Factors affecting metaverse adoption in education: A systematic review, adoption framework, and future research agenda.

The Metaverse, underpinned by its technical infrastructure, heavily relies on user engagement and behavior for successful integration into educational settings. Understanding its driving factors is essential for such a platform to transition from theory to practice, especially in educational settings. However, these factors remain elusive due to inconsistencies in infrastructure and environments. Therefore, this systematic review aims to fill this void by presenting an integrative view on Metaverse adoption in education. This is achieved via three primary dimensions: establishing a taxonomy of the factors influencing Metaverse adoption in education, proposing a framework for Metaverse adoption, and suggesting future research trajectories in this domain. The review systematically classifies the influential factors into four distinct categories: psychological and motivational factors, quality factors, social factors, and inhibiting factors. The proposed framework provides a structured approach for future studies investigating the Metaverse adoption in educational settings. The proposed framework also emphasizes that educational institutions should not only consider the technical prerequisites but also the social, psychological, and motivational aspects of the Metaverse. The study also pinpoints several critical research agendas to enhance our understanding of Metaverse adoption in education. The insights from this review are invaluable for educational institutions, policymakers, developers, and researchers, significantly enriching the emerging field of Metaverse adoption.

Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach.

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs' environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

Medical data integration using HL7 standards for patient's early identification.

Integration between information systems is critical, especially in the healthcare domain, since interoperability requirements are related to patients' data confidentiality, safety, and satisfaction. The goal of this study is to propose a solution based on the integration between queue management solution (QMS) and the electronic medical records (EMR), using Health Level Seven (HL7) protocols and Extensible Markup Language (XML). The proposed solution facilitates the patient's self-check-in within a healthcare organization in UAE. The solution aims to help in minimizing the waiting times within the outpatient department through early identification of patients who hold the Emirates national ID cards, i.e., whether an Emirati or expatriates. The integration components, solution design, and the custom-designed XML and HL7 messages were clarified in this paper. In addition, the study includes a simulation experiment through control and intervention weeks with 517 valid appointments. The experiment goal was to evaluate the patient's total journey and each related clinical stage by comparing the "routine-based identification" with the "patient's self-check-in" processes in case of booked appointments. As a key finding, the proposed solution is efficient and could reduce the "patient's journey time" by more than 14 minutes and "time to identify" patients by 10 minutes. There was also a significant drop in the waiting time to triage and the time to finish the triage process. In conclusion, the proposed solution is considered innovative and can provide a positive added value for the patient's whole journey.

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms.

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.