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
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
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
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
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,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
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
[Objective] The aim was to discuss the spatial pattern changes of land use in Tianjin new coastal area based on fractal dimensions.[Method] By dint of remote and geographic information system technology to obtain the ...[Objective] The aim was to discuss the spatial pattern changes of land use in Tianjin new coastal area based on fractal dimensions.[Method] By dint of remote and geographic information system technology to obtain the data of urban land use in new coastal area from 1993 to 2008,the boundary dimension,radius dimension and information dimension of each land use type were calculated based on fractal dimension.In addition,the revealed land use spatial dimension changes characteristics were analyzed.[Result] The spatial distribution of each land use type in new costal area had distinct fractal characteristics.And,the amount and changes of three types of dimension values effectively revealed the changes of complicatedness,centeredness and evenness of spatial pattern of land use in the study area.The boundary dimension of unused land and salty earth increased incessantly,which suggested its increasing complicatedness.The boundary of the port and wharf and shoal land was getting simpler.The radius dimension of the cultivated land was larger than 2,which suggested that its area spread from center to the surroundings;the one in salty land and waters distributed evenly within different radius space to the center of the city;the one in other land use types reduced gradually from center to the surroundings.The information dimension value in the woodland and orchard land,unused land and shoal land was small,and was in obvious concentrated distribution;the spatial distribution of cultivated and salty land concentrated in the outside area;the construction area in the port and wharf spread gradually on the basis of original state;the spatial distribution of waters and residents and mines were even.[Conclusion] Applying fractal dimensions to the study of spatial pattern changes of urban land use can make up for some disadvantages in classical urban spatial pattern quantitative research,which has favorable practical value.展开更多
“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that ...“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.展开更多
Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstructi...Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstruction model,this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting.This method was applied and validated in the study of the fracture microstructure of the rock after blasting.The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action.The undulation rate of the three-dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology.In terms of tilting trend,the tilting direction also shows gradual disorder,with the tilting angle increasing correspondingly.All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases;the difference between the two-dimensional roughness evaluation indexes and the three-dimensional ones of the same micro-area rock samples also becomes increasingly larger,among which the three-dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation.Compared with the linear fitting formula of the power function relationship,the three-dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs,which renders higher correlation coefficients,and the degree of linear fitting of JRC to the three-dimensional fractal dimension is higher.The fractal characteristics of the blast-affected region form a unity with the three-dimensional roughness evaluation of the fissure surface.展开更多
Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by ...Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.展开更多
Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is...Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.展开更多
The Black–Scholes equation is one of the most important partial differential equations governing the value of financial derivatives in financial markets.The Black–Scholes model for pricing stock options has been app...The Black–Scholes equation is one of the most important partial differential equations governing the value of financial derivatives in financial markets.The Black–Scholes model for pricing stock options has been applied to various payoff structures,and options trading is based on Black and Scholes’principle of dynamic hedging to estimate and assess option prices over time.However,the Black–Scholes model requires severe constraints,assumptions,and conditions to be applied to real-life financial and economic problems.Several methods and approaches have been developed to approach these conditions,such as fractional Black–Scholes models based on fractional derivatives.These fractional models are expected since the Black–Scholes equation is derived using Ito’s lemma from stochastic calculus,where fractional derivatives play a leading role.Hence,a fractional stochastic model that includes the basic Black–Scholes model as a special case is expected.However,these fractional financial models require computational tools and advanced analytical methods to solve the associated fractional Black–Scholes equations.Nevertheless,it is believed that the fractal nature of economic processes permits to model economical and financial markets problems more accurately compared to the conventional model.The relationship between fractional calculus and fractals is well-known in the literature.This study introduces a generalized Black–Scholes equation in fractal dimensions and discusses its role in financial marketing.In our analysis,we consider power-laws properties for volatility,interest rated,and dividend payout,which emerge in several empirical regularities in quantitative finance and economics.We apply our model to study the problem of pricing barrier option and we estimate the values of fractal dimensions in both time and in space.Our model can be used to obtain the prices of many pay-off models.We observe that fractal dimensions considerably affect the solutions of the Black–Scholes equation and that,for fractal dimensions much smaller than unity,the call option increases significantly.We prove that fractal dimensions are a powerful tool to obtain new results.Further details are analyzed and discussed.展开更多
