Background: Over the past decade, there has been a rapid advancement in technology and virtual reality applications, leading to the emergence of the metaverse - a virtual universe where users interact with each other and their surroundings through immersive experiences. In the nursing profession, the metaverse presents unique opportunities to enhance patient care, education, and collaboration. Aim: To analyze and identify the attributes of metaverse in nursing, exploring its dimensions, benefits, challenges, and implications. By examining relevant literature, this study will contribute to a better understanding of the metaverse in nursing. Method/Design: Concept analysis by Walker and Avant (2019). Results: Metaverse in nursing involves three defining attributes: use of advanced technologies, better access to education and healthcare, and collaboration and community building. Antecedents of metaverse in nursing require technological advancements, increased digital literacy, demand for innovative education, globalization of healthcare education Conclusion: Metaverse in Nursing suggests that it is a promising technology that has the potential to enhance nursing practice and improve patient outcomes, but further research is needed to fully explore the impact of its integration.

The digital drug traceability systems ensure the patient-centric dose, dosage form, and strength delivered to the patient as intended in the supply chain. It helps the digital healthcare platforms securely establish drug information supplied to patients for potential treatments. Therefore, it is important for the global supply chain to explore the number of high-end digital health solutions and drug traceability to create an interactive loop on drug security for patients. This article provides an overview of advanced technologies for digital drug traceability, such as blockchain, that would establish a secure pharmaceutical supply chain for the digital world.

Progress in the development and adoption of technological innovations is instrumental in enhancing the efficiency of production systems across the globe. Through the introduction of cost-efficient and high-performing technologies, countries can both reduce the resource use intensity of their economies and boost the global supply of essential products. The focus of this study is to analyze the application of advanced machinery and mechanisms within the agricultural sector, a primary industry that acts as a major contributor to the gross domestic product (GDP) of many nations. Specifically, this paper provides an in-depth review of the latest impact assessments based on analytical and modeling tools conducted on agricultural machinery and production technologies. Our findings highlight the positive role played by scientific progress and innovation in driving the competitiveness, growth and improved sustainability of the agricultural sector. Over the years, advanced technologies have accelerated the development and modernization of machinery, equipment, and processes in farming. Typically, modern machinery and equipment have enabled large-scale production on farms, enhancing the cost-efficient use of both land and labor, as well as the capacity and timeliness in performing essential agricultural operations. The rapid diffusion of technical advancements has further contributed to resource savings, productivity growth, and the overall transformation of agricultural value chains. Accordingly, the implementation of appropriate enabling conditions is of vital importance in encouraging the widespread integration of technologies in agriculture, not only boosting productivity along the agri-food chain but also yielding widespread social, economic, and environmental benefits.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.