This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics o...This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.展开更多
Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's per...Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.展开更多
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between differ...To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Characterization of mechanical alterations of shale constituent phases is critical for an in-depth understanding of the underlying mechanisms of shale softening.In this study,a hydro-thermal reaction system is set up ...Characterization of mechanical alterations of shale constituent phases is critical for an in-depth understanding of the underlying mechanisms of shale softening.In this study,a hydro-thermal reaction system is set up to mimic the interactions between shale and water-based fluids under the subsurface environment in shale formations.Using a coupled analysis of grid nanoindentation and in situ mineralogical identification,mechanical alterations of shale constituent mineral phases are revealed.Mechanical degradation of carbonate and clay phases is 10 times greater than quartz,pyrite and organic phases.The KCl additive greatly mitigates mechanical degradation of the clay phase.The high temperature and pressure results in a mechanical degradation of carbonate minerals as much as three times of that occurs at room temperature and atmospheric pressure.Multiscale mechanical models,which are established based on Mori-Tanaka(MT)and self-consistent(SC)schemes,predict more accurate elastic softening of shale composite than the microindentation experiments,due to the microcracks generated in the experiments.Based on the calculation of the multiscale mechanical model,under the subsurface environment of shale formations(e.g.80℃ and 8 MPa),the carbonate dissolution leads to a reduction in Young's modulus of shale composite by about 30%,while the degradation of clay minerals only causes a reduction by up to 9%.展开更多
With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics fo...With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics for continuous foot gait monitoring.Nevertheless,existing biomass-based TENG often faces challenges of insufficient mechanical robustness and durability in practical applications,where they are prone to surface abrasion and structural fracture under continuous compression and friction,severely limiting their long-term performances.In order to address these challenges,this work proposed a multiscale crosslinking strategy,which strengthened the noncovalent interactions within the polymer by constructing multiple reinforcement networks,successfully fabricating a dual-network C-lignin-based triboelectric material(CLTM)with excellent durability and crack resistance.Among them,the optimal CLTM(PSGCL-0.2)exhibited high mechanical strength(strain 445%,tensile strength 41.56 MPa,Young's modulus 41.25 MPa,toughness 159.67 MJ/m^(3))and excellent cyclic stability(300 cycles)with versatile functionalities,including antibacterial,antioxidant,and UV-shielding properties,water stabilization(255.51 g/m^(2)/d),efficient photothermal conversion,and full recyclability.Furthermore,biomass-based TENG device assembled from PSGCL-0.2 achieved stable triboelectric output properties(102.5 V,2.9μA,and 61.3 nC),and sustainable for 2000 cycles,fast response time(68 ms),and excellent power density(325.9 mW/m^(2)),effectively converting mechanical energy into electrical energy.Especially,PSGCL 0.2 was also integrated into the wireless self-powered smart insole,successfully enabling real-time visual monitoring of plantar pressure distribution and dynamic gait.Meanwhile,combined with the machine learning algorithm,the self-powered smart insole achieved precise recognition and classification of eight different motion states with an accuracy of 98%.This study provides the feasible strategy for developing extremely stable and durable biomass-based TENG,aimed at advancing sustainable intelligent healthcare systems.展开更多
As a representative insensitive high explosive,3-nitro-1,2,4-triazol-5-one(NTO)has garnered significant attention due to its ability to substantially reduce the risk of accidental detonation in munitions.However,its i...As a representative insensitive high explosive,3-nitro-1,2,4-triazol-5-one(NTO)has garnered significant attention due to its ability to substantially reduce the risk of accidental detonation in munitions.However,its inherent acidity induces severe interfacial corrosion of metal casings,thereby limiting its engineering applications.Based on the micro-corrosion mechanism of NTO on carbon steel(CS),this study designs an arginine-derived corrosion inhibitor,N2-[(phenylamino)thioxomethyl]-arginine(PTA).Electrochemical tests reveal that PTA exhibits an outstanding corrosion inhibition efficiency of 98.0%in NTO solution.Density functional theory(DFT)and molecular dynamics(MD)simulations elucidate the inhibition mechanism of PTA,demonstrating that it not only co-adsorbs with NTO^(−) onto the CS surface to form a dense and stable protective film but also disrupts the strong interactions between NTO^(−) and Fe,thereby suppressing nitro group-induced reduction,decomposition,and excessive surface oxidation.Furthermore,a PTA-loaded mesoporous silica(mSiO_(2))nanoparticles(NPs)-reinforced epoxy resin(EP)composite coating was constructed.Benefiting from the enhanced barrier properties of PTA@mSiO_(2) NPs and the synergistic effect between PTA and NTO^(−),the low-frequency impedance of the composite coating remained as high as 1.29×10^(9)Ω·cm^(2) after 30 days of immersion in NTO solution,exhibiting a two-order-of-magnitude improvement compared to the pure EP coating.This study proposes an effective corrosion control strategy to mitigate NTO-induced corrosion,providing insights into the development of advanced corrosion protection strategies for broader applications.