Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences
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Percorrer Matemática e Estatística | Comunicações em congressos, conferências e seminários / Communications in congresses, conferences por Objetivos de Desenvolvimento Sustentável (ODS) "03:Saúde de Qualidade"
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- Comparison of discriminant analysis methods: Application to occupational exposure to particulate matterPublication . Ramos, Maria do Rosário; Carolino, E.; Viegas, Carla; Viegas, SandraHealth effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 μm, 0.5 μm, 1 μm, 2.5 μm, 5 μm and 10 μm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 μm is the parameter that better discriminates industries.
- Spatial and multivariate statistics in assessing water quality in the North SeaPublication . Ody, Christopher; Ramos, Maria do Rosário; Carolino, Elisabete Teresa Mata Almeida; Gervasi , O.; Murgante , B.; Garau , C.; Taniar, D.; Rocha , A. M. A. C.; Lago , M. N. FaginasThe Southern North Sea region plays a vital role for both the economy and society of the surrounding countries. Analyzing the quality of your water is a critical process that involves an assessment of physical, chemical, and biological parameters, essential to guarantee environmental sustainability and the health of local communities and marine ecosystems. Using Multivariate and Spatial Statistics methods, this study seeks to identify spatial patterns and autocorrelations to assess water quality in that region. The data set used was taken on a scientific cruise carried out in December 2020 aboard the RV Meteor vessel, led by a team of German researchers. The raw data went through pretreatment guided by the Data Quality Control protocol of SeaDataNet, an international oceanography project aimed at making European maritime data available. Spike and gradient tests were performed, in addition to data standardization and imputation through inverse distance weighting interpolation. For a better understanding of the scientific area, the data were aggregated by zones for certain analyses, and were sometimes considered globally. An exploratory spatial data analysis (ESDA) was carried out in order to summarize its main characteristics. A reduction in the dimensionality of the original data was carried out through principal component analysis as an auxiliary tool for spatial analysis. The Spatial autocorrelation is analyzed by calculating global and local Moran’s I Statistics. The outcomes indicate a significant spatial autocorrelation for all variables considered in the freshwater areas and a notable range flattening of the variables in the open sea areas, which possibly caused the lack of significant spatial autocorrelation in those areas.
- Two-way MANCOVA: an application to public healthPublication . Ramos, Maria do Rosário; Carolino, E.; Oliveira, T.; Bicho, M.The aim of this work is to use the MANCOVA model to study the influence of the phenotype of an enzyme – Acid phosphatase – and a genetic factor – Haptoglobin genotype – on two dependent variables - Activity of Acid Phosphatase (ACP1) and the Body Mass Index (BMI). Therefore it's used a general linear model, namely a multivariate analysis of covariance (Two-way MANCOVA). The covariate is the age of the subject. This covariate works as control variable for the independent factors, serving to reduce the error term in the model. The main results showed that only the ACP1 phenotype has a significant effect on the activity of ACP1 and the covariate has a significant effect in both dependent variables. The univariate analysis showed that ACP1 phenotype accounts for about 12.5% of the variability in the activity of ACP1. In respect to this covariate it can be seen that accounts for about 4.6% of the variability in the activity of ACP1 and 37.3% in the BMI.