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Relatório de estágio de mestrado em Educação Pré-escolar

Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Homicídio nas relações de intimidade: a construção mediática do crime em homens e mulheres

Dissertação de mestrado integrado em Psicologia

Coagulation/flocculation/flotation/nanofiltration processes using Moringa Oleifera as coagulant of Eutrophized river

This study investigated the efficiency of Moringa oleifera (MO) seeds as natural coagulant in coagulation/flocculation/dissolved air flotation (C/F/DAF), followed by nanofiltration (NF) for Microcystis protocystis and microcystin-LR removal. The methodology adopted in this work was performed in two steps: 1) coagulation/flocculation/dissolved air flotation (C/F/DAF) process using the MO extracted in saline solution of potassium chloride (KCl-1M) and sodium chloride (NaCl-1M) in optimum dosage 50 mg·L-1; 2) nanofiltration process using NF90 and NF270 membrane provided Dow Chemical Company®. A working pressure of 8 bar was applied. In all samples were analyzed color, turbidity, pH, cyanobacterial cells count and microcystin concentration. The use of MO seeds as natural coagulant, obtained satisfactory results in the M. protocystis, color and turbidity removal. NF was able to completely remove cyanobacterial cells and microcystins (100 %) from M. protocystis (always under the quantification limit). Therefore, C/F/DAF+NF sequence is a safe barrier against M. protocystis and microcystins in drinking water.

Melhoria e normalização de ferramentas de apoio à produção aplicando ferramentas Lean

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Assistive locomotion strategies for active lower limb devices

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

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.