With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candida...With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes...Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.展开更多
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior...The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.展开更多
Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive...Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.展开更多
To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and v...To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).展开更多
Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive s...Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.展开更多
Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate ...Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.展开更多
Direct-write atom lithography,one of the potential nanofabrication techniques,is restricted by some difficulties in producing optical masks for the deposition of complex structures.In order to make further progress,a ...Direct-write atom lithography,one of the potential nanofabrication techniques,is restricted by some difficulties in producing optical masks for the deposition of complex structures.In order to make further progress,a structured mirror array is developed to transversely collimate the chromium atomic beam in two dimensions.The best collimation is obtained when the laser red detunes by natural line-width of transition 7S3 → 7P40 of the chromium atom.The collimation ratio is 0.45 vertically（in x axis）,and it is 0.55 horizontally（in y axis）.The theoretical model is also simulated,and success of our structured mirror array is achieved.展开更多
Synthetic two-dimensional（2 D） polymers have totally different topology structures compared with traditional linear or branched polymers. The peculiar 2 D structures bring superior properties. Although, from linear ...Synthetic two-dimensional（2 D） polymers have totally different topology structures compared with traditional linear or branched polymers. The peculiar 2 D structures bring superior properties. Although, from linear to 2 D polymers, the study of these new materials is still in its infancy, they already show potential applications especially in optoelectronics, membranes, energy storage and catalysis, etc. In this review, we summarize the recent progress of the 2 D materials from three respects：（1） Chemistry—different types of polymerization reactions or supramolecular assembly to construct the 2 D networks were described;（2） Preparation methods—surface science, crystal engineering approaches and solution synthesis were introduced;（3） Functionalization and some early applications.展开更多
Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk...Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk materials owing to the atomic nature of 2D materials.Plasmonic nanostructures are usually integrated with 2D materials to enhance the light–matter interactions,offering great opportunities for both fundamental research and technological applications.Nanoparticle-on-mirror(NPo M)structures with extremely confined optical fields are highly desired in this aspect.In addition,2D materials provide a good platform for the study of plasmonic fields with subnanometer resolution and quantum plasmonics down to the characteristic length scale of a single atom.A focused and up-to-date review article is highly desired for a timely summary of the progress in this rapidly growing field and to encourage more research efforts in this direction.In this review,we will first introduce the basic concepts of plasmonic modes in NPo M structures.Interactions between plasmons and quasi-particles in 2D materials,e.g.,excitons and phonons,from weak to strong coupling and potential applications will then be described in detail.Related phenomena in subnanometer metallic gaps separated by 2D materials,such as quantum tunneling,will also be touched.We will finally discuss phenomena and physical processes that have not been understood clearly and provide an outlook for future research.We believe that the hybrid systems of2D materials and NPo M structures will be a promising research field in the future.展开更多
Two-dimensional function photonic crystals, in which the dielectric constants of medium columns are the functions of space coordinates , are proposed and studied numerically. The band gaps structures of the photonic c...Two-dimensional function photonic crystals, in which the dielectric constants of medium columns are the functions of space coordinates , are proposed and studied numerically. The band gaps structures of the photonic crystals for TE and TM waves are different from the two-dimensional conventional photonic crystals. Some absolute band gaps and semiDirac points are found. When the medium column radius and the function form of the dielectric constant are modulated, the numbers, width, and position of band gaps are changed, and the semi-Dirac point can either occur or disappear. Therefore,the special band gaps structures and semi-Dirac points can be achieved through the modulation on the two-dimensional function photonic crystals. The results will provide a new design method of optical devices based on the two-dimensional function photonic crystals.展开更多
