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
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
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
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 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
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
Porous metals fabricated via three-dimensional(3D)printing have attracted extensive attention in many fields owing to their open pores and customization potential.However,dense internal structures produced by the powd...Porous metals fabricated via three-dimensional(3D)printing have attracted extensive attention in many fields owing to their open pores and customization potential.However,dense internal structures produced by the powder bed fusion technique fails to meet the feature of porous materials in scenarios that demand large specific surface areas.Herein,we propose a strategy for 3D printing of titanium scaffolds featuring multiscale porous internal structures via powder modification and digital light processing(DLP).After modification,the titanium powders were composited with acrylic resin and maintained spherical shapes.Compared with the raw powder slurries,the modified powder slurries exhibited higher stability and preferable curing characteristics,and the depth sensitivity of the modified powder slurries with 45 vol%solid loading increased by approximately 72%.Green scaffolds were subsequently printed from the slurries with a solid loading reaching 45 vol%via DLP 3D printing.The scaffolds had macropores(pore diameters of approximately 1 mm)and internal open micropores(pore diameters of approximately 5.7-13.0μm)after sintering.Additionally,these small-featured(approximately 320μm)scaffolds retained sufficient compressive strength((70.01±3.53)MPa)even with high porosity(approximately 73.95%).This work can facilitate the fabrication of multiscale porous metal scaffolds with high solid loading slurries,offering potential for applications requiring high specific surface area ratios.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
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
Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep lear...Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep learning(DL)approaches to automate cataract classification more precisely,leading to the development of the Multiscale Parallel Feature Aggregation Network with Attention Fusion(MPFAN-AF).Focused on improving a model’s performance,this approach applies multiscale feature extraction,parallel feature fusion,along with attention-based fusion to sharpen its focus on salient features,which are crucial in detecting cataracts.Methods:Coarse-level features are captured through the application of convolutional layers,and these features undergo refinement through layered kernels of varying sizes.Moreover,this method captures all the diverse representations of cataracts accurately by parallel feature aggregation.Utilizing the Cataract Eye Dataset available on Kaggle,containing 612 labelled images of eyes with and without cataracts proportionately(normal vs.pathological),this model was trained and tested.Results:Results using the proposed model reflect greater precision over traditional convolutional neural networks(CNNs)models,achieving a classification accuracy of 97.52%.Additionally,the model demonstrated exceptional performance in classification tasks.The ablation studies validated that all applications added value to the prediction process,particularly emphasizing the attention fusion module.Conclusion:The MPFAN-AF model demonstrates high efficiency together with interpretability because it shows promise as an integration solution for real-time mobile cataract detection screening systems.Standard performance indicators indicate that AI-based ophthalmology tools have a promising future for use in remote conditions that lack medical resources.展开更多
The maximal force a muscle can exert depends on its length,which has been explained by the sliding filament theory on the sarcomere level.1 A longer muscle can act over a greater range of motion and has more sarcomere...The maximal force a muscle can exert depends on its length,which has been explained by the sliding filament theory on the sarcomere level.1 A longer muscle can act over a greater range of motion and has more sarcomeres in series,which increases its capacity to produce force at a specific muscle contraction velocity because each sarcomere contracts at a lower velocity.