The high salinity, humidity, and oxygen-rich environments of coastal marine areas pose serious corrosion risks to metal structures, particularly in equipment such as ships, offshore platforms, and port facilities. With the development of artificial intelligence technologies, image recognition-based intelligent detection methods have provided effective support for corrosion monitoring in marine engineering structures. This study aims to explore the performance improvements of different modified YOLOv5 models in small-object corrosion detection tasks, focusing on five IoU-based improved loss functions and their optimization effects on the YOLOv5 model. First, the study utilizes corrosion testing data from the Zhoushan seawater station of the China National Materials Corrosion and Protection Science Data Center to construct a corrosion image dataset containing 1266 labeled images. Then, based on the improved IoU loss functions, five YOLOv5 models were constructed: YOLOv5-NWD, YOLOv5-Shape-IoU, YOLOv5-WIoU, YOLOv5-Focal-EIoU, and YOLOv5-SIoU. These models, along with the traditional YOLOv5 model, were trained using the dataset, and their performance was evaluated using metrics such as precision, recall, F1 score, and FPS. The results showed that YOLOv5-NWD performed the best across all metrics, with a 7.2% increase in precision and a 2.2% increase in F1 score. The YOLOv5-Shape-IoU model followed, with improvements of 4.5% in precision and 2.6% in F1 score. In contrast, the performance improvements of YOLOv5-Focal-EIoU, YOLOv5-SIoU, and YOLOv5-WIoU were more limited. Further analysis revealed that different IoU ratios significantly affected the performance of the YOLOv5-NWD model. Experiments showed that the 4:6 ratio yielded the highest precision, while the 6:4 ratio performed the best in terms of recall, F1 score, and confusion matrix results. In addition, this study conducted an assessment using four datasets of different sizes: 300, 600, 900, and 1266 images. The results indicate that increasing the size of the training dataset enables the model to find a better balance between precision and recall, that is, a higher F1 score, while also effectively improving the model’s processing speed. Therefore, the choice of an appropriate IoU ratio should be based on specific application needs to optimize model performance. This study provides theoretical support for small-object corrosion detection tasks, advances the development of loss function design, and enhances the detection accuracy and reliability of YOLOv5 in practical applications.

Urban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. However, those studies are regardless of the traffic flow theory of the network and the road users’ perspectives on the performance of traffic. This study proposes a multiagent reinforcement learning (MARL) based traffic control strategy, in which each intersection in a macroscopic fundamental diagram (MFD) region was controlled by one agent using the level of services (LOS) and MFD-based parameters as rewards. The proposed MARL strategy was evaluated by simulation in a 3×3 grid network compared with pretimed, actuated, and MFD-based traffic control strategies. The evaluation results showed that, at different demand levels, the proposed MARL strategy outperforms the other three traffic control strategies in terms of average intersection queue length and average intersection waiting time to a different extent. Results also showed that the proposed MARL dissipated the congestion faster than the other three control strategies. Results of the Friedman test indicated that the differences in performances between the proposed MARL and other strategies were statistically significant regardless of the demand level. The MFD in the testbed network controlled by the proposed MARL was different from that controlled by the pretimed strategy, especially the MFD scatter plot. It provides insights on considering the traffic flow theory of the network when applying MARL to traffic control strategies.

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

In the domain of supply chain resilience (SCR), research has disproportionately emphasized the environmental dimensions of environmental, social, and governance (ESG) factors, leading to an oversight regarding the roles played by social and governance factors. To address this gap, a questionnaire survey was conducted among 313 employees from five Chinese supply chain enterprises. Through factor analysis, this study identified four latent variables associated with environmental factors, three with social factors, and four with governance factors. A structural equation model was then developed to present a comprehensive analysis of the impacts of the three ESG dimensions and digital intelligence on SCR, while also examining the interplay among these ESG factors. The findings reveal that environmental factors positively influence SCR, whereas social and governance factors exert a negative impact. This study further observes that digital intelligence enhances ESG factors but does not directly influence SCR. These results underscore the intricate dynamics between ESG indicators, digital intelligence, and SCR, highlighting the imperative for supply chain entities to make balanced decisions. This research offers novel insights into the effects of ESG factors from the employees’ viewpoint, providing implications and recommendations for supply chain management.

Infrastructure‐to‐infrastructure (I2I) communication enables the exchange of traffic data between intersections, which brings a new challenge to urban traffic control. This paper proposes a novel deep reinforcement learning (DRL) framework for urban traffic signal control within the vehicle‐to‐everything (V2X) ecosystem. The framework incorporates a joint‐state representation integrating traffic data from both I2I and vehicle‐to‐infrastructure (V2I) communications, while considering communication range effects. The reward function is designed to optimize both local intersection conditions and global network performance, facilitating adaptive signal coordination. A simulation case study in Huzhou, China, evaluates the proposed model against conventional pretimed, actuated, nonjoint‐state DRL, and joint‐state reinforcement learning (RL) models. Results demonstrate superior performance of the joint‐state DRL model in episode rewards, average speed, and average time loss, particularly during peak traffic periods. To address data limitations, Monte Carlo cross‐validation (MCCV) is employed, further validating the model’s robustness. Results show consistent performance advantages in average speed and time loss across peak and off‐peak periods, with slight variations compared to joint‐state RL models in certain intervals. The impacts of the communication range are also discussed with the proposed model. Pearson correlation analysis reveals a strong positive correlation between the communication range and convergence time across all traffic periods. Meanwhile, correlations between the communication range and reward, average speed, and average time loss vary by traffic period. Findings highlight the transformative potential of integrating DRL with V2X communication technologies for enhancing traffic signal control in complex urban environments. The proposed model offers a flexible, adaptive approach to traffic management, optimizing flow while maintaining safety standards, with implications for future smart city developments.

Connected vehicles (CVs) leverage the vehicle‐to‐everything (V2X) function to interact with various mobility systems. In China, regulations mandate that CVs must possess automatic recording capabilities as stipulated by the “Management Specification for Road Tests of Intelligent CVs.” Yet, public perception of these functionalities and their impact on the acceptance of V2X technology remains unclear. This study explores the acceptance of V2X in China by augmenting the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Utilizing a survey of 567 Chinese drivers, we employed structural equation modeling (SEM) and Multiple Indicators Multiple Causes (MIMIC) analysis to dissect the factors influencing the behavioral intention (BI) to use V2X. Our findings reveal that social influence (SI), facilitating conditions (FC), and effort expectancy (EE) significantly predict the intention to adopt V2X. Interestingly, while trust (T) does not exert a direct influence, its overall effect on BI surpasses those of the previously mentioned factors. Moreover, the MIMIC models highlight that individuals’ understanding of V2X significantly shapes their acceptance attitudes. These insights underscore the importance of enhancing T, particularly in the data security aspects of V2X, to bolster its acceptance in China. By addressing these concerns, stakeholders can pave the way for wider adoption of this pivotal technology.