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.

In order to comprehensively analyze and evaluate the monitoring capabilities of the integrated multi-satellite retrievals for global precipitation measurement (GPM) final run (IMERG-F) satellite retrieval product for daily and sub-daily scale precipitation, as well as various intensity rainfalls, under complex mountainous conditions in southwest China, ground-based dense rain gauge data was used to investigate these aspects. The results show that within the daily and sub-daily scales, the critical success index (CSI) of IMERG for short-term accumulated precipitation primarily ranges from 0.2 to 0.6, with the correlation coefficient fluctuating between 0.25 and 0.5. The daily scale precipitation detection accuracy is the highest, with better performance in summer months compared to winter. For different intensity rainfall events, IMERG exhibits a high probability of detection (POD) for light rainfall, while the false alarm rate (FAR) is relatively low. However, there is an underestimation phenomenon for moderate to heavy rainfall. The elevation difference significantly impacts the stability of IMERG products, but there is no direct linear relationship with the elevation itself. Compared to areas with significant topographic variations, IMERG-F demonstrates higher reliability in detecting weak rainfall events in areas with less topographic variation. It is concluded that the application of IMERG products in southwest China should consider the limitations imposed by seasonal and topographic characteristics.

The Chang 6₃ sand formation in Block A of Ordos Basin is an ultra-low permeability reservoir, which is difficult to be conventionally exploited. At present, horizontal well fracturing is widely used for development. According to the physical characteristics of ultra-low permeability reservoir, the geomechanical model and fracturing simulation software, combined with embedded discrete fracture method were used to characterize the artificial fractures generated by fracturing simulation of Chang 6₃ sand formation, and carried out numerical simulation research based on this. Through this integrated process, the integrated simulation of complex fracturing of horizontal wells were realized, and the efficiency of horizontal well development was improved. The results show that the embedded discrete fracture method can effectively combine fracturing and numerical simulation, and realize the integrated flow from fracturing to numerical simulation of horizontal wells. The accuracy of the numerical model was verified by the historical fitting of the production mode of fixed oil production. By adjusting the relevant parameters to optimize the model, it is more in line with the actual production situation of the well group, which is conducive to the subsequent development plan adjustment and production forecast.

The reservoir of Chang 7 in Heshuinan area of Ordos Basin has high salinity of formation water and strong heterogeneity of mud content, which leads to unclear understanding of oil-water distribution and main controlling factors. It seriously restricts the efficient development of the oilfield. In order to improve the efficiency and accuracy of the identification of oil-water layer and clarify the distribution law of oil and water, the intelligent random forest method was used to interpret the fluid properties on the basis of logging, oil test and production test data, and defined the distribution law of oil and water in Chang 7 in Heshuinan area. In addition, the controlling factors of oil and water distribution were analyzed from various angles by combining the experiments of constant rate mercury injection, CT (computerized tomography) and nuclear magnetic resonance. The results show that the accuracy of the fluid properties interpretation can reach 78.9% by using random forest method. In the vertical direction, the oil layer developed in Chang 7₁ is better than that in Chang 7₂, and the oil-water layer and water layer are mainly developed in Chang 7₂. In the plane direction, the eastern oil layer is thicker and distributed more continuously, and the southwest oil layer near the B60 well area has higher water content. The fluid properties inside the reservoir are controlled by the shale content, the pore structure and the viscosity of crude oil. The pore size and the connectivity of pore throat of the reservoir are affected by the content of mud. The pore structure of the reservoir and the viscosity of crude oil together control the fluid mobility, thus affecting the spatial distribution of oil and water in Heshuinan area.

The study area is located in the southeastern part of the Badanjilin Basin, in the Benbatu region. Multiple airborne radiometric anomalies have been discovered in the area. To further explore the causes of these anomalies and provide references for subsequent uranium exploration work, these airborne radioactive anomalies were classified, summarized, ground verified, and analyzed for their potential for uranium mineralization. The results show that there are two types of anomalies in the area, one is a densely distributed strong anomaly in the central part, which is mainly an airborne radiometric anomaly for exploring hard sandstone-type uranium ores. The other is a weak anomaly distributed in a strip in the southeast, which is mainly an anomaly for exploring in-situ leachable sandstone-type uranium ores and represents a new type of uranium mineralization discovered in the area. By combining regional geological data, the potential for mineralization of the weak anomalies HFU-03 and HFU-04 was analyzed. The research results provide new clues and ideas for the types of uranium exploration in the area.

Errenshan area of Weiningbeishan is located in the southern margin of Alashan microcontinent, which is one of the important hydrothermal polymetallic mineralization areas in Ningxia. In order to serve the next prospecting in the periphery and deep part of the area, the multidimensional anomaly system in this area was discussed on the basis of borehole rock geochemical survey. The results show that in the known polymetallic ore bodies in this area, there are negative anomaly systems characterized by major element Na₂O, mineralization agent element anomaly system represented by S, mineralization and associated element anomaly system, etc., which confirms the existence of multidimensional anomaly system. Under the guidance of the theory of multi-dimensional anomaly system, the metallogenic conditions and favorable areas of metallogenic potential in the study area are further delineated. The research result is a new attempt to optimize geochemical exploration methods for hydrothermal polymetallic deposits in Weiningbeishan area, and has important practical value for geological prospecting in this area.

