Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by d...Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.展开更多
Multicolor fluorescent probes based on small organic molecules have the advantages of low cost, good biocompatibility, easily modifiable molecular structures and adjustable fluorescence performance. In addition, small...Multicolor fluorescent probes based on small organic molecules have the advantages of low cost, good biocompatibility, easily modifiable molecular structures and adjustable fluorescence performance. In addition, small molecule multicolor fluorescent probes generally undergo multi-site or multi-step reactions, which means that they can be used for the specific detection of structurally similar substances in complex bio-systems. In this review, we focus on the design and application of multicolor fluorescent probes based on small organic molecules: single fluorophores with multiple reaction sites, multiple fluorophores with single reaction sites, or multiple fluorophores with multiple reaction sites. Moreover, a design strategy for multicolor fluorescent probes and its application in biological imaging are also summarized, providing a systematic plan for future research on fluorescent probes functionalized by small organic molecules. It will also play an important role in the development of additional functions for small organic molecule fluorescent probes.展开更多
基金This work was supported by the National Key R&D Program of China under Grant 2023YFB2703802the Hunan Province Innovation and Entrepreneurship Training Program for College Students S202311528073.
文摘Sharing data while protecting privacy in the industrial Internet is a significant challenge.Traditional machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy concerns.Federated learning(FL)has gained considerable attention because it allows for decentralized training on multiple local datasets.However,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL performance.This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain technology.To overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local models.This approach can significantly improve the accuracy of FL training when there is relatively large dispersion.In addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model parameters.Finally,we evaluate the performance of the proposed scheme using commonly used open-source image datasets.The simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.
基金This work was supported by the National Natural Science Foundation of China(21672131,21775096)One Hundred People Plan of Shanxi Province,Shanxi Province“1331 Project”Key Innovation Team Construction Plan Cultivation Team(2018-CT-1)+7 种基金2018 Xiangyuan County Solid Waste Comprehensive Utilization Science and Technology Project(2018XYSDJS-05)Shanxi Province Foundation for Returness(2017-026)Shanxi Collaborative Innovation Center of High Value-added Utilization of Coal-related Wastes(2015-10-B3)the Shanxi Province Foundation for Selected(No.2019)the Innovative Talents of Higher Education Institutions of Shanxi,Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(2019L0031)the Key R&D Program of Shanxi Province(201903D421069)the Shanxi Province Science Foundation(201901D111015)China Institute for Radiation Production and Scientific Instrument Center of Shanxi University(201512).
文摘Multicolor fluorescent probes based on small organic molecules have the advantages of low cost, good biocompatibility, easily modifiable molecular structures and adjustable fluorescence performance. In addition, small molecule multicolor fluorescent probes generally undergo multi-site or multi-step reactions, which means that they can be used for the specific detection of structurally similar substances in complex bio-systems. In this review, we focus on the design and application of multicolor fluorescent probes based on small organic molecules: single fluorophores with multiple reaction sites, multiple fluorophores with single reaction sites, or multiple fluorophores with multiple reaction sites. Moreover, a design strategy for multicolor fluorescent probes and its application in biological imaging are also summarized, providing a systematic plan for future research on fluorescent probes functionalized by small organic molecules. It will also play an important role in the development of additional functions for small organic molecule fluorescent probes.