Abstract:In this study, the water level fluctuation zone of the Three Gorges Reservoir (TGR) was selected for investigation. A combination of chemical analysis and biological testing was utilized to investigate the effects of water fluctuation on the compounds and concentrations of estrogenic effector substances, as well as the estrogenic activity in soil (during the non-flooding period) and sediment (during the flooding period). The correlation between typical estrogens and estrogenic activity was analyzed. The study aimed to provide crucial data support for reservoir ecological safety and environmental health management. The distribution and concentration of 8 typical estrogens were analyzed using an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), while estrogenic activity was determined via the yeast estrogen screening assay (YES). Correlation analysis and concentration summation calculations were used to establish the relationships between chemical substances and biological effects. Among the 8 studied estrogens, only estrone (E₁) and ethinylestradiol (EE₂) were detected, with concentrations ranging from 0.025 ng/g to 2.667 ng/g. Sediments during the flooding period showed significantly higher estrogenic activity, with 17β-estradiol equivalents (EEQ) ranging from 0.637 ng/g to 6.987 ng/g. However, correlation analysis did not reveal a distinct correlation between the target compounds and estrogenic effects, as the detected chemicals only accounted for about 29.46% of the estrogenic activity. The results suggest that water fluctuation can influence the type and the concentration of estrogens in the water level fluctuation zone of the TGR, leading to increased estrogenic activity during the flooding period. The direct linkage between typical estrogens and estrogenic activity was not identified. Therefore, new methods and techniques are required to enhance identification accuracy and improve the management of the risk of estrogenic effects in the water level fluctuation zone.

Abstract:Microplastics are emerging pollutants widely present in soil, and their effects on soil phosphorus (P) adsorption are still unclear. This paper examined the effects of 0.1% to 10% microplastics on soil P adsorption and the underlying mechanisms. The results showed that microplastics could significantly increase the rate of liquid film diffusion stage in the first stage of the adsorption process (p<0.05). Compared with the pure soil (q_e=6.456 mg/g), the soil P adsorption capacity was significantly reduced by microplastics with less than 1% concentration (p<0.05). However, the soil P adsorption capacity was significantly increased by microplastic with concentrations higher than 5% (p<0.05). Additionally, the smaller the particle size of microplastics, the greater the P adsorption capacity of soil with the same microplastic concentration. Microplastics could compete with P for adsorption sites, which reduced the adsorption of P in the microplastic-soil system. However, microplastics could also directly adsorb P, leading to an increase in the P adsorption capacity of microplastic-soil system when microplastic concentration were more than 5%. Therefore, microplastics pollution in soil could significantly change P adsorption characteristics, which closely related to the microplastics concentration and particle size.

Abstract:As a typical geological structure, a soft interlayer is a key factor affecting the safety and stability of the entire slope. In order to better analyze the properties of soft interlayers and their influence on slope stability, the orthogonal control method was used to simulate four occurrence states of the sandwich depth, inclination angle, quantity and structural surface spacing between the layers of the slope with a soft interlayer in a mine open-pit rock slope. A 4×4 orthogonal simulation was carried out and the stability and deformation law of the slope were revealed under different occurrence states. The results show that: 1) the occurrence state of the soft and weak interlayer of the slope has a significant impact on the stability of the slope. The safety factor of the slope increases with the increase of the burial depth of the soft interlayer, and the safety factor tends to stabilize after the burial depth reaches a certain level. 2) With the increase in the angle of the soft interlayer, the failure mechanism is manifested as interlayer mismotion, creep slip along the layer and progressive change of shear-slip along the soft structural surface. 3) The increase in the number of soft interlayer layers leads to an overall decline in the slope with uneven deformation, a decrease in the safety factor of the slope, and an increase in horizontal displacement. 4) For the soft sandwich slope with the same buried depth, structural surface spacing has less influence on the slope safety factor when the spacing is small.

