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.

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.

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.

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.

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.

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.

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.

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.

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.

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.