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.

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.

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.

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.

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 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.

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.

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.

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.

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.