“Fantasia Lusitana”, de João Canijo: O Portugal ficcional vs. o país real. O Estado Novo e a ‘portugalidade’. A construção da identidade

ISBN: 978-989-96858-3-3

Contributo para o estudo da tutela do promitente - comprador

Dissertação de mestrado em Direito Judiciário

O documentário, o real e o ponto de vista

Relatório de estágio de mestrado em Ciências da Comunicação (área de especialização em Audiovisual e Multimédia)

A (re)descoberta do(s) percurso(s) de formação e desenvolvimento profissional

Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

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%.

A Real-Time intelligent system for tracking patient condition

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.

The real economic effects of cross-delistingfrom the united states: evidence on post-operating performance, financial constraints and stock crash risk

Tese de Doutoramento em Ciências Empresariais.

Atividades de campo no ensino da Geologia: opiniões de professores portugueses sobre formas ideais de as usar

Devido à elevada dimensão espacial e temporal da maior parte dos fenómenos geológicos, não é possível reproduzir no laboratório os fenómenos a estudar, pelo que, se o professor de Geologia pretender colocar os alunos em contato com esses fenómenos, terá que organizar uma saída de campo para que as necessárias e adequadas atividades sejam realizadas no lugar onde esses fenómenos ocorrem. Contudo, e apesar de professores e alunos reconhecerem diversas potencialidades didáticas das saídas de campo, os professores raramente as organizam e justificam isso com base em diversos impedimentos. Neste artigo relatam-se os resultados de um estudo em que 233 professores portugueses de Biologia e Geologia foram inquiridos acerca de formas ideais de integrar as atividades de campo na componente de Geologia, no 3º ciclo do Ensino Básico (n=102) e no Ensino Secundário (n=131). Os resultados sugerem que as práticas que os professores gostariam de implementar, caso não houvesse constrangimentos à realização de atividades de campo, não seriam, na maior parte dos casos, muito diferentes das práticas implementadas que são relatadas na literatura. Esta falta de exigência e de ousadia por parte dos professores, no que concerne ao modo como as atividades de campo deveriam ser utilizadas, sugere a necessidade de a formação inicial e contínua de professores contemplar uma abordagem adequada das saídas de campo e de as escolas se reorganizarem para facilitarem a organização, fundamentada, das mesmas.