Based on the number of foggy days in Nanjing in December from 1980 to 2011, we analyzed the surface temperature and atmospheric circulation characteristics of foggy years and less-foggy years. Positive anomalies of th...Based on the number of foggy days in Nanjing in December from 1980 to 2011, we analyzed the surface temperature and atmospheric circulation characteristics of foggy years and less-foggy years. Positive anomalies of the Arctic Oscillation(AO) were found to weaken the East Asian trough, which is not conducive to the southward migration of cold air. Simultaneously, this atmospheric condition favors stability as a result of a high-pressure anomaly from the middle Yangtze River Delta region. A portion of La Nia events increases the amount of water vapor in the South China Sea region, so this phenomenon could provide the water vapor condition required for foggy days in Nanjing.Based on the data in December 2007, which contained the greatest number of foggy days for the years studied, the source of fog vapor in Nanjing was primarily from southern China and southwest Taiwan Island based on a synoptic scale study. The water vapor in southern China and in the southwestern flow increased, and after a period of 2-3 days,the humidity in Nanjing increased. Simultaneously, the water vapor from the southwestern of Taiwan Island was directly transported to Nanjing by the southerly wind. Therefore, these two areas are the most important sources of water vapor that results in heavy fog in Nanjing. Using the bivariate Empirical Orthogonal Function(EOF) mode on the surface temperature and precipitable water vapor, the first mode was found to reflect the seasonal variation from early winter to late winter, which reduced the surface temperature on a large scale. The second mode was found to reflect a large-scale,northward, warm and humid airflow that was accompanied by the enhancement of the subtropical high, particularly between December 15-21, which is primarily responsible for the consecutive foggy days in Nanjing.展开更多
Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use ...Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.展开更多
39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.Ar...39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.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
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
The dependence of shrinkage porosities on microstructure characteristics of Mg−12Al alloy was investigated.The distribution,morphology,size,and number density of shrinkage porosities were analyzed under different cool...The dependence of shrinkage porosities on microstructure characteristics of Mg−12Al alloy was investigated.The distribution,morphology,size,and number density of shrinkage porosities were analyzed under different cooling rates.The relationship between shrinkage porosities and microstructure characteristics was discussed in terms of temperature conditions,feeding channel characteristics,and feeding capacity.Further,the feeding behavior of the residual liquid phase in the solid skeleton was quantified by introducing permeability.Results show a strong correlation between the solid microstructure skeleton and shrinkage porosity characteristics.An increase in permeability corresponds to a declining number density of shrinkage porosities.This study aims to provide a more complete understanding how to reduce shrinkage porosities by controlling microstructure characteristics.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
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
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
One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mension...One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mensional time series(TS1d)with the extracted complexity features only at a single scale.Aiming at these problems,a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy(CMEGDE_(2D))is proposed in this paper.First,the TS_(1D) is transformed into a two-dimensional image(I_(2D))by using Gramian angular fields(GAF)with more internal data structures and geometri features,which preserve the global characteristics and time dependence of vibration signals.Second,the I2D is analyzed at multiple scales through the composite coarse-graining method,which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods.Subsequently,a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE_(2D) for fault feature ex-traction and the chicken swarm algorithm optimized support vector machine(CsO-SvM)for fault pattern identification.The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method.The results demonstrate that the proposed method has stronger dis-crimination ability,higher fault diagnosis accuracy and better stability than the other compared methods.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
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
The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to r...The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.展开更多
The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and dispos...The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
Extensive engineering experience and research findings suggest that rock mass instability is typically attributed to human engineering activities and natural disturbances,resulting in general dynamic mechanical proper...Extensive engineering experience and research findings suggest that rock mass instability is typically attributed to human engineering activities and natural disturbances,resulting in general dynamic mechanical properties.This is attributed to external interference resulting from the extensive use of the mechanical and blasting techniques necessary for mineral extraction.Quantifying the impact of dynamic disturbances on rock deformation behavior is essential for comprehending the long-term response of surrounding rock during excavation.This study placed the rock to sustained pressure and investigated the impact of varying hammer heights and dry and wet(W-D)damage on its shear failure behavior.This study investigated the fatigue disturbance studies on W-D damaged sandstone samples via W-D equipment,a disturbance creep device,digital image correlation(DIC),and acoustic emission(AE)technology.The experimental findings suggest that acoustic emission sensors can be utilized to quantify the internal damage of rock samples during cyclic impact,whereas DIC technology(optical measurement)is capable of capturing the surface crack propagation of samples.Under repeated impact and the combined action of W-D conditions,the bearing capacity of sandstone decreases,whereas the deformation capacity increases.Furthermore,the W-D cycles and impact strength are inversely related to the fatigue life.The intensity of W-D damage and disturbances further accelerates the development and propagation of cracks under cyclic disturbances.The research results are of preventive significance to ensure the safety and sustainable development of engineering construction.展开更多
The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by the...The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.展开更多
基金China Meteorological Special Program(GYHY201506013)National Nature Science Foundation of China(41405068,41275151,41475034)+1 种基金Qing-Lan Project of Jiangsu ProvinceNatural Science Foundation of Jiangsu Province(SBK201220841)
文摘Based on the number of foggy days in Nanjing in December from 1980 to 2011, we analyzed the surface temperature and atmospheric circulation characteristics of foggy years and less-foggy years. Positive anomalies of the Arctic Oscillation(AO) were found to weaken the East Asian trough, which is not conducive to the southward migration of cold air. Simultaneously, this atmospheric condition favors stability as a result of a high-pressure anomaly from the middle Yangtze River Delta region. A portion of La Nia events increases the amount of water vapor in the South China Sea region, so this phenomenon could provide the water vapor condition required for foggy days in Nanjing.Based on the data in December 2007, which contained the greatest number of foggy days for the years studied, the source of fog vapor in Nanjing was primarily from southern China and southwest Taiwan Island based on a synoptic scale study. The water vapor in southern China and in the southwestern flow increased, and after a period of 2-3 days,the humidity in Nanjing increased. Simultaneously, the water vapor from the southwestern of Taiwan Island was directly transported to Nanjing by the southerly wind. Therefore, these two areas are the most important sources of water vapor that results in heavy fog in Nanjing. Using the bivariate Empirical Orthogonal Function(EOF) mode on the surface temperature and precipitable water vapor, the first mode was found to reflect the seasonal variation from early winter to late winter, which reduced the surface temperature on a large scale. The second mode was found to reflect a large-scale,northward, warm and humid airflow that was accompanied by the enhancement of the subtropical high, particularly between December 15-21, which is primarily responsible for the consecutive foggy days in Nanjing.
文摘Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.
文摘39 soil samples surrounding a lead-zinc mining area in Guangxi were collected,and the contents of Pb,Hg,Cd,Cr,As,Cu,Zn,and Ni were determined to investigate the pollution characteristics and sources of heavy metals.ArcGIS inverse distance weight difference method was used to analyze the characteristics of pollution distribution,and single-factor pollution index,Nemerow comprehensive pollution index,ground accumulation index,and potential ecological risk index were selected to evaluate the characteristics of heavy metal pollution.Based on correlation analysis,the absolute principal component-multiple linear regression(APCS-MLR)and positive definite matrix factorization(PMF)models were used to analyze the sources of soil heavy metals.The results showed that the average concentrations of all eight heavy metals exceeded both national and Guangxi soil background values.Hg,Cd,and Zn exhibited high variation(greater than 0.5),indicating significant external disturbances,and their spatial distribution was closely related to mining activity locations.The single-factor pollution index evaluation indicated varying degrees of pollution risk for Cd,Zn,and As,with Cd and Zn being the most severe pollutants,as 69.23%and 30.77%of the samples fell into the moderate pollution or higher category.The geoaccumulation index analysis ranked the mean pollution levels of the eight elements as follows:Zn>Cd>Ni>Pb>Cu>Cr>Hg>As,with Cd and Zn showing the most severe contamination,and 51.28%of the samples exhibiting moderate or higher pollution levels.The Nemerow comprehensive pollution index evaluation showed that 74.35%of soil samples were classified as moderate to heavy pollution.The potential ecological risk index assessment indicated significant ecological risks posed by Cd and Zn,with 82.05%and 5.12%of the samples classified as causing strong to extreme ecological risks,respectively.The source apportionment analysis revealed minor differences between the two models.The APCS-MLR model identified three pollution sources and their contribution rates:anthropogenic mining sources(31.13%),parent material sources(40.38%),and unidentified sources(28.49%).The PMF model identified three pollution sources with contribution rates of anthropogenic mining sources(26.10%),parent material sources(46.96%),and a combined traffic and agricultural source(26.61%).Pb,Hg,Cd,and Zn mainly originated from mining activities;Cr,As,and Ni were primarily derived from the parent material,while Cu was predominantly attributed to traffic and agricultural sources.These findings provide a scientific basis for the prevention and control of heavy metal pollution in mining areas.
