Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
In the present paper,a hierarchical multi-scale method is developed for the nonlinear analysis of composite materials undergoing heterogeneity and damage.Starting from the homogenization theory,the energy equivalence ...In the present paper,a hierarchical multi-scale method is developed for the nonlinear analysis of composite materials undergoing heterogeneity and damage.Starting from the homogenization theory,the energy equivalence between scales is developed.Then accompanied with the energy based damage model,the multi-scale damage evolutions are resolved by homogenizing the energy scalar over the meso-cell.The macroscopic behaviors described by the multi-scale damage evolutions represent the mesoscopic heterogeneity and damage of the composites.A rather simple structure made from particle reinforced composite materials is developed as a numerical example.The agreement between the fullscale simulating results and the multi-scale simulating results demonstrates the capacity of the proposed model to simulate nonlinear behaviors of quasi-brittle composite materials within the multi-scale framework.展开更多
A multi-scale damage model of concrete is proposed based on the concept of energy equivalent strain for generic two-or three-dimensional applications.Continuum damage mechanics serves as the framework to describe the ...A multi-scale damage model of concrete is proposed based on the concept of energy equivalent strain for generic two-or three-dimensional applications.Continuum damage mechanics serves as the framework to describe the basic damage variables,namely the tensile and compressive damage.The homogenized Helmholtz free energy is introduced as the bridge to link the micro-cell and macroscopic material.The crack propagation in micro-cells is modeled,and the Helmholtz free energy in the cracked micro-structure is calculated and employed to extract the damage evolution functions in the macroscopic material.Based on the damage energy release rates and damage consistent condition,the energy equivalent strain is used to expand the uniaxial damage model to the multi-dimensional damage model.Agreements with existing experimental data that include uniaxial tensile and compressive tests,biaxial compression and biaxial peak stress envelop demonstrate the capacity of the multi-scale damage model in reproducing the typical nonlinear performances of concrete specimens.The simulation of precast laminated concrete slab further demonstrates its application to concrete structures.展开更多
The service life of refractory brick in the slag tapping hole of gasifiers is a significant concern for long-term and stable operation.This study examined the damage mechanism of high chromia refractory of four commer...The service life of refractory brick in the slag tapping hole of gasifiers is a significant concern for long-term and stable operation.This study examined the damage mechanism of high chromia refractory of four commercial coal-water slurry gasifiers with their corresponding gasification coal samples and the corroded refractory bricks in the slag tapping hole of the gasifier.The slag characteristic,including crystallization and viscosity-temperature of four gasification coal samples were analyzed.The results revealed that the low viscosity slag could lead to more severe damage to refractory bricks.Given the risk of slag crystallization,it is recommended to establish a safe slag tapping temperature range should be set as tICT(initial crystallization temperature)−t_(2.5) when tICT is higher than t_(25).Upon examining interior morphology of these corroded refractory bricks,some cracks were observed within them.The chemical composition of molten slag was analyzed using SEM-EDS.However,XRD results found no spinel containing zirconium in these cracks.This suggests that the emergence of these cracks are mainly attributed to the molten slag penetration and the subsequent reaction with the refractory material.The difference in thermal expansion between the newly formed substances and refractory material is critical in forming these cracks.Furthermore,SEM-EDS analysis was also conducted on the slag-aggregate and the slag-matrix interface.The results reveal that the reduction in Cr_(2)O_(3) content is the earliest characteristic of damage in high chromia refractories.A proposed damage mechanism of refractory brick suggests that the matrix and aggregate of high chromia refractory are initially compromised because of the reduced Cr_(2)O_(3) content.Subsequently,the molten slag penetrates the interior of the refractory brick,forming new substances,leading to damage caused by the difference in thermal expansion between the new substances and the refractory brick.Understanding and preventing the reduction of Cr_(2)O_(3) content is vital to prolonging the service life of refractory brick in the slag tapping hole of the gasifier based on this damage mechanism.展开更多
