DC traction load flow including AC distribution network

In the first part some information and characterisation about an AC distribution network that feeds traction substations and their possible influences on the DC traction load flow are presented. Those influences are investigated and mathematically modelled. To corroborate the mathematical model, an example is presented and their results are confronted with real measurements.

Variety matters: adaptive genetic diversity and parasite load in two mouse opossums from the Brazilian Atlantic forest

The adaptive potential of a species to a changing environment and in disease defence is primarily based on genetic variation. Immune genes, such as genes of the major histocompatibility complex (MHC), may thereby be of particular importance. In marsupials, however, there is very little knowledge about natural levels and functional importance of MHC polymorphism, despite their key role in the mammalian evolution. In a previous study, we discovered remarkable differences in the MHC class II diversity between two species of mouse opossums (Gracilinanus microtarsus, Marmosops incanus) from the Brazilian Atlantic forest, which is one of the most endangered hotspots for biodiversity conservation. Since the main forces in generating MHC diversity are assumed to be pathogens, we investigated in this study gastrointestinal parasite burden and functional associations between the individual MHC constitution and parasite load. We tested two contrasting scenarios, which might explain differences in MHC diversity between species. We predicted that a species with low MHC diversity would either be under relaxed selection pressure by low parasite diversity (`Evolutionary equilibrium` scenario), or there was a recent loss in MHC diversity leading to a lack of resistance alleles and increased parasite burden (`Unbalanced situation` scenario). In both species it became apparent that the MHC class II is functionally important in defence against gastrointestinal helminths, which was shown here for the first time in marsupials. On the population level, parasite diversity did not markedly differ between the two host species. However, we did observe considerable differences in the individual parasite load (parasite prevalence and infection intensity): while M. incanus revealed low MHC DAB diversity and high parasite load, G. microtarsus showed a tenfold higher population wide MHC DAB diversity and lower parasite burden. These results support the second scenario of an unbalanced situation.

Inflammation and adipose tissue: effects of progressive load training in rats

Introduction: Cytokines (IL-6, IL-10 and TNF-alpha) are increased after exhaustive exercise in the rat retroperitoneal (RPAT) and mesenteric adipose tissue (MEAT) pads. On the other hand, these cytokines show decreased expression in these depots in response to a chronic exercise protocol. However, the effect of exercise with overload combined with a short recovery period on pro-and anti-inflammatory cytokine expression is unknown. In the present study, we investigated the regulation of cytokine production in the adipose tissue of rats after an overtraining-inducing exercise protocol. Methods: Male Wistar rats were divided into four groups: Control (C), Trained (Tr), Overtrained (OT) and recovered overtrained (R). Cytokines (IL-6, TNF-alpha and IL-10) levels and Toll Like Receptor 4 (TLR4), Nuclear Factor kBBp65 (NF-kBp65), Hormone Sensitive Lipase (HSL) and, Perilipin protein expression were assessed in the adipose tissue. Furthermore, we analysed plasma lipid profile, insulin, testosterone, corticosterone and endotoxin levels, and liver triacylglycerol, cytokine content, as well as apolipoprotein B (apoB) and TLR4 expression in the liver. Results: OT and R groups exhibited reduced performance accompanied by lower testosterone and increased corticosterone and endotoxin levels when compared with the control and trained groups. IL-6 and IL-10 protein levels were increased in the adipose tissue of the group allowed to recover, in comparison with all the other studied groups. TLR-4 and NF-kBp65 were increased in this same group when compared with both control and trained groups. The protein expression of HSL was increased and that of Perilipin, decreased in the adipose in R in relation to the control. In addition, we found increased liver and serum TAG, along with reduced apoB protein expression and IL-6 and IL-10 levels in the of R in relation to the control and trained groups. Conclusion: In conclusion, we have shown that increases in pro-inflammatory cytokines in the adipose tissue after an overtraining protocol may be mediated via TLR-4 and NF-kBp65 signalling, leading to an inflammatory state in this tissue.

Apoptosis of macrophages during pulmonary Mycobacterium bovis infection: correlation with intracellular bacillary load and cytokine levels

P>Apoptosis of macrophages infected with pathogenic mycobacteria is an alternative host defence capable of removing the environment supporting bacterial growth. In this work the influence of virulence and bacterial load on apoptosis of alveolar macrophages during the initial phase of infection by Mycobacterium bovis was investigated. BALB/c mice were infected intratracheally with high or low doses of the virulent (ATCC19274) or attenuated (bacillus Calmette-Guerin Moreau) strains of M. bovis. The frequency of macrophage apoptosis, the growth of mycobacteria in macrophages, and the in situ levels of the cytokines tumour necrosis factor-alpha (TNF-alpha), interleukin-10 (IL-10) and IL-12 and of the anti-apoptotic protein Bcl-2 were measured at day 3 and day 7 post-infection. An increase of macrophage apoptosis was observed after infection with both strains but the virulent strain induced less apoptosis than the attenuated strain. On the 3rd day after infection with the virulent strain macrophage apoptosis was reduced in the high-dose group, while on the 7th day post-infection macrophage apoptosis was reduced in the low-dose group. Inhibition of apoptosis was correlated with increased production of IL-10, reduced production of TNF-alpha and increased production of Bcl-2. In addition, the production of IL-12 was reduced at points where the lowest levels of macrophage apoptosis were observed. Our results indicate that virulent mycobacteria are able to modulate macrophage apoptosis to an extent dependent on the intracellular bacterial burden, which benefits its intracellular growth and dissemination to adjacent cells.

