Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challen...Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.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
The onset,cessation,and length of the rainy season are crucial for global water resources,agricultural practices,and food security.However,the response of precipitation seasonality to global warming remains uncertain....The onset,cessation,and length of the rainy season are crucial for global water resources,agricultural practices,and food security.However,the response of precipitation seasonality to global warming remains uncertain.In this study,we analyze how global warming levels(GWLs)of 1.5℃ and 2℃ could affect the timing of rainfall onset(RODs),rainfall cessation(RCDs),and the overall duration of the rainy season(LRS)over global land monsoon(GLM)regions using simulations from CMIP6 under the SSP2-4.5 and SSP5-8.5 scenarios.With high model consensus,our results reveal that RODs are projected to occur later over Southern Africa,North Africa,and South America,but earlier over South Asia and Australia,in a warmer climate.The projected early RODs in Australia are more pronounced at the 2℃ GWL under SSP5-8.5.On the other hand,early RCDs are projected over South America and East Asia,while late RCDs are projected over North Africa,with high inter-model agreement.These changes are associated with a future decrease in LRS in most GLM regions.Additionally,we found that continuous warming over 1.5℃ will further reduce the length of the rainy season,especially over the South America,North Africa,and Southern Africa monsoon regions.The findings underscore the urgent need to mitigate global warming.展开更多
This study investigates the impact of vegetation-climate feedback on the global land monsoon system during the Last Interglacial(LIG,127000 years BP)and the mid-Holocene(MH,6000 years BP)using the earth system model E...This study investigates the impact of vegetation-climate feedback on the global land monsoon system during the Last Interglacial(LIG,127000 years BP)and the mid-Holocene(MH,6000 years BP)using the earth system model EC-Earth3.Our findings indicate that vegetation changes significantly influence the global monsoon area and precipitation patterns,especially in the North African and Indian monsoon regions.The North African monsoon region experienced the most substantial increase in vegetation during both the LIG and MH,resulting in significant increases in monsoonal precipitation by 9.8%and 6.0%,respectively.The vegetation feedback also intensified the Saharan Heat Low,strengthened monsoonal flows,and enhanced precipitation over the North African monsoon region.In contrast,the Indian monsoon region exhibited divergent responses to vegetation changes.During the LIG,precipitation in the Indian monsoon region decreased by 2.2%,while it increased by 1.6%during the MH.These differences highlight the complex and region-specific impacts of vegetation feedback on monsoon systems.Overall,this study demonstrates that vegetation feedback exerts distinct influences on the global monsoon during the MH and LIG.These findings highlight the importance of considering vegetation-climate feedback in understanding past monsoon variability and in predicting future climate change impacts on monsoon systems.展开更多
Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emi...Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proo...Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proof on 28 September 2025 without any questions.However,on 13 November 2025,the Editorial Office of Medical Data Mining noticed an inconsistency between the data presented in the main text and Figure 1.Specifically,erroneous Figure 1 states“a total of 56,691 literatures were obtained through database search”,while the main text in the Search results section states“According to the search term,a total of 59,220 publications were retrieved from the database.”The authors acknowledge that the original version of Figure 1 was incorrect and have provided the revised,correct version in this corrigendum.The authors would like to assert that there is no change in the body text of the article.展开更多
Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield netwo...Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield network,this paper proposes a new chaotic annealing neural network (CANN) for global optimization of continuous constrained non linear programming.It is easy to implement,conceptually simple,and generally applicable.Numerical experiments on severe test functions manifest that CANN is efficient and reliable to search for global optimum and outperforms the existing genetic algorithm GAMAS for the same purpose.展开更多
An efficient parallel global router using random optimization that is independent of net ordering is proposed.Parallel approaches are described and strategies guaranteeing the routing quality are discussed.The wire le...An efficient parallel global router using random optimization that is independent of net ordering is proposed.Parallel approaches are described and strategies guaranteeing the routing quality are discussed.The wire length model is implemented on multiprocessor,which enables the algorithm to approach feasibility of large scale problems.Timing driven model on multiprocessor and wire length model on distributed processors are also presented.The parallel algorithm greatly reduces the run time of routing.The experimental results show good speedups with no degradation of the routing quality.展开更多
