Gaussian random field-based log odds occupancy mapping

This paper focuses on mapping problem with known robot pose in static environments and proposes a Gaussian random field-based log odds occupancy mapping (GRF-LOOM). In this method, occupancy probability is regarded as an unknown parameter and the dependence between parameters are considered. Given measurements and the dependence, the parameters of not only observed space but also unobserved space can be predicted. The occupancy probabilities in log odds form are regarded as a GRF. This mapping task can be solved by the well-known prediction equation in Gaussian processes, which involves an inverse problem. Instead of the prediction equation, a new recursive algorithm is also proposed to avoid the inverse problem. Finally, the proposed method is evaluated in simulations

Pastor.i: a smartphone application to facilitate grazing management

Grazing in extensive beef farming systems is often manage in an empirical way based on past experience and on the visual appreciation of animal behavior and forage potential. Records of entrances and exits of the animals in the paddocks are rare. However, knowing the occupation period and the animal density, when coupled with biomass defines the grazing pressure. This knowledge is essential for planning and making informed decisions, that influence the profitability of the farm. Moreover, adequate grazing pressure is crucial for the sustainability of many SSPs where system maintenance is dependent on the balance between grazing pressure and regeneration or maintenance of trees and shrubs. Pastor.i is a smartphone application (APP) designed to allow pasture data logging to be very simple. The application is synchronized with the website and allows the producer to have in his pocket all the farm, being possible to identify the paddock, calculate the area, record the movements of the animals and consult the occupation history of the paddock. The application calculates the actual stocking rate, that can be associated with the location of the animals, obtained if the animals are using collars with GPS, which allows to know the areas of the paddock that are most grazed, visualized through heat maps. The information enables localized actions, such as fertilizing or sowing, to improve areas that are not grazed. The application also allows you to save photos of the sward. This temporal photographic record provides information on the condition of trees, the botanical composition and on the tendency of grazing to improve or to worsen coverage. The APP is available for download, is compatible with Android and is being tested with focus groups

HMM-Based Dynamic Mapping with Gaussian Random Fields

This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments

A grey-box Neural Network Composite Model for an Industrial Heating Furnace

Industrial furnaces consume large amounts of energy and their operating points have a major influence on the quality of the final product. Design- ing a tool that analyzes the combustion process, fluid mechanics and heat transfer and assists the energy audit work is then of the most importance. This work proposes a hybrid composite model for such a tool, having, as its base, two white-box models, namely a detailed Computational Fluid Dynamics (CFD) model and a simplified Reduced-Order (RO) model, plus a black-box model developed using Artificial Neural Networks. The preliminary results presented in this paper show that this composite model is able to improve the accuracy of the RO model without having the high computational load of the CFD model

Adaptações no Serviço de Cirurgia Vascular do CHULN durante a pandemia de COVID-19 e impacto na atividade global

© SPACVWith the onset of the SARS-CoV-2 pandemic in early 2020, health services and personnel adapted their resources to mitigate and control the outbreak. These needs inevitably led to adaptations in most medical and surgical departments, including in our Vascular Surgery department. As we are facing a second outbreak of this pandemic, with unpredictable outcomes and repercussions in health services, it is crucial to learn from previous experiences and share strategies to perform the best care to our patients, despite the restrictions that have been imposed. Through this paper, we review the adaptations in Centro Hospitalar Universitário Lisboa Norte and particularly in our department to overcome the pandemic. We also assess the impact of these changes in our activity and compare with the experience of other fellow surgeons. With an upcoming second outbreak, it is crucial to learn from this and other departments’ experiences to overcome a potential health crisis.info:eu-repo/semantics/publishedVersio

Epidemiology of Superficial Fungal Infections in Portugal: 3-year review (2014-2016)

