1000 resultados para Distribuição binomial


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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O presente estudo cefalométrico retrospectivo teve como objetivo avaliar a influência do padrão esquelético sagital na determinação do padrão esquelético vertical da face (Tipo Facial) em indivíduos com diferentes más oclusões, que procuraram a UMESP para tratamento ortodôntico nos últimos 10 anos. Para isso foram selecionadas as telerradiografias iniciais de 59 pacientes, com idade média de 16 anos e 7 meses variando entre 11 e 25 anos. Estes pacientes foram selecionados após a análise facial subjetiva de 1600 documentações, resultando em 3 grupos. Grupo 1 Padrão I facial; Grupo 2 - Padrão II; e, Grupo 3 - Padrão III. Após esta divisão, foi comparado se a determinação do tipo facial é diferente nas medidas angulares SN.GoGn e SN.Gn. Para testar essa hipótese, utilizou-se uma regressão logística com erros distribuídos de acordo com uma distribuição binomial. Para observar a probabilidade de uma congruência entre SN.Gn e SN.GoGn utilizou-se uma regressão logística individual para cada Padrão Facial. Observou-se que a probabilidade de uma congruência entre SN.Gn e SN.GoGn no Padrão I é relativamente alta (70%), mas para os Padrões II e III essa congruência é relativamente baixa - Padrão II (46%) e Padrão III (37%). O Padrão esquelético sagital da face (Padrão I, II e III) influencia na determinação do Tipo Facial. Utilizar a grandeza SN.Gn parece não ser apropriado para a determinação do Padrão esquelético vertical da face, em virtude do ponto Gn sofrer importantes deslocamentos nos diferentes Padrões Faciais.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant (p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

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It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

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The delay stochastic simulation algorithm (DSSA) by Barrio et al. [Plos Comput. Biol.2, 117–E (2006)] was developed to simulate delayed processes in cell biology in the presence of intrinsic noise, that is, when there are small-to-moderate numbers of certain key molecules present in a chemical reaction system. These delayed processes can faithfully represent complex interactions and mechanisms that imply a number of spatiotemporal processes often not explicitly modeled such as transcription and translation, basic in the modeling of cell signaling pathways. However, for systems with widely varying reaction rate constants or large numbers of molecules, the simulation time steps of both the stochastic simulation algorithm (SSA) and the DSSA can become very small causing considerable computational overheads. In order to overcome the limit of small step sizes, various τ-leap strategies have been suggested for improving computational performance of the SSA. In this paper, we present a binomial τ- DSSA method that extends the τ-leap idea to the delay setting and avoids drawing insufficient numbers of reactions, a common shortcoming of existing binomial τ-leap methods that becomes evident when dealing with complex chemical interactions. The resulting inaccuracies are most evident in the delayed case, even when considering reaction products as potential reactants within the same time step in which they are produced. Moreover, we extend the framework to account for multicellular systems with different degrees of intercellular communication. We apply these ideas to two important genetic regulatory models, namely, the hes1 gene, implicated as a molecular clock, and a Her1/Her 7 model for coupled oscillating cells.

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Consultoria Legislativa - Área XI - Meio Ambiente e Direito Ambiental, Organização Territorial, Desenvolvimento Urbano e Regional.

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Consultoria Legislativa - Área XII - Recursos Minerais, Hídricos e Energéticos.

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Consultoria Legislativa - Área XII - Recursos Minerais, Hídrico e Energéticos.

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Consultoria Legislativa - Área XIX - Ciência Política, Sociologia Política, História, Relações Internacionais.