To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethyle...To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethylene-polypropylene glycol(F127)and reduction of hydrogen.An interesting phenomenon is discovered that F127 can break GeO_(2)polycrystalline microparticles into 100 nm nanoparticles by only physical interaction,which promotes the uniform dispersion of GeO_(2)in a carbon network structure composed of graphene(rGO)and carbon nanotubes(CNTs).As evaluated as anode material of Lithium-ion batteries,Ge/rGO/CNTs nanocomposites exhibit excellent lithium storage performance.The initial specific capacity is high to 1549.7 mAh/g at 0.2 A/g,and the reversible capacity still retains972.4 mAh/g after 100 cycles.The improved lithium storage performance is attributed to that Ge nanoparticles can effectively slow down the volume expansion during charge and discharge processes,and threedimensional carbon networks can improve electrical conductivity and accelerate lithium-ion transfer of anode materials.展开更多
Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-por...Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.展开更多
To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an...To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.展开更多
In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understandin...In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.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
In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisti...In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.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
In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3D...In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3DSIM)has been impeded by challenges arising from the speed and intricacy of polarization modulation.We introduce a high-speed modulation 3DSIM system,leveraging the polarizationmaintaining and modulation capabilities of a digital micromirror device(DMD)in conjunction with an electrooptic modulator.The DMD-3DSIM system yields a twofold enhancement in both lateral(133 nm)and axial(300 nm)resolution compared to wide-field imaging and can acquire a data set comprising 29 sections of 1024 pixels×1024 pixels,with 15 ms exposure time and 6.75 s per volume.The versatility of the DMD-3DSIM approach was exemplified through the imaging of various specimens,including fluorescent beads,nuclear pores,microtubules,actin filaments,and mitochondria within cells,as well as plant and animal tissues.Notably,polarized 3DSIM elucidated the orientation of actin filaments.Furthermore,the implementation of diverse deconvolution algorithms further enhances 3D resolution.The DMD-based 3DSIM system presents a rapid and reliable methodology for investigating biomedical phenomena,boasting capabilities encompassing 3D superresolution,fast temporal resolution,and polarization imaging.展开更多
A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray di...A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray diffraction. The crystal crystallizes in the triclinic system, molecular formula ZnC12H16N2O8, Mr=381.64, space group P with a = 6.338(1), b =6.919(1), c=9.277(1), α=96.28(1), β=104.91(1), γ=112.85(1)°, V=352.12(9)?3, Z=1, Dc=1.80g?cm-3 and F(000)=196, μ =1.791mm-1. The crystal structure was solved by direct methods for final R=0.0204 and Rw=0.0542 for 1258 observed reflections with [Fo>4σ(Fo)]. The crystal structure reveals that zinc ion is trans-octahedral with two pyridyl nitrogens and two aque oxygens at the equational positions and two aqua oxygens at the axial positions. The complex forms a three-dimensional network through intermolecular hydrogen bonds.展开更多
Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors....Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors.This dynamic color-changing capability is crucial for applications that require adaptable optical properties,positioning CLCs as key materials in advanced photonic technologies.This review focuses on the mechanisms of dynamic color tuning in CLCs across various forms,including small molecules,cholesteric liquid crystal elastomers(CLCEs),and cholesteric liquid crystal networks(CLCNs),and emphasizes the distinct responsive coloration each structure provides.Key developments in photochromic mechanisms based on azobenzene,dithienylethene,and molecular motor switches,are discussed for their roles in enhancing the stability and tuning range of CLCs.We examine the color-changing behaviors of CLCEs under mechanical stimuli and CLCNs under swelling,highlighting the advantages of each form.Following this,applications of dynamic color-tuning CLCs in information encryption,adaptive camouflage,and smart sensing technologies are explored.The review concludes with an outlook on current challenges and future directions in CLC research,particularly in biomimetic systems and dynamic photonic devices,aiming to broaden their functional applications and impact.展开更多
