Phylogeography of Japanese encephalitis virus:genotype is associated with climate

The circulation of vector-borne zoonotic viruses is largely determined by the overlap in the geographical distributions of virus-competent vectors and reservoir hosts. What is less clear are the factors influencing the distribution of virus-specific lineages. Japanese encephalitis virus (JEV) is the most important etiologic agent of epidemic encephalitis worldwide, and is primarily maintained between vertebrate reservoir hosts (avian and swine) and culicine mosquitoes. There are five genotypes of JEV: GI-V. In recent years, GI has displaced GIII as the dominant JEV genotype and GV has re-emerged after almost 60 years of undetected virus circulation. JEV is found throughout most of Asia, extending from maritime Siberia in the north to Australia in the south, and as far as Pakistan to the west and Saipan to the east. Transmission of JEV in temperate zones is epidemic with the majority of cases occurring in summer months, while transmission in tropical zones is endemic and occurs year-round at lower rates. To test the hypothesis that viruses circulating in these two geographical zones are genetically distinct, we applied Bayesian phylogeographic, categorical data analysis and phylogeny-trait association test techniques to the largest JEV dataset compiled to date, representing the envelope (E) gene of 487 isolates collected from 12 countries over 75 years. We demonstrated that GIII and the recently emerged GI-b are temperate genotypes likely maintained year-round in northern latitudes, while GI-a and GII are tropical genotypes likely maintained primarily through mosquito-avian and mosquito-swine transmission cycles. This study represents a new paradigm directly linking viral molecular evolution and climate

Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics

The issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems

Bayesian Model Averaging Based Storage Lifetime Assessment Method for Rubber Sealing Rings

Rubber sealing ring is one of the most widely used seals. It is always stored for a period of time before put into use, especially in aeronautic and aerospace applications. It is necessary to evaluate the storage lifetime of rubber sealing rings. However, due to the long storage lifetime of rubber sealing rings, two issues need to be handled, including model uncertainty and lack of storage lifetime data. A Bayesian model averaging based storage lifetime assessment method for rubber sealing rings is proposed in this article. The Gamma distribution model and Weibull distribution model are selected as the candidate models and combined based on Bayesian model averaging method. The Bayesian model averaging method is applied to handle the model uncertainty. Considering the lack of storage lifetime data, the degradation data are utilized to give the priors of model probability and distribution parameters based on the similarity principle. The results indicate that the proposed method has smaller minus log-likelihood value and is better than the other discussed method, considering both goodness of fit and complexity

Remaining useful life and fault detection models for high voltage electrical connectors focused on predictive maintenance

