Gap solitons in Bose-Einstein condensates in linear and nonlinear optical lattices

Properties of localized states on array of BEC confined to a potential, representing superposition of linear and nonlinear optical lattices are investigated. For a shallow lattice case the coupled mode system has been derived. We revealed new types of gap solitons and studied their stability. For the first time a moving soliton solution has been found. Analytical predictions are confirmed by numerical simulations of the Gross-Pitaevskii equation with jointly acting linear and nonlinear periodic potentials. (c) 2007 Elsevier B.V. All rights reserved.

The thrust and the relation factor kr in linear induction machines

This paper shows a new manner to establish the thrust of a linear induction machine. A new factor is established, named ''Relation Factor, which provides conditions to establish the thrust and other important variables of the linear and sector induction machines.

Quasiconformal mappings in octonionic algebra: A graphical and analytical comparison

In this article, we present quasiconformal mappings related to octonionic algebra. Based on the metric definition of quasiconformal mappings and using transformations of the type f(z)=zn, we compare the graphical and analytic results. © 2009 Pushpa Publishing House.

Sobre a produção de significados para a noção de transformação linear em álgebra linear

Pós-graduação em Educação Matemática - IGCE

A cross-sectional electromyography assessment in linear scleroderma patients

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

A note on model uncertainty in linear regression

We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

O papel das tecnologias digitais em disciplinas de álgebra linear a distância: possibilidades, limites e desafios

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Probabilistic crack growth analyses using a boundary element model: Applications in linear elastic fracture and fatigue problems

This paper addresses the numerical solution of random crack propagation problems using the coupling boundary element method (BEM) and reliability algorithms. Crack propagation phenomenon is efficiently modelled using BEM, due to its mesh reduction features. The BEM model is based on the dual BEM formulation, in which singular and hyper-singular integral equations are adopted to construct the system of algebraic equations. Two reliability algorithms are coupled with BEM model. The first is the well known response surface method, in which local, adaptive polynomial approximations of the mechanical response are constructed in search of the design point. Different experiment designs and adaptive schemes are considered. The alternative approach direct coupling, in which the limit state function remains implicit and its gradients are calculated directly from the numerical mechanical response, is also considered. The performance of both coupling methods is compared in application to some crack propagation problems. The investigation shows that direct coupling scheme converged for all problems studied, irrespective of the problem nonlinearity. The computational cost of direct coupling has shown to be a fraction of the cost of response surface solutions, regardless of experiment design or adaptive scheme considered. (C) 2012 Elsevier Ltd. All rights reserved.

Degree of multicollinearity and variables involved in linear dependence in additive-dominant models

The objective of this work was to assess the degree of multicollinearity and to identify the variables involved in linear dependence relations in additive-dominant models. Data of birth weight (n=141,567), yearling weight (n=58,124), and scrotal circumference (n=20,371) of Montana Tropical composite cattle were used. Diagnosis of multicollinearity was based on the variance inflation factor (VIF) and on the evaluation of the condition indexes and eigenvalues from the correlation matrix among explanatory variables. The first model studied (RM) included the fixed effect of dam age class at calving and the covariates associated to the direct and maternal additive and non-additive effects. The second model (R) included all the effects of the RM model except the maternal additive effects. Multicollinearity was detected in both models for all traits considered, with VIF values of 1.03 - 70.20 for RM and 1.03 - 60.70 for R. Collinearity increased with the increase of variables in the model and the decrease in the number of observations, and it was classified as weak, with condition index values between 10.00 and 26.77. In general, the variables associated with additive and non-additive effects were involved in multicollinearity, partially due to the natural connection between these covariables as fractions of the biological types in breed composition.

