In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix sp...The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.展开更多
This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we...This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strateg...Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
Carbon dots(CDs)have been widely studied since their discovery because of simple preparation,low toxicity and excellent luminescence properties.With the deepening of research,the luminescence properties of CDs are not...Carbon dots(CDs)have been widely studied since their discovery because of simple preparation,low toxicity and excellent luminescence properties.With the deepening of research,the luminescence properties of CDs are not only limited to fluorescence,but also their afterglow properties have been widely studied.Many excellent results have been reported for afterglow CDs.Researchers have found that various organic matrixes(OMs)can fix the emission properties of CDs and provide a rigid environment,and the interaction between OMs and CDs can inhibit the non-radiative transition of triplet excitons,which can effectively activate the afterglow performance of CDs.In this review,we provide a detailed introduction to the research progress on afterglow CDs in OMs.The preparation of afterglow CDs and their related properties were analyzed and summarized based on organic polymer matrixes and organic small molecule matrixes.Organic polymer matrixes from synthetic polymers and natural polymers have been introduced.Then,the mechanism of solid and liquid afterglow of CDs by OMs is analyzed,and their applications in the fields of anti-counterfeiting,information encryption,phosphorescence detection,fingerprint recognition,lighting and so on are summarized.Finally,the challenges facing afterglow CDs in OMs are summarized,and future research is proposed.展开更多
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distr...Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
Traditional nerve repair methods,such as autologous nerve grafting and allogeneic nerve grafting,face issues such as donor shortage,functional loss,and immune rejection.Decellularized extracellular matrix-based grafts...Traditional nerve repair methods,such as autologous nerve grafting and allogeneic nerve grafting,face issues such as donor shortage,functional loss,and immune rejection.Decellularized extracellular matrix-based grafts have emerged as highly promising alternatives,capable of uniquely recreating the natural neural mic roenvironment,promoting host cell remodeling,and ultimately enhancing functional neural regeneration.This review comprehensively analyzes the key mechanisms of peripheral nerve injury and regeneration,focusing on contemporary therapeutic strategies for key aspects such as axonal apoptosis inhibition,enhanced intrinsic regenerative capacity,construction of regenerative microenvironment,and prevention of target organ atrophy.Findings from this review has shown that decellularized extra cellular matrix grafts can promote the migration,prolife ration,and differentiation of nerve cells by providing physical suppo rt,chemical signals,and mechanical stability.Decellularized extracellular matrix grafts are mainly used as ne rve conduits,scaffolds,hydrogels,and3D printing inks.Decellularized extra cellular matrix grafts have demonstrated significant advantages in promoting nerve regeneration by regulating the prolife ration and differentiation of Schwann cells,improving the neural microenvironment,reducing inflammato ry responses,and promoting angiogenesis.Additionally,decellularized extracellular matrix grafts can se rve as drug carrie rs,enabling the controlled release of growth factors,which further enhances nerve regeneration.However,these grafts also have some limitations,including the presence of immunogenic residues,inadequate mechanical prope rties,inter-batch variability,and uncontrollable degradation rates.Future research should focus on optimizing the decellularization process,enhancing the mechanical prope rties of decellularized extracellular matrix grafts,reducing immunogenicity,improving biocompatibility and safety,and developing new composite mate rials.Furthermore,exploring their application potential in complex nerve injuries,such as diabetic neuropathy,is crucial to meet the needs of peripheral nerve regeneration and repair.展开更多
We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-l...We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-like state withoutapoptosis.The results obtained by the authors highlight the features of theeffects of senescent drift induction in surrounding tissues.In the light of thesefindings,the role of the properties of extracellular matrix and cellular glycocalyxin responses of human tumors to therapy remain uninvestigated.These extracellularbarriers appear to be significant obstacles to effective cancer therapy,especiallyin relation to the use of unique properties of tumor microenvironment forthe immunotherapy-resistant cancer treatment.展开更多
Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which req...Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.展开更多
The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color...The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.展开更多
With the rapid advancement of electromagnetic launch technology,enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railg...With the rapid advancement of electromagnetic launch technology,enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems.Traditional aluminum alloy armatures often suffer from severe ablation,deformation,and uneven current distribution under high pulsed currents,which limit their performance and service life.To address these challenges,this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions.The temperature,stress,and strain of the armatures during operation were analyzed to investigate the effects of different graphene volume fractions on the deformation and damage behavior of aluminum matrix composite armatures under the multi-field coupling of electromagnetic,thermal,and structural interactions.The results indicate that,compared to the 6061 aluminum alloy matrix,the graphene-reinforced aluminum matrix composite armature significantly suppresses ablation damage at the tail and throat edges.The incorporation of graphene notably reduces the temperature rise during the armature emission process,increases the muzzle velocity under identical current excitation,and mitigates directional deformation of the armature.The 1 wt.% graphene-reinforced aluminum matrix composite armature demonstrates better agreement with experimental results at a strain rate of 2000 s^(-1),while simultaneously improving stress-strain response,reducing temperature rise,and improving velocity performance.展开更多
Let R be a reduced ring,and k,n be integers with 1≤k≤n.We construct a special subring Tk,n(R),relative to endomorphisms of R,of the upper triangular matrix ring Tn(R)over R and show that Tk,n(R)is semicommutative an...Let R be a reduced ring,and k,n be integers with 1≤k≤n.We construct a special subring Tk,n(R),relative to endomorphisms of R,of the upper triangular matrix ring Tn(R)over R and show that Tk,n(R)is semicommutative and Armendariz.Our results yield more examples of semicommutative and Armendariz rings.Also,the maximality of Tk,n(R)in some rings are discussed.展开更多
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singular...In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singularity categories for a formal triangular matrix ring,respectively.As an application,we study the pure global dimension of a formal triangular matrix ring.展开更多
Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces ...Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.展开更多
Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO...Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.展开更多
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金The National Natural Science Foundations of China (12202219)the Natural Science Foundations of Ningxia (2024AAC02009, 2023AAC05001)the Ningxia Youth Top Talents Training Project。
文摘The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.
文摘This work deals with an inverse scattering problem for the Schrodinger operator on a star-shaped graph with one semi-infinite branch.Using the high-frequency asymptotic behaviour of the reflection coefficient,first we provide the identifiability of the geometry of this star-shaped graph:the number of edges and their lengths.Under some assumptions on the geometry of the graph,the main result states that the measurement of one reflection coefficient,together with the scattering data corresponding to the infinite branch,associated with Robin boundary conditions at the external nodes of the graph,can uniquely determine the parameters of the boundary conditions and the potentials on the whole interval which is already known in a half-interval.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.51576097,51976089)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(No.BCXJ24-05)the Aeronautical Science Foundation of China(No.2023L060052001).
文摘Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
基金the Youth Talent Program Startup Foundation of Qufu Normal University(No.602601)the Natural Science Foundation of Rizhao(No.RZ2021ZR37)the Natural Science Foundation of Shandong(No.ZR2022MB047)。
文摘Carbon dots(CDs)have been widely studied since their discovery because of simple preparation,low toxicity and excellent luminescence properties.With the deepening of research,the luminescence properties of CDs are not only limited to fluorescence,but also their afterglow properties have been widely studied.Many excellent results have been reported for afterglow CDs.Researchers have found that various organic matrixes(OMs)can fix the emission properties of CDs and provide a rigid environment,and the interaction between OMs and CDs can inhibit the non-radiative transition of triplet excitons,which can effectively activate the afterglow performance of CDs.In this review,we provide a detailed introduction to the research progress on afterglow CDs in OMs.The preparation of afterglow CDs and their related properties were analyzed and summarized based on organic polymer matrixes and organic small molecule matrixes.Organic polymer matrixes from synthetic polymers and natural polymers have been introduced.Then,the mechanism of solid and liquid afterglow of CDs by OMs is analyzed,and their applications in the fields of anti-counterfeiting,information encryption,phosphorescence detection,fingerprint recognition,lighting and so on are summarized.Finally,the challenges facing afterglow CDs in OMs are summarized,and future research is proposed.
