Interest in the metaverse has been growing worldwide as the virtual environment provides opportunities for highly immersive and interactive experiences. Metaverse has gradually gained acceptance in the medical field with the advancement of technologies such as big data, the Internet of Things, and 5 G mobile networks. The demand for and development of metaverse are different in diverse subspecialties owing to patients with varying degrees of clinical disease. Hence, we aim to explore the application of metaverse in acute medicine by reviewing published studies and the clinical management of patients. Our review examined the published articles about the concept of metaverse roadmap, and four additional domains were extracted: education, prehospital and disaster medicine, diagnosis and treatment application, and administrative affairs. Augmented reality (AR) and virtual reality (VR) integration have broad applications in education and clinical training. VR-related studies surpassed AR-related studies in the emergency medicine field. The metaverse roadmap revealed that lifelogging and mirror world are still developing fields of the metaverse. Our findings provide insight into the features, application, development, and potential of a metaverse in emergency medicine. This study will enable emergency care systems to be better equipped to face future challenges.

Given the widespread application of ChatGPT, we aim to evaluate its proficiency in the emergency medicine specialty written examination. Additionally, we compare the performance of GPT-3.5, GPT-4, GPTs, and GPT-4o. The research seeks to ascertain whether custom GPTs possess the essential capabilities and access to knowledge bases necessary for providing accurate information, and to explore the effectiveness and potential of personalized knowledge bases in supporting the education of medical residents. We evaluated the performance of ChatGPT-3.5, GPT-4, custom GPTs, and GPT-4o on the Emergency Medicine Specialist Examination in Taiwan. Two hundred single-choice exam questions were provided to these AI models, and their responses were recorded. Correct rates were compared among the four models, and the McNemar test was applied to paired model data to determine if there were significant changes in performance. Out of 200 questions, GPT-3.5, GPT-4, custom GPTs, and GPT-4o correctly answered 77, 105, 119, and 138 questions, respectively. GPT-4o demonstrated the highest performance, significantly better than GPT-4, which, in turn, outperformed GPT-3.5, while custom GPTs exhibited superior performance compared to GPT-4 but inferior performance compared to GPT-4o, with all p < 0.05. In the emergency medicine specialty written exam, our findings highlight the value and potential of large language models (LLMs), and highlight their strengths and limitations, especially in question types and image-inclusion capabilities. Not only do GPT-4o and custom GPTs facilitate exam preparation, but they also elevate the evidence level in responses and source accuracy, demonstrating significant potential to transform educational frameworks and clinical practices in medicine.

The aim of this paper is to measure production and marketing efficiencies in the printed circuit board (PCB) industry using grey relation analysis (GRA) and data envelopment analysis (DEA) techniques. A total of 32 efficiency indicators are initially considered and seven are selected. They represent five indicators for production efficiency and two for marketing efficiency. Fifteen of the 23 companies evaluated need improvement in both production and marketing efficiencies, while four companies had their priority production efficiency improvement and the remaining four companies with priority in marketing efficiency improvement.

Journal of the Operational Research Society (JORS) is the world's longest established OR journal and an official journal of The OR Society. It is the aim of the Journal to present papers which are relevant to practitioners, researchers, teachers, students and consumers of operational research, and which cover the theory, practice, history or methodology of OR.

Smart tourism technologies (STTs) are technological media that tourists apply in various stages of the tourism decision-making process. The purpose of this study was to explore how the communication elements of social network sites (SNSs), as a part of STTs, enhance tourists’ motivation and usage intention. A structural framework based on communication elements and the uses and gratification theory with regard to SNSs usage was developed and investigated. An online survey was employed for the data collection, and structural equation modeling was used in the hypotheses analysis. The findings indicated that Internet self-efficacy, information quality, and systems quality trigger the information-seeking motive while service quality and source credibility positively determine the relationship maintenance motive. The information-seeking motive, entertainment motive, relationship maintenance motive, and Internet self-efficacy positively influence the intention to use SNSs for trips. New findings were found in terms of the relationship between the motives. The information-seeking motive and relationship maintenance motive influence the entertainment motive. Moreover, the relationship maintenance motive influences the information-seeking motive.

The development of an efficient and scientific decision rule for selecting Internet stocks is a necessary and valuable investment tool. As a result, this article adopts two mathematical approaches: data envelopment analysis (DEA) and analytical hierarchical process (AHP) to construct a stock selection framework using 31 listed US Internet companies. The empirical results from this study form the basis for discussion of the combined DEA and AHP approach for selecting Internet stocks. The findings confirm the usefulness of the combined approach that incorporates efficiency analysis and structural decision making. Implications of these findings are also discussed.