LIKELIHOOD ESTIMATION OF CONJUGACY RELATIONSHIPS IN LINEAR MODELS WITH APPLICATIONS TO HIGH-THROUGHPUT GENOMICS

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting

Localized short-echo-time (1)H-MR spectra of human brain contain contributions of many low-molecular-weight metabolites and baseline contributions of macromolecules. Two approaches to model such spectra are compared and the data acquisition sequence, optimized for reproducibility, is presented. Modeling relies on prior knowledge constraints and linear combination of metabolite spectra. Investigated was what can be gained by basis parameterization, i.e., description of basis spectra as sums of parametric lineshapes. Effects of basis composition and addition of experimentally measured macromolecular baselines were investigated also. Both fitting methods yielded quantitatively similar values, model deviations, error estimates, and reproducibility in the evaluation of 64 spectra of human gray and white matter from 40 subjects. Major advantages of parameterized basis functions are the possibilities to evaluate fitting parameters separately, to treat subgroup spectra as independent moieties, and to incorporate deviations from straightforward metabolite models. It was found that most of the 22 basis metabolites used may provide meaningful data when comparing patient cohorts. In individual spectra, sums of closely related metabolites are often more meaningful. Inclusion of a macromolecular basis component leads to relatively small, but significantly different tissue content for most metabolites. It provides a means to quantitate baseline contributions that may contain crucial clinical information.

Solving SAT in linear time with a neural-like membrane system

We present in this paper a neural-like membrane system solving the SAT problem in linear time. These neural Psystems are nets of cells working with multisets. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses (?excitations?) to the neighboring cells. The maximal mode of rules application and the replicative mode of communication between cells are at the core of the eficiency of these systems.

SolarPaces 2013: Innovations on direct steam generation in linear fresnel collectors

Direct Steam Generation (DSG) in Linear Fresnel (LF) solar collectors is being consolidated as a feasible technology for Concentrating Solar Power (CSP) plants. The competitiveness of this technology relies on the following main features: water as heat transfer fluid (HTF) in Solar Field (SF), obtaining high superheated steam temperatures and pressures at turbine inlet (500ºC and 90 bar), no heat tracing required to avoid HTF freezing, no HTF degradation, no environmental impacts, any heat exchanger between SF and Balance Of Plant (BOP), and low cost installation and maintenance. Regarding to LF solar collectors, were recently developed as an alternative to Parabolic Trough Collector (PTC) technology. The main advantages of LF are: the reduced collector manufacturing cost and maintenance, linear mirrors shapes versus parabolic mirror, fixed receiver pipes (no ball joints reducing leaking for high pressures), lower susceptibility to wind damages, and light supporting structures allowing reduced driving devices. Companies as Novatec, Areva, Solar Euromed, etc., are investing in LF DSG technology and constructing different pilot plants to demonstrate the benefits and feasibility of this solution for defined locations and conditions (Puerto Errado 1 and 2 in Murcia Spain, Lidellin Newcastle Australia, Kogran Creek in South West Queensland Australia, Kimberlina in Bakersfield California USA, Llo Solar in Pyrénées France,Dhursar in India,etc). There are several critical decisions that must be taken in order to obtain a compromise and optimization between plant performance, cost, and durability. Some of these decisions go through the SF design: proper thermodynamic operational parameters, receiver material selection for high pressures, phase separators and recirculation pumps number and location, pipes distribution to reduce the amount of tubes (reducing possible leaks points and transient time, etc.), etc. Attending to these aspects, the correct design parameters selection and its correct assessment are the main target for designing DSG LF power plants. For this purpose in the recent few years some commercial software tools were developed to simulatesolar thermal power plants, the most focused on LF DSG design are Thermoflex and System Advisor Model (SAM). Once the simulation tool is selected,it is made the study of the proposed SFconfiguration that constitutes the main innovation of this work, and also a comparison with one of the most typical state-of-the-art configuration. The transient analysis must be simulated with high detail level, mainly in the BOP during start up, shut down, stand by, and partial loads are crucial, to obtain the annual plant performance. An innovative SF configurationwas proposed and analyzed to improve plant performance. Finally it was demonstrated thermal inertia and BOP regulation mode are critical points in low sun irradiation day plant behavior, impacting in annual performance depending on power plant location.