Abstract:Cavitation has been applied in medical treatments such as tissue ablation and lithotripsy. Considering that cavitation in medical applications always occurs within the fluid environment of the human body, which exhibits viscoelastic behavior, studying the dynamics of cavitation bubbles in viscoelastic media is essential for optimizing their medical use. This study used numerical simulations based on the open-source CFD platform OpenFOAM to develop a solver for compressible gas-liquid two-phase flows in viscoelastic fluid. Single cavitation bubble near a rigid boundary was simulated under various rheological parameters. Then the comparisons were made with corresponding cases in Newtonian fluid to analyze the effects of viscoelasticity on bubble dynamic characteristics. Results indicate that the viscoelastic effect inhibits the growth of cavitation bubbles and the intensity of the jet, and reduces the pressure load on the boundary caused by the cavitation process. Such influences are affected by the rheological properties of the fluid. As the relaxation time increases, the cavitation bubbles store more elastic potential energy in the early stage of growth and release it in the later stage, which prolongs the growth process of the cavitation bubbles and increases their maximum size. Conversely, when the migration coefficient increases, the energy dissipation during the growth of the cavitation bubbles increases, resulting in a decrease in their maximum size. Meanwhile, it is found that the velocity of the jet formed during the collapse of the cavitation bubble and the impact pressure on the boundary first increase and then decrease as the distance between the cavitation bubble and the boundary increases.This study can provide theoretical support for the application of cavitation bubbles in viscoelastic fluids and may inform both the beneficial use and mitigation of cavitation in fields such as chemical emulsification, seawater desalination, and petroleum transportation.

Abstract:High-temperature environments and variations in hot water premise plumbing create unique microbial community characteristics that impact water safety. This study utilized a simulation platform for non-circulating hot water premise plumbing to investigate microbial growth under the effects of four specific hydraulic regimes: high variable flow (HIG), low variable flow (LOW), steady state (SS), and retention (R) conditions. After 91 days of operation, biofilm and water samples were collected from the simulation platform. The bacterial counts in the samples were measuerd, and the microbial community structure and the presence of potential pathogens genus were evaluated using 16S rRNA gene sequencing. The results showed that the R condition led to the highest microbial counts in both water and biofilm samples, while the continuously varying flow conditions, LOW and HIG, showed lower counts. Live bacteria demonstrated a propensity to colonize biofilms, exhibiting increased numbers and relative abundances of potentially pathogenic genera in the membrane samples. In addition, biofilms under varying flow conditions exhibited higher relative abundances of three typical potentially pathogenic genera, Pseudomonas spp., Legionella spp., and Mycobacterium spp., indicating a greater potential pathogenic risk.

Abstract:Recently, there has been a significant focus on the issue of antibiotic pollution in water bodies. As a novel adsorbent material, magnetic biochar exhibits excellent potential for antibiotic removal due to its exceptional porosity, extensive surface area, abundant functional groups, and ease of recovery. This paper outlines the typical characteristics of magnetic biochar compared to conventional biochar. It summarizes the principles of various preparation methods, including pyrolysis, coprecipitation, hydrothermal synthesis, and ball milling, while comparing the crucial preparation conditions that must be considered for each method. In addition to controlling temperature, preparation time, and the ratio of magnetic material to biochar, further modifications with different reagents are discussed as potential means to enhance antibiotic adsorption capacity. The paper provides a detailed analysis of adsorption mechanisms such as pore filling, π—π conjugation, complexation, and electrostatic interactions, elucidating magnetic materials′ role during adsorption. Furthermore, the study reviews the recent progress in coupling magnetic biochar with other water treatment methods, such as photocatalysis and Fenton reactions. Finally, the paper outlines future research directions and application prospects for magnetic biochar, providing guidance for optimizing its design and enhancing the removal efficacy of antibiotic from water treatment.

