Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting...Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.展开更多
Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in hum...Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.展开更多
Most path integral expressions for quantum open systems are formulated with diagonal systembath coupling,where the influence functional is a functional of scalar-valued trajectories.This formalism is enough if only a ...Most path integral expressions for quantum open systems are formulated with diagonal systembath coupling,where the influence functional is a functional of scalar-valued trajectories.This formalism is enough if only a single bath is under consideration.However,when multiple baths are present,non-diagonal system-bath couplings need to be taken into consideration.In such a situation,using an abstract Liouvillian method,the influence functional can be obtained as a functional of operator-valued trajectories.The value of the influence functional itself also becomes a superoperator rather than an ordinary scalar,whose meaning is ambiguous at first glance and its connection to the conventional understanding of the influence functional needs extra careful consideration.In this article,we present another concrete derivation of the superoperator-valued influence functional based on the straightforward Trotter-Suzuki splitting,which can provide a clear picture to interpret the superoperator-valued influence functional.展开更多
The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in mic...The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array(FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge(SOC)equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.展开更多
The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. V...The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP's Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code's design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies.展开更多
In this paper,we discuss the numerical accuracy of asymptotic homogenization method(AHM)and multiscale finite element method(MsFEM)for periodic composite materials.Through numerical calculation of the model problems f...In this paper,we discuss the numerical accuracy of asymptotic homogenization method(AHM)and multiscale finite element method(MsFEM)for periodic composite materials.Through numerical calculation of the model problems for four kinds of typical periodic composite materials,the main factors to determine the accuracy of first-order AHM and second-order AHM are found,and the physical interpretation of these factors is given.Furthermore,the way to recover multiscale solutions of first-order AHM and MsFEM is theoretically analyzed,and it is found that first-order AHM and MsFEM provide similar multiscale solutions under some assumptions.Finally,numerical experiments verify that MsFEM is essentially a first-order multiscale method for periodic composite materials.展开更多
Earthquake induced liquefaction is one of the main geo-disasters threating urban regions, which not only causes direct damages to buildings, but also delays both real-time disaster relief actions and reconstruction ac...Earthquake induced liquefaction is one of the main geo-disasters threating urban regions, which not only causes direct damages to buildings, but also delays both real-time disaster relief actions and reconstruction activities. It is thus important to assess liquefaction hazard of urban regions effectively and efficiently for disaster prevention and mitigation. Conventional assessment approaches rely on engineering indices such as the factor of safety(FS) against liquefaction, which cannot take into account directly the uncertainties of soils. In contrast, a physics simulation-based approach, by solving soil dynamics problems coupled with excess pore water pressure(EPWP) it is possible to model the uncertainties directly via Monte Carlo simulations. In this study, we demonstrate the capability of such an approach for assessing an urban region with over 10 000 sites. The permeability parameters are assumed to follow a base-10-lognormal distribution among 100 model analyses for each site. A dynamic simulation is conducted for each model analysis to obtain the EPWP results. Based on over 1 million EPWP analysis models, we obtained a probabilistic liquefaction assessment. Empowered by high performance computing, we present for the first time a probabilistic liquefaction hazard assessment for urban regions based on dynamics analysis, which consider soil uncertainties.展开更多
The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. ...The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.展开更多
In this paper,we propose a stable heat jet approach for accurate temperature control of the nonlinear Fermi-Pasta-Ulam beta(FPU-β)chain.First,we design a stable nonlinear boundary condition,with co-efficients determi...In this paper,we propose a stable heat jet approach for accurate temperature control of the nonlinear Fermi-Pasta-Ulam beta(FPU-β)chain.First,we design a stable nonlinear boundary condition,with co-efficients determined by a machine learning technique.Its stability can be proved rigorously.Based on this stable boundary condition,we derive a two-way boundary condition complying with phonon heat source,and construct stable heat jet approach.Numerical tests illustrate the stability of the boundary condition and the effectiveness in eliminating boundary reflections.Furthermore,we extend the bound-ary condition formulation with more atoms,and train the coefficients to eliminate extreme short waves by machine learning technique.Under this extended boundary condition,the heat jet approach is effec-tive for high temperature,and may be adopted for multiscale computation of atomic motion at finite temperature.展开更多
Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid ...Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).展开更多
Surfactant molecules, when dispersed in solution, have been shown to spontaneously form aggregates. Our previous studies on molecular dynamics(MD) calculations have shown that ionic sodium dodecyl sulfate molecules qu...Surfactant molecules, when dispersed in solution, have been shown to spontaneously form aggregates. Our previous studies on molecular dynamics(MD) calculations have shown that ionic sodium dodecyl sulfate molecules quickly aggregated even when the aggregation number is small. The aggregation rate, however, decreased for larger aggregation numbers. In addition, studies have shown that micelle formation was not completed even after a 100 ns-long MD run(Chem. Phys. Lett. 2016, 646, 36). Herein, we analyze the free energy change of micelle formation based on chemical species model combined with molecular dynamics calculations. First, the free energy landscape of the aggregation, ?G_(i+j)^+, where two aggregates with sizes i and j associate to form the(i + j)-mer, was investigated using the free energy of micelle formation of the i-mer, G_i^+, which was obtained through MD calculations. The calculated ?G_(i+j)^+ was negative for all the aggregations where the sum of DS ions in the two aggregates was 60 or less. From the viewpoint of chemical equilibrium, aggregation to the stable micelle is desired. Further, the free energy profile along possible aggregation pathways was investigated, starting from small aggregates and ending with the complete thermodynamically stable micelles in solution. The free energy profiles, G(l, k), of the aggregates at l-th aggregation path and k-th state were evaluated by the formation free energy ∑_in_i( l,k)G_i^+ and the free energy of mixing ∑_in_i( l,k)k_BTln( n_i( l,k)/n( l,k)), where ni(l, k) is the number of i-mer in the system at the l-th i aggregation path and k-th state, with n(l,k)= ∑_n_i( l,k). All the aggregation pathways were obtained from the initial i state of 12 pentamers to the stable micelle with i = 60. All the calculated G(l, k) values monotonically decreased with increasing k. This indicates that there are no free energy barriers along the pathways. Hence, the slowdown is not due to the thermodynamic stability of the aggregates, but rather the kinetics that inhibit the association of the fragments. The time required for a collision between aggregates, one of the kinetic factors, was evaluated using the fast passage time, t_(FPT). The calculated t_(FPT) was about 20 ns for the aggregates with N = 31. Therefore, if aggregation is a diffusion-controlled process, it should be completed within the 100 ns-simulation. However, aggregation does not occur due to the free energy barrier between the aggregates, that is, the repulsive force acting on them. This may be caused by electrostatic repulsions produced by the overlap of the electric double layers, which are formed by the negative charge of the hydrophilic groups and counter sodium ions on the surface of the aggregates.展开更多
A triaxially deformed relativistic Hartree–Bogoliubov theory in the Woods–Saxon basis is developed with the aim of treating the triaxial deformation,pairing correlations and continuum in a unified way.In order to co...A triaxially deformed relativistic Hartree–Bogoliubov theory in the Woods–Saxon basis is developed with the aim of treating the triaxial deformation,pairing correlations and continuum in a unified way.In order to consider the triaxial deformation,the deformed potentials are expanded in terms of spherical harmonic functions in the coordinate space.In order to take the pairing correlations into account and treat the continuum properly,by using the Dirac Woods–Saxon basis,which has correct asymptotic behavior,the relativistic Hartree–Bogoliubov equation with triaxial deformation is solved.The formalism of triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis is presented.Taking an axially deformed nucleus24Ne and a triaxially deformed nucleus76Ge as examples,the numerical checks are performed.A weakly bound nucleus112Ge is taken as an example to carry out the necessary converge checks for the numerical parameters.In addition,the ground-state properties of even–even germanium isotopes are investigated.The evolutions of two-neutron separation energy,deformation,root-mean-square radii and density distribution with mass number are analyzed.The comparison between the calculations from the relativistic Hartree–Bogoliubov theory based on harmonic-oscillator basis and the triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis is performed.It is found that the neutron drip line is extended from114Ge to118Ge in the triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis.展开更多
The work studies model reduction method for nonlinear systems based on proper orthogonal decomposition (POD)and discrete empirical interpolation method (DEIM). Instead of using the classical DEIM to directly approxima...The work studies model reduction method for nonlinear systems based on proper orthogonal decomposition (POD)and discrete empirical interpolation method (DEIM). Instead of using the classical DEIM to directly approximate thenonlinear term of a system, our approach extracts the main part of the nonlinear term with a linear approximation beforeapproximating the residual with the DEIM. We construct the linear term by Taylor series expansion and dynamic modedecomposition (DMD), respectively, so as to obtain a more accurate reconstruction of the nonlinear term. In addition, anovel error prediction model is devised for the POD-DEIM reduced systems by employing neural networks with the aid oferror data. The error model is cheaply computable and can be adopted as a remedy model to enhance the reduction accuracy.Finally, numerical experiments are performed on two nonlinear problems to show the performance of the proposed method.展开更多
The stone chip resistance performance of automotive coatings has attracted increasing attention in academic and industrial communities.Even though traditional gravelometer tests can be used to evaluate stone chip resi...The stone chip resistance performance of automotive coatings has attracted increasing attention in academic and industrial communities.Even though traditional gravelometer tests can be used to evaluate stone chip resistance of automotive coatings,such experiment-based methods suffer from poor repeatability and high cost.The main purpose of this work is to develop a CFD-DEM-wear coupling method to accurately and efficiently simulate stone chipbehaviorof automotive coatings inagravelometer test.Toachieve this end,an approach coupling an unresolved computational fluid dynamics(CFD)method and a discrete element method(DEM)are employed to account for interactions between fluids and large particles.In order to accurately describe large particles,a rigid connection particle method is proposed.In doing so,each actual non-spherical particle can be approximately described by rigidly connecting a group of non-overlapping spheres,and particle-fluid interactions are simulated based on each component sphere.An erosion wear model is used to calculate the impact damage of coatings based on particlecoating interactions.Single spherical particle tests are performed to demonstrate the feasibility of the proposed rigid connection particle method under various air pressure conditions.Then,the developed CFD-DEM-wear model is applied to reproduce the stone chip behavior of two standard tests,i.e.,DIN 55996-1 and SAE-J400-2002 tests.Numerical results are found to be in good agreement with experimental data,which demonstrates the capacity of our developed method in stone chip resistance evaluation.Finally,parametric studies are conducted to numerically investigate the influences of initial velocity and test panel orientation on impact damage of automotive coatings.展开更多
A split-step second-order predictor-corrector method for space-fractional reaction-diffusion equations with nonhomogeneous boundary conditions is presented and analyzed for the stability and convergence.The matrix tra...A split-step second-order predictor-corrector method for space-fractional reaction-diffusion equations with nonhomogeneous boundary conditions is presented and analyzed for the stability and convergence.The matrix transfer technique is used for spatial discretization of the problem.The method is shown to be unconditionally stable and second-order convergent.Numerical experiments are performed to confirm the stability and secondorder convergence of the method.The split-step predictor-corrector method is also compared with an IMEX predictor-corrector method which is found to incur oscillatory behavior for some time steps.Our method is seen to produce reliable and oscillatioresults for any time step when implemented on numerical examples with nonsmooth initial data.We also present a priori reliability constraint for the IMEX predictor-corrector method to avoid unwanted oscillations and show its validity numerically.展开更多
We investigate the restart of the Restarted Shifted GMRES method for solving shifted linear systems. Recently the variant of the GMRES（m） method with the unfixed update has been proposed to improve the convergence o...We investigate the restart of the Restarted Shifted GMRES method for solving shifted linear systems. Recently the variant of the GMRES（m） method with the unfixed update has been proposed to improve the convergence of the GMRES（m） method for solving linear systems, and shown to have an efficient convergence property. In this paper, by applying the unfixed update to the Restarted Shifted GMRES method, we propose a variant of the Restarted Shifted GMRES method. We show a potentiality for efficient convergence within the variant by some numerical results.展开更多
