The quality of internal plunge grinding process is affected by the grinding performance of different grinding wheels. In order to online monitor the grinding performance of different grinding wheels under the same experimental parameters during the internal grinding process. A particle swarm optimization-back propagation(PSO-BP) neural network-based grinding performance monitoring method for different grinding wheels was proposed. Firstly, the feature parameters of acoustic emission signal, power signal, vibration signal, displacement signal and current signal were extracted. Then, according to the eigenvalue data samples of each sensor and the global optimization function of BP neural network by particle swarm optimization algorithm, the PSO-BP online monitoring model was established by using PSO algorithm to optimize the initial weights and thresholds of BP neural network to accurately monitor the grinding performance of different grinding wheels. Finally, the BP neural network model and the PSO-BP model were analyzed and compared with the experimental data. The results show that the PSO-BP monitoring model has higher monitoring accuracy than the BP neural network model, with an average correct rate as high as 97.6%, and the validity of PSO-BP is verified through a large number of experiments, which is able to effectively monitor the grinding performance status of different grinding wheels.

In dynamic wireless environments, the distortion of transmission waveform is inevitably present, deteriorating the accuracy of identifying high altitude electromagnetic pulse (HEMP) parameters. To address this issue, an extreme learning machine parameter identification network (ELM-PInet)-based parameter identification method was investigated, which leverages the characteristics of HEMP waveform and considers the impact of wireless channels, thereby improving the accuracy of HEMP parameter identification. To demonstrate the nonlinear effects of wireless channels, the transmission model of HEMP waveform was first constructed based on wireless transmission theory. Subsequently, an ELM-PInet was developed to suppress waveform distortion and improve the identification accuracy of HEMP parameters. Finally, the proposed method was validated through field irradiation test on the experimental platform. Simulation results demonstrate that compared to classical HEMP parameter identification methods, the identification accuracy of HEMP parameters is enhanced by the proposed method. Furthermore, the ELM-PInet-based parameter identification method exhibits its robustness against the impacts of different parameters. Additionally, the effectiveness of the proposed method is further validated through field irradiation experiments.

To investigate the correlation between aerobic exercise and cardiac function, lipid metabolism, and inflammation in patients with cardiovascular disease, by searching PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI) databases for relevant studies on the effects of aerobic exercise on cardiac function, lipid metabolism, and inflammatory factors in patients with cardiovascular disease, Meta-analysis and correlation analysis were conducted using RevMan5.4 and R software. The results show that aerobic exercise significantly reduces B-type natriuretic peptide (BNP) [SMD=-0.84, 95% CI (-1.34, - 0.34), P=0.001], systolic blood pressure (SBP) [SMD=- 0.55, 95% CI (-0.86, - 0.25), P=0.000 4], and diastolic blood pressure (DBP) [SMD=- 0.99, 95% CI (-1.67, - 0.32), P=0.004], LDL [SMD=- 0.53, 95% CI (- 0.89, - 0.18), P=0.003], and C-reactive protein (CRP) [SMD=-0.53, 95% CI (-0.90, -0.16), P=0.005]. CRP is positively correlated with HDL, LDL, and DBP, with correlation coefficients of 0.35, 0.26, and 0.28, respectively. CRP is negatively correlated with SBP, with correlation coefficients of -0.31. From this, it can be seen that aerobic exercise can improve heart function, lipid metabolism, and levels of inflammatory factors to a certain extent in patients with cardiovascular diseases, and there is a correlation between heart function, lipid metabolism, and inflammation.

In order to study the effect of unsteady flight parameters on the aerodynamic characteristics of simulated butterflies, a flight dynamics model was established with the black-framed blue Morpho butterfly as the research object. Based on the flight principle, the relative coordinates of butterfly wings, body and ground during flight were established, and the kinematic equations of butterfly wings and body during flight were constructed. The aerodynamic characteristics of the simulated butterfly were verified based on the flight principle of the butterfly, and the effects of the change of flutter angle and pitch angle on the lift and drag of the simulated butterfly were studied under the natural environment flow field. The results show that there is a positive correlation between turning angle and lift force, but no correlation with drag. When the flutter angle is less than 120°, the lift is positively correlated, when the flutter angle is greater than 120°, the lift is negatively correlated, and the flutter angle is negatively correlated with the drag. A high pressure area begins to occur at the leading edge of the wings when the downward flapping occurs, and at the edge of the wings when the upward flapping occurs. The research results provide a reference for the control parameters and wing design of flapping wing aircraft, and provide a scientific basis for further optimization of bionic flapping wing flight.

The tight oil reservoirs in the eastern Ordos Basin are characterized by shallow burial, low pressure, small principal geostress, and low fracture pressure, which are significantly different from the general mid-deep tight oil reservoirs. Previously, the development of horizontal wells in this area through hydraulic fracturing was mainly based on field experience, and the design of the fracturing construction lacked a theoretical foundation, making the impact pattern of construction parameters unclear and the enhancement of production effect uncertain. Hence, research on the optimization of key parameters in fracturing construction is urgently needed. To maximize production efficiency, an integrated research method involving fracturing simulation and numerical reservoir simulation has been adopted. FrSmart has been used for fracturing simulation, Petrel for building geological reservoir models, and tNavigator for numerical simulation. Through the comprehensive application of various numerical simulation software, optimal cluster spacing, displacement, and single-segment fluid volume suitable for horizontal well fracturing in the reservoir were determined. By adjusting the conventional volume fracturing process parameters of well YCN-1 in the study area to a cluster spacing of 20 m, a displacement of 12 m³/min, and increasing the single-segment fluid volume to 1 000 m³, significant improvements in fracturing and production enhancement effects were achieved. Field test results show that the production of well YCN-1 after optimizing fracturing parameters is 29.98% and 50.27% higher than that of the unoptimized wells N-2 and N-3, respectively. Therefore, a method of critical significance for guiding the fracturing construction of shallow tight oil reservoirs, enhancing fracturing efficiency, and improving production effects has been proposed.

