Tourism is a pillar industry of the national economy and can reflect the overall economic development level of Sanya.Using the tourism data of Sanya from 2012 to 2023,we applied principal component analysis to extract...Tourism is a pillar industry of the national economy and can reflect the overall economic development level of Sanya.Using the tourism data of Sanya from 2012 to 2023,we applied principal component analysis to extract two principal components from 11 indicators affecting the city’s tourism revenue.Python was employed to develop regression models and GM(1,1)models for predicting Sanya’s tourism revenue.The results show that in recent years,Sanya’s tourism revenue has been on the rise.The number of domestic tourists is the main factor affecting Sanya’s tourism revenue.Sanya’s tourism revenue is mainly influenced by domestic tourism revenue,the number of domestic tourists,the number of domestic overnight tourists in Sanya,the number of inbound overnight tourists in Sanya,and tourism foreign exchange earnings,etc.However,tourism hotels and other factors have no significant impact on Sanya’s tourism revenue.Based on the root mean square error(RMSE)criterion,regression models exhibit superior predictive performance compared to the GM(1,1)model in forecasting Sanya’s tourism revenue.展开更多
On July 24th, Lectra's Board of Directors, chaired by Daniel Harari, reviewed the consolidated financial statements for the first half of 2025, which have been subject to a limited review by the Statutory Auditors.
The implementation of the new revenue standard has a far-reaching impact on the accounting treatment of insurance enterprises,and the application of contract settlement accounts,as the key to connect business and fina...The implementation of the new revenue standard has a far-reaching impact on the accounting treatment of insurance enterprises,and the application of contract settlement accounts,as the key to connect business and financial accounting,needs to be standardized.This paper analyzes the core requirements of the new revenue standard,combines the special characteristics of insurance contracts with both service and financial attributes,and explains the logic of setting up and accounting methods of secondary accounts,such as insurance service income and investment component apportionment.In view of the practical pain points such as variable consideration apportionment and reclassification of inter-period contracts,the paper proposes strategies for system upgrading and internal control strengthening.The study shows that standardizing the application of accounts can help enterprises implement the requirements of the standard and improve the quality of financial information.展开更多
Taihu Snow (838262) is a home textile manufacturing company listed on the Beijing Stock Exchange in 2022.It is a bedding manufacturer focusing on silk products.The company was esta blished on May 18,2006,Centered arou...Taihu Snow (838262) is a home textile manufacturing company listed on the Beijing Stock Exchange in 2022.It is a bedding manufacturer focusing on silk products.The company was esta blished on May 18,2006,Centered around the"Taihu Snow"brand,its products cover suite products (such as pillowcases,quilt covers,sheets),quilt cores,silk scarves and otheremerging retail products.展开更多
The inversion of large sparse matrices poses a major challenge in geophysics,particularly in Bayesian seismic inversion,significantly limiting computational efficiency and practical applicability to largescale dataset...The inversion of large sparse matrices poses a major challenge in geophysics,particularly in Bayesian seismic inversion,significantly limiting computational efficiency and practical applicability to largescale datasets.Existing dimensionality reduction methods have achieved partial success in addressing this issue.However,they remain limited in terms of the achievable degree of dimensionality reduction.An incremental deep dimensionality reduction approach is proposed herein to significantly reduce matrix size and is applied to Bayesian linearized inversion(BLI),a stochastic seismic inversion approach that heavily depends on large sparse matrices inversion.The proposed method first employs a linear transformation based on the discrete cosine transform(DCT)to extract the matrix's essential information and eliminate redundant components,forming the foundation of the dimensionality reduction framework.Subsequently,an innovative iterative DCT-based dimensionality reduction process is applied,where the reduction magnitude is carefully calibrated at each iteration to incrementally reduce dimensionality,thereby effectively eliminating matrix redundancy in depth.This process is referred to as the incremental discrete cosine transform(IDCT).Ultimately,a linear IDCT-based reduction operator is constructed and applied to the kernel matrix inversion in BLI,resulting in a more efficient BLI framework.The proposed method was evaluated through synthetic and field data tests and compared with conventional dimensionality reduction methods.The IDCT approach significantly improves the dimensionality reduction efficiency of the core inversion matrix while preserving inversion accuracy,demonstrating prominent advantages in solving Bayesian inverse problems more efficiently.展开更多