Clinical correlations of human cytomegalovirus strains and viral load in kidney transplant recipients

Little is known about clinical differences associated with cytomegalovirus (CMV) infection by distinct strains in renal transplant patients. Different clinical pictures may be associated with specific viral genotypes. viral load, as well as host factors. The objective of this study was to identify CMV strains to determine viral load (antigenemia), and their correlation with clinical data in renal transplant recipients. Seventy-one patients were enrolled, comprising 91 samples. After selection, polymorphonuclear cells were used to amplify and sequence the gB region of CMV DNA. The sequences were analyzed to ascertain the frequency of different genotypes. Additionally, the results of this Study showed that the gB coding gene presents a great variability, revealing a variety of patterns: classical gB (1.4%), gB1V (46.4%), classical gB2 (35.2%), gB2V (2.8%), gB3 (1.4%), classical gB4 (4.9%) and gB4V (4.9%). The mean viral load in kidney transplant patient was 75.1 positive cells (1-1000). A higher viral load was observed in patients with genotype 4 infection. Statistically significant differences were detected between gB1 and gB4 (p=0.010), and between gB2 and gB4 (p=0.021). The average numbers of positive cells in relation to clinical presentation were: 34.5 in asymptomatic, 49.5 in CMV associated syndrome and 120.7 in patients with invasive disease (p=0.048). As a group, gB1 was the most frequent strain and revealed a potential risk for developing invasive disease. Viral load also seemed to be important as a marker associated with clinical presentation of the disease. (C) 2008 Elsevier B.V. All rights reserved.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

Exact Search Directions for Optimization of Linear and Nonlinear Models Based on Generalized Orthonormal Functions

A novel technique for selecting the poles of orthonormal basis functions (OBF) in Volterra models of any order is presented. It is well-known that the usual large number of parameters required to describe the Volterra kernels can be significantly reduced by representing each kernel using an appropriate basis of orthonormal functions. Such a representation results in the so-called OBF Volterra model, which has a Wiener structure consisting of a linear dynamic generated by the orthonormal basis followed by a nonlinear static mapping given by the Volterra polynomial series. Aiming at optimizing the poles that fully parameterize the orthonormal bases, the exact gradients of the outputs of the orthonormal filters with respect to their poles are computed analytically by using a back-propagation-through-time technique. The expressions relative to the Kautz basis and to generalized orthonormal bases of functions (GOBF) are addressed; the ones related to the Laguerre basis follow straightforwardly as a particular case. The main innovation here is that the dynamic nature of the OBF filters is fully considered in the gradient computations. These gradients provide exact search directions for optimizing the poles of a given orthonormal basis. Such search directions can, in turn, be used as part of an optimization procedure to locate the minimum of a cost-function that takes into account the error of estimation of the system output. The Levenberg-Marquardt algorithm is adopted here as the optimization procedure. Unlike previous related work, the proposed approach relies solely on input-output data measured from the system to be modeled, i.e., no information about the Volterra kernels is required. Examples are presented to illustrate the application of this approach to the modeling of dynamic systems, including a real magnetic levitation system with nonlinear oscillatory behavior.

Global attractor for a model of extensible beam with nonlinear damping and source terms

This paper is concerned with the existence of a global attractor for the nonlinear beam equation, with nonlinear damping and source terms, u(tt) + Delta(2)u -M (integral(Omega)vertical bar del u vertical bar(2)dx) Delta u + f(u) + g(u(t)) = h in Omega x R(+), where Omega is a bounded domain of R(N), M is a nonnegative real function and h is an element of L(2)(Omega). The nonlinearities f(u) and g(u(t)) are essentially vertical bar u vertical bar(rho) u - vertical bar u vertical bar(sigma) u and vertical bar u(t)vertical bar(r) u(t) respectively, with rho, sigma, r > 0 and sigma < rho. This kind of problem models vibrations of extensible beams and plates. (C) 2010 Elsevier Ltd. All rights reserved.

MULTIPLICITY OF POSITIVE SOLUTIONS FOR A CLASS OF NONLINEAR SCHRODINGER EQUATIONS

This paper proves the multiplicity of positive solutions for the following class of quasilinear problems: {-epsilon(p)Delta(p)u+(lambda A(x) + 1)vertical bar u vertical bar(p-2)u = f(u), R(N) u(x)>0 in R(N), where Delta(p) is the p-Laplacian operator, N > p >= 2, lambda and epsilon are positive parameters, A is a nonnegative continuous function and f is a continuous function with subcritical growth. Here, we use variational methods to get multiplicity of positive solutions involving the Lusternick-Schnirelman category of intA(-1)(0) for all sufficiently large lambda and small epsilon.

Positive solutions for a fourth order equation with nonlinear boundary conditions

Existence of positive solutions for a fourth order equation with nonlinear boundary conditions, which models deformations of beams on elastic supports, is considered using fixed points theorems in cones of ordered Banach spaces. Iterative and numerical solutions are also considered. (C) 2010 IMACS. Published by Elsevier B.V. All rights reserved.

Monotone positive solutions for a fourth order equation with nonlinear boundary conditions

This work is concerned with the existence of monotone positive solutions for a class of beam equations with nonlinear boundary conditions. The results are obtained by using the monotone iteration method and they extend early works on beams with null boundary conditions. Numerical simulations are also presented. (C) 2009 Elsevier Ltd. All rights reserved.

Continuity of the dynamics in a localized large diffusion problem with nonlinear boundary conditions

This paper is concerned with singular perturbations in parabolic problems subjected to nonlinear Neumann boundary conditions. We consider the case for which the diffusion coefficient blows up in a subregion Omega(0) which is interior to the physical domain Omega subset of R(n). We prove, under natural assumptions, that the associated attractors behave continuously as the diffusion coefficient blows up locally uniformly in Omega(0) and converges uniformly to a continuous and positive function in Omega(1) = (Omega) over bar\Omega(0). (C) 2009 Elsevier Inc. All rights reserved.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.