To explore the time-varying performance of urban road networks under regular rainfall and establish a reasonable evaluation system for road network resilience, an LSTM model for the rainfall-induced traffic flow degradation in urban road segments was first constructed, and a network performance function with time-varying characteristics was second defined. Then, based on the resilience concept, a time-varying resilience calculation model for road networks was derived. Finally, the effect of rainfall on the time-varying characteristics of road segments and networks was investigated based on rainfall information and traffic data of a certain city and the Sioux Falls network. The results show that when the duration or amount of rainfall increases, the traffic flow degradation of road segments increases on weekdays. The road network resilience is a comprehensive reflection of the synergistic effect of various related road segments, and therefore its response to rainfall is slower than that of traffic flow degradation, and its value is generally not less than 0.9 under regular rainfall conditions. The road network resilience during low rainfall seasons is significantly stronger than that during high rainfall seasons, and the annual resilience of road networks is usually maintained at a higher level when not affected by rainfall disasters, and the difference between adjacent years is not significant.

To address the limitations of relying on manual extraction of knowledge from complaint texts in automotive safety risk management, this study employed LLMs for automated risk discrimination. First, over 50 000 complaint texts covering eight major subsystems such as the engine were collected. A demonstration sampling method based on Bilingual and Crosslingual Embedding(BCEmbedding) model and community detection algorithm was proposed to construct a diverse and high-quality example knowledge base. Secondly, prompts were designed from perspectives such as scenarios, skills, and examples to develop agents capable of performing risk keyword extraction and expansion, as well as subsystem risk categorization. Finally, by analyzing reasoning knowledge from retrieved texts and utilizing chain of thought techniques, a risk level knowledge base for retrieved texts was established. A consensus-seeking multi-agent(MA) system based on LLMs was designed, resulting in a discriminative model for automotive safety risk levels. The results show that the model not only reduces labor costs but also achieves high accuracy and efficiency. It effectively supports risk term extraction, risk categorization, and risk level assessment in complaint incidents, thereby enhancing safety risk management.

This study examines antecedents and underlying mechanisms shaping enterprises' willingness to participate in government-led emergency logistics collaboration. A questionnaire of transportation enterprises in Wuhan was conducted to assess their participation willingness. Drawing on TPB and CSR Pyramid theory, a theoretical model was developed to represent the formation mechanisms of enterprise participation willingness and empirically tested using Logit regression analysis. Results show that the government's multi-party coordination and scheduling capabilities and safety assurance capabilities have significant positive effects on enterprise participation willingness, while the compensation mechanism has a marginally significant effect. Two improvement pathways are proposed: strengthening perceived facilitating factors to reinforce enterprises' trust and confidence, and optimizing enterprises' expectations of long-term positive returns to enhance their recognition of social impact.

To uncover the cross-system cascading propagation mechanism of urban waterlogging disaster chains and enhance blocking effectiveness, a coupled drainage-traffic network model was first constructed based on complex network theory, integrating high-precision topographic data, real-time traffic flow, and historical flooding records. A dynamic cascading-failure algorithm was then employed to quantify node centrality and link vulnerability, thereby dissecting the cross-system propagation path from "rainstorm-runoff-ponding-traffic paralysis". Finally, a three-level resilience blocking strategy—critical-node reinforcement, redundant-road-network optimization, and intelligent-response coordination—is proposed. The results show that pump-station expansion and redundant road design can effectively reduce waterlogged area and shorten traffic-interruption duration. And this enhances the system resilience and proactive immunity of critical urban infrastructures. It shifts the disaster-prevention paradigm from passive emergency response to active immunity.

To prevent coal spontaneous combustion disasters in the gob area and precisely delineate hazardous zones, the variation patterns of CO and O₂ volume fractions along the gob length were statistically analyzed based on field measurements. Critical warning threshold ranges for spontaneous combustion were established and a gob hazard zoning method based on gas volume fraction kernel density was proposed. The results demonstrate that O₂ volume fraction exhibits a linear variation with distinct stages, and the variation rate before the inflection point is lower than that after the point. The CO volume fraction follows a quadratic polynomial variation. The kernel densities of both CO and O₂ volume fractions decrease with increasing gob length. Their highest kernel density distributions occur within 0-10m and 0-30m, respectively. O₂ volume fraction is more stable before the inflection point but exhibits greater fluctuation afterward. The average upper and lower acceptable limits for O₂ volume fraction before and after the inflection point are 3.3% and 6.3%, respectively. The critical CO and O₂ volume fraction values for hazard zoning based on kernel density are 0.009% and 15.1%, which divides the gob area into four quadrants and three risk levels (safe, potentially hazardous, and hazardous).

