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Resumo Objetivo Analisar a prevalência de excesso de peso corporal em adolescentes e fatores associados. Métodos Estudo transversal conduzido com 635 adolescentes de ambos os sexos, com idade entre 10 e 16 anos, em escolas públicas estaduais. A coleta de dados foi realizada no segundo semestre de 2016 por uma equipe multidisciplinar. Coletou-se dados demográficos, da história familiar, desenvolvimento púbere e hábitos comportamentais. Foi feita a classificação dos adolescentes, conforme o estado nutricional. Utilizou-se a análise bivariada e a regressão múltipla hierarquizada de Poisson, o que possibilitou verificar a associação do excesso de peso corporal com as demais variáveis, bem como a razão de prevalência entre elas. Resultados A prevalência de excesso de peso foi de 32,8% dos adolescentes, associada a história familiar de dislipidemia (p=0,003; RP=1,474; IC95%= 1,139-1,907) e a ingestão de álcool (p=0,044; RP=1,430; IC95%= 1,009-2,028). As demais variáveis neste estudo não foram associadas ao EPC. Conclusão Verificou-se altas prevalências de excesso de peso corporal entres os adolescentes, associadas à história familiar de dislipidemia e ingestão de álcool.

The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approach based on the IMM filter and retention models to determine the TPM adaptively and automatically with relatively low computational cost and no need for complex operations or storing the measurement history. The proposed method is compared to the traditional IMM filter, IMM with Bayesian Network (BNs) and a state-of-the-art Adaptive TPM-based parallel IMM (ATPM-PIMM) algorithm. The experiments were carried out in an artificial numerical example as well as in two real-world health monitoring applications: the PRONOSTIA platform and the Li-ion batteries data set provided by NASA. The Retention Interacting Multiple Model (R-IMM) results indicate that a better prediction performance can be obtained when the TPM is not properly adjusted or not precisely known.

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -whenever the corresponding local fuzzy models change -from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.

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