A symmetrical one-dimensional(1D)photonic crystal structure with a Dirac-emimetal-defected layer is proposed.The material properties of the Dirac semimetal are governed by three key parameters:Fermi level,Fermi veloci...A symmetrical one-dimensional(1D)photonic crystal structure with a Dirac-emimetal-defected layer is proposed.The material properties of the Dirac semimetal are governed by three key parameters:Fermi level,Fermi velocity,and degeneracy factor.Simulation results demonstrate that the proposed structure generates multiple photonic bandgaps within the THz frequency range.In the low-THz region,pronounced resonant transmission peaks emerge,enabling near-perfect filtering performance.The positions of these defect modes can be dynamically tuned by adjusting the Fermi level and degeneracy factor.In mid-and high-THz frequency bands,the Dirac semimetal begins to exhibit metallic behavior,leading to attenuation of the transmission peaks and the appearance of absorption.The elevation of the Fermi level delays the critical threshold for the transition from the dielectric state to the metallic state,while an increase in Fermi velocity suppresses metallic behavior.Therefore,enhancing both the Fermi level and Fermi velocity contributes to strengthening the defect peak intensity.Conversely,increasing the degeneracy factor strengthens the metallic characteristics,thereby disrupting the high-frequency photonic bandgap.Notably,the defect layer thickness and incident angle exert significant influence on the transmission behavior:a larger incident angle causes the defect peak to shift toward higher frequencies and reduces its intensity,whereas a thicker defect layer shifts the defect peak toward lower frequencies.The modulation effects of both parameters become more pronounced as frequency increases.Compared with conventional photonic crystals,our work can provide a tunable structure over transmission properties,offering novel strategies for designing tunable filters and optical sensors.展开更多
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of ci...Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.展开更多
A novel complex, [Cu 2(phen)(sal)(Hsal) 2] n(1), was synthesized and structurally characterized. The basic dimeric units are hold by sal ligands and extended into 1-D network. The carboxylate groups of salicylates...A novel complex, [Cu 2(phen)(sal)(Hsal) 2] n(1), was synthesized and structurally characterized. The basic dimeric units are hold by sal ligands and extended into 1-D network. The carboxylate groups of salicylates coordinate to the central ion in three different coordination modes: chelating, bridging and bridging-chelating. In the case of bridging-chelating of the carboxylate group of the salicylate, all three oxygen atoms of salicylate are bidentately coordinated to copper ion, namely, μ 4-η 3 binding mode.展开更多
In sub nanometer carbon nanotubes,water exhibits unique dynamic characteristics,and in the high-frequency region of the infrared spectrum,where the stretching vibrations of the internal oxygen-hydrogen(O-H)bonds are c...In sub nanometer carbon nanotubes,water exhibits unique dynamic characteristics,and in the high-frequency region of the infrared spectrum,where the stretching vibrations of the internal oxygen-hydrogen(O-H)bonds are closely related to the hydrogen bonds(H-bonds)network between water molecules.Therefore,it is crucial to analyze the relationship between these two aspects.In this paper,the infrared spectrum and motion characteristics of the stretching vibrations of the O-H bonds in one-dimensional confined water(1DCW)and bulk water(BW)in(6,6)single-walled carbon nanotubes(SWNT)are studied by molecular dynamics simulations.The results show that the stretching vibrations of the two O-H bonds in 1DCW exhibit different frequencies in the infrared spectrum,while the O-H bonds in BW display two identical main frequency peaks.Further analysis using the spring oscillator model reveals that the difference in the stretching amplitude of the O-H bonds is the main factor causing the change in vibration frequency,where an increase in stretching amplitude leads to a decrease in spring stiffness and,consequently,a lower vibration frequency.A more in-depth study found that the interaction of H-bonds between water molecules is the fundamental cause of the increased stretching amplitude and decreased vibration frequency of the O-H bonds.Finally,by analyzing the motion trajectory of the H atoms,the dynamic differences between 1DCW and BW are clearly revealed.These findings provide a new perspective for understanding the behavior of water molecules at the nanoscale and are of significant importance in advancing the development of infrared spectroscopy detection technology.展开更多