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 sandy conglomerate reservoir is tight and exhibits strong heterogeneity,rendering conventional water flooding and gas drive methods inefficient and challenging for the effective development.CO_(2) water alternatin...The sandy conglomerate reservoir is tight and exhibits strong heterogeneity,rendering conventional water flooding and gas drive methods inefficient and challenging for the effective development.CO_(2) water alternating gas(CO_(2)-WAG)injection as an effective enhanced oil recovery(EOR)method has been applied in heterogeneous reservoirs.Simultaneously,it facilitates carbon sequestration,contributing to the green and low-carbon transformation of energy.However,the EOR mechanisms and influencing factors are still unclear for the development of heterogeneous sandy conglomerate reservoirs.In this paper,we conducted core flooding experiments combined nuclear magnetic resonance(NMR)technology to investigate EOR mechanisms of the CO_(2)-WAG injection on the multiscale(reservoir,layer,and pore).The study compared multiscale oil recovery in sandy conglomerate reservoirs under both miscible and immiscible conditions,while also analyzing the effects of water-gas ratio and injection rate.In the immiscible state,the CO_(2)-WAG displacement achieves an oil recovery of approximately 22.95%,representing a 7.82%increase compared to CO_(2) flooding.This method effectively inhibits CO_(2) breakthrough in high-permeability layers while enhancing the oil recovery in medium-and low-permeability layers.Furthermore,CO_(2)-WAG displacement improves the microscopic oil displacement efficiency within mesopores and micropores.As the water-gas ratio increases,the total oil recovery rises,with enhanced oil recovery in low-permeability layers and micropores.Moreover,a gradual increase in injection rate leads to a decrease in total oil recovery,but it leads to an increase in oil recovery from low-permeability sandy conglomerate layers and micropores.In the miscible state,the displacement efficiency of CO_(2)-WAG is significantly enhanced,the total oil recovery three times higher than that in the immiscible state.In particular,the oil recovery from low permeability layers and micropores has further improved.Additionally,experimental results indicate that parameters such as water-gas ratio and injection rate do not significantly affect the oil recovery of CO_(2)-WAG miscible displacement.Therefore,maintaining the reservoir pressure above the minimum miscible pressure is the key to maximizing ultimate recovery factor in these reservoirs.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
Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like pop...Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.展开更多
Viscoelastic flows play an important role in numerous engineering fields,and the multiscale algorithms for simulating viscoelastic flows have received significant attention in order to deepen our understanding of the ...Viscoelastic flows play an important role in numerous engineering fields,and the multiscale algorithms for simulating viscoelastic flows have received significant attention in order to deepen our understanding of the nonlinear dynamic behaviors of viscoelastic fluids.However,traditional grid-based multiscale methods are confined to simple viscoelastic flows with short relaxation time,and there is a lack of uniform multiscale scheme available for coupling different solvers in the simulations of viscoelastic fluids.In this paper,a universal multiscale method coupling an improved smoothed particle hydrodynamics(SPH)and multiscale universal interface(MUI)library is presented for viscoelastic flows.The proposed multiscale method builds on an improved SPH method and leverages the MUI library to facilitate the exchange of information among different solvers in the overlapping domain.We test the capability and flexibility of the presented multiscale method to deal with complex viscoelastic flows by solving different multiscale problems of viscoelastic flows.In the first example,the simulation of a viscoelastic Poiseuille flow is carried out by two coupled improved SPH methods with different spatial resolutions.The effects of exchanging different physical quantities on the numerical results in both the upper and lower domains are also investigated as well as the absolute errors in the overlapping domain.In the second example,the complex Wannier flow with different Weissenberg numbers is further simulated by two improved SPH methods and coupling the improved SPH method and the dissipative particle dynamics(DPD)method.The numerical results show that the physical quantities for viscoelastic flows obtained by the presented multiscale method are in consistence with those obtained by a single solver in the overlapping domain.Moreover,transferring different physical quantities has an important effect on the numerical results.展开更多
In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in th...In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV,focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022,an enhanced western Pacific subtropical high(WPSH)and anomalous double blockings over the Ural Mountains and Sea of Okhotsk,respectively,were associated with the persistently anomalous high pressure over the YRV,leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the double-blocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale,the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore,the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.展开更多
Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial pertur...Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.展开更多
