781 resultados para social-proximity urban vehicular networks


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'The Princeton Companion to Applied Mathematics' is an introduction to applied mathematics for students, teachers, and professionals. This article is for the "Application Areas" part of the book, which comprises articles on connections between applied mathematics and other disciplines.

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Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

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The paper analyses the emergence of group-specific attitudes and beliefs about tax compliance when individuals interact in a social network. It develops a model in which taxpayers possess a range of individual characteristics – including attitude to risk, potential for success in self-employment, and the weight attached to the social custom for honesty – and make an occupational choice based on these characteristics. Occupations differ in the possibility for evading tax. The social network determines which taxpayers are linked, and information about auditing and compliance is transmitted at meetings between linked taxpayers. Using agent-based simulations, the analysis demonstrates how attitudes and beliefs endogenously emerge that differ across sub-groups of the population. Compliance behaviour is different across occupational groups, and this is reinforced by the development of group-specific attitudes and beliefs. Taxpayers self-select into occupations according to the degree of risk aversion, the subjective probability of audit is sustained above the objective probability, and the weight attached to the social custom differs across occupations. These factors combine to lead to compliance levels that differ across occupations.

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We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.

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Results from two studies on longitudinal friendship networks are presented, exploring the impact of a gratitude intervention on positive and negative affect dynamics in a social network. The gratitude intervention had been previously shown to increase positive affect and decrease negative affect in an individual but dynamic group effects have not been considered. In the first study the intervention was administered to the whole network. In the second study two social networks are considered and in each only a subset of individuals, initially low/high in negative affect respectively received the intervention as `agents of change'. Data was analyzed using stochastic actor based modelling techniques to identify resulting network changes, impact on positive and negative affect and potential contagion of mood within the group. The first study found a group level increase in positive and a decrease in negative affect. Homophily was detected with regard to positive and negative affect but no evidence of contagion was found. The network itself became more volatile along with a fall in rate of change of negative affect. Centrality measures indicated that the best broadcasters were the individuals with the least negative affect levels at the beginning of the study. In the second study, the positive and negative affect levels for the whole group depended on the initial levels of negative affect of the intervention recipients. There was evidence of positive affect contagion in the group where intervention recipients had low initial level of negative affect and contagion in negative affect for the group where recipients had initially high level of negative affect.

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BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.

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Sociedades pós-modernas caracterizam-se pela transição de economias baseadas em ativos tangíveis para economias de conhecimento, onde indivíduos vivenciam uma imprescindível conectividade, mas ao mesmo tempo, experimentam um enfraquecimento das estruturas sociais, que tem generado uma crescente necessidade de se criar bases cognitivas e afetivas para a vida (Rheingold, 1992; Wasko & Farah, 2005; Arvidsson, 2008). Nesse cenário se desenvolve o fenômeno das redes sociais virtuais, agregando milhões de pessoas que compartilham mensagens de texto, imagens e vídeos todos os dias (Nielsen, 2012) fazendo com que organizações privadas foquem cada vez mais seus investimentos para acompanhar as novas tendências (McWilliam, 2000; Reichheld & Schefter, 2000; Yoo, Suh & Lee, 2002; Arvidsson, 2008). Consequentemente, uma das mais importantes questões que vem ganhando importância no meio academico e entre profissionais da área é justamente: por que as pessoas compartilham conhecimento online? (Monge, Fulk, Kalman, Flanigan, Parnassa & Rumsey, 1998; Lin, 2001) Por meio de uma metodologia de estudo de caso conduzida no Brasil e na França, este estudo objetiva produzir uma relevante revisão teórica acerca do tema, trazendo novas idéias de diferentes contextos, e propondo um modelo para avaliar as principais motivações que conduzem indivíduos a compartilhar conhecimento em redes sociais virtuais. Essas razões foram estruturadas em cinco dimensões: capital estrutural, cognitivo e relacional, motivações pessoais e razões monetárias (Nahapiet & Ghoshal, 1998; Wasko & Faraj, 2005; Chiu et al, 2006). As evidências sugerem que o processo de participar e compartilhar conhecimento em redes sociais virtuais é resultado de uma complexa combinação de motivações de orientação pessoal e coletiva, que parecem variar pouco de acordo com os diferentes objetivos e contextos dessas comunidades, onde as razões financeiras parecem ser secundárias.

