Design cycle is one of the key factors that influence the development of customized mechanical products (CMP). Secondary development technology (SDT) is an effective method to enhance the efficiency of CMP. To better promote high-quality development of SDT in CMP applications, first, key technological advancements in SDT were briefly introduced based on the analyzes of the evolution of customized product production and the parameterized design methods. Then, research and application status of SDT in CMP design was analyzed from different perspectives, and existing problems are summarized. Finally, the future trends of SDT were explored in conjunction with modern information technologies, such as artificial intelligence (AI), collaborative design (CD), cloud technology (CT), and digital twins (DT), and the importance of domestic development platforms was emphasized. The results provides a reference for the application and further development of SDT in CMP.

With the continuous development of modern network information technology, the traditional passive network security defences are static defences that can not effectively respond to new types of network threats and can no longer meet the needs of network security. As the main network defence mean, active defence overcomes the many defects of traditional defence, can effectively respond to unknown network activities, showing strong advantages. Starting from the development process of active defense, the main technologies currently existing in network security active defense were sorted out, and the advantages and disadvantages of the main technologies at four levels, namely, network security intrusion defence, network security intrusion detection, network security intrusion prediction, and network security intrusion response, were summarised and analyzed, as well as the analysis and outlook of its future development direction.

To investigate the influence of Allee effect on population dynamics, the center theorem and bifurcation theory were used to study the bifurcation of a discrete predator-prey system with Allee effect on prey. The results indicate that an appropriate increase in the growth rate of the prey population will lead to the emergence of predators, the natural enemies of the prey by the transcritical bifurcation; Under the appropriate Allee effect, the system is stable, but if the Allee effect exceeds the critical value the system will undergo a double period flip bifurcation. The bifurcation parameter diagram also shows that as the Allee effect further strengthens, this flip bifurcation will lead to chaos in the system. From a biological perspective, in a certain ecological environment, a species that reproduces to a certain extent will encounter its natural enemies through “natural selection”. An appropriate Allee effect is beneficial for the stability of the predator-prey system, but if the Allee effect of the prey is too strong, it will cause a sharp decrease in predators and an effective restriction of no natural enemies. The prey will undergo a brief surge, exceed the environmental capacity, and then die out. To reduce the adverse effects of the bifurcations on the system, the state feedback is used to control the transcritical bifurcation and facilitate hybrid control to control the flip bifurcation. The numerical simulation results show complete consistency with the theoretical analyses.

Research on the factors affecting soil organic carbon density is of great significance for regulating climate change and sustainable agricultural development. Previous studies have mainly explored the relationship between various factors (e.g., climate, altitude, soil physicochemical properties, etc.) and the influence of soil organic carbon density, but less involved in the interaction relationship between factors. Typical soil profiles were collected in Anhui Province to estimate the soil organic carbon density (SOCD) in the 0~10 cm, 10~20 cm, 20~30 cm and 30~100 cm soil horizons. The structural equation model was used to analyze the effects of climate, elevation, vegetation, soil water content, human activities and other environmental factors on SOCD. The results are as follows. In the 0~30 cm soil layer, SOCD show a gradually decreasing trend, and the average SOCD in the 0~10 cm, 10~20 cm and 20~30 cm soil layers were 2.09, 1.63 and 1.10 kg/m², respectively. The average SOCD of 30~100 cm soil layer is 4.46 kg/m². The spatial distribution of SOCD in the province gradually increased from north to south. The SOCD of 0~10 cm and 10~20 cm soil layer is higher than 5.00 kg/m^-2, mainly distributed in the Jianghuai hilly downland and the Riverine Plain. The areas with SOCD higher than 3.00 kg/m² in the 20~30 cm soil layer were distributed in the South Anhui hilly region. The high SOCD values of 30~100 cm are mainly distributed in the South Anhui hilly region. In the structural equation model of 0~10 cm, 10~20 cm and 20~30 cm soil layer, land use has the largest positive influence on SOCD, and the influence coefficients are 0.22, 0.20 and 0.22, respectively. The average annual temperature has the largest negative influence on SOCD, and the influence coefficients are -0.04 and -0.03. Annual rainfall was the most significant in 30~100 cm soil layer, but land use and NDVI were not significantly affected (p>0.05). Topography affects SOCD through four paths: land use, NDVI, annual precipitation and average annual temperature. Human footprint affected SOCD through NDVI, and the effect on NDVI reached a very significant level (p<0.001). The structural equation model established in this study initially explained the relationship between different environmental factors, and provid a theoretical basis for SOCD regulation and agricultural sustainable development.

Controlled-source audio-frequency magnetotellurics (CSAMT) uses artificial sources, providing strong anti-interference capabilities. It is widely used in oil exploration, mineral surveys and other areas. Traditional 2D inversion technology is mature, and deep learning has recently made some research advancements in geophysical exploration. There is still a research gap in applying deep learning to CSAMT inversion. Therefore, developing a 2D inversion algorithm for CSAMT based on deep learning is highly significant for advancing the use of deep learning in electromagnetic exploration. The characteristics of deep learning components such as convolutional layers, pooling layers, fully connected layers, and the UNet network were introduced. An explanation was provided on how to construct the training dataset, the UNet network used in this study, and how to set various training parameters. The network was saved after training. When the inversion was needed, the net was loaded and the algorithm could predict the result. Several theoretical models were designed for inversion, and the experiment results verified the reliability and effectiveness of the algorithm. The time of the deep learning inversion and the tranditional inversion was recorded. Building training set needed much time, but the time of deep learning inverison was much less than the tranditional inversion. The deep learning inversion is more efficient than the traditional inversion.

An improved PSPNet(pyramid scene parseing network) network was proposed to automatically identify fractures in electrical imaging logging images, which was difficult to extract fracture features and led to low segmentation accuracy and large calculation of network parameters. Firstly, the backbone network in PSPNet was replaced with the optimized MobileNetV3 network, which could significantly reduce the number of network parameters and the amount of computation. Secondly, the asymptotic feature pyramid network(AFPN) was introduced to increase the interaction of multi-scale information and enhance the recognition ability of small cracks. Then, multi-depthwise Conv head transposed attention(MDTA) was introduced to extract global features and improve the extraction ability of key information. Finally, the combination of Focal Loss and Dice Loss were used as a loss function to solve the problem of unbalanced proportion of data sets. The experimental results show that the improved PSPNet network has a good segmentation effect on the fracture in the electrical imaging logging. Compared with the PSPNet network, mIoU(mean intersection over union) improved by 3.17% and mPA(mean pixel accuracy) improved by 6.38%. In addition, the number of parameters, calculation amount and weight of the proposed algorithm are reduced by 94.3%, 95.7% and 93.8% respectively compared with the original model. At the same time, the crack identification system based on CIFLog is developed, which can meet the practical needs of the electrical imaging logging.