In recent years, industries have chosen to invest in technology with the aim of making their processes more efficient and thus offering market products of higher quality. Nowadays it is very common for companies to use special systems to predict failures and avoid unexpected delays, reduction of costs, etc. SBI Connectors, along with the Universitat Politècnica de Catalunya, have been collaborating to develop research projects for more than 10 years. As a result of the collaboration with the university, patents and international publications have been generated, which have helped to situate and reinforce SBI Connectors leadership in the international market while offering an image of scientific-technical credibility. This research is carried out, with the collaboration of SBI connectors and Universitat Politècnica de Catalunya, in order to develop the Smartconnector project (within the Retos de Colaboración Spanish research frame). The thesis proposed by the author is dedicated to develop and validate RUL (Remaining Useful Life) and fault detection approaches for electrical substation connectors. The RUL approach proposed in this work is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector (main health indicator), which has two parameters, whose values are identified from on-line acquired data. Next, the fault detection chapter is divided into two parts. The first part presents an on-line condition monitoring method to predict early failures in power connectors from on-line acquired data in conjunction with another parametric degradation model of the resistance of the connector, whose parameters are identified by means of the Markov chain Monte Carlo stochastic method. The second part presents, analyzes and compares the performance of three simple and effective methods for online determination of the State of Health (SoH) of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which determines the degradation trajectory. Furthermore, this work includes an in-depth study of the temperature dependence of the electrical contact resistance (ECR). To analyze and validate results presented in this work, data is acquired in real time, including main parameters such as the electrical current and voltage drop across the terminals of the connector, conductor and connector temperature, thus estimating the phase shift between voltage drop and electrical current waveforms and the contact resistance by means of accelerated aging tests (ADT).En los últimos años, la industria ha optado por invertir en tecnología con el objetivo de hacer más eficientes sus procesos y así ofrecer al mercado productos de mayor calidad. Hoy en día es muy habitual que las empresas utilicen sistemas especiales para predecir fallos y evitar retrasos inesperados, reducción de costes, etc. SBI connectors, junto con la Universitat Politècnica de Catalunya, colaboran para desarrollar proyectos de investigación desde hace más de 10 años. Fruto de la colaboración con la universidad se han generado patentes y publicaciones internacionales, que han ayudado a situar y reforzar el liderazgo de SBI Connectors en el mercado internacional, al tiempo que ofrece una imagen de credibilidad científico-técnica. Esta tesis doctoral se realiza con la colaboración de SBI connectors y la Universitat Politècnica de Catalunya, para desarrollar el proyecto Smartconnector (dentro del marco de investigación Retos de Colaboración). La tesis propuesta por el autor está dedicada a desarrollar y validar modelos de RUL (Remaining Useful Life) y detección de fallos para conectores de subestaciones eléctricas enfocador al mantemiento predictive. El enfoque RUL propuesto en este trabajo se basa en un modelo simple y preciso de la degradación de la resistencia eléctrica del conector respecto al tiempo (indicador principal de salud), el cual tiene dos parámetros cuyos valores se identifican a partir de datos adquiridos en línea. A continuación, el capítulo de detección de fallos se divide en dos partes. En la primera parte se presenta un método de monitoreo en línea de condición para predecir fallos tempranos en conectores de potencia a partir de datos adquiridos en línea en conjunto con otro modelo paramétrico de degradación de la resistencia del conector, cuyos parámetros son identificados por medio del algoritmo de Markov Chain Monte Carlo. La segunda parte presenta, analiza y compara las prestaciones de tres métodos simples y efectivos para la determinación en línea del Estado de Salud (SoH) de conectores de potencia con bajos requerimientos computacionales. Los enfoques propuestos se basan en el seguimiento de la evolución de la resistencia eléctrica de los conectores, que determina la trayectoria de degradación. Además, este trabajo incluye un estudio en profundidad de la dependencia de la temperatura de la resistencia eléctrica de contacto (ECR). Para analizar y validar todo el trabajo presentado, se adquieren datos en tiempo real, incluyendo parámetros principales como la corriente eléctrica y la caída de tensión en los terminales del conector, la temperatura del conductor y del conector, estimando así el desfase entre la caída de tensión y la tensión eléctrica, forma de onda de corriente y la resistencia de contacto por medio de ensayos de envejecimiento acelerado (ADT).Postprint (published version

Design and Analysis of Infectious Disease Studies

[no abstract available

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting; Markov chain Monte Carlo

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro- pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis aects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting, Markov chain Monte Carlo.

Reliability Estimation of Rotary Lip Seal in Aircraft Utility System Based on Time-Varying Dependence Degradation Model and Its Experimental Validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViar) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices and two exchange rates????. We examine violation rates, back-testing criteria, market risk charges and quantile loss function to measure the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, which should be useful for financial practitioners.Markov chain Monte Carlo;backtesting;Value-at-Risk;CAViaR model;Skewed-Laplace distribution;intra-day range

A Bayesian Optimal Design for Accelerated Degradation Testing Based on the Inverse Gaussian Process

Accelerated degradation testing (ADT) is commonly used to obtain degradation data of products by exerting loads over usage conditions. Such data can be used for estimating component lifetime and reliability under usage conditions. The design of ADT entails to establish a model of the degradation process and define the test plan to satisfy given criteria under the constraint of limited test resources. Bayesian optimal design is a method of decision theory under uncertainty, which uses historical data and expert information to find the optimal test plan. Different expected utility functions can be selected as objectives. This paper presents a method for Bayesian optimal design of ADT, based on the inverse Gaussian process and considering three objectives for the optimization: Relative entropy, quadratic loss function, and Bayesian D-optimality. The Markov chain Monte Carlo and the surface fitting methods are used to obtain the optimal plan. By sensitivity analysis and a proposed efficiency factor, the Bayesian D-optimality is identified as the most robust and appropriate objective for Bayesian optimization of ADT