJASPSp7Ae8zyTXpaoS0BNjDflIwgI6X1gMOVo6Lz4jBj5YQw1XLBox3owEXmSIHCzKswEv8cHnp4xEi88H7h+Tiq8c+fqrDRgOjMVyBomx6oW8d+nwsJpxjNGxyxjcG2lQPXQL/tXucDmD4iF/LLEWj9FW0xcY+C7eW8+oQgH3oxXuDAyDnkQ/uwQjh0q31qQVjK6Wq0A+ZRgvHnlGPBusvzCjUU2rW9x80+0+419EVFCQMrd2Yu64FJAxFkA3PB988FC0CO9iti5n3Fxwi0ljsjrq6RBxFIEZaRxueiospsMoL2UMMIHcMBZYICX31FFzEmqviKMR299iYLgRNQjgCxi6Dh2Hhcjg4RpWeDQS442WiB0tnhBJ1XW4yKOFr8yauyoORGPQ0OmuO0cG1Be0FyvSxjy6G5AK+bGMkTsxIYUgpQxdG1GiRBq8iNXM5u4edEMsI6gDpt/HjM29hoGV8AokTUjg+A5RxaYpr4Ia+fGRFuoj93KHVyvgIeoJxUTwZ8VbsFbs/o3ckAbyDpqPlRzNB8r7PMdM8vgCGLSGQcB9apfbbrMjGoL2ZniLzTLGyErwHBCrKPjgjYVv+TzADRjLyyMoa20Uie+f//uLyE0aSilP0/B6RAd1MCJ6zKZck+T5LJRZDIqWGQCfSvyUEdvxLtJjXNBvIeVcVBhf0IcObxDWem5wFxJPevXsC0EeX1AB8Dq6EfMzzWtxdNiCPBX2Et8x510evEDjU6ehK7RAaoVLM5BvQDEW0x7jiz53Kf8iJSYVqlpd1RwStMKiGLxLBPy2lJGUVvAOJhQHEEwy1NK773yINVXBnbbbdiedD6D+M55/SZsJ6EmUqEBJKP0zhrRDPZEtel679ZqEpXEIhjqGX7coswiOc2PhMPAXJEFwkXjSYJAYNAS98dC4SSZNCNC3UyZPh9gulAf4IjFUXZUOuBEnHzLkcOC+TVt6tG7xm++WxHHFw/hIVPiV+XaLbpsGFZ8xMr4bS+54Fx0uzWMpD0YintrjOdZ+SLjS9yGsrI63I1EooOoTRvnt3Kk7soIAjrav5BUljXYqmfnCLDGVx3PwpQxr5lP9EvhibXO4iQEmwb+oksGQvVjj0Z8GdVvJyE4sqH4nNQjgsyjPC9Qx5LLAsHjqFDGYREQ7dQ2b1mpNITMOWSEpL8lTeZPbvel6FmFJGls4PHH3eOCYoVFIMpbzbZ61e3GAE4OmGwkeSto86zj+uJPP/v15Ic/mVnPPVeKMamXxBDOiDI/6Y0L6sYFCRewc4nCz36cPgN7KtV3IojQW9VaHFhsa0Sh2nNCSoHZleHiv58k4GrgWW2WLUiSo7rhDE800K89C2OE9NSVxLBmkT3PDXpkEjLil5ufmaprROCKu8HYcV8ajDCVetIjjeKUxLxLSKwY3qnKt1ih43PhXz+OLOi3Be1eEyA71spx0pnIDrmqVDP0UUq8xKaqrGiCFCFRWao6KdmiBZHEbVBkmGKhKnal81NUhT2zS3KDUXNLrKtFi6AFkpXdDnlmV0IZDTSvj3UyqgE3vIDuODSNOE+f4/27fruVQNSyJQwSh/0Nu1Zfw/izJVuLSiK41HpfVFO+uUivU2DTvIdLA+ZwXBn3jVK7Q+YUZL6uNuTpsRCY3KBy6bOMtIJRY143tz/SuQ0XYiG99S1JpcONgHC6Nzxwaxqu96mGNVznvM0jG9luadz6xO9c5NoMdVPAmAK2+GneHq6lJXaRf3z2vuPyaKL5dZ0uoQQDfz4Igi0FmwXqb/1eBCvQDKaBJg2DuJyWABeREhZgXLOan+XkRpPixYyb8bxtSAL4tJS3X/9v+LVCBtoRsnpVLs/lJSXK6zc1WyWj5aX5eFFBD2ozoukkqAN+WkrQP73u5phaoQJsn6dE5XfKnIzv2dJLO/n8A+CxulKW2wO2vPv3MgE/7ONpe+U7y4wjm+X/ldhby//pOkoLwY2klas73q8nWJe3IbMmuimZ1wJ1EbZNpM2jzs05bRdoLS/FcO/eXFnnt8tTPxObR/+blJg3fj97hWhE336KfmkLaJP5ApcTgPvvWbch/gH5mwOeS6bdwaLVLuskh1Mbt/1Y8ztEPZ68