Rational design of electrocatalysts is important for a sustainable oxygen evolution reaction(OER).It is still a huge challenge to engineer active sites in multi-sizes and multi-components simultaneously.Here,a series ...Rational design of electrocatalysts is important for a sustainable oxygen evolution reaction(OER).It is still a huge challenge to engineer active sites in multi-sizes and multi-components simultaneously.Here,a series of CoxP nanoparticles(NPs)confined in an SiO2matrix(SiO2/CoxP)is designed and synthesized as OER electrocatalysts.The phosphorization of the hydrolyzed Co-phyllosilicate promotes the formation of ultrasmall and small Co2P and CoP.These are firmly confined in the SiO2matrix.The coupling of multi-size and multi-component CoxP catalysts can regulate reaction kinetics and electron transfer ability,enrich the active sites,and eventually promote the intrinsic OER activity.The SiO2matrix provides abundant porous structure and oxygen vacancies,and these facilitate the exposure of active sites and improve conductivity.Because of the synergy and interplay of multisized/component CoxP NPs and the porous SiO2matrix,the unique SiO2/CoxP heterostructure exhibits low overpotential(293 m V@10 mA cm-2),and robust stability(decay 12 mV after 5000 CV cycles,97.4%of initial current after 100 h chronoamperometric)for the OER process,exceeding many advanced metal phosphide electrocatalysts.This work provides a novel tactic to design low-cost,simple,and highly efficient OER electrocatalysts.展开更多
With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the netw...With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.展开更多
Aimed at the computational aeroacoustics multi-scale problem of complex configurations discretized with multi-size mesh, the flux reconstruction method based on modified Weight Essentially Non-Oscillatory（WENO） sche...Aimed at the computational aeroacoustics multi-scale problem of complex configurations discretized with multi-size mesh, the flux reconstruction method based on modified Weight Essentially Non-Oscillatory（WENO） scheme is proposed at the interfaces of multi-block grids.With the idea of Dispersion-Relation-Preserving（DRP） scheme, different weight coefficients are obtained by optimization, so that it is in WENO schemes with various characteristics of dispersion and dissipation. On the basis, hybrid flux vector splitting method is utilized to intelligently judge the amplitude of the gap between grid interfaces. After the simulation and analysis of 1D convection equation with different initial conditions, modified WENO scheme is proved to be able to independently distinguish the gap amplitude and generate corresponding dissipation according to the grid resolution. Using the idea of flux reconstruction at grid interfaces, modified WENO scheme with increasing dissipation is applied at grid points, while DRP scheme with low dispersion and dissipation is applied at the inner part of grids. Moreover, Gauss impulse spread and periodic point sound source flow among three cylinders with multi-scale grids are carried out. The results show that the flux reconstruction method at grid interfaces is capable of dealing with Computational Aero Acoustics（CAA） multi-scale problems.展开更多
It has been revealed that the different morphologies of anodized TiO_2 nanotubes, especially nanotube diameters, triggered different cell behaviors. However, the influence of TiO_2 nanotubes with coexisting multi-size...It has been revealed that the different morphologies of anodized TiO_2 nanotubes, especially nanotube diameters, triggered different cell behaviors. However, the influence of TiO_2 nanotubes with coexisting multi-size diameters on cell behaviors is seldom reported. In this work, coexisting four-diameter TiO_2 nanotube samples, namely,one single substrate with the integration of four different nanotube diameters(60, 150, 250, and 350 nm), were prepared by repeated anodization. The boundaries between two different diameter regions show well-organized structure without obvious difference in height. The adhesion behaviors of MC3T3-E1 cells on the coexisting fourdiameter TiO_2 nanotube arrays were investigated. The results exhibit a significant difference of cell density between smaller diameters(60 and 150 nm) and larger diameters(250 and 350 nm) within 24 h incubation with the coexistence of different diameters, which is totally different from that on the single-diameter TiO_2 nanotube arrays. The coexistence of four different diameters does not change greatly the cell morphologies compared with the singlediameter nanotubes. The findings in this work are expected to offer further understanding of the interaction between cells and materials.展开更多
Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditi...Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma.展开更多
