The efficiency, precision, and automation of fruit and vegetable picking are realized through the integration of multiple mechanical arms in multi-mechanical arm cooperative picking technology, effectively addressing the high costs and low efficiency associated with traditional manual picking methods. The research progress in multi-mechanical arm cooperative picking technology was summarized, and the framework of the multi-mechanical arm cooperative picking system was comprehended. In light of the decision-making challenges in cooperative picking, the cooperative methods and task allocation within cooperative picking task planning was analyzed, and the collision detection, obstacle avoidance strategies, and path planning techniques utilized in cooperative picking with multiple robotic arms was reviewed. The future development direction of multi-mechanical arm cooperative picking technology is outlined, with a proposed development trend that envisions the combination of machine and agronomy, human-machine collaboration, decision-making big models, and multi-algorithm fusion.

Strengthening green and low-carbon environmental control technologies is critical because the industrial, agricultural, and construction sectors face three major challenges: high energy consumption, high emissions, and low energy efficiency in controlling temperature and humidity in specific areas. Heat pump-driven desiccant wheel air conditioning provides high evaporative temperature cooling, effective humidity management, and easy integration with renewable energy equipment. The typical heat pump-driven desiccant wheel air conditioning system was analyzed, the research progress of heat pump independently driven desiccant wheel air conditioning system under various coupling methods was reviewed, and the performance, regeneration temperature, and application occasions of heat pump independently driven desiccant wheel air conditioning system under various working conditions were summarized. Furthermore, the solar-assisted heat pump-driven desiccant wheel air conditioning system was introduced. Finally, existing challenges and future developments of heat pump-driven desiccant wheel air conditioning system were analyzed and projected.

The interporosity flow between matrix pores and fractures is the main cause of strong attenuation and dispersion of elastic wave in the seismic frequency band. A fractional equation for interporosity flow is developed. The constitutive relationship of double-porosity medium was improved in the frequency domain, in which three factors representing the effects of fractional interporosity flow were introduced. A wave equation considering fractional interporosity flow for a double-porosity medium was developed by substituting the improved constitutive relationship into the Lagrangian equation. The wave velocity and inverse quality factor was obtained and analyzed by plotting. Comparing to the traditional interporosity flow, the fractional interporosity flow provides a more flexible and accurate description for the dispersion and attenuation of elastic waves in the seismic frequency band.

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.

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 improve the accuracy of geological hazard susceptibility assessment, Fuyang District in Hangzhou, Zhejiang Province was taken as the research area and a random forest method was proposed for evaluating geological hazard susceptibility, considering buffer zone optimization strategies. Firstly, nine evaluation factors were selected: normalized difference vegetation index, distance to roads, distance to faults, rainfall during the flood season, slope, aspect, ruggedness, distance to water systems, and lithology. Multicollinearity analysis was conducted to ensure the independence of the factors. Secondly, buffer zones of 0.5 km, 1 km, 1.5 km, and 2 km were constructed. Negative sample points were generated using random sampling to avoid cross-contamination between positive and negative samples, enhance sample representativeness, and improve the model's discrimination capability. An additional set of random sampling points without buffer zones was also established for comparison. The random forest algorithm was then used to train and test the geological hazard susceptibility model. Results indicated that the buffer zone optimization strategy significantly improved the model's predictive accuracy and that there was an optimal boundary for the buffer zone. The model's AUC(area under curve) value was highest at 0.815 for the 1 km buffer zone, indicating that negative samples collected within this buffer zone could more accurately distinguish geological hazard characteristics. Finally, based on the susceptibility evaluation results of the optimal buffer zone and the random forest model, high susceptibility areas were mainly concentrated in the mountainous regions in the northwest and southeast. The frequency ratio increased with the susceptibility level, validating the scientific validity of this method. This approach can provide a basis for geological hazard prevention and control in Fuyang District.

In order to analyze the instability and failure characteristics of earthquake landslides in the Loess Plateau, the Xinbaocun landslide induced by the 8.5-magnitude Haiyuan earthquake in 1920 was taken as an example. Based on satellite remote sensing interpretation and field investigation of landslides, the original terrain of the Xinbaocun landslide before sliding was restored using the numerical simulation function of MATLAB based on the principle that the volume of the landslide body before and after sliding is equal. On this basis, the numerical simulation method was used to analyze the instability, deformation and failure characteristics of the Xinbaocun landslide under the action of earthquakes and invert the minimum horizontal ground motion of the slope instability. The landslide restoration results show that after the restoration of the Xinbaocun landslide, the slope surface shape is generally concave, and the slope distribution range is 12°~18°.The numerical simulation results show that under natural conditions, the slope deformation of the restored landslide is small, there is no obvious plastic deformation zone,the stability is good, and the overall stability coefficient is 1.355. Under the action of earthquake, when the input earthquake motion is 0.4g, the stability coefficient is 0.887, the slope is in an unstable and damaged state, and the slope body has a large area of deformation. The maximum deformation area is located near the top of the slope,and gradually decreases from the top to the foot of the slope. It is speculated that the landslide is a push-type landslide. The inversion results show that the minimum horizontal earthquake motion for slope instability is 0.36g. The research results of this paper can provide certain basic data and reference for the prevention and control of earthquake disasters in the Loess Plateau.

