In order to explore the causes of miners' risk decision-making and achieve precise pre-control of their risk behaviors, this study first employed RFT, principal component analysis(PCA) and latent profile analysis(LPA) to reduce the dimensionality and classify two types of individual factors—personality and trait mindfulness. Four main factors were extracted: calm awareness, stable responsibility, expressive action, and social agreeableness. Miners were categorized into promotion-focused and prevention-focused types. Next, using eye-tracking technology and behavioral experiments, a sequential risk decision-making task was selected to analyze miners' eye movement and behavioral characteristics during the risk decision-making test, examining the risk tendencies of different regulatory focus types. The results show that promotion-focused miners exhibit greater attention to high-reward options and higher sensitivity to missed opportunities during risk decision-making, whereas prevention-focused miners demonstrate more conservative behavioral traits.

In order to reveal the water-rock coupling mechanism of CO₂ geological storage. This study employed literature research and theoretical analysis to introduce the experimental conditions and methods for CO₂-water-rock reactions, the physicochemical property changes of reservoirs after CO₂ action, the evolution of microstructure, and the deterioration characteristics of mechanical properties. Key issues regarding the multi-field coupling effects of CO₂-water-rock interactions were proposed, and the research progress on each key issue was summarized and analyzed. The results indicate that systematic research on the dissolution effects of CO₂ in different phases and under long-term dynamic conditions is lacking in CO₂ immersion tests. The experimental setups fail to accurately simulate the migration characteristics of CO₂ in real reservoirs. The mechanism governing the generation, transport, and adhesion of precipitates in CO₂-water-rock reactions remains unclear. This leads to limited research on the correlation between pore structure evolution and mechanical property degradation in reservoirs. Furthermore, the substantive relationship between reservoir microstructure and macroscopic mechanical parameters has not yet been established, which hinders the development of multi-scale damage evolution models. Additionally, current models for reservoir mechanical property degradation do not fully account for the multi-field coupling effects of thermal-hydrological-mechanical-chemical-damage interactions.

In order to prevent the accident of people entrapped in the grain mass of warehouse and protect the life safety of workers, the discrete element engineering EDM(EDEM) method was proposed to simulate the distribution of human body pressure. Firstly, the soft sphere particle model and the basic particle contact model Hertz-Mindlin (no slip) were selected to construct the wheat, corn and rice grain particle models, and the multi-body pressure model of the human body was constructed by coupling multi-body dynamics and EDEM. Secondly, the accident area model was constructed and the simulation parameters were set considering the factors such as accident scene and calculation amount. Finally, the simulation of particle motion process and the analysis of human body surface pressure were completed, and the human body force distribution measuring device of people entrapped in the grain mass was developed. The results show that the buried time of different grains is slightly different, with wheat being less than corn and less than rice. The maximum pressure on the surface of the human body can reach 1.4×10⁶ Pa which fluctuates violently. The average maximum pressure of the weighted sum of the wheat warehouse is 9 864 Pa, that of corn warehouse is 8 255 Pa, and that of rice warehouse is 7 283 Pa. The pressure distribution data provides a reference for accident prevention and related safety training, and provides ideas for the development of protective devices.

In order to promote the safe and eco-friendly operation of wet dust collection systems by mitigating hydrogen fire and explosion risks caused by metal waste dust in contact with water, a custom-designed metal-water hydrogen evolution testing system and a water-based Cinnamomum camphor leaf extract (prepared via decoction) were utilized. Experiments assessed the hydrogen suppression efficiency of Cinnamomum camphor leaf extract at various mass concentrations. Surface analysis and theoretical calculations were conducted to study the adsorption behavior, chemical reaction kinetics, and inhibition mechanism of Cinnamomum camphor leaf extract on hydrogen evolution from aluminum-zinc (Al-Zn) dust. The hydrogen suppression stability at the optimal mass concentration was evaluated under varying environmental conditions. Experimental results demonstrated that higher mass concentrations of extract improved its ability to suppress hydrogen evolution from Al-Zn dust. At a mass concentration of 50 mg/L, the hydrogen suppression efficiency reached 97.86%, and the reaction rate constant was nearly zero, suggesting negligible hydrogen production. Cinnamomum camphor leaf extract spontaneously adsorbs onto the Al-Zn dust surface, following the monolayer Langmuir model and involving both physical and chemical adsorption. The surface analysis reveals that Cinnamomum camphor leaf extract forms a protective film on the Al-Zn dust via complexation, effectively preventing dust-water contact and suppressing hydrogen evolution. Tests under varying external conditions demonstrated that Cinnamomum camphor leaf extract consistently suppressed hydrogen evolution from Al-Zn dust, despite variations in temperature fluctuations, pressure changes, and water disturbances in the wet dust collection system.

In order to further enhance the early abnormal warning capability of natural gas compressor units, a novel method was proposed based on the VMD (Variational Mode Decomposition) algorithm, Informer algorithm, 3σ criterion, and Correlation-Weight optimization. A predictive model was constructed using the Informer architecture, in which the monitoring data were decomposed by VMD algorithm into multi-scale features of different frequencies to serve as model inputs. During the training process, the weight coefficients between each decomposed component and the original signal were calculated to optimize and adjust the internal model parameters. Furthermore, the prediction reconstruction results were combined with the 3σ statistical criterion to further improve the warning performance. Two segments of normal and abnormal pressure differential monitoring data from field compressor units were collected for experimental validation. The results show that, compared with other prediction methods, the proposed warning method achieves the lowest prediction errors. For the prediction of normal box pressure differentials, the errors are reduced by 66.67%-71.43% (Mean Squared Error, MSE), 36.67%-45.45% (Mean Absolute Error, MAE), 40.17%-45.42% (Root Mean Squared Error, RMSE), and 36.57%-45.72% (Mean Absolute Percentage Error, MAPE). For the abnormal inlet pressure differentials, the errors are reduced by 64.43%-71.12% (MSE), 44.02%-52.27% (MAE), 40.36%-45.53% (RMSE), and 37.24%-47.79% (MAPE). The proposed method exhibits superior prediction accuracy in both detailed and trend features. In the test set, it provides anomaly warning 60 minutes in advance, thereby improving the reliability of safe and stable operation of the compressor units.

The goals of carbon peaking and carbon neutrality support the development of HFCV industry. Safe parking of HFCV in outdoor three-dimensional parking lot is the key to large-scale promotion. Therefore, a scaled-down physical experimental platform was built to investigate the effects of outdoor multi-story parking lot closure conditions (closed, semi-open, open), leakage location, mass flow rate nd aperture on hydrogen diffusion characteristics. The experimental results show that as the opening degree of the outdoor multi-story parking lot increases, and the related indexes of helium at the leakage source rise. For example, when the leakage occurs at the center with a large aperture at the flow rate is 0.18 kg/s, the maximum volume fractions of helium on the first, second, and third floors are 21.3%, 27% and 45.3%, respectively. The corresponding hydrogen volume fractions far exceed the lower explosive limit, posing an extremely high risk of combustion and explosion. With the increase of leakage mass flow, both the volume fraction and pressure of helium increase, and the safety risk increases. There is an obvious positive correlation between leakage mass flow rate of helium and volume fraction and pressure. Under the same degree of openness, helium tends to accumulate in corners, and its diffusion along walls is more efficient than that in open areas. The diffusion process is guided by the spatial boundary, showing a non-uniform diffusion trend. In addition, the larger the pore size, the higher the helium leakage rate.

In order to enhance the accident monitoring effect and early warning efficiency of lithium-ion battery energy storage power stations and reduce the probability of fire occurrence, aiming at the problems existing in prefabricated cabin LiFePO₄ (LFP) battery energy storage power stations, such as passive monitoring delay, inaccurate monitoring of a single parameter, and ambiguous classification of disaster risks, an active online multi-source monitoring and hierarchical early warning system for LFP energy storage power stations was designed. Firstly, the thermal runaway and fire occurrence process of the energy storage power station was analyzed. The characteristic index set of each level was constructed, which is specific: internal temperature (single), internal pressure-sound (battery Pack), H₂ and CO concentration (battery cluster), smoke characteristic image (battery chamber), and plan a detailed monitoring implementation path. Then, based on the four-level early warning corresponding to the four levels, formulate the early warning strategy and clarify the early warning process. Experimental results demonstrated that: This monitoring and early warning system can effectively achieve the early warning of LFP energy storage power stations. Under 2C overcharging conditions, it can give an early warning of the thermal runaway of 32 Ah battery cells 38.9 min in advance. The combustion of the 344 Ah battery module was warned 53.2 min in advance under the condition of 0.5C overcharging. This study proves that this system can provide an effective guarantee for the safe operation of energy storage power stations.

