Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.

Tempos, movimentos e qualidade da operação de colheita mecanizada de soja em função do formato de talhões

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Electrochemical oxidation of landfill leachate in a flow reactor: optimization using response surface methodology

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The influence of handrim design on the contact forces on hands'surface: a preliminary study

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A relação das tendinopatias do manguito rotador com as atividades cotidianas: uma contribuição para o design ergonômico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de método por cromatografia líquida bidimensional abrange para documentação química de extratos vegetais e estudos metabolômicos

Pós-graduação em Química - IQ

Design emocional e análise observacional: inter-relações entre produto e usuário

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Reguladores vegetais e nutrientes minerais no metabolismo de plantas de tomateiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Design e semântica: investigação de técnicas estatísticas para auxílio no projeto de produto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influência do design de instrumentos de escrita manual na percepção e no desempenho da atividade por pessoas com sem rizartrose

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Design gráfico ergonômico: método para verificação de níveis de usabilidade de fontes tipográficas para texto em suportes impressos e digitais

Pós-graduação em Design - FAAC

Parâmetros nutricionais e metabólicos de novilhos nelore recriados em pastos de diferentes alturas e doses de suplemento

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

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

A novel HS-SBSE system coupled with gas chromatography and mass spectrometry for the analysis of organochlorine pesticides in water samples

A methodology to analyze organochlorine pesticides (OCPs) in water samples has been accomplished by using headspace stir bar sorptive extraction (HS-SBSE). The bars were in house coated with a thick film of PDMS in order to properly work in the headspace mode. Sampling was done by a novel HS-SBSE system whereas the analysis was performed by capillary GC coupled mass spectrometric detection (HS-SBSE-GC-MS). The extraction optimization, using different experimental parameters has been established by a standard equilibrium time of 120 min at 85 degrees C. A mixture of ACN/toluene as back extraction solvent promoted a good performance to remove the OCPs sorbed in the bar. Reproducibility between 2.1 and 14.8% and linearity between 0.96 and 1.0 were obtained for pesticides spiked in a linear range between 5 and 17 ng/g in water samples during the bar evaluation.

Clinical trial design in non-invasive brain stimulation psychiatric research

Major depressive disorder (MDD) trials - investigating either non-pharmacological or pharmacological interventions - have shown mixed results. Many reasons explain this heterogeneity, but one that stands out is the trial design due to specific challenges in the field. We aimed therefore to review the methodology of non-invasive brain stimulation (NIBS) trials and provide a framework to improve clinical trial design. We performed a systematic review for randomized, controlled MDD trials whose intervention was transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) in MEDLINE and other databases from April 2002 to April 2008. We created an unstructured checklist based on CONSORT guidelines to extract items such as power analysis, sham method, blinding assessment, allocation concealment, operational criteria used for MDD, definition of refractory depression and primary study hypotheses. Thirty-one studies were included. We found that the main methodological issues can be divided in to three groups: (1) issues related to phase II/small trials, (2) issues related to MDD trials and, (3) specific issues of NIBS studies. Taken together, they can threaten study validity and lead to inconclusive results. Feasible solutions include: estimating the sample size a priori; measuring the degree of refractoriness of the subjects; specifying the primary hypothesis and statistical tests; controlling predictor variables through stratification randomization methods or using strict eligibility criteria; adjusting the study design to the target population; using adaptive designs and exploring NIBS efficacy employing biological markers. In conclusion, our study summarizes the main methodological issues of NIBS trials and proposes a number of alternatives to manage them. Copyright (C) 2011 John Wiley & Sons, Ltd.