In order to explore the effects of sway on keyboard input of operators in DCS and to reduce human errors, an experimental study was conducted under four sway conditions—static, low, moderate, and high. Operators' performance in numeric, alphabetic, and alphanumeric input tasks, as well as their subjective ratings, were measured. Statistical analyses were employed to examine the effects of sway and to compare performance differences across input types. The results show that sway has a significant main effect on keyboard input performance. Compared with the static, low, and moderate sway conditions, the input time and number of corrections for alphabetic and alphanumeric entries significantly increase under the high-sway condition, whereas the accuracy of numeric and alphanumeric inputs significantly decrease. Significant differences are also found among the three input types: numeric input achieves the highest accuracy and shortest completion time, while alphabetic input requires the longest time. Sway also significantly affects subjective evaluations. Under the high-sway condition, perceived input difficulty, physical discomfort, visual discomfort, and workload ratings are all significantly higher. Under low and moderate sway conditions, input performance and perceived difficulty are largely consistent with the static condition, whereas under moderate sway, the perceived difficulty and workload for alphabetic input are significantly higher than those under low and static conditions. Under high sway, overall input performance markedly declines, accompanied by pronounced increases in difficulty, discomfort, and workload.

To address employee' AI anxiety arising from increased system complexity and heightened information uncertainty in the application of AI to corporate safety management and decision-making, a mechanism model of "AI anxiety-human-AI trust-human-AI collaborative decision quality" was developed based on UMT. It introduced system transparency as a boundary condition to explain how employees appraised and coped with AI-related threats in contexts such as risk warnings and algorithmic black boxes. A questionnaire survey was conducted with a sample of 523 employees from AI-adopting enterprises. Confirmatory factor analysis(CFA) and structural equation modeling were employed to test the measurement model, path relationships, and moderating effects, while controlling for variables such as gender, age, education, job type, and AI usage frequency. The results show that AI anxiety reduces the quality of human-AI collaborative decision-making. Human-AI trust partially mediates the relationship between AI anxiety and collaborative decision-making quality. System transparency positively moderates the effect of human-AI trust on collaborative decision-making quality, such that higher transparency facilitates the translation of trust into higher-quality collaboration.

In order to overcome the limitations of traditional multi-electrode electroencephalography technology in flight applications and effectively assess the mental workload of pilots, 43 pilots were recruited in this study to conduct simulated flight experiments. Each participant was required to perform flight tasks at three different mental workload levels (low, medium, and high) using a Cessna 172R simulator. The low workload task involved a standard five-leg takeoff and landing route, while the medium and high workload tasks were variations of the low workload task, with the addition of one and three malfunctions, respectively. Heart rate signals, EEG signals (collected from the Frontal Pole(FP) 1 electrode), and National Aeronautics and Space Administration-Task Load Index(NASA-TLX) scale data were recorded. The results show that as mental workload increased, both NASA-TLX scale scores and heart rate exhibited an upward trend. The power values in the Alpha and Beta bands significantly increased as mental workload levels increased. A threshold based on the percentage increase in EEG power was established. When the power increase exceeded the threshold, it was classified as medium or high mental workload, triggering an alert. The findings suggest that single-electrode EEG signals based on the FP1 electrode can effectively assess the mental workload levels of pilots.

The 24Model, as an original and systematic accident causation theory in China, has been widely applied in the field of safety science since 2005. However, a comprehensive review of its theoretical development and application trends is still lacking. A systematic search and stratified screening were conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. A scoping review approach was employed to perform structured coding and data extraction from the 357 screened Chinese- and English-language publications. Subsequently, data analyses, including cross-tabulation analysis and chi-square tests, were carried out. The results indicate that research on the 24Model has shown a sustained growth trend and has begun to disseminate globally. Its application domains have expanded from the coal mining industry to complex system industries such as the chemical sector. In addition, application paradigms have evolved from qualitative analysis to quantitative modeling, from post-accident analysis to proactive prevention, and from traditional analytical approaches to digital and intelligent technologies. The systematic model has been further developed and refined in the fifth and sixth editions, providing a new theoretical framework for safety analysis in complex systems. This study suggests that future research on the 24Model may continue to focus on deepening its systematic theoretical foundations, innovating preventive applications, and integrating digital and intelligent technologies.