In order to enrich and improve the geohazard-pregnant mechanism of structural mélange, taking the North Lancang River suture zone as an example, the structural petrology characteristics of the structural mélange were dissected, then the physical and mechanical characteristics were identified through field geological investigation and laboratory test analysis, and finally its geohazard effect were revealed. The results show that the tectonic mélange in the northern Lancang River suture zone is sandwiched between granite and granite gneiss on the southwest and volcano-sedimentary rocks on the northeast as a non-abutment layer. The main rock types are phyllite and slate with a small amount of basalt, limestone and siliceous rock blocks, showing obvious “hybrid accumulation” characteristics. Under the coupling process of tectonic uplift, differential weathering and river erosion, it gradually evolved into a mountain deep canyon landform, which is the topographic basis for the development of geohazard. In terms of structural petrology, the structural mélange in the North Lancang River suture zone shows the characteristics of “one weak and three strong”, that is, weak metamorphism, strong deformation, strong alteration and strong orientation, which leads to serious deterioration of rocks and is the source basis of geohazard. In terms of physical and mechanical properties, the tectonic mélange appears as a set of easy to slip soft rock “formation”, rich in clay minerals, with high porosity and water absorption, low compressive and shear strength, which provides structural conditions for the development of geohazards. Tectonic activity and tectonic stress are strong in the North Lancang River suture zone, which provides dynamic conditions for the development of geohazards. The research results are conducive to further enriching and improving the geohazard breeding mechanism of tectonic mélange.

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.

In order to summarize the distribution characteristics of traditional Chinese medicine (TCM) syndromes and syndrome elements in chronic kidney diseases (CKD). The studies related to TCM syndromes in patients with CKD were searched for in CNKI, WANFANG DATA, VIP, SinoMed, Pubmed, Embase and Cochrane Library from the inception dates to August 21th, 2024, and extracted the relevant information. WPS Excel was used to count the frequency of basic characteristics, syndrome types, and syndrome elements in the literature for establishing a database. The distribution characteristics of syndrome elements were explored by association rule analysis and systematic clustering techniques within data mining. The results show that a total of 126 articles are enrolled, including 45 382 patients with CKD. There are 129 TCM syndrome types and 37 syndrome elements (23 disease nature elements and 14 disease location elements). There are 7 syndrome element combinations types. The 6 common TCM syndrome types of CKD with a composition ratio ≥5% are spleen and kidney qi deficiency syndrome, blood stasis syndrome, damp-heat syndrome, damp turbidity syndrome, qi and yin deficiency syndrome, and spleen and kidney yang deficiency syndrome. Disease nature elements: 16 substantial and 7 insubstantial syndrome elements are included. The 6 disease nature elements with a composition ratio ≥5% are qi deficiency, dampness, yin deficiency, blood stasis, heat, yang deficiency. There are 61 syndrome element combinations types of disease nature elements, with 7 combinations with a composition ratio ≥5%, which are: qi deficiency, blood stasis, dampness + heat, yang deficiency, dampness, yin deficiency, and a combination of qi deficiency + yin deficiency. Disease location elements: the 3 disease location elements with a composition ratio ≥5% are kidney, spleen, and liver. There are 23 syndrome element combinations types with a composition ratio ≥5% are spleen + kidney, kidney, liver + kidney. Syndrome element combinations types: one, two, three, four, five, six, and seven syndrome elements are included, with the two-syndrome element combinations being the most common (46.36%). Correlation analysis indicates that the support for the spleen-stomach is the highest, with the highest confidence in spleen-dampness-heat, meridians-tendons and bones, spleen-fluid retention-qi deficiency, and spleen-stomach-dampness. There are 4 clusters group can be obtained results of the system clustering analysis. It is concluded that the TCM syndromes of CKD is deficienc-excess complex. Spleen-kidney qi deficiency syndrome is the most common syndrome in chronic kidney disease (CKD). The main disease location of CKD are kidney, spleen, and liver. Qi deficiency, dampness, yin deficiency, blood stasis, heat, and yang deficiency are common disease nature elements.

Caragana is the main wind-breaking and sand-fixing vegetation in Inner Mongolia, with a wide planting area and a large area. There is a great demand for harvesting machine in the stubble and harvesting of caragana. Aiming at the problems of high fuel consumption and heavy pollution of traditional caragana harvester, the maximum speed, maximum gradient and pure electric driving range of the extended range hybrid caragana harvester designed by our research group were used as the objective functions to design the dynamic parameters. Firstly, according to the design parameters and performance requirements of the whole machine, the parameters of the range extender, drive motor and battery of the harvester were designed. Combined with the actual working parameters, AVL/Cruise software was used to simulate and find the optimal solution of the overall performance index. Combined with the actual working parameters, AVL/Cruise software was used to simulate and find the optimal solution of the overall performance index. The reasonableness of the simulation results is verified by the field test of the whole machine. Comparing the two results, it is found that the error of the maximum speed is 9.012% and the climbing error is 6.404%. The error of the two results is within the allowable range. The results show that the simulation model is reasonable and can provide reference for the optimization of dynamic parameters of the extended range hybrid Caragana harvester.