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant i...Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant importance.The traditional finite element method(FEM)remains one of the primary approaches for addressing such issues.However,the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision,which inevitably leads to a reduction in computational efficiency.To enhance the computational accuracy and efficiency of the FEM for heterogeneous multi-field coupling problems,this study presents the coupling magneto-electro-elastic multiscale finite element method(CM-MsFEM)for heterogeneous MEE structures.Unlike the conventional multiscale FEM(MsFEM),the proposed algorithm simultaneously constructs displacement,electric,and magnetic potential multiscale basis functions to address the heterogeneity of the corresponding parameters.The macroscale formulation of CM-MsFEM was derived,and the macroscale/microscale responses of the problems were obtained through up/downscaling calculations.Evaluation using numerical examples analyzing the transient behavior of heterogeneous MEE structures demonstrated that the proposed method outperforms traditional FEM in terms of both accuracy and computational efficiency,making it an appropriate choice for numerically modeling the dynamics of heterogeneous MEE structures.展开更多
Layered rocks(LR)exhibit inherent anisotropic stiffness and strength induced by oriented rough weakness planes,along with stress induced anisotropy and friction related plastic deformation occurs during loading.Furthe...Layered rocks(LR)exhibit inherent anisotropic stiffness and strength induced by oriented rough weakness planes,along with stress induced anisotropy and friction related plastic deformation occurs during loading.Furthermore,microcracks located in intact rock matrix(IRM)of LR are also critically important for friction and damage dissipation processes.In this paper,we first present a novel multiscale friction-damage(MFD)model using a two-step Mori-Tanaka homogenization scheme,with the aim of describing the multiscale friction-damage mechanics in LR.Physically,the initiation and propagation of flaws at different scales(i.e.microcracks and weakness planes)induced damage,and the plastic deformation is closely associated with frictional sliding along these flaws.In the thermodynamics framework,the macroscopic stress-strain relations,the local driving forces respectively conjuncted with flaws propagation and plastic deformation are derived.An analytical macroscopic strength criterion is subsequently deduced,which takes into account the variation of inclination angle and confining pressure.Notably,the failure mechanisms of IRM shearing and weakness planes sliding are inherent included in the criterion.As an original contribution,a new multisurface semi-implicit return mapping algorithm(MSRM)is developed to integrate the proposed MFD model.The robustness of MSRM algorithm is assessed by numerical tests with different loading steps sizes and convergence conditions.Finally,the effectiveness of the MFD model is confirmed using data from experiments under conventional triaxial compression,all main features of mechanical behaviors of LR are well captured by the proposed model,including initial anisotropy,stress-induced anisotropy and strain hardening/softening.展开更多
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock st...Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock structural degradation.This may lead to problems in the evaluation of rock structure stability and safe life.Multiscale numerical modeling is regarded as an effective way to gain insight into factors affecting rock properties from a cross-scale view.This study compiles the history of theoretical developments and numerical techniques related to rock multiscale issues according to different modeling architectures,that is,the homogenization theory,the hierarchical approach,and the concurrent approach.For these approaches,their benefits,drawbacks,and application scope are underlined.Despite the considerable attempts that have been made,some key issues still result in multiple challenges.Therefore,this study points out the perspectives of rock multiscale issues so as to provide a research direction for the future.The review results show that,in addition to numerical techniques,for example,high-performance computing,more attention should be paid to the development of an advanced constitutive model with consideration of fine geometrical descriptions of rock to facilitate solutions to multiscale problems in rock mechanics and rock engineering.展开更多
The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus...The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.展开更多