To deeply explore the underlying patterns of road traffic accidents involving Autonomous Vehicles (AV), relying solely on the statistical analysis of individual accident description factors was insufficient. It was necessary to uncover further the comprehensive latent categories reflected by the interactions of multiple factors. Given that AV accident data contained structured information and narrative text, an innovative approach was proposed for type identification combining K-means clustering analysis and LCA. Specifically, the K-means method was used to extract key information from the narrative text, which was then fed into the LCA model to overcome the limitation of LCA being able to utilize only the structured information in existing accident reports. The effectiveness of this combined approach was verified using 437 AV traffic accidents in California, USA. The results show that AV accidents mainly manifest in four comprehensive types. The combined approach of K-means and LCA enables efficient clustering analysis of structured information that includes narrative text.

In order to explore the heterogeneity sources of the influencing factors of traffic accident severity on low-grade highway and analyze the causal mechanisms of accident severity, based on traffic accident records of a low-grade highway in Chongqing city in the past 7 years, combined with the classification of collision vehicle types, the accident severity inducement analysis model was constructed using the fixed parameter Logit, mixed Logit, and random parameter Logit models considering heterogeneity sources. The differences in goodness-of-fit and heterogeneity of different models were analyzed, and the influence of significant variables on accident severity was quantified using average elasticity coefficient. The results show that the Logit model with random parameters considering heterogeneous sources has the highest goodness-of-fit under the same type of accident conditions. In the comprehensive accident model, the non-motor vehicle of the involved party has the greatest influence on the accident severity. The frontal collision is a heterogeneous variable that obeys a normal distribution of (-0.668, 0.749²). The mean and variance of the parameters are positively influenced by summer and large accident-causing vehicles. The season and involved vehicle types exhibit the largest effect intensity in the vehicle-to-vehicle accident model. The parameters associated with summer, mid-sized accident-causing vehicles, and side collisions follow a one-sided triangular distribution with mean (variance) of 0.586, 0.948, and 0.631, and have mean heterogeneity with the factors of night and horizontal curve radius greater than 1 600 m. The severity of vehicle-to-non-motor vehicle accidents is significantly affected by the variables of non-motor vehicle, night, and frontal collision. The parameters corresponding to the night and involved non-motor vehicle variables obey the unilateral triangular distribution of the mean (variance) 2.040 and 1.330, the mean value of parameters is significantly correlated with the weekend and age over 59 years old. Reasonably segmenting the accident dataset of low-grade highways in mountainous areas helps to reduce the heterogeneity effect in the factors affecting accident severity and improve the accuracy and reliability of accident severity cause analysis results.

To solve the problem of difficulty in balancing trajectory optimization and real-time performance in lane change and obstacle avoidance trajectory planning for intelligent vehicles, a CBR mechanism based intelligent vehicle lane changing and obstacle avoidance trajectory planning method was proposed. The optimal trajectories in typical scenarios were obtained by offline optimization of the fifth-degree polynomial trajectory parameters, forming an optimal case library consisting of the state variables and decision variables. The optimal trajectory correction rule in the new case scenario was explored, and a trajectory key parameter update function was established. Thus, existing case trajectories were directly called in similar case scenarios, and the lane changing and obstacle avoidance trajectories were updated according to state variables such as vehicle speed and position in new case scenarios, thus the case library was updated. The comparative test results show that the trajectory performance of proposed method is basically consistent with that of the fifth-degree polynomial online optimization method, and is significantly better than that of A^* and Rapidly-exploring Random Trees-star(RRT^*) methods. Specifically, the maximum curvature of the trajectories from the proposed method and the fifth-degree polynomial online optimization method are both 0.001 2 m^-1, and the maximum lateral acceleration of the trajectories from the two methods are both 1.245 7 m/s² in the similar case scenario. While in the new case scenario, the maximum curvature of the trajectories from the two methods are both 0.001 4 m^-1, and the maximum lateral acceleration is 1.194 9 and 1.1365 m/s², respectively. Meanwhile, compared with the three comparative methods of the fifth-degree polynomial online optimization, A^* and RRT^*, the proposed method has the smallest planning time. The planning time in the new case scenario and the similar case is only 0.001 1 and 0.000 1 s, respectively.