Hélices e Espirais em Micro e Nanofibras Celulósicas

A celulose é o polímero renovável mais abundante do mundo. É conhecido pela sua excelente biocompatibilidade, propriedades térmicas e mecânicas. A celulose assim como os polipéptideos e o ADN, pertence a uma família de moléculas orgânicas que dão origem à formação de fases líquidas cristalinas (LCs) colestéricas. A Passiflora Edulis, tal como outras plantas trepadeiras, possui longas e flexíveis gavinhas que permitem à planta encontrar um suporte para se fixar. As gavinhas podem assumir a forma de espirais ou de hélices consoante sejam sustentadas por apenas uma ou por ambas as extremidades. As hélices apresentam muitas vezes duas porções helicoidais, uma esquerda e outra direita, separadas por um segmento recto denominado perversão. Este comportamento é consequência da curvatura intrínseca das gavinhas produzidas pela planta trepadeira. O mesmo comportamento pode ser observado em micro e nanofibras celulósicas fabricadas a partir de soluções líquido-cristalinas, numa escala três a quatro ordens de grandeza inferior à das gavinhas. Este facto sugere que o modelo físico utilizado tenha invariância de escala. Neste trabalho é feito o estudo de fibras e jactos que imitam as estruturas helicoidais apresentadas pelas gavinhas das plantas trepadeiras. As fibras e jactos são produzidos a partir de soluções líquidas cristalinas celulósicas. De modo a determinar as características morfológicas e estruturais, que contribuem para a curvatura das fibras, foram utilizadas técnicas de imagem por ressonância magnética (MRI), microscopia óptica com luz polarisada (MOP), microscopia electrónica de varrimento (SEM) e microscopia de força atómica (AFM) . A variação da forma das estruturas helicoidais com a temperatura parece ser relevante para o fabrico de membranas não tecidas para aplicação em sensores termo-mecânicos.

Contribuição para a adaptação e validação da versão portuguesa da Motor Assessment Scale

Introdução: A Motor Assessment Scale (MAS) tem mostrado ser um instrumento válido e fidedigno na avaliação do progresso clínico de indivíduos que sofreram um Acidente Vascular Cerebral (AVC). Objectivos: Traduzir e adaptar a MAS à realidade portuguesa e contribuir para a validação da versão portuguesa, avaliando a sua consistência interna. Metodologia: Após um processo de tradução, revisão por peritos, retroversão e comparação com a versão original, obteve-se a versão portuguesa da MAS. Procedeu-se a um estudo correlacional transversal para avaliação da consistência interna; a amostra final incluiu 30 sujeitos, 16 do sexo masculino e 14 do sexo feminino, com idades entre os 42 e 85 anos (média de 64±11,85 anos), com hemiparésia ou hemiplegia decorrente de AVC e que realizavam fisioterapia em um de 6 Hospitais seleccionados por conveniência; a média do tempo de diagnóstico foi de 306±1322,82 dias e do tempo de fisioterapia foi de 47±57,57 dias. Resultados: Obteve-se uma média de 24±14,51 pontos nas pontuações totais e um coeficiente de Alfa de Cronbach de 0,939, sem a exclusão de qualquer item; as correlações inter item variaram entre 0,395 e 0,916. Conclusões: Apesar da reduzida amostra e da sua heterogeneidade nas características e pontuações da escala, a Versão Portuguesa da MAS apresentou uma forte consistência interna, verificando-se que os itens estão, na sua maioria, muito correlacionados entre si, o que sustenta a adequação de cada item e apoia que, de forma geral, esta escala tem uma concepção lógica e estruturada.

Validation of a measurement scale for the relationship between the orientation to corporate social responsibility and other business strategic variables

The importance of Social Responsibility (SR) is higher if this business variable is related with other ones of strategic nature in business activity (competitive success that the company achieved, performance that the firms develop and innovations that they carries out). The hypothesis is that organizations that focus on SR are those who get higher outputs and innovate more, achieving greater competitive success. A scale for measuring the orientation to SR has defined in order to determine the degree of relationship between above elements. This instrument is original because previous scales do not exist in the literature which could measure, on the one hand, the three classics sub-constructs theoretically accepted that SR is made up and, on the other hand, the relationship between SR and the other variables. As a result of causal relationships analysis we conclude with a scale of 21 indicators, validated scale with a sample of firms belonging to the Autonomous Community of Extremadura and it is the first empirical validation of these dimensions we know so far, in this context.