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financially supported by the National Key Research and Development Program of China(No.2021YFB3701000)the National Natural Science Foundation of China(Nos.52471118,52101125,U2037601,and U21A2048)Young Elite Scientists Sponsorship Program by CAST,China(No.2022QNRC001)。
文摘The dependence of shrinkage porosities on microstructure characteristics of Mg−12Al alloy was investigated.The distribution,morphology,size,and number density of shrinkage porosities were analyzed under different cooling rates.The relationship between shrinkage porosities and microstructure characteristics was discussed in terms of temperature conditions,feeding channel characteristics,and feeding capacity.Further,the feeding behavior of the residual liquid phase in the solid skeleton was quantified by introducing permeability.Results show a strong correlation between the solid microstructure skeleton and shrinkage porosity characteristics.An increase in permeability corresponds to a declining number density of shrinkage porosities.This study aims to provide a more complete understanding how to reduce shrinkage porosities by controlling microstructure characteristics.
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.
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 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Supported by the National Natural Science Foundation of China(Grant No.51975004)the Outstanding Youth Fund of Universities in Anhui Province of China(Grant No.2022AH020032).
文摘One-dimensional ensemble dispersion entropy(EDE1D)is an effective nonlinear dynamic analysis method for complexity measurement of time series.However,it is only restricted to assessing the complexity of one-di-mensional time series(TS1d)with the extracted complexity features only at a single scale.Aiming at these problems,a new nonlinear dynamic analysis method termed two-dimensional composite multi-scale ensemble Gramian dispersion entropy(CMEGDE_(2D))is proposed in this paper.First,the TS_(1D) is transformed into a two-dimensional image(I_(2D))by using Gramian angular fields(GAF)with more internal data structures and geometri features,which preserve the global characteristics and time dependence of vibration signals.Second,the I2D is analyzed at multiple scales through the composite coarse-graining method,which overcomes the limitation of a single scale and provides greater stability compared to traditional coarse-graining methods.Subsequently,a new fault diagnosis method of rolling bearing is proposed based on the proposed CMEGDE_(2D) for fault feature ex-traction and the chicken swarm algorithm optimized support vector machine(CsO-SvM)for fault pattern identification.The simulation signals and two data sets of rolling bearings are utilized to verify the effectiveness of the proposed fault diagnosis method.The results demonstrate that the proposed method has stronger dis-crimination ability,higher fault diagnosis accuracy and better stability than the other compared methods.
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.
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.
基金The Central Government Guides Local Foundation for Science and Technology Development(Grant No.YDZJSX2024B004).
文摘The soft actuator is characterized by high safety,flexibility,and adaptability.It is capable of both active and passive defor-mations.This paper presents a discrete degree of freedom(DOF)method for soft actuators to reveal DOF characteristics.The method draws on the superposition mechanism of the deformation characteristics of the sarcomere in the skeletal muscles of living organisms.Firstly,the multi-DOF deformation characteristics of the soft actuator are discretized into superimposed combinations of single-DOF micro-units.Then,the soft actuator was determined to contain deformation characteristics such as extension-contraction,bending,and twisting.Eighteen types of micro-units with basic deforma-tion characteristics were obtained depending on the axis and orientation.Further,the mapping relationship between the combination of micro-units and the motion characteristics of the soft actuator based on the GF set theory was established.Finally,an active-passive DOF co-structured soft actuator(APCSA)was developed.The graphical approach analyzes the experimental results,and it can be concluded that active and passive DOFs can coexist in the composite deformation of the soft actuator.
基金Project(2022YFC2904103)supported by the National Key Research and Development Program of ChinaProjects(52374112,52274108)supported by the National Natural Science Foundation of China+1 种基金Projects(BX20220036,BX20230041)supported by the Postdoctoral Innovation Talents Support Program,ChinaProject(2232080)supported by the Beijing Natural Science Foundation,China。
文摘The development of metallic mineral resources generates a significant amount of solid waste,such as tailings and waste rock.Cemented tailings and waste-rock backfill(CTWB)is an effective method for managing and disposing of this mining waste.This study employs a macro-meso-micro testing method to investigate the effects of the waste rock grading index(WGI)and loading rate(LR)on the uniaxial compressive strength(UCS),pore structure,and micromorphology of CTWB materials.Pore structures were analyzed using scanning electron microscopy(SEM)and mercury intrusion porosimetry(MIP).The particles(pores)and cracks analysis system(PCAS)software was used to quantitatively characterize the multi-scale micropores in the SEM images.The key findings indicate that the macroscopic results(UCS)of CTWB materials correspond to the microscopic results(pore structure and micromorphology).Changes in porosity largely depend on the conditions of waste rock grading index and loading rate.The inclusion of waste rock initially increases and then decreases the UCS,while porosity first decreases and then increases,with a critical waste rock grading index of 0.6.As the loading rate increases,UCS initially rises and then falls,while porosity gradually increases.Based on MIP and SEM results,at waste rock grading index 0.6,the most probable pore diameters,total pore area(TPA),pore number(PN),maximum pore area(MPA),and area probability distribution index(APDI)are minimized,while average pore form factor(APF)and fractal dimension of pore porosity distribution(FDPD)are maximized,indicating the most compact pore structure.At a loading rate of 12.0 mm/min,the most probable pore diameters,TPA,PN,MPA,APF,and APDI reach their maximum values,while FDPD reaches its minimum value.Finally,the mechanism of CTWB materials during compression is analyzed,based on the quantitative results of UCS and porosity.The research findings play a crucial role in ensuring the successful application of CTWB materials in deep metal mines.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
基金supported by National Natural Science Foundation of China(Grant Nos.52364004 and 52264006)The Youth Talent Growth Project of Guizhou Provincial Department of Education(Grant No.QianJiaoJi[2024]18).
文摘Extensive engineering experience and research findings suggest that rock mass instability is typically attributed to human engineering activities and natural disturbances,resulting in general dynamic mechanical properties.This is attributed to external interference resulting from the extensive use of the mechanical and blasting techniques necessary for mineral extraction.Quantifying the impact of dynamic disturbances on rock deformation behavior is essential for comprehending the long-term response of surrounding rock during excavation.This study placed the rock to sustained pressure and investigated the impact of varying hammer heights and dry and wet(W-D)damage on its shear failure behavior.This study investigated the fatigue disturbance studies on W-D damaged sandstone samples via W-D equipment,a disturbance creep device,digital image correlation(DIC),and acoustic emission(AE)technology.The experimental findings suggest that acoustic emission sensors can be utilized to quantify the internal damage of rock samples during cyclic impact,whereas DIC technology(optical measurement)is capable of capturing the surface crack propagation of samples.Under repeated impact and the combined action of W-D conditions,the bearing capacity of sandstone decreases,whereas the deformation capacity increases.Furthermore,the W-D cycles and impact strength are inversely related to the fatigue life.The intensity of W-D damage and disturbances further accelerates the development and propagation of cracks under cyclic disturbances.The research results are of preventive significance to ensure the safety and sustainable development of engineering construction.
基金funded by 2025 Graduate Teaching Construction Project of USST.
文摘The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.
