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1. Hybrid Concurrent Learning for Hybrid Linear Regression

   https://doi.org/10.1109/CDC51059.2022.9992473  

   Johnson, Ryan S. ; Sanfelice, Ricardo G. ( January 2022 , 2022 IEEE 61st Conference on Decision and Control)
2. Hybrid Lipid-Polymer Bilayers: pH-Mediated Interactions between Hybrid Vesicles and Glass

   https://doi.org/10.3390/polym12040745  

   Willes, Keith L. ; Genchev, Jasmyn R. ; Paxton, Walter F. ( April 2020 , Polymers)

   One practical approach towards robust and stable biomimetic platforms is to generate hybrid bilayers that incorporate both lipids and block co-polymer amphiphiles. The currently limited number of reports on the interaction of glass surfaces with hybrid lipid and polymer vesicles—DOPC mixed with amphiphilic poly(ethylene oxide-b-butadiene) (PEO-PBd)—describe substantially different conclusions under very similar conditions (i.e., same pH). In this study, we varied vesicle composition and solution pH in order to generate a broader picture of spontaneous hybrid lipid/polymer vesicle interactions with rigid supports. Using quartz crystal microbalance with dissipation (QCM-D), we followed the interaction of hybrid lipid-polymer vesicles with borosilicate glass as a function of pH. We found pH-dependent adsorption/fusion of hybrid vesicles that accounts for some of the contradictory results observed in previous studies. Our results show that the formation of hybrid lipid-polymer bilayers is highly pH dependent and indicate that the interaction between glass surfaces and hybrid DOPC/PEO-PBd can be tuned with pH. 

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3. A hybrid multivector model predictive control for an inner-interleaved hybrid multilevel converter

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   Li, Yufei ; Diao, Fei ; Zhao, Yue ( February 2022 , IEEE Journal of Emerging and Selected Topics in Industrial Electronics)
4. Hybrid Dynamical System Model and Robust Control of a Hybrid Neuroprosthesis Under Fatigue Based Switching

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   Sheng, Zhiyu ; Molazadeh, Vahidreza ; Sharma, Nitin ( June 2018 , American Control Conference)
5. Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning

   https://doi.org/10.3390/en16020652  

   Yao, Zhengyu ; Yoon, Hwan-Sik ; Hong, Yang-Ki ( January 2023 , Energies)

   Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms. 

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