Uplift piles, in accordance with their structural properties, effectively sustain the structural uplift loads and have emerged as an efficacious solution to address the anti-floating issue. The precise determination of the internal forces within uplift piles is crucial for comprehending their load-bearing characteristics. Nevertheless, the tensile capacity of concrete is relatively feeble. Once the load attains a specific magnitude, its elastic modulus will decline, rendering the traditional axial force calculation methods inapplicable. By leveraging the optical frequency domain reflectometry(OFDR) strain measurement technology and conducting indoor model tests of uplift piles, the strain distribution and evolution patterns of both steel bars and concrete during the pulling process were analyzed. The alterations in the elastic modulus of concrete throughout the tension-failure process were thereby obtained. A method for optimizing the axial force calculation, which exploits the relationship curve between the concrete strain and elastic modulus, was put forward. This enables the accurate acquisition of the axial force of the pile body and its subsequent application in practical engineering projects. The test results indicate that under the condition of small loads, the OFDR technology can identify the locations where concrete cracks emerge based on the strain curve of the pile body. In the event of pile body failure under large loads, the elastic modulus of concrete can be rectified using the relationship curve between strain and elastic modulus. Compared with traditional calculation methods, the relative error of the axial force throughout the entire process can be confined within 5%. The viability of this approach has been corroborated in actual engineering endeavors, and the optimized axial force calculation exhibits enhanced precision.

In order to better study the spatial distribution of co-seismic landslides of the Luding earthquake, satellite images of Sentinel-2 on July 8 before and October 1 after the 2022 Luding earthquake were acquired, and a study area of 145.6 km² was obtained after cropping. Supervised classification was performed on the two images using the minimum distance method, maximum likelihood method, and neural network method, respectively, and the classification results were verified by calculating the overall accuracy and Kappa coefficient. Finally, the supervised classification results of the neural network method of the two images were selected for comparison to obtain the change detection results, and a total of 2 247 co-seismic landslides were identified in the study area, covering an area of 22.61 km², which accounted for 15.53% of the total study area. In the statistics of the slope direction, it was found that the slope direction of the co-seismic landslides radiated in the vertical direction of the originating fault to both sides, indicating that the co-seismic landslides were influenced by the originating fault. The analysis of elevation distribution pointed out that the landslides were mainly concentrated in the elevation range of 1 000 m to 2 000 m, accounting for more than 90% of the total. The analysis with other factors indicated that the landslide events were mainly concentrated near rivers, roads and on mountain slopes. The results of the principal component analysis indicates that the most important factor influencing the incidence of landslides in the same earthquake is the distribution of topographic deposits.

Studying the characteristics of airport traffic flow fluctuation range is fundamental for efficient traffic management and control. Mastering these characteristics plays a crucial role in maintaining the stability and effectiveness of overall airport operations. Considering the irreversibility of time and the cumulative impact of traffic congestion, which occurs when airport traffic exceeds facility capacity within certain time intervals, a method for constructing an adaptive crossing network was proposed. From the perspective of complex network topology, both the overall characteristics of the network and the centrality of nodes were analyzed. The integrated centrality of nodes was calculated using the independent weighting coefficient method, enabling the identification of key time nodes that are core hubs of strong fluctuations within the network. The results show that the adaptive crossing network, mapped based on the traffic data from Beijing Daxing International Airport, exhibits characteristics of complexity and order, featuring scale-free properties, assortativity, and a distinct community structure. The time period from 21:20 to 22:25 (nodes 257~269) ranks highly across various centrality measures, indicating a significant fluctuation impact range, and thus, these nodes are identified as core hub nodes within the network. The integrated centrality synthesizes various topological centrality features of the network, and through quantitative analysis, effectively characterizes the strong fluctuation nodes within the network. This method provides a theoretical basis and practical reference for the optimization of airport traffic flow management and the study of abnormal fluctuations, offering a new perspective for enhancing airport operational efficiency and safety.