A large number of loess landfills have been generated by the Pingshan land formation project carried out in Lanzhou area, which has a large safety hazard due to its low compaction and lack of necessary protection, resulting in the extensive development of geologic hazards such as loess caves, landslides, and so on. The pore microstructure of in-situ loess landfill with different water content was quantitatively studied through scanning electron microscope and Image J software, combined with fractal theory, the pore type, number, area, and change rule of the number of dimensions of the pore of the landfill loess with different water content was obtained, and the dynamic relationship between the pore structure and the wetting of loess and development of the caves were preliminarily analyzed. The results show that: with the increase of water content, the number and area of large and medium-sized hollow pores gradually decrease, while the number and area of small hollow pores decrease, but the area of small hollow pores increase, and the collapse of large and medium-sized hollow pores is the main reason for wet subsidence and deformation of loess; the pore dimension number of landfill topsoil has a linear negative correlation with water content, and a positive correlation with wet subsidence; the average pore dimension number of in-situ topsoil is 1.251, and the average pore structure is 1.251. The average pore dimension is 1.251; based on the characteristics of cave development in loess landfill, it is proposed that the protection treatment should be carried out in three aspects, such as the construction of drainage ditches, the reinforcement of caves, and the protection of slopes. The research results can provide theoretical support for the engineering construction and geologic disaster prevention and control research in Lanzhou landfill loess area.

Machine learning methods have been employed in the study area of Changyang Tujia Autonomous County for landslide hazard assessment, it could provide a scientific basis for geological disaster prevention and control efforts. Through the correlation analysis of 12 evaluation indicators (planar curvature, terrain undulation, surface roughness, slope, vegetation coverage, engineering lithology, distance to fault zone, distance to water system, rainfall, land use type, distance to buildings, and distance to roads) in the study area selected by historical landslide points, they were selected. And the evaluation model of the study area was constructed by calculating the information content of factors and integrate support vector machine (SVM) and gradient boosting decision tree (GBDT) models. The hazard of the study area was classified into four levels: extreme high, high, medium, and low, to generate hazard zoning. Subsequently, an assessment of the evaluation model was conducted. The results indicated that the very high hazard zone was mainly distributed in the southwest, central, and eastern parts of the research area. The distribution percentages of very high, high, medium, and low hazard zones predicted by the I-SVM and I-GBDT models were 15.86%, 21.29%, 33.51%, 28.68%, and 30.08%, 7.41%, 13.28%, 49.22%, respectively. The prediction of hazard zones by the I-SVM model aligned more closely with reality. The AUC values for the I-SVM and I-GBDT models were 0.859 and 0.829, respectively. The prediction of risk zones by the I-SVM model is deemed more reasonable and reliable.

Since 2019, the Jiubaoyan landslide has exhibited continuous and gradual deformation. On September 17, 2021, during the rainy season, the landslide was obviously deformed and slipped due to the continuous heavy rainfall. On the basis of traditional engineering geological exploration methods such as on-site investigation, drilling and displacement monitoring, the finite element simulation method Midas GTS was utilized to simulate and calculate the seepage and displacement field of the slope under different working conditions, the landslide formation mechanism was comprehensively analyzed. Furthermore, the Fast GPU Matrix computing of discrete element method (MatDEM) was introduced to forecast the trend of the landslide sliding evolution under rainstorm working conditions. The results indicate these as follows. ① The finite element numerical simulation results are consistent with the drilling results, revealing that the sliding zone of Jiubaoyan landslide is located at the interface between the quaternary landslide accumulation layer gravel soil (${Q}_{4}^{del}$) and the mudstone of the Jurassic Suining Formation (${J}_{2}^{sn}$); ② Both finite element numerical simulation and on-site investigation suggest that Jiubaoyan landslide is a multi-level shallow soil landslide mainly driven by push forces and secondarily propelled by traction forces. The sliding mass is divided into upper and lower parts, which are closely interconnected. The loose soil structure in the landslide area as well as the abundant water storage ahead and behind the slope serves as material source for the landslide formation and annual heavy rainfall during the rainy season acts as critical external triggers for landslides.③ The finite element numerical simulation results reveal that Jiubaoyan landslide remains relatively stable under the natural conditions but transitions to an unstable state under the rainfall conditions. It is possible to lead to large displacement landslide under the persistent extreme rainstorms. ④ The discrete element simulation results suggest that the slope is in an destabilized state under extreme rainfall conditions, with two parts of sliding mass are penetrated through two sliding zones. This further landslide instability may culminate in significant displacement landslide, resulting in considerable economic and human losses. ⑤ The research method combining finite element analysis with discrete element analysis not only corroborates on-site investigation, drilling, and monitoring, but also enables a quantitatively analysis of the formation mechanism and prediction of potential working conditions in the future. It is hoped that such a study can offer some useful references for studying similar multi-level landslide disasters in mountainous regions.

Recent studies indicate that for early-stage non-small cell lung cancer(NSCLC) classified as T1N0M0, sublobar resection offers long-term outcomes comparable to lobectomy. However, these early-stage patients may still experience pleural invasion, which is associated with poor prognosis. It is necessary to compare the long-term efficacy of sublobar resection versus lobectomy in patients with T≤3 cm N0M0 NSCLC accompanied by pleural invasion. Research data were sourced from the SEER(Surveillance, Epidemiology, and End Results) database. Patients diagnosed were from between 2010 and 2020 with T≤3 cm N0M0 NSCLC and pleural invasion. Patients were divided into sublobar resection and lobectomy groups, and their cancer-specific survival(CSS) and overall survival(OS) were compared. Univariate analysis post-matching reveale no significant differences in CSS and OS between the sublobar resection and lobectomy groups. Multivariate analysis also indicate that the surgical approach is not an independent prognostic factor for CSS(HR=1.185, 95% CI: 0.745~1.885, P=0.472) and OS (HR=1.171, 95% CI: 0.869~1.577, P=0.299)in patients with T≤3 cm N0M0 NSCLC and pleural invasion. Subgroup analyses show no significant differences in CSS and OS between the two groups across various subgroups. Competing risk model multivariate analysis also demonstrate no significant difference in lung cancer-specific mortality between sublobar resection and lobectomy. In conclusion, for patients with T≤3 cm N0M0 NSCLC accompanied by pleural invasion, sublobar resection offers long-term survival outcomes comparable to lobectomy and can be considered a viable surgical option for this patient population.

A new amidoxime small molecule compound was synthesized by oximation addition reaction and the uranium decorporation was evaluated. The changes of endogenous metabolites caused by Uranium decorporation in animals were investigated by metabonomics method, and the related differential metabolites were searched for and their metabolic pathways and mechanisms were explored.The mice were divided into blank group (NG), model group (MG), 0.42 mmol/kg ZnNa₃-DTPA group(YG), 0.21 mmol/kg amidoxime group (CN) and 0.42 mmol/kg amidoxime group (EN), and were injected with positive drug (ZnNa₃-DTPA) and amidoxime compounds in tail vein immediately after the tail vein injection of uranyl acetate and amidoxme compound,the uranium content in the kidney and femur of mice was determined by Inductively-coupled plasma mass spectrometer(ICP-MS) 24 h later. The metabolites in the serum of each group were identified by GC-MS(gas chromatograaphy-mss spectrometer), and screened as potentially differentiated metabolites by orthogonal partial least squares discriminant analysis (OPLS-DA) with variable importance in the projection (VIP) > 1, mass spectrometry database and MetaboAnalyst platform were used to analyze the differential metabolites and their associated pathways. The results show that compared with model group, 0.42 mmol/kg amidoxime compound group decreased uranium content in kidney and femur by 61.70% and 54.74%, and the positive group at the same dose reduced the uranium content in kidney and femur by 60.70% and 40%, respectively.The results indicated that the small molecule compound of aminoxime had significant uranium decorporation. Metabolomic analysis showed that the metabolic profile of the amidoxime group was significantly different from that of the model group, which was closer to that of the normal group than that of the positive group. A total of 14 different metabolites were found after screening, and the enrichment analysis of metabolic pathways showed that the metabolic pathways related to them were mainly tyrosine metabolism. Biosynthesis of phenylalanine, tyrosine and tryptophan, metabolism of glycine, serine and threonine, etc. The small molecule amidoxime compound has a remarkable uranium decorporation effect, which is better than ZnNa₃-DTPA, and has a protective effect on kidney injury caused by uranium.

