14 resultados para separation factor

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Este artigo analisa a questão da eficácia publicitária num contexto de saturação. O reconhecimento da marca ou a recordação da publicidade associada à marca serão sempre, pelo menos, objectivos secundários de uma qualquer campanha publicitária. A eficácia surge assim associada inegavelmente à capacidade de memorização por parte do público-alvo das campanhas. Assim, parece consensual que as bases da eficácia publicitária são a memorização e a frequência publicitária no seu conjunto. Os modelos quantitativos existentes baseados em extensas bases de dados utilizados para estimar os resultados de eficácia utilizam sobretudo a relação cobertura/repetição, mas não chegam para saber qual a real eficácia do anúncio face aos objectivos junto de um determinado target. É possível aumentar a eficácia, através da criatividade, sem aumentar a pressão publicitária. Num contexto de saturação publicitária, o aumento da pressão publicitária não resultará certamente em maior eficácia. A resposta passará pelo poder da criatividade, desde logo nas estratégias do marketing e da comunicação, mas também das próprias mensagens publicitárias e na criatividade ao nível da utilização dos meios, sejam eles mais ou menos tradicionais.

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Large area n-i-p-n-i-p a-SiC:H heterostructures are used as sensing element in a double colour laser scanned photodiode image sensor (D/CLSP). This work aims to clarify possible improvements, physical limits and performance of CLSP image sensor when used as non-pixel image reader. Here, the image capture device and the scanning reader are optimized and the effects of the sensor structure on the output characteristics discussed. The role of the design of the sensing element, the doped layer composition and thickness, the read-out parameters (applied voltage and scanner frequency) on the image acquisition and the colour detection process are analysed. A physical model is presented and supported by a numerical simulation of the output characteristics of the sensor.

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Large area n-i-p-n-i-p a-SiC:H heterostructures are used as sensing element in a Double Color Laser Scanned Photodiode image sensor (D/CLSP). This work aims to clarify possible improvements, physical limits and performance of CLSP image sensor when used as non-pixel image reader. Here, the image capture device and the scanning reader are optimized and the effects of the sensor structure on the output characteristics discussed. The role of the design of the sensing element, the doped layer composition and thickness, the read-out parameters (applied voltage and scanner frequency) on the image acquisition and the color detection process are analyzed. A physical model is presented and supported by a numerical simulation of the output characteristics of the sensor.

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This study examines the role of illiquidity (proxied by the proportion of zero returns) as an additional risk factor in asset pricing. We use Portuguese monthly data, covering the period between January 1988 and December 2008. We compute an illiquidity factor using the Fama and French [Fama, E. F., and K. R. French (1993), "Common risk factors in the returns on stocks and bonds", Journal of Financial Economics, Vol. 33, Nº. 1, pp. 3-56] procedure and analyze the performance of CAPM, Fama-French three-factor model and illiquidity-augmented versions of these models in explaining both the time-series and the cross-section of returns. Our results reveal that the effect of characteristic liquidity is subsumed by the models considered, but the risk of illiquidity is not priced in the Portuguese stock market.

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Mestrado em Contabilidade e Gestão das Instituições Financeiras

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Background: There are now several lines of evidence to suggest that protein synthesis and translation factors are involved in the regulation of cell proliferation and cancer development. Aims: To investigate gene expression patterns of eukaryotic releasing factor 3 (eRF3) in gastric cancer. Methods: RNA was prepared from 25 gastric tumour biopsies and adjacent non-neoplastic mucosa. Real time TaqMan reverse transcription polymerase chain reaction (RT-PCR) was performed to measure the relative gene expression levels. DNA was isolated from tumour and normal tissues and gene dosage was determined by a quantitative real time PCR using SYBR Green dye. Results: Different histological types of gastric tumours were analysed and nine of the 25 tumours revealed eRF3/GSPT1 overexpression; moreover, eight of the 12 intestinal type carcinomas analysed overexpressed the gene, whereas eRF3/GSPT1 was overexpressed in only one of the 10 diffuse type carcinomas (Kruskal-Wallis Test; p , 0.05). No correlation was found between ploidy and transcript expression levels of eRF3/GSPT1. Overexpression of eRF3/GSPT1 was not associated with increased translation rates because the upregulation of eRF3/GSPT1 did not correlate with increased eRF1 levels. Conclusions: Overexpression of eRF3/GSPT1 in intestinal type gastric tumours may lead to an increase in the translation efficiency of specific oncogenic transcripts. Alternatively, eRF3/GSPT1 may be involved in tumorigenesis as a result of its non-translational roles, namely (dis)regulating the cell cycle, apoptosis, or transcription.

