Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,t...The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,the InSight lander collected long-term marsquake data,which improved the Martian interior structure model.B ased on the preliminary analysis of marsquake data,Mars has a molten liquid core with a radius of around 1700 km.As the Martian core has a smaller density and lower temperature than pure iron at corresponding pressure and temperature conditions,some light elements are introduced to reduce the density and liquidus temperature.With various methods for seismic analysis,in-situ high-pressure and high-temperature experiments,and first-principal calculations,the Martian core composition and evolution models have been updated in the past few years.Here,we review those studies on the light elements in the Martian core from four aspects including(1)high-temperature and high-pressure experiments,(2)marsquake data,(3)mineral physics model with molecular dynamics simulations and(4)cosmochemistry investigation.We discussed the effect of different light elements on the Martian core s density,sound velocity and liquidus temperature.Moreover,the review examines the varieties,abundances and forms of light elements in the Martian core.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD9...The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD98M alloy significantly decreases with an increase in impurity content,and the presence of nitrogen leads to an increase in alloy porosity.These porosities promote the rapid diffusion of molten salt and oxygen into the alloy,resulting in a bilateral diffusion of oxygen and sulfur,which leads to an accumulation of these elements at the oxide−matrix interface.This process contributes to the formation and propagation of interfacial cracks.A growth model was developed for hot corrosion products in alloys with varying impurity elements.展开更多
On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked def...On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked deftly at the stove,his hands moving swiftly over the scorching iron wok as tender green tea leaves dance between his fingers.展开更多
We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-...We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.展开更多
Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage se...Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.展开更多
Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most e...Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most effective strategies for addressing these issues.However,typical residual elements(Cu,As,Sn,Sb,Bi,etc.)inherited from scrap could significantly influence the mechanical properties of steel.In this work,we investigate the effects of residual elements on the microstructure evolution and mechanical properties of a quenching and partitioning(Q&P)steel by comparing a commercial QP1180 steel(referred to as QP)to the one containing typical residual elements(Cu+As+Sn+Sb+Bi<0.3wt%)(referred to as QP-R).The results demonstrate that in comparison with the QP steel,the residual elements significantly refine the prior austenite grain(9.7μm vs.14.6μm)due to their strong solute drag effect,leading to a higher volume fraction(13.0%vs.11.8%),a smaller size(473 nm vs.790 nm)and a higher average carbon content(1.26 wt%vs.0.99 wt%)of retained austenite in the QP-R steel.As a result,the QP-R steel exhibits a sustained transformation-induced plasticity(TRIP)effect,leading to an enhanced strain hardening effect and a simultaneous improvement of strength and ductility.Grain boundary segregation of residual elements was not observed at prior austenite grain boundaries in the QP-R steel,primarily due to continuous interface migration during austenitization.This study demonstrates that the residual elements with concentrations comparable to that in scrap result in significant microstructural refinement,causing retained austenite with relatively higher stability and thus offering promising mechanical properties and potential applications.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
Liquid crystal Pacharatnam-Berry phase optical elements(PBOEs)have found promising applications in augmented reality and virtual reality because of their slim formfactor,lightweight,and high optical efficiency.However...Liquid crystal Pacharatnam-Berry phase optical elements(PBOEs)have found promising applications in augmented reality and virtual reality because of their slim formfactor,lightweight,and high optical efficiency.However,chromatic aberration remains a serious longstanding problem for diffractive optics,hindering their broader adoption.To overcome the chromatic aberrations for red,green and blue(RGB)light sources,in this paper,we propose a counterintuitive multi-twist structure to achieve narrowband PBOEs without crosstalk,which plays a vital role to eliminate the chromatic aberration.The performance of our designed and fabricated narrowband Pacharatnam-Berry lenses(PBLs)aligns well with our simulation results.Furthermore,in a feasibility demonstration experiment using a laser projector,our proposed PBL system indeed exhibits a diminished chromatic aberration as compared to a broadband PBL.Additionally,polarization raytracing is implemented to demonstrate the versatility of the multi-twist structure for designing any RGB wavelengths with high contrast ratios.This analysis explores the feasibility of using RGB laser lines and quantum dot light-emitting diodes.Overall,our approach enables high optical efficiency,low fabrication complexity,and high degree of design freedom to accommodate any liquid crystal material and RGB light sources,holding immense potential for widespread applications of achromatic PBOEs.展开更多
The data production elements are driving profound transformations in the real economy across production objects,methods,and tools,generating significant economic effects such as industrial structure upgrading.This pap...The data production elements are driving profound transformations in the real economy across production objects,methods,and tools,generating significant economic effects such as industrial structure upgrading.This paper aims to reveal the impact mechanism of the data elements on the“three transformations”(high-end,intelligent,and green)in the manufacturing sector,theoretically elucidating the intrinsic mechanisms by which the data elements influence these transformations.The study finds that the data elements significantly enhance the high-end,intelligent,and green levels of China's manufacturing industry.In terms of the pathways of impact,the data elements primarily influence the development of high-tech industries and overall green technological innovation,thereby affecting the high-end,intelligent,and green transformation of the industry.展开更多