In order to solve the problem of unbalanced ratio of excavation and anchor and the difficulty of single transportation in fully mechanized excavation roadway, a cooperative anchor truck for fully mechanized excavation roadway was designed. Based on ADAMS, the kinematics model of the cooperative anchor truck was established, and the trajectory planning of the efficient support operation was carried out. The mechanical characteristics and ultimate support distance of each leg during the forward movement of the supporting device and the supporting monomer were analyzed. The results show that during the forward movement of the supporting monomer, when the four legs are fully supported, the force and moment of the front leg increase by 196% and 195% respectively, and the force and moment of the rear leg decrease from 73.5 kN and 238 kN · m to 0 respectively. The limit distance of the support is 15.3 m, which meets the design requirements of the support. When the three legs are supported and the right rear leg is not supported, the force and torque of the left front leg are increased by 566% and 572% respectively, and the force and torque of the left rear leg are reduced from 145 kN and 462 kN · m to 0 respectively. The constant force and torque of the right front leg are 150.8 kN and 569.4 kN · m, and the limit distance is 14.99 m. It can be seen that under the special working condition of three-leg support and no support for the right rear leg, the limit distance of the cooperative transport anchor vehicle exceeds the maximum length of the roadheader, which meets the requirements of efficient support.

The double bottom blowing lead smelting is a representative kind of oxygen-enriched enhanced molten pool smelting process. Comparing with the traditional ore lead smelting,its production operation is more complex for coprocessing lead based solid waste. In order to ensure its production status in a stable and economic state, there are higher requirements for its process control. MetCal desk metallurgical computing platform was used to establish a calculation model for the double bottom blowing lead smelting process for coprocessing lead based solid waste through the principles of material balance, thermal balance and chemical balance in the smelting process. The process parameters of the double bottom blowing lead smelting process for coprocessing lead based solid waste were simulated and calculated. Meanwhile, the model was modified and controlled by the feedback control algorithm to make it more in line with the actual production situation, which can provide guidance for the actual production operation of the process engineers and realize the optimal control of the process.

CO₂ flooding is one of the most effective technical means to improve the recovery of low permeability reservoirs. However, the injected CO₂ will cause asphaltene precipitation in crude oil, resulting in the decrease of reservoir permeability and the change of reservoir heterogeneity. In order to clarify the effect of asphaltene differential precipitation on the heterogeneity of low permeability reservoirs under different pressure conditions, a low permeability reservoir in Jilin Oilfield was taken as the research object. Through the CO₂ long core displacement experiment, the CO₂ displacement characteristics under different pressures were clarified. At the same time, combined with the test results of crude oil and core properties, the changes of crude oil asphaltene content and reservoir properties before and after displacement under different pressure conditions were analyzed, and the distribution law of asphaltene precipitation was further clarified. The results show that under immiscible conditions, the degree of crude oil recovery is low, only 55.11%. The asphaltene precipitation increases first and then decreases with the increase of displacement distance. CO₂ mainly displaces the large pore throat crude oil, and the asphaltene precipitation also mainly occurs in the high permeability area, thus inhibiting the reservoir heterogeneity. Under miscible conditions, the degree of crude oil recovery is high, up to 81.84%. Asphaltene precipitation increases with the increase of displacement distance. CO₂ can displace small pore throat crude oil. Asphaltene precipitation occurs in high permeability and low permeability areas, but low permeability areas are greatly affected, and reservoir heterogeneity is further strengthened.

The silica with the highest content in sandstone was taken as the main research object. The SiO₂ surfaces underwent hydroxyl (—OH) and methyl (—CH₃) treatments to represent the hydrophilic and hydrophobic walls, respectively. LAMMPS software was utilized to implement molecular dynamics simulations and replicate the process of supercritical CO₂ extraction of crude oil components. The results indicate that the temperature is 333.15 K and the pressure is 20 MPa. The hydroxylated silica surface extracted 5.07% more saturated hydrocarbon molecules using supercritical CO₂ compared to the methylated silica surface. The interaction energy between the oil components and the two wall surfaces is mutually attractive. The interaction energy of saturated hydrocarbon molecules decreases by 77.52% for hydroxylated silica surface and 46.04% for methylated silica surface, respectively. Additionally, the interaction energy of methylated silica on saturated hydrocarbon is greater than that of the hydroxylated silica surface.It is important to note that carbon dioxide easily extracts saturated hydrocarbons with short molecular chains. The diffusion coefficients of crude oil components under two surface conditions are saturated hydrocarbons > resins > aromatic hydrocarbons > asphaltenes.