Introdução: As infeções fúngicas superficiais são as dermatoses infeciosas mais frequentes e a sua incidência continua a aumentar. Os dermatófitos são os principais agentes causais apresentando, contudo, uma distribuição geográfica variável. Material e Métodos: O presente estudo teve como objetivo a caracterização epidemiológica das infeções fúngicas superficiais diagnosticadas nos Serviços/Unidades de Dermatologia pertencentes ao Serviço Nacional de Saúde Português entre janeiro de 2014 e dezembro 2016 através da análise retrospetiva dos resultados das culturas realizadas durante esse período. Resultados: Foram estudados 2375 isolamentos, pertencentes a 2319 doentes. O dermatófito mais frequentemente isolado foi o Trichophyton rubrum (53,6%), tendo sido o principal agente causal da tinha da pele glabra (52,4%) e das onicomicoses (51,1%). Relativamente às tinhas do couro cabeludo, globalmente o Microsporum audouinii foi o agente mais prevalente (42,6%), seguido do Trichophyton soudanense (22,1%). Enquanto na área metropolitana de Lisboa estes dermatófitos foram os principais agentes de tinha do couro cabeludo, nas regiões Norte e Centro o agente mais frequente foi o Microsporum canis (58,5%). Os fungos leveduriformes foram os principais responsáveis pelas onicomicoses das mãos (76,7%). Conclusão: Os resultados deste estudo estão globalmente concordantes com a literatura científica. O Trichophyton rubrum apresenta-se como o dermatófito mais frequentemente isolado em cultura. Na tinha do couro cabeludo, na área metropolitana de Lisboa, as espécies antropofílicas de importação assumem particular destaque.info:eu-repo/semantics/publishedVersio

Mahalanobis distance based accuracy prediction models for Sentinel-2 Image Scene Classification

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become a prime component of any problem-solving. Examples include building classifiers for applications such as image/speech recognition, traffic prediction, product recommendation, virtual personal assistant (VPA), online fraud detection and many more. The performance of these developed classifiers is highly dependent upon the training dataset, and subsequently, without human intervention or true labels, the evaluation over unseen observations remains unknown. Using a statistical distance researchers did try to assess the model’s goodness-of-fit and compared multiple independent models. Nonetheless, given a train-test split and different classifiers built over the training set, the question ‘is it possible to find a prediction error using the relation between training and test set?’ remains unsolved. In this article, we propose a generalized statistical distance-based method measuring the prediction uncertainty at a new query point. To be specific, we propose a Mahalanobis distance-based Evidence Function Model to measure the misclassification caused by K-Nearest Neighbours (KNN), Extra Trees (ET), and Convolutional Neural Network (CNN) models when classifying Sentinel-2 image into six scene classes (Water, Shadow, Cirrus, Cloud, Snow, Other). The performance of the proposed method was assessed over two different datasets: (i) the test set, with an overall mean prediction uncertainty detection of 62.99%, 29.80% and 31.51%, leading to a mean micro-F1 performance of 67.89%, 39.30%, and 38.29% for KNN, ET, and CNN, respectively; (ii) a water-body set, with prediction uncertainty detection of 22.27%, 42.08%, and 27.67%, leading to a micro-F1 performance of 34.70%, 58.96%, and 43.32%, respectively

Pastor.i _ Uma aplicação de smartphone para facilitar a gestão do pastoreio

Grazing in extensive beef farming systems is often manage in an empirical way based on past experience and on the visual appreciation of animal behavior and forage potential. Records of entrances and exits of the animals in the paddocks in a regular basis are rare. However, knowing the occupation period and the animal density, when coupled with biomass defines the grazing pressure and carry capacity. This knowledge is essential for planning and making informed decisions, that influence the profitability of the farm. Moreover, adequate grazing pressure is crucial for the sustainability of many SSPs where system maintenance is dependent on the balance between grazing pressure and regeneration or maintenance of trees and shrubs. Pastor.i is a smartphone application (APP) designed to allow pasture data logging to be very simple. The application is synchronized with the website and allows the producer to have in his pocket all the farm, being possible to identify the paddock, calculate the area, record the movements of the animals and consult the occupation history of the paddock. The application calculates the actual stocking rate, that can be associated with the location of the animals, obtained if the animals are using collars with GPS, which allows to know the areas of the paddock that are most grazed, visualized through heat maps. The information enables localized actions, such as fertilizing or sowing, to improve areas that are not grazed. The application also allows you to save photos of the sward. This temporal photographic record provides information on the condition of trees, the botanical composition and on the tendency of grazing to improve or to worsen coverage. The APP is available for download, is compatible with Android and is being tested with focus groups

