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
Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk...Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk materials owing to the atomic nature of 2D materials.Plasmonic nanostructures are usually integrated with 2D materials to enhance the light–matter interactions,offering great opportunities for both fundamental research and technological applications.Nanoparticle-on-mirror(NPo M)structures with extremely confined optical fields are highly desired in this aspect.In addition,2D materials provide a good platform for the study of plasmonic fields with subnanometer resolution and quantum plasmonics down to the characteristic length scale of a single atom.A focused and up-to-date review article is highly desired for a timely summary of the progress in this rapidly growing field and to encourage more research efforts in this direction.In this review,we will first introduce the basic concepts of plasmonic modes in NPo M structures.Interactions between plasmons and quasi-particles in 2D materials,e.g.,excitons and phonons,from weak to strong coupling and potential applications will then be described in detail.Related phenomena in subnanometer metallic gaps separated by 2D materials,such as quantum tunneling,will also be touched.We will finally discuss phenomena and physical processes that have not been understood clearly and provide an outlook for future research.We believe that the hybrid systems of2D materials and NPo M structures will be a promising research field in the future.展开更多
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
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
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
This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly...This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly includes three steps:(1)a ResUNet-involved generative and adversarial network(ResUNet-GAN)is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure,and a fiber orientation chromatogram is presented to represent continuous fiber angles;(2)to avoid the local optimum problem,the independent continuous mapping method(ICM method)considering the improved principal stress orientation interpolated continuous fiber angle optimization(PSO-CFAO)strategy is utilized to construct CFRCS topology optimization dataset;(3)the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations.Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy.Furthermore,the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing.Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced.The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.展开更多
The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challengin...The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challenging to further enhance the low frequency microwave absorption and elucidate the related loss mechanisms.Herein,the chiral CNCs are first synthesized on a threedimensional(3D)carbon foam and then combined with the FeNi/NiFe_(2)O_(4) nanoparticles to form a novel chiral-dielectric-magnetic trinity foam.The 3D porous CNC-carbon foam network provides excellent impedance matching and strong conduction loss.The formation of the FeNi-carbon interfaces induces interfacial polarization loss,which is confirmed by the density functional theory calculations.Further permeability analysis and the micromagnetic simulation indicate that the nanoscale chiral magnetic heterostructures achieve magnetic pinning and coupling effects,which enhance the magnetic anisotropy and magnetic loss capability.Owing to the synergistic effect between dielectricity,chirality,and magnetism,the trinity composite foam exhibits excellent microwave absorption performance with an ultrabroad effective absorption bandwidth(EAB)of 14 GHz and a minimum reflection of loss less than-50 dB.More importantly,the C-band EAB of the foam is extended to 4 GHz,achieving the full C-band coverage.This study provides further guidelines for the microstructure design of the chiral-dielectric-magnetic trinity composites to achieve broadband microwave absorption.展开更多
Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials ...Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.展开更多
Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have b...Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.展开更多
Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To ...Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To facilitate the inspection process,the authors previously developed a rapid building safety assessment system using sparse acceleration measurements for steel framed buildings.The proposed system modeled nonlinearity in the measurement data using a calibrated simplified lumped-mass model and convolutional neural networks(CNNs),based on which the buildinglevel damage index was estimated rapidly after earthquakes.The proposed system was validated for a nonlinear 3D numerical model of a five-story steel building,and later for a large-scale specimen of an 18-story building in Japan tested on the E-Defense shaking table.However,the applicability of the safety assessment system for reinforced concrete(RC)structures with complex hysteretic material nonlinearity has yet to be explored;the previous approach based on a simplified lumpedmass model with a Bouc-Wen hysteretic model does not accurately represent the inherent nonlinear behavior and resulting damage states of RC structures.This study extends the rapid building safety assessment system to low-rise RC moment resisting frame structures representing typical residential apartments in Japan.First,a safety classification for RC structures based on a damage index consistent with the current state of practice is defined.Then,a 3D nonlinear numerical model of a two-story moment frame structure is created.A simplified lumped-mass nonlinear model is developed and calibrated using the 3D model,incorporating the Takeda degradation model for the RC material nonlinearity.This model is used to simulate the seismic response and associated damage sensitive features(DSF)for random ground motion.The resulting database of responses is used to train a convolutional neural network(CNN)that performs rapid safety assessment.The developed system is validated using the 3D nonlinear analysis model subjected to historical earthquakes.The results indicate the applicability of the proposed system for RC structures following seismic events.展开更多
Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the ...Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.展开更多
The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on t...The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on the economic performance of the whole system.Pump network and water turbine network constitute the work network of the circulating water system,that is,the fluid machinery network.Based on the previous studies,this paper proposes a stepwise method to optimize the fluid machinery network,that is,to optimize the network structure by using the recoverable pressure-head curve of the branch,and consider the recovery of adjustable resistance at the valve of each branch,so as to further reduce energy consumption and water consumption.The calculation result of the case shows that the topology structure optimization can further reduce the operation cost and the annual capital cost on the basis of the fixed structure optimization,and the total annualized cost can be reduced by 30.04%.The optimization result of different flow shows that both the pump network and the water turbine network tend to series structure at a low flow rate whereas to parallel structure at a high flow rate.展开更多
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp...Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.展开更多
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supported by the National Natural Science Foundation of China(62205183)the Research Grants Council of Hong Kong(ANR/RGC,Ref.No.A-CUHK404/21).
