Container terminal yard as the hub of the operation area on both land and sea sides, the space allocation will directly affect the overall operation efficiency of the terminal. In order to reasonably allocate the space of U-shaped yard to improve the efficiency of container loading, two blocks sharing one intelligent guided vehicle(IGV) lane were formed into a group. From the perspective of reducing lane congestion, considering the constraints of high and low workloads of adjacent operation areas within the block groups, and with the goal of minimizing the total loading operation time and balancing the number of containers in the block groups, the first stage of the export container allocation model was constructed, and an immunity compensation mechanism and Metropolis criterion were introduced. And an improved adaptive genetic algorithm was designed by introducing immune compensation mechanism and Metropolis criterion. Based on the allocation results of the first stage, a priority descending balanced stockpiling strategy was established to ensure the continuous operation of the yard crane, and the second stage of the container allocation model was constructed, and a heuristic algorithm was designed to solve the problem. The design of large-scale case experiments shows that the algorithm is able to give effective export container stockpiling area delineation and container space allocation scheme, and the performance of the algorithm is verified by different scale comparison experiments, which shows that the algorithm has a faster convergence speed and is able to optimize 10% of the target value, which improves the overall operational efficiency of the U-shaped automated terminal.

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.

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.

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.

In order to address the challenges posed by multifaceted risk factors in air traffic control operations, a comprehensive analysis of unsafe operational incident reports was performed to extract risk-related information and identify underlying patterns. The latent Dirichlet allocation (LDA) model was utilized to uncover key risk topics and associated keywords, and the evolutionary relationships among different risk themes were systematically analyzed. A semantic network for the civil aviation air traffic control domain was constructed using the bidirectional encoder representation from Transformers(BERT) model to examine the interconnections and potential dependencies among risk topics. This network provides a theoretical foundation for quantifying the association between keywords. The findings indicate that the proposed approach enhances the digital representation of safety risks in air traffic control operations. It is concluded that the results offer valuable insights for advancing risk assessment and mitigation strategies in civil aviation air traffic control systems. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

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.

In order to further explore the pipe-soil interaction and the failure mechanism of buried pipelines during the collapse process, the element birth and death technology and the displacement load technology were used to simulate the soil collapse process. The coupling models considering the pipe-soil nonlinear effect were constructed respectively to analyze the failure law of buried pipelines under collapse. The results show that the bottom of the pipe jacking is the control point, the axial stress is the control stress, and the junction of the non-subsidence area and the subsidence area and the center of the subsidence area are dangerous sections. Applying displacement load technology to element birth and death technology, the location of the most dangerous section transitions from the junction of non-subsidence area and subsidence area to the center of subsidence area, and the peak value of Von-Mises stress increases from 4.3 MPa to 6.09 MPa. The application of displacement load technology makes the lower part of the pipeline always in contact with the soil, and the pipeline is subjected to strong shear near the junction of the non-subsidence area and the subsidence area. The element birth and death technology is lost with the soil, and the pipe-soil state gradually changes from contact to separation, which is a weak shear effect. The Von-Mises stress peak is larger and the radial stress is smaller, which can better reflect the actual situation. According to the stress and settlement values, the maximum Von-Mises stress prediction formula is fitted, and the error is within 7%. The research results can provide reference for the safe operation of pipelines and the selection of collapse simulation methods.

According to the fault data of a certain type EMU traction system in China in 2022, the location and frequency distribution of key faulty equipment were analyzed, and the impact duration caused by various failure problems was counted. These two are combined as the spatio-temporal characteristics of EMU traction system faults. The distribution model was selected to compare the duration distribution characteristics of various faults, and the K-S test method was used to analyze the fitting effects. The results show that the fault influence time of pantograph, traction converter and roof high-voltage cable is the most suitable for the logistic distribution model fitting, while the lognormal distribution is most suitable for the traction transformer, main circuit breaker and traction motor. It is of great significance to predict the failure time of EMU traction system, point out the optimization direction of system equipment maintenance, and improve the efficiency of train operation and scheduling.

In the petroleum industry, the CO₂ flooding technology plays an important role in many EOR methods. In recent years, CO₂ flooding technology has attracted more attention because of its positive contribution to carbon storage, but CO₂ injection will greatly increase the risk of corrosion failure of oil casing, and the development of corrosion inhibitors and the research on inhibition mechanism have achieved certain results. The research progress of organic corrosion inhibitors was systematically summarized from the perspective of the inhibition mechanism of organic corrosion inhibitors on CO₂ corrosion in the petroleum industry. The adsorption, reinforcement, bridging and hydrophobic film formation mechanisms of organic corrosion inhibitors were mainly introduced. The inhibition effects of organic amines, imidazolines, surfactants, polymers and carbon dots on CO₂ corrosion were compared and analyzed from the mechanism of functional groups and metal surfaces. The research on the inhibition mechanism of organic corrosion inhibitors in CO₂ environment and the development trend and focus of corrosion inhibitors were prospected.

Structural design is an important part of the architectural engineering design stage, which must ensure that the building is safe, reliable, economical, and durable. Artificial intelligence can replace structural designers with a lot of training and repetitive operations to find the optimal design results and improve design efficiency. In order to comprehensively understand the relevant research and application hotspots of artificial intelligence in structural design, the current research status of artificial intelligence in the three stages of scheme design, preliminary design and construction drawing design was summarized from the perspective of the entire structural design process. Through reviewing literatures, it is found that artificial intelligence methods such as expert systems, decision trees, annealing algorithms, genetic algorithms, neural networks, and linear regression have been widely used in the field of building structure design, which has brought new development directions and approaches. At present, artificial intelligence methods are more widely used in the design of aboveground structures, but less in underground structures (foundations, basements, etc.), and their application in underground structures needs to be strengthened. In addition, the quantitative translation technology of normative provisions is relatively mature, but the qualitative translation technology of normative provisions still needs to be broken, and it is necessary to strengthen the research on rule-based or machine learning-based natural language processing.