Estratégias de tradução em sites das regiões de turismo de Portugal: estudo baseado em corpus

Em Portugal, o turismo é uma actividade económica que gera ganhos significativos e a promoção turística do país no mercado externo assenta cada vez mais na criação de sites multilingues. Este artigo examina um corpus constituído por textos provenientes de sites de Regiões de Turismo de Portugal, em português, e as respectivas traduções para inglês, com o objectivo de demonstrar o modo como os tradutores adicionam informação inexistente no texto original. Através da análise desta característica específica dos sites oficiais traduzidos para promover o destino ―Portugal‖ no mercado externo pretende salientar-se a importância que as estratégias de tradução assumem no marketing do destino turístico, uma vez que a informação adicionada cria uma determinada imagem de uma região. Em termos teóricos e metodológicos, este artigo enquadra-se no âmbito da Linguística de Corpus.

Palynologic data from Aquitaine (SW France) Middle Miocene Sables Fauves Formation. Climatic evolution

Climatic reconstructions based on palynological data from Aquitaine outcrops emphasize an important degradation phase during the Lower Serravallian. Climatic and environmental changes can be related to sea-level variations (Bur 5 / Lan 1, Lan 2 / Ser 1 and Ser 2 cycles). Transgressive phases feature warmer conditions and more open environments whereas regressive phases are marked by a cooler climate and an extent of the forest cover. From Langhian to Middle Serravallian, a general cooling is highlighted, with disappearance of most megathermic taxa and a transition from warm and dry climate to warm-temperate and much more humid conditions. Conclusions are consistent with studies on bordering areas and place the major degradation phase around 14 My. The palynologic data allow filling a gap in the climatic evolution of Southern France, as a connection between Lower and Upper Miocene, both well recorded. These results document, on Western Europe scale, latitudinal climatic gradient across Northern hemisphere while featuring a transition between Mediterranean area and northeastern Atlantic frontage.

ECG biometrics using a dissimilarity space representation

Electrocardiogram (ECG) biometrics are a relatively recent trend in biometric recognition, with at least 13 years of development in peer-reviewed literature. Most of the proposed biometric techniques perform classifi-cation on features extracted from either heartbeats or from ECG based transformed signals. The best representation is yet to be decided. This paper studies an alternative representation, a dissimilarity space, based on the pairwise dissimilarity between templates and subjects' signals. Additionally, this representation can make use of ECG signals sourced from multiple leads. Configurations of three leads will be tested and contrasted with single-lead experiments. Using the same k-NN classifier the results proved superior to those obtained through a similar algorithm which does not employ a dissimilarity representation. The best Authentication EER went as low as 1:53% for a database employing 503 subjects. However, the employment of extra leads did not prove itself advantageous.

As energias renováveis e a eficiência energética na mitigação das alterações climáticas

Comunicação apresentada no VIII Congresso de Geografia Portuguesa, na Reitoria da Universidade de Lisboa a 27 de outubro de 2011

Estrutura a partir do Movimento em Sistemas Autónomos

Mestrado em Engenharia Electrotécnica e de Computadores - Ramo de Sistemas Autónomos

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.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

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.

O cinema ibérico e os óscares: razões para a não selecção dos filmes portugueses ao óscar de melhor filme em língua estrangeira, através da análise comparativa entre as cinematografias de Espanha, Portugal e o modelo de produção de Hollywood

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

Práticas de gestão de recursos humanos em grandes empresas da Cidade da Praia – Cabo Verde

Este estudo tem como objetivo principal caraterizar as práticas de GRH existentes nas grandes empresas da Cidade da Praia. Neste sentido, foi realizada uma abordagem teórica à evolução da GRH e identificadas as práticas de GRH, capazes de reconhecer nas pessoas um recurso determinante no sucesso organizacional. O presente estudo caracteriza as práticas de GRH desenvolvidas pelas empresas da nossa amostra; o grau de intervenção do departamento de recursos humanos no desenvolvimento dessas práticas. Simultaneamente, é apresentada a caracterização das empresas e do departamento de RH. A uma amostra de 40 empresas foi aplicado um inquérito por questionário que permitiu concluir que (1) as práticas mais desenvolvidas são a contratação e as práticas de remuneração direta ou económica; (2) na maioria das práticas de GRH desenvolvidas, o DRH tem um elevado grau de intervenção no desenvolvimento e implementação das práticas de GRH; (3) os responsáveis de RH não possuem formação específica na área de GRH.