Based on the fractal theory,this paper takes the form of performing architecture as the research object,and systematically discusses the application value of fractal dimension in architectural design.By expounding the...Based on the fractal theory,this paper takes the form of performing architecture as the research object,and systematically discusses the application value of fractal dimension in architectural design.By expounding the self-affine,self-similarity,and iterative generation characteristics of fractal geometry,the Box-Counting Dimension method is introduced as a quantitative tool to measure the dimensions of the roof plane,facade,and spatial shape of Wuzhen Grand Theatre and Harbin Grand Theatre.The research shows that the geometric complexity of Wuzhen Grand Theater in the“fifth façade”and multi-faceted façade is significantly higher than that of Harbin Grand Theater,and its morphological design is more inclined to echo the texture of the surrounding water towns.The Harbin Grand Theater realizes the dialogue with the natural environment with simple nonlinear lines.The research proves that fractal dimension can effectively quantify the complexity of architectural form,provide a scientific basis for the form design,environmental integration,and form interpretation of performance architecture,and expand the mathematical analysis dimension of architectural form design.展开更多
In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin...In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.展开更多
With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food tra...With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food trade and security.This study aims to identify the supply-demand match between cropland supply and food consumption and to evaluate sustainable cropland zoning in multiple scenarios and multidimensional assessments.This study uses ecological,environmental and socioeconomic data to quantify diverse food demand patterns into corresponding cropland demands,further mapping the spatio-temporal characteristics of China's cropland supply-demand matches.By utilizing shared socioeconomic pathways(SSPs),this study delineates multiple scenarios to determine the supply-demand of cropland across different Chinese regions from 2030 to 2050.On the basis of ecological,geographical and socioeconomic datasets,this study constructs a multidimensional and multiscenario framework for sustainable agricultural zoning from 2030 to 2050 and proposes a future sustainable agricultural development strategy for each region in different periods.The results indicate that between 2002 and 2022,there was a significant gap between cropland supply and demand.Moreover,an obvious spatial mismatch is observed between cropland supply and demand across various Chinese regions.From 2030 to 2050,there is a noticeable shift in the spatial distribution of cropland supply and demand,with the supply-demand match becoming more strained and varying considerably under different development scenarios.With significant differences between different development scenarios,different regions will have to adopt different development strategies at different periods.This study proposes a multiscenario and multidimensional simulation framework for future agricultural sustainable zoning,which aims to provide scientific insights and policy improvements to promote sustainable agricultural development.展开更多
The phenomenon of photothermally induced transparency(PTIT)arises from the nonlinear behavior of an optical cavity,resulting from the heating of mirrors.By introducing a coupling field in the form of a standing wave,P...The phenomenon of photothermally induced transparency(PTIT)arises from the nonlinear behavior of an optical cavity,resulting from the heating of mirrors.By introducing a coupling field in the form of a standing wave,PTIT can be transitioned into photothermally induced grating(PTIG).A two-dimensional(2D)diffraction pattern is achieved through the adjustment of key parameters such as coupling strength and effective detuning.Notably,we observe first,second,and third-order intensity distributions,with the ability to transfer probe energy predominantly to the third order by fine-tuning the coupling strength.The intensity distribution is characterized by(±m,±n),where m,n=1,2,3.This proposed 2D grating system offers a novel platform for manipulating PTIG,presenting unique possibilities for enhanced functionality and control.展开更多
At a time when global crises continue to intensify,UN Secretary-General António Guterres stated at this year’s high-level plenary meeting to commemorate the 80th anniversary of the establishment of the United Na...At a time when global crises continue to intensify,UN Secretary-General António Guterres stated at this year’s high-level plenary meeting to commemorate the 80th anniversary of the establishment of the United Nations that the core of the 2030 Agenda for Sustainable Development,the Pact for the Future,and the UN 80 Initiative lies in renewing the foundations of international cooperation.展开更多
Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishe...Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishes a pore-scale numerical framework integratingMonte Carlo-generated fractal porousmedia with Volume of Fluid(VOF)simulations to unravel the coupling among pore distribution characterized by fractal dimension(Df),flow dynamics,and displacement efficiency.A pore-scale model based on the computed tomography(CT)microstructure of Berea sandstone is established,and the simulation results are compared with experimental data.Good agreement is found in phase distribution,breakthrough behavior,and flow path morphology,confirming the reliability of the numerical simulation method.Ten fractal porous media models with Df ranging from 1.25~1.7 were constructed using a Monte-Carlo approach.The gas-liquid two-phase flow dynamics was characterized using the VOF solver across gas injection rates of 0.05-5m/s,inwhich the time-resolved two-phase distribution patternswere systematically recorded.The results reveal that smaller fractal dimensions(Df=1.25~1.45)accelerate fingering breakthrough(peak velocity is 1.73 m/s at Df=1.45)due to a bimodal pore size distribution dominated by narrow channels.Increasing Df amplifies vorticity generation by about 3 times(eddy viscosity is 0.033 Pa⋅s at Df=1.7)through reduced interfacial curvature,while tortuosity-driven pressure differentials transition from sharp increases(0.4~6.3 Pa at Df=1.25~1.3)to inertial plateaus(4.8 Pa at Df=1.7).A nonlinear increase in equilibrium gas volume fraction(fav=0.692 at Df=1.7)emerges from residual gas saturation and turbulence-enhanced dispersion.This behavior is further modulated by flow velocity,with fav peaking at 0.72 under capillary-dominated conditions(0.05 m/s),but decreasing to 0.65 in the inertial regime(0.5 m/s).The work quantitatively links fractal topology to multiphase flow regimes,demonstrating the critical role of Df in governing preferential pathways,energy dissipation,and phase distribution.展开更多
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.
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.
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 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.
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.
基金Supported by National Natural Science Fund Program(40705038)~~
文摘[Objective] The aim was to discuss the spatial pattern changes of land use in Tianjin new coastal area based on fractal dimensions.[Method] By dint of remote and geographic information system technology to obtain the data of urban land use in new coastal area from 1993 to 2008,the boundary dimension,radius dimension and information dimension of each land use type were calculated based on fractal dimension.In addition,the revealed land use spatial dimension changes characteristics were analyzed.[Result] The spatial distribution of each land use type in new costal area had distinct fractal characteristics.And,the amount and changes of three types of dimension values effectively revealed the changes of complicatedness,centeredness and evenness of spatial pattern of land use in the study area.The boundary dimension of unused land and salty earth increased incessantly,which suggested its increasing complicatedness.The boundary of the port and wharf and shoal land was getting simpler.The radius dimension of the cultivated land was larger than 2,which suggested that its area spread from center to the surroundings;the one in salty land and waters distributed evenly within different radius space to the center of the city;the one in other land use types reduced gradually from center to the surroundings.The information dimension value in the woodland and orchard land,unused land and shoal land was small,and was in obvious concentrated distribution;the spatial distribution of cultivated and salty land concentrated in the outside area;the construction area in the port and wharf spread gradually on the basis of original state;the spatial distribution of waters and residents and mines were even.[Conclusion] Applying fractal dimensions to the study of spatial pattern changes of urban land use can make up for some disadvantages in classical urban spatial pattern quantitative research,which has favorable practical value.
文摘“Multidimensional international world”refers to understanding the world through multiple dimensions beyond traditional economic or political measures,fostering cross-cultural collaboration,and creating systems that balance global integration with local needs.This also includes management of global business operations across diverse cultures in a multipolar international landscape.The paper briefs the developed and already tested in numerous applications high-level Spatial Grasp Model and Technology(SGT),which can help investigate and manage complex systems with a holistic spatial approach effectively covering various physical and virtual dimensions,their interrelations,and integration as a whole.Different areas will be investigated with examples of practical solutions in them and their combinations in a high-level Spatial Grasp Language(SGL),the key element of SGT.This allows for the creation and distributed management of very large spatial networks with different orientation which can be self-spreading,self-analyzing,self-modifying,and self-recovering in complex terrestrial and celestial environments,and also organize dynamic multi-networking solutions supporting global evolution and integrity.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFC2902103National Natural Science Foundation of China,Grant/Award Number:51934001Fundamental Research Funds for the Central Universities,Grant/Award Number:2023JCCXLJ02。
文摘Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstruction model,this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting.This method was applied and validated in the study of the fracture microstructure of the rock after blasting.The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action.The undulation rate of the three-dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology.In terms of tilting trend,the tilting direction also shows gradual disorder,with the tilting angle increasing correspondingly.All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases;the difference between the two-dimensional roughness evaluation indexes and the three-dimensional ones of the same micro-area rock samples also becomes increasingly larger,among which the three-dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation.Compared with the linear fitting formula of the power function relationship,the three-dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs,which renders higher correlation coefficients,and the degree of linear fitting of JRC to the three-dimensional fractal dimension is higher.The fractal characteristics of the blast-affected region form a unity with the three-dimensional roughness evaluation of the fissure surface.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62031013)Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project(Grant No.2022ZDJS117).