A hybrid algorithm (IWOA-BP) combining the improved whale optimization algorithm (IWOA) and backpropagation neural network (BP) was proposed to offer theoretical support for the formulation of grain strategies in the agriculture sector and its related industries. By introducing an improved convergence factor, nonlinear inertia weight, and optimal neighborhood disturbance strategy into the modified whale optimization algorithm, the optimal solution of the algorithm was obtained. This solution was then utilized as the initial weights and thresholds of the BP neural network, thereby enhancing the convergence speed and accuracy of the IWOA-BP hybrid algorithm. Subsequently, a grain yield prediction model based on the improved whale optimization algorithm was established using data from China’s grain yield over 45 years and seven influencing factors including effective irrigation area, chemical fertilizer application, rural electricity consumption, total power of agricultural machinery, sowing area of grain crops, disaster-affected area, and per capita consumption expenditure in rural areas. Through extensive experiments on a test set, it was found that the IWOA-BP model consistently outperformed other prediction models such as long short-term memory (LSTM), extreme learning machine (ELM), BP neural network with whale optimization algorithm (WOA-BP), and BP neural network with particle swarm optimization (PSO-BP). Compared to the ELM model, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the IWOA-BP model were reduced by 77.12% and 88.18% respectively. When compared to the LSTM model, the RMSE and MAPE of the IWOA-BP model were reduced by 69.11% and 47.36% respectively. Furthermore, in comparison to the WOA-BP model, the mean absolute error (MAE), RMSE, and MAPE of the IWOA-BP model were reduced by 43.78%, 43.22% and 45.96% respectively. Additionally, when compared to the PSO-BP model, the MAE, RMSE, and MAPE of the IWOA-BP model were reduced by 89.67%, 90.61% and 90.82% respectively. Therefore, the proposed IWOA-BP prediction model can be effectively used to predict grain yield due to its higher coefficient of determination, smaller prediction error, and faster convergence speed. It has important technical reference value for agricultural departments and relevant policymakers.

In order to effectively utilize LNG(liquefied natural gas) cold energy and liquefy CO₂ in gas turbine exhaust gas, a new process of LNG cold energy to liquefy CO₂ and CO₂ power cycle was proposed. In this process, Reheat cycle and regenerative cycle were added on the basis of conventional Rankine cycle, and multi-flow strand heat exchanger was set up. Chemical process simulation software was used to simulate the process flow and sensitivity analysis of reflux temperature, interstage cooling temperature and maximum circulating temperature and pressure was carried out to obtain the best operating parameters. Exergy efficiency, specific work and CO₂ liquefaction rate of the system were analyzed and calculated by exergy analysis. Exergy efficiency of exergy was 54.16%, specific work was 335.9 kJ/kg LNG, and CO₂ liquefaction rate was 0.621 7 kg/kg LNG in a new process. The evaluation indexes of the new process were better than those of the existing process. As for the exergic efficiency, exergic efficiency of exergic was as the highest and the temperature of CO₂ after liquefied was as the constraint condition, exergic efficiency of exergic was 54.28% and specific power was 337.5 kJ /kg LNG, so the system performance was further improved.

With the development of the energy industry and the continuous growth of global energy demand, the exploration and development of geothermal resources have become increasingly difficult. Deep and ultra-deep geothermal resources have been identified as a key direction for the development of the new energy industry. As the drilling depth for geothermal wells continues to increase, the thermal and physical properties of the drilling fluid are found to have a more significant impact on the calculation of wellbore temperature and pressure amid changes in temperature and pressure. In A coupled numerical model of transient temperature and pressure in the wellbore during the drilling of geothermal wells was established, and the influence of the density and viscosity of drilling fluid on the calculation of wellbore temperature and pressure with changes in temperature and pressure during the drilling of geothermal wells was studied. It was shown by the calculation results that the viscosity and density of the drilling fluid significantly affected the calculation of wellbore temperature. When the changes in viscosity with temperature are considered, the calculation results of wellbore temperature are found to be 3.1% higher, and when changes in viscosity and density with both temperature and pressure are considered simultaneously, the results are 4.99% higher, compared with the case where changes in viscosity with temperature are not taken into account; To improve the accuracy of calculations, the thermal and physical properties of drilling fluid should be fully considered in calculating the temperature and pressure of geothermal wells.

Zizhou gas field is a multi-layer superposed tight sandstone gas field. The reservoir covers strata from Benxi Formation of Carboniferous system to He8 Formation of lower Shihezi Formation of Permian system. At present, the pore structure of the reservoir and its influence on the reserve utilization are not well understood. It seriously restricts the gas field interlayer digging process. The pore structure of tight sandstone reservoirs in Benxi Formation-He8 Member was comprehensively investigated through the utilization of cast thin sections, reservoir physical property, production data, as well as statistical and correlation analysis methods. The suggestions for further interlayer potential exploitation were also given. Draw four conclusions. The reservoir has a typical 1+1 type pore structure. Among them, the lower formations (Benxi Formation, Taiyuan Formation, Shan2 member) have developed primary intergranular pores, with large primary pore proportion, face rate and pore size, and good pore structure, which is the dominant pore structure of primary pores. In the upper formations (Shan1 member and He8 member), the intragranular dissolve pore- intercrystalline pore- microfissure are developed, the primary pore ratio, face ratio and pore size are smaller, and the pore structure is poor, which is the dominant pore structure of the secondary pores. The difference of rock composition, especially the content of quartz and cuttings, is the main reason for the formation of 1+1 pore structure. Among them, the lower formations are dominated by quartz sandstone with high quartz content and low cuttings content, which is conducive to the preservation of primary pores. The upper formations are dominated by lithic sandstone with high lithic content and low quartz content, which are not conducive to the preservation of primary pores but to the formation of secondary pores. The pore structure of 1+1 type has a significant effect on the exploitation of reserves. Among them, the lower formations have good pore structure, relatively high permeability (0.52 mD), large discharge area (0.70 km²), and high reserve utilization degree (67.9%). The upper formations have poor pore structure, relatively low permeability (0.33mD), small discharge area (0.34 km²), and low reserve utilization degree (26.9%). The analysis shows that the reserves of the upper formations have not been effectively utilized under the current well pattern conditions. In order to reduce the waste of reserves in the upper formation, realize the balanced exploitation of gas fields and improve the overall reserve utilization degree of Zizhou gas field, it is recommended to exploit the upper formations separately with a small well spacing.

