Ditopic molecular architectures for the recognition of anionic species

Dissertation presented to obtain the Ph.D degree in Chemistry

Implementação de um programa de artes plásticas para a promoção da inclusão de alunos com Perturbações do Especto do Autismo

A aprendizagem cooperativa, através da qual os alunos se ajudam no processo de aprendizagem (Argyle, 1991; Balkcom, 1992; Johnson, Johnson, & Holubec, 1994) encontra nas artes plásticas um meio privilegiado de comunicação e estimulação sensorial. Este estudo visa examinar os efeitos da implementação de um programa de atividades cooperativas no âmbito das artes plásticas sobre o processo de inclusão de crianças com Perturbação do Espectro do Autismo (PEA) nas suas turmas de ensino regular, estando sobre análise: (i) o seu envolvimento e satisfação nas atividades dinamizadas; (ii) a interação estabelecida com os pares; (iii) e o apoio/ atitudes dos pares com desenvolvimento típico na relação com os colegas com PEA. Implementado em duas turmas do 1.º Ciclo do Ensino Básico, os efeitos do programa foram estudados, mediante a implementação de um estudo de caso único, tipo AB, com um desenho de múltiplas linhas de base. Para o efeito foram analisados registos de observação de seis crianças com autismo e de seis pares que compunham as díades de trabalho; e da entrevista aos professores das turmas. A socialização e desenvolvimento de atitudes positivas por parte dos alunos com desenvolvimento típico foi também aferida através de uma entrevista dirigida aos próprios. Os resultados são sugestivos de um global aumento da interação, do envolvimento e satisfação dos alunos com PEA – registando-se maior expressão destes indicadores aquando do uso de técnicas de teor mais sensorial (como monotipia, desenho, modelagem). O programa parece ter também promovido comportamentos mais apoiantes por parte dos pares na maximização da participação dos alunos com PEA - parecendo reforçar o reconhecimento das atividades artísticas cooperativas como promotoras de relações de interajuda e de mútuo conhecimento.

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Human activity recognition for an intelligent knee orthosis

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Intention recognition, commitment and their roles in the evolution of cooperation

Dissertação para obtenção do Grau de Doutor em Informática

Bacterial peptidoglycan biosynthesis and recognition by the innate immune system

Dissertation presented to obtain the Ph.D degree in Biology

Software for pattern recognition of the larvae of Aedes aegypti and Aedes albopictus

Software for pattern recognition of the larvae of mosquitoes Aedes aegypti and Aedes albopictus, biological vectors of dengue and yellow fever, has been developed. Rapid field identification of larva using a digital camera linked to a laptop computer equipped with this software may greatly help prevention campaigns.

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Search-for-yield in Portuguese fixed-income mutual funds and monetary policy

This paper studies the effects of monetary policy on mutual fund risk taking using a sample of Portuguese fixed-income mutual funds in the 2000-2012 period. Firstly I estimate time-varying measures of risk exposure (betas) for the individual funds, for the benchmark portfolio, as well as for a representative equally-weighted portfolio, through 24-month rolling regressions of a two-factor model with two systematic risk factors: interest rate risk (TERM) and default risk (DEF). Next, in the second phase, using the estimated betas, I try to understand what portion of the risk exposure is in excess of the benchmark (active risk) and how it relates to monetary policy proxies (one-month rate, Taylor residual, real rate and first principal component of a cross-section of government yields and rates). Using this methodology, I provide empirical evidence that Portuguese fixed-income mutual funds respond to accommodative monetary policy by significantly increasing exposure, in excess of their benchmarks, to default risk rate and slightly to interest risk rate as well. I also find that the increase in funds’ risk exposure to gain a boost in return (search-for-yield) is more pronounced following the 2007-2009 global financial crisis, indicating that the current historic low interest rates may incentivize excessive risk taking. My results suggest that monetary policy affects the risk appetite of non-bank financial intermediaries.

The determinants of mutual fund performance: A cross-country study

We use a new data set to study the determinants of the performance of open–end actively managed equity mutual funds in 27 countries. We find that mutual funds underperform the market overall. The results show important differences in the determinants of fund performance in the USA and elsewhere in the world. The US evidence of diminishing returns to scale is not a universal truth as the performance of funds located outside the USA and funds that invest overseas is not negatively affected by scale. Our findings suggest that the adverse scale effects in the USA are related to liquidity constraints faced by funds that, by virtue of their style, have to invest in small and domestic stocks. Country characteristics also explain fund performance. Funds located in countries with liquid stock markets and strong legal institutions display better performance.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.