Abstract:In order to explore the characteristics of pores and fractures at different scales and their influence on permeability, nitrogen adsorption, mercury injection and CT experiments were carried out on 14 large coal bases in China. Seepage experiments were then conducted on the coal samples after CT scanning. The results showed that the micropores and transition pores in each coal sample are mostly closed pores with poor connectivity, which is not conducive to coal seepage. The pore and fractures volume with r=10 nm and r=100 μm accounts for a large proportion, contributing most to the porosity of coal. Large-scale parallel plate pores in S2 and S3 provide sufficient space for seepage. By dividing dominant pore size segments characterized by three experiments, a method for comprehensively characterizing porosity and fractal dimension is proposed. The porosity range of each coal sample is from 1.62% to 11.60%, and the fractal dimension range is from 2.29 to 2.78. The permeability of coal samples ranges from 0.000 2×10^-15 m² to 0.652 5×10^-15 m², mainly in medium and low permeability. The relationship between the porosity components of r<50 nm, 50 nm≤r≤8.5 μm and r>8.5 μm and permeability is as follows: y=0.274 1x-0.078 1, y=0.067 4x+0.023 7 and y=0.003 9x^{2.598 6}, respectively. The correlation between the porosity component of r>8.5 μm and permeability is the strongest. Compared with nitrogen adsorption and mercury intrusion experiments, the CT experiment is more suitable for analyzing the influence of pore and fractures on water seepage.

Abstract:To improve the protective capabilities of existing low-grade concrete guardrails against heavy vehicles,three types of composite protective plates designed for direct installation on the surface of concrete guardrails were developed. Refined finite element (FE) models of truck-guardrails were established, and the reliability of these models was verified by comparison with crash test results. Six evaluation indexes, including impact force,collision angle variations,vehicle trajectory, height variations of the carriage box tail,vehicle dynamic extroversion value and camber angle, were considered. The anti-collision performance of the three schemes was compared and analyzed. The results indicate that all three kinds of guardrails effectively guide vehicles to turn smoothly during the integral truck impacts. Scheme 1, without hindrance, demonstrates superior performance in key indices compared to the others. Under towed truck impacts, scheme 1 exhibits excellent guiding and blocking capabilities, with an exit angle of only 0.75°.The protection energy of the combined guardrail reaches 650 kJ,providing a protection capacity 4 times greater than the existing low-grade guardrail.

Abstract:This study presents a 3D rough fracture grouting model on an engineering scale, specifically investigating the diffusion mechanism of slurry under different fracture surface roughness, openness, grouting pressure, fracture inclination, and slurry viscosity in fractured rock bodies. Using the software COMSOL Multiphysics, the study examines the slurry flow law within the fractures. It is observed that the profile of the slurry diffusion surface is influenced by the roughness of the fracture surface. A rough fracture surface hinders slurry diffusion and is a key cause of anisotropy in slurry diffusion. Furthermore, a quadratic polynomial relationship is identified between the grouting rate and the degree of opening. Large fracture opening results in smaller losses in grouting rate. Injection pressure is found to weakly affect the slurry diffusion profile, mainly changing the diffusion radius of the slurry. There is a positive correlation between injection pressure and diffusion radius, with a quadratic polynomial relationship with injection rate. Higher injection pressure leads to lower loss in injection rate. The effect of slurry viscosity on the slurry diffusion pattern depends on the range of viscosities, primarily changing the range of slurry diffusion. This study sheds light on the slurry diffusion pattern in rough fissures and provides a theoretical basis for grouting prediction and management in extraction area.

Abstract:In order to accurately and efficiently evaluate the mechanical properties of semi-rigid joints in transmission towers, a method based on surrogate model is proposed to predict the moment-rotation relationship of the semi-rigid joints. By introducing the surrogate model method to approximate the functional relationship between the geometric dimensions, ultimate flexural capacity, and initial rotational stiffness of semi-rigid joints, a prediction model with high accuracy is established. Furthermore, the moment-rotation curves of semi-rigid joints in transmission towers are fitted using the Kish-Chen power function model. The results show that the proposed surrogate model-based prediction method for moment-rotation curves of semi-rigid joints can reduce the cost of experiments and numerical simulations, while accruately approximating the actual force-deformation relationship of semi-rigid joints in transmission towers. This method also provides valuable insights for the engineering design and theoretical research of semi-rigid joints in transmission towers.

