Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.

Traditional learning has faced major changes due to the COVID-19 pandemic, highlighting the necessity for innovative education methods. Virtual reality (VR) technology has the potential to change teaching and learning paradigms by providing a gamified, immersive, and engaging education. The purpose of this study is to evaluate the impact of virtual reality in academic context by using a VR software instrument (called EduAssistant). The system’s features such as virtual amphitheater, search by voice recognition, whiteboard, and a video conference system have fostered a sense of connection and community interaction. The study involved 117 students for VR experience, out of which 97 watched a pre-recorded video and 20 students used the VR headset, and an additional 20 students for traditional learning. The students who used the VR headset achieved a significantly higher mean quiz score of 8.31 compared to 7.55 for the traditional learning group with a two-tailed p-value of 0.0468. Over 80% of the total number of participants were satisfied (4 or 5 out of 5) with the experience and the confidence level when searching through voice recognition was over 90%. The study demonstrates that virtual reality is an excellent approach for changing conventional education. The research results, based on samples, simulations, and surveys, revealed a positive impact of VR and its gamification methods on the students’ cognitive performance, engagement, and learning experience. Immersion provided by a virtual assistant tool helped to promote active and deep learning. Experiments based on EduAssistant features suggest that virtual reality is also an effective strategy for future research related to students with disabilities.

Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches.