Integration of chaos theory and mathematical models in building simulation: Part II: Conceptual frameworks

Current mathematical models in building research have been limited in most studies to linear dynamics systems. A literature review of past studies investigating chaos theory approaches in building simulation models suggests that as a basis chaos model is valid and can handle the increasing complexity of building systems that have dynamic interactions among all the distributed and hierarchical systems on the one hand, and the environment and occupants on the other. The review also identifies the paucity of literature and the need for a suitable methodology of linking chaos theory to mathematical models in building design and management studies. This study is broadly divided into two parts and presented in two companion papers. Part (I), published in the previous issue, reviews the current state of the chaos theory models as a starting point for establishing theories that can be effectively applied to building simulation models. Part (II) develop conceptual frameworks that approach current model methodologies from the theoretical perspective provided by chaos theory, with a focus on the key concepts and their potential to help to better understand the nonlinear dynamic nature of built environment systems. Case studies are also presented which demonstrate the potential usefulness of chaos theory driven models in a wide variety of leading areas of building research. This study distills the fundamental properties and the most relevant characteristics of chaos theory essential to (1) building simulation scientists and designers (2) initiating a dialogue between scientists and engineers, and (3) stimulating future research on a wide range of issues involved in designing and managing building environmental systems.

Hierarchical analysis of production efficiency in a coastal trawl fishery

We present, pedagogically, the Bayesian approach to composed error models under alternative, hierarchical characterizations; demonstrate, briefly, the Bayesian approach to model comparison using recent advances in Markov Chain Monte Carlo (MCMC) methods; and illustrate, empirically, the value of these techniques to natural resource economics and coastal fisheries management, in particular. The Bayesian approach to fisheries efficiency analysis is interesting for at least three reasons. First, it is a robust and highly flexible alternative to commonly applied, frequentist procedures, which dominate the literature. Second,the Bayesian approach is extremely simple to implement, requiring only a modest addition to most natural-resource economist tool-kits. Third, despite its attractions, applications of Bayesian methodology in coastal fisheries management are few.

A multilevel model for the study of breastfeeding determinants in Brazil

The benefits of breastfeeding for the children`s health have been highlighted in many studies. The innovative aspect of the present study lies in its use of a multilevel model, a technique that has rarely been applied to studies on breastfeeding. The data reported were collected from a larger study, the Family Budget Survey-Pesquisa de Orcamentos Familiares, carried out between 2002 and 2003 in Brazil that involved a sample of 48 470 households. A representative national sample of 1477 infants aged 0-6 months was used. The statistical analysis was performed using a multilevel model, with two levels grouped by region. In Brazil, breastfeeding prevalence was 58%. The factors that bore a negative influence on breastfeeding were over four residents living in the same household [odds ratio (OR) = 0.68, 90% confidence interval (CI) = 0.51-0.89] and mothers aged 30 years or more (OR = 0.68, 90% CI = 0.53-0.89). The factors that positively influenced breastfeeding were the following: higher socio-economic levels (OR = 1.37, 90% CI = 1.01-1.88), families with over two infants under 5 years (OR = 1.25, 90% CI = 1.00-1.58) and being a resident in rural areas (OR = 1.25, 90% CI = 1.00-1.58). Although majority of the mothers was aware of the value of maternal milk and breastfed their babies, the prevalence of breastfeeding remains lower than the rate advised by the World Health Organization, and the number of residents living in the same household along with mothers aged 30 years or older were both factors associated with early cessation of infant breastfeeding before 6 months.

Predator-prey models: an application for the plankton dynamics of lake Geneva

In this paper we present a hierarchical Bayesian analysis for a predator-prey model applied to ecology considering the use of Markov Chain Monte Carlo methods. We consider the introduction of a random effect in the model and the presence of a covariate vector. An application to ecology is considered using a data set related to the plankton dynamics of lake Geneva for the year 1990. We also discuss some aspects of discrimination of the proposed models.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss-Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.