Collapse, as a common geological hazard in China, are widely distributed, highly concealed, sudden, and pose significant risks. In southwestern China, where disaster-prone red beds are extensively distributed, high cutting slopes are highly susceptible to landslides and collapses if not promptly managed, especially under the combined effects of back-end catchment and freeze-thaw cycles. Taking the collapse at Shejian Town in Guangyuan as a case study, on-site investigations, image analysis, and formula calculations were conducted to examine the failure mechanisms of red bed high cutting slopes influenced by back-end catchment and freeze-thaw effects, aiming to provide insights for disaster prevention in infrastructure development within red bed regions. The results indicate that the unique topography and climate conditions of the study area contribute to abundant rainfall, creating a water-intensive environment due to the extensive back-end catchment in the collapse area and infiltration from irrigated farmlands. The prolonged saturation of rock masses, combined with repeated freeze-thaw cycles, significantly weakens the rock strength, reducing the tensile strengths of sandstone and mudstone to 166.12 kPa and 72.77 kPa, respectively. Both values are lower than the fracture water pressure of 174.87 kPa in the fractures of the red bed cut slope, ultimately leading to the occurrence of the collapse. Given the variations in failure modes of red bed high cutting slopes with different rock structures under the effects of back-end catchment and freeze-thaw cycles, it is recommended to enhance drainage measures during prevention efforts and to implement targeted treatments based on the identified failure modes.

Cardiac arrest and global cerebral ischemia-reperfusion injury after cardiopulmonary resuscitation (CPR) are common pathological conditions in critically ill patients, with high mortality and disability rates. Currently, the conventional asphyxia method is commonly used to establish a brain injury model after CPR. However, it has a low resuscitation success rate and postoperative survival rate. Therefore, a modified asphyxia model of cardiac arrest and global cerebral ischemia-reperfusion injury after CPR was established and compared it with the conventional asphyxia method to evaluate its advantages. Sprague Dawley(SD) rats were randomly divided into a conventional group and a modified group. The conventional asphyxia method and the modified asphyxia method were used to establish the models, respectively. The resuscitation success rate and 24-hour survival rate were observed. Neurological deficits were assessed using neurological deficit scores. Hematoxylin-eosin staining was used to observe pathological changes in brain tissue. Transmission electron microscopy was used to examine neuronal ultrastructure. Western Blot analysis was performed to detect inflammatory, oxidative stress, and apoptotic markers. The results show that compared with the conventional asphyxia method, the modified asphyxia method has a higher resuscitation success rate and 24-hour survival rate. No significant differences are observed in brain injury, inflammation, oxidative stress, and apoptosis between the two methods. The modified asphyxia method meets the requirements for studying global cerebral ischemia-reperfusion injury.

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.

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.

In order to solve the problem of combining plasma equipment with robots to process casting risers, where variations in riser dimensions due to mold accuracy necessitate precise trajectories and poses for proper arc initiation and operation, a method based on a process parameter library was employed for investigation. This method was comprised of process parameter library, analysis module, similarity measurement module, path generation module, and parameter module. Through the construction of two-stage comparison method, target parameters can be quickly filtered from the library. Using path generation algorithms and deep learning algorithms, working paths and process parameters matching the point cloud model were obtained. The results show that this method can accurately generate operational trajectories and poses based on the workpiece, enabling the reduction of manual intervention, lowering of operational complexity, and enhancement of the reliability and safety of the cleaning operations for coupler castings.

In the natural gas pipeline transportation system, the series elbows is particularly susceptible to erosion due to its special structure and the sand-producing characteristics of natural gas. The erosion behavior of the series elbows is affected by multiple factors, among which the length of the connecting pipe between the two elbows is a variable that cannot be ignored. For this reason, the computational fluid dynamics(CFD)-discrete phase model(DPM) numerical simulation method was used to study the erosion behavior of the series elbows at different spacings of sand-containing natural gas. The results show that when two elbows are installed in series, the corrosion morphology and rate of the second elbow are greatly affected by the distance between the two elbows. With the increase of the length of the middle section of the series elbow, the corrosion morphology of the first elbow is V-shaped, but the corrosion morphology of the second elbow gradually changes from a triangle to a V-shape. In addition, due to the influence of gravity on the migration trajectory of sand particles, the area with the most serious erosion and wear of the second elbow is 5° to 8° behind the upstream elbow. By analyzing the multi-angle section flow field of the second elbow, it is found that the airflow generates a more complex secondary flow at the second elbow after passing through the middle section. Therefore, the maximum erosion rate of the second elbow decreases first and then increases with the increase of the length of the middle section of the series elbows. The research results can provide certain theoretical guidance and basis for optimizing the engineering design and erosion prevention of the double elbow system.

The novel electro-hydraulic composite intelligent completion system primarily involves downhole flow control technology and multi-parameter detection technology and so on. Based on the principles of mechanical structure design, the structural design of the core component of the electro-hydraulic composite intelligent completion flow control valve was conducted, namely the throttle valve sleeve. Finite element analysis was utilized to numerically simulate and study the mechanical performance and fluid flow characteristics of the throttle valve sleeve. The the flow field characteristics under various openings, water cut rates, displacements, and working conditions were analyzed by this method. The results indicate that the downhole temperature and pressure conditions have little effect on the flow control performance of the throttle valve sleeve. The performance is stable and meets the design requirements under high temperature and high pressure (125 ℃, 50 MPa). With different openings, the pressure difference gradually increases as the flow rate increases. At a constant flow rate, the pressure difference decreases with the increase of the opening. When the flow rate and opening are constant, the pressure difference gets greater when water cut becomes larger. When the flow rate and water content are constant, the greater the control series of the throttle valve sleeve, the smaller the pressure difference. The research results can provide theoretical guidance for the structural design of intelligent completion flow control valve.