This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies...This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies,we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints.This approach significantly refines the optimization process,ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints.First,we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials.Then,these local yield-based load factors are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels.This integration is crucial for the practical application of optimized structures in real-world scenarios,where material yield and stability behavior critically influence structural integrity and durability.Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material.Despite the foundational assumption of the separation of scales,the de-homogenized structures,even at relatively coarse length scales,exhibit a remarkably high degree of agreement with the corresponding homogenized predictions.展开更多
Zirconium alloys are critical materials in nuclear engineering due to their exceptional irradiation resistance and corrosion stability.However,prolonged exposure to extreme operational environments,including a high ra...Zirconium alloys are critical materials in nuclear engineering due to their exceptional irradiation resistance and corrosion stability.However,prolonged exposure to extreme operational environments,including a high radiation,mechanical stress,and corrosive media,induces surface degradation mechanisms including stress corrosion cracking and erosion from impurity particle impacts,necessitating advanced surface treatments to improve hardness and corrosion resistance.We explore the application of laser shock peening(LSP)to enhance the surface properties of the Zr4 alloy.Experimental analyses reveal substantial microstructural modifications upon the LSP.The surface grain refinement achieved a maximum reduction of 52.7%in average grain size(from 22.88 to 10.8μm^(2)),accompanied by an increase of 59%in hardness(204 to 326 HV).Additionally,a compressive residual stress layer(approximately-100 MPa)was generated on the treated surface,which reduces the risk of stress corrosion cracking.To elucidate the mechanistic basis of these improvements,a multiscale computational framework was developed,integrating finite-element models for macroscale stress field evolution and molecular dynamics simulations for nanoscale dislocation dynamics.By incorporating the strain rate as a critical variable,this framework bridges microstructure evolution with macroscopic mechanical enhancements.The simulations not only elucidated the dynamic interplay between shockwave-induced plastic deformation and property improvements but also exhibited a good consistency with experimental residual stress profiles.Notably,we propose the application of strain rate-driven multiscale modeling in LSP research for Zr alloys,providing a predictive method to optimize laser parameters for a tailored surface strengthening.This study not only confirms that LSP is a feasible strategy capable of effectively enhancing the comprehensive surface properties of Zr alloys and extending their service life in nuclear environments,but also provides a reliable simulation methodology in the field of laser surface engineering of alloy materials.展开更多
Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution....Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.展开更多
This paper examined how microstructure influences the homogenized thermal conductivity of cellular structures and revealed a surface-induced size-dependent effect.This effect is linked to the porous microstructural fe...This paper examined how microstructure influences the homogenized thermal conductivity of cellular structures and revealed a surface-induced size-dependent effect.This effect is linked to the porous microstructural features of cellular structures,which stems from the degree of porosity and the distri-bution of the pores.Unlike the phonon-driven surface effect at the nanoscale,the macro-scale surface mechanism in thermal cellular structures is found to be the microstructure-induced changes in the heat conduction path based on fully resolved 3D numerical simulations.The surface region is determined by the microstructure,characterized by the intrinsic length.With the coupling between extrinsic and intrinsic length scales under the surface mechanism,a surface-enriched multiscale method was devel-oped to accurately capture the complex size-dependent thermal conductivity.The principle of scale separation required by classical multiscale methods is not necessary to be satisfied by the proposed multiscale method.The significant potential of the surface-enriched multiscale method was demon-strated through simulations of the effective thermal conductivity of a thin-walled metamaterial struc-ture.The surface-enriched multiscale method offers higher accuracy compared with the classical multiscale method and superior efficiency over high-fidelity finite element methods.展开更多
Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study emplo...Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.展开更多
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur...Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant Nos.U2442206,42205067,and 41922035)the National Key R&D Program of China(Grant No.2024YFC3013100)the Key Research Program of Frontier Sciences of CAS(Grant No.QYZDB-SSW-DQC017).