As a large transportation hub, airport terminals have complex structures, and the evacuation efficiency becomes extremely important when an emergency occurs. To improve the evacuation efficiency of the terminal building, an improved A* algorithm has been proposed for selecting the optimal evacuation path based on the actual distribution of personnel. Firstly, the flow of personnel in the terminal building was simulated, and data on the distribution of personnel was obtained. Then the time-varying distribution of personnel in each area was considered in the cost calculation of path selection. Finally, the A* algorithm was improved in terms of traversal methods, network weights, other factors, and congested paths were replanned to avoid congestion. The results indicate that considering the distribution of passengers in the terminal improves the evacuation paths at each node, which leads to shorter evacuation time compared to traditional A* algorithm. It also allows for the avoidance of congested paths. This study can provide theoretical and methodological support for the rapid evacuation of passengers in the terminal under emergencies.

With the increase of air cargo volume, cargo plans are frequently interrupted due to disruptions in cargo demand, so rescheduling flight schedules is the core issue for air cargo recovery. An air cargo recovery model based on spatio-temporal network method was proposed with the goal of maximizing the profits of airlines under the disturbance of temporary increase in demand. Aircraft routes, cargo routes and flights were reorganized in the model and the initial flight plan was preserved as much as possible by adding penalty factors. In order to verify the effectiveness of the model, the model was solved using CPLEX solver. The proposed spatio-temporal network-based air cargo recovery model was compared with the model in reference. The results show that the proposed model has significant advantages in computational efficiency and finding optimal values, and the advantages become more apparent with the increase of the case size. The sensitivity of the model's solution results to the time window width and aircraft carrying capacity was analyzed. The results show that the narrower the time window, the slower the solution speed, while as the time window width increases, the solution speed accelerates and tends to stabilize. As the carrying capacity of the aircraft gradually increases, the solving speed of the model becomes faster and tends to be stable.

Hyperspectral remote sensing widely uses unmanned aerial vehicles (UAV) as flight platforms for data collection, which has the advantages of flexibility and efficiency. However, due to UAV performance and environmental conditions, it is difficult for sensors to maintain a fixed shooting posture during the collection process, resulting in data misalignment, distortion, and deformation. While UAV positioning systems and inertial measurement devices provide real-time position and posture for hyperspectral cameras, achieving high accuracy often necessitates numerous ground control points for auxiliary geometric correction, which is time-consuming and labor-intensive. Therefore, it is necessary to study an efficient and time-saving data processing method to correct distortions in hyperspectral data acquisition. In order to efficiently and time-saving eliminate distortions in hyperspectral data during the acquisition process, an unmanned aerial vehicle (UAV) push scan hyperspectral camera data acquisition system was designed based on the principle of collinearity equations. The system integrates a high-precision inertial measurement system and synchronously collects LiDAR point cloud data in the measurement area. The high-precision terrain information contained in the LiDAR point cloud was used for geometric correction of hyperspectral data, and the influence of different density point cloud data on the geometric correction results was studied. Experiments have shown that using LiDAR point clouds improves accuracy by 67% compared to using average elevation geometric correction results. The use of LiDAR and hyperspectral cameras for synchronous acquisition has a significant effect on improving the accuracy of hyperspectral data.

The oxidation of Sb(III) occurred rapidly in aerobic and dark groundwater environments, with previous studies suggesting that co-oxidation of Fe(II) and Sb(III) may be the predominant driving mechanism. However, there is a lack of field evidence confirming environmental isotope fractionation. Therefore, 20 groups of Magunao aquifer (${\mathrm{Dx}}_{3}^{4}$ water) samples were collected from the North mine of Xikuangshan antimony mining area in Hunan Province to compare the differences in environmental isotopes (δ⁵⁶Fe, δ¹³C, and δ³⁴S) between high- and low-Sb groundwater and investigate the fractionation process of these isotopes. The results reveal that total Sb(^TSb) concentrations ranged from 5.30 μg/L to 20 700 μg/L, with a mean concentration of 3 660.61 μg/L. Additionally, Sb(V) is found to be the most dominant valence state for Sb in ${\mathrm{Dx}}_{3}^{4}$ water. The neutral-alkaline and oxygen-enriched conditions in ${\mathrm{Dx}}_{3}^{4}$ water facilitate the co-oxidation of FeS₂ and Sb₂S₃, as well as induce fractionation of δ¹⁸${\mathrm{O}}_{\mathrm{S}{\mathrm{O}}_{4}}$, δ³⁴${\mathrm{S}}_{\mathrm{S}{\mathrm{O}}_{4}}$and δ⁵⁶Fe between sediments and groundwater, resulting in the increase of $\mathrm{S}{\mathrm{O}}_{4}^{2-}$,total Fe (^TFe) and Sb(Ⅴ) contents in high Sb groundwater. Furthermore, microbial activities promote the oxidative decomposition of organic carbon, thereby enhancing the co-oxidation rate of Fe(Ⅱ) and Sb(Ⅲ). This conclusion unveils a novel mechanism for aerobic oxidation of Sb(III) in dark groundwater environments while providing a scientific foundation for advancing our understanding of the Sb geochemical cycle and preventing environmental pollution from high Sb groundwater.

