Electrical and Magnetic Characterization of BSCCO and YBCO HTS Tapes for Fault Current Limiter Application

Several high temperature superconductor (HTS) tapes have been developed since the late eighties. Due to the new techniques applied for their production, HTS tapes are becoming feasible and practical for many applications. In this work, we present the test results of five commercial HTS tapes from the BSCCO and YBCO families (short samples of 200 mm). We have measured and analyzed their intrinsic and extrinsic properties and compared their behaviors for fault current limiter (FCL) applications. Electrical measurements were performed to determine the critical current and the n value through the V-I relationship under DC and AC magnetic fields. The resistance per unit length was determined as a function of temperature. The magnetic characteristics were analyzed through susceptibility curves as a function of temperature. As transport current generates a magnetic field surrounding the HTS material, the magnetic measurements indicate the magnetic field supported by the tapes under a peak current 1.5 times higher than the critical current, I(c). By pulsed current tests the recovery time and the energy/volume during a current fault were also analyzed. These results are in agreement with the data found in the literature giving the most appropriate performance conductor for a FCL device (I(peak) = 4 kA) to be used in a 220 V-60 Hz grid.

A dedicated setup for the measurement of the electron transport parameters in gases at large electric fields

Electron transport parameters are important in several areas ranging from particle detectors to plasma-assisted processing reactors. Nevertheless, especially at high fields strengths and for complex gases, relatively few data are published. A dedicated setup has been developed to measure the electron drift velocity and the first Townsend coefficient in parallel plate geometry. An RPC-like cell has been adopted to reach high field strengths without the risk of destructive sparks. The validation data obtained with pure Nitrogen will be presented and compared to a selection of the available literature and to calculations performed with Magboltz 2 version 8.6. The new data collected in pure Isobutane will then be discussed. This is the first time the electron drift velocity in pure Isobutane is measured well into the saturation region. Good agreement is found with expectations from Magboltz. (C) 2009 Elsevier B.V. All rights reserved.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Electric Power Forecasting Methodologies of Some South American Countries: A Comparative Analysis

In this paper, a comparative analysis of the long-term electric power forecasting methodologies used in some South American countries, is presented. The purpose of this study is to compare and observe if such methodologies have some similarities, and also examine the behavior of the results when they are applied to the Brazilian electric market. The abovementioned power forecasts were performed regarding the main four consumption classes (residential, industrial, commercial and rural) which are responsible for approximately 90% of the national consumption. The tool used in this analysis was the SAS (c) program. The outcome of this study allowed identifying various methodological similarities, mainly those related to the econometric variables used by these methods. This fact strongly conditioned the comparative results obtained.

Void fraction measurement and signal analysis from multiple-electrode impedance sensors

Void fraction sensors are important instruments not only for monitoring two-phase flow, but for furnishing an important parameter for obtaining flow map pattern and two-phase flow heat transfer coefficient as well. This work presents the experimental results obtained with the analysis of two axially spaced multiple-electrode impedance sensors tested in an upward air-water two-phase flow in a vertical tube for void fraction measurements. An electronic circuit was developed for signal generation and post-treatment of each sensor signal. By phase shifting the electrodes supplying the signal, it was possible to establish a rotating electric field sweeping across the test section. The fundamental principle of using a multiple-electrode configuration is based on reducing signal sensitivity to the non-uniform cross-section void fraction distribution problem. Static calibration curves were obtained for both sensors, and dynamic signal analyses for bubbly, slug, and turbulent churn flows were carried out. Flow parameters such as Taylor bubble velocity and length were obtained by using cross-correlation techniques. As an application of the void fraction tested, vertical flow pattern identification could be established by using the probability density function technique for void fractions ranging from 0% to nearly 70%.

Profiles of functional deficits in mild cognitive impairment and dementia: benefits from objective measurement

The magnitude Of functional impairment that may indicate the threshold between MCI and incipient Alzheimer`s disease (AD) has not been clearly defined. The objective was to examine the pattern of functional impairment in the continuum MCI-AD. Eighty-nine older adults (32 cognitively unimpaired, 31 MCI, and 26 AD patients) were examined with the Brazilian version of the Direct Assessment of Functional Status (DAFS-BR) at a University-based memory clinic. MCI patients were sub-divided according to the progression to AD upon follow-up, and had baseline cognitive, functional and biological variables analyzed. MCI patients displayed mild deficits in functional abilities, with intermediate scores as compared to controls and AD. The DAFS-BR items that differentiated MCI from controls involved the ability to deal with finances and shopping skills. At baseline, scores obtained by MCI patients who converted to AD were not significantly different from scores of nonconverters. The magnitude of functional deficits was associated with AD-like pathological findings in the CSF. In conclusion, MCI patients present with early functional changes in complex, instrumental abilities that require the integrity of memory and executive functions. The objective measurement of the functional state may help identify older adults with increased risk of developing dementia in the MCI-AD continuum. (JINS, 2010, 16, 297-305.)

Robust inference in an heteroscedastic measurement error model

In this paper we deal with robust inference in heteroscedastic measurement error models Rather than the normal distribution we postulate a Student t distribution for the observed variables Maximum likelihood estimates are computed numerically Consistent estimation of the asymptotic covariance matrices of the maximum likelihood and generalized least squares estimators is also discussed Three test statistics are proposed for testing hypotheses of interest with the asymptotic chi-square distribution which guarantees correct asymptotic significance levels Results of simulations and an application to a real data set are also reported (C) 2009 The Korean Statistical Society Published by Elsevier B V All rights reserved

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

Measurement error models with a general class of error distribution

In general, the normal distribution is assumed for the surrogate of the true covariates in the classical error model. This paper considers a class of distributions, which includes the normal one, for the variables subject to error. An estimation approach yielding consistent estimators is developed and simulation studies reported.

Medidas lineares dos estágios de desenvolvimento da dentição permanente humana usando Tomografia Computadorizada de Feixe Cônico: um estudo preliminar

OBJETIVO: determinar as medidas lineares dos estágios de desenvolvimento da dentição permanente humana, usando tomografia computadorizada de feixe cônico (TCFC). MÉTODOS: este estudo foi desenvolvido a partir de bancos de dados de clínicas radiológicas privadas, envolvendo 18 pacientes (13 do sexo masculino, 5 do sexo feminino, com idades variando entre 3 e 20 anos). As imagens das TCFC foram obtidas por meio do sistema i-CAT e medidas com uma função específica do programa desse mesmo sistema. Duzentos e trinta e oito dentes foram analisados, em diferentes estágios de desenvolvimento, nos planos coronal e sagital. O método foi baseado na delimitação e mensuração das distâncias entre pontos anatômicos correspondentes ao desenvolvimento das coroas e raízes dentárias. A partir dos valores obtidos, pôde-se desenvolver um modelo quantitativo para se avaliar os estágios inicial e final de desenvolvimento para todos os grupos dentários. RESULTADOS E CONCLUSÕES: as medidas obtidas dos diferentes grupos dentários estão de acordo com as estimativas das investigações publicadas previamente. As imagens por TCFC dos diferentes estágios de desenvolvimento podem contribuir no diagnóstico, planejamento e resultado dos tratamentos em diversas especialidades odontológicas. As dimensões das coroas e das raízes dentárias podem ter importantes aplicações clínicas e em pesquisas, constituindo uma técnica não invasiva que contribui com estudos in vivo. Entretanto, mais estudos são recomendados a fim de minimizar possíveis variáveis metodológicas.