A deep learning based T-beam formwork polishing robot was designed for the problems of difficult and time consuming polishing of T-beam formwork for variable cross-section. Firstly, an adaptive polishing structure was proposed to solve the technical problem that the existing polishing device cannot fit the inner variable cross-section of the T-beam formwork, and the polishing roller was easy to get stuck in the T-beam formwork partition. Secondly, in order to realize the quantitative monitoring of the polishing quality, a YOLOv8n-DSE algorithm was proposed to identify concrete dirt and stains on the formwork, the DySample dynamic up-sampling module was introduced to enhance the anti-interference ability of the model and accelerate the calculation speed, to improve the accuracy of small target detection, the SOEP (small object enhance pyramid) module was designed to improve the detection performance of small target detection through the SPDConv(space to depth convolution) to obtain the information features of the small target and give them to the CSP(cross stage partial)-Omni-Kernel for the integration of the features. Finally, the EMA(exponential moving average)-SlideLoss was replaced to make the model more concerned with the quantitative monitoring of the concrete, allowed the model to focus more on difficult targets, which can improve the effect on difficult case detection. The accuracy, recall, and mAP(mean average precision) values are improved by 3.1%, 9.7%, and 3.2%, respectively, compared with those before the improvement. The improved model was deployed to the robot and tested in the field. The results show that the equipment meets the plant's needs for polishing variable-section T-beam formwork.

The principle of particle damping energy consumption and inertial capacity efficiency increase are widely used in structural vibration reduction control. Based on the advantages of particle damping and inertial capacity, a particle damping inertial capacity shock damper (PID) was designed, which mainly contained particle damping unit, inertial mass unit and stiffness unit. Firstly, the working principle of PID was elaborated, the mechanical analysis model of single degree of freedom was established, a small PID mock-up was produced, a variety of working conditions were set up to test the mechanical properties of the PID. Then, the mechanical properties of PID were further explored by the combined simulation method of many-body dynamics software and discrete element software. Finally, to verify its engineering application value, dynamic time-course analysis of the damping structures configured with PID and tuned mass damper (TMD) by finite element structural analysis software SAP2000. The results show that PID has excellent damping performance, when the other conditions are certain, the energy consumption effect of PID increases with the increase of vibration displacement amplitude and vibration frequency. In the building structure, PID shows better damping than TMD, it has a high engineering application value.

In order to address the problems of poor flame stability, low combustion efficiency and high lean-combustion limit in traditional direct-fired porous media burners, five porous media burners with different pore arrangements were constructed for low-concentration methane (LCM) combustion experiments, and the effects of porous media arrangements, equivalence ratios and flow rates on the combustion properties of LCM were investigated. CH₄ conversion, pollutant emissions and flue gas temperatures were also analyzed under lean combustion conditions. The results show that the E-type gradually-varied porous media burner exhibits the optimal LCM combustion adaptability, which can partially compensate for the interface temperature perturbation and improve combustion stability during LCM combustion. At an equivalence ratio of 0.39 and a flow rate of 50 L/min, the stationary combustion time of LCM in the burner exceeds 140 min and the flame position is consistently maintained at 60 mm. The LCM combustion in the E-type porous media achieves a CH₄ conversion of 99.99% with CO and NO_x emissions of 531×10^-6 and 23×10^-6, respectively, generating high-quality flue gas with a mean temperature of more than 588 ℃ that can be employed in industrial production. The research results provide an important reference for improving the utilization efficiency of low-concentration methane in coal mines and reducing methane emissions.

A calculation method of effective power based on heat transfer mathematical simulation of intercooling system was proposed for an aviation piston engine, and the heat transfer simulation model of the intercooling system was developed with VB language. The validity of the simulation model was verified by the test data. The results show that the errors between the simulation values and the test values of the intercooler cold side outlet temperature and the hot side outlet temperature are within 1.5%. Using the simulation model, the influence of fan air flow on the effective power of the aviation piston engine was studied, and the air effective power recovery was studied. The results show that with the increase of fan air volume, the amplification of fan power increases, while the amplification of engine power decreases. Under the combined effect of the two, the effective power of the engine first increases and then decreases with the increase of fan air volume. For the aviation piston engine studied, when the fan air volume is 1 400 m³/h, the effective power of the engine reaches the maximum, which is 101.6 kW. When the flight altitude is below 2 000 m, the engine effective power recovery coefficient increases slightly with the increase of flight altitude, and when the flight altitude is above 2 000 m, the engine effective power decreases significantly with the increase of flight altitude. Under the condition of 50 ℃ at sea level, the effective power recovery coefficient of the engine at 7 000 m is only 92.2%.

Sodium fire accidents in sodium technology room can generate harmful aerosols. To analyze the impact of sodium fire aerosol particle migration, a microchannel grid structure similar to real cracks was constructed using computational fluid dynamics(CFD) method based on the actual concrete crack characteristics to simulate the migration process of aerosol particles in the room wall. A two-dimensional horizontal microchannel flow model was established, considering gravity, inertial force, and the influence of Saffman lift and Brownian diffusion on particle motion was studied, and a microchannel particle motion model was constructed to numerically simulate particle retention characteristics for different gap structures. The results indicate that when the gap size is less than submillimeter, it is considered that there is no risk of causing a large amount of aerosol particle leakage in the gap. The branching corners and uneven micro structures within concrete gaps can effectively reduce the penetration coefficient of particles in the gaps and reduce leakage.