Gas turbine rotors are complex dynamic systems with high-dimensional,discrete,and multi-source nonlinear coupling characteristics.Significant amounts of resources and time are spent during the process of solving dynam...Gas turbine rotors are complex dynamic systems with high-dimensional,discrete,and multi-source nonlinear coupling characteristics.Significant amounts of resources and time are spent during the process of solving dynamic characteristics.Therefore,it is necessary to design a lowdimensional model that can well reflect the dynamic characteristics of high-dimensional system.To build such a low-dimensional model,this study developed a dimensionality reduction method considering global order energy distribution by modifying the proper orthogonal decomposition theory.First,sensitivity analysis of key dimensionality reduction parameters to the energy distribution was conducted.Then a high-dimensional rotor-bearing system considering the nonlinear stiffness and oil film force was reduced,and the accuracy and the reusability of the low-dimensional model under different operating conditions were examined.Finally,the response results of a multi-disk rotor-bearing test bench were reduced using the proposed method,and spectrum results were then compared experimentally.Numerical and experimental results demonstrate that,during the dimensionality reduction process,the solution period of dynamic response results has the most significant influence on the accuracy of energy preservation.The transient signal in the transformation matrix mainly affects the high-order energy distribution of the rotor system.The larger the proportion of steady-state signals is,the closer the energy tends to accumulate towards lower orders.The low-dimensional rotor model accurately reflects the frequency response characteristics of the original high-dimensional system with an accuracy of up to 98%.The proposed dimensionality reduction method exhibits significant application potential in the dynamic analysis of highdimensional systems coupled with strong nonlinearities under variable operating conditions.展开更多
The electric double layer(EDL),formed by charge adsorption at the electrolyte–electrode interface,constitutes the microenvironment governing electrochemical reactions.However,due to scale mismatch between the EDL thi...The electric double layer(EDL),formed by charge adsorption at the electrolyte–electrode interface,constitutes the microenvironment governing electrochemical reactions.However,due to scale mismatch between the EDL thickness and electrode topography,solving the two-dimensional(2D)nonhomogeneous Poisson–Nernst–Planck(N-PNP)equations remains computationally intractable.This limitation hinders understanding of fundamental phenomena such as curvature-driven instabilities in 2D EDL.Here,we propose a dimensionality-decomposition strategy embedding a fully connected neural network(FCNN)to solve 2D N-PNP equations,in which the FCNN is trained on key electrochemical parameters by reducing the electrostatic boundary into multiple equivalent 1D representations.Through a representative case of LiPF6 reduction on lithium metal half-cell,nucleus size is unexpectedly found to have an important influence on dendrite morphology and tip kinetics.This work paves the way for bridging nanoscale and macroscale simulations with expandability to 2D situations of other 1D EDL models.展开更多
In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent dev...In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent developments in envelope methods and algorithms,the authors refresh the envelope inverse regression as a flexible alternative to the existing inverse regression methods in dimension reduction.The authors discuss the versatility of the envelope approach and demonstrate the advantages of the envelope dimension reduction through simulation studies.展开更多
The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pil...The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.展开更多
Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(...Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(fMRI),diffusion-weighted MRI,and longitudinal health data.In survival analysis,it is both important and challenging to integrate clinically relevant information,such as gender,age,and disease state along with medical imaging tensor data or longitudinal health data to predict disease outcomes.Most existing higher-order sufficient dimension reduction regressions for matrix-or array-valued data focus solely on tensor data,often neglecting established clinical covariates that are readily available and known to have predictive value.Based on the idea of Folded-Minimum Average Variance Estimation(Folded-MAVE:Xue and Yin,2014),the authors propose a new method,Partial Dimension Folded-MAVE(PF-MAVE),to address regression mean functions with tensor-valued covariates while simultaneously incorporating clinical covariates,which are typically categorical variables.Theorems and simulation studies demonstrate the importance of incorporating these categorical clinical predictors.A survival analysis of a longitudinal study of primary biliary cirrhosis(PBC)data is included for illustration of the proposed method.展开更多
A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisso...A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisson jumps(ISIDSP).The AIIC control strategy inherits the flexibility of aperiodically intermittent control,including the variable control period,adjustable control interval length,and the discretization of impulsive control.In addition,this article introduces a novel mild Itô's formula.By leveraging semigroup theory,the contraction mapping principle,and graph theory,along with constructing the Lyapunov function,the criterion for the existence and uniqueness of a mild solution of ISIDSP is thereby established.Furthermore,the mean-square exponential synchronization problem of the above systems is resolved,and the constraints within the mild solution domain are alleviated.These criteria clarify the impact of control parameters,control intervals and network topology on ESMS.The theoretical results are subsequently applied to a class of neural networks with reaction-diffusion processes,and the validity of the results is verified using numerical simulations.展开更多