Gas storage operations can be significantly impacted by abnormal wellbore temperatures at natural gas storage sites. Accurately predicting wellbore temperatures is of paramount importance for enhancing the safety and efficiency of these operations. Based on the analysis of operational parameter correlations, a gas storage wellbore temperature prediction method was proposed using advanced spatiotemporal graph convolutional neural network (A-SGCN). Both GCN and long short-term memory (LSTM) networks were employed by A-SGCN to capture spatial and temporal dependencies, respectively. Based on this framework, an adaptive residual attention mechanism was incorporated to effectively capture the intricate relationships between spatiotemporal data, ultimately enabling accurate temperature prediction. The effectiveness of the method is validated through its application at the Huangcaoxia gas storage No.2 injection-production station. Accurate prediction of wellhead temperature at Well No.1 is achieved through the association of monitoring parameters between Well No. 1 and Well No.6.

A new anomaly sound detection algorithm was studied that combines attention mechanisms and domain generalization techniques to more accurately identify normal and abnormal sounds in mechanical equipment. Specifically, two neural networks were jointly trained using a sub-cluster Adacos loss function, with features modeled by a Gaussian mixture model (GMM). Anomaly scores were calculated using negative log-likelihood values, and a 90th percentile threshold was set for detection. The algorithm demonstrated strong performance across seven types of machines, including fans and bearings, achieving harmonic mean AUC(area under curve) and pAUC values of 76.69% and 87.99%, with the highest performance observed on valve data. Compared to two baseline systems, the algorithm improved AUC and pAUC by 24.08% and 20.68%, respectively. Ablation studies further confirmed the positive impact of the GMM, attention mechanism, and Scadacos loss function. When tested against eight other algorithms on the same dataset, the proposed method showed a 4.16% improvement in the harmonic mean of AUC and pAUC, highlighting its significant advantage in anomaly sound detection tasks.

Landslides are significant geological hazard, with most events directly related to rainfall. Consequently, rainfall monitoring is critical for landslide forecasting and prediction. A self-powered rainfall sensor based on triboelectric nanogenerator (TENG) technology was developed for landslide monitoring. The sensor consists of a transmission unit and a sensing unit, which enable rainfall measurement and self-powering through rain-induced contact electrification of nanomaterials. Experimental results indicate that the sensor maintains a measurement error of 5% within the 0~300 mL/24 h range, with a linearity of 3.4%. It is capable of being operated in environments where the temperature is below 100 ℃ and the relative humidity is under 90%. Additionally, the output of the sensor can achieve a maximum power of 203.5 nW when an external load resistance of 7.8×10⁷ Ω is applied. The performance of the sensor can meet the practical requirements for field rainfall monitoring of landslides and also show potentials for the development of related rainfall monitoring technologies.

Natural cooling is one of the more energy-efficient and widely used cooling methods in data center air conditioning systems. Based on the climatic characteristics of Suzhou, Beijing, Guiyang, Guangzhou and Urumqi, the natural cooling system model of data centers was established by TRNSYS, and then the energy saving effect of data centers under the same chilled water supply and return temperatures in different regions was studied. The results show that when the chilled water supply/return temperatures are 15/22 ℃, the longest time for complete natural cooling is in Urumqi, accounting for 67.4% of the year, and the shortest time is in Guangzhou, accounting for 10.2% of the year; the longest time for complete mechanical cooling is in Guangzhou, accounting for 63.0% of the year, and the shortest time is in Urumqi, accounting for 4.3% of the year; the longest time for part of natural cooling is in Guiyang, accounting for 31.8% of the year, and the shortest time is in Beijing, accounting for 4.3% of the year. The longest part of natural cooling time is in Guiyang, accounting for 31.8% of the year, and the shortest is in Beijing, accounting for 18.6% of the year. At full load, the lowest annual average system power usage effectiveness (PUE) was in Urumqi at 1.227, and the highest PUE was in Guangzhou at 1.299. The average annual PUE of the five cities decreases as the load factor increases, and the effect of air conditioning load on the average annual PUE becomes smaller as the load factor increases. The findings provide theoretical support for guiding the application of natural cooling technology in data centers in different regions.

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.

To enhance the support for the nuclear safety review of domestic advanced pressurized water reactor (PWR), the reactor coolant pump (RCP) shaft seizure accident for this reactor type was conducted. The impact of the loss of offsite power (LOOP) assumption and moderator temperature feedback on the accident consequences was examined. The findings are as follows. The peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding, recorded during the accident process, met the accident acceptance criteria, enabling the nuclear power plant to eventually stabilize. The LOOP assumption was found to be conservative. For this accident, however, the differences arising from this assumption manifested in the later stages and did not influence key outcome parameters such as system pressure peak, minimum departure from nucleate boiling ratio (DNBR), and peak temperature of hot spot cladding, which occurred early in the accident. Therefore, the inclusion or exclusion of the LOOP assumption was deemed acceptable. The introduction of moderator temperature feedback was considered an unconservative assumption. Nevertheless, even without this feedback, the peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding still met the accident acceptance criteria, maintaining a substantial margin. Additionally, incorporating moderator temperature feedback brought the calculations closer to reality. Overall, the introduction of moderator temperature feedback in this accident was also judged to be acceptable.

In order to strengthen the heat-mass transfer performance of humid air-spray water outside staggered tube bundles (STB), an analytical model was constructed for heat-mass transfer performance of humid air-spray evaporative cooling in staggered tube bundles based on the coupled method of DPM(discrete phase model) and Wall film model. The verification results show that the error was less than 1.1% for simulation and parameter results. Meanwhile, the influences were studied for three key structural parameters on heat-mass transfer performance. The results show that the heat transfer performance is improved between tube wall and spray water with the increase of longitudinal and transverse spacing of tube bundles. However, the mass transfer performance decreases of humid air-spray water with the increase of transverse spacing. Meanwhile, Nusselt number increases by 33.3% with the increase of longitudinal spacing from 30 to 70 mm, increases by 73.5% with the increase of transverse spacing from 10 to 50 mm. Besides, the heat transfer performance proves to be better when contact area increases between tube bundle and spray water with larger pipe diameter. At a certain transverse and longitudinal spacing, the lowest humid air temperature and highest enthalpy are located on the maximum pipe diameter (24 mm), and its decreasing and increasing degrees are 11.2% and 35.6%, respectively. The above results can provide a theoretical basis for optimizing the structure of staggered tube bundles and improving the heat-mass transfer efficiency.

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.

In the event of a public health emergency, the selection of the optimal distribution locations of daily necessities is crucial. Accordingly, the emergency distribution of daily necessities in Changchun City was taken as an example for empirical research and an optimization algorithm was proposed for the location of emergency supplies distribution. Initially, the virtual material warehouses, distribution candidate locations, and service coverage areas were determined by using K-means clustering and the Thiessen polygon method. Then, an optimization model was established with the goals of maximum coverage and minimum cost, and an improved genetic algorithm based on adaptive elite retention strategy was proposed to solve it. Empirical research had shown that the clustering algorithm could obtain 873 virtual material warehouses and distribution candidates, while Thiessen polygons could determine the reasonable coverage range of candidates, and improved genetic algorithms could obtain better 30 material warehouses and distribution locations. Data analysis had also found that there was a time lag between the distribution of supplies and effective control of infection. Therefore, once the rate of new additions exceeds the normal value, emergency supplies should be increased in advance to control further outbreaks of possible infections.The research results are beneficial for enhancing the ability of urban crisis management and emergency response, ensuring timely and efficient supply of daily necessities.

To improve the short-term prediction accuracy of photovoltaic power generation models with multiple input features, a photovoltaic power prediction ensemble model LGGWO-TCN-MHSA based on optimizing TCN hyperparameters was proposed. The model integrated the levy gold grey wolf optimization (LGGWO), temporal convolutional network (TCN), and multi-head self-attention mechanism (MHSA). First, the Spearman correlation coefficient method extracted the main features that significantly affect photovoltaic power, which were then fed into the TCN prediction model. Then, the proposed multi-strategy LGGWO was applied to the TCN for hyperparameter optimization, which improved the model's prediction performance. Finally, the predicted values were input into the multi-head self-attention model to further boost prediction accuracy. The experiment was verified using original Australian photovoltaic data. By comparing with six groups of models including convolutional neural networks (CNN) and long short-term memory neural networks (LSTM), the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model on the test data set were reduced by 2.03%~82.0% and 10.5%~80.1%, respectively. The results show that the proposed method has high prediction accuracy and good stability.

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.

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.

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.

In order to improve the accuracy of network traffic classification, a traffic classification method combining an attention mechanism and a convolutional neural network was proposed. An attention mechanism layer was designed and implemented on the basis of the convolutional neural network model, which received the output of the fully connected layer as input, calculated the weight of the input features, and multiplied it by the original features to strengthen the key features. This, in turn, helped to improve the model's ability to capture key information. Secondly, in order to solve the problem that the model was overfitting to the high-proportion category due to the unbalanced sample number of network traffic categories, and it was difficult to identify the small-proportion categories, a method to augment the dataset was proposed. Considering the perspective of hyperparameter combination optimization, a hyperparameter search strategy based on Bayesian optimization and five-fold cross-validation was proposed to optimize the hyperparameter combination of the model. The combination of hyperparameters of the model was determined by the above methods. The public dataset was used for the above experiments and model tests. The results show that compared with other methods, the overall accuracy, precision, and F₁ score are significantly improved, which verifies that the proposed method has better classification performance.

