A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.

This study concerns the development of an embedded system with low computational resources and low power consumption. It uses the NXP LPC2106 with ARM7 processor architecture, for acquiring, processing and classifying images. This embedded system is design to detect and recognize traffic signs. Taking into account the processor capabilities and the desired features for the embedded system, a set of algorithms was developed that require low computational resources and memory. These features were accomplished using a modified Freeman Method in conjunction with a new algorithm "ear pull" proposed in this work. Each of these algorithms was tested with static images, using code developed for MATLAB and for the CMUcam3. The road environment was simulated and experimental tests were performed to measure traffic signs recognition rate on real environment. The technical limitations imposed by the embedded system led to an increased complexity of the project, however the final results provide a recognition rate of 77% on road tests.Thus, the embedded system features overcome the initial expectations and highlight the potentialities of both algorithms that were developed.

The available data about mobile broadband services / applications is put together, and their characterisation parameters are identified, enabling some insight into new approaches for multi-service traffic analysis in MBS. On one hand, a classification of services and applications is proposed based on the 1.21 1 ITU-T Recommendation. It distinguishes between interactive and distribution services, and also identifies the types of information supporting each one. On the other hand, a taxonomy is proposed for applications’ characterisation parameters, which are divided into five different types: main ones, traffic and communications parameters, service components and operation environments as well. Examples are given for the range of variation of each type of parameters.

Background: General Practice/Family Medicine includes approaches to the biological, technological, behavioural, sociological and anthropological domains. Objectives: To document the domains addressed in the final assignments of the Integrated Master’s Degree in Medicine at the Faculty of Medicine, University of Coimbra, in the area of GP/FM. Material and methods: Observational study of the titles of final assignments, between 2008 and 2017, granted by the Faculty of Medicine of the University of Coimbra. A domain analysis using as codes the International Classification in Primary Care-2 and the Q-Codes, a context classification in Primary Care, year of elaboration and gender of author was carried out for each title of final assignment. A descriptive and inferential analysis was performed through parametric and nonparametric tests. Results: 169 papers were analysed, 23.1% written by male students, with a positive overall growth dynamics (Δ = +7) between 2008 and 2017. Q-Codes were registered 276 times, while the ICPC-2 codes were used 133 times. Under the Q-Codes, “doctor’s issues” is the most frequently addressed (n = 112; 67.2%), and under the International Classification in Primary Care-2 classifications, the chapter “Psychological” was predominant (n = 35; 21%). Under the Q-Codes, subcategories “primary care setting” (n = 26; 15.6%), “health issue management” (n = 23; 13.8%) and “unable to code, unclear” (22; 13.2%) were dominant. Within the International Classification in Primary Care-2, the subcategories “diabetes noninsulin dependent” (n = 22; 13.2%), “depressive disorder” (8, 4.8%) and “hypertension uncomplicated” (8; 4.8%) were predominantly focused on. Conclusions: The 3CGP may become a professional tool, allowing for more precise identification of final works, for a better communication method in medical activity and for avoiding the loss of previously developed works.

Resumo Estudo Um: Validação Preliminar de um Questionário para avaliar a perceção do clima motivacional dos alunos nas aulas de Educação Física A Teoria dos Objetivos de Realização, inicialmente aplicada ao contexto da educação, tornou-se bastante popular no contexto do desporto, estando na base de diversos estudos sobre a motivação dos sujeitos. Esta teoria baseia-se na existência de dois grupos de objetivos de realização: orientação para a tarefa ou para o ego, pelos quais os sujeitos avaliam a sua competência e definem o sucesso da sua participação numa determinada atividade num contexto específico de realização, tendo por base critérios diferenciados. Segundo vários autores, esta teoria centra-se também na forma como os sujeitos percecionam o contexto social no qual atuam, pelo que o clima motivacional irá influenciar os objetivos da ação para uma determinada orientação (ego ou tarefa). Por outro lado, a perceção do clima motivacional também tem um papel importante na satisfação das necessidades psicológicas básicas, que assumem um papel determinante na regulação da motivação. Assim sendo, com o objetivo de avaliar a perceção do clima motivacional dos alunos nas aulas de Educação Física, em Portugal, surge a necessidade de traduzir e validar para a versão Portuguesa o questionário Learning and Performance Orientations in Physical Education Classes Questionnaire (LAPOPECQ) numa amostra de estudantes portugueses do ensino básico português. Participaram no estudo 400 alunos do 2º e 3º ciclo do ensino básico, divididos em 2 subestudos de modo a entender a perceção do clima motivacional induzido pelos alunos (n=200) com idades compreendidas entre os 10 e os 18 anos (M=13.36;SD=1.68) e pelos professores (n=200) com idades compreendidas entre os 10 e os 17 anos (M=13.22;SD=1.62). Em ambos os subestudos, os alunos eram de escolas públicas e tinham diferentes níveis de envolvimento com a prática de atividade física e desporto fora da escola. Os resultados obtidos demonstraram que a versão portuguesa do LAPOPECQ apresenta uma estrutura fatorial igual à da versão original, com índices bastante aceitáveis de validade e de fiabilidade. Desta forma, poderá ser utilizada com um elevado grau de confiança para avaliar a Perceção do Clima Motivacional (induzido pelos professores e/ou pelos alunos) nas aulas de Educação Física.

The analysis of physiological variables of a patient can improve the death risk classification in Intensive Care Units(ICU) and help decision making and resource management. This work proposes a computational approach to death prediction through physiological variables analysis in ICU. Physiological variables that compounds time-series(e.g., blood pressure) are represented as Dependent Gaussian Processes(DGP). Variables that do not represent time-series (e.g., age) are used to cluster DGPs with Decision Trees. Classification is made according to a distance measure that combines Dynamic Time Warping and Kullback-Leibler divergence. The results of this approach are superior to other method already used, SAPS-I, on the considered test dataset.The results are similar to other computational methods published by the research community. The results comparing variations of the proposed method show that there is advatage in using the proposed clustering of DGPs.

A practical way to characterize consumable substances is through its chemical elements in its composition and theirs concentrations. By using these elements as feature variables, it is possible to arrange these substances samples in a data matrix in which data mining and statistical techniques can be applied for predictive analysis. The classification of consumable substances based on its chemical components is an interesting problem and provides useful information for various purposes, as: recognition of geographical origin of a substance; validation and authenticity; determination of the characteristics of a product which can aid companies in the quality control and preservation; differentiation of categories of a product, and others. This study presents a methodology for predictive analysis of substances and food based on its chemical components, using data mining concepts and techniques allied to ICPMS. Four applications of the proposed methodology are described: recognition of the geographical origin of Brazilian white rice produced in São Paulo and Goiás states; differentiation of organic and conventional Brazilian grape juice; differentiation of organic and conventional Brazilian chocolate, and analysis of its toxic and essential elements; recognition of the source of ecstasy tablets apprehended in two cities from Sao Paulo state, Ribeirão Preto and Campinas. For all applications presented, the classification models obtained showed high predictive performance (over 85%), which attest the efficiency of the proposed methodology, and the variable selection techniques used helped us to identify the chemical elements which are more important to the differentiation of the analyzed samples. For the purpose of distinguishing food samples into organic and conventional, our approach is pioneer and yielded good results.