文摘Light–matter interactions in two-dimensional(2D)materials have been the focus of research since the discovery of graphene.The light–matter interaction length in 2D materials is,however,much shorter than that in bulk materials owing to the atomic nature of 2D materials.Plasmonic nanostructures are usually integrated with 2D materials to enhance the light–matter interactions,offering great opportunities for both fundamental research and technological applications.Nanoparticle-on-mirror(NPo M)structures with extremely confined optical fields are highly desired in this aspect.In addition,2D materials provide a good platform for the study of plasmonic fields with subnanometer resolution and quantum plasmonics down to the characteristic length scale of a single atom.A focused and up-to-date review article is highly desired for a timely summary of the progress in this rapidly growing field and to encourage more research efforts in this direction.In this review,we will first introduce the basic concepts of plasmonic modes in NPo M structures.Interactions between plasmons and quasi-particles in 2D materials,e.g.,excitons and phonons,from weak to strong coupling and potential applications will then be described in detail.Related phenomena in subnanometer metallic gaps separated by 2D materials,such as quantum tunneling,will also be touched.We will finally discuss phenomena and physical processes that have not been understood clearly and provide an outlook for future research.We believe that the hybrid systems of2D materials and NPo M structures will be a promising research field in the future.
基金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.
基金supported by the National Natural Science Foundation of China(Grant No.11872080)Beijing Natural Science Foundation(Grant No.3192005).
文摘This paper presents a deep learning-based topology optimization method for the joint design of material layout and fiber orientation in continuous fiber-reinforced composite structure(CFRCS).The proposed method mainly includes three steps:(1)a ResUNet-involved generative and adversarial network(ResUNet-GAN)is developed to establish the end-to-end mapping from structural design parameters to fiber-reinforced composite optimized structure,and a fiber orientation chromatogram is presented to represent continuous fiber angles;(2)to avoid the local optimum problem,the independent continuous mapping method(ICM method)considering the improved principal stress orientation interpolated continuous fiber angle optimization(PSO-CFAO)strategy is utilized to construct CFRCS topology optimization dataset;(3)the well-trained ResUNet-GAN is deployed to design the optimal structural material distribution together with the corresponding continuous fiber orientations.Numerical simulations for benchmark structure verify that the proposed method greatly improves the design efficiency of CFRCS along with high design accuracy.Furthermore,the CFRCS topology configuration designed by ResUNet-GAN is fabricated by additive manufacturing.Compression experiments of the specimens show that both the stiffness structure and peak load of the CFRCS topology configuration designed by the proposed method have significantly enhanced.The proposed deep learning-based topology optimization method will provide great flexibility in CFRCS for engineering applications.
基金supported by the National Natural Science Foundation of China[Grant Nos.52272288 and 51972039]the China Postdoctoral Science Foundation[No.2021M700658].
文摘The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challenging to further enhance the low frequency microwave absorption and elucidate the related loss mechanisms.Herein,the chiral CNCs are first synthesized on a threedimensional(3D)carbon foam and then combined with the FeNi/NiFe_(2)O_(4) nanoparticles to form a novel chiral-dielectric-magnetic trinity foam.The 3D porous CNC-carbon foam network provides excellent impedance matching and strong conduction loss.The formation of the FeNi-carbon interfaces induces interfacial polarization loss,which is confirmed by the density functional theory calculations.Further permeability analysis and the micromagnetic simulation indicate that the nanoscale chiral magnetic heterostructures achieve magnetic pinning and coupling effects,which enhance the magnetic anisotropy and magnetic loss capability.Owing to the synergistic effect between dielectricity,chirality,and magnetism,the trinity composite foam exhibits excellent microwave absorption performance with an ultrabroad effective absorption bandwidth(EAB)of 14 GHz and a minimum reflection of loss less than-50 dB.More importantly,the C-band EAB of the foam is extended to 4 GHz,achieving the full C-band coverage.This study provides further guidelines for the microstructure design of the chiral-dielectric-magnetic trinity composites to achieve broadband microwave absorption.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFE0207300)National Natural Science Foundation of China(Grant Nos.22179142 and 22075314)+1 种基金Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2024ZB051 and 2023ZB836)the technical support for Nano-X from Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences(SINANO).