Aiming at the problems of poor accuracy, slow convergence rate and high jitter of traditional super-twisting sliding mode observer, a permanent magnet synchronous motor(PMSM) speed observation technique based on the improved super-twisting algorithm was proposed. Firstly, a segmented exponential function was used to replace the switching function to eliminate the phase delay problem due to the low-pass filter, and the adaptive sliding mode gain was designed to achieve stable tracking at different speeds. Then, in order to solve the problem that the traditional quadrature phase-locked loop failed when the motor steering was changed, an improved quadrature phase-locked loop was proposed, so that its output was independent of the rotation direction, so as to realized the correct tracking of the forward and reverse rotation. Finally, due to the wrong convergence point of the proposed improved quadrature phase locked loop, there was a 180° phase difference between the estimated position and the actual position, an adjustment function was designed to solve this problem. The simulation results show that compared with the traditional sliding mode observer, the proposed improvement method has faster response and better dynamic performance.

To address limitations in the engineering application of neural network based maximum power point tracking(MPPT) algorithms, an improved lightweight neural network MPPT algorithm was proposed. The complexity and memory usage of the neural network were reduced through a knowledge distillation compression algorithm, and a lightweight model was obtained. The inherent theoretical error of model predictions was corrected using an optimized variable step-size perturb and observe method. In the initial stage, the neural network predicted the voltage range of the maximum power point. In the later stage, disturbance observation progressively refined this range until it converged at the maximum power point. A simulation model was developed in MATLAB/Simulink, and a physical model was constructed for comparative experiments. Results indicate that the proposed algorithm achieves higher tracking efficiency, improved ripple voltage suppression, and lower resource consumption rate in embedded devices.

The advantages of renewable wind energy lead to a rapid growth in the scale of wind power, while lightning strike accidents on wind farm delivery systems have a significant impact on the new power system. The traditional lightning strike warning method requires high data types and sample sizes, and lacks consideration of relative location as well as the distribution of lightning density. A lightning strike warning method for wind farm delivery systems based on the stepped lightning strike probability calculation method was proposed. Firstly, the data of lightning points around a wind farm in Hainan, China in 2020 were analyzed, and the Monte Carlo method was used to find the center of mass of the clusters as well as the density of lightning points to fit the trajectory of the thunderclouds. Then, based on the relative position of the movement trajectory and transmission line, the stepped lightning strike probability calculation method was combined to calculate the value of the lightning strike probability in a short period of time. Finally, the simulation was combined with the operation monitoring data of a wind farm in Hainan from 2020 to 2022. The results show that the relative error of the proposed method is within 15%, and the impact of the difference in the density of lightning points on the warning accuracy is effectively reduced, which ensures the safety of the wind farm delivery system.

In response to the current situation of relying on manual alignment of the optical path in existing velocity interferometer system for any reflector(VISAR) devices, and to meet the future demand for remote automated control, a new method for automatic alignment of the optical path was proposed. The complementary metal oxide semiconductor(CMOS) of this method was measured indirectly, and the pixel deviation of the light spot was used as a system input. Coefficient matrix transformation and discrete fuzzy feedback control methods were used to quickly eliminate the errors. Based on the modules such as vision and motion in the Windows control and automation technology(TwinCAT), each of which was run in a different real-time kernel, the communication link between the vision and motion control modules was eliminated, and fast real-time closed-loop control was realized. After the experimental verification of shock wave velocity measurement, the remote “one-button” automatic alignment was realized. The system can shorten the alignment time to 2 s and improve the alignment accuracy to 4.5 μm. The problem of inefficient manual adjustment of the existing device was solved, and the accuracy and stability of the system were improved.

Dust deposition can affect the normal operation of equipment. To accurately and efficiently detect dust on equipment and formulate a scientific cleaning strategy, a lightweight dust deposition detection method based on Fast-UNet was proposed. By effectively pruning UNet and adopting max pooling and bilinear interpolation for down-sampling and up-sampling operations, the parameter redundancy was reduced, and a compact basic network was obtained. The lightweight Ghost Module was used to replace the ordinary convolution in the basic network, further reducing the complexity of the network. An convolutional block attention module(CBAM) that integrated channel and spatial attention was embedded in the encoding process, which made the network pay more attention to the target area while introducing minimal parameters. Experiments on a simulated dust deposition dataset show that, compared with the original model, Fast-UNet reduces the number of parameters by 99.6%, decreases computational complexity by 98.7%, achieves an inference speed of 94.18 frames per second, and maintains a recognition accuracy of 91.17%. Compared with five other mainstream segmentation models, Fast-UNet also demonstrates advantages in both accuracy and speed. This method meets the needs of dust detection for both accuracy and efficiency, providing a technical reference for dust quantitative analysis.

Tunnel lining detection is an important element of quality management in tunnel construction and maintenance. Due to the variety of internal lining defects and unclear boundaries makes it challenging to identify these problems and train models effectively. Relying on manual detection or existing models, it is not possible to achieve fast and accurate defect detection. To address the above problems, A dataset consisted of 1 922 liner radar samples collected from Yunnan Tunnel B-scan was developed for training the model. A tunnel lining defect detection model YOLO-Tunnel based on YOLOv5 was proposed, which improved the model feature extraction ability, increased the receptive field, and improved the model localization ability by upgraded the Backbone and Neck. And further improved the model detection ability by selected the appropriate model size and balanced weight based on the dataset's scale and target size proportions. The results show that YOLO-Tunnel has better defect detection accuracy compared to YOLOv5s and also meets the real-time detection requirements, in which the precision, recall, and mAP are increased by 2.5, 9.0, and 8.1 percentage points, respectively, with the inference time increases by 2.7 ms to 21.8 ms. The research results provide a reference for further improving the performance of the detection of tunnel lining detection and the direction of optimization of the model reference.