Abstract:To study the seismic performance of coal gangue concrete beam column joints, six full-scale specimens were prepared and subjected to low-cycle repeated loading tests. The analysis focused on various seismic indicators, including failure modes, hysteresis curves, skeleton curves, stiffness degradation, energy dissipation capacity. The effects of coal gangue aggregate replacement rates (0,0%, 50% and 100%) and beam longitudinal reinforcement ratios (0.67%, 1.34%, and 2.09%) on the seismic performance were studied. The results showed that the failure modes of joints are bending failure at the beam end and shear failure at the node area. The failure primarily manifests as extensive cracking in the joint area and significant spalling of concrete at the beam ends, with coal gangue concrete beam-column joints exhibiting more severe damage compared to ordinary concrete. Specimens with low coal gangue replacement rates displayed full hysteretic curves, slow stiffness degradation, good ductility, and good seismic performance. As the replacement rate of coal gangue increases, the specimens exhibits a pronounced pinching effect in the hysteretic curves, along with accelerated stiffness degradation and reduced ductility and energy dissipation. Increasing the longitudinal reinforcement ratio of the beam can improve the damage to the concrete at the end of the beam and enhance its bearing capacity. However, excessively high reinforcement ratio may induce shear failure of the nodes, reducing their energy dissipation capacity and ductility.

Abstract:To investigate the influence of fiber incorporation on the mechanical properties of reinforced grouting sleeves, experimental studies were conducted on the material properties of polypropylene (PP) fiber-reinforced and polyvinyl alcohol (PVA) fiber-reinforced grouting materials. Mechanical property tests were performed on PP and PVA fiber-reinforced grouting materials with varying lengths and volume fractions. Uniaxial tensile tests were carried out on fiber-reinforced grouting sleeves with three different reinforcement anchorage lengths of 4d, 6d, and 8d (where d represents the diameter of the reinforcement bar). The results indicate that the addition of fibers leads to a certain degree of reduction in the fluidity and compressive strength of the grouting material, with insignificant impact on flexural strength. Moreover, as the fiber length and volume fraction increase, the compressive toughness of the grouting material improves significantly. For sleeve specimens with a 4d embedment depth, reinforcement pull-out failure occurred, while reinforcement rupture occurred in specimens with 6d and 8d embedment depths. Additionally, as the fiber length and volume fraction in the grouting material increase, the ultimate bearing capacity and ultimate displacement of the sleeves undergoing reinforcement pull-out failure also increase. There was a significant negative correlation between the compressive strength of the fiber-reinforced grouting material and the ultimate bearing capacity and ultimate displacement of the sleeves, whereas a positive correlation was observed with the compressive toughness of the material. Based on the traditional prediction formula for the ultimate bearing capacity of grouting sleeves, a new prediction formula that comprehensively considers both the compressive strength and compressive toughness of the grouting material is proposed by introducing the compressive toughness index as a parameter.

Abstract:In order to analyze the impact of ambient temperature on the temperature field and long-term deformation of steel-tube-confined concrete-filled steel tube (TCFST), a temperature field analysis model of TCFST were established with ABAQUS finite element software. Taking the Shenzhen region as a case study, the temperature field of exposed TCFST columns under ambient temperature in the Shenzhen area were studied theoretically. Six TCFST short columns (with total steel content of the inner and outer steel tubes at 5.8%, 10.5%, and 15.0%, and a steel ratio of 0.75 between the inner and outer steel tubes) were designed and loaded under sustained axial compression for 850 days at room and changing ambient temperatures. The development law of long-term deformation of TCFST was clarified. Based on three commonly used long-term deformation prediction models for plain concrete, the long-term deformation of TCFST at room temperature was calculated. Real measurement data were used to modify the EC2 model for predicting long-term deformation of plain concrete, establishing a predictive model for long-term deformation of TCFST columns that accounts for temperature effects. The results indicate that when the average annual temperature of the section exceeds 20 ℃, it is advisable to consider the influence of temperature when calculating the long-term deformation of TCFST. The long-term deformation of TCFST columns decreases with an increasing total steel ratio of the inner and outer steel tubes, a higher ratio of steel content between the inner and outer layers, and lower temperatures. Additionally, the steel content of the outer steel tubes and season initial loading have no discernible impact on the long-term deformation of the TCFST.

