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”

Angiostrongylus costaricensis: first record of its occurrence in the State of Espirito Santo, Brazil, and a review of its geographic distribution

Abdominal angiostrongyliasis is a parasitic disease caused by Angiostrongylus costaricensis, a metastrongylid nematode with wide geographic distribution, occurring from the United States to Argentina. In Brazil, the disease has been reported from the States of Rio Grande do Sul, Santa Catarina, Paraná, São Paulo, Federal District of Brasilia and Minas Gerais. We report here a case of abdominal angiostrongyliasis in a 9-year-old girl, from Itatiba, State of Espirito Santo, Brazil, submitted to exploratory laparotomy for acute abdomen. Extensive inflammatory lesions of terminal ileum and cecum, with perforations of the first, were present, and ileocecal resection was performed. The pathological picture was characterized by transmural inflammatory granulomatous reaction, extensive eosinophilic infiltration, eosinophilic vasculitis and the presence of worms within a mesenteric artery branch, with histological features of metastrongylid nematodes. This case report contributes to a better knowledge of the geographic distribution of this parasite in Brazil, suggesting that abdominal angiostrongyliasis may represent a disease of medical importance, more than a rarity of academic interest.

Scorpion sting epidemiology in Montes municipality of the State of Sucre, Venezuela: geographic distribution

Scorpion stings were surveyed in the Montes Municipality of the State of Sucre, Venezuela, aiming to extend the information on these poisonous accidents by characterizing their geographic distribution. From 1980 to 1990, 184 cases of scorpion stings were recorded with an incidence rate of 38.6 cases per 10,000 inhabitants. The locality of San Fernando presented the highest incidence (68.3(0)/000) of poisonous accidents. The highest percentages of severe cases were recorded in the towns of Arenas (27%), San Lorenzo (21%), and Cocollar (19%), which are located at the foot of the Turimiquire Mountains. This region is a dispersion area of scorpions of the Tityus genus. Our results show that this region of the State of Sucre is endemic for scorpion stings which are an important public health problem.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Paracoccidioidomycosis in children in the state of Rio de Janeiro (Brazil): Geographic distribution and the study of a "reservarea"

Thirty six cases of acute disseminated paracoccidioidomycosis in 3 to 12 year-old children, natives of the state of Rio de Janeiro, were seen in the period 1981-1996. All patients were residents in the rural region of 15 counties, scattered on the Southwestern part of this state. The rural region of two neighboring counties, where 16 cases (44.4%) occurred, was visited. It exhibited the environmental conditions that are considered favorable to the survival of P. brasiliensis. The most important of these conditions, abundant watercourses and autochthonous forest, are distributed on well defined and limited areas, in which the dwellings are also localized. Probably, a careful epidemiological study of forthcoming cases of the disease in children may facilitate the search for the micro-niche of the fungus.

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

Analysis of genetic relatedness between populations of Aedes aegypti from different geographic regions of São Paulo state, Brazil

RAPD markers have been used for the analysis of genetic differentiation of Aedes aegypti, because they allow the study of genetic relationships among populations. The aim of this study was to identify populations in different geographic regions of the São Paulo State in order to understand the infestation pattern of A. aegypti. The dendrogram constructed with the combined data set of the RAPD patterns showed that the mosquitoes were segregated into two major clusters. Mosquitoes from the Western region of the São Paulo State constituted one cluster and the other was composed of mosquitoes from a laboratory strain and from a coastal city, where the largest Latin American port is located. These data are in agreement with the report on the infestation in the São Paulo State. The genetic proximity was greater between mosquitoes whose geographic origin was closer. However, mosquitoes from the coastal city were genetically closer to laboratory-reared mosquitoes than to field-collected mosquitoes from the São Paulo State. The origin of the infestation in this place remains unclear, but certainly it is related to mosquitoes of origins different from those that infested the West and North region of the State in the 80's.

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.

Regional change in the Algarve: A Geographic Information System approach

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

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.

Public Participation geographic information systems in developing countries: lessons from Uganda on the impacts of interactive screens PPGIS and community perceptions of space

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.