The algebraic solitary wave and its associated eigenvalue problem in a deep stratified fluid with a free surface and a shallow upper layer were studied. And its vertical structure was examined. An exact solution for t...The algebraic solitary wave and its associated eigenvalue problem in a deep stratified fluid with a free surface and a shallow upper layer were studied. And its vertical structure was examined. An exact solution for the derived 2D Benjamin-Ono equation was obtained, and physical explanation was given with the corresponding dispersion relation. As a special case, the vertical structure of the weakly nonlinear internal wave for the Holmboe density distribution was numerically investigated, and the propagating mechanism of the internal wave was studied by using the ray theory.展开更多
Effect of network structure on plasticity and fracture mode of Zr？Al？Ni？Cu bulk metallic glasses （BMGs） was investigated. The microstructures of transversal and longitudinal sections were exposed by chemical etch...Effect of network structure on plasticity and fracture mode of Zr？Al？Ni？Cu bulk metallic glasses （BMGs） was investigated. The microstructures of transversal and longitudinal sections were exposed by chemical etching and observed by scanning electron microscopy （SEM）. The mechanical properties were examined by room-temperature uniaxial compression test. The results show that both plasticity and fracture mode are significantly affected by the network structure and the alteration occurs when the size of the network structure reaches up to a critical value. When the cell size （dc） of the network structure is ~3μm, Zr-based BMGs characterize in plasticity that decreases with increasingdc. The fracture mode gradually transforms from single 45° shear fracture to double 45° shear fracture and then cleavage fracture with increasingdc. In addition, the mechanisms of the transition of the plasticity and the fracture mode for these Zr-based BMGs are also discussed.展开更多
The multilayered structure of the European airport network（EAN）,composed of connections and flights between European cities,is analyzed through the k-core decomposition of the connections network.This decomposition ...The multilayered structure of the European airport network（EAN）,composed of connections and flights between European cities,is analyzed through the k-core decomposition of the connections network.This decomposition allows to identify the core,bridge and periphery layers of the EAN.The core layer includes the best-connected cities,which include important business air traffic destinations.The periphery layer includes cities with lesser connections,which serve low populated areas where air travel is an economic alternative.The remaining cities form the bridge of the EAN,including important leisure travel origins and destinations.The multilayered structure of the EAN affects network robustness,as the EAN is more robust to isolation of nodes of the core,than to the isolation of a combination of core and bridge nodes.展开更多
基金the financial support provided by the National Natural Science Foundation of China(grant no.22373032)the open research fund of Songshan Lake Materials Laboratory(grant no.2023SLABFK06)。
文摘With the rapid growth of technologies requiring high-power energy storage,achieving long-term cyclic stability under ultra-high current density is a key challenge.Aqueous zinc-ion batteries(AZIBs)are promising candidates due to their intrinsic safety and low cost,but they suffer from severe interfacial instability at rates exceeding 10 mA cm^(-2),which drastically shortens their cycle life.Inspired by theoretical calculations,triglyme(TGDE)additive with strong electron-donating groups into Zn(OTf)_(2) electrolytes effectively disrupts the hydrogen-bond network among free water molecules,while the weak coordination of TGDE with Zn^(2+)promotes the entry of OTf-into the primary Zn^(2+)solvated sheath,thus decreasing the coordination number of water with Zn^(2+).As such,the hydrogen-bond network and the bulk solvated structure are reconstructed with better stability.Moreover,the strong adsorption of TGDE lying on the Zn(002)surface would induce Zn depositions along(002)together with the reduced exposed surface,further effectively inhibiting side reactions.Likewise,TGDE electrolyte induces the formation of such ZnF_(2)-ZnS dual-layer solid electrolyte interface(SEI)with superior chemical stability and ionic conductivity,thereby regulating Zn^(2+)flux with dendrite-free depositions.Based on this electrolyte,Zn‖Zn cells can be stably cycled for 1300 h at a limit of 10 mA cm^(-2) and 10 mAh cm^(-2).The assembled Zn‖V_(2)O_(5) full cells still maintain 99.9%capacity retention after 1000 cycles at 10 A g^(-1).This work provides a feasible approach for designing aqueous electrolytes to reconstruct the hydrogen-bond network and solvated structure,which can be extended to the applications of high-rate and high-temperature scenarios.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金Supported by the National Key Research Program(No.2024-1129-954-112)National Natural Science Foundation of China(No.52372033)Guangxi Science and Technology Major Program(No.AA24263054)。
文摘Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.
基金supported by the National Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by the National Natural Science Foundation of China(No.22373024,22463006,and 52463015)the joint fund between the Gansu Provincial Science and Technology Plan Project(Natural Science Foundation)(No.23JRRA794)the Open Research Fund of the Songshan Lake Materials Laboratory(No.2023SLABFK11)。
文摘The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.
基金Supported by the National Natural Science Foundation of China(U23A20595,52034010,52288101)National Key Research and Development Program of China(2022YFE0203400)+1 种基金Shandong Provincial Natural Science Foundation(ZR2024ZD17)Fundamental Research Funds for the Central Universities(23CX10004A).
