Otimização por Evolução Diferencial aplicada em Controle Preditivo baseado em Modelo

Apesar de amplamente utilizados com resultados satisfatórios, sistemas lineares de controle podem não ser adequados para processos complexos e não lineares. Dessa forma, algoritmos não lineares de otimização se fazem necessários. Neste trabalho, apresenta-se um estudo comparativo entre o uso de um controlador preditivo com otimização por Evolução Diferencial e os controladores lineares PID e DMC. Resultados obtidos por meio de simulações numéricas demonstram vantagens do uso do algoritmo evolutivo que justificam seu maior custo computacional

Incidence and risk factors for Preeclampsia in a cohort of healthy nulliparous pregnant women: a nested case-control study

The objective of this study is to determine the incidence, socio-demographic and clinical risk factors for preeclampsia and associated maternal and perinatal adverse outcomes. This is a nested case-control derived from the multicentre cohort study Preterm SAMBA, in five different centres in Brazil, with nulliparous healthy pregnant women. Clinical data were prospectively collected, and risk factors were assessed comparatively between PE cases and controls using risk ratio (RR) (95% CI) plus multivariate analysis. Complete data were available for 1,165 participants. The incidence of preeclampsia was 7.5%. Body mass index determined at the first medical visit and diastolic blood pressure over 75 mmHg at 20 weeks of gestation were independently associated with the occurrence of preeclampsia. Women with preeclampsia sustained a higher incidence of adverse maternal outcomes, including C-section (3.5 fold), preterm birth below 34 weeks of gestation (3.9 fold) and hospital stay longer than 5 days (5.8 fold) than controls. They also had worse perinatal outcomes, including lower birthweight (a mean 379 g lower), small for gestational age babies (RR 2.45 [1.52-3.95]), 5-minute Apgar score less than 7 (RR 2.11 [1.03-4.29]), NICU admission (RR 3.34 [1.61-6.9]) and Neonatal Near Miss (3.65 [1.78-7.49]). Weight gain rate per week, obesity and diastolic blood pressure equal to or higher than 75 mmHg at 20 weeks of gestation were shown to be associated with preeclampsia. Preeclampsia also led to a higher number of C-sections and prolonged hospital admission, in addition to worse neonatal outcomes9CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ401636/2013-5Bill and Melinda Gates FoundationGates Foundation [OPP1107597]; CNPqNational Council for Scientific and Technological Development (CNPq) [401636/2013-5

Análise do Desempenho de Algoritmos Clássicos de Busca de Caminho em Ambiente de Navegação

A literatura apresenta uma grande variedade de algoritmos de buscade caminho em ambientes de navegação. Esses algoritmos são amplamenteutilizados em jogos, robôs, GPS e diversas outras aplicações. Este trabalhovisa analisar como os algoritmos clássicos Busca Cega em Largura, Busca comCusto Uniforme, A* e Weighted A* desempenham em um cenário com diversoscustos. Apresenta-se uma análise nos objetivos de minimização do custo docaminho e de quantidade de nós expandidos

A Multistage Simulated Annealing for Protein Structure Prediction Using Rosetta

The Protein Structure Prediction problem is currently one of the most challenging open problems in Bioinformatics being a NP-Complete problem. In this work, a Multistage Simulated Annealing (MSA) employing different levels of detail for the potential energy function is applied using the Rosetta framework. The backbone and centroid coordinates model is employed being the side chains repacked at the end of the process. Experiments were conducted using four wellknown proteins with different degrees of complexity, namely: 1ZDD; 1CRN; 1ENH; 1AIL. The results obtained showed that MSA is able to find better energy function values in all four proteins, and better RMSD in three of them

An ant colony based system for data mining: applications to medical data

This work describes an algorithm for rulediscovery in databases called AntMiner. Theobjective of the algorithm is the extraction ofclassification rules to be applied to unseen dataas a decision aid. The algorithm used todiscover such rules is inspired in the behaviorof a real ant colony, as well as some conceptsof information theory and data mining.AntMiner was applied to medical databases toobtain classification rule

An Ant Colony Algorithm for Classification Rule Discovery

Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Hueristic Approach will be a repository for the applications of these techniques in the area of data mining

Mining comprehensible rules from data with an ant colony algorithm

This work describes an algorithm for data mining called Ant-Miner (Ant Colony-based Data Miner).The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: (a) Ant-Miner is competitive with CN2 with respect to predictive accuracy; and (b) The rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2