Synthesis of chemical processes is of non-convex and multi-modal. Deterministic strategies often fail to find global optimum within reasonable time scales. Stochastic methodologies generally approach global solution i...Synthesis of chemical processes is of non-convex and multi-modal. Deterministic strategies often fail to find global optimum within reasonable time scales. Stochastic methodologies generally approach global solution in probability. In recogniting the state of art status in the discipline, a new approach for global optimization of processes, based on sequential number theoretic optimization (SNTO), is proposed. In this approach, subspaces and feasible points are derived from uniformly scattered points, and iterations over passing the corner of local optimum are enhanced via parallel strategy. The efficiency of the approach proposed is verified by results obtained from various case studies.展开更多
Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning usi...Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.展开更多
By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of ...By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.展开更多
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimi...A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.展开更多
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization metho...High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.展开更多
Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to ...Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.展开更多
A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search...A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.展开更多
High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis mode...High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.展开更多
In this paper, an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems. The proposed algorithm is based on the Bernstein poly...In this paper, an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems. The proposed algorithm is based on the Bernstein polynomial approach. Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point, modified rules for the selection of the subdivision direction, and a new acceleration device to avoid some unnecessary subdivisions. The performance of the proposed algorithm is numerically tested on a collection of 16 test problems. The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics.展开更多
基金funded by National Natural Science Foundation of China(grant No.52405255)Special Program of Huzhou(grant No.2023GZ05)+1 种基金Projects of Huzhou Science and Technology Correspondent(grant No.2023KT76)Guangdong Basic and Applied Basic Research Foundation(grant No.2025A1515010487)。
文摘Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.
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 Australian Research Council(Grant No.CE230100012)。
文摘The onset,cessation,and length of the rainy season are crucial for global water resources,agricultural practices,and food security.However,the response of precipitation seasonality to global warming remains uncertain.In this study,we analyze how global warming levels(GWLs)of 1.5℃ and 2℃ could affect the timing of rainfall onset(RODs),rainfall cessation(RCDs),and the overall duration of the rainy season(LRS)over global land monsoon(GLM)regions using simulations from CMIP6 under the SSP2-4.5 and SSP5-8.5 scenarios.With high model consensus,our results reveal that RODs are projected to occur later over Southern Africa,North Africa,and South America,but earlier over South Asia and Australia,in a warmer climate.The projected early RODs in Australia are more pronounced at the 2℃ GWL under SSP5-8.5.On the other hand,early RCDs are projected over South America and East Asia,while late RCDs are projected over North Africa,with high inter-model agreement.These changes are associated with a future decrease in LRS in most GLM regions.Additionally,we found that continuous warming over 1.5℃ will further reduce the length of the rainy season,especially over the South America,North Africa,and Southern Africa monsoon regions.The findings underscore the urgent need to mitigate global warming.
基金supported by the Swedish Research Council(Vetenskapsradet,Grant No.202203129)the Project of Youth Science and Technology Fund of Gansu Province(Grant No.24JRRA439)partially funded by the Swedish Research Council(Vetenskapsradet,Grant No.2022-06725)。
文摘This study investigates the impact of vegetation-climate feedback on the global land monsoon system during the Last Interglacial(LIG,127000 years BP)and the mid-Holocene(MH,6000 years BP)using the earth system model EC-Earth3.Our findings indicate that vegetation changes significantly influence the global monsoon area and precipitation patterns,especially in the North African and Indian monsoon regions.The North African monsoon region experienced the most substantial increase in vegetation during both the LIG and MH,resulting in significant increases in monsoonal precipitation by 9.8%and 6.0%,respectively.The vegetation feedback also intensified the Saharan Heat Low,strengthened monsoonal flows,and enhanced precipitation over the North African monsoon region.In contrast,the Indian monsoon region exhibited divergent responses to vegetation changes.During the LIG,precipitation in the Indian monsoon region decreased by 2.2%,while it increased by 1.6%during the MH.These differences highlight the complex and region-specific impacts of vegetation feedback on monsoon systems.Overall,this study demonstrates that vegetation feedback exerts distinct influences on the global monsoon during the MH and LIG.These findings highlight the importance of considering vegetation-climate feedback in understanding past monsoon variability and in predicting future climate change impacts on monsoon systems.