In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and address the limitation of acquiring limited information from a single sensor in grinding monitoring, this study a method for online recognition of the grinding direction of single crystal diamond tools based on multi-information fusion and particle swarm optimization (PSO) algorithm for optimizing the BP(back propagation) neural network was proposed. Vibration signals and acoustic emission (AE) signals were collected during the grinding process. The wavelet packet decomposition method was applied to analyze the vibration signals of the tool and identify the characteristic frequency bands strongly correlated with the grinding direction. The parameter analysis method was used to analyze the AE signals and extract the characteristic parameters. The energy values of the characteristic frequency bands in the vibration signals and the characteristic parameters of the AE signals were taken as the feature parameters for identifying the grinding direction of the tool. These feature parameters were then used as inputs to the BP neural network model for fusion and online recognition of the grinding direction. To overcome the disadvantage of the BP neural network easily getting stuck in local minima, the PSO algorithm was utilized to optimize the weights and thresholds of the neural network, effectively solving the problem of local minima. The experimental results show that the accuracy of online identification of the grinding direction of single crystal diamond tools is effectively improved by PSO-BP and multi-information fusion, reaching an accuracy of 85%, providing a new method for online identification of the grinding direction of single crystal diamond tools.

Signal processing and deep learning are often combined to achieve better diagnostic results in the field of fault diagnosis. Based on this, the symplectic geometric mode decomposition was improved and the ResNeXt neural network was optimized, and then a gearbox fault diagnosis model was proposed based on the combination of optimized symplectic geometric mode decomposition and ResNeXt neural network was improved. Firstly, the collected vibration signals were filtered and reconstructed by optimized symplectic geometric mode decomposition to obtain the effective components. Then it was sent to the improved ResNeXt neural network for fault recognition and classification. The rolling bearing variable condition data from the University of Ottawa was used to verify the feasibility of the model. The gearbox data from drivetrain dynamics simula (DDS) was used for contrast experiment and anti-noise experiment, which verified the effectiveness of changes and the generalization of the model.

Wall-mounted furnace heating is one of the main ways of winter heating in northern rural areas. Due to the pressure fluctuation of the gas pipeline, the wall-mounted furnace is prone to combustion instability, and even CO poisoning accidents. In order to reveal the variation law of temperature field and combustion products in gas-fired wall-mounted furnace under the change of pipeline pressure, taking the fire row burner as the research object, the distribution of gas composition at the outlet of the ejector device, the temperature in the combustion chamber, and the concentration of CO and NO in the flue gas at the outlet of the combustion chamber under different pipeline pressures were studied by numerical simulation and experiment. The results show that. ① With the decrease of the gas inlet pressure, the methane concentration at the outlet of the fire increases, and the non-uniformity of methane concentration increases. ② As the gas inlet pressure decreases, the CO mass concentration at the outlet of the combustion chamber gradually increases. When the gas inlet pressure is 500 Pa, the CO mass concentration reaches a peak of 25.2 mg/m³, which is higher than the human body CO poisoning accident limit of 23 mg/m³. The mass concentration of NO at the outlet of the combustion chamber increases first and then decreases, reaching a peak of 18.99 mg/m³ at 1 500 Pa. ③ As the gas inlet pressure decreases, the maximum temperature in the combustion chamber increases first and then decreases. The minimum temperature is 1 840 K at 500 Pa. The combustion is not sufficient and the heat generated by combustion is less. It can be seen that the decrease of the pipeline pressure increases the instability of the wall-mounted furnace combustion and the CO concentration also increases significantly. The results can provide some theoretical support for the manufacturers of gas wall-mounted furnaces in enhancing the safety of equipment.

In order to solve the problem of high effective inductor current and peak value in the quadrilateral inductor current control strategy of four-switch Buck-Boost (FSBB) converter, a boundary conduction mode (BCM) control strategy was proposed, which shortened the freewheeling phase without power transmission to zero in the existing quadrilateral inductor current control strategy, so as to reduce the RMS and peak value of inductor current. Firstly, the current waveforms of the FSBB converter in different modes of working modes and inductor currents were analyzed. Secondly, the constraints of the FSBB converter to achieve soft switching under all working conditions were analyzed, and the value rules of the inductor current are obtained. Then, the variation of inductor current in different modes was analyzed, and the control method in critical continuous mode was given, when the input and output voltage difference was small, increase the output power by increasing the duty cycle of the first or third stage, and when the input and output voltage difference was large, the FSBB converter works in the critical continuous state of inductor current, which effectively reduces the effective value and peak value of inductor current. Finally, a simulation model was built. The results show that the proposed BCM control strategy can achieve zero-voltage turn-on and has good dynamic response ability.

The digital twin technology of the distribution network is an important product resulting from the integration and development of the power system and information technology. The technology simulates the physical behavior and operational status of the distribution network in a digital space by constructing a virtual model of the physical distribution network, enabling comprehensive simulation and analysis. Due to the diverse systems and complex states involved, the existing digital twin simulation platform technology for distribution networks still requires improvement. A wavelet-LSTM fusion model for power state and weather factors was constructed based on the existing wavelet transform and long short-term memory (LSTM) neural network. The high-dimensional input data were converted into detail and contour coefficients using discrete wavelet transform. Subsequently, LSTM neural networks were constructed to process the data and fuse the results, thereby forming accurate prediction outcomes. This method was validated on real datasets, showing that the wavelet-LSTM fusion model significantly improves the mean absolute percentage error (MAPE) compared to the existing LSTM network. Additionally, the method was tested on datasets from different industries. Compared to wavelet-Lasso, LSTM, and STL-LSTM, it exhibits better performance in terms of MAPE, demonstrating that the wavelet LSTM prediction method can be applied to state data from various sectors, thereby providing robust support for future state prediction of digital twins.

Neurosurgical procedures like cerebral vascular bypass, brain tissue dissection, and neurorrhaphy often lack microsurgical instruments with delicate force perception. To enhance manual precision and tactile force perception during surgery, a novel multi DOFs rope-driven micromanipulation wrist gripper designed was introduced for surgical robots. This wrist gripper was powered by a screw drive controlling six ropes, facilitating dexterous movements at the distal end of miniature instruments. The high gear reduction ratio of the screw drive enhances driving precision, thereby achieving high operational accuracy and stability of the distal wrist gripper. Moreover, a force sensor was integrated between the ropes’ rear end and the screw drive to monitor the tension in the ropes in real time. Based on the tension in the ropes, a computational model for estimating the contact force at the distal end of the wrist gripper was proposed, enabling the perception of external contact forces. Experimental results show that under open-loop control, the average motion tracking error of the wrist gripper is less than 1°, and the maximum mean value of force estimation error is 53.85 mN.

Process model discovery algorithms are capable of extracting process models from event logs, but different algorithms have varying capabilities in handling event logs. Currently, most research on evaluating these algorithms involves indirect evaluation methods, which have limitations. To address this issue, a method was proposed to directly evaluate the reliability of process model discovery algorithms, using reliability as an important evaluation metric. The original event log was preprocessed to obtain an incremental sub-log collection, the process model discovery algorithm was applied to the incremental sub-logs and the original event log to obtain process models, and the reliability of the business process model discovery algorithm was evaluated through quality assessment. Based on nine public simulation event logs and four real event logs, multiple model discovery algorithms were experimented on from the aspects of weak reliability, noise interference reliability, and strong reliability. The experimental results showed that the reliability values of Heuristic Miner, Inductive Miner-infrequent, Inductive Miner, and Alpha Miner were 4, 3.2, 2.4, and 1.6, respectively. Higher reliability values indicated stronger reliability of the algorithms. Thus, the proposed method can effectively evaluate the reliability of the algorithms.