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Introduction - Obesity became a major public health problem as a result of its increasing prevalence worldwide. Paraoxonase-1 (PON1) is an esterase able to protect membranes and lipoproteins from oxidative modifications. At the PON1 gene, several polymorphisms in the promoter and coding regions have been identified. The aims of this study were i) to assess PON1 L55M and Q192R polymorphisms as a risk factor for obesity in women; ii) to compare PON1 activity according to the expression of each allele in L55M and Q192R polymorphisms; iii) to compare PON1 activity between obese and normal-weight women. Materials and methods - We studied 75 healthy (35.9±8.2 years) and 81 obese women (34.3±8.2 years). Inclusion criteria for obese subjects were body mass index ≥30 kg/m2 and absence of inflammatory/neoplasic conditions or kidney/hepatic dysfunction. The two PON1 polymorphisms were assessed by real-time PCR with TaqMan probes. PON1 enzymatic activity was assessed by spectrophotometric methods, using paraoxon as a substrate. Results - No significant differences were found for PON1 activity between normal and obese women. Nevertheless, PON1 activity was greater (P<0.01) for the RR genotype (in Q192R polymorphism) and for the LL genotype (in L55M polymorphism). The frequency of allele R of Q192R polymorphism was significantly higher in obese women (P<0.05) and was associated with an increased risk of obesity (odds ratio=2.0 – 95% confidence interval (1.04; 3.87)). Conclusion - 55M and Q192R polymorphisms influence PON1 activity. The allele R of the Q192R polymorphism is associated with an increased risk for development of obesity among Portuguese Caucasian premenopausal women.

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The histone deacetylase inhibitors sodium butyrate (NaBu) and trichostatin A (TSA) exhibit anti-proliferative activity by causing cell cycle arrest and apoptosis. The mechanisms by which NaBu and TSA cause apoptosis and cell cycle arrest are not yet completely clarified, although these agents are known to modulate the expression of several genes including cell-cycle- and apoptosis-related genes. The enzymes involved in the process of translation have important roles in controlling cell growth and apoptosis, and several of these translation factors have been described as having a causal role in the development of cancer. The expression patterns of the translation mechanism, namely of the elongation factors eEF1A1 and eEF1A2, and of the termination factors eRF1 and eRF3, were studied in the breast cancer cell line MCF-7 by real-time quantitative reverse transcription-polymerase chain reaction after a 24-h treatment with NaBu and TSA. NaBu induced inhibition of translation factors' transcription, whereas TSA caused an increase in mRNA levels. Thus, these two agents may modulate the expression of translation factors through different pathways. We propose that the inhibition caused by NaBu may, in part, be responsible for the cell cycle arrest and apoptosis induced by this agent in MCF-7 cells.

