The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere i...The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere in Germany as Humboldt fellows had led to the first Sino-German Workshop in Berlin held at the Humboldt-Universitat zu Berlin in 2005. The particular purpose of the second German-Chinese Workshop on Computational and Applied Mathematics was to attract more junior Chinese scientists to the actual research activities in Germany. A summer school in Beijing on adaptive finite element methods with Carsten Carstensen and Roll Rannacher piror to the Hangzhou workshop underlined this activity to foster the collaboration of the new generations in the fields of computational and applied mathematics. This special issue reflects the present topics therein in both countries and can be summarised under five headings （i）-（v）.展开更多
In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Lat...In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Later in October of 1979, based on this office CAS established the Institute of Applied Mathematics （IAM）. The first director of IAM was the world-wide famous mathematician, Professor HUA Loo-Keng, and most faculty members of IAM came from Institute of Mathematics within CAS, which was founded in July of 1952 and was also directed by Prof. HUA.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
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite cons...Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.展开更多
In order to understand the effects of cellular diffusion on the dynamic behaviors of cancer cell subpopulations,we establish a reaction-diffusion model of the competition between drug-sensitive and drug-resistant canc...In order to understand the effects of cellular diffusion on the dynamic behaviors of cancer cell subpopulations,we establish a reaction-diffusion model of the competition between drug-sensitive and drug-resistant cancer cells.Firstly,taking drug dosage and diffusion coefficients as bifurcation parameters,we investigate the Turing instability conditions for the drug-sensitive and drug-resistant cancer cell model driven by passive-diffusion and cross-diffusion factors at the positive steady state solution,and obtain the distribution regions of the model undergoing Turing instability.Secondly,we deduce the wave speed conditions for the three types of traveling wave solutions connecting two nontrivial steady state solutions,and prove the existence of traveling wave solutions driven by passive-diffusion,using the eigenvalue analysis method,the upper and lower solution method,and Schauder’s fixed point theorem.Finally,we perform some numerical simulations to verify the results of the obtained theories and give the spatially inhomogeneous steady state solutions and the traveling wave solutions,as well as the wave solutions of the non-uniformity diffusion with different temporal and spatial locations.展开更多
A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance con...A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance condition.We obtain the well-posedness using nonlinear semigroup theory arguments.Moreover,the exponential stability result of the solution is shown by using the energy method to produce a suitable Lyapunov function.展开更多
In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to...In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.展开更多
Currently,researchers worldwide are conducting theoretical and experimental studies to understand the significance of nanofluids in heat transfer processes.These fluids are created by dispersing nanoparticles in a bas...Currently,researchers worldwide are conducting theoretical and experimental studies to understand the significance of nanofluids in heat transfer processes.These fluids are created by dispersing nanoparticles in a base fluid.Experiments have demonstrated that nanofluids exhibit superior and more attractive thermal properties compared to conventional fluids.In this current study,we discuss about the heat transfer enhancement of unsteady incompressible laminar couple stress nanofluid flow with Magnetohydrodynamics(MHD)between parallel plates.Prescribed temperature boundary conditions of the surface are employed on the porous surface and it is assumed that the temperature changes periodically over time on the plates.The flow is provoked by periodic suction as well as injection at the plates.With the aid of similarity variables,the system of governing transport equations is transformed into a nonlinear system of ordinary differential equations which is subsequently solved using shooting method along with Runge-Kutta fourth order scheme.The obtained results are shown graphically and explained for the non-dimensional velocity,heat profiles with diverse fluid parameters as well as geometric parameters.Nusselt number is calculated at the lower and upper plates.It is found that temperature component of the fluid is augmenting with suction-injection parameter,whereas it is decreasing in nature with respect to Reynolds number.展开更多
Lithium metal batteries(LMBs)represent a promising solution for next-generation energy storage due to their high energy density,but the growth of lithium dendrites presents significant challenges to their performance ...Lithium metal batteries(LMBs)represent a promising solution for next-generation energy storage due to their high energy density,but the growth of lithium dendrites presents significant challenges to their performance and safety.This review provides a comprehensive overview of the mechanisms behind lithium dendrite formation and the role of in situ/operando observation and phase field simulation in understanding and mitigating this issue,The key driving factors of dendrite growth,such as lithium-ion flux heterogeneity,surface defects,and localized stress,are explored through advanced experimental techniques,which enable real-time visualization of dendrite nucleation and growth dynamics.Complementarily,phase field simulations provide insights into subsurface and temporal evolution of dendrites by modeling thermodynamic and kinetic processes,while machine learning techniques optimize simulation accuracy through data-driven parameter refinement.The integration of experimental observations with simulation models holds great potential in improving understanding and predictive capabilities.Despite ongoing progress,challenges remain in resolving technical limitations in observation techniques,improving computational efficiency,and fostering interdisciplinary collaboration.This review highlights the synergy between experimental and computational strategies in advancing the development of LMBs and calls for continued research to overcome existing hurdles and unlock the full potential of lithium metal anodes.展开更多
