Parallel Weibull regression model.

This paper focuses on the lifetime analysis of parallel systems consisting of Weibull components with independent failures and covariates. The performance of the parameter estimates of two and three-component parallel systems at different values of the shape parameter, σ, are compared and some confidence interval procedures are analyzed via a coverage probability study for m = 2, using simulated data

Micro-simulation study of vehicular interactions on upgrades of intercity roads under heterogeneous traffic conditions in India

Purpose: Study of the basic traffic flow characteristics and clear understanding of vehicular interactions are the pre-requisites for highway capacity analysis and to formulate effective traffic management and control measures. The road traffic in India is highly heterogeneous comprising vehicles of wide ranging physical dimensions, weight, power and dynamic characteristics. The problem of measuring volume of such heterogeneous traffic has been addressed by converting the different types of vehicles into equivalent passenger cars and expressing the volume as Passenger Car Unit per hour (PCU/h). Methods: Computer simulation has emerged as an effective technique for modeling traffic flow due to its capability to account for the randomness related to traffic. This paper is concerned with application of a simulation model of heterogeneous traffic flow, named HETEROSIM, to quantify the vehicular interaction, in terms of PCU, for the different categories of vehicles, by considering the traffic flow of representative composition, on upgrades of different magnitudes on intercity roads in India. Results and Conclusions: The PCU estimates, made through microscopic simulation, for the different types of vehicles of heterogeneous traffic, for a wide range of grades and traffic volume, indicate that the PCU value of a vehicle significantly changes with change in traffic volume, magnitude of upgrade and its length. It is found that, the change in PCU value of vehicles is not significant beyond a length of 1600 m on grades. Also, it has been found that the PCU estimates are accurate at 5% level of significance

Interval estimation for parameters of a bivariate time varying covariate model

This paper investigates several asymptotic confidence interval estimates, based on the Wald, likelihood ratio and the score statistics for the parameters of a parallel two-component system model, with dependent failure and a time varying covariate, when data is censored. This model is an extension of the bivariate exponential model. The procedures are investigated via a coverage probability study using the simulated data. The results clearly indicate that the interval estimates, based on the likelihood ratio method, work better than any of the other two methods when dealing with the censored data

Reference Model For Office Defense Cooperation (ODC) Training Support System (OTSS)

The purpose of this project is to develop a reference model and a prototype system for training management activities at the Office of Defense Cooperation (ODC) in US Embassy, Kuala Lumpur. A reference model is important as it serves as a good starting point for system developers to understand users' requirements, and it saves time, resources and cost, reduces risk, improves effectiveness and efficiency, and reduces ambiguity. The prototype system, referred as ODC Training Support System (OTSS), will provide functionality that could not be fulfilled by a primary system, and subsequently complement the primary system in order to achieve synergy and greater productivity. The reference model was developed using a simplified version of Rational Unified Process (RUP) methodology, and Unified Modeling Language's (UML) notations. The database application for the prototype system was developed using Microsoft Access 2000, and the Web interface was developed using MySQL database server, HTML, PHP scripting language, and Apache Web server. The system was validated through system requirement testing and IBM's Computer System Usability Questionnaire. It was concluded that, users' requirements were met and the prototype system was operating well, however, the system's interface and functionality were recommended for further improvement. This project concludes by discussing problems and limitations that were encountered in completing this project, and offers a few recommendations for future development in this subject

Survival model of a parallel system with dependent failures and time varying covariates

In this paper, we extended a parallel system survival model based on the bivariate exponential to incorporate a time varying covariate. We calculated the bias, standard error and rmse of the parameter estimates of this model at different censoring levels using simulated data. We then compared the difference in the total error when a fixed covariate model was used instead of the true time varying covariate model. Following that, we studied three methods of constructing confidence intervals for such models and conclusions were drawn based on the results of the coverage probability study. Finally, the results obtained by fitting the diabetic retinopathy study data to the model were analysed

