Traditional robot localization and navigation methods in complex building environments are characterized by low accuracy, heavy reliance on sensors, and an inability to effectively address dynamic obstacles, making it challenging to achieve satisfactory results in practical applications. To address these issues, building information modeling (BIM) technology was introduced. BIM, with its geometric and semantic information, was utilized to assist robot localization and navigation in complex environments. More accurate environmental perception and optimal path planning were provided to robots, reducing the risk of collisions with environmental components and improving the accuracy and efficiency of task execution. The current status of BIM technology in robot localization, mapping, and path planning was compared, the advantages and challenges of its application in architectural environments were analyzed, and future prospects for its application in intelligent buildings and robotic intelligence were explored.

The civil engineering industry faces with a vast array of unstructured textual information during its digital transformation. Large language models (LLMs) provide a new opportunity for the intelligent transformation of the industry because of its powerful natural language processing capability. A systematic literature review approach was employed, and based on the technical framework of LLMs and the current state of research in vertical domains, four major application scenarios for LLMs in civil engineering were suggested, along with corresponding technological approaches, challenges faced, and research trends. It is found that exploratory research and application of LLMs in civil engineering have been conducted, primarily focusing on content creation, intelligent Q & A, text summarization, and analytical reasoning, covering the entire lifecycle of civil engineering projects and featuring interdisciplinary and multimodal integration. However, the utilization of LLMs struggles with low specificity of knowledge, poor timeliness of information, and inferior data quality and interactivity. Based on this, a series of future research opportunities were proposed to enhance the breadth and depth of LLMs application in the field of civil engineering by using parametric efficient fine-tuning technology to inject expertise in model optimization. Combined with knowledge graph, LLMs can improve the accuracy, interpretability and timeliness of answers. Multi-modal data types were integrated to expand the application scenarios of LLMs in civil engineering. Appropriate model evaluation methods were developed to quantify the value and performance of LLMs applications in civil engineering. In terms of application scenarios, combined with the characteristics of LLMs and civil engineering fields, the application of LLMs in complex tasks such as document generation, question and answer system, information extraction and compliance review can be expanded, and the interaction efficiency between practitioners and data can be improved. The purpose of the study is to provide reference for the academic and business circles to further apply LLMs in the field of civil engineering.

The Shenzijing-Hashituo Subsag is located in the central part of the Changling fault depression in the Songliao Basin, an area with promising oil and gas exploration prospects. It represents a favorable oil and gas accumulation zone within the Songliao Basin. Understanding the tectonic evolution of this region and its influence on hydrocarbon genesis is crucial for guiding future exploration and development efforts in the Changling fault depression. Based on the structural characteristics, ancient drop of faults, activity rate and growth index method, the structural evolution of the depression was analyzed. Combined with the characteristics of stratum erosion, it was determined that the Shenzijing-Hasituo subsag was co-deposited in the early stage, and the Shahezi Formation was affected by compression and uplift at the end, resulting in overall erosion and segmentation. The uplift on the west side of the Hasituo depression was further strengthened during the Denglouku Formation period. At the same time, it is clear that the tectonic evolution of the fault depression layer in the Shenzijing depression-Hatshituo depression of the Changling Fault depression follows the longitudinal evolution law of fault depression-co-deposition-inversion. This early rapid subsidence and late inversion and uplift form a relatively favorable accumulation combination model of lower generation and upper storage. The research results can provide theoretical references for the restoration and further exploration of the ancient structure of the Changling fault depression.

The generalized finite difference method for seismic wavefields numerical simulation is capable of adapting to undulating stratigraphic interfaces, eliminating the staircase scattering effect caused by such interfaces, and enhancing the accuracy of forward modeling. However, when the second-order generalized finite difference method is used to solve the wave equation, low temporal accuracy can lead to temporal dispersion at larger time intervals, affecting the precision of forward simulation. A fourth-order generalized finite-difference forward modeling algorithm for the scalar wave equation was studied, along with its stability conditions and dispersion characteristics. By transferring the fourth-order time derivative to the spatial derivative term, fourth-order accuracy in time was achieved, effectively suppressing temporal dispersion. Compared to the second-order generalized finite-difference method, the fourth-order approach allows for larger time intervals, thereby reducing computational costs to some extent. Experimental results demonstrate that the proposed algorithm effectively mitigates both staircase scattering and temporal dispersion, yielding higher computational accuracy. When applied to reverse time migration, it produces high-quality imaging profiles.

In order to study the relationship between rainfall and regional geological disaster risk, construct the meteorological warning model of geological disasters in Huangshui Basin, and provide reference for the geological disaster warning work in the basin. Huangshui Basin in Qinghai Province was taken as an example, where the susceptibility to geological hazards was evaluated using the information value method. The independence of each factor was tested using the Pearson correlation coefficient, and the accuracy of the evaluation results was assessed through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Subsequently, a geological-rainfall coupling model was also established using the logistic regression method, incorporating rainfall data, to carry out geological disaster meteorological risk warning and verification. The results show that Huangshui Basin geological disasters of very high susceptibility area and high susceptibility area is mainly distributed in the river valley plain zone and its tributaries at all levels in the middle and lower reaches of the two sides, the region is a geological disaster area. The results of the susceptibility evaluation are assessed for precision, and an AUC value of 0.785 is found. Geological-rainfall coupled warning model in the susceptibility value, the day rainfall, the first 1 day rainfall, the first 2 days day rainfall, the first 2 days of rainfall and the first 3 days of rainfall on the disaster are all positive, the model's prediction accuracy for the occurrence of geological disasters is 84.2%, and the AUC value is 0.864. Based on the five typical geological disasters that occurred in Huangshui Basin on 29th August 2020, the established geological-rainfall coupling model was verified, and it is found that with the geological-rainfall coupling model warning, four geological disasters reach the first warning level, and the other one reaches the second warning level. Combined with the warning rainfall threshold, the model was used to achieve the early warning of the landslide in Hongyagou Village on 5 September 2024. It can be seen that the constructed meteorological early warning model for geological disasters in the Huangshui Basin has a good early warning effect and a relatively high prediction accuracy rate, which can provide references for disaster prevention and mitigation for relevant departments.

