Heterostructured materials as a new class can effectively avoid the inverted relationship of the“banana”curve followed by strength-ductility.The difference in grain size is the mainstream idea of the design of heter...Heterostructured materials as a new class can effectively avoid the inverted relationship of the“banana”curve followed by strength-ductility.The difference in grain size is the mainstream idea of the design of heterogeneous zones.However,the synergistic strengthening mechanism and deformation behavior among multi-scale heterostructures are still unclear.In this work,AZ80/AZ31 laminate with a multi-scale heterogeneous distribution of grain size,precipitates,and texture between alternate AZ31 and AZ80 component layers,which was constructed by accumulative extrusion bonding combined with aging treatment.The composite samples after 2-pass extrusion presented an outstanding strength-ductility synergy,which was attributed to the joint action of texture softening and hardening,grain refinement as well as multistage heterogeneous deformation induced(HDI)strengthening and hardening.Multi-types of heterogeneous regions provided more sites for geometrically necessary dislocation accumulation to accommodate multiple strain gradients under the constraint of multi-layer interfaces,enhancing HDI stress.The synergistic effect of great Schmid factor difference and increasing geometric compatibility factor between adjacent grains at the layer interface led to strain transfer behavior,which facilitated strain delocalization.This work expands the design ideas and preparation methods of heterostructured materials and enriches the theory of heterogeneous deformation.展开更多
Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigi...Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.展开更多
Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based elect...Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.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
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
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.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
Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimizat...Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design.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
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
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.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
Sandwich structures are widely favored for their lightweight,high strength and superior impact mitigation capabilities in blast mitigation and transportation safety applications.Their application in large-scale,high-e...Sandwich structures are widely favored for their lightweight,high strength and superior impact mitigation capabilities in blast mitigation and transportation safety applications.Their application in large-scale,high-energy rockfall protection remains limited due to their relatively low volumetric energy absorption efficiency and the complex fabrication processes of key energy-absorbing components.To address these limitations,this study proposes a novel sandwich structure incorporating mild steel tubes as core energy absorbers to efficiently mitigate highenergy rockfall impacts.A finite element model was developed in LS-DYNA to systematically investigate the deformation and energy absorption behaviors.Comprehensive parametric analyses were conducted to quantify the effects of key design variables,including tube wall thickness,tube spacing(number of tubes),and infill materials.The results demonstrate that increasing tube wall thickness significantly enhances ultimate energy absorption,with 12-mm-thick tubes absorbing 2.2 times more energy than 6-mm-thick tubes.Lateral constraints induced by adjacent tubes improve specific energy absorption per unit displacement by approximately 30%-45%.Furthermore,incorporating infill materials considerably enhances energy absorption,with aluminum foam infills achieving an 81%increase compared to empty tubes.Nevertheless,higher energy absorption capacity typically leads to greater peak impact forces,increasing the number of tubes offers a better balance between energy absorption and impact force,optimizing the structural performance.These findings provide valuable theoretical insights and practical guidelines for designing sandwich structures in civil and infrastructure engineering applications for effective rockfall protection.展开更多
Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-ins...Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Anchovies are key forage species in coastal and pelagic food webs,playing crucial ecological and commercial roles in marine ecosystems.Encrasicholina punctifer is a commercially and ecologically important species,yet ...Anchovies are key forage species in coastal and pelagic food webs,playing crucial ecological and commercial roles in marine ecosystems.Encrasicholina punctifer is a commercially and ecologically important species,yet little is known about its osteological development characteristics.Understanding these ontogenetic patterns is essential for explaining phylogenetic relationships within Clupeiformes,informing larval identification in ichthyoplankton surveys,and enhancing aquaculture practices.This study describes the ontogenetic characteristics of the appendicular and median skeletons and fin supports in E.punctifer larvae and juveniles,providing quantitative ontogenetic timelines to address gaps in engraulid developmental biology and contribute to teleost ontogeny research.Larval and juvenile specimens of E.punctifer;size range:3.00-34.56 mm standard length(SL)and notochord length(NL)collected from the Philippine Sea were processed using the standard Alizarin red and Alcian blue clearing-and-staining technique to differentiate