Integration of natural gas and electricity transmission systems has strengthened interdependence between the two systems.Due to the close interconnection through coupling elements between the power system(PS)and natur...Integration of natural gas and electricity transmission systems has strengthened interdependence between the two systems.Due to the close interconnection through coupling elements between the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Therefore,an integrated planning model jointing security-constrained considering cascading effects is proposed in this paper.Meanwhile,natural gas and electricity transmission systems considering stochastic failures and various operating characteristics of components can be viewed as a multistate systems.Moreover,power-to-gas(P2G),as a promising technology proposed to store surplus renewable energy,is considered in the integrated planning.First,multi-state models for different components are developed to describe realistic operating conditions in natural gas and electricity transmission systems.Furthermore,a mixed-integer linear programming(MILP)approach considers N-1 contingency and cascading effects between natural gas and the electrical power systems.Therefore,a security-constrained integrated planning model of natural gas and electricity transmission systems is represented.The proposed methods are validated using an integrated gas and power test system.展开更多
In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which ...In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which the contact conditions are described by a Signorini’s condition and Coulomb’s friction law.We derive the variational form of the contact problem which is a mixed system formulated by variational inequalities and equalities.Then,we use standard results on mixed problems and the Banach fixed-point theorem to prove the existence and uniqueness of the solution to the contact problem.Moreover,we demonstrate the convergence of a penalty method for this contact problem under consideration.Finally,finite element method is applied to the penalty contact problem and a strong convergence theorem is obtained.展开更多
In this paper,a class of discontinuous Cohen-Grossberg neural networks with timevarying delays is considered.Firstly,under the extended Filippov differential inclusions framework,the problem of periodic solutions of t...In this paper,a class of discontinuous Cohen-Grossberg neural networks with timevarying delays is considered.Firstly,under the extended Filippov differential inclusions framework,the problem of periodic solutions of the considered neural networks with more relaxed conditions imposed on the amplification functions is analyzed by using set-valued mapping and Kakutani's fixed point theorem,which has rarely been used to study such problem.Secondly,the fixed-time synchronization of the error system of the considered neural networks is also investigated by designing a novel control strategy,which can improve not only the previous ones with sign function greatly,but also can reduce the chattering phenomenon.Finally,two numerical examples are presented to further illustrate the validity of the obtained results.展开更多
The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource manag...The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource management,and public safety.Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated,stealthy threats.To address these challenges,we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework(Evo-Transformer-DRL)designed for robust and adaptive FDI detection in smart water infrastructures.The proposed architecture integrates three powerful paradigms:a transformer encoder for modeling complex temporal dependencies in multivariate time series,a DRL agent for learning optimal decision policies in dynamic environments,and an evolutionary optimizer to fine-tune model hyper-parameters.This synergy enhances detection performance while maintaining adaptability across varying data distributions.Specifically,hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer(IGWO),ensuring a balanced trade-off between detection accuracy and computational efficiency.The model is trained and evaluated on three realistic Industry 4.0 water datasets:secure water treatment(SWaT),water distribution(WADI),and battle of the attack detection algorithms(BATADAL),which capture diverse attack scenarios in smart treatment and distribution systems.Comparative analysis against state-of-the-art baselines including Transformer,DRL,bidirectional encoder representations from transformers(BERT),convolutional neural network(CNN),long short-term memory(LSTM),and support vector machines(SVM)demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy,recall,area under the curve(AUC),and execution time.Notably,it achieves a maximum detection accuracy of 99.19%,highlighting its strong generalization capability across different testbeds.These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment,where rapid adaptation,scalability,and reliability are paramount for securing critical infrastructure systems.展开更多
Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done mo...Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.展开更多
