140 resultados para 2017 Appropriations
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Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1ºCiclo do Ensino Básico
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Dissertação de mestrado em Técnicas de Caracterização e Análise Química
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Dissertação de mestrado em Técnicas de Caracterização e Análise Química
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Dissertação de mestrado em Química Medicinal
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Dissertação de mestrado em Técnicas de Caracterização e Análise Química
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Dissertação de mestrado em Educação Especial (área de especialização em Intervenção Precoce)
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Dissertação de mestrado em Bioengenharia
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Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.
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Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.
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Dissertação de mestrado em Ciências da Linguagem
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Teses de Doutoramento em Arquitectura.
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Chlorine is the most commonly used agent for general disinfection, particularly for microbial growth control in drinking water distribution systems. The goals of this study were to understand the effects of chlorine, as sodium hypochlorite (NaOCl), on bacterial membrane physicochemical properties (surface charge, surface tension and hydrophobicity) and on motility of two emerging pathogens isolated from drinking water, Acinetobacter calcoaceticus and Stenotrophomonas maltophilia. The effects of NaOCl on the control of single and dual-species monolayer adhered bacteria (2 h incubation) and biofilms (24 h incubation) was also assessed. NaOCl caused significant changes on the surface hydrophobicity and motility of A. calcoaceticus, but not of S. maltophilia. Planktonic and sessile S. maltophilia were significantly more resistant to NaOCl than A. calcoaceticus. Monolayer adhered co-cultures of A. calcoaceticus-S. maltophilia were more resilient than the single species. Oppositely, dual species biofilms were more susceptible to NaOCl than their single species counterparts. In general, biofilm removal and killing demonstrated to be distinct phenomena: total bacterial viability reduction was achieved even if NaOCl at the higher concentrations had a reduced removal efficacy, allowing biofilm reseed. In conclusion, understanding the antimicrobial susceptibility of microorganisms to NaOCl can contribute to the design of effective biofilm control strategies targeting key microorganisms, such as S. maltophilia, and guarantying safe and high-quality drinking water. Moreover, the results reinforce that biofilms should be regarded as chronic contaminants of drinking water distribution systems and accurate methods are needed to quantify their presence as well as strategies complementary/alternative to NaOCl are required to effectively control the microbiological quality of drinking water.
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Natural mineral waters (still), effervescent natural mineral waters (sparkling) and aromatized waters with fruit-flavors (still or sparkling) are an emerging market. In this work, the capability of a potentiometric electronic tongue, comprised with lipid polymeric membranes, to quantitatively estimate routinely quality physicochemical parameters (pH and conductivity) as well as to qualitatively classify water samples according to the type of water was evaluated. The study showed that a linear discriminant model, based on 21 sensors selected by the simulated annealing algorithm, could correctly classify 100 % of the water samples (leave-one out cross-validation). This potential was further demonstrated by applying a repeated K-fold cross-validation (guaranteeing that at least 15 % of independent samples were only used for internal-validation) for which 96 % of correct classifications were attained. The satisfactory recognition performance of the E-tongue could be attributed to the pH, conductivity, sugars and organic acids contents of the studied waters, which turned out in significant differences of sweetness perception indexes and total acid flavor. Moreover, the E-tongue combined with multivariate linear regression models, based on sub-sets of sensors selected by the simulated annealing algorithm, could accurately estimate waters pH (25 sensors: R 2 equal to 0.99 and 0.97 for leave-one-out or repeated K-folds cross-validation) and conductivity (23 sensors: R 2 equal to 0.997 and 0.99 for leave-one-out or repeated K-folds cross-validation). So, the overall satisfactory results achieved, allow envisaging a potential future application of electronic tongue devices for bottled water analysis and classification.
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Projeto de mestrado em Mediação Cultural e Literária
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Doctoral thesis in Marketing and Strategy.