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 frastructure projects such as hydropower, harbours and transport are usually constructed by rockfill of aggregates from local materials, however, the shapes of rockfill of aggregates in different regions are quite different. In order to study the effect of aspect ratio on the K₀ consolidation characteristics of rockfill, firstly, 10 kinds of particle clusters with different aspect ratios were generated by the discrete element method, and then the same equivalent particle size was taken to generate the specimens with the same initial state in the same way, and then the K₀ consolidation test was carried out at last, and the effect of the aspect ratio of the particles on the K₀ consolidation characteristics of the heap stone materials and its micro-mechanisms were explored in detail. The results of the study show these as fouows At the macro level, the specimens with different aspect ratios of rockfill are divided by AR=0.5, when the AR is 0.5~1, the K₀ value of the specimens increases with the increase of aspect ratio, and when the AR is 0.2~0.5, the trend of the change of the K₀ value of the specimens with the increase of the aspect ratio is unknown. This suggests that the particles in the interval of aspect ratio 0.5~1 need to be focused on during engineering design. At the microscopic level, the smaller the particle aspect ratio (the longer the particles), the larger the coordination number, the smaller the average force between the particles, and the smaller the cumulative angle of rotation of the particles, which suggests that the contact between the particles of the specimens with smaller particle aspect ratios is closer, the contact force chain is more dispersed, and the particles are more resistant to rotation. This may be the reason for the differences in K₀ consolidation properties of rockfill with different aspect ratios. The research results can provide theoretical references for the evaluation of K₀ values in areas where there are particle shape differences, and provide a basis for selecting appropriate rockfill materials, ensuring the safety of the project.

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.

In order to study the effect of freeze-thaw cycle on the stability of lime-amended loess slope, the change rule of shear strength and soil-water characteristics of lime-amended loess under different numbers of freeze-thaw cycles was obtained by straight shear test and soil-water characteristics test, and the stability of high-fill loess slope was analysed by using the strength discount method with MIDAS GTS NX software. The results show that: the internal friction angle and cohesion of the soil samples both increase with the increase of the age of maintenance, and with the increase of the number of freeze-thaw cycles, the internal friction angle of the soil samples will first increase and then gradually tend to be stable, and the cohesion will first decrease and then gradually tend to be stable; the lime-amended loess has its matrix suction maximum at the lowest temperature of the first freeze-thaw cycle, and its matrix suction is minimum at the second freeze-thaw cycle, and after that the matrix suction of the soil body increased. Freezing and thawing cycles will cause the slope infiltration flow rate to increase; the trend of pore water pressure changes in the freezing and thawing slope model is the same as that of the unfrozen slope, and the pore water pressure is positive at the bottom of the slope model, and then decreases gradually near the top of the slope, and then reaches the minimum at the top of the slope. The freeze-thaw cycle will cause the strain of the slope to increase under rainfall conditions, which affects the safety and stability of the slope; the shallow freeze-thaw has little effect on the safety of high-fill improved loess slopes.

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.

A mathematical model was established to address the multi-constraint, large-scale three-dimensional bin packing problem. A hybrid metaheuristic algorithm combining an improved whale algorithm with simulated annealing was proposed. The algorithm discretized the whale algorithm, including individual encoding and updating mechanisms, and utilized simulated annealing to overcome local optima traps. Moreover, a heuristic loading rule was designed for decoding and optimizing the packing solution. The algorithm was evaluated using standard packing instances from Bischoff and Ratcliff's OR-Library, as well as real-world cargo order data, covering a range of cargo types from weakly heterogeneous to strongly heterogeneous. The proposed algorithm achieved a balance between global and local search capabilities, resulting in high packing efficiency for various types of containers. Specifically, the average container filling rate were 92.24% for weakly heterogeneous cargo, 88.78% for strongly heterogeneous cargo, and an overall average of 91.29%. This result provides valuable insights and references for the study of three-dimensional bin packing problems.

In order to investigate the aging mechanism of high dosage rubber powder modified asphalt, with the help of 20%, 25% and 30% of three different dosages of rubber asphalt, using four-component analysis experiment and infrared spectroscopy experiment, evaluation of aging rubber asphalt four-component indexes change, and analyze the chemical composition and functional group changes before and after aging, through the comparative study, to reveal high dosage rubber powder modified asphalt aging mechanism under different aging conditions. The aging mechanism of high doped rubber powder modified asphalt under different aging conditions was revealed through comparative study. The results show that: the aging of high dosage rubber powder modified asphalt components more significant. 20% dosage, the saturation fraction, aromatic fraction and gum decreased by 4.4%, 3.4%, 4.3%, respectively, asphaltene increased by 117.7%; 25% dosage of rubber asphalt saturation fraction decreased by 5.0%, the aromatic fraction decreased by 8.4%, gum decreased by 4.9%, asphaltene increased by 119.3%, and 30% dosage, the decrease is greater, respectively, 7.4%, 9.5%, 6.0%, asphaltene increased to 128.9%; aging process, high dosage of rubber powder modified asphalt has less mass loss, the combination of light components of the asphalt and cracked rubber binding reduces the light component activity and enhances the resistance to aging; high dosage of modified asphalt in the aging conditions of carbonyl and sulfoxide group index growth is more significant, which may lead to a decline in the performance of the The compatibility of rubber and asphalt is more susceptible to aging, which is manifested by more intense vulcanization phenomena and changes in molecular chain cross-linking. The research results provide theoretical support for the aging mechanism of rubber asphalt, and for the practical application and maintenance of high dosage of rubber powder asphalt pavement, which is of great significance to improve the performance of rubber asphalt pavement.