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H...Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.展开更多
A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and rel...A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.展开更多
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov...Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.展开更多
Classical linear discriminant analysis(LDA)(Fisher,1936)implicitly assumes the classification boundary depends on only one linear combination of the predictors.This restriction can lead to poor classification in appli...Classical linear discriminant analysis(LDA)(Fisher,1936)implicitly assumes the classification boundary depends on only one linear combination of the predictors.This restriction can lead to poor classification in applications where the decision boundary depends on multiple linear combinations of the predictors.To overcome this challenge,the authors first project the predictors onto an envelope central space and then perform LDA based on the sufficient predictor.The performance of the proposed method in improving classification accuracy is demonstrated in both synthetic data and real applications.展开更多
In our study,we tackle a linear advection-diffusion equation that varies with time and is constrained to one dimension,under the framework of homogeneous Dirichlet boundary conditions.We employ two distinct approaches...In our study,we tackle a linear advection-diffusion equation that varies with time and is constrained to one dimension,under the framework of homogeneous Dirichlet boundary conditions.We employ two distinct approaches for solving this equation:an analytical solution through the method of separation of variables,and a numerical solution utilizing the finite difference method.The computational output includes three dimensional(3D)plots for solutions,focusing on pollutants such as Ammonia,Carbon monoxide,Carbon dioxide,and Sulphur dioxide.Concentrations,along with their respective diffusivities,are analyzed through 3D plots and actual calculations.To comprehend the diffusivity-concentration relationship for predicting pollutant movement in the air,the domain is divided into two halves.The study explores the behavior of pollutants with higher diffusivity entering regions with lower diffusivity,and vice versa,using 2D and 3D plots.This task is crucial for effective pollution control strategies,and safeguarding the environment and public health.展开更多
文摘The 2nd Sino-German Workshop on Computational and Applied Mathematics took place in Hangzhou, China, from October 9-13, 2007. The long list of senior Chinese numerical analysts who had spent a year or more somewhere in Germany as Humboldt fellows had led to the first Sino-German Workshop in Berlin held at the Humboldt-Universitat zu Berlin in 2005. The particular purpose of the second German-Chinese Workshop on Computational and Applied Mathematics was to attract more junior Chinese scientists to the actual research activities in Germany. A summer school in Beijing on adaptive finite element methods with Carsten Carstensen and Roll Rannacher piror to the Hangzhou workshop underlined this activity to foster the collaboration of the new generations in the fields of computational and applied mathematics. This special issue reflects the present topics therein in both countries and can be summarised under five headings （i）-（v）.
文摘In March of 1979, Chinese Academy of Sciences （CAS） established, with the approval of the State Council of China, an office for promoting the application of mathematics and Interdisciplinary studies in practice. Later in October of 1979, based on this office CAS established the Institute of Applied Mathematics （IAM）. The first director of IAM was the world-wide famous mathematician, Professor HUA Loo-Keng, and most faculty members of IAM came from Institute of Mathematics within CAS, which was founded in July of 1952 and was also directed by Prof. HUA.
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 by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
文摘Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.
文摘In order to understand the effects of cellular diffusion on the dynamic behaviors of cancer cell subpopulations,we establish a reaction-diffusion model of the competition between drug-sensitive and drug-resistant cancer cells.Firstly,taking drug dosage and diffusion coefficients as bifurcation parameters,we investigate the Turing instability conditions for the drug-sensitive and drug-resistant cancer cell model driven by passive-diffusion and cross-diffusion factors at the positive steady state solution,and obtain the distribution regions of the model undergoing Turing instability.Secondly,we deduce the wave speed conditions for the three types of traveling wave solutions connecting two nontrivial steady state solutions,and prove the existence of traveling wave solutions driven by passive-diffusion,using the eigenvalue analysis method,the upper and lower solution method,and Schauder’s fixed point theorem.Finally,we perform some numerical simulations to verify the results of the obtained theories and give the spatially inhomogeneous steady state solutions and the traveling wave solutions,as well as the wave solutions of the non-uniformity diffusion with different temporal and spatial locations.
文摘A model for dynamic frictionless contact between a viscoelastic body and foundation is considered.The viscoelastic constitutive law is assumed to be nonlinear and the contact is modelled with the normal compliance condition.We obtain the well-posedness using nonlinear semigroup theory arguments.Moreover,the exponential stability result of the solution is shown by using the energy method to produce a suitable Lyapunov function.
基金supported in part by the National Natural Science Foundation of China under Grant No.12471281in part by the National Statistical Science Research Project under Grant No.2022LD03。
文摘In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.
