This study aimed to uncover the underlying mechanisms of emergency information processing during accidents and to improve the emergency response capabilities of individuals, thereby reducing injury severity. Based on theories of implicit memory and automatic processing, ERPs technology was employed in behavioral and neural experiments. The "reaction time" and "accuracy" were used as measures of information processing capacity. The ERP components P200 (P2), P300 (P3), and Late Positive Potential (LPP) were used to reflect the participants' cognitive and emotional processing. This study explored the automatic processing mechanisms of emergency information and identified its influencing factors. The results show that in frequent accident scenarios, experience-guided implicit memory enhances early attention sensitivity. This reduces conscious brain processing, automates information processing, and demands fewer cognitive resources. Conversely, infrequent accident scenarios trigger panic emotions, increase cognitive resource consumption, lead to cognitive fixation and attachment, and inhibit the automation of information processing.

In order to address the problem of low payload and spatial utilization rates in the air transportation of emergency supplies, this study focuses on mixed loading schemes for transport aircraft, targeting non-standard emergency materials characterized by diverse types, significant variations in size and mass, and the presence of bundling or proportional transportation requirements. A size-based classification criterion was proposed to divide the materials into three categories: large, medium, and small. For medium and small items, a multi-objective two-dimensional loading model was established and solved by integrating the bottom-left algorithm with NSGA-III. The resulting configurations were then treated as large-sized items. Within the aircraft cargo hold, NSGA-III was further employed to generate loading schemes for these large-sized items, with the objectives of maximizing loaded area and payload weight while minimizing center-of-gravity deviation. The results indicate that, through material classification and staged optimization, the proposed method effectively reduces the dimensionality of the solution space and improves computational efficiency. The generated loading schemes achieve an average cargo space utilization rate of 78.09%, an average payload utilization rate of 86.19%, and an average center-of-gravity deviation of only 0.222 meters. This approach significantly enhances the utilization of transport aircraft while ensuring flight safety, and provides timely decision-making support for emergency relief operations.

To accurately assess the impact of noise on the health of workers in fully mechanized mining face, the key influencing factors of noise occupational health were determined by using the theory of man-machine-environment-management system, combined with Fisher Score, maximum information coefficient and approximate Markov blanket method. The prediction model of coal mine noise risk classification based on the GBDT algorithm was constructed, and the Kappa coefficient and its accuracy were used as the index of model efficiency to compare and verify the accuracy of the model. The results show that the occupational health damage of noise in fully mechanized mining face is closely related to individual status, equipment configuration, environmental factors and occupational health management. Among these, job category, individual age, length of service, protection awareness, degree of equipment automation, pass rate of noise monitoring points, noise exposure, reverberation time and management institutions and personnel are the key indicators of occupational health risk classification and prediction. The accuracy of the occupation health risk classification prediction model of coal mine noise based on GBDT is up to 99.6%, and the average accuracy and Kappa coefficient are 98.3% and 0.958, respectively. The evaluation accuracy of six prediction models for noise occupational health risk classification in fully mechanized mining face is ranked as follows: GBDT > Genetic Algorithm optimization Random Forest(GA-RF) > Particle Swarm optimization Least Squares Support Vector Machine(PSO-LSSVM) > Random Forest(RF) > Support Vector Machine(SVM) > Decision tree.

In order to construct the fundamental theory of macrosecurisafetyology (which combines the meanings of security & safety science for the large-scale issues), combined methods of literature review, data statistics, logical deduction and model analysis, etc. were used in this investigation. First, the research process and characteristics on the relevant theory of macrosecurisafety (which means the integrated security & safety for the large-scale issues) in the past decade were reviewed. Then, definitions, connotation, and characteristics of macrosecurisafety were given from multi perspectives, and a group of the basic concepts and their systems of macrosecurisafetyology were put forward. The disciplinary group that constitutes macrosecurisafetyology was built, and a research paradigm and principles of macrosecurisafetyology were summarized. Two macrosecurisafety models and their related definitions were built according to two scenarios. The research results clarify the scope and identification criteria of macrosecurisafety, extract 10 sets of core concept groups supporting 10 macrosecurisafetyology subjects, including macrosecurisafety state studies, macrosecurisafety meta principle studies, macrosecurisafety internal cause studies, macrosecurisafety system studies, macrosecurisafety natural disaster studies, macrosecurisafety man-made disaster studies, macrosecurisafety external cause studies, macrosecurisafety prevention and control studies, macrosecurisafety comprehensive studies, and macrosecurisafety marginal studies, providing their definitions and scopes for these macrosecurisafetyology subjects, form a research paradigm and classification for macrosecurisafetyology principles, and obtain a modeling method and two typical models for macrosecurisafetyology. The above results play an important role in consolidating the foundation of macrosecurisafetyology.

In order to promote the development of theories on the modernization of work safety governance, a four-dimensional model of the work safety governance system—comprising governance actors, governance rules, governance tools, and governance resources—was constructed based on systems theory and governance theory. Using CAS theory, seven fundamental characteristics—aggregation, nonlinearity, flow, diversity, tagging, internal models, and building blocks—were analyzed. Furthermore, by integrating fitness landscape theory and the NK fitness landscape model, an NK landscape model for the evolution of the work safety governance system was developed, revealing 16 state combinations of the system's genetic morphology (A-R-T-S) and their fitness distributions. The results indicate that the system's evolution follows a three-stage mechanism: Initial Stage: Random walk and low-state locking → Development Stage: Adaptive walk and local coordination limitations → Maturity Stage: Adaptive leap and deep coordination. Empirical analysis indicates that the evolution of China's work safety governance system encompasses three periods with corresponding mechanisms: the System Initial Establishment Period (1978—2002), the System Improvement Period (2003—2012), and the System Modernization Advancing Period (2012—2024).