Bayesian estimation for Poisson process models with grouped data and covariate

This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process model for grouped failure data from a repairable system with covariate. The extended powerlaw model with a recurrence rate that incorporates both time and covariate effect is compared to the powerlaw, log-linear and HPP models. We propose the use of both informative and noninformative priors depending on the nature of the parameter. The MCMC technique is utilized to obtain samples from the posterior distribution which was implemented via WinBUGS. We then apply the Bayesian Deviance Information Criteria (DIC) to select the best model for real data from ball bearing failures where information regarding previous failures are available. The credible interval is used to check the significance of the parameters of the selected model. We also used the posterior predictive distribution for model checking by comparing the observed and posterior predictive mean number of failures

Self-awareness Group Counseling Model for Prospective Counselors

AbstractThe self-awareness group counseling model is aimed to promote intrapersonal and interpersonal awareness of counseling students. Since self-awareness is thought to be one of the most influential factors in affecting a counselor's profession, activities that promote personal-understanding needs to be a central component in counseling programs. This paper provides a rationale for the need of self-awareness group counseling, as well as the formal education of counseling skills, in national counselor-education programs. The model employs these scopes for the basic self-awareness group model; (1) the scope of self-awareness, (2) self and other perceptions, (3) the discovery of thoughts, emotions and behaviors, (4) the understanding of communication patterns, (5) the ability to give and receive feedback, (6) self evaluations and (7) experiential learning of counseling skills and processes. Each session of the group counseling is structured by psychodramatic activities. Based on the pilot experience, a discussion is made on the perceived effectiveness of the model. Preliminary findings indicate that the counseling students benefit from the group experience in terms of self-understanding

Bus Priority on Roads Carrying Heterogeneous Traffic: a Study using Computer Simulation

The desirable goal in passenger transportation is moving more people in fewer vehicles. This goal, in respect of road transport, can be attained by encouraging public transport modes like buses by assigning priority. This paper is concerned with application of micro simulation technique to study the impact of provision of reserved bus lanes on the flow of highly heterogeneous traffic on urban roads. The specific objectives of this paper are (i) to modify and validate a newly developed model of heterogeneous traffic flow using field observed data and (ii) to apply the validated simulation model to study the impact of exclusive bus lanes introduced on urban arterials for a wide range of traffic volume levels. The impact of introduction of an exclusive bus lane is measured in terms of the reduction in speed of other categories of motor vehicles, due to the consequent reduction in road space, over a wide range of traffic volume. The main finding of this paper is, that if an exclusive bus lane is provided under highly heterogeneous traffic conditions,(prevailing in Indian cities), then, the maximum permissible volume to capacity ratio that will ensure a level of service of C for the traffic stream comprising all the motor vehicles, except the buses, is about 0.53

Modeling repairable system failures with interval failure data and time dependent covariate

An application of a repairable system model for interval failure data with a time dependent covariate is examined. The performance of several models based on the NHPP when applied to real data on ball bearing failures is also explored. The best model for the data was selected based on results of the likelihood ratio test. The bootstrapping technique was applied to obtain the variance estimate for the estimated expected number of failures. Results demonstrate that the proposed model works well and is easy to implement, in addition the bootstrap variance estimate provides a simple substitute for the traditional estimate

Simulation of interval censored data in medical and biological studies.

This research looks at the simulation of interval censored data when the survivor function of the survival time is known and attendance probability of the subjects for follow-ups can take any number between 0 to 1. Interval censored data often arise in the medical and biological follow-up studies where the event of interest occurs somewhere between two known times. Regardless of the methods used to analyze these types of data, simulation of interval censored data is an important and challenging step toward model building and prediction of survival time. The simulation itself is rather tedious and very computer intensive due to the interval monitoring of subjects at prescheduled times and subject's incomplete attendance to follow-ups. In this paper the simulated data by the proposed method were assessed using the bias, standard error and root mean square error (RMSE) of the parameter estimates where the survival time T is assumed to follow the Gompertz distribution function