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.

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.

In order to reduce the dynamic loads on risers under harsh sea conditions and enhance their adaptability to complex marine environments, a multifunctional suspension system for deepwater riser was proposed, which included a suspension system, a hydraulic compensation system, and a measurement and control system, in order to ensure the safety of the suspended riser under severe sea conditions such as typhoons in the South China Sea. In addition, the system can also be applied to transit with a suspended riser between wells, which can significantly reduce the load of the suspension riser. The suspension system suspends a single bearing riser system, and relieves the stress concentration on the top of the riser by means of a centralizer and a hinge joint; the hydraulic compensation system adopts three working modes of differential throttling compensation, passive non-throttling compensation and throttling shielding to meet the requirements of platform avoidance and inter-well transit. The measurement and control system monitors and warns in real time, regulates and controls the telescopic movement of the hollow hydraulic cylinder, and realizes the real-time compensation of the dynamic load of the riser system. The system has undergone sea trial applications, the results show that the proposed multi-function suspension system can significantly reduce the riser top stress and dynamic load amplitude, especially in the differential throttle compensation and passive non-throttle compensation modes. At the same time, the downstream and upstream speeds of the platform are increased to 1.0 knots, which significantly improves the operation efficiency. The system helps to overcome the challenge of traditional suspension mode, provides a safe and efficient solution for deepwater drilling riser operations, and reduces the disaster risk of suspension riser under extreme sea conditions such as typhoons.

In-service pipelines are subject to complex stresses, and their performance degradation over time constitutes a dynamic, time-varying stochastic process. To address the challenge that traditional deterministic functions struggle to accurately capture its inherent randomness, a dynamic analysis method for failure probability of in-service pipelines based on a dual stochastic process was proposed. The degradation of pipeline performance was simulated using an inverse Gaussian stochastic process, while the variation of internal pressure loads within the pipeline was described by an equal-interval stationary binomial rectangular wave process probability model. A dual stochastic process probability model for pipeline bearing capacity and internal pressure load was then constructed. Based on statistical parameters and performance degradation data from a specific pipeline's service period, inverse Gaussian distribution was used to fit the performance degradation models at six distinct time points, enabling dynamic failure probability prediction. The results show that the pipeline's service life is predicted to be 16 and 14 years using degradation data from 2 and 4 years, respectively. When utilizing degradation data from 6, 8, and 10 years, the predicted service lives are 12, 11, and 10 years, respectively. Sensitivity analysis indicates that wall thickness, yield strength, pipe diameter, and operating pressure have the most significant impacts on the pipeline's failure probability, followed by the initial depth of defects. In contrast, the initial length of defects, depth corrosion rate, and length corrosion rate have relatively minor effects.

To investigate the influence of ammonia admixture on methane explosion propagation characteristics in tunneling tunnels, the explosive behavior of methane/ammonia/air mixtures was systematically examined under varying ammonia blending ratios, initial pressures, and initial temperatures. The explosion overpressure evolution and flame propagation characteristics of the mixtures under varying ammonia blending ratios, initial pressures, and initial temperatures. Results indicate that the incorporation of ammonia exhibits a dual effect on the explosion behavior: initial suppression followed by enhancement. The explosion overpressure is observed to decrease first and then increase with rising ammonia content, while the flame front position and propagation velocity progressively increase with higher ammonia ratios. Regarding the influence of initial pressure, the peak explosion overpressure, flame propagation velocity, and flame position all show linear increasing trends with elevated initial pressure. The influence of initial temperature on explosion characteristics is more complex. Within the range of 300-700 K, the explosion overpressure increases with temperature, and the flame propagation trajectory show a significant positive correlation with temperature. However, when the temperature reaches 900 K, flame propagation is markedly suppressed and manifested by significant decreases in both the flame front position and velocity. The study reveals that the ammonia blending ratio exerts a non-monotonic influence on the methane explosion process, while initial pressure and temperature regulate explosion intensity and flame propagation behavior in linear and nonlinear ways, respectively.