Os efeitos da concentração regional no desempenho das empresas: uma abordagem multinível

O presente trabalho tem por objetivo avaliar o impacto das concentrações regionais no desempenho organizacional das empresas brasileiras com ênfase no setor serviços. Com o intuito de atingir este objetivo realizou-se uma comparação entre o desempenho organizacional das firmas localizadas em áreas de concentração geográficas e aquelas situadas fora destas áreas. Além disso, procurou-se contrastar o efeito da concentração regional sobre o desempenho das empresas de serviços com as empresas do setor industrial. A revisão literária evidenciou a existência de vantagens para empresas concentradas regionalmente, o que levou à principal hipótese deste trabalho, de que tais vantagens ocasionariam melhor desempenho das firmas. Desta forma, buscou-se averiguar a existência de uma relação entre o desempenho organizacional e a localização geográfica das empresas de serviços regionalmente concentradas. O trabalho de identificação das concentrações regionais foi realizado adaptando-se os critérios utilizados no setor industrial para o setor serviços, a partir dos dados de número de estabelecimentos e de funcionários, obtidos através da base dados da Relação Anual de Informações Sociais (RAIS). O desempenho organizacional foi mensurado por dois indicadores: lucratividade e o crescimento de vendas. A fonte de dados de desempenho utilizada foi a base de microdados das seguintes pesquisas do Instituto Brasileiro de Geografia e Estatística (IBGE): Pesquisa Industrial Anual (PIA) e Pesquisa Anual de Serviços (PAS). A amostra utilizada incluiu 78.789 observações de prestadoras de serviços e 22.460 observações de empresas do setor industrial, entre 2001 e 2005. Os resultados foram produzidos por meio da aplicação dos modelos hierárquicos ou modelos multiníveis. Os resultados revelaram um efeito positivo sobre o crescimento das empresas situadas em áreas de concentração regional (tanto do setor serviços quanto da indústria), porém não foram encontradas evidências de maior lucratividade das mesmas. As conclusões deste trabalho contribuem para a tomada de decisão dos gestores, ao avaliar se deverão ou não situar seu empreendimento em uma área de concentração regional. Além de apresentar implicações para as políticas públicas, pois a constatação de um efeito positivo sobre o crescimento das firmas em determinadas concentrações pode direcionar políticas de incentivo, com o objetivo de estimular a formação de tais concentrações em determinadas localidades para desenvolvimento regional.

A moderação da concentração setorial no efeito do ambiente institucional na performance empresarial

