A New Control Chart for Monitoring Reliability Using Sudden Death Testing Under Weibull Distribution

In this paper, a new control chart using sudden death testing is designed by assuming that the lifetime/failure time of the product follows the Weibull distribution. The structure of the proposed chart is presented. The control chart coefficient is determined using some specified average run length for the in control process and the shifted process. Simulation study is given for the illustration purpose.11Ysciescopu

An Attribute Control Chart Based on the Birnbaum-Saunders Distribution Using Repetitive Sampling

In this paper, an attribute control chart using repetitive sampling is proposed when the lifetime of a product follows the Birnbaum Saunders distribution. The number of failures is to be monitored by designing two pairs of upper and lower control limits. The necessary measurements are derived to assess the average run length (ARL). The various tables for ARLs are presented when the scale parameter and/or the shape parameter are shifted. The efficiency of the proposed control chart is compared with an existing chart. The proposed chart is shown to be more efficient than an existing control chart in terms of ARL. A real example is given for illustration purpose.112Ysciescopu

Accuracy of diagnosis and relationship with quality of emergency medicine training program

An indicator for emergency room performance is the ability to establish the correct diagnosis within the emergency room over the years. The authors chose to examine the non-congruence of Emergency Room diagnoses to that established after hospital stay for three selected years. A total of 8488 records were reviewed and all disparate diagnosis were recorded and categorized. Retrospective chart reviews were done from July 2008 to February 2009 at the Aga Khan University Hospital, Karachi. A substantial reduction in the percentage of disparate diagnoses was seen over the years from 41% in the initial year to 14% in the last year evaluated. It was concluded that over the years there has been an improvement in the reliability of Emergency Room diagnoses at the Aga Khan University Hospital, Karachi

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Log-logistic distribution for survival data analysis using MCMC

This paper focuses on the application of Markov Chain Monte Carlo (MCMC) technique for estimating the parameters of log-logistic (LL) distribution which is dependent on a complete sample. To find Bayesian estimates for the parameters of the LL model OpenBUGS—established software for Bayesian analysis based on MCMC technique, is employed. It is presumed that samples for independent non informative set of priors for estimating LL parameters are drawn from posterior density function. A proposed module was developed and incorporated in OpenBUGS to estimate the Bayes estimators of the LL distribution. It is shown that statistically consistent parameter estimates and their respective credible intervals can be constructed through the use of OpenBUGS. Finally comparison of maximum likelihood estimate and Bayes estimates is carried out using three plots. Additively through this research it is established that computationally MCMC technique can be effortlessly put into practice. Elaborate procedure for applying MCMC, to estimate parameters of LL model, is demonstrated by making use of real survival data relating to bladder cancer patients