In order to solve the fault line selection problem of single-phase high resistance grounding in resonant grounded distribution systems, a fault line selection scheme was proposed that utilized the disturbance characteristics of zero-sequence current before and after the neutral point parallel resistor was grounded. The zero-sequence fault model was established for the two conditions before and after the resistor paralleling with the arc suppression coil, and the zero-sequence current variation characteristics of sound and faulty lines were analyzed correspondingly. The amplitude of the zero-sequence current of any healthy line decreased after the neutral resistance was applied, while the current of the faulty line increased. Furthermore, a fault line selection criteria was constructed, which was used the amplitude disturbance characteristics of the zero-sequence currents. Simulations based on MATLAB verified the correctness and effectiveness of the proposed method. The results show that the proposed scheme is able to reliably detect single-phase ground fault with the resistance up to 5 kΩ.

Addressing the lack of clarity regarding spontaneous imbibition displacement mechanisms and production management strategies for shale oil in the Cangdong Sag, a model was established that considered the “synergy of imbibition, flowback, and productivity”. The model was designed to dynamically reflect the mutual constraints and synergies between spontaneous imbibition displacement and engineering parameters such as flowback rates and soaking durations. Through comprehensive numerical simulations integrating geology and engineering across the entire lifecycle, the spontaneous imbibition displacement patterns and optimal flowback regimes for the C1 and C3 sweet spots in the Kong-2 Member, which served as the main development interval in the Cangdong Sag, were elucidated. The results indicate that spontaneous imbibition displacement continues to occur during the flowback process. The optimal soaking durations for the C1 and C3 sweet spots are determined to be between 37 and 42 days, with a reasonable flowback rate ranging from 20 to 40 tons per day. The research findings provide a theoretical foundation for studies on spontaneous imbibition mechanisms and reasonable production management strategies for deep shale oil and gas reservoirs.

In order to identify the characteristics of the geothermal field in Longmuwan, including its temperature field, hydrochemistry, and recharge sources, and to analyse its genetic model. The hydrogeochemistry, isotopes, and geothermal temperature measurement methods were employed, integrated with regional geological characteristics, the hydrochemical characteristics, heat reservoir temperature and recharge sources of the geothermal field were systematically analysed, and a conceptual genetic model was preliminarily constructed. The results indicate that the geothermal gradient of porous stratified reservoir is 5.23~8.25 ℃/100 m, while the fracture zoned reservoir has a gradient of 1.47~4.50 ℃/100 m, the deep heat reservoir temperature range is 87~115 ℃, with geothermal fluid circulation depths reaching 960~2 298 m in Longmuwan geothermal field. The hydrochemical types of geothermal fluid are HCO₃-Na·Ca type and Cl·HCO₃-Na type, primarily recharged by atmospheric precipitation. The calculated recharge elevation is 421~597 m, suggesting the recharge area is the hinterland of Jianfeng Ridge. Isotopic age results show that the formation age of geothermal water exceeds 6 000 a, and it has the characteristics of a long recharge pathway and slow groundwater flow.

Modern air traffic management systems necessitate efficient and accurate identification and classification of hazard-related text data to ensure flight safety. Air traffic control hazard data encompasses information on potential factors, conditions, or events that may adversely impact aviation safety. Existing text classification methods face challenges due to the diversity of data categories and imbalances within classes. An enhanced ensemble model based on the Stacking framework, incorporating a dual-weighting mechanism was proposed for improved performance. A dual-protection strategy was implemented to categorize hazards and safety risks systematically. The methodology employed the term frequency-inverse document frequency(TF-IDF)algorithm to extract and vectorize features from preprocessed hazard texts. To address class imbalance, the synthetic minority over-sampling technique(SMOTE) and adaptive synthetic sampling approach(ADASYN)algorithms were utilized to generate synthetic samples for minority classes. The Stacking ensemble model was refined by dynamically weighting the F₁ scores derived from cross-validation of base learners and integrating a sensitivity assessment mechanism across the ensemble. Experimental results on the constructed dataset demonstrate that the ADASYN-enhanced ensemble model achieves notable improvements in precision, recall, and F₁ scores by 0.9%, 1.1%, and 1.0%, respectively, effectively mitigating overfitting in majority classes. The proposed algorithm significantly enhances the classification performance of imbalanced hazard text categories, contributing to the advancement of safety risk management in air traffic control.

Due to the complex underground environment, low lighting conditions, and the small size of hard hats, the detection results are not ideal. To address low-quality images in complex environments, an improved YOLOv7 for hard hat detection in low-quality images from underground coal mines was proposed. Firstly, addressing the limitation that image features were susceptible to noise interference under low-light conditions, a multi-scale MELAN module was introduced. By constructing a multi-scale attention mechanism, broader contextual information was captured, thereby enhancing feature extraction and effectively suppressing noise interference. Secondly, the OD-SMP module was constructed using soft pooling and full-dimensional dynamic convolution in the backbone network, which reduced information diffusion in feature mappings, retained more contextual information, and enhanced the detection capability for small targets. Finally, to address the varying quality of detection samples caused by the complex backgrounds and environments with different lighting and distances in underground coal mines, Wise-IoU was used as the loss function. Experimental results show that the average precision of the improved model is 94.9%, which is 13.5% higher than the original YOLOv7 model, demonstrating better detection performance.