In order to minimize the influence of construction for airport operation, the construction method of closing part of time or some pavement without stopping is usually used by the airport pavement maintenance office. Therefore, it is necessary to consider the key objectives of construction management, such as quality, duration and safety, and carry out a research on construction organization and management in the maintenance and repair of airport pavement. A multi-objective optimization model was proposed for the non-stop construction organization of airport pavements, focusing on the construction period within airport pavement maintenance management. The construction process was emphasized and functional relationships among cost, quality, and construction time were established to quantify each objective scientifically. Moreover, an optimization model was developed, incorporating the construction window period as a constraint and leveraging the closure of nighttime hours to facilitate uninterrupted construction without disrupting navigation. The approach ensures efficient non-stop construction of airport pavements. In the model, a triple objective optimization strategy was employed, aiming to minimize duration and cost while maximizing quality level. To solve the model, the performance of multiple objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm-Ⅱ(NSGA-II), and NSGA-III algorithms was compared under the 3D objectives. The results demonstrate that all three algorithms show commendable performance in solving the concave optimization problem DTLZ2, with NSGA-III notably outperforming the others in tackling the three-dimensional optimization problem. The capacity of the model to further optimize construction management objectives, while fully adhering to construction constraints, was verified, which provided a decision-making framework for construction organization and management in airport pavement maintenance, catering to diverse requirements.

In order to study the effect of low temperature on mechanical properties, fatigue properties and frost resistance of cement stabilized crushed stone mixture in cold area. Using 5 ℃, 10 ℃, 15 ℃ to simulate the field low temperature curing condition, based on the mechanical properties test, three-point loading fatigue test and freeze-thaw cycle test, the differences of unconfined compressive strength, flexural tensile strength, dynamic resilience modulus, fatigue resistance and anti-freezing durability of cement stabilized gravel mixture under low temperature curing condition, 20 ℃ standard curing condition and 30 ℃ curing condition were compared. The effect of curing temperature on the attenuation law of microvoid structure of cement-stabilized crushed stone mixture during freeze-thaw cycle test was studied. The results show that, compared with the standard curing temperature (20 ℃), the mechanical properties of cement stabilized macadam mixture decrease by 20%~40% and the fatigue life decrease by 40%~50% under the condition of 5~10 ℃. Low temperature curing has a significant deterioration effect on the mechanical properties, fatigue properties and anti-freezing properties of cement stabilized macadam mixture. The greater the decrease of mechanical properties and fatigue characteristics of cement stabilized crushed stone mixture. Increasing the curing temperature is helpful to reduce the decay rate of micro void diameter after freeze-thaw cycle, increase the stability of the void structure of cement stabilized gravel mixture and refine the micro void structure during the freeze-thaw cycle test, and thus increase the mechanical properties, fatigue durability and frost resistance of cement stabilized gravel mixture.

Rock mass classification is a fundamental component in tunnel engineering construction. With the rapid advancement of mechanized and intelligent construction technologies in China, drilling-parameter-based intelligent rock mass classification methods have become crucial in facilitating smart mechanized tunneling. This need is especially pronounced in the mountainous regions of Western China, where complex terrain and challenging construction, combined with limited experience in mechanized tunneling and the restricted applicability of current intelligent rock mass classification methods, make mechanized construction crucial for improving project quality and effectively controlling construction risks. A predictive method was proposed for intelligent rock mass classification using drilling measurement parameters. Focusing on multiple long tunnels as research subjects, on-site drilling parameters were collected and rock mass mechanical tests was conducted to construct a drilling parameter database, then intelligent algorithms was applied, such as support vector regression (SVR) and particle swarm optimization-back propagation (PSO-BP), to develop a predictive model for rock mass classification. The result indicates that the absolute value of correlation coefficient |r_s| between drilling parameters and rock mass classification indices is greater than 0.6, demonstrating a significant correlation, where torque and rotational speed show the strongest correlation with rock mass classification indices. A standardized parameter index database with 574 ideal samples was established through data-cleaning tools. Comparative analysis of predictive accuracy across intelligent algorithms indicated that the PSO-BP model demonstrated the best performance. The PSO-BP neural network-based prediction model was validated by transient electromagnetic (TEM) and tunnel seismic prediction (TSP) advanced geological forecasting, confirming its accuracy in predicting rock mass classification and providing reliable support for mechanized tunnel excavation.

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.

With the rapid advancement of air transport aviation and general aviation, the frequency assignment of air navigation stations has become increasingly crucial. A comprehensive algorithm was presented for assigning frequencies to individual air navigation stations. Subsequently, in order to address the issue of frequency assignment for multiple aviation navigation stations, a model was established specifically for civil aviation navigation stations. Finally, considering the limitations of traditional multi-objective genetic algorithms such as slow convergence speed and susceptibility to local optimal solutions, a multi-objective genetic algorithm along with a multi-objective genetic local search algorithm based on optimal weight allocation was proposed to effectively solve the frequency assignment problem faced by aviation navigation stations. The problem encompasses several objectives including minimizing frequency interference and utilizing the minimum number of frequencies required. Simulation results demonstrate that our proposed multi-objective genetic local search algorithm successfully resolves the frequency assignment problem encountered by air navigation stations. In comparison with traditional multi-objective genetic algorithms and those incorporating optimized weight allocation, our proposed algorithm significantly enhances solution quality, convergence speed, and stability.