Lithium-sulfur(Li-S)chemistry reaction opens a new battery era with high energy density;meanwhile,multiple electrons migration leads to the complex phase transition of sulfur species.To manipulate the binding strength...Lithium-sulfur(Li-S)chemistry reaction opens a new battery era with high energy density;meanwhile,multiple electrons migration leads to the complex phase transition of sulfur species.To manipulate the binding strength of multiple key intermediates more efficiently,the bimetallic TiVC MXene is utilized to realize multi-dimensional catalysis.Based on the macroscopic three-dimensional(3D)structure using two-dimensional(2D)MXene architecture,electron conductivity and sulfur utilization are improved.Microscopically,Ti-V catalytic systems regulate multiple reaction intermediates through intermetallic synergies customized surface properties and atomic scale coordination,thereby improving electronic and ionic conductivity.In-situ Raman spectroscopy and electrochemical analysis show that the conversion rate of polysulfides was accelerated during the charge-discharge process.The Ti-V interaction exhibits unique catalytic activity and regulates multiple continuous processes of sulfur species phase transformation,which are essential for the excellent energy performance of Li-S batteries.This study not only clarifies the catalytic mechanism of Ti-V at different dimensions but also proposes a promising strategy for the design of advanced catalytic systems in energy storage technology.展开更多
Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not ex...Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.展开更多
Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems....Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems.Therefore,there is an urgent need to develop advanced tools to characterize the spatio-temporal variations of three-dimensional(3D)DO.To address this challenge,this study introduces the Light Gradient Boosting Machine(Light-GBM),combining satellite remote sensing and reanalysis data with Biogeochemical Argo data to accurately reconstruct the 3D DO structure in the Mediterranean Sea from 2010 to 2022.Various environmental parameters are incorporated as inputs,including spatiotemporal features,meteorological characteristics,and ocean color properties.The LightGBM model demonstrates excellent performance on the testing dataset with R^(2) of 0.958.The modeled DO agrees better with in-situ measurements than products from numerical models.Using the Shapley Additive exPlanations method,the contributions of input features are assessed.Sea surface temperatures provide a correlation with DO at the sea surface,while spatial coordinates supplement the view of the ocean interior.Based on the reconstructed 3D DO structure,we identify an oxygen minimum zone in the western Mediterranean that expands continuously,reaching depths of approximately 300–800 m.The western Mediterranean exhibits a significant declining trend.This study enhances marine environmental evidence by proposing a precise and cost-effective approach for reconstructing 3D DO,thereby offering insights into the dynamics of DO variations under changing climatic conditions.展开更多
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-...BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.AIM To investigate structural network communication within large-scale brain networks in adolescents with depression.METHODS We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression.Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents,including 17 depressed adolescents without SA or NSSI(major depressive disorder group),27 depressed adolescents with NSSI but no SA(NSSI group),and 41 depressed adolescents with SA and NSSI(NSSI+SA group).RESULTS Significant differences in structural network communication were observed across the four groups,involving spatially widespread brain regions,particularly encompassing cortico-cortical connections(e.g.,dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus;connections based on precentral gyrus)and cortico-subcortical circuits(e.g.,the nucleus accumbens-frontal circuit).In addition,we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents.We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.CONCLUSION This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA,highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms.These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.展开更多