Abstract:To explore the load-bearing performance and the role of plate components in H-shaped section bending members, four-point pure bending tests were conducted on four welded H-shaped steel beams with different flange and web width-to-thickness ratios. The failure mode, force-displacement curve, ultimate bending capacity, and ductility of the test pieces were analyzed. By comparing the strain development and single plate bearing capacity of the web plates under different flange support conditions with the same dimensions, the mechanism of plate component interaction in this situation was revealed. Based on the experimental results, a calibrated finite element model was established using ABAQUS, and parameter analysis was conducted using this modeling method. By separating the flange and web plate bending load capacities of the parameterized components, the law of plate component interaction under bending conditions was further explored. The experimental and parametric analysis results both indicate that the occurrence of local buckling in the plate elements generally corresponds to the ultimate bending capacity of the H-shaped section members. The width-to-thickness ratio of the plates is a significant factor affecting local buckling. Furthermore, the interaction between the plates manifests as plates with different width-to-thickness ratios influencing the buckling timing of adjacent plates by altering the constraints on them, which affects the load-bearing capacity of individual plates and thus impacts the overall load-bearing performance of the member. Finally, based on the parameter analysis results, the ultimate load-bearing capacity of the members was normalized, and an S2-S4 grade section classification method considering the interaction of plate elements under bending conditions for H-shaped sections was proposed. The comparison results show that the section classification limits proposed in this paper match well with the experimental results and can reasonably reflect the impact of plate element interactions on the actual load-bearing capacity of the section.

Abstract:Heliostats, as the core solar energy concentrating elements in tower solar thermal power plants, require accurate evaluation of the wind-resistance reliability to ensure safe and efficient operation of the power plants. To address this, a wind-resistance reliability evaluation method for heliostats considering the shielding effect of heliostat field is proposed. Firstly, wind tunnel pressure measurement tests were conducted on a heliostat field to obtain wind shape coefficients of the mirrors, and measure the shielding effects of the heliostat field based on its spatial variations. Subsequently, by combining random wind field simulation, finite element simulation, and probability density evolution theory, a wind-resistance reliability evaluation method for heliostats considering the shielding effects of heliostat field was established, with the mirror stress, deformation, and surface slope error as response indicators. Finally, taking a group of small heliostats in the heliostat field as an example, the influence of the shielding effects of the heliostat field, mirror pitch angle, wind direction angle and other parameters on its wind-resistance reliability was investigated. The results shows that the reliability of the helopstats on the outer side mirror of the heliostat field was significantly lower than that on the inner side. The reliability of the outer side was higher than when facing the wind compared to when facing away from it. In addition, for the heliostats on the outer side of the heliostat field, their reliability shows significant variations with changes in wind direction and pitch angle, whereas the reliability of the inner heliostats showed minimal variation with these parameters.

Abstract:The drag force of buildings in urban environment represent a complex and critical issue. Currently, most research usually regards buildings with uniform heights, ignoring the effect of the non-uniformity of building heights on the drag force distribution. To address this, this study proposes a height stratification method to calculate the sectional drag coefficient of buildings with non-uniform heights, C_dz. This method employs a sectional correction factor β_z to adjust the drag coefficient of buildings with uniform heights to C_dz of buildings with non-uniform heights. Subquently, wind tunnel experiments are then conducted to investigate the effects of building height category N, layer type, and layout on the sectional drag coefficient of individual buildings and the total building array. The results show that the non-uniformity of building heights has a significant impact on the flow adjustment process. When the building height category, N, is 2 or 3, the layer layout has a minimal impact on β_z. However, when N increases to 4, β_z values of staggered layouts are higher than those of square layouts. When using the height stratification method to calculate C_dz of buildings with non-uniform heights, β_z requires further parameterization if N≥4. The outcome of this study provide theoretical support for estimating drag force in urban buildings, enhancing the accuracy of building effect parameterization, and improving the precision of urban weather forecasting and pollutant dispersion calculations.