In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicti...In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicting long-term roadway stability,necessitating the development of a reliable constitutive creep model and numerical simulation approach.In this study,creep experiments were conducted on pre-damaged rock with varying initial damage levels to investigate the time-dependent mechanical properties.Based on the experimental results,an accelerated-creep criterion was proposed,and an elastic-viscoplastic creep damage model(EVPCD)was established that simultaneously considers the effects of time-dependent damage and instantaneous damage caused by stress disturbances on rock creep behavior.Subsequently,the effectiveness of the proposed creep model was verified using experimental data,and the secondary development of the EVPCD model was completed based on the FLAC3D platform.Following this,a long-term stability analysis method of deep surrounding rock that accounts for excavation-and mining-induced disturbances was proposed.Using the main roadway of Xutuan Coal Mine as a case study,numerical simulations were carried out to investigate the time-dependent deformation and failure characteristics of the surrounding rock following excavation and mining disturbance.Combined with on-site monitoring of the surrounding rock damage areas,the results indicate that the EVPCD outperforms the CVISC and Nishihara models in predicting the time-dependent behavior of deep surrounding rock.展开更多
To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests ...To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests were conducted,and a corresponding piecewise damage constitutive model(PDCM)was established.We found that both dry-wet cycling and precompression stress deteriorate the physical properties,alter the microscopic characteristics,and reduce the mechanical properties of the LHF.These degradations are particularly pronounced under the CEDWCPS,although the magnitude of these changes gradually diminishes with the progression of dry-wet cycling.Meanwhile,they also reduce the deformation degree,prolong the micropore compaction stage,shorten the unstable crack propagation stage,lower the frequency and intensity of AE events,decrease the high-amplitude and high-frequency AE signals,enlarge crack scales,and shorten the crack initiation time.Among the changes of these indicators,the dry-wet cycling plays a dominant role.The crack types of LHF under the CEDWCPS(LHFCEDWCPS)are predominantly tensile cracks,supplemented by shear cracks.The failure mode can be defined as tensileshear composite failure.Finally,the established PDCM effectively captures the nonlinear deformation of micropore and the linear deformation of the matrix in LHFCEDWCPS,with all corresponding R^(2) consistently exceeding 0.97.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.展开更多
In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this pap...In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this paper,these influences are investigated from the perspective of the time domain.First,a novel time-domain model of the multi-VSC system is obtained by using a multi-scale method.On this basis,a stability criterion is proposed to assess the synchronization stability of the system.Then,the accuracy of the time-domain model and its stability criterion in various conditions are discussed.Moreover,the negative impact of the interaction on the system is quantified.Finally,the above theoretical analysis is also verified in the controller hardware-in-the-loop(CHIL)experiments.展开更多
To investigate the long-term stability of soft-hard interbedded rock masses with initial damage induced by earthquakes and periodic drying and wetting,this study prepared samples with different initial damage through ...To investigate the long-term stability of soft-hard interbedded rock masses with initial damage induced by earthquakes and periodic drying and wetting,this study prepared samples with different initial damage through cyclic loading and unloading(CLU)experiments followed by cyclic drying and wetting(CDW)experiments,and finally conducted creep experiments.The study analyzed the effects of initial damage on creep mechanical behavior,crack evolution,and explored failure precursor information,revealing the damage failure mechanisms.The results show that the structural characteristics of the rock mass control its macroscopic failure mode.Initial damage promotes microcrack development,influences the fracture mode,and increases the proportion of high-frequency(200−280 kHz)acoustic emission events during creep.Meanwhile,initial damage exacerbates creep characteristics,increasing the creep rate,shortening total creep failure time,and reducing long-term strength.The damage failure is attributed to:the generation of internal cracks and pores in the rock caused by CLU;mineral hydrolysis and expansion-contraction due to CDW,resulting in weakened intergranular cementation;and full development of cracks and pores under creep stress.Additionally,the deformation difference coefficient and the coefficient of variation of RA/AF values can serve as precursor indicators for creep failure.