Exploring the spatiotemporal evolution of FVC(fractional vegetation cover) in the East Pamirs Plateau and the driving mechanisms of FVC by the driving forces, providing scientific data support and reference for vegetation protection in the study area. Based on Landsat remote sensing image data, meteorological data, DEM (digital elevation model)data and other data in 1993, 2000,2007,2014 and 2021, pixel dichotomous model, Markov transition model, spatial auto-correlation analysis and geodetector were used to research on the spatiotemporal evolution and detection driving forces of vegetation coverage in the East Pamirs Plateau. Results show as follows. The FVC in the East Pamirs Plateau is exhibiting a fluctuating upward trend, in terms of spatial distribution, is overall characterized by “higher in the east, lower in the west and extremely low in the medium”. The spatial autocorrelation analysis of vegetation coverage in the East Pamirs Plateau from 1993 to 2021 showed a significant positive spatial autocorrelation, with the global Moran's index of FVC 0.27~0.40. Local spatial autocorrelation shows that the vegetation coverage in local areas is dominated by low-low aggregation and high-high aggregation. Factor detection results showed that the explanatory power (q) of various driving factors on the spatial heterogeneity of vegetation coverage in the study area fluctuated to varying degrees from 1993 to 2021, with land use and DEM were the main driving factors of FVC in the East Pamirs Plateau. Results of the interaction detection showed that the influence of interaction between driving factors is greater than that of a single driving factor, and the interaction results are all enhanced.

Traditional Chinese medicine is an important cultural and ecological resource in China. Relevant national or local regulations and standards set clear requirements for the packaging label design of traditional Chinese medicine, creating hard constraints that guide traditional Chinese medicine packaging design. creating hard constraints that guide traditional Chinese medicine packaging design. Under these circumstances, the packaging design information for traditional Chinese medicine was organized, a packaging box template that complies with these constraints was created, and a design environment with hard constraints was established. Using the packaging design of “Jiangzhong Jianwei Xiaoshi Tablets” as an example, the existing packaging box was studied as the research object. Eye-tracking technology was employed to capture eye movement data on all six faces of the packaging box and its unfolded view. Key indicators, such as fixation time and heat maps, were used to analyze image attention and the importance of drug information. The current packaging information layout was systematically evaluated, and directions for improvement were proposed. Finally, through design empowerment, the drug information and layout were re-matched and arranged, producing a packaging design plan that guides traditional Chinese medicine packaging design to achieve efficient optimization and innovation of visual information under standardized conditions.

In order to study the influence of rock fragmentation on the bearing capacity of rock-socketed pile, a concrete rock-shear model considering rock fragmentation was established using statistical theory. The calculation results of the model were compared with the experimental results to verify the accuracy of the model. Using a concrete rock shear model considering rock fragmentation and the load transfer mechanism of rock-socketed pile, a load transfer equation of rock socketed pile considering rock fragmentation was established and solved using the Runge-Kutta method. The calculation results of the equation were compared with the results of on-site static load experiments, which verified the accuracy of the equation. The research results indicate that it is feasible to establish a mechanical model for rock fragmentation through statistical theory. The load transfer equation of rock socketed pile considering rock fragmentation is consistent with the results of on-site static load experiments, which can describe the yield deformation stage and strain softening stage of the pile-rock relative displacement-pile side friction curve. The parameter sensitivity research was conducted, and the influence of rock fragmentation on the concrete-rock shear mechanism and rock-socketed pile bearing mechanism was conducted in-depth analysis.

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit (SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.

Air-coupled ground penetrating radar is widely used in areas with poor terrain conditions and many surface obstructions. In order to precisely evaluate the influence of surface obstructions on the air-coupled ground penetrating radar detection, the horizontal distance between surface obstructions and the detection object was quantitatively studied. The air-coupled ground penetrating radar principle was first used to design an indoor experiment. The influence of the distance between the obstacle and the detection object on the depth of detection and the amplitude changes of the reflection interface of the detection object was analyzed. The relevant laws were then verified by forward simulation. Finally, the relationship between the distance between the obstacle and the detection object and the parameters of the air-coupled ground penetrating radar when it was working was derived based on the analysis of the electromagnetic wave propagation laws of the air-coupled ground penetrating radar. The research results show that the physical experiment obstacles used for air-coupled ground-penetrating radar detection of underground objects have no significant impact on the detection depth. As the obstacle gradually moves away from the detection object, the amplitude of the reflection interface of the detection object will first increase and then stabilize. The amplitude increase stage follows an exponential function distribution law, and the amplitude stability stage has the same amplitude value as when there are no obstacles. In this paper, the distance between the obstacle and the target object at which the amplitude is just approaching stability is defined as the critical distance at which the obstacle affects the amplitude of the radar signal. The height of the antenna, the depth of the target object, the dielectric constant of the detection medium, and the angle of incidence of the electromagnetic wave are all related to the critical distance. The result quantifies the influence of surface obstacles on the physical characteristics of the air-coupled ground penetrating radar and provides guidance for the operation of air-coupled ground penetrating radar.