In order to clarify the potential research value and application prospects of LLMs and KG technologies in the field of construction safety, the existing problems in the knowledge-driven digital transformation of this domain were comprehensively analyzed, and the current state of technological development regarding knowledge graphs and large language models within it was reviewed. First, relevant literature was retrieved from the China National Knowledge Infrastructure (CNKI) and Web of Science(WoS) databases to define the scope of analysis. Then, the existing problems and challenges in the digital transformation of construction safety were analyzed, and the necessity of introducing KG and LLM was elaborated. Subsequently, the technological development of KG and LLM, along with the current status of their application research in construction safety, was briefly described. Preliminary achievements in the integrated application of KG and LLM within the construction safety domain were explored. Finally, the shortcomings of existing research were summarized, and future research directions are outlined. The results indicate that LLMs and KG technologies have demonstrated significant application potential in various scenarios within construction safety, including knowledge management, risk identification, and intelligent decision-making, highlighting considerable prospects for future implementation. However, current research still faces challenges such as a lack of real-time capabilities and insufficient integration. Future efforts should focus on establishing comprehensive and precise construction safety KG and exploring novel approaches for the integrated application of LLM and KG.

To effectively suppress the transition of coal spontaneous combustion oxidation in goaf areas into the accelerated stage, a stable and high-heat-storage PCM was prepared using ammonium aluminum sulfate dodecahydrate and magnesium sulfate heptahydrate as raw materials. Through differential scanning calorimetry tests, solid-liquid transition tests, programmed heating tests, infrared spectroscopy, thermal insulation tests, and microscopic analysis, PCM thermal properties, the driving mechanism of solid-liquid transition, heat storage capacity, and coal oxidation inhibitory performance were studied. The test results show that the developed material has a phase transition temperature of 66.8℃, a latent heat of phase transition of 300.708 J/g, and a relatively low specific heat capacity during the heating process. The melting time exhibits an exponential decrease with increasing temperature, and a dense covering film forms after melting. When the material is added at a mass fraction of 15%, the inhibitory rate reaches 41%. Under a 500℃ heat source, with a material thickness of 30 mm, the effective thermal insulation time reaches 65 minutes.

To address the safety risk issues of smart household appliance products, a systematic risk analysis was conducted by taking two sweeping robot-heater fire accidents in a certain location as the research subjects. An integrated method combining the FRAM and PLTS was adopted. FRAM was applied to analyze the accidents, through which nine core functional modules were identified, including navigation module, obstacle avoidance sensor module, and motion control module. Subsequently, PLTS was introduced to quantitatively determine the main characteristics of the input and output terminals of each functional module. Based on this quantitative analysis, four functional resonance modules that led to the accidents in the sweeping robot-heater were confirmed. The results show that the primary causes of the accidents include insufficient sensor accuracy and failure of users to implement the "power-off when leaving" operation. Additionally, the barrier system for risk prevention and control is identified, which consists of physical barriers, functional barriers, intangible barriers, and symbolic barriers.

In order to address the challenges of unclear spatial concentration distribution and uncertain future evolution in high-pressure large-diameter gas pipeline leakage scenarios, a predictive model for gas leakage dispersion was proposed by integrating machine learning-based dimensionality reduction and time series forecasting methods. Firstly, a multi-condition dataset of gas leakage concentration fields was generated using computational fluid dynamics simulations. Subsequently, the dimensionality reduction module and time series forecasting module of the predictive model were separately optimized and trained using this dataset. Finally, the model's predictive accuracy was evaluated on an independent test set, and the prediction errors under various forecast horizons were analyzed. The results show that the model achieves a mean absolute error of 0.000 5 and a mean absolute percentage error (mAPE) of 6.82% on the test set, with the mAPE remaining below 14% across different prediction time steps.

In order to further prevent subway crowd stampede accidents and improve the safety management of metro stations, a metro crowd stampede resilience concept was proposed based on resilience theory. Centered on absorption capacity, resistance capacity, recovery capacity, and adaptation capacity, the developmental stages of metro crowd stampede resilience were analyzed, and a resilience evaluation index system was constructed by identifying core influencing factors related to crowd density. By integrating SD with MEE model, a metro crowd stampede resilience evaluation model was developed. This model was then applied to analyze the Beijing Xizhimen Metro Station. Results show that the model overcomes the limitation of traditional static evaluation models in depicting the dynamic evolution of the metro system's resilience capacity over time, enabling a quantitative assessment of dynamic resilience changes. The overall resilience level of Xizhimen Station is Grade II (relatively high resilience). However, due to delays in real-time monitoring and insufficient emergency response, it drops to Grade III (moderate resilience) during morning peak hours, with adaptability constrained by the low coverage rate of intelligent systems.