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi...Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a mo...The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Background:Human skin is affected by ultraviolet rays on a daily basis,and excessive ultraviolet radiation(UVR)can lead to sunburn erythema,tanning,photoaging,and skin tumors.The combination of Astragali Radix(AR)and ...Background:Human skin is affected by ultraviolet rays on a daily basis,and excessive ultraviolet radiation(UVR)can lead to sunburn erythema,tanning,photoaging,and skin tumors.The combination of Astragali Radix(AR)and Anemarrhenae Rhizoma(AAR)is a common pairing in traditional Chinese medicine(TCM).According to earlier studies,they possess properties capable of alleviating the adverse impacts of UVR on the skin.However,the specific actions and underlying mechanisms require further investigation.The study aims to analyze the efficacy of AR-AAR in preventing UVR-induced skin damage and to clarify the associated molecular mechanisms.Methods:Potential signaling pathways by which AR and AAR may protect against UVR-induced skin damage were identified with network pharmacology,molecular docking techniques and molecular dynamics(MD)simulation.Except the normal group,the back skin of SD rats was exposed to 1.1 mW/cm^(2) UVA combined with 0.1 mW/cm^(2) UVB daily,and the UVR skin damage model was established.Morphological features of skin tissues of different groups were discovered through Hematoxylin and Eosin(HE)staining,Masson staining,Weigert staining.ELISA was utilized to measure the levels of reactive oxygen species(ROS),Interleukin 6(IL-6),Interleukin 1β(IL-1β)and Tumor necrosis factos-α(TNF-α)in skin tissues.RT-PCR and Western blot were employed to quantify the mRNA and protein contents of PI3K,AKT,and MMP-9.Results:Network pharmacology analysis predicts that AR-AAR may improve skin damage induced by UVR through the PI3K/AKT signaling pathway.Histological staining shows that AR-AAR can significantly reduce inflammatory infiltration and fibrosis in damaged skin.Treatment with AR-AAR(2:1)significantly reduced the expression levels of IL-1β,IL-6,TNF-αand ROS in UVR-damaged rat skin.After treatment with AR-AAR(2:1),not only did the relative mRNA expression levels of PI3K and AKT and the protein expression levels of PI3K,AKT,P-PI3K,and P-AKT increase,but the mRNA and protein expression levels of MMP-9 decreased.Conclusion:The study indicate that the AR-AAR combination and its active components may mitigate UVR skin damage by modulating the PI3K/AKT signaling pathway.展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20170680)the National Natural Science Foundation of China(Grant No.51708106)are gratefully appreciated.
文摘In the present paper,a hierarchical multi-scale method is developed for the nonlinear analysis of composite materials undergoing heterogeneity and damage.Starting from the homogenization theory,the energy equivalence between scales is developed.Then accompanied with the energy based damage model,the multi-scale damage evolutions are resolved by homogenizing the energy scalar over the meso-cell.The macroscopic behaviors described by the multi-scale damage evolutions represent the mesoscopic heterogeneity and damage of the composites.A rather simple structure made from particle reinforced composite materials is developed as a numerical example.The agreement between the fullscale simulating results and the multi-scale simulating results demonstrates the capacity of the proposed model to simulate nonlinear behaviors of quasi-brittle composite materials within the multi-scale framework.
基金National Science Foundation of China under Grant No.51808499Science Foundation of Zhejiang Province of China under the Grant No.LQ18E080009 and2018C03033-2 is gratefully acknowledged.
文摘A multi-scale damage model of concrete is proposed based on the concept of energy equivalent strain for generic two-or three-dimensional applications.Continuum damage mechanics serves as the framework to describe the basic damage variables,namely the tensile and compressive damage.The homogenized Helmholtz free energy is introduced as the bridge to link the micro-cell and macroscopic material.The crack propagation in micro-cells is modeled,and the Helmholtz free energy in the cracked micro-structure is calculated and employed to extract the damage evolution functions in the macroscopic material.Based on the damage energy release rates and damage consistent condition,the energy equivalent strain is used to expand the uniaxial damage model to the multi-dimensional damage model.Agreements with existing experimental data that include uniaxial tensile and compressive tests,biaxial compression and biaxial peak stress envelop demonstrate the capacity of the multi-scale damage model in reproducing the typical nonlinear performances of concrete specimens.The simulation of precast laminated concrete slab further demonstrates its application to concrete structures.