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Negócios sociais, empresas autossuficientes com objetivos principalmente sociais estão a surgir e a mudar o cenário económico mundial. No Brasil, o campo de Empreendedorismo Social promete ajudar a resolver os vários problemas sociais do país, mas tal promessa depende do desenvolvimento de um ecossistema de suporte. No entanto, a pesquisa desenvolvida no tópico ainda é limitada, especialmente quando considerando pesquisa em contextos macro como estruturas de suporte ao negócio. O presente estudo explora o ecossistema de suporte aos negócios sociais no Brasil, oferecendo uma análise qualitativa preliminar da eficácia da rede de suporte existente para os negócios sociais, de acordo com as perceções de empreendedores sociais e prestadores de suporte. O estudo é desenvolvido baseando-se no modelo conceptual de Turrini et al. (2010) sobre os determinantes de eficácia de redes a fim de facilitar a captura de padrões. Desta forma, cada variável de eficácia de redes é desenvolvida no contexto da presente investigação e as principais conclusões relativas ao ecossistema de suporte para negócios sociais no Brasil são destacadas. Os resultados sugerem um rápido crescimento da disponibilidade de suporte para negócios sociais, mas indicam que estes serviços ainda são em número limitado e concentrados no Sudeste do país. Adicionalmente, os serviços de suporte são percecionados pelos empreendedores sociais como serviços de alta qualidade e embora se observe um sentimento generalizado de que a colaboração entre organizações de suporte tem sido importante para a construção do campo, os resultados indicam que um maior nível de interação e formalização entre prestadores de suporte levaria a maiores níveis de criação de sinergias e potenciaria a construção do ecossistema. Por último, é observado um sentimento generalizado de que o crescente nível de recursos financeiros, consciência pública e apoio do governo ao campo impactarão positivamente o desenvolvimento do ecossistema.

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Rogram relying on sociological interface between Economic Sociology, Sociology of Moral Theory of Socialization and Social Stratification, this dissertation research makes use of theoretical contributions Luic Boltanski, Charles Taylor, Axel Honneth, Pierre Bourdieu and Bernard Lahire to problematize the generally about the physical and symbolic production and social reproduction of the type of "economic ethics" predominant in the new petite bourgeoisie Brazilian. In other words, the goal is to explain and analyze the objective conditions (economic needs and moral grammar) and intersubjective (modes of socialization and social networks) and update the social genesis and contextual transcontextual beliefs, biases, inclinations and cultural regularities observed the economic behavior of individual profiles for the fractions of the urban petty bourgeoisie and commercial upward Natal / RN. With regard to methodological strategies adopted in data collection will be conducted qualitative interviews (semistructured) and ethnographic notes. In turn, the analytical treatment of the collected empirical content is based on the approach dispositionalist (Pierre Bourdieu, Loïc Wacquant and Bernard Lahire) that emphasizes the study of the past embedded agents and the different contexts of incorporation / activation / inhibition of "provisions" individual cultural

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The relationship between malnutrition and social support was first suggested in the mid-1990s. Despite its plausibility, no empirical studies aimed at obtaining evidence of this association could be located. The goal of the present study was to investigate such evidence. A case-control study was carried out including 101 malnourished children (weight-for-age National Center for Health Statistics/WHO 5th percentile) aged 12-23 months, who were compared with 200 well-nourished children with regard to exposure to a series of factors related to their social support system. Univariate and multiple logistic regressions were carried out, odds ratios being adjusted for per capita family income, mother's schooling, and number of children. The presence of an interaction between income and social support variables was also tested. Absence of a partner living with the mother increased risk of malnutrition (odds ratio 2.4 (95 % CI 1.19, 4.89)), even after adjustment for per capita family income, mother's schooling, and number of children. The lack of economic support during adverse situations accounted for a very high risk of malnutrition (odds ratio 10.1 (95 % CI 3.48, 29.13)) among low-income children, but had no effect on children of higher-income families. Results indicate that receiving economic support is an efficient risk modulator for malnutrition among low-income children. In addition, it was shown that the absence of a partner living with the mother is an important risk factor for malnutrition, with an effect independent from per capita family income, mother's schooling, and number of children.