Existing methods for predicting temperature from subway carriage and tunnel fires mainly rely on physical models and empirical methods that are valid only under narrowly-defined environmental conditions. To solve this issue, this study adopts an artificial-intelligence-based approach. A GA-BPNN network model is constructed by optimizing BPNN using a GA. The GA is employed to global optimize BPNN's weights and thresholds, after which the model is trained to predict the temperature distribution of both the subway carriage and the tunnel, thereby achieving intelligent inversion of the fire temperature field. The results show that, for subway carriage temperature prediction, GA-BPNN model yields a mean absolute error (MAE) of 8.17, a root mean square error (RMSE) of 9.76, and a coefficient of determination (R²) of 0.99. For tunnel temperature prediction, MAE is 3.95, RMSE is 5.63, and R² reaches 0.98. By comparing the results with those of the traditional BPNN, it is found that the GA-BPNN model outperforms the conventional BPNN in both prediction accuracy and generalization capability.

In order to promote the optimization and upgrading of metal dust explosion-proof technology and establishment of a safety protection and control system, this study systematically investigated the reaction kinetics of aluminum powder with excess water under varying particle sizes and stacking masses, based on the self-developed visualization experimental platform for the reaction between stacked metal dust and water. Through quantitative characterization of the effects of particle size and stacking mass on key reaction parameters-including the maximum temperature, total gas production, maximum hydrogen concentration, maximum pressure, and total reaction time-the kinetic mechanism of the aluminum-water reaction was revealed. The results demonstrate that at a fixed particle size, all characteristic parameters exhibit positive correlations with increasing stacking mass. At a fixed stacking mass, the maximum temperature follows a V-shaped trend (decreasing then increasing) with increasing particle size, while gas production, hydrogen concentration, and pressure show inverted U-shaped trends (increasing then decreasing). Total reaction time increases monotonically with increasing particle sizes. The primary reaction products are Al(OH)₃ and H₂, and the reaction process comprises three distinct stages: slow hydrogen evolution, violent reaction, and attenuation and termination.

To address the high-risk characteristics and cascading propagation effects of accident consequences of major hazard installations in chemical parks, the framework of the safety risk prevention and control system was constructed and optimized. The advantages and disadvantages of safety risk analysis methods for major hazard installations and the evolution analysis methods of domino accident chains were systematically reviewed. The definition, classification, and efficacy evaluation indicators and models of safety barriers in chemical parks were summarized. A new classification pattern for safety barriers in chemical parks was proposed, encompassing physical facilities, automated technologies, operational procedures, and organizational management. Key assessment indicators for safety barriers in chemical parks were identified, namely effectiveness and reliability, and future research directions were outlined. The results show that the proposed system reveals the intrinsic mechanism of dynamic closed-loop control among safety risk analysis, domino accident chain evolution, and safety barriers for major hazard installations in chemical parks, thereby effectively overcoming the static limitations of traditional approaches. The new classification pattern and key assessment indicators provide a novel paradigm for quantifying the effectiveness of safety barriers in chemical parks.

In order to address the significant safety risks posed by frequent extreme weather to the source, grid, and load-side equipment of the new power system, a risk assessment model for urban new power systems under extreme weather conditions was proposed. First, risk factors of the urban new power system were identified based on disaster theory, and an ISM was applied to clarify the interrelationships among these risk factors. Subsequently, the topological structure of the disaster chain was mapped into a BN. The prior probabilities of each risk factor node were determined using fuzzy comprehensive evaluation and accident statistics. Sensitivity analysis and scenario analysis were employed to derive key risk nodes for urban new power system accidents and the consequences of multi-hazard coupled accidents. Finally, MC simulation was utilized to conduct operational optimization analysis on “transmission towers” from the perspective of wind resistance level design. The results indicate that the constructed BN-MC coupled model effectively quantifies and enhances the analysis of extreme weather risks in urban new power systems. Under multiple superimposed extreme weather conditions, the failure probability of photovoltaic generators reaches as high as 60%, with strong winds being the key driving factor. Furthermore, improving the wind resistance level of transmission towers significantly reduces their failure probability. At a real-time wind speed of 36 km/h, increasing the wind resistance level from 35 km/h to 40 km/h reduces the failure probability by 59.39%. This effect exhibits a nonlinear characteristic, with a greater reduction in risk probability in the low wind speed range than in the medium wind speed range.