文摘Existing imaging techniques cannot simultaneously achieve high resolution and a wide field of view,and manual multi-mineral segmentation in shale lacks precision.To address these limitations,we propose a comprehensive framework based on generative adversarial network(GAN)for characterizing pore structure properties of shale,which incorporates image augmentation,super-resolution reconstruction,and multi-mineral auto-segmentation.Using real 2D and 3D shale images,the framework was assessed through correlation function,entropy,porosity,pore size distribution,and permeability.The application results show that this framework enables the enhancement of 3D low-resolution digital cores by a scale factor of 8,without paired shale images,effectively reconstructing the unresolved fine-scale pores under a low resolution,rather than merely denoising,deblurring,and edge clarification.The trained GAN-based segmentation model effectively improves manual multi-mineral segmentation results,resulting in a strong resemblance to real samples in terms of pore size distribution and permeability.This framework significantly improves the characterization of complex shale microstructures and can be expanded to other heterogeneous porous media,such as carbonate,coal,and tight sandstone reservoirs.
基金financially supported by the National Key R&D Program of China(No.2022YFB3707405)the National Natural Science Foundation of China(Nos.U22A20113,52171137,52071116)+1 种基金Heilongjiang Provincial Natural Science Foundation,China(No.TD2020E001)Heilongjiang Touyan Team Program,China.
文摘To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).
基金supported by Beijing High Level Public Health Technology Talent Construction Project(Discipline Backbone-01-028)the Beijing Municipal Science&Technology Commission(No.Z181100001518005)+2 种基金the Capital's Funds for Health Improvement and Research(CFH 2024-2-1174)the University of Macao(MYRG-GRG2023-00141-FHS,CPG2025-00021-FHS)the Science and Technology Plan Foundation of Guangzhou(No.202201011663).
文摘Background Post-stroke depression(PSD)is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life(QoL).To date,few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.Aims This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.Methods This study was based on the 2017–2018 collection of data from a large national survey in China.Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale(CESD),while QoL was measured with the World Health Organization Quality of Life-brief version.Network analysis was employed to explore the structure of PSD,using expected influence(EI)to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.Results A total of 1123 stroke survivors were included,with an overall prevalence of depression of 34.3%(n=385;95%confidence interval 31.5%to 37.2%).In the network model of depression,the most central symptoms were CESD3(‘feeling blue/depressed’,EI:1.180),CESD6(‘feeling nervous/fearful’,EI:0.864)and CESD8(‘loneliness’,EI:0.843).In addition,CESD5(‘hopelessness’,EI:−0.195),CESD10(‘sleep disturbances’,EI:−0.169)and CESD4(‘everything was an effort’,EI:−0.150)had strong negative associations with QoL.Conclusion This study found that PSD was common among older Chinese stroke survivors.Given its negative impact on QoL,appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.
基金supported by the Natural Science Foundation of China(No.U22A2099)the Innovation Project of Guangxi Graduate Education(YCBZ2023130).
文摘Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.
基金Project supported by the Shanghai Nanoscience Foundation,China (Grant Nos. 0852nm07000 and 0952nm07000)the National Natural Science Foundation of China (Grant Nos. 10804084 and 91123022)+1 种基金the National Key Technology R & D Program,China (Grant No. 2006BAF06B08)the Specialized Research Fund for the Doctoral Program of Ministry of High Education of China (Grant No. 200802471008)
文摘Direct-write atom lithography,one of the potential nanofabrication techniques,is restricted by some difficulties in producing optical masks for the deposition of complex structures.In order to make further progress,a structured mirror array is developed to transversely collimate the chromium atomic beam in two dimensions.The best collimation is obtained when the laser red detunes by natural line-width of transition 7S3 → 7P40 of the chromium atom.The collimation ratio is 0.45 vertically（in x axis）,and it is 0.55 horizontally（in y axis）.The theoretical model is also simulated,and success of our structured mirror array is achieved.
基金financially supported by the National Natural Science Foundation of China(No.21604046)the National Young Thousand Talents Program,Shandong Provincial Natural Science Foundation,China(No.ZR2016XJ004)
文摘Synthetic two-dimensional（2 D） polymers have totally different topology structures compared with traditional linear or branched polymers. The peculiar 2 D structures bring superior properties. Although, from linear to 2 D polymers, the study of these new materials is still in its infancy, they already show potential applications especially in optoelectronics, membranes, energy storage and catalysis, etc. In this review, we summarize the recent progress of the 2 D materials from three respects：（1） Chemistry—different types of polymerization reactions or supramolecular assembly to construct the 2 D networks were described;（2） Preparation methods—surface science, crystal engineering approaches and solution synthesis were introduced;（3） Functionalization and some early applications.