To explore the factors affecting customers' evaluation of fresh logistics service quality, a logistics service quality evaluation model was proposed and established based on sentiment analysis of online reviews and latent Dirichlet allocation (LDA). A convolutional neural network (CNN) model integrating a multi-head self-attention mechanism and bidirectional long short-term memory network (BiLSTM) was constructed for sentiment analysis of online comments. Additionally, LDA topic model was carried out for positive and negative comments after classification. The key factors affecting the evaluation of fresh product logistics service quality were obtained by exploring the focus of customers' demand for fresh product logistics service. The sentiment analysis based on CNN-BiLSTM-Attention was implemented through Python programming, and the results of sentiment analysis on online comments were compared with those of support vector machine (SVM), CNN, BiLSTM, and CNN-BiLSTM. The comparison results show that, compared with the classification results of other models, the CNN-BiLSTM-Attention model is superior in accuracy, precision, recall rate, F₁, and other indexes, effectively improving the accuracy of text emotion classification. The research results demonstrate that researching the factors affecting the logistics service quality of fresh e-commerce based on online review data can help e-commerce enterprises better improve logistics efficiency and service quality from the perspective of consumer demand.

To address the high cost and low accuracy of manual inspection for steel surface defects, as well as the excessive computational resource requirements caused by complex traditional target detection models,a lightweight defect detection algorithm named YOLOv8n-MDC was proposed by integrating MobileNetv3 with YOLOv8.Firstly, based on YOLOv8n, the original intersection over union(IoU)-based bounding box loss function was replaced with weighted IoU(WIoU), enhancing model robustness through a non-monotonic focusing mechanism. Secondly, the backbone feature extraction network of YOLOv8n was substituted with MobileNetv3, utilizing its lightweight architecture to reduce network complexity and redundant computational overhead. Finally, during the feature fusion stage, depthwise separable convolution (DWConv) and C3Ghost modules replaced the original components, further minimizing model parameters and accelerating detection speed. Evaluated on the NEU-DET steel surface defect dataset, the YOLOv8n-MDC achieves an mAP of 81.3%, representing a 5% improvement over the baseline YOLOv8n, while its parameter count and computational complexity are reduced to 1.02 M and 2.1 GFLOPs (33.9% and 25.9% of the original model, respectively), meeting industrial requirements. This lightweight algorithm significantly reduces computational complexity and resource consumption while enhancing detection accuracy, offering an optimized solution for industrial steel surface defect inspection.

In the field of fingerprint recognition technology, ridge density, as one of the morphological features of fingerprints, has demonstrated increasing research value. Aiming at the problems of time-consuming and labor-intensive existing measurement methods, an algorithm based automated measurement method was proposed. The algorithm first preprocessed fingerprint images, including grayscale conversion, edge detection, noise reduction, and ridge enhancement, to improve image quality and clarity. Subsequently, it strengthened fingerprint features, performed array transformation, determined directional vectors, detects peaks, and finally plotted a grayscale fluctuation diagram to visually present the measurement results. Experimental results show that the automated measurement algorithm performs well in terms of efficiency and accuracy, exhibiting high consistency and significant statistical correlation with manual measurements. This further validates the scientific robustness and effectiveness of the automated measurement method, providing new perspectives and approaches for the automation and intelligence of fingerprint recognition.

In order to better address incidents of online violence resulting from uncontrolled public opinion in the era of self-media, network platforms are involved in decision-making during the early stages of online violence opinion formation. This can effectively prevent the formation of online violence opinions. Firstly, based on the inducement behaviors of online violence and considering the internal self-purification effects among platforms, self-media, and netizens, the costs and benefits of their autonomous behaviors during the initial stages of online violence opinion formation were defined. Next, a “platform-self-media-netizen” three-party evolutionary game model was constructed, and the behaviors of each subject and their evolutionary stable strategies were analyzed. Finally, numerical simulation experiments were conducted using MATLAB to verify the accuracy of the model and evolutionary results. To further reveal the factors influencing the cooperation among these parties, the impact of their initial cooperation willingness and related parameters on the system was explored. Simulation results show that a strong regulatory strategy adopted by network platforms, with clear guidance, can effectively enhance the cooperation willingness of self-media and netizens, effectively curbing the formation of online violence opinions.

As a key link between carbon source and carbon sink in carbon capture, utilization and storage(CCUS) technology, CO₂ pipeline transportation will play an important role in the process of carbon neutralization in the future. For the pipeline water hammer condition, the pressure oscillation may exceed the pressure in the pipe and be lower than the inlet pressure of the pump. At present, the water hammer and control theory of supercritical CO₂ pipeline is not mature. A mathematical model based on the law of conservation of mass, momentum and energy was established to describe the one-dimensional gas flow in the pipeline. The characteristic line method was used to solve the model, and the MATLAB programming was used to calculate. The simulation results were compared with the simulation results of the gas transmission system model proposed by Kiuchi and the simulation results of the commercial software OLGA. The results show that the simulation results are generally consistent with the simulation results of the gas transmission system model. Compared with OLGA software, the maximum relative errors of pressure and flow are 0.02% and 2.32%, respectively, which meet the requirements of engineering calculation accuracy. For the fast transient process of pipeline parameter change caused by pipeline compressor start and stop, valve emergency switch and rapid change of flow in a short time, the rapid change value is set to simulate. The established model can calculate the parameter change of each node with high accuracy, which can provide theoretical support and technical support for the localization of supercritical CO₂ pipeline transportation process simulation software.

