Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Desenvolvimento e teste de um sistema para caracterização de materiais magnetoelétricos

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

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.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Peptaibolin analogues by incorporation of α,α-dialkylglycines: synthesis and study of their membrane permeating ability

Analogues of Peptaibolin, a peptaibol with antibiotic activity, incorporating α,α-dialkylglycines (Deg, Dpg, and Ac6c) at selected positions were synthesised by MW-SPPS and fully characterized. A control analogue incorporating L-alanine was also prepared. The native peptide and the analogues were studied by fluorescence spectroscopy for their membrane permeating activity. Small unilamellar vesicles (SUVs) of egg phosphatidylcholine/ cholesterol (70:30) containing an encapsulated fluorescence probe (6-carboxyfluorescein) were used as membrane models. The assays of carboxyfluorescein release from SUVs upon peptide addition showed that Peptaibolin-Dpg and Peptaibolin-Ac6c are the most active peptides. These results indicate that the structure of the α,α-dialkylglycines is crucial for the membrane permeating ability of these Peptaibolin analogues.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Automatic distinction of Fernando Pessoas’ heteronyms

Text Mining has opened a vast array of possibilities concerning automatic information retrieval from large amounts of text documents. A variety of themes and types of documents can be easily analyzed. More complex features such as those used in Forensic Linguistics can gather deeper understanding from the documents, making possible performing di cult tasks such as author identi cation. In this work we explore the capabilities of simpler Text Mining approaches to author identification of unstructured documents, in particular the ability to distinguish poetic works from two of Fernando Pessoas' heteronyms: Alvaro de Campos and Ricardo Reis. Several processing options were tested and accuracies of 97% were reached, which encourage further developments.

Supercritical fluid and pharmaceutical applications. Part I: Process classification

The supercritical fluid technology has been target of many pharmaceuticals investigations in particles production for almost 35 years. This is due to the great advantages it offers over others technologies currently used for the same purpose. A brief history is presented, as well the classification of supercritical technology based on the role that the supercritical fluid (carbon dioxide) performs in the process.

Ontological representation of tumor-node-metastasis classification and an ontology-driven classifier: a study on colorectal cancer

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

Prospective assessment of different indices of cardiac risk for patients undergoing noncardiac surgeries

OBJECTIVE: To compare the accuracy of 4 different indices of cardiac risk currently used for predicting perioperative cardiac complications. METHODS: We studied 119 patients at a university-affiliated hospital whose cardiac assessment had been required for noncardiac surgery. Predictive factors of high risk for perioperative cardiac complications were assessed through clinical history and physical examination, and the patients were followed up after surgery until the 4th postoperative day to assess the occurrence of cardiac events. All patients were classified according to 4 indices of cardiac risk: the Goldman risk-factor index, Detsky modified risk index, Larsen index, and the American Society of Anesthesiologists' physical status classification and their compared accuracies, examining the areas under their respective receiver operating characteristic (ROC) curves. RESULTS: Cardiac complications occurred in 16% of the patients. The areas under the ROC curves were equal for the Goldman risk-factor index, the Larsen index, and the American Society of Anesthesiologists' physical status classification: 0.48 (SEM ± 0.03). For the Detsky index, the value found was 0.38 (SEM ± 0.03). This difference in the values was not statistically significant. CONCLUSION: The cardiac risk indices currently used did not show a better accuracy than that obtained randomly. None of the indices proved to be significantly better than the others. Studies to improve our ability to predict such complications are still required.

Les diatomées. Histoire naturelle, préparation, classification & description des principales espèces, par J. Pelletan, avec une introduction à l'étude des diatomées par m. J. Deby, et un exposé de la classification des diatomées par m. Paul Petit.

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