Based on the engineering geological conditions of a coal mine in western China, this research focuses on addressing the challenges associated with rock burst prevention in working face of inclined thick coal seams. Theoretical analysis, mechanical modeling, numerical simulation, and field monitoring were employed to analyze the influencing factors and hazardous areas of rock burst in working face of an inclined thick coal seam. Optimization strategies for double entry inward layout were proposed and subsequently validated through engineering application. It is found that the occurrence of rock burst is influenced by several factors including buried depth, thick-hard roof, coal seam dip angle, coal pillars, bottom coal. The belt and track roadways were identified as zones with strong rock burst danger, while the setup entry was categorized as a zone with moderate rock burst danger. Based on an analysis of roof structure evolution and floor stress calculations, the floor was divided into three low-stress zones: the semi-protected zone beneath key blocks (I), the protected zone beneath key blocks (Ⅱ), and the stress release zone of gangue (Ⅲ). Numerical simulations determined that the optimal positioning was within the protected zone beneath key blocks (Ⅱ), specifically at a horizontal distance of 5.15 m from the right of the track roadway and a vertical distance of 3 m from the goaf. Microseismic and pressure monitoring demonstrated that implementing the double entry inward layout transformed the energy release pattern of the surrounding rock from "low-frequency and high-energy" to "high-frequency and low-energy". Furthermore, the duration of continuous pressure was significantly reduced, thereby mitigating the rock burst danger in the working face.

In order to address the requirements of low-carbon operation and safety in aircraft conflict resolution, an IVO method based on adaptive elliptical protected zones was proposed, taking into account aircraft carbon emissions and speed adjustment deviations. First, adaptive elliptical protected zones were constructed according to the velocity differences among aircraft. Second, aircraft flying on the same route with similar velocity vectors and close spatial proximity were grouped into clusters, and key characteristics such as cluster centroids, velocity vectors, and safety regions were defined. Then, a cluster control algorithm was applied to reasonably adjust the velocity vectors of aircraft within each cluster, ensuring the maintenance of safe separation during conflict resolution. Subsequently, velocity obstacle cones were established between clusters. By incorporating constraints on the maximum allowable adjustment per unit time and combining planar geometric analysis with a mathematical optimization model, conflict-free velocities and headings with minimal deviation were determined under low-carbon objectives. Finally, dynamic behaviour analysis was conducted through numerical simulations implemented in Python. The results show that, compared with the traditional velocity obstacle method, the proposed approach improves conflict resolution efficiency by 87.5%, reduces the average adjustment magnitude by 86.67%, and achieves fuel savings of up to 37.36%, demonstrating its effectiveness for aircraft conflict resolution and operational optimization in complex air traffic environments.

In order to realize the effective elimination of CO in the tail gas of mine trackless rubber-tyred vehicle, the development of high-performance CO elimination reaction catalyst was focused on in this study. Co₃O₄/CuO/Ni Foam(NF) supported catalyst was prepared by one-step solvothermal synthesis method. Characterization analyses, including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and specific surface area measurement (BET), were conducted to investigate the effects of synthesis conditions such as synthesis time, synthesis temperature, and growth substrate, as well as physicochemical properties such as catalyst composition and morphological characteristics, on catalytic performance of catalyst. The optimal synthesis conditions and CO elimination performance of catalyst were determined. Hydrogen temperature-programmed reduction (H₂-TPR), oxygen temperature-programmed desorption (O₂-TPD), and in-situ infrared spectroscopy were employed to explore the intermediate processes of CO elimination catalyzed by the catalyst, revealing the catalytic mechanism of CO elimination by the catalyst. The results show that the optimal synthesis condition of Co₃O₄/CuO/NF supported catalyst is 140 ℃ for 4 h. Under this condition, the catalyst can completely eliminate CO at the elimination temperature of 150 ℃, and the elimination process of CO by the catalyst follows the Mars-van-Krevelen(MvK) mechanism.

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.