ossified and cartilaginous elements.Specimens were examined under a stereo microscope and imaged with an HTC1600ISP digital camera for detailed morphological analysis of skeletal structures.Caudal complex development was evident by 5.55 mm NL,with the initial appearance of cartilaginous hypurals 1–2 and parhypural,followed by Hy3–5 at 6.08 mm NL.Cartilaginous Hy6 was visible by 9.58 mm SL in postflexion larvae.Six hypural bones were present from the postflexion to juvenile stage.Principal fin rays formed via intramembranous ossification at 6.32 mm NL,and segmentation was evident by 10.24 mm SL.Neural arch 1 appeared at 7.38 mm NL,preceding full haemal arch development.Uroneurals 1–2 and ural centra ossified by 9.58 mm SL,with Hy5 detached from U1 at 10.24 mm SL.Preural centra ossified before the arches.Epurals 1 and 2 were evident by 10.24 mm SL.Dorsal fin proximal radials developed anteriorly and distal radials posteriorly.Pectoral fin development initiated with an ossified cleithrum and the coracoscapular cartilage at 5.25 mm NL.By 10.24 mm SL,cartilaginous neural and haemal arches and spines were distinct.Uroneural 1 initially develops as cartilage.The formation of a pleurostyle aligns with clupeoid fish skeletal ontogeny.Dorsal and anal fin development follows a distinctive pattern,with variability in fin support development.Our results reveal accelerated caudal complex formation in E.punctifer and contribute new knowledge on the osteological characteristics of the genus Encrasicholina,and aid in understanding larval and juvenile ontogeny of marine teleosts.展开更多
Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2...Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.展开更多
基金financially supported by the National Natural Science Foundation of China(No.52071035)the Doctoral Scientific Research Foundation of Anhui University of Technology(No.RZ2400002557).
文摘Heterostructured materials as a new class can effectively avoid the inverted relationship of the“banana”curve followed by strength-ductility.The difference in grain size is the mainstream idea of the design of heterogeneous zones.However,the synergistic strengthening mechanism and deformation behavior among multi-scale heterostructures are still unclear.In this work,AZ80/AZ31 laminate with a multi-scale heterogeneous distribution of grain size,precipitates,and texture between alternate AZ31 and AZ80 component layers,which was constructed by accumulative extrusion bonding combined with aging treatment.The composite samples after 2-pass extrusion presented an outstanding strength-ductility synergy,which was attributed to the joint action of texture softening and hardening,grain refinement as well as multistage heterogeneous deformation induced(HDI)strengthening and hardening.Multi-types of heterogeneous regions provided more sites for geometrically necessary dislocation accumulation to accommodate multiple strain gradients under the constraint of multi-layer interfaces,enhancing HDI stress.The synergistic effect of great Schmid factor difference and increasing geometric compatibility factor between adjacent grains at the layer interface led to strain transfer behavior,which facilitated strain delocalization.This work expands the design ideas and preparation methods of heterostructured materials and enriches the theory of heterogeneous deformation.
基金supported by the National Natural Science Foundation of China(No.52175269)the Innovative Research Groups of the National Natural Science Foundation of China(No.52021003)+2 种基金Natural Science Foundation of Jilin Province of China(No.20210101052JC)Science and Technology Research Project of Education Department of Jilin Province(JJKH20231146KJ,JJKH20241262KJ)China Postdoctoral Science Foundation(2024M751086).
文摘Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.
基金funded by the Joint Fund for Regional Innovation and Development of National Natural Science Foundation of China(U21A20143)the National Science Fund for Excellent Young Scholars(52322607)the Excellent Youth Foundation of Heilongjiang Scientific Committee(YQ2022E028)。
文摘Improving the volumetric energy density of supercapacitors is essential for practical applications,which highly relies on the dense storage of ions in carbon-based electrodes.The functional units of carbon-based electrode exhibit multi-scale structural characteristics including macroscopic electrode morphologies,mesoscopic microcrystals and pores,and microscopic defects and dopants in the carbon basal plane.Therefore,the ordered combination of multi-scale structures of carbon electrode is crucial for achieving dense energy storage and high volumetric performance by leveraging the functions of various scale structu re.Considering that previous reviews have focused more on the discussion of specific scale structu re of carbon electrodes,this review takes a multi-scale perspective in which recent progresses regarding the structureperformance relationship,underlying mechanism and directional design of carbon-based multi-scale structures including carbon morphology,pore structure,carbon basal plane micro-environment and electrode technology on dense energy storage and volumetric property of supercapacitors are systematically discussed.We analyzed in detail the effects of the morphology,pore,and micro-environment of carbon electrode materials on ion dense storage,summarized the specific effects of different scale structures on volumetric property and recent research progress,and proposed the mutual influence and trade-off relationship between various scale structures.In addition,the challenges and outlooks for improving the dense storage and volumetric performance of carbon-based supercapacitors are analyzed,which can provide feasible technical reference and guidance for the design and manufacture of dense carbon-based electrode materials.
