To enhance China's work safety governance, policy texts related to work safety were analyzed using text analysis and coding methods, aiming to provide an in-depth understanding of the current policy system and to identify existing gaps in China's safety governance policies. Based on Duxiu database and the website of the Central People's Government, 80 policy texts issued between 1949 and 2024 were selected. NVivo 12 plus software was used to conduct coding analysis, which was then employed to summarize the distribution and usage of policy tools in China's work safety governance. The research show that China's work safety governance policies employ a comprehensive set of policy tools. They exhibit a development pattern centered on pre-accident prevention, supported by talents and technology, and complemented by coordinated measures during and after accidents. At the same time, the current policy structure is still unbalanced. Policy tools play a prominent role, while enabling and incentive tools are insufficient. The integration degree of policy tools with governance logic elements is low, affecting the overall effectiveness of governance. In the future, it is necessary to promote the optimization of policy tool structure, balance the distribution of governance elements, and strengthen the deep integration of policies and elements to enhance the level of work safety governance.

In order to accurately identify risks and effectively prevent accidents in confined space operations, a scenario-based accident evolution model grounded in key elements was developed to conduct quantitative risk analysis. This study addressed confined space operations by systematically organizing their key elements, which encompass physical characteristics, spatiotemporal context, environmental conditions, safety management, and emergency decision-making. The Web Ontology Language (OWL) was employed to standardize the description of these elements and represent the scenarios, thereby enabling a structured and standardized characterization of accident evolution. Drawing upon data from nearly 50 domestic confined space accident cases, as well as current standards and expert knowledge, the study performed state-based description and correlation analysis on critical aspects, including gas detection, ventilation, personal protective equipment, and accident consequences throughout the operational process. A BN structure and probability parameters were established accordingly. An empirical analysis of a confined space poisoning accident was conducted to validate the model. The results show that the systematic identification of critical safety factors contributes to the structured management of accident knowledge. Furthermore, the BN model constructed on this basis enables quantitative risk assessment and scenario deduction. The empirical findings demonstrate that risk-ignorant rescue attempts constitute the primary factor exacerbating outcomes in confined space accidents, particularly when multiple non-professional responders enter without personal protective equipment. Therefore, emergency plans should clearly define rescue procedures, responsibilities, and protection requirements.

In order to reveal the influence of small perturbations on the failure and re-initiation of normal detonation and quasi-detonation waves, experimental were carried out on acetylene-oxygen mixtures. Firstly, thin metal plates of different lengths were arranged in the explosion chamber to introduce small-scale perturbations. Then, helical springs with wire diameters of 7 and 9 mm were used to construct rough wall surfaces for generating quasi-detonation. Finally, distributed photoelectric probes were employed to record the arrival times of detonation waves, and a high-speed schlieren system was combined to observe the diffraction and re-initiation processes of detonation waves. The research reveals that introducing minor perturbations significantly reduces the critical initiation pressure threshold. Below this critical initial pressure, re-initiation of the detonation wave is impossible, even with perturbations present. Conversely, above this critical pressure, planar detonation waves within the tube consistently transition to spherical detonation waves in all repeated experiments. The re-initiation site for normal detonation in a smooth tube consistently occurs near the thin plate, whereas the re-initiation location for quasi-detonation in a rough tube exhibits randomness. Quantitative analysis demonstrates distinct critical initiation criteria for the two detonation types: for the successful re-initiation of detonation, the ratio of the critical tube diameter to the detonation cell size must be greater than or equal to 13, while the critical threshold for the successful re-initiation of quasi-detonation is reduced to approximately 8 for the ratio of the critical tube diameter to the cell size.