In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of th...In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.展开更多
In this paper,we study two types of the Ding injective dimensions of complexes.First,we provide some equivalent characterizations of the dimension related to the special Ding injec-tive preenvelopes.Furthermore,we con...In this paper,we study two types of the Ding injective dimensions of complexes.First,we provide some equivalent characterizations of the dimension related to the special Ding injec-tive preenvelopes.Furthermore,we consider the relationship between the dimensions Dipd(Y)and Did(Y)of the complex Y,where Dipd(Y)denotes the dimension associated with special Ding injective preenvelopes,and Did(Y)denotes the dimension associated with DG-injective resolutions.It is demonstrated that Dipd(Y)=Did(Y)for any bounded complex Y.展开更多
In this paper,the authors propose a nonlinear dimension reduction technique based on Fréchet inverse regression to achieve sufficient dimension reduction for responses in metric spaces and predictors in Riemannia...In this paper,the authors propose a nonlinear dimension reduction technique based on Fréchet inverse regression to achieve sufficient dimension reduction for responses in metric spaces and predictors in Riemannian manifolds.The authors rigorously establish statistical properties of the estimators,providing formal proofs of their consistency and asymptotic behaviors.The effectiveness of our method is demonstrated through extensive simulations and applications to real-world datasets which highlight its practical utility for complex data with non-Euclidean structures.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
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods ofte...Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods often overlook the multi-scale nature of bird targets,limiting their ability to capture rich contextual information across different scales.To address this,we propose a cross-dimensional attention network(CDA-Net)for bird detection that integrates spatial and channel information to improve species recognition.The proposed CDA-Net partitions feature maps into multiple channel wise sub-features.Spatial and channel attention are applied to each subfeature,and the resulting features are fused using the Hadamard product.The fused features are then forwarded to the detection head to generate the final detection results.This approach effectively captures and integrates information across spatial and channel dimensions.Experiments on our self-constructed Nanhai Wetland Waterbird Dataset and the public CUB-200-2011 dataset yield precision scores of 91.32%and 81.99%,respectively,outperforming existing methods.Our approach effectively handles scale variation in bird detection and provides a valuable tool for advancing automated wetland waterbird monitoring.展开更多
In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperat...In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperature(T_(c))of 1D superconductors is low.In this work,we theoretically investigate the possible high T_(c) superconductivity of(5,5)carbon nanotube(CNT).The pristine(5,5)CNT is a Dirac semimetal and can be modulated into a semiconductor by full hydrogenation.Interestingly,by further hole doping,it can be regulated into a metallic state with the sp^(3)-hybridized σ electrons metalized,and a giant Kohn anomaly appears in the optical phonons.The two factors together enhance the electron–phonon coupling,and lead to high-T_(c) superconductivity.When the hole doping concentration of hydrogenated-(5,5)CNT is 2.5 hole/cell,the calculated T_(c) is 82.3 K,exceeding the boiling point of liquid nitrogen.Therefore,the predicted hole-doped hydrogenated-(5,5)CNT provides a new platform for 1D high-T_(c) superconductivity and may have potential applications in 1D nanodevices.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patien...Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.展开更多
基金financially supported by the Education Department of Hainan Province(Hnky2024-84)the Higher Education Digital Transformation Research Special Project of the Employment Association for College Graduates(GJX25Z2167).
文摘Tourism is a pillar industry of the national economy and can reflect the overall economic development level of Sanya.Using the tourism data of Sanya from 2012 to 2023,we applied principal component analysis to extract two principal components from 11 indicators affecting the city’s tourism revenue.Python was employed to develop regression models and GM(1,1)models for predicting Sanya’s tourism revenue.The results show that in recent years,Sanya’s tourism revenue has been on the rise.The number of domestic tourists is the main factor affecting Sanya’s tourism revenue.Sanya’s tourism revenue is mainly influenced by domestic tourism revenue,the number of domestic tourists,the number of domestic overnight tourists in Sanya,the number of inbound overnight tourists in Sanya,and tourism foreign exchange earnings,etc.However,tourism hotels and other factors have no significant impact on Sanya’s tourism revenue.Based on the root mean square error(RMSE)criterion,regression models exhibit superior predictive performance compared to the GM(1,1)model in forecasting Sanya’s tourism revenue.