cCcorNuUz1MDJ54nKJteVjUjAZ/AwSn2uQdz8AmbF3pqlvJawPlQJPbXJXX8gPB4uf+eapEOwH5eEud+bo34UStE0apMMv+WkZWlLhvVnTT8/4AOj6+LR76TPPv1K0yP/L7Hp91v2xd8/FosHvJ4i/3nDJ1Qb3SvXke+k5ctWnXvOBS57D2KazINrW7bNT5kjh8eFTz/1N2DZgvlv1O9wh4ewwM0Lzr9YnlIiGetscsRzFPL4ftEbb+f/9UPIZFCDzRf9U5NDm5jNC7zfSR5tnh64f0z+X/+XqEEAn4xU3Xi10Zm9G+uqWu0FNzoCf2XWXP0VcJtWaZCg0U4l6fgmsDRv19XkkWaqPD0GsZDvl96VyIDVUsZiLg2vAho0yExBOwgej+FtOjCuoT+8DFD03IoDgidp2oZ/ZUGqOqt01TbNuKdSuEBVlc3W0XlWlVH9UQe0Rau3T8uYFONteLETRUSDkjJeA5SksY7FnQ6kFxAoc7UxQVNng0E7AiqJbr3rXNVAqaI2jYSUv8PgLqVxHBfthasrNFIu14+R945Sr6rEYt6Xqu+yPFC2Sz5adtbvzlHfhrwd2I2hTRVwCUlObEUkiQP/PvbokxF9GGSbYsXX0Ng0gLj9truUWBYeGHEV7cURgNUDGh1Z2+j5awsWaazVFXYcAl7GPXpLDXS5OAXcG3EpLYoh7fj2MllahPRd0WDZZNeQZjrIXM4VJi1+VCX1pEXLFY2Cwfg6YhItySaN6lW6ekPlBpwpSdrEOPH1ACaPaPH51Zf/tOPgzHo3RZshVUz7ErmmqVHq9iC2XvJofHfvPQ+kaYeEGqZij5Qgvupb/aZS6nPpTyFK/0TUIIDP5ybO0cOO1ZqJLsYYAJvwUE9CGtDtPXB/DfCM51/CzyOPGO7RFkyvy6hHPh5idEh8Dr3fwJoJBucR0+DLcceeJPQR4ghB+uy2h4pWMvy7z94HOASjptUt9t3nwCTNiFDQu+98GMYGVnhyyslniMUzvF0FT9AWsUWCoajOOP33EY2MxDQ9uvc8/LBhwpfWrdpjldbM8QjNhww5PGBkCyXu3Kk7kNondiusELI9ZvjxggzNk5NPOr1d206a1Xp+1JHHhIxnhfSoABpSwesZmzdrlWIMDE2t4Ucf14Q38mlbxyIco2N9LgZoHXLAz5179PKJJineLtiv755BbOMG4PNpGubGgtigg4aU8YI6j9uX6FWPDk/oBHVsFAe5EkZowiMlaphkZC10I7pl8MFDkebRR55QR+VWPr3oECzKGeALefbq2Rc99vlnX4ujlNKL3bCqq5qjD484/GhNcv/fgQ/UonlrmWpredCgywBbAyEEOfWUM20uA8JHVAmDpR5O8v5l1FxMi3zQD7LiVA5gmyTt6RyuKEMPPTKiRwRGSn2rkw2DgRWEUFHsso3vxx93slBSQ4PnaI66wqel3rEjTlQ/27HporoUpe+yc28Z0yEBWoqBcHnvnc2LPXMdDi5KcYEX8CUpTGDSdezQVXXbYfvGaGmH9l1UT/UVegDvBlwbVO7PghoE8GF4sFZjnNav+wIM4dITE3360ouzgSkYsG++3jB2zITdeu+O5xgDAB/SB7yQVHwjxqrjtdBIuZKOk3L7RTJ5pzp0aUTKgw4cfOklV+7Zf+8ojr3cc9c+8hZ6792P5CqIn8s+qcOQ4/tHH36iG+Y/ZpgsXbfct09/sAggTD5tqA9mhckABHirZYs2KxguZcyD41ENANaXX/zjwdEPWQxeAkYBYZYiwf77DcK7qIxPU37M82Y1tWiXphaeHPzbQ9u07qD1wKerFvS+ct5+DaESX5QP6rOS0aIgjcplbRX9wxN0AgVn4yFSoquhdaKrMROQAHIlylr85rshvcEdBtrE51uL30NueBc6KaYKMAiJy3gjOLqud69+KhSsD51ILkRIDLif9fIck+59UexNiTVmv30PWsOgm258CFDHODFzXl0ACQUpUQG0WliMbFH6n2+8FTVEPg9Pewz98MXn36h/fC4AWHhMWq7eN3K0Ov93Z/43gABt0RIitwFkhdE06Z+PhQENWcrQUhsB32233gk2wwIjFwU0PMNrWOUtv8fuA