This study considers the reliability of the stress-strength model in the presence of the fuzziness when the stress and strength variables have weighted exponential distribution with the common shape parameter. It obtains the mean remaining strength and fuzzy mean remaining strength for the weighted exponential distribution in order to calculate how long a component can live on average under the stress. The comparative simulation results with conventional and fuzzy approaches are presented to observe the effect of the parameter changes on the models using the maximum likelihood method for different sample sizes. In addition, the developed models are applied to two real data and estimation results were generated for two approaches.

Ranked set sampling (RSS) is an advanced data collection method when the exact measurement of an observation is difficult and/or expensive used in a number of research areas, e.g., environment, bioinformatics, ecology, etc. In this method, random sets are drawn from a population and the units in sets are ranked with a ranking mechanism which is based on a visual inspection or a concomitant variable. Because of the importance of working with a good design and easy analysis, there is a need for a software tool which provides sampling designs and statistical inferences based on RSS and its modifications. This paper introduces an R package as a free and easy-to-use analysis tool for both sampling processes and statistical inferences based on RSS and its modified versions. For researchers, the RSSampling package provides a sample with RSS, extreme RSS, median RSS, percentile RSS, balanced groups RSS, double versions of RSS, L-RSS, truncation-based RSS, and robust extreme RSS when the judgment rankings are both perfect and imperfect. Researchers can also use this new package to make parametric inferences for the population mean and the variance where the sample is obtained via classical RSS. Moreover, this package includes applications of the nonparametric methods which are one sample sign test, Mann-Whitney-Wilcoxon test, and Wilcoxon signed-rank test procedures. The package is available as RSSampling on CRAN.

In this paper, we consider the estimation of the reliability in a stress–strength model by the maximum likelihood and Bayesian methods under generalized exponential distribution. We provide the estimation of the reliability with simple random sampling and ranked set sampling methods. Lindley’s algorithm is used to obtain the approximate Bayesian estimation of the reliability with gamma priors. The results are compared in terms of relative efficiency in different sample sizes through a simulation study with R-software. Finally, two real data examples are presented to estimate reliability.

AIM: Virtual reality (VR) based technologies have been used in dentistry for almost two decades. Dental simulators, treatment planning software, and CAD/CAM systems have evolved significantly over the years, changing both dental education and clinical practice. The purpose of this survey study is to learn the knowledge, opinions, and thoughts of dental students in our country on the use of VR-based dental simulators in education, and to raise awareness on this issue. METHODS: Questions testing participants’ knowledge were based on the data from peer-reviewed dental journals. The survey questions consisting of a total of 25 questions were delivered online via Google Forms (Google Inc., USA) to students who had preclinical training in the dental faculty before the Covid-19 pandemic. The data obtained were evaluated using the descriptive statistics and Pearson chi-square test. RESULTS: 422 of the 662 students in the study were female and 240 were male students. 82.3% of the study participants were studying at a state university. 74.6% of the participants in the study stated that they needed more preclinical education. While 89.9% of the students participating in the survey stated that they do not have information about preclinical education with virtual reality, 97.4% stated that they have not used a VR-based dental simulator before. 85.5% of them stated that they feel positive about training in virtual environment with VR-based dental simulator and 86% of them prefer using both VR-based training and phantom models in preclinical training. CONCLUSION: Dental students had overall positive attitudes towards VR-based dental simulator but very few used VR-based dental simulators in education and practice. While using VR-based dental simulators as part of undergraduate and continuing education programs is rapidly advancing in the world, in our country having knowledge about VR-based dental simulators will increase awareness for the development of such technologies and their inclusion in dentistry education.

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