In order to study a non-destructive testing method for concrete beam stress, a ultrasonic tail stress identification algorithm coda wave-deep residual shrinkage network (C-DRSN) based on deep residual shrinkage network(DRSN) was proposed. According to the high-dimensional characteristics of the tail wave signal vector, the interference of signal noise to the measurement stress accuracy was reduced by introducing residual contraction block, using soft threshold function and attention mechanism, and the adaptive recognition and extraction of stress features in the signal were realized, and the recognition accuracy was improved. The characteristics were visually analyzed, and the mapping relationship between the tail wave sign and the stress was established. In order to verify the model's ability of stress recognition, ultrasonic tail wave signals of concrete I-beams under three-point bending and eccentric compression loads were collected respectively. The results show that the recognition rate can reach 99% under both loading modes, indicating that the proposed method is feasible in the stress recognition of concrete beams, and the accuracy of the proposed method is higher than that of the tail wave interference method.

To realize the efficient analysis of composite joints with novel side-plate reinforced connections, a macro model of beam-column-slab composite joint was proposed. The optimal realization method of the connection between floor and steel beam was determined. The accuracy and reliability of the macro model was verified. Furthermore, a beam-column joint frame model with traditional side-plate reinforced connections (TSP), a beam-column joint frame model with novel side-plate reinforced connections (FBSP), and a composite joint frame model with novel side-plate reinforced connections (CJ-FBSP) were established. Elastic-plastic time-history analysis was conducted on three frame models considering the joint performance. The top point lateral displacement, inter-story drift angle, plastic energy dissipation, and plastic hinge distribution of different frame models under seismic waves were obtained. The results show that the performance of TSP and FBSP joints can be well simulated according to the spring stiffness calculation method. At the same time, the connection method considering the shear slip and pull-out performance of studs can precisely simulate the mechanical performance of composite joints. The inter-story drift angle of the three joint frame models do not exceed the specification limit (1/50). The maximum inter-story drift angle of FBSP frame model is smaller than that of TSP frame model but larger than that of CJ-FBSP frame model, and the plastic energy dissipation capacity is the best. Due to the strengthening effect of the floor, the plastic hinge rate of CJ-FBSP frame model is the smallest but the plastic energy dissipation capacity is weak.

To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.

In construction safety inspections, visual obstructions often lead to missing features, resulting in dangerous misjudgments. To improve the efficiency of risk identification in construction, a method for occluded feature inference based on amodal completion technology was proposed, using construction fence detection as a case study. First, a dataset of fence detection images with visual obstructions was created using image synthesis techniques. Then, a combination of YOLOv8 instance segmentation and the amodal segmenter based on boundary uncertainty estimation (ASBU) feature completion network was used to infer the visual features of the occluded parts of the fence. The completed features of the occluded construction fences can be applied to various construction safety monitoring tasks, such as closed-loop detection. The approach was validated using fence images from multiple construction sites, achieving precise feature completion for occluded fences (average intersection ratio mIoU>95.5%). The research results provide a framework for feature inference in occluded construction scenes, which enhances the efficiency of intelligent construction safety supervision.

In deep foundation pit engineering, the support form of “bored pile + internal support” is often used for foundation pit support, but the support effect of different support arrangements on the foundation pit is different. Relying on a foundation pit project in a fast-track reconstruction and expansion project in Changzhi City, ABAQUS was used to numerically simulate the whole process of excavation of different steel support layout schemes, and the influence of different internal support layout schemes on the settlement of the surface soil around the excavation of the foundation pit, the horizontal displacement of the supporting structure and the uplift of the bottom of the foundation pit were analyzed, and then the reliability of the model was verified by combining the field monitoring results. The results show that the numerical simulation results can accurately reflect the deformation law and characteristics of the foundation pit in the process of excavation and support. When the depth of the internal support arrangement is small, it has a good control effect on the maximum horizontal displacement of the supporting pile and the surface settlement outside the pit. When the depth of the internal support arrangement is large, the maximum uplift at the bottom of the pit has a certain control effect. Increasing the number of internal supports has different effects on reducing the maximum horizontal displacement of the supporting pile and the maximum uplift at the bottom of the pit.

The accumulated water in the cavity behind the tunnel lining in cold regions may freeze under low-temperature conditions, resulting in local frost heave pressure. Assuming that the cavity behind the tunnel lining was a semi-elliptical space, the interaction between the surrounding rock, ice body, and lining during the frost heave process was simplified as springs in series, and an analytical solution for the local frost heave pressure in the semi-elliptical water-accumulated space was proposed. A three-dimensional numerical model was developed to verify the effectiveness of the analytical solution. Further, the relationships between local water-accumulated frost heave pressure and surrounding rock grade, lining stiffness, water-accumulated depth, and frost heave level were studied, and the influences of frost heave position on the mechanical characteristics of lining structure were analyzed. The results show that the local water-accumulated frost heave pressure of the cold region tunnel is negatively correlated with the surrounding rock grade and positively correlated with the lining stiffness, water-accumulated depth, and frost heave level. The influence degree of various factors on frost heave pressure is as follows: water-accumulated depth > lining stiffness > surrounding rock grade > frost heave level. The impact of water-accumulated frost heave on the lining structure mainly occurs in the contact area between the ice body and the lining, leading to the convergence of the tunnel towards the inner side. The principal stress of the lining is maximum when the water-accumulated space is located at the inverted arch, and the principal stress of the lining is minimum when it is located at the arch foot. Under the action of local frost heave, there is a sudden change in the bending moment and axial force of the lining structure, manifested as the bending moment increase and axial force decrease of the lining under tension on the air side, as well as the bending moment decrease and axial force decrease of the lining under tension on the surrounding rock side. The stiffness differences in different zones of the lining lead to different impacts of frost heave pressure on structure safety. The influence degree of frost heave position on structure safety is as follows: vault > inverted arch > arch shoulder > arch foot > wall foot.