1YxMI9cUP507Y2oEgYUWT3/9xdRedQN/TBwwH54fcBe+4I9wBUYHawxGFYgsvyObLo6+Izsghy6dO6BbJEA78rhDNwOnoH6DwYGp6EsheRDAuSGHNDnGA7gEcTnM884G1/ko4Z5gbqF9HfCYr/jDk0E9/JzwMIDuVhuJEhw4w23pOkVpwVDUwDz4vw/XITvSxkmQMAXMuSMwyue5XyJT5R10omnSeO58opr8QQ1RD9glJOUhTea2g2WGgTwLXrj7Wuuvv7WW/4yetQ4cJtWMHDYM0//feYLs8B86NlixtvSqiIXFnzBi0igNTCkSxCAQyseFvCV9Nl26KzuxWEzbHr7Y3HLrZz4CxLNkMGHCQTlkISUw44aoYVXuODSC1qijRxxwChgQVXJoYsoalhHF27xt8t9xptvuv2Ky6+5+qrrVjDomyqDeaWpK1U3wyCaHo3y77n7fvC3TykALRVSiCK61nrUsCaMn4IMkc/sV+ZdftnV6C6pzJgMN1x/M/h73tzXk1T5kSEqib4VH2M6nXjCqUnG8gK+KC5mQPkFzcSUG/XAWIAOHiITL75MWoEkUD0s+4AbFG1T98EQYBkIqexjGlz3pz+jJvh84vFnsMAAgICtIdVAzFUvvhZ5NYMFYAGzKGOiFZh4aqlWLJ8+G6gq8nE55fAvskW54ASUqP75yx33APFvufkO1BANEd6hOCCmdGrFYgp5zcCdf7lXgIJC64drvuD8i1cxGIHGVL7D77z9ARggQ2fV66+7adrUR9FX115zAwgc4tC7UY1Fnqik4NWgAwPYCcuVmtOt6y4Q/bQCCVYEIjYjTbgUk+sYegDA98bCtyyK7WA5sQcyBIAiGYYYnKMtAnl3uXSbdbmzCe7SrFnPAJEe5WJUQ/3sM96awdAyEPDLGQ5eYVG0DPzjmw0+fWyS1KbRNAxxhr5iaDWajzYKIrGiA7Xr6F2LOevEEf+ffupvDiXKXK82cGoQwAfunDvnNaz/ADiFToOIN3nSw3+88FLManQoMBHySIZeLxgYedEjGVawHPAFVH8gsoE5FK7rU4ZLwgSQyzRmkbxEMUNOP+0sjVkO+KDQgSHAHBv+ucFjYBLIKRZtnaRZIwdM3Qx9xdYx7IpHN2mP244oSBqrgE85eIy58sqsuSBwrWJMgbkD3h6Pimm6io3QCsX+xIK/nrG5fEaRqd9RaMJKxgJxqfvgxRHHnIDeQP7I/IUZL6tKeAt11rsoUcAHOBPwgXelFvl0KwZPn3vOBUIHpYc4gLZMmTx9/EOT5RxmMwQAmomGo26AG4gYPoWvtYxaKrUIdcNwQCAFQco44fhTMI7qVbyICnt5wKdpg4c54HMYmszj2oDSpz/8VzcGvojh8FCQBB81CiAOaQuJMTSrGYtFEKyCUD2UDnSGXDZu7ER8mnRzrN+xDqMSrGFsBSDCY48+GXIXEp9PPvEs8gTUYmjQycgHjIpqIxnSq7H4RGM14i7dxSDxAezEIRCRDht6VIrq82aAD1+QP9YqtAJNQ1WdOCixx8UMo4xKKvKggE9j7THAH4Ru6Z4STr084JMYq25McsdG2o/D3RgkM+JQ3kiMbO+4/W6kx2zCrARroWIeo1GANy679Koc8Kn+aEsB+L4PoQcVBBHqBpZfLKr4AqSDcgTgw0hgckoRw/hhIimK50bAB96SnhUx1K3JqDt+HOoOI4pJItTAiEK78XgWkQM+hZCFUrmaQY3AIgA+ze1ljO2hyRBxswn5g12223Yn8AT0TfyF16GJ1Ac+fUHRKDfFkxPM4XQcqQUTFeDiUohbRod8PFEMEpfB5pJ0kMS8qt9RDiPwlNLpHQr43557oWl1CxRk0l8V82GnHYshxymEKtS3kNujAj7opArcgAoDLDCZgcUoBQqLIhR4RBY0BKVATlkdR/ETxCxnmM+cUCPVCSkx59E0FBowNFYZLVTwCnIIKDgH3G5D1/3+rHO9PODzuQWB5/WBD/9CJcc8hKxRH/jQRogbWDlMHiKhtoBgj1CiObyaIWrQyegl1Ar4rrFTbcFCnzDQLBJraRQBNTwuA+sZQQANRBGY5GnG4FnPyLiQK//62FMGrzfAeoD06Ew