In order to improve the systematicity and objectivity of safety risk identification in civil aviation ATC operations and to explore the causes and interaction relationships of ATC unsafe incidents, a causal analysis method for ATC unsafe incidents was proposed, integrating TM, HFACS, and FRAM. Firstly, TM technology was employed to perform text processing on unsafe incident reports, and high-frequency keywords related to ATC operation risks were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Secondly, semantic attribution was conducted on these keywords, and an improved multi-level HFACS framework for ATC operations was constructed based on the characteristics of ATC work, and the corresponding risk factors were determined. Then, the interdependent relationships between various functional modules in the ATC operation system were analyzed by introducing FRAM to reveal the potential impact of interactions within the complex ATC system on incident occurrence. Finally, two cases of ATC unsafe incidents were investigated to analyze their risk causes. The results show that the proposed method effectively mines the interrelationships between multi-source causes and identifies multi-level risk causal factors, which verifies the effectiveness and scientific rigor of the method in multi-source data processing and complex causal structure mining.

To improve the risk decision-making effectiveness of enterprise safety professionals and curb the occurrence of unsafe behaviors, the theoretical connotation and improvement paths of risk cognitive ability were explored in this study. Based on SRK cognitive theory, the antecedent conditions of risk cognitive ability were classified into skill-based, rule-based, and knowledge-based cognitive abilities. The analysis of risk cognitive dimensions was combined from the perspectives of management systems, cognitive load, and cognitive tasks. Subsequently, a cognitive theoretical model was constructed, covering hazard identification ability, equipment operation ability, hidden danger inspection ability, hidden danger management ability, scenario decision-making ability, and case reasoning ability. NCA and fsQCA were applied to conduct data calibration, necessity analysis, sufficiency analysis, and robustness analysis on the multi-dimensional risk cognitive abilities of 100 safety professionals. Furthermore, the configurational paths of the antecedent conditions were analyzed, and targeted strategies for improving cognitive abilities were proposed. The results show that there are four configurational paths for both high risk cognitive competence and non-high risk cognitive competence. Specifically, high risk cognitive competence encompasses three configurational types: the rule-knowledge dual-driven type, the multi-dimensional cognitive ability synergistic type, and the emergency management ability dominated type. Accordingly, a "precision empowerment-multidimensional synergy-dynamic adaptation" path for improving risk cognitive ability is put forward, thereby enhancing the level of enterprise safety management.

To investigate the failure issues of overdue in-service pressure pipelines, this paper proposed a method based on integration of BOA and SAM for analyzing the failure probability of such pipelines, building upon the FTA framework. First, the causes of failure in overdue in-service pressure pipelines were analyzed, and a fault tree for failure was established. Then, experts were invited to evaluate the basic events of fault tree, and their opinions were aggregated using SAM method and an improved BOA-SAM approach. The accuracy of aggregation was assessed by comparing the uncertainty values of two methods (the lower the uncertainty value, the more precise the aggregation result). Finally, the failure probability of the top event was calculated, and an importance analysis was conducted. The results show that the uncertainty values of the aggregation results obtained using the improved BOA-SAM are significantly lower than those of SAM method. Notably, for the basic event X₆, the uncertainty value decreased by 7.6%. The failure probability of the top event is calculated to be 0.117 641. Stress corrosion cracking, third-party damage, weld cracking and excessive H₂S content in the medium are identified as the key factors contributing to the occurrence of accidents. This study provides a scientific and logical approach for risk assessment and failure analysis of overdue in-service pressure pipelines.

To investigate the influence of coal bedding angle on enhancing CO₂ injection-enhanced coalbed methane recovery efficiency, coal samples from Mine 6 in Sijiazhuang, Jinzhong, Shanxi Province were studied. Using a flow displacement experimental system, CO₂ displacement of CH₄ experiments were conducted under different bedding angles. The variation patterns of displaced gas composition, gas flow rate, displacement efficiency, and storage capacity with bedding angle were analyzed during the displacement process. The results indicate: An increase in the angle between the bedding plane and the seepage direction significantly prolongs the breakthrough time of CO₂. The steeper the bedding angle, the slower the rate of change in outlet gas composition. The relationship between inlet and outlet flow rates follows the pattern 0 > 30 > 60 > 90°. The relationship for outlet CH₄ flow rate aligns with outlet flow rate in the early stage of the experiment, shifting to 90 > 60 > 30 > 0° in the later stage. At the end of the first displacement stage, CH₄ recovery rates followed 0 > 30 > 60 > 90°. By the end of the second stage, recovery rates shifted to 30 > 0 > 60 > 90°. Upon completion of the entire displacement process, the 30°-layered coal achieved the highest CH₄ recovery rate of 67%. The 30° bedded coal exhibited the lowest displacement ratio and the most effective replacement. Throughout the displacement process, CO₂ penetration consistently followed the pattern 0 > 30 > 60 > 90°, and non-0° bedded coal had not reached adsorption equilibrium for CO₂ by the end of displacement. Furthermore, the 90° bed coal achieved a storage rate of 81.59%, demonstrating the best storage effectiveness. These findings provide crucial insights for optimizing CO₂ injection strategies to enhance coalbed methane extraction.

In order to improve the safety management level of high-risk operations, an intelligent management and control method was proposed based on the knowledge graph and the Apriori algorithm. First, some high-risk operation accidents were collected. Then, the accident causes were extracted and classified automatically by Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF) model, which constructed a knowledge graph of high-risk operation accidents, storing relevant accident data. Subsequently, an index system of high-risk operation causative factors was established, combined with the corresponding operation standard specification. Furthermore, the correlation among causative factors was determined by the Apriori algorithm. Finally, some countermeasures were proposed for targeted control. In this paper, the application of confined space operations was used to demonstrate this method. The results showed that the main causes of confined space accidents are not wearing safety protective equipment", blind rescue", not being equipped with gas detection and other equipment", missing safety warning signs", insufficient safety education", and inadequate safety management". Moreover, the correlation between insufficient safety education" and not wearing safety protective equipment" is greater (80.7% support, 60.41% confidence), and the relationship between insufficient safety education" and blind rescue" also has a strong association (80.7% support, 55.88% confidence).