The current acupoint automatic positioning technology for massage robots is faced with issues such as dress restrictions, limited application scope, and poor positioning accuracy. A new method for human back acupoint positioning, based on back morphology classification and a multilayer perceptron network morphology classification-multilayer perceptron-based accurate point location(BMC-MPAPL) has been proposed. A substantial collection of diverse human back images, along with skeletal key point positioning techniques, kernel density estimation, and the maximum interclass variance approach, was used to investigate the statistical distribution and effective categorization of back morphologies. To counteract the restrictions imposed by clothing on positioning, a dataset encompassing key back points and the Dazhui acupoint was developed based on classification outcomes, and the automatic positioning of the Dazhui acupoint was accomplished through a deep learning model of the multi-layer perceptron network. Utilizing the Dazhui acupoint's positioning results, a human coordinate system was established, and the automatic positioning of 60 common back acupoints was achieved with the integration of ancient Chinese bone measurement methods. The results show that the Dazhui acupoint positioning model, tailored to various back morphologies, has realized high-precision positioning, with an average accuracy of 94.87% at an allowable error of 20 mm, marking a 13.37% enhancement over models without back classification. For other common back acupoints, the positioning accuracy stands at 91.58% within an allowable error of 20 mm, irrespective of patient attire or background constraints. It is concluded that the method presented effectively enhances the accuracy and applicability of acupoint automatic positioning.

Forest fires pose a significant threat to human lives and property. Accurate prediction of forest fire risk is crucial for disaster mitigation and prevention. Influenced by factors such as terrain, meteorology, vegetation cover, and human activities, the causes of forest fires exhibit regional differences. This study uses historical forest fire events in Muli County, Sichuan Province as the response variable, with terrain, meteorological data, vegetation cover, and human activity data as explanatory variables. Leveraging CatBoost's strengths in handling high-dimensional sparse data and classification problems, a high-precision forest fire prediction model was constructed based on CatBoost. The experimental results indicate that, compared to random forest (RF), extreme gradient boosting(XGBoost), and gradient boosting decision trees(GBDT) models, the CatBoost model achieves higher modeling accuracy and significantly improves forest fire prediction accuracy, with a prediction accuracy rate of 91.36% and an area under curve(AUC) value of 0.970. Predictions made using this model can provide valuable references for the early prevention of forest fires in Muli County.

In order to meet the needs of fast and efficient transportation for coal mine drilling construction operations with trackless transportation conditions, a trackless rubber tyre vehicle integrated drilling rig was used to travel directly from the ground to the drilling site without the need for secondary transportation. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. Based on the overall technical requirements, multiple key technologies of the trackless rubber tyre vehicle were designed and analyzed, and the static load capacity and dynamic characteristics of the frame under two working conditions, namely the driving process and the drilling process, were simulated and analyzed. The simulation results show that the stress and displacement meet the design requirements of the frame load capacity during the walking and drilling processes, and it has good stability. After the installation and adjustment of the rubber wheel chassis, a climbing test and comprehensive performance test were carried out. The test shows that the whole vehicle meets the 15 ° large angle climbing condition, all data meet the standard requirements, and the performance meets the transportation needs of the coal mine drilling rig. It can adapt to the complex working conditions underground in coal mines.

Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.

In deep heavy oil reservoirs, substantial heat losses during steam injection are often associated with suboptimal steam chamber development, significantly reducing the efficiency of steam flooding. A novel steam chamber expansion model was introduced, incorporating a wellbore heat loss coefficient derived from vapor-liquid interface theory and heat transfer principles. Compared to existing models, the modified model was shown to predict a more pronounced steam override and a larger steam-swept area at the reservoir top. Validation against field monitoring data reveals a deviation of only 7.61%, demonstrating strong agreement with actual development conditions. Further analysis of the wellbore heat loss rate and steam chamber morphology shows that the heat loss rate peaks early in the injection process and subsequently decreases over time. It is observed that the wellbore heat loss rate increases with greater reservoir depth. Additionally, the mobility ratio is found to be negatively correlated with steam chamber development, while the shape factor is positively correlated, with larger shape factors resulting in a wider steam-swept area and a reduced impact of steam override. The research is closely integrated with theoretical concepts and practical applications, enabling rapid and accurate predictions of steam chamber front positions, optimizing steam injection parameters, and informing the design of development strategies for medium to deep heavy oil reservoirs.

The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation, located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region, pose significant challenges to reservoir evaluation and natural gas exploration and development. Traditional diagenetic facies identification methods are often low in accuracy, heavily reliant on specialized personnel, and time-consuming. There is an urgent need for a diagenetic facies identification method that is highly accurate, cost-effective, and fast. Firstly, based on cast thin section identification data, the lithology of the tight sandstone was determined using a ternary plot of components. Image processing techniques were then used to identify the types and proportions of pores and cements, and the diagenetic facies of the tight sandstone were classified. Secondly, the corresponding 1 019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range, median, uniformity, and skewness. These 6 types of well log data were standardized to a 0-1 range, and data imbalance was addressed using synthetic minority over-sampling technique (SMOTE). Finally, 10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison. The study found that ensemble learning algorithms, especially the extreme randomized trees (ET) algorithm, performs best in diagenetic facies identification, achieving higher accuracy and F₁ scores than traditional machine learning algorithms. This significantly improved identification accuracy and stability. The ET model was then used to predict the diagenetic facies of the JQ8 well, validating the feasibility of the method. This study provides effective technical methods and references for diagenetic facies research in tight sandstones.

As large-scale fracturing in the development of deep shale gas results in rapid production decline, the accurate understanding of gas-liquid flow patterns is considered essential for stabilizing gas well production. Gas well models with two different wellbore trajectory structures were established, and OLGA software was applied to conduct transient calculations on models with varying tubing depths. The results indicate that in deep shale gas well A₁, slug flow only occurs in the build-up section and above, while in well B₁, slug flow appears in the horizontal section and near the build-up section. Considering cumulative gas production and liquid loading, the optimal tubing depth for deep shale gas wells A₁ and B₁ is at the heel of the horizontal section, while for conventional shale gas wells A and B, the optimal tubing depths are at the heel of the horizontal section and one-third of the horizontal section. Deep shale gas wells are more favorable for drainage and production compared to conventional shale gas wells.The optimal tubing setting depths for conventional shale gas wells with two deep formation wellbore configurations are at the horizontal section heel and the one-third point of the lateral, respectively. It is determined that deep shale gas wells are more advantageous for drainage and production compared to conventional shale gas wells. As the tubing size decreases, both the gas production and the corresponding critical gas flow rate for liquid carryover are reduced. It is also found that the greater the light hydrocarbon content in the shale gas composition, the higher the gas production. This study is intended to provide a reference for determining the rational tubing placement in the drainage and gas production processes of deep shale gas wells.

