The field of industrial engineering and cyber-physical systems is witnessing significant advancements with the integration of artificial intelligence (AI) and digital twin technology. Researchers are exploring the use of AI and digital twins to enhance predictive maintenance, improve safety assessment, and optimize system performance. The development of cloud-based AI platforms and digital twin-driven approaches is enabling automated monitoring and real-time intervention, reducing the need for manual inspections and improving overall efficiency. Noteworthy papers in this area include:

• A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering, which provides a comprehensive taxonomy and architectural elements for digital twin technology.
• User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality, which proposes a digital twin-based approach for user-centric communication service provision.
• Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC, which demonstrates the potential of reinforcement learning in achieving substantial energy savings in residential settings.