Gas risk assessment of tunnel construction is one of the key issues to ensure the safe construction of tunnels passing through gas sections. Aiming at the three stages of investigation, design and construction in the process of tunnel construction, the interpretative structural model was used to reveal the hierarchical key of gas risk influencing factors in the above three stages. The hierarchical model of risk assessment was established, and the weight calculation method of risk assessment index was defined. Combined with data collection, literature collation and engineering investigation, the assignment standard of risk assessment index was proposed based on membership degree theory, and the gas risk assessment method in tunnel construction process was constructed. Combined with engineering examples, the rationality of the proposed method was verified. The results show that the pregnancy risk environment in the survey stage is the precondition, and the survey disturbance is the inducing factor. The pregnancy risk environment in the design stage is the precondition, and the design factor is the inducing factor. In the construction stage, the pregnancy risk environment is the precondition, the construction disturbance is the inducing factor, and the site management is the root cause. The risk is revealed in the survey stage, the risk is reduced in the design stage, and the safety is ensured in the construction stage. The proposed method is consistent with the on-site disclosure, which verifies the rationality of the proposed method. Through the above research, it can provide a theoretical basis for the risk determination of the tunnel crossing the gas area, and provide a reference for the selection of subsequent engineering measures.

To investigate the psychological load characteristics of drivers on interchange ramp curves, an naturalistic driving test was conducted with 38 participants in a high-density interchange group in Chongqing. The PhysioLAB physiological monitoring system was used to collect electrocardiogram (ECG) data from drivers navigating the ramp curves. Three typical ramps were taken as research objects, a factor analysis model was established based on heart rate (HR), heart rate increment (HRI), and heart rate variability[root mean square of the difference between adjacent normal cardiac cycles(RMSSD) and standard deviation of RR intervals for all sinus heartbeats(SDNN)] to analyze the variations in psychological load and influencing factors for drivers on different ramp curves. The results indicate that the average heart rate for drivers on various ramp scenarios is found to range from 60 to 120 beats per minute, with heart rate increments between 3% and 15%. Psychological load on right-turn ramps is more dispersed, while on left-turn and circular ramps, it is more concentrated, with higher load observed on circular ramps. Differences in psychological load are noted among drivers of different styles and genders. Left-turn ramps impact female drivers more, and right-turn ramps impact aggressive drivers more. Psychological load decreases with increased familiarity and increases with larger turning angles. More ramp lanes lead to more vehicle weaving, increasing psychological load. A negative correlation is observed between psychological load and the radius of right-turn ramps, with no linear relationship for left-turn and circular ramps. Driving on consecutive circular ramps increases psychological load.

Highway maintenance operations occupy existing road infrastructure and have a significant impact on vehicle traffic safety and efficiency. In order to analyze the traffic flow characteristics during maintenance operations on a four-lane highway when the inner lane is closed, a traffic flow modeling and simulation method based on an improved cellular automaton was proposed. According to the Highway Maintenance Safety Operation Regulations (JTG H30—2015), maintenance operation control zones were established, dividing the mainline highway facilities into six traffic scenarios: warning zone, upstream transition zone, buffer zone, work zone, downstream transition zone, and termination zone. By introducing a longitudinal safety distance model and optimizing the lateral lane-changing safety condition determination rule, the NaSch car-following model and symmetric two-lane cellular automata (STCA) lane-changing model were improved. The model was calibrated using field data, and the traffic flow operating conditions in the maintenance work zone were simulated using MATLAB. The results show that when the traffic flow density reaches 1 550 pcu/h per lane, setting the merging start point 1 000 meters downstream of the warning zone, with a speed limit of 60 km/h and an upstream transition zone length of 160 meters, the traffic flow safety and efficiency indices in the maintenance zone are optimized.

The smart skin of an aircraft is realized by integrating distributed sensors, actuators, and controllers into the composite skin, thereby enabling it to monitor its own state and detect damages. The physical field inversion algorithm plays a key role in the signal processing of the smart skin. However, due to factors such as the low sensor density, traditional inversion algorithms exhibit limited accuracy. In order to enhance the monitoring precision of the smart skin, a back propagation(BP) neural network-improved grey wolf optimizer(IGWO) inversion algorithm, which combined a BP neural network with an IGWO-optimized Kriging model, was proposed. A prototype of the smart skin was subsequently fabricated, and wind tunnel tests were conducted to validate the proposed algorithm. The results demonstrate that the BP-IGWO inversion algorithm achieves higher accuracy and superior detail representation compared to traditional inversion algorithms, and can better monitor the state of smart skin.

In order to improve the security performance of civil aviation security personnel, the relationship between security personnel's hazard perception ability and security performance under different factors was explored. Based on the fuzzy signal detection theory(FSDT), the discriminability and judgment criterion were used as indicators to measure the hazard perception ability of security personnel. A mixed experimental design of 2 (experience: novice, veteran) ×2 (time pressure: no time limit, time limit) ×2 (probability of occurrence of contraband: high, low) was employed to examine the impact of experience, time pressure, and probability of contraband on the hazard perception ability of security personnel. The results indicate that experience and time pressure significantly impact security performance, while the main effect of contraband probability on security performance is not significant. Experience has a notable effect on fuzzy discriminability and fuzzy judgment criterion, with veterans demonstrating higher overall hazard perception ability than novices. Additionally, a time limit reduces the discriminating power of security personnel under low contraband probability but improves fuzzy judgment criterion standards under high contraband probability. Attaching importance to security skills training, setting reasonable search time and conveyor speed can effectively improve security personnel's hazard perception ability and ensure aviation safety.

