Aplicação de um ciclo orgânico de Rankine à indústria Naval

Trabalho Final de Mestrado para obtenção de grau de Mestre em Engenharia Mecânica

Análise estática e dinâmica de painéis corrugados construídos em materiais compósitos

Os painéis corrugados têm cada vez maior aplicabilidade em engenharia. São frequentemente utilizados em estruturas sandwich, em telhados e têm muitas outras aplicações nas indústrias civil, mecânica, aeronáutica e naval. Esta crescente aplicação deve-se ao facto de as suas corrugações conferirem maior rigidez ao painel devido ao aumento do rácio resistência/peso, evitando-se assim o recurso a reforços estruturais. Este facto, associado às características dos materiais compósitos laminados, que comparativamente com os materiais “tradicionais”, evidenciam uma relação rigidez/peso, bem como um comportamento mecânico; no mesmo sentido, contribui para uma potencial utilização deste tipo de solução, em variadíssimas situações na área das engenharias. Este tipo de painéis é particularmente adequado em situações de carga em que se verificam esforços de compressão e torção. Com o presente trabalho, pretende-se efectuar um estudo de análise do comportamento mecânico, quer em termos de análise estática linear, quer de vibrações livres de um painel corrugado em material compósito, sendo analisadas várias variáveis, nomeadamente: o tipo de material, a geometria da corrugação, a sequência de empilhamento das camadas de fibra, entre outras. Para este efeito, e após o enquadramento do tipo de problema que se possui, é utilizado o software comercial de análise por elementos finitos, Ansys®.

José Régio e a moda

Qual foi a relação de José Régio com a moda? Dos indícios documentais biográficos, releva uma relação com a indumentária pessoal muito sóbria, muito menos preocupada que a de outros escritores, mas igualmente longe do desleixo. Na sua obra, as descrições de vestuário e as referências à moda são parcas e multo limitadas e nem mesmo a excepção de ter escrito um conto sobre um vestido questiona uma atitude geral de alheamento e desinteresse. ABSTRACT - What was the relationship between José Régio and Fashion? His biography reveals a very sober personal relationship with clothing, much less worried than other writers, but also away from the carelessness. In his work, descriptions of clothing and references to fashion are scarce and limited and not even have written a short story about one dress changes a general attitude of aloofness and detachment.

O estilo interpessoal de comunicação dos líderes transformacionais e transaccionais: seu impacto no empenhamento organizacional

Tendo como moldura teórica o modelo multifactorial de liderança que traça a destrinça entre líderes transformacionais e transaccionais, e dando especial enfoque ao estilo interpessoal de comunicação diferenciado que os mesmos utilizam para comunicar com os seus colaboradores em cenários de trabalho, o presente trabalho de investigação tem como principal objectivo analisar o impacto que as dimensões que compõem as lideranças transformacionais e transaccionais exercem no empenhamento que os colaboradores nutrem em relação às suas organizações de pertença. Para o efeito, foi levado a cabo um estudo comparativo junto de duas organizações situadas em zonas geograficamente distintas, Norte e Centro Litoral de Portugal que se dedicam ao mesmo ramo de actividade: a construção e reparação naval. Através da aplicação de inquéritos por questionário junto de 289 sujeitos, foi possível efectuar a análise empírica. Não obstante a revisão da literatura por nós efectuada indicar que são os líderes transformacionais aqueles que contribuem em maior grau para elevar o empenhamento organizacional dos seus seguidores, os resultados obtidos não confirmam a hipótese de trabalho formulada. Se por um lado, na empresa situada no Centro litoral do nosso país, concluiu-se serem as dimensões transformacionais de liderança que maior correlação positiva estabelece com o empenhamento organizacional, por outro lado, na empresa sediada no Norte litoral, são as dimensões transaccionais de liderança que maior associação estabelece com a referida atitude organizacional. Estamos em crer que esta ambivalência de resultados se deve, primordialmente, à existência de variáveis contingenciais que poderão estar a moderar a relação estudada. Aliás a necessidade de contextualizar a liderança transformacional e transaccional é uma das principais sugestões que indicamos para futuras investigações.

Eça de Queiroz e a moda

RESUMO: Eça de Queiroz foi uma personalidade impar da literatura portuguesa, sendo as suas obras reflexo do tempo que viveu e da sociedade em que se integrou, verificando-se que estas, estão recheadas de referências à indumentária e às modas que se iam sucedendo e que muitas vezes descreveu. Dos indícios documentais biográficos, releva uma relação com a indumentária pessoal muito atenta e cuidada quase podendo ser considerado um dandy.

Garrett e a moda

Almeida Garrett foi uma personalidade fulcral para a cultura da primeira metade do século XIX em Portugal. As suas obras reflectem o tempo em que viveu, as ideias que defendeu e têm também referências à indumentária e à moda. Foi o editor do periódico de moda O Toucador. A sua biografia revela uma relação com a indumentária pessoal muito atenta e cuidada o que permite que seja considerado um dos primeiros dandies portugueses.

Leonel Duarte Ferreira (1894-1959) : vida e obra musical

Tese apresentada para o cumprimento dos requisitos necessários à obtenção do grau de Doutor no ramo de Ciências Musicais

18F-FDG measurement in primary lung cancer: SUV normalization to different distribution volumes

Introduction: Standard Uptake Value (SUV) is a measurement of the uptake in a tumour normalized on the basis of a distribution volume and is used to quantify 18F-Fluorodeoxiglucose (FDG) uptake in tumors, such as primary lung tumor. Several sources of error can affect its accuracy. Normalization can be based on body weight, body surface area (BSA) and lean body mass (LBM). The aim of this study is to compare the influence of 3 normalization volumes in the calculation of SUV: body weight (SUVW), BSA (SUVBSA) and LBM (SUVLBM), with and without glucose correction, in patients with known primary lung tumor. The correlation between SUV and weight, height, blood glucose level, injected activity and time between injection and image acquisition is evaluated. Methods: Sample included 30 subjects (8 female and 22 male) with primary lung tumor, with clinical indication for 18F-FDG Positron Emission Tomography (PET). Images were acquired on a Siemens Biography according to the department’s protocol. Maximum pixel SUVW was obtained for abnormal uptake focus through semiautomatic VOI with Quantification 3D isocontour (threshold 2.5). The concentration of radioactivity (kBq/ml) was obtained from SUVW, SUVBSA, SUVLBM and the glucose corrected SUV were mathematically obtained. Results: Statistically significant differences between SUVW, SUVBSA and SUVLBM and between SUVWgluc, SUVBSAgluc and SUVLBMgluc were observed (p=0.000<0.05). The blood glucose level showed significant positive correlations with SUVW (r=0.371; p=0.043) and SUVLBM (r=0.389; p=0.034). SUVBSA showed independence of variations with the blood glucose level. Conclusion: The measurement of a radiopharmaceutical tumor uptake normalized on the basis of different distribution volumes is still variable. Further investigation on this subject is recommended.

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.