In this paper we analyze connectivity of one-dimensional Vehicular Ad Hoc Networks where vehicle gap distribution can be approximat- ed by an exponential distribution. The probabilities of Vehicular Ad Hoc Network con...In this paper we analyze connectivity of one-dimensional Vehicular Ad Hoc Networks where vehicle gap distribution can be approximat- ed by an exponential distribution. The probabilities of Vehicular Ad Hoc Network connectivity for difference cases are derived. Furthermore we proof that the nodes in a sub-interval [z1, z1 ＋ △z] of interval [0,z],z 〉 0 where all the nodes are independently uniform distributed is a Poisson process and the relationship of Vehicle Ad hoc Networks and one-dimensional Ad Hoc networks where nodes independently uniform distributed in [zl, z1 ＋ △z] is explained. The analysis is validated by computing the probability of network connectivity and comparing it with the Mont Carlo simu- lation results.展开更多
This paper studies the problem applying Radial Basis Function Network(RBFN) which is trained by the Recursive Least Square Algorithm(RLSA) to the recognition of one dimensional images of radar targets. The equivalence...This paper studies the problem applying Radial Basis Function Network(RBFN) which is trained by the Recursive Least Square Algorithm(RLSA) to the recognition of one dimensional images of radar targets. The equivalence between the RBFN and the estimate of Parzen window probabilistic density is proved. It is pointed out that the I/O functions in RBFN hidden units can be generalized to general Parzen window probabilistic kernel function or potential function, too. This paper discusses the effects of the shape parameter a in the RBFN and the forgotten factor A in RLSA on the results of the recognition of three kinds of kernel function such as Gaussian, triangle, double-exponential, at the same time, also discusses the relationship between A and the training time in the RBFN.展开更多
Using the Bose-Fermi mapping method,we obtain the exact ground state wavefunction of one-dimensional(1D)Bose gas with the zero-range dipolar interaction in the strongly repulsive contact interaction limit.Its ground s...Using the Bose-Fermi mapping method,we obtain the exact ground state wavefunction of one-dimensional(1D)Bose gas with the zero-range dipolar interaction in the strongly repulsive contact interaction limit.Its ground state density distributions for both repulsive and attractive dipole interactions are exhibited.It is shown that in the case of the finite dipole interaction the density profiles do not change obviously with the increase of dipole interaction and display the typical shell structure of Tonks-Girardeau gases.As the repulsive dipole interaction is greatly strong,the density decreases at the center of the trap and displays a sunken valley.As the attractive dipole interaction increases,the density displays more oscillations and sharp peaks appear in the strong attraction limit,which mainly originate from the atoms occupying the low single particle levels.展开更多
Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications d...Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications due to their chemical diversity.Converting 2D TMDs into one-dimensional(1D)TMDs nanotubes can not only retain some advantages of 2D nanosheets but also providing a unique direction to explore the novel properties of TMDs materials in the 1D limit.However,the controllable preparation of high-quality nanotubes remains a major challenge.It is very necessary to review the advanced development of one-dimensional transition metal dichalcogenide nanotubes from preparation to application.Here,we first summarize a series of bottom-up synthesis methods of 1D TMDs,such as template growth and metal catalyzed method.Then,top-down synthesis methods are summarized,which included selfcuring and stacking of TMDs nanosheets.In addition,we discuss some key applications that utilize the properties of 1D-TMDs nanotubes in the areas of catalyst preparation,energy storage,and electronic devices.Last but not least,we prospect the preparation methods of high-quality 1D-TMDs nanotubes,which will lay a foundation for the synthesis of high-performance optoelectronic devices,catalysts,and energy storage components.展开更多