文摘Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
基金funded by the National Natural Science Foundation of China(Grant No.42275039)the Meteorological Joint Fund by NSF and CMA(Grant No.U2342224)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3701202)the S&T Development Fund of CAMS(Grant No.2024KJ019)。
文摘Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
文摘Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proof on 28 September 2025 without any questions.However,on 13 November 2025,the Editorial Office of Medical Data Mining noticed an inconsistency between the data presented in the main text and Figure 1.Specifically,erroneous Figure 1 states“a total of 56,691 literatures were obtained through database search”,while the main text in the Search results section states“According to the search term,a total of 59,220 publications were retrieved from the database.”The authors acknowledge that the original version of Figure 1 was incorrect and have provided the revised,correct version in this corrigendum.The authors would like to assert that there is no change in the body text of the article.
基金the National Natural Science Foundation of China(No.79970 0 4 2 )
文摘Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield network,this paper proposes a new chaotic annealing neural network (CANN) for global optimization of continuous constrained non linear programming.It is easy to implement,conceptually simple,and generally applicable.Numerical experiments on severe test functions manifest that CANN is efficient and reliable to search for global optimum and outperforms the existing genetic algorithm GAMAS for the same purpose.
文摘An efficient parallel global router using random optimization that is independent of net ordering is proposed.Parallel approaches are described and strategies guaranteeing the routing quality are discussed.The wire length model is implemented on multiprocessor,which enables the algorithm to approach feasibility of large scale problems.Timing driven model on multiprocessor and wire length model on distributed processors are also presented.The parallel algorithm greatly reduces the run time of routing.The experimental results show good speedups with no degradation of the routing quality.
文摘Synthesis of chemical processes is of non-convex and multi-modal. Deterministic strategies often fail to find global optimum within reasonable time scales. Stochastic methodologies generally approach global solution in probability. In recogniting the state of art status in the discipline, a new approach for global optimization of processes, based on sequential number theoretic optimization (SNTO), is proposed. In this approach, subspaces and feasible points are derived from uniformly scattered points, and iterations over passing the corner of local optimum are enhanced via parallel strategy. The efficiency of the approach proposed is verified by results obtained from various case studies.
基金Supported by State Key Laboratory of Robotics and System (HIT) under Grant No.SKLRS200706the Heilongjiang Scientific Research Foundation for Postdoctoral Financial Assistance under Grant No.323630221the Project of Harbin Technological Talent Research Foundation under Grant No.RC2006QN009015
文摘Path planning is an important issue for autonomous underwater vehicles （AUVs） traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization （ACO） were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
文摘By combining properly the simulated annealing algorithm and the nonlinear programming neural network, a new hybrid method for comtrained global optimization is proposed in this paper. To maintain the applicability of the simulated annealing algorithm used in the hybrid method as general as possible, the nonlinear programming neural network is employed at each iteration to find only a feasible solution to the original constrained problem rather than a local optimal solution. Such a feasible solution is obtained by solving an auxiliary optimization problem with a new objective function. The computational results for two numerical examples indicate that the proposed hybrid method for constrained global optimization is not only highly reliable but also much more effcient than the simulated annealing algorithm using the penalty function method to deal with the constraints.
基金supported by the National Natural Science Foundation of China(6076600161105004)+1 种基金the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ14110)the Program for Innovative Research Team of Guilin University of Electronic Technology(IRTGUET)
文摘A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
基金supported by National Natural Science Foundation of China(Grant No.51105040)Aeronautic Science Foundation of China(Grant No.2011ZA72003)Excellent Young Scholars Research Fund of Beijing Institute of Technology(Grant No.2010Y0102)
文摘High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.
文摘Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.
基金supported by the National Natural Science Foundation of China(60870004)
文摘A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
基金supported by National Natural Science Foundation of China (Grant Nos. 50875024,51105040)Excellent Young Scholars Research Fund of Beijing Institute of Technology,China (Grant No.2010Y0102)Defense Creative Research Group Foundation of China(Grant No. GFTD0803)
文摘High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.
文摘In this paper, an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems. The proposed algorithm is based on the Bernstein polynomial approach. Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point, modified rules for the selection of the subdivision direction, and a new acceleration device to avoid some unnecessary subdivisions. The performance of the proposed algorithm is numerically tested on a collection of 16 test problems. The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics.