文摘Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.
基金support from National Natural Science Foundation of China(Grant Nos.22275145,22305189and 21875184)Natural Science Foundation of Shaanxi Province(Grant Nos.2022JC-10 and 2024JC-YBQN-0112).
文摘Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.
基金Rami Ahmad El-Nabulsi has received funding from the Czech National Agency of Agricultural 533 Research,project QK22020134“Innovative fisheries management of a large reservoir”.
文摘The Black–Scholes equation is one of the most important partial differential equations governing the value of financial derivatives in financial markets.The Black–Scholes model for pricing stock options has been applied to various payoff structures,and options trading is based on Black and Scholes’principle of dynamic hedging to estimate and assess option prices over time.However,the Black–Scholes model requires severe constraints,assumptions,and conditions to be applied to real-life financial and economic problems.Several methods and approaches have been developed to approach these conditions,such as fractional Black–Scholes models based on fractional derivatives.These fractional models are expected since the Black–Scholes equation is derived using Ito’s lemma from stochastic calculus,where fractional derivatives play a leading role.Hence,a fractional stochastic model that includes the basic Black–Scholes model as a special case is expected.However,these fractional financial models require computational tools and advanced analytical methods to solve the associated fractional Black–Scholes equations.Nevertheless,it is believed that the fractal nature of economic processes permits to model economical and financial markets problems more accurately compared to the conventional model.The relationship between fractional calculus and fractals is well-known in the literature.This study introduces a generalized Black–Scholes equation in fractal dimensions and discusses its role in financial marketing.In our analysis,we consider power-laws properties for volatility,interest rated,and dividend payout,which emerge in several empirical regularities in quantitative finance and economics.We apply our model to study the problem of pricing barrier option and we estimate the values of fractal dimensions in both time and in space.Our model can be used to obtain the prices of many pay-off models.We observe that fractal dimensions considerably affect the solutions of the Black–Scholes equation and that,for fractal dimensions much smaller than unity,the call option increases significantly.We prove that fractal dimensions are a powerful tool to obtain new results.Further details are analyzed and discussed.
基金Jiangxi Province Intelligent Building Engineering Research Center Open Fund Project,Fractal Theory of Performing Architectural Form Design Research(Project No.:EZ202111440).
文摘Based on the fractal theory,this paper takes the form of performing architecture as the research object,and systematically discusses the application value of fractal dimension in architectural design.By expounding the self-affine,self-similarity,and iterative generation characteristics of fractal geometry,the Box-Counting Dimension method is introduced as a quantitative tool to measure the dimensions of the roof plane,facade,and spatial shape of Wuzhen Grand Theatre and Harbin Grand Theatre.The research shows that the geometric complexity of Wuzhen Grand Theater in the“fifth façade”and multi-faceted façade is significantly higher than that of Harbin Grand Theater,and its morphological design is more inclined to echo the texture of the surrounding water towns.The Harbin Grand Theater realizes the dialogue with the natural environment with simple nonlinear lines.The research proves that fractal dimension can effectively quantify the complexity of architectural form,provide a scientific basis for the form design,environmental integration,and form interpretation of performance architecture,and expand the mathematical analysis dimension of architectural form design.
文摘In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.
