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.

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.

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.

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.

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 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.

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.

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.

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.

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.