O presente estudo avança a compreensão da performance empresarial ao propor que condições dos setores, especificamente a concentração setorial, moderam a relação entre instituições e desempenho das firmas. Já é sabido que o ambiente institucional impacta o desempenho das firmas (Makino, Isobe, & Chan, 2004) e que as reformas pró-mercado contribuem para que esse efeito seja positivo, tanto para firmas domésticas como estrangeiras (Cuervo-Cazurra & Dau, 2009). A explicação desse efeito é baseada na economia dos custos de transação (Coase, 1937; Commons, 1934). Contudo, não se sabe se o efeito é o mesmo para todos os setores e se fatores moderam a relação. Esta tese contou com 230.222 observações referentes a 10.903 empresas em 64 países em um intervalo de 23 anos coletados em diferentes bancos de dados. Foi testada a interação de seis variáveis institucionais com o índice Herfindahl-Hirschman (HHI) para três variáveis dependentes diferentes: retorno sobre ativos (ROA), retorno sobre patrimônio líquido (ROE) e crescimento de vendas composto de três anos. Duas estratégias empíricas foram utilizadas: modelos com efeitos fixos e modelos hierárquicos (multinível). Os resultados confirmaram a hipótese de que a interação do HHI é significante e negativa com quatro variáveis institucionais: voice and accountability, efetividade do governo, qualidade regulatória e controle da corrupção. Concentração setorial modera o efeito do ambiente institucional na performance empresarial. Em contextos onde as instituições são sólidas, a força de agentes como sindicatos, associações, imprensa e consumidor assume poder de barganha, refreando o poder das empresas e o oportunismo. Regras legais, direito comum e instituições tendem a limitar o poder unilateral em relações contratuais de todos os tipos, independe da fonte do poder (Macneil, 1980). Observou-se adicionalmente que a proteção ao oportunismo se dá principalmente por meio das instituições informais, como a proteção à democracia, direitos do consumidor e controle da corrupção. Ao propiciar poder aos outros agentes que interagem com as empresas, instituições fortes garantem o enforcement de compromissos contratuais, em particular os contratos sociais (Argyres & Liebeskind, 1999). Como implicações, essa tese propõe que estratégias de expansão dentro do setor, aquisição de participação de mercado e fusões e aquisições dentro do setor são mais adequadas em ambientes institucionais mais fracos que em ambientes fortes. Empresas que possuem alta participação de mercado devem reconhecer o impacto negativo que podem sofrer em seu desempenho a partir de melhorias institucionais. Finalmente, o estudo reforça a importância do reconhecimento por parte de governos de que setores e firmas se beneficiam de forma desigual das mudanças institucionais. O conhecimento prévio desses impactos pode servir de direcionamento para a formulação de políticas públicas justas e eficientes. As principais limitações estão relacionadas à base de dados, exclusivamente composta de empresas com capital aberto, a forma pela qual a classificação de algumas empresas diversificadas foi feita e o fato dessa tese não investigar diretamente o poder de barganha nem ao menos o oportunismo, mas somente o poder moderador da concentração setorial no efeito das instituições no desempenho.

Human growth, biological maturation, motor performance and contextual factors in Madeira children

The central aims of this study were: (1) to construct age- and gender-specific percentiles for motor coordination (MC), (2) to analyze the change, stability, and prediction of MC, (3) to investigate the relationship between motor performance and body fatness, and (4) to evaluate the relationships between skeletal maturation and fundamental motor skills (FMS) and MC. The data collected was from the ‘Healthy Growth of Madeira Children Study’ and from the ‘Madeira Child Growth Study’. In these studies, MC, FMS, skeletal age, growth characteristics, motor performance, physical activity, socioeconomic status, and geographical area were assessed/measured. Generalized additive models for location, scale and shape, mixed between-within subjects ANOVA, multilevel models, and hierarchical regression (blocks) were some of the statistical procedures used in the analyses. Scores on walking backwards and moving sideways improved with age. It was also found that boys performed better than girls on moving sideways. Normal-weight children outperformed obese peers in almost all gross MC tests. Inter-age correlations were calculated to be between 0.15 and 0.60. Age was associated with a better performance in catching, scramble, speed run, standing long jump, balance, and tennis ball throwing. Body mass index was positively associated with scramble and speed run, and negatively related to the standing long jump. Physical activity was negatively associated with scramble. Semi-urban children displayed better catching skills relative to their urban peers. The standardized residual of skeletal age on chronological age (SAsr) and its interaction with stature and/or body mass accounted for the maximum of 7.0% of variance in FMS and MC over that attributed to body size per se. SAsr alone accounted for a maximum of 9.0% variance in FMS and MC over that attributed to body size per se and interactions between SAsr and body size. This study demonstrates the need to promote FMS, MC, motor performance, and physical activity in children.

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

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

Class of self-dual models in three dimensions

In the present paper we introduce a hierarchical class of self-dual models in three dimensions, inspired in the original self-dual theory of Towsend-Pilch-Nieuwenhuizen. The basic strategy is to explore the powerful property of the duality transformations in order to generate a new field. The generalized propagator can be written in terms of the primitive one (first order), and also the respective order and disorder correlation functions. Some conclusions about the charge screening and magnetic flux were established. ©1999 The American Physical Society.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Visualization of Protein Folding Funnels in Lattice Models

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