This paper addressed the issues of insufficient safety distance, unsmooth path, and low planning efficiency in AGV path planning in narrow road scenarios. A new path planning method based on Voronoi skeletons for narrow roads was proposed. Firstly, the key nodes of the Voronoi skeleton were extracted to generate a custom Voronoi graph layer, which was then combined with static, obstacle, and expansion layers to iteratively generate a four-layer network structure cost map, accurately distinguishing the influence range of obstacles. Secondly, the new cost map was used as a constraint in conjunction with an improved A^* algorithm for global path planning, guiding the AGV to drive along the center of the road to ensure its safety. Finally, the global path was optimized using B-spline smoothing to improve the AGV's efficiency and stability in navigating narrow roads and other complex scenarios. Experimental results show that, in narrow road scenarios, the safety of the AGV path planning is improved by 82%, the number of spatial corners is reduced by 55.85%, and the path planning time is shortened by 48.98%. The proposed algorithm effectively enhances the robustness, dynamic obstacle avoidance, and real-time performance of path planning in narrow road scenarios, enabling the AGV to move in the safest manner.

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.

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 enhance the detection ability of common faults in the chemical process and ensure the stable and reliable operation, a new fault detection method, termed M-WKPCA, was proposed. The method was based on historical fault data and KPCA, incorporating historical fault data through a weighting mechanism. Initially, kernel principal components(KPC) of normal data and historical fault data were calculated according to KPCA. KPC that can highlight the fault information were selected based on a comparison of the constructed indexes. A weighting matrix was then used to highlight the fault information, and a new statistic was constructed to establish an online fault detection model based on M-WKPCA method. Then, the M-WKPCA method was used to detect common faults in parallel. A parallel WKPCA fault detection strategy was proposed to achieve high precision detection of common faults. Finally, the proposed method was verified using simulated data from the Tennessee Eastman (TE) chemical process. The results show that the proposed method achieves an average accurate detection accuracy of 82.25%. This is much higher than that of the comparison methods, demonstrating its superiority in fault detection. At the same time, since fault information is incorporated during the selection of KPC, the detected fault data are significantly different from the normal data in terms of statistics.

To enhance the long-term stability and safety of lithium-ion batteries, a study on potential safety hazards caused by over-discharge was conducted. Aiming at the over-discharge/recharge phenomenon that the lithium-ion batteries may encounter in practical applications such as new energy electric vehicles and electrochemical energy storage, ternary lithium-ion batteries were examined. Through cycle tests and Hybrid Pulse Power Characteristic(HPPC) tests, adiabatic accelerating rate calorimetry was employed to investigate thermal runaway. The effects of over-discharge/recharge cycles on electrical characteristics—such as discharge capacity, DC internal resistance, and incremental capacity—as well as on the thermal runaway characteristic parameters under adiabatic conditions, were analyzed. The results show that over-discharge cycling accelerates battery capacity decay and increases internal resistance, with a particularly notable rise in DC internal resistance in the low state-of-charge region. However, after short-term cycling, prolonged resting allows partial capacity recovery. Over-discharge cycles also reduce the thermal stability of batteries, lower the self-heating onset temperature, and shorten the thermal runaway time compared to fresh batteries and normally cycled batteries, although the maximum thermal runaway temperature is relatively lower.

In order to address the challenges associated with the long-distance vertical transport of fire suppressant agents delivered by tethered firefighting drones in super high-rise building fires, a vertical transport model for foam extinguishing agents was established. The simulation results were comparatively validated against both empirical model calculations and experimental data. Furthermore, the study conducted an in-depth investigation into the effects of flow rate, gas-liquid ratio, and pipe diameter on pressure loss and flow velocity during the vertical transport of foam extinguishing agents. The findings indicate that the numerical simulation errors remain stable and are consistently less than 3%, demonstrating superior accuracy compared to the empirical model. In terms of flow characteristics, as the flow rate increases, pressure loss gradually rises, with the rate of increase becoming more pronounced at higher flow rates; concurrently, the stabilization time of the flow velocity within the pipe is reduced. Under constant flow rate conditions, when the pipe diameters are 60 mm and 100 mm, respectively, pressure loss decreases with an increase in the gas-liquid ratio. Moreover, increasing the pipe diameter mitigates the influence of the gas-liquid ratio on both frictional pressure drop and flow velocity. The impact of pipe diameter on pressure loss is primarily realized through alterations in the internal flow velocity: as the pipe diameter increases, the flow velocity decreases, leading to a corresponding reduction in frictional pressure drop. Within the pipe diameter range of 40 mm to 60 mm, the effect of pipe diameter on pressure loss and velocity variation is significant; however, when the pipe diameter exceeds 80 mm, this influence gradually diminishes. The research outcomes provide theoretical guidance for the vertical.

To enhance the safety performance of civil aviation pilots, this study constructs a three-dimensional analytical framework for career resilience based on affect, behavior, and cognition(ABC). Integrating machine learning with fsQCA, it empirically analyzes 229 questionnaire responses from Chinese civil aviation pilots. Building upon the measurement of antecedent variable importance weights using the random forest algorithm, the fsQCA method is further applied to decipher the impact mechanisms of different condition configurations on safety performance.The results indicated that no single factor constitutes a necessary condition for either high or non-high safety performance; however, learning willingness and cooperation consciousness play key roles in driving civil aviation pilots to achieve high safety performance. Five configurational paths leading to high safety performance are identified and categorized into three patterns: "emotionally empowered-behaviorally oriented," "resilient collaboration-behaviorally dominant," and "efficiency driven-intrinsically motivated." In contrast, two configurational paths leading to non-high safety performance are classified as "behavior-atrophy" and "affection-deficiency" types. Furthermore, substitution relationships exist among conditional variables in the five configurations for high safety performance.

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.

To prevent interface similarity-induced errors in nuclear power plants, this study investigated the impact of interface similarity on cognitive performance and identified the optimal similarity range using semantic differential scale and scenario-based experiments. Firstly, a multi-dimensional perceptual feature system for interface similarity was developed through literature review, and 10 key similarity variables applicable to industrial control scenarios were identified. Secondly, a normalized equation for estimating the overall perceived similarity of nuclear power interfaces was established using semantic differential questionnaire survey and multiple linear regression analysis. Finally, typical interfaces from nuclear procedure tasks and alarm handling tasks were selected as experimental materials. Interface samples with varying levels of similarity were constructed based on three feature dimensions: color, layout structure, and complexity. Subjective questionnaires and scenario-based task experiments were conducted to measure and analyze the optimal range of similarity. The results indicate that interface similarity has a significant impact on operators' cognitive responses. with color, layout, and complexity as core dimensions. Cognitive performance is optimal when the interface similarity falls within the "generally similar" range (4-6).