Testing Nelder-Mead based repulsion algorithms for multiple roots of nonlinear systems via a two-level factorial design of experiments

This paper addresses the challenging task of computing multiple roots of a system of nonlinear equations. A repulsion algorithm that invokes the Nelder-Mead (N-M) local search method and uses a penalty-type merit function based on the error function, known as 'erf', is presented. In the N-M algorithm context, different strategies are proposed to enhance the quality of the solutions and improve the overall efficiency. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm. The main goal of this paper is to use a two-level factorial design of experiments to analyze the statistical significance of the observed differences in selected performance criteria produced when testing different strategies in the N-M based repulsion algorithm.

Bootstrap performance profiles in stochastic algorithms assessment

Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.

A radiofrequency identification system for the 60 GHz ISM band

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Identification of duplicates of cassava accessions sampled on the North Region of Brazil using microsatellite markers

Duplicates are common in germplasm banks and their identification is needed to facilitate germplasm bank management and to reduce maintenance costs. The aim of this work was to identify duplicates of cassava from a germplasm bank in Eastern Amazon, which had been previously characterized both morphological and agronomically. In order to be genotyped with 15 microsatellite loci, 36 accessions were selected. These accessions were classified into 13 groups of similar morpho-agronomical characteristics. All loci were polymorphic, and 75 alleles were identified, with an average of five alleles per loci and H E = 0.66. There were determined 34 pairs of genotypes with identical multiloci profiles and the probability of genetic identity was 1.1x10-12 with probability of exclusion of 99.9999%. Among these duplicates, there are accessions sampled on different years and places, but with different names and accessions with the same name sampled in different places and years. The study identified genotypes that are grown in different places and that have been maintained over the years by local farmers.

Identification and characterization of deforestation hot spots in Venezuela using MODIS satellite images

Land cover changes over time as a result of human activity. Nowadays deforestation may be considered one of the main environmental problems. The objective of this study was to identify and characterize changes to forest cover in Venezuela between 2005-2010. Two maps of deforestation hot spots were generated on the basis of MODIS data, one using digital techniques and the other by means of direct visual interpretation by experts. These maps were validated against Landsat ETM+ images. The accuracy of the map obtained digitally was estimated by means of a confusion matrix. The overall accuracy of the maps obtained digitally was 92.5%. Expert opinions regarding the hot spots permitted the causes of deforestation to be identified. The main processes of deforestation were concentrated to the north of the Orinoco River, where 8.63% of the country's forests are located. In this region, some places registered an average annual forest change rate of between 0.72% and 2.95%, above the forest change rate for the country as a whole (0.61%). The main causes of deforestation for the period evaluated were agricultural and livestock activities (47.9%), particularly family subsistence farming and extensive farming which were carried out in 94% of the identified areas.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Segmentação do tecido tumoral cerebral em imagens de ressonância magnética

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Use of anatomical root markers for species identification in Catasetum (Orchidaceae) at the Portal da Amazônia region, MT, Brazil

Orchidaceae is one of the largest botanical families, with approximately 780 genera. Among the genera of this family, Catasetum currently comprises 166 species. The aim of this study was to characterize the root anatomy of eight Catasetum species, verifying adaptations related to epiphytic habit and looking for features that could contribute to the vegetative identification of such species. The species studied were collected at the Portal da Amazônia region, Mato Grosso state, Brazil. The roots were fixed in FAA 50, cut freehand, and stained with astra blue/fuchsin. Illustrations were obtained with a digital camera mounted on a photomicroscope. The roots of examined species shared most of the anatomical characteristics observed in other species of the Catasetum genus, and many of them have adaptations to the epiphytic habit, such as presence of secondary thickening in the velamen cell walls, exodermis, cortex, and medulla. Some specific features were recognized as having taxonomic application, such as composition of the thickening of velamen cell walls, ornamentation of absorbent root-hair walls, presence of tilosomes, composition and thickening of the cortical cell walls, presence of mycorrhizae, endodermal cell wall thickening, the number of protoxylem poles, and composition and thickening of the central area of the vascular cylinder. These traits are important anatomical markers to separate the species within the genus and to generate a dichotomous identification key for Catasetum. Thus, providing a useful tool for taxonomists of this group