The offshore heavy oil thermal recovery platform has the characteristics of small space, high steam injection temperature, and high steam injection pressure, with temperatures up to 300 ℃. Once high-temperature and high-pressure steam leaks, it will cause serious consequences and pose a huge threat to equipment and inspection personnel. An effective steam leakage monitoring method was urgently needed. In order to solve these problems, the influence of thermodynamics, fluid mechanics and other factors were considered comprehensively to study the mechanism of steam leakage monitoring in offshore heavy oil thermal recovery. A virtual sensing monitoring method based on mechanism and inference was proposed, and for the first time, the indirect measurement method of steam leakage was applied to steam leakage monitoring in offshore heavy oil thermal recovery. A steam leakage monitoring model was built, and a hybrid sensing technology suitable for steam leakage monitoring in offshore heavy oil thermal recovery was formed for real-time online monitoring of steam leakage. The results show that this method can achieve leak discrimination and leak estimation based on operational data, and directly characterize the failure state of steam leaks online. The minimum detectable leak rate can reach 0.5%, and the accuracy of leak discrimination is above 96.49%. Compared with traditional methods, the minimum detectable leakage rate has increased by 90%, and the leakage discrimination rate has increased by at least 1.6%. This method solves the problems of limited installation of physical sensors on site, difficulty in obtaining effective monitoring data, and limited accuracy due to personnel experience, making up for the shortcomings of on-site monitoring methods for thermal recovery platforms and providing safety guarantees for offshore heavy oil development.

The plunger pump is one of the important power conversion components of the hydraulic system, and its performance directly affects the safety and stability of the hydraulic system. In order to accurately evaluate the operating status of the plunger pump, a plunger pump health status assessment method based on a combination of convolutional neural network(CNN) and long short-term memory network(LSTM) was proposed, and a genetic algorithm was introduced to optimize the parameters of the neural network. The vibration signals of the plunger pump at different operating moments were collected. The energy characteristics of the vibration signals were extracted by using wavelet packets. Combined with the time-frequency domain characteristics of the signals, a dataset of the health status characteristics of the plunger pump was constructed. The health status was identified and classified by the CNN-LSTM method, and the classification results were evaluated by sample entropy. To verify the effectiveness of this health assessment method, it was applied to the experimental test of the plunger pump. The results show that the recognition accuracy of this method reaches 99%, which can effectively improve the accuracy of the health status assessment of the plunger pump.

In order to guide the optimal design and daily maintenance of explosive capacity bombs, the response characteristics of 20 L explosive capacity bombs under the explosion action of explosives were studied. Internal explosion tests of different masses of trinitrotoluene (TNT) explosive capacity bombs were designed. The strain distribution and variation law of the outer wall of the explosive capacity bomb were measured, and the internal explosion impact overpressure of the explosive capacity bomb was obtained. According to the test results, the equal-scale simulation calculation model was calibrated, and the propagation law of shock wave inside the explosive capacitor bomb after the explosive explosion was calculated, and the pressure distribution in the explosion field was obtained. The results show that the simulation results of internal pressure of explosive charge are basically consistent with the experimental results. Under the action of explosive explosion, the internal pressure of explosive capacitor presents a complex structure of multi-peaks, the maximum pressure appears at the corner of explosive capacitor, and the maximum strain of the outer wall of explosive capacitor appears at the lower position of the explosion center plane.

In order to study the influence of floating offshore wind power suction anchor size on the horizontal bearing capacity, based on the ABAQUS finite element software, a three-dimensional finite element model of suction anchor foundation was established by using elastic-plastic constitutive model. The results show that the increase of diameter and height can improve the horizontal bearing capacity of suction anchor, and the increase of height is more obvious. With the change of diameter and height, the position of the mooring point also needs to be adjusted accordingly. When the ratio of suction anchor diameter to height D/H>1, the position of the mooring point needs to be increased correspondingly to make it move in translation. The change of diameter will affect the pressure change of the anchor wall, while the height has little effect on the pressure change of anchor wall. The research results are used in global first offshore floating wind power + aquaculture platform “Guoneng Gongxiang Hao” and can provide a reference for relevant project design.

The cathode flow channel of proton exchange membrane fuel cell (PEMFC) serves as the site of oxidant reduction, and the interaction of the flow channel configuration and operating parameters is one of the keys to enhance the performance of the cell. A three-dimensional proton exchange membrane cell model with cathode sidewall shrinkage runner was established to meet the design requirements of PEMFC sidewall shrinkage runner regarding the operating parameters. The changing rules of electrochemical performance, temperature distribution on the membrane surface and water content distribution were investigated under different temperatures, pressures and cathode stoichiometric ratios. It is shown that under the constant operating parameters, the current density curves of the ridge centerline and the flow channel centerline are impulsively fluctuated, and the temperature curves of the membrane surface and the water content curves of the membrane surface are regularly fluctuated. The current density, temperature and water content at the ridge centerline are obviously higher than those at the flow channel centerline. Under the change of operating parameters, when the pressure is increased from 0.1 MPa to 0.3 MPa, the current density is increased from 0.860 A/cm² to 1.500 A/cm², with an increase of 74.4%. When the temperature is increased from 50 ℃ to 80 ℃, the current density is increased from 0.822 A/cm² to 0.856 A/cm², with an increase of 4.1%. And when the cathode stoichiometry ratio is increased from 10 to 90, the current density is increased from 1.502 A/cm² to 1.568 A/cm², with an increase of 4.4%. Furthermore, a PEMFC output performance evaluation method based on the combined assignment method and the improved radar diagram method has been established. The cathode sidewall-retracted proton exchange membrane fuel cell is shown to exhibit excellent output performance under the operating parameters of 0.25~0.3 MPa, 70~80 ℃ and the stoichiometric ratio in the range of 70~90.

