This study intends to explore the idea of a vocational village strategy to foster sustainable rural development. Vocational villages, offering targeted skills training and economic opportunities, present a compelling soft approach to rural development, addressing the need for sustainable livelihoods and community empowerment. Drawing upon the collaborative governance (the penta-helix model); underpinning the social capital perspective; and highlighting the economic, institutional, cultural, environmental, technological, and institutional dimensions of sustainable development, a vocational village strategy is expected to level up village capacities and facilitate modernization. The research was narratively developed through a qualitative methodology using primary and secondary data sources. Primary empirical data was employed to analyze vocational village practices in Panggungharjo Village, Yogyakarta, Indonesia as a representative example. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) framework provided secondary data to present comparative literature on vocational village development. The findings determined a four-staged vocational village model includes initiation, training, business development, and independence. The success of this model is contingent upon political, bureaucratic, and sociocultural factors (social capital), as well as the effective collaboration of government, academia, industry, and community (penta-helix). This research contributes to the urgency of vocational village practices and models as a viable strategy for achieving equitable and sustainable rural development.

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

European commissioner for the Internal Market, Thierry Breton, told Le Journal du Dimanche in January 2022, “Existing nuclear plants alone will need 50 billion euros of investment from now until 2030. And new generation ones will need 500 billion”. This paper considers whether these values are realistic. Further, it asks whether these investments would yield an internationally competitive European nuclear power infrastructure given that the nuclear power industries in the Organization for Economic Cooperation and Development member countries have lost global nuclear market share to Russian and Chinese firms since 1995.The paper investigates whether the European nuclear industry even with massive investment can compete with the Chinese nuclear industries. It concludes that the European (in particular, the French) nuclear power industry will be unlikely to be cost competitive with the Chinese nuclear power industry unless financing and new plant orders are immediately forthcoming. To achieve carbon neutrality, the issue becomes whether European Union countries can afford indigenous nuclear technologies or will need to import nuclear power plants from Asia.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

Introduction: New energy vehicles (NEVs) refer to automobiles powered by alternative energy sources to reduce reliance on fossil fuels and mitigate environmental impacts. They represent a sustainable transportation solution, aligning with global efforts to promote energy efficiency in the automotive sector. Aim: The purpose of this research is to investigate the influence of social demand on the business model of NEVs. Through a comprehensive analysis of consumer preferences and market dynamics, the research aims to identify strategies for driving the sustainable growth of the NEV industry in respond to societal demands. Research methodology: We conduct a questionnaire survey on 2415 individuals and evaluated that questionnaire data by multifactor analysis of variance to examine individual consumer characteristics. We employed NOVA to evaluate the differences in market penetration factors. Additionally, a regression analysis model is utilized to examine accessibility element’s effects on the consumer’s intensions to buy, addressing categorical and ordered data requirements effectively. Research findings: This research demonstrates that middle-aged and adolescent demographics show the highest willingness to purchase NEV’s, particularly emphasizing technological advancements. Consumer preferences vary based on focus like NEV type, model and brand, necessitating tailored marketing strategies. Conclusion: Improving perception levels and addressing charging convenience and innovative features are vital for enhancing market penetration and sustainable business growth in the NEV industry.