Accommodation management system using mobile devices

Os dispositivos móveis são pessoais, intransmissíveis e cada vez mais utilizados, tornando-se assim numa boa ferramenta para a realização de um conjunto de serviços na indústria hoteleira. Entre esses serviços que necessitam da identificação pessoal, encontram-se a possibilidade do cliente reservar um quarto ou utilizar o serviço de quartos. Atualmente é muito utilizado, nos locais de alojamento, um smart card que possibilite ao cliente ter acesso a alguns dos serviços disponíveis. O objetivo deste documento é apresentar uma alternativa ao sistema de cartões, utilizando para o efeito, dispositivos móveis. De modo a garantir a segurança e uma utilização semelhante ao sistema de cartões existentes foi utilizada a tecnologia NFC (Near Field Communication) que, ao permitir o modo de emulação de cartão, facilita a transação do sistema de smart card existente, para o da utilização de dispositivos móveis na realização das mesmas funções. Mais concretamente, será abordada a utilização de smartphones para o processo de abertura de portas. Para que exista uma melhor compreensão e para que haja um conhecimento das suas capacidades e limites foram estudados casos de uso da tecnologia NFC. Este documento apresenta ainda os processos de desenvolvimento de uma aplicação nativa para o sistema operativo Android, cujo objetivo é proporcionar ao cliente de um local de alojamento um novo modo de acesso ao quarto, utilizando a tecnologia NFC. Para além desta funcionalidade a aplicação permite ainda ao utilizador fazer reservas, fazer o check-in, fazer o check-out entre outras. Posteriormente serão apresentadas as conclusões e possíveis trabalhos futuros.

Fonte de Iluminação LED – Avaliação dos Efeitos Fotobiológicos no Olho e Pele

Tendo por referência a diretiva 2006/95/CE, o trabalho desenvolvido no contexto da disciplina de Dissertação/Projeto/Estágio do Mestrado de Engenharia de Instrumentação e Metrologia, decorreu nas instalações do IEP (Instituto Electrotécnico Português) e teve como objetivo principal o desenvolvimento de um procedimento de avaliação dos efeitos fotobiológicos no olho e pele provocados por fontes de emissão contínua (LED), doravante designado método alternativo ao de referência. Os dois métodos, alternativo e de referência, utilizam respectivamente um foto-radiómetro multicanal e um espetro-radiómetro. O procedimento desenvolvido (método alternativo) de acordo com a norma EN/IEC62471) consiste na aquisição dos valores de irradiância com recurso a um foto-radiómetro e posterior determinação dos valores da radiância, com os quais se faz a avaliação dos efeitos fotobiológicos, para fontes de luz LED (Light Emitting Diode) ou GLS (General Lighting Service). A consulta detalhada da norma EN/IEC62471 e a pesquisa sobre os conceitos, definições, equipamentos e metodologias relacionadas com o tema em causa, constituiu o primeiro passo deste projecto. Com recurso aos dois equipamentos, uma fonte de luz LED (módulo de 12 lâmpadas LED) é avaliada em relação aos perigos (ou riscos) actínico UV e UV-A, ao perigo da luz azul e ainda o perigo térmico na retina e térmico na pele, permitindo fazer uma análise comparativa dos resultados. O método alternativo revelou-se bastante flexível e eficaz, proporcionando bons resultados em termos da irradiância e radiância dos referidos efeitos fotobiológicos. A comparação destes resultados com os valores limites de exposição mencionados na norma EN/IEC6247 permitiu afirmar que a fonte de luz LED avaliada não representa perigo fotobiológico para a saúde humana e classifica-se no grupo de risco “isento”. Uma vez cumpridos os objectivos, entendeu-se que seria uma mais-valia para o trabalho já realizado, estudar outro caso prático. Sendo assim, fez-se a avaliação da radiação de apenas um dos LED´s que constituíam a fonte usada nos ensaios anteriores, com o espetro-radiómetro (método de referência) e com uma distância de 200 mm entre a fonte e o medidor. Neste caso verificaram-se diferenças significativas nas quantidades obtidas quando comparadas com os valores normativos. Concluiu-se que o efeito fotobiológico da luz azul insere-se no grupo de “isento”, sem perigo para a saúde. Contudo, o efeito térmico da retina apresenta um aumento considerável da quantidade de radiância, embora dentro do grupo de risco “isento”. Esta classificação de grupos de risco. Face aos resultados obtidos, pode confirmar-se que as lâmpadas LED apresentam segurança fotobiológica, atendendo aos baixos valores de irradiância e radiância dos efeitos fotobiológicos estudados. Pode ainda afirmar-se que a utilização do foto-radiómetro em alternativa ao espetro-radiómetro se revela mais eficaz do ponto de vista de metodologia prática. Este trabalho demonstra a robustez desses dois equipamentos de avaliação dos efeitos fotobiológicos, e procura estabelecer uma linha de orientação para a prevenção dos efeitos adversos na pele e olhos de todos os seres humanos sujeitos à radiação ótica artificial. Quanto às incertezas de medições, em relação ao processo de medição com foto-radiómetro, a sua estimação não se realizou, devido a não rastreabilidade entre as medições indicadas pelo fabricante, no certificado de calibração e as medidas realizadas por outras entidades. Contudo, é propõe-se a sua realização em trabalhos futuros dentro desse âmbito. As incertezas dos resultados de medições com espetro-radiómetro foram parcialmente estimadas. Atendendo às potencialidades do sistema de medição, propõe-se como trabalho futuro, a aplicação da norma IEC62478, que faz parte da aplicação da norma EN/IEC62471 na avaliação do efeito da luz azul, com base na determinação da temperatura de cor correlacionada (CCT) de lâmpadas ou sistemas de lâmpadas incluindo luminárias. Os valores de irradiância e radiância adquiridos nos processos de avaliação, tanto com foto-radiómetro como espectro-radiómetro foram gravados em ficheiro Excel para um CD e anexados a este trabalho.

