To address the unsustainable problems arising from significant demand gaps, inadequate support, and delayed supply of emergency resources in major disasters, and to tackle the limitation of traditional evaluation indicators that overlook social and environmental sustainability, this study was conducted. Firstly, based on the triple bottom line theory of sustainable development and literature mining method, a comprehensive evaluation index system was constructed from three dimensions: economic support capacity, social support capacity, and environmental support capacity for emergency resource support. Then, the original indicators were revised through expert evaluation to determine the final selected indicators. Finally, the grey relational analysis method was applied to construct an evaluation model for emergency resource support capacity in major disasters oriented towards sustainable development. Taking Henan Province as an example, the model was applied to verify its scientificity and effectiveness. The research shows that, when considering various factors such as economy, society, and environment, the correlation degree of social support is 0.933 90, having the strongest impact on emergency resource support capacity. In addition, indicators such as fixed assets investment in the logistics industry, the number of social organization units, the number of hospital beds in medical and health institutions, and regional GDP rank among the top, making them key influencing factors driving emergency resource support in Henan Province.

In order to address the challenges associated with the long-distance vertical transport of fire suppressant agents delivered by tethered firefighting drones in super high-rise building fires, a vertical transport model for foam extinguishing agents was established. The simulation results were comparatively validated against both empirical model calculations and experimental data. Furthermore, the study conducted an in-depth investigation into the effects of flow rate, gas-liquid ratio, and pipe diameter on pressure loss and flow velocity during the vertical transport of foam extinguishing agents. The findings indicate that the numerical simulation errors remain stable and are consistently less than 3%, demonstrating superior accuracy compared to the empirical model. In terms of flow characteristics, as the flow rate increases, pressure loss gradually rises, with the rate of increase becoming more pronounced at higher flow rates; concurrently, the stabilization time of the flow velocity within the pipe is reduced. Under constant flow rate conditions, when the pipe diameters are 60 mm and 100 mm, respectively, pressure loss decreases with an increase in the gas-liquid ratio. Moreover, increasing the pipe diameter mitigates the influence of the gas-liquid ratio on both frictional pressure drop and flow velocity. The impact of pipe diameter on pressure loss is primarily realized through alterations in the internal flow velocity: as the pipe diameter increases, the flow velocity decreases, leading to a corresponding reduction in frictional pressure drop. Within the pipe diameter range of 40 mm to 60 mm, the effect of pipe diameter on pressure loss and velocity variation is significant; however, when the pipe diameter exceeds 80 mm, this influence gradually diminishes. The research outcomes provide theoretical guidance for the vertical.

To enhance the efficiency of emergency supplies distribution after an earthquake, an emergency vehicle routing optimization problem was investigated under limited transportation capacity and the need to simultaneously deliver multiple categories of emergency supplies, while road conditions and demand urgency were jointly considered. Firstly, vehicle travel speeds were corrected based on road damage rates, and a demand urgency evaluation method is established by incorporating key characteristics of earthquake disasters. Then, a post-earthquake emergency supplies distribution optimization model was formulated to minimize the total delivery time and the total urgency-weighted demand cost, involving multiple depots, multiple affected sites, multiple types of emergency supplies, and heterogeneous vehicle fleets. Next, a HEA integrating the fast non-dominated sorting genetic algorithm (NSGA-II) with variable neighborhood search (VNS), is then designed to solve the model. Finally, a case study based on the 2008 Wenchuan (5.12) earthquake was constructed, and simulation experiments were conducted to validate the effectiveness and feasibility of the proposed model and algorithm. The results show that HEA outperforms three benchmark multi-objective optimization algorithms in both solution-set convergence and overall solution quality, and can provide emergency decision-makers with a diverse set of trade-off solutions within a short computation time (average 115.0 s).

The rapid growth of passenger volume of urban rail transit and the urgent need for intelligent operation have made accurate section passenger flow prediction a key technical challenge to improve the level of dynamic scheduling and safety control. To this end, this paper innovatively integrated the physical information constraint mechanism, data-driven method and SGA, and proposed a new deep learning framework. Firstly, a physical residual term was designed and embedded into memory cells as a regulation signal, forcing the model to learn the physical laws of passenger flow while retaining the temporal characteristics of passenger flow. Secondly, a dual-objective fitness function based on physical loss and data loss was innovatively proposed to achieve further optimization of model performance while establishing a constraint mechanism. Finally, SGA was used to balance the differentiated and synergistic effects of hyperparameters in the model. Experimental results show that the constructed model exhibits good predictive performance on both the training set and the validation set. The improved fitness function can narrow the error range of the model prediction results. In the two-stage ablation experiment, the mean square error range of the proposed deep learning framework is reduced by 71.03% compared with the long short-term memory model, which verifies the synergistic enhancement of the model prediction ability by the simultaneous introduction of physical constraint mechanism and intelligent optimization algorithm.