Aiming at the current aviation typical parts with complex structure and high precision requirements, increased difficulty in quality inspection but backward inspection methods, an inspection plan automatic preparation method was proposed. Firstly, from the information reuse and information integration, the inspection number model was constructed on the basis of the design model, which layed a foundation of data source for the generation of the inspection plan. Secondly, the production method of the inspection plan was formulated, and the rules of the inspection sequence generation were designed, to achieve the automatic generation of inspection sequence number by using the improved Dynamic planning algorithm. Finally, combined with the CAA(component application architecture) secondary development interface and Excel component development, the corresponding function module was developed under the CATIA environment, to achieve the output of the inspection element information and process view, to complete the automatic preparation and export of the inspection plan. Through examples, it is proved that the method can reduce the labour intensity of inspectors and improve the efficiency of the preparation of inspection plans.

In view of the position control accuracy and body chattering of small unmanned helicopter under large disturbance, an improved sliding mode controller (SMC) was proposed. Firstly, for the unknown parameters in the dynamic model of small unmanned helicopter named Align T-REX 300, three flight experiments were designed for the data acquisition, determining the comprehensive aerodynamic parameters. Then, the dual-channels control strategy with an improved SMC was designed to realized the helicopter hovering. The parameters ranges of SMC were presented by combining stability analysis, which can effectively suppress chattering. The model validation experiment shows that the aerodynamic parameters determined by the flight experiments have high fidelity. Moreover, the control simulations show that the steady state error of the step responses is less than 0.02 under the continuous large disturbance. Under the improved SMC with high control accuracy and weak tremor, the servos' control signal curves are smooth, which is more conducive for achieving the flight control of unmanned helicopter under the disturbance in actual engineering.

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 estimate carbon storage and investigate its influencing factors in typical karst rocky desertification regions, Jianshui County, located in the upper reaches of the Yangtze River and within the Nanpan River Basin, was selected for study due to its significant ecological position. Data from Landsat 5 Collection 1 Tier 1 Surface Reflectance, Landsat 8 Collection 1 Tier 1 Surface Reflectance, and digital elevation model (DEM) were used to conduct the analysis. The google earth engine (GEE) platform and the integrated valuation of ecosystem services and trade-offs (InVEST) model was used to estimate and predict carbon storage, while the geodetector tool was employed to analyze influencing factors. The results show that, between 2000 and 2020, Jianshui County was predominantly classified as potential rocky desertification and non-desertification, with the rocky desertification area found to have decreased by 115 km² overall, and the non-desertification area increased by 21 km², reflecting an improvement in desertification conditions. Carbon storage displayed a spatial distribution pattern of “higher in the north and south, lower in the center,” with recorded values of 116.293 t, 113.310 t, and 118.363 t in 2000, 2010, and 2020, respectively, demonstrating an initial decrease followed by an increase. Projections suggest that carbon storage may reach 129.981 t by 2030. Temperature (q=0.672) was identified as the primary factor affecting changes in carbon storage, with the interactions between factors shown to have a stronger effect on carbon storage than individual factors, especially the interaction between slope and temperature (q=0.998), indicating a non-linear enhancement. It is concluded that carbon storage in Jianshui County exhibits a trend of initial decrease followed by increase, influenced by both natural and social factors, with a spatial pattern of “higher in the north and south, lower in the center.”

In order to evaluate the characteristics of fire smoke dispersion in large-space buildings, FDS software was used to investigate the effects of air-inlet position, smoke-proofing depth and smoke exhausting rate to the working efficiency of fire isolation belt. The results show that, by opening the air-inlet in the non-fire zone and closing that in the fire zone, a reverse airflow can be induced below the fire isolation belt, which resists the smoke overflow and reduces the disturbances to the smoke layer. The increase of the smoke-proofing depth can increase the space for smoke storage and reduce the height of the smoke layer, thus delaying the instant of smoke overflow by decreasing the generation rate of fire smoke. In the scenario without mechanical smoke exhausting in the fire isolation belt, the instant of smoke overflow can be delayed by about 50% when the smoke-proofing depth increases from 20% to 40%. By adopting mechanical smoke exhausting in the fire isolation belt, the accumulated smoke can be timely extracted, which can effectively prevent the smoke spilling into other non-fire zone. It is concluded that, through an overall consideration to the smoke isolation efficiency, building functions and construction costs, the combination of “40% smoke-proofing depth + adopting the mechanical smoke exhausting rate with reference to the Smoke Standard + opening the air-inlet in the adjacent non-fire zone” is recommended as an advantageous design principle for fire isolation belts.