In order to solve the problem that the classical model of rocking rigid body has a large error and cannot solve the collision restitution coefficient of heterogeneous and irregular rigid bodies, realize more accurate dynamic response prediction, design, safety evaluation and vibration control of rocking structure, based on the energy conversion analysis of mass element, the heterogeneous and irregular rigid bodies were discretized and the kinetic energy conversion analysis of discrete elements was carried out. The collision process of rigid body was divided into three stages. In the first stage, the residual kinetic energy of each discrete unit of rigid body was calculated after the vertical kinetic energy was dissipated. In the second stage, the residual kinetic energy of each discrete element of the rigid body was calculated after the kinetic energy had been dissipated in the direction of the rotation corner after the collision. In the third stage, the residual kinetic energy of each discrete element was converted and calculated under the action of internal force, and the collision recovery coefficient of the whole process was solved. With the help of the Digital Image Correlation (DIC) measurement system, the swing response test was carried out and the method was verified. The results show that the relative error between the collision recovery coefficient of a homogeneous rectangular rigid body obtained by this method and the experimental value is less than 3%, far less than the relative error between the classical model of a rocking rigid body and the experimental value. The relative error between the calculated collision recovery coefficients of heterogeneous and irregular rigid bodies and the experimental values is less than 5%. By this method, the dynamic response of a rocking structure after impact can be predicted more accurately, and a more reasonable structural design and safety evaluation can be provided.

The 24Model, as an original and systematic accident causation theory in China, has been widely applied in the field of safety science since 2005. However, a comprehensive review of its theoretical development and application trends is still lacking. A systematic search and stratified screening were conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. A scoping review approach was employed to perform structured coding and data extraction from the 357 screened Chinese- and English-language publications. Subsequently, data analyses, including cross-tabulation analysis and chi-square tests, were carried out. The results indicate that research on the 24Model has shown a sustained growth trend and has begun to disseminate globally. Its application domains have expanded from the coal mining industry to complex system industries such as the chemical sector. In addition, application paradigms have evolved from qualitative analysis to quantitative modeling, from post-accident analysis to proactive prevention, and from traditional analytical approaches to digital and intelligent technologies. The systematic model has been further developed and refined in the fifth and sixth editions, providing a new theoretical framework for safety analysis in complex systems. This study suggests that future research on the 24Model may continue to focus on deepening its systematic theoretical foundations, innovating preventive applications, and integrating digital and intelligent technologies.

To address employee' AI anxiety arising from increased system complexity and heightened information uncertainty in the application of AI to corporate safety management and decision-making, a mechanism model of "AI anxiety-human-AI trust-human-AI collaborative decision quality" was developed based on UMT. It introduced system transparency as a boundary condition to explain how employees appraised and coped with AI-related threats in contexts such as risk warnings and algorithmic black boxes. A questionnaire survey was conducted with a sample of 523 employees from AI-adopting enterprises. Confirmatory factor analysis(CFA) and structural equation modeling were employed to test the measurement model, path relationships, and moderating effects, while controlling for variables such as gender, age, education, job type, and AI usage frequency. The results show that AI anxiety reduces the quality of human-AI collaborative decision-making. Human-AI trust partially mediates the relationship between AI anxiety and collaborative decision-making quality. System transparency positively moderates the effect of human-AI trust on collaborative decision-making quality, such that higher transparency facilitates the translation of trust into higher-quality collaboration.

