Multifunctional converter to interface renewable energy sources and electric vehicles with the power grid in smart grids context

This paper proposes a multifunctional converter to interface renewable energy sources (e.g., solar photovoltaic panels) and electric vehicles (EVs) with the power grid in smart grids context. This multifunctional converter allows deliver energy from the solar photovoltaic panels to an EV or to the power grid, and exchange energy in bidirectional mode between the EV and the power grid. Using this multifunctional converter are not required multiple conversion stages, as occurs with the traditional solutions, where are necessary two power converters to integrate the solar photovoltaic system in the power grid and also two power converters to integrate an off-board EV battery charger in the power grid (dc-dc and dc-ac power converters in both cases). Taking into account that the energy provided (or delivered) from the power grid in each moment is function of the EV operation mode and also of the energy produced from the solar photovoltaic system, it is possible to define operation strategies and control algorithms in order to increase the energy efficiency of the global system and to improve the power quality of the electrical system. The proposed multifunctional converter allows the operation in four distinct cases: (a) Transfer of energy from the solar photovoltaic system to the power grid; (b) Transfer of energy from the solar photovoltaic system and from the EV to the power grid; (c) Transfer of energy from the solar photovoltaic system to the EV or to the power grid; (d) Transfer of energy between the EV and the power grid. Along the paper are described the system architecture and the control algorithms, and are also presented some computational simulation results for the four aforementioned cases. It is also presented a comparative analysis between the traditional and the proposed solution in terms of operation efficiency and estimated cost of implementation.

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.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Inovação como fator de competitividade da cadeia de valor da moda

Tese de Doutoramento em Engenharia Têxtil

Haematophagic behavior in laboratory of Lutzomyia cruzi (Mangabeira) (Diptera: Psychodidae) in relation to three mammalian blood sources in Manaus, Brazil

The sand fly Lutzomyia cruzi is considered as one of vectors of visceral leishmaniasis in Brazil. This work examined optimum feeding age, feeding time, host preference, fecundity rates, and female blood meal volume taken by single females from a closed colony of L. cruzi. Mean feeding time was longer on hamsters, 6.6 minutes, than on humans, 5.7 minutes. 49.1% of the 48h-old flies fed on humans and 43.3% of 72h-old flies fed on hamsters. Of a total of 120 females, 61% fed on humans and 25% fed on hamsters. Total fecundity was significantly higher in females fed on hamster than on human or opossum. Laboratory-reared L. cruzi females fed earlier, more promptly, and preferably on humans than on hamsters when offered these blood-meal sources simultaneously. The blood-meal volume is higher in females fed on hamsters than other hosts (human and opossum).

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

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

Contribution to Early Holocene vegetation and climate history of Eastern Orinoco Llanos, Venezuela, from a paleoecological record of a Mauritia L.f. swamp

A palynological analysis of an organic paleosol found at 150-125 cm depth in a Mauritia swamp from the Eastern Orinoco Llanos is presented. The 25 cm pollen record summarizes the vegetation history during the Early Holocene, from 10,225 to 7,800 calendar yr BP. The vegetation was characterized by a Poaceae marsh, where Asteraceae, Melastomataceae, Schefflera-type and Phyllanthus were the most abundant shrubs and trees. Pollen-types richness was lower than that recorded today in similar environments, and Mauritia pollen was absent. Results suggest that climate was as humid as present during the beginning of the Holocene, with a decreasing trend in humidity from around 8,000-7,000 yr BP, in coincidence with the beginning of the "Early-Mid-Holocene Dryness" that affected deeply the Amazon Basin and neighboring areas. Dry climatic conditions could have existed in the study site until the Mid-Late Holocene when a Mauritia swamp developed, and humid conditions similar to present established. Main climate phases inferred in our study site fit well with regional trends recorded in other places located north Amazon Basin. However, conclusions are still limited by the lack of additional Quaternary records in the Orinoco Llanos area, avoiding regional correlations.

Sistema de reposição e de gestão de inventários na rede de lojas de uma empresa de retalho de moda

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Desenvolvimento e teste de um sistema para caracterização de materiais magnetoelétricos

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores

Um estudo sobre gênero no Brasil do século XIX: o caso Anna Jansen, a Rainha do Maranhão

Dissertação de mestrado em Contabilidade

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.

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.

Some notes on a historical perspective of panic disorder

This article aims to describe important points in the history of panic disorder concept, as well as to highlight the importance of its diagnosis for clinical and research developments. Panic disorder has been described in several literary reports and folklore. One of the oldest examples lies in Greek mythology - the god Pan, responsible for the term panic. The first half of the 19th century witnessed the culmination of medical approach. During the second half of the 19th century came the psychological approach of anxiety. The 20th century associated panic disorder to hereditary, organic and psychological factors, dividing anxiety into simple and phobic anxious states. Therapeutic development was also observed in psychopharmacological and psychotherapeutic fields. Official classifications began to include panic disorder as a category since the third edition of the American Classification Manual (1980). Some biological theories dealing with etiology were widely discussed during the last decades of the 20th century. They were based on laboratory studies of physiological, cognitive and biochemical tests, as the false suffocation alarm theory and the fear network. Such theories were important in creating new diagnostic paradigms to modern psychiatry. That suggests the need to consider a wide range of historical variables to understand how particular features for panic disorder diagnosis have been developed and how treatment has emerged.