Adherence to health-related behaviors: effectiveness of implementation intentions and posthypnotic suggestion in college students

Resumo: Com base no conceito de implementação de intenções (Gollwitzer, 1993, 1999) e na teoria do contexto de resposta de Kirsch & Lynn (1997), o presente trabalho testou a eficácia de uma intervenção combinada de implementação de intenções com hipnose e sugestão pós-hipnótica na promoção da adesão a uma tarefa simples (avaliação do humor) e uma tarefa difícil (actividade física). Os participantes são estudantes universitários de uma universidade na Nova Jérsia, (N=124, Estudo 1, EUA) e em Lisboa (N=323, Estudo 2, Portugal). Em ambos os estudos os participantes foram seleccionados a partir de uma amostra mais vasta baseado num escrutínio da sua sugestibilidade hipnótica avaliada por meio da Escala de Grupo de Sugestibilidade Hipnótica de Waterloo-Stanford (WSGC): Forma C. O Estudo 1 usou um desenho factorial do tipo 2x2x3 (tipo de intenção formada x hipnose x nível de sugestionabilidade) e o Estudo 2 usou um desenho factorial do tipo 2 x 2x 2 x 4 (tipo de tarefa x tipo de intenção formada x hipnose x nível de sugestionabilidade). No Estudo 1 foi pedido aos participantes que corressem todos os dias e durante três semanas durante 5 minutos, que medissem a sua pulsação antes e depois da actividade física e que mandassem um e-mail ao experimentador, fornecendo assim uma medida comportamental e uma medida de auto-relato. Aos participantes no grupo de intenções de meta foi apenas pedido que corressem todos os dias. Aos participantes no grupo de implementação de intenções foi pedido que especificasses com exactidão quando e onde iriam correr e enviar o e-mail. Para além disso, cerca de metade dos participantes foram hipnotizados e receberam uma sugestão pós-hipnótica em que lhes foi sugerido que o pensamento de correr todos os dias lhes viria à mente sem esforço no momento apropriado. A outra metade dos participantes não recebeu qualquer sugestão hipnótica. No Estudo 2 foi seguido o mesmo procedimento, mas a cerca de metade dos participantes foi atribuída uma tarefa fácil (enviar um Adherence to health-related behaviors ix SMS com a avaliação diária do seu estado de humor naquele momento) e à outra metade da amostra foi atribuída a tarefa de exercício físico atrás descrita (tarefa difícil). Os resultados do estudo 1 mostraram uma interacção significativa entre o nível de sugestionabilidade dos participantes e a sugestão pós-hipnótica (p<.01) indicando que a administração da sugestão pós-hipnótica aumentou a adesão nos participantes muito sugestionáveis, mas baixou a adesão nos participantes pouco sugestionáveis. Não se encontraram diferenças entre os grupos que formaram intenções de meta e os que formaram implementação de intenções. No Estudo 2 os resultados indicaram que os participantes aderiram significativamente mais à tarefa fácil do que à tarefa difícil (p<.001). Os resultados não revelaram diferenças significativas entre as condições implementações de intenções, hipnose e as duas estratégias combinadas, indicando que a implementação de intenções não foi eficaz no aumento da adesão às duas tarefas propostas e não beneficiou da combinação com as sugestões pós-hipnóticas. A utilização da hipnose com sugestão pós-hipnótica significativamente reduziu a adesão a ambas as tarefas. Dado que não existiam instrumentos em Português destinados a avaliar a sugestionabilidade hipnótica, traduziu-se e adaptou-se para Português Escala de Grupo de sugestibilidade hipnótica de Waterloo-Stanford (WSGC): Forma C. A amostra Portuguesa (N=625) apresentou resultados semelhantes aos encontrados nas amostras de referência em termos do formato da distribuição dos padrões da pontuação e do índice de dificuldade dos itens. Contudo, a proporção de estudantes portugueses encontrada que pontuaram na zona superior de sugestionabilidade foi significativamente inferior à proporção de participantes na mesma zona encontrada nas amostras de referência. No sentido de lançar alguma luz sobre as razões para este resultado, inquiriu-se alguns dos participantes acerca das suas atitudes face à hipnose utilizando uma versão portuguesa da Escala de Valência de Atitudes e Crenças face à Hipnose e comparou-se com a opinião de Adherence to health-related behaviors xAbstract: On the basis of Gollwitzer’s (1993, 1999) implementation intentions’ concept, and Kirsch & Lynn’s (1997) response set theory, this dissertation tested the effectiveness of a combined intervention of implementation intentions with hypnosis with posthypnotic suggestions in enhancing adherence to a simple (mood report) and a difficult (physical activity) health-related task. Participants were enrolled in a university in New Jersey (N=124, Study 1, USA) and in two universities in Lisbon (N=323, Study 2, Portugal). In both studies participants were selected from a broader sample based on their suggestibility scores using the Waterloo-Stanford Group C (WSGC) scale of hypnotic susceptibility and then randomly assigned to the experimental groups. Study 1 used a 2x2x3 factorial design (instruction x hypnosis x level of suggestibility) and Study 2 used a 2 x 2x 2 x 4 factorial design (task x instructions x hypnosis x level of suggestibility). In Study 1 participants were asked to run in place for 5 minutes each day for a three-week period, to take their pulse rate before and after the activity, and to send a daily email report to the experimenter, thus providing both a self-report and a behavioral measure of adherence. Participants in the goal intention condition were simply asked to run in place and send the e-mail once a day. Those in the implementation intention condition were further asked to specify the exact place and time they would perform the physical activity and send the e-mail. In addition, half of the participants were given a post-hypnotic suggestion indicating that the thought of running in place would come to mind without effort at the appropriate moment. The other half did not receive a posthypnotic suggestion. Study 2 followed the same procedure, but additionally half of the participants were instructed to send a mood report by SMS (easy task) and half were assigned to the physical activity task described above (difficult task). Adherence to health-related behaviors vii Study 1 result’s showed a significant interaction between participant’s suggestibility level and posthypnotic suggestion (p<.01) indicating that posthypnotic suggestion enhanced adherence among highly suggestible participants, but lowered it among low suggestible individuals. No differences between the goal intention and the implementation intentions groups were found. In Study 2, participants adhered significantly more (p<.001) to the easy task than to the difficult task. Results did not revealed significant differences between the implementation intentions, hypnosis and the two conditions combined, indicating that implementation intentions was not enhanced by hypnosis with posthypnotic suggestion, neither was effective as single intervention in enhancing adherence to any of the tasks. Hypnosis with posthypnotic suggestion alone significantly reduced adherence to both tasks in comparison with participants that did not receive hypnosis. Since there were no instruments in Portuguese language to asses hypnotic suggestibility, the Waterloo-Stanford Group C (WSGC) scale of hypnotic susceptibility was translated and adapted to Portuguese and was used in the screening of a sample of college students from Lisbon (N=625). Results showed that the Portuguese sample has distribution shapes and difficulty patterns of hypnotic suggestibility scores similar to the reference samples, with the exception of the proportion of Portuguese students scoring in the high range of hypnotic suggestibility, that was found lower than the in reference samples. In order to shed some light on the reasons for this finding participant’s attitudes toward hypnosis were inquired using a Portuguese translation and adaptation of the Escala de Valencia de Actitudes y Creencias Hacia la Hipnosis, Versión Cliente, and compared with participants with no prior hypnosis experience (N=444). Significant differences were found between the two groups with participants without hypnosis experience scoring higher in factors indicating misconceptions and negative attitudes about hypnosis.