Rare earth elements are strategic commodities in many countries, and an important resource for the growing modern technology industry. As such, there is an increasing interest for development of rare earth element pro...Rare earth elements are strategic commodities in many countries, and an important resource for the growing modern technology industry. As such, there is an increasing interest for development of rare earth element processing, and this work is a part of further development of chromatography as a rare earth element separation process method. Process optimization is pivotal for process development, and it is common that several competing objectives must be regarded. Chromatographic separation processes often consider competing objectives, such as productivity, yield, pool concentration and modifier consumption, which leads to Pareto optimal solutions. Adding robustness to a process is of great importance to account for process disturbances and uncertainties but generally comes with reduced performance of the other process objectives as a trade off. In this study, a model-based robust multi-objective optimization was carried out for batch-wise chromatographic separation of the rare earth elements samarium, europium and gadolinium, which was considered highly un-robust due to the neighbouring peaks proximity to the product pooling horizon. The results from the robust optimization were used to chart the required operation point changes for keeping the amount of failed batches at an acceptable level when a certain level of process disturbance was introduced. The loss of process performance due to the gained robustness was found to be in the range of 10% - 20% reduced productivity when comparing the robust and un-robust Pareto solutions at Pareto points with identical yield. The methodology presented shows how to increase robustness to a highly un-robust system while still keeping multiple objectives at their optima.展开更多
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金financially supported by the National Natural Science Foundation of China(grant no.42120104005)Guizhou Provincial 2021 Science and Technology Subsidies(grant no.GZ2021SIG)+1 种基金Guizhou Provincial Science and Technology Projects(grant nos.ZK[2024]087GCC[2023]060)。
文摘The Martian core mainly contains iron,nickel and some light elements.However,controversies remain about the structure and chemical composition of the Martian core due to a lack of samples and marsquake data.Recently,the InSight lander collected long-term marsquake data,which improved the Martian interior structure model.B ased on the preliminary analysis of marsquake data,Mars has a molten liquid core with a radius of around 1700 km.As the Martian core has a smaller density and lower temperature than pure iron at corresponding pressure and temperature conditions,some light elements are introduced to reduce the density and liquidus temperature.With various methods for seismic analysis,in-situ high-pressure and high-temperature experiments,and first-principal calculations,the Martian core composition and evolution models have been updated in the past few years.Here,we review those studies on the light elements in the Martian core from four aspects including(1)high-temperature and high-pressure experiments,(2)marsquake data,(3)mineral physics model with molecular dynamics simulations and(4)cosmochemistry investigation.We discussed the effect of different light elements on the Martian core s density,sound velocity and liquidus temperature.Moreover,the review examines the varieties,abundances and forms of light elements in the Martian core.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金financial support from the National Key Research and Development Project of China(No.2019YFA0705300)the National Natural Science Foundation of China(No.52004051)+1 种基金the Project of Zhongyuan Critical Metals Laboratory,China(No.GJJSGFYQ202321)the Fund for Priority Support of Research Projects by Returned Overseas Scholars in Henan Province,China。
文摘The influence of varying levels of impurity elements on the hot corrosion resistance of the DD98M alloy in Na_(2)SO_(4)+NaCl salt at 950℃ was investigated.The results indicate that the corrosion resistance of the DD98M alloy significantly decreases with an increase in impurity content,and the presence of nitrogen leads to an increase in alloy porosity.These porosities promote the rapid diffusion of molten salt and oxygen into the alloy,resulting in a bilateral diffusion of oxygen and sulfur,which leads to an accumulation of these elements at the oxide−matrix interface.This process contributes to the formation and propagation of interfacial cracks.A growth model was developed for hot corrosion products in alloys with varying impurity elements.
文摘On the stone-paved lanes of Songyang County that date back to ancient times,morning mist lingered as a faint fragrance of tea wafted from a century-old house.Inside,Yang Junjie,a tea maker born in the 1980s,worked deftly at the stove,his hands moving swiftly over the scorching iron wok as tender green tea leaves dance between his fingers.
基金supported in part by the National Key R&D Program of China (Contract Nos.2023YFA1606500,2024YFE0109800,and 2024YFE0110400)Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB34010000)+5 种基金the Gansu Key Project of Science and Technology (Grant No.23ZDGA014)the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2021B0301030006)the National Natural Science Foundation of China (Grant Nos.12105328,W2412040,12475126,12422507,12035011,12375118,12435008,and W2412043)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-002)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant Nos.2020409 and 2023439)the Russian Science Foundation (Grant No.25-42-00003)。
文摘We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.
基金supported by the National Natural Science Foundation of China, No.61932008Natural Science Foundation of Shanghai, No.21ZR1403200 (both to JC)。
文摘Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.