To enhance the safety risk management level of UAV logistics distribution, a cascading failure evolution model for UAV logistics distribution safety risks was constructed based on complex network theory. Through simulated evolution experiments, the risk evolution characteristics in specific scenarios of UAV logistics distribution were revealed. By analyzing 63 interview transcripts, safety risk factors and their interrelationships were identified, and the credibility of these findings was verified by comparing them with relevant literature analysis results. The safety risk factors were categorized into five types: personnel, machinery, materials, methods, and environment. A directed weighted complex network with 69 nodes and 469 edges was then constructed. Using the Gephi platform, the overall topological structure characteristics of the complex network were calculated and analyzed, confirming the necessity of dynamic risk evolution analysis. The importance levels of nodes in the complex network were classified as high, medium, and general, and the development stages were classified as latent, diffusion, and occurrence, which laid the foundation for the construction of the dynamic cascading failure evolution model. Risk propagation probability and risk load redistribution rules were defined to quantitatively characterize risk evolution. A Python program was designed to conduct simulation evolution experiments. Key experimental groups were defined, and the evolution network of UAV logistics distribution safety risks was constructed. The simulated evolution results were analyzed from dimensions such as key evolution nodes and key evolution paths. The results show that in the evolution network, nodes s15, s17, and s18 in the machinery category are the most important, and the key evolution paths starting from this category of factors are the most abundant. The loss of control of nodes s54 and s57 in the environment category is the primary cause of control failure in the machinery category nodes. Therefore, strategies should be formulated to focus on controlling factors in the environment and machinery categories, such as immature external supervision and equipment signal issues, to achieve forward shifting of safety risk control.

In order to address the inherent limitations of traditional analysis methods—such as insufficient integration of professional knowledge and weak interpretability of causal reasoning—when dealing with the complex characteristics of multi-factor nonlinear interactions in power systems, a large model for power production safety accident analysis was proposed that integrates LLM, RAG, and KG. A framework with four core modules was built: knowledge retrieval, knowledge reasoning, answer generation, and performance evaluation. RAG technology was used to accurately retrieve relevant knowledge from professional texts, and KG was leveraged for structured reasoning on accident entities and relationships to make up for retrieval blind spots. Finally, LLM was employed to generate professional and interpretable answers for accident causal analysis. The study comprehensively evaluated the system through subjective expert scoring and objective metrics like ROUGE and BLEU, and results show that in the scenario of power production safety accident analysis, the knowledge enhancement technology of RAG and KG provides universal performance improvement for basic models with a certain parameter scale—it helps models accurately capture professional correlations such as equipment fault transmission chains and enhances the quality of accident cause mining and result evolution reasoning. Large models including DeepSeek-R1 and Qwen2.5-72B significantly improved in the accuracy of parsing professional terms and organizing multi-factor correlations under this mode, among which DeepSeek-R1 achieved a comprehensive score of 4.05, better meeting the precision requirements of the field; meanwhile, there is a model capability threshold for the enhancement effect: after enhancement, Qwen2.5-72B can efficiently parse complex logics like cross-regional power grid fault linkage, balances performance and deployment costs, and is suitable for enterprises' practical needs, while smaller models such as Qwen2.5-14B, due to limited basic reasoning capabilities, fail to process professional information effectively after introducing external knowledge, leading to performance degradation and inability to meet professional requirements.

