In recent years, global geopolitical turmoil, including events like the US–China trade war and the Russia–Ukraine conflict, has significantly reshaped the panorama of the global supply chain (SC). Among these, the chip SC stands out as particularly impacted. Chips form the backbone of all electronic industries, therefore, there is an urgent need for a reassessment of SC security within the chip sector. In this study, we begin by conducting an LDA analysis on 320 relevant news reports to develop a thematic model for the Chinese chip supply chain (CCSC). This approach helps identify the key risk landscape, ultimately distilling 10 major risk factors and four mitigation strategies. Subsequently, we propose an improved multi-layer sequential Bayesian Network (BN) model to assess and quantify risks within CCSC. Lastly, we utilize sensitivity analysis and propagation analysis to examine the impact of risk factors on the ultimate risk of SC disruption and define the resilience and importance of the risk nodes. Our research offers fresh theoretical insight into utilizing BN and LDA methods for modeling SC disruption risk. Furthermore, the study reveals that talent shortage, patent infringement, and insufficient Research and Development (R&D) investment are the three most significant factors contributing to the risk of disruptions in the CCSC. These factors are not only the most critical but also the least resilient, underscoring that enhancing innovation capabilities should be the foremost priority for strengthening the CCSC. Increasing government subsidies is the most effective mitigation measure, providing greater financial support for enterprises, boosting their innovation capabilities and competitiveness, and attracting more investors to the industry.
Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human–robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human–robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human–robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human–robot interface, construction robot applications, human–robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human–robot interface, and communication; and abilities such as safety awareness, continuous learning, problem-solving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human–robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices.
The decarbonization of power and transportation systems faces critical challenges in infrastructure coordination and grid stability, despite rapid growth in electric vehicles (EVs) and renewable energy. This commentary proposes the 5S framework—smart charging, synergistic infrastructure, and storable grid for a stable and sustainable power system—to harmonize these systems across individual, regional, and trans-regional levels. The 5S framework highlights the transformative potential of autonomous vehicles, V2X connectivity, and AI in achieving stable, sustainable, and synergistic energy-transportation systems. This approach offers a scalable roadmap for global stakeholders to accelerate Net Zero Emissions goals while addressing infrastructure gaps and systemic inefficiencies.
Rapid urbanization is reshaping mobility demands, calling for advanced intelligence and management capabilities in urban transport systems. Generative Artificial Intelligence (AI) presents new opportunities to enhance the efficiency and responsiveness of Intelligent Transportation Systems (ITS). This paper reviews the existing literature in transportation and AI to investigate the core technologies of Artificial Intelligence Generated Content (AIGC) – including dialog and reasoning, prediction and decision making, and multimodal generation. Applications are summarized across the four primary ITS subsystems (road subsystem, vehicle subsystem, traveler subsystem and management subsystem). This paper finds that AIGC has become an important way to promote the progress and development of ITS by exploring the research progress of cutting-edge technologies such as data generation, assisted driving decision-making, and intelligent traffic prediction. Meanwhile, this paper explores the potential challenges that AIGC brings to human society from the perspectives of safety risks of fake content, human-machine relationships, social cognition and emotional trust, and related ethical issues, providing insights for the development of safer and more sustainable ITS in the future.
To more accurately estimate and control the magnitude of the shield tail clearance, a hybrid deep learning model with the integration of an online physics-informed deep neural network (online PDNN) and non-dominated sorting genetic algorithm-II (NSGA-II) is developed. The online PDNN has evolved from a deep learning framework constrained by the underlying physical mechanism of shield tail clearance measurements. The algorithm is used to forecast the shield tail clearance in tunnel boring machines (TBMs). The NSGA-II is employed to conduct the multi-objective optimization (MOO) process for shield tail clearance. The proposed method is validated in a tunnel case in China. Experimental results reveal that: (1) In comparison with some state-of-the-art algorithms, the online PDNN model demonstrates superior capability in predicting shield tail clearance above, upper-left, and upper-right, with R2 scores of 0.93, 0.90, and 0.90, respectively; (2) The MOO achieves a comprehensive optimal solution, with the overall improvement percentage of shield tail clearance reaching 30.87% and a hypervolume of 32 under the 20% constraint condition, which surpasses the average performance of other MOO frameworks by 23 and 5.48%, respectively. The novelty of this research lies in coupling the constructed physical constraints and the online update mechanism into a causal analysis-oriented data-driven model, which not only enhances the model’s performance and interpretability but also realizes the control for the shield tail clearance by the integration of NSGA-II.
This paper examines the intricate issue of Optimal Power Flow (OPF) optimization concerning the incorporation of renewable energy sources (RESs) into power networks. We present the Boosting Circulatory System Based Optimization (BCSBO) method, a novel modification of the original Circulatory System Based Optimization (CSBO) algorithm. The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills, making it an effective instrument for addressing intricate optimization challenges. The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples. The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches, including CSBO, Moth-Flame Optimization (MFO), Particle Swarm Optimization (PSO), Thermal Exchange Optimization (TEO), and Elephant Herding Optimization (EHO). For the first case with minimizing the fuel cost associated with the thermal power generators, the total cost reported by the BCBSO is obtained as
Locating the source of diffusion in complex networks is a critical and challenging problem, exemplified by tasks such as identifying the origin of power grid faults or detecting the source of computer viruses. The accuracy of source localization in most existing methods is highly dependent on the number of infected nodes. When there are few infected nodes in the network, the accuracy is relatively limited. This poses a major challenge in identifying the source in the early stages of diffusion. This article presents a novel deep learning-based model for source localization under limited information conditions, denoted as GCN-MSL (Graph Convolutional Networks and network Monitor-based Source Localization model). The GCN-MSL model is less affected by the number of infected nodes and enables the efficient identification of the diffusion source in the early stages. First, pre-deployed monitor nodes, controlled by the network administrator, continuously report real-time data, including node states and the arrival time of anomalous signals. These data, along with the network topology, are used to construct node features. Graph convolutional networks are employed to aggregate information from multiple-order neighbors, thereby forming comprehensive node representations. Subsequently, the model is trained with the true source labeled as the target, allowing it to distinguish the source node from other nodes within the network. Once trained, the model can be applied to locate hidden sources in other diffusion networks. Experimental results across multiple data sets demonstrate the superiority of the GCN-MSL model, especially in the early stages of diffusion, where it significantly enhances both the accuracy and efficiency of source localization. Additionally, the GCN-MSL model exhibits strong robustness and adaptability to variations in external parameters of monitor nodes. The proposed method holds significant value in the timely detection of anomalous signals within complex networks and preventing the spread of harmful information.
This study proposes a comprehensive framework for the joint optimization of maintenance actions and safety stock policies for multi-specification small-batch (MSSB) production. The production system considered consists of multiple machines arranged in a series-parallel configuration. Given the multi-stage nature of the MSSB, a piecewise Gamma process is developed to model the degradation of machines owing to varying product specifications. A quality-based maintenance model is proposed to guide the scheduling of maintenance actions based on the observed product defect rate. The maintenance policy is optimized at two levels: at the machine level, the optimal quality of the produced products is determined, and at the system level, a threshold quality value is established to facilitate the opportunistic maintenance of machines. The relationship between the buffer stock and machine capacity is explicitly modeled to ensure production efficiency. A simulation-based multi-objective algorithm is employed to identify the optimal decision variable levels for the proposed maintenance policy. The numerical results demonstrate that the proposed method effectively balances the conflicting objectives of minimizing the expected operational costs and maximizing production efficiency.