Abstract:Most of the existing constitutive models often relay on parameters that lack actual physical significance to account for the structure of soil, while neglecting the influence of structural yield stress. To address this gap, a structure parameter ξ incoporating the relationship between the shear strength of undistributed and remolded soils is proposed. Within the framework of critical state theory, a modified Cam-clay model is developed. This model employs Caputo-type fractional differential to describe the characteristics of soil plastic flow direction in the soil and the non-orthogonality of the yield surface. Consequently, a fractional constitutive model considering the structural effects in clay is established. The model is validated by Wenzhou clay and Osaka clay. When the stress of the soil is less than the structural yield stress, the structural parameter ξ>1, indicating that the mechanical properties of the soil are significantly affected by the structural properties. Conversely, when the stress of the clay is larger than the structure yield stress, the structural parameter ξ=1, and the structural influence disappears, allowing the model to reduce to a conventional fractional constitutive model. The prediction results of Wenzhou clay show that, when the confining pressure is 0,0, and 200 kPa, the fractional constitutive model considering the structural influence reduces the maximum prediction error by 27.6%, 13.05% and 1.8%, respectively, compared to the model ignoring the structural influence, with an average maximum prediction error of 4.92%. Further validation using prediction results of Osaka clay demonstrates that the model better predicts the mechanical and deformation characteristics of structural clay, exhibiting good applicability and reliability.

Abstract:In view of the high spatial and temporal complexity of intrusion detection caused by high dimensionality of traffic data features in the modern network environment and low classification accuracy caused by the lack of sensitivity of traditional intrusion detection methods to the correlation between traffic data, an intrusion detection method based on feature reduction and improved self-attention mechanism is proposed to improve the efficiency and accuracy of intrusion detection. Firstly, aiming at the problem of high-dimensional data, an auto-encoder with nonlinear feature extraction capability is used to extract features, which reduces data redundancy and ensures classifier performance to be basically unchanged, so as to ensure that intrusion detection methods can effectively identify attacks. Secondly, aiming at the problem that traditional intrusion-detection methods ignore the correlation of traffic data, a self-attention mechanism is introduced in the intrusion detection classification process to learn the correlation of network data over a period of time. The causal convolution is introduced in original self-attention mechanism to calculate the correlation score between data, and integrate the local location information of current traffic data and the correlation between the traffic data in the concerned domain, which comprehensively analyzes current traffic behavior and complete accurate classification. Experimental results on UNSW-NB15 dataset show that the proposed intrusion detection method attains 98.32% accuracy on the binary classification tasks, and outperforms traditional methods on multi-classification tasks as well, indicating promising applicability in modern network environment.

Abstract:In the field of radar communication integration, it is a crucial link to design a common waveform signal that can simultaneously support radar detection function and communication information transmission function. In response to the problem of low autocorrelation performance after modulating communication information within radar signal pulses, an integrated radar and communication signal format based on nonlinear frequency modulation (NLFM) signal with high frequency band utilization and low autocorrelation sidelobe is proposed. The core of establishing NLFM-16QAM integrated radar and communication signal model is to take NLFM signal as the carrier of 16 order quadrature amplitude modulation (QAM) signal. The ambiguity function of NLFM-16QAM signal and the related radar and communication performance are analyzed. Noting that the randomness of the communication baseband signal in the proposed NLFM-16QAM waveform can degrade radar functionality and thus reduce moving-target detection performance, we introduce improvements at the receiver of the integrated system. Specifically, a wavelet packet noise reduction is proposed to process NLFM-16QAM signal in combination with natural gradient separation algorithm. Simulation results show that the proposed signal has larger frequency band utilization in comparison with the integrated radar and communication signals of low-order modulation. Compared with the autocorrelation performance of 16QAM-LFM signal, integral sidelobe ratio and peak sidelobe ratio of proposed signal are reduced by 23.07 dB and 26.08 dB respectively. After NLFM-16QAM signal is processed by the joint algorithm of the receiving end, the detection results of the moving target are significantly improved.

