To explore the impacts of various factors on the performance of concrete, the response surface methodology was adopted to optimize the concrete mix proportion. In the experiment, the water-binder ratio, the dosage of steel slag, and the content of desert sand were taken as variables, with a focus on analyzing the main performance indicators such as the slump of concrete, compressive strength, and splitting tensile strength. The experimental results indicate that the content of desert sand has the most significant influence on the slump of concrete, while the compressive strength and splitting tensile strength are mainly affected by the variation of the water-binder ratio. With the increase of the content of desert sand, the slump, compressive strength, and splitting tensile strength of concrete exhibit a trend of initially increasing and then decreasing. When the content of desert sand reaches 30%, the performance is optimal. The addition of steel slag can enhance the fluidity of desert sand concrete (DSC). As the amount of steel slag increases, the compressive strength of DSC shows a decreasing trend, and the tensile strength increases initially and then decreases. The addition of steel slag interacts with desert sand, particularly on the tensile strength of DSC. Through response analysis, the optimal mix proportion was obtained as a water-binder ratio of 0.39, a dosage of steel slag of 10%, and a content of desert sand of 30%, at which the comprehensive performance of DSC is the best. Finally, non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) was utilized for multi-objective optimization, which yielded a more complete solution set, thereby providing certain technical support for the application of DSC.

Soil seed banks serve as the foundation for plant restoration and succession, playing a crucial role in sustaining biodiversity and ecological balance. The effect of short-term enclosure and grazing ban on soil seed bank was studied in the marsh meadow of Napahai Lake in northwest Yunnan Plateau. Through field sampling and seed germination identification, the effects of grazing and short-term confinement (3 years) on species composition, diversity and functional group structure of topsoil seed bank were compared. The findings show that short-term grazing exclusion significantly alters the diversity of the soil seed bank, reducing the Shannon-Wiener and Pielou indices (P<0.05) and increasing the density of sedge seeds (P<0.05). Weeds dominate in both enclosed and grazed conditions, with grasses and sedges following, and legumes being minimal. The percentage of weed species decreases, while that of sedge species increases under grazing exclusion, with no significant change observed in grasses and legumes. Weed importance values are significantly negatively correlated with grass and sedge importance values (P<0.05), and grass density is significantly positively correlated with sedge density (P<0.05). These results indicate that while short-term grazing exclusion reduces the diversity of the soil seed bank in swamp meadows, it also enhances the replenishment and recovery of sedge seeds, offering new insights for the restoration management of wetland ecosystems.

Second order selective harmonic repetitive control(SOSHRC) strategy with good frequency adaptability and dynamic performance, is widely used in grid-connected inverter control. To address the problems of the traditional SOSHRC strategy, such as the difficulty of using the stability analysis method and the conservative stability criterion, a novel second order selective harmonic repetitive control and proportional control(SOSHRC-PC) was proposed. Firstly, the structure and principle of the novel SOSHRC were designed, the novel SOSHRC-PC control strategy was introduced. Then, the stability and parameter design methods of the novel SOSHRC-PC controller were analyzed. Finally, a three-phase grid-connected inverter based on the novel second-order (6k ± 1) harmonic repetitive control and proportional control was constructed, and the simulation results show that the proposed control strategy has great steady state and dynamic performance.

Aiming at the problem that the unordered charging of large-scale electric truck (ET) increases the peak load of the grid and affects the power quality, a cluster division and two-tier optimal scheduling strategy for peak load balancing scenarios were proposed. Firstly, the demand response model of ET participating in power grid peak regulation was established considering real-time road flow and multi-energy consumption factors. With logistics factors as characteristic quantities, ET was divided into day-ahead clusters by an improved fuzzy clustering algorithm. Secondly, based on the clustering results, combined with the different interests of power grid dispatching and enterprise users, a two-tier scheduling model was established under the framework of master-slave game considering the flexible time window to solve the charging and discharging power of pure electric heavy duty card in the cluster in real time. Finally, particle swarm optimization based on Kriging model was used to speed up the solving of the model. The simulation results of ET data in a logistics area show that the two-tier scheduling strategy based on cluster division and flexible time window can better smooth the load curve and reduce the scheduling deviation of clusters. At the same time, Kriging optimization algorithm is more fast in solving the two-tier optimization model.

The reactive power optimization and reconfiguration of traditional distribution network are mostly studied separately, lacking the coordination and cooperation of different optimization techniques. A mathematical model of reactive power and reconfiguration collaborative optimization of active distribution network was established. Combined with the two optimization methods of reactive power optimization and reconfiguration of distribution network, the coordinated operation of the two was realized according to the actual situation of distribution network. Taking the minimum annual comprehensive cost as the objective function, the improved grey wolf algorithm was used to solve the problem under the constraints of network power balance, node voltage amplitude and network radial operation. Aiming at the problems of low population diversity, easy to fall into local optimal solution and slow running speed of traditional grey wolf algorithm, it is proposed to increase the explosion mechanism of fireworks algorithm on the basis of grey wolf update strategy. At the same time, in order to improve the computational efficiency and solution accuracy, the fireworks algorithm was used for integer solution optimization, and the nonlinear programming algorithm was introduced to optimize the continuous solution. The IEEE33 node distribution network was taken as an example to verify four different scenarios. The results show that the proposed collaborative optimization model can effectively reduce the network loss and annual comprehensive cost, suppress the node voltage fluctuation level, and show the superiority of the improved algorithm in convergence speed and calculation accuracy.