Paleontologia e evolução a problemática da espécie em Paleozoologia

The concept of species in Paleontology is of paramount importance since the correct taxonomic determinations are essential to establish the age of the beds where fossils are collected. Particularly since 1940, the concept of species from a biological context, corresponding to the variability of a set of interpopulation compatibility, led us to a new approach, in which a typological conception has been replaced by a populationist one. If the notion of species is not necessarily identical for all living organisms, the greater the difficulties of interpretation in the private world of cephalopod fossils. The latter, lend themselves well to population systematics, and where this concept of species rests primarily on the morphological similarities. Thus, the introduction of general ideas analyse "typological species", "biological species", the problem of the definition of a "population" in Paleontology, and also the importance of the biometric analysis of fossil associations. The classic examples of polymorphism amd polytypism, in existing or extinct organisms, show that the concept of fossil species, observed in a well-defined period of its lifetime, is no different from that of biological species. The study of the evolution of fossil organisms allow us to understand the modelities of evolution and the mechanisms of speciation here synthesized and fully documented, namely the anagenesis or sequential evolution and the cladogenesis or divergent evoltuion; these mechanisms are the basis of the synthetic or gradualist theory of evolution developed by Dobzhansky, Mayr, Huxley, Rensch and impson. This summary ends with a reference to the theory of punctuated (or intermittent) equilibria proposed by Gould and Eldredge, who presented a more objective interpretation of morphological gaps, considered as elements of evolution itself. The interdisciplinary collaboration between zoologists, geneticists and paleontologists, is compulsory in this domain. Paleozoology has a key role since it conveys the dynamism and depth to the dimension of space-time duality.