In order to further prevent coal mine safety accidents triggered by miners'personality traits and psychological factors, this study adopts the Big Five personality model to assess personality traits. From the perspective of emotions, this study explores the predictive role of the Big Five personality and emotions in miners'unsafe behaviors, while also examining the moderating effect of emotions on the relationship between the Big Five personality traits and unsafe behaviors. First, SPSS 26.0 and AMOS 26.0 were used to analyze the reliability and validity of the questionnaire. Then, a SEM was established to verify the hypotheses. On this basis, the hierarchical regression analysis method was employed to test the moderating effect of emotions. The results show that: the three personality traits of extraversion, neuroticism, and openness, and negative emotions positively influence unsafe behaviors. The two personality traits of agreeableness and conscientiousness, and positive emotions negatively affect unsafe behaviors. Positive emotions play a moderating role in the relationship between the openness personality trait and unsafe behaviors. Negative emotions have a moderating effect in the relationships between the four personality traits of neuroticism, openness, extraversion, and agreeableness and unsafe behaviors. Therefore, coal mining enterprises can identify potential safety hazards and develop strategies to intervene in employees'unsafe behaviors based on workers'personality traits and emotional states, thereby enhancing the on-site safety level.

To address the dynamic risks in hazardous areas and the lack of timely warning of personnel intrusion within deep foundation pit, a digital twin system integrating BIM and UWB technology was developed. Initially, a virtual scene representing the deep foundation pit was constructed using the OurBIM 3D graphics engine. Second, a method for calibrating hazardous areas was proposed, and UWB technology was utilized to locate construction personnel and hazardous sources in real-time. Third, a real-time warning mechanism for personnel intrusion behavior was established, and a digital twin system was designed for regional intrusion warning of personnel in deep foundation pit, incorporating functionalities such as scene visualization, hazardous area definition, and warning data logging. Finally, the system was applied to a specific deep foundation pit in Wuhan. The results demonstrate that the system is capable of monitoring personnel intrusion behavior in two distinct types of hazardous areas within the deep foundation pit, achieving a comprehensive warning success rate of 97.4%. This system validates the feasibility and effectiveness of digital twin technology in the construction safety domain.

To enhance the safety management of operators in machining workshops, an identification model based on YOLOv11 was constructed. The YOLOv11 model was improved by integrating the MetaFormer architecture, Mixed Aggregation Network (MANet) module, and Adaptive Feature Grid Convolution Attention (AFGC Attention) mechanism. A video dataset captured in a real workshop environment was established to validate the identification model. The results show that the improved YOLOv11 model can identify three types of behaviors, namely unattended operation, operating without a face shield, and operating without protective clothing, with F₁scores exceeding 0.93 for all categories. The improved model demonstrates a significant enhancement in identifying small-sized targets, with the F₁ score for identifying glove-wearing behavior increasing from 0.684 to 0.708, and the mAP@0.5 value rising from 0.604 to 0.651. The research findings may provide technical support for the identification and early warning of unsafe behaviors among operators in machining workshops.

To effectively identify and prevent the risks of chemical fire accidents, a comprehensive research framework was proposed, integrating text mining, SIF model hierarchical analysis, and social network analysis based on rich accident investigation reports in the era of big data. Firstly, the key causes of the accidents were systematically extracted through text preprocessing, term frequency-inverse document frequency (TF-IDF) keyword extraction, and latent dirichlet allocation (LDA) topic modeling, combined with 75 representative chemical fire accident investigation reports from 2000 to 2024. Then, based on the SIF model, the extracted causes were classified into the micro, meso, and macro levels. Subsequently, the accident cause network was constructed using social network analysis methods. Core nodes and key influence paths in the accident cause network were identified through frequency statistics, centrality analysis, and key relationship mining. The research results show that the micro-causes accounted for 56.3%, representing the most crucial factors contributing to chemical fire accidents, with individual factors and environmental equipment risks being dominant. At the meso level, insufficient safety supervision has the highest degree centrality. The key cause path for chemical fire accidents is: inadequate organizational management → insufficient safety supervision → insufficient personal preparation → violation of regulations.

To improve the level of process safety management in refining and chemical enterprises and enhance dynamic monitoring and trend warning capabilities for key safety indicators, a multi-model fusion method for process alarm data prediction was proposed. This approach integrated three time series forecasting models: Autoregressive Integrated Moving Average (ARIMA), double exponential smoothing, and particle swarm optimization(PSO)-based support vector regression (SVR). The method effectively modeled and predicted diverse safety indicators by addressing their trend, autocorrelation, and nonlinear characteristics. Initially, outliers in the raw indicator data were processed. Three types of forecasting models were then constructed and their prediction results were computed. The optimal model for trend prediction was automatically selected based on error comparison. Finally, an empirical analysis was conducted using the time-averaged alarm count indicators from a refinery enterprise over one year. The results show that the proposed method dynamically adapts to varying data characteristics, with selected model prediction errors consistently remaining below 0.1, significantly outperforming the existing requirements for alarm magnitude accuracy. This method effectively enhances the accuracy and flexibility of safety indicator prediction in refining and chemical enterprises and enables the timely identification of potential risk indicators.