Adjustable or programmable metamaterials offer versatile functions,while the complex multi-dimensional regulation increases workload,and hinders their applications in practical scenarios.To address these challenges,we...Adjustable or programmable metamaterials offer versatile functions,while the complex multi-dimensional regulation increases workload,and hinders their applications in practical scenarios.To address these challenges,we present a mechanically programmable acoustic metamaterial for real-time focal tuning via one-dimensional phase-gradient modulation in this paper.The device integrates a phase gradient structure with concave cavity channels and an x-shaped telescopic mechanical framework,enabling dynamic adjustment of inter-unit spacing(1 mm-3 mm)through a microcontroller-driven motor.By modulating the spacing between adjacent channels,the phase gradient is precisely controlled,allowing continuous focal shift from 50 mm to 300 mm along the x-axis at 7500 Hz.Broadband focusing is also discussed in the range6800 Hz-8100 Hz,with transmission coefficients exceeding 0.5,ensuring high efficiency and robust performance.Experimental results align closely with simulations,validating the design's effectiveness and adaptability.Unlike conventional programmable metamaterials requiring multi-dimensional parameter optimization,this approach simplifies real-time control through single-axis mechanical adjustment,significantly reducing operational complexity.Due to the advantages of broadband focusing,simple control mode,real-time monitoring,and so on,the device may have extensive applications in the fields of acoustic imaging,nondestructive testing,ultrasound medical treatment,etc.展开更多
The oxygen evolution reaction(OER),a critical half-reaction in water electrolysis,has garnered significant attention.However,sluggish OER kinetics has emerged as a major impediment to efficient electrochemical energy c...The oxygen evolution reaction(OER),a critical half-reaction in water electrolysis,has garnered significant attention.However,sluggish OER kinetics has emerged as a major impediment to efficient electrochemical energy conversion.There is an urgent need to design novel electrocatalysts with optimized OER kinetics and enhanced intrinsic activity to improve overall OER performance.Herein,one-dimensional(1D)nanocomposites with high electrocatalytic activity were developed through the deposition of CoFePBA nanocubes onto the surface of MnO_(2) nanowires.The electronic structure of the nanocomposite surface was modified,and the synergistic effects between transition metals were leveraged to enhance catalytic activity through the deposition of Prussian blue analog(PBA)nanocubes on manganese dioxide nanowires.Specifically,CoFePBA featured an open crystal structure that offiered numerous electrochemical active sites and efficient charge transfer pathways.Additionally,the synergistic interactions between Co and Fe significantly reduced the OER overpotential.Additionally,the 1D rigid MnO_(2) acted as protective armor,ensuring the stability of active sites within CoFePBA during the OER.The synthesized MnO_(2)@CoFePBA achieved an overpotential of 1.614 V at 10 mA/cm^(2) and a small Tafel slope of 94 mV/dec and demonstrated stable performance for over 200 h.This work offers new insights into the rational design of various PBA-based nanocomposites with high activity and stability.展开更多
We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method...We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.展开更多
As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important pos...As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.展开更多
One-dimensional nano-grating standard(ODNGS)is widely recognized as a crucial nanometric standard for metrological technology.However,achieving the ultratiny size of ODNGS with high consistent uniformity and low rough...One-dimensional nano-grating standard(ODNGS)is widely recognized as a crucial nanometric standard for metrological technology.However,achieving the ultratiny size of ODNGS with high consistent uniformity and low roughness by conventional processes such as the inductively coupled plasma(ICP)etching methodpresents a significant challenge in obtaining accurate calibration values.In this work,a 50-nm ODNGS with a conformal buffer layer(Al_(2)O_(3))is successfully obtained,indicating outstanding stability and abrasion resistance.Remarkably,the introduction of hydrogen silsesquioxane(HSQ)and amorphous Al_(2)O_(3)simultaneously guarantees an incredibly small expanded uncertainty(0.5 nm)and repeatability of the standard uniformity(less than 0.3 nm)in