To prevent the risk of abnormal gas emissions during tunnel excavation through unstable coal-bearing strata, this study focused on the inclined shaft section of Baoanying No.1 Tunnel in the Chengdu-Kunming Railway Expansion Project. Through on-site monitoring, numerical simulation, and engineering application, gas emission patterns, risk identification methods, and prevention technologies were investigated. The results indicate that tunnel gas originates from unstable coal seams and carbonaceous rocks within the strata, and gas emissions correlate strongly with coal/carbon content in surrounding rocks and the degree of geological fracturing. Then an integrated geophysical-geological drilling approach was employed to establish an advanced identification method for abnormal gas emissions. This method utilizes multiple indicators: unfavorable geological bodies, lithological variations, borehole gas concentrations, and emission rates. A "borehole curtain" interception technique was developed for high-efficiency gas risk prevention and control. Boreholes arranged in curtain patterns were drilled from both side chambers to intercept and extract coal-rock gas ahead of the advancing tunnel face. Field applications demonstrate that this technology reduces construction interference and increases advance rates.

To address the limitations of traditional models in mining multi-dimensional temporal features and handling class imbalance, A multi-task driving risk prediction model based on Class Balance loss (CB)- Asymmetric Loss (ASL)-TimesNet and is proposed. A sliding time window was employed to extract multi-dimensional temporal features, improving the objectivity and granularity of risk level labeling. Based on these features, four types of labels were constructed: forward collision risk, rear collision risk, lateral collision risk, and steering intention. In terms of model design, the TimesNet architecture was incorporated to effectively capture periodic variations and dynamic evolution in multi-dimensional temporal features, thereby enhancing the modeling capability of temporal information in complex driving scenarios. Meanwhile, a hybrid loss function integrating CB and ASL was devised to improve prediction performance under class-imbalanced conditions. Experimental results demonstrate that the proposed CB-ASL-TimesNet model achieves an average accuracy of 0.908 6 in driving risk and steering intention prediction tasks. Compared with the traditional machine learning model CatBoost, the proposed approach yields a 16% improvement, and it outperforms the mainstream time-series model Gate Recurrent Unit(GRU) by 5.8%, verifying the significant effectiveness of the proposed model in enhancing prediction performance.

TRS is a safety-critical aero-engine system. In response to the inadequacies of conventional safety analysis techniques in addressing the complexities associated with multilevel coupling and cross-linking of multiple systems concerning system interaction and closed-loop design specifications, this study proposes a method that integrates STPA and MBSA. By establishing a whole-process analysis framework from system requirement capture to verification, an overall system model was constructed through the use of SysML to reveal the architectural principles. The STPA method was employed to define 4 types of system-level losses (accidents) and 8 types of system-level hazards, construct a TRS feedback control structure model, identify 11 unsafe control actions (UCAs), derive causal scenarios, and assign respective safety levels. Using the model checking tool new symbolic model verifier (NuSMV), fault and nominal models were constructed to verify critical system safety properties. The results demonstrate that the proposed model possesses logical integrity and correctness, and indicate that the probability of "Thrust Reverser Non-Command Open in Air," as determined from minimal cut set, is 1.95×10^-10 per flight hour, thereby meeting the safety requirement of a failure probability of less than 10^-9 per flight hour.

To investigate the influence of pre-existing fissures on the settlement and deformation behavior of high-fill compacted loess foundations, geotechnical centrifuge model tests were conducted for a flat-terrain high-fill engineering scenario. Two foundation models, an intact model and a fissured model, were designed and comparatively examined during the loading process with respect to deformation development and failure characteristics. The results show that the failure mode is transformed by fissures from a single tensile-cracking pattern to a combined tensile-shear failure, in which fissure propagation, a branched crack network, and stepped surface dislocation are formed. The overall stiffness and stability of the foundation are reduced by fissures; settlement is initiated earlier than in the intact model, the total settlement is increased, and the settlement distribution is more non-uniform. A V-shaped concentration pattern of surface settlement is exhibited and is markedly intensified with increasing load. In addition, a distinct stratified effect on stress distribution is induced by the fissure structure: vertical stress is concentrated in the upper layer, weakened in the middle layer, and only slightly affected in the lower layer, whereas horizontal stress is concentrated near the bottom and released in the middle and upper layers. These changes reconstruct stress-transfer paths and increase the risk of local instability.

To ensure the operational and structural safety of the subway system, an assessment of the resilience of metro shield tunnels under explosive loading was conducted. A resilience assessment framework and grading criteria for shield tunnels under blast loading were established, and a resilience prediction model was developed based on a backpropagation (BP) neural network with a three-input, five-hidden-layer, single-output architecture. This model and evaluation approach were applied in a case study on the Xi'an Metro Line 1 to assess and predict the tunnel's resilience. The results indicate that a shorter standoff distance, a higher explosive yield, and a higher number of explosions each accelerate the decline in the tunnel's resilience. The resilience exhibited the most pronounced drop after the first explosion; the subsequent rate of decline was relatively gradual until the fifth explosion, after which it increased significantly. After the fifth explosion, the tunnel entered a low-resilience state requiring prompt repairs to meet operational requirements, and by the seventh explosion, the resilience had fallen to an extremely low level that could no longer ensure operational safety. The resilience assessment framework and prediction model developed in this study can be used to assess the safety status of metro shield tunnels under repeated external explosions.