1Fj/RWIebDD6l11132Q3Mo4JQDSwh6HMpnqqtmwd8+AQna5cQY6pNZC+ONaKVHiO+hAE+NwI+zBHknOCRmpR9Lw/4pA3gRW3tQZYHHGsEUZCyVR1Qf/yEcgPZUMOH51hpcgwmJs8Bn9pSAL7vQ9p0AN9gvCP6J69niJv99xv08kuv4kn7dp0hYmAUwX+Y9oAbsT4kRKjDDmU3jERN05aQ17Cq77XnPuAerOrg721+swM4BjMB7ypesaYoMtShgcl42UcPOxbcA/6AVIWxB4oNPfRIMVPAbT6M+oC99sUr4ADMN8xkLOyoDOqMQrFEl/BuBBSkOYYmSD2HvrCKkdxLeHuAVlS0RUci+ImWrqaOdtedI/EdOUvXQ88A4iWIicC1yBZTDsv1H877oxoOuRivIM999j5AUwXNREMgm+j6C6A8ikadNZ2AKVDi8AWMq7gsaZ66qP5oNbgcr+viDqg8mEtff7XB5eknpJJPGfsbxSV5IIhsHR5x6DxxPUO0owJmfBMC3l3OmP5Y0jTTPEbukrYlwMLnOf99vsPQDCi0mKFcUNDgg4diRqHCGI6QZ1PoFiwPaR64q1z8i5yXMdqH0BmFQvhFl0KKRz0xZA+Ofkj/rqW9i45BAEZpxlzAT/AD6onWJXh0jmS/HXSI6lnOQ2pk+9yzM8AD6I00z5fLGYALjQWLatnA68gHSwWkvEOGHO7yCBttwVvIEPKaAAvMAEx3if4lPDNFSzFSqPNrCxYhQzBbLqq7y/N9tEJNQ8dKrDN51LOccciRCXjg/feWYNFazXNtMTlmDVjF4JE3qqpzDCTABAGSlvH60JB7u0mGoYV4Dp0dL3bs0HU57zDAPEJZ6FvoOuJbVBX8jCcRQ/W4lD9QBNanRHwc97OgBgF8GNqAIlUJD0nRfdAE9dChfYbNbTsrtgCw40Or3CIj0Qzc4zNsXJKbrAYtZk0aWIl3tb65cTAoTR48xxKNMfZ5OrzD9o213aZX/Diyk889FIeHiWZs/6z9rzTtWjRDhJUpxpLyeKoo2S3BK2j1RfAUxKa80k2QMhf8LkMbCNXTqWePKl7HtAnjMCFGHNDCjY0TVRAqgMU54HUKHvcEVRPtfKUZ/QkV9hgn2eAen0BHXYTpajAYTIbGekgJ+LBoyxLQKsJmVJUgDmzjczcq4EahaqLxMnlY4XGjUIk1CspcvZHg/r0XB/wwubeFZEBeN7aPk6ooiAziKCPKx+fSZdGGyYjvVMLrzWpqk1TAQ56zq1YO7T+wtgnyIh6bBrFVZsQjexUKNtCgqJJOHNpH31VndTt6xuZSh3FRc7T14cZujjmWM+PbqfzYRspikBiNLIAVa5VLdUHMo4Iy8f6vek+jJtYFw/iEOf1Ue9XVSl/KCD1iUQ091BR0bIbmBxZxyuJxkE1WN2lzEzCwoCqgT/yFZqoaZbw/QB2oQh0yrUYwx6sNnBoE8OWYQMwt9spNFbGLmEwsLr53Y7cHcRKGSlgpZkpzp9YnIKZoYQCe1mxUcTn8wkNo1vvuc2BupMUiuUlYSlOPiPe0ZbgFk6TVhUZaZbkEYo8QozzxIhKXMn6RqpGI4yAZNI8yaLrlE23L6LWmxEiJTyuOIiXuFCEBgA8sGNESQjMhwVsIxHlicbyOuS37sgzt9dQ0Ozbg0hxW/sWx11cO3y2CSG4sJJamuD2PzFFEih4CGdpdq2Mj7nyHvFUuZHxdYZl6CZ9AYeG7F+uzQm0NYhRbbtrceRDMKZ/cjFI+BiNcRdxwiGgnKBR2iBGa8Kk4DHKKhjtiCZuWevpM0RRR3IVeymWVolGUGhXQKk3lilVUeaV0qc7nKpBDN3WLnmgQ3TgMF5hTcKbmC9FCmtfYhLNHH3kCaoEgWAjiMRBkyOVHLwLpBGE+z2EkKJTQAADPJUiKqx2iVa756kMk0Kip+QbtUcRFwnF1rHDQYJwxjb4qLODT6zY5XLPDIy/l/vpZUIMAvgIVqEAF+k9SAfgKVKAC/eKoAHwFKlCBfnH08wA+P97gy/+rgZC2RbSrmNtGKVCBCtQw6ecBfMcfd/JRRx6jPfgGSL179fN5uBaFZTp0y09ToAIVqOHQzwP4Xpk1