In recent years, the rapid development of technologies such as virtual reality, augmented reality, and mixed reality, along with the significant increase in publications related to the Metaverse, demonstrates a sustained growth in interest in this field. Some scholars have already performed bibliometric analyses of this emerging field. However, previous analyses have not been comprehensive due to limitations such as the volume of literature, particularly lacking in co-citation analysis, which is crucial for understanding the interconnectedness and impact of research works. In this study, we used the Web of Science as a database to search for topics related to the Metaverse from 1995 to 2023. Subsequently, we employed CiteSpace for co-citation network analysis to supplement previous research. Through our analysis at the journal, author, and literature levels, we identified core journals and key authors in the Metaverse field. We discovered that Extended Reality (XR), education, user privacy, and terminologies related to the Metaverse are significant research themes within the field. This study provides clear and actionable research directions for future papers in the Metaverse field.

This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city's northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model's predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

This paper aims to verify the possibility of utilising water-in-diesel emulsions (WiDE) as an alternative drop-in fuel for diesel engines. An 8% WiDE was produced to be tested in a four-stroke, indirect injection (IDI) diesel engine and compared to EN590 diesel fuel. An eddy current brake and an exhaust gas analyser were utilised to measure different engine parameters such as torque, fuel consumption, and emissions at different engine loads. The results show that the engine running on emulsified fuel leads to a reduction in torque and power, an increase in the specific fuel consumption, and slightly better thermal efficiency. The highest percentual increment of thermal efficiency for WiDE is obtained at 100% engine load, 5.68% higher compared to diesel. The emissions of nitric oxide (NO) and carbon dioxide (CO₂) are reduced, but carbon monoxide (CO) and hydrocarbons (HC) emissions are increased, compared to traditional diesel fuel. The most substantial decrease in NO and CO₂ levels was achieved at 75% engine load with 33.86% and 25.08% respectively, compared to diesel.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. The proposed methodologies and optimization focus on balancing the mean efficiency and variability by adjusting the concentration parameter of the Von Mises distribution, which models directional variability in thermal conductivity. The study highlights the superiority of the Von Mises distribution in achieving more consistent and efficient thermal performance compared to the uniform distribution. We also conducted a sensitivity analysis of the parameters for further insights. The results show that optimal tuning of the concentration parameter can significantly reduce efficiency variability while maintaining a mean efficiency above the desired threshold. This demonstrates the importance of considering both stochastic effects and directional consistency in thermal systems, providing robust and reliable design strategies.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.