In order to solve the problem of poor image denoising performance caused by the simple encoder-decoder structure of the convolutional neural network image denoising model, a residual dense image denoising network (RDIDNet) based on the residual dense network and attention mechanism was proposed. Firstly, the global residual block was used to enhance the nonlinear mapping ability of the network model. Secondly, the double-element convolutional attention module was introduced to realize the adaptive feature fusion in the decoding process of RDIDNet model. Finally, the RDIDNet denoising model was compared with 14 representative denoising methods, and ablation experiments were conducted to verify the effectiveness of using RDU Sub Network, DE-CAM, and PSNRLoss for network optimization on the benchmark model. The experimental results show that in the Set12 dataset and BSD68 dataset, RDIDNet improves the peak signal to noise ratio (PSNR) and structural similarity (SSIM) metrics by an average of 1.03 dB and 0.027 5, respectively, compared to the traditional classical method BM3D. Compared to SwinIR based on Vision Transformers architecture, the average improvement is 0.03 dB and 0.001 4, respectively. Compared to the latest CNN based denoising method NHNet, it has an average improvement of 0.22 dB and 0.008 9. The RDIDNet denoising network focuses more on low-frequency information and has more stable model training. It can effectively eliminate image noise while preserving image details and textures, and has good performance.

At present, the renovation of old residential areas is in a comprehensive promotion stage. Building a systematic and scientific external space renovation system for old residential areas is of great significance for promoting the renovation of old residential areas, improving the quality of life of residents, and optimizing urban image. A comprehensive transformation system covering five criteria layers and twenty-three subcategories was organized and constructed based on a literature review and keyword clustering analysis. Secondly, the weights of various renovation elements from different perspectives were quantitatively analyzed using a questionnaire survey and analytic hierarchy process. At the standard level, residents pay more attention to facility renovation and improving community service quality, with evaluation weights of 0.256 9 and 0.223 1, respectively. Planning and design management personnel pay more attention to transportation and environmental renovation, with evaluation weights of 0.238 2 and 0.231 7, respectively. The factor layer weights indicate that all entities emphasize the importance of landscape greening, activity space quality, environmental sanitation facilities, and facade renovation, which should be given special attention in the external space renovation of old residential areas.

With the continuous promotion of the “dual carbon” strategic goals and the construction of new power systems, traditional distribution networks are gradually transforming into information-based, digital, and intelligent new distribution systems. To accurately characterize and analyze the characteristics of different types of loads in the distribution network, and support efficient operation and control of the distribution network, a data-driven classification method for typical load curves in the distribution network was proposed. Firstly, based on load data, various classification scenarios of typical loads in the distribution network were analyzed, and performance evaluation indicators for classification scenarios including error rate, accuracy, and confusion matrix were proposed. On this basis, a data-driven load classification method for distribution networks was proposed, which converts 24 dimensional daily load vectors into image data and uses convolutional neural networks to identify load curve images, achieving accurate classification of distribution network load curves. Finally, the accuracy and effectiveness of the proposed method were verified by combining actual distribution network load data, and analyzed and compared with existing methods. The results indicate that the proposed method for classifying typical load curves in power distribution networks has better classification speed and accuracy.

Aiming at the problems such as small and medium-sized obstacles on the road are prone to miss detection, small target obstacles are difficult to detect, and the number of model parameters is large in smart driving scenarios, the obstacle target detection algorithm with improved YOLOv8n was proposed. Distribution shifting convolution (DSConv) was used in the backbone network to replace floating point operation with integer operation, reducing the amount of redundant computation, and maintaining the accuracy by imitating the original convolution layer by quantization and distribution shifting. By adding small target detection layer, the feature information of small target can be captured better and the scale characteristics of small target can be adapted. Combined with SimAM parameterless attention mechanism, SPPF-SimAM module was introduced to improve the quality and diversity of feature representation, and the detection accuracy was improved without increasing the number of parameters. By combining ghost-shuffle convolution (GSConv) and VoV-GSCSP modules, the neck feature fusion network was lightweight, reducing the number of parameters and calculation of the model. The experimental results show that the accuracy, recall, and mean average precision of the improved model are improved by 1.6%, 8.0%, and 6.2%, respectively. The number of parameters is reduced by 6.7% compared with the original model, and the proposed algorithm effectively improves the detection accuracy of small and medium-sized obstacles in smart driving scenarios, and achieves a better balance between the detection performance and the model lightweighting.

Concrete arc beams in the support mold is often difficult to ensure the molding accuracy, the production is more difficult to high cost, and 3D printing technology has a construction speed, design freedom and high characteristics, so in order to solve the problems such as the complexity of concrete arc beam support, the effectiveness of 3D printing arc shell-cast-in-place beam construction was studied. According to the existing 3D printing concrete ratio and process parameters, three 3D printing curved beam mold shells were designed and printed, and the printing and molding accuracy was measured. The mold shells were equipped with reinforcing cages and cast-in-place concrete materials, and 3D printing concrete curved mold shells-cast-in-place beams were produced. The beam specimens were subjected to vertical loading tests to validate the effectiveness of the construction method. The results show that the 3D printed curved mold shell is basically the same size as the 3D model, with a maximum error of 4% in the middle, and the overall printing and molding quality is good. Under vertical loading, the damage patterns of the three 3D printed curved mold shell-cast-in-place beam specimens are similar. The cracking load and ultimate capacity of the beam specimen with reinforcement between the curved mold shell and the cast-in-place beam have been significantly improved, with an increase in the ultimate load of about 25%.