A systematic study was conducted on the issue of gate assignment, with the goal of minimizing the number of remote gate assignments and the idle time of near gates. A multi-objective mathematical model was proposed to address the multi-objective and multi-constraint characteristics of the problem. The model was designed to minimize the number of remote gate assignments and the idle time of near gates while taking into account parameters such as actual flight arrival and departure times, aircraft types, and the interrelationships among gates. The gate assignment process was optimized using the deep reinforcement learning method, specifically the deep deterministic policy gradient(DDPG) algorithm. To enhance the optimization ability and performance of the algorithm, an improved DDPG algorithm was developed by incorporating prioritized experience replay and multi-strategy exploration mechanisms. Comparative experiments were conducted, and the results show that the improved algorithm significantly reduces the number of remote gate assignments and optimized time utilization. The algorithm also achieves faster convergence and stronger global optimization capabilities, confirming its effectiveness.

In order to explore the concentration level and health risks of polycyclic aromatic hydrocarbons (PAHs) in cabin of new cars, air samples of 15 newly produced passenger cars in the gaseous and particulate phases under normal temperature and high temperature conditions were collected by using particulate samplers in series with polyurethane foam (PUF) sleeves. The contents of 16 priority PAHs in the samples were determined by GC-MS, and the health risk assessment of drivers and passengers was carried out. The results show that the average detection rates of 16 PAHs in the gaseous and particulate phases is 2.17~50.00.Among them, naphthalene (Nap), phenanthrene (Ace), and phenanthrene (Phe) are detected in some sample cars under high and normal temperature conditions, while phenanthrene (Acy) is detected in all sample cars under high and normal temperature conditions, and the rest of the substances are only detected under high temperature conditions. The concentration of PAHs in cabin of cars under high temperature conditions is higher than that under normal temperature conditions, and the concentration in the gas phase is higher than that in the particulate phase. Overall, Nap exhibites the highest concentration. The carcinogenic health risks of Nap, Ace, Phe, and Acy under high temperature conditions range from 3.53×10^-11 to 4.54×10^-9, while under normal temperature conditions, they range from 1.70×10^-11 to 5.16×10^-9. It can be seen that PAHs in the gas phase and particulate phase in cabin of new cars, can be effectively collected by using particulate matter samplers in series with a polyurethane foam (PUF) sleeves for sampling. The detected concentrations of PAHs in cabin of cars, is low, the overall carcinogenic health risk is less than 10^-6, and the carcinogenic risk is low.

The concept of “resilience” is introduced as a new research direction for cities to withstand uncertain risks in the face of complex challenges such as global environmental changes, accelerated urbanization, and frequent epidemics. To explore the spatiotemporal evolution and obstacle factors of urban natural disaster resilience in Shaanxi Province to enhance resilience against natural disasters. The entropy weight-technique for order preference by similarity to ideal solution(TOPSIS) method was used to assess resilience levels across four dimensions: economy, society, infrastructure, and ecological environment. The spatiotemporal evolution characteristics of resilience in Shaanxi Province from 2018 to 2022 were examined using a combination of GIS, Theil index, and center-standard deviation ellipse. An obstacle degree diagnosis model was employed to analyze influencing factors. The results indicate that over time, the overall resilience level against natural disasters in cities, except for Xianyang, shows an upward trend. Overall resilience, social resilience, and infrastructure resilience increase, while ecological environment resilience slightly declines. Spatially, the resilience in Shaanxi Province follows a pattern of “Guanzhong region > Northern Shaanxi region > Southern Shaanxi region.” The major influencing factors are infrastructure resilience and ecological environment resilience, with the length of urban drainage pipelines and greening coverage area identified as the top obstacles restricting urban resilience. The research findings are expected to provide theoretical references for regional natural disaster management and resilient urban planning in Shaanxi Province.

Oil and gas drilling and production wellsites are complex and have many types of potential safety hazards, in order to improve the accuracy of the identification of potential safety hazards in wellsites, an oil and gas drilling and production wellsite potential safety hazard identification method based on improved YOLOv5 was proposed. Firstly, in order to solve the problem that the background of the picture was complex and the recognition difficulty increased, the SimAM attention mechanism was introduced in the backbone network; secondly, in order to solve the problem that the scales of the types of hidden hazards were different and there were multiple scales in one picture, the original feature fusion was replaced by adaptive spatial fusion of features (ASFF). Lastly, the hidden hazard recognition effect of the improved model was validated by comparing the model with other models. The results show that the improved YOLOv5 model improves the average accuracy value of recognition by 10.4%, and has a better recognition effect on the safety hazards of oil and gas drilling and production well sites. In order to solve the limitation of video monitoring and identification of oil and gas drilling and production wellsite safety hazards, a set of intelligent wearable device was developed, which effectively improved the portability of the identification of wellsite safety hazards.

In order to reduce the risk of logistics drones to the ground after failure or collision in the air, a model for logistics drones to the ground risk assessment was constructed, focusing on the risk analysis of drone failure and crash. Firstly, in the drone failure and crash risk model, the possible failure of drones in different flight stages was discussed in detail, including vertical fall and horizontal fall, as well as the casualties and economic losses that these falls may cause to ground personnel. Secondly, the collision and crash risk model was analyzed the dynamic behavior of drones after a collision in the air and its impact on ground safety, including collision momentum-energy conservation and collision consequence assessment. Finally, a comprehensive three-dimensional risk assessment matrix was constructed, combining the possibility of accidents, the number of deaths on the ground and the economic losses to evaluate the risk level in different scenarios. The case analysis shows that the number of deaths on the ground is within the order of 10^-6, and the risk level in different regions is obtained. It can be seen that this method provides important risk assessment tools and data support for logistics drone operators and relevant policy makers.