基金Zhejiang Provincial Sannong-Jiufang Science and Technology Collaboration Initiative,No.2025SNJF012。
文摘With the rapid advancement of global socio-economy and mounting environmental and ecological risks,China faces challenges in ensuring its food security and sustainable development,which further affects global food trade and security.This study aims to identify the supply-demand match between cropland supply and food consumption and to evaluate sustainable cropland zoning in multiple scenarios and multidimensional assessments.This study uses ecological,environmental and socioeconomic data to quantify diverse food demand patterns into corresponding cropland demands,further mapping the spatio-temporal characteristics of China's cropland supply-demand matches.By utilizing shared socioeconomic pathways(SSPs),this study delineates multiple scenarios to determine the supply-demand of cropland across different Chinese regions from 2030 to 2050.On the basis of ecological,geographical and socioeconomic datasets,this study constructs a multidimensional and multiscenario framework for sustainable agricultural zoning from 2030 to 2050 and proposes a future sustainable agricultural development strategy for each region in different periods.The results indicate that between 2002 and 2022,there was a significant gap between cropland supply and demand.Moreover,an obvious spatial mismatch is observed between cropland supply and demand across various Chinese regions.From 2030 to 2050,there is a noticeable shift in the spatial distribution of cropland supply and demand,with the supply-demand match becoming more strained and varying considerably under different development scenarios.With significant differences between different development scenarios,different regions will have to adopt different development strategies at different periods.This study proposes a multiscenario and multidimensional simulation framework for future agricultural sustainable zoning,which aims to provide scientific insights and policy improvements to promote sustainable agricultural development.
文摘The phenomenon of photothermally induced transparency(PTIT)arises from the nonlinear behavior of an optical cavity,resulting from the heating of mirrors.By introducing a coupling field in the form of a standing wave,PTIT can be transitioned into photothermally induced grating(PTIG).A two-dimensional(2D)diffraction pattern is achieved through the adjustment of key parameters such as coupling strength and effective detuning.Notably,we observe first,second,and third-order intensity distributions,with the ability to transfer probe energy predominantly to the third order by fine-tuning the coupling strength.The intensity distribution is characterized by(±m,±n),where m,n=1,2,3.This proposed 2D grating system offers a novel platform for manipulating PTIG,presenting unique possibilities for enhanced functionality and control.
基金a major project of the National Social Science Fund of China,titled“Research on Contemporary Chinese Perspective on Human Rights and Their International Dissemination”(Project Approval Number 23&ZD151)the Chongqing Social Science Planning Talent Program Project,titled“‘Quantifying Human Rights’:Research on Human Rights Indices and Indicator Systems”(Project Approval Number 2024YC037).
文摘At a time when global crises continue to intensify,UN Secretary-General António Guterres stated at this year’s high-level plenary meeting to commemorate the 80th anniversary of the establishment of the United Nations that the core of the 2030 Agenda for Sustainable Development,the Pact for the Future,and the UN 80 Initiative lies in renewing the foundations of international cooperation.
基金funded by the National Key R&D Program of China,China(Grant No.2023YFB4005500)National Natural Science Foundation of China,China(Grant Nos.52379113 and 52379114).
文摘Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishes a pore-scale numerical framework integratingMonte Carlo-generated fractal porousmedia with Volume of Fluid(VOF)simulations to unravel the coupling among pore distribution characterized by fractal dimension(Df),flow dynamics,and displacement efficiency.A pore-scale model based on the computed tomography(CT)microstructure of Berea sandstone is established,and the simulation results are compared with experimental data.Good agreement is found in phase distribution,breakthrough behavior,and flow path morphology,confirming the reliability of the numerical simulation method.Ten fractal porous media models with Df ranging from 1.25~1.7 were constructed using a Monte-Carlo approach.The gas-liquid two-phase flow dynamics was characterized using the VOF solver across gas injection rates of 0.05-5m/s,inwhich the time-resolved two-phase distribution patternswere systematically recorded.The results reveal that smaller fractal dimensions(Df=1.25~1.45)accelerate fingering breakthrough(peak velocity is 1.73 m/s at Df=1.45)due to a bimodal pore size distribution dominated by narrow channels.Increasing Df amplifies vorticity generation by about 3 times(eddy viscosity is 0.033 Pa⋅s at Df=1.7)through reduced interfacial curvature,while tortuosity-driven pressure differentials transition from sharp increases(0.4~6.3 Pa at Df=1.25~1.3)to inertial plateaus(4.8 Pa at Df=1.7).A nonlinear increase in equilibrium gas volume fraction(fav=0.692 at Df=1.7)emerges from residual gas saturation and turbulence-enhanced dispersion.This behavior is further modulated by flow velocity,with fav peaking at 0.72 under capillary-dominated conditions(0.05 m/s),but decreasing to 0.65 in the inertial regime(0.5 m/s).The work quantitatively links fractal topology to multiphase flow regimes,demonstrating the critical role of Df in governing preferential pathways,energy dissipation,and phase distribution.
