Strong pulsation is one of the most important causes of damage to hydraulic structures such as stilling basins, so it is crucial to clarify the characteristics of pulsation in hydraulic structures to ensure the safe operation of the project. In this study, a fine numerical model of the hydraulic model of a stilling basin was established based on DES (detached eddy simulation) model and VOF (volume of fluid) method, and the simulation results were in good agreement with the experimental results. Based on the numerical simulation results, the distribution of pressure fluctuation, pulsating velocity, vorticity, and turbulent kinetic energy in the stilling basin were analyzed. The results show that the pressure fluctuation in the stilling basin shows a bimodal distribution along the flow direction, and the pulsation accounts for more than 10% in the flow impact area. The spanwise and streamwise velocity pulsation dominate in the stilling basin, and the vertical pulsation is weaker, while the spanwise pulsation at the centerline of the bottom floor in the collision area decreases rapidly. The streamwise and vertical vorticity on the centerline of bottom floor and surface outlet are characterized by “small time-average value and large pulsation value”, and the transverse-axis vortex caused by the drop sill has a large influence on the distribution of the spanwise vortices. The results of turbulent kinetic energy spectrum analysis show that the pulsations in the stilling basin mainly consist of a large number of low-frequency pulsations below 1 Hz, indicating that the turbulent fluctuations in the stilling basin are mainly controlled by large-scale and low-frequency vortices. The analysis helps to deepen the knowledge of the flow characteristics of flood discharge and energy dissipation structures such as stilling basins, and provides a certain reference for design and safety assessment.

In traditional blind deconvolution algorithms, recalculating the gradient or redesigning the optimization approach for filter coefficients becomes necessary when altering the characterization index. This requirement can render the development process of new blind deconvolution algorithms inflexible. To address these issues, a blind deconvolution algorithm that employs NRO(Newton-Raphson optimizer) to seek out the optimal filter coefficients was proposed. Initially, generalized spherical coordinate transformation was used to define the search range for the filter coefficients. Subsequently, the generalized l_p/l_q norm of the envelope spectrum was adopted as the characterization index. The proposed blind deconvolution algorithm is then utilized for the early detection of minor faults in rolling bearings. Both simulation and experimental results confirm the efficacy of the proposed algorithm, demonstrating its faster convergence rate compared to classical PSO(particle swarm optimization).

Hydrogen energy is a promising secondary low-carbon energy source in the 21st century of global energy transition. However, HE hydrogen embrittlement in metallic materials refers to the diffusion of hydrogen into the metal in different forms, either in solid solution or as hydrides, causing severe lattice distortion, reducing ductility and toughness, and leading to embrittlement and fracture. It has raised a number of safety issues and limited the service life of hydrogen storage systems due to its insidious nature, abruptness, and diverse failures. In recent years, scholars have been conducting extensive research on hydrogen embrittlement, benefiting from improved experimental tests and numerical simulation methods. A comprehensive review of the latest advancements in hydrogen embrittlement research was provided: elucidating the concept of hydrogen embrittlement and the prevailing mechanism, analyzing the characteristics and factors influencing hydrogen embrittlement in storage vessels, and summarizing the characteristics and primary application scopes of the existing macro to micro, static and dynamic experimental methods in the evaluation of material hydrogen embrittlement. Special attention is given to the research progress in combining these methods with numerical simulation analyses, including their applicability and limitations in practical engineering. Insights and references might be offered to the ongoing development of evaluation methodologies for the hydrogen embrittlement resistance of metal materials.