To further investigate the effect of ultrasonic stimulation on gas diffusion in coal, this study utilized an ultrasonic stimulation test system for gas-bearing coal to analyze variations in gas diffusion under different ultrasonic conditions. Based on the Langmuir-like model, Quasi-first-order kinetic model, and gas dynamic diffusion model, the influence of ultrasonic stimulation on the kinetic characteristics of gas diffusion was systematically examined. Results demonstrate that increased ultrasonic power, elevated frequency, or prolonged stimulation time significantly enhance gas diffusion. Specifically, when ultrasonic power increases from 250 W to 1 000 W, gas diffusion rises from 0.689 mL/g to 0.981 mL/g, with the diffusion rate increasing from 5.92% to 10.69%. Similarly, gas diffusion escalates from 0.739 mL/g at 20 kHz to 1.074 mL/g at 40 kHz, elevating the diffusion rate from 6.36% to 9.65%. Extending stimulation time from 30 min to 120 min boosts gas diffusion from 0.833 mL/g to 1.100 mL/g, increasing the diffusion rate from 8.50% to 12.65%. The gas dynamic diffusion model exhibits the strongest fit for describing gas diffusion behavior under ultrasonic stimulation, followed by the Langmuir-like and Quasi-first-order kinetic models. Both the initial gas diffusion coefficient D₀ and its attenuation coefficient β demonstrate identical trends under ultrasonic stimulation, showing exponential positive correlations with ultrasonic power, frequency, and stimulation time. Ultrasonic stimulation enhances the kinetic characteristics of gas diffusion in coal by improving pore connectivity through mechanical vibration and pore-cleaning effects.

In order to address the inherent limitations of traditional analysis methods—such as insufficient integration of professional knowledge and weak interpretability of causal reasoning—when dealing with the complex characteristics of multi-factor nonlinear interactions in power systems, a large model for power production safety accident analysis was proposed that integrates LLM, RAG, and KG. A framework with four core modules was built: knowledge retrieval, knowledge reasoning, answer generation, and performance evaluation. RAG technology was used to accurately retrieve relevant knowledge from professional texts, and KG was leveraged for structured reasoning on accident entities and relationships to make up for retrieval blind spots. Finally, LLM was employed to generate professional and interpretable answers for accident causal analysis. The study comprehensively evaluated the system through subjective expert scoring and objective metrics like ROUGE and BLEU, and results show that in the scenario of power production safety accident analysis, the knowledge enhancement technology of RAG and KG provides universal performance improvement for basic models with a certain parameter scale—it helps models accurately capture professional correlations such as equipment fault transmission chains and enhances the quality of accident cause mining and result evolution reasoning. Large models including DeepSeek-R1 and Qwen2.5-72B significantly improved in the accuracy of parsing professional terms and organizing multi-factor correlations under this mode, among which DeepSeek-R1 achieved a comprehensive score of 4.05, better meeting the precision requirements of the field; meanwhile, there is a model capability threshold for the enhancement effect: after enhancement, Qwen2.5-72B can efficiently parse complex logics like cross-regional power grid fault linkage, balances performance and deployment costs, and is suitable for enterprises' practical needs, while smaller models such as Qwen2.5-14B, due to limited basic reasoning capabilities, fail to process professional information effectively after introducing external knowledge, leading to performance degradation and inability to meet professional requirements.

In order to prevent and control the fire hazard of a road tanker coupling with diesel leaking and burning, small-scale experiments were first conducted using a 5 L tank filled with 1.65 L of 0# diesel fuel. The diesel was released from the tank bottom and burned, and the temporal evolution of the fuel temperature, tank pressure, thermal radiation, and flame morphology was investigated. Subsequently, numerical simulations of tank thermal exposure were conducted. The results show that once the temperature of leaking diesel reaches 227.8 ℃, the diesel fuel leaking to the external environment will boil and burn. This leads to a sudden escalation of fire hazard, i.e., a sudden increase in the tank pressure, the thermal radiation and flame size. The onset time of hazard escalation increases exponentially with filling level. When the filling level rises from 33% to 80%, the onset time increases by an average factor of 2.32 across different thermal boundary conditions. Flame offset relative to the tank reduces the thermal load on the tank, thereby lowering the diesel temperature-rise rate and increasing the onset time. Moreover, the rising rate of the onset time grows with the flame offset degree. The increase in the onset time provides more available safe egress time (ASET). Thus, ASET increases as both the filling level and the flame offset degree increase.