This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissibl...This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissible function of the boundary-value problem(BVP),which satisfies all geometrical boundary conditions.We then prove that the admissible neural network solution also satisfies natural boundary conditions,and therefore all boundary conditions,when the stationarity condition of the variational principle is met.Numerical examples are presented to show the advantages and effectiveness of the VPINN in comparison with the physics-informed neural network(PINN).Another contribution of the work is the introduction of Gaussian approximation of the Dirac delta function,which significantly enhances the ability of neural networks to handle singularities,as demonstrated by the examples with concentrated support conditions and loadings.It is hoped that these structural examples are so convincing that engineers would adopt the VPINN method in their structural design practice.展开更多
基金financially supported by National Natural Science Foundation of China(Nos.22379056,52102100)Industry foresight and common key technology research in Carbon Peak and Carbon Neutrality Special Project from Zhenjiang city(No.CG2023003)Research and Practice Innovation Plan of Postgraduate Training Innovation Project in Jiangsu Province(No.SJCX23_2164)。
文摘To solve the volume expansion and poor electrical conductivity of germanium-based anode materials,Ge/rGO/CNTs nanocomposites with three-dimensional network structure are fabricated through the dispersion of polyethylene-polypropylene glycol(F127)and reduction of hydrogen.An interesting phenomenon is discovered that F127 can break GeO_(2)polycrystalline microparticles into 100 nm nanoparticles by only physical interaction,which promotes the uniform dispersion of GeO_(2)in a carbon network structure composed of graphene(rGO)and carbon nanotubes(CNTs).As evaluated as anode material of Lithium-ion batteries,Ge/rGO/CNTs nanocomposites exhibit excellent lithium storage performance.The initial specific capacity is high to 1549.7 mAh/g at 0.2 A/g,and the reversible capacity still retains972.4 mAh/g after 100 cycles.The improved lithium storage performance is attributed to that Ge nanoparticles can effectively slow down the volume expansion during charge and discharge processes,and threedimensional carbon networks can improve electrical conductivity and accelerate lithium-ion transfer of anode materials.
基金This work was financially supported by the Natural Science Foundation of Shandong Province, China (Y2006F03).
文摘Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.
基金financially supported by the National Natural Science Foundation of China(Nos.21876164 and U2030203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.
基金support of the Fundamental Research Funds for the Central Universities(No.E2ET0411X2).
文摘In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.
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 National Natural Science Foundation of China(Nos.51407134,52002196)Natural Science Foundation of Shandong Province(Nos.ZR2019YQ24,ZR2020QF084)+1 种基金Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Qingchuang Talents Induction Program of Shandong Higher Education Institution(Research and Innovation Team of Structural-Functional Polymer Composites)and Special Financial of Shandong Province(Structural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Talent Teams(No.37000022P990304116449)).
文摘In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.
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supported by the National Key R&D Program of China (Award No.2022YFC3401100)the National Natural Science Foundation of China。
文摘In recent years,notable progress has been achieved in both the hardware and algorithms of structured illumination microscopy(SIM).Nevertheless,the advancement of three-dimensional structured illumination microscopy(3DSIM)has been impeded by challenges arising from the speed and intricacy of polarization modulation.We introduce a high-speed modulation 3DSIM system,leveraging the polarizationmaintaining and modulation capabilities of a digital micromirror device(DMD)in conjunction with an electrooptic modulator.The DMD-3DSIM system yields a twofold enhancement in both lateral(133 nm)and axial(300 nm)resolution compared to wide-field imaging and can acquire a data set comprising 29 sections of 1024 pixels×1024 pixels,with 15 ms exposure time and 6.75 s per volume.The versatility of the DMD-3DSIM approach was exemplified through the imaging of various specimens,including fluorescent beads,nuclear pores,microtubules,actin filaments,and mitochondria within cells,as well as plant and animal tissues.Notably,polarized 3DSIM elucidated the orientation of actin filaments.Furthermore,the implementation of diverse deconvolution algorithms further enhances 3D resolution.The DMD-based 3DSIM system presents a rapid and reliable methodology for investigating biomedical phenomena,boasting capabilities encompassing 3D superresolution,fast temporal resolution,and polarization imaging.