Buildings are important carriers of human production activities. Accurate and fast extraction of building areas can play an important role in the field of natural resource management. Although significant progress has been made in building extraction from remote sensing images based on CNN(convolutional neural network), the constructed network model still needs to be optimized in feature extraction and feature fusion. Therefore, a coordinate attention and CCFNet(convolutional enhanced full-scale fusion building extraction network) was proposed. The constructed model consists of a residual encoder enhanced by coordinate attention and convolution and a full-scale fusion decoder. Coordinate attention was used in the encoder to build inter-channel dependencies and capture global information. The asymmetric convolution was used to enhance the edge feature extraction of ground objects, and it is more robust to rotation, flip distortion and uneven aspect ratio of ground objects. The full-scale fusion method used in the decoder helps to reconstruct the buildings. The experimental results on the dataset of typical Chinese city buildings show that compared with other building extraction networks, The CCFNet model constructed in this paper achieves the best experimental Accuracy of 93.84%, 84.08%, 72.53% and 82.59% in the four segmentation evaluation indicators of accuracy, F₁, IOU and MIOU, respectively. Experimental results show that the model can effectively extract building regions.

In order to reveal the spatiotemporal distribution characteristics and causative factors of the ground subsidence in Jiangdong New District, SBAS-InSAR(small baseline subsets interferometric synthetic aperture radar) technology was adopted to process and analyze 175 scenes of Sentinel-1A imagery data from January 2018 to February 2024, extracting the deformation parameters. The study found that the subsidence rate in Jiangdong New District has undergone a variation from very slow to fast and then back to slow, forming several severe subsidence areas mainly distributed along major traffic arteries and reclamation areas. The main causative factors of the subsidence were qualitatively and quantitatively analyzed, which include poor engineering geological conditions, consolidation in the reclamation areas, and land use change, etc. The acceleration of subsidence rate is closely related to the rapid development and construction of the region, among which the excessive load on the ground surface is becoming the main influencing factor of subsidence.

To address the limitations of traditional protocol recognition methods caused by the presence of numerous non-standard protocols in IC (industrial control) sector, a method based on edge-distributed deep learning was studied to enhance IC protocol recognition technology. A recognition method based on CNN (convolutional neural networks) was proposed: real IC protocol data from the network was collected and preprocessed, and an appropriate CNN model was selected according to protocol characteristics to implicitly extract the essential features of the protocols. This achieved classification and recognition of seven types of IC protocols with an accuracy of up to 99.92%. Furthermore, the IC protocol recognition model was deployed at the network edge, leveraging a data-parallel distributed strategy for collaborative training within an edge server computing cluster. This improved the training efficiency of the model by 1.87~2.81 times while maintaining high accuracy. The results show that this method significantly improves the accuracy of IC protocol recognition, greatly enhances model training efficiency, and is well-suited for deployment in edge computing environments. It is evident that this method has significant value in optimizing IC protocol recognition performance.

In recent years, the scale of wind turbine grid connection has been increasing, for the deep learning of wind speed prediction requires a large amount of data, as well as stochastic differential equations for wind power system modeling fail to portray the impact of wind speed correlation on the output power and grid-connection point voltage, a Markov switching stochastic differential equation modeling method considering stochastic factors and wind speed correlation was proposed for power systems containing wind power. The Nataf and LSTM were introduced to construct the wind speed spatio-temporal correlation model, the Markov switching stochastic differential equation was used to segment and linearize the wind power system into various linear segments. Then the effects of wind speed correlation and stochastic excitation strength on the voltage at the grid-connection point were studied, and the critical stable excitation strength of the wind power system was analyzed. Finally, the stochastic simulation of the constructed system model was carried out by numerical analysis methods, and the results show that the system state variable fluctuates in the stable region within the critical value of the random excitation intensity, and the comparison with the stable waveform of voltage in the Simulink simulation circuit verifies the validity of the modeling method in this paper, and provides a theoretical basis for the stability analysis of the new wind farm access to the power system.