To enhance the classification accuracy of lower limb movements, this paper was introduced a hybrid recognition model based on surface electromyography (sEMG) that combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). Initially, sEMG data were collected from 20 subjects performing four types of gait movements: ascending stairs, descending stairs, walking, and squatting. Subsequently, the collected sEMG data underwent preprocessing, and both time domain and frequency domain features were extracted to serve as inputs for the machine learning recognition model. The CNN-LSTM model was then constructed for lower limb action recognition and compared against the performances of CNN, LSTM, and SVM (support vector machine,)models. The results demonstrate that the CNN-LSTM model outperforms the CNN, LSTM, and SVM models by 2.16%, 8.34%, and 11.16% in accuracy, respectively, thereby proving its superior classification performance. This model provides an effective solution for enhancing lower limb motor functions, offering significant benefits for rehabilitation medical equipment and power assist devices.

With the deepening research on federated learning, it has been observed that the privacy protection strategies employed within federated learning fall short of fully guaranteeing the security and confidentiality of user data. Moreover, the training process in federated learning encounters challenges regarding model convergence. In response to these aforementioned issues, an innovative solution termed adaptive differential privacy (DP-AdaMod) was proposed. Primarily, the model training process was fine-tuned by incorporating an adaptive learning rate algorithm to mitigate model fluctuations and the adverse effects of overfitting. Consequently, this enhancement led to improved training efficiency and optimal performance. Secondly, the application of differential privacy techniques ensured the privacy security in federated learning through the deliberate introduction of noise into the model gradients. Additionally, accurate quantification of privacy loss was achieved by implementing the moment accountant mechanism, facilitating a balanced trade-off between privacy preservation and analytical accuracy. This meticulous approach served to fortify system security. Lastly, the efficacy of the proposed solution was ascertained through comprehensive simulation experiments. The results substantiate the superior performance of the proposed method, evident by its exceptional accuracy, efficient utilization of privacy budget, and other notable facets.

With the continuous development of container cloud technology, it is of great significance to predict and analyze the overall trend and peak of cloud resource requests for efficient utilization and reasonable allocation of container cloud resources. Deep learning technology for load prediction has become a key technology to solve the unbalanced utilization of container cloud resources. Aiming at the problems of low prediction accuracy and insufficient capture sequence features existing in the current single model and combination model of load prediction, a cloud resource combination prediction model based on temporal convolutional network-long short-term memory(TCN-LSTM)was proposed. The hollow convolution in the combination model increased the sensitivity field without reducing the feature size to obtain longer time series features. The residual network could transfer information across layers to accelerate the convergence of the network, and the obtained time series features could effectively improve the prediction accuracy of LSTM. Useing Alibaba’s publicly available dataset to make predictions, the experiment shows that the proposed model is compared with the single prediction model and other combined models, and the error index-mean absolute error(MAE) is reduced by 8%~13.7% and root mean squared error (RMSE) by 9.8%~13.1%, which proves the effectiveness of the proposed model.

Due to the increasing scarcity of precious woods and the severe environmental issues caused by overexploitation, it is necessary to mimic the appearance of precious woods by dyeing ordinary wood. Computer-assisted dyeing technology was utilized to achieve high-precision dyeing of ordinary wood, thus creating substitutes that resemble precious woods and reducing dependence on them. Initially, based on the concept of gene expression programming (GEP), a multi-expression programming (MEP) algorithm was proposed to predict dye ratios. Considering the complex interactions among various dyes, multi-gene expression was employed. The MEP algorithm can handle these complex interactions between multiple dyes, resulting in more intuitive functional expressions. To enhance the function mining accuracy of MEP, the probabilities of mutation and recombination operators ware adaptively adjusted, and parallel programming was employed to boost function mining efficiency. Compared to gene expression programming results, MEP delves deeper into functional relationships and achieves a relative deviation of 0.113 in color prediction.

At present, the research on bamboo mainly focuses on large-diameter bamboos such as moso bamboo, and the research and application of square bamboo and small-diameter bamboos are rare. Based on the test method of physical and mechanical properties of bamboo, the mechanical tests were carried out on 111 Zhaotong square bamboo flake samples born in 3~5 years, and the tensile and compressive strength, elastic modulus and other parameters were determined. Based on the experimental data, the intensity probability distribution was determined and its distribution characteristics were verified. Combined with the research results of timber structure and the reliability limit state design method, the strength standard value and design value of square bamboo were obtained, and its feasibility was verified by comparison with moso bamboo, larch and other materials. The reliability index was corrected for the design value of the compressive strength of the grain that did not meet the requirements of the code. The result enriches the database of mechanical properties of small-diameter bamboo, and also opens up a new direction for the further research and application of bamboo.

China’s offshore wind power sector is rapidly progressing with large-scale and efficient projects. To support large-capacity wind turbines, there is an increasing need for advanced tower structures that offer superior structural performance and economic efficiency. One such innovative solution is the hybrid fiber-reinforced composite materials (FRP)-concrete-steel prestressed double-skin wind turbine towers (PDSWTs) proposed by scholars at The Hong Kong Polytechnic University. PDSWTs boast excellent durability, high load-bearing capacity, and exceptional stability, resulting in the potential to reduce production and maintenance costs throughout the service life. A study on the analysis and design procedures of PDSWTs was presented using a tower that supports 12 MW offshore wind turbine as an example, based on the provisions in current design standards and finite element modelling. The results demonstrate that: the tower successfully meets the frequency requirements of wind turbine; the tower possesses high ultimate resistance under compression-bending, shear and torsional loadings of ultimate limit states; the strain, stress, crack width and tower deformation under serviceability limit states meet the requirements of current design standards; furthermore, a simplified method is proposed to check the fatigue resistance of steel and concrete in the tower sections. The result is expected to provide references for the design and application of PDSWTs.

According to the drawing load characteristics of horizontal rectangular anchor plate in red clay foundation, the vertical drawing model test of horizontal rectangular anchor plate in saturated red clay foundation is carried out by using a self-made visual drawing model test system combined with digital photographic measurement technology. The results show that the sliding surface of soil around the anchor shows different shapes with the change of buried depth ratio, but the initial Angle does not change with the change of buried depth ratio. The load displacement curve generally has obvious peak characteristics, but with the increase of the buried depth ratio, the characteristics gradually weaken. When the buried depth ratio is the same, the smaller the length-width ratio is, the more obvious the three-dimensional bearing characteristics of the anchor plate and the peak characteristics of the curve are. The bearing capacity coefficient increases with the increase of buried depth ratio, but the law is different under different aspect ratio. The bearing capacity coefficient decreases with the increase of length-width ratio, and the change law is consistent under different buried depth ratio. Conclusion: For test red clay, the anchor plate can be classified as shallow buried type at least within the range of buried depth ratio 4, and deep buried type if the buried depth ratio is greater than 8. Under the same conditions, under the influence of cohesion and dilatancy, the tensile strength coefficient of anchor plate in red clay foundation is between loose sand foundation and dense sand foundation.