We study the origin of the UV-excess in star clusters by performing N-body simulations of six clusters with N = 10 k and N = 100 k(single stars & binary systems) and metallicities of Z = 0.01, 0.001 and 0.0001, us...We study the origin of the UV-excess in star clusters by performing N-body simulations of six clusters with N = 10 k and N = 100 k(single stars & binary systems) and metallicities of Z = 0.01, 0.001 and 0.0001, using PETAR. All models initially have a 50% primordial binary fraction. Using Galev NB we convert the simulated data into synthetic spectra and photometry for the China Space Station Telescope(CSST) and Hubble Space Telescope(HST). From the spectral energy distributions we identify three stellar populations that contribute to the UVexcess:(1) second asymptotic giant branch stars, which contribute to the UV flux at early times;(2) naked helium stars and(3) white dwarfs, which are long-term contributors to the FUV spectra. Binary stars consisting of a white dwarf and a main sequence star are cataclysmic variable(CV) candidates. The magnitude distribution of CV candidates is bimodal up to 2 Gyr. The bright CV population is particularly bright in FUV-NUV. The FUV-NUV color of our model clusters is 1–2 mag redder than the UV-excess globular clusters in M87 and in the Milky Way. This discrepancy may be induced by helium enrichment in observed clusters. Our simulations are based on simple stellar evolution;we do not include the effects of variations in helium and light elements or multiple stellar populations. A positive radial color gradient is present in CSST NUV-y for main sequence stars in all models with a color difference of 0.2–0.5 mag, up to 4 half-mass radii. The CSST NUV-g color correlates strongly with HST FUV-NUV for NUV-g > 1 mag, with the linear relation FUV-NUV =(1.09 ± 0.12) ×(NUV-g) +(-1.01 ± 0.22). This allows for conversion of future CSST NUV-g colors into HST FUV-NUV colors, which are sensitive to UV-excess features. We find that CSST will be able to detect UVexcess in Galactic/extragalactic star clusters with ages >200 Myr.展开更多
Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling...Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.展开更多
The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architectu...The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.展开更多
The human central nervous system（CNS）has a markedly poor capacity for regenerating its axons following injury.This appears to be due to two main causes：1）a developmentally regulated decline in regenerative capacit...The human central nervous system（CNS）has a markedly poor capacity for regenerating its axons following injury.This appears to be due to two main causes：1）a developmentally regulated decline in regenerative capacity within mature CNS neurons,and 2）the presence of biological components that constitute barriers to axon regeneration（e.g.,growth-inhibitory molecules）.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.
基金Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)the Competitive Research Fund of The University of Aizu,Japan.
文摘Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.
基金supported by the National Natural Science Foundation of China under Grant No.12104328the Sichuan Science and Technology Program under Grant No.2024NSFSC1388。
文摘Most path integral expressions for quantum open systems are formulated with diagonal systembath coupling,where the influence functional is a functional of scalar-valued trajectories.This formalism is enough if only a single bath is under consideration.However,when multiple baths are present,non-diagonal system-bath couplings need to be taken into consideration.In such a situation,using an abstract Liouvillian method,the influence functional can be obtained as a functional of operator-valued trajectories.The value of the influence functional itself also becomes a superoperator rather than an ordinary scalar,whose meaning is ambiguous at first glance and its connection to the conventional understanding of the influence functional needs extra careful consideration.In this article,we present another concrete derivation of the superoperator-valued influence functional based on the straightforward Trotter-Suzuki splitting,which can provide a clear picture to interpret the superoperator-valued influence functional.
基金National Natural Science Foundation of China(51977160)“Voltage Self balancing Control Method for Modular Multilevel Converter Based on Switching State Matrix”.
文摘The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array(FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge(SOC)equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.
基金supported by the National Natural Science Eoundation of China under Grant No.40221503the China National Key Programme for Development Basic Sciences (Abbreviation:973 Project,Grant No.G1999032801)
文摘The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP's Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code's design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies.
基金the National Natural Science Foundation of China(No.11501449 and 11471262)the Center for high performance computing of Northwestern Polytechnical University.
文摘In this paper,we discuss the numerical accuracy of asymptotic homogenization method(AHM)and multiscale finite element method(MsFEM)for periodic composite materials.Through numerical calculation of the model problems for four kinds of typical periodic composite materials,the main factors to determine the accuracy of first-order AHM and second-order AHM are found,and the physical interpretation of these factors is given.Furthermore,the way to recover multiscale solutions of first-order AHM and MsFEM is theoretically analyzed,and it is found that first-order AHM and MsFEM provide similar multiscale solutions under some assumptions.Finally,numerical experiments verify that MsFEM is essentially a first-order multiscale method for periodic composite materials.