Updated List and identification key of 219 tree species of white sand forests of the Allpahuayo Mishana National Reserve, Loreto, Peru

White sand forests, although low in nutrients, are characterized not only by several endemic species of plants but also by several monodominant species. In general, plants in this forest have noticeably thin stems. The aim of this work was to elaborate a parallel dichotomous key for the identification of Angiosperm tree species occurring on white sand forests at the Allpahuayo Mishana National Reserve, Loreto, Peru. We compiled a list of species from several publications in order to have the most comprehensive list of species that occur on white sand forest. We found 219 species of Angiosperm, the more abundant species were Pachira brevipes (26.27%), Caraipa utilis (17.90%), Dicymbe uaiparuensis (13.27%), Dendropanax umbellatus (3.28%), Sloanea spathulata (2.52%), Ternstroemia klugiana (2.30%), Haploclathra cordata (2.28%), Parkia igneiflora (1.20%), Emmotum floribundum (1.06%), Ravenia biramosa (1.04%) among others. Most species of white sand forests can be distinguished using characteristics of stems, branches and leaves. This key is very useful for the development of floristic inventories and related projects on white sand forests from Allpahuayo Mishana National Reserve.

Retroanálise de parâmetros geomecânicos baseada em algoritmos de otimização

Dissertação de mestrado integrado em Engenharia Civil

Alcohol Use Disorder Identification Test (AUDIT): explorando seus parâmetros psicométricos

OBJETIVO: O presente estudo teve como objetivo conhecer evidências de validade e precisão do Alcohol Use Disorder Identification Test (AUDIT). MÉTODOS: Contou-se com uma amostra de conveniência (não probabilística) de 547 estudantes universitários de Fortaleza (CE), com idade média de 21,6 anos (dp = 4,86; amplitude de 18 a 53), a maioria do sexo masculino (51,5%), solteira (91,4%) e católica (62,5%). Os participantes responderam ao AUDIT e a perguntas demográficas. Procurando conhecer a estrutura fatorial, além de estatísticas descritivas, realizou-se uma Análise de Componentes Principais. Adicionalmente, a fim de avaliar a precisão do instrumento, efetuaram-se cálculos de alfa de Cronbach (consistência interna), correlações de r de Pearson e coeficiente de correlação intraclasse - ICC (precisão teste-reteste). RESULTADOS: De acordo com a análise de componentes principais com rotação oblimin, a estrutura bifatorial do AUDIT mostrou-se coerente, com todos os itens apresentando saturações satisfatórias, superior a |0,40|, tendo o Fator 1 explicado 47,5% da variância total com alfa de 0,84 e o Fator 2 explicado 11,6% da variância total com alfa de 0,69. Os resultados do teste-reteste indicaram correlação forte entre os dados obtidos na primeira (t1) e segunda (t2) aplicação (r tt = 0,94, p < 0,01), sem diferença significativa de médias nos dois tempos (m t1 = 0,37, dp = 0,49; m t2 = 0,34, dp2= 0,47; p > 0,05), com ICC satisfatório (0,96). CONCLUSÕES: Os achados apoiaram a adequação psicométrica do AUDIT, com as análises fatoriais exploratórias apontando como mais satisfatória a estrutura com dois fatores, bem como atestaram sua boa estabilidade temporal.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Parameter identification of the fermentative production of fructo-oligosaccharides by Aureobasidium pullulans

In this study, a mathematical model for the production of Fructo-oligosaccharides (FOS) by Aureobasidium pullulans is developed. This model contains a relatively large set of unknown parameters, and the identification problem is analyzed using simulation data, as well as experimental data. Batch experiments were not sufficiently informative to uniquely estimate all the unknown parameters, thus, additional experiments have to be achieved in fed-batch mode to supplement the missing information. © 2015 IEEE.