This paper deals with an integrated and interconnected stochastic queuing-inventory system with a fresh item, a returned item, and a refurbished item. This system provides a multi-type service facility to an arriving multi-class customer through a dedicated channel. It sells fresh and refurbished items, buys used items from customers, refurbishes the used items for resale, and provides a repair service for defective items. The assumption of purchasing a used item from the customer and allowing them to buy a fresh item is a new idea in stochastic queuing-inventory modeling. To do so, this system has four parallel queues to receive four classes of customers and five dedicated servers to provide a multi-type service facility. Customers are classified according to the type of service they require. Each class of arrival follows an independent Poisson process. The service time of each dedicated server is assumed to be exponentially distributed and independent. This system assumes an instantaneous ordering policy for the replenishment of a fresh item. In the long run of this considered system, the joint probability distribution of the seven-dimensional stochastic process, significant system performance measures, and the optimum total cost are to be derived using the Neuts matrix geometric technique. The main objective of the system was to increase the occurrence of all kinds of customers by providing a multi-type service facility in one place. Buying a used item is unavoidable in an emerging society because it helps form a green society. Furthermore, the numerical result shows that the assumption of a system that allows a customer to sell their used item and purchase a new item will increase the number of customers approaching the system.

Cross-cultural competence (CCC) is leaders' most critical skill set in science, technology, engineering, and math (STEM) education because it can help them effectively manage cultural diversity. The research provides insights into the potential of AI to foster more inclusive and culturally aware educational environments for STEM students using a theory that combines Constructivism and Cultural-Historical Activity Theory (CHAT). The study discusses two research questions: (1) How can AI-driven tools be used in STEM education to enhance cross-cultural sensitivity and communication? (2) What is the impact of AI-facilitated reflective practices on students' self-awareness of cultural biases? This research shows how AI-based language translators and cultural simulations enable learners to adapt to different cultures. According to Constructivism, learning occurs when learners actively construct their knowledge from experiences towards cultural sensitivity, understanding, and respecting other cultures while reducing stereotypes. CHAT, on its part, views AI as aligning with AI as one of the artifacts or tools that are at the disposal of human beings in order to facilitate social interaction; thus, it has the potential to enhance intercultural communication where verbal cues are not only linguistic but also non-verbal. A total of 228 respondents were reached through surveys, resulting in preliminary results showing improved CCC due to accommodative devices such as culturally-specific software simulations or language translators employing AI models. This study adds value to the existing literature by presenting empirical evidence of how AI can foster CCC in STEM education. It highlights the imperative of AI's theoretical and practical applications in educational curricula.

In response to the digital revolution, nowadays, many companies operate online and offline businesses in parallel to ensure their future competitiveness. This research examines the inventory strategy for multi-product vendor-buyer supply chain systems, considering space constraints and carbon emissions, in order to improve competence in managing online and offline integrated orders. We amalgamate costs and emissions in transport and storage. Here, we divide the warehouse of the buyer into two stages: one for satisfying online orders and the other for satisfying offline orders. We also assume that additional crashing costs reduce the lead times for receiving products in the buyer’s warehouse. This study demonstrates a mathematical model in the form of a constrained non-linear programme (NLP) and derives a Lagrangian multiplier method to solve it. An iterative solution procedure is designed in order to attain sustainable manufacturing decisions, which are illustrated numerically.

Unlike alliance proponents, we posited that high-tech small firms (HTSFs) may not prefer interfirm collaboration for innovation activities. Drawing on the transaction cost and corporate entrepreneurship perspectives, we proposed that boundary decisions in innovation activities are determined by the perceived uncertainties of innovation projects and entrepreneurial orientation (EO). We also examined the consequences of collaborative innovation. In a sample of 178 Korean HTSFs in software development, we found that some aspects of perceived uncertainty prevented collaborative innovation, and that EO moderated this effect, enhancing the outcomes of collaborative innovation. We discuss theoretical and practical insights of these findings.

This study explores innovative, ethical leadership approaches using artificial intelligence (AI) and fractal geometry in public health while fostering sustainable business practices within public health systems. The research employs a qualitative methodology based on case studies, secondary data analysis, and fractal-based AI algorithm evaluations. It examines advanced algorithms' technical applications in public health settings, improving data privacy, copyright, and intellectual property protection. The study finds that fractal algorithms offer robust solutions for promoting ethical leadership in AI-driven public health systems. Fractal geometry's complexity and self-similarity improve predictive modeling, resource allocation, and system transparency while ensuring legal and ethical compliance. By applying fractal algorithms, public health organizations can improve privacy protection, intellectual property management, and ethical governance. The study highlights the need for further research on practical applications, optimization of fractal algorithms, and overcoming the computational demands associated with their deployment in public health. Ethical leadership approaches supported by fractal algorithms can drive more equitable and secure public health interventions, enhancing trust in AI-driven solutions and reducing healthcare access and outcomes disparities. This research presents a novel integration of fractal geometry and AI to address critical ethical issues in public health, providing innovative solutions for data privacy, intellectual property protection, and ethical leadership practices.

This study explores the perceptions, adoption, and applications of AI in healthcare among regional health professionals in Guangdong, China. It seeks to determine how health professionals view AI, its application in practice, and the different challenges practitioners face regarding its application. The research applies a survey approach in which 204 medical practitioners’ perceptions and ideas on AI applications were collected. This involves using Pearson Chi-square tests to examine whether roles as professionals correlate with their attitudes toward AI, such as trust in its reliability and effectiveness, its influence on the accuracy of diagnosis treatment plans, and the impact of specialized training on confidence in handling faulty AI tools alongside descriptive analysis. The outcomes reveal variations across such respondent-role groups concerning views on AI and trust, where they are more involved in the diagnostic exercise, and treatment planning groups show higher levels of optimism about the impact of AI. Furthermore, the findings exhibit a close association between specialized training in AI and increased confidence levels when utilizing AI tools, emphasizing the significance of directed learning interventions. However, this study is based on self-reported data that biases may influence and only focused on the Guangdong province in China. A strategic integration approach for patient care and operational efficiency through this technology has been recommended, particularly within China and the broader Asia-Pacific region.