文摘On July 24th, Lectra's Board of Directors, chaired by Daniel Harari, reviewed the consolidated financial statements for the first half of 2025, which have been subject to a limited review by the Statutory Auditors.
文摘The implementation of the new revenue standard has a far-reaching impact on the accounting treatment of insurance enterprises,and the application of contract settlement accounts,as the key to connect business and financial accounting,needs to be standardized.This paper analyzes the core requirements of the new revenue standard,combines the special characteristics of insurance contracts with both service and financial attributes,and explains the logic of setting up and accounting methods of secondary accounts,such as insurance service income and investment component apportionment.In view of the practical pain points such as variable consideration apportionment and reclassification of inter-period contracts,the paper proposes strategies for system upgrading and internal control strengthening.The study shows that standardizing the application of accounts can help enterprises implement the requirements of the standard and improve the quality of financial information.
文摘Taihu Snow (838262) is a home textile manufacturing company listed on the Beijing Stock Exchange in 2022.It is a bedding manufacturer focusing on silk products.The company was esta blished on May 18,2006,Centered around the"Taihu Snow"brand,its products cover suite products (such as pillowcases,quilt covers,sheets),quilt cores,silk scarves and otheremerging retail products.
基金partly supported by Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(2021JJLH0052)National Natural Science Foundation of China(42274154,42304116)+2 种基金Natural Science Foundation of Heilongjiang Province,China(LH2024D013)Heilongjiang Postdoctoral Fund(LBHZ23103)Hainan Yazhou Bay Science and Technology City Jingying Talent Project(SKJC-JYRC-2024-05)。
文摘The inversion of large sparse matrices poses a major challenge in geophysics,particularly in Bayesian seismic inversion,significantly limiting computational efficiency and practical applicability to largescale datasets.Existing dimensionality reduction methods have achieved partial success in addressing this issue.However,they remain limited in terms of the achievable degree of dimensionality reduction.An incremental deep dimensionality reduction approach is proposed herein to significantly reduce matrix size and is applied to Bayesian linearized inversion(BLI),a stochastic seismic inversion approach that heavily depends on large sparse matrices inversion.The proposed method first employs a linear transformation based on the discrete cosine transform(DCT)to extract the matrix's essential information and eliminate redundant components,forming the foundation of the dimensionality reduction framework.Subsequently,an innovative iterative DCT-based dimensionality reduction process is applied,where the reduction magnitude is carefully calibrated at each iteration to incrementally reduce dimensionality,thereby effectively eliminating matrix redundancy in depth.This process is referred to as the incremental discrete cosine transform(IDCT).Ultimately,a linear IDCT-based reduction operator is constructed and applied to the kernel matrix inversion in BLI,resulting in a more efficient BLI framework.The proposed method was evaluated through synthetic and field data tests and compared with conventional dimensionality reduction methods.The IDCT approach significantly improves the dimensionality reduction efficiency of the core inversion matrix while preserving inversion accuracy,demonstrating prominent advantages in solving Bayesian inverse problems more efficiently.
