To enhance China's work safety governance level, policy texts on work safety issued by the central government and 30 provincial-level governments from 2014 to 2023, as well as safety accident data from the same period, were used as the research sample. By integrating the Levenshtein distance algorithm, the Jaccard similarity algorithm, and other similarity measures, a "theme-content" two-stage policy synergy analysis model was proposed. Central-local policy synergy degree was quantitatively measured through a weighted evaluation of policy theme matching degree and content similarity. A complex synergy network was then constructed. Using social network analysis and modularity algorithms, the structural characteristics of the central-local safety policy synergy network were analyzed in depth, and its spatiotemporal evolution was revealed. Based on panel data, the impact of central-local synergy outcomes of safety policies on accident incidence were further investigated. The results show that China's central-local safety policy synergy network is exhibiting an increasingly integrated development trend over time. The responsiveness of provincial governments to central policies is continuously improving, evolving from early regional differentiation toward comprehensive nationwide coordination, and an initial "central planning-local response" national work safety policy system has been initially established. Moreover, the central-local policy synergy degree is significantly negatively correlated with the frequency of safety accidents, and improving the level of coordination can effectively reduce the risk of work safety accidents.

In order to address the constraints of limited corpus resources, restricted input capacity, and data privacy in applying LLMs to the field of safety engineering, a localized accident question-answering model integrating the DeepSeek with a RAG mechanism was constructed to enable intelligent parsing and knowledge services for complex texts, thereby supporting safety management decision-making. A semantic-feature corpus was built based on accident investigation reports and laws and regulations released by government emergency management systems, and technologies such as PaddleOCR, LayoutLMv3, and YOLOv8 were incorporated to accomplish document structure reconstruction and semantic modeling. The model encompassed four stages—document parsing, semantic alignment, knowledge-base construction, and hybrid retrieval—and was designed with capabilities for causal-chain extraction, regulation matching, and semantic mapping. The results indicated that, compared with the Deepseek-r1:32b model without the RAG mechanism, the enhanced model achieved improvements of 7.7% in automated scoring and 17.6% in human evaluation, and the response-speed and stability metrics presented higher numerical performance than those of the baseline model. The model performance was still influenced by the local parameter scale and the knowledge-updating mechanism, yet the experimental findings demonstrate that it is capable of fulfilling the intended functions in the present study.

This study aims to systematically explore the temporal and spatial patterns, causal mechanisms, and prevention and control strategies of dust explosion accidents in China. Knowledge graph technology combined with statistical analysis was used to analyze the relevant literature and accident cases of dust explosion accidents in China from 1990 to 2024. A knowledge graph was constructed using CiteSpace visualization software to analyze the spatiotemporal distribution characteristics and the accident causality chain of dust explosion accidents. A four-layer prevention and control system framework of "hazard source-facility-management-emergency" was constructed, and its effectiveness was verified by typical cases. It is found that the risk of dust explosion shows the characteristics of "type differentiation" and "geographical migration", with frequent occurrence of dust, high fatality of coal dust and high secondary injury of grain dust explosions. The accident hotspots are showing a trend of shifting from the eastern to the central and western regions. Inadequate dust cleaning and defective dust removal systems are central causes, while operational violations and use of non-explosion-proof equipment are main triggering conditions, and the causal structure varies significantly with dust types. The implementation of differential and precise prevention and control strategies is the key means to improve the prevention and control efficiency of dust explosion accidents.

To address the structural safety risks in the workforce arising from the increasing proportion of older miners in the mining industry, this study investigated the causal mechanisms underlying unsafe behaviors among older miners. Drawing on the IMB model, a SEM was developed to examine the causal pathways of miners' unsafe behaviors, and multi-group analysis was employed to compare the path coefficient differences between older and younger miners. The results indicate that knowledge and experience factors such as educational attainment and safety knowledge, as well as psychological characteristic factors including safety attitudes, accident experience, mental health, job burnout, and risk attitudes, have significant effects on miners' unsafe behaviors. Notably, work ability mediates both the pathway from knowledge and experience to unsafe behaviors and the pathway from psychological characteristics to unsafe behaviors. In addition, risk attitudes, mental health, job burnout, educational attainment, and safety knowledge exert stronger influences on unsafe behaviors among older miners compared to their younger counterparts.

To reduce the occupational health risks of communication base station maintenance personnel and improve their physical and mental health, a moderated mediation model was built in this study. It was based on COR to explore the psychosocial factors affecting these personnel's physical and mental health. First, the action path of prosocial work orientation on occupational health risks was analyzed based on the COR, and four theoretical hypotheses were proposed. Second, a hypothetical model encompassing prosocial work orientation, meaningful work, neuroticism, and occupational health risks was established. Finally, SPSS and PROCESS were employed to analyze 3 559 valid sample data from a large communication company to verify whether the hypotheses held true, and corresponding management implications were put forward based on the data. The results show that prosocial work orientation significantly negatively predicts occupational health risks. Meaningful work plays a partial mediating role in the relationship between prosocial work orientation and occupational health risks. Neuroticism not only positively moderates the negative relationship between meaningful work and occupational health risks but also further positively moderates the strength of the aforementioned mediating path—specifically, the mediating effect is stronger among individuals with high neuroticism.