文摘Currently,researchers worldwide are conducting theoretical and experimental studies to understand the significance of nanofluids in heat transfer processes.These fluids are created by dispersing nanoparticles in a base fluid.Experiments have demonstrated that nanofluids exhibit superior and more attractive thermal properties compared to conventional fluids.In this current study,we discuss about the heat transfer enhancement of unsteady incompressible laminar couple stress nanofluid flow with Magnetohydrodynamics(MHD)between parallel plates.Prescribed temperature boundary conditions of the surface are employed on the porous surface and it is assumed that the temperature changes periodically over time on the plates.The flow is provoked by periodic suction as well as injection at the plates.With the aid of similarity variables,the system of governing transport equations is transformed into a nonlinear system of ordinary differential equations which is subsequently solved using shooting method along with Runge-Kutta fourth order scheme.The obtained results are shown graphically and explained for the non-dimensional velocity,heat profiles with diverse fluid parameters as well as geometric parameters.Nusselt number is calculated at the lower and upper plates.It is found that temperature component of the fluid is augmenting with suction-injection parameter,whereas it is decreasing in nature with respect to Reynolds number.
基金the financial support of the National Natural Science Foundation of China(Nos.12172206 and 11972218)。
文摘Lithium metal batteries(LMBs)represent a promising solution for next-generation energy storage due to their high energy density,but the growth of lithium dendrites presents significant challenges to their performance and safety.This review provides a comprehensive overview of the mechanisms behind lithium dendrite formation and the role of in situ/operando observation and phase field simulation in understanding and mitigating this issue,The key driving factors of dendrite growth,such as lithium-ion flux heterogeneity,surface defects,and localized stress,are explored through advanced experimental techniques,which enable real-time visualization of dendrite nucleation and growth dynamics.Complementarily,phase field simulations provide insights into subsurface and temporal evolution of dendrites by modeling thermodynamic and kinetic processes,while machine learning techniques optimize simulation accuracy through data-driven parameter refinement.The integration of experimental observations with simulation models holds great potential in improving understanding and predictive capabilities.Despite ongoing progress,challenges remain in resolving technical limitations in observation techniques,improving computational efficiency,and fostering interdisciplinary collaboration.This review highlights the synergy between experimental and computational strategies in advancing the development of LMBs and calls for continued research to overcome existing hurdles and unlock the full potential of lithium metal anodes.
基金supported in part by the Key Projects of National Natural Science Foundation of China under Grant 51936003.
文摘Integration of natural gas and electricity transmission systems has strengthened interdependence between the two systems.Due to the close interconnection through coupling elements between the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Therefore,an integrated planning model jointing security-constrained considering cascading effects is proposed in this paper.Meanwhile,natural gas and electricity transmission systems considering stochastic failures and various operating characteristics of components can be viewed as a multistate systems.Moreover,power-to-gas(P2G),as a promising technology proposed to store surplus renewable energy,is considered in the integrated planning.First,multi-state models for different components are developed to describe realistic operating conditions in natural gas and electricity transmission systems.Furthermore,a mixed-integer linear programming(MILP)approach considers N-1 contingency and cascading effects between natural gas and the electrical power systems.Therefore,a security-constrained integrated planning model of natural gas and electricity transmission systems is represented.The proposed methods are validated using an integrated gas and power test system.
基金supported by the Project for Outstanding Young Talents in Bagui of Guangxi,the Natural Science Foundation of Guangxi(2021GXNSFFA196004,2024GXNSFBA010337)the NSFC(12371312)+2 种基金the Natural Science Foundation of Chongqing(CSTB2024NSCQ-JQX0033)supported by the Postdoctoral Fellowship Program of CPSF(GZC20241534)the Startup Project of Postdoctoral Scientific Research of Zhejiang Normal University(ZC304023924).
文摘In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which the contact conditions are described by a Signorini’s condition and Coulomb’s friction law.We derive the variational form of the contact problem which is a mixed system formulated by variational inequalities and equalities.Then,we use standard results on mixed problems and the Banach fixed-point theorem to prove the existence and uniqueness of the solution to the contact problem.Moreover,we demonstrate the convergence of a penalty method for this contact problem under consideration.Finally,finite element method is applied to the penalty contact problem and a strong convergence theorem is obtained.
基金Supported by the National Natural Science Foundation of China(62576008)University Annual Scientific Research Plan of Anhui Province(2022AH030023)。
文摘In this paper,a class of discontinuous Cohen-Grossberg neural networks with timevarying delays is considered.Firstly,under the extended Filippov differential inclusions framework,the problem of periodic solutions of the considered neural networks with more relaxed conditions imposed on the amplification functions is analyzed by using set-valued mapping and Kakutani's fixed point theorem,which has rarely been used to study such problem.Secondly,the fixed-time synchronization of the error system of the considered neural networks is also investigated by designing a novel control strategy,which can improve not only the previous ones with sign function greatly,but also can reduce the chattering phenomenon.Finally,two numerical examples are presented to further illustrate the validity of the obtained results.