Multi-scale lamellar structure significantly improves toughness of Ti_(2)AlNb based alloys,which are inher-ently brittle intermetallics,without compromising their strength.This structure was achieved through-B2-transu...Multi-scale lamellar structure significantly improves toughness of Ti_(2)AlNb based alloys,which are inher-ently brittle intermetallics,without compromising their strength.This structure was achieved through-B2-transus-forging(TBTF)combined with O+B2 two-phase region heat treatments.Various types of multi-scale lamellar structures were obtained by controlling the cooling rate after TBTF.These variations were mainly attributed to differences in the distribution,content,and size of the thick lamellar O phase and the size and crystallographic orientation of B2 grain.By analyzing the microstructural characteristics and crystallographic orientation near the crack propagation path,it was found that the crack propaga-tion resistance of thick lamellae,sub grain and grain boundaries(GBs)O phase increased sequentially,accompanied by more tortuous crack propagation path.Moreover,B2 grains with high misorientation significantly deflected the crack propagation by cleavage ridges between adjoining cleavage planes.Addi-tionally,the development of numerous secondary cleavage ridges,resulting from the transition through varying secondary cleavage planes in distinct sub B2 grains,further hindered the quick propagation of cracks.It was clarified that the cleavage planes were dominantly belonging to{110}.These findings pro-vided valuable guidance for the design of damage tolerance strategies for Ti_(2)AlNb-based intermetallics.展开更多
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 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supported by the National Natural Science Foundation of China(Nos.52405326,52105588 and 52075421)the Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-222 and 2023-YBSF-276)。
文摘Porous metals fabricated via three-dimensional(3D)printing have attracted extensive attention in many fields owing to their open pores and customization potential.However,dense internal structures produced by the powder bed fusion technique fails to meet the feature of porous materials in scenarios that demand large specific surface areas.Herein,we propose a strategy for 3D printing of titanium scaffolds featuring multiscale porous internal structures via powder modification and digital light processing(DLP).After modification,the titanium powders were composited with acrylic resin and maintained spherical shapes.Compared with the raw powder slurries,the modified powder slurries exhibited higher stability and preferable curing characteristics,and the depth sensitivity of the modified powder slurries with 45 vol%solid loading increased by approximately 72%.Green scaffolds were subsequently printed from the slurries with a solid loading reaching 45 vol%via DLP 3D printing.The scaffolds had macropores(pore diameters of approximately 1 mm)and internal open micropores(pore diameters of approximately 5.7-13.0μm)after sintering.Additionally,these small-featured(approximately 320μm)scaffolds retained sufficient compressive strength((70.01±3.53)MPa)even with high porosity(approximately 73.95%).This work can facilitate the fabrication of multiscale porous metal scaffolds with high solid loading slurries,offering potential for applications requiring high specific surface area ratios.
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.
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.
文摘Background:Early and accurate diagnosis of cataracts,which ranks among the leading preventable causes of blindness,is critical to securing positive outcomes for patients.Recently,eye image analyses have used deep learning(DL)approaches to automate cataract classification more precisely,leading to the development of the Multiscale Parallel Feature Aggregation Network with Attention Fusion(MPFAN-AF).Focused on improving a model’s performance,this approach applies multiscale feature extraction,parallel feature fusion,along with attention-based fusion to sharpen its focus on salient features,which are crucial in detecting cataracts.Methods:Coarse-level features are captured through the application of convolutional layers,and these features undergo refinement through layered kernels of varying sizes.Moreover,this method captures all the diverse representations of cataracts accurately by parallel feature aggregation.Utilizing the Cataract Eye Dataset available on Kaggle,containing 612 labelled images of eyes with and without cataracts proportionately(normal vs.pathological),this model was trained and tested.Results:Results using the proposed model reflect greater precision over traditional convolutional neural networks(CNNs)models,achieving a classification accuracy of 97.52%.Additionally,the model demonstrated exceptional performance in classification tasks.The ablation studies validated that all applications added value to the prediction process,particularly emphasizing the attention fusion module.Conclusion:The MPFAN-AF model demonstrates high efficiency together with interpretability because it shows promise as an integration solution for real-time mobile cataract detection screening systems.Standard performance indicators indicate that AI-based ophthalmology tools have a promising future for use in remote conditions that lack medical resources.