文摘A zinc complex, [Zn(iso)_2(H_2O)_4](iso=C_6H_4NO_2^-), was synthesized and characterized by elemental analysis, thermal analysis and IR spectrum studies. The crystal structure of the complex was determined by X-ray diffraction. The crystal crystallizes in the triclinic system, molecular formula ZnC12H16N2O8, Mr=381.64, space group P with a = 6.338(1), b =6.919(1), c=9.277(1), α=96.28(1), β=104.91(1), γ=112.85(1)°, V=352.12(9)?3, Z=1, Dc=1.80g?cm-3 and F(000)=196, μ =1.791mm-1. The crystal structure was solved by direct methods for final R=0.0204 and Rw=0.0542 for 1258 observed reflections with [Fo>4σ(Fo)]. The crystal structure reveals that zinc ion is trans-octahedral with two pyridyl nitrogens and two aque oxygens at the equational positions and two aqua oxygens at the axial positions. The complex forms a three-dimensional network through intermolecular hydrogen bonds.
基金financially supported by the National Natural Science Foundation of China(Nos.52233001,51927805,and 52173110)the Innovation Program of Shanghai Municipal Education Commission(No.2023ZKZD07)the Shanghai Rising-Star Program(No.22QA1401200)。
文摘Cholesteric liquid crystals(CLCs)exhibit unique helical superstructures that selectively reflect circularly polarized light,enabling them to dynamically respond to environmental changes with tunable structural colors.This dynamic color-changing capability is crucial for applications that require adaptable optical properties,positioning CLCs as key materials in advanced photonic technologies.This review focuses on the mechanisms of dynamic color tuning in CLCs across various forms,including small molecules,cholesteric liquid crystal elastomers(CLCEs),and cholesteric liquid crystal networks(CLCNs),and emphasizes the distinct responsive coloration each structure provides.Key developments in photochromic mechanisms based on azobenzene,dithienylethene,and molecular motor switches,are discussed for their roles in enhancing the stability and tuning range of CLCs.We examine the color-changing behaviors of CLCEs under mechanical stimuli and CLCNs under swelling,highlighting the advantages of each form.Following this,applications of dynamic color-tuning CLCs in information encryption,adaptive camouflage,and smart sensing technologies are explored.The review concludes with an outlook on current challenges and future directions in CLC research,particularly in biomimetic systems and dynamic photonic devices,aiming to broaden their functional applications and impact.
基金the financial support provided by the National Natural Science Foundation of China(No.51932005)the Liaoning Revitalization Talents Program(No.XLYC1807175)+1 种基金the Development Plan of Science and Technology of Jilin Province,China(YDZJ202301ZYTS280)the Natural Science Foundation of Jilin Province(YDZJ202401316ZYTS)。
文摘Lithium-sulfur(Li-S)chemistry reaction opens a new battery era with high energy density;meanwhile,multiple electrons migration leads to the complex phase transition of sulfur species.To manipulate the binding strength of multiple key intermediates more efficiently,the bimetallic TiVC MXene is utilized to realize multi-dimensional catalysis.Based on the macroscopic three-dimensional(3D)structure using two-dimensional(2D)MXene architecture,electron conductivity and sulfur utilization are improved.Microscopically,Ti-V catalytic systems regulate multiple reaction intermediates through intermetallic synergies customized surface properties and atomic scale coordination,thereby improving electronic and ionic conductivity.In-situ Raman spectroscopy and electrochemical analysis show that the conversion rate of polysulfides was accelerated during the charge-discharge process.The Ti-V interaction exhibits unique catalytic activity and regulates multiple continuous processes of sulfur species phase transformation,which are essential for the excellent energy performance of Li-S batteries.This study not only clarifies the catalytic mechanism of Ti-V at different dimensions but also proposes a promising strategy for the design of advanced catalytic systems in energy storage technology.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFC1523300Construction of Innovation Platform Program of Qinghai Province of China,Grant/Award Number:2022-ZJ-T02。
文摘Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.
