To evaluate the micro-structural degradation characteristics and strength attenuation law of multi source solid waste solidified lake sediment under freeze-thaw cycle conditions, the macroscopic engineering characteristics of the solidified sediment material under freeze-thaw cycle conditions, such as unconfined compressive strength, volume deformation and permeability coefficient were observed through multiple repeated freeze-thaw cycle tests on samples of granulated blast furnace slag, desulfurization gypsum, and construction waste co solidified sediment. By combining micro testing methods such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy spectrum analysis, then the mineral composition, functional groups, surface morphology and elemental composition of solidified sediment materials during freeze-thaw cycles were systematically analyzed, revealing the micro mechanism of structural degradation of solidified sediment under freeze-thaw cycles. The results show that with the increase of freeze-thaw cycles, fibrous ettringite and columnar gypsum and other cementitious products fracture and overlapped with each other to form a network, and the internal pores increased. This may be the reason for the decrease in strength and increase in permeability coefficient of the solidified sediment. The obtained mechanical characteristics and degradation patterns of solidified sediment during freeze-thaw cycles can provide basic data for the application and promotion of this material in regions with significant freeze-thaw cycle characteristics such as northwest and northeast of China.

Buildings are important carriers of human production activities. Accurate and fast extraction of building areas can play an important role in the field of natural resource management. Although significant progress has been made in building extraction from remote sensing images based on CNN(convolutional neural network), the constructed network model still needs to be optimized in feature extraction and feature fusion. Therefore, a coordinate attention and CCFNet(convolutional enhanced full-scale fusion building extraction network) was proposed. The constructed model consists of a residual encoder enhanced by coordinate attention and convolution and a full-scale fusion decoder. Coordinate attention was used in the encoder to build inter-channel dependencies and capture global information. The asymmetric convolution was used to enhance the edge feature extraction of ground objects, and it is more robust to rotation, flip distortion and uneven aspect ratio of ground objects. The full-scale fusion method used in the decoder helps to reconstruct the buildings. The experimental results on the dataset of typical Chinese city buildings show that compared with other building extraction networks, The CCFNet model constructed in this paper achieves the best experimental Accuracy of 93.84%, 84.08%, 72.53% and 82.59% in the four segmentation evaluation indicators of accuracy, F₁, IOU and MIOU, respectively. Experimental results show that the model can effectively extract building regions.

When tunnels with extra-large cross-sections pass through layered rock formations, given the unique structural characteristics of these layers, suitable control strategies must be implemented to mitigate any adverse impacts. Based on the extra-large section tunnel project of Chongqing Guobo Center Station passing through layered rock, numerical simulations, and field monitoring are adopted in this study to compare the mechanical differences between balanced and unbalanced anchor cable supports during tunneling. The results show that compared with the balanced anchor cable support, the influence of the unbalanced anchor cable support on the control difference of displacement and plastic zone of surrounding rock is not significant. However, the unbalanced anchor cable supportcould significantly affect the bending moment distribution range of the vault of lining structure, while having little influence on the peak bending moment and the distribution and magnitude of axial force, with the differences between them being 8% and 11.2% respectively. The reduction in the safety redundancy of the lining structure under the unbalanced anchor cable support is less, and the stability of the tunnel in layered rock could still be maintained. From the perspectives of economy and construction convenience, the amount of anchor cable could be saved by 51%, and the economic advantages are more prominent, which is more advantageous in the construction of tunnels with large sections in layered rock. The effectiveness of the unbalanced support in maintaining tunnel stability is confirmed by field monitoring. The discrepancy between the numerical simulation and field monitoring results is merely 2 mm, demonstrating high consistency and validating the accuracy of the calculations. Critical insights for the design and construction of future similar tunnels in layered rock are provided.

In recent years, rapid development has been continuously observed in China’s manufacturing industry, where AGVs automated guided vehicles have been increasingly adopted by enterprises as core equipment in intelligent logistics systems. To ensure the efficiency of warehouse operations, addressing the issue of transportation path conflicts among AGVs has garnered growing attention from researchers. A literature review on the issue of multi-AGV path conflicts in warehouses was conducted from two perspectives. First, from the perspective of conflict types, the research problems were categorized into collision problems and deadlock problems, and the current state of research on multi-AGV collision avoidance strategies under different conflict types was analyzed. Second, from the perspective of model-solving algorithms, the study divides the approaches into heuristic algorithms and reinforcement learning algorithms, analyzing their application in multi-AGV path conflict issues in warehouses in recent years. Finally, the existing literature was summarized, and future directions for addressing multi-AGV path conflicts in warehouses were proposed.

sEMG (surface electromyography) signals are physiological signal closely related to human movement, and the analysis of sEMG signals play an important role in the field of human-machine interaction. Aiming at the difficulty of both efficiency and accuracy of electromyographic signal classification, an upper limb sEMG classification method was innovatively proposed, which combined feature screening with classifier hyperparameter optimization. BPSO (binary particle swarm optimization) algorithm was adopted to screen the features. PSO (particle swarm optimization) algorithm was further utilized to adjust the hyperparameters of the LSSVM (least-squares support vector machine). By collecting sEMG signals from four parts of the human upper body and extracting 48-dimensional features from them, classification experiments were conducted on four common movements of upper limb. The results show that the BPSO-PSO-LSSVM algorithm retains only the 21-dimensional features of the EMG data, and the average classification accuracy obtained reaches 97.54%. It is proved that this method can effectively screen out the optimal combination of features for upper limb motion classification and improve the accuracy of movement classification.

