Estudo geológico e geotécnico de taludes rochosos em S. Pedro da Afurada (Vila Nova de Gaia): contributos para a avaliação da estabilidade estrutural

Mestrado em Engenharia Geotécnica e Geoambiente

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.

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.

Avaliação da participação em crianças e jovens com paralisia cerebral, na ilha de S. Miguel, Açores

Introdução: Nas crianças/jovens com Paralisia Cerebral (PC), as limitações motoras repercutem-se em limitações funcionais e, consequentemente, na diminuição da participação em ocupações. Sendo as manifestações da PC diferentes de indivíduo para indivíduo, estas vão refletir, dependendo da gravidade, quadro motor, ambiente físico e social, diferentes níveis de participação. Objetivo: O objetivo deste estudo foi avaliar a relação entre a idade, sexo e grau de comprometimento motor e a participação em crianças/jovens com diagnóstico de paralisia cerebral com idades compreendidas entre os 5 e os 18 anos na ilha de São Miguel. Amostra e Métodos: 25 crianças de ambos os sexos (5- 18 anos), sinalizadas em instituições especializadas de reabilitação e em Centros de Atividades Ocupações (CAO’s) na Ilha de São Miguel – Açores. Foram aplicados dois instrumentos de avaliação às crianças/jovens, Gross Motor Function Measure e Quality of Upper Extremity Skills Test, e foram entregues aos pais os outros dois instrumentos para autopreenchimento, Assessment of Life Habits e Child Health Questionnaire – Parent- Form 50. Na análise estatística, recorreu-se a testes como o Kolmogorov-Smirnov, Tstudent ou Mann-Whitney, teste de Fisher, teste de Spearman e ANOVA. Resultados: Não foram encontradas relações significativas entre a idade e o sexo e o nível de participação das crianças/jovens com PC. Contrariamente, ao avaliarmos a relação entre o grau de participação e o grau de afetação verificamos que esta é significativa (p=0,004). Conclusão: Na nossa amostra não se encontrou uma influência da idade e do sexo com a frequência da participação (relações não foram significativas). Contudo, pode-se concluir que as crianças/jovens que apresentam menos limitações motoras, como as que se enquadram no nível I/II da Gross Motor Function Classification System, apresentam níveis de participação maiores do que as que apresentam níveis de afetação motora maiores (Nível V)

Potenciar a consciência fonológica de crianças pré-escolares em contexto naturalístico

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação Especialidade Intervenção Precoce

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.

Modelo de data mining para detecção de tumores em exames de rastreio

Dissertação para obtenção do grau de Mestre em Engenharia Informática

Ecotoxicity tests using the green algae chlorella vulgaris — a useful tool in hazardous effluents management

The treatment efficiency of laboratory wastewaters was evaluated and ecotoxicity tests with Chlorella vulgaris were performed on them to assess the safety of their environmental discharge. For chemical oxygen demand wastewaters, chromium (VI), mercury (II) and silver were efficiently removedby chemical treatments.Areduction of ecotoxicitywas achieved; nevertheless, an EC50 (effective concentration that causes a 50% inhibition in the algae growth) of 1.5% (v/v) indicated still high level of ecotoxicity. For chloride determination wastewaters, an efficient reduction of chromium and silver was achieved after treatment. Regarding the reduction of ecotoxicity observed, EC50 increased from 0.059% to 0.5%, only a 0.02% concentration in the aquatic environment would guarantee no effects. Wastewaters containing phenanthroline/iron (II) complex were treated by chemical oxidation. Treatmentwas satisfactory concerning chemical parameters, although an increase in ecotoxicitywas observed (EC50 reduced from 0.31% to 0.21%). The wastes from the kinetic study of persulphate and iodide reaction were treated with sodium bisulphite until colour was removed. Although they did not reveal significant ecotoxicity, only over 1% of the untreated waste produced observable effects over algae. Therefore, ecotoxicity tests could be considered a useful tool not only in laboratory effluents treatment, as shown, but also in hazardous wastewaters management.

Indicadores prognósticos na hipertensão pulmonar

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular - Ramo de especialização: Intervenção Cardiovascular

Estudo e desenvolvimento de modelo de dispositivo controlado por temperatura no contexto de eficiência energética: potencial de aplicação para DSR-SF

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia