Influence of LPG composition on straw applied one componente PU winter foam

O principal objectivo desta tese é obter uma relação directa entre a composição dos gases liquefeitos de petróleo (GLP), propano, n-butano e isobutano, usados como aerossóis propulsores numa lata de poliuretano de um componente, com as propriedades das espumas produzidas por spray. As espumas obtidas, terão de ter como requisito principal, um bom desempenho a temperaturas baixas, -10ºC, sendo por isso designadas por espumas de Inverno. Uma espuma é considerada como tendo um bom desempenho se não apresentar a -10/-10ºC (temperatura lata/ spray) glass bubbles, base holes e cell collapse. As espumas deverão ainda ter densidades do spray no molde a +23/+23ºC abaixo dos 30 g/L, um rendimento superior a 30 L, boa estabilidade dimensional e um caudal de espuma a +5/+5ºC superior a 5 g/s. Os ensaios experimentais foram realizados a +23/+23ºC, +5/+5ºC e a -10/-10ºC. A cada temperatura, as espumas desenvolvidas, foram submetidas a testes que permitiram determinar a sua qualidade. Testes esses que incluem os designados por Quick Tests (QT): o spray no papel e no molde das espumas nas referidas temperaturas. As amostras do papel e no molde são especialmente analisadas, quanto, às glass bubbles, cell collapse, base holes, cell structur e, cutting shrinkage, para além de outras propriedades. Os QT também incluem a análise da densidade no molde (ODM) e o estudo do caudal de espumas. Além dos QT foram realizados os testes da estabilidade dimensional das espumas, testes físicos de compressão e adesão, testes de expansão das espumas após spray e do rendimento por lata de espuma. Em todos os ensaios foi utilizado um tubo adaptador colocado na válvula da lata como método de spray e ainda mantida constante a proporção das matérias-primas (excepto os gases, em estudo). As experiências iniciaram-se com o estudo de GLPs presentes no mercado de aerossóis. Estes resultaram que o GLP: propano/ n-butano/ isobutano: (30/ 0/ 70 w/w%), produz as melhores espumas de inverno a -10/-10ºC, reduzindo desta forma as glass bubbles, base holes e o cell collapse produzido pelos restantes GLP usados como aerossóis nas latas de poliuretano. Testes posteriores tiveram como objectivo estudar a influência directa de cada gás, propano, n-butano e isobutano nas espumas. Para tal, foram usadas duas referências do estudo com GLP comercializáveis, 7396 (30 /0 /70 w/w %) e 7442 (0/ 0/ 100 w/w %). Com estes resultados concluí-se que o n-butano produz más propriedades nas espumas a -10/- 10ºC, formando grandes quantidades de glass bubbles, base holes e cell collapse. Contudo, o uso de propano reduz essas glass bubbles, mas em contrapartida, forma cell collapse.Isobutano, porém diminui o cell collapse mas não as glass bubbles. Dos resultados experimentais podemos constatar que o caudal a +5/+5ºC e densidade das espumas a +23/+23ºC, são influenciados pela composição do GLP. O propano e n-butano aumentam o caudal de espuma das latas e a sua densidade, ao contrário com o que acontece com o isobutano. Todavia, pelos resultados obtidos, o isobutano proporciona os melhores rendimentos de espumas por lata. Podemos concluir que os GLPs que contivessem cerca de 30 w/w % de propano (bons caudais a +5/+5ºC e menos glass bubbles a -10/-10ºC), e cerca 70 w/w % de isobutano (bons rendimentos de espumas, bem como menos cell collapse a -10/-10ºC) produziam as melhores espumas. Também foram desenvolvidos testes sobre a influência da quantidade de gás GLP presente numa lata. A análise do volume de GLP usado, foi realizada com base na melhor espuma obtida nos estudos anteriores, 7396, com um GLP (30 / 0/ 70 w/w%), e foram feitas alterações ao seu volume gás GLP presente no pré-polímero. O estudo concluiu, que o aumento do volume pode diminuir a densidade das espumas, e o seu decréscimo, um aumento da densidade. Também indico u que um mau ajuste do volume poderá causar más propriedades nas espumas. A análise económica, concluiu que o custo das espumas com mais GLP nas suas formulações, reduz-se em cerca de 3%, a quando de um aumento do volume de GLP no pré-polímero de cerca de 8 %. Esta diminuição de custos deveu-se ao facto, de um aumento de volume de gás, implicar uma diminuição na quantidade das restantes matérias-primas, com custos superiores, já que o volume útil total da lata terá de ser sempre mantido nos 750 mL. Com o objectivo de melhorar a qualidade da espuma 7396 (30/0/70 w/w %) obtida nos ensaios anteriores adicionou-se à formulação 7396 o HFC-152a (1,1-di fluoroetano). Os resultados demonstram que se formam espumas com más propriedades, especialmente a -10/-10ºC, contudo proporcionou excelentes shaking rate da lata. Através de uma pequena análise de custos não é aconselhável o seu uso pelos resultados obtidos, não proporcionando um balanço custo/benefício favorável. As três melhores espumas obtidas de todos os estudos foram comparadas com uma espuma de inverno presente no mercado. 7396 e 7638 com um volume de 27 % no prépolímero e uma composição de GLP (30/ 0 / 70 w/w%) e (13,7/ 0/ 86,3 w/w%), respectivamente, e 7690, com 37 % de volume no pré-polímero e GLP (30/ 0 / 70 w/w%), apresentaram em geral melhores resultados, comparando com a espuma benchmark . Contudo, os seus shaking rate a -10/-10ºC, de cada espuma, apresentaram valores bastante inferiores à composição benchmarking.

Estudo de retardantes de chama, isentes de halogéneos, em espumas de poliuretano monocomponente

Estágio de natureza profissional para obtenção do grau de Mestre em Engenharia Química

Liquid-vapor interfaces of patchy colloids

We investigate the liquid-vapor interface of a model of patchy colloids. This model consists of hard spheres decorated with short-ranged attractive sites ("patches") of different types on their surfaces. We focus on a one-component fluid with two patches of type A and nine patches of type B (2A9B colloids), which has been found to exhibit reentrant liquid-vapor coexistence curves and very low-density liquid phases. We have used the density-functional theory form of Wertheim's first-order perturbation theory of association, as implemented by Yu and Wu [J. Chem. Phys. 116, 7094 (2002)], to calculate the surface tension, and the density and degree of association profiles, at the liquid-vapor interface of our model. In reentrant systems, where AB bonds dominate, an unusual thickening of the interface is observed at low temperatures. Furthermore, the surface tension versus temperature curve reaches a maximum, in agreement with Bernardino and Telo da Gama's mesoscopic Landau-Safran theory [Phys. Rev. Lett. 109, 116103 (2012)]. If BB attractions are also present, competition between AB and BB bonds gradually restores the monotonic temperature dependence of the surface tension. Lastly, the interface is "hairy," i.e., it contains a region where the average chain length is close to that in the bulk liquid, but where the density is that of the vapor. Sufficiently strong BB attractions remove these features, and the system reverts to the behavior seen in atomic fluids.

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany

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.

On independent component analysis applied to unmixing hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.

eLearning in the context of a one-year postgraduate course in macroscopy in pathology: benefits of a multifaceted and pluripotent concept

Performing Macroscopy in Pathology implies to plan and implement methods of selection, description and collection of biological material from human organs and tissues, actively contributing to the clinical pathology analysis by preparing macroscopic report and the collection and identification of fragments, according to the standardized protocols and recognizing the criteria internationally established for determining the prognosis. The Macroscopy in Pathology course is a full year program with theoretical and pratical components taught by Pathologists. It is divided by organ/system surgical pathology into weekly modules and includes a practical "hands-on" component in Pathology Departments. The students are 50 biomedical scientists aged from 22 to 50 years old from all across the country that want to acquire competences in macroscopy. A blended learning strategy was used in order to: give students the opportunity to attend from distance; support the contents, lessons and the interaction with colleagues and teachers; facilitate the formative/summative assessment.

Improving Remote Voting Security with Code Voting

One of the major problems that prevents the spread of elections with the possibility of remote voting over electronic networks, also called Internet Voting, is the use of unreliable client platforms, such as the voter's computer and the Internet infrastructure connecting it to the election server. A computer connected to the Internet is exposed to viruses, worms, Trojans, spyware, malware and other threats that can compromise the election's integrity. For instance, it is possible to write a virus that changes the voter's vote to a predetermined vote on election's day. Another possible attack is the creation of a fake election web site where the voter uses a malicious vote program on the web site that manipulates the voter's vote (phishing/pharming attack). Such attacks may not disturb the election protocol, therefore can remain undetected in the eyes of the election auditors. We propose the use of Code Voting to overcome insecurity of the client platform. Code Voting consists in creating a secure communication channel to communicate the voter's vote between the voter and a trusted component attached to the voter's computer. Consequently, no one controlling the voter's computer can change the his/her's vote. The trusted component can then process the vote according to a cryptographic voting protocol to enable cryptographic verification at the server's side.

Burn wood influence on outdoor air quality in a small village: Foros de Arrão, Portugal

One Plus Sequential Air Sampler—Partisol was placed in a small village (Foros de Arrão) in central Portugal to collect PM10 (particles with an aerodynamic diameter below 10 μm), during the winter period for 3 months (December 2009–March 2010). Particles masses were gravimetrically determined and the filters were analyzed by instrumental neutron activation analysis to assess their chemical composition. The water-soluble ion compositions of the collected particles were determined by Ion-exchange Chromatography. Principal component analysis was applied to the data set of chemical elements and soluble ions to assess the main sources of the air pollutants. The use of both analytical techniques provided information about elemental solubility, such as for potassium, which was important to differentiate sources.

Análise de fiabilidade de sistemas e componentes de helicópteros

Este trabalho ocorre face à necessidade da empresa Helisuporte ter uma perspectiva a nível de fiabilidade das suas aeronaves. Para isso, foram traçados como objectivos de estudo a criação de uma base de dados de anomalias; identificação de sistemas e componentes problemáticos; caracterização dos mesmos, avaliar a condição de falha e, com isto, apresentar soluções de controlo de anomalias. Assim, foi desenvolvida uma metodologia que proporciona tratamento de dados com recurso a uma análise não-paramétrica, tendo sido escolhida a estatística de amostra. Esta irá permitir a identificação dos sistemas problemáticos e seus componentes anómalos. Efectuado o tratamento de dados, passamos para a caracterização fiabilística desses componentes, assumindo o tempo de operação e a vida útil específica de cada um. Esta foi possível recorrendo ao cálculo do nível de fiabilidade, MTBF, MTBUR e taxa de avarias. De modo a identificar as diferentes anomalias e caracterizar o “know-how” da equipa de manutenção, implementou-se a análise de condição de falha, mais propriamente a análise dos modos e efeitos de falha. Tendo isso em atenção, foi construído um encadeamento lógico simples, claro e eficaz, face a uma frota complexa. Implementada essa metodologia e analisados os resultados podemos afirmar que os objectivos foram alcançados, concluindo-se que os valores de fiabilidade que caracterizam alguns dos componentes das aeronaves pertencentes à frota em estudo não correspondem ao esperado e idealizado como referência de desempenho dos mesmos. Assim, foram sugeridas alterações no manual de manutenção de forma a melhorar estes índices. Com isto conseguiu-se desenvolver, o que se poderá chamar de, “fiabilidade na óptica do utilizador”.

Positive Solutions of the Dirichlet Problem for the One-dimensional Minkowski-Curvature Equation

We discuss existence and multiplicity of positive solutions of the Dirichlet problem for the quasilinear ordinary differential equation-(u' / root 1 - u'(2))' = f(t, u). Depending on the behaviour of f = f(t, s) near s = 0, we prove the existence of either one, or two, or three, or infinitely many positive solutions. In general, the positivity of f is not required. All results are obtained by reduction to an equivalent non-singular problem to which variational or topological methods apply in a classical fashion.

Study on influence of the new CoDeSys V3.0 on the existing CoDeSys discipline of the Mind8 Engineering Center

CoDeSys "Controller Development Systems" is a development environment for programming in the area of automation controllers. It is an open source solution completely in line with the international industrial standard IEC 61131-3. All five programming languages for application programming as defined in IEC 61131-3 are available in the development environment. These features give professionals greater flexibility with regard to programming and allow control engineers have the ability to program for many different applications in the languages in which they feel most comfortable. Over 200 manufacturers of devices from different industrial sectors offer intelligent automation devices with a CoDeSys programming interface. In 2006, version 3 was released with new updates and tools. One of the great innovations of the new version of CoDeSys is object oriented programming. Object oriented programming (OOP) offers great advantages to the user for example when wanting to reuse existing parts of the application or when working on one application with several developers. For this reuse can be prepared a source code with several well known parts and this is automatically generated where necessary in a project, users can improve then the time/cost/quality management. Until now in version 2 it was necessary to have hardware interface called “Eni-Server” to have access to the generated XML code. Another of the novelties of the new version is a tool called Export PLCopenXML. This tool makes it possible to export the open XML code without the need of specific hardware. This type of code has own requisites to be able to comply with the standard described above. With XML code and with the knowledge how it works it is possible to do component-oriented development of machines with modular programming in an easy way. Eplan Engineering Center (EEC) is a software tool developed by Mind8 GmbH & Co. KG that allows configuring and generating automation projects. Therefore it uses modules of PLC code. The EEC already has a library to generate code for CoDeSys version 2. For version 3 and the constant innovation of drivers by manufacturers, it is necessary to implement a new library in this software. Therefore it is important to study the XML export to be then able to design any type of machine. The purpose of this master thesis is to study the new version of the CoDeSys XML taking into account all aspects and impact on the existing CoDeSys V2 models and libraries in the company Harro Höfliger Verpackungsmaschinen GmbH. For achieve this goal a small sample named “Traffic light” in CoDeSys version 2 will be done and then, using the tools of the new version it there will be a project with version 3 and also the EEC implementation for the automatically generated code.

Impact of maritime air mass trajectories on the Western European coast urban aerosol

Lisbon is the largest urban area in the Western European coast. Due to this geographical position the Atlantic Ocean serves as an important source of particles and plays an important role in many atmospheric processes. The main objectives of this study were to (1) perform a chemical characterization of particulate matter (PM2.5) sampled in Lisbon, (2) identify the main sources of particles, (3) determine PM contribution to this urban area, and (4) assess the impact of maritime air mass trajectories on concentration and composition of respirable PM sampled in Lisbon. During 2007, PM2.5 was collected on a daily basis in the center of Lisbon with a Partisol sampler. The exposed Teflon filters were measured by gravimetry and cut into two parts: one for analysis by instrumental neutron activation analysis (INAA) and the other by ion chromatography (IC). Principal component analysis (PCA) and multilinear regression analysis (MLRA) were used to identify possible sources of PM2.5 and determine mass contribution. Five main groups of sources were identified: secondary aerosols, traffic, calcium, soil, and sea. Four-day backtracking trajectories ending in Lisbon at the starting sampling time were calculated using the HYSPLIT model. Results showed that maritime transport scenarios were frequent. These episodes were characterized by a significant decrease of anthropogenic aerosol concentrations and exerted a significant role on air quality in this urban area.