The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated ...A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated manual transmissions) become continuously variable, isstudied. With specific mechano-mechanical and electromechanical composite CVT systems as detailedexamples, its basic working principles are expatiated. General methods and key points in designingand realizing such systems are also analyzed and discussed.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
The paper considers the methodology for a comprehensive analysis of the stability of an open pit-dump system,using limit equilibrium(LEM)and finite element(FEM)methods in the Russian CAE(computer-aided engineering)sof...The paper considers the methodology for a comprehensive analysis of the stability of an open pit-dump system,using limit equilibrium(LEM)and finite element(FEM)methods in the Russian CAE(computer-aided engineering)software Fidesys.It briefly highlights the issues of comparing limit equilibrium methods using the VNIMI(Research Institute of Geomechanics and Mine Surveying-Intersectoral Scientific Center"VNIMI")methodology and a specialized software product with numerical methods.The main focus of this study is to compare the results of the stability analysis in the volumetric model of the open pit-dump system using limit equilibrium and finite element methods in the CAE software Fidesys.It was found that,when modeling the combined operation of an open pit-dump system in complex terrain,both methods should be used,as each has its own advantages.The finite element method,for instance,has certain features that are not present in the calculations using the limit equilibrium approach.As a key scientific contribution,this paper introduces an automation program for calculating the stability of open-pit walls using the limit equilibrium method in CAE Fidesys,which was not previously integrated in the original software.The calculations performed with the use of this newly developed module were compared to those obtained from other widely used software solutions available on the market.The findings demonstrate a remarkable level of convergence in the calculation results for all relevant parameters,including the safety factor,localization,instability type,and deformation.The proposed approach i mproves the accuracy of calculati ons and ensures consistency between the higher stress design zones and the actual deformation and fracture patterns.It also enhances the ability to predict the behavior of rock mass when calculating stability parameters for facilities,both during operation and desi gn.展开更多
In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introduc...In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.展开更多
The harmonic balance method(HBM)has been widely applied to get the periodic solution of nonlinear systems,however,its convergence rate as well as computation efficiency is dramatically degraded when the system is high...The harmonic balance method(HBM)has been widely applied to get the periodic solution of nonlinear systems,however,its convergence rate as well as computation efficiency is dramatically degraded when the system is highly non-smooth,e.g.,discontinuous.In order to accelerate the convergence,an enriched HBM is developed in this paper where the non-smooth Bernoulli bases are additionally introduced to enrich the conventional Fourier bases.The basic idea behind is that the convergence rate of the HB solution,as a truncated Fourier series,can be improved if the smoothness of the solution becomes finer.Along this line,using non-smooth Bernoulli bases can compensate the highly non-smooth part of the solution and then,the smoothness of the residual part for Fourier approximation is improved so as to achieve accelerated convergence.Numerical examples are conducted on systems with non-smooth restoring and/or external forces.The results confirm that the proposed enriched HBM indeed increases the convergence rate and the increase becomes more significant if more non-smooth bases are used.展开更多
Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classificati...Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.展开更多
Recently,inspired by a modified generalized shift-splitting iteration method for complex symmetric linear systems,we propose two variants of the modified generalized shift-splitting iteration(MGSS)methods for solving ...Recently,inspired by a modified generalized shift-splitting iteration method for complex symmetric linear systems,we propose two variants of the modified generalized shift-splitting iteration(MGSS)methods for solving com-plex symmetric linear systems.One is a parameterized MGSS iteration method and the other is a modified parameterized MGSS iteration method.We prove that the proposed methods are convergent under appropriate constraints on the parameters.In addition,we also give the eigenvalue distributions of differ-ent preconditioned matrices to verify the effectiveness of the preconditioners proposed in this paper.展开更多
Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimi...Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.展开更多
The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital...The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.展开更多
Dear Editor,This letter addresses the robust predefined-time control challenge for leaderless optimal formation in networked mobile vehicle(NMV)systems.The aim is to minimize a composite global cost function derived f...Dear Editor,This letter addresses the robust predefined-time control challenge for leaderless optimal formation in networked mobile vehicle(NMV)systems.The aim is to minimize a composite global cost function derived from individual strongly convex functions of each agent,considering both input disturbances and network communication constraints.A novel predefined-time optimal formation control(PTOFC)algorithm is presented,ensuring agent state convergence to optimal formation positions within an adjustable settling time.Through the integration of an integral sliding mode technique,disturbances are effectively countered.A representative numerical example highlights the effectiveness and robustness of the developed approach.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix sp...The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.展开更多
Unequal virtual water transfer may aggravate local water scarcity risk.However,the quantitative confirmation of a clear geographic convergence between virtual water transfer and water scarcity risk remains undetermine...Unequal virtual water transfer may aggravate local water scarcity risk.However,the quantitative confirmation of a clear geographic convergence between virtual water transfer and water scarcity risk remains undetermined.We present an analytical framework that reveals the spatial matching between global water scarcity risk and virtual water trade inequality.This framework integrates a three-dimensional water scarcity risk assessment,hybrid input-output analysis,pollution trade term construction,and geographic convergence identification.The framework is applied to 123 countries for long-term validation from 1991 to 2021.We show that despite global improvements in water efficiency and security,countries exceeding the maximum water vulnerability threshold have increased by 50%.South Asia is the largest net exporter of virtual water.Central Asia exhibits the most pronounced virtual water trade inequality.To achieve the same economic growth,Central Asia needs to pay several times the local water consumption costs of developed regions(15.9−83.6 times,2021).In the past 30 years,the average geographic convergence index exceeded 0.8.Countries facing severe water scarcity also exhibit pronounced inequalities in virtual water trade,indicating that a significant geographic convergence relationship exists.Effectively responding to this unsustainable relationship necessitates balancing both domestic resource risk management and global virtual water trade regulation.展开更多
AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy s...AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.展开更多
In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which ...In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which the contact conditions are described by a Signorini’s condition and Coulomb’s friction law.We derive the variational form of the contact problem which is a mixed system formulated by variational inequalities and equalities.Then,we use standard results on mixed problems and the Banach fixed-point theorem to prove the existence and uniqueness of the solution to the contact problem.Moreover,we demonstrate the convergence of a penalty method for this contact problem under consideration.Finally,finite element method is applied to the penalty contact problem and a strong convergence theorem is obtained.展开更多
Nereididae is a prolific annelid family widely distributed in the world oceans,especially in the Indo-Pacific Convergence Zone(IPCZ).However,its biogeographic pattern remains unexplored in IPCZ.To contribute to the un...Nereididae is a prolific annelid family widely distributed in the world oceans,especially in the Indo-Pacific Convergence Zone(IPCZ).However,its biogeographic pattern remains unexplored in IPCZ.To contribute to the understanding of biodiversity and biogeography of Nereididae in the IPCZ,we integrated historical data of species distributions with those of model-predicted ones to determine the biogeographic patterns of nereid species,from which we projected to its future distribution patterns for 2090-2100 under different climate scenarios(SSP1-1.9 and SSP5-8.5).Functional diversity within IPCZ was assessed using functional richness,functional evenness,and functional disparity.Divergence times within Nereididae were estimated using three DNA marker genes(COI,16S,and 18S rRNA),and a time tree was constructed based on a strict molecular clock model.The IPCZ was established as a key Nereididae biodiversity hotspot through distribution modelling of 256 species(44 genera),and temperature emerging as the predominant climatic driver of species distribution patterns.The distribution of species and functional diversity is notable for its non-centralized pattern.We projected that by the end of the century,areas of medium-to-high species richness will expand significantly under the low-emission SSP1-1.9 climate scenario.However,under the high-emission SSP5-8.5 scenario,the suitability of these regions significantly declines,posing an increasingly severe threat to biodiversity.In addition,by molecular clock analysis,we revealed that the evolutionary divergence of extant nereidid species occurred mainly in the Cretaceous and Jurassic,suggesting that paleogeographical and environmental events,such as oceanic anoxic events,might have played a pivotal role in shaping the evolutionary trajectory and ecological adaptations of marine annelids.These findings highlight the importance of considering both current biodiversity patterns and historical contexts in conservation planning,and provided insights into the potential factors on the biogeographic distribution and evolutionary processes of Nereididae.展开更多
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
基金This project is supported by National Natural Science Foundation of China (No.50275053) and Provincial Natural Science Fundation of Guangdong (No.020857).