the grating dimensions.TheⅠ-Ⅴcurves of ODNGS with an Al_(2)O_(3)buffer layer at room temperature(RT)and200℃are depicted respectively to showcase the sustained favorable insulation properties.Notably,the nanostructure fluctuation,line edge roughness(LER)and line width roughness(LWR)of the standard can be decreased obviously by 64.1%,63%and 70%,respectively.Our results suggest that the ODNGS with Al_(2)O_(3)exhibits exceptional precision and robust calibration reliability for calibrating nanoscale measuring instruments.It holds tremendous potential for manufacturing high-precision nanostructures and grating arrays with precisely controllable dimensions,which will play a pivotal role in the fabrication of microfluidics chips,metasurface and photodetectors in the future.展开更多
Controlling charge polarity in the semiconducting single-walled carbon nanotubes(CNTs) by substitutional doping is a difficult work due to their extremely strong C–C bonding. In this work, an inner doping strategy is...Controlling charge polarity in the semiconducting single-walled carbon nanotubes(CNTs) by substitutional doping is a difficult work due to their extremely strong C–C bonding. In this work, an inner doping strategy is explored by filling CNTs with one-dimensional(1D)-TM_(6)Te_(6) nanowires to form TM_(6)Te_(6)@CNT-(16,0) 1D van der Waals heterostructures(1D-vd WHs). The systematic first-principles studies on the electronic properties of 1D-vd WHs show that N-type doping CNTs can be formed by charge transfer from TM_(6)Te_(6) nanowires to CNTs, without introducing additional carrier scattering.Particularly, contribution from both T M(e.g., Sc and Y) and Te atoms strengthens the charge transfer. The outside CNTs further confine the dispersion of Te-p orbitals in nanowires that deforms the C-π states at the bottom of the conduction band to quasi sp^(3) hybridization. Our study provides an inner doping strategy that can effectively confine the charge polarity of CNTs and further broaden its applications in some novel nano-devices.展开更多
In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical ...In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical model is proposed,resulting in coupled governing integral equations that account for interfacial normal and shear stresses.To numerically solve these integral equations,an expansion method using orthogonal Chebyshev polynomials is employed.The results provide insights into the interfacial stresses,axial force,as well as axial and vertical deformations of the PQC film.Additionally,fracture criteria,including stress intensity factors,mode angles,and the J-integral,are evaluated.The solution is compared with the membrane theory,neglecting the normal stress and bending deformation.Finally,the effects of stiffness and aspect ratio on the PQC film are thoroughly discussed.This study serves as a valuable guide for controlling the mechanical response and conducting safety assessments of PQC film systems.展开更多
Two types of one-dimensional(1D)anti-PT-symmetric periodic ring optical waveguide networks,consisting of gain and loss materials,are constructed.The singular optical propagation properties of these networks are invest...Two types of one-dimensional(1D)anti-PT-symmetric periodic ring optical waveguide networks,consisting of gain and loss materials,are constructed.The singular optical propagation properties of these networks are investigated.The results show that the system composed of gain materials exhibits characteristics of ultra-strong transmission and bidirectional reflection.Conversely,the system composed of loss materials demonstrates equal transmittance and reflectance at some frequencies.In both the systems,a new type of total reflection phenomenon is observed.When the imaginary part of the refractive indices of waveguide segments is smaller than 10-5,the system shows bidirectional transparency with the transmittance tending to be 1 and reflectivity to be smaller than 10-8 at some bands.When the refractive indices of the waveguide segments are real,the system will be bidirectional transparent at the full band.These findings may deepen the understanding of anti-PT-symmetric optical systems and optical waveguide networks,and possess potential applications in efficient optical energy storage,ultra-sensitive optical filters,ultra-sensitive all-optical switches,integrated optical chips,stealth physics,and so on.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review a...To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