文摘The maximal force a muscle can exert depends on its length,which has been explained by the sliding filament theory on the sarcomere level.1 A longer muscle can act over a greater range of motion and has more sarcomeres in series,which increases its capacity to produce force at a specific muscle contraction velocity because each sarcomere contracts at a lower velocity.
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supported by the National Natural Science Foundation of China(No.52204065,No.52374063)the Independent Innovation Research Project of China University of Petroleum(No.24CX06017A)Shandong Provincial Natural Science Foundation,China(No.ZR2024QE075,No.ZR2023ME049)。
文摘The sandy conglomerate reservoir is tight and exhibits strong heterogeneity,rendering conventional water flooding and gas drive methods inefficient and challenging for the effective development.CO_(2) water alternating gas(CO_(2)-WAG)injection as an effective enhanced oil recovery(EOR)method has been applied in heterogeneous reservoirs.Simultaneously,it facilitates carbon sequestration,contributing to the green and low-carbon transformation of energy.However,the EOR mechanisms and influencing factors are still unclear for the development of heterogeneous sandy conglomerate reservoirs.In this paper,we conducted core flooding experiments combined nuclear magnetic resonance(NMR)technology to investigate EOR mechanisms of the CO_(2)-WAG injection on the multiscale(reservoir,layer,and pore).The study compared multiscale oil recovery in sandy conglomerate reservoirs under both miscible and immiscible conditions,while also analyzing the effects of water-gas ratio and injection rate.In the immiscible state,the CO_(2)-WAG displacement achieves an oil recovery of approximately 22.95%,representing a 7.82%increase compared to CO_(2) flooding.This method effectively inhibits CO_(2) breakthrough in high-permeability layers while enhancing the oil recovery in medium-and low-permeability layers.Furthermore,CO_(2)-WAG displacement improves the microscopic oil displacement efficiency within mesopores and micropores.As the water-gas ratio increases,the total oil recovery rises,with enhanced oil recovery in low-permeability layers and micropores.Moreover,a gradual increase in injection rate leads to a decrease in total oil recovery,but it leads to an increase in oil recovery from low-permeability sandy conglomerate layers and micropores.In the miscible state,the displacement efficiency of CO_(2)-WAG is significantly enhanced,the total oil recovery three times higher than that in the immiscible state.In particular,the oil recovery from low permeability layers and micropores has further improved.Additionally,experimental results indicate that parameters such as water-gas ratio and injection rate do not significantly affect the oil recovery of CO_(2)-WAG miscible displacement.Therefore,maintaining the reservoir pressure above the minimum miscible pressure is the key to maximizing ultimate recovery factor in these reservoirs.
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funded by The Third Comprehensive Scientific Investigation in Xinjiang(Grant No.2021xjkk1001)Program of National Social Science Foundation of China(Grant No.22BJL061)+1 种基金Major Project of Xinjiang Social Science Foundation(Grant No.21AZD008)The National Natural Science Foundation of China(Grant No.41461035).
文摘Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress.Traditional evaluation methods focus on basic economic metrics like population and capital,which may not fully reflect the complexities of economic activities.Nighttime light(NTL)has been validated as an alternative indicator for regional economic development,yet limitations persist in its evaluation.This study integrates OpenStreetMap(OSM)data and NTL data,providing a novel data integration approach for evaluating economic development.The study uses mainland of China as a case,applying ordinary least squares(OLS)and geographically weighted regression(GWR)to evaluate OSM and NTL data across provincial,municipal,and county levels.It compares OSM,NTL,and their combined use,providing key empirical insights for enhancing data fusion models.The study results reveal:(1)NTL data is more accurate for provincial-level economic activity,while OSM data excels at the county level.(2)GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales.(3)Through the integration of both datasets,it is observed that,compared to single-data modeling,the performance is enhanced at the city scale and county scale.The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial,municipal,and county levels.The study offers a straightforward and efficient approach to data integration.The findings offer new research perspectives and scientific support for sustainable regional economic growth,particularly valuable in data-scarce,underdeveloped areas.