To enhance the importance of buildings and bridges as physical hazard-bearing bodies for hazardous chemical accidents in regional risk assessment. In this paper, a physical vulnerability assessment model was established, and a risk assessment method was proposed, which considered the hazardous chemical accident consequences and the physical vulnerability of hazard-bearing bodies. Firstly, areal locations of hazardous atmospheres (ALOHA) was used to simulate the possible risk footprints of hazards. Secondly, a physical vulnerability assessment model including exposure, sensitivity and adaptability was established. Density of structures and distance from the accident center supply were selected as the exposure dimension layer. The age of the structures, building height, seismic grade of building and bridge length were selected as sensitivity dimension layer. Emergency shelter area, road area and infrastructure maintenance funds were selected as the adaptability index layer, and the driving force factors of physical hazard-bearing body vulnerability were analyzed through the geographical detector. Finally, arc geographic information system (ArcGIS) was used to superimpose the accident consequence map and the physical vulnerability map to generate a comprehensive risk map to realize the comprehensive risk visualization of hazard-bearing body. This method was applied to the risk assessment of physical hazard-bearing bodies in a town of Tianjin. The results show that the density of structures and the distance from the accident center have the strongest explanatory power for the vulnerability of physical hazard-bearing bodies. The explanatory power of these two factors is 0.515 and 0.464, respectively. High-risk areas result from the spatial overlap of high hazard and high vulnerability. The comprehensive regional risk resulting from the combination of accident consequences and vulnerability exhibits significant spatial variation. On the accident consequence map, the eastern part of the town near the hazard release point is the most dangerous area. However, owing to the low vulnerability of disaster-bearing bodies in the surrounding area of the release point, it is classified as a medium-risk zone on the comprehensive risk map.

To optimize the aerogel structure and reduce the effective thermal conductivity of the aerogel, the force field, atomic model, heating and cooling calculation modules were added to large-scale atomic/molecular parallel simulator(LAMMPS) to accurately simulate the adsorption of water molecules by silica aerogels and heat transfer processes. The results show that the adsorption capacity of silica aerogels to water molecules increases as the water content increases, showing a significant rise followed by an equilibrium state. With the increase of temperature, the thermal movement of water molecules intensifies, and the adsorption capacity of silica aerogels to water molecules decreases. However, the free water molecular weight increases. As the pressure increases, the collision frequency of water molecules and silica aerogels increases, resulting in an increase in adsorption capacity. When the water content increases and the temperature decreases, the mutual squeezing among water molecules promotes more water molecules to penetrate into the interior of silica aerogels, meanwhile, the thermal motion of water molecules slows down, which is not conducive to their escape from the aerogels pores, resulting in an increase in the number density of the silica aerogels system. Silica aerogels form water films by adsorbing water molecules, and the water films constitute "water bridges". With the increase of water content, the connectivity among "water bridges" is enhanced, and the contact area increases, leading to an increase in effective thermal conductivity of silica aerogels and significant degradation of thermal insulation.

With the global transition of civil aviation navigation systems from magnetic north to true north reference, a dynamic risk assessment model integrating STPA and FBN was proposed to quantify, identify, and effectively control systemic risks induced by the navigation reference transition. A four-level control structure-covering strategic, regional, organizational, and equipment layers-was established to identify seven categories of system-level hazards and twelve types of unsafe control actions. Expert uncertainty was quantified via fuzzy sets, and a Bayesian network (BN) was constructed using the Leaky Noisy-or Gate model. Furthermore, a dynamic Bayesian network (DBN) was developed to simulate risk evolution across five phases (t₀ to t₀+28 years). The results show that technological lag and insufficient policy coordination are the major risk drivers in the early stage (e.g., airspace conflict probability up to 0.852). However, through phased implementation of technology upgrades, policy alignment, and redundancy design, key risks can be reduced to below 0.01 by t₀+28. This study proposes an original strategy integrating the 'phased compliance-fund disbursement' policy linkage mechanism, aircraft service life-based technical iteration path, and the 'inertial navigation + low-orbit satellite' dual-redundancy artificial intelligence (AI) governance system, to systematically resolve policy delays, intergenerational equipment conflicts and operational risks in the true north transition.

In order to improve the efficiency of data fusion in the wireless sensor network (WSN) of a uranium tailings pond, reduce redundant data transmission, and extend network lifespan, an innovative data fusion algorithm was proposed, namely the SAPSO-BP data fusion algorithm based on improved SA and PSO optimized BP neural network. The algorithm integrated the global search capability of the SA algorithm with the efficient optimization mechanism of the PSO algorithm, incorporating dynamic inertia weights and mutation operators to enhance global search ability and avoid local optima. Furthermore, the improved algorithm was used to optimize the weight matrix and threshold parameters of the BP neural network, constructing a high-performance multi-sensor data fusion model, which was applied to radionuclide monitoring in uranium tailings ponds. The results show that the SAPSO-BP algorithm outperforms the compared algorithms in terms of data fusion accuracy, network energy consumption, and network lifespan. Compared with the traditional BP algorithm, it reduces mean relative error(MRE) and root mean square error(RMSE)by up to 40% and 45%, respectively, and improves the goodness of fit to 0.908 3. Additionally, it delays the first node death to approximately 1 180 rounds, extends the overall network lifespan to about 1 500 rounds, and achieves lower node energy consumption and a more balanced energy distribution.