To explore the application regularity and mechanism of formulas containing Huang Qin (Scutellariae radix)-Bai Zhu (Atractylodis macrocephalae Rhizoma) (HQ-BZ) herb pair in the treatment of ICP (intrahepatic cholestasis of pregnancy). All literature on prescriptions containing HQ-BZ herb pairs against ICP was screened from the VIP, Wanfang, and CNKI databases. Subsequently, the R language was employed to analyze and summarize its medication rules and core prescriptions. Network pharmacology was used to predict the mechanism of core prescriptions against ICP, followed by molecular docking and experimental verification to confirm the potential mechanism. A total of 68 prescriptions were included, involving 67 herbs characterized mainly by cold, bitter, and spleen meridian. The core prescription “Artemisiae scopariae Herba-Rhei radix et Rhizoma-Gardeniae fructus-Scutellariae radix-Atractylodis macrocephalae Rhizoma-Poriacocos” was obtained based on the comprehensive analysis of traditional Chinese medicine data, among which quercetin, apigenin, and other key active components may act on core targets such as AKT1 (serine/threonine kinase B1), BAX (BCL2-associated X protein), and participate in PI3K-AKT (phosphatidylinositol 3-kinase/protein kinase B), apoptosis, and other multiple targets and pathways to play the role of ICP therapy. The molecular docking results showed that apigenin demonstrated superior binding affinity with the top 11 core targets compared to quercetin and beta-sitosterol. HTR-8/SVneo cell experiments proved that apigenin significantly reduced the apoptosis rate induced by TCA (taurocholic acid) and elevated the protein expression levels of Bax/Bcl-2 (P<0.01), as well as p-PI3K/PI3K, and p-AKT/AKT (P<0.01). Pre-treatment with LY294002 could reverse the anti-apoptosis effects and the expression levels of the aforementioned proteins induced by apigenin. In summary, the core prescription that includes the HQ-BZ can provide references for the clinical prescription of ICP. Apigenin, a key component of core prescription, can inhibit the apoptosis in HTR-8/SVneo cells induced by TCA and has the potential to treat ICP, and its mechanism may be related to the regulation of the PI3K-AKT signaling pathway.

Aiming at the problems of poor real-time detection, low accuracy, and false detection and omission of pavement disease detection including hole and crack, an improved algorithm based on YOLOv9 was proposed to resolve the problem. Firstly, AKConv (alterable kernel convolution) was introduced into the backbone network to replace the convolution module in RepNCSPELAN4, which improves the feature extraction ability of the network for different diseases and effectively solve the problem that road disease is difficult to distinguish from background environment features. Secondly, selective image attention mechanism (SimAM) and DySample sampling modules were introduced to focus on the key information in the detection head, and the capability to extract information features was enhanced more efficiently. Finally, the inner-IOU function was used to optimize the weight parameters of the model to improve the learning ability of mixed samples. The experimental comparison between YOLOv9-c and our model showed that the accuracy, recall rate and MAP of the improved model are increased by 40.17%, 15.99% and 20.95% respectively. The performance has been significantly improved, and the detection effect is more accurately and efficiently, and the accuracy and generalization ability of pavement disease detection algorithm are improved.

To address the reduction in support bearing capacity caused by concrete shrinkage and to improve the damping performance of supports under mining-induced seismic conditions, this study focuses on the energy-absorbing and shock-resisting performance of single arch sand-filled steel tubular frames and the damping performance of multi-arch combined support systems. Finite element software was employed to establish models of surrounding rock and sand-filled steel tubular frames, as well as multi-arch support systems connected with flexible cables and dampers. The performance of sand-filled steel tubular frames under static and dynamic loading, as well as their seismic resistance under mining-induced tremors, was investigated. The results indicate that the deformation of the tunnel under static loading remains stable, while the support effectiveness is satisfactory under impact loading except for relatively large deformations at the crown. Under static and dynamic loading, the equivalent plastic strain at the crown of the sand-filled steel tubular frames shows a significant increase, while changes in other areas remain minimal, demonstrating good load-bearing capacity. In the three-arch support system, the third arch experiences reduced vibration amplitude due to the dual energy dissipation effects of flexible cables and dampers. Calculations of the safety factor at the maximum shear stress of the tunnel reveal a significant improvement in the seismic performance of adjacent supports, providing insights for further studies on support damping mechanisms.