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.

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.

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.

To investigate the stress characteristics of the primary support in the shallow buried biased section of the tunnel in fully weathered granite strata. Based on the Xiangsishan Tunnel, laboratory tests were conducted to determine the mechanical parameters including cohesion and internal friction angle of fully weathered granite. The construction process was simulated by FLAC 3D numerical software for the shallow buried bias section of the tunnel entrance. To study the effects of varying ground slopes, tunnel depths, and soil-rock interface locations on the stress characteristics of the primary support structure. The results indicate that, under various impact factors, the axial force and bending moment of the primary support exhibit the distribution characteristics of “upper large and lower small”, while the safety factor displays the opposite trend. The internal stresses of the primary support are distributed asymmetrically as the ground slope increases. The positive bending moment shifts toward the deeper-buried side and gradually increases, while the negative bending moment and axial force increase at the arch waist on the shallow-buried side. When the ground slope reaches 40°, the safety factor of the primary support falls below the standard allowable value. However, the stability of the primary support can be enhanced by implementing multi-stage variable slopes. As the depth of the tunnel increases, the asymmetric distribution of internal stresses in the primary support decreases, while the overall magnitude of internal stresses continuously increases. When the soil-rock interface crosses the tunnel at various locations, the magnitude of the internal stresses in the primary support is significantly affected, whereas the distribution pattern remains relatively stable. The reliability of the stress characteristics of the primary support under various impact factors was verified through on-site monitoring of the stress variations in the steel arch and concrete at various locations of the tunnel.

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 railway networks in cold regions, frequent subgrade diseases are observed. To investigate the freeze-thaw characteristics and mechanical properties of subgrade soils in cold regions, a series of laboratory tests were conducted. The effects of moisture content, freezing temperature, and freeze-thaw cycles on soil behavior were systematically investigated. The experimental results indicate that the frost heave ratio and thaw settlement coefficient increase consistently with higher moisture content, lower freezing temperatures, and more freeze-thaw cycles. More pronounced moisture migration is observed under conditions of higher initial moisture content and higher freezing temperatures. The upper part of soil samples shows gradual moisture reduction while the lower part exhibits moisture accumulation with increasing freeze-thaw cycles. The freezing temperatures are measured as -1.96, -1.89, -2.17, -2.06 ℃ for initial moisture contents of 8%, 10%, 12%, and 14% respectively, with the lowest freezing temperature occurring at 12% moisture content. The strength variation ranges are determined as 3.63~6.15 MPa with increasing moisture content, 3.26~6.05 MPa with decreasing freezing temperatures, and 4.49~3.68 MPa with increasing freeze-thaw cycles. These findings are considered significant for ensuring the stability and safety of transportation infrastructure in cold regions.

Large section tunnel in-situ expansion excavation is prone to induce ground settlement, posing a threat to the service safety of surrounding structures. However, the settlement evolution of the overlying strata during tunnel expansion excavation are not yet clear. A method combining theoretical analysis, physical model testing, and engineering practice was adopted to investigate the settlement evolution of the overlying strata during expansion excavation of tunnels. A theoretical model for tunnel expansion excavation settlement was established. The research findings indicate that the settlement of the overlying strata above the tunnel exhibits a sudden increase characteristic, with the expansion excavation settlement zone showing a parabolic distribution, which is primarily related to the cohesive force of the rock mass and its brittle fracture characteristics. The strata settlement shows a nonlinear increasing relationship with the distance from the tunnel, mainly influenced by the non-uniform attenuation of excavation unloading disturbance. The theoretical model curves can reflect the settlement evolution consistent with the physical model tests, with an average deviation of 4.8% between the experimental and theoretical values. Considering the influence of the correction coefficient α for tunnel support on the measured engineering values, the model with α=0.7 and α=0.4 can better predict the range of surface settlement after tunnel expansion excavation and support. The research results provide a theoretical method for calculating strata settlement during tunnel in-situ expansion excavation.

In order to solve the problems of missed detection and false detection in the current remote sensing image small target detection task, a SMCA+CSC+shape-aware intersection over union loss(SIoU)-you only look once(SCS-YOLO) remote sensing image small target detection algorithm was proposed. Firstly, in response to the problem of small and clustered targets in remote sensing images, a spatial multi-scale convolutional attention module(SMCA) was constructed to improve the model’s feature extraction ability of spatial and channel information. Secondly, in order to solve the problem that the semantic information of small targets was easy to be lost during deep network transmission, the aggregation subpixel convolution module concentrated sub-pixel convolution(CSC) was designed, and the multi-scale aggregation feature extraction method was used to enhance the ability of the network to extract semantic information. Finally, the SIoU loss function was used to replace the complete intersection over union loss(CIoU) loss function in the original model, which accelerated the convergence speed of the network. The average of the average precision(mAP)of the SCS-YOLO model reaches 97% and 90.9% on the RSOD and NWPU VHR-10 datasets, respectively, which is 2.2% and 2.7% higher than that of the original model, which shows the effectiveness of the method in the small target detection task of remote sensing images.

Explicit content features of webpages are often unavailable due to distractions such as commercials, insufficient permissions, privacy protection, or deceptive disguises. To address the challenge of classifying webpages with severe content feature deficiency, a method combining graph embedding and extreme gradient boosting(XGBoost) was proposed. This method leveraged implicit relational features in webpage hyperlink networks for multi-classification. Firstly, a hyperlink network was constructed using relationships between webpages. Then, node features were extracted using graph embedding models, and statistical structural features such as clustering coefficients and PageRank values were concatenated to form dense feature vectors. Finally, ensemble learning models, including XGBoost, were trained to classify webpages for prediction. Experiments on a real Wikipedia dataset show that the Struct2Vec*+XGBoost approach achieves excellent classification results, with accuracy, precision, recall, and F₁-score metrics reaching 0.987 5, 0.965 9, 0.971 3, and 0.964 1, respectively. These results are superior to those of comparison models. The findings demonstrate the effectiveness of using implicit link-based features for webpage classification in scenarios with content feature deficiency.

Due to the unclear constitutive relationship between the structure and performance of styrene-butadiene-styrene block copolymer(SBS) modified asphalt, the current way to improve the performance of SBS modified asphalt is still to simply increase its SBS content. However, early pavement diseases are still frequent. To explore the effect of swelling degree of SBS on the rheological properties of modified asphalt and its internal mechanism without increasing SBS content. The microstructure of SBS modified asphalt was observed by fluorescence microscope. The conventional properties and rheological properties of SBS modified asphalt were analyzed by dynamic shear rheometer. The internal mechanism of the influence of SBS swelling degree on the performance of SBS modified asphalt was revealed by molecular dynamics. The results show that the fully swollen star-line blended SBS modified asphalt has a higher swelling area, and has obvious performance advantages in terms of conventional performance, rheological properties and anti-aging properties. Molecular simulation shows that the complete swelling of SBS makes the radial distribution function peak of SBS modified asphalt higher, which improves the interaction between SBS molecules and light components in SBS modified asphalt. On the basis of maintaining the original stable asphalt colloid structure, SBS styrene ends are interconnected to form π-π conjugate, which improves the toughness of SBS network.