The field of cyber-physical systems is witnessing a significant shift towards the adoption of digital twins, which are virtual replicas of physical systems that enable real-time monitoring, simulation, and optimization. This trend is driven by the need for more efficient, adaptive, and resilient systems that can operate in complex and dynamic environments. Recent research has focused on developing innovative digital twin-based frameworks for various applications, including vehicle-to-grid coordination, robotic systems, and network management. These frameworks leverage advanced technologies such as multi-agent reinforcement learning, hybrid system modeling, and edge computing to improve the performance, security, and scalability of cyber-physical systems. Notable papers in this area include: A Digital Twin-based Multi-Agent Reinforcement Learning Framework for Vehicle-to-Grid Coordination, which introduces a novel hybrid architecture for coordinating large-scale decentralised systems. Digital Twin based Automatic Reconfiguration of Robotic Systems in Smart Environments, which proposes a framework for autonomous and dynamic reconfiguration of robotic controllers using digital twin technology.