文摘This study reveals the critical role of multiscale interaction within the westerly wind bursts(WWBs)west of the MJO convection in modulating the prediction skill for the November MJO event during the DYNAMO(Dynamics of the Madden–Julian Oscillation)field campaign.The characteristics of the MJO convection envelope are obtained by the largescale precipitation tracking method,and a novel metric is introduced to quantify the prediction skill for the MJO convection in the ECMWF reforecast.The ECMWF forecast exhibits approximately 17 days in skillful prediction for the MJO convection—significantly lower than that derived from the global measure.The reforecast ensembles are further classified into high and low skill catalogs based on the mean prediction skill during the observed WWBs period.High-skill ensembles exhibit significantly enhanced low-level westerlies,amplified MJO convection,and reduced spatial separation between the low-level westerlies and MJO convection during the WWBs period,indicating stronger coupling between the large-scale circulation and the convection.Mechanistic analysis reveals that enhanced westerlies in high-skill ensembles can transfer more high-frequency energy to the MJO convection through the flux convergence of interaction energy for MJO convection development,resulting in better prediction skill.
基金supported by the National Science and Technology Major Project,China(No.J2019-Ⅱ-0012-0032)。
文摘Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.
基金funded by Ministry of Education Humanities and Social Science Research Project,grant number 23YJAZH034The Postgraduate Research and Practice Innovation Program of Jiangsu Province,grant number SJCX25_17National Computer Basic Education Research Project in Higher Education Institutions,grant number 2024-AFCEC-056,2024-AFCEC-057.
文摘To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects,similar defects and background features,and similarities between different defects,this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network(MSESE),which is built upon the You Only Look Once version 11 nano(YOLOv11n).To address the difficulty of locating defect edges,we first propose an edge enhancement module(EEM),apply it to the process of multiscale feature extraction,and then propose a multiscale edge enhancement module(MSEEM).By obtaining defect features from different scales and enhancing their edge contours,the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features.By fusing the squeeze-and-excitation attention mechanism with the EEM,we obtain a lighter module that can enhance the representation of edge features,which is named the edge enhancement module with squeeze-and-excitation attention(EEMSE).This module was subsequently integrated into the detection head.The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead,while effectively adjusting channel-wise importance and further refining feature representation.Experiments on the NEU-DET dataset show that,compared with the original YOLOv11n,the improved model achieves improvements of 4.1%and 2.2%in terms of mAP@0.5 and mAP@0.5:0.95,respectively,and the GFLOPs value decreases from the original value of 6.4 to 6.2.Furthermore,when compared to current mainstream models,Mamba-YOLOT and RTDETR-R34,our method achieves superior performance with 6.5%and 8.9%higher mAP@0.5,respectively,while maintaining a more compact parameter footprint.These results collectively validate the effectiveness and efficiency of our proposed approach.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金funded by the Open Research Fund Programof State Key Laboratory of Hydroscience and Engineering(Project No.sklhse-2023-D-04)the National Natural Science Foundation of China(Project No.51979144).
文摘Characterization of mechanical alterations of shale constituent phases is critical for an in-depth understanding of the underlying mechanisms of shale softening.In this study,a hydro-thermal reaction system is set up to mimic the interactions between shale and water-based fluids under the subsurface environment in shale formations.Using a coupled analysis of grid nanoindentation and in situ mineralogical identification,mechanical alterations of shale constituent mineral phases are revealed.Mechanical degradation of carbonate and clay phases is 10 times greater than quartz,pyrite and organic phases.The KCl additive greatly mitigates mechanical degradation of the clay phase.The high temperature and pressure results in a mechanical degradation of carbonate minerals as much as three times of that occurs at room temperature and atmospheric pressure.Multiscale mechanical models,which are established based on Mori-Tanaka(MT)and self-consistent(SC)schemes,predict more accurate elastic softening of shale composite than the microindentation experiments,due to the microcracks generated in the experiments.Based on the calculation of the multiscale mechanical model,under the subsurface environment of shale formations(e.g.80℃ and 8 MPa),the carbonate dissolution leads to a reduction in Young's modulus of shale composite by about 30%,while the degradation of clay minerals only causes a reduction by up to 9%.
基金supported by the grants from National Natural Science Foundation of China(Nos.22278091,22278046)Young Elite Sci-entists Sponsorship Program by CAST(No.2024QNRC0387)+2 种基金the Guangxi Natural Science Foundation of China(Nos.2025GXNSFBA069146,2023GXNSFGA026001,GKAD25069076)the Foundation(No.202403)of Tianjin Key Laboratory of Pulp&Paper(Tianjin University of Science&Technology)P.R.China,and the Foundation of Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control,College of Light Industry and Food Engineering,Guangxi University(No.2021KF01).
