An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Search for new resonances decaying via WZ to leptons in proton–proton collisions √s=8TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for resonances and quantum black holes using dijet mass spectra in proton-proton collisions at root s=8 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Probing minimal supergravity at the CERN LHC for large tan beta

For large values of the minimal supergravity model parameter tan beta, the tau lepton and the bottom quark Yukawa couplings become large, leading to reduced masses of tau sleptons and b squarks relative to their first and second generation counterparts, and to enhanced decays of charginos and neutralinos to tau leptons and b quarks. We evaluate the reach of the CERN Large Hadron Collider (LHC) pp collider for supersymmetry in the MSUGRA model parameter space. We find that values of m((g) over tilde) similar to 1500-2000 GeV can be probed with just 10 fb(-1) of integrated luminosity for tan beta values as high as 45, so that MSUGRA cannot escape the scrutiny of LHC experiments by virtue of having a large value of tan beta. We also perform a case study of an MSUGRA model at tan beta = 45 where (Z) over tilde(2)-->tau<(tau)over tilde>(1) and (W) over tilde(1)-->tau(1)nu(tau) with similar to 100% branching fraction. In this case, at least within our simplistic study, we show that a di-tau mass edge, which determines the value of m((Z) over tilde 2) - m((Z) over tilde 1), can still be reconstructed. This information can be used as a starting point for reconstructing SUSY cascade decays on an event-by-event basis, and can provide a strong constraint in determining the underlying model parameters. Finally, we show that for large tan beta, there can be an observable excess of tau leptons, and argue that tau signals might serve to provide new information about the underlying model framework. [S0556-2821(99)04205-8].

Relação ideal dos aminoácidos essenciais para mantença, crescimento e produção de aves

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Seleção genômica para características longitudinais de bovinos da raça Holandesa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Abordagem icônica para modelagem e simulação de ambientes de computação em nuvem ?

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação automatizada do relevo a partir de parâmetros morfométricos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação automatizada do relevo a partir de parâmetros morfométricos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Kumaraswamy Gumbel distribution

The Gumbel distribution is perhaps the most widely applied statistical distribution for problems in engineering. We propose a generalization-referred to as the Kumaraswamy Gumbel distribution-and provide a comprehensive treatment of its structural properties. We obtain the analytical shapes of the density and hazard rate functions. We calculate explicit expressions for the moments and generating function. The variation of the skewness and kurtosis measures is examined and the asymptotic distribution of the extreme values is investigated. Explicit expressions are also derived for the moments of order statistics. The methods of maximum likelihood and parametric bootstrap and a Bayesian procedure are proposed for estimating the model parameters. We obtain the expected information matrix. An application of the new model to a real dataset illustrates the potentiality of the proposed model. Two bivariate generalizations of the model are proposed.

Divertor map with freedom of geometry and safety factor profile

An explicit, area-preserving and integrable magnetic field line map for a single-null divertor tokamak is obtained using a trajectory integration method to represent equilibrium magnetic surfaces. The magnetic surfaces obtained from the map are capable of fitting different geometries with freely specified position of the X-point, by varying free model parameters. The safety factor profile of the map is independent of the geometric parameters and can also be chosen arbitrarily. The divertor integrable map is composed of a nonintegrable map that simulates the effect of external symmetry-breaking resonances, so as to generate a chaotic region near the separatrix passing through the X-point. The composed field line map is used to analyze escape patterns (the connection length distribution and magnetic footprints on the divertor plate) for two equilibrium configurations with different magnetic shear profiles at the plasma edge.

The McDonald inverted beta distribution

We introduce a five-parameter continuous model, called the McDonald inverted beta distribution, to extend the two-parameter inverted beta distribution and provide new four- and three-parameter sub-models. We give a mathematical treatment of the new distribution including expansions for the density function, moments, generating and quantile functions, mean deviations, entropy and reliability. The model parameters are estimated by maximum likelihood and the observed information matrix is derived. An application of the new model to real data shows that it can give consistently a better fit than other important lifetime models. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Generalized beta-generated distributions

This article introduces generalized beta-generated (GBG) distributions. Sub-models include all classical beta-generated, Kumaraswamy-generated and exponentiated distributions. They are maximum entropy distributions under three intuitive conditions, which show that the classical beta generator skewness parameters only control tail entropy and an additional shape parameter is needed to add entropy to the centre of the parent distribution. This parameter controls skewness without necessarily differentiating tail weights. The GBG class also has tractable properties: we present various expansions for moments, generating function and quantiles. The model parameters are estimated by maximum likelihood and the usefulness of the new class is illustrated by means of some real data sets. (c) 2011 Elsevier B.V. All rights reserved.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

The McDonald extended distribution: properties and applications

We study a five-parameter lifetime distribution called the McDonald extended exponential model to generalize the exponential, generalized exponential, Kumaraswamy exponential and beta exponential distributions, among others. We obtain explicit expressions for the moments and incomplete moments, quantile and generating functions, mean deviations, Bonferroni and Lorenz curves and Gini concentration index. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. The applicability of the new model is illustrated by means of a real data set.