To scientifically evaluate and enhance construction enterprises' emergency management capabilities for sudden safety incidents, a comprehensive model integrating qualitative analysis and qunatitative evaluation was proposed, addressing the limitations of traditional static assessment methods, which struggle to capture functional coupling and are easily influenced by subiective weighting. First, based on the theory of balanced emergency management throughout the entire process, a complete evaluation indicators system was established by dentifying 12 secondary indicators across four stages, including preparation and prevention, monitoring and early warning, response and disposal, and recovery and learning, and integrating the trajectory intersection theory and catastrophe theory. Subsequently, FRAM was employed to identify key functions and coupling paths among these indicators. An evaluation model was then developed by integrating an improved K-shell algorithm with BN. Finally, the model was applied to a practical engineering case and its effectiveness was validated through expert review and scenario simulations. The results demonstrate that the selected construction enterprise has a comprehensive emergency management capability of 81.682%, indicating its emergency mechanism can effectively respond to and handle various construction safety incidents. Among the capabilities, recovery and learning performs best (90.855%), while monitoring and early warning remains relatively weak (76.616%). Sensitivity analysis shows that professional team development (F₃) and on-site command decision-making (F₇) contributed most significantly to the overall capability.

To address the challenge of locating the fire source in high-rise building fires, a full-scale indoor fire test platform was constructed and a series of tests were conducted to investigate the feasibility of inferring fire locations from the temperature field on the fire-unexposed surface of window glass in high-rise building fires. By varying the fire location and heat release rate, the temperature field on fire-unexposed surface and fire environment parameters were obtained, and the characteristics of fire-unexposed surface temperature field under different scenarios were analyzed. The results show that window glass regions at higher elevations and on the fire side exhibit significantly higher temperature rise rates under different fire location conditions. At 480 s after ignition, the temperature non-uniformity coefficient of fire-unexposed surface under different fire location conditions is not less than 33.52%, and reductions in the distance between fire location and window lead to a marked increase in the temperature non-uniformity coefficient on the fire-unexposed surface. With increasing heat release rate, the coefficient of variation of the increase in temperature rise rates across different glass regions on the fire-unexposed surface generally exceeds 10%, and this disparity becomes more pronounced with increasing heat release rate. When the normal distance between the fire location and the window decreases, window glass at higher elevations and on the fire side exhibits a greater increase in temperature rise rate. When the radial distance between the fire location and the window decreases, the increase in temperature rise rate at higher elevations is significantly greater than that on the fire side.

In order to improve the rationality and effectiveness of critical infrastructure security resource allocation, and solve the shortcomings of traditional methods in the balance of security resource allocation and protection effectiveness, an optimization model of critical infrastructure security resource allocation was constructed based on PT, ASD and Stackelberg game theory. Firstly, the ASD-Stackelberg game model was constructed to solve the optimal spatial allocation of security resources within critical infrastructure. Secondly, the PT theory was introduced to describe the bounded rationality characteristics of both attack and defense sides, and the optimal security resource investment threshold was determined. Finally, the nuclear power plant security system was taken as a case study. The verification and analysis results show that compared with the traditional empirical configuration method, the optimal resource allocation scheme output by this model has significantly improved the overall security effectiveness of nuclear power plants by 25.9%, and can accurately identify the optimal amount of security resource investment. This result not only confirms the effectiveness and feasibility of the model in solving the optimal configuration scheme, but also quantitatively establishes its significant superiority in enhancing security protection capabilities.

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.

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.

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.

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.

In order to solve the problems of incomplete index system, low accuracy of calculation results and weak applicability of control measures when evaluating the safety of shield tunneling underpasses, a method for evaluating the safety status of shield tunneling underpasses was studied based on entropy weight cloud model. By analyzing various factors affecting the safety status of the project, it was determined that overall safety level was jointly determined by the original safety level of building and disturbance effect of underpass construction process. An original safety status evaluation index system for buildings was established, which consisted of 13 indicators in three categories: the existing deformation resistance of the building, the healthy and intact state of the building, and the importance of building. A safety evaluation index system for underpass construction process was established, which consisted of 22 indicators in five categories: excavation face instability, displacement of soil on side of shield machine ring, subsequent soil consolidation, construction management level, and relationship between building and tunnel. The weights of each index were determined by entropy weight method, the digital characteristics of the index were determined based on the cloud model, safety level and development trend were determined by FSM algorithm, and the weighted fitting weight value was used to judge the primary and secondary risk control points. Taking a subway project under two buildings as an example, the index system and evaluation method were verified. The results show that original safety status of two buildings and the construction process under two buildings are generally safe, and overall risk is within an acceptable range. For secondary indicators that are not inherent attributes of the project, the factors with weighted fitting weight values exceeding the average level (0.04) are used as key control points, those between 0.02 and 0.04 are used as secondary control points, and those between 0 and 0.02 are used as general control points.

A safety culture reshaping model was studied to enable enterprises to carry out safety culture reshaping in a more effective manner and enhance practical outcomes. Typical domestic and international safety culture models were systematically compared and analyzed to summarize their characteristics in terms of element design and logical structure. Based on actual needs of enterprise safety management scenarios, elements adaptation and frameworks integration were carried out, and an excellent safety culture reshaping model. This model incorporated six core elements—leadership, safety philosophy, risk control, communication, systems, and behavior, and deeply integrated within the "being-knowing-doing unity" logical framework. The results show that "excellence" is reflected in ambitious goals, extreme execution, and measurable mechanisms, while the core of "reshaping" lies in "retaining strengths and correcting weaknesses", which involves transforming unscientific safety concepts, revising imperfect systems, improving inefficient communication, and rectifying misleading leadership behaviors. The application of the excellent safety culture reshaping model enables all employees to first establish a solid foundation in values and safety beliefs, then reach a consensus in safety cognition, and finally standardize their safety behaviors, forming a shared behavioral pattern, thereby enhancing the effectiveness of safety culture reshaping.

In order to elucidate the essential differences among safety-I, safety-II, and safety-III, to clarify the operational mechanisms of safety science paradigms, and to promote the sustained development of safety science as a scientific discipline, this study adopted literature review and comparative analysis methods. The theoretical distinctions among the three from epistemological and methodological dimensions were analyzed. Then the construction and transformation of the safety science paradigm from the perspective of philosophy of science were discussed, and their paradigm positions were clarified. The results indicate that: Safety-I represents traditional accident causation theories or models that emphasize causality. Safety-II is a resilience theory that studies safety issues from a positive perspective. Safety-III is a systemic accident model grounded in systems theory and cybernetics. The safety science paradigm comprises one entity with four aspects. These aspects include the paradigm-free stage, paradigm establishment stage, normal science research stage, paradigm crisis stage, and paradigm shift stage. Currently, the safety science paradigm is dominated by accident causation theory and remains in the normal science research stage. Although there are signs of a paradigm crisis, it has not yet entered the paradigm shift period. Safety-I and Safety-III are research contents within the accident causation theory paradigm. However, Safety-II reflects changes in both beliefs and research perspectives. It can be seen as a new research direction in safety science, but it has not yet become a new paradigm. The future development of the safety science paradigm has three possible forms. It requires continuous practice and exploration led by new technologies.