Traditional manual methods for detecting wall hollowing suffered from strong subjectivity, low efficiency, and difficulties in large-scale application. To address these issues, this study proposed an intelligent detection method based on a fully automatic hollowing signal acquisition device and an optimized signal processing algorithm. Firstly, a fully automatic hollowing signal acquisition device capable of stable operation on building walls was designed to achieve standardized tapping and high-precision acoustic signal acquisition. Secondly, VMD and EEMD optimized by Bayesian Optimization (BO) were employed to denoise the original signals, thereby enhancing the features of hollowing signals. Then, MSC and MFCC features of the signals were extracted and fused at the frame level to form an MFCC+MSC feature set. Finally, a majority voting ensemble learning model was utilized for classification, enabling high-precision hollowing detection. The results indicate that the classification accuracy of the proposed method reaches 99.31%, significantly outperforming traditional methods. These results validate the feasibility and effectiveness of combining automated devices with optimized signal processing techniques for wall hollowing detection.

To optimize the layout of the outbound transportation system and enhance system resilience, a "Sensitivity-Response" dual-dimensional evaluation index system was constructed. The comprehensive methods including set pair analysis, spatial Markov chain, and obstacle degree model were used to analyze the dynamic characteristics and driving mechanisms of vulnerability in 225 inland cities in China. The findings reveal that vulnerability demonstrates significant multi-polarization and spatial club convergence. The overall vulnerability decreases annually, evolving from bi-polarization to multi-polarization, with a reduction of absolute difference. Vulnerability exhibits significant spatial auto-correlation and spillover effects, forming a spatial pattern where "high-vulnerability is clustered along the borders, while low-vulnerability diffuses from urban agglomerations". A low-vulnerability neighborhood environment increases the probability of a city's vulnerability transferring downward by 64.5%. The obstructive effects of both freight turnover volume and total import-export trade value weaken annually. In contrast, the hindering effect of shortest inland-port road travel time shifts from a relatively stable to a consistent increase. Furthermore, city hub level and connectivity impose significant constraints on vulnerability of non-hub cities.

To develop reasonable emergency material allocation plans for flood disasters, prospect theory was used to characterize the risk perception levels of decision-makers. The reference points for the risk perception of "disaster relief center-disaster site" two-tier decision-makers were defined. A simulation model was constructed using system dynamics to examine the impact of decision-makers' risk perception on the allocation of emergency supplies for flood disasters. The model's validity was verified with case study of 2023 Zhuozhou flood. The study revealed that satisfaction rate at disaster sites and degree of supply-demand imbalance were influenced by risk perception levels of two-tier decision-makers, with a curve representing these effects. The findings show that the risk perception levels of decision-makers at both disaster sites and relief centers have a marginal increasing effect on the disaster site satisfaction rate and supply-demand imbalance. Additionally, under different levels of material availability, the difference in satisfaction rates at disaster sites becomes smaller as decision-makers' risk perception becomes more pessimistic.

In order to explore the emotional expression characteristics of different types of groups under emergencies, and to deeply analyze the influence of gender, temperament type and accident environment on the stress state emotions of the groups, a stress state emotional stimulation test based on facial expression technology was designed. A cohort of 137 participants was exposed to five categories of accident videos. Facial expression data for six basic emotions were collected using FaceReader software, with the Kruskal-Wallis test employed to analyze differences across gender, temperament types, and accident scenarios. A k-means clustering model was further constructed based on arousal dynamic features. The results show that female participants exhibit significantly higher intensities of sadness and fear, whereas males show stronger anger responses. Sanguine individuals demonstrate the most pronounced emotional reactivity, while phlegmatic types achieve the fastest arousal modulation. Fear responses are most pronounced in building fire scenarios. Males outperform females in arousal self-regulation capacity.

In order to address the limitations of traditional earthquake rescue training disaster site, including low simulation fidelity, poor safety, high costs and inadequate quantitative monitoring, this study developed a standardized, full-process earthquake emergency rescue training system using VR technology based on the Unity 3D engine, creating a virtual training scenario for earthquake disaster scenes. While systematically improving rescuers' tactical skills and practical abilities, the system enables quantitative measurement of training effectiveness. On one hand, this paper describes the model building, scene design, functional modules, and interactive design process of the earthquake emergency rescue training system. On the other hand, through pre-test and post-test control experiments, participants' performance is evaluated in terms of theoretical knowledge of earthquake disaster rescue, operational skills, and rescue strategies. The results show that subjects receiving training through this system significantly outperformed the traditional training group in both immediate knowledge acquisition and two-week knowledge transfer efficiency (P < 0.05^*, Cohen's d = 1.0), particularly in skill and strategy learning outcomes (P < 0.05^*, d = 1.23). Meanwhile, subjective experience evaluations confirmed high satisfaction ratings from subjects, corroborating the system's practical efficiency in knowledge transfer performance (P < 0.01^**, 1.1 ≤ d ≤ 3.9).

