Sistema de exercício online para apoio a aprendizagem de Medicina Legal na Universidade de Brasília

O propósito deste estudo retrospectivo foi avaliar o padrão de utilização do sistema de exercícios on-line, facultativos, assíncronos, para o apoio à aprendizagem da disciplina Medicina Legal e Deontologia Médica na Universidade de Brasília. Os sujeitos foram 38 alunos que cursaram a disciplina no segundo semestre de 2005. O sistema oferecia conteúdos textuais e imagens que podiam ser acessados anonimamente. Para a resolução dos exercícios do tipo "verdadeiro" ou "falso", alguns com imagens, era necessário que os alunos se identificassem por senha, o que permitiu o monitoramento. Os resultados mostraram que 32 alunos (84%) realizaram exercícios on-line, com uma média de 183 respostas por aluno, entre os que aderiram; 52% dos exercícios foram resolvidos nas últimas 24 horas antes da prova; 62,3% dos exercícios foram resolvidos entre 19h e 01h. Conclui-se que os alunos, num sistema facultativo, concentram seus esforços na véspera da prova, o que diminui a eficiência do sistema, sugerindo que técnicas de motivação para o uso regular desse tipo de sistema devem ser implementadas.

Educação médica continuada online: potencial e desafios no cenário brasileiro

Com a intensa produção e veiculação de informações científicas, tornou-se difícil para o profissional médico manter-se atualizado com os recursos habituais. A necessidade de conhecer e de participar de processos de formação continuada se impõe. Entre outras iniciativas, a Associação Médica Brasileira e o Conselho Federal de Medicina lançaram o Programa Nacional de Educação Continuada a Distância para médicos, buscando divulgar o conhecimento produzido nos grandes centros para profissionais de áreas mais remotas ou com reduzida disponibilidade de tempo. Tendo como pressu posto que a Sociedade do Conhecimento requer a formação inicial e continuada de profissionais e de cidadãos com um novo conjunto de competências para atuar com eficiência e responsabilidade, esses programas devem ser desenvolvidos com base em abordagens pedagógicas que efetivamente valorizem, além dos conteúdos de ensino, a disposição para a pesquisa, a autonomia na busca da informação, o espírito colaborativo e a postura ética, entre outras. No intuito de contribuir para essa reflexão, este texto tem por objetivos retomar o processo de formalização da educação médica continuada a distância no Brasil em termos didático-pedagógicos e analisar perspectivas dessas ações educativas.

Arquivamento e comunicação de imagens radiológicas na formação médica online

O estudo investiga as possibilidades de utilização dos recursos de arquivamento e comunicação de imagens nas salas de aula e à distância no processo de formação médica através da telemedicina. Discutem-se as possibilidades de usar programas de informática que reproduzem os recursos de diferentes meios de diagnóstico por imagem como ferramenta didática nas aulas de telemedicina, por meio do acesso a imagens radiológicas utilizando sistemas de informática para fins de emissão de laudos à distância na formação médica. Avaliou-se a apresentação de imagens digitais nas salas de aula dos cursos de saúde a partir da experiência de residentes em formação que atuam na modalidade online, por meio de questionários aplicados com especialistas e residentes que atuam no caso relatado no estudo. Os aspectos de formação docente dos médicos, especialmente para atuar em ambientes online, definição de metodologias de avaliação, interação entre os sujeitos envolvidos foram avaliados para considerar a possibilidade de usar a experiência em cursos de Medicina como um meio de educação à distância (EAD) utilizando Arquivamento e Comunicação de Imagens (PACS)

Colaboração docente online na educação universitária

A colaboração docente on-line permite a criação de comunidades virtuais. As ferramentas virtuais de uma equipe em rede permitem criar aulas virtuais em diferentes localizações geográficas. Estes ambientes permitem na educação a transferência do conhecimento entre alunos e docentes, incluindo atividades síncronas e assíncronas. Neste trabalho descrevemos nossa experiência em colaboração docente on-line, utilizando ferramentas virtuais na educação universitária. Os softwares utilizados foram 1-Skype: Software de aplicação para chamadas na Internet (VoIP). 2- LAN: Aula virtual digital disponibilizado em http://www.cevap.unesp.br/abertura.htm. Estas mídias foram utilizadas em dias e locais de transmissão e recepção diferentes. Os temas foram: 1-"Como preparar uma videoconferência", 2-"Importância das TICs na docência Universitária", 3-"Plataformas virtuais com bases de dados automatizadas na busca bibliográfica", 4-"Uso do laboratório virtual no ensino de Biologia Celular, Histologia e Embriologia". As ferramentas virtuais permitem a colaboração on-line de docentes localizados em diferentes localizações geográficas, além de formar recursos humanos que as usarão para melhorar o desempenho da educação universitária.

Loviisan ydinvoimalaitoksen todennäköisyyspohjaisen riskianalyysin tason 2 epävarmuustarkastelut