基金Supported by Carbon Neutrality and Energy System Transformation (CNEST) ProgramScience and Technology Innovation Project of CHN Energy (GJNY-24-26)。
文摘The service life of refractory brick in the slag tapping hole of gasifiers is a significant concern for long-term and stable operation.This study examined the damage mechanism of high chromia refractory of four commercial coal-water slurry gasifiers with their corresponding gasification coal samples and the corroded refractory bricks in the slag tapping hole of the gasifier.The slag characteristic,including crystallization and viscosity-temperature of four gasification coal samples were analyzed.The results revealed that the low viscosity slag could lead to more severe damage to refractory bricks.Given the risk of slag crystallization,it is recommended to establish a safe slag tapping temperature range should be set as tICT(initial crystallization temperature)−t_(2.5) when tICT is higher than t_(25).Upon examining interior morphology of these corroded refractory bricks,some cracks were observed within them.The chemical composition of molten slag was analyzed using SEM-EDS.However,XRD results found no spinel containing zirconium in these cracks.This suggests that the emergence of these cracks are mainly attributed to the molten slag penetration and the subsequent reaction with the refractory material.The difference in thermal expansion between the newly formed substances and refractory material is critical in forming these cracks.Furthermore,SEM-EDS analysis was also conducted on the slag-aggregate and the slag-matrix interface.The results reveal that the reduction in Cr_(2)O_(3) content is the earliest characteristic of damage in high chromia refractories.A proposed damage mechanism of refractory brick suggests that the matrix and aggregate of high chromia refractory are initially compromised because of the reduced Cr_(2)O_(3) content.Subsequently,the molten slag penetrates the interior of the refractory brick,forming new substances,leading to damage caused by the difference in thermal expansion between the new substances and the refractory brick.Understanding and preventing the reduction of Cr_(2)O_(3) content is vital to prolonging the service life of refractory brick in the slag tapping hole of the gasifier based on this damage mechanism.
基金funded by the National Natural Science Foundation of China(Nos.52004098,U24B2041,and 52274079)the Key Research and Development Program of Henan Province(No.251111320400)+1 种基金the Key Research Project Plan for Higher Education Institutions in Henan Province(Nos.24A570006 and 25A570002)the Scientific and Technological Research Project in Henan Province(No.242102320061).
文摘In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicting long-term roadway stability,necessitating the development of a reliable constitutive creep model and numerical simulation approach.In this study,creep experiments were conducted on pre-damaged rock with varying initial damage levels to investigate the time-dependent mechanical properties.Based on the experimental results,an accelerated-creep criterion was proposed,and an elastic-viscoplastic creep damage model(EVPCD)was established that simultaneously considers the effects of time-dependent damage and instantaneous damage caused by stress disturbances on rock creep behavior.Subsequently,the effectiveness of the proposed creep model was verified using experimental data,and the secondary development of the EVPCD model was completed based on the FLAC3D platform.Following this,a long-term stability analysis method of deep surrounding rock that accounts for excavation-and mining-induced disturbances was proposed.Using the main roadway of Xutuan Coal Mine as a case study,numerical simulations were carried out to investigate the time-dependent deformation and failure characteristics of the surrounding rock following excavation and mining disturbance.Combined with on-site monitoring of the surrounding rock damage areas,the results indicate that the EVPCD outperforms the CVISC and Nishihara models in predicting the time-dependent behavior of deep surrounding rock.
基金supported by the Yunnan Province Science and Technology Plan Project(No.202403AA080001-4)the Key Research and Development Project of Guangxi,China(No.guikeAB24010144)the National Key Research and Development Project of China(Nos.2021YFB3901402 and 2018YFC1504802)。
文摘To reveal the influence of coupled effects of dry-wet cycling and precompression stress(CEDWCPS)on the damage evolution of limestone with horizontal fissure(LHF),a series of degradation and uniaxial compression tests were conducted,and a corresponding piecewise damage constitutive model(PDCM)was established.We found that both dry-wet cycling and precompression stress deteriorate the physical properties,alter the microscopic characteristics,and reduce the mechanical properties of the LHF.These degradations are particularly pronounced under the CEDWCPS,although the magnitude of these changes gradually diminishes with the progression of dry-wet cycling.Meanwhile,they also reduce the deformation degree,prolong the micropore compaction stage,shorten the unstable crack propagation stage,lower the frequency and intensity of AE events,decrease the high-amplitude and high-frequency AE signals,enlarge crack scales,and shorten the crack initiation time.Among the changes of these indicators,the dry-wet cycling plays a dominant role.The crack types of LHF under the CEDWCPS(LHFCEDWCPS)are predominantly tensile cracks,supplemented by shear cracks.The failure mode can be defined as tensileshear composite failure.Finally,the established PDCM effectively captures the nonlinear deformation of micropore and the linear deformation of the matrix in LHFCEDWCPS,with all corresponding R^(2) consistently exceeding 0.97.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
基金supported by the Science and Technology Project of State Grid Corporation of China(5400-202199281A-0-0-00).
