Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
The contribution of this paper is the introduction of three new pseudo-inverse formulations for the real-time control of foot-force distribution in multi-legged walking machines. Three alternative locomotion performance objectives are proposed for the purpose of optimizing the foot-force distribution. An exhaustive search method has been used to obtain truly optimal results, which are then used for comparison with the results obtained using suboptimal pseudo-inverse formulations that are suitable for real-time control. Simulation results show that, by using the appropriate pseudo-inverse formulation, a good approximation to the corresponding optimal foot-force distribution can be obtained. Furthermore, it is clear that the friction duty factor formulation provides an excellent real-time solution for minimizing the risk of foot-slip.
Social network services can not only help people form relationships and make new friends and partners, but also assist in processing personal information, sharing knowledge, and managing social relationships. Social networks achieve valuable communication and collaboration, bring additional business opportunities, and have great social value. Research on social network problems is effective by using assumption, definition, analysis, modeling, and optimization strategies. In this paper, we survey the existing problems of game theory applied to social networks and classify their application scenarios into four categories: information diffusion, behavior analysis, community detection, and information security. Readers can clearly master knowledge application in every category. Finally, we discuss certain limitations of game theory on the basis of research in recent years and propose future directions of social network research.
We have prepared a novel nanobiotherapeutic, Poly-[hemoglobin-superoxide dismutase-catalase-carbonic anhydrase], which not only transports both oxygen and carbon dioxide but also a therapeutic antioxidant. Our previous study in a severe sustained 90 min hemorrhagic shock rat model shows that it has a hepatoprotective effect. We investigate its hepatoprotective effect further in this present report using an alcohol-damaged primary hepatocyte culture model. Results show that it significantly reduced ethanol-induced AST release, lipid peroxidation, and ROS production in rat primary hepatocytes culture. It also significantly enhanced the viability of ethanol-treated hepatocytes. Thus, the result shows that Poly-[hemoglobin-superoxide dismutase-catalase-carbonic anhydrase] also has some hepatoprotective effects against alcohol-induced injury in in vitro rat primary hepatocytes cell culture. This collaborate our previous observation of its hepatoprotective effect in a severe sustained 90-min hemorrhagic shock rat model.
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