In order to further study the connectivity and hydraulic connection between the injection and production wells in geothermal reservoir, a dual media model of fracture-porosity was constructed by using COMSOL Multiphysic.The effects of the aperture of the fissure group in fractured rock mass, the diffusion coefficient of the rock matrix, and the permeability on the tracer breakthrough curve (BTC) at different dip angles with the mainstream direction were analyzed.The results indicate that the convection of water in the fissure is the main factor influencing the concentration migration. With the increase of the fissure aperture, the migration rate and peak concentration of the tracer are enhanced, and the degree of the influence on the tracer migration by the fissure aperture decreases with the increase of the dip angle of the fissure group. The diffusion coefficient and permeability of the rock matrix have a significant impact on the temporal and spatial distribution of the tracer concentration. With the increase of the matrix diffusion coefficient, the delay effect of the tracer migration is elevated. With the increase of the matrix permeability, the anisotropy of the reservoir pressure and concentration distribution is reduced, and the concentration distribution at the outlet boundary becomes more uniform.

To investigate the deformation characteristics of tunnels under impact and blast loads, A combination of model testing and numerical simulation was proposed to analyze the damage behavior and patterns under various dynamic loading conditions. The Hailuogou Tunnel was studied, and a combination of model testing and numerical simulation was utilized. Initially, an impact test was performed on the scaled-down physical tunnel model. Subsequently, numerical analysis of the tunnel model was conducted and verified. Then, a comparison was made between the tunnel deformation results of the scaled model and the prototype model under impact load. Finally, the effect of blasting load on the deformation of the prototype tunnel was analyzed. The results indicated that the proposed method accurately reflected the actual impact load’s effect on tunnel deformation. Additionally, the numerical analysis results of the scaled tunnel model closely matched the test results. Moreover, the deformation of the top of the prototype tunnel under the impact load was approximately 10 times greater than that of the scaled tunnel model, and it aligned well with the deformation caused by a blasting load equivalent to 500 kg of TNT. The impact load effectively simulated damage to the tunnel vault. Increasing the depth of cover and reducing the impact load represented effective measures to mitigate significant tunnel damage. The challenges of on-site testing during surface blasting are surmounted by this study’s findings. Additionally, a cost-effective, safe, and dependable testing approach is furnished for analyzing the destructive behaviors and modes of tunnels under various dynamic loads. Furthermore, technical support is offered for the safe and economical design of tunnels with optimized blasting loads.

Previous studies on visual effects primarily focus on evaluating the overall urban environment, lacking specific research on historical districts within cities. In order to evaluate the visual effects of plantscapes in historic districts, street view images and machine learning methods were used. The ResNeSt model was selected to assess the coordination and health of plantscapes. The results show that the ResNeSt model performs best in classification and regression tasks. Its scores are consistent with expert evaluations and moderately to highly correlated with public evaluations. Additionally, the visual effects of plantscapes are significantly influenced by economic factors, with the visual effect scores of streets outside the historic districts generally higher than those inside. It is concluded that machine learning models are highly effective in evaluating the visual effects of plantscapes in historic districts. This provides a scientific basis for their protection and optimization, with important implications for urban planning and tourism.

During the parallel operation of excavation and concrete lining construction in the diversion tunnel, the quality of construction was significantly affected due to the severe damage to the floor concrete caused by heavy truck crush. In response to the challenging problem of floor concrete protection caused by heavy truck crush during the “excavation-lining” parallel operation in the diversion tunnel, a numerical calculation model for floor protection was established using ABAQUS. Adopting the floor protection measure of overlaying “tunnel excavation debris-concrete” composite protection layer, a numerical simulation study on the protection of tunnel floor concrete was conducted. The influence patterns of the material characteristics of the protection layer and the bias distance of the heavy truck on the stress and deformation of the floor concrete were analyzed, and the effectiveness of protection under different working conditions was evaluated. The study results indicated that the floor protection measures could effectively improve the protection of the floor concrete. As the concrete grade of the protection layer increased, the stress and deformation of the floor concrete decreased, with the reduction rate reaching up to 17.5% and 12.4% respectively. Furthermore, as the bias distance of the heavy truck increased, the maximum deformation of the floor concrete increased, and the range of deformation expanded significantly. Moreover, stress growth was extremely pronounced at the edges, with a maximum growth rate of up to 222.6%. The research results can provide theoretical and technical guidance for the protection of the floor concrete against crush damage in the diversion tunnel.

In the winter water transfer process of the Northwest cold region long-distance water transfer project, channels and hydraulic structures such as gate piers are frequently subjected to damage from flowing ice impacts. To safeguard the stability and security of winter water transfer operations, it is imperative to investigate the mechanical response characteristics of gate piers under the influence of flowing ice impact. ANSYS/LS-DYNA finite element software was employed to establish a refined finite element model of the gate pier under ice-water coupling conditions using the arbitrary Lagrangian-Eulerian (ALE) fluid-solid interaction method. The accuracy and validity of the numerical model are were verified by comparing the impact forces of flowing ice against relevant standards. The mechanical response characteristics of flowing ice on the gate pier by varying models such as the ice-water coupling model, additional mass model, fluid-free model, and flowing ice characteristics (velocity and compression strength)was explored. The findings indicated that the impact damage from flowing ice on the gate pier primarily occurs at the collision contact area between flowing ice and the gate pier. The presence of the water medium significantly mitigates the damage caused by flowing ice, emphasizing its viscous effects. Comparing different collision condition models, the additional mass model exhibits the highest impact force and X-direction displacement peak values, followed by the fluid-free model, with the fluid-solid coupling model showing the least impact, thereby suggesting the suitability of the additional mass model for simulation calculations and structural design. Furthermore, the result revealed that both the peak and mean impact forces increase with higher flow ice velocities and compression strengths, underscoring the importance of considering these factors in impact force assessments. Practical measures such as installing ice stopping ropes are recommended to mitigate flow ice impact forces and ensure structural safety in real-world applications.

Variations in freezing and thawing of the roadbed are known to significantly influence the dynamic behavior of high-speed railway ballast tracks. This phenomenon potentially compromises the safety and efficiency of train operations. A comprehensive vehicle-ballastless track-roadbed spatial dynamic model was employed to examine the effects of different wavelengths, amplitudes, and velocities on track dynamics due to roadbed frost heave. It was found that, with a constant heave amplitude, an increase in wavelength initially boosted and then reduced the car’s vibration acceleration. Concurrently, the vertical wheel-rail force diminished as the wavelength extended, leading to a decrease in both the wheel load reduction rate and the risk of derailment. Conversely, when the wavelength was kept steady and the heave amplitude was increased, the car’s peak vibration acceleration escalated. At a 40 mm amplitude, the vertical wheel-rail force peaked at 198.642 kN before dropping to zero within 1.384 seconds, resulting in a brief airborne phase for the car. An increase in heave amplitude heightened both the wheel load reduction rate and the derailment coefficient, reaching critical safety thresholds at a 35 mm amplitude. Higher driving speeds intensified the dynamic indicators of the rail system.These insights provide crucial guidance for analyzing dynamic challenges in high-speed railway tracks and addressing structural issues effectively.

