Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

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.

Ensaios sobre a responsabilidade social corporativa e sua divulgação no setor portuário

Tese de Doutoramento em Contabilidade

Occupational stress and coping among portuguese military police officers

This study describes the professional experience of military police officers from the Portuguese Republican National Guard (N = 95). We focused on the main sources and consequences of stress and the coping strategies used to deal with stress. The evaluation protocol included one closed-ended question and four open-ended questions. Data analysis of meaningful text segments was conceptually based and data categorization followed deductive content analysis. Results allowed the identification of 483 meaning units. Factors intrinsic to the job and the relationships at work were the main stressors referred by participants. The consequences of stressors were evident at an individual level, affecting family, psychological, and physical/health domains. The coping strategies used to deal with the main source of stress in the professional career were focused on problem solving (e.g., active confrontation) and emotional regulation (e.g., situation acceptance). Practical implications and future avenues of research with these professionals are discussed.

Risco de inundação em áreas rurais: bacia do rio Luís Alves (SC)

O presente trabalho identificou e mapeou as áreas inundáveis da Bacia do Rio Luís Alves. A bacia, de uso da terra predominantemente agrícola, possui um notável histórico de inundações, demandando estudos que subsidiem o planejamento e a gestão do território. A partir da coleta de dados pluviométricos e fluviométricos, associada ao processamento de dados espaciais, foi desenvolvida uma metodologia em ambiente de SIG, que possibilitou a simulação de eventos de inundação, bem como a determinação de áreas de risco.

Estudo das necessidades de médicos especialistas na Região Norte de Portugal

Dissertação de mestrado em Engenharia Industrial

Divulgação de informação sobre o governo das sociedades em Portugal: Extensão e determinantes

Tese de Doutoramento em Contabilidade

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular

Arquitetura e paisagem monásticas no território bracarense: o caso de Dume (S. Martinho)

Dissertação de mestrado em Arqueologia

Indisciplina, violência e delinquência na escola: a perspetiva dos professores

Dissertação de mestrado em Crime, Diferença e Desigualdade

Avaliação da eficiência de projetos de reabilitação ambiental de áreas mineiras abandonadas: o caso da área mineira de Covas

Dissertação de mestrado em Geociências (área de especialização em Valorização de Recursos Geológicos)