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.

To explore more efficient fire extinguishing techniques for Chinese liquor warehouses, based on the similarity theory, an experimental bench for liquid CO₂ fire extinguishing with dimensions of 5 000 mm×2 400 mm×2 900 mm was constructed. An in-depth study was conducted on the changes in Chinese liquor quality, temperature distribution, and gas volume fraction during the process of extinguishing Chinese liquor pool fire using liquid CO₂ fire extinguishing systems with different combustion disk diameters. The research results indicate that liquid CO₂ can effectively extinguish Chinese liquor fires. The larger the diameter of the combustion disk, the more significant the change in mass. The larger the diameter of the combustion disk, the higher the temperature that can be reached by the Chinese liquor pool fire, up to 340 ℃. The extinguishing time under three different combustion disc diameters is 32, 36, and 38s respectively, and there is no re-ignition phenomenon. The highest gas volume fraction of CO₂ in the fire scene space can reach 26%. The distribution of CO₂ content at measurement points G₁-G₄ under different combustion disc diameters is as follows: G₄ > G₃ > G₂ > G₁. The distribution of CO content is: G₃ > G₄ > G₁ > G₂. The distribution of O₂ content is G₁ > G₂ > G₃ > G₄.

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.

In order to understand the characteristics of water transport during spontaneous infiltration and absorption of coal of different rank, coal samples from Mengtai Manlailiang mine, Jiulishan mine, Tiandiwangpo mine, and Pingdingshan No.10 mine were taken as the research objects, The T₂ spectrum of water in the coal sample at different times during the imbibition process was measured by using the low-field NMR experimental system, and the variation of water in the coal sample with time and space during the imbibition process was explored based on the change of water quality in the coal sample. The results show that the wettability of low-rank coal is better than that of high-rank coal and medium-rank coal, and the imbibition capacity of low-rank coal is the largest under the same experimental conditions, followed by high-rank coal, and the imbibition capacity of medium-rank coal is the smallest. According to the change of water in the process of coal sample imbibition with time, the coal sample imbibition process can be divided into three stages: the initial stage of imbibition, the middle stage of imbibition and the late stage of imbibition. In the early stage of imbibition, the sensitivity of low-rank coal to hydraulic action is higher than that of high-rank coal and medium-rank coal, and the imbibition velocity of low-rank coal is the largest, followed by medium-rank coal, and the imbibition velocity of high-rank coal is the smallest. In the middle and late stages of imbibition, the imbibition rate of high-rank coal is larger than that of medium-rank coal. In the process of imbibition and sorption, water will be transported from the pores with smaller pore size to the pores with larger pore size due to capillary force in the micropores/mesopores of the same magnitude, and the resistance of water in the lower rank coal is less than that in the higher rank coal.

Aiming at the problem of non-homogeneity and difficulty in obtaining the mechanical parameters of surrounding rock in the process of coal mining, the experimental device for determining the parameters of loaded coal rock with drilling was developed independently to realize the real-time perception of strength and structure of rock layer. The effects of circumferential pressure, drilling speed, rotational speed and rotational torque on strength of simulated rock specimens were investigated and analyzed by coupling the gray correlation of collected parameters with drilling. Under loaded conditions, stratified specimens of different combinations of types were drilled in order to restore the actual effect of drilling into coal and rock seams in real environments, and to verify the accuracy of identification of compressive strength of specimens by the following drilling parameters. It was shown that the strength of the specimens was positively correlated with the circumferential pressure, rotating torque and drilling speed. The strength of the specimen remained almost constant when the rotational speed varied. The correlations between rotational torque, circumferential pressure, drilling speed, and rotational speed and compressive strength were 0.996, 0.831, 0.739, and 0.347, respectively. The strength of the specimen was linearly related to the rotational torque. By analyzing the features of change in the rotating torque curve, the strength of the rock formation can be identified and thus the location of the stratum boundary can be located.