文摘A new type of composite CVT(continuously variable transmission) systemsfeatured by power flow divergence and dual-mode convergence, capable of improving CVT's efficiencyand power capacity or making AMTs(automated manual transmissions) become continuously variable, isstudied. With specific mechano-mechanical and electromechanical composite CVT systems as detailedexamples, its basic working principles are expatiated. General methods and key points in designingand realizing such systems are also analyzed and discussed.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
文摘The paper considers the methodology for a comprehensive analysis of the stability of an open pit-dump system,using limit equilibrium(LEM)and finite element(FEM)methods in the Russian CAE(computer-aided engineering)software Fidesys.It briefly highlights the issues of comparing limit equilibrium methods using the VNIMI(Research Institute of Geomechanics and Mine Surveying-Intersectoral Scientific Center"VNIMI")methodology and a specialized software product with numerical methods.The main focus of this study is to compare the results of the stability analysis in the volumetric model of the open pit-dump system using limit equilibrium and finite element methods in the CAE software Fidesys.It was found that,when modeling the combined operation of an open pit-dump system in complex terrain,both methods should be used,as each has its own advantages.The finite element method,for instance,has certain features that are not present in the calculations using the limit equilibrium approach.As a key scientific contribution,this paper introduces an automation program for calculating the stability of open-pit walls using the limit equilibrium method in CAE Fidesys,which was not previously integrated in the original software.The calculations performed with the use of this newly developed module were compared to those obtained from other widely used software solutions available on the market.The findings demonstrate a remarkable level of convergence in the calculation results for all relevant parameters,including the safety factor,localization,instability type,and deformation.The proposed approach i mproves the accuracy of calculati ons and ensures consistency between the higher stress design zones and the actual deformation and fracture patterns.It also enhances the ability to predict the behavior of rock mass when calculating stability parameters for facilities,both during operation and desi gn.
基金Supported by the National Natural Science Foundation of China(12061048)NSF of Jiangxi Province(20232BAB201026,20232BAB201018)。
文摘In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.
基金supported by the National Natural Science Foundation of China (Grant No. 12372028)the National Key Research and Development Program of China (Grant No. 2020YFC2201101)the Guangdong Basic and Applied Basic Research Foundation (Grant No.2022A1515011809)。
文摘The harmonic balance method(HBM)has been widely applied to get the periodic solution of nonlinear systems,however,its convergence rate as well as computation efficiency is dramatically degraded when the system is highly non-smooth,e.g.,discontinuous.In order to accelerate the convergence,an enriched HBM is developed in this paper where the non-smooth Bernoulli bases are additionally introduced to enrich the conventional Fourier bases.The basic idea behind is that the convergence rate of the HB solution,as a truncated Fourier series,can be improved if the smoothness of the solution becomes finer.Along this line,using non-smooth Bernoulli bases can compensate the highly non-smooth part of the solution and then,the smoothness of the residual part for Fourier approximation is improved so as to achieve accelerated convergence.Numerical examples are conducted on systems with non-smooth restoring and/or external forces.The results confirm that the proposed enriched HBM indeed increases the convergence rate and the increase becomes more significant if more non-smooth bases are used.
文摘Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.
基金supported by the National Natural Science Foundation of China(Grant No.12371378)by the Natural Science Foundation of Fujian Province(Grant Nos.2024J01980,2024J08242).
文摘Recently,inspired by a modified generalized shift-splitting iteration method for complex symmetric linear systems,we propose two variants of the modified generalized shift-splitting iteration(MGSS)methods for solving com-plex symmetric linear systems.One is a parameterized MGSS iteration method and the other is a modified parameterized MGSS iteration method.We prove that the proposed methods are convergent under appropriate constraints on the parameters.In addition,we also give the eigenvalue distributions of differ-ent preconditioned matrices to verify the effectiveness of the preconditioners proposed in this paper.
基金supported by the National Defense Basic Scientific Research Program(JCKY2021603B030)the National Natural Science Foundation of China(62273118,12150008)the Natural Science Foundation of Heilongjiang Province(LH2022F023).
文摘Dear Editor,This letter proposes a convex optimization-based model predictive control(MPC)autonomous guidance method for the Mars ascent vehicle(MAV).We use the modified chebyshev-picard iteration(MCPI)to solve optimization sub-problems within the MPC framework,eliminating the dynamic constraints in solving the optimal control problem and enhancing the convergence performance of the algorithm.Moreover,this method can repeatedly perform trajectory optimization calculations at a high frequency,achieving timely correction of the optimal control command.Numerical simulations demonstrate that the method can satisfy the requirements of rapid computation and reliability for the MAV system when considering uncertainties and perturbations.