As the trend toward more-electric and all-electric aircraft accelerates, multi-electric engines have become a key technology. A coaxial high-torque permanent magnet synchronous motor based on a new type of rotary cylinder disc engine was designed to achieve the integration of the engine and the motor. Firstly, based on the relationship between the engine performance parameters and the drive shaft, the coaxial structure and motor dimensions were determined. Secondly, the inhibitory effect of the number and size of the flat wire winding layers on copper loss was analyzed through the finite element soft analysis. Meanwhile, the motor topology was optimized by using the rotor segmented inclined pole, auxiliary slot and Taguchi algorithm to reduce the cog-slot torque, rated torque ripple, stator iron loss and air-gap magnetic flux density distortion rate of the motor, significantly improving the electromagnetic performance. Finally, the various working conditions of the motor were simulated, the driving and power generation efficiencies were calculated, and it is verified that the motor does not demagnetize under the condition of large current. Results indicate the motor delivers 200 N·m of torque at a rated speed of 6 000 r/min, with a peak torque of 400 N·m and a maximum power output of 250 kW in high-speed generation mode, meeting all design specifications.

The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.

With the rapid development of the power system, the large-scale integration of new energy into the grid and the coordinated optimization of source-grid-load-storage have increased the proportion of power electronic equipment, making the stability of the power grid, especially the assessment of transient stability, particularly important. Aiming at the problem of insufficient consideration of topological structure in traditional methods, a deep learning method based on Transformer-graph attention network(GAT) parallel feature fusion was proposed for the transient stability evaluation of power systems. The busbar voltage amplitude, phase angle and topological adjacentation matrix were taken as input features. Batch data were generated using the Siemens simulation software PSS/E, and features were extracted in parallel through Transformer and GAT. Weighted fusion was carried out using the attention mechanism. The comparison results with other methods show that this method simulates different load conditions and fault conditions in the IEEE 39-node system. The results indicate that the evaluation accuracy and robustness are relatively high, and it can effectively improve the safety and stability of the power system.

The stepped frequency ground penetrating radar has the advantages of high sensitivity, large dynamic range, and high average power. Radio frequency system on chip(RFSOC) based stepped frequency ground penetrating radar transceiver system design was designed and implemented. The system mainly includes modules such as the transmission link, reception link, clock unit, field programmable gate array(FPGA) control system, and data transmission unit. By setting multiple numerically controlled oscillator (NCO) for synchronization between the transmission and reception ends, the carrier was synchronized on the same frequency and phase, ensuring the phase coherence of radar transmission and reception signals. A receiving time window was also designed in the data transmission unit. The experimental test results show that the system can achieve the transmission and reception of step frequency signals with a frequency range of 200 MHz~2 GHz and a step size of 2 MHz, and can effectively detect multiple scene targets such as free space, sand pits, and asphalt pavements.

In order to solve the problem of uneven data distribution or confusion of characteristics in material supply chain under multi-source data, a data balance processing method of material supply chain based on multi-source data was proposed. The data distribution space of material supply chain was established, non-boundary and boundary areas were set, the center of the area was calibrated, the distances between different characteristic data and the center point were calculated respectively, the unbalanced data was searched in advance by using the edge mixed sampling algorithm, and the boundary area to which it belongs was determined according to the data distance characteristics. The unbalanced data was regarded as the working nodes in the supply chain cluster. When the number of working nodes in the cluster changes, the load generated by each cycle execution node was obtained by flow calculation, and the load value was converted into the execution thread score. The unbalanced data was obtained by comparing the scores, the threshold of data memory and CPU resource consumption in the material supply chain was calculated, and the parallel task thread was established. The scheduler was used to transfer the execution thread in the working node to the next node to achieve the purpose of load balancing. The experimental results show that the proposed method has the advantages of short response time, large data throughput, good processing effect and strong stability, and has good practical application value.

In order to achieve accurate segmentation of surgical instruments, a dual-encoding network surgical instrument segmentation method was proposed based on improved Swin Transformer. By taking advantage of different coding advantages of Swin Transformer and convolutional neural network(CNN), the global semantic information and local details of image features can be effectively captured to improve the expression ability of the model. To compensate for the loss of feature details during the downsampling process as much as possible, the multi-resolution feature pyramid pooling(MFPP) block was constructed to obtain more scale context information by combining different dimensional features and enhance the expression of local detail information. Finally, a coordinate attention block was added in the skip connection to fuse target position information with feature information for precise perception of the surgical instrument targets. The experimental results show that the proposed method achieves more accurate segmentation results in both binary and parts segmentation of surgical instruments, further verifying the effectiveness and accuracy of the proposed method.

Urban roads are recognized to facilitate daily human activities while simultaneously being observed to shape behavioral patterns. Currently, most studies simply analyze the relationship between roads and crime distribution as part of the built environment, and few studies have thoroughly explored the impact of different structural attributes of roads on crime. In order to further explore this impact mechanism and provide guidance for the optimization of police resource allocation by front-line police departments. The operational mechanisms were investigated by which geometric and topological road attributes influence theft distribution across varied transportation modalities. Using crime data from YC District, JB City, spatial crime hotspots and high-risk road segments were initially identified through geospatial analysis. Next, the road structure was systematically decomposed into two distinct dimensions: geometric attributes and topological properties. A space syntax segment model was created to quantitatively assess geometric characteristics and traffic modality-specific topological configurations. Finally, Statistical relationships were investigated using zero-inflated negative binomial regression complemented by multiple linear regression modeling. Research has found that theft incidence is demonstrated to be positively associated with segment length, angular curvature metrics, and branch road density within the network. Elevated crime probabilities are observed in extended roadway segments exhibiting complex geometric configurations and regions characterized by dense branch road networks. The closeness and betweenness of the road's topological structure exert varying significant effects on theft crime across different traffic modes. Specifically, in pedestrian traffic modes, a significant negative correlation is observed between road closeness and betweenness and the occurrence of theft crime. In bicycle traffic modes, road closeness and betweenness are found to have a positive effect on theft crime. In electric vehicle and motor vehicle traffic modes, road closeness have a positive effect on theft crime, while in electric vehicle traffic mode, betweenness was found to have a negative effect on theft crime. These findings contribute novel insights to environmental criminology theory while offering empirically grounded recommendations for strategic police deployment and urban security management.

Image segmentation is a fundamental problem in medical image analysis, the typical deep learning based UNet architecture (UNet) and its variants are widely used in retinal vessel segmentation. However, the UNet network extracts feature information from images through local convolution modules, which makes the global information of the images difficult to be correlated and the long-distance dependencies between pixels difficult to be effectively captured. Considering the problems with the UNet network model and the characteristics of retinal vascular images, an attention module was added to the skip connections of UNet to capture long-distance dependencies between blood vessels. In addition, to enhance the segmentation ability of the network, the group normalization(GN) was used instead of the original batch normalization (BN) of the UNet network model, and the corresponding groups were selected for different channels. To update parameters and optimize the network, the final cross entropy loss function was designed using the side output layer and the final output layer. Experiments are implemented on the DRIVE dataset and CHASEDB1 dataset, and the experimental results show that the proposed model has better image segmentation performance.

