A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-δ as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results.

A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-delta as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results. (C) 2013 The Electrochemical Society. All rights reserved.

POOR PERFORMANCE OF BOOTSTRAP CONFIDENCE INTERVALS FOR THE LOCATION OF A QUANTITATIVE TRAIT LOUCS

The aim of many genetic studies is to locate the genomic regions (called quantitative trait loci, QTLs) that contribute to variation in a quantitative trait (such as body weight). Confidence intervals for the locations of QTLs are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the non-parametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals are poorly behaved and so should not be used in this context. The profile likelihood (or LOD curve) for QTL location has a tendency to peak at genetic markers, and so the distribution of the maximum likelihood estimate (MLE) of QTL location has the unusual feature of point masses at genetic markers; this contributes to the poor behavior of the bootstrap. Likelihood support intervals and approximate Bayes credible intervals, on the other hand, are shown to behave appropriately.

Characterization Of Porous Media In Proton Exchange Membrane Fuel Cell Based on Percolation Studies

Water management in the porous media of proton exchange membrane (PEM) fuel cells, catalyst layer and porous transport layers (PTL) is confronted by two issues, flooding and dry out, both of which result in improper functioning of the fuel cell and lead to poor performance and degradation. The data that has been reported about water percolation and wettability within a fuel cell catalyst layer is limited to porosimetry. A new method and apparatus for measuring the percolation pressure in the catalyst layer has been developed. The experimental setup is similar to a Hele-Shaw experiment where samples are compressed and a fluid is injected into the sample. Pressure-Wetted Volume plots as well as Permeability plots for the catalyst layers were generated from the percolation testing. PTL samples were also characterizes using a Hele-Shaw method. Characterization for the PTLs was completed for the three states: new, conditioned and aged. This is represented in a Ce-t* plots, which show a large offset between new and aged samples.

An urn-based Bayesian block bootstrap

Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary processes. One of the main assumptions in block bootstrapping is that the blocks of observations are exchangeable, i.e. their joint distribution is immune to permutations. In this paper we propose a new Bayesian approach to block bootstrapping, starting from the construction of exchangeable blocks. Our sampling mechanism is based on a particular class of reinforced urn processes

Continuum percolation for Gibbs point processes

We consider percolation properties of the Boolean model generated by a Gibbs point process and balls with deterministic radius. We show that for a large class of Gibbs point processes there exists a critical activity, such that percolation occurs a.s. above criticality. For locally stable Gibbs point processes we show a converse result, i.e. they do not percolate a.s. at low activity.

A Bootstrap-Regression Procedure to Capture Unit Specific Effects in Data Envelopment Analysis

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Análisis de la evolución de los resultados de los alumnos y la metodología Bootstrap para detección de cambios

En este trabajo nos enfocamos en el problema del punto de cambio aplicado al control de calidad del proceso enseñanza-aprendizaje. Para ello se tomo en cuenta la evolución temporal de la proporción de alumnos promocionados, por cuatrimestre, de la asignatura Estadística de la Facultad de Ingeniería de la UNLP, desde el año 2001 al 2008. El objetivo es analizar la posible aparición de cambios en dicha proporción no detectados por las cartas de control convencionales. Se trata de establecer las posibles causas de esos cambios en el marco de las transformaciones ocurridas a partir de la acreditación de las carreras de Ingeniería de la UNLP, usando estas herramientas de estudio. El análisis de punto de cambio es una novedosa herramienta utilizada con el fin de determinar la existencia o no de cambios en procesos de diferente índole. Para su aplicación se emplea un test de hipótesis y la metodología Bootstrap.

Análisis de la evolución de los resultados de los alumnos y la metodología Bootstrap para detección de cambios

En este trabajo nos enfocamos en el problema del punto de cambio aplicado al control de calidad del proceso enseñanza-aprendizaje. Para ello se tomo en cuenta la evolución temporal de la proporción de alumnos promocionados, por cuatrimestre, de la asignatura Estadística de la Facultad de Ingeniería de la UNLP, desde el año 2001 al 2008. El objetivo es analizar la posible aparición de cambios en dicha proporción no detectados por las cartas de control convencionales. Se trata de establecer las posibles causas de esos cambios en el marco de las transformaciones ocurridas a partir de la acreditación de las carreras de Ingeniería de la UNLP, usando estas herramientas de estudio. El análisis de punto de cambio es una novedosa herramienta utilizada con el fin de determinar la existencia o no de cambios en procesos de diferente índole. Para su aplicación se emplea un test de hipótesis y la metodología Bootstrap.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Percolation in soils at Different Bulk Densities: Porosity, Connectivity and Grey Threshold.

To improve percolation modelling on soils the geometrical properties of the pore space must be understood; this includes porosity, particle and pore size distribution and connectivity of the pores. A study was conducted with a soil at different bulk densities based on 3D grey images acquired by X-ray computed tomography. The objective was to analyze the effect in percolation of aspects of pore network geometry and discuss the influence of the grey threshold applied to the images. A model based on random walk algorithms was applied to the images, combining five bulk densities with up to six threshold values per density. This allowed for a dynamical perspective of soil structure in relation to water transport through the inclusion of percolation speed in the analyses. To evaluate separately connectivity and isolate the effect of the grey threshold, a critical value of 35% of porosity was selected for every density. This value was the smallest at which total-percolation walks appeared for the all images of the same porosity and may represent a situation of percolation comparable among bulks densities. This criterion avoided an arbitrary decision in grey thresholds. Besides, a random matrix simulation at 35% of porosity with real images was used to test the existence of pore connectivity as a consequence of a non-random soil structure.

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.

Aplicación de la metodología bootstrap para la estimación de la movilidad de furgonetas

En este trabajo se muestran los resultados de la aplicación de la metodología bootstrap a datos de 3369 encuestas realizadas en 2009 a nivel nacional entre conductores de furgonetas, para obtener datos de movilidad interurbana y total, según edad de los vehículos, uso, conductores y otras características de este tipo de vehículo. Se obtienen estimaciones puntuales e intervalos de confianza para la movilidad total de furgonetas, así como para los cuatro tipos de furgonetas según la clasificación realizada en el proyecto de referencia. Se comparan los resultados obtenidos con estimaciones alternativas realizadas con otras fuentes de datos para el mismo colectivo (encuestas realizadas en inspecciones en carretera realizadas por la ATGC de la DGT e inspecciones en ITV) y datos publicados por fuentes oficiales. Estos resultados de movilidad (en término de vehículo-kilómetro) se usarán para la estimación de ratios de accidentalidad en un estudio comparado con otros colectivos de vehículos.