Assessment of nursing students and nurses' orientation towards patient-centeredness

Background - Being patient centered is a core value for nursing. Patient centered-care has been related to patient and health provider satisfaction, better health outcomes, higher quality of care and more efficient health care delivery. Objectives - The purpose was to assess the orientation adopted by nurses and students in patient care, using The Patient-Practitioner Orientation Scale, as well as to compare the results between resident nurses and students from different academic years. Settings - Public School of Nursing and a Central Hospital, in Lisbon (Portugal). Participants - Students in the first, second and fourth year of nursing school and nurses participated in the study. Methods - For data collection, we used The Patient-Practitioner Orientation Scale (European Portuguese version), an instrument designed to measure individual preferences toward the dimension of caring a sharing in health professional-patient relationship. Students and nurses also filled out two additional questions about their perception of competence in technical and communication skills. Additional demographic information was also collected, including gender, age, academic year and length of professional experience. Results - A total of 525 students (84.7% female) and 108 nurses (77.8% female) participated in this study. In general, caring sub-scores, measuring the preference of about attending to patient emotional aspects, were higher than sharing sub-scores, measuring beliefs about giving information and perceiving patient as a member of the health team. Students were significantly more patient-centered throughout their nursing education (p<0.001). Comparing to students in the second and fourth academic years (p<0.001) nurses' scores were significantly lower both in total PPOS and in caring and sharing subscales. Conclusions - These results reinforce the idea that patient centeredness may be developed in academic context. The scores obtained highlight the importance of studies that aim to identify factors that may explain the decrease of patient centeredness in professional practice.

Development and validation of a psychometric scale for assessing PA chest image quality: a pilot study

Purpose - To develop and validate a psychometric scale for assessing image quality perception for chest X-ray images. Methods - Bandura's theory was used to guide scale development. A review of the literature was undertaken to identify items/factors which could be used to evaluate image quality using a perceptual approach. A draft scale was then created (22 items) and presented to a focus group (student and qualified radiographers). Within the focus group the draft scale was discussed and modified. A series of seven postero-anterior chest images were generated using a phantom with a range of image qualities. Image quality perception was confirmed for the seven images using signal-to-noise ratio (SNR 17.2–36.5). Participants (student and qualified radiographers and radiology trainees) were then invited to independently score each of the seven images using the draft image quality perception scale. Cronbach alpha was used to test interval reliability. Results - Fifty three participants used the scale to grade image quality perception on each of the seven images. Aggregated mean scale score increased with increasing SNR from 42.1 to 87.7 (r = 0.98, P < 0.001). For each of the 22 individual scale items there was clear differentiation of low, mid and high quality images. A Cronbach alpha coefficient of >0.7 was obtained across each of the seven images. Conclusion - This study represents the first development of a chest image quality perception scale based on Bandura's theory. There was excellent correlation between the image quality perception scores derived using the scale and the SNR. Further research will involve a more detailed item and factor analysis.

Study of the production of polyesters for polyurethanes at pilot plant scale

Dissertação para obtenção do grau de Mestre em Engenharia Química

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Contributo para a validação de uma escala pediátrica, Peabody Developmental Motor Scale-2 (versão portuguesa)

Mestrado em Fisioterapia

Development and validation of a psychometric scale for assessing PA chest image quality

Purpose - To develop and validate a psychometric scale for assessing image quality for chest radiographs.

Control of predators in industrial scale microalgae cultures with Pulsed Electric Fields.

This work describes the utilization of Pulsed Electric Fields to control the protozoan contamination of a microalgae culture, in an industrial 2.7m3 microalgae photobioreactor. The contaminated culture was treated with Pulsed Electric Fields, PEF, for 6h with an average of 900V/cm, 65μs pulses of 50Hz. Working with recirculation, all the culture was uniformly exposed to the PEF throughout the assay. The development of the microalgae and protozoan populations was followed and the results showed that PEF is effective on the selective elimination of protozoa from microalgae cultures, inflicting on the protozoa growth halt, death or cell rupture, without affecting microalgae productivity. Specifically, the results show a reduction of the active protozoan population of 87% after 6h treatment and 100% after few days of normal cultivation regime. At the same time, microalgae growth rate remained unaffected. © 2014 Elsevier B.V.

Optimizing memory transactions for large-scale programs

Even though Software Transactional Memory (STM) is one of the most promising approaches to simplify concurrent programming, current STM implementations incur significant overheads that render them impractical for many real-sized programs. The key insight of this work is that we do not need to use the same costly barriers for all the memory managed by a real-sized application, if only a small fraction of the memory is under contention lightweight barriers may be used in this case. In this work, we propose a new solution based on an approach of adaptive object metadata (AOM) to promote the use of a fast path to access objects that are not under contention. We show that this approach is able to make the performance of an STM competitive with the best fine-grained lock-based approaches in some of the more challenging benchmarks. (C) 2015 Elsevier Inc. All rights reserved.

Preserving the validity of the two-Higgs-doublet model up to the Planck scale

We examine the constraints on the two Higgs doublet model (2HDM) due to the stability of the scalar potential and absence of Landau poles at energy scales below the Planck scale. We employ the most general 2HDM that incorporates an approximately Standard Model (SM) Higgs boson with a flavor aligned Yukawa sector to eliminate potential tree-level Higgs-mediated flavor changing neutral currents. Using basis independent techniques, we exhibit robust regimes of the 2HDM parameter space with a 125 GeV SM-like Higgs boson that is stable and perturbative up to the Planck scale. Implications for the heavy scalar spectrum are exhibited.

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.