4Sensing - decentralized processing for participatory sensing data

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

O alisamento dos resultados numa empresa: o caso do Grupo SONANGOL

Dissertação de Mestrado apresentada ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças sob orientação de Professor Doutor Adalmiro Alvaro Malheiro de Castro Andrade Pereira

Study of a 9th century silver earrings set from Mikulčice: corrosion, conservation and maintenance

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

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Viscosity and self-diffusion coefficients of dialkyl adipates: a correlation scheme with predictive capabilities

A correlation and predictive scheme for the viscosity and self-diffusivity of liquid dialkyl adipates is presented. The scheme is based on the kinetic theory for dense hard-sphere fluids, applied to the van der Waals model of a liquid to predict the transport properties. A "universal" curve for a dimensionless viscosity of dialkyl adipates was obtained using recently published experimental viscosity and density data of compressed liquid dimethyl (DMA), dipropyl (DPA), and dibutyl (DBA) adipates. The experimental data are described by the correlation scheme with a root-mean-square deviation of +/- 0.34 %. The parameters describing the temperature dependence of the characteristic volume, V-0, and the roughness parameter, R-eta, for each adipate are well correlated with one single molecular parameter. Recently published experimental self-diffusion coefficients of the same set of liquid dialkyl adipates at atmospheric pressure were correlated using the characteristic volumes obtained from the viscosity data. The roughness factors, R-D, are well correlated with the same single molecular parameter found for viscosity. The root-mean-square deviation of the data from the correlation is less than 1.07 %. Tests are presented in order to assess the capability of the correlation scheme to estimate the viscosity of compressed liquid diethyl adipate (DEA) in a range of temperatures and pressures by comparison with literature data and of its self-diffusivity at atmospheric pressure in a range of temperatures. It is noteworthy that no data for DEA were used to build the correlation scheme. The deviations encountered between predicted and experimental data for the viscosity and self-diffusivity do not exceed 2.0 % and 2.2 %, respectively, which are commensurate with the estimated experimental measurement uncertainty, in both cases.

Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Análise dos métodos de seleção de fornecedores

No ambiente altamente competitivo de hoje, um processo eficaz de seleção de fornecedores é muito importante para o sucesso de qualquer organização [33]. Esta dissertação procura determinar quais os critérios e métodos mais utilizados no problema da seleção de fornecedores, contribuindo assim para o apoio a entidades que pretendam iniciar uma seleção de fornecedores de uma forma mais eficaz. Para atingir os objetivos propostos, foi realizada uma análise de artigos que fazem a revisão literária dos métodos e critérios desde o ano de 1985 até ao ano 2012. Com os dados obtidos destas revisões, foi possível identificar quais os três principais métodos utilizados ao longo dos anos, sendo eles o DEA, AHP e Fuzzy set theory e os principais critérios utilizados na seleção de fornecedores. Nesta dissertação, é apresentada uma visão geral da tomada de decisão e os métodos utilizados na tomada de decisão multicritério. É abordado o problema da seleção de fornecedores, o seu processo de seleção e as revisões literárias dos métodos e critérios de seleção utilizados nos últimos anos. Por fim, é apresentada a contribuição para a seleção de fornecedores do estudo realizado durante o desenvolvimento desta dissertação, sendo apresentados e explicados os principais métodos de seleção de fornecedores, bem como os critérios utilizados.

Every normal logic program has a 2-valued semantics: theory, extensions, applications, implementations

Trabalho apresentado no âmbito do Doutoramento em Informática, como requisito parcial para obtenção do grau de Doutor em Informática

Models and tools for value systems analysis in collaborative environments

Dissertation to obtain the degree of Doctor in Electrical and Computer Engineering, specialization of Collaborative Networks

Os adolescentes portugueses do 9º ano e o uso do preservativo : atitudes, factores sociais e afectivos

RESUMO - A presente investigação pretende caracterizar e relacionar os principais factores que levam o adolescente a usar ou não o preservativo na relação sexual, no âmbito da adaptação da Teoria do Comportamento Interpessoal de Triandis (1977). O enfoque é posto na relação entre as atitudes e os factores afectivos e sociais que estão associados à intenção comportamental. Trata-se de um estudo transversal, correlacional, descritivo e analítico, que recorre à técnica do inquérito por questionário. A amostra é de 2.465 adolescentes de 14, 15 e 16 anos a frequentar o 9.º ano das escolas portuguesas, por regiões. Espera-se uma forte associação entre os factores sociais, afectivos e atitudes, e entre estes individualmente, e a intenção do uso do preservativo. ----------- ABSTRACT - This research aims to explore, characterize and set relationships between the main factors that lead adolescents to use or not use condoms during sexual intercourse, based in an adaptation of the Theory of Interpersonal Behaviour (Triandis, 1977). The focus is put on the relationship between attitudes and emotional and social factors that are associated with behavioral intention. It is a cross-sectional, correlational, descriptive and analytical study that uses the survey questionnaire. The sample is 2.465 adolescents aged 14, 15 and 16 years attending the ninth grade of a Portuguese school, by region. It is expected a strong association between social and emotional factors and attitudes, as well as between them individually and the intention of use condom.

Health care marketing: the theory of planned behavior applied to patients' choise between private and public providers in the portuguese health care system.

Dissertation submitted in partial fulfillment of the requirements for degree of Master in Statistics and Information Management.