To prevent safety accidents such as bursting and leakage caused by wear in mine filling pipelines, CFD-DEM was employed to investigate the wear characteristics of filling slurries on pipelines. An L-shaped pipeline and a solid-liquid two-phase flow model were constructed to conduct numerical simulation experiments. Particle size (0.001-0.1 mm), slurry solid volume fraction (60%-80%), and slurry flow velocity (2-6 m/s) were used as variable parameters to explore the maximum wear rate of the pipeline under different conditions. The results indicate that both particle size and solid volume fraction exhibit a nonlinear relationship with the maximum wear rate. At a particle size of 0.1 mm, the maximum wear rate is 13.4 × 10^-5 kg/m². There is a critical solid volume fraction of 70%, at which the maximum wear rate was 7.15 × 10^-5 kg/m²; beyond this value, the wear rate tended to stabilize. The slurry flow velocity shows a linear relationship with the maximum wear rate; at a flow velocity of 7 m/s, the maximum wear rate is 8.25 × 10^-6 kg/m². The influence of each parameter on pipeline wear was ranked as follows: particle size > solid volume fraction > slurry flow velocity. The interaction between particle size and solid volume fraction has the most significant impact, followed by the interaction between solid volume fraction and slurry flow velocity, while the interaction between particle size and slurry flow velocity had the least effect.

To address the degraded detection performance of insulated glove wearing status caused by small hand regions in complex distribution network operation scenarios, a small-object-oriented detection algorithm, termed LN (LLC (Light-weight, Light, Coordinate) +NWD(Normalized Wasserstein Distance)), -YOLO is proposed.First, an LLC module was designed by integrating lightweight spatial pyramid pooling, a receptive field fusion attention mechanism, and coordinate convolution, thereby enhancing spatial perception through multi-scale feature aggregation and coordinate embedding.Subsequently, the Mixup data augmentation strategy was introduced to improve model robustness, and a NWD loss function is incorporated to optimize small-object detection.Finally, the effectiveness of LLC module was validated through controlled experiments to determine the optimal baseline, followed by ablation and comparative experiments on the proposed method.The results demonstrate that the proposed algorithm achieves a detection accuracy of 90.1%, representing a 2.0% improvement over the baseline, with a detection speed of 56 frames per second and a memory footprint of 15.7 MB, meeting the requirements for accuracy, real-time performance, and edge-device deployment in distribution network operation scenarios.

To address the problems of lagging fault response and insufficient accuracy in the traditional operation and maintenance mode of mine main hoists, a fault diagnosis model for mine main hoists based on BO-XGBoost was constructed, and the SHAP method was integrated to improve the model interpretability. The Bayesian Optimization (BO) algorithm was used to optimize the hyperparameters of the eXtreme Gradient Boosting (XGBoost) model. Based on the monitoring data from an experimental mine main hoist, the XGBoost model combined with the SHAP attribution analysis method was adopted to identify the key influencing factors and their action mechanisms. The results show that compared with the baseline XGBoost model, the BO-XGBoost model increases accuracy by 4.1%, reduces log loss by 41.9%, and shortens model training time by 80.1%. Compared with traditional decision tree, random forest and LightGBM algorithms, the BO-XGBoost model improves precision by 26.5%, 11.9% and 13.6%, respectively, demonstrating excellent test accuracy.Wire rope tension, lower sheave temperature and motor voltage are the three key causal factors of faults. Different fault types are affected by different factors; for instance, excessively high main shaft vibration, motor temperature and excessively low hoisting speed provide greater positive gain for main shaft fault prediction. Three-factor interaction analysis reveals the dominant role and influence patterns of various factors during wire rope faults. The probability of wire rope faults is mainly dominated by tension, motor current and hoisting speed. Excessively low tension or current significantly increases the risk, and rising hoisting speed further aggravates the fault probability, whereas lower sheave temperature has a weak influence.