基金supported by the Training Program for Academic and Technical Leaders of Major Disciplines in Jiangxi Province(No.20212BCJ23020)the Science and Technology Project of Jiangxi Provincial Department of Education(No.GJJ211305)+1 种基金the National Natural Science Foundation of China(No.51671010)the National University Students Innovation and Entrepreneurship Training Program(No.202110408005)。
文摘Rational design of electrocatalysts is important for a sustainable oxygen evolution reaction(OER).It is still a huge challenge to engineer active sites in multi-sizes and multi-components simultaneously.Here,a series of CoxP nanoparticles(NPs)confined in an SiO2matrix(SiO2/CoxP)is designed and synthesized as OER electrocatalysts.The phosphorization of the hydrolyzed Co-phyllosilicate promotes the formation of ultrasmall and small Co2P and CoP.These are firmly confined in the SiO2matrix.The coupling of multi-size and multi-component CoxP catalysts can regulate reaction kinetics and electron transfer ability,enrich the active sites,and eventually promote the intrinsic OER activity.The SiO2matrix provides abundant porous structure and oxygen vacancies,and these facilitate the exposure of active sites and improve conductivity.Because of the synergy and interplay of multisized/component CoxP NPs and the porous SiO2matrix,the unique SiO2/CoxP heterostructure exhibits low overpotential(293 m V@10 mA cm-2),and robust stability(decay 12 mV after 5000 CV cycles,97.4%of initial current after 100 h chronoamperometric)for the OER process,exceeding many advanced metal phosphide electrocatalysts.This work provides a novel tactic to design low-cost,simple,and highly efficient OER electrocatalysts.
文摘With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.
文摘Aimed at the computational aeroacoustics multi-scale problem of complex configurations discretized with multi-size mesh, the flux reconstruction method based on modified Weight Essentially Non-Oscillatory（WENO） scheme is proposed at the interfaces of multi-block grids.With the idea of Dispersion-Relation-Preserving（DRP） scheme, different weight coefficients are obtained by optimization, so that it is in WENO schemes with various characteristics of dispersion and dissipation. On the basis, hybrid flux vector splitting method is utilized to intelligently judge the amplitude of the gap between grid interfaces. After the simulation and analysis of 1D convection equation with different initial conditions, modified WENO scheme is proved to be able to independently distinguish the gap amplitude and generate corresponding dissipation according to the grid resolution. Using the idea of flux reconstruction at grid interfaces, modified WENO scheme with increasing dissipation is applied at grid points, while DRP scheme with low dispersion and dissipation is applied at the inner part of grids. Moreover, Gauss impulse spread and periodic point sound source flow among three cylinders with multi-scale grids are carried out. The results show that the flux reconstruction method at grid interfaces is capable of dealing with Computational Aero Acoustics（CAA） multi-scale problems.
基金supported by the National Natural Science Foundation of China(No.51401126,No.51271117)Shanghai Committee of Science and Technology,China(No.14441901800)
文摘It has been revealed that the different morphologies of anodized TiO_2 nanotubes, especially nanotube diameters, triggered different cell behaviors. However, the influence of TiO_2 nanotubes with coexisting multi-size diameters on cell behaviors is seldom reported. In this work, coexisting four-diameter TiO_2 nanotube samples, namely,one single substrate with the integration of four different nanotube diameters(60, 150, 250, and 350 nm), were prepared by repeated anodization. The boundaries between two different diameter regions show well-organized structure without obvious difference in height. The adhesion behaviors of MC3T3-E1 cells on the coexisting fourdiameter TiO_2 nanotube arrays were investigated. The results exhibit a significant difference of cell density between smaller diameters(60 and 150 nm) and larger diameters(250 and 350 nm) within 24 h incubation with the coexistence of different diameters, which is totally different from that on the single-diameter TiO_2 nanotube arrays. The coexistence of four different diameters does not change greatly the cell morphologies compared with the singlediameter nanotubes. The findings in this work are expected to offer further understanding of the interaction between cells and materials.
基金This paper is supported by the National Youth Natural Science Foundation of China(61802208)the National Natural Science Foundation of China(61873131)+5 种基金the Natural Science Foundation of Anhui(1908085MF207 and 1908085QE217)the Key Research Project of Anhui Natural Science(KJ2020A1215 and KJ2020A1216)the Excellent Youth Talent Support Foundation of Anhui(gxyqZD2019097)the Postdoctoral Foundation of Jiangsu(2018K009B)the Higher Education Quality Project of Anhui(2019sjjd81,2018mooc059,2018kfk009,2018sxzx38 and 2018FXJT02)the Fuyang Normal University Doctoral Startup Foundation(2017KYQD0008).
文摘Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma.
