The situation of road traffic safety of non-motorized vehicles remains severe, and extensive research has been conducted in China and abroad. To comprehensively analyze the research status and prospect, 448 Chinese papers and 860 English papers from 2000 to 2025 were selected from China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Subsequently, the papers were analyzed for keyword co-occurrence and cluster by CiteSpace, and on this basis, the research hotspots and prospect were summarized. The results indicate that current research shows a significant growth trend, and primarily focuses on four areas: risk of riding behaviors, road traffic accidents, mixed traffic flow conflicts, and traffic management strategies. Future studies should expand into diverse scenarios and deepen the application of multimodal data, with methodological support of computer vision technologies and machine learning algorithms to enhance research capabilities.

In order to prevent safety accidents caused by unsafe behaviors of underground personnel and to ensure their safety, by utilizing advanced machine vision and computer technologies, the traditional YOLOv5s algorithm and OpenPose algorithm target detection models were improved, and a dual-model coupled algorithm for identifying unsafe behaviors of underground personnel was proposed. Through statistical analysis of the most common unsafe behaviors in current underground coal mines, the unsafe behaviors of miners were classified, including item-related, action-related, and area-related unsafe behaviors. According to the characteristics of miners' unsafe behaviors, the improved YOLOv5s algorithm and the OpenPose algorithm were coupled for recognition, and training and verification were conducted on public datasets and self-built datasets. The results show that compared with the current mainstream methods, the dual-model coupled recognition method has a significant improvement in recognition accuracy on self-built datasets and public datasets, with an increase of 5% to 10%, and can quickly and effectively identify unsafe behaviors of underground personnel.

In order to address the managerial complexities arising from the multi-causal and dynamic nature of unsafe behaviors in high-risk industries such as mining and construction, and to overcome the "one-size-fits-all" limitations of traditional behavior-based safety management in the era of informatization and intelligence, this paper introduces the concept of behavioral risk profiling based on the developmental trajectory of behavioral safety and risk profiling theory. By employing literature review methodology and integrating interdisciplinary perspectives, this study systematically examines the constituent elements and construction methods of behavioral risk profiling. The findings reveal that behavioral risk profiling effectively addresses the growing limitations of traditional behavior-based safety management, providing a powerful analytical and managerial tool for the new era. Through a four-layer labeling system comprising work scenarios, behavioral manifestations, risk factors, and risk assessment, it systematically reveals the mapping relationships of unsafe behaviors from phenomena to their underlying essence. On this basis, a construction methodology is developed following the logic of hierarchical data collection, risk-based group assessment, and personalized intervention.

To address the challenges in accurately describing uncertainty and multiple failure modes in the risk assessment of embankment slope failures in goaf areas, a risk assessment model based on an improved T-S fuzzy fault tree approach was proposed. Initially, Gaussian fuzzy numbers were introduced to characterize the failure states and occurrence probabilities of basic events, thereby addressing the over-reliance on precise probabilistic data in conventional fault tree analysis and the insufficient representation of intermediate event states. Subsequently, T-S fuzzy model was employed to replace traditional AND/OR relationships in logic gates, capturing the uncertainty and fuzzy characteristics among events, and thereby deriving the failure probability of slope collapse. Finally, an engineering case study was conducted for validation. The results demonstrate that the proposed approach simplifies the fault tree construction, identifies the key risk factors leading to slope failure, provides a ranking of their influence, and reveal the intrinsic relationships between failure events and various factors.

To accurately characterize freely accumulated rock blocks and pore space properties in fully mechanized top-coal caving goafs, a PNM was introduced. Slice tests of free rock block accumulation in fixed space and corresponding image processing were conducted. Pore network information was extracted using Open-source libraries like OpenPNM and PoreSpy, followed by 3D visual reconstruction and quantitative characterization of PNM to analyze topological geometric and statistical features of the reconstructed model. Results show that with increasing freely accumulated rock block size in fixed space, pore-throat size increases significantly: the average diameters of pores and throats rise from 2.46 mm and 1.41 mm to 3.85 mm and 2.73 mm, respectively, with weak correlation to throat length. The numbers of pores and throats decrease from 3 146 and 6 428 to 1 043 and 2 225, respectively, and the pore coordination numbers concentrate between 2 and 5 (peak at 2). The proportion of pore space increases, and the connectivity of porous media is enhanced. The topological property parameters are approximately positively skewed distribution as a whole, with the peak shewed to the left and the concentrated values in the dataset being relatively small. Two-point correlation function calculations reveal that the relative errors of porosity between the original image data and the reconstructed model are less than 2.5%.