基金supported by the China Postdoctoral Science Foundation(No.2024M764171)the Postdoctoral Research Start-up Funds,China(No.AUGA5710027424)+1 种基金the National Natural Science Foundation of China(No.U2341237)the Development and construction funds for the School of Mechatronics Engineering of HIT,China(No.CBQQ8880103624)。
文摘Gas turbine rotors are complex dynamic systems with high-dimensional,discrete,and multi-source nonlinear coupling characteristics.Significant amounts of resources and time are spent during the process of solving dynamic characteristics.Therefore,it is necessary to design a lowdimensional model that can well reflect the dynamic characteristics of high-dimensional system.To build such a low-dimensional model,this study developed a dimensionality reduction method considering global order energy distribution by modifying the proper orthogonal decomposition theory.First,sensitivity analysis of key dimensionality reduction parameters to the energy distribution was conducted.Then a high-dimensional rotor-bearing system considering the nonlinear stiffness and oil film force was reduced,and the accuracy and the reusability of the low-dimensional model under different operating conditions were examined.Finally,the response results of a multi-disk rotor-bearing test bench were reduced using the proposed method,and spectrum results were then compared experimentally.Numerical and experimental results demonstrate that,during the dimensionality reduction process,the solution period of dynamic response results has the most significant influence on the accuracy of energy preservation.The transient signal in the transformation matrix mainly affects the high-order energy distribution of the rotor system.The larger the proportion of steady-state signals is,the closer the energy tends to accumulate towards lower orders.The low-dimensional rotor model accurately reflects the frequency response characteristics of the original high-dimensional system with an accuracy of up to 98%.The proposed dimensionality reduction method exhibits significant application potential in the dynamic analysis of highdimensional systems coupled with strong nonlinearities under variable operating conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.92472207,52472223,and 92572301)。
文摘The electric double layer(EDL),formed by charge adsorption at the electrolyte–electrode interface,constitutes the microenvironment governing electrochemical reactions.However,due to scale mismatch between the EDL thickness and electrode topography,solving the two-dimensional(2D)nonhomogeneous Poisson–Nernst–Planck(N-PNP)equations remains computationally intractable.This limitation hinders understanding of fundamental phenomena such as curvature-driven instabilities in 2D EDL.Here,we propose a dimensionality-decomposition strategy embedding a fully connected neural network(FCNN)to solve 2D N-PNP equations,in which the FCNN is trained on key electrochemical parameters by reducing the electrostatic boundary into multiple equivalent 1D representations.Through a representative case of LiPF6 reduction on lithium metal half-cell,nucleus size is unexpectedly found to have an important influence on dendrite morphology and tip kinetics.This work paves the way for bridging nanoscale and macroscale simulations with expandability to 2D situations of other 1D EDL models.
基金supported by the National Natural Science Foundation of China under Grant No.12301365supported by the National Natural Science Foundation of China under Grant No.2241200071Guangdong Basic and Applied Basic Research Foundation under Grant No.2023A1515110001。
文摘In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent developments in envelope methods and algorithms,the authors refresh the envelope inverse regression as a flexible alternative to the existing inverse regression methods in dimension reduction.The authors discuss the versatility of the envelope approach and demonstrate the advantages of the envelope dimension reduction through simulation studies.
文摘The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.
文摘Multi-dimensional arrays are referred to as tensors.Tensor-valued predictors are commonly encountered in modern biomedical applications,such as electroencephalogram(EEG),magnetic resonance imaging(MRI),functional MRI(fMRI),diffusion-weighted MRI,and longitudinal health data.In survival analysis,it is both important and challenging to integrate clinically relevant information,such as gender,age,and disease state along with medical imaging tensor data or longitudinal health data to predict disease outcomes.Most existing higher-order sufficient dimension reduction regressions for matrix-or array-valued data focus solely on tensor data,often neglecting established clinical covariates that are readily available and known to have predictive value.Based on the idea of Folded-Minimum Average Variance Estimation(Folded-MAVE:Xue and Yin,2014),the authors propose a new method,Partial Dimension Folded-MAVE(PF-MAVE),to address regression mean functions with tensor-valued covariates while simultaneously incorporating clinical covariates,which are typically categorical variables.Theorems and simulation studies demonstrate the importance of incorporating these categorical clinical predictors.A survival analysis of a longitudinal study of primary biliary cirrhosis(PBC)data is included for illustration of the proposed method.
基金supported in part by the National Natural Science Foundation of China(12471422,62573274,12371173)the Natural Science Foundation of Shandong Province of China(ZR2022LLZ003,ZR2024MF001)the Funding for Visiting Studies and Research by Teachers in Ordinary Undergraduate Colleges and Universities in Shandong Province。
文摘A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisson jumps(ISIDSP).The AIIC control strategy inherits the flexibility of aperiodically intermittent control,including the variable control period,adjustable control interval length,and the discretization of impulsive control.In addition,this article introduces a novel mild Itô's formula.By leveraging semigroup theory,the contraction mapping principle,and graph theory,along with constructing the Lyapunov function,the criterion for the existence and uniqueness of a mild solution of ISIDSP is thereby established.Furthermore,the mean-square exponential synchronization problem of the above systems is resolved,and the constraints within the mild solution domain are alleviated.These criteria clarify the impact of control parameters,control intervals and network topology on ESMS.The theoretical results are subsequently applied to a class of neural networks with reaction-diffusion processes,and the validity of the results is verified using numerical simulations.
