Gestão do risco de crédito e rentabilidade dos bancos: o caso português

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 do Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira.

Determinantes da estrutura de capitais do setor do calçado em Portugal

Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação de Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira

A estruturação dos determinantes relevantes no processo de decisão do investimento direto estrangeiro em Portugal e em Espanha

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 do Dr. Luís Pereira Gomes

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

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.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Cultura organizacional e desempenho: o impacto da cultura organizacional no desempenho financeiro

Esta dissertação visa o estudo da influência da cultura organizacional no desempenho financeiro das organizações. Nesse contexto, procuramos analisar qual a cultura predominante das organizações, de forma a estabelecer posteriormente uma relação entre a cultura e o desempenho das empresas. Para isso a metodologia seguida foi a realização de um inquérito por questionário a empresas da região Douro de Portugal no sentido de obter, através de uma adaptação ao instrumento desenvolvido por Cameron e Quinn (2006), a cultura predominante da empresa, os indicadores financeiros necessários ao nosso estudo assim como, uma caracterização da amostra recolhida. Para análise e tratamento dos dados recolhidos através do inquérito por questionário foi utilizada a ferramenta estatística SPSS que nos permitiu retirar ilações sobre as características da amostra, assim como sobre a relação existente entre cultura organizacional e desempenho financeiro, esta relação foi avaliada através de testes de correlação e regressão linear múltipla. Os resultados sugerem que as variáveis culturais, cultura adocrática, mercado e hierárquica e o número de colaboradores explicam em cerca de 20% o resultado líquido ajustado. Também se verificou um efeito positivo da cultura adocrática e de mercado, embora o efeito da cultura de mercado seja mais forte que o da adocrática, e o efeito negativo da cultura hierárquica, ainda que estes resultados não sejam estatisticamente significativos. Não existem evidências que os tipos de cultura analisados (adocrática, de mercado e hierárquica) estão significativamente associados ao desempenho financeiro, avaliado pelos resultados líquidos ajustados, das empresas analisadas, quer pelos testes de correlação quer pelos resultados da estimação do modelo de regressão linear múltipla.

Task partitioning and priority assignment for distributed hard real-time systems

In this paper, we propose the Distributed using Optimal Priority Assignment (DOPA) heuristic that finds a feasible partitioning and priority assignment for distributed applications based on the linear transactional model. DOPA partitions the tasks and messages in the distributed system, and makes use of the Optimal Priority Assignment (OPA) algorithm known as Audsley’s algorithm, to find the priorities for that partition. The experimental results show how the use of the OPA algorithm increases in average the number of schedulable tasks and messages in a distributed system when compared to the use of Deadline Monotonic (DM) usually favoured in other works. Afterwards, we extend these results to the assignment of Parallel/Distributed applications and present a second heuristic named Parallel-DOPA (P-DOPA). In that case, we show how the partitioning process can be simplified by using the Distributed Stretch Transformation (DST), a parallel transaction transformation algorithm introduced in [1].

Continuidade de cuidados numa Unidade de Saúde Familiar : o efeito nos custos

RESUMO - Caracterização do problema: O sistema de saúde português atingiu um patamar de ineficiência tal que urge ser reestruturado de forma a torná-lo sustentável. De forma a atingir este nível de sustentabilidade, uma série de soluções podem ser consideradas das quais destacamos a integração de cuidados. Este conceito exige que os diferentes níveis de saúde sigam um único caminho, trabalhando de forma coordenada e contínua. A integração de cuidados pode ser implementada através de várias tipologias entre as quais se destaca a integração clínica que por sua vez é composta pela continuidade de cuidados. Assim, ao medir a continuidade de cuidados, quantifica-se de certa forma a integração de cuidados. Objetivos: Avaliar o impacto da continuidade de cuidados nos custos. Metodologia: Os dados foram analisados através de estatísticas descritivas para verificar o seu grau de normalidade. Posteriormente foram aplicados testes t-student para analisar a existência de diferenças estatisticamente significativas entre as médias das diferentes variáveis. Foi então estudado o grau de associação entre variáveis através da correlação de spearman. Por fim, foi utilizado o modelo de regressão log-linear para verificar a existência de uma relação entre as várias naturezas de custos e os índices de continuidade. Com base neste modelo foram simulados dois cenários para estimar o impacto da maximização da continuidade de cuidados nas várias naturezas de custos. Conclusões: No geral, verifica-se uma relação muito ligeira entre a continuidade de cuidados e os custos. Mais especificamente, uma relação mais duradoura entre o médico e o doente resulta numa poupança de custos, independentemente da tipologia. Analisando a densidade da relação, observa-se uma relação positiva entre a mesma e os custos totais e o custo com Meios Complementares de Diagnóstico e Terapêutica (MCDT). Contudo verifica-se uma relação médico-doente negativa entre a densidade e os custos com medicamentos e com pessoal. Ao analisar o impacto da continuidade de cuidados nos custos, conclui-se que apenas a duração da relação médico-doente tem um impacto negativo em todas as categorias de custos, exceto o custo com medicamentos. A densidade de cuidados tem um impacto negativo apenas no custo com pessoal, influenciando positivamente as outras categorias de custos. Extrapolando para o nível nacional se o nível de densidade de uma relação fosse maximizado, existiria uma poupança de 0,18 euros, por ano, em custos com pessoal.