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金funded by theWuxi“Taihu Light”Science and Technology Key Project(Basic Research)(K20241046)the National Natural Science Foundation of China(Grant Nos.62102189,62122032,42305158)+1 种基金the Open Project of the National Engineering Research Center for Sensor Networks(2024YJZXKFKT02)Wuxi University Research Start-up Fund for High-Level Talents(No.2022r043).
文摘Generative steganography uses generative stego images to transmit secret message.It also effectively defends against statistical steganalysis.However,most existing methods focus primarily on matching the feature distribution of training data,often neglecting the sequential continuity between moves in the game.This oversight can result in unnatural patterns that deviate from real user behavior,thereby reducing the security of the hidden communication.To address this issue,we design a Gomoku agent based on the AlphaZero algorithm.The model engages in self-play to generate a sequence of plausible moves.These moves formthe basis of the stego images.We then apply an attractionmatrix at each step.It guides themove selection so that themoves appearmore natural.Thismethod helps maintain logical flow between moves.It also extends the game length,which increases the embedding capacity.Next,we filter and prioritize the generated moves.The selected moves are embedded into a move pool.Secret message is mapped to thesemoves.It is then embedded step by step as the game progresses.The finalmove sequence constitutes a complete steganographic game record.The receiver can extract the secret message using this record and a predefined mapping rule.Experiments show that our method reaches a maximum embedding capacity of 223 bits per carrier.Detection accuracy is 0.500 under XuNet and 0.498 under YeNet.These results are equal to random guessing,showing strong imperceptibility.The proposed method demonstrates superior concealment,higher embedding capacity,and greater robustness against common image distortions and steganalysis attacks.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金National Natural Science Foundation of China,No.32130060,No.81901256Jiangsu College Students Innovation and En trepreneurship Training Program,No.202310304120Y,No.202313993004Y2024 Medical Research Project by the Jiangsu Commission of Health,No.M2024009。
文摘Traditional nerve repair methods,such as autologous nerve grafting and allogeneic nerve grafting,face issues such as donor shortage,functional loss,and immune rejection.Decellularized extracellular matrix-based grafts have emerged as highly promising alternatives,capable of uniquely recreating the natural neural mic roenvironment,promoting host cell remodeling,and ultimately enhancing functional neural regeneration.This review comprehensively analyzes the key mechanisms of peripheral nerve injury and regeneration,focusing on contemporary therapeutic strategies for key aspects such as axonal apoptosis inhibition,enhanced intrinsic regenerative capacity,construction of regenerative microenvironment,and prevention of target organ atrophy.Findings from this review has shown that decellularized extra cellular matrix grafts can promote the migration,prolife ration,and differentiation of nerve cells by providing physical suppo rt,chemical signals,and mechanical stability.Decellularized extracellular matrix grafts are mainly used as ne rve conduits,scaffolds,hydrogels,and3D printing inks.Decellularized extra cellular matrix grafts have demonstrated significant advantages in promoting nerve regeneration by regulating the prolife ration and differentiation of Schwann cells,improving the neural microenvironment,reducing inflammato ry responses,and promoting angiogenesis.Additionally,decellularized extracellular matrix grafts can se rve as drug carrie rs,enabling the controlled release of growth factors,which further enhances nerve regeneration.However,these grafts also have some limitations,including the presence of immunogenic residues,inadequate mechanical prope rties,inter-batch variability,and uncontrollable degradation rates.Future research should focus on optimizing the decellularization process,enhancing the mechanical prope rties of decellularized extracellular matrix grafts,reducing immunogenicity,improving biocompatibility and safety,and developing new composite mate rials.Furthermore,exploring their application potential in complex nerve injuries,such as diabetic neuropathy,is crucial to meet the needs of peripheral nerve regeneration and repair.