基金Project supported by the National Natural Science Foundation of China(No.52109068)the Water Conservancy Technology Project of Jiangsu Province of China(No.2022060)。
文摘Viscoelastic flows play an important role in numerous engineering fields,and the multiscale algorithms for simulating viscoelastic flows have received significant attention in order to deepen our understanding of the nonlinear dynamic behaviors of viscoelastic fluids.However,traditional grid-based multiscale methods are confined to simple viscoelastic flows with short relaxation time,and there is a lack of uniform multiscale scheme available for coupling different solvers in the simulations of viscoelastic fluids.In this paper,a universal multiscale method coupling an improved smoothed particle hydrodynamics(SPH)and multiscale universal interface(MUI)library is presented for viscoelastic flows.The proposed multiscale method builds on an improved SPH method and leverages the MUI library to facilitate the exchange of information among different solvers in the overlapping domain.We test the capability and flexibility of the presented multiscale method to deal with complex viscoelastic flows by solving different multiscale problems of viscoelastic flows.In the first example,the simulation of a viscoelastic Poiseuille flow is carried out by two coupled improved SPH methods with different spatial resolutions.The effects of exchanging different physical quantities on the numerical results in both the upper and lower domains are also investigated as well as the absolute errors in the overlapping domain.In the second example,the complex Wannier flow with different Weissenberg numbers is further simulated by two improved SPH methods and coupling the improved SPH method and the dissipative particle dynamics(DPD)method.The numerical results show that the physical quantities for viscoelastic flows obtained by the presented multiscale method are in consistence with those obtained by a single solver in the overlapping domain.Moreover,transferring different physical quantities has an important effect on the numerical results.
基金the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004)the National Natural Science Foundation of China(Grant No.42175056)+3 种基金the Natural Science Foundation of Shanghai(Grant No.21ZR1457600)Review and Summary Project of China Meteorological Administration(Grant No.FPZJ2023-044)the China Meteorological Administration Innovation and Development Project(Grant No.CXFZ2022J009)the Key Innovation Team of Climate Prediction of the China Meteorological Administration(Grant No.CMA2023ZD03).
文摘In the summer of 2022,China(especially the Yangtze River Valley,YRV)suffered its strongest heatwave(HW)event since 1961.In this study,we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV,focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022,an enhanced western Pacific subtropical high(WPSH)and anomalous double blockings over the Ural Mountains and Sea of Okhotsk,respectively,were associated with the persistently anomalous high pressure over the YRV,leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the double-blocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale,the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore,the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.
基金supported by the Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213)the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902)the National Science Foundation for Young Scholars (Grant No. 42205166)。
文摘Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
基金supported by the National Natural Science Foundation of China(No.52275380).
文摘Multi-scale lamellar structure significantly improves toughness of Ti_(2)AlNb based alloys,which are inher-ently brittle intermetallics,without compromising their strength.This structure was achieved through-B2-transus-forging(TBTF)combined with O+B2 two-phase region heat treatments.Various types of multi-scale lamellar structures were obtained by controlling the cooling rate after TBTF.These variations were mainly attributed to differences in the distribution,content,and size of the thick lamellar O phase and the size and crystallographic orientation of B2 grain.By analyzing the microstructural characteristics and crystallographic orientation near the crack propagation path,it was found that the crack propaga-tion resistance of thick lamellae,sub grain and grain boundaries(GBs)O phase increased sequentially,accompanied by more tortuous crack propagation path.Moreover,B2 grains with high misorientation significantly deflected the crack propagation by cleavage ridges between adjoining cleavage planes.Addi-tionally,the development of numerous secondary cleavage ridges,resulting from the transition through varying secondary cleavage planes in distinct sub B2 grains,further hindered the quick propagation of cracks.It was clarified that the cleavage planes were dominantly belonging to{110}.These findings pro-vided valuable guidance for the design of damage tolerance strategies for Ti_(2)AlNb-based intermetallics.