To achieve accurate identification and evaluation of the service state of heavy-haul railway lines, a heavy-haul railway line comprehensive inspection vehicle was designed and developed, integrating six major inspection systems: a track condition inspection system, a rail profile measurement system, a track geometry measurement system, a rail flaw detection system, a wheel-rail force measurement system, and a vibration measurement system. A positioning and synchronization system is employed to enable synchronized acquisition and correlation analysis of multi-source line-condition data, and to provide graded alarms for suspected defects. The results show that, since its commissioning in July 2023, the heavy-haul railway line comprehensive inspection vehicle, operating at speeds up to 80 km/h, has continuously collected multi-source data including track geometry parameters, surface inspection images, internal B-scan images, wheel-rail forces, and vibration signals, and has established a database of typical defects. By combining the positioning and synchronization system with a multi-source spatiotemporal mapping model, the system realizes automatic alignment and correlation analysis of inspection data, enabling accurate tracing of defect causes and reducing both false positives and missed detections. Furthermore, the proposed graded alarm mechanism for suspected defects supports differentiated responses such as speed restriction, reporting, and record-keeping according to exceedance levels, thereby ensuring train operation safety while improving both heavy-haul railway maintenance efficiency and train operation efficiency.

To address the challenges of complex data environments, low accuracy of single-sensor detection, and limited precision in traditional laboratory safety systems, this study presented a multi-sensor fusion early warning model based on an ISBOA and algorithm KELM. First, the KELM framework was employed to integrate heterogeneous sensor data and construct the warning model, where a regularization term was introduced to alleviate overfitting. Then, the improved ISBOA adaptively optimized the regularization coefficient C and kernel parameterσ of the KELM, thereby enhancing parameter robustness and diagnostic accuracy. Finally, simulation and experimental analyses were conducted using both synthetic and real laboratory datasets, and the proposed ISBOA-KELM model was compared with the unimproved Secretary Bird Optimization Algorithm (SBOA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) algorithms. The experimental results show that the ISBOA-KELM model improved accuracy by 4%, 3%, and 2%, respectively, compared with the other three models. In four representative laboratory safety scenarios, including fire and gas leakage, the detection accuracy exceeds 96% with the false negative rate below 6%, which significantly improves the reliability and robustness of safety accident early warning.

In order to improve the identification accuracy of kick risk during drilling, a multi-category kick risk intelligent identification model was proposed by integrating feature engineering and machine learning techniques. Firstly, a wavelet transform was employed to achieve noise suppression based on field-measured kick data. Secondly, the dynamic variation trends of key parameters were extracted using smooth spline functions, and the abnormal fluctuation behaviors of kick-related characteristic parameters were analyzed. Based on this, a three-level risk classification criterion (low, medium, and high) was established, and kick risks were labeled according to the variation features of drilling data. Then, the sparrow search algorithm (SSA) was introduced to optimize the extreme learning machine (ELM), and a multi-classification kick risk intelligent identification model based on IELM was constructed. Finally, the performance of the model was validated through training, tuning, and testing on the constructed risk dataset. The results show that the IELM model outperforms the original ELM and back-propagation (BP) neural network model in terms of classification accuracy and discrimination stability, and is capable of identifying different levels of kick risks more accurately and efficiently.

In order to explore the influence of laying foam slurry insulation layer on the temperature field of surrounding rock in high temperature mines, the heat transfer differential equation of unsteady surrounding rock and the two-dimensional unsteady heat transfer differential equation of foam slurry insulation layer were established by using Fourier 's law. The finite difference method was used to solve the differential equations, so as to explore the temperature change of surrounding rock after laying the foam slurry insulation layer and the influence of initial temperature on foam slurry insulation layer. The results show that the temperature tends to be stable with time due to the small thermal conductivity of the foam slurry insulation layer. The temperature difference between the rock strata adjacent to the foam slurry insulation layer is large, and the larger the thickness of the foam slurry insulation layer, the higher the temperature of the rock mass. It can be seen that the laying of the foam slurry insulation layer hinders the transfer of a certain amount of heat to the roadway so that the temperature gradient of the rock mass increases. When the initial temperature of the foam slurry insulation layer is closer to the air flow temperature, the temperature gradient inside the foam slurry is smaller and the temperature can reach a steady state in a short time.

To address the issue of spontaneous coal combustion caused by air leakage from fissures, grouting is commonly employed to seal fissures. PSES is a new type of air-leakage plugging material, whose durability and reliability of sealing performance depend directly on its intrinsic failure process. Uniaxial compression experiments, in conjunction with a single-hole model, the semi-empirical M-T model, and the CDP model, were employed to investigate the mechanical behavior of PSES across micro-, meso-, and macro-scales. The results reveal that at the microscale, the inner wall of the vesicle constitutes a primary weak point within the PSES structure, and the critical external pressure signifies the onset of localized yielding; at the mesoscale, the semi-empirical M-T model accurately predicts the equivalent effective modulus and confirms its capability to characterize the relationship between porosity and stiffness; at the macroscale, the porosity-corrected CDP model accurately fits the stress-strain curve, with the Mean Absolute Percentage Error (MAPE) ranging from 0.23% to 2.69%. Furthermore, the corrected damage factor closely corresponds with the material's actual condition, effectively characterizing its damage evolution characteristics. This, in turn, provides a reliable basis for evaluating the long-term service performance of the air-leakage plugging material.