文摘The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource management,and public safety.Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated,stealthy threats.To address these challenges,we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework(Evo-Transformer-DRL)designed for robust and adaptive FDI detection in smart water infrastructures.The proposed architecture integrates three powerful paradigms:a transformer encoder for modeling complex temporal dependencies in multivariate time series,a DRL agent for learning optimal decision policies in dynamic environments,and an evolutionary optimizer to fine-tune model hyper-parameters.This synergy enhances detection performance while maintaining adaptability across varying data distributions.Specifically,hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer(IGWO),ensuring a balanced trade-off between detection accuracy and computational efficiency.The model is trained and evaluated on three realistic Industry 4.0 water datasets:secure water treatment(SWaT),water distribution(WADI),and battle of the attack detection algorithms(BATADAL),which capture diverse attack scenarios in smart treatment and distribution systems.Comparative analysis against state-of-the-art baselines including Transformer,DRL,bidirectional encoder representations from transformers(BERT),convolutional neural network(CNN),long short-term memory(LSTM),and support vector machines(SVM)demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy,recall,area under the curve(AUC),and execution time.Notably,it achieves a maximum detection accuracy of 99.19%,highlighting its strong generalization capability across different testbeds.These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment,where rapid adaptation,scalability,and reliability are paramount for securing critical infrastructure systems.
基金supported by the National Natural Science Foundation of China under Grant No.12288201。
文摘Synthesizing images or texts automatically becomes a useful research area in the artificial intelligence nowadays.Generative adversarial networks(GANs),proposed by Goodfellow,et al.in 2014,make this task to be done more efficiently by using deep neural networks(DNNs).The authors consider generating corresponding images from a single-sentence input text description using a GAN.Specifically,the authors analyze the GAN-CLS algorithm,which is a kind of advanced method of GAN proposed by Reed,et al.in 2016.In this paper the authors show the theoretical problem with this algorithm and correct it by modifying the objective function of the model.Experiments are performed on the Oxford-102 dataset and the CUB dataset to support the theoretical results.Since the proposed modification can be seen as an idea which can be used to improve all such kind of GAN models,the authors try two models,GAN-CLS and AttnGAN_(GPT).As a result,in both of the two models,the proposed modified algorithm is more stable and can generate images which are more plausible than the original algorithm.Also,some of the generated images match the input texts better,and the proposed modified algorithm has better performance on the quantitative indicators including FID and Inception Score.Finally,the authors propose some future application prospect of the modification idea,especially in the area of large language models.
基金funded by National Natural Science Foundation of China Key Program(12431014)Key Project of Hunan Education Department(22A0126)+1 种基金Natural Science Foundation of Hunan Province(2022JJ30555)Postgraduate Scientific Research Innovation Project of Xiangtan University(XDCX2024Y172)。
文摘Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.
基金Project supported by the National Natural Science Foundation of China(Grant No.12472033)。
文摘A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.
文摘Classical linear discriminant analysis(LDA)(Fisher,1936)implicitly assumes the classification boundary depends on only one linear combination of the predictors.This restriction can lead to poor classification in applications where the decision boundary depends on multiple linear combinations of the predictors.To overcome this challenge,the authors first project the predictors onto an envelope central space and then perform LDA based on the sufficient predictor.The performance of the proposed method in improving classification accuracy is demonstrated in both synthetic data and real applications.
文摘In our study,we tackle a linear advection-diffusion equation that varies with time and is constrained to one dimension,under the framework of homogeneous Dirichlet boundary conditions.We employ two distinct approaches for solving this equation:an analytical solution through the method of separation of variables,and a numerical solution utilizing the finite difference method.The computational output includes three dimensional(3D)plots for solutions,focusing on pollutants such as Ammonia,Carbon monoxide,Carbon dioxide,and Sulphur dioxide.Concentrations,along with their respective diffusivities,are analyzed through 3D plots and actual calculations.To comprehend the diffusivity-concentration relationship for predicting pollutant movement in the air,the domain is divided into two halves.The study explores the behavior of pollutants with higher diffusivity entering regions with lower diffusivity,and vice versa,using 2D and 3D plots.This task is crucial for effective pollution control strategies,and safeguarding the environment and public health.