基金financially supported by the National Natural Science Foundation of China(Nos.52293395 and 52293393)the Xiongan Science and Technology Innovation Talent Project of MOST,China(No.2022XACX0500).
文摘Producing steel requires large amounts of energy to convert iron ores into steel,which often comes from fossil fuels,leading to carbon emissions and other pollutants.Increasing scrap usage emerges as one of the most effective strategies for addressing these issues.However,typical residual elements(Cu,As,Sn,Sb,Bi,etc.)inherited from scrap could significantly influence the mechanical properties of steel.In this work,we investigate the effects of residual elements on the microstructure evolution and mechanical properties of a quenching and partitioning(Q&P)steel by comparing a commercial QP1180 steel(referred to as QP)to the one containing typical residual elements(Cu+As+Sn+Sb+Bi<0.3wt%)(referred to as QP-R).The results demonstrate that in comparison with the QP steel,the residual elements significantly refine the prior austenite grain(9.7μm vs.14.6μm)due to their strong solute drag effect,leading to a higher volume fraction(13.0%vs.11.8%),a smaller size(473 nm vs.790 nm)and a higher average carbon content(1.26 wt%vs.0.99 wt%)of retained austenite in the QP-R steel.As a result,the QP-R steel exhibits a sustained transformation-induced plasticity(TRIP)effect,leading to an enhanced strain hardening effect and a simultaneous improvement of strength and ductility.Grain boundary segregation of residual elements was not observed at prior austenite grain boundaries in the QP-R steel,primarily due to continuous interface migration during austenitization.This study demonstrates that the residual elements with concentrations comparable to that in scrap result in significant microstructural refinement,causing retained austenite with relatively higher stability and thus offering promising mechanical properties and potential applications.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金supports from the National Key Research and Development Program of China(2023YFB2806803)the National Natural Science Foundation of China(62075127).
文摘Liquid crystal Pacharatnam-Berry phase optical elements(PBOEs)have found promising applications in augmented reality and virtual reality because of their slim formfactor,lightweight,and high optical efficiency.However,chromatic aberration remains a serious longstanding problem for diffractive optics,hindering their broader adoption.To overcome the chromatic aberrations for red,green and blue(RGB)light sources,in this paper,we propose a counterintuitive multi-twist structure to achieve narrowband PBOEs without crosstalk,which plays a vital role to eliminate the chromatic aberration.The performance of our designed and fabricated narrowband Pacharatnam-Berry lenses(PBLs)aligns well with our simulation results.Furthermore,in a feasibility demonstration experiment using a laser projector,our proposed PBL system indeed exhibits a diminished chromatic aberration as compared to a broadband PBL.Additionally,polarization raytracing is implemented to demonstrate the versatility of the multi-twist structure for designing any RGB wavelengths with high contrast ratios.This analysis explores the feasibility of using RGB laser lines and quantum dot light-emitting diodes.Overall,our approach enables high optical efficiency,low fabrication complexity,and high degree of design freedom to accommodate any liquid crystal material and RGB light sources,holding immense potential for widespread applications of achromatic PBOEs.
文摘The data production elements are driving profound transformations in the real economy across production objects,methods,and tools,generating significant economic effects such as industrial structure upgrading.This paper aims to reveal the impact mechanism of the data elements on the“three transformations”(high-end,intelligent,and green)in the manufacturing sector,theoretically elucidating the intrinsic mechanisms by which the data elements influence these transformations.The study finds that the data elements significantly enhance the high-end,intelligent,and green levels of China's manufacturing industry.In terms of the pathways of impact,the data elements primarily influence the development of high-tech industries and overall green technological innovation,thereby affecting the high-end,intelligent,and green transformation of the industry.
文摘Rare earth elements are strategic commodities in many countries, and an important resource for the growing modern technology industry. As such, there is an increasing interest for development of rare earth element processing, and this work is a part of further development of chromatography as a rare earth element separation process method. Process optimization is pivotal for process development, and it is common that several competing objectives must be regarded. Chromatographic separation processes often consider competing objectives, such as productivity, yield, pool concentration and modifier consumption, which leads to Pareto optimal solutions. Adding robustness to a process is of great importance to account for process disturbances and uncertainties but generally comes with reduced performance of the other process objectives as a trade off. In this study, a model-based robust multi-objective optimization was carried out for batch-wise chromatographic separation of the rare earth elements samarium, europium and gadolinium, which was considered highly un-robust due to the neighbouring peaks proximity to the product pooling horizon. The results from the robust optimization were used to chart the required operation point changes for keeping the amount of failed batches at an acceptable level when a certain level of process disturbance was introduced. The loss of process performance due to the gained robustness was found to be in the range of 10% - 20% reduced productivity when comparing the robust and un-robust Pareto solutions at Pareto points with identical yield. The methodology presented shows how to increase robustness to a highly un-robust system while still keeping multiple objectives at their optima.