In order to reveal the spatiotemporal distribution characteristics and causative factors of the ground subsidence in Jiangdong New District, SBAS-InSAR(small baseline subsets interferometric synthetic aperture radar) technology was adopted to process and analyze 175 scenes of Sentinel-1A imagery data from January 2018 to February 2024, extracting the deformation parameters. The study found that the subsidence rate in Jiangdong New District has undergone a variation from very slow to fast and then back to slow, forming several severe subsidence areas mainly distributed along major traffic arteries and reclamation areas. The main causative factors of the subsidence were qualitatively and quantitatively analyzed, which include poor engineering geological conditions, consolidation in the reclamation areas, and land use change, etc. The acceleration of subsidence rate is closely related to the rapid development and construction of the region, among which the excessive load on the ground surface is becoming the main influencing factor of subsidence.

In order to investigate the water-holding mechanism and infiltration law of modified glutinous rice-based reconstructed soil layer under rainfall, soil column infiltration tests were firstly conducted to analyze the influence of modified glutinous rice-based material dosage variations on macroscopic vertical infiltration patterns of reconstructed soil. NMR (nuclear magnetic resonance) and scanning electron microscopy technologies were employed to investigate microporous structure and water-holding characteristics under different material dosages. Based on the findings, reconstructed soil with optimal material dosage (12.5%) was selected for rainfall slope modeling tests, through which moisture transport patterns and post-precipitation water redistribution characteristics in reconstructed soil layers were investigated.The results show as follows. With the increase of the dosage of modified glutinous rice-based materials, the number of effective pores (mesopores) increases and then decreases, the number of small pores gradually increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. Modified glutinous rice-based materials wrap around, adsorb to soil particles, and combine with gravel to form agglomerates, thereby changing the pore structure of the soil, enhancing the soil water retention capacity, and improving the effectiveness of soil water. Under the condition of 25 mm/h rainfall intensity, an increase in slope gradient led to the decrease of infiltration depth of each cross-section, and the infiltration site shifted significantly (from the top to the foot of the slope). The depth of slope infiltration during the entire rainfall period decreased significantly with the increase of slope gradient, and water in the slope was redistributed at the end of rainfall. The average infiltration depth of the slope at 35°, 55°, and 75° was 10 cm, 8 cm, and 5 cm, respectively. This study is significant for improving the technical system of ecological slope restoration and guiding conservation and management efforts.

Aiming at the problems of poor real-time detection, low accuracy, and false detection and omission of pavement disease detection including hole and crack, an improved algorithm based on YOLOv9 was proposed to resolve the problem. Firstly, AKConv (alterable kernel convolution) was introduced into the backbone network to replace the convolution module in RepNCSPELAN4, which improves the feature extraction ability of the network for different diseases and effectively solve the problem that road disease is difficult to distinguish from background environment features. Secondly, selective image attention mechanism (SimAM) and DySample sampling modules were introduced to focus on the key information in the detection head, and the capability to extract information features was enhanced more efficiently. Finally, the inner-IOU function was used to optimize the weight parameters of the model to improve the learning ability of mixed samples. The experimental comparison between YOLOv9-c and our model showed that the accuracy, recall rate and MAP of the improved model are increased by 40.17%, 15.99% and 20.95% respectively. The performance has been significantly improved, and the detection effect is more accurately and efficiently, and the accuracy and generalization ability of pavement disease detection algorithm are improved.

To address the limitations of traditional protocol recognition methods caused by the presence of numerous non-standard protocols in IC (industrial control) sector, a method based on edge-distributed deep learning was studied to enhance IC protocol recognition technology. A recognition method based on CNN (convolutional neural networks) was proposed: real IC protocol data from the network was collected and preprocessed, and an appropriate CNN model was selected according to protocol characteristics to implicitly extract the essential features of the protocols. This achieved classification and recognition of seven types of IC protocols with an accuracy of up to 99.92%. Furthermore, the IC protocol recognition model was deployed at the network edge, leveraging a data-parallel distributed strategy for collaborative training within an edge server computing cluster. This improved the training efficiency of the model by 1.87~2.81 times while maintaining high accuracy. The results show that this method significantly improves the accuracy of IC protocol recognition, greatly enhances model training efficiency, and is well-suited for deployment in edge computing environments. It is evident that this method has significant value in optimizing IC protocol recognition performance.

Under the background of the energy transition, energy storage, as an important technical means to support a high proportion of renewable energy access and consumption, has gradually become an indispensable part of virtual power plants. Among them, energy storage selection is a key issue to ensure the safe and stable operation of the power grid and improve the scheduling efficiency of virtual power plants. Therefore, an energy storage selection method based on game combination weighting and GRA-MARCOS was proposed for virtual power plants. Firstly, the evaluation indicator system of energy storage adaptability was constructed on the basis of considering the technical, economic, security, and environmental protection indicators of energy storage. Secondly, based on game theory, the comprehensive subjective and objective weights of the indicators were obtained by combining the subjective weights from FAHP(fuzzy analytic hierarchy process) method with the objective weights derived from the CRITIC and MEREC methods. Finally, the utility function of each alternative energy storage technology was calculated using the MARCOS method improved by GRA(grey relational analysis), and this was used to rank and make selection decisions for energy storage. The validity and robustness of the proposed energy storage selection method were verified through examples and sensitivity analysis. This method provides a reasonable and effective decision-making scheme for energy storage selection in different scenarios of virtual power plants.