Aiming at the problem of high-precision, high-speed and efficient temperature control caused by multi-point measurement and asymmetric heating condition of input and output, a new algorithm (IGRO-FuzzyN-PID) based on IGRO-PID and multi-layer fuzzy nested algorithm was proposed. Simulation results show that IGRO algorithm is superior to CPO (crested porcupine optimizer)、IPSO(improved particle swarm optimization)、COA(crayfish optimization algorithm)、GA(genetic algorithm) in PID control system. Simulation and experimental results show that compared with the IGRO-PID algorithm, the overshoot, steady-state error and average error of the IGRO-FuzzyN-PID algorithm are optimally increased by 70.91%, 70.69%, 82.35% and 86.89%, 76.23%, 86.56% under symmetric and asymmetric input and output conditions. It is proved that the proposed algorithm meets the control requirements of high precision, high speed and high efficiency under symmetric and asymmetric input and output conditions.

The loess hilly area is one of the areas with a high incidence of geological disasters, and it is urgent to use appropriate evaluation factors and training models to conduct research on the susceptibility assessment of geological disasters. Kangdian Town, Gongyi City, the township hardest hit during the “7·20” extremely heavy rainstorm in Zhengzhou, was taken as the study area. Based on satellite remote sensing interpretation, field survey, UAV aerial photography and relevant data collection, an evaluation system covering 13 influencing factors of three main control factors, namely loess interface, human engineering activities and hydrodynamic effects, was constructed. CatBoost model, XGBoost model and LightGBM model were used to carry out the evaluation study of geological disaster vulnerability. Based on the machine learning model with the best performance, SHAP(shapley additive explanations) algorithm was used to complete the global interpretation of characteristics and dependency analysis. The results show that the CatBoost model has higher accuracy than other models (XGBoost and LightGBM), and performs the best in AUC(area under curve) value, accuracy, precision, recall, F₁ score, and field validation. The proportion of areas with extremely high, high, medium, low, and extremely low susceptibility is 3.19%, 1.40%, 2.04%, 5.93%, and 87.44%, respectively. The extremely high and high susceptibility areas are mainly distributed on both sides of gullies with strong human activities, and slope cutting and building are important causes of geological disasters. The aim of this study is to optimize the modeling approach, investigate the uncertainty and interpretability of the modeling process, explain and analyze the decision-making mechanism of machine learning susceptibility, and provide scientific basis for geological disaster prevention and control in the loess hilly area of western Henan.

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.

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.

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.

In order to meet the transfer and transportation requirements of passengers and to significantly mitigate the loss of passenger flow that exceeds the waiting tolerance threshold, the optimization method of feeder bus scheduling considering the arrival time and passenger flow loss of rail transit trains was studied. The distribution of passenger flow demand was characterized by the passenger transfer demand and the arrival times of rail transit within the study period. The transfer time was described by the alignment between the time passengers arrive at the station of the bus and the bus departure schedule, as well as the operational capacity of the buses. The constraints of bus departure intervals, passenger flow loss and transfer demands were considered, and the multi-objective optimization with the minimum passenger flow loss, bus number and passenger transfer waiting time was realized under the limited number of buses that can be scheduled. Due to the contradictions among the optimization objectives, the model was solved with Non-dominated Sorting Genetic Algorithm II (NSGA-II). Finally, taking the actual bus routes as an example, the results show that the optimized model takes into account the bus operation cost and the passenger transfer time cost, and can obtain the scheduling that meets the passenger flow demand and represents different priorities. When the number of feeder buses is the same, the total transfer waiting time of the optimized method is reduced by 8.0% compared with the uniform headway. The average factor under the uneven is 59.3%, which is better than the average factor of 50.2% under the uniform. The calculation results validate the effectiveness and rationality of the model and algorithm, effectively enhancing the match between the time and capacity of buses and urban rail transit.

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.

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 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.

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.