An analysis of teachers' environmental concerns: use of a conceptual change model

Tese de doutoramento em Ciências da Educação

Brownian motion with drift threshold model

In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.

Stochastic model of transcription initiation of closely spaced promoters in escherichia coli

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Parallel run-time for CO-OPN

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Supply chain integration model: practices and customer values

Dissertation to obtain PhD in Industrial Engineering

Protesting or justifying? A latent class model for contingent valuation with attitudinal data

This article develops a latent class model for estimating willingness-to-pay for public goods using simultaneously contingent valuation (CV) and attitudinal data capturing protest attitudes related to the lack of trust in public institutions providing those goods. A measure of the social cost associated with protest responses and the consequent loss in potential contributions for providing the public good is proposed. The presence of potential justification biases is further considered, that is, the possibility that for psychological reasons the response to the CV question affects the answers to the attitudinal questions. The results from our empirical application suggest that psychological factors should not be ignored in CV estimation for policy purposes, allowing for a correct identification of protest responses.

R&D spillovers in an endogenous growth model with physical capital, human capital and varieties

There is a family of models with Physical, Human capital and R&D for which convergence properties have been discussed (Arnold, 2000a; Gómez, 2005). However, spillovers in R&D have been ignored in this context. We introduce spillovers in this model and derive its steady-state and stability properties. This new feature implies that the model is characterized by a system of four differential equations. A unique Balanced Growth Path along with a two dimensional stable manifold are obtained under simple and reasonable conditions. Transition is oscillatory toward the steady-state for plausible values of parameters.

Absorption in human capital and r&d effects in an endogenous growth model

Until now, in models of endogenous growth with physical capital, human capital and R&D such as in Arnold [Journal of Macroeconomics 20 (1998)] and followers, steady-state growth is independent of innovation activities. We introduce absorption in human capital accumulation and describe the steady-state and transition of the model. We show that this new feature provides an effect of R&D in growth, consumption and welfare. We compare the quantitative effects of R&D productivity with the quantitative effects of Human Capital productivity in wealth and welfare.

Transitional dynamics of an endogenous growth model with an erosion effect

The convergence features of an Endogenous Growth model with Physical capital, Human Capital and R&D have been studied. We add an erosion effect (supported by empirical evidence) to this model, and fully characterize its convergence properties. The dynamics is described by a fourth-order system of differential equations. We show that the model converges along a one-dimensional stable manifold and that its equilibrium is saddle-path stable. We also argue that one of the implications of considering this “erosion effect” is the increase in the adherence of the model to data.

Analysis of the interaction of polycyclic aromatic compounds in a model organism: integration of genotoxic and histopathological effects

Due to their toxicity, especially their carcinogenic potential, polycyclic aromatic hydrocarbons (PAHs) became priority pollutants in biomonitoring programmes and environmental policy, such as the European Water Framework Directive. The model substances tested in this study, namely benzo[b]fluoranthene (B[b]F), considered potentially carcinogenic to humans and an effector carcinogenic PAH to wildlife, and phenanthrene (Phe), deemed a non-carcinogenic PAH, are common PAHs in coastal waters, owning distinct properties reflected in different, albeit overlapping, mechanisms of toxicity. Still, as for similar PAHs, their interaction effects remain largely unknown. In order to study the genotoxic effects of caused by the interaction of carcinogenic and non-carcinogenic PAHs, and their relation to histopathological alterations, juvenile sea basses, Dicentrarchus labrax, a highly ecologically- and economically-relevant marine fish, were injected with different doses (5 and 10 μg.g-1 fish ww) of the two PAHs, isolated or in mixture, and incubated for 48 h. Individuals injected with B[b]F and the PAH mixture exhibited higher clastogenic/aneugenic effects and DNA strand breakage in blood cells, determined through the erythrocytic nuclear abnormalities (ENA) and Comet assays, respectively. Also, hepatic histopathological alterations were found in all animals, especially those injected with B[b]F and the PAH mixture, relating especially to inflammation. Still, Phe also exhibited genotoxic effects in sea bass, especially in higher doses, revealing a very significant acute effect that was accordant with the Microtox test performed undergone in parallel. Overall, sea bass was sensitive to B[b]F (a higher molecular weight PAH), likely due to efficient bioactivation of the pollutant (yielding genotoxic metabolites and reactive oxygen species), when compared to Phe, the latter revealing a more significant acute effect. The results indicate no significant additive effect between the substances, under the current experimental conditions. The present study highlights the importance of understanding PAH interactions in aquatic organisms, since they are usually present in the aquatic environment in complex mixtures.

State detection in a financial portfolio: a self-organizing maps approach for financial time series

This study analyses financial data using the result characterization of a self-organized neural network model. The goal was prototyping a tool that may help an economist or a market analyst to analyse stock market series. To reach this goal, the tool shows economic dependencies and statistics measures over stock market series. The neural network SOM (self-organizing maps) model was used to ex-tract behavioural patterns of the data analysed. Based on this model, it was de-veloped an application to analyse financial data. This application uses a portfo-lio of correlated markets or inverse-correlated markets as input. After the anal-ysis with SOM, the result is represented by micro clusters that are organized by its behaviour tendency. During the study appeared the need of a better analysis for SOM algo-rithm results. This problem was solved with a cluster solution technique, which groups the micro clusters from SOM U-Matrix analyses. The study showed that the correlation and inverse-correlation markets projects multiple clusters of data. These clusters represent multiple trend states that may be useful for technical professionals.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.