Diplomityössä tarkastellaan Loviisan ydinvoimalaitoksen todennäköisyyspohjaisen riskianalyysin tason 2 epävarmuuksia. Tason 2 riskitutkimuksissa tutkitaan ydinvoimalaitosonnettomuuksia, joiden seurauksena osa reaktorin radioaktiivisista aineista vapautuu ympäristöön. Näiden tutkimuksien päätulos on suuren päästön vuotuinen taajuus ja se on pääosin todelliseen laitoshistoriaan perustuva tilastollinen odotusarvo. Tämän odotusarvon uskottavuutta voidaan parantaa huomioimalla merkittävimmät laskentaan liittyvät epävarmuudet. Epävarmuuksia laskentaan aiheutuu muiden muassa vakavan reaktorionnettomuuden ilmiöistä, turvallisuusjärjestelmien laitteista, inhimillisistä toiminnoista sekä luotettavuusmallin määrittelemättömistä osista. Diplomityössä kuvataan, kuinka epävarmuustarkastelut integroidaan osaksi Loviisan ydinvoimalaitoksen todennäköisyyspohjaisia riskianalyysejä. Tämä toteutetaan diplomityössä kehitetyillä apuohjelmilla PRALA:lla ja PRATU:lla, joiden avulla voidaan lisätä laitoshistorian perusteella muodostetut epävarmuusparametrit osaksi riskianalyysien luotettavuusdataa. Lisäksi diplomityössä on laskettu laskentaesimerkkinä Loviisan ydinvoimalaitoksen suuren päästön vuotuisen taajuuden vaihtelua kuvaava luottamusväli. Tämä laskentaesimerkki pohjautuu pääosin konservatiivisiin epävarmuusarvioihin, ei todellisiin tilastollisiin epävarmuuksiin. Laskentaesimerkin tulosten perusteella Loviisan suuren päästön taajuudella on laaja vaihteluväli; virhekertoimeksi saatiin 8,4 nykyisillä epävarmuusparametreilla. Suuren päästön taajuuden luottamusväliä voidaan kuitenkin tulevaisuudessa supistaa, kun hyödynnetään todelliseen laitoshistoriaan perustuvia epävarmuusparametreja.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

ESTIMATION OF CORK PRODUCTION USINGAERIAL IMAGERY1

ABSTRACT Inventory and prediction of cork harvest over time and space is important to forest managers who must plan and organize harvest logistics (transport, storage, etc.). Common field inventory methods including the stem density, diameter and height structure are costly and generally point (plot) based. Furthermore, the irregular horizontal structure of cork oak stands makes it difficult, if not impossible, to interpolate between points. We propose a new method to estimate cork production using digital multispectral aerial imagery. We study the spectral response of individual trees in visible and near infrared spectra and then correlate that response with cork production prior to harvest. We use ground measurements of individual trees production to evaluate the model’s predictive capacity. We propose 14 candidate variables to predict cork production based on crown size in combination with different NDVI index derivates. We use Akaike Information Criteria to choose the best among them. The best model is composed of combinations of different NDVI derivates that include red, green, and blue channels. The proposed model is 15% more accurate than a model that includes only a crown projection without any spectral information.

Estimation of the potential evapotranspiration by a simplified penman method

The numerous methods for calculating the potential or reference evapotranspiration (ETo or ETP) almost always do for a 24-hour period, including values of climatic parameters throughout the nocturnal period (daily averages). These results have a nil effect on transpiration, constituting the main evaporative demand process in cases of localized irrigation. The aim of the current manuscript was to come up with a model rather simplified for the calculation of diurnal daily ETo. It deals with an alternative approach based on the theoretical background of the Penman method without having to consider values of aerodynamic conductance of latent and sensible heat fluxes, as well as data of wind speed and relative humidity of the air. The comparison between the diurnal values of ETo measured in weighing lysimeters with elevated precision and estimated by either the Penman-Monteith method or the Simplified-Penman approach in study also points out a fairly consistent agreement among the potential demand calculation criteria. The Simplified-Penman approach was a feasible alternative to estimate ETo under the local meteorological conditions of two field trials. With the availability of the input data required, such a method could be employed in other climatic regions for scheduling irrigation.

Estimation of transpiration of the 'Valencia' orange young plant using thermal dissipation probe method

Most studies on measures of transpiration of plants, especially woody fruit, relies on methods of heat supply in the trunk. This study aimed to calibrate the Thermal Dissipation Probe Method (TDP) to estimate the transpiration, study the effects of natural thermal gradients and determine the relation between outside diameter and area of xylem in 'Valencia' orange young plants. TDP were installed in 40 orange plants of 15 months old, planted in boxes of 500 L, in a greenhouse. It was tested the correction of the natural thermal differences (DTN) for the estimation based on two unheated probes. The area of the conductive section was related to the outside diameter of the stem by means of polynomial regression. The equation for estimation of sap flow was calibrated having as standard lysimeter measures of a representative plant. The angular coefficient of the equation for estimating sap flow was adjusted by minimizing the absolute deviation between the sap flow and daily transpiration measured by lysimeter. Based on these results, it was concluded that the method of TDP, adjusting the original calibration and correction of the DTN, was effective in transpiration assessment.

Estimation of evaporation in the Banabuiú dam, in the state of Ceará, Brazil, by different combined methods, derived from the Penman equation

The state of Ceará, Brazil, has 75% of its area covered by Brazilian semiarid, with its peculiar features. In this state, the dams are constituted in water structure of strategic importance, ensuring, both in time and space, the development and supply of water to population. However, construction of reservoirs results in various impacts that should be carefully observed when deciding on their implementation. One of the impacts identified as negative is the increased evaporation, which constitutes a major component of water balance in reservoirs, especially in arid regions. Several methods for estimating evaporation have been proposed over time, many of them deriving from the Penman equation. This study evaluated six different methods for estimating evaporation in order to determine the most suitable for use in hydrological models for water balance in reservoirs in the state of Ceará. The tested methods were proposed by Penman, Kohler-Nordenson-Fox, Priestley-Taylor, deBruim-Keijman, Brutsaert-Stricker and deBruim. The methods presented good performance when tested for water balance during the dry season, and the Priestley-Taylor was the most appropriate, since the data from de simulated water balance with evaporation estimated by this method were the closest of the water balance data observed from measures of reservoir level and the elevation-volume curve provided by the Company of Management of Water Resources of the state of Ceará - COGERH.