文摘The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.
基金supported by the National Natural Science Foundation of China(62373162,U24A20268,624B2055)the Shenzhen Science and Technology Program(JCYJ 20240813114007010)the Knowledge Innovation Program of Wuhan-Basic Research(2023010201010100).
文摘Dear Editor,This letter addresses the robust predefined-time control challenge for leaderless optimal formation in networked mobile vehicle(NMV)systems.The aim is to minimize a composite global cost function derived from individual strongly convex functions of each agent,considering both input disturbances and network communication constraints.A novel predefined-time optimal formation control(PTOFC)algorithm is presented,ensuring agent state convergence to optimal formation positions within an adjustable settling time.Through the integration of an integral sliding mode technique,disturbances are effectively countered.A representative numerical example highlights the effectiveness and robustness of the developed approach.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
基金The National Natural Science Foundations of China (12202219)the Natural Science Foundations of Ningxia (2024AAC02009, 2023AAC05001)the Ningxia Youth Top Talents Training Project。
文摘The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.
基金supported by National Natural Science Foundation of China(Grant No.52279027)National Key R&D Program of China(Grant No.2021YFC3200201)+1 种基金Key Research Project on Decision Consultation of the Strategic Development Department of China Association for Science and Technology(Grant No.2023070615CG111504)China Engineering Science and Technology Development Strategy Henan Research Institute Strategic Consulting Research Project(Grant No.2024HENYB01).
文摘Unequal virtual water transfer may aggravate local water scarcity risk.However,the quantitative confirmation of a clear geographic convergence between virtual water transfer and water scarcity risk remains undetermined.We present an analytical framework that reveals the spatial matching between global water scarcity risk and virtual water trade inequality.This framework integrates a three-dimensional water scarcity risk assessment,hybrid input-output analysis,pollution trade term construction,and geographic convergence identification.The framework is applied to 123 countries for long-term validation from 1991 to 2021.We show that despite global improvements in water efficiency and security,countries exceeding the maximum water vulnerability threshold have increased by 50%.South Asia is the largest net exporter of virtual water.Central Asia exhibits the most pronounced virtual water trade inequality.To achieve the same economic growth,Central Asia needs to pay several times the local water consumption costs of developed regions(15.9−83.6 times,2021).In the past 30 years,the average geographic convergence index exceeded 0.8.Countries facing severe water scarcity also exhibit pronounced inequalities in virtual water trade,indicating that a significant geographic convergence relationship exists.Effectively responding to this unsustainable relationship necessitates balancing both domestic resource risk management and global virtual water trade regulation.
基金Supported by Mashhad University of Medical Sciences.
文摘AIM:To evaluate the differences in near point of convergence(NPC),fusional vergence,saccadic eye movements,versional eye movements,and heterophoria between patients diagnosed with Parkinson’s disease(PD)and healthy subjects.METHODS:A cross-sectional comparative study was conducted,enrolling two cohorts:a PD group and a healthy control group.The PD group was recruited via non-random convenience sampling,while the control group was selected randomly from individuals without PD.All participants were screened according to predefined inclusion and exclusion criteria before undergoing a comprehensive optometric assessment,which included measurements of uncorrected visual acuity,corrected visual acuity,and objective and subjective refraction.Subsequently,binocular vision function evaluations were performed,covering NPC measurement,fusional vergence reserve assessment at both distance and near,saccadic eye movement testing,and versional eye movement and heterophoria assessment.RESULTS:A total of 42 PD patients and 41 healthy controls were included in the final analysis.The two groups were well-matched in terms of sex distribution[29 males(69.0%)in the PD group vs 29 males(70.7%)in the control group,P=0.867]and mean age(55.3±9.6y in the PD group vs 54.9±9.8y in the control group,P=0.866).The prevalence of abnormal versional eye movements was significantly higher in the PD group than in the control group(23.81%,95%CI:12.05%-39.45%vs 7.32%,95%CI:1.54%-19.92%;P=0.025).Near exophoria was more prevalent in PD patients(61.90%,95%CI:45.64%-76.43%)than in controls(17.07%,95%CI:7.15%-32.06%),with a significant difference[odds