d+Hri/OfNxA68YRTq6ua27Ri87b4grsCFahAW4saBPB5ccQnWfzqjFx/WTTce+7ZGbNenlPOWCO52FP42aRxwqGBlcwXZPumMBsWrZNSjCaClIlSSzYosv6zeFOXLDAsGlgZ9PgxaBZnxVGDZMMhW4GQ1p7KXKp3zojhuGNPqm3Z1uJdpbJgKFCBCtSQqUEAX/t2nQcO2A+od/11N8ll6rNPv3r3nQ8/eP/jSy+5EvjywoyX57y6ALBSt3KtzIvSvEd1+bJVY8dMkMUWnq9Z/Wku+Ogeuw/4ZOlK+Y0Bqt5/b8nKFWs+/+zrptUt8HzpxyuOPGK4TKhW01917pzXbNqyjn9oMgQ3RR+wGEzl0/VfrqpbhzyvveYG/EQRMtfyeFs2/hp+9HGyE5bvUUHVLVCBGjg1CODrs9seBx04GLgzccLU9979SHAjVKpjSKUZz7+0jKGWBC5ufH8wEtx9130ZXpK7ksFR9FeCt1zbDLQ3+OChQMlceAlBp887uZEbsLWcXvRHHXnMMcOPx/NhR43AW7fe8hc5zCI32WfiXaCnDDXl+4k8u3fbFYUiq+bNWnn0RvrZWXIWqEC/QGoQwLfrLrsNOmgI8GL0qHFAIovRNTw6GwDOgDgvvTj71dnzLRruA4ksukl/8fk3kOAg5f35xlvHPDgeX9bycvt1az8HxkEShCj36CNPSC8WXHr005Zdex0dvJE/0iMrCI+zX5kHyRGvQCQEqK3hBc/4LoP7kfeOwneIe0A9fIEA+NGHnwAioV8fftiw6qrmSCaYLmi7BSpQA6cGAXydO3U/dsSJkJWgZkrVzQFfHSNWznxh1puL3oFY16RxQnGVgVNAHJOhCoCGXTr3QEo5aYV0dSpmQOYe3XsC1/KBD/IaIM9nuB6gm7y+gFkffrB0/ryFSDBk8GEAOOQsfMSTDu27oJJyY5KH2XIGNcGT0087S55PmTjOeH4bC1SgAjUcahDAZzDGrMOrUiC1QcTLAR/ABV9efulV/AXZ7YLzLwY2QTkdN3biXXeOBMYtfH3xJRdf4THyHfAIaaTkKuTkU08+p5hF+cC3gsFEjz/uZAh6eD794b/+7bkXALuf8roWiHtATO0A+vTAB7RB0PMZrxF/AVjxc97c17W3mOG9DYraFP2w214KVKAC/dTUIIAPQKZrKOQ37tGjO2ToAf1byitv+uy2h+5h8Ggy0rXLztkA3LbXslk7q9QpTSaGjTimT+/d5WGO10cccwJkSXxPlVX7TjbgR1RWadludSbrNJ6Ty6ClXvTHy2y67qPEVrXt9t3nwKDe9eyAPP2ETHfpJVf26tlX+eN5+3adjzryGI8XGJm8oMDfgit3Lc91XT80s5nnk+VlPz0Svjjuxgm+haI8yk9ToAIVKEsNAvg8BjF2ed+gTSO4MA6+ZjGuRppRTCLG8/AZJkghgLKKZ2kaoFbaxMxENb6X9qISj9EulVKuFLYTlDvptBX5QEwnSCRtHWhEDLhixDEqTIZ+CRkwQ188xrQQkAnsFMvEqXe7VcAgLhbNX5Sh8PrbKLQiMzCtIOG5aNsmZEPgXWD/i4R9BSpQgX5cahDAlyOP56FmPdc0P74o79vIctPJABASOWZVZbqjW7YJacv0/cAGZaUnI0gFjCKVT8LcHHlxDKVvIye+wFDJJEUKdvMT58g07RKn3PSro1S5620if1RShAROkALlp0kyCGh90TKg38jmK1ygAhVI1OCAT/JU7snmUS+bwHMTgQ/5zCq1o0QQVbbMT5P0szgCFRLyGb6knE1AiUUz5txPn1Yp9Z/kU07plnqeA8qNAHQjMhw7aVVbZjles5x/2WnXJ9RTZDi+ZWcjzeWnEebWFy1t242iND6hRP+L8t4qUIEKJGpYwOcw/GR9c5CQHh35Kf8ngWOaLiQds5lbdNelwzLuJqDKs7N7XgBHSE+ZxlbnxCaAT9pu7qe+bxJ0ciRlPCf3uQxg+Z22LJYdVHhutfHrBU+PrE5t/K8b7+552aZFGSsqS2yiDhYjSNevcDpd5jie5wUF4CtQgb6TtibwAY/+tYcVz9Uw9Fu1654uq015kZdIpbzaNu27WBADs5SV2gLXsJzqpFtppGzXDqocu9pJWUGws1e0evygtyafGCXtikQaKYvDdGhUp43I8xzHNVKZqPl2yaRZVlsS3Ne2WWgmyyw8j2qMahTUJIogCn746nWZ5PaO919uaVVNopFvZt6bdrJnbG+5ifJka9NLJ7NUhi+eAxnSrK5q0apNjyCoSrmuYVhlNe1at2qfFQAdZBZZWcrqvCkzjZaa6cCy3YpkWGFnbjs08+nYIypKi4qNkt+UVZQGZdWl6Rq3HFowct6m3O4e1NhNzW08/5yga490oth1zLLSyoRlRm4UuiXJqlrfvujENlVNIqj5pV4mFexUYdjvTTopslOhmU5b5o5l2Q1Ez6oAAfSdAg4WqED1aGsC378Rgc/3Q8vPtO3QvVnT5k2bNmtZ2zZIVdi5k02ksctc13Y8O5XOns8GfjngqWnFDovHHr126tAWpUVZmQfqIaSu7LFpWanlNUrhtarmJfbJYU1lceJor824sma1iShMpBtFEXAE8lpjJ+qcKfpixlnpRFGJGzVJumkrKg/K35m0T1Xy15DmyoyMaij8dbMSWba2lTUtmjZvXVPdtKq6edNWHQKGPleLCNZR9hW7EsAXOMWpKNipSaqFU/ThhCPrpg9vnip2U61SZva4I4vpVdVNHNeNylKp5KFRt9oSqOjGLbWdzqndOWpsoizDDXcqSSZDI2nvcO2ZPd59fkQbtwjFJRwjlaho5m9f9/jwCq9JyqgoT0alQW6zr7DrV6ACbUxbE/hyIOLmrDes0A6cVp07tevYrbZz6xZdayA9gf5HfbOqPdcI3JLIqXKsjOnbbYKid8YOWnN3+zWPn924ODItz0RC1w0cO+V7tU7YobFTURI0b5K4ubbNvR273962w9P9W3Vq7Ac7BO0Mb+9Ke+jOvVs1Kf/0tYvWv3TaBy/8sdo0jaC0UVlYbRctHb37w1cfFASeaRvA09B2U7Yd2jYQLelVQLQLy4KWXTq0bNOjXcddWnfpGIb4P3s5w/800/UtLyvbZiyvMrJ67dZy9cMHrHx430uPqfWSwMRUhWEiw2TgN48qW+7ktkz4aSs1Il07tmPPa1p3e6Bby8vbdclYVYFV0SLZ5A8tevfMVN12xuClMw5dPWv4ujeusku29fzQT5SfO7zHmnF77d+tyLPTKTOC3Odkz8NNxzOz4t6mjo8LVKBfLG1d4PuXAJXMXicUGUk/aTh26NZ27Ny9x9417bo379HDC8sAL56fvTsNwlzoeo5XlfDLGpUkm5mJN6ac+tnDXVbd13/pwyc0T25nBqZrV1hWOvJqTLcsTHl9i+1bO+59c8fOt3bu+mCL9IbbL7y3V7Bh1NAbutbe0q12YstO13XpHxmmFVqB1/i92RdnEmY6VQ14LXWDP59UXXdv72XThieMzG+sslKvprERBX7GpX2f7WUyUY3lRy06d27VoU+Hzv267NrXMD2Tqi4gJ/BC3w0M001nfD9dlTKty4Z1XTN5r3+M33/F9D+iDOjC0Nyzhzn4EpZVWuGxzTve2Xn3O9p2u7lTm1Gdww13nz/v9G53t205srbbXe1r7m1RPTRT44dBKQDd3m7MX45uBvBNu8kw5UaZxWN+t2HyoLceHZbKpBuVQoysCX0sG6HhZIXfgllMgQpUn7Ym8GkDHtPeMIJ5r73z6rzFCxa9/eLcxX9/YfbMmTPHTnhk3JTn5i16d/b8RbPmLnzplXmlhuvblQGU1KSZTv7qo8dPWDe6//ope62YckSqtMj3kxVWMgq2dUuKKoOU79SUeq4dJh07eUdNtzknDv3qgcO+GnfU12+Mmz39ug1TLtgw7uxrerSt2T60vZQDQa3U9720bxsZO+0ZTheraPmEgZ+OOWjFtL1bOUUZe6dUYBuWaXrpM8+9GFV6feFbM2a9MX/R0seffm7+nBfHjhu/4K1lz86cN/PVhQsWvjNn7uthlBWyAs9vbNp+SdHiUcO+nPrbryf0/OSRg1sWF0X2dl7YCMIduiH0gYBpO5XJOMlOpU0eatVhw60Xr5l46KdTz/tq7oS6CRetm3D0upEnnhDUuG5lYDmBVVZcYqRSzaE7B8b/a57M7LuLv3LCnl+M33XltEObFhdVWI3DEtsrgcyXhm4MbddzNnHmU6AC/WJpawIflEeXwPfirPlPz5j96uvv/e3l1+64768jR4258ooTn37m0TvvGT1y7LQZryycs/Dd52ctmPPaYkhJvmlXlO64cOqZyx7e/fNJ+66cOHTdlD2mX7vvQ9cc+Nilg8Zfs9+oS/evTBaV7GhhwkOFbBJF3bbZbmSXDusfOPrd+4/csOHDDd98tuS+4S+dtlfHHYt8tyKyytOlqEfgNrIi+9e10W+mXD7wo3H7rR4/YMnI/t88uc/b4w54e9KprZNFteEO553/h9mvvzf7jQ9enPXauEefuvP+aZPHT5jwwMVTp90/dtpToyb9deacxS/OefPV+W+9PHu+66Vc06hIbLtw3MmfTh2wbnzfL6YdvGLabx++at+Hr9ln3LUHTrxqyJnHHpxxm7jQnu0y3wojy7qsuvndLcs+erD/qqeu/fLrr1bMu3X5vQfe26tF60S0UxSkvch3UgmzkeeaLTOVtTsWrXri/FWPD107ud/aqX3XjD9w6cRh70z9Q8smRWnzv+zS0lRQCSm4IPEVqED1aWsCn+dkgQ9YNumRJ+94YMLoaU/MnL9o5qK506fc9sELf3xnxuXXnrff4kVLX5759j33PDh6zDP3j3lqu0TTFm7Rx389eO34I5c8OGDF5H3WTtlj/dgh6yfsvXRM13VT9/pq3J4nDNkj8orNdFngl9dst/1B6ej+7h1u26PphrGHrnnwkE/XvLTqk7c+H33I+geOubNNWFP8K+iDKasaSqNpNVk0/bSPJ3ZbO36vurE9l0zp9/nE/ddP7rlhYp/1D++zZOIxsx88e8zIW++8/6E/3zv1wSlPzJn/1qKFH51wRJ/35/xh8ujT3pz36uwF70185LG773vonlHTHpg6taS8eM9M0ZqpR66dNvifY/b4+J49Vkzcd+XY/hvGD/7HpN6fTO6xZOpJluc6jgcsc8OgQ8mO52Qy05t3XnDprsvH7j9/8kUbvvxkwZQRG8Yc8rez+t5Q3abMTTRJmUiaSVdVVThvTTluzfiu66cP3DCm14opey6Zsstn4w5YParj10/0W/HQYa9Nurg8aRmJFNA/v/MLVKBfMm194IPEt2vv/jNnvfbci/PvGzOmZYdO7TsO6Nqjb9M2nZt12L3tLn1mvDbvlTfnPjtrZs9e/aqc4quO6TPtgn5PX7Hno1f1f+ba/Z89N7V86t7rpv52zah+y8bvvX9Xw/ddzwvKrfTeqVZ3dB1wX8udRzftcmfzzG3trRV3Hrp6+jHrHrvu9rbb3NK5YmT7Xf7ctmefdLOsSpi2ykurvaRX4QRXHtHuwzGHrXio38qH+n82fe8PJ59xz0X9m1lFGeNXJSWJU866EELf/IVvnn/9Tc26du24866dO/Zu1rJ7u1327NC912vvfPjMC6+89MqCgf0GpUqbXX5st8eu3G/CJf0evbHPxD/1Xf/Y0XUT+6+Zvt/6SYNWThjSNlWUSBhelKmwo4t23e/2Zr0fatvvrg7d7+xW9ewxLb+5/4j3xx/5zdNn39O14qpurce37ntN177NTc/2yqOU5xlRtdO8acn2VxzZ5ZMnzlr10MA1k3dfMeGgNx4cMeXawS2Nooy5TeQ1LTHSwFYJ1wUqUIFEWxP4cka2kHeMpB+FlR/Ovffjl899b8apHy04d+Wc85Y/d+5niy55c/YoKGthutowLNO2ysNUxqpwooTjV1Sa2y6bMnDd1IHLx+68dNLhbewdvArfsyqjoBpilGMnM06yzCitahLc2Gvnz+87+YvRI944u/faCcdNH9rt4ChtJvzq4uJ04+LsJp/jJTOJFtU7Th59qWHa5b8p+vLh4csfHlA36aDENkV+GHhlLcKK2tJSSGZlrImxauaFdXMuXvTUictePHflrAvfn3H2ivnnXX3eCM+rSDdrkfCdZNIPXSsodbP5V1e7/rbv3Tv0s0n7rn50/9fHDG5jO0FQZdhZD+IaK2hlu2nDiEJ/4LbB7c1rvrrvlA0PnvT5XUM/GbnnbbU7t0edXKsyMM3iUjQ8Y5e55RWOWdS7l5FqWt7Cqfhi6nFfTxrwxuQDMk22L9k+