文摘Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.
基金Project supported by the National Key Research and Development Program of China(Grant No.2022YFB2803900)the National Natural Science Foundation of China(Grant Nos.61974075 and 61704121)+2 种基金the Natural Science Foundation of Tianjin Municipality(Grant Nos.22JCZDJC00460 and 19JCQNJC00700)Tianjin Municipal Education Commission(Grant No.2019KJ028)Fundamental Research Funds for the Central Universities(Grant No.22JCZDJC00460).
文摘Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.
基金supported by a fellowship from Design Department of Taisei Corporation。
文摘Many countries throughout the world have experienced large earthquakes,which cause building damage or collapse.After such earthquakes,structures must be inspected rapidly to judge whether they are safe to reoccupy.To facilitate the inspection process,the authors previously developed a rapid building safety assessment system using sparse acceleration measurements for steel framed buildings.The proposed system modeled nonlinearity in the measurement data using a calibrated simplified lumped-mass model and convolutional neural networks(CNNs),based on which the buildinglevel damage index was estimated rapidly after earthquakes.The proposed system was validated for a nonlinear 3D numerical model of a five-story steel building,and later for a large-scale specimen of an 18-story building in Japan tested on the E-Defense shaking table.However,the applicability of the safety assessment system for reinforced concrete(RC)structures with complex hysteretic material nonlinearity has yet to be explored;the previous approach based on a simplified lumpedmass model with a Bouc-Wen hysteretic model does not accurately represent the inherent nonlinear behavior and resulting damage states of RC structures.This study extends the rapid building safety assessment system to low-rise RC moment resisting frame structures representing typical residential apartments in Japan.First,a safety classification for RC structures based on a damage index consistent with the current state of practice is defined.Then,a 3D nonlinear numerical model of a two-story moment frame structure is created.A simplified lumped-mass nonlinear model is developed and calibrated using the 3D model,incorporating the Takeda degradation model for the RC material nonlinearity.This model is used to simulate the seismic response and associated damage sensitive features(DSF)for random ground motion.The resulting database of responses is used to train a convolutional neural network(CNN)that performs rapid safety assessment.The developed system is validated using the 3D nonlinear analysis model subjected to historical earthquakes.The results indicate the applicability of the proposed system for RC structures following seismic events.
基金National Natural Science Foundation of China under Grant No.61973037China Postdoctoral Science Foundation under Grant No.2022M720419。
文摘Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.
基金Financial support from Key Research and Development Projects of Shaanxi Province(2024GX-YBXM-508)Research Fund for Young Star of Science and Technology in Shaanxi Province(2023KJXX-124)are gratefully acknowledged.
文摘The circulating water system is widely used as the cooling system in the process industry,which has the characteristics of high water and power consumption,and its energy consumption level has an important impact on the economic performance of the whole system.Pump network and water turbine network constitute the work network of the circulating water system,that is,the fluid machinery network.Based on the previous studies,this paper proposes a stepwise method to optimize the fluid machinery network,that is,to optimize the network structure by using the recoverable pressure-head curve of the branch,and consider the recovery of adjustable resistance at the valve of each branch,so as to further reduce energy consumption and water consumption.The calculation result of the case shows that the topology structure optimization can further reduce the operation cost and the annual capital cost on the basis of the fixed structure optimization,and the total annualized cost can be reduced by 30.04%.The optimization result of different flow shows that both the pump network and the water turbine network tend to series structure at a low flow rate whereas to parallel structure at a high flow rate.
基金the National Natural Science Foundation of China(Nos.62272063,62072056 and 61902041)the Natural Science Foundation of Hunan Province(Nos.2022JJ30617 and 2020JJ2029)+4 种基金Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications(No.JZNY202102)the Traffic Science and Technology Project of Hunan Province,China(No.202042)Hunan Provincial Key Research and Development Program(No.2022GK2019)this work was funded by the Researchers Supporting Project Number(RSPD2023R681)King Saud University,Riyadh,Saudi Arabia.
文摘Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.