ratio(OR)=7.99;95%CI:2.83-21.99;P<0.001].The mean NPC was significantly greater(more receded)in the PD group than in the control group(9.01±3.74 cm vs 7.20±2.15 cm;P=0.007).A statistically significant positive correlation was observed between PD severity and NPC values(Pearson’s correlation coefficient=0.309;P=0.046).Except for distance baseout break and distance base-out recovery values,all other fusional vergence parameters were significantly lower in the PD group than in the control group(P<0.05).The mean saccadic test score was significantly lower in PD patients than in controls(3.29±0.57 vs 3.78±0.42;P<0.001).Among all fusional vergence indices,near base-in blur yielded the highest area under the curve(AUC=0.877),with a sensitivity of 69%and specificity of 90%,followed by distance base-out blur(AUC=0.824,sensitivity=97.6%,specificity=66.7%),near base-out blur(AUC=0.814,sensitivity=76.2%,specificity=72.7%),near base-out break(AUC=0.749,sensitivity=78.6%,specificity=67.6%),and near base-out recovery(AUC=0.749,sensitivity=95.2%,specificity=50%).CONCLUSION:PD is associated with significant binocular vision function impairment,with receded NPC and reduced near fusional vergence reserves being the most prominent disorders.These findings highlight the potential value of binocular vision assessment as a non-invasive biomarker for the early detection and clinical monitoring of PD.
基金supported by the Project for Outstanding Young Talents in Bagui of Guangxi,the Natural Science Foundation of Guangxi(2021GXNSFFA196004,2024GXNSFBA010337)the NSFC(12371312)+2 种基金the Natural Science Foundation of Chongqing(CSTB2024NSCQ-JQX0033)supported by the Postdoctoral Fellowship Program of CPSF(GZC20241534)the Startup Project of Postdoctoral Scientific Research of Zhejiang Normal University(ZC304023924).
文摘In this paper,we study a comprehensive mathematical model describing the problem of frictional contact between a nonlinear thermo-piezoelectric body and a rigid foundation with electrically conductive effect,in which the contact conditions are described by a Signorini’s condition and Coulomb’s friction law.We derive the variational form of the contact problem which is a mixed system formulated by variational inequalities and equalities.Then,we use standard results on mixed problems and the Banach fixed-point theorem to prove the existence and uniqueness of the solution to the contact problem.Moreover,we demonstrate the convergence of a penalty method for this contact problem under consideration.Finally,finite element method is applied to the penalty contact problem and a strong convergence theorem is obtained.
基金Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB42000000)the National Natural Science Foundation of China(No.42376092)the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(No.2022QNLM030004)。
文摘Nereididae is a prolific annelid family widely distributed in the world oceans,especially in the Indo-Pacific Convergence Zone(IPCZ).However,its biogeographic pattern remains unexplored in IPCZ.To contribute to the understanding of biodiversity and biogeography of Nereididae in the IPCZ,we integrated historical data of species distributions with those of model-predicted ones to determine the biogeographic patterns of nereid species,from which we projected to its future distribution patterns for 2090-2100 under different climate scenarios(SSP1-1.9 and SSP5-8.5).Functional diversity within IPCZ was assessed using functional richness,functional evenness,and functional disparity.Divergence times within Nereididae were estimated using three DNA marker genes(COI,16S,and 18S rRNA),and a time tree was constructed based on a strict molecular clock model.The IPCZ was established as a key Nereididae biodiversity hotspot through distribution modelling of 256 species(44 genera),and temperature emerging as the predominant climatic driver of species distribution patterns.The distribution of species and functional diversity is notable for its non-centralized pattern.We projected that by the end of the century,areas of medium-to-high species richness will expand significantly under the low-emission SSP1-1.9 climate scenario.However,under the high-emission SSP5-8.5 scenario,the suitability of these regions significantly declines,posing an increasingly severe threat to biodiversity.In addition,by molecular clock analysis,we revealed that the evolutionary divergence of extant nereidid species occurred mainly in the Cretaceous and Jurassic,suggesting that paleogeographical and environmental events,such as oceanic anoxic events,might have played a pivotal role in shaping the evolutionary trajectory and ecological adaptations of marine annelids.These findings highlight the importance of considering both current biodiversity patterns and historical contexts in conservation planning,and provided insights into the potential factors on the biogeographic distribution and evolutionary processes of Nereididae.