Oil-based drilling fluids, favored for their superior stability and inhibitive properties in complex deep oil and gas strata, is constrained by a scarcity of efficient materials for leak prevention and plugging, thereby limiting their utilization. In response to this challenge, a homogenous and stable polymer, SMHDVD, was synthesized via solution polymerization, using acrylate monomers as the primary chain and incorporating functional monomers that offer resistance to high temperatures and enhanced bonding with the formation. The impact of different concentrations (0.1%, 0.3%, and 0.5%) of the crosslinking agent divinylbenzene on the polymer SMHDVD was also investigated. In-house plugging experiments have shown that SMHDVD significantly contributes to the plugging efficacy of oil-based drilling fluid systems, with a maximum reduction in cumulative drilling fluid loss of up to 68.7%, surpassing the performance of conventional plugging materials. It was observed through comparative studies that the incorporation of a crosslinking agent diminishes the sealing capacity of SMHDVD, and an increase in the crosslinking agent's concentration leads to a decline in the polymer's plugging performance. The polymeric plugging agent developed in this research offers a novel perspective for the prevention of oil-based drilling fluid leakage, with promising prospects for practical field application.

On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.

The laminectomy robot is an auxiliary surgical robot developed for laminectomy in recent years. In order to explore the differences in reducing orthopedic surgeons' mental workload between laminectomy robot techniques and traditional laminectomy methods, a multimodal evaluation incorporating an electrocardiogram, eye tracking, and the NASA-TLX scale was utilized to assess the mental workload of orthopedic surgeons undergoing both surgical procedures. Through simulated surgical trials, 12 orthopedic surgeons performed laminectomies employing both the robot-assisted and the conventional techniques, collecting multimodal data for both variance and correlation analyses. The findings indicate significant differences in subjective mental workload between the laminectomy robot and traditional techniques (P<0.05). However, in terms of electrocardiogram indicators such as average heart rate, the low frequency/high frequency ratio (LF/HF), and the standard deviation of NN intervals (SDNN), no significant differences are noted. Significant differences are observed in eye movement indicators, including pupil diameter (P<0.05), fixation rate (P<0.05), and saccade rate (P<0.01). Further correlation analysis underscored a notably significant relationship between pupil diameter and levels of subjective mental workload in both surgery techniques. In conclusion, compared to traditional laminectomy methods, the use of laminectomy robots can alleviate the mental workload on orthopedic surgeons, with both pupil diameter and subjective mental workload levels providing effective reflections of the orthopedic surgeons’mental workload.

The high-altitude & dense-vegetation landslide is difficult to investigate and lack of data, its appearance and subsurface information also difficult to get. Therefore it is difficult to identify and threat the road greatly. A case study of the Xiaojiapo landslide was presented. Detailed on-site geological surveys were performed, and drone-based oblique and orthophoto imaging techniques were employed to get the landslide's characteristics. Historical deformation of the landslide was analyzed using satellite image, airborne LiDAR was used to collect point cloud and digital imagery data of the landslide surface. A digital elevation model and centimeter-level three-dimensional model of the landslide surface were created following vegetation removal to assess the surface characteristics. The combined investigations reveal that the Xiaojiapo landslide is situated at an elevation of 3 030 m with a vegetation coverage reaching up to 90%. It is a typical landslide with high-altitude and dense vegetation. The key internal factors contributing to the landslide including the unique alpine valley topography, the slope structure with advantageous free faces, and easily erodible geological layers. Precipitation induces internal water infiltration, which reduces the strength of the rock and soil mass. Additionally, freeze-thaw cycles further diminish slope strength, while earthquakes and road constructions disturb the internal structure, weakening it further. These combined internal and external factors drive the landslide deformation. This study offers technical insights for the landslide identifying, early-warning, and mitigation of road landslides in high-altitude regions with dense vegetation.

An algorithm has been proposed to detect small targets in unmanned aerial vehicle(UAV) aerial images. The algorithm is based on an improved real-time detection Transformer (RT-DETR) and aims to address the challenges posed by complex backgrounds and a large number of small target samples. To enhance the feature fusion network, a dedicated feature fusion structure for small targets has been incorporated, utilizing rich location information from the shallow feature map to improve the network's ability to detect small targets. Furthermore, the last residual block in the BackBone has been removed to prevent an increase in additional parameters. Additionally, the MCP Block, a reconstructed BasicBlock structure in the backbone network, has been designed, which includes a multi-channel feature partial convolution module (MCPConv) to reduce redundancy in channel features and enhance the acquisition of multi-scale detail features. Moreover, a location encoding mechanism with learning ability has been introduced to obtain more accurate and expressive location information. The normalized weighted deviation(NWD) and mean precision-driven IoU(MPDIoU) positioning loss functions have been incorporated to accelerate the convergence speed of the model and reduce sensitivity to position deviation. Experimental results on the VisDrone2019-DET dataset demonstrate that the improved model reduces parameters by 62% compared to the original model, increases mAP50 by 3.9%, and improves FPS by 17%. The improved model exhibits superior detection performance compared to other mainstream detection models.