Evaluation of thermal remote sensing for detection of thermal anomalies as earthquake precursors: a case study for Malatya-Pütürge-Doganyol (Turkey) Earthquake, July 13, 2003

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

Internationalization strategies and methods of construction firms – a comparative study between Hong Kong - Chinese and Portuguese – European approaches

Dissertação para obtenção do Grau Mestre em Engenharia Civil – Perfil de Construção

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente

Molecular diversity of bla genes in Klebsiella pneumoniae and Escherichia coli isolates

Dissertation presented to obtain a Ph.D. degree in Biology, speciality Microbiology, by Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Derivation methods for hybrid knowledge bases with rules and ontologies

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Papel do tutor num currículo médico pré-graduado que utilize a aprendizagem por problemas

Educação Médica, 1993; 4(3): 169-173.

Part I : Comparing derivatization methods for amino acids – one and two-step procedures using gas chromatography – flame ion detector (GC-FID) Part II: Story of an icon from its execution until its conservation

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro

Applications of Fourier methods to the analysis of some stochastic processes

Dissertação de Doutoramento em Matemática: Processos Estocásticos

Comparative analysis of time-frequency methods estimating the time-varying microstructure of sleep EEG spindles

Proceedings of the Information Technology Applications in Biomedicine, Ioannina - Epirus, Greece, October 26-28, 2006

Prognostic modelling of breast cancer patients: a benchmark of predictive models with external validation

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores – Sistemas Digitais e Percepcionais pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

New ultrasonic-based methods in proteomics

Dissertação apresentada para obtenção do Grau de Doutor em Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia.A presente dissertação foi preparada no âmbito do convénio bilateral existente entre a Universidade Nova de Lisboa e a Universidade de Vigo.

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

A inteligência emocional na formação em gestão da saúde : estudo de caso e proposta de modelo de avaliação

RESUMO - Introdução: A Inteligência Emocional (IE) é considerada um factor preditivo de sucesso, mais significativo do que outros tipos de inteligência e o seu estudo tem recebido cada vez maior relevância com o objectivo de aumentar os níveis de desempenho em gestão (Goleman, 2009). O desenvolvimento da IE no âmbito da formação em gestão apresenta resultados contraditórios sendo necessário confirmar o potencial de desenvolvimento da IE em programas de formação específicos. Objectivos: Confirmar a importância da IE para a gestão da saúde e perceber o seu potencial de desenvolvimento em programas de formação específicos; analisar o módulo opcional de Emoção, Liderança e Coaching na Gestão em Saúde; e construir uma proposta de modelo que avalie se a participação nessa Unidade Curricular permite aumentar os níveis de IE. Metodologia: Realizou-se uma revisão da literatura, que permitiu ter acesso aos conceitos e teorias e, posteriormente, o estudo de caso do módulo opcional que permitiu compará-lo com outras teorias existentes. Finalmente, construiu-se uma proposta de modelo de avaliação da IE, com um desenho quasi-experimental. Conclusões: A IE é um factor essencial para o sucesso, principalmente na Gestão da Saúde, pelas características do mercado e das organizações. Os instrumentos de avaliação da IE com recurso à medição de competências são os que apresentam menos limitações. O peso do módulo opcional no Curso de Mestrado em Gestão da Saúde, é pouco significativo (3,33% dos ECTS) e apenas 36,6% dos alunos o frequentaram. A estrutura do módulo está alinhada com as directrizes de outras teorias, mas a sua curta duração poderá constituir uma limitação. Sugere-se a criação de apoio tutorial individualizado e prolongado. O modelo de avaliação proposto representa a primeira tentativa de avaliação do desenvolvimento da IE na formação em Gestão da Saúde em Portugal e a sua aplicação permitiria a o aprimoramento do potencial de desenvolvimento das competências dos gestores. ---------------------------------- ABSTRACT - Introduction: Emotional Intelligence (EI) is the most predictive factor of success when compared with other types of intelligence. Since it is believed to increase performance levels, EI study has been given more relevance (Goleman, 2009). EI development studies show contradictory results, becoming necessary to prove the benefits of the development programs. Purposes: This study aimed to confirm the importance of the EI in health care management; to perceive the EI development potential of specific programs; to analyze the optional curricular unit of Emotion, Coaching and Leadership in Health Management; and to build a model that proposes to evaluate the student’s EI development. Methods: After the Literature Revision, the Case Study of the Curricular Unit allowed to compare it with other existing theories. The Model of EI evaluation consists on a quasi-experimental study. Conclusions: EI is an essential factor for success, mainly in Health Care Management, because of its market and organizations characteristics. The ability instruments of EI evaluation are those which show the least limitations. The Curricular Unit represents only 3,33% of the ECTS provided by this Health Management Master. Only 36.6% of master’s students chose to participate in this curricular unit. The structure of the curricular unit is lined up with the guide-lines of other theories. However, being a 6 weeks program, it could represent a limitation. It is suggested to create an individual and longitudinal tutorial support. The EI evaluation model proposed represents the first attempt to evaluate de EI development in Health Management programs in Portugal. Its application could increase the manager’s development efficacy.

Industry level foresight: designing foresight methods for Lithuanian energy sector

Enterprise and Work Innovation Studies,6,IET, pp.9-51