Fossil fuel dependence and interfuel substitution in the electricity sector of Western European countries between 1990-2011

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Sexual dysfunction and dissatisfaction in chronic hepatitis C patients

IntroductionThe prevalence of sexual dysfunction (SD) and dissatisfaction with sexual life (DSL) in patients with chronic hepatitis C virus infection (CHC) was jointly investigated via a thorough psychopathological analysis, which included dimensions such as fatigue, impulsiveness, psychiatric comorbidity, health-related quality of life (HRQL) and sociodemographic and clinical characteristics.MethodsMale and female CHC patients from an outpatient referral center were assessed using the Brief Fatigue Inventory, the Barrat Impulsiveness Scale, the Beck Depression Inventory (BDI), the Hospital Anxiety and Depression Scale, the Hamilton Anxiety Scale (HAM-A), and the World Health Organization Quality of Life Scale-Brief Version (WHOQOL-BREF). Structured psychiatric interviews were performed according to the Mini-International Neuropsychiatric Interview. SD was assessed based on specific items in the BDI (item 21) and the HAM-A (item 12). DSL was assessed based on a specific question in the WHOQOL-BREF (item 21). Multivariate analysis was performed according to an ordinal linear regression model in which SD and DSL were considered as outcome variables.ResultsSD was reported by 60 (57.1%) of the patients according to the results of the BDI and by 54 (51.4%) of the patients according to the results of the HAM-A. SD was associated with older age, female gender, viral genotype 2 or 3, interferon-α use, impulsiveness, depressive symptoms, antidepressant and benzodiazepine use, and lower HRQL. DSL was reported by 34 (32.4%) of the patients and was associated with depressive symptoms, anxiety symptoms, antidepressant use, and lower HRQL.ConclusionsThe prevalence of SD and DSL in CHC patients was high and was associated with factors, such as depressive symptoms and antidepressant use. Screening and managing these conditions represent significant steps toward improving medical assistance and the HRQL of CHC patients.

Otimização de esquemas terapêuticos do carcinoma da próstata envolvendo braquiterapia de baixa taxa de dose e radioterapia externa : abordagens física e radiobiológica

RESUMO: Este trabalho teve como objetivo a determinação de esquemas de tratamento alternativos para o carcinoma da próstata com radioterapia externa (EBRT) e braquiterapia de baixa taxa de dose (LDRBT) com implantes permanentes de Iodo-125, biologicamente equivalentes aos convencionalmente usados na prática clínica, com recurso a modelos teóricos e a métodos de Monte Carlo (MC). Os conceitos de dose biológica efetiva (BED) e de dose uniforme equivalente (EUD) foram utilizados, com o modelo linear-quadrático (LQ), para a determinação de regimes de tratamento equivalentes. Numa primeira abordagem, utilizou-se a BED para determinar: 1) esquemas hipofracionados de EBRT mantendo as complicações retais tardias de regimes convencionais com doses totais de 75,6 Gy, 77,4 Gy, 79,2 Gy e 81,0 Gy; e 2) a relação entre as doses totais de EBRT e LDRBT de modo a manter a BED do regime convencional de 45 Gy de EBRT e 110 Gy de LDRBT. Numa segunda abordagem, recorreu-se ao código de MC MCNPX para a simulação de distribuições de dose de EBRT e LDRBT em dois fantomas de voxel segmentados a partir das imagens de tomografia computorizada de pacientes com carcinoma da próstata. Os resultados das simulações de EBRT e LDRBT foram somados e determinada uma EUD total de forma a obterem-se: 1) esquemas equivalentes ao tratamento convencional de 25 frações de 1,8 Gy de EBRT em combinação com 110 Gy de LDRBT; e 2) esquemas equivalentes a EUD na próstata de 67 Gy, 72 Gy, 80 Gy, 90 Gy, 100 Gy e 110 Gy. Em todos os resultados nota-se um ganho terapêutico teórico na utilização de esquemas hipofracionados de EBRT. Para uma BED no reto equivalente ao esquema convencional, tem-se um aumento de 2% na BED da próstata com menos 5 frações. Este incremento dá-se de forma cada vez mais visível à medida que se reduz o número de frações, sendo da ordem dos 10-11% com menos 20 frações e dos 35-45% com menos 40 frações. Considerando os resultados das simulações de EBRT, obteve-se uma EUD média de 107 Gy para a próstata e de 42 Gy para o reto, com o esquema convencional de 110 Gy de LDRBT, seguidos de 25 frações de 1,8 Gy de EBRT. Em termos de probabilidade de controlo tumoral (igual EUD), é equivalente a este tratamento a administração de EBRT em 66 frações de 1,8 Gy, 56 de 2 Gy, 40 de 2,5 Gy, 31 de 3 Gy, 20 de 4 Gy ou 13 de 5 Gy. Relativamente à administração de 66 frações de 1,8 Gy, a EUD generalizada no reto reduz em 6% com o recurso a frações de 2,5 Gy e em 10% com frações de 4 Gy. Determinou-se uma BED total de 162 Gy para a administração de 25 frações de 1,8 Gy de EBRT em combinação com 110 Gy de LDRBT. Variando-se a dose total de LDRBT (TDLDRBT) em função da dose total de EBRT (TDEBRT), de modo a garantir uma BED de 162 Gy, obteve-se a seguinte relação:.......... Os resultados das simulações mostram que a EUD no reto diminui com o aumento da dose total de LDRBT para dose por fração de EBRT (dEBRT) inferiores a 2, Gy e aumenta para dEBRT a partir dos 3 Gy. Para quantidades de TDLDRBT mais baixas (<50 Gy), o reto beneficia de frações maiores de EBRT. À medida que se aumenta a TDLDRBT, a EUD generalizada no reto torna-se menos dependente da dEBRT. Este trabalho mostra que é possível a utilização de diferentes regimes de tratamento para o carcinoma da próstata com radioterapia que possibilitem um ganho terapêutico, quer seja administrando uma maior dose biológica com efeitos tardios constantes, quer mantendo a dose no tumor e diminuindo a toxicidade retal. A utilização com precaução de esquemas hipofracionados de EBRT, para além do benefício terapêutico, pode trazer vantagens ao nível da conveniência para o paciente e economia de custos. Os resultados das simulações deste estudo e conversão para doses de efeito biológico para o tratamento do carcinoma da próstata apresentam linhas de orientação teórica de interesse para novos ensaios clínicos. --------------------------------------------------ABSTRACT: The purpose of this work was to determine alternative radiotherapy regimens for the treatment of prostate cancer using external beam radiotherapy (EBRT) and low dose-rate brachytherapy (LDRBT) with Iodine-125 permanent implants which are biologically equivalent to conventional clinical treatments, by the use of theoretical models and Monte Carlo techniques. The concepts of biological effective dose (BED) and equivalent uniform dose (EUD), together with the linear-quadratic model (LQ), were used for determining equivalent treatment regimens. In a first approach, the BED concept was used to determine: 1) hypofractionated schemes of EBRT maintaining late rectal complications as with the conventional regimens with total doses of 75.6 Gy, 77.4 Gy, 79.2 Gy and 81.0 Gy; and 2) the relationship between total doses of EBRT and LDRBT in order to keep the BED of the conventional treatment of 45 Gy of EBRT and 110 Gy of LDRBT. In a second approach, the MC code MCNPX was used for simulating dose distributions of EBRT and LDRBT in two voxel phantoms segmented from the computed tomography of patients with prostate cancer. The results of the simulations of EBRT and LDRBT were added up and given an overall EUD in order to obtain: 1) equivalent to conventional treatment regimens of 25 fraction of 1.8 Gy of EBRT in combination with 110Gy of LDRBT; and 2) equivalent schemes of EUD of 67 Gy, 72 Gy, 80 Gy, 90 Gy, 100 Gy, and 110Gy to the prostate. In all the results it is noted a therapeutic gain using hypofractionated EBRT schemes. For a rectal BED equivalent to the conventional regimen, an increment of 2% in the prostate BED was achieved with less 5 fractions. This increase is visibly higher as the number of fractions decrease, amounting 10-11% with less 20 fractions and 35-45% with less 20 fractions. Considering the results of the EBRT simulations an average EUD of 107 Gy was achieved for the prostate and of 42 Gy for the rectum with the conventional scheme of 110 Gy of LDRBT followed by 25 fractions of 1.8 Gy of EBRT. In terms of tumor control probability (same EUD) it is equivalent to this treatment, for example, delivering the EBRT in 66 fractions of 1.8 Gy, 56 fractions of 2 Gy, 40 fractions of 2.5 Gy, 31 fractions of 3 Gy, 20 fractions of 4 Gy or 13 fractions of 5 Gy. Regarding the use of 66 fractions of 1.8 Gy, the rectum EUD is reduced to 6% with 2.5 Gy per fraction and to 10% with 4 Gy. A total BED of 162 Gy was achieved for the delivery of 25 fractions of 1.8 Gy of EBRT in combination with 110 Gy of LDRBT. By varying the total dose of LDRBT (TDLDRBT) with the total dose of EBRT (TDEBRT) so as to ensure a BED of 162 Gy, the following relationship was obtained: ....... The simulation results show that the rectum EUD decreases with the increase of the TDLDRBT, for EBRT dose per fracion (dEBRT) less than 2.5 Gy and increases for dEBRT above 3 Gy. For lower amounts of TDLDRBT (< 50Gy), the rectum benefits of larger EBRT fractions. As the TDLDRBT increases, the rectum gEUD becomes less dependent on the dEBRT. The use of different regimens which enable a therapeutic gain, whether deivering a higher dose with the same late biological effects or maintaining the dose to the tumor and reducing rectal toxicity is possible. The use with precaution of hypofractionated regimens, in addition to the therapeutic benefit, can bring advantages in terms of convenience for the patient and cost savings. The simulation results of this study together with the biological dose conversion for the treatment of prostate cancer serve as guidelines of interest for new clinical trials.