文摘A symmetrical one-dimensional(1D)photonic crystal structure with a Dirac-emimetal-defected layer is proposed.The material properties of the Dirac semimetal are governed by three key parameters:Fermi level,Fermi velocity,and degeneracy factor.Simulation results demonstrate that the proposed structure generates multiple photonic bandgaps within the THz frequency range.In the low-THz region,pronounced resonant transmission peaks emerge,enabling near-perfect filtering performance.The positions of these defect modes can be dynamically tuned by adjusting the Fermi level and degeneracy factor.In mid-and high-THz frequency bands,the Dirac semimetal begins to exhibit metallic behavior,leading to attenuation of the transmission peaks and the appearance of absorption.The elevation of the Fermi level delays the critical threshold for the transition from the dielectric state to the metallic state,while an increase in Fermi velocity suppresses metallic behavior.Therefore,enhancing both the Fermi level and Fermi velocity contributes to strengthening the defect peak intensity.Conversely,increasing the degeneracy factor strengthens the metallic characteristics,thereby disrupting the high-frequency photonic bandgap.Notably,the defect layer thickness and incident angle exert significant influence on the transmission behavior:a larger incident angle causes the defect peak to shift toward higher frequencies and reduces its intensity,whereas a thicker defect layer shifts the defect peak toward lower frequencies.The modulation effects of both parameters become more pronounced as frequency increases.Compared with conventional photonic crystals,our work can provide a tunable structure over transmission properties,offering novel strategies for designing tunable filters and optical sensors.
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
基金Supported by National Natural Science Foundation of China(Grant Nos.52272433 and 11874110)Jiangsu Provincial Key R&D Program(Grant No.BE2021084)Technical Support Special Project of State Administration for Market Regulation(Grant No.2022YJ11).
文摘Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.
基金ProjectsupportedbytheNationalNaturalScienceFoundationofChina (No .5 0 0 730 19)andtheScientificResearchFoundationfortheReturnedOverseasChineseScholars,StateEducationMinistryofChina .
文摘A novel complex, [Cu 2(phen)(sal)(Hsal) 2] n(1), was synthesized and structurally characterized. The basic dimeric units are hold by sal ligands and extended into 1-D network. The carboxylate groups of salicylates coordinate to the central ion in three different coordination modes: chelating, bridging and bridging-chelating. In the case of bridging-chelating of the carboxylate group of the salicylate, all three oxygen atoms of salicylate are bidentately coordinated to copper ion, namely, μ 4-η 3 binding mode.
基金Supported by the Natural Science Foundation of China(51705326,52075339)。
文摘In sub nanometer carbon nanotubes,water exhibits unique dynamic characteristics,and in the high-frequency region of the infrared spectrum,where the stretching vibrations of the internal oxygen-hydrogen(O-H)bonds are closely related to the hydrogen bonds(H-bonds)network between water molecules.Therefore,it is crucial to analyze the relationship between these two aspects.In this paper,the infrared spectrum and motion characteristics of the stretching vibrations of the O-H bonds in one-dimensional confined water(1DCW)and bulk water(BW)in(6,6)single-walled carbon nanotubes(SWNT)are studied by molecular dynamics simulations.The results show that the stretching vibrations of the two O-H bonds in 1DCW exhibit different frequencies in the infrared spectrum,while the O-H bonds in BW display two identical main frequency peaks.Further analysis using the spring oscillator model reveals that the difference in the stretching amplitude of the O-H bonds is the main factor causing the change in vibration frequency,where an increase in stretching amplitude leads to a decrease in spring stiffness and,consequently,a lower vibration frequency.A more in-depth study found that the interaction of H-bonds between water molecules is the fundamental cause of the increased stretching amplitude and decreased vibration frequency of the O-H bonds.Finally,by analyzing the motion trajectory of the H atoms,the dynamic differences between 1DCW and BW are clearly revealed.These findings provide a new perspective for understanding the behavior of water molecules at the nanoscale and are of significant importance in advancing the development of infrared spectroscopy detection technology.