基金This research was supported by the FOCUS Establishing Supercomputing Center of Excellence。
文摘Earthquake induced liquefaction is one of the main geo-disasters threating urban regions, which not only causes direct damages to buildings, but also delays both real-time disaster relief actions and reconstruction activities. It is thus important to assess liquefaction hazard of urban regions effectively and efficiently for disaster prevention and mitigation. Conventional assessment approaches rely on engineering indices such as the factor of safety(FS) against liquefaction, which cannot take into account directly the uncertainties of soils. In contrast, a physics simulation-based approach, by solving soil dynamics problems coupled with excess pore water pressure(EPWP) it is possible to model the uncertainties directly via Monte Carlo simulations. In this study, we demonstrate the capability of such an approach for assessing an urban region with over 10 000 sites. The permeability parameters are assumed to follow a base-10-lognormal distribution among 100 model analyses for each site. A dynamic simulation is conducted for each model analysis to obtain the EPWP results. Based on over 1 million EPWP analysis models, we obtained a probabilistic liquefaction assessment. Empowered by high performance computing, we present for the first time a probabilistic liquefaction hazard assessment for urban regions based on dynamics analysis, which consider soil uncertainties.
基金the Deutsche Forschungsgemeinschaft (German Research Foundation),DAAD (German Academic Exchange Service) and FUNDAYACUCHO, and Bayer Technology Services
文摘The design of optimal separation flow sheets for multi-component mixtures is still not a solved problem This is especially the case when non-ideal or azeotropic mixtures or hybrid separation processes are considered. We review recent developments in this field and present a systematic framework for the design of separation flow sheets. This framework proposes a three-step approach. In the first step different flow sheets are generated. In the second step these alternative flow sheet structures are evaluated with shortcut methods. In the third step a rigorous mixed-integer nonlinear programming （MINLP） optimization of the entire flow sheet is executed to determine the best alternative. Since a number of alternative flow sheets have already been eliminated, only a few optimization runs are necessary in this final step. The whole framework thus allows the systematic generation and evaluation of separation processes and is illustrated with the case study of the separation of ethanol and water.
基金partially supported by the National Natural Science Foundation of China (Grants 11988102, 11521202, 11832001, and 11890681)
文摘In this paper,we propose a stable heat jet approach for accurate temperature control of the nonlinear Fermi-Pasta-Ulam beta(FPU-β)chain.First,we design a stable nonlinear boundary condition,with co-efficients determined by a machine learning technique.Its stability can be proved rigorously.Based on this stable boundary condition,we derive a two-way boundary condition complying with phonon heat source,and construct stable heat jet approach.Numerical tests illustrate the stability of the boundary condition and the effectiveness in eliminating boundary reflections.Furthermore,we extend the bound-ary condition formulation with more atoms,and train the coefficients to eliminate extreme short waves by machine learning technique.Under this extended boundary condition,the heat jet approach is effec-tive for high temperature,and may be adopted for multiscale computation of atomic motion at finite temperature.
基金the support of the Leverhulme Centre for Wildfires,Environment and Society through the Leverhulme Trust(RC-2018-023)Sibo Cheng,César Quilodran-Casas,and Rossella Arcucci acknowledge the support of the PREMIERE project(EP/T000414/1)+5 种基金the support of EPSRC grant:PURIFY(EP/V000756/1)the Fundamental Research Funds for the Central Universitiesthe support of the SASIP project(353)funded by Schmidt Futures–a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologiesDFG for the Heisenberg Programm Award(JA 1077/4-1)the National Natural Science Foundation of China(61976120)the Natural Science Key Foundat ion of Jiangsu Education Department(21KJA510004)。
文摘Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).
基金This work was supported by FLAGSHIP2020,MEXT within Priority Study 5(Development of New Fundamental Technologies for High-Efficiency Energy Creation,Conversion/Storage and Use)Using Computational Resources of the K Computer Provided by the RIKEN Advanced
文摘Surfactant molecules, when dispersed in solution, have been shown to spontaneously form aggregates. Our previous studies on molecular dynamics(MD) calculations have shown that ionic sodium dodecyl sulfate molecules quickly aggregated even when the aggregation number is small. The aggregation rate, however, decreased for larger aggregation numbers. In addition, studies have shown that micelle formation was not completed even after a 100 ns-long MD run(Chem. Phys. Lett. 2016, 646, 36). Herein, we analyze the free energy change of micelle formation based on chemical species model combined with molecular dynamics calculations. First, the free energy landscape of the aggregation, ?G_(i+j)^+, where two aggregates with sizes i and j associate to form the(i + j)-mer, was investigated using the free energy of micelle formation of the i-mer, G_i^+, which was obtained through MD calculations. The calculated ?G_(i+j)^+ was negative for all the aggregations where the sum of DS ions in the two aggregates was 60 or less. From the viewpoint of chemical equilibrium, aggregation to the stable micelle is desired. Further, the free energy profile along possible aggregation pathways was investigated, starting from small aggregates and ending with the complete thermodynamically stable micelles in solution. The free energy profiles, G(l, k), of the aggregates at l-th aggregation path and k-th state were evaluated by the formation free energy ∑_in_i( l,k)G_i^+ and the free energy of mixing ∑_in_i( l,k)k_BTln( n_i( l,k)/n( l,k)), where ni(l, k) is the number of i-mer in the system at the l-th i aggregation path and k-th state, with n(l,k)= ∑_n_i( l,k). All the aggregation pathways were obtained from the initial i state of 12 pentamers to the stable micelle with i = 60. All the calculated G(l, k) values monotonically decreased with increasing k. This indicates that there are no free energy barriers along the pathways. Hence, the slowdown is not due to the thermodynamic stability of the aggregates, but rather the kinetics that inhibit the association of the fragments. The time required for a collision between aggregates, one of the kinetic factors, was evaluated using the fast passage time, t_(FPT). The calculated t_(FPT) was about 20 ns for the aggregates with N = 31. Therefore, if aggregation is a diffusion-controlled process, it should be completed within the 100 ns-simulation. However, aggregation does not occur due to the free energy barrier between the aggregates, that is, the repulsive force acting on them. This may be caused by electrostatic repulsions produced by the overlap of the electric double layers, which are formed by the negative charge of the hydrophilic groups and counter sodium ions on the surface of the aggregates.
基金the Sichuan Normal University for financial support(No.341813001)。
文摘A triaxially deformed relativistic Hartree–Bogoliubov theory in the Woods–Saxon basis is developed with the aim of treating the triaxial deformation,pairing correlations and continuum in a unified way.In order to consider the triaxial deformation,the deformed potentials are expanded in terms of spherical harmonic functions in the coordinate space.In order to take the pairing correlations into account and treat the continuum properly,by using the Dirac Woods–Saxon basis,which has correct asymptotic behavior,the relativistic Hartree–Bogoliubov equation with triaxial deformation is solved.The formalism of triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis is presented.Taking an axially deformed nucleus24Ne and a triaxially deformed nucleus76Ge as examples,the numerical checks are performed.A weakly bound nucleus112Ge is taken as an example to carry out the necessary converge checks for the numerical parameters.In addition,the ground-state properties of even–even germanium isotopes are investigated.The evolutions of two-neutron separation energy,deformation,root-mean-square radii and density distribution with mass number are analyzed.The comparison between the calculations from the relativistic Hartree–Bogoliubov theory based on harmonic-oscillator basis and the triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis is performed.It is found that the neutron drip line is extended from114Ge to118Ge in the triaxially deformed relativistic Hartree–Bogoliubov theory in Woods–Saxon basis.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11871400 and 11971386)the Natural Science Foundation of Shaanxi Province,China(Grant No.2017JM1019).
文摘The work studies model reduction method for nonlinear systems based on proper orthogonal decomposition (POD)and discrete empirical interpolation method (DEIM). Instead of using the classical DEIM to directly approximate thenonlinear term of a system, our approach extracts the main part of the nonlinear term with a linear approximation beforeapproximating the residual with the DEIM. We construct the linear term by Taylor series expansion and dynamic modedecomposition (DMD), respectively, so as to obtain a more accurate reconstruction of the nonlinear term. In addition, anovel error prediction model is devised for the POD-DEIM reduced systems by employing neural networks with the aid oferror data. The error model is cheaply computable and can be adopted as a remedy model to enhance the reduction accuracy.Finally, numerical experiments are performed on two nonlinear problems to show the performance of the proposed method.
基金supported by the National Key R&D Program of China(No.2017YFE0117300)the Science and Technology Planning Project of Guangzhou(No.201804020065)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.311021013).
