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The introductory pharmacy practice experiences (IPPE) in Taiwan, which are traditionally conducted in physical hospital settings, incorporated up to 30% distance learning from May 2021 due to Coronavirus Disease 2019 (COVID-19). A web-based cross-sectional survey was adopted to investigate pharmacy students' experiences and perceptions of transitioning from in-hospital internships to distance learning due to COVID-19 in the pharmacy department of a university in Southern Taiwan. We analyzed the results to discover factors that significantly affected students' perceptions of transitioning from in-hospital internships to distance learning. In total, 81 interns from the university's pharmacy department responded to the questionnaire. Approximately half of the participants felt happy when they learned, before the internship began, that the internship would be partially replaced with distance learning. The overall satisfaction rate was 67.9%, and no significant differences was observed in students' satisfaction between hospital size or distance-learning time. However, more students in the medical center felt they had insufficient time to finish assignments compared to those in the regional hospitals, and the students who had 11–15 days of distance learning felt that they interacted more smoothly with their peers compared to those who had other durations. Program designers should make distance internship courses more student-centered, with a focus on increasing interactions between students, teachers, and peers to compensate for the lack of physical presence. [ABSTRACT FROM AUTHOR]

In the entertainment industry, providing games to players is a main business activity with a long history. There are various kinds of game types such as the computer game, web game, pocket game, mobile game developed for play. Virtual reality is a new technology which can integrate into the computer or mobile for generating virtual reality games. The goal of this study is to develop a framework to evaluate and select the virtual reality games development project. In the proposed framework, Linguistic VIKOR is integrated with fuzzy cognitive maps to select the project. For readers to understand the proposed method, a case study has been introduced to evaluate the project based on the proposed method. Finally, the conclusion and future research are discussed as an ending. [ABSTRACT FROM AUTHOR]

Medical data usually have missing values; hence, imputation methods have become an important issue. In previous studies, many imputation methods based on variable data had a multivariate normal distribution, such as expectation-maximization and regression-based imputation. These assumptions may lead to deviations in the results, which sometimes create a bottleneck. In addition, directly deleting instances with missing values may have several problems, such as losing important data, producing invalid research samples, and leading to research deviations. Therefore, this study proposed a safe-region imputation method for handling medical data with missing values; we also built a medical prediction model and compared the removed missing values with imputation methods in terms of the generated rules, accuracy, and AUC. First, this study used the kNN imputation, multiple imputation, and the proposed imputation to impute the missing data and then applied four attribute selection methods to select the important attributes. Then, we used the decision tree (C4.5), random forest, REP tree, and LMT classifier to generate the rules, accuracy, and AUC for comparison. Because there were four datasets with imbalanced classes (asymmetric classes), the AUC was an important criterion. In the experiment, we collected four open medical datasets from UCI and one international stroke trial dataset. The results show that the proposed safe-region imputation is better than the listing imputation methods and after imputing offers better results than directly deleting instances with missing values in the number of rules, accuracy, and AUC. These results will provide a reference for medical stakeholders. [ABSTRACT FROM AUTHOR]