文摘In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this paper,these influences are investigated from the perspective of the time domain.First,a novel time-domain model of the multi-VSC system is obtained by using a multi-scale method.On this basis,a stability criterion is proposed to assess the synchronization stability of the system.Then,the accuracy of the time-domain model and its stability criterion in various conditions are discussed.Moreover,the negative impact of the interaction on the system is quantified.Finally,the above theoretical analysis is also verified in the controller hardware-in-the-loop(CHIL)experiments.
基金Project(U22A20603)supported by the National Natural Science Foundation of ChinaProject(2023YFC3008300)supported by the National Key Research and Development Program of China。
文摘To investigate the long-term stability of soft-hard interbedded rock masses with initial damage induced by earthquakes and periodic drying and wetting,this study prepared samples with different initial damage through cyclic loading and unloading(CLU)experiments followed by cyclic drying and wetting(CDW)experiments,and finally conducted creep experiments.The study analyzed the effects of initial damage on creep mechanical behavior,crack evolution,and explored failure precursor information,revealing the damage failure mechanisms.The results show that the structural characteristics of the rock mass control its macroscopic failure mode.Initial damage promotes microcrack development,influences the fracture mode,and increases the proportion of high-frequency(200−280 kHz)acoustic emission events during creep.Meanwhile,initial damage exacerbates creep characteristics,increasing the creep rate,shortening total creep failure time,and reducing long-term strength.The damage failure is attributed to:the generation of internal cracks and pores in the rock caused by CLU;mineral hydrolysis and expansion-contraction due to CDW,resulting in weakened intergranular cementation;and full development of cracks and pores under creep stress.Additionally,the deformation difference coefficient and the coefficient of variation of RA/AF values can serve as precursor indicators for creep failure.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
基金National Key Research&Development Program of China,No.2021YFC3201201Ningxia Key Research and Development Program(Special Talents),No.2023BSB03021+1 种基金Natural Science Foundation of Ningxia,No.2023AAC05014University First-Class Discipline Construction Project of Ningxia,No.NXYLXK2021A03。
文摘The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the Shaanxi Qinchuang Yuan“scientist+engineer”team construction(No.2023KXJ-080)Shaanxi Chiral Drug Engineering Technology Research Center(Department of Science and Technology of Shaanxi Province.No.[2011]-251).
文摘Background:Human skin is affected by ultraviolet rays on a daily basis,and excessive ultraviolet radiation(UVR)can lead to sunburn erythema,tanning,photoaging,and skin tumors.The combination of Astragali Radix(AR)and Anemarrhenae Rhizoma(AAR)is a common pairing in traditional Chinese medicine(TCM).According to earlier studies,they possess properties capable of alleviating the adverse impacts of UVR on the skin.However,the specific actions and underlying mechanisms require further investigation.The study aims to analyze the efficacy of AR-AAR in preventing UVR-induced skin damage and to clarify the associated molecular mechanisms.Methods:Potential signaling pathways by which AR and AAR may protect against UVR-induced skin damage were identified with network pharmacology,molecular docking techniques and molecular dynamics(MD)simulation.Except the normal group,the back skin of SD rats was exposed to 1.1 mW/cm^(2) UVA combined with 0.1 mW/cm^(2) UVB daily,and the UVR skin damage model was established.Morphological features of skin tissues of different groups were discovered through Hematoxylin and Eosin(HE)staining,Masson staining,Weigert staining.ELISA was utilized to measure the levels of reactive oxygen species(ROS),Interleukin 6(IL-6),Interleukin 1β(IL-1β)and Tumor necrosis factos-α(TNF-α)in skin tissues.RT-PCR and Western blot were employed to quantify the mRNA and protein contents of PI3K,AKT,and MMP-9.Results:Network pharmacology analysis predicts that AR-AAR may improve skin damage induced by UVR through the PI3K/AKT signaling pathway.Histological staining shows that AR-AAR can significantly reduce inflammatory infiltration and fibrosis in damaged skin.Treatment with AR-AAR(2:1)significantly reduced the expression levels of IL-1β,IL-6,TNF-αand ROS in UVR-damaged rat skin.After treatment with AR-AAR(2:1),not only did the relative mRNA expression levels of PI3K and AKT and the protein expression levels of PI3K,AKT,P-PI3K,and P-AKT increase,but the mRNA and protein expression levels of MMP-9 decreased.Conclusion:The study indicate that the AR-AAR combination and its active components may mitigate UVR skin damage by modulating the PI3K/AKT signaling pathway.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