To enhance aviation operation safety, improve airspace management efficiency, and enhance the defense capabilities of system against spoofing and interference, an anomaly data detection model was proposed based on WGAN-XGBoost. Firstly, WGAN was utilized to learn the intrinsic distribution of the preprocessed ADS-B data, generating abnormal data for augmenting and balancing the training dataset. Then, XGBoost algorithm was employed to train the mixed dataset, building the final abnormal classification detector. Finally, the performance comparisons were conducted through experiments with benchmark models such as Naive Bayes, Logistic Regression, and Perceptron. The results show that the performance of XGBoost is superior to that of all comparison models including accuracy, precision, recall, and F₁ score, with accuracy and precision both exceeding 0.999. The total detection time for 243 792 data points is 2.070 2 s, with an average detection time of 0.008 5 ms per data point. It achieves the optimal balance between detection performance and time cost and has been validated by real abnormal events, demonstrating good practicality and applicability.

In order to evaluate comprehensive driving risk in interchange ramp areas, a real-vehicle test was conducted on Chongqing interchange groups, focusing on three typical ramps: right-turn directional, left-turn semi-directional, and small-radius loop ramps. Drivers' electrocardiographic data and vehicle operation status were collected by PhysioLAB and Speedbox, respectively, when passing through ramps. Psychological load and vehicle operation risks of the three ramps were analyzed, and an improved entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method was used to construct and evaluate a comprehensive driving risk model. Results show four HRI change patterns: convex curve, continuous increase, continuous decrease and concave curve. Heart Rate Increase (HRI) in right-turn directional ramps first decreases, then increases, in left-turn semi-directional ramps, it first increases, then decreases near diverging/merging points, in small-radius loop ramps, it fluctuates significantly there. Vehicle operation risk is highest in ramp sections, increasing with smaller radii. The level of vehicle operation risk is small-radius loop ramp greater than left-turn semi-directional ramp, greater than right-turn directional ramp. Comprehensive driving risk peaks in ramp sections, widely distributed in split/confluence areas and peaking shortly after diversion points.

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.

Community fires exhibit high incidence rates and interconnected risk factors. To establish an effective fire prevention and control system aligned with community risk profiles, a hybrid SNA/N-K model was constructed to calculate risk coupling values and network centrality metrics. By adjusting the out-degree values of closeness centrality using coupling values, critical risk factors were identified, providing evidence-based decision-making insights for community fire prevention. Key findings reveal that management deficiencies dominate risk coupling dynamics. An increase coupling factors significantly elevates community fire risk levels. The top three critical risk factors are failure to investigate fire hazards as required, inadequate safety awareness and knowledge of residents and absence of regular fire safety education. Based on this research, a "three-dimensional four-stage" community fire safety management model is proposed. From the dimensions of institutional optimization, technological empowerment and behavioral intervention, a comprehensive strategy is formulated to decouple and interrupt risk coupling chains in community fire prevention and control, encompassing the full cycle of prevention, early warning, response, and recovery. This study contributes a theoretical framework and empirical intervention pathways for community fire risk mitigation, offering actionable guidance for policymakers and practitioners.

In order to enhance the risk control efficiency of civil aviation flight safety in China and accurately identify the key risk factors that cause flight accidents, multi-source text mining of flight accident risks was conducted using Weibo information, news reports, and aviation accident investigation reports as samples. Flight accident risk factors were identified using the bidirectional encoder representations from transformers for topic modeling(BERTopic) . The semantic correlation of risk factors was analyzed using the Word2Vec model and complex network, from which the key risk factors were determined. The bidirectional encoder representations from transformers(BERT) was adopted to mine the personnel risk factors that trigger the most serious negative emotions among the public. The key personnel risk factors in low-altitude airspace were identified through word frequency statistics. The results indicate that personnel risk is the key risk factor leading to flight accidents. Among them, failure to strictly follow operating procedures, bird strikes, and sickness of flight crew are the key risk factors affecting flight safety. The non-strict implementation of operation procedures by flight crew not only arouses the most negative public emotions but also constitutes the key personnel risk factor leading to low-altitude airspace accidents.