To enhance heat transfer efficiency and improve thermal exchange performance, a composite enhanced thermal exchange technology was explored that combined annular internal fins with protruding units, aiming to create an innovative thermal exchange structure. Through numerical simulation methods, the flow and heat transfer characteristics of this structure were studied within the Reynolds number Re range is 8 000~20 000. The analysis results indicate that the layout of the protruding units and four parameters (depth, radius, spacing, and quantity) have a significant impact on thermal performance. The mechanism of enhanced heat transfer was explained using field synergy theory. Under optimal parameters, with a depth of 2 mm, a radius of a specific value, a spacing of 20 mm, and six protruding units, the best thermal exchange performance is achieved, with an overall heat transfer performance improvement of 4.71%~23.59% compared to internal finned tubes. Increasing depth, radius, and quantity, while decreasing spacing, enhances heat transfer but also increases resistance, limiting the growth of overall thermal performance. Field synergy analysis shows that the structure promotes strong secondary vortices, significantly enhancing the synergy effect between the velocity field and the temperature field.

The dispatchable potential of charging stations represents the feasible solution space for optimizing their bidding strategies in the electricity market. However, the uncertainty in the access times of electric vehicles complicates the accurate assessment of this dispatchable potential. To address this issue, an uncertainty analysis method was proposed for evaluating the dispatchable potential of charging stations, taking into account the stochastic nature of electric vehicle charging times. Firstly, a generalized energy storage model for various types of electric vehicle clusters was established using Minkowski summation theory. Secondly, the impact of the randomness in electric vehicle arrival and departure times on the dispatchable potential of charging stations was analyzed. The discretized probability density functions of these times were mapped to the probability distributions of individual electric vehicle model parameters. By integrating these with the generalized energy storage model of electric vehicle clusters, the probabilistic characteristics of the dispatchable potential across various electric vehicle clusters were derived. Furthermore, the probability distribution characteristics of the dispatchable potential of charging stations were derived by aggregating the parameters of various electric vehicle clusters using convolution operations. Finally, simulations were conducted in MATLAB and compared with Monte Carlo simulations to validate the effectiveness of the proposed method.

The unmanned aerial vehicle (UAV) system, with its advantages of flexible deployment and line-of-sight propagation, has become an essential tool for assisting mobile communications in handling high-density data processing and emergency communications. However, the computational processing capabilities and endurance issues of UAVs under complex environments remain significant technological bottlenecks. The development of mobile edge computing (MEC) technology provides an effective solution to address UAVs’ computational and energy consumption challenges. A distributed task offloading strategy based on a multi-agent reinforcement learning algorithm was proposed for MEC-assisted UAV systems. The task offloading and resource allocation process of UAVs was modelled as a Markov game process (MGP) involving multiple MEC nodes. To solve the MGP problem, a distributed reinforcement learning algorithm for multi-agent collaboration was proposed. The algorithm enabled agents to find the optimal strategies through online collaborative learning based on local observation information. In comparative experiments, the convergence and system performance of the proposed scheme were evaluated. The results show that the proposed scheme outperforms the comparison schemes in terms of convergence speed, energy consumption, and unloading rate.

To gain a deeper understanding of the interaction between surface water and groundwater in the water cycle of the inland basins of the Hexi region in Gansu Province, and thereby a scientific basis was provided for water resource management, the interaction and scheduling impacts of surface water and groundwater in the Taolai River Basin using long-term hydrological data combined with the WEAP-MODFLOW model were analyzed. The results indicate that the simulated groundwater extraction volume (2.461×10⁸ m³) closely aligns with the actual extraction volume (2.5×10⁸ m³), with an error of only about 1%, validating the model’s effectiveness in simulating groundwater extraction. Under the projected water use scenario for 2030, without reservoir regulation, the surface water and groundwater supply volumes would be 3.840×10⁸ m³ and 1.982×10⁸ m³ respectively, revealing a certain groundwater imbalance issue. Changes in the storage capacity of the Taolai Gorge Reservoir significantly affect the surface water supply capacity, with increased storage effectively enhancing the regulation and supply capacity of surface water, thereby alleviating the burden on the groundwater system. Adopting a scheduling rule that prioritizes surface water use has a positive impact on groundwater balance, helping to mitigate pressure on the groundwater system and protect groundwater resources. Evidently, the Taolai River Basin exhibits significant variability in its hydrological cycle, and there are distinct differences in the hydrological characteristics between the Hongshui River and the Taolai River, highlighting the necessity for implementing regionalized water resource management strategies.

With the rapid development of the global aviation industry, airport ground operations management is increasingly challenging. Ensuring safety, improving efficiency, and reducing environmental impacts constitute critical tasks. To address this, a mixed-integer linear programming model incorporating taxiway conflict prevention was developed. This model aimed to minimize taxi time and CO₂ emissions through dynamic optimization with the non-dominated sorting genetic algorithm II (NSGA-II). Implementation was conducted in Python for a major Chinese hub airport, with results compared against the commercial optimizer Gurobi. Computational findings reveal a 17.46% reduction in total taxi time and an 18.35% decrease in CO₂ emissions across 14 aircraft. The NSGA-II solution is found to be within 1.083% of Gurobi’s optimal solution, while a 95.0% faster computation time is achieved. The capability of NSGA-II in handling large-scale multi-objective taxi path optimization problems is demonstrated. Operational efficiency is enhanced, and CO₂ emissions are significantly reduced by the proposed approach.