Reliability analysis and allocation were conducted on the propulsion system of multi rotor electric vertical takeoff and landing (eVTOL) aircraft. Firstly, to solve the problem of insufficient accumulation of reliability historical data of multi-rotor eVTOL aircraft, a reliability analysis model was established by using fuzzy Bayesian network (FBN) to supplement the reliability prior data, and reliability posterior inference was carried out to assist the key link of the positioning system. Secondly, based on the FBN reliability analysis model of the system, an improved advisory group on reliability of electronic equipment(AGREE) reliability allocation method was proposed. Reliability distribution of eVTOL propulsion systems with different configurations was carried out. The results show that the FBN reliability analysis model supplements the propulsion system reliability data and can effectively identify the system weak links. The reliability allocation results of the improved AGREE allocation method meet the reliability requirements for eVTOL aircraft in SC-VTOL-01, while the reliability allocation results obtained by this method are more reasonable, reflecting the differences between different configurations, subsystems, and components.

Compressive strength is an important index to characterize the mechanical properties of filling body. It is of great significance to ensure the safety of stope by quickly and accurately determining the compressive strength of filling body. In order to explore the influence law of the strength of multi-source coal-based solid waste filling body and accurately predict the strength of coal-based solid waste filling body to guide the safe, efficient and green mining of coal mine, the influencing factors of the compressive strength of coal-based solid waste filling body were studied by orthogonal test with coal gangue as coarse material, desulfurization gypsum, gasification slag and bottom slag as fine material, fly ash and cement as cementing agent. The grey correlation degree analysis method was used to analyze the correlation between each test factor and the compressive strength of filling body. The strength prediction of coal-based solid waste backfill at different curing ages was carried out by using 5-11-3 three-layer back propagation(BP) neural network structure. The results show that the influence of concentration, gasification slag and desulfurization gypsum content on compressive strength increases with the increase of curing age, and the influence of fly ash and bottom slag content on compressive strength increases first and then decreases with the increase of curing age. Orthogonal test combined with BP neural network can reduce the number of tests without losing generality. The correlation coefficient R of strength prediction of coal-based solid waste backfill is 0.999 87. It can be seen that high concentration and high content of gasification slag and desulfurization gypsum are of great significance for filling body requiring high strength. At the same time, orthogonal test combined with BP neural network can accurately predict the strength of filling body.

The spatial variation characteristics of soil fertility in potato growing area were clarified to provide theoretical basis for soil precise fertilization and fertilizer management in the study area. Taking Keshan Farm in Heilongjiang Province as the study area, 100 sample points were selected in the potato growing area, and soil pH, organic matter, total nitrogen, total phosphorus and total potassium were selected as the indicators to evaluate soil fertility. Geostatistics and geographic information system(GIS) were combined to analyze the spatial variation characteristics of soil nutrients, and soil comprehensive evaluation method was used to evaluate soil fertility in the study area. Results show that the soil is weakly acidic and the pH variation coefficient is small. The contents of organic matter, total nitrogen, total phosphorus and total potassium are at medium and high levels, belonging to moderate intensity variation. Soil pH is a moderate spatial autocorrelation, and the spatial autocorrelation of organic matter, total nitrogen, total phosphorus and total potassium is weak. The spatial accumulation of organic matter and total nitrogen is significant. The spatial variation of soil nutrients in the study area is obvious, showing an east-west direction, and the content of soil nutrients in the middle of the study area is relatively low. The soil fertility in the study area is above the medium level, and the area with good fertility accounts for 72% of the total area. The soil fertility of Keshan farm is good, which can meet the needs of potato growth. Human factors are the main factors affecting soil nutrient content.