基金supported by the NSFC(12461012)and the NSF of Chongqing(CSTB2024NSCQ-MSX1246).
文摘In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.
基金Supported by the National Natural Science Foundation of China(Grant No.12061061)the Young Talents Team Project of Gansu Province(Grant No.2025QNTD49)+1 种基金Lanshan Talent Project of Northwest Minzu University(Grant No.Xbmulsrc202412)Longyuan Young Talents of Gansu Province.
文摘In this paper,we study two types of the Ding injective dimensions of complexes.First,we provide some equivalent characterizations of the dimension related to the special Ding injec-tive preenvelopes.Furthermore,we consider the relationship between the dimensions Dipd(Y)and Did(Y)of the complex Y,where Dipd(Y)denotes the dimension associated with special Ding injective preenvelopes,and Did(Y)denotes the dimension associated with DG-injective resolutions.It is demonstrated that Dipd(Y)=Did(Y)for any bounded complex Y.
文摘In this paper,the authors propose a nonlinear dimension reduction technique based on Fréchet inverse regression to achieve sufficient dimension reduction for responses in metric spaces and predictors in Riemannian manifolds.The authors rigorously establish statistical properties of the estimators,providing formal proofs of their consistency and asymptotic behaviors.The effectiveness of our method is demonstrated through extensive simulations and applications to real-world datasets which highlight its practical utility for complex data with non-Euclidean structures.
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.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
基金supported by the National Natural Science Foundation of China(32371874,32401569)supported by Beijing Natural Science Foundation(6244053)。
文摘Monitoring waterbirds is vital for evaluating the ecological health of wetlands,and object detection offers an automated solution for identifying birds in monitoring imagery.However,conventional detection methods often overlook the multi-scale nature of bird targets,limiting their ability to capture rich contextual information across different scales.To address this,we propose a cross-dimensional attention network(CDA-Net)for bird detection that integrates spatial and channel information to improve species recognition.The proposed CDA-Net partitions feature maps into multiple channel wise sub-features.Spatial and channel attention are applied to each subfeature,and the resulting features are fused using the Hadamard product.The fused features are then forwarded to the detection head to generate the final detection results.This approach effectively captures and integrates information across spatial and channel dimensions.Experiments on our self-constructed Nanhai Wetland Waterbird Dataset and the public CUB-200-2011 dataset yield precision scores of 91.32%and 81.99%,respectively,outperforming existing methods.Our approach effectively handles scale variation in bird detection and provides a valuable tool for advancing automated wetland waterbird monitoring.
基金supported by the National Natural Science Foundation of China (Grant Nos.12074213 and 11574108)the Major Basic Program of Natural Science Foundation of Shandong Province (Grant No.ZR2021ZD01)the Natural Science Foundation of Shandong Province (Grant No.ZR2023MA082)。
文摘In recent years,the research on superconductivity in one-dimensional(1D)materials has been attracting increasing attention due to its potential applications in low-dimensional nanodevices.However,the critical temperature(T_(c))of 1D superconductors is low.In this work,we theoretically investigate the possible high T_(c) superconductivity of(5,5)carbon nanotube(CNT).The pristine(5,5)CNT is a Dirac semimetal and can be modulated into a semiconductor by full hydrogenation.Interestingly,by further hole doping,it can be regulated into a metallic state with the sp^(3)-hybridized σ electrons metalized,and a giant Kohn anomaly appears in the optical phonons.The two factors together enhance the electron–phonon coupling,and lead to high-T_(c) superconductivity.When the hole doping concentration of hydrogenated-(5,5)CNT is 2.5 hole/cell,the calculated T_(c) is 82.3 K,exceeding the boiling point of liquid nitrogen.Therefore,the predicted hole-doped hydrogenated-(5,5)CNT provides a new platform for 1D high-T_(c) superconductivity and may have potential applications in 1D nanodevices.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金supported by the Canadian Institutes of Health Research(DFD-181599)the National Institutes of Health(T32AG058527)to RJB and R0190106435 to VM.
文摘Neurodegenerative disorders represent an increasingly pertinent public health crisis.As a greater proportion of the population ages,neurodegenerative disorders and other diseases of aging place undue burdens on patients,caregivers,and healthcare workers.Alzheimer’s disease(AD)and Parkinson’s disease represent the two most common neurodegenerative disorders in the population,affecting over 65 million people,worldwide.