Noise signals are added to the signal during transmission, resulting in changes in the amplitude of the signal, thus affecting the reception sensitivity. In order to study the relationship between noise and sensitivity, two signal-to-noise ratio calculation methods were used, and digital filtering, pulse peak extraction and half-amplitude point calculation were processed on the signals, which in turn complete the judgment and sensitivity calculation of DME(distance measuring equipment) signals, and based on which the relationship between DME judgment and the size of signal-to-noise ratio was analyzed. The results show that when the signal-to-noise ratio is small and the signal strength is sufficiently small, the accuracy of the DME judgment is greatly affected by the noise signal, which will lead to the reduction of the ranging accuracy.

The strength reduction method is an essential approach for calculating the slope safety factor, with its computation reliant on the criterion for slope instability during the process of strength reduction. Among the commonly employed criteria for slope instability, displacement mutation stands out; however, its determination remains relatively subjective at present. The maximum slope displacement was considered as a function of the strength reduction factor based on the strength reduction method. The standard deviations of the maximum slope displacements for different strength reduction factors were calculated. Consequently, a method for determining the slope safety factor within the strength reduction method, based on the standard deviations of the maximum slope displacements, was proposed. The method was validated through a typical case study and subsequently applied to a practical engineering project. The results demonstrate that the proposed method can objectively identify the occurrence of slope displacement mutation during the strength reduction process and effectively ascertain the slope safety factor. The proposed method is especially applicable for practical slope engineering projects requiring quantitative comparison of safety factors for different cases.

The karst region in Northern Guangxi is one of the most typical and severely rocky desertification areas in China. Research on ecological restoration in this region is of significant importance. The study focused on the moss experimental research area in Pingfeng Mountain, Qixing District, Guilin City. The dominant moss species in four typical habitats were selected using the five-point sampling method. Field cultivation methods for these species were studied. Different slope treatments' effects on moss growth patterns were revealed. Mosses were explored as innovative methods for ecological restoration in rocky desertification areas. Hyophila involuta, Barbula unguiculata, and Bryum paradoxum were identified as dominant species, serving as references for ecological restoration in rocky desertification areas. In the field cultivation experiment, the effects of slope on moss growth indices were considered. The indices analyzed included moss coverage, plant density, plant height, moss crust thickness, and dry weight. Barbula unguiculata and Bryum paradoxum are considered as dominant moss species for ecological restoration of rocky surfaces in desertification areas. They effectively solve the problem of large bare rock surfaces in Northern Guangxi and improve the ecological benefits of karst areas.

In order to solve the problem of difficulty in constructing forecasting models caused by the characteristics of power grid materials, such as many varieties, diverse specifications, huge quantities, wide range of uses, and great influence by policies and investments. Firstly, the factors affecting the quantity of material demand for infrastructure, business expansion, and emergency repair projects were screened by the Delphi method and gray correlation analysis (GRA). Secondly, an improved particle swarm algorithm that introduced adaptive inertia factor and learning factor was utilized to adjust the optimal parameter combinations of the extreme learning machine, and train the material demand prediction models for various distribution network projects. Finally, the results of the GRA-IPSO-ELM (grey relational analysis, improved particle swarm optimization, and extreme learning machines) model were compared with the results of four common forecasting models by taking the demand of 10 kV power cables of a power grid for 2020—2022 infrastructure projects as an example. The results show that the prediction accuracy of the GRA-IPSO-ELM model is improved by 10.38%, 5.37% and 3.83% compared with the ELM model, the support vector machine model and the PSO-ELM model, which shows that the model proposed in this paper realizes accurate and efficient prediction of the quantity of material demand in the distribution network.

The U-shaped geothermal well, as a method for extracting medium-deep geothermal energy, is considered significant for enhancing the energy utilization efficiency of geothermal reservoirs. Geological and reservoir data from the Huangling area in Shaanxi Province were used to establish a numerical model of a U-shaped geothermal well with a depth of 3 500 meters. Field experiment cases were simulated and validated to investigate the heat transfer characteristics of U-shaped geothermal wells and the impact of related factors on thermal extraction efficiency. The effects of well type, reservoir geothermal gradient, and operational conditions on thermal extraction efficiency were analyzed, and the optimization of well type and parameters was further evaluated. The results indicate that U-shaped geothermal wells achieve higher thermal extraction efficiency compared to other types, particularly in areas with high geothermal gradients and depths exceeding 3 000 meters. Within the parameter range of this study (injection flow rates of 40, 60, 80, 100, and 120 m³/h, and injection temperatures of 20, 25, 30, 35, and 40 ℃), it was found that a larger temperature difference between the inlet and outlet fluids of the U-shaped geothermal well leads to a higher heat transfer rate. The optimal injection flow rate and temperature for U-shaped geothermal wells in the study area were determined to be 100 m³/h and 20 ℃, respectively. The selection of the injection flow rate should consider the requirements for outlet temperature, thermal extraction power, and the power consumption of the circulating water pump. The research findings are expected to provide guidance for optimizing medium-deep geothermal extraction in the region.