Abstract:The data distributions of cross-scenario images of strip steel defects vary considerably due to imaging factors such as camera type, parameters, and environmental illumination, resulting in poor generalization performance of defect recognition models based on deep learning. To address this issue, we propose a pseudo label correction and optimization domain adaptation (PLCODA) model for strip steel defect recognition. Firstly, a Retinex image enhancement module based on maximum entropy and brightness constraints was designed to generate an intermediate domain that is consistent with the label information in the source domain while different from the data distribution in the two domains. Second, we develop a dual-prediction adversarial coupling architecture that performs adversarial learning between the target domain and each of the source and intermediate domains to generate initial pseudo-labels for target-domain samples. Finally, we propose a pseudo-label correction and iterative purification strategy: we correct pseudo-labels via an improved noise matrix, then iteratively purify them by reinforcing high-confidence predictions, self-punishing low-confidence predictions, and reducing the discrepancy between pseudo-labels and ground-truth labels using a designed label-difference metric. The method was validated on steel-strip defect datasets from Handan Iron & Steel Group and the publicly Severstal Steel Defect Dataset. Experimental results show that the proposed method is superior to the existing domain adaptation methods for cross-scenario defect recognition.

Abstract:To address the reliance of most deep learning methods for magnetic resonance (MR) imaging (MRI) reconstruction on extensive fully-sampled datasets for training, this study proposes an unsupervised dual-domain N2N network with attention mechanisms (DN2NA) for parallel MRI reconstruction. The proposed DN2NA network can directly reconstruct undersampled k-space data without requiring additional training data. Specifically, we integrate complex-valued convolution and channel attention mechanism into the N2N framework to construct a baseline unsupervised network N2NA. Two physical priors are incorporated to enhance the performance of the frequency-domain (k-space) N2NA network, which is then cascaded with an image-domain N2NA network to form the dual-domain DN2NA architecture. This combination effectively leverages the complementary advantages of frequency-domain and image-domain networks. Given the absence of ground-truth references in practical scenarios, an early-stopping strategy is adopted to prevent overfitting and improve stability. Experiments conducted on three knee and brain datasets demonstrate that DN2NA achieves higher PSNR and SSIM, along with lower HFEN and STD compared to existing unsupervised networks (IUNN and KUNN), indicating superior reconstruction quality and stability in repeated reconstructions. Furthermore, DN2NA exhibits comparable or better performance than the supervised network MICCAN.

Abstract:In the decoder of live video streaming, compressed video often suffers from block loss during recovery. The spatial error concealment utilizes the correlation between blocks in the current frame for the recovery of error image, without requiring information from other frames. Among many spatial error concealment algorithms, sparse representation mechanism further utilizes the sparsity of an image and achieve better recovery quality than pixel-wise interpolation mechanism. The current sparse representation algorithms still face challenges such as inaccurate selection of candidate subregions and parameter sensitivity of correlation model. Therefore, this paper studies the dual space regularization framework according to sparse representation, and focuses on optimizing such two stages as local region matching and local linear correlation modeling in this framework. We proposes a double-sparse spatial error concealment algorithm with adaptive threshold and α-ML kernel function. During the stage of local region matching, the proposed algorithm designs a local region matching method with adaptive threshold, which can flexibly adapt to the missing subregions with different characteristics, and provide more accurate observation space and potential space for dictionary construction and local linear correlation modeling. During the stage of local linear correlation modeling, the proposed algorithm utilizes a kernel ridge regression method with α-ML kernel function as the local linear correlation model. Compared with the Gaussian kernel function, the α-ML kernel function has low parameter sensitivity and good flexibility. Experimental results show that in typical block loss modes, the proposed algorithm outperforms other existing spatial error concealment algorithms in terms of recovery quality.

Abstract:Text in natural-scene images often present characteristics of complex backgrounds, varied shapes, multiple orientations and changing illumination. In order to improve detection performance for scene text, particularly irregular text, we propose the feature guided adaptive network (FGANet), an irregular-scene text detection network based on feature filtering and adaptive fusion mechanisms. In specific, FGANet designs a module that utilizes dilated convolution to enlarge the receptive and enhance the network′s feature representation capability. Its adaptive feature fusion module integrates deep semantic information with shallow detailed information, enabling stronger text-awareness. Experiments results show that for scene text detection, FGANet achieves notable improvements in F-score over comparative methods on four benchmark datasets: ICDAR2015, CTW1500, MSRA-TD500, and TotalText, with gains of 2.4%, 1.3%, 1.8%, and 1.4%, respectively.