Elementos de geoquímica das rochas do maciço ígneo de Sines

The chemical and mineralogical characteristics of the intrusive and extrusive rocks of this igneous complex are presented. The suggest affinities between these rocks. The major and the trace elements lead us to conclude that these rocks were originated in a common magmatic chamber by fractional crystallization.

Performance assessment of a wind energy conversion system using a hierarchical controller structure

This paper deals with a hierarchical structure composed by an event-based supervisor in a higher level and two distinct proportional integral (PI) controllers in a lower level. The controllers are applied to a variable speed wind energy conversion system with doubly-fed induction generator, namely, the fuzzy PI control and the fractional-order PI control. The event-based supervisor analyses the operation state of the wind energy conversion system among four possible operational states: park, start-up, generating or brake and sends the operation state to the controllers in the lower level. In start-up state, the controllers only act on electric torque while pitch angle is equal to zero. In generating state, the controllers must act on the pitch angle of the blades in order to maintain the electric power around the nominal value, thus ensuring that the safety conditions required for integration in the electric grid are met. Comparisons between fuzzy PI and fractional-order PI pitch controllers applied to a wind turbine benchmark model are given and simulation results by Matlab/Simulink are shown. From the results regarding the closed loop point of view, fuzzy PI controller allows a smoother response at the expense of larger number of variations of the pitch angle, implying frequent switches between operational states. On the other hand fractional-order PI controller allows an oscillatory response with less control effort, reducing switches between operational states. (C) 2015 Elsevier Ltd. All rights reserved.

Brownian bridge and other path-dependent Gaussian processes vectorial simulation

The iterative simulation of the Brownian bridge is well known. In this article, we present a vectorial simulation alternative based on Gaussian processes for machine learning regression that is suitable for interpreted programming languages implementations. We extend the vectorial simulation of path-dependent trajectories to other Gaussian processes, namely, sequences of Brownian bridges, geometric Brownian motion, fractional Brownian motion, and Ornstein-Ulenbeck mean reversion process.

New findings on the dynamics of HIV and TB coinfection models

In this paper we study a model for HIV and TB coinfection. We consider the integer order and the fractional order versions of the model. Let α∈[0.78,1.0] be the order of the fractional derivative, then the integer order model is obtained for α=1.0. The model includes vertical transmission for HIV and treatment for both diseases. We compute the reproduction number of the integer order model and HIV and TB submodels, and the stability of the disease free equilibrium. We sketch the bifurcation diagrams of the integer order model, for variation of the average number of sexual partners per person and per unit time, and the tuberculosis transmission rate. We analyze numerical results of the fractional order model for different values of α, including α=1. The results show distinct types of transients, for variation of α. Moreover, we speculate, from observation of the numerical results, that the order of the fractional derivative may behave as a bifurcation parameter for the model. We conclude that the dynamics of the integer and the fractional order versions of the model are very rich and that together these versions may provide a better understanding of the dynamics of HIV and TB coinfection.

Dynamical Stability and Predictability of Football Players: The Study of One Match

The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player’s trajectory. By applying FC, one can benefit from newly considered modeling perspectives, such as the fractional coefficient, to estimate a player’s predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player’s process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match.

On the numerical computation of the Mittag-Leffler function

Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.

Analysis of Forest Fires by means of Pseudo Phase Plane and Multidimensional Scaling Methods

Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

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.

A study on parallel versus sequential relational fuzzy clustering methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Análise e implementação de filtros digitais fraccionários

O projeto realizado teve como tema a aplicação das derivadas e integrais fraccionários para a implementação de filtros digitais numa perspetiva de processamento digital de sinais. Numa primeira fase do trabalho, é efetuado uma abordagem teórica sobre os filtros digitais e o cálculo fraccionário. Estes conceitos teóricos são utilizados posteriormente para o desenvolvimento do presente projeto. Numa segunda fase, é desenvolvida uma interface gráfica em ambiente MatLab, utilizando a ferramenta GUIDE. Esta interface gráfica tem como objetivo a implementação de filtros digitais fraccionários. Na terceira fase deste projeto são implementados os filtros desenvolvidos experimentalmente através do ADSP-2181, onde será possível analisar e comparar os resultados experimentais com os resultados obtidos por simulação no MatLab. Como quarta e última fase deste projeto é efetuado uma reflexão sobre todo o desenvolvimento da Tese e o que esta me proporcionou. Com este relatório pretendo apresentar todo o esforço aplicado na realização deste trabalho, bem como alguns dos conhecimentos adquiridos ao longo do curso.

On the numerical computation of the Mittag-Leffler function

Recently simple limiting functions establishing upper and lower bounds on the Mittag-Leffler function were found. This paper follows those expressions to design an efficient algorithm for the approximate calculation of expressions usual in fractional-order control systems. The numerical experiments demonstrate the superior efficiency of the proposed method.

The Persistence of Memory

This paper analyzes several natural and man-made complex phenomena in the perspective of dynamical systems. Such phenomena are often characterized by the absence of a characteristic length-scale, long range correlations and persistent memory, which are features also associated to fractional order systems. For each system, the output, interpreted as a manifestation of the system dynamics, is analyzed by means of the Fourier transform. The amplitude spectrum is approximated by a power law function and the parameters are interpreted as an underlying signature of the system dynamics. The complex systems under analysis are then compared in a global perspective in order to unveil and visualize hidden relationships among them.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

Influência do ciclo menstrual na fracção exalada de óxido nítrico

RESUMO:A determinação da fracção exalada de óxido nítrico (FENO) é amplamente utilizada como um biomarcador da inflamação eosinofílica das vias aéreas. Alguns estudos sugerem que a produção de óxido nítrico (NO) é influenciada pelas variações cíclicas hormonais na mulher,porém os dados não são consensuais. Deste modo, o objectivo do nosso estudo foi avaliar como varia a FENO ao longo do ciclo menstrual. Com esta finalidade, avaliamos um grupo de 20 voluntárias, em idade fértil, com ciclo menstrual regular, não fumadoras, que não utilizavam contraceptivos hormonais, nem suplementos alimentares e/ou medicamentosos e que não se encontravam grávidas, nem a amamentar. Todas referiram não ter conhecimento de qualquer patologia que afecte a FENO. A existência de atopia foi controlada através de testes cutâneos por prick, tendo-se excluído as participantes que apresentaram testes positivos. Realizamos quatro visitas de estudo, com base na periodicidade do ciclo de cada participante, nas quais, efectuamos a determinação da FENO, a quantificação dos níveis plasmáticos de óxido nítrico e nitratos (NO/NO3 -) e o doseamento hormonal de 17 -estradiol e progesterona. As avaliações realizaram-se no período da manhã, em jejum absoluto, tendo respeitado uma dieta pobre em nitratos no dia anterior e abstido da prática de exercício vigoroso uma hora antes da avaliação. Com este trabalho, verificamos um aumento significativo da FENO na fase secretora (17.97 ppb ± 5.8) comparativamente com a fase menstrual e proliferativa (16.48 ppb ± 3.6 e 15.95 ppb ±2.8, respectivamente). Não observamos variações significativas dos níveis plasmáticos de NO/NO3 - ao longo do ciclo. Constatamos uma correlação positiva entre a FENO e os níveis plasmáticos de NO/NO3 - durante a ovulação e verificamos que, para a nossa amostra, os níveis hormonais de estradiol e progesterona não são preditores do valor da FENO, nem dos níveis plasmáticos de NO/NO3-. Os resultados deste trabalho mostram uma variação da FENO ao longo do ciclo, ainda assim, mantendo-se os seus valores dentro do intervalo de referência, reforçando a fiabilidade deste biomarcador.--ABSTRACT:The determination of fractional exhaled nitric oxide (FENO) is widely used as a biomarker of eosinophilic airway inflammation. Some studies suggest that nitric oxide (NO) is influenced by cyclical hormonal changes in women, but those are not consensual. The aim of our study was to assess how FENO varies throughout the menstrual cycle. With this purpose, we studied a group of 20 volunteers within childbearing age, with regular menstrual cycle, non-smokers, who were not taking any medications including hormonal contraception and food supplements and who were not pregnant or breast-feeding. All participants reported not being aware of any condition that could affect the FENO. The presence of atopy was controlled by a skin prick test, having been excluded participants with positive test. We conducted four study visits, based on the periodicity of the cycle of each participant. In each visit, we made the determination of the FENO, the quantification of plasmatic levels of nitric oxide and nitrates (NO/NO3 -) and the blood levels of hormone estradiol-17 and progesterone. The evaluations occurred at morning, after overnight fasting. The participants were request to follow a low-nitrate diet in the previous day and refrained from vigorous exercise, for at least one hour before the visit We found a significant increase of FENO on secretory phase (17.97 ppb ± 5.8) compared with the menstrual and proliferative phase (16.48 ppb ± 3.6 and 15.95 ppb ± 2.8, espectively). No significant variations were found throughout the menstrual cycle in plasmatic levels of NO/NO3 -. We found a positive correlation between FENO and plasmatic levels of NO/NO3 - during ovulation. Finally, in our sample, the levels of oestradiol and progesterone are not predictors of FENO value nor of plasmatic levels of NO/NO3-. This study shows a variation of FENO over the menstrual cycle, nevertheless, the values remain within the reference range, reinforcing the reliability of this biomarker.

Utilidade do Doppler Tecidular na Predição de Eventos Arrítmicos em Adultos com Tetralogia de Fallot Corrigida

INTRODUCTION: Adults with repaired tetralogy of Fallot (TOF) may be at risk for progressive right ventricular (RV) dilatation and dysfunction, which is commonly associated with arrhythmic events. In frequently volume-overloaded patients with congenital heart disease, tissue Doppler imaging (TDI) is particularly useful for assessing RV function. However, it is not known whether RV TDI can predict outcome in this population. OBJECTIVE: To evaluate whether RV TDI parameters are associated with supraventricular arrhythmic events in adults with repaired TOF. METHODS: We studied 40 consecutive patients with repaired TOF (mean age 35 +/- 11 years, 62% male) referred for routine echocardiographic exam between 2007 and 2008. The following echocardiographic measurements were obtained: left ventricular (LV) ejection fraction, LV end-systolic volume, LV end-diastolic volume, RV fractional area change, RV end-systolic area, RV end-diastolic area, left and right atrial volumes, mitral E and A velocities, RV myocardial performance index (Tei index), tricuspid annular plane systolic excursion (TAPSE), myocardial isovolumic acceleration (IVA), pulmonary regurgitation color flow area, TDI basal lateral, septal and RV lateral peak diastolic and systolic annular velocities (E' 1, A' 1, S' 1, E' s, A' s, S' s, E' rv, A' rv, S' rv), strain, strain rate and tissue tracking of the same segments. QRS duration on resting ECG, total duration of Bruce treadmill exercise stress test and presence of exercise-induced arrhythmias were also analyzed. The patients were subsequently divided into two groups: Group 1--12 patients with previous documented supraventricular arrhythmias (atrial tachycardia, fibrillation or flutter) and Group 2 (control group)--28 patients with no previous arrhythmic events. Univariate and multivariate analysis was used to assess the statistical association between the studied parameters and arrhythmic events. RESULTS: Patients with previous events were older (41 +/- 14 vs. 31 +/- 6 years, p = 0.005), had wider QRS (173 +/- 20 vs. 140 +/- 32 ms, p = 0.01) and lower maximum heart rate on treadmill stress testing (69 +/- 35 vs. 92 +/- 9%, p = 0.03). All patients were in NYHA class I or II. Clinical characteristics including age at corrective surgery, previous palliative surgery and residual defects did not differ significantly between the two groups. Left and right cardiac chamber dimensions and ventricular and valvular function as evaluated by conventional Doppler parameters were also not significantly different. Right ventricular strain and strain rate were similar between the groups. However, right ventricular myocardial TDI systolic (Sa: 5.4+2 vs. 8.5 +/- 3, p = 0.004) and diastolic indices and velocities (Ea, Aa, septal E/Ea, and RV free wall tissue tracking) were significantly reduced in patients with arrhythmias compared to the control group. Multivariate linear regression analysis identified RV early diastolic velocity as the sole variable independently associated with arrhythmic history (RV Ea: 4.5 +/- 1 vs. 6.7 +/- 2 cm/s, p = 0.01). A cut-off for RV Ea of < 6.1 cm/s identified patients in the arrhythmic group with 86% sensitivity and 59% specificity (AUC = 0.8). CONCLUSIONS: Our results suggest that TDI may detect RV dysfunction in patients with apparently normal function as assessed by conventional echocardiographic parameters. Reduction in RV early diastolic velocity appears to be an early abnormality and is associated with occurrence of arrhythmic events. TDI may be useful in risk stratification of patients with repaired tetralogy of Fallot.