Cyber-physical systems (CPSs) play a crucial role in modern transportation, particularly in transportation cyber-physical systems (TCPSs) for emergency supply logistics. By utilizing real-time data collection, analysis, and communication technologies, TCPS improves the efficiency, safety, and reliability of emergency supply transportation systems. However, existing research often overlooks the dynamic nature of the transportation environment and the complexities of joint emergency supply transportation amid uncertainty. The risk factors for both the physical and cyber layers are inadequately addressed. Consequently, assessing the risks associated with transporting emergency materials within CPS frameworks remains a significant challenge. This study proposes a risk assessment model based on TCPS to analyze the risks associated with transporting emergency supplies via various transportation modes. Initially, a comprehensive analysis of risk factors spanning both the cyber and physical layers within the TCPS is conducted. A risk assessment model is subsequently developed by considering transportation time costs, expenses, and delays. A risk area is then introduced to simulate the impact of recurrent emergency events on emergency supply transportation. Finally, we simulate emergency supply transportation scenarios to facilitate effective risk evaluation.

In recent years, improvements in energy storage technology, cost reduction, and the increasing imbalance between power grid supply and demand, along with new incentive policies, have highlighted the benefits of battery energy storage systems. These systems offer long life, low cost, and high energy conversion efficiency. While energy storage is gradually transitioning from demonstration projects to commercial operations, its technical and economic performance is still limited, and it lacks economies of scale. Research on the design and operational optimization of energy storage systems is crucial for advancing project demonstrations and commercial applications. Therefore, this paper aims to provide insights into system configuration and operational optimization. It first summarizes the optimal configuration of energy storage technology for the grid side, user side, and renewable energy generation. It then analyzes and reviews the economic optimization and cybersecurity challenges in power system operations. Finally, this paper discusses unresolved issues in energy storage applications and highlights important considerations for future implementation and expansion.

Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of virtualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.