To study the influence of initial temperature on the combustion behavior of pool fires, pool fire tests at different initial temperatures (5, 10, 20, 40, 60 and 80 ℃ ) were conducted using a self-built initial temperature controlled pool fire test platform. The characteristics of variations in combustion process, mass loss rate, flame height, and plume temperature were analyzed. The results show that when the initial temperature ranges from 5 to 60 ℃, the combustion process of oil pool fire is divided into three stages: growth, steady and decay. When the initial temperature increases to 80 ℃, the combustion process is divided into five stages: growth, steady, boiling transition, boiling and decay. The mass loss rate, flame height and plume temperature are all positively correlated with the initial temperature. When the initial temperature increases from 5 ℃ to 80 ℃, the mass loss rate, flame height and plume temperature increased by 12.13 g/(s·m²), 170.4 mm, and 130 ℃, respectively. The mass loss rate decreases nonlinearly with the temperature difference between the boiling point of n-heptane and the initial temperature. The ratio of flame height to pool diameter follows a power-law function of the ratio between the initial temperature and the boiling point of n-heptane point.

A dual-wavelength photoelectric smoke detection simulation system based on amorphous gallium oxide broadband photodetectors was proposed to resolve the limitations of traditional light-scattering smoke detectors. These traditional detectors were susceptible to interference from dust and moisture, exhibit high false alarm rates, and feature prolonged response times. Based on Mie scattering theory, dual wavelengths (980 and 405 nm) were employed by the system to measure smoke particle volume surface area concentration. And SMD was derived for effective differentiation between fire-related and non-fire-related smoke. Measurement bias was minimized by optimization of detection angle parameters (980 nm: 60°, 405 nm: 120°) through theoretical analysis and simulation experiments. The system's effectiveness was validated by simulation tests. Results indicate that the system maintains low false alarm rates for test smoke (fire tests ≤3.3%, non-fire smoke ≤6.7%), demonstrating high sensitivity and low false alarm rates.

Data reliability is essential for accurate identification of coal mine disaster risks. To accurately evaluate the data reliability of coal mine disaster monitoring and early warning systems, relevant policies, regulations, standards and literatures including the One Regulation and Four Detailed Rules were systematically reviewed, and a reliability evaluation method for coal mine disaster monitoring systems was proposed by integrating big data and GIS spatial analysis technology. The method was verified via field practice in disaster prevention and control at a coal mine in Shanxi Province. Results indicate that extracting the characteristics of imprecision, heterogeneity and conflict from multi-source monitoring information is the core to accurately identify unreliable data, including over-limit values, equipment failures, missing information, positional errors and abnormal frequencies. A reliability evaluation index system covering 3 primary categories (legitimacy, compliance and rationality) and 437 subcategories is constructed, which can fully restore multi-source associated information of the monitoring system throughout its full life cycle. During normal production in February 2025 at the test mine, 56 753 pieces of unreliable information were identified by this method, with a 100% accuracy rate verified by manual inspection. Furthermore, this method can dynamically assess whether existing mine monitoring systems meet disaster early warning requirements during the data preprocessing stage, and timely prompt mine maintenance and system upgrading.

To improve the detection efficiency and automation level of hidden defects on the outer surface of elevator traction steel wire ropes, and reduce the incidence of elevator accidents, an online detection model for defects on the outer surface of elevator traction steel wire ropes based on improved YOLOv5s is constructed. Firstly, the GhostConv module is introduced into the feature extraction layer C3 module to reduce computational complexity, and the Convolutional Block Attention Module (CBAM) is integrated to enhance the feature extraction capability of small-scale defects. A feature extraction module GC-C3 (GhostConv and CBAM-C3) that integrates GhostConv and CBAM is constructed; Secondly, in the feature fusion layer, Path Aggregation Network (PANet) and Bidirectional Feature Pyramid Network (BiFPN) are used to construct a multi-scale feature fusion network PBNet (PANet BiFPN), which combines multi-scale weight allocation strategy to improve the fusion effect of multi-scale defect feature information; Then, dynamically adjusting the quality weights of prediction boxes using Weighted Intersection over Union(WIoU) loss function, reducing the interference of low-quality samples on training. Finally, the model will be deployed to the developed detection system to perform online testing on the surface defects of the traction steel wire rope on the elevator car roof, verify the improvement effect of the model, and provide grading reminders for the defects. The results showed that the average detection accuracy of the improved model was 96.2%, with a detection speed of 192 f/s, which was 4.1% and 12.3% higher than the original model, respectively. The model volume was reduced by 38.9%. According to the online visualization experiment, under the actual operating environment of the traction steel wire rope (light illumination of 200~400 lx, speed of 1.5~2 m/s), the average accuracy of the system for 8 typical external surface defects is still stable at 94.6% or above, which meets the application requirements of online detection of hidden dangers of external surface defects of the traction steel wire rope in service and reduces elevator accidents caused by external surface defects of the traction steel wire rope.

To address the problems of low accuracy and insufficient adaptability in existing methods for determining the parameters of PIM for predicting surface deformation prediction in goaf areas under thick unconsolidated layers, 36 sets of measured surface movement data from coal mining working faces were selected. The core indicators of mining-geological conditions were screened via Hierarchical Cluster Analysis (HCA), Entropy Weight Method(EWM) and Grey Relational Degree (GRD) analysis. Furthermore, the GRNN model was optimized by integrating K-fold cross-validation with the neighborhood perturbation strategy of SAA, and an SAA-GRNN optimization model was constructed for PIM parameter determination. A case study was conducted using 45 sets of data from coal mining working faces with thick unconsolidated layers in the Jining area. The results show that: seven mining-geological condition indicators can be classified into three categories, and five core input indicators were identified screening, namely mining thickness M, coal seam dip angle α, mining depth H, strike mining degree D₃/H, and unconsolidated layer thickness h. The maximum root-mean-squared error (RMSE) of SAA-GRNN model is no more than 0.190 4, the maximum mean absolute error (MAE) is controlled within 0.133 9, the maximum mean absolute percentage error (MAPE) is 0.153 6, and the overall coefficient of determination (R²) is generally above 0.8. Under the same conditions, the prediction errors are greatly reduced compared with those obtained using Back Propagation (BP) neural network and the conventional GRNN model.

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.

With the continuous expansion of electric vehicle charging stations, the power grid faces increasing risks such as power overload, load fluctuations, and uneven demand distribution. To address these issues, this paper proposes an Hybrid Deep Fusion(HDF)-Long Short-Term Memory(LSTM)-based method for load forecasting and graded early warning. The method integrates LSTM, Gated Recurrent Unit(GRU), and Transformer architectures with multi-source data, including historical load, meteorological conditions, and traffic flow. Pearson correlation analysis and a dynamic weight allocation mechanism are employed to improve nonlinear feature representation. Based on the transformer capacity and simultaneity factor specified in the Code for Design of Electric Vehicle Charging Stations, a three-level early warning mechanism is developed for rapid alerting near critical thresholds. Results show that the proposed model outperforms eXtreme Gradient Boosting(XGBoost), GRU, LSTM, and Transformer models, with an Mean Squared Error(MSE) of 0.185 2, an Mean Absolute Error(MAE) of 0.2682, and an R² of 0.985 7. The model also shows good computational efficiency and application potential in charging station load forecasting and operational risk warning.

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 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 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.