基金supported by National Science Fund for Distinguished Young Scholars (No.60525110)National 973 Program (No. 2007CB307100, 2007CB307103)+2 种基金National Natural Science Foundation of China (No. 60902051)Chinese Universities Scientific Fund (BUP-T2009RC0505)Development Fund Project for Electronic and Information Industry (Mobile Service and Application System Based on 3G)
文摘In this paper we analyze connectivity of one-dimensional Vehicular Ad Hoc Networks where vehicle gap distribution can be approximat- ed by an exponential distribution. The probabilities of Vehicular Ad Hoc Network connectivity for difference cases are derived. Furthermore we proof that the nodes in a sub-interval [z1, z1 ＋ △z] of interval [0,z],z 〉 0 where all the nodes are independently uniform distributed is a Poisson process and the relationship of Vehicle Ad hoc Networks and one-dimensional Ad Hoc networks where nodes independently uniform distributed in [zl, z1 ＋ △z] is explained. The analysis is validated by computing the probability of network connectivity and comparing it with the Mont Carlo simu- lation results.
基金Supported by the National Natural Science Foundationthe Doctoral Foundation of the State Education Commission of China
文摘This paper studies the problem applying Radial Basis Function Network(RBFN) which is trained by the Recursive Least Square Algorithm(RLSA) to the recognition of one dimensional images of radar targets. The equivalence between the RBFN and the estimate of Parzen window probabilistic density is proved. It is pointed out that the I/O functions in RBFN hidden units can be generalized to general Parzen window probabilistic kernel function or potential function, too. This paper discusses the effects of the shape parameter a in the RBFN and the forgotten factor A in RLSA on the results of the recognition of three kinds of kernel function such as Gaussian, triangle, double-exponential, at the same time, also discusses the relationship between A and the training time in the RBFN.
基金Project supported by the National Natural Science Foundation of China(Grant No.11174026)。
文摘Using the Bose-Fermi mapping method,we obtain the exact ground state wavefunction of one-dimensional(1D)Bose gas with the zero-range dipolar interaction in the strongly repulsive contact interaction limit.Its ground state density distributions for both repulsive and attractive dipole interactions are exhibited.It is shown that in the case of the finite dipole interaction the density profiles do not change obviously with the increase of dipole interaction and display the typical shell structure of Tonks-Girardeau gases.As the repulsive dipole interaction is greatly strong,the density decreases at the center of the trap and displays a sunken valley.As the attractive dipole interaction increases,the density displays more oscillations and sharp peaks appear in the strong attraction limit,which mainly originate from the atoms occupying the low single particle levels.
基金supported by the National Natural Science Foundation of China(No.22202065).
文摘Two-dimensional(2D)transition metal sulfides(TMDs)are emerging and highly well received 2D materials,which are considered as an ideal 2D platform for studying various electronic properties and potential applications due to their chemical diversity.Converting 2D TMDs into one-dimensional(1D)TMDs nanotubes can not only retain some advantages of 2D nanosheets but also providing a unique direction to explore the novel properties of TMDs materials in the 1D limit.However,the controllable preparation of high-quality nanotubes remains a major challenge.It is very necessary to review the advanced development of one-dimensional transition metal dichalcogenide nanotubes from preparation to application.Here,we first summarize a series of bottom-up synthesis methods of 1D TMDs,such as template growth and metal catalyzed method.Then,top-down synthesis methods are summarized,which included selfcuring and stacking of TMDs nanosheets.In addition,we discuss some key applications that utilize the properties of 1D-TMDs nanotubes in the areas of catalyst preparation,energy storage,and electronic devices.Last but not least,we prospect the preparation methods of high-quality 1D-TMDs nanotubes,which will lay a foundation for the synthesis of high-performance optoelectronic devices,catalysts,and energy storage components.