In order to enhance human-artificial intelligence(AI) collaborative decision-making quality in complex industrial environments, mitigate the deficiency of human trust in AI, and bridge cognitive gaps in human-AI collaboration, based on the theory of mind, the study constructed a model of the relationship between human-AI trust, Human-AI SMM and human-AI collaborative decision-making quality, and task complexity was introduced as a moderating variable. First, hypotheses were proposed based on the theoretical relationships among the variables, and a questionnaire was designed by integrating the human-AI trust scale, Human-AI SMM scale, the human-AI collaborative decision-making quality scale, and the task complexity scale. Then, the questionnaires were distributed to frontline employees of AI-using companies nationwide, and 493 valid samples were collected. Finally, SPSS 26.0, AMOS 24.0 and Process 4.0 were used for data analysis and hypothesis testing on the collected valid samples. The results of the study show that human-AI trust significantly and positively affects human-AI collaborative decision-making quality. Human-AI SMM mediates the relationship between human-AI trust and human-AI collaborative decision-making quality. Task complexity positively moderates the relationship between human-AI trust and Human-AI SMM.

In order to explore the influencing pathways of negative emotions among occupational noise-exposed workers, 493 male workers with high noise exposure from five typical manufacturing enterprises were recruited as subjects. Data were collected through occupational health surveys, noise measurements, and psychological scales. Key variables, including age, cumulative noise exposure (CNE), marital status, and nine other factors, were screened using the least absolute shrinkage and selection operator (LASSO) regression, based on which a Bayesian network model was constructed. The results showed that the detection rate of negative emotions among male workers was 5.7%, with an average noise exposure level of 91.5 dB(A). The model identified multiple influencing pathways, and their probability distributions varied across age groups: in the <30 years group, the pathway "age → marital status → negative emotions" was predominant (16.4%); in the 30-39 years group, the direct pathway "age → negative emotions" was most prominent (30.6%); while in the ≥40 years group, the pathway "age → CNE → negative emotions" was dominant (21.5%-29.4%). Moreover, the high CNE group generally exhibited a higher probability of negative emotions than the medium exposure group. The study indicates that negative emotions among male workers under high noise exposure are interactively influenced by factors such as age, CNE, smoking, drinking, and marital status. The Bayesian network model effectively reveals these complex pathway relationships.

To improve the current situation of frequent disasters and accidents in coal mines, the game behaviors among various stakeholders in the dynamic governance of intelligent coal mine safety risks were investigated. A tripartite evolutionary game model was constructed involving the government, coal mine enterprises, and miners, to analyze the evolutionary stability among the three parties. Additionally, evolutionary game scenario simulations were employed to mimic the actual context of intelligent coal mine safety risk governance. Through this, the influence processes of factors such as governmental regulatory intensity and benefit-sharing ratio on the strategies of the gaming participants were revealed. The results indicate that governments should appropriately apply reward and punishment mechanisms during the regulation of enterprise safety governance. This approach can enhance corporate attention to safety risk management, reduce regulatory costs, and promote synergistic benefits between government and enterprises. High governance costs tend to reduce corporate enthusiasm for compliance and may encourage opportunistic behavior; therefore, incentive mechanisms and cost-sharing strategies are necessary to alleviate the burden on enterprises. Encouraging miner participation and reducing their involvement costs contribute to faster identification of potential safety risks and help prevent accidents.

To standardize the scenario representation of accident disaster emergency rescue systems, improve the efficiency of information transmission and the level of scientific decision-making in the emergency rescue process, literature research and case analysis methods were applied to integrate existing theoretical achievements. The scenario elements of the accident disaster emergency rescue system were extracted from three dimensions: system structure composition, interaction between components, and temporal evolution process. The connotation, extension, structure, and function of each element were clarified. A mathematical model was constructed to demonstrate the feasibility of predicting the next state based on the current state and interaction relationship. The results indicate that the proposed scenario element system is consistent with the knowledge element theory framework, and the dynamic evolution characteristics of the accident disaster emergency rescue system can be systematically characterized.

To develop a novel and effective fire-extinguishing agent dedicated to lithium-ion batteries, this study established an experimental platform. Experiments were conducted on 20 Ah lithium-ion phosphate batteries to investigate the inhibitory efficacy of dry ice on the thermal runaway of lithium-ion batteries. Experimental results indicate that dry ice can successfully inhibit the thermal runaway process of lithium-ion batteries: specifically, spraying 1.5 kg of dry ice in the experiment effectively blocked the early-stage thermal runaway of the battery. Furthermore, the inhibitory efficacy of dry ice on battery thermal runaway shows a positive correlation with its spray amount—increasing the dry ice spray amount to 2.6 kg enabled successful suppression of the battery's severe thermal runaway stage. In addition, the phase change heat absorption rate of dry ice is positively correlated with the ambient temperature gradient; however, the cooling rate and effective utilization rate of dry ice do not increase with the rise in spray amount or ambient temperature. In the experiment, when the dry ice spray amount was increased from 0.65 kg to 2.6 kg before battery pressure relief, both the cooling rate and effective utilization rate of dry ice exhibited a trend of first increasing and then decreasing. Moreover, as the severity of thermal runaway and ambient temperature increased, the two decreased from 67.5% and 7.8% to 15.4% and 4.1%, respectively. This study may provide a reference for the development of lithium-ion batteries fire-extinguishing agents.