Plataforma de gestão de guias de programação de TV digital

Com o consumismo de mais variedade e qualidade de informação, assim como, produtos interativos, surgiu a necessidade de apresentar mais conteúdos, para além da programação de televisão comum. Com os avanços tecnológicos ligados à indústria da televisão e sua distribuição nos lares portugueses pelos operadores de TV, a quantidade de oferta de canais deixou de ser um foco, passando a ser prioritário a melhoria da experiência do cliente. Com a introdução de novas funcionalidades nas caixas recetoras de sinais de transmissão de canais, como por exemplo, a capacidade de apresentar informações adicionais sobre os programas, desde da sua apresentação em modo trailer até ao elenco detalhado que o compõe, os clientes podem ter uma nova experiência de interação com os serviços de TV. A funcionalidade de gravação agendada de programas levou ao próximo ponto de melhoria de experiência do cliente. As gravações que resultavam em programas indevidamente cortados, quer no seu início quer no seu fim, foi um dos motivos que levou os operadores de TV a procurarem um melhor serviço de gestão de guias de programação digitais. A InfoPortugal, entidade detentora do seguinte projeto e EPG Provider de algumas operadoras de TV nacionais, viu-se obrigada a atualizar os seu sistemas de distribuição de conteúdos, para responder à evolução dos requisitos dos seus clientes.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Anti-neoplastic properties of hydralazine in prostate cancer

Prostate cancer (PCa) is a major cause of cancer-related morbidity and mortality worldwide. Although early disease is often efficiently managed therapeutically, available options for advanced disease are mostly ineffective. Aberrant DNA methylation associated with gene-silencing of cancer-related genes is a common feature of PCa. Therefore, DNA methylation inhibitors might constitute an attractive alternative therapy. Herein, we evaluated the anti-cancer properties of hydralazine, a non-nucleoside DNA methyltransferases (DNMT) inhibitor, in PCa cell lines. In vitro assays showed that hydralazine exposure led to a significant dose and time dependent growth inhibition, increased apoptotic rate and decreased invasiveness. Furthermore, it also induced cell cycle arrest and DNA damage. These phenotypic effects were particularly prominent in DU145 cells. Following hydralazine exposure, decreased levels of DNMT1, DNMT3a and DNMT3b mRNA and DNMT1 protein were depicted. Moreover, a significant decrease in GSTP1, BCL2 and CCND2 promoter methylation levels, with concomitant transcript re-expression, was also observed. Interestingly, hydralazine restored androgen receptor expression, with upregulation of its target p21 in DU145 cell line. Protein array analysis suggested that blockage of EGF receptor signaling pathway is likely to be the main mechanism of hydralazine action in DU145 cells. Our data demonstrate that hydralazine attenuated the malignant phenotype of PCa cells, and might constitute a useful therapeutic tool.