基金supported by the National Natural Science Foundation of China(Grant No.12374416)。
文摘Adjustable or programmable metamaterials offer versatile functions,while the complex multi-dimensional regulation increases workload,and hinders their applications in practical scenarios.To address these challenges,we present a mechanically programmable acoustic metamaterial for real-time focal tuning via one-dimensional phase-gradient modulation in this paper.The device integrates a phase gradient structure with concave cavity channels and an x-shaped telescopic mechanical framework,enabling dynamic adjustment of inter-unit spacing(1 mm-3 mm)through a microcontroller-driven motor.By modulating the spacing between adjacent channels,the phase gradient is precisely controlled,allowing continuous focal shift from 50 mm to 300 mm along the x-axis at 7500 Hz.Broadband focusing is also discussed in the range6800 Hz-8100 Hz,with transmission coefficients exceeding 0.5,ensuring high efficiency and robust performance.Experimental results align closely with simulations,validating the design's effectiveness and adaptability.Unlike conventional programmable metamaterials requiring multi-dimensional parameter optimization,this approach simplifies real-time control through single-axis mechanical adjustment,significantly reducing operational complexity.Due to the advantages of broadband focusing,simple control mode,real-time monitoring,and so on,the device may have extensive applications in the fields of acoustic imaging,nondestructive testing,ultrasound medical treatment,etc.
基金supported by the National Natural Science Foundation of China(No.52371240)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘The oxygen evolution reaction(OER),a critical half-reaction in water electrolysis,has garnered significant attention.However,sluggish OER kinetics has emerged as a major impediment to efficient electrochemical energy conversion.There is an urgent need to design novel electrocatalysts with optimized OER kinetics and enhanced intrinsic activity to improve overall OER performance.Herein,one-dimensional(1D)nanocomposites with high electrocatalytic activity were developed through the deposition of CoFePBA nanocubes onto the surface of MnO_(2) nanowires.The electronic structure of the nanocomposite surface was modified,and the synergistic effects between transition metals were leveraged to enhance catalytic activity through the deposition of Prussian blue analog(PBA)nanocubes on manganese dioxide nanowires.Specifically,CoFePBA featured an open crystal structure that offiered numerous electrochemical active sites and efficient charge transfer pathways.Additionally,the synergistic interactions between Co and Fe significantly reduced the OER overpotential.Additionally,the 1D rigid MnO_(2) acted as protective armor,ensuring the stability of active sites within CoFePBA during the OER.The synthesized MnO_(2)@CoFePBA achieved an overpotential of 1.614 V at 10 mA/cm^(2) and a small Tafel slope of 94 mV/dec and demonstrated stable performance for over 200 h.This work offers new insights into the rational design of various PBA-based nanocomposites with high activity and stability.
基金supported by the National Natural Science Foundation of China(Grant No.11974420).
文摘We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.
基金National Natural Science Foundation of China(41901297,41806209)Science and Technology Key Project of Henan Province(202102310017)+1 种基金Key Research Projects for the Universities of Henan Province(20A170013)China Postdoctoral Science Foundation(2021M693201)。
文摘As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.
