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
We successfully incorporated phenyl groups into a small-molecule quaternary ammonium cross-linker and synthesized cross-linked polybenzimidazole membranes via a one-step cross-linking process.Compared with conventiona...We successfully incorporated phenyl groups into a small-molecule quaternary ammonium cross-linker and synthesized cross-linked polybenzimidazole membranes via a one-step cross-linking process.Compared with conventional quaternary ammonium-crosslinked benzimidazole membranes,the introduction of phenyl groups significantly increases the free volume within the membrane.After phosphoric acid doping,the benzimidazole membrane with larger free volume retains more phosphoric acid compared to conventional quaternary ammonium-crosslinked membranes,forming an extensive hydrogen-bonding network that effectively enhances its anhydrous proton conductivity.The anhydrous proton conductivity reaches 91 mS·cm^(-1)at 160℃,substantially higher than that of conventional quaternary ammonium-crosslinked membranes with the same mass fraction.Benefiting from the improved conductivity,the membrane electrode assembly exhibits reduced ohmic polarization,achieving a peak power density of 792 mW·cm^(-2)at 160℃.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
Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has ...Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.展开更多
Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homog...Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homogenous distribution of nanomaterials,which is essential for enhancing their performance while also reducing production costs.In this work,we achieve uniform and stable dispersion of various nano-materials through the confinement effect generated by the stereocomplex cross-linked network formed by the combination of poly(L-lactic)acid and poly(D-lactic)acid.The unique confinement effect of poly-lactic acid(PLA)isomers is universal and significantly enhances the dispersion of nanomaterials in both PLA solutions and films.To demonstrate the efficacy of our approach,we disperse aggregation-induced emission(AIE)molecules within PLA,which leads to the production of PLA films exhibiting improved fluorescence property.This work provides an effective solution for the preparation of nanocomposite ma-terials that are both high-performing and cost-efficient.展开更多
Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network...Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.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
Cross-linkedβ-cyclodextrin polymer/Fe3O4 composite nanoparticles with core-shell structures were prepared via cross linking reaction on the surface of carboxymethylβ-cyclodextrin（CM-β-CD） modified Fe3O4 nanoparti...Cross-linkedβ-cyclodextrin polymer/Fe3O4 composite nanoparticles with core-shell structures were prepared via cross linking reaction on the surface of carboxymethylβ-cyclodextrin（CM-β-CD） modified Fe3O4 nanoparticles inβ-cyclodextrin alkaline solution by using epichlorohydrin as crosslinking agent.The morphology,structure and magnetic properties of the prepared composite nanoparticles were investigated by transmission electron microscopy（TEM）,Fourier transform infrared（FTIR） spectrometry,X-ray diffraction（XRD） measurement,thermogravimetric analysis（TGA） and Vibrating sample magnetometry （VSM）,respectively.展开更多
Cross-linked polyethylene (PEX) pipes used in hot water supply are required for high mechanical strength and high creep resistance at high temperature. Especially PEX-a pipes which are made by peroxide cross-linking h...Cross-linked polyethylene (PEX) pipes used in hot water supply are required for high mechanical strength and high creep resistance at high temperature. Especially PEX-a pipes which are made by peroxide cross-linking have better performance, such as creep resistance and thermal shock resistance than the pipes made by the other cross-linking method. Because the PEX-a pipes indicate the higher cross-link degree as compared with the other PEX pipes. In this study, the PEX-a pipes which were mixed with several stabilizers were tested to evaluate the effects on cross-link degree and the oxygen induction time. And also they are evaluated with the chlorine aqueous solution by the performance of the long-term hydrostatic pressure test and the long-term hydro dynamic pressure test. As a result, it was found that the combination of antioxidants for PEX-a pipes plays an important role to prolong the oxygen induction time without inhibiting the cross-linking. From the results of the 1H pulsed NMR measurement over the melting point of polyethylene, it was found that each peroxide PEX pipe with different antioxidant combinations indicated the different proportion and crosslink density of cross-linked region, in addition, that these pipes had the effective structure of cross-linking for the hydrostatic and hydrodynamic pressure test with the chlorine aqueous solution. Therefore, it was considered to be useful results for studies of the stricture of cross-linking of polyethylene.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Funded in part by the National Key Research and Development Program of China(No.2023YFB4006302)。
文摘We successfully incorporated phenyl groups into a small-molecule quaternary ammonium cross-linker and synthesized cross-linked polybenzimidazole membranes via a one-step cross-linking process.Compared with conventional quaternary ammonium-crosslinked benzimidazole membranes,the introduction of phenyl groups significantly increases the free volume within the membrane.After phosphoric acid doping,the benzimidazole membrane with larger free volume retains more phosphoric acid compared to conventional quaternary ammonium-crosslinked membranes,forming an extensive hydrogen-bonding network that effectively enhances its anhydrous proton conductivity.The anhydrous proton conductivity reaches 91 mS·cm^(-1)at 160℃,substantially higher than that of conventional quaternary ammonium-crosslinked membranes with the same mass fraction.Benefiting from the improved conductivity,the membrane electrode assembly exhibits reduced ohmic polarization,achieving a peak power density of 792 mW·cm^(-2)at 160℃.
