Pt/Carbon materials as Bi-Functional catalysts for N-decane hydroisomerization

The activity and selectivity of bi-functional carbon-supported platinum catalysts for the hydroisomerization of n-alkanes have been studied. The influence of the properties of the carbon support on the performance of the catalysts were investigated by incorporating the metallic function on a series of carbons with varied porosity (microporous: GL-50 from Norit, and mesoporous: CMK-3) and surface chemistry (modified by wet oxidation). The characterization results achieved with H-2 chemisorption and TEM showed differences in surface metal concentrations and metal-support interactions depending on the support composition. The highest metal dispersion was achieved after oxidation of the carbon matrix in concentrated nitric acid, suggesting that the presence of surface functional sites distributed in inner and outer surface favors a homogeneous metal distribution. On the other hand, the higher hydrogenating activity of the catalysts prepared with the mesoporous carbon pointed out that a fast molecular traffic inside the pores plays an important role in the catalysts performance. For n-decane hydroisomerization of long chain n-alkanes, higher activities were obtained for the catalysts with an optimized acidity and metal dispersion along with adequate porosity, pointing out the importance of the support properties in the performance of the catalysts.

Sensitive bi-enzymatic biosensor based on polyphenoloxidases–gold nanoparticles–chitosan hybrid film–graphene doped carbon paste electrode for carbamates detection

A bi-enzymatic biosensor (LACC–TYR–AuNPs–CS/GPE) for carbamates was prepared in a single step by electrodeposition of a hybrid film onto a graphene doped carbon paste electrode (GPE). Graphene and the gold nanoparticles (AuNPs) were morphologically characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, dynamic light scattering and laser Doppler velocimetry. The electrodeposited hybrid film was composed of laccase (LACC), tyrosinase (TYR) and AuNPs entrapped in a chitosan (CS) polymeric matrix. Experimental parameters, namely graphene redox state, AuNPs:CS ratio, enzymes concentration, pH and inhibition time were evaluated. LACC–TYR–AuNPs–CS/GPE exhibited an improved Michaelis–Menten kinetic constant (26.9 ± 0.5 M) when compared with LACC–AuNPs–CS/GPE (37.8 ± 0.2 M) and TYR–AuNPs–CS/GPE (52.3 ± 0.4 M). Using 4-aminophenol as substrate at pH 5.5, the device presented wide linear ranges, low detection limits (1.68×10− 9 ± 1.18×10− 10 – 2.15×10− 7 ± 3.41×10− 9 M), high accuracy, sensitivity (1.13×106 ± 8.11×104 – 2.19×108 ± 2.51×107 %inhibition M− 1), repeatability (1.2–5.8% RSD), reproducibility (3.2–6.5% RSD) and stability (ca. twenty days) to determine carbaryl, formetanate hydrochloride, propoxur and ziram in citrus fruits based on their inhibitory capacity on the polyphenoloxidases activity. Recoveries at two fortified levels ranged from 93.8 ± 0.3% (lemon) to 97.8 ± 0.3% (orange). Glucose, citric acid and ascorbic acid do not interfere significantly in the electroanalysis. The proposed electroanalytical procedure can be a promising tool for food safety control.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Predicting Xerostomia induced by IMRT treatments: A logistic regression approach

Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments’ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”

Double coverage and demand for health care: Evidence from quantile regression

An individual experiences double coverage when he bene ts from more than one health insurance plan at the same time. This paper examines the impact of such supplementary insurance on the demand for health care services. Its novelty is that within the context of count data modelling and without imposing restrictive parametric assumptions, the analysis is carried out for di¤erent points of the conditional distribution, not only for its mean location. Results indicate that moral hazard is present across the whole outcome distribution for both public and private second layers of health insurance coverage but with greater magnitude in the latter group. By looking at di¤erent points we unveil that stronger double coverage e¤ects are smaller for high levels of usage. We use data for Portugal, taking advantage of particular features of the public and private protection schemes on top of the statutory National Health Service. By exploring the last Portuguese Health Survey, we were able to evaluate their impacts on the consumption of doctor visi

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Automated segmentation, tracking and evaluation of bacteria in microscopy images

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

An Open Source BI Approach: Concept Proof Tracking Fleet

É possível assistir nos dias de hoje, a um processo tecnológico evolutivo acentuado por toda a parte do globo. No caso das empresas, quer as pequenas, médias ou de grandes dimensões, estão cada vez mais dependentes dos sistemas informatizados para realizar os seus processos de negócio, e consequentemente à geração de informação referente aos negócios e onde, muitas das vezes, os dados não têm qualquer relacionamento entre si. A maioria dos sistemas convencionais informáticos não são projetados para gerir e armazenar informações estratégicas, impossibilitando assim que esta sirva de apoio como recurso estratégico. Portanto, as decisões são tomadas com base na experiência dos administradores, quando poderiam serem baseadas em factos históricos armazenados pelos diversos sistemas. Genericamente, as organizações possuem muitos dados, mas na maioria dos casos extraem pouca informação, o que é um problema em termos de mercados competitivos. Como as organizações procuram evoluir e superar a concorrência nas tomadas de decisão, surge neste contexto o termo Business Intelligence(BI). A GisGeo Information Systems é uma empresa que desenvolve software baseado em SIG (sistemas de informação geográfica) recorrendo a uma filosofia de ferramentas open-source. O seu principal produto baseia-se na localização geográfica dos vários tipos de viaturas, na recolha de dados, e consequentemente a sua análise (quilómetros percorridos, duração de uma viagem entre dois pontos definidos, consumo de combustível, etc.). Neste âmbito surge o tema deste projeto que tem objetivo de dar uma perspetiva diferente aos dados existentes, cruzando os conceitos BI com o sistema implementado na empresa de acordo com a sua filosofia. Neste projeto são abordados alguns dos conceitos mais importantes adjacentes a BI como, por exemplo, modelo dimensional, data Warehouse, o processo ETL e OLAP, seguindo a metodologia de Ralph Kimball. São também estudadas algumas das principais ferramentas open-source existentes no mercado, assim como quais as suas vantagens/desvantagens relativamente entre elas. Em conclusão, é então apresentada a solução desenvolvida de acordo com os critérios enumerados pela empresa como prova de conceito da aplicabilidade da área Business Intelligence ao ramo de Sistemas de informação Geográfica (SIG), recorrendo a uma ferramenta open-source que suporte visualização dos dados através de dashboards.

Desenvolvimento e análise de um limitador de corrente indutivo baseado em fita supercondutora Bi-2223

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Abordagem do Léxico Toponímico Municipal do Cuito da Província do Bié: Caso de Bairros, Comunas, Embalas e Aldeias

Este estudo, sob o tema “Abordagem do Léxico Toponímico Municipal do Cuito da Província do Bié: Caso de Bairros, Comunas, Embalas e Aldeias”, teve os seguintes objectivos: identificar e inventariar os topónimos do município do Cuito e harmonizar a sua ortografia; analisar os topónimos que apresentam variações e mudanças ortográficas, e propor a sua harmonização ortográfica, assim como os gentílicos; descrever a origem etimológica, a formação lexical, a pronúncia/transcrição fonética e a motivação dos topónimos. A metodologia de trabalho foi baseada na pesquisa bibliográfica e de campo, que foi concretizada através de entrevista aos regedores, aos sobas e aos anciãos, bem como através de conversas informais. Portanto, o objectivo principal foi propor a harmonização ortográfica e criar uma base de dados de topónimos do município do Cuito.