Simulation of a Billet Heating Furnace

This work presents the method developed in the scope of the “Audit Furnace” project to support the manufacturing industry in understanding the energy efficiencies of its furnaces and to identify strategies for the continuous improvement of its processes. A digital representation to support the development, calibration, and training of a physical-based reduced-order model for industrial furnaces is sought by integrating experimental data obtained in energy audits performed at several industrial units with detailed numerical results from computational fluid dynamics simulations of the furnaces. Composite models with two blocks, a physics-based reduced-order block, and a machine learning model block, are proposed in order to simultaneously achieve performance and flexibility in its adaptation to different furnaces, while keeping the computational load in acceptable levels. In this paper, preliminary results of the application of the method to a billet heating furnace are presented, namely the results of the computational fluid dynamics simulations of the furnace and their comparison with the measurements performed in an energy audit. This is the first, essential step of the proposed method. The numerical results generated will allow calibrating and training the reduced-order model and will feed the machine learning model training process

Career satisfaction of medical residents in Portugal

Introdução: A satisfação com a profissão médica tem sido apontada como um fator essencial para a qualidade assistencial, o bemestar dos doentes e a estabilidade dos sistemas de saúde. Estudos recentes têm vindo a enfatizar um crescente descontentamento dos médicos, principalmente como consequência das alterações das relações laborais. Objetivos: Avaliar a perceção dos médicos de formação específica em Portugal, sobre as expectativas e grau de satisfação com a profissão, especialidade e local de formação; razões da insatisfação e intenção de emigrar. Material e Métodos: Estudo transversal. A colheita de dados foi efetuada entre Maio e Agosto de 2014 através de um Inquérito online sobre a “Satisfação com a Especialidade”. Resultados: De uma população total de 5788 médicos, foram obtidas 804 respostas (12,25% do total de médicos internos). Desta amostra, 77% das respostas correspondem a internos dos três primeiros anos de formação. Verificou-se que 90% dos médicos se encontram satisfeitos com a especialidade, tendo-se encontrado também níveis elevados de satisfação com a profissão (85%) e local de formação (86%). Por outro lado, constatou-se que estes diminuíam com a progressão ao longo dos anos de internato. A avaliação global sobre o panorama da prática médica foi negativa e 65% dos médicos responderam que consideram emigrar após conclusão do internato. Conclusão: Os médicos internos em Portugal apresentam níveis positivos de satisfação com a sua profissão. No entanto, a sua opinião sobre o panorama da Medicina e os resultados relativos à intenção de emigrar alertam para a necessidade de tomada de medidas para inverter este cenário.Introduction: The satisfaction with the medical profession has been identified as an essential factor for the quality of care, the wellbeing of patients and the healthcare systems’ stability. Recent studies have emphasized a growing discontent of physicians, mainly as a result of changes in labor relations. Objectives: To assess the perception of Portuguese medical residents about: correspondence of residency with previous expectations; degree of satisfaction with the specialty, profession and place of training; reasons for dissatisfaction; opinion regarding clinical practice in Portugal and emigration intents. Material and Methods: Cross-sectional study. Data collection was conducted through the “Satisfaction with Specialization Survey”, created in an online platform, designed for this purpose, between May and August 2014. Results: From a total population of 5788 medical residents, 804 (12.25 %) responses were obtained. From this sample, 77% of the responses were from residents in the first three years. Results showed that 90% of the residents are satisfied with their specialty, 85% with the medical profession and 86% with their place of training. Nevertheless, results showed a decrease in satisfaction over the final years of residency. The overall assessment of the clinical practice scenario in Portugal was negative and 65% of residents have plans to emigrate after completing their residency. Conclusion: Portuguese residents revealed high satisfaction levels regarding their profession. However, their views on Portuguese clinical practice and the results concerning the intent to emigrate highlight the need to take steps to reverse this scenario