文摘With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics for continuous foot gait monitoring.Nevertheless,existing biomass-based TENG often faces challenges of insufficient mechanical robustness and durability in practical applications,where they are prone to surface abrasion and structural fracture under continuous compression and friction,severely limiting their long-term performances.In order to address these challenges,this work proposed a multiscale crosslinking strategy,which strengthened the noncovalent interactions within the polymer by constructing multiple reinforcement networks,successfully fabricating a dual-network C-lignin-based triboelectric material(CLTM)with excellent durability and crack resistance.Among them,the optimal CLTM(PSGCL-0.2)exhibited high mechanical strength(strain 445%,tensile strength 41.56 MPa,Young's modulus 41.25 MPa,toughness 159.67 MJ/m^(3))and excellent cyclic stability(300 cycles)with versatile functionalities,including antibacterial,antioxidant,and UV-shielding properties,water stabilization(255.51 g/m^(2)/d),efficient photothermal conversion,and full recyclability.Furthermore,biomass-based TENG device assembled from PSGCL-0.2 achieved stable triboelectric output properties(102.5 V,2.9μA,and 61.3 nC),and sustainable for 2000 cycles,fast response time(68 ms),and excellent power density(325.9 mW/m^(2)),effectively converting mechanical energy into electrical energy.Especially,PSGCL 0.2 was also integrated into the wireless self-powered smart insole,successfully enabling real-time visual monitoring of plantar pressure distribution and dynamic gait.Meanwhile,combined with the machine learning algorithm,the self-powered smart insole achieved precise recognition and classification of eight different motion states with an accuracy of 98%.This study provides the feasible strategy for developing extremely stable and durable biomass-based TENG,aimed at advancing sustainable intelligent healthcare systems.
文摘As a representative insensitive high explosive,3-nitro-1,2,4-triazol-5-one(NTO)has garnered significant attention due to its ability to substantially reduce the risk of accidental detonation in munitions.However,its inherent acidity induces severe interfacial corrosion of metal casings,thereby limiting its engineering applications.Based on the micro-corrosion mechanism of NTO on carbon steel(CS),this study designs an arginine-derived corrosion inhibitor,N2-[(phenylamino)thioxomethyl]-arginine(PTA).Electrochemical tests reveal that PTA exhibits an outstanding corrosion inhibition efficiency of 98.0%in NTO solution.Density functional theory(DFT)and molecular dynamics(MD)simulations elucidate the inhibition mechanism of PTA,demonstrating that it not only co-adsorbs with NTO^(−) onto the CS surface to form a dense and stable protective film but also disrupts the strong interactions between NTO^(−) and Fe,thereby suppressing nitro group-induced reduction,decomposition,and excessive surface oxidation.Furthermore,a PTA-loaded mesoporous silica(mSiO_(2))nanoparticles(NPs)-reinforced epoxy resin(EP)composite coating was constructed.Benefiting from the enhanced barrier properties of PTA@mSiO_(2) NPs and the synergistic effect between PTA and NTO^(−),the low-frequency impedance of the composite coating remained as high as 1.29×10^(9)Ω·cm^(2) after 30 days of immersion in NTO solution,exhibiting a two-order-of-magnitude improvement compared to the pure EP coating.This study proposes an effective corrosion control strategy to mitigate NTO-induced corrosion,providing insights into the development of advanced corrosion protection strategies for broader applications.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China(Grant Nos.42102346,42172301).
文摘Magneto-electro-elastic(MEE)materials are widely utilized across various fields due to their multi-field coupling effects.Consequently,investigating the coupling behavior of MEE composite materials is of significant importance.The traditional finite element method(FEM)remains one of the primary approaches for addressing such issues.However,the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision,which inevitably leads to a reduction in computational efficiency.To enhance the computational accuracy and efficiency of the FEM for heterogeneous multi-field coupling problems,this study presents the coupling magneto-electro-elastic multiscale finite element method(CM-MsFEM)for heterogeneous MEE structures.Unlike the conventional multiscale FEM(MsFEM),the proposed algorithm simultaneously constructs displacement,electric,and magnetic potential multiscale basis functions to address the heterogeneity of the corresponding parameters.The macroscale formulation of CM-MsFEM was derived,and the macroscale/microscale responses of the problems were obtained through up/downscaling calculations.Evaluation using numerical examples analyzing the transient behavior of heterogeneous MEE structures demonstrated that the proposed method outperforms traditional FEM in terms of both accuracy and computational efficiency,making it an appropriate choice for numerically modeling the dynamics of heterogeneous MEE structures.