In order to solve the problem that the classical model of rocking rigid body has a large error and cannot solve the collision restitution coefficient of heterogeneous and irregular rigid bodies, realize more accurate dynamic response prediction, design, safety evaluation and vibration control of rocking structure, based on the energy conversion analysis of mass element, the heterogeneous and irregular rigid bodies were discretized and the kinetic energy conversion analysis of discrete elements was carried out. The collision process of rigid body was divided into three stages. In the first stage, the residual kinetic energy of each discrete unit of rigid body was calculated after the vertical kinetic energy was dissipated. In the second stage, the residual kinetic energy of each discrete element of the rigid body was calculated after the kinetic energy had been dissipated in the direction of the rotation corner after the collision. In the third stage, the residual kinetic energy of each discrete element was converted and calculated under the action of internal force, and the collision recovery coefficient of the whole process was solved. With the help of the Digital Image Correlation (DIC) measurement system, the swing response test was carried out and the method was verified. The results show that the relative error between the collision recovery coefficient of a homogeneous rectangular rigid body obtained by this method and the experimental value is less than 3%, far less than the relative error between the classical model of a rocking rigid body and the experimental value. The relative error between the calculated collision recovery coefficients of heterogeneous and irregular rigid bodies and the experimental values is less than 5%. By this method, the dynamic response of a rocking structure after impact can be predicted more accurately, and a more reasonable structural design and safety evaluation can be provided.

To improve the prediction accuracy and reliability of slope instability in mine waste dumps, a hybrid GA-BP model was developed by integrating an improved GA with a BP neural network. The model employed GA to globally optimize the initial weights and thresholds of the BP network, and incorporated the Levenberg-Marquardt (LM) algorithm to enhance convergence speed. Ten key parameters—including bench slope angle, geotechnical internal stress, bench height, surface displacement, and pore water pressure—were selected as inputs, with the slope safety factor as the output. Training and validation was performed using 150 field case datasets. The results show that GA-BP model reduces the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 46.9%, 25.4%, and 5.38%, respectively, compared to the conventional BP model. Predictions are closer to the safety threshold (F_s = 1.2), indicating enhanced sensitivity and stability. Pearson correlation analysis confirms strong relationships between surface and internal displacement (0.98) and between pore water pressure and rainfall (0.75), supporting the rationality of the input indicators. The study demonstrates that GA-BP model effectively overcomes local optima and gradient vanishing issues in BP networks, providing a reliable tool for intelligent slope stability assessment.

In order to solve the problems of high equipment price and large quantity demand in the existing forklift driving obstacle safety early warning distance measurement, a forklift driving obstacle safety distance early warning model based on image information was proposed. Firstly, based on deep learning technology, Squeeze-and-Excitation (SE) networks channel attention mechanism is introduced, and methods such as replacing the Intersection over Union(IoU) localization loss function with the Adaptive Threshold Focal Loss (ATFL) function are employed to improve the YOLOv12 algorithm for identifying obstacle targets in forklift travel. Secondly, on the basis of the improved YOLOv12 algorithm, the Kalman filter was introduced to improve the motion prediction model. And the distance detection method considering the camera pitch angle was used to accurately obtain the actual distance between different types of targets and the driving fork workshop. Thirdly, the kinematic process of forklift braking and forklift obstacle avoidance was analyzed, and the classification criteria of safe braking distance warning level and safety obstacle avoidance distance warning level were established, respectively. Finally, experiments were carried out to verify the feasibility of the safety warning distance of forklift driving obstacles based on image information. The results show that the real-time distance warning model can accurately identify obstacle targets in real-time and precisely determine the distance to obstacles within the permissible error range, enabling risk-level warning for obstacles during forklift operation.

In order to effectively reduce the vaporization rate of LNG, three parameters were proposed and weight factors were introduced. Through Taguchi experiments, the quantitative research was conducted on the foaming ratio and stability of high-pressure foams, the amount of LNG leakage, and the height of foam coverage, to investigate their effects on suppressing LNG evaporation and vapor diffusion. The results show that foams with high foaming ratio and good stability are more conducive to reducing the initial volume fraction of vapor, while foams with poor stability are more conducive to vapor diffusion; the leakage volume has a significant impact on the initial volume fraction of vapor, while the foam coverage has a relatively smaller impact on the later volume fraction of vapor accumulation; the foam height has a minor impact on the initial volume fraction of vapor, but has a significant impact on the ease of vapor diffusion; the foam performance and coverage height at different spatial points have different effects on the speed of the decrease in vapor volume fraction; the leakage volume and foam coverage height have different effects on the speed of the decrease in vapor volume fraction 10 minutes after the cessation of adding foam.