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A literatura descreve que as ações de endomarketing contribuem para a aproximação, interação e o bom relacionamento entre a empresa e os colaboradores. Sendo caracterizada como uma relação de troca, na qual a empresa fornece instrumentos capazes de promover o interesse dos colaboradores a participarem mais ativamente em toda a envolvente organizacional - através de ações que favoreçam o diálogo, sugestões, partilha de conhecimentos e informações, bem como relatos de situações corriqueiras ocorridas no dia-a-dia da empresa. Em contrapartida, à medida que o colaborador se sente valorizado pela empresa e pelos colegas, passa a perceber a importância do seu trabalho dentro da organização, permitindo que identifique o trabalho como algo prazeroso. Esta troca contribui para a produtividade e desenvolvimento da empresa, bem como a satisfação e empenhamento dos colaboradores no trabalho. Neste sentido, visando averiguar como esta situação é vivenciada na prática decidiu-se pela realização de um estágio, tendo como questão de estudo qual o impacto das ações de endomarketing no empenhamento e na satisfação dos clientes internos da PT PRO? Para responder a tal indagação foi realizado um estágio na empresa PT PRO, nomeadamente na Direção de Desenvolvimento de Mercado, Marketing e Formação. Apresentam-se ao longo do presente relatório a descrição das especificidades do estágio, tal como as principais ilações daí retiradas tendo através de pesquisas documentais (arquivo e documentos internos e Intranet), reuniões com a Direção de Marketing e com a tutora do estágio, tal como pela aplicação e análise de um inquérito por questionário aplicado aos colaboradores, chegando-se à conclusão que, de facto, as ações de endomarketing levadas a cabo pela empresa contribuem para o empenhamento e satisfação dos colaboradores.

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Mestrado em Gestão e Empreendedorismo

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We investigate the structural and thermodynamic properties of a model of particles with 2 patches of type A and 10 patches of type B. Particles are placed on the sites of a face centered cubic lattice with the patches oriented along the nearest neighbor directions. The competition between the self- assembly of chains, rings, and networks on the phase diagram is investigated by carrying out a systematic investigation of this class of models, using an extension ofWertheim's theory for associating fluids and Monte Carlo numerical simulations. We varied the ratio r epsilon(AB)/epsilon(AA) of the interaction between patches A and B, epsilon(AB), and between A patches, epsilon(AA) (epsilon(BB) is set to theta) as well as the relative position of the A patches, i.e., the angle. between the (lattice) directions of the A patches. We found that both r and theta (60 degrees, 90 degrees, or 120 degrees) have a profound effect on the phase diagram. In the empty fluid regime (r < 1/2) the phase diagram is reentrant with a closed miscibility loop. The region around the lower critical point exhibits unusual structural and thermodynamic behavior determined by the presence of relatively short rings. The agreement between the results of theory and simulation is excellent for theta = 120 degrees but deteriorates as. decreases, revealing the need for new theoretical approaches to describe the structure and thermodynamics of systems dominated by small rings. (C) 2014 AIP Publishing LLC.

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Purpose: Pressure ulcers are a high cost, high volume issue for health and medical care providers, having a detrimental effect on patients and relatives. Pressure ulcer prevention is widely covered in the literature, but little has been published regarding the risk to patients in the radiographical setting. This review of the current literature is to identify findings relevant to radiographical context. Methods: Literature searching was performed using Science Direct and Medline databases. The search was limited to articles published in the last ten years to remain current and excluded studies containing participants less than 17 years of age. In total 14 studies were acquired; three were excluded as they were not relevant. The remaining 11 studies were compared and reviewed. Discussion: Eight of the studies used ‘healthy’ participants and three used symptomatic participants. Nine studies explored interface pressure with a range of pressure mat technologies, two studies measured shear (MRI finite element modelling, and a non-invasive instrument), and one looked at blood flow and haemoglobin oxygenation. A range of surfaces were considered from trauma, nursing and surgical backgrounds for their ability to reduce pressure including standard mattresses, high specification mattresses, rigid and soft layer spine boards, various overlays (gel, air filled, foam). Conclusion: The current literature is not appropriate for the radiographic patient and cannot be extrapolated to a radiologic context. Sufficient evidence is presented in this review to support the need for further work specific to radiography in order to minimise the development of PU in at risk patients.

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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.