To address the problems of information distortion, transmission interruption, and response failure in emergency information systems during urban extreme rainstorm disasters, the study first identified five categories of risk—information source, information acquisition, information storage, information processing, and information transmission—from the perspective of the information processing lifecycle. Taking "7·20" Zhengzhou rainstorm event as a case, the risk factors of emergency information were systematically revealed. Subsequently, the connotation of emergency information resilience was defined, and a resilience structure was constructed. Finally, a "risk-resilience" coupling theoretical framework of emergency information resilience for urban extreme rainstorm disasters was established. The Results show that emergency information resilience is formed by the coupled effect of institutional resilience, social resilience, organizational resilience, and physical resilience. The theoretical framework of emergency information resilience consists of four stages. Including scientific construction of information resilience, integration of information risk early warning and response, cutting off the path of information risk diffusion, and enhancing the adaptability of information systems. The theoretical framework of the role of urban mega-rainstorm disaster emergency information resilience constructed in the text significantly improves the absorption capacity, recovery capacity and adaptability of information systems in response to complex disaster impacts.

To quantify the rescue effect of medical supplies delivered to demand points at different times, the concept of rescue utility is introduced, and a rescue utility quantification function was constructed based on the time difference of medical supplies arriving at demand points. On this basis, a path planning model for multi-modal distribution of medical supplies in flood disasters was established with the objective of maximizing total rescue utility and minimizing total distribution time. According to the characteristics of the model, a HNSGA-II was designed for solution. Experiments were conducted using multiple types of examples. The results demonstrate that HNSGA-II achieves a 62% and 29% improvement in total rescue utility compared to the traditional Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Artificial Bee Colony Algorithm (MOABCA), respectively. Additionally, the average satisfaction level of material delivery time is enhanced by 13% and 6.1%, respectively. These findings indicate that HNSGA-II significantly improves emergency rescue outcomes, exhibits superior multi-objective optimization capability, and ensures that disaster victims receive timely and effective treatment under emergency conditions.

In order to effectively predict disasters and evaluate the effectiveness of intervention measures, a multi-disaster coupling evolution intelligent deduction method was proposed. The disaster process, multi-disaster coupling, as well as the advantages and difficulties of evolutionary intelligent deduction, were discussed. The disaster evolution process was described, and an intelligent deduction method was established. Taking the mining process of an open-pit mine as an example, the proposed method was applied for analysis, and its effectiveness was verified. The results show that the multi-disaster coupling process is complex and changeable, with characteristics such as uncertainty, network structure effect and spatio-temporal distribution difference. The system fault evolution theory can provide support for the multi-disaster coupling evolution intelligent deduction from the perspectives of conceptual description, topological structure and mathematical analysis. The evolutionary intelligent deduction method is established, and its steps and mathematical model are provided. This method can qualitatively and quantitatively deduce the multi-disaster coupling evolution process, discover hidden disaster processes and generate emergent knowledge.

To optimize the pathways for cross-regional collaboration in natural disaster management, focused on Yangtze River delta—a pioneering region in cross-provincial collaborative practices—and employed social network analysis and QAP analysis to construct a cross-regional emergency collaboration network for natural disaster response in the area. The study examined both the overall and nodal characteristics of the network to assess the current state of cross-regional emergency collaboration and identify the driving factors behind such cooperation. The results indicate that with the establishment and development of Yangtze River delta emergency management task force, intercity collaboration barriers have gradually diminished, and the emergency collaboration network has become increasingly dense, reflecting more frequent interregional cooperative governance. Provincial governments, represented by the three provinces and one municipality (Jiangsu, Zhejiang, Anhui and Shanghai), serve as the dominant actors in the collaborative network, while municipal governments such as Xuancheng, Jiaxing, and Suzhou act as proactive facilitators. Key factors driving the formation of this cross-regional collaborative governance mechanism include geographical proximity, industrial structure, transportation infrastructure, interprovincial administrative relationships, and historical cooperative ties.