Annular flow of viscoelastic fluids: Analytical and numerical solutions

This work provides analytical and numerical solutions for the linear, quadratic and exponential Phan–Thien–Tanner (PTT) viscoelastic models, for axial and helical annular fully-developed flows under no slip and slip boundary conditions, the latter given by the linear and nonlinear Navier slip laws. The rheology of the three PTT model functions is discussed together with the influence of the slip velocity upon the flow velocity and stress fields. For the linear PTT model, full analytical solutions for the inverse problem (unknown velocity) are devised for the linear Navier slip law and two different slip exponents. For the linear PTT model with other values of the slip exponent and for the quadratic PTT model, the polynomial equation for the radial location (β) of the null shear stress must be solved numerically. For both models, the solution of the direct problem is given by an iterative procedure involving three nonlinear equations, one for β, other for the pressure gradient and another for the torque per unit length. For the exponential PTT model we devise a numerical procedure that can easily compute the numerical solution of the pure axial flow problem

The influence of the conservation status and changes in the rainfall regime on forest-savanna mosaic dynamics in Northern Brazilian Amazonia

Coupled carbon/climate models are predicting changes in Amazon carbon and water cycles for the near future, with conversion of forest into savanna-like vegetation. However, empirical data to support these models are still scarce for Amazon. Facing this scenario, we investigated whether conservation status and changes in rainfall regime have influenced the forest-savanna mosaic over 20 years, from 1986 to 2006, in a transitional area in Northern Amazonia. By applying a spectral linear mixture model to a Landsat-5-TM time series, we identified protected savanna enclaves within a strictly protected nature reserve (Maracá Ecological Station - MES) and non-protected forest islands at its outskirts and compared their areas among 1986/1994/2006. The protected savanna enclaves decreased 26% in the 20-years period at an average rate of 0.131 ha year-1, with a greater reduction rate observed during times of higher precipitation, whereas the non-protected forest islands remained stable throughout the period of study, balancing the encroachment of forests into the savanna during humid periods and savannization during reduced rainfall periods. Thus, keeping favorable climate conditions, the MES conservation status would continue to favor the forest encroachment upon savanna, while the non-protected outskirt areas would remain resilient to disturbance regimes. However, if the increases in the frequency of dry periods predicted by climate models for this region are confirmed, future changes in extension and directions of forest limits will be affected, disrupting ecological services as carbon storage and the maintenance of local biodiversity.