目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4....目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4.2.0软件的Bibliometrix包对发文国家、合作网络、作者、机构、期刊、高被引文献、关键词和文献被引频次进行定量和可视化分析,并运用H指数分析作者的学术影响力。结果共纳入1219篇文献,2008—2024年发文量总体呈上升趋势。累计发文量排名前三位的国家分别是美国、中国和意大利;发文量排名前三位的期刊分别为Journal of Cachexia,Sarcopenia and Muscle、International Journal of Molecular Sciences和Experimental Gerontology;H指数排名前六位的作者分别为Marzettie E、Calvani R、Picca A、Van Remmen H、Leeuwenbugh C和Bernabel R;被引频次最高的文献是“Sarcopenia:agingrelated loss of muscle mass and function”;出现频次排名前五的关键词分别为skeletalmuscle、sarcopenia、oxidative stress、exercise和expression。结论老年性肌少症与线粒体相关性研究领域呈现良好的发展态势。未来需加强跨国家、跨机构和跨学科合作,可重点关注线粒体融合蛋白等对线粒体功能的影响,以及饮食和运动对老年性肌少症的干预作用等方面的探索。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
文摘目的分析2008—2024年老年性肌少症与线粒体相关性研究的现状、热点及发展趋势,为该领域的后续研究提供参考。方法检索2008年1月1日至2024年12月31日Web of Science核心合集数据库收录的老年性肌少症与线粒体相关性研究的文献,使用R 4.2.0软件的Bibliometrix包对发文国家、合作网络、作者、机构、期刊、高被引文献、关键词和文献被引频次进行定量和可视化分析,并运用H指数分析作者的学术影响力。结果共纳入1219篇文献,2008—2024年发文量总体呈上升趋势。累计发文量排名前三位的国家分别是美国、中国和意大利;发文量排名前三位的期刊分别为Journal of Cachexia,Sarcopenia and Muscle、International Journal of Molecular Sciences和Experimental Gerontology;H指数排名前六位的作者分别为Marzettie E、Calvani R、Picca A、Van Remmen H、Leeuwenbugh C和Bernabel R;被引频次最高的文献是“Sarcopenia:agingrelated loss of muscle mass and function”;出现频次排名前五的关键词分别为skeletalmuscle、sarcopenia、oxidative stress、exercise和expression。结论老年性肌少症与线粒体相关性研究领域呈现良好的发展态势。未来需加强跨国家、跨机构和跨学科合作,可重点关注线粒体融合蛋白等对线粒体功能的影响,以及饮食和运动对老年性肌少症的干预作用等方面的探索。
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.
