Conditional probability applied to the definition of limestone geochemical facies: A new Methodological Approach

A new method, based on linear correlation and phase diagrams was successfully developed for processes like the sedimentary process, where the deposition phase can have different time duration - represented by repeated values in a series - and where the erosion can play an important rule deleting values of a series. The sampling process itself can be the cause of repeated values - large strata twice sampled - or deleted values: tiny strata fitted between two consecutive samples. What we developed was a mathematical procedure which, based upon the depth chemical composition evolution, allows the establishment of frontiers as well as the periodicity of different sedimentary environments. The basic tool isn't more than a linear correlation analysis which allow us to detect the existence of eventual evolution rules, connected with cyclical phenomena within time series (considering the space assimilated to time), with the final objective of prevision. A very interesting discovery was the phenomenon of repeated sliding windows that represent quasi-cycles of a series of quasi-periods. An accurate forecast can be obtained if we are inside a quasi-cycle (it is possible to predict the other elements of the cycle with the probability related with the number of repeated and deleted points). We deal with an innovator methodology, reason why it's efficiency is being tested in some case studies, with remarkable results that shows it's efficacy. Keywords: sedimentary environments, sequence stratigraphy, data analysis, time-series, conditional probability.

A New Approach in the Design of Air Stripping Columns for the Treatment of Groundwater Contaminated With Volatile Organic Compounds

Volatile organic compounds are a common source of groundwater contamination that can be easily removed by air stripping in columns with random packing and using a counter-current flow between the phases. This work proposes a new methodology for column design for any type of packing and contaminant which avoids the necessity of an arbitrary chosen diameter. It also avoids the employment of the usual graphical Eckert correlations for pressure drop. The hydraulic features are previously chosen as a project criterion. The design procedure was translated into a convenient algorithm in C++ language. A column was built in order to test the design, the theoretical steady-state and dynamic behaviour. The experiments were conducted using a solution of chloroform in distilled water. The results allowed for a correction in the theoretical global mass transfer coefficient previously estimated by the Onda correlations, which depend on several parameters that are not easy to control in experiments. For best describe the column behaviour in stationary and dynamic conditions, an original mathematical model was developed. It consists in a system of two partial non linear differential equations (distributed parameters). Nevertheless, when flows are steady, the system became linear, although there is not an evident solution in analytical terms. In steady state the resulting ODE can be solved by analytical methods, and in dynamic state the discretization of the PDE by finite differences allows for the overcoming of this difficulty. To estimate the contaminant concentrations in both phases in the column, a numerical algorithm was used. The high number of resulting algebraic equations and the impossibility of generating a recursive procedure did not allow the construction of a generalized programme. But an iterative procedure developed in an electronic worksheet allowed for the simulation. The solution is stable only for similar discretizations values. If different values for time/space discretization parameters are used, the solution easily becomes unstable. The system dynamic behaviour was simulated for the common liquid phase perturbations: step, impulse, rectangular pulse and sinusoidal. The final results do not configure strange or non-predictable behaviours.

Internet Geography: New Spaces of Information

The objective of great investments in telecommunication networks is to approach economies and put an end to the asymmetries. The most isolated regions could be the beneficiaries of this new technological investments wave disseminating trough the territories. The new economic scenarios created by globalisation make high capacity backbones and coherent information society polity, two instruments that could change regions fate and launch them in to an economic development context. Technology could bring international projection to services or products and could be the differentiating element between a national and an international economic strategy. So, the networks and its fluxes are becoming two of the most important variables to the economies. Measuring and representing this new informational accessibility, mapping new communities, finding new patterns and localisation models, could be today’s challenge. In the physical and real space, location is defined by two or three geographical co-ordinates. In the network virtual space or in cyberspace, geography seems incapable to define location, because it doesn’t have a good model. Trying to solve the problem and based on geographical theories and concepts, new fields of study came to light. The Internet Geography, Cybergeography or Geography of Cyberspace are only three examples. In this paper and using Internet Geography and informational cartography, it was possible to observe and analyse the spacialisation of the Internet phenomenon trough the distribution of the IP addresses in the Portuguese territory. This work shows the great potential and applicability of this indicator to Internet dissemination and regional development studies. The Portuguese territory is seen in a completely new form: the IP address distribution of Country Code Top Level Domains (.pt) could show new regional hierarchies. The spatial concentration or dispersion of top level domains seems to be a good instrument to reflect the info-structural dynamic and economic development of a territory, especially at regional level.

Mediterranean spotted fever and identification of new agents of rickettsioses in Portugal : epidemiological determinants, host and microbial features in portuguese patients

Resumo: a febre botonosa, também conhecida por febre escaro-nodular (FEN) é uma doença endémica nos Países da bacia do Mediterrâneo, África, Médio Oriente, Índia e Paquistão. O agente etiológico responsável por esta patologia é a bactéria Rickettsia conorii. Contudo, em alguns países, como Portugal e Itália, esta patologia é causada por duas estirpes diferentes: R conorii Malish e R conorii Israeli spotted fever strain. O principal vector e reservatório é o ixodídeo Rhipicephalus sanguineus. Mesmo com uma elevada taxa de subnotificação detectada no nosso País, a taxa incidência da FEN é de 8.4/105 habitantes (1989-2005), uma das mais altas quando comparada coom a de outros países da bacia do Mediterrâneo. De todos os distritos portugueses, Bragança e Beja são aqueles que apresentam as taxas de incidência mais elevadas, 56,8/105 habitantes e 47,4 / 105 habitantes respectivamente. Em Portugal, as alterações climáticas verificadas na última década, nomeadamente a subida das temperaturas médias anuais, parecem ter influenciado o ciclo de vida do vector e a sua dinâmica sazonal, permitindo ao R. sanguineus completar mais de um ciclo de vida por ano. Este facto, e a possibilidade deste vector se manter activo noutros meses do ano, nomeadamente nos meses de inverno, tem influenciado consequentemente o padrão de distribuição anual dos casos de FEN. A febre escaro-nodular caracteriza-se clinicamente como uma doença exantemática, com um processo de vasculite generalizado. Apesar de na generalidade ser considerada uma doença benigna (quando tratada atempadamente e com terapêutica adequada e específica)e de estarem descritos casos graves em cerca de 5-6% dos doentes, em Portugal essa percentagem aumentou e consequentemente levou a um aumento de casos fatais. Este facto tornou-se mais evidente em 1997, no Hospital Distrital de Beja e no Hospital Garcia de Orta, onde a taxa de letalidade atingiu os 32% e 18% respectivamente.Para além dos factores de co-morbilidade encontrados nos doentes mais graves, como diabetes mellitus, ou o atraso na instituição da terapêutica específica, foi colocada de que a estirpe R. conorii Israel spotted fever strain pudesse ser mais virulenta ou então estivesse associada a diferentes manifestações clínicas que dificultassem o diagnóstico clínico e a instituição atempada da terapêutica. Houve ainda a necessidade de avaliar alguns parâmetros imunológicos dos doentes e tentar identificar que factores, nomeadamente que citoquinas, poderiam estar envolvidos na resposta a uma infecção por R.conorii.Face a estas questões foi avaliada e comparada a epidemiologia, manifestações clínicas e laboratoriais de 140 doentes (71 infectados com R. conorii Malish e 69 infectados com R. conorii Israel spotted fever strain). Concluiu-se que existe uma sobreposição de manifestações clinicas entre os dois grupos de doentes, mas que a percentagem da escara de inoculação é significativamente inferior em doentes infectados com R. conorii Israel spotted fever strain. Dos resultados mais importantes encontrados neste estudo concluiu-se que a estirpe R. conorii Malish e é demonstrado, pela primeira vez, estatisticamente que o alcoolismo é um factor de risco para a morte de doentes com FEN. Associadas a factores de um mau prognósitco da doença, estão as manifestações gastrointestinais, que poderão ser ou não reflexo de alterações do sistema nervoso central, e ainda a alteração de parâmetros laboratoriais como a presença de hiperbilirubinemia e aumento dos valores da ureia.A maior parte dos estudos realizados sobre os mecanismos da resposta imunitária à infecção por R. conorii e as interacções hospedeiro - agente etiológico têm sido elucidados com base em modelos animais. Poucos estudos têm sido efectuados em doentes e nenhum estudo prévio tinha sido realizado no sentido de avaliar localmente (escara/pele) quais os mediadores ou outras moléculas envolvidas na resposta imunitária às rickettsioses. Foi avaliado o nível de expressão génica de RNA mensageiro (RNAm)de diferentes citoquinas em amostras de pele de doentes com FEN pela técnica de PCR em tempo real.Os resultados deste estudo mostraram que, quando comparado com o grupo controlo, os 23 doentes analisados apresentavam níveis estatisticamente significativos, mais elevados de expressão génica de interferão (IFN-γ, Tumor necrosis factor (TFN-α, interleucina 10 (IL-10, RANTES (regulated by activation, normal T-cell-expressed and secreted chemokine)e indolamina 2-3 desoxigenase (IDO),uma enzima envolvida no controlo e limitação do crescimento intracelular das rickettsias, através da degradação do triptofano. Seis dos 23 doentes apresentaram ainda niveis de expressão elevados de óxido nítrico indutível (iNOS)que actua como microbicida. Encontrou-se uma correlação positiva entre a expressão de RNAm de TNF-α, γ, iNOS e IDO e os casos menos graves de FEN sugerindo um tipo de resposta imunitária tipo Th1, i.e. com papel protector na resposta à infecção.Verificou-se também que os valores de expressão genética do RNAm de IL-10, estavam inversamente correlacionados com a expressão do RNAm de TNF-α e IFN-γ. Os casos menos graves de FEN parecem assim envolver um balanço entre a resposta pró-inflamatória e anti-inflamatória. Já os níveis de expressão génica do RNAm de IL-10 estavam inversamente correlacionados com a expressão RNAm de TNF-α e IFN-γ. Os casos menos graves de FEN parecem assim envolver um balanço entre uma resposta pró-inflamatória e anti-inflamatória. Já os níveis de expressão RNAm da quimoquina RANTES foram estatisticamente mais elevados em doentes graves.Nesta dissertação é ainda descrita uma nova rickettsiose presente em Portugal, causada pela bactéria R. sibirica mongolitimonae, que foi identificada laboratorialmente por isolamento do agente, e por detecção do DNA em biopsia de pele. A presença deste agente foi ainda corroborada pela detecção em paralelo do mesmo agente no ixodídeos como R. africae like e em pulgas como R. felis e R.typhi alertam para a possibilidade de existência de outras rickettsioses que possam estar diagnosticadas em Portugal. Abstract: Mediterranean spotted fever (MSF), a tick-borne disease caused by Rickettsia conorii, is widley distributed in the Old World, being endemic in the southern Europe, Africa, Middle East, India and Pakistan. In Portugal two strains cause disease: R.conorii Malish and R.conorii Israeli spotted fever.Rhipicephalus sanguineus, the brown dog tick, is considered the main vector and reservoir. MSF is characterized by seasonality, and most of cases are encountered in late spring and summer, peaking in July and August. However, CEVDI/INSA laboratory has observed that the incidence of MSF cases has changed during winter season.The increasing annual averages of air temperatures and warmer and drier winters might have influenced the dynamics of the life cycle and activity of R. sanguineus, and indirectley the number MSF cases during the so called MSF off-season.In the period of 1989-2005, the incidence rate of MSF was 8.4/105 inhabitants, one of the highest rates compared with other endemic countries. In the Portugal during the same period, the highest incidence rates were reported in the districts of Bragança, with 56.8/105 inhabitants, and Beja, with 47.4/105 inhabitants. Severe cases of MSF are reported in 6% of the patients, but it seems that this pattern of disease in Portugal has been changing.This factor became more evident in 1997, with a reported case fatality rate of 32% and 18% in patients with MSF admited at Beja and Garcia Orta Hospitals, respectively. Although it was found that diabetes mellitus and delay in therapy have been implicated as a risk factor for death, the hypothesis was considered, that the new ISF strain isolated from Portugueses patients in the same year (1997)causes different or atypical clinical conorii Malish strain. The local (skin biopsies) immune response to R. conorii infection was also evaluated.A prospective study was performed to characterized epidemiological, clinical, laboratory features and determined risk factors for a fatal outcome. One hundred forty patients (51% patients were infected with Rickettsia conorii Malish stain and 49% with Israeli spotted fever strain)with diagnosis documented with identification of the causative rickettsial strain were admitted to 13 Portugueses Hospitals during 1994-2006.Comparison of the clinical manifestations of MSF caused by Malish and ISF strains revealed tremendous overlap that would not permit clinical recognition of the strain envolved, but an eschar was observed in a significantly higher percentage of patients with Malish than ISF strain.A fatal outcome was significantly more likely for patients with ISF strain infection meaning that ISF strain was more virulent than Malish strain, and also alcoholism was a host risk factor for a fatal outcome.The pathophysiology of a fatal outcome involved significantly greater incidence of petechial rash, gastrointestinal symptoms, confusion/obtundation, dehydration, tachypnea, hepatomegaly, leukocytosis, coagulopathy, azotemia, hyperbilirubinemia, and elevated hepatic enzymes and creatine kinase. Multivariate analysis revealed that acute renal failure and hyperbilirubinemia were most strong associated with a fatal oucome of infections with both strains.The immune response to R. conorii infection determined with both strains. The immune response to R. conorii infection determined by the expression levels of inflammatory and immune mediators in skin biopsies collected from untreated patients with Mediterranean spotted fever reveal that intralesional expression of mRNA of TNF-α, IFN-γ, IL-10, RANTES, and indoleamine-2, 3-dioxygenase (IDO)an enzyme involved in limiting rickettsial growth by tryptophan degradation, were elevated in skin of MSF patients compared to controls. Six patients had elevated levels of inducible nitric oxide synthase (NOS2, a source microbicidal nitric oxide.Positive correlations among TNF-α, IFN-γ, NOS2,IDO and mild-to-moderate disease suggested that type 1 polarization plays a protective role. Significantly high levels of intralesional IL-10 were inversely correlated with IFN-γ and TNF-α. The chemokine RANTES was significantly higher in patients with several MSF. It seems that MSF patients with mild-to-moderate disease have a strong and balanced intralesional pro-inflammatory and anti-inflammatory response, while severe disease is associated with higher chemokine expression.Whether these findings are simply a correlate of mild and severe disease or contribute to anti-rickettsial immunity and pathogenesis remains to be determined.In this dissertation is also described a new rickettsiois present in Portugal caused by R.sibirica mongolitimonae strain, identified based on agent isolation and DNA detection by PCR technique in a skin biopsy.The presence of this agent corroborated by its detection also in Rhipicephalus pusillus tick. Also, pathogenic tick and flea-borne rickettsial agents such as R. africae strain detected in Rhipicephalus bursa tick, and R.felis and R.typhi detected in different fleas species raise the alert for the possible existence of other rickettsioses in Portugal that might be underdiagnosed.

Modelling a cable structure for a new wind energy production device

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Mecânica Especialização em Concepção e Produção

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.

FROM NON-PLACE TO PLACE: A STUDY OF EUROPEAN PUBLIC SPACE AS A SPACE OF IDENTITY

Master Erasmus Mundus Crossways in European Humanities

Investigation of trypanothione synthetase of Leishmania infantum as a potential target for new anti-parasitic drugs

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Design of New Materials for Passive Vibration Control

The aim of this contribution is to extend the techniques of composite materials design to non-linear material behaviour and apply it for design of new materials for passive vibration control. As a first step a computational tool allowing determination of macroscopic optimized one-dimensional isolator behaviour was developed. Voigt, Maxwell, standard and more complex material models can be implemented. Objective function considers minimization of the initial reaction and/or displacement peak as well as minimization of the steady-state amplitude of reaction and/or displacement. The complex stiffness approach is used to formulate the governing equations in an efficient way. Material stiffness parameters are assumed as non-linear functions of the displacement. The numerical solution is performed in the complex space. The steady-state solution in the complex space is obtained by an iterative process based on the shooting method which imposes the conditions of periodicity with respect to the known value of the period. Extension of the shooting method to the complex space is presented and verified. Non-linear behaviour of material parameters is then optimized by generic probabilistic meta-algorithm, simulated annealing. Dependence of the global optimum on several combinations of leading parameters of the simulated annealing procedure, like neighbourhood definition and annealing schedule, is also studied and analyzed. Procedure is programmed in MATLAB environment.

Efficient Legendre spectral tau algorithm for solving two-sided space-time Caputo fractional advection-dispersion equation

In this paper we present the operational matrices of the left Caputo fractional derivative, right Caputo fractional derivative and Riemann–Liouville fractional integral for shifted Legendre polynomials. We develop an accurate numerical algorithm to solve the two-sided space–time fractional advection–dispersion equation (FADE) based on a spectral shifted Legendre tau (SLT) method in combination with the derived shifted Legendre operational matrices. The fractional derivatives are described in the Caputo sense. We propose a spectral SLT method, both in temporal and spatial discretizations for the two-sided space–time FADE. This technique reduces the two-sided space–time FADE to a system of algebraic equations that simplifies the problem. Numerical results carried out to confirm the spectral accuracy and efficiency of the proposed algorithm. By selecting relatively few Legendre polynomial degrees, we are able to get very accurate approximations, demonstrating the utility of the new approach over other numerical methods.

State Space Analysis of Forest Fires

This paper studies forest fires from the perspective of dynamical systems. Burnt area, precipitation and atmospheric temperatures are interpreted as state variables of a complex system and the correlations between them are investigated by means of different mathematical tools. First, we use mutual information to reveal potential relationships in the data. Second, we adopt the state space portrait to characterize the system’s behavior. Third, we compare the annual state space curves and we apply clustering and visualization tools to unveil long-range patterns. We use forest fire data for Portugal, covering the years 1980–2003. The territory is divided into two regions (North and South), characterized by different climates and vegetation. The adopted methodology represents a new viewpoint in the context of forest fires, shedding light on a complex phenomenon that needs to be better understood in order to mitigate its devastating consequences, at both economical and environmental levels.

THE PROCESS OF Leishmania INFECTION - DISEASE AND NEW PERSPECTIVES OF PALEOPARASITOLOGY

Species of the genus Leishmania (Kinetoplastida, Trypanosomatidae) are causative agents of leishmaniasis, a complex disease with variable clinical spectrum and epidemiological diversity, constituting, in some countries, a serious public health problem. The origin and evolution of leishmaniasis has been under discussion regarding some clinical and parasitological aspects. After the introduction of paleoparasitology, molecular methods and immunodiagnostic techniques have been applied allowing the recovery of parasite remains, as well as the diagnosis of past infections in humans and other hosts. The dating of archaeological samples has allowed the parasitological analysis in time and space. This manuscript presents the state of the art of leishmaniasis and prospects related to paleoparasitology studies and their contribution to the evolutionary and phylogenetic clarification of parasites belonging to the genus Leishmania, and the leishmaniasis caused by them.

Renova black toilet paper: The creation of a glocal market space

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

THE U.S. and European M&A cycles: A Markov switching and state space approach

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