On the implementation of a new version of the Weibull distribution and machine learning approach to model the COVID-19 data.

Statistical methodologies have broader applications in almost every sector of life including education, hydrology, reliability, management, and healthcare sciences. Among these sectors, statistical modeling and predicting data in the healthcare sector is very crucial. In this paper, we introduce a new method, namely, a new extended exponential family to update the distributional flexibility of the existing models. Based on this approach, a new version of the Weibull model, namely, a new extended exponential Weibull model is introduced. The applicability of the new extended exponential Weibull model is shown by considering two data sets taken from the health sciences. The first data set represents the mortality rate of the patients infected by the coronavirus disease 2019 (COVID-19) in Mexico. Whereas, the second set represents the mortality rate of COVID-19 patients in Holland. Utilizing the same data sets, we carry out forecasting using three machine learning (ML) methods including support vector regression (SVR), random forest (RF), and neural network autoregression (NNAR). To assess their forecasting performances, two statistical accuracy measures, namely, root mean square error (RMSE) and mean absolute error (MAE) are considered. Based on our findings, it is observed that the RF algorithm is very effective in predicting the death rate of the COVID-19 data in Mexico. Whereas, for the second data, the SVR performs better as compared to the other methods.

A New Generalized-X Family for Analyzing the COVID-19 Data Set: a Case Study

Nowadays, researchers in applied sectors are highly motivated to propose and study new generalizations of the existing distributions to provide the best fit to data. To provide a close fit to data in numerous sectors, a series of new distributions have been proposed. In this study, we propose a new family called the new generalized- X (for short, "NG- X ") family of distributions. Based on the NG- X method, a novel modification of the Weibull model called the new generalized-Weibull (for short, "NG-Weibull") distribution is studied. The heavy-tailed characteristics of the NG- X distributions are derived. The maximum likelihood estimators of the NG- X distributions are also obtained. Based on the special case (i.e., NG-Weibull) of the NG- X family, a simulation study is provided. The practical performance of the new NG-Weibull model is assessed by analyzing the COVID-19 data set. The fitting results of the NG-Weibull model are compared with three other competing models. Based on certain statistical measures, it is observed that the NG-Weibull model is the best competitive model. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Unidirectional Elimination of Hydrogen by a Giant Local Field Saves First- and Last-Mile Performances of Semiconductor Devices.

Hydrogen, the smallest element, easily forms bonds to host/dopant atoms in semiconductors, which strongly passivates the original electronic characteristics and deteriorates the final reliability. Here, we demonstrate a concept of unidirectional elimination of hydrogen from semiconductor wafers as well as electronic chips through a giant local electric field induced by compact chloridions. We reveal an interactive behavior of chloridions, which can rapidly approach and take hydrogen atoms away from the device surface. A universal and simple technique based on a solution-mediated three-electrode system achieves efficient hydrogen elimination from various semiconductor wafers (p-GaN, p-AlGaN, SiC, and AlInP) and also complete light emitting diodes (LEDs). The p-type conductivity and light output efficiency of H-eliminated UVC LEDs have been significantly enhanced, and the lifetime is almost doubled. Moreover, we confirm that under a one-second irradiation of UVC LEDs, bacteria and COVID-19 coronavirus can be completely killed (>99.93%). This technology will accelerate the further development of the semiconductor-based electronic industry.