基金jointly supported by Science and Technology Projects in Guangzhou(Grant No.SL2023A04J01079)Zhejiang ProvincialWater Conservancy Science and Technology Plan Project(Grant No.RC2405)Thematic Five of the Second Scientific Expedition of Qinghai-Tibet Plateau(Grant No.2019QZKK0905).
文摘Layered rocks(LR)exhibit inherent anisotropic stiffness and strength induced by oriented rough weakness planes,along with stress induced anisotropy and friction related plastic deformation occurs during loading.Furthermore,microcracks located in intact rock matrix(IRM)of LR are also critically important for friction and damage dissipation processes.In this paper,we first present a novel multiscale friction-damage(MFD)model using a two-step Mori-Tanaka homogenization scheme,with the aim of describing the multiscale friction-damage mechanics in LR.Physically,the initiation and propagation of flaws at different scales(i.e.microcracks and weakness planes)induced damage,and the plastic deformation is closely associated with frictional sliding along these flaws.In the thermodynamics framework,the macroscopic stress-strain relations,the local driving forces respectively conjuncted with flaws propagation and plastic deformation are derived.An analytical macroscopic strength criterion is subsequently deduced,which takes into account the variation of inclination angle and confining pressure.Notably,the failure mechanisms of IRM shearing and weakness planes sliding are inherent included in the criterion.As an original contribution,a new multisurface semi-implicit return mapping algorithm(MSRM)is developed to integrate the proposed MFD model.The robustness of MSRM algorithm is assessed by numerical tests with different loading steps sizes and convergence conditions.Finally,the effectiveness of the MFD model is confirmed using data from experiments under conventional triaxial compression,all main features of mechanical behaviors of LR are well captured by the proposed model,including initial anisotropy,stress-induced anisotropy and strain hardening/softening.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
基金National Natural Science Foundation of China,Grant/Award Numbers:52192691,52192690。
文摘Rock is geometrically and mechanically multiscale in nature,and the traditional phenomenological laws at the macroscale cannot render a quantitative relationship between microscopic damage of rocks and overall rock structural degradation.This may lead to problems in the evaluation of rock structure stability and safe life.Multiscale numerical modeling is regarded as an effective way to gain insight into factors affecting rock properties from a cross-scale view.This study compiles the history of theoretical developments and numerical techniques related to rock multiscale issues according to different modeling architectures,that is,the homogenization theory,the hierarchical approach,and the concurrent approach.For these approaches,their benefits,drawbacks,and application scope are underlined.Despite the considerable attempts that have been made,some key issues still result in multiple challenges.Therefore,this study points out the perspectives of rock multiscale issues so as to provide a research direction for the future.The review results show that,in addition to numerical techniques,for example,high-performance computing,more attention should be paid to the development of an advanced constitutive model with consideration of fine geometrical descriptions of rock to facilitate solutions to multiscale problems in rock mechanics and rock engineering.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179,11875042,and 12150410309)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900).
文摘The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals,coordinating physiological processes with the 24-hour day–night cycle.Comprising various cell types,the suprachiasmatic nucleus(SCN)integrates environmental signals to maintain complex and robust circadian rhythms.Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function.Synchrony involves coordinated neuronal firing for robust rhythms,while complexity reflects diverse activity patterns and interactions,indicating adaptability.Interestingly,the SCN retains circadian rhythms in vitro,demonstrating intrinsic rhythmicity.This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels,based on Bagrov et al.’s approach.By examining structural complexity and local complexities across scales,we aim to understand how tetrodotoxin,a neurotoxin that inhibits action potentials,affects SCN neurons.Our method captures critical scales in neuronal interactions that traditional methods may overlook.Validation with the Goodwin model confirms the reliability of our observations.By integrating experimental data with theoretical models,this study provides new insights into the effects of tetrodotoxin(TTX)on neuronal complexities,contributing to the understanding of circadian rhythms.
基金supported by Villum Fonden through the Villum Investigator Project“AMSTRAD”(Grant No.VIL54487).