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 evaluate the insulation performance of sleeping bags during actual use in various low-temperatures, wind speeds and wind directions, and to improve the comfort of sleeping bag users, an experimental study on the thermal insulation performance of sleeping bags was conducted. The thermal insulation performance of mummy-style sleeping bags and envelope-style sleeping bags was evaluated using a low-temperature coupled environment chamber and "Newton" thermal manikin system. Thermal resistance parameters of the two types of sleeping bags under varying environmental temperatures (-20, -15, -10, and -5 ℃), wind speeds (1, 2, 3, 5, and 7 m/s), and wind directions (along the head, waist, and foot directions) were measured. The effects of a single low-temperature windless environment and a combined low-temperature windy environment on the thermal insulation performance of sleeping bags were studied. The experimental results show that in a single low-temperature windless environment, the thermal resistance value of the mummy sleeping bag changed more stably than that of the envelope sleeping bag, and the mummy sleeping bag has better thermal insulation performance. In a combined low-temperature windy environment, increasing the wind speed would reduce the thermal resistance of the air layer; and when the wind speed was 7 m/s, the thermal resistance of the air layer decreased by 50%. Wind direction along the head direction has the greatest impact on thermal resistance, with the total thermal resistance of the mummy sleeping bag decreasing by 42.8% and that of the envelope sleeping bag decreasing by 50.1%. Wind direction along the foot direction had the least impact on the thermal resistance value of the sleeping bag, with the total thermal resistance of the mummy sleeping bag decreasing by 23.1% and that of the envelope sleeping bag decreasing by 29.3%.When the wind speed along the head was 7 m/s, the air layer temperature at the feet of the mummy sleeping bag and the envelope sleeping bag dropped to the lowest, which were 21.83 and 26.54 ℃, respectively.

To address problems such as fixed granularity in emergency scenario modelling and unclear descriptions of multi-scenario attributes and structures, a top-down unitary-scenario construction method was proposed from a microscopic perspective based on baseline thinking. Evolution relationships were established based on two types of knowledge elements, namely state elements and trigger elements. Combined with a hierarchical PN and refined layer by layer, the cascading evolution scenarios of emergencies were systematically characterized, and the evolution dynamics of scenarios were simulated. Taking cascading lifeline disaster events under heavy rainfall as an example, a hierarchical scenario model was constructed.. The characteristics of cascading failure, common cause failure and escalation failure features were characterized, and the evolution trends were analyzed. The interlocking dilemma between urban lifeline failure and emergency response was identified. The results indicate that the proposed scenario construction method can meet different levels of decision-making needs through a hierarchical scenario structure. The isomorphic Markov chain can identify the critical elements affecting the scenario evolution.

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 ensure timely rescue of trapped personnel in fire scenarios, a personnel positioning-tracking-stationary alarm system was proposed by combining BP neural networks and DS-TWR technology. The system was developed based on the LabVIEW platform, where the BP neural network learned the error patterns of multipath effects and non-line-of-sight propagation, corrected DS-TWR ranging errors, and achieved precise personnel positioning and trajectory tracking. By calculating the movement distance within a defined time, the system evaluated personnel motion status and established thresholds of displacement and time to trigger alarms, which automatically activated alerts when a person remained stationary beyond the safety threshold. Test results show that the system achieves centimeter-level static and dynamic positioning accuracy under both normal and metal/electromagnetic interference environments. It features high positioning precision and favorable stability, enables accurate trajectory generation, delivers reliable static personnel alarms, and maintains a low response latency.

To investigate the movement conflicts caused by pedestrians driven by panic and the avoidance of locally high-density areas, as well as their impact on crowd evacuation efficiency, the dynamic effects of the distance of pedestrians to the exit, surrounding crowd density and emotional infection on individual panic were considered. In addition, the detouring behavior of pedestrians to avoid the high-density areas and the mutual pushing behavior during evacuation are incorporated. A panic propagation model was constructed based on SFM, and crowding avoidance force and pushing force were introduced to develop a crowd evacuation model considering panic emotion and crowding avoidance behavior. The results indicate that moderate crowding avoidance behavior during evacuation can enhance evacuation efficiency to a certain extent, whereas excessively strong interactions amplify movement conflicts and the accumulation of pushing forces, thereby increasing the risk of stampedes. In low-density evacuation scenarios, individual panic levels are mainly influenced by the distance to exit, and the crowding avoidance force remains relatively weak. In contrast, under high-density situations, the dominant role of exit distance diminishes. The propagation of panic emotions interacts with crowd density to form a "density-emotion" coupled feedback, which significantly increases pedestrian crowding avoidance force and pushing intensity. During evacuation, the maximum crowding avoidance force of pedestrians first increases and then decreases. In the middle and late stages of evacuation, when pushing becomes intense, a larger ankle joint torque weight can significantly reduce the probability of falling.