ZnO is a highly significant II-VI semiconductor known for its excellent optoelectronic properties,making it widely applicable and promising for use in light-emitting devices,solar cells,lasers,and photodetectors.The m...ZnO is a highly significant II-VI semiconductor known for its excellent optoelectronic properties,making it widely applicable and promising for use in light-emitting devices,solar cells,lasers,and photodetectors.The methods for preparing ZnO are diverse,and among them,the hydrothermal method is favored for its simplicity,ease of operation,and low cost,making it an optimal choice for ZnO single-crystal growth.Most studies investigating the effects of different hydrothermal experimental parameters on the morphology and performance of ZnO nano-materials typically focus on only 2—3 variable parameters,with few examining the impact of all possible experimental parameter changes on ZnO nano-mate-rials.The principles of the hydrothermal method and its advantages in nano-material preparation were briefly introduced in this article.The detailed discussion on the influence of various experimental parameters on the preparation of ZnO nano-materials was provided,which including reaction materials,Zn^(2+)/OH^(-)ratio,reaction time and temperature,additives,experimental equipment,and annealing conditions.The review co-vers how different experimental parameters affect the morphology and performance of the materials,as well as how different rare earth doping elements influence the performance of ZnO nano-materials.It is hoped that this work will contribute to future research on the hydrothermal synthesis of nano-materials.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
文摘ZnO is a highly significant II-VI semiconductor known for its excellent optoelectronic properties,making it widely applicable and promising for use in light-emitting devices,solar cells,lasers,and photodetectors.The methods for preparing ZnO are diverse,and among them,the hydrothermal method is favored for its simplicity,ease of operation,and low cost,making it an optimal choice for ZnO single-crystal growth.Most studies investigating the effects of different hydrothermal experimental parameters on the morphology and performance of ZnO nano-materials typically focus on only 2—3 variable parameters,with few examining the impact of all possible experimental parameter changes on ZnO nano-mate-rials.The principles of the hydrothermal method and its advantages in nano-material preparation were briefly introduced in this article.The detailed discussion on the influence of various experimental parameters on the preparation of ZnO nano-materials was provided,which including reaction materials,Zn^(2+)/OH^(-)ratio,reaction time and temperature,additives,experimental equipment,and annealing conditions.The review co-vers how different experimental parameters affect the morphology and performance of the materials,as well as how different rare earth doping elements influence the performance of ZnO nano-materials.It is hoped that this work will contribute to future research on the hydrothermal synthesis of nano-materials.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