文摘This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process.Building upon established multiscale methodologies,we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints.This approach significantly refines the optimization process,ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints.First,we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials.Then,these local yield-based load factors are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels.This integration is crucial for the practical application of optimized structures in real-world scenarios,where material yield and stability behavior critically influence structural integrity and durability.Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material.Despite the foundational assumption of the separation of scales,the de-homogenized structures,even at relatively coarse length scales,exhibit a remarkably high degree of agreement with the corresponding homogenized predictions.
基金Supported by National Key Research and Development Program of China(Grant No.2023YFB4603803)National Natural Science Foundation of China(Grant No.12374295).
文摘Zirconium alloys are critical materials in nuclear engineering due to their exceptional irradiation resistance and corrosion stability.However,prolonged exposure to extreme operational environments,including a high radiation,mechanical stress,and corrosive media,induces surface degradation mechanisms including stress corrosion cracking and erosion from impurity particle impacts,necessitating advanced surface treatments to improve hardness and corrosion resistance.We explore the application of laser shock peening(LSP)to enhance the surface properties of the Zr4 alloy.Experimental analyses reveal substantial microstructural modifications upon the LSP.The surface grain refinement achieved a maximum reduction of 52.7%in average grain size(from 22.88 to 10.8μm^(2)),accompanied by an increase of 59%in hardness(204 to 326 HV).Additionally,a compressive residual stress layer(approximately-100 MPa)was generated on the treated surface,which reduces the risk of stress corrosion cracking.To elucidate the mechanistic basis of these improvements,a multiscale computational framework was developed,integrating finite-element models for macroscale stress field evolution and molecular dynamics simulations for nanoscale dislocation dynamics.By incorporating the strain rate as a critical variable,this framework bridges microstructure evolution with macroscopic mechanical enhancements.The simulations not only elucidated the dynamic interplay between shockwave-induced plastic deformation and property improvements but also exhibited a good consistency with experimental residual stress profiles.Notably,we propose the application of strain rate-driven multiscale modeling in LSP research for Zr alloys,providing a predictive method to optimize laser parameters for a tailored surface strengthening.This study not only confirms that LSP is a feasible strategy capable of effectively enhancing the comprehensive surface properties of Zr alloys and extending their service life in nuclear environments,but also provides a reliable simulation methodology in the field of laser surface engineering of alloy materials.
基金National Natural Science Foundation of China,Grant/Award Number:62173098,62104047Guangdong Provincial Key Laboratory of Cyber-Physical System,Grant/Award Number:2020B1212060069。
文摘Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB1714600)the National Natural Science Foundation of China(Grant No.52175095)the Young Top-Notch Talent Cultivation Program of Hubei Province of China.
文摘This paper examined how microstructure influences the homogenized thermal conductivity of cellular structures and revealed a surface-induced size-dependent effect.This effect is linked to the porous microstructural features of cellular structures,which stems from the degree of porosity and the distri-bution of the pores.Unlike the phonon-driven surface effect at the nanoscale,the macro-scale surface mechanism in thermal cellular structures is found to be the microstructure-induced changes in the heat conduction path based on fully resolved 3D numerical simulations.The surface region is determined by the microstructure,characterized by the intrinsic length.With the coupling between extrinsic and intrinsic length scales under the surface mechanism,a surface-enriched multiscale method was devel-oped to accurately capture the complex size-dependent thermal conductivity.The principle of scale separation required by classical multiscale methods is not necessary to be satisfied by the proposed multiscale method.The significant potential of the surface-enriched multiscale method was demon-strated through simulations of the effective thermal conductivity of a thin-walled metamaterial struc-ture.The surface-enriched multiscale method offers higher accuracy compared with the classical multiscale method and superior efficiency over high-fidelity finite element methods.
基金funded by the National Natural Science Foundation of China(Grant No.51574225)Shandong Energy Group(Grant No.SNKJ2022BJ03-R28)for Caiping Lu+1 种基金the Research Team on MonitoringActivity Mechanisms of Unnatural Earthquakes of Shandong Earthquake Agency(Grant No.TD202301)for Chengyu Liu.
文摘Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.
基金supported by the National Natural Science Foundation of China (Grant Nos.12102021,12372105,12172026,and 12225201)the Fundamental Research Funds for the Central Universities and the Academic Excellence Foundation of BUAA for PhD Students.
文摘Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.