基金supported by the Central Guiding Local Science and Technology Development Fund of Shandong-Yellow River Basin(No.YDZX2023019)Shandong Natural Science Foundation of China(Nos.ZR2020QF067 and ZR2023QD073)+6 种基金the Discipline Cluster Research Project of Qingdao University“Deep mining and intelligent prediction of multimodal big data for marine ecological disasters”(No.20240604)sourced from the International Argo Program and the national programs that contribute to it(https://argo.ucsd.edu)the CMEMS(http://marine.copernicus.eu/)the CDS(https://cds.climate.copernicus.eu/)the EMODnet(https://www.emodnet-chemistry.eu/)obtained from the ERA5(https://www.ecmwf.int)derived from the Glob Colour Project(http://globcolour.info).
文摘Oceanic dissolved oxygen(DO)concentration is crucial for assessing the status of marine ecosystems.Against the backdrop of global warming,DO shows a general decrease,posing a threat to the health of marine ecosystems.Therefore,there is an urgent need to develop advanced tools to characterize the spatio-temporal variations of three-dimensional(3D)DO.To address this challenge,this study introduces the Light Gradient Boosting Machine(Light-GBM),combining satellite remote sensing and reanalysis data with Biogeochemical Argo data to accurately reconstruct the 3D DO structure in the Mediterranean Sea from 2010 to 2022.Various environmental parameters are incorporated as inputs,including spatiotemporal features,meteorological characteristics,and ocean color properties.The LightGBM model demonstrates excellent performance on the testing dataset with R^(2) of 0.958.The modeled DO agrees better with in-situ measurements than products from numerical models.Using the Shapley Additive exPlanations method,the contributions of input features are assessed.Sea surface temperatures provide a correlation with DO at the sea surface,while spatial coordinates supplement the view of the ocean interior.Based on the reconstructed 3D DO structure,we identify an oxygen minimum zone in the western Mediterranean that expands continuously,reaching depths of approximately 300–800 m.The western Mediterranean exhibits a significant declining trend.This study enhances marine environmental evidence by proposing a precise and cost-effective approach for reconstructing 3D DO,thereby offering insights into the dynamics of DO variations under changing climatic conditions.
基金National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金Supported by the National Natural Science Foundation of China,No.81871081 and No.62201265the Fundamental Research Funds for the Central Universities,No.NJ2024029-14the Talent Support Programs of Wuxi Health Commission,No.BJ2023085,No.FZXK2021012,and No.M202358.
文摘BACKGROUND Depression,non-suicidal self-injury(NSSI),and suicide attempts(SA)often co-occur during adolescence and are associated with long-term adverse health outcomes.Unfortunately,neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.AIM To investigate structural network communication within large-scale brain networks in adolescents with depression.METHODS We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression.Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents,including 17 depressed adolescents without SA or NSSI(major depressive disorder group),27 depressed adolescents with NSSI but no SA(NSSI group),and 41 depressed adolescents with SA and NSSI(NSSI+SA group).RESULTS Significant differences in structural network communication were observed across the four groups,involving spatially widespread brain regions,particularly encompassing cortico-cortical connections(e.g.,dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus;connections based on precentral gyrus)and cortico-subcortical circuits(e.g.,the nucleus accumbens-frontal circuit).In addition,we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents.We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.CONCLUSION This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA,highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms.These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.
基金supported by the National Natural Science Foundation of China(Nos.12072118 and12372029)。
文摘This paper presents the variational physics-informed neural network(VPINN)as an effective tool for static structural analyses.One key innovation includes the construction of the neural network solution as an admissible function of the boundary-value problem(BVP),which satisfies all geometrical boundary conditions.We then prove that the admissible neural network solution also satisfies natural boundary conditions,and therefore all boundary conditions,when the stationarity condition of the variational principle is met.Numerical examples are presented to show the advantages and effectiveness of the VPINN in comparison with the physics-informed neural network(PINN).Another contribution of the work is the introduction of Gaussian approximation of the Dirac delta function,which significantly enhances the ability of neural networks to handle singularities,as demonstrated by the examples with concentrated support conditions and loadings.It is hoped that these structural examples are so convincing that engineers would adopt the VPINN method in their structural design practice.
