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
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
The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly...The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly considered the influence of gravity conditions on the constrained melting process of a PCM and not on its unconstrained melting process. In this study, a numerical model was constructed to comprehensively analyze the constrained and unconstrained melting processes of a PCM inside a spherical capsule under low-gravity conditions. After validation, the model was then applied to investigating the effects of low-gravity conditions on the evolution of velocity, temperature, melt layer thickness, heat transfer, liquid fraction, and total melting time. For the unconstrained melting process, low-gravity conditions weaken buoyancy-driven natural convection and slow down the solid PCM downward trend, thereby limiting the melting rate. In addition, the melt layer thickness does not increase linearly with decreasing gravity. Specifically, the increase in melt layer thickness is smaller by about 1.06 mm when the gravity drops from 0.4g to 0.2g compared to when it drops from 0.2g to 0.1g. The local heat flux in the contact melting area gradually decreases with the reduction of gravity during the unconstrained melting process. During the constrained melting process, notable oscillations in the local heat flux were observed. Decreasing the gravity from g to 0g increased the total melting times of the constrained and unconstrained melting processes by 417% and 621%, respectively.展开更多
[目的/意义]当前AI for Science(AI4S)科研范式加速演进,科学研究呈现智能化、平台化与跨学科融合趋势,传统学科情报服务体系面临技术嵌入不足、服务响应滞后、能力结构不适配等问题。明确AI4S背景下学科情报服务的转型方向,对构建高效...[目的/意义]当前AI for Science(AI4S)科研范式加速演进,科学研究呈现智能化、平台化与跨学科融合趋势,传统学科情报服务体系面临技术嵌入不足、服务响应滞后、能力结构不适配等问题。明确AI4S背景下学科情报服务的转型方向,对构建高效科研支持体系具有重要意义。[方法/过程]通过梳理揭示AI4S科研范式下学科情报服务面临的挑战与能力缺口,围绕“数据—信息—知识—情报(DIKI)”链条,设计面向AI4S的新型学科情报服务体系;结合科研组织形态变革,提出服务内容重构、学科馆员能力升级与组织机制创新的路径。[结果/结论]研究认为AI4S驱动下,学科情报服务需从过程嵌入走向系统协同,从被动响应转向智能赋能;学科馆员应完成角色转型,构建以高质量数据保障、智能情报产出和AI素养提升为核心的支撑能力;同时,组织机制需适配动态流程、重构边界与交叉协作模式,为智能科研生态体系提供全链条服务体系支撑。展开更多
针对触摸屏监控系统不能满足大中型立体仓库对数据进行存储和处理的功能需求,在Visual Studio 2019集成开发环境中,采用C#语言开发一套立体仓库上位机控制系统。以多线程的方式实时读取库位信息;以S7-1200 PLC作为主控制器,设计产品的...针对触摸屏监控系统不能满足大中型立体仓库对数据进行存储和处理的功能需求,在Visual Studio 2019集成开发环境中,采用C#语言开发一套立体仓库上位机控制系统。以多线程的方式实时读取库位信息;以S7-1200 PLC作为主控制器,设计产品的自动入库和自动出库程序流程,采用SCL语言设计了库位先进先出的控制程序。C#和S7-1200 PLC之间采用S7通信的方式控制立体仓库的出入库操作和库位信息采集,3年的现场运行情况表明,整个系统能在上位机上对立体仓库进行手动控制和自动控制,能精确快速地进行入库出库操作,运行平稳,上位机上能正确实时显示库位信息,达到了预期的结果。展开更多
食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)...食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)在形成过程中的动态演变,包括其结构表征和功能特性。6 g/100 mL的FP通过酸热处理(pH 2,85℃)不同时间(0~24 h)后得到FPF。处理后的样品通过硫黄素T、荧光、二酪氨酸、透射电子显微镜、傅里叶红外光谱等进行结构表征,结果表明FP先在酸热过程中水解成多肽,再自组装成富含β-折叠结构的FPF(由0 h的34.44%增加到24 h的45.89%)。通过起泡性、乳化性和凝胶特性等对FPF功能特性进行表征,与FP相比,反应24 h后的FPF具有更好的起泡性、乳化性和凝胶特性。此外,FPF在体外细胞实验中没有表现出细胞毒性。研究结果为FPF的形成规律提供了理论支撑。展开更多
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金supported by the National Natural Science Foundation of China under Grant 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 (Grant No.52376181)。
文摘The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly considered the influence of gravity conditions on the constrained melting process of a PCM and not on its unconstrained melting process. In this study, a numerical model was constructed to comprehensively analyze the constrained and unconstrained melting processes of a PCM inside a spherical capsule under low-gravity conditions. After validation, the model was then applied to investigating the effects of low-gravity conditions on the evolution of velocity, temperature, melt layer thickness, heat transfer, liquid fraction, and total melting time. For the unconstrained melting process, low-gravity conditions weaken buoyancy-driven natural convection and slow down the solid PCM downward trend, thereby limiting the melting rate. In addition, the melt layer thickness does not increase linearly with decreasing gravity. Specifically, the increase in melt layer thickness is smaller by about 1.06 mm when the gravity drops from 0.4g to 0.2g compared to when it drops from 0.2g to 0.1g. The local heat flux in the contact melting area gradually decreases with the reduction of gravity during the unconstrained melting process. During the constrained melting process, notable oscillations in the local heat flux were observed. Decreasing the gravity from g to 0g increased the total melting times of the constrained and unconstrained melting processes by 417% and 621%, respectively.
文摘[目的/意义]当前AI for Science(AI4S)科研范式加速演进,科学研究呈现智能化、平台化与跨学科融合趋势,传统学科情报服务体系面临技术嵌入不足、服务响应滞后、能力结构不适配等问题。明确AI4S背景下学科情报服务的转型方向,对构建高效科研支持体系具有重要意义。[方法/过程]通过梳理揭示AI4S科研范式下学科情报服务面临的挑战与能力缺口,围绕“数据—信息—知识—情报(DIKI)”链条,设计面向AI4S的新型学科情报服务体系;结合科研组织形态变革,提出服务内容重构、学科馆员能力升级与组织机制创新的路径。[结果/结论]研究认为AI4S驱动下,学科情报服务需从过程嵌入走向系统协同,从被动响应转向智能赋能;学科馆员应完成角色转型,构建以高质量数据保障、智能情报产出和AI素养提升为核心的支撑能力;同时,组织机制需适配动态流程、重构边界与交叉协作模式,为智能科研生态体系提供全链条服务体系支撑。
文摘针对触摸屏监控系统不能满足大中型立体仓库对数据进行存储和处理的功能需求,在Visual Studio 2019集成开发环境中,采用C#语言开发一套立体仓库上位机控制系统。以多线程的方式实时读取库位信息;以S7-1200 PLC作为主控制器,设计产品的自动入库和自动出库程序流程,采用SCL语言设计了库位先进先出的控制程序。C#和S7-1200 PLC之间采用S7通信的方式控制立体仓库的出入库操作和库位信息采集,3年的现场运行情况表明,整个系统能在上位机上对立体仓库进行手动控制和自动控制,能精确快速地进行入库出库操作,运行平稳,上位机上能正确实时显示库位信息,达到了预期的结果。