基金supported by the National Natural Science Foundation of China(62205183)the Research Grants Council of Hong Kong(ANR/RGC,Ref.No.A-CUHK404/21).
文摘Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk materials owing to the atomic nature of 2D materials.Plasmonic nanostructures are usually integrated with 2D materials to enhance the light–matter interactions,offering great opportunities for both fundamental research and technological applications.Nanoparticle-on-mirror(NPo M)structures with extremely confined optical fields are highly desired in this aspect.In addition,2D materials provide a good platform for the study of plasmonic fields with subnanometer resolution and quantum plasmonics down to the characteristic length scale of a single atom.A focused and up-to-date review article is highly desired for a timely summary of the progress in this rapidly growing field and to encourage more research efforts in this direction.In this review,we will first introduce the basic concepts of plasmonic modes in NPo M structures.Interactions between plasmons and quasi-particles in 2D materials,e.g.,excitons and phonons,from weak to strong coupling and potential applications will then be described in detail.Related phenomena in subnanometer metallic gaps separated by 2D materials,such as quantum tunneling,will also be touched.We will finally discuss phenomena and physical processes that have not been understood clearly and provide an outlook for future research.We believe that the hybrid systems of2D materials and NPo M structures will be a promising research field in the future.
基金Project supported by the National Natural Science Foundations of China(Grant No.61275047)the Research Project of Chinese Ministry of Education(Grant No.213009A)the Scientific and Technological Development Foundation of Jilin Province,China(Grant No.20130101031JC)
文摘Two-dimensional function photonic crystals, in which the dielectric constants of medium columns are the functions of space coordinates , are proposed and studied numerically. The band gaps structures of the photonic crystals for TE and TM waves are different from the two-dimensional conventional photonic crystals. Some absolute band gaps and semiDirac points are found. When the medium column radius and the function form of the dielectric constant are modulated, the numbers, width, and position of band gaps are changed, and the semi-Dirac point can either occur or disappear. Therefore,the special band gaps structures and semi-Dirac points can be achieved through the modulation on the two-dimensional function photonic crystals. The results will provide a new design method of optical devices based on the two-dimensional function photonic crystals.
文摘The algebraic solitary wave and its associated eigenvalue problem in a deep stratified fluid with a free surface and a shallow upper layer were studied. And its vertical structure was examined. An exact solution for the derived 2D Benjamin-Ono equation was obtained, and physical explanation was given with the corresponding dispersion relation. As a special case, the vertical structure of the weakly nonlinear internal wave for the Holmboe density distribution was numerically investigated, and the propagating mechanism of the internal wave was studied by using the ray theory.
基金Projects(50874045,51301194)supported by the National Natural Science Foundation of ChinaProject(2144057)supported by the Natural Science Foundation of Beijing Municipality,China
文摘Effect of network structure on plasticity and fracture mode of Zr？Al？Ni？Cu bulk metallic glasses （BMGs） was investigated. The microstructures of transversal and longitudinal sections were exposed by chemical etching and observed by scanning electron microscopy （SEM）. The mechanical properties were examined by room-temperature uniaxial compression test. The results show that both plasticity and fracture mode are significantly affected by the network structure and the alteration occurs when the size of the network structure reaches up to a critical value. When the cell size （dc） of the network structure is ~3μm, Zr-based BMGs characterize in plasticity that decreases with increasingdc. The fracture mode gradually transforms from single 45° shear fracture to double 45° shear fracture and then cleavage fracture with increasingdc. In addition, the mechanisms of the transition of the plasticity and the fracture mode for these Zr-based BMGs are also discussed.
文摘The multilayered structure of the European airport network（EAN）,composed of connections and flights between European cities,is analyzed through the k-core decomposition of the connections network.This decomposition allows to identify the core,bridge and periphery layers of the EAN.The core layer includes the best-connected cities,which include important business air traffic destinations.The periphery layer includes cities with lesser connections,which serve low populated areas where air travel is an economic alternative.The remaining cities form the bridge of the EAN,including important leisure travel origins and destinations.The multilayered structure of the EAN affects network robustness,as the EAN is more robust to isolation of nodes of the core,than to the isolation of a combination of core and bridge nodes.