In order to scientifically identify the key disaster-causing factors and key disaster-causing paths in the urban rail transit system's flood disaster chain, and enhance the resilience of these systems, a study was conducted based on news report text data related to the urban rail transit flood disasters from 2010 to 2024. Natural language processing technology and a rule-based template matching method were used to extract causal event pairs. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to generalize the extracted event pairs, and the abstract event evolutionary graph of rainstorm disaster in the urban rail transit system was constructed, which was visualized as a disaster chain evolution network combined with Gephi. The complex network theory was introduced to quantitatively analyze the rainstorm disaster chain evolution network of the urban rail transit system, and identify the key disaster factors and key disaster paths in the disaster chain. The results show that the four types of disaster events, rainstorm, station water, subway shutdown and equipment failure, are of high importance and are the key disaster factors in the rainstorm disaster chain evolution network of the urban rail transit system. The three evolutionary paths of "road water → station water", "subway shutdown → economic loss" and "road water → rainwater backpouring" have high vulnerability, which are the key disaster paths in the rainstorm disaster chain evolution network of the urban rail transit system. The identified key disaster-causing factors and key disaster-causing paths can provide decision support for disaster prevention and mitigation.

In order to accurately predict the noise distribution of offshore platforms to prevent occupational noise hazards, a joint simulation method combining the sound ray method and SEA method was proposed to solve the problem that the traditional single noise prediction method was difficult to take into account the structural sound and air sound propagation characteristics and could not describe the large-scale spatial sound field distribution in detail. In this method, SEA was used to simulate the acoustic propagation characteristics of the structure, and the acoustic propagation law of the air in the large space of the deck was simulated by the sound ray method. Taking an offshore central platform as the research object, a joint simulation model was constructed based on the measured vibration and noise source intensity data. The sound field distribution of each deck was simulated and calculated, and the effectiveness of the method was verified by a spatial grid noise test. The results show that the contribution of air acoustic energy to the total noise energy of each deck of the offshore platform is more than 50 %, which is higher than that of structural acoustic energy. Obstacles such as large-scale equipment, production area rooms and firewalls have a significant effect on noise shielding, and air sound propagation has a more prominent impact on the spatial sound field distribution of the deck, the joint simulation results are in good agreement with the measured results, and the overall average difference between the prediction and the test data is within 2 dB, which can realize the high-precision prediction of the spatial sound field distribution of the offshore platform deck.

To effectively identify and control quality and safety risk of intelligent consumer products, a KG for intelligent consumer product quality and safety risk was constructed based on LLMs. Complex network analysis methods were integrated to systematically analyze risk distribution characteristics and propagation mechanisms. Data on intelligent consumer products were collected through multiple channels; the quality and safety risk issues in product accident cases were sorted out. Based on safety theories and the system structure of intelligent products, a multi-level and extensible knowledge ontology for quality and safety risks was constructed. Under the constraints of this ontology, LLMs were utilized to achieve automated knowledge extraction, and a KG containing 16 611 nodes and 32 178 edges was constructed. The KG was mapped into a risk network. Based on complex network theory, centrality indicators of three key node types—hazard factors, accident injuries, and safety events—were calculated to identify critical nodes and propagation paths in risk transmission. The research results show that among various intelligent consumer products, intelligent home products account for the highest proportion of risk entities; physical hazards and information hazards are the main risk types; property damage and physical injury are the primary accident consequences. Typical direct propagation paths and cascading propagation paths can be identified by the risk network.

To enhance the safety performance of civil aviation pilots, this study constructs a three-dimensional analytical framework for career resilience based on affect, behavior, and cognition(ABC). Integrating machine learning with fsQCA, it empirically analyzes 229 questionnaire responses from Chinese civil aviation pilots. Building upon the measurement of antecedent variable importance weights using the random forest algorithm, the fsQCA method is further applied to decipher the impact mechanisms of different condition configurations on safety performance.The results indicated that no single factor constitutes a necessary condition for either high or non-high safety performance; however, learning willingness and cooperation consciousness play key roles in driving civil aviation pilots to achieve high safety performance. Five configurational paths leading to high safety performance are identified and categorized into three patterns: "emotionally empowered-behaviorally oriented," "resilient collaboration-behaviorally dominant," and "efficiency driven-intrinsically motivated." In contrast, two configurational paths leading to non-high safety performance are classified as "behavior-atrophy" and "affection-deficiency" types. Furthermore, substitution relationships exist among conditional variables in the five configurations for high safety performance.

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 prevent interface similarity-induced errors in nuclear power plants, this study investigated the impact of interface similarity on cognitive performance and identified the optimal similarity range using semantic differential scale and scenario-based experiments. Firstly, a multi-dimensional perceptual feature system for interface similarity was developed through literature review, and 10 key similarity variables applicable to industrial control scenarios were identified. Secondly, a normalized equation for estimating the overall perceived similarity of nuclear power interfaces was established using semantic differential questionnaire survey and multiple linear regression analysis. Finally, typical interfaces from nuclear procedure tasks and alarm handling tasks were selected as experimental materials. Interface samples with varying levels of similarity were constructed based on three feature dimensions: color, layout structure, and complexity. Subjective questionnaires and scenario-based task experiments were conducted to measure and analyze the optimal range of similarity. The results indicate that interface similarity has a significant impact on operators' cognitive responses. with color, layout, and complexity as core dimensions. Cognitive performance is optimal when the interface similarity falls within the "generally similar" range (4-6).