The research explores academia and industry experts’ viewpoints regarding the innovative progression of Virtual Reality (VR)-based safety tools customized for technical and vocational education training (TVET) within commercial kitchen contexts. Developing a VR-based safety tools holistic framework is crucial in identifying constructs to mitigate the risks prevalent in commercial kitchens, encompassing physical, chemical, biological, ergonomic, and psychosocial hazards workers encounter. Introducing VR-based safety training represents a proactive strategy to bolster education and training standards, especially given the historically limited attention directed toward workers’ physical and mental well-being in this sector. This study pursues a primary objective: validating a framework for VR-based kitchen safety within TVET’s hospitality programs. In addition to on-site observations, the research conducted semi-structured interviews with 16 participants, including safety training coordinators, food service coordinators, and IT experts. Participants supplemented qualitative insights by completing a 7-Likert scale survey. Utilizing the Fuzzy Delphi technique, seven constructs were delineated. The validation process underscored three pivotal constructs essential for the VR safety framework’s development: VR kitchen design, interactive applications, and hazard identification. These findings significantly affect the hospitality industry’s safety standards and training methodologies within commercial kitchen environments.

The article presents a study of the connectivity and integration of sovereign bond and stock markets in 10 BRICS+ countries in the context of crisis instabilities in 2019−2024. Financial markets are becoming more integrated, and an increasing share of public investments are carried out across borders, which increases not only the opportunities for participants, but also the risks of a new crisis. The work used data on central bank rates of the considered countries, yield indices of 10-year government bonds, gold and Brent oil prices. The methods include the analysis of exchange rate dynamics, connectivity estimates based on the multivariate concordance coefficient and two-factor Friedman rank variance analysis, VAR models, Granger predictability and cointegration. The objective of this study is to analyze the interrelationship and cointegration between the sovereign bond and equity markets of selected BRICS+ countries during crisis periods. Our findings indicate that market interrelationship intensifies during crises, which in turn amplifies volatility. Additionally, we observed that none of the economies within the BRICS+ group can be classified as fully integrated or entirely isolated markets. The disruption of the interrelationship in the sovereign bond markets of the group is primarily reflected in the inconsistency of dynamic changes between Russia, China, and India. During the global shock of 2019–2020, the crisis spread from China, followed by Indonesia, and later to the other countries of the group. The financial and debt markets of the sampled countries were able to quickly cope with the severe shocks of the COVID-2019 period. The 2022–2024 crisis, which lasted significantly longer, began in Russia before spreading to countries across Asia and Africa. By 2024, Russia’s sovereign bond yields showed a marked decline. The increased market volatility following 2022 disrupted the integration and interrelationship of the stock and debt markets within the BRICS+ countries.

The use of public transport is one of the concepts of sustainable transport. However, people prefer to use private vehicles, which causes various problems, one of which is the high carbon emissions produced. This research aims to encourage programs to use passenger public transportation through a carbon tax. The method in this research is descriptive quantitative with primary data and secondary data. Secondary data was developed in the research by collecting literature study sources on the concept of sustainable transportation development as well as primary data carried out by analyzing calculations regarding the implementation of the carbon tax. There are several proposals that can significantly accelerate the achievement of goals, namely a collaborative approach through collaboration between local government agencies, a policy of progressively implementing a carbon tax as a coercive policy and supported by a program to provide supporting facilities for public transportation. Decision making in this research was carried out by looking at the percentage increase in public transportation use based on the application of a carbon tax or carbon tax.

In order to assess the effects of e-learning integration on university performance and competitiveness, this study uses Oman as a model for the Gulf. Analyzing how e-learning impacts technology integration, diversity, community engagement, infrastructure, financial strength, institutional reputation, student outcomes, research and innovation, and academic quality can reveal whether universities are effectively incorporating digital tools to enhance teaching and learning. By offering a framework for comparable institutions in the Gulf area, this study provides insights into optimizing e-learning techniques to improve university performance and competitiveness. This study uses the Structural Equation Modeling (SEM) with a dataset comprising 424 participants and 55 indicators, analyzed using both measurement and structural models. The results of the hypothesis testing, which indicate that e-learning has a positive effect on factors like student outcomes (B = 0.080, t = 2.859, P = 0.004) and institutional reputation (B = 0.058, t = 2.770, P = 0.005), lend credence to these beliefs. Omani universities need culturally sensitive e-learning, stronger institutional support, and training to enhance diversity (B = 0.002, t = 0.456, P = 0.647) and technology integration (B = −0.009, t = 0.864, P = 0.387). These improvements increase the visibility of Gulf institutions abroad, attracting the best students from all around the world and fostering an inclusive learning atmosphere. Financially speaking, e-learning offers reasonably priced solutions such as digital libraries and virtual laboratories, which are especially beneficial in a region where education plays a major role in socioeconomic development.

In recent years, the environment in the manufacturing industry has become strongly competitive, which is why companies have found it necessary to constantly adjust their strategies and take actions aimed at improving their performance and competitiveness in a sustainable way to grow and remain in the market. Therefore, this paper aims to present an analysis to explain the current situation in the manufacturing industry in Aguascalientes, Mexico, by means of a survey in which product eco-innovation (PEI), process eco-innovation (PrEI) and organizational eco-innovation (OEI) and its effect on environmental performance (EP) and sustainable competitive performance (SCP) were measured. The results show that (EP) is positively and significantly influenced by (PEI) and (PrEI), while no significant influence is found for (OE). Furthermore, it is confirmed that environmental performance positively and significantly influences (SCP). The findings obtained from this study point to the relevance of promoting eco-innovation activities in the manufacturing sector, as this will ensure sustainable competitiveness.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.