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,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Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
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supported by the National Natural Science Foundation of China(Grant Nos.12432005 and 12472116)the Fundamental Research Funds for the Central Universities(DUTZD25240).
文摘Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design.
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 Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
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supported by the National Key R&D Program of China(Grant No.2019YFC1509703)the Tianjin Science and Technology Program Project(Grant No.23YFYSHZ00130)。
文摘Sandwich structures are widely favored for their lightweight,high strength and superior impact mitigation capabilities in blast mitigation and transportation safety applications.Their application in large-scale,high-energy rockfall protection remains limited due to their relatively low volumetric energy absorption efficiency and the complex fabrication processes of key energy-absorbing components.To address these limitations,this study proposes a novel sandwich structure incorporating mild steel tubes as core energy absorbers to efficiently mitigate highenergy rockfall impacts.A finite element model was developed in LS-DYNA to systematically investigate the deformation and energy absorption behaviors.Comprehensive parametric analyses were conducted to quantify the effects of key design variables,including tube wall thickness,tube spacing(number of tubes),and infill materials.The results demonstrate that increasing tube wall thickness significantly enhances ultimate energy absorption,with 12-mm-thick tubes absorbing 2.2 times more energy than 6-mm-thick tubes.Lateral constraints induced by adjacent tubes improve specific energy absorption per unit displacement by approximately 30%-45%.Furthermore,incorporating infill materials considerably enhances energy absorption,with aluminum foam infills achieving an 81%increase compared to empty tubes.Nevertheless,higher energy absorption capacity typically leads to greater peak impact forces,increasing the number of tubes offers a better balance between energy absorption and impact force,optimizing the structural performance.These findings provide valuable theoretical insights and practical guidelines for designing sandwich structures in civil and infrastructure engineering applications for effective rockfall protection.
基金supported by the Khalifa University of Science and Technology internal grants(Nos.2021-CIRA-109,2020-CIRA-007,and 2020-CIRA-024).
文摘Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
文摘Anchovies are key forage species in coastal and pelagic food webs,playing crucial ecological and commercial roles in marine ecosystems.Encrasicholina punctifer is a commercially and ecologically important species,yet little is known about its osteological development characteristics.Understanding these ontogenetic patterns is essential for explaining phylogenetic relationships within Clupeiformes,informing larval identification in ichthyoplankton surveys,and enhancing aquaculture practices.This study describes the ontogenetic characteristics of the appendicular and median skeletons and fin supports in E.punctifer larvae and juveniles,providing quantitative ontogenetic timelines to address gaps in engraulid developmental biology and contribute to teleost ontogeny research.Larval and juvenile specimens of E.punctifer;size range:3.00-34.56 mm standard length(SL)and notochord length(NL)collected from the Philippine Sea were processed using the standard Alizarin red and Alcian blue clearing-and-staining technique to differentiate ossified and cartilaginous elements.Specimens were examined under a stereo microscope and imaged with an HTC1600ISP digital camera for detailed morphological analysis of skeletal structures.Caudal complex development was evident by 5.55 mm NL,with the initial appearance of cartilaginous hypurals 1–2 and parhypural,followed by Hy3–5 at 6.08 mm NL.Cartilaginous Hy6 was visible by 9.58 mm SL in postflexion larvae.Six hypural bones were present from the postflexion to juvenile stage.Principal fin rays formed via intramembranous ossification at 6.32 mm NL,and segmentation was evident by 10.24 mm SL.Neural arch 1 appeared at 7.38 mm NL,preceding full haemal arch development.Uroneurals 1–2 and ural centra ossified by 9.58 mm SL,with Hy5 detached from U1 at 10.24 mm SL.Preural centra ossified before the arches.Epurals 1 and 2 were evident by 10.24 mm SL.Dorsal fin proximal radials developed anteriorly and distal radials posteriorly.Pectoral fin development initiated with an ossified cleithrum and the coracoscapular cartilage at 5.25 mm NL.By 10.24 mm SL,cartilaginous neural and haemal arches and spines were distinct.Uroneural 1 initially develops as cartilage.The formation of a pleurostyle aligns with clupeoid fish skeletal ontogeny.Dorsal and anal fin development follows a distinctive pattern,with variability in fin support development.Our results reveal accelerated caudal complex formation in E.punctifer and contribute new knowledge on the osteological characteristics of the genus Encrasicholina,and aid in understanding larval and juvenile ontogeny of marine teleosts.
文摘Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.