To evaluate the application performance of UAVs and rescue robots in earthquake disaster data acquisition and three-dimensional (3D) modeling, an air-ground collaborative emergency response field experiment was conducted at the Beichuan earthquake site. In the experiment, fixed-wing UAVs were deployed to acquire aerial images covering the entire earthquake-affected area, from which high-resolution top-view images were generated, while rotary-wing UAVs were used to supplement data collection in key local regions. Meanwhile, the NuBot rescue robot was deployed to enter damaged buildings and collect indoor disaster images and 3D point cloud data through real-time video transmission and lidar scanning, and 3D reconstructions of interior building structures and artificially constructed collapse scenes were produced. Based on the acquired 3D geographic information, a human-machine interaction platform was developed using virtual reality technology to support visualization and interactive analysis of 3D scenes.The experimental results indicate that multi-scale image data covering both the overall earthquake-affected area and local regions were obtained and corresponding high-precision real-scene models were generated. Indoor images and 3D point cloud data were collected in complex environments, forming 3D models of interior building structures. The 3D scene data were visualized in the virtual reality system and applied to human-machine interaction analysis.

In order to ensure flight safety and enhance operational efficiency, a classification method for civil aviation pilots' psychological health characteristics was developed based on machine learning clustering algorithms. Firstly, 215 active airline pilots were selected as research subjects. Psychological health data across two dimensions—psychological traits and psychological states—were collected using a pilot psychological competency assessment system. Subsequently, principal component analysis was employed for dimensionality reduction, and the elbow method was utilized to determine the optimal number of clusters. The K-means algorithm was applied to classify the samples into four distinct psychological health categories, facilitating the analysis of psychological characteristic differences among groups. Finally, visualization techniques were implemented based on the clustering outcomes to construct individual and group psychological health profiles. Radar charts were adopted to illustrate the distribution patterns of psychological characteristics across different pilot categories. The psychological health structure of pilots has group heterogeneity and diverse expression patterns. The proposed clustering algorithm can transform pilots' psychological health data into observable group feature portraits, achieving systematic identification and visualization of pilot psychological states at the operational level.

To reveal the co-evolutionary relationship between disease transmission intensity and emotional diffusion rate, based on a two-layer complex network structure, this study integrated the classical Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model with a five-state emotional dynamics framework (S_e-E_e-I_e-G_e-R_e) to construct a coupled dynamics model (SEIR-S_eE_eI_eG_eR_e). Theoretical derivation and simulation validation were conducted using the microscopic Markov chain approach and Monte Carlo simulation. The findings reveal that although reducing the disease infection probability can mitigate the scale of emotional propagation, its inhibitory effect on the peak of emotional propagation remains limited. In contrast, shortening the disease incubation period and improving the recovery rate significantly regulate the spread of negative emotions, exhibiting a two-stage propagation pattern characterized by emotional metabolic hysteresis and the density threshold effect of emotional guides. Furthermore, enhancing individuals' compliance with epidemic preventive measures can effectively delay the spread and reduce the scale of negative emotions through three pathways: blocking transmission chains, reinforcing social norms, and correcting cognitive biases.

In order to improve the efficiency of facepiece-wearing detection for tunnel operation workers, a feature fusion-based facepiece detection model was proposed. First, high quality query images were selected, and an image gallery was established. An image retrieval method was adopted to obtain samples and measure the similarity between query and gallery images, thereby iteratively expanding the dataset scale. Then, Histogram of Oriented Gradients (HOG) and Fisher features were extracted from the images. The Ant Lion Optimizer (ALO) was introduced to compute the optimal weight combination for the two types of features, which were subsequently fused. Finally, based on the fused features, a SVM was utilized to train a facepiece detection model, and experimental evaluations were conducted on the self-constructed dataset. The results indicate that the proposed model effectively accomplishes the task of facepiece-wearing detection in tunnel operation scenarios. Feature fusion enhances the image description and improves the detection accuracy of the model. Compared to using only HOG features or Fisher features, the accuracy is increased by 6% and 14%, respectively. The model meets the accuracy requirements for facepiece-wearing detection of workers in tunnel construction environments.