TEReqVtu2c1ts8xzs+aBG3d+gQr0C6atCnwkGbIEdgR6f9ZV6+aOWDdvyMdzD/hq3qFfP3/Y2lmDLjq5awCkc8oElIEDmSdqbJVmrOqLjmi3fsL+66f1WTJ9+AEdjcCyDMeF+JbNE7PdtbNRA5J+85LMjW1rNtzzu5E9mt/dpvX7Fw1afdOpv2vfudypSVluMoCymS738SVz+SltP11wntHYiBLB4xcPfHdsrzWPHBe5acepMN1K1EFGLZ7jWp6/4aWTN8w+6R/zhv9j9mFfvXT4F7OGLXt5SK1flAqbJvx01pHOLbM82l2jwna6qVn01YO7rB23x+JJw1pZRdkW2WWZZNp1otLQ9cxEuZ29KuT0VKv7e7adPrDdzZ1rb+1au27CUX/qVNt0m18ZfjnycRjZBeIe2nrZCZ2m336o4RtVZtP3J57x5YQ93376GGj5KaeTmQkttyyw0qGFHsva3+T3fIEK9IulrQ98bj0nrbRv9u7Wpk11204dOnbv3rVD++49d9kNj5OBb/lm4BqmU501TPONpOE2a/Krugn7fzFuz0WTf+c72wSQ70zOcDcLTKDADv2SCPh4edtdxtaUH5tu36y0tpsVjmrT9fWjB13VvbNlWcCd4jALCk4Q/vdh/ZfNGbxs1qAlf7/Gi5q1DhuvnbL/1cM7uLZjNcmGOQDkhXbW/ATpTS9dUVvZZueWrTo169iua9t2HWq71nasbR75FaUJ23XtlG1XJKImQWVpCN3baOpu+/6k4+vGdnhj9IlZL10/EzoJoJLsnCHvFqeiKGm3284YX9XiyqY1HX+9Y3UydVzQdlTbdi+eNmiwb1TYTdPJikSYrUNUXbZq4b0bFl7y6QvHffDq6CC09+lufz5p38tP6pY9I054vu8D01O2iWp4DOWQ3+0FKtAvlrYu8P3LEY0GfVkqte3GbuMrLjr+tgt73nzxbhef1d9KZjezILN4dhnELqTJyoYOpnTF1FsGL3+g/6hrj0w1KbLMylK7MhuFxcxCidzFoiBT6QfVtn3XrgPbNPZDM9k4TPipsood7VHt+k+p7e7aO4ZGJfLMWCW2bRpmyaoZF6x9ZlhtebHvNtqx2Pxm8pDqYPvGdpPQbwoRz/Qi4B1BBPJpiLql3fRNF4+47ZLut1416KyTDg1STRs1Lva9rISYRTTfsPDFrgTGvfvY7+vG7XnR4QBZwzZ8M5l1I4P4BkxHi6pKKyCglSXMo6pqT+i+e3WJ51dWZ331DHOIXzOqqt2V7XsCptOWCfk0NNM1YfOmme2ee2DQstmnVzUtTZWWeWbRx/fskWq0fbGTCoKEYbTA66YXZuPWQFAtmLMUqED1aOsC38ZEuc+HitqqtsbzSlKpvOAlVnUyKoH+2CZR/Ob0obUl22+cQB4gdAvJipDJoGNZS4URrZ+musQ5tLpLhx0cE9qz56bNrOoKQOm7a3rJO5PKPTMEYthNhh85KOWUZKU8e+NSQFYyiPzsDlq7ti1tG3iXV9usqFWW8CpbmkUfTTnr6D6Bk9k44sD/+MAlvTI71bq8RX4m/ZyyE3sPaF7qpSwsD1nwDcszVYHXMio654QBTtI2AadNfnP2Kcd6ZjFd+rJGy/n5FKhABRI1SOCjq0FpSaPSkp02ShBYZVaQAEg1t53h+6Wd4kR+JgIpoUnKDMutbCyWjYCvwk1VbpNokciG6kNuKNRx0sg84zZKub+uKa+0TSsMPMcozQbJsn3P3oSq6DsZyG6B5zerqXYdy0iW5qeBzAW5sNzeoYNT1MLfzgkqN0pQz/k3dBMuKpyfSY3htgzTUISzNck+iRKml2xcWuZubzcpSgWVhuOXZ/VpI/J0UJ4Ve/PzKVCBCiRqWMCXo1xQz42eZ/35Pdfy7dAJfOiz6ZpNvMtPnSogh1LeMu7+O/Y5jmcFgeF5cgEG9gEpsnthycg1yxNJJ5WqgYZoW3olqy3mF+TQd800smEOsgXFl6D/Wxovu9uIIuwgDZSO/Kr8NLncFHE6/68Sy/LSkEqzteXC4Ed+ReBVZsqqmhQnoTW7hu97ZSAri5vZGFyF2AQFKtBm6P8DjGljZyFCjbsAAAAASUVORK5CYII=>