文摘We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-like state withoutapoptosis.The results obtained by the authors highlight the features of theeffects of senescent drift induction in surrounding tissues.In the light of thesefindings,the role of the properties of extracellular matrix and cellular glycocalyxin responses of human tumors to therapy remain uninvestigated.These extracellularbarriers appear to be significant obstacles to effective cancer therapy,especiallyin relation to the use of unique properties of tumor microenvironment forthe immunotherapy-resistant cancer treatment.
基金supported in part by the National Science Foundation under GrantsDMS 2436630 and 2436629.
文摘Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.
基金supported by the Key R&D Program of Shaanxi Province,China(2024NC-YBXM-146)the Xi’an Agricultural Technology Research and Development Project,China(24NYGG0048)+1 种基金the Key R&D Program of Xianyang,China(L2024-ZDYF-ZDYF-NY-0028)the National Foreign Expert Project of China(G2023172002L)。
文摘The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.
基金funded Basic Research Projects of Higher Education Institutions in Liaoning Province(JYTZD20230004)Future Industry Frontier Technology Project in Liaoning Province in 2025(2025JH2/101330141)Key Research and Development Program of Liaoning Province in 2025.
文摘With the rapid advancement of electromagnetic launch technology,enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems.Traditional aluminum alloy armatures often suffer from severe ablation,deformation,and uneven current distribution under high pulsed currents,which limit their performance and service life.To address these challenges,this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions.The temperature,stress,and strain of the armatures during operation were analyzed to investigate the effects of different graphene volume fractions on the deformation and damage behavior of aluminum matrix composite armatures under the multi-field coupling of electromagnetic,thermal,and structural interactions.The results indicate that,compared to the 6061 aluminum alloy matrix,the graphene-reinforced aluminum matrix composite armature significantly suppresses ablation damage at the tail and throat edges.The incorporation of graphene notably reduces the temperature rise during the armature emission process,increases the muzzle velocity under identical current excitation,and mitigates directional deformation of the armature.The 1 wt.% graphene-reinforced aluminum matrix composite armature demonstrates better agreement with experimental results at a strain rate of 2000 s^(-1),while simultaneously improving stress-strain response,reducing temperature rise,and improving velocity performance.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12161049,12361008).
文摘Let R be a reduced ring,and k,n be integers with 1≤k≤n.We construct a special subring Tk,n(R),relative to endomorphisms of R,of the upper triangular matrix ring Tn(R)over R and show that Tk,n(R)is semicommutative and Armendariz.Our results yield more examples of semicommutative and Armendariz rings.Also,the maximality of Tk,n(R)in some rings are discussed.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
基金Supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2025L092)the National Natural Science Foundation of China(Grant No.12071120).
文摘In this paper,we identify conditions on the change of rings to induce functors between the two pure derived(resp.,pure singularity)categories.Then we construct recollements of pure derived categories and pure singularity categories for a formal triangular matrix ring,respectively.As an application,we study the pure global dimension of a formal triangular matrix ring.
文摘Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.
基金supported by the FNRS-FRFC,the Walloon Region,and the University of Namur(Conventions No.2.5020.11,GEQ U.G006.15,1610468,RW/GEQ2016 et U.G011.22)funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement n°101034383。
文摘Efficient solar light harvesting is essential for high-performance photocatalysts.Here,Rigorous CoupledWave Analysis(RCWA)computational method is used to investigate and optimize the optical absorption of TiO_(2)-BiVO_(4) inverse opal(IO)structures under varying light incidence angles and pore-filling medium(air or water).Simulations were validated against experimental reflectance data.They revealed that small-pore IOs strongly absorb in the UV-C and UV-B regions due to the slow photon effect,making them ideal for sterilization and water disinfection.Medium-and large-pore IOs benefit from additional slow photon effect at the 2nd order photonic band gap,enhancing absorption across both UV and visible regions.Medium-pore IOs are suited for indoor air treatment and water purification,while large-pore IOs with the highest photon flux enhancement enable solar-driven photocatalysis such as outdoor pollutant removal and hydrogen production.For all tested IO designs,the absorbed photon flux exceeds that of equivalent planar slabs,highlighting the advantage of photonic structuring for sustainable photocatalytic applications.