Occluded pedestrian re-identification is a challenging task in the field of computer vision. A method was proposed using the FGMS-Net network, which significantly enhances pedestrian re-identification in occluded environments through several improvements. Firstly, an improved foreground segmentation technique was employed to effectively remove background and other clutter information, resulting in more accurate feature extraction. Secondly, to address the occlusion issue, a multi-scale feature discrimination method was introduced, enabling the model to better capture local features and thereby enhancing identification capability. Finally, an attention mechanism was added to the backbone network, allowing the network to focus more on critical information and improve overall recognition performance. The experimental results show that method proposed has achieved significant performance improvement in the task of pedestrian re recognition with occlusion. On the Occluded-DukeMTMC dataset, the cumulative matching feature Rank-1 and mean average precision (mAP) reach 71.7% and 61.6%, respectively.

Although the multi-task convolutional neural networks (MTCNN) face detection algorithm has achieved good results in some face recognition tasks, the accuracy of face detection needs to be improved in the face of some complex small-scale and multi-person face detection tasks. An improved MTCNN algorithm was proposed. Firstly, the intersection over union (IoU) threshold parameter was fine-tuned when creating the data set to classify face samples more accurately. Secondly, replacing the max pooling layer of the network with convolutional layers can improve network performance. Finally, the squeeze-excitation(SE) attention mechanism was introduced into the O-Net network to improve the feature expression ability of the network. The test results show that compared with the original MTCNN algorithm, the detection accuracy of the P-Net network and R-Net network of the improved algorithm has increased by 1%, and the detection accuracy of the O-Net network has increased by 0.5%. Moreover, the improved algorithm performs better in the actual face detection task.

In dense scenes, the frequent occurrence of occluded or small-scale pedestrian objects poses significant challenges to traditional object detection models, frequently leading to a high number of missed detections and false positives. In order to solve the problem of high false negative rate and false positive rate in pedestrian detection in such dense scenes, a novel dense scene pedestrian detection framework called ST-YOLO was proposed. Firstly, the low-level small object detection layer in YOLOv5's backbone network was integrated into the feature pyramid network and path aggregation network structure, adding a pedestrian detection layer for detecting small objects. Secondly, the neck network of YOLOv5 was improved by utilizing multi-scale global information based on Swin Transformer and local information extracted by convolutional neural networks (CNN) to construct aggregated features and enhance the network's feature extraction capability. And the SIoU (scalable IoU) loss function was introduced in the prediction process to accelerate the convergence speed of the model and improve detection capability. Finally, Soft NMS (soft non maximum suppression) was used instead of the original non maximum suppression (NMS) algorithm to reduce the problem of mistakenly deleting detection boxes during the non maximum suppression stage and lower the false alarm rate of the detection algorithm. A large number of experiments on the Wide Person dataset have shown that the improved ST-YOLO algorithm has improved accuracy and mAP_0.5 by 5.7% and 3.6% respectively compared to the current mainstream YOLOv9 algorithm.

With the expansion of the Internet scale and changes in its topological structure, network management is facing huge challenges. Segment routing (SR) protocols, especially SRv6(segment routing over IPv6), have become a research hotspot due to their high programmability and scalability. The path optimization control mechanism based on SRv6 solves the problem of avoiding and relaying specific nodes in multiple demands and scenarios to improve network performance. A path transfer scheme for fully deployed SRv6 networks was proposed, and the routing overhead was reduced through the optimization of forked paths. For some deployment networks, define the critical path and design the avoidance and relay path forwarding scheme to optimize the path forwarding efficiency. The experimental results show that when SRv6 is fully deployed, the optimization scheme can effectively reduce the depth of the segment list and the routing overhead. In some deployment networks, only a small number of SRv6 nodes can achieve performance close to that of a full SRv6 network, successfully solving the problem of evading and relaying specific nodes. The research results provide effective support for the application of SRv6 in different network deployments.

The previous structural seismic vulnerability analysis is generally based on the characteristics of the structure itself, it is rare to combine with the differences of engineering sites in the study area, the location differences of different engineering sites within the city are ignored. Taking Chengdu City as the research area and the three-story reinforced concrete frame structure as an example, an analysis method for the seismic vulnerability of reinforced concrete frame structures based on peak ground acceleration (PGA) and the maximum inter-story displacement angle θ_max of the structure was proposed. For three-story reinforced concrete frame structures, this method conducts dynamic time-history analysis using the interlayer shear model to obtain θ_max under each seismic response. Then, logarithmic linear fitting is performed on θ_max and its corresponding ground motion to obtain the relationship between the two. For the Chengdu area, this method takes the historical ground motion data of Chengdu as the data basis and combines PGA calculation formula to obtain the PGA of each engineering site location in Chengdu. Furthermore, taking the maximum inter-layer displacement angle as the structural damage index and PGA as the ground motion intensity index, the highest structural failure probabilities of the structure under four different performance levels of full operation, basic operation, life safety and near collapse were studied, which were 94.1%, 89.1%, 74.7% and 40.8% respectively. Moreover, the overall changing trend of the structural failure probability at each performance level of the structure decreases from the west to the east. Therefore, the seismic construction requirements for structures in the western region can be appropriately strengthened, and those for structures in the eastern region can be appropriately relaxed, so as to save economic costs. The proposed method has certain application value in reducing the losses caused by earthquakes and provides a certain theoretical basis for the seismic design of building structures.

With the implementation of river dredging projects in China, a large amount of dredged silt has been generated. The treatment and disposal of silt have gradually attracted great attention. Using solidification technology is one of the effective ways to solve the problems caused by dredged silt. Taking the dredged silt from Beibaidang in Zhejiang as the research object, the solidified products were analyzed through X-ray diffraction (XRD), and the porosity and pore structure of the solidified silt soil were quantitatively analyzed by means of X-ray computed tomography (X-CT) and mercury intrusion porosimetry (MIP) tests. Meanwhile, it explores the mechanical variation laws and solidification mechanism of the soil under soaking and dry-wet cycling conditions. The research shows that the calcite content in the solidified soil increases with the increase of the solidifying agent dosage. The increase of ordinary sand dosage improves the small and medium-sized porosity inside the soil, but the overall porosity shows a decreasing trend. The results of water stability tests indicate that the strength and stability of the solidified silt soil are significantly improved with the increase of the solidifying agent content, and they first increase and then decrease with the increase of sand dosage.