文摘The stone chip resistance performance of automotive coatings has attracted increasing attention in academic and industrial communities.Even though traditional gravelometer tests can be used to evaluate stone chip resistance of automotive coatings,such experiment-based methods suffer from poor repeatability and high cost.The main purpose of this work is to develop a CFD-DEM-wear coupling method to accurately and efficiently simulate stone chipbehaviorof automotive coatings inagravelometer test.Toachieve this end,an approach coupling an unresolved computational fluid dynamics(CFD)method and a discrete element method(DEM)are employed to account for interactions between fluids and large particles.In order to accurately describe large particles,a rigid connection particle method is proposed.In doing so,each actual non-spherical particle can be approximately described by rigidly connecting a group of non-overlapping spheres,and particle-fluid interactions are simulated based on each component sphere.An erosion wear model is used to calculate the impact damage of coatings based on particlecoating interactions.Single spherical particle tests are performed to demonstrate the feasibility of the proposed rigid connection particle method under various air pressure conditions.Then,the developed CFD-DEM-wear model is applied to reproduce the stone chip behavior of two standard tests,i.e.,DIN 55996-1 and SAE-J400-2002 tests.Numerical results are found to be in good agreement with experimental data,which demonstrates the capacity of our developed method in stone chip resistance evaluation.Finally,parametric studies are conducted to numerically investigate the influences of initial velocity and test panel orientation on impact damage of automotive coatings.
文摘A split-step second-order predictor-corrector method for space-fractional reaction-diffusion equations with nonhomogeneous boundary conditions is presented and analyzed for the stability and convergence.The matrix transfer technique is used for spatial discretization of the problem.The method is shown to be unconditionally stable and second-order convergent.Numerical experiments are performed to confirm the stability and secondorder convergence of the method.The split-step predictor-corrector method is also compared with an IMEX predictor-corrector method which is found to incur oscillatory behavior for some time steps.Our method is seen to produce reliable and oscillatioresults for any time step when implemented on numerical examples with nonsmooth initial data.We also present a priori reliability constraint for the IMEX predictor-corrector method to avoid unwanted oscillations and show its validity numerically.
基金Supported by the Strategic Programs for Innovative Research(SPIRE)Field5"The origin of matter and the universe"
文摘We investigate the restart of the Restarted Shifted GMRES method for solving shifted linear systems. Recently the variant of the GMRES（m） method with the unfixed update has been proposed to improve the convergence of the GMRES（m） method for solving linear systems, and shown to have an efficient convergence property. In this paper, by applying the unfixed update to the Restarted Shifted GMRES method, we propose a variant of the Restarted Shifted GMRES method. We show a potentiality for efficient convergence within the variant by some numerical results.
基金Supported by the research grants from the China Manned Space Project with No. CMS-CSST-2021-A08。
文摘We study the origin of the UV-excess in star clusters by performing N-body simulations of six clusters with N = 10 k and N = 100 k(single stars & binary systems) and metallicities of Z = 0.01, 0.001 and 0.0001, using PETAR. All models initially have a 50% primordial binary fraction. Using Galev NB we convert the simulated data into synthetic spectra and photometry for the China Space Station Telescope(CSST) and Hubble Space Telescope(HST). From the spectral energy distributions we identify three stellar populations that contribute to the UVexcess:(1) second asymptotic giant branch stars, which contribute to the UV flux at early times;(2) naked helium stars and(3) white dwarfs, which are long-term contributors to the FUV spectra. Binary stars consisting of a white dwarf and a main sequence star are cataclysmic variable(CV) candidates. The magnitude distribution of CV candidates is bimodal up to 2 Gyr. The bright CV population is particularly bright in FUV-NUV. The FUV-NUV color of our model clusters is 1–2 mag redder than the UV-excess globular clusters in M87 and in the Milky Way. This discrepancy may be induced by helium enrichment in observed clusters. Our simulations are based on simple stellar evolution;we do not include the effects of variations in helium and light elements or multiple stellar populations. A positive radial color gradient is present in CSST NUV-y for main sequence stars in all models with a color difference of 0.2–0.5 mag, up to 4 half-mass radii. The CSST NUV-g color correlates strongly with HST FUV-NUV for NUV-g > 1 mag, with the linear relation FUV-NUV =(1.09 ± 0.12) ×(NUV-g) +(-1.01 ± 0.22). This allows for conversion of future CSST NUV-g colors into HST FUV-NUV colors, which are sensitive to UV-excess features. We find that CSST will be able to detect UVexcess in Galactic/extragalactic star clusters with ages >200 Myr.
基金This work was supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20204010600090).
文摘The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.
文摘The human central nervous system（CNS）has a markedly poor capacity for regenerating its axons following injury.This appears to be due to two main causes：1）a developmentally regulated decline in regenerative capacity within mature CNS neurons,and 2）the presence of biological components that constitute barriers to axon regeneration（e.g.,growth-inhibitory molecules）.
