Environmental and track perturbations on multiple pantograph interaction with catenaries in high-speed trains

The top velocity of high-speed trains is generally limited by the ability to supply the proper amount of energy through the pantograph-catenary interface. The deterioration of this interaction can lead to the loss of contact, which interrupts the energy supply and originates arcing between the pantograph and the catenary, or to excessive contact forces that promote wear between the contacting elements. Another important issue is assessing on how the front pantograph influences the dynamic performance of the rear one in trainsets with two pantographs. In this work, the track and environmental conditions influence on the pantograph-catenary is addressed, with particular emphasis in the multiple pantograph operations. These studies are performed for high speed trains running at 300 km/h with relation to the separation between pantographs. Such studies contribute to identify the service conditions and the external factors influencing the contact quality on the overhead system. (C) 2013 Elsevier Ltd. All rights reserved.

Automatic vehicle detection and classification

This paper presents a proposal for an automatic vehicle detection and classification (AVDC) system. The proposed AVDC should classify vehicles accordingly to the Portuguese legislation (vehicle height over the first axel and number of axels), and should also support profile based classification. The AVDC should also fulfill the needs of the Portuguese motorway operator, Brisa. For the classification based on the profile we propose:he use of Eigenprofiles, a technique based on Principal Components Analysis. The system should also support multi-lane free flow for future integration in this kind of environments.

Decisions with multiple simultaneous goals and uncertain causal effects

A key aspect of decision-making in a disaster response scenario is the capability to evaluate multiple and simultaneously perceived goals. Current competing approaches to build decision-making agents are either mental-state based as BDI, or founded on decision-theoretic models as MDP. The BDI chooses heuristically among several goals and the MDP searches for a policy to achieve a specific goal. In this paper we develop a preferences model to decide among multiple simultaneous goals. We propose a pattern, which follows a decision-theoretic approach, to evaluate the expected causal effects of the observable and non-observable aspects that inform each decision. We focus on yes-or-no (i.e., pursue or ignore a goal) decisions and illustrate the proposal using the RoboCupRescue simulation environment.

Implications of the Intervention Program for Physical Activity (IPPA) in the perception of illness and wellbeing in people with Multiple Sclerosis (MS)

The aim of this study is to examine the implications of the IPPA in the perception of illness and wellbeing in MS patients. Methods - This is a quasi experimental study non-randomized study with 24 MS patients diagnosed at least 1 year before, and with an EDSS score of under 7. We used the IPPA in 3 groups of eight people in 3 Portuguese hospitals (Lisbon, Coimbra, and Porto). The sessions were held once a week for 90 minutes, over a period of 7 weeks. The instruments used were: We asked the subjects the question “Please classify the severity of your disease?” and used the Personal Wellbeing Scale (PWS) at the beginning (time A) and end (time B) of the IPPA. We used the SPSS version 20. A non-parametric statistical hypothesis test (Wilcoxon test) was used for the variable analysis. The intervention followed the recommendations of the Helsinki Declaration. Results – The results suggest that there are differences between time A and B, the perception of illness decreased (p<0.08), while wellbeing increased (p<0.01). Conclusions: The IPPA can play an important role in modifying the perception of disease severity and personal wellbeing.

Classification and staging of chronic liver disease from multimodal data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Comparing the content of instruments assessing environmental factors using the International Classification of Functioning, Disability and Health

Purpose: To describe and compare the content of instruments that assess environmental factors using the International Classification of Functioning, Disability and Health (ICF). Methods: A systematic search of PubMed, CINAHL and PEDro databases was conducted using a pre-determined search strategy. The identified instruments were screened independently by two investigators, and meaningful concepts were linked to the most precise ICF category according to published linking rules. Results: Six instruments were included, containing 526 meaningful concepts. Instruments had between 20% and 98% of items linked to categories in Chapter 1. The highest percentage of items from one instrument linked to categories in Chapters 2–5 varied between 9% and 50%. The presence or absence of environmental factors in a specific context is assessed in 3 instruments, while the other 3 assess the intensity of the impact of environmental factors. Discussion: Instruments differ in their content, type of assessment, and have several items linked to the same ICF category. Most instruments primarily assess products and technology (Chapter 1), highlighting the need to deepen the discussion on the theory that supports the measurement of environmental factors. This discussion should be thorough and lead to the development of methodologies and new tools that capture the underlying concepts of the ICF.

Comorbidity adjustment index for the international classification of diseases, 10

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

Classificação de atividades físicas através do uso do acelerómetro do smartphone

Dissertação de natureza científica realizada para a obtenção do grau de Mestre em Engenharia de redes de comunicação e Multimédia

Factores psicossociais preditivos de ajustamento à vida de pessoas com doenças crónicas

Doenças crónicas são de longa duração, de progressão lenta e induzem alterações na vida das pessoas, que são confrontadas com um conjunto de fatores que exercem um impacto negativo na sua qualidade de vida (QdV). A QdV é um conceito envolvendo componentes essenciais da qualidade humana: físicas, psicológicas, sociais, culturais e espirituais. Após o diagnóstico e com a doença estabilizada, os doentes procuram novas formas de lidar com esta. Este estudo teve como objetivo identificar fatores psicossociais preditivos (otimismo, afeto positivo e negativo, adesão aos tratamentos, suporte social e espiritualidade) da QdV (bem-estar geral, saúde física, saúde mental) e bem-estar subjetivo (BES) em pessoas com doenças crónicas. Amostra constituída por 774 indivíduos [30% diabetes, 27,1% cancro, 17,2% diabetes, 12% epilepsia, 11,5% esclerose múltipla e 2,2% miastenia, 70,5% do sexo feminino, idade M(DP)=42,9(11,6), educação M(DP)=9,6(4,7), anos diagnóstico M(DP)=12,8(9,7), classificação da doença M(DP)=6,6 (2,8)], recrutados nos hospitais centrais portugueses. Aplicando Modelos de Equações Estruturais e ajustando para variáveis sociodemográficas e clínicas, verificou-se que, pessoas mais otimistas, mais ativas e com uma melhor adesão aos tratamentos apresentam um melhor bem-estar geral, uma melhor saúde mental e um melhor bem-estar subjetivo; uma melhor adesão aos tratamentos contribui para uma melhor saúde física; melhor suporte social reflete-se numa melhor saúde mental; pessoas com mais espiritualidade apresentam uma melhor saúde física e uma melhor saúde mental. Estas conclusões contribuem para a definição de uma terapia que pode ajudar a uma melhor adaptação dos protocolos de tratamento para atender às necessidades dos doentes.

Timing analysis of a multiple logical ring wired/wireless PROFIBUS network

Recently, there have been a few research efforts towards extending the capabilities of fieldbus networks to encompass wireless support. In previous works we have proposed a hybrid wired/wireless PROFIBUS network solution where the interconnection between the heterogeneous communication media was accomplished through bridge-like interconnecting devices. The resulting networking architecture embraced a multiple logical ring (MLR) approach, thus with multiple independent tokens, to which a specific bridging protocol extension, the inter-domain protocol (IDP), was proposed. The IDP offers compatibility with standard PROFIBUS, and includes mechanisms to support inter-cell mobility of wireless nodes. We advance that work by proposing a worst-case response timing analysis of the IDP.