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financially supported by the National Natu-ral Science Foundation of China(Nos.22375047,22378068,and 22075046)the Natural Science Foundation of Fujian Province(No.2022J01568)+2 种基金the National Key Research and Development Program of China(Nos.2022YFB3804905 and 2022YFB3804900)China Postdoctoral Science Foundation(No.2023M743437)start-up funding from Wenzhou Institute,University of Chinese Academy of Sciences(No.WIUCASQD2019002).
文摘Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.
基金supported by the National Key R&D Program of China(No.2022YFB3804204)the National Natural Science Foundation of China(Nos.52127805,52102090,12172005,and 12325202)+1 种基金the Fundamental Research Funds for the Central Uni-versities(No.2232022D-04)the Innovation and Development Sup-port Plan for Key Industries in Southern Xinjiang(No.2022DB011).
文摘Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homogenous distribution of nanomaterials,which is essential for enhancing their performance while also reducing production costs.In this work,we achieve uniform and stable dispersion of various nano-materials through the confinement effect generated by the stereocomplex cross-linked network formed by the combination of poly(L-lactic)acid and poly(D-lactic)acid.The unique confinement effect of poly-lactic acid(PLA)isomers is universal and significantly enhances the dispersion of nanomaterials in both PLA solutions and films.To demonstrate the efficacy of our approach,we disperse aggregation-induced emission(AIE)molecules within PLA,which leads to the production of PLA films exhibiting improved fluorescence property.This work provides an effective solution for the preparation of nanocomposite ma-terials that are both high-performing and cost-efficient.
基金supported by the National Natural Science Foundation of China(No.22202033)National College Students Innovation and Entrepreneurship Training Program(No.202310347048)Zhejiang Provincial Training Programs of Innovation and Entrepreneurship for Undergraduates(No.S202310347032)。
文摘Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.
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financially supported by the Guangdong Natural Science Foundation(No.020891)
文摘Cross-linkedβ-cyclodextrin polymer/Fe3O4 composite nanoparticles with core-shell structures were prepared via cross linking reaction on the surface of carboxymethylβ-cyclodextrin（CM-β-CD） modified Fe3O4 nanoparticles inβ-cyclodextrin alkaline solution by using epichlorohydrin as crosslinking agent.The morphology,structure and magnetic properties of the prepared composite nanoparticles were investigated by transmission electron microscopy（TEM）,Fourier transform infrared（FTIR） spectrometry,X-ray diffraction（XRD） measurement,thermogravimetric analysis（TGA） and Vibrating sample magnetometry （VSM）,respectively.
文摘Cross-linked polyethylene (PEX) pipes used in hot water supply are required for high mechanical strength and high creep resistance at high temperature. Especially PEX-a pipes which are made by peroxide cross-linking have better performance, such as creep resistance and thermal shock resistance than the pipes made by the other cross-linking method. Because the PEX-a pipes indicate the higher cross-link degree as compared with the other PEX pipes. In this study, the PEX-a pipes which were mixed with several stabilizers were tested to evaluate the effects on cross-link degree and the oxygen induction time. And also they are evaluated with the chlorine aqueous solution by the performance of the long-term hydrostatic pressure test and the long-term hydro dynamic pressure test. As a result, it was found that the combination of antioxidants for PEX-a pipes plays an important role to prolong the oxygen induction time without inhibiting the cross-linking. From the results of the 1H pulsed NMR measurement over the melting point of polyethylene, it was found that each peroxide PEX pipe with different antioxidant combinations indicated the different proportion and crosslink density of cross-linked region, in addition, that these pipes had the effective structure of cross-linking for the hydrostatic and hydrodynamic pressure test with the chlorine aqueous solution. Therefore, it was considered to be useful results for studies of the stricture of cross-linking of polyethylene.
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.