In order to explore the influence of pore defects on the mechanical properties of concrete road and seek an equivalent model to replace porous concrete road to reduce computational time. Based on micromechanics methods, the effective elastic modulus, Poisson's ratio, coefficient of thermal conductivity and coefficient of thermal expansion of porous concrete were calculated. Three-dimensional double-layer concrete roads with randomly distributed, non-interference and varying sizes of spherical pores and their equivalent models were established to study their mechanical properties under three working conditions, namely, concentrated force, static vehicle load, and temperature-static vehicle load coupling, and further the simulation calculation time for each model was compared. The results show that under the coupling effect of temperature and static vehicle load, the increase of porosity has little effect on the temperature and displacement of porous concrete roads at the same depth and different times. Moreover, for the same porosity, the farther away from the pavement, the peak temperature shifts backward over time. When the porosity is within 8%, the actual porous model can be replaced by the Eshelby equivalent model, Mori-Tanaka equivalent model, or Self-Consistent equivalent model under concentrated force or static vehicle load, and by the equivalent model 2 under temperature-static vehicle load. With fixed computational power and constant porosity, the simulation time for the actual porous model far exceeds its equivalent model. Using equivalent models for research can significantly shorten the calculation time, and the computational efficiency can be approximately improved by about 99.8%.

Under the guidance of the full life cycle design concept, a scheme of moving the existing downspout pipe scheme to the reinforced concrete peripheral column was proposed. The main feature of this scheme is that the downspouts are pre-embedded into the surrounding columns and used for roof drainage during the building's service life. At the end of the building's lifespan, the downspouts were used to remove blast holes. To demonstrate the feasibility of this scheme, first of all, it was verified that the material and diameter of the embedded downspout could simultaneously meet the drainage function and the function of removing the blast hole. Secondly, by drawing on the axial embedded hole blasting technology of reinforced concrete beams and using the new type of blasting equipment, the long bag for loading explosives, the feasibility of using downspouts for blasting demolition was demonstrated from both the charge structure and the blasting operation aspects. Finally, the finite element analysis software ABAQUS was used to analyze the stress distribution and stress-strain laws when two types of cross-sectional peripheral columns were placed in four downspouts with different diameters. The analysis results show that when the hollow rate is small, the embedded pipe has little influence on the specimens. In order to avoid significant impact on the peripheral columns, the hollow rate of the specimens should not be greater than 2%. In combination with the requirements of blasting demolition, the hollow ratio should not be less than 0.18% either. Subsequently, it was pointed out that the relay service duration of pipes and concrete holes is sufficient to reach the building's life cycle. Finally, it is clarified that after the downspout is built in, it can also enhance the aesthetic appeal of the building and avoid the safety risk of thieves climbing along the downspout. Therefore, it is feasible to insert reinforced concrete peripheral columns into the downspout pipe.

To investigate the annual variation characteristics of soil temperature around the medium-shallow coaxial tube ground heat exchanger, a two-dimensional unsteady heat transfer model for the medium-shallow coaxial tube ground heat exchanger was established. The model was solved based on the finite volume method and validated using experimental data from the project. The research findings indicate that under individual heating and cooling conditions, the circulation mode of outer-in and inner-out has a greater impact on soil temperature at a depth of 100 m, while the inner-in and outer-out mode has a more significant effect on soil temperature at a depth of 500 m. During summer conditions, reverse heat transfer is more likely to occur with the outer-in and inner-out circulation mode. At the end of the first winter (or summer) season, the thermal influence radius of the soil is less than 10 m, but this radius increases over time. At the end of one operational cycle, the soil temperature increases at depths shallower than approximately 300 m and decreases at depths deeper than approximately 300 m. Orthogonal experiments reveal that the inlet water temperature in both winter and summer has a notable impact on temperature fluctuations at a soil depth of 100 m, while the inlet water temperature in summer significantly affects temperature fluctuations at a soil depth of 500 m.

Storm rainfall patterns are critical to infrastructure flood control, but the main design methods are hindered by requiring high resolution data and ignoring the impacts of climate change. Taking the typical small and medium-sized river basin (Liudong River Basin) with relatively high flood control pressure in the Karst area of southwest China over the years as an example, based on the Gamma distribution with flexible distribution characteristics and wide application, and using the duration data of the maximum 24-hour rainstorm in the past 60 years, the model performance was evaluated, the parameters of the model were determined, and the evolution characteristics of the parameters were analyzed. Based on the above results, the changing trends of rain pattern uncertainty, complexity and predictability were calculated. The results show that the model has high accuracy, and the average correlation coefficient is greater than 0.92. The evolution trend of the model parameters indicates that under the background of climate change, the rain pattern of the maximum 24-hour rainstorm shows the characteristics of decreasing shape factor and increasing scale factor. The uncertainty of rain patterns has generally increased, and the complexity and predictability show high spatial heterogeneity. The research results will provide references for optimizing the rainstorm rain pattern model and analyzing its dynamic evolution, thereby better preventing and controlling flood disasters, especially in Karst areas with high precipitation uncertainty and complex underlying surface conditions.

Seepage analysis is the key research content of dam safety and stability, and it is of great significance for dam disaster risk control by constructing a high-precision prediction model of seepage quantity for earth-rock dam. In order to further improve the seepage prediction capability of earth-rock dam, a prediction model combining long short-term memory neural(LSTM) networks, convolutional neural(CNN) networks, and attention mechanism (Attention) was proposed. Firstly, CNN was used to mine the deep features of the data, then the time series features of the seepage flow monitoring data was extracted through LSTM, and finally the attention mechanism to the pooling layer and the fully connected layer was added to determine the importance of different time features and assign weights. Through the application analysis of engineering examples, compared with CNN, LSTM and CNN-LSTM models, the CNN-LSTM-Attention model has better prediction effect, and its coefficient of determination R² is as high as more than 0.98, and it can capture the spatial characteristics and temporal dependence of seepage data at the same time, which shows strong reliability and stability in the prediction of seepage flow of earth-rock dam.

