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 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.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

In the context of contemporary global challenges such as the COVID-19 pandemic, geopolitical conflicts, and climate change, food security assumes particular significance, being an integral part of national security. This study aims to investigate the interplay between food security and national security systems, with a focus on identifying gaps in the literature and determining directions for further research. The study conducted a systematic literature review on food security and national security systems employing a rigorous and transparent process. The qualitative analysis is grounded in the quantitative one, encompassing studies from Scopus. The examination of the selected peer-reviewed articles revealed several methodological and thematic limitations in existing research: i Geographic imbalance: There is a predominant focus on developed countries, while food security issues in developing countries remain insufficiently studied; ii Insufficient explication: There is a lack of research dedicated to managerial and economic aspects of food security in the context of national security; iii Methodological constraints: There is a predominance of quantitative methods and retrospective/cross-sectional studies. Recommendations include developing comprehensive strategies at both global and national levels to enhance food stability and accessibility.

Tomato powdery mildew, fruit rot, and twig blight are all managed with Deltamethrin. Its residues could still be present in the crops, posing a health risk. The pesticide residue analysis, dissipation rate, and safety assessments were thus examined in green tomatoes. The analytical method for residue analysis was validated according to international standards. Tomato fruits and soil were used to study the dissipation of Deltamethrin 100 EC (11% w/w) at 12.5 g a.i ha⁻¹ for the recommended dose (RD) and 25.0 g a.i ha⁻¹ for the double of the recommended dose (DD). Ethyl acetate was used to extract residues from tomato fruit, and PSA and magnesium sulphate were used for cleanup.The fruits had recoveries ranging from 83% to 93% and the soil sample from 81.67% to 89.6%, with the limit of detection (LOQ) estimated at 0.01 mg kg⁻¹. The matrix effect (ME) was calculated to be less than 20% for the tomato fruits and the soil.Half-lives for RD and DD were 1.95 and 1.84 days, respectively. All sampling days for both doses had dietary exposures of residues below the maximum permissible intake (MPI) of 0.16 mg person⁻¹ day⁻¹. The most effective method of decontaminating tomato residue containing Deltamethrin is blanching.