Abstract:A nonlinear finite element analysis(FEA) model was proposed to simulate a semiconductor display industrial building equipped with a steel frame brace as a practical engineering case study. The seismic performance of the structure under frequent and rare earthquakes was analyzed. The results show that the overall indexes of the structure meet the requirements of the design codes. However, the inter-story displacement angles of the 3rd and the 6th floors are relatively large. Under rare earthquakes, damage to the members is mainly concentrated in the steel braces. Slight damage occurred in the corner columns at the bottom floor and side beams at middle floors of the structure. The steel braces are shown to effectively improve the structural stiffness and reduce damage to the beams and columns.

Abstract:An adaptive, robust, fault-tolerant formation control approach is proposed to address the challenges of actuator faults, parameter uncertainty, external disturbances, and communication delays within a formation system. The nonlinear dynamics model of spacecraft relative positions is presented. The adaptive robust fault-tolerant controller is designed with bounded input, and adaptive laws are developed to estimate the values of actuator faults, mass, and upper bound of external disturbances, respectively. Additionally, the Lyapunov stability of the closed-loop system is analyzed, and the necessary conditions to ensure system stability are provided. Numerical simulation results show that the presented control method enables effective formation tracking control, with steady state error of location tracking is less than 1.5×10^-3 m and steady state error of velocity tracking is less than 1.8×10^-5 m, validating the effectiveness of the proposed method.

Abstract:To address the challenges posed by environmental noise, complex water surface target distributions, and the blurring of small-scale features in water surface target detection against complex river backgrounds, this paper presents UltraWS, an enhanced water surface target detection algorithm that integrates multi-scale features and attention mechanisms. Firstly, a spatial attention module and multi-head strategy are incorporated into a standard detection network to fuse multi-scale features and improve the detection capability of small targets. Secondly, the UltraLU module is introduced to enhance class activation mapping and reduce the influence of environmental and distribution factors on target detection. Finally, a Tucker tensor decomposition method is applied to achieve model lightweighting, enhancing model interpretability and inference speed. Experimental results demonstrate that the proposed UltraWS algorithm improves resistance to background noise, enhances small target detection, and achieves a balance between detection speed and accuracy suitable for edge deployment requirements. On the WSODD dataset, the algorithm achieves the highest mAP value of 84.5%, outperforming other mainstream methods by a considerable improvement. This proposed algorithm, coupled with the established channel safety inspection system and evaluation method, contributes significantly to the advancement of intelligent river transportation.

Abstract:The State Grid has continuously improved its material procurement management level and refined its online procurement processes. However, inaccurate estimation of procurement plans, has led suppliers to engage in price games using the general bidding and tendering mechanism during the bidding process. This has resulted in increased procurement costs of the power grid company. Therefore, it is of great significance to establish an accurate and effective electricity material demand forecasting model. In respose to the instability, volatility and intermittency of power material sequences, this paper proposes a forecasting method for power material demand based on parameter-optimized variational mode decomposition (VMD) and long short-term memory neural network (LSTM). Typical power materials from the State Grid e-commerce zone platform were selected. VMD, optimized by using the whale optimization algorithm(WOA) parameters, was adopted to perform modal decomposition on the original sequence. LSTM models were then constructed for each modal component obtained from the decomposition. Finally, the predicted values of each mode were superimposed and reconstructed into the predicted value of power materials. Experimental results show that the proposed method achieves higher prediction accuracy compared to LSTM, EMD-LSTM,VMD-LSTM, PSO-VMD-LSTM and SSA-VMD-LSTM. This approach holds practical significance for the forecast of power grid material purchase.

Abstract:A multi-modal information fusion based object recognition method for autonomous driving is proposed to address the vehicle and pedestrian detection challenge in autonomous driving environments. The method first improves ResNet50 network based on spatial attention mechanism and hybrid null convolution. The standard convolution is replaced by selective kernel convolution, which allows the network to dynamically adjust the size of the perceptual field according to the feature size. Then, the sawtooth hybrid null convolution is used to enable the network to capture multi-scale contextual information and improve the network feature extraction capability. The localization loss function in YOLOv3 is replaced with the GIoU loss function, which has better operability in practical applications. Finally, human-vehicle target classification and recognition algorithm based on two kinds of data fusion is proposed, which can improve the accuracy of the target detection. Experimental results show that compared with OFTNet, VoxelNet and FASTERRCNN, the mAP index can be improved by 0.05 during daytime and 0.09 in the evening, and the convergence effect is good.