In order to help enterprises better adapt to the dynamic environment in the real business, a multiperiod intermodal routing and storage co-optimization model with transport price uncertainty was investigated. Firstly, an integer programming mathematical model was established in the environment of transport price certainty. Secondly, a robust optimization model was established in the environment of uncertain transport prices, and the robust optimization model was transformed into an equivalent linear robust peer-to-peer problem. Subsequently, on the basis of the traditional k-shortest algorithm, iterative greedy algorithm (IG) and adaptive large neighbourhood search algorithm (ALNS), a hybrid heuristic algorithm of MKIGALNS was proposed to solve the problems. Finally, the correctness of the proposed model as well as the effectiveness of the algorithm were verified by different sizes of arithmetic case experiments. The experimental results indicate that in 10 sets of arithmetic cases, the average total operating cost is CNY 439 191 when storage is not allowed and CNY 391 378 when storage is allowed, so the storage decision should be made, which is conducive to the reduction of operating cost. And through the related robust experiments, the total operating cost as well as the multiperiod intermodal operation strategy changes with the change of the uncertain budget value, which reveals the intrinsic connection.

The super-large cross-section tunnel is prone to large deformation when passing through soft rock stratum. The reasonable selection of excavation method is of great significance for construction safety. In order to explore the applicability of the double-side nine-step excavation method to the construction of super-large cross-section tunnels, based on a 500 m² super-large cross-section soft rock tunnel under construction in Chongqing, the mechanical properties of sandy mudstone were revealed by laboratory experiments. The deformation characteristics of surface and super-large cross-section tunnel structures were compared and analyzed by numerical simulation and field monitoring. The excavation sequence, temporary support measures and excavation step length were optimized. The results show that the stress-strain curves of sandy mudstone samples under different confining pressures and different unloading rates are similar, and the triaxial compressive strength and deformation characteristics of rock samples change significantly. With the excavation of the core rock mass of the upper step, the displacement of the super-large section tunnel is abruptly changed. When the temporary support measures are removed, the deformation of the super-large section tunnel is further aggravated. Different excavation steps cause successive disturbance of surrounding rock, resulting in different unloading rates of surrounding rock and affecting the deformation of surface and tunnel structure. The temporary transverse bracing effectively limits the convergence of the arch waist, and the convergence of the arch waist is reduced by about 10.0 mm under all the layout conditions. In addition, the shorter the length of the excavation step, the smaller the deformation of the surface and the super large section tunnel.

In order to solve the defects of poor water stability and easy disintegration of red clay, industrial solid waste [fly ash (FA), phosphogypsum] combined with cement (C) was used to improve red clay. The mechanical properties, water stability and micro-mechanisms of the industrial solid waste-cement amended red clay were investigated through indoor tests. The results show that the strength of the improved soil shows a trend of increasing and then decreasing with the increase of the ratio (R) of phosphogypsum replacing fly ash.When the cement doping is 7% and R=7%, the maximum dry density of improved soil increases by 2.6%, the 7-day unconfined compressive strength (UCS) increases by 11%, and the 28-day UCS increases by 57%, which meets the bearing standard of subgrade for road use. There is no obvious change in the water-filled specimen after the 7-day maintenance, the resistance to disintegration is enhanced, and the water stability coefficient of the specimen reaches 92.9% in the 28-day maintenance. The water stability coefficient of the specimen reaches 92.9%, and the water stability coefficient increases 1.61 times. The microscopic analysis shows that the replacement of fly ash by phosphogypsum promoted the generation of new hydration products of ettringite and calcium-silicate-hydrate (C-S-H), which transforms the soil body from fragmented granular to a denser gel network structure, enhances the bonding between the red clay particles, and fills up the pore space at the same time. The results verifies the feasibility of industrial solid waste-cement-amended red clay as roadbed fill, provides a solid theoretical foundation and basis for engineering practice, and broadened the reuse of industrial solid waste.

To investigate the impact of temperature on axial stress in large-span concrete variable-section continuous beam bridges under various wind speed fields, a method was proposed to calculate vertical temperature gradients separately based on inconsistent deck slab thickness and simulate lateral fluctuating wind speed fields using the spectral method. Firstly, vertical temperature gradient variations and their depths were calculated by employing the concrete heat-conduction equation, daily maximum and minimum temperatures, and deck slab thickness of the variable-section continuous beam bridge. Secondly, bridge modeling was performed using MIDAS Civil, and ZKH standard static live loads were simulated to represent moving train loads. Finally, static array wind loads and pulsating wind loads were applied to the bridge. The results indicate that the axial stresses in the left and right lanes obtained from the proposed method, which uses vertical temperature gradients and their depths derived from the concrete heat-conduction equation, daily temperature extremes, and bridge deck slab thickness, are larger compared to existing studies. Under the same wind speed field model, the harm to the bridge is greatest under gradient heating, followed by gradient cooling, while no temperature change results in the least impact. When the bridge is subject to the same temperature model, both the axial stress values and amplitudes of the bridge under pulsating wind loads are larger and more severe than those under no wind or static wind conditions, posing greater hazards to the bridge. The research findings can provide references for the structural design and safe operation of large-span concrete variable-section continuous beam bridges.

The alignment monitoring of steel arch bridges constitutes an essential component of bridge health monitoring systems. Three-dimensional laser scanning technology was utilized, and the traditional density-based spatial clustering of applications with noise(DBSCAN) algorithm was improved by integrating the random sample consensus(RANSAC) algorithm to extract the alignment of steel arch bridge ribs. Three-dimensional laser point cloud data, characterized by its comprehensiveness and detailed representation, is capable of fully presenting the structural shape and deformation information of the bridge. The RANSAC-integrated improved DBSCAN algorithm, constrained by the structural features of the steel arch bridge, effectively achieves the removal of discrete points as well as point clouds from the bridge deck, cross bracing, lateral connections, and web members. Point clouds extracted using the RANSAC-integrated improved DBSCAN algorithm are fitted to identify key points, and a comparison is made with results obtained manually. The extraction errors for the key points of the arch ribs are all within the millimeter range, with the maximum error being 9.2 mm and the minimum error being 0.1 mm. This extraction method is demonstrated to more accurately and effectively accomplish the alignment extraction of steel arch bridges, achieving millimeter-level precision in alignment extraction. It significantly reduces labor and time costs, provides better robustness for the complex structures of steel arch bridges, and adapts well to practical production demands.