To address the problems of incomplete unimodal feature representation and low image-text fusion efficiency in construction safety hazard identification for hydropower projects, an intelligent image-text multimodal intelligent identification method was proposed. First, 12 categories of construction safety hazards were defined according to hydropower construction characteristics, and an image-text multimodal dataset was established. Second, bidirectional encoder representations from transformers (BERT) model and vision transformer (ViT) model were employed to extract hazard text and image features respectively. GFN was then introduced to dynamically adjust the contribution of image and text features and capture cross-modal correlated feature information, while a multi-layer perceptron was used to improve classification accuracy. Comparative experiments were conducted to verify the model's accuracy and reliability. The results show the method optimizes the contribution of multimodal features by enhancing identification stability. The multimodal hazard identification accuracy reaches 84.99%, representing an improvement of 1.73% over the text-based model and 12.24% over the image-based model.. The proposed approach outperforms existing benchmark models in hazard classification and improves the robustness of intelligent hazard identification.

In order to effectively reduce the vaporization rate of LNG, three parameters were proposed and weight factors were introduced. Through Taguchi experiments, the quantitative research was conducted on the foaming ratio and stability of high-pressure foams, the amount of LNG leakage, and the height of foam coverage, to investigate their effects on suppressing LNG evaporation and vapor diffusion. The results show that foams with high foaming ratio and good stability are more conducive to reducing the initial volume fraction of vapor, while foams with poor stability are more conducive to vapor diffusion; the leakage volume has a significant impact on the initial volume fraction of vapor, while the foam coverage has a relatively smaller impact on the later volume fraction of vapor accumulation; the foam height has a minor impact on the initial volume fraction of vapor, but has a significant impact on the ease of vapor diffusion; the foam performance and coverage height at different spatial points have different effects on the speed of the decrease in vapor volume fraction; the leakage volume and foam coverage height have different effects on the speed of the decrease in vapor volume fraction 10 minutes after the cessation of adding foam.

To address the new demands for professionals in the fire protection industry and improve the quality of talent development, this study adopted the OBE framework. It drew on interviews with industry organizations, practitioners, graduates, and educators, along with an analysis of relevant laws, regulations, and standards. The curriculum systems of 29 domestic and international universities offering fire engineering programs were compared, and the competency requirements for talent in the context of fire protection and artificial intelligence integration were examined. The study identifies key job roles and core competencies in fire engineering, defines corresponding educational objectives and graduation outcomes, and maps their interrelationships. Furthermore, it proposes a curriculum system and knowledge structure suited to the evolving needs of the industry, designates eight core courses such as Fire Combustion Science, and develops new engineering-oriented fire protection courses that respond to emerging safety challenges and integrate artificial intelligence. Finally, it is suggested that fire engineering majors in different universities should leverage their respective institutional and disciplinary strengths to develop distinctive features, serve the industry, and foster mutual support.

To address the inability of existing battery management systems in acquiring internal temperature gradients, structural strain, and gas generation signals in lithium-ion cells, which result in inadequate safety monitoring and early-warning capability against thermal runaway, a systematic review of fiber optic sensors for lithium-ion battery safety monitoring and the challenges they face was provided. First, key safety-related monitoring parameters, including temperature, strain, gas evolution, and electrolyte state, were identified. Second, the principles and characteristics of representative sensing technologies, such as Fiber Bragg Gratings(FBG), Tilted Fiber Bragg Gratings(TFBG), Fiber Optic Evanescent Wave Sensing(FOEW), and Distributed Optic Fiber Sensing(DOFS), were reviewed. Third, the research progress in in-situ temperature and strain monitoring, electrolyte state evaluation, gas detection, and thermal runaway risk identification for each technology was summarized. Finally, major challenges in practical application, including integration compatibility, multi-parameter cross-sensitivity, long-term stability, and cost, were discussed. The survey reveals that fiber-optic sensing enables multi-point, millisecond-scale temperature monitoring with ±0.1 ℃ accuracy, strain mapping at ±0.1 με resolution, in-situ gas detection at 0.12% precision, and electrolyte refractive-index tracking down to 10^-3. Feeding these multi-parameter, in-situ, real-time data into advanced algorithms can significantly enhance early-warning capability for lithium-ion battery thermal runaway.