Partial classification of Lorenz knots: Syllable permutations of torus knots words

We define families of aperiodic words associated to Lorenz knots that arise naturally as syllable permutations of symbolic words corresponding to torus knots. An algorithm to construct symbolic words of satellite Lorenz knots is defined. We prove, subject to the validity of a previous conjecture, that Lorenz knots coded by some of these families of words are hyperbolic, by showing that they are neither satellites nor torus knots and making use of Thurston's theorem. Infinite families of hyperbolic Lorenz knots are generated in this way, to our knowledge, for the first time. The techniques used can be generalized to study other families of Lorenz knots.

Learning and Knowledge in the Context of Absorptive Capacity

Develop a new model of Absorptive Capacity taking into account two variables namely Learning and knowledge to explain how companies transform information into knowledge

Web 2.0 and its impact on knowledge and business organizations

Today, information overload and the lack of systems that enable locating employees with the right knowledge or skills are common challenges that large organisations face. This makes knowledge workers to re-invent the wheel and have problems to retrieve information from both internal and external resources. In addition, information is dynamically changing and ownership of data is moving from corporations to the individuals. However, there is a set of web based tools that may cause a major progress in the way people collaborate and share their knowledge. This article aims to analyse the impact of ‘Web 2.0’ on organisational knowledge strategies. A comprehensive literature review was done to present the academic background followed by a review of current ‘Web 2.0’ technologies and assessment of their strengths and weaknesses. As the framework of this study is oriented to business applications, the characteristics of the involved segments and tools were reviewed from an organisational point of view. Moreover, the ‘Enterprise 2.0’ paradigm does not only imply tools but also changes the way people collaborate, the way the work is done (processes) and finally impacts on other technologies. Finally, gaps in the literature in this area are outlined.

Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Exercício físico e cancro da mama: uma revisão

Introdução – Os efeitos fisiológicos da atividade física e do treino são atualmente motivo de extensa investigação cujos resultados mostraram já de forma incontroversa os seus benefícios em diferentes condições clínicas. Diferentes estudos mostraram já os efeitos benéficos do exercício regular de intensidade leve a moderada na diminuição do risco de cancro, bem como na aptidão física de indivíduos portadores de cancro, submetidos ou não a cirurgia. A prescrição do exercício mais adequado para a sua maior eficácia na melhoria da aptidão física e para a diminuição da fadiga não é, no entanto, ainda consensual. O objetivo deste estudo foi o de rever o conhecimento atual sobre os benefícios do exercício físico em sobreviventes de cancro da mama, bem como sistematizar as linhas orientadoras atuais para a prescrição do exercício físico na referida população. Metodologia – Recorreu-se a uma revisão da literatura, tendo como base as palavras-chave: cancro da mama, sobreviventes de cancro da mama, risco de cancro, exercício físico, atividade física e treino, dando preferência a estudos que, na classificação de Oxford, correspondessem aos níveis I (ensaios clínicos randomizados e revisões sistemáticas) e II de evidência científica (ensaios clínicos não randomizados). Conclusão – Embora se reconheça que o exercício físico é benéfico para a população em geral e existam linhas orientadoras para a prescrição do exercício físico em indivíduos com cancro, estas não são ainda absolutamente consensuais, necessitando sempre de individualização no treino. A investigação em torno das questões que envolvem a adequada prescrição do exercício físico em indivíduos com ou em risco de desenvolver cancro é primordial. ABSTRACT - Introduction – Physiological effects of physical activity and training are currently subject of extensive research which has already showed uncontroversial benefits in different clinical conditions. Different studies have already shown the beneficial effects of mild to moderate regular exercise in decreasing cancer risk and increasing physical fitness of individuals suffering from cancer, undergoing surgery or not. However, the appropriate exercise prescription for greater efficacy in improving physical fitness and decreasing fatigue is not yet consensus. The aim of this study was to review current knowledge about the benefits of exercise on breast cancer survivors and systematize the existing guidelines for prescribing exercise in this population. Methodology – A literature review was conducted based on the keywords: breast cancer, breast cancer survivors, cancer risk, physical exercise, physical activity and training, giving preference to studies in the classification of Oxford corresponded to level I (randomized clinical trials and systematic reviews) and II (no randomized clinical trials) scientific evidence. Conclusion – Although it is recognized that exercise is beneficial for general population and that there are guidelines for exercise prescription for individuals with cancer, there is no absolute agreement and they constantly require individual adaptations in training. Research on issues involving the correct prescription of exercise for individuals with or at risk of developing cancer is vital.

Integrated vehicle classification system

This paper presents an integrated system for vehicle classification. This system aims to classify vehicles using different approaches: 1) based on the height of the first axle and_the number of axles; 2) based on volumetric measurements and; 3) based on features extracted from the captured image of the vehicle. The system uses a laser sensor for measurements and a set of image analysis algorithms to compute some visual features. By combining different classification methods, it is shown that the system improves its accuracy and robustness, enabling its usage in more difficult environments satisfying the proposed requirements established by the Portuguese motorway contractor BRISA.

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Caracterização profissional e grau de satisfação dos licenciados em fisioterapia da Escola Superior de Tecnologia da Saúde de Lisboa

Mestrado em Fisioterapia.

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.

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.