A divulgação de informação sobre planos de benefícios de reforma em Portugal e a sua relação com o endividamento: Uma análise do PSI geral

Mestrado em Contabilidade e Gestão das Instituições Financeiras

A Evolução da divulgação de informação sobre os benefícios dos empregados: o caso das empresas do PSI 20 nos anos de 2007 a 2009

Mestrado em Contabilidade

Two-way MANCOVA: an application to public health

The aim of this work is to use the MANCOVA model to study the influence of the phenotype of an enzyme - Acid phosphatase - and a genetic factor - Haptoglobin genotype - on two dependent variables - Activity of Acid Phosphatase (ACP1) and the Body Mass Index (BMI). Therefore it's used a general linear model, namely a multivariate analysis of covariance (Two-way MANCOVA). The covariate is the age of the subject. This covariate works as control variable for the independent factors, serving to reduce the error term in the model. The main results showed that only the ACP1 phenotype has a significant effect on the activity of ACP1 and the covariate has a significant effect in both dependent variables. The univariate analysis showed that ACP1 phenotype accounts for about 12.5% of the variability in the activity of ACP1. In respect to this covariate it can be seen that accounts for about 4.6% of the variability in the activity of ACP1 and 37.3% in the BMI.

Nutritional status and overhydration: can bioimpedance spectroscopy be useful in haemodialysis patients?

Background: Protein-energy wasting (PEW), associated with inflammation and overhydration, is common in haemodialysis (HD) patients and is associated with high morbidity and mortality. Objective: Assess the relationship between nutritional status, markers of inflammation and body composition through bioimpedance spectroscopy (BIS) in HD patients. Methods: This observational, cross-sectional, single centre study, carried out in an HD centre in Forte da Casa (Portugal), involved 75 patients on an HD programme. In all participating patients, the following laboratory tests were conducted: haemoglobin, albumin, C-reactive protein (CRP) and 25-hydroxyvitamin D3 [25(OH)D3]. The body mass index of all patients was calculated and a modified version of subjective global assessment (SGA) was produced for patients on dialysis. Intracellular water (ICW) and extracellular water (ECW) were measured by BIS (Body Composition Monitor®, Fresenius Medical Care®) after the HD session. In statistical analysis, Spearman’s correlation was used for the univariate analysis and linear regression for the multivariate analysis (SPSS 14.0). A P value of <.05 was considered statistically significant. Results: PEW, inversely assessed through the ICW/body weight (BW) ratio, was positively related to age (P<.001), presence of diabetes (P=.004), BMI (P=.01) and CRP (P=.008) and negatively related to albumin (p=.006) and 25(OH)D3 (P=.007). Overhydration, assessed directly through the ECW/BW ratio, was positively related with CRP (P=.009) and SGA (P=.03), and negatively with 25(OH)D3 (P=.006) and BMI (P=.01). In multivariate analysis, PEW was associated with older age (P<.001), the presence of diabetes (P=.003), lower 25(OH)D3 (P=.008), higher CRP (P=.001) and lower albumin levels (P=.004). Over-hydration was associated with higher CRP (P=.001) and lower levels of 25(OH)D3 (P=.003). Conclusions: Taking these results into account, the ICW/BW and ECW/BW ratios, assessed with BIS, have proven to be good markers of the nutritional and inflammatory status of HD patients. BIS may be a useful tool for regularly assessing the nutritional and hydration status in these patients and may allow nutritional advice to be improved and adjusted.

Divulgação de informação sobre instrumentos derivados: evidência empírica nas empresas do PSI 20

Mestrado em Contabilidade

O papel do educador no envolvimento das famílias no contexto educativo: a interação dos agentes educativos

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Establishment of optimal physical assets inspection frequency based on risk principles

Risk Based Inspection (RBI) is a risk methodology used as the basis for prioritizing and managing the efforts for an inspection program allowing the allocation of resources to provide a higher level of coverage on physical assets with higher risk. The main goal of RBI is to increase equipment availability while improving or maintaining the accepted level of risk. This paper presents the concept of risk, risk analysis and RBI methodology and shows an approach to determine the optimal inspection frequency for physical assets based on the potential risk and mainly on the quantification of the probability of failure. It makes use of some assumptions in a structured decision making process. The proposed methodology allows an optimization of inspection intervals deciding when the first inspection must be performed as well as the subsequent intervals of inspection. A demonstrative example is also presented to illustrate the application of the proposed methodology.

Exposure assessment: the influence of environmental monitoring methodology

Exposure assessment is an important step of risk assessment process and has evolved more quickly than perhaps any aspect of the four-step risk paradigm (hazard identification, exposure assessment, dose-response analysis, and risk characterization). Nevertheless, some epidemiological studies have associated adverse health effects to a chemical exposure with an inadequate or absent exposure quantification. In addition to the metric used, the truly representation of exposure by measurements depends on: the strategy of sampling, random collection of measurements, and similarity between the measured and unmeasured exposure groups. Two environmental monitoring methodologies for formaldehyde occupational exposure were used to assess the influence of metric selection in exposure assessment and, consequently, in risk assessment process.

The influence of experience and training in a group of novice observers: a jackknife alternative free-response receiver operating characteristic analysis

Purpose - The study evaluates the pre- and post-training lesion localisation ability of a group of novice observers. Parallels are drawn with the performance of inexperienced radiographers taking part in preliminary clinical evaluation (PCE) and ‘red-dot’ systems, operating within radiography practice. Materials and methods - Thirty-four novice observers searched 92 images for simulated lesions. Pre-training and post-training evaluations were completed following the free-response the receiver operating characteristic (FROC) method. Training consisted of observer performance methodology, the characteristics of the simulated lesions and information on lesion frequency. Jackknife alternative FROC (JAFROC) and highest rating inferred ROC analyses were performed to evaluate performance difference on lesion-based and case-based decisions. The significance level of the test was set at 0.05 to control the probability of Type I error. Results - JAFROC analysis (F(3,33) = 26.34, p < 0.0001) and highest-rating inferred ROC analysis (F(3,33) = 10.65, p = 0.0026) revealed a statistically significant difference in lesion detection performance. The JAFROC figure-of-merit was 0.563 (95% CI 0.512,0.614) pre-training and 0.677 (95% CI 0.639,0.715) post-training. Highest rating inferred ROC figure-of-merit was 0.728 (95% CI 0.701,0.755) pre-training and 0.772 (95% CI 0.750,0.793) post-training. Conclusions - This study has demonstrated that novice observer performance can improve significantly. This study design may have relevance in the assessment of inexperienced radiographers taking part in PCE or commenting scheme for trauma.

Market power analysis in the Iberian electricity market using a conjectural variations model

In the last years the electricity industry has faced a restructuring process. Among the aims of this process was the increase in competition, especially in the generation activity where firms would have an incentive to become more efficient. However, the competitive behavior of generating firms might jeopardize the expected benefits of the electricity industry liberalization. The present paper proposes a conjectural variations model to study the competitive behavior of generating firms acting in liberalized electricity markets. The model computes a parameter that represents the degree of competition of each generating firm in each trading period. In this regard, the proposed model provides a powerful methodology for regulatory and competition authorities to monitor the competitive behavior of generating firms. As an application of the model, a study of the day-ahead Iberian electricity market (MIBEL) was conducted to analyze the impact of the integration of the Portuguese and Spanish electricity markets on the behavior of generating firms taking into account the hourly results of the months of June and July of 2007. The advantages of the proposed methodology over other methodologies used to address market power, namely Residual Supply index and Lerner index are highlighted. (C) 2014 Elsevier Ltd. All rights reserved.

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.