Generalized Weibull–Lindley (GWL) Distribution in Modeling Lifetime Data

In this manuscript, we have derived a new lifetime distribution named generalized Weibull–Lindley (GWL) distribution based on the T-X family of distribution specifically the generalized Weibull-X family of distribution. We derived and investigated the shapes of its probability density function (pdf), hazard rate function, and survival function. Some statistical properties such as quantile function, mode, median, order statistics, Shannon entropy, Galton skewness, and Moors kurtosis have been derived. Parameter estimation was done through maximum likelihood estimation (MLE) method. Monte Carlo simulation was conducted to check the performance of the parameter estimates. For the inference purpose, two real-life datasets were applied and generalized Weibull–Lindley (GWL) distribution appeared to be superior over its competitors including Lindley distribution, Akash distribution, new Weibull-F distribution, Weibull–Lindley (WL) distribution, and two-parameter Lindley (TPL) distribution.

Computed tomography stroke findings and population demographics at Pelonomi Hospital, Bloemfontein

Background: Stroke remains the highest cause of death in patients more than 50 years old in South Africa, and the fourth highest cause of death overall. There is a paucity of information regarding this disease in the Free State Province.Objectives: To assess the stroke profile of patients referred for computed tomography (CT) imaging to our institution along with evaluating factors that could improve stroke management.Method: The demographic information, stroke risk factors, stroke types and time to imaging were evaluated for all patients who presented for CT stroke imaging from July 2014 until July 2015. Information was gathered prospectively from the hospital and radiology information systems.Results: The study included 174 patients (53.5% female, 46.5% male). Their mean age was 59 years (standard deviation (SD) 14.6). The most prevalent risk factors were hypertension (83.7%), smoking (20.5%) and diabetes (15.0%). The population group consisted of 67.8% ischaemic (n = 118) and 32.2% (n = 56) haemorrhagic strokes. The majority of patients with a known time of symptom onset (n = 102) presented after 8 hours (82.4%). The median order to report time (ORT) was 61 min (range 18 min–1361 min). The median arrival to report time (ART) was 32 min (range 4 min–893 min).Conclusion: Our stroke population did not differ significantly from others in South Africa and Africa overall. Pre- and in-hospital delays significantly influenced patient numbers qualifying for thrombolysis.

Reliability Model Choice Based on AHP and Entropy Weight-TOPSIS

Aiming at the problem that some products show no failure in the random truncation test period,the mean rank order method is introduced to determine the sequence number of failure time and the method of approximate median order is used to calculate the empirical cumulative distribution function.To solve the poser that model of failure distribution of the same batch of data is not the single one,this study uses analytic hierarchy process (AHP) and entropy weight-TOPSIS,including both objective and subjective factors,to find out the datas optimun distribution.Through taking an example,It proved that this method is simple to use and has good applicability.