The current safety evaluation standards lack guidance on the alignment design specific to lower-grade highways, such as four-lane and non-standard highways. To address this issue, a method for assessing the driving safety of mountainous highways with low index and complex alignment was proposed. The method involved building a human-vehicle-road system simulation using the CarSIM/TruckSIM simulation software platform, selecting representative car models, and using realing vehicle driving data as simulation parameters to carry out virtual vehicle driving tests. Four categories of indicators, including speed, lateral stability, driver handling, and speed regulation, were used to examine and analyze the compliance of the tested highway’s geometric design, speed coordination, driving operation load, identification of accident-prone sections, and safety of continuous uphill sections. The feasibility of the “human-vehicle-road” simulation method was verified by running virtual simulation driving tests similar to the real-vehicle test, and one four-lane highway and one non-standard highway were selected as examples. The results demonstrate that this method can effectively identify alignment design combinations that are below the limit and inconsistent, as well as road sections with high driver handling loads and poor balance. It can also identify dangerous road sections, corresponding vehicle accident patterns, critical safety speeds, and positions and widths where heavy-duty trucks deviate from the travel lane when passing through hairpin curves. Based on the driving/accident conditions of dangerous road sections, targeted geometric parameter improvement measures can be proposed to enhance the trafficability and stability of vehicles on ordinary mountainous highways with low index and complex alignment, ultimately improving the quality of alignment design and driving safety for low-index highways in mountainous areas.

To solve the issue of insufficient durability for steel bridge deck pavement, two types of double-layer stone mastic asphalt (SMA) pavement structures were used as research objects. Firstly, the most unfavorable loading position of the typical bridge deck was determined through the finite element analysis method; and the mechanical response of the above two structures at this loading position was calculated, thus the optimal structural combination for steel bridge deck pavement and its design index requirements were proposed. Secondly, two types of high viscosity and elasticity modified asphalt (A and B) were prepared; and then, taking the road performance of asphalt binders and their mixtures as the evaluation criteria, effects of asphalt binder’s types on the road performance of steel bridge deck pavement asphalt mixtures were compared, thus the asphalt binder with the best properties was selected. Finally, the bonding performance between the pavement layer and the steel plate was evaluated by using the indoor pull-out and oblique shear tests. Meanwhile, the bonding performance of the pavement layer under the most unfavorable temperature conditions was tested with the actual engineering. Test results show that the middle position is the most unfavorable load position on the steel bridge deck. Therefore, the tensile stress, vertical displacement, and bottom shear stress of the pavement layer at this location can be selected as the main design indicators for steel bridge deck pavement. In addition, the two designed pavement structures exhibit the consistent mechanical response patterns, among which the vertical displacement and layer bottom shear stress of structure 2 (SMA-13+SMA-10+asphalt mortar) are relatively smaller. As for the asphalt binders, comparing with SBS (styrene butadiene styrene triblock copolymer) modified asphalt, the prepared high viscosity and elasticity modified asphalt (A and B) have the better road properties, among which the road property of A modified asphalt is the best. The pull-out test results show that, under the temperature conditions of 25 ℃ and 60 ℃, the bonding strength between the pavement layer and the steel plate can all meet the design requirements. The actual engineering test result show that temperature inside the pavement structure layer exhibits the periodic variation pattern, with the highest temperature not exceeding 60 ℃. Therefore, the design index based on the interlayer bonding strength at this temperature is scientific and reasonable, and meanwhile, the interlayer bonding strength of various structural layers in the actual engineering meets the design requirements under this unfavorable temperatures.

To achieve safe and efficient replacement of stay cables that exceeded their designed service life or suffer serious damage, a stay-cable replacement system (SRS) was proposed which used the existing cable-stayed anchor plate (ECAP) as a reaction force system. The scaled model of the typical main girder segment and the full-scale model of the SRS were designed and produced. The axial compression and eccentric compression static tests were carried out under the conditions of dry contact and adhesive contact between the steel anchor barrel (SAB) of SRS and the ECAP. The results show that, under dry contact conditions, the failure mode of the SAB is the buckling of the contact point between the eccentric compression side of the SAB end and the ECAP. Under adhesive contact conditions, the failure mode of the SAB is the buckling at the interface between the eccentric compression side of the SAB and the epoxy resin glue. After pouring of the epoxy resin glue, the displacement and maximum compressive stress of the SAB are smaller, and the compressive safety and stability of the SAB are better. The proposed SRS can be applied in stay-cable replacement.

Efficient and accurate crack detection can provide a basis for assessing the structural safety of tunnels. Aiming at the shortcomings of traditional crack detection methods, which are complex and weak in generalization ability, an improved algorithm YOLOv5-CT(YOLOv5 CBAM Transformer) for tunnel lining crack detection was proposed.Considering the slender morphology of the cracks, the network introduced the Transformer module to improve the crack detection effect.The strong long-range dependency capture ability of the Transformer module enabled the proposed detection model to fully learn the contextual information of the crack region. In addition, the network integrated the convolutional attention mechanism CBAM(convolutional block attention module) in neck.The experiment shows that the YOLOv5-CT can achieve AP50 and AP of 85.2% and 51.3%, respectively, which is an improvement of 8.9% and 12.1% compared to the baseline model YOLOv5. It is better than other one-stage object detection networks in terms of accuracy, and the inference speed reaches 161.3 fps under 640×640 pixel conditions, which meets real-time detection of tunnel lining cracks.

In recent years, spatial-temporal graph convolutional network (STGCN) has been introduced into traffic flow prediction, which has good spatial-temporal traffic data modeling ability and has achieved advanced performance, but there are still two problems: ①Traffic flow data have strong temporal and spatial correlation; ②Static pre-defined graphs are difficult to capture the spatio-temporal dependence of dynamic changes in traffic flow over time. To solve the above problems, a new spatial-temporal decomposed framework (STDF) was proposed, which used residual connection, forgetting gate and update gate to organically connect time module and space module to decompose and predict input information in multiple dimensions. In addition, by instantiating STDF, a new traffic prediction model based on input traffic signal decomposition decomposed dynamic spatial-temporal graph convolutional network (DDSTGCN) was proposed. It captured the spatiotemporal dependencies of traffic and designed a dynamic graph learning module that takes into account the dynamic nature of spatial dependencies. Finally, two real traffic flow data were used to compare with the existing traffic flow prediction algorithms. The experimental results show that the proposed method has good performance in the accuracy of traffic flow prediction and can effectively complete the traffic flow prediction in the real scenario.

The evaluation of on-street parking efficiency is considered a necessary basis for the optimization and management of on-street parking as well as the development of urban intelligent parking systems. Therefore, an evaluation method of on-street parking efficiency based on the entropy weight Critic-Topsis model was proposed. Firstly, nine indexes, such as the number of parking spaces, the remaining width of the road, and the cumulative number of parking, were selected from three aspects: parking space attributes, operating characteristics, and traffic impact, to construct the evaluation index system of on-street parking efficiency. Secondly, an evaluation method of the entropy weight Critic-Topsis model was proposed based on the data characteristics of the evaluation index of on-street parking efficiency. The entropy weight method and Critic method were employed to calculate the weight of each index, and then the improved topsis model was utilized to quantitatively evaluate the parking lots. Finally, this model was utilized to analyze the data of 10 on-street parking lots in Beijing, resulting in the evaluation results and rankings for each parking lot. It is found that in the evaluation index system of on-street parking efficiency, the number of parking spaces, the utilization rate of parking spaces, and the remaining width of the road have significant influences on the evaluation results, with weights of 22.99%, 13.72%, and 13.71% respectively. In the case analysis of 10 on-street parking lots in Beijing, higher scores are observed for on-street parking lots such as Gongti East Road, Keyuan Road, and Huayuan North Road, which are 0.659 4, 0.611 3, and 0.608 1, respectively. Moreover, relevant suggestions and optimization measures are provided for the on-street parking lots of Chunhe Road and Lingnan Road, which have lower scores, aiming to enhance the long-term management system of urban on-street parking and improve the level of urban traffic management.

The selection of variables affecting fuel consumption in the existing studies usually has no clear criteria, and it is difficult to combine the research results with actual flight. The flight training data of a Cessna 172 was used to predict the fuel consumption during the airborne phase of general aviation trainer aircraft. Firstly, based on the authors’ flight experience as well as correlation analysis, the features that influence fuel flow rate were selected from the pilot’s operational perspective. Secondly, a regression tree model was used to predict fuel flow rate under different flight conditions, correlating the aircraft’s actual flight status with the predicted fuel flow rate, in order to facilitate subsequent research on specific fuel-saving strategies from the flight technique perspective. Finally, a random forest model optimized with hyperparameter tuning was used to predict the fuel flow rate. The experimental results show that the accuracy of the model used is better than that of the existing research results, with a mean absolute error of 0.286 gallon/h, a root mean squared error of 0.496 gallon/h, a residual sum of squares of 0.968 4, and a mean absolute percentage error of 4.00%.

In order to solve the problem of uneven allocation of airspace resources in traditional artificial sectors based on subjective experience, and to meet the needs of today’s air traffic operation, the problem of three-dimensional sectorization in terminal areas was studied by improving Agent method. Firstly, while adhering to traditional sectoring constraints, the objective was to enhance sector adaptability to traffic flows and achieve a reduction and balance in air traffic control workload. Subsequently, the traditional Agent method was improved by using genetic algorithm to determine the location of Agent initial solution, so that it could enhance computational efficiency, designing and optimizing Agent growth rules and spatial filling rules. Finally, using the Shanghai terminal area as a case study, the results indicated that the improved Agent method yields sector planning scheme with respective improvements of 25.84% and 18.54% in sector shape characteristics and adaptability to airborne traffic flows. Simultaneously, while reducing the overall terminal area air traffic control workload, the standard deviation of control workload among sectors was reduced by 53.33% and 36.58%, respectively, compared to the existing and traditional Agent methods.It can be seen that the Research on Improved Agent-Based Sectorization Method provides reference for the local characteristic airspace planning of our country.

Under the condition of airport autonomous operation, perception of the operational environment is a crucial factor constraining the realization of autonomous airport operations. In the process of airport surface traffic operation, understanding the utilization of surface movement resources is a key step in establishing a comprehensive operational environment. The surface movement process at airports is first focused on in this study, and an ontology model for airport surface movement processes is constructed. Based on the structural layout of the airport surface road network, the movement paths were divided, and a "node-edge" model based on the connection between network nodes was established. Meanwhile, building upon the ontology model, dynamic and static attributes of the surface road network were defined as the basic properties of network nodes. With network nodes as the research object, various conflict scenarios existing in aircraft surface movement processes were modeled based on the dynamic attributes of network nodes, thus achieving a dynamic representation of aircraft movement processes at network nodes. Using speed data generated by aircraft dynamics models as a basis, a visualization representation of dynamic graphs of surface movement resource utilization in the presence of aircraft conflict scenarios was designed.Experimental results demonstrate that the model effectively represents both conflict and conflict-free scenarios in surface operations. This enhances the overall perception of surface movement resource utilization among participants in airport surface traffic.

As a key device for rocket launch, the erecting device’s load-bearing performance is crucial for the success. In response to the instability phenomenon of the vertical plate of a certain rocket erecting device under the loading condition at the moment of erection, in order to analyze the reasons for the instability of the vertical plate, a finite element simulation model of the erecting device was established considering the actual load situation. The compressive load on both sides of the vertical plate under the working condition of erection was extracted, and based on the theory of small deflection thin plate elastic stability, the local instability of the vertical plate was explained: the compressive load of the vertical plate at the moment of erection exceeded its critical instability load, manifested as the characteristic of lateral bending deformation instability. Based on the analysis results, local reinforcement measures for the vertical plate were proposed. Finite element analysis and erecting loading tests were conducted on the vertical device after reinforcement. The results indicate that the stress consistency at the corresponding measurement points of the left and right vertical plates is good, and the stress deviation between the simulation calculation and the test result is not more than 10%. The lateral bending deformation decreases from 5.3 mm before reinforcement to 1.6 mm after reinforcement, proving the effectiveness of structural reinforcement. The relevant conclusions provide a reference for the local stability analysis of large and complex structures.

In view of the problems of large lag and nonlinearity in aeration control systems for wastewater treatment. The principles of aeration control systems were analyzed, meanwhile, mathematical model for such systems was established. Based on traditional PID(proportion,integration,differential) control algorithms, particle swarm optimization algorithms, and fuzzy control algorithms, an improved particle swarm optimized fuzzy PID algorithm was proposed to overcome the drawbacks of expert-dependency and lack of dynamic performance in fuzzy PID control. The system was simulated using MATLAB to compare the speed, accuracy, and stability of the three control methods in terms of step response, disturbance rejection, and robustness under model mismatch conditions. The results indicate that the improved particle swarm optimized fuzzy PID algorithm outperforms traditional PID and fuzzy PID control algorithms in terms of step response, disturbance rejection, and robustness. It achieves faster and more stable regulation of dissolved oxygen, thereby enhancing control system performance. The improvement is expected to reduce operational costs at wastewater treatment plants, as well as improve system reliability and economic efficiency.

To address the fuzziness and randomness in safety evaluations of liquefied natural gas (LNG) chemical plants, a novel risk assessment method was proposed based on the normal cloud model. The proposed method was based on the selection of evaluation indicators from the perspective of intrinsic safety, which was used to evaluate the severity and likelihood of accidents through expert judgment. The weights of the indicators were determined by combining the analytic hierarchy process (AHP) and the criteria importance though intercrieria correlation (CRITIC). The risk matrix thresholds were softened using a forward cloud generator, and the evaluation results were optimized through a backward cloud generator to obtain cloud characteristic numbers and generate corresponding two-dimensional cloud maps. By comparing the actual cloud map with the standard cloud map, the risk levels of the evaluation indicators were determined. The results show that the model effectively integrates the expert opinions with aims of retaining the randomness and fuzziness, and providing the visualizes the risk assessment results. The novel method also provides a new risk assessment tool for the safety management of LNG chemical plants.