To mitigate the impact of highway accidents on traffic capacity and driving safety, a coordinated control strategy was proposed involving both service areas and toll stations. Firstly, the proposed coordinated control strategy was described in detail. Secondly, to simulate traffic flow more accurately under highway accident scenarios, the cellular automata model was enhanced by introducing different random deceleration probabilities, acceleration/deceleration rates, and lane-changing conditions for different vehicle types. Finally, the effectiveness of the proposed control strategy was validated through simulation. The results indicate that, compared to scenarios without control measures, implementing service area control can reduce average vehicle delay, fuel consumption, and cumulative carbon emissions by 62.90%, 69.50%, and 69.50% respectively. Moreover, using the coordinated control strategy of service areas and toll stations can further reduce these metrics by 55.76%, 59.58%, and 59.58% respectively. Precise control measures can significantly reduce the impact of accidents.

The rudder in the coaxial twin-rotor is a complex mechatronic position-following control system, and its control accuracy plays a key role in manipulating the flight attitude. Because the common rudder is lacking in adapting to the unique flight environment of the aircraft, the accuracy of tracking declination and the stability performance need to improve. The manipulation principle and structure of micro UAV were analyzed, the mathematical models of position loop, current loop double PID steering gear control system and transition position loop Fuzzy PID steering gear control system were established respectively. Combined with the actual flight conditions, the dynamic and static characteristics of the coaxial twin-rotor steering gear control system were analyzed by using Fuzzy editor and Simulink module. The results show that the rudder control system with current loop PID and position loop Fuzzy PID control has 28.6% less overshoot, 28% less adjustment time and faster response than the dual PID control system. Meanwhile, the Fuzzy PID parameters are adjusted in real time to track the changes, which can adapt to the complex and variable flight conditions of the coaxial twin-rotor more quickly. The obtained control system based on dual-loop Fuzzy PID shows high accuracy and meets the requirements of stable, accurate and robust working under complex working conditions, which is of great significance for the control system design of coaxial dual-rotor aircraft.

The development of hydrogen-powered aircraft is a key strategy for the aviation industry to achieve carbon neutrality. Compared to high-pressure gaseous hydrogen, cryogenic liquid hydrogen will be the main fuel for future hydrogen-powered commercial aviation. However, the occurrence of cavitation in liquid hydrogen during transport has the potential to result in an unstable or even interrupted fuel supply to the engine, which could ultimately lead to catastrophic risks to flight safety. Using numerical simulation method, based on homogeneous mixed flow model, Navier-Stokes (RANS) method and Zwart cavitation model, the cavitation flow characteristics and development law of liquid hydrogen in aircraft transport pipelines were deeply studied, and partially compared with normal temperature water. The results show that the cavitation number, the outlet/inlet pressure ratio, and the length/diameter ratio have a significant influence on the occurrence and development of cavitation. The condensation process of liquid hydrogen is considerably slower than the evaporation process. The effect of the cavitation number on the evaporation process is minimal, but it has a significant effect on the maximum condensation rate. The critical pressure ratio for the disappearance of cavitation in liquid hydrogen is lower than in water. At the same pressure ratio, water cavitates more easily than liquid hydrogen, with a greater number of cavitation bubbles and a thicker cavitation region. Reducing the length/diameter ratio can inhibit the occurrence and development of cavitation in liquid hydrogen. It is recommended that the diameter of the contraction section be increased to achieve a higher outlet flow, rather than shortening the length of the pipeline.

In order to enhance the safety and efficiency of operations in the double-channel U-shaped apron area of large airports, an optimized operational procedure for the double-channel U-shaped area was studied. Firstly, the utilization and partitioning of taxiways in the double-channel U-shaped apron area were designed, and the positions of pushback holding points were optimized. Secondly, based on the partitioning of the double-channel U-shaped apron area and the optimized positions of pushback holding points, different operational procedures for aircraft were designed for various scenarios. Then, evaluation indicators were designed from the perspectives of safety and efficiency, and corresponding evaluation models were established. Finally, simulation experiments were conducted using Wuhan Tianhe Airport as the object. The results show that the proposed optimized operational procedure can reduce the total operation time by 13.3%, total waiting time by 31.4%, and waiting rate by 22.4%. The flight density was gradually increased until reaching the maximum theoretical capacity of the U-shaped apron area, and further verification was conducted. The results indicate that the proposed optimized operational procedure performs better across different indicators under varying flight volumes, verifying its effectiveness and providing theoretical references for current and future operational procedures of double-channel U-shaped apron areas.

The decomposition of submerged plants such as Potamogeton crispus releases a large amount of nutrients into the water body, which has a negative impact on aquatic ecosystems. To investigate the slowing effect of filter feeding benthic animal, the Hyriopsis cumingii, on the deterioration of water quality after the decomposition of submerged plants, a 45 day experimental chamber simulation experiment was conducted from May to July 2023 using different specifications and densities of clams and seagrass combinations to monitor changes in water quality indicators and phytoplankton community structure. It was found that filtration through the Hyriopsis cumingii can reduce the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and chlorophyll a (CHLA) in the water to a certain extent. To investigate the density effect and size effect of its Hyriopsis cumingii, low, medium, and high-density triangular sail clams were released. The experimental results show that different sizes and densities of Hyriopsis cumingii can significantly control the biomass of phytoplankton, and Hyriopsis cumingii have a significant impact on the community structure of phytoplankton. Among them, releasing small-sized (shell length 4 cm) low-density (1 g/L) Hyriopsis cumingii has the best effect on improving water quality and controlling phytoplankton biomass.

To improve the utilization rate of waste clay, a composite curing agent composed of cement, alkali excitation agent, siliceous solid waste material and sulfate solid waste material was used to cure a waste clay. Unconfined compressive strength test (UCS), water stability test, X-ray diffraction test and scanning electron microscope(SEM) test were carried out to capture the mechanical behavior and reinforcing mechanism of the solidified clay. The results show that there is a significant solidification effect on the clay when the dosage of curing agent is from 10% to 12%, and the water stability coefficient of solidified clay is larger than 80%. The UCS of the solidified clay with 12% curing agent content is 2.45 MPa after curing for 28 days, which is 2.33 times the UCS of the solidified clay at a curing time of 7 days (1.05 MPa). A large number of hydration products such as calcium silicate (aluminate) hydrate and ettringite produced in the solidified clay can improve the performance of solidified soil by improving the micro-pore structure of soil and strengthening the cementation between aggregates. The present research can provide reference for the resource utilization of waste clay.