基金financially supported by the National Natural Science Foundation of China(No.52175434)the National Key Research and Development Program of China(No.2022YFB3204801)
文摘One-dimensional nano-grating standard(ODNGS)is widely recognized as a crucial nanometric standard for metrological technology.However,achieving the ultratiny size of ODNGS with high consistent uniformity and low roughness by conventional processes such as the inductively coupled plasma(ICP)etching methodpresents a significant challenge in obtaining accurate calibration values.In this work,a 50-nm ODNGS with a conformal buffer layer(Al_(2)O_(3))is successfully obtained,indicating outstanding stability and abrasion resistance.Remarkably,the introduction of hydrogen silsesquioxane(HSQ)and amorphous Al_(2)O_(3)simultaneously guarantees an incredibly small expanded uncertainty(0.5 nm)and repeatability of the standard uniformity(less than 0.3 nm)in the grating dimensions.TheⅠ-Ⅴcurves of ODNGS with an Al_(2)O_(3)buffer layer at room temperature(RT)and200℃are depicted respectively to showcase the sustained favorable insulation properties.Notably,the nanostructure fluctuation,line edge roughness(LER)and line width roughness(LWR)of the standard can be decreased obviously by 64.1%,63%and 70%,respectively.Our results suggest that the ODNGS with Al_(2)O_(3)exhibits exceptional precision and robust calibration reliability for calibrating nanoscale measuring instruments.It holds tremendous potential for manufacturing high-precision nanostructures and grating arrays with precisely controllable dimensions,which will play a pivotal role in the fabrication of microfluidics chips,metasurface and photodetectors in the future.
基金Project supported by the National Natural Science Foundation of China (Grant No. 92477205)。
文摘Controlling charge polarity in the semiconducting single-walled carbon nanotubes(CNTs) by substitutional doping is a difficult work due to their extremely strong C–C bonding. In this work, an inner doping strategy is explored by filling CNTs with one-dimensional(1D)-TM_(6)Te_(6) nanowires to form TM_(6)Te_(6)@CNT-(16,0) 1D van der Waals heterostructures(1D-vd WHs). The systematic first-principles studies on the electronic properties of 1D-vd WHs show that N-type doping CNTs can be formed by charge transfer from TM_(6)Te_(6) nanowires to CNTs, without introducing additional carrier scattering.Particularly, contribution from both T M(e.g., Sc and Y) and Te atoms strengthens the charge transfer. The outside CNTs further confine the dispersion of Te-p orbitals in nanowires that deforms the C-π states at the bottom of the conduction band to quasi sp^(3) hybridization. Our study provides an inner doping strategy that can effectively confine the charge polarity of CNTs and further broaden its applications in some novel nano-devices.
基金Supported by the National Natural Science Foundation of China (Nos. 11902293 and 12272353)。
文摘In this paper,the mechanical response of a one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)thin film is analyzed under electric and temperature loads.Based on the Euler-Bernoulli beam theory,a theoretical model is proposed,resulting in coupled governing integral equations that account for interfacial normal and shear stresses.To numerically solve these integral equations,an expansion method using orthogonal Chebyshev polynomials is employed.The results provide insights into the interfacial stresses,axial force,as well as axial and vertical deformations of the PQC film.Additionally,fracture criteria,including stress intensity factors,mode angles,and the J-integral,are evaluated.The solution is compared with the membrane theory,neglecting the normal stress and bending deformation.Finally,the effects of stiffness and aspect ratio on the PQC film are thoroughly discussed.This study serves as a valuable guide for controlling the mechanical response and conducting safety assessments of PQC film systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11674107,61475049,11775083,61774062,and 61771205).
文摘Two types of one-dimensional(1D)anti-PT-symmetric periodic ring optical waveguide networks,consisting of gain and loss materials,are constructed.The singular optical propagation properties of these networks are investigated.The results show that the system composed of gain materials exhibits characteristics of ultra-strong transmission and bidirectional reflection.Conversely,the system composed of loss materials demonstrates equal transmittance and reflectance at some frequencies.In both the systems,a new type of total reflection phenomenon is observed.When the imaginary part of the refractive indices of waveguide segments is smaller than 10-5,the system shows bidirectional transparency with the transmittance tending to be 1 and reflectivity to be smaller than 10-8 at some bands.When the refractive indices of the waveguide segments are real,the system will be bidirectional transparent at the full band.These findings may deepen the understanding of anti-PT-symmetric optical systems and optical waveguide networks,and possess potential applications in efficient optical energy storage,ultra-sensitive optical filters,ultra-sensitive all-optical switches,integrated optical chips,stealth physics,and so on.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
